= 0.11.2)\\n\"\n msg += \"\\t\\t2: demultiplexing per lane (with demultiplex.py v >= 1.14.0)\\n\"\n msg += \"\\t\\t3: cleaning per sample (with CutAdapt v >= 1.8)\\n\"\n msg += \"\\t\\t4: alignment per sample (with BWA MEM v >= 0.7.12, Samtools v >= 1.3, Picard and R v >= 3)\\n\"\n msg += \"\\t\\t5: local realignment per sample (with GATK v >= 3.5)\\n\"\n msg += \"\\t\\t6: local realignment per genotype (with GATK v >= 3.5)\\n\"\n msg += \"\\t\\t7: variant and genotype calling per genotype (with GATK HaplotypeCaller v >= 3.5)\\n\"\n msg += \"\\t\\t8: variant and genotype calling jointly across genotypes (with GATK GenotypeGVCFs v >= 3.5)\\n\"\n msg += \"\\t\\t9: variant and genotype filtering (with GATK v >= 3.5)\\n\"\n msg += \" --samples\\tpath to the 'samples' file\\n\"\n msg += \"\\t\\tcompulsory for all steps, but can differ between steps\\n\"\n msg += \"\\t\\t e.g. if samples come from different species or are aligned\\n\"\n msg += \"\\t\\t on different ref genomes, different samples file should\\n\"\n msg += \"\\t\\t be used for steps 4-9, representing different subsets of\\n\"\n msg += \"\\t\\t the file used for steps 1-3\\n\"\n msg += \"\\t\\tthe file should be encoded in ASCII\\n\"\n msg += \"\\t\\tthe first row should be a header with column names\\n\"\n msg += \"\\t\\teach 'sample' (see details below) should have one and only one row\\n\"\n msg += \"\\t\\tany two columns should be separated with one tabulation\\n\"\n msg += \"\\t\\tcolumns can be in any order\\n\"\n msg += \"\\t\\trows starting by '#' are skipped\\n\"\n msg += \"\\t\\t12 columns are compulsory (but there can be more):\\n\"\n msg += \"\\t\\t genotype (see details below, e.g. 'Col-0', but use neither underscore '_' nor space ' ' nor dot '.', use dash '-' instead)\\n\"\n msg += \"\\t\\t ref_genome (identifier of the reference genome used for alignment, e.g. 'Atha_v2', but use neither space ' ' nor dot '.'; the full species name, e.g. 'Arabidopsis thaliana', will be present in the file given to --dict)\\n\"\n msg += \"\\t\\t library (e.g. can be the same as 'genotype')\\n\"\n msg += \"\\t\\t barcode (e.g. 'ATGG')\\n\"\n msg += \"\\t\\t seq_center (e.g. 'Broad Institute', 'GenoToul', etc)\\n\"\n msg += \"\\t\\t seq_platform (e.g. 'ILLUMINA', see SAM format specification)\\n\"\n msg += \"\\t\\t seq_platform_model (e.g. 'HiSeq 2000')\\n\"\n msg += \"\\t\\t flowcell (e.g. 'C5YMDACXX')\\n\"\n msg += \"\\t\\t lane (e.g. '3', can be '31' if a first demultiplexing was done per index)\\n\"\n msg += \"\\t\\t date (e.g. '2015-01-15', see SAM format specification)\\n\"\n msg += \"\\t\\t fastq_file_R1 (filename, one per lane, gzip-compressed)\\n\"\n msg += \"\\t\\t fastq_file_R2 (filename, one per lane, gzip-compressed)\\n\"\n msg += \" --fcln\\tidentifier of a flowcell and lane number\\n\"\n msg += \"\\t\\tformat as <flowcell>_<lane-number>, e.g. 'C5YMDACXX_1'\\n\"\n msg += \"\\t\\tif set, only the samples from this lane will be analyzed\\n\"\n msg += \" --pird\\tpath to the input reads directory\\n\"\n msg += \"\\t\\tcompulsory for steps 1 and 2\\n\"\n msg += \"\\t\\twill be added to the columns 'fastq_file_R*' from the sample file\\n\"\n msg += \"\\t\\tif not set, input read files should be in current directory\\n\"\n msg += \" --enz\\tname of the restriction enzyme\\n\"\n msg += \"\\t\\tcompulsory for step 2\\n\"\n msg += \"\\t\\tdefault=ApeKI\\n\"\n msg += \" --dmxmet\\tmethod used to demultiplex\\n\"\n msg += \"\\t\\tcompulsory for step 2\\n\"\n msg += \"\\t\\tdefault=4c (see the help of demultiplex.py to know more)\\n\"\n msg += \" --subst\\tnumber of substitutions allowed during demultiplexing\\n\"\n msg += \"\\t\\tcompulsory for step 2\\n\"\n msg += \"\\t\\tdefault=2\\n\"\n msg += \" --ensubst\\tenforce the nb of substitutions allowed\\n\"\n msg += \"\\t\\tcompulsory for step 2\\n\"\n msg += \"\\t\\tdefault=lenient/strict\\n\"\n msg += \" --adp\\tpath to the file containing the adapters\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tsame format as FastQC: name<tab>sequence\\n\"\n msg += \"\\t\\tname: at least 'adpR1' (also 'adpR2' if paired-end)\\n\"\n msg += \"\\t\\tsequence: from 5' (left) to 3' (right)\\n\"\n msg += \" --errtol\\terror tolerance to find adapters\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=0.2\\n\"\n msg += \" --minovl\\tminimum overlap length between reads and adapters\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=3 (in bases)\\n\"\n msg += \" --minrl\\tminimum length to keep a read\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=35 (in bases)\\n\"\n msg += \" --minq\\tminimum quality to trim a read\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=20 (used for both reads if paired-end)\\n\"\n msg += \" --maxNp\\tmaximum percentage of N to keep a read\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=0.2\\n\"\n msg += \" --ref\\tpath to the prefix of files for the reference genome\\n\"\n msg += \"\\t\\tcompulsory for steps 4, 5, 6, 7, 8, 9\\n\"\n msg += \"\\t\\tshould correspond to the 'ref_genome' column in --samples\\n\"\n msg += \"\\t\\te.g. '/data/Atha_v2' for '/data/Atha_v2.fa', '/data/Atha_v2.bwt', etc\\n\"\n msg += \"\\t\\tthese files are produced via 'bwa index ...'\\n\"\n msg += \" --dict\\tpath to the 'dict' file (SAM header with @SQ tags)\\n\"\n msg += \"\\t\\tcompulsory for step 4\\n\"\n msg += \"\\t\\tsee 'CreateSequenceDictionary' in the Picard software\\n\"\n msg += \" --jgid\\tcohort identifier to use for joint genotyping\\n\"\n msg += \"\\t\\tcompulsory for steps 8, 9\\n\"\n msg += \"\\t\\tuseful to launch several, different cohorts in parallel\\n\"\n msg += \" --rat\\trestrict alleles to be of a particular allelicity\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tdefault=ALL/BIALLELIC/MULTIALLELIC\\n\"\n msg += \"\\t\\tsee '--restrictAllelesTo' in GATK's SelectVariant\\n\"\n msg += \" --mdp\\tminimum value for DP (read depth; e.g. 10)\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee GATK's VariantFiltration\\n\"\n msg += \" --mgq\\tminimum value for GQ (genotype quality; e.g. 20)\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee GATK's VariantFiltration\\n\"\n msg += \" --mnfg\\tmaximum number of filtered genotypes to keep a variant\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee '--maxFilteredGenotypes' in GATK's SelectVariants\\n\"\n msg += \" --mffg\\tmaximum fraction of filtered genotypes to keep a variant\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee '--maxFractionFilteredGenotypes' in GATK's SelectVariants\\n\"\n msg += \" --mnnc\\tmaximum number of not-called genotypes to keep a variant\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \" --mfnc\\tmaximum fraction of not-called genotypes to keep a variant\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee '--maxNOCALLfraction' in GATK's SelectVariants\\n\"\n msg += \" --fam\\tpath to the file containing pedigree information\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tdiscard variants with Mendelian violations (see Semler et al, 2012)\\n\"\n msg += \"\\t\\tshould be in the 'fam' format specified by PLINK\\n\"\n msg += \"\\t\\tvalidation strictness (GATK '-pedValidationType') is set at 'SILENT'\\n\"\n msg += \"\\t\\t allowing some samples to be absent from the pedigree\\n\"\n msg += \" --mvq\\tminimum GQ for each trio member to accept a variant as a Mendelian violation\\n\"\n msg += \"\\t\\tused in step 9 if '--fam' is specified\\n\"\n msg += \"\\t\\tdefault=0\\n\"\n msg += \" --xlssf\\tpath to the file with genotypes to exclude\\n\"\n msg += \"\\t\\tused in step 9 (can be especially useful if '--fam' is specified)\\n\"\n msg += \" --tmpd\\tpath to a temporary directory on child nodes (default=.)\\n\"\n msg += \"\\t\\te.g. it can be /tmp or /scratch\\n\"\n msg += \"\\t\\tused in step 4 for 'samtools sort'\\n\"\n msg += \"\\t\\tused in step 7 for 'GATK HaplotypeCaller'\\n\"\n msg += \" --jvmXms\\tinitial memory allocated to the Java Virtual Machine\\n\"\n msg += \"\\t\\tdefault=512m (can also be specified as 1024k, 1g, etc)\\n\"\n msg += \"\\t\\tused in steps 4, 5, 6, 7 and 8 for Picard and GATK\\n\"\n msg += \" --jvmXmx\\tmaximum memory allocated to the Java Virtual Machine\\n\"\n msg += \"\\t\\tdefault=4g\\n\"\n msg += \"\\t\\tused in steps 4, 5, 6, 7 and 8 for Picard and GATK\\n\"\n msg += \" --queue2\\tname of the second cluster queue (default=bigmem.q)\\n\"\n msg += \"\\t\\tused in step 4 for Picard to collect insert sizes\\n\"\n msg += \" --knowni\\tpath to a VCF file with known indels (for local realignment)\\n\"\n msg += \" --known\\tpath to a VCF file with known variants (e.g. from dbSNP)\\n\"\n msg += \" --force\\tforce to re-run step(s)\\n\"\n msg += \"\\t\\tthis removes without warning the step directory if it exists\\n\"\n msg += \"\\n\"\n msg += \"Examples:\\n\"\n msg += \" %s --step 1 --samples samples.txt\\n\" % os.path.basename(sys.argv[0])\n msg += \"\\n\"\n msg += \"Details:\\n\"\n msg += \"This program aims at genotyping a set of 'genotypes' using data from\\n\"\n msg += \"a restriction-assisted DNA sequencing (RAD-seq) experiment, also known\\n\"\n msg += \"as a genotyping-by-sequencing (GBS) experiment.\\n\"\n msg += \"Here, by 'genotype', we mean the entity which is the focus of the\\n\"\n msg += \"study. For instance, it can be a plant variety (or a human being), or\\n\"\n msg += \"the specific clone of a given plant variety (or a specific tumor of a\\n\"\n msg += \"given human being), etc.\\n\"\n msg += \"Importantly, note that the content of the 'genotype' column will\\n\"\n msg += \"be used to set the 'SM' (sample) tag of the 'RG' (read group) header\\n\"\n msg += \"record type of the SAM format (see http://www.htslib.org/). However,\\n\"\n msg += \"internal to this program, the term 'sample' corresponds to the unique\\n\"\n msg += \"quadruplet (genotype,flowcell,lane,barcode) for steps 1 and 2, and to\\n\"\n msg += \"the unique triplet (genotype,flowcell,lane) for the others.\\n\"\n msg += \"Jobs are executed in parallel (--schdlr). Their return status is\\n\"\n msg += \"recorded in a SQLite database which is removed at the end. If a job\\n\"\n msg += \"fails, the whole script stops with an error.\\n\"\n msg += \"\\n\"\n msg += \"Dependencies:\\n\"\n msg += \"Python >= 2.7; Biopython; pyutilstimflutre >= 0.5\\n\"\n msg += \"\\n\"\n msg += \"Report bugs to <timothee.flutre@inra.fr>.\"\n print(msg); sys.stdout.flush()","def main(argv):\n version = \"0.1.2\"\n interval = 1\n max_run_time = 0\n finished = 0\n first_time = 1\n output_file = 0\n output_file_enabled = 0\n output_path = 0\n header_row = 1\n\n #*** Get the hostname for use in filenames etc:\n hostname = socket.gethostname()\n\n #*** Start by parsing command line parameters:\n try:\n opts, args = getopt.getopt(argv, \"hu:m:ni:w:Wb:jv\",\n [\"help\",\n \"url=\",\n \"max-run-time=\",\n \"no-keepalive\",\n \"interval=\",\n \"output-file=\",\n \"output-path=\",\n \"no-header-row\",\n \"version\"])\n except getopt.GetoptError as err:\n print \"mosp: Error with options:\", err\n print_help()\n sys.exit(2)\n for opt, arg in opts:\n if opt in (\"-h\", \"--help\"):\n print_help()\n sys.exit()\n elif opt in (\"-v\", \"--version\"):\n print 'mosp.py version', version\n sys.exit()\n elif opt in (\"-m\", \"--max-run-time\"):\n max_run_time = float(arg)\n elif opt in (\"-i\", \"--interval\"):\n interval = float(arg)\n elif opt in (\"-w\", \"--output-file\"):\n output_file = arg\n output_file_enabled = 1\n elif opt == \"-W\":\n output_file = \"mosp-\" + hostname + \"-\" + \\\n time.strftime(\"%Y%m%d-%H%M%S.csv\")\n output_file_enabled = 1\n elif opt in (\"-b\", \"--output-path\"):\n output_path = arg\n elif opt in (\"-j\", \"--no-header-row\"):\n header_row = 0\n\n print \"\\nMeasure Operating System Performance (mosp) version\", \\\n version\n\n #*** Display output filename:\n if output_file_enabled:\n if output_path:\n output_file = os.path.join(output_path, output_file)\n print \"Results filename is\", output_file\n else:\n print \"Not outputing results to file, as option not selected\"\n\n if not header_row:\n print \"Not writing a header row to CSV\"\n\n #*** Use this if max_run_time is set:\n initial_time = time.time()\n\n #*** Instantiate classes:\n cpus = CPUs()\n swap = Swap()\n nics = NICs()\n\n #*** Start the loop:\n while not finished:\n timenow = datetime.datetime.now()\n timestamp = timenow.strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]\n start_time = time.time()\n\n #*** Update CPU measurements:\n cpus.update()\n\n #*** Update swap measurements:\n swap.update()\n\n #*** Update network measurements:\n nics.update()\n\n #*** Put the stats into a nice string for printing and\n #*** writing to file:\n result_csv = str(timestamp) + \",\" \\\n + cpus.csv() \\\n + swap.csv() \\\n + nics.csv() \\\n + \"\\n\"\n result_kvp = str(timestamp) + \" \" \\\n + cpus.kvp() \\\n + swap.kvp() \\\n + nics.kvp()\n print result_kvp\n if output_file_enabled:\n #*** Header row in CSV:\n if first_time and header_row:\n #*** Write a header row to CSV:\n header_csv = \"time,\" + cpus.csv_header(hostname) + \\\n swap.csv_header(hostname) + \\\n nics.csv_header(hostname) + \\\n \"\\n\"\n first_time = 0\n with open(output_file, 'a') as the_file:\n the_file.write(header_csv)\n\n #*** Write a data row to CSV:\n with open(output_file, 'a') as the_file:\n the_file.write(result_csv)\n\n if max_run_time:\n if (start_time - initial_time) > max_run_time:\n break\n\n #*** Sleep for interval seconds:\n time.sleep(interval)","def _cmd_metrics(args):\n if (\n len(args.cnarrays) > 1\n and args.segments\n and len(args.segments) > 1\n and len(args.cnarrays) != len(args.segments)\n ):\n raise ValueError(\n \"Number of coverage/segment filenames given must be \"\n \"equal, if more than 1 segment file is given.\"\n )\n\n cnarrs = map(read_cna, args.cnarrays)\n if args.segments:\n args.segments = map(read_cna, args.segments)\n table = metrics.do_metrics(cnarrs, args.segments, args.drop_low_coverage)\n write_dataframe(args.output, table)","def define_options(self):\n\n from clinica.engine.cmdparser import PIPELINE_CATEGORIES\n\n clinica_comp = self._args.add_argument_group(PIPELINE_CATEGORIES['CLINICA_COMPULSORY'])\n clinica_comp.add_argument(\"caps_directory\",\n help='Path to the CAPS directory.')\n clinica_comp.add_argument(\"list_bvalues\", type=str,\n help='String listing all the shells (i.e. the b-values) in the corrected DWI datasets comma separated (e.g, 0,300,700,2200)')\n # Optional arguments\n clinica_opt = self._args.add_argument_group(PIPELINE_CATEGORIES['CLINICA_OPTIONAL'])\n\n clinica_opt.add_argument(\"-wd\", \"--working_directory\",\n help='Temporary directory to store pipeline intermediate results')\n clinica_opt.add_argument(\"-np\", \"--n_procs\", type=int, default=4,\n help='Number of cores used to run in parallel')\n clinica_opt.add_argument(\"-tsv\", \"--subjects_sessions_tsv\",\n help='TSV file containing a list of subjects with their sessions.')","def cmd_help(args):","def reports_cli():","def printOptions(opts,subject_ids,session_ids,task_list, run_list, acq, rec):\n uname = os.popen('uname -s -n -r').read()\n print \"\\n\"\n print \"* Pipeline started at \"+time.strftime(\"%c\")+\"on \"+uname\n print \"* Command line is : \\n \"+str(sys.argv)+\"\\n\"\n print \"* The source directory is : \"+opts.sourceDir\n print \"* The target directory is : \"+opts.targetDir+\"\\n\"\n print \"* Data-set Subject ID(s) is/are : \"+str(', '.join(subject_ids))+\"\\n\"\n # print \"* PET conditions : \"+ ','.join(opts.condiList)+\"\\n\"\n print \"* Sessions : \", session_ids, \"\\n\"\n print \"* Tasks : \" , task_list , \"\\n\"\n print \"* Runs : \" , run_list , \"\\n\"\n print \"* Acquisition : \" , acq , \"\\n\"\n print \"* Reconstruction : \" , rec , \"\\n\"","def getHelp(self):\r\n help_str =\\\r\n \"\"\"##########################################################################################\r\n#\r\n# Required:\r\n#\r\n# --query_NAST multi-fasta file containing query sequences in alignment format\r\n#\r\n# Common opts:\r\n#\r\n# --db_NAST db in NAST format\r\n# --db_FASTA db in fasta format (megablast formatted)\r\n#\r\n#\r\n# -n number of top matching database sequences to compare to (default 15)\r\n# -R min divergence ratio default: 1.007\r\n# -P min percent identity among matching sequences (default: 90)\r\n#\r\n# ## parameters to tune ChimeraParentSelector:\r\n#\r\n# Scoring parameters:\r\n# -M match score (default: +5)\r\n# -N mismatch penalty (default: -4)\r\n# -Q min query coverage by matching database sequence (default: 70)\r\n# -T maximum traverses of the multiple alignment (default: 1)\r\n\r\n#\r\n# ## parameters to tune ChimeraPhyloChecker:\r\n#\r\n#\r\n# --windowSize default 50\r\n# --windowStep default 5\r\n# --minBS minimum bootstrap support for calling chimera (default: 90)\r\n# -S percent of SNPs to sample on each side of breakpoint for computing bootstrap support (default: 10)\r\n# --num_parents_test number of potential parents to test for chimeras (default: 3)\r\n# --MAX_CHIMERA_PARENT_PER_ID Chimera/Parent alignments with perID above this are considered non-chimeras (default 100; turned off)\r\n#\r\n# ## misc opts\r\n#\r\n# --printFinalAlignments shows alignment between query sequence and pair of candidate chimera parents\r\n# --printCSalignments print ChimeraSlayer alignments in ChimeraSlayer output\r\n# --exec_dir chdir to here before running\r\n#\r\n#########################################################################################\r\n \"\"\"\r\n return help_str","def procs_calculate_axyzc(molecules, n_cores=-1, show_progress=True, scr=None, cmd=XTB_CMD):\n results = None\n return results","def _get_metrics_options(metrics):\n metrics_options = []\n if metrics is None:\n metrics = []\n for static_metric in metrics:\n metrics_options += [\n \"-m\",\n static_metric.metric.mp_metric_name,\n str(static_metric.value),\n ]\n return metrics_options","def main():\n\n args = parse_args()\n metric_sender = MetricSender(verbose=args.verbose, debug=args.debug)\n\n discovery_key_disk = 'disc.disk'\n interval = 3\n pcp_disk_dev_metrics = ['disk.dev.total', 'disk.dev.avactive']\n item_prototype_macro_disk = '#OSO_DISK'\n item_prototype_key_tps = 'disc.disk.tps'\n item_prototype_key_putil = 'disc.disk.putil'\n\n disk_metrics = pminfo.get_sampled_data(pcp_disk_dev_metrics, interval, 2)\n\n pcp_metrics_divided = {}\n for metric in pcp_disk_dev_metrics:\n pcp_metrics_divided[metric] = {k: v for k, v in disk_metrics.items() if metric in k}\n\n # do TPS checks; use disk.dev.total\n filtered_disk_totals = clean_up_metric_dict(pcp_metrics_divided[pcp_disk_dev_metrics[0]],\n pcp_disk_dev_metrics[0] + '.')\n\n # Add dynamic items\n metric_sender.add_dynamic_metric(discovery_key_disk, item_prototype_macro_disk, filtered_disk_totals.keys())\n\n # calculate the TPS and add them to the ZaggSender\n for disk, totals in filtered_disk_totals.iteritems():\n disk_tps = (totals[1] - totals[0]) / interval\n metric_sender.add_metric({'%s[%s]' % (item_prototype_key_tps, disk): disk_tps})\n\n # do % Util checks; use disk.dev.avactive\n filtered_disk_totals = clean_up_metric_dict(pcp_metrics_divided[pcp_disk_dev_metrics[1]],\n pcp_disk_dev_metrics[1] + '.')\n\n # calculate the % Util and add them to the ZaggSender\n for disk, totals in filtered_disk_totals.iteritems():\n total_active = (float)(totals[1] - totals[0]) / 1000.0\n putil = 100 * total_active / interval\n\n metric_sender.add_metric({'%s[%s]' % (item_prototype_key_putil, disk): putil})\n\n metric_sender.send_metrics()","def cvv_report(argv):\n p = optparse.OptionParser()\n p.add_option('-c', '--cfg',\n action='store', default='', dest='config',\n help='config file name')\n p.add_option('-d', '--debug',\n action='store_true', default=False, dest='debug',\n help='run the debugger')\n p.add_option('-p', '--prefix',\n action='store', default='', dest='prefix',\n help='table name prefix')\n p.add_option('-v', '--verbose',\n action='store_true', default=False, dest='verbose',\n help='pass verbose flag to HSI object')\n try:\n (o, a) = p.parse_args(argv)\n except SystemExit:\n return\n\n if o.debug:\n pdb.set_trace()\n\n if o.config != '':\n cfg = CrawlConfig.get_config(o.config)\n else:\n cfg = CrawlConfig.get_config()\n\n if o.prefix != '':\n cfg.set('dbi', 'tbl_prefix', o.prefix)\n\n dim = {}\n dim['cos'] = Dimension.get_dim('cos')\n dim['ttypes'] = Dimension.get_dim('ttypes')\n\n print dim['cos'].report()\n print dim['ttypes'].report()","def _show_help(self):\r\n info = {\"/contexts/<context>/[orgs/[<org_name>]]/[spaces/[<space_name>]]\": \"reports\",\r\n \"/contexts/<context>/orgs_metadata/[<org_name>]\": \"metadata\",\r\n \"/contexts/<context>/orgs/<org_name>/director\": \"org/director mapping\",\r\n \"/reader_status\": \"status of Bitbucket reader cache\"}\r\n if self._cache_refresh:\r\n info['/refresh'] = \"force cache refresh from BitBucket\"\r\n return info","def info():\n f = Figlet(font='standard')\n click.echo(f.renderText('covtool'))\n click.secho(\n \"covtool: a simple CLI for fetching covid data\", fg='cyan')\n click.echo(\n \"Data Sources: https://www.worldometers.info/coronavirus\\nJohn Hopkins [https://github.com/CSSEGISandData/COVID-19] \")\n click.secho(\"Author: Amayo II <amayomordecai@gmail.com>\", fg='magenta')","def cmdopt(request):\n return request.config.getoption(\"-c\")","def show(self):\n prev_queries = 0\n prev_cpu_sys = 0\n prev_cpu_user = 0\n \n lines = {\n \"Uptime (seconds)\": \"--\",\n \"Number of queries\": \"--\",\n \"Query per second\": \"--\",\n \"ACL drops\": \"--\",\n \"Dynamic drops\": \"--\",\n \"Rule drops\": \"--\",\n \"CPU Usage (%s)\": \"--\",\n \"Cache hitrate\": \"--\"\n }\n\n while True:\n try:\n # get stats from dnsdist\n stats = Statistics(console=self.console)\n global_stats = stats[\"global\"]\n \n qps = int(global_stats[\"queries\"]) - prev_queries\n prev_queries = int(global_stats[\"queries\"])\n cpu = (int(global_stats[\"cpu-sys-msec\"])+int(global_stats[\"cpu-user-msec\"]) - prev_cpu_sys - prev_cpu_user) / 10\n prev_cpu_sys = int(global_stats[\"cpu-sys-msec\"])\n prev_cpu_user = int(global_stats[\"cpu-user-msec\"])\n \n lines[\"Uptime (seconds)\"] = global_stats[\"uptime\"]\n lines[\"Number of queries\"] = global_stats[\"queries\"]\n lines[\"Query per second\"] = qps\n lines[\"CPU Usage (%s)\"] = cpu\n lines[\"ACL drops\"] = global_stats[\"acl-drops\"]\n lines[\"Rule drops\"] = global_stats[\"rule-drop\"]\n lines[\"Cache hitrate\"] = global_stats[\"cache-hits\"]\n lines[\"Dynamic drops\"] = global_stats[\"dyn-blocked\"]\n\n # reprint the lines \n sys.stdout.write(\"\\033[1mDashboard for dnsdist\\033[0m\\n\")\n sys.stdout.write(\"\\n\")\n sys.stdout.write(\"Global:\\n\")\n for k,v in lines.items():\n sys.stdout.write(\"\\t%s: %s\\n\" % (k,v))\n sys.stdout.write(\"Backends:\\n\")\n for s in stats[\"backends\"]:\n if not len(s[\"name\"]):\n s[\"name\"] = \"--\"\n if not len(s[\"pools\"]):\n s[\"pools\"] = \"--\"\n sys.stdout.write(\"\\t#%s / %s / %s / %s\\n\" % (s[\"#\"],s[\"address\"],s[\"name\"],s[\"pools\"]) )\n sys.stdout.write(\"\\t\\tNumber of queries: %s\\n\" % s[\"queries\"])\n sys.stdout.write(\"\\t\\tQuery per second: %s\\n\" % s[\"qps\"])\n sys.stdout.write(\"\\t\\tNumber of drops: %s\\n\" % s[\"drops\"])\n sys.stdout.write(\"\\n\")\n sys.stdout.write(\"Ctrl+C to exit\\n\")\n \n time.sleep(1)\n \n \n # move up cursor and delete whole line\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\") \n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n for k,v in lines.items():\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\") \n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n for s in stats[\"backends\"]:\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\") \n sys.stdout.write(\"\\x1b[1A\\x1b[2K\") \n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n \n del stats\n except KeyboardInterrupt:\n break","def printHelp():\n print(\"amqWorkApiMass.py -n <msgcnt> -b <body> -m <headers> -s <path/to/bodyandheaders>\")","def do_config():\n\n tracking = get_tracking()\n for unit in (\"ppm\", \"sec\"):\n\ttunit = unit\n\tif unit == \"sec\":\n\t tunit = \"seconds\"\n\tprint \"multigraph chrony_%s\" % unit\n\tprint \"graph_title NTP (Chrony) Statistics (%s)\" % unit\n\tprint \"graph_vlabel %s\" % unit\n\tprint \"graph_args --base 1000\"\n\tprint \"graph_category time\"\n\tprint \"graph_info NTP (Chrony) tracking statistics (the ones measured in %s)\" % tunit\n\tfor key in tracking[tunit]:\n\t item = tracking[tunit][key]\n\t print \"\"\"%s.label %s\n%s.draw LINE2\n%s.info %s\"\"\" % (key, item[\"label\"], key, key, item[\"label\"])\n\tprint\n return 0","def cli(argv):\r\n argv.append(\"--exhaust-materials\")\r\n cltestbench.cli(argv)","def collect_cluster_info(output_dir, k8s_cli):\n collect_helper(output_dir, cmd=\"{} cluster-info\".format(k8s_cli),\n file_name=\"cluster_info\", resource_name=\"cluster-info\")","def main(cls):\n parser = cls.make_argument_parser()\n args = parser.parse_args()\n args.device = make_hoomd_device(args)\n benchmark = cls(**vars(args))\n performance = benchmark.execute()\n\n if args.device.communicator.rank == 0:\n print(f'{numpy.mean(performance)}')","def help(update, context):\n msg = \"\"\n msg += \"\\n/covid 7-Day-Incident per Million\"\n msg += \"\\n/daylio What did I do a year ago today?\"\n msg += \"\\n/f1last Results of the last race\"\n msg += \"\\n/f1stand Driver standings\"\n msg += \"\\n/f1next Time and place of the next race\"\n msg += \"\\n/fuel prices and consump. (args: Xeur Ykm)\"\n msg += \"\\n/ip Outside ip address\"\n msg += \"\\n/rate Exchange rates (args: Xeur/Yhuf)\"\n msg += \"\\n/rss check rss feeds for new content\"\n msg += \"\\n/sun Time of sunrise and sunset\"\n msg += \"\\n/xkcd Sends last comic image and alt\"\n msg.rstrip()\n update.message.reply_text(msg)","def show(self, options=None):\n\n # # IMPLEMENTATION NOTE: Stub for implementing options:\n # if options and self.InspectOptions.ALL_OUTPUT_LABELS in options:\n # pass\n\n print (\"\\n---------------------------------------------------------\")\n print (\"\\n{0}\".format(self.name))\n\n\n print (\"\\n\\tControl enabled: {0}\".format(self.enable_controller))\n print (\"\\n\\tProcesses:\")\n\n for process in self.processes:\n print (\"\\t\\t{} [learning enabled: {}]\".format(process.name, process._learning_enabled))\n\n\n # Print execution_sets (output of toposort)\n print (\"\\n\\tExecution sets: \".format(self.name))\n # Sort for consistency of output\n execution_sets_sorted = sorted(self.execution_sets)\n for i in range(len(execution_sets_sorted)):\n # for i in range(len(self.execution_sets)):\n print (\"\\t\\tSet {0}:\\n\\t\\t\\t\".format(i),end='')\n print(\"{ \",end='')\n sorted_mechs_names_in_set = sorted(list(mech_tuple.mechanism.name\n for mech_tuple in self.execution_sets[i]))\n for name in sorted_mechs_names_in_set:\n print(\"{0} \".format(name), end='')\n print(\"}\")\n\n # Print executionList sorted by phase and including EVC mechanism\n\n # Sort executionList by phase\n sorted_execution_list = self.executionList.copy()\n\n\n # Sort by phaseSpec and, within each phase, by mechanism name\n sorted_execution_list.sort(key=lambda mech_tuple: mech_tuple.phase)\n\n\n # Add controller to execution list for printing if enabled\n if self.enable_controller:\n sorted_execution_list.append(MechanismTuple(self.controller, None, self.controller.phaseSpec))\n\n\n mech_names_from_exec_list = list(mech_tuple.mechanism.name for mech_tuple in self.executionList)\n mech_names_from_sorted_exec_list = list(mech_tuple.mechanism.name for mech_tuple in sorted_execution_list)\n\n print (\"\\n\\tExecution list: \".format(self.name))\n phase = 0\n print(\"\\t\\tPhase {}:\".format(phase))\n for mech_tuple in sorted_execution_list:\n if mech_tuple.phase != phase:\n phase = mech_tuple.phase\n print(\"\\t\\tPhase {}:\".format(phase))\n print (\"\\t\\t\\t{}\".format(mech_tuple.mechanism.name))\n\n print (\"\\n\\tOrigin mechanisms: \".format(self.name))\n for mech_tuple in self.originMechanisms.mech_tuples_sorted:\n print(\"\\t\\t{0} (phase: {1})\".format(mech_tuple.mechanism.name, mech_tuple.phase))\n\n print (\"\\n\\tTerminal mechanisms: \".format(self.name))\n for mech_tuple in self.terminalMechanisms.mech_tuples_sorted:\n print(\"\\t\\t{0} (phase: {1})\".format(mech_tuple.mechanism.name, mech_tuple.phase))\n for output_state_name in mech_tuple.mechanism.outputStates:\n print(\"\\t\\t\\t{0}\".format(output_state_name))\n\n # if any(process.learning for process in self.processes):\n if self.learning:\n print (\"\\n\\tTarget mechanisms: \".format(self.name))\n for mech_tuple in self.targetMechanisms.mech_tuples:\n print(\"\\t\\t{0} (phase: {1})\".format(mech_tuple.mechanism.name, mech_tuple.phase))\n\n print (\"\\n---------------------------------------------------------\")","def usage(err=''):\r\n m = '%s\\n' %err\r\n m += 'Default usage is to list Cases closed for the 30 days\\n'\r\n m += '\\n Example:\\n'\r\n m += ' closedcases -n 90 \\n' \r\n m += ' \\n'\r\n# m += ' closedcases -n 60 -s blast5 \\n'\r\n return m","def _cmd_segmetrics(args):\n if not 0.0 < args.alpha <= 1.0:\n raise RuntimeError(\"alpha must be between 0 and 1.\")\n\n if not any((args.location_stats, args.spread_stats, args.interval_stats)):\n logging.info(\"No stats specified\")\n return\n\n # Calculate all metrics\n cnarr = read_cna(args.cnarray)\n segarr = read_cna(args.segments)\n segarr = do_segmetrics(\n cnarr,\n segarr,\n args.location_stats,\n args.spread_stats,\n args.interval_stats,\n args.alpha,\n args.bootstrap,\n args.smooth_bootstrap,\n skip_low=args.drop_low_coverage,\n )\n tabio.write(segarr, args.output or segarr.sample_id + \".segmetrics.cns\")","def help(bin_name='windmill'):\n bin_name = 'windmill'\n module = sys.modules[__name__]\n from windmill.conf import global_settings\n all_option_names = []\n options_string = []\n for option in [getattr(module, x) for x in dir(module) if (\n hasattr(getattr(module, x), 'option_names')) and (\n getattr(module, x).__doc__ is not None ) ]:\n all_option_names.append(option.option_names)\n if hasattr(option, 'setting'):\n if getattr(global_settings, option.setting, None) is not None:\n default = ' Defaults to %s' % str(getattr(global_settings, option.setting, None))\n else:\n default = ''\n else:\n default = ''\n if option.option_names[0] is None:\n if not issubclass(option, GeneralBool):\n options_string.append(' '+''.join([str(option.option_names[1])+'='+' :: ', \n option.__doc__]) + default)\n else:\n options_string.append(' '+''.join([str(option.option_names[1])+' :: ', \n option.__doc__]) + default)\n else:\n if not issubclass(option, GeneralBool):\n options_string.append(' '+''.join([\n '-'+str(option.option_names[0])+', '\n +str(option.option_names[1])+'='+' :: ',\n option.__doc__]) + default)\n else:\n options_string.append(' '+''.join([\n '-'+str(option.option_names[0])+', '\n +str(option.option_names[1])+' :: ',\n option.__doc__]) + default)\n\n preamble = \"\"\"windmill web test automation system.\n %s [-%s] action [option=value] [firefox|ie|safari] [http://www.example.com]\n \nAvailable Actions:\n shell Enter the windmilll shell environment (modified python shell). \n Uses ipython if installed. Exit using ^d\n run_service Run the windmill service in foreground. Kill using ^c.\n \nAvailable Options:\"\"\" % ( bin_name,\n ''.join([ o[0] for o in all_option_names if o[0] is not None ]) \n )\n print preamble\n print '\\n'.join(options_string)","def build_cmdline():\n\tcmd=optparse.OptionParser(version=__version__)\n\tcmd.add_option('-c', '', dest='config_fname',type=\"string\", help='WHM/WHMCS configuration file', metavar=\"FILE\")\n\tcmd.add_option('-s', '', dest=\"whm_section\", type=\"string\", help=\"WHM server to use. Specify section name. eg: -s ds01\", metavar=\"SERVER\")\n\tcmd.add_option('','--search', action=\"store\", dest='search', type=\"string\", help=\"Search client by DNS domain name or cPanel username\", metavar=\"STRING\")\n\tcmd.add_option('-d', '', dest='whmcs_deptid', type=\"int\", help=\"WHMCS Department ID\", metavar=\"INT\") \n\tcmd.add_option('-m', '', dest='whmcs_ticketmsg_fname', type=\"string\", help=\"WHMCS abuse ticket template file\", metavar='FILE')\n\tcmd.add_option('-r', '', dest='whm_suspendmsg_fname', type=\"string\", help='cPanel account suspension reason template file', metavar='FILE')\n\tcmd.add_option('-f', '', dest='whmcs_proofmsg_fname', type=\"string\", help='Abuse proof file which will be appended to abuse ticket message', metavar='FILE')\n\tcmd.add_option('', '--subject', dest='whmcs_subject', type=\"string\", help='Specify abuse ticket subject title.', metavar=\"STRING\")\n\tcmd.add_option('-y', '--allyes', dest='allyes', action=\"store_true\", default=False, help='Assume yes as an answer to any question which would be asked')\n\treturn cmd","def cmdline(self, executable, options, task, rlimits):\n data_model_param = get_data_model_from_task(task, {ILP32: \"-m32\", LP64: \"-m64\"})\n print(options)\n if data_model_param and not any(\n option.startswith(\"--clang-options=\") for option in options\n ):\n options += [\"--clang-options=\" + data_model_param]\n\n if task.property_file:\n options += [\"--svcomp-property\", task.property_file]\n else:\n raise UnsupportedFeatureException(\n \"SMACK can't execute without a property file.\"\n )\n\n options += [task.single_input_file]\n\n return [executable] + options","def display_memcache_info(request):\n # pylint: disable-msg=E1101\n return utility.respond(request, 'admin/memcache_info',\n {'memcache_info': memcache.get_stats()})","def execute(self, context):\n logger.info(\"///////////////////////////////////////////////////////////////////////\")\n logger.info(\"[ChainedTransfig] Collectd-Jmx w/ Common MBeans Generation...\")\n logger.info(\"///////////////////////////////////////////////////////////////////////\")\n\n #insert common to assemble collectd.genericjmx.common.conf in later step\n context[CTX_KEY_COMMON_COLLECTD_JMX_APP_PREFIX] = 'common'\n super().execute(context)\n\n logger.info(\"///////////////////////////////////////////////////////////////////////\")\n logger.info(\"[ChainedTransfig] Collectd-Jmx w/ Common MBeans Generation... COMPLETES\")\n logger.info(\"///////////////////////////////////////////////////////////////////////\")","def ShowIPCVoucherAttributeControl(cmd_args=[], cmd_options={}):\n if not cmd_args:\n raise ArgumentError(\"Please provide correct arguments.\")\n ivac = kern.GetValueFromAddress(cmd_args[0], 'ipc_voucher_attr_control_t')\n print GetIPCVoucherAttrControlSummary.header\n print GetIPCVoucherAttrControlSummary(ivac)\n if config['verbosity'] > vHUMAN:\n cur_entry_index = 0\n last_entry_index = unsigned(ivac.ivac_table_size)\n print \"index \" + GetIPCVoucherAttributeEntrySummary.header\n while cur_entry_index < last_entry_index:\n print \"{: <5d} \".format(cur_entry_index) + GetIPCVoucherAttributeEntrySummary(addressof(ivac.ivac_table[cur_entry_index]))\n cur_entry_index += 1","def host_report(args):\n config = Kconfig(client=args.client, debug=args.debug, region=args.region, zone=args.zone, namespace=args.namespace)\n k = config.k\n k.report()","def main(argv=None):\n\n if argv is None:\n argv = sys.argv\n\n # setup command line parser\n parser = E.OptionParser(version=\"%prog version: $Id$\",\n usage=globals()[\"__doc__\"])\n\n parser.add_option(\"-t\", \"--test\", dest=\"test\", type=\"string\",\n help=\"supply help\")\n\n parser.add_option(\"--method\", dest=\"method\", type=\"choice\",\n choices=(\"metrics\", \"summary\", \"module_summary\"),\n help=\"method to summarise clustering\")\n\n parser.add_option(\"--ref-gtf-files\", dest=\"ref_gtf\", type=\"string\",\n help=\"comma separated list of reference gtf files\")\n\n # add common options (-h/--help, ...) and parse command line\n (options, args) = E.Start(parser, argv=argv)\n\n if options.method == \"metrics\":\n infile = argv[-1]\n E.info(\"loading input file: %s\" % infile)\n assert infile\n\n df = pd.read_table(infile,\n sep=\"\\t\",\n header=None,\n index_col=0)\n\n df = df.ix[:, :50]\n cluster_combs = (x for x in itertools.combinations(df.columns,\n 2))\n genes = df.index\n results_dict = {}\n all_clusts = {}\n\n E.info(\"setting up cluster containers\")\n for i in df.columns:\n clusters = set(df[i].values.tolist())\n cluster_dict = {}\n for clust in clusters:\n cluster_dict[clust] = []\n for gene in genes:\n cluster_dict[df[i][gene]].append(gene)\n\n for col in clusters:\n col_set = set()\n clust_col = cluster_dict[col]\n gene_members = itertools.combinations(clust_col,\n 2)\n col_set.update(gene_members)\n cluster_dict[col] = col_set\n all_clusts[i] = cluster_dict\n E.info(\"generating all pair-wise cluster comparisons\")\n E.info(\"calculating adjusted mutual information\")\n for k in cluster_combs:\n clusters1 = all_clusts[k[0]]\n clusters2 = all_clusts[k[1]]\n metric_dict = {}\n metric_dict['AMI'] = TS.adjustedMutualInformation(clusters1,\n clusters2)\n results_dict[k] = metric_dict\n\n res_frame = pd.DataFrame(results_dict).T\n res_frame = res_frame.reset_index()\n res_frame.drop(['level_0'], inplace=True, axis=1)\n res_frame.drop(['level_1'], inplace=True, axis=1)\n\n # flatten rand indices and add to output dataframe\n rand_arrays = TS.randIndexes(df)\n flat_adj_rand = TS.unravel_arrays(rand_arrays[0])\n flat_rand = TS.unravel_arrays(rand_arrays[1])\n res_frame['Rand_Index'] = flat_rand\n res_frame['Adjusted_Rand_Index'] = flat_adj_rand\n E.info(\"aggregating results\")\n\n res_frame.to_csv(options.stdout,\n sep=\"\\t\",\n index_label='idx')\n\n elif options.method == \"summary\":\n infiles = argv[-1]\n list_of_files = infiles.split(\",\")\n\n file_dict = {}\n for fle in list_of_files:\n fname = fle.split(\"/\")[-1]\n condition = fname.split(\"-\")[0]\n ref = fname.split(\"-\")[1]\n df_ = pd.read_table(fle,\n sep=\"\\t\",\n header=0,\n index_col=0)\n df_.columns = ['gene_id', 'cluster']\n clust_dict = {}\n for idx in df_.index:\n cluster = df_.loc[idx]['cluster']\n gene = df_.loc[idx]['gene_id']\n try:\n clust_dict[cluster] += 1\n except KeyError:\n clust_dict[cluster] = 1\n med_size = np.median(clust_dict.values())\n file_dict[fname] = {'condition': condition,\n 'reference': ref,\n 'median_cluster_size': med_size}\n\n outframe = pd.DataFrame(file_dict).T\n outframe.to_csv(options.stdout,\n sep=\"\\t\",\n index_label='idx')\n\n elif options.method == \"module_summary\":\n # get lncRNA/gene lengths from reference gtfs\n ref_gtfs = options.ref_gtf.split(\",\")\n length_dict = {}\n for ref in ref_gtfs:\n oref = IOTools.openFile(ref, \"rb\")\n git = GTF.transcript_iterator(GTF.iterator(oref))\n for gene in git:\n for trans in gene:\n length = trans.end - trans.start\n try:\n length_dict[trans.gene_id] += length\n except KeyError:\n length_dict[trans.gene_id] = length\n oref.close()\n\n infiles = argv[-1]\n list_of_files = infiles.split(\",\")\n\n fdfs = []\n for fle in list_of_files:\n cond = fle.split(\"/\")[-1].split(\"-\")[0]\n refer = fle.split(\"/\")[-1].split(\"-\")[1]\n _df = pd.read_table(fle, sep=\"\\t\",\n header=0, index_col=0)\n _df.columns = ['gene_id', 'cluster']\n clusters = set(_df['cluster'])\n c_dict = {}\n # summarize over each cluster\n for clust in clusters:\n lengths = []\n c_df = _df[_df['cluster'] == clust]\n for lid in c_df['gene_id']:\n lengths.append(length_dict[lid])\n c_dict[clust] = {'cluster_size': len(c_df['gene_id']),\n 'mean_length': np.mean(lengths),\n 'index': (cond, refer),\n 'module': clust}\n cdf = pd.DataFrame(c_dict).T\n # use a multindex for hierarchical indexing\n midx = pd.MultiIndex.from_tuples(cdf['index'])\n cdf.index = midx\n cdf.drop(['index'], inplace=True, axis=1)\n fdfs.append(cdf)\n\n # generate a single output df\n s_df = fdfs[0]\n fdfs.pop(0)\n for df in fdfs:\n s_df = s_df.append(df)\n\n s_df.to_csv(options.stdout,\n index_label=(\"condition\", \"reference\"),\n sep=\"\\t\")\n\n # write footer and output benchmark information.\n E.Stop()","def about( cls, ):\n url = r\"http://www.opencircuits.com/Python_Smart_Terminal\"\n __, mem_msg = cls.show_process_memory( )\n msg = ( f\"{cls.controller.app_name} version:{cls.controller.version} \\nmode: {cls.parameters.mode}\"\n f\"\\n by Russ Hensel\"\n f\"\\nMemory in use {mem_msg} \\nCheck <Help> or \\n{url} \\nfor more info.\" )\n messagebox.showinfo( \"About\", msg )","def stats(caesar, input):\n commands = {}\n users = {}\n channels = {}\n\n ignore = set(['f_note', 'startup', 'message', 'noteuri'])\n for (name, user), count in caesar.stats.items(): \n if name in ignore: continue\n if not user: continue\n\n if not user.startswith('#'): \n try: users[user] += count\n except KeyError: users[user] = count\n else: \n try: commands[name] += count\n except KeyError: commands[name] = count\n\n try: channels[user] += count\n except KeyError: channels[user] = count\n\n comrank = sorted([(b, a) for (a, b) in commands.iteritems()], reverse=True)\n userank = sorted([(b, a) for (a, b) in users.iteritems()], reverse=True)\n charank = sorted([(b, a) for (a, b) in channels.iteritems()], reverse=True)\n\n # most heavily used commands\n creply = 'most used commands: '\n for count, command in comrank[:10]: \n creply += '%s (%s), ' % (command, count)\n caesar.say(creply.rstrip(', '))\n\n # most heavy users\n reply = 'power users: '\n for count, user in userank[:10]: \n reply += '%s (%s), ' % (user, count)\n caesar.say(reply.rstrip(', '))\n\n # most heavy channels\n chreply = 'power channels: '\n for count, channel in charank[:3]: \n chreply += '%s (%s), ' % (channel, count)\n caesar.say(chreply.rstrip(', '))","def help_help(self):\n print(\"List commands or print details about a command\")","def help(self):\n self._log.info('### Help for the class %s ###',\n self.__class__)\n self._log.info(self.__doc__)\n \n print ('## job property:',self._context_name, self.statusOn, self.StoredValue)\n print ('## allowed Values:',self.allowedValues)\n print ('## default Value:', self.__class__.StoredValue )\n print ('## allowed Types :',self.allowedTypes )\n self._log.info('### End of the help for the class %s ###',\n self.__class__)","def cli_help(self):\n self._generate_cli_version()\n self._generate_cli_help()\n sys.exit(0)","async def eventstats(self, ctx):\n if ctx.invoked_subcommand is None:\n await ctx.send_help(ctx.command)","def command_help(args):\n\tprint_usage()\n\treturn 0","def print_help():\n print(\"Archive generated report to a web server. e.g.\")\n print(\"rm -rf /cs-shared/contrail_code_coverage/test_coverage\")\n print(\"cp -a build/coverage/controller/test_coverage \" +\n \"/cs-shared/contrail_code_coverage/\")\n print(\"http://10.84.5.100/cs-shared/contrail_code_coverage/test_coverage\")","def opc_calcs(df_param_indexed):\n \n df_param_indexed = df_param_indexed.copy()\n \n ''' commented 20180210 after Calmetrix update\n # Remove for cc1 data exported with cc2\n mix_start = datetime.strptime(\n df_param_indexed.loc['Mix Time', 1], \"%d-%b-%Y %H:%M:%S\")\n log_start = datetime.strptime(\n df_param_indexed.loc['Start Time', 1], \"%d-%b-%Y %H:%M:%S\")\n time_difference = (log_start - mix_start).total_seconds()\n '''\n # select values from DataFrame and calculate mass of binder in sample\n # may be worth checking if any of these values are 0 at some point in the future\n \n m_water = float(df_param_indexed.loc['Water Mass, g', 1])\n m_cem = float(df_param_indexed.loc['Cement Mass, g', 1])\n m_sample = float(df_param_indexed.loc['Sample Mass, g', 1])\n m_sample_cem = m_sample / (m_cem + m_water) * m_cem\n \n return m_sample_cem","def config_pbc_md(self):\n\n self._config_md()\n self.title = \"PBC MD Simulation\"\n self.cntrl[\"cut\"] = 8.0\n self.cntrl[\"igb\"] = 0\n self.cntrl[\"iwrap\"] = 1\n self.cntrl[\"ntp\"] = 1\n self.cntrl[\"barostat\"] = 2","def cli(argv):\r\n args = get_args(argv)\r\n verbosity = \"summary\"\r\n if args.verbose:\r\n verbosity = \"report\"\r\n report = evaluate(args.design, verbosity)\r\n print json.dumps(report, indent=4)","def qsub_cmmd(self):\n temp = 'qsub -l mem=1G,time=:5: -cwd -j y -o {log} {job}'.format(**self.dict)\n return temp.format(fnum=self.fnum)","def runMCMC(df, cents, show=False):\n if type(cents) is not list:\n cents = [cents]\n numCents = len(cents)\n p = None\n \n # Tau = the precision of the normal distribution (of the above peaks)\n taus = 1. / pm.Uniform('stds', 0, 100, size=numCents)**2 # tau = 1/sigma**2\n centers = pm.Normal('centers', cents, [0.0025 for i in cents],\n size=numCents)\n \n if numCents == 2: # Assignment probability\n p = pm.Uniform('p', 0, 1)\n assignment = pm.Categorical('asisgnment', [p, 1-p],\n size=len(df.intervals))\n @pm.deterministic\n def center_i(assignment=assignment, centers=centers):\n return centers[assignment]\n @pm.deterministic\n def tau_i(assignment=assignment, taus=taus):\n return taus[assignment]\n observations = pm.Normal('obs', center_i, tau_i, value=df.intervals,\n observed=True)\n # Create the model 2 peaks\n mcmc = pm.MCMC([p, assignment, observations, taus, centers])\n \n else:\n observations = pm.Normal('obs', value=df.intervals, observed=True)\n mcmc = pm.MCMC([observations, taus, centers]) # Create model, 1 peak\n \n # Run the model\n mcmc.sample(50000)\n center_trace = mcmc.trace(\"centers\")[:]\n try:\n clusts = [center_trace[:,i] for i in range(numCents)]\n except:\n clusts = [center_trace]\n \n if show:\n for i in range(numCents):\n plt.hist(center_trace[:,i], bins=50, histtype='stepfilled',\n color=['blue', 'red'][i], alpha=0.7)\n plt.show()\n \n print('Evolved clusters at:')\n print([np.mean(c) for c in clusts])\n return clusts","def test_cli_help(self):\n output = self.update_command('-h')","def explainerdashboard_cli(ctx):","def execute(self, context):\n app = context[CTX_KEY_COMMON_COLLECTD_JMX_APP_PREFIX]\n logger.info(\"///////////////////////////////////////////////////////////////////////\")\n logger.info(\"[ChainedTransfig] Collectd-Jmx w/ Application-wise [%s] ...\", app)\n logger.info(\"///////////////////////////////////////////////////////////////////////\")\n\n super().execute(context)\n\n logger.info(\"///////////////////////////////////////////////////////////////////////\")\n logger.info(\"[ChainedTransfig] Collectd-Jmx w/ Application-wise [%s] ... \"\n \"COMPLETES\", app)\n logger.info(\"///////////////////////////////////////////////////////////////////////\")","def print_help_info(self, global_options):\r\n usage = ['',\"Type '%s help <subcommand>' for help on a specific subcommand.\" % self.prog_name,'']\r\n usage.append('Available subcommands:')\r\n commands = self.get_commands(global_options).keys()\r\n commands.sort()\r\n for cmd in commands:\r\n usage.append(' %s' % cmd)\r\n return '\\n'.join(usage)","def print_performance_info(self):\n pass","def help_text(command):\n\n courses_list = ('ENPM611', 'ENPM613', 'ENPM631', 'ENPM687',\\\n 'ENPM691', 'ENPM693', 'ENPM694', 'ENPM696',\\\n 'ENPM809J','ENPM809R', 'ENPM809W')\n\n response = 'I have course descriptions for: '\n for course_name in courses_list:\n response = response + course_name + ' '\n\n response = response + '\\nTo get the course description, execute command: about ENPM<course_number>'\n\n return response","def HMC_Help():\n os.system(\"cls\")\n while True:\n print((\"\\n\\n\",\"Help\".center(50)))\n print_list = [\"ManagedSystem\",\"LogicalPartition\",\"VirtualIOServer\",\"Cluster\",\"Performance Capaity Monitoring\",\"Return to Main Menu\"]\n choice = int(print_obj.print_on_screen(print_list))\n directory = os.path.dirname(os.path.dirname(__file__))\n if choice == 1:\n path = directory+\"/help/ManagedSystem\"\n files = [f for f in os.listdir(path)if os.path.isfile(os.path.join(path,f))]\n for f in files :\n print((open(path+\"/%s\"%(f)).read()))\n elif choice == 2:\n path = directory+\"/help/LogicalPartition\"\n files = [f for f in os.listdir(path)if os.path.isfile(os.path.join(path,f))]\n for f in files :\n print((open(path+\"/%s\"%(f)).read()))\n elif choice == 3:\n path = directory+\"/help/VirtualIOServer\"\n files = [f for f in os.listdir(path)if os.path.isfile(os.path.join(path,f))]\n for f in files :\n print((open(path+\"/%s\"%(f)).read()))\n elif choice == 4:\n print((open(directory+\"/help/Cluster.txt\").read()))\n elif choice == 5:\n print((open(directory+\"/help/PerformanceCapacityMonitoring.txt\").read()))\n elif choice == 6:\n os.system(\"cls\")\n return\n else:\n print(\"\\nTry using Valid option\")\n back_to_menu()","def stats(self, d_raw_materials=None):\n cm_stats = 'sugar {0} tablespoons remaining\\n'.format(d_raw_materials['sugar'])\n cm_stats += 'butter {0} teaspoons remaining\\n'.format(d_raw_materials['butter'])\n cm_stats += 'dark chocolate {0} tablespoons remaining\\n'.format(d_raw_materials['dark chocolate'])\n cm_stats += 'mint chocolate {0} tablespoons remaining\\n'.format(d_raw_materials['mint chocolate'])\n cm_stats += 'milk chocolate {0} tablespoons remaining\\n'.format(d_raw_materials['milk chocolate'])\n cm_stats += 'light corn syrup {0} teaspoons remaining\\n'.format(d_raw_materials['light corn syrup'])\n cm_stats += 'sweetened condensed milk {0} teaspoons remaining\\n'.format(d_raw_materials[\n 'sweetened condensed milk'])\n cm_stats += 'vanilla extract {0} teaspoons remaining\\n'.format(d_raw_materials['vanilla extract'])\n cm_stats += 'Reese\\'s Pieces {0} tablespoons remaining\\n'.format(d_raw_materials['Reese\\'s Pieces'])\n cm_stats += super(ChocolateMachine, self).stats()\n return cm_stats","def options():\n print \"\"\"Options summary:\n -h, --help\n -u, --usage\n -v, --verbose <verb_level>\n -t, --transaction\n -e, --endpoint <endpoint>\n -i, --interface-type <iface_type>\n -f, --from-file <filename>\n --not-error-tolerant\n \"\"\"","def DoHelp(options, args):\n __pychecker__ = 'unusednames=options'\n if len(args) == 1 and args[0] in COMMAND_USAGE_TEXT:\n print(COMMAND_USAGE_TEXT[args[0]])\n else:\n raise gclient_utils.Error(\"unknown subcommand '%s'; see 'gclient help'\" %\n args[0])","def show_help(argv=None):\n if argv:\n if \"list_datasets\" in argv:\n karr, _, desc = COMMANDS_TABLE[\"list_datasets\"]\n sdm_util.print_message(\"command : %s\" % (\" | \".join(karr)))\n sdm_util.print_message(\"usage : sdm ls\")\n sdm_util.print_message(\"\")\n sdm_util.print_message(desc)\n return 0\n elif \"search_datasets\" in argv:\n karr, _, desc = COMMANDS_TABLE[\"search_datasets\"]\n sdm_util.print_message(\"command : %s\" % (\" | \".join(karr)))\n sdm_util.print_message(\"usage : sdm search <keyword>\")\n sdm_util.print_message(\"\")\n sdm_util.print_message(desc)\n return 0\n elif \"show_mounts\" in argv:\n karr, _, desc = COMMANDS_TABLE[\"show_mounts\"]\n sdm_util.print_message(\"command : %s\" % (\" | \".join(karr)))\n sdm_util.print_message(\"usage : sdm ps\")\n sdm_util.print_message(\"\")\n sdm_util.print_message(desc)\n return 0\n elif \"mount\" in argv:\n karr, _, desc = COMMANDS_TABLE[\"mount\"]\n sdm_util.print_message(\"command : %s\" % (\" | \".join(karr)))\n sdm_util.print_message(\"usage : sdm mount <dataset_name> [<mount_path>]\")\n sdm_util.print_message(\"\")\n sdm_util.print_message(desc)\n return 0\n elif \"mmount\" in argv:\n karr, _, desc = COMMANDS_TABLE[\"mmount\"]\n sdm_util.print_message(\"command : %s\" % (\" | \".join(karr)))\n sdm_util.print_message(\"usage : sdm mmount <dataset_name> [<dataset_name> ...]\")\n sdm_util.print_message(\"\")\n sdm_util.print_message(desc)\n return 0\n elif \"unmount\" in argv:\n karr, _, desc = COMMANDS_TABLE[\"unmount\"]\n sdm_util.print_message(\"command : %s\" % (\" | \".join(karr)))\n sdm_util.print_message(\"usage : sdm unmount <mount_id> [<cleanup_flag>]\")\n sdm_util.print_message(\"\")\n sdm_util.print_message(desc)\n return 0\n elif \"munmount\" in argv:\n karr, _, desc = COMMANDS_TABLE[\"munmount\"]\n sdm_util.print_message(\"command : %s\" % (\" | \".join(karr)))\n sdm_util.print_message(\"usage : sdm munmount <mount_id> [<mount_id> ...]\")\n sdm_util.print_message(\"\")\n sdm_util.print_message(desc)\n return 0\n elif \"clean\" in argv:\n karr, _, desc = COMMANDS_TABLE[\"clean\"]\n sdm_util.print_message(\"command : %s\" % (\" | \".join(karr)))\n sdm_util.print_message(\"usage : sdm clean\")\n sdm_util.print_message(\"\")\n sdm_util.print_message(desc)\n return 0\n else:\n sdm_util.print_message(\"Unrecognized command\")\n return 1\n else:\n sdm_util.print_message(\"command : sdm <COMMAND> [<COMMAND_SPECIFIC_ARGS> ...]