None:\n # Get initial time\n start_time: float = time.time()\n\n # Extract the abstracts and save to csv in a multiprocess manner\n with Pool(processes=self.processes) as pool:\n pool.map(self.single_run, os.listdir(self.input_path))\n\n # Print the run time\n print(\"--- %s seconds ---\" % (time.time() - start_time))","title":""},{"docid":"7554b106b9828bdff1bc31043c9f6ec0","score":"0.6423451","text":"def job(self):","title":""},{"docid":"17942c34c233f3e96f9902c998ee5b1e","score":"0.6319304","text":"def _jobs(self):\n return []","title":""},{"docid":"b7da140356313340c4e559a24ee5754d","score":"0.63056195","text":"def run(self):\n\n threadlist = [None] * self.jobs\n\n for i in range(self.jobs):\n threadlist[i] = Thread(target=self.__run_tests, daemon=True)\n threadlist[i].start()\n\n for thread in threadlist:\n thread.join()\n\n self.result = sum(self.failed)\n self.tests[\"algorithm\"].append(self.algorithm.__name__)\n self.tests[\"dimension\"].append(self.dim)\n self.tests[\"failed\"].append(self.result)\n if self.algorithm.__name__ == \"jacobi\":\n self.tests[\"maxiter\"].append(None)\n else:\n self.tests[\"maxiter\"].append(self.maxiter)","title":""},{"docid":"2594624b2094460d4030a1c064752036","score":"0.628158","text":"def _run_steps(self):\n self.step_results = [\n *nmslib.batch_query_index(index=self.index,\n dataset=self.dataset.test,\n k=self.service_config.k)\n ]","title":""},{"docid":"4bb1c8026a4a44ff1d4727fd260bec29","score":"0.6236874","text":"def run(self):\n \n self.read_data()\n self.fast_assign_jobs()\n self.display_output()","title":""},{"docid":"7e9ae111bd9afc396c9e7ef6d4e6a91e","score":"0.6220301","text":"def declare_train_with_data(self):\n\n results = []\n # Separately for every split. Reduce total number of file parsing.\n for split_nr in range(1, 6):\n train_df, test_df = ExperimentRunner.read_declare_log(self.results_folder, split_nr)\n payload_train_df, payload_test_df = ExperimentRunner.read_payload_log(self.results_folder, split_nr)\n\n\n tr_result = self.train(train_df, test_df, payload_train_df, payload_test_df,\n split_nr=split_nr, exp_name=\"declare_data\")\n\n\n result = {\n \"result\": tr_result,\n \"split\": split_nr\n }\n\n results.append(result)\n\n return results","title":""},{"docid":"921e1036eeebb7fb0ca6e056440f69e2","score":"0.62187403","text":"def test_get_exec_batch_results(self):\n pass","title":""},{"docid":"5443d875c8c95d662ebc0976c865385f","score":"0.6202834","text":"def load_all_data(self):\n sttime = datetime.now()\n logger.debug('Starting loading all at: '+str(sttime))\n max_simul_jobs = len(self.simulations_list)\n jobs = []\n manager = Manager()\n params = manager.Queue()\n result_list = manager.list()\n for i in range(max_simul_jobs):\n p = Process(target=self.load_data, args=(params, result_list))\n jobs.append(p)\n p.start()\n iters = itertools.chain(self.simulations_list,(None,) * max_simul_jobs)\n for iter in iters:\n params.put(iter)\n for j in jobs:\n j.join()\n cube_list = iris.cube.CubeList(list(result_list))\n entime = datetime.now()\n logger.debug('Finished loading all at: '+str(entime))\n return cube_list","title":""},{"docid":"642b5d8138b4b8c59ad3e544e41ccc85","score":"0.6197176","text":"def batch(self):\n return","title":""},{"docid":"e0d1f981cab7778fba9253f7fd2763b5","score":"0.6163524","text":"def multi(self):\n\n # multiprocessing parameters\n nz = len(self.toprocess)\n t = np.linspace(0, nz, self.nproc+1, dtype='int')\n #print('multi', nz, t)\n result_queue = multiprocessing.Queue()\n\n procs = [multiprocessing.Process(name='Subprocess-'+str(j), target=self.process,\n args=(self.toprocess[t[j]:t[j+1]], self.params, j, result_queue))\n for j in range(self.nproc)]\n\n for p in procs:\n p.start()\n\n resultdict = {}\n # get the results in a dict\n\n for i in range(self.nproc):\n resultdict.update(result_queue.get())\n\n for p in multiprocessing.active_children():\n p.join()\n\n restot = pd.DataFrame()\n\n # gather the results\n for key, vals in resultdict.items():\n restot = pd.concat((restot, vals), sort=False)\n \"\"\"\n if restot is None:\n restot = vals\n else:\n restot = np.concatenate((restot, vals))\n \"\"\"\n return restot","title":""},{"docid":"78c0058b2e3c5cdf1d511270fd8e6d64","score":"0.6159943","text":"def execute(self):\n total_api_calls=0\n for measureCode in self.measure_codes:\n self._construct_series_id_list(measureCode)\n chunked_list = self._chunk_list(self._series_id_list,50)\n for list in chunked_list:\n if total_api_calls < 500:\n data = self._blsGetData(list)\n self._bls_parse_data(data)\n total_api_calls += 1\n else:\n #log api limit reached here\n break\n self.recurse_parse()\n self._consolidate_gaps()","title":""},{"docid":"344b26f505a37d3f913eae924a801d2d","score":"0.61341053","text":"def run(self):\n\n # I do this because, I tried to split them in memory, but I got\n # problems to count the rows, I think there might be some problems\n # with some column values, so to basically speed up the process I save\n # sub parts of the original dataset into several files to try to apply\n # KNN on one or several datasets, however I will aim to do it on 1 to begin with.\n # The idea is to perform KNN on each and every file to then stack then combine\n # them to get a solid model. I would like to implement a SGD\n # approach on the whole dataset.\n #\n # This also gives me a fairly good overview of the data and I am able to analyze\n # subparts of the data to get a better understanding of it\n if not os.path.isfile(TRAIN_CSV_PATH):\n df = self.extract(DEF_CSV_PATH)\n df = self.transform(df)\n dfs = split_dataframe(df, True)\n write_split_dataset(dfs)\n\n df = self.extract(TRAIN_CSV_PATH)\n\n return self.transform(df)","title":""},{"docid":"2f356a122c98f83da0b17e477ac83cf1","score":"0.61234033","text":"def process(self, toproc, params, j=0, output_q=None):\n\n metricTot = pd.DataFrame()\n\n for val in toproc:\n tab = self.func(val, params)\n if tab is not None:\n metricTot = pd.concat((metricTot, tab), sort=False)\n\n if output_q is not None:\n return output_q.put({j: metricTot})\n else:\n return metricTot","title":""},{"docid":"a987ea671e38eb8ffd1f0bec964edcd0","score":"0.60552084","text":"def generateJobs(self):\n jobs = []\n trace = np.loadtxt(self.output_filename, comments=';')\n for row in trace:\n job = self.generateJob(row)\n jobs.append(job)\n return jobs","title":""},{"docid":"f8fee0c7d98394288d388fcef0714246","score":"0.6031838","text":"def run_all_analyses(self):\n # List all analysis functions to be run here\n self.results.append(CountLipids(membrane=self.membrane).results)\n self.results.append(SurfaceArea(membrane=self.membrane).results)\n self.results.append(OrderParam(membrane=self.membrane).results)\n self.results.append(Thickness(membrane=self.membrane).results)\n # self.results.append(Domains(membrane=self.membrane).results)\n\n # Collect all the results into one dataframe\n self.results = pd.concat(self.results)","title":""},{"docid":"09807d7299b9e043cd21024f51e89565","score":"0.60176617","text":"def gatherResults(\n lvls,\n outputdir,\n splitdata,\n adata,\n dozip=True,\n verbose=2,\n nparetodiff=1,\n paretomethod=1,\n gatherpareto=True,\n gatherJstats=True,\n legacy=False,\n ncores=1,\n queue=\"q72h\",\n quiet=False,\n):\n joblist = []\n\n tag = splitTag(lvls)\n edir0 = splitdir(lvls)\n level = len(lvls)\n kmin = splitdata[\"levels\"][level + 1] + 1\n kmax = splitdata[\"kmax\"]\n prfile = os.path.join(outputdir, edir0, processingtimeFile(lvls))\n nfile = os.path.join(outputdir, edir0, numbersFile(lvls))\n nfilej = os.path.join(outputdir, edir0, numbersFile(lvls, tag=\"jstats\"))\n zipfile0 = \"splitted-%s.zip\" % tag\n zipfile = join(outputdir, edir0, zipfile0)\n lockfile = gatherLockfile(outputdir, lvls)\n plog = os.path.join(outputdir, edir0, \"gather-log-%s.txt\" % tag)\n jlog = os.path.join(outputdir, edir0, \"gather-log-jstats-%s.txt\" % tag)\n\n if os.path.exists(lockfile):\n print(\"gatherResults: lockfile %s exists\" % lockfile)\n return []\n pythonbinary = pythonBinary()\n if len(lvls) > 0:\n ii = lvls[0]\n else:\n ii = -1\n if len(lvls) > 1:\n jj = lvls[1]\n else:\n jj = -1\n if len(lvls) > 2:\n kk = lvls[2]\n else:\n kk = -1\n nsplitstr = \" \".join([\"--nsplit%d %d\" % (x, splitdata[x][\"n\"]) for x in range(4) if x in splitdata])\n cmd = f'echo \"Calculating pareto optimal arrays\"; cd {join(outputdir, edir0)};\\n'\n cmd += (\"touch %s\" % lockfile) + os.linesep\n\n lvloptions = \" --split0 %d --split1 %d --split2 %d %s --kmax %d --kmin %d\" % (ii, jj, kk, nsplitstr, kmax, kmin)\n if gatherpareto:\n cmd += (\n \"oaclustergather -c %s -b %s -f BINARY --paretomethod %d --nparetodiff %d --numbersfile %s %s --cleanrun 1 -o %s | tee %s;\"\n % (\n join(outputdir, \"oaconfig.txt\"),\n join(outputdir),\n paretomethod,\n nparetodiff,\n nfile,\n lvloptions,\n \"pareto\",\n plog,\n )\n )\n cmd += os.linesep\n if gatherJstats:\n cmd += os.linesep + \"# j-statistics \" + os.linesep\n cmd += \"oaclustergather -c {} -b {} --method 1 --numbersfile {} {} --cleanrun 1 -o {} | tee {};\".format(\n join(outputdir, \"oaconfig.txt\"),\n join(outputdir),\n nfilej,\n lvloptions,\n \"jstats\",\n jlog,\n )\n cmd += os.linesep\n if isVSCcluster():\n cmd += check_pipe_cmd(tag=\"oaclustergather\", errorcode=1)\n\n cmd += os.linesep + 'echo \"paretomethod %d\" > %s\\n' % (paretomethod, join(outputdir, edir0, \"gatheroptions.txt\"))\n cmd += gzipOA(join(outputdir, edir0))\n cmd += os.linesep\n cmd += \"cd %s; tail -n 3 sp%d-split-*/sp0*extend.txt > %s;\" % (\n join(outputdir, edir0),\n level,\n os.path.join(\"timelog-split-%s.txt\" % tag),\n )\n cmd += \"cd %s;\" % outputdir\n cmd += os.linesep\n\n # FIXME: hard-coded\n codedir = os.path.join(os.path.expanduser(\"~\"), \"projects/oapackage\")\n if not os.path.exists(codedir):\n raise Exception(\"update hard-coded location!\")\n gfile = os.path.join(codedir, \"pythondevelop\", \"gather_processing_times.py\")\n cmd += \"\\n%s %s -b %s -n %d %s\" % (\n pythonbinary,\n gfile,\n outputdir,\n splitdata[level][\"n\"],\n \" \".join([str(x) for x in lvls]),\n )\n cmd += os.linesep\n\n if dozip:\n zipstr = \"\".join([\"sp%d-split*\" % i for i in range(level + 1)]) + \".oa*\"\n # zipstr = '-'.join(['sp%d-split-%d' % (i, lvls[i]) for i in range(level) ]) + '-sp%d-split-*.oa*' % (level+1)\n\n cmdz = \"\\n# compress subdirs\" + os.linesep\n cmdz += (\"PFILE=%s\" % prfile) + os.linesep\n cmdz += \"if [ -f $PFILE ];\" + os.linesep\n cmdz += \"then\" + os.linesep\n if quiet:\n qstr = \" -q \"\n else:\n qstr = \"\"\n cmdz += (\n 'echo \"Compressing directory %s\"; cd %s; zip -r -m %s -T %s sp%d-split-* %s'\n % (os.path.join(outputdir, edir0), os.path.join(outputdir, edir0), qstr, zipfile, level, zipstr)\n + os.linesep\n )\n cmdz += os.linesep\n cmdz += \"else\" + os.linesep\n cmdz += (' echo \"Could not find file %s\"' % prfile) + os.linesep\n\n cmdz += \"fi\" + os.linesep\n\n cmd += cmdz\n else:\n cmd += os.linesep + \"# no compressing of subdirs (dozip=False)\" + os.linesep\n\n cmd += (\"rm -f %s\" % lockfile) + os.linesep\n # runcommand(cmd, dryrun=dryrun, idstr='job-compress-%d (depends on job-%d-x and job-%d-statistics)' % (ii,ii, ii), verbose=verbose, logfile=allcmdlogfile)\n\n for kk in range(splitdata[level][\"n\"]):\n lvlsn = lvls + [kk]\n edirX = splitdir(lvlsn)\n\n lfile = os.path.join(os.path.join(outputdir, edirX), \"lock-extend.txt\")\n\n if os.path.exists(lfile):\n print(f\"gatherResults {tag}: lock file for {kk} exists, skipping\")\n return joblist\n\n checkfilesstart = gatherFilesList(lvls, outputdir, splitdata, adata, verbose=0, legacy=legacy)\n if dozip:\n checkfiles = [nfile, prfile, zipfile]\n else:\n checkfiles = [nfile, prfile]\n j = job(\n cmd=cmd,\n jobtype=\"gather %s\" % tag,\n queue=queue,\n shorttag=\"G%s\" % tag,\n ncores=ncores,\n checkfiles=checkfiles,\n checkfilesstart=checkfilesstart,\n )\n\n if not j.complete() or True:\n joblist.append(j)\n return joblist","title":""},{"docid":"ab901bfcf04a0ce8de427d0175c6aee6","score":"0.6005265","text":"def compute_results(self):\n pass","title":""},{"docid":"0f12a81d22e0fc34d62b40b8ecec60d9","score":"0.59998673","text":"def load_data_wrapper():\r\n\r\n comm = MPI.COMM_WORLD\r\n comm.Barrier()\r\n wt = MPI.Wtime()\r\n if comm.rank == 0:\r\n # read data from gzip\r\n tr_d, va_d, te_d = load_data()\r\n\r\n # calculate the set length that each process has to calculate\r\n tr_set_length = np.ceil(len(tr_d[0])*1.0 / comm.size)\r\n va_set_length = np.ceil(len(va_d[0])*1.0 / comm.size)\r\n te_set_length = np.ceil(len(te_d[0])*1.0 / comm.size)\r\n\r\n # send only the corresponding data to each process\r\n for rank in range(1, comm.size):\r\n comm.send(tr_d[0][rank * tr_set_length:(rank + 1) * tr_set_length], dest=rank, tag=11)\r\n comm.send(tr_d[1][rank * tr_set_length:(rank + 1) * tr_set_length], dest=rank, tag=12)\r\n comm.send(va_d[0][rank * va_set_length:(rank + 1) * va_set_length], dest=rank, tag=13)\r\n comm.send(te_d[0][rank * te_set_length:(rank + 1) * te_set_length], dest=rank, tag=14)\r\n\r\n # process the corresponding part of the arrays\r\n training_inputs = tr_d[0][0:tr_set_length]\r\n training_results = tr_d[1][0:tr_set_length]\r\n validation_inputs = va_d[0][0:va_set_length]\r\n test_inputs = te_d[0][0:te_set_length]\r\n all_training_inputs = []\r\n all_training_results = []\r\n all_validation_inputs = []\r\n all_test_inputs = []\r\n for x in training_inputs:\r\n # if x.shape != (784,1):\r\n # print x.shape\r\n all_training_inputs.append(reshape_as_matrix(x))\r\n for y in training_results:\r\n all_training_results.append(vectorized_result(y))\r\n for x in validation_inputs:\r\n # if x.shape != (784,1):\r\n # print \"validation da\"\r\n # print x.shape\r\n all_validation_inputs.append(reshape_as_matrix(x))\r\n for x in test_inputs:\r\n # if x.shape != (784,1):\r\n # print \"test: da\"\r\n # print x.shape\r\n all_test_inputs.append(reshape_as_matrix(x))\r\n # all_training_inputs = [np.reshape(x, (784,1)) for x in tr_d[0][0:tr_set_length]]\r\n # all_training_results = [vectorized_result(y) for y in tr_d[1][0:tr_set_length]]\r\n # all_validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0][0:va_set_length]]\r\n # all_test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0][0:te_set_length]]\r\n\r\n total_messages = (comm.size - 1) * 4\r\n while total_messages > 0:\r\n status = MPI.Status()\r\n data = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)\r\n source = status.Get_source()\r\n tag = status.Get_tag()\r\n if tag == 11:\r\n all_training_inputs.extend(data)\r\n # TODO push back to training_inputs - might not work properly\r\n elif tag == 12:\r\n all_training_results.extend(data)\r\n # TODO push back to training_results - might not work properly\r\n elif tag == 13:\r\n all_validation_inputs.extend(data)\r\n # TODO push back to validation_inputs - might not work properly\r\n elif tag == 14:\r\n all_test_inputs.extend(data)\r\n # TODO push back to test_inputs - might not work properly\r\n total_messages -= 1\r\n\r\n training_data = zip(all_training_inputs, all_training_results)\r\n validation_data = zip(all_validation_inputs, va_d[1])\r\n test_data = zip(all_test_inputs, te_d[1])\r\n\r\n else:\r\n # receive arrays from master and process the corresponding part\r\n training_inputs=[]\r\n training_results=[]\r\n validation_inputs=[]\r\n test_inputs=[]\r\n\r\n training_data = None\r\n validation_data = None\r\n test_data = None\r\n\r\n for i in range(0,4):\r\n status = MPI.Status()\r\n data = comm.recv(source=0, tag=MPI.ANY_TAG, status=status)\r\n tag = status.Get_tag()\r\n if tag == 11:\r\n for x in data:\r\n training_inputs.append(np.reshape(x, (784,1)))\r\n # training_inputs = [np.reshape(x, (784,1)) for x in data]\r\n elif tag == 12:\r\n for x in data:\r\n training_results.append(vectorized_result(x))\r\n # training_results = [vectorized_result(y) for y in data]\r\n elif tag == 13:\r\n for x in data:\r\n validation_inputs.append(np.reshape(x, (784,1)))\r\n # validation_inputs = [np.reshape(x, (784, 1)) for x in data]\r\n elif tag == 14:\r\n for x in data:\r\n test_inputs.append(np.reshape(x, (784,1)))\r\n # test_inputs = [np.reshape(x, (784, 1)) for x in data]\r\n\r\n comm.send(training_inputs, dest=0, tag=11)\r\n comm.send(training_results, dest=0, tag=12)\r\n comm.send(validation_inputs, dest=0, tag=13)\r\n comm.send(test_inputs, dest=0, tag=14)\r\n\r\n if comm.rank == 0:\r\n comm.bcast(training_data, root=0)\r\n comm.bcast(validation_data, root=0)\r\n comm.bcast(test_data, root=0)\r\n print \"MNIST_data_transformation\", MPI.Wtime() - wt\r\n else:\r\n training_data = comm.bcast(training_data, root=0)\r\n validation_data = comm.bcast(validation_data, root=0)\r\n test_data = comm.bcast(test_data, root=0)\r\n return (training_data, validation_data, test_data)","title":""},{"docid":"4b99f5ad25737d6f79056a8dbef8fb21","score":"0.59788","text":"def func_run_many_jobs(self):\n\n try:\n pool = multiprocessing.Pool()\n lrslt_success = pool.map_async(func_run_job,\n self.func_get_job_commands())\n lf_success = [f_success for f_success in lrslt_success.get()]\n except Exception as e:\n f_error_occured = True\n self.logr_job.info(\"\".join([\"PipelineRunner.func_run_many_jobs:: \",\n \"Serious error occured for job\"]))\n self.logr_job.info(\"PipelineRunner.func_run_many_jobs:: \\n\" + str(e))","title":""},{"docid":"999bc240f80ae009232cda40cb445d67","score":"0.5952506","text":"def run_all(self):\n\n run_iter_args, total_iterations = self._make_model_args_mp()\n # register the process pool and init a queue\n # store results in ordered dictionary\n results = {}\n\n if self.processes > 1:\n with tqdm(total_iterations, disable=not self.display_progress) as pbar:\n for params, model in self.pool.imap_unordered(\n self._run_wrappermp, run_iter_args\n ):\n results[params] = model\n pbar.update()\n\n self._result_prep_mp(results)\n # For debugging model due to difficulty of getting errors during multiprocessing\n else:\n for run in run_iter_args:\n params, model_data = self._run_wrappermp(run)\n results[params] = model_data\n\n self._result_prep_mp(results)\n\n # Close multi-processing\n self.pool.close()\n\n return (\n getattr(self, \"model_vars\", None),\n getattr(self, \"agent_vars\", None),\n getattr(self, \"datacollector_model_reporters\", None),\n getattr(self, \"datacollector_agent_reporters\", None),\n )","title":""},{"docid":"3962bf86133ae0952b9f624b6e4223de","score":"0.5949363","text":"def process_jobs_(jobs):\n\n pass","title":""},{"docid":"4726c70303ee4e3bb0c76d1cd7cd21cf","score":"0.5924849","text":"def run(self):\n self._setup()\n for i in xrange(self.job_number - 1, len(self.input_file_pathes), self.job_count):\n self._local_job(i)","title":""},{"docid":"f49c7d1d697c884a2168b5c83c987321","score":"0.5920149","text":"def execute(self):\r\n self.parallelize(self.core_calc_task,\r\n self.task_arg_gen(),\r\n self.task_completed)","title":""},{"docid":"148f3b476a7e82c79a9c86e10c804abb","score":"0.5917715","text":"def work(dataset,op_parse,task_list):\n \n logfile = '%s.log' % dataset.replace('/','_')\n output = file(logfile,'w')\n \n previous = {}\n for t in task_list:\n\n t.options = copy.deepcopy(op_parse.options)\n t.dataset, t.user = splitUser(dataset,op_parse.user)\n t.dataset, t.options.tier = splitTier(t.dataset, t.options.tier)\n t.options.tier = addRunRange(t.options.tier, t.options.min_run, t.options.max_run)\n\n log(output,'%s: [%s] %s:' % (dataset,time.asctime(),t.getname()))\n if t.__doc__:\n log(output,'%s: %s' % (dataset,t.__doc__) )\n try:\n previous[t.getname()] = t.run(previous)\n log(output,'%s: \\t%s' % (dataset,previous[t.getname()]),tostdout=False)\n except Exception, e:\n\n import traceback, StringIO\n sb = StringIO.StringIO()\n traceback.print_exc(file=sb)\n tb = sb.getvalue()\n sb.close()\n \n log(output,'%s: [%s] %s FAILED:' % (dataset,time.asctime(),t.getname()))\n log(output,\"%s: Error was '%s'\" % (dataset,str(e)))\n log(output,\"%s: Traceback was '%s'\" % (dataset,tb))\n\n #TODO: Perhaps some cleaning?\n break\n \n output.close()\n \n #dump the output in a python friendly format\n import pickle\n dumpfile = '%s.pkl' % dataset.replace('/','_')\n output = file(dumpfile,'wb')\n pickle.dump(previous, output)\n output.close()\n \n return logfile","title":""},{"docid":"50b805c6c1cb9f1bce62083f032bf46a","score":"0.5914648","text":"def run(self):\n # Get some initialization info required for the pipeline to run\n initializer_obj = initialize.Initializer(self.instr, self.cfg)\n initializer_obj.initialize_dates()\n initializer_obj.get_date_ranges()\n initializer_obj.get_processed_ranges()\n\n if self.initialize:\n initializer_obj.initialize_HDF5(chunks=self.chunks)\n\n # Initialize the downloader\n downloader = download.Downloader(instr=self.instr,\n instr_cfg=self.instr_cfg)\n\n # Divide up the dates into chunks\n date_chunks = np.array_split(initializer_obj.dates, self.chunks)\n for i, chunk in enumerate(date_chunks):\n for (start, stop) in chunk:\n failed = False\n results = None\n if '{} {}'.format(start.iso, stop.iso) in \\\n initializer_obj.previously_analyzed:\n LOG.info('Already analyzed {} to {}\\n'.format(start.iso,\n stop.iso))\n continue\n\n # Start the analysis\n LOG.info('Analyzing data from {} to {}'.format(start.iso,\n stop.iso))\n if self.download:\n download_time = self.run_downloader(date_range=(start, stop),\n downloader=downloader)\n self.processing_times['download'] = download_time\n\n self.flist = glob.glob(self.search_pattern)\n\n if self.process:\n process_time = self.run_processing(start, stop)\n self.processing_times['cr_rejection'] = process_time\n\n # Analyze the images and extract the results iff files\n # were successfully processed through CR rejection AND\n # the analyze flag is True.\n if self.analyze and self.flist:\n analysis_time, results = self.run_labeling_all(\n chunk_num=i + 1\n )\n self.processing_times['analysis'] = analysis_time\n else:\n failed = True\n\n self.processing_times['total'] = sum(\n list(self.processing_times.values())\n )\n\n # Clean up downloaded files and write out the range we just processed\n self._pipeline_cleanup(start, stop, failed)\n\n # Send the final email iff there were results computed\n if results:\n self.send_email(start, stop, results)","title":""},{"docid":"a5e5f0f6c37cfd109b6f62069e824a5d","score":"0.5910644","text":"def _process_dataset(self):\n raise NotImplementedError","title":""},{"docid":"a5e5f0f6c37cfd109b6f62069e824a5d","score":"0.5910644","text":"def _process_dataset(self):\n raise NotImplementedError","title":""},{"docid":"98f53f279d11b165a26acca3bebda535","score":"0.59061104","text":"def main(self):\n optDataList = []\n \n windowSize = 3\n\n processedData = readFile(self.DATASET, f\"{FILE_PATH_CORE}{self.DATASET}{self.INPUT_FILE}\")\n\n newData = self.processControlMeasures(processedData, windowSize)\n\n #Log the current dataset before further processing\n self.writeFile(newData, \"/2. Rt/after_control_measures.csv\")\n\n regionalIndexList = self.getRegionalIndexs(newData)\n \n splitList = []\n \n for currStartIndex, currEndIndex in regionalIndexList:\n splitList.append(newData[currStartIndex:currEndIndex])\n\n numProcesses = 10\n\n with multiprocessing.Pool(numProcesses) as p:\n result = p.map(self.calculateRt, splitList)\n\n for currRegionalList in result:\n for row in currRegionalList:\n optDataList.append(row)\n #optDataList = self.calculateRt(newData)\n\n optDataList = self.filterDate(optDataList)\n \n \"\"\"optRegionalIndex = self.getRegionalIndexs(optDataList)\n results = []\n for currIndex in range(len(optRegionalIndex)):\n optStart, optEnd = optRegionalIndex[currIndex]\n orgStart, orgEnd = regionalIndexList[currIndex]\n results.insert(0, [(33 - currIndex), f\"{orgEnd - orgStart}\", f\"{optEnd - optStart}\"])\n \n with open(FILE_PATH_CORE + self.DATASET + \"/Rt/test.csv\", \"w\") as optFile:\n labels = [\"Region No\", \"Original Size\", \"New Size\"]\n\n myWriter = csv.writer(optFile, labels)\n myWriter.writerow(labels)\n for row in results:\n myWriter.writerow(row)\"\"\"\n self.writeFile(optDataList, self.OUTPUT_FILE, True)","title":""},{"docid":"845960149ff3620e6b725403a96c9794","score":"0.58925414","text":"def run_methods(self):\n try:\n # dictionaries of population time series\n self.batch_maxsus()\n except Exception as e:\n print(str(e))","title":""},{"docid":"de983d98d636968ee96d9cb329b4d228","score":"0.5885412","text":"def execute_worker(self):","title":""},{"docid":"c626bca72872887b3dee0b38c4193a9b","score":"0.5853928","text":"def algorithm(self, parameters):\n try:\n logging.info(\"Getting Agent info ...\")\n agentInfo = self.collectAgentInfo()\n \n #jobs per request info\n logging.info(\"Getting Job Couch Data ...\")\n jobInfoFromCouch = self.localCouchDB.getJobSummaryByWorkflowAndSite()\n\n #fwjr per request info\n logging.info(\"Getting FWJRJob Couch Data ...\")\n\n #fwjrInfoFromCouch = self.localCouchDB.getEventSummaryByWorkflow()\n fwjrInfoFromCouch = self.localCouchDB.getJobPerformanceByTaskAndSite()\n \n logging.info(\"Getting Batch Job Data ...\")\n batchJobInfo = self.wmagentDB.getBatchJobInfo()\n \n logging.info(\"Getting Finished Task Data ...\")\n finishedTasks = self.wmagentDB.getFinishedSubscriptionByTask()\n\n # get the data from local workqueue:\n # request name, input dataset, inWMBS, inQueue\n logging.info(\"Getting Local Queue Data ...\")\n localQInfo = self.localQueue.getAnalyticsData()\n\n # combine all the data from 3 sources\n logging.info(\"\"\"Combining data from\n Job Couch(%s),\n FWJR(%s), \n Batch Job(%s),\n Finished Tasks(%s),\n Local Queue(%s) ...\"\"\" \n % (len(jobInfoFromCouch), len(fwjrInfoFromCouch), len(batchJobInfo), len(finishedTasks), len(localQInfo)))\n\n tempCombinedData = combineAnalyticsData(jobInfoFromCouch, batchJobInfo)\n combinedRequests = combineAnalyticsData(tempCombinedData, localQInfo)\n\n #set the uploadTime - should be the same for all docs\n uploadTime = int(time.time())\n \n self.uploadAgentInfoToCentralWMStats(agentInfo, uploadTime)\n \n logging.info(\"%s requests Data combined,\\n uploading request data...\" % len(combinedRequests))\n requestDocs = convertToRequestCouchDoc(combinedRequests, fwjrInfoFromCouch, finishedTasks,\n self.agentInfo, uploadTime, self.summaryLevel)\n\n\n if self.plugin != None:\n self.plugin(requestDocs, self.localSummaryCouchDB, self.centralWMStatsCouchDB)\n\n self.localSummaryCouchDB.uploadData(requestDocs)\n logging.info(\"Request data upload success\\n %s request, \\nsleep for next cycle\" % len(requestDocs))\n\n except Exception, ex:\n logging.error(\"Error occurred, will retry later:\")\n logging.error(str(ex))\n logging.error(\"Trace back: \\n%s\" % traceback.format_exc())","title":""},{"docid":"84bdaff2a639a8ff01ea1907b0fcd223","score":"0.58449954","text":"def _run_raw(self, *args, **kwargs):\n \n for job in self.jobs:\n job._run_raw(*args, **kwargs)","title":""},{"docid":"ba57fa230f1ed044185601e86f1bcdd0","score":"0.58430576","text":"def _fetch_jobs_for_work(self):\n raise NotImplementedError","title":""},{"docid":"569c038dd0c07ba127e40a0244594530","score":"0.5827205","text":"def algorithm(self, *args, **kwargs):\n myThread = threading.currentThread()\n \n filesPerJob = int(kwargs.get(\"files_per_job\", 10))\n jobsPerGroup = int(kwargs.get(\"jobs_per_group\", 0))\n filesInJob = 0\n totalJobs = 0\n listOfFiles = []\n\n #Get a dictionary of sites, files\n locationDict = self.sortByLocation()\n\n for location in locationDict.keys():\n #Now we have all the files in a certain location\n fileList = locationDict[location]\n filesInJob = 0\n jobsInGroup = 0\n self.newGroup()\n if len(fileList) == 0:\n #No files for this location\n #This isn't supposed to happen, but better safe then sorry\n continue\n for file in fileList:\n if filesInJob == 0 or filesInJob == filesPerJob:\n if jobsPerGroup:\n if jobsInGroup > jobsPerGroup:\n self.newGroup()\n jobsInGroup = 0\n\n self.newJob(name = self.getJobName(length=totalJobs))\n \n filesInJob = 0\n totalJobs += 1\n jobsInGroup += 1\n \n filesInJob += 1\n self.currentJob.addFile(file)\n \n listOfFiles.append(file)\n\n return","title":""},{"docid":"175af69e5ff75ebbec513da1800cead7","score":"0.58132476","text":"def execute_grid_job(self):\n # preprocess\n if self.m_args.sub_task == 'preprocess':\n self.m_tool_chain.preprocess_data(\n self.m_preprocessor,\n indices = self.indices(self.m_file_selector.original_data_list(), self.m_grid.number_of_preprocessings_per_job),\n force = self.m_args.force)\n\n elif self.m_args.sub_task == 'train-extractor':\n self.m_tool_chain.train_extractor(\n self.m_extractor,\n self.m_preprocessor,\n force = self.m_args.force)\n\n # extract features\n elif self.m_args.sub_task == 'extract':\n self.m_tool_chain.extract_features(\n self.m_extractor,\n self.m_preprocessor,\n indices = self.indices(self.m_file_selector.preprocessed_data_list(), self.m_grid.number_of_extracted_features_per_job),\n force = self.m_args.force)\n\n # train the feature projector\n elif self.m_args.sub_task == 'train-projector':\n self.m_tool_chain.train_projector(\n self.m_tool,\n self.m_extractor,\n force = self.m_args.force)\n\n # project the features\n elif self.m_args.sub_task == 'project':\n self.m_tool_chain.project_features(\n self.m_tool,\n self.m_extractor,\n indices = self.indices(self.m_file_selector.preprocessed_data_list(), self.m_grid.number_of_projected_features_per_job),\n force = self.m_args.force)\n\n # train model enroller\n elif self.m_args.sub_task == 'train-enroller':\n self.m_tool_chain.train_enroller(\n self.m_tool,\n self.m_extractor,\n force = self.m_args.force)\n\n # enroll models\n elif self.m_args.sub_task == 'enroll':\n self.m_tool_chain.enroll_models(\n self.m_tool,\n self.m_extractor,\n groups = (self.m_args.group,),\n compute_zt_norm = False,\n indices = self.indices(self.m_file_selector.model_ids(self.m_args.group), self.m_grid.number_of_enrolled_models_per_job),\n force = self.m_args.force)\n\n # compute scores\n elif self.m_args.sub_task == 'compute-scores':\n self.m_tool_chain.compute_scores(\n self.m_tool,\n groups = (self.m_args.group,),\n compute_zt_norm = False,\n indices = self.indices(self.m_file_selector.model_ids(self.m_args.group), self.m_grid.number_of_models_per_scoring_job),\n preload_probes = self.m_args.preload_probes,\n force = self.m_args.force)\n\n # concatenate\n elif self.m_args.sub_task == 'concatenate':\n self.m_tool_chain.concatenate(compute_zt_norm = False)\n\n # average\n elif self.m_args.sub_task == 'average-results':\n self.average_results()\n\n # Test if the keyword was processed\n else:\n raise ValueError(\"The given subtask '%s' could not be processed. THIS IS A BUG. Please report this to the authors.\" % self.m_args.sub_task)","title":""},{"docid":"c851faf32178fe261593717eece516e4","score":"0.58044916","text":"def collect_batch(self):\n pass","title":""},{"docid":"ded38aa20607659f6923d6c93bf36d3e","score":"0.5800215","text":"def run(self):\n pool = mpr.Pool(mpr.cpu_count()-1)\n handles = []\n for i, fn in zip(self.identifiers, self.fn_inputs):\n handle = pool.apply_async(parse_blast,args=(fn,))\n handles.append(handle)\n pool.close()\n pool.join()\n results = []\n for i, r in zip(self.identifiers,handles):\n try:\n blast_result = r.get()\n results.append((i,blast_result))\n except:\n log.error(\"Problem with sequence %s\",i)\n self.failed_processes.add(i)\n self.fn_inputs = []\n return results","title":""},{"docid":"54c82e92657e618437b4f2968ab34e00","score":"0.5800151","text":"def run_all(self, **run_args):\n\n print(\"\\n========== ModelManager: Running {} active space locations ==========\".format(len(self.coords_ls)))\n print(\"\\tInitiating {} process(es)...\\n\\n\".format(self.n_jobs))\n\n kwargs = {'timeout': self.timeout} # timeout limit\n\n if run_args is not None and len(run_args) > 0:\n self.run_args = run_args\n\n if self.n_jobs == 1:\n gdf_list = []\n\n for input_args in self.coords_ls:\n # call the helper run function with the timeout wrapper\n gdf_list.append(timeout_wrapper(self._run, input_args, **kwargs))\n\n else: # start multiprocessing\n # create a pool to multiprocess this task\n with NestablePool(processes=self.n_jobs, maxtasksperchild=1) as p:\n # call the timeout_wrapper function to run the CreateActiveSpace model with each coordinate\n res_ls = [p.apply_async(timeout_wrapper, args=(self._run, coord, ), kwds=kwargs)\n for coord in self.coords_ls]\n\n # wait for results to come in asynchronously\n gdf_list = [res.get() for res in res_ls]\n\n p.close() # not sure what these do, but I'm hopping on the bandwagon\n p.join()\n\n return pd.concat(gdf_list) # returns the list as one GeoDataFrame","title":""},{"docid":"d13f724c99372fe85c0bf253ada4bda3","score":"0.5791606","text":"def declare_train(self):\n\n results = []\n\n # Separately for every split. Reduce total number of file parsing.\n for split_nr in range(1, 6):\n train_df, test_df = ExperimentRunner.read_declare_log(self.results_folder, split_nr)\n tr_result = self.train(train_df, test_df, split_nr=split_nr, exp_name=\"declare\")\n\n result = {\n \"result\": tr_result,\n \"split\": split_nr\n }\n\n results.append(result)\n\n return results","title":""},{"docid":"7e954ef11df63742be22f0c2d3aa0cba","score":"0.5789156","text":"def runall(self):\n multi_process_tasks(self.test_f1, self.test_accuracy, self.test_recall, self.test_precision)\n log.info(\"Parallel runs of the classification_tests are completed.\")","title":""},{"docid":"dc716b3396e40e6d3009e8d8cc86ab4e","score":"0.5786649","text":"def __call__(self):\r\n self.create_dataframes()\r\n self.get_feature_by_shap()\r\n self.one_factor_calculate_score()\r\n self.one_factor_selection()","title":""},{"docid":"a8962af98b97faeb5cd09547a466343d","score":"0.57859856","text":"def execute(self, webpages):\n results = []\n\n self.__start_threading(webpages)\n self.__populate_result_array(results, webpages)\n\n return results","title":""},{"docid":"173b4a2bfc521185e724afcda3d33da1","score":"0.5784341","text":"def run(self, *args, **kw):\n super(Fork, self).run(*args, **kw)\n\n for i in self.output_ports:\n self._output_data[i] = self.get_input_data()","title":""},{"docid":"9f2645b1a4add1bacea1cea3a408d13c","score":"0.57748777","text":"def get_jobs(self):\n run_ids = self.run_ids\n input_json = construct_copy_outputs_input(run_ids)\n\n mapping_file_content, pairing_file_content, data_clinical_content = generate_sample_pairing_and_mapping_files(\n run_ids\n )\n\n input_json[\"meta\"] = [\n {\"class\": \"File\", \"basename\": \"sample_mapping.txt\", \"contents\": mapping_file_content},\n {\"class\": \"File\", \"basename\": \"sample_pairing.txt\", \"contents\": pairing_file_content},\n {\"class\": \"File\", \"basename\": \"sample_data_clinical.txt\", \"contents\": data_clinical_content},\n ]\n\n number_of_runs = len(run_ids)\n name = \"ARGOS COPY OUTPUTS %s runs [%s,..] \" % (number_of_runs, run_ids[0])\n\n app = self.get_pipeline_id()\n pipeline = Pipeline.objects.get(id=app)\n pipeline_version = pipeline.version\n project_prefix = input_json[\"project_prefix\"]\n\n tags = {\"run_ids\": run_ids}\n\n copy_outputs_job_data = {\"app\": app, \"inputs\": input_json, \"name\": name, \"tags\": tags}\n\n \"\"\"\n If project_prefix and job_group_id, write output to a directory\n that uses both\n \"\"\"\n output_directory = None\n if project_prefix:\n tags[\"project_prefix\"] = project_prefix\n if self.job_group_id:\n jg = JobGroup.objects.get(id=self.job_group_id)\n jg_created_date = jg.created_date.strftime(\"%Y%m%d_%H_%M_%f\")\n output_directory = os.path.join(\n pipeline.output_directory, \"argos\", project_prefix, pipeline_version, jg_created_date\n )\n copy_outputs_job_data[\"output_directory\"] = output_directory\n copy_outputs_job = [RunCreator(**copy_outputs_job_data)]\n\n return copy_outputs_job","title":""},{"docid":"46f2426abcf95fcec532b1d76f811589","score":"0.5770437","text":"def do_processing(self):\n\n # Read the full dataset following the 'next' url until it is empty\n print(\"Reading data from \" + self.data_url)\n df = self.read_data_frame(self.data_url)\n output_df = pd.DataFrame(df[Converter.RESULTS_FIELD_NAME].tolist())\n while not pd.isna(df[Converter.NEXT_FIELD_NAME].iloc[0]):\n next_url = df[Converter.NEXT_FIELD_NAME].iloc[0]\n df = self.read_data_frame(next_url)\n output_df = output_df.append(pd.DataFrame(df[Converter.RESULTS_FIELD_NAME].tolist()))\n # Add computed columns\n output_df[Converter.DURATION_MINUTES_FIELD_NAME] = output_df[Converter.DURATION_TEXT_FIELD_NAME].apply(Converter.duration_text_2_minutes)\n output_df[Converter.DISTANCE_METERS_FIELD_NAME] = output_df[Converter.DISTANCE_TEXT_FIELD_NAME].apply(Converter.distance_text_2_meters)\n # Save result as CSV\n output_df.to_csv(self.output_filename, index=False)\n print(str(len(output_df.index)) + \" row(s) saved to \" + self.output_filename)","title":""},{"docid":"6bcc91505197feb4220c3f7738023179","score":"0.57679707","text":"def _do_train_job(self, data_iterable, job_parameters, thread_private_mem):\n raise NotImplementedError()","title":""},{"docid":"af4a83badafbc0c40e032bf464a6dc9b","score":"0.57520294","text":"def _data_process(self):\n self._data_process_name()\n self._data_process_context()\n self._data_process_content()\n self._data_process_parent()\n self._data_process_timedata()\n self._data_process_various()","title":""},{"docid":"64ce903e98cf918b3c788ef3d2198923","score":"0.57510227","text":"def submitJobs(self, jobs):\n err_jobs = set([])\n for job_name, job_type, job_args in jobs:\n sleep(1)\n job_handler = JOB_TYPE_LOOKUP.get(job_type, None)\n \n if job_handler is None:\n sys.stderr.write(\"Unknown job type %s for job %s\\n\" % \\\n (job_type, job_name))\n err_jobs.add(job_name)\n continue\n \n job = job_handler(job_name, job_args)\n cmd = job()\n sys.stdout.write('Job command: %s' % cmd)\n \n #### decompose job submission\n # submit job - if loading into DB use singleq\n if job_type=='LoadAnalysisOTUTableHandler':\n submit_queue = 'singleq'\n pvmem='48gb'\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\n elif job_type=='LoadSplitLibSeqsHandler':\n submit_queue = 'singleq'\n pvmem='48gb'\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\n elif job_type=='ProcessSFFHandler':\n submit_queue = 'no_bad_touching'\n pvmem='48gb'\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\n elif job_type=='betaDiversityThroughPlots':\n submit_queue = 'no_bad_touching'\n pvmem='64gb'\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\n elif job_type=='makeOTUHeatmap':\n submit_queue = 'no_bad_touching'\n pvmem='4gb'\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\n elif job_type=='alphaRarefaction':\n submit_queue = 'no_bad_touching'\n pvmem='4gb'\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\n elif job_type=='summarizeTaxa':\n submit_queue = 'no_bad_touching'\n pvmem='16gb'\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\n elif job_type=='generateMapOTUTableSubmitJobs':\n submit_queue = 'no_bad_touching'\n pvmem='64gb'\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\n else:\n submit_queue = 'no_bad_touching'\n pvmem='8gb'\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\n \n res_lines = res.splitlines()\n\n if len(res_lines) == 0 or len(res_lines) > 1:\n # job did not start\n err_jobs.add(job_name)\n sys.stderr.write('Unable to start %s\\n\\tError:%s\\n' % \\\n (cmd, res))\n else:\n # job started\n pbs_id = res_lines[0].split('.',1)[0]\n job.setTorqueJobId(pbs_id, 'NEW')\n self.Jobs[pbs_id] = job\n \n sys.stdout.write('Started PBS job id %s, ORA job id %d\\n' %\\\n (pbs_id, job_name))\n\n return list(err_jobs)","title":""},{"docid":"f0f5cd6edc9b0c5dc4af758be090b802","score":"0.575074","text":"def process_data(self, list_of_directories):\n\n # get the file names\n file_names = get_data_files(list_of_directories)\n\n values = [delayed(self.process_file)(f) for f in file_names]\n\n # seizure_df = pd.DataFrame()\n # failures = []\n results = compute(*values, get=dask.multiprocessing.get)\n results = [res for res in results if res is not None]\n return pd.concat(results)","title":""},{"docid":"70f0ac28a6475d090c6d100a383a74df","score":"0.57377684","text":"def process_multiple_data(files):\n # set up the compute graph\n graph = delayed([delayed(process_data)(file_) for file_ in files])\n # compute the graph\n results = compute(graph)\n\n return pd.concat([results[0][i] for i in range(len(files))])","title":""},{"docid":"943b8146281b7d9606dc3ef32f115361","score":"0.57279444","text":"def _fetch_results_from_workers(self):\n if self.n_workers > 1 or isinstance(self.client, Client):\n done_list = [(i, future) for i, future in enumerate(self.futures) if future.done()]\n else:\n # Dask not invoked in the synchronous case\n done_list = [(i, future) for i, future in enumerate(self.futures)]\n if len(done_list) > 0:\n self.logger.debug(\n \"Collecting {} of the {} job(s) active.\".format(len(done_list), len(self.futures))\n )\n for _, future in done_list:\n if self.n_workers > 1 or isinstance(self.client, Client):\n run_info = future.result()\n if \"device_id\" in run_info:\n # updating GPU usage\n self.gpu_usage[run_info[\"device_id\"]] -= 1\n self.logger.debug(\"GPU device released: {}\".format(run_info[\"device_id\"]))\n future.release()\n else:\n # Dask not invoked in the synchronous case\n run_info = future\n # update bracket information\n fitness, cost = run_info[\"fitness\"], run_info[\"cost\"]\n info = run_info[\"info\"] if \"info\" in run_info else dict()\n budget, parent_id = run_info[\"budget\"], run_info[\"parent_id\"]\n config = run_info[\"config\"]\n bracket_id = run_info[\"bracket_id\"]\n for bracket in self.active_brackets:\n if bracket.bracket_id == bracket_id:\n # bracket job complete\n bracket.complete_job(budget) # IMPORTANT to perform synchronous SH\n\n # carry out DE selection\n if fitness <= self.de[budget].fitness[parent_id]:\n self.de[budget].population[parent_id] = config\n self.de[budget].fitness[parent_id] = fitness\n # updating incumbents\n if self.de[budget].fitness[parent_id] < self.inc_score:\n self._update_incumbents(\n config=self.de[budget].population[parent_id],\n score=self.de[budget].fitness[parent_id],\n info=info\n )\n # book-keeping\n self._update_trackers(\n traj=self.inc_score, runtime=cost, history=(\n config.tolist(), float(fitness), float(cost), float(budget), info\n )\n )\n # remove processed future\n self.futures = np.delete(self.futures, [i for i, _ in done_list]).tolist()","title":""},{"docid":"88fafdfd41a6da9fc17fa90f83712e9b","score":"0.5722823","text":"def determine_job_array_parameters(self):\n if self.execution_type == 'incore':\n return None\n if len(self.job_types) > 1:\n self.iterate_by.append('job_types')\n\n for job_type in self.job_types:\n if self.species is not None:\n if len(self.species) > 1:\n self.iterate_by.append('species')\n if job_type == 'conformers':\n if self.species is not None and sum(len(species.conformers) for species in self.species) > 10:\n self.iterate_by.append('conformers')\n self.number_of_processes += sum([len(species.conformers) for species in self.species])\n for species in self.species:\n if job_type in ['sp', 'opt', 'freq', 'optfreq', 'composite', 'ornitals', 'onedmin', 'irc']:\n self.number_of_processes += 1\n # elif job_type == 'scan' and rotor_dict['directed_scan_type'] != 'ess': # Todo: implement directed scans\n elif job_type == 'scan' and len(species.rotors_dict.keys()) > 1000: # Todo: Modify when pipe is implemented\n self.iterate_by.append('scan')\n scan_points_per_dimension = 360.0 / self.scan_res\n for rotor_dict in species.rotors_dict.values():\n if rotor_dict['directed_scan_type'] == 'ess':\n self.number_of_processes += 1\n elif 'cont_opt' in rotor_dict['directed_scan_type']:\n # A single calculation per species for a continuous scan, either diagonal or not.\n self.number_of_processes += 1\n elif 'brute_force' in rotor_dict['directed_scan_type']:\n if 'diagonal' in rotor_dict['directed_scan_type']:\n self.number_of_processes += scan_points_per_dimension\n else:\n self.number_of_processes += \\\n sum([scan_points_per_dimension ** len(rotor_dict['scan'])])\n\n elif self.reactions is not None:\n if len(self.reactions) > 1:\n self.iterate_by.append('reactions')\n self.number_of_processes += len(self.reactions)\n\n if self.number_of_processes > self.incore_capacity:\n self.execution_type = 'pipe'\n self._determine_workers()\n self.write_hdf5()","title":""},{"docid":"9d81b7efd0f9552d24535d1b6527abc3","score":"0.57169366","text":"def test_half_job(spark_session, mocker):\n df = spark_session.createDataFrame(\n [(1, \"2009-10-01\", \"2009-10-05\", 10), (2, \"2009-10-08\", \"2009-10-02\", 10)],\n [\"id\", \"srch_ci\", \"srch_co\", \"hotel_id\"],\n )\n\n result = []\n\n mocker.patch.object(batch_hw, \"_extract_expedia\")\n batch_hw._extract_expedia.return_value = df\n\n def save_result(xdf):\n result.append(xdf)\n return xdf\n\n mocker.patch.object(batch_hw, \"_get_invalid_hotels\", new=save_result)\n\n # ignore other steps\n mocker.patch.object(batch_hw, \"_extract_hotels_topic\")\n batch_hw._extract_expedia.return_value = df\n\n mocker.patch.object(batch_hw, \"_extract_hotels_data\", new=lambda x: x)\n mocker.patch.object(batch_hw, \"_join_hotels_data\", new=lambda x, y: x)\n\n mocker.patch.object(batch_hw, \"_show_invalid\", new=lambda x: x)\n\n mocker.patch.object(batch_hw, \"_clear_invalid_data\", new=lambda x, y: x)\n\n mocker.patch.object(batch_hw, \"_show_stats\", new=lambda x: x)\n\n mocker.patch.object(batch_hw, \"_load_data\", new=lambda x, y: y)\n\n # run job\n batch_hw.run_job(spark_session, {})\n # check result of first part\n expected_df = spark_session.createDataFrame(\n [(1, datetime.date(2009, 10, 1), datetime.date(2009, 10, 5), 10, None, None, None),\n (2, datetime.date(2009, 10, 2), datetime.date(2009, 10, 8), 10, True, datetime.date(2009, 10, 1), 1)],\n [\"id\", \"srch_ci\", \"srch_co\", \"hotel_id\", \"swap_ci_co\", \"pred_ci\", \"idle_days\"],\n ).toPandas()\n\n assert len(result) == 1\n pd.testing.assert_frame_equal(result[0].toPandas(), expected_df, check_dtype=True)","title":""},{"docid":"65c0bb90b309c8300319eceea0f709e0","score":"0.5716509","text":"def test_run(self):\n plugin_names, plugin_classes = self.get_available_plugins()\n for idx in range(len(plugin_names)):\n adapter = batchui_ctrl.BatchPluginAdapter(plugin_names[idx], self.datafile)\n plugin_cls_inst = plugin_classes[idx]()\n plugin_cls_inst._data = dataio.get_data(self.datafile)\n plugin_cls_inst.run()\n expected_data = plugin_cls_inst._data\n adapter.run()\n returned_data = adapter.data\n self.assertTrue(np.array_equal(expected_data, returned_data))","title":""},{"docid":"3c3f55dc5972d6ea37c38cb21cdd3957","score":"0.57080185","text":"def pull_results(self):\n output = []\n for n in self.results:\n for x in n:\n output.append(x)\n output = np.asarray(output)\n self.results = [-1 for _ in range(self.n_workers)]\n return output","title":""},{"docid":"1fcadfb565c0a4558e90bc91e32606d2","score":"0.5706631","text":"def run(self):\r\n self.sweep_all_parameters()\r\n self.get_final_results()","title":""},{"docid":"0cfbb21af063ff8b7708eda209dd8852","score":"0.5704449","text":"def evaluate_in_parallel(self):\n # Purge job queue if necessary\n RpcClient(None, None, **self.credentials).purge()\n jobs = queue.Queue() # \"Counter\" of pending jobs, shared between threads\n responses = queue.Queue() # Collect fitness values from workers\n for i, individual in enumerate(self.individuals):\n if not individual.get_fitness_status():\n job_order = json.dumps([i, individual.get_genes(), individual.get_additional_parameters()])\n jobs.put(True)\n client = RpcClient(jobs, responses, **self.credentials)\n communication_thread = threading.Thread(target=client.call, args=[job_order])\n communication_thread.daemon = True\n communication_thread.start()\n jobs.join() # Block here until all jobs are completed\n # Collect results and assign them to their respective individuals\n while not responses.empty():\n response = responses.get(False)\n i, value = json.loads(response)\n self.individuals[i].set_fitness(value)","title":""},{"docid":"5896f2e3c4aafe63c5c54d3d41fef550","score":"0.5703514","text":"def run():\n\n for snapid in range(1000):\n\n timestamp = datetime.datetime.now()\n\n # Call a celery task to process the dataset!\n tasks.process_data.delay(snapid, timestamp) # calls to delay are completely async - doesn't block!","title":""},{"docid":"35501a60c31ec5a2e0ec6deea8607097","score":"0.56903654","text":"def run(self, dry=False):\n msg = \"Spawning %d job arrays\" % self.njobs\n if self.njobs == 1:\n msg = msg[:-1]\n elif self.njobs < 1:\n return\n\n for key, vals in self.items():\n # check if values are chunks\n if isinstance(vals[0], tuple):\n for chunk in vals:\n self._queue(key, chunk, dry)\n else:\n self._queue(key, vals, dry)","title":""},{"docid":"e853e2d364198b91ba0115575d570bd7","score":"0.5687988","text":"def run(self):\n # Create sub-lists\n for i in range(0, self.jobs):\n sub_list = [self.dirs[j] for j in range(0, len(self.dirs))\n if j % self.jobs == i]\n\n # Create new process\n if len(sub_list) > 0:\n p = SmaliParserProcess(sub_list, self.suffix, self.result_queue)\n self.processes.append(p)\n\n # Start processes\n for p in self.processes:\n p.start()\n\n # Exit the completed processes\n for p in self.processes:\n p.join()\n\n # Get results","title":""},{"docid":"128b661971bbd386beda22485b6cc7d4","score":"0.5681116","text":"def processing(self):\n\n # NOTE: can be some problems with paths here...\n\n # do the parsing of the file from the Mega -----------------------------\n path_in = self.main_path + '/Data/'\n path_out_logger = self.main_path + '/ResultAnalyzis/'\n\n data_parser = Parser_logger(verbose=1)\n data_parser.load_file(path_in + self.filename)\n data_parser.parse_current_data(path_out_logger + self.filename)\n\n # do the analyzis of the data ------------------------------------------\n path_in_processing = path_out_logger\n if self.debug:\n print(\"CAREFUL: IN DEBUG MODE! USE DUMMY DATA\")\n instance_compute_statistics = WaveStatistics(path_in=path_in_processing, filename='test_00470_00471.csv', verbose=self.verbose)\n else:\n instance_compute_statistics = WaveStatistics(path_in=path_in_processing, filename=self.filename + \"_B\", verbose=self.verbose)\n\n instance_compute_statistics.perform_all_processing()\n\n instance_compute_statistics.writeData(path_output=path_out_logger + self.filename + \".bin\")","title":""},{"docid":"47a4e85499781979b9e3f73b26dc6bf4","score":"0.56803954","text":"def __process_all_threads(self):\n for t in self.thread_list:\n self.thread_data[t] = self.__process_csv_directory(t)","title":""},{"docid":"0e5ebd12cf72af95340bded07ed75046","score":"0.56737584","text":"def do_batch(self, elements):\n return batch.do_batch(self, elements)","title":""},{"docid":"2b2478c6804e9532dfd8eac706bb210e","score":"0.5673665","text":"def evaluate_batch(self):\n # if split mentioned for clf training\n X_train, y_train, X_val, y_val, stream, training_ids = self.read_data_as_stream()\n self.opts.X_train = X_train\n self.opts.y_train = y_train.reshape(-1, 1)\n self.opts.clf, y_pred = self.classification(X_train, y_train, X_val, y_val)\n self.opts.clf_init = clone(self.opts.clf)\n self.opts.init_x = deepcopy(X_train)\n self.opts.init_y = deepcopy(y_train)\n self.opts.clf_init = self.opts.clf_init.fit(self.opts.init_x, self.opts.init_y)\n # self.adr_classification(y_pred, y_train, training_ids)\n queried_ids = list()\n queries_asked = 0\n while not stream.empty():\n testing_set = stream.read_next_from_stream()\n self.opts.clf = copy(self.opts.clf_init)\n self.opts.clf = self.opts.clf.fit(self.opts.init_x, self.opts.init_y)\n x_test_cur, y_test_cur, ids_test_cur = testing_set.x, testing_set.y, testing_set.ids\n self.opts.x_dim = x_test_cur.shape[1]\n self.opts.y_dim = 1\n self.opts.ids_dim = 1\n y_pred = self.opts.clf.predict_proba(x_test_cur)\n self.opts.combined_data = np.hstack((x_test_cur, y_test_cur.reshape(-1, 1), ids_test_cur.reshape(-1, 1)))\n self.adr_classification(y_pred, y_test_cur, ids_test_cur, budget=self.opts.query_budget)\n self.opts.current_yr += 1","title":""},{"docid":"cc8b2c7a44ccc15b5a9427316b0c989d","score":"0.5671535","text":"def __call__(self, **kwargs):\n from subprocess import Popen, PIPE\n\n # Load raw data into numpy using shell commands; there may be a faster\n # way to do this; but this seems faster than np.loadtxt()\n # followed by np.concanenate() for multiple files\n command = \"cat {0} | sed ':a;N;$!ba;s/\\\\n//g'\".format(\n \" \".join(self.infiles))\n process = Popen(command, stdout = PIPE, shell = True)\n input_bytes = bytearray(process.stdout.read())\n dataset = np.array(np.frombuffer(input_bytes, dtype = \"S8\",\n count = int((len(input_bytes) -1) / 8)), np.float32)\n\n # np.loadtxt alternative; keep here for future testing\n # dataset = []\n # for infile in self.infiles:\n # dataset += [np.loadtxt(infile)]\n # dataset = np.concatenate(dataset)\n\n # Reshape if necessary\n if len(self.dimensions) != 0:\n dataset = dataset.reshape(\n [dataset.size / np.product(self.dimensions)] + self.dimensions)\n\n # Store in instance variable\n self.datasets[self.output][\"data\"] = dataset","title":""},{"docid":"44be03b9e6ba39b7728190d342e9be46","score":"0.565891","text":"def process_jobs(jobs):\n for row in jobs:\n import_replay(row)","title":""},{"docid":"ab2894caa05c1ff82c3c55b403f765a6","score":"0.5658359","text":"def run(self, *args, **kwargs):\n \n for job in self.jobs:\n job.run(*args, **kwargs)","title":""},{"docid":"d4430ef7c2b32c5fbb02d03e73c2a1bf","score":"0.5650312","text":"def _run_tasks(configuration, n_jobs, data, vector_source):\n logging.info('running tasks')\n\n # retrieve the actions that should be run by the framework\n #actions = configuration.keys()\n\n # **********************************\n # LOADING RAW TEXT\n # **********************************\n x_tr, y_tr, x_test, y_test = data\n\n # CROSSVALIDATION\n # **********************************\n scores = []\n for i, clf_name in enumerate(configuration['classifiers']):\n if not configuration['classifiers'][clf_name]:\n continue\n\n # ignore disabled classifiers\n if not configuration['classifiers'][clf_name]['run']:\n logging.warn('Ignoring classifier %s' % clf_name)\n continue\n\n logging.info('--------------------------------------------------------')\n logging.info('Building pipeline')\n\n # CREATE CROSSVALIDATION ITERATOR\n cv_iterator, validate_indices, x_vals_seen, y_vals_seen = \\\n _build_crossvalidation_iterator(configuration['crossvalidation'],\n x_tr, y_tr, x_test,\n y_test)\n\n logging.info('Assigning id %d to classifier %s', i, clf_name)\n pipeline, fit_params = _build_pipeline(i, vector_source, clf_name,\n configuration['feature_extraction'],\n configuration['feature_selection'],\n configuration['dimensionality_reduction'],\n configuration['classifiers'],\n configuration['output_dir'],\n configuration['debug'],\n exp_name=configuration['name'])\n\n # pass the (feature selector + classifier) pipeline for evaluation\n logging.info('***Fitting pipeline for %s' % clf_name)\n\n # the pipeline is cloned for each fold of the CV iterator, but its fit arguments aren't\n # I've added a clone call for the fit args to the CV function, so that the fit arguments are not\n # going to be shared between pipeline folds, but are shared between all estimators inside\n # a pipeline during a fold\n logging.debug('Identity of vector source is %d', id(vector_source))\n if vector_source:\n try:\n logging.debug('The BallTree is %s', vector_source.nbrs)\n except AttributeError:\n logging.debug('The vector source is is %s', vector_source)\n # pass the same vector source to the vectorizer, feature selector and metadata stripper\n # that way the stripper can call vector_source.populate() after the feature selector has had its say,\n # and that update source will then be available to the vectorizer at decode time\n #fit_params = {\n # 'vect__vector_source': vector_source,\n # 'fs__vector_source': vector_source,\n # 'stripper__vector_source': vector_source,\n #}\n scores_this_clf = naming_cross_val_score(\n pipeline, x_vals_seen,\n y_vals_seen,\n ChainCallable(configuration['evaluation']),\n cv=cv_iterator, n_jobs=n_jobs,\n verbose=0, fit_params=fit_params) # pass resource here\n\n for run_number, a in scores_this_clf:\n # If there is just one metric specified in the conf file a is a\n # 0-D numpy array and needs to be indexed as [()]. Otherwise it\n # is a dict\n mydict = a[()] if hasattr(a, 'shape') and len(a.shape) < 1 else a\n for metric, score in mydict.items():\n scores.append(\n [clf_name.split('.')[-1],\n run_number,\n metric.split('.')[-1],\n score])\n del pipeline\n del scores_this_clf\n logging.info('Classifier scores are %s', scores)\n return 0, _analyze(scores, configuration['output_dir'], configuration['name'])","title":""},{"docid":"05608e74b8b8901fdca7de8177c2865d","score":"0.56433636","text":"def runParallel(self,):\n \n #\n # imports\n #\n import multiprocessing\n \n #\n # drop old data and create table for new data\n #\n if self.logfile: self.logfile.write('Create reads table (and drop old one if needed) ...\\n')\n self.analysisfolder.database.getConnection()\n self.analysisfolder.database.c.execute(\"DROP TABLE IF EXISTS reads\")\n self.analysisfolder.database.c.execute('''CREATE TABLE reads (id, header, sequenceR1, sequenceR2, qualR1, qualR2, direction, h1, h2, h3, constructType, dbsMatch, dbsSeq, dbsQual, mappingFlagR1, refNameR1, refPosR1, mapQR1, cigarR1, mappingFlagR2, refNameR2, refPosR2, mapQR2, cigarR2,insertSize, clusterId, annotations, fromFastqId, r1PositionInFile, r2PositionInFile, bamFilePos, individual_id, PRIMARY KEY (id))''')\n if self.logfile: self.logfile.write('commiting changes to database.\\n')\n self.analysisfolder.database.commitAndClose()\n \n #\n # Write log message\n #\n if self.logfile: self.logfile.write('Adding reads to database table (working in parallel).\\n')\n \n # set up the pool of processes to run the analysis for each read pair\n poolOfProcesses = multiprocessing.Pool(int(self.analysisfolder.settings.parallelProcesses),maxtasksperchild=100000000)\n self.parallelResults = poolOfProcesses.imap_unordered(foreachRead,self.readPairGenerator(),chunksize=10000)\n \n #\n # Parse through files and add to database in chunks of \"self.appendChunkSize\" reads\n #\n adapterCount = 0\n with self.progress:\n for pair in self.parallelResults:\t \n\n #\n # post procesing for each read, preparing and adding to db\n #\n self.foreachProcessedPair(pair)\n\n if 'Read1IsIlluminaAdapter' in pair.annotations or 'Read2IsIlluminaAdapter' in pair.annotations: adapterCount += 1\n \n # Set the adapter count to the results value in the results database table\n self.analysisfolder.results.setResult('readPairsAreIlluminaAdapters',adapterCount)\n\n #\n # add final chunk to db and close the multiprocessing pool\n #\n if self.readsToAdd: self.chunkToDb()\n poolOfProcesses.close()\n poolOfProcesses.join()\n \n #\n # Done write to log and return\n #\n if self.logfile: self.logfile.write('Files '+', '.join([str(key) for key in self.filesAdded.keys()])+' added sucesfully to the database.\\n')\n if self.logfile: self.logfile.write('Saving totalReadCount etc to results table in database.\\n')\n self.analysisfolder.results.setResult('minR1readLength',self.minR1ReadLength)\n self.analysisfolder.results.setResult('minR2readLength',self.minR2ReadLength)\n self.analysisfolder.results.saveToDb()\n self.resultsummary.printsummary()\n \n return 0","title":""},{"docid":"c24e4c42d6e898e7d48df9e6a7d6039d","score":"0.56423473","text":"def runAll(self,tempprefix,finalprefix):\n system = STP(self.main_dir,self.cif_dir,self.temp_dir,self.pot_dir,self.systemcommand)\n #GENERATE JOBS\n for i in range(self.number()):\n system.genJob(tempprefix,finalprefix,self._cifs[i])\n #SUBMIT JOBS\n for j in range(self.number()):\n status = system.runJob(self._cifs[j],finalprefix,self.keyphrase)\n return","title":""},{"docid":"0dd9ede2fe3f74f9efd8748bee73ec38","score":"0.5630406","text":"def evaluate_multiple_datasets(LOG, dataloaders, model, opt, evaltype='Class', save=True, give_return=False, epoch=0):\n start = time.time()\n\n csv_data = [epoch]\n\n\n with torch.no_grad():\n for i,dataloader in enumerate(dataloaders):\n print('Working on Set {}/{}'.format(i+1, len(dataloaders)))\n image_paths = np.array(dataloader.dataset.image_list)\n #Compute Metrics for specific testset.\n F1, NMI, recall_at_ks, feature_matrix_all = aux.eval_metrics_one_dataset(model, dataloader, device=opt.device, k_vals = opt.k_vals, evaltype=evaltype)\n #Generate printable summary string.\n result_str = ', '.join('@{0}: {1:.4f}'.format(k,rec) for k,rec in zip(opt.k_vals, recall_at_ks))\n result_str = 'SET {0}: Epoch (Test) {1}: NMI [{2:.4f}] | F1 {3:.4f}| Recall [{4}]'.format(i+1, epoch, NMI, F1, result_str)\n\n if LOG is not None:\n if save:\n if not len(LOG['Class'].progress_saver['val']['Set {} Recall @ 1'.format(i)]) or recall_at_ks[0]>np.max(LOG['Class'].progress_saver['val']['Set {} Recall @ 1'.format(i)]):\n #Save Checkpoint for specific test set.\n aux.set_checkpoint(model, opt, LOG['Class'].progress_saver, LOG['Class'].prop.save_path+'/checkpoint_set{}.pth.tar'.format(i+1))\n aux.recover_closest_one_dataset(feature_matrix_all, image_paths, LOG['Class'].prop.save_path+'/sample_recoveries_set{}.png'.format(i+1))\n\n csv_data += [NMI, F1]+recall_at_ks\n print(result_str)\n\n csv_data.insert(0, np.round(time.time()-start))\n #Update logs.\n LOG[evaltype].log('val', LOG[evaltype].metrics_to_log['val'], csv_data)\n\n\n if give_return:\n return csv_data[2:]\n else:\n None","title":""},{"docid":"81660cb743d058bf500d280feb27aa0a","score":"0.5630072","text":"def run_experiment(self):\n X_train, X_test, y_train, y_test = train_test_split(self.features,self.target, test_size=self.test_size)\n\n max_score = -float('inf')\n self.results = list()\n for i in range(self.pipelines_to_test):\n start = time.time()\n n_pre_estimators = random.randint(1, len(self.pre_estimators))\n pre_estimators = random.sample(self.pre_estimators, n_pre_estimators)\n estimator = random.sample(self.estimators, 1)\n\n pre_estimators = parameters_select(pre_estimators)\n estimator = parameters_select(estimator)\n print(estimator)\n print(pre_estimators)\n pipeline = MLProcess(estimator[0],\n pre_estimators,\n self.score_func)\n pipeline.fit(X=X_train, y=y_train)\n score_train = pipeline.score(X=X_train, y=y_train)\n score_test = pipeline.score(X=X_test, y=y_test)\n y_pred = pipeline.predict(X_test)\n show_results(y_test, y_pred, estimator)\n end = time.time()\n print(score_train)\n print(score_test)\n if score_test > max_score:\n self.pipeline = pipeline\n\n self.results.append({'pre_estimators':pre_estimators,\n 'estimator':estimator,\n 'score_train':score_train,\n 'score_test':score_test,\n 'execution_time':end-start\n })","title":""},{"docid":"a13fb8dfe06f879d12f6214b3ba5ec7d","score":"0.56296027","text":"def iterate_batch_data(self):\n pass","title":""},{"docid":"2992cb72f1ba8a77479b9292e6db53da","score":"0.56286234","text":"def process_dataset(self, dataset):\n pass","title":""},{"docid":"24b2b1cd023a0eec1c385e8abafa0827","score":"0.56274986","text":"def run_data_cleaning_job(data_cleaning_job):\n dataset_path = \"\"\n if data_cleaning_job[\"dataset\"][\"type\"] == \"csv\":\n dataset_path = os.path.abspath(data_cleaning_job[\"dataset\"][\"param\"][0])\n\n return_list = []\n if data_cleaning_job[\"tool\"][\"name\"] == \"dboost\":\n return_list = run_dboost(dataset_path, data_cleaning_job[\"tool\"][\"param\"])\n if data_cleaning_job[\"tool\"][\"name\"] == \"nadeef\":\n return_list = run_nadeef(dataset_path, data_cleaning_job[\"tool\"][\"param\"])\n if data_cleaning_job[\"tool\"][\"name\"] == \"openrefine\":\n return_list = run_openrefine(dataset_path, data_cleaning_job[\"tool\"][\"param\"])\n if data_cleaning_job[\"tool\"][\"name\"] == \"katara\":\n return_list = run_katara(dataset_path, data_cleaning_job[\"tool\"][\"param\"])\n if data_cleaning_job[\"tool\"][\"name\"] == \"holoclean\":\n return_list = run_holoclean(dataset_path, data_cleaning_job[\"tool\"][\"param\"])\n return return_list","title":""},{"docid":"1192f9ed2dba3fe0efa462c5e0f6e18d","score":"0.5626362","text":"def initiate_job_lists(self):\n self.fetch_jobs = []\n self.skipped_fetch_jobs = []\n self.index_jobs = []\n self.skipped_index_jobs = []\n for dm in self.data_managers:\n jobs, skipped_jobs = self.get_dm_jobs(dm)\n if self.dm_is_fetcher(dm):\n self.fetch_jobs.extend(jobs)\n self.skipped_fetch_jobs.extend(skipped_jobs)\n else:\n self.index_jobs.extend(jobs)\n self.skipped_index_jobs.extend(skipped_jobs)","title":""},{"docid":"08c69598eefb3a7a62f1a1e0a3c52488","score":"0.56202024","text":"def test_all_sets(self):\n self.training_results.append(self.test_data(self.training_data))\n self.validation_results.append(self.test_data(self.validation_data))\n self.testing_results.append(self.test_data(self.testing_data))","title":""},{"docid":"92cb63e9a86f2599e698566fffc5c648","score":"0.5619495","text":"def fillDataSet ( chain , variables , selection , ppservers = () ) :\n \n task = FillTask ( variables , selection )\n wmgr = Parallel.WorkManager( ppservers = ppservers )\n \n cname = chain.GetName() \n files = chain.files() \n pairs = [ ( cname,i ) for i in files ] \n\n wmgr.process( task, pairs )\n\n return task.output","title":""},{"docid":"76ec176ecb93566cbc70a4be45df2700","score":"0.5606346","text":"def execute(self):","title":""},{"docid":"3d6ed10d64c931cdb0fc10f660515262","score":"0.5597856","text":"def __call__(self, prediction_batches, dataloader, split_name):\n for method in self.reporting_methods:\n if method in self.reporting_method_dict:\n if split_name == 'test' and method not in self.test_reporting_constraint:\n tqdm.write(\"Reporting method {} not in test set reporting \"\n \"methods (reporter.py); skipping\".format(method))\n continue\n tqdm.write(\"Reporting {} on split {}\".format(method, split_name))\n self.reporting_method_dict[method](prediction_batches\n , dataloader, split_name)\n else:\n tqdm.write('[WARNING] Reporting method not known: {}; skipping'.format(method))","title":""},{"docid":"564027e74b497bd53a8a0f52afc9a0f0","score":"0.5597412","text":"def task_5_2_2():\n results = [None, None, None, None]\n # TODO Task 5.2.2: Your code goes here\n\n # print and return results\n print('BATCH SIZE: 100; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): ' + str(results[0] / 1000))\n print('BATCH SIZE: 100; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): ' + str(results[1] / 1000))\n print('BATCH SIZE: 1000; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): ' + str(results[2] / 1000))\n print('BATCH SIZE: 1000; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): ' + str(results[3] / 1000))\n return results","title":""},{"docid":"5590e416079743cb1be5b6bda353dad7","score":"0.55917346","text":"def _calculateJobs(self):\n ndim = len(self._min_params)\n linspaces = []\n for i in range(ndim):\n linspaces.append(\n self._permuteSequence(\n list(linspace(self._min_params[i], self._max_params[i], self._n_steps[i]))))\n# print(linspaces; exit(0))\n# linspaces = array(linspaces,float)\n nr_c = len(linspaces[0])\n nr_g = len(linspaces[1])\n i = 0\n j = 0\n jobs = []\n\n while i < nr_c or j < nr_g:\n if i / float(nr_c) < j / float(nr_g):\n line = []\n for k in range(0, j):\n line.append((linspaces[0][i], linspaces[1][k]))\n i += 1\n jobs.append(line)\n else:\n line = []\n for k in range(0, i):\n line.append((linspaces[0][k], linspaces[1][j]))\n j += 1\n jobs.append(line)\n return jobs","title":""},{"docid":"255466882843aa808536dc5d60d8ce0d","score":"0.55833364","text":"def main(data_dir, json_out, param_file, n_splits=5, rho=0.1, nu=0.5):\n params = _read_params(param_file)\n method_names = params[\"evaluation\"][\"methods\"]\n n_methods = len(method_names)\n\n results = dict()\n results[\"auc\"] = dict()\n results[\"time\"] = dict()\n results[\"methods\"] = method_names[:n_methods]\n results[\"datasize\"] = dict()\n results[\"n_classes\"] = dict()\n\n # chain the generators\n datasets = gen_dataset(data_dir)\n data_gen = gen_data(datasets)\n\n logger.debug(\"starting...\")\n ot_start = time.time()\n\n for X, y, dataset_name in data_gen:\n results[\"auc\"][dataset_name] = [None] * n_methods\n results[\"time\"][dataset_name] = [None] * n_methods\n\n classes = list(set(y))\n outlier_class = classes[-1]\n y[y != outlier_class] = classes[0]\n\n classes = list(set(y))\n results[\"n_classes\"][dataset_name] = len(classes)\n\n results[\"datasize\"][dataset_name] = X.shape\n sigma = lsanomaly.lengthscale_approx.median_kneighbour_distance(X)\n\n for method, method_name in enumerate(method_names):\n logger.debug(\n \"dataset: {}, method: {}\".format(dataset_name, method_name)\n )\n current_method_aucs = list()\n start_time = time.time()\n\n kf = model_selection.StratifiedKFold(n_splits=n_splits)\n for train, test in kf.split(X, y):\n X_train = X[train, :]\n y_train = y[train]\n X_train = X_train[y_train != outlier_class]\n y_train = y_train[y_train != outlier_class]\n\n X_test = X[test, :]\n y_test = y[test]\n\n evaluate(\n X_train,\n y_train,\n X_test,\n y_test,\n outlier_class,\n method_name,\n current_method_aucs,\n sigma,\n rho=rho,\n nu=nu,\n )\n elapsed = time.time() - start_time\n logger.debug(\n \"\\tavg AUC : {:>8.4f}\".format(np.mean(current_method_aucs))\n )\n logger.debug(\"\\ttotal time : {:>8.4f}s\".format(elapsed))\n logger.debug(\"\\tavg time : {:>8.4f}s\".format(elapsed / n_splits))\n results[\"time\"][dataset_name][method] = time.time() - start_time\n results[\"auc\"][dataset_name][method] = copy.copy(\n current_method_aucs\n )\n with open(json_out, \"w\") as fp:\n json.dump(results, fp)\n logger.debug(\n \"Total evaluation time was about {:>4.2f}m\".format(\n (time.time() - ot_start) / 60.0\n )\n )\n logger.debug(\"Results written to {}\".format(json_out))","title":""},{"docid":"be2a82afd35ab2c7b924108cc45a7cf9","score":"0.55814594","text":"def runner(self):\n printtime('Starting {} analysis pipeline'.format(self.analysistype), self.starttime)\n # Create the objects to be used in the analyses (if required)\n general = None\n for sample in self.runmetadata.samples:\n general = getattr(sample, 'general')\n if general is None:\n # Create the objects to be used in the analyses\n objects = Objectprep(self)\n objects.objectprep()\n self.runmetadata = objects.samples\n # Run the analyses\n mlst = MLSTmap(self, self.analysistype, self.cutoff)\n # Create the reports\n self.reporter()\n mlst.clear()\n # Print the metadata to a .json file\n MetadataPrinter(self)","title":""},{"docid":"8e9bcbcd4056870531318baa287705ed","score":"0.5577686","text":"def pre_process_data(dataset):\n res_set = []\n res_target = []\n for x in dataset:\n #TODO: We need to assign a number to all the instances. Can we do this in place rather than do this offline?\n break","title":""},{"docid":"663134206da3d4c90e553b11b3df3633","score":"0.5575616","text":"def run(self):\n\n data_dict = self.data_plus_meta_[self.rack_order_ -1].data_\n cfg = self.data_plus_meta_[self.rack_order_].config_\n # Used for optimizing the hyperparameters of the ml algorithm\n # Uses the train data folds\n if cfg['if_optimize']:\n print (\"Running hyperparameter optimization\")\n self.data_plus_meta_[self.rack_order_].config_[\"hyperparameters\"] = self.optimize(data_dict.train_set_dict_.copy(), cfg)\n # Validation fold is for checking the pipeline\n # Used for comparing multiple paths in a pipeline\n # If validation folds are required by config, the score is the aggregation function used for cv for hyperparameter tuning\n print (\"Running cross-validation\")\n score = self.run_cv(data_dict.validate_set_dict_, cfg, self.data_plus_meta_[self.rack_order_].config_[\"hyperparameters\"])\n with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n print (\"\\nCV metrics - Validation sets:\\n\", score)\n\n if cfg['VALIDATION_MODEL']:\n self.data_plus_meta_[self.rack_order_].score_test_ = score[cfg['validate_metric']].agg(cfg['validate_aggregation'])\n print (\"Validation score:\", self.data_plus_meta_[self.rack_order_].score_test_)","title":""},{"docid":"c75b1abbc7cd606c3376610e800fae68","score":"0.55648845","text":"def test():\n machine = JobMachine()\n jobs = []\n \n #first round\n for i in range(10):\n job = IlastikJob(testFunction, [\"Processing task \",str(i)])\n jobs.append(job)\n\n results = machine.process(jobs)\n print \"finished:\"\n \n for r in results:\n print r\n\n GLOBAL_WM.setThreadCount(0) #set unthreaded mode\n \n #second round\n for i in range(10):\n job = IlastikJob(testFunction, [\"#2 Processing task \",str(i)])\n jobs.append(job)\n\n results = machine.process(jobs)\n print \"finished:\"\n \n for r in results:\n print r","title":""},{"docid":"0722726926648b61d4c97c42dea0bfe6","score":"0.5550157","text":"def func_parse_jobs(self):\n\n # Parse job info, if no sample file is given then return [None].\n # Does not do this immediately beause there are other variables\n # to manage depending in the number of samples or existence of a sample\n # sheet.\n llstr_sample_data = [None]\n if self.ns_arguments.str_sample_file:\n with open(self.ns_arguments.str_sample_file, \"r\") as hndl_samples:\n llstr_sample_data = [lstr_row for lstr_row in csv.reader(hndl_samples, delimiter = b\"\\t\")]\n\n # Turn on Job running\n if len(llstr_sample_data) > 1 and self.ns_arguments.i_number_jobs > 1:\n self.f_is_multi_job = True\n else:\n self.f_is_multi_job = False\n\n # No more job threads than number of jobs requested\n self.ns_arguments.i_number_jobs = min(self.ns_arguments.i_number_jobs,\n len(llstr_sample_data))\n\n # To run multiple samples, a pipeline config is required\n if self.ns_arguments.str_pipeline_config_file:\n self.str_possible_config_file = os.path.realpath(self.ns_arguments.str_pipeline_config_file)\n else:\n self.str_possible_config_file = os.path.splitext(os.path.realpath(sys.argv[0]))[0] + C_STR_CONFIG_EXTENSION\n if self.ns_arguments.str_sample_file and (not os.path.exists(self.str_possible_config_file)):\n self.logr_job.error(\"All jobs with sample files need pipeline config files.\")\n exit(997)\n\n return(llstr_sample_data)","title":""},{"docid":"021f7a48e738ec2e321c5728b01cb816","score":"0.5547924","text":"def process(self, jobs):\n \n self.numWorkers = GLOBAL_WM.threads\n self.sem = threading.Semaphore(self.numWorkers)\n self.workers = deque()\n for i in range(self.numWorkers):\n worker = GLOBAL_WM.workerPool[i]\n #TODO: only append free workers !\n self.workers.append(worker)\n \n #do the work\n while len(jobs) > 0:\n self.sem.acquire()\n job = jobs.pop()\n job.run(self)\n \n #make sure all workers finished: \n for i in range(self.numWorkers):\n self.sem.acquire()\n\n #release the semaphore finally: \n for i in range(self.numWorkers):\n self.sem.release() \n \n results = self.results\n self.results = deque() \n return results","title":""},{"docid":"f0d7d8371563139764cf7f9145032c8c","score":"0.554417","text":"def run_data_cleaning_job(data_cleaning_job):\n dataset_path = \"\"\n if data_cleaning_job[\"dataset\"][\"type\"] == \"csv\":\n dataset_path = os.path.abspath(data_cleaning_job[\"dataset\"][\"param\"][0])\n\n return_list = []\n if data_cleaning_job[\"tool\"][\"name\"] == \"dboost\":\n return_list = run_dboost(dataset_path, data_cleaning_job[\"tool\"][\"param\"])\n if data_cleaning_job[\"tool\"][\"name\"] == \"nadeef\":\n return_list = run_nadeef(dataset_path, data_cleaning_job[\"tool\"][\"param\"])\n if data_cleaning_job[\"tool\"][\"name\"] == \"openrefine\":\n return_list = run_openrefine(dataset_path, data_cleaning_job[\"tool\"][\"param\"])\n if data_cleaning_job[\"tool\"][\"name\"] == \"katara\":\n return_list = run_katara(dataset_path, data_cleaning_job[\"tool\"][\"param\"])\n return return_list","title":""},{"docid":"5126bfa320bc1b25e145d8177b9e107f","score":"0.5540038","text":"def processTask():","title":""},{"docid":"78b5d2a62034a40b219973e0826b8d54","score":"0.553229","text":"def main():\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')\n maisons.pipeline()\n appartements.pipeline()","title":""},{"docid":"3459d6d8bca9c5d317f224563e6ed7bc","score":"0.55315953","text":"def job(self):\n pass","title":""},{"docid":"2d1a4e90c094769383f80ecdda63de4d","score":"0.5529511","text":"def run(self, inputs):\n with self as process:\n for data in inputs:\n process(data)","title":""},{"docid":"687b9fbf65020492df54c3684221ba31","score":"0.55291593","text":"def results():","title":""},{"docid":"95fdc92bfc7e48ae8482fc61a0f32b11","score":"0.5526919","text":"def split_processing(dataset, name, description, api, args, resume,\n session_file=None, path=None, log=None):\n train_dataset = None\n test_dataset = None\n sample_rate = 1 - args.test_split\n # if resuming, try to extract train dataset form log files\n if resume:\n message = u.dated(\"Dataset not found. Resuming.\\n\")\n resume, train_dataset = c.checkpoint(\n c.is_dataset_created, path, \"_train\", debug=args.debug,\n message=message, log_file=session_file, console=args.verbosity)\n\n if train_dataset is None:\n dataset_split_args = r.set_dataset_split_args(\n \"%s - train (%s %%)\" % (name,\n int(sample_rate * 100)), description, args,\n sample_rate, out_of_bag=False)\n train_dataset = r.create_dataset(\n dataset, dataset_split_args, args, api, path, session_file,\n log, \"train\")\n if train_dataset:\n train_dataset = r.get_dataset(train_dataset, api,\n args.verbosity, session_file)\n\n # if resuming, try to extract test dataset form log files\n if resume:\n message = u.dated(\"Dataset not found. Resuming.\\n\")\n resume, test_dataset = c.checkpoint(\n c.is_dataset_created, path, \"_test\", debug=args.debug,\n message=message, log_file=session_file, console=args.verbosity)\n\n if test_dataset is None:\n dataset_split_args = r.set_dataset_split_args(\n \"%s - test (%s %%)\" % (name,\n int(args.test_split * 100)), description, args,\n sample_rate, out_of_bag=True)\n test_dataset = r.create_dataset(\n dataset, dataset_split_args, args, api, path, session_file,\n log, \"test\")\n if test_dataset:\n test_dataset = r.get_dataset(test_dataset, api, args.verbosity,\n session_file)\n return train_dataset, test_dataset, resume","title":""},{"docid":"961b7ba39ffd1c9445cd4b375ed7e239","score":"0.5521262","text":"def execute(self, work_unit):","title":""}],"string":"[\n {\n \"docid\": \"2c75164bb06e2fe93a8887b597c49827\",\n \"score\": \"0.6873918\",\n \"text\": \"def batch_run(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e4fb49c095b7635f67d8e053f02338\",\n \"score\": \"0.65535104\",\n \"text\": \"def run_parallel(self, **kwargs):\\n\\n duts=kwargs.get('duts') \\n method=kwargs.get('method','run') \\n max_jobs=kwargs.get('max_jobs',None) \\n if max_jobs is None:\\n max_jobs = len(duts)\\n nbatch = int(np.ceil(len(duts)/max_jobs))\\n for j in range(nbatch):\\n dutrange = range(j*max_jobs,(j+1)*max_jobs)\\n if dutrange.stop > len(duts):\\n dutrange = range(j*max_jobs,len(duts))\\n que=[]\\n proc=[]\\n for i in dutrange:\\n self.print_log(type='I', msg='Starting parallel run %d/%d' % (i+1,len(duts)))\\n que.append(multiprocessing.Queue())\\n proc.append(multiprocessing.Process(target=getattr(duts[i],method)))\\n duts[i].par = True\\n duts[i].queue = que[-1]\\n proc[-1].start()\\n n=0\\n for i in dutrange:\\n ret_dict=que[n].get() # returned dictionary\\n self.print_log(type='I', msg='Saving results from parallel run of %s' %(duts[i]))\\n for key,value in ret_dict.items():\\n if key in duts[i].IOS.Members:\\n duts[i].IOS.Members[key] = value\\n elif hasattr(duts[i],key):\\n setattr(duts[i],key,value)\\n else:\\n duts[i].extracts.Members[key] = value\\n proc[n].join()\\n n+=1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47af1ee6d547c61c7513923d34b0442b\",\n \"score\": \"0.6431891\",\n \"text\": \"def _split_data(self, job):\\n\\n # 1. Extracts all of the raw data from raw data filepath\\n assert job.raw_filepath, \\\\\\n \\\"Raw data filepath has not been set!\\\"\\n files = os.listdir(job.raw_filepath)\\n files = list(filter(lambda x: x.endswith('.csv'), files))\\n assert len(files) == 1, \\\\\\n \\\"Only supporting one file per dataset folder!\\\"\\n data_filepath = os.path.join(\\n job.raw_filepath,\\n files[0]\\n )\\n raw_data = pd.read_csv(data_filepath)\\n\\n # 2. Create transformed folder, if it doesn't exist.\\n transformed_filepath = os.path.join(job.raw_filepath, \\\"transformed\\\")\\n if not os.path.isdir(transformed_filepath):\\n os.makedirs(transformed_filepath)\\n\\n # 3. Create session folder using timestamp and random characters.\\n new_name = str(uuid.uuid4())\\n session_filepath = os.path.join(transformed_filepath, new_name)\\n os.makedirs(session_filepath)\\n\\n # 4. Retrieve train-test split from job.\\n split = job.hyperparams['split']\\n\\n # 5. Shuffle raw data, then split into train and test set.\\n raw_data = raw_data.sample(frac=1)\\n split_index = int(len(raw_data)*split)\\n train = raw_data.iloc[:split_index] \\n test = raw_data.iloc[split_index:]\\n\\n # 6. Create train.csv and test.csv in data folder.\\n train.to_csv(\\n os.path.join(session_filepath, 'train.csv'),\\n index=False\\n )\\n\\n test.to_csv(\\n os.path.join(session_filepath, 'test.csv'),\\n index=False\\n )\\n\\n # 7. Get datapoint count to be used in future jobs\\n datapoint_count = count_datapoints(session_filepath)\\n\\n # 8. Return job with assigned session folder filepath and\\n # datapoint count.\\n results = DMLResult(\\n status='successful',\\n job=job,\\n results = {\\n 'session_filepath': session_filepath,\\n 'datapoint_count': datapoint_count\\n },\\n error_message=\\\"\\\"\\n )\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4df2021bdab3b036d0b04d1f3e9363\",\n \"score\": \"0.6424601\",\n \"text\": \"def batch_run(self) -> None:\\n # Get initial time\\n start_time: float = time.time()\\n\\n # Extract the abstracts and save to csv in a multiprocess manner\\n with Pool(processes=self.processes) as pool:\\n pool.map(self.single_run, os.listdir(self.input_path))\\n\\n # Print the run time\\n print(\\\"--- %s seconds ---\\\" % (time.time() - start_time))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7554b106b9828bdff1bc31043c9f6ec0\",\n \"score\": \"0.6423451\",\n \"text\": \"def job(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17942c34c233f3e96f9902c998ee5b1e\",\n \"score\": \"0.6319304\",\n \"text\": \"def _jobs(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7da140356313340c4e559a24ee5754d\",\n \"score\": \"0.63056195\",\n \"text\": \"def run(self):\\n\\n threadlist = [None] * self.jobs\\n\\n for i in range(self.jobs):\\n threadlist[i] = Thread(target=self.__run_tests, daemon=True)\\n threadlist[i].start()\\n\\n for thread in threadlist:\\n thread.join()\\n\\n self.result = sum(self.failed)\\n self.tests[\\\"algorithm\\\"].append(self.algorithm.__name__)\\n self.tests[\\\"dimension\\\"].append(self.dim)\\n self.tests[\\\"failed\\\"].append(self.result)\\n if self.algorithm.__name__ == \\\"jacobi\\\":\\n self.tests[\\\"maxiter\\\"].append(None)\\n else:\\n self.tests[\\\"maxiter\\\"].append(self.maxiter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2594624b2094460d4030a1c064752036\",\n \"score\": \"0.628158\",\n \"text\": \"def _run_steps(self):\\n self.step_results = [\\n *nmslib.batch_query_index(index=self.index,\\n dataset=self.dataset.test,\\n k=self.service_config.k)\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bb1c8026a4a44ff1d4727fd260bec29\",\n \"score\": \"0.6236874\",\n \"text\": \"def run(self):\\n \\n self.read_data()\\n self.fast_assign_jobs()\\n self.display_output()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e9ae111bd9afc396c9e7ef6d4e6a91e\",\n \"score\": \"0.6220301\",\n \"text\": \"def declare_train_with_data(self):\\n\\n results = []\\n # Separately for every split. Reduce total number of file parsing.\\n for split_nr in range(1, 6):\\n train_df, test_df = ExperimentRunner.read_declare_log(self.results_folder, split_nr)\\n payload_train_df, payload_test_df = ExperimentRunner.read_payload_log(self.results_folder, split_nr)\\n\\n\\n tr_result = self.train(train_df, test_df, payload_train_df, payload_test_df,\\n split_nr=split_nr, exp_name=\\\"declare_data\\\")\\n\\n\\n result = {\\n \\\"result\\\": tr_result,\\n \\\"split\\\": split_nr\\n }\\n\\n results.append(result)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"921e1036eeebb7fb0ca6e056440f69e2\",\n \"score\": \"0.62187403\",\n \"text\": \"def test_get_exec_batch_results(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5443d875c8c95d662ebc0976c865385f\",\n \"score\": \"0.6202834\",\n \"text\": \"def load_all_data(self):\\n sttime = datetime.now()\\n logger.debug('Starting loading all at: '+str(sttime))\\n max_simul_jobs = len(self.simulations_list)\\n jobs = []\\n manager = Manager()\\n params = manager.Queue()\\n result_list = manager.list()\\n for i in range(max_simul_jobs):\\n p = Process(target=self.load_data, args=(params, result_list))\\n jobs.append(p)\\n p.start()\\n iters = itertools.chain(self.simulations_list,(None,) * max_simul_jobs)\\n for iter in iters:\\n params.put(iter)\\n for j in jobs:\\n j.join()\\n cube_list = iris.cube.CubeList(list(result_list))\\n entime = datetime.now()\\n logger.debug('Finished loading all at: '+str(entime))\\n return cube_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"642b5d8138b4b8c59ad3e544e41ccc85\",\n \"score\": \"0.6197176\",\n \"text\": \"def batch(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d1f981cab7778fba9253f7fd2763b5\",\n \"score\": \"0.6163524\",\n \"text\": \"def multi(self):\\n\\n # multiprocessing parameters\\n nz = len(self.toprocess)\\n t = np.linspace(0, nz, self.nproc+1, dtype='int')\\n #print('multi', nz, t)\\n result_queue = multiprocessing.Queue()\\n\\n procs = [multiprocessing.Process(name='Subprocess-'+str(j), target=self.process,\\n args=(self.toprocess[t[j]:t[j+1]], self.params, j, result_queue))\\n for j in range(self.nproc)]\\n\\n for p in procs:\\n p.start()\\n\\n resultdict = {}\\n # get the results in a dict\\n\\n for i in range(self.nproc):\\n resultdict.update(result_queue.get())\\n\\n for p in multiprocessing.active_children():\\n p.join()\\n\\n restot = pd.DataFrame()\\n\\n # gather the results\\n for key, vals in resultdict.items():\\n restot = pd.concat((restot, vals), sort=False)\\n \\\"\\\"\\\"\\n if restot is None:\\n restot = vals\\n else:\\n restot = np.concatenate((restot, vals))\\n \\\"\\\"\\\"\\n return restot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78c0058b2e3c5cdf1d511270fd8e6d64\",\n \"score\": \"0.6159943\",\n \"text\": \"def execute(self):\\n total_api_calls=0\\n for measureCode in self.measure_codes:\\n self._construct_series_id_list(measureCode)\\n chunked_list = self._chunk_list(self._series_id_list,50)\\n for list in chunked_list:\\n if total_api_calls < 500:\\n data = self._blsGetData(list)\\n self._bls_parse_data(data)\\n total_api_calls += 1\\n else:\\n #log api limit reached here\\n break\\n self.recurse_parse()\\n self._consolidate_gaps()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"344b26f505a37d3f913eae924a801d2d\",\n \"score\": \"0.61341053\",\n \"text\": \"def run(self):\\n\\n # I do this because, I tried to split them in memory, but I got\\n # problems to count the rows, I think there might be some problems\\n # with some column values, so to basically speed up the process I save\\n # sub parts of the original dataset into several files to try to apply\\n # KNN on one or several datasets, however I will aim to do it on 1 to begin with.\\n # The idea is to perform KNN on each and every file to then stack then combine\\n # them to get a solid model. I would like to implement a SGD\\n # approach on the whole dataset.\\n #\\n # This also gives me a fairly good overview of the data and I am able to analyze\\n # subparts of the data to get a better understanding of it\\n if not os.path.isfile(TRAIN_CSV_PATH):\\n df = self.extract(DEF_CSV_PATH)\\n df = self.transform(df)\\n dfs = split_dataframe(df, True)\\n write_split_dataset(dfs)\\n\\n df = self.extract(TRAIN_CSV_PATH)\\n\\n return self.transform(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f356a122c98f83da0b17e477ac83cf1\",\n \"score\": \"0.61234033\",\n \"text\": \"def process(self, toproc, params, j=0, output_q=None):\\n\\n metricTot = pd.DataFrame()\\n\\n for val in toproc:\\n tab = self.func(val, params)\\n if tab is not None:\\n metricTot = pd.concat((metricTot, tab), sort=False)\\n\\n if output_q is not None:\\n return output_q.put({j: metricTot})\\n else:\\n return metricTot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a987ea671e38eb8ffd1f0bec964edcd0\",\n \"score\": \"0.60552084\",\n \"text\": \"def generateJobs(self):\\n jobs = []\\n trace = np.loadtxt(self.output_filename, comments=';')\\n for row in trace:\\n job = self.generateJob(row)\\n jobs.append(job)\\n return jobs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8fee0c7d98394288d388fcef0714246\",\n \"score\": \"0.6031838\",\n \"text\": \"def run_all_analyses(self):\\n # List all analysis functions to be run here\\n self.results.append(CountLipids(membrane=self.membrane).results)\\n self.results.append(SurfaceArea(membrane=self.membrane).results)\\n self.results.append(OrderParam(membrane=self.membrane).results)\\n self.results.append(Thickness(membrane=self.membrane).results)\\n # self.results.append(Domains(membrane=self.membrane).results)\\n\\n # Collect all the results into one dataframe\\n self.results = pd.concat(self.results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09807d7299b9e043cd21024f51e89565\",\n \"score\": \"0.60176617\",\n \"text\": \"def gatherResults(\\n lvls,\\n outputdir,\\n splitdata,\\n adata,\\n dozip=True,\\n verbose=2,\\n nparetodiff=1,\\n paretomethod=1,\\n gatherpareto=True,\\n gatherJstats=True,\\n legacy=False,\\n ncores=1,\\n queue=\\\"q72h\\\",\\n quiet=False,\\n):\\n joblist = []\\n\\n tag = splitTag(lvls)\\n edir0 = splitdir(lvls)\\n level = len(lvls)\\n kmin = splitdata[\\\"levels\\\"][level + 1] + 1\\n kmax = splitdata[\\\"kmax\\\"]\\n prfile = os.path.join(outputdir, edir0, processingtimeFile(lvls))\\n nfile = os.path.join(outputdir, edir0, numbersFile(lvls))\\n nfilej = os.path.join(outputdir, edir0, numbersFile(lvls, tag=\\\"jstats\\\"))\\n zipfile0 = \\\"splitted-%s.zip\\\" % tag\\n zipfile = join(outputdir, edir0, zipfile0)\\n lockfile = gatherLockfile(outputdir, lvls)\\n plog = os.path.join(outputdir, edir0, \\\"gather-log-%s.txt\\\" % tag)\\n jlog = os.path.join(outputdir, edir0, \\\"gather-log-jstats-%s.txt\\\" % tag)\\n\\n if os.path.exists(lockfile):\\n print(\\\"gatherResults: lockfile %s exists\\\" % lockfile)\\n return []\\n pythonbinary = pythonBinary()\\n if len(lvls) > 0:\\n ii = lvls[0]\\n else:\\n ii = -1\\n if len(lvls) > 1:\\n jj = lvls[1]\\n else:\\n jj = -1\\n if len(lvls) > 2:\\n kk = lvls[2]\\n else:\\n kk = -1\\n nsplitstr = \\\" \\\".join([\\\"--nsplit%d %d\\\" % (x, splitdata[x][\\\"n\\\"]) for x in range(4) if x in splitdata])\\n cmd = f'echo \\\"Calculating pareto optimal arrays\\\"; cd {join(outputdir, edir0)};\\\\n'\\n cmd += (\\\"touch %s\\\" % lockfile) + os.linesep\\n\\n lvloptions = \\\" --split0 %d --split1 %d --split2 %d %s --kmax %d --kmin %d\\\" % (ii, jj, kk, nsplitstr, kmax, kmin)\\n if gatherpareto:\\n cmd += (\\n \\\"oaclustergather -c %s -b %s -f BINARY --paretomethod %d --nparetodiff %d --numbersfile %s %s --cleanrun 1 -o %s | tee %s;\\\"\\n % (\\n join(outputdir, \\\"oaconfig.txt\\\"),\\n join(outputdir),\\n paretomethod,\\n nparetodiff,\\n nfile,\\n lvloptions,\\n \\\"pareto\\\",\\n plog,\\n )\\n )\\n cmd += os.linesep\\n if gatherJstats:\\n cmd += os.linesep + \\\"# j-statistics \\\" + os.linesep\\n cmd += \\\"oaclustergather -c {} -b {} --method 1 --numbersfile {} {} --cleanrun 1 -o {} | tee {};\\\".format(\\n join(outputdir, \\\"oaconfig.txt\\\"),\\n join(outputdir),\\n nfilej,\\n lvloptions,\\n \\\"jstats\\\",\\n jlog,\\n )\\n cmd += os.linesep\\n if isVSCcluster():\\n cmd += check_pipe_cmd(tag=\\\"oaclustergather\\\", errorcode=1)\\n\\n cmd += os.linesep + 'echo \\\"paretomethod %d\\\" > %s\\\\n' % (paretomethod, join(outputdir, edir0, \\\"gatheroptions.txt\\\"))\\n cmd += gzipOA(join(outputdir, edir0))\\n cmd += os.linesep\\n cmd += \\\"cd %s; tail -n 3 sp%d-split-*/sp0*extend.txt > %s;\\\" % (\\n join(outputdir, edir0),\\n level,\\n os.path.join(\\\"timelog-split-%s.txt\\\" % tag),\\n )\\n cmd += \\\"cd %s;\\\" % outputdir\\n cmd += os.linesep\\n\\n # FIXME: hard-coded\\n codedir = os.path.join(os.path.expanduser(\\\"~\\\"), \\\"projects/oapackage\\\")\\n if not os.path.exists(codedir):\\n raise Exception(\\\"update hard-coded location!\\\")\\n gfile = os.path.join(codedir, \\\"pythondevelop\\\", \\\"gather_processing_times.py\\\")\\n cmd += \\\"\\\\n%s %s -b %s -n %d %s\\\" % (\\n pythonbinary,\\n gfile,\\n outputdir,\\n splitdata[level][\\\"n\\\"],\\n \\\" \\\".join([str(x) for x in lvls]),\\n )\\n cmd += os.linesep\\n\\n if dozip:\\n zipstr = \\\"\\\".join([\\\"sp%d-split*\\\" % i for i in range(level + 1)]) + \\\".oa*\\\"\\n # zipstr = '-'.join(['sp%d-split-%d' % (i, lvls[i]) for i in range(level) ]) + '-sp%d-split-*.oa*' % (level+1)\\n\\n cmdz = \\\"\\\\n# compress subdirs\\\" + os.linesep\\n cmdz += (\\\"PFILE=%s\\\" % prfile) + os.linesep\\n cmdz += \\\"if [ -f $PFILE ];\\\" + os.linesep\\n cmdz += \\\"then\\\" + os.linesep\\n if quiet:\\n qstr = \\\" -q \\\"\\n else:\\n qstr = \\\"\\\"\\n cmdz += (\\n 'echo \\\"Compressing directory %s\\\"; cd %s; zip -r -m %s -T %s sp%d-split-* %s'\\n % (os.path.join(outputdir, edir0), os.path.join(outputdir, edir0), qstr, zipfile, level, zipstr)\\n + os.linesep\\n )\\n cmdz += os.linesep\\n cmdz += \\\"else\\\" + os.linesep\\n cmdz += (' echo \\\"Could not find file %s\\\"' % prfile) + os.linesep\\n\\n cmdz += \\\"fi\\\" + os.linesep\\n\\n cmd += cmdz\\n else:\\n cmd += os.linesep + \\\"# no compressing of subdirs (dozip=False)\\\" + os.linesep\\n\\n cmd += (\\\"rm -f %s\\\" % lockfile) + os.linesep\\n # runcommand(cmd, dryrun=dryrun, idstr='job-compress-%d (depends on job-%d-x and job-%d-statistics)' % (ii,ii, ii), verbose=verbose, logfile=allcmdlogfile)\\n\\n for kk in range(splitdata[level][\\\"n\\\"]):\\n lvlsn = lvls + [kk]\\n edirX = splitdir(lvlsn)\\n\\n lfile = os.path.join(os.path.join(outputdir, edirX), \\\"lock-extend.txt\\\")\\n\\n if os.path.exists(lfile):\\n print(f\\\"gatherResults {tag}: lock file for {kk} exists, skipping\\\")\\n return joblist\\n\\n checkfilesstart = gatherFilesList(lvls, outputdir, splitdata, adata, verbose=0, legacy=legacy)\\n if dozip:\\n checkfiles = [nfile, prfile, zipfile]\\n else:\\n checkfiles = [nfile, prfile]\\n j = job(\\n cmd=cmd,\\n jobtype=\\\"gather %s\\\" % tag,\\n queue=queue,\\n shorttag=\\\"G%s\\\" % tag,\\n ncores=ncores,\\n checkfiles=checkfiles,\\n checkfilesstart=checkfilesstart,\\n )\\n\\n if not j.complete() or True:\\n joblist.append(j)\\n return joblist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab901bfcf04a0ce8de427d0175c6aee6\",\n \"score\": \"0.6005265\",\n \"text\": \"def compute_results(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f12a81d22e0fc34d62b40b8ecec60d9\",\n \"score\": \"0.59998673\",\n \"text\": \"def load_data_wrapper():\\r\\n\\r\\n comm = MPI.COMM_WORLD\\r\\n comm.Barrier()\\r\\n wt = MPI.Wtime()\\r\\n if comm.rank == 0:\\r\\n # read data from gzip\\r\\n tr_d, va_d, te_d = load_data()\\r\\n\\r\\n # calculate the set length that each process has to calculate\\r\\n tr_set_length = np.ceil(len(tr_d[0])*1.0 / comm.size)\\r\\n va_set_length = np.ceil(len(va_d[0])*1.0 / comm.size)\\r\\n te_set_length = np.ceil(len(te_d[0])*1.0 / comm.size)\\r\\n\\r\\n # send only the corresponding data to each process\\r\\n for rank in range(1, comm.size):\\r\\n comm.send(tr_d[0][rank * tr_set_length:(rank + 1) * tr_set_length], dest=rank, tag=11)\\r\\n comm.send(tr_d[1][rank * tr_set_length:(rank + 1) * tr_set_length], dest=rank, tag=12)\\r\\n comm.send(va_d[0][rank * va_set_length:(rank + 1) * va_set_length], dest=rank, tag=13)\\r\\n comm.send(te_d[0][rank * te_set_length:(rank + 1) * te_set_length], dest=rank, tag=14)\\r\\n\\r\\n # process the corresponding part of the arrays\\r\\n training_inputs = tr_d[0][0:tr_set_length]\\r\\n training_results = tr_d[1][0:tr_set_length]\\r\\n validation_inputs = va_d[0][0:va_set_length]\\r\\n test_inputs = te_d[0][0:te_set_length]\\r\\n all_training_inputs = []\\r\\n all_training_results = []\\r\\n all_validation_inputs = []\\r\\n all_test_inputs = []\\r\\n for x in training_inputs:\\r\\n # if x.shape != (784,1):\\r\\n # print x.shape\\r\\n all_training_inputs.append(reshape_as_matrix(x))\\r\\n for y in training_results:\\r\\n all_training_results.append(vectorized_result(y))\\r\\n for x in validation_inputs:\\r\\n # if x.shape != (784,1):\\r\\n # print \\\"validation da\\\"\\r\\n # print x.shape\\r\\n all_validation_inputs.append(reshape_as_matrix(x))\\r\\n for x in test_inputs:\\r\\n # if x.shape != (784,1):\\r\\n # print \\\"test: da\\\"\\r\\n # print x.shape\\r\\n all_test_inputs.append(reshape_as_matrix(x))\\r\\n # all_training_inputs = [np.reshape(x, (784,1)) for x in tr_d[0][0:tr_set_length]]\\r\\n # all_training_results = [vectorized_result(y) for y in tr_d[1][0:tr_set_length]]\\r\\n # all_validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0][0:va_set_length]]\\r\\n # all_test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0][0:te_set_length]]\\r\\n\\r\\n total_messages = (comm.size - 1) * 4\\r\\n while total_messages > 0:\\r\\n status = MPI.Status()\\r\\n data = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)\\r\\n source = status.Get_source()\\r\\n tag = status.Get_tag()\\r\\n if tag == 11:\\r\\n all_training_inputs.extend(data)\\r\\n # TODO push back to training_inputs - might not work properly\\r\\n elif tag == 12:\\r\\n all_training_results.extend(data)\\r\\n # TODO push back to training_results - might not work properly\\r\\n elif tag == 13:\\r\\n all_validation_inputs.extend(data)\\r\\n # TODO push back to validation_inputs - might not work properly\\r\\n elif tag == 14:\\r\\n all_test_inputs.extend(data)\\r\\n # TODO push back to test_inputs - might not work properly\\r\\n total_messages -= 1\\r\\n\\r\\n training_data = zip(all_training_inputs, all_training_results)\\r\\n validation_data = zip(all_validation_inputs, va_d[1])\\r\\n test_data = zip(all_test_inputs, te_d[1])\\r\\n\\r\\n else:\\r\\n # receive arrays from master and process the corresponding part\\r\\n training_inputs=[]\\r\\n training_results=[]\\r\\n validation_inputs=[]\\r\\n test_inputs=[]\\r\\n\\r\\n training_data = None\\r\\n validation_data = None\\r\\n test_data = None\\r\\n\\r\\n for i in range(0,4):\\r\\n status = MPI.Status()\\r\\n data = comm.recv(source=0, tag=MPI.ANY_TAG, status=status)\\r\\n tag = status.Get_tag()\\r\\n if tag == 11:\\r\\n for x in data:\\r\\n training_inputs.append(np.reshape(x, (784,1)))\\r\\n # training_inputs = [np.reshape(x, (784,1)) for x in data]\\r\\n elif tag == 12:\\r\\n for x in data:\\r\\n training_results.append(vectorized_result(x))\\r\\n # training_results = [vectorized_result(y) for y in data]\\r\\n elif tag == 13:\\r\\n for x in data:\\r\\n validation_inputs.append(np.reshape(x, (784,1)))\\r\\n # validation_inputs = [np.reshape(x, (784, 1)) for x in data]\\r\\n elif tag == 14:\\r\\n for x in data:\\r\\n test_inputs.append(np.reshape(x, (784,1)))\\r\\n # test_inputs = [np.reshape(x, (784, 1)) for x in data]\\r\\n\\r\\n comm.send(training_inputs, dest=0, tag=11)\\r\\n comm.send(training_results, dest=0, tag=12)\\r\\n comm.send(validation_inputs, dest=0, tag=13)\\r\\n comm.send(test_inputs, dest=0, tag=14)\\r\\n\\r\\n if comm.rank == 0:\\r\\n comm.bcast(training_data, root=0)\\r\\n comm.bcast(validation_data, root=0)\\r\\n comm.bcast(test_data, root=0)\\r\\n print \\\"MNIST_data_transformation\\\", MPI.Wtime() - wt\\r\\n else:\\r\\n training_data = comm.bcast(training_data, root=0)\\r\\n validation_data = comm.bcast(validation_data, root=0)\\r\\n test_data = comm.bcast(test_data, root=0)\\r\\n return (training_data, validation_data, test_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b99f5ad25737d6f79056a8dbef8fb21\",\n \"score\": \"0.59788\",\n \"text\": \"def func_run_many_jobs(self):\\n\\n try:\\n pool = multiprocessing.Pool()\\n lrslt_success = pool.map_async(func_run_job,\\n self.func_get_job_commands())\\n lf_success = [f_success for f_success in lrslt_success.get()]\\n except Exception as e:\\n f_error_occured = True\\n self.logr_job.info(\\\"\\\".join([\\\"PipelineRunner.func_run_many_jobs:: \\\",\\n \\\"Serious error occured for job\\\"]))\\n self.logr_job.info(\\\"PipelineRunner.func_run_many_jobs:: \\\\n\\\" + str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"999bc240f80ae009232cda40cb445d67\",\n \"score\": \"0.5952506\",\n \"text\": \"def run_all(self):\\n\\n run_iter_args, total_iterations = self._make_model_args_mp()\\n # register the process pool and init a queue\\n # store results in ordered dictionary\\n results = {}\\n\\n if self.processes > 1:\\n with tqdm(total_iterations, disable=not self.display_progress) as pbar:\\n for params, model in self.pool.imap_unordered(\\n self._run_wrappermp, run_iter_args\\n ):\\n results[params] = model\\n pbar.update()\\n\\n self._result_prep_mp(results)\\n # For debugging model due to difficulty of getting errors during multiprocessing\\n else:\\n for run in run_iter_args:\\n params, model_data = self._run_wrappermp(run)\\n results[params] = model_data\\n\\n self._result_prep_mp(results)\\n\\n # Close multi-processing\\n self.pool.close()\\n\\n return (\\n getattr(self, \\\"model_vars\\\", None),\\n getattr(self, \\\"agent_vars\\\", None),\\n getattr(self, \\\"datacollector_model_reporters\\\", None),\\n getattr(self, \\\"datacollector_agent_reporters\\\", None),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3962bf86133ae0952b9f624b6e4223de\",\n \"score\": \"0.5949363\",\n \"text\": \"def process_jobs_(jobs):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4726c70303ee4e3bb0c76d1cd7cd21cf\",\n \"score\": \"0.5924849\",\n \"text\": \"def run(self):\\n self._setup()\\n for i in xrange(self.job_number - 1, len(self.input_file_pathes), self.job_count):\\n self._local_job(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f49c7d1d697c884a2168b5c83c987321\",\n \"score\": \"0.5920149\",\n \"text\": \"def execute(self):\\r\\n self.parallelize(self.core_calc_task,\\r\\n self.task_arg_gen(),\\r\\n self.task_completed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"148f3b476a7e82c79a9c86e10c804abb\",\n \"score\": \"0.5917715\",\n \"text\": \"def work(dataset,op_parse,task_list):\\n \\n logfile = '%s.log' % dataset.replace('/','_')\\n output = file(logfile,'w')\\n \\n previous = {}\\n for t in task_list:\\n\\n t.options = copy.deepcopy(op_parse.options)\\n t.dataset, t.user = splitUser(dataset,op_parse.user)\\n t.dataset, t.options.tier = splitTier(t.dataset, t.options.tier)\\n t.options.tier = addRunRange(t.options.tier, t.options.min_run, t.options.max_run)\\n\\n log(output,'%s: [%s] %s:' % (dataset,time.asctime(),t.getname()))\\n if t.__doc__:\\n log(output,'%s: %s' % (dataset,t.__doc__) )\\n try:\\n previous[t.getname()] = t.run(previous)\\n log(output,'%s: \\\\t%s' % (dataset,previous[t.getname()]),tostdout=False)\\n except Exception, e:\\n\\n import traceback, StringIO\\n sb = StringIO.StringIO()\\n traceback.print_exc(file=sb)\\n tb = sb.getvalue()\\n sb.close()\\n \\n log(output,'%s: [%s] %s FAILED:' % (dataset,time.asctime(),t.getname()))\\n log(output,\\\"%s: Error was '%s'\\\" % (dataset,str(e)))\\n log(output,\\\"%s: Traceback was '%s'\\\" % (dataset,tb))\\n\\n #TODO: Perhaps some cleaning?\\n break\\n \\n output.close()\\n \\n #dump the output in a python friendly format\\n import pickle\\n dumpfile = '%s.pkl' % dataset.replace('/','_')\\n output = file(dumpfile,'wb')\\n pickle.dump(previous, output)\\n output.close()\\n \\n return logfile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b805c6c1cb9f1bce62083f032bf46a\",\n \"score\": \"0.5914648\",\n \"text\": \"def run(self):\\n # Get some initialization info required for the pipeline to run\\n initializer_obj = initialize.Initializer(self.instr, self.cfg)\\n initializer_obj.initialize_dates()\\n initializer_obj.get_date_ranges()\\n initializer_obj.get_processed_ranges()\\n\\n if self.initialize:\\n initializer_obj.initialize_HDF5(chunks=self.chunks)\\n\\n # Initialize the downloader\\n downloader = download.Downloader(instr=self.instr,\\n instr_cfg=self.instr_cfg)\\n\\n # Divide up the dates into chunks\\n date_chunks = np.array_split(initializer_obj.dates, self.chunks)\\n for i, chunk in enumerate(date_chunks):\\n for (start, stop) in chunk:\\n failed = False\\n results = None\\n if '{} {}'.format(start.iso, stop.iso) in \\\\\\n initializer_obj.previously_analyzed:\\n LOG.info('Already analyzed {} to {}\\\\n'.format(start.iso,\\n stop.iso))\\n continue\\n\\n # Start the analysis\\n LOG.info('Analyzing data from {} to {}'.format(start.iso,\\n stop.iso))\\n if self.download:\\n download_time = self.run_downloader(date_range=(start, stop),\\n downloader=downloader)\\n self.processing_times['download'] = download_time\\n\\n self.flist = glob.glob(self.search_pattern)\\n\\n if self.process:\\n process_time = self.run_processing(start, stop)\\n self.processing_times['cr_rejection'] = process_time\\n\\n # Analyze the images and extract the results iff files\\n # were successfully processed through CR rejection AND\\n # the analyze flag is True.\\n if self.analyze and self.flist:\\n analysis_time, results = self.run_labeling_all(\\n chunk_num=i + 1\\n )\\n self.processing_times['analysis'] = analysis_time\\n else:\\n failed = True\\n\\n self.processing_times['total'] = sum(\\n list(self.processing_times.values())\\n )\\n\\n # Clean up downloaded files and write out the range we just processed\\n self._pipeline_cleanup(start, stop, failed)\\n\\n # Send the final email iff there were results computed\\n if results:\\n self.send_email(start, stop, results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e5f0f6c37cfd109b6f62069e824a5d\",\n \"score\": \"0.5910644\",\n \"text\": \"def _process_dataset(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e5f0f6c37cfd109b6f62069e824a5d\",\n \"score\": \"0.5910644\",\n \"text\": \"def _process_dataset(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98f53f279d11b165a26acca3bebda535\",\n \"score\": \"0.59061104\",\n \"text\": \"def main(self):\\n optDataList = []\\n \\n windowSize = 3\\n\\n processedData = readFile(self.DATASET, f\\\"{FILE_PATH_CORE}{self.DATASET}{self.INPUT_FILE}\\\")\\n\\n newData = self.processControlMeasures(processedData, windowSize)\\n\\n #Log the current dataset before further processing\\n self.writeFile(newData, \\\"/2. Rt/after_control_measures.csv\\\")\\n\\n regionalIndexList = self.getRegionalIndexs(newData)\\n \\n splitList = []\\n \\n for currStartIndex, currEndIndex in regionalIndexList:\\n splitList.append(newData[currStartIndex:currEndIndex])\\n\\n numProcesses = 10\\n\\n with multiprocessing.Pool(numProcesses) as p:\\n result = p.map(self.calculateRt, splitList)\\n\\n for currRegionalList in result:\\n for row in currRegionalList:\\n optDataList.append(row)\\n #optDataList = self.calculateRt(newData)\\n\\n optDataList = self.filterDate(optDataList)\\n \\n \\\"\\\"\\\"optRegionalIndex = self.getRegionalIndexs(optDataList)\\n results = []\\n for currIndex in range(len(optRegionalIndex)):\\n optStart, optEnd = optRegionalIndex[currIndex]\\n orgStart, orgEnd = regionalIndexList[currIndex]\\n results.insert(0, [(33 - currIndex), f\\\"{orgEnd - orgStart}\\\", f\\\"{optEnd - optStart}\\\"])\\n \\n with open(FILE_PATH_CORE + self.DATASET + \\\"/Rt/test.csv\\\", \\\"w\\\") as optFile:\\n labels = [\\\"Region No\\\", \\\"Original Size\\\", \\\"New Size\\\"]\\n\\n myWriter = csv.writer(optFile, labels)\\n myWriter.writerow(labels)\\n for row in results:\\n myWriter.writerow(row)\\\"\\\"\\\"\\n self.writeFile(optDataList, self.OUTPUT_FILE, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"845960149ff3620e6b725403a96c9794\",\n \"score\": \"0.58925414\",\n \"text\": \"def run_methods(self):\\n try:\\n # dictionaries of population time series\\n self.batch_maxsus()\\n except Exception as e:\\n print(str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de983d98d636968ee96d9cb329b4d228\",\n \"score\": \"0.5885412\",\n \"text\": \"def execute_worker(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c626bca72872887b3dee0b38c4193a9b\",\n \"score\": \"0.5853928\",\n \"text\": \"def algorithm(self, parameters):\\n try:\\n logging.info(\\\"Getting Agent info ...\\\")\\n agentInfo = self.collectAgentInfo()\\n \\n #jobs per request info\\n logging.info(\\\"Getting Job Couch Data ...\\\")\\n jobInfoFromCouch = self.localCouchDB.getJobSummaryByWorkflowAndSite()\\n\\n #fwjr per request info\\n logging.info(\\\"Getting FWJRJob Couch Data ...\\\")\\n\\n #fwjrInfoFromCouch = self.localCouchDB.getEventSummaryByWorkflow()\\n fwjrInfoFromCouch = self.localCouchDB.getJobPerformanceByTaskAndSite()\\n \\n logging.info(\\\"Getting Batch Job Data ...\\\")\\n batchJobInfo = self.wmagentDB.getBatchJobInfo()\\n \\n logging.info(\\\"Getting Finished Task Data ...\\\")\\n finishedTasks = self.wmagentDB.getFinishedSubscriptionByTask()\\n\\n # get the data from local workqueue:\\n # request name, input dataset, inWMBS, inQueue\\n logging.info(\\\"Getting Local Queue Data ...\\\")\\n localQInfo = self.localQueue.getAnalyticsData()\\n\\n # combine all the data from 3 sources\\n logging.info(\\\"\\\"\\\"Combining data from\\n Job Couch(%s),\\n FWJR(%s), \\n Batch Job(%s),\\n Finished Tasks(%s),\\n Local Queue(%s) ...\\\"\\\"\\\" \\n % (len(jobInfoFromCouch), len(fwjrInfoFromCouch), len(batchJobInfo), len(finishedTasks), len(localQInfo)))\\n\\n tempCombinedData = combineAnalyticsData(jobInfoFromCouch, batchJobInfo)\\n combinedRequests = combineAnalyticsData(tempCombinedData, localQInfo)\\n\\n #set the uploadTime - should be the same for all docs\\n uploadTime = int(time.time())\\n \\n self.uploadAgentInfoToCentralWMStats(agentInfo, uploadTime)\\n \\n logging.info(\\\"%s requests Data combined,\\\\n uploading request data...\\\" % len(combinedRequests))\\n requestDocs = convertToRequestCouchDoc(combinedRequests, fwjrInfoFromCouch, finishedTasks,\\n self.agentInfo, uploadTime, self.summaryLevel)\\n\\n\\n if self.plugin != None:\\n self.plugin(requestDocs, self.localSummaryCouchDB, self.centralWMStatsCouchDB)\\n\\n self.localSummaryCouchDB.uploadData(requestDocs)\\n logging.info(\\\"Request data upload success\\\\n %s request, \\\\nsleep for next cycle\\\" % len(requestDocs))\\n\\n except Exception, ex:\\n logging.error(\\\"Error occurred, will retry later:\\\")\\n logging.error(str(ex))\\n logging.error(\\\"Trace back: \\\\n%s\\\" % traceback.format_exc())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84bdaff2a639a8ff01ea1907b0fcd223\",\n \"score\": \"0.58449954\",\n \"text\": \"def _run_raw(self, *args, **kwargs):\\n \\n for job in self.jobs:\\n job._run_raw(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba57fa230f1ed044185601e86f1bcdd0\",\n \"score\": \"0.58430576\",\n \"text\": \"def _fetch_jobs_for_work(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"569c038dd0c07ba127e40a0244594530\",\n \"score\": \"0.5827205\",\n \"text\": \"def algorithm(self, *args, **kwargs):\\n myThread = threading.currentThread()\\n \\n filesPerJob = int(kwargs.get(\\\"files_per_job\\\", 10))\\n jobsPerGroup = int(kwargs.get(\\\"jobs_per_group\\\", 0))\\n filesInJob = 0\\n totalJobs = 0\\n listOfFiles = []\\n\\n #Get a dictionary of sites, files\\n locationDict = self.sortByLocation()\\n\\n for location in locationDict.keys():\\n #Now we have all the files in a certain location\\n fileList = locationDict[location]\\n filesInJob = 0\\n jobsInGroup = 0\\n self.newGroup()\\n if len(fileList) == 0:\\n #No files for this location\\n #This isn't supposed to happen, but better safe then sorry\\n continue\\n for file in fileList:\\n if filesInJob == 0 or filesInJob == filesPerJob:\\n if jobsPerGroup:\\n if jobsInGroup > jobsPerGroup:\\n self.newGroup()\\n jobsInGroup = 0\\n\\n self.newJob(name = self.getJobName(length=totalJobs))\\n \\n filesInJob = 0\\n totalJobs += 1\\n jobsInGroup += 1\\n \\n filesInJob += 1\\n self.currentJob.addFile(file)\\n \\n listOfFiles.append(file)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"175af69e5ff75ebbec513da1800cead7\",\n \"score\": \"0.58132476\",\n \"text\": \"def execute_grid_job(self):\\n # preprocess\\n if self.m_args.sub_task == 'preprocess':\\n self.m_tool_chain.preprocess_data(\\n self.m_preprocessor,\\n indices = self.indices(self.m_file_selector.original_data_list(), self.m_grid.number_of_preprocessings_per_job),\\n force = self.m_args.force)\\n\\n elif self.m_args.sub_task == 'train-extractor':\\n self.m_tool_chain.train_extractor(\\n self.m_extractor,\\n self.m_preprocessor,\\n force = self.m_args.force)\\n\\n # extract features\\n elif self.m_args.sub_task == 'extract':\\n self.m_tool_chain.extract_features(\\n self.m_extractor,\\n self.m_preprocessor,\\n indices = self.indices(self.m_file_selector.preprocessed_data_list(), self.m_grid.number_of_extracted_features_per_job),\\n force = self.m_args.force)\\n\\n # train the feature projector\\n elif self.m_args.sub_task == 'train-projector':\\n self.m_tool_chain.train_projector(\\n self.m_tool,\\n self.m_extractor,\\n force = self.m_args.force)\\n\\n # project the features\\n elif self.m_args.sub_task == 'project':\\n self.m_tool_chain.project_features(\\n self.m_tool,\\n self.m_extractor,\\n indices = self.indices(self.m_file_selector.preprocessed_data_list(), self.m_grid.number_of_projected_features_per_job),\\n force = self.m_args.force)\\n\\n # train model enroller\\n elif self.m_args.sub_task == 'train-enroller':\\n self.m_tool_chain.train_enroller(\\n self.m_tool,\\n self.m_extractor,\\n force = self.m_args.force)\\n\\n # enroll models\\n elif self.m_args.sub_task == 'enroll':\\n self.m_tool_chain.enroll_models(\\n self.m_tool,\\n self.m_extractor,\\n groups = (self.m_args.group,),\\n compute_zt_norm = False,\\n indices = self.indices(self.m_file_selector.model_ids(self.m_args.group), self.m_grid.number_of_enrolled_models_per_job),\\n force = self.m_args.force)\\n\\n # compute scores\\n elif self.m_args.sub_task == 'compute-scores':\\n self.m_tool_chain.compute_scores(\\n self.m_tool,\\n groups = (self.m_args.group,),\\n compute_zt_norm = False,\\n indices = self.indices(self.m_file_selector.model_ids(self.m_args.group), self.m_grid.number_of_models_per_scoring_job),\\n preload_probes = self.m_args.preload_probes,\\n force = self.m_args.force)\\n\\n # concatenate\\n elif self.m_args.sub_task == 'concatenate':\\n self.m_tool_chain.concatenate(compute_zt_norm = False)\\n\\n # average\\n elif self.m_args.sub_task == 'average-results':\\n self.average_results()\\n\\n # Test if the keyword was processed\\n else:\\n raise ValueError(\\\"The given subtask '%s' could not be processed. THIS IS A BUG. Please report this to the authors.\\\" % self.m_args.sub_task)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c851faf32178fe261593717eece516e4\",\n \"score\": \"0.58044916\",\n \"text\": \"def collect_batch(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded38aa20607659f6923d6c93bf36d3e\",\n \"score\": \"0.5800215\",\n \"text\": \"def run(self):\\n pool = mpr.Pool(mpr.cpu_count()-1)\\n handles = []\\n for i, fn in zip(self.identifiers, self.fn_inputs):\\n handle = pool.apply_async(parse_blast,args=(fn,))\\n handles.append(handle)\\n pool.close()\\n pool.join()\\n results = []\\n for i, r in zip(self.identifiers,handles):\\n try:\\n blast_result = r.get()\\n results.append((i,blast_result))\\n except:\\n log.error(\\\"Problem with sequence %s\\\",i)\\n self.failed_processes.add(i)\\n self.fn_inputs = []\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54c82e92657e618437b4f2968ab34e00\",\n \"score\": \"0.5800151\",\n \"text\": \"def run_all(self, **run_args):\\n\\n print(\\\"\\\\n========== ModelManager: Running {} active space locations ==========\\\".format(len(self.coords_ls)))\\n print(\\\"\\\\tInitiating {} process(es)...\\\\n\\\\n\\\".format(self.n_jobs))\\n\\n kwargs = {'timeout': self.timeout} # timeout limit\\n\\n if run_args is not None and len(run_args) > 0:\\n self.run_args = run_args\\n\\n if self.n_jobs == 1:\\n gdf_list = []\\n\\n for input_args in self.coords_ls:\\n # call the helper run function with the timeout wrapper\\n gdf_list.append(timeout_wrapper(self._run, input_args, **kwargs))\\n\\n else: # start multiprocessing\\n # create a pool to multiprocess this task\\n with NestablePool(processes=self.n_jobs, maxtasksperchild=1) as p:\\n # call the timeout_wrapper function to run the CreateActiveSpace model with each coordinate\\n res_ls = [p.apply_async(timeout_wrapper, args=(self._run, coord, ), kwds=kwargs)\\n for coord in self.coords_ls]\\n\\n # wait for results to come in asynchronously\\n gdf_list = [res.get() for res in res_ls]\\n\\n p.close() # not sure what these do, but I'm hopping on the bandwagon\\n p.join()\\n\\n return pd.concat(gdf_list) # returns the list as one GeoDataFrame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d13f724c99372fe85c0bf253ada4bda3\",\n \"score\": \"0.5791606\",\n \"text\": \"def declare_train(self):\\n\\n results = []\\n\\n # Separately for every split. Reduce total number of file parsing.\\n for split_nr in range(1, 6):\\n train_df, test_df = ExperimentRunner.read_declare_log(self.results_folder, split_nr)\\n tr_result = self.train(train_df, test_df, split_nr=split_nr, exp_name=\\\"declare\\\")\\n\\n result = {\\n \\\"result\\\": tr_result,\\n \\\"split\\\": split_nr\\n }\\n\\n results.append(result)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e954ef11df63742be22f0c2d3aa0cba\",\n \"score\": \"0.5789156\",\n \"text\": \"def runall(self):\\n multi_process_tasks(self.test_f1, self.test_accuracy, self.test_recall, self.test_precision)\\n log.info(\\\"Parallel runs of the classification_tests are completed.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc716b3396e40e6d3009e8d8cc86ab4e\",\n \"score\": \"0.5786649\",\n \"text\": \"def __call__(self):\\r\\n self.create_dataframes()\\r\\n self.get_feature_by_shap()\\r\\n self.one_factor_calculate_score()\\r\\n self.one_factor_selection()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8962af98b97faeb5cd09547a466343d\",\n \"score\": \"0.57859856\",\n \"text\": \"def execute(self, webpages):\\n results = []\\n\\n self.__start_threading(webpages)\\n self.__populate_result_array(results, webpages)\\n\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"173b4a2bfc521185e724afcda3d33da1\",\n \"score\": \"0.5784341\",\n \"text\": \"def run(self, *args, **kw):\\n super(Fork, self).run(*args, **kw)\\n\\n for i in self.output_ports:\\n self._output_data[i] = self.get_input_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f2645b1a4add1bacea1cea3a408d13c\",\n \"score\": \"0.57748777\",\n \"text\": \"def get_jobs(self):\\n run_ids = self.run_ids\\n input_json = construct_copy_outputs_input(run_ids)\\n\\n mapping_file_content, pairing_file_content, data_clinical_content = generate_sample_pairing_and_mapping_files(\\n run_ids\\n )\\n\\n input_json[\\\"meta\\\"] = [\\n {\\\"class\\\": \\\"File\\\", \\\"basename\\\": \\\"sample_mapping.txt\\\", \\\"contents\\\": mapping_file_content},\\n {\\\"class\\\": \\\"File\\\", \\\"basename\\\": \\\"sample_pairing.txt\\\", \\\"contents\\\": pairing_file_content},\\n {\\\"class\\\": \\\"File\\\", \\\"basename\\\": \\\"sample_data_clinical.txt\\\", \\\"contents\\\": data_clinical_content},\\n ]\\n\\n number_of_runs = len(run_ids)\\n name = \\\"ARGOS COPY OUTPUTS %s runs [%s,..] \\\" % (number_of_runs, run_ids[0])\\n\\n app = self.get_pipeline_id()\\n pipeline = Pipeline.objects.get(id=app)\\n pipeline_version = pipeline.version\\n project_prefix = input_json[\\\"project_prefix\\\"]\\n\\n tags = {\\\"run_ids\\\": run_ids}\\n\\n copy_outputs_job_data = {\\\"app\\\": app, \\\"inputs\\\": input_json, \\\"name\\\": name, \\\"tags\\\": tags}\\n\\n \\\"\\\"\\\"\\n If project_prefix and job_group_id, write output to a directory\\n that uses both\\n \\\"\\\"\\\"\\n output_directory = None\\n if project_prefix:\\n tags[\\\"project_prefix\\\"] = project_prefix\\n if self.job_group_id:\\n jg = JobGroup.objects.get(id=self.job_group_id)\\n jg_created_date = jg.created_date.strftime(\\\"%Y%m%d_%H_%M_%f\\\")\\n output_directory = os.path.join(\\n pipeline.output_directory, \\\"argos\\\", project_prefix, pipeline_version, jg_created_date\\n )\\n copy_outputs_job_data[\\\"output_directory\\\"] = output_directory\\n copy_outputs_job = [RunCreator(**copy_outputs_job_data)]\\n\\n return copy_outputs_job\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f2426abcf95fcec532b1d76f811589\",\n \"score\": \"0.5770437\",\n \"text\": \"def do_processing(self):\\n\\n # Read the full dataset following the 'next' url until it is empty\\n print(\\\"Reading data from \\\" + self.data_url)\\n df = self.read_data_frame(self.data_url)\\n output_df = pd.DataFrame(df[Converter.RESULTS_FIELD_NAME].tolist())\\n while not pd.isna(df[Converter.NEXT_FIELD_NAME].iloc[0]):\\n next_url = df[Converter.NEXT_FIELD_NAME].iloc[0]\\n df = self.read_data_frame(next_url)\\n output_df = output_df.append(pd.DataFrame(df[Converter.RESULTS_FIELD_NAME].tolist()))\\n # Add computed columns\\n output_df[Converter.DURATION_MINUTES_FIELD_NAME] = output_df[Converter.DURATION_TEXT_FIELD_NAME].apply(Converter.duration_text_2_minutes)\\n output_df[Converter.DISTANCE_METERS_FIELD_NAME] = output_df[Converter.DISTANCE_TEXT_FIELD_NAME].apply(Converter.distance_text_2_meters)\\n # Save result as CSV\\n output_df.to_csv(self.output_filename, index=False)\\n print(str(len(output_df.index)) + \\\" row(s) saved to \\\" + self.output_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bcc91505197feb4220c3f7738023179\",\n \"score\": \"0.57679707\",\n \"text\": \"def _do_train_job(self, data_iterable, job_parameters, thread_private_mem):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4a83badafbc0c40e032bf464a6dc9b\",\n \"score\": \"0.57520294\",\n \"text\": \"def _data_process(self):\\n self._data_process_name()\\n self._data_process_context()\\n self._data_process_content()\\n self._data_process_parent()\\n self._data_process_timedata()\\n self._data_process_various()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64ce903e98cf918b3c788ef3d2198923\",\n \"score\": \"0.57510227\",\n \"text\": \"def submitJobs(self, jobs):\\n err_jobs = set([])\\n for job_name, job_type, job_args in jobs:\\n sleep(1)\\n job_handler = JOB_TYPE_LOOKUP.get(job_type, None)\\n \\n if job_handler is None:\\n sys.stderr.write(\\\"Unknown job type %s for job %s\\\\n\\\" % \\\\\\n (job_type, job_name))\\n err_jobs.add(job_name)\\n continue\\n \\n job = job_handler(job_name, job_args)\\n cmd = job()\\n sys.stdout.write('Job command: %s' % cmd)\\n \\n #### decompose job submission\\n # submit job - if loading into DB use singleq\\n if job_type=='LoadAnalysisOTUTableHandler':\\n submit_queue = 'singleq'\\n pvmem='48gb'\\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\\n elif job_type=='LoadSplitLibSeqsHandler':\\n submit_queue = 'singleq'\\n pvmem='48gb'\\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\\n elif job_type=='ProcessSFFHandler':\\n submit_queue = 'no_bad_touching'\\n pvmem='48gb'\\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\\n elif job_type=='betaDiversityThroughPlots':\\n submit_queue = 'no_bad_touching'\\n pvmem='64gb'\\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\\n elif job_type=='makeOTUHeatmap':\\n submit_queue = 'no_bad_touching'\\n pvmem='4gb'\\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\\n elif job_type=='alphaRarefaction':\\n submit_queue = 'no_bad_touching'\\n pvmem='4gb'\\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\\n elif job_type=='summarizeTaxa':\\n submit_queue = 'no_bad_touching'\\n pvmem='16gb'\\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\\n elif job_type=='generateMapOTUTableSubmitJobs':\\n submit_queue = 'no_bad_touching'\\n pvmem='64gb'\\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\\n else:\\n submit_queue = 'no_bad_touching'\\n pvmem='8gb'\\n res = getoutput(QSUB_CMD % (cmd, job_name,pvmem,submit_queue))\\n \\n res_lines = res.splitlines()\\n\\n if len(res_lines) == 0 or len(res_lines) > 1:\\n # job did not start\\n err_jobs.add(job_name)\\n sys.stderr.write('Unable to start %s\\\\n\\\\tError:%s\\\\n' % \\\\\\n (cmd, res))\\n else:\\n # job started\\n pbs_id = res_lines[0].split('.',1)[0]\\n job.setTorqueJobId(pbs_id, 'NEW')\\n self.Jobs[pbs_id] = job\\n \\n sys.stdout.write('Started PBS job id %s, ORA job id %d\\\\n' %\\\\\\n (pbs_id, job_name))\\n\\n return list(err_jobs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0f5cd6edc9b0c5dc4af758be090b802\",\n \"score\": \"0.575074\",\n \"text\": \"def process_data(self, list_of_directories):\\n\\n # get the file names\\n file_names = get_data_files(list_of_directories)\\n\\n values = [delayed(self.process_file)(f) for f in file_names]\\n\\n # seizure_df = pd.DataFrame()\\n # failures = []\\n results = compute(*values, get=dask.multiprocessing.get)\\n results = [res for res in results if res is not None]\\n return pd.concat(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70f0ac28a6475d090c6d100a383a74df\",\n \"score\": \"0.57377684\",\n \"text\": \"def process_multiple_data(files):\\n # set up the compute graph\\n graph = delayed([delayed(process_data)(file_) for file_ in files])\\n # compute the graph\\n results = compute(graph)\\n\\n return pd.concat([results[0][i] for i in range(len(files))])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"943b8146281b7d9606dc3ef32f115361\",\n \"score\": \"0.57279444\",\n \"text\": \"def _fetch_results_from_workers(self):\\n if self.n_workers > 1 or isinstance(self.client, Client):\\n done_list = [(i, future) for i, future in enumerate(self.futures) if future.done()]\\n else:\\n # Dask not invoked in the synchronous case\\n done_list = [(i, future) for i, future in enumerate(self.futures)]\\n if len(done_list) > 0:\\n self.logger.debug(\\n \\\"Collecting {} of the {} job(s) active.\\\".format(len(done_list), len(self.futures))\\n )\\n for _, future in done_list:\\n if self.n_workers > 1 or isinstance(self.client, Client):\\n run_info = future.result()\\n if \\\"device_id\\\" in run_info:\\n # updating GPU usage\\n self.gpu_usage[run_info[\\\"device_id\\\"]] -= 1\\n self.logger.debug(\\\"GPU device released: {}\\\".format(run_info[\\\"device_id\\\"]))\\n future.release()\\n else:\\n # Dask not invoked in the synchronous case\\n run_info = future\\n # update bracket information\\n fitness, cost = run_info[\\\"fitness\\\"], run_info[\\\"cost\\\"]\\n info = run_info[\\\"info\\\"] if \\\"info\\\" in run_info else dict()\\n budget, parent_id = run_info[\\\"budget\\\"], run_info[\\\"parent_id\\\"]\\n config = run_info[\\\"config\\\"]\\n bracket_id = run_info[\\\"bracket_id\\\"]\\n for bracket in self.active_brackets:\\n if bracket.bracket_id == bracket_id:\\n # bracket job complete\\n bracket.complete_job(budget) # IMPORTANT to perform synchronous SH\\n\\n # carry out DE selection\\n if fitness <= self.de[budget].fitness[parent_id]:\\n self.de[budget].population[parent_id] = config\\n self.de[budget].fitness[parent_id] = fitness\\n # updating incumbents\\n if self.de[budget].fitness[parent_id] < self.inc_score:\\n self._update_incumbents(\\n config=self.de[budget].population[parent_id],\\n score=self.de[budget].fitness[parent_id],\\n info=info\\n )\\n # book-keeping\\n self._update_trackers(\\n traj=self.inc_score, runtime=cost, history=(\\n config.tolist(), float(fitness), float(cost), float(budget), info\\n )\\n )\\n # remove processed future\\n self.futures = np.delete(self.futures, [i for i, _ in done_list]).tolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88fafdfd41a6da9fc17fa90f83712e9b\",\n \"score\": \"0.5722823\",\n \"text\": \"def determine_job_array_parameters(self):\\n if self.execution_type == 'incore':\\n return None\\n if len(self.job_types) > 1:\\n self.iterate_by.append('job_types')\\n\\n for job_type in self.job_types:\\n if self.species is not None:\\n if len(self.species) > 1:\\n self.iterate_by.append('species')\\n if job_type == 'conformers':\\n if self.species is not None and sum(len(species.conformers) for species in self.species) > 10:\\n self.iterate_by.append('conformers')\\n self.number_of_processes += sum([len(species.conformers) for species in self.species])\\n for species in self.species:\\n if job_type in ['sp', 'opt', 'freq', 'optfreq', 'composite', 'ornitals', 'onedmin', 'irc']:\\n self.number_of_processes += 1\\n # elif job_type == 'scan' and rotor_dict['directed_scan_type'] != 'ess': # Todo: implement directed scans\\n elif job_type == 'scan' and len(species.rotors_dict.keys()) > 1000: # Todo: Modify when pipe is implemented\\n self.iterate_by.append('scan')\\n scan_points_per_dimension = 360.0 / self.scan_res\\n for rotor_dict in species.rotors_dict.values():\\n if rotor_dict['directed_scan_type'] == 'ess':\\n self.number_of_processes += 1\\n elif 'cont_opt' in rotor_dict['directed_scan_type']:\\n # A single calculation per species for a continuous scan, either diagonal or not.\\n self.number_of_processes += 1\\n elif 'brute_force' in rotor_dict['directed_scan_type']:\\n if 'diagonal' in rotor_dict['directed_scan_type']:\\n self.number_of_processes += scan_points_per_dimension\\n else:\\n self.number_of_processes += \\\\\\n sum([scan_points_per_dimension ** len(rotor_dict['scan'])])\\n\\n elif self.reactions is not None:\\n if len(self.reactions) > 1:\\n self.iterate_by.append('reactions')\\n self.number_of_processes += len(self.reactions)\\n\\n if self.number_of_processes > self.incore_capacity:\\n self.execution_type = 'pipe'\\n self._determine_workers()\\n self.write_hdf5()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d81b7efd0f9552d24535d1b6527abc3\",\n \"score\": \"0.57169366\",\n \"text\": \"def test_half_job(spark_session, mocker):\\n df = spark_session.createDataFrame(\\n [(1, \\\"2009-10-01\\\", \\\"2009-10-05\\\", 10), (2, \\\"2009-10-08\\\", \\\"2009-10-02\\\", 10)],\\n [\\\"id\\\", \\\"srch_ci\\\", \\\"srch_co\\\", \\\"hotel_id\\\"],\\n )\\n\\n result = []\\n\\n mocker.patch.object(batch_hw, \\\"_extract_expedia\\\")\\n batch_hw._extract_expedia.return_value = df\\n\\n def save_result(xdf):\\n result.append(xdf)\\n return xdf\\n\\n mocker.patch.object(batch_hw, \\\"_get_invalid_hotels\\\", new=save_result)\\n\\n # ignore other steps\\n mocker.patch.object(batch_hw, \\\"_extract_hotels_topic\\\")\\n batch_hw._extract_expedia.return_value = df\\n\\n mocker.patch.object(batch_hw, \\\"_extract_hotels_data\\\", new=lambda x: x)\\n mocker.patch.object(batch_hw, \\\"_join_hotels_data\\\", new=lambda x, y: x)\\n\\n mocker.patch.object(batch_hw, \\\"_show_invalid\\\", new=lambda x: x)\\n\\n mocker.patch.object(batch_hw, \\\"_clear_invalid_data\\\", new=lambda x, y: x)\\n\\n mocker.patch.object(batch_hw, \\\"_show_stats\\\", new=lambda x: x)\\n\\n mocker.patch.object(batch_hw, \\\"_load_data\\\", new=lambda x, y: y)\\n\\n # run job\\n batch_hw.run_job(spark_session, {})\\n # check result of first part\\n expected_df = spark_session.createDataFrame(\\n [(1, datetime.date(2009, 10, 1), datetime.date(2009, 10, 5), 10, None, None, None),\\n (2, datetime.date(2009, 10, 2), datetime.date(2009, 10, 8), 10, True, datetime.date(2009, 10, 1), 1)],\\n [\\\"id\\\", \\\"srch_ci\\\", \\\"srch_co\\\", \\\"hotel_id\\\", \\\"swap_ci_co\\\", \\\"pred_ci\\\", \\\"idle_days\\\"],\\n ).toPandas()\\n\\n assert len(result) == 1\\n pd.testing.assert_frame_equal(result[0].toPandas(), expected_df, check_dtype=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65c0bb90b309c8300319eceea0f709e0\",\n \"score\": \"0.5716509\",\n \"text\": \"def test_run(self):\\n plugin_names, plugin_classes = self.get_available_plugins()\\n for idx in range(len(plugin_names)):\\n adapter = batchui_ctrl.BatchPluginAdapter(plugin_names[idx], self.datafile)\\n plugin_cls_inst = plugin_classes[idx]()\\n plugin_cls_inst._data = dataio.get_data(self.datafile)\\n plugin_cls_inst.run()\\n expected_data = plugin_cls_inst._data\\n adapter.run()\\n returned_data = adapter.data\\n self.assertTrue(np.array_equal(expected_data, returned_data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3f55dc5972d6ea37c38cb21cdd3957\",\n \"score\": \"0.57080185\",\n \"text\": \"def pull_results(self):\\n output = []\\n for n in self.results:\\n for x in n:\\n output.append(x)\\n output = np.asarray(output)\\n self.results = [-1 for _ in range(self.n_workers)]\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fcadfb565c0a4558e90bc91e32606d2\",\n \"score\": \"0.5706631\",\n \"text\": \"def run(self):\\r\\n self.sweep_all_parameters()\\r\\n self.get_final_results()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cfbb21af063ff8b7708eda209dd8852\",\n \"score\": \"0.5704449\",\n \"text\": \"def evaluate_in_parallel(self):\\n # Purge job queue if necessary\\n RpcClient(None, None, **self.credentials).purge()\\n jobs = queue.Queue() # \\\"Counter\\\" of pending jobs, shared between threads\\n responses = queue.Queue() # Collect fitness values from workers\\n for i, individual in enumerate(self.individuals):\\n if not individual.get_fitness_status():\\n job_order = json.dumps([i, individual.get_genes(), individual.get_additional_parameters()])\\n jobs.put(True)\\n client = RpcClient(jobs, responses, **self.credentials)\\n communication_thread = threading.Thread(target=client.call, args=[job_order])\\n communication_thread.daemon = True\\n communication_thread.start()\\n jobs.join() # Block here until all jobs are completed\\n # Collect results and assign them to their respective individuals\\n while not responses.empty():\\n response = responses.get(False)\\n i, value = json.loads(response)\\n self.individuals[i].set_fitness(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5896f2e3c4aafe63c5c54d3d41fef550\",\n \"score\": \"0.5703514\",\n \"text\": \"def run():\\n\\n for snapid in range(1000):\\n\\n timestamp = datetime.datetime.now()\\n\\n # Call a celery task to process the dataset!\\n tasks.process_data.delay(snapid, timestamp) # calls to delay are completely async - doesn't block!\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35501a60c31ec5a2e0ec6deea8607097\",\n \"score\": \"0.56903654\",\n \"text\": \"def run(self, dry=False):\\n msg = \\\"Spawning %d job arrays\\\" % self.njobs\\n if self.njobs == 1:\\n msg = msg[:-1]\\n elif self.njobs < 1:\\n return\\n\\n for key, vals in self.items():\\n # check if values are chunks\\n if isinstance(vals[0], tuple):\\n for chunk in vals:\\n self._queue(key, chunk, dry)\\n else:\\n self._queue(key, vals, dry)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e853e2d364198b91ba0115575d570bd7\",\n \"score\": \"0.5687988\",\n \"text\": \"def run(self):\\n # Create sub-lists\\n for i in range(0, self.jobs):\\n sub_list = [self.dirs[j] for j in range(0, len(self.dirs))\\n if j % self.jobs == i]\\n\\n # Create new process\\n if len(sub_list) > 0:\\n p = SmaliParserProcess(sub_list, self.suffix, self.result_queue)\\n self.processes.append(p)\\n\\n # Start processes\\n for p in self.processes:\\n p.start()\\n\\n # Exit the completed processes\\n for p in self.processes:\\n p.join()\\n\\n # Get results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128b661971bbd386beda22485b6cc7d4\",\n \"score\": \"0.5681116\",\n \"text\": \"def processing(self):\\n\\n # NOTE: can be some problems with paths here...\\n\\n # do the parsing of the file from the Mega -----------------------------\\n path_in = self.main_path + '/Data/'\\n path_out_logger = self.main_path + '/ResultAnalyzis/'\\n\\n data_parser = Parser_logger(verbose=1)\\n data_parser.load_file(path_in + self.filename)\\n data_parser.parse_current_data(path_out_logger + self.filename)\\n\\n # do the analyzis of the data ------------------------------------------\\n path_in_processing = path_out_logger\\n if self.debug:\\n print(\\\"CAREFUL: IN DEBUG MODE! USE DUMMY DATA\\\")\\n instance_compute_statistics = WaveStatistics(path_in=path_in_processing, filename='test_00470_00471.csv', verbose=self.verbose)\\n else:\\n instance_compute_statistics = WaveStatistics(path_in=path_in_processing, filename=self.filename + \\\"_B\\\", verbose=self.verbose)\\n\\n instance_compute_statistics.perform_all_processing()\\n\\n instance_compute_statistics.writeData(path_output=path_out_logger + self.filename + \\\".bin\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47a4e85499781979b9e3f73b26dc6bf4\",\n \"score\": \"0.56803954\",\n \"text\": \"def __process_all_threads(self):\\n for t in self.thread_list:\\n self.thread_data[t] = self.__process_csv_directory(t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5ebd12cf72af95340bded07ed75046\",\n \"score\": \"0.56737584\",\n \"text\": \"def do_batch(self, elements):\\n return batch.do_batch(self, elements)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b2478c6804e9532dfd8eac706bb210e\",\n \"score\": \"0.5673665\",\n \"text\": \"def evaluate_batch(self):\\n # if split mentioned for clf training\\n X_train, y_train, X_val, y_val, stream, training_ids = self.read_data_as_stream()\\n self.opts.X_train = X_train\\n self.opts.y_train = y_train.reshape(-1, 1)\\n self.opts.clf, y_pred = self.classification(X_train, y_train, X_val, y_val)\\n self.opts.clf_init = clone(self.opts.clf)\\n self.opts.init_x = deepcopy(X_train)\\n self.opts.init_y = deepcopy(y_train)\\n self.opts.clf_init = self.opts.clf_init.fit(self.opts.init_x, self.opts.init_y)\\n # self.adr_classification(y_pred, y_train, training_ids)\\n queried_ids = list()\\n queries_asked = 0\\n while not stream.empty():\\n testing_set = stream.read_next_from_stream()\\n self.opts.clf = copy(self.opts.clf_init)\\n self.opts.clf = self.opts.clf.fit(self.opts.init_x, self.opts.init_y)\\n x_test_cur, y_test_cur, ids_test_cur = testing_set.x, testing_set.y, testing_set.ids\\n self.opts.x_dim = x_test_cur.shape[1]\\n self.opts.y_dim = 1\\n self.opts.ids_dim = 1\\n y_pred = self.opts.clf.predict_proba(x_test_cur)\\n self.opts.combined_data = np.hstack((x_test_cur, y_test_cur.reshape(-1, 1), ids_test_cur.reshape(-1, 1)))\\n self.adr_classification(y_pred, y_test_cur, ids_test_cur, budget=self.opts.query_budget)\\n self.opts.current_yr += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8b2c7a44ccc15b5a9427316b0c989d\",\n \"score\": \"0.5671535\",\n \"text\": \"def __call__(self, **kwargs):\\n from subprocess import Popen, PIPE\\n\\n # Load raw data into numpy using shell commands; there may be a faster\\n # way to do this; but this seems faster than np.loadtxt()\\n # followed by np.concanenate() for multiple files\\n command = \\\"cat {0} | sed ':a;N;$!ba;s/\\\\\\\\n//g'\\\".format(\\n \\\" \\\".join(self.infiles))\\n process = Popen(command, stdout = PIPE, shell = True)\\n input_bytes = bytearray(process.stdout.read())\\n dataset = np.array(np.frombuffer(input_bytes, dtype = \\\"S8\\\",\\n count = int((len(input_bytes) -1) / 8)), np.float32)\\n\\n # np.loadtxt alternative; keep here for future testing\\n # dataset = []\\n # for infile in self.infiles:\\n # dataset += [np.loadtxt(infile)]\\n # dataset = np.concatenate(dataset)\\n\\n # Reshape if necessary\\n if len(self.dimensions) != 0:\\n dataset = dataset.reshape(\\n [dataset.size / np.product(self.dimensions)] + self.dimensions)\\n\\n # Store in instance variable\\n self.datasets[self.output][\\\"data\\\"] = dataset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44be03b9e6ba39b7728190d342e9be46\",\n \"score\": \"0.565891\",\n \"text\": \"def process_jobs(jobs):\\n for row in jobs:\\n import_replay(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab2894caa05c1ff82c3c55b403f765a6\",\n \"score\": \"0.5658359\",\n \"text\": \"def run(self, *args, **kwargs):\\n \\n for job in self.jobs:\\n job.run(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4430ef7c2b32c5fbb02d03e73c2a1bf\",\n \"score\": \"0.5650312\",\n \"text\": \"def _run_tasks(configuration, n_jobs, data, vector_source):\\n logging.info('running tasks')\\n\\n # retrieve the actions that should be run by the framework\\n #actions = configuration.keys()\\n\\n # **********************************\\n # LOADING RAW TEXT\\n # **********************************\\n x_tr, y_tr, x_test, y_test = data\\n\\n # CROSSVALIDATION\\n # **********************************\\n scores = []\\n for i, clf_name in enumerate(configuration['classifiers']):\\n if not configuration['classifiers'][clf_name]:\\n continue\\n\\n # ignore disabled classifiers\\n if not configuration['classifiers'][clf_name]['run']:\\n logging.warn('Ignoring classifier %s' % clf_name)\\n continue\\n\\n logging.info('--------------------------------------------------------')\\n logging.info('Building pipeline')\\n\\n # CREATE CROSSVALIDATION ITERATOR\\n cv_iterator, validate_indices, x_vals_seen, y_vals_seen = \\\\\\n _build_crossvalidation_iterator(configuration['crossvalidation'],\\n x_tr, y_tr, x_test,\\n y_test)\\n\\n logging.info('Assigning id %d to classifier %s', i, clf_name)\\n pipeline, fit_params = _build_pipeline(i, vector_source, clf_name,\\n configuration['feature_extraction'],\\n configuration['feature_selection'],\\n configuration['dimensionality_reduction'],\\n configuration['classifiers'],\\n configuration['output_dir'],\\n configuration['debug'],\\n exp_name=configuration['name'])\\n\\n # pass the (feature selector + classifier) pipeline for evaluation\\n logging.info('***Fitting pipeline for %s' % clf_name)\\n\\n # the pipeline is cloned for each fold of the CV iterator, but its fit arguments aren't\\n # I've added a clone call for the fit args to the CV function, so that the fit arguments are not\\n # going to be shared between pipeline folds, but are shared between all estimators inside\\n # a pipeline during a fold\\n logging.debug('Identity of vector source is %d', id(vector_source))\\n if vector_source:\\n try:\\n logging.debug('The BallTree is %s', vector_source.nbrs)\\n except AttributeError:\\n logging.debug('The vector source is is %s', vector_source)\\n # pass the same vector source to the vectorizer, feature selector and metadata stripper\\n # that way the stripper can call vector_source.populate() after the feature selector has had its say,\\n # and that update source will then be available to the vectorizer at decode time\\n #fit_params = {\\n # 'vect__vector_source': vector_source,\\n # 'fs__vector_source': vector_source,\\n # 'stripper__vector_source': vector_source,\\n #}\\n scores_this_clf = naming_cross_val_score(\\n pipeline, x_vals_seen,\\n y_vals_seen,\\n ChainCallable(configuration['evaluation']),\\n cv=cv_iterator, n_jobs=n_jobs,\\n verbose=0, fit_params=fit_params) # pass resource here\\n\\n for run_number, a in scores_this_clf:\\n # If there is just one metric specified in the conf file a is a\\n # 0-D numpy array and needs to be indexed as [()]. Otherwise it\\n # is a dict\\n mydict = a[()] if hasattr(a, 'shape') and len(a.shape) < 1 else a\\n for metric, score in mydict.items():\\n scores.append(\\n [clf_name.split('.')[-1],\\n run_number,\\n metric.split('.')[-1],\\n score])\\n del pipeline\\n del scores_this_clf\\n logging.info('Classifier scores are %s', scores)\\n return 0, _analyze(scores, configuration['output_dir'], configuration['name'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05608e74b8b8901fdca7de8177c2865d\",\n \"score\": \"0.56433636\",\n \"text\": \"def runParallel(self,):\\n \\n #\\n # imports\\n #\\n import multiprocessing\\n \\n #\\n # drop old data and create table for new data\\n #\\n if self.logfile: self.logfile.write('Create reads table (and drop old one if needed) ...\\\\n')\\n self.analysisfolder.database.getConnection()\\n self.analysisfolder.database.c.execute(\\\"DROP TABLE IF EXISTS reads\\\")\\n self.analysisfolder.database.c.execute('''CREATE TABLE reads (id, header, sequenceR1, sequenceR2, qualR1, qualR2, direction, h1, h2, h3, constructType, dbsMatch, dbsSeq, dbsQual, mappingFlagR1, refNameR1, refPosR1, mapQR1, cigarR1, mappingFlagR2, refNameR2, refPosR2, mapQR2, cigarR2,insertSize, clusterId, annotations, fromFastqId, r1PositionInFile, r2PositionInFile, bamFilePos, individual_id, PRIMARY KEY (id))''')\\n if self.logfile: self.logfile.write('commiting changes to database.\\\\n')\\n self.analysisfolder.database.commitAndClose()\\n \\n #\\n # Write log message\\n #\\n if self.logfile: self.logfile.write('Adding reads to database table (working in parallel).\\\\n')\\n \\n # set up the pool of processes to run the analysis for each read pair\\n poolOfProcesses = multiprocessing.Pool(int(self.analysisfolder.settings.parallelProcesses),maxtasksperchild=100000000)\\n self.parallelResults = poolOfProcesses.imap_unordered(foreachRead,self.readPairGenerator(),chunksize=10000)\\n \\n #\\n # Parse through files and add to database in chunks of \\\"self.appendChunkSize\\\" reads\\n #\\n adapterCount = 0\\n with self.progress:\\n for pair in self.parallelResults:\\t \\n\\n #\\n # post procesing for each read, preparing and adding to db\\n #\\n self.foreachProcessedPair(pair)\\n\\n if 'Read1IsIlluminaAdapter' in pair.annotations or 'Read2IsIlluminaAdapter' in pair.annotations: adapterCount += 1\\n \\n # Set the adapter count to the results value in the results database table\\n self.analysisfolder.results.setResult('readPairsAreIlluminaAdapters',adapterCount)\\n\\n #\\n # add final chunk to db and close the multiprocessing pool\\n #\\n if self.readsToAdd: self.chunkToDb()\\n poolOfProcesses.close()\\n poolOfProcesses.join()\\n \\n #\\n # Done write to log and return\\n #\\n if self.logfile: self.logfile.write('Files '+', '.join([str(key) for key in self.filesAdded.keys()])+' added sucesfully to the database.\\\\n')\\n if self.logfile: self.logfile.write('Saving totalReadCount etc to results table in database.\\\\n')\\n self.analysisfolder.results.setResult('minR1readLength',self.minR1ReadLength)\\n self.analysisfolder.results.setResult('minR2readLength',self.minR2ReadLength)\\n self.analysisfolder.results.saveToDb()\\n self.resultsummary.printsummary()\\n \\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24e4c42d6e898e7d48df9e6a7d6039d\",\n \"score\": \"0.56423473\",\n \"text\": \"def runAll(self,tempprefix,finalprefix):\\n system = STP(self.main_dir,self.cif_dir,self.temp_dir,self.pot_dir,self.systemcommand)\\n #GENERATE JOBS\\n for i in range(self.number()):\\n system.genJob(tempprefix,finalprefix,self._cifs[i])\\n #SUBMIT JOBS\\n for j in range(self.number()):\\n status = system.runJob(self._cifs[j],finalprefix,self.keyphrase)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dd9ede2fe3f74f9efd8748bee73ec38\",\n \"score\": \"0.5630406\",\n \"text\": \"def evaluate_multiple_datasets(LOG, dataloaders, model, opt, evaltype='Class', save=True, give_return=False, epoch=0):\\n start = time.time()\\n\\n csv_data = [epoch]\\n\\n\\n with torch.no_grad():\\n for i,dataloader in enumerate(dataloaders):\\n print('Working on Set {}/{}'.format(i+1, len(dataloaders)))\\n image_paths = np.array(dataloader.dataset.image_list)\\n #Compute Metrics for specific testset.\\n F1, NMI, recall_at_ks, feature_matrix_all = aux.eval_metrics_one_dataset(model, dataloader, device=opt.device, k_vals = opt.k_vals, evaltype=evaltype)\\n #Generate printable summary string.\\n result_str = ', '.join('@{0}: {1:.4f}'.format(k,rec) for k,rec in zip(opt.k_vals, recall_at_ks))\\n result_str = 'SET {0}: Epoch (Test) {1}: NMI [{2:.4f}] | F1 {3:.4f}| Recall [{4}]'.format(i+1, epoch, NMI, F1, result_str)\\n\\n if LOG is not None:\\n if save:\\n if not len(LOG['Class'].progress_saver['val']['Set {} Recall @ 1'.format(i)]) or recall_at_ks[0]>np.max(LOG['Class'].progress_saver['val']['Set {} Recall @ 1'.format(i)]):\\n #Save Checkpoint for specific test set.\\n aux.set_checkpoint(model, opt, LOG['Class'].progress_saver, LOG['Class'].prop.save_path+'/checkpoint_set{}.pth.tar'.format(i+1))\\n aux.recover_closest_one_dataset(feature_matrix_all, image_paths, LOG['Class'].prop.save_path+'/sample_recoveries_set{}.png'.format(i+1))\\n\\n csv_data += [NMI, F1]+recall_at_ks\\n print(result_str)\\n\\n csv_data.insert(0, np.round(time.time()-start))\\n #Update logs.\\n LOG[evaltype].log('val', LOG[evaltype].metrics_to_log['val'], csv_data)\\n\\n\\n if give_return:\\n return csv_data[2:]\\n else:\\n None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81660cb743d058bf500d280feb27aa0a\",\n \"score\": \"0.5630072\",\n \"text\": \"def run_experiment(self):\\n X_train, X_test, y_train, y_test = train_test_split(self.features,self.target, test_size=self.test_size)\\n\\n max_score = -float('inf')\\n self.results = list()\\n for i in range(self.pipelines_to_test):\\n start = time.time()\\n n_pre_estimators = random.randint(1, len(self.pre_estimators))\\n pre_estimators = random.sample(self.pre_estimators, n_pre_estimators)\\n estimator = random.sample(self.estimators, 1)\\n\\n pre_estimators = parameters_select(pre_estimators)\\n estimator = parameters_select(estimator)\\n print(estimator)\\n print(pre_estimators)\\n pipeline = MLProcess(estimator[0],\\n pre_estimators,\\n self.score_func)\\n pipeline.fit(X=X_train, y=y_train)\\n score_train = pipeline.score(X=X_train, y=y_train)\\n score_test = pipeline.score(X=X_test, y=y_test)\\n y_pred = pipeline.predict(X_test)\\n show_results(y_test, y_pred, estimator)\\n end = time.time()\\n print(score_train)\\n print(score_test)\\n if score_test > max_score:\\n self.pipeline = pipeline\\n\\n self.results.append({'pre_estimators':pre_estimators,\\n 'estimator':estimator,\\n 'score_train':score_train,\\n 'score_test':score_test,\\n 'execution_time':end-start\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a13fb8dfe06f879d12f6214b3ba5ec7d\",\n \"score\": \"0.56296027\",\n \"text\": \"def iterate_batch_data(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2992cb72f1ba8a77479b9292e6db53da\",\n \"score\": \"0.56286234\",\n \"text\": \"def process_dataset(self, dataset):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24b2b1cd023a0eec1c385e8abafa0827\",\n \"score\": \"0.56274986\",\n \"text\": \"def run_data_cleaning_job(data_cleaning_job):\\n dataset_path = \\\"\\\"\\n if data_cleaning_job[\\\"dataset\\\"][\\\"type\\\"] == \\\"csv\\\":\\n dataset_path = os.path.abspath(data_cleaning_job[\\\"dataset\\\"][\\\"param\\\"][0])\\n\\n return_list = []\\n if data_cleaning_job[\\\"tool\\\"][\\\"name\\\"] == \\\"dboost\\\":\\n return_list = run_dboost(dataset_path, data_cleaning_job[\\\"tool\\\"][\\\"param\\\"])\\n if data_cleaning_job[\\\"tool\\\"][\\\"name\\\"] == \\\"nadeef\\\":\\n return_list = run_nadeef(dataset_path, data_cleaning_job[\\\"tool\\\"][\\\"param\\\"])\\n if data_cleaning_job[\\\"tool\\\"][\\\"name\\\"] == \\\"openrefine\\\":\\n return_list = run_openrefine(dataset_path, data_cleaning_job[\\\"tool\\\"][\\\"param\\\"])\\n if data_cleaning_job[\\\"tool\\\"][\\\"name\\\"] == \\\"katara\\\":\\n return_list = run_katara(dataset_path, data_cleaning_job[\\\"tool\\\"][\\\"param\\\"])\\n if data_cleaning_job[\\\"tool\\\"][\\\"name\\\"] == \\\"holoclean\\\":\\n return_list = run_holoclean(dataset_path, data_cleaning_job[\\\"tool\\\"][\\\"param\\\"])\\n return return_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1192f9ed2dba3fe0efa462c5e0f6e18d\",\n \"score\": \"0.5626362\",\n \"text\": \"def initiate_job_lists(self):\\n self.fetch_jobs = []\\n self.skipped_fetch_jobs = []\\n self.index_jobs = []\\n self.skipped_index_jobs = []\\n for dm in self.data_managers:\\n jobs, skipped_jobs = self.get_dm_jobs(dm)\\n if self.dm_is_fetcher(dm):\\n self.fetch_jobs.extend(jobs)\\n self.skipped_fetch_jobs.extend(skipped_jobs)\\n else:\\n self.index_jobs.extend(jobs)\\n self.skipped_index_jobs.extend(skipped_jobs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08c69598eefb3a7a62f1a1e0a3c52488\",\n \"score\": \"0.56202024\",\n \"text\": \"def test_all_sets(self):\\n self.training_results.append(self.test_data(self.training_data))\\n self.validation_results.append(self.test_data(self.validation_data))\\n self.testing_results.append(self.test_data(self.testing_data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92cb63e9a86f2599e698566fffc5c648\",\n \"score\": \"0.5619495\",\n \"text\": \"def fillDataSet ( chain , variables , selection , ppservers = () ) :\\n \\n task = FillTask ( variables , selection )\\n wmgr = Parallel.WorkManager( ppservers = ppservers )\\n \\n cname = chain.GetName() \\n files = chain.files() \\n pairs = [ ( cname,i ) for i in files ] \\n\\n wmgr.process( task, pairs )\\n\\n return task.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ec176ecb93566cbc70a4be45df2700\",\n \"score\": \"0.5606346\",\n \"text\": \"def execute(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d6ed10d64c931cdb0fc10f660515262\",\n \"score\": \"0.5597856\",\n \"text\": \"def __call__(self, prediction_batches, dataloader, split_name):\\n for method in self.reporting_methods:\\n if method in self.reporting_method_dict:\\n if split_name == 'test' and method not in self.test_reporting_constraint:\\n tqdm.write(\\\"Reporting method {} not in test set reporting \\\"\\n \\\"methods (reporter.py); skipping\\\".format(method))\\n continue\\n tqdm.write(\\\"Reporting {} on split {}\\\".format(method, split_name))\\n self.reporting_method_dict[method](prediction_batches\\n , dataloader, split_name)\\n else:\\n tqdm.write('[WARNING] Reporting method not known: {}; skipping'.format(method))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564027e74b497bd53a8a0f52afc9a0f0\",\n \"score\": \"0.5597412\",\n \"text\": \"def task_5_2_2():\\n results = [None, None, None, None]\\n # TODO Task 5.2.2: Your code goes here\\n\\n # print and return results\\n print('BATCH SIZE: 100; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): ' + str(results[0] / 1000))\\n print('BATCH SIZE: 100; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): ' + str(results[1] / 1000))\\n print('BATCH SIZE: 1000; ALPHA: 10%; TOTAL SIMULATION TIME (SECONDS): ' + str(results[2] / 1000))\\n print('BATCH SIZE: 1000; ALPHA: 5%; TOTAL SIMULATION TIME (SECONDS): ' + str(results[3] / 1000))\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5590e416079743cb1be5b6bda353dad7\",\n \"score\": \"0.55917346\",\n \"text\": \"def _calculateJobs(self):\\n ndim = len(self._min_params)\\n linspaces = []\\n for i in range(ndim):\\n linspaces.append(\\n self._permuteSequence(\\n list(linspace(self._min_params[i], self._max_params[i], self._n_steps[i]))))\\n# print(linspaces; exit(0))\\n# linspaces = array(linspaces,float)\\n nr_c = len(linspaces[0])\\n nr_g = len(linspaces[1])\\n i = 0\\n j = 0\\n jobs = []\\n\\n while i < nr_c or j < nr_g:\\n if i / float(nr_c) < j / float(nr_g):\\n line = []\\n for k in range(0, j):\\n line.append((linspaces[0][i], linspaces[1][k]))\\n i += 1\\n jobs.append(line)\\n else:\\n line = []\\n for k in range(0, i):\\n line.append((linspaces[0][k], linspaces[1][j]))\\n j += 1\\n jobs.append(line)\\n return jobs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"255466882843aa808536dc5d60d8ce0d\",\n \"score\": \"0.55833364\",\n \"text\": \"def main(data_dir, json_out, param_file, n_splits=5, rho=0.1, nu=0.5):\\n params = _read_params(param_file)\\n method_names = params[\\\"evaluation\\\"][\\\"methods\\\"]\\n n_methods = len(method_names)\\n\\n results = dict()\\n results[\\\"auc\\\"] = dict()\\n results[\\\"time\\\"] = dict()\\n results[\\\"methods\\\"] = method_names[:n_methods]\\n results[\\\"datasize\\\"] = dict()\\n results[\\\"n_classes\\\"] = dict()\\n\\n # chain the generators\\n datasets = gen_dataset(data_dir)\\n data_gen = gen_data(datasets)\\n\\n logger.debug(\\\"starting...\\\")\\n ot_start = time.time()\\n\\n for X, y, dataset_name in data_gen:\\n results[\\\"auc\\\"][dataset_name] = [None] * n_methods\\n results[\\\"time\\\"][dataset_name] = [None] * n_methods\\n\\n classes = list(set(y))\\n outlier_class = classes[-1]\\n y[y != outlier_class] = classes[0]\\n\\n classes = list(set(y))\\n results[\\\"n_classes\\\"][dataset_name] = len(classes)\\n\\n results[\\\"datasize\\\"][dataset_name] = X.shape\\n sigma = lsanomaly.lengthscale_approx.median_kneighbour_distance(X)\\n\\n for method, method_name in enumerate(method_names):\\n logger.debug(\\n \\\"dataset: {}, method: {}\\\".format(dataset_name, method_name)\\n )\\n current_method_aucs = list()\\n start_time = time.time()\\n\\n kf = model_selection.StratifiedKFold(n_splits=n_splits)\\n for train, test in kf.split(X, y):\\n X_train = X[train, :]\\n y_train = y[train]\\n X_train = X_train[y_train != outlier_class]\\n y_train = y_train[y_train != outlier_class]\\n\\n X_test = X[test, :]\\n y_test = y[test]\\n\\n evaluate(\\n X_train,\\n y_train,\\n X_test,\\n y_test,\\n outlier_class,\\n method_name,\\n current_method_aucs,\\n sigma,\\n rho=rho,\\n nu=nu,\\n )\\n elapsed = time.time() - start_time\\n logger.debug(\\n \\\"\\\\tavg AUC : {:>8.4f}\\\".format(np.mean(current_method_aucs))\\n )\\n logger.debug(\\\"\\\\ttotal time : {:>8.4f}s\\\".format(elapsed))\\n logger.debug(\\\"\\\\tavg time : {:>8.4f}s\\\".format(elapsed / n_splits))\\n results[\\\"time\\\"][dataset_name][method] = time.time() - start_time\\n results[\\\"auc\\\"][dataset_name][method] = copy.copy(\\n current_method_aucs\\n )\\n with open(json_out, \\\"w\\\") as fp:\\n json.dump(results, fp)\\n logger.debug(\\n \\\"Total evaluation time was about {:>4.2f}m\\\".format(\\n (time.time() - ot_start) / 60.0\\n )\\n )\\n logger.debug(\\\"Results written to {}\\\".format(json_out))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2a82afd35ab2c7b924108cc45a7cf9\",\n \"score\": \"0.55814594\",\n \"text\": \"def runner(self):\\n printtime('Starting {} analysis pipeline'.format(self.analysistype), self.starttime)\\n # Create the objects to be used in the analyses (if required)\\n general = None\\n for sample in self.runmetadata.samples:\\n general = getattr(sample, 'general')\\n if general is None:\\n # Create the objects to be used in the analyses\\n objects = Objectprep(self)\\n objects.objectprep()\\n self.runmetadata = objects.samples\\n # Run the analyses\\n mlst = MLSTmap(self, self.analysistype, self.cutoff)\\n # Create the reports\\n self.reporter()\\n mlst.clear()\\n # Print the metadata to a .json file\\n MetadataPrinter(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e9bcbcd4056870531318baa287705ed\",\n \"score\": \"0.5577686\",\n \"text\": \"def pre_process_data(dataset):\\n res_set = []\\n res_target = []\\n for x in dataset:\\n #TODO: We need to assign a number to all the instances. Can we do this in place rather than do this offline?\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"663134206da3d4c90e553b11b3df3633\",\n \"score\": \"0.5575616\",\n \"text\": \"def run(self):\\n\\n data_dict = self.data_plus_meta_[self.rack_order_ -1].data_\\n cfg = self.data_plus_meta_[self.rack_order_].config_\\n # Used for optimizing the hyperparameters of the ml algorithm\\n # Uses the train data folds\\n if cfg['if_optimize']:\\n print (\\\"Running hyperparameter optimization\\\")\\n self.data_plus_meta_[self.rack_order_].config_[\\\"hyperparameters\\\"] = self.optimize(data_dict.train_set_dict_.copy(), cfg)\\n # Validation fold is for checking the pipeline\\n # Used for comparing multiple paths in a pipeline\\n # If validation folds are required by config, the score is the aggregation function used for cv for hyperparameter tuning\\n print (\\\"Running cross-validation\\\")\\n score = self.run_cv(data_dict.validate_set_dict_, cfg, self.data_plus_meta_[self.rack_order_].config_[\\\"hyperparameters\\\"])\\n with pd.option_context('display.max_rows', None, 'display.max_columns', None):\\n print (\\\"\\\\nCV metrics - Validation sets:\\\\n\\\", score)\\n\\n if cfg['VALIDATION_MODEL']:\\n self.data_plus_meta_[self.rack_order_].score_test_ = score[cfg['validate_metric']].agg(cfg['validate_aggregation'])\\n print (\\\"Validation score:\\\", self.data_plus_meta_[self.rack_order_].score_test_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c75b1abbc7cd606c3376610e800fae68\",\n \"score\": \"0.55648845\",\n \"text\": \"def test():\\n machine = JobMachine()\\n jobs = []\\n \\n #first round\\n for i in range(10):\\n job = IlastikJob(testFunction, [\\\"Processing task \\\",str(i)])\\n jobs.append(job)\\n\\n results = machine.process(jobs)\\n print \\\"finished:\\\"\\n \\n for r in results:\\n print r\\n\\n GLOBAL_WM.setThreadCount(0) #set unthreaded mode\\n \\n #second round\\n for i in range(10):\\n job = IlastikJob(testFunction, [\\\"#2 Processing task \\\",str(i)])\\n jobs.append(job)\\n\\n results = machine.process(jobs)\\n print \\\"finished:\\\"\\n \\n for r in results:\\n print r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0722726926648b61d4c97c42dea0bfe6\",\n \"score\": \"0.5550157\",\n \"text\": \"def func_parse_jobs(self):\\n\\n # Parse job info, if no sample file is given then return [None].\\n # Does not do this immediately beause there are other variables\\n # to manage depending in the number of samples or existence of a sample\\n # sheet.\\n llstr_sample_data = [None]\\n if self.ns_arguments.str_sample_file:\\n with open(self.ns_arguments.str_sample_file, \\\"r\\\") as hndl_samples:\\n llstr_sample_data = [lstr_row for lstr_row in csv.reader(hndl_samples, delimiter = b\\\"\\\\t\\\")]\\n\\n # Turn on Job running\\n if len(llstr_sample_data) > 1 and self.ns_arguments.i_number_jobs > 1:\\n self.f_is_multi_job = True\\n else:\\n self.f_is_multi_job = False\\n\\n # No more job threads than number of jobs requested\\n self.ns_arguments.i_number_jobs = min(self.ns_arguments.i_number_jobs,\\n len(llstr_sample_data))\\n\\n # To run multiple samples, a pipeline config is required\\n if self.ns_arguments.str_pipeline_config_file:\\n self.str_possible_config_file = os.path.realpath(self.ns_arguments.str_pipeline_config_file)\\n else:\\n self.str_possible_config_file = os.path.splitext(os.path.realpath(sys.argv[0]))[0] + C_STR_CONFIG_EXTENSION\\n if self.ns_arguments.str_sample_file and (not os.path.exists(self.str_possible_config_file)):\\n self.logr_job.error(\\\"All jobs with sample files need pipeline config files.\\\")\\n exit(997)\\n\\n return(llstr_sample_data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"021f7a48e738ec2e321c5728b01cb816\",\n \"score\": \"0.5547924\",\n \"text\": \"def process(self, jobs):\\n \\n self.numWorkers = GLOBAL_WM.threads\\n self.sem = threading.Semaphore(self.numWorkers)\\n self.workers = deque()\\n for i in range(self.numWorkers):\\n worker = GLOBAL_WM.workerPool[i]\\n #TODO: only append free workers !\\n self.workers.append(worker)\\n \\n #do the work\\n while len(jobs) > 0:\\n self.sem.acquire()\\n job = jobs.pop()\\n job.run(self)\\n \\n #make sure all workers finished: \\n for i in range(self.numWorkers):\\n self.sem.acquire()\\n\\n #release the semaphore finally: \\n for i in range(self.numWorkers):\\n self.sem.release() \\n \\n results = self.results\\n self.results = deque() \\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0d7d8371563139764cf7f9145032c8c\",\n \"score\": \"0.554417\",\n \"text\": \"def run_data_cleaning_job(data_cleaning_job):\\n dataset_path = \\\"\\\"\\n if data_cleaning_job[\\\"dataset\\\"][\\\"type\\\"] == \\\"csv\\\":\\n dataset_path = os.path.abspath(data_cleaning_job[\\\"dataset\\\"][\\\"param\\\"][0])\\n\\n return_list = []\\n if data_cleaning_job[\\\"tool\\\"][\\\"name\\\"] == \\\"dboost\\\":\\n return_list = run_dboost(dataset_path, data_cleaning_job[\\\"tool\\\"][\\\"param\\\"])\\n if data_cleaning_job[\\\"tool\\\"][\\\"name\\\"] == \\\"nadeef\\\":\\n return_list = run_nadeef(dataset_path, data_cleaning_job[\\\"tool\\\"][\\\"param\\\"])\\n if data_cleaning_job[\\\"tool\\\"][\\\"name\\\"] == \\\"openrefine\\\":\\n return_list = run_openrefine(dataset_path, data_cleaning_job[\\\"tool\\\"][\\\"param\\\"])\\n if data_cleaning_job[\\\"tool\\\"][\\\"name\\\"] == \\\"katara\\\":\\n return_list = run_katara(dataset_path, data_cleaning_job[\\\"tool\\\"][\\\"param\\\"])\\n return return_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5126bfa320bc1b25e145d8177b9e107f\",\n \"score\": \"0.5540038\",\n \"text\": \"def processTask():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78b5d2a62034a40b219973e0826b8d54\",\n \"score\": \"0.553229\",\n \"text\": \"def main():\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\\n maisons.pipeline()\\n appartements.pipeline()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3459d6d8bca9c5d317f224563e6ed7bc\",\n \"score\": \"0.55315953\",\n \"text\": \"def job(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d1a4e90c094769383f80ecdda63de4d\",\n \"score\": \"0.5529511\",\n \"text\": \"def run(self, inputs):\\n with self as process:\\n for data in inputs:\\n process(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"687b9fbf65020492df54c3684221ba31\",\n \"score\": \"0.55291593\",\n \"text\": \"def results():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95fdc92bfc7e48ae8482fc61a0f32b11\",\n \"score\": \"0.5526919\",\n \"text\": \"def split_processing(dataset, name, description, api, args, resume,\\n session_file=None, path=None, log=None):\\n train_dataset = None\\n test_dataset = None\\n sample_rate = 1 - args.test_split\\n # if resuming, try to extract train dataset form log files\\n if resume:\\n message = u.dated(\\\"Dataset not found. Resuming.\\\\n\\\")\\n resume, train_dataset = c.checkpoint(\\n c.is_dataset_created, path, \\\"_train\\\", debug=args.debug,\\n message=message, log_file=session_file, console=args.verbosity)\\n\\n if train_dataset is None:\\n dataset_split_args = r.set_dataset_split_args(\\n \\\"%s - train (%s %%)\\\" % (name,\\n int(sample_rate * 100)), description, args,\\n sample_rate, out_of_bag=False)\\n train_dataset = r.create_dataset(\\n dataset, dataset_split_args, args, api, path, session_file,\\n log, \\\"train\\\")\\n if train_dataset:\\n train_dataset = r.get_dataset(train_dataset, api,\\n args.verbosity, session_file)\\n\\n # if resuming, try to extract test dataset form log files\\n if resume:\\n message = u.dated(\\\"Dataset not found. Resuming.