\")\n sdm_util.print_message(\"\")\n sdm_util.print_message(\"Available Commands\")\n\n tbl = PrettyTable()\n tbl.field_names = [\"COMMAND\", \"DESCRIPTION\"]\n for cmd in COMMANDS:\n command, _, desc = cmd\n command_str = \" | \".join(command)\n tbl.add_row([command_str, desc])\n\n sdm_util.print_message(tbl)\n sdm_util.print_message(\"\")\n return 0","def printMCLStatus(self):\n print()\n print(\"====================\")\n print(\"Cycle\", self.countCycles)\n self._printHist()","def get_hmcs(self):\n url = '%s/ibm-hmcs' % self.catalog['compute']\n res = self.get(url)\n if res['status'] == 200:\n return json.loads(res['body'])['hmcs']\n else:\n LOG.error('Get HMCs failed: %s %s %s' %\n (res['status'], res['reason'], res['body']))\n raise InvalidResponse(res)","def qc_metrics(self, files_in, qc_files):\n self.cmd(\"{samtools} index {bam_in}\"\n .format(\n samtools=self.cmds[\"samtools\"],\n bam_in=files_in[0],\n ),\n shell=True)\n self.cmd(\"{samtools} idxstats {bam_in} | tee {qc_file}\"\n .format(\n samtools=self.cmds[\"samtools\"],\n bam_in = files_in[0],\n qc_file = qc_files[0],\n ),\n shell=True,\n log_output=True)\n self.cmd(\"{samtools} flagstat {bam_in} | tee {qc_file}\"\n .format(\n samtools=self.cmds[\"samtools\"],\n bam_in = files_in[0],\n qc_file = qc_files[1],\n ),\n shell=True,\n log_output=True)\n \n self.checkpoint(qc_files[0])\n self.checkpoint(qc_files[1])\n self.checkpoint(qc_files[2])","def main():\n configuration = {'resource-folder': 'resources',\n 'build-folder': 'build',\n 'log-folder': 'logfiles',\n 'use-preloaded': False,\n 'addi-metrics': 'addi-metrics.json',\n 'jenkins': {'dependency-filename': 'dependencies.txt',\n 'server': 'http://is.dbc.dk',\n 'repository-project': 'opensearch-3rd-party-dependencies'},\n 'log-zip-file':'logs.zip'}\n configuration.update(cli())\n setup_logger(configuration['verbose'])\n run_performance_test(configuration)","def cli():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"tic\", help=\"TIC number\")\n parser.add_argument(\"-L\", \"--LIST\", help=\"Only fit the LC\", action=\"store_true\")\n parser.add_argument(\"-S\", \"--SAVEGAIA\", help=\"Save Gaia sources\", action=\"store_true\")\n parser.add_argument(\"-C\", \"--COORD\", help=\"Use coordinates\", default=False)\n parser.add_argument(\"-n\", \"--name\", help=\"Target name to be plotted in title\", default=False)\n parser.add_argument(\"-D2\", \"--DR2\", help=\"Use Gaia DR2 catalog instead of DR3\", action=\"store_true\")\n parser.add_argument(\"-PM\", \"--PM\", help=\"Add proper motion direction arrows in the plot\", action=\"store_true\")\n parser.add_argument(\"--maglim\", default=5., help=\"Maximum magnitude contrast respect to TIC\")\n parser.add_argument(\"--sector\", default=None, help=\"Select Sector if more than one\")\n parser.add_argument(\"--gid\", default=None, help=\"Gaia ID\")\n parser.add_argument(\"--gmag\", default=None, help=\"Gaia mag\")\n parser.add_argument(\"--sradius\", default=10., type=float, help=\"Search radius (in arcsec) for the get_gaia_data function\")\n parser.add_argument(\"--legend\", default='best', help=\"Legend location\")\n args = parser.parse_args()\n return args","def cli(*args, **kwargs):\n logger.debug('Global options: %s %s', args, kwargs)","def main():\n logging.basicConfig(level=logging.INFO, format=\"%(message)s\")\n _makeEnvir()\n try:\n opts, args = getopt.getopt(sys.argv[1:], \"hv:f:r:\", [\"help\", \"vista=\", \"fmqlep=\", \"report=\", \"host=\", \"port=\", \"access=\", \"verify=\"])\n except getopt.GetoptError, err:\n print str(err)\n print __doc__\n sys.exit(2)\n # Default VistA to Caregraf Demo VistA\n vista = \"CGVISTA\"\n # Will match to CGVISTA's if no other VistA name is specified\n fmqlEP = \"\"\n host = \"\"\n port = -1\n access = \"\"\n verify = \"\"\n report = \"\"\n for o, a in opts:\n if o in [\"-v\", \"--vista\"]:\n vista = a\n elif o in [\"-f\", \"--fmqlep\"]:\n fmqlEP = a\n elif o in [\"--host\"]:\n host = a\n elif o in [\"--port\"]:\n port = a \n elif o in [\"--access\"]:\n access = a\n elif o in [\"--verify\"]:\n verify = a\n elif o in [\"-r\", \"--report\"]:\n report = a\n elif o in [\"-h\", \"--help\"]:\n print __doc__\n sys.exit()\n if not report:\n sys.exit()\n if vista == \"CGVISTA\":\n print \"Defaulting to Caregraf's demo VistA, 'CGVISTA'\"\n fmqlEP = \"http://vista.caregraf.org/fmqlEP\"\n print \"VDM - comparing %s against GOLD\" % vista\n goldCacher = FMQLCacher(\"Caches\")\n goldCacher.setVista(\"GOLD\")\n otherCacher = FMQLCacher(\"Caches\")\n otherCacher.setVista(vista, fmqlEP=fmqlEP, host=host, port=int(port), access=access, verify=verify)\n _runReport(report, goldCacher, otherCacher)","def process_cl_args():\n\n parser = argparse.ArgumentParser(add_help=False)\n parser.add_argument('commands', nargs='*')\n parser.add_argument('--help', '-h', action='store_true')\n parser.add_argument('--version', '-v', action='store_true')\n parser.add_argument('--debug', '-d', action='store_true')\n parser.add_argument('--logging', '-l', action='store_true')\n parser.add_argument('--no-autosize', action='store_true')\n parser.add_argument('--no-preload', action='store_true')\n args = parser.parse_args()\n\n if args.version:\n xprint(get_version_info())\n xprint(\"\")\n sys.exit()\n\n elif args.help:\n for x in helptext():\n xprint(x[2])\n sys.exit()\n\n if args.debug or os.environ.get(\"mpsytdebug\") == \"1\":\n xprint(get_version_info())\n g.debug_mode = True\n g.no_clear_screen = True\n logfile = os.path.join(tempfile.gettempdir(), \"mpsyt.log\")\n logging.basicConfig(level=logging.DEBUG, filename=logfile)\n logging.getLogger(\"pafy\").setLevel(logging.DEBUG)\n\n elif args.logging or os.environ.get(\"mpsytlog\") == \"1\":\n logfile = os.path.join(tempfile.gettempdir(), \"mpsyt.log\")\n logging.basicConfig(level=logging.DEBUG, filename=logfile)\n logging.getLogger(\"pafy\").setLevel(logging.DEBUG)\n\n if args.no_autosize:\n g.detectable_size = False\n\n g.command_line = \"playurl\" in args.commands or \"dlurl\" in args.commands\n if g.command_line:\n g.no_clear_screen = True\n\n if args.no_preload:\n g.preload_disabled = True\n\n g.argument_commands = args.commands","def get_cp_info(self):\n return self.get(COMMAND_CPM, 'GetCpInfo')","def help(self):\r\n self._short_help(None, None, None, None)","def test_cw_metrics(self):\n\n instances = set()\n result = self.cw_client.list_metrics(Namespace=\"CWAgent\", MetricName=\"cpu_usage_system\")\n for i in result[\"Metrics\"]:\n instances.add(i[\"Dimensions\"][0][\"Value\"])\n\n for key, value in self.cdk_output_map.items():\n if \"Instance\" in key:\n self.assertTrue(value in instances)","def help():\n description = \"\"\"\n Get the observed and expected enrichment trend plot based on contact matrix.\n Example:\n getSigEnrich.py -d GM12878_Trac -o GM12878_Trac -cut 0 -p 10 \n \"\"\"\n parser = argparse.ArgumentParser(description=description,\n formatter_class=RawTextHelpFormatter)\n parser.add_argument(\"-d\",\n dest=\"dir\",\n required=True,\n type=str,\n help=\"Directory for cLoops2 pre generated.\")\n parser.add_argument(\"-o\",\n dest=\"output\",\n required=True,\n type=str,\n help=\"Output prefix.\")\n parser.add_argument(\n \"-c\",\n dest=\"chroms\",\n required=False,\n default=\"\",\n type=str,\n help=\n \"Whether to process limited chroms, specify it as chr1,chr2,chr3, default is not. Use this to save time for quite big data.\"\n )\n parser.add_argument(\n \"-bs\",\n dest=\"binSize\",\n required=False,\n default=5000,\n type=int,\n help=\n \"Bin size (bp) to generate the contact matrix for estimation, default is 5000 bp.\"\n )\n parser.add_argument(\n \"-cut\",\n dest=\"cut\",\n type=int,\n default=0,\n help=\"Distance cutoff for PETs to filter, default is 0.\")\n parser.add_argument('-p',\n dest=\"cpu\",\n required=False,\n default=1,\n type=int,\n help=\"Number of CPUs to run the job, default is 1.\")\n parser.add_argument(\n '-r',\n dest=\"repeats\",\n required=False,\n default=10,\n type=int,\n help=\n \"The reapet times to shuffle PETs to get the mean expected background,default is 10.\"\n )\n parser.add_argument('-plot',\n dest=\"plot\",\n required=False,\n action=\"store_true\",\n help=\"Set to plot the result.\")\n op = parser.parse_args()\n return op","def metrics(_):\r\n collector = BuildsCollector()\r\n build_metrics, headers = collector.get_metrics_table()\r\n print(tabulate(build_metrics, headers=headers))","def HelpCommand(self, unused_args, unused_sub_opts=None, unused_headers=None,\n unused_debug=None):\n self.OutputUsageAndExit()","async def sysinfo(self, ctx: Context):\n\t\tstart = time.perf_counter()\n\t\tend = time.perf_counter()\n\t\tduration = (end - start) * 1000\n\t\tcpuavg = psutil.cpu_percent(interval=None)\n\t\tmem = psutil.virtual_memory()[2]\n\t\tdurround = round(duration, 3)\n\t\tosun = os.uname()\n\t\tawait self.send(f\"System Info | CPU: {cpuavg}% | RAM: {mem}% | Latency: {durround * 1000}ms | OS: {sys.platform}\", whisper=[ctx.author.id])","def diagnostics(self,\n *opts, # type: DiagnosticsOptions\n **kwargs # type: Dict[str, Any]\n ) -> DiagnosticsResult:\n\n return super().diagnostics(*opts, **kwargs)","def get_help(self) -> None: \n print(messages.get_help())","def optionHelp(self):\n return {}","def help():","def metrics_adapter(cmd_ctx, cpc, adapter, **options):\n cmd_ctx.execute_cmd(\n lambda: cmd_metrics_adapter(cmd_ctx, cpc, adapter, options))"],"string":"[\n \"def metrics_cpc(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_cpc(cmd_ctx, cpc, options))\",\n \"def metrics_channel(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_channel(cmd_ctx, cpc, options))\",\n \"def metrics_proc(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_proc(cmd_ctx, cpc, options))\",\n \"def metrics_crypto(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_crypto(cmd_ctx, cpc, options))\",\n \"def print_help():\\n print \\\"\\\"\\\"\\nMeasure Operating System Performance (mosp)\\n-------------------------------------------\\n\\nUse this program to measure and report on operating system\\nperformance.\\n\\nThis code measures operating system performance,\\nincluding CPU, memory, disk and network, and\\noutputs stats to screen and optionally to file\\ntoo for use in performance analysis\\n\\nUses the psutil library\\n\\nInstall psutil (Ubuntu) if you don't already have it:\\n sudo apt-get install python-dev\\n sudo pip install psutil\\n\\nUsage:\\n python mosp.py [options]\\n\\nExample usage:\\n python mosp.py -W -i 2\\n\\nOptions:\\n -h --help Display this help and exit\\n -m --max-run-time Maximum time to run for before exiting\\n (default is infinite)\\n -i --interval Interval between requests in seconds\\n (default is 1)\\n -w --output-file Specify an output filename\\n -W Output results to default filename\\n default format is:\\n mosp-HOSTNAME-YYYYMMDD-HHMMSS.csv\\n -b --output-path Specify path to output file directory\\n -j --no-header-row Suppress writing header row into CSV\\n -v --version Output version information and exit\\n\\n \\\"\\\"\\\"\\n return()\",\n \"def metrics_flash(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_flash(cmd_ctx, cpc, options))\",\n \"def print_help(self):\\r\\n\\t\\ttext = \\\"\\\\tName: ml_scikit_OPTICS\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\tThis machine learning plugin uses scikit-learn's OPTICS algorithm.\\\\n\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\tOptional Parameters:\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\t\\\\tOPTICS_skip_normalization: Do NOT perform normalization (scaling) of data, skip this step.\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\t\\\\OPTICS_eps: Specify eps parameter (default is 1.0).\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\t\\\\OPTICS_min_samples: Specify min_samples parameter (default is 5).\\\"\\r\\n#\\r\\n# OPTICS (with memory complexity n) is an alternative to DBSCAN (with memory complexity n^2)\\r\\n# which has time complexity n^2 in general with the default max_eps = np.inf. \\r\\n# We will set max_eps = eps to reduce the run-time.\\r\\n#\\r\\n\\t\\treturn text\",\n \"def phast_cmmd(self):\\n temp = '{prog} -R {rho} -C {ecov} -E {elen} -N {chrom} -i MAF {maf} {model} > {wig}\\\\n'.format(**self.dict)\\n return temp.format(fnum=self.fnum)\",\n \"def help_opt(self):\\n print(OPTIONS)\",\n \"def treatCmdOpts(argv):\\n baseName = os.path.basename(__file__)\\n amc.cBaseName = colored(baseName, 'yellow')\\n\\n helpTxt = amc.cBaseName + ' analyses observation statistics file for selected GNSSs'\\n\\n # create the parser for command line arguments\\n parser = argparse.ArgumentParser(description=helpTxt)\\n\\n parser.add_argument('--obsstat', help='observation statistics file', type=str, required=True)\\n\\n parser.add_argument('--freqs', help='select frequencies to use (out of {freqs:s}, default {freq:s})'.format(freqs='|'.join(gfzc.lst_freqs), freq=colored(gfzc.lst_freqs[0], 'green')), default=gfzc.lst_freqs[0], type=str, required=False, action=gco.freqtype_action, nargs='+')\\n\\n parser.add_argument('--cutoff', help='cutoff angle in degrees (default {mask:s})'.format(mask=colored('0', 'green')), default=0, type=int, required=False, action=gco.cutoff_action)\\n\\n parser.add_argument('--dbcvs', help='Add information to CVS database (default {cvsdb:s})'.format(cvsdb=colored(gco.CVSDB_OBSTLE, 'green')), required=False, type=str, default=gco.CVSDB_OBSTLE)\\n\\n parser.add_argument('--plot', help='displays interactive plots (default False)', action='store_true', required=False, default=False)\\n\\n parser.add_argument('--logging', help='specify logging level console/file (two of {choices:s}, default {choice:s})'.format(choices='|'.join(gco.lst_logging_choices), choice=colored(' '.join(gco.lst_logging_choices[3:5]), 'green')), nargs=2, required=False, default=gco.lst_logging_choices[3:5], action=gco.logging_action)\\n\\n # drop argv[0]\\n args = parser.parse_args(argv[1:])\\n\\n # return arguments\\n return args.obsstat, args.freqs, args.cutoff, args.dbcvs, args.plot, args.logging\",\n \"async def stats(self, ctx):\\n if ctx.invoked_subcommand is None:\\n await send_cmd_help(ctx)\",\n \"def commandline_options():\\n parser = argparse.ArgumentParser(\\n description='ocn_diags_generator: CESM wrapper python program for Ocean Diagnostics packages.')\\n\\n parser.add_argument('--backtrace', action='store_true',\\n help='show exception backtraces as extra debugging '\\n 'output')\\n\\n parser.add_argument('--debug', action='store_true',\\n help='extra debugging output')\\n\\n #parser.add_argument('--config', nargs=1, required=True, help='path to config file')\\n\\n options = parser.parse_args()\\n return options\",\n \"def mc(self, *args) -> None:\\n env = os.environ.copy()\\n env['MC_HOST_minio'] = self.auth_url\\n # --config-dir is set just to prevent any config set by the user\\n # from interfering with the test.\\n try:\\n subprocess.run(\\n [\\n 'mc', '--quiet', '--no-color', f'--config-dir={self.path}',\\n *args\\n ],\\n stdout=subprocess.DEVNULL,\\n stderr=subprocess.PIPE,\\n env=env,\\n encoding='utf-8',\\n errors='replace',\\n check=True\\n )\\n except OSError as exc:\\n raise MissingProgram(f'mc could not be run: {exc}') from exc\\n except subprocess.CalledProcessError as exc:\\n raise ProgramFailed(exc.stderr) from exc\",\n \"def main( argv = None ):\\n\\n if argv == None: argv = sys.argv\\n\\n # setup command line parser\\n parser = E.OptionParser( version = \\\"%prog version: $Id$\\\", \\n usage = globals()[\\\"__doc__\\\"] )\\n\\n parser.add_option(\\\"--category\\\", dest=\\\"category\\\", type=\\\"choice\\\",\\n choices = (\\\"B\\\", \\\"C\\\"), help=\\\"supply help\\\" )\\n\\n ## add common options (-h/--help, ...) and parse command line \\n (options, args) = E.Start( parser, argv = argv )\\n\\n data = getData(options.stdin)\\n if options.category == \\\"B\\\":\\n options.stdout.write(\\\"Category B pathway\\\\tKO\\\\tGenes\\\\tDescriptions\\\\n\\\")\\n for pathway, descriptions in b2ko(data).iteritems():\\n options.stdout.write(\\\"\\\\t\\\".join([pathway, \\\"; \\\".join(descriptions[0]), \\\"; \\\".join(descriptions[1]), \\\"; \\\".join(descriptions[2])]) + \\\"\\\\n\\\")\\n\\n elif options.category == \\\"C\\\":\\n options.stdout.write(\\\"Category C pathway\\\\tKO\\\\tGenes\\\\tDescriptions\\\\n\\\")\\n for pathway, descriptions in c2ko(data).iteritems():\\n options.stdout.write(\\\"\\\\t\\\".join([pathway, \\\"; \\\".join(descriptions[0]), \\\"; \\\".join(descriptions[1]), \\\"; \\\".join(descriptions[2])]) + \\\"\\\\n\\\")\\n else:\\n raise ValueError(\\\"must specify the category of pathway\\\")\\n\\n\\n ## write footer and output benchmark information.\\n E.Stop()\",\n \"def measure(self,command_exe, command_args, measure_out):\\n pass\",\n \"def main():\\n test_cases = ast.literal_eval(sys.argv[1])\\n results = str(my_info()) + '\\\\t\\\\t'\\n for test_case in test_cases:\\n mode = test_case[0]\\n id_1 = int(test_case[1])\\n id_2 = int(test_case[2])\\n if mode == 'jc':\\n results += str(Jaccard_Coefficient(id_1, id_2)) + '\\\\t\\\\t'\\n elif mode == 'cc':\\n results += str(Correlation_Coefficient(id_1, id_2)) + '\\\\t\\\\t'\\n else:\\n exit('bad command')\\n print results + '\\\\n'\",\n \"def do_hostinfo(self, args):\\n host = opts = None\\n if args:\\n args = args.split()\\n host = args.pop()\\n\\n if not host:\\n print('Usage: hostinfo [-cdmu] host_name_or_ip')\\n print(' uptime and load stats returned if no options specified')\\n return\\n\\n try:\\n ip = socket.gethostbyname(host)\\n except socket.gaierror:\\n print('cannot resolve', host, file=sys.stderr)\\n return\\n\\n opts = []\\n while args:\\n arg = args.pop(0)\\n if arg.startswith('--'):\\n if arg == '--cpu':\\n opts.append('c')\\n elif arg == '--disk':\\n opts.append('d')\\n elif arg == '--memory':\\n opts.append('m')\\n elif arg == '--uptime':\\n opts.append('u')\\n else:\\n print('unrecognized option:', arg, file=sys.stderr)\\n return\\n else:\\n if arg[0] == '-':\\n for ch in arg[1:]:\\n if ch in ('cdmu') and ch not in opts:\\n opts.append(ch)\\n else:\\n print('unrecognized option:', ch, file=sys.stderr)\\n return\\n\\n stats = self._qm.get_host_stats(ip)\\n\\n if not opts:\\n # Get uptime and load averages.\\n up = stats['uptime']\\n load = stats['cpu_load']\\n print('Up for %s days, %s hours, %s minutes, '\\n 'load averages: %s, %s, %s'\\n % (up['days'], up['hours'], up['minutes'], load['one'],\\n load['five'], load['fifteen']))\\n return\\n\\n all_stats = []\\n for opt in opts:\\n if opt == 'd':\\n # Get disk usage.\\n disks = stats['disk_usage']\\n st = ['Disk Usage:']\\n for mount, disk_info in disks.viewitems():\\n st.append(' Usage for: %s' % mount)\\n for k, v in disk_info.viewitems():\\n st.append(' %s: %s' % (k, v))\\n all_stats.append('\\\\n'.join(st))\\n all_stats.append('')\\n elif opt == 'c':\\n # Get CPU load.\\n load_stats = stats['cpu_load']\\n st = ['CPU Load Average:']\\n st.append(' last one minute: %s' % load_stats['one'])\\n st.append(' last five minutes: %s' % load_stats['five'])\\n st.append(' last fifteen minutes: %s' % load_stats['fifteen'])\\n all_stats.append('\\\\n'.join(st))\\n all_stats.append('')\\n elif opt == 'm':\\n # Get Memory Usage.\\n memory_usage = stats['memory_usage']\\n st = ['Memory usage:']\\n for k, v in memory_usage.viewitems():\\n st.append(' %s: %s' % (k, v))\\n all_stats.append('\\\\n'.join(st))\\n all_stats.append('')\\n elif opt == 'u':\\n # Get uptime.\\n up = stats['uptime']\\n st = ['Uptime:']\\n st.append(' Up for %s days, %s hours and %s minutes'\\n % (up['days'], up['hours'], up['minutes']))\\n all_stats.append('\\\\n'.join(st))\\n all_stats.append('')\\n\\n print('\\\\n'.join(all_stats))\",\n \"def report_on_config( args ):\\n\\n from khmer.utils import print_error\\n\\n if args.quiet: return\\n\\n print_error( \\\"\\\\nPARAMETERS:\\\" )\\n print_error( \\\" - kmer size = {0} \\\\t\\\\t(-k)\\\".format( args.ksize ) )\\n print_error( \\\" - n hashes = {0} \\\\t\\\\t(-N)\\\".format( args.n_hashes ) )\\n print_error(\\n \\\" - min hashsize = {0:5.2g} \\\\t(-x)\\\".format( args.min_hashsize )\\n )\\n print_error( \\\"\\\" )\\n print_error(\\n \\\"Estimated memory usage is {0:.2g} bytes \\\"\\n \\\"(n_hashes x min_hashsize)\\\".format( args.n_hashes * args.min_hashsize )\\n )\\n print_error( \\\"-\\\" * 8 )\\n\\n if DEFAULT_MIN_HASHSIZE == args.min_hashsize:\\n print_error(\\n \\\"** WARNING: hashsize is default! \\\" \\n \\\"You absodefly want to increase this!\\\\n** \\\"\\n \\\"Please read the docs!\\\"\\n )\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description=main.__doc__,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n epilog=\\\"Homepage: https://github.com/kbat/mc-tools\\\")\\n parser.add_argument('com', type=str, help='plot requests file name', nargs='?', default=\\\"/tmp/foo.c\\\")\\n parser.add_argument('comout', type=str, help='COMOUT file name', nargs='?', default=\\\"zoom\\\")\\n\\n args = parser.parse_args()\\n\\n cmd = {} # dictionary of commands\\n\\n bas = False\\n plane = False\\n \\n with open(args.com) as f:\\n for line in f.readlines():\\n words = line.strip().split()\\n if len(words) is 0:\\n continue\\n\\n for i,w in enumerate(words):\\n if re.search(\\\"^bas\\\", w):\\n cmd['bas'] = list(map(float, words[i+1:i+7]))\\n if plane is False: bas = True # basis was before plane cuts\\n elif re.search(\\\"^or\\\", w):\\n cmd['or'] = list(map(float, words[i+1:i+4]))\\n elif re.search(\\\"^ex\\\", w):\\n try: # both x and y scales are given\\n cmd['ex'] = list(map(float, words[i+1:i+3]))\\n continue\\n except ValueError: # just 1 scale is given\\n cmd['ex'] = list(map(float, words[i+1:i+2]))\\n elif re.search(\\\"^lab\\\", w):\\n cmd['label'] = list(map(int, map(float, words[i+1:i+3]))) #+ [words[i+3]]\\n elif re.search(\\\"^p[xyz]\\\", w):\\n cmd[w] = [float(words[i+1])]\\n if bas is False: plane = True # plane cuts were before basis\\n elif re.search(\\\"^legend\\\", w):\\n cmd[w] = [words[i+1]]\\n elif w == \\\"scale\\\":\\n print(w)\\n if int(words[i+1]): # no need to put 'scale 0'\\n cmd[w] = [words[i+1]]\\n elif w in (\\\"mesh\\\"):\\n if int(words[i+1])==1: # no need to put 'mesh 1'\\n cmd[w] = [words[i+1]]\\n\\n print(bas, plane)\\n\\n if plane: # bas was first\\n keys = ('bas', 'or', 'ex', 'px', 'py', 'pz', 'label', 'mesh', 'legend', 'scale')\\n elif bas:\\n keys = ('or', 'ex', 'px', 'py', 'pz', 'bas', 'label', 'mesh', 'legend', 'scale')\\n else:\\n keys = {'or', 'ex', 'label', 'mesh', 'legend', 'scale'}\\n \\n with open(args.comout, 'w') as f:\\n for key in keys:\\n if key in cmd:\\n # newline required by mcplot:\\n if key in ('mesh', 'legend', 'scale', 'label'):\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"%s %s \\\" % (key,\\\" \\\".join(str(e) for e in cmd[key]),))\\n f.write(\\\"\\\\n\\\")\",\n \"def metrics_roce(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_roce(cmd_ctx, cpc, options))\",\n \"def options():\\n print \\\"\\\"\\\"Options summary:\\n -h, --help\\n -u, --usage\\n -v, --verbose <verb_level>\\n -e, --endpoint <endpoint>\\n -i, --interface-type <iface_type>\\n -r, --recursive\\n --dbs-conf <conf_file>\\n --show-prod\\n --show-caf\\n --only-subscribed\\n --only-custodial\\n \\\"\\\"\\\"\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\\n )\\n add_common_snmp_args(parser)\\n parser.add_argument(\\n \\\"-w\\\",\\n \\\"--warning\\\",\\n type=int,\\n default=70,\\n help=\\\"Warning memory usage percentage (0-100)\\\",\\n )\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--critical\\\",\\n type=int,\\n default=90,\\n help=\\\"Critical memory usage percentage (0-100)\\\",\\n )\\n parser.add_argument(\\n \\\"-f\\\",\\n \\\"--family\\\",\\n required=True,\\n help=\\\"Switch family. Supported families: 1910, 1920, 1920S\\\",\\n )\\n\\n config = vars(parser.parse_args())\\n check_snmp_args(config)\\n check_thresholds(config)\\n\\n dataset = {}\\n\\n if config[\\\"family\\\"] == \\\"1920S\\\":\\n cpu = ObjectType(\\n ObjectIdentity(\\n \\\"HP-SWITCHING-MIB\\\", \\\"agentSwitchCpuProcessTotalUtilization\\\", 0\\n )\\n )\\n elif config[\\\"family\\\"] in [\\\"1910\\\", \\\"1920\\\"]:\\n cpu = ObjectType(\\n ObjectIdentity(\\\"HH3C-ENTITY-EXT-MIB\\\", \\\"hh3cEntityExtCpuUsage\\\", 8)\\n )\\n else:\\n unknown_exit(SERVICE, f\\\"Switch family {config['family']} NOT known\\\")\\n\\n try:\\n raw_data = get_snmp_data(config, cpu)\\n except ValueError as err:\\n unknown_exit(SERVICE, err)\\n add_vars_to_dataset(dataset, raw_data)\\n\\n if config[\\\"family\\\"] == \\\"1920S\\\":\\n dataset[\\\"cpu_usage\\\"] = get_hp_cpu_usage(\\n dataset[\\\"agentSwitchCpuProcessTotalUtilization\\\"]\\n )\\n elif config[\\\"family\\\"] in [\\\"1910\\\", \\\"1920\\\"]:\\n dataset[\\\"cpu_usage\\\"] = int(dataset[\\\"hh3cEntityExtCpuUsage\\\"])\\n else:\\n unknown_exit(SERVICE, f\\\"Switch family {config['family']} NOT known\\\")\\n\\n state, message = generate_output(config, dataset)\\n report(state, message)\",\n \"def help(self, args):\\n print('No commands available for this consumer')\",\n \"def help(self):\\n msg = \\\"`%s' performs the computational aspects of genotyping-by-sequencing.\\\\n\\\" % os.path.basename(sys.argv[0])\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Usage: %s [OPTIONS] ...\\\\n\\\" % os.path.basename(sys.argv[0])\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Options:\\\\n\\\"\\n msg += \\\" -h, --help\\\\tdisplay the help and exit\\\\n\\\"\\n msg += \\\" -V, --version\\\\toutput version information and exit\\\\n\\\"\\n msg += \\\" -v, --verbose\\\\tverbosity level (0/default=1/2/3)\\\\n\\\"\\n msg += \\\" --proj1\\\\tname of the project used for steps 1 to 4\\\\n\\\"\\n msg += \\\"\\\\t\\\\tmention a reference genome only if all samples belong to\\\\n\\\"\\n msg += \\\"\\\\t\\\\t the same species, and will be mapped to the same ref genome\\\\n\\\"\\n msg += \\\" --proj2\\\\tname of the project used for steps 4 to 8\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcan be the same as --proj1, or can be different\\\\n\\\"\\n msg +=\\\"\\\\t\\\\t notably when samples come from different species\\\\n\\\"\\n msg += \\\"\\\\t\\\\t or if one wants to align reads to different ref genomes\\\\n\\\"\\n msg += \\\" --schdlr\\\\tname of the cluster scheduler (default=SGE)\\\\n\\\"\\n msg += \\\" --queue\\\\tname of the cluster queue (default=normal.q)\\\\n\\\"\\n msg += \\\" --resou\\\\tcluster resources (e.g. 'test' for 'qsub -l test')\\\\n\\\"\\n msg += \\\" --rmvb\\\\tremove bash scripts for jobs launched in parallel\\\\n\\\"\\n msg += \\\" --step\\\\tstep to perform (1/2/3/.../9)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t1: raw read quality per lane (with FastQC v >= 0.11.2)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t2: demultiplexing per lane (with demultiplex.py v >= 1.14.0)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t3: cleaning per sample (with CutAdapt v >= 1.8)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t4: alignment per sample (with BWA MEM v >= 0.7.12, Samtools v >= 1.3, Picard and R v >= 3)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t5: local realignment per sample (with GATK v >= 3.5)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t6: local realignment per genotype (with GATK v >= 3.5)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t7: variant and genotype calling per genotype (with GATK HaplotypeCaller v >= 3.5)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t8: variant and genotype calling jointly across genotypes (with GATK GenotypeGVCFs v >= 3.5)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t9: variant and genotype filtering (with GATK v >= 3.5)\\\\n\\\"\\n msg += \\\" --samples\\\\tpath to the 'samples' file\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for all steps, but can differ between steps\\\\n\\\"\\n msg += \\\"\\\\t\\\\t e.g. if samples come from different species or are aligned\\\\n\\\"\\n msg += \\\"\\\\t\\\\t on different ref genomes, different samples file should\\\\n\\\"\\n msg += \\\"\\\\t\\\\t be used for steps 4-9, representing different subsets of\\\\n\\\"\\n msg += \\\"\\\\t\\\\t the file used for steps 1-3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tthe file should be encoded in ASCII\\\\n\\\"\\n msg += \\\"\\\\t\\\\tthe first row should be a header with column names\\\\n\\\"\\n msg += \\\"\\\\t\\\\teach 'sample' (see details below) should have one and only one row\\\\n\\\"\\n msg += \\\"\\\\t\\\\tany two columns should be separated with one tabulation\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcolumns can be in any order\\\\n\\\"\\n msg += \\\"\\\\t\\\\trows starting by '#' are skipped\\\\n\\\"\\n msg += \\\"\\\\t\\\\t12 columns are compulsory (but there can be more):\\\\n\\\"\\n msg += \\\"\\\\t\\\\t genotype (see details below, e.g. 'Col-0', but use neither underscore '_' nor space ' ' nor dot '.', use dash '-' instead)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t ref_genome (identifier of the reference genome used for alignment, e.g. 'Atha_v2', but use neither space ' ' nor dot '.'; the full species name, e.g. 'Arabidopsis thaliana', will be present in the file given to --dict)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t library (e.g. can be the same as 'genotype')\\\\n\\\"\\n msg += \\\"\\\\t\\\\t barcode (e.g. 'ATGG')\\\\n\\\"\\n msg += \\\"\\\\t\\\\t seq_center (e.g. 'Broad Institute', 'GenoToul', etc)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t seq_platform (e.g. 'ILLUMINA', see SAM format specification)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t seq_platform_model (e.g. 'HiSeq 2000')\\\\n\\\"\\n msg += \\\"\\\\t\\\\t flowcell (e.g. 'C5YMDACXX')\\\\n\\\"\\n msg += \\\"\\\\t\\\\t lane (e.g. '3', can be '31' if a first demultiplexing was done per index)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t date (e.g. '2015-01-15', see SAM format specification)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t fastq_file_R1 (filename, one per lane, gzip-compressed)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t fastq_file_R2 (filename, one per lane, gzip-compressed)\\\\n\\\"\\n msg += \\\" --fcln\\\\tidentifier of a flowcell and lane number\\\\n\\\"\\n msg += \\\"\\\\t\\\\tformat as <flowcell>_<lane-number>, e.g. 'C5YMDACXX_1'\\\\n\\\"\\n msg += \\\"\\\\t\\\\tif set, only the samples from this lane will be analyzed\\\\n\\\"\\n msg += \\\" --pird\\\\tpath to the input reads directory\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for steps 1 and 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\twill be added to the columns 'fastq_file_R*' from the sample file\\\\n\\\"\\n msg += \\\"\\\\t\\\\tif not set, input read files should be in current directory\\\\n\\\"\\n msg += \\\" --enz\\\\tname of the restriction enzyme\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=ApeKI\\\\n\\\"\\n msg += \\\" --dmxmet\\\\tmethod used to demultiplex\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=4c (see the help of demultiplex.py to know more)\\\\n\\\"\\n msg += \\\" --subst\\\\tnumber of substitutions allowed during demultiplexing\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=2\\\\n\\\"\\n msg += \\\" --ensubst\\\\tenforce the nb of substitutions allowed\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=lenient/strict\\\\n\\\"\\n msg += \\\" --adp\\\\tpath to the file containing the adapters\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsame format as FastQC: name<tab>sequence\\\\n\\\"\\n msg += \\\"\\\\t\\\\tname: at least 'adpR1' (also 'adpR2' if paired-end)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsequence: from 5' (left) to 3' (right)\\\\n\\\"\\n msg += \\\" --errtol\\\\terror tolerance to find adapters\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=0.2\\\\n\\\"\\n msg += \\\" --minovl\\\\tminimum overlap length between reads and adapters\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=3 (in bases)\\\\n\\\"\\n msg += \\\" --minrl\\\\tminimum length to keep a read\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=35 (in bases)\\\\n\\\"\\n msg += \\\" --minq\\\\tminimum quality to trim a read\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=20 (used for both reads if paired-end)\\\\n\\\"\\n msg += \\\" --maxNp\\\\tmaximum percentage of N to keep a read\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=0.2\\\\n\\\"\\n msg += \\\" --ref\\\\tpath to the prefix of files for the reference genome\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for steps 4, 5, 6, 7, 8, 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tshould correspond to the 'ref_genome' column in --samples\\\\n\\\"\\n msg += \\\"\\\\t\\\\te.g. '/data/Atha_v2' for '/data/Atha_v2.fa', '/data/Atha_v2.bwt', etc\\\\n\\\"\\n msg += \\\"\\\\t\\\\tthese files are produced via 'bwa index ...'\\\\n\\\"\\n msg += \\\" --dict\\\\tpath to the 'dict' file (SAM header with @SQ tags)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 4\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee 'CreateSequenceDictionary' in the Picard software\\\\n\\\"\\n msg += \\\" --jgid\\\\tcohort identifier to use for joint genotyping\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for steps 8, 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tuseful to launch several, different cohorts in parallel\\\\n\\\"\\n msg += \\\" --rat\\\\trestrict alleles to be of a particular allelicity\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=ALL/BIALLELIC/MULTIALLELIC\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee '--restrictAllelesTo' in GATK's SelectVariant\\\\n\\\"\\n msg += \\\" --mdp\\\\tminimum value for DP (read depth; e.g. 10)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee GATK's VariantFiltration\\\\n\\\"\\n msg += \\\" --mgq\\\\tminimum value for GQ (genotype quality; e.g. 20)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee GATK's VariantFiltration\\\\n\\\"\\n msg += \\\" --mnfg\\\\tmaximum number of filtered genotypes to keep a variant\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee '--maxFilteredGenotypes' in GATK's SelectVariants\\\\n\\\"\\n msg += \\\" --mffg\\\\tmaximum fraction of filtered genotypes to keep a variant\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee '--maxFractionFilteredGenotypes' in GATK's SelectVariants\\\\n\\\"\\n msg += \\\" --mnnc\\\\tmaximum number of not-called genotypes to keep a variant\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\" --mfnc\\\\tmaximum fraction of not-called genotypes to keep a variant\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee '--maxNOCALLfraction' in GATK's SelectVariants\\\\n\\\"\\n msg += \\\" --fam\\\\tpath to the file containing pedigree information\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdiscard variants with Mendelian violations (see Semler et al, 2012)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tshould be in the 'fam' format specified by PLINK\\\\n\\\"\\n msg += \\\"\\\\t\\\\tvalidation strictness (GATK '-pedValidationType') is set at 'SILENT'\\\\n\\\"\\n msg += \\\"\\\\t\\\\t allowing some samples to be absent from the pedigree\\\\n\\\"\\n msg += \\\" --mvq\\\\tminimum GQ for each trio member to accept a variant as a Mendelian violation\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9 if '--fam' is specified\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=0\\\\n\\\"\\n msg += \\\" --xlssf\\\\tpath to the file with genotypes to exclude\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9 (can be especially useful if '--fam' is specified)\\\\n\\\"\\n msg += \\\" --tmpd\\\\tpath to a temporary directory on child nodes (default=.)\\\\n\\\"\\n msg += \\\"\\\\t\\\\te.g. it can be /tmp or /scratch\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 4 for 'samtools sort'\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 7 for 'GATK HaplotypeCaller'\\\\n\\\"\\n msg += \\\" --jvmXms\\\\tinitial memory allocated to the Java Virtual Machine\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=512m (can also be specified as 1024k, 1g, etc)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in steps 4, 5, 6, 7 and 8 for Picard and GATK\\\\n\\\"\\n msg += \\\" --jvmXmx\\\\tmaximum memory allocated to the Java Virtual Machine\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=4g\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in steps 4, 5, 6, 7 and 8 for Picard and GATK\\\\n\\\"\\n msg += \\\" --queue2\\\\tname of the second cluster queue (default=bigmem.q)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 4 for Picard to collect insert sizes\\\\n\\\"\\n msg += \\\" --knowni\\\\tpath to a VCF file with known indels (for local realignment)\\\\n\\\"\\n msg += \\\" --known\\\\tpath to a VCF file with known variants (e.g. from dbSNP)\\\\n\\\"\\n msg += \\\" --force\\\\tforce to re-run step(s)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tthis removes without warning the step directory if it exists\\\\n\\\"\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Examples:\\\\n\\\"\\n msg += \\\" %s --step 1 --samples samples.txt\\\\n\\\" % os.path.basename(sys.argv[0])\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Details:\\\\n\\\"\\n msg += \\\"This program aims at genotyping a set of 'genotypes' using data from\\\\n\\\"\\n msg += \\\"a restriction-assisted DNA sequencing (RAD-seq) experiment, also known\\\\n\\\"\\n msg += \\\"as a genotyping-by-sequencing (GBS) experiment.\\\\n\\\"\\n msg += \\\"Here, by 'genotype', we mean the entity which is the focus of the\\\\n\\\"\\n msg += \\\"study. For instance, it can be a plant variety (or a human being), or\\\\n\\\"\\n msg += \\\"the specific clone of a given plant variety (or a specific tumor of a\\\\n\\\"\\n msg += \\\"given human being), etc.\\\\n\\\"\\n msg += \\\"Importantly, note that the content of the 'genotype' column will\\\\n\\\"\\n msg += \\\"be used to set the 'SM' (sample) tag of the 'RG' (read group) header\\\\n\\\"\\n msg += \\\"record type of the SAM format (see http://www.htslib.org/). However,\\\\n\\\"\\n msg += \\\"internal to this program, the term 'sample' corresponds to the unique\\\\n\\\"\\n msg += \\\"quadruplet (genotype,flowcell,lane,barcode) for steps 1 and 2, and to\\\\n\\\"\\n msg += \\\"the unique triplet (genotype,flowcell,lane) for the others.\\\\n\\\"\\n msg += \\\"Jobs are executed in parallel (--schdlr). Their return status is\\\\n\\\"\\n msg += \\\"recorded in a SQLite database which is removed at the end. If a job\\\\n\\\"\\n msg += \\\"fails, the whole script stops with an error.\\\\n\\\"\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Dependencies:\\\\n\\\"\\n msg += \\\"Python >= 2.7; Biopython; pyutilstimflutre >= 0.5\\\\n\\\"\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Report bugs to <timothee.flutre@inra.fr>.\\\"\\n print(msg); sys.stdout.flush()\",\n \"def main(argv):\\n version = \\\"0.1.2\\\"\\n interval = 1\\n max_run_time = 0\\n finished = 0\\n first_time = 1\\n output_file = 0\\n output_file_enabled = 0\\n output_path = 0\\n header_row = 1\\n\\n #*** Get the hostname for use in filenames etc:\\n hostname = socket.gethostname()\\n\\n #*** Start by parsing command line parameters:\\n try:\\n opts, args = getopt.getopt(argv, \\\"hu:m:ni:w:Wb:jv\\\",\\n [\\\"help\\\",\\n \\\"url=\\\",\\n \\\"max-run-time=\\\",\\n \\\"no-keepalive\\\",\\n \\\"interval=\\\",\\n \\\"output-file=\\\",\\n \\\"output-path=\\\",\\n \\\"no-header-row\\\",\\n \\\"version\\\"])\\n except getopt.GetoptError as err:\\n print \\\"mosp: Error with options:\\\", err\\n print_help()\\n sys.exit(2)\\n for opt, arg in opts:\\n if opt in (\\\"-h\\\", \\\"--help\\\"):\\n print_help()\\n sys.exit()\\n elif opt in (\\\"-v\\\", \\\"--version\\\"):\\n print 'mosp.py version', version\\n sys.exit()\\n elif opt in (\\\"-m\\\", \\\"--max-run-time\\\"):\\n max_run_time = float(arg)\\n elif opt in (\\\"-i\\\", \\\"--interval\\\"):\\n interval = float(arg)\\n elif opt in (\\\"-w\\\", \\\"--output-file\\\"):\\n output_file = arg\\n output_file_enabled = 1\\n elif opt == \\\"-W\\\":\\n output_file = \\\"mosp-\\\" + hostname + \\\"-\\\" + \\\\\\n time.strftime(\\\"%Y%m%d-%H%M%S.csv\\\")\\n output_file_enabled = 1\\n elif opt in (\\\"-b\\\", \\\"--output-path\\\"):\\n output_path = arg\\n elif opt in (\\\"-j\\\", \\\"--no-header-row\\\"):\\n header_row = 0\\n\\n print \\\"\\\\nMeasure Operating System Performance (mosp) version\\\", \\\\\\n version\\n\\n #*** Display output filename:\\n if output_file_enabled:\\n if output_path:\\n output_file = os.path.join(output_path, output_file)\\n print \\\"Results filename is\\\", output_file\\n else:\\n print \\\"Not outputing results to file, as option not selected\\\"\\n\\n if not header_row:\\n print \\\"Not writing a header row to CSV\\\"\\n\\n #*** Use this if max_run_time is set:\\n initial_time = time.time()\\n\\n #*** Instantiate classes:\\n cpus = CPUs()\\n swap = Swap()\\n nics = NICs()\\n\\n #*** Start the loop:\\n while not finished:\\n timenow = datetime.datetime.now()\\n timestamp = timenow.strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]\\n start_time = time.time()\\n\\n #*** Update CPU measurements:\\n cpus.update()\\n\\n #*** Update swap measurements:\\n swap.update()\\n\\n #*** Update network measurements:\\n nics.update()\\n\\n #*** Put the stats into a nice string for printing and\\n #*** writing to file:\\n result_csv = str(timestamp) + \\\",\\\" \\\\\\n + cpus.csv() \\\\\\n + swap.csv() \\\\\\n + nics.csv() \\\\\\n + \\\"\\\\n\\\"\\n result_kvp = str(timestamp) + \\\" \\\" \\\\\\n + cpus.kvp() \\\\\\n + swap.kvp() \\\\\\n + nics.kvp()\\n print result_kvp\\n if output_file_enabled:\\n #*** Header row in CSV:\\n if first_time and header_row:\\n #*** Write a header row to CSV:\\n header_csv = \\\"time,\\\" + cpus.csv_header(hostname) + \\\\\\n swap.csv_header(hostname) + \\\\\\n nics.csv_header(hostname) + \\\\\\n \\\"\\\\n\\\"\\n first_time = 0\\n with open(output_file, 'a') as the_file:\\n the_file.write(header_csv)\\n\\n #*** Write a data row to CSV:\\n with open(output_file, 'a') as the_file:\\n the_file.write(result_csv)\\n\\n if max_run_time:\\n if (start_time - initial_time) > max_run_time:\\n break\\n\\n #*** Sleep for interval seconds:\\n time.sleep(interval)\",\n \"def _cmd_metrics(args):\\n if (\\n len(args.cnarrays) > 1\\n and args.segments\\n and len(args.segments) > 1\\n and len(args.cnarrays) != len(args.segments)\\n ):\\n raise ValueError(\\n \\\"Number of coverage/segment filenames given must be \\\"\\n \\\"equal, if more than 1 segment file is given.\\\"\\n )\\n\\n cnarrs = map(read_cna, args.cnarrays)\\n if args.segments:\\n args.segments = map(read_cna, args.segments)\\n table = metrics.do_metrics(cnarrs, args.segments, args.drop_low_coverage)\\n write_dataframe(args.output, table)\",\n \"def define_options(self):\\n\\n from clinica.engine.cmdparser import PIPELINE_CATEGORIES\\n\\n clinica_comp = self._args.add_argument_group(PIPELINE_CATEGORIES['CLINICA_COMPULSORY'])\\n clinica_comp.add_argument(\\\"caps_directory\\\",\\n help='Path to the CAPS directory.')\\n clinica_comp.add_argument(\\\"list_bvalues\\\", type=str,\\n help='String listing all the shells (i.e. the b-values) in the corrected DWI datasets comma separated (e.g, 0,300,700,2200)')\\n # Optional arguments\\n clinica_opt = self._args.add_argument_group(PIPELINE_CATEGORIES['CLINICA_OPTIONAL'])\\n\\n clinica_opt.add_argument(\\\"-wd\\\", \\\"--working_directory\\\",\\n help='Temporary directory to store pipeline intermediate results')\\n clinica_opt.add_argument(\\\"-np\\\", \\\"--n_procs\\\", type=int, default=4,\\n help='Number of cores used to run in parallel')\\n clinica_opt.add_argument(\\\"-tsv\\\", \\\"--subjects_sessions_tsv\\\",\\n help='TSV file containing a list of subjects with their sessions.')\",\n \"def cmd_help(args):\",\n \"def reports_cli():\",\n \"def printOptions(opts,subject_ids,session_ids,task_list, run_list, acq, rec):\\n uname = os.popen('uname -s -n -r').read()\\n print \\\"\\\\n\\\"\\n print \\\"* Pipeline started at \\\"+time.strftime(\\\"%c\\\")+\\\"on \\\"+uname\\n print \\\"* Command line is : \\\\n \\\"+str(sys.argv)+\\\"\\\\n\\\"\\n print \\\"* The source directory is : \\\"+opts.sourceDir\\n print \\\"* The target directory is : \\\"+opts.targetDir+\\\"\\\\n\\\"\\n print \\\"* Data-set Subject ID(s) is/are : \\\"+str(', '.join(subject_ids))+\\\"\\\\n\\\"\\n # print \\\"* PET conditions : \\\"+ ','.join(opts.condiList)+\\\"\\\\n\\\"\\n print \\\"* Sessions : \\\", session_ids, \\\"\\\\n\\\"\\n print \\\"* Tasks : \\\" , task_list , \\\"\\\\n\\\"\\n print \\\"* Runs : \\\" , run_list , \\\"\\\\n\\\"\\n print \\\"* Acquisition : \\\" , acq , \\\"\\\\n\\\"\\n print \\\"* Reconstruction : \\\" , rec , \\\"\\\\n\\\"\",\n \"def getHelp(self):\\r\\n help_str =\\\\\\r\\n \\\"\\\"\\\"##########################################################################################\\r\\n#\\r\\n# Required:\\r\\n#\\r\\n# --query_NAST multi-fasta file containing query sequences in alignment format\\r\\n#\\r\\n# Common opts:\\r\\n#\\r\\n# --db_NAST db in NAST format\\r\\n# --db_FASTA db in fasta format (megablast formatted)\\r\\n#\\r\\n#\\r\\n# -n number of top matching database sequences to compare to (default 15)\\r\\n# -R min divergence ratio default: 1.007\\r\\n# -P min percent identity among matching sequences (default: 90)\\r\\n#\\r\\n# ## parameters to tune ChimeraParentSelector:\\r\\n#\\r\\n# Scoring parameters:\\r\\n# -M match score (default: +5)\\r\\n# -N mismatch penalty (default: -4)\\r\\n# -Q min query coverage by matching database sequence (default: 70)\\r\\n# -T maximum traverses of the multiple alignment (default: 1)\\r\\n\\r\\n#\\r\\n# ## parameters to tune ChimeraPhyloChecker:\\r\\n#\\r\\n#\\r\\n# --windowSize default 50\\r\\n# --windowStep default 5\\r\\n# --minBS minimum bootstrap support for calling chimera (default: 90)\\r\\n# -S percent of SNPs to sample on each side of breakpoint for computing bootstrap support (default: 10)\\r\\n# --num_parents_test number of potential parents to test for chimeras (default: 3)\\r\\n# --MAX_CHIMERA_PARENT_PER_ID Chimera/Parent alignments with perID above this are considered non-chimeras (default 100; turned off)\\r\\n#\\r\\n# ## misc opts\\r\\n#\\r\\n# --printFinalAlignments shows alignment between query sequence and pair of candidate chimera parents\\r\\n# --printCSalignments print ChimeraSlayer alignments in ChimeraSlayer output\\r\\n# --exec_dir chdir to here before running\\r\\n#\\r\\n#########################################################################################\\r\\n \\\"\\\"\\\"\\r\\n return help_str\",\n \"def procs_calculate_axyzc(molecules, n_cores=-1, show_progress=True, scr=None, cmd=XTB_CMD):\\n results = None\\n return results\",\n \"def _get_metrics_options(metrics):\\n metrics_options = []\\n if metrics is None:\\n metrics = []\\n for static_metric in metrics:\\n metrics_options += [\\n \\\"-m\\\",\\n static_metric.metric.mp_metric_name,\\n str(static_metric.value),\\n ]\\n return metrics_options\",\n \"def main():\\n\\n args = parse_args()\\n metric_sender = MetricSender(verbose=args.verbose, debug=args.debug)\\n\\n discovery_key_disk = 'disc.disk'\\n interval = 3\\n pcp_disk_dev_metrics = ['disk.dev.total', 'disk.dev.avactive']\\n item_prototype_macro_disk = '#OSO_DISK'\\n item_prototype_key_tps = 'disc.disk.tps'\\n item_prototype_key_putil = 'disc.disk.putil'\\n\\n disk_metrics = pminfo.get_sampled_data(pcp_disk_dev_metrics, interval, 2)\\n\\n pcp_metrics_divided = {}\\n for metric in pcp_disk_dev_metrics:\\n pcp_metrics_divided[metric] = {k: v for k, v in disk_metrics.items() if metric in k}\\n\\n # do TPS checks; use disk.dev.total\\n filtered_disk_totals = clean_up_metric_dict(pcp_metrics_divided[pcp_disk_dev_metrics[0]],\\n pcp_disk_dev_metrics[0] + '.')\\n\\n # Add dynamic items\\n metric_sender.add_dynamic_metric(discovery_key_disk, item_prototype_macro_disk, filtered_disk_totals.keys())\\n\\n # calculate the TPS and add them to the ZaggSender\\n for disk, totals in filtered_disk_totals.iteritems():\\n disk_tps = (totals[1] - totals[0]) / interval\\n metric_sender.add_metric({'%s[%s]' % (item_prototype_key_tps, disk): disk_tps})\\n\\n # do % Util checks; use disk.dev.avactive\\n filtered_disk_totals = clean_up_metric_dict(pcp_metrics_divided[pcp_disk_dev_metrics[1]],\\n pcp_disk_dev_metrics[1] + '.')\\n\\n # calculate the % Util and add them to the ZaggSender\\n for disk, totals in filtered_disk_totals.iteritems():\\n total_active = (float)(totals[1] - totals[0]) / 1000.0\\n putil = 100 * total_active / interval\\n\\n metric_sender.add_metric({'%s[%s]' % (item_prototype_key_putil, disk): putil})\\n\\n metric_sender.send_metrics()\",\n \"def cvv_report(argv):\\n p = optparse.OptionParser()\\n p.add_option('-c', '--cfg',\\n action='store', default='', dest='config',\\n help='config file name')\\n p.add_option('-d', '--debug',\\n action='store_true', default=False, dest='debug',\\n help='run the debugger')\\n p.add_option('-p', '--prefix',\\n action='store', default='', dest='prefix',\\n help='table name prefix')\\n p.add_option('-v', '--verbose',\\n action='store_true', default=False, dest='verbose',\\n help='pass verbose flag to HSI object')\\n try:\\n (o, a) = p.parse_args(argv)\\n except SystemExit:\\n return\\n\\n if o.debug:\\n pdb.set_trace()\\n\\n if o.config != '':\\n cfg = CrawlConfig.get_config(o.config)\\n else:\\n cfg = CrawlConfig.get_config()\\n\\n if o.prefix != '':\\n cfg.set('dbi', 'tbl_prefix', o.prefix)\\n\\n dim = {}\\n dim['cos'] = Dimension.get_dim('cos')\\n dim['ttypes'] = Dimension.get_dim('ttypes')\\n\\n print dim['cos'].report()\\n print dim['ttypes'].report()\",\n \"def _show_help(self):\\r\\n info = {\\\"/contexts/<context>/[orgs/[<org_name>]]/[spaces/[<space_name>]]\\\": \\\"reports\\\",\\r\\n \\\"/contexts/<context>/orgs_metadata/[<org_name>]\\\": \\\"metadata\\\",\\r\\n \\\"/contexts/<context>/orgs/<org_name>/director\\\": \\\"org/director mapping\\\",\\r\\n \\\"/reader_status\\\": \\\"status of Bitbucket reader cache\\\"}\\r\\n if self._cache_refresh:\\r\\n info['/refresh'] = \\\"force cache refresh from BitBucket\\\"\\r\\n return info\",\n \"def info():\\n f = Figlet(font='standard')\\n click.echo(f.renderText('covtool'))\\n click.secho(\\n \\\"covtool: a simple CLI for fetching covid data\\\", fg='cyan')\\n click.echo(\\n \\\"Data Sources: https://www.worldometers.info/coronavirus\\\\nJohn Hopkins [https://github.com/CSSEGISandData/COVID-19] \\\")\\n click.secho(\\\"Author: Amayo II <amayomordecai@gmail.com>\\\", fg='magenta')\",\n \"def cmdopt(request):\\n return request.config.getoption(\\\"-c\\\")\",\n \"def show(self):\\n prev_queries = 0\\n prev_cpu_sys = 0\\n prev_cpu_user = 0\\n \\n lines = {\\n \\\"Uptime (seconds)\\\": \\\"--\\\",\\n \\\"Number of queries\\\": \\\"--\\\",\\n \\\"Query per second\\\": \\\"--\\\",\\n \\\"ACL drops\\\": \\\"--\\\",\\n \\\"Dynamic drops\\\": \\\"--\\\",\\n \\\"Rule drops\\\": \\\"--\\\",\\n \\\"CPU Usage (%s)\\\": \\\"--\\\",\\n \\\"Cache hitrate\\\": \\\"--\\\"\\n }\\n\\n while True:\\n try:\\n # get stats from dnsdist\\n stats = Statistics(console=self.console)\\n global_stats = stats[\\\"global\\\"]\\n \\n qps = int(global_stats[\\\"queries\\\"]) - prev_queries\\n prev_queries = int(global_stats[\\\"queries\\\"])\\n cpu = (int(global_stats[\\\"cpu-sys-msec\\\"])+int(global_stats[\\\"cpu-user-msec\\\"]) - prev_cpu_sys - prev_cpu_user) / 10\\n prev_cpu_sys = int(global_stats[\\\"cpu-sys-msec\\\"])\\n prev_cpu_user = int(global_stats[\\\"cpu-user-msec\\\"])\\n \\n lines[\\\"Uptime (seconds)\\\"] = global_stats[\\\"uptime\\\"]\\n lines[\\\"Number of queries\\\"] = global_stats[\\\"queries\\\"]\\n lines[\\\"Query per second\\\"] = qps\\n lines[\\\"CPU Usage (%s)\\\"] = cpu\\n lines[\\\"ACL drops\\\"] = global_stats[\\\"acl-drops\\\"]\\n lines[\\\"Rule drops\\\"] = global_stats[\\\"rule-drop\\\"]\\n lines[\\\"Cache hitrate\\\"] = global_stats[\\\"cache-hits\\\"]\\n lines[\\\"Dynamic drops\\\"] = global_stats[\\\"dyn-blocked\\\"]\\n\\n # reprint the lines \\n sys.stdout.write(\\\"\\\\033[1mDashboard for dnsdist\\\\033[0m\\\\n\\\")\\n sys.stdout.write(\\\"\\\\n\\\")\\n sys.stdout.write(\\\"Global:\\\\n\\\")\\n for k,v in lines.items():\\n sys.stdout.write(\\\"\\\\t%s: %s\\\\n\\\" % (k,v))\\n sys.stdout.write(\\\"Backends:\\\\n\\\")\\n for s in stats[\\\"backends\\\"]:\\n if not len(s[\\\"name\\\"]):\\n s[\\\"name\\\"] = \\\"--\\\"\\n if not len(s[\\\"pools\\\"]):\\n s[\\\"pools\\\"] = \\\"--\\\"\\n sys.stdout.write(\\\"\\\\t#%s / %s / %s / %s\\\\n\\\" % (s[\\\"#\\\"],s[\\\"address\\\"],s[\\\"name\\\"],s[\\\"pools\\\"]) )\\n sys.stdout.write(\\\"\\\\t\\\\tNumber of queries: %s\\\\n\\\" % s[\\\"queries\\\"])\\n sys.stdout.write(\\\"\\\\t\\\\tQuery per second: %s\\\\n\\\" % s[\\\"qps\\\"])\\n sys.stdout.write(\\\"\\\\t\\\\tNumber of drops: %s\\\\n\\\" % s[\\\"drops\\\"])\\n sys.stdout.write(\\\"\\\\n\\\")\\n sys.stdout.write(\\\"Ctrl+C to exit\\\\n\\\")\\n \\n time.sleep(1)\\n \\n \\n # move up cursor and delete whole line\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\") \\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n for k,v in lines.items():\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\") \\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n for s in stats[\\\"backends\\\"]:\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\") \\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\") \\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n \\n del stats\\n except KeyboardInterrupt:\\n break\",\n \"def printHelp():\\n print(\\\"amqWorkApiMass.py -n <msgcnt> -b <body> -m <headers> -s <path/to/bodyandheaders>\\\")\",\n \"def do_config():\\n\\n tracking = get_tracking()\\n for unit in (\\\"ppm\\\", \\\"sec\\\"):\\n\\ttunit = unit\\n\\tif unit == \\\"sec\\\":\\n\\t tunit = \\\"seconds\\\"\\n\\tprint \\\"multigraph chrony_%s\\\" % unit\\n\\tprint \\\"graph_title NTP (Chrony) Statistics (%s)\\\" % unit\\n\\tprint \\\"graph_vlabel %s\\\" % unit\\n\\tprint \\\"graph_args --base 1000\\\"\\n\\tprint \\\"graph_category time\\\"\\n\\tprint \\\"graph_info NTP (Chrony) tracking statistics (the ones measured in %s)\\\" % tunit\\n\\tfor key in tracking[tunit]:\\n\\t item = tracking[tunit][key]\\n\\t print \\\"\\\"\\\"%s.label %s\\n%s.draw LINE2\\n%s.info %s\\\"\\\"\\\" % (key, item[\\\"label\\\"], key, key, item[\\\"label\\\"])\\n\\tprint\\n return 0\",\n \"def cli(argv):\\r\\n argv.append(\\\"--exhaust-materials\\\")\\r\\n cltestbench.cli(argv)\",\n \"def collect_cluster_info(output_dir, k8s_cli):\\n collect_helper(output_dir, cmd=\\\"{} cluster-info\\\".format(k8s_cli),\\n file_name=\\\"cluster_info\\\", resource_name=\\\"cluster-info\\\")\",\n \"def main(cls):\\n parser = cls.make_argument_parser()\\n args = parser.parse_args()\\n args.device = make_hoomd_device(args)\\n benchmark = cls(**vars(args))\\n performance = benchmark.execute()\\n\\n if args.device.communicator.rank == 0:\\n print(f'{numpy.mean(performance)}')\",\n \"def help(update, context):\\n msg = \\\"\\\"\\n msg += \\\"\\\\n/covid 7-Day-Incident per Million\\\"\\n msg += \\\"\\\\n/daylio What did I do a year ago today?\\\"\\n msg += \\\"\\\\n/f1last Results of the last race\\\"\\n msg += \\\"\\\\n/f1stand Driver standings\\\"\\n msg += \\\"\\\\n/f1next Time and place of the next race\\\"\\n msg += \\\"\\\\n/fuel prices and consump. (args: Xeur Ykm)\\\"\\n msg += \\\"\\\\n/ip Outside ip address\\\"\\n msg += \\\"\\\\n/rate Exchange rates (args: Xeur/Yhuf)\\\"\\n msg += \\\"\\\\n/rss check rss feeds for new content\\\"\\n msg += \\\"\\\\n/sun Time of sunrise and sunset\\\"\\n msg += \\\"\\\\n/xkcd Sends last comic image and alt\\\"\\n msg.rstrip()\\n update.message.reply_text(msg)\",\n \"def show(self, options=None):\\n\\n # # IMPLEMENTATION NOTE: Stub for implementing options:\\n # if options and self.InspectOptions.ALL_OUTPUT_LABELS in options:\\n # pass\\n\\n print (\\\"\\\\n---------------------------------------------------------\\\")\\n print (\\\"\\\\n{0}\\\".format(self.name))\\n\\n\\n print (\\\"\\\\n\\\\tControl enabled: {0}\\\".format(self.enable_controller))\\n print (\\\"\\\\n\\\\tProcesses:\\\")\\n\\n for process in self.processes:\\n print (\\\"\\\\t\\\\t{} [learning enabled: {}]\\\".format(process.name, process._learning_enabled))\\n\\n\\n # Print execution_sets (output of toposort)\\n print (\\\"\\\\n\\\\tExecution sets: \\\".format(self.name))\\n # Sort for consistency of output\\n execution_sets_sorted = sorted(self.execution_sets)\\n for i in range(len(execution_sets_sorted)):\\n # for i in range(len(self.execution_sets)):\\n print (\\\"\\\\t\\\\tSet {0}:\\\\n\\\\t\\\\t\\\\t\\\".format(i),end='')\\n print(\\\"{ \\\",end='')\\n sorted_mechs_names_in_set = sorted(list(mech_tuple.mechanism.name\\n for mech_tuple in self.execution_sets[i]))\\n for name in sorted_mechs_names_in_set:\\n print(\\\"{0} \\\".format(name), end='')\\n print(\\\"}\\\")\\n\\n # Print executionList sorted by phase and including EVC mechanism\\n\\n # Sort executionList by phase\\n sorted_execution_list = self.executionList.copy()\\n\\n\\n # Sort by phaseSpec and, within each phase, by mechanism name\\n sorted_execution_list.sort(key=lambda mech_tuple: mech_tuple.phase)\\n\\n\\n # Add controller to execution list for printing if enabled\\n if self.enable_controller:\\n sorted_execution_list.append(MechanismTuple(self.controller, None, self.controller.phaseSpec))\\n\\n\\n mech_names_from_exec_list = list(mech_tuple.mechanism.name for mech_tuple in self.executionList)\\n mech_names_from_sorted_exec_list = list(mech_tuple.mechanism.name for mech_tuple in sorted_execution_list)\\n\\n print (\\\"\\\\n\\\\tExecution list: \\\".format(self.name))\\n phase = 0\\n print(\\\"\\\\t\\\\tPhase {}:\\\".format(phase))\\n for mech_tuple in sorted_execution_list:\\n if mech_tuple.phase != phase:\\n phase = mech_tuple.phase\\n print(\\\"\\\\t\\\\tPhase {}:\\\".format(phase))\\n print (\\\"\\\\t\\\\t\\\\t{}\\\".format(mech_tuple.mechanism.name))\\n\\n print (\\\"\\\\n\\\\tOrigin mechanisms: \\\".format(self.name))\\n for mech_tuple in self.originMechanisms.mech_tuples_sorted:\\n print(\\\"\\\\t\\\\t{0} (phase: {1})\\\".format(mech_tuple.mechanism.name, mech_tuple.phase))\\n\\n print (\\\"\\\\n\\\\tTerminal mechanisms: \\\".format(self.name))\\n for mech_tuple in self.terminalMechanisms.mech_tuples_sorted:\\n print(\\\"\\\\t\\\\t{0} (phase: {1})\\\".format(mech_tuple.mechanism.name, mech_tuple.phase))\\n for output_state_name in mech_tuple.mechanism.outputStates:\\n print(\\\"\\\\t\\\\t\\\\t{0}\\\".format(output_state_name))\\n\\n # if any(process.learning for process in self.processes):\\n if self.learning:\\n print (\\\"\\\\n\\\\tTarget mechanisms: \\\".format(self.name))\\n for mech_tuple in self.targetMechanisms.mech_tuples:\\n print(\\\"\\\\t\\\\t{0} (phase: {1})\\\".format(mech_tuple.mechanism.name, mech_tuple.phase))\\n\\n print (\\\"\\\\n---------------------------------------------------------\\\")\",\n \"def usage(err=''):\\r\\n m = '%s\\\\n' %err\\r\\n m += 'Default usage is to list Cases closed for the 30 days\\\\n'\\r\\n m += '\\\\n Example:\\\\n'\\r\\n m += ' closedcases -n 90 \\\\n' \\r\\n m += ' \\\\n'\\r\\n# m += ' closedcases -n 60 -s blast5 \\\\n'\\r\\n return m\",\n \"def _cmd_segmetrics(args):\\n if not 0.0 < args.alpha <= 1.0:\\n raise RuntimeError(\\\"alpha must be between 0 and 1.\\\")\\n\\n if not any((args.location_stats, args.spread_stats, args.interval_stats)):\\n logging.info(\\\"No stats specified\\\")\\n return\\n\\n # Calculate all metrics\\n cnarr = read_cna(args.cnarray)\\n segarr = read_cna(args.segments)\\n segarr = do_segmetrics(\\n cnarr,\\n segarr,\\n args.location_stats,\\n args.spread_stats,\\n args.interval_stats,\\n args.alpha,\\n args.bootstrap,\\n args.smooth_bootstrap,\\n skip_low=args.drop_low_coverage,\\n )\\n tabio.write(segarr, args.output or segarr.sample_id + \\\".segmetrics.cns\\\")\",\n \"def help(bin_name='windmill'):\\n bin_name = 'windmill'\\n module = sys.modules[__name__]\\n from windmill.conf import global_settings\\n all_option_names = []\\n options_string = []\\n for option in [getattr(module, x) for x in dir(module) if (\\n hasattr(getattr(module, x), 'option_names')) and (\\n getattr(module, x).__doc__ is not None ) ]:\\n all_option_names.append(option.option_names)\\n if hasattr(option, 'setting'):\\n if getattr(global_settings, option.setting, None) is not None:\\n default = ' Defaults to %s' % str(getattr(global_settings, option.setting, None))\\n else:\\n default = ''\\n else:\\n default = ''\\n if option.option_names[0] is None:\\n if not issubclass(option, GeneralBool):\\n options_string.append(' '+''.join([str(option.option_names[1])+'='+' :: ', \\n option.__doc__]) + default)\\n else:\\n options_string.append(' '+''.join([str(option.option_names[1])+' :: ', \\n option.__doc__]) + default)\\n else:\\n if not issubclass(option, GeneralBool):\\n options_string.append(' '+''.join([\\n '-'+str(option.option_names[0])+', '\\n +str(option.option_names[1])+'='+' :: ',\\n option.__doc__]) + default)\\n else:\\n options_string.append(' '+''.join([\\n '-'+str(option.option_names[0])+', '\\n +str(option.option_names[1])+' :: ',\\n option.__doc__]) + default)\\n\\n preamble = \\\"\\\"\\\"windmill web test automation system.\\n %s [-%s] action [option=value] [firefox|ie|safari] [http://www.example.com]\\n \\nAvailable Actions:\\n shell Enter the windmilll shell environment (modified python shell). \\n Uses ipython if installed. Exit using ^d\\n run_service Run the windmill service in foreground. Kill using ^c.\\n \\nAvailable Options:\\\"\\\"\\\" % ( bin_name,\\n ''.join([ o[0] for o in all_option_names if o[0] is not None ]) \\n )\\n print preamble\\n print '\\\\n'.join(options_string)\",\n \"def build_cmdline():\\n\\tcmd=optparse.OptionParser(version=__version__)\\n\\tcmd.add_option('-c', '', dest='config_fname',type=\\\"string\\\", help='WHM/WHMCS configuration file', metavar=\\\"FILE\\\")\\n\\tcmd.add_option('-s', '', dest=\\\"whm_section\\\", type=\\\"string\\\", help=\\\"WHM server to use. Specify section name. eg: -s ds01\\\", metavar=\\\"SERVER\\\")\\n\\tcmd.add_option('','--search', action=\\\"store\\\", dest='search', type=\\\"string\\\", help=\\\"Search client by DNS domain name or cPanel username\\\", metavar=\\\"STRING\\\")\\n\\tcmd.add_option('-d', '', dest='whmcs_deptid', type=\\\"int\\\", help=\\\"WHMCS Department ID\\\", metavar=\\\"INT\\\") \\n\\tcmd.add_option('-m', '', dest='whmcs_ticketmsg_fname', type=\\\"string\\\", help=\\\"WHMCS abuse ticket template file\\\", metavar='FILE')\\n\\tcmd.add_option('-r', '', dest='whm_suspendmsg_fname', type=\\\"string\\\", help='cPanel account suspension reason template file', metavar='FILE')\\n\\tcmd.add_option('-f', '', dest='whmcs_proofmsg_fname', type=\\\"string\\\", help='Abuse proof file which will be appended to abuse ticket message', metavar='FILE')\\n\\tcmd.add_option('', '--subject', dest='whmcs_subject', type=\\\"string\\\", help='Specify abuse ticket subject title.', metavar=\\\"STRING\\\")\\n\\tcmd.add_option('-y', '--allyes', dest='allyes', action=\\\"store_true\\\", default=False, help='Assume yes as an answer to any question which would be asked')\\n\\treturn cmd\",\n \"def cmdline(self, executable, options, task, rlimits):\\n data_model_param = get_data_model_from_task(task, {ILP32: \\\"-m32\\\", LP64: \\\"-m64\\\"})\\n print(options)\\n if data_model_param and not any(\\n option.startswith(\\\"--clang-options=\\\") for option in options\\n ):\\n options += [\\\"--clang-options=\\\" + data_model_param]\\n\\n if task.property_file:\\n options += [\\\"--svcomp-property\\\", task.property_file]\\n else:\\n raise UnsupportedFeatureException(\\n \\\"SMACK can't execute without a property file.\\\"\\n )\\n\\n options += [task.single_input_file]\\n\\n return [executable] + options\",\n \"def display_memcache_info(request):\\n # pylint: disable-msg=E1101\\n return utility.respond(request, 'admin/memcache_info',\\n {'memcache_info': memcache.get_stats()})\",\n \"def execute(self, context):\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\\n logger.info(\\\"[ChainedTransfig] Collectd-Jmx w/ Common MBeans Generation...\\\")\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\\n\\n #insert common to assemble collectd.genericjmx.common.conf in later step\\n context[CTX_KEY_COMMON_COLLECTD_JMX_APP_PREFIX] = 'common'\\n super().execute(context)\\n\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\\n logger.info(\\\"[ChainedTransfig] Collectd-Jmx w/ Common MBeans Generation... COMPLETES\\\")\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\",\n \"def ShowIPCVoucherAttributeControl(cmd_args=[], cmd_options={}):\\n if not cmd_args:\\n raise ArgumentError(\\\"Please provide correct arguments.\\\")\\n ivac = kern.GetValueFromAddress(cmd_args[0], 'ipc_voucher_attr_control_t')\\n print GetIPCVoucherAttrControlSummary.header\\n print GetIPCVoucherAttrControlSummary(ivac)\\n if config['verbosity'] > vHUMAN:\\n cur_entry_index = 0\\n last_entry_index = unsigned(ivac.ivac_table_size)\\n print \\\"index \\\" + GetIPCVoucherAttributeEntrySummary.header\\n while cur_entry_index < last_entry_index:\\n print \\\"{: <5d} \\\".format(cur_entry_index) + GetIPCVoucherAttributeEntrySummary(addressof(ivac.ivac_table[cur_entry_index]))\\n cur_entry_index += 1\",\n \"def host_report(args):\\n config = Kconfig(client=args.client, debug=args.debug, region=args.region, zone=args.zone, namespace=args.namespace)\\n k = config.k\\n k.report()\",\n \"def main(argv=None):\\n\\n if argv is None:\\n argv = sys.argv\\n\\n # setup command line parser\\n parser = E.OptionParser(version=\\\"%prog version: $Id$\\\",\\n usage=globals()[\\\"__doc__\\\"])\\n\\n parser.add_option(\\\"-t\\\", \\\"--test\\\", dest=\\\"test\\\", type=\\\"string\\\",\\n help=\\\"supply help\\\")\\n\\n parser.add_option(\\\"--method\\\", dest=\\\"method\\\", type=\\\"choice\\\",\\n choices=(\\\"metrics\\\", \\\"summary\\\", \\\"module_summary\\\"),\\n help=\\\"method to summarise clustering\\\")\\n\\n parser.add_option(\\\"--ref-gtf-files\\\", dest=\\\"ref_gtf\\\", type=\\\"string\\\",\\n help=\\\"comma separated list of reference gtf files\\\")\\n\\n # add common options (-h/--help, ...) and parse command line\\n (options, args) = E.Start(parser, argv=argv)\\n\\n if options.method == \\\"metrics\\\":\\n infile = argv[-1]\\n E.info(\\\"loading input file: %s\\\" % infile)\\n assert infile\\n\\n df = pd.read_table(infile,\\n sep=\\\"\\\\t\\\",\\n header=None,\\n index_col=0)\\n\\n df = df.ix[:, :50]\\n cluster_combs = (x for x in itertools.combinations(df.columns,\\n 2))\\n genes = df.index\\n results_dict = {}\\n all_clusts = {}\\n\\n E.info(\\\"setting up cluster containers\\\")\\n for i in df.columns:\\n clusters = set(df[i].values.tolist())\\n cluster_dict = {}\\n for clust in clusters:\\n cluster_dict[clust] = []\\n for gene in genes:\\n cluster_dict[df[i][gene]].append(gene)\\n\\n for col in clusters:\\n col_set = set()\\n clust_col = cluster_dict[col]\\n gene_members = itertools.combinations(clust_col,\\n 2)\\n col_set.update(gene_members)\\n cluster_dict[col] = col_set\\n all_clusts[i] = cluster_dict\\n E.info(\\\"generating all pair-wise cluster comparisons\\\")\\n E.info(\\\"calculating adjusted mutual information\\\")\\n for k in cluster_combs:\\n clusters1 = all_clusts[k[0]]\\n clusters2 = all_clusts[k[1]]\\n metric_dict = {}\\n metric_dict['AMI'] = TS.adjustedMutualInformation(clusters1,\\n clusters2)\\n results_dict[k] = metric_dict\\n\\n res_frame = pd.DataFrame(results_dict).T\\n res_frame = res_frame.reset_index()\\n res_frame.drop(['level_0'], inplace=True, axis=1)\\n res_frame.drop(['level_1'], inplace=True, axis=1)\\n\\n # flatten rand indices and add to output dataframe\\n rand_arrays = TS.randIndexes(df)\\n flat_adj_rand = TS.unravel_arrays(rand_arrays[0])\\n flat_rand = TS.unravel_arrays(rand_arrays[1])\\n res_frame['Rand_Index'] = flat_rand\\n res_frame['Adjusted_Rand_Index'] = flat_adj_rand\\n E.info(\\\"aggregating results\\\")\\n\\n res_frame.to_csv(options.stdout,\\n sep=\\\"\\\\t\\\",\\n index_label='idx')\\n\\n elif options.method == \\\"summary\\\":\\n infiles = argv[-1]\\n list_of_files = infiles.split(\\\",\\\")\\n\\n file_dict = {}\\n for fle in list_of_files:\\n fname = fle.split(\\\"/\\\")[-1]\\n condition = fname.split(\\\"-\\\")[0]\\n ref = fname.split(\\\"-\\\")[1]\\n df_ = pd.read_table(fle,\\n sep=\\\"\\\\t\\\",\\n header=0,\\n index_col=0)\\n df_.columns = ['gene_id', 'cluster']\\n clust_dict = {}\\n for idx in df_.index:\\n cluster = df_.loc[idx]['cluster']\\n gene = df_.loc[idx]['gene_id']\\n try:\\n clust_dict[cluster] += 1\\n except KeyError:\\n clust_dict[cluster] = 1\\n med_size = np.median(clust_dict.values())\\n file_dict[fname] = {'condition': condition,\\n 'reference': ref,\\n 'median_cluster_size': med_size}\\n\\n outframe = pd.DataFrame(file_dict).T\\n outframe.to_csv(options.stdout,\\n sep=\\\"\\\\t\\\",\\n index_label='idx')\\n\\n elif options.method == \\\"module_summary\\\":\\n # get lncRNA/gene lengths from reference gtfs\\n ref_gtfs = options.ref_gtf.split(\\\",\\\")\\n length_dict = {}\\n for ref in ref_gtfs:\\n oref = IOTools.openFile(ref, \\\"rb\\\")\\n git = GTF.transcript_iterator(GTF.iterator(oref))\\n for gene in git:\\n for trans in gene:\\n length = trans.end - trans.start\\n try:\\n length_dict[trans.gene_id] += length\\n except KeyError:\\n length_dict[trans.gene_id] = length\\n oref.close()\\n\\n infiles = argv[-1]\\n list_of_files = infiles.split(\\\",\\\")\\n\\n fdfs = []\\n for fle in list_of_files:\\n cond = fle.split(\\\"/\\\")[-1].split(\\\"-\\\")[0]\\n refer = fle.split(\\\"/\\\")[-1].split(\\\"-\\\")[1]\\n _df = pd.read_table(fle, sep=\\\"\\\\t\\\",\\n header=0, index_col=0)\\n _df.columns = ['gene_id', 'cluster']\\n clusters = set(_df['cluster'])\\n c_dict = {}\\n # summarize over each cluster\\n for clust in clusters:\\n lengths = []\\n c_df = _df[_df['cluster'] == clust]\\n for lid in c_df['gene_id']:\\n lengths.append(length_dict[lid])\\n c_dict[clust] = {'cluster_size': len(c_df['gene_id']),\\n 'mean_length': np.mean(lengths),\\n 'index': (cond, refer),\\n 'module': clust}\\n cdf = pd.DataFrame(c_dict).T\\n # use a multindex for hierarchical indexing\\n midx = pd.MultiIndex.from_tuples(cdf['index'])\\n cdf.index = midx\\n cdf.drop(['index'], inplace=True, axis=1)\\n fdfs.append(cdf)\\n\\n # generate a single output df\\n s_df = fdfs[0]\\n fdfs.pop(0)\\n for df in fdfs:\\n s_df = s_df.append(df)\\n\\n s_df.to_csv(options.stdout,\\n index_label=(\\\"condition\\\", \\\"reference\\\"),\\n sep=\\\"\\\\t\\\")\\n\\n # write footer and output benchmark information.\\n E.Stop()\",\n \"def about( cls, ):\\n url = r\\\"http://www.opencircuits.com/Python_Smart_Terminal\\\"\\n __, mem_msg = cls.show_process_memory( )\\n msg = ( f\\\"{cls.controller.app_name} version:{cls.controller.version} \\\\nmode: {cls.parameters.mode}\\\"\\n f\\\"\\\\n by Russ Hensel\\\"\\n f\\\"\\\\nMemory in use {mem_msg} \\\\nCheck <Help> or \\\\n{url} \\\\nfor more info.\\\" )\\n messagebox.showinfo( \\\"About\\\", msg )\",\n \"def stats(caesar, input):\\n commands = {}\\n users = {}\\n channels = {}\\n\\n ignore = set(['f_note', 'startup', 'message', 'noteuri'])\\n for (name, user), count in caesar.stats.items(): \\n if name in ignore: continue\\n if not user: continue\\n\\n if not user.startswith('#'): \\n try: users[user] += count\\n except KeyError: users[user] = count\\n else: \\n try: commands[name] += count\\n except KeyError: commands[name] = count\\n\\n try: channels[user] += count\\n except KeyError: channels[user] = count\\n\\n comrank = sorted([(b, a) for (a, b) in commands.iteritems()], reverse=True)\\n userank = sorted([(b, a) for (a, b) in users.iteritems()], reverse=True)\\n charank = sorted([(b, a) for (a, b) in channels.iteritems()], reverse=True)\\n\\n # most heavily used commands\\n creply = 'most used commands: '\\n for count, command in comrank[:10]: \\n creply += '%s (%s), ' % (command, count)\\n caesar.say(creply.rstrip(', '))\\n\\n # most heavy users\\n reply = 'power users: '\\n for count, user in userank[:10]: \\n reply += '%s (%s), ' % (user, count)\\n caesar.say(reply.rstrip(', '))\\n\\n # most heavy channels\\n chreply = 'power channels: '\\n for count, channel in charank[:3]: \\n chreply += '%s (%s), ' % (channel, count)\\n caesar.say(chreply.rstrip(', '))\",\n \"def help_help(self):\\n print(\\\"List commands or print details about a command\\\")\",\n \"def help(self):\\n self._log.info('### Help for the class %s ###',\\n self.__class__)\\n self._log.info(self.__doc__)\\n \\n print ('## job property:',self._context_name, self.statusOn, self.StoredValue)\\n print ('## allowed Values:',self.allowedValues)\\n print ('## default Value:', self.__class__.StoredValue )\\n print ('## allowed Types :',self.allowedTypes )\\n self._log.info('### End of the help for the class %s ###',\\n self.__class__)\",\n \"def cli_help(self):\\n self._generate_cli_version()\\n self._generate_cli_help()\\n sys.exit(0)\",\n \"async def eventstats(self, ctx):\\n if ctx.invoked_subcommand is None:\\n await ctx.send_help(ctx.command)\",\n \"def command_help(args):\\n\\tprint_usage()\\n\\treturn 0\",\n \"def print_help():\\n print(\\\"Archive generated report to a web server. e.g.\\\")\\n print(\\\"rm -rf /cs-shared/contrail_code_coverage/test_coverage\\\")\\n print(\\\"cp -a build/coverage/controller/test_coverage \\\" +\\n \\\"/cs-shared/contrail_code_coverage/\\\")\\n print(\\\"http://10.84.5.100/cs-shared/contrail_code_coverage/test_coverage\\\")\",\n \"def opc_calcs(df_param_indexed):\\n \\n df_param_indexed = df_param_indexed.copy()\\n \\n ''' commented 20180210 after Calmetrix update\\n # Remove for cc1 data exported with cc2\\n mix_start = datetime.strptime(\\n df_param_indexed.loc['Mix Time', 1], \\\"%d-%b-%Y %H:%M:%S\\\")\\n log_start = datetime.strptime(\\n df_param_indexed.loc['Start Time', 1], \\\"%d-%b-%Y %H:%M:%S\\\")\\n time_difference = (log_start - mix_start).total_seconds()\\n '''\\n # select values from DataFrame and calculate mass of binder in sample\\n # may be worth checking if any of these values are 0 at some point in the future\\n \\n m_water = float(df_param_indexed.loc['Water Mass, g', 1])\\n m_cem = float(df_param_indexed.loc['Cement Mass, g', 1])\\n m_sample = float(df_param_indexed.loc['Sample Mass, g', 1])\\n m_sample_cem = m_sample / (m_cem + m_water) * m_cem\\n \\n return m_sample_cem\",\n \"def config_pbc_md(self):\\n\\n self._config_md()\\n self.title = \\\"PBC MD Simulation\\\"\\n self.cntrl[\\\"cut\\\"] = 8.0\\n self.cntrl[\\\"igb\\\"] = 0\\n self.cntrl[\\\"iwrap\\\"] = 1\\n self.cntrl[\\\"ntp\\\"] = 1\\n self.cntrl[\\\"barostat\\\"] = 2\",\n \"def cli(argv):\\r\\n args = get_args(argv)\\r\\n verbosity = \\\"summary\\\"\\r\\n if args.verbose:\\r\\n verbosity = \\\"report\\\"\\r\\n report = evaluate(args.design, verbosity)\\r\\n print json.dumps(report, indent=4)\",\n \"def qsub_cmmd(self):\\n temp = 'qsub -l mem=1G,time=:5: -cwd -j y -o {log} {job}'.format(**self.dict)\\n return temp.format(fnum=self.fnum)\",\n \"def runMCMC(df, cents, show=False):\\n if type(cents) is not list:\\n cents = [cents]\\n numCents = len(cents)\\n p = None\\n \\n # Tau = the precision of the normal distribution (of the above peaks)\\n taus = 1. / pm.Uniform('stds', 0, 100, size=numCents)**2 # tau = 1/sigma**2\\n centers = pm.Normal('centers', cents, [0.0025 for i in cents],\\n size=numCents)\\n \\n if numCents == 2: # Assignment probability\\n p = pm.Uniform('p', 0, 1)\\n assignment = pm.Categorical('asisgnment', [p, 1-p],\\n size=len(df.intervals))\\n @pm.deterministic\\n def center_i(assignment=assignment, centers=centers):\\n return centers[assignment]\\n @pm.deterministic\\n def tau_i(assignment=assignment, taus=taus):\\n return taus[assignment]\\n observations = pm.Normal('obs', center_i, tau_i, value=df.intervals,\\n observed=True)\\n # Create the model 2 peaks\\n mcmc = pm.MCMC([p, assignment, observations, taus, centers])\\n \\n else:\\n observations = pm.Normal('obs', value=df.intervals, observed=True)\\n mcmc = pm.MCMC([observations, taus, centers]) # Create model, 1 peak\\n \\n # Run the model\\n mcmc.sample(50000)\\n center_trace = mcmc.trace(\\\"centers\\\")[:]\\n try:\\n clusts = [center_trace[:,i] for i in range(numCents)]\\n except:\\n clusts = [center_trace]\\n \\n if show:\\n for i in range(numCents):\\n plt.hist(center_trace[:,i], bins=50, histtype='stepfilled',\\n color=['blue', 'red'][i], alpha=0.7)\\n plt.show()\\n \\n print('Evolved clusters at:')\\n print([np.mean(c) for c in clusts])\\n return clusts\",\n \"def test_cli_help(self):\\n output = self.update_command('-h')\",\n \"def explainerdashboard_cli(ctx):\",\n \"def execute(self, context):\\n app = context[CTX_KEY_COMMON_COLLECTD_JMX_APP_PREFIX]\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\\n logger.info(\\\"[ChainedTransfig] Collectd-Jmx w/ Application-wise [%s] ...\\\", app)\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\\n\\n super().execute(context)\\n\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\\n logger.info(\\\"[ChainedTransfig] Collectd-Jmx w/ Application-wise [%s] ... \\\"\\n \\\"COMPLETES\\\", app)\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\",\n \"def print_help_info(self, global_options):\\r\\n usage = ['',\\\"Type '%s help <subcommand>' for help on a specific subcommand.\\\" % self.prog_name,'']\\r\\n usage.append('Available subcommands:')\\r\\n commands = self.get_commands(global_options).keys()\\r\\n commands.sort()\\r\\n for cmd in commands:\\r\\n usage.append(' %s' % cmd)\\r\\n return '\\\\n'.join(usage)\",\n \"def print_performance_info(self):\\n pass\",\n \"def help_text(command):\\n\\n courses_list = ('ENPM611', 'ENPM613', 'ENPM631', 'ENPM687',\\\\\\n 'ENPM691', 'ENPM693', 'ENPM694', 'ENPM696',\\\\\\n 'ENPM809J','ENPM809R', 'ENPM809W')\\n\\n response = 'I have course descriptions for: '\\n for course_name in courses_list:\\n response = response + course_name + ' '\\n\\n response = response + '\\\\nTo get the course description, execute command: about ENPM<course_number>'\\n\\n return response\",\n \"def HMC_Help():\\n os.system(\\\"cls\\\")\\n while True:\\n print((\\\"\\\\n\\\\n\\\",\\\"Help\\\".center(50)))\\n print_list = [\\\"ManagedSystem\\\",\\\"LogicalPartition\\\",\\\"VirtualIOServer\\\",\\\"Cluster\\\",\\\"Performance Capaity Monitoring\\\",\\\"Return to Main Menu\\\"]\\n choice = int(print_obj.print_on_screen(print_list))\\n directory = os.path.dirname(os.path.dirname(__file__))\\n if choice == 1:\\n path = directory+\\\"/help/ManagedSystem\\\"\\n files = [f for f in os.listdir(path)if os.path.isfile(os.path.join(path,f))]\\n for f in files :\\n print((open(path+\\\"/%s\\\"%(f)).read()))\\n elif choice == 2:\\n path = directory+\\\"/help/LogicalPartition\\\"\\n files = [f for f in os.listdir(path)if os.path.isfile(os.path.join(path,f))]\\n for f in files :\\n print((open(path+\\\"/%s\\\"%(f)).read()))\\n elif choice == 3:\\n path = directory+\\\"/help/VirtualIOServer\\\"\\n files = [f for f in os.listdir(path)if os.path.isfile(os.path.join(path,f))]\\n for f in files :\\n print((open(path+\\\"/%s\\\"%(f)).read()))\\n elif choice == 4:\\n print((open(directory+\\\"/help/Cluster.txt\\\").read()))\\n elif choice == 5:\\n print((open(directory+\\\"/help/PerformanceCapacityMonitoring.txt\\\").read()))\\n elif choice == 6:\\n os.system(\\\"cls\\\")\\n return\\n else:\\n print(\\\"\\\\nTry using Valid option\\\")\\n back_to_menu()\",\n \"def stats(self, d_raw_materials=None):\\n cm_stats = 'sugar {0} tablespoons remaining\\\\n'.format(d_raw_materials['sugar'])\\n cm_stats += 'butter {0} teaspoons remaining\\\\n'.format(d_raw_materials['butter'])\\n cm_stats += 'dark chocolate {0} tablespoons remaining\\\\n'.format(d_raw_materials['dark chocolate'])\\n cm_stats += 'mint chocolate {0} tablespoons remaining\\\\n'.format(d_raw_materials['mint chocolate'])\\n cm_stats += 'milk chocolate {0} tablespoons remaining\\\\n'.format(d_raw_materials['milk chocolate'])\\n cm_stats += 'light corn syrup {0} teaspoons remaining\\\\n'.format(d_raw_materials['light corn syrup'])\\n cm_stats += 'sweetened condensed milk {0} teaspoons remaining\\\\n'.format(d_raw_materials[\\n 'sweetened condensed milk'])\\n cm_stats += 'vanilla extract {0} teaspoons remaining\\\\n'.format(d_raw_materials['vanilla extract'])\\n cm_stats += 'Reese\\\\'s Pieces {0} tablespoons remaining\\\\n'.format(d_raw_materials['Reese\\\\'s Pieces'])\\n cm_stats += super(ChocolateMachine, self).stats()\\n return cm_stats\",\n \"def options():\\n print \\\"\\\"\\\"Options summary:\\n -h, --help\\n -u, --usage\\n -v, --verbose <verb_level>\\n -t, --transaction\\n -e, --endpoint <endpoint>\\n -i, --interface-type <iface_type>\\n -f, --from-file <filename>\\n --not-error-tolerant\\n \\\"\\\"\\\"\",\n \"def DoHelp(options, args):\\n __pychecker__ = 'unusednames=options'\\n if len(args) == 1 and args[0] in COMMAND_USAGE_TEXT:\\n print(COMMAND_USAGE_TEXT[args[0]])\\n else:\\n raise gclient_utils.Error(\\\"unknown subcommand '%s'; see 'gclient help'\\\" %\\n args[0])\",\n \"def show_help(argv=None):\\n if argv:\\n if \\\"list_datasets\\\" in argv:\\n karr, _, desc = COMMANDS_TABLE[\\\"list_datasets\\\"]\\n sdm_util.print_message(\\\"command : %s\\\" % (\\\" | \\\".join(karr)))\\n sdm_util.print_message(\\\"usage : sdm ls\\\")\\n sdm_util.print_message(\\\"\\\")\\n sdm_util.print_message(desc)\\n return 0\\n elif \\\"search_datasets\\\" in argv:\\n karr, _, desc = COMMANDS_TABLE[\\\"search_datasets\\\"]\\n sdm_util.print_message(\\\"command : %s\\\" % (\\\" | \\\".join(karr)))\\n sdm_util.print_message(\\\"usage : sdm search <keyword>\\\")\\n sdm_util.print_message(\\\"\\\")\\n sdm_util.print_message(desc)\\n return 0\\n elif \\\"show_mounts\\\" in argv:\\n karr, _, desc = COMMANDS_TABLE[\\\"show_mounts\\\"]\\n sdm_util.print_message(\\\"command : %s\\\" % (\\\" | \\\".join(karr)))\\n sdm_util.print_message(\\\"usage : sdm ps\\\")\\n sdm_util.print_message(\\\"\\\")\\n sdm_util.print_message(desc)\\n return 0\\n elif \\\"mount\\\" in argv:\\n karr, _, desc = COMMANDS_TABLE[\\\"mount\\\"]\\n sdm_util.print_message(\\\"command : %s\\\" % (\\\" | \\\".join(karr)))\\n sdm_util.print_message(\\\"usage : sdm mount <dataset_name> [<mount_path>]\\\")\\n sdm_util.print_message(\\\"\\\")\\n sdm_util.print_message(desc)\\n return 0\\n elif \\\"mmount\\\" in argv:\\n karr, _, desc = COMMANDS_TABLE[\\\"mmount\\\"]\\n sdm_util.print_message(\\\"command : %s\\\" % (\\\" | \\\".join(karr)))\\n sdm_util.print_message(\\\"usage : sdm mmount <dataset_name> [<dataset_name> ...]\\\")\\n sdm_util.print_message(\\\"\\\")\\n sdm_util.print_message(desc)\\n return 0\\n elif \\\"unmount\\\" in argv:\\n karr, _, desc = COMMANDS_TABLE[\\\"unmount\\\"]\\n sdm_util.print_message(\\\"command : %s\\\" % (\\\" | \\\".join(karr)))\\n sdm_util.print_message(\\\"usage : sdm unmount <mount_id> [<cleanup_flag>]\\\")\\n sdm_util.print_message(\\\"\\\")\\n sdm_util.print_message(desc)\\n return 0\\n elif \\\"munmount\\\" in argv:\\n karr, _, desc = COMMANDS_TABLE[\\\"munmount\\\"]\\n sdm_util.print_message(\\\"command : %s\\\" % (\\\" | \\\".join(karr)))\\n sdm_util.print_message(\\\"usage : sdm munmount <mount_id> [<mount_id> ...]\\\")\\n sdm_util.print_message(\\\"\\\")\\n sdm_util.print_message(desc)\\n return 0\\n elif \\\"clean\\\" in argv:\\n karr, _, desc = COMMANDS_TABLE[\\\"clean\\\"]\\n sdm_util.print_message(\\\"command : %s\\\" % (\\\" | \\\".join(karr)))\\n sdm_util.print_message(\\\"usage : sdm clean\\\")\\n sdm_util.print_message(\\\"\\\")\\n sdm_util.print_message(desc)\\n return 0\\n else:\\n sdm_util.print_message(\\\"Unrecognized command\\\")\\n return 1\\n else:\\n sdm_util.print_message(\\\"command : sdm <COMMAND> [<COMMAND_SPECIFIC_ARGS> ...]\\\")\\n sdm_util.print_message(\\\"\\\")\\n sdm_util.print_message(\\\"Available Commands\\\")\\n\\n tbl = PrettyTable()\\n tbl.field_names = [\\\"COMMAND\\\", \\\"DESCRIPTION\\\"]\\n for cmd in COMMANDS:\\n command, _, desc = cmd\\n command_str = \\\" | \\\".join(command)\\n tbl.add_row([command_str, desc])\\n\\n sdm_util.print_message(tbl)\\n sdm_util.print_message(\\\"\\\")\\n return 0\",\n \"def printMCLStatus(self):\\n print()\\n print(\\\"====================\\\")\\n print(\\\"Cycle\\\", self.countCycles)\\n self._printHist()\",\n \"def get_hmcs(self):\\n url = '%s/ibm-hmcs' % self.catalog['compute']\\n res = self.get(url)\\n if res['status'] == 200:\\n return json.loads(res['body'])['hmcs']\\n else:\\n LOG.error('Get HMCs failed: %s %s %s' %\\n (res['status'], res['reason'], res['body']))\\n raise InvalidResponse(res)\",\n \"def qc_metrics(self, files_in, qc_files):\\n self.cmd(\\\"{samtools} index {bam_in}\\\"\\n .format(\\n samtools=self.cmds[\\\"samtools\\\"],\\n bam_in=files_in[0],\\n ),\\n shell=True)\\n self.cmd(\\\"{samtools} idxstats {bam_in} | tee {qc_file}\\\"\\n .format(\\n samtools=self.cmds[\\\"samtools\\\"],\\n bam_in = files_in[0],\\n qc_file = qc_files[0],\\n ),\\n shell=True,\\n log_output=True)\\n self.cmd(\\\"{samtools} flagstat {bam_in} | tee {qc_file}\\\"\\n .format(\\n samtools=self.cmds[\\\"samtools\\\"],\\n bam_in = files_in[0],\\n qc_file = qc_files[1],\\n ),\\n shell=True,\\n log_output=True)\\n \\n self.checkpoint(qc_files[0])\\n self.checkpoint(qc_files[1])\\n self.checkpoint(qc_files[2])\",\n \"def main():\\n configuration = {'resource-folder': 'resources',\\n 'build-folder': 'build',\\n 'log-folder': 'logfiles',\\n 'use-preloaded': False,\\n 'addi-metrics': 'addi-metrics.json',\\n 'jenkins': {'dependency-filename': 'dependencies.txt',\\n 'server': 'http://is.dbc.dk',\\n 'repository-project': 'opensearch-3rd-party-dependencies'},\\n 'log-zip-file':'logs.zip'}\\n configuration.update(cli())\\n setup_logger(configuration['verbose'])\\n run_performance_test(configuration)\",\n \"def cli():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"tic\\\", help=\\\"TIC number\\\")\\n parser.add_argument(\\\"-L\\\", \\\"--LIST\\\", help=\\\"Only fit the LC\\\", action=\\\"store_true\\\")\\n parser.add_argument(\\\"-S\\\", \\\"--SAVEGAIA\\\", help=\\\"Save Gaia sources\\\", action=\\\"store_true\\\")\\n parser.add_argument(\\\"-C\\\", \\\"--COORD\\\", help=\\\"Use coordinates\\\", default=False)\\n parser.add_argument(\\\"-n\\\", \\\"--name\\\", help=\\\"Target name to be plotted in title\\\", default=False)\\n parser.add_argument(\\\"-D2\\\", \\\"--DR2\\\", help=\\\"Use Gaia DR2 catalog instead of DR3\\\", action=\\\"store_true\\\")\\n parser.add_argument(\\\"-PM\\\", \\\"--PM\\\", help=\\\"Add proper motion direction arrows in the plot\\\", action=\\\"store_true\\\")\\n parser.add_argument(\\\"--maglim\\\", default=5., help=\\\"Maximum magnitude contrast respect to TIC\\\")\\n parser.add_argument(\\\"--sector\\\", default=None, help=\\\"Select Sector if more than one\\\")\\n parser.add_argument(\\\"--gid\\\", default=None, help=\\\"Gaia ID\\\")\\n parser.add_argument(\\\"--gmag\\\", default=None, help=\\\"Gaia mag\\\")\\n parser.add_argument(\\\"--sradius\\\", default=10., type=float, help=\\\"Search radius (in arcsec) for the get_gaia_data function\\\")\\n parser.add_argument(\\\"--legend\\\", default='best', help=\\\"Legend location\\\")\\n args = parser.parse_args()\\n return args\",\n \"def cli(*args, **kwargs):\\n logger.debug('Global options: %s %s', args, kwargs)\",\n \"def main():\\n logging.basicConfig(level=logging.INFO, format=\\\"%(message)s\\\")\\n _makeEnvir()\\n try:\\n opts, args = getopt.getopt(sys.argv[1:], \\\"hv:f:r:\\\", [\\\"help\\\", \\\"vista=\\\", \\\"fmqlep=\\\", \\\"report=\\\", \\\"host=\\\", \\\"port=\\\", \\\"access=\\\", \\\"verify=\\\"])\\n except getopt.GetoptError, err:\\n print str(err)\\n print __doc__\\n sys.exit(2)\\n # Default VistA to Caregraf Demo VistA\\n vista = \\\"CGVISTA\\\"\\n # Will match to CGVISTA's if no other VistA name is specified\\n fmqlEP = \\\"\\\"\\n host = \\\"\\\"\\n port = -1\\n access = \\\"\\\"\\n verify = \\\"\\\"\\n report = \\\"\\\"\\n for o, a in opts:\\n if o in [\\\"-v\\\", \\\"--vista\\\"]:\\n vista = a\\n elif o in [\\\"-f\\\", \\\"--fmqlep\\\"]:\\n fmqlEP = a\\n elif o in [\\\"--host\\\"]:\\n host = a\\n elif o in [\\\"--port\\\"]:\\n port = a \\n elif o in [\\\"--access\\\"]:\\n access = a\\n elif o in [\\\"--verify\\\"]:\\n verify = a\\n elif o in [\\\"-r\\\", \\\"--report\\\"]:\\n report = a\\n elif o in [\\\"-h\\\", \\\"--help\\\"]:\\n print __doc__\\n sys.exit()\\n if not report:\\n sys.exit()\\n if vista == \\\"CGVISTA\\\":\\n print \\\"Defaulting to Caregraf's demo VistA, 'CGVISTA'\\\"\\n fmqlEP = \\\"http://vista.caregraf.org/fmqlEP\\\"\\n print \\\"VDM - comparing %s against GOLD\\\" % vista\\n goldCacher = FMQLCacher(\\\"Caches\\\")\\n goldCacher.setVista(\\\"GOLD\\\")\\n otherCacher = FMQLCacher(\\\"Caches\\\")\\n otherCacher.setVista(vista, fmqlEP=fmqlEP, host=host, port=int(port), access=access, verify=verify)\\n _runReport(report, goldCacher, otherCacher)\",\n \"def process_cl_args():\\n\\n parser = argparse.ArgumentParser(add_help=False)\\n parser.add_argument('commands', nargs='*')\\n parser.add_argument('--help', '-h', action='store_true')\\n parser.add_argument('--version', '-v', action='store_true')\\n parser.add_argument('--debug', '-d', action='store_true')\\n parser.add_argument('--logging', '-l', action='store_true')\\n parser.add_argument('--no-autosize', action='store_true')\\n parser.add_argument('--no-preload', action='store_true')\\n args = parser.parse_args()\\n\\n if args.version:\\n xprint(get_version_info())\\n xprint(\\\"\\\")\\n sys.exit()\\n\\n elif args.help:\\n for x in helptext():\\n xprint(x[2])\\n sys.exit()\\n\\n if args.debug or os.environ.get(\\\"mpsytdebug\\\") == \\\"1\\\":\\n xprint(get_version_info())\\n g.debug_mode = True\\n g.no_clear_screen = True\\n logfile = os.path.join(tempfile.gettempdir(), \\\"mpsyt.log\\\")\\n logging.basicConfig(level=logging.DEBUG, filename=logfile)\\n logging.getLogger(\\\"pafy\\\").setLevel(logging.DEBUG)\\n\\n elif args.logging or os.environ.get(\\\"mpsytlog\\\") == \\\"1\\\":\\n logfile = os.path.join(tempfile.gettempdir(), \\\"mpsyt.log\\\")\\n logging.basicConfig(level=logging.DEBUG, filename=logfile)\\n logging.getLogger(\\\"pafy\\\").setLevel(logging.DEBUG)\\n\\n if args.no_autosize:\\n g.detectable_size = False\\n\\n g.command_line = \\\"playurl\\\" in args.commands or \\\"dlurl\\\" in args.commands\\n if g.command_line:\\n g.no_clear_screen = True\\n\\n if args.no_preload:\\n g.preload_disabled = True\\n\\n g.argument_commands = args.commands\",\n \"def get_cp_info(self):\\n return self.get(COMMAND_CPM, 'GetCpInfo')\",\n \"def help(self):\\r\\n self._short_help(None, None, None, None)\",\n \"def test_cw_metrics(self):\\n\\n instances = set()\\n result = self.cw_client.list_metrics(Namespace=\\\"CWAgent\\\", MetricName=\\\"cpu_usage_system\\\")\\n for i in result[\\\"Metrics\\\"]:\\n instances.add(i[\\\"Dimensions\\\"][0][\\\"Value\\\"])\\n\\n for key, value in self.cdk_output_map.items():\\n if \\\"Instance\\\" in key:\\n self.assertTrue(value in instances)\",\n \"def help():\\n description = \\\"\\\"\\\"\\n Get the observed and expected enrichment trend plot based on contact matrix.\\n Example:\\n getSigEnrich.py -d GM12878_Trac -o GM12878_Trac -cut 0 -p 10 \\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=description,\\n formatter_class=RawTextHelpFormatter)\\n parser.add_argument(\\\"-d\\\",\\n dest=\\\"dir\\\",\\n required=True,\\n type=str,\\n help=\\\"Directory for cLoops2 pre generated.\\\")\\n parser.add_argument(\\\"-o\\\",\\n dest=\\\"output\\\",\\n required=True,\\n type=str,\\n help=\\\"Output prefix.\\\")\\n parser.add_argument(\\n \\\"-c\\\",\\n dest=\\\"chroms\\\",\\n required=False,\\n default=\\\"\\\",\\n type=str,\\n help=\\n \\\"Whether to process limited chroms, specify it as chr1,chr2,chr3, default is not. Use this to save time for quite big data.\\\"\\n )\\n parser.add_argument(\\n \\\"-bs\\\",\\n dest=\\\"binSize\\\",\\n required=False,\\n default=5000,\\n type=int,\\n help=\\n \\\"Bin size (bp) to generate the contact matrix for estimation, default is 5000 bp.\\\"\\n )\\n parser.add_argument(\\n \\\"-cut\\\",\\n dest=\\\"cut\\\",\\n type=int,\\n default=0,\\n help=\\\"Distance cutoff for PETs to filter, default is 0.\\\")\\n parser.add_argument('-p',\\n dest=\\\"cpu\\\",\\n required=False,\\n default=1,\\n type=int,\\n help=\\\"Number of CPUs to run the job, default is 1.\\\")\\n parser.add_argument(\\n '-r',\\n dest=\\\"repeats\\\",\\n required=False,\\n default=10,\\n type=int,\\n help=\\n \\\"The reapet times to shuffle PETs to get the mean expected background,default is 10.\\\"\\n )\\n parser.add_argument('-plot',\\n dest=\\\"plot\\\",\\n required=False,\\n action=\\\"store_true\\\",\\n help=\\\"Set to plot the result.\\\")\\n op = parser.parse_args()\\n return op\",\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 HelpCommand(self, unused_args, unused_sub_opts=None, unused_headers=None,\\n unused_debug=None):\\n self.OutputUsageAndExit()\",\n \"async def sysinfo(self, ctx: Context):\\n\\t\\tstart = time.perf_counter()\\n\\t\\tend = time.perf_counter()\\n\\t\\tduration = (end - start) * 1000\\n\\t\\tcpuavg = psutil.cpu_percent(interval=None)\\n\\t\\tmem = psutil.virtual_memory()[2]\\n\\t\\tdurround = round(duration, 3)\\n\\t\\tosun = os.uname()\\n\\t\\tawait self.send(f\\\"System Info | CPU: {cpuavg}% | RAM: {mem}% | Latency: {durround * 1000}ms | OS: {sys.platform}\\\", whisper=[ctx.author.id])\",\n \"def diagnostics(self,\\n *opts, # type: DiagnosticsOptions\\n **kwargs # type: Dict[str, Any]\\n ) -> DiagnosticsResult:\\n\\n return super().diagnostics(*opts, **kwargs)\",\n \"def get_help(self) -> None: \\n print(messages.get_help())\",\n \"def optionHelp(self):\\n return {}\",\n \"def help():\",\n \"def metrics_adapter(cmd_ctx, cpc, adapter, **options):\\n cmd_ctx.execute_cmd(\\n lambda: cmd_metrics_adapter(cmd_ctx, cpc, adapter, options))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.629929","0.6273303","0.60736","0.5926187","0.5641638","0.55025315","0.5396064","0.53120846","0.52493966","0.5243705","0.523837","0.5222159","0.51953936","0.519269","0.5182496","0.5178438","0.51700777","0.5129585","0.51175195","0.51148605","0.51081395","0.5094479","0.5088534","0.50876766","0.5083076","0.50563556","0.5043129","0.5018782","0.50118124","0.5006939","0.49899745","0.49886495","0.49876744","0.49844867","0.4984234","0.49684325","0.49650833","0.49590605","0.49570918","0.4928168","0.4922446","0.4919366","0.4916089","0.4908699","0.49069148","0.49012366","0.48999208","0.4895324","0.48884052","0.48766488","0.48741058","0.4873641","0.48608923","0.48579937","0.48553276","0.48514852","0.4843435","0.48313886","0.48263013","0.4812996","0.48095083","0.48004547","0.48003402","0.47984856","0.4797413","0.4791654","0.47847703","0.47708428","0.4768093","0.47652674","0.4755939","0.4754236","0.47494218","0.47474626","0.4732156","0.47295058","0.47291747","0.47262752","0.47110453","0.47055694","0.46991584","0.4696264","0.4693831","0.46922427","0.4688808","0.4683552","0.4682853","0.46808046","0.46804628","0.46799797","0.4673382","0.46684214","0.46664265","0.46621153","0.46611106","0.4658123","0.4655142","0.46526244","0.46523097","0.46492645"],"string":"[\n \"0.629929\",\n \"0.6273303\",\n \"0.60736\",\n \"0.5926187\",\n \"0.5641638\",\n \"0.55025315\",\n \"0.5396064\",\n \"0.53120846\",\n \"0.52493966\",\n \"0.5243705\",\n \"0.523837\",\n \"0.5222159\",\n \"0.51953936\",\n \"0.519269\",\n \"0.5182496\",\n \"0.5178438\",\n \"0.51700777\",\n \"0.5129585\",\n \"0.51175195\",\n \"0.51148605\",\n \"0.51081395\",\n \"0.5094479\",\n \"0.5088534\",\n \"0.50876766\",\n \"0.5083076\",\n \"0.50563556\",\n \"0.5043129\",\n \"0.5018782\",\n \"0.50118124\",\n \"0.5006939\",\n \"0.49899745\",\n \"0.49886495\",\n \"0.49876744\",\n \"0.49844867\",\n \"0.4984234\",\n \"0.49684325\",\n \"0.49650833\",\n \"0.49590605\",\n \"0.49570918\",\n \"0.4928168\",\n \"0.4922446\",\n \"0.4919366\",\n \"0.4916089\",\n \"0.4908699\",\n \"0.49069148\",\n \"0.49012366\",\n \"0.48999208\",\n \"0.4895324\",\n \"0.48884052\",\n \"0.48766488\",\n \"0.48741058\",\n \"0.4873641\",\n \"0.48608923\",\n \"0.48579937\",\n \"0.48553276\",\n \"0.48514852\",\n \"0.4843435\",\n \"0.48313886\",\n \"0.48263013\",\n \"0.4812996\",\n \"0.48095083\",\n \"0.48004547\",\n \"0.48003402\",\n \"0.47984856\",\n \"0.4797413\",\n \"0.4791654\",\n \"0.47847703\",\n \"0.47708428\",\n \"0.4768093\",\n \"0.47652674\",\n \"0.4755939\",\n \"0.4754236\",\n \"0.47494218\",\n \"0.47474626\",\n \"0.4732156\",\n \"0.47295058\",\n \"0.47291747\",\n \"0.47262752\",\n \"0.47110453\",\n \"0.47055694\",\n \"0.46991584\",\n \"0.4696264\",\n \"0.4693831\",\n \"0.46922427\",\n \"0.4688808\",\n \"0.4683552\",\n \"0.4682853\",\n \"0.46808046\",\n \"0.46804628\",\n \"0.46799797\",\n \"0.4673382\",\n \"0.46684214\",\n \"0.46664265\",\n \"0.46621153\",\n \"0.46611106\",\n \"0.4658123\",\n \"0.4655142\",\n \"0.46526244\",\n \"0.46523097\",\n \"0.46492645\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.649805"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":1599,"cells":{"query":{"kind":"string","value":"Report processor usage metrics for CPCs. In addition to the commandspecific options shown in this help text, the general options (see 'zhmc help') can also be specified right after the 'zhmc' command name."},"document":{"kind":"string","value":"def metrics_proc(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_proc(cmd_ctx, cpc, options))"},"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 metrics_cpc(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_cpc(cmd_ctx, cpc, options))","def metrics_channel(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_channel(cmd_ctx, cpc, options))","def metrics_crypto(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_crypto(cmd_ctx, cpc, options))","def do_hostinfo(self, args):\n host = opts = None\n if args:\n args = args.split()\n host = args.pop()\n\n if not host:\n print('Usage: hostinfo [-cdmu] host_name_or_ip')\n print(' uptime and load stats returned if no options specified')\n return\n\n try:\n ip = socket.gethostbyname(host)\n except socket.gaierror:\n print('cannot resolve', host, file=sys.stderr)\n return\n\n opts = []\n while args:\n arg = args.pop(0)\n if arg.startswith('--'):\n if arg == '--cpu':\n opts.append('c')\n elif arg == '--disk':\n opts.append('d')\n elif arg == '--memory':\n opts.append('m')\n elif arg == '--uptime':\n opts.append('u')\n else:\n print('unrecognized option:', arg, file=sys.stderr)\n return\n else:\n if arg[0] == '-':\n for ch in arg[1:]:\n if ch in ('cdmu') and ch not in opts:\n opts.append(ch)\n else:\n print('unrecognized option:', ch, file=sys.stderr)\n return\n\n stats = self._qm.get_host_stats(ip)\n\n if not opts:\n # Get uptime and load averages.\n up = stats['uptime']\n load = stats['cpu_load']\n print('Up for %s days, %s hours, %s minutes, '\n 'load averages: %s, %s, %s'\n % (up['days'], up['hours'], up['minutes'], load['one'],\n load['five'], load['fifteen']))\n return\n\n all_stats = []\n for opt in opts:\n if opt == 'd':\n # Get disk usage.\n disks = stats['disk_usage']\n st = ['Disk Usage:']\n for mount, disk_info in disks.viewitems():\n st.append(' Usage for: %s' % mount)\n for k, v in disk_info.viewitems():\n st.append(' %s: %s' % (k, v))\n all_stats.append('\\n'.join(st))\n all_stats.append('')\n elif opt == 'c':\n # Get CPU load.\n load_stats = stats['cpu_load']\n st = ['CPU Load Average:']\n st.append(' last one minute: %s' % load_stats['one'])\n st.append(' last five minutes: %s' % load_stats['five'])\n st.append(' last fifteen minutes: %s' % load_stats['fifteen'])\n all_stats.append('\\n'.join(st))\n all_stats.append('')\n elif opt == 'm':\n # Get Memory Usage.\n memory_usage = stats['memory_usage']\n st = ['Memory usage:']\n for k, v in memory_usage.viewitems():\n st.append(' %s: %s' % (k, v))\n all_stats.append('\\n'.join(st))\n all_stats.append('')\n elif opt == 'u':\n # Get uptime.\n up = stats['uptime']\n st = ['Uptime:']\n st.append(' Up for %s days, %s hours and %s minutes'\n % (up['days'], up['hours'], up['minutes']))\n all_stats.append('\\n'.join(st))\n all_stats.append('')\n\n print('\\n'.join(all_stats))","def _cmd_metrics(args):\n if (\n len(args.cnarrays) > 1\n and args.segments\n and len(args.segments) > 1\n and len(args.cnarrays) != len(args.segments)\n ):\n raise ValueError(\n \"Number of coverage/segment filenames given must be \"\n \"equal, if more than 1 segment file is given.\"\n )\n\n cnarrs = map(read_cna, args.cnarrays)\n if args.segments:\n args.segments = map(read_cna, args.segments)\n table = metrics.do_metrics(cnarrs, args.segments, args.drop_low_coverage)\n write_dataframe(args.output, table)","def print_help():\n print \"\"\"\nMeasure Operating System Performance (mosp)\n-------------------------------------------\n\nUse this program to measure and report on operating system\nperformance.\n\nThis code measures operating system performance,\nincluding CPU, memory, disk and network, and\noutputs stats to screen and optionally to file\ntoo for use in performance analysis\n\nUses the psutil library\n\nInstall psutil (Ubuntu) if you don't already have it:\n sudo apt-get install python-dev\n sudo pip install psutil\n\nUsage:\n python mosp.py [options]\n\nExample usage:\n python mosp.py -W -i 2\n\nOptions:\n -h --help Display this help and exit\n -m --max-run-time Maximum time to run for before exiting\n (default is infinite)\n -i --interval Interval between requests in seconds\n (default is 1)\n -w --output-file Specify an output filename\n -W Output results to default filename\n default format is:\n mosp-HOSTNAME-YYYYMMDD-HHMMSS.csv\n -b --output-path Specify path to output file directory\n -j --no-header-row Suppress writing header row into CSV\n -v --version Output version information and exit\n\n \"\"\"\n return()","def metrics_env(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_env(cmd_ctx, cpc, options))","def print_help(self):\r\n\t\ttext = \"\\tName: ml_scikit_OPTICS\"\r\n\t\ttext += \"\\n\\t\\tThis machine learning plugin uses scikit-learn's OPTICS algorithm.\\n\"\r\n\t\ttext += \"\\n\\t\\tOptional Parameters:\"\r\n\t\ttext += \"\\n\\t\\t\\tOPTICS_skip_normalization: Do NOT perform normalization (scaling) of data, skip this step.\"\r\n\t\ttext += \"\\n\\t\\t\\OPTICS_eps: Specify eps parameter (default is 1.0).\"\r\n\t\ttext += \"\\n\\t\\t\\OPTICS_min_samples: Specify min_samples parameter (default is 5).\"\r\n#\r\n# OPTICS (with memory complexity n) is an alternative to DBSCAN (with memory complexity n^2)\r\n# which has time complexity n^2 in general with the default max_eps = np.inf. \r\n# We will set max_eps = eps to reduce the run-time.\r\n#\r\n\t\treturn text","def measure(self,command_exe, command_args, measure_out):\n pass","def do_stats(self, args):\n total_cpu = free_cpu = in_use_cpu = 0\n\n summary = self._qm.get_all_host_summary()\n for host_id, host_info in summary.viewitems():\n host_cpu = int(host_info['total cores'])\n total_cpu += host_cpu\n locked = host_info.get('locked by')\n if locked:\n # If host is locked then all CPUs are in use.\n in_use_cpu += host_cpu\n else:\n free_host_cpu = int(host_info['free cores'])\n in_use_cpu += (host_cpu - free_host_cpu)\n free_cpu += free_host_cpu\n\n print('total CPU: ', total_cpu)\n print('used/locked CPU: ', in_use_cpu)\n print('free CPU: ', free_cpu)\n capacity = float(in_use_cpu) / float(total_cpu)\n print('capacity used: %.1f%%' % (capacity * 100,))\n capacity = float(free_cpu) / float(total_cpu)\n print('capacity remaining: %.1f%%' % (capacity * 100,))","def metrics_roce(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_roce(cmd_ctx, cpc, options))","def stats(caesar, input):\n commands = {}\n users = {}\n channels = {}\n\n ignore = set(['f_note', 'startup', 'message', 'noteuri'])\n for (name, user), count in caesar.stats.items(): \n if name in ignore: continue\n if not user: continue\n\n if not user.startswith('#'): \n try: users[user] += count\n except KeyError: users[user] = count\n else: \n try: commands[name] += count\n except KeyError: commands[name] = count\n\n try: channels[user] += count\n except KeyError: channels[user] = count\n\n comrank = sorted([(b, a) for (a, b) in commands.iteritems()], reverse=True)\n userank = sorted([(b, a) for (a, b) in users.iteritems()], reverse=True)\n charank = sorted([(b, a) for (a, b) in channels.iteritems()], reverse=True)\n\n # most heavily used commands\n creply = 'most used commands: '\n for count, command in comrank[:10]: \n creply += '%s (%s), ' % (command, count)\n caesar.say(creply.rstrip(', '))\n\n # most heavy users\n reply = 'power users: '\n for count, user in userank[:10]: \n reply += '%s (%s), ' % (user, count)\n caesar.say(reply.rstrip(', '))\n\n # most heavy channels\n chreply = 'power channels: '\n for count, channel in charank[:3]: \n chreply += '%s (%s), ' % (channel, count)\n caesar.say(chreply.rstrip(', '))","def main():\n processor.custom_config = parse_arguments()\n processor.process()\n logger.info(processor.statistics)\n logger.info(processor.custom_config)","def procs_calculate_axyzc(molecules, n_cores=-1, show_progress=True, scr=None, cmd=XTB_CMD):\n results = None\n return results","async def stats(self, ctx):\n if ctx.invoked_subcommand is None:\n await send_cmd_help(ctx)","def main():\n\n args = parse_args()\n metric_sender = MetricSender(verbose=args.verbose, debug=args.debug)\n\n discovery_key_disk = 'disc.disk'\n interval = 3\n pcp_disk_dev_metrics = ['disk.dev.total', 'disk.dev.avactive']\n item_prototype_macro_disk = '#OSO_DISK'\n item_prototype_key_tps = 'disc.disk.tps'\n item_prototype_key_putil = 'disc.disk.putil'\n\n disk_metrics = pminfo.get_sampled_data(pcp_disk_dev_metrics, interval, 2)\n\n pcp_metrics_divided = {}\n for metric in pcp_disk_dev_metrics:\n pcp_metrics_divided[metric] = {k: v for k, v in disk_metrics.items() if metric in k}\n\n # do TPS checks; use disk.dev.total\n filtered_disk_totals = clean_up_metric_dict(pcp_metrics_divided[pcp_disk_dev_metrics[0]],\n pcp_disk_dev_metrics[0] + '.')\n\n # Add dynamic items\n metric_sender.add_dynamic_metric(discovery_key_disk, item_prototype_macro_disk, filtered_disk_totals.keys())\n\n # calculate the TPS and add them to the ZaggSender\n for disk, totals in filtered_disk_totals.iteritems():\n disk_tps = (totals[1] - totals[0]) / interval\n metric_sender.add_metric({'%s[%s]' % (item_prototype_key_tps, disk): disk_tps})\n\n # do % Util checks; use disk.dev.avactive\n filtered_disk_totals = clean_up_metric_dict(pcp_metrics_divided[pcp_disk_dev_metrics[1]],\n pcp_disk_dev_metrics[1] + '.')\n\n # calculate the % Util and add them to the ZaggSender\n for disk, totals in filtered_disk_totals.iteritems():\n total_active = (float)(totals[1] - totals[0]) / 1000.0\n putil = 100 * total_active / interval\n\n metric_sender.add_metric({'%s[%s]' % (item_prototype_key_putil, disk): putil})\n\n metric_sender.send_metrics()","def process_info(process):\n\thelp(process)","def metrics_flash(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_flash(cmd_ctx, cpc, options))","def phast_cmmd(self):\n temp = '{prog} -R {rho} -C {ecov} -E {elen} -N {chrom} -i MAF {maf} {model} > {wig}\\n'.format(**self.dict)\n return temp.format(fnum=self.fnum)","def help(self, args):\n print('No commands available for this consumer')","def treatCmdOpts(argv):\n baseName = os.path.basename(__file__)\n amc.cBaseName = colored(baseName, 'yellow')\n\n helpTxt = amc.cBaseName + ' analyses observation statistics file for selected GNSSs'\n\n # create the parser for command line arguments\n parser = argparse.ArgumentParser(description=helpTxt)\n\n parser.add_argument('--obsstat', help='observation statistics file', type=str, required=True)\n\n parser.add_argument('--freqs', help='select frequencies to use (out of {freqs:s}, default {freq:s})'.format(freqs='|'.join(gfzc.lst_freqs), freq=colored(gfzc.lst_freqs[0], 'green')), default=gfzc.lst_freqs[0], type=str, required=False, action=gco.freqtype_action, nargs='+')\n\n parser.add_argument('--cutoff', help='cutoff angle in degrees (default {mask:s})'.format(mask=colored('0', 'green')), default=0, type=int, required=False, action=gco.cutoff_action)\n\n parser.add_argument('--dbcvs', help='Add information to CVS database (default {cvsdb:s})'.format(cvsdb=colored(gco.CVSDB_OBSTLE, 'green')), required=False, type=str, default=gco.CVSDB_OBSTLE)\n\n parser.add_argument('--plot', help='displays interactive plots (default False)', action='store_true', required=False, default=False)\n\n parser.add_argument('--logging', help='specify logging level console/file (two of {choices:s}, default {choice:s})'.format(choices='|'.join(gco.lst_logging_choices), choice=colored(' '.join(gco.lst_logging_choices[3:5]), 'green')), nargs=2, required=False, default=gco.lst_logging_choices[3:5], action=gco.logging_action)\n\n # drop argv[0]\n args = parser.parse_args(argv[1:])\n\n # return arguments\n return args.obsstat, args.freqs, args.cutoff, args.dbcvs, args.plot, args.logging","def main(cls):\n parser = cls.make_argument_parser()\n args = parser.parse_args()\n args.device = make_hoomd_device(args)\n benchmark = cls(**vars(args))\n performance = benchmark.execute()\n\n if args.device.communicator.rank == 0:\n print(f'{numpy.mean(performance)}')","def help(self):\n msg = \"`%s' performs the computational aspects of genotyping-by-sequencing.\\n\" % os.path.basename(sys.argv[0])\n msg += \"\\n\"\n msg += \"Usage: %s [OPTIONS] ...\\n\" % os.path.basename(sys.argv[0])\n msg += \"\\n\"\n msg += \"Options:\\n\"\n msg += \" -h, --help\\tdisplay the help and exit\\n\"\n msg += \" -V, --version\\toutput version information and exit\\n\"\n msg += \" -v, --verbose\\tverbosity level (0/default=1/2/3)\\n\"\n msg += \" --proj1\\tname of the project used for steps 1 to 4\\n\"\n msg += \"\\t\\tmention a reference genome only if all samples belong to\\n\"\n msg += \"\\t\\t the same species, and will be mapped to the same ref genome\\n\"\n msg += \" --proj2\\tname of the project used for steps 4 to 8\\n\"\n msg += \"\\t\\tcan be the same as --proj1, or can be different\\n\"\n msg +=\"\\t\\t notably when samples come from different species\\n\"\n msg += \"\\t\\t or if one wants to align reads to different ref genomes\\n\"\n msg += \" --schdlr\\tname of the cluster scheduler (default=SGE)\\n\"\n msg += \" --queue\\tname of the cluster queue (default=normal.q)\\n\"\n msg += \" --resou\\tcluster resources (e.g. 