\\\\n\\\")\\n resume, test_dataset = c.checkpoint(\\n c.is_dataset_created, path, \\\"_test\\\", debug=args.debug,\\n message=message, log_file=session_file, console=args.verbosity)\\n\\n if test_dataset is None:\\n dataset_split_args = r.set_dataset_split_args(\\n \\\"%s - test (%s %%)\\\" % (name,\\n int(args.test_split * 100)), description, args,\\n sample_rate, out_of_bag=True)\\n test_dataset = r.create_dataset(\\n dataset, dataset_split_args, args, api, path, session_file,\\n log, \\\"test\\\")\\n if test_dataset:\\n test_dataset = r.get_dataset(test_dataset, api, args.verbosity,\\n session_file)\\n return train_dataset, test_dataset, resume\",\n \"title\": \"\"\n },\n {\n \"docid\": \"961b7ba39ffd1c9445cd4b375ed7e239\",\n \"score\": \"0.5521262\",\n \"text\": \"def execute(self, work_unit):\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1681,"cells":{"query_id":{"kind":"string","value":"31ece964885d2dce60226c5e8cdbc050"},"query":{"kind":"string","value":"Emit ``CREATE TYPE`` for this"},"positive_passages":{"kind":"list like","value":[{"docid":"4deeed2a3eb4a6af3b3697f5038a011a","score":"0.0","text":"def create(self, bind=None, checkfirst=True):\n if not bind.dialect.supports_native_enum:\n return\n\n super().create(bind, checkfirst=checkfirst)","title":""}],"string":"[\n {\n \"docid\": \"4deeed2a3eb4a6af3b3697f5038a011a\",\n \"score\": \"0.0\",\n \"text\": \"def create(self, bind=None, checkfirst=True):\\n if not bind.dialect.supports_native_enum:\\n return\\n\\n super().create(bind, checkfirst=checkfirst)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f6f886f23fbe426eefa80c709ee61745","score":"0.66751873","text":"def create(self):\n return CreateDatatypes([self])[0]","title":""},{"docid":"4e275bd469fadcc00e9677156359e143","score":"0.66318345","text":"def create_type(self, create_type):\n\n self._create_type = create_type","title":""},{"docid":"525c59659d1bfe4368d4d9b19edc3cef","score":"0.6471718","text":"def GenerateType(self, field):\n pass","title":""},{"docid":"6e19f2d7499757c2838e84e3b33b0149","score":"0.6424601","text":"def _generate_create(create_op: ccdag.Create):\n\n col_type_str = \", \".join(\n [col.type_str for col in create_op.out_rel.columns])\n return \"CREATE RELATION {} WITH COLUMNS ({})\\n\".format(\n create_op.out_rel.dbg_str(),\n col_type_str\n )","title":""},{"docid":"6c60f68ec86ed1798f83ad84da47c925","score":"0.63537914","text":"def do_type_create(cs, args):\n vtype = cs.volume_types.create(args.name)\n _print_volume_type_list([vtype])","title":""},{"docid":"6c60f68ec86ed1798f83ad84da47c925","score":"0.63537914","text":"def do_type_create(cs, args):\n vtype = cs.volume_types.create(args.name)\n _print_volume_type_list([vtype])","title":""},{"docid":"730e8dd2b59b217401eb73a8023a7e39","score":"0.6259442","text":"def GenerateType(self, message):\n pass","title":""},{"docid":"c63faf3d37ad5576be65e2cde82a0b86","score":"0.62456167","text":"def construct_type(self, **kw):\n return self.type.model(**kw).save()","title":""},{"docid":"64b2bf809e196709a59ae865ff1e98db","score":"0.61108524","text":"def _insert_type(uid=None, owner=None, type=None, prop=None,\n name=None,\n type_of=None, is_class=False,\n prop_keys=None,\n custom=None):\n\n # default uid creation inherited from SMDynDoc\n\n if custom is None:\n custom = {}\n\n print(type)\n\n sm_type = SMType(uid=uid, owner=owner, type=type, prop=prop,\n name=name,\n type_of=type_of, is_class=is_class,\n prop_keys=prop_keys,\n **custom)\n\n sm_type.save(validate=True, write_concert={\"w\": 1})\n logger.debug('Inserted SMType with uid %s', sm_type.uid)\n\n return sm_type","title":""},{"docid":"d4f81ea95da993583f9a090bfb49aea1","score":"0.5834776","text":"def insert_datatype(self, **kwargs):\n # Prepare the DDL schema templates if it has not already been done\n if not TABLE_TEMPLATES:\n _set_templates()\n sql = \"\"\"INSERT INTO datatype (name, struct, units, description)\n VALUES (%(name)s, %(struct)s, %(units)s, %(description)s)\"\"\"\n self.execute(sql, kwargs)\n datatype = self.fetch_datatype(kwargs)\n template = TABLE_TEMPLATES[datatype['struct']]\n create_ddl = template % datatype # Set %(name) and %(id) in table creation DDL\n self.execute(create_ddl)\n return datatype","title":""},{"docid":"e2131fd5e58d39f32bd5d1d95e845288","score":"0.5710527","text":"def type(*args, **kwargs):\n \n pass","title":""},{"docid":"12c2b00e569162c0c588f17589dd742f","score":"0.5705717","text":"def serialized_type_declaration(self) -> str:\n return render_type_declaration(\n name=self.serializer_field_name,\n optional=self.serializer_field.allow_null,\n readonly=self.serializer_field.read_only,\n type_=self._serialized_type\n )","title":""},{"docid":"b3c83207cb6f0fc4992661a34eca0f1b","score":"0.56991327","text":"def addTypeId(*args, **kwargs):\n \n pass","title":""},{"docid":"47d98f31481c4d476966a656cc8bc8ef","score":"0.5691845","text":"def create_record(self, type, name, content):\n return self._change_record_sets('CREATE', type, name, content)","title":""},{"docid":"db3d00831dea18af5a133f6eb649a0a3","score":"0.569033","text":"def create(self, props):\n return self.model_type(**props)","title":""},{"docid":"0fbe00eb4461e73489dc89e543ca5a7c","score":"0.56720513","text":"def create(self, dbversion=None):\n dflt = ''\n if self.default:\n dflt = \"DEFAULT \"\n clauses = []\n for (strat, oper) in list(self.operators.items()):\n clauses.append(\"OPERATOR %d %s\" % (strat, oper))\n for (supp, func) in list(self.functions.items()):\n clauses.append(\"FUNCTION %d %s\" % (supp, func))\n if self.storage is not None:\n clauses.append(\"STORAGE %s\" % self.storage)\n return [\"CREATE OPERATOR CLASS %s\\n %sFOR TYPE %s USING %s \"\n \"AS\\n %s\" % (\n self.qualname(), dflt,\n self.qualname(self.schema, self.type), self.index_method,\n ',\\n ' .join(clauses))]","title":""},{"docid":"71451039b6acb3e2d04d02aa9220b9cb","score":"0.5665075","text":"def creatortype2id(self):\n return self._maps.creatortype2id","title":""},{"docid":"8a3550b493a2712412db1375334d4d6d","score":"0.5653759","text":"def type_declaration(f):\n f.write( ('{' + '\\n' ) )\n f.write( ('type particleCollector;' + '\\n') )\n f.write( ('mode polygon;' + '\\n') )\n f.write( ('polygons' + '\\n') ) \n f.write( ('(' + '\\n') )\n f.write( ('(' + '\\n') )","title":""},{"docid":"e8e73dd686e80e76a2fa15140cb5d88a","score":"0.56230026","text":"def Type(tpe):\r\n return {'_type': tpe}","title":""},{"docid":"96c0c0dbe0f73cc756fb31a6fce4575d","score":"0.5578257","text":"def getType():","title":""},{"docid":"768aef51c34ccac9af42a77a7671b598","score":"0.556209","text":"def startType(self):","title":""},{"docid":"6438b41d53e085b5b1785d8a5fa74926","score":"0.55409884","text":"def __init__(__self__, *,\n type: pulumi.Input[str]):\n pulumi.set(__self__, \"type\", 'Avro')","title":""},{"docid":"a2f7039ec4a3149d437b82c854d005fe","score":"0.5526266","text":"def __init__(__self__, *,\n name: Optional[pulumi.Input[str]] = None,\n type: Optional[pulumi.Input['StandardSqlDataTypeArgs']] = None):\n if name is not None:\n pulumi.set(__self__, \"name\", name)\n if type is not None:\n pulumi.set(__self__, \"type\", type)","title":""},{"docid":"7d7046dd26485533451bfedb8aa2cfdb","score":"0.5521865","text":"def createString(self):\n return self.name + \" \" + self.type","title":""},{"docid":"2d5698b51bdf2654d4b2c2d2ca09f785","score":"0.5521394","text":"def create(type_dict, *type_parameters, **kwargs):\n raise NotImplementedError(\"create unimplemented for: %s\" % repr(type_parameters))","title":""},{"docid":"23d48fb5867753c3fc5830ca11634c83","score":"0.55052954","text":"def __init__(self, process, new_type):\n self.new_type = new_type\n # TODO: Validate type.\n\n super().__init__(process=process, schema_type=\"output\")","title":""},{"docid":"38eb866a1784da25d8dee363547718e7","score":"0.55039626","text":"def get_creation_command(self) -> str:\n\n command = '{} {}'.format(self.title, self.type)\n if self.condition:\n command += ' {}'.format(self.condition)\n if self.default_value:\n command += ' DEFAULT {}'.format(self.default_value)\n return command","title":""},{"docid":"4ce57eb497ec8f1a455396d30c908081","score":"0.54850966","text":"def create_tables(self):\n map_sql_types = {str: \"TEXT\", int: \"INTEGER\"}\n statements = []\n for t in self.types.values():\n fields = [\"id INTEGER PRIMARY KEY\"]\n for field in t.fields:\n if field.fieldtype in map_sql_types:\n fields.append(f\"{field.name} {map_sql_types[field.fieldtype]}\")\n # TODO: nullable?\n else:\n # TODO: foreign key\n fields.append(f\"{field.name} INTEGER\")\n fields.append(\n f\"FOREIGN KEY({field.name}) REFERENCES {field.fieldtype.name}(id)\"\n )\n fields_string = \",\\n\".join(fields)\n statements.append(\n f\"CREATE TABLE IF NOT EXISTS {t.name} (\\n{fields_string}\\n);\"\n )\n return \"\\n\".join(statements)","title":""},{"docid":"442434683b6d921a5a15104b250fb252","score":"0.5480817","text":"def createNode(self, nodetype: str) -> TGNode:","title":""},{"docid":"6442aa8c9f6e9f610b423e4b5d8ff6ed","score":"0.5480077","text":"def addNew(self, typ=None):\n self.sigAddNew.emit(self, typ)","title":""},{"docid":"a91c4ca53d46eb671e212b63bc3a5573","score":"0.54759026","text":"def id2creatortype(self):\n return self._maps.id2creatortype","title":""},{"docid":"45e10101f353a3ca214603d45a1d606b","score":"0.54746526","text":"def create_type(parent,idx,opc_type):\n \n try:\n class_of_entity = Factory.get_entity_class(opc_type)\n\n logger.info(\"Inicio criando tipo {}-{} !\".format(opc_type,class_of_entity.__name__))\n \n # cria o tipo de objeto\n type_obj = class_of_entity.create(parent,idx)\n\n except IOError as e:\n\n type_obj = None\n logger.warning(\"Não foi possível criar : Parent {} - Tipo {} class {}\".format(parent,opc_type,class_of_entity.__name__)) \n print(e)\n\n return type_obj","title":""},{"docid":"e1ffe9f4030aaf80dcd6ec9ed0609b98","score":"0.545072","text":"def add_type(self, obc_type=1):\n\n # Feels a bit ridiculous having a whole method for this...\n setattr(self, 'type', obc_type)","title":""},{"docid":"d45ed722753266aba42eee9dd709016c","score":"0.5446835","text":"def __init__(self, name, info=None, constr=None, id=None):\n Type.__init__(self, name, 0, info, constr, id)","title":""},{"docid":"1cf784e3233119aaee41a0ef1591154f","score":"0.5443688","text":"def createFromType(self, tokenType, text):\r\n\r\n raise NotImplementedError","title":""},{"docid":"045a045daef2f4efcbf7dbd9a55040a4","score":"0.54109013","text":"async def create(self, payload, data, query_type):\n type_class = self.__get_type(query_type)\n await type_class.create(payload, data)","title":""},{"docid":"f9204979255b5e4afe8a02a5829c5fe9","score":"0.5404765","text":"def create(self, type_, *args, **kwargs):\n attribute = type_(self, *args, **kwargs)\n self.append(attribute)\n return attribute","title":""},{"docid":"7c443061a2e83349ded6005b31c6c679","score":"0.5402147","text":"def __init__(__self__, *,\n type: pulumi.Input[str],\n name: Optional[pulumi.Input[str]] = None,\n source_field: Optional[pulumi.Input[str]] = None):\n pulumi.set(__self__, \"type\", 'Dynamic')\n if name is not None:\n pulumi.set(__self__, \"name\", name)\n if source_field is not None:\n pulumi.set(__self__, \"source_field\", source_field)","title":""},{"docid":"4ccbc1022bb64bdddc012420394b1dcb","score":"0.5398705","text":"def type(self) -> str:\r\n ...","title":""},{"docid":"c29e8bf7ba4e46aeff2f0de7e5bb6fab","score":"0.5397507","text":"def __init__(self, name, info=None, constr=None, id=None):\n Type.__init__(self, name, 10, info, constr, id)","title":""},{"docid":"3939f517a336d64c1c7b01471d90af7d","score":"0.5396055","text":"def create(self, dbversion=None):\n dflt = ''\n if self.default:\n dflt = 'DEFAULT '\n return [\"CREATE %sCONVERSION %s\\n FOR '%s' TO '%s' FROM %s\" % (\n dflt, self.qualname(), self.source_encoding,\n self.dest_encoding, self.function)]","title":""},{"docid":"de7b74545d17d1a69b37c7a384296210","score":"0.53780836","text":"def add_type_sig(self, name, n):\n self.add_data(\"type_sig\", name, n)","title":""},{"docid":"8dca002210206a10b680df289e908fc1","score":"0.53732693","text":"def newType(self):\n dlg = NameEntryDialog(_('Add Type'), _('Enter new type name:'), '', '',\n ConfigDialog.formatsRef.typeNames(), self)\n if dlg.exec_() == QDialog.Accepted:\n newFormat = nodeformat.NodeFormat(dlg.text,\n ConfigDialog.formatsRef, None,\n True)\n ConfigDialog.formatsRef[dlg.text] = newFormat\n ConfigDialog.currentTypeName = dlg.text\n ConfigDialog.currentFieldName = newFormat.fieldNames()[0]\n self.updateContent()\n self.mainDialogRef.setModified()","title":""},{"docid":"3561849a4bb975cca0e189f9edfc474e","score":"0.5366511","text":"def getEntityOfTypeCreateEvent(self, entType):\n return 'entityOfTypeCreate-%s-%s' % (self.levelId, entType)","title":""},{"docid":"8f5f8e717cdfd8ca99998a02d67cd8b1","score":"0.5364333","text":"def __init__(self, type:str, name:str):\n self.type = type\n self.name = name","title":""},{"docid":"bd60c3328e2d2ca3ebe849acc556fd02","score":"0.5323545","text":"def create(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"bd60c3328e2d2ca3ebe849acc556fd02","score":"0.5323545","text":"def create(self): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"57da7a492abc614ef2d1d94093211170","score":"0.53162694","text":"def type(self, identity):\n self.startType()\n self.topicRef(identity)\n self.endType()","title":""},{"docid":"0a5012b404915fbaa8ed6c830b222488","score":"0.52962255","text":"def type_def(self):\n return self._type_def","title":""},{"docid":"62c825e8500746f2d84fb7a94a76231a","score":"0.5287055","text":"def create(self, product_type_info):\n product_type_create = \"product-type\"\n return self.connection.make_request(\n \"{}/{}\".format(self.resource_parent, product_type_create), \"POST\", data=product_type_info\n )","title":""},{"docid":"6474a92fe3c5a7e161c0f2374f526960","score":"0.5285874","text":"def type_(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"type\"),\n )","title":""},{"docid":"6474a92fe3c5a7e161c0f2374f526960","score":"0.5285874","text":"def type_(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"type\"),\n )","title":""},{"docid":"6474a92fe3c5a7e161c0f2374f526960","score":"0.5285874","text":"def type_(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"type\"),\n )","title":""},{"docid":"6474a92fe3c5a7e161c0f2374f526960","score":"0.5285874","text":"def type_(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"type\"),\n )","title":""},{"docid":"6474a92fe3c5a7e161c0f2374f526960","score":"0.5285874","text":"def type_(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"type\"),\n )","title":""},{"docid":"6474a92fe3c5a7e161c0f2374f526960","score":"0.5285874","text":"def type_(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"type\"),\n )","title":""},{"docid":"6474a92fe3c5a7e161c0f2374f526960","score":"0.5285874","text":"def type_(self) -> str:\n return typing.cast(\n str,\n self._properties.get(\"type\"),\n )","title":""},{"docid":"2781e2bb46c60e48146e1017ac682113","score":"0.52776563","text":"def _create_instance(self):\n return type(str(\"\"), (), {})","title":""},{"docid":"fd2ff34b637e8ac0aa4fb303e2f42319","score":"0.52776384","text":"def makeTypeElement(self, name, extra_attrs={}):\n for key in extra_attrs.keys():\n extra_attrs[key] = str(extra_attrs[key]) # convert everything to strings\n return le.SubElement(self.__root, \"type\", name=name, **extra_attrs)","title":""},{"docid":"56ac3af91ea168e17915ff312b933784","score":"0.5273929","text":"def get_create_table_stmt(data_type):\n\tcol_list = map(lambda a:'%s VARCHAR(1024)'%a,COLUMN_MAP[data_type].keys())\n\tcreate_stmt = 'create table %s(%s)'%(data_type, \",\".join(col_list))\n\treturn create_stmt","title":""},{"docid":"5a3d637d0a280475e420a85b8c053aef","score":"0.5268393","text":"def GetType(self, *args, **kwargs):\n pass","title":""},{"docid":"4608b8efafbb1cbd30d916c1fec4ce2e","score":"0.5258888","text":"def getType(self):","title":""},{"docid":"603277c456c13dbfabdfc1cf92d2e3be","score":"0.5250335","text":"def gettype(self):\n return 'type'","title":""},{"docid":"1bb96a9b7aa758639bc6aca665831f8d","score":"0.52470636","text":"def register_type(self, oids, name, casting):\n assert type(oids) is tuple\n assert type(name) in (unicode, str)\n assert hasattr(casting, \"__call__\")\n self._register_types.append((oids, name, casting))\n if self._db is not None:\n psycopg2.extensions.register_type(psycopg2.extensions.new_type(oids, name, casting))","title":""},{"docid":"6dc10201148e0c4aecaf1cbef5f62c9b","score":"0.5243135","text":"def writeTypeDefs(self) :\n output = \"\\n\"\n for eachClass in cppClasses:\n output += eachClass.writeClassTypeDef()\n return output","title":""},{"docid":"bde24ec52bb19c22770f3bdcf338ab74","score":"0.52385294","text":"def __call__(self, name: str) -> typing.Type:\n ...","title":""},{"docid":"162a2595cab833f5d11d2bfe677cca01","score":"0.523457","text":"def generate_schema() -> StructType:\n\n schema = StructType(\n [StructField(\"text\", StringType()), StructField(\"created_at\", StringType())],\n )\n\n return schema","title":""},{"docid":"5ff8eaf530dfcc68e0c8a2d7ddf35f53","score":"0.5226337","text":"def __init__(self):\n Type.__init__(self, '1', 16, 'A type with a single member',\n constr=None, id=None)","title":""},{"docid":"051eb5199fed6677e52c9ef3c00c239a","score":"0.5219687","text":"def __init__(self, type):\n self.__type = type","title":""},{"docid":"e1f8da56560958248870a7e0dfd69a39","score":"0.5209874","text":"def create(self, table, name):\n\n columns = [\"{0} {1}\".format(name, ctype) for name, ctype in table.items()]\n create = SQLite.CREATE_TABLE.format(table=name, fields=\", \".join(columns))\n\n # pylint: disable=W0703\n try:\n self.cur.execute(create)\n except Exception as e:\n print(create)\n print(\"Failed to create table: \" + e)","title":""},{"docid":"f538611f3742d2efc341c3863dc3eb78","score":"0.5204573","text":"def generate(self):\n def v(node):\n if node.is_internal():\n self._generate_node(node.name)\n else:\n self.writer.put_line(\"// {0}\".format(node.name))\n self.writer.put_line(\"template<> struct {0}<{1}> {{\".format(CLASS,node.name))\n self.writer.put_line(\"static bool Test(IRType type) {\",1)\n self.writer.put_line(\"return type == {0}<{1}>::Value;\".format(CLASS2TYPE,node.name),2)\n self.writer.put_line(\"}\",1)\n self.writer.put_line(\"};\",0)\n\n self.model.visit(v)","title":""},{"docid":"0c23a71098ef096dc76b76488c569e64","score":"0.5199731","text":"def create():","title":""},{"docid":"e0c21eafbdf30aa64cb465a8e03b24a7","score":"0.5193321","text":"def create(self, identity, data=None, record=None, **kwargs):\n self._set_type(data, record)","title":""},{"docid":"804e6f25d1c107a3afd3db1a7cafccb4","score":"0.51912457","text":"def get_type(self) -> str:\n pass","title":""},{"docid":"8535d7006b00184c327588a653c42d2a","score":"0.51888454","text":"def type(self) -> str:\n return self.__type","title":""},{"docid":"8535d7006b00184c327588a653c42d2a","score":"0.51888454","text":"def type(self) -> str:\n return self.__type","title":""},{"docid":"8535d7006b00184c327588a653c42d2a","score":"0.51888454","text":"def type(self) -> str:\n return self.__type","title":""},{"docid":"68d3f93265014b75bd71b21f6b20afb0","score":"0.5182294","text":"def ptype(self):\n\t\tpass","title":""},{"docid":"c0727c9309c4f5fcd046661fc3890e38","score":"0.51788837","text":"def __create_node__(self, node_type, name):\n\n return maya.cmds.createNode(node_type, name=name), True","title":""},{"docid":"5f52b757fb2f30f9b11ea22ffcab9b14","score":"0.5172284","text":"def create(self):\n pass","title":""},{"docid":"5f52b757fb2f30f9b11ea22ffcab9b14","score":"0.5172284","text":"def create(self):\n pass","title":""},{"docid":"5f52b757fb2f30f9b11ea22ffcab9b14","score":"0.5172284","text":"def create(self):\n pass","title":""},{"docid":"5f52b757fb2f30f9b11ea22ffcab9b14","score":"0.5172284","text":"def create(self):\n pass","title":""},{"docid":"c8e8a8071769a421510615f7aead4a03","score":"0.5162163","text":"def register_type(self, name, target):\n self.types[name] = target","title":""},{"docid":"6b363101763f6b192cad06292ae42f07","score":"0.5156602","text":"def type_name(self) -> str:\n return self._type_name","title":""},{"docid":"20c1726c2cc63e83035f75dab663ebb1","score":"0.5146935","text":"def get_type():\n pass","title":""},{"docid":"04577d015c001a5d804566b6f6272030","score":"0.51455396","text":"def type(self) -> type:","title":""},{"docid":"66844de0da2cd3fa60c93baa57ad409b","score":"0.5141454","text":"def create(doc_type: str, name: str):\n doc_type = doc_type.lower().strip()\n create_template(doc_type, name)","title":""},{"docid":"62405d0e700086b1ecf96b01595fee16","score":"0.51379836","text":"def __schemaCreate(self, schemaDefObj):\n try:\n tableIdList = schemaDefObj.getSchemaIdList()\n sqlGen = SqlGenAdmin(self.__verbose)\n sqlL = sqlGen.createDatabaseSQL(schemaDefObj.getDatabaseName())\n for tableId in tableIdList:\n tableDefObj = schemaDefObj.getSchemaObject(tableId)\n sqlL.extend(sqlGen.createTableSQL(databaseName=schemaDefObj.getDatabaseName(), tableDefObj=tableDefObj))\n\n logger.debug(\"Schema creation SQL string\\n %s\\n\\n\", \"\\n\".join(sqlL))\n with Connection(cfgOb=self.__cfgOb, resourceName=self.__resourceName) as client:\n myQ = MyDbQuery(dbcon=client, verbose=self.__verbose)\n #\n # Permit warnings to support \"drop table if exists\" for missing tables.\n #\n myQ.setWarning(\"ignore\")\n ret = myQ.sqlCommand(sqlCommandList=sqlL)\n logger.debug(\"\\n\\n+INFO mysql server returns %r\\n\", ret)\n self.assertTrue(ret)\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n self.fail()","title":""},{"docid":"3cd7e58bbd43c165a5021d2c22ba5618","score":"0.51324487","text":"def to_sql_statement_create(self) -> TextClause:\n\n # Remove possible semicolon from definition because we're adding a \"WITH DATA\" clause\n definition = self.definition.rstrip().rstrip(\";\")\n\n return sql_text(\n f'CREATE MATERIALIZED VIEW {self.literal_schema}.\"{self.signature}\" AS {definition} WITH {\"NO\" if not self.with_data else \"\"} DATA;'\n )","title":""},{"docid":"489fd351b26bd6bf2af879a0afd9e014","score":"0.51305354","text":"def generate_structure(dbo):\n dbo.create_tables()","title":""},{"docid":"4999e5342b2bc8e363f0f69a8b05b53f","score":"0.512845","text":"def get_type(self) -> str:\n return self.TYPE","title":""},{"docid":"69c414eeba011099d0d7a5ec960d825b","score":"0.51261026","text":"def type():\n raise NotImplementedError()","title":""},{"docid":"d7abfa82f3628eba28d9d7ffaa2505c3","score":"0.5121354","text":"def type(self):\n pass","title":""},{"docid":"d7abfa82f3628eba28d9d7ffaa2505c3","score":"0.5121354","text":"def type(self):\n pass","title":""},{"docid":"6c527468828625a5d195fe2ffc6c9211","score":"0.5119413","text":"def type(self):","title":""},{"docid":"7308ff3740b257615fb81f332fc23181","score":"0.5119267","text":"def add(self, content_type_info):\n return_type = ContentType(self.context)\n self.add_child(return_type)\n params = content_type_info.to_json()\n for k, v in params.items():\n if k == \"Id\":\n return_type.set_property(k, {\"StringValue\": v}, True)\n else:\n return_type.set_property(k, v, True)\n qry = CreateEntityQuery(self, return_type, return_type)\n self.context.add_query(qry)\n return return_type","title":""},{"docid":"94b52dfa1aaae6662ac114152b9772f2","score":"0.5118612","text":"def addType(self, trajType):\n self.type = trajType","title":""},{"docid":"e498d1c0ba911b7899079bb2c375c444","score":"0.5118403","text":"def visit_type(self, name, type_):\n\n child_indent = Indenter(self.type_callback)\n grandchild_indent = Indenter(child_indent)\n\n print()\n self.type_callback('Type: {}'.format(name))\n\n if type_['sources']:\n child_indent('Sources:')\n for name in natural_sorted(type_['sources']):\n grandchild_indent('{}: {}'.format(name, type_['sources'][name]))","title":""},{"docid":"014198b45f647aa0e5e9b3bfce09d687","score":"0.5113016","text":"def type_(self, value: str):\n self._properties[\"type\"] = value","title":""},{"docid":"014198b45f647aa0e5e9b3bfce09d687","score":"0.5113016","text":"def type_(self, value: str):\n self._properties[\"type\"] = value","title":""}],"string":"[\n {\n \"docid\": \"f6f886f23fbe426eefa80c709ee61745\",\n \"score\": \"0.66751873\",\n \"text\": \"def create(self):\\n return CreateDatatypes([self])[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e275bd469fadcc00e9677156359e143\",\n \"score\": \"0.66318345\",\n \"text\": \"def create_type(self, create_type):\\n\\n self._create_type = create_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"525c59659d1bfe4368d4d9b19edc3cef\",\n \"score\": \"0.6471718\",\n \"text\": \"def GenerateType(self, field):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e19f2d7499757c2838e84e3b33b0149\",\n \"score\": \"0.6424601\",\n \"text\": \"def _generate_create(create_op: ccdag.Create):\\n\\n col_type_str = \\\", \\\".join(\\n [col.type_str for col in create_op.out_rel.columns])\\n return \\\"CREATE RELATION {} WITH COLUMNS ({})\\\\n\\\".format(\\n create_op.out_rel.dbg_str(),\\n col_type_str\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c60f68ec86ed1798f83ad84da47c925\",\n \"score\": \"0.63537914\",\n \"text\": \"def do_type_create(cs, args):\\n vtype = cs.volume_types.create(args.name)\\n _print_volume_type_list([vtype])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c60f68ec86ed1798f83ad84da47c925\",\n \"score\": \"0.63537914\",\n \"text\": \"def do_type_create(cs, args):\\n vtype = cs.volume_types.create(args.name)\\n _print_volume_type_list([vtype])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"730e8dd2b59b217401eb73a8023a7e39\",\n \"score\": \"0.6259442\",\n \"text\": \"def GenerateType(self, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c63faf3d37ad5576be65e2cde82a0b86\",\n \"score\": \"0.62456167\",\n \"text\": \"def construct_type(self, **kw):\\n return self.type.model(**kw).save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b2bf809e196709a59ae865ff1e98db\",\n \"score\": \"0.61108524\",\n \"text\": \"def _insert_type(uid=None, owner=None, type=None, prop=None,\\n name=None,\\n type_of=None, is_class=False,\\n prop_keys=None,\\n custom=None):\\n\\n # default uid creation inherited from SMDynDoc\\n\\n if custom is None:\\n custom = {}\\n\\n print(type)\\n\\n sm_type = SMType(uid=uid, owner=owner, type=type, prop=prop,\\n name=name,\\n type_of=type_of, is_class=is_class,\\n prop_keys=prop_keys,\\n **custom)\\n\\n sm_type.save(validate=True, write_concert={\\\"w\\\": 1})\\n logger.debug('Inserted SMType with uid %s', sm_type.uid)\\n\\n return sm_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4f81ea95da993583f9a090bfb49aea1\",\n \"score\": \"0.5834776\",\n \"text\": \"def insert_datatype(self, **kwargs):\\n # Prepare the DDL schema templates if it has not already been done\\n if not TABLE_TEMPLATES:\\n _set_templates()\\n sql = \\\"\\\"\\\"INSERT INTO datatype (name, struct, units, description)\\n VALUES (%(name)s, %(struct)s, %(units)s, %(description)s)\\\"\\\"\\\"\\n self.execute(sql, kwargs)\\n datatype = self.fetch_datatype(kwargs)\\n template = TABLE_TEMPLATES[datatype['struct']]\\n create_ddl = template % datatype # Set %(name) and %(id) in table creation DDL\\n self.execute(create_ddl)\\n return datatype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2131fd5e58d39f32bd5d1d95e845288\",\n \"score\": \"0.5710527\",\n \"text\": \"def type(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12c2b00e569162c0c588f17589dd742f\",\n \"score\": \"0.5705717\",\n \"text\": \"def serialized_type_declaration(self) -> str:\\n return render_type_declaration(\\n name=self.serializer_field_name,\\n optional=self.serializer_field.allow_null,\\n readonly=self.serializer_field.read_only,\\n type_=self._serialized_type\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c83207cb6f0fc4992661a34eca0f1b\",\n \"score\": \"0.56991327\",\n \"text\": \"def addTypeId(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47d98f31481c4d476966a656cc8bc8ef\",\n \"score\": \"0.5691845\",\n \"text\": \"def create_record(self, type, name, content):\\n return self._change_record_sets('CREATE', type, name, content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3d00831dea18af5a133f6eb649a0a3\",\n \"score\": \"0.569033\",\n \"text\": \"def create(self, props):\\n return self.model_type(**props)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fbe00eb4461e73489dc89e543ca5a7c\",\n \"score\": \"0.56720513\",\n \"text\": \"def create(self, dbversion=None):\\n dflt = ''\\n if self.default:\\n dflt = \\\"DEFAULT \\\"\\n clauses = []\\n for (strat, oper) in list(self.operators.items()):\\n clauses.append(\\\"OPERATOR %d %s\\\" % (strat, oper))\\n for (supp, func) in list(self.functions.items()):\\n clauses.append(\\\"FUNCTION %d %s\\\" % (supp, func))\\n if self.storage is not None:\\n clauses.append(\\\"STORAGE %s\\\" % self.storage)\\n return [\\\"CREATE OPERATOR CLASS %s\\\\n %sFOR TYPE %s USING %s \\\"\\n \\\"AS\\\\n %s\\\" % (\\n self.qualname(), dflt,\\n self.qualname(self.schema, self.type), self.index_method,\\n ',\\\\n ' .join(clauses))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71451039b6acb3e2d04d02aa9220b9cb\",\n \"score\": \"0.5665075\",\n \"text\": \"def creatortype2id(self):\\n return self._maps.creatortype2id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a3550b493a2712412db1375334d4d6d\",\n \"score\": \"0.5653759\",\n \"text\": \"def type_declaration(f):\\n f.write( ('{' + '\\\\n' ) )\\n f.write( ('type particleCollector;' + '\\\\n') )\\n f.write( ('mode polygon;' + '\\\\n') )\\n f.write( ('polygons' + '\\\\n') ) \\n f.write( ('(' + '\\\\n') )\\n f.write( ('(' + '\\\\n') )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e73dd686e80e76a2fa15140cb5d88a\",\n \"score\": \"0.56230026\",\n \"text\": \"def Type(tpe):\\r\\n return {'_type': tpe}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96c0c0dbe0f73cc756fb31a6fce4575d\",\n \"score\": \"0.5578257\",\n \"text\": \"def getType():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"768aef51c34ccac9af42a77a7671b598\",\n \"score\": \"0.556209\",\n \"text\": \"def startType(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6438b41d53e085b5b1785d8a5fa74926\",\n \"score\": \"0.55409884\",\n \"text\": \"def __init__(__self__, *,\\n type: pulumi.Input[str]):\\n pulumi.set(__self__, \\\"type\\\", 'Avro')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f7039ec4a3149d437b82c854d005fe\",\n \"score\": \"0.5526266\",\n \"text\": \"def __init__(__self__, *,\\n name: Optional[pulumi.Input[str]] = None,\\n type: Optional[pulumi.Input['StandardSqlDataTypeArgs']] = None):\\n if name is not None:\\n pulumi.set(__self__, \\\"name\\\", name)\\n if type is not None:\\n pulumi.set(__self__, \\\"type\\\", type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d7046dd26485533451bfedb8aa2cfdb\",\n \"score\": \"0.5521865\",\n \"text\": \"def createString(self):\\n return self.name + \\\" \\\" + self.type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d5698b51bdf2654d4b2c2d2ca09f785\",\n \"score\": \"0.5521394\",\n \"text\": \"def create(type_dict, *type_parameters, **kwargs):\\n raise NotImplementedError(\\\"create unimplemented for: %s\\\" % repr(type_parameters))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d48fb5867753c3fc5830ca11634c83\",\n \"score\": \"0.55052954\",\n \"text\": \"def __init__(self, process, new_type):\\n self.new_type = new_type\\n # TODO: Validate type.\\n\\n super().__init__(process=process, schema_type=\\\"output\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38eb866a1784da25d8dee363547718e7\",\n \"score\": \"0.55039626\",\n \"text\": \"def get_creation_command(self) -> str:\\n\\n command = '{} {}'.format(self.title, self.type)\\n if self.condition:\\n command += ' {}'.format(self.condition)\\n if self.default_value:\\n command += ' DEFAULT {}'.format(self.default_value)\\n return command\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce57eb497ec8f1a455396d30c908081\",\n \"score\": \"0.54850966\",\n \"text\": \"def create_tables(self):\\n map_sql_types = {str: \\\"TEXT\\\", int: \\\"INTEGER\\\"}\\n statements = []\\n for t in self.types.values():\\n fields = [\\\"id INTEGER PRIMARY KEY\\\"]\\n for field in t.fields:\\n if field.fieldtype in map_sql_types:\\n fields.append(f\\\"{field.name} {map_sql_types[field.fieldtype]}\\\")\\n # TODO: nullable?\\n else:\\n # TODO: foreign key\\n fields.append(f\\\"{field.name} INTEGER\\\")\\n fields.append(\\n f\\\"FOREIGN KEY({field.name}) REFERENCES {field.fieldtype.name}(id)\\\"\\n )\\n fields_string = \\\",\\\\n\\\".join(fields)\\n statements.append(\\n f\\\"CREATE TABLE IF NOT EXISTS {t.name} (\\\\n{fields_string}\\\\n);\\\"\\n )\\n return \\\"\\\\n\\\".join(statements)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"442434683b6d921a5a15104b250fb252\",\n \"score\": \"0.5480817\",\n \"text\": \"def createNode(self, nodetype: str) -> TGNode:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6442aa8c9f6e9f610b423e4b5d8ff6ed\",\n \"score\": \"0.5480077\",\n \"text\": \"def addNew(self, typ=None):\\n self.sigAddNew.emit(self, typ)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a91c4ca53d46eb671e212b63bc3a5573\",\n \"score\": \"0.54759026\",\n \"text\": \"def id2creatortype(self):\\n return self._maps.id2creatortype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e10101f353a3ca214603d45a1d606b\",\n \"score\": \"0.54746526\",\n \"text\": \"def create_type(parent,idx,opc_type):\\n \\n try:\\n class_of_entity = Factory.get_entity_class(opc_type)\\n\\n logger.info(\\\"Inicio criando tipo {}-{} !\\\".format(opc_type,class_of_entity.__name__))\\n \\n # cria o tipo de objeto\\n type_obj = class_of_entity.create(parent,idx)\\n\\n except IOError as e:\\n\\n type_obj = None\\n logger.warning(\\\"Não foi possível criar : Parent {} - Tipo {} class {}\\\".format(parent,opc_type,class_of_entity.__name__)) \\n print(e)\\n\\n return type_obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ffe9f4030aaf80dcd6ec9ed0609b98\",\n \"score\": \"0.545072\",\n \"text\": \"def add_type(self, obc_type=1):\\n\\n # Feels a bit ridiculous having a whole method for this...\\n setattr(self, 'type', obc_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45ed722753266aba42eee9dd709016c\",\n \"score\": \"0.5446835\",\n \"text\": \"def __init__(self, name, info=None, constr=None, id=None):\\n Type.__init__(self, name, 0, info, constr, id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cf784e3233119aaee41a0ef1591154f\",\n \"score\": \"0.5443688\",\n \"text\": \"def createFromType(self, tokenType, text):\\r\\n\\r\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"045a045daef2f4efcbf7dbd9a55040a4\",\n \"score\": \"0.54109013\",\n \"text\": \"async def create(self, payload, data, query_type):\\n type_class = self.__get_type(query_type)\\n await type_class.create(payload, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9204979255b5e4afe8a02a5829c5fe9\",\n \"score\": \"0.5404765\",\n \"text\": \"def create(self, type_, *args, **kwargs):\\n attribute = type_(self, *args, **kwargs)\\n self.append(attribute)\\n return attribute\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c443061a2e83349ded6005b31c6c679\",\n \"score\": \"0.5402147\",\n \"text\": \"def __init__(__self__, *,\\n type: pulumi.Input[str],\\n name: Optional[pulumi.Input[str]] = None,\\n source_field: Optional[pulumi.Input[str]] = None):\\n pulumi.set(__self__, \\\"type\\\", 'Dynamic')\\n if name is not None:\\n pulumi.set(__self__, \\\"name\\\", name)\\n if source_field is not None:\\n pulumi.set(__self__, \\\"source_field\\\", source_field)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ccbc1022bb64bdddc012420394b1dcb\",\n \"score\": \"0.5398705\",\n \"text\": \"def type(self) -> str:\\r\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29e8bf7ba4e46aeff2f0de7e5bb6fab\",\n \"score\": \"0.5397507\",\n \"text\": \"def __init__(self, name, info=None, constr=None, id=None):\\n Type.__init__(self, name, 10, info, constr, id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3939f517a336d64c1c7b01471d90af7d\",\n \"score\": \"0.5396055\",\n \"text\": \"def create(self, dbversion=None):\\n dflt = ''\\n if self.default:\\n dflt = 'DEFAULT '\\n return [\\\"CREATE %sCONVERSION %s\\\\n FOR '%s' TO '%s' FROM %s\\\" % (\\n dflt, self.qualname(), self.source_encoding,\\n self.dest_encoding, self.function)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de7b74545d17d1a69b37c7a384296210\",\n \"score\": \"0.53780836\",\n \"text\": \"def add_type_sig(self, name, n):\\n self.add_data(\\\"type_sig\\\", name, n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dca002210206a10b680df289e908fc1\",\n \"score\": \"0.53732693\",\n \"text\": \"def newType(self):\\n dlg = NameEntryDialog(_('Add Type'), _('Enter new type name:'), '', '',\\n ConfigDialog.formatsRef.typeNames(), self)\\n if dlg.exec_() == QDialog.Accepted:\\n newFormat = nodeformat.NodeFormat(dlg.text,\\n ConfigDialog.formatsRef, None,\\n True)\\n ConfigDialog.formatsRef[dlg.text] = newFormat\\n ConfigDialog.currentTypeName = dlg.text\\n ConfigDialog.currentFieldName = newFormat.fieldNames()[0]\\n self.updateContent()\\n self.mainDialogRef.setModified()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3561849a4bb975cca0e189f9edfc474e\",\n \"score\": \"0.5366511\",\n \"text\": \"def getEntityOfTypeCreateEvent(self, entType):\\n return 'entityOfTypeCreate-%s-%s' % (self.levelId, entType)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5f8e717cdfd8ca99998a02d67cd8b1\",\n \"score\": \"0.5364333\",\n \"text\": \"def __init__(self, type:str, name:str):\\n self.type = type\\n self.name = name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd60c3328e2d2ca3ebe849acc556fd02\",\n \"score\": \"0.5323545\",\n \"text\": \"def create(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd60c3328e2d2ca3ebe849acc556fd02\",\n \"score\": \"0.5323545\",\n \"text\": \"def create(self): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57da7a492abc614ef2d1d94093211170\",\n \"score\": \"0.53162694\",\n \"text\": \"def type(self, identity):\\n self.startType()\\n self.topicRef(identity)\\n self.endType()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a5012b404915fbaa8ed6c830b222488\",\n \"score\": \"0.52962255\",\n \"text\": \"def type_def(self):\\n return self._type_def\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62c825e8500746f2d84fb7a94a76231a\",\n \"score\": \"0.5287055\",\n \"text\": \"def create(self, product_type_info):\\n product_type_create = \\\"product-type\\\"\\n return self.connection.make_request(\\n \\\"{}/{}\\\".format(self.resource_parent, product_type_create), \\\"POST\\\", data=product_type_info\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6474a92fe3c5a7e161c0f2374f526960\",\n \"score\": \"0.5285874\",\n \"text\": \"def type_(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"type\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6474a92fe3c5a7e161c0f2374f526960\",\n \"score\": \"0.5285874\",\n \"text\": \"def type_(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"type\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6474a92fe3c5a7e161c0f2374f526960\",\n \"score\": \"0.5285874\",\n \"text\": \"def type_(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"type\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6474a92fe3c5a7e161c0f2374f526960\",\n \"score\": \"0.5285874\",\n \"text\": \"def type_(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"type\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6474a92fe3c5a7e161c0f2374f526960\",\n \"score\": \"0.5285874\",\n \"text\": \"def type_(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"type\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6474a92fe3c5a7e161c0f2374f526960\",\n \"score\": \"0.5285874\",\n \"text\": \"def type_(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"type\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6474a92fe3c5a7e161c0f2374f526960\",\n \"score\": \"0.5285874\",\n \"text\": \"def type_(self) -> str:\\n return typing.cast(\\n str,\\n self._properties.get(\\\"type\\\"),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2781e2bb46c60e48146e1017ac682113\",\n \"score\": \"0.52776563\",\n \"text\": \"def _create_instance(self):\\n return type(str(\\\"\\\"), (), {})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2ff34b637e8ac0aa4fb303e2f42319\",\n \"score\": \"0.52776384\",\n \"text\": \"def makeTypeElement(self, name, extra_attrs={}):\\n for key in extra_attrs.keys():\\n extra_attrs[key] = str(extra_attrs[key]) # convert everything to strings\\n return le.SubElement(self.__root, \\\"type\\\", name=name, **extra_attrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"56ac3af91ea168e17915ff312b933784\",\n \"score\": \"0.5273929\",\n \"text\": \"def get_create_table_stmt(data_type):\\n\\tcol_list = map(lambda a:'%s VARCHAR(1024)'%a,COLUMN_MAP[data_type].keys())\\n\\tcreate_stmt = 'create table %s(%s)'%(data_type, \\\",\\\".join(col_list))\\n\\treturn create_stmt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a3d637d0a280475e420a85b8c053aef\",\n \"score\": \"0.5268393\",\n \"text\": \"def GetType(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4608b8efafbb1cbd30d916c1fec4ce2e\",\n \"score\": \"0.5258888\",\n \"text\": \"def getType(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"603277c456c13dbfabdfc1cf92d2e3be\",\n \"score\": \"0.5250335\",\n \"text\": \"def gettype(self):\\n return 'type'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bb96a9b7aa758639bc6aca665831f8d\",\n \"score\": \"0.52470636\",\n \"text\": \"def register_type(self, oids, name, casting):\\n assert type(oids) is tuple\\n assert type(name) in (unicode, str)\\n assert hasattr(casting, \\\"__call__\\\")\\n self._register_types.append((oids, name, casting))\\n if self._db is not None:\\n psycopg2.extensions.register_type(psycopg2.extensions.new_type(oids, name, casting))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dc10201148e0c4aecaf1cbef5f62c9b\",\n \"score\": \"0.5243135\",\n \"text\": \"def writeTypeDefs(self) :\\n output = \\\"\\\\n\\\"\\n for eachClass in cppClasses:\\n output += eachClass.writeClassTypeDef()\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde24ec52bb19c22770f3bdcf338ab74\",\n \"score\": \"0.52385294\",\n \"text\": \"def __call__(self, name: str) -> typing.Type:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"162a2595cab833f5d11d2bfe677cca01\",\n \"score\": \"0.523457\",\n \"text\": \"def generate_schema() -> StructType:\\n\\n schema = StructType(\\n [StructField(\\\"text\\\", StringType()), StructField(\\\"created_at\\\", StringType())],\\n )\\n\\n return schema\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ff8eaf530dfcc68e0c8a2d7ddf35f53\",\n \"score\": \"0.5226337\",\n \"text\": \"def __init__(self):\\n Type.__init__(self, '1', 16, 'A type with a single member',\\n constr=None, id=None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"051eb5199fed6677e52c9ef3c00c239a\",\n \"score\": \"0.5219687\",\n \"text\": \"def __init__(self, type):\\n self.__type = type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1f8da56560958248870a7e0dfd69a39\",\n \"score\": \"0.5209874\",\n \"text\": \"def create(self, table, name):\\n\\n columns = [\\\"{0} {1}\\\".format(name, ctype) for name, ctype in table.items()]\\n create = SQLite.CREATE_TABLE.format(table=name, fields=\\\", \\\".join(columns))\\n\\n # pylint: disable=W0703\\n try:\\n self.cur.execute(create)\\n except Exception as e:\\n print(create)\\n print(\\\"Failed to create table: \\\" + e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f538611f3742d2efc341c3863dc3eb78\",\n \"score\": \"0.5204573\",\n \"text\": \"def generate(self):\\n def v(node):\\n if node.is_internal():\\n self._generate_node(node.name)\\n else:\\n self.writer.put_line(\\\"// {0}\\\".format(node.name))\\n self.writer.put_line(\\\"template<> struct {0}<{1}> {{\\\".format(CLASS,node.name))\\n self.writer.put_line(\\\"static bool Test(IRType type) {\\\",1)\\n self.writer.put_line(\\\"return type == {0}<{1}>::Value;\\\".format(CLASS2TYPE,node.name),2)\\n self.writer.put_line(\\\"}\\\",1)\\n self.writer.put_line(\\\"};\\\",0)\\n\\n self.model.visit(v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c23a71098ef096dc76b76488c569e64\",\n \"score\": \"0.5199731\",\n \"text\": \"def create():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0c21eafbdf30aa64cb465a8e03b24a7\",\n \"score\": \"0.5193321\",\n \"text\": \"def create(self, identity, data=None, record=None, **kwargs):\\n self._set_type(data, record)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"804e6f25d1c107a3afd3db1a7cafccb4\",\n \"score\": \"0.51912457\",\n \"text\": \"def get_type(self) -> str:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8535d7006b00184c327588a653c42d2a\",\n \"score\": \"0.51888454\",\n \"text\": \"def type(self) -> str:\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8535d7006b00184c327588a653c42d2a\",\n \"score\": \"0.51888454\",\n \"text\": \"def type(self) -> str:\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8535d7006b00184c327588a653c42d2a\",\n \"score\": \"0.51888454\",\n \"text\": \"def type(self) -> str:\\n return self.__type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d3f93265014b75bd71b21f6b20afb0\",\n \"score\": \"0.5182294\",\n \"text\": \"def ptype(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0727c9309c4f5fcd046661fc3890e38\",\n \"score\": \"0.51788837\",\n \"text\": \"def __create_node__(self, node_type, name):\\n\\n return maya.cmds.createNode(node_type, name=name), True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f52b757fb2f30f9b11ea22ffcab9b14\",\n \"score\": \"0.5172284\",\n \"text\": \"def create(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f52b757fb2f30f9b11ea22ffcab9b14\",\n \"score\": \"0.5172284\",\n \"text\": \"def create(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f52b757fb2f30f9b11ea22ffcab9b14\",\n \"score\": \"0.5172284\",\n \"text\": \"def create(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f52b757fb2f30f9b11ea22ffcab9b14\",\n \"score\": \"0.5172284\",\n \"text\": \"def create(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8e8a8071769a421510615f7aead4a03\",\n \"score\": \"0.5162163\",\n \"text\": \"def register_type(self, name, target):\\n self.types[name] = target\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b363101763f6b192cad06292ae42f07\",\n \"score\": \"0.5156602\",\n \"text\": \"def type_name(self) -> str:\\n return self._type_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c1726c2cc63e83035f75dab663ebb1\",\n \"score\": \"0.5146935\",\n \"text\": \"def get_type():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04577d015c001a5d804566b6f6272030\",\n \"score\": \"0.51455396\",\n \"text\": \"def type(self) -> type:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66844de0da2cd3fa60c93baa57ad409b\",\n \"score\": \"0.5141454\",\n \"text\": \"def create(doc_type: str, name: str):\\n doc_type = doc_type.lower().strip()\\n create_template(doc_type, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62405d0e700086b1ecf96b01595fee16\",\n \"score\": \"0.51379836\",\n \"text\": \"def __schemaCreate(self, schemaDefObj):\\n try:\\n tableIdList = schemaDefObj.getSchemaIdList()\\n sqlGen = SqlGenAdmin(self.__verbose)\\n sqlL = sqlGen.createDatabaseSQL(schemaDefObj.getDatabaseName())\\n for tableId in tableIdList:\\n tableDefObj = schemaDefObj.getSchemaObject(tableId)\\n sqlL.extend(sqlGen.createTableSQL(databaseName=schemaDefObj.getDatabaseName(), tableDefObj=tableDefObj))\\n\\n logger.debug(\\\"Schema creation SQL string\\\\n %s\\\\n\\\\n\\\", \\\"\\\\n\\\".join(sqlL))\\n with Connection(cfgOb=self.__cfgOb, resourceName=self.__resourceName) as client:\\n myQ = MyDbQuery(dbcon=client, verbose=self.__verbose)\\n #\\n # Permit warnings to support \\\"drop table if exists\\\" for missing tables.\\n #\\n myQ.setWarning(\\\"ignore\\\")\\n ret = myQ.sqlCommand(sqlCommandList=sqlL)\\n logger.debug(\\\"\\\\n\\\\n+INFO mysql server returns %r\\\\n\\\", ret)\\n self.assertTrue(ret)\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n self.fail()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd7e58bbd43c165a5021d2c22ba5618\",\n \"score\": \"0.51324487\",\n \"text\": \"def to_sql_statement_create(self) -> TextClause:\\n\\n # Remove possible semicolon from definition because we're adding a \\\"WITH DATA\\\" clause\\n definition = self.definition.rstrip().rstrip(\\\";\\\")\\n\\n return sql_text(\\n f'CREATE MATERIALIZED VIEW {self.literal_schema}.\\\"{self.signature}\\\" AS {definition} WITH {\\\"NO\\\" if not self.with_data else \\\"\\\"} DATA;'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489fd351b26bd6bf2af879a0afd9e014\",\n \"score\": \"0.51305354\",\n \"text\": \"def generate_structure(dbo):\\n dbo.create_tables()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4999e5342b2bc8e363f0f69a8b05b53f\",\n \"score\": \"0.512845\",\n \"text\": \"def get_type(self) -> str:\\n return self.TYPE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c414eeba011099d0d7a5ec960d825b\",\n \"score\": \"0.51261026\",\n \"text\": \"def type():\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7abfa82f3628eba28d9d7ffaa2505c3\",\n \"score\": \"0.5121354\",\n \"text\": \"def type(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7abfa82f3628eba28d9d7ffaa2505c3\",\n \"score\": \"0.5121354\",\n \"text\": \"def type(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c527468828625a5d195fe2ffc6c9211\",\n \"score\": \"0.5119413\",\n \"text\": \"def type(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7308ff3740b257615fb81f332fc23181\",\n \"score\": \"0.5119267\",\n \"text\": \"def add(self, content_type_info):\\n return_type = ContentType(self.context)\\n self.add_child(return_type)\\n params = content_type_info.to_json()\\n for k, v in params.items():\\n if k == \\\"Id\\\":\\n return_type.set_property(k, {\\\"StringValue\\\": v}, True)\\n else:\\n return_type.set_property(k, v, True)\\n qry = CreateEntityQuery(self, return_type, return_type)\\n self.context.add_query(qry)\\n return return_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b52dfa1aaae6662ac114152b9772f2\",\n \"score\": \"0.5118612\",\n \"text\": \"def addType(self, trajType):\\n self.type = trajType\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e498d1c0ba911b7899079bb2c375c444\",\n \"score\": \"0.5118403\",\n \"text\": \"def visit_type(self, name, type_):\\n\\n child_indent = Indenter(self.type_callback)\\n grandchild_indent = Indenter(child_indent)\\n\\n print()\\n self.type_callback('Type: {}'.format(name))\\n\\n if type_['sources']:\\n child_indent('Sources:')\\n for name in natural_sorted(type_['sources']):\\n grandchild_indent('{}: {}'.format(name, type_['sources'][name]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"014198b45f647aa0e5e9b3bfce09d687\",\n \"score\": \"0.5113016\",\n \"text\": \"def type_(self, value: str):\\n self._properties[\\\"type\\\"] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"014198b45f647aa0e5e9b3bfce09d687\",\n \"score\": \"0.5113016\",\n \"text\": \"def type_(self, value: str):\\n self._properties[\\\"type\\\"] = value\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1682,"cells":{"query_id":{"kind":"string","value":"b91cdbd5e0f55cb1591ad023618cb050"},"query":{"kind":"string","value":"Add a morph.DFMRotate object or create a new one. If no argument is given, a new instance of morph.DFMRotate will be created."},"positive_passages":{"kind":"list like","value":[{"docid":"8abe78c38d9fcefb8cf2294aa8859ef7","score":"0.0","text":"def add_rotate(rotate_obj):","title":""}],"string":"[\n {\n \"docid\": \"8abe78c38d9fcefb8cf2294aa8859ef7\",\n \"score\": \"0.0\",\n \"text\": \"def add_rotate(rotate_obj):\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"976dffd0871d2fa44491dc8d42c37db7","score":"0.5145458","text":"def MorphParamCreateDFMRotate(name, rotate_args, entities, bounds, autobounds):","title":""},{"docid":"fe8b8d14829630d2c7752af44f9745ef","score":"0.502624","text":"def add(self) -> None:\n\n # verify that the object's designations are not already in the database\n n: int = (\n self.db.session.query(Designation)\n .filter(Designation.desg.in_(self.designations))\n .count()\n )\n if n > 0:\n raise DesignationError(f\"{n} of the target designations are already used\")\n\n obj: Obj = Obj()\n self.db.session.add(obj)\n self.db.session.commit()\n\n i: int\n for i in range(len(self.designations)):\n desg: Designation = Designation(\n object_id=obj.object_id, desg=self.designations[i], primary=i == 0\n )\n self.db.session.add(desg)\n\n self.db.session.commit()\n self._object_id = obj.object_id","title":""},{"docid":"f724ed3901003a438f2d2a6aab044848","score":"0.4858118","text":"def _add_dmi_object(self, dmi, allow_overwrites=False):\n name = dmi.name\n self.dmis[name] = dmi\n self._type_to_id_map[dmi.type].append(name)","title":""},{"docid":"0a06a4906eff96a9d7e6c363b764c195","score":"0.48215377","text":"def __add__(self, DBZ2):\n return DBZ(dataTime=self.dataTime, matrix=self.matrix+DBZ2.matrix,\\\n name=self.name+\"_plus_\"+DBZ2.name, \\\n dt=self.dt, dx=self.dx, dy=self.dy,\\\n dataPath =self.outputPath+\"_plus_\"+DBZ2.name+\".dat\",\\\n outputPath=self.outputPath+\"_plus_\"+DBZ2.name+\".dat\",\\\n imagePath =self.imagePath +\"_plus_\"+DBZ2.name+\".png\",\\\n database =self.database,\\\n cmap=self.cmap, verbose=self.verbose)","title":""},{"docid":"ecab0049f67e9cdf643e78e9a5b13bb5","score":"0.48210606","text":"def add_data(self, name, f, d, params=None):\n if self._model.independent == \"f\":\n return self._add_data(name, f, d, params)\n else:\n return self._add_data(name, d, f, params)","title":""},{"docid":"664b02da5d680aec00ab44dfdc3afefe","score":"0.47280216","text":"def NewParameterDFMRotate(args, entities, bounds, autobounds, name):\n\n\timport warnings; warnings.warn(\"Deprecated since version 21.0.0. Use class DFM instead.\", DeprecationWarning)","title":""},{"docid":"ae54f2ae6c0acc4ee56150d698f4510a","score":"0.46424422","text":"def _add_dmig_object(self, dmig, allow_overwrites=False):\n name = dmig.name\n self.dmigs[name] = dmig\n self._type_to_id_map[dmig.type].append(name)","title":""},{"docid":"7b6f2f9168ad90e571b32657a0d40c74","score":"0.46394476","text":"def addDrip(self, date):\n newDrip = self._createTrade(\"*\", \"*\", date, \"drip\")\n #TODO: Possibly add a flag to automatically add dividends if set\n pass","title":""},{"docid":"639b23dd8ccf7cb9656889b8c2b17ecd","score":"0.4624708","text":"def __add__(self, o):\n if self.phc != o.phc:\n raise (ValueError(\"Unable to add fields over different crystals.\"))\n if all(self.res != o.res):\n raise (ValueError(\"Unable to add fields with different resolutions.\"))\n if self.polarization != o.polarization:\n raise (ValueError(\"Unable to add fields with different polarizations.\"))\n if self.z_dimension != o.z_dimension:\n raise (ValueError(\"Unable to add fields with different z dimension.\"))\n\n field = lambda t: self._field(t) + o._field(t)\n return XYField(res = self.res, phc=self.phc, z_dimension=self.z_dimension,\n polarization=self.polarization, meshgrid=self.meshgrid, field=field)","title":""},{"docid":"f0ecf5abdd60dc0d7ed61c46279494ef","score":"0.45418882","text":"def add(self, title, date, runtime):\r\n return self.bst.add(Movie(title, date, runtime))","title":""},{"docid":"b256b9126ef6b903fd2d6cc434adac2c","score":"0.453784","text":"def _add_flutter_object(self, flutter):\n key = flutter.sid\n assert key not in self.flutters, 'FLUTTER=%s old=\\n%snew=\\n%s' % (key, self.flutters[key], flutter)\n assert key > 0\n self.flutters[key] = flutter\n self._type_to_id_map[flutter.type].append(key)","title":""},{"docid":"598a03eb841a1a717df3506ed5fc553d","score":"0.45344514","text":"def add(self, d):\n self.data.append(d)","title":""},{"docid":"2e8b982ee53ad33f96a6cabe532b5f37","score":"0.45325243","text":"def add(self, dpid=None):\n return self._send_flow_mods_from_request(dpid, \"add\")","title":""},{"docid":"814131f289f4909a775b74df55e97f8c","score":"0.45318726","text":"def addAttr(self, attr):\n\n if isinstance(attr, str):\n node, attr = attr.rsplit(\".\", 1)\n node = encode(node)\n attr = node[attr]\n\n mobj = attr\n\n xattr = None\n if isinstance(mobj, _AbstractAttribute):\n xattr = mobj\n mobj = attr.create()\n\n assert isinstance(mobj, om.MObject)\n\n try:\n self._fn.addAttribute(mobj)\n\n except RuntimeError:\n # Maya doesn't tell you *why* it fails,\n # so let's do some investigating\n\n # But we can only do that if we've\n # got something to work with.\n if xattr is not None:\n # fn = om.MFnAttribute(mobj)\n if xattr[\"shortName\"] in self:\n raise ExistError(\n \"%s['%s'] already exists!\"\n % (self.name(), xattr[\"shortName\"])\n )\n\n if xattr[\"name\"] in self:\n raise ExistError(\n \"%s['%s'] already exists!\"\n % (self.name(), xattr[\"name\"])\n )\n\n # We didn't find anything wrong, proceed with generic error\n raise\n\n # These don't natively support defaults by Maya\n # They aren't being saved with the file, unless\n # we explicitly set it after creation.\n if isinstance(attr, String) and attr[\"default\"]:\n self[attr[\"name\"]] = attr[\"default\"]","title":""},{"docid":"e2f8cca5ba4f4a5e326f8477c056d5be","score":"0.4527011","text":"def _add_dmik_object(self, dmik, allow_overwrites=False):\n name = dmik.name\n self.dmiks[name] = dmik\n self._type_to_id_map[dmik.type].append(name)","title":""},{"docid":"bcca4956cab6c0ec572f609aecf8f2d4","score":"0.4520015","text":"def append_mlf(self, mlf):\n self.mlfs.append(mlf)","title":""},{"docid":"4713c94781898059ca1ad5bc77181d99","score":"0.45039982","text":"def MorphCreateDFMRotateArgs(rotated, x, y, z, dx, dy, dz):","title":""},{"docid":"32c04601c58d8f423517707b877a8e69","score":"0.4497843","text":"def Voicelead_addOctave(*args):\n return _CsoundAC.Voicelead_addOctave(*args)","title":""},{"docid":"c23e4a11c24fdbcfb2c234b8f6ad64db","score":"0.44710323","text":"def _add_dvmrel_object(self, dvmrel):\n key = dvmrel.oid\n assert key not in self.dvmrels, 'DVMRELx=%s old=\\n%snew=\\n%s' % (\n key, self.dvmrels[key], dvmrel)\n assert key not in self.dvmrels\n assert key > 0\n self.dvmrels[key] = dvmrel\n self._type_to_id_map[dvmrel.type].append(key)","title":""},{"docid":"6d30988e034a887b2b977fa85dfc487b","score":"0.44519797","text":"def add(self, *, database_object_dict) -> object:\n\t\traise NotImplementedError()","title":""},{"docid":"695783530130dd9e2d8056b5825a0767","score":"0.4441959","text":"def __add__(self, other):\n diff = len(self) - len(other)\n if diff > 0:\n new = Motif((other << diff).pfm + self.pfm)\n elif diff < 0:\n new = Motif((self << diff).pf + other.pfm)\n else:\n new = Motif(self.pfm + other.pfm)\n\n new.id = new.consensus\n return new","title":""},{"docid":"b7bc5a04478ca3bb87874e0959ff3a4f","score":"0.44392937","text":"def add_movie(self, id):\n\n movie = Movie.get(id_=id)\n movie.arca_up()\n self.movies_saved.add(movie)\n\n #mudar datetime","title":""},{"docid":"b19ae3ee6e3095f470f63ffda7b2d48f","score":"0.4418368","text":"def __rmul__(self, M):\n if type(M)==type(1) or type(M)==type(1.1) or type(M)==type(self.matrix) : \n matrix = self.matrix * M\n name=self.name+\"_times_\"+ str(M)\n if type(M)==type(self):\n matrix = self.matrix * M.matrix\n name=self.name+\"_times_\"+ M.name\n return DBZ(dataTime=self.dataTime, matrix=matrix,\\\n dt=self.dt, dx=self.dx, dy=self.dy,\\\n name =name,\n dataPath =self.outputPath+\"_times_\"+str(M)+\".dat\",\\\n outputPath=self.outputPath+\"_times_\"+str(M)+\".dat\",\\\n imagePath =self.imagePath +\"_times_\"+str(M)+\".png\",\\\n database =self.database,\\\n cmap=self.cmap, verbose=self.verbose)","title":""},{"docid":"6d9b3f5605ef7018d2f1c20ecf530645","score":"0.44103634","text":"def create(self, *args, **kargs):\n return getattr(odml, self.__class__.__name__)(*args, **kargs)","title":""},{"docid":"a7e5b97b74caa72e2551602110e49e21","score":"0.4397141","text":"def add_memo():\n try:\n date = arrow.get(request.args.get('date', 0, type=str),\n 'YYYY/MM/DD').naive\n text = request.args.get('text', 0, type=str)\n\n record = {\"type\": \"dated_memo\",\n \"date\": date,\n \"text\": text}\n result = collection.insert_one(record)\n record_id = str(result.inserted_id)\n\n message = 'Memo added.'\n worked = True\n except:\n message = 'Memo not added.'\n record_id = ''\n worked = False\n\n return flask.jsonify(message=message, record_id=record_id, result=worked)","title":""},{"docid":"6ce5def3345dba2aaf769748604a3b1c","score":"0.439125","text":"def add(self, *args, **kwargs):\n raise NotImplementedError()","title":""},{"docid":"81acd19fd3df555fff248824c040e135","score":"0.43864262","text":"def add(self, *args, **kwargs):\n\n pass","title":""},{"docid":"4ebfbe3cd07c0cbd0d7f994d6eb5bfa2","score":"0.4376674","text":"def add_dna(self, dna):\n\n dna._model = self\n dna.init_variable()\n\n self.dna = dna","title":""},{"docid":"8791bfd099b6f554a283430d6e13e8c6","score":"0.4367991","text":"def _add_dlink_object(self, dlink):\n key = dlink.oid\n assert key not in self.dlinks, 'DLINK=%s old=\\n%snew=\\n%s' % (\n key, self.dlinks[key], dlink)\n assert key > 0\n self.dlinks[key] = dlink\n self._type_to_id_map[dlink.type].append(key)","title":""},{"docid":"50c796422e62bc5255f6adc80f4c5fa5","score":"0.4356571","text":"def add(cls):\n raise NotImplementedError()","title":""},{"docid":"3b816cf73ea8be02939292ebeba47669","score":"0.43449968","text":"def pitchfx_add(db, hdb, date1, date2, prompt):\n # intial pitch data vector of nulls\n comm = \"PRAGMA table_info(pitchfx)\"\n hdb.execute(comm)\n npfx = len(hdb.fetchall())\n pfxinit = [None]*npfx\n\n # game types\n gtypes = ['R', 'F', 'D', 'L', 'W']\n\n # pitchfx variables: sqlite3 database indices\n pfxkeys = {\n 'game_id': 0,\n 'id': 1,\n 'at_bat': 2,\n 'time': 3,\n 'cur_event': 4,\n 'des': 5,\n 'type': 6,\n 'pre_balls': 7,\n 'post_balls': 8,\n 'pre_strikes': 9,\n 'post_strikes': 10,\n 'start_speed': 11,\n 'end_speed': 12,\n 'sz_top': 13,\n 'sz_bot': 14,\n 'pfx_x': 15,\n 'pfx_z': 16,\n 'px': 17,\n 'pz': 18,\n 'x': 19,\n 'y': 20,\n 'x0': 21,\n 'y0': 22,\n 'z0': 23,\n 'vx0': 24,\n 'vy0': 25,\n 'vz0': 26,\n 'ax': 27,\n 'ay': 28,\n 'az': 29,\n 'break_y': 30,\n 'break_angle': 31,\n 'break_length': 32,\n 'spin_dir': 33,\n 'spin_rate': 34,\n 'pitch_type': 35\n }\n\n # determine which dates already exist in the database\n query = \"SELECT min(date) FROM games\" \n hdb.execute(query) \n dbmin = hdb.fetchone()[0] \n # empty database \n if dbmin == None:\n date_start = date1 \n date_end = date2 \n else: \n query = \"SELECT max(date) FROM games\" \n hdb.execute(query)\n dbmax = hdb.fetchone()[0]\n # date1 date2 dbmin dbmax \n if date2 < dbmin: \n date_start = date1 \n date_end = date2 \n # dbmin dbmax date1 date2 \n elif date1 > dbmax: \n date_start = date1 \n date_end = date2 \n # date1 dbmin date2 dbmax \n # date1 dbmin dbmax date2 \n elif date1 < dbmin: \n date_start = date1 \n if date2 <= dbmax: \n date_end = dbmin \n else: \n date_end = date2 \n # dbmin date1 dbmax date2 \n elif dbmin <= date1: \n if date2 >= dbmax: \n date_start = dbmax \n date_end = date2 \n # dbmin date1 date2 dbmax \n else: \n date_start = dbmin \n date_end = dbmax \n\n # loop through dates (as defined by the date_start and date_end values)\n sdate_start = str(date_start)\n sdate_end = str(date_end)\n years = [sdate_start[:4], sdate_end[:4]]\n months = [sdate_start[4:6], sdate_end[4:6]]\n month = int(months[0])\n days = [sdate_start[-2:], sdate_end[-2:]]\n day = int(days[0])\n\n # set up regular expression\n str_game = re.compile(\"\"\"^.+href=\"(gid_\\d+_\\d+_\\d+_.+)/\".+$\"\"\")\n str_xml = re.compile(\"\"\"^.+href=\"(.+\\.xml)\".+$\"\"\")\n\n # loop over years, months, days for games\n for year in range(int(years[0]), int(years[1])+1):\n while month < 13:\n while day < 32:\n # determine whether there are any files to look at on given day\n try:\n url_root = \"http://gd2.mlb.com/components/game/mlb/year_%s/month_%s/day_%s\" %(year, str(month).zfill(2), str(day).zfill(2))\n output = urllib.urlopen(url_root)\n except:\n day += 1\n continue\n # create list of game ids on given day\n date = int(str(year).zfill(4)+str(month).zfill(2)+str(day).zfill(2))\n ggs = []\n for line in output:\n find = str_game.search(line)\n if find:\n ggs.append(find.group(1))\n\n # read in game and team information from game url\n for gg in ggs:\n url_game = url_root+\"/%s\" %(gg)\n url = url_game+\"/game.xml\"\n try:\n output = urllib.urlopen(url).read()\n log = ET.fromstring(output)\n except:\n continue\n gdict = log.attrib\n game_type = gdict['type']\n # determine what sort of game it is\n if game_type not in gtypes:\n continue\n try:\n game_id = int(gdict['game_pk'])\n #game_id_prev = game_id\n except:\n continue\n #except:\n # game_id = game_id_prev+1\n # check if game already exists in table\n comm = \"SELECT date FROM games WHERE game_id=%s\" %(game_id)\n hdb.execute(comm)\n if hdb.fetchone() != None:\n continue\n print gg\n temp_time = gdict['local_game_time']\n gtime = int(temp_time[0:2]+temp_time[3:5])\n for ginfo in log:\n if ginfo.tag == 'team':\n ggdict = ginfo.attrib\n if ggdict[\"type\"] == 'home':\n hid = ggdict['id']\n try:\n tname = ggdict['name_full']\n except:\n tname = ggdict['name']\n tid = int(hid)\n tabbrv = ggdict['abbrev']\n hw = int(ggdict['w'])\n hl = int(ggdict['l'])\n else:\n vid = ggdict['id']\n try:\n tname = ggdict['name_full']\n except:\n tname = ggdict['name']\n tid = int(vid)\n tabbrv = ggdict['abbrev']\n vw = int(ggdict['w'])\n vl = int(ggdict['l'])\n # fill in team table\n info = (tid, tname, tabbrv)\n insert = \"INSERT OR IGNORE INTO teams VALUES \" \\\n \"(?, ?, ?)\"\n hdb.execute(insert, info)\n # stadium information\n elif ginfo.tag == 'stadium':\n ggdict = ginfo.attrib\n sid = int(ggdict['id'])\n sname = ggdict['name']\n # fill in stadium table\n info = (sid, sname)\n insert = \"INSERT OR IGNORE INTO stadiums VALUES \" \\\n \"(?, ?)\"\n hdb.execute(insert, info)\n\n # read in player info from players xml\n url = url_game+'/players.xml'\n output = urllib.urlopen(url).read()\n log = ET.fromstring(output)\n for dinfo in log:\n for pinfo in dinfo:\n if pinfo.tag == 'player':\n try:\n pdict = pinfo.attrib\n player_id = pdict['id'] \n first = pdict['first'] \n last = pdict['last'] \n pos = pdict['position'] \n bat = pdict['bats'] \n throw = pdict['rl'] \n except:\n continue\n try:\n info = (player_id, first, last, pos, bat, throw, -1)\n insert = \"INSERT INTO players VALUES \" \\\n \"(?, ?, ?, ?, ?, ?, ?)\"\n hdb.execute(insert, info)\n dob_flag = 1\n except:\n dob_flag = 0\n # grab dob from individual player urls\n if dob_flag == 1:\n url_player = url_game+'/batters/%s.xml' %(player_id)\n poutput = urllib.urlopen(url_player).read()\n plog = ET.fromstring(poutput)\n ppdict = plog.attrib\n pdob = ppdict['dob']\n yy = pdob[-4:] \n mm = pdob[:2] \n dd = pdob[3:5] \n dob = int(yy+mm+dd)\n # update player table\n comm = \"UPDATE players SET dob=%s \" \\\n \"WHERE player_id=%s AND position='%s'\" %(dob, player_id, pos)\n hdb.execute(comm)\n elif pinfo.tag == 'umpire':\n udict = pinfo.attrib\n umpire_name = udict['name']\n umpire_position = udict['position']\n try:\n umpire_id = int(udict['id'])\n # fill in umpires table\n info = (umpire_id, umpire_name)\n insert = \"INSERT OR IGNORE INTO umpires VALUES \" \\\n \"(?, ?)\"\n hdb.execute(insert, info)\n except:\n umpire_id = -1\n if umpire_position == 'home':\n u_home = umpire_id\n elif umpire_position == 'first':\n u_first = umpire_id\n elif umpire_position == 'second':\n u_second = umpire_id\n elif umpire_position == 'third':\n u_third = umpire_id\n\n # fill in game table if game does not already exist\n info = (game_id, game_type, date, gtime, hid, hw, hl, vid, vw, vl, sid, u_home, u_first, u_second, u_third)\n insert = \"INSERT OR IGNORE INTO games VALUES \" \\\n \"(?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\"\n hdb.execute(insert, info)\n\n # read in pitchfx and event tables\n url = url_game+\"/inning/inning_all.xml\"\n try:\n output = urllib.urlopen(url).read()\n log = ET.fromstring(output)\n except:\n continue\n # intialize events and scores\n event_id = 0\n action_flag = 0\n pre_home_score = 0\n post_home_score = 0\n pre_away_score = 0\n post_away_score = 0\n # loop over inning\n for header in log:\n iinfo = header.attrib\n inning_num = iinfo['num']\n for inning in header:\n # new inning flag\n inning_flag = 1\n post_outs = 0\n pre_1b = 0\n post_1b = 0\n pre_2b = 0\n post_2b = 0\n pre_3b = 0\n post_3b = 0\n post_home = 0\n if inning.tag == 'top':\n is_top = 1\n else:\n is_top = 0\n # loop over events in an inning\n for etype in inning:\n # keep track of in-game player changes\n if etype.tag == 'action':\n edict = etype.attrib\n action_description = edict['des']\n if 'Pinch-Hitter' in action_description:\n action_event = edict['event']\n action_flag = 1\n elif 'Change' in action_description:\n action_event = edict['event']\n action_flag = 1\n elif etype.tag == 'atbat':\n pre_outs = post_outs\n # flag for events with multiple runners\n runner_flag = 1\n # reset count at beginning of at-bat\n pre_balls = 0\n post_balls = 0\n pre_strikes = 0\n post_stikes = 0\n edict = etype.attrib\n # to account for strikeouts\n max_strikes = int(edict['s'])\n ab = int(edict['num'])\n pitcher_id = int(edict['pitcher'])\n batter_id = int(edict['batter'])\n end_of_ab_outs = int(edict['o'])\n event_description_ab = edict['event']\n # if there is a previous action then write current ab info as event\n if action_flag == 1:\n info = (game_id, event_id, event_description, inning_num, is_top, pre_outs, post_outs, pitcher_id, batter_id, pre_1b, post_1b, pre_2b, post_2b, pre_3b, post_3b, post_home, pre_home_score, post_home_score, pre_away_score, post_away_score)\n insert = \"INSERT OR IGNORE INTO events VALUES (\" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \" \\\n \"?, ?, ? ,?, ?, ?, ?, ?, ?, ?\" \\\n \")\"\n hdb.execute(insert, info)\n event_id += 1\n #if inning_flag == 1:\n # event_id += 1\n # info = (game_id, event_id, -1, inning_num, is_top, pre_outs, post_outs, pitcher_id, batter_id, -1, -1, -1, pre_home_score, post_home_score, pre_away_score, post_away_score)\n # insert = \"INSERT OR IGNORE INTO events VALUES (\" \\\n # \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \" \\\n # \"?, ?, ?, ?, ?, ?\" \\\n # \")\"\n # hdb.execute(insert, info)\n try:\n pre_home_score = post_home_score\n pre_away_score = post_away_score\n post_home_score = edict['home_team_runs']\n post_away_score = edict['away_team_runs']\n except:\n pass\n # reset inning and action flag\n action_flag = 0\n inning_flag = 0\n runner_flag = 0\n # read in pitches\n for event in etype:\n # update runners if necessary\n if (event.tag != \"runner\") and (runner_flag == 0):\n pre_1b = post_1b\n pre_2b = post_2b\n pre_3b = post_3b\n post_home = 0\n event_id += 1\n # registered pitch\n if event.tag == 'pitch':\n runner_flag = 1\n # initialize pitchfx list and read in basic info\n info = list(pfxinit)\n info[pfxkeys['game_id']] = game_id\n info[pfxkeys['at_bat']] = ab\n info[pfxkeys['cur_event']] = event_id\n idict = event.attrib\n try:\n time_stamp = int(idict['sv_id'][-6:])\n info[pfxkeys['time']] = time_stamp\n except:\n pass\n # only fill in pitchfx info that exist\n common = set(idict.keys()).intersection(pfxkeys)\n for pfxvars in common:\n info[pfxkeys[pfxvars]] = idict[pfxvars]\n # update balls and strikes information\n if idict['type'] == 'B':\n post_balls = min(pre_balls+1,4)\n post_strikes = pre_strikes\n elif idict['type'] == 'S':\n post_balls = pre_balls\n if idict['des'] == 'Foul':\n post_strikes = min(pre_strikes+1,2)\n else:\n post_strikes = min(pre_strikes+1,max_strikes)\n else:\n post_balls = pre_balls\n post_strikes = pre_strikes\n info[pfxkeys['pre_balls']] = pre_balls\n info[pfxkeys['post_balls']] = post_balls\n info[pfxkeys['pre_strikes']] = pre_strikes\n info[pfxkeys['post_strikes']] = post_strikes\n # fill in pitchfx table\n insert = \"INSERT OR IGNORE INTO pitchfx VALUES (\" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \" \\\n \"?, ?, ?, ?, ?, ?\" \\\n \")\"\n hdb.execute(insert, info)\n # update balls and strikes again\n pre_balls = post_balls\n pre_strikes = post_strikes\n # initialize runner events(s)\n elif (event.tag == 'runner'):\n rdict = event.attrib\n runner_id = rdict['id']\n event_description = rdict['event']\n base_start = rdict['start']\n flag1 = 0\n flag2 = 0\n flag3 = 0\n if base_start == \"1B\":\n pre_1b = runner_id\n flag1 = 1\n elif base_start == \"2B\":\n pre_2b = runner_id\n flag2 = 1\n elif base_start == \"3B\":\n pre_3b = runner_id\n flag3 = 1\n #if base_start == \"\":\n # base_start = \"H\"\n base_end = rdict['end']\n if base_end == \"\":\n try:\n if rdict['score'] == \"T\":\n #base_end = \"H\"\n post_home = runner_id\n except:\n #base_end = \"0\"\n post_outs = pre_outs+1\n elif base_end == \"1B\":\n post_1b = runner_id\n elif base_end == \"2B\":\n post_2b = runner_id\n elif base_end == \"3B\":\n post_3b = runner_id\n if base_start != base_end:\n if flag1 == 1:\n post_1b = 0\n elif flag2 == 1:\n post_2b = 0\n elif flag3 == 1:\n post_3b = 0\n # fill in event table\n info = (game_id, event_id, event_description, inning_num, is_top, pre_outs, post_outs, pitcher_id, batter_id, pre_1b, post_1b, pre_2b, post_2b, pre_3b, post_3b, post_home, pre_home_score, post_home_score, pre_away_score, post_away_score)\n insert = \"INSERT OR REPLACE INTO events VALUES (\" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?\" \\\n \")\"\n hdb.execute(insert, info)\n ## update event id if necessary\n #if runner_flag == 1:\n # event_id += 1\n # upcoming runner tags are not separate events\n runner_flag = 0\n # handle pitch-outs\n elif event.tag == 'po':\n runner_flag = 1\n rdict = event.attrib\n event_description = rdict['des']\n # fill in event table\n info = (game_id, event_id, event_description, inning_num, is_top, pre_outs, post_outs, pitcher_id, batter_id, pre_1b, post_1b, pre_2b, post_2b, pre_3b, post_3b, post_home, pre_home_score, post_home_score, pre_away_score, post_away_score)\n insert = \"INSERT OR IGNORE INTO events VALUES (\" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?\" \\\n \")\" \n hdb.execute(insert, info)\n event_id += 1\n # add end of at-bat information only if an out\n if end_of_ab_outs > pre_outs:\n # if last entry in ab is not runner then there was nobody on base (new event)\n # fill in event table\n info = (game_id, event_id, event_description_ab, inning_num, is_top, pre_outs, end_of_ab_outs, pitcher_id, batter_id, pre_1b, post_1b, pre_2b, post_2b, pre_3b, post_3b, post_home, pre_home_score, post_home_score, pre_away_score, post_away_score)\n #info = (game_id, event_id, event_description_ab, inning_num, is_top, pre_outs, end_of_ab_outs, pitcher_id, batter_id, batter_id, 'H', '0', pre_home_score, post_home_score, pre_away_score, post_away_score)\n insert = \"INSERT OR REPLACE INTO events VALUES (\" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \" \\\n \"?, ?, ?, ?, ?, ?, ?, ?, ?, ?\" \\\n \")\" \n hdb.execute(insert, info)\n #if event.tag != 'runner':\n # event_id += 1\n # update outs\n pre_outs = end_of_ab_outs\n post_outs = end_of_ab_outs\n # if last entry in ab is runner then there was someone on base (already registered as event)\n #if event.tag == 'runner':\n # event_id += 1\n ## temporary break from games\n # commit game\n db.commit()\n #return\n # check whether to break out\n #if date >= date_end:\n if date >= date_end:\n return\n # update day\n day += 1\n # update day and month\n day = 1\n month += 1\n # reset month\n month = 1","title":""},{"docid":"f0a620d47920ecd7a11772e587a20052","score":"0.43337896","text":"def add_medical_note(app_dt, doctor_id, medical_note):\n\n app = Appointment.query.filter(Appointment.doctor_id.like(\n doctor_id), Appointment.start_datetime.like(app_dt)).first()\n app.medical_note = medical_note\n db.session.commit()","title":""},{"docid":"b1a803987e7360865409e91832bf2b14","score":"0.43265975","text":"def _add_dvcrel_object(self, dvcrel):\n key = dvcrel.oid\n assert key not in self.dvcrels, 'DVCRELx=%s old\\n%snew=\\n%s' % (\n key, self.dvcrels[key], dvcrel)\n assert key > 0\n self.dvcrels[key] = dvcrel\n self._type_to_id_map[dvcrel.type].append(key)","title":""},{"docid":"32c68e332db27c13a59a0a58725d1fbf","score":"0.43222874","text":"def add(self):\n if self.in_reference_to:\n # This is a meta fact\n return self.__add_meta()\n\n # This is not a meta fact\n return self.__add_fact()","title":""},{"docid":"fa77e371384e00116de87b678e0e45da","score":"0.4315649","text":"def addAttr(self, node, attr):\n\n assert isinstance(node, Node), \"%s was not a cmdx.Node\"\n\n if SAFE_MODE:\n assert _isalive(node._mobject)\n\n mobj = attr\n\n if isinstance(attr, _AbstractAttribute):\n mobj = attr.create()\n\n this_name = om.MFnAttribute(mobj).name\n if node.hasAttr(this_name):\n raise ExistError(\n \"Attribute already exists: %s\" % node[this_name].path()\n )\n\n # Ensure it isn't in the process of being added\n for _node, _mattr in self._attributesBeingAdded:\n name_being_added = om.MFnAttribute(_mattr).name\n if node == _node and this_name == name_being_added:\n raise ExistError(\n \"Same attribute added twice: %s.%s\" % (\n node.path(), name_being_added)\n )\n\n status = self._modifier.addAttribute(node._mobject, mobj)\n\n if not status:\n raise ValueError(\n \"Could not add the attribute: %s.%s\" % (node, attr)\n )\n\n if isinstance(attr, String) and attr[\"default\"]:\n log.warning(\"Strings don't support default values with modifier\")\n\n # Protect against user error\n # This one is especially pesky; Maya will go ahead and create\n # the same attribute name multiple times, only to complain when\n # you try and undo. Bad, Maya, bad!\n self._attributesBeingAdded += [(node, mobj)]\n\n if SAFE_MODE:\n self._modifier.doIt()\n\n return mobj","title":""},{"docid":"83f3973bc26071c10702dd1281944677","score":"0.4314483","text":"def _add_dresp_object(self, dresp):\n key = dresp.dresp_id\n assert key not in self.dresps, 'DRESPx=%s old=\\n%snew=\\n%s' % (\n key, self.dresps[key], dresp)\n assert key > 0\n self.dresps[key] = dresp\n self._type_to_id_map[dresp.type].append(key)","title":""},{"docid":"98b8f60ceed72ec68189ae8df70a2908","score":"0.4311742","text":"def __add__(self, obj):\n if isinstance(obj, Number):\n if obj:\n self.numerator += obj * self.denominator\n return self\n assert(obj.vartype == 3)\n \n if self.denominator != obj.denominator:\n return NotImplemented('The Denominator should be the Same')\n else:\n new = FracVariable(self.numerator + obj.numerator, self.denominator)\n return new","title":""},{"docid":"1950031b153576432b61091dfd737860","score":"0.43108582","text":"def add_drone(self):\r\n # Start a new drone instance\r\n #print('TODO: Start a new drone')\r\n #drone = Drone(id, name, class_type, rescue, operator)\r\n drone = Drone(None)\r\n # Display the drone\r\n self.view_drone(drone, self._save_new_drone)","title":""},{"docid":"9800bdaa8c85cdb7cdb6f2eda2ea89d1","score":"0.43034083","text":"def add_fibre(self, data):\n\n # Type casting:\n f_id = int(data[1])\n radius = float(data[2])\n\n # Any argument after the 2nd is regarded as a fibre:\n nodes = []\n for i in range(3, len(data)):\n n_id = int(data[i])\n nodes.append(n_id)\n\n # Add an instance of the fibre class to fibre_dict:\n self.fibre_dict[f_id] = Fibre(f_id, radius, *nodes)\n\n return","title":""},{"docid":"b2a6c250d1cac9606aab9aa7537c5db7","score":"0.4267027","text":"def AddDEM(self, dem):\n\n\t\tself.date.append(dem.date)\n\t\tself.uncertainty.append(dem.uncertainty)\n\t\t# Add the first band, and then replace NoDataValue by np.nan\n\t\tdem_array = dem.ReadAsArray()\n\t\tdem_array[dem_array == dem.GetNoDataValue()] = np.nan\n\t\treturn TimeSeriesDEM(array=np.dstack([self, dem_array]), date=self.date, uncertainty=self.uncertainty)","title":""},{"docid":"f31c5753a3f8848463b8b33ab826a094","score":"0.42633322","text":"def add(self, new_movie):\n self.__formatter.format(new_movie)\n self.__validator.validate(new_movie)\n\n self.__repository.add(new_movie)","title":""},{"docid":"5c26f3cd52ae675a0afe5d0080c1ebc8","score":"0.4262025","text":"def series(dtype='mri', in_study:Study=None, in_database:Database=None, **kwargs)->Series:\n if dtype not in ['mri', 'MRImage']:\n message = 'dbdicom can only create images of type MRImage at this stage'\n raise ValueError(message)\n \n if in_study is not None:\n series = in_study.new_series()\n else:\n if in_database is None:\n _database = database()\n else:\n _database = in_database\n patient = _database.new_patient()\n study = patient.new_study()\n series = study.new_series(**kwargs)\n if in_database is None:\n _database.save()\n return series","title":""},{"docid":"693125378a893ca9207609c4630b4c46","score":"0.42376268","text":"def __add__(self, other: Formula, /) -> Formula:\n if not isinstance(other, Formula):\n raise TypeError('can only add Formula instance')\n return Formula(\n join_charge(\n f'({self._formula_nocharge})({other._formula_nocharge})',\n self._charge + other.charge,\n )\n )","title":""},{"docid":"62b2f155e7a4397f4de63a1d60706a4c","score":"0.42301017","text":"def add(self, usr, opt):\n return RatingData.objects.add(self.instance, usr, opt)","title":""},{"docid":"3df620a74f92516ecacf2beacf444c03","score":"0.42292303","text":"def save(self, *args, **kwargs):\n def match_date(date):\n \"\"\"Function to parse string-dates into python date objects.\n \"\"\"\n date = date.strip()\n date = date.replace('-', '.')\n if re.match(r'[0-9]{4}$', date):\n dr = datetime.strptime(date, '%Y')\n dr2 = date\n elif re.match(r'[0-9]{1,2}\\.[0-9]{1,2}\\.[0-9]{4}$', date):\n dr = datetime.strptime(date, '%d.%m.%Y')\n dr2 = date\n elif re.match(r'[0-9]{4}\\.\\.\\.$', date):\n dr = datetime.strptime(date, '%Y...')\n dr2 = re.match(r'([0-9]{4})\\.\\.\\.$', date).group(1)\n elif re.match(r'[0-9]{4}\\.[0-9]{1,2}\\.\\.$', date):\n dr = datetime.strptime(date, '%Y.%m..')\n dr2 = re.match(r'([0-9]{4})\\.([0-9]{1,2})\\.\\.$', date).group(2)\n +'.' + \\\n re.match(r'([0-9]{4})\\.([0-9]{1,2})\\.\\.$', date).group(1)\n elif re.match(r'[0-9]{4}\\.[0-9]{1,2}\\.[0-9]{1,2}$', date):\n dr = datetime.strptime(date, '%Y.%m.%d')\n dr3 = re.match(\n r'([0-9]{4})\\.([0-9]{1,2})\\.([0-9]{1,2})$', date)\n dr2 = dr3.group(3) + '.' + dr3.group(2) + '.' + dr3.group(1)\n else:\n dr = None\n dr2 = dr2 = date\n return dr, dr2\n if self.start_date_written:\n self.start_date, self.start_date_written = match_date(\n self.start_date_written)\n if self.end_date_written:\n self.end_date, self.end_date_written = match_date(\n self.end_date_written)\n super(Area, self).save(*args, **kwargs)\n return self","title":""},{"docid":"cd60922db29769fd92d83823e59c42bd","score":"0.42245078","text":"def _add_dmij_object(self, dmij, allow_overwrites=False):\n name = dmij.name\n self.dmijs[name] = dmij\n self._type_to_id_map[dmij.type].append(name)","title":""},{"docid":"8e79b8605e786ed33655fe8bc477b8ce","score":"0.4224313","text":"def add(self, *mobjects):\n mobjects = [*mobjects, *self.foreground_mobjects]\n self.restructure_mobjects(to_remove=mobjects)\n self.mobjects += mobjects\n return self","title":""},{"docid":"8e79b8605e786ed33655fe8bc477b8ce","score":"0.4224313","text":"def add(self, *mobjects):\n mobjects = [*mobjects, *self.foreground_mobjects]\n self.restructure_mobjects(to_remove=mobjects)\n self.mobjects += mobjects\n return self","title":""},{"docid":"a59199e3ab5a5e968d609f908ab35829","score":"0.4198464","text":"def add(self, graphic):\n\n if self.is_locked():\n warn(\"The Capture is locked, no changes can be done\", RuntimeWarning)\n return False\n\n if not hasattr(self, graphic.label):\n setattr(self, graphic.label, graphic)\n self.__graphics[graphic.type].append(graphic)\n\n if graphic.is_point():\n self.__camera.set_lkpoint(graphic)\n else:\n warn(\"The Graphic %s already exists. It wont be added\" % graphic.label, RuntimeWarning)\n return False","title":""},{"docid":"6701f85d521206c6ce0268a0f24218b4","score":"0.4195164","text":"def add_deme(\n self,\n name: str,\n *,\n description: str = None,\n ancestors: list = None,\n proportions: list = None,\n start_time: float = None,\n epochs: list = None,\n defaults: dict = None,\n ):\n deme: MutableMapping[str, Any] = dict(name=name)\n if description is not None:\n deme[\"description\"] = description\n if ancestors is not None:\n deme[\"ancestors\"] = ancestors\n if proportions is not None:\n deme[\"proportions\"] = proportions\n if start_time is not None:\n deme[\"start_time\"] = start_time\n if epochs is not None:\n deme[\"epochs\"] = epochs\n if defaults is not None:\n deme[\"defaults\"] = defaults\n\n if \"demes\" not in self.data:\n self.data[\"demes\"] = []\n self.data[\"demes\"].append(deme)","title":""},{"docid":"90a5d4c95bc6bf39a32d8302d5bb7ed7","score":"0.41905457","text":"def add_migration(\n self,\n *,\n rate: float = None,\n # We use a special NO_DEFAULT value here, to distinguish between the user\n # not specifying anything, and specifying the value None (which may be\n # necessary to override a 'defaults' value set in the data dictionary).\n demes: list = NO_DEFAULT, # type: ignore\n source: str = NO_DEFAULT, # type: ignore\n dest: str = NO_DEFAULT, # type: ignore\n start_time: float = None,\n end_time: float = None,\n ):\n migration: MutableMapping[str, Any] = dict()\n if rate is not None:\n migration[\"rate\"] = rate\n if demes is not NO_DEFAULT:\n migration[\"demes\"] = demes\n if source is not NO_DEFAULT:\n migration[\"source\"] = source\n if dest is not NO_DEFAULT:\n migration[\"dest\"] = dest\n if start_time is not None:\n migration[\"start_time\"] = start_time\n if end_time is not None:\n migration[\"end_time\"] = end_time\n\n if \"migrations\" not in self.data:\n self.data[\"migrations\"] = []\n self.data[\"migrations\"].append(migration)","title":""},{"docid":"cd49a1ce234bd52349b35ceb5807e5f6","score":"0.4185431","text":"def __add__(self, other):\r\n if isinstance(other, Frac):\r\n return Frac(self.x * other.y + self.y * other.x, self.y * other.y)\r\n elif isinstance(other, int):\r\n return Frac(self.x + (other*self.y), self.y)\r\n elif isinstance(other, float):\r\n new_frac = other.as_integer_ratio()\r\n other = Frac(new_frac[0], new_frac[1])\r\n return Frac(self.x * other.y + self.y * other.x, self.y * other.y)\r\n else:\r\n raise ValueError(\"Niewłaściwy argument w dodawaniu\")","title":""},{"docid":"739b44fb6456233121bff892d55e473d","score":"0.41777584","text":"def add(self, other):\n if other._x0 is not None:\n self.add_point(other._x0, other._y0)\n self.add_point(other._x1, other._y1)\n return self","title":""},{"docid":"16a4ac8f13188614ed9980e71b5d7436","score":"0.41691804","text":"def createFOP(verbose=False, usefmm=False, fatray=False):\n if usefmm:\n from .FMModelling import TravelTimeFMM\n fop = TravelTimeFMM(verbose=verbose)\n else:\n if fatray:\n fop = FatrayDijkstraModelling(verbose=verbose)\n else:\n fop = pg.TravelTimeDijkstraModelling(verbose=verbose)\n\n return fop","title":""},{"docid":"b903ac1595ffcb020f200cd94629e83a","score":"0.41689783","text":"def add_fiber(self, fiber_id, y, z, area, material_class, w, h):\n if not isinstance(material_class, UniaxialIncrementalMaterial):\n raise ValueError(\"material_class is not of type : UniaxialIncrementalMaterial\")\n if fiber_id in self._fibers:\n raise RuntimeError(f\"Section already contains fiber with id {fiber_id}\")\n self._fibers[fiber_id] = Fiber(fiber_id, y, z, area, material_class, w, h)","title":""},{"docid":"cda9d6376040ebf9fc52fa56be20b7e0","score":"0.41636315","text":"def _add_islamic_revolution_day(self, name: str) -> Optional[date]:\n return self._add_persian_calendar_holiday(\n name, self._persian_calendar.persian_to_gregorian(self._year - 1, 11, 22)\n )","title":""},{"docid":"c93e9e0f4c6b4c4fffa132c14646184b","score":"0.4160634","text":"def add_station(self, df_orient=None, stationname=None, orientsingle_path=None, period=None):\n\n in_parentheses = self.dict_OBStypes.get(period)\n\n if orientsingle_path is not None:\n orientsingle = pd.read_pickle(orientsingle_path)\n else:\n if (df_orient is None) or (stationname is None):\n raise ValueError('Both df_orient and stationname must be given.')\n \n try:\n orientsingle = OrientSingle(\n df_orient, stationname, \n self.if_selection, \n in_parentheses=in_parentheses,\n **self._kw_orientsingle,\n )\n except IndexError:\n pass\n except PvalueError:\n pass\n\n self.stations.append(orientsingle)","title":""},{"docid":"f48d7d7ef7f65430b021632a09c6daa7","score":"0.41593266","text":"def add(self, value: object) -> None:\n self.da.append(value)\n return","title":""},{"docid":"24f5cf0a077d5358972158c168d0dd4f","score":"0.4157909","text":"def add(self, child):\n\n if not isinstance(child, FPNode):\n raise TypeError(\"Can only add other FPNodes as children\")\n\n if not child.item in self._children:\n self._children[child.item] = child\n child.parent = self","title":""},{"docid":"24f5cf0a077d5358972158c168d0dd4f","score":"0.4157909","text":"def add(self, child):\n\n if not isinstance(child, FPNode):\n raise TypeError(\"Can only add other FPNodes as children\")\n\n if not child.item in self._children:\n self._children[child.item] = child\n child.parent = self","title":""},{"docid":"24f5cf0a077d5358972158c168d0dd4f","score":"0.4157909","text":"def add(self, child):\n\n if not isinstance(child, FPNode):\n raise TypeError(\"Can only add other FPNodes as children\")\n\n if not child.item in self._children:\n self._children[child.item] = child\n child.parent = self","title":""},{"docid":"fb9b676192a8662668b6b9c143a4fff6","score":"0.4156987","text":"def add(self,node,**kwargs):\n if node not in self.graph: #handles adding a new node with new features\n self.graph[node] = dict()\n self.keys.append(node)\n self.graph[node] = kwargs\n self.graph[node][\"neighbors\"] = set()\n self.nodes.append(node)\n else: #if node is already in graph, handles updating features.\n for key in kwargs.items():\n try:\n if key[1] != self.graph[node][key[0]]: #handles update\n self.graph[node][key[0]] = key[1]\n except KeyError:\n self.graph[node][key[0]] = key[1] #handles new feature to\n #node that exits.","title":""},{"docid":"8aa26e9d5d749de0ae863e70652124c3","score":"0.4153644","text":"def add_child(self, **kwargs):\n newobj = self.__class__(**kwargs)\n newobj.parent = self\n newobj._create_path()\n return newobj","title":""},{"docid":"f94e07bddbe80cdb926d802d3f4e3837","score":"0.414829","text":"def add_shape(self, shape):\n if isinstance(shape, Shape):\n self.shapes.append(shape)\n shape.set_id(len(self.shapes))\n else:\n raise TypeError","title":""},{"docid":"512732955eab320b5d540d3c68029b3d","score":"0.41481784","text":"def __add__(self, other):\n if not is_formula(other):\n raise ValueError('only Formula objects can be added to a Formula')\n f = Formula(np.hstack([self.terms, other.terms]))\n return f","title":""},{"docid":"f5419341b2270b1ce5404e061d2ed34b","score":"0.4143523","text":"def add(**kwargs):\n store = Store.create(**kwargs)\n return store","title":""},{"docid":"d341874410e396cd71aae84dd16dd509","score":"0.41411453","text":"def add_child(self, child):\n if self.type.lower() != 'directory':\n raise ValueError(\"Only directory objects can have children\")\n if child is self:\n raise ValueError('Cannot be a child of itself!')\n if child not in self._children:\n self._children.append(child)\n child.parent = self\n return child","title":""},{"docid":"2b6996c0a7f470e9244928c240a1efa5","score":"0.4141001","text":"def AddDelta(self, fn, next_state):\n self.deltas.append((fn, next_state))","title":""},{"docid":"b6c6fd10e1428bc794d076cd42c0cae3","score":"0.41403237","text":"def addDataType(self, dt: ghidra.program.model.data.DataType) -> None:\n ...","title":""},{"docid":"4cf2e1001e70ca0c05c6254676075f13","score":"0.41393378","text":"def add(self, obj):\n raise NotImplementedError","title":""},{"docid":"f37666ebf18204eb9e978a91b8de436a","score":"0.41273922","text":"def add():\n calendar_service = Calendar_Service(MONGO)\n date = request.form['date']\n vacation = request.form['vacation']\n title = request.form['title']\n remark = request.form['remark']\n attendance_time = request.form['attendance_time']\n closing_time = request.form['closing_time']\n data, code, message = calendar_service.add(date, vacation, title,\n remark, attendance_time, closing_time)\n return __result(data, code, message)","title":""},{"docid":"92fedce66803d4e0af4845b9b6bb38e1","score":"0.41220674","text":"def add_instance(self, head_id, instance_id: str, role, argument_instance):\n argument_alias = get_variable_name(instance_id)\n\n # If an instance of a word already exists, then it should not get added\n if not self.has_instance(instance_id):\n self.__g.triples.append(penman.Triple(argument_alias, self.instance_role, argument_instance))\n\n self.__g.triples.append(penman.Triple(get_variable_name(head_id), role, argument_alias))","title":""},{"docid":"956ef341140a8407910604f824076ef0","score":"0.41219705","text":"def __add__(self, other):\n if other == 0:\n return self\n\n assert self.framerate == other.framerate\n\n # make an array of times that covers both waves\n start = min(self.start, other.start)\n end = max(self.end, other.end)\n n = int(round((end - start) * self.framerate)) + 1\n\n # initially set to 0\n ys = np.zeros(n)\n ts = start + np.arange(n) / self.framerate\n\n newWave = FWave(ys, ts, self.framerate);\n newWave.addWave(self)\n newWave.addWave(other)\n\n return newWave","title":""},{"docid":"58fc6fd572ce08e248a063bc064583ff","score":"0.4115445","text":"def add_document(self, definition):\n type_ = self.from_doc(definition)\n return self.add(type_)","title":""},{"docid":"58fc6fd572ce08e248a063bc064583ff","score":"0.4115445","text":"def add_document(self, definition):\n type_ = self.from_doc(definition)\n return self.add(type_)","title":""},{"docid":"1ff4c22b1bbc457ef9114e8eb261ce39","score":"0.41133165","text":"def addRental(self, id, bookId, clientId, rentalDate=datetime.timedelta(0), dueDate=datetime.timedelta(0),\r\n returnedDate=\"\", recordForUndo=True):\r\n if self.checkId(id) == False:\r\n raise InvalidIdException\r\n if rentalDate == datetime.timedelta(0):\r\n rentalDate = datetime.datetime.now().date()\r\n if dueDate == datetime.timedelta(0):\r\n dueDate = rentalDate + datetime.timedelta(days=10)\r\n rental = self.validation(id, bookId, clientId, rentalDate, dueDate, returnedDate)\r\n self._rentalRepo.add(rental)\r\n if recordForUndo == True:\r\n undo = FunctionCall(self.removeRental, id, False)\r\n redo = FunctionCall(self.addRental, id, bookId, clientId, rentalDate, dueDate, returnedDate, False)\r\n operation = Operation(redo, undo)\r\n self._undoController.recordOperation(operation)\r\n\r\n return rental","title":""},{"docid":"9c40e72c3d34a886caf9de7c71bf8481","score":"0.41110054","text":"def add_mrnas(self, mrna_list, add_degradation=True):\n if not hasattr(mrna_list, '__iter__'):\n mrna_list = [mrna_list]\n if len(mrna_list) == 0:\n return None\n\n # First check whether the mRNAs exist in the model\n mrna_list = [x for x in mrna_list if x.id not in self.mrnas]\n\n for mrna in mrna_list:\n mrna._model = self\n mrna.init_variable()\n\n if add_degradation:\n self._add_mrna_degradation(mrna, scaled=True, queue=True)\n\n self.mrnas += mrna_list\n self._push_queue","title":""},{"docid":"493019478bcf84e75e4e58c5d7b04174","score":"0.4109323","text":"def add(self, object_: Shape) -> None:\n self.objects.append(object_)","title":""},{"docid":"793350270268ae89fa1dbb9b13b0931b","score":"0.41086546","text":"def add_shape(self, shape):\r\n self.shapes.append(shape)","title":""},{"docid":"ef24c6314faaedd2d3cfd67cc05fbf83","score":"0.41068408","text":"def add(self, definition_id=None, title=None):\n def decorator(cls):\n if definition_id is None:\n name = cls.__name__\n else:\n name = definition_id\n if title is not None:\n cls._title = title\n self._registry[name] = cls\n self._rev_registry[cls] = name\n # cls._schema = self\n # for k, v in six.iteritems(cls.__dict__):\n # if isinstance(v, Document):\n # print(k)\n return cls\n return decorator","title":""},{"docid":"55f8597c02f73981b567e0e46277d353","score":"0.4100982","text":"def dm_add(self,moar_content):\n self.player[-1].add(moar_content)\n return self","title":""},{"docid":"1b74172efd7fa22e1c184a360d6a1090","score":"0.40980515","text":"def add_movie(self, movie: Movie):\n raise NotImplementedError","title":""},{"docid":"d99cf250ac0428a024d1f725e224b7f1","score":"0.40884656","text":"def addOctave(*args):\n return _CsoundAC.Voicelead_addOctave(*args)","title":""},{"docid":"dcc5296165d26b8591bae9fd04baf836","score":"0.40795213","text":"def __init__(self,\n pmf_remove: pld_pmf.PLDPmf,\n pmf_add: Optional[pld_pmf.PLDPmf] = None):\n self._pmf_remove = pmf_remove\n self._symmetric = pmf_add is None\n self._pmf_add = pmf_remove if self._symmetric else pmf_add","title":""},{"docid":"acebb076aa9897c5f45a9687185afbab","score":"0.40781176","text":"def __add__(self, other):\n result = self.__class__(self.lines, self.density, self.relax, self.mink)\n result += other.lines if hasattr(other, 'lines') else other\n return result","title":""},{"docid":"aaa23319f49352731d44d4a4f0cfbea4","score":"0.40771043","text":"def add_record(self, record=None, name=None, style=None, content=None):\n try: \n add_record = self.__db_module.add_record\n except AttributeError:\n raise AttributeError('Database (' + self.style + ') has no attribute add_record')\n else:\n return add_record(self.__db_info, record=record, name=name, style=style, content=content)","title":""},{"docid":"d3681492b96dc5f4c89fd91e8688290e","score":"0.40748632","text":"def addFirst(self, dt: ghidra.program.model.data.DataType) -> None:\n ...","title":""},{"docid":"a3d169a7bce928483f9ee71b4c808fd3","score":"0.40711415","text":"def add(self):\n\n self._add()","title":""},{"docid":"c1c79d00fe353ea6025b577f4e973777","score":"0.40692812","text":"def add_dmi(self, periods=14, str='{name}({period})',\n name='', **kwargs):\n study = {'kind': 'dmi',\n 'name': name,\n 'params': {'periods': periods, 'high': self._d['high'], 'low': self._d['low'],\n 'close': self._d['close'],\n 'str': str},\n 'display': utils.merge_dict({'legendgroup': False}, kwargs)}\n self._add_study(study)","title":""},{"docid":"670fd9a954ce2a4b2be39ac4e444161d","score":"0.40688488","text":"def insert_fed_object_version(\n fed_object_id: int,\n fed_version_id: int,\n component_id: int,\n action_id: typing.Optional[int],\n schema: typing.Optional[typing.Dict[str, typing.Any]],\n data: typing.Optional[typing.Dict[str, typing.Any]],\n user_id: typing.Optional[int],\n utc_datetime: typing.Optional[datetime.datetime],\n allow_disabled_languages: bool = False\n) -> typing.Optional[Object]:\n if action_id is not None:\n actions.check_action_exists(action_id)\n if user_id is not None:\n users.check_user_exists(user_id)\n object = Objects.insert_fed_object_version(\n data=data,\n schema=schema,\n user_id=user_id,\n action_id=action_id,\n utc_datetime=utc_datetime,\n fed_object_id=fed_object_id,\n fed_version_id=fed_version_id,\n component_id=component_id,\n allow_disabled_languages=allow_disabled_languages\n )\n if object is not None:\n tags.update_object_tag_usage(object)\n return object","title":""},{"docid":"7f1f4b6311e7198611c02fb9b681c433","score":"0.40656993","text":"def _add_flfact_object(self, flfact):\n key = flfact.sid\n #assert key not in self.flfacts\n assert key > 0\n self.flfacts[key] = flfact # set id...\n self._type_to_id_map[flfact.type].append(key)","title":""},{"docid":"152edec6563907f44585810a16602ad7","score":"0.4065446","text":"def _add_ddval_object(self, ddval):\n key = ddval.oid\n assert key not in self.ddvals, 'DDVAL=%s old=\\n%snew=\\n%s' % (\n key, self.ddvals[key], ddval)\n assert key > 0\n self.ddvals[key] = ddval\n self._type_to_id_map[ddval.type].append(key)","title":""},{"docid":"c4441cbdbe6fc9b7cd89d9571a211407","score":"0.40651438","text":"def addDoor(self, door, isMain=False):\n\t\tif not self.mainDoor and isMain: self.mainDoor = door\n\t\tself.doors.append(door)","title":""},{"docid":"923fee96fd63ab83da596cb10a02e759","score":"0.40641314","text":"def add(cls: Type[ModelInterface], data: dict, session: _Session = None) -> ModelInterface:\n record, = cls.add_all([data, ], session=session)\n return record","title":""},{"docid":"2e9cd8d758b475e60257279c24606d93","score":"0.40630695","text":"def __add__(\n self: IRoDType,\n other: BaseRepresentationOrDifferential | IRoDType,\n ) -> IRoDType:\n if isinstance(other, InterpolatedBaseRepresentationOrDifferential) and not array_equal(\n other.affine,\n self.affine,\n ):\n raise ValueError(_op_msg.format(\"add\", self._class_))\n # Add, then re-interpolate\n return self._realize_class(self.data.__add__(other), self.affine)","title":""},{"docid":"b8de1873078831c34de09703bd13202d","score":"0.4062445","text":"def add_dos(self, label, dos):\n\n densities = dos.get_smeared_densities(self.sigma) if self.sigma \\\n else dos.densities\n self._doses[label] = {'frequencies': dos.frequencies, 'densities': densities}","title":""},{"docid":"0c2704e012a2b51ac10582bb418ecd46","score":"0.40588906","text":"def __add__(self,other,/):\n\t\ttry:\n\t\t\treturn Complex(self.re+other.re,self.im+other.im)\n\t\texcept:\n\t\t\treturn Complex(self.re+other,self.im)","title":""},{"docid":"36f1cac0cf4303613cf7984d2f8e49ed","score":"0.40550447","text":"def add_fwf_asset(\n self,\n name: str,\n filepath_or_buffer: pydantic.FilePath | pydantic.AnyUrl,\n **kwargs,\n ) -> FeatherAsset: # type: ignore[valid-type]\n asset = FWFAsset(\n name=name,\n filepath_or_buffer=filepath_or_buffer,\n **kwargs,\n )\n return self._add_asset(asset=asset)","title":""},{"docid":"b3b1630389187862e7220acd23ceb1e6","score":"0.40546027","text":"def addFilm(self, id, titel, rating) -> bool:\n if self.films.tableRetrieve(id)[1]: # Kijkt na of film-id al bestaat\n print(f\"\\033[1;31;49mThe id: {id}, is already in use! The movie {titel} has NOT been created!\\033[0m\")\n return False\n film = Film(id, titel, rating) # Nieuwe film aanmaken\n self.films.tableInsert(film) # Film toevoegen aan ADT van films\n print(f\"The movie {titel} has been created!\")\n return True","title":""},{"docid":"50f50e59cab318324e0500eef912497f","score":"0.40533546","text":"def addShape(self, sh):\n\t\tif sh in self.set:\n\t\t\traise ValueError('duplicate shape')\n\t\telse:\n\t\t\tself.set.append(sh)","title":""},{"docid":"cbbbded34a6eeb4c7f3ff9cd512c85b9","score":"0.40445527","text":"def add(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"976dffd0871d2fa44491dc8d42c37db7\",\n \"score\": \"0.5145458\",\n \"text\": \"def MorphParamCreateDFMRotate(name, rotate_args, entities, bounds, autobounds):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe8b8d14829630d2c7752af44f9745ef\",\n \"score\": \"0.502624\",\n \"text\": \"def add(self) -> None:\\n\\n # verify that the object's designations are not already in the database\\n n: int = (\\n self.db.session.query(Designation)\\n .filter(Designation.desg.in_(self.designations))\\n .count()\\n )\\n if n > 0:\\n raise DesignationError(f\\\"{n} of the target designations are already used\\\")\\n\\n obj: Obj = Obj()\\n self.db.session.add(obj)\\n self.db.session.commit()\\n\\n i: int\\n for i in range(len(self.designations)):\\n desg: Designation = Designation(\\n object_id=obj.object_id, desg=self.designations[i], primary=i == 0\\n )\\n self.db.session.add(desg)\\n\\n self.db.session.commit()\\n self._object_id = obj.object_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f724ed3901003a438f2d2a6aab044848\",\n \"score\": \"0.4858118\",\n \"text\": \"def _add_dmi_object(self, dmi, allow_overwrites=False):\\n name = dmi.name\\n self.dmis[name] = dmi\\n self._type_to_id_map[dmi.type].append(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a06a4906eff96a9d7e6c363b764c195\",\n \"score\": \"0.48215377\",\n \"text\": \"def __add__(self, DBZ2):\\n return DBZ(dataTime=self.dataTime, matrix=self.matrix+DBZ2.matrix,\\\\\\n name=self.name+\\\"_plus_\\\"+DBZ2.name, \\\\\\n dt=self.dt, dx=self.dx, dy=self.dy,\\\\\\n dataPath =self.outputPath+\\\"_plus_\\\"+DBZ2.name+\\\".dat\\\",\\\\\\n outputPath=self.outputPath+\\\"_plus_\\\"+DBZ2.name+\\\".dat\\\",\\\\\\n imagePath =self.imagePath +\\\"_plus_\\\"+DBZ2.name+\\\".png\\\",\\\\\\n database =self.database,\\\\\\n cmap=self.cmap, verbose=self.verbose)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecab0049f67e9cdf643e78e9a5b13bb5\",\n \"score\": \"0.48210606\",\n \"text\": \"def add_data(self, name, f, d, params=None):\\n if self._model.independent == \\\"f\\\":\\n return self._add_data(name, f, d, params)\\n else:\\n return self._add_data(name, d, f, params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"664b02da5d680aec00ab44dfdc3afefe\",\n \"score\": \"0.47280216\",\n \"text\": \"def NewParameterDFMRotate(args, entities, bounds, autobounds, name):\\n\\n\\timport warnings; warnings.warn(\\\"Deprecated since version 21.0.0. Use class DFM instead.\\\", DeprecationWarning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae54f2ae6c0acc4ee56150d698f4510a\",\n \"score\": \"0.46424422\",\n \"text\": \"def _add_dmig_object(self, dmig, allow_overwrites=False):\\n name = dmig.name\\n self.dmigs[name] = dmig\\n self._type_to_id_map[dmig.type].append(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b6f2f9168ad90e571b32657a0d40c74\",\n \"score\": \"0.46394476\",\n \"text\": \"def addDrip(self, date):\\n newDrip = self._createTrade(\\\"*\\\", \\\"*\\\", date, \\\"drip\\\")\\n #TODO: Possibly add a flag to automatically add dividends if set\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"639b23dd8ccf7cb9656889b8c2b17ecd\",\n \"score\": \"0.4624708\",\n \"text\": \"def __add__(self, o):\\n if self.phc != o.phc:\\n raise (ValueError(\\\"Unable to add fields over different crystals.\\\"))\\n if all(self.res != o.res):\\n raise (ValueError(\\\"Unable to add fields with different resolutions.\\\"))\\n if self.polarization != o.polarization:\\n raise (ValueError(\\\"Unable to add fields with different polarizations.\\\"))\\n if self.z_dimension != o.z_dimension:\\n raise (ValueError(\\\"Unable to add fields with different z dimension.\\\"))\\n\\n field = lambda t: self._field(t) + o._field(t)\\n return XYField(res = self.res, phc=self.phc, z_dimension=self.z_dimension,\\n polarization=self.polarization, meshgrid=self.meshgrid, field=field)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0ecf5abdd60dc0d7ed61c46279494ef\",\n \"score\": \"0.45418882\",\n \"text\": \"def add(self, title, date, runtime):\\r\\n return self.bst.add(Movie(title, date, runtime))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b256b9126ef6b903fd2d6cc434adac2c\",\n \"score\": \"0.453784\",\n \"text\": \"def _add_flutter_object(self, flutter):\\n key = flutter.sid\\n assert key not in self.flutters, 'FLUTTER=%s old=\\\\n%snew=\\\\n%s' % (key, self.flutters[key], flutter)\\n assert key > 0\\n self.flutters[key] = flutter\\n self._type_to_id_map[flutter.type].append(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598a03eb841a1a717df3506ed5fc553d\",\n \"score\": \"0.45344514\",\n \"text\": \"def add(self, d):\\n self.data.append(d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8b982ee53ad33f96a6cabe532b5f37\",\n \"score\": \"0.45325243\",\n \"text\": \"def add(self, dpid=None):\\n return self._send_flow_mods_from_request(dpid, \\\"add\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"814131f289f4909a775b74df55e97f8c\",\n \"score\": \"0.45318726\",\n \"text\": \"def addAttr(self, attr):\\n\\n if isinstance(attr, str):\\n node, attr = attr.rsplit(\\\".\\\", 1)\\n node = encode(node)\\n attr = node[attr]\\n\\n mobj = attr\\n\\n xattr = None\\n if isinstance(mobj, _AbstractAttribute):\\n xattr = mobj\\n mobj = attr.create()\\n\\n assert isinstance(mobj, om.MObject)\\n\\n try:\\n self._fn.addAttribute(mobj)\\n\\n except RuntimeError:\\n # Maya doesn't tell you *why* it fails,\\n # so let's do some investigating\\n\\n # But we can only do that if we've\\n # got something to work with.\\n if xattr is not None:\\n # fn = om.MFnAttribute(mobj)\\n if xattr[\\\"shortName\\\"] in self:\\n raise ExistError(\\n \\\"%s['%s'] already exists!\\\"\\n % (self.name(), xattr[\\\"shortName\\\"])\\n )\\n\\n if xattr[\\\"name\\\"] in self:\\n raise ExistError(\\n \\\"%s['%s'] already exists!\\\"\\n % (self.name(), xattr[\\\"name\\\"])\\n )\\n\\n # We didn't find anything wrong, proceed with generic error\\n raise\\n\\n # These don't natively support defaults by Maya\\n # They aren't being saved with the file, unless\\n # we explicitly set it after creation.\\n if isinstance(attr, String) and attr[\\\"default\\\"]:\\n self[attr[\\\"name\\\"]] = attr[\\\"default\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2f8cca5ba4f4a5e326f8477c056d5be\",\n \"score\": \"0.4527011\",\n \"text\": \"def _add_dmik_object(self, dmik, allow_overwrites=False):\\n name = dmik.name\\n self.dmiks[name] = dmik\\n self._type_to_id_map[dmik.type].append(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcca4956cab6c0ec572f609aecf8f2d4\",\n \"score\": \"0.4520015\",\n \"text\": \"def append_mlf(self, mlf):\\n self.mlfs.append(mlf)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4713c94781898059ca1ad5bc77181d99\",\n \"score\": \"0.45039982\",\n \"text\": \"def MorphCreateDFMRotateArgs(rotated, x, y, z, dx, dy, dz):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32c04601c58d8f423517707b877a8e69\",\n \"score\": \"0.4497843\",\n \"text\": \"def Voicelead_addOctave(*args):\\n return _CsoundAC.Voicelead_addOctave(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c23e4a11c24fdbcfb2c234b8f6ad64db\",\n \"score\": \"0.44710323\",\n \"text\": \"def _add_dvmrel_object(self, dvmrel):\\n key = dvmrel.oid\\n assert key not in self.dvmrels, 'DVMRELx=%s old=\\\\n%snew=\\\\n%s' % (\\n key, self.dvmrels[key], dvmrel)\\n assert key not in self.dvmrels\\n assert key > 0\\n self.dvmrels[key] = dvmrel\\n self._type_to_id_map[dvmrel.type].append(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d30988e034a887b2b977fa85dfc487b\",\n \"score\": \"0.44519797\",\n \"text\": \"def add(self, *, database_object_dict) -> object:\\n\\t\\traise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695783530130dd9e2d8056b5825a0767\",\n \"score\": \"0.4441959\",\n \"text\": \"def __add__(self, other):\\n diff = len(self) - len(other)\\n if diff > 0:\\n new = Motif((other << diff).pfm + self.pfm)\\n elif diff < 0:\\n new = Motif((self << diff).pf + other.pfm)\\n else:\\n new = Motif(self.pfm + other.pfm)\\n\\n new.id = new.consensus\\n return new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7bc5a04478ca3bb87874e0959ff3a4f\",\n \"score\": \"0.44392937\",\n \"text\": \"def add_movie(self, id):\\n\\n movie = Movie.get(id_=id)\\n movie.arca_up()\\n self.movies_saved.add(movie)\\n\\n #mudar datetime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19ae3ee6e3095f470f63ffda7b2d48f\",\n \"score\": \"0.4418368\",\n \"text\": \"def __rmul__(self, M):\\n if type(M)==type(1) or type(M)==type(1.1) or type(M)==type(self.matrix) : \\n matrix = self.matrix * M\\n name=self.name+\\\"_times_\\\"+ str(M)\\n if type(M)==type(self):\\n matrix = self.matrix * M.matrix\\n name=self.name+\\\"_times_\\\"+ M.name\\n return DBZ(dataTime=self.dataTime, matrix=matrix,\\\\\\n dt=self.dt, dx=self.dx, dy=self.dy,\\\\\\n name =name,\\n dataPath =self.outputPath+\\\"_times_\\\"+str(M)+\\\".dat\\\",\\\\\\n outputPath=self.outputPath+\\\"_times_\\\"+str(M)+\\\".dat\\\",\\\\\\n imagePath =self.imagePath +\\\"_times_\\\"+str(M)+\\\".png\\\",\\\\\\n database =self.database,\\\\\\n cmap=self.cmap, verbose=self.verbose)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d9b3f5605ef7018d2f1c20ecf530645\",\n \"score\": \"0.44103634\",\n \"text\": \"def create(self, *args, **kargs):\\n return getattr(odml, self.__class__.__name__)(*args, **kargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7e5b97b74caa72e2551602110e49e21\",\n \"score\": \"0.4397141\",\n \"text\": \"def add_memo():\\n try:\\n date = arrow.get(request.args.get('date', 0, type=str),\\n 'YYYY/MM/DD').naive\\n text = request.args.get('text', 0, type=str)\\n\\n record = {\\\"type\\\": \\\"dated_memo\\\",\\n \\\"date\\\": date,\\n \\\"text\\\": text}\\n result = collection.insert_one(record)\\n record_id = str(result.inserted_id)\\n\\n message = 'Memo added.'\\n worked = True\\n except:\\n message = 'Memo not added.'\\n record_id = ''\\n worked = False\\n\\n return flask.jsonify(message=message, record_id=record_id, result=worked)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ce5def3345dba2aaf769748604a3b1c\",\n \"score\": \"0.439125\",\n \"text\": \"def add(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81acd19fd3df555fff248824c040e135\",\n \"score\": \"0.43864262\",\n \"text\": \"def add(self, *args, **kwargs):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ebfbe3cd07c0cbd0d7f994d6eb5bfa2\",\n \"score\": \"0.4376674\",\n \"text\": \"def add_dna(self, dna):\\n\\n dna._model = self\\n dna.init_variable()\\n\\n self.dna = dna\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8791bfd099b6f554a283430d6e13e8c6\",\n \"score\": \"0.4367991\",\n \"text\": \"def _add_dlink_object(self, dlink):\\n key = dlink.oid\\n assert key not in self.dlinks, 'DLINK=%s old=\\\\n%snew=\\\\n%s' % (\\n key, self.dlinks[key], dlink)\\n assert key > 0\\n self.dlinks[key] = dlink\\n self._type_to_id_map[dlink.type].append(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50c796422e62bc5255f6adc80f4c5fa5\",\n \"score\": \"0.4356571\",\n \"text\": \"def add(cls):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b816cf73ea8be02939292ebeba47669\",\n \"score\": \"0.43449968\",\n \"text\": \"def pitchfx_add(db, hdb, date1, date2, prompt):\\n # intial pitch data vector of nulls\\n comm = \\\"PRAGMA table_info(pitchfx)\\\"\\n hdb.execute(comm)\\n npfx = len(hdb.fetchall())\\n pfxinit = [None]*npfx\\n\\n # game types\\n gtypes = ['R', 'F', 'D', 'L', 'W']\\n\\n # pitchfx variables: sqlite3 database indices\\n pfxkeys = {\\n 'game_id': 0,\\n 'id': 1,\\n 'at_bat': 2,\\n 'time': 3,\\n 'cur_event': 4,\\n 'des': 5,\\n 'type': 6,\\n 'pre_balls': 7,\\n 'post_balls': 8,\\n 'pre_strikes': 9,\\n 'post_strikes': 10,\\n 'start_speed': 11,\\n 'end_speed': 12,\\n 'sz_top': 13,\\n 'sz_bot': 14,\\n 'pfx_x': 15,\\n 'pfx_z': 16,\\n 'px': 17,\\n 'pz': 18,\\n 'x': 19,\\n 'y': 20,\\n 'x0': 21,\\n 'y0': 22,\\n 'z0': 23,\\n 'vx0': 24,\\n 'vy0': 25,\\n 'vz0': 26,\\n 'ax': 27,\\n 'ay': 28,\\n 'az': 29,\\n 'break_y': 30,\\n 'break_angle': 31,\\n 'break_length': 32,\\n 'spin_dir': 33,\\n 'spin_rate': 34,\\n 'pitch_type': 35\\n }\\n\\n # determine which dates already exist in the database\\n query = \\\"SELECT min(date) FROM games\\\" \\n hdb.execute(query) \\n dbmin = hdb.fetchone()[0] \\n # empty database \\n if dbmin == None:\\n date_start = date1 \\n date_end = date2 \\n else: \\n query = \\\"SELECT max(date) FROM games\\\" \\n hdb.execute(query)\\n dbmax = hdb.fetchone()[0]\\n # date1 date2 dbmin dbmax \\n if date2 < dbmin: \\n date_start = date1 \\n date_end = date2 \\n # dbmin dbmax date1 date2 \\n elif date1 > dbmax: \\n date_start = date1 \\n date_end = date2 \\n # date1 dbmin date2 dbmax \\n # date1 dbmin dbmax date2 \\n elif date1 < dbmin: \\n date_start = date1 \\n if date2 <= dbmax: \\n date_end = dbmin \\n else: \\n date_end = date2 \\n # dbmin date1 dbmax date2 \\n elif dbmin <= date1: \\n if date2 >= dbmax: \\n date_start = dbmax \\n date_end = date2 \\n # dbmin date1 date2 dbmax \\n else: \\n date_start = dbmin \\n date_end = dbmax \\n\\n # loop through dates (as defined by the date_start and date_end values)\\n sdate_start = str(date_start)\\n sdate_end = str(date_end)\\n years = [sdate_start[:4], sdate_end[:4]]\\n months = [sdate_start[4:6], sdate_end[4:6]]\\n month = int(months[0])\\n days = [sdate_start[-2:], sdate_end[-2:]]\\n day = int(days[0])\\n\\n # set up regular expression\\n str_game = re.compile(\\\"\\\"\\\"^.+href=\\\"(gid_\\\\d+_\\\\d+_\\\\d+_.+)/\\\".+$\\\"\\\"\\\")\\n str_xml = re.compile(\\\"\\\"\\\"^.+href=\\\"(.+\\\\.xml)\\\".+$\\\"\\\"\\\")\\n\\n # loop over years, months, days for games\\n for year in range(int(years[0]), int(years[1])+1):\\n while month < 13:\\n while day < 32:\\n # determine whether there are any files to look at on given day\\n try:\\n url_root = \\\"http://gd2.mlb.com/components/game/mlb/year_%s/month_%s/day_%s\\\" %(year, str(month).zfill(2), str(day).zfill(2))\\n output = urllib.urlopen(url_root)\\n except:\\n day += 1\\n continue\\n # create list of game ids on given day\\n date = int(str(year).zfill(4)+str(month).zfill(2)+str(day).zfill(2))\\n ggs = []\\n for line in output:\\n find = str_game.search(line)\\n if find:\\n ggs.append(find.group(1))\\n\\n # read in game and team information from game url\\n for gg in ggs:\\n url_game = url_root+\\\"/%s\\\" %(gg)\\n url = url_game+\\\"/game.xml\\\"\\n try:\\n output = urllib.urlopen(url).read()\\n log = ET.fromstring(output)\\n except:\\n continue\\n gdict = log.attrib\\n game_type = gdict['type']\\n # determine what sort of game it is\\n if game_type not in gtypes:\\n continue\\n try:\\n game_id = int(gdict['game_pk'])\\n #game_id_prev = game_id\\n except:\\n continue\\n #except:\\n # game_id = game_id_prev+1\\n # check if game already exists in table\\n comm = \\\"SELECT date FROM games WHERE game_id=%s\\\" %(game_id)\\n hdb.execute(comm)\\n if hdb.fetchone() != None:\\n continue\\n print gg\\n temp_time = gdict['local_game_time']\\n gtime = int(temp_time[0:2]+temp_time[3:5])\\n for ginfo in log:\\n if ginfo.tag == 'team':\\n ggdict = ginfo.attrib\\n if ggdict[\\\"type\\\"] == 'home':\\n hid = ggdict['id']\\n try:\\n tname = ggdict['name_full']\\n except:\\n tname = ggdict['name']\\n tid = int(hid)\\n tabbrv = ggdict['abbrev']\\n hw = int(ggdict['w'])\\n hl = int(ggdict['l'])\\n else:\\n vid = ggdict['id']\\n try:\\n tname = ggdict['name_full']\\n except:\\n tname = ggdict['name']\\n tid = int(vid)\\n tabbrv = ggdict['abbrev']\\n vw = int(ggdict['w'])\\n vl = int(ggdict['l'])\\n # fill in team table\\n info = (tid, tname, tabbrv)\\n insert = \\\"INSERT OR IGNORE INTO teams VALUES \\\" \\\\\\n \\\"(?, ?, ?)\\\"\\n hdb.execute(insert, info)\\n # stadium information\\n elif ginfo.tag == 'stadium':\\n ggdict = ginfo.attrib\\n sid = int(ggdict['id'])\\n sname = ggdict['name']\\n # fill in stadium table\\n info = (sid, sname)\\n insert = \\\"INSERT OR IGNORE INTO stadiums VALUES \\\" \\\\\\n \\\"(?, ?)\\\"\\n hdb.execute(insert, info)\\n\\n # read in player info from players xml\\n url = url_game+'/players.xml'\\n output = urllib.urlopen(url).read()\\n log = ET.fromstring(output)\\n for dinfo in log:\\n for pinfo in dinfo:\\n if pinfo.tag == 'player':\\n try:\\n pdict = pinfo.attrib\\n player_id = pdict['id'] \\n first = pdict['first'] \\n last = pdict['last'] \\n pos = pdict['position'] \\n bat = pdict['bats'] \\n throw = pdict['rl'] \\n except:\\n continue\\n try:\\n info = (player_id, first, last, pos, bat, throw, -1)\\n insert = \\\"INSERT INTO players VALUES \\\" \\\\\\n \\\"(?, ?, ?, ?, ?, ?, ?)\\\"\\n hdb.execute(insert, info)\\n dob_flag = 1\\n except:\\n dob_flag = 0\\n # grab dob from individual player urls\\n if dob_flag == 1:\\n url_player = url_game+'/batters/%s.xml' %(player_id)\\n poutput = urllib.urlopen(url_player).read()\\n plog = ET.fromstring(poutput)\\n ppdict = plog.attrib\\n pdob = ppdict['dob']\\n yy = pdob[-4:] \\n mm = pdob[:2] \\n dd = pdob[3:5] \\n dob = int(yy+mm+dd)\\n # update player table\\n comm = \\\"UPDATE players SET dob=%s \\\" \\\\\\n \\\"WHERE player_id=%s AND position='%s'\\\" %(dob, player_id, pos)\\n hdb.execute(comm)\\n elif pinfo.tag == 'umpire':\\n udict = pinfo.attrib\\n umpire_name = udict['name']\\n umpire_position = udict['position']\\n try:\\n umpire_id = int(udict['id'])\\n # fill in umpires table\\n info = (umpire_id, umpire_name)\\n insert = \\\"INSERT OR IGNORE INTO umpires VALUES \\\" \\\\\\n \\\"(?, ?)\\\"\\n hdb.execute(insert, info)\\n except:\\n umpire_id = -1\\n if umpire_position == 'home':\\n u_home = umpire_id\\n elif umpire_position == 'first':\\n u_first = umpire_id\\n elif umpire_position == 'second':\\n u_second = umpire_id\\n elif umpire_position == 'third':\\n u_third = umpire_id\\n\\n # fill in game table if game does not already exist\\n info = (game_id, game_type, date, gtime, hid, hw, hl, vid, vw, vl, sid, u_home, u_first, u_second, u_third)\\n insert = \\\"INSERT OR IGNORE INTO games VALUES \\\" \\\\\\n \\\"(?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\\\"\\n hdb.execute(insert, info)\\n\\n # read in pitchfx and event tables\\n url = url_game+\\\"/inning/inning_all.xml\\\"\\n try:\\n output = urllib.urlopen(url).read()\\n log = ET.fromstring(output)\\n except:\\n continue\\n # intialize events and scores\\n event_id = 0\\n action_flag = 0\\n pre_home_score = 0\\n post_home_score = 0\\n pre_away_score = 0\\n post_away_score = 0\\n # loop over inning\\n for header in log:\\n iinfo = header.attrib\\n inning_num = iinfo['num']\\n for inning in header:\\n # new inning flag\\n inning_flag = 1\\n post_outs = 0\\n pre_1b = 0\\n post_1b = 0\\n pre_2b = 0\\n post_2b = 0\\n pre_3b = 0\\n post_3b = 0\\n post_home = 0\\n if inning.tag == 'top':\\n is_top = 1\\n else:\\n is_top = 0\\n # loop over events in an inning\\n for etype in inning:\\n # keep track of in-game player changes\\n if etype.tag == 'action':\\n edict = etype.attrib\\n action_description = edict['des']\\n if 'Pinch-Hitter' in action_description:\\n action_event = edict['event']\\n action_flag = 1\\n elif 'Change' in action_description:\\n action_event = edict['event']\\n action_flag = 1\\n elif etype.tag == 'atbat':\\n pre_outs = post_outs\\n # flag for events with multiple runners\\n runner_flag = 1\\n # reset count at beginning of at-bat\\n pre_balls = 0\\n post_balls = 0\\n pre_strikes = 0\\n post_stikes = 0\\n edict = etype.attrib\\n # to account for strikeouts\\n max_strikes = int(edict['s'])\\n ab = int(edict['num'])\\n pitcher_id = int(edict['pitcher'])\\n batter_id = int(edict['batter'])\\n end_of_ab_outs = int(edict['o'])\\n event_description_ab = edict['event']\\n # if there is a previous action then write current ab info as event\\n if action_flag == 1:\\n info = (game_id, event_id, event_description, inning_num, is_top, pre_outs, post_outs, pitcher_id, batter_id, pre_1b, post_1b, pre_2b, post_2b, pre_3b, post_3b, post_home, pre_home_score, post_home_score, pre_away_score, post_away_score)\\n insert = \\\"INSERT OR IGNORE INTO events VALUES (\\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \\\" \\\\\\n \\\"?, ?, ? ,?, ?, ?, ?, ?, ?, ?\\\" \\\\\\n \\\")\\\"\\n hdb.execute(insert, info)\\n event_id += 1\\n #if inning_flag == 1:\\n # event_id += 1\\n # info = (game_id, event_id, -1, inning_num, is_top, pre_outs, post_outs, pitcher_id, batter_id, -1, -1, -1, pre_home_score, post_home_score, pre_away_score, post_away_score)\\n # insert = \\\"INSERT OR IGNORE INTO events VALUES (\\\" \\\\\\n # \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \\\" \\\\\\n # \\\"?, ?, ?, ?, ?, ?\\\" \\\\\\n # \\\")\\\"\\n # hdb.execute(insert, info)\\n try:\\n pre_home_score = post_home_score\\n pre_away_score = post_away_score\\n post_home_score = edict['home_team_runs']\\n post_away_score = edict['away_team_runs']\\n except:\\n pass\\n # reset inning and action flag\\n action_flag = 0\\n inning_flag = 0\\n runner_flag = 0\\n # read in pitches\\n for event in etype:\\n # update runners if necessary\\n if (event.tag != \\\"runner\\\") and (runner_flag == 0):\\n pre_1b = post_1b\\n pre_2b = post_2b\\n pre_3b = post_3b\\n post_home = 0\\n event_id += 1\\n # registered pitch\\n if event.tag == 'pitch':\\n runner_flag = 1\\n # initialize pitchfx list and read in basic info\\n info = list(pfxinit)\\n info[pfxkeys['game_id']] = game_id\\n info[pfxkeys['at_bat']] = ab\\n info[pfxkeys['cur_event']] = event_id\\n idict = event.attrib\\n try:\\n time_stamp = int(idict['sv_id'][-6:])\\n info[pfxkeys['time']] = time_stamp\\n except:\\n pass\\n # only fill in pitchfx info that exist\\n common = set(idict.keys()).intersection(pfxkeys)\\n for pfxvars in common:\\n info[pfxkeys[pfxvars]] = idict[pfxvars]\\n # update balls and strikes information\\n if idict['type'] == 'B':\\n post_balls = min(pre_balls+1,4)\\n post_strikes = pre_strikes\\n elif idict['type'] == 'S':\\n post_balls = pre_balls\\n if idict['des'] == 'Foul':\\n post_strikes = min(pre_strikes+1,2)\\n else:\\n post_strikes = min(pre_strikes+1,max_strikes)\\n else:\\n post_balls = pre_balls\\n post_strikes = pre_strikes\\n info[pfxkeys['pre_balls']] = pre_balls\\n info[pfxkeys['post_balls']] = post_balls\\n info[pfxkeys['pre_strikes']] = pre_strikes\\n info[pfxkeys['post_strikes']] = post_strikes\\n # fill in pitchfx table\\n insert = \\\"INSERT OR IGNORE INTO pitchfx VALUES (\\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?\\\" \\\\\\n \\\")\\\"\\n hdb.execute(insert, info)\\n # update balls and strikes again\\n pre_balls = post_balls\\n pre_strikes = post_strikes\\n # initialize runner events(s)\\n elif (event.tag == 'runner'):\\n rdict = event.attrib\\n runner_id = rdict['id']\\n event_description = rdict['event']\\n base_start = rdict['start']\\n flag1 = 0\\n flag2 = 0\\n flag3 = 0\\n if base_start == \\\"1B\\\":\\n pre_1b = runner_id\\n flag1 = 1\\n elif base_start == \\\"2B\\\":\\n pre_2b = runner_id\\n flag2 = 1\\n elif base_start == \\\"3B\\\":\\n pre_3b = runner_id\\n flag3 = 1\\n #if base_start == \\\"\\\":\\n # base_start = \\\"H\\\"\\n base_end = rdict['end']\\n if base_end == \\\"\\\":\\n try:\\n if rdict['score'] == \\\"T\\\":\\n #base_end = \\\"H\\\"\\n post_home = runner_id\\n except:\\n #base_end = \\\"0\\\"\\n post_outs = pre_outs+1\\n elif base_end == \\\"1B\\\":\\n post_1b = runner_id\\n elif base_end == \\\"2B\\\":\\n post_2b = runner_id\\n elif base_end == \\\"3B\\\":\\n post_3b = runner_id\\n if base_start != base_end:\\n if flag1 == 1:\\n post_1b = 0\\n elif flag2 == 1:\\n post_2b = 0\\n elif flag3 == 1:\\n post_3b = 0\\n # fill in event table\\n info = (game_id, event_id, event_description, inning_num, is_top, pre_outs, post_outs, pitcher_id, batter_id, pre_1b, post_1b, pre_2b, post_2b, pre_3b, post_3b, post_home, pre_home_score, post_home_score, pre_away_score, post_away_score)\\n insert = \\\"INSERT OR REPLACE INTO events VALUES (\\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?\\\" \\\\\\n \\\")\\\"\\n hdb.execute(insert, info)\\n ## update event id if necessary\\n #if runner_flag == 1:\\n # event_id += 1\\n # upcoming runner tags are not separate events\\n runner_flag = 0\\n # handle pitch-outs\\n elif event.tag == 'po':\\n runner_flag = 1\\n rdict = event.attrib\\n event_description = rdict['des']\\n # fill in event table\\n info = (game_id, event_id, event_description, inning_num, is_top, pre_outs, post_outs, pitcher_id, batter_id, pre_1b, post_1b, pre_2b, post_2b, pre_3b, post_3b, post_home, pre_home_score, post_home_score, pre_away_score, post_away_score)\\n insert = \\\"INSERT OR IGNORE INTO events VALUES (\\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?\\\" \\\\\\n \\\")\\\" \\n hdb.execute(insert, info)\\n event_id += 1\\n # add end of at-bat information only if an out\\n if end_of_ab_outs > pre_outs:\\n # if last entry in ab is not runner then there was nobody on base (new event)\\n # fill in event table\\n info = (game_id, event_id, event_description_ab, inning_num, is_top, pre_outs, end_of_ab_outs, pitcher_id, batter_id, pre_1b, post_1b, pre_2b, post_2b, pre_3b, post_3b, post_home, pre_home_score, post_home_score, pre_away_score, post_away_score)\\n #info = (game_id, event_id, event_description_ab, inning_num, is_top, pre_outs, end_of_ab_outs, pitcher_id, batter_id, batter_id, 'H', '0', pre_home_score, post_home_score, pre_away_score, post_away_score)\\n insert = \\\"INSERT OR REPLACE INTO events VALUES (\\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?, \\\" \\\\\\n \\\"?, ?, ?, ?, ?, ?, ?, ?, ?, ?\\\" \\\\\\n \\\")\\\" \\n hdb.execute(insert, info)\\n #if event.tag != 'runner':\\n # event_id += 1\\n # update outs\\n pre_outs = end_of_ab_outs\\n post_outs = end_of_ab_outs\\n # if last entry in ab is runner then there was someone on base (already registered as event)\\n #if event.tag == 'runner':\\n # event_id += 1\\n ## temporary break from games\\n # commit game\\n db.commit()\\n #return\\n # check whether to break out\\n #if date >= date_end:\\n if date >= date_end:\\n return\\n # update day\\n day += 1\\n # update day and month\\n day = 1\\n month += 1\\n # reset month\\n month = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a620d47920ecd7a11772e587a20052\",\n \"score\": \"0.43337896\",\n \"text\": \"def add_medical_note(app_dt, doctor_id, medical_note):\\n\\n app = Appointment.query.filter(Appointment.doctor_id.like(\\n doctor_id), Appointment.start_datetime.like(app_dt)).first()\\n app.medical_note = medical_note\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1a803987e7360865409e91832bf2b14\",\n \"score\": \"0.43265975\",\n \"text\": \"def _add_dvcrel_object(self, dvcrel):\\n key = dvcrel.oid\\n assert key not in self.dvcrels, 'DVCRELx=%s old\\\\n%snew=\\\\n%s' % (\\n key, self.dvcrels[key], dvcrel)\\n assert key > 0\\n self.dvcrels[key] = dvcrel\\n self._type_to_id_map[dvcrel.type].append(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32c68e332db27c13a59a0a58725d1fbf\",\n \"score\": \"0.43222874\",\n \"text\": \"def add(self):\\n if self.in_reference_to:\\n # This is a meta fact\\n return self.__add_meta()\\n\\n # This is not a meta fact\\n return self.__add_fact()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa77e371384e00116de87b678e0e45da\",\n \"score\": \"0.4315649\",\n \"text\": \"def addAttr(self, node, attr):\\n\\n assert isinstance(node, Node), \\\"%s was not a cmdx.Node\\\"\\n\\n if SAFE_MODE:\\n assert _isalive(node._mobject)\\n\\n mobj = attr\\n\\n if isinstance(attr, _AbstractAttribute):\\n mobj = attr.create()\\n\\n this_name = om.MFnAttribute(mobj).name\\n if node.hasAttr(this_name):\\n raise ExistError(\\n \\\"Attribute already exists: %s\\\" % node[this_name].path()\\n )\\n\\n # Ensure it isn't in the process of being added\\n for _node, _mattr in self._attributesBeingAdded:\\n name_being_added = om.MFnAttribute(_mattr).name\\n if node == _node and this_name == name_being_added:\\n raise ExistError(\\n \\\"Same attribute added twice: %s.%s\\\" % (\\n node.path(), name_being_added)\\n )\\n\\n status = self._modifier.addAttribute(node._mobject, mobj)\\n\\n if not status:\\n raise ValueError(\\n \\\"Could not add the attribute: %s.%s\\\" % (node, attr)\\n )\\n\\n if isinstance(attr, String) and attr[\\\"default\\\"]:\\n log.warning(\\\"Strings don't support default values with modifier\\\")\\n\\n # Protect against user error\\n # This one is especially pesky; Maya will go ahead and create\\n # the same attribute name multiple times, only to complain when\\n # you try and undo. Bad, Maya, bad!\\n self._attributesBeingAdded += [(node, mobj)]\\n\\n if SAFE_MODE:\\n self._modifier.doIt()\\n\\n return mobj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83f3973bc26071c10702dd1281944677\",\n \"score\": \"0.4314483\",\n \"text\": \"def _add_dresp_object(self, dresp):\\n key = dresp.dresp_id\\n assert key not in self.dresps, 'DRESPx=%s old=\\\\n%snew=\\\\n%s' % (\\n key, self.dresps[key], dresp)\\n assert key > 0\\n self.dresps[key] = dresp\\n self._type_to_id_map[dresp.type].append(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b8f60ceed72ec68189ae8df70a2908\",\n \"score\": \"0.4311742\",\n \"text\": \"def __add__(self, obj):\\n if isinstance(obj, Number):\\n if obj:\\n self.numerator += obj * self.denominator\\n return self\\n assert(obj.vartype == 3)\\n \\n if self.denominator != obj.denominator:\\n return NotImplemented('The Denominator should be the Same')\\n else:\\n new = FracVariable(self.numerator + obj.numerator, self.denominator)\\n return new\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1950031b153576432b61091dfd737860\",\n \"score\": \"0.43108582\",\n \"text\": \"def add_drone(self):\\r\\n # Start a new drone instance\\r\\n #print('TODO: Start a new drone')\\r\\n #drone = Drone(id, name, class_type, rescue, operator)\\r\\n drone = Drone(None)\\r\\n # Display the drone\\r\\n self.view_drone(drone, self._save_new_drone)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9800bdaa8c85cdb7cdb6f2eda2ea89d1\",\n \"score\": \"0.43034083\",\n \"text\": \"def add_fibre(self, data):\\n\\n # Type casting:\\n f_id = int(data[1])\\n radius = float(data[2])\\n\\n # Any argument after the 2nd is regarded as a fibre:\\n nodes = []\\n for i in range(3, len(data)):\\n n_id = int(data[i])\\n nodes.append(n_id)\\n\\n # Add an instance of the fibre class to fibre_dict:\\n self.fibre_dict[f_id] = Fibre(f_id, radius, *nodes)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2a6c250d1cac9606aab9aa7537c5db7\",\n \"score\": \"0.4267027\",\n \"text\": \"def AddDEM(self, dem):\\n\\n\\t\\tself.date.append(dem.date)\\n\\t\\tself.uncertainty.append(dem.uncertainty)\\n\\t\\t# Add the first band, and then replace NoDataValue by np.nan\\n\\t\\tdem_array = dem.ReadAsArray()\\n\\t\\tdem_array[dem_array == dem.GetNoDataValue()] = np.nan\\n\\t\\treturn TimeSeriesDEM(array=np.dstack([self, dem_array]), date=self.date, uncertainty=self.uncertainty)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f31c5753a3f8848463b8b33ab826a094\",\n \"score\": \"0.42633322\",\n \"text\": \"def add(self, new_movie):\\n self.__formatter.format(new_movie)\\n self.__validator.validate(new_movie)\\n\\n self.__repository.add(new_movie)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c26f3cd52ae675a0afe5d0080c1ebc8\",\n \"score\": \"0.4262025\",\n \"text\": \"def series(dtype='mri', in_study:Study=None, in_database:Database=None, **kwargs)->Series:\\n if dtype not in ['mri', 'MRImage']:\\n message = 'dbdicom can only create images of type MRImage at this stage'\\n raise ValueError(message)\\n \\n if in_study is not None:\\n series = in_study.new_series()\\n else:\\n if in_database is None:\\n _database = database()\\n else:\\n _database = in_database\\n patient = _database.new_patient()\\n study = patient.new_study()\\n series = study.new_series(**kwargs)\\n if in_database is None:\\n _database.save()\\n return series\",\n \"title\": \"\"\n },\n {\n \"docid\": \"693125378a893ca9207609c4630b4c46\",\n \"score\": \"0.42376268\",\n \"text\": \"def __add__(self, other: Formula, /) -> Formula:\\n if not isinstance(other, Formula):\\n raise TypeError('can only add Formula instance')\\n return Formula(\\n join_charge(\\n f'({self._formula_nocharge})({other._formula_nocharge})',\\n self._charge + other.charge,\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62b2f155e7a4397f4de63a1d60706a4c\",\n \"score\": \"0.42301017\",\n \"text\": \"def add(self, usr, opt):\\n return RatingData.objects.add(self.instance, usr, opt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3df620a74f92516ecacf2beacf444c03\",\n \"score\": \"0.42292303\",\n \"text\": \"def save(self, *args, **kwargs):\\n def match_date(date):\\n \\\"\\\"\\\"Function to parse string-dates into python date objects.\\n \\\"\\\"\\\"\\n date = date.strip()\\n date = date.replace('-', '.')\\n if re.match(r'[0-9]{4}$', date):\\n dr = datetime.strptime(date, '%Y')\\n dr2 = date\\n elif re.match(r'[0-9]{1,2}\\\\.[0-9]{1,2}\\\\.[0-9]{4}$', date):\\n dr = datetime.strptime(date, '%d.%m.%Y')\\n dr2 = date\\n elif re.match(r'[0-9]{4}\\\\.\\\\.\\\\.$', date):\\n dr = datetime.strptime(date, '%Y...')\\n dr2 = re.match(r'([0-9]{4})\\\\.\\\\.\\\\.$', date).group(1)\\n elif re.match(r'[0-9]{4}\\\\.[0-9]{1,2}\\\\.\\\\.$', date):\\n dr = datetime.strptime(date, '%Y.%m..')\\n dr2 = re.match(r'([0-9]{4})\\\\.([0-9]{1,2})\\\\.\\\\.$', date).group(2)\\n +'.' + \\\\\\n re.match(r'([0-9]{4})\\\\.([0-9]{1,2})\\\\.\\\\.$', date).group(1)\\n elif re.match(r'[0-9]{4}\\\\.[0-9]{1,2}\\\\.[0-9]{1,2}$', date):\\n dr = datetime.strptime(date, '%Y.%m.%d')\\n dr3 = re.match(\\n r'([0-9]{4})\\\\.([0-9]{1,2})\\\\.([0-9]{1,2})$', date)\\n dr2 = dr3.group(3) + '.' + dr3.group(2) + '.' + dr3.group(1)\\n else:\\n dr = None\\n dr2 = dr2 = date\\n return dr, dr2\\n if self.start_date_written:\\n self.start_date, self.start_date_written = match_date(\\n self.start_date_written)\\n if self.end_date_written:\\n self.end_date, self.end_date_written = match_date(\\n self.end_date_written)\\n super(Area, self).save(*args, **kwargs)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd60922db29769fd92d83823e59c42bd\",\n \"score\": \"0.42245078\",\n \"text\": \"def _add_dmij_object(self, dmij, allow_overwrites=False):\\n name = dmij.name\\n self.dmijs[name] = dmij\\n self._type_to_id_map[dmij.type].append(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e79b8605e786ed33655fe8bc477b8ce\",\n \"score\": \"0.4224313\",\n \"text\": \"def add(self, *mobjects):\\n mobjects = [*mobjects, *self.foreground_mobjects]\\n self.restructure_mobjects(to_remove=mobjects)\\n self.mobjects += mobjects\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e79b8605e786ed33655fe8bc477b8ce\",\n \"score\": \"0.4224313\",\n \"text\": \"def add(self, *mobjects):\\n mobjects = [*mobjects, *self.foreground_mobjects]\\n self.restructure_mobjects(to_remove=mobjects)\\n self.mobjects += mobjects\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a59199e3ab5a5e968d609f908ab35829\",\n \"score\": \"0.4198464\",\n \"text\": \"def add(self, graphic):\\n\\n if self.is_locked():\\n warn(\\\"The Capture is locked, no changes can be done\\\", RuntimeWarning)\\n return False\\n\\n if not hasattr(self, graphic.label):\\n setattr(self, graphic.label, graphic)\\n self.__graphics[graphic.type].append(graphic)\\n\\n if graphic.is_point():\\n self.__camera.set_lkpoint(graphic)\\n else:\\n warn(\\\"The Graphic %s already exists. It wont be added\\\" % graphic.label, RuntimeWarning)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6701f85d521206c6ce0268a0f24218b4\",\n \"score\": \"0.4195164\",\n \"text\": \"def add_deme(\\n self,\\n name: str,\\n *,\\n description: str = None,\\n ancestors: list = None,\\n proportions: list = None,\\n start_time: float = None,\\n epochs: list = None,\\n defaults: dict = None,\\n ):\\n deme: MutableMapping[str, Any] = dict(name=name)\\n if description is not None:\\n deme[\\\"description\\\"] = description\\n if ancestors is not None:\\n deme[\\\"ancestors\\\"] = ancestors\\n if proportions is not None:\\n deme[\\\"proportions\\\"] = proportions\\n if start_time is not None:\\n deme[\\\"start_time\\\"] = start_time\\n if epochs is not None:\\n deme[\\\"epochs\\\"] = epochs\\n if defaults is not None:\\n deme[\\\"defaults\\\"] = defaults\\n\\n if \\\"demes\\\" not in self.data:\\n self.data[\\\"demes\\\"] = []\\n self.data[\\\"demes\\\"].append(deme)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90a5d4c95bc6bf39a32d8302d5bb7ed7\",\n \"score\": \"0.41905457\",\n \"text\": \"def add_migration(\\n self,\\n *,\\n rate: float = None,\\n # We use a special NO_DEFAULT value here, to distinguish between the user\\n # not specifying anything, and specifying the value None (which may be\\n # necessary to override a 'defaults' value set in the data dictionary).\\n demes: list = NO_DEFAULT, # type: ignore\\n source: str = NO_DEFAULT, # type: ignore\\n dest: str = NO_DEFAULT, # type: ignore\\n start_time: float = None,\\n end_time: float = None,\\n ):\\n migration: MutableMapping[str, Any] = dict()\\n if rate is not None:\\n migration[\\\"rate\\\"] = rate\\n if demes is not NO_DEFAULT:\\n migration[\\\"demes\\\"] = demes\\n if source is not NO_DEFAULT:\\n migration[\\\"source\\\"] = source\\n if dest is not NO_DEFAULT:\\n migration[\\\"dest\\\"] = dest\\n if start_time is not None:\\n migration[\\\"start_time\\\"] = start_time\\n if end_time is not None:\\n migration[\\\"end_time\\\"] = end_time\\n\\n if \\\"migrations\\\" not in self.data:\\n self.data[\\\"migrations\\\"] = []\\n self.data[\\\"migrations\\\"].append(migration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd49a1ce234bd52349b35ceb5807e5f6\",\n \"score\": \"0.4185431\",\n \"text\": \"def __add__(self, other):\\r\\n if isinstance(other, Frac):\\r\\n return Frac(self.x * other.y + self.y * other.x, self.y * other.y)\\r\\n elif isinstance(other, int):\\r\\n return Frac(self.x + (other*self.y), self.y)\\r\\n elif isinstance(other, float):\\r\\n new_frac = other.as_integer_ratio()\\r\\n other = Frac(new_frac[0], new_frac[1])\\r\\n return Frac(self.x * other.y + self.y * other.x, self.y * other.y)\\r\\n else:\\r\\n raise ValueError(\\\"Niewłaściwy argument w dodawaniu\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"739b44fb6456233121bff892d55e473d\",\n \"score\": \"0.41777584\",\n \"text\": \"def add(self, other):\\n if other._x0 is not None:\\n self.add_point(other._x0, other._y0)\\n self.add_point(other._x1, other._y1)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a4ac8f13188614ed9980e71b5d7436\",\n \"score\": \"0.41691804\",\n \"text\": \"def createFOP(verbose=False, usefmm=False, fatray=False):\\n if usefmm:\\n from .FMModelling import TravelTimeFMM\\n fop = TravelTimeFMM(verbose=verbose)\\n else:\\n if fatray:\\n fop = FatrayDijkstraModelling(verbose=verbose)\\n else:\\n fop = pg.TravelTimeDijkstraModelling(verbose=verbose)\\n\\n return fop\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b903ac1595ffcb020f200cd94629e83a\",\n \"score\": \"0.41689783\",\n \"text\": \"def add_fiber(self, fiber_id, y, z, area, material_class, w, h):\\n if not isinstance(material_class, UniaxialIncrementalMaterial):\\n raise ValueError(\\\"material_class is not of type : UniaxialIncrementalMaterial\\\")\\n if fiber_id in self._fibers:\\n raise RuntimeError(f\\\"Section already contains fiber with id {fiber_id}\\\")\\n self._fibers[fiber_id] = Fiber(fiber_id, y, z, area, material_class, w, h)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cda9d6376040ebf9fc52fa56be20b7e0\",\n \"score\": \"0.41636315\",\n \"text\": \"def _add_islamic_revolution_day(self, name: str) -> Optional[date]:\\n return self._add_persian_calendar_holiday(\\n name, self._persian_calendar.persian_to_gregorian(self._year - 1, 11, 22)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93e9e0f4c6b4c4fffa132c14646184b\",\n \"score\": \"0.4160634\",\n \"text\": \"def add_station(self, df_orient=None, stationname=None, orientsingle_path=None, period=None):\\n\\n in_parentheses = self.dict_OBStypes.get(period)\\n\\n if orientsingle_path is not None:\\n orientsingle = pd.read_pickle(orientsingle_path)\\n else:\\n if (df_orient is None) or (stationname is None):\\n raise ValueError('Both df_orient and stationname must be given.')\\n \\n try:\\n orientsingle = OrientSingle(\\n df_orient, stationname, \\n self.if_selection, \\n in_parentheses=in_parentheses,\\n **self._kw_orientsingle,\\n )\\n except IndexError:\\n pass\\n except PvalueError:\\n pass\\n\\n self.stations.append(orientsingle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f48d7d7ef7f65430b021632a09c6daa7\",\n \"score\": \"0.41593266\",\n \"text\": \"def add(self, value: object) -> None:\\n self.da.append(value)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f5cf0a077d5358972158c168d0dd4f\",\n \"score\": \"0.4157909\",\n \"text\": \"def add(self, child):\\n\\n if not isinstance(child, FPNode):\\n raise TypeError(\\\"Can only add other FPNodes as children\\\")\\n\\n if not child.item in self._children:\\n self._children[child.item] = child\\n child.parent = self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f5cf0a077d5358972158c168d0dd4f\",\n \"score\": \"0.4157909\",\n \"text\": \"def add(self, child):\\n\\n if not isinstance(child, FPNode):\\n raise TypeError(\\\"Can only add other FPNodes as children\\\")\\n\\n if not child.item in self._children:\\n self._children[child.item] = child\\n child.parent = self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24f5cf0a077d5358972158c168d0dd4f\",\n \"score\": \"0.4157909\",\n \"text\": \"def add(self, child):\\n\\n if not isinstance(child, FPNode):\\n raise TypeError(\\\"Can only add other FPNodes as children\\\")\\n\\n if not child.item in self._children:\\n self._children[child.item] = child\\n child.parent = self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb9b676192a8662668b6b9c143a4fff6\",\n \"score\": \"0.4156987\",\n \"text\": \"def add(self,node,**kwargs):\\n if node not in self.graph: #handles adding a new node with new features\\n self.graph[node] = dict()\\n self.keys.append(node)\\n self.graph[node] = kwargs\\n self.graph[node][\\\"neighbors\\\"] = set()\\n self.nodes.append(node)\\n else: #if node is already in graph, handles updating features.\\n for key in kwargs.items():\\n try:\\n if key[1] != self.graph[node][key[0]]: #handles update\\n self.graph[node][key[0]] = key[1]\\n except KeyError:\\n self.graph[node][key[0]] = key[1] #handles new feature to\\n #node that exits.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa26e9d5d749de0ae863e70652124c3\",\n \"score\": \"0.4153644\",\n \"text\": \"def add_child(self, **kwargs):\\n newobj = self.__class__(**kwargs)\\n newobj.parent = self\\n newobj._create_path()\\n return newobj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f94e07bddbe80cdb926d802d3f4e3837\",\n \"score\": \"0.414829\",\n \"text\": \"def add_shape(self, shape):\\n if isinstance(shape, Shape):\\n self.shapes.append(shape)\\n shape.set_id(len(self.shapes))\\n else:\\n raise TypeError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"512732955eab320b5d540d3c68029b3d\",\n \"score\": \"0.41481784\",\n \"text\": \"def __add__(self, other):\\n if not is_formula(other):\\n raise ValueError('only Formula objects can be added to a Formula')\\n f = Formula(np.hstack([self.terms, other.terms]))\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5419341b2270b1ce5404e061d2ed34b\",\n \"score\": \"0.4143523\",\n \"text\": \"def add(**kwargs):\\n store = Store.create(**kwargs)\\n return store\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d341874410e396cd71aae84dd16dd509\",\n \"score\": \"0.41411453\",\n \"text\": \"def add_child(self, child):\\n if self.type.lower() != 'directory':\\n raise ValueError(\\\"Only directory objects can have children\\\")\\n if child is self:\\n raise ValueError('Cannot be a child of itself!')\\n if child not in self._children:\\n self._children.append(child)\\n child.parent = self\\n return child\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b6996c0a7f470e9244928c240a1efa5\",\n \"score\": \"0.4141001\",\n \"text\": \"def AddDelta(self, fn, next_state):\\n self.deltas.append((fn, next_state))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6c6fd10e1428bc794d076cd42c0cae3\",\n \"score\": \"0.41403237\",\n \"text\": \"def addDataType(self, dt: ghidra.program.model.data.DataType) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cf2e1001e70ca0c05c6254676075f13\",\n \"score\": \"0.41393378\",\n \"text\": \"def add(self, obj):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f37666ebf18204eb9e978a91b8de436a\",\n \"score\": \"0.41273922\",\n \"text\": \"def add():\\n calendar_service = Calendar_Service(MONGO)\\n date = request.form['date']\\n vacation = request.form['vacation']\\n title = request.form['title']\\n remark = request.form['remark']\\n attendance_time = request.form['attendance_time']\\n closing_time = request.form['closing_time']\\n data, code, message = calendar_service.add(date, vacation, title,\\n remark, attendance_time, closing_time)\\n return __result(data, code, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92fedce66803d4e0af4845b9b6bb38e1\",\n \"score\": \"0.41220674\",\n \"text\": \"def add_instance(self, head_id, instance_id: str, role, argument_instance):\\n argument_alias = get_variable_name(instance_id)\\n\\n # If an instance of a word already exists, then it should not get added\\n if not self.has_instance(instance_id):\\n self.__g.triples.append(penman.Triple(argument_alias, self.instance_role, argument_instance))\\n\\n self.__g.triples.append(penman.Triple(get_variable_name(head_id), role, argument_alias))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"956ef341140a8407910604f824076ef0\",\n \"score\": \"0.41219705\",\n \"text\": \"def __add__(self, other):\\n if other == 0:\\n return self\\n\\n assert self.framerate == other.framerate\\n\\n # make an array of times that covers both waves\\n start = min(self.start, other.start)\\n end = max(self.end, other.end)\\n n = int(round((end - start) * self.framerate)) + 1\\n\\n # initially set to 0\\n ys = np.zeros(n)\\n ts = start + np.arange(n) / self.framerate\\n\\n newWave = FWave(ys, ts, self.framerate);\\n newWave.addWave(self)\\n newWave.addWave(other)\\n\\n return newWave\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58fc6fd572ce08e248a063bc064583ff\",\n \"score\": \"0.4115445\",\n \"text\": \"def add_document(self, definition):\\n type_ = self.from_doc(definition)\\n return self.add(type_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58fc6fd572ce08e248a063bc064583ff\",\n \"score\": \"0.4115445\",\n \"text\": \"def add_document(self, definition):\\n type_ = self.from_doc(definition)\\n return self.add(type_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ff4c22b1bbc457ef9114e8eb261ce39\",\n \"score\": \"0.41133165\",\n \"text\": \"def addRental(self, id, bookId, clientId, rentalDate=datetime.timedelta(0), dueDate=datetime.timedelta(0),\\r\\n returnedDate=\\\"\\\", recordForUndo=True):\\r\\n if self.checkId(id) == False:\\r\\n raise InvalidIdException\\r\\n if rentalDate == datetime.timedelta(0):\\r\\n rentalDate = datetime.datetime.now().date()\\r\\n if dueDate == datetime.timedelta(0):\\r\\n dueDate = rentalDate + datetime.timedelta(days=10)\\r\\n rental = self.validation(id, bookId, clientId, rentalDate, dueDate, returnedDate)\\r\\n self._rentalRepo.add(rental)\\r\\n if recordForUndo == True:\\r\\n undo = FunctionCall(self.removeRental, id, False)\\r\\n redo = FunctionCall(self.addRental, id, bookId, clientId, rentalDate, dueDate, returnedDate, False)\\r\\n operation = Operation(redo, undo)\\r\\n self._undoController.recordOperation(operation)\\r\\n\\r\\n return rental\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c40e72c3d34a886caf9de7c71bf8481\",\n \"score\": \"0.41110054\",\n \"text\": \"def add_mrnas(self, mrna_list, add_degradation=True):\\n if not hasattr(mrna_list, '__iter__'):\\n mrna_list = [mrna_list]\\n if len(mrna_list) == 0:\\n return None\\n\\n # First check whether the mRNAs exist in the model\\n mrna_list = [x for x in mrna_list if x.id not in self.mrnas]\\n\\n for mrna in mrna_list:\\n mrna._model = self\\n mrna.init_variable()\\n\\n if add_degradation:\\n self._add_mrna_degradation(mrna, scaled=True, queue=True)\\n\\n self.mrnas += mrna_list\\n self._push_queue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"493019478bcf84e75e4e58c5d7b04174\",\n \"score\": \"0.4109323\",\n \"text\": \"def add(self, object_: Shape) -> None:\\n self.objects.append(object_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"793350270268ae89fa1dbb9b13b0931b\",\n \"score\": \"0.41086546\",\n \"text\": \"def add_shape(self, shape):\\r\\n self.shapes.append(shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef24c6314faaedd2d3cfd67cc05fbf83\",\n \"score\": \"0.41068408\",\n \"text\": \"def add(self, definition_id=None, title=None):\\n def decorator(cls):\\n if definition_id is None:\\n name = cls.__name__\\n else:\\n name = definition_id\\n if title is not None:\\n cls._title = title\\n self._registry[name] = cls\\n self._rev_registry[cls] = name\\n # cls._schema = self\\n # for k, v in six.iteritems(cls.__dict__):\\n # if isinstance(v, Document):\\n # print(k)\\n return cls\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55f8597c02f73981b567e0e46277d353\",\n \"score\": \"0.4100982\",\n \"text\": \"def dm_add(self,moar_content):\\n self.player[-1].add(moar_content)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b74172efd7fa22e1c184a360d6a1090\",\n \"score\": \"0.40980515\",\n \"text\": \"def add_movie(self, movie: Movie):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d99cf250ac0428a024d1f725e224b7f1\",\n \"score\": \"0.40884656\",\n \"text\": \"def addOctave(*args):\\n return _CsoundAC.Voicelead_addOctave(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcc5296165d26b8591bae9fd04baf836\",\n \"score\": \"0.40795213\",\n \"text\": \"def __init__(self,\\n pmf_remove: pld_pmf.PLDPmf,\\n pmf_add: Optional[pld_pmf.PLDPmf] = None):\\n self._pmf_remove = pmf_remove\\n self._symmetric = pmf_add is None\\n self._pmf_add = pmf_remove if self._symmetric else pmf_add\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acebb076aa9897c5f45a9687185afbab\",\n \"score\": \"0.40781176\",\n \"text\": \"def __add__(self, other):\\n result = self.__class__(self.lines, self.density, self.relax, self.mink)\\n result += other.lines if hasattr(other, 'lines') else other\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa23319f49352731d44d4a4f0cfbea4\",\n \"score\": \"0.40771043\",\n \"text\": \"def add_record(self, record=None, name=None, style=None, content=None):\\n try: \\n add_record = self.__db_module.add_record\\n except AttributeError:\\n raise AttributeError('Database (' + self.style + ') has no attribute add_record')\\n else:\\n return add_record(self.__db_info, record=record, name=name, style=style, content=content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3681492b96dc5f4c89fd91e8688290e\",\n \"score\": \"0.40748632\",\n \"text\": \"def addFirst(self, dt: ghidra.program.model.data.DataType) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3d169a7bce928483f9ee71b4c808fd3\",\n \"score\": \"0.40711415\",\n \"text\": \"def add(self):\\n\\n self._add()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1c79d00fe353ea6025b577f4e973777\",\n \"score\": \"0.40692812\",\n \"text\": \"def add_dmi(self, periods=14, str='{name}({period})',\\n name='', **kwargs):\\n study = {'kind': 'dmi',\\n 'name': name,\\n 'params': {'periods': periods, 'high': self._d['high'], 'low': self._d['low'],\\n 'close': self._d['close'],\\n 'str': str},\\n 'display': utils.merge_dict({'legendgroup': False}, kwargs)}\\n self._add_study(study)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"670fd9a954ce2a4b2be39ac4e444161d\",\n \"score\": \"0.40688488\",\n \"text\": \"def insert_fed_object_version(\\n fed_object_id: int,\\n fed_version_id: int,\\n component_id: int,\\n action_id: typing.Optional[int],\\n schema: typing.Optional[typing.Dict[str, typing.Any]],\\n data: typing.Optional[typing.Dict[str, typing.Any]],\\n user_id: typing.Optional[int],\\n utc_datetime: typing.Optional[datetime.datetime],\\n allow_disabled_languages: bool = False\\n) -> typing.Optional[Object]:\\n if action_id is not None:\\n actions.check_action_exists(action_id)\\n if user_id is not None:\\n users.check_user_exists(user_id)\\n object = Objects.insert_fed_object_version(\\n data=data,\\n schema=schema,\\n user_id=user_id,\\n action_id=action_id,\\n utc_datetime=utc_datetime,\\n fed_object_id=fed_object_id,\\n fed_version_id=fed_version_id,\\n component_id=component_id,\\n allow_disabled_languages=allow_disabled_languages\\n )\\n if object is not None:\\n tags.update_object_tag_usage(object)\\n return object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f1f4b6311e7198611c02fb9b681c433\",\n \"score\": \"0.40656993\",\n \"text\": \"def _add_flfact_object(self, flfact):\\n key = flfact.sid\\n #assert key not in self.flfacts\\n assert key > 0\\n self.flfacts[key] = flfact # set id...\\n self._type_to_id_map[flfact.type].append(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"152edec6563907f44585810a16602ad7\",\n \"score\": \"0.4065446\",\n \"text\": \"def _add_ddval_object(self, ddval):\\n key = ddval.oid\\n assert key not in self.ddvals, 'DDVAL=%s old=\\\\n%snew=\\\\n%s' % (\\n key, self.ddvals[key], ddval)\\n assert key > 0\\n self.ddvals[key] = ddval\\n self._type_to_id_map[ddval.type].append(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4441cbdbe6fc9b7cd89d9571a211407\",\n \"score\": \"0.40651438\",\n \"text\": \"def addDoor(self, door, isMain=False):\\n\\t\\tif not self.mainDoor and isMain: self.mainDoor = door\\n\\t\\tself.doors.append(door)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"923fee96fd63ab83da596cb10a02e759\",\n \"score\": \"0.40641314\",\n \"text\": \"def add(cls: Type[ModelInterface], data: dict, session: _Session = None) -> ModelInterface:\\n record, = cls.add_all([data, ], session=session)\\n return record\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e9cd8d758b475e60257279c24606d93\",\n \"score\": \"0.40630695\",\n \"text\": \"def __add__(\\n self: IRoDType,\\n other: BaseRepresentationOrDifferential | IRoDType,\\n ) -> IRoDType:\\n if isinstance(other, InterpolatedBaseRepresentationOrDifferential) and not array_equal(\\n other.affine,\\n self.affine,\\n ):\\n raise ValueError(_op_msg.format(\\\"add\\\", self._class_))\\n # Add, then re-interpolate\\n return self._realize_class(self.data.__add__(other), self.affine)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8de1873078831c34de09703bd13202d\",\n \"score\": \"0.4062445\",\n \"text\": \"def add_dos(self, label, dos):\\n\\n densities = dos.get_smeared_densities(self.sigma) if self.sigma \\\\\\n else dos.densities\\n self._doses[label] = {'frequencies': dos.frequencies, 'densities': densities}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c2704e012a2b51ac10582bb418ecd46\",\n \"score\": \"0.40588906\",\n \"text\": \"def __add__(self,other,/):\\n\\t\\ttry:\\n\\t\\t\\treturn Complex(self.re+other.re,self.im+other.im)\\n\\t\\texcept:\\n\\t\\t\\treturn Complex(self.re+other,self.im)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36f1cac0cf4303613cf7984d2f8e49ed\",\n \"score\": \"0.40550447\",\n \"text\": \"def add_fwf_asset(\\n self,\\n name: str,\\n filepath_or_buffer: pydantic.FilePath | pydantic.AnyUrl,\\n **kwargs,\\n ) -> FeatherAsset: # type: ignore[valid-type]\\n asset = FWFAsset(\\n name=name,\\n filepath_or_buffer=filepath_or_buffer,\\n **kwargs,\\n )\\n return self._add_asset(asset=asset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3b1630389187862e7220acd23ceb1e6\",\n \"score\": \"0.40546027\",\n \"text\": \"def addFilm(self, id, titel, rating) -> bool:\\n if self.films.tableRetrieve(id)[1]: # Kijkt na of film-id al bestaat\\n print(f\\\"\\\\033[1;31;49mThe id: {id}, is already in use! The movie {titel} has NOT been created!\\\\033[0m\\\")\\n return False\\n film = Film(id, titel, rating) # Nieuwe film aanmaken\\n self.films.tableInsert(film) # Film toevoegen aan ADT van films\\n print(f\\\"The movie {titel} has been created!\\\")\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f50e59cab318324e0500eef912497f\",\n \"score\": \"0.40533546\",\n \"text\": \"def addShape(self, sh):\\n\\t\\tif sh in self.set:\\n\\t\\t\\traise ValueError('duplicate shape')\\n\\t\\telse:\\n\\t\\t\\tself.set.append(sh)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbbbded34a6eeb4c7f3ff9cd512c85b9\",\n \"score\": \"0.40445527\",\n \"text\": \"def add(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1683,"cells":{"query_id":{"kind":"string","value":"8e915c9bed9fe88cb18c1dc152baa1e4"},"query":{"kind":"string","value":"Generacion de hinchas de River"},"positive_passages":{"kind":"list like","value":[{"docid":"043b0f1bef0ba62cfa505cba8d4eeee7","score":"0.0","text":"def barra_brava_river(viajes, lugares_bote, mutex):\n while viajes < 20:\n time.sleep(random.randrange(0, 5))\n hr = threading.Thread(target=hincha_river, args=(lugares_bote, mutex,))\n hr.start()\n # --------------------------------------------------\n #mutex.acquire()\n #hinchas.append(hr)\n #mutex.release()\n hr.join()","title":""}],"string":"[\n {\n \"docid\": \"043b0f1bef0ba62cfa505cba8d4eeee7\",\n \"score\": \"0.0\",\n \"text\": \"def barra_brava_river(viajes, lugares_bote, mutex):\\n while viajes < 20:\\n time.sleep(random.randrange(0, 5))\\n hr = threading.Thread(target=hincha_river, args=(lugares_bote, mutex,))\\n hr.start()\\n # --------------------------------------------------\\n #mutex.acquire()\\n #hinchas.append(hr)\\n #mutex.release()\\n hr.join()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0cc41407da05b64dc4ef2d3a5f2701d2","score":"0.67497545","text":"def hincha_river():","title":""},{"docid":"3b0bb52fa9521499ef508c6bb7079ab1","score":"0.60572916","text":"def genemap(self):\n\t\treturn [('W1',8, 'Weighted'), ('L1',8, 'Weighted'), ('R1',8, 'Weighted')] # For OTA simulation\n\t\t#return [('R1',8, 'Weighted'), ('R2',8, 'Weighted')] #For resistive divider simulation","title":""},{"docid":"34c549b2b49dcdeff0c197648a5ace0f","score":"0.5819491","text":"def _assemble_Hankel(self):\n # To understand the default choice of mc and mo,\n # consider (let sample_interval=P=1 w.l.o.g.)\n # sequences\n # [0 1 1 2 2 3] => H = [[0 1][1 2]] H2 = [[1 2][2 3]], mc=mo=1, nt=6\n # [0 1 1 2 2 3 3 4 4 5] => H=[[0 1 2][1 2 3][2 3 4]]\n # and H2=[[1 2 3][2 3 4][3 4 5]], mc=mo=2,nt=10\n # Thus, using all available data means mc=mo=(nt-2)/4.\n # For additional output times, (nt-2)/4 rounds down, so\n # we use this formula.\n\n if self.mo is None or self.mc is None:\n # Set mo and mc, time_steps is always in format [0, 1, P, P+1, ...]\n self.mo = (self.num_time_steps-2)//4\n self.mc = self.mo\n\n if (self.mo + self.mc) + 2 > self.num_time_steps:\n raise ValueError(\n 'mo + mc + 2= %d and must be <= than the number of samples %d'\n % (self.mo + self.mc + 2, self.num_time_steps))\n\n self.Hankel_array = np.zeros(\n (self.num_Markovs * self.mo, self.num_inputs * self.mc))\n self.Hankel_array2 = np.zeros(self.Hankel_array.shape)\n #Markovs_flattened = \\\n # self.Markovs.swapaxes(0,1).reshape((num_Markovs, -1))\n for row in range(self.mo):\n row_start = row * self.num_Markovs\n row_end = row_start + self.num_Markovs\n for col in range(self.mc):\n col_start = col * self.num_inputs\n col_end = col_start + self.num_inputs\n self.Hankel_array[\n row_start:row_end, col_start:col_end] = self.Markovs[\n 2 * (row + col)]\n self.Hankel_array2[\n row_start:row_end, col_start:col_end] = self.Markovs[\n 2 * (row + col) + 1]","title":""},{"docid":"ac96a3d58150692c9c30e7fcbcd1e463","score":"0.58036286","text":"def barra_brava_river():","title":""},{"docid":"8e6a9ad35cca64dee3e434b9c023c27c","score":"0.5776637","text":"def raie_H(n1,n2): \n return 1/(RH*((1/(n1**2))-(1/(n2**2))))","title":""},{"docid":"dd6cdeeb61784dd07c9973a52d6faf7b","score":"0.57670236","text":"def produce(self):\n r, g, b = (0, 0, 0)\n\n r = 0 if not self.r else int(self.r / LASER_POWER_DENOM)\n g = 0 if not self.g or LASER_POWER_DENOM > 4 else self.g\n b = 0 if not self.b else int(self.b / LASER_POWER_DENOM)\n\n\n if self.anchored:\n key_points = [(-32600, 0)] + self.key_points + [(32600, 0)]\n else:\n key_points = self.key_points\n\n key_points.sort(lambda a,b: cmp(a[0],b[0]))\n\n\n def make_line(pt1, pt2, steps=200):\n xdiff = pt1[0] - pt2[0]\n ydiff = pt1[1] - pt2[1]\n line = []\n for i in xrange(0, steps, 1):\n j = float(i)/steps\n x = pt1[0] - (xdiff * j)\n y = pt1[1] - (ydiff * j)\n line.append((x, y, r, g, b)) \n return line\n\n def make_curvy_line(pt1, pt2, steps=200):\n xdiff = pt1[0] - pt2[0]\n ydiff = pt1[1] - pt2[1]\n line = []\n for i in xrange(0, steps, 1):\n j = float(i)/steps\n x_add = xdiff * j\n x = pt1[0] - (x_add)\n y_extra = math.sin(x/self.x_wave_scale) * self.y_wave_scale\n y_add = ydiff * j\n y = pt1[1] - (y_add + y_extra)\n line.append((x, y, r, g, b)) \n return line \n\n\n i = 0\n while i + 1 < len(key_points):\n p1 = key_points[i]\n p2 = key_points[i+1]\n\n #print \"%s - %s\" % (p1, p2)\n i += 1\n\n for p in make_curvy_line(p1, p2, self.step): # 50 is cool\n yield p\n\n\n \"\"\"\n\n # Generate points\n ed = self.radius\n\n pts = []\n pts.append({0: ed, 1: ed})\n pts.append({0: -ed, 1: ed})\n pts.append({0: -ed, 1: -ed})\n pts.append({0: ed, 1: -ed})\n\n # Rotate points\n for p in pts:\n x = p[0]\n y = p[1]\n p[0] = x*math.cos(self.theta) - y*math.sin(self.theta)\n p[1] = y*math.cos(self.theta) + x*math.sin(self.theta)\n\n # Translate points\n for pt in pts:\n pt[0] += self.x\n pt[1] += self.y\n\n\n\n\n # DRAW THE SHAPE\n\n p = None # Save in scope\n\n for p in make_line(pts[0], pts[1], SQUARE_EDGE_SAMPLE_PTS):\n break\n for i in range(int(round(SQUARE_VERTEX_SAMPLE_PTS/2.0))):\n yield p\n for p in make_line(pts[0], pts[1], SQUARE_EDGE_SAMPLE_PTS):\n yield p\n for i in range(SQUARE_VERTEX_SAMPLE_PTS):\n yield p\n for p in make_line(pts[1], pts[2], SQUARE_EDGE_SAMPLE_PTS):\n yield p\n for i in range(SQUARE_VERTEX_SAMPLE_PTS):\n yield p\n for p in make_line(pts[2], pts[3], SQUARE_EDGE_SAMPLE_PTS):\n yield p\n for i in range(SQUARE_VERTEX_SAMPLE_PTS):\n yield p\n for p in make_line(pts[3], pts[0], SQUARE_EDGE_SAMPLE_PTS):\n self.lastPt = p # KEEP BOTH\n yield p\n for i in range(int(round(SQUARE_VERTEX_SAMPLE_PTS/2.0))):\n self.lastPt = p # KEEP BOTH\n yield p\n\n self.drawn = True\n\n \"\"\"","title":""},{"docid":"b7a8b9e1882d441d175ebc4bad05fb38","score":"0.5751102","text":"def generate(self):","title":""},{"docid":"8c632e78593d5bdbc208230d1d04c7b3","score":"0.57292694","text":"def makeHiersFromHier(self,pythonHier):\n isAPairs = pythonHier.isARelationships\n numberPairs = len(isAPairs)\n for pairNumber in range (0,numberPairs):\n subCatName = isAPairs[pairNumber][0]\n superCatName = isAPairs[pairNumber][1]\n subCatNumber = pythonHier.getUnitNumber(subCatName)\n superCatNumber = pythonHier.getUnitNumber(superCatName)\n #print subCatName,superCatName,subCatNumber,superCatNumber\n #make one hierarchical relations as a half CA connection\n self.fsa.stateHalfTurnsOnState(self.cells,subCatNumber,\n self.cells,superCatNumber)","title":""},{"docid":"2fd67e786ea4dd9aa711cbc17b51822a","score":"0.56716526","text":"def hadamards(length):\n for i in range(length):\n for j in range(length):\n yield cirq.HGate()(cirq.GridQubit(i, j))","title":""},{"docid":"41c4047af6c25028450a5fcf0c422504","score":"0.56567967","text":"def huygstein(area,deltax,deltay):\r\n import numpy as np\r\n huygens=np.zeros((1, 4))\r\n huygens[0,0]=area*(deltax**2)\r\n huygens[0,1]=area*(deltax*deltay)\r\n huygens[0,2]=huygens[0,1]\r\n huygens[0,3]=area*(deltay**2)\r\n \"\"\"\"print(3333333333)\r\n print(huygens)\"\"\"\r\n return huygens","title":""},{"docid":"3ffb5c735a156c4e2333a4eeab0238bc","score":"0.5601081","text":"def hlowerofp2(p):\r\n\r\n pi = p / PSTAR2\r\n q1 = 572.54459862746 + 31.3220101646784*(-13.91883977887 + pi) ** 0.5\r\n q2 = -0.5 + 540./q1\r\n o = numpy.zeros(18)\r\n o[0]= q1 * q1\r\n o[1]= o[0] * o[0]\r\n o[2]= o[1] * o[1]\r\n o[3]= pi * pi\r\n o[4]= o[3] * o[3]\r\n o[5]= q2 * q2\r\n o[6]= o[5] * o[5]\r\n o[7]= o[5] * o[6]\r\n o[8]= o[4] * o[4]\r\n o[9]= o[6] * o[6]\r\n o[10]= o[8] * o[8]\r\n o[11]= o[9] * o[9]\r\n o[12]= o[11] * o[11]\r\n o[13]= o[6] * q2\r\n o[14]= o[5] * q2\r\n o[15]= o[9] * o[5]\r\n o[16]= o[12] * o[5]\r\n o[17]= o[12] * o[5] * q2\r\n\r\n h = (4.63697573303507e9 + 3.74686560065793*o[1] + 3.57966647812489e-6*o[0]*o[1] + 2.81881548488163e-13*o[2] - 7.64652332452145e7*q1 -0.00450789338787835*o[1]*q1 - 1.55131504410292e-9*o[0]*o[1]*q1 + o[0]*(2.51383707870341e6 - 4.78198198764471e6*o[9]*o[10]*o[11]*o[12]*o[3]+ 49.9651389369988*o[10]*o[11]*o[12]*o[3]*o[4]*o[6] + o[14]*o[3]*(1.03746636552761e-13 - 0.00349547959376899*o[15] - 2.55074501962569e-7*o[7])*o[8] + (-242662.235426958*o[9]*o[11] - 3.46022402653609*o[15])*o[3]*o[4]*pi + o[3]*(0.109336249381227 -2248.08924686956*o[13] - 354742.725841972*o[16] - 24.1331193696374*o[5])*pi - 3.09081828396912e-19*o[10]*o[11]*o[4]*o[6]*pi -1.24107527851371e-8*o[10]*o[12]*o[3]*o[4]*o[5]*o[6]*pi + 3.99891272904219*o[4]*o[7]*pi + 0.0641817365250892*o[9]*o[6]*o[8]*pi+ pi*(-4444.87643334512 - 75253.6156722047*o[13] - 43051.9020511789*o[5] - 22926.6247146068*q2) + o[3]*(-8.23252840892034 -3927.0508365636*o[14] - 239.325789467604*o[17] - 76407.3727417716*o[7] - 94.4508644545118*q2) + 0.360567666582363*o[4]*(-0.0161221195808321 + q2)*(0.0338039844460968 + q2) + o[10]*(-0.000584580992538624*o[9]*o[11]*o[6] + 1.33248030241755e6*o[11]*o[12]*q2) + o[8]*(-7.38502736990986e7*o[17] + 0.0000224425477627799*o[5]*o[6]*q2) + o[3]*o[4]*(-2.08438767026518e8*o[16] - 0.0000124971648677697*o[5] - 8442.30378348203*o[9]*o[5]*o[6]*q2) + o[10]*o[8]*(4.73594929247646e-22*o[9]*o[11]*q2 -13.6411358215175*o[9]*o[11]*o[12]*q2 + 5.52427169406836e-10*o[12]*o[5]*o[6]*q2) + o[10]*o[4]*(2.67174673301715e-6*o[16] +4.44545133805865e-18*o[11]*o[5]*q2 - 50.2465185106411*o[9]*o[12]*o[5]*o[6]*q2)))/o[0]\r\n return h","title":""},{"docid":"ddfcf346c523615ca84f211c98997741","score":"0.55728126","text":"def H(self):\n return 0.785 * (self.G ** 2) * self.P","title":""},{"docid":"1bae93e24df7c3a6301098936c46f7bc","score":"0.55433327","text":"def create_hypnogram(total_sleep_hours):\n\n total_sleep_min = total_sleep_hours * 60\n\n sleep_cycle = total_sleep_min // 90\n\n time_dict = {}\n\n i = 0\n while i < sleep_cycle:\n\n if i < 1:\n awake_stage = randint(12, 15)\n\n time_dict[\"awake\"] = [awake_stage]\n\n time_dict[\"REM\"] = [10]\n\n stage_1 = randint(5, 10)\n\n time_dict[\"NREM1\"] = stage_1\n\n stage_2 = randint(10, 25)\n\n time_dict[\"NREM2\"] = [stage_2]\n\n total_sleep_before_rem = awake_stage + stage_1 + stage_2 + 10\n\n time_difference_before_rem_stage = 90 - total_sleep_before_rem\n\n time_dict[\"NREM3\"] = [time_difference_before_rem_stage]\n\n i += 1\n\n elif i < (sleep_cycle - 1):\n\n stage_2 = randint(30, 60)\n\n time_dict[\"NREM2\"].append(stage_2)\n\n stage_3_4 = randint(10, 20)\n\n time_dict[\"NREM3\"].append(stage_3_4)\n\n total_sleep_before_rem = stage_2 + stage_3_4\n\n time_difference_before_rem_stage = 90 - total_sleep_before_rem\n\n time_dict[\"REM\"].append(time_difference_before_rem_stage)\n\n i += 1\n\n else:\n\n awake_stage = randint(12, 15)\n\n time_dict[\"awake\"].append(awake_stage)\n\n stage_2 = randint(20, 40)\n\n time_dict[\"NREM2\"].append(stage_2)\n\n stage_3_4 = randint(10, 20)\n\n time_dict[\"NREM3\"].append(stage_3_4)\n\n total_sleep_before_rem = awake_stage + stage_2 + stage_3_4\n\n time_difference_before_rem_stage = 90 - total_sleep_before_rem\n\n time_dict[\"REM\"].append(time_difference_before_rem_stage)\n\n i += 1\n\n return time_dict","title":""},{"docid":"34605525860d8831bb3fc3db96d6228d","score":"0.55324537","text":"def wer(r, h):\n # initialisation\n d = np.zeros((len(r)+1)*(len(h)+1), dtype=np.uint8)\n d = d.reshape((len(r)+1, len(h)+1))\n for i in range(len(r)+1):\n for j in range(len(h)+1):\n if i == 0:\n d[0][j] = j\n elif j == 0:\n d[i][0] = i\n\n # computation\n for i in range(1, len(r)+1):\n for j in range(1, len(h)+1):\n if r[i-1] == h[j-1]:\n d[i][j] = d[i-1][j-1]\n else:\n substitution = d[i-1][j-1] + 1\n insertion = d[i][j-1] + 1\n deletion = d[i-1][j] + 1\n d[i][j] = min(substitution, insertion, deletion)\n return d[len(r)][len(h)]","title":""},{"docid":"1d5da7570cdec4a0bc83cc2d7d86573f","score":"0.55225617","text":"def _H(self):\r\n\t\tp = self.P\r\n\t\tm = self.m\r\n\t\tv = self.V\r\n\t\treturn p.dot(p)/(2*m) + v","title":""},{"docid":"0be0eab1fe07515cf47344da757aa8b3","score":"0.55130965","text":"def generuj_cervene():\n vygenerovane_pole_cervenych = []\n\n global NESPRAVNE_VYGENEROVANE\n global POLE_SURADNIC\n\n while len(vygenerovane_pole_cervenych) < MAX_POCET_BODOV_TRIEDY:\n spravne = pravdepodobnost()\n\n if spravne:\n x = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\n y = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\n while [x, y, CERVENA] in vygenerovane_pole_cervenych or [x, y] in POLE_SURADNIC:\n x = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\n y = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\n else:\n NESPRAVNE_VYGENEROVANE += 1\n x, y = generuj_nespravne_suradnice(R_x_graf, R_y_graf)\n while [x, y, CERVENA] in vygenerovane_pole_cervenych:\n x, y = generuj_nespravne_suradnice(R_x_graf, R_y_graf)\n\n vygenerovane_pole_cervenych.append([x, y, CERVENA])\n\n POLE_SURADNIC += [[a[0], a[1]] for a in vygenerovane_pole_cervenych]\n\n return vygenerovane_pole_cervenych","title":""},{"docid":"8adeab41dd037a63bb966cb55c263aea","score":"0.548377","text":"def vykresli_hranice(matica):\n for y in range(DOLNA_HRANICA, OHRANICENIE + 1):\n vloz_do_matice(matica, OHRANICENIE, y, CERVENA)\n vloz_do_matice(matica, y, OHRANICENIE, CERVENA)\n\n vloz_do_matice(matica, -OHRANICENIE, y, ZELENA)\n vloz_do_matice(matica, y, -OHRANICENIE, MODRA)\n\n for y in range(-OHRANICENIE, HORNA_HRANICA + 1):\n vloz_do_matice(matica, -OHRANICENIE, y, FIALOVA)\n vloz_do_matice(matica, y, -OHRANICENIE, FIALOVA)\n\n vloz_do_matice(matica, OHRANICENIE, y, MODRA)\n vloz_do_matice(matica, y, OHRANICENIE, ZELENA)","title":""},{"docid":"960e48285676f71cebb92f676748fdee","score":"0.54747546","text":"def create_ham(self):\n from tcc.interaction import HAM_SPINLESS_RI_CORE\n return HAM_SPINLESS_RI_CORE(self)","title":""},{"docid":"960e48285676f71cebb92f676748fdee","score":"0.54747546","text":"def create_ham(self):\n from tcc.interaction import HAM_SPINLESS_RI_CORE\n return HAM_SPINLESS_RI_CORE(self)","title":""},{"docid":"3603632ed0d4bbcaa6b65ac1849670c4","score":"0.5473399","text":"def __init__(self, harshness=1.0):\n self.harshness = harshness","title":""},{"docid":"4afb41934ff50daf24ba40dfc067db0a","score":"0.546286","text":"def gurdy_hop(self, num_hops=15):\n\n upper_delta = 1\n basic_angle = -1.55\n angle_change = random.uniform(0.2, 0.7)\n upperlegM_angle = basic_angle\n lowerlegM_angle = basic_angle - upper_delta * angle_change * 2.0\n\n headarm_angle = upperlegM_angle\n headforearm_angle = lowerlegM_angle\n\n upper_yaw_angle = 0.7\n upperleg_yaw_angle = upper_yaw_angle\n\n basefoot_max = 0.05\n basefoot_min = 0.0\n basefoot_peg_M1_value = basefoot_min\n basefoot_peg_M2_value = basefoot_min\n basefoot_peg_M3_value = basefoot_min\n\n #self.gurdy_init_pos_sequence()\n for repetitions in range(num_hops):\n self.gurdy_movement_look(upperlegM_angle,\n upperlegM_angle,\n upperlegM_angle,\n lowerlegM_angle,\n lowerlegM_angle,\n lowerlegM_angle,\n headarm_angle,\n headforearm_angle,\n upperleg_yaw_angle,\n upperleg_yaw_angle,\n upperleg_yaw_angle,\n upperleg_yaw_angle,\n basefoot_peg_M1_value,\n basefoot_peg_M2_value,\n basefoot_peg_M3_value)\n\n upper_delta *= -1\n if upper_delta < 0:\n upperlegM_angle = basic_angle + angle_change\n upperleg_yaw_angle = upper_yaw_angle\n basefoot_peg_M1_value = basefoot_min\n basefoot_peg_M2_value = basefoot_min\n basefoot_peg_M3_value = basefoot_min\n else:\n upperlegM_angle = basic_angle\n upperleg_yaw_angle = -1* upper_yaw_angle\n basefoot_peg_M1_value = basefoot_max\n basefoot_peg_M2_value = basefoot_max\n basefoot_peg_M3_value = basefoot_max\n\n lowerlegM_angle = basic_angle - upper_delta * angle_change * 2.0\n\n headarm_angle = upperlegM_angle\n headforearm_angle = lowerlegM_angle","title":""},{"docid":"a8dcf31285714d06d3927c1343a0f07a","score":"0.5434556","text":"def algorithm(self):","title":""},{"docid":"a8dcf31285714d06d3927c1343a0f07a","score":"0.5434556","text":"def algorithm(self):","title":""},{"docid":"a2070c8d233c2d246b7ab06775dc6074","score":"0.54323447","text":"def __init__(self):\n self.min_h = []\n self.max_h = []","title":""},{"docid":"fd1d0ba1cd018f46e2ab21a5fbd55ee3","score":"0.5430339","text":"def test_ilustrace_vypoctu_husty_graf():\n sekvence_hran = [(1, 2), (1, 4), (3, 4), (2, 3), (5, 6), (5, 7), (6, 7), (4, 5), (1, 3), (7, 4)]\n husty_graf_rychly(7, sekvence_hran)","title":""},{"docid":"99a2f97d53b2f52ff66a8735407be40c","score":"0.54174936","text":"def get_ph(self, repeats=5, wait=0):\n return {'pH': 7}","title":""},{"docid":"de6d9f0d9d75f6b64ab14594e6a057f2","score":"0.5391293","text":"def helium_test2():\n H11 = pk_he.H11_pm\n H12 = pk_he.H12_pm\n H22 = pk_he.H22_pm\n H = np.array([[H11, H12], [H12, H22]])","title":""},{"docid":"b8ee429d40cf3d2ce0b6fcd505a95f05","score":"0.5391135","text":"def randoramized(self):\n pass","title":""},{"docid":"a30e3b2ef68c49ebbd53074bfb8c7055","score":"0.53861606","text":"def wer(r, h):\n # initialisation\n # d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint8)\n d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint16)\n # d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint32)\n d = d.reshape((len(r)+1, len(h)+1))\n for i in range(len(r)+1):\n for j in range(len(h)+1):\n if i == 0:\n d[0][j] = j\n elif j == 0:\n d[i][0] = i\n\n # computation\n for i in range(1, len(r)+1):\n for j in range(1, len(h)+1):\n if r[i-1] == h[j-1]:\n d[i][j] = d[i-1][j-1]\n else:\n substitution = d[i-1][j-1] + 1\n insertion = d[i][j-1] + 1\n deletion = d[i-1][j] + 1\n d[i][j] = min(substitution, insertion, deletion)\n\n return d[len(r)][len(h)]","title":""},{"docid":"29e09e7915637a8088ee95daf0ab6085","score":"0.53747","text":"def _createrhythms():\n #toks = [(s, Fraction(1, 2**n)) for n,s in enumerate('whqestx')]\n tab = {}\n # Iterate the rhythmic letters (w=whole, h=half ... x=sixty-fourth)\n for i,t in enumerate('whqestx'):\n sym = t\n # the rhythmic value of each type w=1/1, h=1/2 ...