'test' for 'qsub -l test')\\n\"\n msg += \" --rmvb\\tremove bash scripts for jobs launched in parallel\\n\"\n msg += \" --step\\tstep to perform (1/2/3/.../9)\\n\"\n msg += \"\\t\\t1: raw read quality per lane (with FastQC v >= 0.11.2)\\n\"\n msg += \"\\t\\t2: demultiplexing per lane (with demultiplex.py v >= 1.14.0)\\n\"\n msg += \"\\t\\t3: cleaning per sample (with CutAdapt v >= 1.8)\\n\"\n msg += \"\\t\\t4: alignment per sample (with BWA MEM v >= 0.7.12, Samtools v >= 1.3, Picard and R v >= 3)\\n\"\n msg += \"\\t\\t5: local realignment per sample (with GATK v >= 3.5)\\n\"\n msg += \"\\t\\t6: local realignment per genotype (with GATK v >= 3.5)\\n\"\n msg += \"\\t\\t7: variant and genotype calling per genotype (with GATK HaplotypeCaller v >= 3.5)\\n\"\n msg += \"\\t\\t8: variant and genotype calling jointly across genotypes (with GATK GenotypeGVCFs v >= 3.5)\\n\"\n msg += \"\\t\\t9: variant and genotype filtering (with GATK v >= 3.5)\\n\"\n msg += \" --samples\\tpath to the 'samples' file\\n\"\n msg += \"\\t\\tcompulsory for all steps, but can differ between steps\\n\"\n msg += \"\\t\\t e.g. if samples come from different species or are aligned\\n\"\n msg += \"\\t\\t on different ref genomes, different samples file should\\n\"\n msg += \"\\t\\t be used for steps 4-9, representing different subsets of\\n\"\n msg += \"\\t\\t the file used for steps 1-3\\n\"\n msg += \"\\t\\tthe file should be encoded in ASCII\\n\"\n msg += \"\\t\\tthe first row should be a header with column names\\n\"\n msg += \"\\t\\teach 'sample' (see details below) should have one and only one row\\n\"\n msg += \"\\t\\tany two columns should be separated with one tabulation\\n\"\n msg += \"\\t\\tcolumns can be in any order\\n\"\n msg += \"\\t\\trows starting by '#' are skipped\\n\"\n msg += \"\\t\\t12 columns are compulsory (but there can be more):\\n\"\n msg += \"\\t\\t genotype (see details below, e.g. 'Col-0', but use neither underscore '_' nor space ' ' nor dot '.', use dash '-' instead)\\n\"\n msg += \"\\t\\t ref_genome (identifier of the reference genome used for alignment, e.g. 'Atha_v2', but use neither space ' ' nor dot '.'; the full species name, e.g. 'Arabidopsis thaliana', will be present in the file given to --dict)\\n\"\n msg += \"\\t\\t library (e.g. can be the same as 'genotype')\\n\"\n msg += \"\\t\\t barcode (e.g. 'ATGG')\\n\"\n msg += \"\\t\\t seq_center (e.g. 'Broad Institute', 'GenoToul', etc)\\n\"\n msg += \"\\t\\t seq_platform (e.g. 'ILLUMINA', see SAM format specification)\\n\"\n msg += \"\\t\\t seq_platform_model (e.g. 'HiSeq 2000')\\n\"\n msg += \"\\t\\t flowcell (e.g. 'C5YMDACXX')\\n\"\n msg += \"\\t\\t lane (e.g. '3', can be '31' if a first demultiplexing was done per index)\\n\"\n msg += \"\\t\\t date (e.g. '2015-01-15', see SAM format specification)\\n\"\n msg += \"\\t\\t fastq_file_R1 (filename, one per lane, gzip-compressed)\\n\"\n msg += \"\\t\\t fastq_file_R2 (filename, one per lane, gzip-compressed)\\n\"\n msg += \" --fcln\\tidentifier of a flowcell and lane number\\n\"\n msg += \"\\t\\tformat as <flowcell>_<lane-number>, e.g. 'C5YMDACXX_1'\\n\"\n msg += \"\\t\\tif set, only the samples from this lane will be analyzed\\n\"\n msg += \" --pird\\tpath to the input reads directory\\n\"\n msg += \"\\t\\tcompulsory for steps 1 and 2\\n\"\n msg += \"\\t\\twill be added to the columns 'fastq_file_R*' from the sample file\\n\"\n msg += \"\\t\\tif not set, input read files should be in current directory\\n\"\n msg += \" --enz\\tname of the restriction enzyme\\n\"\n msg += \"\\t\\tcompulsory for step 2\\n\"\n msg += \"\\t\\tdefault=ApeKI\\n\"\n msg += \" --dmxmet\\tmethod used to demultiplex\\n\"\n msg += \"\\t\\tcompulsory for step 2\\n\"\n msg += \"\\t\\tdefault=4c (see the help of demultiplex.py to know more)\\n\"\n msg += \" --subst\\tnumber of substitutions allowed during demultiplexing\\n\"\n msg += \"\\t\\tcompulsory for step 2\\n\"\n msg += \"\\t\\tdefault=2\\n\"\n msg += \" --ensubst\\tenforce the nb of substitutions allowed\\n\"\n msg += \"\\t\\tcompulsory for step 2\\n\"\n msg += \"\\t\\tdefault=lenient/strict\\n\"\n msg += \" --adp\\tpath to the file containing the adapters\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tsame format as FastQC: name<tab>sequence\\n\"\n msg += \"\\t\\tname: at least 'adpR1' (also 'adpR2' if paired-end)\\n\"\n msg += \"\\t\\tsequence: from 5' (left) to 3' (right)\\n\"\n msg += \" --errtol\\terror tolerance to find adapters\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=0.2\\n\"\n msg += \" --minovl\\tminimum overlap length between reads and adapters\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=3 (in bases)\\n\"\n msg += \" --minrl\\tminimum length to keep a read\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=35 (in bases)\\n\"\n msg += \" --minq\\tminimum quality to trim a read\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=20 (used for both reads if paired-end)\\n\"\n msg += \" --maxNp\\tmaximum percentage of N to keep a read\\n\"\n msg += \"\\t\\tcompulsory for step 3\\n\"\n msg += \"\\t\\tdefault=0.2\\n\"\n msg += \" --ref\\tpath to the prefix of files for the reference genome\\n\"\n msg += \"\\t\\tcompulsory for steps 4, 5, 6, 7, 8, 9\\n\"\n msg += \"\\t\\tshould correspond to the 'ref_genome' column in --samples\\n\"\n msg += \"\\t\\te.g. '/data/Atha_v2' for '/data/Atha_v2.fa', '/data/Atha_v2.bwt', etc\\n\"\n msg += \"\\t\\tthese files are produced via 'bwa index ...'\\n\"\n msg += \" --dict\\tpath to the 'dict' file (SAM header with @SQ tags)\\n\"\n msg += \"\\t\\tcompulsory for step 4\\n\"\n msg += \"\\t\\tsee 'CreateSequenceDictionary' in the Picard software\\n\"\n msg += \" --jgid\\tcohort identifier to use for joint genotyping\\n\"\n msg += \"\\t\\tcompulsory for steps 8, 9\\n\"\n msg += \"\\t\\tuseful to launch several, different cohorts in parallel\\n\"\n msg += \" --rat\\trestrict alleles to be of a particular allelicity\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tdefault=ALL/BIALLELIC/MULTIALLELIC\\n\"\n msg += \"\\t\\tsee '--restrictAllelesTo' in GATK's SelectVariant\\n\"\n msg += \" --mdp\\tminimum value for DP (read depth; e.g. 10)\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee GATK's VariantFiltration\\n\"\n msg += \" --mgq\\tminimum value for GQ (genotype quality; e.g. 20)\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee GATK's VariantFiltration\\n\"\n msg += \" --mnfg\\tmaximum number of filtered genotypes to keep a variant\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee '--maxFilteredGenotypes' in GATK's SelectVariants\\n\"\n msg += \" --mffg\\tmaximum fraction of filtered genotypes to keep a variant\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee '--maxFractionFilteredGenotypes' in GATK's SelectVariants\\n\"\n msg += \" --mnnc\\tmaximum number of not-called genotypes to keep a variant\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \" --mfnc\\tmaximum fraction of not-called genotypes to keep a variant\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tsee '--maxNOCALLfraction' in GATK's SelectVariants\\n\"\n msg += \" --fam\\tpath to the file containing pedigree information\\n\"\n msg += \"\\t\\tused in step 9\\n\"\n msg += \"\\t\\tdiscard variants with Mendelian violations (see Semler et al, 2012)\\n\"\n msg += \"\\t\\tshould be in the 'fam' format specified by PLINK\\n\"\n msg += \"\\t\\tvalidation strictness (GATK '-pedValidationType') is set at 'SILENT'\\n\"\n msg += \"\\t\\t allowing some samples to be absent from the pedigree\\n\"\n msg += \" --mvq\\tminimum GQ for each trio member to accept a variant as a Mendelian violation\\n\"\n msg += \"\\t\\tused in step 9 if '--fam' is specified\\n\"\n msg += \"\\t\\tdefault=0\\n\"\n msg += \" --xlssf\\tpath to the file with genotypes to exclude\\n\"\n msg += \"\\t\\tused in step 9 (can be especially useful if '--fam' is specified)\\n\"\n msg += \" --tmpd\\tpath to a temporary directory on child nodes (default=.)\\n\"\n msg += \"\\t\\te.g. it can be /tmp or /scratch\\n\"\n msg += \"\\t\\tused in step 4 for 'samtools sort'\\n\"\n msg += \"\\t\\tused in step 7 for 'GATK HaplotypeCaller'\\n\"\n msg += \" --jvmXms\\tinitial memory allocated to the Java Virtual Machine\\n\"\n msg += \"\\t\\tdefault=512m (can also be specified as 1024k, 1g, etc)\\n\"\n msg += \"\\t\\tused in steps 4, 5, 6, 7 and 8 for Picard and GATK\\n\"\n msg += \" --jvmXmx\\tmaximum memory allocated to the Java Virtual Machine\\n\"\n msg += \"\\t\\tdefault=4g\\n\"\n msg += \"\\t\\tused in steps 4, 5, 6, 7 and 8 for Picard and GATK\\n\"\n msg += \" --queue2\\tname of the second cluster queue (default=bigmem.q)\\n\"\n msg += \"\\t\\tused in step 4 for Picard to collect insert sizes\\n\"\n msg += \" --knowni\\tpath to a VCF file with known indels (for local realignment)\\n\"\n msg += \" --known\\tpath to a VCF file with known variants (e.g. from dbSNP)\\n\"\n msg += \" --force\\tforce to re-run step(s)\\n\"\n msg += \"\\t\\tthis removes without warning the step directory if it exists\\n\"\n msg += \"\\n\"\n msg += \"Examples:\\n\"\n msg += \" %s --step 1 --samples samples.txt\\n\" % os.path.basename(sys.argv[0])\n msg += \"\\n\"\n msg += \"Details:\\n\"\n msg += \"This program aims at genotyping a set of 'genotypes' using data from\\n\"\n msg += \"a restriction-assisted DNA sequencing (RAD-seq) experiment, also known\\n\"\n msg += \"as a genotyping-by-sequencing (GBS) experiment.\\n\"\n msg += \"Here, by 'genotype', we mean the entity which is the focus of the\\n\"\n msg += \"study. For instance, it can be a plant variety (or a human being), or\\n\"\n msg += \"the specific clone of a given plant variety (or a specific tumor of a\\n\"\n msg += \"given human being), etc.\\n\"\n msg += \"Importantly, note that the content of the 'genotype' column will\\n\"\n msg += \"be used to set the 'SM' (sample) tag of the 'RG' (read group) header\\n\"\n msg += \"record type of the SAM format (see http://www.htslib.org/). However,\\n\"\n msg += \"internal to this program, the term 'sample' corresponds to the unique\\n\"\n msg += \"quadruplet (genotype,flowcell,lane,barcode) for steps 1 and 2, and to\\n\"\n msg += \"the unique triplet (genotype,flowcell,lane) for the others.\\n\"\n msg += \"Jobs are executed in parallel (--schdlr). Their return status is\\n\"\n msg += \"recorded in a SQLite database which is removed at the end. If a job\\n\"\n msg += \"fails, the whole script stops with an error.\\n\"\n msg += \"\\n\"\n msg += \"Dependencies:\\n\"\n msg += \"Python >= 2.7; Biopython; pyutilstimflutre >= 0.5\\n\"\n msg += \"\\n\"\n msg += \"Report bugs to <timothee.flutre@inra.fr>.\"\n print(msg); sys.stdout.flush()","def qc_metrics(self, files_in, qc_files):\n self.cmd(\"{samtools} index {bam_in}\"\n .format(\n samtools=self.cmds[\"samtools\"],\n bam_in=files_in[0],\n ),\n shell=True)\n self.cmd(\"{samtools} idxstats {bam_in} | tee {qc_file}\"\n .format(\n samtools=self.cmds[\"samtools\"],\n bam_in = files_in[0],\n qc_file = qc_files[0],\n ),\n shell=True,\n log_output=True)\n self.cmd(\"{samtools} flagstat {bam_in} | tee {qc_file}\"\n .format(\n samtools=self.cmds[\"samtools\"],\n bam_in = files_in[0],\n qc_file = qc_files[1],\n ),\n shell=True,\n log_output=True)\n \n self.checkpoint(qc_files[0])\n self.checkpoint(qc_files[1])\n self.checkpoint(qc_files[2])","def usage(progname):\n \n sys.stderr.write(\"Usage: \" +progname + \" [-cmnv] [-z score] \"\n \" <outdir>\\n\")\n sys.stderr.write(' -c class level not fold level evaluation\\n')\n sys.stderr.write(' -m read multiquery file on stdin\\n')\n sys.stderr.write(' -n negate scores (so that most -ve is best)\\n')\n sys.stderr.write(' -v verbose messages to stderr\\n')\n sys.stderr.write(' -z score : assign identifiers not present in the output a score of score\\n')\n sys.exit(1)","def _cmd_segmetrics(args):\n if not 0.0 < args.alpha <= 1.0:\n raise RuntimeError(\"alpha must be between 0 and 1.\")\n\n if not any((args.location_stats, args.spread_stats, args.interval_stats)):\n logging.info(\"No stats specified\")\n return\n\n # Calculate all metrics\n cnarr = read_cna(args.cnarray)\n segarr = read_cna(args.segments)\n segarr = do_segmetrics(\n cnarr,\n segarr,\n args.location_stats,\n args.spread_stats,\n args.interval_stats,\n args.alpha,\n args.bootstrap,\n args.smooth_bootstrap,\n skip_low=args.drop_low_coverage,\n )\n tabio.write(segarr, args.output or segarr.sample_id + \".segmetrics.cns\")","def main(argv=None):\n\n if argv is None:\n argv = sys.argv\n\n # setup command line parser\n parser = E.OptionParser(version=\"%prog version: $Id$\",\n usage=globals()[\"__doc__\"])\n\n parser.add_option(\"-t\", \"--test\", dest=\"test\", type=\"string\",\n help=\"supply help\")\n\n parser.add_option(\"--method\", dest=\"method\", type=\"choice\",\n choices=(\"metrics\", \"summary\", \"module_summary\"),\n help=\"method to summarise clustering\")\n\n parser.add_option(\"--ref-gtf-files\", dest=\"ref_gtf\", type=\"string\",\n help=\"comma separated list of reference gtf files\")\n\n # add common options (-h/--help, ...) and parse command line\n (options, args) = E.Start(parser, argv=argv)\n\n if options.method == \"metrics\":\n infile = argv[-1]\n E.info(\"loading input file: %s\" % infile)\n assert infile\n\n df = pd.read_table(infile,\n sep=\"\\t\",\n header=None,\n index_col=0)\n\n df = df.ix[:, :50]\n cluster_combs = (x for x in itertools.combinations(df.columns,\n 2))\n genes = df.index\n results_dict = {}\n all_clusts = {}\n\n E.info(\"setting up cluster containers\")\n for i in df.columns:\n clusters = set(df[i].values.tolist())\n cluster_dict = {}\n for clust in clusters:\n cluster_dict[clust] = []\n for gene in genes:\n cluster_dict[df[i][gene]].append(gene)\n\n for col in clusters:\n col_set = set()\n clust_col = cluster_dict[col]\n gene_members = itertools.combinations(clust_col,\n 2)\n col_set.update(gene_members)\n cluster_dict[col] = col_set\n all_clusts[i] = cluster_dict\n E.info(\"generating all pair-wise cluster comparisons\")\n E.info(\"calculating adjusted mutual information\")\n for k in cluster_combs:\n clusters1 = all_clusts[k[0]]\n clusters2 = all_clusts[k[1]]\n metric_dict = {}\n metric_dict['AMI'] = TS.adjustedMutualInformation(clusters1,\n clusters2)\n results_dict[k] = metric_dict\n\n res_frame = pd.DataFrame(results_dict).T\n res_frame = res_frame.reset_index()\n res_frame.drop(['level_0'], inplace=True, axis=1)\n res_frame.drop(['level_1'], inplace=True, axis=1)\n\n # flatten rand indices and add to output dataframe\n rand_arrays = TS.randIndexes(df)\n flat_adj_rand = TS.unravel_arrays(rand_arrays[0])\n flat_rand = TS.unravel_arrays(rand_arrays[1])\n res_frame['Rand_Index'] = flat_rand\n res_frame['Adjusted_Rand_Index'] = flat_adj_rand\n E.info(\"aggregating results\")\n\n res_frame.to_csv(options.stdout,\n sep=\"\\t\",\n index_label='idx')\n\n elif options.method == \"summary\":\n infiles = argv[-1]\n list_of_files = infiles.split(\",\")\n\n file_dict = {}\n for fle in list_of_files:\n fname = fle.split(\"/\")[-1]\n condition = fname.split(\"-\")[0]\n ref = fname.split(\"-\")[1]\n df_ = pd.read_table(fle,\n sep=\"\\t\",\n header=0,\n index_col=0)\n df_.columns = ['gene_id', 'cluster']\n clust_dict = {}\n for idx in df_.index:\n cluster = df_.loc[idx]['cluster']\n gene = df_.loc[idx]['gene_id']\n try:\n clust_dict[cluster] += 1\n except KeyError:\n clust_dict[cluster] = 1\n med_size = np.median(clust_dict.values())\n file_dict[fname] = {'condition': condition,\n 'reference': ref,\n 'median_cluster_size': med_size}\n\n outframe = pd.DataFrame(file_dict).T\n outframe.to_csv(options.stdout,\n sep=\"\\t\",\n index_label='idx')\n\n elif options.method == \"module_summary\":\n # get lncRNA/gene lengths from reference gtfs\n ref_gtfs = options.ref_gtf.split(\",\")\n length_dict = {}\n for ref in ref_gtfs:\n oref = IOTools.openFile(ref, \"rb\")\n git = GTF.transcript_iterator(GTF.iterator(oref))\n for gene in git:\n for trans in gene:\n length = trans.end - trans.start\n try:\n length_dict[trans.gene_id] += length\n except KeyError:\n length_dict[trans.gene_id] = length\n oref.close()\n\n infiles = argv[-1]\n list_of_files = infiles.split(\",\")\n\n fdfs = []\n for fle in list_of_files:\n cond = fle.split(\"/\")[-1].split(\"-\")[0]\n refer = fle.split(\"/\")[-1].split(\"-\")[1]\n _df = pd.read_table(fle, sep=\"\\t\",\n header=0, index_col=0)\n _df.columns = ['gene_id', 'cluster']\n clusters = set(_df['cluster'])\n c_dict = {}\n # summarize over each cluster\n for clust in clusters:\n lengths = []\n c_df = _df[_df['cluster'] == clust]\n for lid in c_df['gene_id']:\n lengths.append(length_dict[lid])\n c_dict[clust] = {'cluster_size': len(c_df['gene_id']),\n 'mean_length': np.mean(lengths),\n 'index': (cond, refer),\n 'module': clust}\n cdf = pd.DataFrame(c_dict).T\n # use a multindex for hierarchical indexing\n midx = pd.MultiIndex.from_tuples(cdf['index'])\n cdf.index = midx\n cdf.drop(['index'], inplace=True, axis=1)\n fdfs.append(cdf)\n\n # generate a single output df\n s_df = fdfs[0]\n fdfs.pop(0)\n for df in fdfs:\n s_df = s_df.append(df)\n\n s_df.to_csv(options.stdout,\n index_label=(\"condition\", \"reference\"),\n sep=\"\\t\")\n\n # write footer and output benchmark information.\n E.Stop()","def main():\n\n parser = argparse.ArgumentParser(description=main.__doc__,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n epilog=\"Homepage: https://github.com/kbat/mc-tools\")\n parser.add_argument('com', type=str, help='plot requests file name', nargs='?', default=\"/tmp/foo.c\")\n parser.add_argument('comout', type=str, help='COMOUT file name', nargs='?', default=\"zoom\")\n\n args = parser.parse_args()\n\n cmd = {} # dictionary of commands\n\n bas = False\n plane = False\n \n with open(args.com) as f:\n for line in f.readlines():\n words = line.strip().split()\n if len(words) is 0:\n continue\n\n for i,w in enumerate(words):\n if re.search(\"^bas\", w):\n cmd['bas'] = list(map(float, words[i+1:i+7]))\n if plane is False: bas = True # basis was before plane cuts\n elif re.search(\"^or\", w):\n cmd['or'] = list(map(float, words[i+1:i+4]))\n elif re.search(\"^ex\", w):\n try: # both x and y scales are given\n cmd['ex'] = list(map(float, words[i+1:i+3]))\n continue\n except ValueError: # just 1 scale is given\n cmd['ex'] = list(map(float, words[i+1:i+2]))\n elif re.search(\"^lab\", w):\n cmd['label'] = list(map(int, map(float, words[i+1:i+3]))) #+ [words[i+3]]\n elif re.search(\"^p[xyz]\", w):\n cmd[w] = [float(words[i+1])]\n if bas is False: plane = True # plane cuts were before basis\n elif re.search(\"^legend\", w):\n cmd[w] = [words[i+1]]\n elif w == \"scale\":\n print(w)\n if int(words[i+1]): # no need to put 'scale 0'\n cmd[w] = [words[i+1]]\n elif w in (\"mesh\"):\n if int(words[i+1])==1: # no need to put 'mesh 1'\n cmd[w] = [words[i+1]]\n\n print(bas, plane)\n\n if plane: # bas was first\n keys = ('bas', 'or', 'ex', 'px', 'py', 'pz', 'label', 'mesh', 'legend', 'scale')\n elif bas:\n keys = ('or', 'ex', 'px', 'py', 'pz', 'bas', 'label', 'mesh', 'legend', 'scale')\n else:\n keys = {'or', 'ex', 'label', 'mesh', 'legend', 'scale'}\n \n with open(args.comout, 'w') as f:\n for key in keys:\n if key in cmd:\n # newline required by mcplot:\n if key in ('mesh', 'legend', 'scale', 'label'):\n f.write(\"\\n\")\n f.write(\"%s %s \" % (key,\" \".join(str(e) for e in cmd[key]),))\n f.write(\"\\n\")","def main():\n parser = argparse.ArgumentParser(\n description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\n )\n add_common_snmp_args(parser)\n parser.add_argument(\n \"-w\",\n \"--warning\",\n type=int,\n default=70,\n help=\"Warning memory usage percentage (0-100)\",\n )\n parser.add_argument(\n \"-c\",\n \"--critical\",\n type=int,\n default=90,\n help=\"Critical memory usage percentage (0-100)\",\n )\n parser.add_argument(\n \"-f\",\n \"--family\",\n required=True,\n help=\"Switch family. Supported families: 1910, 1920, 1920S\",\n )\n\n config = vars(parser.parse_args())\n check_snmp_args(config)\n check_thresholds(config)\n\n dataset = {}\n\n if config[\"family\"] == \"1920S\":\n cpu = ObjectType(\n ObjectIdentity(\n \"HP-SWITCHING-MIB\", \"agentSwitchCpuProcessTotalUtilization\", 0\n )\n )\n elif config[\"family\"] in [\"1910\", \"1920\"]:\n cpu = ObjectType(\n ObjectIdentity(\"HH3C-ENTITY-EXT-MIB\", \"hh3cEntityExtCpuUsage\", 8)\n )\n else:\n unknown_exit(SERVICE, f\"Switch family {config['family']} NOT known\")\n\n try:\n raw_data = get_snmp_data(config, cpu)\n except ValueError as err:\n unknown_exit(SERVICE, err)\n add_vars_to_dataset(dataset, raw_data)\n\n if config[\"family\"] == \"1920S\":\n dataset[\"cpu_usage\"] = get_hp_cpu_usage(\n dataset[\"agentSwitchCpuProcessTotalUtilization\"]\n )\n elif config[\"family\"] in [\"1910\", \"1920\"]:\n dataset[\"cpu_usage\"] = int(dataset[\"hh3cEntityExtCpuUsage\"])\n else:\n unknown_exit(SERVICE, f\"Switch family {config['family']} NOT known\")\n\n state, message = generate_output(config, dataset)\n report(state, message)","def cmd_help(args):","def eval_cpuset():\n\tnum_cpu = run('grep -c ^processor /proc/cpuinfo',quiet=True,warn_only=True)\n\tprint(red('Number of cpus : \\t'+num_cpu))","def handle_program_options():\n parser = argparse.ArgumentParser(description=\"Gather numeric information \\\n about the processed sequence data in an \\\n MG-RAST project.\")\n parser.add_argument('project_id',\n help=\"The project identifier (MG-RAST ID)\")\n parser.add_argument('-a', '--auth_key',\n help=\"An MG-RAST API authorization key. This is \\\n necessary to access projects marked as private.\")\n parser.add_argument('-g', '--group_by', action='append',\n help=\"A string that matches some part of the \\\n 'Metagenome Name' field. All matching project \\\n metagenomes will be grouped by this identifier \\\n and their stats will be summed. This option can \\\n be specified multiple times to create multiple \\\n groups. All non-matching metagenomes will \\\n appear separately in the table. NOTE: \\\n Strings will be matched longest first. This \\\n allows for matching names that might be a \\\n substring of another match. For example: -g S \\\n -g NS. The name field will first be matched \\\n against the longest string (NS) first and then \\\n each smaller string in order.\")\n parser.add_argument('-o', '--output_filename', default='meta_stats.txt',\n help=\"The name of the file the project summary \\\n information will be written to.\")\n\n# parser.add_argument('-v', '--verbose', action='store_true')\n\n return parser.parse_args()","def qsub_cmmd(self):\n temp = 'qsub -l mem=1G,time=:5: -cwd -j y -o {log} {job}'.format(**self.dict)\n return temp.format(fnum=self.fnum)","def process_cl_args():\n\n parser = argparse.ArgumentParser(add_help=False)\n parser.add_argument('commands', nargs='*')\n parser.add_argument('--help', '-h', action='store_true')\n parser.add_argument('--version', '-v', action='store_true')\n parser.add_argument('--debug', '-d', action='store_true')\n parser.add_argument('--logging', '-l', action='store_true')\n parser.add_argument('--no-autosize', action='store_true')\n parser.add_argument('--no-preload', action='store_true')\n args = parser.parse_args()\n\n if args.version:\n xprint(get_version_info())\n xprint(\"\")\n sys.exit()\n\n elif args.help:\n for x in helptext():\n xprint(x[2])\n sys.exit()\n\n if args.debug or os.environ.get(\"mpsytdebug\") == \"1\":\n xprint(get_version_info())\n g.debug_mode = True\n g.no_clear_screen = True\n logfile = os.path.join(tempfile.gettempdir(), \"mpsyt.log\")\n logging.basicConfig(level=logging.DEBUG, filename=logfile)\n logging.getLogger(\"pafy\").setLevel(logging.DEBUG)\n\n elif args.logging or os.environ.get(\"mpsytlog\") == \"1\":\n logfile = os.path.join(tempfile.gettempdir(), \"mpsyt.log\")\n logging.basicConfig(level=logging.DEBUG, filename=logfile)\n logging.getLogger(\"pafy\").setLevel(logging.DEBUG)\n\n if args.no_autosize:\n g.detectable_size = False\n\n g.command_line = \"playurl\" in args.commands or \"dlurl\" in args.commands\n if g.command_line:\n g.no_clear_screen = True\n\n if args.no_preload:\n g.preload_disabled = True\n\n g.argument_commands = args.commands","def main():\n parser = argparse.ArgumentParser(description='investigate code health and random statistics')\n sub_parsers = parser.add_subparsers(dest='command_name', title='Commands', help='', metavar='<command>')\n\n sub = sub_parsers.add_parser('line-count', help='list line counts')\n sub.add_argument('files', nargs='+', help='files or folders to look in')\n sub.add_argument('--each', type=int, default=1)\n sub.add_argument('--show', action='store_true')\n sub.add_argument('--include-empty', dest='discard_empty', action='store_false')\n sub.set_defaults(func=handle_line_count)\n\n sub = sub_parsers.add_parser('include-list', help='list headers from files')\n cc.add_argument(sub)\n sub.add_argument('files', nargs='+')\n sub.add_argument('--print', dest='print_files', action='store_true')\n sub.add_argument('--print-stats', dest='print_stats', action='store_true')\n sub.add_argument('--print-max', dest='print_max', action='store_true')\n sub.add_argument('--no-list', dest='print_list', action='store_false')\n sub.add_argument('--count', default=2, type=int, help=\"only print includes that are more or equal to <count>\")\n sub.add_argument('--limit', nargs='+', help=\"limit search to theese files and folders\")\n sub.set_defaults(func=handle_list)\n\n sub = sub_parsers.add_parser('include-gv', help='generate a graphviz of the includes')\n cc.add_argument(sub)\n sub.add_argument('files', nargs='+')\n sub.add_argument('--limit', nargs='+', help=\"limit search to theese files and folders\")\n sub.add_argument('--group', action='store_true', help=\"group output\")\n sub.add_argument('--cluster', action='store_true', help=\"group output into clusters\")\n sub.set_defaults(func=handle_gv)\n\n sub = sub_parsers.add_parser('list-indents', help='list the files with the maximum indents')\n sub.add_argument('files', nargs='+')\n sub.add_argument('--each', type=int, default=1, help='group counts')\n sub.add_argument('--show', action='store_true', help='include files in list')\n sub.add_argument('--hist', action='store_true', help='show simple histogram')\n sub.add_argument('--include-empty', dest='discard_empty', action='store_false')\n sub.set_defaults(func=handle_list_indents)\n\n sub = sub_parsers.add_parser('missing-pragma-once', help='find headers with missing include guards')\n sub.add_argument('files', nargs='+')\n sub.set_defaults(func=handle_missing_include_guards)\n\n sub = sub_parsers.add_parser('missing-in-cmake', help='find files that existis on disk but missing in cmake')\n sub.add_argument('files', nargs='+')\n cc.add_argument(sub)\n sub.set_defaults(func=handle_missing_in_cmake)\n\n sub = sub_parsers.add_parser('list-no-project-folders', help='find projects that have not set the solution folder')\n sub.add_argument('files', nargs='+')\n cc.add_argument(sub)\n sub.set_defaults(func=handle_list_no_project_folder)\n\n sub = sub_parsers.add_parser('check-files', help=\"find files that doesn't match the name style\")\n sub.add_argument('files', nargs='+')\n cc.add_argument(sub)\n sub.set_defaults(func=handle_check_files)\n\n args = parser.parse_args()\n if args.command_name is not None:\n args.func(args)\n else:\n parser.print_help()","def metrics_networkport(cmd_ctx, cpc, adapter, **options):\n cmd_ctx.execute_cmd(\n lambda: cmd_metrics_networkport(cmd_ctx, cpc, adapter, options))","def HelpCommand(self, unused_args, unused_sub_opts=None, unused_headers=None,\n unused_debug=None):\n self.OutputUsageAndExit()","def command_help(args):\n\tprint_usage()\n\treturn 0","def main():\n known_args, unknown_args = parse_known_args()\n if not unknown_args:\n # return an error message if no command is provided\n sys.exit(\"Please provide a command to benchmark: $ humann_benchmark COMMAND\")\n try:\n process = subprocess.Popen(\" \".join(unknown_args),shell=True)\n except (EnvironmentError, subprocess.CalledProcessError):\n sys.exit(\"Unable to execute command: \" + \" \".join(unknown_args))\n pid=str(process.pid)\n start=time.time()\n max_memory=0\n while process.poll() is None:\n time.sleep(1)\n # while the process is running check on the memory use\n # get the pids of the main process and all children (and their children)\n pids=get_pids(pid)\n stdout=subprocess.check_output([\"ps\",\"--pid\",\",\".join(pids),\"-o\",\"pid,rss,command\"]).decode(\"utf-8\")\n print(\"\\n\"+stdout+\"\\n\")\n # remove the header from the process output\n status=[i.split() for i in filter(lambda x: x, stdout.split(\"\\n\")[1:])]\n # memory is the sum of all rss\n memory=sum(int(i[1]) for i in status)\n if memory > max_memory:\n max_memory=memory\n \n end=time.time()\n print(\"Time: {:.0f} minutes\".format((end-start)/60))\n print(\"Max Memory (RSS): {:.1f} GB\".format(max_memory*1.0/1024**2))","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 printHelp():\n print(\"amqWorkApiMass.py -n <msgcnt> -b <body> -m <headers> -s <path/to/bodyandheaders>\")","def _usage_options_example(self):\n pass","def main(argv):\n version = \"0.1.2\"\n interval = 1\n max_run_time = 0\n finished = 0\n first_time = 1\n output_file = 0\n output_file_enabled = 0\n output_path = 0\n header_row = 1\n\n #*** Get the hostname for use in filenames etc:\n hostname = socket.gethostname()\n\n #*** Start by parsing command line parameters:\n try:\n opts, args = getopt.getopt(argv, \"hu:m:ni:w:Wb:jv\",\n [\"help\",\n \"url=\",\n \"max-run-time=\",\n \"no-keepalive\",\n \"interval=\",\n \"output-file=\",\n \"output-path=\",\n \"no-header-row\",\n \"version\"])\n except getopt.GetoptError as err:\n print \"mosp: Error with options:\", err\n print_help()\n sys.exit(2)\n for opt, arg in opts:\n if opt in (\"-h\", \"--help\"):\n print_help()\n sys.exit()\n elif opt in (\"-v\", \"--version\"):\n print 'mosp.py version', version\n sys.exit()\n elif opt in (\"-m\", \"--max-run-time\"):\n max_run_time = float(arg)\n elif opt in (\"-i\", \"--interval\"):\n interval = float(arg)\n elif opt in (\"-w\", \"--output-file\"):\n output_file = arg\n output_file_enabled = 1\n elif opt == \"-W\":\n output_file = \"mosp-\" + hostname + \"-\" + \\\n time.strftime(\"%Y%m%d-%H%M%S.csv\")\n output_file_enabled = 1\n elif opt in (\"-b\", \"--output-path\"):\n output_path = arg\n elif opt in (\"-j\", \"--no-header-row\"):\n header_row = 0\n\n print \"\\nMeasure Operating System Performance (mosp) version\", \\\n version\n\n #*** Display output filename:\n if output_file_enabled:\n if output_path:\n output_file = os.path.join(output_path, output_file)\n print \"Results filename is\", output_file\n else:\n print \"Not outputing results to file, as option not selected\"\n\n if not header_row:\n print \"Not writing a header row to CSV\"\n\n #*** Use this if max_run_time is set:\n initial_time = time.time()\n\n #*** Instantiate classes:\n cpus = CPUs()\n swap = Swap()\n nics = NICs()\n\n #*** Start the loop:\n while not finished:\n timenow = datetime.datetime.now()\n timestamp = timenow.strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]\n start_time = time.time()\n\n #*** Update CPU measurements:\n cpus.update()\n\n #*** Update swap measurements:\n swap.update()\n\n #*** Update network measurements:\n nics.update()\n\n #*** Put the stats into a nice string for printing and\n #*** writing to file:\n result_csv = str(timestamp) + \",\" \\\n + cpus.csv() \\\n + swap.csv() \\\n + nics.csv() \\\n + \"\\n\"\n result_kvp = str(timestamp) + \" \" \\\n + cpus.kvp() \\\n + swap.kvp() \\\n + nics.kvp()\n print result_kvp\n if output_file_enabled:\n #*** Header row in CSV:\n if first_time and header_row:\n #*** Write a header row to CSV:\n header_csv = \"time,\" + cpus.csv_header(hostname) + \\\n swap.csv_header(hostname) + \\\n nics.csv_header(hostname) + \\\n \"\\n\"\n first_time = 0\n with open(output_file, 'a') as the_file:\n the_file.write(header_csv)\n\n #*** Write a data row to CSV:\n with open(output_file, 'a') as the_file:\n the_file.write(result_csv)\n\n if max_run_time:\n if (start_time - initial_time) > max_run_time:\n break\n\n #*** Sleep for interval seconds:\n time.sleep(interval)","def printOptions(opts,subject_ids,session_ids,task_list, run_list, acq, rec):\n uname = os.popen('uname -s -n -r').read()\n print \"\\n\"\n print \"* Pipeline started at \"+time.strftime(\"%c\")+\"on \"+uname\n print \"* Command line is : \\n \"+str(sys.argv)+\"\\n\"\n print \"* The source directory is : \"+opts.sourceDir\n print \"* The target directory is : \"+opts.targetDir+\"\\n\"\n print \"* Data-set Subject ID(s) is/are : \"+str(', '.join(subject_ids))+\"\\n\"\n # print \"* PET conditions : \"+ ','.join(opts.condiList)+\"\\n\"\n print \"* Sessions : \", session_ids, \"\\n\"\n print \"* Tasks : \" , task_list , \"\\n\"\n print \"* Runs : \" , run_list , \"\\n\"\n print \"* Acquisition : \" , acq , \"\\n\"\n print \"* Reconstruction : \" , rec , \"\\n\"","def usage():","def usage():","def help(cls, extra_args=None):\n if (_is_text_interface()):\n return _create_text_help_str(cls, cls._TEXT_USAGE)\n else:\n return cls._GRAPHICAL_USAGE","def metrics_lpar(cmd_ctx, cpc, lpar, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_lpar(cmd_ctx, cpc, lpar, options))","def collect_cluster_info(output_dir, k8s_cli):\n collect_helper(output_dir, cmd=\"{} cluster-info\".format(k8s_cli),\n file_name=\"cluster_info\", resource_name=\"cluster-info\")","def print_performance_info(self):\n pass","def collect_usage_pieces(self, ctx):\n pieces = super(ProfilingCommand, self).collect_usage_pieces(ctx)\n assert pieces[-1] == '[ARGV]...'\n pieces.insert(-1, '[--]')\n return pieces","def metrics_adapter(cmd_ctx, cpc, adapter, **options):\n cmd_ctx.execute_cmd(\n lambda: cmd_metrics_adapter(cmd_ctx, cpc, adapter, options))","async def sysinfo(self, ctx: Context):\n\t\tstart = time.perf_counter()\n\t\tend = time.perf_counter()\n\t\tduration = (end - start) * 1000\n\t\tcpuavg = psutil.cpu_percent(interval=None)\n\t\tmem = psutil.virtual_memory()[2]\n\t\tdurround = round(duration, 3)\n\t\tosun = os.uname()\n\t\tawait self.send(f\"System Info | CPU: {cpuavg}% | RAM: {mem}% | Latency: {durround * 1000}ms | OS: {sys.platform}\", whisper=[ctx.author.id])","def usage(err=''):\r\n m = '%s\\n' %err\r\n m += 'Default usage is to list Cases closed for the 30 days\\n'\r\n m += '\\n Example:\\n'\r\n m += ' closedcases -n 90 \\n' \r\n m += ' \\n'\r\n# m += ' closedcases -n 60 -s blast5 \\n'\r\n return m","def cmdline(self, executable, options, task, rlimits):\n data_model_param = get_data_model_from_task(task, {ILP32: \"-m32\", LP64: \"-m64\"})\n print(options)\n if data_model_param and not any(\n option.startswith(\"--clang-options=\") for option in options\n ):\n options += [\"--clang-options=\" + data_model_param]\n\n if task.property_file:\n options += [\"--svcomp-property\", task.property_file]\n else:\n raise UnsupportedFeatureException(\n \"SMACK can't execute without a property file.\"\n )\n\n options += [task.single_input_file]\n\n return [executable] + options","def usage():\n print(\"[1] Getting help from a cipher \")\n print(\" ---> ./cryptogra.py caesar -h \")\n print(\"\")","def _get_metrics_options(metrics):\n metrics_options = []\n if metrics is None:\n metrics = []\n for static_metric in metrics:\n metrics_options += [\n \"-m\",\n static_metric.metric.mp_metric_name,\n str(static_metric.value),\n ]\n return metrics_options","def getHelp(self):\r\n help_str =\\\r\n \"\"\"##########################################################################################\r\n#\r\n# Required:\r\n#\r\n# --query_NAST multi-fasta file containing query sequences in alignment format\r\n#\r\n# Common opts:\r\n#\r\n# --db_NAST db in NAST format\r\n# --db_FASTA db in fasta format (megablast formatted)\r\n#\r\n#\r\n# -n number of top matching database sequences to compare to (default 15)\r\n# -R min divergence ratio default: 1.007\r\n# -P min percent identity among matching sequences (default: 90)\r\n#\r\n# ## parameters to tune ChimeraParentSelector:\r\n#\r\n# Scoring parameters:\r\n# -M match score (default: +5)\r\n# -N mismatch penalty (default: -4)\r\n# -Q min query coverage by matching database sequence (default: 70)\r\n# -T maximum traverses of the multiple alignment (default: 1)\r\n\r\n#\r\n# ## parameters to tune ChimeraPhyloChecker:\r\n#\r\n#\r\n# --windowSize default 50\r\n# --windowStep default 5\r\n# --minBS minimum bootstrap support for calling chimera (default: 90)\r\n# -S percent of SNPs to sample on each side of breakpoint for computing bootstrap support (default: 10)\r\n# --num_parents_test number of potential parents to test for chimeras (default: 3)\r\n# --MAX_CHIMERA_PARENT_PER_ID Chimera/Parent alignments with perID above this are considered non-chimeras (default 100; turned off)\r\n#\r\n# ## misc opts\r\n#\r\n# --printFinalAlignments shows alignment between query sequence and pair of candidate chimera parents\r\n# --printCSalignments print ChimeraSlayer alignments in ChimeraSlayer output\r\n# --exec_dir chdir to here before running\r\n#\r\n#########################################################################################\r\n \"\"\"\r\n return help_str","def usage(msgarg):\n if msgarg:\n sys.stderr.write(\"error: %s\\n\" % msgarg)\n print(\"\"\"\\\n usage: %s [options]\n\n options:\n -d increase debug msg verbosity level\n -c N emit N classes (def: 500) per instances\n -I N emit N instances\n\n \"\"\" % os.path.basename(sys.argv[0]))\n sys.exit(1)","def DoHelp(options, args):\n __pychecker__ = 'unusednames=options'\n if len(args) == 1 and args[0] in COMMAND_USAGE_TEXT:\n print(COMMAND_USAGE_TEXT[args[0]])\n else:\n raise gclient_utils.Error(\"unknown subcommand '%s'; see 'gclient help'\" %\n args[0])","def run(self, line):\n LOGGER.info(\"Scalable PMEM: {}\".format(self.name))\n try:\n (options, _) = self._parse_arglist(line)\n except:\n if (\"-h\" in line) or (\"--help\" in line):\n return ReturnCodes.SUCCESS\n else:\n raise InvalidCommandLineErrorOPTS(\"\")\n\n if len(args):\n InvalidCommandLineError(\"This command takes no parameters.\")\n\n LOGGER.info(\"Options: {}\".format(options))\n\n if not self._chif_lib:\n self._helpers.failNoChifLibrary()\n\n enable = True\n if options.enableFeature is False:\n enable = False\n\n self.enableOrDisableFeature(enable)\n\n #Return code\n return ReturnCodes.SUCCESS","def help_opt(self):\n print(OPTIONS)","def usage(self, host):","def print_usage_command(self):\n print self.get_usage_command()","def print_usage_command(self):\n print self.get_usage_command()","def main():\n DataClasses = [FamilyStats, SeqHdrStats, UniProtStats]\n CmdLineOps, Args = parse_command_line_options()\n ThreadManager(DataClasses)\n print_results(DataClasses, CmdLineOps.output_file)\n return","def Usage(shorthelp=0, writeto_stdout=0, detailed_error=None,\n exitcode=None, show_cmd=None, show_global_flags=False):\n printer('%s: Incorrect usage; details below.' % show_cmd)\n printer('Correct usage is as follows:')\n printer('')\n for line in (' ' + cmd.__doc__.rstrip()).splitlines():\n printer(line)\n # Print out str(FLAGS) for just the UICmd-specific flags.\n tmp_flags = flags.FlagValues()\n unused_cmd = type(cmd)(show_cmd, tmp_flags)\n prefix = _UICMD_MODULE_NAME + ':\\n'\n flag_str = tmp_flags.ModuleHelp(_UICMD_MODULE_NAME)\n flag_str = flag_str.lstrip()\n if flag_str.startswith(prefix):\n flag_str = flag_str[len(prefix):]\n if flag_str:\n printer('')\n printer('flags:')\n for line in flag_str.splitlines():\n printer(line)\n if detailed_error is not None:\n printer('')\n printer('The incorrect usage is as follows:')\n printer('')\n for line in unicode(detailed_error).splitlines():\n printer(' ' + line)","def parsing_arguments(args=None):\n description = ''\n parser = argparse.ArgumentParser(\n prog='hatchet plot-cn',\n description=description,\n formatter_class=argparse.RawTextHelpFormatter,\n )\n parser.add_argument('INPUT', help='One or more space-separated files in CN_BBC format')\n parser.add_argument(\n '-n',\n '--patientnames',\n required=False,\n default=config.plot_cn.patientnames,\n type=str,\n help='One or more space-separated patient names (default: inferred from filenames)',\n )\n parser.add_argument(\n '-u',\n '--minu',\n required=False,\n default=config.plot_cn.minu,\n type=float,\n help='Minimum proportion of a CNA to be considered subclonal (default: 0.2)\"',\n )\n parser.add_argument(\n '-x',\n '--rundir',\n required=False,\n default=config.plot_cn.rundir,\n type=str,\n help='Running directory (default: current directory)',\n )\n parser.add_argument(\n '-b',\n '--baseCN',\n required=False,\n default=config.plot_cn.basecn,\n type=int,\n help='Base copy number (default: inferred from tumor ploidy)',\n )\n parser.add_argument(\n '-sC',\n '--figsizeclones',\n required=False,\n default=config.plot_cn.figsizeclones,\n type=str,\n help='Size of clone plots in the form \"(X-SIZE, Y-SIZE)\"',\n )\n parser.add_argument(\n '-sP',\n '--figsizecn',\n required=False,\n default=config.plot_cn.figsizecn,\n type=str,\n help='Size of CN plots in the form \"(X-SIZE, Y-SIZE)\"',\n )\n parser.add_argument(\n '-sG',\n '--figsizegrid',\n required=False,\n default=config.plot_cn.figsizegrid,\n type=str,\n help='Size of grid plots in the form \"(X-SIZE, Y-SIZE)\"',\n )\n parser.add_argument(\n '-rC',\n '--resolutionclones',\n required=False,\n default=config.plot_cn.resolutionclones,\n type=int,\n help='Number of bins to merge together for plotting clone profiles (default: 100)\"',\n )\n parser.add_argument(\n '-rP',\n '--resolutioncn',\n required=False,\n default=config.plot_cn.resolutioncn,\n type=int,\n help='Number of bins to merge together for plotting proportions (default: 500)\"',\n )\n parser.add_argument(\n '-rG',\n '--resolutiongrid',\n required=False,\n default=config.plot_cn.resolutiongrid,\n type=int,\n help='Number of bins to merge together in grids (default: 100)\"',\n )\n parser.add_argument(\n '-e',\n '--threshold',\n required=False,\n default=config.plot_cn.threshold,\n type=float,\n help='Threshold used to classify a tumor into either diploid or tetraploid (default: 3.0)\"',\n )\n parser.add_argument(\n '--ymax',\n required=False,\n default=config.plot_cn.ymax,\n type=int,\n help='Maximum values in y-axis (default: automatically inferred)\"',\n )\n parser.add_argument(\n '--ymin',\n required=False,\n default=config.plot_cn.ymin,\n type=int,\n help='Minimum values in y-axis (default: automatically inferred)\"',\n )\n parser.add_argument(\n '--clonepalette',\n required=False,\n default=config.plot_cn.clonepalette,\n type=str,\n help='Palette for coloring the clones among Set1, Set2, Set3, Paired (default: Set1)\"',\n )\n parser.add_argument(\n '--linkage',\n required=False,\n default=config.plot_cn.linkage,\n type=str,\n help=(\n 'Linkage method used for clustering (default: single, available (single, complete, average, weighted, '\n 'centroid, median, ward) from SciPy)\"'\n ),\n )\n parser.add_argument('-V', '--version', action='version', version=f'%(prog)s {__version__}')\n args = parser.parse_args(args)\n\n if len(args.INPUT.split()) == 0:\n raise ValueError(error('Please specify at least one sample as input!'))\n if args.patientnames is None:\n patientnames = {fil: os.path.basename(fil) for fil in args.INPUT.split()}\n else:\n patientnames = {f: n for f, n in zip(args.INPUT.split(), args.patientnames.split())}\n if len(args.INPUT.split()) != len(set(patientnames.values())):\n raise ValueError(error('Multiple patients have the same name but they should unique!'))\n if args.figsizeclones is not None:\n figsizeclones = to_tuple(args.figsizeclones, error_message='Wrong format of figsizeclones!')\n if args.figsizecn is not None:\n figsizecn = to_tuple(args.figsizecn, error_message='Wrong format of figsizecn!')\n if args.figsizegrid is not None:\n figsizegrid = to_tuple(args.figsizegrid, error_message='Wrong format of figsizegrid!')\n\n if not os.path.isdir(args.rundir):\n raise ValueError(error('Running directory does not exist!'))\n if not 0.0 <= args.minu <= 1.0:\n raise ValueError(error('The minimum proportion for subclonal CNAs must be in [0, 1]!'))\n if args.baseCN is not None and args.baseCN < 2:\n raise ValueError(error('Base CN must be greater or equal than 2!'))\n if args.resolutionclones is not None and args.resolutionclones < 1:\n raise ValueError(error('Resolution must be greater than 1!'))\n if args.resolutioncn is not None and args.resolutioncn < 1:\n raise ValueError(error('Resolution must be greater than 1!'))\n if args.resolutiongrid is not None and args.resolutiongrid < 1:\n raise ValueError(error('Resolution must be greater than 1!'))\n if args.threshold < 0:\n raise ValueError(error('Threshold must be positive!'))\n if args.linkage not in {\n 'single',\n 'complete',\n 'average',\n 'weighted',\n 'centroid',\n 'median',\n 'ward',\n }:\n raise ValueError(error('Unknown linkage method!'))\n\n if args.clonepalette == 'Set1':\n pal = plt.cm.Set1\n elif args.clonepalette == 'Set2':\n pal = plt.cm.Set2\n elif args.clonepalette == 'Set3':\n pal = plt.cm.Set3\n elif args.clonepalette == 'Paired':\n pal = plt.cm.Paired\n else:\n raise ValueError(error('Unknown clone palette!'))\n\n return {\n 'input': args.INPUT.split(),\n 'names': patientnames,\n 'rundir': args.rundir,\n 'minu': args.minu,\n 'base': args.baseCN,\n 'clonefigsize': figsizeclones,\n 'propsfigsize': figsizecn,\n 'clusterfigsize': figsizegrid,\n 'profileres': args.resolutionclones,\n 'cnres': args.resolutioncn,\n 'clusterres': args.resolutiongrid,\n 'threshold': args.threshold,\n 'linkage': args.linkage,\n 'ymax': args.ymax,\n 'ymin': args.ymin,\n 'clonepalette': pal,\n }","def usage(msg):\n ap.print_usage()\n print \"-\"*40\n print msg\n exit(1)","def get_cp_info(self):\n return self.get(COMMAND_CPM, 'GetCpInfo')","def _memtop_setup_parser(parser):\n parser.add_argument('file', nargs=1, help='Python script to check for memory usage.')\n parser.add_argument('-o', default=None, action='store', dest='outfile',\n help='Name of output file. By default, output goes to stdout.')\n parser.add_argument('-l', '--limit', action='store', type=int, default=20, dest='limit',\n help='Limit the number of lines in the output.')","def metrics_partition(cmd_ctx, cpc, partition, **options):\n cmd_ctx.execute_cmd(\n lambda: cmd_metrics_partition(cmd_ctx, cpc, partition, options))","def show_mem(cmd, cnt, args):\n if cpu is None:\n log(\"Load program first\") \n return\n elif len(cpu.memory) == 0:\n log(\"Load program first\") \n return \n chunk = 0\n chunk_count = len(cpu.memory)\n while chunk < chunk_count: \n chunk_start = cpu.memory[chunk][MEMADDR]\n chunk_end = chunk_start + cpu.memory[chunk][MEMSIZE] \n log(\"{:d} {:#x}..{:#x}\".format(chunk, chunk_start, chunk_end)) \n chunk += 1\n if machine == \"ARM\":\n if len(cpu.high_memory) != 0:\n log(\"High memory\")\n for addr in sorted(cpu.high_memory):\n log(\"{:#x}\".format(addr))","def usage(self):\n\n # header\n self.usage_header()\n\n print _(\"\"\"Screen: %(screen)s\nDescription: %(description)s\n\nUsage: %(app_name)s %(screen)s [options]\"\"\") % {\n 'app_name': constants.App.NAME,\n 'screen': self.name,\n 'description': self.description,\n }\n # any additional info in between (see other classes for reference)\n self._usage_options_example()\n\n #footer\n self.usage_footer()","def gather_info_and_display():\n # Obtain total rss displayed in memory.stat for each group,\n # container and service.\n try:\n output_mem = pipe_command(GREP_CMD, AWK_CMD, cwd=MEMPATH)\n LOG.debug(\n 'command: %s\\n%s',\n \"grep -rs total_rss '/sys/fs/cgroup/memory/' \"\n \"| awk '$2>0{print$0}' \",\n output_mem)\n except subprocess.CalledProcessError as error:\n LOG.error('Could not get total_rss memory, error=%s', error)\n return 1\n\n mem_info = get_meminfo()\n pt_groups = gather_groups_memory(output_mem)\n pt_cont = gather_containers_memory(output_mem)\n pt_serv = sys_service_memory()\n\n # Dump the tables out\n print('\\nPer groups memory usage:')\n\n # Get string to be printed and create list of elements separated by \\n\n list_of_table_lines = pt_groups.get_string().split('\\n')\n\n # Use the first line (+---+-- ...) as horizontal rule to insert later\n horizontal_line = list_of_table_lines[0]\n\n # Print the table, except last two lines ( \"Total\" row + final separator).\n print(\"\\n\".join(list_of_table_lines[:-2]))\n # Print separator, and finally the \"Total\" row.\n print(horizontal_line)\n print(\"\\n\".join(list_of_table_lines[-2:]))\n\n pt_namespc = prettytable.PrettyTable(\n ['Namespace',\n 'Resident Set Size (MiB)',\n ], caching=False)\n pt_namespc.align = 'l'\n pt_namespc.align['Resident Set Size (MiB)'] = 'r'\n\n print('\\nPer namespace memory usage:')\n for n_s in MEMORY['namespaces']:\n pt_namespc.add_row(\n [n_s,\n MEMORY['namespaces'][n_s],\n ])\n print(pt_namespc)\n\n print('\\nPer container memory usage:')\n print(pt_cont)\n\n print('\\nPer service memory usage:')\n print(pt_serv)\n\n base_mebib = 0.0\n k8s_system = 0.0\n k8s_addon = 0.0\n platform_memory_percent = 0.0\n\n # Calculate base memory usage (i.e., normal memory, exclude K8S and VMs)\n # e.g., docker, system.slice, user.slice\n for group in MEMORY['cgroups']:\n if group in BASE_GROUPS:\n base_mebib += float(MEMORY['cgroups'][group])\n\n # K8S platform system usage (essential) and addons usage (non-essential)\n for n_s in MEMORY['namespaces']:\n if n_s in K8S_NAMESPACE_SYSTEM:\n k8s_system += MEMORY['namespaces'][n_s]\n elif n_s in K8S_NAMESPACE_ADDON:\n k8s_addon += MEMORY['namespaces'][n_s]\n\n # Calculate platform memory usage\n platform_mebib = base_mebib + k8s_system\n\n anon_mebib = float(mem_to_mebibytes(\n mem_info['Active(anon)'] + mem_info['Inactive(anon)'])) * KBYTE\n avail_mebib = float(mem_to_mebibytes(\n mem_info['MemAvailable'])) * KBYTE\n total_mebib = float(anon_mebib + avail_mebib)\n\n anon_percent = py2_round(100 * anon_mebib / total_mebib, DECIMAL_DIGITS) # pylint: disable=W1619\n\n reserved_mebib = get_platform_reserved_memory()\n # Calculate platform memory in terms of percent reserved\n if reserved_mebib > 0.0:\n platform_memory_percent = py2_round(\n 100 * platform_mebib / reserved_mebib, DECIMAL_DIGITS) # pylint: disable=W1619\n\n pt_platf = prettytable.PrettyTable(\n ['Reserved',\n 'Platform',\n 'Base',\n 'K8s Platform system',\n 'k8s-addon'\n ], caching=False)\n pt_platf.align = 'l'\n\n pt_platf.add_row(\n [reserved_mebib,\n '{} ({}%)'.format(platform_mebib, platform_memory_percent),\n base_mebib,\n k8s_system,\n k8s_addon\n ])\n print('\\nPlatform memory usage in MiB:')\n print(pt_platf)\n\n pt_4k = prettytable.PrettyTable(\n ['Anon',\n 'Cgroup-rss',\n 'Available',\n 'Total'\n ], caching=False)\n pt_4k.align = 'l'\n\n pt_4k.add_row(\n ['{} ({}%)'.format(anon_mebib, anon_percent),\n MEMORY['cgroups']['total_rss'],\n avail_mebib,\n total_mebib\n ])\n\n print('\\n4K memory usage in MiB:')\n print(pt_4k)\n\n return 0","def usage():\n pass","def main(argv):\n args = parse_command_line(argv)\n return show_scale_file_info(args.info_file) or 0","def mc(self, *args) -> None:\n env = os.environ.copy()\n env['MC_HOST_minio'] = self.auth_url\n # --config-dir is set just to prevent any config set by the user\n # from interfering with the test.\n try:\n subprocess.run(\n [\n 'mc', '--quiet', '--no-color', f'--config-dir={self.path}',\n *args\n ],\n stdout=subprocess.DEVNULL,\n stderr=subprocess.PIPE,\n env=env,\n encoding='utf-8',\n errors='replace',\n check=True\n )\n except OSError as exc:\n raise MissingProgram(f'mc could not be run: {exc}') from exc\n except subprocess.CalledProcessError as exc:\n raise ProgramFailed(exc.stderr) from exc","def collect(self):\n if not self._btime:\n return\n\n try:\n with open(\"/proc/self/stat\", 'rb') as stat:\n parts = (stat.read().split(b')')[-1].split())\n\n self.process_metrics[\"vmem\"].set(\"\", float(parts[20]))\n self.process_metrics[\"rss\"].set(\"\", float(parts[21]) * _PAGESIZE)\n start_time_secs = float(parts[19]) / self._ticks\n self.process_metrics[\"start_time\"].set(\n \"\", start_time_secs + self._btime\n )\n utime = float(parts[11]) / self._ticks\n stime = float(parts[12]) / self._ticks\n self.process_metrics[\"cpu\"].set(\"\", utime + stime)\n except IOError:\n pass\n\n try:\n with open(\"/proc/self/limits\", 'rb') as limits:\n for line in limits:\n if line.startswith(b'Max open file'):\n self.process_metrics[\"max_fds\"].set(\n \"\", float(line.split()[3])\n )\n break\n\n self.process_metrics[\"open_fds\"].set(\n \"\", float(len(os.listdir(\"/proc/self/fd\")))\n )\n except (IOError, OSError):\n pass\n\n # Update gc metrics if enabled.\n if \"collected\" in self.process_metrics:\n for generation, stat in enumerate(gc.get_stats()):\n generation = {\"generation\": str(generation)}\n self.process_metrics[\"collected\"].set(\n generation, stat[\"collected\"]\n )\n self.process_metrics[\"uncollectable\"].set(\n generation, stat[\"uncollectable\"]\n )\n self.process_metrics[\"collections\"].set(\n generation, stat[\"collections\"]\n )","def info(self):\n import string\n results = self.info_list()\n labels = \"%-8s %-9s %-4s %-8s %-8s %-4s\" % \\\n ('MACHINE','CPU','GHZ','MB TOTAL',\n 'MB FREE','LOAD')\n print labels\n for i in range(len(self.workers)):\n name = string.split(self.workers[i].host,'.')[0]\n res = results[i]\n s = \"%-8s %2dx%-6s %4.1f %8.1f %8.1f %4.2f\" % \\\n (name[-8:], res['cpu_count'],res['cpu_type'][-6:], \\\n res['cpu_speed'],res['mem_total'],res['mem_free'],\\\n res['load_1'])\n print s","def report_on_config( args ):\n\n from khmer.utils import print_error\n\n if args.quiet: return\n\n print_error( \"\\nPARAMETERS:\" )\n print_error( \" - kmer size = {0} \\t\\t(-k)\".format( args.ksize ) )\n print_error( \" - n hashes = {0} \\t\\t(-N)\".format( args.n_hashes ) )\n print_error(\n \" - min hashsize = {0:5.2g} \\t(-x)\".format( args.min_hashsize )\n )\n print_error( \"\" )\n print_error(\n \"Estimated memory usage is {0:.2g} bytes \"\n \"(n_hashes x min_hashsize)\".format( args.n_hashes * args.min_hashsize )\n )\n print_error( \"-\" * 8 )\n\n if DEFAULT_MIN_HASHSIZE == args.min_hashsize:\n print_error(\n \"** WARNING: hashsize is default! \" \n \"You absodefly want to increase this!\\n** \"\n \"Please read the docs!\"\n )","def execute(self):\n\n if not os.path.exists(self._source_file_name):\n logger.info(\"Did not find the aicpu profiling source file\")\n return\n\n with open(self._source_file_name, 'rb') as ai_cpu_data:\n content = ai_cpu_data.read()\n if content[0:2].hex().upper() == \"5A5A\":\n ai_cpu_total_time_summary, result_list = self.parser_binary_file(content)\n else:\n ai_cpu_total_time_summary, result_list = self.parser_txt_file(content)\n\n os.chmod(self._source_file_name, stat.S_IREAD)\n\n if result_list:\n ai_cpu_total_time = format(ai_cpu_total_time_summary, '.6f')\n result_list.append([\"AI CPU Total Time(ms):\", ai_cpu_total_time])\n fwrite_format(self._output_filename, \" \".join(self._dst_file_column_title), is_start=True, is_print=True)\n fwrite_format(self._output_filename, result_list, is_print=True)\n\n # For timeline display.\n self._result_list = result_list","def display_memcache_info(request):\n # pylint: disable-msg=E1101\n return utility.respond(request, 'admin/memcache_info',\n {'memcache_info': memcache.get_stats()})","async def run_mpc(self) -> Dict[str, Dict[Metric, int]]:\n pass","def test_cw_metrics(self):\n\n instances = set()\n result = self.cw_client.list_metrics(Namespace=\"CWAgent\", MetricName=\"cpu_usage_system\")\n for i in result[\"Metrics\"]:\n instances.add(i[\"Dimensions\"][0][\"Value\"])\n\n for key, value in self.cdk_output_map.items():\n if \"Instance\" in key:\n self.assertTrue(value in instances)","def mcmc(self, assign_step_methods=True, *args, **kwargs):\n\n self.mc = pm.MCMC(self.nodes_db.node.values, *args, **kwargs)\n\n self.pre_sample()\n\n return self.mc","def main():\n test_cases = ast.literal_eval(sys.argv[1])\n results = str(my_info()) + '\\t\\t'\n for test_case in test_cases:\n mode = test_case[0]\n id_1 = int(test_case[1])\n id_2 = int(test_case[2])\n if mode == 'jc':\n results += str(Jaccard_Coefficient(id_1, id_2)) + '\\t\\t'\n elif mode == 'cc':\n results += str(Correlation_Coefficient(id_1, id_2)) + '\\t\\t'\n else:\n exit('bad command')\n print results + '\\n'","def do_stats(cs, args):\n stats_info = cs.containers.stats(args.container)\n utils.print_dict(stats_info)","def options():\n print \"\"\"Options summary:\n -h, --help\n -u, --usage\n -v, --verbose <verb_level>\n -e, --endpoint <endpoint>\n -i, --interface-type <iface_type>\n -r, --recursive\n --dbs-conf <conf_file>\n --show-prod\n --show-caf\n --only-subscribed\n --only-custodial\n \"\"\"","def usage():\n\n # Local constants\n\n # Local variables\n\n #****** start usage() ******#\n print()\n print(\" Usage: python TCGCardTracker.py <arguement below> <optional-argument-1>\")\n print(\"\\tadd (Optional): Add a card to your collection. Requires TCGPlayer URL.\")\n print(\"\\tdelete (Optional): Delete a card from your collection. Requires TCGPlayer URL.\")\n print(\"\\tupdate (Optional): Updates pricing data for every card in your collection.\")\n print(\"\\ttop25 (Optional): Outputs the 25 most valuable cards from your collection.\")\n print(\"\\texport (Optional): Exports a list of TCGPlayer URLs to a text file.\")\n print(\"\\texport_collection (Optional): Exports your collection to a .csv including most recent price data.\")\n print(\"\\timport (Optional): Imports a text file of TCGPlayer URLs to bulk import cards into your collection. Requires text file.\")\n print(\"\\tworth (Optional): Ouputs how much your collection is worth using latest price data.\")\n print(\"\\tgraph (Optional): Outputs historical pricing data for a given card. Requires TCGPlayer URL.\")\n print(\"\\tgraph (Optional): Outputs historical pricing data for a given card. Requires TCGPlayer URL.\")\n print(\"\\tticker (Optional): Displays a ticker grid of the change in value over a given time. If run without the days back parameter it will default to 7 days.\")\n sys.exit()","def stat_cuda(msg: str) -> None:\n print(f'-- {msg:<35} allocated: %dM, max allocated: %dM, cached: %dM, max cached: %dM' % (\n torch.cuda.memory_allocated() / 1024 / 1024,\n torch.cuda.max_memory_allocated() / 1024 / 1024,\n torch.cuda.memory_cached() / 1024 / 1024,\n torch.cuda.max_memory_cached() / 1024 / 1024\n ))","def do_config():\n\n tracking = get_tracking()\n for unit in (\"ppm\", \"sec\"):\n\ttunit = unit\n\tif unit == \"sec\":\n\t tunit = \"seconds\"\n\tprint \"multigraph chrony_%s\" % unit\n\tprint \"graph_title NTP (Chrony) Statistics (%s)\" % unit\n\tprint \"graph_vlabel %s\" % unit\n\tprint \"graph_args --base 1000\"\n\tprint \"graph_category time\"\n\tprint \"graph_info NTP (Chrony) tracking statistics (the ones measured in %s)\" % tunit\n\tfor key in tracking[tunit]:\n\t item = tracking[tunit][key]\n\t print \"\"\"%s.label %s\n%s.draw LINE2\n%s.info %s\"\"\" % (key, item[\"label\"], key, key, item[\"label\"])\n\tprint\n return 0","def help(update, context):\n msg = \"\"\n msg += \"\\n/covid 7-Day-Incident per Million\"\n msg += \"\\n/daylio What did I do a year ago today?\"\n msg += \"\\n/f1last Results of the last race\"\n msg += \"\\n/f1stand Driver standings\"\n msg += \"\\n/f1next Time and place of the next race\"\n msg += \"\\n/fuel prices and consump. (args: Xeur Ykm)\"\n msg += \"\\n/ip Outside ip address\"\n msg += \"\\n/rate Exchange rates (args: Xeur/Yhuf)\"\n msg += \"\\n/rss check rss feeds for new content\"\n msg += \"\\n/sun Time of sunrise and sunset\"\n msg += \"\\n/xkcd Sends last comic image and alt\"\n msg.rstrip()\n update.message.reply_text(msg)","def show(self):\n prev_queries = 0\n prev_cpu_sys = 0\n prev_cpu_user = 0\n \n lines = {\n \"Uptime (seconds)\": \"--\",\n \"Number of queries\": \"--\",\n \"Query per second\": \"--\",\n \"ACL drops\": \"--\",\n \"Dynamic drops\": \"--\",\n \"Rule drops\": \"--\",\n \"CPU Usage (%s)\": \"--\",\n \"Cache hitrate\": \"--\"\n }\n\n while True:\n try:\n # get stats from dnsdist\n stats = Statistics(console=self.console)\n global_stats = stats[\"global\"]\n \n qps = int(global_stats[\"queries\"]) - prev_queries\n prev_queries = int(global_stats[\"queries\"])\n cpu = (int(global_stats[\"cpu-sys-msec\"])+int(global_stats[\"cpu-user-msec\"]) - prev_cpu_sys - prev_cpu_user) / 10\n prev_cpu_sys = int(global_stats[\"cpu-sys-msec\"])\n prev_cpu_user = int(global_stats[\"cpu-user-msec\"])\n \n lines[\"Uptime (seconds)\"] = global_stats[\"uptime\"]\n lines[\"Number of queries\"] = global_stats[\"queries\"]\n lines[\"Query per second\"] = qps\n lines[\"CPU Usage (%s)\"] = cpu\n lines[\"ACL drops\"] = global_stats[\"acl-drops\"]\n lines[\"Rule drops\"] = global_stats[\"rule-drop\"]\n lines[\"Cache hitrate\"] = global_stats[\"cache-hits\"]\n lines[\"Dynamic drops\"] = global_stats[\"dyn-blocked\"]\n\n # reprint the lines \n sys.stdout.write(\"\\033[1mDashboard for dnsdist\\033[0m\\n\")\n sys.stdout.write(\"\\n\")\n sys.stdout.write(\"Global:\\n\")\n for k,v in lines.items():\n sys.stdout.write(\"\\t%s: %s\\n\" % (k,v))\n sys.stdout.write(\"Backends:\\n\")\n for s in stats[\"backends\"]:\n if not len(s[\"name\"]):\n s[\"name\"] = \"--\"\n if not len(s[\"pools\"]):\n s[\"pools\"] = \"--\"\n sys.stdout.write(\"\\t#%s / %s / %s / %s\\n\" % (s[\"#\"],s[\"address\"],s[\"name\"],s[\"pools\"]) )\n sys.stdout.write(\"\\t\\tNumber of queries: %s\\n\" % s[\"queries\"])\n sys.stdout.write(\"\\t\\tQuery per second: %s\\n\" % s[\"qps\"])\n sys.stdout.write(\"\\t\\tNumber of drops: %s\\n\" % s[\"drops\"])\n sys.stdout.write(\"\\n\")\n sys.stdout.write(\"Ctrl+C to exit\\n\")\n \n time.sleep(1)\n \n \n # move up cursor and delete whole line\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\") \n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n for k,v in lines.items():\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\") \n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n for s in stats[\"backends\"]:\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\") \n sys.stdout.write(\"\\x1b[1A\\x1b[2K\") \n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n \n del stats\n except KeyboardInterrupt:\n break","def _cmd_genemetrics(args):\n cnarr = read_cna(args.filename)\n segarr = read_cna(args.segment) if args.segment else None\n is_sample_female = verify_sample_sex(cnarr, args.sample_sex, args.male_reference, args.diploid_parx_genome)\n # TODO use the stats args\n table = do_genemetrics(\n cnarr,\n segarr,\n args.threshold,\n args.min_probes,\n args.drop_low_coverage,\n args.male_reference,\n is_sample_female,\n args.diploid_parx_genome,\n )\n logging.info(\"Found %d gene-level gains and losses\", len(table))\n write_dataframe(args.output, table)","def execute(self, context):\n logger.info(\"///////////////////////////////////////////////////////////////////////\")\n logger.info(\"[ChainedTransfig] Collectd-Jmx w/ Common MBeans Generation...\")\n logger.info(\"///////////////////////////////////////////////////////////////////////\")\n\n #insert common to assemble collectd.genericjmx.common.conf in later step\n context[CTX_KEY_COMMON_COLLECTD_JMX_APP_PREFIX] = 'common'\n super().execute(context)\n\n logger.info(\"///////////////////////////////////////////////////////////////////////\")\n logger.info(\"[ChainedTransfig] Collectd-Jmx w/ Common MBeans Generation... COMPLETES\")\n logger.info(\"///////////////////////////////////////////////////////////////////////\")","def info():\n f = Figlet(font='standard')\n click.echo(f.renderText('covtool'))\n click.secho(\n \"covtool: a simple CLI for fetching covid data\", fg='cyan')\n click.echo(\n \"Data Sources: https://www.worldometers.info/coronavirus\\nJohn Hopkins [https://github.com/CSSEGISandData/COVID-19] \")\n click.secho(\"Author: Amayo II <amayomordecai@gmail.com>\", fg='magenta')","def help(bot, sender, sendmsg, label, args):\n\n clist = commands.commands\n csort = sorted(clist.values(), key=lambda c: c.__name__.lower())\n\n if len(args) > 0:\n page = int(args[0]) - 1\n else:\n page = 0\n\n pages = len(clist) // 10 + 1\n\n sendmsg(\"-- Help (Page {} of {}) --\".format(page + 1, pages))\n for i in range(10):\n if i >= len(csort):\n break\n\n command = csort[i + (page * 10)]\n sendmsg(\"{}: {}\".format(command.__name__, command.__doc__))","def _cmd_coverage(args):\n pset = coverage.do_coverage(\n args.interval,\n args.bam_file,\n args.count,\n args.min_mapq,\n args.processes,\n args.fasta,\n )\n if not args.output:\n # Create an informative but unique name for the coverage output file\n bambase = core.fbase(args.bam_file)\n bedbase = core.fbase(args.interval)\n tgtbase = (\n \"antitargetcoverage\" if \"anti\" in bedbase.lower() else \"targetcoverage\"\n )\n args.output = f\"{bambase}.{tgtbase}.cnn\"\n if os.path.exists(args.output):\n args.output = f\"{bambase}.{bedbase}.cnn\"\n core.ensure_path(args.output)\n tabio.write(pset, args.output)","def profiler(*args, addCategory: AnyStr=\"\", allCategories: bool=True, bufferSize: Union[int,\n bool]=0, categoryIndex: Union[int, bool]=0, categoryIndexToName: Union[int,\n bool]=0, categoryInfo: Union[AnyStr, bool]=\"\", categoryName: Union[AnyStr,\n bool]=\"\", categoryNameToIndex: Union[AnyStr, bool]=\"\", categoryRecording:\n bool=True, clearAllMelInstrumentation: bool=True, colorIndex: int=0, eventCPUId:\n bool=True, eventCategory: bool=True, eventColor: bool=True, eventCount: bool=True,\n eventDescription: bool=True, eventDuration: bool=True, eventIndex: Union[int,\n bool]=0, eventName: bool=True, eventStartTime: bool=True, eventThreadId: bool=True,\n instrumentMel: bool=True, load: Union[AnyStr, bool]=\"\", output: Union[AnyStr,\n bool]=\"\", procedureDescription: AnyStr=\"\", procedureName: AnyStr=\"\",\n removeCategory: AnyStr=\"\", reset: bool=True, sampling: bool=True, signalEvent:\n bool=True, signalMelEvent: bool=True, q=True, query=True, **kwargs)->Union[None,\n Any]:\n pass"],"string":"[\n \"def metrics_cpc(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_cpc(cmd_ctx, cpc, options))\",\n \"def metrics_channel(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_channel(cmd_ctx, cpc, options))\",\n \"def metrics_crypto(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_crypto(cmd_ctx, cpc, options))\",\n \"def do_hostinfo(self, args):\\n host = opts = None\\n if args:\\n args = args.split()\\n host = args.pop()\\n\\n if not host:\\n print('Usage: hostinfo [-cdmu] host_name_or_ip')\\n print(' uptime and load stats returned if no options specified')\\n return\\n\\n try:\\n ip = socket.gethostbyname(host)\\n except socket.gaierror:\\n print('cannot resolve', host, file=sys.stderr)\\n return\\n\\n opts = []\\n while args:\\n arg = args.pop(0)\\n if arg.startswith('--'):\\n if arg == '--cpu':\\n opts.append('c')\\n elif arg == '--disk':\\n opts.append('d')\\n elif arg == '--memory':\\n opts.append('m')\\n elif arg == '--uptime':\\n opts.append('u')\\n else:\\n print('unrecognized option:', arg, file=sys.stderr)\\n return\\n else:\\n if arg[0] == '-':\\n for ch in arg[1:]:\\n if ch in ('cdmu') and ch not in opts:\\n opts.append(ch)\\n else:\\n print('unrecognized option:', ch, file=sys.stderr)\\n return\\n\\n stats = self._qm.get_host_stats(ip)\\n\\n if not opts:\\n # Get uptime and load averages.\\n up = stats['uptime']\\n load = stats['cpu_load']\\n print('Up for %s days, %s hours, %s minutes, '\\n 'load averages: %s, %s, %s'\\n % (up['days'], up['hours'], up['minutes'], load['one'],\\n load['five'], load['fifteen']))\\n return\\n\\n all_stats = []\\n for opt in opts:\\n if opt == 'd':\\n # Get disk usage.\\n disks = stats['disk_usage']\\n st = ['Disk Usage:']\\n for mount, disk_info in disks.viewitems():\\n st.append(' Usage for: %s' % mount)\\n for k, v in disk_info.viewitems():\\n st.append(' %s: %s' % (k, v))\\n all_stats.append('\\\\n'.join(st))\\n all_stats.append('')\\n elif opt == 'c':\\n # Get CPU load.\\n load_stats = stats['cpu_load']\\n st = ['CPU Load Average:']\\n st.append(' last one minute: %s' % load_stats['one'])\\n st.append(' last five minutes: %s' % load_stats['five'])\\n st.append(' last fifteen minutes: %s' % load_stats['fifteen'])\\n all_stats.append('\\\\n'.join(st))\\n all_stats.append('')\\n elif opt == 'm':\\n # Get Memory Usage.\\n memory_usage = stats['memory_usage']\\n st = ['Memory usage:']\\n for k, v in memory_usage.viewitems():\\n st.append(' %s: %s' % (k, v))\\n all_stats.append('\\\\n'.join(st))\\n all_stats.append('')\\n elif opt == 'u':\\n # Get uptime.\\n up = stats['uptime']\\n st = ['Uptime:']\\n st.append(' Up for %s days, %s hours and %s minutes'\\n % (up['days'], up['hours'], up['minutes']))\\n all_stats.append('\\\\n'.join(st))\\n all_stats.append('')\\n\\n print('\\\\n'.join(all_stats))\",\n \"def _cmd_metrics(args):\\n if (\\n len(args.cnarrays) > 1\\n and args.segments\\n and len(args.segments) > 1\\n and len(args.cnarrays) != len(args.segments)\\n ):\\n raise ValueError(\\n \\\"Number of coverage/segment filenames given must be \\\"\\n \\\"equal, if more than 1 segment file is given.\\\"\\n )\\n\\n cnarrs = map(read_cna, args.cnarrays)\\n if args.segments:\\n args.segments = map(read_cna, args.segments)\\n table = metrics.do_metrics(cnarrs, args.segments, args.drop_low_coverage)\\n write_dataframe(args.output, table)\",\n \"def print_help():\\n print \\\"\\\"\\\"\\nMeasure Operating System Performance (mosp)\\n-------------------------------------------\\n\\nUse this program to measure and report on operating system\\nperformance.\\n\\nThis code measures operating system performance,\\nincluding CPU, memory, disk and network, and\\noutputs stats to screen and optionally to file\\ntoo for use in performance analysis\\n\\nUses the psutil library\\n\\nInstall psutil (Ubuntu) if you don't already have it:\\n sudo apt-get install python-dev\\n sudo pip install psutil\\n\\nUsage:\\n python mosp.py [options]\\n\\nExample usage:\\n python mosp.py -W -i 2\\n\\nOptions:\\n -h --help Display this help and exit\\n -m --max-run-time Maximum time to run for before exiting\\n (default is infinite)\\n -i --interval Interval between requests in seconds\\n (default is 1)\\n -w --output-file Specify an output filename\\n -W Output results to default filename\\n default format is:\\n mosp-HOSTNAME-YYYYMMDD-HHMMSS.csv\\n -b --output-path Specify path to output file directory\\n -j --no-header-row Suppress writing header row into CSV\\n -v --version Output version information and exit\\n\\n \\\"\\\"\\\"\\n return()\",\n \"def metrics_env(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_env(cmd_ctx, cpc, options))\",\n \"def print_help(self):\\r\\n\\t\\ttext = \\\"\\\\tName: ml_scikit_OPTICS\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\tThis machine learning plugin uses scikit-learn's OPTICS algorithm.\\\\n\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\tOptional Parameters:\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\t\\\\tOPTICS_skip_normalization: Do NOT perform normalization (scaling) of data, skip this step.\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\t\\\\OPTICS_eps: Specify eps parameter (default is 1.0).\\\"\\r\\n\\t\\ttext += \\\"\\\\n\\\\t\\\\t\\\\OPTICS_min_samples: Specify min_samples parameter (default is 5).\\\"\\r\\n#\\r\\n# OPTICS (with memory complexity n) is an alternative to DBSCAN (with memory complexity n^2)\\r\\n# which has time complexity n^2 in general with the default max_eps = np.inf. \\r\\n# We will set max_eps = eps to reduce the run-time.\\r\\n#\\r\\n\\t\\treturn text\",\n \"def measure(self,command_exe, command_args, measure_out):\\n pass\",\n \"def do_stats(self, args):\\n total_cpu = free_cpu = in_use_cpu = 0\\n\\n summary = self._qm.get_all_host_summary()\\n for host_id, host_info in summary.viewitems():\\n host_cpu = int(host_info['total cores'])\\n total_cpu += host_cpu\\n locked = host_info.get('locked by')\\n if locked:\\n # If host is locked then all CPUs are in use.\\n in_use_cpu += host_cpu\\n else:\\n free_host_cpu = int(host_info['free cores'])\\n in_use_cpu += (host_cpu - free_host_cpu)\\n free_cpu += free_host_cpu\\n\\n print('total CPU: ', total_cpu)\\n print('used/locked CPU: ', in_use_cpu)\\n print('free CPU: ', free_cpu)\\n capacity = float(in_use_cpu) / float(total_cpu)\\n print('capacity used: %.1f%%' % (capacity * 100,))\\n capacity = float(free_cpu) / float(total_cpu)\\n print('capacity remaining: %.1f%%' % (capacity * 100,))\",\n \"def metrics_roce(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_roce(cmd_ctx, cpc, options))\",\n \"def stats(caesar, input):\\n commands = {}\\n users = {}\\n channels = {}\\n\\n ignore = set(['f_note', 'startup', 'message', 'noteuri'])\\n for (name, user), count in caesar.stats.items(): \\n if name in ignore: continue\\n if not user: continue\\n\\n if not user.startswith('#'): \\n try: users[user] += count\\n except KeyError: users[user] = count\\n else: \\n try: commands[name] += count\\n except KeyError: commands[name] = count\\n\\n try: channels[user] += count\\n except KeyError: channels[user] = count\\n\\n comrank = sorted([(b, a) for (a, b) in commands.iteritems()], reverse=True)\\n userank = sorted([(b, a) for (a, b) in users.iteritems()], reverse=True)\\n charank = sorted([(b, a) for (a, b) in channels.iteritems()], reverse=True)\\n\\n # most heavily used commands\\n creply = 'most used commands: '\\n for count, command in comrank[:10]: \\n creply += '%s (%s), ' % (command, count)\\n caesar.say(creply.rstrip(', '))\\n\\n # most heavy users\\n reply = 'power users: '\\n for count, user in userank[:10]: \\n reply += '%s (%s), ' % (user, count)\\n caesar.say(reply.rstrip(', '))\\n\\n # most heavy channels\\n chreply = 'power channels: '\\n for count, channel in charank[:3]: \\n chreply += '%s (%s), ' % (channel, count)\\n caesar.say(chreply.rstrip(', '))\",\n \"def main():\\n processor.custom_config = parse_arguments()\\n processor.process()\\n logger.info(processor.statistics)\\n logger.info(processor.custom_config)\",\n \"def procs_calculate_axyzc(molecules, n_cores=-1, show_progress=True, scr=None, cmd=XTB_CMD):\\n results = None\\n return results\",\n \"async def stats(self, ctx):\\n if ctx.invoked_subcommand is None:\\n await send_cmd_help(ctx)\",\n \"def main():\\n\\n args = parse_args()\\n metric_sender = MetricSender(verbose=args.verbose, debug=args.debug)\\n\\n discovery_key_disk = 'disc.disk'\\n interval = 3\\n pcp_disk_dev_metrics = ['disk.dev.total', 'disk.dev.avactive']\\n item_prototype_macro_disk = '#OSO_DISK'\\n item_prototype_key_tps = 'disc.disk.tps'\\n item_prototype_key_putil = 'disc.disk.putil'\\n\\n disk_metrics = pminfo.get_sampled_data(pcp_disk_dev_metrics, interval, 2)\\n\\n pcp_metrics_divided = {}\\n for metric in pcp_disk_dev_metrics:\\n pcp_metrics_divided[metric] = {k: v for k, v in disk_metrics.items() if metric in k}\\n\\n # do TPS checks; use disk.dev.total\\n filtered_disk_totals = clean_up_metric_dict(pcp_metrics_divided[pcp_disk_dev_metrics[0]],\\n pcp_disk_dev_metrics[0] + '.')\\n\\n # Add dynamic items\\n metric_sender.add_dynamic_metric(discovery_key_disk, item_prototype_macro_disk, filtered_disk_totals.keys())\\n\\n # calculate the TPS and add them to the ZaggSender\\n for disk, totals in filtered_disk_totals.iteritems():\\n disk_tps = (totals[1] - totals[0]) / interval\\n metric_sender.add_metric({'%s[%s]' % (item_prototype_key_tps, disk): disk_tps})\\n\\n # do % Util checks; use disk.dev.avactive\\n filtered_disk_totals = clean_up_metric_dict(pcp_metrics_divided[pcp_disk_dev_metrics[1]],\\n pcp_disk_dev_metrics[1] + '.')\\n\\n # calculate the % Util and add them to the ZaggSender\\n for disk, totals in filtered_disk_totals.iteritems():\\n total_active = (float)(totals[1] - totals[0]) / 1000.0\\n putil = 100 * total_active / interval\\n\\n metric_sender.add_metric({'%s[%s]' % (item_prototype_key_putil, disk): putil})\\n\\n metric_sender.send_metrics()\",\n \"def process_info(process):\\n\\thelp(process)\",\n \"def metrics_flash(cmd_ctx, cpc, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_flash(cmd_ctx, cpc, options))\",\n \"def phast_cmmd(self):\\n temp = '{prog} -R {rho} -C {ecov} -E {elen} -N {chrom} -i MAF {maf} {model} > {wig}\\\\n'.format(**self.dict)\\n return temp.format(fnum=self.fnum)\",\n \"def help(self, args):\\n print('No commands available for this consumer')\",\n \"def treatCmdOpts(argv):\\n baseName = os.path.basename(__file__)\\n amc.cBaseName = colored(baseName, 'yellow')\\n\\n helpTxt = amc.cBaseName + ' analyses observation statistics file for selected GNSSs'\\n\\n # create the parser for command line arguments\\n parser = argparse.ArgumentParser(description=helpTxt)\\n\\n parser.add_argument('--obsstat', help='observation statistics file', type=str, required=True)\\n\\n parser.add_argument('--freqs', help='select frequencies to use (out of {freqs:s}, default {freq:s})'.format(freqs='|'.join(gfzc.lst_freqs), freq=colored(gfzc.lst_freqs[0], 'green')), default=gfzc.lst_freqs[0], type=str, required=False, action=gco.freqtype_action, nargs='+')\\n\\n parser.add_argument('--cutoff', help='cutoff angle in degrees (default {mask:s})'.format(mask=colored('0', 'green')), default=0, type=int, required=False, action=gco.cutoff_action)\\n\\n parser.add_argument('--dbcvs', help='Add information to CVS database (default {cvsdb:s})'.format(cvsdb=colored(gco.CVSDB_OBSTLE, 'green')), required=False, type=str, default=gco.CVSDB_OBSTLE)\\n\\n parser.add_argument('--plot', help='displays interactive plots (default False)', action='store_true', required=False, default=False)\\n\\n parser.add_argument('--logging', help='specify logging level console/file (two of {choices:s}, default {choice:s})'.format(choices='|'.join(gco.lst_logging_choices), choice=colored(' '.join(gco.lst_logging_choices[3:5]), 'green')), nargs=2, required=False, default=gco.lst_logging_choices[3:5], action=gco.logging_action)\\n\\n # drop argv[0]\\n args = parser.parse_args(argv[1:])\\n\\n # return arguments\\n return args.obsstat, args.freqs, args.cutoff, args.dbcvs, args.plot, args.logging\",\n \"def main(cls):\\n parser = cls.make_argument_parser()\\n args = parser.parse_args()\\n args.device = make_hoomd_device(args)\\n benchmark = cls(**vars(args))\\n performance = benchmark.execute()\\n\\n if args.device.communicator.rank == 0:\\n print(f'{numpy.mean(performance)}')\",\n \"def help(self):\\n msg = \\\"`%s' performs the computational aspects of genotyping-by-sequencing.\\\\n\\\" % os.path.basename(sys.argv[0])\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Usage: %s [OPTIONS] ...\\\\n\\\" % os.path.basename(sys.argv[0])\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Options:\\\\n\\\"\\n msg += \\\" -h, --help\\\\tdisplay the help and exit\\\\n\\\"\\n msg += \\\" -V, --version\\\\toutput version information and exit\\\\n\\\"\\n msg += \\\" -v, --verbose\\\\tverbosity level (0/default=1/2/3)\\\\n\\\"\\n msg += \\\" --proj1\\\\tname of the project used for steps 1 to 4\\\\n\\\"\\n msg += \\\"\\\\t\\\\tmention a reference genome only if all samples belong to\\\\n\\\"\\n msg += \\\"\\\\t\\\\t the same species, and will be mapped to the same ref genome\\\\n\\\"\\n msg += \\\" --proj2\\\\tname of the project used for steps 4 to 8\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcan be the same as --proj1, or can be different\\\\n\\\"\\n msg +=\\\"\\\\t\\\\t notably when samples come from different species\\\\n\\\"\\n msg += \\\"\\\\t\\\\t or if one wants to align reads to different ref genomes\\\\n\\\"\\n msg += \\\" --schdlr\\\\tname of the cluster scheduler (default=SGE)\\\\n\\\"\\n msg += \\\" --queue\\\\tname of the cluster queue (default=normal.q)\\\\n\\\"\\n msg += \\\" --resou\\\\tcluster resources (e.g. 'test' for 'qsub -l test')\\\\n\\\"\\n msg += \\\" --rmvb\\\\tremove bash scripts for jobs launched in parallel\\\\n\\\"\\n msg += \\\" --step\\\\tstep to perform (1/2/3/.../9)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t1: raw read quality per lane (with FastQC v >= 0.11.2)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t2: demultiplexing per lane (with demultiplex.py v >= 1.14.0)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t3: cleaning per sample (with CutAdapt v >= 1.8)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t4: alignment per sample (with BWA MEM v >= 0.7.12, Samtools v >= 1.3, Picard and R v >= 3)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t5: local realignment per sample (with GATK v >= 3.5)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t6: local realignment per genotype (with GATK v >= 3.5)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t7: variant and genotype calling per genotype (with GATK HaplotypeCaller v >= 3.5)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t8: variant and genotype calling jointly across genotypes (with GATK GenotypeGVCFs v >= 3.5)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t9: variant and genotype filtering (with GATK v >= 3.5)\\\\n\\\"\\n msg += \\\" --samples\\\\tpath to the 'samples' file\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for all steps, but can differ between steps\\\\n\\\"\\n msg += \\\"\\\\t\\\\t e.g. if samples come from different species or are aligned\\\\n\\\"\\n msg += \\\"\\\\t\\\\t on different ref genomes, different samples file should\\\\n\\\"\\n msg += \\\"\\\\t\\\\t be used for steps 4-9, representing different subsets of\\\\n\\\"\\n msg += \\\"\\\\t\\\\t the file used for steps 1-3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tthe file should be encoded in ASCII\\\\n\\\"\\n msg += \\\"\\\\t\\\\tthe first row should be a header with column names\\\\n\\\"\\n msg += \\\"\\\\t\\\\teach 'sample' (see details below) should have one and only one row\\\\n\\\"\\n msg += \\\"\\\\t\\\\tany two columns should be separated with one tabulation\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcolumns can be in any order\\\\n\\\"\\n msg += \\\"\\\\t\\\\trows starting by '#' are skipped\\\\n\\\"\\n msg += \\\"\\\\t\\\\t12 columns are compulsory (but there can be more):\\\\n\\\"\\n msg += \\\"\\\\t\\\\t genotype (see details below, e.g. 'Col-0', but use neither underscore '_' nor space ' ' nor dot '.', use dash '-' instead)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t ref_genome (identifier of the reference genome used for alignment, e.g. 'Atha_v2', but use neither space ' ' nor dot '.'; the full species name, e.g. 'Arabidopsis thaliana', will be present in the file given to --dict)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t library (e.g. can be the same as 'genotype')\\\\n\\\"\\n msg += \\\"\\\\t\\\\t barcode (e.g. 'ATGG')\\\\n\\\"\\n msg += \\\"\\\\t\\\\t seq_center (e.g. 'Broad Institute', 'GenoToul', etc)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t seq_platform (e.g. 'ILLUMINA', see SAM format specification)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t seq_platform_model (e.g. 'HiSeq 2000')\\\\n\\\"\\n msg += \\\"\\\\t\\\\t flowcell (e.g. 'C5YMDACXX')\\\\n\\\"\\n msg += \\\"\\\\t\\\\t lane (e.g. '3', can be '31' if a first demultiplexing was done per index)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t date (e.g. '2015-01-15', see SAM format specification)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t fastq_file_R1 (filename, one per lane, gzip-compressed)\\\\n\\\"\\n msg += \\\"\\\\t\\\\t fastq_file_R2 (filename, one per lane, gzip-compressed)\\\\n\\\"\\n msg += \\\" --fcln\\\\tidentifier of a flowcell and lane number\\\\n\\\"\\n msg += \\\"\\\\t\\\\tformat as <flowcell>_<lane-number>, e.g. 'C5YMDACXX_1'\\\\n\\\"\\n msg += \\\"\\\\t\\\\tif set, only the samples from this lane will be analyzed\\\\n\\\"\\n msg += \\\" --pird\\\\tpath to the input reads directory\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for steps 1 and 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\twill be added to the columns 'fastq_file_R*' from the sample file\\\\n\\\"\\n msg += \\\"\\\\t\\\\tif not set, input read files should be in current directory\\\\n\\\"\\n msg += \\\" --enz\\\\tname of the restriction enzyme\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=ApeKI\\\\n\\\"\\n msg += \\\" --dmxmet\\\\tmethod used to demultiplex\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=4c (see the help of demultiplex.py to know more)\\\\n\\\"\\n msg += \\\" --subst\\\\tnumber of substitutions allowed during demultiplexing\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=2\\\\n\\\"\\n msg += \\\" --ensubst\\\\tenforce the nb of substitutions allowed\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 2\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=lenient/strict\\\\n\\\"\\n msg += \\\" --adp\\\\tpath to the file containing the adapters\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsame format as FastQC: name<tab>sequence\\\\n\\\"\\n msg += \\\"\\\\t\\\\tname: at least 'adpR1' (also 'adpR2' if paired-end)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsequence: from 5' (left) to 3' (right)\\\\n\\\"\\n msg += \\\" --errtol\\\\terror tolerance to find adapters\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=0.2\\\\n\\\"\\n msg += \\\" --minovl\\\\tminimum overlap length between reads and adapters\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=3 (in bases)\\\\n\\\"\\n msg += \\\" --minrl\\\\tminimum length to keep a read\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=35 (in bases)\\\\n\\\"\\n msg += \\\" --minq\\\\tminimum quality to trim a read\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=20 (used for both reads if paired-end)\\\\n\\\"\\n msg += \\\" --maxNp\\\\tmaximum percentage of N to keep a read\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 3\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=0.2\\\\n\\\"\\n msg += \\\" --ref\\\\tpath to the prefix of files for the reference genome\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for steps 4, 5, 6, 7, 8, 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tshould correspond to the 'ref_genome' column in --samples\\\\n\\\"\\n msg += \\\"\\\\t\\\\te.g. '/data/Atha_v2' for '/data/Atha_v2.fa', '/data/Atha_v2.bwt', etc\\\\n\\\"\\n msg += \\\"\\\\t\\\\tthese files are produced via 'bwa index ...'\\\\n\\\"\\n msg += \\\" --dict\\\\tpath to the 'dict' file (SAM header with @SQ tags)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for step 4\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee 'CreateSequenceDictionary' in the Picard software\\\\n\\\"\\n msg += \\\" --jgid\\\\tcohort identifier to use for joint genotyping\\\\n\\\"\\n msg += \\\"\\\\t\\\\tcompulsory for steps 8, 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tuseful to launch several, different cohorts in parallel\\\\n\\\"\\n msg += \\\" --rat\\\\trestrict alleles to be of a particular allelicity\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=ALL/BIALLELIC/MULTIALLELIC\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee '--restrictAllelesTo' in GATK's SelectVariant\\\\n\\\"\\n msg += \\\" --mdp\\\\tminimum value for DP (read depth; e.g. 10)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee GATK's VariantFiltration\\\\n\\\"\\n msg += \\\" --mgq\\\\tminimum value for GQ (genotype quality; e.g. 20)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee GATK's VariantFiltration\\\\n\\\"\\n msg += \\\" --mnfg\\\\tmaximum number of filtered genotypes to keep a variant\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee '--maxFilteredGenotypes' in GATK's SelectVariants\\\\n\\\"\\n msg += \\\" --mffg\\\\tmaximum fraction of filtered genotypes to keep a variant\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee '--maxFractionFilteredGenotypes' in GATK's SelectVariants\\\\n\\\"\\n msg += \\\" --mnnc\\\\tmaximum number of not-called genotypes to keep a variant\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\" --mfnc\\\\tmaximum fraction of not-called genotypes to keep a variant\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tsee '--maxNOCALLfraction' in GATK's SelectVariants\\\\n\\\"\\n msg += \\\" --fam\\\\tpath to the file containing pedigree information\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdiscard variants with Mendelian violations (see Semler et al, 2012)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tshould be in the 'fam' format specified by PLINK\\\\n\\\"\\n msg += \\\"\\\\t\\\\tvalidation strictness (GATK '-pedValidationType') is set at 'SILENT'\\\\n\\\"\\n msg += \\\"\\\\t\\\\t allowing some samples to be absent from the pedigree\\\\n\\\"\\n msg += \\\" --mvq\\\\tminimum GQ for each trio member to accept a variant as a Mendelian violation\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9 if '--fam' is specified\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=0\\\\n\\\"\\n msg += \\\" --xlssf\\\\tpath to the file with genotypes to exclude\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 9 (can be especially useful if '--fam' is specified)\\\\n\\\"\\n msg += \\\" --tmpd\\\\tpath to a temporary directory on child nodes (default=.)\\\\n\\\"\\n msg += \\\"\\\\t\\\\te.g. it can be /tmp or /scratch\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 4 for 'samtools sort'\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 7 for 'GATK HaplotypeCaller'\\\\n\\\"\\n msg += \\\" --jvmXms\\\\tinitial memory allocated to the Java Virtual Machine\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=512m (can also be specified as 1024k, 1g, etc)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in steps 4, 5, 6, 7 and 8 for Picard and GATK\\\\n\\\"\\n msg += \\\" --jvmXmx\\\\tmaximum memory allocated to the Java Virtual Machine\\\\n\\\"\\n msg += \\\"\\\\t\\\\tdefault=4g\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in steps 4, 5, 6, 7 and 8 for Picard and GATK\\\\n\\\"\\n msg += \\\" --queue2\\\\tname of the second cluster queue (default=bigmem.q)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tused in step 4 for Picard to collect insert sizes\\\\n\\\"\\n msg += \\\" --knowni\\\\tpath to a VCF file with known indels (for local realignment)\\\\n\\\"\\n msg += \\\" --known\\\\tpath to a VCF file with known variants (e.g. from dbSNP)\\\\n\\\"\\n msg += \\\" --force\\\\tforce to re-run step(s)\\\\n\\\"\\n msg += \\\"\\\\t\\\\tthis removes without warning the step directory if it exists\\\\n\\\"\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Examples:\\\\n\\\"\\n msg += \\\" %s --step 1 --samples samples.txt\\\\n\\\" % os.path.basename(sys.argv[0])\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Details:\\\\n\\\"\\n msg += \\\"This program aims at genotyping a set of 'genotypes' using data from\\\\n\\\"\\n msg += \\\"a restriction-assisted DNA sequencing (RAD-seq) experiment, also known\\\\n\\\"\\n msg += \\\"as a genotyping-by-sequencing (GBS) experiment.\\\\n\\\"\\n msg += \\\"Here, by 'genotype', we mean the entity which is the focus of the\\\\n\\\"\\n msg += \\\"study. For instance, it can be a plant variety (or a human being), or\\\\n\\\"\\n msg += \\\"the specific clone of a given plant variety (or a specific tumor of a\\\\n\\\"\\n msg += \\\"given human being), etc.\\\\n\\\"\\n msg += \\\"Importantly, note that the content of the 'genotype' column will\\\\n\\\"\\n msg += \\\"be used to set the 'SM' (sample) tag of the 'RG' (read group) header\\\\n\\\"\\n msg += \\\"record type of the SAM format (see http://www.htslib.org/). However,\\\\n\\\"\\n msg += \\\"internal to this program, the term 'sample' corresponds to the unique\\\\n\\\"\\n msg += \\\"quadruplet (genotype,flowcell,lane,barcode) for steps 1 and 2, and to\\\\n\\\"\\n msg += \\\"the unique triplet (genotype,flowcell,lane) for the others.\\\\n\\\"\\n msg += \\\"Jobs are executed in parallel (--schdlr). Their return status is\\\\n\\\"\\n msg += \\\"recorded in a SQLite database which is removed at the end. If a job\\\\n\\\"\\n msg += \\\"fails, the whole script stops with an error.\\\\n\\\"\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Dependencies:\\\\n\\\"\\n msg += \\\"Python >= 2.7; Biopython; pyutilstimflutre >= 0.5\\\\n\\\"\\n msg += \\\"\\\\n\\\"\\n msg += \\\"Report bugs to <timothee.flutre@inra.fr>.\\\"\\n print(msg); sys.stdout.flush()\",\n \"def qc_metrics(self, files_in, qc_files):\\n self.cmd(\\\"{samtools} index {bam_in}\\\"\\n .format(\\n samtools=self.cmds[\\\"samtools\\\"],\\n bam_in=files_in[0],\\n ),\\n shell=True)\\n self.cmd(\\\"{samtools} idxstats {bam_in} | tee {qc_file}\\\"\\n .format(\\n samtools=self.cmds[\\\"samtools\\\"],\\n bam_in = files_in[0],\\n qc_file = qc_files[0],\\n ),\\n shell=True,\\n log_output=True)\\n self.cmd(\\\"{samtools} flagstat {bam_in} | tee {qc_file}\\\"\\n .format(\\n samtools=self.cmds[\\\"samtools\\\"],\\n bam_in = files_in[0],\\n qc_file = qc_files[1],\\n ),\\n shell=True,\\n log_output=True)\\n \\n self.checkpoint(qc_files[0])\\n self.checkpoint(qc_files[1])\\n self.checkpoint(qc_files[2])\",\n \"def usage(progname):\\n \\n sys.stderr.write(\\\"Usage: \\\" +progname + \\\" [-cmnv] [-z score] \\\"\\n \\\" <outdir>\\\\n\\\")\\n sys.stderr.write(' -c class level not fold level evaluation\\\\n')\\n sys.stderr.write(' -m read multiquery file on stdin\\\\n')\\n sys.stderr.write(' -n negate scores (so that most -ve is best)\\\\n')\\n sys.stderr.write(' -v verbose messages to stderr\\\\n')\\n sys.stderr.write(' -z score : assign identifiers not present in the output a score of score\\\\n')\\n sys.exit(1)\",\n \"def _cmd_segmetrics(args):\\n if not 0.0 < args.alpha <= 1.0:\\n raise RuntimeError(\\\"alpha must be between 0 and 1.\\\")\\n\\n if not any((args.location_stats, args.spread_stats, args.interval_stats)):\\n logging.info(\\\"No stats specified\\\")\\n return\\n\\n # Calculate all metrics\\n cnarr = read_cna(args.cnarray)\\n segarr = read_cna(args.segments)\\n segarr = do_segmetrics(\\n cnarr,\\n segarr,\\n args.location_stats,\\n args.spread_stats,\\n args.interval_stats,\\n args.alpha,\\n args.bootstrap,\\n args.smooth_bootstrap,\\n skip_low=args.drop_low_coverage,\\n )\\n tabio.write(segarr, args.output or segarr.sample_id + \\\".segmetrics.cns\\\")\",\n \"def main(argv=None):\\n\\n if argv is None:\\n argv = sys.argv\\n\\n # setup command line parser\\n parser = E.OptionParser(version=\\\"%prog version: $Id$\\\",\\n usage=globals()[\\\"__doc__\\\"])\\n\\n parser.add_option(\\\"-t\\\", \\\"--test\\\", dest=\\\"test\\\", type=\\\"string\\\",\\n help=\\\"supply help\\\")\\n\\n parser.add_option(\\\"--method\\\", dest=\\\"method\\\", type=\\\"choice\\\",\\n choices=(\\\"metrics\\\", \\\"summary\\\", \\\"module_summary\\\"),\\n help=\\\"method to summarise clustering\\\")\\n\\n parser.add_option(\\\"--ref-gtf-files\\\", dest=\\\"ref_gtf\\\", type=\\\"string\\\",\\n help=\\\"comma separated list of reference gtf files\\\")\\n\\n # add common options (-h/--help, ...) and parse command line\\n (options, args) = E.Start(parser, argv=argv)\\n\\n if options.method == \\\"metrics\\\":\\n infile = argv[-1]\\n E.info(\\\"loading input file: %s\\\" % infile)\\n assert infile\\n\\n df = pd.read_table(infile,\\n sep=\\\"\\\\t\\\",\\n header=None,\\n index_col=0)\\n\\n df = df.ix[:, :50]\\n cluster_combs = (x for x in itertools.combinations(df.columns,\\n 2))\\n genes = df.index\\n results_dict = {}\\n all_clusts = {}\\n\\n E.info(\\\"setting up cluster containers\\\")\\n for i in df.columns:\\n clusters = set(df[i].values.tolist())\\n cluster_dict = {}\\n for clust in clusters:\\n cluster_dict[clust] = []\\n for gene in genes:\\n cluster_dict[df[i][gene]].append(gene)\\n\\n for col in clusters:\\n col_set = set()\\n clust_col = cluster_dict[col]\\n gene_members = itertools.combinations(clust_col,\\n 2)\\n col_set.update(gene_members)\\n cluster_dict[col] = col_set\\n all_clusts[i] = cluster_dict\\n E.info(\\\"generating all pair-wise cluster comparisons\\\")\\n E.info(\\\"calculating adjusted mutual information\\\")\\n for k in cluster_combs:\\n clusters1 = all_clusts[k[0]]\\n clusters2 = all_clusts[k[1]]\\n metric_dict = {}\\n metric_dict['AMI'] = TS.adjustedMutualInformation(clusters1,\\n clusters2)\\n results_dict[k] = metric_dict\\n\\n res_frame = pd.DataFrame(results_dict).T\\n res_frame = res_frame.reset_index()\\n res_frame.drop(['level_0'], inplace=True, axis=1)\\n res_frame.drop(['level_1'], inplace=True, axis=1)\\n\\n # flatten rand indices and add to output dataframe\\n rand_arrays = TS.randIndexes(df)\\n flat_adj_rand = TS.unravel_arrays(rand_arrays[0])\\n flat_rand = TS.unravel_arrays(rand_arrays[1])\\n res_frame['Rand_Index'] = flat_rand\\n res_frame['Adjusted_Rand_Index'] = flat_adj_rand\\n E.info(\\\"aggregating results\\\")\\n\\n res_frame.to_csv(options.stdout,\\n sep=\\\"\\\\t\\\",\\n index_label='idx')\\n\\n elif options.method == \\\"summary\\\":\\n infiles = argv[-1]\\n list_of_files = infiles.split(\\\",\\\")\\n\\n file_dict = {}\\n for fle in list_of_files:\\n fname = fle.split(\\\"/\\\")[-1]\\n condition = fname.split(\\\"-\\\")[0]\\n ref = fname.split(\\\"-\\\")[1]\\n df_ = pd.read_table(fle,\\n sep=\\\"\\\\t\\\",\\n header=0,\\n index_col=0)\\n df_.columns = ['gene_id', 'cluster']\\n clust_dict = {}\\n for idx in df_.index:\\n cluster = df_.loc[idx]['cluster']\\n gene = df_.loc[idx]['gene_id']\\n try:\\n clust_dict[cluster] += 1\\n except KeyError:\\n clust_dict[cluster] = 1\\n med_size = np.median(clust_dict.values())\\n file_dict[fname] = {'condition': condition,\\n 'reference': ref,\\n 'median_cluster_size': med_size}\\n\\n outframe = pd.DataFrame(file_dict).T\\n outframe.to_csv(options.stdout,\\n sep=\\\"\\\\t\\\",\\n index_label='idx')\\n\\n elif options.method == \\\"module_summary\\\":\\n # get lncRNA/gene lengths from reference gtfs\\n ref_gtfs = options.ref_gtf.split(\\\",\\\")\\n length_dict = {}\\n for ref in ref_gtfs:\\n oref = IOTools.openFile(ref, \\\"rb\\\")\\n git = GTF.transcript_iterator(GTF.iterator(oref))\\n for gene in git:\\n for trans in gene:\\n length = trans.end - trans.start\\n try:\\n length_dict[trans.gene_id] += length\\n except KeyError:\\n length_dict[trans.gene_id] = length\\n oref.close()\\n\\n infiles = argv[-1]\\n list_of_files = infiles.split(\\\",\\\")\\n\\n fdfs = []\\n for fle in list_of_files:\\n cond = fle.split(\\\"/\\\")[-1].split(\\\"-\\\")[0]\\n refer = fle.split(\\\"/\\\")[-1].split(\\\"-\\\")[1]\\n _df = pd.read_table(fle, sep=\\\"\\\\t\\\",\\n header=0, index_col=0)\\n _df.columns = ['gene_id', 'cluster']\\n clusters = set(_df['cluster'])\\n c_dict = {}\\n # summarize over each cluster\\n for clust in clusters:\\n lengths = []\\n c_df = _df[_df['cluster'] == clust]\\n for lid in c_df['gene_id']:\\n lengths.append(length_dict[lid])\\n c_dict[clust] = {'cluster_size': len(c_df['gene_id']),\\n 'mean_length': np.mean(lengths),\\n 'index': (cond, refer),\\n 'module': clust}\\n cdf = pd.DataFrame(c_dict).T\\n # use a multindex for hierarchical indexing\\n midx = pd.MultiIndex.from_tuples(cdf['index'])\\n cdf.index = midx\\n cdf.drop(['index'], inplace=True, axis=1)\\n fdfs.append(cdf)\\n\\n # generate a single output df\\n s_df = fdfs[0]\\n fdfs.pop(0)\\n for df in fdfs:\\n s_df = s_df.append(df)\\n\\n s_df.to_csv(options.stdout,\\n index_label=(\\\"condition\\\", \\\"reference\\\"),\\n sep=\\\"\\\\t\\\")\\n\\n # write footer and output benchmark information.\\n E.Stop()\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description=main.__doc__,\\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\\n epilog=\\\"Homepage: https://github.com/kbat/mc-tools\\\")\\n parser.add_argument('com', type=str, help='plot requests file name', nargs='?', default=\\\"/tmp/foo.c\\\")\\n parser.add_argument('comout', type=str, help='COMOUT file name', nargs='?', default=\\\"zoom\\\")\\n\\n args = parser.parse_args()\\n\\n cmd = {} # dictionary of commands\\n\\n bas = False\\n plane = False\\n \\n with open(args.com) as f:\\n for line in f.readlines():\\n words = line.strip().split()\\n if len(words) is 0:\\n continue\\n\\n for i,w in enumerate(words):\\n if re.search(\\\"^bas\\\", w):\\n cmd['bas'] = list(map(float, words[i+1:i+7]))\\n if plane is False: bas = True # basis was before plane cuts\\n elif re.search(\\\"^or\\\", w):\\n cmd['or'] = list(map(float, words[i+1:i+4]))\\n elif re.search(\\\"^ex\\\", w):\\n try: # both x and y scales are given\\n cmd['ex'] = list(map(float, words[i+1:i+3]))\\n continue\\n except ValueError: # just 1 scale is given\\n cmd['ex'] = list(map(float, words[i+1:i+2]))\\n elif re.search(\\\"^lab\\\", w):\\n cmd['label'] = list(map(int, map(float, words[i+1:i+3]))) #+ [words[i+3]]\\n elif re.search(\\\"^p[xyz]\\\", w):\\n cmd[w] = [float(words[i+1])]\\n if bas is False: plane = True # plane cuts were before basis\\n elif re.search(\\\"^legend\\\", w):\\n cmd[w] = [words[i+1]]\\n elif w == \\\"scale\\\":\\n print(w)\\n if int(words[i+1]): # no need to put 'scale 0'\\n cmd[w] = [words[i+1]]\\n elif w in (\\\"mesh\\\"):\\n if int(words[i+1])==1: # no need to put 'mesh 1'\\n cmd[w] = [words[i+1]]\\n\\n print(bas, plane)\\n\\n if plane: # bas was first\\n keys = ('bas', 'or', 'ex', 'px', 'py', 'pz', 'label', 'mesh', 'legend', 'scale')\\n elif bas:\\n keys = ('or', 'ex', 'px', 'py', 'pz', 'bas', 'label', 'mesh', 'legend', 'scale')\\n else:\\n keys = {'or', 'ex', 'label', 'mesh', 'legend', 'scale'}\\n \\n with open(args.comout, 'w') as f:\\n for key in keys:\\n if key in cmd:\\n # newline required by mcplot:\\n if key in ('mesh', 'legend', 'scale', 'label'):\\n f.write(\\\"\\\\n\\\")\\n f.write(\\\"%s %s \\\" % (key,\\\" \\\".join(str(e) for e in cmd[key]),))\\n f.write(\\\"\\\\n\\\")\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\\n )\\n add_common_snmp_args(parser)\\n parser.add_argument(\\n \\\"-w\\\",\\n \\\"--warning\\\",\\n type=int,\\n default=70,\\n help=\\\"Warning memory usage percentage (0-100)\\\",\\n )\\n parser.add_argument(\\n \\\"-c\\\",\\n \\\"--critical\\\",\\n type=int,\\n default=90,\\n help=\\\"Critical memory usage percentage (0-100)\\\",\\n )\\n parser.add_argument(\\n \\\"-f\\\",\\n \\\"--family\\\",\\n required=True,\\n help=\\\"Switch family. Supported families: 1910, 1920, 1920S\\\",\\n )\\n\\n config = vars(parser.parse_args())\\n check_snmp_args(config)\\n check_thresholds(config)\\n\\n dataset = {}\\n\\n if config[\\\"family\\\"] == \\\"1920S\\\":\\n cpu = ObjectType(\\n ObjectIdentity(\\n \\\"HP-SWITCHING-MIB\\\", \\\"agentSwitchCpuProcessTotalUtilization\\\", 0\\n )\\n )\\n elif config[\\\"family\\\"] in [\\\"1910\\\", \\\"1920\\\"]:\\n cpu = ObjectType(\\n ObjectIdentity(\\\"HH3C-ENTITY-EXT-MIB\\\", \\\"hh3cEntityExtCpuUsage\\\", 8)\\n )\\n else:\\n unknown_exit(SERVICE, f\\\"Switch family {config['family']} NOT known\\\")\\n\\n try:\\n raw_data = get_snmp_data(config, cpu)\\n except ValueError as err:\\n unknown_exit(SERVICE, err)\\n add_vars_to_dataset(dataset, raw_data)\\n\\n if config[\\\"family\\\"] == \\\"1920S\\\":\\n dataset[\\\"cpu_usage\\\"] = get_hp_cpu_usage(\\n dataset[\\\"agentSwitchCpuProcessTotalUtilization\\\"]\\n )\\n elif config[\\\"family\\\"] in [\\\"1910\\\", \\\"1920\\\"]:\\n dataset[\\\"cpu_usage\\\"] = int(dataset[\\\"hh3cEntityExtCpuUsage\\\"])\\n else:\\n unknown_exit(SERVICE, f\\\"Switch family {config['family']} NOT known\\\")\\n\\n state, message = generate_output(config, dataset)\\n report(state, message)\",\n \"def cmd_help(args):\",\n \"def eval_cpuset():\\n\\tnum_cpu = run('grep -c ^processor /proc/cpuinfo',quiet=True,warn_only=True)\\n\\tprint(red('Number of cpus : \\\\t'+num_cpu))\",\n \"def handle_program_options():\\n parser = argparse.ArgumentParser(description=\\\"Gather numeric information \\\\\\n about the processed sequence data in an \\\\\\n MG-RAST project.\\\")\\n parser.add_argument('project_id',\\n help=\\\"The project identifier (MG-RAST ID)\\\")\\n parser.add_argument('-a', '--auth_key',\\n help=\\\"An MG-RAST API authorization key. This is \\\\\\n necessary to access projects marked as private.\\\")\\n parser.add_argument('-g', '--group_by', action='append',\\n help=\\\"A string that matches some part of the \\\\\\n 'Metagenome Name' field. All matching project \\\\\\n metagenomes will be grouped by this identifier \\\\\\n and their stats will be summed. This option can \\\\\\n be specified multiple times to create multiple \\\\\\n groups. All non-matching metagenomes will \\\\\\n appear separately in the table. NOTE: \\\\\\n Strings will be matched longest first. This \\\\\\n allows for matching names that might be a \\\\\\n substring of another match. For example: -g S \\\\\\n -g NS. The name field will first be matched \\\\\\n against the longest string (NS) first and then \\\\\\n each smaller string in order.\\\")\\n parser.add_argument('-o', '--output_filename', default='meta_stats.txt',\\n help=\\\"The name of the file the project summary \\\\\\n information will be written to.