\n rat = Fraction(1, 2**i)\n # create the dotted, but dont go smaller than 1/64.\n # NB: when d is 0 there is no dot.\n for d in range(0,7-i):\n #print(sym+('.'*d), rat * (2 - Fraction(1, 2**d)))\n #_rhythms[sym+('.'*d)] = rat * (2 - Fraction(1, 2**d))\n tab[sym+('.'*d)] = rat * (2 - Fraction(1, 2**d))\n for pre in [3]:\n amt = Fraction(pre-1,pre)\n tab[str(pre)+sym+('.'*d)] = (amt * rat * (2 - Fraction(1, 2**d)))\n return tab","title":""},{"docid":"2f5ff92ebad2b0d04b6dea2a80eaf1af","score":"0.53691757","text":"def hall_sensor() -> int:\n ...","title":""},{"docid":"2ad9bebec27bc42ee7493c8ec97a3dd3","score":"0.5368942","text":"def generuj_zelene():\n vygenerovane_pole_zelenych = []\n\n global NESPRAVNE_VYGENEROVANE\n global POLE_SURADNIC\n\n while len(vygenerovane_pole_zelenych) < MAX_POCET_BODOV_TRIEDY:\n spravne = pravdepodobnost()\n\n if spravne:\n x = np.random.randint(-OHRANICENIE + 1, HORNA_HRANICA + 1)\n y = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\n while [x, y, ZELENA] in vygenerovane_pole_zelenych or [x, y] in POLE_SURADNIC:\n x = np.random.randint(-OHRANICENIE + 1, HORNA_HRANICA + 1)\n y = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\n else:\n NESPRAVNE_VYGENEROVANE += 1\n x, y = generuj_nespravne_suradnice(G_x_graf, G_x_graf)\n while [x, y, ZELENA] in vygenerovane_pole_zelenych:\n x, y = generuj_nespravne_suradnice(G_x_graf, G_x_graf)\n\n vygenerovane_pole_zelenych.append([x, y, ZELENA])\n\n POLE_SURADNIC += [[a[0], a[1]] for a in vygenerovane_pole_zelenych]\n\n return vygenerovane_pole_zelenych","title":""},{"docid":"2b357a05ef693188d70a6f89f4418b27","score":"0.5367623","text":"def testHp(self):\n randomizer = random.Random()\n randomizer.seed(0)\n rnd = randomizer.random\n for H, P, R in itertools.permutations(range(3)):\n tb = geo.TaitBryan(H, P, R)\n hprs = ((rnd(), rnd(), rnd()) for _ in range(100))\n for h, p, r in hprs:\n h = (h - .5) * geo.TAU # Between -tau/2 and tau/2.\n p = (p - .5) * geo.TAU / 2 # Between -tau/4 and tau/4.\n r *= geo.TAU # Totally free, should not matter.\n vr = tb.rot(h, p, r).dot(tb.rest)\n hr, pr = tb.hp(vr)\n self.assertAlmostEqual(hr, h, 7)\n self.assertAlmostEqual(pr, p, 7)","title":""},{"docid":"a6736189478f79b92e7683e0c3ab84a3","score":"0.5362178","text":"def create_ham(self):\n from tcc.interaction import HAM_SPINLESS_RI_CORE_HUBBARD\n return HAM_SPINLESS_RI_CORE_HUBBARD(self)","title":""},{"docid":"3bfc6a8f449ab9ebe0977f5f0a1fda8d","score":"0.53568757","text":"def sort(self):\n nheav = self.nheav\n ias_heav = list(self.ias_heav)\n g = np.zeros((nheav, nheav))\n xhs = [] # X-H bonds\n ih = nheav\n coords = []; coords_H = []\n chgs = []; chgs_H = []\n dic = dict( zip(ias_heav, range(nheav)) )\n# print ' *** dic = ', dic\n for i, ia in enumerate( ias_heav ):\n coords.append( self.coords[ia] )\n chgs.append( self.chgs[ia] )\n jas = self.ias[ self.bom[ia,:] > 0 ]\n for ja in jas:\n if self.zs[ja] == 1:\n coords_H.append( self.coords[ja] )\n chgs_H.append( self.chgs[ja] )\n xhs.append([i,ih]); ih += 1\n else:\n g[i,dic[ja]] = g[dic[ja],i] = self.bom[ia,ja]\n coords_U = np.concatenate( (coords, coords_H) )\n self.coords = coords_U\n chgs_U = np.concatenate( (chgs, chgs_H) )\n self.chgs = chgs_U\n g2 = np.zeros((ih,ih))\n g2[:nheav, :nheav] = g\n for xh in xhs:\n i,j = xh\n g2[i,j] = g2[j,i] = 1\n self.bom = g2\n nh = ih - nheav\n zsU = np.array( list(self.zs[ias_heav]) + [1,]*nh )\n self.zs = zsU\n self.ias_heav = self.ias[ self.zs > 1 ]\n blk = write_ctab(zsU, chgs_U, g2, coords_U, sdf=None)\n m0 = obconv(blk)\n m1 = clone(m0)\n# print ' *** ', Chem.MolToSmiles(m1)\n m1.DeleteHydrogens()\n self.m0 = m0; self.m = m1","title":""},{"docid":"29c9bb978cb112f791fb96e5685f2728","score":"0.5344258","text":"def calc_ham_wo_k(self):\n def get_dir_cos(dist_vec):\n \"\"\" return directional cos of distance vector \"\"\"\n if np.linalg.norm(dist_vec) == 0:\n return 0., 0., 0.\n else:\n return dist_vec / np.linalg.norm(dist_vec)\n\n def get_ind(atom_1_i, orbit_1_i, element_1, orbit_1):\n return self.system.all_iter.index((atom_1_i, orbit_1_i, element_1, orbit_1))\n\n # params = self.system.params\n\n # TODO spin interactions\n\n # off-diagonal\n bond_mat = self.system.structure.bond_mat\n\n for atom_1_i, atom_1 in enumerate(self.system.structure.atoms):\n for atom_2_i, atom_2 in enumerate(self.system.structure.atoms):\n for image_ind in range(self.system.structure.max_image):\n if image_ind == self.system.structure.max_image/2 and atom_1_i == atom_2_i:\n continue\n\n if bond_mat[image_ind, atom_1_i, atom_2_i] == 0:\n continue\n param_element = self.system.get_hop_params(atom_1_i, atom_2_i, image_ind)\n\n # get direction cosines\n l, m, n = self.system.structure.get_dir_cos(image_ind, atom_1_i, atom_2_i)\n param_lmn = dict({'l': l, 'm': m, 'n': n,})\n param_element.update(param_lmn)\n hop_int_pair = get_hop_int(**param_element)\n\n for orbit_1_i, orbit_1 in enumerate(atom_1.orbitals):\n for orbit_2_i, orbit_2 in enumerate(atom_2.orbitals):\n hop_int_ = hop_int_pair[Hamiltonian.get_orb_ind(orbit_1)][Hamiltonian.get_orb_ind(orbit_2)] \n ind_1 = get_ind(atom_1_i, orbit_1_i, atom_1.element, orbit_1)\n ind_2 = get_ind(atom_2_i, orbit_2_i, atom_2.element, orbit_2)\n self.H_wo_g[image_ind, ind_1, ind_2] = hop_int_\n \n # real hermitian -> symmetric\n # self.H_wo_g += np.transpose(self.H_wo_g, [0, 2, 1])#[range(self.H_wo_g.shape[0])[::-1],:,:]\n\n # diagonal\n H_ind = 0\n for atom_i, atom in enumerate(self.system.structure.atoms):\n len_orbitals = len(atom.orbitals)\n # assert len_orbitals == 10, 'now # of orbitals == {}'.format(len(atom.orbitals))\n\n onsite_i = self.system.get_onsite_term(atom_i)\n\n self.H_wo_g[self.system.structure.max_image/2, \n H_ind: H_ind+len_orbitals, H_ind: H_ind+len_orbitals] = onsite_i\n H_ind += len_orbitals","title":""},{"docid":"fffc22dbf248b4a6414af5284d980caa","score":"0.5339243","text":"def get_ham(self, h):\n loaded_params, JZZ, ZZ, Mz, Ms = tfim.load_diag_ME(\"../hamiltonian/matrix\")\n Mx = tfim.load_Mx(\"../hamiltonian/matrix\")\n print(JZZ.shape)\n H = -JZZ - h*Mx\n return H","title":""},{"docid":"0d482c7c46e9c3154f2cfe3b3b5b22e2","score":"0.5336185","text":"def hair_additions(chromo1, chromo2):\n # Determine if the horse is white\n kit = [ge.discrete_allele(cr, 'kit') for cr in (chromo1, chromo2)]\n if 'W' in kit:\n return ''\n\n output = ''\n\n sooty = 'sty'\n if 'Sty' in [ge.discrete_allele(cr, 'sooty') for cr in (chromo1, chromo2)]:\n sooty = 'Sty'\n\n rabi = 'rb'\n if 'Rb' in [ge.discrete_allele(cr, 'rabicano') for cr in (chromo1, chromo2)]:\n rabi = 'Rb'\n\n overo = 'o'\n if 'O' in [ge.discrete_allele(cr, 'overo') for cr in (chromo1, chromo2)]:\n overo = 'O'\n\n splash = 'Spl'\n if 'spl' in [ge.discrete_allele(cr, 'splashed') for cr in (chromo1, chromo2)]:\n splash = 'spl'\n\n sabino = 'sb'\n if 'Sb' in kit:\n sabino = 'Sb'\n\n roan = 'rn'\n if 'Rn' in kit:\n roan = 'Rn'\n\n tabino = 'tb'\n if 'Tb' in kit:\n tabino = 'Tb'\n\n leopard = 'LpLp'\n ls = [ge.discrete_allele(cr, 'leopard_complex') for cr in (chromo1, chromo2)]\n if 'lp' in ls:\n if 'Lp' not in ls:\n leopard = 'lp'\n else:\n leopard = 'Lp'\n\n p1 = 'patn1'\n if 'PATN1' in [ge.discrete_allele(cr, 'pattern1') for cr in (chromo1, chromo2)]:\n p1 = 'PATN1'\n\n p2 = 'patn2'\n if 'PATN2' in [ge.discrete_allele(cr, 'pattern2') for cr in (chromo1, chromo2)]:\n p2 = 'PATN2'","title":""},{"docid":"1759e95019d630297cd2ff860317a5ab","score":"0.53332025","text":"def PORCENTAJE_HISTERESIS():\n return 10.0","title":""},{"docid":"0fc9b71df1fb9ae1766499a919a0583b","score":"0.532652","text":"def generate_routefile(self, seed):\n np.random.seed(seed) # make tests reproducible\n\n # the generation of cars is distributed according to a weibull distribution\n timings = np.random.weibull(2, self._n_cars_generated)\n timings = np.sort(timings)\n\n # reshape the distribution to fit the interval 0:max_steps\n car_gen_steps = []\n min_old = math.floor(timings[1])\n max_old = math.ceil(timings[-1])\n min_new = 0\n max_new = self._max_steps\n for value in timings:\n car_gen_steps = np.append(car_gen_steps, ((max_new - min_new) / (max_old - min_old)) * (value - max_old) + max_new)\n\n car_gen_steps = np.rint(car_gen_steps) # round every value to int -> effective steps when a car will be generated\n\n # produce the file for cars generation, one car per line\n with open(\"intersection/episode_routes.rou.xml\", \"w\") as routes:\n print(\"\"\"<routes>\n <vType accel=\"1.0\" decel=\"4.5\" id=\"standard_car\" length=\"5.0\" minGap=\"2.5\" maxSpeed=\"25\" sigma=\"0.5\" />\n\n <route id=\"W_N\" edges=\"W2TL TL2N\"/>\n <route id=\"W_E\" edges=\"W2TL TL2E\"/>\n <route id=\"W_S\" edges=\"W2TL TL2S\"/>\n <route id=\"N_W\" edges=\"N2TL TL2W\"/>\n <route id=\"N_E\" edges=\"N2TL TL2E\"/>\n <route id=\"N_S\" edges=\"N2TL TL2S\"/>\n <route id=\"E_W\" edges=\"E2TL TL2W\"/>\n <route id=\"E_N\" edges=\"E2TL TL2N\"/>\n <route id=\"E_S\" edges=\"E2TL TL2S\"/>\n <route id=\"S_W\" edges=\"S2TL TL2W\"/>\n <route id=\"S_N\" edges=\"S2TL TL2N\"/>\n <route id=\"S_E\" edges=\"S2TL TL2E\"/>\"\"\", file=routes)\n\n \n for counter, step in enumerate(car_gen_steps):\n i = np.random.randint(0,4)\n j = np.random.randint(0,3)\n\n if(array[i][j]>0):\n if(i,j==0,0):\n print(' <vehicle id=\"W_N_%i\" type=\"standard_car\" route=\"W_N\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==0,1):\n print(' <vehicle id=\"W_E_%i\" type=\"standard_car\" route=\"W_E\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==0,2):\n print(' <vehicle id=\"W_S_%i\" type=\"standard_car\" route=\"W_S\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==1,0): \n print(' <vehicle id=\"N_W_%i\" type=\"standard_car\" route=\"N_W\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==1,1):\n print(' <vehicle id=\"N_E_%i\" type=\"standard_car\" route=\"N_E\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes) \n elif(i,j==1,2):\n print(' <vehicle id=\"N_S_%i\" type=\"standard_car\" route=\"N_S\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==2,0):\n print(' <vehicle id=\"E_W_%i\" type=\"standard_car\" route=\"E_W\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==2,1):\n print(' <vehicle id=\"E_N_%i\" type=\"standard_car\" route=\"E_N\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==2,2):\n print(' <vehicle id=\"E_S_%i\" type=\"standard_car\" route=\"E_S\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==3,0):\n print(' <vehicle id=\"S_W_%i\" type=\"standard_car\" route=\"S_W\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==3,1):\n print(' <vehicle id=\"S_N_%i\" type=\"standard_car\" route=\"S_N\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n elif(i,j==3,2):\n print(' <vehicle id=\"S_E_%i\" type=\"standard_car\" route=\"S_E\" depart=\"%s\" departLane=\"random\" departSpeed=\"10\" />' % (car_counter, step), file=routes)\n else:\n print(\"error\")\n\n array[i][j]-=1\n\n\n print(\"</routes>\", file=routes)","title":""},{"docid":"0ab931d370c0bc193ec0a8c29bac35bd","score":"0.53103614","text":"def makeCorridor(self):\n clength = randrange(18) + 3\n heading = randrange(4)\n if heading == 0: # North\n wd = 1\n lg = -clength\n elif heading == 1: # East\n wd = clength\n lg = 1\n elif heading == 2: # South\n wd = 1\n lg = clength\n elif heading == 3: # West\n wd = -clength\n lg = 1\n return wd, lg, heading","title":""},{"docid":"72f1267fe844933f4ede74025df020f1","score":"0.52999496","text":"def _init_h(self):\n h=np.array([.5,1.])\n _self=self\n _self.h=h;_self.form_operators()\n _,logr=self.MH_step()\n a=2.*(np.exp(logr)>0.5)-1.\n while a*logr>-a*np.log(2):\n h*=pow(2.,a)\n _self=self\n _self.h=h;_self.form_operators()\n _,logr=self.MH_step()\n return h","title":""},{"docid":"473779689990ef915bbe0d2b82ebf1b2","score":"0.5297422","text":"def init_heur():\n \n global count\n global random_numbers\n\n random.seed(0)\n random_numbers = [random.random() for i in range(long(1e7))]\n count = 0","title":""},{"docid":"ab770b2e1f1b4c26e439fac1af09f91f","score":"0.52946675","text":"def generate_moves(self):","title":""},{"docid":"f10908b18ba6e74ed1164ed9b70eabcb","score":"0.529333","text":"def GEOM_8x10():","title":""},{"docid":"48d0c28aea250a2f15b85dacaf475340","score":"0.52921313","text":"def roty(angle):\n ca = cos(angle)\n sa = sin(angle)\n H = array(\n [[ ca, 0, sa, 0],\n [ 0, 1, 0, 0],\n [-sa, 0, ca, 0],\n [ 0, 0, 0, 1]])\n return H","title":""},{"docid":"e547927e631ac9d2486676b9fd158702","score":"0.52844083","text":"def heat_or_not_room(self):\n h = 0\n t = 0\n hv = {}\n for k, v in self.eq3.rooms.iteritems():\n if self.setup.preference == \"per\":\n # and valve position is over single valve position no matter what\n if v[4] > self.setup.svpnmw:\n h = 3\n # or valve is over desired position to switch heating on\n if v[4] > self.setup.valve_switch:\n hv.update({k: [v[0], v[0], v[0]]})\n if len(hv) >= self.setup.valve_num:\n h = 1\n elif self.setup.preference == \"total\":\n t += v[0]\n if t >= self.setup.total_switch:\n h = 2\n return [h, t, hv]","title":""},{"docid":"dd2af1edd6baeca800570eaf84cdac16","score":"0.52816343","text":"def calc_hog(im,numorient=9):\n sz = cv.GetSize(im)\n gr = cv.CreateImage(sz, 8, 1)\n gx = cv.CreateImage(sz, 32, 1)\n gy = cv.CreateImage(sz, 32, 1)\n \n #convert to grayscale\n cv.CvtColor(im, gr, cv.CV_BGR2GRAY)\n \n #calc gradient using sobel\n cv.Sobel(gr, gx, 1, 0, 3)\n cv.Sobel(gr, gy, 0, 1, 3)\n \n #calc initial result\n hog = np.zeros((sz[1], sz[0], numorient))\n mid = numorient/2\n for y in xrange(0, sz[1]-1):\n for x in xrange(0, sz[0]-1):\n angle = int(round(mid*np.arctan2(gy[y,x], gx[y,x])/np.pi))+mid\n magnitude = np.sqrt(gx[y,x]*gx[y,x]+gy[y,x]*gy[y,x])\n hog[y,x,angle] += magnitude\n \n \n #build integral image\n for x in xrange(1, sz[0]-1):\n for ang in xrange(numorient):\n hog[y,x,ang] += hog[y,x-1,ang]\n for y in xrange(1, sz[1]-1):\n for ang in xrange(numorient):\n hog[y,x,ang] += hog[y-1,x,ang]\n for y in xrange(1, sz[1]-1):\n for x in xrange(1, sz[0]-1):\n for ang in xrange(numorient):\n #tambah kiri dan atas, kurangi dengan kiri-atas\n hog[y,x,ang] += hog[y-1,x,ang] + hog[y,x-1,ang] - hog[y-1,x-1,ang]\n return hog","title":""},{"docid":"c8c5e9ba48fad1b9c14b39d8751eb5a2","score":"0.52748704","text":"def heuristique_PCM(self,draw=False):\n if draw:\n try:\n os.makedirs(self.dirname+\"/H_ShortestPath\")\n except:\n pass\n \n #Graphe de depart contenant tout les noeuds \n # Individu = Graphe_Individu(self,self.wholeGraphDict)\n # G = Individu.get_graphe()\n #\n # if draw:\n # self.drawGraph(\"/H_ShortestPath/G0\",G.get_edges())\n\n G1,graphPaths = self.construireGrapheDistance()\n if draw:\n self.drawGraph(\"/H_ShortestPath/G1\",G1.graphe.get_edges())\n\n \"\"\"\n Etape 2\n \"\"\"\n G2 = G1.get_MST()\n if draw:\n self.drawGraph(\"/H_ShortestPath/G2_%d\"%G2.get_total_weight(),G2.get_edges())\n\n G3 = self.reconstruireGrapheChemins(G2.get_edges(),graphPaths)\n if draw:\n self.drawGraph(\"/H_ShortestPath/G3_%d\"%G3.get_MST().get_total_weight(),G3.graphe.get_edges()) \n\n \"\"\"\n Etape 4\n \"\"\" \n G4 = G3.get_MST()\n if draw:\n self.drawGraph(\"/H_ShortestPath/G4_%d\"%G4.get_total_weight(),G4.get_edges())\n\n dictSteinerNodes = self.eliminationFeuilles(G4.get_edges(),G3.get_dictSteinerNodes().keys())\n\n G5 = Graphe_Individu(self,dictSteinerNodes)\n if draw:\n self.drawGraph(\"/H_ShortestPath/G5_%d\"%G5.get_MST().get_total_weight(),G5.graphe.get_edges())\n\n for i in self.steinerNodes:\n if i not in dictSteinerNodes.keys():\n dictSteinerNodes[i] = 0\n\n for i in self.steinerNodes :\n if i not in dictSteinerNodes.keys():\n dictSteinerNodes[i] = 0\n\n return dictSteinerNodes","title":""},{"docid":"3d71bd04146e03892a13157f07d1852b","score":"0.5266146","text":"def __init__(self, D_in, H, D_out,prob):\n super(Highway, self).__init__()","title":""},{"docid":"29e6f394e2833a9dc1437fb275062a4e","score":"0.5261538","text":"def displayH(self):\n Matrix = self.getMatrix()\n Matrix = [normalizeList(i,width) for i in Matrix]\n shiftY = 0\n spaceY = 1.0*height/Y\n param = [0,0]\n for row in Matrix:\n shiftX = 0\n param[0] = 0\n for i in row:\n with pushMatrix():\n #scale(1,1,555*noise(scl*param[1]+cos(TWO_PI*a),scl*param[0]+sin(TWO_PI*a)))\n #self.module(shiftX,shiftY,i,spaceY)\n scale(1,1,255*noise(radius*cos(TWO_PI*a)+sum(param)))\n if(i > 0): self.module(self.modules[self.choices[param[1]][param[0]]],shiftX,shiftY,i,spaceY)\n shiftX += i\n param[0]+=1\n shiftY += spaceY\n param[1]+=1","title":""},{"docid":"f9586c4fe14c93493f44a493ccfc7c82","score":"0.525852","text":"def geom_create():\n r_chord = 1\n taper_ratio = 1\n t_chord = r_chord/taper_ratio\n b = 10\n sweep_angle = 20 \n # Wing edge co-ordinates\n r_le = (0.0, 0.0)\n r_te = (r_chord, 0.0)\n t_le = (0.5*b*tan(math.radians(sweep_angle)), 0.5*b) \n t_te = (0.5*b*tan(math.radians(sweep_angle)) + t_chord, 0.5*b)\n S = 0.5*b*t_le[0] + b*(t_te[0]-t_le[0]) + 0.5*b*(r_chord-t_te[0])\n AR = b**2/S\n return r_le, r_te, t_le, t_te, sweep_angle","title":""},{"docid":"05e97c434e1dc810603c2df113b899bf","score":"0.52567554","text":"def main():\n print \"Routes through a 20x20 grid = \",binom(40,20)","title":""},{"docid":"21b6fa5481e5278e31ae1b7d5f207aa1","score":"0.5256023","text":"def creation_agent(nb_Ah, nb_Ac, nb_V, nb_M):\r\n assert isinstance(nb_Ah, int),'nb_Ah doit être un entier'\r\n assert isinstance(nb_Ac, int),'nb_Ac doit être un entier'\r\n assert isinstance(nb_V, int),'nb_V doit être un entier'\r\n assert isinstance(nb_M, int),'nb_M doit être un entier'\r\n\r\n total_agent = nb_Ah +nb_Ac +nb_V +nb_M # total agent\r\n \r\n if total_agent>=600:\r\n print(\"Il y a trop d'agent dans l'environnement !\")\r\n\r\n else:\r\n \r\n liste_pos = []\r\n\r\n while len(liste_pos)!= total_agent: \r\n x= randint(0,24)\r\n y= randint(0,24)\r\n pos=[x,y]\r\n if pos in liste_pos: #verifier qu'il n'y ait pas de doublon\r\n pass\r\n #print('DOUBLE')\r\n else:\r\n liste_pos.append(pos)\r\n for i in range(len(liste_pos)): #positionner agent sur la grille + creation objet agent\r\n if i<nb_Ah-1:\r\n world[x][y] = 'Ah'\r\n #animal_herbivore = Animal(2,2,2,5,0,'herbivore',pos, 'present',fonctioncreerfield('animal'pos) )\r\n if i<nb_Ah+nb_Ac-1 and i>=nb_Ah:\r\n world[x][y] = 'Ac'\r\n #animal_carnivore = Animal(2,2,2,5,0,'carnivore')\r\n if i<nb_Ah+nb_Ac+nb_V-1 and i>=nb_Ah+nb_Ac:\r\n world[x][y] = 'V'\r\n #vegetal = Vegetal(2,2,2,5,0)\r\n if i<total_agent-1 and i>=nb_Ah+nb_Ac+nb_V:\r\n world[x][y] = 'M'\r\n #mineral = Mineral()\r\n \r\n \r\n \r\n print('nb_total agent =',total_agent)\r\n print('longeur liste pos =',len(liste_pos))\r\n print('liste pos = ',liste_pos)\r\n print('espace')\r\n print(world)\r\n\r\n #debugueur avec agent\r","title":""},{"docid":"634a93aa07a8677198dd8201aee4dbdf","score":"0.5255234","text":"def movern_calculation(self, print_litho_info, litho_raster, print_junctions_to_csv, n_movern = 18, start_movern = 0.1, delta_movern = 0.05, print_basin_raster = True, print_segmented_M_chi_map_to_csv =False, print_chi_data_maps = True, print_simple_chi_map_with_basins_to_csv =True, minimum_basin_size_pixels = 10000, maximum_basin_size_pixels = 90000000, threshold_contributing_pixels = 5000, only_take_largest_basin = True, write_hillshade = True, plot = True, minimum_elevation = 0, maximum_elevation= 30000,use_precipitation_raster_for_chi = False,precipitation_fname = \"NULL\",burn_raster_to_csv=False, burn_raster_prefix=\"NULL\",secondary_burn_raster_to_csv=False,secondary_burn_raster_prefix=\"Null\"):\n\t\tself.chi_mapping_tool_full(burn_raster_to_csv=burn_raster_to_csv,burn_raster_prefix=burn_raster_prefix, secondary_burn_raster_to_csv=secondary_burn_raster_to_csv,secondary_burn_raster_prefix=secondary_burn_raster_prefix, print_litho_info=print_litho_info, litho_raster=litho_raster, print_junctions_to_csv=print_junctions_to_csv, n_movern = n_movern,start_movern = start_movern,delta_movern = delta_movern,estimate_best_fit_movern= True,print_basin_raster = print_basin_raster,minimum_basin_size_pixels = minimum_basin_size_pixels, maximum_basin_size_pixels = maximum_basin_size_pixels,threshold_contributing_pixels = threshold_contributing_pixels,only_take_largest_basin=only_take_largest_basin,write_hillshade = write_hillshade, minimum_elevation = minimum_elevation, maximum_elevation = maximum_elevation, use_precipitation_raster_for_chi = use_precipitation_raster_for_chi,precipitation_fname = precipitation_fname)\n\t\tif (plot):\n\t\t\tplotting_command = \"python %sPlotMOverNAnalysis.py -dir %s -fname %s -ALL True\" %(self.LSDMT_path,self.wpath,self.wprefix)\n\t\t\tsub.call(plotting_command, shell = True)","title":""},{"docid":"149cebe898813735a2762e96850e58f8","score":"0.5254368","text":"def HEAT(NRX, NCX, LX, X, NRY, NCY, LY, Y, LG, G):\n G = ZERO(NRX * NRY * LG, G)\n # print(NRX, NRY, LG)\n # G = numpy.zeros((NRX * NRY * LG, ))\n MIN = MIN0(LG, LX)\n for M in range(NRX): \n for N in range(NRY):\n for L in range(NCX): \n for J in range(MIN): \n LDOT = MIN0(LY, LX - J)\n for I in range(LDOT): \n# print(\"M, N, L, J, I : \", M, N, L, J, I)\n K = I + J \n MNJ = M + N * NRX + J * NRX * NRY\n MLK = M + L * NRX + K * NRX * NCX\n NLI = N + L * NRY + I * NRY * NCX\n G[MNJ] += X[MLK] * Y[NLI]\n return G","title":""},{"docid":"9f57ef473123fb25c97130935814d70a","score":"0.5254356","text":"def hom(p):\n ph=np.concatenate((p,np.ones((1,p.shape[1]))))\n return ph","title":""},{"docid":"361b4034cb75c61ca168b2ce6619d6c4","score":"0.5245675","text":"def hodnik ( iX=0 , iZ=0 , iY=0 , duzina = 10 ):\n #gdje sam\n orMj = gdjeSam ()\n orSm = gdjeGledam ()\n \n orMj = premjesti_origin ( orMj , iX , iZ , iY , orSm ) #mice ishodiste na centar kupole\n \n crtaj_kvadar ( orMj , ( 1 , -3 , -1 ) , ( duzina , 3 , 5 ) , orSm , 98 , 0 ) # stonebrick tijelo\n crtaj_kvadar ( orMj , ( 1 , -2 , 0 ) , ( duzina , 2 , 4 ) , orSm , AIR.id , 0 ) # stonebrick rupa\n crtaj_kvadar ( orMj , ( 1 , -2 , 0 ) , ( duzina , -2 , 0 ) , orSm , 44 , 5 ) # stonebrick slab lijevi dolje\n crtaj_kvadar ( orMj , ( 1 , 2 , 0 ) , ( duzina , 2 , 0 ) , orSm , 44 , 5 ) # stonebrick slab desni dolje\n crtaj_stepenice ( orMj ,( 1 , -2 , 4 ) , ( duzina , -2 , 4 ) , orSm , blok_id = 109 , rel_smjer = \"desno\" , gore_dolje = \"da\" ) \n crtaj_stepenice ( orMj ,( 1 , 2 , 4 ) , ( duzina , 2 , 4 ) , orSm , blok_id = 109 , rel_smjer = \"lijevo\" , gore_dolje = \"da\" ) \n \n \n \n for dX in range( 3 , duzina , 5 ) : \t\t# prodji cijeli pravokutnik\n crtaj_kvadar ( orMj , (dX,0,5) , (dX,0,5) , orSm , 89 )\n \n\n return 1","title":""},{"docid":"33a5ffd3a03af8b449b83e0b8fbb3827","score":"0.5244088","text":"def _hupperofp1(p):\r\n\r\n pi1 = 7.1 - p / PSTAR1\r\n o = numpy.zeros(3)\r\n o[0] = pi1 ** 2\r\n o[1] = o[0] ** 2\r\n o[2] = o[1] ** 2\r\n\r\n h = 639675.036*(2.42896927729349 + pi1*(-0.00141131225285294 + pi1*(0.00143759406818289 + pi1*(0.000125338925082983 + pi1*(0.0000123617764767172 + pi1*(3.17834967400818e-6 + o[0]*pi1*(1.46754947271665e-8 + o[1]*o[2]*pi1*(1.86779322717506e-17 + o[0]*(-4.18568363667416e-19 + o[0]*o[1]*(-9.19148577641497e-22 + pi1*(4.27026404402408e-22 + (-6.66749357417962e-23 + 3.49930466305574e-24*pi1)*pi1)))))))))))\r\n return h","title":""},{"docid":"9c74ba78bf94b4b821094c4316e372c9","score":"0.52419484","text":"def exring():\n ra, rb=2.,3.\n rf = pspdf.pdf_uniform(ra,rb)\n pf = pspdf.pdf_uniform(-pi,pi)\n N = 10000\n rs = rf.random(N); phis = pf.random(N) \n xs = map(lambda r,phi: r*cos(phi),rs,phis)\n ys = map(lambda r,phi: r*sin(phi),rs,phis)\n h1 = TH2F('r','r',40,-1.2*rb,1.2*rb,40,-1.2*rb,1.2*rb)\n map(lambda x,y: h1.Fill(x,y),xs,ys)\n cov = pslist.cov(xs,ys); rho = pslist.rho(xs,ys)\n print ' cov ',cov,' rho ',rho\n return h1","title":""},{"docid":"a9f13bdf52c9ee5451771617b15d6d93","score":"0.52413726","text":"def swissPairings():","title":""},{"docid":"febcdb7455af48072c872bf1c567571b","score":"0.5236832","text":"def yag_s():\n yagtgx.put(tgx())\n yaghx.put(hexx.get())\n yaghy.put(hexy.get())\n yaghz.put(hexz.get())","title":""},{"docid":"ff81f4dde343219a4189868f02348ca8","score":"0.5231859","text":"def getHeteroProb(k, m, n):\n\n # Probability of parent X being D, H or R\n D = k/(k+m+n)\n H = m/(k+m+n)\n R = n/(k+m+n)\n\n # Probability of parent Y being D, H or R\n DD = (k-1)/((k+m+n)-1)\n Dx = (k)/((k+m+n)-1)\n HH = (m-1)/((k+m+n)-1)\n Hx = (m)/((k+m+n)-1)\n RR = (n-1)/((k+m+n)-1)\n Rx = (n)/((k+m+n)-1)\n\n # Probabilities of dominant positive for pairings\n pDD = 1\n pDH = 1\n pDR = 1\n pHH = 0.75\n pHR = 0.5\n pRR = 0\n\n # Full calculation\n prob = (D*DD*pDD)+(D*Dx*pDH)+(D*Dx*pDR)+(H*HH*pHH)+(H*Hx*pDH)+(H*Hx*pHR)+(R*RR*pRR)+(R*Rx*pDR)+(R*Rx*pHR)\n print(prob)","title":""},{"docid":"73493dd22943fc68d818b5c9fe1ec5f5","score":"0.5228056","text":"def makeD02HH(name, D0MassWin, D0PT, D0VtxChi2Ndof, D0Bpvdira, D0Bpvdls,\n D0daughterPT, D0daughterP, D0daughterTrkChi2,\n D0daughterTrkGhostProb, D0daughterBpvIpChi2,\n### D0daughterKaonPIDK, D0daughterPionPIDK,\n D0daughterKaonProbNNk, D0daughterPionProbNNpi, \n ) :\n\n _motherCuts = \"(ADMASS('D0')<%(D0MassWin)s) & (PT>%(D0PT)s) & (VFASPF(VCHI2PDOF)<%(D0VtxChi2Ndof)s) \" % locals()\n _motherCuts += \"& (BPVDIRA>%(D0Bpvdira)s) & (BPVDLS>%(D0Bpvdls)s)\" % locals()\n _daughtersCuts = \"(PT> %(D0daughterPT)s) & (P>%(D0daughterP)s) & (TRCHI2DOF<%(D0daughterTrkChi2)s) \" % locals()\n _daughtersCuts += \"& (TRGHP<%(D0daughterTrkGhostProb)s) & (BPVIPCHI2()>%(D0daughterBpvIpChi2)s)\" % locals()\n### _KCutsPID = \"&( (PIDK>%(D0daughterKaonPIDK)s) | (PROBNNk>%(D0daughterKaonProbNNk)s) )\" % locals()\n### _PiCutsPID = \"&( (PIDK<%(D0daughterPionPIDK)s) | (PROBNNpi>%(D0daughterPionProbNNpi)s) )\" % locals()\n _KCutsPID = \"&(PROBNNk>%(D0daughterKaonProbNNk)s)\" % locals()\n _PiCutsPID = \"&(PROBNNpi>%(D0daughterPionProbNNpi)s)\" % locals()\n _KCuts = _daughtersCuts + _KCutsPID\n _PiCuts = _daughtersCuts + _PiCutsPID\n _D0Filter = CombineParticles( DecayDescriptor = \"[D0 -> K- pi+]cc\",\n MotherCut = _motherCuts,\n DaughtersCuts = { \"pi+\" : _PiCuts, \"K+\" : _KCuts }\n )\n\n# _stdTightKaons = DataOnDemand(Location = \"Phys/StdAllLooseKaons/Particles\")\n# _stdTightPions = DataOnDemand(Location = \"Phys/StdAllLoosePions/Particles\")\n _stdTightKaons = DataOnDemand(Location = \"Phys/StdAllLooseANNKaons/Particles\")\n _stdTightPions = DataOnDemand(Location = \"Phys/StdAllLooseANNPions/Particles\")\n\n return Selection (name,\n Algorithm = _D0Filter,\n RequiredSelections = [_stdTightKaons, _stdTightPions])","title":""},{"docid":"787a6d44c156002826b6e481d6475820","score":"0.52275974","text":"def chi_mapping_tool_full(self,print_litho_info=False,litho_raster='NULL',minimum_elevation = 0, maximum_elevation = 30000, min_slope_for_fill = 0.0001, \n raster_is_filled = False, remove_seas = True ,only_check_parameters = False,print_raster_without_seas= False, \n CHeads_file = 'NULL', threshold_contributing_pixels = 200,minimum_basin_size_pixels = 5000, \n maximum_basin_size_pixels = 8000, test_drainage_boundaries = True, only_take_largest_basin = False, \n BaselevelJunctions_file = \"NULL\", extend_channel_to_node_before_receiver_junction = True,find_complete_basins_in_window = True, \n find_largest_complete_basins = False, print_basin_raster = False, convert_csv_to_geojson = False,\n print_stream_order_raster = False,print_channels_to_csv = False, print_junction_index_raster= False,\n print_junctions_to_csv =False,print_fill_raster = False,print_DrainageArea_raster = False, \n write_hillshade = True,print_basic_M_chi_map_to_csv = False, ksn_knickpoint_analysis = False, A_0 = 1,m_over_n = 0.5,threshold_pixels_for_chi = 0, basic_Mchi_regression_nodes = 11,burn_raster_to_csv = False,burn_raster_prefix = 'NULL',\n burn_data_csv_column_header = 'burned_data',secondary_burn_raster_to_csv = False,\n secondary_burn_raster_prefix = 'NULL',secondary_burn_data_csv_column_header = 'secondary_burned_data', \n n_movern = 8, start_movern = 0.1, delta_movern = 0.1, only_use_mainstem_as_reference = True,calculate_MLE_collinearity = False,\n collinearity_MLE_sigma = 1000, print_profiles_fxn_movern_csv = False, calculate_MLE_collinearity_with_points = False, \n calculate_MLE_collinearity_with_points_MC = False, MC_point_fractions = 5, MC_point_iterations = 1000,\n max_MC_point_fraction = 0.5, movern_residuals_test = False, MCMC_movern_analysis = False, \n MCMC_movern_minimum = 0.05, MCMC_movern_maximum = 1.5, MCMC_chain_links = 5000, estimate_best_fit_movern = True,\n SA_vertical_interval = 20,log_A_bin_width = 0.1,print_slope_area_data = False, segment_slope_area_data = False, \n slope_area_minimum_segment_length = 3, bootstrap_SA_data =False, N_SA_bootstrap_iterations = 1000,\n SA_bootstrap_retain_node_prbability = 0.5,n_iterations = 20,minimum_segment_length = 10, maximum_segment_length = 100000, n_nodes_to_visit = 10, target_nodes = 80,skip = 2, sigma = 20,print_chi_coordinate_raster = False, print_simple_chi_map_to_csv = True, \n print_chi_data_maps = True, print_simple_chi_map_with_basins_to_csv = True,\n print_segmented_M_chi_map_to_csv =True, print_source_keys = True, print_sources_to_csv = False, \n print_sources_to_raster =False, print_baselevel_keys = False, use_precipitation_raster_for_chi = False, \n print_discharge_raster = False,print_chi_no_discharge = False,check_chi_maps = False,\n precipitation_fname = \"NULL\", print_segments = False, print_segments_raster = False):\n\t\t# writing the file\n\n\t\tparam_name = self.wpath+self.wprefix+\"_Chiculations.param\"\n\t\tfile = open(param_name, 'w')\n\t\tfile.write('# This is a parameter file for the chi_mapping_tool \\n')\n\t\tfile.write('# Documentation is here: https://lsdtopotools.github.io/LSDTT_documentation/LSDTT_chi_analysis.html \\n')\n\t\tfile.write(\" \\n\")\n\t\tfile.write('# These are parameters for the file i/o \\n')\n\t\tfile.write(\"# IMPORTANT: You MUST make the write directory: the code will not work if it doens't exist. \\n\")\n\t\tfile.write('read path: %s \\n'%(self.rpath))\n\t\tfile.write('write path: %s \\n'%(self.wpath))\n\t\tfile.write('read fname: %s \\n'%(self.rprefix))\n\t\tfile.write('write fname: %s \\n'%(self.wprefix))\n\t\tfile.write(\" \\n\")\n\t\tfile.write('minimum_elevation: %s \\n'%(minimum_elevation))\n\t\tfile.write('maximum_elevation: %s \\n'%(maximum_elevation))\n\t\tfile.write('min_slope_for_fill: %s \\n'%(min_slope_for_fill))\n\t\tfile.write('raster_is_filled: %s \\n'%(to_cpp(raster_is_filled)))\n\t\tfile.write('remove_seas: %s \\n'%(to_cpp(remove_seas)))\n\t\tfile.write('only_check_parameters: %s \\n'%(to_cpp(only_check_parameters)))\n\t\tfile.write('CHeads_file: %s \\n'%(CHeads_file))\n\t\tfile.write('start_movern: %s \\n'%(to_cpp(start_movern)))\n\t\tfile.write('delta_movern: %s \\n'%(to_cpp(delta_movern)))\n\t\tfile.write('only_use_mainstem_as_reference: %s \\n'%(to_cpp(only_use_mainstem_as_reference)))\n\t\tfile.write('calculate_MLE_collinearity: %s \\n'%(to_cpp(calculate_MLE_collinearity)))\n\t\tfile.write('collinearity_MLE_sigma: %s \\n'%(to_cpp(collinearity_MLE_sigma)))\n\t\tfile.write('print_profiles_fxn_movern_csv: %s \\n'%(to_cpp(print_profiles_fxn_movern_csv)))\n\t\tfile.write('calculate_MLE_collinearity_with_points: %s \\n'%(to_cpp(calculate_MLE_collinearity_with_points)))\n\t\tfile.write('calculate_MLE_collinearity_with_points_MC: %s \\n'%(to_cpp(calculate_MLE_collinearity_with_points_MC)))\n\t\tfile.write('MC_point_fractions: %s \\n'%(to_cpp(MC_point_fractions)))\n\t\tfile.write('print_chi_coordinate_raster: %s \\n'%(to_cpp(print_chi_coordinate_raster)))\n\t\tfile.write('print_chi_data_maps: %s \\n'%(to_cpp(print_chi_data_maps)))\n\t\tfile.write('print_simple_chi_map_to_csv: %s \\n'%(to_cpp(print_simple_chi_map_to_csv)))\n\t\tfile.write('print_simple_chi_map_with_basins_to_csv: %s \\n'%(to_cpp(print_simple_chi_map_with_basins_to_csv)))\n\t\tfile.write('print_segmented_M_chi_map_to_csv: %s \\n'%(to_cpp(print_segmented_M_chi_map_to_csv)))\n\t\tfile.write('print_basic_M_chi_map_to_csv: %s \\n'%(to_cpp(print_basic_M_chi_map_to_csv)))\n\t\tfile.write('print_source_keys: %s \\n'%(to_cpp(print_source_keys)))\n\t\tfile.write('print_sources_to_csv: %s \\n'%(to_cpp(print_sources_to_csv)))\n\t\tfile.write('print_sources_to_raster: %s \\n'%(to_cpp(print_sources_to_raster)))\n\t\tfile.write('print_baselevel_keys: %s \\n'%(to_cpp(print_baselevel_keys)))\n\t\tfile.write('use_precipitation_raster_for_chi: %s \\n'%(to_cpp(use_precipitation_raster_for_chi)))\n\t\tfile.write('print_discharge_raster: %s \\n'%(to_cpp(print_discharge_raster)))\n\t\tfile.write('print_chi_no_discharge: %s \\n'%(to_cpp(print_chi_no_discharge)))\n\t\tfile.write('check_chi_maps: %s \\n'%(to_cpp(check_chi_maps)))\n\t\tfile.write('precipitation_fname: %s \\n'%(to_cpp(precipitation_fname)))\n\t\tfile.write('print_segments: %s \\n'%(to_cpp(print_segments)))\n\t\tfile.write('print_segments_raster: %s \\n'%(to_cpp(print_segments_raster)))\n\t\tfile.write('print_litho_info: %s \\n'%(to_cpp(print_litho_info)))\n\t\tfile.write('litho_raster: %s \\n'%(to_cpp(litho_raster)))\n\t\tfile.write('estimate_best_fit_movern: %s \\n'%(to_cpp(estimate_best_fit_movern)))\n\t\tfile.write('SA_vertical_interval: %s \\n'%(to_cpp(SA_vertical_interval)))\n\t\tfile.write('log_A_bin_width: %s \\n'%(to_cpp(log_A_bin_width)))\n\t\tfile.write('print_slope_area_data: %s \\n'%(to_cpp(print_slope_area_data)))\n\t\tfile.write('segment_slope_area_data: %s \\n'%(to_cpp(segment_slope_area_data)))\n\t\tfile.write('slope_area_minimum_segment_length: %s \\n'%(to_cpp(slope_area_minimum_segment_length)))\n\t\tfile.write('bootstrap_SA_data: %s \\n'%(to_cpp(bootstrap_SA_data)))\n\t\tfile.write('N_SA_bootstrap_iterations: %s \\n'%(to_cpp(N_SA_bootstrap_iterations)))\n\t\tfile.write('SA_bootstrap_retain_node_prbability: %s \\n'%(to_cpp(SA_bootstrap_retain_node_prbability)))\n\t\tfile.write('n_iterations: %s \\n'%(to_cpp(n_iterations)))\n\t\tfile.write('minimum_segment_length: %s \\n'%(to_cpp(minimum_segment_length)))\n\t\tfile.write('maximum_segment_length: %s \\n'%(to_cpp(maximum_segment_length)))\n\t\tfile.write('n_nodes_to_visit: %s \\n'%(to_cpp(n_nodes_to_visit)))\n\t\tfile.write('target_nodes: %s \\n'%(to_cpp(target_nodes)))\n\t\tfile.write('skip: %s \\n'%(to_cpp(skip)))\n\t\tfile.write('MC_point_iterations: %s \\n'%(to_cpp(MC_point_iterations)))\n\t\tfile.write('max_MC_point_fraction: %s \\n'%(to_cpp(max_MC_point_fraction)))\n\t\tfile.write('movern_residuals_test: %s \\n'%(to_cpp(movern_residuals_test)))\n\t\tfile.write('MCMC_movern_analysis: %s \\n'%(to_cpp(MCMC_movern_analysis)))\n\t\tfile.write('MCMC_movern_minimum: %s \\n'%(to_cpp(MCMC_movern_minimum)))\n\t\tfile.write('MCMC_movern_maximum: %s \\n'%(to_cpp(MCMC_movern_maximum)))\n\t\tfile.write('MCMC_chain_links: %s \\n'%(to_cpp(MCMC_chain_links)))\n\t\tfile.write('print_stream_order_raster: %s \\n'%(to_cpp(print_stream_order_raster)))\n\t\tfile.write('print_raster_without_seas: %s \\n'%(to_cpp(print_raster_without_seas)))\n\t\tfile.write('threshold_contributing_pixels: %s \\n'%(threshold_contributing_pixels))\n\t\tfile.write('minimum_basin_size_pixels: %s \\n'%(minimum_basin_size_pixels))\n\t\tfile.write('maximum_basin_size_pixels: %s \\n'%(maximum_basin_size_pixels))\n\t\tfile.write('test_drainage_boundaries: %s \\n'%(to_cpp(test_drainage_boundaries)))\n\t\tfile.write('only_take_largest_basin: %s \\n'%(to_cpp(only_take_largest_basin)))\n\t\tfile.write('BaselevelJunctions_file: %s \\n'%(BaselevelJunctions_file))\n\t\tfile.write('extend_channel_to_node_before_receiver_junction: %s \\n'%(to_cpp(extend_channel_to_node_before_receiver_junction)))\n\t\tfile.write('find_complete_basins_in_window: %s \\n'%(to_cpp(find_complete_basins_in_window)))\n\t\tfile.write('find_largest_complete_basins: %s \\n'%(to_cpp(find_largest_complete_basins)))\n\t\tfile.write('print_basin_raster: %s \\n'%(to_cpp(print_basin_raster)))\n\t\tfile.write('convert_csv_to_geojson: %s \\n'%(to_cpp(convert_csv_to_geojson)))\n\t\tfile.write('print_channels_to_csv: %s \\n'%(to_cpp(print_channels_to_csv)))\n\t\tfile.write('print_junction_index_raster: %s \\n'%(to_cpp(print_junction_index_raster)))\n\t\tfile.write('print_junctions_to_csv: %s \\n'%(to_cpp(print_junctions_to_csv)))\n\t\tfile.write('print_fill_raster: %s \\n'%(to_cpp(print_fill_raster)))\n\t\tfile.write('write_hillshade: %s \\n'%(to_cpp(write_hillshade)))\n\t\tfile.write('print_basic_M_chi_map_to_csv: %s \\n'%(to_cpp(print_basic_M_chi_map_to_csv)))\n\t\tfile.write('ksn_knickpoint_analysis: %s \\n'%(to_cpp(ksn_knickpoint_analysis)))\n\t\tfile.write('A_0: %s \\n'%(to_cpp(A_0)))\n\t\tfile.write('m_over_n: %s \\n'%(to_cpp(m_over_n)))\n\t\tfile.write('threshold_pixels_for_chi: %s \\n'%(to_cpp(threshold_pixels_for_chi)))\n\t\tfile.write('basic_Mchi_regression_nodes: %s \\n'%(to_cpp(basic_Mchi_regression_nodes)))\n\t\tfile.write('burn_raster_to_csv: %s \\n'%(to_cpp(burn_raster_to_csv)))\n\t\tfile.write('burn_raster_prefix: %s \\n'%(to_cpp(burn_raster_prefix)))\n\t\tfile.write('burn_data_csv_column_header: %s \\n'%(to_cpp(burn_data_csv_column_header)))\n\t\tfile.write('secondary_burn_raster_to_csv: %s \\n'%(to_cpp(secondary_burn_raster_to_csv)))\n\t\tfile.write('secondary_burn_raster_prefix: %s \\n'%(to_cpp(secondary_burn_raster_prefix)))\n\t\tfile.write('secondary_burn_data_csv_column_header: %s \\n'%(to_cpp(secondary_burn_data_csv_column_header)))\n\t\tfile.write('n_movern: %s \\n'%(to_cpp(n_movern)))\n\t\tfile.write('print_stream_order_raster: %s \\n'%(to_cpp(print_stream_order_raster)))\n\n\n\n\t\tfile.write(\" \\n\")\n\t\tfile.close()\n\t\t# done with writing the parameter file\n\n\t\t#analysis command\n\n\t\tchi_mapping_tool_command = \"%sdriver_functions_MuddChi2014/chi_mapping_tool.exe %s %s\" %(self.LSDTT_path, self.wpath, self.wprefix+\"_Chiculations.param\")\n\t\tsub.call(chi_mapping_tool_command, shell = True)","title":""},{"docid":"a58b5e53032180a74d606858454c36d7","score":"0.52266","text":"def getH(at1, at2, verbose=False):\r\n \r\n if verbose:\r\n print 'Computing coordinates for H atom'\r\n if at1.species == 'Si':\r\n dHr = 1.48\r\n elif at1.species == 'O':\r\n dHr = 1.09\r\n else :\r\n dHr = 1.3\r\n\r\n dx = at1.x - at2.x\r\n dy = at1.y - at2.y\r\n dz = at1.z - at2.z\r\n \r\n dr = np.sqrt(dx*dx + dy*dy + dz*dz)\r\n \r\n dHx = dHr*dx/dr \r\n dHy = dHr*dy/dr \r\n dHz = dHr*dz/dr \r\n\r\n H = Atom('H', at1.x + dHx, at1.y + dHy, at1.z + dHz)\r\n\r\n if verbose:\r\n print '--> DISTANCE from main atom:', distance(at1, H), ' Angstroem'\r\n print '--> Colinearity area of points (the closer to 0 the better) :',\r\n # Check (18) @ http://mathworld.wolfram.com/TriangleArea.html \r\n m1 = np.array([[at1.y, at1.z,1],[at2.y,at2.z,1],[H.y,H.z,1]])\r\n m2 = np.array([[at1.z, at1.x,1],[at2.z,at2.x,1],[H.z,H.x,1]])\r\n m3 = np.array([[at1.x, at1.y,1],[at2.x,at2.y,1],[H.x,H.y,1]])\r\n delta = 0.5*np.sqrt(np.linalg.det(m1)**2 + np.linalg.det(m2)**2 + \r\n np.linalg.det(m3)**2) \r\n print delta, ' A^3'\r\n print \r\n return H","title":""},{"docid":"3cd35f716f02221dbcda47525757f238","score":"0.52180487","text":"def get_hepburntab():\n return \"\"\"\nぁ xa あ a ぃ xi い i ぅ xu\nう u う゛ vu う゛ぁ va う゛ぃ vi う゛ぇ ve\nう゛ぉ vo ぇ xe え e ぉ xo お o\n \n\nか ka が ga き ki きゃ kya きゅ kyu\nきょ kyo ぎ gi ぎゃ gya ぎゅ gyu ぎょ gyo\nく ku ぐ gu け ke げ ge こ ko\nご go \n\nさ sa ざ za し shi しゃ sha しゅ shu\nしょ sho じ ji じゃ ja じゅ ju じょ jo\nす su ず zu せ se ぜ ze そ so\nぞ zo \n\nた ta だ da ち chi ちゃ cha ちゅ chu\nちょ cho ぢ di ぢゃ dya ぢゅ dyu ぢょ dyo\n\nっ xtsu \nっう゛ vvu っう゛ぁ vva っう゛ぃ vvi \nっう゛ぇ vve っう゛ぉ vvo \nっか kka っが gga っき kki っきゃ kkya \nっきゅ kkyu っきょ kkyo っぎ ggi っぎゃ ggya \nっぎゅ ggyu っぎょ ggyo っく kku っぐ ggu \nっけ kke っげ gge っこ kko っご ggo っさ ssa\nっざ zza っし sshi っしゃ ssha \nっしゅ sshu っしょ ssho \nっじ jji っじゃ jja っじゅ jju っじょ jjo \nっす ssu っず zzu っせ sse っぜ zze っそ sso\nっぞ zzo った tta っだ dda っち cchi \nっちゃ ccha っちゅ cchu っちょ ccho っぢ ddi \nっぢゃ ddya っぢゅ ddyu っぢょ ddyo っつ ttsu \nっづ ddu って tte っで dde っと tto っど ddo\nっは hha っば bba っぱ ppa っひ hhi \nっひゃ hhya っひゅ hhyu っひょ hhyo っび bbi \nっびゃ bbya っびゅ bbyu っびょ bbyo っぴ ppi \nっぴゃ ppya っぴゅ ppyu っぴょ ppyo っふ ffu \nっふぁ ffa っふぃ ffi っふぇ ffe っふぉ ffo \nっぶ bbu っぷ ppu っへ hhe っべ bbe っぺ ppe\nっほ hho っぼ bbo っぽ ppo っや yya っゆ yyu\nっよ yyo っら rra っり rri っりゃ rrya \nっりゅ rryu っりょ rryo っる rru っれ rre \nっろ rro \n\nつ tsu づ du て te で de と to\nど do \n\nな na に ni にゃ nya にゅ nyu にょ nyo\nぬ nu ね ne の no \n\nは ha ば ba ぱ pa ひ hi ひゃ hya\nひゅ hyu ひょ hyo び bi びゃ bya びゅ byu\nびょ byo ぴ pi ぴゃ pya ぴゅ pyu ぴょ pyo\nふ fu ふぁ fa ふぃ fi ふぇ fe ふぉ fo\nぶ bu ぷ pu へ he べ be ぺ pe\nほ ho ぼ bo ぽ po \n\nま ma み mi みゃ mya みゅ myu みょ myo\nむ mu め me も mo\n\nゃ xya や ya ゅ xyu ゆ yu ょ xyo\nよ yo \n\nら ra り ri りゃ rya りゅ ryu りょ ryo\nる ru れ re ろ ro \n\nゎ xwa わ wa ゐ wi ゑ we\nを wo ん n \n\nん n'\nでぃ dyi\nー -\nちぇ che\nっちぇ cche\n\"\"\"","title":""},{"docid":"25d36f3ac086565cbef6d2c1870558a6","score":"0.52176076","text":"def H(n):\n\t# Euler-Mascheroni constant\n\tgamma = 0.57721566490153286060651209008240243104215933593992\n\treturn gamma + np.log(n) + 0.5/n - 1./(12*n**2) + 1./(120*n**4)","title":""},{"docid":"b98e14f20fcf52831814ed04bb61700d","score":"0.5213762","text":"def cs_h(c, R):\n if c >= 2*R:\n print('WARNING: the chord is greater than the diameter!')\n print('WARNING: returning maximum height, the radius')\n return R\n else:\n return R - math.sqrt( R**2 - (c**2/4) )","title":""},{"docid":"62da46cd02b241999b3b2ca4586754c9","score":"0.5207297","text":"def automatic_simulation():\n river = '20'\n repeats = '5'\n bears = '4'\n fish = '6'\n return river, repeats, bears, fish","title":""},{"docid":"84eda26d12dc4af3337bd1c65c536dad","score":"0.5200412","text":"def makeDp2HHH(name, DpmMassWin, DpmPT, DpmVtxChi2Ndof, DpmBpvdira, DpmBpvdls,\n DpmdaughterPT, DpmdaughterP, DpmdaughterTrkChi2,\n DpmdaughterTrkGhostProb, DpmdaughterBpvIpChi2,\n### DpmdaughterKaonPIDK, DpmdaughterPionPIDK,\n DpmdaughterKaonProbNNk, DpmdaughterPionProbNNpi,\n ) :\n\n _DpmotherCuts = \"(ADMASS('D+')<%(DpmMassWin)s) & (PT> %(DpmPT)s) & (VFASPF(VCHI2PDOF) < %(DpmVtxChi2Ndof)s) \" % locals()\n _DpmotherCuts += \"& (BPVDIRA>%(DpmBpvdira)s) & (BPVDLS > %(DpmBpvdls)s)\" % locals()\n _DpdaughtersCuts = \"(PT>%(DpmdaughterPT)s) & (P>%(DpmdaughterP)s) & (TRCHI2DOF<%(DpmdaughterTrkChi2)s) \" % locals()\n _DpdaughtersCuts += \"& (TRGHP<%(DpmdaughterTrkGhostProb)s) & (BPVIPCHI2()>%(DpmdaughterBpvIpChi2)s)\" % locals()\n### _KCutsPID = \"&( (PIDK>%(DpmdaughterKaonPIDK)s) | (PROBNNk>%(DpmdaughterKaonProbNNk)s) )\" % locals()\n### _PiCutsPID = \"&( (PIDK<%(DpmdaughterPionPIDK)s) | (PROBNNpi>%(DpmdaughterPionProbNNpi)s) )\" % locals()\n _KCutsPID = \"&(PROBNNk>%(DpmdaughterKaonProbNNk)s)\" % locals()\n _PiCutsPID = \"&(PROBNNpi>%(DpmdaughterPionProbNNpi)s)\" % locals()\n _DpKCuts = _DpdaughtersCuts + _KCutsPID\n _DpPiCuts = _DpdaughtersCuts + _PiCutsPID\n\n _DplusFilter = CombineParticles( DecayDescriptor = \"[D+ -> K- pi+ pi+]cc\",\n MotherCut = _DpmotherCuts,\n DaughtersCuts = { \"pi+\" : _DpPiCuts, \"K+\" : _DpKCuts }\n )\n\n _stdTightKaons = DataOnDemand(Location = \"Phys/StdAllLooseANNKaons/Particles\")\n _stdTightPions = DataOnDemand(Location = \"Phys/StdAllLooseANNPions/Particles\")\n\n return Selection (name,\n Algorithm = _DplusFilter,\n RequiredSelections = [_stdTightKaons, _stdTightPions])","title":""},{"docid":"77065c33805d67b465c3b6641e3056e5","score":"0.51946056","text":"def __init__(self, i, h, o):\n self.Whf = np.random.normal(size=(h + i, h))\n self.Whb = np.random.normal(size=(h + i, h))\n self.Wy = np.random.normal(size=(h + h, o))\n self.bhf = np.zeros((1, h))\n self.bhb = np.zeros((1, h))\n self.by = np.zeros((1, o))","title":""},{"docid":"4bcfc63975342c70b8ceb4b471b9ba2c","score":"0.5186168","text":"def makeLand(self):\r\n for n in range(200):\r\n _x = randint(0, 299)\r\n _y = randint(0, 299)\r\n _size = randint(10, 16) * 8\r\n _height = randint(1, 15)\r\n self.addHill((_x, _y), _size, _height)","title":""},{"docid":"68ab456403fadfaec1fc4c9d7d7f8ddb","score":"0.51828945","text":"def keepH(self,conditions='T_180 P_250'):\n print 'generate trajectory containing only the hydrogens'\n from amber.amber10 import top,crd,ptraj\n pt=self.conditions2dict(conditions)\n pt['onlyH']={'simdir':joink(pt['simdir'],'onlyH')}\n pH=pt['onlyH']\n if not isthere(pH['simdir']): os.system('mkdir -p '+pH['simdir'])\n #check number of hydrogens read\n Nhydr=top(self.dry['top']).numberAtoms(mask='@H*')\n pH['Nhydr']=Nhydr #store also here\n if 'Nhydr' in self.__dict__ and self.Nhydr!=Nhydr:\n buf='different number of hydrogens than stored!\\n%d vs %d'%\\\n (self.Nhydr,Nhydr)\n Bye(buf)\n else:\n self.Nhydr=Nhydr\n Prod=pt['Prod'] #Production object\n for keyword in ('crdfile','fit2prev','fit2centr'):\n print 'processing '+keyword\n crdf=joink(Prod.simdir,Prod.strip[keyword])\n outf=basename(crdf.replace('.crd.gz','.H.crd'))\n outf=joink(pH['simdir'],outf)\n ptr=ptraj(self.dry['top'],crdf)\n ptr.strip(entities=['!@H*',]) #keep only hydrogens\n ptr.go(outcrd=outf,outfile='./junk.log')\n print 'gzip H-only trajectory'\n os.system('gzip -f '+outf)\n pH['crd.'+keyword]=outf+'.gz'\n return True","title":""},{"docid":"20b01ad24b887963852a30231f2ce4e8","score":"0.51809806","text":"def __A_hill(self, g2, g1, u, w, i, j):\n hill = (self.a_voigt[u, w, i, j] + self.A_reus(g2, g1, u, w, i, j)) / 2\n return hill","title":""},{"docid":"58a3c968f7e99d85e4cda1fb860e0d24","score":"0.5179428","text":"def t_Hohmann(r1, r2, muPlanet = 3.986e14):\n\tt = np.pi * np.sqrt((r1 + r2)**3 / (8 * muPlanet))\n\treturn t","title":""},{"docid":"92271a74623cc1903dd6fc67fceeb210","score":"0.5178114","text":"def __init__(self):\n # Constants\n self.SEUILV = 50.\n self.FSIGMA = 1.\n self.HALO_BORNE_TRAME = 4\n self.RAPP_GLITCH = 0.05\n self.ELIM_GLITCH = True\n\n # Array of pitch values\n self.hzptr = []\n self.nval = 0\n self.delta = 0.01\n\n # Output of cible:\n self.cib = []\n # Output of reduc:\n self.cibred = []\n # Output of reduc2:\n self.cibred2 = []\n\n # cible window length (lfen1 est en pointes echantillon,\n # pas milliseconds)\n self.lfen1 = 30\n # f0 threshold\n self.hzinf = 50\n # f0 ceiling (Hirst, Di Cristo et Espesser :\n # hzsup est calcule automatiquement)\n self.hzsup = 600\n # maximum error (Maxec est 1+Delta en Hirst, Di Cristo et Espesser)\n self.maxec = 1.04\n # reduc window length (lfen2 est en pointes echantillon,\n # pas milliseconds)\n self.lfen2 = 20\n # minimal distance (seuildiff_x est en pointes echantillon,\n # pas milliseconds)\n self.seuildiff_x = 5\n # minimal frequency ratio\n self.seuilrapp_y = 0.05\n\n self.a0 = 0\n self.a1 = 0\n self.a2 = 0","title":""},{"docid":"fdb6deddabb78b78011846466106f676","score":"0.5170811","text":"def h(self, node):\r\n sick = 0\r\n count_0 = 0\r\n count_1 = 0\r\n count_2 = 0\r\n count_H_0 = 0\r\n count_H_1 = 0\r\n count_H_2 = 0\r\n will_be_sick = 0\r\n #healthy_nbrs_0 = 0\r\n #healthy_nbrs_1 = 0\r\n #healthy_nbrs_2 = 0\r\n\r\n #print(node.state,self._sick,self._healthy)\r\n\r\n for k in range(self._rows):\r\n for t in range(self._columns):\r\n if node.state[k, t][0] == 'S':\r\n sick += 1\r\n flag = 0\r\n if node.state[k, t][1] == 0:\r\n count_0 += 1\r\n flag = 1\r\n if node.state[k, t][1] == 1:\r\n count_1 += 1\r\n flag = 2\r\n if node.state[k, t][1] == 2:\r\n count_2 += 1\r\n flag = 3\r\n if k+1 < self._rows and node.state[k+1, t][0] == 'H':\r\n if flag == 1:\r\n count_H_0 += 1\r\n #healthy_nbrs_0 += count_helthy_nbrs(self._rows, self._columns, node.state, k + 1, t)\r\n elif flag == 2:\r\n count_H_1 += 1\r\n #healthy_nbrs_1 += count_helthy_nbrs(self._rows, self._columns, node.state, k + 1, t)\r\n else:\r\n count_H_2 += 1\r\n #healthy_nbrs_2 += count_helthy_nbrs(self._rows, self._columns, node.state, k + 1, t)\r\n\r\n will_be_sick += 1\r\n if k != 0 and node.state[k-1, t][0] == 'H':\r\n if flag == 1:\r\n count_H_0 += 1\r\n #healthy_nbrs_0 += count_helthy_nbrs(self._rows, self._columns, node.state, k - 1, t)\r\n elif flag == 2:\r\n count_H_1 += 1\r\n #healthy_nbrs_1 += count_helthy_nbrs(self._rows, self._columns, node.state, k - 1, t)\r\n else:\r\n count_H_2 += 1\r\n #healthy_nbrs_2 += count_helthy_nbrs(self._rows, self._columns, node.state, k - 1, t)\r\n will_be_sick += 1\r\n\r\n if t+1 < self._columns and node.state[k, t+1][0] == 'H':\r\n if flag == 1:\r\n count_H_0 += 1\r\n #healthy_nbrs_0 += count_helthy_nbrs(self._rows, self._columns, node.state, k , t+1)\r\n elif flag == 2:\r\n count_H_1 += 1\r\n #healthy_nbrs_1 += count_helthy_nbrs(self._rows, self._columns, node.state, k , t+1)\r\n else:\r\n count_H_2 += 1\r\n #healthy_nbrs_2 += count_helthy_nbrs(self._rows, self._columns, node.state, k , t+1)\r\n will_be_sick += 1\r\n\r\n if t != 0 and node.state[k, t-1][0] == 'H':\r\n if flag == 1:\r\n count_H_0 += 1\r\n #healthy_nbrs_0 += count_helthy_nbrs(self._rows, self._columns, node.state, k, t-1)\r\n elif flag == 2:\r\n count_H_1 += 1\r\n #healthy_nbrs_1 += count_helthy_nbrs(self._rows, self._columns, node.state, k, t-1)\r\n else:\r\n count_H_2 += 1\r\n #healthy_nbrs_2 += count_helthy_nbrs(self._rows, self._columns, node.state, k, t-1)\r\n will_be_sick += 1\r\n bonus=0\r\n if sick <= self.police:\r\n return 0\r\n else:\r\n return count_0*6+count_1*2+count_2 + count_H_0*6+count_H_1*2+count_H_2 + will_be_sick*6\r\n #return 0\r","title":""},{"docid":"1241882237e5cd528018992835572f61","score":"0.5170061","text":"def gen(self, n):\n raise NotImplementedError(\"homology classes not yet implemented\")","title":""},{"docid":"b380f04a3e9f8893cc9ddc1f4d09e192","score":"0.5165206","text":"def makeLc2HHH(name, LcMassWin, LcPT, LcVtxChi2Ndof, LcBpvdira, LcBpvdls,\n LcdaughterPT, LcdaughterP, LcdaughterTrkChi2,\n LcdaughterTrkGhostProb, LcdaughterBpvIpChi2,\n### LcdaughterKaonPIDK, LcdaughterPionPIDK,\n LcdaughterProtonProbNNp,\n LcdaughterKaonProbNNk, LcdaughterPionProbNNpi,\n ) :\n\n### _LcotherCuts = \"(ALL)\" # no cuts\n _LcotherCuts = \"( (ADMASS('Lambda_c+')<%(LcMassWin)s) | (ADMASS('Xi_c+')<%(LcMassWin)s) ) \" % locals()\n _LcotherCuts += \"& (PT> %(LcPT)s) & (VFASPF(VCHI2PDOF) < %(LcVtxChi2Ndof)s) \" % locals()\n _LcotherCuts += \"& (BPVDIRA>%(LcBpvdira)s) & (BPVDLS > %(LcBpvdls)s)\" % locals()\n### _LcdaughtersCuts = \"(ALL)\" # no cuts\n _LcdaughtersCuts = \"(PT>%(LcdaughterPT)s) & (P>%(LcdaughterP)s) & (TRCHI2DOF<%(LcdaughterTrkChi2)s) \" % locals()\n _LcdaughtersCuts += \"& (TRGHP<%(LcdaughterTrkGhostProb)s) & (BPVIPCHI2()>%(LcdaughterBpvIpChi2)s)\" % locals()\n\n### _KCutsPID = \"&( (PIDK>%(LcdaughterKaonPIDK)s) | (PROBNNk>%(LcdaughterKaonProbNNk)s) )\" % locals()\n### _PiCutsPID = \"&( (PIDK<%(LcdaughterPionPIDK)s) | (PROBNNpi>%(LcdaughterPionProbNNpi)s) )\" % locals()\n _PCutsPID = \"&(PROBNNp>%(LcdaughterProtonProbNNp)s)\" % locals()\n _KCutsPID = \"&(PROBNNk>%(LcdaughterKaonProbNNk)s)\" % locals()\n _PiCutsPID = \"&(PROBNNpi>%(LcdaughterPionProbNNpi)s)\" % locals()\n _LcPCuts = _LcdaughtersCuts + _PCutsPID\n _LcKCuts = _LcdaughtersCuts + _KCutsPID\n _LcPiCuts = _LcdaughtersCuts + _PiCutsPID\n\n _LcFilter = CombineParticles( DecayDescriptor = \"[Lambda_c+ -> p+ K- pi+]cc\",\n MotherCut = _LcotherCuts,\n DaughtersCuts = { \"p+\" : _LcPCuts, \"pi+\" : _LcPiCuts, \"K+\" : _LcKCuts }\n )\n# _stdTightProtons = DataOnDemand(Location = \"Phys/StdAllLooseProtons/Particles\")\n# _stdTightKaons = DataOnDemand(Location = \"Phys/StdAllLooseKaons/Particles\")\n# _stdTightPions = DataOnDemand(Location = \"Phys/StdAllLoosePions/Particles\")\n _stdTightProtons = DataOnDemand(Location = \"Phys/StdAllLooseANNProtons/Particles\")\n _stdTightKaons = DataOnDemand(Location = \"Phys/StdAllLooseANNKaons/Particles\")\n _stdTightPions = DataOnDemand(Location = \"Phys/StdAllLooseANNPions/Particles\")\n\n return Selection (name,\n Algorithm = _LcFilter,\n RequiredSelections = [_stdTightProtons, _stdTightKaons, _stdTightPions])","title":""},{"docid":"4cde6c9d0c3ed4a269686e71dc455922","score":"0.5165196","text":"def handhog(BHs):\n results = {}\n counter = 0\n for bh in BHs:\n for pl in bh.players:\n if not results.get(pl):\n results[pl] = (0,0)\n if bh.players[REVPLAYERS[bh.declarer]] == pl:\n results[pl] = (results[pl][0] + 1, results[pl][1] + 1)\n else:\n results[pl] = (results[pl][0], results[pl][1] + 1)\n\n\n counter = counter + 1\n if counter % 1000 == 0:\n print(counter)\n return results","title":""},{"docid":"51c37f667977ab9361f3b257e7231d80","score":"0.5163456","text":"def execute(self):\n\t\tself.h = self.g*self.g","title":""},{"docid":"de6eeeb6535e323e9d1b916badfb28e1","score":"0.5155058","text":"def wer(self, r, h):\n #\n #Store the diff text\n\n import sys\n import StringIO\n stdout = sys.stdout # keep a handle on the real standard output\n sys.stdout = StringIO.StringIO() # Choose a file-like object to write to\n\n #build the matrix\n r = r.split()\n h = h.split()\n d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint8).reshape((len(r)+1, len(h)+1))\n for i in range(len(r)+1):\n for j in range(len(h)+1):\n if i == 0: d[0][j] = j\n elif j == 0: d[i][0] = i\n for i in range(1,len(r)+1):\n for j in range(1, len(h)+1):\n if r[i-1] == h[j-1]:\n d[i][j] = d[i-1][j-1]\n else:\n substitute = d[i-1][j-1] + 1\n insert = d[i][j-1] + 1\n delete = d[i-1][j] + 1\n d[i][j] = min(substitute, insert, delete)\n result = float(d[len(r)][len(h)]) / len(r) * 100\n result = str(\"%.2f\" % result) + \"%\"\n\n #find out the manipulation steps\n x = len(r)\n y = len(h)\n list = []\n while True:\n if x == 0 and y == 0:\n break\n else:\n if d[x][y] == d[x-1][y-1] and r[x-1] == h[y-1]:\n list.append(\"e\")\n x = x-1\n y = y-1\n elif d[x][y] == d[x][y-1]+1:\n list.append(\"i\")\n x = x\n y = y-1\n elif d[x][y] == d[x-1][y-1]+1:\n list.append(\"s\")\n x = x-1\n y = y-1\n else:\n list.append(\"d\")\n x = x-1\n y = y\n list = list[::-1]\n\n #print the result in aligned way\n print \"REF:\",\n for i in range(len(list)):\n if list[i] == \"i\":\n count = 0\n for j in range(i):\n if list[j] == \"d\":\n count += 1;\n index = i - count\n print \" \"*(len(h[index])),\n elif list[i] == \"s\":\n count1 = 0\n for j in range(i):\n if list[j] == \"i\":\n count1 += 1;\n index1 = i - count1\n count2 = 0\n for j in range(i):\n if list[j] == \"d\":\n count2 += 1;\n index2 = i - count2\n if len(r[index1])<len(h[index2]):\n print r[index1]+\" \"*(len(h[index2])-len(r[index1])),\n\n else:\n print r[index1],\n else:\n count = 0\n for j in range(i):\n if list[j] == \"i\":\n count += 1;\n index = i - count\n print r[index],\n print\n print \"HYP:\",\n for i in range(len(list)):\n if list[i] == \"d\":\n count = 0\n for j in range(i):\n if list[j] == \"i\":\n count += 1;\n index = i - count\n print \" \"*(len(r[index])),\n elif list[i] == \"s\":\n count1 = 0\n for j in range(i):\n if list[j] == \"i\":\n count1 += 1;\n index1 = i - count1\n count2 = 0\n for j in range(i):\n if list[j] == \"d\":\n count2 += 1;\n index2 = i - count2\n if len(r[index1])>len(h[index2]):\n print h[index2]+\" \"*(len(r[index1])-len(h[index2])),\n else:\n print h[index2],\n else:\n count = 0\n for j in range(i):\n if list[j] == \"d\":\n count += 1;\n index = i - count\n print h[index],\n print\n print \"EVA:\",\n for i in range(len(list)):\n if list[i] == \"d\":\n count = 0\n for j in range(i):\n if list[j] == \"i\":\n count += 1;\n index = i - count\n print \"D\"+\" \"*(len(r[index])-1),\n elif list[i] == \"i\":\n count = 0\n for j in range(i):\n if list[j] == \"d\":\n count += 1;\n index = i - count\n print \"I\"+\" \"*(len(h[index])-1),\n elif list[i] == \"s\":\n count1 = 0\n for j in range(i):\n if list[j] == \"i\":\n count1 += 1;\n index1 = i - count1\n count2 = 0\n for j in range(i):\n if list[j] == \"d\":\n count2 += 1;\n index2 = i - count2\n if len(r[index1])>len(h[index2]):\n print \"S\"+\" \"*(len(r[index1])-1),\n else:\n print \"S\"+\" \"*(len(h[index2])-1),\n else:\n count = 0\n for j in range(i):\n if list[j] == \"i\":\n count += 1;\n index = i - count\n print \" \"*(len(r[index])),\n print\n print \"WER: \"+result\n diffText = sys.stdout.getvalue()\n sys.stdout = stdout\n\n resultDict = {\n 'diff': diffText,\n 'wer': result\n }\n return resultDict","title":""},{"docid":"e595c16bc979ca2f1e74108ce2cb4a8b","score":"0.51543844","text":"def hommel(p, n):\n raise NotImplementedError","title":""},{"docid":"c239a355ade7a6678c16c46b50180a3c","score":"0.5153726","text":"def world1(w, h, base=0):\n plates = generate_noise_map(w, h, w/3, 1, base=base)\n hills = generate_noise_map(w, h, w/5, 2, 0.44, base=base)\n trees = generate_noise_map(w, h, w/8, 3, 0.76, base=base)\n speckle = generate_noise_map(w, h, w/150, octaves=2, persistence=0.1, base=base)\n\n for vertical in range(h):\n for horizontal in range(w):\n \"\"\"Now the fun custom parts begin. These will be differnet per world type\"\"\"\n base_value = next(plates)\n value = base_value + next(hills) / 2.2\n speckle_value = next(speckle) / 4\n\n if base_value < 0.144 and base_value > -0.2:\n value += speckle_value\n\n tree_value = next(trees)\n if (tree_value > 0):\n value += tree_value\n\n if value > 1:\n value = 1\n\n if value < -1:\n value = -1\n\n value = (value + 1) / 2\n\n yield value","title":""},{"docid":"b0dbdf95f1e7e2a08e641042fa78c5b7","score":"0.5150863","text":"def set_barehopping(self):\n R=(0,0,0)\n h_onsite=numpy.zeros((self.Atoms.nspinorbitals,self.Atoms.nspinorbitals),dtype=numpy.complex)\n if R not in self.Hr: ## onsite part is not in the hoppings\n return h_onsite\n\n # correct only in H[R=0] is diagonal\n #h_onsite[:]=numpy.diag(self.Hr[R])[:]\n #self.Hr[R]-=numpy.diag(h_onsite)\n\n #\n k=0\n for ispin in xrange(len(self.Atoms.spin)):\n for ia in xrange(len(self.Atoms.orbitals)): #\n norb=len(self.Atoms.orbitals[ia])\n h_onsite[k:k+norb,k:k+norb]=self.Hr[R][k:k+norb,k:k+norb]\n k+=norb\n self.Hr[R]-=h_onsite\n\n return h_onsite","title":""},{"docid":"b5643a520418bf25e1c607b7f200eeac","score":"0.5149517","text":"def zihan_giwaxs_2023_1(t=0.5, name='test', dist='unspecified'):\n\n\n incident_angles = [0.5, 1.5, 4.36,6.2]\n waxs_arc = [0, 20]\n user_name = \"ZZ\"\n\n # Sample flat at ai0\n ai0 = stage.th.position\n\n for wa in waxs_arc:\n yield from bps.mv(waxs, wa)\n \n dets = [pil900KW] #if waxs.arc.position < 15 else [pil900KW, pil1M]\n det_exposure_time(t, t)\n\n for ai in incident_angles:\n yield from bps.mv(stage.th, ai0 + ai)\n\n # Metadata\n e = energy.position.energy / 1000\n sdd = pil1m_pos.z.position / 1000\n wa = waxs.arc.position + 0.001\n wa = str(np.round(float(wa), 1)).zfill(4)\n\n # Sample name\n name_fmt = \"{sample}_{dist}mm_{energy}keV_wa{wax}_sdd{sdd}m_ai{ai}\"\n sample_name = name_fmt.format(\n sample=name,\n dist=dist,\n energy=\"%.2f\" % e,\n wax=wa,\n sdd=\"%.1f\" % sdd,\n ai=ai,\n )\n sample_name = sample_name.translate(\n {ord(c): \"_\" for c in \"!@#$%^&*{}:/<>?\\|`~+ =,\"}\n )\n sample_id(user_name=user_name, sample_name=sample_name)\n print(f\"\\n\\n\\n\\t=== Sample: {sample_name} ===\")\n yield from bp.count(dets)\n\n yield from bps.mv(stage.th, ai0)\n sample_id(user_name=\"test\", sample_name=\"test\")\n det_exposure_time(0.5, 0.5)","title":""},{"docid":"89e8b292b37099f5f815720e310df8fa","score":"0.5146213","text":"def __init__(self, low=16, high=33, k=3, p=0.5, r=8, sigma=0.25, t=1024, threshold=128,\n alpha_values=np.arange(0.1, 1.1, 0.1)):\n self.low = low\n self.high = high\n self.k = k\n self.p = p\n self.r = r\n self.sigma = sigma\n self.T = t\n self.threshold = threshold\n self.alpha_values = alpha_values\n\n # Fix the seed for debugging\n # # np.random.seed(123457)\n\n # Generate a network\n self.num_switches = np.random.randint(self.low, self.high)\n self.num_hosts = np.random.poisson(self.num_switches)\n self.num_nodes = self.num_switches + self.num_hosts\n self.G = nx.connected_watts_strogatz_graph(self.num_switches, self.k, self.p)\n\n self.switches = range(self.num_switches) # The list of switch ids\n self.hosts = range(self.num_switches, self.num_nodes) # The list of host ids\n\n # Append hosts\n host_switches = np.random.choice(self.num_switches, self.num_hosts)\n for i in range(self.num_hosts):\n host = self.num_switches + i\n self.G.add_node(host)\n self.G.add_edge(host_switches[i], host)\n\n # The number of switches to be measured in each time step\n self.n = self.num_switches / self.r\n\n # Draw the graph\n # nx.draw(self.G)\n # plt.show()\n self.draw()\n\n print 'Number of Switches:', self.num_switches\n print 'Number of hosts:', self.num_hosts\n print 'Number of links:', len(self.G.edges()) + self.num_hosts\n print '=========='\n\n # Create flow paths\n self.flows = {}\n self.paths = {}\n for src in self.hosts:\n for dest in self.hosts:\n if src == dest:\n continue\n self.paths[(src, dest)] = nx.shortest_path(self.G, src, dest)\n self.flows[(src, dest)] = 0.","title":""},{"docid":"387eb47e48655bd82caa6c6c225cda45","score":"0.51462024","text":"def circ2elip_Hohmann(r1, r2, muPlanet = 3.986e14):\n\tv1 = np.sqrt(muPlanet/r1) * (np.sqrt(2*r2/(r1 + r2)) - 1)\n\treturn v1","title":""},{"docid":"6e3d63691bf4df9d633f0026fc9d79d2","score":"0.5144528","text":"def getsldonations():","title":""},{"docid":"7527e42a2e4d245732cd9cf9e83d38dd","score":"0.51440126","text":"def orifice_h_per_row(self):\n height_orifices = (np.linspace(0, self.row_n - 1, self.row_n)) * \\\n self.row_b + 0.5 * self.orifice_d\n return height_orifices","title":""},{"docid":"10b5c4f4150dcc6629ef928047af9541","score":"0.5139847","text":"def create_generation_by_harem(self, parents):\n\n # Determine mating probability of all parents\n parents[\"mating_prob\"]=parents[\"fitness\"]/sum(parents[\"fitness\"])\n \n # The top 5% become our \"alphas\" and everone else is a \"beta\"\n parents = parents.sort_values(by='fitness', ascending=False)\n alpha_count = ceil(len(parents)*0.05)\n alphas = parents[:alpha_count]\n betas = parents[alpha_count:]\n \n # generate eims df as we generate the eims themselves\n eim_dna = []\n eim_conflicts = []\n eim_fitness = []\n eim_is_solution = [] \n eim_mom = []\n eim_dad = []\n eim_mom_chromosome = []\n eim_dad_chromosome = []\n eim_mutated = [] \n \n # keep breeding until we reach the target population \n i = 0\n while(i < self.parent.parent.settings[\"eims_per_generation\"]):\n i += 1 \n \n # select one of our alphas to be dad\n dad = alphas.sample(n=2, \n replace=True,\n weights=parents[\"mating_prob\"])\n \n # select one of our betas to be mom\n mom = betas.sample(n=2, \n replace=True,\n weights=parents[\"mating_prob\"])\n \n # breed the selected pair\n params = {\"eimtype\":'breeding',\n \"mom\":mom[\"dna\"].iloc[0],\n \"dad\":dad[\"dna\"].iloc[1]} \n baby = Eim(self,params)\n eim_dna.append(baby.dna)\n eim_conflicts.append(baby.conflicts) \n eim_fitness.append(baby.fitness)\n eim_is_solution.append(baby.is_solution) \n eim_mom.append(mom[\"dna\"].astype(str))\n eim_dad.append(dad[\"dna\"].astype(str)) \n eim_mom_chromosome.append(baby.mom_chromosome)\n eim_dad_chromosome.append(baby.dad_chromosome)\n eim_mutated.append(baby.mutated)\n \n # create dataframe\n self.set_eims_df([eim_dna,eim_conflicts,eim_fitness,eim_is_solution,eim_mom,eim_dad,eim_mom_chromosome,eim_dad_chromosome,eim_mutated], extended=True) # order matters ","title":""},{"docid":"da4fe173be35c4534d50731355d56b36","score":"0.5139621","text":"def helix_assignement(hbond_turn):\r\n\r\n for i in range(0, len(hbond_turn)-1):\r\n if ((hbond_turn[i+1][0] - hbond_turn[i][0] == 1 and\r\n hbond_turn[i][3] == hbond_turn[i+1][3]) or\r\n (hbond_turn[i][0] - hbond_turn[i-1][0] == 1 and\r\n hbond_turn[i][3] == hbond_turn[i-1][3])):\r\n if (hbond_turn[i][1] - hbond_turn[i][0] == 4):\r\n hbond_turn[i][3] = \"H\"\r\n hbond_turn[i+1][3] = \"H\"\r\n elif (hbond_turn[i][1] - hbond_turn[i][0] == 3):\r\n hbond_turn[i][3] = \"G\"\r\n hbond_turn[i+1][3] = \"G\"\r\n elif (hbond_turn[i][1] - hbond_turn[i][0] == 5):\r\n hbond_turn[i][3] = \"I\"\r\n hbond_turn[i][3] = \"I\"\r\n\r\n return(hbond_turn)","title":""},{"docid":"7313eef756f42f75222b4aff2cfb41a6","score":"0.5138997","text":"def generate_cue_spikes():\n\n spike_times = np.asarray(hom_poisson(20.0, 10, t_max=0.2, seed=12345))\n spiking_neurons = np.zeros_like(spike_times)\n for neuron_id in range(1, 100):\n spike_times_tmp = np.asarray(hom_poisson(20.0, 10, t_max=0.2, seed=12345+neuron_id))\n spike_times = np.concatenate((spike_times, spike_times_tmp), axis=0)\n spiking_neurons_tmp = neuron_id * np.ones_like(spike_times_tmp)\n spiking_neurons = np.concatenate((spiking_neurons, spiking_neurons_tmp), axis=0)\n\n return spike_times, spiking_neurons","title":""},{"docid":"73daa342fa1e12f01db18a1b34fffad6","score":"0.5130493","text":"def heuristique_ACPM(self,draw=False):\n \n def getVertricesOfPath(edges):\n set_node = set()\n for e in edges:\n id1,id2 = self.getIdVerticesOfEdge(e)\n set_node.add(id1)\n set_node.add(id2)\n return set_node \n \n \n if draw:\n try:\n os.makedirs(self.dirname+\"/H_ACPM\")\n except:\n pass\n \n \n #Graphe de depart contenant tout les noeuds \n G = Graphe_Individu(self,self.wholeGraphDict)\n if draw:\n self.drawGraph(\"/H_ACPM/G0\",G.get_graphe().get_edges())\n \n \n nb_vertices = 1\n nb_tmp_vertices = 0\n i=0\n \n #Tant que le nombre de noeuds du graphe decroit, appliquer kruskal\n while nb_vertices > nb_tmp_vertices :\n g_acpm = G.get_MST()\n\n \n nb_vertices = len(G.get_dictSteinerNodes())\n set_Node = set(G.get_dictSteinerNodes().keys())\n dictSteinerNodez = self.eliminationFeuilles(g_acpm.get_edges(),set_Node)\n G = Graphe_Individu(self,dictSteinerNodez)\n nb_tmp_vertices = len(dictSteinerNodez)\n \n if draw:\n i+= 1 \n self.drawGraph(\"/H_ACPM/G%d\"%i+\"_%d\"%g_acpm.get_total_weight(),g_acpm.get_edges())\n \n \n if draw:\n self.drawGraph(\"/H_ACPM/GFinal_%d\"%G.get_MST().get_total_weight(),G.get_MST().get_edges())\n \n for i in self.steinerNodes:\n if i not in dictSteinerNodez.keys():\n dictSteinerNodez[i] = 0\n\n # return Graphe_Individu(self,dictSteinerNodez)\n return dictSteinerNodez","title":""},{"docid":"014570b37eff851750eb1d81a2c05944","score":"0.5129009","text":"def gyros(self, time):","title":""},{"docid":"fecedaa7e56cccb693137b4f2b64af24","score":"0.5125265","text":"def FRO_HALS_update_H(self):\n self.gather_W_H(gH=False)\n WTW = self.global_gram(self.W_ij)\n AtW = self.ATW_glob()\n for kk in iter(range(0, self.k)):\n temp_vec = self.H_ij[kk, :] + AtW[kk, :] - WTW[kk, :].dot(self.H_ij)\n self.H_ij[kk, :] = np.maximum(temp_vec, self.eps)","title":""},{"docid":"1f4c40349be4142540dbf7949263161f","score":"0.5124931","text":"def ehess2rhess(self, X, Hess):","title":""},{"docid":"c7cc3903139e88c8e5ba2bb43d178656","score":"0.51243556","text":"def homard_exec(theStudy):\n error = 0\n#\n while not error :\n #\n homard.SetCurrentStudy(theStudy)\n #\n # Creation of the zones\n # =====================\n # Creation of the box Zone_1_1\n Zone_1_1 = homard.CreateZoneBox('Zone_1_1', -0.01, 1.01, -0.01, 0.4, -0.01, 0.6)\n\n # Creation of the sphere Zone_1_2\n Zone_1_2 = homard.CreateZoneSphere('Zone_1_2', 0.5, 0.6, 0.7, 0.75)\n #\n # Creation of the hypotheses\n # ==========================\n # Creation of the hypothesis a10_1pc_de_mailles_a_raffiner_sur_ERRE_ELEM_SIGM\n HypoName_1 = \"a10_1pc_de_mailles_a_raffiner_sur_ERRE_ELEM_SIGM\"\n print \"-------- Creation of the hypothesis\", HypoName_1\n Hypo_1_1 = homard.CreateHypothesis(HypoName_1)\n Hypo_1_1.SetField('RESU____ERRE_ELEM_SIGM__________')\n Hypo_1_1.SetUseComp(0)\n Hypo_1_1.AddComp('ERREST')\n Hypo_1_1.SetRefinThr(3, 10.1)\n Hypo_1_1.AddFieldInterp('RESU____DEPL____________________')\n Hypo_1_1.AddFieldInterp('RESU____ERRE_ELEM_SIGM__________')\n print HypoName_1, \" : zones utilisées :\", Hypo_1_1.GetZones()\n print HypoName_1, \" : champ utilisé :\", Hypo_1_1.GetFieldName()\n print HypoName_1, \" : composantes utilisées :\", Hypo_1_1.GetComps()\n if ( len (Hypo_1_1.GetFieldName()) > 0 ) :\n print \".. caractéristiques de l'adaptation :\", Hypo_1_1.GetField()\n print HypoName_1, \" : champs interpolés :\", Hypo_1_1.GetFieldInterps()\n # Creation of the hypothesis Zones_1_et_2\n HypoName_2 = \"Zones_1_et_2\"\n print \"-------- Creation of the hypothesis\", HypoName_2\n Zones_1_et_2 = homard.CreateHypothesis(HypoName_2)\n Zones_1_et_2.AddZone('Zone_1_1', 1)\n Zones_1_et_2.AddZone('Zone_1_2', 1)\n print HypoName_2, \" : zones utilisées :\", Zones_1_et_2.GetZones()\n print HypoName_2, \" : champ utilisé :\", Zones_1_et_2.GetFieldName()\n if ( len (Zones_1_et_2.GetFieldName()) > 0 ) :\n print \".. caractéristiques de l'adaptation :\", Zones_1_et_2.GetField()\n print HypoName_2, \" : champs interpolés :\", Zones_1_et_2.GetFieldInterps()\n #\n # Creation of the cases\n # =====================\n # Creation of the case\n CaseName = \"Case_\" + Test_Name\n print \"-------- Creation of the case\", CaseName\n MeshFile = os.path.join(Rep_Test, Test_Name + '.00.med')\n Case_test_1 = homard.CreateCase(CaseName, 'MAILL', MeshFile)\n Case_test_1.SetDirName(dircase)\n Case_test_1.SetConfType(1)\n #\n # Creation of the iterations\n # ==========================\n # Creation of the iteration 1\n IterName = \"I_\" + Test_Name + \"_1\"\n print \"-------- Creation of the iteration\", IterName\n Iter_test_1_1 = Case_test_1.NextIteration(IterName)\n Iter_test_1_1.AssociateHypo(HypoName_1)\n print \". Hypothese :\", HypoName_1\n Iter_test_1_1.SetMeshName('M1')\n Iter_test_1_1.SetMeshFile(os.path.join(dircase, 'maill.01.med'))\n Iter_test_1_1.SetFieldFile(os.path.join(Rep_Test, Test_Name + '.00.med'))\n Iter_test_1_1.SetTimeStepRank(1, 1)\n Iter_test_1_1.SetFieldInterpTimeStep('RESU____DEPL____________________', 1)\n Iter_test_1_1.SetFieldInterpTimeStepRank('RESU____ERRE_ELEM_SIGM__________', 1, 1)\n print \". Instants d'interpolation :\", Iter_test_1_1.GetFieldInterpsTimeStepRank()\n error = Iter_test_1_1.Compute(1, 1)\n if error :\n error = 1\n break\n\n # Creation of the iteration 2\n IterName = \"I_\" + Test_Name + \"_2\"\n print \"-------- Creation of the iteration\", IterName\n Iter_test_1_2 = Iter_test_1_1.NextIteration(IterName)\n Iter_test_1_2.AssociateHypo(HypoName_1)\n print \". Hypothese :\", HypoName_1\n Iter_test_1_2.SetMeshName('M2')\n Iter_test_1_2.SetMeshFile(os.path.join(dircase, 'maill.02.med'))\n Iter_test_1_2.SetFieldFile(os.path.join(Rep_Test, Test_Name + '.01.med'))\n Iter_test_1_2.SetTimeStepRank(1, 1)\n Iter_test_1_2.SetFieldInterpTimeStep('RESU____DEPL____________________', 1)\n Iter_test_1_2.SetFieldInterpTimeStepRank('RESU____ERRE_ELEM_SIGM__________', 1, 1)\n print \". Instants d'interpolation :\", Iter_test_1_2.GetFieldInterpsTimeStepRank()\n error = Iter_test_1_2.Compute(1, 1)\n if error :\n error = 2\n break\n\n # Creation of the iteration 3\n IterName = \"I_\" + Test_Name + \"_3\"\n print \"-------- Creation of the iteration\", IterName\n Iter_test_1_3 = Iter_test_1_2.NextIteration(IterName)\n Iter_test_1_3.AssociateHypo(HypoName_2)\n print \". Hypothese :\", HypoName_2\n Iter_test_1_3.SetMeshName('M3')\n Iter_test_1_3.SetMeshFile(os.path.join(dircase, 'maill.03.med'))\n Iter_test_1_2.SetFieldFile(os.path.join(Rep_Test, Test_Name + '.02.med'))\n print \". Instants d'interpolation :\", Iter_test_1_3.GetFieldInterpsTimeStepRank()\n error = Iter_test_1_3.Compute(1, 1)\n if error :\n error = 3\n break\n #\n # Creation of the schema YACS\n # ===========================\n ScriptFile = os.path.join(pathHomard, \"share\", \"doc\", \"salome\", \"gui\", \"HOMARD\", \"en\", \"_downloads\", \"yacs_script_test.py\")\n ScriptFile = os.path.normpath(ScriptFile)\n DirName = dircase\n YACS_test_1 = Case_test_1.CreateYACSSchema(\"YACS_test_1\", ScriptFile, DirName, MeshFile)\n error = YACS_test_1.Write()\n if error :\n error = 4\n break\n #\n break\n #\n return error","title":""},{"docid":"47762a48a13de82cd68e2618cda5b4a8","score":"0.51222813","text":"def wichmann_hill_custom_1(seed1, seed2, seed3, n):\n m1, m2, m3 = 39343, 39367, 39373\n a1, a2, a3 = 123, 122, 121\n return __wh(seed1,seed2,seed3,m1, m2,m3,a1,a2,a3,n)","title":""},{"docid":"cf24c04efde3a1bd8c89d3ee5399b29a","score":"0.51183164","text":"def bycie_skontrowanym(self):#bycie skontrowanym 158, 160, 162, krecenie w glowie 164, 166\n self.stan = 'h'\n for i in range(1, 41):\n if not self.jestem_graczem and self.jestem_kopniety and self.energia <=30:\n self.jestem_uderzony, self.jestem_kopniety = False, False\n return self.szuraj_po_ziemii()\n if self.czy_koniec():\n return False\n self.aaanimacja = 158 + 2*int((i)/14)\n self.podejmij_centrowanie_postaci()\n time.sleep(0.005)\n for i in range(0, random.randint(20, 100)):\n if not self.jestem_graczem and self.jestem_kopniety:\n self.jestem_uderzony, self.jestem_kopniety = False, False\n if self.energia <=30:\n return self.szuraj_po_ziemii()\n else:\n return self.animuj_bycie_uderzonym()\n if not self.jestem_graczem and self.jestem_uderzony:\n self.jestem_uderzony, self.jestem_kopniety = False, False\n return self.animuj_bycie_uderzonym()\n if self.czy_koniec():\n return False\n if i%5 == 0:\n if self.aaanimacja == 164:\n self.aaanimacja = 166\n else:\n self.aaanimacja = 164\n time.sleep(0.05)\n self.reset_animacji()","title":""}],"string":"[\n {\n \"docid\": \"0cc41407da05b64dc4ef2d3a5f2701d2\",\n \"score\": \"0.67497545\",\n \"text\": \"def hincha_river():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b0bb52fa9521499ef508c6bb7079ab1\",\n \"score\": \"0.60572916\",\n \"text\": \"def genemap(self):\\n\\t\\treturn [('W1',8, 'Weighted'), ('L1',8, 'Weighted'), ('R1',8, 'Weighted')] # For OTA simulation\\n\\t\\t#return [('R1',8, 'Weighted'), ('R2',8, 'Weighted')] #For resistive divider simulation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c549b2b49dcdeff0c197648a5ace0f\",\n \"score\": \"0.5819491\",\n \"text\": \"def _assemble_Hankel(self):\\n # To understand the default choice of mc and mo,\\n # consider (let sample_interval=P=1 w.l.o.g.)\\n # sequences\\n # [0 1 1 2 2 3] => H = [[0 1][1 2]] H2 = [[1 2][2 3]], mc=mo=1, nt=6\\n # [0 1 1 2 2 3 3 4 4 5] => H=[[0 1 2][1 2 3][2 3 4]]\\n # and H2=[[1 2 3][2 3 4][3 4 5]], mc=mo=2,nt=10\\n # Thus, using all available data means mc=mo=(nt-2)/4.\\n # For additional output times, (nt-2)/4 rounds down, so\\n # we use this formula.\\n\\n if self.mo is None or self.mc is None:\\n # Set mo and mc, time_steps is always in format [0, 1, P, P+1, ...]\\n self.mo = (self.num_time_steps-2)//4\\n self.mc = self.mo\\n\\n if (self.mo + self.mc) + 2 > self.num_time_steps:\\n raise ValueError(\\n 'mo + mc + 2= %d and must be <= than the number of samples %d'\\n % (self.mo + self.mc + 2, self.num_time_steps))\\n\\n self.Hankel_array = np.zeros(\\n (self.num_Markovs * self.mo, self.num_inputs * self.mc))\\n self.Hankel_array2 = np.zeros(self.Hankel_array.shape)\\n #Markovs_flattened = \\\\\\n # self.Markovs.swapaxes(0,1).reshape((num_Markovs, -1))\\n for row in range(self.mo):\\n row_start = row * self.num_Markovs\\n row_end = row_start + self.num_Markovs\\n for col in range(self.mc):\\n col_start = col * self.num_inputs\\n col_end = col_start + self.num_inputs\\n self.Hankel_array[\\n row_start:row_end, col_start:col_end] = self.Markovs[\\n 2 * (row + col)]\\n self.Hankel_array2[\\n row_start:row_end, col_start:col_end] = self.Markovs[\\n 2 * (row + col) + 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac96a3d58150692c9c30e7fcbcd1e463\",\n \"score\": \"0.58036286\",\n \"text\": \"def barra_brava_river():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e6a9ad35cca64dee3e434b9c023c27c\",\n \"score\": \"0.5776637\",\n \"text\": \"def raie_H(n1,n2): \\n return 1/(RH*((1/(n1**2))-(1/(n2**2))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd6cdeeb61784dd07c9973a52d6faf7b\",\n \"score\": \"0.57670236\",\n \"text\": \"def produce(self):\\n r, g, b = (0, 0, 0)\\n\\n r = 0 if not self.r else int(self.r / LASER_POWER_DENOM)\\n g = 0 if not self.g or LASER_POWER_DENOM > 4 else self.g\\n b = 0 if not self.b else int(self.b / LASER_POWER_DENOM)\\n\\n\\n if self.anchored:\\n key_points = [(-32600, 0)] + self.key_points + [(32600, 0)]\\n else:\\n key_points = self.key_points\\n\\n key_points.sort(lambda a,b: cmp(a[0],b[0]))\\n\\n\\n def make_line(pt1, pt2, steps=200):\\n xdiff = pt1[0] - pt2[0]\\n ydiff = pt1[1] - pt2[1]\\n line = []\\n for i in xrange(0, steps, 1):\\n j = float(i)/steps\\n x = pt1[0] - (xdiff * j)\\n y = pt1[1] - (ydiff * j)\\n line.append((x, y, r, g, b)) \\n return line\\n\\n def make_curvy_line(pt1, pt2, steps=200):\\n xdiff = pt1[0] - pt2[0]\\n ydiff = pt1[1] - pt2[1]\\n line = []\\n for i in xrange(0, steps, 1):\\n j = float(i)/steps\\n x_add = xdiff * j\\n x = pt1[0] - (x_add)\\n y_extra = math.sin(x/self.x_wave_scale) * self.y_wave_scale\\n y_add = ydiff * j\\n y = pt1[1] - (y_add + y_extra)\\n line.append((x, y, r, g, b)) \\n return line \\n\\n\\n i = 0\\n while i + 1 < len(key_points):\\n p1 = key_points[i]\\n p2 = key_points[i+1]\\n\\n #print \\\"%s - %s\\\" % (p1, p2)\\n i += 1\\n\\n for p in make_curvy_line(p1, p2, self.step): # 50 is cool\\n yield p\\n\\n\\n \\\"\\\"\\\"\\n\\n # Generate points\\n ed = self.radius\\n\\n pts = []\\n pts.append({0: ed, 1: ed})\\n pts.append({0: -ed, 1: ed})\\n pts.append({0: -ed, 1: -ed})\\n pts.append({0: ed, 1: -ed})\\n\\n # Rotate points\\n for p in pts:\\n x = p[0]\\n y = p[1]\\n p[0] = x*math.cos(self.theta) - y*math.sin(self.theta)\\n p[1] = y*math.cos(self.theta) + x*math.sin(self.theta)\\n\\n # Translate points\\n for pt in pts:\\n pt[0] += self.x\\n pt[1] += self.y\\n\\n\\n\\n\\n # DRAW THE SHAPE\\n\\n p = None # Save in scope\\n\\n for p in make_line(pts[0], pts[1], SQUARE_EDGE_SAMPLE_PTS):\\n break\\n for i in range(int(round(SQUARE_VERTEX_SAMPLE_PTS/2.0))):\\n yield p\\n for p in make_line(pts[0], pts[1], SQUARE_EDGE_SAMPLE_PTS):\\n yield p\\n for i in range(SQUARE_VERTEX_SAMPLE_PTS):\\n yield p\\n for p in make_line(pts[1], pts[2], SQUARE_EDGE_SAMPLE_PTS):\\n yield p\\n for i in range(SQUARE_VERTEX_SAMPLE_PTS):\\n yield p\\n for p in make_line(pts[2], pts[3], SQUARE_EDGE_SAMPLE_PTS):\\n yield p\\n for i in range(SQUARE_VERTEX_SAMPLE_PTS):\\n yield p\\n for p in make_line(pts[3], pts[0], SQUARE_EDGE_SAMPLE_PTS):\\n self.lastPt = p # KEEP BOTH\\n yield p\\n for i in range(int(round(SQUARE_VERTEX_SAMPLE_PTS/2.0))):\\n self.lastPt = p # KEEP BOTH\\n yield p\\n\\n self.drawn = True\\n\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7a8b9e1882d441d175ebc4bad05fb38\",\n \"score\": \"0.5751102\",\n \"text\": \"def generate(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c632e78593d5bdbc208230d1d04c7b3\",\n \"score\": \"0.57292694\",\n \"text\": \"def makeHiersFromHier(self,pythonHier):\\n isAPairs = pythonHier.isARelationships\\n numberPairs = len(isAPairs)\\n for pairNumber in range (0,numberPairs):\\n subCatName = isAPairs[pairNumber][0]\\n superCatName = isAPairs[pairNumber][1]\\n subCatNumber = pythonHier.getUnitNumber(subCatName)\\n superCatNumber = pythonHier.getUnitNumber(superCatName)\\n #print subCatName,superCatName,subCatNumber,superCatNumber\\n #make one hierarchical relations as a half CA connection\\n self.fsa.stateHalfTurnsOnState(self.cells,subCatNumber,\\n self.cells,superCatNumber)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fd67e786ea4dd9aa711cbc17b51822a\",\n \"score\": \"0.56716526\",\n \"text\": \"def hadamards(length):\\n for i in range(length):\\n for j in range(length):\\n yield cirq.HGate()(cirq.GridQubit(i, j))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41c4047af6c25028450a5fcf0c422504\",\n \"score\": \"0.56567967\",\n \"text\": \"def huygstein(area,deltax,deltay):\\r\\n import numpy as np\\r\\n huygens=np.zeros((1, 4))\\r\\n huygens[0,0]=area*(deltax**2)\\r\\n huygens[0,1]=area*(deltax*deltay)\\r\\n huygens[0,2]=huygens[0,1]\\r\\n huygens[0,3]=area*(deltay**2)\\r\\n \\\"\\\"\\\"\\\"print(3333333333)\\r\\n print(huygens)\\\"\\\"\\\"\\r\\n return huygens\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ffb5c735a156c4e2333a4eeab0238bc\",\n \"score\": \"0.5601081\",\n \"text\": \"def hlowerofp2(p):\\r\\n\\r\\n pi = p / PSTAR2\\r\\n q1 = 572.54459862746 + 31.3220101646784*(-13.91883977887 + pi) ** 0.5\\r\\n q2 = -0.5 + 540./q1\\r\\n o = numpy.zeros(18)\\r\\n o[0]= q1 * q1\\r\\n o[1]= o[0] * o[0]\\r\\n o[2]= o[1] * o[1]\\r\\n o[3]= pi * pi\\r\\n o[4]= o[3] * o[3]\\r\\n o[5]= q2 * q2\\r\\n o[6]= o[5] * o[5]\\r\\n o[7]= o[5] * o[6]\\r\\n o[8]= o[4] * o[4]\\r\\n o[9]= o[6] * o[6]\\r\\n o[10]= o[8] * o[8]\\r\\n o[11]= o[9] * o[9]\\r\\n o[12]= o[11] * o[11]\\r\\n o[13]= o[6] * q2\\r\\n o[14]= o[5] * q2\\r\\n o[15]= o[9] * o[5]\\r\\n o[16]= o[12] * o[5]\\r\\n o[17]= o[12] * o[5] * q2\\r\\n\\r\\n h = (4.63697573303507e9 + 3.74686560065793*o[1] + 3.57966647812489e-6*o[0]*o[1] + 2.81881548488163e-13*o[2] - 7.64652332452145e7*q1 -0.00450789338787835*o[1]*q1 - 1.55131504410292e-9*o[0]*o[1]*q1 + o[0]*(2.51383707870341e6 - 4.78198198764471e6*o[9]*o[10]*o[11]*o[12]*o[3]+ 49.9651389369988*o[10]*o[11]*o[12]*o[3]*o[4]*o[6] + o[14]*o[3]*(1.03746636552761e-13 - 0.00349547959376899*o[15] - 2.55074501962569e-7*o[7])*o[8] + (-242662.235426958*o[9]*o[11] - 3.46022402653609*o[15])*o[3]*o[4]*pi + o[3]*(0.109336249381227 -2248.08924686956*o[13] - 354742.725841972*o[16] - 24.1331193696374*o[5])*pi - 3.09081828396912e-19*o[10]*o[11]*o[4]*o[6]*pi -1.24107527851371e-8*o[10]*o[12]*o[3]*o[4]*o[5]*o[6]*pi + 3.99891272904219*o[4]*o[7]*pi + 0.0641817365250892*o[9]*o[6]*o[8]*pi+ pi*(-4444.87643334512 - 75253.6156722047*o[13] - 43051.9020511789*o[5] - 22926.6247146068*q2) + o[3]*(-8.23252840892034 -3927.0508365636*o[14] - 239.325789467604*o[17] - 76407.3727417716*o[7] - 94.4508644545118*q2) + 0.360567666582363*o[4]*(-0.0161221195808321 + q2)*(0.0338039844460968 + q2) + o[10]*(-0.000584580992538624*o[9]*o[11]*o[6] + 1.33248030241755e6*o[11]*o[12]*q2) + o[8]*(-7.38502736990986e7*o[17] + 0.0000224425477627799*o[5]*o[6]*q2) + o[3]*o[4]*(-2.08438767026518e8*o[16] - 0.0000124971648677697*o[5] - 8442.30378348203*o[9]*o[5]*o[6]*q2) + o[10]*o[8]*(4.73594929247646e-22*o[9]*o[11]*q2 -13.6411358215175*o[9]*o[11]*o[12]*q2 + 5.52427169406836e-10*o[12]*o[5]*o[6]*q2) + o[10]*o[4]*(2.67174673301715e-6*o[16] +4.44545133805865e-18*o[11]*o[5]*q2 - 50.2465185106411*o[9]*o[12]*o[5]*o[6]*q2)))/o[0]\\r\\n return h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddfcf346c523615ca84f211c98997741\",\n \"score\": \"0.55728126\",\n \"text\": \"def H(self):\\n return 0.785 * (self.G ** 2) * self.P\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bae93e24df7c3a6301098936c46f7bc\",\n \"score\": \"0.55433327\",\n \"text\": \"def create_hypnogram(total_sleep_hours):\\n\\n total_sleep_min = total_sleep_hours * 60\\n\\n sleep_cycle = total_sleep_min // 90\\n\\n time_dict = {}\\n\\n i = 0\\n while i < sleep_cycle:\\n\\n if i < 1:\\n awake_stage = randint(12, 15)\\n\\n time_dict[\\\"awake\\\"] = [awake_stage]\\n\\n time_dict[\\\"REM\\\"] = [10]\\n\\n stage_1 = randint(5, 10)\\n\\n time_dict[\\\"NREM1\\\"] = stage_1\\n\\n stage_2 = randint(10, 25)\\n\\n time_dict[\\\"NREM2\\\"] = [stage_2]\\n\\n total_sleep_before_rem = awake_stage + stage_1 + stage_2 + 10\\n\\n time_difference_before_rem_stage = 90 - total_sleep_before_rem\\n\\n time_dict[\\\"NREM3\\\"] = [time_difference_before_rem_stage]\\n\\n i += 1\\n\\n elif i < (sleep_cycle - 1):\\n\\n stage_2 = randint(30, 60)\\n\\n time_dict[\\\"NREM2\\\"].append(stage_2)\\n\\n stage_3_4 = randint(10, 20)\\n\\n time_dict[\\\"NREM3\\\"].append(stage_3_4)\\n\\n total_sleep_before_rem = stage_2 + stage_3_4\\n\\n time_difference_before_rem_stage = 90 - total_sleep_before_rem\\n\\n time_dict[\\\"REM\\\"].append(time_difference_before_rem_stage)\\n\\n i += 1\\n\\n else:\\n\\n awake_stage = randint(12, 15)\\n\\n time_dict[\\\"awake\\\"].append(awake_stage)\\n\\n stage_2 = randint(20, 40)\\n\\n time_dict[\\\"NREM2\\\"].append(stage_2)\\n\\n stage_3_4 = randint(10, 20)\\n\\n time_dict[\\\"NREM3\\\"].append(stage_3_4)\\n\\n total_sleep_before_rem = awake_stage + stage_2 + stage_3_4\\n\\n time_difference_before_rem_stage = 90 - total_sleep_before_rem\\n\\n time_dict[\\\"REM\\\"].append(time_difference_before_rem_stage)\\n\\n i += 1\\n\\n return time_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34605525860d8831bb3fc3db96d6228d\",\n \"score\": \"0.55324537\",\n \"text\": \"def wer(r, h):\\n # initialisation\\n d = np.zeros((len(r)+1)*(len(h)+1), dtype=np.uint8)\\n d = d.reshape((len(r)+1, len(h)+1))\\n for i in range(len(r)+1):\\n for j in range(len(h)+1):\\n if i == 0:\\n d[0][j] = j\\n elif j == 0:\\n d[i][0] = i\\n\\n # computation\\n for i in range(1, len(r)+1):\\n for j in range(1, len(h)+1):\\n if r[i-1] == h[j-1]:\\n d[i][j] = d[i-1][j-1]\\n else:\\n substitution = d[i-1][j-1] + 1\\n insertion = d[i][j-1] + 1\\n deletion = d[i-1][j] + 1\\n d[i][j] = min(substitution, insertion, deletion)\\n return d[len(r)][len(h)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d5da7570cdec4a0bc83cc2d7d86573f\",\n \"score\": \"0.55225617\",\n \"text\": \"def _H(self):\\r\\n\\t\\tp = self.P\\r\\n\\t\\tm = self.m\\r\\n\\t\\tv = self.V\\r\\n\\t\\treturn p.dot(p)/(2*m) + v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be0eab1fe07515cf47344da757aa8b3\",\n \"score\": \"0.55130965\",\n \"text\": \"def generuj_cervene():\\n vygenerovane_pole_cervenych = []\\n\\n global NESPRAVNE_VYGENEROVANE\\n global POLE_SURADNIC\\n\\n while len(vygenerovane_pole_cervenych) < MAX_POCET_BODOV_TRIEDY:\\n spravne = pravdepodobnost()\\n\\n if spravne:\\n x = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\\n y = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\\n while [x, y, CERVENA] in vygenerovane_pole_cervenych or [x, y] in POLE_SURADNIC:\\n x = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\\n y = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\\n else:\\n NESPRAVNE_VYGENEROVANE += 1\\n x, y = generuj_nespravne_suradnice(R_x_graf, R_y_graf)\\n while [x, y, CERVENA] in vygenerovane_pole_cervenych:\\n x, y = generuj_nespravne_suradnice(R_x_graf, R_y_graf)\\n\\n vygenerovane_pole_cervenych.append([x, y, CERVENA])\\n\\n POLE_SURADNIC += [[a[0], a[1]] for a in vygenerovane_pole_cervenych]\\n\\n return vygenerovane_pole_cervenych\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8adeab41dd037a63bb966cb55c263aea\",\n \"score\": \"0.548377\",\n \"text\": \"def vykresli_hranice(matica):\\n for y in range(DOLNA_HRANICA, OHRANICENIE + 1):\\n vloz_do_matice(matica, OHRANICENIE, y, CERVENA)\\n vloz_do_matice(matica, y, OHRANICENIE, CERVENA)\\n\\n vloz_do_matice(matica, -OHRANICENIE, y, ZELENA)\\n vloz_do_matice(matica, y, -OHRANICENIE, MODRA)\\n\\n for y in range(-OHRANICENIE, HORNA_HRANICA + 1):\\n vloz_do_matice(matica, -OHRANICENIE, y, FIALOVA)\\n vloz_do_matice(matica, y, -OHRANICENIE, FIALOVA)\\n\\n vloz_do_matice(matica, OHRANICENIE, y, MODRA)\\n vloz_do_matice(matica, y, OHRANICENIE, ZELENA)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"960e48285676f71cebb92f676748fdee\",\n \"score\": \"0.54747546\",\n \"text\": \"def create_ham(self):\\n from tcc.interaction import HAM_SPINLESS_RI_CORE\\n return HAM_SPINLESS_RI_CORE(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"960e48285676f71cebb92f676748fdee\",\n \"score\": \"0.54747546\",\n \"text\": \"def create_ham(self):\\n from tcc.interaction import HAM_SPINLESS_RI_CORE\\n return HAM_SPINLESS_RI_CORE(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3603632ed0d4bbcaa6b65ac1849670c4\",\n \"score\": \"0.5473399\",\n \"text\": \"def __init__(self, harshness=1.0):\\n self.harshness = harshness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4afb41934ff50daf24ba40dfc067db0a\",\n \"score\": \"0.546286\",\n \"text\": \"def gurdy_hop(self, num_hops=15):\\n\\n upper_delta = 1\\n basic_angle = -1.55\\n angle_change = random.uniform(0.2, 0.7)\\n upperlegM_angle = basic_angle\\n lowerlegM_angle = basic_angle - upper_delta * angle_change * 2.0\\n\\n headarm_angle = upperlegM_angle\\n headforearm_angle = lowerlegM_angle\\n\\n upper_yaw_angle = 0.7\\n upperleg_yaw_angle = upper_yaw_angle\\n\\n basefoot_max = 0.05\\n basefoot_min = 0.0\\n basefoot_peg_M1_value = basefoot_min\\n basefoot_peg_M2_value = basefoot_min\\n basefoot_peg_M3_value = basefoot_min\\n\\n #self.gurdy_init_pos_sequence()\\n for repetitions in range(num_hops):\\n self.gurdy_movement_look(upperlegM_angle,\\n upperlegM_angle,\\n upperlegM_angle,\\n lowerlegM_angle,\\n lowerlegM_angle,\\n lowerlegM_angle,\\n headarm_angle,\\n headforearm_angle,\\n upperleg_yaw_angle,\\n upperleg_yaw_angle,\\n upperleg_yaw_angle,\\n upperleg_yaw_angle,\\n basefoot_peg_M1_value,\\n basefoot_peg_M2_value,\\n basefoot_peg_M3_value)\\n\\n upper_delta *= -1\\n if upper_delta < 0:\\n upperlegM_angle = basic_angle + angle_change\\n upperleg_yaw_angle = upper_yaw_angle\\n basefoot_peg_M1_value = basefoot_min\\n basefoot_peg_M2_value = basefoot_min\\n basefoot_peg_M3_value = basefoot_min\\n else:\\n upperlegM_angle = basic_angle\\n upperleg_yaw_angle = -1* upper_yaw_angle\\n basefoot_peg_M1_value = basefoot_max\\n basefoot_peg_M2_value = basefoot_max\\n basefoot_peg_M3_value = basefoot_max\\n\\n lowerlegM_angle = basic_angle - upper_delta * angle_change * 2.0\\n\\n headarm_angle = upperlegM_angle\\n headforearm_angle = lowerlegM_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8dcf31285714d06d3927c1343a0f07a\",\n \"score\": \"0.5434556\",\n \"text\": \"def algorithm(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8dcf31285714d06d3927c1343a0f07a\",\n \"score\": \"0.5434556\",\n \"text\": \"def algorithm(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2070c8d233c2d246b7ab06775dc6074\",\n \"score\": \"0.54323447\",\n \"text\": \"def __init__(self):\\n self.min_h = []\\n self.max_h = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd1d0ba1cd018f46e2ab21a5fbd55ee3\",\n \"score\": \"0.5430339\",\n \"text\": \"def test_ilustrace_vypoctu_husty_graf():\\n sekvence_hran = [(1, 2), (1, 4), (3, 4), (2, 3), (5, 6), (5, 7), (6, 7), (4, 5), (1, 3), (7, 4)]\\n husty_graf_rychly(7, sekvence_hran)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a2f97d53b2f52ff66a8735407be40c\",\n \"score\": \"0.54174936\",\n \"text\": \"def get_ph(self, repeats=5, wait=0):\\n return {'pH': 7}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6d9f0d9d75f6b64ab14594e6a057f2\",\n \"score\": \"0.5391293\",\n \"text\": \"def helium_test2():\\n H11 = pk_he.H11_pm\\n H12 = pk_he.H12_pm\\n H22 = pk_he.H22_pm\\n H = np.array([[H11, H12], [H12, H22]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8ee429d40cf3d2ce0b6fcd505a95f05\",\n \"score\": \"0.5391135\",\n \"text\": \"def randoramized(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30e3b2ef68c49ebbd53074bfb8c7055\",\n \"score\": \"0.53861606\",\n \"text\": \"def wer(r, h):\\n # initialisation\\n # d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint8)\\n d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint16)\\n # d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint32)\\n d = d.reshape((len(r)+1, len(h)+1))\\n for i in range(len(r)+1):\\n for j in range(len(h)+1):\\n if i == 0:\\n d[0][j] = j\\n elif j == 0:\\n d[i][0] = i\\n\\n # computation\\n for i in range(1, len(r)+1):\\n for j in range(1, len(h)+1):\\n if r[i-1] == h[j-1]:\\n d[i][j] = d[i-1][j-1]\\n else:\\n substitution = d[i-1][j-1] + 1\\n insertion = d[i][j-1] + 1\\n deletion = d[i-1][j] + 1\\n d[i][j] = min(substitution, insertion, deletion)\\n\\n return d[len(r)][len(h)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29e09e7915637a8088ee95daf0ab6085\",\n \"score\": \"0.53747\",\n \"text\": \"def _createrhythms():\\n #toks = [(s, Fraction(1, 2**n)) for n,s in enumerate('whqestx')]\\n tab = {}\\n # Iterate the rhythmic letters (w=whole, h=half ... x=sixty-fourth)\\n for i,t in enumerate('whqestx'):\\n sym = t\\n # the rhythmic value of each type w=1/1, h=1/2 ...\\n rat = Fraction(1, 2**i)\\n # create the dotted, but dont go smaller than 1/64.\\n # NB: when d is 0 there is no dot.\\n for d in range(0,7-i):\\n #print(sym+('.'*d), rat * (2 - Fraction(1, 2**d)))\\n #_rhythms[sym+('.'*d)] = rat * (2 - Fraction(1, 2**d))\\n tab[sym+('.'*d)] = rat * (2 - Fraction(1, 2**d))\\n for pre in [3]:\\n amt = Fraction(pre-1,pre)\\n tab[str(pre)+sym+('.'*d)] = (amt * rat * (2 - Fraction(1, 2**d)))\\n return tab\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f5ff92ebad2b0d04b6dea2a80eaf1af\",\n \"score\": \"0.53691757\",\n \"text\": \"def hall_sensor() -> int:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ad9bebec27bc42ee7493c8ec97a3dd3\",\n \"score\": \"0.5368942\",\n \"text\": \"def generuj_zelene():\\n vygenerovane_pole_zelenych = []\\n\\n global NESPRAVNE_VYGENEROVANE\\n global POLE_SURADNIC\\n\\n while len(vygenerovane_pole_zelenych) < MAX_POCET_BODOV_TRIEDY:\\n spravne = pravdepodobnost()\\n\\n if spravne:\\n x = np.random.randint(-OHRANICENIE + 1, HORNA_HRANICA + 1)\\n y = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\\n while [x, y, ZELENA] in vygenerovane_pole_zelenych or [x, y] in POLE_SURADNIC:\\n x = np.random.randint(-OHRANICENIE + 1, HORNA_HRANICA + 1)\\n y = np.random.randint(DOLNA_HRANICA, OHRANICENIE)\\n else:\\n NESPRAVNE_VYGENEROVANE += 1\\n x, y = generuj_nespravne_suradnice(G_x_graf, G_x_graf)\\n while [x, y, ZELENA] in vygenerovane_pole_zelenych:\\n x, y = generuj_nespravne_suradnice(G_x_graf, G_x_graf)\\n\\n vygenerovane_pole_zelenych.append([x, y, ZELENA])\\n\\n POLE_SURADNIC += [[a[0], a[1]] for a in vygenerovane_pole_zelenych]\\n\\n return vygenerovane_pole_zelenych\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b357a05ef693188d70a6f89f4418b27\",\n \"score\": \"0.5367623\",\n \"text\": \"def testHp(self):\\n randomizer = random.Random()\\n randomizer.seed(0)\\n rnd = randomizer.random\\n for H, P, R in itertools.permutations(range(3)):\\n tb = geo.TaitBryan(H, P, R)\\n hprs = ((rnd(), rnd(), rnd()) for _ in range(100))\\n for h, p, r in hprs:\\n h = (h - .5) * geo.TAU # Between -tau/2 and tau/2.\\n p = (p - .5) * geo.TAU / 2 # Between -tau/4 and tau/4.\\n r *= geo.TAU # Totally free, should not matter.\\n vr = tb.rot(h, p, r).dot(tb.rest)\\n hr, pr = tb.hp(vr)\\n self.assertAlmostEqual(hr, h, 7)\\n self.assertAlmostEqual(pr, p, 7)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6736189478f79b92e7683e0c3ab84a3\",\n \"score\": \"0.5362178\",\n \"text\": \"def create_ham(self):\\n from tcc.interaction import HAM_SPINLESS_RI_CORE_HUBBARD\\n return HAM_SPINLESS_RI_CORE_HUBBARD(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bfc6a8f449ab9ebe0977f5f0a1fda8d\",\n \"score\": \"0.53568757\",\n \"text\": \"def sort(self):\\n nheav = self.nheav\\n ias_heav = list(self.ias_heav)\\n g = np.zeros((nheav, nheav))\\n xhs = [] # X-H bonds\\n ih = nheav\\n coords = []; coords_H = []\\n chgs = []; chgs_H = []\\n dic = dict( zip(ias_heav, range(nheav)) )\\n# print ' *** dic = ', dic\\n for i, ia in enumerate( ias_heav ):\\n coords.append( self.coords[ia] )\\n chgs.append( self.chgs[ia] )\\n jas = self.ias[ self.bom[ia,:] > 0 ]\\n for ja in jas:\\n if self.zs[ja] == 1:\\n coords_H.append( self.coords[ja] )\\n chgs_H.append( self.chgs[ja] )\\n xhs.append([i,ih]); ih += 1\\n else:\\n g[i,dic[ja]] = g[dic[ja],i] = self.bom[ia,ja]\\n coords_U = np.concatenate( (coords, coords_H) )\\n self.coords = coords_U\\n chgs_U = np.concatenate( (chgs, chgs_H) )\\n self.chgs = chgs_U\\n g2 = np.zeros((ih,ih))\\n g2[:nheav, :nheav] = g\\n for xh in xhs:\\n i,j = xh\\n g2[i,j] = g2[j,i] = 1\\n self.bom = g2\\n nh = ih - nheav\\n zsU = np.array( list(self.zs[ias_heav]) + [1,]*nh )\\n self.zs = zsU\\n self.ias_heav = self.ias[ self.zs > 1 ]\\n blk = write_ctab(zsU, chgs_U, g2, coords_U, sdf=None)\\n m0 = obconv(blk)\\n m1 = clone(m0)\\n# print ' *** ', Chem.MolToSmiles(m1)\\n m1.DeleteHydrogens()\\n self.m0 = m0; self.m = m1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29c9bb978cb112f791fb96e5685f2728\",\n \"score\": \"0.5344258\",\n \"text\": \"def calc_ham_wo_k(self):\\n def get_dir_cos(dist_vec):\\n \\\"\\\"\\\" return directional cos of distance vector \\\"\\\"\\\"\\n if np.linalg.norm(dist_vec) == 0:\\n return 0., 0., 0.\\n else:\\n return dist_vec / np.linalg.norm(dist_vec)\\n\\n def get_ind(atom_1_i, orbit_1_i, element_1, orbit_1):\\n return self.system.all_iter.index((atom_1_i, orbit_1_i, element_1, orbit_1))\\n\\n # params = self.system.params\\n\\n # TODO spin interactions\\n\\n # off-diagonal\\n bond_mat = self.system.structure.bond_mat\\n\\n for atom_1_i, atom_1 in enumerate(self.system.structure.atoms):\\n for atom_2_i, atom_2 in enumerate(self.system.structure.atoms):\\n for image_ind in range(self.system.structure.max_image):\\n if image_ind == self.system.structure.max_image/2 and atom_1_i == atom_2_i:\\n continue\\n\\n if bond_mat[image_ind, atom_1_i, atom_2_i] == 0:\\n continue\\n param_element = self.system.get_hop_params(atom_1_i, atom_2_i, image_ind)\\n\\n # get direction cosines\\n l, m, n = self.system.structure.get_dir_cos(image_ind, atom_1_i, atom_2_i)\\n param_lmn = dict({'l': l, 'm': m, 'n': n,})\\n param_element.update(param_lmn)\\n hop_int_pair = get_hop_int(**param_element)\\n\\n for orbit_1_i, orbit_1 in enumerate(atom_1.orbitals):\\n for orbit_2_i, orbit_2 in enumerate(atom_2.orbitals):\\n hop_int_ = hop_int_pair[Hamiltonian.get_orb_ind(orbit_1)][Hamiltonian.get_orb_ind(orbit_2)] \\n ind_1 = get_ind(atom_1_i, orbit_1_i, atom_1.element, orbit_1)\\n ind_2 = get_ind(atom_2_i, orbit_2_i, atom_2.element, orbit_2)\\n self.H_wo_g[image_ind, ind_1, ind_2] = hop_int_\\n \\n # real hermitian -> symmetric\\n # self.H_wo_g += np.transpose(self.H_wo_g, [0, 2, 1])#[range(self.H_wo_g.shape[0])[::-1],:,:]\\n\\n # diagonal\\n H_ind = 0\\n for atom_i, atom in enumerate(self.system.structure.atoms):\\n len_orbitals = len(atom.orbitals)\\n # assert len_orbitals == 10, 'now # of orbitals == {}'.format(len(atom.orbitals))\\n\\n onsite_i = self.system.get_onsite_term(atom_i)\\n\\n self.H_wo_g[self.system.structure.max_image/2, \\n H_ind: H_ind+len_orbitals, H_ind: H_ind+len_orbitals] = onsite_i\\n H_ind += len_orbitals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fffc22dbf248b4a6414af5284d980caa\",\n \"score\": \"0.5339243\",\n \"text\": \"def get_ham(self, h):\\n loaded_params, JZZ, ZZ, Mz, Ms = tfim.load_diag_ME(\\\"../hamiltonian/matrix\\\")\\n Mx = tfim.load_Mx(\\\"../hamiltonian/matrix\\\")\\n print(JZZ.shape)\\n H = -JZZ - h*Mx\\n return H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d482c7c46e9c3154f2cfe3b3b5b22e2\",\n \"score\": \"0.5336185\",\n \"text\": \"def hair_additions(chromo1, chromo2):\\n # Determine if the horse is white\\n kit = [ge.discrete_allele(cr, 'kit') for cr in (chromo1, chromo2)]\\n if 'W' in kit:\\n return ''\\n\\n output = ''\\n\\n sooty = 'sty'\\n if 'Sty' in [ge.discrete_allele(cr, 'sooty') for cr in (chromo1, chromo2)]:\\n sooty = 'Sty'\\n\\n rabi = 'rb'\\n if 'Rb' in [ge.discrete_allele(cr, 'rabicano') for cr in (chromo1, chromo2)]:\\n rabi = 'Rb'\\n\\n overo = 'o'\\n if 'O' in [ge.discrete_allele(cr, 'overo') for cr in (chromo1, chromo2)]:\\n overo = 'O'\\n\\n splash = 'Spl'\\n if 'spl' in [ge.discrete_allele(cr, 'splashed') for cr in (chromo1, chromo2)]:\\n splash = 'spl'\\n\\n sabino = 'sb'\\n if 'Sb' in kit:\\n sabino = 'Sb'\\n\\n roan = 'rn'\\n if 'Rn' in kit:\\n roan = 'Rn'\\n\\n tabino = 'tb'\\n if 'Tb' in kit:\\n tabino = 'Tb'\\n\\n leopard = 'LpLp'\\n ls = [ge.discrete_allele(cr, 'leopard_complex') for cr in (chromo1, chromo2)]\\n if 'lp' in ls:\\n if 'Lp' not in ls:\\n leopard = 'lp'\\n else:\\n leopard = 'Lp'\\n\\n p1 = 'patn1'\\n if 'PATN1' in [ge.discrete_allele(cr, 'pattern1') for cr in (chromo1, chromo2)]:\\n p1 = 'PATN1'\\n\\n p2 = 'patn2'\\n if 'PATN2' in [ge.discrete_allele(cr, 'pattern2') for cr in (chromo1, chromo2)]:\\n p2 = 'PATN2'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1759e95019d630297cd2ff860317a5ab\",\n \"score\": \"0.53332025\",\n \"text\": \"def PORCENTAJE_HISTERESIS():\\n return 10.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fc9b71df1fb9ae1766499a919a0583b\",\n \"score\": \"0.532652\",\n \"text\": \"def generate_routefile(self, seed):\\n np.random.seed(seed) # make tests reproducible\\n\\n # the generation of cars is distributed according to a weibull distribution\\n timings = np.random.weibull(2, self._n_cars_generated)\\n timings = np.sort(timings)\\n\\n # reshape the distribution to fit the interval 0:max_steps\\n car_gen_steps = []\\n min_old = math.floor(timings[1])\\n max_old = math.ceil(timings[-1])\\n min_new = 0\\n max_new = self._max_steps\\n for value in timings:\\n car_gen_steps = np.append(car_gen_steps, ((max_new - min_new) / (max_old - min_old)) * (value - max_old) + max_new)\\n\\n car_gen_steps = np.rint(car_gen_steps) # round every value to int -> effective steps when a car will be generated\\n\\n # produce the file for cars generation, one car per line\\n with open(\\\"intersection/episode_routes.rou.xml\\\", \\\"w\\\") as routes:\\n print(\\\"\\\"\\\"<routes>\\n <vType accel=\\\"1.0\\\" decel=\\\"4.5\\\" id=\\\"standard_car\\\" length=\\\"5.0\\\" minGap=\\\"2.5\\\" maxSpeed=\\\"25\\\" sigma=\\\"0.5\\\" />\\n\\n <route id=\\\"W_N\\\" edges=\\\"W2TL TL2N\\\"/>\\n <route id=\\\"W_E\\\" edges=\\\"W2TL TL2E\\\"/>\\n <route id=\\\"W_S\\\" edges=\\\"W2TL TL2S\\\"/>\\n <route id=\\\"N_W\\\" edges=\\\"N2TL TL2W\\\"/>\\n <route id=\\\"N_E\\\" edges=\\\"N2TL TL2E\\\"/>\\n <route id=\\\"N_S\\\" edges=\\\"N2TL TL2S\\\"/>\\n <route id=\\\"E_W\\\" edges=\\\"E2TL TL2W\\\"/>\\n <route id=\\\"E_N\\\" edges=\\\"E2TL TL2N\\\"/>\\n <route id=\\\"E_S\\\" edges=\\\"E2TL TL2S\\\"/>\\n <route id=\\\"S_W\\\" edges=\\\"S2TL TL2W\\\"/>\\n <route id=\\\"S_N\\\" edges=\\\"S2TL TL2N\\\"/>\\n <route id=\\\"S_E\\\" edges=\\\"S2TL TL2E\\\"/>\\\"\\\"\\\", file=routes)\\n\\n \\n for counter, step in enumerate(car_gen_steps):\\n i = np.random.randint(0,4)\\n j = np.random.randint(0,3)\\n\\n if(array[i][j]>0):\\n if(i,j==0,0):\\n print(' <vehicle id=\\\"W_N_%i\\\" type=\\\"standard_car\\\" route=\\\"W_N\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==0,1):\\n print(' <vehicle id=\\\"W_E_%i\\\" type=\\\"standard_car\\\" route=\\\"W_E\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==0,2):\\n print(' <vehicle id=\\\"W_S_%i\\\" type=\\\"standard_car\\\" route=\\\"W_S\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==1,0): \\n print(' <vehicle id=\\\"N_W_%i\\\" type=\\\"standard_car\\\" route=\\\"N_W\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==1,1):\\n print(' <vehicle id=\\\"N_E_%i\\\" type=\\\"standard_car\\\" route=\\\"N_E\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes) \\n elif(i,j==1,2):\\n print(' <vehicle id=\\\"N_S_%i\\\" type=\\\"standard_car\\\" route=\\\"N_S\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==2,0):\\n print(' <vehicle id=\\\"E_W_%i\\\" type=\\\"standard_car\\\" route=\\\"E_W\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==2,1):\\n print(' <vehicle id=\\\"E_N_%i\\\" type=\\\"standard_car\\\" route=\\\"E_N\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==2,2):\\n print(' <vehicle id=\\\"E_S_%i\\\" type=\\\"standard_car\\\" route=\\\"E_S\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==3,0):\\n print(' <vehicle id=\\\"S_W_%i\\\" type=\\\"standard_car\\\" route=\\\"S_W\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==3,1):\\n print(' <vehicle id=\\\"S_N_%i\\\" type=\\\"standard_car\\\" route=\\\"S_N\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n elif(i,j==3,2):\\n print(' <vehicle id=\\\"S_E_%i\\\" type=\\\"standard_car\\\" route=\\\"S_E\\\" depart=\\\"%s\\\" departLane=\\\"random\\\" departSpeed=\\\"10\\\" />' % (car_counter, step), file=routes)\\n else:\\n print(\\\"error\\\")\\n\\n array[i][j]-=1\\n\\n\\n print(\\\"</routes>\\\", file=routes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab931d370c0bc193ec0a8c29bac35bd\",\n \"score\": \"0.53103614\",\n \"text\": \"def makeCorridor(self):\\n clength = randrange(18) + 3\\n heading = randrange(4)\\n if heading == 0: # North\\n wd = 1\\n lg = -clength\\n elif heading == 1: # East\\n wd = clength\\n lg = 1\\n elif heading == 2: # South\\n wd = 1\\n lg = clength\\n elif heading == 3: # West\\n wd = -clength\\n lg = 1\\n return wd, lg, heading\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72f1267fe844933f4ede74025df020f1\",\n \"score\": \"0.52999496\",\n \"text\": \"def _init_h(self):\\n h=np.array([.5,1.])\\n _self=self\\n _self.h=h;_self.form_operators()\\n _,logr=self.MH_step()\\n a=2.*(np.exp(logr)>0.5)-1.\\n while a*logr>-a*np.log(2):\\n h*=pow(2.,a)\\n _self=self\\n _self.h=h;_self.form_operators()\\n _,logr=self.MH_step()\\n return h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"473779689990ef915bbe0d2b82ebf1b2\",\n \"score\": \"0.5297422\",\n \"text\": \"def init_heur():\\n \\n global count\\n global random_numbers\\n\\n random.seed(0)\\n random_numbers = [random.random() for i in range(long(1e7))]\\n count = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab770b2e1f1b4c26e439fac1af09f91f\",\n \"score\": \"0.52946675\",\n \"text\": \"def generate_moves(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f10908b18ba6e74ed1164ed9b70eabcb\",\n \"score\": \"0.529333\",\n \"text\": \"def GEOM_8x10():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48d0c28aea250a2f15b85dacaf475340\",\n \"score\": \"0.52921313\",\n \"text\": \"def roty(angle):\\n ca = cos(angle)\\n sa = sin(angle)\\n H = array(\\n [[ ca, 0, sa, 0],\\n [ 0, 1, 0, 0],\\n [-sa, 0, ca, 0],\\n [ 0, 0, 0, 1]])\\n return H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e547927e631ac9d2486676b9fd158702\",\n \"score\": \"0.52844083\",\n \"text\": \"def heat_or_not_room(self):\\n h = 0\\n t = 0\\n hv = {}\\n for k, v in self.eq3.rooms.iteritems():\\n if self.setup.preference == \\\"per\\\":\\n # and valve position is over single valve position no matter what\\n if v[4] > self.setup.svpnmw:\\n h = 3\\n # or valve is over desired position to switch heating on\\n if v[4] > self.setup.valve_switch:\\n hv.update({k: [v[0], v[0], v[0]]})\\n if len(hv) >= self.setup.valve_num:\\n h = 1\\n elif self.setup.preference == \\\"total\\\":\\n t += v[0]\\n if t >= self.setup.total_switch:\\n h = 2\\n return [h, t, hv]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd2af1edd6baeca800570eaf84cdac16\",\n \"score\": \"0.52816343\",\n \"text\": \"def calc_hog(im,numorient=9):\\n sz = cv.GetSize(im)\\n gr = cv.CreateImage(sz, 8, 1)\\n gx = cv.CreateImage(sz, 32, 1)\\n gy = cv.CreateImage(sz, 32, 1)\\n \\n #convert to grayscale\\n cv.CvtColor(im, gr, cv.CV_BGR2GRAY)\\n \\n #calc gradient using sobel\\n cv.Sobel(gr, gx, 1, 0, 3)\\n cv.Sobel(gr, gy, 0, 1, 3)\\n \\n #calc initial result\\n hog = np.zeros((sz[1], sz[0], numorient))\\n mid = numorient/2\\n for y in xrange(0, sz[1]-1):\\n for x in xrange(0, sz[0]-1):\\n angle = int(round(mid*np.arctan2(gy[y,x], gx[y,x])/np.pi))+mid\\n magnitude = np.sqrt(gx[y,x]*gx[y,x]+gy[y,x]*gy[y,x])\\n hog[y,x,angle] += magnitude\\n \\n \\n #build integral image\\n for x in xrange(1, sz[0]-1):\\n for ang in xrange(numorient):\\n hog[y,x,ang] += hog[y,x-1,ang]\\n for y in xrange(1, sz[1]-1):\\n for ang in xrange(numorient):\\n hog[y,x,ang] += hog[y-1,x,ang]\\n for y in xrange(1, sz[1]-1):\\n for x in xrange(1, sz[0]-1):\\n for ang in xrange(numorient):\\n #tambah kiri dan atas, kurangi dengan kiri-atas\\n hog[y,x,ang] += hog[y-1,x,ang] + hog[y,x-1,ang] - hog[y-1,x-1,ang]\\n return hog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c5e9ba48fad1b9c14b39d8751eb5a2\",\n \"score\": \"0.52748704\",\n \"text\": \"def heuristique_PCM(self,draw=False):\\n if draw:\\n try:\\n os.makedirs(self.dirname+\\\"/H_ShortestPath\\\")\\n except:\\n pass\\n \\n #Graphe de depart contenant tout les noeuds \\n # Individu = Graphe_Individu(self,self.wholeGraphDict)\\n # G = Individu.get_graphe()\\n #\\n # if draw:\\n # self.drawGraph(\\\"/H_ShortestPath/G0\\\",G.get_edges())\\n\\n G1,graphPaths = self.construireGrapheDistance()\\n if draw:\\n self.drawGraph(\\\"/H_ShortestPath/G1\\\",G1.graphe.get_edges())\\n\\n \\\"\\\"\\\"\\n Etape 2\\n \\\"\\\"\\\"\\n G2 = G1.get_MST()\\n if draw:\\n self.drawGraph(\\\"/H_ShortestPath/G2_%d\\\"%G2.get_total_weight(),G2.get_edges())\\n\\n G3 = self.reconstruireGrapheChemins(G2.get_edges(),graphPaths)\\n if draw:\\n self.drawGraph(\\\"/H_ShortestPath/G3_%d\\\"%G3.get_MST().get_total_weight(),G3.graphe.get_edges()) \\n\\n \\\"\\\"\\\"\\n Etape 4\\n \\\"\\\"\\\" \\n G4 = G3.get_MST()\\n if draw:\\n self.drawGraph(\\\"/H_ShortestPath/G4_%d\\\"%G4.get_total_weight(),G4.get_edges())\\n\\n dictSteinerNodes = self.eliminationFeuilles(G4.get_edges(),G3.get_dictSteinerNodes().keys())\\n\\n G5 = Graphe_Individu(self,dictSteinerNodes)\\n if draw:\\n self.drawGraph(\\\"/H_ShortestPath/G5_%d\\\"%G5.get_MST().get_total_weight(),G5.graphe.get_edges())\\n\\n for i in self.steinerNodes:\\n if i not in dictSteinerNodes.keys():\\n dictSteinerNodes[i] = 0\\n\\n for i in self.steinerNodes :\\n if i not in dictSteinerNodes.keys():\\n dictSteinerNodes[i] = 0\\n\\n return dictSteinerNodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d71bd04146e03892a13157f07d1852b\",\n \"score\": \"0.5266146\",\n \"text\": \"def __init__(self, D_in, H, D_out,prob):\\n super(Highway, self).__init__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29e6f394e2833a9dc1437fb275062a4e\",\n \"score\": \"0.5261538\",\n \"text\": \"def displayH(self):\\n Matrix = self.getMatrix()\\n Matrix = [normalizeList(i,width) for i in Matrix]\\n shiftY = 0\\n spaceY = 1.0*height/Y\\n param = [0,0]\\n for row in Matrix:\\n shiftX = 0\\n param[0] = 0\\n for i in row:\\n with pushMatrix():\\n #scale(1,1,555*noise(scl*param[1]+cos(TWO_PI*a),scl*param[0]+sin(TWO_PI*a)))\\n #self.module(shiftX,shiftY,i,spaceY)\\n scale(1,1,255*noise(radius*cos(TWO_PI*a)+sum(param)))\\n if(i > 0): self.module(self.modules[self.choices[param[1]][param[0]]],shiftX,shiftY,i,spaceY)\\n shiftX += i\\n param[0]+=1\\n shiftY += spaceY\\n param[1]+=1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9586c4fe14c93493f44a493ccfc7c82\",\n \"score\": \"0.525852\",\n \"text\": \"def geom_create():\\n r_chord = 1\\n taper_ratio = 1\\n t_chord = r_chord/taper_ratio\\n b = 10\\n sweep_angle = 20 \\n # Wing edge co-ordinates\\n r_le = (0.0, 0.0)\\n r_te = (r_chord, 0.0)\\n t_le = (0.5*b*tan(math.radians(sweep_angle)), 0.5*b) \\n t_te = (0.5*b*tan(math.radians(sweep_angle)) + t_chord, 0.5*b)\\n S = 0.5*b*t_le[0] + b*(t_te[0]-t_le[0]) + 0.5*b*(r_chord-t_te[0])\\n AR = b**2/S\\n return r_le, r_te, t_le, t_te, sweep_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05e97c434e1dc810603c2df113b899bf\",\n \"score\": \"0.52567554\",\n \"text\": \"def main():\\n print \\\"Routes through a 20x20 grid = \\\",binom(40,20)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21b6fa5481e5278e31ae1b7d5f207aa1\",\n \"score\": \"0.5256023\",\n \"text\": \"def creation_agent(nb_Ah, nb_Ac, nb_V, nb_M):\\r\\n assert isinstance(nb_Ah, int),'nb_Ah doit être un entier'\\r\\n assert isinstance(nb_Ac, int),'nb_Ac doit être un entier'\\r\\n assert isinstance(nb_V, int),'nb_V doit être un entier'\\r\\n assert isinstance(nb_M, int),'nb_M doit être un entier'\\r\\n\\r\\n total_agent = nb_Ah +nb_Ac +nb_V +nb_M # total agent\\r\\n \\r\\n if total_agent>=600:\\r\\n print(\\\"Il y a trop d'agent dans l'environnement !\\\")\\r\\n\\r\\n else:\\r\\n \\r\\n liste_pos = []\\r\\n\\r\\n while len(liste_pos)!= total_agent: \\r\\n x= randint(0,24)\\r\\n y= randint(0,24)\\r\\n pos=[x,y]\\r\\n if pos in liste_pos: #verifier qu'il n'y ait pas de doublon\\r\\n pass\\r\\n #print('DOUBLE')\\r\\n else:\\r\\n liste_pos.append(pos)\\r\\n for i in range(len(liste_pos)): #positionner agent sur la grille + creation objet agent\\r\\n if i<nb_Ah-1:\\r\\n world[x][y] = 'Ah'\\r\\n #animal_herbivore = Animal(2,2,2,5,0,'herbivore',pos, 'present',fonctioncreerfield('animal'pos) )\\r\\n if i<nb_Ah+nb_Ac-1 and i>=nb_Ah:\\r\\n world[x][y] = 'Ac'\\r\\n #animal_carnivore = Animal(2,2,2,5,0,'carnivore')\\r\\n if i<nb_Ah+nb_Ac+nb_V-1 and i>=nb_Ah+nb_Ac:\\r\\n world[x][y] = 'V'\\r\\n #vegetal = Vegetal(2,2,2,5,0)\\r\\n if i<total_agent-1 and i>=nb_Ah+nb_Ac+nb_V:\\r\\n world[x][y] = 'M'\\r\\n #mineral = Mineral()\\r\\n \\r\\n \\r\\n \\r\\n print('nb_total agent =',total_agent)\\r\\n print('longeur liste pos =',len(liste_pos))\\r\\n print('liste pos = ',liste_pos)\\r\\n print('espace')\\r\\n print(world)\\r\\n\\r\\n #debugueur avec agent\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"634a93aa07a8677198dd8201aee4dbdf\",\n \"score\": \"0.5255234\",\n \"text\": \"def movern_calculation(self, print_litho_info, litho_raster, print_junctions_to_csv, n_movern = 18, start_movern = 0.1, delta_movern = 0.05, print_basin_raster = True, print_segmented_M_chi_map_to_csv =False, print_chi_data_maps = True, print_simple_chi_map_with_basins_to_csv =True, minimum_basin_size_pixels = 10000, maximum_basin_size_pixels = 90000000, threshold_contributing_pixels = 5000, only_take_largest_basin = True, write_hillshade = True, plot = True, minimum_elevation = 0, maximum_elevation= 30000,use_precipitation_raster_for_chi = False,precipitation_fname = \\\"NULL\\\",burn_raster_to_csv=False, burn_raster_prefix=\\\"NULL\\\",secondary_burn_raster_to_csv=False,secondary_burn_raster_prefix=\\\"Null\\\"):\\n\\t\\tself.chi_mapping_tool_full(burn_raster_to_csv=burn_raster_to_csv,burn_raster_prefix=burn_raster_prefix, secondary_burn_raster_to_csv=secondary_burn_raster_to_csv,secondary_burn_raster_prefix=secondary_burn_raster_prefix, print_litho_info=print_litho_info, litho_raster=litho_raster, print_junctions_to_csv=print_junctions_to_csv, n_movern = n_movern,start_movern = start_movern,delta_movern = delta_movern,estimate_best_fit_movern= True,print_basin_raster = print_basin_raster,minimum_basin_size_pixels = minimum_basin_size_pixels, maximum_basin_size_pixels = maximum_basin_size_pixels,threshold_contributing_pixels = threshold_contributing_pixels,only_take_largest_basin=only_take_largest_basin,write_hillshade = write_hillshade, minimum_elevation = minimum_elevation, maximum_elevation = maximum_elevation, use_precipitation_raster_for_chi = use_precipitation_raster_for_chi,precipitation_fname = precipitation_fname)\\n\\t\\tif (plot):\\n\\t\\t\\tplotting_command = \\\"python %sPlotMOverNAnalysis.py -dir %s -fname %s -ALL True\\\" %(self.LSDMT_path,self.wpath,self.wprefix)\\n\\t\\t\\tsub.call(plotting_command, shell = True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"149cebe898813735a2762e96850e58f8\",\n \"score\": \"0.5254368\",\n \"text\": \"def HEAT(NRX, NCX, LX, X, NRY, NCY, LY, Y, LG, G):\\n G = ZERO(NRX * NRY * LG, G)\\n # print(NRX, NRY, LG)\\n # G = numpy.zeros((NRX * NRY * LG, ))\\n MIN = MIN0(LG, LX)\\n for M in range(NRX): \\n for N in range(NRY):\\n for L in range(NCX): \\n for J in range(MIN): \\n LDOT = MIN0(LY, LX - J)\\n for I in range(LDOT): \\n# print(\\\"M, N, L, J, I : \\\", M, N, L, J, I)\\n K = I + J \\n MNJ = M + N * NRX + J * NRX * NRY\\n MLK = M + L * NRX + K * NRX * NCX\\n NLI = N + L * NRY + I * NRY * NCX\\n G[MNJ] += X[MLK] * Y[NLI]\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f57ef473123fb25c97130935814d70a\",\n \"score\": \"0.5254356\",\n \"text\": \"def hom(p):\\n ph=np.concatenate((p,np.ones((1,p.shape[1]))))\\n return ph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"361b4034cb75c61ca168b2ce6619d6c4\",\n \"score\": \"0.5245675\",\n \"text\": \"def hodnik ( iX=0 , iZ=0 , iY=0 , duzina = 10 ):\\n #gdje sam\\n orMj = gdjeSam ()\\n orSm = gdjeGledam ()\\n \\n orMj = premjesti_origin ( orMj , iX , iZ , iY , orSm ) #mice ishodiste na centar kupole\\n \\n crtaj_kvadar ( orMj , ( 1 , -3 , -1 ) , ( duzina , 3 , 5 ) , orSm , 98 , 0 ) # stonebrick tijelo\\n crtaj_kvadar ( orMj , ( 1 , -2 , 0 ) , ( duzina , 2 , 4 ) , orSm , AIR.id , 0 ) # stonebrick rupa\\n crtaj_kvadar ( orMj , ( 1 , -2 , 0 ) , ( duzina , -2 , 0 ) , orSm , 44 , 5 ) # stonebrick slab lijevi dolje\\n crtaj_kvadar ( orMj , ( 1 , 2 , 0 ) , ( duzina , 2 , 0 ) , orSm , 44 , 5 ) # stonebrick slab desni dolje\\n crtaj_stepenice ( orMj ,( 1 , -2 , 4 ) , ( duzina , -2 , 4 ) , orSm , blok_id = 109 , rel_smjer = \\\"desno\\\" , gore_dolje = \\\"da\\\" ) \\n crtaj_stepenice ( orMj ,( 1 , 2 , 4 ) , ( duzina , 2 , 4 ) , orSm , blok_id = 109 , rel_smjer = \\\"lijevo\\\" , gore_dolje = \\\"da\\\" ) \\n \\n \\n \\n for dX in range( 3 , duzina , 5 ) : \\t\\t# prodji cijeli pravokutnik\\n crtaj_kvadar ( orMj , (dX,0,5) , (dX,0,5) , orSm , 89 )\\n \\n\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33a5ffd3a03af8b449b83e0b8fbb3827\",\n \"score\": \"0.5244088\",\n \"text\": \"def _hupperofp1(p):\\r\\n\\r\\n pi1 = 7.1 - p / PSTAR1\\r\\n o = numpy.zeros(3)\\r\\n o[0] = pi1 ** 2\\r\\n o[1] = o[0] ** 2\\r\\n o[2] = o[1] ** 2\\r\\n\\r\\n h = 639675.036*(2.42896927729349 + pi1*(-0.00141131225285294 + pi1*(0.00143759406818289 + pi1*(0.000125338925082983 + pi1*(0.0000123617764767172 + pi1*(3.17834967400818e-6 + o[0]*pi1*(1.46754947271665e-8 + o[1]*o[2]*pi1*(1.86779322717506e-17 + o[0]*(-4.18568363667416e-19 + o[0]*o[1]*(-9.19148577641497e-22 + pi1*(4.27026404402408e-22 + (-6.66749357417962e-23 + 3.49930466305574e-24*pi1)*pi1)))))))))))\\r\\n return h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c74ba78bf94b4b821094c4316e372c9\",\n \"score\": \"0.52419484\",\n \"text\": \"def exring():\\n ra, rb=2.,3.\\n rf = pspdf.pdf_uniform(ra,rb)\\n pf = pspdf.pdf_uniform(-pi,pi)\\n N = 10000\\n rs = rf.random(N); phis = pf.random(N) \\n xs = map(lambda r,phi: r*cos(phi),rs,phis)\\n ys = map(lambda r,phi: r*sin(phi),rs,phis)\\n h1 = TH2F('r','r',40,-1.2*rb,1.2*rb,40,-1.2*rb,1.2*rb)\\n map(lambda x,y: h1.Fill(x,y),xs,ys)\\n cov = pslist.cov(xs,ys); rho = pslist.rho(xs,ys)\\n print ' cov ',cov,' rho ',rho\\n return h1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f13bdf52c9ee5451771617b15d6d93\",\n \"score\": \"0.52413726\",\n \"text\": \"def swissPairings():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"febcdb7455af48072c872bf1c567571b\",\n \"score\": \"0.5236832\",\n \"text\": \"def yag_s():\\n yagtgx.put(tgx())\\n yaghx.put(hexx.get())\\n yaghy.put(hexy.get())\\n yaghz.put(hexz.get())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff81f4dde343219a4189868f02348ca8\",\n \"score\": \"0.5231859\",\n \"text\": \"def getHeteroProb(k, m, n):\\n\\n # Probability of parent X being D, H or R\\n D = k/(k+m+n)\\n H = m/(k+m+n)\\n R = n/(k+m+n)\\n\\n # Probability of parent Y being D, H or R\\n DD = (k-1)/((k+m+n)-1)\\n Dx = (k)/((k+m+n)-1)\\n HH = (m-1)/((k+m+n)-1)\\n Hx = (m)/((k+m+n)-1)\\n RR = (n-1)/((k+m+n)-1)\\n Rx = (n)/((k+m+n)-1)\\n\\n # Probabilities of dominant positive for pairings\\n pDD = 1\\n pDH = 1\\n pDR = 1\\n pHH = 0.75\\n pHR = 0.5\\n pRR = 0\\n\\n # Full calculation\\n prob = (D*DD*pDD)+(D*Dx*pDH)+(D*Dx*pDR)+(H*HH*pHH)+(H*Hx*pDH)+(H*Hx*pHR)+(R*RR*pRR)+(R*Rx*pDR)+(R*Rx*pHR)\\n print(prob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73493dd22943fc68d818b5c9fe1ec5f5\",\n \"score\": \"0.5228056\",\n \"text\": \"def makeD02HH(name, D0MassWin, D0PT, D0VtxChi2Ndof, D0Bpvdira, D0Bpvdls,\\n D0daughterPT, D0daughterP, D0daughterTrkChi2,\\n D0daughterTrkGhostProb, D0daughterBpvIpChi2,\\n### D0daughterKaonPIDK, D0daughterPionPIDK,\\n D0daughterKaonProbNNk, D0daughterPionProbNNpi, \\n ) :\\n\\n _motherCuts = \\\"(ADMASS('D0')<%(D0MassWin)s) & (PT>%(D0PT)s) & (VFASPF(VCHI2PDOF)<%(D0VtxChi2Ndof)s) \\\" % locals()\\n _motherCuts += \\\"& (BPVDIRA>%(D0Bpvdira)s) & (BPVDLS>%(D0Bpvdls)s)\\\" % locals()\\n _daughtersCuts = \\\"(PT> %(D0daughterPT)s) & (P>%(D0daughterP)s) & (TRCHI2DOF<%(D0daughterTrkChi2)s) \\\" % locals()\\n _daughtersCuts += \\\"& (TRGHP<%(D0daughterTrkGhostProb)s) & (BPVIPCHI2()>%(D0daughterBpvIpChi2)s)\\\" % locals()\\n### _KCutsPID = \\\"&( (PIDK>%(D0daughterKaonPIDK)s) | (PROBNNk>%(D0daughterKaonProbNNk)s) )\\\" % locals()\\n### _PiCutsPID = \\\"&( (PIDK<%(D0daughterPionPIDK)s) | (PROBNNpi>%(D0daughterPionProbNNpi)s) )\\\" % locals()\\n _KCutsPID = \\\"&(PROBNNk>%(D0daughterKaonProbNNk)s)\\\" % locals()\\n _PiCutsPID = \\\"&(PROBNNpi>%(D0daughterPionProbNNpi)s)\\\" % locals()\\n _KCuts = _daughtersCuts + _KCutsPID\\n _PiCuts = _daughtersCuts + _PiCutsPID\\n _D0Filter = CombineParticles( DecayDescriptor = \\\"[D0 -> K- pi+]cc\\\",\\n MotherCut = _motherCuts,\\n DaughtersCuts = { \\\"pi+\\\" : _PiCuts, \\\"K+\\\" : _KCuts }\\n )\\n\\n# _stdTightKaons = DataOnDemand(Location = \\\"Phys/StdAllLooseKaons/Particles\\\")\\n# _stdTightPions = DataOnDemand(Location = \\\"Phys/StdAllLoosePions/Particles\\\")\\n _stdTightKaons = DataOnDemand(Location = \\\"Phys/StdAllLooseANNKaons/Particles\\\")\\n _stdTightPions = DataOnDemand(Location = \\\"Phys/StdAllLooseANNPions/Particles\\\")\\n\\n return Selection (name,\\n Algorithm = _D0Filter,\\n RequiredSelections = [_stdTightKaons, _stdTightPions])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787a6d44c156002826b6e481d6475820\",\n \"score\": \"0.52275974\",\n \"text\": \"def chi_mapping_tool_full(self,print_litho_info=False,litho_raster='NULL',minimum_elevation = 0, maximum_elevation = 30000, min_slope_for_fill = 0.0001, \\n raster_is_filled = False, remove_seas = True ,only_check_parameters = False,print_raster_without_seas= False, \\n CHeads_file = 'NULL', threshold_contributing_pixels = 200,minimum_basin_size_pixels = 5000, \\n maximum_basin_size_pixels = 8000, test_drainage_boundaries = True, only_take_largest_basin = False, \\n BaselevelJunctions_file = \\\"NULL\\\", extend_channel_to_node_before_receiver_junction = True,find_complete_basins_in_window = True, \\n find_largest_complete_basins = False, print_basin_raster = False, convert_csv_to_geojson = False,\\n print_stream_order_raster = False,print_channels_to_csv = False, print_junction_index_raster= False,\\n print_junctions_to_csv =False,print_fill_raster = False,print_DrainageArea_raster = False, \\n write_hillshade = True,print_basic_M_chi_map_to_csv = False, ksn_knickpoint_analysis = False, A_0 = 1,m_over_n = 0.5,threshold_pixels_for_chi = 0, basic_Mchi_regression_nodes = 11,burn_raster_to_csv = False,burn_raster_prefix = 'NULL',\\n burn_data_csv_column_header = 'burned_data',secondary_burn_raster_to_csv = False,\\n secondary_burn_raster_prefix = 'NULL',secondary_burn_data_csv_column_header = 'secondary_burned_data', \\n n_movern = 8, start_movern = 0.1, delta_movern = 0.1, only_use_mainstem_as_reference = True,calculate_MLE_collinearity = False,\\n collinearity_MLE_sigma = 1000, print_profiles_fxn_movern_csv = False, calculate_MLE_collinearity_with_points = False, \\n calculate_MLE_collinearity_with_points_MC = False, MC_point_fractions = 5, MC_point_iterations = 1000,\\n max_MC_point_fraction = 0.5, movern_residuals_test = False, MCMC_movern_analysis = False, \\n MCMC_movern_minimum = 0.05, MCMC_movern_maximum = 1.5, MCMC_chain_links = 5000, estimate_best_fit_movern = True,\\n SA_vertical_interval = 20,log_A_bin_width = 0.1,print_slope_area_data = False, segment_slope_area_data = False, \\n slope_area_minimum_segment_length = 3, bootstrap_SA_data =False, N_SA_bootstrap_iterations = 1000,\\n SA_bootstrap_retain_node_prbability = 0.5,n_iterations = 20,minimum_segment_length = 10, maximum_segment_length = 100000, n_nodes_to_visit = 10, target_nodes = 80,skip = 2, sigma = 20,print_chi_coordinate_raster = False, print_simple_chi_map_to_csv = True, \\n print_chi_data_maps = True, print_simple_chi_map_with_basins_to_csv = True,\\n print_segmented_M_chi_map_to_csv =True, print_source_keys = True, print_sources_to_csv = False, \\n print_sources_to_raster =False, print_baselevel_keys = False, use_precipitation_raster_for_chi = False, \\n print_discharge_raster = False,print_chi_no_discharge = False,check_chi_maps = False,\\n precipitation_fname = \\\"NULL\\\", print_segments = False, print_segments_raster = False):\\n\\t\\t# writing the file\\n\\n\\t\\tparam_name = self.wpath+self.wprefix+\\\"_Chiculations.param\\\"\\n\\t\\tfile = open(param_name, 'w')\\n\\t\\tfile.write('# This is a parameter file for the chi_mapping_tool \\\\n')\\n\\t\\tfile.write('# Documentation is here: https://lsdtopotools.github.io/LSDTT_documentation/LSDTT_chi_analysis.html \\\\n')\\n\\t\\tfile.write(\\\" \\\\n\\\")\\n\\t\\tfile.write('# These are parameters for the file i/o \\\\n')\\n\\t\\tfile.write(\\\"# IMPORTANT: You MUST make the write directory: the code will not work if it doens't exist. \\\\n\\\")\\n\\t\\tfile.write('read path: %s \\\\n'%(self.rpath))\\n\\t\\tfile.write('write path: %s \\\\n'%(self.wpath))\\n\\t\\tfile.write('read fname: %s \\\\n'%(self.rprefix))\\n\\t\\tfile.write('write fname: %s \\\\n'%(self.wprefix))\\n\\t\\tfile.write(\\\" \\\\n\\\")\\n\\t\\tfile.write('minimum_elevation: %s \\\\n'%(minimum_elevation))\\n\\t\\tfile.write('maximum_elevation: %s \\\\n'%(maximum_elevation))\\n\\t\\tfile.write('min_slope_for_fill: %s \\\\n'%(min_slope_for_fill))\\n\\t\\tfile.write('raster_is_filled: %s \\\\n'%(to_cpp(raster_is_filled)))\\n\\t\\tfile.write('remove_seas: %s \\\\n'%(to_cpp(remove_seas)))\\n\\t\\tfile.write('only_check_parameters: %s \\\\n'%(to_cpp(only_check_parameters)))\\n\\t\\tfile.write('CHeads_file: %s \\\\n'%(CHeads_file))\\n\\t\\tfile.write('start_movern: %s \\\\n'%(to_cpp(start_movern)))\\n\\t\\tfile.write('delta_movern: %s \\\\n'%(to_cpp(delta_movern)))\\n\\t\\tfile.write('only_use_mainstem_as_reference: %s \\\\n'%(to_cpp(only_use_mainstem_as_reference)))\\n\\t\\tfile.write('calculate_MLE_collinearity: %s \\\\n'%(to_cpp(calculate_MLE_collinearity)))\\n\\t\\tfile.write('collinearity_MLE_sigma: %s \\\\n'%(to_cpp(collinearity_MLE_sigma)))\\n\\t\\tfile.write('print_profiles_fxn_movern_csv: %s \\\\n'%(to_cpp(print_profiles_fxn_movern_csv)))\\n\\t\\tfile.write('calculate_MLE_collinearity_with_points: %s \\\\n'%(to_cpp(calculate_MLE_collinearity_with_points)))\\n\\t\\tfile.write('calculate_MLE_collinearity_with_points_MC: %s \\\\n'%(to_cpp(calculate_MLE_collinearity_with_points_MC)))\\n\\t\\tfile.write('MC_point_fractions: %s \\\\n'%(to_cpp(MC_point_fractions)))\\n\\t\\tfile.write('print_chi_coordinate_raster: %s \\\\n'%(to_cpp(print_chi_coordinate_raster)))\\n\\t\\tfile.write('print_chi_data_maps: %s \\\\n'%(to_cpp(print_chi_data_maps)))\\n\\t\\tfile.write('print_simple_chi_map_to_csv: %s \\\\n'%(to_cpp(print_simple_chi_map_to_csv)))\\n\\t\\tfile.write('print_simple_chi_map_with_basins_to_csv: %s \\\\n'%(to_cpp(print_simple_chi_map_with_basins_to_csv)))\\n\\t\\tfile.write('print_segmented_M_chi_map_to_csv: %s \\\\n'%(to_cpp(print_segmented_M_chi_map_to_csv)))\\n\\t\\tfile.write('print_basic_M_chi_map_to_csv: %s \\\\n'%(to_cpp(print_basic_M_chi_map_to_csv)))\\n\\t\\tfile.write('print_source_keys: %s \\\\n'%(to_cpp(print_source_keys)))\\n\\t\\tfile.write('print_sources_to_csv: %s \\\\n'%(to_cpp(print_sources_to_csv)))\\n\\t\\tfile.write('print_sources_to_raster: %s \\\\n'%(to_cpp(print_sources_to_raster)))\\n\\t\\tfile.write('print_baselevel_keys: %s \\\\n'%(to_cpp(print_baselevel_keys)))\\n\\t\\tfile.write('use_precipitation_raster_for_chi: %s \\\\n'%(to_cpp(use_precipitation_raster_for_chi)))\\n\\t\\tfile.write('print_discharge_raster: %s \\\\n'%(to_cpp(print_discharge_raster)))\\n\\t\\tfile.write('print_chi_no_discharge: %s \\\\n'%(to_cpp(print_chi_no_discharge)))\\n\\t\\tfile.write('check_chi_maps: %s \\\\n'%(to_cpp(check_chi_maps)))\\n\\t\\tfile.write('precipitation_fname: %s \\\\n'%(to_cpp(precipitation_fname)))\\n\\t\\tfile.write('print_segments: %s \\\\n'%(to_cpp(print_segments)))\\n\\t\\tfile.write('print_segments_raster: %s \\\\n'%(to_cpp(print_segments_raster)))\\n\\t\\tfile.write('print_litho_info: %s \\\\n'%(to_cpp(print_litho_info)))\\n\\t\\tfile.write('litho_raster: %s \\\\n'%(to_cpp(litho_raster)))\\n\\t\\tfile.write('estimate_best_fit_movern: %s \\\\n'%(to_cpp(estimate_best_fit_movern)))\\n\\t\\tfile.write('SA_vertical_interval: %s \\\\n'%(to_cpp(SA_vertical_interval)))\\n\\t\\tfile.write('log_A_bin_width: %s \\\\n'%(to_cpp(log_A_bin_width)))\\n\\t\\tfile.write('print_slope_area_data: %s \\\\n'%(to_cpp(print_slope_area_data)))\\n\\t\\tfile.write('segment_slope_area_data: %s \\\\n'%(to_cpp(segment_slope_area_data)))\\n\\t\\tfile.write('slope_area_minimum_segment_length: %s \\\\n'%(to_cpp(slope_area_minimum_segment_length)))\\n\\t\\tfile.write('bootstrap_SA_data: %s \\\\n'%(to_cpp(bootstrap_SA_data)))\\n\\t\\tfile.write('N_SA_bootstrap_iterations: %s \\\\n'%(to_cpp(N_SA_bootstrap_iterations)))\\n\\t\\tfile.write('SA_bootstrap_retain_node_prbability: %s \\\\n'%(to_cpp(SA_bootstrap_retain_node_prbability)))\\n\\t\\tfile.write('n_iterations: %s \\\\n'%(to_cpp(n_iterations)))\\n\\t\\tfile.write('minimum_segment_length: %s \\\\n'%(to_cpp(minimum_segment_length)))\\n\\t\\tfile.write('maximum_segment_length: %s \\\\n'%(to_cpp(maximum_segment_length)))\\n\\t\\tfile.write('n_nodes_to_visit: %s \\\\n'%(to_cpp(n_nodes_to_visit)))\\n\\t\\tfile.write('target_nodes: %s \\\\n'%(to_cpp(target_nodes)))\\n\\t\\tfile.write('skip: %s \\\\n'%(to_cpp(skip)))\\n\\t\\tfile.write('MC_point_iterations: %s \\\\n'%(to_cpp(MC_point_iterations)))\\n\\t\\tfile.write('max_MC_point_fraction: %s \\\\n'%(to_cpp(max_MC_point_fraction)))\\n\\t\\tfile.write('movern_residuals_test: %s \\\\n'%(to_cpp(movern_residuals_test)))\\n\\t\\tfile.write('MCMC_movern_analysis: %s \\\\n'%(to_cpp(MCMC_movern_analysis)))\\n\\t\\tfile.write('MCMC_movern_minimum: %s \\\\n'%(to_cpp(MCMC_movern_minimum)))\\n\\t\\tfile.write('MCMC_movern_maximum: %s \\\\n'%(to_cpp(MCMC_movern_maximum)))\\n\\t\\tfile.write('MCMC_chain_links: %s \\\\n'%(to_cpp(MCMC_chain_links)))\\n\\t\\tfile.write('print_stream_order_raster: %s \\\\n'%(to_cpp(print_stream_order_raster)))\\n\\t\\tfile.write('print_raster_without_seas: %s \\\\n'%(to_cpp(print_raster_without_seas)))\\n\\t\\tfile.write('threshold_contributing_pixels: %s \\\\n'%(threshold_contributing_pixels))\\n\\t\\tfile.write('minimum_basin_size_pixels: %s \\\\n'%(minimum_basin_size_pixels))\\n\\t\\tfile.write('maximum_basin_size_pixels: %s \\\\n'%(maximum_basin_size_pixels))\\n\\t\\tfile.write('test_drainage_boundaries: %s \\\\n'%(to_cpp(test_drainage_boundaries)))\\n\\t\\tfile.write('only_take_largest_basin: %s \\\\n'%(to_cpp(only_take_largest_basin)))\\n\\t\\tfile.write('BaselevelJunctions_file: %s \\\\n'%(BaselevelJunctions_file))\\n\\t\\tfile.write('extend_channel_to_node_before_receiver_junction: %s \\\\n'%(to_cpp(extend_channel_to_node_before_receiver_junction)))\\n\\t\\tfile.write('find_complete_basins_in_window: %s \\\\n'%(to_cpp(find_complete_basins_in_window)))\\n\\t\\tfile.write('find_largest_complete_basins: %s \\\\n'%(to_cpp(find_largest_complete_basins)))\\n\\t\\tfile.write('print_basin_raster: %s \\\\n'%(to_cpp(print_basin_raster)))\\n\\t\\tfile.write('convert_csv_to_geojson: %s \\\\n'%(to_cpp(convert_csv_to_geojson)))\\n\\t\\tfile.write('print_channels_to_csv: %s \\\\n'%(to_cpp(print_channels_to_csv)))\\n\\t\\tfile.write('print_junction_index_raster: %s \\\\n'%(to_cpp(print_junction_index_raster)))\\n\\t\\tfile.write('print_junctions_to_csv: %s \\\\n'%(to_cpp(print_junctions_to_csv)))\\n\\t\\tfile.write('print_fill_raster: %s \\\\n'%(to_cpp(print_fill_raster)))\\n\\t\\tfile.write('write_hillshade: %s \\\\n'%(to_cpp(write_hillshade)))\\n\\t\\tfile.write('print_basic_M_chi_map_to_csv: %s \\\\n'%(to_cpp(print_basic_M_chi_map_to_csv)))\\n\\t\\tfile.write('ksn_knickpoint_analysis: %s \\\\n'%(to_cpp(ksn_knickpoint_analysis)))\\n\\t\\tfile.write('A_0: %s \\\\n'%(to_cpp(A_0)))\\n\\t\\tfile.write('m_over_n: %s \\\\n'%(to_cpp(m_over_n)))\\n\\t\\tfile.write('threshold_pixels_for_chi: %s \\\\n'%(to_cpp(threshold_pixels_for_chi)))\\n\\t\\tfile.write('basic_Mchi_regression_nodes: %s \\\\n'%(to_cpp(basic_Mchi_regression_nodes)))\\n\\t\\tfile.write('burn_raster_to_csv: %s \\\\n'%(to_cpp(burn_raster_to_csv)))\\n\\t\\tfile.write('burn_raster_prefix: %s \\\\n'%(to_cpp(burn_raster_prefix)))\\n\\t\\tfile.write('burn_data_csv_column_header: %s \\\\n'%(to_cpp(burn_data_csv_column_header)))\\n\\t\\tfile.write('secondary_burn_raster_to_csv: %s \\\\n'%(to_cpp(secondary_burn_raster_to_csv)))\\n\\t\\tfile.write('secondary_burn_raster_prefix: %s \\\\n'%(to_cpp(secondary_burn_raster_prefix)))\\n\\t\\tfile.write('secondary_burn_data_csv_column_header: %s \\\\n'%(to_cpp(secondary_burn_data_csv_column_header)))\\n\\t\\tfile.write('n_movern: %s \\\\n'%(to_cpp(n_movern)))\\n\\t\\tfile.write('print_stream_order_raster: %s \\\\n'%(to_cpp(print_stream_order_raster)))\\n\\n\\n\\n\\t\\tfile.write(\\\" \\\\n\\\")\\n\\t\\tfile.close()\\n\\t\\t# done with writing the parameter file\\n\\n\\t\\t#analysis command\\n\\n\\t\\tchi_mapping_tool_command = \\\"%sdriver_functions_MuddChi2014/chi_mapping_tool.exe %s %s\\\" %(self.LSDTT_path, self.wpath, self.wprefix+\\\"_Chiculations.param\\\")\\n\\t\\tsub.call(chi_mapping_tool_command, shell = True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58b5e53032180a74d606858454c36d7\",\n \"score\": \"0.52266\",\n \"text\": \"def getH(at1, at2, verbose=False):\\r\\n \\r\\n if verbose:\\r\\n print 'Computing coordinates for H atom'\\r\\n if at1.species == 'Si':\\r\\n dHr = 1.48\\r\\n elif at1.species == 'O':\\r\\n dHr = 1.09\\r\\n else :\\r\\n dHr = 1.3\\r\\n\\r\\n dx = at1.x - at2.x\\r\\n dy = at1.y - at2.y\\r\\n dz = at1.z - at2.z\\r\\n \\r\\n dr = np.sqrt(dx*dx + dy*dy + dz*dz)\\r\\n \\r\\n dHx = dHr*dx/dr \\r\\n dHy = dHr*dy/dr \\r\\n dHz = dHr*dz/dr \\r\\n\\r\\n H = Atom('H', at1.x + dHx, at1.y + dHy, at1.z + dHz)\\r\\n\\r\\n if verbose:\\r\\n print '--> DISTANCE from main atom:', distance(at1, H), ' Angstroem'\\r\\n print '--> Colinearity area of points (the closer to 0 the better) :',\\r\\n # Check (18) @ http://mathworld.wolfram.com/TriangleArea.html \\r\\n m1 = np.array([[at1.y, at1.z,1],[at2.y,at2.z,1],[H.y,H.z,1]])\\r\\n m2 = np.array([[at1.z, at1.x,1],[at2.z,at2.x,1],[H.z,H.x,1]])\\r\\n m3 = np.array([[at1.x, at1.y,1],[at2.x,at2.y,1],[H.x,H.y,1]])\\r\\n delta = 0.5*np.sqrt(np.linalg.det(m1)**2 + np.linalg.det(m2)**2 + \\r\\n np.linalg.det(m3)**2) \\r\\n print delta, ' A^3'\\r\\n print \\r\\n return H\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cd35f716f02221dbcda47525757f238\",\n \"score\": \"0.52180487\",\n \"text\": \"def get_hepburntab():\\n return \\\"\\\"\\\"\\nぁ xa あ a ぃ xi い i ぅ xu\\nう u う゛ vu う゛ぁ va う゛ぃ vi う゛ぇ ve\\nう゛ぉ vo ぇ xe え e ぉ xo お o\\n \\n\\nか ka が ga き ki きゃ kya きゅ kyu\\nきょ kyo ぎ gi ぎゃ gya ぎゅ gyu ぎょ gyo\\nく ku ぐ gu け ke げ ge こ ko\\nご go \\n\\nさ sa ざ za し shi しゃ sha しゅ shu\\nしょ sho じ ji じゃ ja じゅ ju じょ jo\\nす su ず zu せ se ぜ ze そ so\\nぞ zo \\n\\nた ta だ da ち chi ちゃ cha ちゅ chu\\nちょ cho ぢ di ぢゃ dya ぢゅ dyu ぢょ dyo\\n\\nっ xtsu \\nっう゛ vvu っう゛ぁ vva っう゛ぃ vvi \\nっう゛ぇ vve っう゛ぉ vvo \\nっか kka っが gga っき kki っきゃ kkya \\nっきゅ kkyu っきょ kkyo っぎ ggi っぎゃ ggya \\nっぎゅ ggyu っぎょ ggyo っく kku っぐ ggu \\nっけ kke っげ gge っこ kko っご ggo っさ ssa\\nっざ zza っし sshi っしゃ ssha \\nっしゅ sshu っしょ ssho \\nっじ jji っじゃ jja っじゅ jju っじょ jjo \\nっす ssu っず zzu っせ sse っぜ zze っそ sso\\nっぞ zzo った tta っだ dda っち cchi \\nっちゃ ccha っちゅ cchu っちょ ccho っぢ ddi \\nっぢゃ ddya っぢゅ ddyu っぢょ ddyo っつ ttsu \\nっづ ddu って tte っで dde っと tto っど ddo\\nっは hha っば bba っぱ ppa っひ hhi \\nっひゃ hhya っひゅ hhyu っひょ hhyo っび bbi \\nっびゃ bbya っびゅ bbyu っびょ bbyo っぴ ppi \\nっぴゃ ppya っぴゅ ppyu っぴょ ppyo っふ ffu \\nっふぁ ffa っふぃ ffi っふぇ ffe っふぉ ffo \\nっぶ bbu っぷ ppu っへ hhe っべ bbe っぺ ppe\\nっほ hho っぼ bbo っぽ ppo っや yya っゆ yyu\\nっよ yyo っら rra っり rri っりゃ rrya \\nっりゅ rryu っりょ rryo っる rru っれ rre \\nっろ rro \\n\\nつ tsu づ du て te で de と to\\nど do \\n\\nな na に ni にゃ nya にゅ nyu にょ nyo\\nぬ nu ね ne の no \\n\\nは ha ば ba ぱ pa ひ hi ひゃ hya\\nひゅ hyu ひょ hyo び bi びゃ bya びゅ byu\\nびょ byo ぴ pi ぴゃ pya ぴゅ pyu ぴょ pyo\\nふ fu ふぁ fa ふぃ fi ふぇ fe ふぉ fo\\nぶ bu ぷ pu へ he べ be ぺ pe\\nほ ho ぼ bo ぽ po \\n\\nま ma み mi みゃ mya みゅ myu みょ myo\\nむ mu め me も mo\\n\\nゃ xya や ya ゅ xyu ゆ yu ょ xyo\\nよ yo \\n\\nら ra り ri りゃ rya りゅ ryu りょ ryo\\nる ru れ re ろ ro \\n\\nゎ xwa わ wa ゐ wi ゑ we\\nを wo ん n \\n\\nん n'\\nでぃ dyi\\nー -\\nちぇ che\\nっちぇ cche\\n\\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25d36f3ac086565cbef6d2c1870558a6\",\n \"score\": \"0.52176076\",\n \"text\": \"def H(n):\\n\\t# Euler-Mascheroni constant\\n\\tgamma = 0.57721566490153286060651209008240243104215933593992\\n\\treturn gamma + np.log(n) + 0.5/n - 1./(12*n**2) + 1./(120*n**4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b98e14f20fcf52831814ed04bb61700d\",\n \"score\": \"0.5213762\",\n \"text\": \"def cs_h(c, R):\\n if c >= 2*R:\\n print('WARNING: the chord is greater than the diameter!')\\n print('WARNING: returning maximum height, the radius')\\n return R\\n else:\\n return R - math.sqrt( R**2 - (c**2/4) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62da46cd02b241999b3b2ca4586754c9\",\n \"score\": \"0.5207297\",\n \"text\": \"def automatic_simulation():\\n river = '20'\\n repeats = '5'\\n bears = '4'\\n fish = '6'\\n return river, repeats, bears, fish\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84eda26d12dc4af3337bd1c65c536dad\",\n \"score\": \"0.5200412\",\n \"text\": \"def makeDp2HHH(name, DpmMassWin, DpmPT, DpmVtxChi2Ndof, DpmBpvdira, DpmBpvdls,\\n DpmdaughterPT, DpmdaughterP, DpmdaughterTrkChi2,\\n DpmdaughterTrkGhostProb, DpmdaughterBpvIpChi2,\\n### DpmdaughterKaonPIDK, DpmdaughterPionPIDK,\\n DpmdaughterKaonProbNNk, DpmdaughterPionProbNNpi,\\n ) :\\n\\n _DpmotherCuts = \\\"(ADMASS('D+')<%(DpmMassWin)s) & (PT> %(DpmPT)s) & (VFASPF(VCHI2PDOF) < %(DpmVtxChi2Ndof)s) \\\" % locals()\\n _DpmotherCuts += \\\"& (BPVDIRA>%(DpmBpvdira)s) & (BPVDLS > %(DpmBpvdls)s)\\\" % locals()\\n _DpdaughtersCuts = \\\"(PT>%(DpmdaughterPT)s) & (P>%(DpmdaughterP)s) & (TRCHI2DOF<%(DpmdaughterTrkChi2)s) \\\" % locals()\\n _DpdaughtersCuts += \\\"& (TRGHP<%(DpmdaughterTrkGhostProb)s) & (BPVIPCHI2()>%(DpmdaughterBpvIpChi2)s)\\\" % locals()\\n### _KCutsPID = \\\"&( (PIDK>%(DpmdaughterKaonPIDK)s) | (PROBNNk>%(DpmdaughterKaonProbNNk)s) )\\\" % locals()\\n### _PiCutsPID = \\\"&( (PIDK<%(DpmdaughterPionPIDK)s) | (PROBNNpi>%(DpmdaughterPionProbNNpi)s) )\\\" % locals()\\n _KCutsPID = \\\"&(PROBNNk>%(DpmdaughterKaonProbNNk)s)\\\" % locals()\\n _PiCutsPID = \\\"&(PROBNNpi>%(DpmdaughterPionProbNNpi)s)\\\" % locals()\\n _DpKCuts = _DpdaughtersCuts + _KCutsPID\\n _DpPiCuts = _DpdaughtersCuts + _PiCutsPID\\n\\n _DplusFilter = CombineParticles( DecayDescriptor = \\\"[D+ -> K- pi+ pi+]cc\\\",\\n MotherCut = _DpmotherCuts,\\n DaughtersCuts = { \\\"pi+\\\" : _DpPiCuts, \\\"K+\\\" : _DpKCuts }\\n )\\n\\n _stdTightKaons = DataOnDemand(Location = \\\"Phys/StdAllLooseANNKaons/Particles\\\")\\n _stdTightPions = DataOnDemand(Location = \\\"Phys/StdAllLooseANNPions/Particles\\\")\\n\\n return Selection (name,\\n Algorithm = _DplusFilter,\\n RequiredSelections = [_stdTightKaons, _stdTightPions])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77065c33805d67b465c3b6641e3056e5\",\n \"score\": \"0.51946056\",\n \"text\": \"def __init__(self, i, h, o):\\n self.Whf = np.random.normal(size=(h + i, h))\\n self.Whb = np.random.normal(size=(h + i, h))\\n self.Wy = np.random.normal(size=(h + h, o))\\n self.bhf = np.zeros((1, h))\\n self.bhb = np.zeros((1, h))\\n self.by = np.zeros((1, o))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bcfc63975342c70b8ceb4b471b9ba2c\",\n \"score\": \"0.5186168\",\n \"text\": \"def makeLand(self):\\r\\n for n in range(200):\\r\\n _x = randint(0, 299)\\r\\n _y = randint(0, 299)\\r\\n _size = randint(10, 16) * 8\\r\\n _height = randint(1, 15)\\r\\n self.addHill((_x, _y), _size, _height)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68ab456403fadfaec1fc4c9d7d7f8ddb\",\n \"score\": \"0.51828945\",\n \"text\": \"def keepH(self,conditions='T_180 P_250'):\\n print 'generate trajectory containing only the hydrogens'\\n from amber.amber10 import top,crd,ptraj\\n pt=self.conditions2dict(conditions)\\n pt['onlyH']={'simdir':joink(pt['simdir'],'onlyH')}\\n pH=pt['onlyH']\\n if not isthere(pH['simdir']): os.system('mkdir -p '+pH['simdir'])\\n #check number of hydrogens read\\n Nhydr=top(self.dry['top']).numberAtoms(mask='@H*')\\n pH['Nhydr']=Nhydr #store also here\\n if 'Nhydr' in self.__dict__ and self.Nhydr!=Nhydr:\\n buf='different number of hydrogens than stored!\\\\n%d vs %d'%\\\\\\n (self.Nhydr,Nhydr)\\n Bye(buf)\\n else:\\n self.Nhydr=Nhydr\\n Prod=pt['Prod'] #Production object\\n for keyword in ('crdfile','fit2prev','fit2centr'):\\n print 'processing '+keyword\\n crdf=joink(Prod.simdir,Prod.strip[keyword])\\n outf=basename(crdf.replace('.crd.gz','.H.crd'))\\n outf=joink(pH['simdir'],outf)\\n ptr=ptraj(self.dry['top'],crdf)\\n ptr.strip(entities=['!@H*',]) #keep only hydrogens\\n ptr.go(outcrd=outf,outfile='./junk.log')\\n print 'gzip H-only trajectory'\\n os.system('gzip -f '+outf)\\n pH['crd.'+keyword]=outf+'.gz'\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b01ad24b887963852a30231f2ce4e8\",\n \"score\": \"0.51809806\",\n \"text\": \"def __A_hill(self, g2, g1, u, w, i, j):\\n hill = (self.a_voigt[u, w, i, j] + self.A_reus(g2, g1, u, w, i, j)) / 2\\n return hill\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58a3c968f7e99d85e4cda1fb860e0d24\",\n \"score\": \"0.5179428\",\n \"text\": \"def t_Hohmann(r1, r2, muPlanet = 3.986e14):\\n\\tt = np.pi * np.sqrt((r1 + r2)**3 / (8 * muPlanet))\\n\\treturn t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92271a74623cc1903dd6fc67fceeb210\",\n \"score\": \"0.5178114\",\n \"text\": \"def __init__(self):\\n # Constants\\n self.SEUILV = 50.\\n self.FSIGMA = 1.\\n self.HALO_BORNE_TRAME = 4\\n self.RAPP_GLITCH = 0.05\\n self.ELIM_GLITCH = True\\n\\n # Array of pitch values\\n self.hzptr = []\\n self.nval = 0\\n self.delta = 0.01\\n\\n # Output of cible:\\n self.cib = []\\n # Output of reduc:\\n self.cibred = []\\n # Output of reduc2:\\n self.cibred2 = []\\n\\n # cible window length (lfen1 est en pointes echantillon,\\n # pas milliseconds)\\n self.lfen1 = 30\\n # f0 threshold\\n self.hzinf = 50\\n # f0 ceiling (Hirst, Di Cristo et Espesser :\\n # hzsup est calcule automatiquement)\\n self.hzsup = 600\\n # maximum error (Maxec est 1+Delta en Hirst, Di Cristo et Espesser)\\n self.maxec = 1.04\\n # reduc window length (lfen2 est en pointes echantillon,\\n # pas milliseconds)\\n self.lfen2 = 20\\n # minimal distance (seuildiff_x est en pointes echantillon,\\n # pas milliseconds)\\n self.seuildiff_x = 5\\n # minimal frequency ratio\\n self.seuilrapp_y = 0.05\\n\\n self.a0 = 0\\n self.a1 = 0\\n self.a2 = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdb6deddabb78b78011846466106f676\",\n \"score\": \"0.5170811\",\n \"text\": \"def h(self, node):\\r\\n sick = 0\\r\\n count_0 = 0\\r\\n count_1 = 0\\r\\n count_2 = 0\\r\\n count_H_0 = 0\\r\\n count_H_1 = 0\\r\\n count_H_2 = 0\\r\\n will_be_sick = 0\\r\\n #healthy_nbrs_0 = 0\\r\\n #healthy_nbrs_1 = 0\\r\\n #healthy_nbrs_2 = 0\\r\\n\\r\\n #print(node.state,self._sick,self._healthy)\\r\\n\\r\\n for k in range(self._rows):\\r\\n for t in range(self._columns):\\r\\n if node.state[k, t][0] == 'S':\\r\\n sick += 1\\r\\n flag = 0\\r\\n if node.state[k, t][1] == 0:\\r\\n count_0 += 1\\r\\n flag = 1\\r\\n if node.state[k, t][1] == 1:\\r\\n count_1 += 1\\r\\n flag = 2\\r\\n if node.state[k, t][1] == 2:\\r\\n count_2 += 1\\r\\n flag = 3\\r\\n if k+1 < self._rows and node.state[k+1, t][0] == 'H':\\r\\n if flag == 1:\\r\\n count_H_0 += 1\\r\\n #healthy_nbrs_0 += count_helthy_nbrs(self._rows, self._columns, node.state, k + 1, t)\\r\\n elif flag == 2:\\r\\n count_H_1 += 1\\r\\n #healthy_nbrs_1 += count_helthy_nbrs(self._rows, self._columns, node.state, k + 1, t)\\r\\n else:\\r\\n count_H_2 += 1\\r\\n #healthy_nbrs_2 += count_helthy_nbrs(self._rows, self._columns, node.state, k + 1, t)\\r\\n\\r\\n will_be_sick += 1\\r\\n if k != 0 and node.state[k-1, t][0] == 'H':\\r\\n if flag == 1:\\r\\n count_H_0 += 1\\r\\n #healthy_nbrs_0 += count_helthy_nbrs(self._rows, self._columns, node.state, k - 1, t)\\r\\n elif flag == 2:\\r\\n count_H_1 += 1\\r\\n #healthy_nbrs_1 += count_helthy_nbrs(self._rows, self._columns, node.state, k - 1, t)\\r\\n else:\\r\\n count_H_2 += 1\\r\\n #healthy_nbrs_2 += count_helthy_nbrs(self._rows, self._columns, node.state, k - 1, t)\\r\\n will_be_sick += 1\\r\\n\\r\\n if t+1 < self._columns and node.state[k, t+1][0] == 'H':\\r\\n if flag == 1:\\r\\n count_H_0 += 1\\r\\n #healthy_nbrs_0 += count_helthy_nbrs(self._rows, self._columns, node.state, k , t+1)\\r\\n elif flag == 2:\\r\\n count_H_1 += 1\\r\\n #healthy_nbrs_1 += count_helthy_nbrs(self._rows, self._columns, node.state, k , t+1)\\r\\n else:\\r\\n count_H_2 += 1\\r\\n #healthy_nbrs_2 += count_helthy_nbrs(self._rows, self._columns, node.state, k , t+1)\\r\\n will_be_sick += 1\\r\\n\\r\\n if t != 0 and node.state[k, t-1][0] == 'H':\\r\\n if flag == 1:\\r\\n count_H_0 += 1\\r\\n #healthy_nbrs_0 += count_helthy_nbrs(self._rows, self._columns, node.state, k, t-1)\\r\\n elif flag == 2:\\r\\n count_H_1 += 1\\r\\n #healthy_nbrs_1 += count_helthy_nbrs(self._rows, self._columns, node.state, k, t-1)\\r\\n else:\\r\\n count_H_2 += 1\\r\\n #healthy_nbrs_2 += count_helthy_nbrs(self._rows, self._columns, node.state, k, t-1)\\r\\n will_be_sick += 1\\r\\n bonus=0\\r\\n if sick <= self.police:\\r\\n return 0\\r\\n else:\\r\\n return count_0*6+count_1*2+count_2 + count_H_0*6+count_H_1*2+count_H_2 + will_be_sick*6\\r\\n #return 0\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1241882237e5cd528018992835572f61\",\n \"score\": \"0.5170061\",\n \"text\": \"def gen(self, n):\\n raise NotImplementedError(\\\"homology classes not yet implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b380f04a3e9f8893cc9ddc1f4d09e192\",\n \"score\": \"0.5165206\",\n \"text\": \"def makeLc2HHH(name, LcMassWin, LcPT, LcVtxChi2Ndof, LcBpvdira, LcBpvdls,\\n LcdaughterPT, LcdaughterP, LcdaughterTrkChi2,\\n LcdaughterTrkGhostProb, LcdaughterBpvIpChi2,\\n### LcdaughterKaonPIDK, LcdaughterPionPIDK,\\n LcdaughterProtonProbNNp,\\n LcdaughterKaonProbNNk, LcdaughterPionProbNNpi,\\n ) :\\n\\n### _LcotherCuts = \\\"(ALL)\\\" # no cuts\\n _LcotherCuts = \\\"( (ADMASS('Lambda_c+')<%(LcMassWin)s) | (ADMASS('Xi_c+')<%(LcMassWin)s) ) \\\" % locals()\\n _LcotherCuts += \\\"& (PT> %(LcPT)s) & (VFASPF(VCHI2PDOF) < %(LcVtxChi2Ndof)s) \\\" % locals()\\n _LcotherCuts += \\\"& (BPVDIRA>%(LcBpvdira)s) & (BPVDLS > %(LcBpvdls)s)\\\" % locals()\\n### _LcdaughtersCuts = \\\"(ALL)\\\" # no cuts\\n _LcdaughtersCuts = \\\"(PT>%(LcdaughterPT)s) & (P>%(LcdaughterP)s) & (TRCHI2DOF<%(LcdaughterTrkChi2)s) \\\" % locals()\\n _LcdaughtersCuts += \\\"& (TRGHP<%(LcdaughterTrkGhostProb)s) & (BPVIPCHI2()>%(LcdaughterBpvIpChi2)s)\\\" % locals()\\n\\n### _KCutsPID = \\\"&( (PIDK>%(LcdaughterKaonPIDK)s) | (PROBNNk>%(LcdaughterKaonProbNNk)s) )\\\" % locals()\\n### _PiCutsPID = \\\"&( (PIDK<%(LcdaughterPionPIDK)s) | (PROBNNpi>%(LcdaughterPionProbNNpi)s) )\\\" % locals()\\n _PCutsPID = \\\"&(PROBNNp>%(LcdaughterProtonProbNNp)s)\\\" % locals()\\n _KCutsPID = \\\"&(PROBNNk>%(LcdaughterKaonProbNNk)s)\\\" % locals()\\n _PiCutsPID = \\\"&(PROBNNpi>%(LcdaughterPionProbNNpi)s)\\\" % locals()\\n _LcPCuts = _LcdaughtersCuts + _PCutsPID\\n _LcKCuts = _LcdaughtersCuts + _KCutsPID\\n _LcPiCuts = _LcdaughtersCuts + _PiCutsPID\\n\\n _LcFilter = CombineParticles( DecayDescriptor = \\\"[Lambda_c+ -> p+ K- pi+]cc\\\",\\n MotherCut = _LcotherCuts,\\n DaughtersCuts = { \\\"p+\\\" : _LcPCuts, \\\"pi+\\\" : _LcPiCuts, \\\"K+\\\" : _LcKCuts }\\n )\\n# _stdTightProtons = DataOnDemand(Location = \\\"Phys/StdAllLooseProtons/Particles\\\")\\n# _stdTightKaons = DataOnDemand(Location = \\\"Phys/StdAllLooseKaons/Particles\\\")\\n# _stdTightPions = DataOnDemand(Location = \\\"Phys/StdAllLoosePions/Particles\\\")\\n _stdTightProtons = DataOnDemand(Location = \\\"Phys/StdAllLooseANNProtons/Particles\\\")\\n _stdTightKaons = DataOnDemand(Location = \\\"Phys/StdAllLooseANNKaons/Particles\\\")\\n _stdTightPions = DataOnDemand(Location = \\\"Phys/StdAllLooseANNPions/Particles\\\")\\n\\n return Selection (name,\\n Algorithm = _LcFilter,\\n RequiredSelections = [_stdTightProtons, _stdTightKaons, _stdTightPions])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cde6c9d0c3ed4a269686e71dc455922\",\n \"score\": \"0.5165196\",\n \"text\": \"def handhog(BHs):\\n results = {}\\n counter = 0\\n for bh in BHs:\\n for pl in bh.players:\\n if not results.get(pl):\\n results[pl] = (0,0)\\n if bh.players[REVPLAYERS[bh.declarer]] == pl:\\n results[pl] = (results[pl][0] + 1, results[pl][1] + 1)\\n else:\\n results[pl] = (results[pl][0], results[pl][1] + 1)\\n\\n\\n counter = counter + 1\\n if counter % 1000 == 0:\\n print(counter)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51c37f667977ab9361f3b257e7231d80\",\n \"score\": \"0.5163456\",\n \"text\": \"def execute(self):\\n\\t\\tself.h = self.g*self.g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de6eeeb6535e323e9d1b916badfb28e1\",\n \"score\": \"0.5155058\",\n \"text\": \"def wer(self, r, h):\\n #\\n #Store the diff text\\n\\n import sys\\n import StringIO\\n stdout = sys.stdout # keep a handle on the real standard output\\n sys.stdout = StringIO.StringIO() # Choose a file-like object to write to\\n\\n #build the matrix\\n r = r.split()\\n h = h.split()\\n d = numpy.zeros((len(r)+1)*(len(h)+1), dtype=numpy.uint8).reshape((len(r)+1, len(h)+1))\\n for i in range(len(r)+1):\\n for j in range(len(h)+1):\\n if i == 0: d[0][j] = j\\n elif j == 0: d[i][0] = i\\n for i in range(1,len(r)+1):\\n for j in range(1, len(h)+1):\\n if r[i-1] == h[j-1]:\\n d[i][j] = d[i-1][j-1]\\n else:\\n substitute = d[i-1][j-1] + 1\\n insert = d[i][j-1] + 1\\n delete = d[i-1][j] + 1\\n d[i][j] = min(substitute, insert, delete)\\n result = float(d[len(r)][len(h)]) / len(r) * 100\\n result = str(\\\"%.2f\\\" % result) + \\\"%\\\"\\n\\n #find out the manipulation steps\\n x = len(r)\\n y = len(h)\\n list = []\\n while True:\\n if x == 0 and y == 0:\\n break\\n else:\\n if d[x][y] == d[x-1][y-1] and r[x-1] == h[y-1]:\\n list.append(\\\"e\\\")\\n x = x-1\\n y = y-1\\n elif d[x][y] == d[x][y-1]+1:\\n list.append(\\\"i\\\")\\n x = x\\n y = y-1\\n elif d[x][y] == d[x-1][y-1]+1:\\n list.append(\\\"s\\\")\\n x = x-1\\n y = y-1\\n else:\\n list.append(\\\"d\\\")\\n x = x-1\\n y = y\\n list = list[::-1]\\n\\n #print the result in aligned way\\n print \\\"REF:\\\",\\n for i in range(len(list)):\\n if list[i] == \\\"i\\\":\\n count = 0\\n for j in range(i):\\n if list[j] == \\\"d\\\":\\n count += 1;\\n index = i - count\\n print \\\" \\\"*(len(h[index])),\\n elif list[i] == \\\"s\\\":\\n count1 = 0\\n for j in range(i):\\n if list[j] == \\\"i\\\":\\n count1 += 1;\\n index1 = i - count1\\n count2 = 0\\n for j in range(i):\\n if list[j] == \\\"d\\\":\\n count2 += 1;\\n index2 = i - count2\\n if len(r[index1])<len(h[index2]):\\n print r[index1]+\\\" \\\"*(len(h[index2])-len(r[index1])),\\n\\n else:\\n print r[index1],\\n else:\\n count = 0\\n for j in range(i):\\n if list[j] == \\\"i\\\":\\n count += 1;\\n index = i - count\\n print r[index],\\n print\\n print \\\"HYP:\\\",\\n for i in range(len(list)):\\n if list[i] == \\\"d\\\":\\n count = 0\\n for j in range(i):\\n if list[j] == \\\"i\\\":\\n count += 1;\\n index = i - count\\n print \\\" \\\"*(len(r[index])),\\n elif list[i] == \\\"s\\\":\\n count1 = 0\\n for j in range(i):\\n if list[j] == \\\"i\\\":\\n count1 += 1;\\n index1 = i - count1\\n count2 = 0\\n for j in range(i):\\n if list[j] == \\\"d\\\":\\n count2 += 1;\\n index2 = i - count2\\n if len(r[index1])>len(h[index2]):\\n print h[index2]+\\\" \\\"*(len(r[index1])-len(h[index2])),\\n else:\\n print h[index2],\\n else:\\n count = 0\\n for j in range(i):\\n if list[j] == \\\"d\\\":\\n count += 1;\\n index = i - count\\n print h[index],\\n print\\n print \\\"EVA:\\\",\\n for i in range(len(list)):\\n if list[i] == \\\"d\\\":\\n count = 0\\n for j in range(i):\\n if list[j] == \\\"i\\\":\\n count += 1;\\n index = i - count\\n print \\\"D\\\"+\\\" \\\"*(len(r[index])-1),\\n elif list[i] == \\\"i\\\":\\n count = 0\\n for j in range(i):\\n if list[j] == \\\"d\\\":\\n count += 1;\\n index = i - count\\n print \\\"I\\\"+\\\" \\\"*(len(h[index])-1),\\n elif list[i] == \\\"s\\\":\\n count1 = 0\\n for j in range(i):\\n if list[j] == \\\"i\\\":\\n count1 += 1;\\n index1 = i - count1\\n count2 = 0\\n for j in range(i):\\n if list[j] == \\\"d\\\":\\n count2 += 1;\\n index2 = i - count2\\n if len(r[index1])>len(h[index2]):\\n print \\\"S\\\"+\\\" \\\"*(len(r[index1])-1),\\n else:\\n print \\\"S\\\"+\\\" \\\"*(len(h[index2])-1),\\n else:\\n count = 0\\n for j in range(i):\\n if list[j] == \\\"i\\\":\\n count += 1;\\n index = i - count\\n print \\\" \\\"*(len(r[index])),\\n print\\n print \\\"WER: \\\"+result\\n diffText = sys.stdout.getvalue()\\n sys.stdout = stdout\\n\\n resultDict = {\\n 'diff': diffText,\\n 'wer': result\\n }\\n return resultDict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e595c16bc979ca2f1e74108ce2cb4a8b\",\n \"score\": \"0.51543844\",\n \"text\": \"def hommel(p, n):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c239a355ade7a6678c16c46b50180a3c\",\n \"score\": \"0.5153726\",\n \"text\": \"def world1(w, h, base=0):\\n plates = generate_noise_map(w, h, w/3, 1, base=base)\\n hills = generate_noise_map(w, h, w/5, 2, 0.44, base=base)\\n trees = generate_noise_map(w, h, w/8, 3, 0.76, base=base)\\n speckle = generate_noise_map(w, h, w/150, octaves=2, persistence=0.1, base=base)\\n\\n for vertical in range(h):\\n for horizontal in range(w):\\n \\\"\\\"\\\"Now the fun custom parts begin. These will be differnet per world type\\\"\\\"\\\"\\n base_value = next(plates)\\n value = base_value + next(hills) / 2.2\\n speckle_value = next(speckle) / 4\\n\\n if base_value < 0.144 and base_value > -0.2:\\n value += speckle_value\\n\\n tree_value = next(trees)\\n if (tree_value > 0):\\n value += tree_value\\n\\n if value > 1:\\n value = 1\\n\\n if value < -1:\\n value = -1\\n\\n value = (value + 1) / 2\\n\\n yield value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dbdf95f1e7e2a08e641042fa78c5b7\",\n \"score\": \"0.5150863\",\n \"text\": \"def set_barehopping(self):\\n R=(0,0,0)\\n h_onsite=numpy.zeros((self.Atoms.nspinorbitals,self.Atoms.nspinorbitals),dtype=numpy.complex)\\n if R not in self.Hr: ## onsite part is not in the hoppings\\n return h_onsite\\n\\n # correct only in H[R=0] is diagonal\\n #h_onsite[:]=numpy.diag(self.Hr[R])[:]\\n #self.Hr[R]-=numpy.diag(h_onsite)\\n\\n #\\n k=0\\n for ispin in xrange(len(self.Atoms.spin)):\\n for ia in xrange(len(self.Atoms.orbitals)): #\\n norb=len(self.Atoms.orbitals[ia])\\n h_onsite[k:k+norb,k:k+norb]=self.Hr[R][k:k+norb,k:k+norb]\\n k+=norb\\n self.Hr[R]-=h_onsite\\n\\n return h_onsite\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5643a520418bf25e1c607b7f200eeac\",\n \"score\": \"0.5149517\",\n \"text\": \"def zihan_giwaxs_2023_1(t=0.5, name='test', dist='unspecified'):\\n\\n\\n incident_angles = [0.5, 1.5, 4.36,6.2]\\n waxs_arc = [0, 20]\\n user_name = \\\"ZZ\\\"\\n\\n # Sample flat at ai0\\n ai0 = stage.th.position\\n\\n for wa in waxs_arc:\\n yield from bps.mv(waxs, wa)\\n \\n dets = [pil900KW] #if waxs.arc.position < 15 else [pil900KW, pil1M]\\n det_exposure_time(t, t)\\n\\n for ai in incident_angles:\\n yield from bps.mv(stage.th, ai0 + ai)\\n\\n # Metadata\\n e = energy.position.energy / 1000\\n sdd = pil1m_pos.z.position / 1000\\n wa = waxs.arc.position + 0.001\\n wa = str(np.round(float(wa), 1)).zfill(4)\\n\\n # Sample name\\n name_fmt = \\\"{sample}_{dist}mm_{energy}keV_wa{wax}_sdd{sdd}m_ai{ai}\\\"\\n sample_name = name_fmt.format(\\n sample=name,\\n dist=dist,\\n energy=\\\"%.2f\\\" % e,\\n wax=wa,\\n sdd=\\\"%.1f\\\" % sdd,\\n ai=ai,\\n )\\n sample_name = sample_name.translate(\\n {ord(c): \\\"_\\\" for c in \\\"!@#$%^&*{}:/<>?\\\\|`~+ =,\\\"}\\n )\\n sample_id(user_name=user_name, sample_name=sample_name)\\n print(f\\\"\\\\n\\\\n\\\\n\\\\t=== Sample: {sample_name} ===\\\")\\n yield from bp.count(dets)\\n\\n yield from bps.mv(stage.th, ai0)\\n sample_id(user_name=\\\"test\\\", sample_name=\\\"test\\\")\\n det_exposure_time(0.5, 0.5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89e8b292b37099f5f815720e310df8fa\",\n \"score\": \"0.5146213\",\n \"text\": \"def __init__(self, low=16, high=33, k=3, p=0.5, r=8, sigma=0.25, t=1024, threshold=128,\\n alpha_values=np.arange(0.1, 1.1, 0.1)):\\n self.low = low\\n self.high = high\\n self.k = k\\n self.p = p\\n self.r = r\\n self.sigma = sigma\\n self.T = t\\n self.threshold = threshold\\n self.alpha_values = alpha_values\\n\\n # Fix the seed for debugging\\n # # np.random.seed(123457)\\n\\n # Generate a network\\n self.num_switches = np.random.randint(self.low, self.high)\\n self.num_hosts = np.random.poisson(self.num_switches)\\n self.num_nodes = self.num_switches + self.num_hosts\\n self.G = nx.connected_watts_strogatz_graph(self.num_switches, self.k, self.p)\\n\\n self.switches = range(self.num_switches) # The list of switch ids\\n self.hosts = range(self.num_switches, self.num_nodes) # The list of host ids\\n\\n # Append hosts\\n host_switches = np.random.choice(self.num_switches, self.num_hosts)\\n for i in range(self.num_hosts):\\n host = self.num_switches + i\\n self.G.add_node(host)\\n self.G.add_edge(host_switches[i], host)\\n\\n # The number of switches to be measured in each time step\\n self.n = self.num_switches / self.r\\n\\n # Draw the graph\\n # nx.draw(self.G)\\n # plt.show()\\n self.draw()\\n\\n print 'Number of Switches:', self.num_switches\\n print 'Number of hosts:', self.num_hosts\\n print 'Number of links:', len(self.G.edges()) + self.num_hosts\\n print '=========='\\n\\n # Create flow paths\\n self.flows = {}\\n self.paths = {}\\n for src in self.hosts:\\n for dest in self.hosts:\\n if src == dest:\\n continue\\n self.paths[(src, dest)] = nx.shortest_path(self.G, src, dest)\\n self.flows[(src, dest)] = 0.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"387eb47e48655bd82caa6c6c225cda45\",\n \"score\": \"0.51462024\",\n \"text\": \"def circ2elip_Hohmann(r1, r2, muPlanet = 3.986e14):\\n\\tv1 = np.sqrt(muPlanet/r1) * (np.sqrt(2*r2/(r1 + r2)) - 1)\\n\\treturn v1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3d63691bf4df9d633f0026fc9d79d2\",\n \"score\": \"0.5144528\",\n \"text\": \"def getsldonations():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7527e42a2e4d245732cd9cf9e83d38dd\",\n \"score\": \"0.51440126\",\n \"text\": \"def orifice_h_per_row(self):\\n height_orifices = (np.linspace(0, self.row_n - 1, self.row_n)) * \\\\\\n self.row_b + 0.5 * self.orifice_d\\n return height_orifices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b5c4f4150dcc6629ef928047af9541\",\n \"score\": \"0.5139847\",\n \"text\": \"def create_generation_by_harem(self, parents):\\n\\n # Determine mating probability of all parents\\n parents[\\\"mating_prob\\\"]=parents[\\\"fitness\\\"]/sum(parents[\\\"fitness\\\"])\\n \\n # The top 5% become our \\\"alphas\\\" and everone else is a \\\"beta\\\"\\n parents = parents.sort_values(by='fitness', ascending=False)\\n alpha_count = ceil(len(parents)*0.05)\\n alphas = parents[:alpha_count]\\n betas = parents[alpha_count:]\\n \\n # generate eims df as we generate the eims themselves\\n eim_dna = []\\n eim_conflicts = []\\n eim_fitness = []\\n eim_is_solution = [] \\n eim_mom = []\\n eim_dad = []\\n eim_mom_chromosome = []\\n eim_dad_chromosome = []\\n eim_mutated = [] \\n \\n # keep breeding until we reach the target population \\n i = 0\\n while(i < self.parent.parent.settings[\\\"eims_per_generation\\\"]):\\n i += 1 \\n \\n # select one of our alphas to be dad\\n dad = alphas.sample(n=2, \\n replace=True,\\n weights=parents[\\\"mating_prob\\\"])\\n \\n # select one of our betas to be mom\\n mom = betas.sample(n=2, \\n replace=True,\\n weights=parents[\\\"mating_prob\\\"])\\n \\n # breed the selected pair\\n params = {\\\"eimtype\\\":'breeding',\\n \\\"mom\\\":mom[\\\"dna\\\"].iloc[0],\\n \\\"dad\\\":dad[\\\"dna\\\"].iloc[1]} \\n baby = Eim(self,params)\\n eim_dna.append(baby.dna)\\n eim_conflicts.append(baby.conflicts) \\n eim_fitness.append(baby.fitness)\\n eim_is_solution.append(baby.is_solution) \\n eim_mom.append(mom[\\\"dna\\\"].astype(str))\\n eim_dad.append(dad[\\\"dna\\\"].astype(str)) \\n eim_mom_chromosome.append(baby.mom_chromosome)\\n eim_dad_chromosome.append(baby.dad_chromosome)\\n eim_mutated.append(baby.mutated)\\n \\n # create dataframe\\n self.set_eims_df([eim_dna,eim_conflicts,eim_fitness,eim_is_solution,eim_mom,eim_dad,eim_mom_chromosome,eim_dad_chromosome,eim_mutated], extended=True) # order matters \",\n \"title\": \"\"\n },\n {\n \"docid\": \"da4fe173be35c4534d50731355d56b36\",\n \"score\": \"0.5139621\",\n \"text\": \"def helix_assignement(hbond_turn):\\r\\n\\r\\n for i in range(0, len(hbond_turn)-1):\\r\\n if ((hbond_turn[i+1][0] - hbond_turn[i][0] == 1 and\\r\\n hbond_turn[i][3] == hbond_turn[i+1][3]) or\\r\\n (hbond_turn[i][0] - hbond_turn[i-1][0] == 1 and\\r\\n hbond_turn[i][3] == hbond_turn[i-1][3])):\\r\\n if (hbond_turn[i][1] - hbond_turn[i][0] == 4):\\r\\n hbond_turn[i][3] = \\\"H\\\"\\r\\n hbond_turn[i+1][3] = \\\"H\\\"\\r\\n elif (hbond_turn[i][1] - hbond_turn[i][0] == 3):\\r\\n hbond_turn[i][3] = \\\"G\\\"\\r\\n hbond_turn[i+1][3] = \\\"G\\\"\\r\\n elif (hbond_turn[i][1] - hbond_turn[i][0] == 5):\\r\\n hbond_turn[i][3] = \\\"I\\\"\\r\\n hbond_turn[i][3] = \\\"I\\\"\\r\\n\\r\\n return(hbond_turn)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7313eef756f42f75222b4aff2cfb41a6\",\n \"score\": \"0.5138997\",\n \"text\": \"def generate_cue_spikes():\\n\\n spike_times = np.asarray(hom_poisson(20.0, 10, t_max=0.2, seed=12345))\\n spiking_neurons = np.zeros_like(spike_times)\\n for neuron_id in range(1, 100):\\n spike_times_tmp = np.asarray(hom_poisson(20.0, 10, t_max=0.2, seed=12345+neuron_id))\\n spike_times = np.concatenate((spike_times, spike_times_tmp), axis=0)\\n spiking_neurons_tmp = neuron_id * np.ones_like(spike_times_tmp)\\n spiking_neurons = np.concatenate((spiking_neurons, spiking_neurons_tmp), axis=0)\\n\\n return spike_times, spiking_neurons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73daa342fa1e12f01db18a1b34fffad6\",\n \"score\": \"0.5130493\",\n \"text\": \"def heuristique_ACPM(self,draw=False):\\n \\n def getVertricesOfPath(edges):\\n set_node = set()\\n for e in edges:\\n id1,id2 = self.getIdVerticesOfEdge(e)\\n set_node.add(id1)\\n set_node.add(id2)\\n return set_node \\n \\n \\n if draw:\\n try:\\n os.makedirs(self.dirname+\\\"/H_ACPM\\\")\\n except:\\n pass\\n \\n \\n #Graphe de depart contenant tout les noeuds \\n G = Graphe_Individu(self,self.wholeGraphDict)\\n if draw:\\n self.drawGraph(\\\"/H_ACPM/G0\\\",G.get_graphe().get_edges())\\n \\n \\n nb_vertices = 1\\n nb_tmp_vertices = 0\\n i=0\\n \\n #Tant que le nombre de noeuds du graphe decroit, appliquer kruskal\\n while nb_vertices > nb_tmp_vertices :\\n g_acpm = G.get_MST()\\n\\n \\n nb_vertices = len(G.get_dictSteinerNodes())\\n set_Node = set(G.get_dictSteinerNodes().keys())\\n dictSteinerNodez = self.eliminationFeuilles(g_acpm.get_edges(),set_Node)\\n G = Graphe_Individu(self,dictSteinerNodez)\\n nb_tmp_vertices = len(dictSteinerNodez)\\n \\n if draw:\\n i+= 1 \\n self.drawGraph(\\\"/H_ACPM/G%d\\\"%i+\\\"_%d\\\"%g_acpm.get_total_weight(),g_acpm.get_edges())\\n \\n \\n if draw:\\n self.drawGraph(\\\"/H_ACPM/GFinal_%d\\\"%G.get_MST().get_total_weight(),G.get_MST().get_edges())\\n \\n for i in self.steinerNodes:\\n if i not in dictSteinerNodez.keys():\\n dictSteinerNodez[i] = 0\\n\\n # return Graphe_Individu(self,dictSteinerNodez)\\n return dictSteinerNodez\",\n \"title\": \"\"\n },\n {\n \"docid\": \"014570b37eff851750eb1d81a2c05944\",\n \"score\": \"0.5129009\",\n \"text\": \"def gyros(self, time):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fecedaa7e56cccb693137b4f2b64af24\",\n \"score\": \"0.5125265\",\n \"text\": \"def FRO_HALS_update_H(self):\\n self.gather_W_H(gH=False)\\n WTW = self.global_gram(self.W_ij)\\n AtW = self.ATW_glob()\\n for kk in iter(range(0, self.k)):\\n temp_vec = self.H_ij[kk, :] + AtW[kk, :] - WTW[kk, :].dot(self.H_ij)\\n self.H_ij[kk, :] = np.maximum(temp_vec, self.eps)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f4c40349be4142540dbf7949263161f\",\n \"score\": \"0.5124931\",\n \"text\": \"def ehess2rhess(self, X, Hess):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7cc3903139e88c8e5ba2bb43d178656\",\n \"score\": \"0.51243556\",\n \"text\": \"def homard_exec(theStudy):\\n error = 0\\n#\\n while not error :\\n #\\n homard.SetCurrentStudy(theStudy)\\n #\\n # Creation of the zones\\n # =====================\\n # Creation of the box Zone_1_1\\n Zone_1_1 = homard.CreateZoneBox('Zone_1_1', -0.01, 1.01, -0.01, 0.4, -0.01, 0.6)\\n\\n # Creation of the sphere Zone_1_2\\n Zone_1_2 = homard.CreateZoneSphere('Zone_1_2', 0.5, 0.6, 0.7, 0.75)\\n #\\n # Creation of the hypotheses\\n # ==========================\\n # Creation of the hypothesis a10_1pc_de_mailles_a_raffiner_sur_ERRE_ELEM_SIGM\\n HypoName_1 = \\\"a10_1pc_de_mailles_a_raffiner_sur_ERRE_ELEM_SIGM\\\"\\n print \\\"-------- Creation of the hypothesis\\\", HypoName_1\\n Hypo_1_1 = homard.CreateHypothesis(HypoName_1)\\n Hypo_1_1.SetField('RESU____ERRE_ELEM_SIGM__________')\\n Hypo_1_1.SetUseComp(0)\\n Hypo_1_1.AddComp('ERREST')\\n Hypo_1_1.SetRefinThr(3, 10.1)\\n Hypo_1_1.AddFieldInterp('RESU____DEPL____________________')\\n Hypo_1_1.AddFieldInterp('RESU____ERRE_ELEM_SIGM__________')\\n print HypoName_1, \\\" : zones utilisées :\\\", Hypo_1_1.GetZones()\\n print HypoName_1, \\\" : champ utilisé :\\\", Hypo_1_1.GetFieldName()\\n print HypoName_1, \\\" : composantes utilisées :\\\", Hypo_1_1.GetComps()\\n if ( len (Hypo_1_1.GetFieldName()) > 0 ) :\\n print \\\".. caractéristiques de l'adaptation :\\\", Hypo_1_1.GetField()\\n print HypoName_1, \\\" : champs interpolés :\\\", Hypo_1_1.GetFieldInterps()\\n # Creation of the hypothesis Zones_1_et_2\\n HypoName_2 = \\\"Zones_1_et_2\\\"\\n print \\\"-------- Creation of the hypothesis\\\", HypoName_2\\n Zones_1_et_2 = homard.CreateHypothesis(HypoName_2)\\n Zones_1_et_2.AddZone('Zone_1_1', 1)\\n Zones_1_et_2.AddZone('Zone_1_2', 1)\\n print HypoName_2, \\\" : zones utilisées :\\\", Zones_1_et_2.GetZones()\\n print HypoName_2, \\\" : champ utilisé :\\\", Zones_1_et_2.GetFieldName()\\n if ( len (Zones_1_et_2.GetFieldName()) > 0 ) :\\n print \\\".. caractéristiques de l'adaptation :\\\", Zones_1_et_2.GetField()\\n print HypoName_2, \\\" : champs interpolés :\\\", Zones_1_et_2.GetFieldInterps()\\n #\\n # Creation of the cases\\n # =====================\\n # Creation of the case\\n CaseName = \\\"Case_\\\" + Test_Name\\n print \\\"-------- Creation of the case\\\", CaseName\\n MeshFile = os.path.join(Rep_Test, Test_Name + '.00.med')\\n Case_test_1 = homard.CreateCase(CaseName, 'MAILL', MeshFile)\\n Case_test_1.SetDirName(dircase)\\n Case_test_1.SetConfType(1)\\n #\\n # Creation of the iterations\\n # ==========================\\n # Creation of the iteration 1\\n IterName = \\\"I_\\\" + Test_Name + \\\"_1\\\"\\n print \\\"-------- Creation of the iteration\\\", IterName\\n Iter_test_1_1 = Case_test_1.NextIteration(IterName)\\n Iter_test_1_1.AssociateHypo(HypoName_1)\\n print \\\". Hypothese :\\\", HypoName_1\\n Iter_test_1_1.SetMeshName('M1')\\n Iter_test_1_1.SetMeshFile(os.path.join(dircase, 'maill.01.med'))\\n Iter_test_1_1.SetFieldFile(os.path.join(Rep_Test, Test_Name + '.00.med'))\\n Iter_test_1_1.SetTimeStepRank(1, 1)\\n Iter_test_1_1.SetFieldInterpTimeStep('RESU____DEPL____________________', 1)\\n Iter_test_1_1.SetFieldInterpTimeStepRank('RESU____ERRE_ELEM_SIGM__________', 1, 1)\\n print \\\". Instants d'interpolation :\\\", Iter_test_1_1.GetFieldInterpsTimeStepRank()\\n error = Iter_test_1_1.Compute(1, 1)\\n if error :\\n error = 1\\n break\\n\\n # Creation of the iteration 2\\n IterName = \\\"I_\\\" + Test_Name + \\\"_2\\\"\\n print \\\"-------- Creation of the iteration\\\", IterName\\n Iter_test_1_2 = Iter_test_1_1.NextIteration(IterName)\\n Iter_test_1_2.AssociateHypo(HypoName_1)\\n print \\\". Hypothese :\\\", HypoName_1\\n Iter_test_1_2.SetMeshName('M2')\\n Iter_test_1_2.SetMeshFile(os.path.join(dircase, 'maill.02.med'))\\n Iter_test_1_2.SetFieldFile(os.path.join(Rep_Test, Test_Name + '.01.med'))\\n Iter_test_1_2.SetTimeStepRank(1, 1)\\n Iter_test_1_2.SetFieldInterpTimeStep('RESU____DEPL____________________', 1)\\n Iter_test_1_2.SetFieldInterpTimeStepRank('RESU____ERRE_ELEM_SIGM__________', 1, 1)\\n print \\\". Instants d'interpolation :\\\", Iter_test_1_2.GetFieldInterpsTimeStepRank()\\n error = Iter_test_1_2.Compute(1, 1)\\n if error :\\n error = 2\\n break\\n\\n # Creation of the iteration 3\\n IterName = \\\"I_\\\" + Test_Name + \\\"_3\\\"\\n print \\\"-------- Creation of the iteration\\\", IterName\\n Iter_test_1_3 = Iter_test_1_2.NextIteration(IterName)\\n Iter_test_1_3.AssociateHypo(HypoName_2)\\n print \\\". Hypothese :\\\", HypoName_2\\n Iter_test_1_3.SetMeshName('M3')\\n Iter_test_1_3.SetMeshFile(os.path.join(dircase, 'maill.03.med'))\\n Iter_test_1_2.SetFieldFile(os.path.join(Rep_Test, Test_Name + '.02.med'))\\n print \\\". Instants d'interpolation :\\\", Iter_test_1_3.GetFieldInterpsTimeStepRank()\\n error = Iter_test_1_3.Compute(1, 1)\\n if error :\\n error = 3\\n break\\n #\\n # Creation of the schema YACS\\n # ===========================\\n ScriptFile = os.path.join(pathHomard, \\\"share\\\", \\\"doc\\\", \\\"salome\\\", \\\"gui\\\", \\\"HOMARD\\\", \\\"en\\\", \\\"_downloads\\\", \\\"yacs_script_test.py\\\")\\n ScriptFile = os.path.normpath(ScriptFile)\\n DirName = dircase\\n YACS_test_1 = Case_test_1.CreateYACSSchema(\\\"YACS_test_1\\\", ScriptFile, DirName, MeshFile)\\n error = YACS_test_1.Write()\\n if error :\\n error = 4\\n break\\n #\\n break\\n #\\n return error\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47762a48a13de82cd68e2618cda5b4a8\",\n \"score\": \"0.51222813\",\n \"text\": \"def wichmann_hill_custom_1(seed1, seed2, seed3, n):\\n m1, m2, m3 = 39343, 39367, 39373\\n a1, a2, a3 = 123, 122, 121\\n return __wh(seed1,seed2,seed3,m1, m2,m3,a1,a2,a3,n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf24c04efde3a1bd8c89d3ee5399b29a\",\n \"score\": \"0.51183164\",\n \"text\": \"def bycie_skontrowanym(self):#bycie skontrowanym 158, 160, 162, krecenie w glowie 164, 166\\n self.stan = 'h'\\n for i in range(1, 41):\\n if not self.jestem_graczem and self.jestem_kopniety and self.energia <=30:\\n self.jestem_uderzony, self.jestem_kopniety = False, False\\n return self.szuraj_po_ziemii()\\n if self.czy_koniec():\\n return False\\n self.aaanimacja = 158 + 2*int((i)/14)\\n self.podejmij_centrowanie_postaci()\\n time.sleep(0.005)\\n for i in range(0, random.randint(20, 100)):\\n if not self.jestem_graczem and self.jestem_kopniety:\\n self.jestem_uderzony, self.jestem_kopniety = False, False\\n if self.energia <=30:\\n return self.szuraj_po_ziemii()\\n else:\\n return self.animuj_bycie_uderzonym()\\n if not self.jestem_graczem and self.jestem_uderzony:\\n self.jestem_uderzony, self.jestem_kopniety = False, False\\n return self.animuj_bycie_uderzonym()\\n if self.czy_koniec():\\n return False\\n if i%5 == 0:\\n if self.aaanimacja == 164:\\n self.aaanimacja = 166\\n else:\\n self.aaanimacja = 164\\n time.sleep(0.05)\\n self.reset_animacji()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1684,"cells":{"query_id":{"kind":"string","value":"4c3f1e80e1a13647cfac3dc3f583becc"},"query":{"kind":"string","value":"Test for searching and finding through the children of a node with location specified."},"positive_passages":{"kind":"list like","value":[{"docid":"040f8892cd1ebc22e86bd7fd0ef294be","score":"0.5892507","text":"def test_find_node(self):\n # Assign\n node2 = Node(\"Child node 2\")\n node3 = Node(\"Child node 3\")\n node2.add_or_replace_child(node=node3, index=2)\n\n node1 = Node(\"Child node 1\")\n node1.add_or_replace_child(node=node2, index=0)\n\n root = Node(\"Root node\")\n root.add_or_replace_child(node=node1, index=3)\n\n # Act\n retrieved_node1 = root.find_node([3])\n retrieved_node2 = root.find_node([3, 0])\n retrieved_node3 = root.find_node([3, 0, 2])\n\n # Assert\n self.assertEqual(node1, retrieved_node1)\n self.assertEqual(node2, retrieved_node2)\n self.assertEqual(node3, retrieved_node3)","title":""}],"string":"[\n {\n \"docid\": \"040f8892cd1ebc22e86bd7fd0ef294be\",\n \"score\": \"0.5892507\",\n \"text\": \"def test_find_node(self):\\n # Assign\\n node2 = Node(\\\"Child node 2\\\")\\n node3 = Node(\\\"Child node 3\\\")\\n node2.add_or_replace_child(node=node3, index=2)\\n\\n node1 = Node(\\\"Child node 1\\\")\\n node1.add_or_replace_child(node=node2, index=0)\\n\\n root = Node(\\\"Root node\\\")\\n root.add_or_replace_child(node=node1, index=3)\\n\\n # Act\\n retrieved_node1 = root.find_node([3])\\n retrieved_node2 = root.find_node([3, 0])\\n retrieved_node3 = root.find_node([3, 0, 2])\\n\\n # Assert\\n self.assertEqual(node1, retrieved_node1)\\n self.assertEqual(node2, retrieved_node2)\\n self.assertEqual(node3, retrieved_node3)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"402682c267e585a31521634972681bfe","score":"0.63392574","text":"def getChildren(node, *args, **kwargs):","title":""},{"docid":"47f5e66c1cce9bfeecaeb53fb73a0493","score":"0.63026905","text":"def findChildren(self, name='None', attrs='{}', recursive='True', text='None', limit='None', **kwargs):\n \n pass","title":""},{"docid":"685bddfc7f8acd0ec7d47ec4455f4ed4","score":"0.629981","text":"def test_childLookup(self):\n self.navpage.factory = tcf = TestClientFactory(\n 'client-of-livepage', 'I AM A CLIENT OF THE LIVEPAGE')\n\n self.assertEqual(self.navpage.locateChild(None,\n ('child-of-fragment',)),\n ('I AM A CHILD OF THE FRAGMENT', ()))\n self.assertEqual(self.navpage.locateChild(None,\n (tcf.magicSegment,)),\n (tcf.returnValue, ()))","title":""},{"docid":"ed4689f762e8dcb895ced4a0d3bbedef","score":"0.625426","text":"def locateChild(ctx, segments):","title":""},{"docid":"2eb123051c984814d6fa75305c32846f","score":"0.62259406","text":"def test_node_has_correct_children(full_tree):\n node = full_tree.search(6)\n assert node.left.val == 4 and node.right.val == 8","title":""},{"docid":"1ca1cb104961e5a9e08fd95b4251fb93","score":"0.60317063","text":"def node_children(node, all_hierarchy=True, full_path=True):\n\n raise NotImplementedError()","title":""},{"docid":"2737e3c256626da7300a649ff3b0beee","score":"0.5995648","text":"def findChild(self, name='None', attrs='{}', recursive='True', text='None', **kwargs):\n \n pass","title":""},{"docid":"9ed612ffbe6224af871af581ef2f2981","score":"0.5937417","text":"def get_children(self, node):\n raise NotImplementedError","title":""},{"docid":"4b155cea05c39cd319a511dcc6affde6","score":"0.59342474","text":"def get_children(self, child_name = None):\n children = []\n try:\n sibling = self.get_first_child()\n except PixeliseElementSearchError:\n raise PixeliseElementSearchError, \"There are no children.\"\n while sibling:\n if child_name:\n if sibling.get_node_name() == child_name:\n children.append(sibling)\n else:\n children.append(sibling)\n try:\n sibling = sibling.get_next_sibling()\n except PixeliseElementSearchError:\n # No more siblings so lets end the while loop\n sibling = None\n return children","title":""},{"docid":"328c748cd9402d068574c2c0ad940f59","score":"0.5921426","text":"def get_child_nodes(node, node_ids):\n if node['childNodes']:\n for child in node['childNodes']:\n node_ids.append(child['id'])\n get_child_nodes(child, node_ids)","title":""},{"docid":"e2ec83969e242cb01f96eb3126f6b92a","score":"0.5915198","text":"def find_children_named(self, parent, name):\n for node in parent.childNodes:\n if node.nodeName == name:\n yield node","title":""},{"docid":"5cda562d890edf1f251b577f3b2d5b14","score":"0.58905506","text":"def children(self, id_, **kwargs):\n if \"parent\" in self.data.columns:\n query = dict(parent=id_, **kwargs)\n _df = self.data\n conditions = [(_df[k] == v) for (k, v) in query.items()]\n mask = multiple_and(*conditions)\n children = _df[mask].index.tolist()\n\n return [x for x in children]\n else:\n return []","title":""},{"docid":"c24e8bcbae3819892dc50c7fe76a5497","score":"0.58869016","text":"def search(self, keyword, children=None, arg=None):\n if children is None:\n children = self.substmts\n return [ch for ch in children\n if ch.keyword == keyword and (arg is None or ch.arg == arg)]","title":""},{"docid":"f1d32e44830d8be1c5ed6f04a3810b2f","score":"0.5882318","text":"def return_children(self):\n return True","title":""},{"docid":"0d1db8d4199da5e29cf95d419a71f202","score":"0.58799654","text":"def find_all(self, node_type):\n for child in self.iter_child_nodes():\n if isinstance(child, node_type):\n yield child\n for result in child.find_all(node_type):\n yield result","title":""},{"docid":"ce43dea7f181edfbcfa31286fb98ee89","score":"0.5866933","text":"def walkChildren(elem):\n return IterWalkChildren(elem)","title":""},{"docid":"0f21ca25c748fc3e0c1d3eb80cc47b13","score":"0.584094","text":"def list_children(node, all_hierarchy=True, full_path=True, children_type=None):\n\n raise NotImplementedError()","title":""},{"docid":"665d5447f53f579aa2f276a25987cfca","score":"0.58373964","text":"def find_children(self):\n for i in range(len(self.vertices)):\n self.vertices[i].children = []\n for i in range(len(self.vertices)):\n for parent in self.vertices[i].parents:\n if i not in self.vertices[parent].children:\n self.vertices[parent].children.append(i)","title":""},{"docid":"62a823faf0f219fe14b0ce54d37facdd","score":"0.5823317","text":"def get_children(self, refobj): #pragma: no cover\n raise NotImplementedError","title":""},{"docid":"bbe4818630122f60b6bde56132df1e4e","score":"0.58139014","text":"def fetch_new_children(self, ):\n root = self.get_root()\n refobjinter = self.get_refobjinter()\n unwrapped = self.get_unwrapped(root, refobjinter)\n self.wrap(self.get_root(), self.get_refobjinter(), unwrapped)\n\n suggestions = self.get_suggestions()\n for typ, element in suggestions:\n for c in self._children:\n if typ == c.get_typ() and element == c.get_element():\n break\n else:\n Reftrack(root=root, refobjinter=refobjinter, typ=typ, element=element, parent=self)","title":""},{"docid":"783e409c369d6740a359c1290f19e843","score":"0.5812229","text":"def _findNode(self, tree, path):\n for item in tree['children']:\n if path.startswith(item['path']):\n return self._findNode(item, path)\n return tree","title":""},{"docid":"c3fce45cea74fc242574a6f030677afd","score":"0.5804516","text":"def get_children(self, node, crawler):\n\n graph = crawler.get_graph()\n state = crawler.get_state()\n self.sleep()\n if graph.node[node]['bipartite'] == 0:\n return self.get_child0(node, graph, state)\n\n else:\n return self.get_child1(node, graph, state)","title":""},{"docid":"ee56c8bce163496b6c0c8491a78ed488","score":"0.580129","text":"def generate_children(self, node):\n children = []\n # North\n # If the node would be outside of the board, or contain a wall, we do not generate it.\n if node.state[1] > 0:\n if not self.world[node.state[0]][node.state[1] - 1] == \"#\":\n state = (node.state[0], node.state[1] - 1)\n child = search_node.SearchNode(state, node.g + self.get_cost(state), self.heuristic_eval(state), node)\n children.append(child)\n\n # East\n if node.state[0] < len(self.world) - 1:\n if not self.world[node.state[0] + 1][node.state[1]] == \"#\":\n state = (node.state[0] + 1, node.state[1])\n child = search_node.SearchNode(state, node.g + self.get_cost(state), self.heuristic_eval(state), node)\n children.append(child)\n\n # West\n if node.state[0] > 0:\n if not self.world[node.state[0] - 1][node.state[1]] == \"#\":\n state = (node.state[0] - 1, node.state[1])\n child = search_node.SearchNode(state, node.g + self.get_cost(state), self.heuristic_eval(state), node)\n children.append(child)\n\n # South\n if node.state[1] < len(self.world[0]) - 1:\n if not self.world[node.state[0]][node.state[1] + 1] == \"#\":\n state = (node.state[0], node.state[1] + 1)\n child = search_node.SearchNode(state, node.g + self.get_cost(state), self.heuristic_eval(state), node)\n children.append(child)\n\n return children","title":""},{"docid":"f50ca2322c493e0c01b7b701046ad30e","score":"0.5784027","text":"def children(self) -> Iterable[\"Node\"]:\n\t\tpass","title":""},{"docid":"8a79a7ee7dce15ae623005ecd3cc73e5","score":"0.57702726","text":"def test_find_elements(self):\n tree = BinarySearchTree()\n for element in (1, 10, 12, 5, 8):\n tree.add(element)\n self.assertTrue(tree.find(element))","title":""},{"docid":"f61738f5aadf5a4d348f2ea490d88446","score":"0.57560235","text":"def findChildren(self,**kwargs):\r\n\r\n\t\tchildren = []\r\n\t\tdef _childCollector(widget):\r\n\t\t\tif widget.match(**kwargs):\r\n\t\t\t\tchildren.append(widget)\r\n\t\tself.deepApply(_childCollector)\r\n\t\treturn children","title":""},{"docid":"13e3f974cf814dde0e02e03e1d7e8342","score":"0.57319283","text":"def get_children(*a): \n _str_func = 'get_children'\n _d = validate_arg(*a) \n \n if '[' in _d['attr']:\n return False\n \n try:return mc.attributeQuery(_d['attr'], node = _d['node'], listChildren=True) or [] \n except Exception,err:\n log.error(\"|{0}| >> {1} | {2}\".format(_str_func,_d['combined'],err))\n return False","title":""},{"docid":"162099eced56b86f4c1265c43904b7cf","score":"0.57305413","text":"def find_child_expr(cfunc, e, q, parents=False):\r\n\r\n class expr_finder_t(hx.ctree_visitor_t):\r\n def __init__(self, cfunc, q, parents):\r\n hx.ctree_visitor_t.__init__(self,\r\n hx.CV_PARENTS if parents else hx.CV_FAST)\r\n\r\n self.cfunc = cfunc\r\n self.query = q\r\n self.found = list()\r\n return\r\n\r\n def visit_expr(self, e):\r\n \"\"\"process cexpr_t elements\"\"\"\r\n try:\r\n if self.query(self.cfunc, e):\r\n self.found.append(query_result_t(self.cfunc, e))\r\n except:\r\n pass\r\n return 0\r\n\r\n if cfunc:\r\n expfinder = expr_finder_t(cfunc, q, parents)\r\n expfinder.apply_to_exprs(e, None)\r\n return expfinder.found\r\n return list()","title":""},{"docid":"6309cdfdf41352168fbf13a4f691df54","score":"0.57216424","text":"def GetChildren(self, item, children):","title":""},{"docid":"3324423a8f6d4f973883612e34d9a655","score":"0.57093817","text":"def get_children(self, points, masses, ids, leaves):\n #index of the children\n index = (points > self.cen)\n \n #loop through 8 children\n for i in range(2):\n for j in range(2):\n for k in range(2):\n \n #check if there is a galaxy in the child node\n in_node = np.all(index == np.bool_([i,j,k]), axis=0)\n \n #if theres no point in the node\n if not np.any(in_node): \n continue\n # offset between parent and child box centers\n offset = 0.5*self.size*(np.array([i,j,k])-0.5) \n self.children.append(Node(self.cen + offset,\n self.size/2,\n masses[in_node],\n points[in_node],\n ids[in_node],\n leaves))","title":""},{"docid":"e7adca38ceb9912979d5cc332f62c678","score":"0.56792617","text":"def test_navigation_children(chikin):\n assert len(list(chikin.children)) == 2\n docclass, document = chikin.children\n assert document.name == 'document'\n assert len(list(chikin.document.children)) == 7","title":""},{"docid":"cd63f0d7748fda35f9177c4e74202b23","score":"0.56589663","text":"def find_childs(self, search_term, search_matches, ignore_case=False):\n\n if ignore_case:\n search_term = search_term.lower()\n gid = self.gid.lower()\n name = self.name.lower()\n else:\n gid = self.gid\n name = self.name\n \n if search_term in gid or search_term in name:\n search_matches.append(self)\n\n for c in self.childs:\n search_matches = c.find_childs(search_term, search_matches, ignore_case)\n\n return search_matches","title":""},{"docid":"69c76e9855f382d96433cbe125d8785d","score":"0.56486493","text":"def get_children(self, id, level=None):\n\n db = self.db\n table = db.gis_location\n\n query = (table.deleted == False)\n if level:\n query = query & (table.level == level)\n term = str(id)\n query = query & ((table.path.like(term + \"/%\")) | (table.path.like(\"%/\" + term + \"/%\")))\n children = db(query).select(table.id,\n table.name)\n return children","title":""},{"docid":"5a6e1f96330c24870d9569f2ec0ee40f","score":"0.56354845","text":"def find_child_item(cfunc, i, q, parents=False):\r\n\r\n class citem_finder_t(hx.ctree_visitor_t):\r\n def __init__(self, cfunc, q, parents):\r\n hx.ctree_visitor_t.__init__(self,\r\n hx.CV_PARENTS if parents else hx.CV_FAST)\r\n\r\n self.cfunc = cfunc\r\n self.query = q\r\n self.found = list()\r\n return\r\n\r\n def process(self, i):\r\n \"\"\"process cinsn_t and cexpr_t elements alike\"\"\"\r\n\r\n try:\r\n if self.query(self.cfunc, i):\r\n self.found.append(query_result_t(self.cfunc, i))\r\n except:\r\n pass\r\n return 0\r\n\r\n def visit_insn(self, i):\r\n return self.process(i)\r\n\r\n def visit_expr(self, e):\r\n return self.process(e)\r\n\r\n if cfunc:\r\n itfinder = citem_finder_t(cfunc, q, parents)\r\n itfinder.apply_to(i, None)\r\n return itfinder.found\r\n return list()","title":""},{"docid":"e2335278368c480be46897b7c5a8b0ec","score":"0.5628231","text":"def get_children(self):\r\n return _RMF.TraverseHelper_get_children(self)","title":""},{"docid":"131001bf8104dcce861efbc6f44a9284","score":"0.5596992","text":"def get_children(self, path):\n raise NotImplementedError","title":""},{"docid":"90b15b283eccbd93d81ae6691783135e","score":"0.55948013","text":"def get_children(self):\n\t\treturn self.marker_list[:]","title":""},{"docid":"9f9a908e86aee78c155cc14d7b5b1462","score":"0.55831885","text":"def children(self, p):\n if self.left(p) is not None:\n yield self.left(p)\n if self.right(p) is not None:\n yield self.right(p)","title":""},{"docid":"372a32ad5059b9e1967ce3926955fbd4","score":"0.5581325","text":"def findChildren(self, nodeType = None):\n if nodeType is None:\n nodeType = DocNode\n return [x for x in self.children if x.isA(nodeType)]","title":""},{"docid":"c96848fea1ceed018304f752dbe0cda7","score":"0.55774796","text":"def get_location_children(client: SymphonyClient, location_id: str) -> List[Location]:\n location_with_children = LocationChildrenQuery.execute(\n client, id=location_id\n ).location\n if not location_with_children:\n raise EntityNotFoundError(entity=Entity.Location, entity_id=location_id)\n\n return [\n Location(\n name=location.name,\n id=location.id,\n latitude=location.latitude,\n longitude=location.longitude,\n externalId=location.externalId,\n locationTypeName=location.locationType.name,\n )\n for location in location_with_children.children\n ]","title":""},{"docid":"f6b85326cfdb2cbf6d626a384fe446f6","score":"0.5571778","text":"def children(self, p):\n pass","title":""},{"docid":"32aacaba32f7b3f8d6b81f582a180ebe","score":"0.5571309","text":"def processChildren(self, children, visitor):\n for c in children:\n if c.visit(visitor):\n return True\n return False","title":""},{"docid":"6fe3a7e3c2fea535107ff95b4bbb2983","score":"0.5567861","text":"def test_node_get(self):\n\n node3_children = self.node3.get_children()\n self.assertEqual(next(node3_children), self.node7)\n self.assertEqual(next(node3_children), self.node8)","title":""},{"docid":"69e58d2ae5e1776a9c97844593cb33f4","score":"0.5566528","text":"def test_ui_object2_oid_get_children_get(self):\n pass","title":""},{"docid":"3d43c0c66d8090591d9a9f80d5276ea6","score":"0.55541646","text":"def has_children(self):\n return self.root in self.nparents","title":""},{"docid":"29d5528a5d1053aa18de0eb991713172","score":"0.5524883","text":"def locate_child(self, child):\n for field in self._astroid_fields:\n node_or_sequence = getattr(self, field)\n # /!\\ compiler.ast Nodes have an __iter__ walking over child nodes\n if child is node_or_sequence:\n return field, child\n if (\n isinstance(node_or_sequence, (tuple, list))\n and child in node_or_sequence\n ):\n return field, node_or_sequence\n msg = \"Could not find %s in %s's children\"\n raise AstroidError(msg % (repr(child), repr(self)))","title":""},{"docid":"3fd84abd8f192562d7eadb834d860c3e","score":"0.5520942","text":"def findChild(self, predicate, creator='None'):\n \n pass","title":""},{"docid":"9bcd555ab9f5c5e55353e71cdb8cbf81","score":"0.55177575","text":"def visit(node, depth=1):\n for child in node.get_children():\n # in case the children are visible to staff only, skip them\n if child.visible_to_staff_only:\n continue\n start = get_override_for_ccx(ccx, child, 'start', None)\n if start:\n start = str(start)[:-9]\n due = get_override_for_ccx(ccx, child, 'due', None)\n if due:\n due = str(due)[:-9]\n hidden = get_override_for_ccx(\n ccx, child, 'visible_to_staff_only',\n child.visible_to_staff_only)\n visited = {\n 'location': str(child.location),\n 'display_name': child.display_name,\n 'category': child.category,\n 'start': start,\n 'due': due,\n 'hidden': hidden,\n }\n if depth < 3:\n children = tuple(visit(child, depth + 1))\n if children:\n visited['children'] = children\n yield visited\n else:\n yield visited","title":""},{"docid":"8f5f5a184d693b5ac23548536f842254","score":"0.55044585","text":"def __contains__(self, node):","title":""},{"docid":"82fc4d0b710ba8ece68ddaf04b638f1f","score":"0.5495379","text":"def find(self, cls=object, test=lambda node: True):\n for parent, node, idx in walk_tree_pre(self):\n if isinstance(node, cls) and test(node):\n yield node","title":""},{"docid":"2f6fafbf4c5cc55cb871c050746367d7","score":"0.54836357","text":"def getTargetInChildren(element, x, y):\n return_elt = None\n for elt in DOM.IterWalkChildren(element):\n hit = isIn(elt, x, y)\n if hit:\n return_elt = elt\n return return_elt","title":""},{"docid":"fc312f184cc1db1ca9f810bb0d429d74","score":"0.547992","text":"def children(node, graph):\n\tchilds = graph[node]\n\tif len(childs) == 0:\n\t\tprint \"node %d is a dead end\" %node\n\treturn childs","title":""},{"docid":"beddbd77f85dbcd364f0780174ff8719","score":"0.5470476","text":"def childrenIndicesByLabel(parsed_tree, label, search_range=None):\n # words\n if not isinstance(parsed_tree, Tree):\n return []\n\n # terminal nodes\n if not isinstance(parsed_tree[0], Tree):\n return []\n\n if search_range is not None:\n return [i for i in search_range\n if parsed_tree[i].label() == label]\n else:\n return [i for i in range(len(parsed_tree)) \n if parsed_tree[i].label() == label]","title":""},{"docid":"0ffe999d7f9e3300bd1d87c719a5a64b","score":"0.5464434","text":"def children(self, member_name):\n\n current = self.somelist.front\n while (current.name != member_name and current is not None):\n current = current.next_\n return current.children","title":""},{"docid":"ac834f06514187c8b447402b5eceb494","score":"0.54544234","text":"def visit_elems(self, node, visited_children):\n return visited_children","title":""},{"docid":"7f5725b1b849928a1f9e77884d85fe20","score":"0.54532886","text":"def get_children(self, token):\n for n in self.children:\n if n.match(token):\n return n\n return None","title":""},{"docid":"5d0a4481bfda3382ba54daee662eddd3","score":"0.5451801","text":"def test_tree_find():\n expected = Tree('assignment', ['x'])\n tree = Tree('start', [Tree('block', [Tree('line', [expected])])])\n assert tree.find('assignment') == [expected]","title":""},{"docid":"abbe606c4915fad8cfb92a733f784b8e","score":"0.54495955","text":"def test_get_child_child_exists(self):\n # Assign\n root = Node(\"Root node\")\n node1 = Node(\"Child node 1\")\n root.add_or_replace_child(node=node1, index=0)\n\n # Act\n retrieved_child = root.get_child(index=0)\n\n # Assert\n self.assertEqual(retrieved_child, node1)","title":""},{"docid":"68b29d71a6b31d862834aa28dba2af40","score":"0.54405564","text":"def children(self, p):\n if self.left(p) is not None:\n yield self.left(p)\n if self.right(p) is not None:\n yield self.right(p)","title":""},{"docid":"68b29d71a6b31d862834aa28dba2af40","score":"0.54405564","text":"def children(self, p):\n if self.left(p) is not None:\n yield self.left(p)\n if self.right(p) is not None:\n yield self.right(p)","title":""},{"docid":"d72c7a487213563bc6a47b7ee12053ed","score":"0.5432187","text":"def test_fetch_all_path_descendants(self):\r\n self.assertPaginatedResult('fetch_all_path_descendants', '2P33/0')","title":""},{"docid":"aa09208a2d2ce704ea925e40b413410c","score":"0.5428874","text":"def find_nodes(nodes,coords):\n return concatenate([ find_row(nodes,c) for c in coords])","title":""},{"docid":"1ea1e6517ea0d4b92816d3658e810f3c","score":"0.54191244","text":"def get_object_children(cmis_id):","title":""},{"docid":"bebd8a501dbc6c3c249bf4db293c0b49","score":"0.54082495","text":"def has_children(self) -> bool:\n return DB.session.query(\n sqlalchemy.literal(True)\n ).filter(\n self.children.exists()\n ).scalar()","title":""},{"docid":"2ab69bdc8483010aa0b268fc0d6e3c6c","score":"0.5401585","text":"def _first_child_found_in(self, *tagnames):\n for tagname in tagnames:\n child = self.find(qn(tagname))\n if child is not None:\n return child\n return None","title":""},{"docid":"2ab69bdc8483010aa0b268fc0d6e3c6c","score":"0.5401585","text":"def _first_child_found_in(self, *tagnames):\n for tagname in tagnames:\n child = self.find(qn(tagname))\n if child is not None:\n return child\n return None","title":""},{"docid":"8ee06db28c594aec926a04c44250a5b7","score":"0.5398356","text":"def nodeHasChildren(VO, lnum): #{{{2\n levels = VO.levels\n if lnum==1 or lnum==len(levels): return False\n elif levels[lnum-1] < levels[lnum]: return True\n else: return False","title":""},{"docid":"01b3997bdc7a05da109f6b40d485ec9a","score":"0.5397548","text":"def list_children_shapes(node, all_hierarchy=True, full_path=True):\n\n raise NotImplementedError()","title":""},{"docid":"8042c160b99ea7257afd9e33e6d0f515","score":"0.53967535","text":"def find_all_paths(coordinate, path=[]):\n path = path + [coordinate]\n word = cobble(path)\n if word not in indices:\n return\n if word in words:\n paths.append(path)\n\n for child in successors(coordinate):\n if child not in path:\n find_all_paths(child, path)","title":""},{"docid":"55c4539bcd627f83881df8727bb62bfc","score":"0.53964573","text":"def _has_children(node):\n return len(list(node.get_children()))","title":""},{"docid":"3eedea601498e716188fc117696b3962","score":"0.53846735","text":"def recurse(parent):\n visited = []\n for child in parent.get_children():\n assert parent.location == child.get_parent().location\n visited.append(child)\n visited += recurse(child)\n return visited","title":""},{"docid":"00537237d27772babef54ee6129afd22","score":"0.5384416","text":"def generate_children(self, node):\n\n moves = [\"up\", \"down\", \"left\", \"right\"]\n children = []\n for move in moves:\n child = copy.deepcopy(node)\n if child.move(move):\n child.parent = node\n child.path_direction = move\n children.append(child)\n return children","title":""},{"docid":"bfa18b74fd7ddb92875cb5311c2ce100","score":"0.5384292","text":"def get_children(node: fbx.FbxNode) -> list[fbx.FbxNode]:\n return [node.GetChild(i) for i in range(node.GetChildCount())]","title":""},{"docid":"0adf585e292b75f5dac147c4394de528","score":"0.538409","text":"def get_children_of(self, identifier):\n\t\treturn [marker.identifier for marker in self.marker_dict[identifier].marker_list]","title":""},{"docid":"3d337348c7a161671308773b1b9d43d8","score":"0.5371615","text":"def processChildren(self, children, visitor):\n for c in children:\n if not c.visit(visitor):\n return False\n return True","title":""},{"docid":"8b675b60ab35cbf90a2447cdd9f1dbcb","score":"0.5365453","text":"def _get_children(self):\n return self._children","title":""},{"docid":"ecdcbaa14d72e70a1619a0bfc58b8522","score":"0.5365169","text":"def load_children(self) -> None:\n pass","title":""},{"docid":"ae088b896cb30ea28437660242985bb4","score":"0.5360459","text":"def _get_children_nodes(self, document):\n ret = []\n for child in self.children:\n if child.submenu:\n node = child.submenu.to_xml(document)\n else:\n node = child.to_xml(document)\n\n if node:\n ret.append(node)\n return ret","title":""},{"docid":"3e93cfe8a2161f3e2756c51c36756d58","score":"0.53564805","text":"def get_node_children(self, node_handle):\n return self.tk.call(node_handle, \"children\")","title":""},{"docid":"2228e9860eaf22c74bcf64a365ffbed6","score":"0.53492546","text":"def test_find_node_works_while_node_exist(filled_list):\n assert filled_list.find_node(67)","title":""},{"docid":"abce7feeb13b231f262da08ff27278dc","score":"0.53473216","text":"def findChildren(self, name):\n\n # Note: this returns a list of all the children of a given\n # name, irrespective of the depth of look-up.\n \n children = []\n \n for child in self.getAllChildren():\n if child.getName() == name:\n children.append(child)\n\n return children","title":""},{"docid":"c0b42a5544b9e15f8087b6dceadfe01b","score":"0.53314596","text":"def test_navigation_descendants(chikin):\n print(list(chikin.descendants))\n assert len(list(chikin.descendants)) == 28","title":""},{"docid":"400c5f0a8f509e4c93fcf2ee3a6b096d","score":"0.5323211","text":"def test_contain(self):\n self.assertTrue(self.tree.contains(0))\n self.assertFalse(self.tree.contains(1))","title":""},{"docid":"8130484dac0be69b72c6ce79f61d9e71","score":"0.5322364","text":"def children(self, node):\n\n return [x for x in tuple(node.children) if x]","title":""},{"docid":"df1a02f72cbb6665abf28464240a56b7","score":"0.53202313","text":"def findall(parent, name, ns=''):\n return [ \n node for node in parent.childNodes\n if node.nodeType == Node.ELEMENT_NODE and \\\n node.nodeName == name\n ]","title":""},{"docid":"266520595191b6491619162bc786acdf","score":"0.53175855","text":"def childs(self):\n \n pass","title":""},{"docid":"266520595191b6491619162bc786acdf","score":"0.53175855","text":"def childs(self):\n \n pass","title":""},{"docid":"266520595191b6491619162bc786acdf","score":"0.53175855","text":"def childs(self):\n \n pass","title":""},{"docid":"266520595191b6491619162bc786acdf","score":"0.53175855","text":"def childs(self):\n \n pass","title":""},{"docid":"4e4128543546fd9635268702bb025c84","score":"0.5314826","text":"def find_subtree(root, label_parts):\n cursor = root\n while cursor and cursor['label'] != label_parts:\n next_cursor = None\n for child in cursor['children']:\n if child['label'] == label_parts[:len(child['label'])]:\n next_cursor = child\n cursor = next_cursor\n return cursor","title":""},{"docid":"67368325ea3c14bc2b6b549a35cc75b3","score":"0.53103274","text":"def has_children ( self ):\n return True","title":""},{"docid":"e3727beb860bbef1d3183931b21e54b9","score":"0.5308358","text":"def yield_children(logger: ConsolePrinter, data: Any,\n terms: SearchTerms, pathsep: PathSeparators,\n build_path: str, seen_anchors: List[str],\n **kwargs: bool) -> Generator[YAMLPath, None, None]:\n include_key_aliases: bool = kwargs.pop(\"include_key_aliases\", True)\n include_value_aliases: bool = kwargs.pop(\"include_value_aliases\", False)\n search_anchors: bool = kwargs.pop(\"search_anchors\", False)\n logger.debug(\n \"Dumping all children in data of type, {}:\"\n .format(type(data)), data=data,\n prefix=\"yaml_paths::yield_children: \")\n\n exclude_alias_matchers = [AnchorMatches.UNSEARCHABLE_ALIAS,\n AnchorMatches.ALIAS_EXCLUDED]\n\n if isinstance(data, CommentedSeq):\n if not build_path and pathsep is PathSeparators.FSLASH:\n build_path = str(pathsep)\n build_path += \"[\"\n\n for idx, ele in enumerate(data):\n anchor_matched = Searches.search_anchor(\n ele, terms, seen_anchors, search_anchors=search_anchors,\n include_aliases=include_value_aliases)\n logger.debug(\n (\"yaml_paths::yield_children<list>: \"\n + \"element[{}] has anchor search => {}.\")\n .format(idx, anchor_matched))\n\n # Build the temporary YAML Path using either Anchor or Index\n if anchor_matched is AnchorMatches.NO_ANCHOR:\n # Not an anchor/alias, so ref this node by its index\n tmp_path = build_path + str(idx) + \"]\"\n else:\n tmp_path = \"{}&{}]\".format(\n build_path,\n YAMLPath.escape_path_section(ele.anchor.value, pathsep)\n )\n\n if (not include_value_aliases\n and anchor_matched in exclude_alias_matchers):\n continue\n\n if isinstance(ele, (CommentedMap, CommentedSeq)):\n for path in yield_children(\n logger, ele, terms, pathsep, tmp_path, seen_anchors,\n search_anchors=search_anchors,\n include_key_aliases=include_key_aliases,\n include_value_aliases=include_value_aliases):\n yield path\n else:\n yield YAMLPath(tmp_path)\n\n elif isinstance(data, CommentedMap):\n if build_path:\n build_path += str(pathsep)\n elif pathsep is PathSeparators.FSLASH:\n build_path = str(pathsep)\n\n pool = data.non_merged_items()\n if include_key_aliases or include_value_aliases:\n pool = data.items()\n\n for key, val in pool:\n tmp_path = build_path + YAMLPath.escape_path_section(key, pathsep)\n\n key_anchor_matched = Searches.search_anchor(\n key, terms, seen_anchors, search_anchors=search_anchors,\n include_aliases=include_key_aliases)\n val_anchor_matched = Searches.search_anchor(\n val, terms, seen_anchors, search_anchors=search_anchors,\n include_aliases=include_value_aliases)\n logger.debug(\n (\"yaml_paths::yield_children<dict>: \"\n + \"key[{}]:value have value anchor search => {}:{}.\")\n .format(key, key_anchor_matched, val_anchor_matched))\n\n if (\n (not include_key_aliases\n and key_anchor_matched in exclude_alias_matchers)\n or (not include_value_aliases\n and val_anchor_matched in exclude_alias_matchers)\n ):\n continue\n\n if isinstance(val, (CommentedSeq, CommentedMap)):\n for path in yield_children(\n logger, val, terms, pathsep, tmp_path, seen_anchors,\n search_anchors=search_anchors,\n include_key_aliases=include_key_aliases,\n include_value_aliases=include_value_aliases):\n yield path\n else:\n yield YAMLPath(tmp_path)\n\n else:\n if not build_path and pathsep is PathSeparators.FSLASH:\n build_path = str(pathsep)\n yield YAMLPath(build_path)","title":""},{"docid":"4e18dcea3a1696682924c1a2aaa3247d","score":"0.53059286","text":"def search(self, value, node=None):\n if node is None:\n node = self.root\n\n if node.keys:\n return True\n\n elif node.leaf:\n # If we are in a leaf, there is no more to check.\n return False\n\n else:\n i = 0\n while i < node.size:\n i += 1\n return self.search(value, node.children[i])","title":""},{"docid":"4bd7fa58cd00dd8acf8a47b59dd0e5a1","score":"0.53005666","text":"def test_has_children_parent_node(self):\n ### Arrange ###########################################################\n tree = Tree()\n tree.insert(Tree.ROOT_PATH)\n tree.insert(Tree.ROOT_PATH + (0,))\n\n ### Assert ############################################################\n self.assertTrue(tree.has_children(Tree.ROOT_PATH))","title":""},{"docid":"6b2ad6818ccb7d72da237edf12650a70","score":"0.5295838","text":"def get_children(self, ident=None):\n if ident is None:\n ident = self.root\n out = [\n (self.ntypes[i], self.nnames[i], self.addresses[i])\n for i in [\n index for index, parent in enumerate(self.nparents) if parent == ident\n ]\n ]\n return out","title":""},{"docid":"7295a7ee8132fe461ec051017be6887f","score":"0.5285472","text":"def find_all_paths(parent_to_child, start, path=[], path_dict=defaultdict()):\n p = path.copy()\n p.append(start)\n if start not in parent_to_child:\n return p, path_dict\n paths = []\n for node in parent_to_child[start]:\n if node in path_dict:\n new_paths = path_dict[node]\n else:\n new_paths, path_dict = find_all_paths(parent_to_child, node, p, path_dict)\n path_dict[node] = new_paths.copy()\n if type(new_paths[0]) is int:\n # there is only one downstream path from current node\n paths.append(new_paths)\n else:\n # there are multiple downstream paths from current node\n for new_path in new_paths:\n paths.append(new_path)\n # LOG.info(\"Returning paths for node: %d\", start)\n return paths, path_dict","title":""},{"docid":"67b9ea151c4aae54f5b8f9c33ec782de","score":"0.5283161","text":"def deep_query(query, nodes):\n def inner(children):\n results = []\n for c in children:\n if query([c]):\n results.append(c)\n results.extend(inner(c.children))\n return results\n return inner(nodes)","title":""},{"docid":"cea22f911f67d5030f5757a58da0f0f4","score":"0.52827543","text":"def get_children(self):\n for node in self.children:\n yield node","title":""},{"docid":"279316c131b487f1795dda663954ea29","score":"0.52708036","text":"def get_children_of_class_node(classNode):\n\tchildRefNodes = classNode.findall(XPATH_CHILD)\n\treturn childRefNodes","title":""},{"docid":"dc987918ecc512946f273bbe3e2f5892","score":"0.52690476","text":"def findParents(self, name='None', attrs='{}', limit='None', **kwargs):\n \n pass","title":""}],"string":"[\n {\n \"docid\": \"402682c267e585a31521634972681bfe\",\n \"score\": \"0.63392574\",\n \"text\": \"def getChildren(node, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47f5e66c1cce9bfeecaeb53fb73a0493\",\n \"score\": \"0.63026905\",\n \"text\": \"def findChildren(self, name='None', attrs='{}', recursive='True', text='None', limit='None', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"685bddfc7f8acd0ec7d47ec4455f4ed4\",\n \"score\": \"0.629981\",\n \"text\": \"def test_childLookup(self):\\n self.navpage.factory = tcf = TestClientFactory(\\n 'client-of-livepage', 'I AM A CLIENT OF THE LIVEPAGE')\\n\\n self.assertEqual(self.navpage.locateChild(None,\\n ('child-of-fragment',)),\\n ('I AM A CHILD OF THE FRAGMENT', ()))\\n self.assertEqual(self.navpage.locateChild(None,\\n (tcf.magicSegment,)),\\n (tcf.returnValue, ()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed4689f762e8dcb895ced4a0d3bbedef\",\n \"score\": \"0.625426\",\n \"text\": \"def locateChild(ctx, segments):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eb123051c984814d6fa75305c32846f\",\n \"score\": \"0.62259406\",\n \"text\": \"def test_node_has_correct_children(full_tree):\\n node = full_tree.search(6)\\n assert node.left.val == 4 and node.right.val == 8\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca1cb104961e5a9e08fd95b4251fb93\",\n \"score\": \"0.60317063\",\n \"text\": \"def node_children(node, all_hierarchy=True, full_path=True):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2737e3c256626da7300a649ff3b0beee\",\n \"score\": \"0.5995648\",\n \"text\": \"def findChild(self, name='None', attrs='{}', recursive='True', text='None', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed612ffbe6224af871af581ef2f2981\",\n \"score\": \"0.5937417\",\n \"text\": \"def get_children(self, node):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b155cea05c39cd319a511dcc6affde6\",\n \"score\": \"0.59342474\",\n \"text\": \"def get_children(self, child_name = None):\\n children = []\\n try:\\n sibling = self.get_first_child()\\n except PixeliseElementSearchError:\\n raise PixeliseElementSearchError, \\\"There are no children.\\\"\\n while sibling:\\n if child_name:\\n if sibling.get_node_name() == child_name:\\n children.append(sibling)\\n else:\\n children.append(sibling)\\n try:\\n sibling = sibling.get_next_sibling()\\n except PixeliseElementSearchError:\\n # No more siblings so lets end the while loop\\n sibling = None\\n return children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"328c748cd9402d068574c2c0ad940f59\",\n \"score\": \"0.5921426\",\n \"text\": \"def get_child_nodes(node, node_ids):\\n if node['childNodes']:\\n for child in node['childNodes']:\\n node_ids.append(child['id'])\\n get_child_nodes(child, node_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ec83969e242cb01f96eb3126f6b92a\",\n \"score\": \"0.5915198\",\n \"text\": \"def find_children_named(self, parent, name):\\n for node in parent.childNodes:\\n if node.nodeName == name:\\n yield node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cda562d890edf1f251b577f3b2d5b14\",\n \"score\": \"0.58905506\",\n \"text\": \"def children(self, id_, **kwargs):\\n if \\\"parent\\\" in self.data.columns:\\n query = dict(parent=id_, **kwargs)\\n _df = self.data\\n conditions = [(_df[k] == v) for (k, v) in query.items()]\\n mask = multiple_and(*conditions)\\n children = _df[mask].index.tolist()\\n\\n return [x for x in children]\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24e8bcbae3819892dc50c7fe76a5497\",\n \"score\": \"0.58869016\",\n \"text\": \"def search(self, keyword, children=None, arg=None):\\n if children is None:\\n children = self.substmts\\n return [ch for ch in children\\n if ch.keyword == keyword and (arg is None or ch.arg == arg)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1d32e44830d8be1c5ed6f04a3810b2f\",\n \"score\": \"0.5882318\",\n \"text\": \"def return_children(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d1db8d4199da5e29cf95d419a71f202\",\n \"score\": \"0.58799654\",\n \"text\": \"def find_all(self, node_type):\\n for child in self.iter_child_nodes():\\n if isinstance(child, node_type):\\n yield child\\n for result in child.find_all(node_type):\\n yield result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce43dea7f181edfbcfa31286fb98ee89\",\n \"score\": \"0.5866933\",\n \"text\": \"def walkChildren(elem):\\n return IterWalkChildren(elem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f21ca25c748fc3e0c1d3eb80cc47b13\",\n \"score\": \"0.584094\",\n \"text\": \"def list_children(node, all_hierarchy=True, full_path=True, children_type=None):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"665d5447f53f579aa2f276a25987cfca\",\n \"score\": \"0.58373964\",\n \"text\": \"def find_children(self):\\n for i in range(len(self.vertices)):\\n self.vertices[i].children = []\\n for i in range(len(self.vertices)):\\n for parent in self.vertices[i].parents:\\n if i not in self.vertices[parent].children:\\n self.vertices[parent].children.append(i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a823faf0f219fe14b0ce54d37facdd\",\n \"score\": \"0.5823317\",\n \"text\": \"def get_children(self, refobj): #pragma: no cover\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe4818630122f60b6bde56132df1e4e\",\n \"score\": \"0.58139014\",\n \"text\": \"def fetch_new_children(self, ):\\n root = self.get_root()\\n refobjinter = self.get_refobjinter()\\n unwrapped = self.get_unwrapped(root, refobjinter)\\n self.wrap(self.get_root(), self.get_refobjinter(), unwrapped)\\n\\n suggestions = self.get_suggestions()\\n for typ, element in suggestions:\\n for c in self._children:\\n if typ == c.get_typ() and element == c.get_element():\\n break\\n else:\\n Reftrack(root=root, refobjinter=refobjinter, typ=typ, element=element, parent=self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"783e409c369d6740a359c1290f19e843\",\n \"score\": \"0.5812229\",\n \"text\": \"def _findNode(self, tree, path):\\n for item in tree['children']:\\n if path.startswith(item['path']):\\n return self._findNode(item, path)\\n return tree\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3fce45cea74fc242574a6f030677afd\",\n \"score\": \"0.5804516\",\n \"text\": \"def get_children(self, node, crawler):\\n\\n graph = crawler.get_graph()\\n state = crawler.get_state()\\n self.sleep()\\n if graph.node[node]['bipartite'] == 0:\\n return self.get_child0(node, graph, state)\\n\\n else:\\n return self.get_child1(node, graph, state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee56c8bce163496b6c0c8491a78ed488\",\n \"score\": \"0.580129\",\n \"text\": \"def generate_children(self, node):\\n children = []\\n # North\\n # If the node would be outside of the board, or contain a wall, we do not generate it.\\n if node.state[1] > 0:\\n if not self.world[node.state[0]][node.state[1] - 1] == \\\"#\\\":\\n state = (node.state[0], node.state[1] - 1)\\n child = search_node.SearchNode(state, node.g + self.get_cost(state), self.heuristic_eval(state), node)\\n children.append(child)\\n\\n # East\\n if node.state[0] < len(self.world) - 1:\\n if not self.world[node.state[0] + 1][node.state[1]] == \\\"#\\\":\\n state = (node.state[0] + 1, node.state[1])\\n child = search_node.SearchNode(state, node.g + self.get_cost(state), self.heuristic_eval(state), node)\\n children.append(child)\\n\\n # West\\n if node.state[0] > 0:\\n if not self.world[node.state[0] - 1][node.state[1]] == \\\"#\\\":\\n state = (node.state[0] - 1, node.state[1])\\n child = search_node.SearchNode(state, node.g + self.get_cost(state), self.heuristic_eval(state), node)\\n children.append(child)\\n\\n # South\\n if node.state[1] < len(self.world[0]) - 1:\\n if not self.world[node.state[0]][node.state[1] + 1] == \\\"#\\\":\\n state = (node.state[0], node.state[1] + 1)\\n child = search_node.SearchNode(state, node.g + self.get_cost(state), self.heuristic_eval(state), node)\\n children.append(child)\\n\\n return children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f50ca2322c493e0c01b7b701046ad30e\",\n \"score\": \"0.5784027\",\n \"text\": \"def children(self) -> Iterable[\\\"Node\\\"]:\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a79a7ee7dce15ae623005ecd3cc73e5\",\n \"score\": \"0.57702726\",\n \"text\": \"def test_find_elements(self):\\n tree = BinarySearchTree()\\n for element in (1, 10, 12, 5, 8):\\n tree.add(element)\\n self.assertTrue(tree.find(element))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61738f5aadf5a4d348f2ea490d88446\",\n \"score\": \"0.57560235\",\n \"text\": \"def findChildren(self,**kwargs):\\r\\n\\r\\n\\t\\tchildren = []\\r\\n\\t\\tdef _childCollector(widget):\\r\\n\\t\\t\\tif widget.match(**kwargs):\\r\\n\\t\\t\\t\\tchildren.append(widget)\\r\\n\\t\\tself.deepApply(_childCollector)\\r\\n\\t\\treturn children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13e3f974cf814dde0e02e03e1d7e8342\",\n \"score\": \"0.57319283\",\n \"text\": \"def get_children(*a): \\n _str_func = 'get_children'\\n _d = validate_arg(*a) \\n \\n if '[' in _d['attr']:\\n return False\\n \\n try:return mc.attributeQuery(_d['attr'], node = _d['node'], listChildren=True) or [] \\n except Exception,err:\\n log.error(\\\"|{0}| >> {1} | {2}\\\".format(_str_func,_d['combined'],err))\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"162099eced56b86f4c1265c43904b7cf\",\n \"score\": \"0.57305413\",\n \"text\": \"def find_child_expr(cfunc, e, q, parents=False):\\r\\n\\r\\n class expr_finder_t(hx.ctree_visitor_t):\\r\\n def __init__(self, cfunc, q, parents):\\r\\n hx.ctree_visitor_t.__init__(self,\\r\\n hx.CV_PARENTS if parents else hx.CV_FAST)\\r\\n\\r\\n self.cfunc = cfunc\\r\\n self.query = q\\r\\n self.found = list()\\r\\n return\\r\\n\\r\\n def visit_expr(self, e):\\r\\n \\\"\\\"\\\"process cexpr_t elements\\\"\\\"\\\"\\r\\n try:\\r\\n if self.query(self.cfunc, e):\\r\\n self.found.append(query_result_t(self.cfunc, e))\\r\\n except:\\r\\n pass\\r\\n return 0\\r\\n\\r\\n if cfunc:\\r\\n expfinder = expr_finder_t(cfunc, q, parents)\\r\\n expfinder.apply_to_exprs(e, None)\\r\\n return expfinder.found\\r\\n return list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6309cdfdf41352168fbf13a4f691df54\",\n \"score\": \"0.57216424\",\n \"text\": \"def GetChildren(self, item, children):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3324423a8f6d4f973883612e34d9a655\",\n \"score\": \"0.57093817\",\n \"text\": \"def get_children(self, points, masses, ids, leaves):\\n #index of the children\\n index = (points > self.cen)\\n \\n #loop through 8 children\\n for i in range(2):\\n for j in range(2):\\n for k in range(2):\\n \\n #check if there is a galaxy in the child node\\n in_node = np.all(index == np.bool_([i,j,k]), axis=0)\\n \\n #if theres no point in the node\\n if not np.any(in_node): \\n continue\\n # offset between parent and child box centers\\n offset = 0.5*self.size*(np.array([i,j,k])-0.5) \\n self.children.append(Node(self.cen + offset,\\n self.size/2,\\n masses[in_node],\\n points[in_node],\\n ids[in_node],\\n leaves))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7adca38ceb9912979d5cc332f62c678\",\n \"score\": \"0.56792617\",\n \"text\": \"def test_navigation_children(chikin):\\n assert len(list(chikin.children)) == 2\\n docclass, document = chikin.children\\n assert document.name == 'document'\\n assert len(list(chikin.document.children)) == 7\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd63f0d7748fda35f9177c4e74202b23\",\n \"score\": \"0.56589663\",\n \"text\": \"def find_childs(self, search_term, search_matches, ignore_case=False):\\n\\n if ignore_case:\\n search_term = search_term.lower()\\n gid = self.gid.lower()\\n name = self.name.lower()\\n else:\\n gid = self.gid\\n name = self.name\\n \\n if search_term in gid or search_term in name:\\n search_matches.append(self)\\n\\n for c in self.childs:\\n search_matches = c.find_childs(search_term, search_matches, ignore_case)\\n\\n return search_matches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c76e9855f382d96433cbe125d8785d\",\n \"score\": \"0.56486493\",\n \"text\": \"def get_children(self, id, level=None):\\n\\n db = self.db\\n table = db.gis_location\\n\\n query = (table.deleted == False)\\n if level:\\n query = query & (table.level == level)\\n term = str(id)\\n query = query & ((table.path.like(term + \\\"/%\\\")) | (table.path.like(\\\"%/\\\" + term + \\\"/%\\\")))\\n children = db(query).select(table.id,\\n table.name)\\n return children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a6e1f96330c24870d9569f2ec0ee40f\",\n \"score\": \"0.56354845\",\n \"text\": \"def find_child_item(cfunc, i, q, parents=False):\\r\\n\\r\\n class citem_finder_t(hx.ctree_visitor_t):\\r\\n def __init__(self, cfunc, q, parents):\\r\\n hx.ctree_visitor_t.__init__(self,\\r\\n hx.CV_PARENTS if parents else hx.CV_FAST)\\r\\n\\r\\n self.cfunc = cfunc\\r\\n self.query = q\\r\\n self.found = list()\\r\\n return\\r\\n\\r\\n def process(self, i):\\r\\n \\\"\\\"\\\"process cinsn_t and cexpr_t elements alike\\\"\\\"\\\"\\r\\n\\r\\n try:\\r\\n if self.query(self.cfunc, i):\\r\\n self.found.append(query_result_t(self.cfunc, i))\\r\\n except:\\r\\n pass\\r\\n return 0\\r\\n\\r\\n def visit_insn(self, i):\\r\\n return self.process(i)\\r\\n\\r\\n def visit_expr(self, e):\\r\\n return self.process(e)\\r\\n\\r\\n if cfunc:\\r\\n itfinder = citem_finder_t(cfunc, q, parents)\\r\\n itfinder.apply_to(i, None)\\r\\n return itfinder.found\\r\\n return list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2335278368c480be46897b7c5a8b0ec\",\n \"score\": \"0.5628231\",\n \"text\": \"def get_children(self):\\r\\n return _RMF.TraverseHelper_get_children(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"131001bf8104dcce861efbc6f44a9284\",\n \"score\": \"0.5596992\",\n \"text\": \"def get_children(self, path):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90b15b283eccbd93d81ae6691783135e\",\n \"score\": \"0.55948013\",\n \"text\": \"def get_children(self):\\n\\t\\treturn self.marker_list[:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f9a908e86aee78c155cc14d7b5b1462\",\n \"score\": \"0.55831885\",\n \"text\": \"def children(self, p):\\n if self.left(p) is not None:\\n yield self.left(p)\\n if self.right(p) is not None:\\n yield self.right(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372a32ad5059b9e1967ce3926955fbd4\",\n \"score\": \"0.5581325\",\n \"text\": \"def findChildren(self, nodeType = None):\\n if nodeType is None:\\n nodeType = DocNode\\n return [x for x in self.children if x.isA(nodeType)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c96848fea1ceed018304f752dbe0cda7\",\n \"score\": \"0.55774796\",\n \"text\": \"def get_location_children(client: SymphonyClient, location_id: str) -> List[Location]:\\n location_with_children = LocationChildrenQuery.execute(\\n client, id=location_id\\n ).location\\n if not location_with_children:\\n raise EntityNotFoundError(entity=Entity.Location, entity_id=location_id)\\n\\n return [\\n Location(\\n name=location.name,\\n id=location.id,\\n latitude=location.latitude,\\n longitude=location.longitude,\\n externalId=location.externalId,\\n locationTypeName=location.locationType.name,\\n )\\n for location in location_with_children.children\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6b85326cfdb2cbf6d626a384fe446f6\",\n \"score\": \"0.5571778\",\n \"text\": \"def children(self, p):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32aacaba32f7b3f8d6b81f582a180ebe\",\n \"score\": \"0.5571309\",\n \"text\": \"def processChildren(self, children, visitor):\\n for c in children:\\n if c.visit(visitor):\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fe3a7e3c2fea535107ff95b4bbb2983\",\n \"score\": \"0.5567861\",\n \"text\": \"def test_node_get(self):\\n\\n node3_children = self.node3.get_children()\\n self.assertEqual(next(node3_children), self.node7)\\n self.assertEqual(next(node3_children), self.node8)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e58d2ae5e1776a9c97844593cb33f4\",\n \"score\": \"0.5566528\",\n \"text\": \"def test_ui_object2_oid_get_children_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d43c0c66d8090591d9a9f80d5276ea6\",\n \"score\": \"0.55541646\",\n \"text\": \"def has_children(self):\\n return self.root in self.nparents\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d5528a5d1053aa18de0eb991713172\",\n \"score\": \"0.5524883\",\n \"text\": \"def locate_child(self, child):\\n for field in self._astroid_fields:\\n node_or_sequence = getattr(self, field)\\n # /!\\\\ compiler.ast Nodes have an __iter__ walking over child nodes\\n if child is node_or_sequence:\\n return field, child\\n if (\\n isinstance(node_or_sequence, (tuple, list))\\n and child in node_or_sequence\\n ):\\n return field, node_or_sequence\\n msg = \\\"Could not find %s in %s's children\\\"\\n raise AstroidError(msg % (repr(child), repr(self)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd84abd8f192562d7eadb834d860c3e\",\n \"score\": \"0.5520942\",\n \"text\": \"def findChild(self, predicate, creator='None'):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bcd555ab9f5c5e55353e71cdb8cbf81\",\n \"score\": \"0.55177575\",\n \"text\": \"def visit(node, depth=1):\\n for child in node.get_children():\\n # in case the children are visible to staff only, skip them\\n if child.visible_to_staff_only:\\n continue\\n start = get_override_for_ccx(ccx, child, 'start', None)\\n if start:\\n start = str(start)[:-9]\\n due = get_override_for_ccx(ccx, child, 'due', None)\\n if due:\\n due = str(due)[:-9]\\n hidden = get_override_for_ccx(\\n ccx, child, 'visible_to_staff_only',\\n child.visible_to_staff_only)\\n visited = {\\n 'location': str(child.location),\\n 'display_name': child.display_name,\\n 'category': child.category,\\n 'start': start,\\n 'due': due,\\n 'hidden': hidden,\\n }\\n if depth < 3:\\n children = tuple(visit(child, depth + 1))\\n if children:\\n visited['children'] = children\\n yield visited\\n else:\\n yield visited\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f5f5a184d693b5ac23548536f842254\",\n \"score\": \"0.55044585\",\n \"text\": \"def __contains__(self, node):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82fc4d0b710ba8ece68ddaf04b638f1f\",\n \"score\": \"0.5495379\",\n \"text\": \"def find(self, cls=object, test=lambda node: True):\\n for parent, node, idx in walk_tree_pre(self):\\n if isinstance(node, cls) and test(node):\\n yield node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f6fafbf4c5cc55cb871c050746367d7\",\n \"score\": \"0.54836357\",\n \"text\": \"def getTargetInChildren(element, x, y):\\n return_elt = None\\n for elt in DOM.IterWalkChildren(element):\\n hit = isIn(elt, x, y)\\n if hit:\\n return_elt = elt\\n return return_elt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc312f184cc1db1ca9f810bb0d429d74\",\n \"score\": \"0.547992\",\n \"text\": \"def children(node, graph):\\n\\tchilds = graph[node]\\n\\tif len(childs) == 0:\\n\\t\\tprint \\\"node %d is a dead end\\\" %node\\n\\treturn childs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beddbd77f85dbcd364f0780174ff8719\",\n \"score\": \"0.5470476\",\n \"text\": \"def childrenIndicesByLabel(parsed_tree, label, search_range=None):\\n # words\\n if not isinstance(parsed_tree, Tree):\\n return []\\n\\n # terminal nodes\\n if not isinstance(parsed_tree[0], Tree):\\n return []\\n\\n if search_range is not None:\\n return [i for i in search_range\\n if parsed_tree[i].label() == label]\\n else:\\n return [i for i in range(len(parsed_tree)) \\n if parsed_tree[i].label() == label]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ffe999d7f9e3300bd1d87c719a5a64b\",\n \"score\": \"0.5464434\",\n \"text\": \"def children(self, member_name):\\n\\n current = self.somelist.front\\n while (current.name != member_name and current is not None):\\n current = current.next_\\n return current.children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac834f06514187c8b447402b5eceb494\",\n \"score\": \"0.54544234\",\n \"text\": \"def visit_elems(self, node, visited_children):\\n return visited_children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f5725b1b849928a1f9e77884d85fe20\",\n \"score\": \"0.54532886\",\n \"text\": \"def get_children(self, token):\\n for n in self.children:\\n if n.match(token):\\n return n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0a4481bfda3382ba54daee662eddd3\",\n \"score\": \"0.5451801\",\n \"text\": \"def test_tree_find():\\n expected = Tree('assignment', ['x'])\\n tree = Tree('start', [Tree('block', [Tree('line', [expected])])])\\n assert tree.find('assignment') == [expected]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abbe606c4915fad8cfb92a733f784b8e\",\n \"score\": \"0.54495955\",\n \"text\": \"def test_get_child_child_exists(self):\\n # Assign\\n root = Node(\\\"Root node\\\")\\n node1 = Node(\\\"Child node 1\\\")\\n root.add_or_replace_child(node=node1, index=0)\\n\\n # Act\\n retrieved_child = root.get_child(index=0)\\n\\n # Assert\\n self.assertEqual(retrieved_child, node1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b29d71a6b31d862834aa28dba2af40\",\n \"score\": \"0.54405564\",\n \"text\": \"def children(self, p):\\n if self.left(p) is not None:\\n yield self.left(p)\\n if self.right(p) is not None:\\n yield self.right(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68b29d71a6b31d862834aa28dba2af40\",\n \"score\": \"0.54405564\",\n \"text\": \"def children(self, p):\\n if self.left(p) is not None:\\n yield self.left(p)\\n if self.right(p) is not None:\\n yield self.right(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d72c7a487213563bc6a47b7ee12053ed\",\n \"score\": \"0.5432187\",\n \"text\": \"def test_fetch_all_path_descendants(self):\\r\\n self.assertPaginatedResult('fetch_all_path_descendants', '2P33/0')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa09208a2d2ce704ea925e40b413410c\",\n \"score\": \"0.5428874\",\n \"text\": \"def find_nodes(nodes,coords):\\n return concatenate([ find_row(nodes,c) for c in coords])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ea1e6517ea0d4b92816d3658e810f3c\",\n \"score\": \"0.54191244\",\n \"text\": \"def get_object_children(cmis_id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bebd8a501dbc6c3c249bf4db293c0b49\",\n \"score\": \"0.54082495\",\n \"text\": \"def has_children(self) -> bool:\\n return DB.session.query(\\n sqlalchemy.literal(True)\\n ).filter(\\n self.children.exists()\\n ).scalar()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab69bdc8483010aa0b268fc0d6e3c6c\",\n \"score\": \"0.5401585\",\n \"text\": \"def _first_child_found_in(self, *tagnames):\\n for tagname in tagnames:\\n child = self.find(qn(tagname))\\n if child is not None:\\n return child\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ab69bdc8483010aa0b268fc0d6e3c6c\",\n \"score\": \"0.5401585\",\n \"text\": \"def _first_child_found_in(self, *tagnames):\\n for tagname in tagnames:\\n child = self.find(qn(tagname))\\n if child is not None:\\n return child\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ee06db28c594aec926a04c44250a5b7\",\n \"score\": \"0.5398356\",\n \"text\": \"def nodeHasChildren(VO, lnum): #{{{2\\n levels = VO.levels\\n if lnum==1 or lnum==len(levels): return False\\n elif levels[lnum-1] < levels[lnum]: return True\\n else: return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b3997bdc7a05da109f6b40d485ec9a\",\n \"score\": \"0.5397548\",\n \"text\": \"def list_children_shapes(node, all_hierarchy=True, full_path=True):\\n\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8042c160b99ea7257afd9e33e6d0f515\",\n \"score\": \"0.53967535\",\n \"text\": \"def find_all_paths(coordinate, path=[]):\\n path = path + [coordinate]\\n word = cobble(path)\\n if word not in indices:\\n return\\n if word in words:\\n paths.append(path)\\n\\n for child in successors(coordinate):\\n if child not in path:\\n find_all_paths(child, path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c4539bcd627f83881df8727bb62bfc\",\n \"score\": \"0.53964573\",\n \"text\": \"def _has_children(node):\\n return len(list(node.get_children()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eedea601498e716188fc117696b3962\",\n \"score\": \"0.53846735\",\n \"text\": \"def recurse(parent):\\n visited = []\\n for child in parent.get_children():\\n assert parent.location == child.get_parent().location\\n visited.append(child)\\n visited += recurse(child)\\n return visited\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00537237d27772babef54ee6129afd22\",\n \"score\": \"0.5384416\",\n \"text\": \"def generate_children(self, node):\\n\\n moves = [\\\"up\\\", \\\"down\\\", \\\"left\\\", \\\"right\\\"]\\n children = []\\n for move in moves:\\n child = copy.deepcopy(node)\\n if child.move(move):\\n child.parent = node\\n child.path_direction = move\\n children.append(child)\\n return children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfa18b74fd7ddb92875cb5311c2ce100\",\n \"score\": \"0.5384292\",\n \"text\": \"def get_children(node: fbx.FbxNode) -> list[fbx.FbxNode]:\\n return [node.GetChild(i) for i in range(node.GetChildCount())]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0adf585e292b75f5dac147c4394de528\",\n \"score\": \"0.538409\",\n \"text\": \"def get_children_of(self, identifier):\\n\\t\\treturn [marker.identifier for marker in self.marker_dict[identifier].marker_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d337348c7a161671308773b1b9d43d8\",\n \"score\": \"0.5371615\",\n \"text\": \"def processChildren(self, children, visitor):\\n for c in children:\\n if not c.visit(visitor):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b675b60ab35cbf90a2447cdd9f1dbcb\",\n \"score\": \"0.5365453\",\n \"text\": \"def _get_children(self):\\n return self._children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecdcbaa14d72e70a1619a0bfc58b8522\",\n \"score\": \"0.5365169\",\n \"text\": \"def load_children(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae088b896cb30ea28437660242985bb4\",\n \"score\": \"0.5360459\",\n \"text\": \"def _get_children_nodes(self, document):\\n ret = []\\n for child in self.children:\\n if child.submenu:\\n node = child.submenu.to_xml(document)\\n else:\\n node = child.to_xml(document)\\n\\n if node:\\n ret.append(node)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e93cfe8a2161f3e2756c51c36756d58\",\n \"score\": \"0.53564805\",\n \"text\": \"def get_node_children(self, node_handle):\\n return self.tk.call(node_handle, \\\"children\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2228e9860eaf22c74bcf64a365ffbed6\",\n \"score\": \"0.53492546\",\n \"text\": \"def test_find_node_works_while_node_exist(filled_list):\\n assert filled_list.find_node(67)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abce7feeb13b231f262da08ff27278dc\",\n \"score\": \"0.53473216\",\n \"text\": \"def findChildren(self, name):\\n\\n # Note: this returns a list of all the children of a given\\n # name, irrespective of the depth of look-up.\\n \\n children = []\\n \\n for child in self.getAllChildren():\\n if child.getName() == name:\\n children.append(child)\\n\\n return children\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0b42a5544b9e15f8087b6dceadfe01b\",\n \"score\": \"0.53314596\",\n \"text\": \"def test_navigation_descendants(chikin):\\n print(list(chikin.descendants))\\n assert len(list(chikin.descendants)) == 28\",\n \"title\": \"\"\n },\n {\n \"docid\": \"400c5f0a8f509e4c93fcf2ee3a6b096d\",\n \"score\": \"0.5323211\",\n \"text\": \"def test_contain(self):\\n self.assertTrue(self.tree.contains(0))\\n self.assertFalse(self.tree.contains(1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8130484dac0be69b72c6ce79f61d9e71\",\n \"score\": \"0.5322364\",\n \"text\": \"def children(self, node):\\n\\n return [x for x in tuple(node.children) if x]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1a02f72cbb6665abf28464240a56b7\",\n \"score\": \"0.53202313\",\n \"text\": \"def findall(parent, name, ns=''):\\n return [ \\n node for node in parent.childNodes\\n if node.nodeType == Node.ELEMENT_NODE and \\\\\\n node.nodeName == name\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266520595191b6491619162bc786acdf\",\n \"score\": \"0.53175855\",\n \"text\": \"def childs(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266520595191b6491619162bc786acdf\",\n \"score\": \"0.53175855\",\n \"text\": \"def childs(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266520595191b6491619162bc786acdf\",\n \"score\": \"0.53175855\",\n \"text\": \"def childs(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"266520595191b6491619162bc786acdf\",\n \"score\": \"0.53175855\",\n \"text\": \"def childs(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e4128543546fd9635268702bb025c84\",\n \"score\": \"0.5314826\",\n \"text\": \"def find_subtree(root, label_parts):\\n cursor = root\\n while cursor and cursor['label'] != label_parts:\\n next_cursor = None\\n for child in cursor['children']:\\n if child['label'] == label_parts[:len(child['label'])]:\\n next_cursor = child\\n cursor = next_cursor\\n return cursor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67368325ea3c14bc2b6b549a35cc75b3\",\n \"score\": \"0.53103274\",\n \"text\": \"def has_children ( self ):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3727beb860bbef1d3183931b21e54b9\",\n \"score\": \"0.5308358\",\n \"text\": \"def yield_children(logger: ConsolePrinter, data: Any,\\n terms: SearchTerms, pathsep: PathSeparators,\\n build_path: str, seen_anchors: List[str],\\n **kwargs: bool) -> Generator[YAMLPath, None, None]:\\n include_key_aliases: bool = kwargs.pop(\\\"include_key_aliases\\\", True)\\n include_value_aliases: bool = kwargs.pop(\\\"include_value_aliases\\\", False)\\n search_anchors: bool = kwargs.pop(\\\"search_anchors\\\", False)\\n logger.debug(\\n \\\"Dumping all children in data of type, {}:\\\"\\n .format(type(data)), data=data,\\n prefix=\\\"yaml_paths::yield_children: \\\")\\n\\n exclude_alias_matchers = [AnchorMatches.UNSEARCHABLE_ALIAS,\\n AnchorMatches.ALIAS_EXCLUDED]\\n\\n if isinstance(data, CommentedSeq):\\n if not build_path and pathsep is PathSeparators.FSLASH:\\n build_path = str(pathsep)\\n build_path += \\\"[\\\"\\n\\n for idx, ele in enumerate(data):\\n anchor_matched = Searches.search_anchor(\\n ele, terms, seen_anchors, search_anchors=search_anchors,\\n include_aliases=include_value_aliases)\\n logger.debug(\\n (\\\"yaml_paths::yield_children<list>: \\\"\\n + \\\"element[{}] has anchor search => {}.\\\")\\n .format(idx, anchor_matched))\\n\\n # Build the temporary YAML Path using either Anchor or Index\\n if anchor_matched is AnchorMatches.NO_ANCHOR:\\n # Not an anchor/alias, so ref this node by its index\\n tmp_path = build_path + str(idx) + \\\"]\\\"\\n else:\\n tmp_path = \\\"{}&{}]\\\".format(\\n build_path,\\n YAMLPath.escape_path_section(ele.anchor.value, pathsep)\\n )\\n\\n if (not include_value_aliases\\n and anchor_matched in exclude_alias_matchers):\\n continue\\n\\n if isinstance(ele, (CommentedMap, CommentedSeq)):\\n for path in yield_children(\\n logger, ele, terms, pathsep, tmp_path, seen_anchors,\\n search_anchors=search_anchors,\\n include_key_aliases=include_key_aliases,\\n include_value_aliases=include_value_aliases):\\n yield path\\n else:\\n yield YAMLPath(tmp_path)\\n\\n elif isinstance(data, CommentedMap):\\n if build_path:\\n build_path += str(pathsep)\\n elif pathsep is PathSeparators.FSLASH:\\n build_path = str(pathsep)\\n\\n pool = data.non_merged_items()\\n if include_key_aliases or include_value_aliases:\\n pool = data.items()\\n\\n for key, val in pool:\\n tmp_path = build_path + YAMLPath.escape_path_section(key, pathsep)\\n\\n key_anchor_matched = Searches.search_anchor(\\n key, terms, seen_anchors, search_anchors=search_anchors,\\n include_aliases=include_key_aliases)\\n val_anchor_matched = Searches.search_anchor(\\n val, terms, seen_anchors, search_anchors=search_anchors,\\n include_aliases=include_value_aliases)\\n logger.debug(\\n (\\\"yaml_paths::yield_children<dict>: \\\"\\n + \\\"key[{}]:value have value anchor search => {}:{}.\\\")\\n .format(key, key_anchor_matched, val_anchor_matched))\\n\\n if (\\n (not include_key_aliases\\n and key_anchor_matched in exclude_alias_matchers)\\n or (not include_value_aliases\\n and val_anchor_matched in exclude_alias_matchers)\\n ):\\n continue\\n\\n if isinstance(val, (CommentedSeq, CommentedMap)):\\n for path in yield_children(\\n logger, val, terms, pathsep, tmp_path, seen_anchors,\\n search_anchors=search_anchors,\\n include_key_aliases=include_key_aliases,\\n include_value_aliases=include_value_aliases):\\n yield path\\n else:\\n yield YAMLPath(tmp_path)\\n\\n else:\\n if not build_path and pathsep is PathSeparators.FSLASH:\\n build_path = str(pathsep)\\n yield YAMLPath(build_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e18dcea3a1696682924c1a2aaa3247d\",\n \"score\": \"0.53059286\",\n \"text\": \"def search(self, value, node=None):\\n if node is None:\\n node = self.root\\n\\n if node.keys:\\n return True\\n\\n elif node.leaf:\\n # If we are in a leaf, there is no more to check.\\n return False\\n\\n else:\\n i = 0\\n while i < node.size:\\n i += 1\\n return self.search(value, node.children[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bd7fa58cd00dd8acf8a47b59dd0e5a1\",\n \"score\": \"0.53005666\",\n \"text\": \"def test_has_children_parent_node(self):\\n ### Arrange ###########################################################\\n tree = Tree()\\n tree.insert(Tree.ROOT_PATH)\\n tree.insert(Tree.ROOT_PATH + (0,))\\n\\n ### Assert ############################################################\\n self.assertTrue(tree.has_children(Tree.ROOT_PATH))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b2ad6818ccb7d72da237edf12650a70\",\n \"score\": \"0.5295838\",\n \"text\": \"def get_children(self, ident=None):\\n if ident is None:\\n ident = self.root\\n out = [\\n (self.ntypes[i], self.nnames[i], self.addresses[i])\\n for i in [\\n index for index, parent in enumerate(self.nparents) if parent == ident\\n ]\\n ]\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7295a7ee8132fe461ec051017be6887f\",\n \"score\": \"0.5285472\",\n \"text\": \"def find_all_paths(parent_to_child, start, path=[], path_dict=defaultdict()):\\n p = path.copy()\\n p.append(start)\\n if start not in parent_to_child:\\n return p, path_dict\\n paths = []\\n for node in parent_to_child[start]:\\n if node in path_dict:\\n new_paths = path_dict[node]\\n else:\\n new_paths, path_dict = find_all_paths(parent_to_child, node, p, path_dict)\\n path_dict[node] = new_paths.copy()\\n if type(new_paths[0]) is int:\\n # there is only one downstream path from current node\\n paths.append(new_paths)\\n else:\\n # there are multiple downstream paths from current node\\n for new_path in new_paths:\\n paths.append(new_path)\\n # LOG.info(\\\"Returning paths for node: %d\\\", start)\\n return paths, path_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67b9ea151c4aae54f5b8f9c33ec782de\",\n \"score\": \"0.5283161\",\n \"text\": \"def deep_query(query, nodes):\\n def inner(children):\\n results = []\\n for c in children:\\n if query([c]):\\n results.append(c)\\n results.extend(inner(c.children))\\n return results\\n return inner(nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cea22f911f67d5030f5757a58da0f0f4\",\n \"score\": \"0.52827543\",\n \"text\": \"def get_children(self):\\n for node in self.children:\\n yield node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"279316c131b487f1795dda663954ea29\",\n \"score\": \"0.52708036\",\n \"text\": \"def get_children_of_class_node(classNode):\\n\\tchildRefNodes = classNode.findall(XPATH_CHILD)\\n\\treturn childRefNodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc987918ecc512946f273bbe3e2f5892\",\n \"score\": \"0.52690476\",\n \"text\": \"def findParents(self, name='None', attrs='{}', limit='None', **kwargs):\\n \\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1685,"cells":{"query_id":{"kind":"string","value":"faf67ca2c6b38c34528540e8f97c1612"},"query":{"kind":"string","value":"Checks that _ViewerApplet doesn't crash on the given model file."},"positive_passages":{"kind":"list like","value":[{"docid":"b1121d0ebf7609c6e9b520ac4d1b5a01","score":"0.57598865","text":"def _check_viewer_applet_on_model(self, resource):\n dut = mut.Meldis()\n lcm = dut._lcm\n diagram = self._make_diagram(\n resource=resource,\n visualizer_params=DrakeVisualizerParams(),\n lcm=lcm)\n diagram.ForcedPublish(diagram.CreateDefaultContext())\n lcm.HandleSubscriptions(timeout_millis=0)\n dut._invoke_subscriptions()\n self.assertEqual(dut.meshcat.HasPath(\"/DRAKE_VIEWER\"), True)","title":""}],"string":"[\n {\n \"docid\": \"b1121d0ebf7609c6e9b520ac4d1b5a01\",\n \"score\": \"0.57598865\",\n \"text\": \"def _check_viewer_applet_on_model(self, resource):\\n dut = mut.Meldis()\\n lcm = dut._lcm\\n diagram = self._make_diagram(\\n resource=resource,\\n visualizer_params=DrakeVisualizerParams(),\\n lcm=lcm)\\n diagram.ForcedPublish(diagram.CreateDefaultContext())\\n lcm.HandleSubscriptions(timeout_millis=0)\\n dut._invoke_subscriptions()\\n self.assertEqual(dut.meshcat.HasPath(\\\"/DRAKE_VIEWER\\\"), True)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6b58e04ec2a8a0bd02e23457960f62ba","score":"0.61653244","text":"def test_viewer_applet_plain_meshes(self):\n self._check_viewer_applet_on_model(\n \"drake/manipulation/models/iiwa_description/urdf/\"\n \"iiwa14_no_collision.urdf\")","title":""},{"docid":"8b403daceb7df7e05332928e36f1ac4d","score":"0.60473186","text":"def _check_model(self):\n if self.pymc_model is None:\n raise ValueError('Must first call `Model._initialize_model` first.')","title":""},{"docid":"c2b9ece37474a0db02d201220cf98fe7","score":"0.56465256","text":"def check_model(self):\n # Read in the network\n try:\n self.model = IENetwork(model=self.model_xml, weights=self.model_bin)\n except Exception as loading_error:\n log.error(f\"Unable to load the IR model: {loading_error}\")\n exit()\n\n # Check the layers\n supported_layers = self.core.query_network(network=self.model, device_name=self.device)\n unsupported_layers = [layer for layer in self.model.layers.keys() if layer not in supported_layers]\n if len(unsupported_layers) != 0:\n log.warn(f\"Unsupported layers found: {unsupported_layers}\")\n log.warn(\"Check whether extensions are available to add to IECore!\")\n log.error(f\"Unsupported layers found in: {self.__class__.__name__}\")\n exit()","title":""},{"docid":"5ba1f9c041d52ec1893a01b2abcd72d4","score":"0.55065256","text":"def valid_data_in_viewer(self):\n frame = self.frame()\n\n if frame:\n self._set_frameinfo()\n if self._viewer[frame]['filename']:\n return True\n else:\n try:\n if self._viewer[frame]['user_array'].any():\n return True\n except AttributeError as ValueError:\n print(\"error in array\")\n return False","title":""},{"docid":"c10a16ed123bdb15619899fc5bba188d","score":"0.53177315","text":"def check_crash(self, process, test):\n return False","title":""},{"docid":"c1d6d3fa39c8e91e5ff35e54c5368a69","score":"0.52565676","text":"def check_is_corrupt(file: Path) -> bool:\n try:\n _ = loadmat(file)\n return False\n except OSError: # a partially-downloaded file\n print(f\"Corrupt: {file.name}\")\n return True\n except MatReadError: # 0 byte file (initiated download but not executed)\n print(f\"Empty: {file.name}\")\n return True","title":""},{"docid":"515292254ea1b3304b245635f52b70e8","score":"0.5235406","text":"def test_viewer_applet_textured_meshes(self):\n self._check_viewer_applet_on_model(\n \"drake/manipulation/models/ycb/sdf/004_sugar_box.sdf\")","title":""},{"docid":"d66759b77b75f190579e7fd6fdb1e087","score":"0.52289504","text":"def bad_run(self):\n return self.exception is not None","title":""},{"docid":"c03338316859556da3d5dbd7918be800","score":"0.5163987","text":"def _check_experiments(self):\n\n if self.exp_id is None:\n if len(self.experiments) != 1:\n raise ValueError(\"Please select a single experiment for export\")\n else:\n self.exp_id = 0\n\n try:\n self.experiment = self.experiments[self.exp_id]\n except IndexError as e:\n raise IndexError(\n f\"Unable to select experiment {self.exp_id} for export\"\n ) from e\n\n # Check the unit cell is static\n crystal = self.experiment.crystal\n if crystal.num_scan_points > 0:\n Bmat = crystal.get_B()\n if not all(\n np.allclose(crystal.get_B_at_scan_point(i), Bmat)\n for i in range(crystal.num_scan_points)\n ):\n raise ValueError(\n \"The crystal must not have a scan-varying unit cell for export\"\n )\n\n # Check no other models are scan-varying\n if (\n self.experiment.goniometer.num_scan_points > 0\n or self.experiment.beam.num_scan_points > 0\n ):\n raise ValueError(\"Only scan-varying crystal orientation is supported\")","title":""},{"docid":"facc0627ddde789975721b11ae2d414c","score":"0.51451147","text":"def check_model() -> str:\n if path.exists(SAVED_MODEL_PATH):\n print('model exists')\n else:\n print('Model does not exist, training model')\n model.train_model()\n return \"Model check done\"","title":""},{"docid":"6232ae3af35aed656dfb16cfa532dcb4","score":"0.51146424","text":"def fileValidation(self, filename):\n if not os.path.exists(filename):\n print(\"error: file '{}' does not exist\".format (filename), file = sys.stderr)\n sys.exit(1)","title":""},{"docid":"7f79c9db2a5f92ab5d62c2b5e78331a4","score":"0.511017","text":"def detect_crashes(self):\r\n \r\n if self.game_state.snake_crash() == True:\r\n self.snake_crashed(self.screen)\r\n return True\r\n \r\n return False","title":""},{"docid":"03a147fa3aadf39fd95596633ad43751","score":"0.5109062","text":"def _check_model(mdf_model):\n\n # Generate JSON\n json_str = mdf_model.to_json()\n\n # Load the JSON\n # load_mdf_json()","title":""},{"docid":"6255167ad5370722a75fbfb6761bbdbd","score":"0.5096999","text":"def _check_first_stage_model(self):\n m = self.models[0] if type(self.models[0]) != list else self.models[0][0]\n if m.n_samples != 1:\n raise Exception(\"First stage must be deterministic!\")\n # else:\n # m._update_uncertainty(0)\n # m.update()","title":""},{"docid":"f35d879bdd64d35fbe1e988c04d41fea","score":"0.50749916","text":"def _check_args(self, args_):\n\n if not os.path.isfile(args_.scenario_file):\n raise ValueError(\"Not found: %s\" % (args_.scenario_file))","title":""},{"docid":"bb73b45141c005f52faf25d62d296a55","score":"0.50393146","text":"def checkSettings(self):\n if self.name == '':\n raise Exception( \"File object declared at line {0} was not given a name\".format( self.declarationLine ) )","title":""},{"docid":"f28435b8a77d36737ee4cf1b18741d20","score":"0.502647","text":"def is_valid(self):\n return self.__class__.file_is_valid(self.view, self.file_path)","title":""},{"docid":"bd34fa7ff5825cb6ce1536a00a1b08f3","score":"0.49814165","text":"def test_filesystem_load_invalid(self):\n docpath = \"/foo/bar/baz\"\n with self.assertRaises(VogelerException):\n self.conn.load_views(docpath)","title":""},{"docid":"f9f1b71672d83fb25f7b03d031818669","score":"0.49612394","text":"def crash(self):\n self.runit('crash')","title":""},{"docid":"e65b1657ba03f4529681e2b1df7d9cc3","score":"0.49502423","text":"def is_model_loaded():\n if _model is None:\n return False\n return True","title":""},{"docid":"5ec54d8e0cecbae509501d60197eadf2","score":"0.49394098","text":"def new_model_needed(model_name):\n return os.path.isfile(model_name_2_file_name(model_name)) is False","title":""},{"docid":"e62ef7ad445b8e841d3e3692002d82b4","score":"0.49375534","text":"def is_crash(mutate_dict):\n return 'crashes' in os.path.basename(os.path.dirname(mutate_dict['path']))","title":""},{"docid":"91cebeb66ab35a9b52a2901322f7177f","score":"0.4936424","text":"def check_file(self, filename):\n pass","title":""},{"docid":"217fe924d5795cfbca3c53b32a5831ec","score":"0.4925335","text":"def check(self, evidence, path_on_disk):\n return True","title":""},{"docid":"217fe924d5795cfbca3c53b32a5831ec","score":"0.4925335","text":"def check(self, evidence, path_on_disk):\n return True","title":""},{"docid":"689692b39864ed90d3255205bb89a903","score":"0.49252918","text":"def has_pixelization_from(model: af.Collection) -> bool:\r\n mesh_list = mesh_list_from(model=model)\r\n\r\n return len(mesh_list) > 0","title":""},{"docid":"adcfb8cb226fd132b57404e016c7e850","score":"0.49176133","text":"def is_valid_ts(self):\n \n # clean the errors \n self.selected_file.error_messages = None\n \n # avoid bug at start \n if not self.v_model:\n return True\n \n folder = Path(self.v_model)\n \n valid = True\n dirs = [d for d in folder.glob('*/')]\n if len(dirs) == 0: \n valid = False\n else:\n for d in dirs:\n try:\n n = int(d.stem)\n except:\n valid = False\n break\n \n # write an error message \n self.selected_file.error_messages = None if valid else cm.widget.folder.no_ts.format('') \n \n return valid","title":""},{"docid":"abe31976941cf00a5c27d54ed860e782","score":"0.489239","text":"def Check(self):\n\n # Generate a dummy file to validate the parameters\n file = TimeBasedAsciiFileWriterUtility(self.model_part, self.file_settings, \"\").file\n file.close()\n DeleteFileIfExisting(file.name)","title":""},{"docid":"e2143759d98b63daf3606942d509dfca","score":"0.488229","text":"def _validate_libsvm_format(file_path):\n with open(file_path, \"r\", errors=\"ignore\") as read_file:\n for line_to_validate in read_file:\n num_sparse_libsvm_features = _get_num_valid_libsvm_features(line_to_validate)\n\n if num_sparse_libsvm_features > 1:\n # Return after first valid LIBSVM line with features\n return\n elif num_sparse_libsvm_features < 0:\n raise SMDebugError(\n _get_invalid_libsvm_error_msg(\n line_snippet=line_to_validate[:50], file_name=file_path.split(\"/\")[-1]\n )\n )\n\n logging.warning(\n \"File {} is not an invalid LIBSVM file but has no features. Accepting simple validation.\".format(\n file_path.split(\"/\")[-1]\n )\n )","title":""},{"docid":"73297ed1440ffd03a134a63b971dc516","score":"0.48741412","text":"def _check_for_error():\n\terr = _exempi.xmp_get_error()\n\tif err != 0:\n\t\traise XMPError( _XMP_ERROR_CODES[err] )","title":""},{"docid":"a4d2479b797063a30b51daff06566f35","score":"0.4863167","text":"def check_model_object(self, context):\n\n mcell = context.scene.mcell\n model_object_list = mcell.model_objects.object_list\n model_object = model_object_list[mcell.model_objects.active_obj_index]\n\n # print (\"Checking name \" + model_object.name )\n\n status = \"\"\n\n # Check for illegal names (Starts with a letter. No special characters.)\n model_object_filter = r\"(^[A-Za-z]+[0-9A-Za-z_.]*$)\"\n m = re.match(model_object_filter, model_object.name)\n if m is None:\n status = \"Object name error: %s\" % (model_object.name)\n\n model_object.status = status\n\n return","title":""},{"docid":"5c62c199b593e46222b255b5806e4c9e","score":"0.48549753","text":"def fileLoad(self, filename:str):\n QApplication.setOverrideCursor(Qt.WaitCursor)\n try:\n self.scene.loadFromFile(filename)\n self.filename = filename\n self.scene.history.clear()\n self.scene.history.storeInitialHistoryStamp()\n return True\n except FileNotFoundError as e:\n dumpException(e)\n QMessageBox.warning(self, \"Error loading %s\" % os.path.basename(filename), str(e).replace('[Errno 2]',''))\n return False\n except InvalidFile as e:\n dumpException(e)\n # QApplication.restoreOverrideCursor()\n QMessageBox.warning(self, \"Error loading %s\" % os.path.basename(filename), str(e))\n return False\n finally:\n QApplication.restoreOverrideCursor()","title":""},{"docid":"ce40aa1f2818606984b7e4c6175f998f","score":"0.48517334","text":"def check_model(self, model):\n if model.status != GRB.Status.OPTIMAL:\n if model.status == GRB.Status.INFEASIBLE:\n self.log(\"Model is infeasible.\", error=True)\n elif model.status == GRB.Status.INF_OR_UNBD:\n self.log(\"Solution found was infinite or unbounded.\", error=True)\n else:\n self.log(\"Gurobi exited with status {}.\".format(model.status), error=True)\n return False\n return True","title":""},{"docid":"ad0c78a75ff014d62567fc0f6d7b40f1","score":"0.48510015","text":"def at_exception(self, *args, **kwargs):\n self.exception_occured = True\n if hasattr(self, \"root\"):\n self.save_meta()\n\n self.logger.info(\"Warning: Evaluation data is incomplete!\")","title":""},{"docid":"8719e2b87ced85f450f1b63e97678a84","score":"0.4841089","text":"def _validate_libsvm_format(file_path):\n with open(file_path, 'r', errors='ignore') as read_file:\n for line_to_validate in read_file:\n num_sparse_libsvm_features = _get_num_valid_libsvm_features(line_to_validate)\n\n if num_sparse_libsvm_features > 1:\n # Return after first valid LIBSVM line with features\n return\n elif num_sparse_libsvm_features < 0:\n raise exc.UserError(_get_invalid_libsvm_error_msg(\n line_snippet=line_to_validate[:50], file_name=file_path.split('/')[-1]))\n\n logging.warning(\"File {} is not an invalid LIBSVM file but has no features. Accepting simple validation.\".format(\n file_path.split('/')[-1]))","title":""},{"docid":"3b8cc52aed32b8faf791377ec23fcd8a","score":"0.4834981","text":"def check_if_model_fitted(self, model: Any) -> bool:","title":""},{"docid":"eeb976b7d652b135b7b5b7752611cfe7","score":"0.48082227","text":"def load_model_file(self, fname):\n model = serial.load(fname)\n if not isinstance(model, Model):\n QtGui.QMessageBox.warning(self, 'Error',\n 'Expecting a model object; got a %s' %\n str(model.__class__))\n else:\n path, name = os.path.split(fname)\n self.settings.setValue('mw/model_path', path)\n self.setWindowTitle('[%s] - Visualize model' % name)\n self.load_model(model)","title":""},{"docid":"79ea48f62ad6ad37e945ebbc416f112a","score":"0.47999752","text":"def _check_before_run(self):\n if not osp.exists(self.train_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.train_dir))\n '''\n if not osp.exists(self.gallery_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.gallery_dir))\n if not osp.exists(self.probe_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.probe_dir))\n '''","title":""},{"docid":"4ae0fcc6197a3bcd3e6003a1745fd547","score":"0.47893187","text":"def test_validate_model(model_file):\n model = callbacks.validate_model(None, \"model\", model_file)\n assert model.id == \"e_coli_core\"\n assert len(model.metabolites) == 72\n assert len(model.reactions) == 95","title":""},{"docid":"9706b49506933f2500c11efe48be1511","score":"0.4780359","text":"def test_model_in_file(self):\n model_runpaths = [\n # Simple SDFormat file.\n \"drake/multibody/benchmarks/acrobot/acrobot.sdf\",\n # Simple URDF file.\n \"drake/multibody/benchmarks/acrobot/acrobot.urdf\",\n # Nested SDFormat file.\n \"drake/manipulation/util/test/simple_nested_model.sdf\",\n # SDFormat world file with multiple models.\n \"drake/manipulation/util/test/simple_world_with_two_models.sdf\",\n ]\n for model_runpath in model_runpaths:\n dut = mut.ModelVisualizer(visualize_frames=True)\n dut.AddModels(FindResourceOrThrow(model_runpath))\n dut.Run(loop_once=True)","title":""},{"docid":"8477ec644c16d8b78203b35a4f2b74bd","score":"0.4778561","text":"def has_collision(self, model):\n return self._collision_checker.check_collision_with_current_scene(model)","title":""},{"docid":"5b12407c5f534ec13458d2979f744c74","score":"0.47776297","text":"def check_datacube(self):\n extension = os.path.splitext(self.file_path[0])\n print(f\"Attempting to load {extension} format files\")\n try:\n self.load_datacube()\n except RuntimeError:\n raise ValueError(\"File isn't supported by Xarray install:\", self.file_path)","title":""},{"docid":"d94975efba0167c4cc0ecb5d0bf503a8","score":"0.47734094","text":"def verify_sensitive_file_is_missing(self) -> bool:\n return self.package_listing_component.verify_sensitive_file_is_missing()","title":""},{"docid":"7d397f470a23bf79f2225143ed2e270f","score":"0.47596085","text":"def _checkObject(self, elem):\n if elem.tag == patchTag():\n filename = elem.get(nameAttr())\n url = elem.get(urlAttr())\n if filename and not url:\n # if url is not given, we should check that file is present locally\n filepath = os.path.join(self.patchesDir(), filename)\n if not os.path.exists(filepath):\n raise Exception(\"patch file %s is not found\" % filepath)\n pass\n else:\n # TODO: we might check validity of URL here (see urlparse)!\n pass\n pass\n pass","title":""},{"docid":"2f8c282e6ed2355e3302f235bcf46cc1","score":"0.4758809","text":"def showLoadErrorMessage(self, file, err, details):\n warning=False\n q = self.loadErrorBox(file, (err, details if details else None),\n warning)\n q.addButton('Ignore' if warning else 'Skip file',\n QMessageBox.AcceptRole)\n abort = q.addButton('Abort', QMessageBox.AcceptRole)\n q.exec()\n if q.clickedButton() == abort:\n self.stopDC()","title":""},{"docid":"399e987ead36fe18c0e84a38f54fd08c","score":"0.47558758","text":"def model_sanity(self, model, train=True):\n # run extra before/after training tests if train=True\n if train:\n model.build_vocab(list_corpus)\n orig0 = np.copy(model.wv.syn0[0])\n model.train(list_corpus)\n self.assertFalse((orig0 == model.wv.syn0[1]).all()) # vector should vary after training\n sims = model.most_similar('war', topn=len(model.index2word))\n t_rank = [word for word, score in sims].index('terrorism')\n # in >200 calibration runs w/ calling parameters, 'terrorism' in 50-most_sim for 'war'\n self.assertLess(t_rank, 50)\n war_vec = model['war']\n sims2 = model.most_similar([war_vec], topn=51)\n self.assertTrue('war' in [word for word, score in sims2])\n self.assertTrue('terrorism' in [word for word, score in sims2])","title":""},{"docid":"7112bc43608aec629754d07122658790","score":"0.47514474","text":"def _check_recording_stopped(self):\n if self.recording:\n raise PiCameraRuntimeError(\"Recording is currently running\")","title":""},{"docid":"6d10d118a9c3491951e2e739317a5175","score":"0.47444692","text":"def check_build_modal_fail(self) -> bool:\n return self.package_listing_component.check_build_modal_fail()","title":""},{"docid":"6aff598a543ecc75e5ac976a0fef07dc","score":"0.47430515","text":"def check_for_setup_error(self):\n pass","title":""},{"docid":"6aff598a543ecc75e5ac976a0fef07dc","score":"0.47430515","text":"def check_for_setup_error(self):\n pass","title":""},{"docid":"e69c1b5dde85ff36780f829cf0adc5f0","score":"0.47414747","text":"def test_missing_h5m_file_error_handling():\n\n import shutil\n\n shutil.copy(self.h5m_filename_smaller, \".\")\n\n # creates xml files so that the code passes the xml file check\n os.system(\"touch geometry.xml\")\n os.system(\"touch materials.xml\")\n os.system(\"touch settings.xml\")\n os.system(\"touch tallies.xml\")\n os.system(\"rm dagmc.h5m\")\n\n odw.Materials(\n h5m_filename=\"dagmc.h5m\",\n correspondence_dict={\n \"mat1\": \"Be\"})","title":""},{"docid":"68d06c8020076c1d3260ecf90a10c150","score":"0.47157216","text":"def _check(filename):\n if not os.path.isfile(filename):\n raise Exception(\"%s not found\" % filename)","title":""},{"docid":"f314fa5724647ec0efe22eb78fe2d30b","score":"0.4707291","text":"def read_null(self):\n ok = QtGui.QMessageBox.critical( None, \n 'Experimental segfault',\n 'Are you sure you want to segfault the application',\n buttons = QtGui.QMessageBox.No | QtGui.QMessageBox.Yes )\n if ok == QtGui.QMessageBox.Yes:\n import faulthandler\n faulthandler._read_null()","title":""},{"docid":"c6531285edda578a87f36401b2e025f4","score":"0.46978906","text":"def validate(self):\n allowed_backbones = ['vgg16', 'vgg19', 'unet']\n if self.backbone_name not in allowed_backbones:\n raise ValueError('Backbone (\\'{}\\') not in allowed backbones ({}).'.format(self.backbone_name,\n allowed_backbones))","title":""},{"docid":"2a5842f0a60a1f021fffafc2bf0b54bb","score":"0.469458","text":"def debug(self):\n print \"Error occured in importer\", __file__\n from tkMessageBox import askokcancel\n res = askokcancel(\"Import Error\",\n \"The existing graphical file could not be imported\\n\" \\\n \"Press Ok to proceed normally, Cancel to dump error to console\")\n \n # Raise the 'caught' error\n if(res == False): raise","title":""},{"docid":"11031fd13dc7539aa63809a9a4d3dca6","score":"0.4693559","text":"def handle_crash_dialog(self, test_id=None, db=True, test=False):\n crashes = False\n count = 1\n print \"checking if there was a crash\"\n while exists(Pattern(\"internal_error.png\"),5):\n if count > 1:\n click(\"Ignore\")\n break\n else:\n crashes = True\n tmpr = Region(getLastMatch().nearby(800))\n if db == True:\n click(\"Include\")\n try:\n time.sleep(3)\n tmpr.find(\"Include\")\n click(tmpr.getLastMatch().below(120))\n type(\"Sikuli Automated test crashed:\" +str(test_id))\n finally:\n if exists(\"button_submit_crash_report.png\") or exists(\"Submit Crash\"):\n click(getLastMatch())\n time.sleep(5)\n count = count + 1\n \n if crashes == True and test == False:\n print \"miro crashed\"\n type(Key.ESC) # close any leftover dialogs\n time.sleep(20) #give it some time to send the report before shutting down.\n print(\"Got a crash report - check bogon\")\n else:\n print \"no crashes\"","title":""},{"docid":"2f83cf9f636ef354078664bab7af51eb","score":"0.4680011","text":"def test_invalid_init():\n\n payload = get_vw_payload(1)\n payload['local_path'] = ' '\n\n # test invalid initialization\n with pytest.raises(RuntimeError):\n VWPredictor(model=DeployedModel(**payload))","title":""},{"docid":"5bb6584dc16acceeaa62105465265455","score":"0.46778414","text":"def check_filesepc(self):\n return True","title":""},{"docid":"af4a6e8ec92bfa1badba889909a5e03f","score":"0.46728885","text":"def _check_import():\n if InputDevice is None:\n raise ImportError('evdev was not imported successfully, nothing will work.')","title":""},{"docid":"21a48bcec4c1a948d8575426dc2b51e6","score":"0.46712875","text":"def test_doesnt_crash(self):\n Waypoint, Segment, generate_trajectory, generate_tank_trajectory, _ = set_cdll_path()\n\n trajectory = generate_trajectory([\n Waypoint(x=-4, y=-1, angle=math.radians(45)),\n Waypoint(x=-1, y=2, angle=0),\n Waypoint(x=4, y=20, angle=0),\n ],\n timestep=0.01,\n max_vel=15.0,\n max_accel=10.0,\n max_jerk=60.0,\n )\n\n self.validate_trajectory(trajectory, 0.01, 15.0, 10.0, 60.0)","title":""},{"docid":"3d4958c6d23d800d50c4a042183901e4","score":"0.46709457","text":"def can_handle_model(cls, model: Any) -> bool:\n return isinstance(model, onnx.ModelProto)","title":""},{"docid":"9ad587ed4379ce9876e6667462ab2a56","score":"0.46685535","text":"def testValidationErrorNoVersion(self):\n jsonData = '{ \"type\": \"probeMap\" }'\n filename = self.randomFileContaining(jsonData)\n errorCaught = False\n try:\n metaObj = Metadata(filename)\n except ucscCancer.cgData.Metadata.ValidationFailed:\n errorCaught = True\n finally:\n os.remove(filename)\n self.assertTrue(errorCaught)","title":""},{"docid":"18fefe696d13e41a0f126e4d7bf4c5f5","score":"0.46342897","text":"def smil_probe(self):\n b_return = False\n if (not self._fcheck() or\n not self._fname() or\n not self._fcontent()):\n log2log(\"warning\", \"SmilParse: \" + 'Something is wrong with {}'.format(self.full_path))\n else:\n b_return = True\n return b_return","title":""},{"docid":"f3aceba79e24cbacc1d1840c576c0fc2","score":"0.4626978","text":"def check_integrity (self, features):\n\n for feature in features:\n if feature.feature_type == GenUtil.POINT:\n if ( not feature.almost_equals(Point(feature.coords_dual),6) ):\n raise InternalError (\"Internal corruption of MA_Point coords structure\")\n\n if feature.feature_type == GenUtil.LINE_STRING:\n if ( not feature.almost_equals(LineString(feature.coords_dual),6) ):\n raise InternalError (\"Internal corruption of MA_LineString coords structure\")\n\n return","title":""},{"docid":"583c4ce1ee08829371a2badc9c80ce36","score":"0.46124458","text":"def crash(self):\n self.node.sys_crash()","title":""},{"docid":"3e477603cc904eee9cdbd98f9382347e","score":"0.46046856","text":"def is_model_in_workspace(self, model):\n meshes = model.get_meshes() \n for mesh in model.get_meshes():\n if not self._workspace.contains_mesh(mesh):\n return False\n return True","title":""},{"docid":"0745bc12fb75174699f014f7cafcd6eb","score":"0.46040538","text":"def _check_model_name_is_valid(cls, model_name):\n valid_models = ['vgg' + str(i) for i in [\"11\", \"11_bn\",\n \"13\", \"13_bn\",\n \"16\", \"16_bn\",\n \"19\", \"19_bn\"]]\n if model_name not in valid_models:\n raise ValueError('model_name should be one of: ' + ', '.join(valid_models))","title":""},{"docid":"1850d7075d6e0c94ebc1530e17e5649e","score":"0.4600341","text":"def load_model(self, model_file):\n pass","title":""},{"docid":"ac5f7574af3bc3f6ab107387d7b64cdf","score":"0.45932883","text":"def _CheckExceptions(self):\n if self.exceptions:\n raise RuntimeError('Exception in invocation thread: %r' %\n self.exceptions)","title":""},{"docid":"9b59bbdd2b03d54745e5769fcf40ff33","score":"0.45893168","text":"def crash_prompt(self, exception, message, tb):\n if not self.crashed:\n self.crashed = True # Don't want to show additional errors directly caused by the first one.\n\n self.create_crash_log(exception, message, tb)\n\n res = messagebox.showerror('Fatal Error', \"Unfortunately, SMBX2 Tileset Importer has crashed. Would you \"\n \"like to report this error on the software's GitHub page? If so, \"\n \"be sure to include the contents of crash_latest.log (located in \"\n \"the logs folder of this program's directory) in your report.\",\n type=messagebox.YESNO)\n if res == 'yes':\n webbrowser.open_new('https://github.com/Sambo3975/SMBX2-Tileset-Creator/issues/new?assignees=&labels'\n '=&template=bug_report.md&title=')\n\n self.destroy()","title":""},{"docid":"7b8bc7ed9b301db17b0e313a316f1065","score":"0.45847043","text":"def test_bad_argument(self):\n if False:\n cpap_extraction.sys.argv = [ \"cpap_extraction.py\", \"inputfile\", \"extrastuff\"]\n with self.assertRaises(SystemExit):\n cpap_extraction.setup_args()","title":""},{"docid":"5f2d394594bf968f9bbf6870e297f8c5","score":"0.45839468","text":"def validate_notebook(filename: str) -> bool:\n try:\n stream: typing.TextIO\n with open(filename, 'rt', encoding='utf8') as stream:\n nbformat.read(stream, nbformat.NO_CONVERT)\n except Exception as error: # pylint: disable=broad-except\n logger.error('Invalid notebook file \"%s\": %s', filename, error)\n logger.info('Use --force to upload the file anyways')\n return False\n return True","title":""},{"docid":"62573f45555379430522fe90166c7442","score":"0.45785657","text":"def check(self):\r\n self._print(\"Checking that studyrlt works fine...\")\r\n success, _ = self._run_studyrlt(None)\r\n return success","title":""},{"docid":"a5e498eee5e6bffcd518d804ead3fd34","score":"0.4571754","text":"def test_vw_verify_on_load():\n\n model = DeployedModel(**get_vw_payload(1))\n model.example = '|1'\n model.output = None\n\n assert model.to_dict() == VWPredictor(model=model, verify_on_load=False).get_model().to_dict()","title":""},{"docid":"f8fe0c48f78aed57514a094979b7f6f3","score":"0.45666975","text":"def test_read_gemmi_model_oserror(self):\n path = \"non-existing-file.pdb\"\n expected = \"File could not be found.\"\n with pytest.raises(OSError) as exception_context:\n _ = read_gemmi_model(path)\n actual = str(exception_context.value)\n assert expected in actual","title":""},{"docid":"cb4b3da243640d1a878d0432b4da41c0","score":"0.45625824","text":"def can_restore_from(self, store):\n self.logger.info('Checking if model can resume from {}'.format(store))\n try:\n check_compatibility(self.snapshot(), store)\n return True\n except:\n self.logger.warning('Model & stored data not compatible',\n exc_info=True)\n return False","title":""},{"docid":"97fb163d6690365067cbe461cec2ff33","score":"0.45515788","text":"def _validate_model_conf(self):\n models = self.model.cascade if self.model.cascade \\\n else [self.model]\n\n if self.model.cascade and len(models) == 1:\n logger.warning('Cascade is defined with single model')\n\n if not models:\n raise RuntimeError('Path to input model xml and bin is required.')\n\n for model in models:\n if len(models) > 1 and not model.name:\n raise RuntimeError('Name of input model is required.')\n if not model.model:\n raise RuntimeError('Path to input model xml is required.')\n if not model.weights:\n raise RuntimeError('Path to input model bin is required.')","title":""},{"docid":"a7dd32aa295b68720d78b281cc5a2d66","score":"0.45496726","text":"def verify_data(filename):\r\n\r\n\tdata_pair = np.load(os.path.join(SER_DST_PATH, filename))\r\n\tif data_pair.shape[1] != 16384:\r\n\t\tprint('Snippet length not 16384 : {} instead'.format(data_pair.shape[1]))\r\n\t\treturn -1","title":""},{"docid":"2b17d931e524196c81f326151e05261d","score":"0.45410818","text":"def _report_file_save_error(msg):\n root = tkinter.Tk()\n root.withdraw()\n tk_message_box.showerror(title='File Save Error', message=msg)\n sys.exit()","title":""},{"docid":"bf72d085812ce409a1ad68e39ce6c102","score":"0.4537832","text":"def validate_args(args):\n if not os.path.isfile(args.field):\n print \"Error: The field file does not exist: \" + args.field\n sys.exit(-1)\n if not os.path.isfile(args.script):\n print \"Error: The script file does not exist: \" + args.script\n sys.exit(-1)","title":""},{"docid":"de991d54f4feb3f074c1062a10f2048f","score":"0.45373088","text":"def _check_model_size(self):\n errors = []\n no_sp = self._model.getNumSpecies()\n no_par = self._model.getNumParameters() # global parameters\n for r in self._model.getListOfReactions(): # local parameters\n no_par += r.getKineticLaw().getNumParameters()\n if no_sp*no_par > self._maximal_model_size:\n errors.append('Model is larger than maximal allowed size.')\n return errors","title":""},{"docid":"0474b723b7c5cfa6aaa26939bfa40047","score":"0.45348296","text":"def checke_submission_legal(submission_id, problem_id):\n return os.path.exists(g_config[\"filePath\"][\"submissionFolder\"] + str(submission_id)) \\\n os.path.exists(g_config[\"filePath\"][\"standardForlder\"] + str(problem_id))","title":""},{"docid":"21ed219059d6d0de6f7237572645ab63","score":"0.45318416","text":"def check_saved_model_full(savedmodel,trim=True):\n ok = True\n expected_files = ['model.p','params.p','stats.p']\n variable_files = ['starttime_*.txt']\n # expected files\n for f in expected_files:\n if not os.path.exists(os.path.join(savedmodel,f)):\n ok = False\n break\n # variable files, files with *\n if ok:\n for f in variable_files:\n if len(glob.glob(os.path.join(savedmodel,f))) == 0:\n ok = False\n break\n # trim if required\n if trim and not ok:\n print 'triming folder',savedmodel,'because of missing files'\n shutil.rmtree(savedmodel)\n # done, retutn\n return ok","title":""},{"docid":"53696304c2b21a1d89ffc6e0d3b1d14c","score":"0.45308194","text":"def check_integrity(self):\n if IS_DEBUG is False:\n return\n\n para_objs = self.attr['para_objs']\n\n for para_obj in para_objs:\n para_obj.check_integrity()","title":""},{"docid":"11c09a706df4ee610c936bb4a6d8b9ba","score":"0.45275727","text":"def _check_version(self):\n from panda3d.core import Texture\n if not hasattr(Texture, \"F_r16i\"):\n return False\n return True","title":""},{"docid":"3e905c2b90b225003bff5a3abba6f360","score":"0.45245638","text":"def check_download( model_name ):\n if os.path.isdir( model_name ):\n print( 'Model already downloaded, using existing version' )\n else:\n print( 'Requested model not found, attempting to download' )\n download_pretrained_model( model_name )","title":""},{"docid":"25bac954222dceecc24636963f4a4b96","score":"0.45240483","text":"def test_egg5(self):\n egg_name = '%s/brokenapp.egg' % self.egg_dir\n sys.path.append(egg_name)\n self.assertRaises(ImportError, app_cache.load_app, 'broken_app')\n raised = None\n try:\n app_cache.load_app('broken_app')\n except ImportError as e:\n raised = e\n\n # Make sure the message is indicating the actual\n # problem in the broken app.\n self.assertTrue(raised is not None)\n self.assertTrue(\"modelz\" in raised.args[0])","title":""},{"docid":"bd566c1c82052d661170bfa8f5179dd5","score":"0.45236096","text":"def ensure_models(models):\n def all_models_ok(ms):\n for m in ms:\n if (not A.anki_model_exists(m)):\n L.w(\"Target data model '{}' does not yet exist. Will attempt to get it by importing the default APKG file.\".format(m))\n return False\n return True\n \n if A.IN_ANKI:\n if not all_models_ok(models):\n no_hourglass()\n try:\n # Import the APKG.\n fp = A.get_filepath(A.APKG_PATH)\n delete_failure = A.import_apkg_model(fp, True)\n L.w(\"Done importing APKG file.\")\n mw.col.models.flush()\n mw.reset(True)\n mw.reset()\n if delete_failure:\n L.warn(delete_failure) # not a big deal\n msgbox(delete_failure)\n else:\n L.w(\"Successfully deleted existing records.\")\n except Exception as e:\n # We were unable to automatically import the APKG\n L.error(e)\n L.warn(A.NO_MODEL_MSG)\n msgbox(A.NO_MODEL_MSG + \"\\nError message: {}\".format(e))\n return False\n return all_models_ok(models) # verify\n return True","title":""},{"docid":"c31b6bec8bab650fa8a18bbe7b03b4db","score":"0.45228386","text":"def validate(args):\n if not args.source_path:\n print(\"Error: You need to specify a source path.\")\n return False\n\n return True","title":""},{"docid":"ceb36dea65c06cdf6bf52e238f9f44e9","score":"0.45215508","text":"def validate(self):\n self.__validate_files()\n\n while self.need_validate():\n for guid, obj in self.__objects.guid_items():\n try:\n obj.validate()\n except PBXValidationError as e:\n self.remove_object(obj)\n \n isolate_objs = [obj for guid, obj in self.__objects.guid_items() \\\n if not obj == self.pbx_rootObject and len(obj.referrers()) == 0]\n if len(isolate_objs) > 0:\n logger.warn(u'[XcodeProj] isolate objects:\\n\\t{0}'\\\n .format(u'\\n\\t'.join([str(o) for o in isolate_objs])))","title":""},{"docid":"b95a625721bdf1e00e01049532a4f415","score":"0.45201305","text":"def check_valid_vertex_file(gui):\n\n niq_misc.remove_curly(gui.vertex_file_E)\n vertex_path = Path(gui.vertex_file_E.get())\n\n # Check if path is empty\n if vertex_path.name == \"\":\n messagebox.showerror(\"Vertex File Error\", \"Please provide a vertex file.\")\n return False\n\n # Check if path has invalid path\n if vertex_path.suffix not in (\".html\", \"\"):\n messagebox.showerror(\"Vertex Selection Error\", r'Vertex selection file must have \".html\" extension.')\n return False\n\n # Check if path exists\n if not vertex_path.exists():\n messagebox.showerror(\"Vertex Selection Error\", \"Provided vertex selection file not found.\")\n return False\n\n with open(vertex_path, \"r\") as original_file:\n original_lines = original_file.readlines()\n\n # Remove extra table data lines if present\n cleaned_content = str()\n found = False\n for line in original_lines:\n if \"<div class\" in line:\n found = True\n if found:\n cleaned_content += line\n\n # Get datapoints\n tokens = re.finditer(r\">([\\d\\.-]+)</span>\", cleaned_content)\n\n token_num = 0\n try:\n # Every other value in tokens will be temperature and so is ignored\n for counter, match in enumerate(tokens):\n token_num = counter\n if not (counter % 2) == 0:\n round(float(match.group(1)))\n except:\n messagebox.showerror((\"Vertex File Error\"), \"Vertex file is unreadable. Please try another.\")\n return False\n\n if token_num < 2:\n messagebox.showerror(\n \"Vertex File Error\", \n \"No vertices detected in vertex file.\\n\\n\" + \n 'When saving plots, ensure the file type option is set to \\\"Webpage, Complete\\\" not \\\"Webpage, HTML only\\\".'\n )\n return False\n\n return True","title":""},{"docid":"b93d976b6137ed485867cc79027491bc","score":"0.45143694","text":"def _check_model_name_is_valid(cls, model_name, also_need_pretrained_weights=False):\n num_models = 4 if also_need_pretrained_weights else 8\n valid_models = ['efficientnet_b'+str(i) for i in range(num_models)]\n if model_name.replace('-','_') not in valid_models:\n raise ValueError('model_name should be one of: ' + ', '.join(valid_models))","title":""},{"docid":"49b943425c01825a6a5306b274bed1e8","score":"0.45141453","text":"def _verify_processed_files(self, show_data:object) -> None:\n mistimed_files = self.segment_verifer(show_data).verify_show()\n if mistimed_files:\n self._delete_bad_files(mistimed_files)\n self.logger.warn('Retrying processing')\n self._process_one_show(show_data, any_file_mistimed=True)","title":""},{"docid":"c5adca754c7aa6943e57a175daccdcde","score":"0.45134374","text":"def _validate(self, fileChooser):\n global curdir\n global mp_queue\n global cnn_queue\n global cnn_results\n\n mp.freeze_support()\n curdir = fileChooser.path\n\n #Initiates functions.\n self._queue_photos()\n #self._multiprocessing(self._handle_photos, mp_queue)\n self._multiprocessing(self._cnn_guesser, cnn_queue)\n self._sidepanel()","title":""},{"docid":"e340e64234cf23fe871fbb5f112af833","score":"0.45118174","text":"def has_errors (self):;\n return self.error_model is not NoError;","title":""},{"docid":"89ee39e073a02056547efa407371d47e","score":"0.45114073","text":"def files_validation(self):\n f1,f2,sampleinfo = generate_sample_info(self.id)\n self._check_equal_file_length(f1,f2)\n self._check_primers_match(f1,f2,sampleinfo)","title":""},{"docid":"42f28867d1734ca1a58fc458efd7ee34","score":"0.45095626","text":"def _doRun(self, model: Model):\n raise Exception(\"Not implemented\")","title":""},{"docid":"23cf568bd9bc072e7b527e5a9eb91582","score":"0.45038092","text":"def raise_dlg_if_failed_plots(self):\n if self.model.failed_plots:\n error_msgs = []\n for desc in self.model.failed_plots:\n msg = \"Failed to recreate plot number {} ({} of '{}' vs '{\" \\\n \"}', z_col '{}'). It will need to be recreated manually.\"\n msg = msg.format(desc.id, desc.plot_type, desc.x_col_name,\n desc.y_col_name, desc.z_col_name)\n error_msgs.append(msg)\n\n msg = \"\\n\\n\".join(error_msgs)\n warning(None, msg)","title":""},{"docid":"1b40bb31e371ba310657a7be5e7b28c9","score":"0.45017707","text":"def validate(self):\n\n\tif self.__fid <= 0:\n\t raise RuntimeError, self.mkerr('fid <= 0')\n\tif self.__fversion <= 0:\n\t raise RuntimeError, self.mkerr('fversion <= 0')\n\tif self.__iid < 0:\n\t raise RuntimeError, self.mkerr('iid < 0')\n\tif self.__depth not in (3, 2, 1, 0):\n\t raise RuntimeError, self.mkerr('depth not in bounds')\n\tif self.__type not in ('data', 'icon', 'submit'):\n\t raise RuntimeError, self.mkerr('type not in permitted set')","title":""},{"docid":"03dd6fb79a59f6717010453f64b103d9","score":"0.4500935","text":"def __validate(self):\n # check technical break\n try:\n if self.driver.find_element_by_xpath('/html/body/div[2]/div/img').get_property('alt') == 'Przerwa techniczna':\n raise TechnicalBreakException()\n except TechnicalBreakException:\n raise\n except Exception:\n pass\n # check error 503... (haven't saved the webpage...)\n # ok\n return","title":""}],"string":"[\n {\n \"docid\": \"6b58e04ec2a8a0bd02e23457960f62ba\",\n \"score\": \"0.61653244\",\n \"text\": \"def test_viewer_applet_plain_meshes(self):\\n self._check_viewer_applet_on_model(\\n \\\"drake/manipulation/models/iiwa_description/urdf/\\\"\\n \\\"iiwa14_no_collision.urdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b403daceb7df7e05332928e36f1ac4d\",\n \"score\": \"0.60473186\",\n \"text\": \"def _check_model(self):\\n if self.pymc_model is None:\\n raise ValueError('Must first call `Model._initialize_model` first.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2b9ece37474a0db02d201220cf98fe7\",\n \"score\": \"0.56465256\",\n \"text\": \"def check_model(self):\\n # Read in the network\\n try:\\n self.model = IENetwork(model=self.model_xml, weights=self.model_bin)\\n except Exception as loading_error:\\n log.error(f\\\"Unable to load the IR model: {loading_error}\\\")\\n exit()\\n\\n # Check the layers\\n supported_layers = self.core.query_network(network=self.model, device_name=self.device)\\n unsupported_layers = [layer for layer in self.model.layers.keys() if layer not in supported_layers]\\n if len(unsupported_layers) != 0:\\n log.warn(f\\\"Unsupported layers found: {unsupported_layers}\\\")\\n log.warn(\\\"Check whether extensions are available to add to IECore!\\\")\\n log.error(f\\\"Unsupported layers found in: {self.__class__.__name__}\\\")\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba1f9c041d52ec1893a01b2abcd72d4\",\n \"score\": \"0.55065256\",\n \"text\": \"def valid_data_in_viewer(self):\\n frame = self.frame()\\n\\n if frame:\\n self._set_frameinfo()\\n if self._viewer[frame]['filename']:\\n return True\\n else:\\n try:\\n if self._viewer[frame]['user_array'].any():\\n return True\\n except AttributeError as ValueError:\\n print(\\\"error in array\\\")\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10a16ed123bdb15619899fc5bba188d\",\n \"score\": \"0.53177315\",\n \"text\": \"def check_crash(self, process, test):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d6d3fa39c8e91e5ff35e54c5368a69\",\n \"score\": \"0.52565676\",\n \"text\": \"def check_is_corrupt(file: Path) -> bool:\\n try:\\n _ = loadmat(file)\\n return False\\n except OSError: # a partially-downloaded file\\n print(f\\\"Corrupt: {file.name}\\\")\\n return True\\n except MatReadError: # 0 byte file (initiated download but not executed)\\n print(f\\\"Empty: {file.name}\\\")\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"515292254ea1b3304b245635f52b70e8\",\n \"score\": \"0.5235406\",\n \"text\": \"def test_viewer_applet_textured_meshes(self):\\n self._check_viewer_applet_on_model(\\n \\\"drake/manipulation/models/ycb/sdf/004_sugar_box.sdf\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d66759b77b75f190579e7fd6fdb1e087\",\n \"score\": \"0.52289504\",\n \"text\": \"def bad_run(self):\\n return self.exception is not None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c03338316859556da3d5dbd7918be800\",\n \"score\": \"0.5163987\",\n \"text\": \"def _check_experiments(self):\\n\\n if self.exp_id is None:\\n if len(self.experiments) != 1:\\n raise ValueError(\\\"Please select a single experiment for export\\\")\\n else:\\n self.exp_id = 0\\n\\n try:\\n self.experiment = self.experiments[self.exp_id]\\n except IndexError as e:\\n raise IndexError(\\n f\\\"Unable to select experiment {self.exp_id} for export\\\"\\n ) from e\\n\\n # Check the unit cell is static\\n crystal = self.experiment.crystal\\n if crystal.num_scan_points > 0:\\n Bmat = crystal.get_B()\\n if not all(\\n np.allclose(crystal.get_B_at_scan_point(i), Bmat)\\n for i in range(crystal.num_scan_points)\\n ):\\n raise ValueError(\\n \\\"The crystal must not have a scan-varying unit cell for export\\\"\\n )\\n\\n # Check no other models are scan-varying\\n if (\\n self.experiment.goniometer.num_scan_points > 0\\n or self.experiment.beam.num_scan_points > 0\\n ):\\n raise ValueError(\\\"Only scan-varying crystal orientation is supported\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"facc0627ddde789975721b11ae2d414c\",\n \"score\": \"0.51451147\",\n \"text\": \"def check_model() -> str:\\n if path.exists(SAVED_MODEL_PATH):\\n print('model exists')\\n else:\\n print('Model does not exist, training model')\\n model.train_model()\\n return \\\"Model check done\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6232ae3af35aed656dfb16cfa532dcb4\",\n \"score\": \"0.51146424\",\n \"text\": \"def fileValidation(self, filename):\\n if not os.path.exists(filename):\\n print(\\\"error: file '{}' does not exist\\\".format (filename), file = sys.stderr)\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f79c9db2a5f92ab5d62c2b5e78331a4\",\n \"score\": \"0.511017\",\n \"text\": \"def detect_crashes(self):\\r\\n \\r\\n if self.game_state.snake_crash() == True:\\r\\n self.snake_crashed(self.screen)\\r\\n return True\\r\\n \\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a147fa3aadf39fd95596633ad43751\",\n \"score\": \"0.5109062\",\n \"text\": \"def _check_model(mdf_model):\\n\\n # Generate JSON\\n json_str = mdf_model.to_json()\\n\\n # Load the JSON\\n # load_mdf_json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6255167ad5370722a75fbfb6761bbdbd\",\n \"score\": \"0.5096999\",\n \"text\": \"def _check_first_stage_model(self):\\n m = self.models[0] if type(self.models[0]) != list else self.models[0][0]\\n if m.n_samples != 1:\\n raise Exception(\\\"First stage must be deterministic!\\\")\\n # else:\\n # m._update_uncertainty(0)\\n # m.update()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f35d879bdd64d35fbe1e988c04d41fea\",\n \"score\": \"0.50749916\",\n \"text\": \"def _check_args(self, args_):\\n\\n if not os.path.isfile(args_.scenario_file):\\n raise ValueError(\\\"Not found: %s\\\" % (args_.scenario_file))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb73b45141c005f52faf25d62d296a55\",\n \"score\": \"0.50393146\",\n \"text\": \"def checkSettings(self):\\n if self.name == '':\\n raise Exception( \\\"File object declared at line {0} was not given a name\\\".format( self.declarationLine ) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f28435b8a77d36737ee4cf1b18741d20\",\n \"score\": \"0.502647\",\n \"text\": \"def is_valid(self):\\n return self.__class__.file_is_valid(self.view, self.file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd34fa7ff5825cb6ce1536a00a1b08f3\",\n \"score\": \"0.49814165\",\n \"text\": \"def test_filesystem_load_invalid(self):\\n docpath = \\\"/foo/bar/baz\\\"\\n with self.assertRaises(VogelerException):\\n self.conn.load_views(docpath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9f1b71672d83fb25f7b03d031818669\",\n \"score\": \"0.49612394\",\n \"text\": \"def crash(self):\\n self.runit('crash')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e65b1657ba03f4529681e2b1df7d9cc3\",\n \"score\": \"0.49502423\",\n \"text\": \"def is_model_loaded():\\n if _model is None:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec54d8e0cecbae509501d60197eadf2\",\n \"score\": \"0.49394098\",\n \"text\": \"def new_model_needed(model_name):\\n return os.path.isfile(model_name_2_file_name(model_name)) is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e62ef7ad445b8e841d3e3692002d82b4\",\n \"score\": \"0.49375534\",\n \"text\": \"def is_crash(mutate_dict):\\n return 'crashes' in os.path.basename(os.path.dirname(mutate_dict['path']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91cebeb66ab35a9b52a2901322f7177f\",\n \"score\": \"0.4936424\",\n \"text\": \"def check_file(self, filename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"217fe924d5795cfbca3c53b32a5831ec\",\n \"score\": \"0.4925335\",\n \"text\": \"def check(self, evidence, path_on_disk):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"217fe924d5795cfbca3c53b32a5831ec\",\n \"score\": \"0.4925335\",\n \"text\": \"def check(self, evidence, path_on_disk):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"689692b39864ed90d3255205bb89a903\",\n \"score\": \"0.49252918\",\n \"text\": \"def has_pixelization_from(model: af.Collection) -> bool:\\r\\n mesh_list = mesh_list_from(model=model)\\r\\n\\r\\n return len(mesh_list) > 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adcfb8cb226fd132b57404e016c7e850\",\n \"score\": \"0.49176133\",\n \"text\": \"def is_valid_ts(self):\\n \\n # clean the errors \\n self.selected_file.error_messages = None\\n \\n # avoid bug at start \\n if not self.v_model:\\n return True\\n \\n folder = Path(self.v_model)\\n \\n valid = True\\n dirs = [d for d in folder.glob('*/')]\\n if len(dirs) == 0: \\n valid = False\\n else:\\n for d in dirs:\\n try:\\n n = int(d.stem)\\n except:\\n valid = False\\n break\\n \\n # write an error message \\n self.selected_file.error_messages = None if valid else cm.widget.folder.no_ts.format('') \\n \\n return valid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abe31976941cf00a5c27d54ed860e782\",\n \"score\": \"0.489239\",\n \"text\": \"def Check(self):\\n\\n # Generate a dummy file to validate the parameters\\n file = TimeBasedAsciiFileWriterUtility(self.model_part, self.file_settings, \\\"\\\").file\\n file.close()\\n DeleteFileIfExisting(file.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2143759d98b63daf3606942d509dfca\",\n \"score\": \"0.488229\",\n \"text\": \"def _validate_libsvm_format(file_path):\\n with open(file_path, \\\"r\\\", errors=\\\"ignore\\\") as read_file:\\n for line_to_validate in read_file:\\n num_sparse_libsvm_features = _get_num_valid_libsvm_features(line_to_validate)\\n\\n if num_sparse_libsvm_features > 1:\\n # Return after first valid LIBSVM line with features\\n return\\n elif num_sparse_libsvm_features < 0:\\n raise SMDebugError(\\n _get_invalid_libsvm_error_msg(\\n line_snippet=line_to_validate[:50], file_name=file_path.split(\\\"/\\\")[-1]\\n )\\n )\\n\\n logging.warning(\\n \\\"File {} is not an invalid LIBSVM file but has no features. Accepting simple validation.\\\".format(\\n file_path.split(\\\"/\\\")[-1]\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73297ed1440ffd03a134a63b971dc516\",\n \"score\": \"0.48741412\",\n \"text\": \"def _check_for_error():\\n\\terr = _exempi.xmp_get_error()\\n\\tif err != 0:\\n\\t\\traise XMPError( _XMP_ERROR_CODES[err] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d2479b797063a30b51daff06566f35\",\n \"score\": \"0.4863167\",\n \"text\": \"def check_model_object(self, context):\\n\\n mcell = context.scene.mcell\\n model_object_list = mcell.model_objects.object_list\\n model_object = model_object_list[mcell.model_objects.active_obj_index]\\n\\n # print (\\\"Checking name \\\" + model_object.name )\\n\\n status = \\\"\\\"\\n\\n # Check for illegal names (Starts with a letter. No special characters.)\\n model_object_filter = r\\\"(^[A-Za-z]+[0-9A-Za-z_.]*$)\\\"\\n m = re.match(model_object_filter, model_object.name)\\n if m is None:\\n status = \\\"Object name error: %s\\\" % (model_object.name)\\n\\n model_object.status = status\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c62c199b593e46222b255b5806e4c9e\",\n \"score\": \"0.48549753\",\n \"text\": \"def fileLoad(self, filename:str):\\n QApplication.setOverrideCursor(Qt.WaitCursor)\\n try:\\n self.scene.loadFromFile(filename)\\n self.filename = filename\\n self.scene.history.clear()\\n self.scene.history.storeInitialHistoryStamp()\\n return True\\n except FileNotFoundError as e:\\n dumpException(e)\\n QMessageBox.warning(self, \\\"Error loading %s\\\" % os.path.basename(filename), str(e).replace('[Errno 2]',''))\\n return False\\n except InvalidFile as e:\\n dumpException(e)\\n # QApplication.restoreOverrideCursor()\\n QMessageBox.warning(self, \\\"Error loading %s\\\" % os.path.basename(filename), str(e))\\n return False\\n finally:\\n QApplication.restoreOverrideCursor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce40aa1f2818606984b7e4c6175f998f\",\n \"score\": \"0.48517334\",\n \"text\": \"def check_model(self, model):\\n if model.status != GRB.Status.OPTIMAL:\\n if model.status == GRB.Status.INFEASIBLE:\\n self.log(\\\"Model is infeasible.\\\", error=True)\\n elif model.status == GRB.Status.INF_OR_UNBD:\\n self.log(\\\"Solution found was infinite or unbounded.\\\", error=True)\\n else:\\n self.log(\\\"Gurobi exited with status {}.\\\".format(model.status), error=True)\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad0c78a75ff014d62567fc0f6d7b40f1\",\n \"score\": \"0.48510015\",\n \"text\": \"def at_exception(self, *args, **kwargs):\\n self.exception_occured = True\\n if hasattr(self, \\\"root\\\"):\\n self.save_meta()\\n\\n self.logger.info(\\\"Warning: Evaluation data is incomplete!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8719e2b87ced85f450f1b63e97678a84\",\n \"score\": \"0.4841089\",\n \"text\": \"def _validate_libsvm_format(file_path):\\n with open(file_path, 'r', errors='ignore') as read_file:\\n for line_to_validate in read_file:\\n num_sparse_libsvm_features = _get_num_valid_libsvm_features(line_to_validate)\\n\\n if num_sparse_libsvm_features > 1:\\n # Return after first valid LIBSVM line with features\\n return\\n elif num_sparse_libsvm_features < 0:\\n raise exc.UserError(_get_invalid_libsvm_error_msg(\\n line_snippet=line_to_validate[:50], file_name=file_path.split('/')[-1]))\\n\\n logging.warning(\\\"File {} is not an invalid LIBSVM file but has no features. Accepting simple validation.\\\".format(\\n file_path.split('/')[-1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b8cc52aed32b8faf791377ec23fcd8a\",\n \"score\": \"0.4834981\",\n \"text\": \"def check_if_model_fitted(self, model: Any) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb976b7d652b135b7b5b7752611cfe7\",\n \"score\": \"0.48082227\",\n \"text\": \"def load_model_file(self, fname):\\n model = serial.load(fname)\\n if not isinstance(model, Model):\\n QtGui.QMessageBox.warning(self, 'Error',\\n 'Expecting a model object; got a %s' %\\n str(model.__class__))\\n else:\\n path, name = os.path.split(fname)\\n self.settings.setValue('mw/model_path', path)\\n self.setWindowTitle('[%s] - Visualize model' % name)\\n self.load_model(model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ea48f62ad6ad37e945ebbc416f112a\",\n \"score\": \"0.47999752\",\n \"text\": \"def _check_before_run(self):\\n if not osp.exists(self.train_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.train_dir))\\n '''\\n if not osp.exists(self.gallery_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.gallery_dir))\\n if not osp.exists(self.probe_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.probe_dir))\\n '''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae0fcc6197a3bcd3e6003a1745fd547\",\n \"score\": \"0.47893187\",\n \"text\": \"def test_validate_model(model_file):\\n model = callbacks.validate_model(None, \\\"model\\\", model_file)\\n assert model.id == \\\"e_coli_core\\\"\\n assert len(model.metabolites) == 72\\n assert len(model.reactions) == 95\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9706b49506933f2500c11efe48be1511\",\n \"score\": \"0.4780359\",\n \"text\": \"def test_model_in_file(self):\\n model_runpaths = [\\n # Simple SDFormat file.\\n \\\"drake/multibody/benchmarks/acrobot/acrobot.sdf\\\",\\n # Simple URDF file.\\n \\\"drake/multibody/benchmarks/acrobot/acrobot.urdf\\\",\\n # Nested SDFormat file.\\n \\\"drake/manipulation/util/test/simple_nested_model.sdf\\\",\\n # SDFormat world file with multiple models.\\n \\\"drake/manipulation/util/test/simple_world_with_two_models.sdf\\\",\\n ]\\n for model_runpath in model_runpaths:\\n dut = mut.ModelVisualizer(visualize_frames=True)\\n dut.AddModels(FindResourceOrThrow(model_runpath))\\n dut.Run(loop_once=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8477ec644c16d8b78203b35a4f2b74bd\",\n \"score\": \"0.4778561\",\n \"text\": \"def has_collision(self, model):\\n return self._collision_checker.check_collision_with_current_scene(model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b12407c5f534ec13458d2979f744c74\",\n \"score\": \"0.47776297\",\n \"text\": \"def check_datacube(self):\\n extension = os.path.splitext(self.file_path[0])\\n print(f\\\"Attempting to load {extension} format files\\\")\\n try:\\n self.load_datacube()\\n except RuntimeError:\\n raise ValueError(\\\"File isn't supported by Xarray install:\\\", self.file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d94975efba0167c4cc0ecb5d0bf503a8\",\n \"score\": \"0.47734094\",\n \"text\": \"def verify_sensitive_file_is_missing(self) -> bool:\\n return self.package_listing_component.verify_sensitive_file_is_missing()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d397f470a23bf79f2225143ed2e270f\",\n \"score\": \"0.47596085\",\n \"text\": \"def _checkObject(self, elem):\\n if elem.tag == patchTag():\\n filename = elem.get(nameAttr())\\n url = elem.get(urlAttr())\\n if filename and not url:\\n # if url is not given, we should check that file is present locally\\n filepath = os.path.join(self.patchesDir(), filename)\\n if not os.path.exists(filepath):\\n raise Exception(\\\"patch file %s is not found\\\" % filepath)\\n pass\\n else:\\n # TODO: we might check validity of URL here (see urlparse)!\\n pass\\n pass\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f8c282e6ed2355e3302f235bcf46cc1\",\n \"score\": \"0.4758809\",\n \"text\": \"def showLoadErrorMessage(self, file, err, details):\\n warning=False\\n q = self.loadErrorBox(file, (err, details if details else None),\\n warning)\\n q.addButton('Ignore' if warning else 'Skip file',\\n QMessageBox.AcceptRole)\\n abort = q.addButton('Abort', QMessageBox.AcceptRole)\\n q.exec()\\n if q.clickedButton() == abort:\\n self.stopDC()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"399e987ead36fe18c0e84a38f54fd08c\",\n \"score\": \"0.47558758\",\n \"text\": \"def model_sanity(self, model, train=True):\\n # run extra before/after training tests if train=True\\n if train:\\n model.build_vocab(list_corpus)\\n orig0 = np.copy(model.wv.syn0[0])\\n model.train(list_corpus)\\n self.assertFalse((orig0 == model.wv.syn0[1]).all()) # vector should vary after training\\n sims = model.most_similar('war', topn=len(model.index2word))\\n t_rank = [word for word, score in sims].index('terrorism')\\n # in >200 calibration runs w/ calling parameters, 'terrorism' in 50-most_sim for 'war'\\n self.assertLess(t_rank, 50)\\n war_vec = model['war']\\n sims2 = model.most_similar([war_vec], topn=51)\\n self.assertTrue('war' in [word for word, score in sims2])\\n self.assertTrue('terrorism' in [word for word, score in sims2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7112bc43608aec629754d07122658790\",\n \"score\": \"0.47514474\",\n \"text\": \"def _check_recording_stopped(self):\\n if self.recording:\\n raise PiCameraRuntimeError(\\\"Recording is currently running\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d10d118a9c3491951e2e739317a5175\",\n \"score\": \"0.47444692\",\n \"text\": \"def check_build_modal_fail(self) -> bool:\\n return self.package_listing_component.check_build_modal_fail()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aff598a543ecc75e5ac976a0fef07dc\",\n \"score\": \"0.47430515\",\n \"text\": \"def check_for_setup_error(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aff598a543ecc75e5ac976a0fef07dc\",\n \"score\": \"0.47430515\",\n \"text\": \"def check_for_setup_error(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e69c1b5dde85ff36780f829cf0adc5f0\",\n \"score\": \"0.47414747\",\n \"text\": \"def test_missing_h5m_file_error_handling():\\n\\n import shutil\\n\\n shutil.copy(self.h5m_filename_smaller, \\\".\\\")\\n\\n # creates xml files so that the code passes the xml file check\\n os.system(\\\"touch geometry.xml\\\")\\n os.system(\\\"touch materials.xml\\\")\\n os.system(\\\"touch settings.xml\\\")\\n os.system(\\\"touch tallies.xml\\\")\\n os.system(\\\"rm dagmc.h5m\\\")\\n\\n odw.Materials(\\n h5m_filename=\\\"dagmc.h5m\\\",\\n correspondence_dict={\\n \\\"mat1\\\": \\\"Be\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68d06c8020076c1d3260ecf90a10c150\",\n \"score\": \"0.47157216\",\n \"text\": \"def _check(filename):\\n if not os.path.isfile(filename):\\n raise Exception(\\\"%s not found\\\" % filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f314fa5724647ec0efe22eb78fe2d30b\",\n \"score\": \"0.4707291\",\n \"text\": \"def read_null(self):\\n ok = QtGui.QMessageBox.critical( None, \\n 'Experimental segfault',\\n 'Are you sure you want to segfault the application',\\n buttons = QtGui.QMessageBox.No | QtGui.QMessageBox.Yes )\\n if ok == QtGui.QMessageBox.Yes:\\n import faulthandler\\n faulthandler._read_null()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6531285edda578a87f36401b2e025f4\",\n \"score\": \"0.46978906\",\n \"text\": \"def validate(self):\\n allowed_backbones = ['vgg16', 'vgg19', 'unet']\\n if self.backbone_name not in allowed_backbones:\\n raise ValueError('Backbone (\\\\'{}\\\\') not in allowed backbones ({}).'.format(self.backbone_name,\\n allowed_backbones))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a5842f0a60a1f021fffafc2bf0b54bb\",\n \"score\": \"0.469458\",\n \"text\": \"def debug(self):\\n print \\\"Error occured in importer\\\", __file__\\n from tkMessageBox import askokcancel\\n res = askokcancel(\\\"Import Error\\\",\\n \\\"The existing graphical file could not be imported\\\\n\\\" \\\\\\n \\\"Press Ok to proceed normally, Cancel to dump error to console\\\")\\n \\n # Raise the 'caught' error\\n if(res == False): raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11031fd13dc7539aa63809a9a4d3dca6\",\n \"score\": \"0.4693559\",\n \"text\": \"def handle_crash_dialog(self, test_id=None, db=True, test=False):\\n crashes = False\\n count = 1\\n print \\\"checking if there was a crash\\\"\\n while exists(Pattern(\\\"internal_error.png\\\"),5):\\n if count > 1:\\n click(\\\"Ignore\\\")\\n break\\n else:\\n crashes = True\\n tmpr = Region(getLastMatch().nearby(800))\\n if db == True:\\n click(\\\"Include\\\")\\n try:\\n time.sleep(3)\\n tmpr.find(\\\"Include\\\")\\n click(tmpr.getLastMatch().below(120))\\n type(\\\"Sikuli Automated test crashed:\\\" +str(test_id))\\n finally:\\n if exists(\\\"button_submit_crash_report.png\\\") or exists(\\\"Submit Crash\\\"):\\n click(getLastMatch())\\n time.sleep(5)\\n count = count + 1\\n \\n if crashes == True and test == False:\\n print \\\"miro crashed\\\"\\n type(Key.ESC) # close any leftover dialogs\\n time.sleep(20) #give it some time to send the report before shutting down.\\n print(\\\"Got a crash report - check bogon\\\")\\n else:\\n print \\\"no crashes\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f83cf9f636ef354078664bab7af51eb\",\n \"score\": \"0.4680011\",\n \"text\": \"def test_invalid_init():\\n\\n payload = get_vw_payload(1)\\n payload['local_path'] = ' '\\n\\n # test invalid initialization\\n with pytest.raises(RuntimeError):\\n VWPredictor(model=DeployedModel(**payload))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bb6584dc16acceeaa62105465265455\",\n \"score\": \"0.46778414\",\n \"text\": \"def check_filesepc(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4a6e8ec92bfa1badba889909a5e03f\",\n \"score\": \"0.46728885\",\n \"text\": \"def _check_import():\\n if InputDevice is None:\\n raise ImportError('evdev was not imported successfully, nothing will work.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21a48bcec4c1a948d8575426dc2b51e6\",\n \"score\": \"0.46712875\",\n \"text\": \"def test_doesnt_crash(self):\\n Waypoint, Segment, generate_trajectory, generate_tank_trajectory, _ = set_cdll_path()\\n\\n trajectory = generate_trajectory([\\n Waypoint(x=-4, y=-1, angle=math.radians(45)),\\n Waypoint(x=-1, y=2, angle=0),\\n Waypoint(x=4, y=20, angle=0),\\n ],\\n timestep=0.01,\\n max_vel=15.0,\\n max_accel=10.0,\\n max_jerk=60.0,\\n )\\n\\n self.validate_trajectory(trajectory, 0.01, 15.0, 10.0, 60.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d4958c6d23d800d50c4a042183901e4\",\n \"score\": \"0.46709457\",\n \"text\": \"def can_handle_model(cls, model: Any) -> bool:\\n return isinstance(model, onnx.ModelProto)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ad587ed4379ce9876e6667462ab2a56\",\n \"score\": \"0.46685535\",\n \"text\": \"def testValidationErrorNoVersion(self):\\n jsonData = '{ \\\"type\\\": \\\"probeMap\\\" }'\\n filename = self.randomFileContaining(jsonData)\\n errorCaught = False\\n try:\\n metaObj = Metadata(filename)\\n except ucscCancer.cgData.Metadata.ValidationFailed:\\n errorCaught = True\\n finally:\\n os.remove(filename)\\n self.assertTrue(errorCaught)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18fefe696d13e41a0f126e4d7bf4c5f5\",\n \"score\": \"0.46342897\",\n \"text\": \"def smil_probe(self):\\n b_return = False\\n if (not self._fcheck() or\\n not self._fname() or\\n not self._fcontent()):\\n log2log(\\\"warning\\\", \\\"SmilParse: \\\" + 'Something is wrong with {}'.format(self.full_path))\\n else:\\n b_return = True\\n return b_return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3aceba79e24cbacc1d1840c576c0fc2\",\n \"score\": \"0.4626978\",\n \"text\": \"def check_integrity (self, features):\\n\\n for feature in features:\\n if feature.feature_type == GenUtil.POINT:\\n if ( not feature.almost_equals(Point(feature.coords_dual),6) ):\\n raise InternalError (\\\"Internal corruption of MA_Point coords structure\\\")\\n\\n if feature.feature_type == GenUtil.LINE_STRING:\\n if ( not feature.almost_equals(LineString(feature.coords_dual),6) ):\\n raise InternalError (\\\"Internal corruption of MA_LineString coords structure\\\")\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"583c4ce1ee08829371a2badc9c80ce36\",\n \"score\": \"0.46124458\",\n \"text\": \"def crash(self):\\n self.node.sys_crash()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e477603cc904eee9cdbd98f9382347e\",\n \"score\": \"0.46046856\",\n \"text\": \"def is_model_in_workspace(self, model):\\n meshes = model.get_meshes() \\n for mesh in model.get_meshes():\\n if not self._workspace.contains_mesh(mesh):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0745bc12fb75174699f014f7cafcd6eb\",\n \"score\": \"0.46040538\",\n \"text\": \"def _check_model_name_is_valid(cls, model_name):\\n valid_models = ['vgg' + str(i) for i in [\\\"11\\\", \\\"11_bn\\\",\\n \\\"13\\\", \\\"13_bn\\\",\\n \\\"16\\\", \\\"16_bn\\\",\\n \\\"19\\\", \\\"19_bn\\\"]]\\n if model_name not in valid_models:\\n raise ValueError('model_name should be one of: ' + ', '.join(valid_models))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1850d7075d6e0c94ebc1530e17e5649e\",\n \"score\": \"0.4600341\",\n \"text\": \"def load_model(self, model_file):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5f7574af3bc3f6ab107387d7b64cdf\",\n \"score\": \"0.45932883\",\n \"text\": \"def _CheckExceptions(self):\\n if self.exceptions:\\n raise RuntimeError('Exception in invocation thread: %r' %\\n self.exceptions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b59bbdd2b03d54745e5769fcf40ff33\",\n \"score\": \"0.45893168\",\n \"text\": \"def crash_prompt(self, exception, message, tb):\\n if not self.crashed:\\n self.crashed = True # Don't want to show additional errors directly caused by the first one.\\n\\n self.create_crash_log(exception, message, tb)\\n\\n res = messagebox.showerror('Fatal Error', \\\"Unfortunately, SMBX2 Tileset Importer has crashed. Would you \\\"\\n \\\"like to report this error on the software's GitHub page? If so, \\\"\\n \\\"be sure to include the contents of crash_latest.log (located in \\\"\\n \\\"the logs folder of this program's directory) in your report.\\\",\\n type=messagebox.YESNO)\\n if res == 'yes':\\n webbrowser.open_new('https://github.com/Sambo3975/SMBX2-Tileset-Creator/issues/new?assignees=&labels'\\n '=&template=bug_report.md&title=')\\n\\n self.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b8bc7ed9b301db17b0e313a316f1065\",\n \"score\": \"0.45847043\",\n \"text\": \"def test_bad_argument(self):\\n if False:\\n cpap_extraction.sys.argv = [ \\\"cpap_extraction.py\\\", \\\"inputfile\\\", \\\"extrastuff\\\"]\\n with self.assertRaises(SystemExit):\\n cpap_extraction.setup_args()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f2d394594bf968f9bbf6870e297f8c5\",\n \"score\": \"0.45839468\",\n \"text\": \"def validate_notebook(filename: str) -> bool:\\n try:\\n stream: typing.TextIO\\n with open(filename, 'rt', encoding='utf8') as stream:\\n nbformat.read(stream, nbformat.NO_CONVERT)\\n except Exception as error: # pylint: disable=broad-except\\n logger.error('Invalid notebook file \\\"%s\\\": %s', filename, error)\\n logger.info('Use --force to upload the file anyways')\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62573f45555379430522fe90166c7442\",\n \"score\": \"0.45785657\",\n \"text\": \"def check(self):\\r\\n self._print(\\\"Checking that studyrlt works fine...\\\")\\r\\n success, _ = self._run_studyrlt(None)\\r\\n return success\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5e498eee5e6bffcd518d804ead3fd34\",\n \"score\": \"0.4571754\",\n \"text\": \"def test_vw_verify_on_load():\\n\\n model = DeployedModel(**get_vw_payload(1))\\n model.example = '|1'\\n model.output = None\\n\\n assert model.to_dict() == VWPredictor(model=model, verify_on_load=False).get_model().to_dict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8fe0c48f78aed57514a094979b7f6f3\",\n \"score\": \"0.45666975\",\n \"text\": \"def test_read_gemmi_model_oserror(self):\\n path = \\\"non-existing-file.pdb\\\"\\n expected = \\\"File could not be found.\\\"\\n with pytest.raises(OSError) as exception_context:\\n _ = read_gemmi_model(path)\\n actual = str(exception_context.value)\\n assert expected in actual\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb4b3da243640d1a878d0432b4da41c0\",\n \"score\": \"0.45625824\",\n \"text\": \"def can_restore_from(self, store):\\n self.logger.info('Checking if model can resume from {}'.format(store))\\n try:\\n check_compatibility(self.snapshot(), store)\\n return True\\n except:\\n self.logger.warning('Model & stored data not compatible',\\n exc_info=True)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97fb163d6690365067cbe461cec2ff33\",\n \"score\": \"0.45515788\",\n \"text\": \"def _validate_model_conf(self):\\n models = self.model.cascade if self.model.cascade \\\\\\n else [self.model]\\n\\n if self.model.cascade and len(models) == 1:\\n logger.warning('Cascade is defined with single model')\\n\\n if not models:\\n raise RuntimeError('Path to input model xml and bin is required.')\\n\\n for model in models:\\n if len(models) > 1 and not model.name:\\n raise RuntimeError('Name of input model is required.')\\n if not model.model:\\n raise RuntimeError('Path to input model xml is required.')\\n if not model.weights:\\n raise RuntimeError('Path to input model bin is required.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7dd32aa295b68720d78b281cc5a2d66\",\n \"score\": \"0.45496726\",\n \"text\": \"def verify_data(filename):\\r\\n\\r\\n\\tdata_pair = np.load(os.path.join(SER_DST_PATH, filename))\\r\\n\\tif data_pair.shape[1] != 16384:\\r\\n\\t\\tprint('Snippet length not 16384 : {} instead'.format(data_pair.shape[1]))\\r\\n\\t\\treturn -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b17d931e524196c81f326151e05261d\",\n \"score\": \"0.45410818\",\n \"text\": \"def _report_file_save_error(msg):\\n root = tkinter.Tk()\\n root.withdraw()\\n tk_message_box.showerror(title='File Save Error', message=msg)\\n sys.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf72d085812ce409a1ad68e39ce6c102\",\n \"score\": \"0.4537832\",\n \"text\": \"def validate_args(args):\\n if not os.path.isfile(args.field):\\n print \\\"Error: The field file does not exist: \\\" + args.field\\n sys.exit(-1)\\n if not os.path.isfile(args.script):\\n print \\\"Error: The script file does not exist: \\\" + args.script\\n sys.exit(-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de991d54f4feb3f074c1062a10f2048f\",\n \"score\": \"0.45373088\",\n \"text\": \"def _check_model_size(self):\\n errors = []\\n no_sp = self._model.getNumSpecies()\\n no_par = self._model.getNumParameters() # global parameters\\n for r in self._model.getListOfReactions(): # local parameters\\n no_par += r.getKineticLaw().getNumParameters()\\n if no_sp*no_par > self._maximal_model_size:\\n errors.append('Model is larger than maximal allowed size.')\\n return errors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0474b723b7c5cfa6aaa26939bfa40047\",\n \"score\": \"0.45348296\",\n \"text\": \"def checke_submission_legal(submission_id, problem_id):\\n return os.path.exists(g_config[\\\"filePath\\\"][\\\"submissionFolder\\\"] + str(submission_id)) \\\\\\n os.path.exists(g_config[\\\"filePath\\\"][\\\"standardForlder\\\"] + str(problem_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21ed219059d6d0de6f7237572645ab63\",\n \"score\": \"0.45318416\",\n \"text\": \"def check_saved_model_full(savedmodel,trim=True):\\n ok = True\\n expected_files = ['model.p','params.p','stats.p']\\n variable_files = ['starttime_*.txt']\\n # expected files\\n for f in expected_files:\\n if not os.path.exists(os.path.join(savedmodel,f)):\\n ok = False\\n break\\n # variable files, files with *\\n if ok:\\n for f in variable_files:\\n if len(glob.glob(os.path.join(savedmodel,f))) == 0:\\n ok = False\\n break\\n # trim if required\\n if trim and not ok:\\n print 'triming folder',savedmodel,'because of missing files'\\n shutil.rmtree(savedmodel)\\n # done, retutn\\n return ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53696304c2b21a1d89ffc6e0d3b1d14c\",\n \"score\": \"0.45308194\",\n \"text\": \"def check_integrity(self):\\n if IS_DEBUG is False:\\n return\\n\\n para_objs = self.attr['para_objs']\\n\\n for para_obj in para_objs:\\n para_obj.check_integrity()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c09a706df4ee610c936bb4a6d8b9ba\",\n \"score\": \"0.45275727\",\n \"text\": \"def _check_version(self):\\n from panda3d.core import Texture\\n if not hasattr(Texture, \\\"F_r16i\\\"):\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e905c2b90b225003bff5a3abba6f360\",\n \"score\": \"0.45245638\",\n \"text\": \"def check_download( model_name ):\\n if os.path.isdir( model_name ):\\n print( 'Model already downloaded, using existing version' )\\n else:\\n print( 'Requested model not found, attempting to download' )\\n download_pretrained_model( model_name )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25bac954222dceecc24636963f4a4b96\",\n \"score\": \"0.45240483\",\n \"text\": \"def test_egg5(self):\\n egg_name = '%s/brokenapp.egg' % self.egg_dir\\n sys.path.append(egg_name)\\n self.assertRaises(ImportError, app_cache.load_app, 'broken_app')\\n raised = None\\n try:\\n app_cache.load_app('broken_app')\\n except ImportError as e:\\n raised = e\\n\\n # Make sure the message is indicating the actual\\n # problem in the broken app.\\n self.assertTrue(raised is not None)\\n self.assertTrue(\\\"modelz\\\" in raised.args[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd566c1c82052d661170bfa8f5179dd5\",\n \"score\": \"0.45236096\",\n \"text\": \"def ensure_models(models):\\n def all_models_ok(ms):\\n for m in ms:\\n if (not A.anki_model_exists(m)):\\n L.w(\\\"Target data model '{}' does not yet exist. Will attempt to get it by importing the default APKG file.\\\".format(m))\\n return False\\n return True\\n \\n if A.IN_ANKI:\\n if not all_models_ok(models):\\n no_hourglass()\\n try:\\n # Import the APKG.\\n fp = A.get_filepath(A.APKG_PATH)\\n delete_failure = A.import_apkg_model(fp, True)\\n L.w(\\\"Done importing APKG file.\\\")\\n mw.col.models.flush()\\n mw.reset(True)\\n mw.reset()\\n if delete_failure:\\n L.warn(delete_failure) # not a big deal\\n msgbox(delete_failure)\\n else:\\n L.w(\\\"Successfully deleted existing records.\\\")\\n except Exception as e:\\n # We were unable to automatically import the APKG\\n L.error(e)\\n L.warn(A.NO_MODEL_MSG)\\n msgbox(A.NO_MODEL_MSG + \\\"\\\\nError message: {}\\\".format(e))\\n return False\\n return all_models_ok(models) # verify\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c31b6bec8bab650fa8a18bbe7b03b4db\",\n \"score\": \"0.45228386\",\n \"text\": \"def validate(args):\\n if not args.source_path:\\n print(\\\"Error: You need to specify a source path.\\\")\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb36dea65c06cdf6bf52e238f9f44e9\",\n \"score\": \"0.45215508\",\n \"text\": \"def validate(self):\\n self.__validate_files()\\n\\n while self.need_validate():\\n for guid, obj in self.__objects.guid_items():\\n try:\\n obj.validate()\\n except PBXValidationError as e:\\n self.remove_object(obj)\\n \\n isolate_objs = [obj for guid, obj in self.__objects.guid_items() \\\\\\n if not obj == self.pbx_rootObject and len(obj.referrers()) == 0]\\n if len(isolate_objs) > 0:\\n logger.warn(u'[XcodeProj] isolate objects:\\\\n\\\\t{0}'\\\\\\n .format(u'\\\\n\\\\t'.join([str(o) for o in isolate_objs])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b95a625721bdf1e00e01049532a4f415\",\n \"score\": \"0.45201305\",\n \"text\": \"def check_valid_vertex_file(gui):\\n\\n niq_misc.remove_curly(gui.vertex_file_E)\\n vertex_path = Path(gui.vertex_file_E.get())\\n\\n # Check if path is empty\\n if vertex_path.name == \\\"\\\":\\n messagebox.showerror(\\\"Vertex File Error\\\", \\\"Please provide a vertex file.\\\")\\n return False\\n\\n # Check if path has invalid path\\n if vertex_path.suffix not in (\\\".html\\\", \\\"\\\"):\\n messagebox.showerror(\\\"Vertex Selection Error\\\", r'Vertex selection file must have \\\".html\\\" extension.')\\n return False\\n\\n # Check if path exists\\n if not vertex_path.exists():\\n messagebox.showerror(\\\"Vertex Selection Error\\\", \\\"Provided vertex selection file not found.\\\")\\n return False\\n\\n with open(vertex_path, \\\"r\\\") as original_file:\\n original_lines = original_file.readlines()\\n\\n # Remove extra table data lines if present\\n cleaned_content = str()\\n found = False\\n for line in original_lines:\\n if \\\"<div class\\\" in line:\\n found = True\\n if found:\\n cleaned_content += line\\n\\n # Get datapoints\\n tokens = re.finditer(r\\\">([\\\\d\\\\.-]+)</span>\\\", cleaned_content)\\n\\n token_num = 0\\n try:\\n # Every other value in tokens will be temperature and so is ignored\\n for counter, match in enumerate(tokens):\\n token_num = counter\\n if not (counter % 2) == 0:\\n round(float(match.group(1)))\\n except:\\n messagebox.showerror((\\\"Vertex File Error\\\"), \\\"Vertex file is unreadable. Please try another.\\\")\\n return False\\n\\n if token_num < 2:\\n messagebox.showerror(\\n \\\"Vertex File Error\\\", \\n \\\"No vertices detected in vertex file.\\\\n\\\\n\\\" + \\n 'When saving plots, ensure the file type option is set to \\\\\\\"Webpage, Complete\\\\\\\" not \\\\\\\"Webpage, HTML only\\\\\\\".'\\n )\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b93d976b6137ed485867cc79027491bc\",\n \"score\": \"0.45143694\",\n \"text\": \"def _check_model_name_is_valid(cls, model_name, also_need_pretrained_weights=False):\\n num_models = 4 if also_need_pretrained_weights else 8\\n valid_models = ['efficientnet_b'+str(i) for i in range(num_models)]\\n if model_name.replace('-','_') not in valid_models:\\n raise ValueError('model_name should be one of: ' + ', '.join(valid_models))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b943425c01825a6a5306b274bed1e8\",\n \"score\": \"0.45141453\",\n \"text\": \"def _verify_processed_files(self, show_data:object) -> None:\\n mistimed_files = self.segment_verifer(show_data).verify_show()\\n if mistimed_files:\\n self._delete_bad_files(mistimed_files)\\n self.logger.warn('Retrying processing')\\n self._process_one_show(show_data, any_file_mistimed=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5adca754c7aa6943e57a175daccdcde\",\n \"score\": \"0.45134374\",\n \"text\": \"def _validate(self, fileChooser):\\n global curdir\\n global mp_queue\\n global cnn_queue\\n global cnn_results\\n\\n mp.freeze_support()\\n curdir = fileChooser.path\\n\\n #Initiates functions.\\n self._queue_photos()\\n #self._multiprocessing(self._handle_photos, mp_queue)\\n self._multiprocessing(self._cnn_guesser, cnn_queue)\\n self._sidepanel()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e340e64234cf23fe871fbb5f112af833\",\n \"score\": \"0.45118174\",\n \"text\": \"def has_errors (self):;\\n return self.error_model is not NoError;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89ee39e073a02056547efa407371d47e\",\n \"score\": \"0.45114073\",\n \"text\": \"def files_validation(self):\\n f1,f2,sampleinfo = generate_sample_info(self.id)\\n self._check_equal_file_length(f1,f2)\\n self._check_primers_match(f1,f2,sampleinfo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42f28867d1734ca1a58fc458efd7ee34\",\n \"score\": \"0.45095626\",\n \"text\": \"def _doRun(self, model: Model):\\n raise Exception(\\\"Not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23cf568bd9bc072e7b527e5a9eb91582\",\n \"score\": \"0.45038092\",\n \"text\": \"def raise_dlg_if_failed_plots(self):\\n if self.model.failed_plots:\\n error_msgs = []\\n for desc in self.model.failed_plots:\\n msg = \\\"Failed to recreate plot number {} ({} of '{}' vs '{\\\" \\\\\\n \\\"}', z_col '{}'). It will need to be recreated manually.\\\"\\n msg = msg.format(desc.id, desc.plot_type, desc.x_col_name,\\n desc.y_col_name, desc.z_col_name)\\n error_msgs.append(msg)\\n\\n msg = \\\"\\\\n\\\\n\\\".join(error_msgs)\\n warning(None, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b40bb31e371ba310657a7be5e7b28c9\",\n \"score\": \"0.45017707\",\n \"text\": \"def validate(self):\\n\\n\\tif self.__fid <= 0:\\n\\t raise RuntimeError, self.mkerr('fid <= 0')\\n\\tif self.__fversion <= 0:\\n\\t raise RuntimeError, self.mkerr('fversion <= 0')\\n\\tif self.__iid < 0:\\n\\t raise RuntimeError, self.mkerr('iid < 0')\\n\\tif self.__depth not in (3, 2, 1, 0):\\n\\t raise RuntimeError, self.mkerr('depth not in bounds')\\n\\tif self.__type not in ('data', 'icon', 'submit'):\\n\\t raise RuntimeError, self.mkerr('type not in permitted set')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03dd6fb79a59f6717010453f64b103d9\",\n \"score\": \"0.4500935\",\n \"text\": \"def __validate(self):\\n # check technical break\\n try:\\n if self.driver.find_element_by_xpath('/html/body/div[2]/div/img').get_property('alt') == 'Przerwa techniczna':\\n raise TechnicalBreakException()\\n except TechnicalBreakException:\\n raise\\n except Exception:\\n pass\\n # check error 503... (haven't saved the webpage...)\\n # ok\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1686,"cells":{"query_id":{"kind":"string","value":"4589f6947d22d318bcdf8a791f92482b"},"query":{"kind":"string","value":"compute boundary of disjoint union of polygons, return union polygon(s) and list of boundary pieces in the same order as corresponding input polygons"},"positive_passages":{"kind":"list like","value":[{"docid":"1257ff00729ac91d555243cdc1ab5e4f","score":"0.83428013","text":"def compute_disjoint_union(*polygons):\n # note: the outputs that are holes are correctly returned in clockwise\n # i.e. opposite direction. this can be identified with the is_a_hole\n # function below.\n\n # determine edges that do not feature twice\n edges = {} # boundary edge: polygon index\n boundaries = [set() for i in polygons]\n for i, ply in enumerate(polygons):\n for e in ply.edges:\n e1 = e[::-1]\n if e1 in edges:\n i1 = edges[e1]\n edges.pop(e1)\n boundaries[i1].remove(e1)\n else:\n edges[e] = i\n boundaries[i].add(e)\n\n polys = []\n # construct connected subsets of unit polygon\n # start with any boundary edge and iteratively add all connected edges\n # repeat until no edges left\n while edges:\n e = next(iter(edges))\n edges.pop(e)\n v0 = e[0]\n v = e[-1]\n nodes = [v0]\n while v != v0:\n nodes.append(v)\n e = next(e1 for e1 in edges if e1[0]==v)\n edges.pop(e)\n v = e[-1]\n polys.append(Polygon(nodes))\n\n return polys, boundaries","title":""}],"string":"[\n {\n \"docid\": \"1257ff00729ac91d555243cdc1ab5e4f\",\n \"score\": \"0.83428013\",\n \"text\": \"def compute_disjoint_union(*polygons):\\n # note: the outputs that are holes are correctly returned in clockwise\\n # i.e. opposite direction. this can be identified with the is_a_hole\\n # function below.\\n\\n # determine edges that do not feature twice\\n edges = {} # boundary edge: polygon index\\n boundaries = [set() for i in polygons]\\n for i, ply in enumerate(polygons):\\n for e in ply.edges:\\n e1 = e[::-1]\\n if e1 in edges:\\n i1 = edges[e1]\\n edges.pop(e1)\\n boundaries[i1].remove(e1)\\n else:\\n edges[e] = i\\n boundaries[i].add(e)\\n\\n polys = []\\n # construct connected subsets of unit polygon\\n # start with any boundary edge and iteratively add all connected edges\\n # repeat until no edges left\\n while edges:\\n e = next(iter(edges))\\n edges.pop(e)\\n v0 = e[0]\\n v = e[-1]\\n nodes = [v0]\\n while v != v0:\\n nodes.append(v)\\n e = next(e1 for e1 in edges if e1[0]==v)\\n edges.pop(e)\\n v = e[-1]\\n polys.append(Polygon(nodes))\\n\\n return polys, boundaries\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6c3d5b45fb28b128783dbaf7b86098fd","score":"0.6554626","text":"def polygon_iou(list1, list2):\n polygon_points1 = np.array(list1).reshape(4, 2)\n poly1 = Polygon(polygon_points1).convex_hull\n polygon_points2 = np.array(list2).reshape(4, 2)\n poly2 = Polygon(polygon_points2).convex_hull\n union_poly = np.concatenate((polygon_points1, polygon_points2))\n if not poly1.intersects(poly2): # this test is fast and can accelerate calculation\n iou = 0\n else:\n try:\n inter_area = poly1.intersection(poly2).area\n #union_area = poly1.area + poly2.area - inter_area\n union_area = MultiPoint(union_poly).convex_hull.area\n if union_area == 0:\n return 0\n iou = float(inter_area) / union_area\n except shapely.geos.TopologicalError:\n print('shapely.geos.TopologicalError occured, iou set to 0')\n iou = 0\n return iou","title":""},{"docid":"bbcfdad84f62be73d91f7d6e2255959c","score":"0.6500648","text":"def polyobj_to_poly(poly):\n\n \"\"\"\n It used to be as simple as\n\n out = []\n for i in range(len(poly)):\n\n cxy = poly.contour(i)\n if cxy[0] != cxy[-1]:\n cxy.append(cxy[0])\n\n cxy = np.asarray(cxy).T\n out.append((poly.isSolid(i), cxy))\n\n return out\n\n \"\"\"\n\n # Not sure what the best way to identify \"components\"\n # here. Is there something in the semantics of the GPC\n # polygon object that would tell us?\n #\n # Since the inclusive polygons do not overlap, by construction,\n # then use the bounding box of each polygon to determine the\n # association between include and exclude polygons.\n #\n incl = {}\n excl = {}\n\n for i in range(len(poly)):\n\n cxy = poly.contour(i)\n if cxy[0] != cxy[-1]:\n cxy.append(cxy[0])\n\n cxy = np.asarray(cxy).T\n\n incflag = poly.isSolid(i)\n bbox = poly.boundingBox(i)\n\n if incflag:\n store = incl\n else:\n store = excl\n\n assert bbox not in store\n store[bbox] = cxy\n\n assert len(incl) > 0\n\n # Match exclude with include shapes. This does a bit more than\n # is needed, to check the assumptions being used.\n #\n def is_inside(bbox_excl, bbox_incl):\n \"\"\"Return True if bbox_excl is within bbox_incl.\"\"\"\n\n xmin_i, xmax_i, ymin_i, ymax_i = bbox_incl\n xmin_e, xmax_e, ymin_e, ymax_e = bbox_excl\n\n return (xmin_e >= xmin_i) and (xmax_e <= xmax_i) \\\n and (ymin_e >= ymin_i) and (ymax_e <= ymax_i)\n\n matches = {}\n for bb_e in excl.keys():\n match = None\n for bb_i in incl.keys():\n if is_inside(bb_e, bb_i):\n assert match is None\n match = bb_i\n\n assert match is not None\n try:\n l = matches[match]\n except:\n l = []\n\n l.append(bb_e)\n matches[match] = l\n\n out = []\n for bbox_i, p_i in incl.items():\n try:\n l = matches[bbox_i]\n except:\n l = []\n\n excls = [excl[idx] for idx in l]\n store = {'polygon': p_i, 'exclude': excls}\n out.append(store)\n\n return out","title":""},{"docid":"9469139354aff0b8ebfc3c732e0c8895","score":"0.64717835","text":"def poly_union(poly_det, poly_gt):\n assert isinstance(poly_det, Polygon)\n assert isinstance(poly_gt, Polygon)\n\n area_det = poly_det.area\n area_gt = poly_gt.area\n area_inters, _ = poly_intersection(poly_det, poly_gt)\n return area_det + area_gt - area_inters","title":""},{"docid":"357590e588e32c3910924f2d1cc5e2f6","score":"0.6433988","text":"def calculate_disjoint_polygon_set(shapefile_uri):\n shapefile = ogr.Open(shapefile_uri)\n shapefile_layer = shapefile.GetLayer()\n\n poly_intersect_lookup = {}\n for poly_feat in shapefile_layer:\n poly_wkt = poly_feat.GetGeometryRef().ExportToWkt()\n shapely_polygon = shapely.wkt.loads(poly_wkt)\n poly_fid = poly_feat.GetFID()\n poly_intersect_lookup[poly_fid] = {\n 'poly': shapely_polygon,\n 'prepared': shapely.prepared.prep(shapely_polygon),\n 'intersects': set(),\n }\n shapefile_layer.ResetReading()\n\n for poly_fid in poly_intersect_lookup:\n for intersect_poly_fid in poly_intersect_lookup:\n polygon = poly_intersect_lookup[poly_fid]['prepared']\n if polygon.intersects(\n poly_intersect_lookup[intersect_poly_fid]['poly']):\n poly_intersect_lookup[poly_fid]['intersects'].add(\n intersect_poly_fid)\n\n # Build maximal subsets\n subset_list = []\n while len(poly_intersect_lookup) > 0:\n # sort polygons by increasing number of intersections\n heap = []\n for poly_fid, poly_dict in poly_intersect_lookup.iteritems():\n heapq.heappush(\n heap, (len(poly_dict['intersects']), poly_fid, poly_dict))\n\n # build maximal subset\n maximal_set = set()\n while len(heap) > 0:\n _, poly_fid, poly_dict = heapq.heappop(heap)\n for maxset_fid in maximal_set:\n if maxset_fid in poly_intersect_lookup[poly_fid]['intersects']:\n # it intersects and can't be part of the maximal subset\n break\n else:\n # we made it through without an intersection, add poly_fid to\n # the maximal set\n maximal_set.add(poly_fid)\n # remove that polygon and update the intersections\n del poly_intersect_lookup[poly_fid]\n # remove all the polygons from intersections once they're compuated\n for maxset_fid in maximal_set:\n for poly_dict in poly_intersect_lookup.itervalues():\n poly_dict['intersects'].discard(maxset_fid)\n subset_list.append(maximal_set)\n return subset_list","title":""},{"docid":"66118676fe5a85be2a66fc53747a7197","score":"0.6293607","text":"def boundary(simplices):\n # Set of boundary simplices, with vertex indices sorted\n boundary_set = set()\n # Dictionary mapping sorted boundary simplices to the original boundary simplex, and its ordering number\n boundary_dictionary = {}\n idx = 0\n for simplex in simplices:\n for bs in simplex_boundary(simplex):\n sbs = tuple(sorted(bs))\n if sbs in boundary_set:\n # Simplex occurs twice, so it's not a boundary simplex\n boundary_set.remove(sbs)\n boundary_dictionary.pop(sbs)\n else:\n # Simplex not in the boundary set, so it's a potential boundary simplex\n boundary_set.add(sbs)\n boundary_dictionary[sbs] = (bs, idx)\n idx += 1\n sorted_boundary_simplices = sorted(boundary_dictionary.values(), key=lambda bsi: bsi[1])\n boundary_list = np.array([bsi[0] for bsi in sorted_boundary_simplices], dtype=int)\n return boundary_list","title":""},{"docid":"4918949bed1e0b432ac8c4922e183d09","score":"0.62510735","text":"def _join(self, polygons):\n def do_join(polygons):\n all_polygons = polygons[:]\n\n skipped = 0\n\n polyA = polygons.pop(0)\n while len(polygons):\n polyB = polygons.pop(0)\n\n # If fewer than 3 edges, it's not a polygon,\n # just throw it out\n if len(polyB) < 4:\n continue\n\n # Find the closest set of vertices between polyA and\n # polyB that don't cross any of the edges in *any* of\n # the polygons\n closest = np.inf\n closest_pair_idx = (None, None)\n for a in xrange(len(polyA) - 1):\n A = polyA[a]\n distances = great_circle_arc.length(A, polyB[:-1])\n b = np.argmin(distances)\n distance = distances[b]\n if distance < closest:\n B = polyB[b]\n # Does this candidate line cross other edges?