\\\")\\n\\n# parser.add_argument('-v', '--verbose', action='store_true')\\n\\n return parser.parse_args()\",\n \"def qsub_cmmd(self):\\n temp = 'qsub -l mem=1G,time=:5: -cwd -j y -o {log} {job}'.format(**self.dict)\\n return temp.format(fnum=self.fnum)\",\n \"def process_cl_args():\\n\\n parser = argparse.ArgumentParser(add_help=False)\\n parser.add_argument('commands', nargs='*')\\n parser.add_argument('--help', '-h', action='store_true')\\n parser.add_argument('--version', '-v', action='store_true')\\n parser.add_argument('--debug', '-d', action='store_true')\\n parser.add_argument('--logging', '-l', action='store_true')\\n parser.add_argument('--no-autosize', action='store_true')\\n parser.add_argument('--no-preload', action='store_true')\\n args = parser.parse_args()\\n\\n if args.version:\\n xprint(get_version_info())\\n xprint(\\\"\\\")\\n sys.exit()\\n\\n elif args.help:\\n for x in helptext():\\n xprint(x[2])\\n sys.exit()\\n\\n if args.debug or os.environ.get(\\\"mpsytdebug\\\") == \\\"1\\\":\\n xprint(get_version_info())\\n g.debug_mode = True\\n g.no_clear_screen = True\\n logfile = os.path.join(tempfile.gettempdir(), \\\"mpsyt.log\\\")\\n logging.basicConfig(level=logging.DEBUG, filename=logfile)\\n logging.getLogger(\\\"pafy\\\").setLevel(logging.DEBUG)\\n\\n elif args.logging or os.environ.get(\\\"mpsytlog\\\") == \\\"1\\\":\\n logfile = os.path.join(tempfile.gettempdir(), \\\"mpsyt.log\\\")\\n logging.basicConfig(level=logging.DEBUG, filename=logfile)\\n logging.getLogger(\\\"pafy\\\").setLevel(logging.DEBUG)\\n\\n if args.no_autosize:\\n g.detectable_size = False\\n\\n g.command_line = \\\"playurl\\\" in args.commands or \\\"dlurl\\\" in args.commands\\n if g.command_line:\\n g.no_clear_screen = True\\n\\n if args.no_preload:\\n g.preload_disabled = True\\n\\n g.argument_commands = args.commands\",\n \"def main():\\n parser = argparse.ArgumentParser(description='investigate code health and random statistics')\\n sub_parsers = parser.add_subparsers(dest='command_name', title='Commands', help='', metavar='<command>')\\n\\n sub = sub_parsers.add_parser('line-count', help='list line counts')\\n sub.add_argument('files', nargs='+', help='files or folders to look in')\\n sub.add_argument('--each', type=int, default=1)\\n sub.add_argument('--show', action='store_true')\\n sub.add_argument('--include-empty', dest='discard_empty', action='store_false')\\n sub.set_defaults(func=handle_line_count)\\n\\n sub = sub_parsers.add_parser('include-list', help='list headers from files')\\n cc.add_argument(sub)\\n sub.add_argument('files', nargs='+')\\n sub.add_argument('--print', dest='print_files', action='store_true')\\n sub.add_argument('--print-stats', dest='print_stats', action='store_true')\\n sub.add_argument('--print-max', dest='print_max', action='store_true')\\n sub.add_argument('--no-list', dest='print_list', action='store_false')\\n sub.add_argument('--count', default=2, type=int, help=\\\"only print includes that are more or equal to <count>\\\")\\n sub.add_argument('--limit', nargs='+', help=\\\"limit search to theese files and folders\\\")\\n sub.set_defaults(func=handle_list)\\n\\n sub = sub_parsers.add_parser('include-gv', help='generate a graphviz of the includes')\\n cc.add_argument(sub)\\n sub.add_argument('files', nargs='+')\\n sub.add_argument('--limit', nargs='+', help=\\\"limit search to theese files and folders\\\")\\n sub.add_argument('--group', action='store_true', help=\\\"group output\\\")\\n sub.add_argument('--cluster', action='store_true', help=\\\"group output into clusters\\\")\\n sub.set_defaults(func=handle_gv)\\n\\n sub = sub_parsers.add_parser('list-indents', help='list the files with the maximum indents')\\n sub.add_argument('files', nargs='+')\\n sub.add_argument('--each', type=int, default=1, help='group counts')\\n sub.add_argument('--show', action='store_true', help='include files in list')\\n sub.add_argument('--hist', action='store_true', help='show simple histogram')\\n sub.add_argument('--include-empty', dest='discard_empty', action='store_false')\\n sub.set_defaults(func=handle_list_indents)\\n\\n sub = sub_parsers.add_parser('missing-pragma-once', help='find headers with missing include guards')\\n sub.add_argument('files', nargs='+')\\n sub.set_defaults(func=handle_missing_include_guards)\\n\\n sub = sub_parsers.add_parser('missing-in-cmake', help='find files that existis on disk but missing in cmake')\\n sub.add_argument('files', nargs='+')\\n cc.add_argument(sub)\\n sub.set_defaults(func=handle_missing_in_cmake)\\n\\n sub = sub_parsers.add_parser('list-no-project-folders', help='find projects that have not set the solution folder')\\n sub.add_argument('files', nargs='+')\\n cc.add_argument(sub)\\n sub.set_defaults(func=handle_list_no_project_folder)\\n\\n sub = sub_parsers.add_parser('check-files', help=\\\"find files that doesn't match the name style\\\")\\n sub.add_argument('files', nargs='+')\\n cc.add_argument(sub)\\n sub.set_defaults(func=handle_check_files)\\n\\n args = parser.parse_args()\\n if args.command_name is not None:\\n args.func(args)\\n else:\\n parser.print_help()\",\n \"def metrics_networkport(cmd_ctx, cpc, adapter, **options):\\n cmd_ctx.execute_cmd(\\n lambda: cmd_metrics_networkport(cmd_ctx, cpc, adapter, options))\",\n \"def HelpCommand(self, unused_args, unused_sub_opts=None, unused_headers=None,\\n unused_debug=None):\\n self.OutputUsageAndExit()\",\n \"def command_help(args):\\n\\tprint_usage()\\n\\treturn 0\",\n \"def main():\\n known_args, unknown_args = parse_known_args()\\n if not unknown_args:\\n # return an error message if no command is provided\\n sys.exit(\\\"Please provide a command to benchmark: $ humann_benchmark COMMAND\\\")\\n try:\\n process = subprocess.Popen(\\\" \\\".join(unknown_args),shell=True)\\n except (EnvironmentError, subprocess.CalledProcessError):\\n sys.exit(\\\"Unable to execute command: \\\" + \\\" \\\".join(unknown_args))\\n pid=str(process.pid)\\n start=time.time()\\n max_memory=0\\n while process.poll() is None:\\n time.sleep(1)\\n # while the process is running check on the memory use\\n # get the pids of the main process and all children (and their children)\\n pids=get_pids(pid)\\n stdout=subprocess.check_output([\\\"ps\\\",\\\"--pid\\\",\\\",\\\".join(pids),\\\"-o\\\",\\\"pid,rss,command\\\"]).decode(\\\"utf-8\\\")\\n print(\\\"\\\\n\\\"+stdout+\\\"\\\\n\\\")\\n # remove the header from the process output\\n status=[i.split() for i in filter(lambda x: x, stdout.split(\\\"\\\\n\\\")[1:])]\\n # memory is the sum of all rss\\n memory=sum(int(i[1]) for i in status)\\n if memory > max_memory:\\n max_memory=memory\\n \\n end=time.time()\\n print(\\\"Time: {:.0f} minutes\\\".format((end-start)/60))\\n print(\\\"Max Memory (RSS): {:.1f} GB\\\".format(max_memory*1.0/1024**2))\",\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 printHelp():\\n print(\\\"amqWorkApiMass.py -n <msgcnt> -b <body> -m <headers> -s <path/to/bodyandheaders>\\\")\",\n \"def _usage_options_example(self):\\n pass\",\n \"def main(argv):\\n version = \\\"0.1.2\\\"\\n interval = 1\\n max_run_time = 0\\n finished = 0\\n first_time = 1\\n output_file = 0\\n output_file_enabled = 0\\n output_path = 0\\n header_row = 1\\n\\n #*** Get the hostname for use in filenames etc:\\n hostname = socket.gethostname()\\n\\n #*** Start by parsing command line parameters:\\n try:\\n opts, args = getopt.getopt(argv, \\\"hu:m:ni:w:Wb:jv\\\",\\n [\\\"help\\\",\\n \\\"url=\\\",\\n \\\"max-run-time=\\\",\\n \\\"no-keepalive\\\",\\n \\\"interval=\\\",\\n \\\"output-file=\\\",\\n \\\"output-path=\\\",\\n \\\"no-header-row\\\",\\n \\\"version\\\"])\\n except getopt.GetoptError as err:\\n print \\\"mosp: Error with options:\\\", err\\n print_help()\\n sys.exit(2)\\n for opt, arg in opts:\\n if opt in (\\\"-h\\\", \\\"--help\\\"):\\n print_help()\\n sys.exit()\\n elif opt in (\\\"-v\\\", \\\"--version\\\"):\\n print 'mosp.py version', version\\n sys.exit()\\n elif opt in (\\\"-m\\\", \\\"--max-run-time\\\"):\\n max_run_time = float(arg)\\n elif opt in (\\\"-i\\\", \\\"--interval\\\"):\\n interval = float(arg)\\n elif opt in (\\\"-w\\\", \\\"--output-file\\\"):\\n output_file = arg\\n output_file_enabled = 1\\n elif opt == \\\"-W\\\":\\n output_file = \\\"mosp-\\\" + hostname + \\\"-\\\" + \\\\\\n time.strftime(\\\"%Y%m%d-%H%M%S.csv\\\")\\n output_file_enabled = 1\\n elif opt in (\\\"-b\\\", \\\"--output-path\\\"):\\n output_path = arg\\n elif opt in (\\\"-j\\\", \\\"--no-header-row\\\"):\\n header_row = 0\\n\\n print \\\"\\\\nMeasure Operating System Performance (mosp) version\\\", \\\\\\n version\\n\\n #*** Display output filename:\\n if output_file_enabled:\\n if output_path:\\n output_file = os.path.join(output_path, output_file)\\n print \\\"Results filename is\\\", output_file\\n else:\\n print \\\"Not outputing results to file, as option not selected\\\"\\n\\n if not header_row:\\n print \\\"Not writing a header row to CSV\\\"\\n\\n #*** Use this if max_run_time is set:\\n initial_time = time.time()\\n\\n #*** Instantiate classes:\\n cpus = CPUs()\\n swap = Swap()\\n nics = NICs()\\n\\n #*** Start the loop:\\n while not finished:\\n timenow = datetime.datetime.now()\\n timestamp = timenow.strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]\\n start_time = time.time()\\n\\n #*** Update CPU measurements:\\n cpus.update()\\n\\n #*** Update swap measurements:\\n swap.update()\\n\\n #*** Update network measurements:\\n nics.update()\\n\\n #*** Put the stats into a nice string for printing and\\n #*** writing to file:\\n result_csv = str(timestamp) + \\\",\\\" \\\\\\n + cpus.csv() \\\\\\n + swap.csv() \\\\\\n + nics.csv() \\\\\\n + \\\"\\\\n\\\"\\n result_kvp = str(timestamp) + \\\" \\\" \\\\\\n + cpus.kvp() \\\\\\n + swap.kvp() \\\\\\n + nics.kvp()\\n print result_kvp\\n if output_file_enabled:\\n #*** Header row in CSV:\\n if first_time and header_row:\\n #*** Write a header row to CSV:\\n header_csv = \\\"time,\\\" + cpus.csv_header(hostname) + \\\\\\n swap.csv_header(hostname) + \\\\\\n nics.csv_header(hostname) + \\\\\\n \\\"\\\\n\\\"\\n first_time = 0\\n with open(output_file, 'a') as the_file:\\n the_file.write(header_csv)\\n\\n #*** Write a data row to CSV:\\n with open(output_file, 'a') as the_file:\\n the_file.write(result_csv)\\n\\n if max_run_time:\\n if (start_time - initial_time) > max_run_time:\\n break\\n\\n #*** Sleep for interval seconds:\\n time.sleep(interval)\",\n \"def printOptions(opts,subject_ids,session_ids,task_list, run_list, acq, rec):\\n uname = os.popen('uname -s -n -r').read()\\n print \\\"\\\\n\\\"\\n print \\\"* Pipeline started at \\\"+time.strftime(\\\"%c\\\")+\\\"on \\\"+uname\\n print \\\"* Command line is : \\\\n \\\"+str(sys.argv)+\\\"\\\\n\\\"\\n print \\\"* The source directory is : \\\"+opts.sourceDir\\n print \\\"* The target directory is : \\\"+opts.targetDir+\\\"\\\\n\\\"\\n print \\\"* Data-set Subject ID(s) is/are : \\\"+str(', '.join(subject_ids))+\\\"\\\\n\\\"\\n # print \\\"* PET conditions : \\\"+ ','.join(opts.condiList)+\\\"\\\\n\\\"\\n print \\\"* Sessions : \\\", session_ids, \\\"\\\\n\\\"\\n print \\\"* Tasks : \\\" , task_list , \\\"\\\\n\\\"\\n print \\\"* Runs : \\\" , run_list , \\\"\\\\n\\\"\\n print \\\"* Acquisition : \\\" , acq , \\\"\\\\n\\\"\\n print \\\"* Reconstruction : \\\" , rec , \\\"\\\\n\\\"\",\n \"def usage():\",\n \"def usage():\",\n \"def help(cls, extra_args=None):\\n if (_is_text_interface()):\\n return _create_text_help_str(cls, cls._TEXT_USAGE)\\n else:\\n return cls._GRAPHICAL_USAGE\",\n \"def metrics_lpar(cmd_ctx, cpc, lpar, **options):\\n cmd_ctx.execute_cmd(lambda: cmd_metrics_lpar(cmd_ctx, cpc, lpar, options))\",\n \"def collect_cluster_info(output_dir, k8s_cli):\\n collect_helper(output_dir, cmd=\\\"{} cluster-info\\\".format(k8s_cli),\\n file_name=\\\"cluster_info\\\", resource_name=\\\"cluster-info\\\")\",\n \"def print_performance_info(self):\\n pass\",\n \"def collect_usage_pieces(self, ctx):\\n pieces = super(ProfilingCommand, self).collect_usage_pieces(ctx)\\n assert pieces[-1] == '[ARGV]...'\\n pieces.insert(-1, '[--]')\\n return pieces\",\n \"def metrics_adapter(cmd_ctx, cpc, adapter, **options):\\n cmd_ctx.execute_cmd(\\n lambda: cmd_metrics_adapter(cmd_ctx, cpc, adapter, options))\",\n \"async def sysinfo(self, ctx: Context):\\n\\t\\tstart = time.perf_counter()\\n\\t\\tend = time.perf_counter()\\n\\t\\tduration = (end - start) * 1000\\n\\t\\tcpuavg = psutil.cpu_percent(interval=None)\\n\\t\\tmem = psutil.virtual_memory()[2]\\n\\t\\tdurround = round(duration, 3)\\n\\t\\tosun = os.uname()\\n\\t\\tawait self.send(f\\\"System Info | CPU: {cpuavg}% | RAM: {mem}% | Latency: {durround * 1000}ms | OS: {sys.platform}\\\", whisper=[ctx.author.id])\",\n \"def usage(err=''):\\r\\n m = '%s\\\\n' %err\\r\\n m += 'Default usage is to list Cases closed for the 30 days\\\\n'\\r\\n m += '\\\\n Example:\\\\n'\\r\\n m += ' closedcases -n 90 \\\\n' \\r\\n m += ' \\\\n'\\r\\n# m += ' closedcases -n 60 -s blast5 \\\\n'\\r\\n return m\",\n \"def cmdline(self, executable, options, task, rlimits):\\n data_model_param = get_data_model_from_task(task, {ILP32: \\\"-m32\\\", LP64: \\\"-m64\\\"})\\n print(options)\\n if data_model_param and not any(\\n option.startswith(\\\"--clang-options=\\\") for option in options\\n ):\\n options += [\\\"--clang-options=\\\" + data_model_param]\\n\\n if task.property_file:\\n options += [\\\"--svcomp-property\\\", task.property_file]\\n else:\\n raise UnsupportedFeatureException(\\n \\\"SMACK can't execute without a property file.\\\"\\n )\\n\\n options += [task.single_input_file]\\n\\n return [executable] + options\",\n \"def usage():\\n print(\\\"[1] Getting help from a cipher \\\")\\n print(\\\" ---> ./cryptogra.py caesar -h \\\")\\n print(\\\"\\\")\",\n \"def _get_metrics_options(metrics):\\n metrics_options = []\\n if metrics is None:\\n metrics = []\\n for static_metric in metrics:\\n metrics_options += [\\n \\\"-m\\\",\\n static_metric.metric.mp_metric_name,\\n str(static_metric.value),\\n ]\\n return metrics_options\",\n \"def getHelp(self):\\r\\n help_str =\\\\\\r\\n \\\"\\\"\\\"##########################################################################################\\r\\n#\\r\\n# Required:\\r\\n#\\r\\n# --query_NAST multi-fasta file containing query sequences in alignment format\\r\\n#\\r\\n# Common opts:\\r\\n#\\r\\n# --db_NAST db in NAST format\\r\\n# --db_FASTA db in fasta format (megablast formatted)\\r\\n#\\r\\n#\\r\\n# -n number of top matching database sequences to compare to (default 15)\\r\\n# -R min divergence ratio default: 1.007\\r\\n# -P min percent identity among matching sequences (default: 90)\\r\\n#\\r\\n# ## parameters to tune ChimeraParentSelector:\\r\\n#\\r\\n# Scoring parameters:\\r\\n# -M match score (default: +5)\\r\\n# -N mismatch penalty (default: -4)\\r\\n# -Q min query coverage by matching database sequence (default: 70)\\r\\n# -T maximum traverses of the multiple alignment (default: 1)\\r\\n\\r\\n#\\r\\n# ## parameters to tune ChimeraPhyloChecker:\\r\\n#\\r\\n#\\r\\n# --windowSize default 50\\r\\n# --windowStep default 5\\r\\n# --minBS minimum bootstrap support for calling chimera (default: 90)\\r\\n# -S percent of SNPs to sample on each side of breakpoint for computing bootstrap support (default: 10)\\r\\n# --num_parents_test number of potential parents to test for chimeras (default: 3)\\r\\n# --MAX_CHIMERA_PARENT_PER_ID Chimera/Parent alignments with perID above this are considered non-chimeras (default 100; turned off)\\r\\n#\\r\\n# ## misc opts\\r\\n#\\r\\n# --printFinalAlignments shows alignment between query sequence and pair of candidate chimera parents\\r\\n# --printCSalignments print ChimeraSlayer alignments in ChimeraSlayer output\\r\\n# --exec_dir chdir to here before running\\r\\n#\\r\\n#########################################################################################\\r\\n \\\"\\\"\\\"\\r\\n return help_str\",\n \"def usage(msgarg):\\n if msgarg:\\n sys.stderr.write(\\\"error: %s\\\\n\\\" % msgarg)\\n print(\\\"\\\"\\\"\\\\\\n usage: %s [options]\\n\\n options:\\n -d increase debug msg verbosity level\\n -c N emit N classes (def: 500) per instances\\n -I N emit N instances\\n\\n \\\"\\\"\\\" % os.path.basename(sys.argv[0]))\\n sys.exit(1)\",\n \"def DoHelp(options, args):\\n __pychecker__ = 'unusednames=options'\\n if len(args) == 1 and args[0] in COMMAND_USAGE_TEXT:\\n print(COMMAND_USAGE_TEXT[args[0]])\\n else:\\n raise gclient_utils.Error(\\\"unknown subcommand '%s'; see 'gclient help'\\\" %\\n args[0])\",\n \"def run(self, line):\\n LOGGER.info(\\\"Scalable PMEM: {}\\\".format(self.name))\\n try:\\n (options, _) = self._parse_arglist(line)\\n except:\\n if (\\\"-h\\\" in line) or (\\\"--help\\\" in line):\\n return ReturnCodes.SUCCESS\\n else:\\n raise InvalidCommandLineErrorOPTS(\\\"\\\")\\n\\n if len(args):\\n InvalidCommandLineError(\\\"This command takes no parameters.\\\")\\n\\n LOGGER.info(\\\"Options: {}\\\".format(options))\\n\\n if not self._chif_lib:\\n self._helpers.failNoChifLibrary()\\n\\n enable = True\\n if options.enableFeature is False:\\n enable = False\\n\\n self.enableOrDisableFeature(enable)\\n\\n #Return code\\n return ReturnCodes.SUCCESS\",\n \"def help_opt(self):\\n print(OPTIONS)\",\n \"def usage(self, host):\",\n \"def print_usage_command(self):\\n print self.get_usage_command()\",\n \"def print_usage_command(self):\\n print self.get_usage_command()\",\n \"def main():\\n DataClasses = [FamilyStats, SeqHdrStats, UniProtStats]\\n CmdLineOps, Args = parse_command_line_options()\\n ThreadManager(DataClasses)\\n print_results(DataClasses, CmdLineOps.output_file)\\n return\",\n \"def Usage(shorthelp=0, writeto_stdout=0, detailed_error=None,\\n exitcode=None, show_cmd=None, show_global_flags=False):\\n printer('%s: Incorrect usage; details below.' % show_cmd)\\n printer('Correct usage is as follows:')\\n printer('')\\n for line in (' ' + cmd.__doc__.rstrip()).splitlines():\\n printer(line)\\n # Print out str(FLAGS) for just the UICmd-specific flags.\\n tmp_flags = flags.FlagValues()\\n unused_cmd = type(cmd)(show_cmd, tmp_flags)\\n prefix = _UICMD_MODULE_NAME + ':\\\\n'\\n flag_str = tmp_flags.ModuleHelp(_UICMD_MODULE_NAME)\\n flag_str = flag_str.lstrip()\\n if flag_str.startswith(prefix):\\n flag_str = flag_str[len(prefix):]\\n if flag_str:\\n printer('')\\n printer('flags:')\\n for line in flag_str.splitlines():\\n printer(line)\\n if detailed_error is not None:\\n printer('')\\n printer('The incorrect usage is as follows:')\\n printer('')\\n for line in unicode(detailed_error).splitlines():\\n printer(' ' + line)\",\n \"def parsing_arguments(args=None):\\n description = ''\\n parser = argparse.ArgumentParser(\\n prog='hatchet plot-cn',\\n description=description,\\n formatter_class=argparse.RawTextHelpFormatter,\\n )\\n parser.add_argument('INPUT', help='One or more space-separated files in CN_BBC format')\\n parser.add_argument(\\n '-n',\\n '--patientnames',\\n required=False,\\n default=config.plot_cn.patientnames,\\n type=str,\\n help='One or more space-separated patient names (default: inferred from filenames)',\\n )\\n parser.add_argument(\\n '-u',\\n '--minu',\\n required=False,\\n default=config.plot_cn.minu,\\n type=float,\\n help='Minimum proportion of a CNA to be considered subclonal (default: 0.2)\\\"',\\n )\\n parser.add_argument(\\n '-x',\\n '--rundir',\\n required=False,\\n default=config.plot_cn.rundir,\\n type=str,\\n help='Running directory (default: current directory)',\\n )\\n parser.add_argument(\\n '-b',\\n '--baseCN',\\n required=False,\\n default=config.plot_cn.basecn,\\n type=int,\\n help='Base copy number (default: inferred from tumor ploidy)',\\n )\\n parser.add_argument(\\n '-sC',\\n '--figsizeclones',\\n required=False,\\n default=config.plot_cn.figsizeclones,\\n type=str,\\n help='Size of clone plots in the form \\\"(X-SIZE, Y-SIZE)\\\"',\\n )\\n parser.add_argument(\\n '-sP',\\n '--figsizecn',\\n required=False,\\n default=config.plot_cn.figsizecn,\\n type=str,\\n help='Size of CN plots in the form \\\"(X-SIZE, Y-SIZE)\\\"',\\n )\\n parser.add_argument(\\n '-sG',\\n '--figsizegrid',\\n required=False,\\n default=config.plot_cn.figsizegrid,\\n type=str,\\n help='Size of grid plots in the form \\\"(X-SIZE, Y-SIZE)\\\"',\\n )\\n parser.add_argument(\\n '-rC',\\n '--resolutionclones',\\n required=False,\\n default=config.plot_cn.resolutionclones,\\n type=int,\\n help='Number of bins to merge together for plotting clone profiles (default: 100)\\\"',\\n )\\n parser.add_argument(\\n '-rP',\\n '--resolutioncn',\\n required=False,\\n default=config.plot_cn.resolutioncn,\\n type=int,\\n help='Number of bins to merge together for plotting proportions (default: 500)\\\"',\\n )\\n parser.add_argument(\\n '-rG',\\n '--resolutiongrid',\\n required=False,\\n default=config.plot_cn.resolutiongrid,\\n type=int,\\n help='Number of bins to merge together in grids (default: 100)\\\"',\\n )\\n parser.add_argument(\\n '-e',\\n '--threshold',\\n required=False,\\n default=config.plot_cn.threshold,\\n type=float,\\n help='Threshold used to classify a tumor into either diploid or tetraploid (default: 3.0)\\\"',\\n )\\n parser.add_argument(\\n '--ymax',\\n required=False,\\n default=config.plot_cn.ymax,\\n type=int,\\n help='Maximum values in y-axis (default: automatically inferred)\\\"',\\n )\\n parser.add_argument(\\n '--ymin',\\n required=False,\\n default=config.plot_cn.ymin,\\n type=int,\\n help='Minimum values in y-axis (default: automatically inferred)\\\"',\\n )\\n parser.add_argument(\\n '--clonepalette',\\n required=False,\\n default=config.plot_cn.clonepalette,\\n type=str,\\n help='Palette for coloring the clones among Set1, Set2, Set3, Paired (default: Set1)\\\"',\\n )\\n parser.add_argument(\\n '--linkage',\\n required=False,\\n default=config.plot_cn.linkage,\\n type=str,\\n help=(\\n 'Linkage method used for clustering (default: single, available (single, complete, average, weighted, '\\n 'centroid, median, ward) from SciPy)\\\"'\\n ),\\n )\\n parser.add_argument('-V', '--version', action='version', version=f'%(prog)s {__version__}')\\n args = parser.parse_args(args)\\n\\n if len(args.INPUT.split()) == 0:\\n raise ValueError(error('Please specify at least one sample as input!'))\\n if args.patientnames is None:\\n patientnames = {fil: os.path.basename(fil) for fil in args.INPUT.split()}\\n else:\\n patientnames = {f: n for f, n in zip(args.INPUT.split(), args.patientnames.split())}\\n if len(args.INPUT.split()) != len(set(patientnames.values())):\\n raise ValueError(error('Multiple patients have the same name but they should unique!'))\\n if args.figsizeclones is not None:\\n figsizeclones = to_tuple(args.figsizeclones, error_message='Wrong format of figsizeclones!')\\n if args.figsizecn is not None:\\n figsizecn = to_tuple(args.figsizecn, error_message='Wrong format of figsizecn!')\\n if args.figsizegrid is not None:\\n figsizegrid = to_tuple(args.figsizegrid, error_message='Wrong format of figsizegrid!')\\n\\n if not os.path.isdir(args.rundir):\\n raise ValueError(error('Running directory does not exist!'))\\n if not 0.0 <= args.minu <= 1.0:\\n raise ValueError(error('The minimum proportion for subclonal CNAs must be in [0, 1]!'))\\n if args.baseCN is not None and args.baseCN < 2:\\n raise ValueError(error('Base CN must be greater or equal than 2!'))\\n if args.resolutionclones is not None and args.resolutionclones < 1:\\n raise ValueError(error('Resolution must be greater than 1!'))\\n if args.resolutioncn is not None and args.resolutioncn < 1:\\n raise ValueError(error('Resolution must be greater than 1!'))\\n if args.resolutiongrid is not None and args.resolutiongrid < 1:\\n raise ValueError(error('Resolution must be greater than 1!'))\\n if args.threshold < 0:\\n raise ValueError(error('Threshold must be positive!'))\\n if args.linkage not in {\\n 'single',\\n 'complete',\\n 'average',\\n 'weighted',\\n 'centroid',\\n 'median',\\n 'ward',\\n }:\\n raise ValueError(error('Unknown linkage method!'))\\n\\n if args.clonepalette == 'Set1':\\n pal = plt.cm.Set1\\n elif args.clonepalette == 'Set2':\\n pal = plt.cm.Set2\\n elif args.clonepalette == 'Set3':\\n pal = plt.cm.Set3\\n elif args.clonepalette == 'Paired':\\n pal = plt.cm.Paired\\n else:\\n raise ValueError(error('Unknown clone palette!'))\\n\\n return {\\n 'input': args.INPUT.split(),\\n 'names': patientnames,\\n 'rundir': args.rundir,\\n 'minu': args.minu,\\n 'base': args.baseCN,\\n 'clonefigsize': figsizeclones,\\n 'propsfigsize': figsizecn,\\n 'clusterfigsize': figsizegrid,\\n 'profileres': args.resolutionclones,\\n 'cnres': args.resolutioncn,\\n 'clusterres': args.resolutiongrid,\\n 'threshold': args.threshold,\\n 'linkage': args.linkage,\\n 'ymax': args.ymax,\\n 'ymin': args.ymin,\\n 'clonepalette': pal,\\n }\",\n \"def usage(msg):\\n ap.print_usage()\\n print \\\"-\\\"*40\\n print msg\\n exit(1)\",\n \"def get_cp_info(self):\\n return self.get(COMMAND_CPM, 'GetCpInfo')\",\n \"def _memtop_setup_parser(parser):\\n parser.add_argument('file', nargs=1, help='Python script to check for memory usage.')\\n parser.add_argument('-o', default=None, action='store', dest='outfile',\\n help='Name of output file. By default, output goes to stdout.')\\n parser.add_argument('-l', '--limit', action='store', type=int, default=20, dest='limit',\\n help='Limit the number of lines in the output.')\",\n \"def metrics_partition(cmd_ctx, cpc, partition, **options):\\n cmd_ctx.execute_cmd(\\n lambda: cmd_metrics_partition(cmd_ctx, cpc, partition, options))\",\n \"def show_mem(cmd, cnt, args):\\n if cpu is None:\\n log(\\\"Load program first\\\") \\n return\\n elif len(cpu.memory) == 0:\\n log(\\\"Load program first\\\") \\n return \\n chunk = 0\\n chunk_count = len(cpu.memory)\\n while chunk < chunk_count: \\n chunk_start = cpu.memory[chunk][MEMADDR]\\n chunk_end = chunk_start + cpu.memory[chunk][MEMSIZE] \\n log(\\\"{:d} {:#x}..{:#x}\\\".format(chunk, chunk_start, chunk_end)) \\n chunk += 1\\n if machine == \\\"ARM\\\":\\n if len(cpu.high_memory) != 0:\\n log(\\\"High memory\\\")\\n for addr in sorted(cpu.high_memory):\\n log(\\\"{:#x}\\\".format(addr))\",\n \"def usage(self):\\n\\n # header\\n self.usage_header()\\n\\n print _(\\\"\\\"\\\"Screen: %(screen)s\\nDescription: %(description)s\\n\\nUsage: %(app_name)s %(screen)s [options]\\\"\\\"\\\") % {\\n 'app_name': constants.App.NAME,\\n 'screen': self.name,\\n 'description': self.description,\\n }\\n # any additional info in between (see other classes for reference)\\n self._usage_options_example()\\n\\n #footer\\n self.usage_footer()\",\n \"def gather_info_and_display():\\n # Obtain total rss displayed in memory.stat for each group,\\n # container and service.\\n try:\\n output_mem = pipe_command(GREP_CMD, AWK_CMD, cwd=MEMPATH)\\n LOG.debug(\\n 'command: %s\\\\n%s',\\n \\\"grep -rs total_rss '/sys/fs/cgroup/memory/' \\\"\\n \\\"| awk '$2>0{print$0}' \\\",\\n output_mem)\\n except subprocess.CalledProcessError as error:\\n LOG.error('Could not get total_rss memory, error=%s', error)\\n return 1\\n\\n mem_info = get_meminfo()\\n pt_groups = gather_groups_memory(output_mem)\\n pt_cont = gather_containers_memory(output_mem)\\n pt_serv = sys_service_memory()\\n\\n # Dump the tables out\\n print('\\\\nPer groups memory usage:')\\n\\n # Get string to be printed and create list of elements separated by \\\\n\\n list_of_table_lines = pt_groups.get_string().split('\\\\n')\\n\\n # Use the first line (+---+-- ...) as horizontal rule to insert later\\n horizontal_line = list_of_table_lines[0]\\n\\n # Print the table, except last two lines ( \\\"Total\\\" row + final separator).\\n print(\\\"\\\\n\\\".join(list_of_table_lines[:-2]))\\n # Print separator, and finally the \\\"Total\\\" row.\\n print(horizontal_line)\\n print(\\\"\\\\n\\\".join(list_of_table_lines[-2:]))\\n\\n pt_namespc = prettytable.PrettyTable(\\n ['Namespace',\\n 'Resident Set Size (MiB)',\\n ], caching=False)\\n pt_namespc.align = 'l'\\n pt_namespc.align['Resident Set Size (MiB)'] = 'r'\\n\\n print('\\\\nPer namespace memory usage:')\\n for n_s in MEMORY['namespaces']:\\n pt_namespc.add_row(\\n [n_s,\\n MEMORY['namespaces'][n_s],\\n ])\\n print(pt_namespc)\\n\\n print('\\\\nPer container memory usage:')\\n print(pt_cont)\\n\\n print('\\\\nPer service memory usage:')\\n print(pt_serv)\\n\\n base_mebib = 0.0\\n k8s_system = 0.0\\n k8s_addon = 0.0\\n platform_memory_percent = 0.0\\n\\n # Calculate base memory usage (i.e., normal memory, exclude K8S and VMs)\\n # e.g., docker, system.slice, user.slice\\n for group in MEMORY['cgroups']:\\n if group in BASE_GROUPS:\\n base_mebib += float(MEMORY['cgroups'][group])\\n\\n # K8S platform system usage (essential) and addons usage (non-essential)\\n for n_s in MEMORY['namespaces']:\\n if n_s in K8S_NAMESPACE_SYSTEM:\\n k8s_system += MEMORY['namespaces'][n_s]\\n elif n_s in K8S_NAMESPACE_ADDON:\\n k8s_addon += MEMORY['namespaces'][n_s]\\n\\n # Calculate platform memory usage\\n platform_mebib = base_mebib + k8s_system\\n\\n anon_mebib = float(mem_to_mebibytes(\\n mem_info['Active(anon)'] + mem_info['Inactive(anon)'])) * KBYTE\\n avail_mebib = float(mem_to_mebibytes(\\n mem_info['MemAvailable'])) * KBYTE\\n total_mebib = float(anon_mebib + avail_mebib)\\n\\n anon_percent = py2_round(100 * anon_mebib / total_mebib, DECIMAL_DIGITS) # pylint: disable=W1619\\n\\n reserved_mebib = get_platform_reserved_memory()\\n # Calculate platform memory in terms of percent reserved\\n if reserved_mebib > 0.0:\\n platform_memory_percent = py2_round(\\n 100 * platform_mebib / reserved_mebib, DECIMAL_DIGITS) # pylint: disable=W1619\\n\\n pt_platf = prettytable.PrettyTable(\\n ['Reserved',\\n 'Platform',\\n 'Base',\\n 'K8s Platform system',\\n 'k8s-addon'\\n ], caching=False)\\n pt_platf.align = 'l'\\n\\n pt_platf.add_row(\\n [reserved_mebib,\\n '{} ({}%)'.format(platform_mebib, platform_memory_percent),\\n base_mebib,\\n k8s_system,\\n k8s_addon\\n ])\\n print('\\\\nPlatform memory usage in MiB:')\\n print(pt_platf)\\n\\n pt_4k = prettytable.PrettyTable(\\n ['Anon',\\n 'Cgroup-rss',\\n 'Available',\\n 'Total'\\n ], caching=False)\\n pt_4k.align = 'l'\\n\\n pt_4k.add_row(\\n ['{} ({}%)'.format(anon_mebib, anon_percent),\\n MEMORY['cgroups']['total_rss'],\\n avail_mebib,\\n total_mebib\\n ])\\n\\n print('\\\\n4K memory usage in MiB:')\\n print(pt_4k)\\n\\n return 0\",\n \"def usage():\\n pass\",\n \"def main(argv):\\n args = parse_command_line(argv)\\n return show_scale_file_info(args.info_file) or 0\",\n \"def mc(self, *args) -> None:\\n env = os.environ.copy()\\n env['MC_HOST_minio'] = self.auth_url\\n # --config-dir is set just to prevent any config set by the user\\n # from interfering with the test.\\n try:\\n subprocess.run(\\n [\\n 'mc', '--quiet', '--no-color', f'--config-dir={self.path}',\\n *args\\n ],\\n stdout=subprocess.DEVNULL,\\n stderr=subprocess.PIPE,\\n env=env,\\n encoding='utf-8',\\n errors='replace',\\n check=True\\n )\\n except OSError as exc:\\n raise MissingProgram(f'mc could not be run: {exc}') from exc\\n except subprocess.CalledProcessError as exc:\\n raise ProgramFailed(exc.stderr) from exc\",\n \"def collect(self):\\n if not self._btime:\\n return\\n\\n try:\\n with open(\\\"/proc/self/stat\\\", 'rb') as stat:\\n parts = (stat.read().split(b')')[-1].split())\\n\\n self.process_metrics[\\\"vmem\\\"].set(\\\"\\\", float(parts[20]))\\n self.process_metrics[\\\"rss\\\"].set(\\\"\\\", float(parts[21]) * _PAGESIZE)\\n start_time_secs = float(parts[19]) / self._ticks\\n self.process_metrics[\\\"start_time\\\"].set(\\n \\\"\\\", start_time_secs + self._btime\\n )\\n utime = float(parts[11]) / self._ticks\\n stime = float(parts[12]) / self._ticks\\n self.process_metrics[\\\"cpu\\\"].set(\\\"\\\", utime + stime)\\n except IOError:\\n pass\\n\\n try:\\n with open(\\\"/proc/self/limits\\\", 'rb') as limits:\\n for line in limits:\\n if line.startswith(b'Max open file'):\\n self.process_metrics[\\\"max_fds\\\"].set(\\n \\\"\\\", float(line.split()[3])\\n )\\n break\\n\\n self.process_metrics[\\\"open_fds\\\"].set(\\n \\\"\\\", float(len(os.listdir(\\\"/proc/self/fd\\\")))\\n )\\n except (IOError, OSError):\\n pass\\n\\n # Update gc metrics if enabled.\\n if \\\"collected\\\" in self.process_metrics:\\n for generation, stat in enumerate(gc.get_stats()):\\n generation = {\\\"generation\\\": str(generation)}\\n self.process_metrics[\\\"collected\\\"].set(\\n generation, stat[\\\"collected\\\"]\\n )\\n self.process_metrics[\\\"uncollectable\\\"].set(\\n generation, stat[\\\"uncollectable\\\"]\\n )\\n self.process_metrics[\\\"collections\\\"].set(\\n generation, stat[\\\"collections\\\"]\\n )\",\n \"def info(self):\\n import string\\n results = self.info_list()\\n labels = \\\"%-8s %-9s %-4s %-8s %-8s %-4s\\\" % \\\\\\n ('MACHINE','CPU','GHZ','MB TOTAL',\\n 'MB FREE','LOAD')\\n print labels\\n for i in range(len(self.workers)):\\n name = string.split(self.workers[i].host,'.')[0]\\n res = results[i]\\n s = \\\"%-8s %2dx%-6s %4.1f %8.1f %8.1f %4.2f\\\" % \\\\\\n (name[-8:], res['cpu_count'],res['cpu_type'][-6:], \\\\\\n res['cpu_speed'],res['mem_total'],res['mem_free'],\\\\\\n res['load_1'])\\n print s\",\n \"def report_on_config( args ):\\n\\n from khmer.utils import print_error\\n\\n if args.quiet: return\\n\\n print_error( \\\"\\\\nPARAMETERS:\\\" )\\n print_error( \\\" - kmer size = {0} \\\\t\\\\t(-k)\\\".format( args.ksize ) )\\n print_error( \\\" - n hashes = {0} \\\\t\\\\t(-N)\\\".format( args.n_hashes ) )\\n print_error(\\n \\\" - min hashsize = {0:5.2g} \\\\t(-x)\\\".format( args.min_hashsize )\\n )\\n print_error( \\\"\\\" )\\n print_error(\\n \\\"Estimated memory usage is {0:.2g} bytes \\\"\\n \\\"(n_hashes x min_hashsize)\\\".format( args.n_hashes * args.min_hashsize )\\n )\\n print_error( \\\"-\\\" * 8 )\\n\\n if DEFAULT_MIN_HASHSIZE == args.min_hashsize:\\n print_error(\\n \\\"** WARNING: hashsize is default! \\\" \\n \\\"You absodefly want to increase this!\\\\n** \\\"\\n \\\"Please read the docs!\\\"\\n )\",\n \"def execute(self):\\n\\n if not os.path.exists(self._source_file_name):\\n logger.info(\\\"Did not find the aicpu profiling source file\\\")\\n return\\n\\n with open(self._source_file_name, 'rb') as ai_cpu_data:\\n content = ai_cpu_data.read()\\n if content[0:2].hex().upper() == \\\"5A5A\\\":\\n ai_cpu_total_time_summary, result_list = self.parser_binary_file(content)\\n else:\\n ai_cpu_total_time_summary, result_list = self.parser_txt_file(content)\\n\\n os.chmod(self._source_file_name, stat.S_IREAD)\\n\\n if result_list:\\n ai_cpu_total_time = format(ai_cpu_total_time_summary, '.6f')\\n result_list.append([\\\"AI CPU Total Time(ms):\\\", ai_cpu_total_time])\\n fwrite_format(self._output_filename, \\\" \\\".join(self._dst_file_column_title), is_start=True, is_print=True)\\n fwrite_format(self._output_filename, result_list, is_print=True)\\n\\n # For timeline display.\\n self._result_list = result_list\",\n \"def display_memcache_info(request):\\n # pylint: disable-msg=E1101\\n return utility.respond(request, 'admin/memcache_info',\\n {'memcache_info': memcache.get_stats()})\",\n \"async def run_mpc(self) -> Dict[str, Dict[Metric, int]]:\\n pass\",\n \"def test_cw_metrics(self):\\n\\n instances = set()\\n result = self.cw_client.list_metrics(Namespace=\\\"CWAgent\\\", MetricName=\\\"cpu_usage_system\\\")\\n for i in result[\\\"Metrics\\\"]:\\n instances.add(i[\\\"Dimensions\\\"][0][\\\"Value\\\"])\\n\\n for key, value in self.cdk_output_map.items():\\n if \\\"Instance\\\" in key:\\n self.assertTrue(value in instances)\",\n \"def mcmc(self, assign_step_methods=True, *args, **kwargs):\\n\\n self.mc = pm.MCMC(self.nodes_db.node.values, *args, **kwargs)\\n\\n self.pre_sample()\\n\\n return self.mc\",\n \"def main():\\n test_cases = ast.literal_eval(sys.argv[1])\\n results = str(my_info()) + '\\\\t\\\\t'\\n for test_case in test_cases:\\n mode = test_case[0]\\n id_1 = int(test_case[1])\\n id_2 = int(test_case[2])\\n if mode == 'jc':\\n results += str(Jaccard_Coefficient(id_1, id_2)) + '\\\\t\\\\t'\\n elif mode == 'cc':\\n results += str(Correlation_Coefficient(id_1, id_2)) + '\\\\t\\\\t'\\n else:\\n exit('bad command')\\n print results + '\\\\n'\",\n \"def do_stats(cs, args):\\n stats_info = cs.containers.stats(args.container)\\n utils.print_dict(stats_info)\",\n \"def options():\\n print \\\"\\\"\\\"Options summary:\\n -h, --help\\n -u, --usage\\n -v, --verbose <verb_level>\\n -e, --endpoint <endpoint>\\n -i, --interface-type <iface_type>\\n -r, --recursive\\n --dbs-conf <conf_file>\\n --show-prod\\n --show-caf\\n --only-subscribed\\n --only-custodial\\n \\\"\\\"\\\"\",\n \"def usage():\\n\\n # Local constants\\n\\n # Local variables\\n\\n #****** start usage() ******#\\n print()\\n print(\\\" Usage: python TCGCardTracker.py <arguement below> <optional-argument-1>\\\")\\n print(\\\"\\\\tadd (Optional): Add a card to your collection. Requires TCGPlayer URL.\\\")\\n print(\\\"\\\\tdelete (Optional): Delete a card from your collection. Requires TCGPlayer URL.\\\")\\n print(\\\"\\\\tupdate (Optional): Updates pricing data for every card in your collection.\\\")\\n print(\\\"\\\\ttop25 (Optional): Outputs the 25 most valuable cards from your collection.\\\")\\n print(\\\"\\\\texport (Optional): Exports a list of TCGPlayer URLs to a text file.\\\")\\n print(\\\"\\\\texport_collection (Optional): Exports your collection to a .csv including most recent price data.\\\")\\n print(\\\"\\\\timport (Optional): Imports a text file of TCGPlayer URLs to bulk import cards into your collection. Requires text file.\\\")\\n print(\\\"\\\\tworth (Optional): Ouputs how much your collection is worth using latest price data.\\\")\\n print(\\\"\\\\tgraph (Optional): Outputs historical pricing data for a given card. Requires TCGPlayer URL.\\\")\\n print(\\\"\\\\tgraph (Optional): Outputs historical pricing data for a given card. Requires TCGPlayer URL.\\\")\\n print(\\\"\\\\tticker (Optional): Displays a ticker grid of the change in value over a given time. If run without the days back parameter it will default to 7 days.\\\")\\n sys.exit()\",\n \"def stat_cuda(msg: str) -> None:\\n print(f'-- {msg:<35} allocated: %dM, max allocated: %dM, cached: %dM, max cached: %dM' % (\\n torch.cuda.memory_allocated() / 1024 / 1024,\\n torch.cuda.max_memory_allocated() / 1024 / 1024,\\n torch.cuda.memory_cached() / 1024 / 1024,\\n torch.cuda.max_memory_cached() / 1024 / 1024\\n ))\",\n \"def do_config():\\n\\n tracking = get_tracking()\\n for unit in (\\\"ppm\\\", \\\"sec\\\"):\\n\\ttunit = unit\\n\\tif unit == \\\"sec\\\":\\n\\t tunit = \\\"seconds\\\"\\n\\tprint \\\"multigraph chrony_%s\\\" % unit\\n\\tprint \\\"graph_title NTP (Chrony) Statistics (%s)\\\" % unit\\n\\tprint \\\"graph_vlabel %s\\\" % unit\\n\\tprint \\\"graph_args --base 1000\\\"\\n\\tprint \\\"graph_category time\\\"\\n\\tprint \\\"graph_info NTP (Chrony) tracking statistics (the ones measured in %s)\\\" % tunit\\n\\tfor key in tracking[tunit]:\\n\\t item = tracking[tunit][key]\\n\\t print \\\"\\\"\\\"%s.label %s\\n%s.draw LINE2\\n%s.info %s\\\"\\\"\\\" % (key, item[\\\"label\\\"], key, key, item[\\\"label\\\"])\\n\\tprint\\n return 0\",\n \"def help(update, context):\\n msg = \\\"\\\"\\n msg += \\\"\\\\n/covid 7-Day-Incident per Million\\\"\\n msg += \\\"\\\\n/daylio What did I do a year ago today?\\\"\\n msg += \\\"\\\\n/f1last Results of the last race\\\"\\n msg += \\\"\\\\n/f1stand Driver standings\\\"\\n msg += \\\"\\\\n/f1next Time and place of the next race\\\"\\n msg += \\\"\\\\n/fuel prices and consump. (args: Xeur Ykm)\\\"\\n msg += \\\"\\\\n/ip Outside ip address\\\"\\n msg += \\\"\\\\n/rate Exchange rates (args: Xeur/Yhuf)\\\"\\n msg += \\\"\\\\n/rss check rss feeds for new content\\\"\\n msg += \\\"\\\\n/sun Time of sunrise and sunset\\\"\\n msg += \\\"\\\\n/xkcd Sends last comic image and alt\\\"\\n msg.rstrip()\\n update.message.reply_text(msg)\",\n \"def show(self):\\n prev_queries = 0\\n prev_cpu_sys = 0\\n prev_cpu_user = 0\\n \\n lines = {\\n \\\"Uptime (seconds)\\\": \\\"--\\\",\\n \\\"Number of queries\\\": \\\"--\\\",\\n \\\"Query per second\\\": \\\"--\\\",\\n \\\"ACL drops\\\": \\\"--\\\",\\n \\\"Dynamic drops\\\": \\\"--\\\",\\n \\\"Rule drops\\\": \\\"--\\\",\\n \\\"CPU Usage (%s)\\\": \\\"--\\\",\\n \\\"Cache hitrate\\\": \\\"--\\\"\\n }\\n\\n while True:\\n try:\\n # get stats from dnsdist\\n stats = Statistics(console=self.console)\\n global_stats = stats[\\\"global\\\"]\\n \\n qps = int(global_stats[\\\"queries\\\"]) - prev_queries\\n prev_queries = int(global_stats[\\\"queries\\\"])\\n cpu = (int(global_stats[\\\"cpu-sys-msec\\\"])+int(global_stats[\\\"cpu-user-msec\\\"]) - prev_cpu_sys - prev_cpu_user) / 10\\n prev_cpu_sys = int(global_stats[\\\"cpu-sys-msec\\\"])\\n prev_cpu_user = int(global_stats[\\\"cpu-user-msec\\\"])\\n \\n lines[\\\"Uptime (seconds)\\\"] = global_stats[\\\"uptime\\\"]\\n lines[\\\"Number of queries\\\"] = global_stats[\\\"queries\\\"]\\n lines[\\\"Query per second\\\"] = qps\\n lines[\\\"CPU Usage (%s)\\\"] = cpu\\n lines[\\\"ACL drops\\\"] = global_stats[\\\"acl-drops\\\"]\\n lines[\\\"Rule drops\\\"] = global_stats[\\\"rule-drop\\\"]\\n lines[\\\"Cache hitrate\\\"] = global_stats[\\\"cache-hits\\\"]\\n lines[\\\"Dynamic drops\\\"] = global_stats[\\\"dyn-blocked\\\"]\\n\\n # reprint the lines \\n sys.stdout.write(\\\"\\\\033[1mDashboard for dnsdist\\\\033[0m\\\\n\\\")\\n sys.stdout.write(\\\"\\\\n\\\")\\n sys.stdout.write(\\\"Global:\\\\n\\\")\\n for k,v in lines.items():\\n sys.stdout.write(\\\"\\\\t%s: %s\\\\n\\\" % (k,v))\\n sys.stdout.write(\\\"Backends:\\\\n\\\")\\n for s in stats[\\\"backends\\\"]:\\n if not len(s[\\\"name\\\"]):\\n s[\\\"name\\\"] = \\\"--\\\"\\n if not len(s[\\\"pools\\\"]):\\n s[\\\"pools\\\"] = \\\"--\\\"\\n sys.stdout.write(\\\"\\\\t#%s / %s / %s / %s\\\\n\\\" % (s[\\\"#\\\"],s[\\\"address\\\"],s[\\\"name\\\"],s[\\\"pools\\\"]) )\\n sys.stdout.write(\\\"\\\\t\\\\tNumber of queries: %s\\\\n\\\" % s[\\\"queries\\\"])\\n sys.stdout.write(\\\"\\\\t\\\\tQuery per second: %s\\\\n\\\" % s[\\\"qps\\\"])\\n sys.stdout.write(\\\"\\\\t\\\\tNumber of drops: %s\\\\n\\\" % s[\\\"drops\\\"])\\n sys.stdout.write(\\\"\\\\n\\\")\\n sys.stdout.write(\\\"Ctrl+C to exit\\\\n\\\")\\n \\n time.sleep(1)\\n \\n \\n # move up cursor and delete whole line\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\") \\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n for k,v in lines.items():\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\") \\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n for s in stats[\\\"backends\\\"]:\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\") \\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\") \\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n sys.stdout.write(\\\"\\\\x1b[1A\\\\x1b[2K\\\")\\n \\n del stats\\n except KeyboardInterrupt:\\n break\",\n \"def _cmd_genemetrics(args):\\n cnarr = read_cna(args.filename)\\n segarr = read_cna(args.segment) if args.segment else None\\n is_sample_female = verify_sample_sex(cnarr, args.sample_sex, args.male_reference, args.diploid_parx_genome)\\n # TODO use the stats args\\n table = do_genemetrics(\\n cnarr,\\n segarr,\\n args.threshold,\\n args.min_probes,\\n args.drop_low_coverage,\\n args.male_reference,\\n is_sample_female,\\n args.diploid_parx_genome,\\n )\\n logging.info(\\\"Found %d gene-level gains and losses\\\", len(table))\\n write_dataframe(args.output, table)\",\n \"def execute(self, context):\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\\n logger.info(\\\"[ChainedTransfig] Collectd-Jmx w/ Common MBeans Generation...\\\")\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\\n\\n #insert common to assemble collectd.genericjmx.common.conf in later step\\n context[CTX_KEY_COMMON_COLLECTD_JMX_APP_PREFIX] = 'common'\\n super().execute(context)\\n\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\\n logger.info(\\\"[ChainedTransfig] Collectd-Jmx w/ Common MBeans Generation... COMPLETES\\\")\\n logger.info(\\\"///////////////////////////////////////////////////////////////////////\\\")\",\n \"def info():\\n f = Figlet(font='standard')\\n click.echo(f.renderText('covtool'))\\n click.secho(\\n \\\"covtool: a simple CLI for fetching covid data\\\", fg='cyan')\\n click.echo(\\n \\\"Data Sources: https://www.worldometers.info/coronavirus\\\\nJohn Hopkins [https://github.com/CSSEGISandData/COVID-19] \\\")\\n click.secho(\\\"Author: Amayo II <amayomordecai@gmail.com>\\\", fg='magenta')\",\n \"def help(bot, sender, sendmsg, label, args):\\n\\n clist = commands.commands\\n csort = sorted(clist.values(), key=lambda c: c.__name__.lower())\\n\\n if len(args) > 0:\\n page = int(args[0]) - 1\\n else:\\n page = 0\\n\\n pages = len(clist) // 10 + 1\\n\\n sendmsg(\\\"-- Help (Page {} of {}) --\\\".format(page + 1, pages))\\n for i in range(10):\\n if i >= len(csort):\\n break\\n\\n command = csort[i + (page * 10)]\\n sendmsg(\\\"{}: {}\\\".format(command.__name__, command.__doc__))\",\n \"def _cmd_coverage(args):\\n pset = coverage.do_coverage(\\n args.interval,\\n args.bam_file,\\n args.count,\\n args.min_mapq,\\n args.processes,\\n args.fasta,\\n )\\n if not args.output:\\n # Create an informative but unique name for the coverage output file\\n bambase = core.fbase(args.bam_file)\\n bedbase = core.fbase(args.interval)\\n tgtbase = (\\n \\\"antitargetcoverage\\\" if \\\"anti\\\" in bedbase.lower() else \\\"targetcoverage\\\"\\n )\\n args.output = f\\\"{bambase}.{tgtbase}.cnn\\\"\\n if os.path.exists(args.output):\\n args.output = f\\\"{bambase}.{bedbase}.cnn\\\"\\n core.ensure_path(args.output)\\n tabio.write(pset, args.output)\",\n \"def profiler(*args, addCategory: AnyStr=\\\"\\\", allCategories: bool=True, bufferSize: Union[int,\\n bool]=0, categoryIndex: Union[int, bool]=0, categoryIndexToName: Union[int,\\n bool]=0, categoryInfo: Union[AnyStr, bool]=\\\"\\\", categoryName: Union[AnyStr,\\n bool]=\\\"\\\", categoryNameToIndex: Union[AnyStr, bool]=\\\"\\\", categoryRecording:\\n bool=True, clearAllMelInstrumentation: bool=True, colorIndex: int=0, eventCPUId:\\n bool=True, eventCategory: bool=True, eventColor: bool=True, eventCount: bool=True,\\n eventDescription: bool=True, eventDuration: bool=True, eventIndex: Union[int,\\n bool]=0, eventName: bool=True, eventStartTime: bool=True, eventThreadId: bool=True,\\n instrumentMel: bool=True, load: Union[AnyStr, bool]=\\\"\\\", output: Union[AnyStr,\\n bool]=\\\"\\\", procedureDescription: AnyStr=\\\"\\\", procedureName: AnyStr=\\\"\\\",\\n removeCategory: AnyStr=\\\"\\\", reset: bool=True, sampling: bool=True, signalEvent:\\n bool=True, signalMelEvent: bool=True, q=True, query=True, **kwargs)->Union[None,\\n Any]:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65378803","0.627548","0.6020114","0.5702169","0.56018233","0.55116755","0.55000883","0.5320139","0.5315897","0.5299815","0.52958316","0.5274619","0.52392995","0.52203125","0.52038133","0.5199431","0.5187151","0.5178949","0.5170392","0.5164013","0.5105565","0.5103818","0.50898266","0.5052539","0.5035622","0.502821","0.501706","0.5015949","0.49987954","0.4995435","0.4994893","0.49872065","0.49857652","0.49823377","0.49802217","0.49538845","0.4953671","0.49489754","0.49483764","0.49386388","0.49346632","0.4929749","0.49271503","0.4909886","0.49063715","0.49063715","0.4888474","0.4883137","0.4876717","0.48731402","0.48707312","0.48706523","0.48664415","0.48657164","0.48556435","0.48515126","0.48501876","0.4835232","0.48343426","0.48284125","0.48110816","0.4809616","0.48017365","0.4801053","0.4801053","0.47884846","0.47855777","0.47798613","0.4779617","0.47782972","0.47773227","0.47709617","0.47623646","0.47552183","0.4751337","0.47482225","0.47429726","0.47399902","0.47312617","0.47309047","0.4730584","0.47276497","0.47267857","0.47211823","0.4715795","0.47132322","0.47124293","0.47105163","0.47093555","0.47082168","0.470178","0.47009042","0.46667597","0.4663084","0.46617326","0.46565914","0.46551764","0.46529984","0.46527383","0.46503717"],"string":"[\n \"0.65378803\",\n \"0.627548\",\n \"0.6020114\",\n \"0.5702169\",\n \"0.56018233\",\n \"0.55116755\",\n \"0.55000883\",\n \"0.5320139\",\n \"0.5315897\",\n \"0.5299815\",\n \"0.52958316\",\n \"0.5274619\",\n \"0.52392995\",\n \"0.52203125\",\n \"0.52038133\",\n \"0.5199431\",\n \"0.5187151\",\n \"0.5178949\",\n \"0.5170392\",\n \"0.5164013\",\n \"0.5105565\",\n \"0.5103818\",\n \"0.50898266\",\n \"0.5052539\",\n \"0.5035622\",\n \"0.502821\",\n \"0.501706\",\n \"0.5015949\",\n \"0.49987954\",\n \"0.4995435\",\n \"0.4994893\",\n \"0.49872065\",\n \"0.49857652\",\n \"0.49823377\",\n \"0.49802217\",\n \"0.49538845\",\n \"0.4953671\",\n \"0.49489754\",\n \"0.49483764\",\n \"0.49386388\",\n \"0.49346632\",\n \"0.4929749\",\n \"0.49271503\",\n \"0.4909886\",\n \"0.49063715\",\n \"0.49063715\",\n \"0.4888474\",\n \"0.4883137\",\n \"0.4876717\",\n \"0.48731402\",\n \"0.48707312\",\n \"0.48706523\",\n \"0.48664415\",\n \"0.48657164\",\n \"0.48556435\",\n \"0.48515126\",\n \"0.48501876\",\n \"0.4835232\",\n \"0.48343426\",\n \"0.48284125\",\n \"0.48110816\",\n \"0.4809616\",\n \"0.48017365\",\n \"0.4801053\",\n \"0.4801053\",\n \"0.47884846\",\n \"0.47855777\",\n \"0.47798613\",\n \"0.4779617\",\n \"0.47782972\",\n \"0.47773227\",\n \"0.47709617\",\n \"0.47623646\",\n \"0.47552183\",\n \"0.4751337\",\n \"0.47482225\",\n \"0.47429726\",\n \"0.47399902\",\n \"0.47312617\",\n \"0.47309047\",\n \"0.4730584\",\n \"0.47276497\",\n \"0.47267857\",\n \"0.47211823\",\n \"0.4715795\",\n \"0.47132322\",\n \"0.47124293\",\n \"0.47105163\",\n \"0.47093555\",\n \"0.47082168\",\n \"0.470178\",\n \"0.47009042\",\n \"0.46667597\",\n \"0.4663084\",\n \"0.46617326\",\n \"0.46565914\",\n \"0.46551764\",\n \"0.46529984\",\n \"0.46527383\",\n \"0.46503717\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66221523"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":15,"numItemsPerPage":100,"numTotalItems":95772,"offset":1500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzYwODQ4Niwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzM2MTIwODYsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.go_izm59NuFxUSPU2fJt8_OOGgKsjnm9Q5AK8PBbDBZtrYF_63_CWcJQ7ohC_-_In8EYMq1BprBX9TY5G1sfAg","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 |
|---|---|---|---|---|---|---|
Sets the 'reindex_data' value in the REST API to 0 to clear it. Splunk then automatically restarts the input. | def clear_checkbox(session_key, stanza):
url = f'https://localhost:8089/servicesNS/nobody/TA-strava-for-splunk/data/inputs/strava_api/{stanza}'
headers = {'Authorization': f'Splunk {session_key}'}
payload = 'reindex_data=0'
helper.send_http_request(url, "POST", headers=headers, payload=payload, verify=False, use_proxy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def restart(self):\n # Note: No need to regenerate the data, just reset the idx\n self.prepare_for_use()",
"def restart(self):\n # Note: No need to regenerate the data, just reset the idx\n self.prepare_for_use()",
"def restart(self):\n # Note: No need to regenerate the data,... | [
"0.6248837",
"0.6248837",
"0.6248837",
"0.6241557",
"0.62066907",
"0.6205658",
"0.6196166",
"0.61584055",
"0.6134224",
"0.61318415",
"0.610773",
"0.6060318",
"0.60403645",
"0.60403645",
"0.5998498",
"0.5993323",
"0.5940291",
"0.5935767",
"0.5879322",
"0.5871808",
"0.5868277",... | 0.0 | -1 |
Gets all activities, 30 per page as per Strava's default. | def get_activities(ts_activity, access_token):
params = {'after': ts_activity, 'access_token': access_token}
url = "https://www.strava.com/api/v3/activities"
response = return_json(url, "GET", parameters=params)
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_page_of_activities_return_all(self, StravaTokens1, Activity1, Activity2, Activity3):\n self.mock_get.return_value = Mock(ok=True)\n self.mock_get.return_value.json.return_value = [Activity1, Activity2, Activity3]\n strava_tokens = StravaTokens1\n response = get_page_of_acti... | [
"0.72672695",
"0.70619905",
"0.704842",
"0.704842",
"0.704842",
"0.704842",
"0.7015815",
"0.6921878",
"0.6793498",
"0.6755012",
"0.67519844",
"0.6731834",
"0.6626525",
"0.65563905",
"0.6493611",
"0.6484105",
"0.6461981",
"0.64476633",
"0.642003",
"0.638269",
"0.6301335",
"0... | 0.69375056 | 7 |
Gets the activity stream for given activity id. | def get_activity_stream(token, activity, types, series_type='time', resolution='high'):
types = ','.join(types)
params = {'access_token': token}
url = f'https://www.strava.com/api/v3/activities/{activity}/streams/{types}&series_type={series_type}&resolution={resolution}&key_by_type='
response = return_json(url, "GET", parameters=params, timeout=10)
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def activity(self, activity_id):\r\n return activities.Activity(self, activity_id)",
"def get(self, id):\n activity = Activity().get(id)\n if not activity:\n abort(404, \"Activity not found\")\n return activity",
"def activity(self, activity_id):\r\n return resourc... | [
"0.67917997",
"0.6699494",
"0.65698034",
"0.6276299",
"0.59187925",
"0.5873506",
"0.58734196",
"0.56921214",
"0.56341624",
"0.5584019",
"0.54834574",
"0.54745483",
"0.5373628",
"0.5367137",
"0.53547525",
"0.53142124",
"0.52937365",
"0.5270165",
"0.52317965",
"0.5202036",
"0.5... | 0.59823203 | 4 |
Gets details on currently logged in athlete. | def get_athlete(token):
url = "https://www.strava.com/api/v3/athlete"
params = {'access_token': token}
response = return_json(url, "GET", parameters=params, timeout=10)
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_athlete(response):\n name = response['athlete']['firstname'] + \" \" + response['athlete']['lastname']\n athlete = {\n 'id': response['athlete']['id'],\n 'name': name,\n 'access_token': response['access_token'],\n 'refresh_to... | [
"0.62125313",
"0.603639",
"0.5979062",
"0.5663854",
"0.5658526",
"0.5654593",
"0.55721027",
"0.5450002",
"0.5400566",
"0.5396792",
"0.5345879",
"0.5323987",
"0.53042555",
"0.52950203",
"0.5274707",
"0.5259616",
"0.52581507",
"0.5236326",
"0.52285975",
"0.52084494",
"0.519947"... | 0.636169 | 0 |
Converts Strava datetime to epoch timestamp | def get_epoch(timestamp):
timestamp_dt = datetime.datetime.strptime(timestamp, "%Y-%m-%dT%H:%M:%SZ")
epoch = calendar.timegm(timestamp_dt.timetuple())
return epoch | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def epoch2datetime(t):\n return datetime.fromtimestamp(t/1000.0)",
"def datetime_to_epoch(datetime):\n return datetime.astype('int64') // 1e9",
"def epoch(value):\n if isinstance(value, datetime.datetime):\n return int(calendar.timegm(value.timetuple())*1000)\n return '' #fails silently for ... | [
"0.82143974",
"0.8190802",
"0.78351754",
"0.7794575",
"0.76652604",
"0.7584979",
"0.7570103",
"0.7562643",
"0.75359434",
"0.7468649",
"0.7423047",
"0.73089755",
"0.73089755",
"0.724977",
"0.71811444",
"0.71131325",
"0.71113986",
"0.7073459",
"0.7016049",
"0.7004898",
"0.69872... | 0.746134 | 10 |
Get or refresh access token from Strava API. | def get_token(client_id, client_secret, token, renewal):
url = "https://www.strava.com/api/v3/oauth/token"
if renewal:
payload = {
'client_id': client_id,
'client_secret': client_secret,
'refresh_token': token,
'grant_type': 'refresh_token'}
message = "Successfully refreshed Strava token."
else:
payload = {
'client_id': client_id,
'client_secret': client_secret,
'code': token,
'grant_type': 'authorization_code'}
message = "Successfully authenticated with Strava using access code."
response = return_json(url, "POST", payload=payload)
helper.log_info(message)
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def refresh_token(self):\n # basic function to get an access token\n api_response = requests.get(\n self.api_config.get_api_url() + \"authentication/g?username=\" + self.api_config.get_api_username() + \"&password=\" + self.api_config.get_api_password())\n\n if api_response.status_c... | [
"0.75720465",
"0.7227093",
"0.7181543",
"0.713469",
"0.7129814",
"0.7058425",
"0.7026847",
"0.6986984",
"0.6963477",
"0.6925943",
"0.6916204",
"0.68907243",
"0.6879203",
"0.6810618",
"0.67986274",
"0.67908436",
"0.67369515",
"0.6724379",
"0.67114365",
"0.67013663",
"0.6646587... | 0.7200718 | 2 |
Stores athlete's id, first name, last name, weight and ftp into strava_athlete KV Store collection. | def kvstore_save_athlete(session_key, athlete_id, firstname, lastname, weight, ftp): # pylint: disable=too-many-arguments
url = 'https://localhost:8089/servicesNS/nobody/TA-strava-for-splunk/storage/collections/data/strava_athlete/batch_save'
headers = {'Content-Type': 'application/json', 'Authorization': f'Splunk {session_key}'}
payload = [{"_key": athlete_id, "id": athlete_id, "firstname": firstname, "lastname": lastname, "fullname": firstname + " " + lastname, "weight": weight, "ftp": ftp}]
helper.send_http_request(url, "POST", headers=headers, payload=payload, verify=False, use_proxy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_athlete(response):\n name = response['athlete']['firstname'] + \" \" + response['athlete']['lastname']\n athlete = {\n 'id': response['athlete']['id'],\n 'name': name,\n 'access_token': response['access_token'],\n 'refresh_to... | [
"0.65488034",
"0.5803945",
"0.56478375",
"0.52747154",
"0.5271561",
"0.52378386",
"0.51092994",
"0.50849545",
"0.50378954",
"0.5033445",
"0.4992036",
"0.49815983",
"0.4975764",
"0.4963135",
"0.49445814",
"0.48963758",
"0.48822185",
"0.48742172",
"0.48721516",
"0.48705444",
"0... | 0.8156723 | 0 |
Gets raw JSON data, parses it into events and writes those to Splunk. | def parse_data(data, activity_id, activity_start_date):
data_dict = {}
final_dict = {}
for i in data:
data_dict[i['type']] = i['data']
counter = 1
nrange = len(data_dict['time'])
for item in range(1, nrange + 1):
final_dict[item] = {}
for key, value in data_dict.items():
counter = 1
for i in value:
final_dict[counter][key] = i
final_dict[counter]['activity_id'] = activity_id
if 'time' in key:
final_dict[counter]['time'] = final_dict[counter]['time'] + activity_start_date
final_dict[counter]['time'] = time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime(final_dict[counter]['time']))
if 'latlng' in key:
final_dict[counter]['lat'] = final_dict[counter]['latlng'][0]
final_dict[counter]['lon'] = final_dict[counter]['latlng'][1]
final_dict[counter].pop('latlng')
counter += 1
result_list = [value for key, value in final_dict.items()]
for event in result_list:
write_to_splunk(index=helper.get_output_index(), sourcetype='strava:activities:stream', data=json.dumps(event))
helper.log_info(f'Added activity stream {activity_id} for {athlete_id}.')