\n crosses = False\n for poly in all_polygons:\n if np.any(\n great_circle_arc.intersects(\n A, B, poly[:-1], poly[1:])):\n crosses = True\n break\n if not crosses:\n closest = distance\n closest_pair_idx = (a, b)\n\n if not np.isfinite(closest):\n # We didn't find a pair of points that don't cross\n # something else, so we want to try to join another\n # polygon. Defer the current polygon until later.\n if len(polygons) in (0, skipped):\n return None\n polygons.append(polyB)\n skipped += 1\n else:\n # Splice the two polygons together using a cut\n # line\n a, b = closest_pair_idx\n new_poly = np.vstack((\n # polyA up to and including the cut point\n polyA[:a+1],\n\n # polyB starting with the cut point and\n # wrapping around back to the cut point.\n # Ignore the last point in polyB, because it\n # is the same as the first\n np.roll(polyB[:-1], -b, 0),\n\n # The cut point on polyB\n polyB[b:b+1],\n\n # the rest of polyA, starting with the cut\n # point\n polyA[a:]\n ))\n\n skipped = 0\n polyA = new_poly\n\n return polyA\n\n for permutation in itertools.permutations(polygons):\n poly = do_join(list(permutation))\n if poly is not None:\n return poly\n\n raise RuntimeError(\"Could not find cut points\")","title":""},{"docid":"d119c94f5bae1680e5584af57e20e1a8","score":"0.62217265","text":"def calculate_boundary_weights(polygons, scale):\n # Scale up all polygons around their center, until they start overlapping\n # NOTE: scale factor should be matched to resolution and type of forest\n scaled_polys = gpd.GeoDataFrame(\n {\n \"geometry\":\n polygons.geometry.scale(xfact=scale, yfact=scale, origin=\"center\")\n },\n crs=polygons.crs,\n )\n\n # Get intersections of scaled polygons, which are the boundaries.\n boundaries = []\n for i in range(len(scaled_polys)):\n\n # For each scaled polygon, get all nearby scaled polygons that intersect with it\n nearby_polys = scaled_polys[scaled_polys.geometry.intersects(\n scaled_polys.iloc[i].geometry)]\n\n # Add intersections of scaled polygon with nearby polygons [except the intersection with itself!]\n for j in range(len(nearby_polys)):\n if nearby_polys.iloc[j].name != scaled_polys.iloc[i].name:\n boundaries.append(scaled_polys.iloc[i].geometry.intersection(\n nearby_polys.iloc[j].geometry))\n\n # Convert to df and ensure we only return Polygons (sometimes it can be a Point, which breaks things)\n boundaries = gpd.GeoDataFrame(\n {\n \"geometry\": gpd.GeoSeries(boundaries)\n },\n crs=polygons.crs,\n ).explode()\n boundaries = boundaries[boundaries.type == \"Polygon\"]\n\n # If we have boundaries, difference overlay them with original polygons to ensure boundaries don't cover labels\n if len(boundaries) > 0:\n boundaries = gpd.overlay(boundaries, polygons, how=\"difference\")\n else:\n boundaries = boundaries.append({\"geometry\": box(0, 0, 0, 0)}, ignore_index=True)\n\n return boundaries","title":""},{"docid":"4707340a7a1ec37f7bd82478172cef3d","score":"0.6090396","text":"def test_populate_polygon_with_exclude(self):\r\n\r\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1]]\r\n ex_poly = [[0, 0], [0.5, 0], [0.5, 0.5], [0, 0.5]] # SW quarter\r\n points = populate_polygon(polygon, 5, exclude=[ex_poly])\r\n\r\n assert len(points) == 5\r\n for point in points:\r\n assert is_inside_polygon(point, polygon)\r\n assert not is_inside_polygon(point, ex_poly)\r\n\r\n # Overlap\r\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1]]\r\n ex_poly = [[-1, -1], [0.5, 0], [0.5, 0.5], [-1, 0.5]]\r\n points = populate_polygon(polygon, 5, exclude=[ex_poly])\r\n\r\n assert len(points) == 5\r\n for point in points:\r\n assert is_inside_polygon(point, polygon)\r\n assert not is_inside_polygon(point, ex_poly)\r\n\r\n # Multiple\r\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1]]\r\n ex_poly1 = [[0, 0], [0.5, 0], [0.5, 0.5], [0, 0.5]] # SW quarter\r\n ex_poly2 = [[0.5, 0.5], [0.5, 1], [1, 1], [1, 0.5]] # NE quarter\r\n\r\n points = populate_polygon(polygon, 20, exclude=[ex_poly1, ex_poly2])\r\n\r\n assert len(points) == 20\r\n for point in points:\r\n assert is_inside_polygon(point, polygon)\r\n assert not is_inside_polygon(point, ex_poly1)\r\n assert not is_inside_polygon(point, ex_poly2)\r\n\r\n # Very convoluted polygon\r\n polygon = [[0, 0], [10, 10], [15, 5], [20, 10], [25, 0],\r\n [30, 10], [40, -10]]\r\n ex_poly = [[-1, -1], [5, 0], [5, 5], [-1, 5]]\r\n points = populate_polygon(polygon, 20, exclude=[ex_poly])\r\n\r\n assert len(points) == 20\r\n for point in points:\r\n assert is_inside_polygon(point, polygon)\r\n assert not is_inside_polygon(point, ex_poly), '%s' % str(point)","title":""},{"docid":"91a0068fde56cbe8b18495d29616f3c7","score":"0.60570765","text":"def find_shapes(city_test, boundary):\r\n GISJOIN=city_test['GISJOIN'].iloc[0]\r\n city=city_test['geometry'].iloc[0]\r\n city=city.buffer(0)\r\n box_min = city.minimum_rotated_rectangle\r\n x, y = box_min.exterior.coords.xy\r\n edge_length = (Point(x[0], y[0]).distance(Point(x[1], y[1])), Point(x[1], y[1]).distance(Point(x[2], y[2])))\r\n width=min(edge_length)/2\r\n city_buffer_reduce=city.buffer(width).buffer(-1.01*width)\r\n city_buffer_reduce=city_buffer_reduce.difference(boundary)\r\n city_buffer_temp=city.buffer(width/4)\r\n city_buffer_temp_2=city_buffer_temp.difference(boundary)\r\n city_buffer_reduce_2=city_buffer_reduce.buffer(1)\r\n city_buffer=city_buffer_temp_2.difference(city_buffer_reduce_2)\r\n \r\n city_buffer_temp_3=city_buffer_temp.difference(city)\r\n other_cities=city_buffer_temp_3.buffer(-1).intersection(boundary)\r\n return city, city_buffer, city_buffer_reduce, other_cities, GISJOIN","title":""},{"docid":"7cae98b6bdfb4960b7bab0f709fe17bf","score":"0.5941276","text":"def test_polygons_with_inner_rings(self):\r\n\r\n # Define two (closed) outer rings - clock wise direction\r\n outer_rings = [numpy.array([[106.79, -6.233],\r\n [106.80, -6.24],\r\n [106.78, -6.23],\r\n [106.77, -6.21],\r\n [106.79, -6.233]]),\r\n numpy.array([[106.76, -6.23],\r\n [106.72, -6.23],\r\n [106.72, -6.22],\r\n [106.72, -6.21],\r\n [106.76, -6.23]])]\r\n\r\n tmp_filename = unique_filename(suffix='.shp')\r\n\r\n # Do outer rings first (use default geometry type polygon)\r\n v_ref = Vector(geometry=outer_rings)\r\n assert v_ref.is_polygon_data\r\n\r\n v_ref.write_to_file(tmp_filename)\r\n v_file = read_layer(tmp_filename)\r\n assert v_file == v_ref\r\n assert v_file.is_polygon_data\r\n\r\n # Do it again but with (closed) inner rings as well\r\n\r\n # Define inner rings (counter clock wise)\r\n inner_rings = [\r\n # 2 rings for feature 0\r\n [numpy.array([[106.77827, -6.2252],\r\n [106.77775, -6.22378],\r\n [106.78, -6.22311],\r\n [106.78017, -6.22530],\r\n [106.77827, -6.2252]])[::-1],\r\n numpy.array([[106.78652, -6.23215],\r\n [106.78642, -6.23075],\r\n [106.78746, -6.23143],\r\n [106.78831, -6.23307],\r\n [106.78652, -6.23215]])[::-1]],\r\n # 1 ring for feature 1\r\n [numpy.array([[106.73709, -6.22752],\r\n [106.73911, -6.22585],\r\n [106.74265, -6.22814],\r\n [106.73971, -6.22926],\r\n [106.73709, -6.22752]])[::-1]]]\r\n\r\n polygons = []\r\n for i, outer_ring in enumerate(outer_rings):\r\n p = Polygon(outer_ring=outer_ring, inner_rings=inner_rings[i])\r\n polygons.append(p)\r\n\r\n v_ref = Vector(geometry=polygons)\r\n assert v_ref.is_polygon_data\r\n data_bbox = v_ref.get_bounding_box()\r\n\r\n # Check data from Vector object\r\n geometry = v_ref.get_geometry(as_geometry_objects=True)\r\n for i, g in enumerate(geometry):\r\n assert numpy.allclose(g.outer_ring, outer_rings[i])\r\n if i == 0:\r\n assert len(g.inner_rings) == 2\r\n else:\r\n assert len(g.inner_rings) == 1\r\n\r\n for j, ring in enumerate(inner_rings[i]):\r\n assert numpy.allclose(ring, g.inner_rings[j])\r\n\r\n # Write to file and read again\r\n v_ref.write_to_file(tmp_filename)\r\n #print 'With inner rings, written to ', tmp_filename\r\n v_file = read_layer(tmp_filename)\r\n assert v_file == v_ref\r\n assert v_file.is_polygon_data\r\n assert numpy.allclose(v_file.get_bounding_box(), data_bbox,\r\n rtol=1.0e-12, atol=1.0e-12)\r\n\r\n # Check data from file\r\n geometry = v_file.get_geometry(as_geometry_objects=True)\r\n for i, g in enumerate(geometry):\r\n assert numpy.allclose(g.outer_ring, outer_rings[i])\r\n if i == 0:\r\n assert len(g.inner_rings) == 2\r\n else:\r\n assert len(g.inner_rings) == 1\r\n\r\n for j, ring in enumerate(inner_rings[i]):\r\n assert numpy.allclose(ring, g.inner_rings[j])","title":""},{"docid":"3ef40b34ee8055ade7a4a4df8fc01bc6","score":"0.5939508","text":"def check_polygons_intersecting(poly_a, poly_b,closed=True):\n for polygon in (poly_a, poly_b):\n #print \"\\nPolygon\",\n\n # This loop is assuming last point is not the repeated first point:\n # for i1 in range(len(polygon)):\n # i2 = (i1 + 1) % len(polygon)\n \n # This loop assumes the last point is the repeated first point to form closed polygon:\n # for i1 in range(len(polygon)-1):\n # i2 = (i1 + 1)\n \n # This loop combines both loops above:\n for i1 in range(len(polygon)-1*closed):\n i2 = (i1 + 1) % len(polygon)\n \n #print(\"i1={} i2={}\".format(polygon[i1], i2))\n p1 = polygon[i1]\n p2 = polygon[i2]\n\n normal = pcbnew.wxPoint(p2.y - p1.y, p1.x - p2.x)\n\n minA, maxA, minB, maxB = (None,) * 4\n\n for p in poly_a:\n projected = normal.x * p.x + normal.y * p.y\n\n if not minA or projected < minA:\n minA = projected\n if not maxA or projected > maxA:\n maxA = projected\n\n for p in poly_b:\n projected = normal.x * p.x + normal.y * p.y\n\n if not minB or projected < minB:\n minB = projected\n if not maxB or projected > maxB:\n maxB = projected\n\n #print(\"maxA={} minB={} -- maxB={} minA={}\".format(maxA, minB, maxB, minA))\n if maxA < minB or maxB < minA:\n return False\n #print(\" Nope\\n\")\n\n return True","title":""},{"docid":"6c2a46010c95132c7f203b4c0867b3ff","score":"0.59158343","text":"def triangulation(boundary, holes = [], polyline_features = [], point_features = []):\n\n # get polyline points for Delaunay triangulation\n delaunay_points = []\n delaunay_points += boundary + point_features\n for polyline in holes:\n delaunay_points += polyline\n for polyline in polyline_features:\n delaunay_points += polyline\n\n # remove duplicates based on their geometric keys\n delaunay_point_map = {}\n for point in delaunay_points:\n point = [float(point[0]), float(point[1]), float(point[2])]\n geom_key = geometric_key(point)\n if geom_key not in delaunay_point_map:\n delaunay_point_map[geom_key] = point\n delaunay_points = list(delaunay_point_map.values())\n\n holes = [hole[: -1] for hole in holes]\n \n # generate Delaunay mesh\n delaunay_faces = delaunay_from_points_2(delaunay_points, boundary = boundary[: -1], holes = holes)\n delaunay_mesh = Mesh.from_vertices_and_faces(delaunay_points, delaunay_faces)\n\n # topological cut along the feature polylines through unwelding\n vertex_map = {geometric_key(delaunay_mesh.vertex_coordinates(vkey)): vkey for vkey in delaunay_mesh.vertices()}\n\n edge_paths = []\n for polyline in polyline_features:\n edge_path = []\n vertex_path = [vertex_map[geometric_key([float(x), float(y), float(z)])] for x, y, z in polyline]\n for i in range(len(vertex_path) - 1):\n if vertex_path[i + 1] in delaunay_mesh.halfedge[vertex_path[i]]:\n edge_path.append([vertex_path[i], vertex_path[i + 1]])\n edge_paths.append(edge_path)\n\n for edge_path in edge_paths:\n unweld_mesh_along_edge_path(delaunay_mesh, edge_path)\n\n return delaunay_mesh","title":""},{"docid":"979d010068c95173a6450cf6abcd44f4","score":"0.5872019","text":"def delaunay_triangulation(point_list, bb_triangle):\n return {Polygon}","title":""},{"docid":"1ba5458465d10e31b58829dd3675429d","score":"0.58213145","text":"def _get_bound(objects):\n if hasattr(objects,'__iter__'):\n bbs = [_get_bound(o) for o in objects]\n if len(bbs) == 1:\n return bbs[0]\n return collide.bb_union(*bbs)\n else:\n return objects.geometry().getBBTight()","title":""},{"docid":"e3e7687bb5d7fd6663804b3649c2210b","score":"0.5791099","text":"def bbox_giou(self, boxes_1, boxes_2):\n # transform [x, y, w, h] to [x_min, y_min, x_max, y_max]\n boxes_1 = tf.concat([boxes_1[..., :2] - boxes_1[..., 2:] * 0.5,\n boxes_1[..., :2] + boxes_1[..., 2:] * 0.5], axis=-1)\n boxes_2 = tf.concat([boxes_2[..., :2] - boxes_2[..., 2:] * 0.5,\n boxes_2[..., :2] + boxes_2[..., 2:] * 0.5], axis=-1)\n boxes_1 = tf.concat([tf.minimum(boxes_1[..., :2], boxes_1[..., 2:]),\n tf.maximum(boxes_1[..., :2], boxes_1[..., 2:])], axis=-1)\n boxes_2 = tf.concat([tf.minimum(boxes_2[..., :2], boxes_2[..., 2:]),\n tf.maximum(boxes_2[..., :2], boxes_2[..., 2:])], axis=-1)\n\n # calculate area of boxes_1 boxes_2\n boxes_1_area = (boxes_1[..., 2] - boxes_1[..., 0]) * (boxes_1[..., 3] - boxes_1[..., 1])\n boxes_2_area = (boxes_2[..., 2] - boxes_2[..., 0]) * (boxes_2[..., 3] - boxes_2[..., 1])\n\n # calculate the two corners of the intersection\n left_up = tf.maximum(boxes_1[..., :2], boxes_2[..., :2])\n right_down = tf.minimum(boxes_1[..., 2:], boxes_2[..., 2:])\n\n # calculate area of intersection\n inter_section = tf.maximum(right_down - left_up, 0.0)\n inter_area = inter_section[..., 0] * inter_section[..., 1]\n\n # calculate union area\n union_area = boxes_1_area + boxes_2_area - inter_area\n\n # calculate iou add epsilon in denominator to avoid dividing by 0\n iou = inter_area / (union_area + tf.keras.backend.epsilon())\n\n # calculate the upper left and lower right corners of the minimum closed convex surface\n enclose_left_up = tf.minimum(boxes_1[..., :2], boxes_2[..., :2])\n enclose_right_down = tf.maximum(boxes_1[..., 2:], boxes_2[..., 2:])\n\n # calculate width and height of the minimun closed convex surface\n enclose_wh = tf.maximum(enclose_right_down - enclose_left_up, 0.0)\n\n # calculate area of the minimun closed convex surface\n enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]\n\n # calculate the giou add epsilon in denominator to avoid dividing by 0\n giou = iou - 1.0 * (enclose_area - union_area) / (enclose_area + tf.keras.backend.epsilon())\n\n return giou","title":""},{"docid":"6dbd80c62f615f25044d35a8f42260cc","score":"0.57526714","text":"def extract_constraints(hull):\n A = []\n b = []\n pairs = []\n angles = []\n\n perimeter = []\n\n angle_intersection = []\n\n for simplex1, simplex2 in itertools.combinations(hull.simplices, 2):\n intersect = set(simplex1).intersection(simplex2)\n\n for p in simplex1:\n perimeter.append((angle(hull.points[p]), list(hull.points[p])))\n\n for p in simplex2:\n perimeter.append((angle(hull.points[p]), list(hull.points[p])))\n\n if intersect:\n v1, v2 = hull.points[[simplex1[0], simplex1[1]]]\n diff = v1-v2\n normal1 = np.array([diff[1],-diff[0]])\n\n # find a point not in the simplex\n i = 0\n while True:\n s = hull.points[i]\n if i not in simplex1:\n break\n i += 1 \n if np.dot(normal1, s-hull.points[simplex1[0]]) > 0:\n normal1 = -normal1\n \n v1, v2 = hull.points[[simplex2[0], simplex2[1]]]\n diff = v1-v2\n normal2 = np.array([diff[1],-diff[0]])\n \n # find a point not in the simplex\n i = 0\n while True:\n s = hull.points[i]\n if i not in simplex2:\n break\n i += 1\n \n if np.dot(normal2, s-hull.points[simplex2[0]]) > 0:\n normal2 = -normal2\n \n dual_basis = np.vstack([normal1, normal2])\n angles.extend([angle(normal1), angle(normal2)])\n angle_intersection.append([angle(normal1), angle(normal2), intersect])\n pairs.append((hull.points[list(intersect)[0]], dual_basis))\n\n for simplex in hull.simplices:\n v1, v2 = hull.points[[simplex[0], simplex[1]]]\n diff = v1-v2\n normal = np.array([diff[1],-diff[0]])\n offset = -np.dot(normal, v1)\n scale = np.linalg.norm(normal)\n if offset < 0:\n scale *= -1\n normal /= scale\n offset /= scale\n A.append(normal)\n b.append(offset)\n\n # crude rounding\n angles = np.array(angles)\n angles *= 50000\n angles = np.unique(angles.astype(np.int))\n angles = angles / 50000.\n\n return np.array(A), np.array(b), pairs, angles, sorted(perimeter), angle_intersection","title":""},{"docid":"57b6abe7ddafc80313c89dccdbe45f95","score":"0.57490575","text":"def disjoint_union(metagraph, graphs):\n ...","title":""},{"docid":"d00d261bf1cef924b35e6ac29e200358","score":"0.57404125","text":"def fix_segments_intersections(polygons, height, width, img_name, use_background_label,\n threshold=0.0, ratio_tolerance=0.001):\n converted_polygons = []\n empty_polygon = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n # Convert points of polygons from string to coco's array.\n # All polygons must be sorted in order from bottom to top\n for polygon in polygons:\n label = polygon['label']\n points = polygon['points'].split(';')\n new_polygon = []\n for xy in points:\n x = float(xy.split(',')[0])\n y = float(xy.split(',')[1])\n new_polygon.append(x)\n new_polygon.append(y)\n converted_polygons.append({'label': label, 'points': new_polygon})\n\n for i in range(0, len(converted_polygons)):\n rle_bottom = mask_util.frPyObjects([converted_polygons[i]['points']], height, width)\n segment_overlapped = False\n for j in range(i + 1, len(converted_polygons)):\n rle_top = mask_util.frPyObjects([converted_polygons[j]['points']], height, width)\n iou = mask_util.iou(rle_bottom, rle_top, [0, 0])\n area_top = sum(mask_util.area(rle_top))\n area_bottom = sum(mask_util.area(rle_bottom))\n if area_bottom == 0:\n continue\n area_ratio = area_top / area_bottom\n sum_iou = sum(iou)\n\n # If segment is fully inside another one, save this segment as is\n if area_ratio - ratio_tolerance < sum_iou[0] < area_ratio + ratio_tolerance:\n continue\n # Check situation when bottom segment is fully inside top.\n # It means that in annotation is mistake. Save this segment as is\n if 1 / area_ratio - ratio_tolerance < sum_iou[0] < 1 / area_ratio + ratio_tolerance:\n continue\n\n if sum_iou[0] > threshold:\n segment_overlapped = True\n bottom_mask = np.array(mask_util.decode(rle_bottom), dtype=np.uint8)\n top_mask = np.array(mask_util.decode(rle_top), dtype=np.uint8)\n\n bottom_mask = np.subtract(bottom_mask, top_mask)\n bottom_mask[bottom_mask > 1] = 0\n\n bottom_mask = np.sum(bottom_mask, axis=2)\n bottom_mask = np.array(bottom_mask > 0, dtype=np.uint8)\n converted_polygons[i]['points'] = mask_to_polygon(bottom_mask)\n # If some segment is empty, do small fix to avoid error in cocoapi function\n if len(converted_polygons[i]['points']) == 0:\n converted_polygons[i]['points'] = [empty_polygon]\n rle_bottom = mask_util.frPyObjects(converted_polygons[i]['points'], height, width)\n if not segment_overlapped:\n converted_polygons[i]['points'] = [converted_polygons[i]['points']]\n\n output_polygons = []\n for i in range(0, len(converted_polygons)):\n if not use_background_label and converted_polygons[i]['label'] == 'background':\n continue\n poly_len = len(converted_polygons[i]['points'])\n if poly_len == 0 or converted_polygons[i]['points'] == [empty_polygon]:\n log.warning('Image <{}> has an empty polygon with label <{}>. '\n 'Perhaps there is a mistake in annotation'.\n format(img_name, converted_polygons[i]['label']))\n else:\n output_polygons.append(converted_polygons[i])\n\n return output_polygons","title":""},{"docid":"29d0478c7b2552eca3d0b93489bf2e4c","score":"0.57062614","text":"def test_split_by_polygon_in_out(self):\r\n\r\n raster_name = os.path.join(\r\n UNITDATA,\r\n 'hazard',\r\n 'jakarta_flood_design.tif')\r\n\r\n exposure_name = os.path.join(\r\n UNITDATA,\r\n 'exposure',\r\n 'roads_osm_4326.shp')\r\n qgis_exposure = QgsVectorLayer(\r\n exposure_name,\r\n 'EXPOSURE',\r\n 'ogr')\r\n\r\n inside_file_name, inside_layer_name, outside_file_name, \\\r\n outside_layer_name = polygonize_thresholds(raster_name, 0.1)\r\n\r\n polygon_in = \\\r\n QgsVectorLayer(inside_file_name, inside_layer_name, 'ogr')\r\n polygon_out = \\\r\n QgsVectorLayer(outside_file_name, outside_layer_name, 'ogr')\r\n\r\n layer = split_by_polygon_in_out(\r\n qgis_exposure, polygon_in, polygon_out, 'flooded', 1)\r\n\r\n feature_count = layer.featureCount()\r\n self.assertEqual(feature_count, 184)\r\n\r\n flooded = 0\r\n iterator = layer.getFeatures()\r\n for feature in iterator:\r\n attributes = feature.attributes()\r\n if attributes[3] == 1:\r\n flooded += 1\r\n self.assertEqual(flooded, 25)","title":""},{"docid":"cbe6a4a4dee2e86946e1b73eb077ba6d","score":"0.5661395","text":"def computeIntersectionOverUnion(boundingBox1, boundingBox2):\n # dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])\n\n bb1=dict([('x1', boundingBox1[0][0]), ('x2', boundingBox1[1][0]), ('y1', boundingBox1[0][1]), ('y2', boundingBox1[1][1])])\n bb2=dict([('x1', boundingBox2[0][0]), ('x2', boundingBox2[1][0]), ('y1', boundingBox2[0][1]), ('y2', boundingBox2[1][1])])\n \n assert bb1['x1'] < bb1['x2']\n assert bb1['y1'] < bb1['y2']\n assert bb2['x1'] < bb2['x2']\n assert bb2['y1'] < bb2['y2']\n\n # determine the coordinates of the intersection rectangle\n x_left = max(bb1['x1'], bb2['x1'])\n y_top = max(bb1['y1'], bb2['y1'])\n x_right = min(bb1['x2'], bb2['x2'])\n y_bottom = min(bb1['y2'], bb2['y2'])\n\n if x_right < x_left or y_bottom < y_top:\n return 0.0\n\n # The intersection of two axis-aligned bounding boxes is always an\n # axis-aligned bounding box\n intersection_area = (x_right - x_left) * (y_bottom - y_top)\n\n # compute the area of both AABBs\n bb1_area = (bb1['x2'] - bb1['x1']) * (bb1['y2'] - bb1['y1'])\n bb2_area = (bb2['x2'] - bb2['x1']) * (bb2['y2'] - bb2['y1'])\n\n # compute the intersection over union by taking the intersection\n # area and dividing it by the sum of prediction + ground-truth\n # areas - the interesection area\n iou = intersection_area / float(bb1_area + bb2_area - intersection_area)\n assert iou >= 0.0\n assert iou <= 1.0\n return iou","title":""},{"docid":"e5f62ac95e94559ac75242df6031bc70","score":"0.5658646","text":"def bbox_iou(self, boxes_1, boxes_2):\n # transform [x, y, w, h] to [x_min, y_min, x_max, y_max]\n boxes_1 = tf.concat([boxes_1[..., :2] - boxes_1[..., 2:] * 0.5,\n boxes_1[..., :2] + boxes_1[..., 2:] * 0.5], axis=-1)\n boxes_2 = tf.concat([boxes_2[..., :2] - boxes_2[..., 2:] * 0.5,\n boxes_2[..., :2] + boxes_2[..., 2:] * 0.5], axis=-1)\n boxes_1 = tf.concat([tf.minimum(boxes_1[..., :2], boxes_1[..., 2:]),\n tf.maximum(boxes_1[..., :2], boxes_1[..., 2:])], axis=-1)\n boxes_2 = tf.concat([tf.minimum(boxes_2[..., :2], boxes_2[..., 2:]),\n tf.maximum(boxes_2[..., :2], boxes_2[..., 2:])], axis=-1)\n\n # calculate area of boxes_1 boxes_2\n boxes_1_area = (boxes_1[..., 2] - boxes_1[..., 0]) * (boxes_1[..., 3] - boxes_1[..., 1])\n boxes_2_area = (boxes_2[..., 2] - boxes_2[..., 0]) * (boxes_2[..., 3] - boxes_2[..., 1])\n\n # calculate the two corners of the intersection\n left_up = tf.maximum(boxes_1[..., :2], boxes_2[..., :2])\n right_down = tf.minimum(boxes_1[..., 2:], boxes_2[..., 2:])\n\n # calculate area of intersection\n inter_section = tf.maximum(right_down - left_up, 0.0)\n inter_area = inter_section[..., 0] * inter_section[..., 1]\n\n # calculate union area\n union_area = boxes_1_area + boxes_2_area - inter_area\n\n # calculate iou add epsilon in denominator to avoid dividing by 0\n iou = inter_area / (union_area + tf.keras.backend.epsilon())\n\n return iou","title":""},{"docid":"01a44ccf09490cde12cfec7cc6446c32","score":"0.5652409","text":"def rasterize_polygons(\n self,\n polygons: Iterable[Iterable[Iterable[Number]]],\n holes: Iterable[Iterable[Iterable[Number]]] = None,\n ) -> np.ndarray:\n size = self.shape[0:2][::-1]\n polygons = [self.xy_to_rowcol(xy)[:, ::-1] + 0.5 for xy in polygons]\n if holes is not None:\n holes = [self.xy_to_rowcol(xy)[:, ::-1] + 0.5 for xy in holes]\n return helpers.polygons_to_mask(polygons, size=size, holes=holes)","title":""},{"docid":"33f6fd1872182d61b89335595cc4de28","score":"0.564399","text":"def poly_union(poly_det, poly_gt, invalid_ret=None, return_poly=False):\n assert isinstance(poly_det, plg)\n assert isinstance(poly_gt, plg)\n assert invalid_ret is None or isinstance(invalid_ret, float) or \\\n isinstance(invalid_ret, int)\n\n if invalid_ret is None:\n poly_det = poly_make_valid(poly_det)\n poly_gt = poly_make_valid(poly_gt)\n\n poly_obj = None\n area = invalid_ret\n if poly_det.is_valid and poly_gt.is_valid:\n poly_obj = poly_det.union(poly_gt)\n area = poly_obj.area\n return (area, poly_obj) if return_poly else area","title":""},{"docid":"53975b26c3627527977fcf34d8f4f32f","score":"0.5641766","text":"def test_populate_polygon_with_exclude2(self):\r\n\r\n M = 200 # Number of points\r\n min_outer = 0\r\n max_outer = 1000\r\n polygon_outer = [[min_outer, min_outer], [max_outer, min_outer],\r\n [max_outer, max_outer], [min_outer, max_outer]]\r\n\r\n delta = 10\r\n min_inner1 = min_outer + delta\r\n max_inner1 = max_outer - delta\r\n inner1_polygon = [[min_inner1, min_inner1], [max_inner1, min_inner1],\r\n [max_inner1, max_inner1], [min_inner1, max_inner1]]\r\n\r\n min_inner2 = min_outer + 2 * delta\r\n max_inner2 = max_outer - 2 * delta\r\n inner2_polygon = [[min_inner2, min_inner2], [max_inner2, min_inner2],\r\n [max_inner2, max_inner2], [min_inner2, max_inner2]]\r\n\r\n points = populate_polygon(polygon_outer, M,\r\n exclude=[inner1_polygon, inner2_polygon])\r\n\r\n assert len(points) == M\r\n for point in points:\r\n assert is_inside_polygon(point, polygon_outer)\r\n assert not is_inside_polygon(point, inner1_polygon)\r\n assert not is_inside_polygon(point, inner2_polygon)\r\n\r\n # Very convoluted polygon\r\n polygon = [[0, 0], [10, 10], [15, 5], [20, 10], [25, 0],\r\n [30, 10], [40, -10]]\r\n ex_poly = [[-1, -1], [5, 0], [5, 5], [-1, 5]]\r\n points = populate_polygon(polygon, M, exclude=[ex_poly])\r\n\r\n assert len(points) == M\r\n for point in points:\r\n assert is_inside_polygon(point, polygon)\r\n assert not is_inside_polygon(point, ex_poly), '%s' % str(point)","title":""},{"docid":"89aba08e13e4ba24bfee1d01111d2f7f","score":"0.5601758","text":"def IntersectionOverUnion(boxes_prds, boxes_trgt, box_format=\"centers\"):\n # boxes_prds: (N, 4) where N is numof Bounding boxes.\n # boxes_labels: (N, 4)\n # use [..., 0:1] same as [:, 0:1], but : can take as one dim,\n # ... means all dims above\n # and [..., 0:1] means colum list, [..., 0] means row list.\n if box_format == \"centers\":\n box1_xc = boxes_prds[..., 0:1]\n box1_yc = boxes_prds[..., 1:2]\n box1_H = boxes_prds[..., 2:3]\n box1_W = boxes_prds[..., 3:4]\n box2_xc = boxes_trgt[..., 0:1]\n box2_yc = boxes_trgt[..., 1:2]\n box2_H = boxes_trgt[..., 2:3]\n box2_W = boxes_trgt[..., 3:4]\n\n box1_x1 = box1_xc - box1_W / 2.0\n box1_y1 = box1_yc - box1_H / 2.0\n box1_x2 = box1_xc + box1_W / 2.0\n box1_y2 = box1_yc + box1_H / 2.0\n box2_x1 = box2_xc - box2_W / 2.0\n box2_y1 = box2_yc - box2_H / 2.0\n box2_x2 = box2_xc + box2_W / 2.0\n box2_y2 = box2_yc + box2_H / 2.0\n\n if box_format == \"corners\":\n box1_x1 = boxes_prds[..., 0:1]\n box1_y1 = boxes_prds[..., 1:2]\n box1_x2 = boxes_prds[..., 2:3]\n box1_y2 = boxes_prds[..., 3:4]\n box2_x1 = boxes_trgt[..., 0:1]\n box2_y1 = boxes_trgt[..., 1:2]\n box2_x2 = boxes_trgt[..., 2:3]\n box2_y2 = boxes_trgt[..., 3:4]\n\n I_x1 = torch.max(box1_x1, box2_x1)\n I_y1 = torch.max(box1_y1, box2_y1)\n I_x2 = torch.min(box1_x2, box2_x2)\n I_y2 = torch.min(box1_y2, box2_y2)\n\n # clamp(0) makes less than 0 value to 0, so non-intersection will be 0\n Intersection = (I_x2 - I_x1).clamp(0) * (I_y2 - I_y1).clamp(0)\n\n box1_area = abs((box1_x2 - box1_x1) * (box1_y2 - box1_y1))\n box2_area = abs((box2_x2 - box2_x1) * (box2_y2 - box2_y1))\n\n epsilon = 1e-6\n\n return Intersection / (box1_area + box2_area - Intersection + epsilon)","title":""},{"docid":"2908a091fe5abeb2bc09ffdea117ea6b","score":"0.5600189","text":"def union(self):\n self._remove_cut_lines()\n self._sanity_check(\"union - remove cut lines\")\n self._find_all_intersections()\n self._sanity_check(\"union - find all intersections\")\n self._remove_interior_edges()\n self._sanity_check(\"union - remove interior edges\")\n self._remove_3ary_edges()\n self._sanity_check(\"union - remove 3ary edges\")\n self._remove_orphaned_nodes()\n self._sanity_check(\"union - remove orphan nodes\", True)\n return self._trace()","title":""},{"docid":"5806a83d9d16c015105624c5a5fb8ee7","score":"0.5599756","text":"def test_booleans():\n half = S.Half\n\n p1, p2, p3, p4 = map(Point, [(0, 0), (1, 0), (5, 1), (0, 1)])\n p5, p6, p7 = map(Point, [(3, 2), (1, -1), (0, 2)])\n l1 = Line(Point(0,0), Point(1,1))\n l2 = Line(Point(half, half), Point(5,5))\n l3 = Line(p2, p3)\n l4 = Line(p3, p4)\n poly1 = Polygon(p1, p2, p3, p4)\n poly2 = Polygon(p5, p6, p7)\n poly3 = Polygon(p1, p2, p5)\n assert Union(l1, l2).equals(l1)\n assert Intersection(l1, l2).equals(l1)\n assert Intersection(l1, l4) == FiniteSet(Point(1,1))\n assert Intersection(Union(l1, l4), l3) == FiniteSet(Point(Rational(-1, 3), Rational(-1, 3)), Point(5, 1))\n assert Intersection(l1, FiniteSet(Point(7,-7))) == EmptySet\n assert Intersection(Circle(Point(0,0), 3), Line(p1,p2)) == FiniteSet(Point(-3,0), Point(3,0))\n assert Intersection(l1, FiniteSet(p1)) == FiniteSet(p1)\n assert Union(l1, FiniteSet(p1)) == l1\n\n fs = FiniteSet(Point(Rational(1, 3), 1), Point(Rational(2, 3), 0), Point(Rational(9, 5), Rational(1, 5)), Point(Rational(7, 3), 1))\n # test the intersection of polygons\n assert Intersection(poly1, poly2) == fs\n # make sure if we union polygons with subsets, the subsets go away\n assert Union(poly1, poly2, fs) == Union(poly1, poly2)\n # make sure that if we union with a FiniteSet that isn't a subset,\n # that the points in the intersection stop being listed\n assert Union(poly1, FiniteSet(Point(0,0), Point(3,5))) == Union(poly1, FiniteSet(Point(3,5)))\n # intersect two polygons that share an edge\n assert Intersection(poly1, poly3) == Union(FiniteSet(Point(Rational(3, 2), 1), Point(2, 1)), Segment(Point(0, 0), Point(1, 0)))","title":""},{"docid":"bc665dcf09bce408bea579313280980c","score":"0.5597862","text":"def voronoi(point_list, boundary):\n return {Polygon}","title":""},{"docid":"87fd1355d5d85cdb3262e3448e9ced46","score":"0.55957127","text":"def segmentation_to_mask(polys, height, width):\n polys = [p.flatten().tolist() for p in polys]\n assert len(polys) > 0, \"Polygons are empty!\"\n\n import pycocotools.mask as cocomask\n rles = cocomask.frPyObjects(polys, height, width)\n rle = cocomask.merge(rles)\n return cocomask.decode(rle)","title":""},{"docid":"827b30c03f1a4ac2ee8ffa5b836ffab6","score":"0.5594046","text":"def bb_intersection_over_union(boxA, boxB):\n # determine the (x, y)-coordinates of the intersection rectangle\n xA = max(boxA[0], boxB[0])\n yA = max(boxA[1], boxB[1])\n xB = min(boxA[2], boxB[2])\n yB = min(boxA[3], boxB[3])\n\n # compute the area of intersection rectangle\n interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)\n\n # compute the area of both the prediction and ground-truth\n # rectangles\n boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)\n boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)\n\n # compute the intersection over union by taking the intersection\n # area and dividing it by the sum of prediction + ground-truth\n # areas - the interesection area\n print(boxAArea,boxBArea,interArea)\n iou = interArea / float(boxAArea + boxBArea - interArea)\n\n # return the intersection over union value\n return iou","title":""},{"docid":"e1892817c2382820f2228b6cc5d33cf6","score":"0.5581676","text":"def create_simplified_geometry(self):\n \n ## Replace MultiPolygons by Polygons ##\n nfeat = self.FeatureCount() # somehow necessary for the loop on features to work \n print(\"%i features to process\" %nfeat)\n\n out_ds = ogr.GetDriverByName('Memory').CreateDataSource('')\n out_lyr = out_ds.CreateLayer('poly', srs=self.srs)\n multipoly2poly(self.layer, out_lyr)\n\n ## Create a single geometry ##\n union = ogr.Geometry(ogr.wkbMultiPolygon)\n for feat in out_lyr:\n union.AddGeometry(feat.GetGeometryRef())\n union=union.Simplify(0)\n\n # Check that geometry is valid (otherwise will fail later)\n if not union.IsValid():\n print(\"ERROR with geometry\")\n return 0\n else:\n return union","title":""},{"docid":"ef6c4c5a8666913fc71719062d788f70","score":"0.5575433","text":"def boundary_substraction(\n *,\n boundary: tuple[tuple[int, ...], ...],\n subtracted: tuple[tuple[int, ...], ...],\n) -> tuple[tuple[int, ...], ...]:\n output = tuple(\n boundary_i for boundary_i in boundary if boundary_i not in subtracted\n )\n return output","title":""},{"docid":"eeb79ab0e1a792756699362a954f35d2","score":"0.55568624","text":"def bbox_iou(box1, box2, order=\"xyxy\"):\n assert order in {\"xyxy\", \"xywh\"}\n\n if order == \"xywh\":\n b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2\n b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2\n b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2\n b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2\n else:\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\n\n # get the corrdinates of the intersection rectangle\n inter_rect_x1 = torch.max(b1_x1, b2_x1)\n inter_rect_y1 = torch.max(b1_y1, b2_y1)\n inter_rect_x2 = torch.min(b1_x2, b2_x2)\n inter_rect_y2 = torch.min(b1_y2, b2_y2)\n # Intersection area\n inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(\n inter_rect_y2 - inter_rect_y1 + 1, min=0\n )\n # Union Area\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\n\n iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)\n\n return iou","title":""},{"docid":"ef24eaeebd85a66c3fd42d0070c2e7ae","score":"0.5553224","text":"def bbox_overlaps(bboxes1, bboxes2, mode='iou'):\n\n assert mode in ['iou', 'iof','overlap']\n\n bboxes1 = bboxes1.astype(np.float32)\n bboxes2 = bboxes2.astype(np.float32)\n rows = bboxes1.shape[0]\n cols = bboxes2.shape[0]\n ious = np.zeros((rows, cols), dtype=np.float32)\n if rows * cols == 0:\n return ious\n exchange = True\n if bboxes1.shape[0] > bboxes2.shape[0]:\n bboxes1, bboxes2 = bboxes2, bboxes1\n ious = np.zeros((cols, rows), dtype=np.float32)\n exchange = False\n area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (\n bboxes1[:, 3] - bboxes1[:, 1] + 1)\n area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (\n bboxes2[:, 3] - bboxes2[:, 1] + 1)\n for i in range(bboxes1.shape[0]):\n x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])\n y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])\n x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])\n y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])\n overlap = np.maximum(x_end - x_start + 1, 0) * np.maximum(\n y_end - y_start + 1, 0)\n if mode == 'iou':\n union = area1[i] + area2 - overlap\n if mode == 'overlap':\n return overlap\n else:\n union = area1[i] if not exchange else area2\n ious[i, :] = overlap / union\n if exchange:\n ious = ious.T\n return ious","title":""},{"docid":"0938abe360aab4d4890b095804424c6e","score":"0.55488","text":"def unionall(self, other_rects):\n result = FRect(self)\n result.unionall_ip(other_rects)\n return result","title":""},{"docid":"e0406cd12d4465d4daca5445a9f9eb9c","score":"0.5547007","text":"def unionDeuxCouches(a,b):\r\n #Lire le shapefile 1 et prendre en parametre a\r\n shapefile1 = gpd.read_file(a)\r\n #Lire le shapefile 2 et prendre en parametre b\r\n shapefile2 = gpd.read_file(b)\r\n #Verifier si les deux couches vecteurs ont de la même systeme de coordonnées\r\n if shapefile1.crs==shapefile2.crs:\r\n #Créer l'union des deux couches\r\n couche_union = gpd.overlay(shapefile1, shapefile2, how='union')\r\n #couche_union.plot(alpha=0.5, edgecolor='k', cmap='tab10'); pour l' afficher en plot\r\n #Definir le nom de la sortie\r\n out = r\"shp/couche_union.shp\"\r\n #Creer le fichier en sortie\r\n ecrire_union=couche_union[:]\r\n ecrire_union.to_file(out)\r\n else:\r\n print(\"Vérifier le systeme de coordonnées de ces deux couches vecteurs\")","title":""},{"docid":"71d94847de5bd82f5f44babf356a528c","score":"0.55420196","text":"def test_valid_polygon_pair_union(self):\n\n response = self.app.post(\n '/polygonAction/union',\n content_type='application/json',\n data=self.get_json_data(\"fixtures/validPolygonPair.json\")\n )\n geoJson = json.loads(response.data.decode('utf8'))\n assert response.status_code == 200\n assert geoJson['operation'] == 'union'","title":""},{"docid":"e4fa23b47d4644dcb2eee9801ad6e5fe","score":"0.5531045","text":"def process_polygons(self, polygons):\n\n polygons = [np.array(p).astype(np.float32) for p in polygons]\n valid_polygons = []\n for polygon in polygons:\n if len(polygon) % 2 == 0 and len(polygon) >= 6:\n valid_polygons.append(polygon)\n return valid_polygons","title":""},{"docid":"3c1623fe0b1f6b82635b9bdd192b1315","score":"0.5526814","text":"def voronoi_polygons(voronoi, boundary, diameter=1000000):\n polygons = []\n\n centroid = voronoi.points.mean(axis=0)\n\n # Mapping from (input point index, Voronoi point index) to list of\n # unit vectors in the directions of the infinite ridges starting\n # at the Voronoi point and neighbouring the input point.\n ridge_direction = defaultdict(list)\n for (p, q), rv in zip(voronoi.ridge_points, voronoi.ridge_vertices):\n u, v = sorted(rv)\n if u == -1:\n # Infinite ridge starting at ridge point with index v,\n # equidistant from input points with indexes p and q.\n t = voronoi.points[q] - voronoi.points[p] # tangent\n n = np.array([-t[1], t[0]]) / np.linalg.norm(t) # normal\n midpoint = voronoi.points[[p, q]].mean(axis=0)\n direction = np.sign(np.dot(midpoint - centroid, n)) * n\n ridge_direction[p, v].append(direction)\n ridge_direction[q, v].append(direction)\n\n for i, r in enumerate(voronoi.point_region):\n region = voronoi.regions[r]\n if -1 not in region:\n # Finite region.\n polygons.append(\n Polygon(voronoi.vertices[region]).intersection(boundary)\n )\n continue\n # Infinite region.\n inf = region.index(-1) # Index of vertex at infinity.\n j = region[(inf - 1) % len(region)] # Index of previous vertex.\n k = region[(inf + 1) % len(region)] # Index of next vertex.\n if j == k:\n # Region has one Voronoi vertex with two ridges.\n dir_j, dir_k = ridge_direction[i, j]\n else:\n # Region has two Voronoi vertices, each with one ridge.\n (dir_j,) = ridge_direction[i, j]\n (dir_k,) = ridge_direction[i, k]\n\n # Length of ridges needed for the extra edge to lie at least\n # 'diameter' away from all Voronoi vertices.\n length = 2 * diameter / np.linalg.norm(dir_j + dir_k)\n\n # Polygon consists of finite part plus an extra edge.\n finite_part = voronoi.vertices[region[inf + 1 :] + region[:inf]]\n extra_edge = [\n voronoi.vertices[j] + dir_j * length,\n voronoi.vertices[k] + dir_k * length,\n ]\n polygons.append(\n Polygon(np.concatenate((finite_part, extra_edge))).intersection(\n boundary\n )\n )\n\n return polygons","title":""},{"docid":"ec78d9e524e8e62b1fa8e5c776f538c7","score":"0.5525387","text":"def get_boundary_polygon(self):\n return simplify_polygon([node.pos for node in self.boundary_nodes])","title":""},{"docid":"b309b280a67704e86086b7afa633db59","score":"0.5522205","text":"def merge_bounding_boxes(bb1, bb2, mode):\n less_than_or_equal = lambda x, y: x if x <= y else y\n greater_than = lambda x, y: x if x > y else y\n\n if mode == \"union\":\n comparison_ops = [\n less_than_or_equal, greater_than, greater_than,\n less_than_or_equal]\n if mode == \"intersection\":\n comparison_ops = [\n greater_than, less_than_or_equal, less_than_or_equal,\n greater_than]\n\n bb_out = [op(x, y) for op, x, y in zip(comparison_ops, bb1, bb2)]\n return bb_out","title":""},{"docid":"31cf511748fac7254a981ee7045faaa3","score":"0.5513587","text":"def bb_intersect_over_union(boxA: BoundingBox, boxB: BoundingBox):\n boxA = [boxA.top, boxA.left, boxA.top + boxA.width, boxA.left + boxA.height]\n boxB = [boxB.top, boxB.left, boxB.top + boxB.width, boxB.left + boxB.height]\n\n # Getting X,Y of Intersection Rectangle\n xA = max(boxA[0], boxB[0])\n yA = max(boxA[1], boxB[1])\n xB = max(boxA[2], boxB[2])\n yB = max(boxA[3], boxB[3])\n\n # Compute area of the rectangle\n interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)\n\n # Compute area of pred box and gt box\n boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)\n boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)\n\n # Compute IoU\n # Intersection area / Sum (gt+pred) area\n iou = interArea / float(boxAArea + boxBArea - interArea)\n\n # idk ;_;\n iou = 1 - math.modf(iou)[0]\n\n return iou","title":""},{"docid":"7846f70363c8a4b37349482056415f04","score":"0.55080426","text":"def test_separate_points_by_polygon1(self):\r\n\r\n # Unit square\r\n U = [[0, 0], [1, 0], [1, 1], [0, 1]]\r\n\r\n inside, outside = separate_points_by_polygon([[0.5, 0.5]], U)\r\n assert len(inside) == 1\r\n assert numpy.allclose(inside, [0])\r\n\r\n inside, outside = separate_points_by_polygon([[0.5, 0.5],\r\n [0.3, 0.2]], U)\r\n assert len(inside) == 2\r\n assert numpy.allclose(inside, [0, 1])\r\n\r\n inside, outside = separate_points_by_polygon([[0.5, 0.5],\r\n [0.3, 0.2],\r\n [0.6, 0.7]], U)\r\n assert len(inside) == 3\r\n assert numpy.allclose(inside, [0, 1, 2])\r\n\r\n inside, outside = separate_points_by_polygon([[0.3, 0.2]], U)\r\n assert len(inside) == 1\r\n assert numpy.allclose(inside, [0])\r\n\r\n inside, outside = separate_points_by_polygon([[0.3, 0.2],\r\n [1, -0.5]], U)\r\n assert len(inside) == 1\r\n assert numpy.allclose(inside, [0])\r\n assert numpy.allclose(outside, [1])\r\n\r\n inside, outside = separate_points_by_polygon([[0.5, 0.5],\r\n [1, -0.5],\r\n [0.3, 0.2]], U)\r\n assert numpy.allclose(inside, [0, 2])\r\n assert numpy.allclose(outside, [1])\r\n\r\n inside, outside = separate_points_by_polygon([[0.1, 0.1],\r\n [0.5, 0.5],\r\n [1, -0.5],\r\n [0.3, 0.2]], U)\r\n assert numpy.allclose(inside, [0, 1, 3])\r\n assert numpy.allclose(outside, [2])\r\n\r\n # Try with boundary (edge) point\r\n inside, outside = separate_points_by_polygon([[0, 0.5],\r\n [0.1, 0.2]], U)\r\n assert numpy.allclose(inside, [0, 1])\r\n\r\n # Try with boundary (corner) point\r\n inside, outside = separate_points_by_polygon([[0, 0],\r\n [0.1, 0.2]], U)\r\n assert numpy.allclose(inside, [0, 1])\r\n\r\n # Try with a range of cases point\r\n inside, outside = separate_points_by_polygon([[0, 0], # corner\r\n [0, 0.5], # edge\r\n [0.1, 0.2]], U)\r\n assert numpy.allclose(inside, [0, 1, 2])\r\n\r\n # One more test of vector formulation returning indices\r\n polygon = [[0, 0], [1, 0], [0.5, -1], [2, -1], [2, 1], [0, 1]]\r\n points = [[0.5, 0.5], [1, -0.5], [1.5, 0], [0.5, 1.5], [0.5, -0.5]]\r\n inside, outside = separate_points_by_polygon(points, polygon)\r\n assert numpy.allclose(inside, [0, 1, 2])\r\n assert numpy.allclose(outside, [3, 4])\r\n\r\n polygon = [[0, 0], [1, 0], [0.5, -1], [2, -1], [2, 1], [0, 1]]\r\n points = [[0.5, 1.4], [0.5, 0.5], [1, -0.5], [1.5, 0],\r\n [0.5, 1.5], [0.5, -0.5]]\r\n inside, outside = separate_points_by_polygon(points, polygon)\r\n assert numpy.allclose(inside, [1, 2, 3])\r\n assert numpy.allclose(outside, [0, 4, 5])","title":""},{"docid":"a650d1c5f022a964933047e7869e1f80","score":"0.5484844","text":"def bbox_iou(box1, box2):\n #Get the coordinates of bounding boxes\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:,0], box1[:,1], box1[:,2], box1[:,3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:,0], box2[:,1], box2[:,2], box2[:,3]\n \n #get the corrdinates of the intersection rectangle\n inter_rect_x1 = torch.max(b1_x1, b2_x1)\n inter_rect_y1 = torch.max(b1_y1, b2_y1)\n inter_rect_x2 = torch.min(b1_x2, b2_x2)\n inter_rect_y2 = torch.min(b1_y2, b2_y2)\n \n #Intersection area\n inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(inter_rect_y2 - inter_rect_y1 + 1, min=0)\n\n #Union Area\n b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)\n b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)\n \n iou = inter_area / (b1_area + b2_area - inter_area)\n \n return iou","title":""},{"docid":"59bbee8b8f915bb9dc8c82ee6429a66c","score":"0.5476246","text":"def selectBoundaryEdges(components):\n\n\tcmds.select(cmds.polyListComponentConversion(components, te=True, bo=True))","title":""},{"docid":"3a335ace891b6e915c09a22a0e93031b","score":"0.5473155","text":"def multipoly2poly(in_lyr, out_lyr):\n k=0\n for in_feat in in_lyr:\n geom = in_feat.GetGeometryRef()\n if geom.GetGeometryName() == 'MULTIPOLYGON':\n geom_part=geom.GetGeometryRef(0)\n addPolygon(geom_part.ExportToWkb(), out_lyr)\n else:\n addPolygon(geom.ExportToWkb(), out_lyr)\n k+=1","title":""},{"docid":"628d96bf0326d7a9be1a9f738b8fabba","score":"0.5465427","text":"def process_polygons(self, polygons):\n\n polygons = [np.array(p) for p in polygons]\n valid_polygons = []\n for polygon in polygons:\n if len(polygon) % 2 == 0 and len(polygon) >= 6:\n valid_polygons.append(polygon)\n return valid_polygons","title":""},{"docid":"628d96bf0326d7a9be1a9f738b8fabba","score":"0.5465427","text":"def process_polygons(self, polygons):\n\n polygons = [np.array(p) for p in polygons]\n valid_polygons = []\n for polygon in polygons:\n if len(polygon) % 2 == 0 and len(polygon) >= 6:\n valid_polygons.append(polygon)\n return valid_polygons","title":""},{"docid":"379d6a0bf9ecaf391a34a49b86e3fe90","score":"0.5461095","text":"def bbox_overlaps(bboxes1, bboxes2, mode='iou'):\n\n assert mode in ['iou', 'iof']\n\n bboxes1 = bboxes1.astype(np.float32)\n bboxes2 = bboxes2.astype(np.float32)\n rows = bboxes1.shape[0]\n cols = bboxes2.shape[0]\n ious = np.zeros((rows, cols), dtype=np.float32)\n if rows * cols == 0:\n return ious\n exchange = False\n if bboxes1.shape[0] > bboxes2.shape[0]:\n bboxes1, bboxes2 = bboxes2, bboxes1\n ious = np.zeros((cols, rows), dtype=np.float32)\n exchange = True\n area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (\n bboxes1[:, 3] - bboxes1[:, 1] + 1)\n area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (\n bboxes2[:, 3] - bboxes2[:, 1] + 1)\n for i in range(bboxes1.shape[0]):\n x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])\n y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])\n x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])\n y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])\n overlap = np.maximum(x_end - x_start + 1, 0) * np.maximum(\n y_end - y_start + 1, 0)\n if mode == 'iou':\n union = area1[i] + area2 - overlap\n else:\n union = area1[i] if not exchange else area2\n ious[i, :] = overlap / union\n if exchange:\n ious = ious.T\n return ious","title":""},{"docid":"cbb4b66db922c14869e2da7e260ac303","score":"0.54547054","text":"def bbox_iou(box1, box2):\n b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2\n b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2\n b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2\n b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2\n\n inter_rect_x1 = torch.max(b1_x1, b2_x1)\n inter_rect_y1 = torch.max(b1_y1, b2_y1)\n inter_rect_x2 = torch.min(b1_x2, b2_x2)\n inter_rect_y2 = torch.min(b1_y2, b2_y2)\n # Intersection area\n inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(\n inter_rect_y2 - inter_rect_y1 + 1, min=0\n )\n # Union Area\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\n\n iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)\n\n return iou","title":""},{"docid":"47be844c287d5c564c71bef0193b42c9","score":"0.5451837","text":"def uniteRectangles(a, b, inputType=\"cv2\"):\n\n if inputType == \"dlib\":\n return a+b\n x = min(a[0], b[0])\n y = min(a[1], b[1])\n w = max(a[0]+a[2], b[0]+b[2]) - x\n h = max(a[1]+a[3], b[1]+b[3]) - y\n return [x, y, w, h]","title":""},{"docid":"7a1d7c3c95864302d4c9f66f9ed4a5b6","score":"0.5451298","text":"def union_bbox(elements, transformed=True, with_stroke=False):\n boxes = []\n for e in elements:\n if not hasattr(e, \"bbox\") or isinstance(e, (Group, Use)):\n continue\n box = e.bbox(transformed=transformed, with_stroke=with_stroke)\n if box is None:\n continue\n boxes.append(box)\n if len(boxes) == 0:\n return None\n (xmins, ymins, xmaxs, ymaxs) = zip(*boxes)\n return min(xmins), min(ymins), max(xmaxs), max(ymaxs)","title":""},{"docid":"7a1d7c3c95864302d4c9f66f9ed4a5b6","score":"0.5451298","text":"def union_bbox(elements, transformed=True, with_stroke=False):\n boxes = []\n for e in elements:\n if not hasattr(e, \"bbox\") or isinstance(e, (Group, Use)):\n continue\n box = e.bbox(transformed=transformed, with_stroke=with_stroke)\n if box is None:\n continue\n boxes.append(box)\n if len(boxes) == 0:\n return None\n (xmins, ymins, xmaxs, ymaxs) = zip(*boxes)\n return min(xmins), min(ymins), max(xmaxs), max(ymaxs)","title":""},{"docid":"3630b5f0a701c86ea8c37c1f3f4979d2","score":"0.54487246","text":"def get_bounding_box_parameterization(self, polygon, height, width):\n outline = np.array(polygon)\n # rrr, ccc = outline[:,1], outline[:,0]\n # rr = []\n # cc = []\n # for r in rrr:\n # if r < 0:\n # r = 0\n # if r > height-1:\n # r = height-1\n # rr.append(r)\n # for c in ccc:\n # if c < 0:\n # c = 0\n # if c > width-1:\n # c = width-1\n # cc.append(c)\n # rr = np.array(rr)\n # cc = np.array(cc)\n rr, cc = outline[:,1], outline[:,0]\n xmin = max(min(cc), 0)\n xmax = min(max(cc), width-1)\n ymin = max(min(rr), 0)\n ymax = min(max(rr), height-1)\n width = xmax - xmin\n height = ymax - ymin\n center_x = xmin + 0.5*width \n center_y = ymin + 0.5*height\n mask_vector = [xmin, ymin, xmax, ymax, center_x, center_y, width, height]\n return mask_vector","title":""},{"docid":"3d42105424bd99d6339a530b3d27817a","score":"0.5445908","text":"def _prepare_polygon_layer(self, layer):\r\n# import time\r\n# startTime = time.clock()\r\n\r\n message = m.Message(\r\n m.Heading(\r\n self.tr('Pre-clipping input data...'),\r\n **PROGRESS_UPDATE_STYLE),\r\n m.Paragraph(self.tr(\r\n 'Modifying %s to avoid intersections with the aggregation '\r\n 'layer'\r\n ) % (layer.name())))\r\n #noinspection PyTypeChecker\r\n self._send_message(message)\r\n\r\n layer_filename = str(layer.source())\r\n postprocessing_polygons = self.safe_layer.get_geometry()\r\n polygons_layer = safe_read_layer(layer_filename)\r\n remaining_polygons = numpy.array(\r\n polygons_layer.get_geometry(), dtype=list)\r\n# myRemainingAttributes = numpy.array(polygons_layer.get_data())\r\n remaining_indexes = numpy.array(range(len(remaining_polygons)))\r\n\r\n #used for unit tests only\r\n self.preprocessed_feature_count = 0\r\n\r\n # TODO (MB) the intersecting array is used only for debugging and\r\n # could be safely removed\r\n intersecting_polygons = []\r\n inside_polygons = []\r\n\r\n # TODO (MB) maybe do raw geos without qgis\r\n #select all post processing polygons with no attributes\r\n aggregation_provider = self.layer.dataProvider()\r\n aggregation_request = QgsFeatureRequest()\r\n aggregation_request.setSubsetOfAttributes([])\r\n\r\n # copy polygons to a memory layer\r\n qgis_memory_layer = create_memory_layer(layer)\r\n\r\n polygons_provider = qgis_memory_layer.dataProvider()\r\n polygons_request = QgsFeatureRequest()\r\n qgis_feature = QgsFeature()\r\n inside_feature = QgsFeature()\r\n fields = polygons_provider.fields()\r\n temporary_dir = temp_dir(sub_dir='pre-process')\r\n out_filename = unique_filename(suffix='.shp', dir=temporary_dir)\r\n\r\n self.copy_keywords(layer, out_filename)\r\n shape_writer = QgsVectorFileWriter(\r\n out_filename,\r\n 'UTF-8',\r\n fields,\r\n polygons_provider.geometryType(),\r\n polygons_provider.crs())\r\n if shape_writer.hasError():\r\n raise InvalidParameterError(shape_writer.errorMessage())\r\n # end TODO\r\n\r\n for (polygon_index, postprocessing_polygon) in enumerate(\r\n postprocessing_polygons):\r\n LOGGER.debug('Post Processing Polygon %s' % polygon_index)\r\n polygon_count = len(remaining_polygons)\r\n aggregation_request.setFilterFid(polygon_index)\r\n aggregation_polygon = aggregation_provider.getFeatures(\r\n aggregation_request).next()\r\n geometry = QgsGeometry(aggregation_polygon.geometry())\r\n\r\n # polygon bounding box values\r\n array = numpy.array(postprocessing_polygon)\r\n minx = miny = sys.maxint\r\n maxx = maxy = -minx\r\n postprocessing_minimum_x = min(minx, min(array[:, 0]))\r\n postprocessing_maximum_x = max(maxx, max(array[:, 0]))\r\n postprocessing_minimum_y = min(miny, min(array[:, 1]))\r\n postprocessing_maximum_y = max(maxy, max(array[:, 1]))\r\n\r\n # create an array full of False to store if a BB vertex is inside\r\n # or outside the polygon\r\n inside_vertices = numpy.zeros(polygon_count * 4, dtype=numpy.bool)\r\n\r\n # Create Nx2 vector of vertices of bounding boxes\r\n bounding_vertices = []\r\n # Compute bounding box for each geometry type\r\n for remaining_polygon in remaining_polygons:\r\n minx = miny = sys.maxint\r\n maxx = maxy = -minx\r\n # Do outer ring only as the BB is outside anyway\r\n array = numpy.array(remaining_polygon)\r\n minx = min(minx, numpy.min(array[:, 0]))\r\n maxx = max(maxx, numpy.max(array[:, 0]))\r\n miny = min(miny, numpy.min(array[:, 1]))\r\n maxy = max(maxy, numpy.max(array[:, 1]))\r\n bounding_vertices.extend([\r\n (minx, miny),\r\n (minx, maxy),\r\n (maxx, maxy),\r\n (maxx, miny)])\r\n\r\n # see if BB vertices are in polygon\r\n bounding_vertices = numpy.array(bounding_vertices)\r\n inside, _ = points_in_and_outside_polygon(\r\n bounding_vertices, postprocessing_polygon)\r\n # make True if the vertex was in polygon\r\n inside_vertices[inside] = True\r\n\r\n # next_iteration_polygons has the 0:count indexes\r\n # outside_polygons has the mapped to original indexes\r\n # and is overwritten at every iteration because we care only of\r\n # the outside polygons remaining after the last iteration\r\n next_iteration_polygons = []\r\n outside_polygons = []\r\n\r\n for i in range(polygon_count):\r\n k = i * 4\r\n mapped_index = remaining_indexes[i]\r\n # memory layers counting starts at 1 instead of 0 as in our\r\n # indexes\r\n feature_id = mapped_index + 1\r\n do_intersection = False\r\n # sum the isInside bool for each of the bounding box vertices\r\n # of each polygon. for example True + True + False + True is 3\r\n polygon_location = numpy.sum(inside_vertices[k:k + 4])\r\n\r\n if polygon_location == 4:\r\n # all vertices are inside -> polygon is inside\r\n # ignore this polygon from further analysis\r\n inside_polygons.append(mapped_index)\r\n polygons_request.setFilterFid(feature_id)\r\n qgis_feature = polygons_provider.getFeatures(\r\n polygons_request).next()\r\n shape_writer.addFeature(qgis_feature)\r\n self.preprocessed_feature_count += 1\r\n# LOGGER.debug('Polygon %s is fully inside' %mapped_index)\r\n# tmpWriter.addFeature(qgis_feature)\r\n\r\n elif polygon_location == 0:\r\n # all vertices are outside\r\n # check if the polygon BB is completely outside of the\r\n # polygon BB.\r\n polygon_min_x = numpy.min(bounding_vertices[k:k + 4, 0])\r\n polygon_max_x = numpy.max(bounding_vertices[k:k + 4, 0])\r\n polygon_min_y = numpy.min(bounding_vertices[k:k + 4, 1])\r\n polygon_max_y = numpy.max(bounding_vertices[k:k + 4, 1])\r\n\r\n # check if remaining_polygon is all E,W,N,S of polygon\r\n if ((polygon_min_x > postprocessing_maximum_x) or\r\n (polygon_max_x < postprocessing_minimum_x) or\r\n (polygon_min_y > postprocessing_maximum_y) or\r\n (polygon_max_y < postprocessing_minimum_y)):\r\n # polygon is surely outside\r\n outside_polygons.append(mapped_index)\r\n # we need this polygon in the next iteration\r\n next_iteration_polygons.append(i)\r\n else:\r\n # polygon might be outside or intersecting. consider\r\n # it intersecting so it goes into further analysis\r\n do_intersection = True\r\n else:\r\n # some vertices are outside some inside -> polygon is\r\n # intersecting\r\n do_intersection = True\r\n\r\n # intersect using qgis\r\n if do_intersection:\r\n# LOGGER.debug('Intersecting polygon %s' % mapped_index)\r\n intersecting_polygons.append(mapped_index)\r\n\r\n polygons_request.setFilterFid(feature_id)\r\n try:\r\n qgis_feature = polygons_provider.getFeatures(\r\n polygons_request).next()\r\n except StopIteration:\r\n LOGGER.debug(\r\n 'Could not fetch feature: %s' % feature_id)\r\n LOGGER.debug([str(error) for error in\r\n polygons_provider.errors()])\r\n\r\n qgis_polygon_geometry = QgsGeometry(\r\n qgis_feature.geometry())\r\n attribute_map = qgis_feature.attributes()\r\n# for (k, attr) in attribute_map.iteritems():\r\n# LOGGER.debug( \"%d: %s\" % (k, attr.toString()))\r\n\r\n # make intersection of the qgis_feature and the\r\n # post processing polygon\r\n # write the inside part to a shp file and the outside part\r\n # back to the original QGIS layer\r\n try:\r\n intersection = geometry.intersection(\r\n qgis_polygon_geometry)\r\n# if intersection is not None:\r\n intersection_geometry = QgsGeometry(intersection)\r\n\r\n #from ftools\r\n unknown_geometry_type = 0\r\n geometry_type = intersection_geometry.wkbType()\r\n if geometry_type == unknown_geometry_type:\r\n int_com = geometry.combine(\r\n qgis_polygon_geometry)\r\n int_sym = geometry.symDifference(\r\n qgis_polygon_geometry)\r\n intersection_geometry = QgsGeometry(\r\n int_com.difference(int_sym))\r\n #LOGGER.debug('wkbType type of intersection: %s' %\r\n # intersection_geometry.wkbType())\r\n polygon_types = [QGis.WKBPolygon, QGis.WKBMultiPolygon]\r\n if intersection_geometry.wkbType() in polygon_types:\r\n inside_feature.setGeometry(intersection_geometry)\r\n inside_feature.setAttributes(attribute_map)\r\n shape_writer.addFeature(inside_feature)\r\n self.preprocessed_feature_count += 1\r\n else:\r\n #LOGGER.debug(\r\n # 'Intersection not a polygon so the two '\r\n # 'polygons either touch only or do not '\r\n # 'intersect. Not adding this to the inside '\r\n # 'list')\r\n pass\r\n # Part of the polygon that is outside the post\r\n # processing polygon\r\n outside = qgis_polygon_geometry.difference(\r\n intersection_geometry)\r\n# if outside is not None:\r\n outside_geometry = QgsGeometry(outside)\r\n\r\n if outside_geometry.wkbType() in polygon_types:\r\n # modify the original geometry to the part\r\n # outside of the post processing polygon\r\n polygons_provider.changeGeometryValues(\r\n {feature_id: outside_geometry})\r\n # we need this polygon in the next iteration\r\n outside_polygons.append(mapped_index)\r\n next_iteration_polygons.append(i)\r\n\r\n except TypeError:\r\n LOGGER.debug('ERROR with FID %s', mapped_index)\r\n\r\n #LOGGER.debug('Inside %s' % inside_polygons)\r\n #LOGGER.debug('Outside %s' % outside_polygons)\r\n #LOGGER.debug('Intersection %s' % intersecting_polygons)\r\n if len(next_iteration_polygons) > 0:\r\n # some polygons are still completely outside of the\r\n # post processing polygon so go on and reiterate using only\r\n # these\r\n next_iteration_index = numpy.array(next_iteration_polygons)\r\n\r\n remaining_polygons = remaining_polygons[\r\n next_iteration_index]\r\n #myRemainingAttributes = myRemainingAttributes[\r\n # next_iteration_index]\r\n remaining_indexes = remaining_indexes[next_iteration_index]\r\n LOGGER.debug('Remaining: %s' % len(remaining_polygons))\r\n else:\r\n print 'no more polygons to be checked'\r\n break\r\n #del tmpWriter\r\n\r\n # here the full polygon set is represented by:\r\n # inside_polygons + intersecting_polygons + next_iteration_polygons\r\n # the a polygon intersecting multiple post processing polygons appears\r\n # multiple times in the array\r\n # noinspection PyUnboundLocalVariable\r\n LOGGER.debug('Results:\\nInside: %s\\nIntersect: %s\\nOutside: %s' % (\r\n inside_polygons, intersecting_polygons, outside_polygons))\r\n\r\n # add in-and outside polygons\r\n for i in outside_polygons:\r\n feature_id = i + 1\r\n polygons_request.setFilterFid(feature_id)\r\n qgis_feature = polygons_provider.getFeatures(\r\n polygons_request).next()\r\n shape_writer.addFeature(qgis_feature)\r\n self.preprocessed_feature_count += 1\r\n\r\n del shape_writer\r\n# LOGGER.debug('Created: %s' % self.preprocessed_feature_count)\r\n\r\n name = '%s %s' % (layer.name(), self.tr('preprocessed'))\r\n output_layer = QgsVectorLayer(out_filename, name, 'ogr')\r\n if not output_layer.isValid():\r\n #TODO (MB) use a better exception\r\n raise Exception('Invalid qgis Layer')\r\n\r\n if self.show_intermediate_layers:\r\n self.update_keywords(output_layer, {'title': name})\r\n #noinspection PyArgumentList\r\n QgsMapLayerRegistry.instance().addMapLayer(output_layer)\r\n\r\n return output_layer","title":""},{"docid":"1a232ad18f36f8846b11c8aa92b2f0ab","score":"0.54436153","text":"def bbox_iou(box1, box2):\n inter_max_xy = torch.min(box1[:, 2:4], box2[:, 2:4])\n inter_min_xy = torch.max(box1[:, 0:2], box2[:, 0:2])\n\n inter_size = torch.clamp((inter_max_xy-inter_min_xy), min=0)\n inter_area = inter_size[:, 0]*inter_size[:, 1] # 1 by num_boxes\n\n # # Original codes\n # # Get the coordinates of bounding boxes\n # b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]\n # b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\n\n # # Get the coordinates of the intersection rectangle\n # # Can take multi-row tensors\n # inter_rect_x1 = torch.max(b1_x1, b2_x1)\n # inter_rect_y1 = torch.max(b1_y1, b2_y1)\n # inter_rect_x2 = torch.min(b1_x2, b2_x2)\n # inter_rect_y2 = torch.min(b1_y2, b2_y2)\n\n # # Intersection area\n # inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * \\\n # torch.clamp(inter_rect_y2 - inter_rect_y1 + 1, min=0)\n\n # # Union Area\n # b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)\n # b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)\n\n b1_area = (box1[:, 2]-box1[:, 0])*(box1[:, 3]-box1[:, 1])\n b2_area = (box2[:, 2]-box2[:, 0])*(box2[:, 3]-box2[:, 1])\n\n iou = inter_area / (b1_area + b2_area - inter_area)\n\n return iou","title":""},{"docid":"c207bdaf64789d61c0f041cd8d3b0eaa","score":"0.5435567","text":"def bbox_overlaps_iof(bboxes1, bboxes2, eps=1e-6):\n rows = bboxes1.shape[0]\n cols = bboxes2.shape[0]\n\n if rows * cols == 0:\n return np.zeros((rows, cols), dtype=np.float32)\n\n hbboxes1 = poly2hbb(bboxes1)\n hbboxes2 = bboxes2\n hbboxes1 = hbboxes1[:, None, :]\n lt = np.maximum(hbboxes1[..., :2], hbboxes2[..., :2])\n rb = np.minimum(hbboxes1[..., 2:], hbboxes2[..., 2:])\n wh = np.clip(rb - lt, 0, np.inf)\n h_overlaps = wh[..., 0] * wh[..., 1]\n\n l, t, r, b = [bboxes2[..., i] for i in range(4)]\n polys2 = np.stack([l, t, r, t, r, b, l, b], axis=-1)\n if shgeo is None:\n raise ImportError('Please run \"pip install shapely\" '\n 'to install shapely first.')\n sg_polys1 = [shgeo.Polygon(p) for p in bboxes1.reshape(rows, -1, 2)]\n sg_polys2 = [shgeo.Polygon(p) for p in polys2.reshape(cols, -1, 2)]\n overlaps = np.zeros(h_overlaps.shape)\n for p in zip(*np.nonzero(h_overlaps)):\n overlaps[p] = sg_polys1[p[0]].intersection(sg_polys2[p[-1]]).area\n unions = np.array([p.area for p in sg_polys1], dtype=np.float32)\n unions = unions[..., None]\n\n unions = np.clip(unions, eps, np.inf)\n outputs = overlaps / unions\n if outputs.ndim == 1:\n outputs = outputs[..., None]\n return outputs","title":""},{"docid":"e4d35d0c2e13e68f4e1f6f836498b25d","score":"0.54283994","text":"def polygon():\n return RepMulti(point(), 3, 8) ^ Polygon","title":""},{"docid":"708bdb8535f25fb9783575dfbd4fc776","score":"0.5420734","text":"def test_three_polygon_intersection():\n res = geom_util.rhs_split(\n SQUARE,\n LineString([(0.5, -0.1), (0.5, 1.1), (0.7, 1.1), (0.7, -0.1)]),\n )\n assert res is None","title":""},{"docid":"e307c8cfc750688ccaaa77e0eb977ce4","score":"0.5419182","text":"def convex_overlaps(gt_bboxes, points):\n overlaps = convex_iou(points, gt_bboxes)\n overlaps = overlaps.transpose(1, 0)\n return overlaps","title":""},{"docid":"de3f77a9882a7388a822652efba31fcc","score":"0.5407142","text":"def subselect_polygon(self, poly_obj):\r\n\t\tmaster_catalog = self.get_master_catalog()\r\n\t\tmaster_catalog_inside = master_catalog.subselect_polygon(poly_obj)\r\n\t\tmerged_events = []\r\n\t\tfor ev in self.merged_events:\r\n\t\t\tmaster_solution = ev.get_master_solution()\r\n\t\t\tif master_solution in master_catalog_inside:\r\n\t\t\t\tmerged_events.append(ev)\r\n\r\n\t\treturn self.__class__(merged_events,\r\n\t\t\t\t\t\t\t\t\tdefault_completeness=self.default_completeness,\r\n\t\t\t\t\t\t\t\t\tdefault_Mrelations=self.default_Mrelations,\r\n\t\t\t\t\t\t\t\t\tagency_areas=self.agency_areas)","title":""},{"docid":"488106706c5bb0538dfbafcc7f059135","score":"0.54064023","text":"def test_get_boundary_features(self):\n geojson_path = os.path.join(os.path.join(os.path.dirname(__file__), 'boundary_polygons'),\n 'us_boundaries.geojson')\n buffer_dist = 0.1 # default\n with open(geojson_path) as geojson_file:\n boundary_features = get_boundary_features(geojson_file.read(), buffer_dist)\n self.assertEqual(len(boundary_features), 1)\n self.assertEqual(boundary_features[0].geom_type, 'MultiPolygon')","title":""},{"docid":"d335c6fbfaa127be87e58dd56e40b100","score":"0.54017967","text":"def find_local_boundary(tri, triangles):\n edges = []\n\n for triangle in triangles:\n\n for i in range(3):\n\n pt1 = tri.simplices[triangle][i]\n pt2 = tri.simplices[triangle][(i + 1) % 3]\n\n if (pt1, pt2) in edges:\n edges.remove((pt1, pt2))\n\n elif (pt2, pt1) in edges:\n edges.remove((pt2, pt1))\n\n else:\n edges.append((pt1, pt2))\n\n return edges","title":""},{"docid":"de939a8c10fe41d39144aa0945d11bb4","score":"0.5388202","text":"def find_deforming_mesh_geometry(polygons, names):\n\n if isinstance(names, str):\n names = [names]\n\n selected_polygons = []\n for name in names:\n for polygon in polygons:\n if polygon.get_feature().get_name() == name:\n selected_polygons.append(polygon.get_resolved_geometry())\n\n return selected_polygons","title":""},{"docid":"188ab9f7dc049749352dab078f6ce175","score":"0.53823173","text":"def test_separate_points_by_polygon_edge(self):\r\n\r\n # Unit square\r\n U = [[0, 0], [1, 0], [1, 1], [0, 1]]\r\n\r\n # Try with boundary (vertical edge) point\r\n inside, outside = separate_points_by_polygon([[0, 0.5],\r\n [0.3, 0.3],\r\n [0.1, 0.6]],\r\n U,\r\n closed=True)\r\n assert len(inside) == 3\r\n assert numpy.allclose(inside, [0, 1, 2])\r\n\r\n inside, outside = separate_points_by_polygon([[0, 0.5],\r\n [0.3, 0.3],\r\n [0.1, 0.6]],\r\n U,\r\n closed=False)\r\n assert len(inside) == 2\r\n assert numpy.allclose(inside, [1, 2])\r\n\r\n # Try with boundary (horizontal edge) point\r\n inside, outside = separate_points_by_polygon([[0.5, 0.0],\r\n [0.3, 0.3],\r\n [0.1, 0.6]], U)\r\n assert len(outside) == 0\r\n assert len(inside) == 3\r\n assert numpy.allclose(inside, [0, 1, 2])\r\n\r\n # Try with boundary (corner) point\r\n inside, outside = separate_points_by_polygon([[0.0, 0.0],\r\n [0.3, 0.3],\r\n [0.1, 0.6]], U)\r\n assert len(outside) == 0\r\n assert len(inside) == 3\r\n assert numpy.allclose(inside, [0, 1, 2])\r\n\r\n # One outside\r\n inside, outside = separate_points_by_polygon([[0, 0.5],\r\n [1.3, 0.3],\r\n [0.1, 0.6]], U)\r\n assert len(outside) == 1\r\n assert len(inside) == 2\r\n assert numpy.allclose(inside, [0, 2])","title":""},{"docid":"396e5b498841e363d3dc8eb4fee8910f","score":"0.5379921","text":"def polygons_to_bitmask(polygons, height, width):\n if any(len(p) <= 4 for p in polygons):\n polygons = [p for p in polygons if len(p) > 4]\n if len(polygons) == 0:\n return np.zeros((height, width)).astype(int)\n\n rles = mask_util.frPyObjects(polygons, height, width)\n rle = mask_util.merge(rles)\n return mask_util.decode(rle).astype(int)","title":""},{"docid":"96731468cda630f42c22e471a0c47cd6","score":"0.5348795","text":"def test_multipart_polygon_can_be_read(self):\r\n\r\n filename = ('%s/boundaries/rw_jakarta.shp' % DATADIR)\r\n L0 = read_layer(filename)\r\n assert len(L0) == 2685\r\n assert L0.is_polygon_data\r\n\r\n geometry = L0.get_geometry(as_geometry_objects=True)\r\n attributes = L0.get_data()\r\n projection = L0.get_projection()\r\n keywords = L0.get_keywords()\r\n\r\n # Store temporarily e.g. for inspection with QGIS\r\n tmp_filename = unique_filename(suffix='.shp')\r\n Vector(geometry=geometry, data=attributes,\r\n projection=projection,\r\n keywords=keywords).write_to_file(tmp_filename)\r\n\r\n # Read again\r\n L1 = read_layer(tmp_filename)\r\n\r\n assert len(L1) == len(L0)\r\n assert L1.is_polygon_data\r\n\r\n for i in range(len(L0)):\r\n # Check geometry\r\n g0 = L0.get_geometry(as_geometry_objects=True)[i]\r\n g1 = L1.get_geometry(as_geometry_objects=True)[i]\r\n assert numpy.allclose(g0.outer_ring, g1.outer_ring)\r\n assert len(g0.inner_rings) == len(g1.inner_rings)\r\n\r\n for j in range(len(g0.inner_rings)):\r\n assert numpy.allclose(g0.inner_rings[j], g1.inner_rings[j])\r\n\r\n # Check attributes\r\n v0 = L0.get_data()[i]\r\n v1 = L1.get_data()[i]\r\n assert v0 == v1\r\n\r\n # Check projection\r\n assert L0.projection == L1.projection\r\n\r\n # Compare all\r\n assert L0 == L1","title":""},{"docid":"04406a775c987c0b049b15d5b9885d89","score":"0.53465843","text":"def npbbox_iou(box1, box2):\r\n n, items1 = box1.shape\r\n m, items2 = box2.shape\r\n # box1_clone = boxes1.reshape(n, 1, items1)\r\n\r\n # Get the coordinates of bounding boxes\r\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0:1], box1[:, 1:2], box1[:, 2:3], box1[:, 3:4]\r\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\r\n # np.tile(b1_x1, (1, m)) np.tile(b2_x1, (n,1))\r\n # model = np.ones((1,n, m,items2), dtype=box1.dtype)\r\n model = np.ones((n, m), dtype=box1.dtype)\r\n bd_model = np.zeros((n, m), dtype=box1.dtype)\r\n # get the corrdinates of the intersection rectangle\r\n inter_rect_x1 = np.max(np.stack([b1_x1 * model, b2_x1 * model]), 0) # np.max(b1_x1, b2_x1)\r\n inter_rect_y1 = np.max(np.stack([b1_y1 * model, b2_y1 * model]), 0) # np.max(b1_y1, b2_y1)\r\n inter_rect_x2 = np.min(np.stack([b1_x2 * model, b2_x2 * model]), 0) # np.min(b1_x2, b2_x2)\r\n inter_rect_y2 = np.min(np.stack([b1_y2 * model, b2_y2 * model]), 0) # np.min(b1_y2, b2_y2)\r\n\r\n inter_rect_x = inter_rect_x2 - inter_rect_x1 + 1\r\n inter_rect_y = inter_rect_y2 - inter_rect_y1 + 1\r\n # inter_rect_x = inter_rect_x.reshape(model.shape)\r\n # inter_rect_y = inter_rect_y.reshape(model.shape)\r\n # Intersection area\r\n\r\n inter_area = np.max(np.stack([inter_rect_x, bd_model]), 0) * \\\r\n np.max(np.stack([inter_rect_y, bd_model]), 0)\r\n # inter_area = inter_area.reshape(model.shape)\r\n # Union Area\r\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\r\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\r\n\r\n iou = inter_area / (b1_area + b2_area - inter_area)\r\n\r\n return iou","title":""},{"docid":"0225d5fc6cd7af07badad050cdbe3d40","score":"0.5339997","text":"def union_overlapping(intervals):\n disjoint_intervals = []\n\n for interval in intervals:\n if disjoint_intervals and disjoint_intervals[-1].overlaps(interval):\n disjoint_intervals[-1] = disjoint_intervals[-1].union(interval)\n else:\n disjoint_intervals.append(interval)\n\n return disjoint_intervals","title":""},{"docid":"a9f9195c6b93b1ef72ce397d4a87bf12","score":"0.533952","text":"def bbox_iou(box1, box2):\n intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])\n intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])\n intersect = intersect_w * intersect_h\n w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin\n w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin\n union = w1*h1 + w2*h2 - intersect\n try:\n res = float(intersect) / union\n except ZeroDivisionError:\n res = 0\n return res","title":""},{"docid":"e5c8009b8d39c96872f8226e5dbab873","score":"0.5335932","text":"def test_polygon_clipping(self):\r\n\r\n # Test data\r\n polygon_filename = ('%s/test_poly.txt' % TESTDATA) # Polygon 799\r\n points_filename = ('%s/test_points.txt' % TESTDATA)\r\n\r\n # Read\r\n polygon = []\r\n fid = open(polygon_filename)\r\n for line in fid.readlines():\r\n fields = line.strip().split(',')\r\n polygon.append([float(fields[0]), float(fields[1])])\r\n polygon = ensure_numeric(polygon)\r\n\r\n points = []\r\n fid = open(points_filename)\r\n for line in fid.readlines():\r\n fields = line.strip().split(',')\r\n points.append([float(fields[0]), float(fields[1])])\r\n points = ensure_numeric(points)\r\n\r\n # Clip\r\n inside, outside = separate_points_by_polygon(points, polygon)\r\n\r\n # Expected number of points inside\r\n assert len(inside) == 458\r\n\r\n # First 10 inside\r\n assert numpy.alltrue(inside[:10] == [2279, 2290, 2297, 2306, 2307,\r\n 2313, 2316, 2319, 2321, 2322])\r\n\r\n # Last 10 outside\r\n assert numpy.alltrue(outside[-10:] == [3519, 3520, 3521, 3522, 3523,\r\n 3524, 3525, 3526, 3527, 3528])\r\n # Store for viewing in e.g. QGis\r\n if False: # True:\r\n Vector(geometry=[polygon]).write_to_file('test_poly.shp')\r\n pts_inside = points[inside]\r\n Vector(geometry=pts_inside).write_to_file('test_points_in.shp')\r\n pts_outside = points[outside]\r\n Vector(geometry=pts_outside).write_to_file('test_points_out.shp')","title":""},{"docid":"f7c982baa285de27c4df11201a3b6caa","score":"0.53303385","text":"def edgepolygon():\r\n pass","title":""},{"docid":"7b02b114943eeceb9bef06f4b998c168","score":"0.53204334","text":"def bbox_diou(self, boxes_1, boxes_2):\n # calculate center distance\n center_distance = tf.reduce_sum(tf.square(boxes_1[..., :2] - boxes_2[..., :2]), axis=-1)\n\n # transform [x, y, w, h] to [x_min, y_min, x_max, y_max]\n boxes_1 = tf.concat([boxes_1[..., :2] - boxes_1[..., 2:] * 0.5,\n boxes_1[..., :2] + boxes_1[..., 2:] * 0.5], axis=-1)\n boxes_2 = tf.concat([boxes_2[..., :2] - boxes_2[..., 2:] * 0.5,\n boxes_2[..., :2] + boxes_2[..., 2:] * 0.5], axis=-1)\n boxes_1 = tf.concat([tf.minimum(boxes_1[..., :2], boxes_1[..., 2:]),\n tf.maximum(boxes_1[..., :2], boxes_1[..., 2:])], axis=-1)\n boxes_2 = tf.concat([tf.minimum(boxes_2[..., :2], boxes_2[..., 2:]),\n tf.maximum(boxes_2[..., :2], boxes_2[..., 2:])], axis=-1)\n\n # calculate area of boxes_1 boxes_2\n boxes_1_area = (boxes_1[..., 2] - boxes_1[..., 0]) * (boxes_1[..., 3] - boxes_1[..., 1])\n boxes_2_area = (boxes_2[..., 2] - boxes_2[..., 0]) * (boxes_2[..., 3] - boxes_2[..., 1])\n\n # calculate the two corners of the intersection\n left_up = tf.maximum(boxes_1[..., :2], boxes_2[..., :2])\n right_down = tf.minimum(boxes_1[..., 2:], boxes_2[..., 2:])\n\n # calculate area of intersection\n inter_section = tf.maximum(right_down - left_up, 0.0)\n inter_area = inter_section[..., 0] * inter_section[..., 1]\n\n # calculate union area\n union_area = boxes_1_area + boxes_2_area - inter_area\n\n # calculate IoU, add epsilon in denominator to avoid dividing by 0\n iou = inter_area / (union_area + tf.keras.backend.epsilon())\n\n # calculate the upper left and lower right corners of the minimum closed convex surface\n enclose_left_up = tf.minimum(boxes_1[..., :2], boxes_2[..., :2])\n enclose_right_down = tf.maximum(boxes_1[..., 2:], boxes_2[..., 2:])\n\n # calculate width and height of the minimun closed convex surface\n enclose_wh = tf.maximum(enclose_right_down - enclose_left_up, 0.0)\n\n # calculate enclosed diagonal distance\n enclose_diagonal = tf.reduce_sum(tf.square(enclose_wh), axis=-1)\n\n # calculate diou add epsilon in denominator to avoid dividing by 0\n diou = iou - 1.0 * center_distance / (enclose_diagonal + tf.keras.backend.epsilon())\n\n return diou","title":""},{"docid":"6288686b0476d853e19fa92f4806d593","score":"0.5320281","text":"def _intersect_cut(shape, cutting_shape):\n inside = shape.intersection(cutting_shape)\n outside = shape.difference(cutting_shape)\n return inside, outside","title":""},{"docid":"57021f66d3667ca239f77ace0c8ee7b9","score":"0.53162813","text":"def bbox_to_poly(north, south, east, west):\n return Polygon([(west, south), (east, south), (east, north), (west, north)])","title":""},{"docid":"444510e471ba4fcc8c24ec160466930e","score":"0.5313836","text":"def regenerate_unpaved_boundaries(self):\n # print \"Top of regenerate_unpaved_boundaries()\"\n\n candidate_edges = np.any( self.edges[:,3:5] == trigrid.UNMESHED, axis=1 )\n candidate_edges = candidate_edges & (self.edges[:,2] != trigrid.DELETED_EDGE)\n rings_and_holes = self.edges_to_rings_and_holes(candidate_edges)\n\n rings = []\n \n for exterior,interiors in rings_and_holes:\n rings.append( exterior )\n rings += interiors\n\n # print \"%d rings in total\"%len(rings)\n\n # dllist of the portion that has not been paved\n self.unpaved = [None] * len(rings)\n\n for ri in range(len(rings)):\n cl = CList()\n for i in rings[ri]:\n cl.append( i, metric=0 )\n \n self.unpaved[ri] = cl\n # print \"Done with regenerating unpaved boundaries\"","title":""},{"docid":"3632a3595ccc8176d3abba1d00e9f3d5","score":"0.5310764","text":"def test_all_outside_polygon(self):\r\n\r\n # unit square\r\n U = [[0, 0], [1, 0], [1, 1], [0, 1]]\r\n\r\n points = [[2, 2], [1, 3], [-1, 1], [0, 2]] # All outside\r\n\r\n inside, outside = separate_points_by_polygon(points, U)\r\n assert len(inside) == 0 # None inside\r\n assert numpy.allclose(outside, [0, 1, 2, 3])\r\n\r\n indices = outside_polygon(points, U, closed=True)\r\n assert numpy.allclose(indices, [0, 1, 2, 3])\r\n\r\n indices = inside_polygon(points, U, closed=True)\r\n assert numpy.allclose(indices, [])","title":""},{"docid":"591453f0bfc7fe6e93aac1993861b489","score":"0.53024197","text":"def merge(b4):\n j, tmp, n = 0, [], len(b4)\n while j < n:\n a = b4[j]\n if j < n - 1:\n b = b4[j + 1]\n if c := a.also.merged(b.also):\n a = Bin(a.down, b.up)\n a.also = c\n j += 1\n tmp += [a]\n j += 1\n return merge(tmp) if len(tmp) < len(b4) else b4","title":""},{"docid":"70166165b60b630150e47975f69696cb","score":"0.5299959","text":"def test_inside_polygon_main2(self):\r\n\r\n # Simplest case: Polygon is the unit square\r\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1]]\r\n\r\n # From real example (that failed)\r\n polygon = [[20, 20], [40, 20], [40, 40], [20, 40]]\r\n points = [[40, 50]]\r\n res = inside_polygon(points, polygon)\r\n assert len(res) == 0\r\n\r\n polygon = [[20, 20], [40, 20], [40, 40], [20, 40]]\r\n points = [[25, 25], [30, 20], [40, 50], [90, 20], [40, 90]]\r\n res = inside_polygon(points, polygon)\r\n assert len(res) == 2\r\n assert numpy.allclose(res, [0, 1])\r\n\r\n # More convoluted and non convex polygon\r\n polygon = [[0, 0], [1, 0], [0.5, -1], [2, -1], [2, 1], [0, 1]]\r\n assert is_inside_polygon((0.5, 0.5), polygon)\r\n assert is_inside_polygon((1, -0.5), polygon)\r\n assert is_inside_polygon((1.5, 0), polygon)\r\n\r\n assert not is_inside_polygon((0.5, 1.5), polygon)\r\n assert not is_inside_polygon((0.5, -0.5), polygon)\r\n\r\n # Very convoluted polygon\r\n polygon = [[0, 0], [10, 10], [15, 5], [20, 10], [25, 0],\r\n [30, 10], [40, -10]]\r\n assert is_inside_polygon((5, 5), polygon)\r\n assert is_inside_polygon((17, 7), polygon)\r\n assert is_inside_polygon((27, 2), polygon)\r\n assert is_inside_polygon((35, -5), polygon)\r\n assert not is_inside_polygon((15, 7), polygon)\r\n assert not is_inside_polygon((24, 3), polygon)\r\n assert not is_inside_polygon((25, -10), polygon)\r\n\r\n # Another combination (that failed)\r\n polygon = [[0, 0], [10, 0], [10, 10], [0, 10]]\r\n assert is_inside_polygon((5, 5), polygon)\r\n assert is_inside_polygon((7, 7), polygon)\r\n assert not is_inside_polygon((-17, 7), polygon)\r\n assert not is_inside_polygon((7, 17), polygon)\r\n assert not is_inside_polygon((17, 7), polygon)\r\n assert not is_inside_polygon((27, 8), polygon)\r\n assert not is_inside_polygon((35, -5), polygon)\r\n\r\n # Multiple polygons\r\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1], [0, 0], [10, 10],\r\n [11, 10], [11, 11], [10, 11], [10, 10]]\r\n assert is_inside_polygon((0.5, 0.5), polygon)\r\n assert is_inside_polygon((10.5, 10.5), polygon)\r\n assert not is_inside_polygon((0, 5.5), polygon)\r\n assert is_inside_polygon((5.5, 5.5), polygon)\r\n\r\n # Polygon with a hole\r\n polygon = [[-1, -1], [2, -1], [2, 2], [-1, 2], [-1, -1],\r\n [0, 0], [1, 0], [1, 1], [0, 1], [0, 0]]\r\n\r\n assert is_inside_polygon((0, -0.5), polygon)\r\n assert not is_inside_polygon((0.5, 0.5), polygon)","title":""},{"docid":"5d0664902dd98ef3cf6053fc65a722aa","score":"0.5295191","text":"def test_afw_union_intersecting(self):\n AFW.rename_afw_states(self.afw_union_3_test_01, 'a_')\n union = AFW.afw_union(self.afw_union_1_test_01,\n self.afw_union_3_test_01)\n\n i = 0\n last = 7\n while i <= last:\n base = list(itertools.repeat('a', i))\n base += list(itertools.repeat('b', i))\n # build all permutation of 'a' and 'b' till length i\n word_set = set(itertools.permutations(base, i))\n for word in word_set:\n word = list(word)\n # print(word)\n original_acceptance_1 = AFW.afw_word_acceptance(\n self.afw_union_1_test_01, word)\n original_acceptance_2 = AFW.afw_word_acceptance(\n self.afw_union_3_test_01, word)\n union_acceptance = AFW.afw_word_acceptance(union, word)\n self.assertEqual(\n original_acceptance_1 or original_acceptance_2,\n union_acceptance)\n i += 1","title":""},{"docid":"f2d67fc05535314467b35e5a35cad1c8","score":"0.5293673","text":"def bbox_iou(box1, box2):\n # Get the coordinates of bounding boxes\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\n\n # get the corrdinates of the intersection rectangle\n inter_rect_x1 = torch.max(b1_x1, b2_x1)\n inter_rect_y1 = torch.max(b1_y1, b2_y1)\n inter_rect_x2 = torch.min(b1_x2, b2_x2)\n inter_rect_y2 = torch.min(b1_y2, b2_y2)\n\n # Intersection area\n inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(inter_rect_y2 - inter_rect_y1 + 1,\n min=0)\n\n # Union Area\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\n\n iou = inter_area / (b1_area + b2_area - inter_area)\n\n return iou","title":""},{"docid":"828324479d5bd1b2aff59fb6d0c581fd","score":"0.52932256","text":"def intersect_polyAs(paths, outdir, region):\n\n # Send in A and [B,C]\n paths = intersect_wrap(paths, outdir, extendby=10)\n\n # Do a wc of all the paths in paths\n wc = {}\n for pathname, path in paths.items():\n wc[pathname] = sum((1 for line in open(path, 'rb')))\n\n # make a printout of wc for comparing with the final plots\n for name, count in wc.items():\n print name + ':\\t' + str(count)\n print('\\n')\n\n A = region\n B = 'gencode'\n C = 'polyAdb'\n\n AIBIC = '_I_'.join(sorted([A,B,C]))\n\n venncount = {}\n venncount[AIBIC] = wc[AIBIC] # we don't need to compute this one\n\n # A\\(BUC) = A - AIB - AIC - AIBIC (Just A)\n for let in [A, B, C]:\n compl = [A, B, C]\n compl.remove(let)\n compl.sort()\n\n # initialize with all poly(A) sites\n venncount[let] = wc[let]\n # remove the two \"BIC\" or \"AIC\" types\n for k in compl:\n venncount[let] -= wc['_I_'.join(sorted([let, k]))]\n # add the union of the three types, since it was subtracted twice\n venncount[let] += wc[AIBIC]\n\n # (AUB)\\C = AIB - AIBIC (Just A and B)\n for let in [A, B, C]:\n compl = [A, B, C]\n compl.remove(let)\n (c1, c2) = sorted(compl)\n\n key = '{0}_I_{1}'.format(c1, c2) # one of (BUC)\\A\n venncount[key] = wc['_I_'.join(sorted([c1, c2]))] - wc[AIBIC]\n\n return paths, venncount","title":""},{"docid":"9c0c47a76d3bdce34cb0d1152f0cad1a","score":"0.5289822","text":"def _stitch_boundaries(edges):\n edge_set = edges.copy()\n boundary_lst = []\n while edge_set:\n boundary = []\n edge0 = edge_set.pop()\n boundary.append(edge0)\n last_edge = edge0\n while edge_set:\n _, j = last_edge\n j_first, j_second = _find_edges_with(j, edge_set)\n if j_first:\n edge_set.remove((j, j_first[0]))\n edge_with_j = (j, j_first[0])\n boundary.append(edge_with_j)\n last_edge = edge_with_j\n elif j_second:\n edge_set.remove((j_second[0], j))\n edge_with_j = (j, j_second[0]) # flip edge rep\n boundary.append(edge_with_j)\n last_edge = edge_with_j\n if edge0[0] == last_edge[1]:\n break\n boundary_lst.append(boundary)\n\n return boundary_lst","title":""},{"docid":"b4cc37134c0effc054b8375fdc893c98","score":"0.52830285","text":"def erase_overlapped_regions(rects, im_w, im_h):\n\n index_list=[]\n ext_rects = rects[:]\n overlapped = []\n# a =\n for i in range(0,len(ext_rects)):\n overlapped[:] = []\n b = len(ext_rects)\n for jj in range (0, b):\n if ext_rects[i]!=\"False\" and ext_rects[jj]!=\"False\":\n\n x1, y1, w1, h1 = ext_rects[i]\n x2, y2, w2, h2 = ext_rects[jj]\n\n if x1>x2 and abs(x1-x2-w2)<0.040*im_w and -0.01*im_w<y1-y2<0.01*im_h:\n overlapped.append(jj)\n\n else:\n overlapped.append(-1)\n else:\n overlapped.append(-1)\n pass\n\n index_list[:]=[]\n\n overlapped_list = [item != -1 for item in overlapped]\n elem_overlapped = list(itertools.compress(ext_rects, overlapped_list))\n for el in overlapped:\n if el != -1:\n index_list.append(el)\n else:\n pass\n for it in range(len(elem_overlapped)):\n if it != \"False\" and ext_rects[i] != \"False\":\n ext_rects = remove_overlapped(elem_overlapped[it], ext_rects, i, index_list[it])\n rectangles_no_overlapped = [item != \"False\" for item in ext_rects]\n ext_rects = list(itertools.compress(ext_rects, rectangles_no_overlapped))\n if False in rectangles_no_overlapped:\n ext_rects = erase_overlapped_regions(ext_rects, im_w, im_h)\n else:\n pass\n\n\n\n return ext_rects","title":""},{"docid":"eabcb22d06713ff994adf63d7d154439","score":"0.52803683","text":"def compute_iou(box, boxes):\n\n x_min_A, y_min_A, x_max_A, y_max_A = box[:4]\n x_min_B, y_min_B = boxes[:, 0], boxes[:, 1]\n x_max_B, y_max_B = boxes[:, 2], boxes[:, 3]\n # calculating the intersection\n inner_x_min = np.maximum(x_min_B, x_min_A)\n inner_y_min = np.maximum(y_min_B, y_min_A)\n inner_x_max = np.minimum(x_max_B, x_max_A)\n inner_y_max = np.minimum(y_max_B, y_max_A)\n inner_w = np.maximum((inner_x_max - inner_x_min), 0)\n inner_h = np.maximum((inner_y_max - inner_y_min), 0)\n intersection_area = inner_w * inner_h\n # calculating the union\n box_area_B = (x_max_B - x_min_B) * (y_max_B - y_min_B)\n box_area_A = (x_max_A - x_min_A) * (y_max_A - y_min_A)\n union_area = box_area_A + box_area_B - intersection_area\n intersection_over_union = intersection_area / union_area\n return intersection_over_union","title":""},{"docid":"7c2672b8ef17dc193a2d050cb9145cb8","score":"0.5278561","text":"def rectangles_right_intersection(Rectangles, x):\n\n U = Interval_Union();\n for rec in Rectangles:\n if(rec.intersects_right(x)):\n U.append(rec.intersection_right(x));\n\n return U;","title":""},{"docid":"c671e33f557e817d18ccf1bda7705338","score":"0.5275798","text":"def bounding_box(self, *vertices):\n\n vec1 = [(vertex[0]) for vertex in vertices]\n vec2 = [(vertex[1]) for vertex in vertices]\n vec1.sort()\n vec2.sort()\n return [vec1[0], vec2[0]], [vec1[-1], vec2[-1]]","title":""},{"docid":"9746f75a26503e84a5b8c4e5fe978f27","score":"0.5274264","text":"def simplex_boundary(simplex):\n # Special handling of vertices\n if len(simplex) == 1:\n return np.array([])\n boundary_simplices = np.empty((len(simplex), len(simplex) - 1), dtype=int)\n for i in range(len(simplex)):\n boundary_simplex = np.delete(simplex, i)\n if i % 2 == 1 and len(boundary_simplex) > 1:\n boundary_simplex[-1], boundary_simplex[-2] = boundary_simplex[-2], boundary_simplex[-1]\n boundary_simplices[i] = boundary_simplex\n return boundary_simplices","title":""},{"docid":"7fdb81dfc6686113e1a310d765e64c94","score":"0.52713543","text":"def in_and_outside_polygon(\r\n points, polygon,\r\n closed=True,\r\n holes=None,\r\n check_input=True):\r\n\r\n # Get separation by outer_ring\r\n inside, outside = separate_points_by_polygon(points, polygon,\r\n closed=closed,\r\n check_input=check_input)\r\n\r\n # Take care of holes\r\n if holes is not None:\r\n msg = ('Argument holes must be a list of polygons, '\r\n 'I got %s' % holes)\r\n if not isinstance(holes, list):\r\n raise InaSAFEError(msg)\r\n\r\n for hole in holes:\r\n in_hole, out_hole = separate_points_by_polygon(points[inside],\r\n hole,\r\n closed=not closed,\r\n check_input=True)\r\n\r\n in_hole = inside[in_hole] # Inside hole\r\n inside = inside[out_hole] # Inside outer_ring but outside hole\r\n\r\n # Add holde indices to outside\r\n outside = numpy.concatenate((outside, in_hole))\r\n\r\n return inside, outside","title":""},{"docid":"92cc8d261ca4edddf77b1739094a968c","score":"0.52705306","text":"def clip_polygon(subject, clipper, operation = 'difference'):\n Subject = Polygon()\n Clipper = Polygon()\n\n for s in subject:\n Subject.add(Vertex(s))\n\n for c in clipper:\n Clipper.add(Vertex(c))\n\n clipped = Clipper.difference(Subject)\\\n if operation == 'reversed-diff'\\\n else Subject.__getattribute__(operation)(Clipper)\n\n clipped = [(ext.points,[hole.points for hole in holes]) for ext,holes in clipped]\n return clipped","title":""},{"docid":"c1d04b7cd242b5d2ffabeaa925b3d251","score":"0.52692235","text":"def poly2obb(polys, version='oc'):\n if version == 'oc':\n results = poly2obb_oc(polys)\n elif version == 'le135':\n results = poly2obb_le135(polys)\n elif version == 'le90':\n results = poly2obb_le90(polys)\n else:\n raise NotImplementedError\n return results","title":""},{"docid":"77f76e361fd2af95a892970dbdfab85d","score":"0.52612066","text":"def poly2hbb(polys):\n shape = polys.shape\n polys = polys.reshape(*shape[:-1], shape[-1] // 2, 2)\n lt_point = np.min(polys, axis=-2)\n rb_point = np.max(polys, axis=-2)\n return np.concatenate([lt_point, rb_point], axis=-1)","title":""},{"docid":"d9d3f9cd57c27fe278ee5bff8fdb06dc","score":"0.5258611","text":"def binary_mask_to_polygon(binary_mask, tolerance=0):\n polygons = []\n # pad mask to close contours of shapes which start and end at an edge\n padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)\n contours = measure.find_contours(padded_binary_mask, 0.5)\n contours = np.subtract(contours, 1)\n for contour in contours:\n contour = close_contour(contour)\n contour = measure.approximate_polygon(contour, tolerance)\n if len(contour) < 3:\n continue\n contour = np.flip(contour, axis=1)\n segmentation = contour.ravel().tolist()\n # after padding and subtracting 1 we may get -0.5 points in our segmentation\n segmentation = [0 if i < 0 else i for i in segmentation]\n polygons.append(segmentation)\n\n return polygons","title":""},{"docid":"d9d3f9cd57c27fe278ee5bff8fdb06dc","score":"0.5258611","text":"def binary_mask_to_polygon(binary_mask, tolerance=0):\n polygons = []\n # pad mask to close contours of shapes which start and end at an edge\n padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)\n contours = measure.find_contours(padded_binary_mask, 0.5)\n contours = np.subtract(contours, 1)\n for contour in contours:\n contour = close_contour(contour)\n contour = measure.approximate_polygon(contour, tolerance)\n if len(contour) < 3:\n continue\n contour = np.flip(contour, axis=1)\n segmentation = contour.ravel().tolist()\n # after padding and subtracting 1 we may get -0.5 points in our segmentation\n segmentation = [0 if i < 0 else i for i in segmentation]\n polygons.append(segmentation)\n\n return polygons","title":""},{"docid":"718a1c372119a195856ebf76458402f5","score":"0.52573603","text":"def underbounded_simple(city, city_buffer_reduce, boundary, GISJOIN, epsg):\r\n if boundary.type=='Polygon':\r\n cities_DF=gpd.GeoDataFrame([boundary])\r\n else:\r\n cities_DF=gpd.GeoDataFrame(boundary)\r\n \r\n if len(cities_DF)==0:\r\n cities_DF['geometry']=\"\"\r\n cities_DF=cities_DF.set_geometry('geometry')\r\n cities_DF=cities_DF.set_crs(epsg)\r\n else:\r\n cities_DF=cities_DF.rename(columns={0:'geometry'})\r\n cities_DF=cities_DF.set_geometry('geometry')\r\n cities_DF=cities_DF.set_crs(epsg)\r\n cities_DF['GISJOIN']=GISJOIN\r\n cities_DF['geometry']=cities_DF.buffer(1)\r\n if city_buffer_reduce.type=='Polygon':\r\n city_buffer_reduce_DF=gpd.GeoDataFrame([city_buffer_reduce])\r\n else:\r\n city_buffer_reduce_DF=gpd.GeoDataFrame(city_buffer_reduce)\r\n \r\n city_buffer_reduce_DF=city_buffer_reduce_DF.rename(columns={0:'geometry'})\r\n city_buffer_reduce_DF=city_buffer_reduce_DF.set_geometry('geometry')\r\n city_buffer_reduce_DF=city_buffer_reduce_DF.set_crs(epsg)\r\n city_buffer_reduce_DF=city_buffer_reduce_DF.explode()\r\n city_buffer_reduce_DF=city_buffer_reduce_DF.reset_index()\r\n city_buffer_reduce_DF['index'] = city_buffer_reduce_DF.index\r\n city_buffer_reduce_DF['AreaAcres']=city_buffer_reduce_DF.area*0.000247105\r\n too_small=city_buffer_reduce_DF[city_buffer_reduce_DF['AreaAcres']<1].copy()\r\n too_small['Type']='Unaffected'\r\n \r\n city_buffer_reduce_DF=city_buffer_reduce_DF[city_buffer_reduce_DF['AreaAcres']>=1].copy()\r\n city_buffer_reduce_DF['Type']=\"Underbounded\"\r\n\r\n\r\n results=pd.concat([city_buffer_reduce_DF, too_small])\r\n results['GISJOIN']=GISJOIN\r\n results=results[['GISJOIN', 'geometry', 'AreaAcres', 'Type']].copy()\r\n return results","title":""},{"docid":"a2b770df8fb84d988dca47bac7ea275c","score":"0.5251293","text":"def get_polygon_outer_bounding_box(polygon):\n\n xs = [i[0] for i in polygon] # may be improved if using numpy methods directly\n ys = [i[1] for i in polygon]\n\n mx = min(xs)\n my = min(ys)\n return mx, my, max(xs) - mx, max(ys) - my","title":""},{"docid":"7bcd98570db5340294c79b3909377076","score":"0.52475697","text":"def test_all_inside_polygon(self):\r\n\r\n # unit square\r\n U = [[0, 0], [1, 0], [1, 1], [0, 1]]\r\n\r\n points = [[0.5, 0.5], [0.2, 0.3], [0, 0.5]] # All inside (or on edge)\r\n\r\n inside, outside = separate_points_by_polygon(points, U)\r\n assert len(outside) == 0 # All inside\r\n assert numpy.allclose(inside, [0, 1, 2])\r\n\r\n indices = outside_polygon(points, U, closed=True)\r\n assert numpy.allclose(indices, [])\r\n\r\n indices = inside_polygon(points, U, closed=True)\r\n assert numpy.allclose(indices, [0, 1, 2])","title":""},{"docid":"54ce64417b756cc294f42b48f6ed0286","score":"0.5246818","text":"def test_valid_polygon_pair_no_union(self):\n\n response = self.app.post(\n '/polygonAction/union',\n content_type='application/json',\n data=self.get_json_data(\n \"fixtures/validPolygonPairNoIntersection.json\"\n )\n )\n assert response.status_code == 204","title":""},{"docid":"09f6cd9a96b7a21e945a8473da338840","score":"0.52406543","text":"def bbox_ioa(box1, box2):\n\n # Get the coordinates of bounding boxes\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[0] + box1[2], box1[1] + box1[3]\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[0] + box2[2], box2[1] + box2[3]\n\n # get the coordinates of the intersection rectangle\n inter_rect_x1 = max(b1_x1, b2_x1)\n inter_rect_y1 = max(b1_y1, b2_y1)\n inter_rect_x2 = min(b1_x2, b2_x2)\n inter_rect_y2 = min(b1_y2, b2_y2)\n\n # Intersection area\n inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, 1920) * np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0,\n 1080)\n\n # Union Area\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\n\n iou = max(inter_area / b1_area, inter_area / b2_area)\n\n return iou","title":""},{"docid":"aec1b63b3e8939d31dc6317f65f5f13d","score":"0.5238896","text":"def check_poly_intersect(poly_1, poly_2):\n ### reference: https://www.codeproject.com/Articles/15573/2D-Polygon-Collision-Detection\n overlap_flag = True\n #### loop over each edge\n for poly_cur in [poly_1, poly_2]:\n npt = poly_cur.shape[0]\n for pid, pt in enumerate(poly_cur):\n #### calculate the projection axis\n pt_next = poly_cur[ (pid+1) % npt ]\n vector = pt_next - pt\n vector_perp_norm = np.array([ -vector[1], vector[0] ]) / (np.sqrt(vector[0]**2+vector[1]**2 ) + 1e-6 )\n\n #### project both polygons to the axis\n pmin_1, pmax_1 = project_to_nvector(vector_perp_norm, poly_1)\n pmin_2, pmax_2 = project_to_nvector(vector_perp_norm, poly_2)\n\n #### check overlap in the projection\n overlap_flag = check_project_intersect(pmin_1, pmax_1, pmin_2, pmax_2)\n if not overlap_flag:\n break\n if not overlap_flag:\n break\n \n return overlap_flag","title":""}],"string":"[\n {\n \"docid\": \"6c3d5b45fb28b128783dbaf7b86098fd\",\n \"score\": \"0.6554626\",\n \"text\": \"def polygon_iou(list1, list2):\\n polygon_points1 = np.array(list1).reshape(4, 2)\\n poly1 = Polygon(polygon_points1).convex_hull\\n polygon_points2 = np.array(list2).reshape(4, 2)\\n poly2 = Polygon(polygon_points2).convex_hull\\n union_poly = np.concatenate((polygon_points1, polygon_points2))\\n if not poly1.intersects(poly2): # this test is fast and can accelerate calculation\\n iou = 0\\n else:\\n try:\\n inter_area = poly1.intersection(poly2).area\\n #union_area = poly1.area + poly2.area - inter_area\\n union_area = MultiPoint(union_poly).convex_hull.area\\n if union_area == 0:\\n return 0\\n iou = float(inter_area) / union_area\\n except shapely.geos.TopologicalError:\\n print('shapely.geos.TopologicalError occured, iou set to 0')\\n iou = 0\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbcfdad84f62be73d91f7d6e2255959c\",\n \"score\": \"0.6500648\",\n \"text\": \"def polyobj_to_poly(poly):\\n\\n \\\"\\\"\\\"\\n It used to be as simple as\\n\\n out = []\\n for i in range(len(poly)):\\n\\n cxy = poly.contour(i)\\n if cxy[0] != cxy[-1]:\\n cxy.append(cxy[0])\\n\\n cxy = np.asarray(cxy).T\\n out.append((poly.isSolid(i), cxy))\\n\\n return out\\n\\n \\\"\\\"\\\"\\n\\n # Not sure what the best way to identify \\\"components\\\"\\n # here. Is there something in the semantics of the GPC\\n # polygon object that would tell us?\\n #\\n # Since the inclusive polygons do not overlap, by construction,\\n # then use the bounding box of each polygon to determine the\\n # association between include and exclude polygons.\\n #\\n incl = {}\\n excl = {}\\n\\n for i in range(len(poly)):\\n\\n cxy = poly.contour(i)\\n if cxy[0] != cxy[-1]:\\n cxy.append(cxy[0])\\n\\n cxy = np.asarray(cxy).T\\n\\n incflag = poly.isSolid(i)\\n bbox = poly.boundingBox(i)\\n\\n if incflag:\\n store = incl\\n else:\\n store = excl\\n\\n assert bbox not in store\\n store[bbox] = cxy\\n\\n assert len(incl) > 0\\n\\n # Match exclude with include shapes. This does a bit more than\\n # is needed, to check the assumptions being used.\\n #\\n def is_inside(bbox_excl, bbox_incl):\\n \\\"\\\"\\\"Return True if bbox_excl is within bbox_incl.\\\"\\\"\\\"\\n\\n xmin_i, xmax_i, ymin_i, ymax_i = bbox_incl\\n xmin_e, xmax_e, ymin_e, ymax_e = bbox_excl\\n\\n return (xmin_e >= xmin_i) and (xmax_e <= xmax_i) \\\\\\n and (ymin_e >= ymin_i) and (ymax_e <= ymax_i)\\n\\n matches = {}\\n for bb_e in excl.keys():\\n match = None\\n for bb_i in incl.keys():\\n if is_inside(bb_e, bb_i):\\n assert match is None\\n match = bb_i\\n\\n assert match is not None\\n try:\\n l = matches[match]\\n except:\\n l = []\\n\\n l.append(bb_e)\\n matches[match] = l\\n\\n out = []\\n for bbox_i, p_i in incl.items():\\n try:\\n l = matches[bbox_i]\\n except:\\n l = []\\n\\n excls = [excl[idx] for idx in l]\\n store = {'polygon': p_i, 'exclude': excls}\\n out.append(store)\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9469139354aff0b8ebfc3c732e0c8895\",\n \"score\": \"0.64717835\",\n \"text\": \"def poly_union(poly_det, poly_gt):\\n assert isinstance(poly_det, Polygon)\\n assert isinstance(poly_gt, Polygon)\\n\\n area_det = poly_det.area\\n area_gt = poly_gt.area\\n area_inters, _ = poly_intersection(poly_det, poly_gt)\\n return area_det + area_gt - area_inters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"357590e588e32c3910924f2d1cc5e2f6\",\n \"score\": \"0.6433988\",\n \"text\": \"def calculate_disjoint_polygon_set(shapefile_uri):\\n shapefile = ogr.Open(shapefile_uri)\\n shapefile_layer = shapefile.GetLayer()\\n\\n poly_intersect_lookup = {}\\n for poly_feat in shapefile_layer:\\n poly_wkt = poly_feat.GetGeometryRef().ExportToWkt()\\n shapely_polygon = shapely.wkt.loads(poly_wkt)\\n poly_fid = poly_feat.GetFID()\\n poly_intersect_lookup[poly_fid] = {\\n 'poly': shapely_polygon,\\n 'prepared': shapely.prepared.prep(shapely_polygon),\\n 'intersects': set(),\\n }\\n shapefile_layer.ResetReading()\\n\\n for poly_fid in poly_intersect_lookup:\\n for intersect_poly_fid in poly_intersect_lookup:\\n polygon = poly_intersect_lookup[poly_fid]['prepared']\\n if polygon.intersects(\\n poly_intersect_lookup[intersect_poly_fid]['poly']):\\n poly_intersect_lookup[poly_fid]['intersects'].add(\\n intersect_poly_fid)\\n\\n # Build maximal subsets\\n subset_list = []\\n while len(poly_intersect_lookup) > 0:\\n # sort polygons by increasing number of intersections\\n heap = []\\n for poly_fid, poly_dict in poly_intersect_lookup.iteritems():\\n heapq.heappush(\\n heap, (len(poly_dict['intersects']), poly_fid, poly_dict))\\n\\n # build maximal subset\\n maximal_set = set()\\n while len(heap) > 0:\\n _, poly_fid, poly_dict = heapq.heappop(heap)\\n for maxset_fid in maximal_set:\\n if maxset_fid in poly_intersect_lookup[poly_fid]['intersects']:\\n # it intersects and can't be part of the maximal subset\\n break\\n else:\\n # we made it through without an intersection, add poly_fid to\\n # the maximal set\\n maximal_set.add(poly_fid)\\n # remove that polygon and update the intersections\\n del poly_intersect_lookup[poly_fid]\\n # remove all the polygons from intersections once they're compuated\\n for maxset_fid in maximal_set:\\n for poly_dict in poly_intersect_lookup.itervalues():\\n poly_dict['intersects'].discard(maxset_fid)\\n subset_list.append(maximal_set)\\n return subset_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66118676fe5a85be2a66fc53747a7197\",\n \"score\": \"0.6293607\",\n \"text\": \"def boundary(simplices):\\n # Set of boundary simplices, with vertex indices sorted\\n boundary_set = set()\\n # Dictionary mapping sorted boundary simplices to the original boundary simplex, and its ordering number\\n boundary_dictionary = {}\\n idx = 0\\n for simplex in simplices:\\n for bs in simplex_boundary(simplex):\\n sbs = tuple(sorted(bs))\\n if sbs in boundary_set:\\n # Simplex occurs twice, so it's not a boundary simplex\\n boundary_set.remove(sbs)\\n boundary_dictionary.pop(sbs)\\n else:\\n # Simplex not in the boundary set, so it's a potential boundary simplex\\n boundary_set.add(sbs)\\n boundary_dictionary[sbs] = (bs, idx)\\n idx += 1\\n sorted_boundary_simplices = sorted(boundary_dictionary.values(), key=lambda bsi: bsi[1])\\n boundary_list = np.array([bsi[0] for bsi in sorted_boundary_simplices], dtype=int)\\n return boundary_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4918949bed1e0b432ac8c4922e183d09\",\n \"score\": \"0.62510735\",\n \"text\": \"def _join(self, polygons):\\n def do_join(polygons):\\n all_polygons = polygons[:]\\n\\n skipped = 0\\n\\n polyA = polygons.pop(0)\\n while len(polygons):\\n polyB = polygons.pop(0)\\n\\n # If fewer than 3 edges, it's not a polygon,\\n # just throw it out\\n if len(polyB) < 4:\\n continue\\n\\n # Find the closest set of vertices between polyA and\\n # polyB that don't cross any of the edges in *any* of\\n # the polygons\\n closest = np.inf\\n closest_pair_idx = (None, None)\\n for a in xrange(len(polyA) - 1):\\n A = polyA[a]\\n distances = great_circle_arc.length(A, polyB[:-1])\\n b = np.argmin(distances)\\n distance = distances[b]\\n if distance < closest:\\n B = polyB[b]\\n # Does this candidate line cross other edges?\\n crosses = False\\n for poly in all_polygons:\\n if np.any(\\n great_circle_arc.intersects(\\n A, B, poly[:-1], poly[1:])):\\n crosses = True\\n break\\n if not crosses:\\n closest = distance\\n closest_pair_idx = (a, b)\\n\\n if not np.isfinite(closest):\\n # We didn't find a pair of points that don't cross\\n # something else, so we want to try to join another\\n # polygon. Defer the current polygon until later.\\n if len(polygons) in (0, skipped):\\n return None\\n polygons.append(polyB)\\n skipped += 1\\n else:\\n # Splice the two polygons together using a cut\\n # line\\n a, b = closest_pair_idx\\n new_poly = np.vstack((\\n # polyA up to and including the cut point\\n polyA[:a+1],\\n\\n # polyB starting with the cut point and\\n # wrapping around back to the cut point.\\n # Ignore the last point in polyB, because it\\n # is the same as the first\\n np.roll(polyB[:-1], -b, 0),\\n\\n # The cut point on polyB\\n polyB[b:b+1],\\n\\n # the rest of polyA, starting with the cut\\n # point\\n polyA[a:]\\n ))\\n\\n skipped = 0\\n polyA = new_poly\\n\\n return polyA\\n\\n for permutation in itertools.permutations(polygons):\\n poly = do_join(list(permutation))\\n if poly is not None:\\n return poly\\n\\n raise RuntimeError(\\\"Could not find cut points\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d119c94f5bae1680e5584af57e20e1a8\",\n \"score\": \"0.62217265\",\n \"text\": \"def calculate_boundary_weights(polygons, scale):\\n # Scale up all polygons around their center, until they start overlapping\\n # NOTE: scale factor should be matched to resolution and type of forest\\n scaled_polys = gpd.GeoDataFrame(\\n {\\n \\\"geometry\\\":\\n polygons.geometry.scale(xfact=scale, yfact=scale, origin=\\\"center\\\")\\n },\\n crs=polygons.crs,\\n )\\n\\n # Get intersections of scaled polygons, which are the boundaries.\\n boundaries = []\\n for i in range(len(scaled_polys)):\\n\\n # For each scaled polygon, get all nearby scaled polygons that intersect with it\\n nearby_polys = scaled_polys[scaled_polys.geometry.intersects(\\n scaled_polys.iloc[i].geometry)]\\n\\n # Add intersections of scaled polygon with nearby polygons [except the intersection with itself!]\\n for j in range(len(nearby_polys)):\\n if nearby_polys.iloc[j].name != scaled_polys.iloc[i].name:\\n boundaries.append(scaled_polys.iloc[i].geometry.intersection(\\n nearby_polys.iloc[j].geometry))\\n\\n # Convert to df and ensure we only return Polygons (sometimes it can be a Point, which breaks things)\\n boundaries = gpd.GeoDataFrame(\\n {\\n \\\"geometry\\\": gpd.GeoSeries(boundaries)\\n },\\n crs=polygons.crs,\\n ).explode()\\n boundaries = boundaries[boundaries.type == \\\"Polygon\\\"]\\n\\n # If we have boundaries, difference overlay them with original polygons to ensure boundaries don't cover labels\\n if len(boundaries) > 0:\\n boundaries = gpd.overlay(boundaries, polygons, how=\\\"difference\\\")\\n else:\\n boundaries = boundaries.append({\\\"geometry\\\": box(0, 0, 0, 0)}, ignore_index=True)\\n\\n return boundaries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4707340a7a1ec37f7bd82478172cef3d\",\n \"score\": \"0.6090396\",\n \"text\": \"def test_populate_polygon_with_exclude(self):\\r\\n\\r\\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1]]\\r\\n ex_poly = [[0, 0], [0.5, 0], [0.5, 0.5], [0, 0.5]] # SW quarter\\r\\n points = populate_polygon(polygon, 5, exclude=[ex_poly])\\r\\n\\r\\n assert len(points) == 5\\r\\n for point in points:\\r\\n assert is_inside_polygon(point, polygon)\\r\\n assert not is_inside_polygon(point, ex_poly)\\r\\n\\r\\n # Overlap\\r\\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1]]\\r\\n ex_poly = [[-1, -1], [0.5, 0], [0.5, 0.5], [-1, 0.5]]\\r\\n points = populate_polygon(polygon, 5, exclude=[ex_poly])\\r\\n\\r\\n assert len(points) == 5\\r\\n for point in points:\\r\\n assert is_inside_polygon(point, polygon)\\r\\n assert not is_inside_polygon(point, ex_poly)\\r\\n\\r\\n # Multiple\\r\\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1]]\\r\\n ex_poly1 = [[0, 0], [0.5, 0], [0.5, 0.5], [0, 0.5]] # SW quarter\\r\\n ex_poly2 = [[0.5, 0.5], [0.5, 1], [1, 1], [1, 0.5]] # NE quarter\\r\\n\\r\\n points = populate_polygon(polygon, 20, exclude=[ex_poly1, ex_poly2])\\r\\n\\r\\n assert len(points) == 20\\r\\n for point in points:\\r\\n assert is_inside_polygon(point, polygon)\\r\\n assert not is_inside_polygon(point, ex_poly1)\\r\\n assert not is_inside_polygon(point, ex_poly2)\\r\\n\\r\\n # Very convoluted polygon\\r\\n polygon = [[0, 0], [10, 10], [15, 5], [20, 10], [25, 0],\\r\\n [30, 10], [40, -10]]\\r\\n ex_poly = [[-1, -1], [5, 0], [5, 5], [-1, 5]]\\r\\n points = populate_polygon(polygon, 20, exclude=[ex_poly])\\r\\n\\r\\n assert len(points) == 20\\r\\n for point in points:\\r\\n assert is_inside_polygon(point, polygon)\\r\\n assert not is_inside_polygon(point, ex_poly), '%s' % str(point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a0068fde56cbe8b18495d29616f3c7\",\n \"score\": \"0.60570765\",\n \"text\": \"def find_shapes(city_test, boundary):\\r\\n GISJOIN=city_test['GISJOIN'].iloc[0]\\r\\n city=city_test['geometry'].iloc[0]\\r\\n city=city.buffer(0)\\r\\n box_min = city.minimum_rotated_rectangle\\r\\n x, y = box_min.exterior.coords.xy\\r\\n edge_length = (Point(x[0], y[0]).distance(Point(x[1], y[1])), Point(x[1], y[1]).distance(Point(x[2], y[2])))\\r\\n width=min(edge_length)/2\\r\\n city_buffer_reduce=city.buffer(width).buffer(-1.01*width)\\r\\n city_buffer_reduce=city_buffer_reduce.difference(boundary)\\r\\n city_buffer_temp=city.buffer(width/4)\\r\\n city_buffer_temp_2=city_buffer_temp.difference(boundary)\\r\\n city_buffer_reduce_2=city_buffer_reduce.buffer(1)\\r\\n city_buffer=city_buffer_temp_2.difference(city_buffer_reduce_2)\\r\\n \\r\\n city_buffer_temp_3=city_buffer_temp.difference(city)\\r\\n other_cities=city_buffer_temp_3.buffer(-1).intersection(boundary)\\r\\n return city, city_buffer, city_buffer_reduce, other_cities, GISJOIN\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cae98b6bdfb4960b7bab0f709fe17bf\",\n \"score\": \"0.5941276\",\n \"text\": \"def test_polygons_with_inner_rings(self):\\r\\n\\r\\n # Define two (closed) outer rings - clock wise direction\\r\\n outer_rings = [numpy.array([[106.79, -6.233],\\r\\n [106.80, -6.24],\\r\\n [106.78, -6.23],\\r\\n [106.77, -6.21],\\r\\n [106.79, -6.233]]),\\r\\n numpy.array([[106.76, -6.23],\\r\\n [106.72, -6.23],\\r\\n [106.72, -6.22],\\r\\n [106.72, -6.21],\\r\\n [106.76, -6.23]])]\\r\\n\\r\\n tmp_filename = unique_filename(suffix='.shp')\\r\\n\\r\\n # Do outer rings first (use default geometry type polygon)\\r\\n v_ref = Vector(geometry=outer_rings)\\r\\n assert v_ref.is_polygon_data\\r\\n\\r\\n v_ref.write_to_file(tmp_filename)\\r\\n v_file = read_layer(tmp_filename)\\r\\n assert v_file == v_ref\\r\\n assert v_file.is_polygon_data\\r\\n\\r\\n # Do it again but with (closed) inner rings as well\\r\\n\\r\\n # Define inner rings (counter clock wise)\\r\\n inner_rings = [\\r\\n # 2 rings for feature 0\\r\\n [numpy.array([[106.77827, -6.2252],\\r\\n [106.77775, -6.22378],\\r\\n [106.78, -6.22311],\\r\\n [106.78017, -6.22530],\\r\\n [106.77827, -6.2252]])[::-1],\\r\\n numpy.array([[106.78652, -6.23215],\\r\\n [106.78642, -6.23075],\\r\\n [106.78746, -6.23143],\\r\\n [106.78831, -6.23307],\\r\\n [106.78652, -6.23215]])[::-1]],\\r\\n # 1 ring for feature 1\\r\\n [numpy.array([[106.73709, -6.22752],\\r\\n [106.73911, -6.22585],\\r\\n [106.74265, -6.22814],\\r\\n [106.73971, -6.22926],\\r\\n [106.73709, -6.22752]])[::-1]]]\\r\\n\\r\\n polygons = []\\r\\n for i, outer_ring in enumerate(outer_rings):\\r\\n p = Polygon(outer_ring=outer_ring, inner_rings=inner_rings[i])\\r\\n polygons.append(p)\\r\\n\\r\\n v_ref = Vector(geometry=polygons)\\r\\n assert v_ref.is_polygon_data\\r\\n data_bbox = v_ref.get_bounding_box()\\r\\n\\r\\n # Check data from Vector object\\r\\n geometry = v_ref.get_geometry(as_geometry_objects=True)\\r\\n for i, g in enumerate(geometry):\\r\\n assert numpy.allclose(g.outer_ring, outer_rings[i])\\r\\n if i == 0:\\r\\n assert len(g.inner_rings) == 2\\r\\n else:\\r\\n assert len(g.inner_rings) == 1\\r\\n\\r\\n for j, ring in enumerate(inner_rings[i]):\\r\\n assert numpy.allclose(ring, g.inner_rings[j])\\r\\n\\r\\n # Write to file and read again\\r\\n v_ref.write_to_file(tmp_filename)\\r\\n #print 'With inner rings, written to ', tmp_filename\\r\\n v_file = read_layer(tmp_filename)\\r\\n assert v_file == v_ref\\r\\n assert v_file.is_polygon_data\\r\\n assert numpy.allclose(v_file.get_bounding_box(), data_bbox,\\r\\n rtol=1.0e-12, atol=1.0e-12)\\r\\n\\r\\n # Check data from file\\r\\n geometry = v_file.get_geometry(as_geometry_objects=True)\\r\\n for i, g in enumerate(geometry):\\r\\n assert numpy.allclose(g.outer_ring, outer_rings[i])\\r\\n if i == 0:\\r\\n assert len(g.inner_rings) == 2\\r\\n else:\\r\\n assert len(g.inner_rings) == 1\\r\\n\\r\\n for j, ring in enumerate(inner_rings[i]):\\r\\n assert numpy.allclose(ring, g.inner_rings[j])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef40b34ee8055ade7a4a4df8fc01bc6\",\n \"score\": \"0.5939508\",\n \"text\": \"def check_polygons_intersecting(poly_a, poly_b,closed=True):\\n for polygon in (poly_a, poly_b):\\n #print \\\"\\\\nPolygon\\\",\\n\\n # This loop is assuming last point is not the repeated first point:\\n # for i1 in range(len(polygon)):\\n # i2 = (i1 + 1) % len(polygon)\\n \\n # This loop assumes the last point is the repeated first point to form closed polygon:\\n # for i1 in range(len(polygon)-1):\\n # i2 = (i1 + 1)\\n \\n # This loop combines both loops above:\\n for i1 in range(len(polygon)-1*closed):\\n i2 = (i1 + 1) % len(polygon)\\n \\n #print(\\\"i1={} i2={}\\\".format(polygon[i1], i2))\\n p1 = polygon[i1]\\n p2 = polygon[i2]\\n\\n normal = pcbnew.wxPoint(p2.y - p1.y, p1.x - p2.x)\\n\\n minA, maxA, minB, maxB = (None,) * 4\\n\\n for p in poly_a:\\n projected = normal.x * p.x + normal.y * p.y\\n\\n if not minA or projected < minA:\\n minA = projected\\n if not maxA or projected > maxA:\\n maxA = projected\\n\\n for p in poly_b:\\n projected = normal.x * p.x + normal.y * p.y\\n\\n if not minB or projected < minB:\\n minB = projected\\n if not maxB or projected > maxB:\\n maxB = projected\\n\\n #print(\\\"maxA={} minB={} -- maxB={} minA={}\\\".format(maxA, minB, maxB, minA))\\n if maxA < minB or maxB < minA:\\n return False\\n #print(\\\" Nope\\\\n\\\")\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c2a46010c95132c7f203b4c0867b3ff\",\n \"score\": \"0.59158343\",\n \"text\": \"def triangulation(boundary, holes = [], polyline_features = [], point_features = []):\\n\\n # get polyline points for Delaunay triangulation\\n delaunay_points = []\\n delaunay_points += boundary + point_features\\n for polyline in holes:\\n delaunay_points += polyline\\n for polyline in polyline_features:\\n delaunay_points += polyline\\n\\n # remove duplicates based on their geometric keys\\n delaunay_point_map = {}\\n for point in delaunay_points:\\n point = [float(point[0]), float(point[1]), float(point[2])]\\n geom_key = geometric_key(point)\\n if geom_key not in delaunay_point_map:\\n delaunay_point_map[geom_key] = point\\n delaunay_points = list(delaunay_point_map.values())\\n\\n holes = [hole[: -1] for hole in holes]\\n \\n # generate Delaunay mesh\\n delaunay_faces = delaunay_from_points_2(delaunay_points, boundary = boundary[: -1], holes = holes)\\n delaunay_mesh = Mesh.from_vertices_and_faces(delaunay_points, delaunay_faces)\\n\\n # topological cut along the feature polylines through unwelding\\n vertex_map = {geometric_key(delaunay_mesh.vertex_coordinates(vkey)): vkey for vkey in delaunay_mesh.vertices()}\\n\\n edge_paths = []\\n for polyline in polyline_features:\\n edge_path = []\\n vertex_path = [vertex_map[geometric_key([float(x), float(y), float(z)])] for x, y, z in polyline]\\n for i in range(len(vertex_path) - 1):\\n if vertex_path[i + 1] in delaunay_mesh.halfedge[vertex_path[i]]:\\n edge_path.append([vertex_path[i], vertex_path[i + 1]])\\n edge_paths.append(edge_path)\\n\\n for edge_path in edge_paths:\\n unweld_mesh_along_edge_path(delaunay_mesh, edge_path)\\n\\n return delaunay_mesh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"979d010068c95173a6450cf6abcd44f4\",\n \"score\": \"0.5872019\",\n \"text\": \"def delaunay_triangulation(point_list, bb_triangle):\\n return {Polygon}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ba5458465d10e31b58829dd3675429d\",\n \"score\": \"0.58213145\",\n \"text\": \"def _get_bound(objects):\\n if hasattr(objects,'__iter__'):\\n bbs = [_get_bound(o) for o in objects]\\n if len(bbs) == 1:\\n return bbs[0]\\n return collide.bb_union(*bbs)\\n else:\\n return objects.geometry().getBBTight()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3e7687bb5d7fd6663804b3649c2210b\",\n \"score\": \"0.5791099\",\n \"text\": \"def bbox_giou(self, boxes_1, boxes_2):\\n # transform [x, y, w, h] to [x_min, y_min, x_max, y_max]\\n boxes_1 = tf.concat([boxes_1[..., :2] - boxes_1[..., 2:] * 0.5,\\n boxes_1[..., :2] + boxes_1[..., 2:] * 0.5], axis=-1)\\n boxes_2 = tf.concat([boxes_2[..., :2] - boxes_2[..., 2:] * 0.5,\\n boxes_2[..., :2] + boxes_2[..., 2:] * 0.5], axis=-1)\\n boxes_1 = tf.concat([tf.minimum(boxes_1[..., :2], boxes_1[..., 2:]),\\n tf.maximum(boxes_1[..., :2], boxes_1[..., 2:])], axis=-1)\\n boxes_2 = tf.concat([tf.minimum(boxes_2[..., :2], boxes_2[..., 2:]),\\n tf.maximum(boxes_2[..., :2], boxes_2[..., 2:])], axis=-1)\\n\\n # calculate area of boxes_1 boxes_2\\n boxes_1_area = (boxes_1[..., 2] - boxes_1[..., 0]) * (boxes_1[..., 3] - boxes_1[..., 1])\\n boxes_2_area = (boxes_2[..., 2] - boxes_2[..., 0]) * (boxes_2[..., 3] - boxes_2[..., 1])\\n\\n # calculate the two corners of the intersection\\n left_up = tf.maximum(boxes_1[..., :2], boxes_2[..., :2])\\n right_down = tf.minimum(boxes_1[..., 2:], boxes_2[..., 2:])\\n\\n # calculate area of intersection\\n inter_section = tf.maximum(right_down - left_up, 0.0)\\n inter_area = inter_section[..., 0] * inter_section[..., 1]\\n\\n # calculate union area\\n union_area = boxes_1_area + boxes_2_area - inter_area\\n\\n # calculate iou add epsilon in denominator to avoid dividing by 0\\n iou = inter_area / (union_area + tf.keras.backend.epsilon())\\n\\n # calculate the upper left and lower right corners of the minimum closed convex surface\\n enclose_left_up = tf.minimum(boxes_1[..., :2], boxes_2[..., :2])\\n enclose_right_down = tf.maximum(boxes_1[..., 2:], boxes_2[..., 2:])\\n\\n # calculate width and height of the minimun closed convex surface\\n enclose_wh = tf.maximum(enclose_right_down - enclose_left_up, 0.0)\\n\\n # calculate area of the minimun closed convex surface\\n enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]\\n\\n # calculate the giou add epsilon in denominator to avoid dividing by 0\\n giou = iou - 1.0 * (enclose_area - union_area) / (enclose_area + tf.keras.backend.epsilon())\\n\\n return giou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dbd80c62f615f25044d35a8f42260cc\",\n \"score\": \"0.57526714\",\n \"text\": \"def extract_constraints(hull):\\n A = []\\n b = []\\n pairs = []\\n angles = []\\n\\n perimeter = []\\n\\n angle_intersection = []\\n\\n for simplex1, simplex2 in itertools.combinations(hull.simplices, 2):\\n intersect = set(simplex1).intersection(simplex2)\\n\\n for p in simplex1:\\n perimeter.append((angle(hull.points[p]), list(hull.points[p])))\\n\\n for p in simplex2:\\n perimeter.append((angle(hull.points[p]), list(hull.points[p])))\\n\\n if intersect:\\n v1, v2 = hull.points[[simplex1[0], simplex1[1]]]\\n diff = v1-v2\\n normal1 = np.array([diff[1],-diff[0]])\\n\\n # find a point not in the simplex\\n i = 0\\n while True:\\n s = hull.points[i]\\n if i not in simplex1:\\n break\\n i += 1 \\n if np.dot(normal1, s-hull.points[simplex1[0]]) > 0:\\n normal1 = -normal1\\n \\n v1, v2 = hull.points[[simplex2[0], simplex2[1]]]\\n diff = v1-v2\\n normal2 = np.array([diff[1],-diff[0]])\\n \\n # find a point not in the simplex\\n i = 0\\n while True:\\n s = hull.points[i]\\n if i not in simplex2:\\n break\\n i += 1\\n \\n if np.dot(normal2, s-hull.points[simplex2[0]]) > 0:\\n normal2 = -normal2\\n \\n dual_basis = np.vstack([normal1, normal2])\\n angles.extend([angle(normal1), angle(normal2)])\\n angle_intersection.append([angle(normal1), angle(normal2), intersect])\\n pairs.append((hull.points[list(intersect)[0]], dual_basis))\\n\\n for simplex in hull.simplices:\\n v1, v2 = hull.points[[simplex[0], simplex[1]]]\\n diff = v1-v2\\n normal = np.array([diff[1],-diff[0]])\\n offset = -np.dot(normal, v1)\\n scale = np.linalg.norm(normal)\\n if offset < 0:\\n scale *= -1\\n normal /= scale\\n offset /= scale\\n A.append(normal)\\n b.append(offset)\\n\\n # crude rounding\\n angles = np.array(angles)\\n angles *= 50000\\n angles = np.unique(angles.astype(np.int))\\n angles = angles / 50000.\\n\\n return np.array(A), np.array(b), pairs, angles, sorted(perimeter), angle_intersection\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57b6abe7ddafc80313c89dccdbe45f95\",\n \"score\": \"0.57490575\",\n \"text\": \"def disjoint_union(metagraph, graphs):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d00d261bf1cef924b35e6ac29e200358\",\n \"score\": \"0.57404125\",\n \"text\": \"def fix_segments_intersections(polygons, height, width, img_name, use_background_label,\\n threshold=0.0, ratio_tolerance=0.001):\\n converted_polygons = []\\n empty_polygon = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\\n # Convert points of polygons from string to coco's array.\\n # All polygons must be sorted in order from bottom to top\\n for polygon in polygons:\\n label = polygon['label']\\n points = polygon['points'].split(';')\\n new_polygon = []\\n for xy in points:\\n x = float(xy.split(',')[0])\\n y = float(xy.split(',')[1])\\n new_polygon.append(x)\\n new_polygon.append(y)\\n converted_polygons.append({'label': label, 'points': new_polygon})\\n\\n for i in range(0, len(converted_polygons)):\\n rle_bottom = mask_util.frPyObjects([converted_polygons[i]['points']], height, width)\\n segment_overlapped = False\\n for j in range(i + 1, len(converted_polygons)):\\n rle_top = mask_util.frPyObjects([converted_polygons[j]['points']], height, width)\\n iou = mask_util.iou(rle_bottom, rle_top, [0, 0])\\n area_top = sum(mask_util.area(rle_top))\\n area_bottom = sum(mask_util.area(rle_bottom))\\n if area_bottom == 0:\\n continue\\n area_ratio = area_top / area_bottom\\n sum_iou = sum(iou)\\n\\n # If segment is fully inside another one, save this segment as is\\n if area_ratio - ratio_tolerance < sum_iou[0] < area_ratio + ratio_tolerance:\\n continue\\n # Check situation when bottom segment is fully inside top.\\n # It means that in annotation is mistake. Save this segment as is\\n if 1 / area_ratio - ratio_tolerance < sum_iou[0] < 1 / area_ratio + ratio_tolerance:\\n continue\\n\\n if sum_iou[0] > threshold:\\n segment_overlapped = True\\n bottom_mask = np.array(mask_util.decode(rle_bottom), dtype=np.uint8)\\n top_mask = np.array(mask_util.decode(rle_top), dtype=np.uint8)\\n\\n bottom_mask = np.subtract(bottom_mask, top_mask)\\n bottom_mask[bottom_mask > 1] = 0\\n\\n bottom_mask = np.sum(bottom_mask, axis=2)\\n bottom_mask = np.array(bottom_mask > 0, dtype=np.uint8)\\n converted_polygons[i]['points'] = mask_to_polygon(bottom_mask)\\n # If some segment is empty, do small fix to avoid error in cocoapi function\\n if len(converted_polygons[i]['points']) == 0:\\n converted_polygons[i]['points'] = [empty_polygon]\\n rle_bottom = mask_util.frPyObjects(converted_polygons[i]['points'], height, width)\\n if not segment_overlapped:\\n converted_polygons[i]['points'] = [converted_polygons[i]['points']]\\n\\n output_polygons = []\\n for i in range(0, len(converted_polygons)):\\n if not use_background_label and converted_polygons[i]['label'] == 'background':\\n continue\\n poly_len = len(converted_polygons[i]['points'])\\n if poly_len == 0 or converted_polygons[i]['points'] == [empty_polygon]:\\n log.warning('Image <{}> has an empty polygon with label <{}>. '\\n 'Perhaps there is a mistake in annotation'.\\n format(img_name, converted_polygons[i]['label']))\\n else:\\n output_polygons.append(converted_polygons[i])\\n\\n return output_polygons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d0478c7b2552eca3d0b93489bf2e4c\",\n \"score\": \"0.57062614\",\n \"text\": \"def test_split_by_polygon_in_out(self):\\r\\n\\r\\n raster_name = os.path.join(\\r\\n UNITDATA,\\r\\n 'hazard',\\r\\n 'jakarta_flood_design.tif')\\r\\n\\r\\n exposure_name = os.path.join(\\r\\n UNITDATA,\\r\\n 'exposure',\\r\\n 'roads_osm_4326.shp')\\r\\n qgis_exposure = QgsVectorLayer(\\r\\n exposure_name,\\r\\n 'EXPOSURE',\\r\\n 'ogr')\\r\\n\\r\\n inside_file_name, inside_layer_name, outside_file_name, \\\\\\r\\n outside_layer_name = polygonize_thresholds(raster_name, 0.1)\\r\\n\\r\\n polygon_in = \\\\\\r\\n QgsVectorLayer(inside_file_name, inside_layer_name, 'ogr')\\r\\n polygon_out = \\\\\\r\\n QgsVectorLayer(outside_file_name, outside_layer_name, 'ogr')\\r\\n\\r\\n layer = split_by_polygon_in_out(\\r\\n qgis_exposure, polygon_in, polygon_out, 'flooded', 1)\\r\\n\\r\\n feature_count = layer.featureCount()\\r\\n self.assertEqual(feature_count, 184)\\r\\n\\r\\n flooded = 0\\r\\n iterator = layer.getFeatures()\\r\\n for feature in iterator:\\r\\n attributes = feature.attributes()\\r\\n if attributes[3] == 1:\\r\\n flooded += 1\\r\\n self.assertEqual(flooded, 25)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe6a4a4dee2e86946e1b73eb077ba6d\",\n \"score\": \"0.5661395\",\n \"text\": \"def computeIntersectionOverUnion(boundingBox1, boundingBox2):\\n # dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])\\n\\n bb1=dict([('x1', boundingBox1[0][0]), ('x2', boundingBox1[1][0]), ('y1', boundingBox1[0][1]), ('y2', boundingBox1[1][1])])\\n bb2=dict([('x1', boundingBox2[0][0]), ('x2', boundingBox2[1][0]), ('y1', boundingBox2[0][1]), ('y2', boundingBox2[1][1])])\\n \\n assert bb1['x1'] < bb1['x2']\\n assert bb1['y1'] < bb1['y2']\\n assert bb2['x1'] < bb2['x2']\\n assert bb2['y1'] < bb2['y2']\\n\\n # determine the coordinates of the intersection rectangle\\n x_left = max(bb1['x1'], bb2['x1'])\\n y_top = max(bb1['y1'], bb2['y1'])\\n x_right = min(bb1['x2'], bb2['x2'])\\n y_bottom = min(bb1['y2'], bb2['y2'])\\n\\n if x_right < x_left or y_bottom < y_top:\\n return 0.0\\n\\n # The intersection of two axis-aligned bounding boxes is always an\\n # axis-aligned bounding box\\n intersection_area = (x_right - x_left) * (y_bottom - y_top)\\n\\n # compute the area of both AABBs\\n bb1_area = (bb1['x2'] - bb1['x1']) * (bb1['y2'] - bb1['y1'])\\n bb2_area = (bb2['x2'] - bb2['x1']) * (bb2['y2'] - bb2['y1'])\\n\\n # compute the intersection over union by taking the intersection\\n # area and dividing it by the sum of prediction + ground-truth\\n # areas - the interesection area\\n iou = intersection_area / float(bb1_area + bb2_area - intersection_area)\\n assert iou >= 0.0\\n assert iou <= 1.0\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5f62ac95e94559ac75242df6031bc70\",\n \"score\": \"0.5658646\",\n \"text\": \"def bbox_iou(self, boxes_1, boxes_2):\\n # transform [x, y, w, h] to [x_min, y_min, x_max, y_max]\\n boxes_1 = tf.concat([boxes_1[..., :2] - boxes_1[..., 2:] * 0.5,\\n boxes_1[..., :2] + boxes_1[..., 2:] * 0.5], axis=-1)\\n boxes_2 = tf.concat([boxes_2[..., :2] - boxes_2[..., 2:] * 0.5,\\n boxes_2[..., :2] + boxes_2[..., 2:] * 0.5], axis=-1)\\n boxes_1 = tf.concat([tf.minimum(boxes_1[..., :2], boxes_1[..., 2:]),\\n tf.maximum(boxes_1[..., :2], boxes_1[..., 2:])], axis=-1)\\n boxes_2 = tf.concat([tf.minimum(boxes_2[..., :2], boxes_2[..., 2:]),\\n tf.maximum(boxes_2[..., :2], boxes_2[..., 2:])], axis=-1)\\n\\n # calculate area of boxes_1 boxes_2\\n boxes_1_area = (boxes_1[..., 2] - boxes_1[..., 0]) * (boxes_1[..., 3] - boxes_1[..., 1])\\n boxes_2_area = (boxes_2[..., 2] - boxes_2[..., 0]) * (boxes_2[..., 3] - boxes_2[..., 1])\\n\\n # calculate the two corners of the intersection\\n left_up = tf.maximum(boxes_1[..., :2], boxes_2[..., :2])\\n right_down = tf.minimum(boxes_1[..., 2:], boxes_2[..., 2:])\\n\\n # calculate area of intersection\\n inter_section = tf.maximum(right_down - left_up, 0.0)\\n inter_area = inter_section[..., 0] * inter_section[..., 1]\\n\\n # calculate union area\\n union_area = boxes_1_area + boxes_2_area - inter_area\\n\\n # calculate iou add epsilon in denominator to avoid dividing by 0\\n iou = inter_area / (union_area + tf.keras.backend.epsilon())\\n\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01a44ccf09490cde12cfec7cc6446c32\",\n \"score\": \"0.5652409\",\n \"text\": \"def rasterize_polygons(\\n self,\\n polygons: Iterable[Iterable[Iterable[Number]]],\\n holes: Iterable[Iterable[Iterable[Number]]] = None,\\n ) -> np.ndarray:\\n size = self.shape[0:2][::-1]\\n polygons = [self.xy_to_rowcol(xy)[:, ::-1] + 0.5 for xy in polygons]\\n if holes is not None:\\n holes = [self.xy_to_rowcol(xy)[:, ::-1] + 0.5 for xy in holes]\\n return helpers.polygons_to_mask(polygons, size=size, holes=holes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f6fd1872182d61b89335595cc4de28\",\n \"score\": \"0.564399\",\n \"text\": \"def poly_union(poly_det, poly_gt, invalid_ret=None, return_poly=False):\\n assert isinstance(poly_det, plg)\\n assert isinstance(poly_gt, plg)\\n assert invalid_ret is None or isinstance(invalid_ret, float) or \\\\\\n isinstance(invalid_ret, int)\\n\\n if invalid_ret is None:\\n poly_det = poly_make_valid(poly_det)\\n poly_gt = poly_make_valid(poly_gt)\\n\\n poly_obj = None\\n area = invalid_ret\\n if poly_det.is_valid and poly_gt.is_valid:\\n poly_obj = poly_det.union(poly_gt)\\n area = poly_obj.area\\n return (area, poly_obj) if return_poly else area\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53975b26c3627527977fcf34d8f4f32f\",\n \"score\": \"0.5641766\",\n \"text\": \"def test_populate_polygon_with_exclude2(self):\\r\\n\\r\\n M = 200 # Number of points\\r\\n min_outer = 0\\r\\n max_outer = 1000\\r\\n polygon_outer = [[min_outer, min_outer], [max_outer, min_outer],\\r\\n [max_outer, max_outer], [min_outer, max_outer]]\\r\\n\\r\\n delta = 10\\r\\n min_inner1 = min_outer + delta\\r\\n max_inner1 = max_outer - delta\\r\\n inner1_polygon = [[min_inner1, min_inner1], [max_inner1, min_inner1],\\r\\n [max_inner1, max_inner1], [min_inner1, max_inner1]]\\r\\n\\r\\n min_inner2 = min_outer + 2 * delta\\r\\n max_inner2 = max_outer - 2 * delta\\r\\n inner2_polygon = [[min_inner2, min_inner2], [max_inner2, min_inner2],\\r\\n [max_inner2, max_inner2], [min_inner2, max_inner2]]\\r\\n\\r\\n points = populate_polygon(polygon_outer, M,\\r\\n exclude=[inner1_polygon, inner2_polygon])\\r\\n\\r\\n assert len(points) == M\\r\\n for point in points:\\r\\n assert is_inside_polygon(point, polygon_outer)\\r\\n assert not is_inside_polygon(point, inner1_polygon)\\r\\n assert not is_inside_polygon(point, inner2_polygon)\\r\\n\\r\\n # Very convoluted polygon\\r\\n polygon = [[0, 0], [10, 10], [15, 5], [20, 10], [25, 0],\\r\\n [30, 10], [40, -10]]\\r\\n ex_poly = [[-1, -1], [5, 0], [5, 5], [-1, 5]]\\r\\n points = populate_polygon(polygon, M, exclude=[ex_poly])\\r\\n\\r\\n assert len(points) == M\\r\\n for point in points:\\r\\n assert is_inside_polygon(point, polygon)\\r\\n assert not is_inside_polygon(point, ex_poly), '%s' % str(point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89aba08e13e4ba24bfee1d01111d2f7f\",\n \"score\": \"0.5601758\",\n \"text\": \"def IntersectionOverUnion(boxes_prds, boxes_trgt, box_format=\\\"centers\\\"):\\n # boxes_prds: (N, 4) where N is numof Bounding boxes.\\n # boxes_labels: (N, 4)\\n # use [..., 0:1] same as [:, 0:1], but : can take as one dim,\\n # ... means all dims above\\n # and [..., 0:1] means colum list, [..., 0] means row list.\\n if box_format == \\\"centers\\\":\\n box1_xc = boxes_prds[..., 0:1]\\n box1_yc = boxes_prds[..., 1:2]\\n box1_H = boxes_prds[..., 2:3]\\n box1_W = boxes_prds[..., 3:4]\\n box2_xc = boxes_trgt[..., 0:1]\\n box2_yc = boxes_trgt[..., 1:2]\\n box2_H = boxes_trgt[..., 2:3]\\n box2_W = boxes_trgt[..., 3:4]\\n\\n box1_x1 = box1_xc - box1_W / 2.0\\n box1_y1 = box1_yc - box1_H / 2.0\\n box1_x2 = box1_xc + box1_W / 2.0\\n box1_y2 = box1_yc + box1_H / 2.0\\n box2_x1 = box2_xc - box2_W / 2.0\\n box2_y1 = box2_yc - box2_H / 2.0\\n box2_x2 = box2_xc + box2_W / 2.0\\n box2_y2 = box2_yc + box2_H / 2.0\\n\\n if box_format == \\\"corners\\\":\\n box1_x1 = boxes_prds[..., 0:1]\\n box1_y1 = boxes_prds[..., 1:2]\\n box1_x2 = boxes_prds[..., 2:3]\\n box1_y2 = boxes_prds[..., 3:4]\\n box2_x1 = boxes_trgt[..., 0:1]\\n box2_y1 = boxes_trgt[..., 1:2]\\n box2_x2 = boxes_trgt[..., 2:3]\\n box2_y2 = boxes_trgt[..., 3:4]\\n\\n I_x1 = torch.max(box1_x1, box2_x1)\\n I_y1 = torch.max(box1_y1, box2_y1)\\n I_x2 = torch.min(box1_x2, box2_x2)\\n I_y2 = torch.min(box1_y2, box2_y2)\\n\\n # clamp(0) makes less than 0 value to 0, so non-intersection will be 0\\n Intersection = (I_x2 - I_x1).clamp(0) * (I_y2 - I_y1).clamp(0)\\n\\n box1_area = abs((box1_x2 - box1_x1) * (box1_y2 - box1_y1))\\n box2_area = abs((box2_x2 - box2_x1) * (box2_y2 - box2_y1))\\n\\n epsilon = 1e-6\\n\\n return Intersection / (box1_area + box2_area - Intersection + epsilon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2908a091fe5abeb2bc09ffdea117ea6b\",\n \"score\": \"0.5600189\",\n \"text\": \"def union(self):\\n self._remove_cut_lines()\\n self._sanity_check(\\\"union - remove cut lines\\\")\\n self._find_all_intersections()\\n self._sanity_check(\\\"union - find all intersections\\\")\\n self._remove_interior_edges()\\n self._sanity_check(\\\"union - remove interior edges\\\")\\n self._remove_3ary_edges()\\n self._sanity_check(\\\"union - remove 3ary edges\\\")\\n self._remove_orphaned_nodes()\\n self._sanity_check(\\\"union - remove orphan nodes\\\", True)\\n return self._trace()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5806a83d9d16c015105624c5a5fb8ee7\",\n \"score\": \"0.5599756\",\n \"text\": \"def test_booleans():\\n half = S.Half\\n\\n p1, p2, p3, p4 = map(Point, [(0, 0), (1, 0), (5, 1), (0, 1)])\\n p5, p6, p7 = map(Point, [(3, 2), (1, -1), (0, 2)])\\n l1 = Line(Point(0,0), Point(1,1))\\n l2 = Line(Point(half, half), Point(5,5))\\n l3 = Line(p2, p3)\\n l4 = Line(p3, p4)\\n poly1 = Polygon(p1, p2, p3, p4)\\n poly2 = Polygon(p5, p6, p7)\\n poly3 = Polygon(p1, p2, p5)\\n assert Union(l1, l2).equals(l1)\\n assert Intersection(l1, l2).equals(l1)\\n assert Intersection(l1, l4) == FiniteSet(Point(1,1))\\n assert Intersection(Union(l1, l4), l3) == FiniteSet(Point(Rational(-1, 3), Rational(-1, 3)), Point(5, 1))\\n assert Intersection(l1, FiniteSet(Point(7,-7))) == EmptySet\\n assert Intersection(Circle(Point(0,0), 3), Line(p1,p2)) == FiniteSet(Point(-3,0), Point(3,0))\\n assert Intersection(l1, FiniteSet(p1)) == FiniteSet(p1)\\n assert Union(l1, FiniteSet(p1)) == l1\\n\\n fs = FiniteSet(Point(Rational(1, 3), 1), Point(Rational(2, 3), 0), Point(Rational(9, 5), Rational(1, 5)), Point(Rational(7, 3), 1))\\n # test the intersection of polygons\\n assert Intersection(poly1, poly2) == fs\\n # make sure if we union polygons with subsets, the subsets go away\\n assert Union(poly1, poly2, fs) == Union(poly1, poly2)\\n # make sure that if we union with a FiniteSet that isn't a subset,\\n # that the points in the intersection stop being listed\\n assert Union(poly1, FiniteSet(Point(0,0), Point(3,5))) == Union(poly1, FiniteSet(Point(3,5)))\\n # intersect two polygons that share an edge\\n assert Intersection(poly1, poly3) == Union(FiniteSet(Point(Rational(3, 2), 1), Point(2, 1)), Segment(Point(0, 0), Point(1, 0)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc665dcf09bce408bea579313280980c\",\n \"score\": \"0.5597862\",\n \"text\": \"def voronoi(point_list, boundary):\\n return {Polygon}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87fd1355d5d85cdb3262e3448e9ced46\",\n \"score\": \"0.55957127\",\n \"text\": \"def segmentation_to_mask(polys, height, width):\\n polys = [p.flatten().tolist() for p in polys]\\n assert len(polys) > 0, \\\"Polygons are empty!\\\"\\n\\n import pycocotools.mask as cocomask\\n rles = cocomask.frPyObjects(polys, height, width)\\n rle = cocomask.merge(rles)\\n return cocomask.decode(rle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"827b30c03f1a4ac2ee8ffa5b836ffab6\",\n \"score\": \"0.5594046\",\n \"text\": \"def bb_intersection_over_union(boxA, boxB):\\n # determine the (x, y)-coordinates of the intersection rectangle\\n xA = max(boxA[0], boxB[0])\\n yA = max(boxA[1], boxB[1])\\n xB = min(boxA[2], boxB[2])\\n yB = min(boxA[3], boxB[3])\\n\\n # compute the area of intersection rectangle\\n interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)\\n\\n # compute the area of both the prediction and ground-truth\\n # rectangles\\n boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)\\n boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)\\n\\n # compute the intersection over union by taking the intersection\\n # area and dividing it by the sum of prediction + ground-truth\\n # areas - the interesection area\\n print(boxAArea,boxBArea,interArea)\\n iou = interArea / float(boxAArea + boxBArea - interArea)\\n\\n # return the intersection over union value\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1892817c2382820f2228b6cc5d33cf6\",\n \"score\": \"0.5581676\",\n \"text\": \"def create_simplified_geometry(self):\\n \\n ## Replace MultiPolygons by Polygons ##\\n nfeat = self.FeatureCount() # somehow necessary for the loop on features to work \\n print(\\\"%i features to process\\\" %nfeat)\\n\\n out_ds = ogr.GetDriverByName('Memory').CreateDataSource('')\\n out_lyr = out_ds.CreateLayer('poly', srs=self.srs)\\n multipoly2poly(self.layer, out_lyr)\\n\\n ## Create a single geometry ##\\n union = ogr.Geometry(ogr.wkbMultiPolygon)\\n for feat in out_lyr:\\n union.AddGeometry(feat.GetGeometryRef())\\n union=union.Simplify(0)\\n\\n # Check that geometry is valid (otherwise will fail later)\\n if not union.IsValid():\\n print(\\\"ERROR with geometry\\\")\\n return 0\\n else:\\n return union\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef6c4c5a8666913fc71719062d788f70\",\n \"score\": \"0.5575433\",\n \"text\": \"def boundary_substraction(\\n *,\\n boundary: tuple[tuple[int, ...], ...],\\n subtracted: tuple[tuple[int, ...], ...],\\n) -> tuple[tuple[int, ...], ...]:\\n output = tuple(\\n boundary_i for boundary_i in boundary if boundary_i not in subtracted\\n )\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeb79ab0e1a792756699362a954f35d2\",\n \"score\": \"0.55568624\",\n \"text\": \"def bbox_iou(box1, box2, order=\\\"xyxy\\\"):\\n assert order in {\\\"xyxy\\\", \\\"xywh\\\"}\\n\\n if order == \\\"xywh\\\":\\n b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2\\n b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2\\n b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2\\n b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2\\n else:\\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]\\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\\n\\n # get the corrdinates of the intersection rectangle\\n inter_rect_x1 = torch.max(b1_x1, b2_x1)\\n inter_rect_y1 = torch.max(b1_y1, b2_y1)\\n inter_rect_x2 = torch.min(b1_x2, b2_x2)\\n inter_rect_y2 = torch.min(b1_y2, b2_y2)\\n # Intersection area\\n inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(\\n inter_rect_y2 - inter_rect_y1 + 1, min=0\\n )\\n # Union Area\\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\\n\\n iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)\\n\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef24eaeebd85a66c3fd42d0070c2e7ae\",\n \"score\": \"0.5553224\",\n \"text\": \"def bbox_overlaps(bboxes1, bboxes2, mode='iou'):\\n\\n assert mode in ['iou', 'iof','overlap']\\n\\n bboxes1 = bboxes1.astype(np.float32)\\n bboxes2 = bboxes2.astype(np.float32)\\n rows = bboxes1.shape[0]\\n cols = bboxes2.shape[0]\\n ious = np.zeros((rows, cols), dtype=np.float32)\\n if rows * cols == 0:\\n return ious\\n exchange = True\\n if bboxes1.shape[0] > bboxes2.shape[0]:\\n bboxes1, bboxes2 = bboxes2, bboxes1\\n ious = np.zeros((cols, rows), dtype=np.float32)\\n exchange = False\\n area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (\\n bboxes1[:, 3] - bboxes1[:, 1] + 1)\\n area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (\\n bboxes2[:, 3] - bboxes2[:, 1] + 1)\\n for i in range(bboxes1.shape[0]):\\n x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])\\n y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])\\n x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])\\n y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])\\n overlap = np.maximum(x_end - x_start + 1, 0) * np.maximum(\\n y_end - y_start + 1, 0)\\n if mode == 'iou':\\n union = area1[i] + area2 - overlap\\n if mode == 'overlap':\\n return overlap\\n else:\\n union = area1[i] if not exchange else area2\\n ious[i, :] = overlap / union\\n if exchange:\\n ious = ious.T\\n return ious\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0938abe360aab4d4890b095804424c6e\",\n \"score\": \"0.55488\",\n \"text\": \"def unionall(self, other_rects):\\n result = FRect(self)\\n result.unionall_ip(other_rects)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0406cd12d4465d4daca5445a9f9eb9c\",\n \"score\": \"0.5547007\",\n \"text\": \"def unionDeuxCouches(a,b):\\r\\n #Lire le shapefile 1 et prendre en parametre a\\r\\n shapefile1 = gpd.read_file(a)\\r\\n #Lire le shapefile 2 et prendre en parametre b\\r\\n shapefile2 = gpd.read_file(b)\\r\\n #Verifier si les deux couches vecteurs ont de la même systeme de coordonnées\\r\\n if shapefile1.crs==shapefile2.crs:\\r\\n #Créer l'union des deux couches\\r\\n couche_union = gpd.overlay(shapefile1, shapefile2, how='union')\\r\\n #couche_union.plot(alpha=0.5, edgecolor='k', cmap='tab10'); pour l' afficher en plot\\r\\n #Definir le nom de la sortie\\r\\n out = r\\\"shp/couche_union.shp\\\"\\r\\n #Creer le fichier en sortie\\r\\n ecrire_union=couche_union[:]\\r\\n ecrire_union.to_file(out)\\r\\n else:\\r\\n print(\\\"Vérifier le systeme de coordonnées de ces deux couches vecteurs\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d94847de5bd82f5f44babf356a528c\",\n \"score\": \"0.55420196\",\n \"text\": \"def test_valid_polygon_pair_union(self):\\n\\n response = self.app.post(\\n '/polygonAction/union',\\n content_type='application/json',\\n data=self.get_json_data(\\\"fixtures/validPolygonPair.json\\\")\\n )\\n geoJson = json.loads(response.data.decode('utf8'))\\n assert response.status_code == 200\\n assert geoJson['operation'] == 'union'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4fa23b47d4644dcb2eee9801ad6e5fe\",\n \"score\": \"0.5531045\",\n \"text\": \"def process_polygons(self, polygons):\\n\\n polygons = [np.array(p).astype(np.float32) for p in polygons]\\n valid_polygons = []\\n for polygon in polygons:\\n if len(polygon) % 2 == 0 and len(polygon) >= 6:\\n valid_polygons.append(polygon)\\n return valid_polygons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c1623fe0b1f6b82635b9bdd192b1315\",\n \"score\": \"0.5526814\",\n \"text\": \"def voronoi_polygons(voronoi, boundary, diameter=1000000):\\n polygons = []\\n\\n centroid = voronoi.points.mean(axis=0)\\n\\n # Mapping from (input point index, Voronoi point index) to list of\\n # unit vectors in the directions of the infinite ridges starting\\n # at the Voronoi point and neighbouring the input point.\\n ridge_direction = defaultdict(list)\\n for (p, q), rv in zip(voronoi.ridge_points, voronoi.ridge_vertices):\\n u, v = sorted(rv)\\n if u == -1:\\n # Infinite ridge starting at ridge point with index v,\\n # equidistant from input points with indexes p and q.\\n t = voronoi.points[q] - voronoi.points[p] # tangent\\n n = np.array([-t[1], t[0]]) / np.linalg.norm(t) # normal\\n midpoint = voronoi.points[[p, q]].mean(axis=0)\\n direction = np.sign(np.dot(midpoint - centroid, n)) * n\\n ridge_direction[p, v].append(direction)\\n ridge_direction[q, v].append(direction)\\n\\n for i, r in enumerate(voronoi.point_region):\\n region = voronoi.regions[r]\\n if -1 not in region:\\n # Finite region.\\n polygons.append(\\n Polygon(voronoi.vertices[region]).intersection(boundary)\\n )\\n continue\\n # Infinite region.\\n inf = region.index(-1) # Index of vertex at infinity.\\n j = region[(inf - 1) % len(region)] # Index of previous vertex.\\n k = region[(inf + 1) % len(region)] # Index of next vertex.\\n if j == k:\\n # Region has one Voronoi vertex with two ridges.\\n dir_j, dir_k = ridge_direction[i, j]\\n else:\\n # Region has two Voronoi vertices, each with one ridge.\\n (dir_j,) = ridge_direction[i, j]\\n (dir_k,) = ridge_direction[i, k]\\n\\n # Length of ridges needed for the extra edge to lie at least\\n # 'diameter' away from all Voronoi vertices.\\n length = 2 * diameter / np.linalg.norm(dir_j + dir_k)\\n\\n # Polygon consists of finite part plus an extra edge.\\n finite_part = voronoi.vertices[region[inf + 1 :] + region[:inf]]\\n extra_edge = [\\n voronoi.vertices[j] + dir_j * length,\\n voronoi.vertices[k] + dir_k * length,\\n ]\\n polygons.append(\\n Polygon(np.concatenate((finite_part, extra_edge))).intersection(\\n boundary\\n )\\n )\\n\\n return polygons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec78d9e524e8e62b1fa8e5c776f538c7\",\n \"score\": \"0.5525387\",\n \"text\": \"def get_boundary_polygon(self):\\n return simplify_polygon([node.pos for node in self.boundary_nodes])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b309b280a67704e86086b7afa633db59\",\n \"score\": \"0.5522205\",\n \"text\": \"def merge_bounding_boxes(bb1, bb2, mode):\\n less_than_or_equal = lambda x, y: x if x <= y else y\\n greater_than = lambda x, y: x if x > y else y\\n\\n if mode == \\\"union\\\":\\n comparison_ops = [\\n less_than_or_equal, greater_than, greater_than,\\n less_than_or_equal]\\n if mode == \\\"intersection\\\":\\n comparison_ops = [\\n greater_than, less_than_or_equal, less_than_or_equal,\\n greater_than]\\n\\n bb_out = [op(x, y) for op, x, y in zip(comparison_ops, bb1, bb2)]\\n return bb_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31cf511748fac7254a981ee7045faaa3\",\n \"score\": \"0.5513587\",\n \"text\": \"def bb_intersect_over_union(boxA: BoundingBox, boxB: BoundingBox):\\n boxA = [boxA.top, boxA.left, boxA.top + boxA.width, boxA.left + boxA.height]\\n boxB = [boxB.top, boxB.left, boxB.top + boxB.width, boxB.left + boxB.height]\\n\\n # Getting X,Y of Intersection Rectangle\\n xA = max(boxA[0], boxB[0])\\n yA = max(boxA[1], boxB[1])\\n xB = max(boxA[2], boxB[2])\\n yB = max(boxA[3], boxB[3])\\n\\n # Compute area of the rectangle\\n interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)\\n\\n # Compute area of pred box and gt box\\n boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)\\n boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)\\n\\n # Compute IoU\\n # Intersection area / Sum (gt+pred) area\\n iou = interArea / float(boxAArea + boxBArea - interArea)\\n\\n # idk ;_;\\n iou = 1 - math.modf(iou)[0]\\n\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7846f70363c8a4b37349482056415f04\",\n \"score\": \"0.55080426\",\n \"text\": \"def test_separate_points_by_polygon1(self):\\r\\n\\r\\n # Unit square\\r\\n U = [[0, 0], [1, 0], [1, 1], [0, 1]]\\r\\n\\r\\n inside, outside = separate_points_by_polygon([[0.5, 0.5]], U)\\r\\n assert len(inside) == 1\\r\\n assert numpy.allclose(inside, [0])\\r\\n\\r\\n inside, outside = separate_points_by_polygon([[0.5, 0.5],\\r\\n [0.3, 0.2]], U)\\r\\n assert len(inside) == 2\\r\\n assert numpy.allclose(inside, [0, 1])\\r\\n\\r\\n inside, outside = separate_points_by_polygon([[0.5, 0.5],\\r\\n [0.3, 0.2],\\r\\n [0.6, 0.7]], U)\\r\\n assert len(inside) == 3\\r\\n assert numpy.allclose(inside, [0, 1, 2])\\r\\n\\r\\n inside, outside = separate_points_by_polygon([[0.3, 0.2]], U)\\r\\n assert len(inside) == 1\\r\\n assert numpy.allclose(inside, [0])\\r\\n\\r\\n inside, outside = separate_points_by_polygon([[0.3, 0.2],\\r\\n [1, -0.5]], U)\\r\\n assert len(inside) == 1\\r\\n assert numpy.allclose(inside, [0])\\r\\n assert numpy.allclose(outside, [1])\\r\\n\\r\\n inside, outside = separate_points_by_polygon([[0.5, 0.5],\\r\\n [1, -0.5],\\r\\n [0.3, 0.2]], U)\\r\\n assert numpy.allclose(inside, [0, 2])\\r\\n assert numpy.allclose(outside, [1])\\r\\n\\r\\n inside, outside = separate_points_by_polygon([[0.1, 0.1],\\r\\n [0.5, 0.5],\\r\\n [1, -0.5],\\r\\n [0.3, 0.2]], U)\\r\\n assert numpy.allclose(inside, [0, 1, 3])\\r\\n assert numpy.allclose(outside, [2])\\r\\n\\r\\n # Try with boundary (edge) point\\r\\n inside, outside = separate_points_by_polygon([[0, 0.5],\\r\\n [0.1, 0.2]], U)\\r\\n assert numpy.allclose(inside, [0, 1])\\r\\n\\r\\n # Try with boundary (corner) point\\r\\n inside, outside = separate_points_by_polygon([[0, 0],\\r\\n [0.1, 0.2]], U)\\r\\n assert numpy.allclose(inside, [0, 1])\\r\\n\\r\\n # Try with a range of cases point\\r\\n inside, outside = separate_points_by_polygon([[0, 0], # corner\\r\\n [0, 0.5], # edge\\r\\n [0.1, 0.2]], U)\\r\\n assert numpy.allclose(inside, [0, 1, 2])\\r\\n\\r\\n # One more test of vector formulation returning indices\\r\\n polygon = [[0, 0], [1, 0], [0.5, -1], [2, -1], [2, 1], [0, 1]]\\r\\n points = [[0.5, 0.5], [1, -0.5], [1.5, 0], [0.5, 1.5], [0.5, -0.5]]\\r\\n inside, outside = separate_points_by_polygon(points, polygon)\\r\\n assert numpy.allclose(inside, [0, 1, 2])\\r\\n assert numpy.allclose(outside, [3, 4])\\r\\n\\r\\n polygon = [[0, 0], [1, 0], [0.5, -1], [2, -1], [2, 1], [0, 1]]\\r\\n points = [[0.5, 1.4], [0.5, 0.5], [1, -0.5], [1.5, 0],\\r\\n [0.5, 1.5], [0.5, -0.5]]\\r\\n inside, outside = separate_points_by_polygon(points, polygon)\\r\\n assert numpy.allclose(inside, [1, 2, 3])\\r\\n assert numpy.allclose(outside, [0, 4, 5])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a650d1c5f022a964933047e7869e1f80\",\n \"score\": \"0.5484844\",\n \"text\": \"def bbox_iou(box1, box2):\\n #Get the coordinates of bounding boxes\\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:,0], box1[:,1], box1[:,2], box1[:,3]\\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:,0], box2[:,1], box2[:,2], box2[:,3]\\n \\n #get the corrdinates of the intersection rectangle\\n inter_rect_x1 = torch.max(b1_x1, b2_x1)\\n inter_rect_y1 = torch.max(b1_y1, b2_y1)\\n inter_rect_x2 = torch.min(b1_x2, b2_x2)\\n inter_rect_y2 = torch.min(b1_y2, b2_y2)\\n \\n #Intersection area\\n inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(inter_rect_y2 - inter_rect_y1 + 1, min=0)\\n\\n #Union Area\\n b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)\\n b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)\\n \\n iou = inter_area / (b1_area + b2_area - inter_area)\\n \\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59bbee8b8f915bb9dc8c82ee6429a66c\",\n \"score\": \"0.5476246\",\n \"text\": \"def selectBoundaryEdges(components):\\n\\n\\tcmds.select(cmds.polyListComponentConversion(components, te=True, bo=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a335ace891b6e915c09a22a0e93031b\",\n \"score\": \"0.5473155\",\n \"text\": \"def multipoly2poly(in_lyr, out_lyr):\\n k=0\\n for in_feat in in_lyr:\\n geom = in_feat.GetGeometryRef()\\n if geom.GetGeometryName() == 'MULTIPOLYGON':\\n geom_part=geom.GetGeometryRef(0)\\n addPolygon(geom_part.ExportToWkb(), out_lyr)\\n else:\\n addPolygon(geom.ExportToWkb(), out_lyr)\\n k+=1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"628d96bf0326d7a9be1a9f738b8fabba\",\n \"score\": \"0.5465427\",\n \"text\": \"def process_polygons(self, polygons):\\n\\n polygons = [np.array(p) for p in polygons]\\n valid_polygons = []\\n for polygon in polygons:\\n if len(polygon) % 2 == 0 and len(polygon) >= 6:\\n valid_polygons.append(polygon)\\n return valid_polygons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"628d96bf0326d7a9be1a9f738b8fabba\",\n \"score\": \"0.5465427\",\n \"text\": \"def process_polygons(self, polygons):\\n\\n polygons = [np.array(p) for p in polygons]\\n valid_polygons = []\\n for polygon in polygons:\\n if len(polygon) % 2 == 0 and len(polygon) >= 6:\\n valid_polygons.append(polygon)\\n return valid_polygons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379d6a0bf9ecaf391a34a49b86e3fe90\",\n \"score\": \"0.5461095\",\n \"text\": \"def bbox_overlaps(bboxes1, bboxes2, mode='iou'):\\n\\n assert mode in ['iou', 'iof']\\n\\n bboxes1 = bboxes1.astype(np.float32)\\n bboxes2 = bboxes2.astype(np.float32)\\n rows = bboxes1.shape[0]\\n cols = bboxes2.shape[0]\\n ious = np.zeros((rows, cols), dtype=np.float32)\\n if rows * cols == 0:\\n return ious\\n exchange = False\\n if bboxes1.shape[0] > bboxes2.shape[0]:\\n bboxes1, bboxes2 = bboxes2, bboxes1\\n ious = np.zeros((cols, rows), dtype=np.float32)\\n exchange = True\\n area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (\\n bboxes1[:, 3] - bboxes1[:, 1] + 1)\\n area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (\\n bboxes2[:, 3] - bboxes2[:, 1] + 1)\\n for i in range(bboxes1.shape[0]):\\n x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])\\n y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])\\n x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])\\n y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])\\n overlap = np.maximum(x_end - x_start + 1, 0) * np.maximum(\\n y_end - y_start + 1, 0)\\n if mode == 'iou':\\n union = area1[i] + area2 - overlap\\n else:\\n union = area1[i] if not exchange else area2\\n ious[i, :] = overlap / union\\n if exchange:\\n ious = ious.T\\n return ious\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbb4b66db922c14869e2da7e260ac303\",\n \"score\": \"0.54547054\",\n \"text\": \"def bbox_iou(box1, box2):\\n b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2\\n b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2\\n b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2\\n b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2\\n\\n inter_rect_x1 = torch.max(b1_x1, b2_x1)\\n inter_rect_y1 = torch.max(b1_y1, b2_y1)\\n inter_rect_x2 = torch.min(b1_x2, b2_x2)\\n inter_rect_y2 = torch.min(b1_y2, b2_y2)\\n # Intersection area\\n inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(\\n inter_rect_y2 - inter_rect_y1 + 1, min=0\\n )\\n # Union Area\\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\\n\\n iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)\\n\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47be844c287d5c564c71bef0193b42c9\",\n \"score\": \"0.5451837\",\n \"text\": \"def uniteRectangles(a, b, inputType=\\\"cv2\\\"):\\n\\n if inputType == \\\"dlib\\\":\\n return a+b\\n x = min(a[0], b[0])\\n y = min(a[1], b[1])\\n w = max(a[0]+a[2], b[0]+b[2]) - x\\n h = max(a[1]+a[3], b[1]+b[3]) - y\\n return [x, y, w, h]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1d7c3c95864302d4c9f66f9ed4a5b6\",\n \"score\": \"0.5451298\",\n \"text\": \"def union_bbox(elements, transformed=True, with_stroke=False):\\n boxes = []\\n for e in elements:\\n if not hasattr(e, \\\"bbox\\\") or isinstance(e, (Group, Use)):\\n continue\\n box = e.bbox(transformed=transformed, with_stroke=with_stroke)\\n if box is None:\\n continue\\n boxes.append(box)\\n if len(boxes) == 0:\\n return None\\n (xmins, ymins, xmaxs, ymaxs) = zip(*boxes)\\n return min(xmins), min(ymins), max(xmaxs), max(ymaxs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a1d7c3c95864302d4c9f66f9ed4a5b6\",\n \"score\": \"0.5451298\",\n \"text\": \"def union_bbox(elements, transformed=True, with_stroke=False):\\n boxes = []\\n for e in elements:\\n if not hasattr(e, \\\"bbox\\\") or isinstance(e, (Group, Use)):\\n continue\\n box = e.bbox(transformed=transformed, with_stroke=with_stroke)\\n if box is None:\\n continue\\n boxes.append(box)\\n if len(boxes) == 0:\\n return None\\n (xmins, ymins, xmaxs, ymaxs) = zip(*boxes)\\n return min(xmins), min(ymins), max(xmaxs), max(ymaxs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3630b5f0a701c86ea8c37c1f3f4979d2\",\n \"score\": \"0.54487246\",\n \"text\": \"def get_bounding_box_parameterization(self, polygon, height, width):\\n outline = np.array(polygon)\\n # rrr, ccc = outline[:,1], outline[:,0]\\n # rr = []\\n # cc = []\\n # for r in rrr:\\n # if r < 0:\\n # r = 0\\n # if r > height-1:\\n # r = height-1\\n # rr.append(r)\\n # for c in ccc:\\n # if c < 0:\\n # c = 0\\n # if c > width-1:\\n # c = width-1\\n # cc.append(c)\\n # rr = np.array(rr)\\n # cc = np.array(cc)\\n rr, cc = outline[:,1], outline[:,0]\\n xmin = max(min(cc), 0)\\n xmax = min(max(cc), width-1)\\n ymin = max(min(rr), 0)\\n ymax = min(max(rr), height-1)\\n width = xmax - xmin\\n height = ymax - ymin\\n center_x = xmin + 0.5*width \\n center_y = ymin + 0.5*height\\n mask_vector = [xmin, ymin, xmax, ymax, center_x, center_y, width, height]\\n return mask_vector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d42105424bd99d6339a530b3d27817a\",\n \"score\": \"0.5445908\",\n \"text\": \"def _prepare_polygon_layer(self, layer):\\r\\n# import time\\r\\n# startTime = time.clock()\\r\\n\\r\\n message = m.Message(\\r\\n m.Heading(\\r\\n self.tr('Pre-clipping input data...'),\\r\\n **PROGRESS_UPDATE_STYLE),\\r\\n m.Paragraph(self.tr(\\r\\n 'Modifying %s to avoid intersections with the aggregation '\\r\\n 'layer'\\r\\n ) % (layer.name())))\\r\\n #noinspection PyTypeChecker\\r\\n self._send_message(message)\\r\\n\\r\\n layer_filename = str(layer.source())\\r\\n postprocessing_polygons = self.safe_layer.get_geometry()\\r\\n polygons_layer = safe_read_layer(layer_filename)\\r\\n remaining_polygons = numpy.array(\\r\\n polygons_layer.get_geometry(), dtype=list)\\r\\n# myRemainingAttributes = numpy.array(polygons_layer.get_data())\\r\\n remaining_indexes = numpy.array(range(len(remaining_polygons)))\\r\\n\\r\\n #used for unit tests only\\r\\n self.preprocessed_feature_count = 0\\r\\n\\r\\n # TODO (MB) the intersecting array is used only for debugging and\\r\\n # could be safely removed\\r\\n intersecting_polygons = []\\r\\n inside_polygons = []\\r\\n\\r\\n # TODO (MB) maybe do raw geos without qgis\\r\\n #select all post processing polygons with no attributes\\r\\n aggregation_provider = self.layer.dataProvider()\\r\\n aggregation_request = QgsFeatureRequest()\\r\\n aggregation_request.setSubsetOfAttributes([])\\r\\n\\r\\n # copy polygons to a memory layer\\r\\n qgis_memory_layer = create_memory_layer(layer)\\r\\n\\r\\n polygons_provider = qgis_memory_layer.dataProvider()\\r\\n polygons_request = QgsFeatureRequest()\\r\\n qgis_feature = QgsFeature()\\r\\n inside_feature = QgsFeature()\\r\\n fields = polygons_provider.fields()\\r\\n temporary_dir = temp_dir(sub_dir='pre-process')\\r\\n out_filename = unique_filename(suffix='.shp', dir=temporary_dir)\\r\\n\\r\\n self.copy_keywords(layer, out_filename)\\r\\n shape_writer = QgsVectorFileWriter(\\r\\n out_filename,\\r\\n 'UTF-8',\\r\\n fields,\\r\\n polygons_provider.geometryType(),\\r\\n polygons_provider.crs())\\r\\n if shape_writer.hasError():\\r\\n raise InvalidParameterError(shape_writer.errorMessage())\\r\\n # end TODO\\r\\n\\r\\n for (polygon_index, postprocessing_polygon) in enumerate(\\r\\n postprocessing_polygons):\\r\\n LOGGER.debug('Post Processing Polygon %s' % polygon_index)\\r\\n polygon_count = len(remaining_polygons)\\r\\n aggregation_request.setFilterFid(polygon_index)\\r\\n aggregation_polygon = aggregation_provider.getFeatures(\\r\\n aggregation_request).next()\\r\\n geometry = QgsGeometry(aggregation_polygon.geometry())\\r\\n\\r\\n # polygon bounding box values\\r\\n array = numpy.array(postprocessing_polygon)\\r\\n minx = miny = sys.maxint\\r\\n maxx = maxy = -minx\\r\\n postprocessing_minimum_x = min(minx, min(array[:, 0]))\\r\\n postprocessing_maximum_x = max(maxx, max(array[:, 0]))\\r\\n postprocessing_minimum_y = min(miny, min(array[:, 1]))\\r\\n postprocessing_maximum_y = max(maxy, max(array[:, 1]))\\r\\n\\r\\n # create an array full of False to store if a BB vertex is inside\\r\\n # or outside the polygon\\r\\n inside_vertices = numpy.zeros(polygon_count * 4, dtype=numpy.bool)\\r\\n\\r\\n # Create Nx2 vector of vertices of bounding boxes\\r\\n bounding_vertices = []\\r\\n # Compute bounding box for each geometry type\\r\\n for remaining_polygon in remaining_polygons:\\r\\n minx = miny = sys.maxint\\r\\n maxx = maxy = -minx\\r\\n # Do outer ring only as the BB is outside anyway\\r\\n array = numpy.array(remaining_polygon)\\r\\n minx = min(minx, numpy.min(array[:, 0]))\\r\\n maxx = max(maxx, numpy.max(array[:, 0]))\\r\\n miny = min(miny, numpy.min(array[:, 1]))\\r\\n maxy = max(maxy, numpy.max(array[:, 1]))\\r\\n bounding_vertices.extend([\\r\\n (minx, miny),\\r\\n (minx, maxy),\\r\\n (maxx, maxy),\\r\\n (maxx, miny)])\\r\\n\\r\\n # see if BB vertices are in polygon\\r\\n bounding_vertices = numpy.array(bounding_vertices)\\r\\n inside, _ = points_in_and_outside_polygon(\\r\\n bounding_vertices, postprocessing_polygon)\\r\\n # make True if the vertex was in polygon\\r\\n inside_vertices[inside] = True\\r\\n\\r\\n # next_iteration_polygons has the 0:count indexes\\r\\n # outside_polygons has the mapped to original indexes\\r\\n # and is overwritten at every iteration because we care only of\\r\\n # the outside polygons remaining after the last iteration\\r\\n next_iteration_polygons = []\\r\\n outside_polygons = []\\r\\n\\r\\n for i in range(polygon_count):\\r\\n k = i * 4\\r\\n mapped_index = remaining_indexes[i]\\r\\n # memory layers counting starts at 1 instead of 0 as in our\\r\\n # indexes\\r\\n feature_id = mapped_index + 1\\r\\n do_intersection = False\\r\\n # sum the isInside bool for each of the bounding box vertices\\r\\n # of each polygon. for example True + True + False + True is 3\\r\\n polygon_location = numpy.sum(inside_vertices[k:k + 4])\\r\\n\\r\\n if polygon_location == 4:\\r\\n # all vertices are inside -> polygon is inside\\r\\n # ignore this polygon from further analysis\\r\\n inside_polygons.append(mapped_index)\\r\\n polygons_request.setFilterFid(feature_id)\\r\\n qgis_feature = polygons_provider.getFeatures(\\r\\n polygons_request).next()\\r\\n shape_writer.addFeature(qgis_feature)\\r\\n self.preprocessed_feature_count += 1\\r\\n# LOGGER.debug('Polygon %s is fully inside' %mapped_index)\\r\\n# tmpWriter.addFeature(qgis_feature)\\r\\n\\r\\n elif polygon_location == 0:\\r\\n # all vertices are outside\\r\\n # check if the polygon BB is completely outside of the\\r\\n # polygon BB.\\r\\n polygon_min_x = numpy.min(bounding_vertices[k:k + 4, 0])\\r\\n polygon_max_x = numpy.max(bounding_vertices[k:k + 4, 0])\\r\\n polygon_min_y = numpy.min(bounding_vertices[k:k + 4, 1])\\r\\n polygon_max_y = numpy.max(bounding_vertices[k:k + 4, 1])\\r\\n\\r\\n # check if remaining_polygon is all E,W,N,S of polygon\\r\\n if ((polygon_min_x > postprocessing_maximum_x) or\\r\\n (polygon_max_x < postprocessing_minimum_x) or\\r\\n (polygon_min_y > postprocessing_maximum_y) or\\r\\n (polygon_max_y < postprocessing_minimum_y)):\\r\\n # polygon is surely outside\\r\\n outside_polygons.append(mapped_index)\\r\\n # we need this polygon in the next iteration\\r\\n next_iteration_polygons.append(i)\\r\\n else:\\r\\n # polygon might be outside or intersecting. consider\\r\\n # it intersecting so it goes into further analysis\\r\\n do_intersection = True\\r\\n else:\\r\\n # some vertices are outside some inside -> polygon is\\r\\n # intersecting\\r\\n do_intersection = True\\r\\n\\r\\n # intersect using qgis\\r\\n if do_intersection:\\r\\n# LOGGER.debug('Intersecting polygon %s' % mapped_index)\\r\\n intersecting_polygons.append(mapped_index)\\r\\n\\r\\n polygons_request.setFilterFid(feature_id)\\r\\n try:\\r\\n qgis_feature = polygons_provider.getFeatures(\\r\\n polygons_request).next()\\r\\n except StopIteration:\\r\\n LOGGER.debug(\\r\\n 'Could not fetch feature: %s' % feature_id)\\r\\n LOGGER.debug([str(error) for error in\\r\\n polygons_provider.errors()])\\r\\n\\r\\n qgis_polygon_geometry = QgsGeometry(\\r\\n qgis_feature.geometry())\\r\\n attribute_map = qgis_feature.attributes()\\r\\n# for (k, attr) in attribute_map.iteritems():\\r\\n# LOGGER.debug( \\\"%d: %s\\\" % (k, attr.toString()))\\r\\n\\r\\n # make intersection of the qgis_feature and the\\r\\n # post processing polygon\\r\\n # write the inside part to a shp file and the outside part\\r\\n # back to the original QGIS layer\\r\\n try:\\r\\n intersection = geometry.intersection(\\r\\n qgis_polygon_geometry)\\r\\n# if intersection is not None:\\r\\n intersection_geometry = QgsGeometry(intersection)\\r\\n\\r\\n #from ftools\\r\\n unknown_geometry_type = 0\\r\\n geometry_type = intersection_geometry.wkbType()\\r\\n if geometry_type == unknown_geometry_type:\\r\\n int_com = geometry.combine(\\r\\n qgis_polygon_geometry)\\r\\n int_sym = geometry.symDifference(\\r\\n qgis_polygon_geometry)\\r\\n intersection_geometry = QgsGeometry(\\r\\n int_com.difference(int_sym))\\r\\n #LOGGER.debug('wkbType type of intersection: %s' %\\r\\n # intersection_geometry.wkbType())\\r\\n polygon_types = [QGis.WKBPolygon, QGis.WKBMultiPolygon]\\r\\n if intersection_geometry.wkbType() in polygon_types:\\r\\n inside_feature.setGeometry(intersection_geometry)\\r\\n inside_feature.setAttributes(attribute_map)\\r\\n shape_writer.addFeature(inside_feature)\\r\\n self.preprocessed_feature_count += 1\\r\\n else:\\r\\n #LOGGER.debug(\\r\\n # 'Intersection not a polygon so the two '\\r\\n # 'polygons either touch only or do not '\\r\\n # 'intersect. Not adding this to the inside '\\r\\n # 'list')\\r\\n pass\\r\\n # Part of the polygon that is outside the post\\r\\n # processing polygon\\r\\n outside = qgis_polygon_geometry.difference(\\r\\n intersection_geometry)\\r\\n# if outside is not None:\\r\\n outside_geometry = QgsGeometry(outside)\\r\\n\\r\\n if outside_geometry.wkbType() in polygon_types:\\r\\n # modify the original geometry to the part\\r\\n # outside of the post processing polygon\\r\\n polygons_provider.changeGeometryValues(\\r\\n {feature_id: outside_geometry})\\r\\n # we need this polygon in the next iteration\\r\\n outside_polygons.append(mapped_index)\\r\\n next_iteration_polygons.append(i)\\r\\n\\r\\n except TypeError:\\r\\n LOGGER.debug('ERROR with FID %s', mapped_index)\\r\\n\\r\\n #LOGGER.debug('Inside %s' % inside_polygons)\\r\\n #LOGGER.debug('Outside %s' % outside_polygons)\\r\\n #LOGGER.debug('Intersection %s' % intersecting_polygons)\\r\\n if len(next_iteration_polygons) > 0:\\r\\n # some polygons are still completely outside of the\\r\\n # post processing polygon so go on and reiterate using only\\r\\n # these\\r\\n next_iteration_index = numpy.array(next_iteration_polygons)\\r\\n\\r\\n remaining_polygons = remaining_polygons[\\r\\n next_iteration_index]\\r\\n #myRemainingAttributes = myRemainingAttributes[\\r\\n # next_iteration_index]\\r\\n remaining_indexes = remaining_indexes[next_iteration_index]\\r\\n LOGGER.debug('Remaining: %s' % len(remaining_polygons))\\r\\n else:\\r\\n print 'no more polygons to be checked'\\r\\n break\\r\\n #del tmpWriter\\r\\n\\r\\n # here the full polygon set is represented by:\\r\\n # inside_polygons + intersecting_polygons + next_iteration_polygons\\r\\n # the a polygon intersecting multiple post processing polygons appears\\r\\n # multiple times in the array\\r\\n # noinspection PyUnboundLocalVariable\\r\\n LOGGER.debug('Results:\\\\nInside: %s\\\\nIntersect: %s\\\\nOutside: %s' % (\\r\\n inside_polygons, intersecting_polygons, outside_polygons))\\r\\n\\r\\n # add in-and outside polygons\\r\\n for i in outside_polygons:\\r\\n feature_id = i + 1\\r\\n polygons_request.setFilterFid(feature_id)\\r\\n qgis_feature = polygons_provider.getFeatures(\\r\\n polygons_request).next()\\r\\n shape_writer.addFeature(qgis_feature)\\r\\n self.preprocessed_feature_count += 1\\r\\n\\r\\n del shape_writer\\r\\n# LOGGER.debug('Created: %s' % self.preprocessed_feature_count)\\r\\n\\r\\n name = '%s %s' % (layer.name(), self.tr('preprocessed'))\\r\\n output_layer = QgsVectorLayer(out_filename, name, 'ogr')\\r\\n if not output_layer.isValid():\\r\\n #TODO (MB) use a better exception\\r\\n raise Exception('Invalid qgis Layer')\\r\\n\\r\\n if self.show_intermediate_layers:\\r\\n self.update_keywords(output_layer, {'title': name})\\r\\n #noinspection PyArgumentList\\r\\n QgsMapLayerRegistry.instance().addMapLayer(output_layer)\\r\\n\\r\\n return output_layer\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a232ad18f36f8846b11c8aa92b2f0ab\",\n \"score\": \"0.54436153\",\n \"text\": \"def bbox_iou(box1, box2):\\n inter_max_xy = torch.min(box1[:, 2:4], box2[:, 2:4])\\n inter_min_xy = torch.max(box1[:, 0:2], box2[:, 0:2])\\n\\n inter_size = torch.clamp((inter_max_xy-inter_min_xy), min=0)\\n inter_area = inter_size[:, 0]*inter_size[:, 1] # 1 by num_boxes\\n\\n # # Original codes\\n # # Get the coordinates of bounding boxes\\n # b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]\\n # b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\\n\\n # # Get the coordinates of the intersection rectangle\\n # # Can take multi-row tensors\\n # inter_rect_x1 = torch.max(b1_x1, b2_x1)\\n # inter_rect_y1 = torch.max(b1_y1, b2_y1)\\n # inter_rect_x2 = torch.min(b1_x2, b2_x2)\\n # inter_rect_y2 = torch.min(b1_y2, b2_y2)\\n\\n # # Intersection area\\n # inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * \\\\\\n # torch.clamp(inter_rect_y2 - inter_rect_y1 + 1, min=0)\\n\\n # # Union Area\\n # b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)\\n # b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)\\n\\n b1_area = (box1[:, 2]-box1[:, 0])*(box1[:, 3]-box1[:, 1])\\n b2_area = (box2[:, 2]-box2[:, 0])*(box2[:, 3]-box2[:, 1])\\n\\n iou = inter_area / (b1_area + b2_area - inter_area)\\n\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c207bdaf64789d61c0f041cd8d3b0eaa\",\n \"score\": \"0.5435567\",\n \"text\": \"def bbox_overlaps_iof(bboxes1, bboxes2, eps=1e-6):\\n rows = bboxes1.shape[0]\\n cols = bboxes2.shape[0]\\n\\n if rows * cols == 0:\\n return np.zeros((rows, cols), dtype=np.float32)\\n\\n hbboxes1 = poly2hbb(bboxes1)\\n hbboxes2 = bboxes2\\n hbboxes1 = hbboxes1[:, None, :]\\n lt = np.maximum(hbboxes1[..., :2], hbboxes2[..., :2])\\n rb = np.minimum(hbboxes1[..., 2:], hbboxes2[..., 2:])\\n wh = np.clip(rb - lt, 0, np.inf)\\n h_overlaps = wh[..., 0] * wh[..., 1]\\n\\n l, t, r, b = [bboxes2[..., i] for i in range(4)]\\n polys2 = np.stack([l, t, r, t, r, b, l, b], axis=-1)\\n if shgeo is None:\\n raise ImportError('Please run \\\"pip install shapely\\\" '\\n 'to install shapely first.')