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_data(self, raw_data):\n data = json.loads(raw_data)\n\n tweet = None\n includes = {}\n errors = []\n matching_rules = []\n\n if \"data\" in data:\n tweet = Tweet(data[\"data\"])\n self.on_tweet(tweet)\n if \"includes\" in data:\n ... | [
"0.5950303",
"0.58063334",
"0.5770824",
"0.5747445",
"0.56922185",
"0.5671852",
"0.5653861",
"0.56337476",
"0.5627374",
"0.5618672",
"0.553584",
"0.55230635",
"0.54872155",
"0.5470855",
"0.54696006",
"0.5466001",
"0.54565036",
"0.5411228",
"0.53876317",
"0.53832644",
"0.53710... | 0.5168414 | 48 |
Gets JSON from URL and parses it for potential error messages. | def return_json(url, method, **kwargs):
response = helper.send_http_request(url, method, use_proxy=False, **kwargs)
try:
response.raise_for_status()
except requests.HTTPError as ex:
# status code 429 means we hit Strava's API limit, wait till next 15 minute mark (+5 seconds) and try again
if ex.response.status_code == 429:
# Get the 15m/24h API limits for this user
api_usage_15m = response.headers['X-RateLimit-Usage'].split(",")[0]
api_usage_24h = response.headers['X-RateLimit-Usage'].split(",")[1]
api_limit_15m = response.headers['X-RateLimit-Limit'].split(",")[0]
api_limit_24h = response.headers['X-RateLimit-Limit'].split(",")[1]
timestamp_now = int(time.time())
modulus_time = timestamp_now % 900
sleepy_time = 0 if modulus_time == 0 else (900 - modulus_time + 5)
helper.log_warning(f'Strava API rate limit hit. Used {api_usage_15m}/15min (limit {api_limit_15m}), {api_usage_24h}/24h (limit {api_limit_24h}). Sleeping for {sleepy_time} seconds.')
time.sleep(sleepy_time)
response = return_json(url, method, **kwargs)
helper.log_debug(f'429 detail: {response}')
return response
if ex.response.status_code in (400, 401):
helper.log_error(f'{ex.response.status_code} Error: Strava API credentials invalid or session expired. Make sure Client ID & Client Secret have been added to the Configuration -> Add-On Parameters tab and your access code is valid.')
sys.exit(1)
if ex.response.status_code == 404:
helper.log_warning(f'404 Error: no stream data for url {url}, can happen for manually added activities.')
return False
if ex.response.status_code == 500:
helper.log_warning(f'500 Error: no data received from Strava API for url {url}, it might be corrupt or invalid. Skipping activity.')
return False
# In case there's any other error than the ones described above, log the error and exit.
helper.log_error(f'Error: {ex}')
sys.exit(1)
# Must have been a 200 status code
return response.json() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _process_url(self, url):\n response = requests.get(url, timeout=self.TIMEOUT)\n try:\n ret = response.json()\n except JSONDecodeError:\n self.log.exception(\"JSONDecodeError, response: %r, response.text: %r\", response, response.text)\n ret = {\"error\": \"... | [
"0.79039305",
"0.7806594",
"0.77826935",
"0.74478185",
"0.7402855",
"0.73680866",
"0.7356292",
"0.73440784",
"0.73038524",
"0.729605",
"0.729605",
"0.729605",
"0.729605",
"0.72762716",
"0.7274411",
"0.7236561",
"0.7228294",
"0.7226799",
"0.7206968",
"0.71908146",
"0.7171582",... | 0.0 | -1 |
Creates dict with athlete details, including token expiry. | def set_athlete(response):
name = response['athlete']['firstname'] + " " + response['athlete']['lastname']
athlete = {
'id': response['athlete']['id'],
'name': name,
'access_token': response['access_token'],
'refresh_token': response['refresh_token'],
'expires_at': response['expires_at'],
'ts_activity': 0}
return athlete | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_athlete(token):\n url = \"https://www.strava.com/api/v3/athlete\"\n params = {'access_token': token}\n response = return_json(url, \"GET\", parameters=params, timeout=10)\n return response",
"def asdict(self):\n return {\n \"access_token\": se... | [
"0.6095435",
"0.5521886",
"0.5451787",
"0.5388297",
"0.5352944",
"0.5180581",
"0.5166357",
"0.5155647",
"0.5131322",
"0.51014733",
"0.50938517",
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"0.505904",
"0.50572944",
"0.5016414",
"0.50079054",
"0.49948767",
"0.49728522",
"0.4964966... | 0.7620931 | 0 |
Writes activity to Splunk index. | def write_to_splunk(**kwargs):
event = helper.new_event(**kwargs)
ew.write_event(event) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, data):\n activities = [json.loads(activity['Json']) for activity in data]\n\n for i in range(len(activities)):\n activities[i]['created_at'] = to_datetime(activities[i]['created_at'])\n\n with Elastic(index='wink', doc_type='activity') as elastic:\n elastic... | [
"0.6238934",
"0.5661036",
"0.56116706",
"0.55407304",
"0.54127765",
"0.5401947",
"0.5389567",
"0.5381006",
"0.53470606",
"0.53407896",
"0.5288701",
"0.5285223",
"0.5240927",
"0.5197521",
"0.5191341",
"0.51774335",
"0.5176707",
"0.51560175",
"0.5123677",
"0.50669056",
"0.50669... | 0.5662735 | 1 |
hamgiin baga yoronhii huvaagdagchiig oloh function | def lcm(*values):
values = set([abs(int(v)) for v in values])
if values and 0 not in values:
n = n0 = max(values)
values.remove(n)
while any( n % m for m in values ):
n += n0
return n
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pohyb(seznam_tahu, seznam_ovoce, tah,radky, sloupce):\n\n x= seznam_tahu [len(seznam_tahu)-1][0] # [x,y] souradnice noveho tahu\n y= seznam_tahu [len(seznam_tahu)-1][1]\n\n if tah == \"s\": #sever\n y -= 1\n elif tah == \"j\": ... | [
"0.73375344",
"0.6515627",
"0.6515627",
"0.6515627",
"0.6515627",
"0.6515627",
"0.63205355",
"0.63011354",
"0.62365353",
"0.60986763",
"0.60985714",
"0.60890335",
"0.5958645",
"0.5890988",
"0.5886411",
"0.58524626",
"0.58322126",
"0.58077747",
"0.57773924",
"0.5761866",
"0.57... | 0.0 | -1 |
objectiin querysetiig avna. Tuhain querysetiin date_time uy deh datag excel export hiine | def export_to_excel(self, worksheet, row_start, col_start, queryset, date_time=timezone.now()):
if queryset:
[row_write, col_write] = self.excel_write_header_and_format(worksheet, row_start, col_start)
for q in queryset:
# object_excel_write function---date_time uyiin history objectiig excel -ruu horvuulne
[row_write, col_write] = q.object_excel_write(worksheet, row_write, col_write, date_time=date_time)
else:
worksheet.write_string(row_start, col_start, u'Мэдээлэл байхгүй') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def export_ho_dan_as_excel_action(fields=None, exclude=None, header=True):\n def export_as_excel(modeladmin, request, queryset):\n opts = modeladmin.model._meta\n field_names = [\"name\", \"status\", \"location\", \"tinh\",\n \"xa\", \"huyen\", \"phone\", \"cuuho\", \"update_... | [
"0.679835",
"0.6526606",
"0.6513021",
"0.63559425",
"0.6347008",
"0.6267613",
"0.61500996",
"0.604096",
"0.59455335",
"0.5921458",
"0.58053875",
"0.5804869",
"0.57998806",
"0.5739592",
"0.57366",
"0.5705012",
"0.5652193",
"0.5652061",
"0.564152",
"0.56380385",
"0.5635472",
... | 0.75960785 | 0 |
objectiin querysetiig avna. Tuhain querysetiin date_time uy deh datag excel export hiine | def export_to_excel(self, workbook, tailan_queryset):
# workbook argumentdaa avna
if tailan_queryset:
#[row_write, col_write] = self.excel_write_header_and_format(worksheet, row_start, col_start)
worksheet = workbook.add_worksheet(u'Гүний худаг')
queryset = Hudag.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = Hudag.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.gunii_hudags:
queryset = tailan.gunii_hudags.hudags.all()
[row_write, col_write] = Hudag.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Цэвэршүүлэх байгууламж')
queryset = Ts_baiguulamj.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = Ts_baiguulamj.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.tsevershuuleh:
queryset = tailan.tsevershuuleh.tsevershuuleh.all()
[row_write, col_write] = Ts_baiguulamj.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Цэвэрлэх байгууламж')
queryset = Ts_baiguulamj.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = Ts_baiguulamj.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.tseverleh:
queryset = tailan.tseverleh.tseverleh.all()
[row_write, col_write] = Ts_baiguulamj.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Усан сан')
queryset = UsanSan.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = UsanSan.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.usansan:
queryset = tailan.usansan.usan_sans.all()
[row_write, col_write] = UsanSan.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Цэвэр усны насос станц')
queryset = NasosStants.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = NasosStants.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.tsever_nasos_stants:
queryset = tailan.tsever_nasos_stants.nasos_stantss.all()
[row_write, col_write] = NasosStants.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Бохир усны насос станц')
queryset = NasosStants.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = NasosStants.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.bohir_nasos_stants:
queryset = tailan.bohir_nasos_stants.nasos_stantss.all()
[row_write, col_write] = NasosStants.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Лаборатори')
queryset = Lab.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = Lab.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.lab:
queryset = tailan.lab.labs.all()
[row_write, col_write] = Lab.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Цэвэр усны шугам')
queryset = Sh_suljee.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = Sh_suljee.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.tsever_usnii_shugam:
queryset = tailan.tsever_usnii_shugam.sh_suljees.all()
[row_write, col_write] = Sh_suljee.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Бохир усны шугам')
queryset = Sh_suljee.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = Sh_suljee.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.bohir_usnii_shugam:
queryset = tailan.bohir_usnii_shugam.sh_suljees.all()
[row_write, col_write] = Sh_suljee.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'АХББ')
queryset = ABB.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = ABB.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.abb:
queryset = tailan.abb.abbs.all()
[row_write, col_write] = ABB.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Ус, дулаан дамжуулах төв')
queryset = UsDamjuulahBair.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = UsDamjuulahBair.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.us_damjuulah_tov:
queryset = tailan.us_damjuulah_tov.usDamjuulahBair.all()
[row_write, col_write] = UsDamjuulahBair.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Ус түгээх байр')
queryset = UsTugeehBair.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = UsTugeehBair.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.us_tugeeh:
queryset = tailan.us_tugeeh.us_tugeeh_bairs.all()
[row_write, col_write] = UsTugeehBair.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Цэвэр усны машин')
queryset = WaterCar.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = WaterCar.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.water_car:
queryset = tailan.water_car.water_cars.all()
[row_write, col_write] = WaterCar.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Бохир усны машин')
queryset = BohirCar.objects.none()
row_write = 5
col_write = 1
[row_write, col_write] = BohirCar.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.bohir_car:
queryset = tailan.bohir_car.bohir_cars.all()
[row_write, col_write] = BohirCar.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
worksheet = workbook.add_worksheet(u'Ажилчдын судалгаа')
row_write = 5
col_write = 1
[row_write, col_write] = Ajiltan.excel_write_header_and_format(worksheet = worksheet, row_start = row_write, col_start = col_write)
for tailan in tailan_queryset:
if tailan.ajiltans:
queryset = tailan.ajiltans.ajiltans.all()
[row_write, col_write] = Ajiltan.export_to_excel_without_header(worksheet = worksheet, row_start=row_write, col_start=col_write, queryset = queryset, date_time = tailan.tailan_date)
else:
worksheet.write_string(row_start, col_start, u'Мэдээлэл байхгүй') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def export_to_excel(self, worksheet, row_start, col_start, queryset, date_time=timezone.now()):\n\t\tif queryset:\n\t\t\t[row_write, col_write] = self.excel_write_header_and_format(worksheet, row_start, col_start)\n\t\t\tfor q in queryset:\n\t\t\t\t# object_excel_write function---date_time uyiin history objectiig ... | [
"0.7596945",
"0.6799895",
"0.65263826",
"0.6513705",
"0.6357183",
"0.62692904",
"0.61503154",
"0.60432583",
"0.5946482",
"0.5921336",
"0.58059806",
"0.5805394",
"0.5800253",
"0.5739457",
"0.57385826",
"0.57053816",
"0.5653567",
"0.56533146",
"0.56427974",
"0.5640495",
"0.5637... | 0.6348618 | 5 |
Originates from external call from trigger system | def start(self, data):
log.info(data)
self.stop()
self.time_start = time.time() - data.get('time_offset', 0) - self.time_offset
self.bpm = float(data.get('bpm', self.DEFAULT_BPM))
self.timesigniture = parse_timesigniture(data.get('timesigniture', DEFAULT_TIMESIGNITURE))
if data.get('sequence'):
sequence_name = data.get('sequence')
assert sequence_name in self.sequences, '{0} is not a known sequence'.format(sequence_name)
self.sequence = self.sequences[sequence_name]
if data.get('scene'):
# Single scene - Fake the sequence list by inserting the name of the single scene required
self.sequence = (data.get('scene', self.DEFAULT_SCENE_NAME), )
self.sequence_index = 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, trigger, type, event):",
"def triggered(self, *args, **kwargs): # real signature unknown\n pass",
"def call(self):",
"def trigger(self, type, event):",
"def fire(self):",
"def __do_trigger(self, request):\n dmp_trigger.DmpTrigger().trigger(request)\n return defines... | [
"0.70280033",
"0.6894477",
"0.6630215",
"0.65832853",
"0.6425316",
"0.64154744",
"0.6267138",
"0.62295663",
"0.616958",
"0.6122845",
"0.6121851",
"0.6086105",
"0.60141927",
"0.5989397",
"0.5989397",
"0.5989397",
"0.5989397",
"0.5890496",
"0.5888235",
"0.5888235",
"0.5888235",... | 0.0 | -1 |
Originates from external call from trigger system | def stop(self, data={}):
self.time_start = 0
self.time_mutator = 0
self.sequence = ()
self.sequence_index = None
self.bpm = self.DEFAULT_BPM
self.timesigniture = DEFAULT_TIMESIGNITURE_ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, trigger, type, event):",
"def triggered(self, *args, **kwargs): # real signature unknown\n pass",
"def call(self):",
"def trigger(self, type, event):",
"def fire(self):",
"def __do_trigger(self, request):\n dmp_trigger.DmpTrigger().trigger(request)\n return defines... | [
"0.70280033",
"0.6894477",
"0.6630215",
"0.65832853",
"0.6425316",
"0.64154744",
"0.6267138",
"0.62295663",
"0.616958",
"0.6122845",
"0.6121851",
"0.6086105",
"0.60141927",
"0.5989397",
"0.5989397",
"0.5989397",
"0.5989397",
"0.5890496",
"0.5888235",
"0.5888235",
"0.5888235",... | 0.0 | -1 |
When a track is started we receve a lighting.start event The audio in the html5 player may have a seek time. This is to start part way into a test recording This seek time update is fired the moment the audio starts playing and within milliseconds of the lighting.start event. This 'first seek' sent within milliseconds of the 'start' event should not actually seek in the lighting scene. As self.time_start is calculated with global offset. We need to know the ACTUAL time since the 'trigger started' to detect our seek to ignore | def _seek(self, time_offset):
if (time.time() - (self.time_start + self.time_offset)) < 0.1:
log.info('Seek recived within 100ms of start - Assuming this is a bounceback from test_audio - applying automatic time mutator of {0}s'.format(time_offset))
self.time_mutator = time_offset
self.time_start = time.time() - time_offset
log.info('seek {0}'.format(time_offset)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setTrackStartTime() :\n s.startTrack()",
"def audio_cd_start_track_time(self, audio_cd_start_track_time):\n self._audio_cd_start_track_time = audio_cd_start_track_time",
"def seek_to_start_time(self):\n return 0",
"def start_time(self):\n return RPR.GetAudioAccessorStartTime(self.... | [
"0.680665",
"0.66605866",
"0.6150333",
"0.6049713",
"0.59493154",
"0.58593935",
"0.58006895",
"0.57242954",
"0.56212443",
"0.55866975",
"0.5570046",
"0.55602914",
"0.55241376",
"0.5506695",
"0.55048025",
"0.55008477",
"0.54706705",
"0.54700154",
"0.5436743",
"0.538599",
"0.53... | 0.5973443 | 4 |
Durations are 'dict string keys'. The keys need to be converted to floats. The keys need to be ordered and the scenes returned with calculated durations | def parse_scene_order(self, data, timesigniture):
if not data:
return ()
num_scenes = len(data)
def attempt_parse_key_timecode(value):
if not value:
return value
try:
return float(value)
except (ValueError, TypeError):
pass
try:
return timecode_to_beat(value, timesigniture)
except (AssertionError, ValueError, AttributeError):
pass
return value
# Surface the original key value in the dict (useful for debugging)
for key, value in data.items():
if value:
value['key'] = key
data_float_indexed = {attempt_parse_key_timecode(k): v for k, v in data.items()}
assert len(data_float_indexed) == num_scenes
sorted_keys = sorted(data_float_indexed.keys())
assert len(sorted_keys) == num_scenes
def normalise_duration(index):
"""
Convert any time code or alias to a linear float value. e.g.
'1.2' parses to -> 1.5
'match_next' resolves to -> 4.0
"""
key = sorted_keys[index]
item = data_float_indexed[key]
if not item:
item = {'duration': 'auto'}
data_float_indexed[key] = item
duration = attempt_parse_key_timecode(item.get('duration'))
if duration == 'match_next':
duration = normalise_duration(index+1)
if duration == 'match_prev':
duration = normalise_duration(index-1)
if isinstance(duration, str) and duration.startswith('match '):
duration = normalise_duration(sorted_keys.index(float(duration.strip('match '))))
if (not duration or duration == 'auto') and index < len(sorted_keys)-1:
duration = sorted_keys[index+1] - key
if not isinstance(duration, float):
#log.info('Unparsed duration: {0}'.format(duration))
duration = self.DEFAULT_DURATION
if duration != item.get('duration'):
item['duration'] = duration
return duration
for index in range(len(sorted_keys)):
normalise_duration(index)
scene_items = []
for key in sorted_keys:
scene_item = data_float_indexed[key]
assert scene_item and scene_item.get('duration') >= 0, "All scene must have durations. Something has failed in parsing. {0}:{1}".format(key, scene_item)
scene_items.append(scene_item)
return scene_items | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalise_duration(index):\n key = sorted_keys[index]\n item = data_float_indexed[key]\n if not item:\n item = {'duration': 'auto'}\n data_float_indexed[key] = item\n duration = attempt_parse_key_timecode(item.get('duration'))\n ... | [
"0.6176038",
"0.59368414",
"0.5897897",
"0.58419317",
"0.5807034",
"0.5672501",
"0.56141585",
"0.5577464",
"0.555346",
"0.5521533",
"0.5519391",
"0.55059147",
"0.5491283",
"0.54520303",
"0.5369046",
"0.5366904",
"0.53086877",
"0.52912253",
"0.52870804",
"0.5256063",
"0.522170... | 0.6748647 | 0 |
Convert any time code or alias to a linear float value. e.g. '1.2' parses to > 1.5 'match_next' resolves to > 4.0 | def normalise_duration(index):
key = sorted_keys[index]
item = data_float_indexed[key]
if not item:
item = {'duration': 'auto'}
data_float_indexed[key] = item
duration = attempt_parse_key_timecode(item.get('duration'))
if duration == 'match_next':
duration = normalise_duration(index+1)
if duration == 'match_prev':
duration = normalise_duration(index-1)
if isinstance(duration, str) and duration.startswith('match '):
duration = normalise_duration(sorted_keys.index(float(duration.strip('match '))))
if (not duration or duration == 'auto') and index < len(sorted_keys)-1:
duration = sorted_keys[index+1] - key
if not isinstance(duration, float):
#log.info('Unparsed duration: {0}'.format(duration))
duration = self.DEFAULT_DURATION
if duration != item.get('duration'):
item['duration'] = duration
return duration | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_speed(as_str: str) -> float:\n return float(as_str.rstrip(\"x\"))",
"def _fs (v):\r\n try : \r\n v = float(v)\r\n except : \r\n v = tuple([float (ss) for ss in \r\n v.replace('(', '').replace(')', '').split(',')])\r\n ... | [
"0.6765329",
"0.66060674",
"0.6591223",
"0.6582236",
"0.65468556",
"0.65312254",
"0.6518258",
"0.645661",
"0.6408871",
"0.6333807",
"0.63263595",
"0.62676144",
"0.62387794",
"0.6207294",
"0.61648494",
"0.61467373",
"0.61412436",
"0.61278975",
"0.6122148",
"0.6112401",
"0.6082... | 0.0 | -1 |
Once the order of the items is known, we can iterate over the scenes calculating/prerendering the dmx state for each section This make seeking much faster | def pre_render_scene_item(self, current_scene_item, previous_scene_item):
assert current_scene_item
current_scene_dmx = current_scene_item.setdefault(Scene.SCENE_ITEM_DMX_STATE_KEY, {})
# Aquire a reference to the previous DMX state
current_scene_dmx['previous'] = copy.copy(previous_scene_item.get(Scene.SCENE_ITEM_DMX_STATE_KEY, {})['target']) if previous_scene_item else AbstractDMXRenderer.new_dmx_array()
# The target state is a copy of the previous state
current_scene_dmx['target'] = copy.copy(current_scene_dmx['previous'])
# Modify the starting/previous state based on any overrides in this scene (this is a shortcut feature as I kept requireing this)
self.render_state_dict(current_scene_item.get('state_start'), current_scene_dmx['previous'])
# Modify the target state based on this scene item
self.render_state_dict(current_scene_item.get('state'), current_scene_dmx['target']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run():\n scene = lm.scene_object()\n copy_latest_low()\n copy_latest_high()",
"def loadData(self, actions):\n # begin to clear the scene\n self.scene.clear()\n self.scene.drawGrid()\n \n # and draw all items\n maxItemId = self.itemId\n for graphicalIt... | [
"0.5440126",
"0.5405451",
"0.5320529",
"0.5313562",
"0.5288161",
"0.52768517",
"0.52676857",
"0.52513534",
"0.52176017",
"0.521186",
"0.5171025",
"0.5155609",
"0.5147708",
"0.51323056",
"0.51169014",
"0.5096112",
"0.5093142",
"0.5089115",
"0.50864947",
"0.5068929",
"0.5037355... | 0.56032324 | 0 |
Given a state dict in the form of | def render_state_dict(self, target_state, dmx_universe_target):
if not target_state:
return
# Copy the alias over this bytearray
if isinstance(target_state, str):
target_state = {'use': target_state}
alias_name = target_state.get('use')
if alias_name:
assert alias_name in self.dmx_universe_alias, "alias '{0}' not defined".format(alias_name)
dmx_universe_target[:] = self.dmx_universe_alias[alias_name]
# Render items
for dmx_device_name, color_value in target_state.items():
self.config.render_device(dmx_universe_target, dmx_device_name, color_value)
# Mute items
for dmx_device_name in self.mute_devices:
self.config.render_device(dmx_universe_target, dmx_device_name, None)
# Add an alias for this state if a name is provided
if target_state.get('name'):
self.dmx_universe_alias[target_state.get('name')] = dmx_universe_target | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fromState(state):",
"def load_state_dict(self, state_dict: Dict[str, torch.Tensor]):\n pass",
"def load_from_state_dict(self, state_dict):\n raise NotImplementedError",
"def load_state_dict(\n self,\n state_dict: Mapping[str, Any],\n *args,\n **kwargs,\n ) -> ... | [
"0.77548754",
"0.7265476",
"0.6977761",
"0.69326776",
"0.69324243",
"0.69324243",
"0.693159",
"0.68787336",
"0.68787336",
"0.6834486",
"0.6827123",
"0.6820761",
"0.6802007",
"0.6770729",
"0.6729137",
"0.6722943",
"0.6710804",
"0.6681896",
"0.6674843",
"0.66132194",
"0.6602126... | 0.0 | -1 |
Given a list of parsed scene_items (a plain list of dicts) Provide methods for redering that data timesigniture is only used for debug printing | def __init__(self, scene_items, timesigniture=DEFAULT_TIMESIGNITURE_):
self.scene_items = scene_items
self.total_beats = sum(scene_item['duration'] for scene_item in self.scene_items)
self.timesigniture = timesigniture | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_items_function(self):\n ars = self.ar[2009][11]['general']\n self.assertEqual(list(ars.items()), [('LastLine', ['20091202000343', '1011585', '206082338', '54716901457']), ('FirstTime', ['20091101000237']), ('LastTime', ['20091130234113']), ('LastUpdate', ['20091201094510', '1011585', '0', '8... | [
"0.5740542",
"0.56621575",
"0.5524638",
"0.5478715",
"0.54087716",
"0.53677016",
"0.53536004",
"0.52064437",
"0.51765895",
"0.5169679",
"0.51359755",
"0.51294976",
"0.5126739",
"0.5117502",
"0.51156247",
"0.511032",
"0.49845767",
"0.49424937",
"0.49424642",
"0.49300858",
"0.4... | 0.64365894 | 0 |
Return a list of all live Python objects, not including the list itself. | def get_all_objects():
gc.collect()
gcl = gc.get_objects()
olist = []
seen = {}
# Just in case:
seen[id(gcl)] = None
seen[id(olist)] = None
seen[id(seen)] = None
# _getr does the real work.
_getr(gcl, olist, seen)
return olist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_objects():\n gcl = gc.get_objects()\n olist = []\n seen = {}\n # Just in case:\n seen[id(gcl)] = None\n seen[id(olist)] = None\n seen[id(seen)] = None\n # _getr does the real work.\n _getr(gcl, olist, seen)\n return olist",
"def get_all_objects():\n gcl = gc.get_o... | [
"0.736436",
"0.729643",
"0.6785282",
"0.6629329",
"0.65524584",
"0.6406",
"0.6404724",
"0.63662046",
"0.6298751",
"0.62828684",
"0.62828684",
"0.62828684",
"0.62828684",
"0.62828684",
"0.62828684",
"0.626183",
"0.6255329",
"0.6242195",
"0.61547345",
"0.6152571",
"0.6122982",
... | 0.7565201 | 0 |
Convert um to m. | def convert_units(self):
for prod in ("ier", "ier_inc_rain"):
self.data[prod].data[:] /= 1e6 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def NM2m(NM):\n return NM * 1852",
"def m_to_mm(meters):\n return meters * 1000.0",
"def nm2m(self):\n return self._nm2m",
"def m_to_mm(): \n # Set blender unit in mm\n bpy.context.scene.unit_settings.scale_length = 0.001\n bpy.context.scene.unit_settings.length_unit = 'MIL... | [
"0.69458026",
"0.68633324",
"0.68530375",
"0.66572696",
"0.65932775",
"0.65664166",
"0.6551626",
"0.65338874",
"0.63150936",
"0.62804407",
"0.6278391",
"0.62339175",
"0.62174183",
"0.6206542",
"0.61768055",
"0.61204433",
"0.6057365",
"0.60438204",
"0.59712124",
"0.5944185",
"... | 0.0 | -1 |
Adds 8% tax to a restaurant bill. | def tax(bill):
bill *= 1.08
print "With tax: %.2f" % bill
return bill | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_tax(self,tax):\n return self.price + (self.price * tax)",
"def tax(bill):\r\n bill *= 1.08\r\n print(\"With tax: %f\" % bill)\r\n return bill",
"def tax(bill):\n bill *= 1.08\n print \"With tax: %f\" % bill\n return bill",
"def tax(bill):\n bill *= 1.08\n print \"With t... | [
"0.7644522",
"0.758284",
"0.7505996",
"0.7505996",
"0.7157527",
"0.7034859",
"0.70066726",
"0.69643134",
"0.65901643",
"0.65687346",
"0.64239335",
"0.6421261",
"0.6313586",
"0.62876344",
"0.62859863",
"0.6272636",
"0.6272636",
"0.6179959",
"0.605302",
"0.60120994",
"0.5976049... | 0.7485274 | 4 |
Adds 15% tip to a restaurant bill. | def tip(bill):
bill *= 1.15
print "With tip: %.2f" % bill
return bill | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tip(bill):\r\n bill *= 1.15\r\n print(\"With tip: %f\" % bill)\r\n return bill",
"def tip(bill):\n bill *= 1.15\n print \"With tip: %f\" % bill\n return bill",
"def tip(bill):\n bill *= 1.15\n print \"With tip: %f\" % bill\n return bill",
"def tip(bill):\n bill *= 1.15\n ... | [
"0.7132613",
"0.70187575",
"0.70187575",
"0.70187575",
"0.6926353",
"0.63815933",
"0.6208463",
"0.6160494",
"0.6047981",
"0.6044608",
"0.6044608",
"0.601777",
"0.58578914",
"0.5761912",
"0.5701724",
"0.56948024",
"0.565889",
"0.55814856",
"0.5531272",
"0.55295736",
"0.550463"... | 0.6996032 | 4 |
Returns the square of a number. | def square(n):
squared = n**2
print "%d squared is %d." % (n, squared)
return squared | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def square(num):\n square = num ** 2\n return square",
"def square_number(number: int) -> int:\n return number * number",
"def square(num):\n return num * num",
"def square(value):\n return value ** 2",
"def my_square(x):\n return x ** 2",
"def square(n: int) -> int:\n return int(n *... | [
"0.86342794",
"0.8617322",
"0.8552498",
"0.80486435",
"0.80462384",
"0.79968894",
"0.7947229",
"0.79286706",
"0.7911237",
"0.7883522",
"0.78671736",
"0.78318316",
"0.78140074",
"0.78127563",
"0.7788222",
"0.77676326",
"0.7688305",
"0.7677109",
"0.76735294",
"0.763642",
"0.762... | 0.78975934 | 11 |
The constructor for Particle Class | def __init__(self, position=np.array([0,0,0], dtype=float),
velocity=np.array([0,0,0], dtype=float), acceleration=np.array([0,0,0],
dtype=float), name='A Particle', restMass=1.0, charge=const.elementary_charge):
self.name = name
self.position = np.array(position, dtype=float)
self.velocity = np.array(velocity, dtype=float)
self.acceleration = np.array(acceleration, dtype=float)
self.restMass = restMass
self.charge = charge
self.electricField = PointElectricFieldClass(sourceParticle=self
, name='Field from %s'%(self.name))
self.magneticField = PointMagneticFieldClass(sourceParticle=self
, name='Field from %s'%(self.name)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, particles):\n self.particles = particles",
"def __init__(self,particle):\n self.par = particle",
"def particle(self) -> Particle:\n return Particle()",
"def particle(self) -> Particle:\n return Particle()",
"def __init__(self, init_pos_1, init_pos_2, M_1, M_2,... | [
"0.846487",
"0.8411835",
"0.7966551",
"0.7966551",
"0.7707181",
"0.76299787",
"0.74913836",
"0.7429638",
"0.73224574",
"0.72718245",
"0.7198867",
"0.699789",
"0.6965769",
"0.6918702",
"0.6917369",
"0.68837965",
"0.68785137",
"0.68018293",
"0.67048806",
"0.6683209",
"0.6675657... | 0.7450836 | 7 |
Method that returns the rest energy of the particle. | def RestEnergy(self):
return (self.restMass * const.speed_of_light * const.speed_of_light) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def energy(self):\n energy = -0.5*np.sum(self.phi)+0.5*np.sum(self.mass*np.sqrt(self.particles.momentum[:,0]**2+self.particles.momentum[:,1]**2)**2)\n return energy",
"def TotalEnergy(self):\n return (math.sqrt((Particle.RestEnergy(self) ** 2)\n + (np.linalg.norm(Particle.Momentum(sel... | [
"0.7668576",
"0.7595888",
"0.7482029",
"0.7454896",
"0.74329084",
"0.74117565",
"0.7405934",
"0.7287761",
"0.7283499",
"0.70380706",
"0.70380706",
"0.6973054",
"0.69607884",
"0.6895652",
"0.67723006",
"0.6750274",
"0.67392176",
"0.67377317",
"0.6689237",
"0.6675212",
"0.66727... | 0.7802598 | 0 |
Method that returns Beta (velocity/speed of light) as a float | def BetaVelocity(self):
return np.linalg.norm(self.velocity) / const.speed_of_light | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def betaT(self):\n if self.maTail > 1:\n return 0\n else:\n return sqrt(1 - self.maTail**2)",
"def getBeta(self, alpha):\n return 2.0*(2.0-alpha) + -4.0*np.sqrt(1.0-alpha)",
"def beta(self):\n eTheta = self.eTheta()\n cosOmg = np.cos(self.omega())\n ... | [
"0.7552084",
"0.754992",
"0.7535426",
"0.7439897",
"0.74009764",
"0.7362183",
"0.7362183",
"0.71039945",
"0.707216",
"0.707216",
"0.707216",
"0.7020982",
"0.69366413",
"0.6877576",
"0.6866696",
"0.67877996",
"0.6773138",
"0.6760468",
"0.6758375",
"0.67385536",
"0.670362",
"... | 0.8313839 | 0 |
Method that returns the Lorentz Factor of the particle. | def LorentzFactor(self):
# Use of abs() and x ** 0.5 provides a more stable calculation of lorentz
# factor than math.sqrt() at high velocities.
return 1 / abs( 1 - Particle.BetaVelocity(self) * Particle.BetaVelocity(self))**0.5 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def L(self) -> float:\n return self._L",
"def Lorentz(x, x0, A, B, d):\n return B + A / (((x - x0) / d) ** 2 + 1)",
"def lorentz(x, gamma):\n return 1 / cs.pi * 0.5 * gamma / ((0.5 * gamma**2) + x**2)",
"def relu(z: float) -> float:\n return z if z > 0 else 0.01 * z",
"def lorentz(x, x0... | [
"0.65476906",
"0.65036815",
"0.6469627",
"0.638012",
"0.6348522",
"0.6347106",
"0.6341828",
"0.6334289",
"0.6333599",
"0.63280964",
"0.62715966",
"0.62416214",
"0.6189796",
"0.61773413",
"0.61520237",
"0.6139124",
"0.6098995",
"0.60558134",
"0.60534275",
"0.5988797",
"0.59480... | 0.8493009 | 0 |
Method that returns the relativistic mass of the particle | def RelativisticMass(self):
return Particle.LorentzFactor(self) * self.restMass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def particleMass(self):\n return self.params['particleMass']",
"def mass(self):\n\t\treturn self.volume*self.density",
"def get_mass(self):\n return self.m",
"def Mass(self):\n mpa = self.MassPerLength()\n if mpa == 0.0:\n return 0.\n L = self.Length()\n m... | [
"0.796206",
"0.75452185",
"0.7294323",
"0.72193676",
"0.7178756",
"0.7156736",
"0.7112089",
"0.7013868",
"0.697946",
"0.69783187",
"0.6944408",
"0.6921542",
"0.6921542",
"0.6899986",
"0.6871825",
"0.6846594",
"0.68247277",
"0.6723331",
"0.6718318",
"0.6706135",
"0.67014885",
... | 0.74535996 | 2 |
Method that returns the relativistic momentum of the particle | def Momentum(self):
return (np.multiply(Particle.LorentzFactor(self)
, np.array(self.velocity,dtype=float))* self.restMass) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getMomentum(self):\n return self.p",
"def calcMomentumFromVelocity(self):\n if self.mass is None:\n raise CoordinateVector(\"The particle mass needs to be specified to calculate the particle momentum from velocity.\")\n values = {}\n for direction in self.v.order:\n gamma = self.calcLor... | [
"0.74322164",
"0.73959345",
"0.73226726",
"0.72440016",
"0.69533795",
"0.69061005",
"0.6754555",
"0.665831",
"0.6585926",
"0.6467245",
"0.6458798",
"0.64507335",
"0.6449878",
"0.64052224",
"0.63391316",
"0.63144106",
"0.6260632",
"0.61805636",
"0.61436313",
"0.6129185",
"0.61... | 0.75053847 | 0 |
Method that returns the total energy of the particle | def TotalEnergy(self):
return (math.sqrt((Particle.RestEnergy(self) ** 2)
+ (np.linalg.norm(Particle.Momentum(self)) * const.speed_of_light) ** 2)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def energy(self):\n energy = -0.5*np.sum(self.phi)+0.5*np.sum(self.mass*np.sqrt(self.particles.momentum[:,0]**2+self.particles.momentum[:,1]**2)**2)\n return energy",
"def calcEnergy(self):\n speed_light = constants.physical_constants[\"speed of light in vacuum\"][0]#m/sec by default\n if sel... | [
"0.84132123",
"0.8266951",
"0.7991302",
"0.7920243",
"0.7864987",
"0.7718018",
"0.76988596",
"0.76899403",
"0.76686484",
"0.76565665",
"0.76121676",
"0.7568719",
"0.7546202",
"0.75326943",
"0.75047314",
"0.7501236",
"0.74609464",
"0.7411283",
"0.7411283",
"0.74109715",
"0.738... | 0.821929 | 2 |
Method that returns the kinetic energy of the particle | def KineticEnergy(self):
return Particle.TotalEnergy(self) - Particle.RestEnergy(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def kineticEnergy(self):\n return self.params['kinetic']",
"def kinetic_energy(self):\r\n return self.mass * np.dot(self.vel, self.vel) / 2",
"def kinetic_energy(self):\r\n position, velocity, escaped_particles,impact, wall_collision,mom = self.box_collision_info()\r\n for j in xran... | [
"0.85219467",
"0.85176307",
"0.84117717",
"0.7932918",
"0.7827377",
"0.7754195",
"0.7666235",
"0.7638704",
"0.74971205",
"0.7490171",
"0.74387",
"0.7403423",
"0.7370556",
"0.72361696",
"0.7233435",
"0.7216185",
"0.7176531",
"0.7157923",
"0.715126",
"0.70478064",
"0.7043319",
... | 0.8440398 | 2 |
Method that updates the particle's velocity and position with the Euler Cromer method | def UpdateCromer(self, deltaT):
self.velocity += self.acceleration * deltaT
self.position += self.velocity * deltaT | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def EulerN(particle,dt):\n particle.acc = particle.acc\n particle.vel = particle.vel + particle.acc*dt\n particle.pos = particle.pos + particle.vel*dt\n\n return particle",
"def update(self, delta):\n # Computes new positions\n for part in self.particles:\n part.set_xyvxvy(se... | [
"0.72924954",
"0.6944585",
"0.66634274",
"0.65291256",
"0.6407229",
"0.640443",
"0.6364964",
"0.6361303",
"0.6322322",
"0.63013875",
"0.627813",
"0.62754977",
"0.62680924",
"0.626102",
"0.62548554",
"0.6244272",
"0.62382376",
"0.62382376",
"0.62364995",
"0.61982566",
"0.61757... | 0.64788157 | 4 |
Method that updates the particle's velocity and position with the Euler Forward method | def UpdateForward(self, deltaT):
self.position += self.velocity * deltaT
self.velocity += self.acceleration * deltaT | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def EulerN(particle,dt):\n particle.acc = particle.acc\n particle.vel = particle.vel + particle.acc*dt\n particle.pos = particle.pos + particle.vel*dt\n\n return particle",
"def update_position(self):\n self.position[0] += self.velocity[0]\n self.position[1] += self.velocity[1]",
"def... | [
"0.7147672",
"0.71063995",
"0.68057793",
"0.6710489",
"0.6636017",
"0.6588007",
"0.65872145",
"0.65763974",
"0.65613395",
"0.6534683",
"0.65088755",
"0.6481874",
"0.6460222",
"0.64475214",
"0.6444629",
"0.63995236",
"0.6384533",
"0.63719004",
"0.63658047",
"0.6355781",
"0.633... | 0.6623841 | 5 |
Method that returns the electric field from the particle that affects another particle. | def GenerateElectricField(self, affectedParticle):
return self.electricField.GenerateField(affectedParticle) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def electric_field(self, xyz):\n dxyz = self.vector_distance(xyz)\n r = spatial.repeat_scalar(self.distance(xyz))\n kr = self.wavenumber*r\n ikr = 1j * kr\n\n front_term = (\n (1j * self.omega * self.mu * self.moment) / (4. * np.pi * r**2) *\n (ikr + 1) * np... | [
"0.6565109",
"0.6523056",
"0.6255628",
"0.62081003",
"0.61325175",
"0.6086595",
"0.5954075",
"0.5868522",
"0.58501065",
"0.5768421",
"0.5700194",
"0.5639009",
"0.5562006",
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"0.55293006",
"0.54763085",
"0.5419618",
"0.53979456",
"0.53979456",
"0.53847796",
"0.5378... | 0.72869897 | 0 |
Method that returns the magnetic field from the particle that affects another particle. | def GenerateMagneticField(self, affectedParticle):
return self.magneticField.GenerateField(affectedParticle) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def magnetic_field(self, xyz):\n dxyz = self.vector_distance(xyz)\n r = spatial.repeat_scalar(self.distance(xyz))\n kr = self.wavenumber * r\n ikr = 1j*kr\n\n front_term = (\n self.current * self.length / (4 * np.pi * r**2) * (ikr + 1) *\n np.exp(-ikr)\n ... | [
"0.67325306",
"0.65006876",
"0.64733946",
"0.64133286",
"0.61525905",
"0.6089795",
"0.594535",
"0.5899009",
"0.58383423",
"0.5798657",
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"0.55965346",
"0.5590425",
"0.5575774",
"0.5534189",
"0.5524397",
"0.551204... | 0.6843076 | 0 |
Returns the initialized component manager. This is used as FastAPI dependency and called for every request. | def get_component_manager(
token: str = Depends(get_api_token),
) -> ComponentOperations:
session = BaseUrlSession(base_url=CONTAXY_API_ENDPOINT)
session.headers = {"Authorization": f"Bearer {token}"}
return ComponentClient(session) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_manager():\n\n return multiprocessing.Manager()",
"def GetManager(self):\r\n\r\n return self.manager",
"def get_manager():\n return __manager__",
"def getManager(self):\n return self._manager",
"def core(self):\n return CoreManager(self)",
"def manager(self):\n ... | [
"0.68361783",
"0.6697513",
"0.66576326",
"0.660257",
"0.6231092",
"0.61051804",
"0.61051804",
"0.6104129",
"0.5990772",
"0.5973677",
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"0.5933692",
"0.588824",
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"0.57823783",
"0.5773982",
"0.5772796",
"0.57626957",
"0.5757743",
"0.57147866",
"0.5693136"... | 0.69490683 | 0 |
Return a boolean mask for a circular sector. The start/stop angles in `angle_range` should be given in clockwise order. | def sector_mask(shape, centre, radius, angle_range):
x,y = np.ogrid[:shape[0],:shape[1]]
cx,cy = centre
tmin,tmax = np.deg2rad(angle_range)
# ensure stop angle > start angle
if tmax < tmin:
tmax += 2*np.pi
# convert cartesian --> polar coordinates
r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)
theta = np.arctan2(x-cx, y-cy) - tmin
# wrap angles between 0 and 2*pi
theta %= (2*np.pi)
# circular mask
circmask = r2 <= radius*radius
# angular mask
anglemask = theta <= (tmax-tmin)
return circmask*anglemask | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sector_mask(shape,centre,radius,angle_range):\n\n x,y = np.ogrid[:shape[0],:shape[1]]\n cx,cy = centre\n tmin,tmax = np.deg2rad(angle_range)\n\n # ensure stop angle > start angle\n if tmax < tmin:\n tmax += 2*np.pi\n\n # convert cartesian --> polar coordinates\n r2 = (x-cx)*(x-c... | [
"0.7897219",
"0.7897219",
"0.7897219",
"0.76970416",
"0.6498443",
"0.6337913",
"0.6234694",
"0.6167133",
"0.6006037",
"0.59997934",
"0.58989346",
"0.5845237",
"0.5817369",
"0.5690966",
"0.5672399",
"0.55962497",
"0.552139",
"0.5515878",
"0.5482021",
"0.54450697",
"0.54447085"... | 0.7855404 | 3 |
Parses a region description to a dict | def parse_region(region, wcs):
logger = logging.getLogger(__name__)
shape = region.split(',')[0]
coord = region.split(',')[1]
frame = region.split(',')[2]
try:
params = region.split(',')[3:]
except IndexError:
logger.error('No coordinates given.')
logger.error('Will exit now.')
sys.exit(1)
if 'sky' in coord.lower() and frame == '':
logger.error('No frame specified.')
logger.error('Will exit now.')
sys.exit(1)
if shape == 'point':
# Convert sky coordinates to pixels if required
if 'sky' in coord.lower():
coo_sky = SkyCoord(params[0], params[1], frame=frame)
params[0:2] = coo_sky.to_pixel(wcs)
params = [int(round(float(x))) for x in params]
rgn = {'shape':'point',
'params':{'cx':params[0], 'cy':params[1]}}
elif shape == 'box':
# Convert sky coordinates to pixels if required
if 'sky' in coord.lower():
blc_sky = SkyCoord(params[0], params[1], frame=frame)
trc_sky = SkyCoord(params[2], params[3], frame=frame)
params[0:2] = blc_sky.to_pixel(wcs)
params[2:] = trc_sky.to_pixel(wcs)
params = [int(round(float(x))) for x in params]
rgn = {'shape':'box',
'params':{'blcx':params[0], 'blcy':params[1],
'trcx':params[2], 'trcy':params[3]}}
elif shape == 'circle':
# Convert sky coordinates to pixels if required
if 'sky' in coord.lower():
coo_sky = SkyCoord(params[0], params[1], frame=frame)
params[0:2] = coo_sky.to_pixel(wcs)
lscale = abs(wcs.pixel_scale_matrix[0,0])*u.deg
val, uni = split_str(params[2])
# Add units to the radius
r = add_radius_units(val, uni)
logger.debug('lscale: {0}'.format(lscale))
logger.debug('radius: {0}'.format(r))
params[2] = (r/lscale).cgs.value
params = [float(x) for x in params]
rgn = {'shape':'circle',
'params':{'cx':params[0], 'cy':params[1], 'r':params[2]}}
elif shape == 'ellipse':
# Convert sky coordinates to pixels if required
if 'sky' in coord.lower():
coo_sky = SkyCoord(params[0], params[1], frame=frame)
params[0:2] = coo_sky.to_pixel(wcs)
lscale = abs(wcs.pixel_scale_matrix[0,0])*u.deg
logger.debug('lscale: {0}'.format(lscale))
# Major axis.
val, uni = split_str(params[2])
# Add units to the major axis.
r = add_radius_units(val, uni)
logger.debug('major axis: {0}'.format(r))
params[2] = (r/lscale).cgs.value
# Minor axis.
val, uni = split_str(params[3])
# Add units to the minor axis.
r = add_radius_units(val, uni)
logger.debug('minor axis: {0}'.format(r))
params[3] = (r/lscale).cgs.value
params = [float(x) for x in params]
rgn = {'shape':'ellipse',
'params':{'cx':params[0], 'cy':params[1],
'bmaj':params[2], 'bmin':params[3],
'theta':params[4]}}
elif shape == 'crtf':
# CASA region files are always in sky coordinates
polys = ci.read_casa_polys(params[0], wcs=wcs)
rgn = {'shape':'polygon',
'params':{'Polygons':polys}}
elif shape == 'all':
rgn = {'shape':'all',
'params':'all'}
else:
print('region description not supported.')
print('Will exit now.')
logger.error('Region description not supported.')
logger.error('Will exit now.')
sys.exit(1)
return rgn | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _decode_region_details(self, region):\r\n\r\n # This whole thing is a bit of a mess but it preserves backwards compat but also allows for\r\n # ad-hoc addition of new regions without code changes\r\n # TODO: Move EC2 builtin details to a config file\r\n region_details = None\r\n\r\n... | [
"0.70874363",
"0.70591384",
"0.60175997",
"0.5904459",
"0.585444",
"0.5795512",
"0.5608974",
"0.5576179",
"0.5553111",
"0.54601663",
"0.5458088",
"0.54207695",
"0.54005855",
"0.5378389",
"0.5374614",
"0.5348017",
"0.53426313",
"0.5309667",
"0.5308136",
"0.53045267",
"0.529966... | 0.5485982 | 9 |
Constructs a cube axis header fits cube header header pyfits header axis axis to reconstruct axis int cube axis numpy array | def get_axis(header, axis):
logger = logging.getLogger(__name__)
logger.debug("Will extract axis: {}.".format(axis))
wcs = WCS(header)
wcs_arr_shape = wcs.array_shape
logger.debug("WCS array shape: {}".format(wcs_arr_shape))
n_axis = wcs.array_shape[-axis]
logger.debug("Axis should have {} elements.".format(n_axis))
if len(wcs_arr_shape) > 3:
axis_vals = wcs.pixel_to_world_values(np.c_[np.zeros(n_axis), np.zeros(n_axis), np.arange(0,n_axis), np.zeros(n_axis)])
else:
axis_vals = wcs.pixel_to_world_values(np.c_[np.zeros(n_axis), np.zeros(n_axis), np.arange(0,n_axis)])
axis_vals = np.asarray(axis_vals)
axis_vals = axis_vals[:,axis-1]
return axis_vals | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_phi_to_fits_header(fits_header, phi_array):\n if len(phi_array) < 2:\n raise ShapeError('RM cube should have two or more frames to be a cube')\n fhdr = fits_header.copy()\n fhdr.set('NAXIS3', len(phi_array))\n fhdr.set('CRPIX3', 1.0)\n fhdr.set('CRVAL3', phi_array[0])\n fhdr.set('C... | [
"0.66289526",
"0.652385",
"0.63435376",
"0.5962099",
"0.59471786",
"0.5827295",
"0.5715055",
"0.5618013",
"0.55621886",
"0.55530864",
"0.5483086",
"0.54277635",
"0.5331385",
"0.5288723",
"0.5281522",
"0.5281522",
"0.5277689",
"0.524827",
"0.52445453",
"0.5223636",
"0.52164155... | 0.6036392 | 3 |
Sums the pixels inside a region preserving the spectral axis. | def extract_spec(data, region, naxis, mode):
logger = logging.getLogger(__name__)
logger.debug('Data shape: {0}'.format(data.shape))
if region['shape'] == 'point':
if naxis > 3:
spec = data[:,:,region['params']['cy'],region['params']['cx']]
if mode == 'sum':
spec = spec.sum(axis=0)
elif mode == 'avg':
spec = spec.mean(axis=0)
elif 'flux' in mode.lower():
spec = spec.sum(axis=0)/region['barea']
else:
logger.error('Mode not supported.')
logger.error('Will exit now.')
sys.exit(1)
elif naxis == 3:
spec = data[:,region['params']['cy'],region['params']['cx']]
else:
spec = data[region['params']['cy'],region['params']['cx']]
elif region['shape'] == 'box':
area = (region['params']['trcy'] - region['params']['blcy']) * \
(region['params']['trcx'] - region['params']['blcx'])
if naxis > 3:
spec = data[0,:,region['params']['blcy']:region['params']['trcy'],
region['params']['blcx']:region['params']['trcx']]
if mode == 'sum':
spec = spec.sum(axis=2).sum(axis=1)
elif mode == 'avg':
spec = spec.mean(axis=2).mean(axis=1)
elif 'flux' in mode.lower():
spec = spec.sum(axis=2).sum(axis=1)/region['barea']
else:
logger.error('Mode not supported.')
logger.error('Will exit now.')
sys.exit(1)
elif naxis == 3:
spec = data[:,region['params']['blcy']:region['params']['trcy'],
region['params']['blcx']:region['params']['trcx']]
if mode == 'sum':
spec = spec.sum(axis=2).sum(axis=1)#/area
elif mode == 'avg':
spec = spec.mean(axis=2).mean(axis=1)#/area
elif 'flux' in mode.lower():
summ = spec.sum(axis=2).sum(axis=1)
logger.info('Sum of pixels: {0}'.format(summ))
spec = summ/region['barea']
else:
logger.error('Mode not supported.')
logger.error('Will exit now.')
sys.exit(1)
else:
spec = data[region['params']['blcy']:region['params']['trcy'],
region['params']['blcx']:region['params']['trcx']]
if mode == 'sum':
spec = spec.sum()
elif mode == 'avg':
spec = spec.mean()
elif 'flux' in mode.lower():
spec = spec.sum()/region['barea']
else:
logger.error('Mode not supported.')
logger.error('Will exit now.')
sys.exit(1)
elif region['shape'] == 'circle':
logger.info("Circular region has a center " \
"at pixel ({0},{1}) with radius " \
"{2}".format(region['params']['cx'],
region['params']['cy'],
region['params']['r']))
if naxis > 3:
logger.debug("The image has more than 3 axes.")
mask = sector_mask(data[0,0].shape,
(region['params']['cy'], region['params']['cx']),
region['params']['r'],
(0, 360))
mdata = data[0][:,mask]
logger.debug("Masked data shape: {0}".format(mdata.shape))
if 'sum' in mode.lower():
spec = mdata.sum(axis=1)
elif 'avg' in mode.lower():
spec = mdata.mean(axis=1)
elif 'flux' in mode.lower():
spec = mdata.sum(axis=1)/region['barea']
elif naxis == 3:
mask = sector_mask(data[0].shape,
(region['params']['cy'], region['params']['cx']),
region['params']['r'],
(0, 360))
mdata = data[:,mask]
logger.debug("Masked data shape: {0}".format(mdata.shape))
if 'sum' in mode.lower():
spec = mdata.sum(axis=1)#/len(np.where(mask.flatten() == 1)[0])
elif 'avg' in mode.lower():
spec = mdata.mean(axis=1)
elif 'flux' in mode.lower():
spec = mdata.sum(axis=1)/region['barea']
else:
logger.error('Mode not supported.')
logger.error('Will exit now.')
sys.exit(1)
else:
mask = sector_mask(data.shape,
(region['params']['cy'], region['params']['cx']),
region['params']['r'],
(0, 360))
mdata = np.ma.masked_invalid(data[mask])
logger.debug("Masked data shape: {0}".format(mdata.shape))
logger.debug("Masked data sum: {0}".format(mdata))
if 'sum' in mode.lower():
spec = mdata.sum()#/len(np.where(mask.flatten() == 1)[0])
elif 'avg' in mode.lower():
spec = mdata.mean()
elif 'flux' in mode.lower():
spec = mdata.sum()/region['barea']
else:
logger.error('Mode not supported.')
logger.error('Will exit now.')
sys.exit(1)
elif region['shape'] == 'ellipse':
logger.info("Elliptical region has a center " \
"at pixel ({0},{1}) with major and minor axes " \
"{2} and {3} at an angle {4}".format(region['params']['cx'],
region['params']['cy'],
region['params']['bmaj'],
region['params']['bmin'],
region['params']['theta']))
logger.debug("Mask shape: {}".format(data.shape[-2:]))
mask = ellipse_mask(data.shape[-2:],
region['params']['cy'], region['params']['cx'],
region['params']['bmaj']/2., region['params']['bmin']/2.,
region['params']['theta'])
logger.debug('Elements in mask: {}'.format(mask.sum()))
if naxis > 3:
mdata = data[0][:,mask]
axis = 1
elif naxis == 3:
mdata = data[:,mask]
axis = 1
else:
mdata = data[mask]
axis = 0
logger.debug("Masked data shape: {0}".format(mdata.shape))
if 'sum' in mode.lower():
spec = mdata.sum(axis=axis)
elif 'avg' in mode.lower():
spec = mdata.mean(axis=axis)
elif 'flux' in mode.lower():
spec = mdata.sum(axis=axis)/region['barea']
else:
logger.error('Mode not supported.')
logger.error('Will exit now.')
sys.exit(1)
elif 'poly' in region['shape']:
npolys = len(region['params']['Polygons'])
if naxis > 3:
shape = data[0][0].shape
npix3 = data[0].shape[0]
elif naxis == 3:
shape = data[0].shape
npix3 = data.shape[0]
else:
shape = data.shape
npix3 = 0
mask = np.zeros(shape)
for poly in region['params']['Polygons']:
# Add all the polygons together
logger.info("Adding polygons to the mask.")
mask += poly.make_mask(shape)
logger.info("Normalizing the mask to unity.")
mask = np.ceil(mask/npolys)
if naxis > 3:
mdata = data[0]*np.tile(mask, (npix3,1,1))
else:
mdata = data*np.tile(mask, (npix3,1,1))
if mode == 'sum':
spec = mdata.sum(axis=1).sum(axis=1)
elif 'avg' in mode.lower():
spec = mdata.mean(axis=1).mean(axis=1)
elif 'flux' in mode.lower():
spec = mdata.sum(axis=1).sum(axis=1)/region['barea']
else:
logger.error('Mode not supported.')
logger.error('Will exit now.')
sys.exit(1)
elif 'all' in region['shape']:
if naxis > 3:
data = data[0]
spec = proc_data(data, mode, region)
elif naxis == 3:
data = data
spec = proc_data(data, mode, region)
else:
spec = proc_data(data, mode, region)
return spec | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sumRegion(self, row1, col1, row2, col2):\n\n if not self.sums:\n return 0\n\n r1, c1 = row1+1, col1+1\n r2, c2 = row2+1, col2+1\n return self.sums[r2][c2] + self.sums[r1-1][c1-1] - self.sums[r2][c1-1] - self.sums[r1-1][c2]",
"def sumRegion(self, row1, col1, row2, col2):... | [
"0.6154293",
"0.603963",
"0.600956",
"0.59598166",
"0.5920878",
"0.59154606",
"0.59146667",
"0.58369637",
"0.5797925",
"0.57950807",
"0.5758008",
"0.5724144",
"0.56694096",
"0.55569243",
"0.5546531",
"0.54882556",
"0.54564047",
"0.5439962",
"0.5425233",
"0.541275",
"0.5411576... | 0.0 | -1 |
Build a WCS object given the spatial header parameters. | def set_wcs(head):
logger = logging.getLogger(__name__)
# Create a new WCS object.
wcs = WCS(head)
if wcs.naxis > 3:
wcs = wcs.dropaxis(2)
logger.debug('WCS contains {0} axes.'.format(wcs.naxis))
return wcs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readWCS(header):\r\n # Creating the WCS instance\r\n w = wcs.WCS(header = header)\r\n # Filling the WCS\r\n w.wcs.crval = N.array([float(header[\"CRVAL1\"]), float(header[\"CRVAL2\"])])\r\n w.wcs.crpix = N.array([float(header[\"CRPIX1\"]), float(header[\"CRPIX2\"])])\r\n w.wcs.cdelt = N.array... | [
"0.76218075",
"0.6315264",
"0.62880224",
"0.61376697",
"0.6050542",
"0.59938204",
"0.599007",
"0.59843117",
"0.5939777",
"0.59356636",
"0.5899153",
"0.5899032",
"0.58228743",
"0.58159834",
"0.5745454",
"0.5663349",
"0.565065",
"0.5642652",
"0.55831504",
"0.55391955",
"0.55139... | 0.60295844 | 5 |
Plots the extraction region. | def show_rgn(ax, rgn, **kwargs):
alpha = 0.1
#lw = 0.1
if rgn['shape'] == 'box':
ax.plot([rgn['params']['blcx']]*2,
[rgn['params']['blcy'],rgn['params']['trcy']], 'r-', **kwargs)
ax.plot([rgn['params']['blcx'],rgn['params']['trcx']],
[rgn['params']['blcy']]*2, 'r-', **kwargs)
ax.plot([rgn['params']['blcx'],rgn['params']['trcx']],
[rgn['params']['trcy']]*2, 'r-', **kwargs)
ax.plot([rgn['params']['trcx']]*2,
[rgn['params']['blcy'],rgn['params']['trcy']], 'r-', **kwargs)
elif rgn['shape'] == 'circle':
patch = mpatches.Circle((rgn['params']['cx'], rgn['params']['cy']),
rgn['params']['r'], alpha=alpha, transform=ax.transData)
#plt.figure().artists.append(patch)
ax.add_patch(patch)
elif rgn['shape'] == 'polygon':
for poly in rgn['params']['Polygons']:
patch = mpatches.Polygon(poly.get_vertices(), closed=True,
alpha=alpha, transform=ax.transData)
ax.add_patch(patch)
elif rgn['shape'] == 'pixel':
ax.plot(region['params']['cy'], region['params']['cx'], 'rs', ms=5) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot(self):\n pass",
"def plot(self):\n\t\tself.plotOfXray().plot()",
"def show(self):\n \n \n \n \n \n \n r = 4\n f, axarr = plt.subplots(r, r, figsize=(8,8))\n counter = 0\n for i in range(r):\n for j in range(r):\n ... | [
"0.64836633",
"0.63798314",
"0.63485694",
"0.63415164",
"0.62981063",
"0.6228685",
"0.6214679",
"0.61689097",
"0.6165796",
"0.61613774",
"0.611915",
"0.60805905",
"0.60388714",
"0.6032662",
"0.6006002",
"0.5967216",
"0.5963205",
"0.59328747",
"0.5918682",
"0.59050053",
"0.589... | 0.0 | -1 |
Splits text from digits in a string. | def split_str(str):
logger = logging.getLogger(__name__)
logger.debug('{0}'.format(str))
match = re.match(r"([0-9]+.?\d{0,32}?)(d|m|s)", str)
if match:
items = match.groups()
return items[0], items[1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_str_digit(s):\n res = []\n for m in re.finditer('(\\d*)(\\D*)', s):\n for g in m.groups():\n if g != '':\n try:\n res.append(int(g))\n except ValueError:\n res.append(g)\n return tuple(res)",
"def split_num(s... | [
"0.69932103",
"0.69409627",
"0.6516405",
"0.6497158",
"0.6497158",
"0.6333978",
"0.62500066",
"0.62345093",
"0.6181007",
"0.61332566",
"0.6114605",
"0.60910285",
"0.6013863",
"0.59716225",
"0.59670913",
"0.59426206",
"0.59077483",
"0.5873073",
"0.58616483",
"0.58398443",
"0.5... | 0.6069437 | 12 |
Get the current status of the pool. | def get_pool_status(self, mission):
# initialize node status
states = dict(
idle=0, rebooting=0, reimaging=0, running=0, unusable=0, creating=0,
starting=0, waiting_for_start_task=0, start_task_failed=0, unknown=0,
leaving_pool=0, offline=0, preempted=0)
# if the pool does not exist
if not self.batch_client.pool.exists(pool_id=mission.pool_name):
return "N/A", "N/A", states
# get pool info
the_pool = self.batch_client.pool.get(pool_id=mission.pool_name)
state = the_pool.state.name
allocation_state = the_pool.allocation_state.name
# get the list of node at current time point
# we check the existance of the pool again to avoid coincidence
if self.batch_client.pool.exists(pool_id=mission.pool_name):
node_list = self.batch_client.compute_node.list(
pool_id=mission.pool_name)
# calculate the number of nodes in each status
for node in node_list:
states[node.state.name] += 1
node_list.reset()
return state, allocation_state, states | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_pool_status():\n pools_status = split_status_pools(fork_and_get_output(\"zpool status\".split()))\n pools = []\n for p in pools_status:\n pools.append(status.PoolStatus(p))\n return pools",
"def status(self):\n self._refresh_state()\n return self._data.get('status')",
"... | [
"0.75640756",
"0.7310082",
"0.73063296",
"0.7286715",
"0.7214205",
"0.7214205",
"0.7214205",
"0.72092366",
"0.71747327",
"0.71360385",
"0.7126846",
"0.71214384",
"0.7102766",
"0.7091428",
"0.70774436",
"0.70774436",
"0.7071764",
"0.70652586",
"0.70541",
"0.70418775",
"0.70287... | 0.71968305 | 8 |
Get a string for the status overview of the pool and nodes. | def get_pool_overview_string(self, mission):
# get statuses
pool_status, allocation_status, node_status = self.get_pool_status(mission)
s = "Pool status: {}\n".format(pool_status)
s += "Allocation status: {}".format(allocation_status)
if pool_status != "N/A":
other = sum(node_status.values()) - node_status["idle"] - \
node_status["running"] - node_status["unusable"]
s += "\n"
s += "Node status: "
s += "{} idle; ".format(node_status["idle"])
s += "{} running; ".format(node_status["running"])
s += "{} unusable; ".format(node_status["unusable"])
s += "{} other;".format(other)
return s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def status_str(self, spaced=False):\n if self.args.vverbose:\n ## Print profile of all nodes\n status = self.pool.status(string=True)\n\n elif self.args.verbose:\n ## Print profile of usable nodes\n status = self.pool.status(min_state=PLNodeState.usable, string=True)\n\n ... | [
"0.78566015",
"0.72541404",
"0.690066",
"0.68025947",
"0.67186147",
"0.6582966",
"0.65206933",
"0.65195024",
"0.65195024",
"0.6434992",
"0.64222366",
"0.6364477",
"0.6364477",
"0.6364477",
"0.6364477",
"0.6364477",
"0.6364477",
"0.6364477",
"0.6364477",
"0.6364477",
"0.636447... | 0.7833251 | 1 |
Get the current status of the job (task scheduler) and tasks. | def get_job_status(self, mission):
# initialize task status
status = dict(active=0, running=0, succeeded=0, failed=0)
# get job status if it exists. Otherwise, return N/A
try:
the_job = self.batch_client.job.get(job_id=mission.job_name)
# get counts of tasks in different statuses
status_counts = self.batch_client.job.get_task_counts(mission.job_name)
except azure.batch.models.BatchErrorException as err:
if err.message.value.startswith("The specified job does not exist"):
return "N/A", status
# raise an exception for other kinds of errors
raise
# update the dictionary
status["active"] = status_counts.active
status["running"] = status_counts.running
status["succeeded"] = status_counts.succeeded
status["failed"] = status_counts.failed
return the_job.state.name, status | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def status(self):\n return self.job_proto.status",
"def get_status(self):\n\t\treturn call_sdk_function('PrlJob_GetStatus', self.handle)",
"def celery_task_status(self):\n return self._get_celery_queue_data()",
"def get_job_status(self):\n if self.worker_thread is None:\n retu... | [
"0.7439859",
"0.74113566",
"0.73972505",
"0.73632085",
"0.7306718",
"0.7229825",
"0.7227331",
"0.719492",
"0.71917385",
"0.7006022",
"0.69687855",
"0.69370997",
"0.6901852",
"0.6846558",
"0.68459725",
"0.68419343",
"0.6834033",
"0.6797795",
"0.67972744",
"0.6779665",
"0.67757... | 0.6771893 | 22 |
Get a string for the status overview of the job and tasks. | def get_job_overview_string(self, mission):
# get statuses
job_status, task_status = self.get_job_status(mission)
s = "Job status: {}".format(job_status)
if job_status != "N/A":
s += "\n"
s += "Tasks status: "
s += "{} active; ".format(task_status["active"])
s += "{} running; ".format(task_status["running"])
s += "{} succeeded; ".format(task_status["succeeded"])
s += "{} failed;".format(task_status["failed"])
return s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_job_status(self):\n total_hits = session.query(BoxHit).filter_by(training_job_id=self.id).count()\n num_hits_left = session.query(BoxHit).filter_by(training_job_id=self.id, outstanding=True).count()\n total_urls = self.num_urls\n num_urls_left = session.query(VideoTrainingURL).... | [
"0.74661416",
"0.7176071",
"0.7176071",
"0.71116614",
"0.7109996",
"0.7087766",
"0.7026905",
"0.69023955",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
"0.6890844",
... | 0.7895506 | 0 |
Get the status of a mission's storage container. | def get_storage_container_status(self, mission):
if self.storage_client.exists(container_name=mission.container_name):
return "available"
# TODO: calculate space used in the container
return "N/A" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_storage_container_overview_string(self, mission):\n\n status = self.get_storage_container_status(mission)\n s = \"Storage container status: {}\".format(status)\n return s",
"def container_status(self) -> str:\n return pulumi.get(self, \"container_status\")",
"def container_s... | [
"0.7662295",
"0.68757343",
"0.65628034",
"0.61149687",
"0.61096156",
"0.60097855",
"0.59428936",
"0.5903649",
"0.58938575",
"0.5616815",
"0.560521",
"0.5599396",
"0.55950266",
"0.5577565",
"0.55629945",
"0.556032",
"0.5544695",
"0.5521543",
"0.5521207",
"0.551155",
"0.5508292... | 0.85038316 | 0 |
Get a string for the status of the storage container. | def get_storage_container_overview_string(self, mission):
status = self.get_storage_container_status(mission)
s = "Storage container status: {}".format(status)
return s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def container_status(self) -> str:\n return pulumi.get(self, \"container_status\")",
"def storage_bytes_status(self) -> str:\n return pulumi.get(self, \"storage_bytes_status\")",
"def status(self) -> str:\n return pulumi.get(self, \"status\")",
"def status(self) -> str:\n return p... | [
"0.8081375",
"0.7568696",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
"0.72568953",
... | 0.7668451 | 1 |
Get the string of an overview to all resources. | def get_overview_string(self, mission):
s = self.get_pool_overview_string(mission) + "\n\n"
s += self.get_job_overview_string(mission) + "\n\n"
s += self.get_storage_container_overview_string(mission)
return s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def overview():\n return render_template('api/api.html', title='API Overview')",
"def __str__(self):\n return gettext('List of %s') % self.resource.__name__",
"def get_overview():\n from app.core.api_views import Api\n from app.modules.overview import inc\n sar = inc.main()\n api = Api()\... | [
"0.6887148",
"0.6648697",
"0.65732783",
"0.63620585",
"0.6329359",
"0.6313626",
"0.6280509",
"0.6225066",
"0.6117485",
"0.6111171",
"0.61043644",
"0.60946447",
"0.6082813",
"0.60682595",
"0.6067177",
"0.6052863",
"0.6049613",
"0.59981334",
"0.59859276",
"0.59684676",
"0.59644... | 0.64852643 | 3 |
A generator that can be used in a loop. | def status_generator(self, mission):
while True:
status = {}
status["timestamp"] = datetime.datetime.utcnow().replace(
microsecond=0, tzinfo=datetime.timezone.utc).strftime(
"%a %b %d %H:%M:%S %Z %Y")
status["pool_status"], status["allocation_status"], \
status["node_status"] = self.get_pool_status(mission)
status["job_status"], status["task_status"] = \
self.get_job_status(mission)
status["storage_status"] = \
self.get_storage_container_status(mission)
yield status | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __iter__(self):\n yield from self.gen",
"def _mc_gen():\r\n n = 1\r\n while True:\r\n yield n\r\n n += 1",
"def __iter__(self):\n return self.new_generator()",
"def generator():\n mygenerator = (x for x in range(3))\n for element in mygenerator:\n print 'pop... | [
"0.77754736",
"0.76618725",
"0.7656073",
"0.75702596",
"0.74272484",
"0.74266475",
"0.73606414",
"0.7329172",
"0.7289084",
"0.7237256",
"0.72144675",
"0.7204889",
"0.71852434",
"0.7164605",
"0.7125203",
"0.7109289",
"0.7065946",
"0.70342624",
"0.7027617",
"0.70258605",
"0.702... | 0.0 | -1 |
Prints summary statistics for every column of X, split into 2 classes X is the data, y is the class labels, assumed to be 0 or 1. This will also plot the data. | def summarize_source(X, y, decimals=4):
# Assumes y is either 1 or 0
pos_idxs = np.where(y == 1)[0]
neg_idxs = np.where(y == 0)[0]
# Divide dataset into positive and negatives
Xs = (X[neg_idxs, :], X[pos_idxs, :])
Ys = (y[neg_idxs], y[pos_idxs])
# Make format string
numstr = ", ".join(["{" + str(i) + ":10." + str(decimals) + "f}" for i
in range(X.shape[1])])
# Output results
print("Total number of samples: " + str(len(y)))
print()
print(str(len(Ys[1])) + " Positive Samples:")
print("\tMin : " + numstr.format( *np.min(Xs[1], axis=0)))
print("\tMean : " + numstr.format(*np.mean(Xs[1], axis=0)))
print("\tMax : " + numstr.format( *np.max(Xs[1], axis=0)))
print()
print("\tStdev : " + numstr.format(*np.sqrt(np.var(Xs[1], axis=0))))
print("\tVar : " + numstr.format( *np.var(Xs[1], axis=0)))
print()
print(str(len(Ys[0])) + " Negative Samples:")
print("\tMin : " + numstr.format( *np.min(Xs[0], axis=0)))
print("\tMean : " + numstr.format(*np.mean(Xs[0], axis=0)))
print("\tMax : " + numstr.format( *np.max(Xs[0], axis=0)))
print()
print("\tStdev : " + numstr.format(*np.sqrt(np.var(Xs[0], axis=0))))
print("\tVar : " + numstr.format( *np.var(Xs[0], axis=0))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def basic_summary(data):\n headers = [\"Split\", \"Samples\", \"Height\", \"Width\", \"Channels\", \"Classes\"]\n print(table_format(headers, header = True))\n for split in [\"train\", \"valid\", \"test\"]:\n X, y = data[split]\n n, h, w, c = X.shape\n n_classes = np.unique(y).shape[0... | [
"0.66654617",
"0.6197573",
"0.59729",
"0.58434284",
"0.58352256",
"0.58352256",
"0.58282775",
"0.5750776",
"0.57281625",
"0.57217586",
"0.5707557",
"0.5698241",
"0.56464404",
"0.56453407",
"0.5628074",
"0.5584864",
"0.55809045",
"0.55757785",
"0.55118465",
"0.55106294",
"0.54... | 0.5336147 | 32 |
Shows a simple scatterplot of X, colored by the classes in y. Technically, this shows the 1st three principal components of X if X has more than 3 dimensions. If X only has 2 dimensions, then just a 2dimensional scatterplot is returned. This will not produce a plot for 1 dimensional data. | def plot_data(X, y):
x_dim = X.shape[1]
# Ignore 1 dimensional data
if x_dim == 1:
print("plot_data not gonna bother with 1 dimensional data")
return
# For 2 dimensional data, just plot it
if x_dim == 2:
plt.scatter(X[:,0], X[:,1], c=y)
plt.show()
return
# For at least 4 dimensions, do PCA
if x_dim >= 4:
pca = PCA(n_components=3)
pca.fit(X)
plot_x = pca.transform(X)
else:
plot_x = X
# Assumes y is either 1 or 0
pos_idxs = np.where(y == 1)[0]
neg_idxs = np.where(y == 0)[0]
# Plot the now 3 dimensional data
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
Xs = plot_x[neg_idxs, :]
ax.scatter(Xs[:,0], Xs[:,1], Xs[:,2], color='orange')
Xs = plot_x[pos_idxs, :]
ax.scatter(Xs[:,0], Xs[:,1], Xs[:,2], color='purple')
# Label plot
if x_dim >= 4:
ax.set_title("PCA of Generated Data")
ax.set_xlabel("1st Principal Component")
ax.set_ylabel("2nd Principal Component")
ax.set_zlabel("3rd Principal Component")
else:
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
# Display!
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scatter_plot(self):\n\n X = self.reduce_dimension(n_components=2)\n\n plt.figure()\n plt.scatter(X[:,0], X[:,1])\n\n return plt",
"def plot_dataset(X, classes):\n data = pd.DataFrame(X, columns=['x', 'y'])\n data['dataset'] = classes\n sns.lmplot('x', 'y', data=data, hue=... | [
"0.68430007",
"0.66364133",
"0.6596344",
"0.6580677",
"0.65084815",
"0.645392",
"0.6410972",
"0.6298724",
"0.6219223",
"0.621192",
"0.61827666",
"0.61542743",
"0.6125981",
"0.60232717",
"0.59843695",
"0.590425",
"0.59014237",
"0.5894517",
"0.58731264",
"0.58717024",
"0.586803... | 0.7091436 | 0 |
This function reads the data from database where the clean data is stored. | def load_data(database_filepath):
# load data from database
engine = create_engine('sqlite:///' + database_filepath)
df = pd.read_sql_table('figure-eight', engine)
# There is only one classification class for child_alone which is 0 which indicates that there is no message classified into this class.
del df['child_alone']
X = df.message.values
Y = df[np.delete(df.columns.values, [0,1,2,3])].values
category_names = np.delete(df.columns.values, [0,1,2,3])
return X,Y,category_names | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleandata():\n engine = create_engine('sqlite:///../data/disaster_db.db')\n df = pd.read_sql_table('disaster_db', engine)\n\n return df",
"def read_db(self):\n with open(self.filename, 'r') as database:\n data = json.load(database)\n self.data = data",
"def read_sql(self):... | [
"0.6639239",
"0.65105474",
"0.63020116",
"0.6263526",
"0.61224455",
"0.61113507",
"0.60522664",
"0.59786123",
"0.59628767",
"0.5925373",
"0.59235024",
"0.5909515",
"0.5893247",
"0.5887125",
"0.5887125",
"0.58866704",
"0.58852226",
"0.5885046",
"0.5872037",
"0.5848737",
"0.583... | 0.0 | -1 |
This function tokenizes the input text and performs necessary cleaning. | def tokenize(text):
url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'
detected_urls = re.findall(url_regex, text)
for url in detected_urls:
text = text.replace(url, "urlplaceholder")
tokens = word_tokenize(text)
lemmatizer = WordNetLemmatizer()
clean_tokens = []
for tok in tokens:
clean_tok = lemmatizer.lemmatize(tok).lower().strip()
clean_tokens.append(clean_tok)
return clean_tokens | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_text(text):\n global cleaned_text\n # remove numbers\n text_nonum = re.sub(r'\\d+', '', text)\n # remove punctuations and convert characters to lower case\n text_nopunct = \"\".join([char.lower() for char in text_nonum if char not in string.punctuation]) \n # sub... | [
"0.75570065",
"0.7090177",
"0.7041805",
"0.7033546",
"0.6981678",
"0.696288",
"0.6919464",
"0.691603",
"0.6914198",
"0.6900118",
"0.6900118",
"0.6900118",
"0.6900118",
"0.6900118",
"0.6900118",
"0.68975586",
"0.6874092",
"0.6874056",
"0.6841664",
"0.6819344",
"0.6789257",
"... | 0.0 | -1 |
This function evaluates the new model and generates classification report containing precision, recall, fscore and accuracy information for individual classes. | def evaluate_model(model, X_test, Y_test, category_names):
y_pred = model.predict(X_test)
for x in range(0, len(category_names)):
print(category_names[x])
print(classification_report(Y_test[:,x], y_pred[:,x]))
print("Accuracy: " + str(accuracy_score(Y_test[:, x], y_pred[:, x]))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate_model(model, X_test, Y_test, category_names):\n# Print out Precision , recall F1_score and support for each column using classification_report function\n y_pred_test = model.predict(X_test)\n print(classification_report(Y_test, y_pred_test, target_names=category_names))",
"def evaluate_model(m... | [
"0.78544277",
"0.7645207",
"0.7580781",
"0.7521916",
"0.73978734",
"0.7377645",
"0.7346552",
"0.73453677",
"0.73044485",
"0.7295265",
"0.7271339",
"0.7254532",
"0.7244622",
"0.72413814",
"0.7187265",
"0.7177157",
"0.71704125",
"0.71259546",
"0.7120069",
"0.7119113",
"0.708959... | 0.70245725 | 28 |
This function packages the trained model into the pickle file. | def save_model(model, model_filepath):
# save the classifier
with open(model_filepath, 'wb') as fid:
pkl.dump(model, fid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_model(self):\n\n # =============================================================\n # Default : pickle the trained model. Change this (and the load\n # function, below) only if the library you used does not support\n # pickling.\n # self.Model_made.save(\"Model_made.h5\")... | [
"0.7843905",
"0.7577629",
"0.73923266",
"0.73051053",
"0.727403",
"0.7219735",
"0.71813995",
"0.71445173",
"0.7117435",
"0.7111384",
"0.710956",
"0.71073145",
"0.7095668",
"0.70784366",
"0.7034732",
"0.7018957",
"0.7018404",
"0.70135003",
"0.70103824",
"0.700899",
"0.6993362"... | 0.67822295 | 36 |
Get current date and time string. | def now_short(_format="%Y%m%d-%H%M%S"):
return time.strftime(_format, time.localtime()) + "\t" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_current_datetime_string ( ) :\n return get_current_datetime( ).strftime( \"%Y%m%d-%H%M%S\" )",
"def get_current_time():\n return time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime())",
"def get_now():\r\n now = dt.datetime.now()\r\n now_str = now.strftime(\"%d/%m %H:%M\")\r\n return now_... | [
"0.8746683",
"0.8295901",
"0.8274269",
"0.8213029",
"0.81925744",
"0.81858903",
"0.8185167",
"0.81688607",
"0.80856556",
"0.8042373",
"0.7992371",
"0.78575885",
"0.7848898",
"0.78472054",
"0.78358054",
"0.779838",
"0.7789519",
"0.77884716",
"0.77856934",
"0.7751212",
"0.77394... | 0.0 | -1 |
Log and assert based on condition. If condition True, log message as PASS to testcase log file. If condition False, Assert and Print message with status FAIL. | def logfile_assert_message(s, condition, message):
if not condition:
s.log_to_file += now_short() + message + ": FAIL\r\n"
assert 0, message + ": FAIL\r\n"
else:
log_message(s, message + ": PASS") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self):\n self.info(\"LOGGING: Testing log messages\")\n self.debug(\"This is a debugging message\")\n self.info(\"This is an informational message\")\n self.warning(\"This is a warning message\")\n self.error(\"This is an error message\")\n self.critical(\"This is... | [
"0.6288022",
"0.61218476",
"0.6107574",
"0.60947496",
"0.5991102",
"0.5953129",
"0.5885416",
"0.587344",
"0.5861646",
"0.58180374",
"0.5753832",
"0.5752025",
"0.5750852",
"0.5717795",
"0.57175326",
"0.570502",
"0.5684395",
"0.5653539",
"0.5651103",
"0.5643155",
"0.5638683",
... | 0.78782284 | 1 |
Write detailed log file for given test. | def write_test_log(t, output_dir):
if t.log_to_file is not None and hasattr(t, "stop_time"):
filename = type(t).__name__ + "-" + time.strftime("%Y%m%d-%H%M%S") + ".txt"
testtime = t.stop_time - t.start_time
with open(os.path.join(output_dir, filename), "w") as log:
log.write("\t=======================================================")
log.write(f"\n\tTest case ID: {type(t).__name__}")
log.write(f"\n\tTest case Description: {type(t).__doc__}")
log.write("\n\t=======================================================\n")
log.write(t.log_to_file)
log.write("\n\t=======================================================")
log.write(f"\n\t{type(t).__name__} test result: {t.result_grade}")
log.write(f"\n\tTotal test time: {testtime} seconds")
log.write("\n\t=======================================================") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_test_log(t, output_dir):\n if t.log_to_file is not None and hasattr(t, \"stop_time\"):\n filename = type(t).__name__ + \"-\" + time.strftime(\"%Y%m%d-%H%M%S\") + \".txt\"\n testtime = t.stop_time - t.start_time\n with open(os.path.join(output_dir, filename), \"w\") as log:\n ... | [
"0.77759415",
"0.6469761",
"0.6405734",
"0.63734347",
"0.6358866",
"0.6292069",
"0.627844",
"0.6241813",
"0.62395686",
"0.6190359",
"0.618326",
"0.6178866",
"0.6163366",
"0.61453724",
"0.61151177",
"0.6075739",
"0.6044056",
"0.6033274",
"0.5966068",
"0.59578186",
"0.5957704",... | 0.7797923 | 0 |
Write log messages to console and to log file(with timestamp). | def log_message(s, msg, header=False):
if s.log_to_file is None:
s.log_to_file = ""
line_sep = "=" * min(len(msg), 80)
full_msg = "\n\t\t" + line_sep + "\n\t\t" + msg + "\n\t\t" + line_sep + "\n"
if header:
logger.debug("\n\n\t\t\t***" + msg + "***\n\n")
s.log_to_file += now_short() + full_msg + "\r\n"
else:
logger.debug(full_msg)
s.log_to_file += now_short() + msg + "\r\n" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _log_to_file(self, message):\n if self.log is not None:\n message = \"[%s] %s\" % (datetime.datetime.utcnow().strftime('%H:%M:%S'), message)\n self.log.write(\"%s\\n\" % (message,))\n self.log.flush()\n print message",
"def writeToLogFile(self, event):\n ... | [
"0.76535463",
"0.7256361",
"0.7207056",
"0.71898395",
"0.7076056",
"0.70700955",
"0.70448047",
"0.7034109",
"0.70302784",
"0.70248175",
"0.69756",
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"0.6955353",
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"0.6922523",
"0.6889003",
"0.68705016",
"0.67957056",
"0.67859244",
"0.6784583",
"0.67739... | 0.0 | -1 |
Instance initialisation to handle the output logging. | def __init__(self, parent, out, color):
self.color = color or "white"
self.out = out
self.parent = parent
self.first_write = True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n\n self.logger = logging.getLogger('sound-count')\n\n self.logger.setLevel(logging.DEBUG)\n\n self.formatter = logging.Formatter('%(asctime)s - %(name)s - %(message)s')\n\n self.stdout_handler = logging.StreamHandler()\n self.stdout_handler.setFormatter(self.... | [
"0.7915068",
"0.78774565",
"0.7758291",
"0.7710645",
"0.76501584",
"0.76135844",
"0.75693744",
"0.7531081",
"0.7513416",
"0.7488386",
"0.74827266",
"0.74706954",
"0.7453568",
"0.7426138",
"0.7380624",
"0.73672324",
"0.7357756",
"0.7321987",
"0.73012114",
"0.7299283",
"0.72518... | 0.0 | -1 |
Write or stdout input messages in colored(if defined). Create the file if not already present. | def write(self, string):
if self.out is not None:
if self.first_write:
self.first_write = False
string = "\r\n" + string
if self.color is not None:
self.out.write(colored(string, self.color))
else:
self.out.write(string)
current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
# check for the split case
if (
len(self.parent.log) > 1
and self.parent.log[-1] == "\r"
and string[0] == "\n"
):
string = f"\n{current_time} {string[1:]}"
to_log = re.sub("\r\n", f"\r\n{current_time} ", string)
self.parent.log += to_log
if hasattr(self.parent, "test_to_log"):
self.parent.test_to_log.log += re.sub(
r"\r\n\[", f"\r\n{self.parent.test_prefix}: [", to_log
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def color_print(*args, **kwargs):\n file = kwargs.get('file', sys.stdout)\n\n end = kwargs.get('end', '\\n')\n\n write = file.write\n if file.isatty():\n for i in range(0, len(args), 2):\n msg = args[i]\n if i + 1 == len(args):\n color = ''\n else:... | [
"0.664176",
"0.58514845",
"0.573148",
"0.56499183",
"0.5588023",
"0.5570282",
"0.55647844",
"0.55574226",
"0.5555951",
"0.5462549",
"0.5433804",
"0.542239",
"0.53665817",
"0.5349737",
"0.5295903",
"0.5280991",
"0.52719",
"0.5243998",
"0.52435213",
"0.52247983",
"0.5207164",
... | 0.0 | -1 |
Add process time with the log messages. | def extra_log(self, string):
if hasattr(self.parent, "log"):
self.parent.log += f"\r\n[{time.process_time()}] "
self.parent.log += string + "\r\n" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_line_in_log():\n logging.info(' ' + '-' * 60 + '\\n')",
"def add_log(self, log):\n log = str(datetime.datetime.now()) + \": \"+log+\"\\n\"\n print(log)\n self.logs.append(log)\n if len(self.logs) > 10:\n self.append_to_logfile()",
"def extra_log(self, string):\... | [
"0.67774904",
"0.6688818",
"0.6468294",
"0.64525807",
"0.6431555",
"0.6324461",
"0.6312171",
"0.6251729",
"0.6245165",
"0.62316775",
"0.6187182",
"0.61647475",
"0.6160086",
"0.61188847",
"0.6091051",
"0.60706383",
"0.6057694",
"0.60288566",
"0.60258675",
"0.60083884",
"0.6007... | 0.66905326 | 1 |
Flushes the buffer storage in console before pexpect. | def flush(self):
if self.out is not None:
self.out.flush() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _flush_buffer(self):\n self.pexpect_child.logfile = None\n flushedStuff = \"\"\n while self.pexpect_child.expect([pexpect.TIMEOUT, r\".+\"], timeout=1):\n flushedStuff += self.pexpect_child.match.group(0)\n self.pexpect_child.logfile = self.log_file",
"def _flush():\n ... | [
"0.7308612",
"0.7289075",
"0.7045256",
"0.6931466",
"0.6834416",
"0.6816106",
"0.6713305",
"0.66353625",
"0.6627915",
"0.65735334",
"0.65354127",
"0.64686835",
"0.6459456",
"0.6449715",
"0.6398675",
"0.6393837",
"0.6390865",
"0.6362393",
"0.6325738",
"0.6310141",
"0.6291992",... | 0.0 | -1 |
Add and write log messages to a combined list. | def create_file_logs(config, board, tests_to_run, logger):
combined_list = []
def add_to_combined_list(log, name, combined_list=combined_list):
for line in log.split("\r\n"):
try:
if line == "":
continue
if line.startswith("\n"):
line = line[1:]
if line.startswith(" ["):
line = line[1:]
ts, text = line.split("]", 1)
timestamp = float(ts[1:-1])
else:
text = line
timestamp = 0.0
combined_list.append(
{"time": timestamp, "text": str(text), "name": name}
)
except Exception as error:
logger.error(error)
logger.debug(f"Failed to parse log line = {repr(line)}")
idx = 1
console_combined = []
for console in board.consoles:
with open(os.path.join(config.output_dir, f"console-{idx}.log"), "w") as clog:
clog.write(console.log)
add_to_combined_list(console.log, f"console-{idx}")
add_to_combined_list(console.log_calls, f"console-{idx}")
add_to_combined_list(console.log, "", console_combined)
idx = idx + 1
def write_combined_log(combined_list, fname):
with open(os.path.join(config.output_dir, fname), "w") as clog:
for e in combined_list:
try:
if e["name"] == "":
clog.write(f"[{e['time']}]{repr(e['text'])}\r\n")
else:
clog.write(f"{e['name']}: [{e['time']}] {repr(e['text'])}\n")
except Exception as error:
logger.error(error)
logger.debug(f"failed to parse line: {repr(e)}")
import operator
console_combined.sort(key=operator.itemgetter("time"))
write_combined_log(console_combined, "console-combined.log")
for device in config.devices:
with open(os.path.join(config.output_dir, device + ".log"), "w") as clog:
d = getattr(config, device)
if hasattr(d, "log"):
clog.write(d.log)
add_to_combined_list(d.log, device)
add_to_combined_list(d.log_calls, device)
for test in tests_to_run:
if hasattr(test, "log") and test.log != "":
with open(
os.path.join(config.output_dir, f"{test.__class__.__name__}.log"), "w"
) as clog:
clog.write(test.log)
if hasattr(test, "log_calls"):
add_to_combined_list(test.log_calls, test.__class__.__name__)
combined_list.sort(key=operator.itemgetter("time"))
write_combined_log(combined_list, "all.log") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_list(self,list_,level='INFO'):\r\n logger.write('\\n'.join(self._log_list(list_)),level)",
"def CombineLogFiles(list_of_lists, logger):\n cur_device_log = ['']\n for cur_file, cur_file_lines in list_of_lists:\n # Ignore files with just the logcat header\n if len(cur_file_lines) < 2:\n ... | [
"0.64969385",
"0.6466005",
"0.62025017",
"0.6156893",
"0.6059565",
"0.6025947",
"0.60147756",
"0.5977898",
"0.5976505",
"0.5960277",
"0.59393877",
"0.5859047",
"0.5806393",
"0.57645357",
"0.5670112",
"0.56171393",
"0.5605485",
"0.55584806",
"0.55563587",
"0.5537255",
"0.55362... | 0.5024609 | 64 |
Set members based using inventory. | def _configure(self):
from .topology import FieldBase
Component._configure(self)
mapBasis = {
"simplex": FieldBase.SIMPLEX_BASIS,
"tensor": FieldBase.TENSOR_BASIS,
"default": FieldBase.DEFAULT_BASIS,
}
self.cellBasis = mapBasis[self.inventory.cellBasisStr]
mapSpace = {
"polynomial": FieldBase.POLYNOMIAL_SPACE,
"point": FieldBase.POINT_SPACE,
}
self.feSpace = mapSpace[self.inventory.feSpaceStr]
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def members(self, members):\n\n self._members = members",
"def members(self, members: object):\n\n self._members = members",
"def members(self, items):\n pass",
"def inventory_items(self, inventory_items):\n\n self._inventory_items = inventory_items",
"def inventory(self, invent... | [
"0.6458913",
"0.6443288",
"0.64391935",
"0.63899845",
"0.6246272",
"0.6177047",
"0.6176741",
"0.6058151",
"0.6013703",
"0.6013703",
"0.6013703",
"0.58846337",
"0.56970346",
"0.56590396",
"0.5608252",
"0.5567245",
"0.5558885",
"0.55403244",
"0.5475591",
"0.54485273",
"0.544436... | 0.0 | -1 |
Factory for subfield items. | def subfieldFactory(name):
from pythia.pyre.inventory import facility
return facility(name, family="subfield", factory=Subfield) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subfield():\n return Subfield()",
"def __init__(self, *args, **kwargs):\n super(ListFieldType, self).__init__(*args, **kwargs)\n\n self.item_info = self.field_info.get('items')",
"def create_subspecialty(sub_data):\n return get_or_create_object(sub_data, Subspecialty)",
"def test_cust... | [
"0.7079664",
"0.60948706",
"0.57870966",
"0.5703622",
"0.5617625",
"0.56146836",
"0.56075746",
"0.5595222",
"0.554094",
"0.546303",
"0.54049605",
"0.53629017",
"0.53175354",
"0.5301199",
"0.5221799",
"0.5221799",
"0.51944876",
"0.51886076",
"0.5142889",
"0.5127228",
"0.512640... | 0.7673134 | 0 |
Factory associated with Subfield. | def subfield():
return Subfield() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subfieldFactory(name):\n from pythia.pyre.inventory import facility\n return facility(name, family=\"subfield\", factory=Subfield)",
"def add_sub_factories(self) -> None:\n for field in get_model_fields(self.model, base=False, foreign=True, m2m=False):\n if not hasattr(self.factory, f... | [
"0.8500797",
"0.66517395",
"0.6441253",
"0.6377734",
"0.59815156",
"0.58264554",
"0.5662838",
"0.5660655",
"0.56087625",
"0.5576944",
"0.5576053",
"0.5484294",
"0.54726523",
"0.5440579",
"0.5439738",
"0.5400487",
"0.528335",
"0.52721506",
"0.52566725",
"0.52566725",
"0.519902... | 0.77598864 | 1 |
Gets the short path name of a given long path. | def get_short_path_name(long_name: str):
output_buf_size = _GetShortPathNameW(long_name, None, 0)
if output_buf_size <= 0:
return None
output_buf = ctypes.create_unicode_buffer(output_buf_size)
needed = _GetShortPathNameW(long_name, output_buf, output_buf_size)
assert 0 < needed < output_buf_size
return output_buf.value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_short_path_name(long_name):\n output_buf_size = 0\n while True:\n output_buf = ctypes.create_unicode_buffer(output_buf_size)\n needed = _GetShortPathNameW(long_name, output_buf, output_buf_size)\n if output_buf_size >= needed:\n return output_buf.value\n else:\n... | [
"0.8374927",
"0.692538",
"0.6779394",
"0.6752157",
"0.6703952",
"0.65418506",
"0.6538999",
"0.6492663",
"0.64758646",
"0.6439164",
"0.63933027",
"0.6386224",
"0.636427",
"0.6346333",
"0.6328241",
"0.63225263",
"0.6192011",
"0.61536336",
"0.6148895",
"0.6138921",
"0.61234444",... | 0.8415998 | 0 |
get img_type file list such as get jpg files | def get_path_list(self, suffix=img_type):
img_list = list(filter(lambda x: x.endswith(suffix), self.path_list))
return img_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_input_files():\n\n raw_list = abspath(get('input_files'))\n valid_types = ['image/jpeg', 'image/tiff']\n images = [x for x in raw_list if mimetypes.guess_type(x)[0] in valid_types]\n print('* Input images: {}'.format(len(images)))\n return images",
"def img_extensions():\n return [\".JP... | [
"0.7476918",
"0.7081896",
"0.6956454",
"0.6846246",
"0.6845208",
"0.6776346",
"0.6740786",
"0.6740108",
"0.66788965",
"0.6673548",
"0.6597972",
"0.6581447",
"0.6568904",
"0.65615165",
"0.6554064",
"0.6504134",
"0.64887166",
"0.6465466",
"0.6461444",
"0.643755",
"0.64279115",
... | 0.0 | -1 |
open jpg file or merge several jpg file then open it | def execute_file(self, event=None):
file_list = self.get_path_list()
print(file_list)
if not file_list:
return
# merge image
# 修复内存泄露的bug，由于没有清除之前打开的图片，第二次打开的图片仍然为之前的图片
try:
self.photos.destroy()
except:
pass
self.photos.imgs = file_list
merged_photo = self.photos.merge_photos()
# show image
try:
window.destroy()
except:
import traceback
traceback.print_exc()
window.build_img_canvas()
window.show_img_in_canvas(merged_photo) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open_frame(path,number):\n num=str(number).zfill(3) #Zero filling\n name = glob.glob(path+\"/*\"+num+\"*\")\n if len(name)==0:\n name = glob.glob(path+\"/\"+str(number)+\".png\")\n if len(name)>1:\n print \"too many matches \",len(name),\" found\"\n name = name[0]\n img = Imag... | [
"0.58855605",
"0.5591409",
"0.55525833",
"0.5439224",
"0.5414354",
"0.5400621",
"0.5384213",
"0.53814346",
"0.5351015",
"0.5304343",
"0.52945495",
"0.5280849",
"0.52760863",
"0.52742296",
"0.527252",
"0.5259538",
"0.5245993",
"0.52299297",
"0.5225909",
"0.52070266",
"0.519107... | 0.60279435 | 0 |
Scroll canvas horizontally and redraw the image | def __scroll_x(self, *args, **kwargs):
self.canvas_image.xview(*args) # scroll horizontally
self.__show_image() # redraw the image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __scroll_x(self, *args, **kwargs):\n self.canvas.xview(*args) # scroll horizontally\n self.__show_image() # redraw the image",
"def continuous_scroll(self, context):\n\n self.drawing.redraw_canvas(self.dy)\n \n return True",
"def refresh(self):\n\n # Delete old i... | [
"0.7781856",
"0.6695677",
"0.6625122",
"0.66034824",
"0.64015204",
"0.6336621",
"0.62105596",
"0.6134658",
"0.6117314",
"0.6087952",
"0.60482293",
"0.5974955",
"0.59536266",
"0.59532565",
"0.5856979",
"0.58364534",
"0.58310264",
"0.58037436",
"0.5784162",
"0.5746283",
"0.5735... | 0.78580177 | 0 |
Scroll canvas vertically and redraw the image | def __scroll_y(self, *args, **kwargs):
self.canvas_image.yview(*args) # scroll vertically
self.__show_image() # redraw the image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __scroll_y(self, *args, **kwargs):\n self.canvas.yview(*args) # scroll vertically\n self.__show_image() # redraw the image",
"def refresh(self):\n\n # Delete old image (if needed) \n if self.canvas_image_id:\n self.canvas.delete(self.canvas_image_id)\n if debug... | [
"0.78763294",
"0.7029265",
"0.6990234",
"0.6822299",
"0.67670774",
"0.6654772",
"0.6486098",
"0.63472086",
"0.6317045",
"0.6261508",
"0.62313896",
"0.61648124",
"0.6163939",
"0.613647",
"0.61271584",
"0.61243415",
"0.61053514",
"0.6080344",
"0.60629725",
"0.606112",
"0.605199... | 0.79207057 | 0 |
Remember previous coordinates for scrolling with the mouse | def __move_from(self, event):
self.canvas_image.scan_mark(event.x, event.y)
self.from_coord = (event.x, event.y) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def store_current_position_as_previous(self):\n pos = self.get_current_position()\n self.previous_xloc = pos[0]\n self.previous_yloc = pos[1]\n self.previous_zloc = pos[2]\n return pos",
"def mousePosition(self):",
"def new_previous_position(self, pos):\n self.previous... | [
"0.68040586",
"0.66060555",
"0.65336144",
"0.647154",
"0.61659133",
"0.61659133",
"0.61084694",
"0.60922366",
"0.60622627",
"0.60567003",
"0.5945194",
"0.59199005",
"0.5912067",
"0.5889155",
"0.58295685",
"0.5828022",
"0.5808109",
"0.57255214",
"0.5721227",
"0.5708124",
"0.57... | 0.0 | -1 |
Drag (move) canvas to the new position | def __move_to(self, event):
self.canvas_image.scan_dragto(event.x, event.y, gain=1)
self.to_coord = (event.x, event.y)
self.__show_image() # zoom tile and show it on the canvas | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_move(self, event):\n self.canvas.scan_dragto(event.x, event.y, gain=1)",
"def drag(self,x,y):\n self.x=x\n self.y=y",
"def drag(self, event):\n self.yview('scroll', self.ypos - event.y, 'units')\n self.xview('scroll', self.xpos - event.x, 'units')\n self.ypos ... | [
"0.7642598",
"0.7116813",
"0.6957533",
"0.6895421",
"0.6839819",
"0.6795433",
"0.6787461",
"0.66360825",
"0.6598902",
"0.65643734",
"0.65615577",
"0.6521887",
"0.64005405",
"0.6353929",
"0.63048756",
"0.62917954",
"0.627049",
"0.6246554",
"0.6198148",
"0.6198148",
"0.6198148"... | 0.67894125 | 6 |
Checks if the point (x,y) is outside the image area | def outside(self, x, y):
bbox = self.canvas_image.coords(self.container) # get image area
if bbox[0] < x < bbox[2] and bbox[1] < y < bbox[3]:
return False # point (x,y) is inside the image area
else:
return True # point (x,y) is outside the image area | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def outside(self, x, y):\n bbox = self.canvas.coords(self.container) # get image area\n if bbox[0] < x < bbox[2] and bbox[1] < y < bbox[3]:\n return False # point (x,y) is inside the image area\n else:\n return True # point (x,y) is outside the image area",
"def poin... | [
"0.87402356",
"0.7378075",
"0.7371467",
"0.7322463",
"0.7271476",
"0.71669763",
"0.7123005",
"0.70656526",
"0.7022389",
"0.7014238",
"0.696292",
"0.6954585",
"0.69515514",
"0.69209486",
"0.6896473",
"0.68796045",
"0.6856969",
"0.68375915",
"0.67989755",
"0.6798606",
"0.678162... | 0.8744287 | 0 |
Zoom with mouse wheel | def __wheel(self, event):
x = self.canvas_image.canvasx(event.x) # get coordinates of the event on the canvas
y = self.canvas_image.canvasy(event.y)
if self.outside(x, y): return # zoom only inside image area
scale = 1.0
# Respond to Linux (event.num) or Windows (event.delta) wheel event
if event.num == 5 or event.delta == -120: # scroll down, smaller
if round(self.__min_side * self.imscale) < 30: return # image is less than 30 pixels
self.imscale /= self.__delta
scale /= self.__delta
if event.num == 4 or event.delta == 120: # scroll up, bigger
i = min(self.canvas_image.winfo_width(), self.canvas_image.winfo_height()) >> 1
if i < self.imscale: return # 1 pixel is bigger than the visible area
self.imscale *= self.__delta
scale *= self.__delta
# Take appropriate image from the pyramid
k = self.imscale * self.__ratio # temporary coefficient
self.__curr_img = min((-1) * int(math.log(k, self.__reduction)), len(self.__pyramid) - 1)
self.__scale = k * math.pow(self.__reduction, max(0, self.__curr_img))
#
self.canvas_image.scale('all', x, y, scale, scale) # rescale all objects
# Redraw some figures before showing image on the screen
self.redraw_figures() # method for child classes
self.__show_image() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_mouse_wheel(self, event):\n delta = event.delta[1]\n if delta > 0: # Zoom in\n factor = 0.9\n elif delta < 0: # Zoom out\n factor = 1 / 0.9\n for _ in range(int(abs(delta))):\n self.zoom(factor, event.pos)",
"def set_zooming_wheel(self):\n ... | [
"0.8572107",
"0.8045329",
"0.80039686",
"0.7584533",
"0.7354916",
"0.7326063",
"0.7284798",
"0.72583723",
"0.72249573",
"0.7202118",
"0.72010577",
"0.719273",
"0.71766204",
"0.705019",
"0.70209515",
"0.6953993",
"0.6904493",
"0.6904493",
"0.68883395",
"0.68644863",
"0.6851454... | 0.7094766 | 13 |
Scrolling with the keyboard. Independent from the language of the keyboard, CapsLock, +, etc. | def __keystroke(self, event):
if event.state - self.__previous_state == 4: # means that the Control key is pressed
pass # do nothing if Control key is pressed
else:
self.__previous_state = event.state # remember the last keystroke state
# Up, Down, Left, Right keystrokes
if event.keycode in [68, 39, 102]: # scroll right, keys 'd' or 'Right'
self.__scroll_x('scroll', 1, 'unit', event=event)
elif event.keycode in [65, 37, 100]: # scroll left, keys 'a' or 'Left'
self.__scroll_x('scroll', -1, 'unit', event=event)
elif event.keycode in [87, 38, 104]: # scroll up, keys 'w' or 'Up'
self.__scroll_y('scroll', -1, 'unit', event=event)
elif event.keycode in [83, 40, 98]: # scroll down, keys 's' or 'Down'
self.__scroll_y('scroll', 1, 'unit', event=event) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_key(self, event):\n if event.key() == QtCore.Qt.Key_Up:\n self.model.channel_Scroll_Up('page')\n elif event.key() == QtCore.Qt.Key_PageUp:\n self.model.channel_Scroll_Up('page')\n elif event.key() == QtCore.Qt.Key_Down:\n self.model.channel_Scroll_Down('... | [
"0.6526636",
"0.6466856",
"0.61477494",
"0.60642177",
"0.60475975",
"0.6046673",
"0.5949585",
"0.59238994",
"0.59151155",
"0.5871811",
"0.5852923",
"0.57747257",
"0.57678497",
"0.5751938",
"0.5697251",
"0.5689333",
"0.5646427",
"0.5634948",
"0.5631655",
"0.56251585",
"0.56207... | 0.563615 | 17 |
Dummy function to redraw figures in the children classes | def redraw_figures(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def redraw(event):\n if np.size(plt.get_figlabels()):\n #Need to check if figure is closed or not and only then do the following\n #operations. Else, the following operations will create a new figure\n ax.clear()\n drawRectangle(ax)\n fig.canvas.draw()\n else:\n pas... | [
"0.69585615",
"0.68990314",
"0.6801519",
"0.67597973",
"0.6633574",
"0.64148325",
"0.6317707",
"0.6250047",
"0.6198424",
"0.61798114",
"0.61377364",
"0.6077331",
"0.60669315",
"0.60655534",
"0.60549927",
"0.6053331",
"0.60529304",
"0.6050833",
"0.604263",
"0.6037915",
"0.6037... | 0.78338176 | 1 |
Crop rectangle from the image and return it | def crop(self, bbox):
return self.__pyramid[0].crop(bbox) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def crop(self, image):\n\t\treturn image.copy()[self.ymin:self.ymax,self.xmin:self.xmax]",
"def crop_bounding_box(im, x, y, w, h):\n return im[y:y+h, x:x+w]",
"def doCrop(image, x, y, w, h):\n\tcrop_height = int((config.FACE_HEIGHT / float(config.FACE_WIDTH)) * w)\n\tmidy = y + h/2\n\ty1 = max(0, midy-c... | [
"0.7942811",
"0.79130894",
"0.7909533",
"0.7762608",
"0.77604425",
"0.768507",
"0.7674636",
"0.7651629",
"0.7633949",
"0.76148933",
"0.7608375",
"0.75981236",
"0.75507385",
"0.74944735",
"0.74684227",
"0.7441258",
"0.7434214",
"0.7434214",
"0.741522",
"0.741522",
"0.73019385"... | 0.7051762 | 31 |
Show image on the Canvas. Implements correct image zoom almost like in Google Maps | def __show_image(self):
box_image = self.canvas_image.coords(self.container) # get image area
box_canvas = (self.canvas_image.canvasx(0), # get visible area of the canvas
self.canvas_image.canvasy(0),
self.canvas_image.canvasx(self.canvas_image.winfo_width()),
self.canvas_image.canvasy(self.canvas_image.winfo_height()))
self.box_img_int = tuple(map(int, box_image)) # convert to integer or it will not work properly
# Get scroll region box
box_scroll = [min(self.box_img_int[0], box_canvas[0]), min(self.box_img_int[1], box_canvas[1]),
max(self.box_img_int[2], box_canvas[2]), max(self.box_img_int[3], box_canvas[3])]
# Horizontal part of the image is in the visible area
if box_scroll[0] == box_canvas[0] and box_scroll[2] == box_canvas[2]:
box_scroll[0] = self.box_img_int[0]
box_scroll[2] = self.box_img_int[2]
# Vertical part of the image is in the visible area
if box_scroll[1] == box_canvas[1] and box_scroll[3] == box_canvas[3]:
box_scroll[1] = self.box_img_int[1]
box_scroll[3] = self.box_img_int[3]
# Convert scroll region to tuple and to integer
self.canvas_image.configure(scrollregion=tuple(map(int, box_scroll))) # set scroll region
x1 = max(box_canvas[0] - box_image[0], 0) # get coordinates (x1,y1,x2,y2) of the image tile
y1 = max(box_canvas[1] - box_image[1], 0)
x2 = min(box_canvas[2], box_image[2]) - box_image[0]
y2 = min(box_canvas[3], box_image[3]) - box_image[1]
if int(x2 - x1) > 0 and int(y2 - y1) > 0: # show image if it in the visible area
image = self.__pyramid[max(0, self.__curr_img)].crop( # crop current img from pyramid
(int(x1 / self.__scale), int(y1 / self.__scale),
int(x2 / self.__scale), int(y2 / self.__scale)))
#
imagetk = ImageTk.PhotoImage(image.resize((int(x2 - x1), int(y2 - y1)), self.__filter))
self.imageid = self.canvas_image.create_image(max(box_canvas[0], self.box_img_int[0]),
max(box_canvas[1], self.box_img_int[1]),
anchor='nw', image=imagetk)
self.canvas_image.lower(self.imageid) # set image into background
self.canvas_image.imagetk = imagetk # keep an extra reference to prevent garbage-collection | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __draw_image(self):\n if self.image_name is not None:\n img = mpimg.imread(self.image_name)\n extent = (0.5, self.xmax+0.5, -0.5, self.ymax-0.5)\n self.ax.imshow(img, extent=extent, origin='lower',\n alpha=self.image_alpha)",
"def showImage(se... | [
"0.6751016",
"0.6738683",
"0.66990304",
"0.66660595",
"0.65815693",
"0.64749587",
"0.6393063",
"0.63925314",
"0.63365763",
"0.6326308",
"0.62597394",
"0.6224787",
"0.62157637",
"0.6211517",
"0.6196586",
"0.61757445",
"0.6165809",
"0.61133194",
"0.6083976",
"0.6078011",
"0.607... | 0.636585 | 8 |
Provides consistant naming to statistic descriptors | def stat_by_group(stat: str, group: str) -> str:
return f'{stat} by {group}' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def DescriptiveName(self):\r\n\t\treturn self._get_attribute('descriptiveName')",
"def name(self):\n return 'data_extraction_for_' + '_'.join(self.names).lower()",
"def DescriptiveName(self):\n return self._get_attribute('descriptiveName')",
"def name(self):\n return '{} {} {}'.format(se... | [
"0.667977",
"0.6442585",
"0.6435272",
"0.6424433",
"0.6370413",
"0.6370413",
"0.6370413",
"0.6370413",
"0.6370413",
"0.6370413",
"0.6352662",
"0.63082206",
"0.62384665",
"0.6236644",
"0.62084746",
"0.62084746",
"0.62084746",
"0.62084746",
"0.61990166",
"0.6192866",
"0.6161828... | 0.0 | -1 |
Repeat the retrieval of the metrics of a metrics context until at least one of the specified metric group names has data. Returns the MetricGroupValues object for the metric group that has data. | def wait_for_metrics(metric_context, metric_groups):
retries = 0
got_data = False
while not got_data:
mr_str = metric_context.get_metrics()
mr = zhmcclient.MetricsResponse(metric_context, mr_str)
for mg_values in mr.metric_group_values:
if mg_values.name in metric_groups:
got_data = True
if DEBUG_METRICS_RESPONSE:
print("Debug: MetricsResponse:")
print(mr_str)
break
if not got_data:
if retries > GET_METRICS_MAX_RETRIES:
return None
time.sleep(GET_METRICS_RETRY_TIME) # avoid hot spin loop
retries += 1
return mg_values | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def result_group(group_id, failures=False, wait=0, count=None, cached=Conf.CACHED):\n if cached:\n return result_group_cached(group_id, failures, wait, count)\n start = time.time()\n if count:\n while 1:\n if count_group(group_id) == count or wait and (time.time() - start) * 1000 ... | [
"0.55873525",
"0.55362415",
"0.54371434",
"0.51211834",
"0.50748",
"0.50633067",
"0.50494426",
"0.50236344",
"0.5014017",
"0.49106795",
"0.4888227",
"0.48790106",
"0.48743725",
"0.4858078",
"0.48575234",
"0.48438308",
"0.48435786",
"0.4804029",
"0.47981688",
"0.47835502",
"0.... | 0.72703934 | 0 |
Print a metric group for a list of resources in the desired output format. | def print_object_values(
object_values_list, metric_group_definition, resource_classes,
output_format, transposed):
if output_format in TABLE_FORMATS:
if output_format == 'table':
output_format = 'psql'
print_object_values_as_table(
object_values_list, metric_group_definition, resource_classes,
output_format, transposed)
elif output_format == 'json':
print_object_values_as_json(
object_values_list, metric_group_definition, resource_classes)
else:
raise InvalidOutputFormatError(output_format) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_metric_groups(cmd_ctx, client, metric_groups, resource_filter):\n\n if not isinstance(metric_groups, (list, tuple)):\n metric_groups = [metric_groups]\n\n properties = {\n 'anticipated-frequency-seconds': MIN_ANTICIPATED_FREQUENCY,\n 'metric-groups': metric_groups,\n }\n ... | [
"0.751741",
"0.63145715",
"0.5910002",
"0.59023666",
"0.59002185",
"0.5885719",
"0.5866339",
"0.5861136",
"0.58524024",
"0.5828134",
"0.57840455",
"0.5753333",
"0.57369936",
"0.57281035",
"0.57204556",
"0.56444025",
"0.56444025",
"0.557295",
"0.5570822",
"0.5553722",
"0.55439... | 0.48897582 | 93 |
Print a list of object values in a tabular output format. | def print_object_values_as_table(
object_values_list, metric_group_definition, resource_classes,
table_format, transposed):
if object_values_list:
metric_definitions = metric_group_definition.metric_definitions
sorted_metric_names = [md.name for md in
sorted(metric_definitions.values(),
key=lambda md: md.index)]
table = list()
headers = list()
for i, ov in enumerate(object_values_list):
row = list()
# Add resource names up to the CPC
res = ov.resource
while res:
if i == 0:
name_prop = res.manager.class_name + '-name'
headers.insert(0, name_prop)
row.insert(0, res.name)
res = res.manager.parent # CPC has None as parent
# Add the metric values
for name in sorted_metric_names:
if i == 0:
m_def = metric_definitions[name]
header_str = name
if m_def.unit:
header_str += u" [{}]".format(m_def.unit)
headers.append(header_str)
value = ov.metrics[name]
row.append(value)
table.append(row)
# Sort the table by the resource name columns
n_sort_cols = len(resource_classes)
table = sorted(table, key=lambda row: row[0:n_sort_cols])
if transposed:
table.insert(0, headers)
table = [list(col) for col in zip(*table)]
headers = []
if not table:
click.echo("No {} resources with metrics data for metric group {}.".
format(metric_group_definition.resource_class,
metric_group_definition.name))
else:
click.echo(tabulate(table, headers, tablefmt=table_format)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def table_print_object(self, obj_name, header, values_list):\n def terminal_size():\n import fcntl, termios, struct\n h, w, hp, wp = struct.unpack('HHHH',\n fcntl.ioctl(0, termios.TIOCGWINSZ,\n ... | [
"0.7586605",
"0.75449604",
"0.72875947",
"0.71443856",
"0.7099981",
"0.6986745",
"0.6952594",
"0.6931348",
"0.6849189",
"0.68258506",
"0.6799973",
"0.67922515",
"0.67875236",
"0.67875236",
"0.6745301",
"0.67065424",
"0.66971654",
"0.6696674",
"0.6684991",
"0.66635174",
"0.664... | 0.7025772 | 5 |
Print a list of object values in JSON output format. | def print_object_values_as_json(
object_values_list, metric_group_definition, resource_classes):
if object_values_list:
metric_definitions = metric_group_definition.metric_definitions
sorted_metric_names = [md.name for md in
sorted(metric_definitions.values(),
key=lambda md: md.index)]
json_obj = list()
for i, ov in enumerate(object_values_list):
resource_obj = OrderedDict()
# Add resource names up to the CPC
res = ov.resource
while res:
name_prop = res.manager.class_name + '-name'
resource_obj[name_prop] = res.name
res = res.manager.parent # CPC has None as parent
# Add the metric values
for name in sorted_metric_names:
m_def = metric_definitions[name]
value = ov.metrics[name]
resource_obj[name] = OrderedDict(value=value, unit=m_def.unit)
json_obj.append(resource_obj)
json_str = json.dumps(json_obj)
click.echo(json_str) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def jprint(obj):\n #\n text = json.dumps(obj, sort_keys=True, indent=4)\n print(text)",
"def print_json(obj):\n print(json.dumps(obj, sort_keys=True, indent=2, separators=(',', ': ')))",
"def print_json(obj):\n print(json.dumps(obj, indent=2))",
"def pprint(self,obj):\n return(json.dump... | [
"0.75082326",
"0.7412574",
"0.7307961",
"0.7016454",
"0.6846915",
"0.6846915",
"0.6722339",
"0.6652273",
"0.6616749",
"0.660433",
"0.65436924",
"0.65436924",
"0.65428746",
"0.65419024",
"0.64811295",
"0.64317226",
"0.64314044",
"0.64107096",
"0.6371735",
"0.6338294",
"0.62911... | 0.6860965 | 4 |
Retrieve and print metric groups. | def print_metric_groups(cmd_ctx, client, metric_groups, resource_filter):
if not isinstance(metric_groups, (list, tuple)):
metric_groups = [metric_groups]
properties = {
'anticipated-frequency-seconds': MIN_ANTICIPATED_FREQUENCY,
'metric-groups': metric_groups,
}
mc = client.metrics_contexts.create(properties)
mg_values = wait_for_metrics(mc, metric_groups)
filtered_object_values = list() # of MetricObjectValues
if not mg_values:
mg_name = metric_groups[0] # just pick any
res_class = zhmcclient._metrics._resource_class_from_group(mg_name)
mg_def = zhmcclient.MetricGroupDefinition(
name=mg_name, resource_class=res_class, metric_definitions=[])
else:
mg_def = mc.metric_group_definitions[mg_values.name]
filter_cpc = None
filter_partition = None
filter_lpar = None
filter_adapter = None
filter_nic = None
for r_class, r_name in resource_filter:
if r_class == 'cpc' and r_name:
filter_cpc = client.cpcs.find(name=r_name)
elif r_class == 'partition' and r_name:
assert filter_cpc
filter_partition = filter_cpc.partitions.find(name=r_name)
elif r_class == 'logical-partition' and r_name:
assert filter_cpc
filter_lpar = filter_cpc.lpars.find(name=r_name)
elif r_class == 'adapter' and r_name:
assert filter_cpc
filter_adapter = filter_cpc.adapters.find(name=r_name)
elif r_class == 'nic' and r_name:
assert filter_partition
filter_nic = filter_partition.nics.find(name=r_name)
resource_class = mg_def.resource_class
for ov in mg_values.object_values:
included = False
if resource_class == 'cpc':
if not filter_cpc:
included = True
elif ov.resource_uri == filter_cpc.uri:
included = True
elif resource_class == 'partition':
if not filter_cpc:
included = True
elif ov.resource.manager.cpc.uri == filter_cpc.uri:
if not filter_partition:
included = True
elif ov.resource_uri == filter_partition.uri:
included = True
elif resource_class == 'logical-partition':
if not filter_cpc:
included = True
elif ov.resource.manager.cpc.uri == filter_cpc.uri:
if not filter_lpar:
included = True
elif ov.resource_uri == filter_lpar.uri:
included = True
elif resource_class == 'adapter':
if not filter_cpc:
included = True
elif ov.resource.manager.cpc.uri == filter_cpc.uri:
if not filter_adapter:
included = True
elif ov.resource_uri == filter_adapter.uri:
included = True
elif resource_class == 'nic':
if not filter_cpc:
included = True
elif ov.resource.manager.partition.manager.cpc.uri == \
filter_cpc.uri:
if not filter_partition:
included = True
elif ov.resource.manager.partition.uri == \
filter_partition.uri:
if not filter_nic:
included = True
elif ov.resource_uri == filter_nic.uri:
included = True
else:
raise ValueError(
"Invalid resource class: {}".format(resource_class))
if included:
filtered_object_values.append(ov)
resource_classes = [f[0] for f in resource_filter]
cmd_ctx.spinner.stop()
print_object_values(filtered_object_values, mg_def, resource_classes,
cmd_ctx.output_format, cmd_ctx.transpose)
mc.delete() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_groups(self):\n\n for counter, label in enumerate(self.exp_labels_list):\n print('Key {}: {} \\n'.format(str(counter), label))",
"def metrics_group():",
"def groups(self):\n #return self.get('{}/groups'.format(ApiVersion.A1.value))\n return self.get('{}/groups'.format(ApiVersio... | [
"0.72279793",
"0.70860595",
"0.674111",
"0.66355723",
"0.64703333",
"0.6281449",
"0.62582123",
"0.62421024",
"0.62346387",
"0.6213498",
"0.61584836",
"0.61584836",
"0.6125847",
"0.6110818",
"0.61033535",
"0.60848945",
"0.6041761",
"0.6026084",
"0.599124",
"0.5982218",
"0.5980... | 0.73456234 | 0 |
Command group for reporting metrics. In addition to the commandspecific options shown in this help text, the general options (see 'zhmc help') can also be specified right after the 'zhmc' command name. | def metrics_group(): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cmd_help(args):",
"def help_opt(self):\n print(OPTIONS)",
"def command_help(self, *args, **kwargs):\n print(\"Commands available:\\n\")\n for name in dir(self):\n if not name.startswith(\"command_\"):\n continue\n name_clean = name[len(\"command_\")... | [
"0.589855",
"0.58264685",
"0.58036107",
"0.57175773",
"0.56941164",
"0.56822515",
"0.56753105",
"0.567416",
"0.56666476",
"0.5660978",
"0.5652861",
"0.56472456",
"0.56321657",
"0.56308264",
"0.55924976",
"0.55864775",
"0.5582454",
"0.5558841",
"0.55458933",
"0.55455136",
"0.5... | 0.0 | -1 |
Report usage overview metrics for CPCs. In addition to the commandspecific options shown in this help text, the general options (see 'zhmc help') can also be specified right after the 'zhmc' command name. | def metrics_cpc(cmd_ctx, cpc, **options):
cmd_ctx.execute_cmd(lambda: cmd_metrics_cpc(cmd_ctx, cpc, options)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metrics_channel(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_channel(cmd_ctx, cpc, options))",
"def do_hostinfo(self, args):\n host = opts = None\n if args:\n args = args.split()\n host = args.pop()\n\n if not host:\n print('Usag... | [
"0.6095599",
"0.59995025",
"0.5967684",
"0.58918285",
"0.5879187",
"0.58760214",
"0.58532023",
"0.58369166",
"0.58359265",
"0.5832341",
"0.58278644",
"0.58267254",
"0.582368",
"0.57165456",
"0.5707957",
"0.56925315",
"0.5681756",
"0.5681756",
"0.5672487",
"0.5671114",
"0.5671... | 0.60818744 | 1 |
Report usage metrics for active partitions of CPCs in DPM mode. In addition to the commandspecific options shown in this help text, the general options (see 'zhmc help') can also be specified right after the 'zhmc' command name. | def metrics_partition(cmd_ctx, cpc, partition, **options):
cmd_ctx.execute_cmd(
lambda: cmd_metrics_partition(cmd_ctx, cpc, partition, options)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metrics_cpc(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_cpc(cmd_ctx, cpc, options))",
"def metrics_proc(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_proc(cmd_ctx, cpc, options))",
"def metrics_channel(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(la... | [
"0.59879315",
"0.59793746",
"0.5929683",
"0.5513063",
"0.5448064",
"0.54282194",
"0.54047555",
"0.5269779",
"0.5266438",
"0.5178241",
"0.5156107",
"0.5139908",
"0.5117616",
"0.51164675",
"0.5078646",
"0.50614786",
"0.5020368",
"0.5004413",
"0.5001407",
"0.4972005",
"0.4944734... | 0.5978439 | 2 |
Report usage metrics for active LPARs of CPCs in classic mode. In addition to the commandspecific options shown in this help text, the general options (see 'zhmc help') can also be specified right after the 'zhmc' command name. | def metrics_lpar(cmd_ctx, cpc, lpar, **options):
cmd_ctx.execute_cmd(lambda: cmd_metrics_lpar(cmd_ctx, cpc, lpar, options)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_help(self):\r\n\t\ttext = \"\\tName: ml_scikit_OPTICS\"\r\n\t\ttext += \"\\n\\t\\tThis machine learning plugin uses scikit-learn's OPTICS algorithm.\\n\"\r\n\t\ttext += \"\\n\\t\\tOptional Parameters:\"\r\n\t\ttext += \"\\n\\t\\t\\tOPTICS_skip_normalization: Do NOT perform normalization (scaling) of dat... | [
"0.56626374",
"0.5658966",
"0.5620566",
"0.55107105",
"0.5491458",
"0.5334808",
"0.53031045",
"0.52814096",
"0.5276708",
"0.52741003",
"0.52695084",
"0.5258748",
"0.5241154",
"0.5233588",
"0.51980907",
"0.5171323",
"0.5130707",
"0.5097048",
"0.5062551",
"0.504902",
"0.5048986... | 0.54307204 | 5 |
Report usage metrics for active adapters of CPCs in DPM mode. In addition to the commandspecific options shown in this help text, the general options (see 'zhmc help') can also be specified right after the 'zhmc' command name. | def metrics_adapter(cmd_ctx, cpc, adapter, **options):
cmd_ctx.execute_cmd(
lambda: cmd_metrics_adapter(cmd_ctx, cpc, adapter, options)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metrics_channel(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_channel(cmd_ctx, cpc, options))",
"def metrics_proc(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_proc(cmd_ctx, cpc, options))",
"def metrics_networkport(cmd_ctx, cpc, adapter, **options):\n c... | [
"0.6317614",
"0.59251046",
"0.58999896",
"0.58430314",
"0.57148993",
"0.56526655",
"0.547031",
"0.53368515",
"0.5278745",
"0.52745694",
"0.52628577",
"0.51370335",
"0.5132186",
"0.51290196",
"0.512005",
"0.5110605",
"0.51074606",
"0.5080317",
"0.5074927",
"0.50636065",
"0.504... | 0.643978 | 0 |
Report usage metrics for all channels of CPCs in classic mode. In addition to the commandspecific options shown in this help text, the general options (see 'zhmc help') can also be specified right after the 'zhmc' command name. | def metrics_channel(cmd_ctx, cpc, **options):
cmd_ctx.execute_cmd(lambda: cmd_metrics_channel(cmd_ctx, cpc, options)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stats(caesar, input):\n commands = {}\n users = {}\n channels = {}\n\n ignore = set(['f_note', 'startup', 'message', 'noteuri'])\n for (name, user), count in caesar.stats.items(): \n if name in ignore: continue\n if not user: continue\n\n if not user.startswith('#'): \n try: us... | [
"0.59195566",
"0.5586245",
"0.5444794",
"0.54191935",
"0.53922415",
"0.5380868",
"0.53543603",
"0.52876854",
"0.52462244",
"0.51891714",
"0.5112807",
"0.51073605",
"0.50244516",
"0.49929634",
"0.49870348",
"0.4977524",
"0.49535966",
"0.4952735",
"0.4930333",
"0.4928074",
"0.4... | 0.6753259 | 0 |
Report environmental and power consumption metrics for CPCs. In addition to the commandspecific options shown in this help text, the general options (see 'zhmc help') can also be specified right after the 'zhmc' command name. | def metrics_env(cmd_ctx, cpc, **options):
cmd_ctx.execute_cmd(lambda: cmd_metrics_env(cmd_ctx, cpc, options)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metrics_cpc(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_cpc(cmd_ctx, cpc, options))",
"def metrics_channel(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_channel(cmd_ctx, cpc, options))",
"def metrics_proc(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd... | [
"0.629929",
"0.6273303",
"0.60736",
"0.5926187",
"0.5641638",
"0.55025315",
"0.5396064",
"0.53120846",
"0.52493966",
"0.5243705",
"0.523837",
"0.5222159",
"0.51953936",
"0.519269",
"0.5182496",
"0.5178438",
"0.51700777",
"0.5129585",
"0.51175195",
"0.51148605",
"0.51081395",
... | 0.649805 | 0 |
Report processor usage metrics for CPCs. In addition to the commandspecific options shown in this help text, the general options (see 'zhmc help') can also be specified right after the 'zhmc' command name. | def metrics_proc(cmd_ctx, cpc, **options):
cmd_ctx.execute_cmd(lambda: cmd_metrics_proc(cmd_ctx, cpc, options)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def metrics_cpc(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_cpc(cmd_ctx, cpc, options))",
"def metrics_channel(cmd_ctx, cpc, **options):\n cmd_ctx.execute_cmd(lambda: cmd_metrics_channel(cmd_ctx, cpc, options))",
"def metrics_crypto(cmd_ctx, cpc, **options):\n cmd_ctx.execute_c... | [
"0.65378803",
"0.627548",
"0.6020114",
"0.5702169",
"0.56018233",
"0.55116755",
"0.55000883",
"0.5320139",
"0.5315897",
"0.5299815",
"0.52958316",
"0.5274619",
"0.52392995",
"0.52203125",
"0.52038133",
"0.5199431",
"0.5187151",
"0.5178949",
"0.5170392",
"0.5164013",
"0.510556... | 0.66221523 | 0 |
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