\\n sg_polys1 = [shgeo.Polygon(p) for p in bboxes1.reshape(rows, -1, 2)]\\n sg_polys2 = [shgeo.Polygon(p) for p in polys2.reshape(cols, -1, 2)]\\n overlaps = np.zeros(h_overlaps.shape)\\n for p in zip(*np.nonzero(h_overlaps)):\\n overlaps[p] = sg_polys1[p[0]].intersection(sg_polys2[p[-1]]).area\\n unions = np.array([p.area for p in sg_polys1], dtype=np.float32)\\n unions = unions[..., None]\\n\\n unions = np.clip(unions, eps, np.inf)\\n outputs = overlaps / unions\\n if outputs.ndim == 1:\\n outputs = outputs[..., None]\\n return outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4d35d0c2e13e68f4e1f6f836498b25d\",\n \"score\": \"0.54283994\",\n \"text\": \"def polygon():\\n return RepMulti(point(), 3, 8) ^ Polygon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"708bdb8535f25fb9783575dfbd4fc776\",\n \"score\": \"0.5420734\",\n \"text\": \"def test_three_polygon_intersection():\\n res = geom_util.rhs_split(\\n SQUARE,\\n LineString([(0.5, -0.1), (0.5, 1.1), (0.7, 1.1), (0.7, -0.1)]),\\n )\\n assert res is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e307c8cfc750688ccaaa77e0eb977ce4\",\n \"score\": \"0.5419182\",\n \"text\": \"def convex_overlaps(gt_bboxes, points):\\n overlaps = convex_iou(points, gt_bboxes)\\n overlaps = overlaps.transpose(1, 0)\\n return overlaps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de3f77a9882a7388a822652efba31fcc\",\n \"score\": \"0.5407142\",\n \"text\": \"def subselect_polygon(self, poly_obj):\\r\\n\\t\\tmaster_catalog = self.get_master_catalog()\\r\\n\\t\\tmaster_catalog_inside = master_catalog.subselect_polygon(poly_obj)\\r\\n\\t\\tmerged_events = []\\r\\n\\t\\tfor ev in self.merged_events:\\r\\n\\t\\t\\tmaster_solution = ev.get_master_solution()\\r\\n\\t\\t\\tif master_solution in master_catalog_inside:\\r\\n\\t\\t\\t\\tmerged_events.append(ev)\\r\\n\\r\\n\\t\\treturn self.__class__(merged_events,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tdefault_completeness=self.default_completeness,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tdefault_Mrelations=self.default_Mrelations,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tagency_areas=self.agency_areas)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"488106706c5bb0538dfbafcc7f059135\",\n \"score\": \"0.54064023\",\n \"text\": \"def test_get_boundary_features(self):\\n geojson_path = os.path.join(os.path.join(os.path.dirname(__file__), 'boundary_polygons'),\\n 'us_boundaries.geojson')\\n buffer_dist = 0.1 # default\\n with open(geojson_path) as geojson_file:\\n boundary_features = get_boundary_features(geojson_file.read(), buffer_dist)\\n self.assertEqual(len(boundary_features), 1)\\n self.assertEqual(boundary_features[0].geom_type, 'MultiPolygon')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d335c6fbfaa127be87e58dd56e40b100\",\n \"score\": \"0.54017967\",\n \"text\": \"def find_local_boundary(tri, triangles):\\n edges = []\\n\\n for triangle in triangles:\\n\\n for i in range(3):\\n\\n pt1 = tri.simplices[triangle][i]\\n pt2 = tri.simplices[triangle][(i + 1) % 3]\\n\\n if (pt1, pt2) in edges:\\n edges.remove((pt1, pt2))\\n\\n elif (pt2, pt1) in edges:\\n edges.remove((pt2, pt1))\\n\\n else:\\n edges.append((pt1, pt2))\\n\\n return edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de939a8c10fe41d39144aa0945d11bb4\",\n \"score\": \"0.5388202\",\n \"text\": \"def find_deforming_mesh_geometry(polygons, names):\\n\\n if isinstance(names, str):\\n names = [names]\\n\\n selected_polygons = []\\n for name in names:\\n for polygon in polygons:\\n if polygon.get_feature().get_name() == name:\\n selected_polygons.append(polygon.get_resolved_geometry())\\n\\n return selected_polygons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"188ab9f7dc049749352dab078f6ce175\",\n \"score\": \"0.53823173\",\n \"text\": \"def test_separate_points_by_polygon_edge(self):\\r\\n\\r\\n # Unit square\\r\\n U = [[0, 0], [1, 0], [1, 1], [0, 1]]\\r\\n\\r\\n # Try with boundary (vertical edge) point\\r\\n inside, outside = separate_points_by_polygon([[0, 0.5],\\r\\n [0.3, 0.3],\\r\\n [0.1, 0.6]],\\r\\n U,\\r\\n closed=True)\\r\\n assert len(inside) == 3\\r\\n assert numpy.allclose(inside, [0, 1, 2])\\r\\n\\r\\n inside, outside = separate_points_by_polygon([[0, 0.5],\\r\\n [0.3, 0.3],\\r\\n [0.1, 0.6]],\\r\\n U,\\r\\n closed=False)\\r\\n assert len(inside) == 2\\r\\n assert numpy.allclose(inside, [1, 2])\\r\\n\\r\\n # Try with boundary (horizontal edge) point\\r\\n inside, outside = separate_points_by_polygon([[0.5, 0.0],\\r\\n [0.3, 0.3],\\r\\n [0.1, 0.6]], U)\\r\\n assert len(outside) == 0\\r\\n assert len(inside) == 3\\r\\n assert numpy.allclose(inside, [0, 1, 2])\\r\\n\\r\\n # Try with boundary (corner) point\\r\\n inside, outside = separate_points_by_polygon([[0.0, 0.0],\\r\\n [0.3, 0.3],\\r\\n [0.1, 0.6]], U)\\r\\n assert len(outside) == 0\\r\\n assert len(inside) == 3\\r\\n assert numpy.allclose(inside, [0, 1, 2])\\r\\n\\r\\n # One outside\\r\\n inside, outside = separate_points_by_polygon([[0, 0.5],\\r\\n [1.3, 0.3],\\r\\n [0.1, 0.6]], U)\\r\\n assert len(outside) == 1\\r\\n assert len(inside) == 2\\r\\n assert numpy.allclose(inside, [0, 2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"396e5b498841e363d3dc8eb4fee8910f\",\n \"score\": \"0.5379921\",\n \"text\": \"def polygons_to_bitmask(polygons, height, width):\\n if any(len(p) <= 4 for p in polygons):\\n polygons = [p for p in polygons if len(p) > 4]\\n if len(polygons) == 0:\\n return np.zeros((height, width)).astype(int)\\n\\n rles = mask_util.frPyObjects(polygons, height, width)\\n rle = mask_util.merge(rles)\\n return mask_util.decode(rle).astype(int)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96731468cda630f42c22e471a0c47cd6\",\n \"score\": \"0.5348795\",\n \"text\": \"def test_multipart_polygon_can_be_read(self):\\r\\n\\r\\n filename = ('%s/boundaries/rw_jakarta.shp' % DATADIR)\\r\\n L0 = read_layer(filename)\\r\\n assert len(L0) == 2685\\r\\n assert L0.is_polygon_data\\r\\n\\r\\n geometry = L0.get_geometry(as_geometry_objects=True)\\r\\n attributes = L0.get_data()\\r\\n projection = L0.get_projection()\\r\\n keywords = L0.get_keywords()\\r\\n\\r\\n # Store temporarily e.g. for inspection with QGIS\\r\\n tmp_filename = unique_filename(suffix='.shp')\\r\\n Vector(geometry=geometry, data=attributes,\\r\\n projection=projection,\\r\\n keywords=keywords).write_to_file(tmp_filename)\\r\\n\\r\\n # Read again\\r\\n L1 = read_layer(tmp_filename)\\r\\n\\r\\n assert len(L1) == len(L0)\\r\\n assert L1.is_polygon_data\\r\\n\\r\\n for i in range(len(L0)):\\r\\n # Check geometry\\r\\n g0 = L0.get_geometry(as_geometry_objects=True)[i]\\r\\n g1 = L1.get_geometry(as_geometry_objects=True)[i]\\r\\n assert numpy.allclose(g0.outer_ring, g1.outer_ring)\\r\\n assert len(g0.inner_rings) == len(g1.inner_rings)\\r\\n\\r\\n for j in range(len(g0.inner_rings)):\\r\\n assert numpy.allclose(g0.inner_rings[j], g1.inner_rings[j])\\r\\n\\r\\n # Check attributes\\r\\n v0 = L0.get_data()[i]\\r\\n v1 = L1.get_data()[i]\\r\\n assert v0 == v1\\r\\n\\r\\n # Check projection\\r\\n assert L0.projection == L1.projection\\r\\n\\r\\n # Compare all\\r\\n assert L0 == L1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04406a775c987c0b049b15d5b9885d89\",\n \"score\": \"0.53465843\",\n \"text\": \"def npbbox_iou(box1, box2):\\r\\n n, items1 = box1.shape\\r\\n m, items2 = box2.shape\\r\\n # box1_clone = boxes1.reshape(n, 1, items1)\\r\\n\\r\\n # Get the coordinates of bounding boxes\\r\\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0:1], box1[:, 1:2], box1[:, 2:3], box1[:, 3:4]\\r\\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\\r\\n # np.tile(b1_x1, (1, m)) np.tile(b2_x1, (n,1))\\r\\n # model = np.ones((1,n, m,items2), dtype=box1.dtype)\\r\\n model = np.ones((n, m), dtype=box1.dtype)\\r\\n bd_model = np.zeros((n, m), dtype=box1.dtype)\\r\\n # get the corrdinates of the intersection rectangle\\r\\n inter_rect_x1 = np.max(np.stack([b1_x1 * model, b2_x1 * model]), 0) # np.max(b1_x1, b2_x1)\\r\\n inter_rect_y1 = np.max(np.stack([b1_y1 * model, b2_y1 * model]), 0) # np.max(b1_y1, b2_y1)\\r\\n inter_rect_x2 = np.min(np.stack([b1_x2 * model, b2_x2 * model]), 0) # np.min(b1_x2, b2_x2)\\r\\n inter_rect_y2 = np.min(np.stack([b1_y2 * model, b2_y2 * model]), 0) # np.min(b1_y2, b2_y2)\\r\\n\\r\\n inter_rect_x = inter_rect_x2 - inter_rect_x1 + 1\\r\\n inter_rect_y = inter_rect_y2 - inter_rect_y1 + 1\\r\\n # inter_rect_x = inter_rect_x.reshape(model.shape)\\r\\n # inter_rect_y = inter_rect_y.reshape(model.shape)\\r\\n # Intersection area\\r\\n\\r\\n inter_area = np.max(np.stack([inter_rect_x, bd_model]), 0) * \\\\\\r\\n np.max(np.stack([inter_rect_y, bd_model]), 0)\\r\\n # inter_area = inter_area.reshape(model.shape)\\r\\n # Union Area\\r\\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\\r\\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\\r\\n\\r\\n iou = inter_area / (b1_area + b2_area - inter_area)\\r\\n\\r\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0225d5fc6cd7af07badad050cdbe3d40\",\n \"score\": \"0.5339997\",\n \"text\": \"def union_overlapping(intervals):\\n disjoint_intervals = []\\n\\n for interval in intervals:\\n if disjoint_intervals and disjoint_intervals[-1].overlaps(interval):\\n disjoint_intervals[-1] = disjoint_intervals[-1].union(interval)\\n else:\\n disjoint_intervals.append(interval)\\n\\n return disjoint_intervals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9f9195c6b93b1ef72ce397d4a87bf12\",\n \"score\": \"0.533952\",\n \"text\": \"def bbox_iou(box1, box2):\\n intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])\\n intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])\\n intersect = intersect_w * intersect_h\\n w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin\\n w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin\\n union = w1*h1 + w2*h2 - intersect\\n try:\\n res = float(intersect) / union\\n except ZeroDivisionError:\\n res = 0\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5c8009b8d39c96872f8226e5dbab873\",\n \"score\": \"0.5335932\",\n \"text\": \"def test_polygon_clipping(self):\\r\\n\\r\\n # Test data\\r\\n polygon_filename = ('%s/test_poly.txt' % TESTDATA) # Polygon 799\\r\\n points_filename = ('%s/test_points.txt' % TESTDATA)\\r\\n\\r\\n # Read\\r\\n polygon = []\\r\\n fid = open(polygon_filename)\\r\\n for line in fid.readlines():\\r\\n fields = line.strip().split(',')\\r\\n polygon.append([float(fields[0]), float(fields[1])])\\r\\n polygon = ensure_numeric(polygon)\\r\\n\\r\\n points = []\\r\\n fid = open(points_filename)\\r\\n for line in fid.readlines():\\r\\n fields = line.strip().split(',')\\r\\n points.append([float(fields[0]), float(fields[1])])\\r\\n points = ensure_numeric(points)\\r\\n\\r\\n # Clip\\r\\n inside, outside = separate_points_by_polygon(points, polygon)\\r\\n\\r\\n # Expected number of points inside\\r\\n assert len(inside) == 458\\r\\n\\r\\n # First 10 inside\\r\\n assert numpy.alltrue(inside[:10] == [2279, 2290, 2297, 2306, 2307,\\r\\n 2313, 2316, 2319, 2321, 2322])\\r\\n\\r\\n # Last 10 outside\\r\\n assert numpy.alltrue(outside[-10:] == [3519, 3520, 3521, 3522, 3523,\\r\\n 3524, 3525, 3526, 3527, 3528])\\r\\n # Store for viewing in e.g. QGis\\r\\n if False: # True:\\r\\n Vector(geometry=[polygon]).write_to_file('test_poly.shp')\\r\\n pts_inside = points[inside]\\r\\n Vector(geometry=pts_inside).write_to_file('test_points_in.shp')\\r\\n pts_outside = points[outside]\\r\\n Vector(geometry=pts_outside).write_to_file('test_points_out.shp')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7c982baa285de27c4df11201a3b6caa\",\n \"score\": \"0.53303385\",\n \"text\": \"def edgepolygon():\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b02b114943eeceb9bef06f4b998c168\",\n \"score\": \"0.53204334\",\n \"text\": \"def bbox_diou(self, boxes_1, boxes_2):\\n # calculate center distance\\n center_distance = tf.reduce_sum(tf.square(boxes_1[..., :2] - boxes_2[..., :2]), axis=-1)\\n\\n # transform [x, y, w, h] to [x_min, y_min, x_max, y_max]\\n boxes_1 = tf.concat([boxes_1[..., :2] - boxes_1[..., 2:] * 0.5,\\n boxes_1[..., :2] + boxes_1[..., 2:] * 0.5], axis=-1)\\n boxes_2 = tf.concat([boxes_2[..., :2] - boxes_2[..., 2:] * 0.5,\\n boxes_2[..., :2] + boxes_2[..., 2:] * 0.5], axis=-1)\\n boxes_1 = tf.concat([tf.minimum(boxes_1[..., :2], boxes_1[..., 2:]),\\n tf.maximum(boxes_1[..., :2], boxes_1[..., 2:])], axis=-1)\\n boxes_2 = tf.concat([tf.minimum(boxes_2[..., :2], boxes_2[..., 2:]),\\n tf.maximum(boxes_2[..., :2], boxes_2[..., 2:])], axis=-1)\\n\\n # calculate area of boxes_1 boxes_2\\n boxes_1_area = (boxes_1[..., 2] - boxes_1[..., 0]) * (boxes_1[..., 3] - boxes_1[..., 1])\\n boxes_2_area = (boxes_2[..., 2] - boxes_2[..., 0]) * (boxes_2[..., 3] - boxes_2[..., 1])\\n\\n # calculate the two corners of the intersection\\n left_up = tf.maximum(boxes_1[..., :2], boxes_2[..., :2])\\n right_down = tf.minimum(boxes_1[..., 2:], boxes_2[..., 2:])\\n\\n # calculate area of intersection\\n inter_section = tf.maximum(right_down - left_up, 0.0)\\n inter_area = inter_section[..., 0] * inter_section[..., 1]\\n\\n # calculate union area\\n union_area = boxes_1_area + boxes_2_area - inter_area\\n\\n # calculate IoU, add epsilon in denominator to avoid dividing by 0\\n iou = inter_area / (union_area + tf.keras.backend.epsilon())\\n\\n # calculate the upper left and lower right corners of the minimum closed convex surface\\n enclose_left_up = tf.minimum(boxes_1[..., :2], boxes_2[..., :2])\\n enclose_right_down = tf.maximum(boxes_1[..., 2:], boxes_2[..., 2:])\\n\\n # calculate width and height of the minimun closed convex surface\\n enclose_wh = tf.maximum(enclose_right_down - enclose_left_up, 0.0)\\n\\n # calculate enclosed diagonal distance\\n enclose_diagonal = tf.reduce_sum(tf.square(enclose_wh), axis=-1)\\n\\n # calculate diou add epsilon in denominator to avoid dividing by 0\\n diou = iou - 1.0 * center_distance / (enclose_diagonal + tf.keras.backend.epsilon())\\n\\n return diou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6288686b0476d853e19fa92f4806d593\",\n \"score\": \"0.5320281\",\n \"text\": \"def _intersect_cut(shape, cutting_shape):\\n inside = shape.intersection(cutting_shape)\\n outside = shape.difference(cutting_shape)\\n return inside, outside\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57021f66d3667ca239f77ace0c8ee7b9\",\n \"score\": \"0.53162813\",\n \"text\": \"def bbox_to_poly(north, south, east, west):\\n return Polygon([(west, south), (east, south), (east, north), (west, north)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"444510e471ba4fcc8c24ec160466930e\",\n \"score\": \"0.5313836\",\n \"text\": \"def regenerate_unpaved_boundaries(self):\\n # print \\\"Top of regenerate_unpaved_boundaries()\\\"\\n\\n candidate_edges = np.any( self.edges[:,3:5] == trigrid.UNMESHED, axis=1 )\\n candidate_edges = candidate_edges & (self.edges[:,2] != trigrid.DELETED_EDGE)\\n rings_and_holes = self.edges_to_rings_and_holes(candidate_edges)\\n\\n rings = []\\n \\n for exterior,interiors in rings_and_holes:\\n rings.append( exterior )\\n rings += interiors\\n\\n # print \\\"%d rings in total\\\"%len(rings)\\n\\n # dllist of the portion that has not been paved\\n self.unpaved = [None] * len(rings)\\n\\n for ri in range(len(rings)):\\n cl = CList()\\n for i in rings[ri]:\\n cl.append( i, metric=0 )\\n \\n self.unpaved[ri] = cl\\n # print \\\"Done with regenerating unpaved boundaries\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3632a3595ccc8176d3abba1d00e9f3d5\",\n \"score\": \"0.5310764\",\n \"text\": \"def test_all_outside_polygon(self):\\r\\n\\r\\n # unit square\\r\\n U = [[0, 0], [1, 0], [1, 1], [0, 1]]\\r\\n\\r\\n points = [[2, 2], [1, 3], [-1, 1], [0, 2]] # All outside\\r\\n\\r\\n inside, outside = separate_points_by_polygon(points, U)\\r\\n assert len(inside) == 0 # None inside\\r\\n assert numpy.allclose(outside, [0, 1, 2, 3])\\r\\n\\r\\n indices = outside_polygon(points, U, closed=True)\\r\\n assert numpy.allclose(indices, [0, 1, 2, 3])\\r\\n\\r\\n indices = inside_polygon(points, U, closed=True)\\r\\n assert numpy.allclose(indices, [])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"591453f0bfc7fe6e93aac1993861b489\",\n \"score\": \"0.53024197\",\n \"text\": \"def merge(b4):\\n j, tmp, n = 0, [], len(b4)\\n while j < n:\\n a = b4[j]\\n if j < n - 1:\\n b = b4[j + 1]\\n if c := a.also.merged(b.also):\\n a = Bin(a.down, b.up)\\n a.also = c\\n j += 1\\n tmp += [a]\\n j += 1\\n return merge(tmp) if len(tmp) < len(b4) else b4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70166165b60b630150e47975f69696cb\",\n \"score\": \"0.5299959\",\n \"text\": \"def test_inside_polygon_main2(self):\\r\\n\\r\\n # Simplest case: Polygon is the unit square\\r\\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1]]\\r\\n\\r\\n # From real example (that failed)\\r\\n polygon = [[20, 20], [40, 20], [40, 40], [20, 40]]\\r\\n points = [[40, 50]]\\r\\n res = inside_polygon(points, polygon)\\r\\n assert len(res) == 0\\r\\n\\r\\n polygon = [[20, 20], [40, 20], [40, 40], [20, 40]]\\r\\n points = [[25, 25], [30, 20], [40, 50], [90, 20], [40, 90]]\\r\\n res = inside_polygon(points, polygon)\\r\\n assert len(res) == 2\\r\\n assert numpy.allclose(res, [0, 1])\\r\\n\\r\\n # More convoluted and non convex polygon\\r\\n polygon = [[0, 0], [1, 0], [0.5, -1], [2, -1], [2, 1], [0, 1]]\\r\\n assert is_inside_polygon((0.5, 0.5), polygon)\\r\\n assert is_inside_polygon((1, -0.5), polygon)\\r\\n assert is_inside_polygon((1.5, 0), polygon)\\r\\n\\r\\n assert not is_inside_polygon((0.5, 1.5), polygon)\\r\\n assert not is_inside_polygon((0.5, -0.5), polygon)\\r\\n\\r\\n # Very convoluted polygon\\r\\n polygon = [[0, 0], [10, 10], [15, 5], [20, 10], [25, 0],\\r\\n [30, 10], [40, -10]]\\r\\n assert is_inside_polygon((5, 5), polygon)\\r\\n assert is_inside_polygon((17, 7), polygon)\\r\\n assert is_inside_polygon((27, 2), polygon)\\r\\n assert is_inside_polygon((35, -5), polygon)\\r\\n assert not is_inside_polygon((15, 7), polygon)\\r\\n assert not is_inside_polygon((24, 3), polygon)\\r\\n assert not is_inside_polygon((25, -10), polygon)\\r\\n\\r\\n # Another combination (that failed)\\r\\n polygon = [[0, 0], [10, 0], [10, 10], [0, 10]]\\r\\n assert is_inside_polygon((5, 5), polygon)\\r\\n assert is_inside_polygon((7, 7), polygon)\\r\\n assert not is_inside_polygon((-17, 7), polygon)\\r\\n assert not is_inside_polygon((7, 17), polygon)\\r\\n assert not is_inside_polygon((17, 7), polygon)\\r\\n assert not is_inside_polygon((27, 8), polygon)\\r\\n assert not is_inside_polygon((35, -5), polygon)\\r\\n\\r\\n # Multiple polygons\\r\\n polygon = [[0, 0], [1, 0], [1, 1], [0, 1], [0, 0], [10, 10],\\r\\n [11, 10], [11, 11], [10, 11], [10, 10]]\\r\\n assert is_inside_polygon((0.5, 0.5), polygon)\\r\\n assert is_inside_polygon((10.5, 10.5), polygon)\\r\\n assert not is_inside_polygon((0, 5.5), polygon)\\r\\n assert is_inside_polygon((5.5, 5.5), polygon)\\r\\n\\r\\n # Polygon with a hole\\r\\n polygon = [[-1, -1], [2, -1], [2, 2], [-1, 2], [-1, -1],\\r\\n [0, 0], [1, 0], [1, 1], [0, 1], [0, 0]]\\r\\n\\r\\n assert is_inside_polygon((0, -0.5), polygon)\\r\\n assert not is_inside_polygon((0.5, 0.5), polygon)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0664902dd98ef3cf6053fc65a722aa\",\n \"score\": \"0.5295191\",\n \"text\": \"def test_afw_union_intersecting(self):\\n AFW.rename_afw_states(self.afw_union_3_test_01, 'a_')\\n union = AFW.afw_union(self.afw_union_1_test_01,\\n self.afw_union_3_test_01)\\n\\n i = 0\\n last = 7\\n while i <= last:\\n base = list(itertools.repeat('a', i))\\n base += list(itertools.repeat('b', i))\\n # build all permutation of 'a' and 'b' till length i\\n word_set = set(itertools.permutations(base, i))\\n for word in word_set:\\n word = list(word)\\n # print(word)\\n original_acceptance_1 = AFW.afw_word_acceptance(\\n self.afw_union_1_test_01, word)\\n original_acceptance_2 = AFW.afw_word_acceptance(\\n self.afw_union_3_test_01, word)\\n union_acceptance = AFW.afw_word_acceptance(union, word)\\n self.assertEqual(\\n original_acceptance_1 or original_acceptance_2,\\n union_acceptance)\\n i += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d67fc05535314467b35e5a35cad1c8\",\n \"score\": \"0.5293673\",\n \"text\": \"def bbox_iou(box1, box2):\\n # Get the coordinates of bounding boxes\\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]\\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\\n\\n # get the corrdinates of the intersection rectangle\\n inter_rect_x1 = torch.max(b1_x1, b2_x1)\\n inter_rect_y1 = torch.max(b1_y1, b2_y1)\\n inter_rect_x2 = torch.min(b1_x2, b2_x2)\\n inter_rect_y2 = torch.min(b1_y2, b2_y2)\\n\\n # Intersection area\\n inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(inter_rect_y2 - inter_rect_y1 + 1,\\n min=0)\\n\\n # Union Area\\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\\n\\n iou = inter_area / (b1_area + b2_area - inter_area)\\n\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"828324479d5bd1b2aff59fb6d0c581fd\",\n \"score\": \"0.52932256\",\n \"text\": \"def intersect_polyAs(paths, outdir, region):\\n\\n # Send in A and [B,C]\\n paths = intersect_wrap(paths, outdir, extendby=10)\\n\\n # Do a wc of all the paths in paths\\n wc = {}\\n for pathname, path in paths.items():\\n wc[pathname] = sum((1 for line in open(path, 'rb')))\\n\\n # make a printout of wc for comparing with the final plots\\n for name, count in wc.items():\\n print name + ':\\\\t' + str(count)\\n print('\\\\n')\\n\\n A = region\\n B = 'gencode'\\n C = 'polyAdb'\\n\\n AIBIC = '_I_'.join(sorted([A,B,C]))\\n\\n venncount = {}\\n venncount[AIBIC] = wc[AIBIC] # we don't need to compute this one\\n\\n # A\\\\(BUC) = A - AIB - AIC - AIBIC (Just A)\\n for let in [A, B, C]:\\n compl = [A, B, C]\\n compl.remove(let)\\n compl.sort()\\n\\n # initialize with all poly(A) sites\\n venncount[let] = wc[let]\\n # remove the two \\\"BIC\\\" or \\\"AIC\\\" types\\n for k in compl:\\n venncount[let] -= wc['_I_'.join(sorted([let, k]))]\\n # add the union of the three types, since it was subtracted twice\\n venncount[let] += wc[AIBIC]\\n\\n # (AUB)\\\\C = AIB - AIBIC (Just A and B)\\n for let in [A, B, C]:\\n compl = [A, B, C]\\n compl.remove(let)\\n (c1, c2) = sorted(compl)\\n\\n key = '{0}_I_{1}'.format(c1, c2) # one of (BUC)\\\\A\\n venncount[key] = wc['_I_'.join(sorted([c1, c2]))] - wc[AIBIC]\\n\\n return paths, venncount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c0c47a76d3bdce34cb0d1152f0cad1a\",\n \"score\": \"0.5289822\",\n \"text\": \"def _stitch_boundaries(edges):\\n edge_set = edges.copy()\\n boundary_lst = []\\n while edge_set:\\n boundary = []\\n edge0 = edge_set.pop()\\n boundary.append(edge0)\\n last_edge = edge0\\n while edge_set:\\n _, j = last_edge\\n j_first, j_second = _find_edges_with(j, edge_set)\\n if j_first:\\n edge_set.remove((j, j_first[0]))\\n edge_with_j = (j, j_first[0])\\n boundary.append(edge_with_j)\\n last_edge = edge_with_j\\n elif j_second:\\n edge_set.remove((j_second[0], j))\\n edge_with_j = (j, j_second[0]) # flip edge rep\\n boundary.append(edge_with_j)\\n last_edge = edge_with_j\\n if edge0[0] == last_edge[1]:\\n break\\n boundary_lst.append(boundary)\\n\\n return boundary_lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4cc37134c0effc054b8375fdc893c98\",\n \"score\": \"0.52830285\",\n \"text\": \"def erase_overlapped_regions(rects, im_w, im_h):\\n\\n index_list=[]\\n ext_rects = rects[:]\\n overlapped = []\\n# a =\\n for i in range(0,len(ext_rects)):\\n overlapped[:] = []\\n b = len(ext_rects)\\n for jj in range (0, b):\\n if ext_rects[i]!=\\\"False\\\" and ext_rects[jj]!=\\\"False\\\":\\n\\n x1, y1, w1, h1 = ext_rects[i]\\n x2, y2, w2, h2 = ext_rects[jj]\\n\\n if x1>x2 and abs(x1-x2-w2)<0.040*im_w and -0.01*im_w<y1-y2<0.01*im_h:\\n overlapped.append(jj)\\n\\n else:\\n overlapped.append(-1)\\n else:\\n overlapped.append(-1)\\n pass\\n\\n index_list[:]=[]\\n\\n overlapped_list = [item != -1 for item in overlapped]\\n elem_overlapped = list(itertools.compress(ext_rects, overlapped_list))\\n for el in overlapped:\\n if el != -1:\\n index_list.append(el)\\n else:\\n pass\\n for it in range(len(elem_overlapped)):\\n if it != \\\"False\\\" and ext_rects[i] != \\\"False\\\":\\n ext_rects = remove_overlapped(elem_overlapped[it], ext_rects, i, index_list[it])\\n rectangles_no_overlapped = [item != \\\"False\\\" for item in ext_rects]\\n ext_rects = list(itertools.compress(ext_rects, rectangles_no_overlapped))\\n if False in rectangles_no_overlapped:\\n ext_rects = erase_overlapped_regions(ext_rects, im_w, im_h)\\n else:\\n pass\\n\\n\\n\\n return ext_rects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eabcb22d06713ff994adf63d7d154439\",\n \"score\": \"0.52803683\",\n \"text\": \"def compute_iou(box, boxes):\\n\\n x_min_A, y_min_A, x_max_A, y_max_A = box[:4]\\n x_min_B, y_min_B = boxes[:, 0], boxes[:, 1]\\n x_max_B, y_max_B = boxes[:, 2], boxes[:, 3]\\n # calculating the intersection\\n inner_x_min = np.maximum(x_min_B, x_min_A)\\n inner_y_min = np.maximum(y_min_B, y_min_A)\\n inner_x_max = np.minimum(x_max_B, x_max_A)\\n inner_y_max = np.minimum(y_max_B, y_max_A)\\n inner_w = np.maximum((inner_x_max - inner_x_min), 0)\\n inner_h = np.maximum((inner_y_max - inner_y_min), 0)\\n intersection_area = inner_w * inner_h\\n # calculating the union\\n box_area_B = (x_max_B - x_min_B) * (y_max_B - y_min_B)\\n box_area_A = (x_max_A - x_min_A) * (y_max_A - y_min_A)\\n union_area = box_area_A + box_area_B - intersection_area\\n intersection_over_union = intersection_area / union_area\\n return intersection_over_union\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c2672b8ef17dc193a2d050cb9145cb8\",\n \"score\": \"0.5278561\",\n \"text\": \"def rectangles_right_intersection(Rectangles, x):\\n\\n U = Interval_Union();\\n for rec in Rectangles:\\n if(rec.intersects_right(x)):\\n U.append(rec.intersection_right(x));\\n\\n return U;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c671e33f557e817d18ccf1bda7705338\",\n \"score\": \"0.5275798\",\n \"text\": \"def bounding_box(self, *vertices):\\n\\n vec1 = [(vertex[0]) for vertex in vertices]\\n vec2 = [(vertex[1]) for vertex in vertices]\\n vec1.sort()\\n vec2.sort()\\n return [vec1[0], vec2[0]], [vec1[-1], vec2[-1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9746f75a26503e84a5b8c4e5fe978f27\",\n \"score\": \"0.5274264\",\n \"text\": \"def simplex_boundary(simplex):\\n # Special handling of vertices\\n if len(simplex) == 1:\\n return np.array([])\\n boundary_simplices = np.empty((len(simplex), len(simplex) - 1), dtype=int)\\n for i in range(len(simplex)):\\n boundary_simplex = np.delete(simplex, i)\\n if i % 2 == 1 and len(boundary_simplex) > 1:\\n boundary_simplex[-1], boundary_simplex[-2] = boundary_simplex[-2], boundary_simplex[-1]\\n boundary_simplices[i] = boundary_simplex\\n return boundary_simplices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fdb81dfc6686113e1a310d765e64c94\",\n \"score\": \"0.52713543\",\n \"text\": \"def in_and_outside_polygon(\\r\\n points, polygon,\\r\\n closed=True,\\r\\n holes=None,\\r\\n check_input=True):\\r\\n\\r\\n # Get separation by outer_ring\\r\\n inside, outside = separate_points_by_polygon(points, polygon,\\r\\n closed=closed,\\r\\n check_input=check_input)\\r\\n\\r\\n # Take care of holes\\r\\n if holes is not None:\\r\\n msg = ('Argument holes must be a list of polygons, '\\r\\n 'I got %s' % holes)\\r\\n if not isinstance(holes, list):\\r\\n raise InaSAFEError(msg)\\r\\n\\r\\n for hole in holes:\\r\\n in_hole, out_hole = separate_points_by_polygon(points[inside],\\r\\n hole,\\r\\n closed=not closed,\\r\\n check_input=True)\\r\\n\\r\\n in_hole = inside[in_hole] # Inside hole\\r\\n inside = inside[out_hole] # Inside outer_ring but outside hole\\r\\n\\r\\n # Add holde indices to outside\\r\\n outside = numpy.concatenate((outside, in_hole))\\r\\n\\r\\n return inside, outside\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92cc8d261ca4edddf77b1739094a968c\",\n \"score\": \"0.52705306\",\n \"text\": \"def clip_polygon(subject, clipper, operation = 'difference'):\\n Subject = Polygon()\\n Clipper = Polygon()\\n\\n for s in subject:\\n Subject.add(Vertex(s))\\n\\n for c in clipper:\\n Clipper.add(Vertex(c))\\n\\n clipped = Clipper.difference(Subject)\\\\\\n if operation == 'reversed-diff'\\\\\\n else Subject.__getattribute__(operation)(Clipper)\\n\\n clipped = [(ext.points,[hole.points for hole in holes]) for ext,holes in clipped]\\n return clipped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1d04b7cd242b5d2ffabeaa925b3d251\",\n \"score\": \"0.52692235\",\n \"text\": \"def poly2obb(polys, version='oc'):\\n if version == 'oc':\\n results = poly2obb_oc(polys)\\n elif version == 'le135':\\n results = poly2obb_le135(polys)\\n elif version == 'le90':\\n results = poly2obb_le90(polys)\\n else:\\n raise NotImplementedError\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f76e361fd2af95a892970dbdfab85d\",\n \"score\": \"0.52612066\",\n \"text\": \"def poly2hbb(polys):\\n shape = polys.shape\\n polys = polys.reshape(*shape[:-1], shape[-1] // 2, 2)\\n lt_point = np.min(polys, axis=-2)\\n rb_point = np.max(polys, axis=-2)\\n return np.concatenate([lt_point, rb_point], axis=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9d3f9cd57c27fe278ee5bff8fdb06dc\",\n \"score\": \"0.5258611\",\n \"text\": \"def binary_mask_to_polygon(binary_mask, tolerance=0):\\n polygons = []\\n # pad mask to close contours of shapes which start and end at an edge\\n padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)\\n contours = measure.find_contours(padded_binary_mask, 0.5)\\n contours = np.subtract(contours, 1)\\n for contour in contours:\\n contour = close_contour(contour)\\n contour = measure.approximate_polygon(contour, tolerance)\\n if len(contour) < 3:\\n continue\\n contour = np.flip(contour, axis=1)\\n segmentation = contour.ravel().tolist()\\n # after padding and subtracting 1 we may get -0.5 points in our segmentation\\n segmentation = [0 if i < 0 else i for i in segmentation]\\n polygons.append(segmentation)\\n\\n return polygons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9d3f9cd57c27fe278ee5bff8fdb06dc\",\n \"score\": \"0.5258611\",\n \"text\": \"def binary_mask_to_polygon(binary_mask, tolerance=0):\\n polygons = []\\n # pad mask to close contours of shapes which start and end at an edge\\n padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)\\n contours = measure.find_contours(padded_binary_mask, 0.5)\\n contours = np.subtract(contours, 1)\\n for contour in contours:\\n contour = close_contour(contour)\\n contour = measure.approximate_polygon(contour, tolerance)\\n if len(contour) < 3:\\n continue\\n contour = np.flip(contour, axis=1)\\n segmentation = contour.ravel().tolist()\\n # after padding and subtracting 1 we may get -0.5 points in our segmentation\\n segmentation = [0 if i < 0 else i for i in segmentation]\\n polygons.append(segmentation)\\n\\n return polygons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"718a1c372119a195856ebf76458402f5\",\n \"score\": \"0.52573603\",\n \"text\": \"def underbounded_simple(city, city_buffer_reduce, boundary, GISJOIN, epsg):\\r\\n if boundary.type=='Polygon':\\r\\n cities_DF=gpd.GeoDataFrame([boundary])\\r\\n else:\\r\\n cities_DF=gpd.GeoDataFrame(boundary)\\r\\n \\r\\n if len(cities_DF)==0:\\r\\n cities_DF['geometry']=\\\"\\\"\\r\\n cities_DF=cities_DF.set_geometry('geometry')\\r\\n cities_DF=cities_DF.set_crs(epsg)\\r\\n else:\\r\\n cities_DF=cities_DF.rename(columns={0:'geometry'})\\r\\n cities_DF=cities_DF.set_geometry('geometry')\\r\\n cities_DF=cities_DF.set_crs(epsg)\\r\\n cities_DF['GISJOIN']=GISJOIN\\r\\n cities_DF['geometry']=cities_DF.buffer(1)\\r\\n if city_buffer_reduce.type=='Polygon':\\r\\n city_buffer_reduce_DF=gpd.GeoDataFrame([city_buffer_reduce])\\r\\n else:\\r\\n city_buffer_reduce_DF=gpd.GeoDataFrame(city_buffer_reduce)\\r\\n \\r\\n city_buffer_reduce_DF=city_buffer_reduce_DF.rename(columns={0:'geometry'})\\r\\n city_buffer_reduce_DF=city_buffer_reduce_DF.set_geometry('geometry')\\r\\n city_buffer_reduce_DF=city_buffer_reduce_DF.set_crs(epsg)\\r\\n city_buffer_reduce_DF=city_buffer_reduce_DF.explode()\\r\\n city_buffer_reduce_DF=city_buffer_reduce_DF.reset_index()\\r\\n city_buffer_reduce_DF['index'] = city_buffer_reduce_DF.index\\r\\n city_buffer_reduce_DF['AreaAcres']=city_buffer_reduce_DF.area*0.000247105\\r\\n too_small=city_buffer_reduce_DF[city_buffer_reduce_DF['AreaAcres']<1].copy()\\r\\n too_small['Type']='Unaffected'\\r\\n \\r\\n city_buffer_reduce_DF=city_buffer_reduce_DF[city_buffer_reduce_DF['AreaAcres']>=1].copy()\\r\\n city_buffer_reduce_DF['Type']=\\\"Underbounded\\\"\\r\\n\\r\\n\\r\\n results=pd.concat([city_buffer_reduce_DF, too_small])\\r\\n results['GISJOIN']=GISJOIN\\r\\n results=results[['GISJOIN', 'geometry', 'AreaAcres', 'Type']].copy()\\r\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2b770df8fb84d988dca47bac7ea275c\",\n \"score\": \"0.5251293\",\n \"text\": \"def get_polygon_outer_bounding_box(polygon):\\n\\n xs = [i[0] for i in polygon] # may be improved if using numpy methods directly\\n ys = [i[1] for i in polygon]\\n\\n mx = min(xs)\\n my = min(ys)\\n return mx, my, max(xs) - mx, max(ys) - my\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bcd98570db5340294c79b3909377076\",\n \"score\": \"0.52475697\",\n \"text\": \"def test_all_inside_polygon(self):\\r\\n\\r\\n # unit square\\r\\n U = [[0, 0], [1, 0], [1, 1], [0, 1]]\\r\\n\\r\\n points = [[0.5, 0.5], [0.2, 0.3], [0, 0.5]] # All inside (or on edge)\\r\\n\\r\\n inside, outside = separate_points_by_polygon(points, U)\\r\\n assert len(outside) == 0 # All inside\\r\\n assert numpy.allclose(inside, [0, 1, 2])\\r\\n\\r\\n indices = outside_polygon(points, U, closed=True)\\r\\n assert numpy.allclose(indices, [])\\r\\n\\r\\n indices = inside_polygon(points, U, closed=True)\\r\\n assert numpy.allclose(indices, [0, 1, 2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54ce64417b756cc294f42b48f6ed0286\",\n \"score\": \"0.5246818\",\n \"text\": \"def test_valid_polygon_pair_no_union(self):\\n\\n response = self.app.post(\\n '/polygonAction/union',\\n content_type='application/json',\\n data=self.get_json_data(\\n \\\"fixtures/validPolygonPairNoIntersection.json\\\"\\n )\\n )\\n assert response.status_code == 204\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09f6cd9a96b7a21e945a8473da338840\",\n \"score\": \"0.52406543\",\n \"text\": \"def bbox_ioa(box1, box2):\\n\\n # Get the coordinates of bounding boxes\\n b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[0] + box1[2], box1[1] + box1[3]\\n b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[0] + box2[2], box2[1] + box2[3]\\n\\n # get the coordinates of the intersection rectangle\\n inter_rect_x1 = max(b1_x1, b2_x1)\\n inter_rect_y1 = max(b1_y1, b2_y1)\\n inter_rect_x2 = min(b1_x2, b2_x2)\\n inter_rect_y2 = min(b1_y2, b2_y2)\\n\\n # Intersection area\\n inter_area = np.clip(inter_rect_x2 - inter_rect_x1 + 1, 0, 1920) * np.clip(inter_rect_y2 - inter_rect_y1 + 1, 0,\\n 1080)\\n\\n # Union Area\\n b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\\n b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\\n\\n iou = max(inter_area / b1_area, inter_area / b2_area)\\n\\n return iou\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aec1b63b3e8939d31dc6317f65f5f13d\",\n \"score\": \"0.5238896\",\n \"text\": \"def check_poly_intersect(poly_1, poly_2):\\n ### reference: https://www.codeproject.com/Articles/15573/2D-Polygon-Collision-Detection\\n overlap_flag = True\\n #### loop over each edge\\n for poly_cur in [poly_1, poly_2]:\\n npt = poly_cur.shape[0]\\n for pid, pt in enumerate(poly_cur):\\n #### calculate the projection axis\\n pt_next = poly_cur[ (pid+1) % npt ]\\n vector = pt_next - pt\\n vector_perp_norm = np.array([ -vector[1], vector[0] ]) / (np.sqrt(vector[0]**2+vector[1]**2 ) + 1e-6 )\\n\\n #### project both polygons to the axis\\n pmin_1, pmax_1 = project_to_nvector(vector_perp_norm, poly_1)\\n pmin_2, pmax_2 = project_to_nvector(vector_perp_norm, poly_2)\\n\\n #### check overlap in the projection\\n overlap_flag = check_project_intersect(pmin_1, pmax_1, pmin_2, pmax_2)\\n if not overlap_flag:\\n break\\n if not overlap_flag:\\n break\\n \\n return overlap_flag\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1687,"cells":{"query_id":{"kind":"string","value":"db7e74bcfd026b96bad79174d35983aa"},"query":{"kind":"string","value":"create a new branch that merges the clusters of two or more branches that have different clusters"},"positive_passages":{"kind":"list like","value":[{"docid":"3d8533a6bbc0b74951d479f93c58ce05","score":"0.6773576","text":"def create_merge_branch(self, cid, merge_child, occluded_parents, fathers, line_num, point_list):\n\n saved_parent_nodes = []\n\n for oparent in occluded_parents:\n\n parent_node = [pnode for pnode in fathers if pnode.flat_children == oparent][0]\n parent_node.cluster_id = cid # !!!!!!!!!! Questionable cid change??\n\n if parent_node not in saved_parent_nodes:\n saved_parent_nodes.append(parent_node)\n\n for node_points in point_list: # get the children in Point class form\n try:\n flat_children = [cpoint.flat_value for cpoint in node_points]\n except:\n flat_children = node_points.flat_value\n\n if merge_child == flat_children:\n child_points = node_points\n break\n\n if type(merge_child) == list:\n merge_child = tuple(merge_child) # tupleate the bid if necessary\n\n merge_node = Node(cid, merge_child, saved_parent_nodes, line_num, child_points)\n Graph.add_node(cid, merge_child, merge_node) # start itself\n\n for par in saved_parent_nodes:\n bra_id = par.branch_id\n if bra_id not in Graph.closed_keys:\n Graph.closed_keys.append(bra_id)\n\n return merge_node","title":""}],"string":"[\n {\n \"docid\": \"3d8533a6bbc0b74951d479f93c58ce05\",\n \"score\": \"0.6773576\",\n \"text\": \"def create_merge_branch(self, cid, merge_child, occluded_parents, fathers, line_num, point_list):\\n\\n saved_parent_nodes = []\\n\\n for oparent in occluded_parents:\\n\\n parent_node = [pnode for pnode in fathers if pnode.flat_children == oparent][0]\\n parent_node.cluster_id = cid # !!!!!!!!!! Questionable cid change??\\n\\n if parent_node not in saved_parent_nodes:\\n saved_parent_nodes.append(parent_node)\\n\\n for node_points in point_list: # get the children in Point class form\\n try:\\n flat_children = [cpoint.flat_value for cpoint in node_points]\\n except:\\n flat_children = node_points.flat_value\\n\\n if merge_child == flat_children:\\n child_points = node_points\\n break\\n\\n if type(merge_child) == list:\\n merge_child = tuple(merge_child) # tupleate the bid if necessary\\n\\n merge_node = Node(cid, merge_child, saved_parent_nodes, line_num, child_points)\\n Graph.add_node(cid, merge_child, merge_node) # start itself\\n\\n for par in saved_parent_nodes:\\n bra_id = par.branch_id\\n if bra_id not in Graph.closed_keys:\\n Graph.closed_keys.append(bra_id)\\n\\n return merge_node\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"a3c052576b04bd128432dd0a90f4608b","score":"0.6381184","text":"def mergeBranchToCluster(self, mcid, cluster_id, branch_id, close=1):\n\n node_list = Graph.cluster_dict[cluster_id][branch_id]\n # make all the cluster_id's the same\n for node in node_list:\n if type(node)==list: #so if we hit a closed branch\n for closure_node in node:\n closure_node.cluster_id = mcid\n else:\n node.cluster_id = mcid\n\n Graph.cluster_dict[mcid][branch_id] = node_list\n if close==1:\n if branch_id not in Graph.closed_keys:\n Graph.closed_keys.append(branch_id) # then close the key","title":""},{"docid":"e0c2eed2572a879537af1b9f53a013df","score":"0.6221104","text":"def create_split_branch(self, cid, child_father, father_node, line_num, point_list, lame_parent=0):\n\n child, parent = child_father\n cid_father = father_node.cluster_id\n bid_father = father_node.branch_id\n\n # create the new branch key from the flat child value\n if type(child)==int: # if its an int\n new_branch_id = child\n else:\n new_branch_id = tuple(child) # keys must be hashable\n\n # pull out points from diagonal clustered point row\n for point_class_list in point_list:\n\n try:\n if child == point_class_list.flat_value:\n child_points = point_class_list\n\n except:\n if child == [x.flat_value for x in point_class_list]:\n child_points = point_class_list\n\n new_node = Node(cid, new_branch_id, father_node, line_num, child_points)\n\n if bid_father not in Graph.closed_keys:\n\n Graph.closed_keys.append(bid_father)\n\n if lame_parent == 1:\n if father_node not in Graph.lame_duck_parents:\n Graph.lame_duck_parents.append(father_node)\n\n # add the new orphan node\n Graph.cluster_dict[cid_father][new_branch_id] = [new_node]\n return new_node","title":""},{"docid":"de2bf463ab95a68066ef9d2acb511bc8","score":"0.61214715","text":"def create_cluster_branch(self, orphan, line_num): # create cluster\n\n try:\n cluster_id = orphan.flat_value\n except:\n cluster_id = tuple([x.flat_value for x in orphan])\n\n # create a node instance and add it to the graph\n orphan_node = Node(cluster_id, cluster_id, cluster_id, line_num, orphan)\n Graph.cluster_dict[cluster_id][cluster_id] = [orphan_node]","title":""},{"docid":"f5d776e694ddf53f5ed38c55614bf0fb","score":"0.609247","text":"def create_new_branch(self, newbranch):\n # self.update(self.branch)\n try:\n self.hg_branch(newbranch)\n return 'succes'\n except Exception as e:\n print(e)\n return 'failure'","title":""},{"docid":"010fe6d75e4164b6d2bca7b0aad97e9c","score":"0.60218775","text":"def test_branch_new():\n atom = ATOMClassifier(X10, y10, random_state=1)\n atom.clean()\n atom.branch = \"b2\"\n assert len(atom._branches) == 2","title":""},{"docid":"82040a95d0372dc244287b54f5aac2fd","score":"0.5968368","text":"def merge_cnvs_clusters(vcfs,temp_dir, output_directory, cluster_merge_slop=0):\n\n # Quality is lierally the sum of the previous VCF files.\n\n basenames = [os.path.basename(x) for x in vcfs]\n __bedtools_duplication_string__ = \"\"\"awk '{{if ($0 !~ /^#/ && $6 > 100){{ split($8,a,\";\"); split(a[2],b,\"=\");print $1,$2,$2+b[2],$3,FILENAME}}}}' {0} | grep duplication | tr ' ' '\\t' | sort -k 1,1 -k 2,2g | bedtools cluster -i - -d {1} > tmp_clusters_duplication.txt\"\"\" \n __bedtools_deletion_string__ = \"\"\"awk '{{if ($0 !~ /^#/ && $6 > 100){{ split($8,a,\";\"); split(a[2],b,\"=\");print $1,$2,$2+b[2],$3,FILENAME}}}}' {0} | grep deletion | tr ' ' '\\t' | sort -k 1,1 -k 2,2g | bedtools cluster -i - -d {1} > tmp_clusters_deletion.txt\"\"\" \n __bedtools_all__= \"\"\"awk '{{if ($0 !~ /^#/ && $6 > 100 ){{ split($8,a,\";\"); split(a[2],b,\"=\");print $1,$2,$2+b[2],$3,FILENAME}}}}' {0} |tr ' ' '\\t' | sort -k 1,1 -k 2,2g | bedtools cluster -i - -d {1} > tmp_clusters.txt\"\"\" \n bedtools_cmd = __bedtools_deletion_string__.format(\" \".join(vcfs), cluster_merge_slop)\n subprocess.check_call(bedtools_cmd, shell=True)\n bedtools_cmd = __bedtools_duplication_string__.format(\" \".join(vcfs), cluster_merge_slop)\n subprocess.check_call(bedtools_cmd, shell=True)\n bedtools_cmd = __bedtools_all__.format(\" \".join(vcfs), cluster_merge_slop)\n subprocess.check_call(bedtools_cmd, shell=True)\n all_vcfs = {}\n __vcf_sort__ =\"vcf-sort {0} | bgzip -c > {0}.gz && tabix -fp vcf {0}.gz\" \n for vcf_f in vcfs:\n vcf_sort_command = __vcf_sort__.format(vcf_f)\n subprocess.check_call(vcf_sort_command,shell=True)\n all_vcfs[vcf_f] = vcf.VCFSimple(vcf_f + \".gz\")\n # Ok now we have all the VCFs in this format.\n # Fix final files\n try:\n os.mkdir(os.path.join(output_directory,\"vcfs\"))\n except OSError:\n pass\n _process_clustering(\"tmp_clusters_duplication.txt\",all_vcfs,os.path.join(output_directory,\"vcfs\",\"duplications.vcf\"))\n _process_clustering(\"tmp_clusters_deletion.txt\",all_vcfs, os.path.join(output_directory, \"vcfs\",\"deletions.vcf\"))\n _process_clustering(\"tmp_clusters.txt\",all_vcfs, os.path.join(output_directory,\"vcfs\", \"all.vcf\"))","title":""},{"docid":"98d48fa6cbe3b5935aae5b996cdfb2d8","score":"0.5958116","text":"def branches(self, nodes):","title":""},{"docid":"401114e45e25f041422d275f653b53e9","score":"0.5903465","text":"def merge_clusters(self, c1, c2, new_name=None):\n if new_name is None:\n new_name = c1\n\n new_clus = self.clu2elm_dict[c1] | self.clu2elm_dict[c2]\n del self.clu2elm_dict[c1]\n del self.clu2elm_dict[c2]\n\n self.clu2elm_dict[new_name] = new_clus\n self.from_clu2elm_dict(self.clu2elm_dict)\n return self","title":""},{"docid":"842134ebd98ecfe066185cddc9c690d7","score":"0.5872413","text":"def create_leaf_clusters(module):\n output = ''\n non_clustered_leafs = find_non_clustered_leafs(module)\n non_clustered_leafs_count = 0\n cli = pn_cli(module)\n clicopy = cli\n\n while non_clustered_leafs_count == 0:\n if len(non_clustered_leafs) == 0:\n non_clustered_leafs_count += 1\n else:\n node1 = non_clustered_leafs[0]\n non_clustered_leafs.remove(node1)\n\n cli = clicopy\n cli += ' switch %s lldp-show ' % node1\n cli += ' format sys-name no-show-headers '\n system_names = run_cli(module, cli).split()\n system_names = list(set(system_names))\n\n cli = clicopy\n cli += ' switch %s fabric-node-show ' % node1\n cli += ' format name no-show-headers '\n nodes_in_fabric = run_cli(module, cli).split()\n nodes_in_fabric = list(set(nodes_in_fabric))\n\n for system in system_names:\n if system not in nodes_in_fabric:\n system_names.remove(system)\n\n terminate_flag = 0\n node_count = 0\n while (node_count < len(system_names)) and (terminate_flag == 0):\n node2 = system_names[node_count]\n if node2 in non_clustered_leafs:\n # Cluster creation\n cluster_name = node1 + '-to-' + node2 + '-cluster'\n output += create_cluster(module, cluster_name, node1, node2)\n\n non_clustered_leafs.remove(node2)\n terminate_flag += 1\n\n node_count += 1\n\n return output","title":""},{"docid":"69a38e84d519d9599d84867f44c051d4","score":"0.58268297","text":"def test_branch_same():\n atom = ATOMClassifier(X10, y10, random_state=1)\n atom.branch = \"master\"\n assert atom.branch.name == \"master\"","title":""},{"docid":"0cef1e5b57168c164e34e9e84871eddc","score":"0.57808805","text":"def new_branch_on_tree(\n self,\n branch_id,\n models,\n pairs_to_compare,\n model_storage_instances,\n precomputed_models,\n spawning_branch,\n **kwargs\n ):\n\n # Instantiate branch\n branch = BranchQMLA(\n branch_id=branch_id,\n models=models,\n model_storage_instances=model_storage_instances,\n precomputed_models=precomputed_models,\n tree=self,\n spawning_branch=spawning_branch,\n pairs_to_compare=pairs_to_compare,\n **kwargs\n )\n\n # Update trees records\n self.branches[branch_id] = branch\n for m in model_storage_instances:\n self.model_storage_instances[m] = model_storage_instances[m]\n\n return branch","title":""},{"docid":"ed885cb5463080ddee9d0839dc146113","score":"0.57504135","text":"def create_cluster(module, switch, name, node1, node2):\n global CHANGED_FLAG\n cli = pn_cli(module)\n clicopy = cli\n cli += ' switch %s cluster-show format name no-show-headers ' % node1\n cluster_list = run_cli(module, cli).split()\n if name not in cluster_list:\n cli = clicopy\n cli += ' switch %s cluster-create name %s ' % (switch, name)\n cli += ' cluster-node-1 %s cluster-node-2 %s ' % (node1, node2)\n if 'Success' in run_cli(module, cli):\n CHANGED_FLAG.append(True)\n return ' %s: %s created successfully \\n' % (switch, name)\n else:\n return ' %s: %s already exists \\n' % (switch, name)","title":""},{"docid":"aebf5280dcf7c9e6489550609cdc3d6d","score":"0.5667581","text":"def create_branch(self, branch_name: str = \"\") -> str:\n with ChangeCWD(self.content):\n if not branch_name:\n branch_name = get_uuid()\n self.branches.append(branch_name)\n self.run_command(f\"git checkout -b {branch_name}\")\n return branch_name","title":""},{"docid":"d356b06e3754dd3ad2dcd2fd40e19a55","score":"0.5651428","text":"def clone_parent_cluster_2_child_cluster(self):\n\n compartment_list_parent = self.inventory.getClusterCells(self.parentCell.clusterId)\n compartment_list_child = self.inventory.getClusterCells(self.childCell.clusterId)\n\n self.clone_cluster_attributes(source_cell_cluster=compartment_list_parent,\n target_cell_cluster=compartment_list_child,\n no_clone_key_dict_list=[])","title":""},{"docid":"fa72da09233e69bc192e7053c9749c47","score":"0.5650963","text":"def merge_nodes(self, node1, node2, initial_commit_graph, df):\n\n new_commit_graph = copy.deepcopy(initial_commit_graph)\n\n # Etapes pour merger les nodes\n # 1. Get list of out connections with a dict\n # eg. {node3 : 5, node4 : 6}\n # 2. Get list of in connections with a dict\n # 3. Merge nodes\n\n # 1 and 2\n\n connections = {}\n\n index = list(df.index)\n new_node_row = []\n\n for column in df.columns:\n if df.at[node1, column] == 1 or df.at[node2, column] == 1:\n new_node_row.append(1)\n for neighbor in index:\n if df.at[neighbor, column] == 1 and neighbor not in [node1, node2]:\n if neighbor not in connections:\n connections[neighbor] = 1\n else:\n connections[neighbor] += 1\n else:\n new_node_row.append(0)\n\n new_node_row = [new_node_row]\n\n\n '''\n for neighbor in initial_commit_graph.adj[node1]:\n if neighbor != node2:\n if neighbor not in connections:\n connections[neighbor] = initial_commit_graph.edges[node1, neighbor]['number_modifications_same_commit']\n else:\n connections[neighbor] += initial_commit_graph.edges[node1, neighbor]['number_modifications_same_commit']\n \n for neighbor in initial_commit_graph.adj[node2]:\n if neighbor != node1:\n if neighbor not in connections:\n connections[neighbor] = initial_commit_graph.edges[node2, neighbor]['number_modifications_same_commit']\n else:\n connections[neighbor] += initial_commit_graph.edges[node2, neighbor]['number_modifications_same_commit']\n '''\n\n\n new_commit_graph.remove_node(node1)\n new_commit_graph.remove_node(node2)\n\n new_node = f'{node1}:{node2}'\n new_commit_graph.add_node(new_node)\n\n new_row = pd.DataFrame(new_node_row, columns=list(df.columns), index=[new_node])\n new_df = df.drop(labels=[node1, node2])\n new_df = new_df.append(new_row)\n\n for neighbor, num_mod in connections.items():\n new_commit_graph.add_edge(new_node, neighbor)\n new_commit_graph.edges[new_node, neighbor]['number_modifications_same_commit'] = num_mod\n\n \n return new_commit_graph, new_df","title":""},{"docid":"661956842ea3ef80a761c9dfc0dfe6fa","score":"0.5607364","text":"def major_branch():","title":""},{"docid":"3b2eb186a162ca8f6a7b2885af6762ec","score":"0.5588885","text":"def setbranch(self, branch, pbranches):","title":""},{"docid":"e7454929742310d787e36bdc03da763c","score":"0.5582486","text":"def create_cluster(module, name, node1, node2):\n global CHANGED_FLAG\n cli = pn_cli(module)\n clicopy = cli\n cli += ' switch %s cluster-show format name no-show-headers ' % node1\n cluster_list = run_cli(module, cli).split()\n if name not in cluster_list:\n cli = clicopy\n cli += ' switch %s cluster-create name %s ' % (node1, name)\n cli += ' cluster-node-1 %s cluster-node-2 %s ' % (node1, node2)\n if 'Success' in run_cli(module, cli):\n CHANGED_FLAG.append(True)\n return ' %s: Created %s \\n' % (node1, name)\n else:\n return ''","title":""},{"docid":"0730c4a471d9c9ca37b02c57cfa37d9a","score":"0.554833","text":"def merge_similar_branches(branches, orientation_threshold=0.7, distance_threshold=8, branch_number_threshold=10):\n branches.sort(key=lambda x: -len(x))\n i = 0\n while i < len(branches) and len(branches[i]) > branch_number_threshold:\n i += 1\n\n while i < len(branches):\n j = i + 1\n while j < len(branches):\n br_1 = branches[i]\n br_2 = branches[j]\n if np.linalg.norm(br_1.center - br_2.center) < distance_threshold and \\\n sum(br_1.direction * br_2.direction) > orientation_threshold:\n br_1.merge(br_2)\n del branches[j]\n else:\n j += 1\n i += 1\n return branches","title":""},{"docid":"1e2a81f7e6c961a0d71466d5771a4769","score":"0.54898673","text":"def set_branch_coordinates(branch,xcoord,ycoord,sepsize,tdistmax):\n if branch.is_leaf():\n stempath=PathXYCoords( xvals=[xcoord,xcoord+branch.dist],yvals=[ycoord,ycoord] )\n branch.r0framecoords=FrameCoords(stem=stempath)\n #nodebox_xmax=max(xcoord+branch.dist,xcoord+tdistmax*0.025)\n #nodebox_xmax=max(xcoord+branch.dist,1.025*tdistmax)\n nodebox_xmax=1.025*tdistmax\n branch.r0node_edgecoords=PathXYCoords(xyvals=[(xcoord,ycoord-0.5*sepsize),(nodebox_xmax,ycoord-0.5*sepsize),\n (nodebox_xmax,ycoord+0.5*sepsize),(xcoord,ycoord+0.5*sepsize)])\n branch.r0branch_edgecoords=branch.r0node_edgecoords.copy()\n return ycoord,ycoord-sepsize\n else:\n xcoord=xcoord+branch.dist\n sub_branches=branch.children\n #sorted_sub_branches=depth_sort(sub_branches)\n ycoord_ascends=[]\n for sub_branch in sub_branches:#sorted_sub_branches:\n ycoord_ascend,ycoord_descend=set_branch_coordinates(sub_branch,xcoord,ycoord,sepsize,tdistmax)\n ycoord=ycoord_descend\n ycoord_ascends.append(ycoord_ascend)\n\n branchstempath=PathXYCoords( xvals=[xcoord-branch.dist,xcoord],\n yvals=[np.mean(ycoord_ascends),np.mean(ycoord_ascends)] )\n branchbasepath=PathXYCoords( xvals=[xcoord,xcoord], \n yvals=[min(ycoord_ascends),max(ycoord_ascends)] )\n branch.r0framecoords=FrameCoords(stem=branchstempath,base=branchbasepath) \n node_edgecoords=PathXYCoords(xyvals=[(xcoord-branch.dist,min(ycoord_ascends)),(xcoord,min(ycoord_ascends)),\n (xcoord,max(ycoord_ascends)),(xcoord-branch.dist,max(ycoord_ascends)) ])\n branch.r0node_edgecoords=node_edgecoords\n branch_ymin=min(node_edgecoords.boundbox.ymin,*[subx.r0branch_edgecoords.boundbox.ymin for subx in branch.children])\n branch_ymax=max(node_edgecoords.boundbox.ymax,*[subx.r0branch_edgecoords.boundbox.ymax for subx in branch.children])\n branch_xmin=min(node_edgecoords.boundbox.xmin,*[subx.r0branch_edgecoords.boundbox.xmin for subx in branch.children])\n branch_xmax=max(node_edgecoords.boundbox.xmax,*[subx.r0branch_edgecoords.boundbox.xmax for subx in branch.children])\n branch.r0branch_edgecoords=PathXYCoords(xyvals=[(branch_xmin,branch_ymin),(branch_xmax,branch_ymin),\n (branch_xmax,branch_ymax),(branch_xmin,branch_ymax)] ) \n\n return np.mean(ycoord_ascends),ycoord_descend","title":""},{"docid":"ba98dd8a587528d8c8db95694d7afc1c","score":"0.5484931","text":"def join_clusters(self, tuple_cluster):\n c1 = tuple_cluster[0]\n c2 = tuple_cluster[1]\n self.clusters.remove(c1)\n self.clusters.remove(c2)\n self.clusters.append(c1 + c2)","title":""},{"docid":"30be337261a2bcbf8609ecf9f4dc19e5","score":"0.5481414","text":"def _new_branch_node(key, value):\n return {\n key: value,\n \"_subgroup\": []\n }","title":""},{"docid":"c0de0246265953ea696efec2beefd67f","score":"0.54665995","text":"def branches(self, local=True):\n pass","title":""},{"docid":"a70279f591f6aa6190e7dd3aad16ed90","score":"0.5458468","text":"def draw_hierarchical_edge_bundle(self, name, load_existing=False, join_clusterless_samples=True):\n\n if not load_existing:\n df, commit_to_files, files_mod_dates = self.analyze_correlation(\n treecommit_analysis=False,\n commit_analysis=True,\n commit_lines_analysis=False,\n get_dataframe=True,\n get_commit_to_files_dict=True,\n get_dates=True)\n # df = self.create_commits_dataframe()\n df.to_csv(f'./df_{name}.csv')\n else:\n df = pd.read_csv(f'./df_{name}', index_col=0)\n\n if not load_existing:\n distance = self.get_distance_matrix(df)\n distance.to_csv(f'./df_distance_{name}.csv')\n else:\n distance = pd.read_csv(f'./df_distance_{name}.csv', index_col=0)\n \n clusters, _ = self.cluster_dataframe(\n distance,\n method='AggClustering',\n distance_matrix=True,\n min_size=3,\n max_eps=1,\n join_clusterless_samples=join_clusterless_samples)\n\n\n with open(\"./clusters_{name}.txt\", \"wb\") as fp:\n pickle.dump(clusters, fp) \n\n template = '['\n\n first_file = True\n for cluster in clusters.keys():\n for file_path in clusters[cluster]:\n\n parsed_file_path = file_path.replace(\"\\\\\", \"/\")\n\n if first_file:\n template += '{\"name\": ' + f'\"root${cluster}${parsed_file_path}\", \"couplings\":['\n first_file = False\n else:\n template += ',\\n{\"name\": ' + f'\"root${cluster}${parsed_file_path}\", \"couplings\":['\n\n first = True\n for neighbor in self.commit_graph[file_path]:\n\n n_cluster = -1\n for c in clusters.keys():\n if neighbor in clusters[c]:\n n_cluster = c\n break\n \n parsed_neighbor = neighbor.replace(\"\\\\\", \"/\")\n\n if first:\n template += f'\"root${n_cluster}${parsed_neighbor}\"'\n first = False\n else:\n template += f',\"root${n_cluster}${parsed_neighbor}\"'\n \n \n template += ']}'\n\n template += ']'\n\n with open('data.json', \"w\") as f:\n f.write(template)","title":""},{"docid":"4820eb8409bca7a6e4dba32fcec97fbe","score":"0.5444103","text":"def new_branching(self, name, *, leaf=None):\n raise NotImplementedError(\"Branching not implemented in this tree, \"\n \"please implement the 'new_branching' method\")","title":""},{"docid":"aad7d67034fd748c8dea0d78a1c6e888","score":"0.54238266","text":"def __init__(\n self,\n branch_id,\n models,\n model_storage_instances,\n pairs_to_compare,\n tree,\n precomputed_models,\n spawning_branch,\n ):\n\n self.branch_id = branch_id\n\n # Get attributes from this branch's tree\n self.tree = tree\n self.log_file = self.tree.log_file\n self.exploration_class = self.tree.exploration_class\n self.exploration_strategy = self.exploration_class.exploration_rules\n\n self.log_print(\n [\n \"Branch {} on tree {}\".format(self.branch_id, self.tree),\n ]\n )\n\n # Get parent branch of this branch\n try:\n self.parent_branch = self.tree.branches[spawning_branch]\n self.log_print(\n [\n \"Setting parent branch of {} -> parent is {}\".format(\n self.branch_id, self.parent_branch.branch_id\n )\n ]\n )\n except BaseException:\n self.parent_branch = None\n self.log_print([\"Failed to set parent branch for \", branch_id])\n\n # Models to place on the branch\n # TODO tidy up how models passed etc. (currently a bit redundant)\n self.model_storage_instances = model_storage_instances\n self.models = models\n self.models_by_id = models\n self.model_id_by_name = {self.models_by_id[m]: m for m in self.models_by_id}\n self.resident_model_ids = sorted(self.models_by_id.keys())\n self.resident_models = list(self.models_by_id.values())\n self.num_models = len(self.resident_models)\n\n # Choose models to compare within this branch\n if pairs_to_compare == \"all\":\n self.log_print([\"All pairs to be compared on branch \", self.branch_id])\n self.pairs_to_compare = list(\n itertools.combinations(self.resident_model_ids, 2)\n )\n else:\n self.log_print([\"Comparison pairs passed:\", pairs_to_compare])\n self.pairs_to_compare = pairs_to_compare\n # order pairs so they read (low, high)\n self.pairs_to_compare = [(min(p), max(p)) for p in self.pairs_to_compare]\n self.num_model_pairs = len(self.pairs_to_compare)\n\n # Models already considered on a previous branch\n self.precomputed_models = precomputed_models\n self.num_precomputed_models = len(self.precomputed_models)\n self.unlearned_models = list(\n set(self.resident_models) - set(self.precomputed_models)\n )\n\n # Interface with QMLA: attributes tp be called/edited continuously\n self.model_learning_complete = False\n self.comparisons_complete = False\n self.bayes_points = {}\n self.rankings = [] # ordered from best to worst\n self.result_counter = 0\n\n self.log_print(\n [\"New branch {}; models: {}\".format(self.branch_id, self.models)]\n )","title":""},{"docid":"94f572aeacc292a305fc4fae64a59d1d","score":"0.5375775","text":"def create_branch_nodes(self):\n from spira.yevon.gdsii.sref import SRef\n from spira.yevon.geometry.ports import Port\n branch_nodes = list()\n for n in self.g.nodes():\n if 'device_reference' in self.g.node[n]:\n D = self.g.node[n]['device_reference']\n if isinstance(D, SRef):\n branch_nodes.append(n)\n if isinstance(D, Port):\n branch_nodes.append(n)\n return branch_nodes","title":""},{"docid":"71a597c596704f5577312f210c483223","score":"0.5373747","text":"def create_branch(repositoryName=None, branchName=None, commitId=None):\n pass","title":""},{"docid":"c281f75d79d317de6ec6115df85bda44","score":"0.5356272","text":"def mergeClusters(self, c1, c2):\n\t\tassert (c1 in self.members), 'Cluster is not in Cluster Set!'\n\t\tassert (c2 in self.members), 'Cluster is not in Cluster Set!'\n\n\t\tpoints = [] \t\t# list to record points in c1 and c2\n\t\tfor p in c1.members():\n\t\t\tpoints.append(p)\n\t\tfor p in c2.members():\n\t\t\tpoints.append(p)\n\t\tnewC = Cluster(points, type(p))\n\t\t# remove old cluster\n\t\tself.members.remove(c1)\n\t\tself.members.remove(c2) \n\t\tself.add(newC)\n\n\t\treturn c1, c2","title":""},{"docid":"99f8ce4985e2a65c947fcb7af5cd4bf9","score":"0.53385556","text":"def create_branching_model(self):\n click.secho(\"Applying branching model...\", fg='green', nl=False)\n r = requests.put(\n f\"{self.repo_endpoint}/branching-model/settings\",\n data = json.dumps(self.merged_config['branching-model']),\n auth = (self.login, self.passwd)\n )\n if str(r.status_code).startswith('20'):\n click.secho(\"done.\", fg='green')\n else:\n click.secho(\"HTTP {}\".format(r.status_code), fg='bright_red')","title":""},{"docid":"25e1391b5859cb0fa54b63a2ad5895dd","score":"0.53306574","text":"def __branch_point_dfs(dfs_data):\n u = dfs_data['ordering'][0]\n large_n = {}\n large_n[u] = 0\n stem = {}\n stem[u] = u\n b = {}\n b[u] = 1\n __branch_point_dfs_recursive(u, large_n, b, stem, dfs_data)\n dfs_data['N_u_lookup'] = large_n\n dfs_data['b_u_lookup'] = b\n return","title":""},{"docid":"84bc28d370a0683049c1a602192c62ec","score":"0.5322581","text":"def create_cluster(self, label, rids, **attributes):\n\n # default attributes\n default_attributes = dict(\n fontsize = 24,\n style = 'rounded,filled',\n color = 'lightgrey', )\n\n default_attributes.update( **attributes )\n\n # if node exists in graph\n # united_nnames = list( set(self._rid_to_nname.keys()) & set(rids) )\n united_nnames = [ self._rid_to_nname[rid]\n for rid in rids if self._rid_to_nname.get(rid) ]\n\n # create new graph\n self._subcount += 1\n name = 'cluster' + str(self._subcount)\n # name = 'a' + str(self._subcount)\n g = self.__graph.subgraph(nbunch=united_nnames, # rank='same',\n name=name, label=label, **default_attributes)\n self.__label_to_subgraph[label] = g\n # g.graph_attr['rank'] = 'same'\n return g","title":""},{"docid":"4a025f38e30384a3043a80d0222daa19","score":"0.5314774","text":"def build_cluster_and_modify_delta_matrix(clusters, delta_matrix, i1, i2):\r\n assert i1 != i2\r\n # if the two values to concatenate are single values...\r\n print(i1)\r\n print(i2)\r\n print(clusters)\r\n if len(clusters[i1]) == 1 and len(clusters[i2]) == 1:\r\n # ...just cluster them\r\n clusters.append([clusters[i1][0], clusters[i2][0]])\r\n else:\r\n # get the index values to compare\r\n indices = [[None, None], [None, None]]\r\n # populate the upper left intersection of index array\r\n indices[0][0] = clusters[i1][0]\r\n # populate the upper right intersection of index array (if necessary)\r\n if len(clusters[i1]):\r\n indices[0][1] = clusters[i1][-1]\r\n # populate the lower left intersection of index array\r\n indices[1][0] = clusters[i2][0]\r\n # populate the lower right intersection of index array (if necessary)\r\n if len(clusters[i2]):\r\n indices[1][1] = clusters[i2][-1]\r\n # lookup the intersections of the index values in the distance matrix\r\n values = [[None, None], [None, None]]\r\n values[0][0] = delta_matrix[indices[0][0]][indices[1][0]]\r\n if indices[0][1] != None:\r\n values[0][1] = delta_matrix[indices[0][1]][indices[1][0]]\r\n if indices[1][1] != None:\r\n values[1][0] = delta_matrix[indices[0][0]][indices[1][1]]\r\n if indices[0][1] != None and indices[1][1] != None:\r\n values[1][1] = delta_matrix[indices[0][1]][indices[1][1]]\r\n\r\n # find the index of the lowest value in the value array\r\n lowest = values[0][0]\r\n low_row = 0\r\n low_col = 0\r\n for j in range(2):\r\n for i in range(2):\r\n if values[j][i] < lowest:\r\n lowest = values[j][i]\r\n low_row = i\r\n low_col = j\r\n\r\n # create the new concatenation based on which\r\n # index of intValue was the lowest\r\n if low_row == 0 and low_col == 0:\r\n clusters.append([clusters[i1][-1], clusters[i2][-1]])\r\n elif low_row == 0 and low_col == 1:\r\n clusters.append([clusters[i1][0], clusters[i2][0]])\r\n elif low_row == 1 and low_col == 0:\r\n clusters.append([clusters[i1][-1], clusters[i2][-1]])\r\n else:\r\n clusters.append([clusters[i1][0], clusters[i2][-1]])\r\n\r\n if i1 < i2:\r\n delta_matrix = remove_row_col(delta_matrix, i1)\r\n delta_matrix = remove_row_col(delta_matrix, i2)\r\n clusters.pop(i1)\r\n clusters.pop(i2)\r\n else:\r\n delta_matrix = remove_row_col(delta_matrix, i2)\r\n delta_matrix = remove_row_col(delta_matrix, i1)\r\n clusters.pop(i2)\r\n clusters.pop(i1)\r\n return clusters, delta_matrix","title":""},{"docid":"fd99e7d1eee7934c32b692bcd68f71c9","score":"0.5311303","text":"def merge_small_branches(branches, distance_threshold=20, branch_number_threshold=2):\n merged_branches = [br for br in branches if len(br) > branch_number_threshold]\n for i, br in enumerate(branches):\n if len(br) <= branch_number_threshold:\n distances = [np.linalg.norm(br.center - br_m.center) for br_m in merged_branches]\n if min(distances) < distance_threshold:\n j = distances.index(min(distances))\n merged_branches[j].merge(br)\n else:\n merged_branches.append(br)\n return merged_branches","title":""},{"docid":"fafafce908f0b546a7e795e2adfa249a","score":"0.52979225","text":"def branches(self):\n raise NotImplementedError","title":""},{"docid":"fafafce908f0b546a7e795e2adfa249a","score":"0.52979225","text":"def branches(self):\n raise NotImplementedError","title":""},{"docid":"d6e63529023e5a2245ccd241dd11538a","score":"0.52936536","text":"def build_common_part(branch_levels_list, feature_base_count,\n name=\"\", weight_decay=None):\n res_levels = len(branch_levels_list[0])\n with tf.variable_scope(name):\n print_debug(name)\n # Concat branches at each level + add conv layers\n levels = []\n for level_index in range(res_levels):\n print_debug(\"\\tlevel {}:\".format(level_index))\n concat_a_b = tf.concat([branch_levels[level_index][0] for branch_levels in branch_levels_list], axis=-1,\n name=\"concat_a_b_{}\".format(level_index))\n print_debug(\"\\t\\tconcat_a_b: {}\".format(concat_a_b))\n feature_count = feature_base_count * math.pow(2, level_index)\n concat_a_b_conv, _ = conv_conv_pool(concat_a_b, [feature_count, feature_count],\n name=\"concat_a_b_conv{}\".format(level_index), pool=False,\n weight_decay=weight_decay)\n print_debug(\"\\t\\tconcat_a_b_conv: {}\".format(concat_a_b_conv))\n levels.append(concat_a_b_conv)\n\n return levels","title":""},{"docid":"97c73d03845410e7d311656d37d64b5c","score":"0.5275886","text":"def build_multibranch_unet(input_branch_params_list, pool_count, common_feature_base_count, output_branch_params_list,\n weight_decay=None):\n # Dropout - controls the complexity of the model, prevents co-adaptation of features.\n with tf.name_scope('dropout'):\n keep_prob = tf.placeholder(tf.float32)\n\n # Build the separate simple convolution networks for each input:\n input_branch_levels_list = []\n for params in input_branch_params_list:\n tf.summary.histogram(\"input_{}\".format(params[\"name\"]), params[\"tensor\"])\n branch_levels = build_input_branch(params[\"tensor\"], params[\"feature_base_count\"], pool_count,\n name=\"branch_{}\".format(params[\"name\"]),\n weight_decay=weight_decay)\n input_branch_levels_list.append(branch_levels)\n\n # Build the common part of the network, concatenating inout branches at all levels\n common_part_levels = build_common_part(input_branch_levels_list,\n common_feature_base_count,\n name=\"common_part\",\n weight_decay=weight_decay)\n\n # Build the splitting part of the network, each level (except the last one) finishing with output branches.\n # Each branch is like the upsampling part of a U-Net\n outputs = []\n for params in output_branch_params_list:\n branch_levels = build_output_branch(common_part_levels,\n params[\"feature_base_count\"],\n name=\"branch_{}\".format(params[\"name\"]),\n weight_decay=weight_decay)\n # Add the last layers for prediction, then upsample each levels' prediction to level 0's resolution\n # TODO: keep this for legacy reasons:\n if params[\"activation\"] == tf.identity:\n name = \"branch_{}_pred_logit\".format(params[\"name\"])\n else:\n name = \"branch_{}_pred\".format(params[\"name\"])\n level_0_pred, stacked_pred_logits = build_pred_branch(branch_levels,\n output_channels=params[\"channel_count\"],\n name=name)\n # Apply activation function to logits\n stacked_preds = params[\"activation\"](stacked_pred_logits)\n output = (stacked_pred_logits, stacked_preds, stacked_preds[:, 0, ...])\n outputs.append(output)\n\n return outputs, keep_prob","title":""},{"docid":"a715411d4d8cd193a6d91c8478cd0a1a","score":"0.526745","text":"def crossingchromosome(self, a, b):\n newchromosome = [0] * self.dimChromomsome # new empty crhomosome\n for x in range(self.dimChromomsome):\n if random.randint(0, 9) == 1: # 10% chromosome from parent 1\n newchromosome[x] = self.mutation(a[x])\n # print(\"Gene from Parent_1 =\",a[x])\n elif random.randint(0, 9) == 2: # 10% chromosome from parent 2\n newchromosome[x] = self.mutation(b[x])\n # print(\"Gene from Parent_2 =\",b[x])\n else:\n # 80% to avarage 2 Chromosome parents values\n newchromosome[x] = (a[x] + b[x]) / 2\n # print(\"Gene from Merged genes =\",newchromosome[x])\n newchromosome[x] += self.mutation(newchromosome[x])\n # print(\"newchromosome = \",newchromosome)\n GeneticTreePlot.addedge(str(a), str(newchromosome))\n GeneticTreePlot.addedge(str(b), str(newchromosome))\n return newchromosome","title":""},{"docid":"19685687517c3d58d99c8a3e729cb907","score":"0.5255168","text":"def test_load_transform_data_multiple_branches():\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\n atom.prune()\n atom.branch = \"b2\"\n atom.balance()\n atom.feature_generation(strategy=\"dfs\", n_features=5)\n atom.branch = \"b3\"\n atom.feature_selection(strategy=\"sfm\", solver=\"lgb\", n_features=20)\n atom.save(\"atom_2\", save_data=False)\n\n atom2 = ATOMClassifier.load(\"atom_2\", data=(X_bin, y_bin), transform_data=True)\n for branch in atom._branches:\n pd.testing.assert_frame_equal(\n left=atom2._branches[branch.name]._data,\n right=atom._branches[branch.name]._data,\n check_dtype=False,\n )","title":""},{"docid":"49bc6d969f6191687f64510bf0afaa66","score":"0.52381176","text":"def branchmap(self):","title":""},{"docid":"22434a092e0041addb53d51af92fbb4b","score":"0.52338576","text":"def _initialize_new_branches(self, branches, birth, prev, A, S):\n\t\tg = self.genealogy; m = self.m\n\t\tfor branch in branches:\n\t\t\tparent, child = branch\n\t\t\tg[parent][child]['s'] = [ 1.]\n\t\t\tg[parent][child]['S'] = [1.]\n\t\t\tg[parent][child]['heights'] = [child.height]\n\t\t\tif child.is_terminal():\n\t\t\t\tg[parent][child]['pik'] = list()\n\t\t\t\ttry:\n\t\t\t\t\tg[parent][child]['pik'].append( array(child.state) )\n\t\t\t\texcept:\n\t\t\t\t\tpdb.set_trace()\n\t\t\t\t\n\t\t\t\tself.p_alpha_k = None\n\t\t\telse:# child is internal node\n\t\t\t\tself.p_alpha_k = self._state_at_internal_node_and_likelihood(child, self.genealogy.successors(child), birth, prev, A, S)\n\t\t\t\tg[parent][child]['pik'] = [ self.p_alpha_k ] \n\t\t\t#\n\t\t#","title":""},{"docid":"ee2e68e16d87b5e7ba936a711c5047ac","score":"0.52258694","text":"def __embed_branch(dfs_data):\n u = dfs_data['ordering'][0]\n dfs_data['LF'] = []\n dfs_data['RF'] = []\n dfs_data['FG'] = {}\n n = dfs_data['graph'].num_nodes()\n f0 = (0, n)\n g0 = (0, n)\n L0 = {'u': 0, 'v': n}\n R0 = {'x': 0, 'y': n}\n dfs_data['LF'].append(f0)\n dfs_data['RF'].append(g0)\n dfs_data['FG'][0] = [L0, R0]\n dfs_data['FG']['m'] = 0\n dfs_data['FG']['l'] = 0\n dfs_data['FG']['r'] = 0\n\n #print 'DFS Ordering: {}'.format(dfs_data['ordering'])\n #for node in dfs_data['ordering']:\n #print '{}: {}'.format(node, dfs_data['adj'][node])\n\n nonplanar = __embed_branch_recursive(u, dfs_data)\n\n #print \"Nonplanar:\", nonplanar\n\n return not nonplanar","title":""},{"docid":"811d7a2c68b65df9eeaa95f17678fb87","score":"0.5218667","text":"def test_branch_without_remote(git_worktree):\n foo1 = git_worktree.create_git_project(\"foo\")\n branch = Branch()\n branch.name = \"master\"\n branch.default = True\n foo1.branches = [branch]\n foo1.apply_config()","title":""},{"docid":"270ca5439662cd88e8b5823be80c1d2c","score":"0.51959795","text":"def breed(tree1, tree2):\n cpy = tree1.copy()\n\n start_depth = random.randint(0, MAX_DEPTH-2)\n\n node1_parent = cpy.get_left_node_at_depth(start_depth)\n node2 = tree2.get_left_node_at_depth(start_depth+1)\n node1_parent.left = node2\n\n return cpy","title":""},{"docid":"f883a2b400d483c4a92039aee1c35e43","score":"0.51894337","text":"def hierarchical_clustering(features, k):\n\n\n\n N, D = features.shape\n\n assert N >= k, 'Number of clusters cannot be greater than number of points'\n\n # Assign each point to its own cluster\n assignments = np.arange(N)\n centers = np.copy(features)\n n_clusters = N\n\n while n_clusters > k:\n ### YOUR CODE HERE\n\n # 构建邻近度矩阵\n # print(centers.shape)\n dist = pdist(centers) # 欧氏距离\n # print(dist.shape)\n distMatrix = squareform(dist)\n distMatrix = np.where(distMatrix!=0.0, distMatrix, 1e5) # 因为squareform构建的邻近度矩阵上对角线是零，影响了最短距离的判断，此处做处理\n # print(distMatrix.shape)\n\n # 获取最小值所在的行和列，即距离最近的两个簇的index，取较小的那个作为保留，较大的那个进行合并\n minRow,minCol = np.unravel_index(distMatrix.argmin(), distMatrix.shape)\n # print(minRow,minCol)\n saveIdx = min(minRow,minCol)\n mergeIdx = max(minRow,minCol)\n # 将簇mergeIdx的点分配给saveIdx所在簇\n assignments = np.where(assignments != mergeIdx, assignments, saveIdx)\n # 因为要删除一个簇mergeIdx，所以下标的变化为:小于mergeIdx的不改变，大于saveIdx的需要减一\n assignments = np.where(assignments < mergeIdx, assignments, assignments - 1)\n\n # 删除被合并的簇所在中心\n centers = np.delete(centers,mergeIdx, axis=0)\n\n # 重新计算新的簇的中心\n # 题目提示中提到了，考虑到运行效率，只需要对进行归并后的簇计算新的中心，别的簇并没有进行操作，所以不影响中心的变化\n saveIdxIndecies = np.where(assignments == saveIdx)\n centers[saveIdx] = np.mean(features[saveIdxIndecies],axis=0)\n\n # 别忘了迭代终止条件\n n_clusters -= 1\n\n\n ### END YOUR CODE\n\n return assignments","title":""},{"docid":"3699f37a73cb5118f27be9f34ed2acd5","score":"0.5180988","text":"def build_branch(self, left_node, right_node):\n if len(self.elems) > 1:\n print('Operation is valir for singe node tree only')\n return self\n self.elems.extend(left_node.elems)\n self.elems.extend(right_node.elems)\n self.elems[0].left = self.elems[1]\n self.elems[1].parent = self.elems[0]\n self.elems[0].right = self.elems[len(left_node.elems)+1]\n self.elems[len(left_node.elems) + 1].parent = self.elems[0]\n self.elems[0].weight = left_node.elems[0].weight + right_node.elems[0].weight\n return self","title":""},{"docid":"95ec19904ea663aed4ff0ac99ee5b133","score":"0.51808137","text":"def create_new_group(self, a, b):\n self.groups[self.group_id] = set([a,b])\n self.node_id[a] = self.node_id[b] = self.group_id\n self.group_id += 1","title":""},{"docid":"fff87163af0fea04b44db119809cd82c","score":"0.5172267","text":"def join(g1, g2, L_max=0.99):\n node_prefix_1 = tools.get_node_prefix(g1)\n node_prefix_2 = tools.get_node_prefix(g2)\n if node_prefix_1 != node_prefix_2:\n for item in g2.keys():\n _, prop = item.split('.', 1)\n g2[node_prefix_1 + '.' + item] = g2.pop(item)\n node_prefix = node_prefix_1\n edge_prefix_1 = tools.get_edge_prefix(g1)\n edge_prefix_2 = tools.get_edge_prefix(g2)\n if edge_prefix_1 != edge_prefix_2:\n for item in g2.keys():\n _, prop = item.split('.', 1)\n g2[edge_prefix_1 + '.' + item] = g2.pop(item)\n edge_prefix = edge_prefix_1\n\n # Perform neighbor query\n from scipy.spatial import KDTree\n t1 = KDTree(g1[node_prefix+'.coords'])\n t2 = KDTree(g2[node_prefix+'.coords'])\n pairs = t1.query_ball_tree(t2, r=L_max)\n # Combine existing network data\n net3 = {}\n Np1 = g1[node_prefix+'.coords'].shape[0]\n Np2 = g2[node_prefix+'.coords'].shape[0]\n Nt1 = g1[edge_prefix+'.conns'].shape[0]\n Nt2 = g2[edge_prefix+'.conns'].shape[0]\n net3[node_prefix+'.coords'] = \\\n np.vstack((g1.pop(node_prefix+'.coords'),\n g2.pop(node_prefix+'.coords')))\n net3[edge_prefix+'.conns'] = \\\n np.vstack((g1.pop(edge_prefix+'.conns'),\n g2.pop(edge_prefix+'.conns') + Np1))\n # Convert kdtree result into new connections\n nnz = sum([len(row) for row in pairs])\n conns = np.zeros((nnz, 2), dtype=int)\n i = 0\n for j, row in enumerate(pairs):\n for col in row:\n conns[i, :] = j, col + Np1\n i += 1\n # Add new connections to network\n net3[edge_prefix+'.conns'] = \\\n np.vstack((net3.pop(edge_prefix+'.conns'), conns))\n\n # Finally, expand any other data arrays on given networks\n items = list(g1.keys()) + list(g2.keys())\n N = {node_prefix: [Np1, Np2], edge_prefix: [Nt1, Nt2]}\n for item in items:\n prefix, attr = item.split('.', 1)\n if prefix == node_prefix:\n temp1 = g1.pop(item, None)\n temp2 = g2.pop(item, None)\n if prefix == edge_prefix:\n temp1 = g1.pop(item, None)\n temp2 = g2.pop(item, None)\n if temp1 is None:\n blank = False if temp2.dtype == bool else np.nan\n temp1 = np.zeros(N[prefix][0], dtype=type(blank))*blank\n elif temp2 is None:\n blank = False if temp1.dtype == bool else np.nan\n temp2 = np.zeros(N[prefix][1], dtype=type(blank))*blank\n net3[item] = np.concatenate((temp1, temp2), axis=0)\n return net3","title":""},{"docid":"b6dd2bdfe6ac3f41064ded99a0eb40fc","score":"0.5162444","text":"def hierarchical_clustering(cluster_list, num_clusters):\n \n \n while len(cluster_list) > num_clusters:\n cluster_list.sort(key = lambda x:x.horiz_center())\n dist = fast_closest_pair(cluster_list)\n \n cluster_list[dist[1]].merge_clusters(cluster_list[dist[2]])\n del cluster_list[dist[2]]\n return cluster_list","title":""},{"docid":"f2ce830cbb4a9c04d06265264022cbec","score":"0.51514846","text":"def hierarchical_clustering(cluster_list, num_clusters):\n while len(cluster_list) > num_clusters:\n closest_pair = fast_closest_pair(cluster_list)\n cluster_i = cluster_list[closest_pair[1]]\n cluster_j = cluster_list[closest_pair[2]]\n cluster_i_union_j = cluster_i.merge_clusters(cluster_j)\n cluster_list.append(cluster_i_union_j)\n cluster_list.remove(cluster_i)\n cluster_list.remove(cluster_j)\n return cluster_list","title":""},{"docid":"5822ccef51a274bcc6eee2cd5f3f2ef4","score":"0.51499665","text":"def join_cells(args):\n\tglobal conn, cur\n\tnewid, cellids = args\n\n\t#add new cell at centroid of the cluster\n\tcur.execute(\"WITH clustered_antennas AS (SELECT ST_Union(eant_pos_original.geom) AS geom FROM eant_pos_original WHERE eant_pos_original.id IN %(cluster)s) \\\n\t\t\t\t INSERT INTO eant_pos (id, lon, lat, geom) \\\n\t\t\t\t SELECT %(id)s AS id, \\\n\t\t\t\t \t\tST_X(ST_Centroid(clustered_antennas.geom)) AS lon, \\\n\t\t\t\t \t\tST_Y(ST_Centroid(clustered_antennas.geom)) AS lat, \\\n\t\t\t\t \t\tST_Centroid(clustered_antennas.geom) AS geom \\\n\t\t\t\t FROM clustered_antennas\", {\"cluster\": tuple(cellids), \"id\": newid})\n\tconn.commit()","title":""},{"docid":"8d054ddee2621a68f627d283aeda6a09","score":"0.5144397","text":"def new_branches(node): \n for pot_node in not_in_min_span_tree: \n pot_weight = random.uniform(0.0, 1.0)\n if pot_weight < branches[pot_node][0]:\n branches[pot_node] = (pot_weight, node)","title":""},{"docid":"85a169011ca78fc14d37c7a9d28b6a6e","score":"0.51311713","text":"def checkout_new_branch(self, index: int) -> None:\n executor.check_call([\"git\", \"branch\", \"-f\", self.sub_branch(index)])\n executor.check_call([\"git\", \"checkout\", self.sub_branch(index)])","title":""},{"docid":"748f16cce4140edf9e52ab1ab31a231b","score":"0.5131006","text":"def cross_river(_starting_bank, _ending_bank, operator):\r\n bank1 = bank2 = []\r\n _starting_bank = list(_starting_bank)\r\n _ending_bank = list(_ending_bank)\r\n operator = list(operator)\r\n\r\n if 'b' in _starting_bank:\r\n bank1 = _starting_bank\r\n bank2 = _ending_bank\r\n elif 'b' in _ending_bank:\r\n bank1 = _ending_bank\r\n bank2 = _starting_bank\r\n\r\n bank1.remove('b')\r\n bank2.append('b')\r\n\r\n while any(operator):\r\n op = operator.pop()\r\n if op in bank1:\r\n bank1.remove(op)\r\n bank2.append(op)\r\n else:\r\n return None\r\n\r\n return build_node(_starting_bank, _ending_bank)","title":""},{"docid":"c2c82285b573d0957911a88004ed0bf3","score":"0.5124543","text":"def _new_branching_node(self, branching, branch_name):\n if branching is None:\n branching = self.new_branching(branch_name)\n elif isinstance(branching, self.leafnode_type):\n branching = self.new_branching(branch_name, leaf=branching)\n elif isinstance(branching, self.branching_type):\n pass\n else:\n raise TypeError(\"Branching can only be typ: \",\n f\"'None', '{self.leafnode_type}', '{self.branching_type}'\")\n #\n return branching","title":""},{"docid":"f3115235a5aed5048bafebdb2b13dd66","score":"0.5122976","text":"def test_branch_change():\n atom = ATOMClassifier(X10, y10, random_state=1)\n atom.branch = \"b2\"\n atom.clean()\n atom.branch = \"master\"\n assert atom.pipeline.empty # Has no Cleaner","title":""},{"docid":"1707fb4ec6ccba28aa84568818b622f3","score":"0.51163745","text":"def create_worktree(branch=BRANCH, print_help=True, *, b=False):\n # If using master branch, then simply symlink to the existing clone\n\n branch_directory = WORKTREES_DIRECTORY / branch\n\n def _create_worktree():\n if branch == \"master\":\n def symlink_master():\n branch_directory.symlink_to(\"../repo\", target_is_directory=True)\n\n guarded_call(\"symlink_master\", symlink_master, \"Symlinking workspace for master\")\n\n else:\n if b:\n subprocess.run(\n [\"git\", \"worktree\", \"add\", str(branch_directory), \"-b\", branch],\n cwd=str(REPO_PATH),\n check=True,\n )\n\n subprocess.run(\n [\"git\", \"push\", \"-u\", \"origin\", branch],\n cwd=str(REPO_PATH),\n check=True,\n )\n else:\n subprocess.run(\n [\"git\", \"fetch\", \"origin\", \"{}:{}\".format(branch, branch), \"--\"],\n cwd=str(REPO_PATH),\n check=True,\n )\n\n subprocess.run(\n [\"git\", \"worktree\", \"add\", str(branch_directory), branch],\n cwd=str(REPO_PATH),\n check=True,\n )\n\n subprocess.run(\n [\"git\", \"branch\", \"-u\", \"origin/{}\".format(branch), branch],\n cwd=str(REPO_PATH),\n check=True,\n )\n\n # Change git paths to relative paths so they work inside the docker container\n (branch_directory / \".git\").write_text(\"gitdir: ../../repo/.git/worktrees/{}\".format(branch))\n (REPO_PATH / \".git\" / \"worktrees\" / branch / \"gitdir\").write_text(\"../worktrees/{}\".format(branch))\n\n guarded_call(\n get_worktree_guard(branch),\n _create_worktree,\n \"Creating workspace for branch {}\".format(branch)\n )\n\n if print_help:\n print('\\nYou can now run this command to move to the worktree:\\n\\n' +\n 'cd {}\\n\\n'.format(branch_directory) +\n 'Add this line to your .bashrc or .zshrc for a shortcut:\\n\\n' +\n 'ccd() {\\n' +\n ' $HOME/cuauv/workspaces/repo/docker/auv-docker.py create-worktree $1 False\\n' +\n ' cd $HOME/cuauv/workspaces/worktrees/$1\\n' +\n '}\\n'\n )","title":""},{"docid":"7165f06caae3aa79e6b21010e640b037","score":"0.51103425","text":"def push_newbranch(self):\n try:\n self.hg_command('push', '--new-branch')\n except Exception as e:\n print(e)\n return","title":""},{"docid":"dab3ec91eaddad7ce407ad1610874c1c","score":"0.5089777","text":"def insertB(self, kwargs = {} ):\n with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:\n try:\n \"\"\" create an empty branch \"\"\"\n cur.execute(\"INSERT INTO branches DEFAULT VALUES RETURNING id\")\n b_id = cur.fetchone()['id']\n self.conn.commit()\n newB = self.getB(b_id)\n except psycopg2.Error as e:\n raise CantExecuteQ(e, self.conn)\n else:\n try:\n \"\"\" fill in the new branch \"\"\"\n for key in kwargs :\n newB.__setattr__(key, kwargs[key])\n\n if newB.parent_id != None:\n \"\"\" couple the new branch to the tree \"\"\"\n parentB = self.getB(newB.parent_id)\n parentB.subbs_id = parentB.subbs_id + [newB.id]\n\n return newB \n except:\n self.removeB(newB.id)\n raise CantExecuteQ()","title":""},{"docid":"c2663d911c56dc386e85d8c65a152b9f","score":"0.5089191","text":"def merge(self, a, b):\n old_id, target_id = sorted((self.node_id[a], self.node_id[b]), key = lambda id: len(self.groups[id]))\n for node in self.groups[old_id]:\n self.node_id[node] = target_id\n self.groups[target_id] |= self.groups[old_id]\n del self.groups[old_id]","title":""},{"docid":"ce33ec37a1dc1508538507f9e865e073","score":"0.50758666","text":"def extract_branches(self, params):\n self.logger.info(\"Split and group centerlines along branches ...\");\n branch_extractor = vmtkscripts.vmtkBranchExtractor()\n branch_extractor.Centerlines = self.geometry \n branch_extractor.Execute()\n self.branch_geometry = branch_extractor.Centerlines\n #print(self.centerlines_branch_geometry)\n self.logger.info(\"The centerlines branches have been calculated.\");","title":""},{"docid":"e1ec5483e24215723ff24c03e3b44a96","score":"0.50720096","text":"def merging_branches(self, branch2, branch3) -> pd.DataFrame:\n self.df = self.df.merge(\n branch2[[\"score2\", \"Img_number\"]],\n how=\"left\",\n on=\"Img_number\",\n left_index=True,\n )\n self.df = self.df.merge(\n branch3[[\"score3\", \"Img_number\"]],\n how=\"left\",\n on=\"Img_number\",\n left_index=True,\n )\n return self.df","title":""},{"docid":"8b923d484bddc1bdc89061d3ac9a873a","score":"0.5071974","text":"def test_two_clusters(self) -> None:\n with Cluster():\n with Cluster():\n pass","title":""},{"docid":"76eb5d0227602128b825e95fda313586","score":"0.50619376","text":"def _getBranchMode(scriptargs):\n if scriptargs.has_key(\"branch\") and scriptargs[\"branch\"]:\n return hou.stateGenerateMode.Branch\n else:\n return hou.stateGenerateMode.Insert","title":""},{"docid":"c382401437494da99688831a8575c7c8","score":"0.50610524","text":"def cluster_join(self):\n cluster_add_node = netapp_utils.zapi.NaElement.create_node_with_children(\n 'cluster-join', **{'cluster-ip-address': self.cluster_ip_address})\n\n try:\n self.server.invoke_successfully(cluster_add_node, enable_tunneling=True)\n return True\n except netapp_utils.zapi.NaApiError as error:\n # Error 36503 denotes node already being used.\n if to_native(error.code) == \"36503\":\n return False\n else:\n self.module.fail_json(msg='Error adding node to cluster %s: %s'\n % (self.cluster_name, to_native(error)),\n exception=traceback.format_exc())","title":""},{"docid":"96ecb36d0fdcc6c9c5e0088122bbd35f","score":"0.50466996","text":"def _merge_clusters(self,dict_x,dict_reduced_cost,dict_dual_val):\n list_added = self.cluster_merger.merge_clusters(dict_x,dict_reduced_cost,self.iteration)\n for new_cluster_id in list_added:\n self.cluster_operation.improve_cluster(cluster_id=new_cluster_id,\n stop_dual_cost= dict_dual_val,\n iteration=self.iteration)","title":""},{"docid":"f1a7a2125c94a4df0ab201a106461f5e","score":"0.5044488","text":"def merge_and_delete_branch(self, branch: str) -> None:\n self.command(\"checkout\", self._default_branch)\n self.command(\"merge\", branch)\n self.command(\"push\", \"origin\", self._default_branch)\n self.command(\"push\", \"-d\", \"origin\", branch)","title":""},{"docid":"3207443ea5acdeca264d4419218dffe2","score":"0.5040457","text":"def add_node(cls, cluster_id, branch_id, node):\n\n try: # try to pull the node list out\n\n node_list = cls.cluster_dict[cluster_id][branch_id]\n\n if node not in node_list:\n node_list.append(node)\n cls.cluster_dict[cluster_id][branch_id]= node_list\n\n except: # if no node list exists then just add\n cls.cluster_dict[cluster_id][branch_id] = [node]","title":""},{"docid":"d05fb2c0f98b8020038215ac5b574b8a","score":"0.50328434","text":"def create_branch(version):\n run('svn cp %(svn_repository)s/trunk %(svn_repository)s/branches/%(version)s '\n '-m \"[ADD] Create new branch %(version)s\"'\n % {'svn_repository': env.svn_repository, 'version': version})","title":""},{"docid":"a89296103e0c93d6415ba097e6dd027f","score":"0.50177205","text":"def addBranch(self, branch):\n if self._isLeaf:\n return false\n else:\n self._branches.append(branch)\n self._branches[-1].setParent(self)\n if (self._label == None):\n self._label = branch.getLabel()\n else:\n self._label = self.mergeLabels(self._label, branch.getLabel()) #","title":""},{"docid":"9ac12989b5d79a86ac625db0186bbd1c","score":"0.501689","text":"def create_branch(branch_name: str, repo_dir: str):\n\n logger.info(f\"Creating {branch_name}\")\n git_new_branch = subprocess.run(\n [\"git\", \"checkout\", \"-b\", branch_name],\n cwd=Path(working_dir, repo_dir),\n capture_output=True,\n )\n logger(git_new_branch.stdout.decode(\"utf-8\").strip(\"\\n\").replace(\"\\n\", \" \"))\n\n # If error, write it out.\n if git_new_branch.returncode:\n err_msg = git_new_branch.stderr.decode(\"utf-8\").strip(\"\\n\").replace(\"\\n\", \" \")\n logger.error(err_msg)\n raise RuntimeError(err_msg)\n\n else:\n logger.info(\n git_new_branch.stdout.decode(\"utf-8\").strip(\"\\n\").replace(\"\\n\", \" \")\n )","title":""},{"docid":"9eeb0ade802bb02984fc7e52dcff4a39","score":"0.50124407","text":"def df_add_cluster(df,arr_clusters):\n\n if 'cluster' in df.columns:\n del(df['cluster'])\n\n #------------------------------------------------------------------- \n # Checking for Nan values \n #------------------------------------------------------------------- \n is_nan_in_arr=np.isnan(df.values)\n\n df_segment=pd.DataFrame(arr_clusters, index=df.index, columns=['cluster'])\n df_=pd.concat([df_segment,df], axis=1, join='inner')\n\n \n return df_","title":""},{"docid":"b48bbae001c717b358696a8180582254","score":"0.50122","text":"def inflate_agent_clusters(cp, cs):\n\n # node clusters with agent positions\n clusters = {\n c: set(cp.graph.agents[r] for r in r_list)\n for c, r_list in cs.agent_clusters.items()\n }\n\n child_clusters = {c: set() for c in cs.agent_clusters.keys()}\n parent_clusters = {}\n\n # start with master cluster active\n active_clusters = [\n c for c, r_list in cs.agent_clusters.items() if cp.master in r_list\n ]\n\n while True:\n # check if active cluster can activate other clusters directly\n found_new = True\n while found_new:\n found_new = False\n for c0, c1 in product(active_clusters, clusters.keys()):\n if c0 == c1 or c1 in active_clusters:\n continue\n for v0, v1 in product(clusters[c0], clusters[c1]):\n if cp.graph.has_conn_edge(v0, v1):\n active_clusters.append(c1)\n child_clusters[c0].add((c1, v1))\n parent_clusters[c1] = (c0, v0)\n found_new = True\n\n # all active nodes\n active_nodes = set.union(*[clusters[c] for c in active_clusters])\n free_nodes = set(cp.graph.nodes) - set.union(*clusters.values())\n if cs.dead_nodes is not None:\n free_nodes -= cs.dead_nodes\n\n # make sure clusters are connected via transitions, if not split\n for c, v_set in clusters.items():\n c_subg = cp.graph_tran.subgraph(v_set | free_nodes).to_undirected()\n\n comps = nx.connected_components(c_subg)\n comp_dict = {\n i: [r for r in cs.agent_clusters[c] if cp.graph.agents[r] in comp]\n for i, comp in enumerate(comps)\n }\n\n comp_dict = {i: l for i, l in comp_dict.items() if len(l) > 0}\n\n if len(comp_dict) > 1:\n # split and restart\n del cs.agent_clusters[c]\n for i, r_list in comp_dict.items():\n cs.agent_clusters[\"{}_{}\".format(c, i + 1)] = r_list\n return inflate_agent_clusters(cp, cs) # recursive call\n\n # find closest neighbor and activate it\n neighbors = cp.graph.post_tran(active_nodes) - active_nodes\n if cs.dead_nodes is not None:\n neighbors -= cs.dead_nodes\n\n if len(neighbors) == 0:\n break # nothing more to do\n\n new_cluster, new_node, min_dist = -1, -1, 99999\n for c in active_clusters:\n c_neighbors = cp.graph.post_tran(clusters[c])\n\n agent_positions = [cp.graph.agents[r] for r in cs.agent_clusters[c]]\n\n for n in neighbors & c_neighbors:\n\n dist, _ = nx.multi_source_dijkstra(\n cp.graph_tran, sources=agent_positions, target=n\n )\n if dist < min_dist:\n min_dist, new_node, new_cluster = dist, n, c\n\n clusters[new_cluster].add(new_node)\n\n cs.subgraphs = clusters\n cs.child_clusters = child_clusters\n cs.parent_clusters = parent_clusters\n\n return cs","title":""},{"docid":"83c4abb1735e700d7b8deddb0570bba9","score":"0.5011986","text":"def branches(web):\n entries = webutil.branchentries(web.repo, web.stripecount)\n latestentry = webutil.branchentries(web.repo, web.stripecount, 1)\n\n return web.sendtemplate(\n b'branches',\n node=hex(web.repo.changelog.tip()),\n entries=entries,\n latestentry=latestentry,\n )","title":""},{"docid":"37c4b783c9a7151449f919387b2d496c","score":"0.5005945","text":"def test_branch_creation(self):\n resp = self.client().post('/branches/', data=self.branch)\n self.assertEqual(resp.status_code, 201)\n self.assertIn('London', str(resp.data))\n self.assertIn('SW4 0PE', str(resp.data))","title":""},{"docid":"0ff3a5cfd8a282818b85c9681ac1da43","score":"0.49959946","text":"def branch_filter(new_node):\n branches[new_node] = (2, 0)","title":""},{"docid":"84c6d2c8139ec5550ff6a75160745eeb","score":"0.49932057","text":"def create_cluster(self, context, server_list):\n # Add hostname here.\n for ser in server_list:\n ser_ref = db.init_node_get(context, ser['id'])\n ser['host'] = ser_ref['host']\n\n\n\n def _update(status):\n LOG.debug('status = %s' % status)\n self._update_server_list_status(context,\n server_list,\n status)\n if status.lower().find('error') != -1:\n raise\n\n # Set at least 3 mons when creating cluster\n pool_default_size = db.vsm_settings_get_by_name(context,'osd_pool_default_size')\n pool_default_size = int(pool_default_size.value)\n nums = len(server_list)\n if nums >= pool_default_size:\n count = 0\n rest_mon_num = 0\n for ser in server_list:\n if ser['is_monitor'] == True:\n count += 1\n if count < pool_default_size:\n rest_mon_num = pool_default_size - count\n if rest_mon_num > 0:\n for ser in server_list:\n if ser['is_monitor'] == False:\n ser['is_monitor'] = True\n rest_mon_num -= 1\n if rest_mon_num <= 0:\n break\n # Use mkcephfs to set up ceph system.\n LOG.info('server_list111 = %s' % server_list)\n monitor_node = self._select_monitor(context, server_list)\n LOG.info('Choose monitor node = %s' % monitor_node)\n # Clean ceph data.\n def __clean_data(host):\n self._agent_rpcapi.update_ssh_keys(context, host)\n self._agent_rpcapi.clean_ceph_data(context, host)\n\n def __create_crushmap(context, server_list, host):\n self._agent_rpcapi.create_crushmap(context,\n server_list=server_list,\n host=host)\n\n try:\n _update(\"Cleaning\")\n thd_list = []\n for ser in server_list:\n thd = utils.MultiThread(__clean_data, host=ser['host'])\n thd_list.append(thd)\n utils.start_threads(thd_list)\n _update(\"Clean success\")\n except:\n _update(\"ERROR: Cleaning\")\n\n # When clean data, we also begin to create ceph.conf\n # and init osd in db.\n # Do not run with the same time as clean_data.\n # It maybe cleaned by clean_data.\n try:\n _update(\"Create ceph.conf\")\n self._agent_rpcapi.inital_ceph_osd_db_conf(context,\n server_list=server_list,\n host=monitor_node['host'])\n _update(\"Create ceph.conf success\")\n except:\n _update(\"ERROR: ceph.conf\")\n\n try:\n _update(\"create crushmap\")\n # Then begin to create crush map file.\n create_crushmap = utils.MultiThread(__create_crushmap,\n context=context,\n server_list=server_list,\n host=monitor_node['host'])\n create_crushmap.start()\n except:\n _update(\"ERROR: crushmap\")\n\n try:\n # Begin to mount disks on the mount_point.\n _update(\"Mount disks\")\n def __mount_disk(host):\n self._agent_rpcapi.mount_disks(context, host)\n\n thd_list = []\n for ser in server_list:\n thd = utils.MultiThread(__mount_disk, host=ser['host'])\n thd_list.append(thd)\n utils.start_threads(thd_list)\n _update(\"Mount disks success\")\n except:\n _update(\"ERROR: mount disk\")\n\n # Generate monitor keyring file.\n try:\n _update(\"start montior\")\n monitor_keyring = utils.gen_mon_keyring()\n def __write_monitor_keyring(host):\n self._agent_rpcapi.write_monitor_keyring(context,\n monitor_keyring,\n host)\n\n thd_list = []\n for ser in server_list:\n thd = utils.MultiThread(__write_monitor_keyring, host=ser['host'])\n thd_list.append(thd)\n utils.start_threads(thd_list)\n _update(\"start monitor success\")\n except:\n _update(\"ERROR: start monitor\")\n\n try:\n _update(\"Create keyring\")\n self._track_monitors(context, server_list)\n\n # Here we use our self-define dir for ceph-monitor services.\n # So we need to create the key ring by command.\n self._agent_rpcapi.create_keyring(context,\n host=monitor_node['host'])\n\n self._agent_rpcapi.upload_keyring_admin_into_db(context,\n host=monitor_node['host'])\n\n def _update_keyring_from_db(host):\n self._agent_rpcapi.update_keyring_admin_from_db(context,\n host=host)\n\n thd_list = []\n for ser in server_list:\n thd = utils.MultiThread(_update_keyring_from_db,\n host=ser['host'])\n thd_list.append(thd)\n utils.start_threads(thd_list)\n _update(\"Success: keyring\")\n except:\n _update(\"ERROR: keyring\")\n\n try:\n self._agent_rpcapi.prepare_osds(context,\n server_list,\n host=monitor_node['host'])\n\n # Begin to start osd service.\n _update('Start osds')\n def __start_osd(host):\n self._agent_rpcapi.start_osd(context, host)\n\n thd_list = []\n for ser in server_list:\n thd = utils.MultiThread(__start_osd, host=ser['host'])\n thd_list.append(thd)\n utils.start_threads(thd_list)\n _update('OSD success')\n except:\n _update(\"ERROR: start osd\")\n\n # add mds service\n try:\n _update(\"Start mds\")\n LOG.info('start mds services, host = %s' % monitor_node['host'])\n self._agent_rpcapi.add_mds(context, host=monitor_node['host'])\n except:\n _update(\"ERROR: mds\")\n # Created begin to get ceph status\n\n try:\n _update('Ceph status')\n stat = self._agent_rpcapi.get_ceph_health(context,\n monitor_node['host'])\n except:\n _update('ERROR: ceph -s')\n\n if stat == False:\n self._update_server_list_status(context,\n server_list,\n \"Ceph Start Error\")\n LOG.error('Ceph starting failed!')\n raise\n\n try:\n _update('Set crushmap')\n # Wait until it's created over.\n while create_crushmap.is_alive():\n time.sleep(1)\n\n def __set_crushmap(context, host):\n self._agent_rpcapi.set_crushmap(context,\n host)\n _update('Active')\n set_crushmap = utils.MultiThread(__set_crushmap,\n context=context,\n host=monitor_node['host'])\n set_crushmap.start()\n except:\n _update('ERROR: set crushmap')\n\n self._update_init_node(context, server_list)\n while set_crushmap.is_alive():\n time.sleep(1)\n self._agent_rpcapi.update_all_status(context,\n host=monitor_node['host'])\n self._agent_rpcapi.update_zones_from_crushmap_to_db(context,None,\n monitor_node['host'])\n self._agent_rpcapi.update_storage_groups_from_crushmap_to_db(context,None,\n monitor_node['host'])\n self._judge_drive_ext_threshold(context)\n self._update_drive_ext_threshold(context)\n return {'message':'res'}","title":""},{"docid":"f908c9b6701adc09a281783e91d07d31","score":"0.49922085","text":"def collapse_branches(overlay):\n structure_map = {}\n\n for node in overlay.nodes:\n if len(node.inputs) <= 1: continue\n gcd = node.gcd\n if all(inp == gcd or inp.inputs == set([gcd]) for inp in node.inputs):\n branches = [inp if inp != gcd else None\n for inp in overlay.sorted(node.inputs)]\n structure_map[node] = OverlayStructure(\"HeadBranch\", {\"branches\" : branches, \"head\": node})\n\n for node in overlay.nodes:\n if len(node.consumers) <= 1: continue\n if all(len(out.consumers) == 0 for out in node.consumers):\n branches = overlay.sorted(node.consumers)\n max_node = overlay.sorted(branches)[-1]\n structure_map[max_node] = OverlayStructure(\"TailBranch\", {\"branches\" : branches, \"tail\": node})\n\n return overlay.collapse_structures(structure_map)","title":""},{"docid":"1c4c1b3919450a02480bc59cc0c42d2e","score":"0.49843","text":"def branch(*args, **kwargs) -> Any:\n pass","title":""},{"docid":"6e6a07afca2f253acd268d468292d60c","score":"0.4981726","text":"def __insert_branch(u, v, dfs_data):\n w = L1(v, dfs_data)\n d_u = D(u, dfs_data)\n d_w = D(w, dfs_data)\n\n # Embed uw\n successful = __embed_frond(u, w, dfs_data)\n if not successful:\n return False\n\n # Embed a branch marker uu on the side opposite to uw, in the same frond block\n #successful = __embed_frond(u, v, dfs_data, as_branch_marker=True)\n successful = __embed_frond(u, u, dfs_data, as_branch_marker=True)\n if not successful:\n return False\n\n return True","title":""},{"docid":"ce0398a488fa868b32226fa4b7112825","score":"0.49749976","text":"def _create_new_clusters(self,dict_reduced_cost,dict_dual_val):\n list_created = self._create_clusters_based_covertness(dict_reduced_cost, dict_dual_val)\n list_created.extend(self._create_cluster_based_dual_value(dict_dual_val))\n for cluster_id in list_created:\n self.cluster_operation.improve_cluster(cluster_id=cluster_id,\n stop_dual_cost= dict_dual_val,\n iteration=self.iteration)","title":""},{"docid":"0d9906a2099d9c80eaa5d2bcb9233fb5","score":"0.4972335","text":"def join_dqlite_master_node(info, master_ip):\n\n # The cluster we want to join may be either token-auth based or x509-auth based.\n # The way to identify the cluster type is to look for the \"admin_token\" in the info\n # we got back from the cluster we try to join.\n # In the case of token-auth we need to:\n # - create the known_tokens.csv file (if it does not exist) with the admin token\n # - turn on token-auth on kube-apiserver\n # - create the token based admin kubeconfig\n # - recreate the kubelet, proxy, scheduler, controller kubeconfigs for the new ca\n # - restart kubelite\n # In the case of x509-auth we need to:\n # - recreate the admin/client, kubelet, proxy, scheduler, controller kubeconfigs for the new ca\n # - restart kubelite\n\n hostname_override = info[\"hostname_override\"]\n store_cert(\"ca.crt\", info[\"ca\"])\n store_cert(\"ca.key\", info[\"ca_key\"])\n store_cert(\"serviceaccount.key\", info[\"service_account_key\"])\n\n if \"admin_token\" in info:\n # We try to join a cluster where token-auth is in place.\n rebuild_token_based_auth_configs(info)\n else:\n # We are joining a x509-auth based cluster\n rebuild_x509_auth_client_configs()\n\n update_apiserver(info.get(\"api_authz_mode\"), info.get(\"apiport\"))\n store_base_kubelet_args(info[\"kubelet_args\"])\n update_kubelet_node_ip(info[\"kubelet_args\"], hostname_override)\n update_kubelet_hostname_override(info[\"kubelet_args\"])\n store_callback_token(info[\"callback_token\"])\n\n if \"etcd_servers\" in info:\n set_arg(\"--etcd-servers\", info[\"etcd_servers\"], \"kube-apiserver\")\n if info.get(\"etcd_ca\"):\n store_cert(\"remote-etcd-ca.crt\", info[\"etcd_ca\"])\n set_arg(\"--etcd-cafile\", \"${SNAP_DATA}/certs/remote-etcd-ca.crt\", \"kube-apiserver\")\n if info.get(\"etcd_cert\"):\n store_cert(\"remote-etcd.crt\", info[\"etcd_cert\"])\n set_arg(\"--etcd-certfile\", \"${SNAP_DATA}/certs/remote-etcd.crt\", \"kube-apiserver\")\n if info.get(\"etcd_key\"):\n store_cert(\"remote-etcd.key\", info[\"etcd_key\"])\n set_arg(\"--etcd-keyfile\", \"${SNAP_DATA}/certs/remote-etcd.key\", \"kube-apiserver\")\n\n mark_no_dqlite()\n service(\"restart\", \"k8s-dqlite\")\n service(\"restart\", \"apiserver\")\n else:\n update_dqlite(info[\"cluster_cert\"], info[\"cluster_key\"], info[\"voters\"], hostname_override)\n\n # We want to update the local CNI yaml but we do not want to apply it.\n # The cni is applied already in the cluster we join\n try_initialise_cni_autodetect_for_clustering(master_ip, apply_cni=False)\n mark_no_cert_reissue()","title":""},{"docid":"8262f6f1a3d5364aca92fa22f705e44a","score":"0.4970496","text":"def compare_branches(self, revision_to_check, branch_base):\n raise NotImplementedError(\"Abstract method\")","title":""},{"docid":"c79566d1f6505043a5646a76967827fd","score":"0.49671867","text":"def draw_clusters(clusters):\n bjp_pos = read_file(collect.BJP_POS_USER_FILE)['results']\n set_bjp_pos = set(bjp_pos)\n bjp_neg = read_file(collect.BJP_NEG_USER_FILE)['results']\n set_bjp_neg = set(bjp_neg)\n con_pos = read_file(collect.CON_POS_USER_FILE)['results']\n set_con_pos = set(con_pos)\n con_neg = read_file(collect.CON_NEG_USER_FILE)['results']\n set_con_neg = set(con_neg)\n count = 2\n for cluster in clusters:\n cluster_bjp_pos = set()\n cluster_bjp_neg = set()\n cluster_con_pos = set()\n cluster_con_neg = set()\n cluster_neutral = set()\n for n in cluster.nodes():\n if n in set_bjp_pos:\n cluster_bjp_pos.add(n)\n elif n in set_bjp_neg:\n cluster_bjp_neg.add(n)\n elif n in set_con_pos:\n cluster_con_pos.add(n)\n elif n in set_con_neg:\n cluster_con_neg.add(n)\n else:\n cluster_neutral.add(n)\n draw_graph(cluster, cluster_bjp_neg, cluster_bjp_pos, cluster_con_neg, cluster_con_pos, cluster_neutral, count,\n 'cluster_' + str(count - 1), 'community detection - cluster '+ str(count - 1) + '\\n Neutral Users - Purple | '\n 'Positive for BJP - Green | '\n 'Negative for BJP - Red | \\n '\n 'Positive for Congress - Blue | '\n 'Negative for Congress - Yellow ')\n count += 1","title":""},{"docid":"66f15a11a23dc98957e678af5829fa20","score":"0.496507","text":"def change_branch(self, *args, **kwargs):\n raise Unimplemented('Unimplemented in dlkit.services - args=' + str(args) + ', kwargs=' + str(kwargs))","title":""},{"docid":"66f15a11a23dc98957e678af5829fa20","score":"0.496507","text":"def change_branch(self, *args, **kwargs):\n raise Unimplemented('Unimplemented in dlkit.services - args=' + str(args) + ', kwargs=' + str(kwargs))","title":""},{"docid":"de75f566a1e8f21634f386d6ae8ddd57","score":"0.49609905","text":"def prune_branches(skel, branch_points=None, iterations=1):\n skel = skel > 0\n if skel.ndim == 2:\n from skimage.morphology import square as cube\n else:\n from skimage.morphology import cube\n # Create empty image to house results\n im_result = sp.zeros_like(skel)\n # If branch points are not supplied, attempt to find them\n if branch_points is None:\n branch_points = spim.convolve(skel*1.0, weights=cube(3)) > 3\n branch_points = branch_points*skel\n # Store original branch points before dilating\n pts_orig = branch_points\n # Find arcs of skeleton by deleting branch points\n arcs = skel*(~branch_points)\n # Label arcs\n arc_labels = spim.label(arcs, structure=cube(3))[0]\n # Dilate branch points so they overlap with the arcs\n branch_points = spim.binary_dilation(branch_points, structure=cube(3))\n pts_labels = spim.label(branch_points, structure=cube(3))[0]\n # Now scan through each arc to see if it's connected to two branch points\n slices = spim.find_objects(arc_labels)\n label_num = 0\n for s in slices:\n label_num += 1\n # Find branch point labels the overlap current arc\n hits = pts_labels[s]*(arc_labels[s] == label_num)\n # If image contains 2 branch points, then it's not a tail.\n if len(sp.unique(hits)) == 3:\n im_result[s] += arc_labels[s] == label_num\n # Add missing branch points back to arc image to make complete skeleton\n im_result += skel*pts_orig\n if iterations > 1:\n iterations -= 1\n im_temp = sp.copy(im_result)\n im_result = prune_branches(skel=im_result,\n branch_points=None,\n iterations=iterations)\n if sp.all(im_temp == im_result):\n iterations = 0\n return im_result","title":""},{"docid":"96b8fdeeaa6899dd7aee2d794763a571","score":"0.49608564","text":"def create_release_branch(self, branch_name):\n self.call_in_repo_dir(['git', 'checkout', self.remote_master_branch])\n self.call_in_repo_dir(['git', 'checkout', '-b', branch_name])","title":""},{"docid":"caa5ce8194fa5bf1262f877e2d99a6a2","score":"0.49593517","text":"def cluster(data, base_labels, config, params):\n\n if isinstance(data, list):\n modifier = 'mini'\n else:\n modifier = 'full'\n\n seed = params.get('seed', 42)\n random.seed(seed)\n\n num_clusters = 0\n\n clustering_labels, birch = test_clusters(config, data, modifier, params)\n\n model_file = os.path.join(\n constants.dir_d,\n constants.dir_mod,\n 'birch_{}_{}_{}.pkl'.format(modifier, num_clusters, len(data))\n )\n joblib.dump(birch, model_file)\n\n if modifier == 'mini':\n data = None\n\n return clustering_labels, birch, modifier, data, 'euclidean'","title":""},{"docid":"65e8490d6109558222635da72d7104ab","score":"0.49574256","text":"def generate_clusters(self):\n\n self.cluster_labels = None","title":""},{"docid":"28b5b820761a5a9ec43b9eb0a762183b","score":"0.49568474","text":"def shallow_clone(self, remote_location, branch):\n\n parsed = urllib.parse.urlparse(remote_location)\n\n pool_manager = PoolManager(ca_certs=certifi.where())\n pool_manager.headers['Cookie'] = self.auth_cookie\n # Suppress ResourceWarning\n pool_manager.headers['Connection'] = 'close'\n\n client = HttpGitClient.from_parsedurl(\n parsed, config=self.get_config_stack(), pool_manager=pool_manager)\n fetch_result = client.fetch(\n parsed.path, self, determine_wants=lambda mapping:\n [mapping[REF_HEADS_PREFIX + _B(branch)]], depth=1)\n stripped_refs = strip_peeled_refs(fetch_result.refs)\n branches = {\n n[len(REF_HEADS_PREFIX):]: v\n for (n, v) in stripped_refs.items()\n if n.startswith(REF_HEADS_PREFIX)\n }\n self.refs.import_refs(REF_REMOTES_PREFIX + DEFAULT_REMOTE_NAME, branches)\n self[HEAD] = self[REF_REMOTES_PREFIX + DEFAULT_REMOTE_NAME + b'/' +\n _B(branch)]","title":""},{"docid":"281b301185bbd5e5f085b4bd2e7bb54d","score":"0.49451664","text":"def create(self, name, base=None, fetch=False):\n # :todo:rethink: feature branch is not required to start at `develop`\n return super(FeatureBranchManager, self).create(\n name, base, fetch=fetch, must_be_on_default_base=False)","title":""},{"docid":"2e8aebf32869ba48b249d409918af95e","score":"0.49421617","text":"def step(self):\n distance_list = []\n min_distance = float(\"inf\")\n \n length = len(self.clusters)\n for i, element in enumerate(self.clusters):\n for otherelement in self.clusters[i+1:]:\n #print \"%s - %s = %s\" % (element.elements[0].value, otherelement.elements[0].value, element - otherelement)\n if element is not otherelement:\n distance = element - otherelement\n if distance < min_distance:\n # a new minimum record\n distance_list = [ (element, otherelement, distance) ]\n min_distance = distance\n elif distance == min_distance:\n # Same value, this will be merged too.\n distance_list.append( (element, otherelement, distance) )\n \n if distance_list:\n element = distance_list.pop(0) \n c1 = element[0]\n c2 = element[1]\n distance = element[2]\n \n self.output.write(\"%0.4f %0.4f %0.4f;\" % (c1.id, c2.id, distance))\n \n c_new = c1 + c2\n c_new.id = self.last_cluster_id\n self.last_cluster_id += 1\n self.clusters.append(c_new)\n \n self.clusters.remove(c1) # remove cluster as it is merged. Following steps wont see it.\n self.clusters.remove(c2)\n \n return True\n else:\n return False","title":""},{"docid":"406b354c1877e77c381de16d3a396648","score":"0.49404743","text":"def distributed_tree_rBCM(experts, weightfunction, \n clusterspec, worker_job='worker', param_server_job='ps', \n target_job=None, target_protocol='grpc'):\n num_worker_tasks = len(clusterspec.job_tasks(worker_job))\n if isinstance(experts, GPModel):\n architecture = [num_worker_tasks]\n else:\n assert len(experts) % num_worker_tasks == 0\n architecture = [num_worker_tasks, len(experts) // num_worker_tasks]\n\n rBCM = tree_rBCM(experts, weightfunction, architecture)\n\n rBCM.tf_graph = tf.Graph()\n \n target = (clusterspec.job_tasks(target_job)[0] if target_job \n else clusterspec.job_tasks(parameter_server_job)[0])\n rBCM.tf_session_target = '{}://{}'.format(target_protocol, target)\n\n for index, child in enumerate(rBCM.child.children):\n child.tf_device = tf.DeviceSpec(job=worker_job, task=index)\n\n for index, param in enumerate(rBCM.params):\n i = index % len(clusterspec.job_tasks(param_server_job))\n param.tf_device = tf.DeviceSpec(job=param_server_job, task=i)\n\n return rBCM","title":""},{"docid":"2a044674c3a251a860ba97fc916b4528","score":"0.4929801","text":"def get_super_regions(dsets, settings, batch_key):\n super_features = {}\n\n # 1) super cluster key: 'chr1-10518929', for example\n # 2) the other datasets where this cluster appears (hela nucl etc)\n\n cluster_file = os.path.join(settings.here, 'merge_dir', 'merged_pA_' + batch_key)\n temp_cluster_file = cluster_file + '_temp'\n\n out_handle = open(cluster_file, 'wb')\n\n for cell_line, comp_dict in dsets.items():\n for comp, utr_dict in comp_dict.items():\n for feature_id, feature in utr_dict.iteritems():\n\n # store every foudn feature in super_features for lalter\n if feature_id not in super_features:\n feature.super_clusters = [] # superclusters later\n super_features[feature_id] = feature\n\n # Create the super_key and store the super_cluster information\n for cls in feature.clusters:\n fromwhere = '_'.join([cell_line, comp])\n\n PAS = '--'.join(cls.nearby_PAS)\n PAS_distance = '--'.join([str(d) for d in cls.PAS_distance])\n\n name = '%'.join([fromwhere,\n feature_id,\n cls.tail_type,\n cls.tail_info,\n PAS,\n PAS_distance,\n str(cls.annotated_polyA_distance)])\n # also add PAS_distance and PAS_type\n out_handle.write('\\t'.join([cls.chrm,\n str(cls.polyA_coordinate),\n str(cls.polyA_coordinate+1),\n name,\n str(cls.nr_support_reads),\n cls.strand+'\\n'\n ]))\n out_handle.close()\n\n junctions = os.path.join(settings.here, 'junctions',\n 'splice_junctions_merged.bed')\n\n # i.5) cut away the exon-exon noise\n cmd = ['subtractBed', '-a', cluster_file, '-b', junctions]\n #p1 = Popen(cmd, stdout=open(temp_cluster_file, 'wb'), stderr=PIPE)\n p1 = Popen(cmd, stdout=open(temp_cluster_file, 'wb'))\n p1.wait()\n #err1 = p1.stderr.read()\n #if err1:\n #debug()\n\n # ii) expand the entries\n cmd = ['slopBed', '-i', temp_cluster_file, '-g', settings.hg19_path, '-b', '15']\n p2 = Popen(cmd, stdout=open(cluster_file, 'wb'))\n p2.wait()\n #err2 = p2.stderr.read()\n #if err2:\n #debug()\n\n # iii) merge and max scores\n cmd = ['mergeBed', '-nms', '-s', '-scores', 'sum', '-i', cluster_file]\n #cmd = ['mergeBed', '-nms', '-s', '-scores', 'max', '-i', cluster_file]\n #p3 = Popen(cmd, stdout=PIPE, stderr=PIPE)\n p3 = Popen(cmd, stdout=PIPE)\n\n # iv) read therough the centered output and update the super_cluster\n for line in p3.stdout:\n (chrm, beg, end, names, maxcovrg, strand) = line.split()\n\n center = int((int(beg)+int(end))/2)\n polyA_coverage = int(float(maxcovrg))\n\n dsets = []\n f_ids = []\n tail_types = []\n tail_infos = []\n nearby_PASes = []\n PAS_distances = []\n annot_distances = []\n\n name_list = names.split(';')\n for names2 in name_list:\n (dset, f_id, t_type, t_info, nearby_PAS, PAS_distance, annot_dist)\\\n = names2.split('%')\n dsets.append(dset)\n f_ids.append(f_id)\n tail_types.append(t_type)\n tail_infos.append(t_info)\n nearby_PASes.append(nearby_PAS)\n PAS_distances.append(PAS_distance)\n annot_distances.append(annot_dist)\n\n feature_id = set(f_ids).pop()\n\n # create cluster object\n cls = Super(dsets, center, polyA_coverage, tail_types, tail_infos,\n strand, nearby_PASes, PAS_distances, annot_distances)\n\n # add the cluster to super_featuress\n super_features[feature_id].super_clusters.append(cls)\n\n # any errors?\n #err3 = p3.stderr.read()\n #if err3:\n #debug()\n\n return super_features","title":""},{"docid":"a698c40bc14c60d75d76ffc6de5cf3a7","score":"0.49256802","text":"def test_can_assign_branch(self):\n self.client().post('/cars/', data=self.car)\n self.client().post('/branches/', data=self.branch)\n resp = self.client().put('/cars/1/branches/1')\n self.assertEqual(resp.status_code, 200)\n self.assertEqual(json.loads(resp.data)['currently_with'],\n '\"{ '\n 'type: branch, '\n 'id: 1, '\n 'city: London, '\n 'postcode: SW4 0PE '\n '}\"'\n )","title":""},{"docid":"5dfd3f02e7a84d75f8690c2fb4a1774a","score":"0.49248463","text":"def create_deploy_branch(deploy_branch, push=True):\n if not deploy_branch_exists(deploy_branch):\n print(\"Creating {} branch on doctr_remote\".format(deploy_branch))\n clear_working_branch()\n run(['git', 'checkout', '--orphan', DOCTR_WORKING_BRANCH])\n # delete everything in the new ref. this is non-destructive to existing\n # refs/branches, etc...\n run(['git', 'rm', '-rf', '.'])\n print(\"Adding .nojekyll file to working branch\")\n run(['touch', '.nojekyll'])\n run(['git', 'add', '.nojekyll'])\n run(['git', 'commit', '-m', 'Create new {} branch with .nojekyll'.format(deploy_branch)])\n if push:\n print(\"Pushing working branch to remote {} branch\".format(deploy_branch))\n run(['git', 'push', '-u', 'doctr_remote', '{}:{}'.format(DOCTR_WORKING_BRANCH, deploy_branch)])\n # return to master branch and clear the working branch\n run(['git', 'checkout', 'master'])\n run(['git', 'branch', '-D', DOCTR_WORKING_BRANCH])\n # fetch the remote so that doctr_remote/{deploy_branch} is resolved\n run(['git', 'fetch', 'doctr_remote'])\n\n return True\n return False","title":""},{"docid":"b86922d4db8a9f7b7bd3a41d850c047b","score":"0.49045464","text":"def create_json_topology():\n\n # Instantiate the topology with JSON structure.\n # \"entity\" will contain nodes of the cluster\n # \"link\" will contain network connexions between nodes\n topology_json = {\"entity\": [],\n \"link\": []}\n\n sensor_dev = {\"id\": 0, \"model\": \"sensor-device\", \"IPT\": 100 * 10 ^ 6, \"RAM\": 4000, \"COST\": 3, \"WATT\": 40.0}\n cloud_dev_1 = {\"id\": 1, \"model\": \"cloud\", \"mytag\": \"cloud1\", \"IPT\": 5000 * 10 ^ 6, \"RAM\": 40000, \"COST\": 3, \"WATT\": 20.0}\n cloud_dev_2 = {\"id\": 2, \"model\": \"cloud\", \"mytag\": \"cloud2\", \"IPT\": 5000 * 10 ^ 6, \"RAM\": 40000, \"COST\": 3, \"WATT\": 20.0}\n actuator_dev_1 = {\"id\": 3, \"model\": \"actuator-device-1\", \"IPT\": 100 * 10 ^ 6, \"RAM\": 4000, \"COST\": 3, \"WATT\": 40.0}\n actuator_dev_2 = {\"id\": 4, \"model\": \"actuator-device-2\", \"IPT\": 100 * 10 ^ 6, \"RAM\": 4000, \"COST\": 3, \"WATT\": 40.0}\n\n link1 = {\"s\": 0, \"d\": 1, \"BW\": 1, \"PR\": 2}\n link2 = {\"s\": 0, \"d\": 2, \"BW\": 1, \"PR\": 4}\n link3 = {\"s\": 1, \"d\": 3, \"BW\": 1, \"PR\": 2}\n link4 = {\"s\": 1, \"d\": 4, \"BW\": 1, \"PR\": 10}\n link5 = {\"s\": 2, \"d\": 4, \"BW\": 1, \"PR\": 6}\n\n topology_json[\"entity\"].append(sensor_dev)\n topology_json[\"entity\"].append(cloud_dev_1)\n topology_json[\"entity\"].append(cloud_dev_2)\n topology_json[\"entity\"].append(actuator_dev_1)\n topology_json[\"entity\"].append(actuator_dev_2)\n\n topology_json[\"link\"].append(link1)\n topology_json[\"link\"].append(link2)\n topology_json[\"link\"].append(link3)\n topology_json[\"link\"].append(link4)\n topology_json[\"link\"].append(link5)\n\n return topology_json","title":""},{"docid":"5d01db9a025e06b058e6b5e105cbd6f3","score":"0.49044645","text":"def test_branch_existing_name():\n atom = ATOMClassifier(X10, y10, random_state=1)\n atom.branch = \"b2\"\n with pytest.raises(ValueError, match=\".*already exists.*\"):\n atom.branch = \"b2_from_master\"","title":""}],"string":"[\n {\n \"docid\": \"a3c052576b04bd128432dd0a90f4608b\",\n \"score\": \"0.6381184\",\n \"text\": \"def mergeBranchToCluster(self, mcid, cluster_id, branch_id, close=1):\\n\\n node_list = Graph.cluster_dict[cluster_id][branch_id]\\n # make all the cluster_id's the same\\n for node in node_list:\\n if type(node)==list: #so if we hit a closed branch\\n for closure_node in node:\\n closure_node.cluster_id = mcid\\n else:\\n node.cluster_id = mcid\\n\\n Graph.cluster_dict[mcid][branch_id] = node_list\\n if close==1:\\n if branch_id not in Graph.closed_keys:\\n Graph.closed_keys.append(branch_id) # then close the key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0c2eed2572a879537af1b9f53a013df\",\n \"score\": \"0.6221104\",\n \"text\": \"def create_split_branch(self, cid, child_father, father_node, line_num, point_list, lame_parent=0):\\n\\n child, parent = child_father\\n cid_father = father_node.cluster_id\\n bid_father = father_node.branch_id\\n\\n # create the new branch key from the flat child value\\n if type(child)==int: # if its an int\\n new_branch_id = child\\n else:\\n new_branch_id = tuple(child) # keys must be hashable\\n\\n # pull out points from diagonal clustered point row\\n for point_class_list in point_list:\\n\\n try:\\n if child == point_class_list.flat_value:\\n child_points = point_class_list\\n\\n except:\\n if child == [x.flat_value for x in point_class_list]:\\n child_points = point_class_list\\n\\n new_node = Node(cid, new_branch_id, father_node, line_num, child_points)\\n\\n if bid_father not in Graph.closed_keys:\\n\\n Graph.closed_keys.append(bid_father)\\n\\n if lame_parent == 1:\\n if father_node not in Graph.lame_duck_parents:\\n Graph.lame_duck_parents.append(father_node)\\n\\n # add the new orphan node\\n Graph.cluster_dict[cid_father][new_branch_id] = [new_node]\\n return new_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de2bf463ab95a68066ef9d2acb511bc8\",\n \"score\": \"0.61214715\",\n \"text\": \"def create_cluster_branch(self, orphan, line_num): # create cluster\\n\\n try:\\n cluster_id = orphan.flat_value\\n except:\\n cluster_id = tuple([x.flat_value for x in orphan])\\n\\n # create a node instance and add it to the graph\\n orphan_node = Node(cluster_id, cluster_id, cluster_id, line_num, orphan)\\n Graph.cluster_dict[cluster_id][cluster_id] = [orphan_node]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5d776e694ddf53f5ed38c55614bf0fb\",\n \"score\": \"0.609247\",\n \"text\": \"def create_new_branch(self, newbranch):\\n # self.update(self.branch)\\n try:\\n self.hg_branch(newbranch)\\n return 'succes'\\n except Exception as e:\\n print(e)\\n return 'failure'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"010fe6d75e4164b6d2bca7b0aad97e9c\",\n \"score\": \"0.60218775\",\n \"text\": \"def test_branch_new():\\n atom = ATOMClassifier(X10, y10, random_state=1)\\n atom.clean()\\n atom.branch = \\\"b2\\\"\\n assert len(atom._branches) == 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82040a95d0372dc244287b54f5aac2fd\",\n \"score\": \"0.5968368\",\n \"text\": \"def merge_cnvs_clusters(vcfs,temp_dir, output_directory, cluster_merge_slop=0):\\n\\n # Quality is lierally the sum of the previous VCF files.\\n\\n basenames = [os.path.basename(x) for x in vcfs]\\n __bedtools_duplication_string__ = \\\"\\\"\\\"awk '{{if ($0 !~ /^#/ && $6 > 100){{ split($8,a,\\\";\\\"); split(a[2],b,\\\"=\\\");print $1,$2,$2+b[2],$3,FILENAME}}}}' {0} | grep duplication | tr ' ' '\\\\t' | sort -k 1,1 -k 2,2g | bedtools cluster -i - -d {1} > tmp_clusters_duplication.txt\\\"\\\"\\\" \\n __bedtools_deletion_string__ = \\\"\\\"\\\"awk '{{if ($0 !~ /^#/ && $6 > 100){{ split($8,a,\\\";\\\"); split(a[2],b,\\\"=\\\");print $1,$2,$2+b[2],$3,FILENAME}}}}' {0} | grep deletion | tr ' ' '\\\\t' | sort -k 1,1 -k 2,2g | bedtools cluster -i - -d {1} > tmp_clusters_deletion.txt\\\"\\\"\\\" \\n __bedtools_all__= \\\"\\\"\\\"awk '{{if ($0 !~ /^#/ && $6 > 100 ){{ split($8,a,\\\";\\\"); split(a[2],b,\\\"=\\\");print $1,$2,$2+b[2],$3,FILENAME}}}}' {0} |tr ' ' '\\\\t' | sort -k 1,1 -k 2,2g | bedtools cluster -i - -d {1} > tmp_clusters.txt\\\"\\\"\\\" \\n bedtools_cmd = __bedtools_deletion_string__.format(\\\" \\\".join(vcfs), cluster_merge_slop)\\n subprocess.check_call(bedtools_cmd, shell=True)\\n bedtools_cmd = __bedtools_duplication_string__.format(\\\" \\\".join(vcfs), cluster_merge_slop)\\n subprocess.check_call(bedtools_cmd, shell=True)\\n bedtools_cmd = __bedtools_all__.format(\\\" \\\".join(vcfs), cluster_merge_slop)\\n subprocess.check_call(bedtools_cmd, shell=True)\\n all_vcfs = {}\\n __vcf_sort__ =\\\"vcf-sort {0} | bgzip -c > {0}.gz && tabix -fp vcf {0}.gz\\\" \\n for vcf_f in vcfs:\\n vcf_sort_command = __vcf_sort__.format(vcf_f)\\n subprocess.check_call(vcf_sort_command,shell=True)\\n all_vcfs[vcf_f] = vcf.VCFSimple(vcf_f + \\\".gz\\\")\\n # Ok now we have all the VCFs in this format.\\n # Fix final files\\n try:\\n os.mkdir(os.path.join(output_directory,\\\"vcfs\\\"))\\n except OSError:\\n pass\\n _process_clustering(\\\"tmp_clusters_duplication.txt\\\",all_vcfs,os.path.join(output_directory,\\\"vcfs\\\",\\\"duplications.vcf\\\"))\\n _process_clustering(\\\"tmp_clusters_deletion.txt\\\",all_vcfs, os.path.join(output_directory, \\\"vcfs\\\",\\\"deletions.vcf\\\"))\\n _process_clustering(\\\"tmp_clusters.txt\\\",all_vcfs, os.path.join(output_directory,\\\"vcfs\\\", \\\"all.vcf\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98d48fa6cbe3b5935aae5b996cdfb2d8\",\n \"score\": \"0.5958116\",\n \"text\": \"def branches(self, nodes):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"401114e45e25f041422d275f653b53e9\",\n \"score\": \"0.5903465\",\n \"text\": \"def merge_clusters(self, c1, c2, new_name=None):\\n if new_name is None:\\n new_name = c1\\n\\n new_clus = self.clu2elm_dict[c1] | self.clu2elm_dict[c2]\\n del self.clu2elm_dict[c1]\\n del self.clu2elm_dict[c2]\\n\\n self.clu2elm_dict[new_name] = new_clus\\n self.from_clu2elm_dict(self.clu2elm_dict)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842134ebd98ecfe066185cddc9c690d7\",\n \"score\": \"0.5872413\",\n \"text\": \"def create_leaf_clusters(module):\\n output = ''\\n non_clustered_leafs = find_non_clustered_leafs(module)\\n non_clustered_leafs_count = 0\\n cli = pn_cli(module)\\n clicopy = cli\\n\\n while non_clustered_leafs_count == 0:\\n if len(non_clustered_leafs) == 0:\\n non_clustered_leafs_count += 1\\n else:\\n node1 = non_clustered_leafs[0]\\n non_clustered_leafs.remove(node1)\\n\\n cli = clicopy\\n cli += ' switch %s lldp-show ' % node1\\n cli += ' format sys-name no-show-headers '\\n system_names = run_cli(module, cli).split()\\n system_names = list(set(system_names))\\n\\n cli = clicopy\\n cli += ' switch %s fabric-node-show ' % node1\\n cli += ' format name no-show-headers '\\n nodes_in_fabric = run_cli(module, cli).split()\\n nodes_in_fabric = list(set(nodes_in_fabric))\\n\\n for system in system_names:\\n if system not in nodes_in_fabric:\\n system_names.remove(system)\\n\\n terminate_flag = 0\\n node_count = 0\\n while (node_count < len(system_names)) and (terminate_flag == 0):\\n node2 = system_names[node_count]\\n if node2 in non_clustered_leafs:\\n # Cluster creation\\n cluster_name = node1 + '-to-' + node2 + '-cluster'\\n output += create_cluster(module, cluster_name, node1, node2)\\n\\n non_clustered_leafs.remove(node2)\\n terminate_flag += 1\\n\\n node_count += 1\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a38e84d519d9599d84867f44c051d4\",\n \"score\": \"0.58268297\",\n \"text\": \"def test_branch_same():\\n atom = ATOMClassifier(X10, y10, random_state=1)\\n atom.branch = \\\"master\\\"\\n assert atom.branch.name == \\\"master\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cef1e5b57168c164e34e9e84871eddc\",\n \"score\": \"0.57808805\",\n \"text\": \"def new_branch_on_tree(\\n self,\\n branch_id,\\n models,\\n pairs_to_compare,\\n model_storage_instances,\\n precomputed_models,\\n spawning_branch,\\n **kwargs\\n ):\\n\\n # Instantiate branch\\n branch = BranchQMLA(\\n branch_id=branch_id,\\n models=models,\\n model_storage_instances=model_storage_instances,\\n precomputed_models=precomputed_models,\\n tree=self,\\n spawning_branch=spawning_branch,\\n pairs_to_compare=pairs_to_compare,\\n **kwargs\\n )\\n\\n # Update trees records\\n self.branches[branch_id] = branch\\n for m in model_storage_instances:\\n self.model_storage_instances[m] = model_storage_instances[m]\\n\\n return branch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed885cb5463080ddee9d0839dc146113\",\n \"score\": \"0.57504135\",\n \"text\": \"def create_cluster(module, switch, name, node1, node2):\\n global CHANGED_FLAG\\n cli = pn_cli(module)\\n clicopy = cli\\n cli += ' switch %s cluster-show format name no-show-headers ' % node1\\n cluster_list = run_cli(module, cli).split()\\n if name not in cluster_list:\\n cli = clicopy\\n cli += ' switch %s cluster-create name %s ' % (switch, name)\\n cli += ' cluster-node-1 %s cluster-node-2 %s ' % (node1, node2)\\n if 'Success' in run_cli(module, cli):\\n CHANGED_FLAG.append(True)\\n return ' %s: %s created successfully \\\\n' % (switch, name)\\n else:\\n return ' %s: %s already exists \\\\n' % (switch, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aebf5280dcf7c9e6489550609cdc3d6d\",\n \"score\": \"0.5667581\",\n \"text\": \"def create_branch(self, branch_name: str = \\\"\\\") -> str:\\n with ChangeCWD(self.content):\\n if not branch_name:\\n branch_name = get_uuid()\\n self.branches.append(branch_name)\\n self.run_command(f\\\"git checkout -b {branch_name}\\\")\\n return branch_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d356b06e3754dd3ad2dcd2fd40e19a55\",\n \"score\": \"0.5651428\",\n \"text\": \"def clone_parent_cluster_2_child_cluster(self):\\n\\n compartment_list_parent = self.inventory.getClusterCells(self.parentCell.clusterId)\\n compartment_list_child = self.inventory.getClusterCells(self.childCell.clusterId)\\n\\n self.clone_cluster_attributes(source_cell_cluster=compartment_list_parent,\\n target_cell_cluster=compartment_list_child,\\n no_clone_key_dict_list=[])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa72da09233e69bc192e7053c9749c47\",\n \"score\": \"0.5650963\",\n \"text\": \"def merge_nodes(self, node1, node2, initial_commit_graph, df):\\n\\n new_commit_graph = copy.deepcopy(initial_commit_graph)\\n\\n # Etapes pour merger les nodes\\n # 1. Get list of out connections with a dict\\n # eg. {node3 : 5, node4 : 6}\\n # 2. Get list of in connections with a dict\\n # 3. Merge nodes\\n\\n # 1 and 2\\n\\n connections = {}\\n\\n index = list(df.index)\\n new_node_row = []\\n\\n for column in df.columns:\\n if df.at[node1, column] == 1 or df.at[node2, column] == 1:\\n new_node_row.append(1)\\n for neighbor in index:\\n if df.at[neighbor, column] == 1 and neighbor not in [node1, node2]:\\n if neighbor not in connections:\\n connections[neighbor] = 1\\n else:\\n connections[neighbor] += 1\\n else:\\n new_node_row.append(0)\\n\\n new_node_row = [new_node_row]\\n\\n\\n '''\\n for neighbor in initial_commit_graph.adj[node1]:\\n if neighbor != node2:\\n if neighbor not in connections:\\n connections[neighbor] = initial_commit_graph.edges[node1, neighbor]['number_modifications_same_commit']\\n else:\\n connections[neighbor] += initial_commit_graph.edges[node1, neighbor]['number_modifications_same_commit']\\n \\n for neighbor in initial_commit_graph.adj[node2]:\\n if neighbor != node1:\\n if neighbor not in connections:\\n connections[neighbor] = initial_commit_graph.edges[node2, neighbor]['number_modifications_same_commit']\\n else:\\n connections[neighbor] += initial_commit_graph.edges[node2, neighbor]['number_modifications_same_commit']\\n '''\\n\\n\\n new_commit_graph.remove_node(node1)\\n new_commit_graph.remove_node(node2)\\n\\n new_node = f'{node1}:{node2}'\\n new_commit_graph.add_node(new_node)\\n\\n new_row = pd.DataFrame(new_node_row, columns=list(df.columns), index=[new_node])\\n new_df = df.drop(labels=[node1, node2])\\n new_df = new_df.append(new_row)\\n\\n for neighbor, num_mod in connections.items():\\n new_commit_graph.add_edge(new_node, neighbor)\\n new_commit_graph.edges[new_node, neighbor]['number_modifications_same_commit'] = num_mod\\n\\n \\n return new_commit_graph, new_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"661956842ea3ef80a761c9dfc0dfe6fa\",\n \"score\": \"0.5607364\",\n \"text\": \"def major_branch():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b2eb186a162ca8f6a7b2885af6762ec\",\n \"score\": \"0.5588885\",\n \"text\": \"def setbranch(self, branch, pbranches):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7454929742310d787e36bdc03da763c\",\n \"score\": \"0.5582486\",\n \"text\": \"def create_cluster(module, name, node1, node2):\\n global CHANGED_FLAG\\n cli = pn_cli(module)\\n clicopy = cli\\n cli += ' switch %s cluster-show format name no-show-headers ' % node1\\n cluster_list = run_cli(module, cli).split()\\n if name not in cluster_list:\\n cli = clicopy\\n cli += ' switch %s cluster-create name %s ' % (node1, name)\\n cli += ' cluster-node-1 %s cluster-node-2 %s ' % (node1, node2)\\n if 'Success' in run_cli(module, cli):\\n CHANGED_FLAG.append(True)\\n return ' %s: Created %s \\\\n' % (node1, name)\\n else:\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0730c4a471d9c9ca37b02c57cfa37d9a\",\n \"score\": \"0.554833\",\n \"text\": \"def merge_similar_branches(branches, orientation_threshold=0.7, distance_threshold=8, branch_number_threshold=10):\\n branches.sort(key=lambda x: -len(x))\\n i = 0\\n while i < len(branches) and len(branches[i]) > branch_number_threshold:\\n i += 1\\n\\n while i < len(branches):\\n j = i + 1\\n while j < len(branches):\\n br_1 = branches[i]\\n br_2 = branches[j]\\n if np.linalg.norm(br_1.center - br_2.center) < distance_threshold and \\\\\\n sum(br_1.direction * br_2.direction) > orientation_threshold:\\n br_1.merge(br_2)\\n del branches[j]\\n else:\\n j += 1\\n i += 1\\n return branches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e2a81f7e6c961a0d71466d5771a4769\",\n \"score\": \"0.54898673\",\n \"text\": \"def set_branch_coordinates(branch,xcoord,ycoord,sepsize,tdistmax):\\n if branch.is_leaf():\\n stempath=PathXYCoords( xvals=[xcoord,xcoord+branch.dist],yvals=[ycoord,ycoord] )\\n branch.r0framecoords=FrameCoords(stem=stempath)\\n #nodebox_xmax=max(xcoord+branch.dist,xcoord+tdistmax*0.025)\\n #nodebox_xmax=max(xcoord+branch.dist,1.025*tdistmax)\\n nodebox_xmax=1.025*tdistmax\\n branch.r0node_edgecoords=PathXYCoords(xyvals=[(xcoord,ycoord-0.5*sepsize),(nodebox_xmax,ycoord-0.5*sepsize),\\n (nodebox_xmax,ycoord+0.5*sepsize),(xcoord,ycoord+0.5*sepsize)])\\n branch.r0branch_edgecoords=branch.r0node_edgecoords.copy()\\n return ycoord,ycoord-sepsize\\n else:\\n xcoord=xcoord+branch.dist\\n sub_branches=branch.children\\n #sorted_sub_branches=depth_sort(sub_branches)\\n ycoord_ascends=[]\\n for sub_branch in sub_branches:#sorted_sub_branches:\\n ycoord_ascend,ycoord_descend=set_branch_coordinates(sub_branch,xcoord,ycoord,sepsize,tdistmax)\\n ycoord=ycoord_descend\\n ycoord_ascends.append(ycoord_ascend)\\n\\n branchstempath=PathXYCoords( xvals=[xcoord-branch.dist,xcoord],\\n yvals=[np.mean(ycoord_ascends),np.mean(ycoord_ascends)] )\\n branchbasepath=PathXYCoords( xvals=[xcoord,xcoord], \\n yvals=[min(ycoord_ascends),max(ycoord_ascends)] )\\n branch.r0framecoords=FrameCoords(stem=branchstempath,base=branchbasepath) \\n node_edgecoords=PathXYCoords(xyvals=[(xcoord-branch.dist,min(ycoord_ascends)),(xcoord,min(ycoord_ascends)),\\n (xcoord,max(ycoord_ascends)),(xcoord-branch.dist,max(ycoord_ascends)) ])\\n branch.r0node_edgecoords=node_edgecoords\\n branch_ymin=min(node_edgecoords.boundbox.ymin,*[subx.r0branch_edgecoords.boundbox.ymin for subx in branch.children])\\n branch_ymax=max(node_edgecoords.boundbox.ymax,*[subx.r0branch_edgecoords.boundbox.ymax for subx in branch.children])\\n branch_xmin=min(node_edgecoords.boundbox.xmin,*[subx.r0branch_edgecoords.boundbox.xmin for subx in branch.children])\\n branch_xmax=max(node_edgecoords.boundbox.xmax,*[subx.r0branch_edgecoords.boundbox.xmax for subx in branch.children])\\n branch.r0branch_edgecoords=PathXYCoords(xyvals=[(branch_xmin,branch_ymin),(branch_xmax,branch_ymin),\\n (branch_xmax,branch_ymax),(branch_xmin,branch_ymax)] ) \\n\\n return np.mean(ycoord_ascends),ycoord_descend\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba98dd8a587528d8c8db95694d7afc1c\",\n \"score\": \"0.5484931\",\n \"text\": \"def join_clusters(self, tuple_cluster):\\n c1 = tuple_cluster[0]\\n c2 = tuple_cluster[1]\\n self.clusters.remove(c1)\\n self.clusters.remove(c2)\\n self.clusters.append(c1 + c2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30be337261a2bcbf8609ecf9f4dc19e5\",\n \"score\": \"0.5481414\",\n \"text\": \"def _new_branch_node(key, value):\\n return {\\n key: value,\\n \\\"_subgroup\\\": []\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0de0246265953ea696efec2beefd67f\",\n \"score\": \"0.54665995\",\n \"text\": \"def branches(self, local=True):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a70279f591f6aa6190e7dd3aad16ed90\",\n \"score\": \"0.5458468\",\n \"text\": \"def draw_hierarchical_edge_bundle(self, name, load_existing=False, join_clusterless_samples=True):\\n\\n if not load_existing:\\n df, commit_to_files, files_mod_dates = self.analyze_correlation(\\n treecommit_analysis=False,\\n commit_analysis=True,\\n commit_lines_analysis=False,\\n get_dataframe=True,\\n get_commit_to_files_dict=True,\\n get_dates=True)\\n # df = self.create_commits_dataframe()\\n df.to_csv(f'./df_{name}.csv')\\n else:\\n df = pd.read_csv(f'./df_{name}', index_col=0)\\n\\n if not load_existing:\\n distance = self.get_distance_matrix(df)\\n distance.to_csv(f'./df_distance_{name}.csv')\\n else:\\n distance = pd.read_csv(f'./df_distance_{name}.csv', index_col=0)\\n \\n clusters, _ = self.cluster_dataframe(\\n distance,\\n method='AggClustering',\\n distance_matrix=True,\\n min_size=3,\\n max_eps=1,\\n join_clusterless_samples=join_clusterless_samples)\\n\\n\\n with open(\\\"./clusters_{name}.txt\\\", \\\"wb\\\") as fp:\\n pickle.dump(clusters, fp) \\n\\n template = '['\\n\\n first_file = True\\n for cluster in clusters.keys():\\n for file_path in clusters[cluster]:\\n\\n parsed_file_path = file_path.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n\\n if first_file:\\n template += '{\\\"name\\\": ' + f'\\\"root${cluster}${parsed_file_path}\\\", \\\"couplings\\\":['\\n first_file = False\\n else:\\n template += ',\\\\n{\\\"name\\\": ' + f'\\\"root${cluster}${parsed_file_path}\\\", \\\"couplings\\\":['\\n\\n first = True\\n for neighbor in self.commit_graph[file_path]:\\n\\n n_cluster = -1\\n for c in clusters.keys():\\n if neighbor in clusters[c]:\\n n_cluster = c\\n break\\n \\n parsed_neighbor = neighbor.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n\\n if first:\\n template += f'\\\"root${n_cluster}${parsed_neighbor}\\\"'\\n first = False\\n else:\\n template += f',\\\"root${n_cluster}${parsed_neighbor}\\\"'\\n \\n \\n template += ']}'\\n\\n template += ']'\\n\\n with open('data.json', \\\"w\\\") as f:\\n f.write(template)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4820eb8409bca7a6e4dba32fcec97fbe\",\n \"score\": \"0.5444103\",\n \"text\": \"def new_branching(self, name, *, leaf=None):\\n raise NotImplementedError(\\\"Branching not implemented in this tree, \\\"\\n \\\"please implement the 'new_branching' method\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aad7d67034fd748c8dea0d78a1c6e888\",\n \"score\": \"0.54238266\",\n \"text\": \"def __init__(\\n self,\\n branch_id,\\n models,\\n model_storage_instances,\\n pairs_to_compare,\\n tree,\\n precomputed_models,\\n spawning_branch,\\n ):\\n\\n self.branch_id = branch_id\\n\\n # Get attributes from this branch's tree\\n self.tree = tree\\n self.log_file = self.tree.log_file\\n self.exploration_class = self.tree.exploration_class\\n self.exploration_strategy = self.exploration_class.exploration_rules\\n\\n self.log_print(\\n [\\n \\\"Branch {} on tree {}\\\".format(self.branch_id, self.tree),\\n ]\\n )\\n\\n # Get parent branch of this branch\\n try:\\n self.parent_branch = self.tree.branches[spawning_branch]\\n self.log_print(\\n [\\n \\\"Setting parent branch of {} -> parent is {}\\\".format(\\n self.branch_id, self.parent_branch.branch_id\\n )\\n ]\\n )\\n except BaseException:\\n self.parent_branch = None\\n self.log_print([\\\"Failed to set parent branch for \\\", branch_id])\\n\\n # Models to place on the branch\\n # TODO tidy up how models passed etc. (currently a bit redundant)\\n self.model_storage_instances = model_storage_instances\\n self.models = models\\n self.models_by_id = models\\n self.model_id_by_name = {self.models_by_id[m]: m for m in self.models_by_id}\\n self.resident_model_ids = sorted(self.models_by_id.keys())\\n self.resident_models = list(self.models_by_id.values())\\n self.num_models = len(self.resident_models)\\n\\n # Choose models to compare within this branch\\n if pairs_to_compare == \\\"all\\\":\\n self.log_print([\\\"All pairs to be compared on branch \\\", self.branch_id])\\n self.pairs_to_compare = list(\\n itertools.combinations(self.resident_model_ids, 2)\\n )\\n else:\\n self.log_print([\\\"Comparison pairs passed:\\\", pairs_to_compare])\\n self.pairs_to_compare = pairs_to_compare\\n # order pairs so they read (low, high)\\n self.pairs_to_compare = [(min(p), max(p)) for p in self.pairs_to_compare]\\n self.num_model_pairs = len(self.pairs_to_compare)\\n\\n # Models already considered on a previous branch\\n self.precomputed_models = precomputed_models\\n self.num_precomputed_models = len(self.precomputed_models)\\n self.unlearned_models = list(\\n set(self.resident_models) - set(self.precomputed_models)\\n )\\n\\n # Interface with QMLA: attributes tp be called/edited continuously\\n self.model_learning_complete = False\\n self.comparisons_complete = False\\n self.bayes_points = {}\\n self.rankings = [] # ordered from best to worst\\n self.result_counter = 0\\n\\n self.log_print(\\n [\\\"New branch {}; models: {}\\\".format(self.branch_id, self.models)]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94f572aeacc292a305fc4fae64a59d1d\",\n \"score\": \"0.5375775\",\n \"text\": \"def create_branch_nodes(self):\\n from spira.yevon.gdsii.sref import SRef\\n from spira.yevon.geometry.ports import Port\\n branch_nodes = list()\\n for n in self.g.nodes():\\n if 'device_reference' in self.g.node[n]:\\n D = self.g.node[n]['device_reference']\\n if isinstance(D, SRef):\\n branch_nodes.append(n)\\n if isinstance(D, Port):\\n branch_nodes.append(n)\\n return branch_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71a597c596704f5577312f210c483223\",\n \"score\": \"0.5373747\",\n \"text\": \"def create_branch(repositoryName=None, branchName=None, commitId=None):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c281f75d79d317de6ec6115df85bda44\",\n \"score\": \"0.5356272\",\n \"text\": \"def mergeClusters(self, c1, c2):\\n\\t\\tassert (c1 in self.members), 'Cluster is not in Cluster Set!'\\n\\t\\tassert (c2 in self.members), 'Cluster is not in Cluster Set!'\\n\\n\\t\\tpoints = [] \\t\\t# list to record points in c1 and c2\\n\\t\\tfor p in c1.members():\\n\\t\\t\\tpoints.append(p)\\n\\t\\tfor p in c2.members():\\n\\t\\t\\tpoints.append(p)\\n\\t\\tnewC = Cluster(points, type(p))\\n\\t\\t# remove old cluster\\n\\t\\tself.members.remove(c1)\\n\\t\\tself.members.remove(c2) \\n\\t\\tself.add(newC)\\n\\n\\t\\treturn c1, c2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99f8ce4985e2a65c947fcb7af5cd4bf9\",\n \"score\": \"0.53385556\",\n \"text\": \"def create_branching_model(self):\\n click.secho(\\\"Applying branching model...\\\", fg='green', nl=False)\\n r = requests.put(\\n f\\\"{self.repo_endpoint}/branching-model/settings\\\",\\n data = json.dumps(self.merged_config['branching-model']),\\n auth = (self.login, self.passwd)\\n )\\n if str(r.status_code).startswith('20'):\\n click.secho(\\\"done.\\\", fg='green')\\n else:\\n click.secho(\\\"HTTP {}\\\".format(r.status_code), fg='bright_red')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25e1391b5859cb0fa54b63a2ad5895dd\",\n \"score\": \"0.53306574\",\n \"text\": \"def __branch_point_dfs(dfs_data):\\n u = dfs_data['ordering'][0]\\n large_n = {}\\n large_n[u] = 0\\n stem = {}\\n stem[u] = u\\n b = {}\\n b[u] = 1\\n __branch_point_dfs_recursive(u, large_n, b, stem, dfs_data)\\n dfs_data['N_u_lookup'] = large_n\\n dfs_data['b_u_lookup'] = b\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84bc28d370a0683049c1a602192c62ec\",\n \"score\": \"0.5322581\",\n \"text\": \"def create_cluster(self, label, rids, **attributes):\\n\\n # default attributes\\n default_attributes = dict(\\n fontsize = 24,\\n style = 'rounded,filled',\\n color = 'lightgrey', )\\n\\n default_attributes.update( **attributes )\\n\\n # if node exists in graph\\n # united_nnames = list( set(self._rid_to_nname.keys()) & set(rids) )\\n united_nnames = [ self._rid_to_nname[rid]\\n for rid in rids if self._rid_to_nname.get(rid) ]\\n\\n # create new graph\\n self._subcount += 1\\n name = 'cluster' + str(self._subcount)\\n # name = 'a' + str(self._subcount)\\n g = self.__graph.subgraph(nbunch=united_nnames, # rank='same',\\n name=name, label=label, **default_attributes)\\n self.__label_to_subgraph[label] = g\\n # g.graph_attr['rank'] = 'same'\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a025f38e30384a3043a80d0222daa19\",\n \"score\": \"0.5314774\",\n \"text\": \"def build_cluster_and_modify_delta_matrix(clusters, delta_matrix, i1, i2):\\r\\n assert i1 != i2\\r\\n # if the two values to concatenate are single values...\\r\\n print(i1)\\r\\n print(i2)\\r\\n print(clusters)\\r\\n if len(clusters[i1]) == 1 and len(clusters[i2]) == 1:\\r\\n # ...just cluster them\\r\\n clusters.append([clusters[i1][0], clusters[i2][0]])\\r\\n else:\\r\\n # get the index values to compare\\r\\n indices = [[None, None], [None, None]]\\r\\n # populate the upper left intersection of index array\\r\\n indices[0][0] = clusters[i1][0]\\r\\n # populate the upper right intersection of index array (if necessary)\\r\\n if len(clusters[i1]):\\r\\n indices[0][1] = clusters[i1][-1]\\r\\n # populate the lower left intersection of index array\\r\\n indices[1][0] = clusters[i2][0]\\r\\n # populate the lower right intersection of index array (if necessary)\\r\\n if len(clusters[i2]):\\r\\n indices[1][1] = clusters[i2][-1]\\r\\n # lookup the intersections of the index values in the distance matrix\\r\\n values = [[None, None], [None, None]]\\r\\n values[0][0] = delta_matrix[indices[0][0]][indices[1][0]]\\r\\n if indices[0][1] != None:\\r\\n values[0][1] = delta_matrix[indices[0][1]][indices[1][0]]\\r\\n if indices[1][1] != None:\\r\\n values[1][0] = delta_matrix[indices[0][0]][indices[1][1]]\\r\\n if indices[0][1] != None and indices[1][1] != None:\\r\\n values[1][1] = delta_matrix[indices[0][1]][indices[1][1]]\\r\\n\\r\\n # find the index of the lowest value in the value array\\r\\n lowest = values[0][0]\\r\\n low_row = 0\\r\\n low_col = 0\\r\\n for j in range(2):\\r\\n for i in range(2):\\r\\n if values[j][i] < lowest:\\r\\n lowest = values[j][i]\\r\\n low_row = i\\r\\n low_col = j\\r\\n\\r\\n # create the new concatenation based on which\\r\\n # index of intValue was the lowest\\r\\n if low_row == 0 and low_col == 0:\\r\\n clusters.append([clusters[i1][-1], clusters[i2][-1]])\\r\\n elif low_row == 0 and low_col == 1:\\r\\n clusters.append([clusters[i1][0], clusters[i2][0]])\\r\\n elif low_row == 1 and low_col == 0:\\r\\n clusters.append([clusters[i1][-1], clusters[i2][-1]])\\r\\n else:\\r\\n clusters.append([clusters[i1][0], clusters[i2][-1]])\\r\\n\\r\\n if i1 < i2:\\r\\n delta_matrix = remove_row_col(delta_matrix, i1)\\r\\n delta_matrix = remove_row_col(delta_matrix, i2)\\r\\n clusters.pop(i1)\\r\\n clusters.pop(i2)\\r\\n else:\\r\\n delta_matrix = remove_row_col(delta_matrix, i2)\\r\\n delta_matrix = remove_row_col(delta_matrix, i1)\\r\\n clusters.pop(i2)\\r\\n clusters.pop(i1)\\r\\n return clusters, delta_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd99e7d1eee7934c32b692bcd68f71c9\",\n \"score\": \"0.5311303\",\n \"text\": \"def merge_small_branches(branches, distance_threshold=20, branch_number_threshold=2):\\n merged_branches = [br for br in branches if len(br) > branch_number_threshold]\\n for i, br in enumerate(branches):\\n if len(br) <= branch_number_threshold:\\n distances = [np.linalg.norm(br.center - br_m.center) for br_m in merged_branches]\\n if min(distances) < distance_threshold:\\n j = distances.index(min(distances))\\n merged_branches[j].merge(br)\\n else:\\n merged_branches.append(br)\\n return merged_branches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafafce908f0b546a7e795e2adfa249a\",\n \"score\": \"0.52979225\",\n \"text\": \"def branches(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafafce908f0b546a7e795e2adfa249a\",\n \"score\": \"0.52979225\",\n \"text\": \"def branches(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6e63529023e5a2245ccd241dd11538a\",\n \"score\": \"0.52936536\",\n \"text\": \"def build_common_part(branch_levels_list, feature_base_count,\\n name=\\\"\\\", weight_decay=None):\\n res_levels = len(branch_levels_list[0])\\n with tf.variable_scope(name):\\n print_debug(name)\\n # Concat branches at each level + add conv layers\\n levels = []\\n for level_index in range(res_levels):\\n print_debug(\\\"\\\\tlevel {}:\\\".format(level_index))\\n concat_a_b = tf.concat([branch_levels[level_index][0] for branch_levels in branch_levels_list], axis=-1,\\n name=\\\"concat_a_b_{}\\\".format(level_index))\\n print_debug(\\\"\\\\t\\\\tconcat_a_b: {}\\\".format(concat_a_b))\\n feature_count = feature_base_count * math.pow(2, level_index)\\n concat_a_b_conv, _ = conv_conv_pool(concat_a_b, [feature_count, feature_count],\\n name=\\\"concat_a_b_conv{}\\\".format(level_index), pool=False,\\n weight_decay=weight_decay)\\n print_debug(\\\"\\\\t\\\\tconcat_a_b_conv: {}\\\".format(concat_a_b_conv))\\n levels.append(concat_a_b_conv)\\n\\n return levels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97c73d03845410e7d311656d37d64b5c\",\n \"score\": \"0.5275886\",\n \"text\": \"def build_multibranch_unet(input_branch_params_list, pool_count, common_feature_base_count, output_branch_params_list,\\n weight_decay=None):\\n # Dropout - controls the complexity of the model, prevents co-adaptation of features.\\n with tf.name_scope('dropout'):\\n keep_prob = tf.placeholder(tf.float32)\\n\\n # Build the separate simple convolution networks for each input:\\n input_branch_levels_list = []\\n for params in input_branch_params_list:\\n tf.summary.histogram(\\\"input_{}\\\".format(params[\\\"name\\\"]), params[\\\"tensor\\\"])\\n branch_levels = build_input_branch(params[\\\"tensor\\\"], params[\\\"feature_base_count\\\"], pool_count,\\n name=\\\"branch_{}\\\".format(params[\\\"name\\\"]),\\n weight_decay=weight_decay)\\n input_branch_levels_list.append(branch_levels)\\n\\n # Build the common part of the network, concatenating inout branches at all levels\\n common_part_levels = build_common_part(input_branch_levels_list,\\n common_feature_base_count,\\n name=\\\"common_part\\\",\\n weight_decay=weight_decay)\\n\\n # Build the splitting part of the network, each level (except the last one) finishing with output branches.\\n # Each branch is like the upsampling part of a U-Net\\n outputs = []\\n for params in output_branch_params_list:\\n branch_levels = build_output_branch(common_part_levels,\\n params[\\\"feature_base_count\\\"],\\n name=\\\"branch_{}\\\".format(params[\\\"name\\\"]),\\n weight_decay=weight_decay)\\n # Add the last layers for prediction, then upsample each levels' prediction to level 0's resolution\\n # TODO: keep this for legacy reasons:\\n if params[\\\"activation\\\"] == tf.identity:\\n name = \\\"branch_{}_pred_logit\\\".format(params[\\\"name\\\"])\\n else:\\n name = \\\"branch_{}_pred\\\".format(params[\\\"name\\\"])\\n level_0_pred, stacked_pred_logits = build_pred_branch(branch_levels,\\n output_channels=params[\\\"channel_count\\\"],\\n name=name)\\n # Apply activation function to logits\\n stacked_preds = params[\\\"activation\\\"](stacked_pred_logits)\\n output = (stacked_pred_logits, stacked_preds, stacked_preds[:, 0, ...])\\n outputs.append(output)\\n\\n return outputs, keep_prob\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a715411d4d8cd193a6d91c8478cd0a1a\",\n \"score\": \"0.526745\",\n \"text\": \"def crossingchromosome(self, a, b):\\n newchromosome = [0] * self.dimChromomsome # new empty crhomosome\\n for x in range(self.dimChromomsome):\\n if random.randint(0, 9) == 1: # 10% chromosome from parent 1\\n newchromosome[x] = self.mutation(a[x])\\n # print(\\\"Gene from Parent_1 =\\\",a[x])\\n elif random.randint(0, 9) == 2: # 10% chromosome from parent 2\\n newchromosome[x] = self.mutation(b[x])\\n # print(\\\"Gene from Parent_2 =\\\",b[x])\\n else:\\n # 80% to avarage 2 Chromosome parents values\\n newchromosome[x] = (a[x] + b[x]) / 2\\n # print(\\\"Gene from Merged genes =\\\",newchromosome[x])\\n newchromosome[x] += self.mutation(newchromosome[x])\\n # print(\\\"newchromosome = \\\",newchromosome)\\n GeneticTreePlot.addedge(str(a), str(newchromosome))\\n GeneticTreePlot.addedge(str(b), str(newchromosome))\\n return newchromosome\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19685687517c3d58d99c8a3e729cb907\",\n \"score\": \"0.5255168\",\n \"text\": \"def test_load_transform_data_multiple_branches():\\n atom = ATOMClassifier(X_bin, y_bin, random_state=1)\\n atom.prune()\\n atom.branch = \\\"b2\\\"\\n atom.balance()\\n atom.feature_generation(strategy=\\\"dfs\\\", n_features=5)\\n atom.branch = \\\"b3\\\"\\n atom.feature_selection(strategy=\\\"sfm\\\", solver=\\\"lgb\\\", n_features=20)\\n atom.save(\\\"atom_2\\\", save_data=False)\\n\\n atom2 = ATOMClassifier.load(\\\"atom_2\\\", data=(X_bin, y_bin), transform_data=True)\\n for branch in atom._branches:\\n pd.testing.assert_frame_equal(\\n left=atom2._branches[branch.name]._data,\\n right=atom._branches[branch.name]._data,\\n check_dtype=False,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49bc6d969f6191687f64510bf0afaa66\",\n \"score\": \"0.52381176\",\n \"text\": \"def branchmap(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22434a092e0041addb53d51af92fbb4b\",\n \"score\": \"0.52338576\",\n \"text\": \"def _initialize_new_branches(self, branches, birth, prev, A, S):\\n\\t\\tg = self.genealogy; m = self.m\\n\\t\\tfor branch in branches:\\n\\t\\t\\tparent, child = branch\\n\\t\\t\\tg[parent][child]['s'] = [ 1.]\\n\\t\\t\\tg[parent][child]['S'] = [1.]\\n\\t\\t\\tg[parent][child]['heights'] = [child.height]\\n\\t\\t\\tif child.is_terminal():\\n\\t\\t\\t\\tg[parent][child]['pik'] = list()\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tg[parent][child]['pik'].append( array(child.state) )\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tpdb.set_trace()\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tself.p_alpha_k = None\\n\\t\\t\\telse:# child is internal node\\n\\t\\t\\t\\tself.p_alpha_k = self._state_at_internal_node_and_likelihood(child, self.genealogy.successors(child), birth, prev, A, S)\\n\\t\\t\\t\\tg[parent][child]['pik'] = [ self.p_alpha_k ] \\n\\t\\t\\t#\\n\\t\\t#\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee2e68e16d87b5e7ba936a711c5047ac\",\n \"score\": \"0.52258694\",\n \"text\": \"def __embed_branch(dfs_data):\\n u = dfs_data['ordering'][0]\\n dfs_data['LF'] = []\\n dfs_data['RF'] = []\\n dfs_data['FG'] = {}\\n n = dfs_data['graph'].num_nodes()\\n f0 = (0, n)\\n g0 = (0, n)\\n L0 = {'u': 0, 'v': n}\\n R0 = {'x': 0, 'y': n}\\n dfs_data['LF'].append(f0)\\n dfs_data['RF'].append(g0)\\n dfs_data['FG'][0] = [L0, R0]\\n dfs_data['FG']['m'] = 0\\n dfs_data['FG']['l'] = 0\\n dfs_data['FG']['r'] = 0\\n\\n #print 'DFS Ordering: {}'.format(dfs_data['ordering'])\\n #for node in dfs_data['ordering']:\\n #print '{}: {}'.format(node, dfs_data['adj'][node])\\n\\n nonplanar = __embed_branch_recursive(u, dfs_data)\\n\\n #print \\\"Nonplanar:\\\", nonplanar\\n\\n return not nonplanar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"811d7a2c68b65df9eeaa95f17678fb87\",\n \"score\": \"0.5218667\",\n \"text\": \"def test_branch_without_remote(git_worktree):\\n foo1 = git_worktree.create_git_project(\\\"foo\\\")\\n branch = Branch()\\n branch.name = \\\"master\\\"\\n branch.default = True\\n foo1.branches = [branch]\\n foo1.apply_config()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270ca5439662cd88e8b5823be80c1d2c\",\n \"score\": \"0.51959795\",\n \"text\": \"def breed(tree1, tree2):\\n cpy = tree1.copy()\\n\\n start_depth = random.randint(0, MAX_DEPTH-2)\\n\\n node1_parent = cpy.get_left_node_at_depth(start_depth)\\n node2 = tree2.get_left_node_at_depth(start_depth+1)\\n node1_parent.left = node2\\n\\n return cpy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f883a2b400d483c4a92039aee1c35e43\",\n \"score\": \"0.51894337\",\n \"text\": \"def hierarchical_clustering(features, k):\\n\\n\\n\\n N, D = features.shape\\n\\n assert N >= k, 'Number of clusters cannot be greater than number of points'\\n\\n # Assign each point to its own cluster\\n assignments = np.arange(N)\\n centers = np.copy(features)\\n n_clusters = N\\n\\n while n_clusters > k:\\n ### YOUR CODE HERE\\n\\n # 构建邻近度矩阵\\n # print(centers.shape)\\n dist = pdist(centers) # 欧氏距离\\n # print(dist.shape)\\n distMatrix = squareform(dist)\\n distMatrix = np.where(distMatrix!=0.0, distMatrix, 1e5) # 因为squareform构建的邻近度矩阵上对角线是零，影响了最短距离的判断，此处做处理\\n # print(distMatrix.shape)\\n\\n # 获取最小值所在的行和列，即距离最近的两个簇的index，取较小的那个作为保留，较大的那个进行合并\\n minRow,minCol = np.unravel_index(distMatrix.argmin(), distMatrix.shape)\\n # print(minRow,minCol)\\n saveIdx = min(minRow,minCol)\\n mergeIdx = max(minRow,minCol)\\n # 将簇mergeIdx的点分配给saveIdx所在簇\\n assignments = np.where(assignments != mergeIdx, assignments, saveIdx)\\n # 因为要删除一个簇mergeIdx，所以下标的变化为:小于mergeIdx的不改变，大于saveIdx的需要减一\\n assignments = np.where(assignments < mergeIdx, assignments, assignments - 1)\\n\\n # 删除被合并的簇所在中心\\n centers = np.delete(centers,mergeIdx, axis=0)\\n\\n # 重新计算新的簇的中心\\n # 题目提示中提到了，考虑到运行效率，只需要对进行归并后的簇计算新的中心，别的簇并没有进行操作，所以不影响中心的变化\\n saveIdxIndecies = np.where(assignments == saveIdx)\\n centers[saveIdx] = np.mean(features[saveIdxIndecies],axis=0)\\n\\n # 别忘了迭代终止条件\\n n_clusters -= 1\\n\\n\\n ### END YOUR CODE\\n\\n return assignments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3699f37a73cb5118f27be9f34ed2acd5\",\n \"score\": \"0.5180988\",\n \"text\": \"def build_branch(self, left_node, right_node):\\n if len(self.elems) > 1:\\n print('Operation is valir for singe node tree only')\\n return self\\n self.elems.extend(left_node.elems)\\n self.elems.extend(right_node.elems)\\n self.elems[0].left = self.elems[1]\\n self.elems[1].parent = self.elems[0]\\n self.elems[0].right = self.elems[len(left_node.elems)+1]\\n self.elems[len(left_node.elems) + 1].parent = self.elems[0]\\n self.elems[0].weight = left_node.elems[0].weight + right_node.elems[0].weight\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ec19904ea663aed4ff0ac99ee5b133\",\n \"score\": \"0.51808137\",\n \"text\": \"def create_new_group(self, a, b):\\n self.groups[self.group_id] = set([a,b])\\n self.node_id[a] = self.node_id[b] = self.group_id\\n self.group_id += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fff87163af0fea04b44db119809cd82c\",\n \"score\": \"0.5172267\",\n \"text\": \"def join(g1, g2, L_max=0.99):\\n node_prefix_1 = tools.get_node_prefix(g1)\\n node_prefix_2 = tools.get_node_prefix(g2)\\n if node_prefix_1 != node_prefix_2:\\n for item in g2.keys():\\n _, prop = item.split('.', 1)\\n g2[node_prefix_1 + '.' + item] = g2.pop(item)\\n node_prefix = node_prefix_1\\n edge_prefix_1 = tools.get_edge_prefix(g1)\\n edge_prefix_2 = tools.get_edge_prefix(g2)\\n if edge_prefix_1 != edge_prefix_2:\\n for item in g2.keys():\\n _, prop = item.split('.', 1)\\n g2[edge_prefix_1 + '.' + item] = g2.pop(item)\\n edge_prefix = edge_prefix_1\\n\\n # Perform neighbor query\\n from scipy.spatial import KDTree\\n t1 = KDTree(g1[node_prefix+'.coords'])\\n t2 = KDTree(g2[node_prefix+'.coords'])\\n pairs = t1.query_ball_tree(t2, r=L_max)\\n # Combine existing network data\\n net3 = {}\\n Np1 = g1[node_prefix+'.coords'].shape[0]\\n Np2 = g2[node_prefix+'.coords'].shape[0]\\n Nt1 = g1[edge_prefix+'.conns'].shape[0]\\n Nt2 = g2[edge_prefix+'.conns'].shape[0]\\n net3[node_prefix+'.coords'] = \\\\\\n np.vstack((g1.pop(node_prefix+'.coords'),\\n g2.pop(node_prefix+'.coords')))\\n net3[edge_prefix+'.conns'] = \\\\\\n np.vstack((g1.pop(edge_prefix+'.conns'),\\n g2.pop(edge_prefix+'.conns') + Np1))\\n # Convert kdtree result into new connections\\n nnz = sum([len(row) for row in pairs])\\n conns = np.zeros((nnz, 2), dtype=int)\\n i = 0\\n for j, row in enumerate(pairs):\\n for col in row:\\n conns[i, :] = j, col + Np1\\n i += 1\\n # Add new connections to network\\n net3[edge_prefix+'.conns'] = \\\\\\n np.vstack((net3.pop(edge_prefix+'.conns'), conns))\\n\\n # Finally, expand any other data arrays on given networks\\n items = list(g1.keys()) + list(g2.keys())\\n N = {node_prefix: [Np1, Np2], edge_prefix: [Nt1, Nt2]}\\n for item in items:\\n prefix, attr = item.split('.', 1)\\n if prefix == node_prefix:\\n temp1 = g1.pop(item, None)\\n temp2 = g2.pop(item, None)\\n if prefix == edge_prefix:\\n temp1 = g1.pop(item, None)\\n temp2 = g2.pop(item, None)\\n if temp1 is None:\\n blank = False if temp2.dtype == bool else np.nan\\n temp1 = np.zeros(N[prefix][0], dtype=type(blank))*blank\\n elif temp2 is None:\\n blank = False if temp1.dtype == bool else np.nan\\n temp2 = np.zeros(N[prefix][1], dtype=type(blank))*blank\\n net3[item] = np.concatenate((temp1, temp2), axis=0)\\n return net3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6dd2bdfe6ac3f41064ded99a0eb40fc\",\n \"score\": \"0.5162444\",\n \"text\": \"def hierarchical_clustering(cluster_list, num_clusters):\\n \\n \\n while len(cluster_list) > num_clusters:\\n cluster_list.sort(key = lambda x:x.horiz_center())\\n dist = fast_closest_pair(cluster_list)\\n \\n cluster_list[dist[1]].merge_clusters(cluster_list[dist[2]])\\n del cluster_list[dist[2]]\\n return cluster_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2ce830cbb4a9c04d06265264022cbec\",\n \"score\": \"0.51514846\",\n \"text\": \"def hierarchical_clustering(cluster_list, num_clusters):\\n while len(cluster_list) > num_clusters:\\n closest_pair = fast_closest_pair(cluster_list)\\n cluster_i = cluster_list[closest_pair[1]]\\n cluster_j = cluster_list[closest_pair[2]]\\n cluster_i_union_j = cluster_i.merge_clusters(cluster_j)\\n cluster_list.append(cluster_i_union_j)\\n cluster_list.remove(cluster_i)\\n cluster_list.remove(cluster_j)\\n return cluster_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5822ccef51a274bcc6eee2cd5f3f2ef4\",\n \"score\": \"0.51499665\",\n \"text\": \"def join_cells(args):\\n\\tglobal conn, cur\\n\\tnewid, cellids = args\\n\\n\\t#add new cell at centroid of the cluster\\n\\tcur.execute(\\\"WITH clustered_antennas AS (SELECT ST_Union(eant_pos_original.geom) AS geom FROM eant_pos_original WHERE eant_pos_original.id IN %(cluster)s) \\\\\\n\\t\\t\\t\\t INSERT INTO eant_pos (id, lon, lat, geom) \\\\\\n\\t\\t\\t\\t SELECT %(id)s AS id, \\\\\\n\\t\\t\\t\\t \\t\\tST_X(ST_Centroid(clustered_antennas.geom)) AS lon, \\\\\\n\\t\\t\\t\\t \\t\\tST_Y(ST_Centroid(clustered_antennas.geom)) AS lat, \\\\\\n\\t\\t\\t\\t \\t\\tST_Centroid(clustered_antennas.geom) AS geom \\\\\\n\\t\\t\\t\\t FROM clustered_antennas\\\", {\\\"cluster\\\": tuple(cellids), \\\"id\\\": newid})\\n\\tconn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d054ddee2621a68f627d283aeda6a09\",\n \"score\": \"0.5144397\",\n \"text\": \"def new_branches(node): \\n for pot_node in not_in_min_span_tree: \\n pot_weight = random.uniform(0.0, 1.0)\\n if pot_weight < branches[pot_node][0]:\\n branches[pot_node] = (pot_weight, node)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85a169011ca78fc14d37c7a9d28b6a6e\",\n \"score\": \"0.51311713\",\n \"text\": \"def checkout_new_branch(self, index: int) -> None:\\n executor.check_call([\\\"git\\\", \\\"branch\\\", \\\"-f\\\", self.sub_branch(index)])\\n executor.check_call([\\\"git\\\", \\\"checkout\\\", self.sub_branch(index)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"748f16cce4140edf9e52ab1ab31a231b\",\n \"score\": \"0.5131006\",\n \"text\": \"def cross_river(_starting_bank, _ending_bank, operator):\\r\\n bank1 = bank2 = []\\r\\n _starting_bank = list(_starting_bank)\\r\\n _ending_bank = list(_ending_bank)\\r\\n operator = list(operator)\\r\\n\\r\\n if 'b' in _starting_bank:\\r\\n bank1 = _starting_bank\\r\\n bank2 = _ending_bank\\r\\n elif 'b' in _ending_bank:\\r\\n bank1 = _ending_bank\\r\\n bank2 = _starting_bank\\r\\n\\r\\n bank1.remove('b')\\r\\n bank2.append('b')\\r\\n\\r\\n while any(operator):\\r\\n op = operator.pop()\\r\\n if op in bank1:\\r\\n bank1.remove(op)\\r\\n bank2.append(op)\\r\\n else:\\r\\n return None\\r\\n\\r\\n return build_node(_starting_bank, _ending_bank)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2c82285b573d0957911a88004ed0bf3\",\n \"score\": \"0.5124543\",\n \"text\": \"def _new_branching_node(self, branching, branch_name):\\n if branching is None:\\n branching = self.new_branching(branch_name)\\n elif isinstance(branching, self.leafnode_type):\\n branching = self.new_branching(branch_name, leaf=branching)\\n elif isinstance(branching, self.branching_type):\\n pass\\n else:\\n raise TypeError(\\\"Branching can only be typ: \\\",\\n f\\\"'None', '{self.leafnode_type}', '{self.branching_type}'\\\")\\n #\\n return branching\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3115235a5aed5048bafebdb2b13dd66\",\n \"score\": \"0.5122976\",\n \"text\": \"def test_branch_change():\\n atom = ATOMClassifier(X10, y10, random_state=1)\\n atom.branch = \\\"b2\\\"\\n atom.clean()\\n atom.branch = \\\"master\\\"\\n assert atom.pipeline.empty # Has no Cleaner\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1707fb4ec6ccba28aa84568818b622f3\",\n \"score\": \"0.51163745\",\n \"text\": \"def create_worktree(branch=BRANCH, print_help=True, *, b=False):\\n # If using master branch, then simply symlink to the existing clone\\n\\n branch_directory = WORKTREES_DIRECTORY / branch\\n\\n def _create_worktree():\\n if branch == \\\"master\\\":\\n def symlink_master():\\n branch_directory.symlink_to(\\\"../repo\\\", target_is_directory=True)\\n\\n guarded_call(\\\"symlink_master\\\", symlink_master, \\\"Symlinking workspace for master\\\")\\n\\n else:\\n if b:\\n subprocess.run(\\n [\\\"git\\\", \\\"worktree\\\", \\\"add\\\", str(branch_directory), \\\"-b\\\", branch],\\n cwd=str(REPO_PATH),\\n check=True,\\n )\\n\\n subprocess.run(\\n [\\\"git\\\", \\\"push\\\", \\\"-u\\\", \\\"origin\\\", branch],\\n cwd=str(REPO_PATH),\\n check=True,\\n )\\n else:\\n subprocess.run(\\n [\\\"git\\\", \\\"fetch\\\", \\\"origin\\\", \\\"{}:{}\\\".format(branch, branch), \\\"--\\\"],\\n cwd=str(REPO_PATH),\\n check=True,\\n )\\n\\n subprocess.run(\\n [\\\"git\\\", \\\"worktree\\\", \\\"add\\\", str(branch_directory), branch],\\n cwd=str(REPO_PATH),\\n check=True,\\n )\\n\\n subprocess.run(\\n [\\\"git\\\", \\\"branch\\\", \\\"-u\\\", \\\"origin/{}\\\".format(branch), branch],\\n cwd=str(REPO_PATH),\\n check=True,\\n )\\n\\n # Change git paths to relative paths so they work inside the docker container\\n (branch_directory / \\\".git\\\").write_text(\\\"gitdir: ../../repo/.git/worktrees/{}\\\".format(branch))\\n (REPO_PATH / \\\".git\\\" / \\\"worktrees\\\" / branch / \\\"gitdir\\\").write_text(\\\"../worktrees/{}\\\".format(branch))\\n\\n guarded_call(\\n get_worktree_guard(branch),\\n _create_worktree,\\n \\\"Creating workspace for branch {}\\\".format(branch)\\n )\\n\\n if print_help:\\n print('\\\\nYou can now run this command to move to the worktree:\\\\n\\\\n' +\\n 'cd {}\\\\n\\\\n'.format(branch_directory) +\\n 'Add this line to your .bashrc or .zshrc for a shortcut:\\\\n\\\\n' +\\n 'ccd() {\\\\n' +\\n ' $HOME/cuauv/workspaces/repo/docker/auv-docker.py create-worktree $1 False\\\\n' +\\n ' cd $HOME/cuauv/workspaces/worktrees/$1\\\\n' +\\n '}\\\\n'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7165f06caae3aa79e6b21010e640b037\",\n \"score\": \"0.51103425\",\n \"text\": \"def push_newbranch(self):\\n try:\\n self.hg_command('push', '--new-branch')\\n except Exception as e:\\n print(e)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dab3ec91eaddad7ce407ad1610874c1c\",\n \"score\": \"0.5089777\",\n \"text\": \"def insertB(self, kwargs = {} ):\\n with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:\\n try:\\n \\\"\\\"\\\" create an empty branch \\\"\\\"\\\"\\n cur.execute(\\\"INSERT INTO branches DEFAULT VALUES RETURNING id\\\")\\n b_id = cur.fetchone()['id']\\n self.conn.commit()\\n newB = self.getB(b_id)\\n except psycopg2.Error as e:\\n raise CantExecuteQ(e, self.conn)\\n else:\\n try:\\n \\\"\\\"\\\" fill in the new branch \\\"\\\"\\\"\\n for key in kwargs :\\n newB.__setattr__(key, kwargs[key])\\n\\n if newB.parent_id != None:\\n \\\"\\\"\\\" couple the new branch to the tree \\\"\\\"\\\"\\n parentB = self.getB(newB.parent_id)\\n parentB.subbs_id = parentB.subbs_id + [newB.id]\\n\\n return newB \\n except:\\n self.removeB(newB.id)\\n raise CantExecuteQ()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2663d911c56dc386e85d8c65a152b9f\",\n \"score\": \"0.5089191\",\n \"text\": \"def merge(self, a, b):\\n old_id, target_id = sorted((self.node_id[a], self.node_id[b]), key = lambda id: len(self.groups[id]))\\n for node in self.groups[old_id]:\\n self.node_id[node] = target_id\\n self.groups[target_id] |= self.groups[old_id]\\n del self.groups[old_id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce33ec37a1dc1508538507f9e865e073\",\n \"score\": \"0.50758666\",\n \"text\": \"def extract_branches(self, params):\\n self.logger.info(\\\"Split and group centerlines along branches ...\\\");\\n branch_extractor = vmtkscripts.vmtkBranchExtractor()\\n branch_extractor.Centerlines = self.geometry \\n branch_extractor.Execute()\\n self.branch_geometry = branch_extractor.Centerlines\\n #print(self.centerlines_branch_geometry)\\n self.logger.info(\\\"The centerlines branches have been calculated.\\\");\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1ec5483e24215723ff24c03e3b44a96\",\n \"score\": \"0.50720096\",\n \"text\": \"def merging_branches(self, branch2, branch3) -> pd.DataFrame:\\n self.df = self.df.merge(\\n branch2[[\\\"score2\\\", \\\"Img_number\\\"]],\\n how=\\\"left\\\",\\n on=\\\"Img_number\\\",\\n left_index=True,\\n )\\n self.df = self.df.merge(\\n branch3[[\\\"score3\\\", \\\"Img_number\\\"]],\\n how=\\\"left\\\",\\n on=\\\"Img_number\\\",\\n left_index=True,\\n )\\n return self.df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b923d484bddc1bdc89061d3ac9a873a\",\n \"score\": \"0.5071974\",\n \"text\": \"def test_two_clusters(self) -> None:\\n with Cluster():\\n with Cluster():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76eb5d0227602128b825e95fda313586\",\n \"score\": \"0.50619376\",\n \"text\": \"def _getBranchMode(scriptargs):\\n if scriptargs.has_key(\\\"branch\\\") and scriptargs[\\\"branch\\\"]:\\n return hou.stateGenerateMode.Branch\\n else:\\n return hou.stateGenerateMode.Insert\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c382401437494da99688831a8575c7c8\",\n \"score\": \"0.50610524\",\n \"text\": \"def cluster_join(self):\\n cluster_add_node = netapp_utils.zapi.NaElement.create_node_with_children(\\n 'cluster-join', **{'cluster-ip-address': self.cluster_ip_address})\\n\\n try:\\n self.server.invoke_successfully(cluster_add_node, enable_tunneling=True)\\n return True\\n except netapp_utils.zapi.NaApiError as error:\\n # Error 36503 denotes node already being used.\\n if to_native(error.code) == \\\"36503\\\":\\n return False\\n else:\\n self.module.fail_json(msg='Error adding node to cluster %s: %s'\\n % (self.cluster_name, to_native(error)),\\n exception=traceback.format_exc())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96ecb36d0fdcc6c9c5e0088122bbd35f\",\n \"score\": \"0.50466996\",\n \"text\": \"def _merge_clusters(self,dict_x,dict_reduced_cost,dict_dual_val):\\n list_added = self.cluster_merger.merge_clusters(dict_x,dict_reduced_cost,self.iteration)\\n for new_cluster_id in list_added:\\n self.cluster_operation.improve_cluster(cluster_id=new_cluster_id,\\n stop_dual_cost= dict_dual_val,\\n iteration=self.iteration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1a7a2125c94a4df0ab201a106461f5e\",\n \"score\": \"0.5044488\",\n \"text\": \"def merge_and_delete_branch(self, branch: str) -> None:\\n self.command(\\\"checkout\\\", self._default_branch)\\n self.command(\\\"merge\\\", branch)\\n self.command(\\\"push\\\", \\\"origin\\\", self._default_branch)\\n self.command(\\\"push\\\", \\\"-d\\\", \\\"origin\\\", branch)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3207443ea5acdeca264d4419218dffe2\",\n \"score\": \"0.5040457\",\n \"text\": \"def add_node(cls, cluster_id, branch_id, node):\\n\\n try: # try to pull the node list out\\n\\n node_list = cls.cluster_dict[cluster_id][branch_id]\\n\\n if node not in node_list:\\n node_list.append(node)\\n cls.cluster_dict[cluster_id][branch_id]= node_list\\n\\n except: # if no node list exists then just add\\n cls.cluster_dict[cluster_id][branch_id] = [node]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d05fb2c0f98b8020038215ac5b574b8a\",\n \"score\": \"0.50328434\",\n \"text\": \"def create_branch(version):\\n run('svn cp %(svn_repository)s/trunk %(svn_repository)s/branches/%(version)s '\\n '-m \\\"[ADD] Create new branch %(version)s\\\"'\\n % {'svn_repository': env.svn_repository, 'version': version})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a89296103e0c93d6415ba097e6dd027f\",\n \"score\": \"0.50177205\",\n \"text\": \"def addBranch(self, branch):\\n if self._isLeaf:\\n return false\\n else:\\n self._branches.append(branch)\\n self._branches[-1].setParent(self)\\n if (self._label == None):\\n self._label = branch.getLabel()\\n else:\\n self._label = self.mergeLabels(self._label, branch.getLabel()) #\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ac12989b5d79a86ac625db0186bbd1c\",\n \"score\": \"0.501689\",\n \"text\": \"def create_branch(branch_name: str, repo_dir: str):\\n\\n logger.info(f\\\"Creating {branch_name}\\\")\\n git_new_branch = subprocess.run(\\n [\\\"git\\\", \\\"checkout\\\", \\\"-b\\\", branch_name],\\n cwd=Path(working_dir, repo_dir),\\n capture_output=True,\\n )\\n logger(git_new_branch.stdout.decode(\\\"utf-8\\\").strip(\\\"\\\\n\\\").replace(\\\"\\\\n\\\", \\\" \\\"))\\n\\n # If error, write it out.\\n if git_new_branch.returncode:\\n err_msg = git_new_branch.stderr.decode(\\\"utf-8\\\").strip(\\\"\\\\n\\\").replace(\\\"\\\\n\\\", \\\" \\\")\\n logger.error(err_msg)\\n raise RuntimeError(err_msg)\\n\\n else:\\n logger.info(\\n git_new_branch.stdout.decode(\\\"utf-8\\\").strip(\\\"\\\\n\\\").replace(\\\"\\\\n\\\", \\\" \\\")\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eeb0ade802bb02984fc7e52dcff4a39\",\n \"score\": \"0.50124407\",\n \"text\": \"def df_add_cluster(df,arr_clusters):\\n\\n if 'cluster' in df.columns:\\n del(df['cluster'])\\n\\n #------------------------------------------------------------------- \\n # Checking for Nan values \\n #------------------------------------------------------------------- \\n is_nan_in_arr=np.isnan(df.values)\\n\\n df_segment=pd.DataFrame(arr_clusters, index=df.index, columns=['cluster'])\\n df_=pd.concat([df_segment,df], axis=1, join='inner')\\n\\n \\n return df_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b48bbae001c717b358696a8180582254\",\n \"score\": \"0.50122\",\n \"text\": \"def inflate_agent_clusters(cp, cs):\\n\\n # node clusters with agent positions\\n clusters = {\\n c: set(cp.graph.agents[r] for r in r_list)\\n for c, r_list in cs.agent_clusters.items()\\n }\\n\\n child_clusters = {c: set() for c in cs.agent_clusters.keys()}\\n parent_clusters = {}\\n\\n # start with master cluster active\\n active_clusters = [\\n c for c, r_list in cs.agent_clusters.items() if cp.master in r_list\\n ]\\n\\n while True:\\n # check if active cluster can activate other clusters directly\\n found_new = True\\n while found_new:\\n found_new = False\\n for c0, c1 in product(active_clusters, clusters.keys()):\\n if c0 == c1 or c1 in active_clusters:\\n continue\\n for v0, v1 in product(clusters[c0], clusters[c1]):\\n if cp.graph.has_conn_edge(v0, v1):\\n active_clusters.append(c1)\\n child_clusters[c0].add((c1, v1))\\n parent_clusters[c1] = (c0, v0)\\n found_new = True\\n\\n # all active nodes\\n active_nodes = set.union(*[clusters[c] for c in active_clusters])\\n free_nodes = set(cp.graph.nodes) - set.union(*clusters.values())\\n if cs.dead_nodes is not None:\\n free_nodes -= cs.dead_nodes\\n\\n # make sure clusters are connected via transitions, if not split\\n for c, v_set in clusters.items():\\n c_subg = cp.graph_tran.subgraph(v_set | free_nodes).to_undirected()\\n\\n comps = nx.connected_components(c_subg)\\n comp_dict = {\\n i: [r for r in cs.agent_clusters[c] if cp.graph.agents[r] in comp]\\n for i, comp in enumerate(comps)\\n }\\n\\n comp_dict = {i: l for i, l in comp_dict.items() if len(l) > 0}\\n\\n if len(comp_dict) > 1:\\n # split and restart\\n del cs.agent_clusters[c]\\n for i, r_list in comp_dict.items():\\n cs.agent_clusters[\\\"{}_{}\\\".format(c, i + 1)] = r_list\\n return inflate_agent_clusters(cp, cs) # recursive call\\n\\n # find closest neighbor and activate it\\n neighbors = cp.graph.post_tran(active_nodes) - active_nodes\\n if cs.dead_nodes is not None:\\n neighbors -= cs.dead_nodes\\n\\n if len(neighbors) == 0:\\n break # nothing more to do\\n\\n new_cluster, new_node, min_dist = -1, -1, 99999\\n for c in active_clusters:\\n c_neighbors = cp.graph.post_tran(clusters[c])\\n\\n agent_positions = [cp.graph.agents[r] for r in cs.agent_clusters[c]]\\n\\n for n in neighbors & c_neighbors:\\n\\n dist, _ = nx.multi_source_dijkstra(\\n cp.graph_tran, sources=agent_positions, target=n\\n )\\n if dist < min_dist:\\n min_dist, new_node, new_cluster = dist, n, c\\n\\n clusters[new_cluster].add(new_node)\\n\\n cs.subgraphs = clusters\\n cs.child_clusters = child_clusters\\n cs.parent_clusters = parent_clusters\\n\\n return cs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c4abb1735e700d7b8deddb0570bba9\",\n \"score\": \"0.5011986\",\n \"text\": \"def branches(web):\\n entries = webutil.branchentries(web.repo, web.stripecount)\\n latestentry = webutil.branchentries(web.repo, web.stripecount, 1)\\n\\n return web.sendtemplate(\\n b'branches',\\n node=hex(web.repo.changelog.tip()),\\n entries=entries,\\n latestentry=latestentry,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37c4b783c9a7151449f919387b2d496c\",\n \"score\": \"0.5005945\",\n \"text\": \"def test_branch_creation(self):\\n resp = self.client().post('/branches/', data=self.branch)\\n self.assertEqual(resp.status_code, 201)\\n self.assertIn('London', str(resp.data))\\n self.assertIn('SW4 0PE', str(resp.data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ff3a5cfd8a282818b85c9681ac1da43\",\n \"score\": \"0.49959946\",\n \"text\": \"def branch_filter(new_node):\\n branches[new_node] = (2, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84c6d2c8139ec5550ff6a75160745eeb\",\n \"score\": \"0.49932057\",\n \"text\": \"def create_cluster(self, context, server_list):\\n # Add hostname here.\\n for ser in server_list:\\n ser_ref = db.init_node_get(context, ser['id'])\\n ser['host'] = ser_ref['host']\\n\\n\\n\\n def _update(status):\\n LOG.debug('status = %s' % status)\\n self._update_server_list_status(context,\\n server_list,\\n status)\\n if status.lower().find('error') != -1:\\n raise\\n\\n # Set at least 3 mons when creating cluster\\n pool_default_size = db.vsm_settings_get_by_name(context,'osd_pool_default_size')\\n pool_default_size = int(pool_default_size.value)\\n nums = len(server_list)\\n if nums >= pool_default_size:\\n count = 0\\n rest_mon_num = 0\\n for ser in server_list:\\n if ser['is_monitor'] == True:\\n count += 1\\n if count < pool_default_size:\\n rest_mon_num = pool_default_size - count\\n if rest_mon_num > 0:\\n for ser in server_list:\\n if ser['is_monitor'] == False:\\n ser['is_monitor'] = True\\n rest_mon_num -= 1\\n if rest_mon_num <= 0:\\n break\\n # Use mkcephfs to set up ceph system.\\n LOG.info('server_list111 = %s' % server_list)\\n monitor_node = self._select_monitor(context, server_list)\\n LOG.info('Choose monitor node = %s' % monitor_node)\\n # Clean ceph data.\\n def __clean_data(host):\\n self._agent_rpcapi.update_ssh_keys(context, host)\\n self._agent_rpcapi.clean_ceph_data(context, host)\\n\\n def __create_crushmap(context, server_list, host):\\n self._agent_rpcapi.create_crushmap(context,\\n server_list=server_list,\\n host=host)\\n\\n try:\\n _update(\\\"Cleaning\\\")\\n thd_list = []\\n for ser in server_list:\\n thd = utils.MultiThread(__clean_data, host=ser['host'])\\n thd_list.append(thd)\\n utils.start_threads(thd_list)\\n _update(\\\"Clean success\\\")\\n except:\\n _update(\\\"ERROR: Cleaning\\\")\\n\\n # When clean data, we also begin to create ceph.conf\\n # and init osd in db.\\n # Do not run with the same time as clean_data.\\n # It maybe cleaned by clean_data.\\n try:\\n _update(\\\"Create ceph.conf\\\")\\n self._agent_rpcapi.inital_ceph_osd_db_conf(context,\\n server_list=server_list,\\n host=monitor_node['host'])\\n _update(\\\"Create ceph.conf success\\\")\\n except:\\n _update(\\\"ERROR: ceph.conf\\\")\\n\\n try:\\n _update(\\\"create crushmap\\\")\\n # Then begin to create crush map file.\\n create_crushmap = utils.MultiThread(__create_crushmap,\\n context=context,\\n server_list=server_list,\\n host=monitor_node['host'])\\n create_crushmap.start()\\n except:\\n _update(\\\"ERROR: crushmap\\\")\\n\\n try:\\n # Begin to mount disks on the mount_point.\\n _update(\\\"Mount disks\\\")\\n def __mount_disk(host):\\n self._agent_rpcapi.mount_disks(context, host)\\n\\n thd_list = []\\n for ser in server_list:\\n thd = utils.MultiThread(__mount_disk, host=ser['host'])\\n thd_list.append(thd)\\n utils.start_threads(thd_list)\\n _update(\\\"Mount disks success\\\")\\n except:\\n _update(\\\"ERROR: mount disk\\\")\\n\\n # Generate monitor keyring file.\\n try:\\n _update(\\\"start montior\\\")\\n monitor_keyring = utils.gen_mon_keyring()\\n def __write_monitor_keyring(host):\\n self._agent_rpcapi.write_monitor_keyring(context,\\n monitor_keyring,\\n host)\\n\\n thd_list = []\\n for ser in server_list:\\n thd = utils.MultiThread(__write_monitor_keyring, host=ser['host'])\\n thd_list.append(thd)\\n utils.start_threads(thd_list)\\n _update(\\\"start monitor success\\\")\\n except:\\n _update(\\\"ERROR: start monitor\\\")\\n\\n try:\\n _update(\\\"Create keyring\\\")\\n self._track_monitors(context, server_list)\\n\\n # Here we use our self-define dir for ceph-monitor services.\\n # So we need to create the key ring by command.\\n self._agent_rpcapi.create_keyring(context,\\n host=monitor_node['host'])\\n\\n self._agent_rpcapi.upload_keyring_admin_into_db(context,\\n host=monitor_node['host'])\\n\\n def _update_keyring_from_db(host):\\n self._agent_rpcapi.update_keyring_admin_from_db(context,\\n host=host)\\n\\n thd_list = []\\n for ser in server_list:\\n thd = utils.MultiThread(_update_keyring_from_db,\\n host=ser['host'])\\n thd_list.append(thd)\\n utils.start_threads(thd_list)\\n _update(\\\"Success: keyring\\\")\\n except:\\n _update(\\\"ERROR: keyring\\\")\\n\\n try:\\n self._agent_rpcapi.prepare_osds(context,\\n server_list,\\n host=monitor_node['host'])\\n\\n # Begin to start osd service.\\n _update('Start osds')\\n def __start_osd(host):\\n self._agent_rpcapi.start_osd(context, host)\\n\\n thd_list = []\\n for ser in server_list:\\n thd = utils.MultiThread(__start_osd, host=ser['host'])\\n thd_list.append(thd)\\n utils.start_threads(thd_list)\\n _update('OSD success')\\n except:\\n _update(\\\"ERROR: start osd\\\")\\n\\n # add mds service\\n try:\\n _update(\\\"Start mds\\\")\\n LOG.info('start mds services, host = %s' % monitor_node['host'])\\n self._agent_rpcapi.add_mds(context, host=monitor_node['host'])\\n except:\\n _update(\\\"ERROR: mds\\\")\\n # Created begin to get ceph status\\n\\n try:\\n _update('Ceph status')\\n stat = self._agent_rpcapi.get_ceph_health(context,\\n monitor_node['host'])\\n except:\\n _update('ERROR: ceph -s')\\n\\n if stat == False:\\n self._update_server_list_status(context,\\n server_list,\\n \\\"Ceph Start Error\\\")\\n LOG.error('Ceph starting failed!')\\n raise\\n\\n try:\\n _update('Set crushmap')\\n # Wait until it's created over.\\n while create_crushmap.is_alive():\\n time.sleep(1)\\n\\n def __set_crushmap(context, host):\\n self._agent_rpcapi.set_crushmap(context,\\n host)\\n _update('Active')\\n set_crushmap = utils.MultiThread(__set_crushmap,\\n context=context,\\n host=monitor_node['host'])\\n set_crushmap.start()\\n except:\\n _update('ERROR: set crushmap')\\n\\n self._update_init_node(context, server_list)\\n while set_crushmap.is_alive():\\n time.sleep(1)\\n self._agent_rpcapi.update_all_status(context,\\n host=monitor_node['host'])\\n self._agent_rpcapi.update_zones_from_crushmap_to_db(context,None,\\n monitor_node['host'])\\n self._agent_rpcapi.update_storage_groups_from_crushmap_to_db(context,None,\\n monitor_node['host'])\\n self._judge_drive_ext_threshold(context)\\n self._update_drive_ext_threshold(context)\\n return {'message':'res'}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f908c9b6701adc09a281783e91d07d31\",\n \"score\": \"0.49922085\",\n \"text\": \"def collapse_branches(overlay):\\n structure_map = {}\\n\\n for node in overlay.nodes:\\n if len(node.inputs) <= 1: continue\\n gcd = node.gcd\\n if all(inp == gcd or inp.inputs == set([gcd]) for inp in node.inputs):\\n branches = [inp if inp != gcd else None\\n for inp in overlay.sorted(node.inputs)]\\n structure_map[node] = OverlayStructure(\\\"HeadBranch\\\", {\\\"branches\\\" : branches, \\\"head\\\": node})\\n\\n for node in overlay.nodes:\\n if len(node.consumers) <= 1: continue\\n if all(len(out.consumers) == 0 for out in node.consumers):\\n branches = overlay.sorted(node.consumers)\\n max_node = overlay.sorted(branches)[-1]\\n structure_map[max_node] = OverlayStructure(\\\"TailBranch\\\", {\\\"branches\\\" : branches, \\\"tail\\\": node})\\n\\n return overlay.collapse_structures(structure_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c4c1b3919450a02480bc59cc0c42d2e\",\n \"score\": \"0.49843\",\n \"text\": \"def branch(*args, **kwargs) -> Any:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e6a07afca2f253acd268d468292d60c\",\n \"score\": \"0.4981726\",\n \"text\": \"def __insert_branch(u, v, dfs_data):\\n w = L1(v, dfs_data)\\n d_u = D(u, dfs_data)\\n d_w = D(w, dfs_data)\\n\\n # Embed uw\\n successful = __embed_frond(u, w, dfs_data)\\n if not successful:\\n return False\\n\\n # Embed a branch marker uu on the side opposite to uw, in the same frond block\\n #successful = __embed_frond(u, v, dfs_data, as_branch_marker=True)\\n successful = __embed_frond(u, u, dfs_data, as_branch_marker=True)\\n if not successful:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce0398a488fa868b32226fa4b7112825\",\n \"score\": \"0.49749976\",\n \"text\": \"def _create_new_clusters(self,dict_reduced_cost,dict_dual_val):\\n list_created = self._create_clusters_based_covertness(dict_reduced_cost, dict_dual_val)\\n list_created.extend(self._create_cluster_based_dual_value(dict_dual_val))\\n for cluster_id in list_created:\\n self.cluster_operation.improve_cluster(cluster_id=cluster_id,\\n stop_dual_cost= dict_dual_val,\\n iteration=self.iteration)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9906a2099d9c80eaa5d2bcb9233fb5\",\n \"score\": \"0.4972335\",\n \"text\": \"def join_dqlite_master_node(info, master_ip):\\n\\n # The cluster we want to join may be either token-auth based or x509-auth based.\\n # The way to identify the cluster type is to look for the \\\"admin_token\\\" in the info\\n # we got back from the cluster we try to join.\\n # In the case of token-auth we need to:\\n # - create the known_tokens.csv file (if it does not exist) with the admin token\\n # - turn on token-auth on kube-apiserver\\n # - create the token based admin kubeconfig\\n # - recreate the kubelet, proxy, scheduler, controller kubeconfigs for the new ca\\n # - restart kubelite\\n # In the case of x509-auth we need to:\\n # - recreate the admin/client, kubelet, proxy, scheduler, controller kubeconfigs for the new ca\\n # - restart kubelite\\n\\n hostname_override = info[\\\"hostname_override\\\"]\\n store_cert(\\\"ca.crt\\\", info[\\\"ca\\\"])\\n store_cert(\\\"ca.key\\\", info[\\\"ca_key\\\"])\\n store_cert(\\\"serviceaccount.key\\\", info[\\\"service_account_key\\\"])\\n\\n if \\\"admin_token\\\" in info:\\n # We try to join a cluster where token-auth is in place.\\n rebuild_token_based_auth_configs(info)\\n else:\\n # We are joining a x509-auth based cluster\\n rebuild_x509_auth_client_configs()\\n\\n update_apiserver(info.get(\\\"api_authz_mode\\\"), info.get(\\\"apiport\\\"))\\n store_base_kubelet_args(info[\\\"kubelet_args\\\"])\\n update_kubelet_node_ip(info[\\\"kubelet_args\\\"], hostname_override)\\n update_kubelet_hostname_override(info[\\\"kubelet_args\\\"])\\n store_callback_token(info[\\\"callback_token\\\"])\\n\\n if \\\"etcd_servers\\\" in info:\\n set_arg(\\\"--etcd-servers\\\", info[\\\"etcd_servers\\\"], \\\"kube-apiserver\\\")\\n if info.get(\\\"etcd_ca\\\"):\\n store_cert(\\\"remote-etcd-ca.crt\\\", info[\\\"etcd_ca\\\"])\\n set_arg(\\\"--etcd-cafile\\\", \\\"${SNAP_DATA}/certs/remote-etcd-ca.crt\\\", \\\"kube-apiserver\\\")\\n if info.get(\\\"etcd_cert\\\"):\\n store_cert(\\\"remote-etcd.crt\\\", info[\\\"etcd_cert\\\"])\\n set_arg(\\\"--etcd-certfile\\\", \\\"${SNAP_DATA}/certs/remote-etcd.crt\\\", \\\"kube-apiserver\\\")\\n if info.get(\\\"etcd_key\\\"):\\n store_cert(\\\"remote-etcd.key\\\", info[\\\"etcd_key\\\"])\\n set_arg(\\\"--etcd-keyfile\\\", \\\"${SNAP_DATA}/certs/remote-etcd.key\\\", \\\"kube-apiserver\\\")\\n\\n mark_no_dqlite()\\n service(\\\"restart\\\", \\\"k8s-dqlite\\\")\\n service(\\\"restart\\\", \\\"apiserver\\\")\\n else:\\n update_dqlite(info[\\\"cluster_cert\\\"], info[\\\"cluster_key\\\"], info[\\\"voters\\\"], hostname_override)\\n\\n # We want to update the local CNI yaml but we do not want to apply it.\\n # The cni is applied already in the cluster we join\\n try_initialise_cni_autodetect_for_clustering(master_ip, apply_cni=False)\\n mark_no_cert_reissue()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8262f6f1a3d5364aca92fa22f705e44a\",\n \"score\": \"0.4970496\",\n \"text\": \"def compare_branches(self, revision_to_check, branch_base):\\n raise NotImplementedError(\\\"Abstract method\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c79566d1f6505043a5646a76967827fd\",\n \"score\": \"0.49671867\",\n \"text\": \"def draw_clusters(clusters):\\n bjp_pos = read_file(collect.BJP_POS_USER_FILE)['results']\\n set_bjp_pos = set(bjp_pos)\\n bjp_neg = read_file(collect.BJP_NEG_USER_FILE)['results']\\n set_bjp_neg = set(bjp_neg)\\n con_pos = read_file(collect.CON_POS_USER_FILE)['results']\\n set_con_pos = set(con_pos)\\n con_neg = read_file(collect.CON_NEG_USER_FILE)['results']\\n set_con_neg = set(con_neg)\\n count = 2\\n for cluster in clusters:\\n cluster_bjp_pos = set()\\n cluster_bjp_neg = set()\\n cluster_con_pos = set()\\n cluster_con_neg = set()\\n cluster_neutral = set()\\n for n in cluster.nodes():\\n if n in set_bjp_pos:\\n cluster_bjp_pos.add(n)\\n elif n in set_bjp_neg:\\n cluster_bjp_neg.add(n)\\n elif n in set_con_pos:\\n cluster_con_pos.add(n)\\n elif n in set_con_neg:\\n cluster_con_neg.add(n)\\n else:\\n cluster_neutral.add(n)\\n draw_graph(cluster, cluster_bjp_neg, cluster_bjp_pos, cluster_con_neg, cluster_con_pos, cluster_neutral, count,\\n 'cluster_' + str(count - 1), 'community detection - cluster '+ str(count - 1) + '\\\\n Neutral Users - Purple | '\\n 'Positive for BJP - Green | '\\n 'Negative for BJP - Red | \\\\n '\\n 'Positive for Congress - Blue | '\\n 'Negative for Congress - Yellow ')\\n count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f15a11a23dc98957e678af5829fa20\",\n \"score\": \"0.496507\",\n \"text\": \"def change_branch(self, *args, **kwargs):\\n raise Unimplemented('Unimplemented in dlkit.services - args=' + str(args) + ', kwargs=' + str(kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f15a11a23dc98957e678af5829fa20\",\n \"score\": \"0.496507\",\n \"text\": \"def change_branch(self, *args, **kwargs):\\n raise Unimplemented('Unimplemented in dlkit.services - args=' + str(args) + ', kwargs=' + str(kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de75f566a1e8f21634f386d6ae8ddd57\",\n \"score\": \"0.49609905\",\n \"text\": \"def prune_branches(skel, branch_points=None, iterations=1):\\n skel = skel > 0\\n if skel.ndim == 2:\\n from skimage.morphology import square as cube\\n else:\\n from skimage.morphology import cube\\n # Create empty image to house results\\n im_result = sp.zeros_like(skel)\\n # If branch points are not supplied, attempt to find them\\n if branch_points is None:\\n branch_points = spim.convolve(skel*1.0, weights=cube(3)) > 3\\n branch_points = branch_points*skel\\n # Store original branch points before dilating\\n pts_orig = branch_points\\n # Find arcs of skeleton by deleting branch points\\n arcs = skel*(~branch_points)\\n # Label arcs\\n arc_labels = spim.label(arcs, structure=cube(3))[0]\\n # Dilate branch points so they overlap with the arcs\\n branch_points = spim.binary_dilation(branch_points, structure=cube(3))\\n pts_labels = spim.label(branch_points, structure=cube(3))[0]\\n # Now scan through each arc to see if it's connected to two branch points\\n slices = spim.find_objects(arc_labels)\\n label_num = 0\\n for s in slices:\\n label_num += 1\\n # Find branch point labels the overlap current arc\\n hits = pts_labels[s]*(arc_labels[s] == label_num)\\n # If image contains 2 branch points, then it's not a tail.\\n if len(sp.unique(hits)) == 3:\\n im_result[s] += arc_labels[s] == label_num\\n # Add missing branch points back to arc image to make complete skeleton\\n im_result += skel*pts_orig\\n if iterations > 1:\\n iterations -= 1\\n im_temp = sp.copy(im_result)\\n im_result = prune_branches(skel=im_result,\\n branch_points=None,\\n iterations=iterations)\\n if sp.all(im_temp == im_result):\\n iterations = 0\\n return im_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96b8fdeeaa6899dd7aee2d794763a571\",\n \"score\": \"0.49608564\",\n \"text\": \"def create_release_branch(self, branch_name):\\n self.call_in_repo_dir(['git', 'checkout', self.remote_master_branch])\\n self.call_in_repo_dir(['git', 'checkout', '-b', branch_name])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"caa5ce8194fa5bf1262f877e2d99a6a2\",\n \"score\": \"0.49593517\",\n \"text\": \"def cluster(data, base_labels, config, params):\\n\\n if isinstance(data, list):\\n modifier = 'mini'\\n else:\\n modifier = 'full'\\n\\n seed = params.get('seed', 42)\\n random.seed(seed)\\n\\n num_clusters = 0\\n\\n clustering_labels, birch = test_clusters(config, data, modifier, params)\\n\\n model_file = os.path.join(\\n constants.dir_d,\\n constants.dir_mod,\\n 'birch_{}_{}_{}.pkl'.format(modifier, num_clusters, len(data))\\n )\\n joblib.dump(birch, model_file)\\n\\n if modifier == 'mini':\\n data = None\\n\\n return clustering_labels, birch, modifier, data, 'euclidean'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65e8490d6109558222635da72d7104ab\",\n \"score\": \"0.49574256\",\n \"text\": \"def generate_clusters(self):\\n\\n self.cluster_labels = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28b5b820761a5a9ec43b9eb0a762183b\",\n \"score\": \"0.49568474\",\n \"text\": \"def shallow_clone(self, remote_location, branch):\\n\\n parsed = urllib.parse.urlparse(remote_location)\\n\\n pool_manager = PoolManager(ca_certs=certifi.where())\\n pool_manager.headers['Cookie'] = self.auth_cookie\\n # Suppress ResourceWarning\\n pool_manager.headers['Connection'] = 'close'\\n\\n client = HttpGitClient.from_parsedurl(\\n parsed, config=self.get_config_stack(), pool_manager=pool_manager)\\n fetch_result = client.fetch(\\n parsed.path, self, determine_wants=lambda mapping:\\n [mapping[REF_HEADS_PREFIX + _B(branch)]], depth=1)\\n stripped_refs = strip_peeled_refs(fetch_result.refs)\\n branches = {\\n n[len(REF_HEADS_PREFIX):]: v\\n for (n, v) in stripped_refs.items()\\n if n.startswith(REF_HEADS_PREFIX)\\n }\\n self.refs.import_refs(REF_REMOTES_PREFIX + DEFAULT_REMOTE_NAME, branches)\\n self[HEAD] = self[REF_REMOTES_PREFIX + DEFAULT_REMOTE_NAME + b'/' +\\n _B(branch)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"281b301185bbd5e5f085b4bd2e7bb54d\",\n \"score\": \"0.49451664\",\n \"text\": \"def create(self, name, base=None, fetch=False):\\n # :todo:rethink: feature branch is not required to start at `develop`\\n return super(FeatureBranchManager, self).create(\\n name, base, fetch=fetch, must_be_on_default_base=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e8aebf32869ba48b249d409918af95e\",\n \"score\": \"0.49421617\",\n \"text\": \"def step(self):\\n distance_list = []\\n min_distance = float(\\\"inf\\\")\\n \\n length = len(self.clusters)\\n for i, element in enumerate(self.clusters):\\n for otherelement in self.clusters[i+1:]:\\n #print \\\"%s - %s = %s\\\" % (element.elements[0].value, otherelement.elements[0].value, element - otherelement)\\n if element is not otherelement:\\n distance = element - otherelement\\n if distance < min_distance:\\n # a new minimum record\\n distance_list = [ (element, otherelement, distance) ]\\n min_distance = distance\\n elif distance == min_distance:\\n # Same value, this will be merged too.\\n distance_list.append( (element, otherelement, distance) )\\n \\n if distance_list:\\n element = distance_list.pop(0) \\n c1 = element[0]\\n c2 = element[1]\\n distance = element[2]\\n \\n self.output.write(\\\"%0.4f %0.4f %0.4f;\\\" % (c1.id, c2.id, distance))\\n \\n c_new = c1 + c2\\n c_new.id = self.last_cluster_id\\n self.last_cluster_id += 1\\n self.clusters.append(c_new)\\n \\n self.clusters.remove(c1) # remove cluster as it is merged. Following steps wont see it.\\n self.clusters.remove(c2)\\n \\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"406b354c1877e77c381de16d3a396648\",\n \"score\": \"0.49404743\",\n \"text\": \"def distributed_tree_rBCM(experts, weightfunction, \\n clusterspec, worker_job='worker', param_server_job='ps', \\n target_job=None, target_protocol='grpc'):\\n num_worker_tasks = len(clusterspec.job_tasks(worker_job))\\n if isinstance(experts, GPModel):\\n architecture = [num_worker_tasks]\\n else:\\n assert len(experts) % num_worker_tasks == 0\\n architecture = [num_worker_tasks, len(experts) // num_worker_tasks]\\n\\n rBCM = tree_rBCM(experts, weightfunction, architecture)\\n\\n rBCM.tf_graph = tf.Graph()\\n \\n target = (clusterspec.job_tasks(target_job)[0] if target_job \\n else clusterspec.job_tasks(parameter_server_job)[0])\\n rBCM.tf_session_target = '{}://{}'.format(target_protocol, target)\\n\\n for index, child in enumerate(rBCM.child.children):\\n child.tf_device = tf.DeviceSpec(job=worker_job, task=index)\\n\\n for index, param in enumerate(rBCM.params):\\n i = index % len(clusterspec.job_tasks(param_server_job))\\n param.tf_device = tf.DeviceSpec(job=param_server_job, task=i)\\n\\n return rBCM\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a044674c3a251a860ba97fc916b4528\",\n \"score\": \"0.4929801\",\n \"text\": \"def get_super_regions(dsets, settings, batch_key):\\n super_features = {}\\n\\n # 1) super cluster key: 'chr1-10518929', for example\\n # 2) the other datasets where this cluster appears (hela nucl etc)\\n\\n cluster_file = os.path.join(settings.here, 'merge_dir', 'merged_pA_' + batch_key)\\n temp_cluster_file = cluster_file + '_temp'\\n\\n out_handle = open(cluster_file, 'wb')\\n\\n for cell_line, comp_dict in dsets.items():\\n for comp, utr_dict in comp_dict.items():\\n for feature_id, feature in utr_dict.iteritems():\\n\\n # store every foudn feature in super_features for lalter\\n if feature_id not in super_features:\\n feature.super_clusters = [] # superclusters later\\n super_features[feature_id] = feature\\n\\n # Create the super_key and store the super_cluster information\\n for cls in feature.clusters:\\n fromwhere = '_'.join([cell_line, comp])\\n\\n PAS = '--'.join(cls.nearby_PAS)\\n PAS_distance = '--'.join([str(d) for d in cls.PAS_distance])\\n\\n name = '%'.join([fromwhere,\\n feature_id,\\n cls.tail_type,\\n cls.tail_info,\\n PAS,\\n PAS_distance,\\n str(cls.annotated_polyA_distance)])\\n # also add PAS_distance and PAS_type\\n out_handle.write('\\\\t'.join([cls.chrm,\\n str(cls.polyA_coordinate),\\n str(cls.polyA_coordinate+1),\\n name,\\n str(cls.nr_support_reads),\\n cls.strand+'\\\\n'\\n ]))\\n out_handle.close()\\n\\n junctions = os.path.join(settings.here, 'junctions',\\n 'splice_junctions_merged.bed')\\n\\n # i.5) cut away the exon-exon noise\\n cmd = ['subtractBed', '-a', cluster_file, '-b', junctions]\\n #p1 = Popen(cmd, stdout=open(temp_cluster_file, 'wb'), stderr=PIPE)\\n p1 = Popen(cmd, stdout=open(temp_cluster_file, 'wb'))\\n p1.wait()\\n #err1 = p1.stderr.read()\\n #if err1:\\n #debug()\\n\\n # ii) expand the entries\\n cmd = ['slopBed', '-i', temp_cluster_file, '-g', settings.hg19_path, '-b', '15']\\n p2 = Popen(cmd, stdout=open(cluster_file, 'wb'))\\n p2.wait()\\n #err2 = p2.stderr.read()\\n #if err2:\\n #debug()\\n\\n # iii) merge and max scores\\n cmd = ['mergeBed', '-nms', '-s', '-scores', 'sum', '-i', cluster_file]\\n #cmd = ['mergeBed', '-nms', '-s', '-scores', 'max', '-i', cluster_file]\\n #p3 = Popen(cmd, stdout=PIPE, stderr=PIPE)\\n p3 = Popen(cmd, stdout=PIPE)\\n\\n # iv) read therough the centered output and update the super_cluster\\n for line in p3.stdout:\\n (chrm, beg, end, names, maxcovrg, strand) = line.split()\\n\\n center = int((int(beg)+int(end))/2)\\n polyA_coverage = int(float(maxcovrg))\\n\\n dsets = []\\n f_ids = []\\n tail_types = []\\n tail_infos = []\\n nearby_PASes = []\\n PAS_distances = []\\n annot_distances = []\\n\\n name_list = names.split(';')\\n for names2 in name_list:\\n (dset, f_id, t_type, t_info, nearby_PAS, PAS_distance, annot_dist)\\\\\\n = names2.split('%')\\n dsets.append(dset)\\n f_ids.append(f_id)\\n tail_types.append(t_type)\\n tail_infos.append(t_info)\\n nearby_PASes.append(nearby_PAS)\\n PAS_distances.append(PAS_distance)\\n annot_distances.append(annot_dist)\\n\\n feature_id = set(f_ids).pop()\\n\\n # create cluster object\\n cls = Super(dsets, center, polyA_coverage, tail_types, tail_infos,\\n strand, nearby_PASes, PAS_distances, annot_distances)\\n\\n # add the cluster to super_featuress\\n super_features[feature_id].super_clusters.append(cls)\\n\\n # any errors?\\n #err3 = p3.stderr.read()\\n #if err3:\\n #debug()\\n\\n return super_features\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a698c40bc14c60d75d76ffc6de5cf3a7\",\n \"score\": \"0.49256802\",\n \"text\": \"def test_can_assign_branch(self):\\n self.client().post('/cars/', data=self.car)\\n self.client().post('/branches/', data=self.branch)\\n resp = self.client().put('/cars/1/branches/1')\\n self.assertEqual(resp.status_code, 200)\\n self.assertEqual(json.loads(resp.data)['currently_with'],\\n '\\\"{ '\\n 'type: branch, '\\n 'id: 1, '\\n 'city: London, '\\n 'postcode: SW4 0PE '\\n '}\\\"'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dfd3f02e7a84d75f8690c2fb4a1774a\",\n \"score\": \"0.49248463\",\n \"text\": \"def create_deploy_branch(deploy_branch, push=True):\\n if not deploy_branch_exists(deploy_branch):\\n print(\\\"Creating {} branch on doctr_remote\\\".format(deploy_branch))\\n clear_working_branch()\\n run(['git', 'checkout', '--orphan', DOCTR_WORKING_BRANCH])\\n # delete everything in the new ref. this is non-destructive to existing\\n # refs/branches, etc...\\n run(['git', 'rm', '-rf', '.'])\\n print(\\\"Adding .nojekyll file to working branch\\\")\\n run(['touch', '.nojekyll'])\\n run(['git', 'add', '.nojekyll'])\\n run(['git', 'commit', '-m', 'Create new {} branch with .nojekyll'.format(deploy_branch)])\\n if push:\\n print(\\\"Pushing working branch to remote {} branch\\\".format(deploy_branch))\\n run(['git', 'push', '-u', 'doctr_remote', '{}:{}'.format(DOCTR_WORKING_BRANCH, deploy_branch)])\\n # return to master branch and clear the working branch\\n run(['git', 'checkout', 'master'])\\n run(['git', 'branch', '-D', DOCTR_WORKING_BRANCH])\\n # fetch the remote so that doctr_remote/{deploy_branch} is resolved\\n run(['git', 'fetch', 'doctr_remote'])\\n\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b86922d4db8a9f7b7bd3a41d850c047b\",\n \"score\": \"0.49045464\",\n \"text\": \"def create_json_topology():\\n\\n # Instantiate the topology with JSON structure.\\n # \\\"entity\\\" will contain nodes of the cluster\\n # \\\"link\\\" will contain network connexions between nodes\\n topology_json = {\\\"entity\\\": [],\\n \\\"link\\\": []}\\n\\n sensor_dev = {\\\"id\\\": 0, \\\"model\\\": \\\"sensor-device\\\", \\\"IPT\\\": 100 * 10 ^ 6, \\\"RAM\\\": 4000, \\\"COST\\\": 3, \\\"WATT\\\": 40.0}\\n cloud_dev_1 = {\\\"id\\\": 1, \\\"model\\\": \\\"cloud\\\", \\\"mytag\\\": \\\"cloud1\\\", \\\"IPT\\\": 5000 * 10 ^ 6, \\\"RAM\\\": 40000, \\\"COST\\\": 3, \\\"WATT\\\": 20.0}\\n cloud_dev_2 = {\\\"id\\\": 2, \\\"model\\\": \\\"cloud\\\", \\\"mytag\\\": \\\"cloud2\\\", \\\"IPT\\\": 5000 * 10 ^ 6, \\\"RAM\\\": 40000, \\\"COST\\\": 3, \\\"WATT\\\": 20.0}\\n actuator_dev_1 = {\\\"id\\\": 3, \\\"model\\\": \\\"actuator-device-1\\\", \\\"IPT\\\": 100 * 10 ^ 6, \\\"RAM\\\": 4000, \\\"COST\\\": 3, \\\"WATT\\\": 40.0}\\n actuator_dev_2 = {\\\"id\\\": 4, \\\"model\\\": \\\"actuator-device-2\\\", \\\"IPT\\\": 100 * 10 ^ 6, \\\"RAM\\\": 4000, \\\"COST\\\": 3, \\\"WATT\\\": 40.0}\\n\\n link1 = {\\\"s\\\": 0, \\\"d\\\": 1, \\\"BW\\\": 1, \\\"PR\\\": 2}\\n link2 = {\\\"s\\\": 0, \\\"d\\\": 2, \\\"BW\\\": 1, \\\"PR\\\": 4}\\n link3 = {\\\"s\\\": 1, \\\"d\\\": 3, \\\"BW\\\": 1, \\\"PR\\\": 2}\\n link4 = {\\\"s\\\": 1, \\\"d\\\": 4, \\\"BW\\\": 1, \\\"PR\\\": 10}\\n link5 = {\\\"s\\\": 2, \\\"d\\\": 4, \\\"BW\\\": 1, \\\"PR\\\": 6}\\n\\n topology_json[\\\"entity\\\"].append(sensor_dev)\\n topology_json[\\\"entity\\\"].append(cloud_dev_1)\\n topology_json[\\\"entity\\\"].append(cloud_dev_2)\\n topology_json[\\\"entity\\\"].append(actuator_dev_1)\\n topology_json[\\\"entity\\\"].append(actuator_dev_2)\\n\\n topology_json[\\\"link\\\"].append(link1)\\n topology_json[\\\"link\\\"].append(link2)\\n topology_json[\\\"link\\\"].append(link3)\\n topology_json[\\\"link\\\"].append(link4)\\n topology_json[\\\"link\\\"].append(link5)\\n\\n return topology_json\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d01db9a025e06b058e6b5e105cbd6f3\",\n \"score\": \"0.49044645\",\n \"text\": \"def test_branch_existing_name():\\n atom = ATOMClassifier(X10, y10, random_state=1)\\n atom.branch = \\\"b2\\\"\\n with pytest.raises(ValueError, match=\\\".*already exists.*\\\"):\\n atom.branch = \\\"b2_from_master\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1688,"cells":{"query_id":{"kind":"string","value":"3755e75101ba7ee74b0543b79d5cc561"},"query":{"kind":"string","value":"Return list of GET parameters that should be removed from querystring"},"positive_passages":{"kind":"list like","value":[{"docid":"d53a7ac1ac0903af71c3a29c647c9a20","score":"0.7678245","text":"def get_querystring_parameter_to_remove(self):\n return self.del_query_parameters + [self.sort_parameter]","title":""}],"string":"[\n {\n \"docid\": \"d53a7ac1ac0903af71c3a29c647c9a20\",\n \"score\": \"0.7678245\",\n \"text\": \"def get_querystring_parameter_to_remove(self):\\n return self.del_query_parameters + [self.sort_parameter]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6d9cb29b157f9a0901208652d3c8ceb7","score":"0.7658783","text":"def remove_from_query(context, *args, **kwargs):\n query_params = []\n # go through current query params..\n for key, value_list in context[\"request\"].GET._iterlists():\n # skip keys mentioned in the args\n if not key in args:\n for value in value_list:\n # skip key-value pairs mentioned in kwargs\n if not (key in kwargs and unicode(value) == unicode(kwargs[key])):\n query_params.append((key, value))\n # empty values will be removed\n query_string = context[\"request\"].path\n if len(query_params):\n query_string = \"?%s\" % urllib.urlencode([\n (key, force_str(value)) for (key, value) in query_params if value\n ]).replace(\"&\", \"&\")\n return query_string","title":""},{"docid":"4ba6f66cd5e06aa27f122018f77c3167","score":"0.75023717","text":"def get_querystring(self):\n to_remove = self.get_querystring_parameter_to_remove()\n query_string = urlparse(self.request.get_full_path()).query\n if six.PY2:\n query_dict = parse_qs(query_string.encode('utf-8'))\n else:\n query_dict = parse_qs(query_string)\n for arg in to_remove:\n if arg in query_dict:\n del query_dict[arg]\n clean_query_string = urlencode(query_dict, doseq=True)\n return clean_query_string","title":""},{"docid":"895bf5ed9541a3738713f8cdb8d1991e","score":"0.72274697","text":"def remove_from_query(context, *args, **kwargs):\n query_params = []\n # go through current query params..\n for key, value_list in context[\"request\"].GET.lists():\n # skip keys mentioned in the args\n if key not in args:\n for value in value_list:\n # skip key-value pairs mentioned in kwargs\n if not (key in kwargs and\n str(value) == str(kwargs[key])):\n query_params.append((key, value))\n return construct_query_string(context, query_params)","title":""},{"docid":"7bb5c4cbc0bac794d325468877807a7e","score":"0.7213651","text":"def query_params(context, exclude=[\"page\"]):\n params = context[\"request\"].GET.copy()\n\n if \"page\" in params:\n del params[\"page\"]\n\n return params.urlencode()","title":""},{"docid":"1c7200ca9eb32199a7edabfe9f2fe888","score":"0.7164342","text":"def _clean_query_params(self) -> dict:\n\n new_query_params = {}\n query_params = dict(self._request.query_params)\n for key, value in query_params.items():\n new_query_params[key] = value.pop(0)\n\n return new_query_params","title":""},{"docid":"07b81e4c317c316e5a926e2f3e697c59","score":"0.68729335","text":"def get_query_string(self, new_params=None, remove=None):\n if new_params is None:\n new_params = {}\n if remove is None:\n remove = []\n p = self.params.copy()\n for r in remove:\n for k in list(p):\n if k.startswith(r):\n del p[k]\n for k, v in new_params.items():\n if v is None:\n if k in p:\n del p[k]\n else:\n p[k] = v\n return '?%s' % urlencode(sorted(p.items()))","title":""},{"docid":"442da788b60b4e478992d53e1ce45783","score":"0.68486035","text":"def clean_query_params(self, params):\n allowed_query_params = [\n \"limit\",\n \"offset\",\n \"current\",\n \"future\",\n \"coords\",\n \"metadata\",\n \"identifier_type\",\n ]\n return QueryParams(\n {k: v for k, v in params.items() if k in allowed_query_params}\n )","title":""},{"docid":"b9bc83218e5fdc9801da750eac831a19","score":"0.67253816","text":"def modify_query(context, *params_to_remove, **params_to_change):\n query_params = []\n for key, value_list in context[\"request\"].GET._iterlists():\n if not key in params_to_remove:\n # don't add key-value pairs for params to change\n if key in params_to_change:\n query_params.append((key, params_to_change[key]))\n params_to_change.pop(key)\n else:\n # leave existing parameters as they were if not mentioned in the params_to_change\n for value in value_list:\n query_params.append((key, value))\n # attach new params\n for key, value in params_to_change.items():\n query_params.append((key, value))\n query_string = context[\"request\"].path\n if len(query_params):\n query_string += \"?%s\" % urllib.urlencode([\n (key, force_str(value)) for (key, value) in query_params if value\n ]).replace(\"&\", \"&\")\n return query_string","title":""},{"docid":"d0534621e1702727fd3d661eb2f95d75","score":"0.6686654","text":"def modify_query(context, *params_to_remove, **params_to_change):\n query_params = []\n for key, value_list in context[\"request\"].GET.lists():\n if not key in params_to_remove:\n # don't add key-value pairs for params_to_remove\n if key in params_to_change:\n # update values for keys in params_to_change\n query_params.append((key, params_to_change[key]))\n params_to_change.pop(key)\n else:\n # leave existing parameters as they were\n # if not mentioned in the params_to_change\n for value in value_list:\n query_params.append((key, value))\n # attach new params\n for key, value in params_to_change.items():\n query_params.append((key, value))\n return construct_query_string(context, query_params)","title":""},{"docid":"79527020a2f6e5ae18db80a9d76d9276","score":"0.66291","text":"def remove_params(url, params_list):\r\n\r\n for param in params_list:\r\n url = remove_param(url, param)\r\n return url","title":""},{"docid":"0f168753aa8dc2ece36264f3f8a565ef","score":"0.64378446","text":"def parameter_controls(self):\r\n parameters = [[k, v] for k, v in self.req.items()\r\n if k not in ('page') and v not in ('any')]\r\n for p in parameters:\r\n j = {k: v for k, v in self.req.items() if k != p[0]}\r\n argstring = '&'.join(['%s=%s' % (k, v) for k, v in j.items()])\r\n p.append(argstring)\r\n parameters.sort(key=lambda p: p[0])\r\n return parameters","title":""},{"docid":"9d0a77d441aa18c075e75d95be6ff51a","score":"0.63298804","text":"def _make_query_string(self) -> str:\n pairs = []\n if self.request.META['QUERY_STRING'] == '':\n return self.request.META['QUERY_STRING']\n for kv_pair in self.request.META['QUERY_STRING'].split('&'):\n if '=' not in kv_pair:\n kv_pair = kv_pair + '='\n pairs.append(kv_pair)\n pairs.sort()\n return '&'.join(pairs)","title":""},{"docid":"a0bfeb61ebe67f6ae99012c3fd08c5c0","score":"0.6308458","text":"def clear_all_query_param(self):\n self._query_params.clear()","title":""},{"docid":"0cdec3dceda9038b642631cb5aaa43ff","score":"0.61881155","text":"def get_preserved_filters(self, request):\n match = request.resolver_match\n if self.preserve_filters and match:\n opts = self.model._meta\n current_url = '%s:%s' % (match.app_name, match.url_name)\n changelist_url = self.changelist_url_format() % (opts.app_label, opts.model_name)\n if current_url == changelist_url:\n preserved_filters = request.GET.urlencode()\n else:\n preserved_filters = request.GET.get('_changelist_filters')\n\n if preserved_filters:\n return urlencode({'_changelist_filters': preserved_filters})\n return ''","title":""},{"docid":"123ce4e8d2725cd901b861135bf6add6","score":"0.61700314","text":"async def filter_only(self, params, types):\n omitted_params = await self.filter_omit(params, types)\n omitted_param_ids = set([get_param_id(param) for param in omitted_params])\n\n new_params = []\n for param in params:\n if get_param_id(param) not in omitted_param_ids:\n new_params.append(param)\n return new_params","title":""},{"docid":"91e74f5f4acc5d9ce09c17913cc53ed8","score":"0.61581457","text":"def add_to_query(context, *params_to_remove, **params_to_add):\n query_params = []\n # go through current query params..\n for key, value_list in context[\"request\"].GET._iterlists():\n if not key in params_to_remove:\n # don't add key-value pairs which already exist in the query\n if key in params_to_add and unicode(params_to_add[key]) in value_list:\n params_to_add.pop(key)\n for value in value_list:\n query_params.append((key, value))\n # add the rest key-value pairs\n for key, value in params_to_add.items():\n query_params.append((key, value))\n # empty values will be removed\n query_string = context[\"request\"].path\n if len(query_params):\n query_string += \"?%s\" % urllib.urlencode([\n (key, force_str(value)) for (key, value) in query_params if value\n ]).replace(\"&\", \"&\")\n return query_string","title":""},{"docid":"f788e499dd9875cb6226f1ac0aed56f0","score":"0.6096771","text":"def strip_query_string(url):\n # type: (str) -> str\n hqs, fs, f = url.partition(\"#\")\n h, _, _ = hqs.partition(\"?\")\n if not f:\n return h\n return h + fs + f","title":""},{"docid":"17ab0bf23b44b61da71c745e7ccaf448","score":"0.6050196","text":"def remove_qs(url):\n return urlparse.urljoin(url, urlparse.urlparse(url).path)","title":""},{"docid":"68a5b2a2420cef2d9c6573c48c4925b3","score":"0.6042088","text":"def getQueryStringArgs():","title":""},{"docid":"c4d10a10003406ee93c1f8e1659a5a3d","score":"0.6031594","text":"def strip_arg(self, to_del):\n arglist = self.args.split('&')\n arglist.remove(to_del)\n self.args = '&'.join(arglist)","title":""},{"docid":"9df73fc86cb86cac8c4705f6b62f0243","score":"0.60309255","text":"def remove_facet(request, facet_value):\n params = {}\n for param in request.GET.lists():\n # reconstruct the non-selected_facets params\n if param[0] != 'selected_facets':\n for v in param[1]:\n params[param[0]] = v\n else:\n print param[0]\n for v in param[1]:\n # exclude the selected_facet param that matches the supplied\n # facet_value\n if facet_value != v.split(':')[1]:\n params[param[0]] = v\n qs = '?%s' % urlencode(params)\n return qs","title":""},{"docid":"6e71ea2acc3de66531b10608568f6c0b","score":"0.60123557","text":"def add_to_query(context, *params_to_remove, **params_to_add):\n query_params = []\n # go through current query params..\n for key, value_list in context[\"request\"].GET.lists():\n if key not in params_to_remove:\n # don't add key-value pairs which already\n # exist in the query\n if (key in params_to_add\n and params_to_add[key] in value_list):\n params_to_add.pop(key)\n for value in value_list:\n query_params.append((key, value))\n # add the rest key-value pairs\n for key, value in params_to_add.items():\n query_params.append((key, value))\n return construct_query_string(context, query_params)","title":""},{"docid":"c0a612055b72cd496d6945e37fea656e","score":"0.6006518","text":"def strip_redirect_url(request, **kwargs):\n # Get a copy of the request.GET queryDict so that it is mutable\n url = request.GET.copy()\n\n # delete the redirect_url and redirect_params parameters if they exist\n try:\n del url['redirect_url']\n del url['redirect_params']\n except KeyError:\n pass\n\n # return the updated url\n return url.urlencode()","title":""},{"docid":"3c6c7e81a00f4c8de2f47e07e85dae59","score":"0.5967444","text":"def path(self, url):\n d, l = parse_qs(urlparse(unquote(url.encode('ascii'))).query), []\n for a in self.path.cols.split():\n if a in d:\n l.append(d[a][0])\n del d[a]\n else:\n l.append('')\n l[-2] = url\n\n # this function is a little more complicated than seemed needed\n # due to the need to remember the undocumented or unknown vars\n l[-1] = '&'.join([ '%s=%s' % (k, v[0]) for k,v in sorted(d.items())])\n return l","title":""},{"docid":"80fd6fb9122b26f44f91afccb8f13d99","score":"0.5961224","text":"def narrow_querystring(url, **kwargs):\n parts = list(urlparse.urlparse(url))\n queryargs = urlparse.parse_qs(parts[4], keep_blank_values=False)\n for k, v in kwargs.iteritems():\n new = convert_to_list(v)\n if k in queryargs:\n existing = convert_to_list(queryargs[k])\n if new:\n # no use of sets here, order matters\n new = [o for o in new if o in existing]\n if not new:\n new = NO_MATCH\n else:\n new = existing\n queryargs[k] = new or NO_MATCH\n parts[4] = urllib.urlencode(queryargs, doseq=True)\n return urlparse.urlunparse(parts)","title":""},{"docid":"02bf26860431948433f8252a34beb3f1","score":"0.5934293","text":"def generate_url_query_params(self, url):\n url = '{}?'.format(url)\n for f in self.and_filter_list:\n if isinstance(f.values, list):\n url = '{}{}={}&'.format(url, f.field, ','.join(f.values))\n else:\n url = '{}{}={}&'.format(url, f.field, f.values)\n return '{}&pageOffset={}&pageSize={}'.format(url, self.page, self.page_size)","title":""},{"docid":"65f4a21cab834c8ba50aff227a96a4bb","score":"0.5921915","text":"def get_url_params(request, response):\n\n _ = response\n \n data = urllib.parse.parse_qs(urllib.parse.urlparse(request.path).query)\n\n return {x: data[x][0] for x in data}","title":""},{"docid":"3d4b6ab4a3c20dccb7a4a6ee9a7d6805","score":"0.5915279","text":"def get_params(**params) -> dict:\n\n return {k: v for k, v in params.items() if v is not UNSET}","title":""},{"docid":"5bbf35b095e3fdcbb2f21d70036456ee","score":"0.58862484","text":"def pop_query_params_with_prefix(environ, prefix):\n orig_qs = environ[\"QUERY_STRING\"]\n parsed_query_items = parse_qsl(orig_qs, keep_blank_values=True)\n popped_params = {}\n updated_query_items = []\n\n for key, value in parsed_query_items:\n if key.startswith(prefix):\n popped_params[key] = value\n else:\n updated_query_items.append((key, value))\n\n updated_qs = urlencode(updated_query_items)\n environ[\"QUERY_STRING\"] = updated_qs\n\n if \"REQUEST_URI\" in environ:\n # If using uwsgi, update `REQUEST_URI`.\n environ[\"REQUEST_URI\"] = environ[\"REQUEST_URI\"][: -len(orig_qs)] + updated_qs\n\n return popped_params","title":""},{"docid":"dc249a941ceb7e8d81a518c2a587661a","score":"0.5870563","text":"def _get_query_params(self, req):\n params = {'filters': self._get_filters(req)}\n\n for PARAM in SUPPORTED_PARAMS:\n if PARAM in req.params:\n params[PARAM] = req.params.get(PARAM)\n return params","title":""},{"docid":"dc249a941ceb7e8d81a518c2a587661a","score":"0.5870563","text":"def _get_query_params(self, req):\n params = {'filters': self._get_filters(req)}\n\n for PARAM in SUPPORTED_PARAMS:\n if PARAM in req.params:\n params[PARAM] = req.params.get(PARAM)\n return params","title":""},{"docid":"494b349fb314c6467d7c99fd671d8281","score":"0.5830052","text":"def _remove_noref_params(params, graph):\n arg_list = set(graph.symbol.list_input_names())\n\n if params:\n param_keys = list(params.keys())\n for key in param_keys:\n if key not in arg_list:\n params.pop(key)","title":""},{"docid":"1f3f4acde1235ce6b04cfa02510c4322","score":"0.58223855","text":"def _filter_params(self, params):\n return dict((k, v) for k, v in params.iteritems() if v is not None)","title":""},{"docid":"71fa95f5acdf918f9776119dd78d15e3","score":"0.5803123","text":"def querystring(context, **kwargs):\n request = context['request']\n updated = request.GET.copy()\n for k, v in kwargs.items(): # have to iterate over and not use .update as it's a QueryDict not a dict\n updated[k] = v\n\n return '?{}'.format(updated.urlencode()) if updated else ''","title":""},{"docid":"4c5081170aa387e694afaa161c05aa0a","score":"0.57814795","text":"def set_query_values(context, **kwargs):\n query_dict = context.request.GET.copy()\n\n for k, v in kwargs.items():\n if is_iterable(v):\n query_dict.setlist(k, v)\n else:\n query_dict[k] = v\n\n return query_dict.urlencode()","title":""},{"docid":"6bcf075fc812ed7f51be9e547ea40f0f","score":"0.577332","text":"def _strip_empty_keys(self, params):\n keys = [k for k, v in params.items() if v == '']\n for key in keys:\n del params[key]","title":""},{"docid":"fa5619399e49084a85f8d6014856e724","score":"0.57531065","text":"def get_remove_filter() :\n\n return []","title":""},{"docid":"fa5619399e49084a85f8d6014856e724","score":"0.57531065","text":"def get_remove_filter() :\n\n return []","title":""},{"docid":"fa5619399e49084a85f8d6014856e724","score":"0.57531065","text":"def get_remove_filter() :\n\n return []","title":""},{"docid":"fa5619399e49084a85f8d6014856e724","score":"0.57531065","text":"def get_remove_filter() :\n\n return []","title":""},{"docid":"05856f639b36e9b6a2c73f7ee83dbe0f","score":"0.5749235","text":"def new_params(since, params):\n return [p for p in params if p[\"since\"] != since]","title":""},{"docid":"36d28d68288cadaee14e3a06ed2f1e47","score":"0.57151556","text":"def free_params(self):\n return [p for p in self._paramlist if p.free]","title":""},{"docid":"4c6fe5ce35d719cebe6fd8358160d0f0","score":"0.5705012","text":"def untrainable_params(self, recurse=True):\n return list(filter(lambda x: not x.requires_grad, self.get_parameters(expand=recurse)))","title":""},{"docid":"16dbac3409fa2ee5924dff30c0916b0b","score":"0.5697507","text":"def append_query_values(context, **kwargs):\n query_dict = context.request.GET.copy()\n\n for k, v in kwargs.items():\n if is_iterable(v):\n for v_item in v:\n query_dict.appendlist(k, v_item)\n else:\n query_dict.appendlist(k, v)\n\n return query_dict.urlencode()","title":""},{"docid":"3136b2c580863fdd37ca5390b3ae368a","score":"0.5683289","text":"def _remove_omitted(params, omit):\n if omit is not None and isinstance(omit, (list, tuple)):\n for item in omit:\n try:\n del params[item]\n except KeyError:\n continue","title":""},{"docid":"5441b9c3fb7821650ee9f31b663ab227","score":"0.5643049","text":"def append_GET_params(url, dictionary):\n url = list(urllib.parse.urlparse(url))\n if url[2] is '':\n url[2] = '/'\n if url[4] is not '':\n url[4] = url[4] + '&'\n params = []\n for key in dictionary:\n key_encoded = urllib.parse.quote(key)\n val_encoded = urllib.parse.quote(str(dictionary[key]))\n params += [key_encoded + '=' + val_encoded]\n url[4] += '&'.join(params)\n return urllib.parse.urlunparse(url)","title":""},{"docid":"1c2a7ef2ca703247bac86c446ff64658","score":"0.564043","text":"def _sanitize_params(self):","title":""},{"docid":"bfc161867284c683d8a09a2778d4b6a8","score":"0.56305015","text":"def admin_extra_filters(cl):\n used_parameters = list(itertools.chain(*(s.used_parameters.keys()\n for s in cl.filter_specs)))\n return dict(\n (k, v) for k, v in cl.params.items() if k not in used_parameters)","title":""},{"docid":"528b20086b94c0c1e6e7ef93184f7348","score":"0.56301606","text":"def get_param_values(self):\n return []","title":""},{"docid":"ea7e3eb9cbba00302fb26b09b5b594a0","score":"0.56196666","text":"def validate_query_params(self):\n allowed_params = set(self.get_filters().keys())\n allowed_params.update(self.get_always_allowed_arguments())\n\n unallowed = set(self.request.query_params.keys()) - allowed_params\n\n if unallowed:\n msg = (\n \"Unsupported parameter(s): {}. Please use a combination of: {}.\".format(\n \", \".join(unallowed), \", \".join(allowed_params)\n )\n )\n self.form.add_error(field=None, error=msg)","title":""},{"docid":"9e3f90b61e31836355f05415b489d1b2","score":"0.5614368","text":"def _get_params(self, method, param=None):\r\n\t\tif method.upper() == 'GET':\r\n\t\t\tif param != None:\r\n\t\t\t\treq_parse = self.request.path.split('/')\r\n\t\t\t\tvalue = None\r\n\t\t\t\tif len(req_parse) >= 3:\r\n\t\t\t\t\tvalue = str(req_parse[2])\r\n\t\t\t\tquery = \"%s=%s\"%(param.replace(':',''),value)\r\n\t\t\t\tif len(self.request.query) > 0:\r\n\t\t\t\t\tquery = query+\"&\"+self.request.query\r\n\t\t\t\t\r\n\t\t\t\treturn query\r\n\t\t\telse:\r\n\t\t\t\treturn self.request.query\r\n\t\telif method.upper() == 'POST':\r\n\t\t\tquery = ''\r\n\t\t\tfor name in self.request.arguments.keys():\r\n\t\t\t\tquery += name+'='+self.get_argument(name)+'&'\r\n\t\t\t\t\r\n\t\t\treturn query[0:len(query)-1]","title":""},{"docid":"e4bc55d4f6991ea2229ec61f61b6b4a2","score":"0.5611185","text":"def add_query_params(request, **kwargs):\n updated = request.GET.copy()\n for key, value in kwargs.items():\n updated[key] = value\n return request.build_absolute_uri('?'+updated.urlencode())","title":""},{"docid":"954ba341d59ebb990fe1cc1d9fcd1f6e","score":"0.56105226","text":"def query_params(self):\n return self._query_params","title":""},{"docid":"c69376ec2be0125e93d315bd6afe7405","score":"0.56065536","text":"def paramsToQueryString (paramMap):\n\tparams=[];add=params.append\n\tfor key in paramMap:\n\t\tval = paramMap[key]\n\t\tif type(val) in [type(\"\"), type(u\"\")]:\n\t\t\tadd (\"%s=%s\" % (key, val))\n\t\tif type(val) == type([]):\n\t\t\tfor v in val:\n\t\t\t\tadd (\"%s=%s\" % (key, urllib.quote(v)))\n\treturn '&'.join (params)","title":""},{"docid":"fec6518b1a0f9dae54af274e5205d63f","score":"0.55968285","text":"def get_search_terms(self, request):\n params = request.QUERY_PARAMS.get(self.search_param, '')\n return [params] #.replace(',', ' ').split()","title":""},{"docid":"95f508f0ed59473a01d2f74ff9b73661","score":"0.55598676","text":"def get_keep_filter() :\n\n return []","title":""},{"docid":"95f508f0ed59473a01d2f74ff9b73661","score":"0.55598676","text":"def get_keep_filter() :\n\n return []","title":""},{"docid":"95f508f0ed59473a01d2f74ff9b73661","score":"0.55598676","text":"def get_keep_filter() :\n\n return []","title":""},{"docid":"d3bafab86045f66f6422aeb560ecc49e","score":"0.5533564","text":"def get_params(self):\r\n return []","title":""},{"docid":"b896e16b3e4cdcc0aaae471db74f4c6b","score":"0.5507502","text":"def get_queryset(self):\n new_query = {key:self.request.query_params.get(key) for key in self.request.query_params} # this line to convert the values of the return object of query param from list of string to string\n queryset = self.get_serializer_class().Meta.model.objects.filter(**new_query)\n return queryset","title":""},{"docid":"ccc0bc698652b0cde47905e6a9daff8c","score":"0.5494568","text":"def get_normalized_params(params):\n return urlparse.urlencode(sorted(params))","title":""},{"docid":"1854ad851144329e45c80ca62bdb2547","score":"0.5485816","text":"def get_query_params(self):\n\n if self.next_page_token:\n return {'pageToken': self.next_page_token}","title":""},{"docid":"e177e5491e6d19684b498af071bdbeef","score":"0.54793346","text":"def get_param_list(self):\n\t\treturn [k for k, v in self.para.items()]","title":""},{"docid":"59efc98f1eac8a8bae15ac87dff42b56","score":"0.5473872","text":"def _sanitize_url_params(req):\n params = dict(req.args)\n\n src_params = {k: v[0] for k, v in params.items()\n if k != 'share'}\n src_url = _add_params(req.base_url, **src_params)\n\n href_params = {k: v[0] for k, v in params.items()\n if k not in ['width', 'height']}\n href_url = _add_params(req.base_url, **href_params)\n\n return src_url, href_url","title":""},{"docid":"ec497613827d31aacb9b36baa81abd0e","score":"0.5458749","text":"def get_search_parameters(request):\n search_filter = \"\"\n page_len = \"\"\n\n if request.args:\n page_len = request.args.get(\"show\", \"\")\n search_filter = \" \".join(\n [arg for arg in request.args if arg != \"show\"]\n )\n\n return search_filter, page_len","title":""},{"docid":"6d5c353cf237a049ac3894667f552531","score":"0.54559433","text":"def uri_query(self):\n value = []\n for option in self.options:\n if option.number == defines.OptionRegistry.URI_QUERY.number:\n value.append(str(option.value))\n return \"&\".join(value)","title":""},{"docid":"ab23af951068ba700136915b964852c0","score":"0.5455822","text":"def unset_kuenstler_params(self, params=None):\n if not params:\n params = self.kuenstler.keys()\n for p in params:\n self.kuenstler[p] = ''","title":""},{"docid":"562c5038ffadd2905e431ce056d578c5","score":"0.5434189","text":"def url_query_params(url):\n return dict(parse_qsl(urlparse(url).query, True))","title":""},{"docid":"a7157a0d5fd81ef98107abca4c9a2011","score":"0.54331154","text":"def args(self):\r\n return url_decode(self.environ.get('QUERY_STRING', ''),\r\n self.url_charset, errors=self.encoding_errors,\r\n cls=self.parameter_storage_class)","title":""},{"docid":"3a2993da76da7d7328ad5dfb54b74376","score":"0.54275715","text":"def test_get_params_excludes_unmentioned(self):\n handler = self.post_json({\n 'foo': 'bar',\n 'baz': 'qux',\n })\n\n self.assertEqual(\n handler.get_params({'foo': str}), # lacks baz\n {'foo': 'bar'} # also lacks baz\n )","title":""},{"docid":"e3f91cb58c9de8075b3d99ad7bc5768d","score":"0.54269546","text":"def querystring(self):\n return {key: value for (key, value) in self.qs.items() if key.startswith(self.MANAGED_KEYS)}","title":""},{"docid":"1e1425052e0a135853735e96a9b2cc71","score":"0.5425071","text":"def my_pagination_for(context, current_page, page_var=\"page\", exclude_vars=\"\"):\n querystring = context[\"request\"].GET.copy()\n exclude_vars = [v for v in exclude_vars.split(\",\") if v] + [page_var]\n for exclude_var in exclude_vars:\n if exclude_var in querystring:\n del querystring[exclude_var]\n querystring = querystring.urlencode()\n result = {\n \"current_page\": current_page,\n \"querystring\": querystring,\n \"page_var\": page_var,\n }\n return result","title":""},{"docid":"abafe80875b9bff34fca288dc397aacb","score":"0.5419454","text":"def append_query_params(original_url, **kwargs):\n scheme, netloc, path, query_string, fragment = urlsplit(original_url)\n query_params = parse_qs(query_string)\n if kwargs is not None:\n for key, value in kwargs.items():\n query_params[key] = [value]\n\n new_query_string = urlencode(query_params, doseq=True)\n new_url = urlunsplit((scheme, netloc, path, new_query_string, fragment))\n return new_url","title":""},{"docid":"8b554bad60311d8939d3885c35f1515f","score":"0.5406037","text":"def get_search_terms(self, request):\n params = request.query_params.get(self.search_param, '')\n return params.replace(',', ' ').strip()","title":""},{"docid":"2f28c755db1316cb3467648451b8121e","score":"0.539024","text":"def clean(self):\n self.request_parameters = OrderedDict()\n self.request_type = \"\"","title":""},{"docid":"728e704afc55c06050ae17e40d76cb4d","score":"0.5372662","text":"def pop_from_GET(obj, attr):\r\n if obj.get(attr, None):\r\n obj_copy = obj.copy()\r\n del obj_copy[attr]\r\n return '&%s' % obj_copy.urlencode()\r\n if not obj:\r\n return ''\r\n return '&%s' % obj.urlencode()","title":""},{"docid":"83fdfa43a494ee5f6b76c703b351a52f","score":"0.5352228","text":"def preserve_parameters(preserve=freeze([])):\n snippet = []\n\n form = get_context().request.get_form()\n for k, v in form.items():\n for prefix in preserve:\n if k.startswith(prefix):\n snippet.append('<input type=\"hidden\" name=\"%s\" value=\"%s\">'\n % (k, quote(v)))\n break\n\n return '\\n'.join(snippet)","title":""},{"docid":"c69f2755dd0bbe48378eba9f75f53861","score":"0.5336839","text":"def get_unpinned_params(opts, params):\n return params - set([p for p, v in opts.__dict__.items() if p in LIKELIHOOD_PINNABLE_PARAMS and v is not None])","title":""},{"docid":"9019c6410eac7fc3061665e80112a346","score":"0.5333564","text":"def _get_param_list(self) -> list:\n param_list = [\n name\n for name in self.__dir__()\n if not name.startswith(\"_\") and not callable(getattr(self, name))\n ]\n return param_list","title":""},{"docid":"9b8a91c94076a14cc2e1615e3f98b8d1","score":"0.53288966","text":"def get_cleaned_kwargs(self, kwargs):\n cleaned_kwargs = kwargs.copy()\n\n if kwargs is not None:\n for k in kwargs:\n if self.is_linguist_lookup(k):\n del cleaned_kwargs[k]\n\n return cleaned_kwargs","title":""},{"docid":"6935c016867b9a8311b7ac51263d37c4","score":"0.5327639","text":"def missing_params(self):\n ret = []\n for (param, pinfo) in self._valid_params.items():\n if pinfo[5]:\n if (not self._params.has_key(param) or\n not self._params[param]):\n ret.append(param)\n # Check \"require_oneof\" list, if necessary\n if len(ret) == 0:\n for param in self._require_oneof_params:\n if self._params.has_key(param):\n return []\n return self._require_oneof_params\n else:\n return ret","title":""},{"docid":"fa39086379b8b9512849766c835e0963","score":"0.53228766","text":"def buildQS(self, params):\n queryString = \"\"\n amp = \"\"\n # keys = params.keys()\n keys = list(params.keys())\n keys.sort()\n for key in keys:\n value = params.get(key)\n if value is not None:\n queryString += amp + key + \"=\" + self.UrlEncode(value)\n amp = \"&\"\n\n return queryString","title":""},{"docid":"492f22942ebdd9b2c6451009e1451e61","score":"0.53176075","text":"def get_params():\n param = []\n paramstring = sys.argv[2]\n if len(paramstring) >= 2:\n params = sys.argv[2]\n cleanedparams = params.replace('?', '')\n if params[len(params) - 1] == '/':\n params = params[0:len(params) - 2]\n pairsofparams = cleanedparams.split('&')\n param = {}\n for i in range(len(pairsofparams)):\n splitparams = {}\n splitparams = pairsofparams[i].split('=')\n if (len(splitparams)) == 2:\n param[splitparams[0]] = splitparams[1]\n return param","title":""},{"docid":"ddfccbd32dde0d1d68be5c01643c7797","score":"0.5314787","text":"def strip_nodeid_parameters(nodeid):\n\treturn REGEX_PARAMETERS.sub('', nodeid)","title":""},{"docid":"0df79d95683de3d8dfb159f7df6d05ea","score":"0.53137153","text":"def update_url_query(context, param_name=None, param_value=None, **kwargs):\n # request.GET is a QueryDict, which is immutable unless we make a copy\n query_dict = context['request'].GET.copy()\n # set variable parameter name and value\n if param_name:\n query_dict[param_name] = param_value\n if param_value == 'remove':\n # remove the parameter altogether\n query_dict.pop(param_name, None)\n # for a normal dict we would use update(kwargs), but QueryDict.update()\n # appends values, instead of replacing them, so we need to set each key\n # explicitly\n for key, value in kwargs.items():\n query_dict[key] = value\n # urlencode converts the dict to a query string\n return query_dict.urlencode()","title":""},{"docid":"9419dc17f0c2f8c474be4e33d9ff7460","score":"0.53083116","text":"def _get_param_list(self) -> list:\n param_list = [\n name for name in self.__dir__()\n if not name.startswith(\"__\") and not callable(getattr(self, name))\n ]\n return param_list","title":""},{"docid":"71948e92b6ee926ef16147a8fbd3be87","score":"0.5303549","text":"def get_all_params(self):\n return {k: v for k, v in self.params.items()}","title":""},{"docid":"866d36d55fd1f24850ddd8b90622e20d","score":"0.53032476","text":"def _extract_paging_params(request):\n count = request.url.query.get('count', DEFAULT_PAGE_SIZE)\n page_num = request.url.query.get('page', 0)\n\n return RouteHandler._check_paging_params(count, page_num)","title":""},{"docid":"071cba7ef32ad5b8db789851168d1499","score":"0.5292904","text":"def get_params(self):\n return list(self._params)","title":""},{"docid":"e685140892bb22831c862cc64f8313ee","score":"0.5289206","text":"def _GetClearedFieldsForUrlRewrite(url_rewrite, field_prefix):\n cleared_fields = []\n if not url_rewrite.pathPrefixRewrite:\n cleared_fields.append(field_prefix + 'pathPrefixRewrite')\n if not url_rewrite.hostRewrite:\n cleared_fields.append(field_prefix + 'hostRewrite')\n return cleared_fields","title":""},{"docid":"10d706b5ed190eabcb619b1833aba3a6","score":"0.52889234","text":"def get_dict_of_all_args(self):\n dictionary = {}\n for arg in [arg for arg in self.request.arguments if arg not in self.settings.get(\"reserved_query_string_params\", [])]:\n val = self.get_argument(arg, default=None)\n if val:\n dictionary[arg] = val\n return dictionary","title":""},{"docid":"457d9c0dcbe6a1fc6b893cc1979ced99","score":"0.5287862","text":"def get_keep_filter() :\n\n return ['.*']","title":""},{"docid":"bef7901d6884d8351e19c78c527b6cfe","score":"0.5286536","text":"def parse_querystring(self, req, name, arg):\n return core.get_value(req.GET, name, arg.multiple)","title":""},{"docid":"e17c64dc05dcf3c33a4ffb1ce9dfe39b","score":"0.52736485","text":"def remove_empty_kwargs(self, **kwargs):\n good_kwargs = {}\n if kwargs is not None:\n for key, value in kwargs.items():\n if value:\n good_kwargs[key] = value\n return good_kwargs","title":""},{"docid":"127a21a76d75f5b88608cab518a2648f","score":"0.5269207","text":"def remove(self, key: str) -> \"QueryParams\":\n q = QueryParams()\n q._dict = dict(self._dict)\n q._dict.pop(str(key), None)\n return q","title":""},{"docid":"d7318a4de5a083460f5671350f90417f","score":"0.52671456","text":"def remove_param(url, param):\r\n url_obj= urlparse(url)\r\n query = parse_qs(url_obj.query)\r\n query.pop(param, None)\r\n url_obj= url_obj._replace(query=urlencode(query, True))\r\n return urlunparse(url_obj)","title":""},{"docid":"1d2c9bf43299816716602ebd9f7059ee","score":"0.52641964","text":"def _remove_kwarg(args, kwarg):\n args = [arg for arg in args if '%s=' % kwarg not in arg]\n # If kwarg is by self, remove it and its value\n try:\n index = args.index(kwarg)\n except ValueError:\n pass\n else:\n args = args[:index] + args[index + 2:]\n return args","title":""},{"docid":"be3e25ad07a61769d22049d87992538a","score":"0.52516925","text":"def _parse_args(self):\n args = {}\n if '?' not in self.request.path_qs:\n args['new_search'] = True\n return args\n\n if len(self.request.get('tags')) > 0:\n tags = self.request.get('tags').split(',')\n args['tags'] = [t.strip() for t in tags]\n\n # All string params are handled the same way\n params = [\n 'user_id',\n 'author',\n 'source',\n 'rating',\n 'count',\n 'text_dump',\n 'spec_op',\n ]\n for p in params:\n if len(self.request.get(p)) > 0:\n args[p] = self.request.get(p)\n\n return args","title":""},{"docid":"461f65127a6669450875e6807021436e","score":"0.52441776","text":"def strip_ticket(self):\n url = request.url\n if url is None:\n return \"something is badly wrong\"\n url = sub(r'ticket=[^&]*&?', '', url)\n url = sub(r'\\?&?$|&$', '', url)\n return url","title":""},{"docid":"ba9b1f3ddcf039f9c0a7c95f852eb1e4","score":"0.52407527","text":"def get_search_params(url_path):\n # Remove the '/search.json' part to get the given options\n query_params = url_path[len('/search.json?'):]\n\n # Find the 'q=' section, what we care about\n queryPos = query_params.find('q=')\n if queryPos == -1:\n # There's no 'q=' to grab items from, fail\n return []\n query_params = query_params[queryPos+2:]\n\n # Find the end of the search terms, if not the end of the string\n endPos = query_params.find('&')\n if endPos != -1:\n query_params = query_params[:endPos]\n\n return query_params","title":""}],"string":"[\n {\n \"docid\": \"6d9cb29b157f9a0901208652d3c8ceb7\",\n \"score\": \"0.7658783\",\n \"text\": \"def remove_from_query(context, *args, **kwargs):\\n query_params = []\\n # go through current query params..\\n for key, value_list in context[\\\"request\\\"].GET._iterlists():\\n # skip keys mentioned in the args\\n if not key in args:\\n for value in value_list:\\n # skip key-value pairs mentioned in kwargs\\n if not (key in kwargs and unicode(value) == unicode(kwargs[key])):\\n query_params.append((key, value))\\n # empty values will be removed\\n query_string = context[\\\"request\\\"].path\\n if len(query_params):\\n query_string = \\\"?%s\\\" % urllib.urlencode([\\n (key, force_str(value)) for (key, value) in query_params if value\\n ]).replace(\\\"&\\\", \\\"&\\\")\\n return query_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba6f66cd5e06aa27f122018f77c3167\",\n \"score\": \"0.75023717\",\n \"text\": \"def get_querystring(self):\\n to_remove = self.get_querystring_parameter_to_remove()\\n query_string = urlparse(self.request.get_full_path()).query\\n if six.PY2:\\n query_dict = parse_qs(query_string.encode('utf-8'))\\n else:\\n query_dict = parse_qs(query_string)\\n for arg in to_remove:\\n if arg in query_dict:\\n del query_dict[arg]\\n clean_query_string = urlencode(query_dict, doseq=True)\\n return clean_query_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"895bf5ed9541a3738713f8cdb8d1991e\",\n \"score\": \"0.72274697\",\n \"text\": \"def remove_from_query(context, *args, **kwargs):\\n query_params = []\\n # go through current query params..\\n for key, value_list in context[\\\"request\\\"].GET.lists():\\n # skip keys mentioned in the args\\n if key not in args:\\n for value in value_list:\\n # skip key-value pairs mentioned in kwargs\\n if not (key in kwargs and\\n str(value) == str(kwargs[key])):\\n query_params.append((key, value))\\n return construct_query_string(context, query_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb5c4cbc0bac794d325468877807a7e\",\n \"score\": \"0.7213651\",\n \"text\": \"def query_params(context, exclude=[\\\"page\\\"]):\\n params = context[\\\"request\\\"].GET.copy()\\n\\n if \\\"page\\\" in params:\\n del params[\\\"page\\\"]\\n\\n return params.urlencode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c7200ca9eb32199a7edabfe9f2fe888\",\n \"score\": \"0.7164342\",\n \"text\": \"def _clean_query_params(self) -> dict:\\n\\n new_query_params = {}\\n query_params = dict(self._request.query_params)\\n for key, value in query_params.items():\\n new_query_params[key] = value.pop(0)\\n\\n return new_query_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07b81e4c317c316e5a926e2f3e697c59\",\n \"score\": \"0.68729335\",\n \"text\": \"def get_query_string(self, new_params=None, remove=None):\\n if new_params is None:\\n new_params = {}\\n if remove is None:\\n remove = []\\n p = self.params.copy()\\n for r in remove:\\n for k in list(p):\\n if k.startswith(r):\\n del p[k]\\n for k, v in new_params.items():\\n if v is None:\\n if k in p:\\n del p[k]\\n else:\\n p[k] = v\\n return '?%s' % urlencode(sorted(p.items()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"442da788b60b4e478992d53e1ce45783\",\n \"score\": \"0.68486035\",\n \"text\": \"def clean_query_params(self, params):\\n allowed_query_params = [\\n \\\"limit\\\",\\n \\\"offset\\\",\\n \\\"current\\\",\\n \\\"future\\\",\\n \\\"coords\\\",\\n \\\"metadata\\\",\\n \\\"identifier_type\\\",\\n ]\\n return QueryParams(\\n {k: v for k, v in params.items() if k in allowed_query_params}\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9bc83218e5fdc9801da750eac831a19\",\n \"score\": \"0.67253816\",\n \"text\": \"def modify_query(context, *params_to_remove, **params_to_change):\\n query_params = []\\n for key, value_list in context[\\\"request\\\"].GET._iterlists():\\n if not key in params_to_remove:\\n # don't add key-value pairs for params to change\\n if key in params_to_change:\\n query_params.append((key, params_to_change[key]))\\n params_to_change.pop(key)\\n else:\\n # leave existing parameters as they were if not mentioned in the params_to_change\\n for value in value_list:\\n query_params.append((key, value))\\n # attach new params\\n for key, value in params_to_change.items():\\n query_params.append((key, value))\\n query_string = context[\\\"request\\\"].path\\n if len(query_params):\\n query_string += \\\"?%s\\\" % urllib.urlencode([\\n (key, force_str(value)) for (key, value) in query_params if value\\n ]).replace(\\\"&\\\", \\\"&\\\")\\n return query_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0534621e1702727fd3d661eb2f95d75\",\n \"score\": \"0.6686654\",\n \"text\": \"def modify_query(context, *params_to_remove, **params_to_change):\\n query_params = []\\n for key, value_list in context[\\\"request\\\"].GET.lists():\\n if not key in params_to_remove:\\n # don't add key-value pairs for params_to_remove\\n if key in params_to_change:\\n # update values for keys in params_to_change\\n query_params.append((key, params_to_change[key]))\\n params_to_change.pop(key)\\n else:\\n # leave existing parameters as they were\\n # if not mentioned in the params_to_change\\n for value in value_list:\\n query_params.append((key, value))\\n # attach new params\\n for key, value in params_to_change.items():\\n query_params.append((key, value))\\n return construct_query_string(context, query_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79527020a2f6e5ae18db80a9d76d9276\",\n \"score\": \"0.66291\",\n \"text\": \"def remove_params(url, params_list):\\r\\n\\r\\n for param in params_list:\\r\\n url = remove_param(url, param)\\r\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f168753aa8dc2ece36264f3f8a565ef\",\n \"score\": \"0.64378446\",\n \"text\": \"def parameter_controls(self):\\r\\n parameters = [[k, v] for k, v in self.req.items()\\r\\n if k not in ('page') and v not in ('any')]\\r\\n for p in parameters:\\r\\n j = {k: v for k, v in self.req.items() if k != p[0]}\\r\\n argstring = '&'.join(['%s=%s' % (k, v) for k, v in j.items()])\\r\\n p.append(argstring)\\r\\n parameters.sort(key=lambda p: p[0])\\r\\n return parameters\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0a77d441aa18c075e75d95be6ff51a\",\n \"score\": \"0.63298804\",\n \"text\": \"def _make_query_string(self) -> str:\\n pairs = []\\n if self.request.META['QUERY_STRING'] == '':\\n return self.request.META['QUERY_STRING']\\n for kv_pair in self.request.META['QUERY_STRING'].split('&'):\\n if '=' not in kv_pair:\\n kv_pair = kv_pair + '='\\n pairs.append(kv_pair)\\n pairs.sort()\\n return '&'.join(pairs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0bfeb61ebe67f6ae99012c3fd08c5c0\",\n \"score\": \"0.6308458\",\n \"text\": \"def clear_all_query_param(self):\\n self._query_params.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cdec3dceda9038b642631cb5aaa43ff\",\n \"score\": \"0.61881155\",\n \"text\": \"def get_preserved_filters(self, request):\\n match = request.resolver_match\\n if self.preserve_filters and match:\\n opts = self.model._meta\\n current_url = '%s:%s' % (match.app_name, match.url_name)\\n changelist_url = self.changelist_url_format() % (opts.app_label, opts.model_name)\\n if current_url == changelist_url:\\n preserved_filters = request.GET.urlencode()\\n else:\\n preserved_filters = request.GET.get('_changelist_filters')\\n\\n if preserved_filters:\\n return urlencode({'_changelist_filters': preserved_filters})\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"123ce4e8d2725cd901b861135bf6add6\",\n \"score\": \"0.61700314\",\n \"text\": \"async def filter_only(self, params, types):\\n omitted_params = await self.filter_omit(params, types)\\n omitted_param_ids = set([get_param_id(param) for param in omitted_params])\\n\\n new_params = []\\n for param in params:\\n if get_param_id(param) not in omitted_param_ids:\\n new_params.append(param)\\n return new_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e74f5f4acc5d9ce09c17913cc53ed8\",\n \"score\": \"0.61581457\",\n \"text\": \"def add_to_query(context, *params_to_remove, **params_to_add):\\n query_params = []\\n # go through current query params..\\n for key, value_list in context[\\\"request\\\"].GET._iterlists():\\n if not key in params_to_remove:\\n # don't add key-value pairs which already exist in the query\\n if key in params_to_add and unicode(params_to_add[key]) in value_list:\\n params_to_add.pop(key)\\n for value in value_list:\\n query_params.append((key, value))\\n # add the rest key-value pairs\\n for key, value in params_to_add.items():\\n query_params.append((key, value))\\n # empty values will be removed\\n query_string = context[\\\"request\\\"].path\\n if len(query_params):\\n query_string += \\\"?%s\\\" % urllib.urlencode([\\n (key, force_str(value)) for (key, value) in query_params if value\\n ]).replace(\\\"&\\\", \\\"&\\\")\\n return query_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f788e499dd9875cb6226f1ac0aed56f0\",\n \"score\": \"0.6096771\",\n \"text\": \"def strip_query_string(url):\\n # type: (str) -> str\\n hqs, fs, f = url.partition(\\\"#\\\")\\n h, _, _ = hqs.partition(\\\"?\\\")\\n if not f:\\n return h\\n return h + fs + f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ab0bf23b44b61da71c745e7ccaf448\",\n \"score\": \"0.6050196\",\n \"text\": \"def remove_qs(url):\\n return urlparse.urljoin(url, urlparse.urlparse(url).path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68a5b2a2420cef2d9c6573c48c4925b3\",\n \"score\": \"0.6042088\",\n \"text\": \"def getQueryStringArgs():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d10a10003406ee93c1f8e1659a5a3d\",\n \"score\": \"0.6031594\",\n \"text\": \"def strip_arg(self, to_del):\\n arglist = self.args.split('&')\\n arglist.remove(to_del)\\n self.args = '&'.join(arglist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9df73fc86cb86cac8c4705f6b62f0243\",\n \"score\": \"0.60309255\",\n \"text\": \"def remove_facet(request, facet_value):\\n params = {}\\n for param in request.GET.lists():\\n # reconstruct the non-selected_facets params\\n if param[0] != 'selected_facets':\\n for v in param[1]:\\n params[param[0]] = v\\n else:\\n print param[0]\\n for v in param[1]:\\n # exclude the selected_facet param that matches the supplied\\n # facet_value\\n if facet_value != v.split(':')[1]:\\n params[param[0]] = v\\n qs = '?%s' % urlencode(params)\\n return qs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e71ea2acc3de66531b10608568f6c0b\",\n \"score\": \"0.60123557\",\n \"text\": \"def add_to_query(context, *params_to_remove, **params_to_add):\\n query_params = []\\n # go through current query params..\\n for key, value_list in context[\\\"request\\\"].GET.lists():\\n if key not in params_to_remove:\\n # don't add key-value pairs which already\\n # exist in the query\\n if (key in params_to_add\\n and params_to_add[key] in value_list):\\n params_to_add.pop(key)\\n for value in value_list:\\n query_params.append((key, value))\\n # add the rest key-value pairs\\n for key, value in params_to_add.items():\\n query_params.append((key, value))\\n return construct_query_string(context, query_params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0a612055b72cd496d6945e37fea656e\",\n \"score\": \"0.6006518\",\n \"text\": \"def strip_redirect_url(request, **kwargs):\\n # Get a copy of the request.GET queryDict so that it is mutable\\n url = request.GET.copy()\\n\\n # delete the redirect_url and redirect_params parameters if they exist\\n try:\\n del url['redirect_url']\\n del url['redirect_params']\\n except KeyError:\\n pass\\n\\n # return the updated url\\n return url.urlencode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c6c7e81a00f4c8de2f47e07e85dae59\",\n \"score\": \"0.5967444\",\n \"text\": \"def path(self, url):\\n d, l = parse_qs(urlparse(unquote(url.encode('ascii'))).query), []\\n for a in self.path.cols.split():\\n if a in d:\\n l.append(d[a][0])\\n del d[a]\\n else:\\n l.append('')\\n l[-2] = url\\n\\n # this function is a little more complicated than seemed needed\\n # due to the need to remember the undocumented or unknown vars\\n l[-1] = '&'.join([ '%s=%s' % (k, v[0]) for k,v in sorted(d.items())])\\n return l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80fd6fb9122b26f44f91afccb8f13d99\",\n \"score\": \"0.5961224\",\n \"text\": \"def narrow_querystring(url, **kwargs):\\n parts = list(urlparse.urlparse(url))\\n queryargs = urlparse.parse_qs(parts[4], keep_blank_values=False)\\n for k, v in kwargs.iteritems():\\n new = convert_to_list(v)\\n if k in queryargs:\\n existing = convert_to_list(queryargs[k])\\n if new:\\n # no use of sets here, order matters\\n new = [o for o in new if o in existing]\\n if not new:\\n new = NO_MATCH\\n else:\\n new = existing\\n queryargs[k] = new or NO_MATCH\\n parts[4] = urllib.urlencode(queryargs, doseq=True)\\n return urlparse.urlunparse(parts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02bf26860431948433f8252a34beb3f1\",\n \"score\": \"0.5934293\",\n \"text\": \"def generate_url_query_params(self, url):\\n url = '{}?'.format(url)\\n for f in self.and_filter_list:\\n if isinstance(f.values, list):\\n url = '{}{}={}&'.format(url, f.field, ','.join(f.values))\\n else:\\n url = '{}{}={}&'.format(url, f.field, f.values)\\n return '{}&pageOffset={}&pageSize={}'.format(url, self.page, self.page_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65f4a21cab834c8ba50aff227a96a4bb\",\n \"score\": \"0.5921915\",\n \"text\": \"def get_url_params(request, response):\\n\\n _ = response\\n \\n data = urllib.parse.parse_qs(urllib.parse.urlparse(request.path).query)\\n\\n return {x: data[x][0] for x in data}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d4b6ab4a3c20dccb7a4a6ee9a7d6805\",\n \"score\": \"0.5915279\",\n \"text\": \"def get_params(**params) -> dict:\\n\\n return {k: v for k, v in params.items() if v is not UNSET}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bbf35b095e3fdcbb2f21d70036456ee\",\n \"score\": \"0.58862484\",\n \"text\": \"def pop_query_params_with_prefix(environ, prefix):\\n orig_qs = environ[\\\"QUERY_STRING\\\"]\\n parsed_query_items = parse_qsl(orig_qs, keep_blank_values=True)\\n popped_params = {}\\n updated_query_items = []\\n\\n for key, value in parsed_query_items:\\n if key.startswith(prefix):\\n popped_params[key] = value\\n else:\\n updated_query_items.append((key, value))\\n\\n updated_qs = urlencode(updated_query_items)\\n environ[\\\"QUERY_STRING\\\"] = updated_qs\\n\\n if \\\"REQUEST_URI\\\" in environ:\\n # If using uwsgi, update `REQUEST_URI`.\\n environ[\\\"REQUEST_URI\\\"] = environ[\\\"REQUEST_URI\\\"][: -len(orig_qs)] + updated_qs\\n\\n return popped_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc249a941ceb7e8d81a518c2a587661a\",\n \"score\": \"0.5870563\",\n \"text\": \"def _get_query_params(self, req):\\n params = {'filters': self._get_filters(req)}\\n\\n for PARAM in SUPPORTED_PARAMS:\\n if PARAM in req.params:\\n params[PARAM] = req.params.get(PARAM)\\n return params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc249a941ceb7e8d81a518c2a587661a\",\n \"score\": \"0.5870563\",\n \"text\": \"def _get_query_params(self, req):\\n params = {'filters': self._get_filters(req)}\\n\\n for PARAM in SUPPORTED_PARAMS:\\n if PARAM in req.params:\\n params[PARAM] = req.params.get(PARAM)\\n return params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"494b349fb314c6467d7c99fd671d8281\",\n \"score\": \"0.5830052\",\n \"text\": \"def _remove_noref_params(params, graph):\\n arg_list = set(graph.symbol.list_input_names())\\n\\n if params:\\n param_keys = list(params.keys())\\n for key in param_keys:\\n if key not in arg_list:\\n params.pop(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3f4acde1235ce6b04cfa02510c4322\",\n \"score\": \"0.58223855\",\n \"text\": \"def _filter_params(self, params):\\n return dict((k, v) for k, v in params.iteritems() if v is not None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71fa95f5acdf918f9776119dd78d15e3\",\n \"score\": \"0.5803123\",\n \"text\": \"def querystring(context, **kwargs):\\n request = context['request']\\n updated = request.GET.copy()\\n for k, v in kwargs.items(): # have to iterate over and not use .update as it's a QueryDict not a dict\\n updated[k] = v\\n\\n return '?{}'.format(updated.urlencode()) if updated else ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c5081170aa387e694afaa161c05aa0a\",\n \"score\": \"0.57814795\",\n \"text\": \"def set_query_values(context, **kwargs):\\n query_dict = context.request.GET.copy()\\n\\n for k, v in kwargs.items():\\n if is_iterable(v):\\n query_dict.setlist(k, v)\\n else:\\n query_dict[k] = v\\n\\n return query_dict.urlencode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bcf075fc812ed7f51be9e547ea40f0f\",\n \"score\": \"0.577332\",\n \"text\": \"def _strip_empty_keys(self, params):\\n keys = [k for k, v in params.items() if v == '']\\n for key in keys:\\n del params[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5619399e49084a85f8d6014856e724\",\n \"score\": \"0.57531065\",\n \"text\": \"def get_remove_filter() :\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5619399e49084a85f8d6014856e724\",\n \"score\": \"0.57531065\",\n \"text\": \"def get_remove_filter() :\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5619399e49084a85f8d6014856e724\",\n \"score\": \"0.57531065\",\n \"text\": \"def get_remove_filter() :\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa5619399e49084a85f8d6014856e724\",\n \"score\": \"0.57531065\",\n \"text\": \"def get_remove_filter() :\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05856f639b36e9b6a2c73f7ee83dbe0f\",\n \"score\": \"0.5749235\",\n \"text\": \"def new_params(since, params):\\n return [p for p in params if p[\\\"since\\\"] != since]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36d28d68288cadaee14e3a06ed2f1e47\",\n \"score\": \"0.57151556\",\n \"text\": \"def free_params(self):\\n return [p for p in self._paramlist if p.free]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c6fe5ce35d719cebe6fd8358160d0f0\",\n \"score\": \"0.5705012\",\n \"text\": \"def untrainable_params(self, recurse=True):\\n return list(filter(lambda x: not x.requires_grad, self.get_parameters(expand=recurse)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16dbac3409fa2ee5924dff30c0916b0b\",\n \"score\": \"0.5697507\",\n \"text\": \"def append_query_values(context, **kwargs):\\n query_dict = context.request.GET.copy()\\n\\n for k, v in kwargs.items():\\n if is_iterable(v):\\n for v_item in v:\\n query_dict.appendlist(k, v_item)\\n else:\\n query_dict.appendlist(k, v)\\n\\n return query_dict.urlencode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3136b2c580863fdd37ca5390b3ae368a\",\n \"score\": \"0.5683289\",\n \"text\": \"def _remove_omitted(params, omit):\\n if omit is not None and isinstance(omit, (list, tuple)):\\n for item in omit:\\n try:\\n del params[item]\\n except KeyError:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5441b9c3fb7821650ee9f31b663ab227\",\n \"score\": \"0.5643049\",\n \"text\": \"def append_GET_params(url, dictionary):\\n url = list(urllib.parse.urlparse(url))\\n if url[2] is '':\\n url[2] = '/'\\n if url[4] is not '':\\n url[4] = url[4] + '&'\\n params = []\\n for key in dictionary:\\n key_encoded = urllib.parse.quote(key)\\n val_encoded = urllib.parse.quote(str(dictionary[key]))\\n params += [key_encoded + '=' + val_encoded]\\n url[4] += '&'.join(params)\\n return urllib.parse.urlunparse(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c2a7ef2ca703247bac86c446ff64658\",\n \"score\": \"0.564043\",\n \"text\": \"def _sanitize_params(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc161867284c683d8a09a2778d4b6a8\",\n \"score\": \"0.56305015\",\n \"text\": \"def admin_extra_filters(cl):\\n used_parameters = list(itertools.chain(*(s.used_parameters.keys()\\n for s in cl.filter_specs)))\\n return dict(\\n (k, v) for k, v in cl.params.items() if k not in used_parameters)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"528b20086b94c0c1e6e7ef93184f7348\",\n \"score\": \"0.56301606\",\n \"text\": \"def get_param_values(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea7e3eb9cbba00302fb26b09b5b594a0\",\n \"score\": \"0.56196666\",\n \"text\": \"def validate_query_params(self):\\n allowed_params = set(self.get_filters().keys())\\n allowed_params.update(self.get_always_allowed_arguments())\\n\\n unallowed = set(self.request.query_params.keys()) - allowed_params\\n\\n if unallowed:\\n msg = (\\n \\\"Unsupported parameter(s): {}. Please use a combination of: {}.\\\".format(\\n \\\", \\\".join(unallowed), \\\", \\\".join(allowed_params)\\n )\\n )\\n self.form.add_error(field=None, error=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e3f90b61e31836355f05415b489d1b2\",\n \"score\": \"0.5614368\",\n \"text\": \"def _get_params(self, method, param=None):\\r\\n\\t\\tif method.upper() == 'GET':\\r\\n\\t\\t\\tif param != None:\\r\\n\\t\\t\\t\\treq_parse = self.request.path.split('/')\\r\\n\\t\\t\\t\\tvalue = None\\r\\n\\t\\t\\t\\tif len(req_parse) >= 3:\\r\\n\\t\\t\\t\\t\\tvalue = str(req_parse[2])\\r\\n\\t\\t\\t\\tquery = \\\"%s=%s\\\"%(param.replace(':',''),value)\\r\\n\\t\\t\\t\\tif len(self.request.query) > 0:\\r\\n\\t\\t\\t\\t\\tquery = query+\\\"&\\\"+self.request.query\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\treturn query\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\treturn self.request.query\\r\\n\\t\\telif method.upper() == 'POST':\\r\\n\\t\\t\\tquery = ''\\r\\n\\t\\t\\tfor name in self.request.arguments.keys():\\r\\n\\t\\t\\t\\tquery += name+'='+self.get_argument(name)+'&'\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\treturn query[0:len(query)-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bc55d4f6991ea2229ec61f61b6b4a2\",\n \"score\": \"0.5611185\",\n \"text\": \"def add_query_params(request, **kwargs):\\n updated = request.GET.copy()\\n for key, value in kwargs.items():\\n updated[key] = value\\n return request.build_absolute_uri('?'+updated.urlencode())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"954ba341d59ebb990fe1cc1d9fcd1f6e\",\n \"score\": \"0.56105226\",\n \"text\": \"def query_params(self):\\n return self._query_params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69376ec2be0125e93d315bd6afe7405\",\n \"score\": \"0.56065536\",\n \"text\": \"def paramsToQueryString (paramMap):\\n\\tparams=[];add=params.append\\n\\tfor key in paramMap:\\n\\t\\tval = paramMap[key]\\n\\t\\tif type(val) in [type(\\\"\\\"), type(u\\\"\\\")]:\\n\\t\\t\\tadd (\\\"%s=%s\\\" % (key, val))\\n\\t\\tif type(val) == type([]):\\n\\t\\t\\tfor v in val:\\n\\t\\t\\t\\tadd (\\\"%s=%s\\\" % (key, urllib.quote(v)))\\n\\treturn '&'.join (params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fec6518b1a0f9dae54af274e5205d63f\",\n \"score\": \"0.55968285\",\n \"text\": \"def get_search_terms(self, request):\\n params = request.QUERY_PARAMS.get(self.search_param, '')\\n return [params] #.replace(',', ' ').split()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f508f0ed59473a01d2f74ff9b73661\",\n \"score\": \"0.55598676\",\n \"text\": \"def get_keep_filter() :\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f508f0ed59473a01d2f74ff9b73661\",\n \"score\": \"0.55598676\",\n \"text\": \"def get_keep_filter() :\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95f508f0ed59473a01d2f74ff9b73661\",\n \"score\": \"0.55598676\",\n \"text\": \"def get_keep_filter() :\\n\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3bafab86045f66f6422aeb560ecc49e\",\n \"score\": \"0.5533564\",\n \"text\": \"def get_params(self):\\r\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b896e16b3e4cdcc0aaae471db74f4c6b\",\n \"score\": \"0.5507502\",\n \"text\": \"def get_queryset(self):\\n new_query = {key:self.request.query_params.get(key) for key in self.request.query_params} # this line to convert the values of the return object of query param from list of string to string\\n queryset = self.get_serializer_class().Meta.model.objects.filter(**new_query)\\n return queryset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccc0bc698652b0cde47905e6a9daff8c\",\n \"score\": \"0.5494568\",\n \"text\": \"def get_normalized_params(params):\\n return urlparse.urlencode(sorted(params))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1854ad851144329e45c80ca62bdb2547\",\n \"score\": \"0.5485816\",\n \"text\": \"def get_query_params(self):\\n\\n if self.next_page_token:\\n return {'pageToken': self.next_page_token}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e177e5491e6d19684b498af071bdbeef\",\n \"score\": \"0.54793346\",\n \"text\": \"def get_param_list(self):\\n\\t\\treturn [k for k, v in self.para.items()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59efc98f1eac8a8bae15ac87dff42b56\",\n \"score\": \"0.5473872\",\n \"text\": \"def _sanitize_url_params(req):\\n params = dict(req.args)\\n\\n src_params = {k: v[0] for k, v in params.items()\\n if k != 'share'}\\n src_url = _add_params(req.base_url, **src_params)\\n\\n href_params = {k: v[0] for k, v in params.items()\\n if k not in ['width', 'height']}\\n href_url = _add_params(req.base_url, **href_params)\\n\\n return src_url, href_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec497613827d31aacb9b36baa81abd0e\",\n \"score\": \"0.5458749\",\n \"text\": \"def get_search_parameters(request):\\n search_filter = \\\"\\\"\\n page_len = \\\"\\\"\\n\\n if request.args:\\n page_len = request.args.get(\\\"show\\\", \\\"\\\")\\n search_filter = \\\" \\\".join(\\n [arg for arg in request.args if arg != \\\"show\\\"]\\n )\\n\\n return search_filter, page_len\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d5c353cf237a049ac3894667f552531\",\n \"score\": \"0.54559433\",\n \"text\": \"def uri_query(self):\\n value = []\\n for option in self.options:\\n if option.number == defines.OptionRegistry.URI_QUERY.number:\\n value.append(str(option.value))\\n return \\\"&\\\".join(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab23af951068ba700136915b964852c0\",\n \"score\": \"0.5455822\",\n \"text\": \"def unset_kuenstler_params(self, params=None):\\n if not params:\\n params = self.kuenstler.keys()\\n for p in params:\\n self.kuenstler[p] = ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"562c5038ffadd2905e431ce056d578c5\",\n \"score\": \"0.5434189\",\n \"text\": \"def url_query_params(url):\\n return dict(parse_qsl(urlparse(url).query, True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7157a0d5fd81ef98107abca4c9a2011\",\n \"score\": \"0.54331154\",\n \"text\": \"def args(self):\\r\\n return url_decode(self.environ.get('QUERY_STRING', ''),\\r\\n self.url_charset, errors=self.encoding_errors,\\r\\n cls=self.parameter_storage_class)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2993da76da7d7328ad5dfb54b74376\",\n \"score\": \"0.54275715\",\n \"text\": \"def test_get_params_excludes_unmentioned(self):\\n handler = self.post_json({\\n 'foo': 'bar',\\n 'baz': 'qux',\\n })\\n\\n self.assertEqual(\\n handler.get_params({'foo': str}), # lacks baz\\n {'foo': 'bar'} # also lacks baz\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f91cb58c9de8075b3d99ad7bc5768d\",\n \"score\": \"0.54269546\",\n \"text\": \"def querystring(self):\\n return {key: value for (key, value) in self.qs.items() if key.startswith(self.MANAGED_KEYS)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e1425052e0a135853735e96a9b2cc71\",\n \"score\": \"0.5425071\",\n \"text\": \"def my_pagination_for(context, current_page, page_var=\\\"page\\\", exclude_vars=\\\"\\\"):\\n querystring = context[\\\"request\\\"].GET.copy()\\n exclude_vars = [v for v in exclude_vars.split(\\\",\\\") if v] + [page_var]\\n for exclude_var in exclude_vars:\\n if exclude_var in querystring:\\n del querystring[exclude_var]\\n querystring = querystring.urlencode()\\n result = {\\n \\\"current_page\\\": current_page,\\n \\\"querystring\\\": querystring,\\n \\\"page_var\\\": page_var,\\n }\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abafe80875b9bff34fca288dc397aacb\",\n \"score\": \"0.5419454\",\n \"text\": \"def append_query_params(original_url, **kwargs):\\n scheme, netloc, path, query_string, fragment = urlsplit(original_url)\\n query_params = parse_qs(query_string)\\n if kwargs is not None:\\n for key, value in kwargs.items():\\n query_params[key] = [value]\\n\\n new_query_string = urlencode(query_params, doseq=True)\\n new_url = urlunsplit((scheme, netloc, path, new_query_string, fragment))\\n return new_url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b554bad60311d8939d3885c35f1515f\",\n \"score\": \"0.5406037\",\n \"text\": \"def get_search_terms(self, request):\\n params = request.query_params.get(self.search_param, '')\\n return params.replace(',', ' ').strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f28c755db1316cb3467648451b8121e\",\n \"score\": \"0.539024\",\n \"text\": \"def clean(self):\\n self.request_parameters = OrderedDict()\\n self.request_type = \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"728e704afc55c06050ae17e40d76cb4d\",\n \"score\": \"0.5372662\",\n \"text\": \"def pop_from_GET(obj, attr):\\r\\n if obj.get(attr, None):\\r\\n obj_copy = obj.copy()\\r\\n del obj_copy[attr]\\r\\n return '&%s' % obj_copy.urlencode()\\r\\n if not obj:\\r\\n return ''\\r\\n return '&%s' % obj.urlencode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83fdfa43a494ee5f6b76c703b351a52f\",\n \"score\": \"0.5352228\",\n \"text\": \"def preserve_parameters(preserve=freeze([])):\\n snippet = []\\n\\n form = get_context().request.get_form()\\n for k, v in form.items():\\n for prefix in preserve:\\n if k.startswith(prefix):\\n snippet.append('<input type=\\\"hidden\\\" name=\\\"%s\\\" value=\\\"%s\\\">'\\n % (k, quote(v)))\\n break\\n\\n return '\\\\n'.join(snippet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c69f2755dd0bbe48378eba9f75f53861\",\n \"score\": \"0.5336839\",\n \"text\": \"def get_unpinned_params(opts, params):\\n return params - set([p for p, v in opts.__dict__.items() if p in LIKELIHOOD_PINNABLE_PARAMS and v is not None])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9019c6410eac7fc3061665e80112a346\",\n \"score\": \"0.5333564\",\n \"text\": \"def _get_param_list(self) -> list:\\n param_list = [\\n name\\n for name in self.__dir__()\\n if not name.startswith(\\\"_\\\") and not callable(getattr(self, name))\\n ]\\n return param_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b8a91c94076a14cc2e1615e3f98b8d1\",\n \"score\": \"0.53288966\",\n \"text\": \"def get_cleaned_kwargs(self, kwargs):\\n cleaned_kwargs = kwargs.copy()\\n\\n if kwargs is not None:\\n for k in kwargs:\\n if self.is_linguist_lookup(k):\\n del cleaned_kwargs[k]\\n\\n return cleaned_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6935c016867b9a8311b7ac51263d37c4\",\n \"score\": \"0.5327639\",\n \"text\": \"def missing_params(self):\\n ret = []\\n for (param, pinfo) in self._valid_params.items():\\n if pinfo[5]:\\n if (not self._params.has_key(param) or\\n not self._params[param]):\\n ret.append(param)\\n # Check \\\"require_oneof\\\" list, if necessary\\n if len(ret) == 0:\\n for param in self._require_oneof_params:\\n if self._params.has_key(param):\\n return []\\n return self._require_oneof_params\\n else:\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa39086379b8b9512849766c835e0963\",\n \"score\": \"0.53228766\",\n \"text\": \"def buildQS(self, params):\\n queryString = \\\"\\\"\\n amp = \\\"\\\"\\n # keys = params.keys()\\n keys = list(params.keys())\\n keys.sort()\\n for key in keys:\\n value = params.get(key)\\n if value is not None:\\n queryString += amp + key + \\\"=\\\" + self.UrlEncode(value)\\n amp = \\\"&\\\"\\n\\n return queryString\",\n \"title\": \"\"\n },\n {\n \"docid\": \"492f22942ebdd9b2c6451009e1451e61\",\n \"score\": \"0.53176075\",\n \"text\": \"def get_params():\\n param = []\\n paramstring = sys.argv[2]\\n if len(paramstring) >= 2:\\n params = sys.argv[2]\\n cleanedparams = params.replace('?', '')\\n if params[len(params) - 1] == '/':\\n params = params[0:len(params) - 2]\\n pairsofparams = cleanedparams.split('&')\\n param = {}\\n for i in range(len(pairsofparams)):\\n splitparams = {}\\n splitparams = pairsofparams[i].split('=')\\n if (len(splitparams)) == 2:\\n param[splitparams[0]] = splitparams[1]\\n return param\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddfccbd32dde0d1d68be5c01643c7797\",\n \"score\": \"0.5314787\",\n \"text\": \"def strip_nodeid_parameters(nodeid):\\n\\treturn REGEX_PARAMETERS.sub('', nodeid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0df79d95683de3d8dfb159f7df6d05ea\",\n \"score\": \"0.53137153\",\n \"text\": \"def update_url_query(context, param_name=None, param_value=None, **kwargs):\\n # request.GET is a QueryDict, which is immutable unless we make a copy\\n query_dict = context['request'].GET.copy()\\n # set variable parameter name and value\\n if param_name:\\n query_dict[param_name] = param_value\\n if param_value == 'remove':\\n # remove the parameter altogether\\n query_dict.pop(param_name, None)\\n # for a normal dict we would use update(kwargs), but QueryDict.update()\\n # appends values, instead of replacing them, so we need to set each key\\n # explicitly\\n for key, value in kwargs.items():\\n query_dict[key] = value\\n # urlencode converts the dict to a query string\\n return query_dict.urlencode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9419dc17f0c2f8c474be4e33d9ff7460\",\n \"score\": \"0.53083116\",\n \"text\": \"def _get_param_list(self) -> list:\\n param_list = [\\n name for name in self.__dir__()\\n if not name.startswith(\\\"__\\\") and not callable(getattr(self, name))\\n ]\\n return param_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71948e92b6ee926ef16147a8fbd3be87\",\n \"score\": \"0.5303549\",\n \"text\": \"def get_all_params(self):\\n return {k: v for k, v in self.params.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"866d36d55fd1f24850ddd8b90622e20d\",\n \"score\": \"0.53032476\",\n \"text\": \"def _extract_paging_params(request):\\n count = request.url.query.get('count', DEFAULT_PAGE_SIZE)\\n page_num = request.url.query.get('page', 0)\\n\\n return RouteHandler._check_paging_params(count, page_num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"071cba7ef32ad5b8db789851168d1499\",\n \"score\": \"0.5292904\",\n \"text\": \"def get_params(self):\\n return list(self._params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e685140892bb22831c862cc64f8313ee\",\n \"score\": \"0.5289206\",\n \"text\": \"def _GetClearedFieldsForUrlRewrite(url_rewrite, field_prefix):\\n cleared_fields = []\\n if not url_rewrite.pathPrefixRewrite:\\n cleared_fields.append(field_prefix + 'pathPrefixRewrite')\\n if not url_rewrite.hostRewrite:\\n cleared_fields.append(field_prefix + 'hostRewrite')\\n return cleared_fields\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10d706b5ed190eabcb619b1833aba3a6\",\n \"score\": \"0.52889234\",\n \"text\": \"def get_dict_of_all_args(self):\\n dictionary = {}\\n for arg in [arg for arg in self.request.arguments if arg not in self.settings.get(\\\"reserved_query_string_params\\\", [])]:\\n val = self.get_argument(arg, default=None)\\n if val:\\n dictionary[arg] = val\\n return dictionary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457d9c0dcbe6a1fc6b893cc1979ced99\",\n \"score\": \"0.5287862\",\n \"text\": \"def get_keep_filter() :\\n\\n return ['.*']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef7901d6884d8351e19c78c527b6cfe\",\n \"score\": \"0.5286536\",\n \"text\": \"def parse_querystring(self, req, name, arg):\\n return core.get_value(req.GET, name, arg.multiple)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e17c64dc05dcf3c33a4ffb1ce9dfe39b\",\n \"score\": \"0.52736485\",\n \"text\": \"def remove_empty_kwargs(self, **kwargs):\\n good_kwargs = {}\\n if kwargs is not None:\\n for key, value in kwargs.items():\\n if value:\\n good_kwargs[key] = value\\n return good_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127a21a76d75f5b88608cab518a2648f\",\n \"score\": \"0.5269207\",\n \"text\": \"def remove(self, key: str) -> \\\"QueryParams\\\":\\n q = QueryParams()\\n q._dict = dict(self._dict)\\n q._dict.pop(str(key), None)\\n return q\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7318a4de5a083460f5671350f90417f\",\n \"score\": \"0.52671456\",\n \"text\": \"def remove_param(url, param):\\r\\n url_obj= urlparse(url)\\r\\n query = parse_qs(url_obj.query)\\r\\n query.pop(param, None)\\r\\n url_obj= url_obj._replace(query=urlencode(query, True))\\r\\n return urlunparse(url_obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d2c9bf43299816716602ebd9f7059ee\",\n \"score\": \"0.52641964\",\n \"text\": \"def _remove_kwarg(args, kwarg):\\n args = [arg for arg in args if '%s=' % kwarg not in arg]\\n # If kwarg is by self, remove it and its value\\n try:\\n index = args.index(kwarg)\\n except ValueError:\\n pass\\n else:\\n args = args[:index] + args[index + 2:]\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be3e25ad07a61769d22049d87992538a\",\n \"score\": \"0.52516925\",\n \"text\": \"def _parse_args(self):\\n args = {}\\n if '?' not in self.request.path_qs:\\n args['new_search'] = True\\n return args\\n\\n if len(self.request.get('tags')) > 0:\\n tags = self.request.get('tags').split(',')\\n args['tags'] = [t.strip() for t in tags]\\n\\n # All string params are handled the same way\\n params = [\\n 'user_id',\\n 'author',\\n 'source',\\n 'rating',\\n 'count',\\n 'text_dump',\\n 'spec_op',\\n ]\\n for p in params:\\n if len(self.request.get(p)) > 0:\\n args[p] = self.request.get(p)\\n\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"461f65127a6669450875e6807021436e\",\n \"score\": \"0.52441776\",\n \"text\": \"def strip_ticket(self):\\n url = request.url\\n if url is None:\\n return \\\"something is badly wrong\\\"\\n url = sub(r'ticket=[^&]*&?', '', url)\\n url = sub(r'\\\\?&?$|&$', '', url)\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba9b1f3ddcf039f9c0a7c95f852eb1e4\",\n \"score\": \"0.52407527\",\n \"text\": \"def get_search_params(url_path):\\n # Remove the '/search.json' part to get the given options\\n query_params = url_path[len('/search.json?'):]\\n\\n # Find the 'q=' section, what we care about\\n queryPos = query_params.find('q=')\\n if queryPos == -1:\\n # There's no 'q=' to grab items from, fail\\n return []\\n query_params = query_params[queryPos+2:]\\n\\n # Find the end of the search terms, if not the end of the string\\n endPos = query_params.find('&')\\n if endPos != -1:\\n query_params = query_params[:endPos]\\n\\n return query_params\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1689,"cells":{"query_id":{"kind":"string","value":"05820bf0cda8ff6e6985ae5b64e56305"},"query":{"kind":"string","value":"used to print fraction"},"positive_passages":{"kind":"list like","value":[{"docid":"a7ef7522c1dba33096776029f106df1d","score":"0.67656446","text":"def __str__(self):\n x = self.numerator / self.denominator\n if x != 0:\n return \"%s and %s / %s\" % (x, self.numerator % self.denominator, self.denominator)\n else:\n return \"%s / %s\" % (self.numerator, self.denominator)","title":""}],"string":"[\n {\n \"docid\": \"a7ef7522c1dba33096776029f106df1d\",\n \"score\": \"0.67656446\",\n \"text\": \"def __str__(self):\\n x = self.numerator / self.denominator\\n if x != 0:\\n return \\\"%s and %s / %s\\\" % (x, self.numerator % self.denominator, self.denominator)\\n else:\\n return \\\"%s / %s\\\" % (self.numerator, self.denominator)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e8e765c12dee572b44efd64798c47853","score":"0.7505799","text":"def printFrac(self, num, den):\n # Wrap LaTeX in \\frac tag\n code = r\"\\frac{%s}{%s}\" % (num, den)\n\n # Append to editor\n self.ui.text_equation.append(code)","title":""},{"docid":"c4d771d1e914a9f050da2f7b4f34d063","score":"0.7426337","text":"def fractionToString(x):\n if x.denominator == 1:\n x = x.numerator\n else:\n x = f\"{x.numerator}/{x.denominator}\"\n return x","title":""},{"docid":"e045f34be867af6bd8c7e952bf6b8dfe","score":"0.7067178","text":"def __repr__(self):\n return str(self.numerator)+\"/\"+str(self.denominator)","title":""},{"docid":"b0de6a29ad123ea677ae13ee5416460c","score":"0.7056627","text":"def my_pretty(frac):\n num, denom = sympy.fraction(sympy.factor(frac))\n return sympy.pretty(num) + \"\\n\" + \"/\\n\" + sympy.pretty(denom) + \"\\n\\n\\n\\n\\n\"","title":""},{"docid":"2812086ed022b6d5cfa04a8c8b5b342e","score":"0.6953772","text":"def __str__(self):\n return str(self.numerator)+\"/\"+str(self.denominator)","title":""},{"docid":"e45dbc25244c481717aa38d4f273d613","score":"0.69442856","text":"def __str__(self):\n return '{} / {}'.format(self.num, self.den)","title":""},{"docid":"9bd2362441107a765a94937ae862ba92","score":"0.6936911","text":"def __str__(self):\n if self.denominator == 1 or self.numerator == 0:\n return str(int(self.numerator))\n else:\n return '{}/{}'.format(int(self.numerator),int(self.denominator))","title":""},{"docid":"b712cbd5b6eb4935d3d2a43cf3e985b7","score":"0.6861012","text":"def __str__(self):\n if self.denominator == 1:\n # show value as integer\n return str(self.numerator)\n return '{0}/{1}'.format(self.numerator, self.denominator)","title":""},{"docid":"a2c188f47771833358859bf78b56e463","score":"0.6850174","text":"def fraction(value):\n fraction = fractions.Fraction(value).limit_denominator(4)\n if fraction.numerator == 0:\n return \"0\"\n if fraction.numerator < fraction.denominator:\n return str(fraction.numerator) + \"/\" + str(fraction.denominator)\n whole = fraction.numerator / fraction.denominator\n if fraction.denominator == 1:\n return str(whole)\n numerator = fraction.numerator - (whole * fraction.denominator)\n return str(whole) + \" \" + str(numerator) + \"/\" + str(fraction.denominator)","title":""},{"docid":"f87832544f2d9ff255419b3192fb2f4c","score":"0.6834446","text":"def fraction(tokens):\r\n base, scaling = base_scaling(tokens)\r\n return base * scaling","title":""},{"docid":"0e49e2c66ed883c97e2eee455b6d1a49","score":"0.6796108","text":"def texFrac(frac):\n return [\"\\\\frac{\" , str(frac._num) , \"}{\" , str(frac._denom) , \"}\"]","title":""},{"docid":"c1148e965cf183aa63858db22d74cd84","score":"0.67821825","text":"def format_fraction(value):\r\n if value == 0:\r\n return \"0\"\r\n elif isinstance(value, Fraction):\r\n return \"{0}/{1}\".format(value.numerator, value.denominator)\r\n else:\r\n return \"{0}/1\".format(value)","title":""},{"docid":"3bbe617991a93adae7d11cfc3df46e4f","score":"0.67652345","text":"def __str__(self):\n if Fraction.display == \"improper\":\n return f\"{self.get_num()}/{self.get_denom()}\"\n elif Fraction.display == \"mixed\":\n m = self.get_num() // self.get_denom()\n if m == 0:\n return f\"{self.get_num()}/{self.get_denom()}\"\n else:\n n = self.get_num() - m*self.get_denom()\n if n == 0:\n return f\"{m}\"\n else:\n return f\"{m}, {n}/{self.get_denom()}\"","title":""},{"docid":"40c22723a833f37985e5509a91618c47","score":"0.67407453","text":"def fraction(self):\r\n base, scaling = self.base_scaling\r\n return base * scaling","title":""},{"docid":"c52b9aa02ac7d02a4d5b3d9203042f44","score":"0.6718211","text":"def fraction(self):\r\n return self._fraction","title":""},{"docid":"24c52778cc799852ee351d764cb3cbb9","score":"0.65996605","text":"def unitFractionString(dd):\n if dd == 1:\n return \"1\"\n else:\n return \"1/\" + intString(dd)","title":""},{"docid":"8356ab02b64bc746e58395b60e15f15d","score":"0.65783906","text":"def __str__(self):\n return str(self.num) + \"/\" + str(self.denom)","title":""},{"docid":"66bde25fca79bad87048883593ed9988","score":"0.6578014","text":"def fraction( self ):\n return self.value()/self.duration()","title":""},{"docid":"32fb078e9d2b6dd16dc0f1899f17e0be","score":"0.6570177","text":"def __str__(self):\n return str(self.num) + \"/\" + str(self.den)","title":""},{"docid":"7f49b3bb174f5c5fcd3f235c14556836","score":"0.6552754","text":"def __str__(self):\n if self.numerator == 0:\n if self.denominator == 0:\n # If 0/0 show nan\n return f'{math.nan}'\n else:\n return '0'\n elif self.denominator == 0 and self.numerator > 0:\n return f'{math.inf}'\n elif self.denominator == 0 and self.numerator < 0:\n return f'{-math.inf}'\n elif self.denominator == 1:\n return f'{self.numerator}'\n else:\n return f'{self.numerator}/{self.denominator}'","title":""},{"docid":"0e444274f990abdb57e4b50955a2a1b5","score":"0.6489734","text":"def __str__(self):\n self.__reduce__()\n return str(int(self.numerator)) + '/' + str(int(self.denominator))","title":""},{"docid":"02480e1f467954e493454fc506b2a12e","score":"0.6434899","text":"def __str__(self) -> str:\n return str(self.num) + \"/\" + str(self.denom)","title":""},{"docid":"9bd95b787e9a15639e500af8251d7255","score":"0.64214236","text":"def fraction_to_string(fraction):\n return '({}) / ({})'.format(fraction.num, fraction.denum)","title":""},{"docid":"9dc86b509d8f16ddd87ce226f2b5ed62","score":"0.6354297","text":"def fractions(self):\n return self.accumulators / self.n","title":""},{"docid":"54469e3cdb7e618d2646a249237c3825","score":"0.6307001","text":"def str_rat(x): #from lecture notes\n return '{0}/{1}'.format(num(x), den(x))","title":""},{"docid":"67774662a85390ca6261846aed83cce6","score":"0.629963","text":"def VertexFormat(self, frac: int) -> None:\n ...","title":""},{"docid":"e47bc83f70afdc24b2cb8e9327bd4835","score":"0.628328","text":"def __str__(self) -> str:\n return f\"{self.num}/{self.denom}\"","title":""},{"docid":"3259e31239b1b940cc6915b0552017e9","score":"0.6281963","text":"def egyptianFractionString(xx):\n return \" + \".join(map(unitFractionString,xx))","title":""},{"docid":"25041e946a2b8d18218e8eace4c05120","score":"0.6271916","text":"def printtree(self):\n for i in range(self.n + 1):\n print(\"Period = \" + str(i))\n print(self.tree[i][: i + 1].round(3))","title":""},{"docid":"9db05f9c69c8a551641d2393e1981e6c","score":"0.62271434","text":"def fractation(x):\n dev=2\n while x>1:\n if x%dev == 0:\n x //= dev\n print(f\"{dev}\", end=' ')\n else:\n dev += 1","title":""},{"docid":"534209582f4c968290f4a0484ada6119","score":"0.6199866","text":"def frac(self):\n return self.quadirrat","title":""},{"docid":"adbf3b2f1d9a443c41f7379460239e68","score":"0.6194349","text":"def format_ratio(numerator, denominator):\n if denominator == 0:\n return float(\"nan\")\n return float(numerator) / denominator","title":""},{"docid":"ff8e7d707009077b2a343bd7f3979fb0","score":"0.61942446","text":"def printpercentcomposition(self):\n if self.__dict__.has_key('sys') is False:\n self.sys = __import__('sys')\n self.sys.stdout.write('\\nelemental percent composition:\\n')\n for key,val in sorted(self.pcomp.items()):\n self.sys.stdout.write('%3s: %7.3f %%\\n' %(key,self.pcomp[key]*100))","title":""},{"docid":"9f45e6d13ee655f8140cc888c27965c5","score":"0.6185754","text":"def show(self):\n formatStr = '{:' + str(8) + '.' + str(self.prec) + 'f}'\n for i, m in enumerate(self.M):\n print(\"|\", end=\"\")\n for n in m:\n print(\" \" if (self.prec > 4) else \"\", end=\"\")\n print(formatStr.format(n if not(n == 0.0) else 0.0),end=\"\")\n print(\" |\")\n print(\"\\n\")","title":""},{"docid":"2f7fbd6a2052f55ecd56cff9333586ce","score":"0.61693066","text":"def contfrac_inaccurate(n):\n while True:\n whole = int(n)\n yield whole\n remainder = n - int(n)\n reciproc = 1 / remainder\n n = reciproc\n print(whole, remainder, reciproc)","title":""},{"docid":"ef79bfc188bafd7cc935d810db97936b","score":"0.6163692","text":"def unpack_fraction(num: str) -> str:\n nums = [int(n) for n in num.split('/')]\n if nums[0] > nums[1]:\n over = nums[0] // nums[1]\n rem = nums[0] % nums[1]\n return '{} {}/{}'.format(over, rem, nums[1])\n return num","title":""},{"docid":"0279f89b49f87f29cb5e97c61564bacd","score":"0.61628515","text":"def percentagify(numerator, denominator):\n if denominator:\n return '{:04.2f}%'.format((numerator / denominator) * 100)\n else:\n return 'N/A'","title":""},{"docid":"c4fd4ad8861f8239a06a7de8d0fb1fe7","score":"0.6152728","text":"def print_percents(x,y):\n print(\"deposit = \" + str(x) + \" in this moment \" + str(y) + \" result in procents \" + \"\"),(str(difine_color(percents(x, y))))","title":""},{"docid":"ada30ed51be57245d49aae971fca4a63","score":"0.6149142","text":"def fraction(self):\r\n if self.duration:\r\n return self.duration.base_scaling[0]","title":""},{"docid":"14a93ab70593b71e7a30f70f1ee1c546","score":"0.6113385","text":"def calc_and_print(self):\n\n self.t = self.find_t_of_s(self.Temp)\n\n print(\" \")\n print(\" T T_n/T t \")\n print(\"-------------------------------------\")\n\n for i in range(len(self.Temp-1)):\n self.T_neutrino[i] = self.find_neutrino_temperature(self.Temp[i])\n self.Splot[i] = self.find_S_of_x(self.find_x_of_T(self.Temp[i]))\n\n print(\"{:.2e} | {:.5f} | {:.4e} \"\\\n .format(self.Temp[i],\n self.T_neutrino[i]/self.Temp[i],\n self.t[i]))","title":""},{"docid":"311b5901bda35368f7ea50b972b22c82","score":"0.61032516","text":"def percentage(numerator, denominator):\n if not denominator:\n return \"0.00%\"\n val = (numerator / Decimal(denominator)) * 100\n return \"%.2f%%\" % val","title":""},{"docid":"613ff22d7d8d008475d69303768362a4","score":"0.6056618","text":"def __float__(self):\n return self.num / self.denom","title":""},{"docid":"3a78d4885300405dc7b345a009bcd990","score":"0.5993808","text":"def print_format(self):\n \n return \"Left Motor: %f%%\\nRight Motor: %f%%\" % (self.left, self.right)","title":""},{"docid":"1bbca2372d45051d643fdfe318cce6eb","score":"0.59848547","text":"def build(self, fraction): #zbudowanie budynku i odjęcie jego kosztu od materiałów frakcji\r\n\r\n pass","title":""},{"docid":"0d50629591fc4876d53b7fbad511fa7b","score":"0.5977914","text":"def _rfr_fraction_ ( self , var1 , var2 ) :\n if isinstance ( var1 , str ) : var1 = self.param ( var1 ) [1]\n if isinstance ( var2 , str ) : var2 = self.param ( var2 ) [1] \n _av1 = abs ( var1.value.value() ) \n _av2 = abs ( var2.value.value() ) \n if _av1 > _av2 : return 1 / ( 1 + self.ratio ( var2 , var1 ) )\n return 1.0 - self.fraction ( var2 , var1 )","title":""},{"docid":"c1f65da4bd9a4cfdf1e4025622b9d832","score":"0.5961971","text":"def fraction_digits(self):\n return self._fraction_digits","title":""},{"docid":"4dd1039b6e5ab35c3e8e1c5a6900e6ef","score":"0.59456325","text":"def packing_fraction(self):\n total_rad = 0\n for i in self.elements:\n total_rad = total_rad + Specie(i).atomic_rad ** 3\n pf = np.array([4 * np.pi * total_rad / (3 * self.volume)])\n return round(pf[0], 5)","title":""},{"docid":"0f85950ce77963004c1fb50312d35124","score":"0.5944779","text":"def print_percents(x,y):\n print(\"deposit = \" + str(x) + \" in this moment \" + str(y) + \" result in procent \" + str(percents(x, y)))","title":""},{"docid":"3eccdb0ad6e84726717fc222aba0d446","score":"0.59192246","text":"def display_fraction(value, limit_denominator=None, allow_mixed_numbers=True,\n coerce_thirds=True):\n\n try:\n whole_number, numerator, denominator = get_fraction_parts(value,\n allow_mixed_numbers,\n limit_denominator,\n coerce_thirds)\n unicode_entity = get_fraction_unicode_entity(fractions.Fraction(numerator, denominator))\n except (ValueError, InvalidOperation) as e:\n whole_number, numerator, denominator, unicode_entity = (value, 0, 0, None)\n\n return {\n 'whole_number': whole_number,\n 'numerator': numerator,\n 'denominator': denominator,\n 'unicode_entity': unicode_entity,\n 'allow_mixed_numbers': allow_mixed_numbers,\n }","title":""},{"docid":"fb5647a89949b600fb33559ed5d5901d","score":"0.5907708","text":"def numer(f):\n return f.half_per(f.num)","title":""},{"docid":"f3552009736b6182ce231ae45afa4a68","score":"0.58914167","text":"def fraction_string(duration):\r\n base, scaling = base_scaling_string(duration)\r\n return base * scaling","title":""},{"docid":"375e6d123eb7f58fb5c9eb445ae51b04","score":"0.5881315","text":"def show_float(self, value):\n self.shreg.write_byte(SEGMENTS[value % 1])\n self.shreg.write_byte(DP | SEGMENTS[value // 1])","title":""},{"docid":"4c65302815229752d600b2fe024c851a","score":"0.58630604","text":"def quotient(a, b):\n quotient = a / b\n print(\"Der Quotient der beiden Zahlen lautet\", quotient)","title":""},{"docid":"43e4d7b43de2dc841b1bbe42484206e3","score":"0.58260185","text":"def format_proc(temp: int) -> str:\n return str(round(temp)) + \"%\"","title":""},{"docid":"6c2228eb912f933e2541c74520ccd4a1","score":"0.58079684","text":"def calcFractions(self):\n self.resultView.clear()\n text = self.exprEdit.text()\n try:\n num = float(text)\n except ValueError:\n try:\n num = float(eval(text))\n output = [_('Entry'), '{0}'.format(num)]\n self.resultView.addTopLevelItem(QTreeWidgetItem(output))\n except:\n QMessageBox.warning(self, 'ConvertAll',\n _('Invalid expresssion'))\n return\n QApplication.setOverrideCursor(Qt.WaitCursor)\n powerOfTwo = self.powerTwoCtrl.isChecked()\n for numer, denom in listFractions(num, powerOfTwo):\n output = ['{0}/{1}'.format(numer, denom),\n '{0}'.format(numer / denom)]\n self.resultView.addTopLevelItem(QTreeWidgetItem(output))\n QApplication.restoreOverrideCursor()","title":""},{"docid":"92a2c80dc8a8a38511b8b6451f4b7bc3","score":"0.58073425","text":"def show(p):\n rows = ['[' + ', '.join(map(lambda x: '{0:.5f}'.format(x), r)) + ']' for r in p]\n print('\\n'.join(rows) + '\\n')","title":""},{"docid":"085a75c0db185f0af3e358a6b4420ab0","score":"0.57816243","text":"def asymptotic_triangle_fraction():\n return 1./3","title":""},{"docid":"73c4e917ce66e065fda0f5e482ae3b1d","score":"0.5780224","text":"def div():\n global str_display_calc\n global nb2\n global operator\n nb2 = float(str_display_calc)\n str_display_calc = \"\"\n operator = 4","title":""},{"docid":"379d21f19598e40b5851a84733d03229","score":"0.5775186","text":"def pcStr(n, d):\n try:\n s = \"%6.2f\" % (100.0*n/d,)\n except:\n s = \"\"\n return s","title":""},{"docid":"1e69327c0da4ab802ceaa947d8fa7fb2","score":"0.57658064","text":"def _print_GoldenRatio(self, e):\n x = self.dom.createElement('cn')\n x.appendChild(self.dom.createTextNode(\"\\N{GREEK SMALL LETTER PHI}\"))\n return x","title":""},{"docid":"0f68e53e43fb63f0b9e2b4ae82d200a9","score":"0.57643324","text":"def calcPercent(f, sigfig=2):\n return str(round(f, sigfig)*100)+\" %\"","title":""},{"docid":"926f8b78f0ec005d073eae5c55c92169","score":"0.5739741","text":"def test_shutter_value_fraction():\n shutter = shutter_value('1/4')\n\n assert shutter == 0.25","title":""},{"docid":"12cecab147ec1418df41fa795b9963a5","score":"0.5738268","text":"def plot_mode_fraction(s):\n fig, ax = plt.subplots(figsize=(4, 3))\n ax.set_xlim(0, 600)\n ax.set_ylim(1e-4,1)\n ax.set_yscale('log')\n ax.grid()\n f = 1 - np.cumsum(s**2) / np.sum(s**2)\n\n ax.set_xlabel('Modes')\n ax.set_ylabel('Missing mode power')\n\n ax.plot(f)\n fig.savefig('HW4/figures/mode_frac.pdf', bbox_inches='tight')","title":""},{"docid":"852505ed01bc4a412f89b8ee4dd9343e","score":"0.572525","text":"def showFrac(self):\n # Create new dialog, setup using about.py\n dialog = QtGui.QDialog()\n frac = Ui_Frac()\n frac.setupUi(dialog)\n\n # Connect event handler\n frac.buttonBox.accepted.connect(lambda: self.printFrac(frac.line_num.text(), frac.line_den.text()))\n\n # Execute new dialog box\n dialog.exec_()","title":""},{"docid":"4fd650010eea556c6e4bc5c661d07ba3","score":"0.57222855","text":"def display_improper_fraction(value, limit_denominator=None, coerce_thirds=True):\n return display_fraction(value, limit_denominator=limit_denominator,\n allow_mixed_numbers=False, coerce_thirds=coerce_thirds)","title":""},{"docid":"94667a77430c0b7e35236930812b5aed","score":"0.5715667","text":"def decimalString(self, N):\n n = self.numerator\n d = self.denominator\n L = []\n if n < 0:\n L.append('-')\n n = -n\n i = 1\n L.append(str(n//d))\n L.append('.')\n while i <= N:\n n = n % d * 10\n L.append(str(n//d))\n i += 1\n return ''.join(L)","title":""},{"docid":"64af99b7c3d56c090a4c57cf9bd174d9","score":"0.570654","text":"def test_str(self):\r\n f = Fraction(3, 4)\r\n self.assertEqual(str(f), '3/4')","title":""},{"docid":"9f1468a73fe3d8fc6cb2952a55397520","score":"0.5683918","text":"def disp(self, verb_modulo=1):\n\n if verb_modulo is None:\n verb_modulo = 20\n if not verb_modulo:\n return\n iteration = self.counteval / self.params.lam\n\n do_print = False\n do_print |= iteration <= 3\n do_print |= iteration % verb_modulo < 1\n\n if iteration == 1 or iteration % (10 * verb_modulo) < 1:\n print(\"evals: sigma ax-ratio std (min/max) f-value t(mm:ss)\")\n\n if do_print:\n frac_inf = np.sum(np.isinf(self.fs))\n diag_sqrt = np.diagonal(self.C) ** 0.5\n info_str = (\n str(self.counteval).rjust(5)\n + \": \"\n + \" %2.1e %6.1e %7.0e %7.0e %8.8e \"\n % (\n self.sigma,\n np.linalg.cond(self.C) ** 0.5,\n self.sigma * np.min(diag_sqrt),\n self.sigma * np.max(diag_sqrt),\n self.fs[0],\n )\n )\n info_str += \" (%02d:%02d)\" % divmod(time.time() - self.stime, 60)\n info_str += (\n \" -> %02d%% inf\" % (frac_inf / self.params.lam * 100)\n if frac_inf\n else \"\"\n )\n\n print(info_str)","title":""},{"docid":"9191c61ba9df332276c6092d8c79b6a1","score":"0.56828725","text":"def fpcnt(dec, precision=2):\n\n pcnt = (dec * 100)\n if pcnt == 0 or pcnt == 100:\n precision = 0\n return '%.*f%%' % (precision, pcnt)","title":""},{"docid":"4893e20611ff227e5403641d36ba59a0","score":"0.5677824","text":"def __str__(self):\n return \"{:.2f}%\".format(self.load())","title":""},{"docid":"0923e7ca92f89b204cc9135e060ffa92","score":"0.56775814","text":"def print_format(self):\n \n return \"Steering: %f%%\\nThrottle: %f%%\\nBrake: %f%%\" % (\n self._steering, self._throttle, self._brake)","title":""},{"docid":"4c436dc503aa58c77ab9132394cad661","score":"0.56712234","text":"def __str__(self):\r\n return \"{:.2f}%\".format(self.load())","title":""},{"docid":"d3997b9679074d9619fb6bde5971ca86","score":"0.56587464","text":"def p33():\n from functools import reduce\n\n fractions = []\n\n for cancel in range(1, 10):\n for num in range(1, 10):\n for den in range(num + 1, 10):\n div = num / den\n\n numerator = num * 10 + cancel\n denominator = cancel * 10 + den\n if numerator / denominator == div:\n fractions.append((numerator, denominator))\n\n numerator = cancel * 10 + num\n denominator = den * 10 + cancel\n if numerator / denominator == div:\n fractions.append((numerator, denominator))\n\n def gcd(a, b):\n \"\"\" Return the greatest common divisor of the given integers\n Euclidean algorithm \n \"\"\"\n if b == 0:\n return a\n else:\n return gcd(b, a % b)\n\n product = reduce(lambda x, y: (x[0] * y[0], x[1] * y[1]), fractions)\n product_simplified = list(map(lambda x: x // gcd(*product), product))\n\n return \"Fraction: {numerator}/{denominator} = {num}/{den}\\nDenominator: {den}\".format(\n numerator=product[0],\n denominator=product[1],\n num=product_simplified[0],\n den=product_simplified[1])","title":""},{"docid":"1705f351964c2e8f55c4ba58b9771073","score":"0.56492007","text":"def golden_ratio():\n print((math.sqrt(5) + 1) / 2)","title":""},{"docid":"c9ee39ec82c4e5d50acc1918f5142084","score":"0.5637647","text":"def percent(num):\n return \"{:.2f}%\".format(num * 100)","title":""},{"docid":"f24ca89a89e2ff636038ae0becf6ff57","score":"0.5633803","text":"def test_print_divisions(self):\n pass","title":""},{"docid":"d8a519618c9cd4496b94e8ad1d99c459","score":"0.56326526","text":"def __str__(self):\n\n return f\"{self.t:0.5f}\"","title":""},{"docid":"72e11b646ded79e943b238dc8502a08e","score":"0.5627867","text":"def get_display_rate(self):\n\n\ttot = self.total_samples()\n ret = \"\"\n ret += `self.lower - self.width*2` + \"\\t\" + `1.0*self.under()/tot` + \"\\n\"\n\tfor i in range(self.n):\n\t ret += `self.lower + self.width * i` +\"\\t\" + `1.0*self.within(i)/tot` + \"\\n\"\n\tret += `self.upper + self.width` + \"\\t\" + `1.0*self.over()/tot`\n return ret","title":""},{"docid":"891860562ccb0c3428c90c1d38696f00","score":"0.5618849","text":"def ffloat(s):\n return float(Fraction(s))","title":""},{"docid":"22e44c4d6fe0febc544dddf5af023a96","score":"0.56139606","text":"def update_dump_pbar(self, frac):\n\n self.gmap.dump_pbar.set_fraction(frac)\n self.flush_updates()","title":""},{"docid":"897d5555f4326d44687913e337f43042","score":"0.5609429","text":"def format(self):\n\n\t\tif self._imperial:\n\t\t\tif self._pounds > 0:\n\t\t\t\tif self._ounces > 0:\n\t\t\t\t\treturn '%dlbs %doz' % ( self._pounds, self._ounces, )\n\t\t\t\treturn '%dlbs' % ( self._pounds, )\n\t\t\telif self._ounces > 0:\n\t\t\t\treturn '%doz' % ( self._ounces, )\n\t\t\treturn ''\n\n\t\telse:\n\t\t\tif self._grams >= 1000:\n\t\t\t\treturn '%.3fkg' % ( self._grams/1000, )\n\t\t\telse:\n\t\t\t\treturn '%dg' % ( self._grams, )","title":""},{"docid":"c221513bcaff9c418a0f2ee3351b7333","score":"0.56044996","text":"def test_repr(self):\r\n self.assertTrue(isinstance(self.fraction_x.__repr__(), str))","title":""},{"docid":"867fc174ef9ad23b36e5e67f804f7e57","score":"0.56030667","text":"def denom(f):\n return f.half_per(f.den)","title":""},{"docid":"d4a6d539b868fdd8ddb08fec8865668f","score":"0.5599813","text":"def get_base_conversion(self):\n total = self.get_value(self.transition_dict)\n for k,v in self.transition_dict.items():\n print \"%s num %.2f\" % (k,(100 * v/float(total)))","title":""},{"docid":"fba9708c73b00e9fec965f97a9b77b79","score":"0.5591018","text":"def print_divider():\n print(\"\\n\"+\"*\"*50+\"\\n\")","title":""},{"docid":"970c01cb1c3a4a82c8abf72167ba2f2b","score":"0.5588731","text":"def forg(x, prec=3):\n if prec == 3:\n # for 3 decimals\n if (abs(x) >= 1e4) or (abs(x) < 1e-4):\n return \"%9.3g\" % x\n else:\n return \"%9.3f\" % x\n elif prec == 4:\n if (abs(x) >= 1e4) or (abs(x) < 1e-4):\n return \"%10.4g\" % x\n else:\n return \"%10.4f\" % x\n else:\n raise NotImplementedError","title":""},{"docid":"d56b57181e83654729b0b5e410dc69bb","score":"0.5584459","text":"def rationalize(*args, **kwargs):\n \n pass","title":""},{"docid":"d56b57181e83654729b0b5e410dc69bb","score":"0.5584459","text":"def rationalize(*args, **kwargs):\n \n pass","title":""},{"docid":"c044e9615dd2454a80eff74a88f62e93","score":"0.5584444","text":"def clean_output(to_fraction):\n output = simple_math.evaluateStack()\n if type(output) == float:\n if float.is_integer(output):\n output = int(output)\n if to_fraction:\n output = simple_math.decimal_to_fraction(output)\n else:\n output = simple_math.sci_notation(output)\n output = unicode(output).encode('utf-8')\n return output","title":""},{"docid":"a613faec61a97a6f2966762b55c9ca83","score":"0.55730736","text":"def print_percents(x, y):\n print(str(y) + \" is \" + str(result) + \" % of \" + str(x))","title":""},{"docid":"d9afefbf83199985439e9bd96afa0c8d","score":"0.55650824","text":"def persents (x, y):\n one_percent = x / 100\n result = y / one_percent\n print(str (y) + \" is \" + str(result) + \"percents of\" + str(x))","title":""},{"docid":"a63458aca6d5ed103b14663d718a87da","score":"0.55594546","text":"def fraction(values):\r\n return np.sum(values) / np.size(values)","title":""},{"docid":"bfd44d24616bc5a665d5ae107c7259d6","score":"0.5559218","text":"def prograssBar(val, final):\n maxlen = 50\n step = final // maxlen\n\n print(\"\\r[ \" + \"#\" * (val // step) + \" ] \" +\n str(int(val * 100.0 / final)) + \"% \", end=\"\")","title":""},{"docid":"f580a464eb7ff45ec2e67c16e1ef63f0","score":"0.55585515","text":"def percentage(part, whole):\n if whole <= 0:\n return -1.00\n return format(100 * (float(part) / (float(whole) if whole != 0 else 1)), '.2f')","title":""},{"docid":"59ea386b1dda8916186e89c3fdfa280b","score":"0.55580324","text":"def Fraction(self, token):\r\n if self.prev_command == '\\\\time':\r\n self.mediator.new_time(token)\r\n self.prev_command = ''\r\n elif self.prev_command == '\\\\numericTimeSignature':\r\n self.mediator.new_time(token, numeric=True)\r\n self.prev_command = ''\r\n else:\r\n self.fraction = token","title":""},{"docid":"59ea386b1dda8916186e89c3fdfa280b","score":"0.55580324","text":"def Fraction(self, token):\r\n if self.prev_command == '\\\\time':\r\n self.mediator.new_time(token)\r\n self.prev_command = ''\r\n elif self.prev_command == '\\\\numericTimeSignature':\r\n self.mediator.new_time(token, numeric=True)\r\n self.prev_command = ''\r\n else:\r\n self.fraction = token","title":""},{"docid":"286dca29bae5a1428a46cb3a2342f707","score":"0.5555828","text":"def FracFormatter(symbol='', number=1):\n def f(x, pos): # must accept location argument\n frac = Fraction(x/number).limit_denominator()\n if x == 0: # zero\n string = '0'\n elif frac.denominator == 1: # denominator is one\n if frac.numerator == 1 and symbol:\n string = f'{symbol:s}'\n elif frac.numerator == -1 and symbol:\n string = f'-{symbol:s}'\n else:\n string = f'{frac.numerator:d}{symbol:s}'\n elif frac.numerator == 1 and symbol: # numerator is +/-1\n string = f'{symbol:s}/{frac.denominator:d}'\n elif frac.numerator == -1 and symbol:\n string = f'-{symbol:s}/{frac.denominator:d}'\n else: # and again make sure we use unicode minus!\n string = f'{frac.numerator:d}{symbol:s}/{frac.denominator:d}'\n return string.replace('-', '\\N{MINUS SIGN}')\n # And create FuncFormatter class\n return mticker.FuncFormatter(f)","title":""},{"docid":"cd8461fea4645e1e378e718b8baf9801","score":"0.5547084","text":"def perc(part, whole):\n return 100 * float(part)/float(whole)","title":""},{"docid":"ef2e6c807db794462085c35c01b987f7","score":"0.553497","text":"def update_frac(self, time_frac):\n log = 'No fraction of timestep available'\n\n return log","title":""},{"docid":"47e1ad0ec04189b605edce345f687a10","score":"0.55333376","text":"def GC_fraction(row):\n\tif isinstance(row,str): row = row.split()\n\tstart,end,GC = map(float,row[1:3] + [row[4]])\n\treturn round(GC / (end - start + 1),1)*100","title":""}],"string":"[\n {\n \"docid\": \"e8e765c12dee572b44efd64798c47853\",\n \"score\": \"0.7505799\",\n \"text\": \"def printFrac(self, num, den):\\n # Wrap LaTeX in \\\\frac tag\\n code = r\\\"\\\\frac{%s}{%s}\\\" % (num, den)\\n\\n # Append to editor\\n self.ui.text_equation.append(code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4d771d1e914a9f050da2f7b4f34d063\",\n \"score\": \"0.7426337\",\n \"text\": \"def fractionToString(x):\\n if x.denominator == 1:\\n x = x.numerator\\n else:\\n x = f\\\"{x.numerator}/{x.denominator}\\\"\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e045f34be867af6bd8c7e952bf6b8dfe\",\n \"score\": \"0.7067178\",\n \"text\": \"def __repr__(self):\\n return str(self.numerator)+\\\"/\\\"+str(self.denominator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0de6a29ad123ea677ae13ee5416460c\",\n \"score\": \"0.7056627\",\n \"text\": \"def my_pretty(frac):\\n num, denom = sympy.fraction(sympy.factor(frac))\\n return sympy.pretty(num) + \\\"\\\\n\\\" + \\\"/\\\\n\\\" + sympy.pretty(denom) + \\\"\\\\n\\\\n\\\\n\\\\n\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2812086ed022b6d5cfa04a8c8b5b342e\",\n \"score\": \"0.6953772\",\n \"text\": \"def __str__(self):\\n return str(self.numerator)+\\\"/\\\"+str(self.denominator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e45dbc25244c481717aa38d4f273d613\",\n \"score\": \"0.69442856\",\n \"text\": \"def __str__(self):\\n return '{} / {}'.format(self.num, self.den)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd2362441107a765a94937ae862ba92\",\n \"score\": \"0.6936911\",\n \"text\": \"def __str__(self):\\n if self.denominator == 1 or self.numerator == 0:\\n return str(int(self.numerator))\\n else:\\n return '{}/{}'.format(int(self.numerator),int(self.denominator))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b712cbd5b6eb4935d3d2a43cf3e985b7\",\n \"score\": \"0.6861012\",\n \"text\": \"def __str__(self):\\n if self.denominator == 1:\\n # show value as integer\\n return str(self.numerator)\\n return '{0}/{1}'.format(self.numerator, self.denominator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2c188f47771833358859bf78b56e463\",\n \"score\": \"0.6850174\",\n \"text\": \"def fraction(value):\\n fraction = fractions.Fraction(value).limit_denominator(4)\\n if fraction.numerator == 0:\\n return \\\"0\\\"\\n if fraction.numerator < fraction.denominator:\\n return str(fraction.numerator) + \\\"/\\\" + str(fraction.denominator)\\n whole = fraction.numerator / fraction.denominator\\n if fraction.denominator == 1:\\n return str(whole)\\n numerator = fraction.numerator - (whole * fraction.denominator)\\n return str(whole) + \\\" \\\" + str(numerator) + \\\"/\\\" + str(fraction.denominator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f87832544f2d9ff255419b3192fb2f4c\",\n \"score\": \"0.6834446\",\n \"text\": \"def fraction(tokens):\\r\\n base, scaling = base_scaling(tokens)\\r\\n return base * scaling\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e49e2c66ed883c97e2eee455b6d1a49\",\n \"score\": \"0.6796108\",\n \"text\": \"def texFrac(frac):\\n return [\\\"\\\\\\\\frac{\\\" , str(frac._num) , \\\"}{\\\" , str(frac._denom) , \\\"}\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1148e965cf183aa63858db22d74cd84\",\n \"score\": \"0.67821825\",\n \"text\": \"def format_fraction(value):\\r\\n if value == 0:\\r\\n return \\\"0\\\"\\r\\n elif isinstance(value, Fraction):\\r\\n return \\\"{0}/{1}\\\".format(value.numerator, value.denominator)\\r\\n else:\\r\\n return \\\"{0}/1\\\".format(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bbe617991a93adae7d11cfc3df46e4f\",\n \"score\": \"0.67652345\",\n \"text\": \"def __str__(self):\\n if Fraction.display == \\\"improper\\\":\\n return f\\\"{self.get_num()}/{self.get_denom()}\\\"\\n elif Fraction.display == \\\"mixed\\\":\\n m = self.get_num() // self.get_denom()\\n if m == 0:\\n return f\\\"{self.get_num()}/{self.get_denom()}\\\"\\n else:\\n n = self.get_num() - m*self.get_denom()\\n if n == 0:\\n return f\\\"{m}\\\"\\n else:\\n return f\\\"{m}, {n}/{self.get_denom()}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40c22723a833f37985e5509a91618c47\",\n \"score\": \"0.67407453\",\n \"text\": \"def fraction(self):\\r\\n base, scaling = self.base_scaling\\r\\n return base * scaling\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c52b9aa02ac7d02a4d5b3d9203042f44\",\n \"score\": \"0.6718211\",\n \"text\": \"def fraction(self):\\r\\n return self._fraction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24c52778cc799852ee351d764cb3cbb9\",\n \"score\": \"0.65996605\",\n \"text\": \"def unitFractionString(dd):\\n if dd == 1:\\n return \\\"1\\\"\\n else:\\n return \\\"1/\\\" + intString(dd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8356ab02b64bc746e58395b60e15f15d\",\n \"score\": \"0.65783906\",\n \"text\": \"def __str__(self):\\n return str(self.num) + \\\"/\\\" + str(self.denom)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66bde25fca79bad87048883593ed9988\",\n \"score\": \"0.6578014\",\n \"text\": \"def fraction( self ):\\n return self.value()/self.duration()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32fb078e9d2b6dd16dc0f1899f17e0be\",\n \"score\": \"0.6570177\",\n \"text\": \"def __str__(self):\\n return str(self.num) + \\\"/\\\" + str(self.den)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f49b3bb174f5c5fcd3f235c14556836\",\n \"score\": \"0.6552754\",\n \"text\": \"def __str__(self):\\n if self.numerator == 0:\\n if self.denominator == 0:\\n # If 0/0 show nan\\n return f'{math.nan}'\\n else:\\n return '0'\\n elif self.denominator == 0 and self.numerator > 0:\\n return f'{math.inf}'\\n elif self.denominator == 0 and self.numerator < 0:\\n return f'{-math.inf}'\\n elif self.denominator == 1:\\n return f'{self.numerator}'\\n else:\\n return f'{self.numerator}/{self.denominator}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e444274f990abdb57e4b50955a2a1b5\",\n \"score\": \"0.6489734\",\n \"text\": \"def __str__(self):\\n self.__reduce__()\\n return str(int(self.numerator)) + '/' + str(int(self.denominator))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02480e1f467954e493454fc506b2a12e\",\n \"score\": \"0.6434899\",\n \"text\": \"def __str__(self) -> str:\\n return str(self.num) + \\\"/\\\" + str(self.denom)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bd95b787e9a15639e500af8251d7255\",\n \"score\": \"0.64214236\",\n \"text\": \"def fraction_to_string(fraction):\\n return '({}) / ({})'.format(fraction.num, fraction.denum)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dc86b509d8f16ddd87ce226f2b5ed62\",\n \"score\": \"0.6354297\",\n \"text\": \"def fractions(self):\\n return self.accumulators / self.n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54469e3cdb7e618d2646a249237c3825\",\n \"score\": \"0.6307001\",\n \"text\": \"def str_rat(x): #from lecture notes\\n return '{0}/{1}'.format(num(x), den(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67774662a85390ca6261846aed83cce6\",\n \"score\": \"0.629963\",\n \"text\": \"def VertexFormat(self, frac: int) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e47bc83f70afdc24b2cb8e9327bd4835\",\n \"score\": \"0.628328\",\n \"text\": \"def __str__(self) -> str:\\n return f\\\"{self.num}/{self.denom}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3259e31239b1b940cc6915b0552017e9\",\n \"score\": \"0.6281963\",\n \"text\": \"def egyptianFractionString(xx):\\n return \\\" + \\\".join(map(unitFractionString,xx))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25041e946a2b8d18218e8eace4c05120\",\n \"score\": \"0.6271916\",\n \"text\": \"def printtree(self):\\n for i in range(self.n + 1):\\n print(\\\"Period = \\\" + str(i))\\n print(self.tree[i][: i + 1].round(3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9db05f9c69c8a551641d2393e1981e6c\",\n \"score\": \"0.62271434\",\n \"text\": \"def fractation(x):\\n dev=2\\n while x>1:\\n if x%dev == 0:\\n x //= dev\\n print(f\\\"{dev}\\\", end=' ')\\n else:\\n dev += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"534209582f4c968290f4a0484ada6119\",\n \"score\": \"0.6199866\",\n \"text\": \"def frac(self):\\n return self.quadirrat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adbf3b2f1d9a443c41f7379460239e68\",\n \"score\": \"0.6194349\",\n \"text\": \"def format_ratio(numerator, denominator):\\n if denominator == 0:\\n return float(\\\"nan\\\")\\n return float(numerator) / denominator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff8e7d707009077b2a343bd7f3979fb0\",\n \"score\": \"0.61942446\",\n \"text\": \"def printpercentcomposition(self):\\n if self.__dict__.has_key('sys') is False:\\n self.sys = __import__('sys')\\n self.sys.stdout.write('\\\\nelemental percent composition:\\\\n')\\n for key,val in sorted(self.pcomp.items()):\\n self.sys.stdout.write('%3s: %7.3f %%\\\\n' %(key,self.pcomp[key]*100))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f45e6d13ee655f8140cc888c27965c5\",\n \"score\": \"0.6185754\",\n \"text\": \"def show(self):\\n formatStr = '{:' + str(8) + '.' + str(self.prec) + 'f}'\\n for i, m in enumerate(self.M):\\n print(\\\"|\\\", end=\\\"\\\")\\n for n in m:\\n print(\\\" \\\" if (self.prec > 4) else \\\"\\\", end=\\\"\\\")\\n print(formatStr.format(n if not(n == 0.0) else 0.0),end=\\\"\\\")\\n print(\\\" |\\\")\\n print(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f7fbd6a2052f55ecd56cff9333586ce\",\n \"score\": \"0.61693066\",\n \"text\": \"def contfrac_inaccurate(n):\\n while True:\\n whole = int(n)\\n yield whole\\n remainder = n - int(n)\\n reciproc = 1 / remainder\\n n = reciproc\\n print(whole, remainder, reciproc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef79bfc188bafd7cc935d810db97936b\",\n \"score\": \"0.6163692\",\n \"text\": \"def unpack_fraction(num: str) -> str:\\n nums = [int(n) for n in num.split('/')]\\n if nums[0] > nums[1]:\\n over = nums[0] // nums[1]\\n rem = nums[0] % nums[1]\\n return '{} {}/{}'.format(over, rem, nums[1])\\n return num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0279f89b49f87f29cb5e97c61564bacd\",\n \"score\": \"0.61628515\",\n \"text\": \"def percentagify(numerator, denominator):\\n if denominator:\\n return '{:04.2f}%'.format((numerator / denominator) * 100)\\n else:\\n return 'N/A'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4fd4ad8861f8239a06a7de8d0fb1fe7\",\n \"score\": \"0.6152728\",\n \"text\": \"def print_percents(x,y):\\n print(\\\"deposit = \\\" + str(x) + \\\" in this moment \\\" + str(y) + \\\" result in procents \\\" + \\\"\\\"),(str(difine_color(percents(x, y))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ada30ed51be57245d49aae971fca4a63\",\n \"score\": \"0.6149142\",\n \"text\": \"def fraction(self):\\r\\n if self.duration:\\r\\n return self.duration.base_scaling[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14a93ab70593b71e7a30f70f1ee1c546\",\n \"score\": \"0.6113385\",\n \"text\": \"def calc_and_print(self):\\n\\n self.t = self.find_t_of_s(self.Temp)\\n\\n print(\\\" \\\")\\n print(\\\" T T_n/T t \\\")\\n print(\\\"-------------------------------------\\\")\\n\\n for i in range(len(self.Temp-1)):\\n self.T_neutrino[i] = self.find_neutrino_temperature(self.Temp[i])\\n self.Splot[i] = self.find_S_of_x(self.find_x_of_T(self.Temp[i]))\\n\\n print(\\\"{:.2e} | {:.5f} | {:.4e} \\\"\\\\\\n .format(self.Temp[i],\\n self.T_neutrino[i]/self.Temp[i],\\n self.t[i]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"311b5901bda35368f7ea50b972b22c82\",\n \"score\": \"0.61032516\",\n \"text\": \"def percentage(numerator, denominator):\\n if not denominator:\\n return \\\"0.00%\\\"\\n val = (numerator / Decimal(denominator)) * 100\\n return \\\"%.2f%%\\\" % val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"613ff22d7d8d008475d69303768362a4\",\n \"score\": \"0.6056618\",\n \"text\": \"def __float__(self):\\n return self.num / self.denom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a78d4885300405dc7b345a009bcd990\",\n \"score\": \"0.5993808\",\n \"text\": \"def print_format(self):\\n \\n return \\\"Left Motor: %f%%\\\\nRight Motor: %f%%\\\" % (self.left, self.right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bbca2372d45051d643fdfe318cce6eb\",\n \"score\": \"0.59848547\",\n \"text\": \"def build(self, fraction): #zbudowanie budynku i odjęcie jego kosztu od materiałów frakcji\\r\\n\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d50629591fc4876d53b7fbad511fa7b\",\n \"score\": \"0.5977914\",\n \"text\": \"def _rfr_fraction_ ( self , var1 , var2 ) :\\n if isinstance ( var1 , str ) : var1 = self.param ( var1 ) [1]\\n if isinstance ( var2 , str ) : var2 = self.param ( var2 ) [1] \\n _av1 = abs ( var1.value.value() ) \\n _av2 = abs ( var2.value.value() ) \\n if _av1 > _av2 : return 1 / ( 1 + self.ratio ( var2 , var1 ) )\\n return 1.0 - self.fraction ( var2 , var1 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1f65da4bd9a4cfdf1e4025622b9d832\",\n \"score\": \"0.5961971\",\n \"text\": \"def fraction_digits(self):\\n return self._fraction_digits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dd1039b6e5ab35c3e8e1c5a6900e6ef\",\n \"score\": \"0.59456325\",\n \"text\": \"def packing_fraction(self):\\n total_rad = 0\\n for i in self.elements:\\n total_rad = total_rad + Specie(i).atomic_rad ** 3\\n pf = np.array([4 * np.pi * total_rad / (3 * self.volume)])\\n return round(pf[0], 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f85950ce77963004c1fb50312d35124\",\n \"score\": \"0.5944779\",\n \"text\": \"def print_percents(x,y):\\n print(\\\"deposit = \\\" + str(x) + \\\" in this moment \\\" + str(y) + \\\" result in procent \\\" + str(percents(x, y)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eccdb0ad6e84726717fc222aba0d446\",\n \"score\": \"0.59192246\",\n \"text\": \"def display_fraction(value, limit_denominator=None, allow_mixed_numbers=True,\\n coerce_thirds=True):\\n\\n try:\\n whole_number, numerator, denominator = get_fraction_parts(value,\\n allow_mixed_numbers,\\n limit_denominator,\\n coerce_thirds)\\n unicode_entity = get_fraction_unicode_entity(fractions.Fraction(numerator, denominator))\\n except (ValueError, InvalidOperation) as e:\\n whole_number, numerator, denominator, unicode_entity = (value, 0, 0, None)\\n\\n return {\\n 'whole_number': whole_number,\\n 'numerator': numerator,\\n 'denominator': denominator,\\n 'unicode_entity': unicode_entity,\\n 'allow_mixed_numbers': allow_mixed_numbers,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb5647a89949b600fb33559ed5d5901d\",\n \"score\": \"0.5907708\",\n \"text\": \"def numer(f):\\n return f.half_per(f.num)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3552009736b6182ce231ae45afa4a68\",\n \"score\": \"0.58914167\",\n \"text\": \"def fraction_string(duration):\\r\\n base, scaling = base_scaling_string(duration)\\r\\n return base * scaling\",\n \"title\": \"\"\n },\n {\n \"docid\": \"375e6d123eb7f58fb5c9eb445ae51b04\",\n \"score\": \"0.5881315\",\n \"text\": \"def show_float(self, value):\\n self.shreg.write_byte(SEGMENTS[value % 1])\\n self.shreg.write_byte(DP | SEGMENTS[value // 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c65302815229752d600b2fe024c851a\",\n \"score\": \"0.58630604\",\n \"text\": \"def quotient(a, b):\\n quotient = a / b\\n print(\\\"Der Quotient der beiden Zahlen lautet\\\", quotient)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43e4d7b43de2dc841b1bbe42484206e3\",\n \"score\": \"0.58260185\",\n \"text\": \"def format_proc(temp: int) -> str:\\n return str(round(temp)) + \\\"%\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c2228eb912f933e2541c74520ccd4a1\",\n \"score\": \"0.58079684\",\n \"text\": \"def calcFractions(self):\\n self.resultView.clear()\\n text = self.exprEdit.text()\\n try:\\n num = float(text)\\n except ValueError:\\n try:\\n num = float(eval(text))\\n output = [_('Entry'), '{0}'.format(num)]\\n self.resultView.addTopLevelItem(QTreeWidgetItem(output))\\n except:\\n QMessageBox.warning(self, 'ConvertAll',\\n _('Invalid expresssion'))\\n return\\n QApplication.setOverrideCursor(Qt.WaitCursor)\\n powerOfTwo = self.powerTwoCtrl.isChecked()\\n for numer, denom in listFractions(num, powerOfTwo):\\n output = ['{0}/{1}'.format(numer, denom),\\n '{0}'.format(numer / denom)]\\n self.resultView.addTopLevelItem(QTreeWidgetItem(output))\\n QApplication.restoreOverrideCursor()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a2c80dc8a8a38511b8b6451f4b7bc3\",\n \"score\": \"0.58073425\",\n \"text\": \"def show(p):\\n rows = ['[' + ', '.join(map(lambda x: '{0:.5f}'.format(x), r)) + ']' for r in p]\\n print('\\\\n'.join(rows) + '\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"085a75c0db185f0af3e358a6b4420ab0\",\n \"score\": \"0.57816243\",\n \"text\": \"def asymptotic_triangle_fraction():\\n return 1./3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c4e917ce66e065fda0f5e482ae3b1d\",\n \"score\": \"0.5780224\",\n \"text\": \"def div():\\n global str_display_calc\\n global nb2\\n global operator\\n nb2 = float(str_display_calc)\\n str_display_calc = \\\"\\\"\\n operator = 4\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379d21f19598e40b5851a84733d03229\",\n \"score\": \"0.5775186\",\n \"text\": \"def pcStr(n, d):\\n try:\\n s = \\\"%6.2f\\\" % (100.0*n/d,)\\n except:\\n s = \\\"\\\"\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e69327c0da4ab802ceaa947d8fa7fb2\",\n \"score\": \"0.57658064\",\n \"text\": \"def _print_GoldenRatio(self, e):\\n x = self.dom.createElement('cn')\\n x.appendChild(self.dom.createTextNode(\\\"\\\\N{GREEK SMALL LETTER PHI}\\\"))\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f68e53e43fb63f0b9e2b4ae82d200a9\",\n \"score\": \"0.57643324\",\n \"text\": \"def calcPercent(f, sigfig=2):\\n return str(round(f, sigfig)*100)+\\\" %\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926f8b78f0ec005d073eae5c55c92169\",\n \"score\": \"0.5739741\",\n \"text\": \"def test_shutter_value_fraction():\\n shutter = shutter_value('1/4')\\n\\n assert shutter == 0.25\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12cecab147ec1418df41fa795b9963a5\",\n \"score\": \"0.5738268\",\n \"text\": \"def plot_mode_fraction(s):\\n fig, ax = plt.subplots(figsize=(4, 3))\\n ax.set_xlim(0, 600)\\n ax.set_ylim(1e-4,1)\\n ax.set_yscale('log')\\n ax.grid()\\n f = 1 - np.cumsum(s**2) / np.sum(s**2)\\n\\n ax.set_xlabel('Modes')\\n ax.set_ylabel('Missing mode power')\\n\\n ax.plot(f)\\n fig.savefig('HW4/figures/mode_frac.pdf', bbox_inches='tight')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"852505ed01bc4a412f89b8ee4dd9343e\",\n \"score\": \"0.572525\",\n \"text\": \"def showFrac(self):\\n # Create new dialog, setup using about.py\\n dialog = QtGui.QDialog()\\n frac = Ui_Frac()\\n frac.setupUi(dialog)\\n\\n # Connect event handler\\n frac.buttonBox.accepted.connect(lambda: self.printFrac(frac.line_num.text(), frac.line_den.text()))\\n\\n # Execute new dialog box\\n dialog.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fd650010eea556c6e4bc5c661d07ba3\",\n \"score\": \"0.57222855\",\n \"text\": \"def display_improper_fraction(value, limit_denominator=None, coerce_thirds=True):\\n return display_fraction(value, limit_denominator=limit_denominator,\\n allow_mixed_numbers=False, coerce_thirds=coerce_thirds)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94667a77430c0b7e35236930812b5aed\",\n \"score\": \"0.5715667\",\n \"text\": \"def decimalString(self, N):\\n n = self.numerator\\n d = self.denominator\\n L = []\\n if n < 0:\\n L.append('-')\\n n = -n\\n i = 1\\n L.append(str(n//d))\\n L.append('.')\\n while i <= N:\\n n = n % d * 10\\n L.append(str(n//d))\\n i += 1\\n return ''.join(L)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64af99b7c3d56c090a4c57cf9bd174d9\",\n \"score\": \"0.570654\",\n \"text\": \"def test_str(self):\\r\\n f = Fraction(3, 4)\\r\\n self.assertEqual(str(f), '3/4')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f1468a73fe3d8fc6cb2952a55397520\",\n \"score\": \"0.5683918\",\n \"text\": \"def disp(self, verb_modulo=1):\\n\\n if verb_modulo is None:\\n verb_modulo = 20\\n if not verb_modulo:\\n return\\n iteration = self.counteval / self.params.lam\\n\\n do_print = False\\n do_print |= iteration <= 3\\n do_print |= iteration % verb_modulo < 1\\n\\n if iteration == 1 or iteration % (10 * verb_modulo) < 1:\\n print(\\\"evals: sigma ax-ratio std (min/max) f-value t(mm:ss)\\\")\\n\\n if do_print:\\n frac_inf = np.sum(np.isinf(self.fs))\\n diag_sqrt = np.diagonal(self.C) ** 0.5\\n info_str = (\\n str(self.counteval).rjust(5)\\n + \\\": \\\"\\n + \\\" %2.1e %6.1e %7.0e %7.0e %8.8e \\\"\\n % (\\n self.sigma,\\n np.linalg.cond(self.C) ** 0.5,\\n self.sigma * np.min(diag_sqrt),\\n self.sigma * np.max(diag_sqrt),\\n self.fs[0],\\n )\\n )\\n info_str += \\\" (%02d:%02d)\\\" % divmod(time.time() - self.stime, 60)\\n info_str += (\\n \\\" -> %02d%% inf\\\" % (frac_inf / self.params.lam * 100)\\n if frac_inf\\n else \\\"\\\"\\n )\\n\\n print(info_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9191c61ba9df332276c6092d8c79b6a1\",\n \"score\": \"0.56828725\",\n \"text\": \"def fpcnt(dec, precision=2):\\n\\n pcnt = (dec * 100)\\n if pcnt == 0 or pcnt == 100:\\n precision = 0\\n return '%.*f%%' % (precision, pcnt)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4893e20611ff227e5403641d36ba59a0\",\n \"score\": \"0.5677824\",\n \"text\": \"def __str__(self):\\n return \\\"{:.2f}%\\\".format(self.load())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0923e7ca92f89b204cc9135e060ffa92\",\n \"score\": \"0.56775814\",\n \"text\": \"def print_format(self):\\n \\n return \\\"Steering: %f%%\\\\nThrottle: %f%%\\\\nBrake: %f%%\\\" % (\\n self._steering, self._throttle, self._brake)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c436dc503aa58c77ab9132394cad661\",\n \"score\": \"0.56712234\",\n \"text\": \"def __str__(self):\\r\\n return \\\"{:.2f}%\\\".format(self.load())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3997b9679074d9619fb6bde5971ca86\",\n \"score\": \"0.56587464\",\n \"text\": \"def p33():\\n from functools import reduce\\n\\n fractions = []\\n\\n for cancel in range(1, 10):\\n for num in range(1, 10):\\n for den in range(num + 1, 10):\\n div = num / den\\n\\n numerator = num * 10 + cancel\\n denominator = cancel * 10 + den\\n if numerator / denominator == div:\\n fractions.append((numerator, denominator))\\n\\n numerator = cancel * 10 + num\\n denominator = den * 10 + cancel\\n if numerator / denominator == div:\\n fractions.append((numerator, denominator))\\n\\n def gcd(a, b):\\n \\\"\\\"\\\" Return the greatest common divisor of the given integers\\n Euclidean algorithm \\n \\\"\\\"\\\"\\n if b == 0:\\n return a\\n else:\\n return gcd(b, a % b)\\n\\n product = reduce(lambda x, y: (x[0] * y[0], x[1] * y[1]), fractions)\\n product_simplified = list(map(lambda x: x // gcd(*product), product))\\n\\n return \\\"Fraction: {numerator}/{denominator} = {num}/{den}\\\\nDenominator: {den}\\\".format(\\n numerator=product[0],\\n denominator=product[1],\\n num=product_simplified[0],\\n den=product_simplified[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1705f351964c2e8f55c4ba58b9771073\",\n \"score\": \"0.56492007\",\n \"text\": \"def golden_ratio():\\n print((math.sqrt(5) + 1) / 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ee39ec82c4e5d50acc1918f5142084\",\n \"score\": \"0.5637647\",\n \"text\": \"def percent(num):\\n return \\\"{:.2f}%\\\".format(num * 100)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f24ca89a89e2ff636038ae0becf6ff57\",\n \"score\": \"0.5633803\",\n \"text\": \"def test_print_divisions(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8a519618c9cd4496b94e8ad1d99c459\",\n \"score\": \"0.56326526\",\n \"text\": \"def __str__(self):\\n\\n return f\\\"{self.t:0.5f}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72e11b646ded79e943b238dc8502a08e\",\n \"score\": \"0.5627867\",\n \"text\": \"def get_display_rate(self):\\n\\n\\ttot = self.total_samples()\\n ret = \\\"\\\"\\n ret += `self.lower - self.width*2` + \\\"\\\\t\\\" + `1.0*self.under()/tot` + \\\"\\\\n\\\"\\n\\tfor i in range(self.n):\\n\\t ret += `self.lower + self.width * i` +\\\"\\\\t\\\" + `1.0*self.within(i)/tot` + \\\"\\\\n\\\"\\n\\tret += `self.upper + self.width` + \\\"\\\\t\\\" + `1.0*self.over()/tot`\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"891860562ccb0c3428c90c1d38696f00\",\n \"score\": \"0.5618849\",\n \"text\": \"def ffloat(s):\\n return float(Fraction(s))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22e44c4d6fe0febc544dddf5af023a96\",\n \"score\": \"0.56139606\",\n \"text\": \"def update_dump_pbar(self, frac):\\n\\n self.gmap.dump_pbar.set_fraction(frac)\\n self.flush_updates()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897d5555f4326d44687913e337f43042\",\n \"score\": \"0.5609429\",\n \"text\": \"def format(self):\\n\\n\\t\\tif self._imperial:\\n\\t\\t\\tif self._pounds > 0:\\n\\t\\t\\t\\tif self._ounces > 0:\\n\\t\\t\\t\\t\\treturn '%dlbs %doz' % ( self._pounds, self._ounces, )\\n\\t\\t\\t\\treturn '%dlbs' % ( self._pounds, )\\n\\t\\t\\telif self._ounces > 0:\\n\\t\\t\\t\\treturn '%doz' % ( self._ounces, )\\n\\t\\t\\treturn ''\\n\\n\\t\\telse:\\n\\t\\t\\tif self._grams >= 1000:\\n\\t\\t\\t\\treturn '%.3fkg' % ( self._grams/1000, )\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn '%dg' % ( self._grams, )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c221513bcaff9c418a0f2ee3351b7333\",\n \"score\": \"0.56044996\",\n \"text\": \"def test_repr(self):\\r\\n self.assertTrue(isinstance(self.fraction_x.__repr__(), str))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"867fc174ef9ad23b36e5e67f804f7e57\",\n \"score\": \"0.56030667\",\n \"text\": \"def denom(f):\\n return f.half_per(f.den)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4a6d539b868fdd8ddb08fec8865668f\",\n \"score\": \"0.5599813\",\n \"text\": \"def get_base_conversion(self):\\n total = self.get_value(self.transition_dict)\\n for k,v in self.transition_dict.items():\\n print \\\"%s num %.2f\\\" % (k,(100 * v/float(total)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fba9708c73b00e9fec965f97a9b77b79\",\n \"score\": \"0.5591018\",\n \"text\": \"def print_divider():\\n print(\\\"\\\\n\\\"+\\\"*\\\"*50+\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"970c01cb1c3a4a82c8abf72167ba2f2b\",\n \"score\": \"0.5588731\",\n \"text\": \"def forg(x, prec=3):\\n if prec == 3:\\n # for 3 decimals\\n if (abs(x) >= 1e4) or (abs(x) < 1e-4):\\n return \\\"%9.3g\\\" % x\\n else:\\n return \\\"%9.3f\\\" % x\\n elif prec == 4:\\n if (abs(x) >= 1e4) or (abs(x) < 1e-4):\\n return \\\"%10.4g\\\" % x\\n else:\\n return \\\"%10.4f\\\" % x\\n else:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d56b57181e83654729b0b5e410dc69bb\",\n \"score\": \"0.5584459\",\n \"text\": \"def rationalize(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d56b57181e83654729b0b5e410dc69bb\",\n \"score\": \"0.5584459\",\n \"text\": \"def rationalize(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c044e9615dd2454a80eff74a88f62e93\",\n \"score\": \"0.5584444\",\n \"text\": \"def clean_output(to_fraction):\\n output = simple_math.evaluateStack()\\n if type(output) == float:\\n if float.is_integer(output):\\n output = int(output)\\n if to_fraction:\\n output = simple_math.decimal_to_fraction(output)\\n else:\\n output = simple_math.sci_notation(output)\\n output = unicode(output).encode('utf-8')\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a613faec61a97a6f2966762b55c9ca83\",\n \"score\": \"0.55730736\",\n \"text\": \"def print_percents(x, y):\\n print(str(y) + \\\" is \\\" + str(result) + \\\" % of \\\" + str(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9afefbf83199985439e9bd96afa0c8d\",\n \"score\": \"0.55650824\",\n \"text\": \"def persents (x, y):\\n one_percent = x / 100\\n result = y / one_percent\\n print(str (y) + \\\" is \\\" + str(result) + \\\"percents of\\\" + str(x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a63458aca6d5ed103b14663d718a87da\",\n \"score\": \"0.55594546\",\n \"text\": \"def fraction(values):\\r\\n return np.sum(values) / np.size(values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfd44d24616bc5a665d5ae107c7259d6\",\n \"score\": \"0.5559218\",\n \"text\": \"def prograssBar(val, final):\\n maxlen = 50\\n step = final // maxlen\\n\\n print(\\\"\\\\r[ \\\" + \\\"#\\\" * (val // step) + \\\" ] \\\" +\\n str(int(val * 100.0 / final)) + \\\"% \\\", end=\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f580a464eb7ff45ec2e67c16e1ef63f0\",\n \"score\": \"0.55585515\",\n \"text\": \"def percentage(part, whole):\\n if whole <= 0:\\n return -1.00\\n return format(100 * (float(part) / (float(whole) if whole != 0 else 1)), '.2f')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ea386b1dda8916186e89c3fdfa280b\",\n \"score\": \"0.55580324\",\n \"text\": \"def Fraction(self, token):\\r\\n if self.prev_command == '\\\\\\\\time':\\r\\n self.mediator.new_time(token)\\r\\n self.prev_command = ''\\r\\n elif self.prev_command == '\\\\\\\\numericTimeSignature':\\r\\n self.mediator.new_time(token, numeric=True)\\r\\n self.prev_command = ''\\r\\n else:\\r\\n self.fraction = token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ea386b1dda8916186e89c3fdfa280b\",\n \"score\": \"0.55580324\",\n \"text\": \"def Fraction(self, token):\\r\\n if self.prev_command == '\\\\\\\\time':\\r\\n self.mediator.new_time(token)\\r\\n self.prev_command = ''\\r\\n elif self.prev_command == '\\\\\\\\numericTimeSignature':\\r\\n self.mediator.new_time(token, numeric=True)\\r\\n self.prev_command = ''\\r\\n else:\\r\\n self.fraction = token\",\n \"title\": \"\"\n },\n {\n \"docid\": \"286dca29bae5a1428a46cb3a2342f707\",\n \"score\": \"0.5555828\",\n \"text\": \"def FracFormatter(symbol='', number=1):\\n def f(x, pos): # must accept location argument\\n frac = Fraction(x/number).limit_denominator()\\n if x == 0: # zero\\n string = '0'\\n elif frac.denominator == 1: # denominator is one\\n if frac.numerator == 1 and symbol:\\n string = f'{symbol:s}'\\n elif frac.numerator == -1 and symbol:\\n string = f'-{symbol:s}'\\n else:\\n string = f'{frac.numerator:d}{symbol:s}'\\n elif frac.numerator == 1 and symbol: # numerator is +/-1\\n string = f'{symbol:s}/{frac.denominator:d}'\\n elif frac.numerator == -1 and symbol:\\n string = f'-{symbol:s}/{frac.denominator:d}'\\n else: # and again make sure we use unicode minus!\\n string = f'{frac.numerator:d}{symbol:s}/{frac.denominator:d}'\\n return string.replace('-', '\\\\N{MINUS SIGN}')\\n # And create FuncFormatter class\\n return mticker.FuncFormatter(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd8461fea4645e1e378e718b8baf9801\",\n \"score\": \"0.5547084\",\n \"text\": \"def perc(part, whole):\\n return 100 * float(part)/float(whole)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef2e6c807db794462085c35c01b987f7\",\n \"score\": \"0.553497\",\n \"text\": \"def update_frac(self, time_frac):\\n log = 'No fraction of timestep available'\\n\\n return log\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47e1ad0ec04189b605edce345f687a10\",\n \"score\": \"0.55333376\",\n \"text\": \"def GC_fraction(row):\\n\\tif isinstance(row,str): row = row.split()\\n\\tstart,end,GC = map(float,row[1:3] + [row[4]])\\n\\treturn round(GC / (end - start + 1),1)*100\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1690,"cells":{"query_id":{"kind":"string","value":"26bf712cdea56b737a3a82b2da71dbc5"},"query":{"kind":"string","value":"Control the normals of an object. This command works on faces or polygonal objects."},"positive_passages":{"kind":"list like","value":[{"docid":"e9126d584ef78d5ce88a4c8db5fd9b97","score":"0.52289","text":"def polyNormal(*args, **kwargs):\n\n pass","title":""}],"string":"[\n {\n \"docid\": \"e9126d584ef78d5ce88a4c8db5fd9b97\",\n \"score\": \"0.52289\",\n \"text\": \"def polyNormal(*args, **kwargs):\\n\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1bbb05d3a41115c7ef5c1abf20d11d98","score":"0.73524904","text":"def setNormals(self):\n self.Normals[0] = normalize(cross(map(operator.sub, self.V1[0], self.V1[2]), map(operator.sub, self.V1[0], self.V1[1])))\n self.Normals[1] = normalize(cross(map(operator.sub, self.V1[0], self.V1[3]), map(operator.sub, self.V1[0], self.V1[2])))\n self.Normals[2] = normalize(cross(map(operator.sub, self.V1[0], self.V1[1]), map(operator.sub, self.V1[0], self.V1[3])))\n self.Normals[3] = normalize(cross(map(operator.sub, self.V1[2], self.V1[3]), map(operator.sub, self.V1[2], self.V1[1])))","title":""},{"docid":"89fe35e6898d9ccd8cc1353db404cafe","score":"0.7059347","text":"def normalsPerVertex(*args, **kwargs):\n \n pass","title":""},{"docid":"993b8fb71076c9222d04009235c43fb2","score":"0.6869938","text":"def compute_normals(self):\n\n if self.has_normal_array:\n self.normals = generate_vertex_normals(self.vertices.reshape(-1, 3), self.indices.reshape(-1, 3))","title":""},{"docid":"3799ece07d510f0da264bdfe8989bfdc","score":"0.6835605","text":"def _normals_changed( self, *args ):\n norm_enabled = self.GetValue( \"NormalsCheck\" )\n config_enabled = self.GetValue( \"configCheck\" )\n\n self.SetEnabled( \"NormalsBox\", norm_enabled and not config_enabled )\n self.SetEnabled( \"NormalsLabel\", norm_enabled and not config_enabled )","title":""},{"docid":"8d7670685ed1040e1ec90723f028de9d","score":"0.67559737","text":"def normals(self,inside=False):\n if invert:\n return self.pointAngles(90)\n return self.pointAngles(90)","title":""},{"docid":"316156990f38cd666c95cc0409860909","score":"0.65043473","text":"def normals(facets, points):\n nv = [vo.normal(points[i], points[j], points[k])\n for i, j, k in facets]\n return vo.indexate(nv)","title":""},{"docid":"0d57f7ec89b9f554a0e3f992d86c2453","score":"0.6373106","text":"def flip_normals(self):\n if self.faces.size % 4:\n raise Exception('Can only flip normals on an all triangular mesh')\n\n f = self.faces.reshape((-1, 4))\n f[:, 1:] = f[:, 1:][:, ::-1]","title":""},{"docid":"5c885b22eae6d23c3c10e44bed0acfea","score":"0.63323236","text":"def plot_normals(self, show_mesh=True, mag=1.0, flip=False,\n use_every=1, **kwargs):\n plotter = pyvista.Plotter(off_screen=kwargs.pop('off_screen', False),\n notebook=kwargs.pop('notebook', None))\n if show_mesh:\n plotter.add_mesh(self, **kwargs)\n\n normals = self.point_normals\n if flip:\n normals *= -1\n plotter.add_arrows(self.points[::use_every],\n normals[::use_every], mag=mag)\n return plotter.show()","title":""},{"docid":"0de02b6a8cd064eb0fa70d8aa748f197","score":"0.6320519","text":"def setNormal(*args, **kwargs):\n \n pass","title":""},{"docid":"0de02b6a8cd064eb0fa70d8aa748f197","score":"0.6320519","text":"def setNormal(*args, **kwargs):\n \n pass","title":""},{"docid":"210f2917a4339476b0a0c0703dc26e9c","score":"0.6172681","text":"def normals(self):\n for vertex in self._normals:\n yield tuple(vertex)","title":""},{"docid":"f6198805e59b443add5d88ba305ccb71","score":"0.61535007","text":"def normal(obj, params, **kwargs):\n normalize = kwargs.get('normalize', True)\n if isinstance(obj, abstract.Curve):\n raise GeomdlException(\"Not implemented for curves\")\n if isinstance(obj, abstract.Surface):\n if isinstance(params[0], float):\n return ops.normal_surface_single(obj, params, normalize)\n else:\n return ops.normal_surface_single_list(obj, params, normalize)","title":""},{"docid":"934f17a6dd1a17a0cd180527d57ece6f","score":"0.6111544","text":"def getNormals(self):\n return _bullet.btBox2dShape_getNormals(self)","title":""},{"docid":"2593bef141c69a2a41a3a9cb2395f436","score":"0.608826","text":"def appendNormals(self):\n for triangle in self.triangles:\n normals = triangle.normals\n\n for j in range(3):\n if normals[j] in self.normals:\n continue\n\n elif normals[j].point.x in self.verticalBorders or normals[j].point.y in self.horizontalBorders:\n self.normals.append(normals[j])\n\n else:\n self.appendNormal(normals[j])","title":""},{"docid":"c3e6e22475a915aa17345072ddd29608","score":"0.60852736","text":"def surface_normals(points, normalize=True):\n points, _, transform_result = columnize(points, (-1, 3, 3), name=\"points\")\n\n p1s = points[:, 0]\n p2s = points[:, 1]\n p3s = points[:, 2]\n v1s = p2s - p1s\n v2s = p3s - p1s\n normals = vg.cross(v1s, v2s)\n\n if normalize:\n normals = vg.normalize(normals)\n\n return transform_result(normals)","title":""},{"docid":"79beb3f72e2fdbf38a6b7c95210824e0","score":"0.6034521","text":"def hasNormals(self):\n return _bullet.btVertexBufferDescriptor_hasNormals(self)","title":""},{"docid":"f7550cb1894eee5e10eab2d8859e5918","score":"0.6021934","text":"def surface_normals_object_model_3d(\n object_model_3d: MaybeSequence[HHandle],\n method: str,\n gen_param_name: Sequence[str],\n gen_param_value: Sequence[Union[float, int, str]]\n) -> Sequence[HHandle]:\n with HalconOperator(515) as proc:\n proc.set_input_tuple(0, object_model_3d)\n proc.set_input_tuple(1, method)\n proc.set_input_tuple(2, gen_param_name)\n proc.set_input_tuple(3, gen_param_value)\n proc.init_oct(0)\n proc.execute()\n object_model_3dnormals = proc.get_output_tuple_m(0)\n return object_model_3dnormals # type: ignore","title":""},{"docid":"d056a1adc7496c0bfe815e8c671f54e1","score":"0.5991372","text":"def compute_normals(self, vertex_positions):\n a = vertex_positions[:, self.mesh.faces[:, 0]]\n b = vertex_positions[:, self.mesh.faces[:, 1]]\n c = vertex_positions[:, self.mesh.faces[:, 2]]\n v1 = b - a\n v2 = c - a\n normal = torch.cross(v1, v2, dim=2)\n return F.normalize(normal, dim=2)","title":""},{"docid":"3f69f50da527ca5be61f756b1cd28bae","score":"0.5959781","text":"def computePointNormals(self):\n # Make sure the geometry is not read only.\n if self.isReadOnly():\n raise hou.GeometryPermissionError()\n\n _cpp_methods.computePointNormals(self)","title":""},{"docid":"8b1aa52949cfc5e2eb5b78d4b14cfd02","score":"0.5912431","text":"def unit_outer_normals(self):\n pass","title":""},{"docid":"3d4921388cd165656dea3c4414fbfb74","score":"0.590263","text":"def compute_normals(cone_edges):\n # ------------------------------------------------\n facet_normals = np.cross(cone_edges, np.roll(cone_edges, -1, axis=0))\n # ------------------------------------------------\n assert isinstance(facet_normals, np.ndarray), 'Wrong return type for compute_normals. Make sure it is a np.ndarray'\n return facet_normals","title":""},{"docid":"43de92f4d515b1af4144b494982dc8d8","score":"0.588539","text":"def point_normals(self) -> 'pyvista.pyvista_ndarray':\n if 'Normals' in self.point_data:\n normals = self.point_data['Normals']\n else:\n normals = self.compute_normals(cell_normals=False, inplace=False).point_data['Normals']\n return normals","title":""},{"docid":"9a48e31cb7992d88fbb959517c2339db","score":"0.5865561","text":"def computeNormals(cloud: o3d.geometry.PointCloud, orient_normals=False): \n cloud.estimate_normals()\n if orient_normals is True: \n cloud.orient_normals_towards_camera_location() \n return cloud","title":""},{"docid":"3a0880950e3879f517347c93bc52b562","score":"0.5826057","text":"def surface_normals_object_model_3d_s(\n object_model_3d: MaybeSequence[HHandle],\n method: str,\n gen_param_name: Sequence[str],\n gen_param_value: Sequence[Union[float, int, str]]\n) -> HHandle:\n with HalconOperator(515) as proc:\n proc.set_input_tuple(0, object_model_3d)\n proc.set_input_tuple(1, method)\n proc.set_input_tuple(2, gen_param_name)\n proc.set_input_tuple(3, gen_param_value)\n proc.init_oct(0)\n proc.execute()\n object_model_3dnormals = proc.get_output_tuple_s(0)\n return object_model_3dnormals # type: ignore","title":""},{"docid":"1dc66dfe365915747150f7c6a4de916c","score":"0.5822876","text":"def compute_normals(self, max_nn):\n estimate_normals(self.point_cloud, KDTreeSearchParamHybrid(radius=self.NORMALS_RADIUS, max_nn=max_nn))","title":""},{"docid":"b660783457652c1ac3322ae797fbcb63","score":"0.5819201","text":"def compute_face_normals(vertices_zyx, faces, normalize=False):\n # numpy is faster than numba for face normals.\n # Always use numpy.\n return compute_face_normals_numpy_chunked(vertices_zyx, faces, normalize)","title":""},{"docid":"65071d4885e2b7ac40fd6bf3f0d85cca","score":"0.5810126","text":"def get_normals(self, orientation=None):\n # Get orientation\n if orientation is None:\n orientation = self.orientation\n\n # Compute blobs coordinates\n rotation_matrix = orientation.rotation_matrix()\n normals = np.array([np.dot(rotation_matrix, vec) for vec in self.reference_normals])\n return normals","title":""},{"docid":"b54fd9a20765dcdac70f3d535df064b4","score":"0.57970876","text":"def normal(self, intersection_point, ray):\r\n pass","title":""},{"docid":"a923bdcc0f511d9f621a07e5463f9ed9","score":"0.57925946","text":"def compute_normals(self, cell_normals=True, point_normals=True,\n split_vertices=False, flip_normals=False,\n consistent_normals=True,\n auto_orient_normals=False,\n non_manifold_traversal=True,\n feature_angle=30.0, inplace=False):\n normal = vtk.vtkPolyDataNormals()\n normal.SetComputeCellNormals(cell_normals)\n normal.SetComputePointNormals(point_normals)\n normal.SetSplitting(split_vertices)\n normal.SetFlipNormals(flip_normals)\n normal.SetConsistency(consistent_normals)\n normal.SetAutoOrientNormals(auto_orient_normals)\n normal.SetNonManifoldTraversal(non_manifold_traversal)\n normal.SetFeatureAngle(feature_angle)\n normal.SetInputData(self)\n normal.Update()\n\n mesh = _get_output(normal)\n if point_normals:\n mesh.GetPointData().SetActiveNormals('Normals')\n if cell_normals:\n mesh.GetCellData().SetActiveNormals('Normals')\n\n\n if inplace:\n self.overwrite(mesh)\n else:\n return mesh","title":""},{"docid":"351c34734d3a39d82102341d254a7b8d","score":"0.57842606","text":"def point_normals(self,\n points: np.ndarray,\n normals: np.ndarray,\n color: mt.Array = WHITE,\n point_size: float = 1) \\\n -> object.PointNormals:\n ro = object.PointNormals(self.visu, points, normals, color, point_size)\n self.append_render_object(ro)\n return ro","title":""},{"docid":"0fa58b16f9ace971161f2ebe85d4302f","score":"0.57615024","text":"def vertex_normals(vertices, faces):\n\n def normalize_v3(arr):\n \"\"\" Normalize a numpy array of 3 component vectors shape=(n,3) \"\"\"\n lens = np.sqrt(arr[:, 0] ** 2 + arr[:, 1] ** 2 + arr[:, 2] ** 2)\n arr /= lens[:, np.newaxis]\n\n tris = vertices[faces]\n facenorms = np.cross(tris[::, 1] - tris[::, 0], tris[::, 2] - tris[::, 0])\n normalize_v3(facenorms)\n\n norm = np.zeros(vertices.shape, dtype=vertices.dtype)\n norm[faces[:, 0]] += facenorms\n norm[faces[:, 1]] += facenorms\n norm[faces[:, 2]] += facenorms\n normalize_v3(norm)\n return norm","title":""},{"docid":"79ca0f5cd8640d4a2a3f1ec31655688f","score":"0.5749858","text":"def draw_vertexnormals(self,\n vertices: Optional[List[int]] = None,\n color: Optional[Union[str, RGBColor, List[RGBColor], Dict[int, RGBColor]]] = None,\n scale: float = 1.0) -> List[bpy.types.Object]:\n vertices = vertices or self.vertices\n vertex_color = colordict(color, vertices, default=(0., 1., 0.))\n lines = []\n for vertex in vertices:\n a = self.vertex_xyz[vertex]\n n = self.mesh.vertex_normal(vertex)\n b = add_vectors(a, scale_vector(n, scale))\n lines.append({\n 'start': a,\n 'end': b,\n 'color': vertex_color[vertex],\n 'name': \"{}.vertexnormal.{}\".format(self.mesh.name, vertex)\n })\n return compas_blender.draw_lines(lines, collection=self.vertexnormalcollection)","title":""},{"docid":"c8bb300686a816844403bd0ebc9250a2","score":"0.5705078","text":"def draw_vertexnormals(self, keys=None, color=None, scale=None):\n vertices = keys or list(self.mesh.vertices())\n scale = scale or self.settings['scale.vertexnormals']\n color = color or self.settings['color.vertexnormals']\n\n lines = []\n for vertex in vertices:\n a = self.mesh.vertex_coordinates(vertex)\n n = self.mesh.vertex_normal(vertex)\n b = add_vectors(a, scale_vector(n, scale))\n lines.append({\n 'start': a,\n 'end': b,\n 'color': color,\n 'name': \"{}.vertexnormal.{}\".format(self.mesh.name, vertex),\n 'arrow': 'end'})\n\n guids = compas_rhino.draw_lines(lines, layer=self.layer, clear=False, redraw=False)\n self.guid_vertexnormal = zip(guids, vertices)\n return guids","title":""},{"docid":"28c18d43a53d715a88bafced3df1f114","score":"0.5703718","text":"def normals_2d(self):\n\n normal_x = [1, 0]\n normal_y = [0, 1]\n\n cs = cos(self.__rotation)\n sn = sin(self.__rotation)\n rot_normal_x = np.array([normal_x[0] * cs - normal_x[1] * sn, normal_x[0] * sn + normal_x[1] * cs])\n rot_normal_y = np.array([normal_y[0] * cs - normal_y[1] * sn, normal_y[0] * sn + normal_y[1] * cs])\n\n return rot_normal_x, rot_normal_y","title":""},{"docid":"5fd0b1d8db3085d7aa7d3bcea24c27a9","score":"0.56881946","text":"def face_normals(self) -> 'pyvista.pyvista_ndarray':\n return self.cell_normals","title":""},{"docid":"713d62bbe398b1c744e4ea54a8153352","score":"0.56808436","text":"def pts_normals(pts1, pts2, pts3, normalize=True):\n # should I normalize these...?\n return vec_crosses(pts2-pts1, pts3-pts1, normalize=normalize)","title":""},{"docid":"4b32520a49f1cd3353839fab49e5ebe4","score":"0.5658754","text":"def cell_normals(self) -> 'pyvista.pyvista_ndarray':\n if 'Normals' in self.cell_data:\n normals = self.cell_data['Normals']\n else:\n normals = self.compute_normals(point_normals=False, inplace=False).cell_data['Normals']\n return normals","title":""},{"docid":"4bc9d34151d90d1917c257ae261ce96f","score":"0.55510193","text":"def SetNormal1InObjectSpace(self, newV1: 'itkCovariantVectorD3') -> \"void\":\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint3_SetNormal1InObjectSpace(self, newV1)","title":""},{"docid":"f0a9a3c4d463e4f6e4d27ffb50c6f7e4","score":"0.5527974","text":"def setNormalizeDirection(*args, **kwargs):\n \n pass","title":""},{"docid":"9d86cfee66900b763433840241fb36f0","score":"0.5526525","text":"def normal(self, intersections):\n pass","title":""},{"docid":"9314ccdbfb829d2387625b6f8b4d238b","score":"0.55201524","text":"def normal_vectors(triangles) :\r\n normals = []\r\n for triangle in triangles :\r\n vec = np.cross(triangle[1]-triangle[0], triangle[2]-triangle[0])\r\n normals.append(vec/np.linalg.norm(vec))\r\n return normals","title":""},{"docid":"fb2cc6160cbfa0d622d2728548d1f6da","score":"0.5502883","text":"def normal_vectors(triangles) :\n normals = []\n for triangle in triangles :\n vec = np.cross(triangle[1]-triangle[0], triangle[2]-triangle[0])\n normals.append(vec/np.linalg.norm(vec))\n return normals","title":""},{"docid":"ecf67212a4ef8b6782c413365facedb0","score":"0.54810864","text":"def normal (p1, p2, p3):\n p12 = point(p2.x-p1.x,p2.y-p1.y,p2.z-p1.z)\n p13 = point(p3.x-p1.x,p3.y-p1.y,p3.z-p1.z)\n normal_vector = determ(p12,p13)\n normal_size = magnitude(normal_vector.x, normal_vector.y, normal_vector.z)\n return point(normal_vector.x/normal_size, normal_vector.y/normal_size, normal_vector.z/normal_size)","title":""},{"docid":"b50f3cc2308ff3a065027813e1a3cccc","score":"0.54581505","text":"def normalVectors(self):\n axesReverse = zeros(3)\n axesReverse[:] = 1/self.axes[:]\n self.normalvectors[0:self.numpoints,0:3] = 0.\n X = zeros(3)\n for i in range(0,self.numpoints,1):\n X[0:3] = self.points[i,0:3] * (axesReverse[0:3]) * (axesReverse[0:3])\n normXReverse = 1./ math.sqrt(X[0]*X[0]+X[1]*X[1]+X[2]*X[2])\n self.normalvectors[i,0:3] = X[0:3] * normXReverse\n return","title":""},{"docid":"4c3b57096e845be3ab4d884583219afb","score":"0.5451476","text":"def setNormalise(self, normalise):\n self.set(normalise=normalise)","title":""},{"docid":"8f88863ebcec35dc944eb7e12b7989a0","score":"0.544015","text":"def SetNormal1InObjectSpace(self, newV1: 'itkCovariantVectorD2') -> \"void\":\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint2_SetNormal1InObjectSpace(self, newV1)","title":""},{"docid":"443f20ce65109090fdbcfb91b9401fee","score":"0.5436328","text":"def invert_normal( plane ):\n # flip the normal, and the distance\n return -plane","title":""},{"docid":"c29b49159e879c77a33ed260f8cb3c21","score":"0.5419106","text":"def orient_normals(normals, points, view_point):\n view_point = np.array(view_point).reshape(1, 3)\n val = np.sum(normals * (view_point - points), axis=-1)\n \n signs = (val > 0).astype(int)*2 - 1\n return -signs.reshape(-1, 1)*normals","title":""},{"docid":"1f9083882126a26509d19e1d6ffd2559","score":"0.53924966","text":"def polySetToFaceNormal(*args, **kwargs):\n\n pass","title":""},{"docid":"197da153836e7b903705641774cbbcb3","score":"0.5375626","text":"def calcNormal(self, normal):\n return _bullet.btTriangleShape_calcNormal(self, normal)","title":""},{"docid":"d0eee7750b87483e80421d5596522f19","score":"0.53743446","text":"def remove_zero_normals(self):\n points_of_interest = np.where(\n np.linalg.norm(self._data, axis=0) != 0.0\n )[0]\n self._data = self._data[:, points_of_interest]","title":""},{"docid":"8f4b02461a3c9ae32e24d7ab59cbba2d","score":"0.53709245","text":"def glorot_normal(self, mode='fan_in', scale=2.0):\n shape = self.shape\n return init_ops_lib.GlorotNormal \\\n .instantiate(\n ndim=len(shape),\n dtype=self.dtype,\n mode=mode.lower(),\n scale=float(scale),\n ).apply(shape, out=self)","title":""},{"docid":"078b003ccf9d9485c5558e7537c3eb3a","score":"0.53696793","text":"def mesh_normalization(meshes):\n if not hasattr(meshes, '__iter__'):\n meshes = [meshes]\n meshes[:] = [mesh_norm(mesh) for mesh in meshes]","title":""},{"docid":"2b856e274fb9a378f1a4e70e23b6b68f","score":"0.5350126","text":"def flip_normal(self): ## TODO verify\n # reverse the lower and upper quad faces\n n1, n2, n3, n4, n5, n6, n7, n8 = self.nodes\n self.nodes = [n1, n4, n3, n2,\n n5, n8, n7, n6,]\n if self.nodes_ref is not None:\n n1_ref, n2_ref, n3_ref, n4_ref, n5_ref, n6_ref, n7_ref, n8_ref = self.nodes_ref\n self.nodes_ref = [\n n1_ref, n4_ref, n3_ref, n2_ref,\n n5_ref, n8_ref, n7_ref, n6_ref,]","title":""},{"docid":"2b856e274fb9a378f1a4e70e23b6b68f","score":"0.5350126","text":"def flip_normal(self): ## TODO verify\n # reverse the lower and upper quad faces\n n1, n2, n3, n4, n5, n6, n7, n8 = self.nodes\n self.nodes = [n1, n4, n3, n2,\n n5, n8, n7, n6,]\n if self.nodes_ref is not None:\n n1_ref, n2_ref, n3_ref, n4_ref, n5_ref, n6_ref, n7_ref, n8_ref = self.nodes_ref\n self.nodes_ref = [\n n1_ref, n4_ref, n3_ref, n2_ref,\n n5_ref, n8_ref, n7_ref, n6_ref,]","title":""},{"docid":"262c37a90930a26020e641a9571b9326","score":"0.53198797","text":"def set_normal(x):\n global normal\n if isinstance(x,list) == False:\n x = x.tolist()\n normal = x\n return normal","title":""},{"docid":"e186560003d29704441f890a58baf2ba","score":"0.5317049","text":"def color_normals(normals):\n return 128-128*normals","title":""},{"docid":"dc49bbdff5c5af770d79ccce04b7f8d7","score":"0.53114444","text":"def get_normal(self):\n vertex_a = [self.halfedge.vertex.x, self.halfedge.vertex.y,\n self.halfedge.vertex.z]\n\n vertex_b = [self.halfedge.next.vertex.x, self.halfedge.next.vertex.y,\n self.halfedge.next.vertex.z]\n\n vertex_c = [self.halfedge.prev.vertex.x, self.halfedge.prev.vertex.y,\n self.halfedge.prev.vertex.z]\n\n # create edge 1 with vector difference\n edge1 = [u - v for u, v in zip(vertex_b, vertex_a)]\n edge1 = normalize(edge1)\n # create edge 2 ...\n edge2 = [u - v for u, v in zip(vertex_c, vertex_b)]\n edge2 = normalize(edge2)\n\n # cross product\n normal = cross_product(edge1, edge2)\n\n normal = normalize(normal)\n\n return normal","title":""},{"docid":"16748ca38a705cef6907e40fcc497f30","score":"0.5270243","text":"def flip_normal(\n self, normal: List[float], point=None, transform_all_input_vectors=False, inplace=False\n ):\n if point is None:\n point = self.center\n check_valid_vector(normal, 'normal')\n check_valid_vector(point, 'point')\n t = transformations.reflection(normal, point=point)\n return self.transform(\n t, transform_all_input_vectors=transform_all_input_vectors, inplace=inplace\n )","title":""},{"docid":"e93318632932c6d11a00aa72cfead2f6","score":"0.5270167","text":"def normal(self, element_index):\n return self._normals[:, element_index]","title":""},{"docid":"f4a44b4b7f6fdef84325fb2585b701b4","score":"0.5265045","text":"def normalize(self):\n prefactor = 1.0 / self.norm\n for primitive in self.primitives:\n primitive.coeff *= prefactor","title":""},{"docid":"17b4de0e831304e893517bed13e58bdb","score":"0.525014","text":"def SetNormal1InWorldSpace(self, newV1: 'itkCovariantVectorD3') -> \"void\":\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint3_SetNormal1InWorldSpace(self, newV1)","title":""},{"docid":"034aca0a48cb005f6c946909b7e893e2","score":"0.5233855","text":"def get_normal_distributions(centroid) -> List[norm]:\n normals = []\n\n #Loop for each dimension\n for i in range(len(centroid.values)):\n normals.append(norm(loc=centroid.values[i], scale=centroid.variance[i]**0.5))\n\n return normals","title":""},{"docid":"0086503ffec690d39a9eaa6c22bbe7f8","score":"0.5218651","text":"def createNormaldata(self, face_normals_to_draw):\n mesh_normals = np.repeat(face_normals_to_draw, 3, axis=0)\n data_mesh_normals = mesh_normals.flatten()\n\n return data_mesh_normals","title":""},{"docid":"567b90a2787c4c5f666b7315276c3ef9","score":"0.51899606","text":"def normal( plane ):\n return plane[ :3 ]","title":""},{"docid":"1a59e701339d6a390ad7505f0441f2c7","score":"0.51579285","text":"def set_document_norms(self):\r\n pass","title":""},{"docid":"1e0e23ed89d67399914c754e3bf7e866","score":"0.51484644","text":"def get_normals(points, knn=None, radius=None):\n assert not (radius is None and knn is None)\n if knn is None:\n method = o3d.geometry.KDTreeSearchParamRadius(radius=radius)\n elif radius is None:\n method = o3d.geometry.KDTreeSearchParamKNN(knn=knn)\n else:\n method = o3d.geometry.KDTreeSearchParamHybrid(max_nn=knn, radius=radius)\n \n pcd = points2PointCloud(points)\n pcd.estimate_normals(method)\n\n if isinstance(points, o3d.geometry.PointCloud):\n return pcd\n \n return np.asarray(pcd.normals)","title":""},{"docid":"f6c446a0fcaf646d07d630d0599e7ad1","score":"0.51391566","text":"def flip_normal(self): ## TODO verify\n # flip n2 with n3\n n1, n2, n3, n4 = self.nodes\n self.nodes = [n1, n3, n2, n4]\n if self.nodes_ref is not None:\n n1_ref, n2_ref, n3_ref, n4_ref = self.nodes_ref\n self.nodes_ref = [n1_ref, n3_ref, n2_ref, n4_ref]","title":""},{"docid":"05c1e1a46cea0c9ddbf458d73ddce598","score":"0.5119353","text":"def calculateNormal(self):\n\n\t\t# Determine view Vector\n\t\tself.__viewX = self.__centerX - self.__eyeX\n\t\tself.__viewY = self.__centerY - self.__eyeY\n\t\tself.__viewZ = self.__centerZ - self.__eyeZ\n\n\t\t# Calculate cross product of the view and up vectors\n\t\tself.__normalX = (self.__viewY * self.__upZ) - (self.__upY * self.__viewZ)\n\t\tself.__normalY = (self.__viewZ * self.__upX) - (self.__upZ * self.__viewX)\n\t\tself.__normalZ = (self.__viewX * self.__upY) - (self.__upX * self.__viewY)\n\n\t\t# Set to be a unit normal\t\t\n\t\tmagnitude = float(math.sqrt(pow(self.__normalX,2) + pow(self.__normalY,2) + pow(self.__normalZ,2)))\n\t\t\n\t\tself.__normalX = float(float(self.__normalX)/magnitude)\n\t\tself.__normalY = float(float(self.__normalY)/magnitude)\n\t\tself.__normalZ = float(float(self.__normalZ)/magnitude)\n\t\tpass","title":""},{"docid":"de3ceee9aa8ed87db0ba5ae380a32cec","score":"0.51135767","text":"def point_normals_colored(self,\n points: np.ndarray,\n normals: np.ndarray,\n colors: np.ndarray,\n point_size: float = 1) \\\n -> object.PointNormalsColored:\n ro = object.PointNormalsColored(self.visu, points, normals, colors, point_size)\n self.append_render_object(ro)\n return ro","title":""},{"docid":"3a815ef66724a2f3193097462655171a","score":"0.5107662","text":"def face_normals(self):\n if self.dim == 2:\n nX = np.c_[np.ones(self.n_faces_x), np.zeros(self.n_faces_x)]\n nY = np.c_[np.zeros(self.n_faces_y), np.ones(self.n_faces_y)]\n return np.r_[nX, nY]\n elif self.dim == 3:\n nX = np.c_[\n np.ones(self.n_faces_x),\n np.zeros(self.n_faces_x),\n np.zeros(self.n_faces_x),\n ]\n nY = np.c_[\n np.zeros(self.n_faces_y),\n np.ones(self.n_faces_y),\n np.zeros(self.n_faces_y),\n ]\n nZ = np.c_[\n np.zeros(self.n_faces_z),\n np.zeros(self.n_faces_z),\n np.ones(self.n_faces_z),\n ]\n return np.r_[nX, nY, nZ]","title":""},{"docid":"1da45b1027318bca21de51ba484366c2","score":"0.50925696","text":"def normals_curve_2d(x):\n b = x.shape[0]\n n_x = x.shape[1]\n\n n = torch.zeros(x.shape)\n n[:, :-1, :] = x[:, 1:, :] - x[:, :-1, :]\n n[:, -1, :] = (x[:, -1, :] - x[:, -2, :])\n n = n[:, :, [1, 0]]\n n[:, :, 0] = -n[:, :, 0]\n n = n / torch.norm(n, dim=2).view(b, n_x, 1)\n n[:, 1:, :] = 0.5*(n[:, 1:, :] + n[:, :-1, :])\n n = n / torch.norm(n, dim=2).view(b, n_x, 1)\n return n","title":""},{"docid":"5f84a9cb9820b8a4dfb9ccc494ce8ebf","score":"0.50755423","text":"def compute_vertex_normals(vertices_zyx, faces, weight_by_face_area=False, face_normals=None):\n if face_normals is None:\n face_normals = compute_face_normals_numpy_chunked(vertices_zyx, faces, not weight_by_face_area)\n\n # numba is slightly faster for vertex normals, but not face normals\n if _numba_available:\n assert vertices_zyx.dtype == np.float32, \\\n f\"Our numba implementation requires float32 vertices, not {vertices_zyx.dtype}\"\n return compute_vertex_normals_numba(vertices_zyx, faces, weight_by_face_area, face_normals)\n else:\n return compute_vertex_normals_numpy(vertices_zyx, faces, weight_by_face_area, face_normals)","title":""},{"docid":"9317e44f17621211211ea5e944f0ce8e","score":"0.5061683","text":"def SetNormal1InWorldSpace(self, newV1: 'itkCovariantVectorD2') -> \"void\":\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint2_SetNormal1InWorldSpace(self, newV1)","title":""},{"docid":"4d938eb972e291e0d891254aae49ecf5","score":"0.50433093","text":"def depth_to_normals(depth: torch.Tensor, camera_matrix: torch.Tensor) -> torch.Tensor:\n if not isinstance(depth, torch.Tensor):\n raise TypeError(f\"Input depht type is not a torch.Tensor. Got {type(depth)}.\")\n\n if not len(depth.shape) == 4 and depth.shape[-3] == 1:\n raise ValueError(f\"Input depth musth have a shape (B, 1, H, W). Got: {depth.shape}\")\n\n if not isinstance(camera_matrix, torch.Tensor):\n raise TypeError(f\"Input camera_matrix type is not a torch.Tensor. \"\n f\"Got {type(camera_matrix)}.\")\n\n if not len(camera_matrix.shape) == 3 and camera_matrix.shape[-2:] == (3, 3):\n raise ValueError(f\"Input camera_matrix must have a shape (B, 3, 3). \"\n f\"Got: {camera_matrix.shape}.\")\n\n # compute the 3d points from depth\n xyz: torch.Tensor = depth_to_3d(depth, camera_matrix) # Bx3xHxW\n\n # compute the pointcloud spatial gradients\n gradients: torch.Tensor = spatial_gradient(xyz) # Bx3x2xHxW\n\n # compute normals\n a, b = gradients[:, :, 0], gradients[:, :, 1] # Bx3xHxW\n\n normals: torch.Tensor = torch.cross(a, b, dim=1) # Bx3xHxW\n return F.normalize(normals, dim=1, p=2)","title":""},{"docid":"9b2d9390e9115803ba435eeebd895eb9","score":"0.50397146","text":"def update_normalization(self, vector_obs: List[torch.Tensor]) -> None:\n pass","title":""},{"docid":"ff06519aa2d8c915e9cdc809a12c47f6","score":"0.50364494","text":"def Normalize(toNormal):\n distance = 0\n for value in toNormal:\n if value <= 0:\n distance -= value\n else:\n distance += value\n if distance > 0:\n for index in range(len(toNormal)):\n toNormal[index] = toNormal[index]/distance\n return toNormal","title":""},{"docid":"83e032b44fc53fa39dc62b1a48884d77","score":"0.5022148","text":"def active_normals(self) -> Optional[pyvista_ndarray]:\n if self.point_data.active_normals is not None:\n return self.point_data.active_normals\n return self.cell_data.active_normals","title":""},{"docid":"6eaef61a815453c455ed2b6ea6c11c28","score":"0.5006172","text":"def SetNormal2InObjectSpace(self, newV2: 'itkCovariantVectorD3') -> \"void\":\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint3_SetNormal2InObjectSpace(self, newV2)","title":""},{"docid":"e0c026b73fffb7db154a3ea261a017d6","score":"0.4982009","text":"def get_normal_direction(self, points, point_id):\n va = points[self.shape_info.point_info[point_id].connect_from] - points[point_id]\n vb = points[self.shape_info.point_info[point_id].connect_to] - points[point_id]\n\n magnitude = np.linalg.norm(va)\n if magnitude > 1e-10:\n va = va / magnitude\n magnitude = np.linalg.norm(vb)\n if magnitude > 1e-10:\n vb = vb / magnitude\n\n direction = np.zeros(2)\n direction[0] = - va[1] + vb[1]\n direction[1] = va[0] - vb[0]\n\n if np.linalg.norm(direction) < 1e-10:\n if np.linalg.norm(va) > 1e-10:\n direction = - va\n else:\n direction = np.array([1, 0])\n else:\n direction = direction / np.linalg.norm(direction)\n\n return direction","title":""},{"docid":"98bf99e7a10f99392ebb15e8874c478f","score":"0.49758577","text":"def reflect(direction, normal):\n return direction - (2 * direction.dot(normal) * normal)","title":""},{"docid":"85319b0acde5514328057bd2a2f90653","score":"0.4968423","text":"def displayAlbedosNormals(albedos, normals, s):\n albedoIm = np.reshape(albedos,s)\n normals += abs(np.min(normals))\n normals /= np.max(normals)\n\n d1 = normals[0,:].reshape(s)\n d2 = normals[1,:].reshape(s)\n d3 = normals[2,:].reshape(s)\n\n normalIm = np.dstack((d1,d2))\n normalIm = np.dstack((normalIm,d3))\n\n plt.imshow(albedoIm, cmap='gray')\n plt.show()\n\n plt.imshow(normalIm, cmap='rainbow')\n plt.show()\n\n return albedoIm, normalIm","title":""},{"docid":"36c39625e55da8a93bc542ef6a297cdf","score":"0.49445993","text":"def draw_facenormals(self,\n faces: Optional[List[List[int]]] = None,\n color: Optional[Union[str, RGBColor, List[RGBColor], Dict[int, RGBColor]]] = None,\n scale: float = 1.0) -> List[bpy.types.Object]:\n faces = faces or self.faces\n face_color = colordict(color, faces, default=(0., 1., 1.))\n lines = []\n for face in faces:\n a = centroid_points([self.vertex_xyz[vertex] for vertex in self.mesh.face_vertices(face)])\n n = self.mesh.face_normal(face)\n b = add_vectors(a, scale_vector(n, scale))\n lines.append({\n 'start': a,\n 'end': b,\n 'name': \"{}.facenormal.{}\".format(self.mesh.name, face),\n 'color': face_color[face]\n })\n return compas_blender.draw_lines(lines, collection=self.facenormalcollection)","title":""},{"docid":"d0015e1bd5f048d70ef592ff3a2c1178","score":"0.49423563","text":"def ComputeTangentAndNormals(self) -> \"bool\":\n return _itkTubeSpatialObjectPython.itkTubeSpatialObject3_ComputeTangentAndNormals(self)","title":""},{"docid":"9d0f0e05336063797ae0a0f2edb45869","score":"0.4924066","text":"def normalize(self):\n norm = self.norm()\n if abs(norm) > 0.0001:\n self.x /= norm\n self.y /= norm\n self.z /= norm","title":""},{"docid":"214c549e61c1dd93514a94856719ed9f","score":"0.49198687","text":"def normal(self, surface_point):\n return (surface_point-self.center).normalize()","title":""},{"docid":"5d56bd191be42835a8252a456d021cd8","score":"0.49172565","text":"def raw_mesh(self,\n points: np.ndarray,\n normals: np.ndarray,\n color: mt.Array = WHITE) \\\n -> object.RawMesh:\n ro = object.RawMesh(self.visu, points, normals, color)\n self.append_render_object(ro)\n return ro","title":""},{"docid":"20b435cc14b6eb1316f0b2eba8ec5a69","score":"0.48866308","text":"def normal(self):\n return self._normal","title":""},{"docid":"182571ae5be91ca7ad3165331ff7098b","score":"0.487705","text":"def compute_face_normals_numpy_chunked(vertices_zyx, faces, normalize=False, chunksize=50_000):\n normals = []\n for i in range(0, len(faces), chunksize):\n n = compute_face_normals_numpy(vertices_zyx, faces[i:i+chunksize], normalize)\n normals.append(n)\n return np.concatenate(normals)","title":""},{"docid":"43c9e5054cffed399528171d2afa3a81","score":"0.48769465","text":"def PlaneFromNormal(origin, normal, xaxis=None):","title":""},{"docid":"0178ff39edb696e04c7ccbce6d642a5a","score":"0.48691598","text":"def test_normal(self):\n env = self.notebook_locals[\"env\"]\n f = self.notebook_locals[\"estimate_normal_by_nearest_pixels\"]\n pC = self.notebook_locals[\"pC\"]\n\n student_sol = f(env.X_WC, pC, uv_step=10)\n reference_sol = np.load(\n FindResource(\"exercises/clutter/normal_solution.npy\")\n )\n\n self.assertTrue(\n len(student_sol) == len(reference_sol),\n \"the number of the normals is incorrect\",\n )\n\n for X, X_sol in zip(student_sol, reference_sol):\n # check only points within the bounding box\n pos_x, pos_y, pos_z = X_sol[0:3, 3]\n if abs(pos_x) < 1.0 and abs(pos_y) < 1.0:\n sol_v = X_sol[0:3, 2]\n # check only the normal vector, not the entire frame\n student_v = X.GetAsMatrix4()[0:3, 2]\n # normalize the vector in case it is not normalized\n student_v = student_v / np.linalg.norm(student_v)\n test = np.dot(student_v, sol_v)\n self.assertTrue(\n test > 0.7,\n \"error at the point: \\n {} \\\n \\n normal is: {} \\\n \\n solution normal is: {}\".format(\n [pos_x, pos_y, pos_z], student_v, sol_v\n ),\n )","title":""},{"docid":"5b57d65c4681cc72bb0927988752a716","score":"0.48630533","text":"def get_normal():\n global normal\n return normal","title":""},{"docid":"3fa6cd444e9ef1bd3f9ac10d7f3ed221","score":"0.4843683","text":"def glorot_normal(shape, scale=2.0, mode='fan_in', dtype='float32', **kwargs):\n args = ArgHelper.parse(locals())\n args['scale'] = float(scale)\n args['mode'] = mode.lower()\n trainable = args.pop('trainable') if 'trainable' in args else False\n op_lib = init_ops_lib.GlorotNormal\n if context.executing_eagerly():\n return op_lib \\\n .instantiate(\n ndim=len(args['dims']),\n scale=args['scale'],\n mode=args['mode'],\n dtype=dtype,\n ).apply(args['dims'], trainable=trainable)\n else:\n return op_lib.blend(**args)","title":""},{"docid":"802f7f6318a5b043ddf5c63c0ba0caf1","score":"0.48322183","text":"def NormalAmps(*args):\n # Getter\n if len(args) == 0:\n return CheckForError(lib.CktElement_Get_NormalAmps())\n\n # Setter\n Value, = args\n CheckForError(lib.CktElement_Set_NormalAmps(Value))","title":""},{"docid":"fb934cf932f439de3ce34f2d04cfd222","score":"0.48255867","text":"def unit_outer_normals(self):\n return self._unit_outer_normals()","title":""},{"docid":"41032dff8363a4fb6eefe67d13cb4419","score":"0.4824227","text":"def SetNormalComponents(self, p_int, p_int_1, p_int_2, p_int_3, p_int_4, p_int_5):\n ...","title":""},{"docid":"6596fd4fdf0e927a2ec209a248734c2d","score":"0.4812352","text":"def norm_mask(self: VecT, normal: 'Vec | FrozenVec') -> VecT:\n norm = normal.norm()\n return type(self)(norm * self.dot(norm))","title":""},{"docid":"000d331fbd7fee7c08e3d7436c92365a","score":"0.48105526","text":"def normalize(self):\n self.coeff /= self.norm","title":""},{"docid":"c074abf0754c95fcad7fe8f5ae6935b4","score":"0.4806936","text":"def polyNormalPerVertex(*args, **kwargs):\n\n pass","title":""},{"docid":"cf72d5d3785adbba4ee295d3bbebf852","score":"0.48026657","text":"def ComputeTangentAndNormals(self) -> \"bool\":\n return _itkTubeSpatialObjectPython.itkTubeSpatialObject2_ComputeTangentAndNormals(self)","title":""}],"string":"[\n {\n \"docid\": \"1bbb05d3a41115c7ef5c1abf20d11d98\",\n \"score\": \"0.73524904\",\n \"text\": \"def setNormals(self):\\n self.Normals[0] = normalize(cross(map(operator.sub, self.V1[0], self.V1[2]), map(operator.sub, self.V1[0], self.V1[1])))\\n self.Normals[1] = normalize(cross(map(operator.sub, self.V1[0], self.V1[3]), map(operator.sub, self.V1[0], self.V1[2])))\\n self.Normals[2] = normalize(cross(map(operator.sub, self.V1[0], self.V1[1]), map(operator.sub, self.V1[0], self.V1[3])))\\n self.Normals[3] = normalize(cross(map(operator.sub, self.V1[2], self.V1[3]), map(operator.sub, self.V1[2], self.V1[1])))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89fe35e6898d9ccd8cc1353db404cafe\",\n \"score\": \"0.7059347\",\n \"text\": \"def normalsPerVertex(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"993b8fb71076c9222d04009235c43fb2\",\n \"score\": \"0.6869938\",\n \"text\": \"def compute_normals(self):\\n\\n if self.has_normal_array:\\n self.normals = generate_vertex_normals(self.vertices.reshape(-1, 3), self.indices.reshape(-1, 3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3799ece07d510f0da264bdfe8989bfdc\",\n \"score\": \"0.6835605\",\n \"text\": \"def _normals_changed( self, *args ):\\n norm_enabled = self.GetValue( \\\"NormalsCheck\\\" )\\n config_enabled = self.GetValue( \\\"configCheck\\\" )\\n\\n self.SetEnabled( \\\"NormalsBox\\\", norm_enabled and not config_enabled )\\n self.SetEnabled( \\\"NormalsLabel\\\", norm_enabled and not config_enabled )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7670685ed1040e1ec90723f028de9d\",\n \"score\": \"0.67559737\",\n \"text\": \"def normals(self,inside=False):\\n if invert:\\n return self.pointAngles(90)\\n return self.pointAngles(90)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"316156990f38cd666c95cc0409860909\",\n \"score\": \"0.65043473\",\n \"text\": \"def normals(facets, points):\\n nv = [vo.normal(points[i], points[j], points[k])\\n for i, j, k in facets]\\n return vo.indexate(nv)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d57f7ec89b9f554a0e3f992d86c2453\",\n \"score\": \"0.6373106\",\n \"text\": \"def flip_normals(self):\\n if self.faces.size % 4:\\n raise Exception('Can only flip normals on an all triangular mesh')\\n\\n f = self.faces.reshape((-1, 4))\\n f[:, 1:] = f[:, 1:][:, ::-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c885b22eae6d23c3c10e44bed0acfea\",\n \"score\": \"0.63323236\",\n \"text\": \"def plot_normals(self, show_mesh=True, mag=1.0, flip=False,\\n use_every=1, **kwargs):\\n plotter = pyvista.Plotter(off_screen=kwargs.pop('off_screen', False),\\n notebook=kwargs.pop('notebook', None))\\n if show_mesh:\\n plotter.add_mesh(self, **kwargs)\\n\\n normals = self.point_normals\\n if flip:\\n normals *= -1\\n plotter.add_arrows(self.points[::use_every],\\n normals[::use_every], mag=mag)\\n return plotter.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0de02b6a8cd064eb0fa70d8aa748f197\",\n \"score\": \"0.6320519\",\n \"text\": \"def setNormal(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0de02b6a8cd064eb0fa70d8aa748f197\",\n \"score\": \"0.6320519\",\n \"text\": \"def setNormal(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"210f2917a4339476b0a0c0703dc26e9c\",\n \"score\": \"0.6172681\",\n \"text\": \"def normals(self):\\n for vertex in self._normals:\\n yield tuple(vertex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6198805e59b443add5d88ba305ccb71\",\n \"score\": \"0.61535007\",\n \"text\": \"def normal(obj, params, **kwargs):\\n normalize = kwargs.get('normalize', True)\\n if isinstance(obj, abstract.Curve):\\n raise GeomdlException(\\\"Not implemented for curves\\\")\\n if isinstance(obj, abstract.Surface):\\n if isinstance(params[0], float):\\n return ops.normal_surface_single(obj, params, normalize)\\n else:\\n return ops.normal_surface_single_list(obj, params, normalize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"934f17a6dd1a17a0cd180527d57ece6f\",\n \"score\": \"0.6111544\",\n \"text\": \"def getNormals(self):\\n return _bullet.btBox2dShape_getNormals(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2593bef141c69a2a41a3a9cb2395f436\",\n \"score\": \"0.608826\",\n \"text\": \"def appendNormals(self):\\n for triangle in self.triangles:\\n normals = triangle.normals\\n\\n for j in range(3):\\n if normals[j] in self.normals:\\n continue\\n\\n elif normals[j].point.x in self.verticalBorders or normals[j].point.y in self.horizontalBorders:\\n self.normals.append(normals[j])\\n\\n else:\\n self.appendNormal(normals[j])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3e6e22475a915aa17345072ddd29608\",\n \"score\": \"0.60852736\",\n \"text\": \"def surface_normals(points, normalize=True):\\n points, _, transform_result = columnize(points, (-1, 3, 3), name=\\\"points\\\")\\n\\n p1s = points[:, 0]\\n p2s = points[:, 1]\\n p3s = points[:, 2]\\n v1s = p2s - p1s\\n v2s = p3s - p1s\\n normals = vg.cross(v1s, v2s)\\n\\n if normalize:\\n normals = vg.normalize(normals)\\n\\n return transform_result(normals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79beb3f72e2fdbf38a6b7c95210824e0\",\n \"score\": \"0.6034521\",\n \"text\": \"def hasNormals(self):\\n return _bullet.btVertexBufferDescriptor_hasNormals(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7550cb1894eee5e10eab2d8859e5918\",\n \"score\": \"0.6021934\",\n \"text\": \"def surface_normals_object_model_3d(\\n object_model_3d: MaybeSequence[HHandle],\\n method: str,\\n gen_param_name: Sequence[str],\\n gen_param_value: Sequence[Union[float, int, str]]\\n) -> Sequence[HHandle]:\\n with HalconOperator(515) as proc:\\n proc.set_input_tuple(0, object_model_3d)\\n proc.set_input_tuple(1, method)\\n proc.set_input_tuple(2, gen_param_name)\\n proc.set_input_tuple(3, gen_param_value)\\n proc.init_oct(0)\\n proc.execute()\\n object_model_3dnormals = proc.get_output_tuple_m(0)\\n return object_model_3dnormals # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d056a1adc7496c0bfe815e8c671f54e1\",\n \"score\": \"0.5991372\",\n \"text\": \"def compute_normals(self, vertex_positions):\\n a = vertex_positions[:, self.mesh.faces[:, 0]]\\n b = vertex_positions[:, self.mesh.faces[:, 1]]\\n c = vertex_positions[:, self.mesh.faces[:, 2]]\\n v1 = b - a\\n v2 = c - a\\n normal = torch.cross(v1, v2, dim=2)\\n return F.normalize(normal, dim=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f69f50da527ca5be61f756b1cd28bae\",\n \"score\": \"0.5959781\",\n \"text\": \"def computePointNormals(self):\\n # Make sure the geometry is not read only.\\n if self.isReadOnly():\\n raise hou.GeometryPermissionError()\\n\\n _cpp_methods.computePointNormals(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1aa52949cfc5e2eb5b78d4b14cfd02\",\n \"score\": \"0.5912431\",\n \"text\": \"def unit_outer_normals(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d4921388cd165656dea3c4414fbfb74\",\n \"score\": \"0.590263\",\n \"text\": \"def compute_normals(cone_edges):\\n # ------------------------------------------------\\n facet_normals = np.cross(cone_edges, np.roll(cone_edges, -1, axis=0))\\n # ------------------------------------------------\\n assert isinstance(facet_normals, np.ndarray), 'Wrong return type for compute_normals. Make sure it is a np.ndarray'\\n return facet_normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43de92f4d515b1af4144b494982dc8d8\",\n \"score\": \"0.588539\",\n \"text\": \"def point_normals(self) -> 'pyvista.pyvista_ndarray':\\n if 'Normals' in self.point_data:\\n normals = self.point_data['Normals']\\n else:\\n normals = self.compute_normals(cell_normals=False, inplace=False).point_data['Normals']\\n return normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a48e31cb7992d88fbb959517c2339db\",\n \"score\": \"0.5865561\",\n \"text\": \"def computeNormals(cloud: o3d.geometry.PointCloud, orient_normals=False): \\n cloud.estimate_normals()\\n if orient_normals is True: \\n cloud.orient_normals_towards_camera_location() \\n return cloud\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a0880950e3879f517347c93bc52b562\",\n \"score\": \"0.5826057\",\n \"text\": \"def surface_normals_object_model_3d_s(\\n object_model_3d: MaybeSequence[HHandle],\\n method: str,\\n gen_param_name: Sequence[str],\\n gen_param_value: Sequence[Union[float, int, str]]\\n) -> HHandle:\\n with HalconOperator(515) as proc:\\n proc.set_input_tuple(0, object_model_3d)\\n proc.set_input_tuple(1, method)\\n proc.set_input_tuple(2, gen_param_name)\\n proc.set_input_tuple(3, gen_param_value)\\n proc.init_oct(0)\\n proc.execute()\\n object_model_3dnormals = proc.get_output_tuple_s(0)\\n return object_model_3dnormals # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dc66dfe365915747150f7c6a4de916c\",\n \"score\": \"0.5822876\",\n \"text\": \"def compute_normals(self, max_nn):\\n estimate_normals(self.point_cloud, KDTreeSearchParamHybrid(radius=self.NORMALS_RADIUS, max_nn=max_nn))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b660783457652c1ac3322ae797fbcb63\",\n \"score\": \"0.5819201\",\n \"text\": \"def compute_face_normals(vertices_zyx, faces, normalize=False):\\n # numpy is faster than numba for face normals.\\n # Always use numpy.\\n return compute_face_normals_numpy_chunked(vertices_zyx, faces, normalize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65071d4885e2b7ac40fd6bf3f0d85cca\",\n \"score\": \"0.5810126\",\n \"text\": \"def get_normals(self, orientation=None):\\n # Get orientation\\n if orientation is None:\\n orientation = self.orientation\\n\\n # Compute blobs coordinates\\n rotation_matrix = orientation.rotation_matrix()\\n normals = np.array([np.dot(rotation_matrix, vec) for vec in self.reference_normals])\\n return normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b54fd9a20765dcdac70f3d535df064b4\",\n \"score\": \"0.57970876\",\n \"text\": \"def normal(self, intersection_point, ray):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a923bdcc0f511d9f621a07e5463f9ed9\",\n \"score\": \"0.57925946\",\n \"text\": \"def compute_normals(self, cell_normals=True, point_normals=True,\\n split_vertices=False, flip_normals=False,\\n consistent_normals=True,\\n auto_orient_normals=False,\\n non_manifold_traversal=True,\\n feature_angle=30.0, inplace=False):\\n normal = vtk.vtkPolyDataNormals()\\n normal.SetComputeCellNormals(cell_normals)\\n normal.SetComputePointNormals(point_normals)\\n normal.SetSplitting(split_vertices)\\n normal.SetFlipNormals(flip_normals)\\n normal.SetConsistency(consistent_normals)\\n normal.SetAutoOrientNormals(auto_orient_normals)\\n normal.SetNonManifoldTraversal(non_manifold_traversal)\\n normal.SetFeatureAngle(feature_angle)\\n normal.SetInputData(self)\\n normal.Update()\\n\\n mesh = _get_output(normal)\\n if point_normals:\\n mesh.GetPointData().SetActiveNormals('Normals')\\n if cell_normals:\\n mesh.GetCellData().SetActiveNormals('Normals')\\n\\n\\n if inplace:\\n self.overwrite(mesh)\\n else:\\n return mesh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"351c34734d3a39d82102341d254a7b8d\",\n \"score\": \"0.57842606\",\n \"text\": \"def point_normals(self,\\n points: np.ndarray,\\n normals: np.ndarray,\\n color: mt.Array = WHITE,\\n point_size: float = 1) \\\\\\n -> object.PointNormals:\\n ro = object.PointNormals(self.visu, points, normals, color, point_size)\\n self.append_render_object(ro)\\n return ro\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa58b16f9ace971161f2ebe85d4302f\",\n \"score\": \"0.57615024\",\n \"text\": \"def vertex_normals(vertices, faces):\\n\\n def normalize_v3(arr):\\n \\\"\\\"\\\" Normalize a numpy array of 3 component vectors shape=(n,3) \\\"\\\"\\\"\\n lens = np.sqrt(arr[:, 0] ** 2 + arr[:, 1] ** 2 + arr[:, 2] ** 2)\\n arr /= lens[:, np.newaxis]\\n\\n tris = vertices[faces]\\n facenorms = np.cross(tris[::, 1] - tris[::, 0], tris[::, 2] - tris[::, 0])\\n normalize_v3(facenorms)\\n\\n norm = np.zeros(vertices.shape, dtype=vertices.dtype)\\n norm[faces[:, 0]] += facenorms\\n norm[faces[:, 1]] += facenorms\\n norm[faces[:, 2]] += facenorms\\n normalize_v3(norm)\\n return norm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79ca0f5cd8640d4a2a3f1ec31655688f\",\n \"score\": \"0.5749858\",\n \"text\": \"def draw_vertexnormals(self,\\n vertices: Optional[List[int]] = None,\\n color: Optional[Union[str, RGBColor, List[RGBColor], Dict[int, RGBColor]]] = None,\\n scale: float = 1.0) -> List[bpy.types.Object]:\\n vertices = vertices or self.vertices\\n vertex_color = colordict(color, vertices, default=(0., 1., 0.))\\n lines = []\\n for vertex in vertices:\\n a = self.vertex_xyz[vertex]\\n n = self.mesh.vertex_normal(vertex)\\n b = add_vectors(a, scale_vector(n, scale))\\n lines.append({\\n 'start': a,\\n 'end': b,\\n 'color': vertex_color[vertex],\\n 'name': \\\"{}.vertexnormal.{}\\\".format(self.mesh.name, vertex)\\n })\\n return compas_blender.draw_lines(lines, collection=self.vertexnormalcollection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8bb300686a816844403bd0ebc9250a2\",\n \"score\": \"0.5705078\",\n \"text\": \"def draw_vertexnormals(self, keys=None, color=None, scale=None):\\n vertices = keys or list(self.mesh.vertices())\\n scale = scale or self.settings['scale.vertexnormals']\\n color = color or self.settings['color.vertexnormals']\\n\\n lines = []\\n for vertex in vertices:\\n a = self.mesh.vertex_coordinates(vertex)\\n n = self.mesh.vertex_normal(vertex)\\n b = add_vectors(a, scale_vector(n, scale))\\n lines.append({\\n 'start': a,\\n 'end': b,\\n 'color': color,\\n 'name': \\\"{}.vertexnormal.{}\\\".format(self.mesh.name, vertex),\\n 'arrow': 'end'})\\n\\n guids = compas_rhino.draw_lines(lines, layer=self.layer, clear=False, redraw=False)\\n self.guid_vertexnormal = zip(guids, vertices)\\n return guids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28c18d43a53d715a88bafced3df1f114\",\n \"score\": \"0.5703718\",\n \"text\": \"def normals_2d(self):\\n\\n normal_x = [1, 0]\\n normal_y = [0, 1]\\n\\n cs = cos(self.__rotation)\\n sn = sin(self.__rotation)\\n rot_normal_x = np.array([normal_x[0] * cs - normal_x[1] * sn, normal_x[0] * sn + normal_x[1] * cs])\\n rot_normal_y = np.array([normal_y[0] * cs - normal_y[1] * sn, normal_y[0] * sn + normal_y[1] * cs])\\n\\n return rot_normal_x, rot_normal_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fd0b1d8db3085d7aa7d3bcea24c27a9\",\n \"score\": \"0.56881946\",\n \"text\": \"def face_normals(self) -> 'pyvista.pyvista_ndarray':\\n return self.cell_normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"713d62bbe398b1c744e4ea54a8153352\",\n \"score\": \"0.56808436\",\n \"text\": \"def pts_normals(pts1, pts2, pts3, normalize=True):\\n # should I normalize these...?\\n return vec_crosses(pts2-pts1, pts3-pts1, normalize=normalize)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b32520a49f1cd3353839fab49e5ebe4\",\n \"score\": \"0.5658754\",\n \"text\": \"def cell_normals(self) -> 'pyvista.pyvista_ndarray':\\n if 'Normals' in self.cell_data:\\n normals = self.cell_data['Normals']\\n else:\\n normals = self.compute_normals(point_normals=False, inplace=False).cell_data['Normals']\\n return normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bc9d34151d90d1917c257ae261ce96f\",\n \"score\": \"0.55510193\",\n \"text\": \"def SetNormal1InObjectSpace(self, newV1: 'itkCovariantVectorD3') -> \\\"void\\\":\\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint3_SetNormal1InObjectSpace(self, newV1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a9a3c4d463e4f6e4d27ffb50c6f7e4\",\n \"score\": \"0.5527974\",\n \"text\": \"def setNormalizeDirection(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d86cfee66900b763433840241fb36f0\",\n \"score\": \"0.5526525\",\n \"text\": \"def normal(self, intersections):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9314ccdbfb829d2387625b6f8b4d238b\",\n \"score\": \"0.55201524\",\n \"text\": \"def normal_vectors(triangles) :\\r\\n normals = []\\r\\n for triangle in triangles :\\r\\n vec = np.cross(triangle[1]-triangle[0], triangle[2]-triangle[0])\\r\\n normals.append(vec/np.linalg.norm(vec))\\r\\n return normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb2cc6160cbfa0d622d2728548d1f6da\",\n \"score\": \"0.5502883\",\n \"text\": \"def normal_vectors(triangles) :\\n normals = []\\n for triangle in triangles :\\n vec = np.cross(triangle[1]-triangle[0], triangle[2]-triangle[0])\\n normals.append(vec/np.linalg.norm(vec))\\n return normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecf67212a4ef8b6782c413365facedb0\",\n \"score\": \"0.54810864\",\n \"text\": \"def normal (p1, p2, p3):\\n p12 = point(p2.x-p1.x,p2.y-p1.y,p2.z-p1.z)\\n p13 = point(p3.x-p1.x,p3.y-p1.y,p3.z-p1.z)\\n normal_vector = determ(p12,p13)\\n normal_size = magnitude(normal_vector.x, normal_vector.y, normal_vector.z)\\n return point(normal_vector.x/normal_size, normal_vector.y/normal_size, normal_vector.z/normal_size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50f3cc2308ff3a065027813e1a3cccc\",\n \"score\": \"0.54581505\",\n \"text\": \"def normalVectors(self):\\n axesReverse = zeros(3)\\n axesReverse[:] = 1/self.axes[:]\\n self.normalvectors[0:self.numpoints,0:3] = 0.\\n X = zeros(3)\\n for i in range(0,self.numpoints,1):\\n X[0:3] = self.points[i,0:3] * (axesReverse[0:3]) * (axesReverse[0:3])\\n normXReverse = 1./ math.sqrt(X[0]*X[0]+X[1]*X[1]+X[2]*X[2])\\n self.normalvectors[i,0:3] = X[0:3] * normXReverse\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c3b57096e845be3ab4d884583219afb\",\n \"score\": \"0.5451476\",\n \"text\": \"def setNormalise(self, normalise):\\n self.set(normalise=normalise)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f88863ebcec35dc944eb7e12b7989a0\",\n \"score\": \"0.544015\",\n \"text\": \"def SetNormal1InObjectSpace(self, newV1: 'itkCovariantVectorD2') -> \\\"void\\\":\\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint2_SetNormal1InObjectSpace(self, newV1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"443f20ce65109090fdbcfb91b9401fee\",\n \"score\": \"0.5436328\",\n \"text\": \"def invert_normal( plane ):\\n # flip the normal, and the distance\\n return -plane\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29b49159e879c77a33ed260f8cb3c21\",\n \"score\": \"0.5419106\",\n \"text\": \"def orient_normals(normals, points, view_point):\\n view_point = np.array(view_point).reshape(1, 3)\\n val = np.sum(normals * (view_point - points), axis=-1)\\n \\n signs = (val > 0).astype(int)*2 - 1\\n return -signs.reshape(-1, 1)*normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f9083882126a26509d19e1d6ffd2559\",\n \"score\": \"0.53924966\",\n \"text\": \"def polySetToFaceNormal(*args, **kwargs):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"197da153836e7b903705641774cbbcb3\",\n \"score\": \"0.5375626\",\n \"text\": \"def calcNormal(self, normal):\\n return _bullet.btTriangleShape_calcNormal(self, normal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0eee7750b87483e80421d5596522f19\",\n \"score\": \"0.53743446\",\n \"text\": \"def remove_zero_normals(self):\\n points_of_interest = np.where(\\n np.linalg.norm(self._data, axis=0) != 0.0\\n )[0]\\n self._data = self._data[:, points_of_interest]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f4b02461a3c9ae32e24d7ab59cbba2d\",\n \"score\": \"0.53709245\",\n \"text\": \"def glorot_normal(self, mode='fan_in', scale=2.0):\\n shape = self.shape\\n return init_ops_lib.GlorotNormal \\\\\\n .instantiate(\\n ndim=len(shape),\\n dtype=self.dtype,\\n mode=mode.lower(),\\n scale=float(scale),\\n ).apply(shape, out=self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"078b003ccf9d9485c5558e7537c3eb3a\",\n \"score\": \"0.53696793\",\n \"text\": \"def mesh_normalization(meshes):\\n if not hasattr(meshes, '__iter__'):\\n meshes = [meshes]\\n meshes[:] = [mesh_norm(mesh) for mesh in meshes]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b856e274fb9a378f1a4e70e23b6b68f\",\n \"score\": \"0.5350126\",\n \"text\": \"def flip_normal(self): ## TODO verify\\n # reverse the lower and upper quad faces\\n n1, n2, n3, n4, n5, n6, n7, n8 = self.nodes\\n self.nodes = [n1, n4, n3, n2,\\n n5, n8, n7, n6,]\\n if self.nodes_ref is not None:\\n n1_ref, n2_ref, n3_ref, n4_ref, n5_ref, n6_ref, n7_ref, n8_ref = self.nodes_ref\\n self.nodes_ref = [\\n n1_ref, n4_ref, n3_ref, n2_ref,\\n n5_ref, n8_ref, n7_ref, n6_ref,]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b856e274fb9a378f1a4e70e23b6b68f\",\n \"score\": \"0.5350126\",\n \"text\": \"def flip_normal(self): ## TODO verify\\n # reverse the lower and upper quad faces\\n n1, n2, n3, n4, n5, n6, n7, n8 = self.nodes\\n self.nodes = [n1, n4, n3, n2,\\n n5, n8, n7, n6,]\\n if self.nodes_ref is not None:\\n n1_ref, n2_ref, n3_ref, n4_ref, n5_ref, n6_ref, n7_ref, n8_ref = self.nodes_ref\\n self.nodes_ref = [\\n n1_ref, n4_ref, n3_ref, n2_ref,\\n n5_ref, n8_ref, n7_ref, n6_ref,]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262c37a90930a26020e641a9571b9326\",\n \"score\": \"0.53198797\",\n \"text\": \"def set_normal(x):\\n global normal\\n if isinstance(x,list) == False:\\n x = x.tolist()\\n normal = x\\n return normal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e186560003d29704441f890a58baf2ba\",\n \"score\": \"0.5317049\",\n \"text\": \"def color_normals(normals):\\n return 128-128*normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc49bbdff5c5af770d79ccce04b7f8d7\",\n \"score\": \"0.53114444\",\n \"text\": \"def get_normal(self):\\n vertex_a = [self.halfedge.vertex.x, self.halfedge.vertex.y,\\n self.halfedge.vertex.z]\\n\\n vertex_b = [self.halfedge.next.vertex.x, self.halfedge.next.vertex.y,\\n self.halfedge.next.vertex.z]\\n\\n vertex_c = [self.halfedge.prev.vertex.x, self.halfedge.prev.vertex.y,\\n self.halfedge.prev.vertex.z]\\n\\n # create edge 1 with vector difference\\n edge1 = [u - v for u, v in zip(vertex_b, vertex_a)]\\n edge1 = normalize(edge1)\\n # create edge 2 ...\\n edge2 = [u - v for u, v in zip(vertex_c, vertex_b)]\\n edge2 = normalize(edge2)\\n\\n # cross product\\n normal = cross_product(edge1, edge2)\\n\\n normal = normalize(normal)\\n\\n return normal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16748ca38a705cef6907e40fcc497f30\",\n \"score\": \"0.5270243\",\n \"text\": \"def flip_normal(\\n self, normal: List[float], point=None, transform_all_input_vectors=False, inplace=False\\n ):\\n if point is None:\\n point = self.center\\n check_valid_vector(normal, 'normal')\\n check_valid_vector(point, 'point')\\n t = transformations.reflection(normal, point=point)\\n return self.transform(\\n t, transform_all_input_vectors=transform_all_input_vectors, inplace=inplace\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e93318632932c6d11a00aa72cfead2f6\",\n \"score\": \"0.5270167\",\n \"text\": \"def normal(self, element_index):\\n return self._normals[:, element_index]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4a44b4b7f6fdef84325fb2585b701b4\",\n \"score\": \"0.5265045\",\n \"text\": \"def normalize(self):\\n prefactor = 1.0 / self.norm\\n for primitive in self.primitives:\\n primitive.coeff *= prefactor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17b4de0e831304e893517bed13e58bdb\",\n \"score\": \"0.525014\",\n \"text\": \"def SetNormal1InWorldSpace(self, newV1: 'itkCovariantVectorD3') -> \\\"void\\\":\\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint3_SetNormal1InWorldSpace(self, newV1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"034aca0a48cb005f6c946909b7e893e2\",\n \"score\": \"0.5233855\",\n \"text\": \"def get_normal_distributions(centroid) -> List[norm]:\\n normals = []\\n\\n #Loop for each dimension\\n for i in range(len(centroid.values)):\\n normals.append(norm(loc=centroid.values[i], scale=centroid.variance[i]**0.5))\\n\\n return normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0086503ffec690d39a9eaa6c22bbe7f8\",\n \"score\": \"0.5218651\",\n \"text\": \"def createNormaldata(self, face_normals_to_draw):\\n mesh_normals = np.repeat(face_normals_to_draw, 3, axis=0)\\n data_mesh_normals = mesh_normals.flatten()\\n\\n return data_mesh_normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"567b90a2787c4c5f666b7315276c3ef9\",\n \"score\": \"0.51899606\",\n \"text\": \"def normal( plane ):\\n return plane[ :3 ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a59e701339d6a390ad7505f0441f2c7\",\n \"score\": \"0.51579285\",\n \"text\": \"def set_document_norms(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e0e23ed89d67399914c754e3bf7e866\",\n \"score\": \"0.51484644\",\n \"text\": \"def get_normals(points, knn=None, radius=None):\\n assert not (radius is None and knn is None)\\n if knn is None:\\n method = o3d.geometry.KDTreeSearchParamRadius(radius=radius)\\n elif radius is None:\\n method = o3d.geometry.KDTreeSearchParamKNN(knn=knn)\\n else:\\n method = o3d.geometry.KDTreeSearchParamHybrid(max_nn=knn, radius=radius)\\n \\n pcd = points2PointCloud(points)\\n pcd.estimate_normals(method)\\n\\n if isinstance(points, o3d.geometry.PointCloud):\\n return pcd\\n \\n return np.asarray(pcd.normals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6c446a0fcaf646d07d630d0599e7ad1\",\n \"score\": \"0.51391566\",\n \"text\": \"def flip_normal(self): ## TODO verify\\n # flip n2 with n3\\n n1, n2, n3, n4 = self.nodes\\n self.nodes = [n1, n3, n2, n4]\\n if self.nodes_ref is not None:\\n n1_ref, n2_ref, n3_ref, n4_ref = self.nodes_ref\\n self.nodes_ref = [n1_ref, n3_ref, n2_ref, n4_ref]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05c1e1a46cea0c9ddbf458d73ddce598\",\n \"score\": \"0.5119353\",\n \"text\": \"def calculateNormal(self):\\n\\n\\t\\t# Determine view Vector\\n\\t\\tself.__viewX = self.__centerX - self.__eyeX\\n\\t\\tself.__viewY = self.__centerY - self.__eyeY\\n\\t\\tself.__viewZ = self.__centerZ - self.__eyeZ\\n\\n\\t\\t# Calculate cross product of the view and up vectors\\n\\t\\tself.__normalX = (self.__viewY * self.__upZ) - (self.__upY * self.__viewZ)\\n\\t\\tself.__normalY = (self.__viewZ * self.__upX) - (self.__upZ * self.__viewX)\\n\\t\\tself.__normalZ = (self.__viewX * self.__upY) - (self.__upX * self.__viewY)\\n\\n\\t\\t# Set to be a unit normal\\t\\t\\n\\t\\tmagnitude = float(math.sqrt(pow(self.__normalX,2) + pow(self.__normalY,2) + pow(self.__normalZ,2)))\\n\\t\\t\\n\\t\\tself.__normalX = float(float(self.__normalX)/magnitude)\\n\\t\\tself.__normalY = float(float(self.__normalY)/magnitude)\\n\\t\\tself.__normalZ = float(float(self.__normalZ)/magnitude)\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de3ceee9aa8ed87db0ba5ae380a32cec\",\n \"score\": \"0.51135767\",\n \"text\": \"def point_normals_colored(self,\\n points: np.ndarray,\\n normals: np.ndarray,\\n colors: np.ndarray,\\n point_size: float = 1) \\\\\\n -> object.PointNormalsColored:\\n ro = object.PointNormalsColored(self.visu, points, normals, colors, point_size)\\n self.append_render_object(ro)\\n return ro\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a815ef66724a2f3193097462655171a\",\n \"score\": \"0.5107662\",\n \"text\": \"def face_normals(self):\\n if self.dim == 2:\\n nX = np.c_[np.ones(self.n_faces_x), np.zeros(self.n_faces_x)]\\n nY = np.c_[np.zeros(self.n_faces_y), np.ones(self.n_faces_y)]\\n return np.r_[nX, nY]\\n elif self.dim == 3:\\n nX = np.c_[\\n np.ones(self.n_faces_x),\\n np.zeros(self.n_faces_x),\\n np.zeros(self.n_faces_x),\\n ]\\n nY = np.c_[\\n np.zeros(self.n_faces_y),\\n np.ones(self.n_faces_y),\\n np.zeros(self.n_faces_y),\\n ]\\n nZ = np.c_[\\n np.zeros(self.n_faces_z),\\n np.zeros(self.n_faces_z),\\n np.ones(self.n_faces_z),\\n ]\\n return np.r_[nX, nY, nZ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1da45b1027318bca21de51ba484366c2\",\n \"score\": \"0.50925696\",\n \"text\": \"def normals_curve_2d(x):\\n b = x.shape[0]\\n n_x = x.shape[1]\\n\\n n = torch.zeros(x.shape)\\n n[:, :-1, :] = x[:, 1:, :] - x[:, :-1, :]\\n n[:, -1, :] = (x[:, -1, :] - x[:, -2, :])\\n n = n[:, :, [1, 0]]\\n n[:, :, 0] = -n[:, :, 0]\\n n = n / torch.norm(n, dim=2).view(b, n_x, 1)\\n n[:, 1:, :] = 0.5*(n[:, 1:, :] + n[:, :-1, :])\\n n = n / torch.norm(n, dim=2).view(b, n_x, 1)\\n return n\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f84a9cb9820b8a4dfb9ccc494ce8ebf\",\n \"score\": \"0.50755423\",\n \"text\": \"def compute_vertex_normals(vertices_zyx, faces, weight_by_face_area=False, face_normals=None):\\n if face_normals is None:\\n face_normals = compute_face_normals_numpy_chunked(vertices_zyx, faces, not weight_by_face_area)\\n\\n # numba is slightly faster for vertex normals, but not face normals\\n if _numba_available:\\n assert vertices_zyx.dtype == np.float32, \\\\\\n f\\\"Our numba implementation requires float32 vertices, not {vertices_zyx.dtype}\\\"\\n return compute_vertex_normals_numba(vertices_zyx, faces, weight_by_face_area, face_normals)\\n else:\\n return compute_vertex_normals_numpy(vertices_zyx, faces, weight_by_face_area, face_normals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9317e44f17621211211ea5e944f0ce8e\",\n \"score\": \"0.5061683\",\n \"text\": \"def SetNormal1InWorldSpace(self, newV1: 'itkCovariantVectorD2') -> \\\"void\\\":\\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint2_SetNormal1InWorldSpace(self, newV1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d938eb972e291e0d891254aae49ecf5\",\n \"score\": \"0.50433093\",\n \"text\": \"def depth_to_normals(depth: torch.Tensor, camera_matrix: torch.Tensor) -> torch.Tensor:\\n if not isinstance(depth, torch.Tensor):\\n raise TypeError(f\\\"Input depht type is not a torch.Tensor. Got {type(depth)}.\\\")\\n\\n if not len(depth.shape) == 4 and depth.shape[-3] == 1:\\n raise ValueError(f\\\"Input depth musth have a shape (B, 1, H, W). Got: {depth.shape}\\\")\\n\\n if not isinstance(camera_matrix, torch.Tensor):\\n raise TypeError(f\\\"Input camera_matrix type is not a torch.Tensor. \\\"\\n f\\\"Got {type(camera_matrix)}.\\\")\\n\\n if not len(camera_matrix.shape) == 3 and camera_matrix.shape[-2:] == (3, 3):\\n raise ValueError(f\\\"Input camera_matrix must have a shape (B, 3, 3). \\\"\\n f\\\"Got: {camera_matrix.shape}.\\\")\\n\\n # compute the 3d points from depth\\n xyz: torch.Tensor = depth_to_3d(depth, camera_matrix) # Bx3xHxW\\n\\n # compute the pointcloud spatial gradients\\n gradients: torch.Tensor = spatial_gradient(xyz) # Bx3x2xHxW\\n\\n # compute normals\\n a, b = gradients[:, :, 0], gradients[:, :, 1] # Bx3xHxW\\n\\n normals: torch.Tensor = torch.cross(a, b, dim=1) # Bx3xHxW\\n return F.normalize(normals, dim=1, p=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b2d9390e9115803ba435eeebd895eb9\",\n \"score\": \"0.50397146\",\n \"text\": \"def update_normalization(self, vector_obs: List[torch.Tensor]) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff06519aa2d8c915e9cdc809a12c47f6\",\n \"score\": \"0.50364494\",\n \"text\": \"def Normalize(toNormal):\\n distance = 0\\n for value in toNormal:\\n if value <= 0:\\n distance -= value\\n else:\\n distance += value\\n if distance > 0:\\n for index in range(len(toNormal)):\\n toNormal[index] = toNormal[index]/distance\\n return toNormal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83e032b44fc53fa39dc62b1a48884d77\",\n \"score\": \"0.5022148\",\n \"text\": \"def active_normals(self) -> Optional[pyvista_ndarray]:\\n if self.point_data.active_normals is not None:\\n return self.point_data.active_normals\\n return self.cell_data.active_normals\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eaef61a815453c455ed2b6ea6c11c28\",\n \"score\": \"0.5006172\",\n \"text\": \"def SetNormal2InObjectSpace(self, newV2: 'itkCovariantVectorD3') -> \\\"void\\\":\\n return _itkTubeSpatialObjectPointPython.itkTubeSpatialObjectPoint3_SetNormal2InObjectSpace(self, newV2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0c026b73fffb7db154a3ea261a017d6\",\n \"score\": \"0.4982009\",\n \"text\": \"def get_normal_direction(self, points, point_id):\\n va = points[self.shape_info.point_info[point_id].connect_from] - points[point_id]\\n vb = points[self.shape_info.point_info[point_id].connect_to] - points[point_id]\\n\\n magnitude = np.linalg.norm(va)\\n if magnitude > 1e-10:\\n va = va / magnitude\\n magnitude = np.linalg.norm(vb)\\n if magnitude > 1e-10:\\n vb = vb / magnitude\\n\\n direction = np.zeros(2)\\n direction[0] = - va[1] + vb[1]\\n direction[1] = va[0] - vb[0]\\n\\n if np.linalg.norm(direction) < 1e-10:\\n if np.linalg.norm(va) > 1e-10:\\n direction = - va\\n else:\\n direction = np.array([1, 0])\\n else:\\n direction = direction / np.linalg.norm(direction)\\n\\n return direction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98bf99e7a10f99392ebb15e8874c478f\",\n \"score\": \"0.49758577\",\n \"text\": \"def reflect(direction, normal):\\n return direction - (2 * direction.dot(normal) * normal)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85319b0acde5514328057bd2a2f90653\",\n \"score\": \"0.4968423\",\n \"text\": \"def displayAlbedosNormals(albedos, normals, s):\\n albedoIm = np.reshape(albedos,s)\\n normals += abs(np.min(normals))\\n normals /= np.max(normals)\\n\\n d1 = normals[0,:].reshape(s)\\n d2 = normals[1,:].reshape(s)\\n d3 = normals[2,:].reshape(s)\\n\\n normalIm = np.dstack((d1,d2))\\n normalIm = np.dstack((normalIm,d3))\\n\\n plt.imshow(albedoIm, cmap='gray')\\n plt.show()\\n\\n plt.imshow(normalIm, cmap='rainbow')\\n plt.show()\\n\\n return albedoIm, normalIm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36c39625e55da8a93bc542ef6a297cdf\",\n \"score\": \"0.49445993\",\n \"text\": \"def draw_facenormals(self,\\n faces: Optional[List[List[int]]] = None,\\n color: Optional[Union[str, RGBColor, List[RGBColor], Dict[int, RGBColor]]] = None,\\n scale: float = 1.0) -> List[bpy.types.Object]:\\n faces = faces or self.faces\\n face_color = colordict(color, faces, default=(0., 1., 1.))\\n lines = []\\n for face in faces:\\n a = centroid_points([self.vertex_xyz[vertex] for vertex in self.mesh.face_vertices(face)])\\n n = self.mesh.face_normal(face)\\n b = add_vectors(a, scale_vector(n, scale))\\n lines.append({\\n 'start': a,\\n 'end': b,\\n 'name': \\\"{}.facenormal.{}\\\".format(self.mesh.name, face),\\n 'color': face_color[face]\\n })\\n return compas_blender.draw_lines(lines, collection=self.facenormalcollection)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0015e1bd5f048d70ef592ff3a2c1178\",\n \"score\": \"0.49423563\",\n \"text\": \"def ComputeTangentAndNormals(self) -> \\\"bool\\\":\\n return _itkTubeSpatialObjectPython.itkTubeSpatialObject3_ComputeTangentAndNormals(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0f0e05336063797ae0a0f2edb45869\",\n \"score\": \"0.4924066\",\n \"text\": \"def normalize(self):\\n norm = self.norm()\\n if abs(norm) > 0.0001:\\n self.x /= norm\\n self.y /= norm\\n self.z /= norm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214c549e61c1dd93514a94856719ed9f\",\n \"score\": \"0.49198687\",\n \"text\": \"def normal(self, surface_point):\\n return (surface_point-self.center).normalize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d56bd191be42835a8252a456d021cd8\",\n \"score\": \"0.49172565\",\n \"text\": \"def raw_mesh(self,\\n points: np.ndarray,\\n normals: np.ndarray,\\n color: mt.Array = WHITE) \\\\\\n -> object.RawMesh:\\n ro = object.RawMesh(self.visu, points, normals, color)\\n self.append_render_object(ro)\\n return ro\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20b435cc14b6eb1316f0b2eba8ec5a69\",\n \"score\": \"0.48866308\",\n \"text\": \"def normal(self):\\n return self._normal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"182571ae5be91ca7ad3165331ff7098b\",\n \"score\": \"0.487705\",\n \"text\": \"def compute_face_normals_numpy_chunked(vertices_zyx, faces, normalize=False, chunksize=50_000):\\n normals = []\\n for i in range(0, len(faces), chunksize):\\n n = compute_face_normals_numpy(vertices_zyx, faces[i:i+chunksize], normalize)\\n normals.append(n)\\n return np.concatenate(normals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43c9e5054cffed399528171d2afa3a81\",\n \"score\": \"0.48769465\",\n \"text\": \"def PlaneFromNormal(origin, normal, xaxis=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0178ff39edb696e04c7ccbce6d642a5a\",\n \"score\": \"0.48691598\",\n \"text\": \"def test_normal(self):\\n env = self.notebook_locals[\\\"env\\\"]\\n f = self.notebook_locals[\\\"estimate_normal_by_nearest_pixels\\\"]\\n pC = self.notebook_locals[\\\"pC\\\"]\\n\\n student_sol = f(env.X_WC, pC, uv_step=10)\\n reference_sol = np.load(\\n FindResource(\\\"exercises/clutter/normal_solution.npy\\\")\\n )\\n\\n self.assertTrue(\\n len(student_sol) == len(reference_sol),\\n \\\"the number of the normals is incorrect\\\",\\n )\\n\\n for X, X_sol in zip(student_sol, reference_sol):\\n # check only points within the bounding box\\n pos_x, pos_y, pos_z = X_sol[0:3, 3]\\n if abs(pos_x) < 1.0 and abs(pos_y) < 1.0:\\n sol_v = X_sol[0:3, 2]\\n # check only the normal vector, not the entire frame\\n student_v = X.GetAsMatrix4()[0:3, 2]\\n # normalize the vector in case it is not normalized\\n student_v = student_v / np.linalg.norm(student_v)\\n test = np.dot(student_v, sol_v)\\n self.assertTrue(\\n test > 0.7,\\n \\\"error at the point: \\\\n {} \\\\\\n \\\\n normal is: {} \\\\\\n \\\\n solution normal is: {}\\\".format(\\n [pos_x, pos_y, pos_z], student_v, sol_v\\n ),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b57d65c4681cc72bb0927988752a716\",\n \"score\": \"0.48630533\",\n \"text\": \"def get_normal():\\n global normal\\n return normal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa6cd444e9ef1bd3f9ac10d7f3ed221\",\n \"score\": \"0.4843683\",\n \"text\": \"def glorot_normal(shape, scale=2.0, mode='fan_in', dtype='float32', **kwargs):\\n args = ArgHelper.parse(locals())\\n args['scale'] = float(scale)\\n args['mode'] = mode.lower()\\n trainable = args.pop('trainable') if 'trainable' in args else False\\n op_lib = init_ops_lib.GlorotNormal\\n if context.executing_eagerly():\\n return op_lib \\\\\\n .instantiate(\\n ndim=len(args['dims']),\\n scale=args['scale'],\\n mode=args['mode'],\\n dtype=dtype,\\n ).apply(args['dims'], trainable=trainable)\\n else:\\n return op_lib.blend(**args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"802f7f6318a5b043ddf5c63c0ba0caf1\",\n \"score\": \"0.48322183\",\n \"text\": \"def NormalAmps(*args):\\n # Getter\\n if len(args) == 0:\\n return CheckForError(lib.CktElement_Get_NormalAmps())\\n\\n # Setter\\n Value, = args\\n CheckForError(lib.CktElement_Set_NormalAmps(Value))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb934cf932f439de3ce34f2d04cfd222\",\n \"score\": \"0.48255867\",\n \"text\": \"def unit_outer_normals(self):\\n return self._unit_outer_normals()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41032dff8363a4fb6eefe67d13cb4419\",\n \"score\": \"0.4824227\",\n \"text\": \"def SetNormalComponents(self, p_int, p_int_1, p_int_2, p_int_3, p_int_4, p_int_5):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6596fd4fdf0e927a2ec209a248734c2d\",\n \"score\": \"0.4812352\",\n \"text\": \"def norm_mask(self: VecT, normal: 'Vec | FrozenVec') -> VecT:\\n norm = normal.norm()\\n return type(self)(norm * self.dot(norm))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"000d331fbd7fee7c08e3d7436c92365a\",\n \"score\": \"0.48105526\",\n \"text\": \"def normalize(self):\\n self.coeff /= self.norm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c074abf0754c95fcad7fe8f5ae6935b4\",\n \"score\": \"0.4806936\",\n \"text\": \"def polyNormalPerVertex(*args, **kwargs):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf72d5d3785adbba4ee295d3bbebf852\",\n \"score\": \"0.48026657\",\n \"text\": \"def ComputeTangentAndNormals(self) -> \\\"bool\\\":\\n return _itkTubeSpatialObjectPython.itkTubeSpatialObject2_ComputeTangentAndNormals(self)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1691,"cells":{"query_id":{"kind":"string","value":"f049ba5d88464a89918b015e73f5e21b"},"query":{"kind":"string","value":"Return the string representation of the model."},"positive_passages":{"kind":"list like","value":[{"docid":"0f283634439620e9ecd215d87804ec00","score":"0.0","text":"def to_str(self):\n return pprint.pformat(self.to_dict())","title":""}],"string":"[\n {\n \"docid\": \"0f283634439620e9ecd215d87804ec00\",\n \"score\": \"0.0\",\n \"text\": \"def to_str(self):\\n return pprint.pformat(self.to_dict())\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0be70d3e77e77041a9ddcf1c23fe823d","score":"0.848826","text":"def __str__(self):\n\t\treturn str(self.__model)","title":""},{"docid":"0fbfd148e937420716433d412dc3b6bf","score":"0.8422646","text":"def __str__(self):\n\n return str(self.model)","title":""},{"docid":"90ca23e8c8e1cb8bd8f270f97a73576c","score":"0.78718925","text":"def __repr__(self: BaseModel) -> str:\n repr_items = [f\"{key}={repr(value)}\" for key, value in self.to_dict().items()]\n return f\"{type(self).__name__}({repr_items})\"","title":""},{"docid":"3e2dc8ccbfa4e0f01c26fadaceb701ec","score":"0.7776631","text":"def __str__(self):\n return self.to_string(plain=True)","title":""},{"docid":"ebc31c7bd4f9f34007a3042b9f7f209c","score":"0.7770617","text":"def __str__(self):\n return self.dumps()","title":""},{"docid":"a19f036b41f8b90b4a2d92dbae171dcc","score":"0.7754224","text":"def __str__(self):\n return str(self.serialize())","title":""},{"docid":"3bf79d1f7c912bb697b66624caa67d96","score":"0.775232","text":"def __str__(self):\n return self._get_compact_representation()","title":""},{"docid":"6a252d160158f6bb3818cf69ba0a96fe","score":"0.77432114","text":"def __str__(self):\n obj_str = '-----------------------------------------------------------------------------------\\n'\n class_name = self.__class__.__name__\n obj_str += '\\t\\t'+class_name +' instance\\n'\n obj_str += '-----------------------------------------------------------------------------------\\n'\n \n # Print pickled attributes\n for att in self.__toprint:\n obj_str += att + ': ' + str(self.__dict__[att])\n obj_str += '\\n'\n \n obj_str += '\\n'\n obj_str += 'MODEL PARAMETERS:\\n'\n obj_str += str(self.params)\n obj_str += '\\n'\n \n obj_str += '-----------------------------------------------------------------------------------'\n \n return obj_str","title":""},{"docid":"7a703d001e7a087bb79c489f7897c480","score":"0.77245307","text":"def __str__(self) -> str:\n return stringify_object(self)","title":""},{"docid":"c1cd4f716a4bf5e723c274bbaf6ab603","score":"0.7722586","text":"def to_string(self):\n return self.__str__()","title":""},{"docid":"a8c315336607f5f84c329a7040cc7a53","score":"0.77128124","text":"def __repr__(self):\n return '<{}>'.format(self.model_type)","title":""},{"docid":"b9005c9aa5a23b6f5a218792dcaff4ce","score":"0.77124935","text":"def __str__(self):\n return self.to_string()","title":""},{"docid":"b9005c9aa5a23b6f5a218792dcaff4ce","score":"0.77124935","text":"def __str__(self):\n return self.to_string()","title":""},{"docid":"b9005c9aa5a23b6f5a218792dcaff4ce","score":"0.77124935","text":"def __str__(self):\n return self.to_string()","title":""},{"docid":"6c5beda380ebcaf3ae2e5b29d680b32e","score":"0.7692923","text":"def __str__(self):#diplays simple representation of a model\n return self.text","title":""},{"docid":"64efbb37fe5045e209c1d2dc7eb142d6","score":"0.76923835","text":"def to_string(self):\r\n return self.__str__()","title":""},{"docid":"f65796c63fb017a482e427b2476c46b4","score":"0.76908946","text":"def to_str(self):\n return self.__str__()","title":""},{"docid":"fe934f0a69f78d0f08666635db8d0c72","score":"0.76837736","text":"def __str__(self) -> str:\n return self.to_string()","title":""},{"docid":"446f186a9708b5d2f43a3e381f2a498a","score":"0.7672617","text":"def __str__(self):\n return self.tostring()","title":""},{"docid":"2121fff839c675d840e80f475ff9d747","score":"0.7658412","text":"def __str__(self):\n return self.to_str()","title":""},{"docid":"e1465884dc89dbbea4e3e89a7ef3d636","score":"0.76560986","text":"def __str__(self):\n return self.object_repr","title":""},{"docid":"fe86f4613c23ca1bb447edfb142d9149","score":"0.7653324","text":"def __str__(self):\n return str(self.to_dict())","title":""},{"docid":"fe86f4613c23ca1bb447edfb142d9149","score":"0.7653324","text":"def __str__(self):\n return str(self.to_dict())","title":""},{"docid":"ca0a3caf00d984a0e2f029c7ef3e26f0","score":"0.7651397","text":"def __str__(self):\n return self.as_string()","title":""},{"docid":"ca0a3caf00d984a0e2f029c7ef3e26f0","score":"0.7651397","text":"def __str__(self):\n return self.as_string()","title":""},{"docid":"20c18c75b69c88420d8c2be583d58fd1","score":"0.7626825","text":"def __str__(self):\n\n return self.toString()","title":""},{"docid":"d3ef754d93725ddba397db755cb86424","score":"0.7617941","text":"def to_string(self) -> str:\n return self.__str__()","title":""},{"docid":"a2f39b7424a3e93c998e9a3659290fb0","score":"0.76178896","text":"def dumps(self):\n return self.local_model.dumps()","title":""},{"docid":"bb9d5717ad09991e25bbc9269b510740","score":"0.7617043","text":"def __str__(self):\n\n attr_str = basic_backend.get_str(self)\n return attr_str","title":""},{"docid":"9f7a1fe277348ae6c9e9fb85eff6d81c","score":"0.76131314","text":"def to_string(self):\n return str(self)","title":""},{"docid":"9f7a1fe277348ae6c9e9fb85eff6d81c","score":"0.76131314","text":"def to_string(self):\n return str(self)","title":""},{"docid":"e25b211736a3d7b028e95df4928f3bc0","score":"0.7612115","text":"def to_string(self):\n return stringify(self, type(self).fields)","title":""},{"docid":"e25b211736a3d7b028e95df4928f3bc0","score":"0.7612115","text":"def to_string(self):\n return stringify(self, type(self).fields)","title":""},{"docid":"2f0b1e4c2ddb7d8b9fb8b1d14e86858c","score":"0.76100063","text":"def to_str(self):\n return str(self)","title":""},{"docid":"874c5576c99ef409975d1f18a7c5b78e","score":"0.75998664","text":"def __str__(self):\n return str(self.__dict__)","title":""},{"docid":"874c5576c99ef409975d1f18a7c5b78e","score":"0.75998664","text":"def __str__(self):\n return str(self.__dict__)","title":""},{"docid":"874c5576c99ef409975d1f18a7c5b78e","score":"0.75998664","text":"def __str__(self):\n return str(self.__dict__)","title":""},{"docid":"b897dbf5ce813ad9d6a05bed7f6eac32","score":"0.7591581","text":"def __str__(self):\n return self.repr()","title":""},{"docid":"7b3fdb305fac51aaacede6984e423aa5","score":"0.7576867","text":"def __str__(self):\n return str(self.__unicode__())","title":""},{"docid":"7b3fdb305fac51aaacede6984e423aa5","score":"0.7576867","text":"def __str__(self):\n return str(self.__unicode__())","title":""},{"docid":"62ea407ca745fa72130ab3af96017128","score":"0.7568822","text":"def __str__(self):\n return str(next(iter(self.model_dict.values()))).split(\"(\")[0]","title":""},{"docid":"34b5d8c159c6b7568788e0ffe598ffa4","score":"0.7567269","text":"def __str__(self):\n\n # This is just going to be the same as the representation.\n return self.__repr__()","title":""},{"docid":"be0e3d93493ae7474f704c8d6655117e","score":"0.7566711","text":"def __str__(self):\n return self.dumpattrs()","title":""},{"docid":"f4caafd301921be1946c707549a37b59","score":"0.7562034","text":"def __repr__(self):\r\n\r\n s = \"\\nModel name: \" + str(self.name) + \"\\n\"\r\n\r\n s += \" n. of words: \" + str(len(self.words)) + \"\\n\"\r\n\r\n s += \" n. of word lengths: \" + str(len(self.wordlengths)) + \"\\n\"\r\n\r\n s += \" n. of sentence lengths: \" + str(len(self.sentencelengths)) + \"\\n\"\r\n\r\n s += \" n. of stems: \" + str(len(self.stems)) + \"\\n\"\r\n\r\n # you will likely want another line for your custom text-feature!\r\n\r\n return s","title":""},{"docid":"38b728d6d72a89b8b93b90d5f2ebb8b3","score":"0.7554271","text":"def to_string(self) -> str:\n return str(self)","title":""},{"docid":"5c65cef57f6617d22670a31b3ee2869c","score":"0.7552576","text":"def __str__(self):\n return self.toString()","title":""},{"docid":"5c65cef57f6617d22670a31b3ee2869c","score":"0.7552576","text":"def __str__(self):\n return self.toString()","title":""},{"docid":"1af3f46fd260e9398115347c73639d7e","score":"0.75460976","text":"def __str__(self) -> str:\n return str(self.__dict__)","title":""},{"docid":"1af3f46fd260e9398115347c73639d7e","score":"0.75460976","text":"def __str__(self) -> str:\n return str(self.__dict__)","title":""},{"docid":"2e0085a4fa34e52f98be099dbb4a3114","score":"0.7544857","text":"def __str__(self):\n return str(self.as_dict())","title":""},{"docid":"011c22541b6815f0ab74b57643ac94ad","score":"0.7525316","text":"def __str__(self):\n\n return self.__repr__()","title":""},{"docid":"f74c932832419ef7a1ba4a3397f831eb","score":"0.749556","text":"def __str__(self):\n return str(self.__repr__())","title":""},{"docid":"d9dda7dd05edcb3aaed5edcd01549464","score":"0.7490514","text":"def to_str(self):\n pass","title":""},{"docid":"1f2f9fe4276249d952faf4aeb42e4e1b","score":"0.7488841","text":"def serialize(self):\n return repr(self)","title":""},{"docid":"a34e73564cef9a7d648a1160f9648590","score":"0.74754584","text":"def __str__(self):\n\t\treturn str(self.__dict__)","title":""},{"docid":"5f4f6cb41976132a9a08fa5c332893b8","score":"0.7463021","text":"def to_string(self):\n pass","title":""},{"docid":"5f4f6cb41976132a9a08fa5c332893b8","score":"0.7463021","text":"def to_string(self):\n pass","title":""},{"docid":"106526c192039ff61551aeb051a004d0","score":"0.7461948","text":"def __str__(self):\n return self.pretty()","title":""},{"docid":"b8341e4736352ff8755d5bc531ff1fd3","score":"0.7459795","text":"def __str__(self):\n return self.text_repr()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"cc56604d6bfa131dee74369a2ec636f8","score":"0.7457378","text":"def __str__(self):\n return self.__repr__()","title":""},{"docid":"5ad04151299c9d6b05490b25a8059aa7","score":"0.7456706","text":"def __repr__(self):\n\t\ts = \"\\nModel name: \" + str(self.name) + \"\\n\"\n\t\ts += \" n. of words: \" + str(len(self.words)) + \"\\n\"\n\t\ts += \" n. of word lengths: \" + str(len(self.wordlengths)) + \"\\n\"\n\t\ts += \" n. of sentence lengths: \" + str(len(self.sentencelengths)) + \"\\n\"\n\t\ts += \" n. of stems: \" + str(len(self.stems)) + \"\\n\"\n\t\ts += \" n. of dialogue lengths: \" + str(len(self.dialogue)) + \"\\n\"\n\t\t# you will likely want another line for your custom text-feature!\n\t\treturn s","title":""},{"docid":"2412e681de152a360c302f68087b2a49","score":"0.7453264","text":"def __str__(self):\n return str(self.obj)","title":""},{"docid":"8fe2a2240008f015e0eefbe90ecb2292","score":"0.74496144","text":"def __str__(self):\n return self.to_message().as_string()","title":""},{"docid":"8fe2a2240008f015e0eefbe90ecb2292","score":"0.74496144","text":"def __str__(self):\n return self.to_message().as_string()","title":""},{"docid":"9af6d1c158f80cd438c4eee67fd6ac70","score":"0.7431591","text":"def to_string(self) -> str:\n raise NotImplementedError","title":""},{"docid":"2b438527f63eb0c81b22b74396f12f06","score":"0.74312055","text":"def __repr__(self) -> str:\n return self.to_string()","title":""},{"docid":"5012398d8e11a1245fe62420f868e810","score":"0.7429622","text":"def to_string(self):\n raise NotImplementedError","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""},{"docid":"bff2052ac2ba06621cc51030f2133214","score":"0.7427959","text":"def __repr__(self):\n return self.to_str()","title":""}],"string":"[\n {\n \"docid\": \"0be70d3e77e77041a9ddcf1c23fe823d\",\n \"score\": \"0.848826\",\n \"text\": \"def __str__(self):\\n\\t\\treturn str(self.__model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fbfd148e937420716433d412dc3b6bf\",\n \"score\": \"0.8422646\",\n \"text\": \"def __str__(self):\\n\\n return str(self.model)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90ca23e8c8e1cb8bd8f270f97a73576c\",\n \"score\": \"0.78718925\",\n \"text\": \"def __repr__(self: BaseModel) -> str:\\n repr_items = [f\\\"{key}={repr(value)}\\\" for key, value in self.to_dict().items()]\\n return f\\\"{type(self).__name__}({repr_items})\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e2dc8ccbfa4e0f01c26fadaceb701ec\",\n \"score\": \"0.7776631\",\n \"text\": \"def __str__(self):\\n return self.to_string(plain=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebc31c7bd4f9f34007a3042b9f7f209c\",\n \"score\": \"0.7770617\",\n \"text\": \"def __str__(self):\\n return self.dumps()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a19f036b41f8b90b4a2d92dbae171dcc\",\n \"score\": \"0.7754224\",\n \"text\": \"def __str__(self):\\n return str(self.serialize())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf79d1f7c912bb697b66624caa67d96\",\n \"score\": \"0.775232\",\n \"text\": \"def __str__(self):\\n return self._get_compact_representation()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a252d160158f6bb3818cf69ba0a96fe\",\n \"score\": \"0.77432114\",\n \"text\": \"def __str__(self):\\n obj_str = '-----------------------------------------------------------------------------------\\\\n'\\n class_name = self.__class__.__name__\\n obj_str += '\\\\t\\\\t'+class_name +' instance\\\\n'\\n obj_str += '-----------------------------------------------------------------------------------\\\\n'\\n \\n # Print pickled attributes\\n for att in self.__toprint:\\n obj_str += att + ': ' + str(self.__dict__[att])\\n obj_str += '\\\\n'\\n \\n obj_str += '\\\\n'\\n obj_str += 'MODEL PARAMETERS:\\\\n'\\n obj_str += str(self.params)\\n obj_str += '\\\\n'\\n \\n obj_str += '-----------------------------------------------------------------------------------'\\n \\n return obj_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a703d001e7a087bb79c489f7897c480\",\n \"score\": \"0.77245307\",\n \"text\": \"def __str__(self) -> str:\\n return stringify_object(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1cd4f716a4bf5e723c274bbaf6ab603\",\n \"score\": \"0.7722586\",\n \"text\": \"def to_string(self):\\n return self.__str__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8c315336607f5f84c329a7040cc7a53\",\n \"score\": \"0.77128124\",\n \"text\": \"def __repr__(self):\\n return '<{}>'.format(self.model_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9005c9aa5a23b6f5a218792dcaff4ce\",\n \"score\": \"0.77124935\",\n \"text\": \"def __str__(self):\\n return self.to_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9005c9aa5a23b6f5a218792dcaff4ce\",\n \"score\": \"0.77124935\",\n \"text\": \"def __str__(self):\\n return self.to_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9005c9aa5a23b6f5a218792dcaff4ce\",\n \"score\": \"0.77124935\",\n \"text\": \"def __str__(self):\\n return self.to_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5beda380ebcaf3ae2e5b29d680b32e\",\n \"score\": \"0.7692923\",\n \"text\": \"def __str__(self):#diplays simple representation of a model\\n return self.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64efbb37fe5045e209c1d2dc7eb142d6\",\n \"score\": \"0.76923835\",\n \"text\": \"def to_string(self):\\r\\n return self.__str__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f65796c63fb017a482e427b2476c46b4\",\n \"score\": \"0.76908946\",\n \"text\": \"def to_str(self):\\n return self.__str__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe934f0a69f78d0f08666635db8d0c72\",\n \"score\": \"0.76837736\",\n \"text\": \"def __str__(self) -> str:\\n return self.to_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"446f186a9708b5d2f43a3e381f2a498a\",\n \"score\": \"0.7672617\",\n \"text\": \"def __str__(self):\\n return self.tostring()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2121fff839c675d840e80f475ff9d747\",\n \"score\": \"0.7658412\",\n \"text\": \"def __str__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1465884dc89dbbea4e3e89a7ef3d636\",\n \"score\": \"0.76560986\",\n \"text\": \"def __str__(self):\\n return self.object_repr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe86f4613c23ca1bb447edfb142d9149\",\n \"score\": \"0.7653324\",\n \"text\": \"def __str__(self):\\n return str(self.to_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe86f4613c23ca1bb447edfb142d9149\",\n \"score\": \"0.7653324\",\n \"text\": \"def __str__(self):\\n return str(self.to_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca0a3caf00d984a0e2f029c7ef3e26f0\",\n \"score\": \"0.7651397\",\n \"text\": \"def __str__(self):\\n return self.as_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca0a3caf00d984a0e2f029c7ef3e26f0\",\n \"score\": \"0.7651397\",\n \"text\": \"def __str__(self):\\n return self.as_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20c18c75b69c88420d8c2be583d58fd1\",\n \"score\": \"0.7626825\",\n \"text\": \"def __str__(self):\\n\\n return self.toString()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ef754d93725ddba397db755cb86424\",\n \"score\": \"0.7617941\",\n \"text\": \"def to_string(self) -> str:\\n return self.__str__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2f39b7424a3e93c998e9a3659290fb0\",\n \"score\": \"0.76178896\",\n \"text\": \"def dumps(self):\\n return self.local_model.dumps()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb9d5717ad09991e25bbc9269b510740\",\n \"score\": \"0.7617043\",\n \"text\": \"def __str__(self):\\n\\n attr_str = basic_backend.get_str(self)\\n return attr_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f7a1fe277348ae6c9e9fb85eff6d81c\",\n \"score\": \"0.76131314\",\n \"text\": \"def to_string(self):\\n return str(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f7a1fe277348ae6c9e9fb85eff6d81c\",\n \"score\": \"0.76131314\",\n \"text\": \"def to_string(self):\\n return str(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e25b211736a3d7b028e95df4928f3bc0\",\n \"score\": \"0.7612115\",\n \"text\": \"def to_string(self):\\n return stringify(self, type(self).fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e25b211736a3d7b028e95df4928f3bc0\",\n \"score\": \"0.7612115\",\n \"text\": \"def to_string(self):\\n return stringify(self, type(self).fields)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f0b1e4c2ddb7d8b9fb8b1d14e86858c\",\n \"score\": \"0.76100063\",\n \"text\": \"def to_str(self):\\n return str(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874c5576c99ef409975d1f18a7c5b78e\",\n \"score\": \"0.75998664\",\n \"text\": \"def __str__(self):\\n return str(self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874c5576c99ef409975d1f18a7c5b78e\",\n \"score\": \"0.75998664\",\n \"text\": \"def __str__(self):\\n return str(self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874c5576c99ef409975d1f18a7c5b78e\",\n \"score\": \"0.75998664\",\n \"text\": \"def __str__(self):\\n return str(self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b897dbf5ce813ad9d6a05bed7f6eac32\",\n \"score\": \"0.7591581\",\n \"text\": \"def __str__(self):\\n return self.repr()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3fdb305fac51aaacede6984e423aa5\",\n \"score\": \"0.7576867\",\n \"text\": \"def __str__(self):\\n return str(self.__unicode__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3fdb305fac51aaacede6984e423aa5\",\n \"score\": \"0.7576867\",\n \"text\": \"def __str__(self):\\n return str(self.__unicode__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62ea407ca745fa72130ab3af96017128\",\n \"score\": \"0.7568822\",\n \"text\": \"def __str__(self):\\n return str(next(iter(self.model_dict.values()))).split(\\\"(\\\")[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34b5d8c159c6b7568788e0ffe598ffa4\",\n \"score\": \"0.7567269\",\n \"text\": \"def __str__(self):\\n\\n # This is just going to be the same as the representation.\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be0e3d93493ae7474f704c8d6655117e\",\n \"score\": \"0.7566711\",\n \"text\": \"def __str__(self):\\n return self.dumpattrs()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4caafd301921be1946c707549a37b59\",\n \"score\": \"0.7562034\",\n \"text\": \"def __repr__(self):\\r\\n\\r\\n s = \\\"\\\\nModel name: \\\" + str(self.name) + \\\"\\\\n\\\"\\r\\n\\r\\n s += \\\" n. of words: \\\" + str(len(self.words)) + \\\"\\\\n\\\"\\r\\n\\r\\n s += \\\" n. of word lengths: \\\" + str(len(self.wordlengths)) + \\\"\\\\n\\\"\\r\\n\\r\\n s += \\\" n. of sentence lengths: \\\" + str(len(self.sentencelengths)) + \\\"\\\\n\\\"\\r\\n\\r\\n s += \\\" n. of stems: \\\" + str(len(self.stems)) + \\\"\\\\n\\\"\\r\\n\\r\\n # you will likely want another line for your custom text-feature!\\r\\n\\r\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38b728d6d72a89b8b93b90d5f2ebb8b3\",\n \"score\": \"0.7554271\",\n \"text\": \"def to_string(self) -> str:\\n return str(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c65cef57f6617d22670a31b3ee2869c\",\n \"score\": \"0.7552576\",\n \"text\": \"def __str__(self):\\n return self.toString()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c65cef57f6617d22670a31b3ee2869c\",\n \"score\": \"0.7552576\",\n \"text\": \"def __str__(self):\\n return self.toString()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af3f46fd260e9398115347c73639d7e\",\n \"score\": \"0.75460976\",\n \"text\": \"def __str__(self) -> str:\\n return str(self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af3f46fd260e9398115347c73639d7e\",\n \"score\": \"0.75460976\",\n \"text\": \"def __str__(self) -> str:\\n return str(self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e0085a4fa34e52f98be099dbb4a3114\",\n \"score\": \"0.7544857\",\n \"text\": \"def __str__(self):\\n return str(self.as_dict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"011c22541b6815f0ab74b57643ac94ad\",\n \"score\": \"0.7525316\",\n \"text\": \"def __str__(self):\\n\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f74c932832419ef7a1ba4a3397f831eb\",\n \"score\": \"0.749556\",\n \"text\": \"def __str__(self):\\n return str(self.__repr__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9dda7dd05edcb3aaed5edcd01549464\",\n \"score\": \"0.7490514\",\n \"text\": \"def to_str(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f2f9fe4276249d952faf4aeb42e4e1b\",\n \"score\": \"0.7488841\",\n \"text\": \"def serialize(self):\\n return repr(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a34e73564cef9a7d648a1160f9648590\",\n \"score\": \"0.74754584\",\n \"text\": \"def __str__(self):\\n\\t\\treturn str(self.__dict__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f4f6cb41976132a9a08fa5c332893b8\",\n \"score\": \"0.7463021\",\n \"text\": \"def to_string(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f4f6cb41976132a9a08fa5c332893b8\",\n \"score\": \"0.7463021\",\n \"text\": \"def to_string(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"106526c192039ff61551aeb051a004d0\",\n \"score\": \"0.7461948\",\n \"text\": \"def __str__(self):\\n return self.pretty()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8341e4736352ff8755d5bc531ff1fd3\",\n \"score\": \"0.7459795\",\n \"text\": \"def __str__(self):\\n return self.text_repr()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc56604d6bfa131dee74369a2ec636f8\",\n \"score\": \"0.7457378\",\n \"text\": \"def __str__(self):\\n return self.__repr__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad04151299c9d6b05490b25a8059aa7\",\n \"score\": \"0.7456706\",\n \"text\": \"def __repr__(self):\\n\\t\\ts = \\\"\\\\nModel name: \\\" + str(self.name) + \\\"\\\\n\\\"\\n\\t\\ts += \\\" n. of words: \\\" + str(len(self.words)) + \\\"\\\\n\\\"\\n\\t\\ts += \\\" n. of word lengths: \\\" + str(len(self.wordlengths)) + \\\"\\\\n\\\"\\n\\t\\ts += \\\" n. of sentence lengths: \\\" + str(len(self.sentencelengths)) + \\\"\\\\n\\\"\\n\\t\\ts += \\\" n. of stems: \\\" + str(len(self.stems)) + \\\"\\\\n\\\"\\n\\t\\ts += \\\" n. of dialogue lengths: \\\" + str(len(self.dialogue)) + \\\"\\\\n\\\"\\n\\t\\t# you will likely want another line for your custom text-feature!\\n\\t\\treturn s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2412e681de152a360c302f68087b2a49\",\n \"score\": \"0.7453264\",\n \"text\": \"def __str__(self):\\n return str(self.obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe2a2240008f015e0eefbe90ecb2292\",\n \"score\": \"0.74496144\",\n \"text\": \"def __str__(self):\\n return self.to_message().as_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe2a2240008f015e0eefbe90ecb2292\",\n \"score\": \"0.74496144\",\n \"text\": \"def __str__(self):\\n return self.to_message().as_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9af6d1c158f80cd438c4eee67fd6ac70\",\n \"score\": \"0.7431591\",\n \"text\": \"def to_string(self) -> str:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b438527f63eb0c81b22b74396f12f06\",\n \"score\": \"0.74312055\",\n \"text\": \"def __repr__(self) -> str:\\n return self.to_string()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5012398d8e11a1245fe62420f868e810\",\n \"score\": \"0.7429622\",\n \"text\": \"def to_string(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bff2052ac2ba06621cc51030f2133214\",\n \"score\": \"0.7427959\",\n \"text\": \"def __repr__(self):\\n return self.to_str()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1692,"cells":{"query_id":{"kind":"string","value":"83be23f920b87d1cbb6264dd415dccd4"},"query":{"kind":"string","value":"Return keyword arguments bound to given logger"},"positive_passages":{"kind":"list like","value":[{"docid":"ec185db085a952fee750b32b67b30585","score":"0.75169855","text":"def bound_log_kwargs(log):\n f = log.msg\n kwargs_list = []\n while True:\n try:\n kwargs_list.append(f.keywords)\n except AttributeError:\n break\n else:\n f = f.func\n # combine them in order they were bound\n kwargs = {}\n for kwa in reversed(kwargs_list):\n kwargs.update(kwa)\n return kwargs","title":""}],"string":"[\n {\n \"docid\": \"ec185db085a952fee750b32b67b30585\",\n \"score\": \"0.75169855\",\n \"text\": \"def bound_log_kwargs(log):\\n f = log.msg\\n kwargs_list = []\\n while True:\\n try:\\n kwargs_list.append(f.keywords)\\n except AttributeError:\\n break\\n else:\\n f = f.func\\n # combine them in order they were bound\\n kwargs = {}\\n for kwa in reversed(kwargs_list):\\n kwargs.update(kwa)\\n return kwargs\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2e798378f8e489aba849418d7a681020","score":"0.6344701","text":"def parseKwargs(cls, config_stats, logger=None):\n kwargs = {}\n kwargs.update(config_stats)\n kwargs.pop('type',None)\n return kwargs","title":""},{"docid":"09192833047de487d28bfa04681dd7ad","score":"0.6314909","text":"def log_record(**kwargs) -> dict:\n return kwargs","title":""},{"docid":"bcae69d3667fd8edb74049655382f508","score":"0.61579067","text":"def set_logger(*args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"ff5cdcb7d8582c6ca823bd16389c61f6","score":"0.59713715","text":"def kw_args(self):\n return self[\"kw_args\"]","title":""},{"docid":"504e7247608ae3733b45191cbec5adfc","score":"0.5923924","text":"def _kwargs_to_log_raw(self, log, sensitive_=False, secrets_=None, **kwargs):\n if kwargs is None:\n return\n\n for key, value in iteritems(kwargs):\n val = self._valueize(value)\n if sensitive_:\n val = self._sec_fix(val, secrets=secrets_)\n\n log[self._valueize_key(key)] = val","title":""},{"docid":"80c42ea995c0360eee9cb0bea9496987","score":"0.57750446","text":"def log_params(self, params: Mapping[str, Any], prefix: Optional[str] = None) -> None:","title":""},{"docid":"535a839594b4871e2e163e9946acffbd","score":"0.57079977","text":"def get_param_logger() -> Logger:\n\n return get_default_logger('Parameter')","title":""},{"docid":"69c7ad5a7fc42d9983a45904af1a93af","score":"0.56830215","text":"def _args_to_log_raw(self, log, sensitive_=False, secrets_=None, *args):\n if args is None:\n return\n\n for idx, arg in enumerate(args):\n val = self._valueize(arg)\n if sensitive_:\n val = self._sec_fix(val, secrets=secrets_)\n\n log['arg%d' % idx] = val","title":""},{"docid":"3d026c33a0dcdf7fee5829bdee19b711","score":"0.5661435","text":"def test_merge_arg_kwarg(logger_mock, warnings_mock):\n lambda_ = lambda a, b='abc': (a, b)\n parameters = list(inspect.signature(lambda_).parameters.values())\n\n # Prefer `args` over `other_kwargs`\n merged = _merge_args(parameters, ['a', 'abc'], {}, {'b': 'xyz'}, {}, '')\n assert merged == (['a', 'abc'], {})\n assert logger_mock.warning.call_count == 0\n assert warnings_mock.warn.call_count == 1\n warnings_mock.reset_mock()\n\n # Prefer `kwargs` over `other_kwargs`\n merged = _merge_args(parameters, ['a'], {'b': 'abc'}, {'b': 'xyz'}, {}, '')\n assert merged == (['a'], {'b': 'abc'})\n assert logger_mock.warning.call_count == 0\n assert warnings_mock.warn.call_count == 1\n warnings_mock.reset_mock()\n\n # Prefer `other_kwargs` over default argument\n merged = _merge_args(parameters, ['a'], {}, {'b': 'xyz'}, {'b': 'abc'}, '')\n assert merged == (['a'], {'b': 'xyz'})\n warnings_mock.warn.assert_not_called()\n warnings_mock.warn.assert_not_called()\n\n # Prefer default argument over nothing\n merged = _merge_args(parameters, ['a'], {}, {}, {'b': 'abc'}, '')\n assert merged == (['a'], {'b': 'abc'})\n warnings_mock.warn.assert_not_called()\n warnings_mock.warn.assert_not_called()","title":""},{"docid":"5b018becff972e15fb7c15ea0438e732","score":"0.56051356","text":"def test_merge_kwarg_variable(logger_mock, warnings_mock):\n lambda_ = lambda a, b, **kwargs: (a, b, kwargs)\n parameters = list(inspect.signature(lambda_).parameters.values())\n\n merged = _merge_args(parameters, ['a', 'b'], {}, {'b': 'xyz'}, {}, '')\n assert merged == (['a', 'b'], {})\n assert logger_mock.warning.call_count == 0\n assert warnings_mock.warn.call_count == 1\n warnings_mock.reset_mock()\n\n merged = _merge_args(parameters, ['a'], {}, {'b': 'xyz'}, {}, '')\n assert merged == (['a'], {'b': 'xyz'})\n logger_mock.warning.assert_not_called()\n warnings_mock.warn.assert_not_called()\n\n merged = _merge_args(parameters, ['a'], {}, {'b': 'xyz', 'c': 'abc'}, {}, '')\n assert merged == (['a'], {'b': 'xyz', 'c': 'abc'})\n logger_mock.warning.assert_not_called()\n warnings_mock.warn.assert_not_called()","title":""},{"docid":"b9321c14e8b073a47cefd731ff91a76e","score":"0.55887884","text":"def extract_extra_args_from_record(record: logging.LogRecord):\n default_attrs = logging.LogRecord(\"\", 0, \"\", 0, None, None, None).__dict__.keys()\n extra_keys = set(record.__dict__.keys()) - default_attrs\n return {k: str(getattr(record, k)) for k in extra_keys if hasattr(record, k)}","title":""},{"docid":"fb452acab41376fca40d71348de87b90","score":"0.553083","text":"def __init__(self, params) :\n for k, v in params.items() :\n self.__dict__[k] = v\n self.logger = logging.getLogger(self.__class__.__name__)","title":""},{"docid":"bbbba64191f54f04cbe68507f0170aa7","score":"0.5522225","text":"def get_kwargs(self, keyword=WHERE_KWARG, **kwargs):\n kwargs[keyword] = self._output()\n return kwargs","title":""},{"docid":"d8c411137076d1a7cae98d830a25d965","score":"0.5513681","text":"def _arg_values(self, **kwargs):\n if self.name in kwargs:\n new = kwargs.pop(self.name)\n if isinstance(new, Constant):\n return new._arg_defaults(alias=self)\n else:\n return {self.name: new}\n else:\n return self._arg_defaults()","title":""},{"docid":"53dd8ba2e0f3de5644be837206fcf653","score":"0.5513545","text":"def GetKeywordArgs(prop, include_default=True):\n kwargs = {\n 'required': prop._required,\n 'repeated': prop._repeated,\n }\n if include_default and hasattr(prop, '_default'):\n kwargs['default'] = prop._default\n if hasattr(prop, '_variant'):\n kwargs['variant'] = prop._variant\n return kwargs","title":""},{"docid":"b96aeaa409db94f9e32f0cd6444c0e9d","score":"0.5510769","text":"def test_configurable__override_with_kwarg(logger_mock, warnings_mock):\n\n @configurable\n def configured(arg=HParam()):\n return arg\n\n add_config({configured: HParams()})\n\n logger_mock.reset_mock()\n warnings_mock.reset_mock()\n configured(arg='a')\n assert logger_mock.warning.call_count == 0\n assert warnings_mock.warn.call_count == 1","title":""},{"docid":"4c43fe014187cc94143499a15414895d","score":"0.55036783","text":"def _loggerPrep(self, level, args):\n if not self._isNum(level): # fallback\n if args:\n args=list(args)\n if level is not None: args.insert(0, level)\n else: args=[level] if level is not None else []\n level=None\n if not args: args=[]\n elif not self._isArray(args): args=list(args)\n return level, args","title":""},{"docid":"e0ab5a98df96dec8fc917fb1b9bba7aa","score":"0.5493007","text":"def inject_kwargs(**mapping):\n def _get_kwargs(f):\n ARG_TYPES = [\n Parameter.POSITIONAL_ONLY,\n Parameter.POSITIONAL_OR_KEYWORD,\n Parameter.KEYWORD_ONLY,\n ]\n args = [n for n, p in inspect.signature(f).parameters.items() if p.kind in ARG_TYPES]\n kwargs = [n for n, p in inspect.signature(f).parameters.items() if p.kind in ARG_TYPES and p.default != inspect._empty]\n return args, kwargs\n\n def wrapper(fun):\n argnames, kwargnames = _get_kwargs(fun)\n def wrapped(*args, **kwargs):\n namedargs = dict(list(zip(argnames[:len(args)], args)))\n config_dict = _config.__dict__ if _config is not None else {}\n for name in kwargnames:\n if name not in kwargs and name not in namedargs:\n if name == \"config\": # Legacy mode (inject the config object)\n kwargs[name] = _config\n else: # New mode (overwrites kwargs with config values if available)\n config_name = mapping[name] if name in mapping else name\n if config_name in config_dict:\n kwargs[name] = config_dict[config_name]\n return fun(*args, **kwargs)\n return wrapped\n return wrapper","title":""},{"docid":"23b62baf530a572b802b8c3269cb0e82","score":"0.54908353","text":"def log(*args, **kwargs):\n \n pass","title":""},{"docid":"bd625f956a782fc199592f49fabf261a","score":"0.5448175","text":"def pass_kwargs(self, **kwargs):\n\n\t\t# log given **kwargs\n\t\tprint 'kwargs is = ', kwargs\n\t\tif kwargs != {}:\n\t\t\tprint \"This keyword was called with this **kwargs:\"\n\t\t\tfor key, value in kwargs.iteritems():\n\t\t\t\tprint \"- kwargs: %s = %s\" % (key, value)\n\t\telse:\n\t\t\tprint 'NO **kwargs passed.'","title":""},{"docid":"3b4edb4eb9c0903f5a83aa5e89acd277","score":"0.5434425","text":"def get_kwargs(self) -> dict:\n raise NotImplementedError()","title":""},{"docid":"40ae43809a2882cacbdbb29b28e6712e","score":"0.5423856","text":"def parse_known_args(self, args=None, namespace=None):\n global crit, error, warn, info, debug, log\n # bring in the global variables containing the logger's methods\n def _export_namespace_dict(func):\n def _get_locals():\n try:\n locals_result = {}\n frame = inspect.currentframe()\n frame = frame.f_back\n # from _get_locals() to _export_namespace_dict()\n frame = frame.f_back\n # from _export_namespace_dict() to __wrap_log_fnc()\n frame = frame.f_back\n # from __wrap_log_fnc() to the function where crit or error was actually called.\n locals_result = frame.f_locals\n finally:\n del frame\n return locals_result\n def __wrapped_fnc(*args,**kwargs):\n # print(\"__wrapped_fnc:{}\".format(func))\n # print(\"__wrapped_fnc:{}\".format(kwargs))\n foo = None\n if \"extra\" not in kwargs:\n kwargs[\"extra\"] = {}\n locals_result = {}\n globals_result = {}\n try:\n frame = inspect.currentframe()\n # frame = frame.f_back\n # from __wrapped_fnc() to _export_namespace_dict()\n frame = frame.f_back\n # from _export_namespace_dict() to __wrap_log_fnc()\n frame = frame.f_back\n # from __wrap_log_fnc() to the function where crit or error was actually called.\n locals_result = frame.f_locals\n globals_result = frame.f_globals\n except Exception as err:\n debug(\"Failed to grab locals and globals:{}\".format(str(err)))\n finally:\n del frame\n kwargs[\"extra\"][\"locals\"] = locals_result\n kwargs[\"extra\"][\"globals\"] = globals_result\n # print(\"__wrapped_fnc:{}\".format(kwargs))\n return func(*args,**kwargs)\n return __wrapped_fnc\n def _wrap_log_fnc(func,lvl,n_args=1):\n \"\"\"\n Accept a log event emitting function,\n and set it to not do anything if it's\n verbosity is not met in the current namespace.\n \"\"\"\n # print(\"Wrapping {}\\n\\t{}\".format(func,trace_fnc_path()))\n def __wrap_log_fnc(*args,**kwargs):\n \"\"\"\n Accept args and kwargs, do a little preprocessing,\n and determine if its allowed to be passed to the \n logger method. \n \"\"\"\n # print(namespace.verbosity, args[0])\n if (namespace.verbosity <= lvl and n_args == 1) or (n_args > 1 and namespace.verbosity <= args[0]):\n if args and len(args) > 0:\n if(n_args<1):\n # if the wrapped method accepts no arg\n # shouldn't happen\n return func(**kwargs)\n if(n_args==1):\n # if the wrapped method only accepts 1 arg\n return func(stringify(args),**kwargs)\n else:\n # print(namespace.verbosity, lvl, n_args, args[0])\n # if the wrapped method accepts many arg\n first_set = args[0:n_args-1]\n second_set = args[n_args-1:]\n # print(\"Logger.log was explicitly called\")\n args = tuple([a for a in first_set]+[stringify(second_set)])\n if len(args) == 1:\n args = args[0]\n return func(*args,**kwargs)\n else:\n return func(*args,**kwargs)\n else:\n return\n return __wrap_log_fnc\n if args is not None and len(args) > 0:\n namespace, args = super(ScreenshotArgumentParser, self).parse_known_args(args=args)\n else:\n namespace, args = super(ScreenshotArgumentParser, self).parse_known_args()\n # doing everything the ArgumentParser class would do,\n # and get the namespace it returns\n if namespace.verbose:\n # if logging everything,\n namespace.verbosity = 1\n # set verbosity/logging level to 1\n if not namespace.verbosity:\n # if no verbosity is set yet,\n namespace.verbosity = 60\n # set it to be quiet\n logr.setLevel(namespace.verbosity)\n # set logging level\n if crit == getattr(logr,\"critical\"):\n # if we haven't wrapped the functions yet\n crit = _export_namespace_dict(crit)\n error = _export_namespace_dict(error)\n # setup error and crit to automatically grab locals and globals from the stack they're called in\n crit = _wrap_log_fnc(crit, 50)\n error = _wrap_log_fnc(error,40)\n warn = _wrap_log_fnc(warn, 30)\n info = _wrap_log_fnc(info, 20)\n debug = _wrap_log_fnc(debug,10)\n log = _wrap_log_fnc(log, 0, n_args=2)\n # crit is now _wrap_log_fnc() with the the \n # Logger method \"critical\" called inside it.\n try:\n # write any special checks or attributes needed for namespace\n pass\n except Exception as err:\n crit(\"Failed to setup namespace.\", extra={ \"err\": err, \"tb\": get_tb()})\n namespace = None\n return namespace, args","title":""},{"docid":"f4df1ede3ecb666e64ac7eac0830b842","score":"0.5417456","text":"def logOpts(self, logger):\n\n for _option in sorted(self.options.keys(), key=str.lower):\n logger.write('%s = %s' % (_option, str(self.options[_option])), doPrint=False)\n\n return","title":""},{"docid":"969b81da6e7394e21b0a19c5db49cfe6","score":"0.54138196","text":"def _get_report_params(params):\n kwargs = {\n 'critical': None,\n 'hostname': None,\n 'checkname': None\n }\n\n for arg in kwargs.keys():\n if params.get(arg, None):\n kwargs[arg] = params[arg]\n\n return kwargs","title":""},{"docid":"2746b8f272aad670f895d62f84ee8361","score":"0.5387787","text":"def test_using_kwargs(**kwargs):\n print 'arg1: ', kwargs['arg1']","title":""},{"docid":"bbd85d6235acd430791404990753d4b3","score":"0.5370849","text":"def log_options(log, args):\n log.info(\"Loglevel: \" + logging.getLevelName(args.loglevel))\n log.info(\"Using hostname: \" + args.hostname)\n log.info(\"Using ip address plugin: \" + args.address_plugin)\n log.info(\"Using service plugin: \" + args.service_plugin)\n log.info(\"Service options: \" +\n (' '.join(args.service_options) if args.service_options else ''))\n log.info(\"Address options: \" +\n (' '.join(args.address_options) if args.address_options else ''))","title":""},{"docid":"8b6bf3b4fe7385c30ac6a4193e7767d1","score":"0.5365794","text":"def log_args(args):\n fields = [f for f in vars(args)]\n longest = max(fields, key=len)\n format_str = '{:>%s} {:}' % len(longest)\n for f in fields:\n msg = format_str.format(f, getattr(args, f))\n log(msg)","title":""},{"docid":"0bb40511979d44a0f8b35ae5c5cdc162","score":"0.53439116","text":"def arguments():\n frame = inspect.currentframe()\n try:\n args = frame.f_back.f_locals[\"kwargs\"]\n except KeyError:\n args = {}\n\n try:\n cls = frame.f_back.f_locals[\"__class__\"]\n DEFAULTS = cls.DEFAULTS.copy()\n except KeyError:\n DEFAULTS = {}\n\n\n \"\"\"self = frame.f_back.f_locals[\"self\"]\n named_args = {k: v for k, v in frame.f_back.f_locals.copy().items() if k != \"kwargs\"}\n\n if not hasattr(self, \"_hyperparameters\"):\n setattr(self, \"_hyperparameters\", dict())\n\n _hyperparameters = getattr(self, \"_hyperparameters\")\n _hyperparameters.update(named_args)\"\"\"\n\n del frame\n\n args = update(DEFAULTS, args)\n return args","title":""},{"docid":"547847fd5c7aa3850f39034c9abb3c31","score":"0.53406125","text":"def keyword_arguments(self, attributes):\n section = ''\n template = ':param {type} {name}: {description} (defaults to {default}).\\n'\n\n for attr in attributes:\n section += template.format(\n type=self._generate_field('type', attr['type']),\n name=self._generate_field('name', attr['name']),\n description=self._generate_field('description'),\n default=self._generate_field('default', attr['default']),\n )\n\n return section","title":""},{"docid":"5cec3a80c6a43f4be870ddad82007c23","score":"0.53369635","text":"def log(level, *args, **kwargs):\n {'debug': logger.debug,\n 'error': logger.error,\n 'exception': logger.exception,\n 'warn': logger.warn}[level](*args, **kwargs)","title":""},{"docid":"acb4ced37f1aa17d30a5f7c3e453023c","score":"0.53357375","text":"def kwargs(self) -> Dict[str, utils.Variable[Any]]:\n return {name: arg for name, arg in zip(self.names, self.variables) if name is not None}","title":""},{"docid":"28308c8acf08774330d2fe0a10f37d6f","score":"0.5325438","text":"def log_arguments(self, in_func, *args, **kwargs):\n\n\t\t# log given *args\n\t\tprint \"*args in '%s()':\" % in_func\n\t\tif args is not None:\n\t\t\ti = 0\n\t\t\tfor arg in args:\n\t\t\t\tprint \"- args[%i] = %s\" % (i, arg)\n\t\t\t\ti += 1\n\t\tprint '\\n'\n\n\t\t# log given **kwargs\n\t\tif kwargs is not None:\n\t\t\tprint \"**kwargs in '%s()': \" % in_func\n\t\t\tfor key, value in kwargs.iteritems():\n\t\t\t\tprint \"- kwargs: %s = %s\" % (key, value)\n\t\tprint '\\n'\n\n\t\t# log __init__ vars\n\t\tprint \"__init__(vars) in '%s()':\" % in_func\n\t\tprint '- self.timeout:\\t\\t\\t', self.timeout\n\t\tprint '- self.action:\\t\\t\\t', self.action\n\t\tprint '- self.click_target:\\t\\t', self.click_target\n\t\tprint '- self.post_condition:\\t\\t', self.post_condition\n\t\tprint '- self.similarity:\\t\\t', self.similarity\n\t\tprint '- self.location:\\t\\t', self.location\n\t\tprint '- self.single_key:\\t\\t', self.single_key\n\t\tprint '\\n'","title":""},{"docid":"7fca6d901bc1530fb2e4f3b4aa548f8d","score":"0.53121096","text":"def _log_args(self, arg_values_list, level=0, logged=None):\n if log.isEnabledFor(logging.DEBUG):\n if logged is None:\n logged = set()\n for i, arg_values in enumerate(arg_values_list):\n arg_values = list(arg_values)\n if level:\n if arg_values and any(v.data not in logged for v in arg_values):\n log.debug(\"%s%s:\", \" \" * level, arg_values[0].variable.id)\n else:\n log.debug(\"Arg %d\", i)\n for value in arg_values:\n if value.data not in logged:\n log.debug(\"%s%s [var %d]\", \" \" * (level + 1), value.data,\n value.variable.id)\n self._log_args(value.data.unique_parameter_values(), level + 2,\n logged | {value.data})","title":""},{"docid":"0d10576e5448e1781cfaf3ef0a38a782","score":"0.5307226","text":"def kwargsVarLenArgsAndRegArgs(one, two, three, *args, **kwargs):\n\tprint(one, two, three, args, kwargs)","title":""},{"docid":"76e5978f7685af37aab7a1e712420335","score":"0.5304121","text":"def find_keywords(args, key_filter=lambda x: not x.startswith(\"$\")):\r\n return dict(x.split('=', 1) for x in args if '=' in x and key_filter(x.split('=')[0]))","title":""},{"docid":"803362b819e12ee11150b89a2708e33d","score":"0.5296548","text":"def get_global_log_properties(logger_name=None):\n if _global_log_props_is_raw_dict:\n global_log_properties = copy.copy(_global_log_props)\n else:\n global_log_properties = {}\n for k, v in _global_log_props.items():\n if callable(v):\n v = v()\n if v is None:\n continue\n global_log_properties[k] = v\n\n if logger_name:\n global_log_properties[\"LoggerName\"] = logger_name\n\n return global_log_properties","title":""},{"docid":"b018d88bf9d7c4ef0305ee34a2ede41b","score":"0.52931553","text":"def kwargs_test(this=5, **kwargs):\n print(kwargs)","title":""},{"docid":"5683fe243dc922dcb2d12190bdaa60de","score":"0.52886945","text":"def get_args():\n kargs = dict(parse_arguments()._get_kwargs());\n return kargs;","title":""},{"docid":"1ba335de5c6e22a9baf60959469bc366","score":"0.5278747","text":"def __init__(self,*args,**kwargs) :\n #init the Logger\n super().__init__(*args,**kwargs)\n #A list of keys whose values have been recognized and used in reading the record\n self.keys_used = []","title":""},{"docid":"7b970ad4b9718beb4abdcb0fb16d7d7e","score":"0.5257787","text":"def init_logger_from_args(self, args):\n if hasattr(args, log_dir_arg_string) and args.logdir is not None:\n self.log_directory = args.logdir\n if hasattr(args, verbose_arg_string) and args.verbose is True:\n self.verbose = True\n if hasattr(args, delete_existing_arg_string) and args.dellog is True:\n self.delete_existing_log_files = True\n self.init_logger()","title":""},{"docid":"7b970ad4b9718beb4abdcb0fb16d7d7e","score":"0.5257787","text":"def init_logger_from_args(self, args):\n if hasattr(args, log_dir_arg_string) and args.logdir is not None:\n self.log_directory = args.logdir\n if hasattr(args, verbose_arg_string) and args.verbose is True:\n self.verbose = True\n if hasattr(args, delete_existing_arg_string) and args.dellog is True:\n self.delete_existing_log_files = True\n self.init_logger()","title":""},{"docid":"f31a629da2b3b62894e883e016ff523a","score":"0.5243751","text":"def build_args_kwargs(self):\n kwarg_args = []\n kwarg_vals = []\n for key, val in self.kwarg_pairs.items():\n kwarg_args.append(key)\n kwarg_vals.append(val)\n args = self.arg_names + kwarg_args\n spec = inspect.ArgSpec(args=args,\n varargs=self.varargs,\n keywords=self.keywords,\n defaults=kwarg_vals)\n return inspect.formatargspec(*spec)","title":""},{"docid":"557f22bbb05873be5cd79a902b1d6ce1","score":"0.52387905","text":"def __init__(self, kwarg_path: Tuple[str]):\n self.kwarg_path = kwarg_path","title":""},{"docid":"3727e595324a052536705cdb02e90c66","score":"0.5235186","text":"def kwargs(self):\n\t\treturn self._kwargs","title":""},{"docid":"426b5288193cb4a07e57558b25ea691c","score":"0.52300596","text":"def get_field_kwargs(field_name, model_field):\n kwargs = {}\n\n return kwargs","title":""},{"docid":"26be4169ecd99aabdd2d801e69c810fb","score":"0.521831","text":"def log_hparams(self, args):\n\n hparams = {}\n args_dict = vars(args)\n for key in args_dict:\n hparams.update({'hparams/' + key: args_dict[key]})\n\n self._log_text(hparams)","title":""},{"docid":"07911a14e4babd77509c43f3b1cc33ca","score":"0.5204924","text":"def logging_arguments(record):\n\n\t\trecord.extra['Name'] = row[0]\t\t#['Name']\n\t\trecord.extra['Title'] = row[1]\t\t#['Title']\n\t\trecord.extra['Domain'] = row[2]\t\t#['Domain']\n\t\trecord.extra['BreachDate'] = row[3]\t\t#['BreachDate']\n\t\trecord.extra['AddedDate'] = row[4]\t\t#['AddedDate']\n\t\trecord.extra['ModifiedDate'] = row[5]\t\t#['ModifiedDate']\n\t\trecord.extra['PwnCount'] = row[6]\t\t#['PwnCount']\n\t\trecord.extra['Description'] = row[7]\t\t#['Description']\n\t\trecord.extra['LogoPath'] = row[8]\t\t#['LogoPath']\n\t\trecord.extra['DataClasses'] = row[9]\t\t#['DataClasses']\n\t\trecord.extra['IsVerified'] = row[10]\t\t#['IsVerified']\n\t\trecord.extra['IsFabricated'] = row[11]\t\t#['IsFabricated']\n\t\trecord.extra['IsSensitive'] = row[12]\t\t#['IsSensitive']\n\t\trecord.extra['IsRetired'] = row[13]\t\t#['IsRetired']\n\t\trecord.extra['IsSpamList'] = row[14]\t\t#['IsSpamList']","title":""},{"docid":"79dd29f7d747ed2d71e83fc14c2c6a76","score":"0.5191766","text":"def add_extra_kwargs(self, kwargs):\n return kwargs","title":""},{"docid":"df55314fa407e5a5083774d0c0ce7106","score":"0.5187425","text":"def build_kwargs(self):\n\t\t\t## not sure if this function is necessary since I originally used a dict...\n\t\t\treturn self.dict","title":""},{"docid":"467a6b95cd8cb60a7ae5fd97a087fc83","score":"0.5149986","text":"def rebind_logger(self, **values: Optional[str]) -> None:\n self.logger = self.logger.bind(**values)","title":""},{"docid":"2d54110da6856250f7202842f6ab028a","score":"0.5147647","text":"def getKeywordArgConstraint(argname, num_posargs=0, previous_kwargs=[]):","title":""},{"docid":"ad6467a90dd02b0dba8d149ea4b9e8c4","score":"0.5144516","text":"def get_field_kwargs(self):\n return self.field_kwargs","title":""},{"docid":"8dce2243a3b7cade8f260ea2df7da489","score":"0.5137142","text":"def kwargs(self):\n return self._callable_information.kwargs","title":""},{"docid":"506677204a66e15b450b275252f55d6e","score":"0.51238185","text":"def _log(attrs, inputs, proto_obj):\n return 'log', attrs, inputs","title":""},{"docid":"a7ec68b1700ec72b0a0848a6deaad347","score":"0.5123651","text":"def kwargs(self):\n return self._kwargs","title":""},{"docid":"d1018943812f090ae6981d6de1ea297a","score":"0.51195467","text":"def static_args(**kwargs):\n def decorate(func):\n for k in kwargs:\n setattr(func, k, kwargs[k])\n return func\n\n return decorate","title":""},{"docid":"b47d7dd2a9d20c939386f2a32dfe1c26","score":"0.5112985","text":"def set_logger(l):\r\n logger = l","title":""},{"docid":"d195061b1b616d9c54ce75dd89461483","score":"0.510295","text":"def variable_key_args(** kwargs):\n for key, value in kwargs.items():\n print(key, value)","title":""},{"docid":"c414403c0ff5f4c5ded2cb10be57703d","score":"0.5098381","text":"def _shrink_kwargs(op, kwargs):\n if hasattr(op, '_apply'): #NNOp\n op = op._apply\n elif isinstance(op, functionable): # functionable\n op = op.function\n elif not isinstance(op, types.FunctionType): # callable object\n op = op.__call__\n spec = inspect.getargspec(op)\n keywords = {i: j for i, j in kwargs.iteritems()\n if spec.keywords is not None or i in spec.args}\n return keywords","title":""},{"docid":"79db2d61ce9f3a170d003d89793bbe53","score":"0.50756115","text":"def __init__(self, logger, level):\n self.logger = logger\n self.level = level","title":""},{"docid":"79db2d61ce9f3a170d003d89793bbe53","score":"0.50756115","text":"def __init__(self, logger, level):\n self.logger = logger\n self.level = level","title":""},{"docid":"79db2d61ce9f3a170d003d89793bbe53","score":"0.50756115","text":"def __init__(self, logger, level):\n self.logger = logger\n self.level = level","title":""},{"docid":"4c0390cc1fe01553f2ef1878ca4e3abf","score":"0.50606006","text":"def getLogger(name=None, project='unknown', version='unknown'):\n # NOTE(dhellmann): To maintain backwards compatibility with the\n # old oslo namespace package logger configurations, and to make it\n # possible to control all oslo logging with one logger node, we\n # replace \"oslo_\" with \"oslo.\" so that modules under the new\n # non-namespaced packages get loggers as though they are.\n if name and name.startswith('oslo_'):\n name = 'oslo.' + name[5:]\n if name not in _loggers:\n _loggers[name] = KeywordArgumentAdapter(logging.getLogger(name),\n {'project': project,\n 'version': version})\n return _loggers[name]","title":""},{"docid":"29880aa1eddae96feb26781695ab653f","score":"0.5058516","text":"def _check_kwargs(self, kwargs: Dict[str, Any]) -> None:\n if self._strict_kwargs_check:\n if len(kwargs) > 0:\n raise ValueError(\"Unused arguments: {}\".format(kwargs))\n else:\n logger = logging.getLogger(__name__)\n for k, v in kwargs.items():\n logger.warning(\"[PathManager] {}={} argument ignored\".format(k, v))","title":""},{"docid":"cd8a83f527e761f211e8b7daee045bad","score":"0.5045541","text":"def extract_uniform_args_kwargs(f, *args, ignore=None, **kwargs):\n if ignore is None:\n ignore = set()\n\n sig = signature(f)\n name_kind = {p.name: p.kind for p in sig.parameters.values()}\n\n bound_args = sig.bind(*args, **kwargs)\n bound_args.apply_defaults()\n\n # Possible argument types:\n #\n # KEYWORD_ONLY\n # POSITIONAL_ONLY\n # POSITIONAL_OR_KEYWORD\n # VAR_KEYWORD\n # VAR_POSITIONAL\n #\n # Accumulate POSITIONAL_ONLY, POSITIONAL_OR_KEYWORD, and VAR_POSITIONAL in the\n # order given in `arguments`.\n new_args = []\n pos_kind = (\n Parameter.POSITIONAL_ONLY,\n Parameter.POSITIONAL_OR_KEYWORD,\n Parameter.VAR_POSITIONAL,\n )\n for name, value in bound_args.arguments.items():\n if name in ignore:\n continue\n if name_kind[name] not in pos_kind:\n break\n if name_kind[name] == Parameter.VAR_POSITIONAL:\n new_args.extend(value)\n else:\n new_args.append(value)\n\n # Accumulate KEYWORD_ONLY and VAR_KEYWORD in the\n # order given in `arguments`.\n new_kwargs = {}\n kw_kind = (Parameter.KEYWORD_ONLY, Parameter.VAR_KEYWORD)\n for name, value in bound_args.arguments.items():\n if name in ignore:\n continue\n if name_kind[name] in pos_kind:\n continue\n assert name_kind[name] in kw_kind\n if name_kind[name] == Parameter.VAR_KEYWORD:\n new_kwargs.update(value)\n else:\n new_kwargs[name] = value\n\n return new_args, new_kwargs","title":""},{"docid":"8b1f81f647d1e3526994eff75bf1f209","score":"0.5042938","text":"def kw_only(*args):\n def decorator(func):\n argspec = getfullargspec(func)\n kw_args = (argspec.args[-len(argspec.defaults):]\n if argspec.defaults else [])\n for kw in args:\n if kw not in kw_args:\n raise Exception(\n \"Invalid kw-only annotation of argument {0} for \"\n \"function {1}\".format(kw, func.__name__))\n func.kw_only = dict((kw, argspec.defaults[kw_args.index(kw)])\n for kw in args)\n return func\n return decorator","title":""},{"docid":"d1a11c9a99ad15c8eab44830163708b5","score":"0.50429076","text":"def log(self, msg: Union[str, Dict[str, Any]], **kwargs):\n pass","title":""},{"docid":"5442a91661206ef036d083b0b4d4457c","score":"0.50379974","text":"def _set_kw_xx(self, **kwargs):\n \n pass","title":""},{"docid":"446236b951bc3a3788e601746a965dd9","score":"0.5035791","text":"def customize_log(log=None):\n def arghandler(args=None, levelnum=None, prefix=None):\n largs = list(args)\n slargs = str(largs[0]).split('\\n')\n for line in slargs:\n if prefix:\n line = prefix + line\n if len(line) == 0:\n log.log(levelnum, '')\n else:\n log.log(levelnum, line)\n\n def ccritical(*args):\n arghandler(args, ll['CRITICAL'], 'CRITICAL - ' )\n def cerror(*args):\n arghandler(args, ll['ERROR'], 'ERROR - ')\n def cwarning(*args):\n arghandler(args, ll['WARNING'], 'WARNING - ')\n def cstatus(*args):\n arghandler(args, ll['STATUS'])\n def cstdinfo(*args):\n arghandler(args, ll['STDINFO'])\n def cinfo(*args):\n arghandler(args, ll['INFO'])\n def cfullinfo(*args):\n arghandler(args, ll['FULLINFO'])\n def cdebug(*args):\n arghandler(args, ll['DEBUG'], 'DEBUG - ')\n\n setattr(log, 'critical', ccritical)\n setattr(log, 'error', cerror)\n setattr(log, 'warning', cwarning)\n setattr(log, 'status', cstatus)\n setattr(log, 'stdinfo', cstdinfo)\n setattr(log, 'info', cinfo)\n setattr(log, 'fullinfo', cfullinfo)\n setattr(log, 'debug', cdebug)\n\n add_filter(DuplicateWarningFilter, log)\n\n return","title":""},{"docid":"ef570160ec0c0a4251bc788e6930a56c","score":"0.501676","text":"def __init__(self, logger, level):\n self.logger = logger\n self.level = level","title":""},{"docid":"40b5f26c1088fd68d525feefa347080c","score":"0.5012756","text":"def _create_config_from_args(self, args, kwargs):\n if not self.has_fn and args:\n raise Exception(\"Builders with fn=None only accept keyword arguments\")\n ba = self.fn_signature.bind(*args, **kwargs)\n ba.apply_defaults()\n a = ba.arguments\n if self.fn_argspec.varkw:\n kwargs = a.pop(self.fn_argspec.varkw, {})\n a.update(kwargs)\n return a","title":""},{"docid":"f74d4895552f91e92113c0dcf6d4f994","score":"0.500969","text":"def kwmapper (*args):\n\tkw = dict()\n\tfor d in args:\n\t\tif isinstance(d, dict):\n\t\t\tkw.update(d)\n\t\telif hasattr(d, '__dict__'):\n\t\t\tfor k in d.__dict__:\n\t\t\t\tif k.startswith('_'):\n\t\t\t\t\tcontinue\n\t\t\t\tkw[k] = getattr(d, k)\n\t\telse:\n\t\t\traise TypeError('can not map: {0}'.format(type(d)))\n\treturn kw","title":""},{"docid":"671c21db249e5fb6af7914fb9a13a691","score":"0.50074905","text":"def _create_arguments_dictionary(parameter_extractors, environment, context):\n arguments = {}\n for parameter_extractor in parameter_extractors:\n arguments.update(parameter_extractor(environment, context))\n return arguments","title":""},{"docid":"46e93fbb5f89fe4fa67b77cd468eefa2","score":"0.5002678","text":"def __init__(self, logger):","title":""},{"docid":"ab28d1a7351d6324a347f15809ebc5e9","score":"0.49996233","text":"def kwargs(self):\n return self._kwargs","title":""},{"docid":"ab28d1a7351d6324a347f15809ebc5e9","score":"0.49996233","text":"def kwargs(self):\n return self._kwargs","title":""},{"docid":"e570b831fb2ebeb557fa851d3dc564bf","score":"0.49982288","text":"def debug_args(argd, **kwargs):\n debug('Arguments:')\n for name, info in kwargs.items():\n debug(\n '{:<12} ({:<12}) = {!r}'.format(\n name,\n info['flag'],\n info.get('parsed', argd[info['flag']]),\n ),\n align=True,\n )","title":""},{"docid":"62dae295d526f9864a4a2f157c7d48ff","score":"0.49975967","text":"def log_debug(*args, **kwargs):\n pass","title":""},{"docid":"3d0b849e66b177594753c5bd404e40d2","score":"0.49943867","text":"def process_logging(log_bucket, log_object_prefix):\n clear_log_bucket = log_bucket == user_request_args_factory.CLEAR\n clear_log_object_prefix = log_object_prefix == user_request_args_factory.CLEAR\n if clear_log_bucket and clear_log_object_prefix:\n return user_request_args_factory.CLEAR\n\n logging_config = {}\n if log_bucket and not clear_log_bucket:\n logging_config['TargetBucket'] = log_bucket\n if log_object_prefix and not clear_log_object_prefix:\n logging_config['TargetPrefix'] = log_object_prefix\n return {'LoggingEnabled': logging_config}","title":""},{"docid":"cb5388c1e31985a2ba5ec4018e0018cb","score":"0.4986229","text":"def get_log_params(config):\n params = {}\n for sec in config:\n for k, v in config[sec].items():\n params[sec + \"/\" + k] = v\n return params","title":""},{"docid":"63dd1a80648dac6ff83d6cb9d76a4c1f","score":"0.49819893","text":"def S(**kwargs):\n return kwargs","title":""},{"docid":"2e41baad21e632880909963f1cfbdda3","score":"0.49803057","text":"def __init__(self, logger, level):\n self.logger = logger\n self.level = level","title":""},{"docid":"6e11d2b097da2d3e29a88f89a31f62e9","score":"0.4975983","text":"def _arbitrary_three_xr_func_args_keys(ds, da=None, other_da=None, **kwargs):\n return ds, da, other_da, kwargs","title":""},{"docid":"8e78a186cfa773bbf39ed2be5531b758","score":"0.49728063","text":"def get_alg_kwargs(alg_cls: Type[Algorithm]) -> Dict[str, Any]:\n return _get_alg_settings(alg_cls)['kwargs']","title":""},{"docid":"14e4d3b2556345d387ba7e30af26b8fb","score":"0.49725124","text":"def log_flags(FLAGS):\n param_list = []\n #for k, v in FLAGS.__flags.items():\n for k in FLAGS:\n v = FLAGS[k].value\n param_list.append(\"'--{}': {}\".format(k, v))\n logging.info('Parameters:\\n' + '\\n'.join(param_list))","title":""},{"docid":"94da3235f1406c9cb82734c95de4f828","score":"0.4964443","text":"def __wrap_log_fnc(*args,**kwargs):\n # print(namespace.verbosity, args[0])\n if (namespace.verbosity <= lvl and n_args == 1) or (n_args > 1 and namespace.verbosity <= args[0]):\n if args and len(args) > 0:\n if(n_args<1):\n # if the wrapped method accepts no arg\n # shouldn't happen\n return func(**kwargs)\n if(n_args==1):\n # if the wrapped method only accepts 1 arg\n return func(stringify(args),**kwargs)\n else:\n # print(namespace.verbosity, lvl, n_args, args[0])\n # if the wrapped method accepts many arg\n first_set = args[0:n_args-1]\n second_set = args[n_args-1:]\n # print(\"Logger.log was explicitly called\")\n args = tuple([a for a in first_set]+[stringify(second_set)])\n if len(args) == 1:\n args = args[0]\n return func(*args,**kwargs)\n else:\n return func(*args,**kwargs)\n else:\n return","title":""},{"docid":"35dd5854af16d0a6c0dd19e0033d01b4","score":"0.49508324","text":"def add_argument_kwargs(self):\n kwargs = {}\n\n if self.description is not None:\n kwargs['help'] = self.description\n if self.parser is not None:\n kwargs['type'] = self.parser\n if self.choices is not None:\n kwargs['choices'] = self.choices\n if self.nargs is not None:\n kwargs['nargs'] = self.nargs\n\n return kwargs","title":""},{"docid":"ec15c584ab249003a5cf555fc80db52a","score":"0.49498397","text":"def log_dict(trainer, to_log, is_param):\n try:\n if is_param:\n trainer.logger.log_hyperparams(to_log)\n else:\n trainer.logger.log_metrics(to_log)\n except:\n pass","title":""},{"docid":"5412215a366d74129e9d5741529cdbba","score":"0.49432275","text":"def get_filter_form_kwargs(self, **kwargs):\n return kwargs","title":""},{"docid":"050ed6ed333cd8d789679418e4531253","score":"0.49391744","text":"def _get_method_params(self):\n caller = sys._getframe(2)\n var_names = list(caller.f_code.co_varnames)\n caller_locals = caller.f_locals\n\n var_names.remove('self')\n kwargs = {key: value for key, value in caller_locals.items()\n if key in var_names and value is not None}\n return kwargs","title":""},{"docid":"bd043a1689626709003929fa605daaef","score":"0.49377203","text":"def _extract_csv_writer_kwargs(self):\n kwargs = {}\n\n if getattr(self.args, 'line_numbers', None):\n kwargs['line_numbers'] = True\n\n return kwargs","title":""},{"docid":"72eb66c655a182e99b912902150c4b46","score":"0.49311846","text":"def handle_kwargs(func, defaults, input_kwargs):\n # Get arguments that appear in function signature.\n args, _, _, kwarg_defaults, _, _, _ = inspect.getfullargspec(unwrap_py2(func))\n # Add defaults\n kwargs = { k : v for (k,v) in defaults.items() }\n # Override those that appear in args\n kwargs.update({ k : v for (k,v) in input_kwargs.items() if k in args })\n\n return kwargs","title":""},{"docid":"ef10b1483638e9633badc3dd4b2aa4ee","score":"0.49281985","text":"def set_and_reflect(self, *args, **kwargs):\n self.kwargs = kwargs\n return args","title":""},{"docid":"b86734738408f392c7cc03e3116d2023","score":"0.492707","text":"def get_logger(*name: str) -> LogGetter:\n return LogGetter(*name)","title":""},{"docid":"5f333ee8a5af6bd9dc02b5f8b34beb16","score":"0.49238986","text":"def get_arg_docs():\n return {\n }","title":""},{"docid":"1fa2902d03cb25cea98e676b78424b1a","score":"0.49200174","text":"def kwargs(self):\n return {kw: next(arg) for kw, arg in self._kwargs.items()}","title":""},{"docid":"f75df3bdfca627b7bd15037d2b8523ae","score":"0.49176526","text":"def filter_args(self, f, kwargs):\n known = set(inspect.signature(f).parameters)\n return {name: kwargs[name] for name in kwargs if name in known}","title":""},{"docid":"71ba6c6a8dd7b9ac90d0631b54e7c1ea","score":"0.49163133","text":"def _log_args(self, args):\n for name, value in sorted(args.items()):\n input_data = self.ij.params_dict.get(name)\n if input_data is None and name not in self.default_args:\n # if name is not in install.json and not a default arg log a warning\n if name not in ['tcex_testing_context']: # values that should not cause a warning\n self.log.data('run', 'input', f'{name}: unknown arg.', 'warning')\n elif input_data is not None and input_data.get('encrypt') is True:\n self.log.data('run', 'input', f'{name}: ***')\n else:\n self.log.data('run', 'input', f'{name}: {value}')","title":""},{"docid":"2914e5365090572ee05245a807d0e798","score":"0.49159974","text":"def configure_logger(name: str | None, level: str) \\\n -> Tuple[logging.Logger, int]:\n logger = logging.getLogger(name)\n level_ = getattr(logging, level.upper())\n logger.setLevel(level_)\n return logger, level","title":""},{"docid":"5337717f0a4f3cc6d6dc390e0625fe51","score":"0.490873","text":"def inject(func, kwargs):\n args = []\n for name in inspect.getargspec(func).args:\n args.append(kwargs.get(name))\n return func(*args)","title":""}],"string":"[\n {\n \"docid\": \"2e798378f8e489aba849418d7a681020\",\n \"score\": \"0.6344701\",\n \"text\": \"def parseKwargs(cls, config_stats, logger=None):\\n kwargs = {}\\n kwargs.update(config_stats)\\n kwargs.pop('type',None)\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09192833047de487d28bfa04681dd7ad\",\n \"score\": \"0.6314909\",\n \"text\": \"def log_record(**kwargs) -> dict:\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcae69d3667fd8edb74049655382f508\",\n \"score\": \"0.61579067\",\n \"text\": \"def set_logger(*args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff5cdcb7d8582c6ca823bd16389c61f6\",\n \"score\": \"0.59713715\",\n \"text\": \"def kw_args(self):\\n return self[\\\"kw_args\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"504e7247608ae3733b45191cbec5adfc\",\n \"score\": \"0.5923924\",\n \"text\": \"def _kwargs_to_log_raw(self, log, sensitive_=False, secrets_=None, **kwargs):\\n if kwargs is None:\\n return\\n\\n for key, value in iteritems(kwargs):\\n val = self._valueize(value)\\n if sensitive_:\\n val = self._sec_fix(val, secrets=secrets_)\\n\\n log[self._valueize_key(key)] = val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80c42ea995c0360eee9cb0bea9496987\",\n \"score\": \"0.57750446\",\n \"text\": \"def log_params(self, params: Mapping[str, Any], prefix: Optional[str] = None) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"535a839594b4871e2e163e9946acffbd\",\n \"score\": \"0.57079977\",\n \"text\": \"def get_param_logger() -> Logger:\\n\\n return get_default_logger('Parameter')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69c7ad5a7fc42d9983a45904af1a93af\",\n \"score\": \"0.56830215\",\n \"text\": \"def _args_to_log_raw(self, log, sensitive_=False, secrets_=None, *args):\\n if args is None:\\n return\\n\\n for idx, arg in enumerate(args):\\n val = self._valueize(arg)\\n if sensitive_:\\n val = self._sec_fix(val, secrets=secrets_)\\n\\n log['arg%d' % idx] = val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d026c33a0dcdf7fee5829bdee19b711\",\n \"score\": \"0.5661435\",\n \"text\": \"def test_merge_arg_kwarg(logger_mock, warnings_mock):\\n lambda_ = lambda a, b='abc': (a, b)\\n parameters = list(inspect.signature(lambda_).parameters.values())\\n\\n # Prefer `args` over `other_kwargs`\\n merged = _merge_args(parameters, ['a', 'abc'], {}, {'b': 'xyz'}, {}, '')\\n assert merged == (['a', 'abc'], {})\\n assert logger_mock.warning.call_count == 0\\n assert warnings_mock.warn.call_count == 1\\n warnings_mock.reset_mock()\\n\\n # Prefer `kwargs` over `other_kwargs`\\n merged = _merge_args(parameters, ['a'], {'b': 'abc'}, {'b': 'xyz'}, {}, '')\\n assert merged == (['a'], {'b': 'abc'})\\n assert logger_mock.warning.call_count == 0\\n assert warnings_mock.warn.call_count == 1\\n warnings_mock.reset_mock()\\n\\n # Prefer `other_kwargs` over default argument\\n merged = _merge_args(parameters, ['a'], {}, {'b': 'xyz'}, {'b': 'abc'}, '')\\n assert merged == (['a'], {'b': 'xyz'})\\n warnings_mock.warn.assert_not_called()\\n warnings_mock.warn.assert_not_called()\\n\\n # Prefer default argument over nothing\\n merged = _merge_args(parameters, ['a'], {}, {}, {'b': 'abc'}, '')\\n assert merged == (['a'], {'b': 'abc'})\\n warnings_mock.warn.assert_not_called()\\n warnings_mock.warn.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b018becff972e15fb7c15ea0438e732\",\n \"score\": \"0.56051356\",\n \"text\": \"def test_merge_kwarg_variable(logger_mock, warnings_mock):\\n lambda_ = lambda a, b, **kwargs: (a, b, kwargs)\\n parameters = list(inspect.signature(lambda_).parameters.values())\\n\\n merged = _merge_args(parameters, ['a', 'b'], {}, {'b': 'xyz'}, {}, '')\\n assert merged == (['a', 'b'], {})\\n assert logger_mock.warning.call_count == 0\\n assert warnings_mock.warn.call_count == 1\\n warnings_mock.reset_mock()\\n\\n merged = _merge_args(parameters, ['a'], {}, {'b': 'xyz'}, {}, '')\\n assert merged == (['a'], {'b': 'xyz'})\\n logger_mock.warning.assert_not_called()\\n warnings_mock.warn.assert_not_called()\\n\\n merged = _merge_args(parameters, ['a'], {}, {'b': 'xyz', 'c': 'abc'}, {}, '')\\n assert merged == (['a'], {'b': 'xyz', 'c': 'abc'})\\n logger_mock.warning.assert_not_called()\\n warnings_mock.warn.assert_not_called()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9321c14e8b073a47cefd731ff91a76e\",\n \"score\": \"0.55887884\",\n \"text\": \"def extract_extra_args_from_record(record: logging.LogRecord):\\n default_attrs = logging.LogRecord(\\\"\\\", 0, \\\"\\\", 0, None, None, None).__dict__.keys()\\n extra_keys = set(record.__dict__.keys()) - default_attrs\\n return {k: str(getattr(record, k)) for k in extra_keys if hasattr(record, k)}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb452acab41376fca40d71348de87b90\",\n \"score\": \"0.553083\",\n \"text\": \"def __init__(self, params) :\\n for k, v in params.items() :\\n self.__dict__[k] = v\\n self.logger = logging.getLogger(self.__class__.__name__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbbba64191f54f04cbe68507f0170aa7\",\n \"score\": \"0.5522225\",\n \"text\": \"def get_kwargs(self, keyword=WHERE_KWARG, **kwargs):\\n kwargs[keyword] = self._output()\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c411137076d1a7cae98d830a25d965\",\n \"score\": \"0.5513681\",\n \"text\": \"def _arg_values(self, **kwargs):\\n if self.name in kwargs:\\n new = kwargs.pop(self.name)\\n if isinstance(new, Constant):\\n return new._arg_defaults(alias=self)\\n else:\\n return {self.name: new}\\n else:\\n return self._arg_defaults()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53dd8ba2e0f3de5644be837206fcf653\",\n \"score\": \"0.5513545\",\n \"text\": \"def GetKeywordArgs(prop, include_default=True):\\n kwargs = {\\n 'required': prop._required,\\n 'repeated': prop._repeated,\\n }\\n if include_default and hasattr(prop, '_default'):\\n kwargs['default'] = prop._default\\n if hasattr(prop, '_variant'):\\n kwargs['variant'] = prop._variant\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b96aeaa409db94f9e32f0cd6444c0e9d\",\n \"score\": \"0.5510769\",\n \"text\": \"def test_configurable__override_with_kwarg(logger_mock, warnings_mock):\\n\\n @configurable\\n def configured(arg=HParam()):\\n return arg\\n\\n add_config({configured: HParams()})\\n\\n logger_mock.reset_mock()\\n warnings_mock.reset_mock()\\n configured(arg='a')\\n assert logger_mock.warning.call_count == 0\\n assert warnings_mock.warn.call_count == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c43fe014187cc94143499a15414895d\",\n \"score\": \"0.55036783\",\n \"text\": \"def _loggerPrep(self, level, args):\\n if not self._isNum(level): # fallback\\n if args:\\n args=list(args)\\n if level is not None: args.insert(0, level)\\n else: args=[level] if level is not None else []\\n level=None\\n if not args: args=[]\\n elif not self._isArray(args): args=list(args)\\n return level, args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0ab5a98df96dec8fc917fb1b9bba7aa\",\n \"score\": \"0.5493007\",\n \"text\": \"def inject_kwargs(**mapping):\\n def _get_kwargs(f):\\n ARG_TYPES = [\\n Parameter.POSITIONAL_ONLY,\\n Parameter.POSITIONAL_OR_KEYWORD,\\n Parameter.KEYWORD_ONLY,\\n ]\\n args = [n for n, p in inspect.signature(f).parameters.items() if p.kind in ARG_TYPES]\\n kwargs = [n for n, p in inspect.signature(f).parameters.items() if p.kind in ARG_TYPES and p.default != inspect._empty]\\n return args, kwargs\\n\\n def wrapper(fun):\\n argnames, kwargnames = _get_kwargs(fun)\\n def wrapped(*args, **kwargs):\\n namedargs = dict(list(zip(argnames[:len(args)], args)))\\n config_dict = _config.__dict__ if _config is not None else {}\\n for name in kwargnames:\\n if name not in kwargs and name not in namedargs:\\n if name == \\\"config\\\": # Legacy mode (inject the config object)\\n kwargs[name] = _config\\n else: # New mode (overwrites kwargs with config values if available)\\n config_name = mapping[name] if name in mapping else name\\n if config_name in config_dict:\\n kwargs[name] = config_dict[config_name]\\n return fun(*args, **kwargs)\\n return wrapped\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23b62baf530a572b802b8c3269cb0e82\",\n \"score\": \"0.54908353\",\n \"text\": \"def log(*args, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd625f956a782fc199592f49fabf261a\",\n \"score\": \"0.5448175\",\n \"text\": \"def pass_kwargs(self, **kwargs):\\n\\n\\t\\t# log given **kwargs\\n\\t\\tprint 'kwargs is = ', kwargs\\n\\t\\tif kwargs != {}:\\n\\t\\t\\tprint \\\"This keyword was called with this **kwargs:\\\"\\n\\t\\t\\tfor key, value in kwargs.iteritems():\\n\\t\\t\\t\\tprint \\\"- kwargs: %s = %s\\\" % (key, value)\\n\\t\\telse:\\n\\t\\t\\tprint 'NO **kwargs passed.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b4edb4eb9c0903f5a83aa5e89acd277\",\n \"score\": \"0.5434425\",\n \"text\": \"def get_kwargs(self) -> dict:\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40ae43809a2882cacbdbb29b28e6712e\",\n \"score\": \"0.5423856\",\n \"text\": \"def parse_known_args(self, args=None, namespace=None):\\n global crit, error, warn, info, debug, log\\n # bring in the global variables containing the logger's methods\\n def _export_namespace_dict(func):\\n def _get_locals():\\n try:\\n locals_result = {}\\n frame = inspect.currentframe()\\n frame = frame.f_back\\n # from _get_locals() to _export_namespace_dict()\\n frame = frame.f_back\\n # from _export_namespace_dict() to __wrap_log_fnc()\\n frame = frame.f_back\\n # from __wrap_log_fnc() to the function where crit or error was actually called.\\n locals_result = frame.f_locals\\n finally:\\n del frame\\n return locals_result\\n def __wrapped_fnc(*args,**kwargs):\\n # print(\\\"__wrapped_fnc:{}\\\".format(func))\\n # print(\\\"__wrapped_fnc:{}\\\".format(kwargs))\\n foo = None\\n if \\\"extra\\\" not in kwargs:\\n kwargs[\\\"extra\\\"] = {}\\n locals_result = {}\\n globals_result = {}\\n try:\\n frame = inspect.currentframe()\\n # frame = frame.f_back\\n # from __wrapped_fnc() to _export_namespace_dict()\\n frame = frame.f_back\\n # from _export_namespace_dict() to __wrap_log_fnc()\\n frame = frame.f_back\\n # from __wrap_log_fnc() to the function where crit or error was actually called.\\n locals_result = frame.f_locals\\n globals_result = frame.f_globals\\n except Exception as err:\\n debug(\\\"Failed to grab locals and globals:{}\\\".format(str(err)))\\n finally:\\n del frame\\n kwargs[\\\"extra\\\"][\\\"locals\\\"] = locals_result\\n kwargs[\\\"extra\\\"][\\\"globals\\\"] = globals_result\\n # print(\\\"__wrapped_fnc:{}\\\".format(kwargs))\\n return func(*args,**kwargs)\\n return __wrapped_fnc\\n def _wrap_log_fnc(func,lvl,n_args=1):\\n \\\"\\\"\\\"\\n Accept a log event emitting function,\\n and set it to not do anything if it's\\n verbosity is not met in the current namespace.\\n \\\"\\\"\\\"\\n # print(\\\"Wrapping {}\\\\n\\\\t{}\\\".format(func,trace_fnc_path()))\\n def __wrap_log_fnc(*args,**kwargs):\\n \\\"\\\"\\\"\\n Accept args and kwargs, do a little preprocessing,\\n and determine if its allowed to be passed to the \\n logger method. \\n \\\"\\\"\\\"\\n # print(namespace.verbosity, args[0])\\n if (namespace.verbosity <= lvl and n_args == 1) or (n_args > 1 and namespace.verbosity <= args[0]):\\n if args and len(args) > 0:\\n if(n_args<1):\\n # if the wrapped method accepts no arg\\n # shouldn't happen\\n return func(**kwargs)\\n if(n_args==1):\\n # if the wrapped method only accepts 1 arg\\n return func(stringify(args),**kwargs)\\n else:\\n # print(namespace.verbosity, lvl, n_args, args[0])\\n # if the wrapped method accepts many arg\\n first_set = args[0:n_args-1]\\n second_set = args[n_args-1:]\\n # print(\\\"Logger.log was explicitly called\\\")\\n args = tuple([a for a in first_set]+[stringify(second_set)])\\n if len(args) == 1:\\n args = args[0]\\n return func(*args,**kwargs)\\n else:\\n return func(*args,**kwargs)\\n else:\\n return\\n return __wrap_log_fnc\\n if args is not None and len(args) > 0:\\n namespace, args = super(ScreenshotArgumentParser, self).parse_known_args(args=args)\\n else:\\n namespace, args = super(ScreenshotArgumentParser, self).parse_known_args()\\n # doing everything the ArgumentParser class would do,\\n # and get the namespace it returns\\n if namespace.verbose:\\n # if logging everything,\\n namespace.verbosity = 1\\n # set verbosity/logging level to 1\\n if not namespace.verbosity:\\n # if no verbosity is set yet,\\n namespace.verbosity = 60\\n # set it to be quiet\\n logr.setLevel(namespace.verbosity)\\n # set logging level\\n if crit == getattr(logr,\\\"critical\\\"):\\n # if we haven't wrapped the functions yet\\n crit = _export_namespace_dict(crit)\\n error = _export_namespace_dict(error)\\n # setup error and crit to automatically grab locals and globals from the stack they're called in\\n crit = _wrap_log_fnc(crit, 50)\\n error = _wrap_log_fnc(error,40)\\n warn = _wrap_log_fnc(warn, 30)\\n info = _wrap_log_fnc(info, 20)\\n debug = _wrap_log_fnc(debug,10)\\n log = _wrap_log_fnc(log, 0, n_args=2)\\n # crit is now _wrap_log_fnc() with the the \\n # Logger method \\\"critical\\\" called inside it.\\n try:\\n # write any special checks or attributes needed for namespace\\n pass\\n except Exception as err:\\n crit(\\\"Failed to setup namespace.\\\", extra={ \\\"err\\\": err, \\\"tb\\\": get_tb()})\\n namespace = None\\n return namespace, args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4df1ede3ecb666e64ac7eac0830b842\",\n \"score\": \"0.5417456\",\n \"text\": \"def logOpts(self, logger):\\n\\n for _option in sorted(self.options.keys(), key=str.lower):\\n logger.write('%s = %s' % (_option, str(self.options[_option])), doPrint=False)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"969b81da6e7394e21b0a19c5db49cfe6\",\n \"score\": \"0.54138196\",\n \"text\": \"def _get_report_params(params):\\n kwargs = {\\n 'critical': None,\\n 'hostname': None,\\n 'checkname': None\\n }\\n\\n for arg in kwargs.keys():\\n if params.get(arg, None):\\n kwargs[arg] = params[arg]\\n\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2746b8f272aad670f895d62f84ee8361\",\n \"score\": \"0.5387787\",\n \"text\": \"def test_using_kwargs(**kwargs):\\n print 'arg1: ', kwargs['arg1']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbd85d6235acd430791404990753d4b3\",\n \"score\": \"0.5370849\",\n \"text\": \"def log_options(log, args):\\n log.info(\\\"Loglevel: \\\" + logging.getLevelName(args.loglevel))\\n log.info(\\\"Using hostname: \\\" + args.hostname)\\n log.info(\\\"Using ip address plugin: \\\" + args.address_plugin)\\n log.info(\\\"Using service plugin: \\\" + args.service_plugin)\\n log.info(\\\"Service options: \\\" +\\n (' '.join(args.service_options) if args.service_options else ''))\\n log.info(\\\"Address options: \\\" +\\n (' '.join(args.address_options) if args.address_options else ''))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b6bf3b4fe7385c30ac6a4193e7767d1\",\n \"score\": \"0.5365794\",\n \"text\": \"def log_args(args):\\n fields = [f for f in vars(args)]\\n longest = max(fields, key=len)\\n format_str = '{:>%s} {:}' % len(longest)\\n for f in fields:\\n msg = format_str.format(f, getattr(args, f))\\n log(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bb40511979d44a0f8b35ae5c5cdc162\",\n \"score\": \"0.53439116\",\n \"text\": \"def arguments():\\n frame = inspect.currentframe()\\n try:\\n args = frame.f_back.f_locals[\\\"kwargs\\\"]\\n except KeyError:\\n args = {}\\n\\n try:\\n cls = frame.f_back.f_locals[\\\"__class__\\\"]\\n DEFAULTS = cls.DEFAULTS.copy()\\n except KeyError:\\n DEFAULTS = {}\\n\\n\\n \\\"\\\"\\\"self = frame.f_back.f_locals[\\\"self\\\"]\\n named_args = {k: v for k, v in frame.f_back.f_locals.copy().items() if k != \\\"kwargs\\\"}\\n\\n if not hasattr(self, \\\"_hyperparameters\\\"):\\n setattr(self, \\\"_hyperparameters\\\", dict())\\n\\n _hyperparameters = getattr(self, \\\"_hyperparameters\\\")\\n _hyperparameters.update(named_args)\\\"\\\"\\\"\\n\\n del frame\\n\\n args = update(DEFAULTS, args)\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"547847fd5c7aa3850f39034c9abb3c31\",\n \"score\": \"0.53406125\",\n \"text\": \"def keyword_arguments(self, attributes):\\n section = ''\\n template = ':param {type} {name}: {description} (defaults to {default}).\\\\n'\\n\\n for attr in attributes:\\n section += template.format(\\n type=self._generate_field('type', attr['type']),\\n name=self._generate_field('name', attr['name']),\\n description=self._generate_field('description'),\\n default=self._generate_field('default', attr['default']),\\n )\\n\\n return section\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cec3a80c6a43f4be870ddad82007c23\",\n \"score\": \"0.53369635\",\n \"text\": \"def log(level, *args, **kwargs):\\n {'debug': logger.debug,\\n 'error': logger.error,\\n 'exception': logger.exception,\\n 'warn': logger.warn}[level](*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb4ced37f1aa17d30a5f7c3e453023c\",\n \"score\": \"0.53357375\",\n \"text\": \"def kwargs(self) -> Dict[str, utils.Variable[Any]]:\\n return {name: arg for name, arg in zip(self.names, self.variables) if name is not None}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28308c8acf08774330d2fe0a10f37d6f\",\n \"score\": \"0.5325438\",\n \"text\": \"def log_arguments(self, in_func, *args, **kwargs):\\n\\n\\t\\t# log given *args\\n\\t\\tprint \\\"*args in '%s()':\\\" % in_func\\n\\t\\tif args is not None:\\n\\t\\t\\ti = 0\\n\\t\\t\\tfor arg in args:\\n\\t\\t\\t\\tprint \\\"- args[%i] = %s\\\" % (i, arg)\\n\\t\\t\\t\\ti += 1\\n\\t\\tprint '\\\\n'\\n\\n\\t\\t# log given **kwargs\\n\\t\\tif kwargs is not None:\\n\\t\\t\\tprint \\\"**kwargs in '%s()': \\\" % in_func\\n\\t\\t\\tfor key, value in kwargs.iteritems():\\n\\t\\t\\t\\tprint \\\"- kwargs: %s = %s\\\" % (key, value)\\n\\t\\tprint '\\\\n'\\n\\n\\t\\t# log __init__ vars\\n\\t\\tprint \\\"__init__(vars) in '%s()':\\\" % in_func\\n\\t\\tprint '- self.timeout:\\\\t\\\\t\\\\t', self.timeout\\n\\t\\tprint '- self.action:\\\\t\\\\t\\\\t', self.action\\n\\t\\tprint '- self.click_target:\\\\t\\\\t', self.click_target\\n\\t\\tprint '- self.post_condition:\\\\t\\\\t', self.post_condition\\n\\t\\tprint '- self.similarity:\\\\t\\\\t', self.similarity\\n\\t\\tprint '- self.location:\\\\t\\\\t', self.location\\n\\t\\tprint '- self.single_key:\\\\t\\\\t', self.single_key\\n\\t\\tprint '\\\\n'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fca6d901bc1530fb2e4f3b4aa548f8d\",\n \"score\": \"0.53121096\",\n \"text\": \"def _log_args(self, arg_values_list, level=0, logged=None):\\n if log.isEnabledFor(logging.DEBUG):\\n if logged is None:\\n logged = set()\\n for i, arg_values in enumerate(arg_values_list):\\n arg_values = list(arg_values)\\n if level:\\n if arg_values and any(v.data not in logged for v in arg_values):\\n log.debug(\\\"%s%s:\\\", \\\" \\\" * level, arg_values[0].variable.id)\\n else:\\n log.debug(\\\"Arg %d\\\", i)\\n for value in arg_values:\\n if value.data not in logged:\\n log.debug(\\\"%s%s [var %d]\\\", \\\" \\\" * (level + 1), value.data,\\n value.variable.id)\\n self._log_args(value.data.unique_parameter_values(), level + 2,\\n logged | {value.data})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d10576e5448e1781cfaf3ef0a38a782\",\n \"score\": \"0.5307226\",\n \"text\": \"def kwargsVarLenArgsAndRegArgs(one, two, three, *args, **kwargs):\\n\\tprint(one, two, three, args, kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76e5978f7685af37aab7a1e712420335\",\n \"score\": \"0.5304121\",\n \"text\": \"def find_keywords(args, key_filter=lambda x: not x.startswith(\\\"$\\\")):\\r\\n return dict(x.split('=', 1) for x in args if '=' in x and key_filter(x.split('=')[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"803362b819e12ee11150b89a2708e33d\",\n \"score\": \"0.5296548\",\n \"text\": \"def get_global_log_properties(logger_name=None):\\n if _global_log_props_is_raw_dict:\\n global_log_properties = copy.copy(_global_log_props)\\n else:\\n global_log_properties = {}\\n for k, v in _global_log_props.items():\\n if callable(v):\\n v = v()\\n if v is None:\\n continue\\n global_log_properties[k] = v\\n\\n if logger_name:\\n global_log_properties[\\\"LoggerName\\\"] = logger_name\\n\\n return global_log_properties\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b018d88bf9d7c4ef0305ee34a2ede41b\",\n \"score\": \"0.52931553\",\n \"text\": \"def kwargs_test(this=5, **kwargs):\\n print(kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5683fe243dc922dcb2d12190bdaa60de\",\n \"score\": \"0.52886945\",\n \"text\": \"def get_args():\\n kargs = dict(parse_arguments()._get_kwargs());\\n return kargs;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ba335de5c6e22a9baf60959469bc366\",\n \"score\": \"0.5278747\",\n \"text\": \"def __init__(self,*args,**kwargs) :\\n #init the Logger\\n super().__init__(*args,**kwargs)\\n #A list of keys whose values have been recognized and used in reading the record\\n self.keys_used = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b970ad4b9718beb4abdcb0fb16d7d7e\",\n \"score\": \"0.5257787\",\n \"text\": \"def init_logger_from_args(self, args):\\n if hasattr(args, log_dir_arg_string) and args.logdir is not None:\\n self.log_directory = args.logdir\\n if hasattr(args, verbose_arg_string) and args.verbose is True:\\n self.verbose = True\\n if hasattr(args, delete_existing_arg_string) and args.dellog is True:\\n self.delete_existing_log_files = True\\n self.init_logger()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b970ad4b9718beb4abdcb0fb16d7d7e\",\n \"score\": \"0.5257787\",\n \"text\": \"def init_logger_from_args(self, args):\\n if hasattr(args, log_dir_arg_string) and args.logdir is not None:\\n self.log_directory = args.logdir\\n if hasattr(args, verbose_arg_string) and args.verbose is True:\\n self.verbose = True\\n if hasattr(args, delete_existing_arg_string) and args.dellog is True:\\n self.delete_existing_log_files = True\\n self.init_logger()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f31a629da2b3b62894e883e016ff523a\",\n \"score\": \"0.5243751\",\n \"text\": \"def build_args_kwargs(self):\\n kwarg_args = []\\n kwarg_vals = []\\n for key, val in self.kwarg_pairs.items():\\n kwarg_args.append(key)\\n kwarg_vals.append(val)\\n args = self.arg_names + kwarg_args\\n spec = inspect.ArgSpec(args=args,\\n varargs=self.varargs,\\n keywords=self.keywords,\\n defaults=kwarg_vals)\\n return inspect.formatargspec(*spec)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"557f22bbb05873be5cd79a902b1d6ce1\",\n \"score\": \"0.52387905\",\n \"text\": \"def __init__(self, kwarg_path: Tuple[str]):\\n self.kwarg_path = kwarg_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3727e595324a052536705cdb02e90c66\",\n \"score\": \"0.5235186\",\n \"text\": \"def kwargs(self):\\n\\t\\treturn self._kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"426b5288193cb4a07e57558b25ea691c\",\n \"score\": \"0.52300596\",\n \"text\": \"def get_field_kwargs(field_name, model_field):\\n kwargs = {}\\n\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26be4169ecd99aabdd2d801e69c810fb\",\n \"score\": \"0.521831\",\n \"text\": \"def log_hparams(self, args):\\n\\n hparams = {}\\n args_dict = vars(args)\\n for key in args_dict:\\n hparams.update({'hparams/' + key: args_dict[key]})\\n\\n self._log_text(hparams)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07911a14e4babd77509c43f3b1cc33ca\",\n \"score\": \"0.5204924\",\n \"text\": \"def logging_arguments(record):\\n\\n\\t\\trecord.extra['Name'] = row[0]\\t\\t#['Name']\\n\\t\\trecord.extra['Title'] = row[1]\\t\\t#['Title']\\n\\t\\trecord.extra['Domain'] = row[2]\\t\\t#['Domain']\\n\\t\\trecord.extra['BreachDate'] = row[3]\\t\\t#['BreachDate']\\n\\t\\trecord.extra['AddedDate'] = row[4]\\t\\t#['AddedDate']\\n\\t\\trecord.extra['ModifiedDate'] = row[5]\\t\\t#['ModifiedDate']\\n\\t\\trecord.extra['PwnCount'] = row[6]\\t\\t#['PwnCount']\\n\\t\\trecord.extra['Description'] = row[7]\\t\\t#['Description']\\n\\t\\trecord.extra['LogoPath'] = row[8]\\t\\t#['LogoPath']\\n\\t\\trecord.extra['DataClasses'] = row[9]\\t\\t#['DataClasses']\\n\\t\\trecord.extra['IsVerified'] = row[10]\\t\\t#['IsVerified']\\n\\t\\trecord.extra['IsFabricated'] = row[11]\\t\\t#['IsFabricated']\\n\\t\\trecord.extra['IsSensitive'] = row[12]\\t\\t#['IsSensitive']\\n\\t\\trecord.extra['IsRetired'] = row[13]\\t\\t#['IsRetired']\\n\\t\\trecord.extra['IsSpamList'] = row[14]\\t\\t#['IsSpamList']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79dd29f7d747ed2d71e83fc14c2c6a76\",\n \"score\": \"0.5191766\",\n \"text\": \"def add_extra_kwargs(self, kwargs):\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df55314fa407e5a5083774d0c0ce7106\",\n \"score\": \"0.5187425\",\n \"text\": \"def build_kwargs(self):\\n\\t\\t\\t## not sure if this function is necessary since I originally used a dict...\\n\\t\\t\\treturn self.dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"467a6b95cd8cb60a7ae5fd97a087fc83\",\n \"score\": \"0.5149986\",\n \"text\": \"def rebind_logger(self, **values: Optional[str]) -> None:\\n self.logger = self.logger.bind(**values)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d54110da6856250f7202842f6ab028a\",\n \"score\": \"0.5147647\",\n \"text\": \"def getKeywordArgConstraint(argname, num_posargs=0, previous_kwargs=[]):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad6467a90dd02b0dba8d149ea4b9e8c4\",\n \"score\": \"0.5144516\",\n \"text\": \"def get_field_kwargs(self):\\n return self.field_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dce2243a3b7cade8f260ea2df7da489\",\n \"score\": \"0.5137142\",\n \"text\": \"def kwargs(self):\\n return self._callable_information.kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"506677204a66e15b450b275252f55d6e\",\n \"score\": \"0.51238185\",\n \"text\": \"def _log(attrs, inputs, proto_obj):\\n return 'log', attrs, inputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7ec68b1700ec72b0a0848a6deaad347\",\n \"score\": \"0.5123651\",\n \"text\": \"def kwargs(self):\\n return self._kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1018943812f090ae6981d6de1ea297a\",\n \"score\": \"0.51195467\",\n \"text\": \"def static_args(**kwargs):\\n def decorate(func):\\n for k in kwargs:\\n setattr(func, k, kwargs[k])\\n return func\\n\\n return decorate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b47d7dd2a9d20c939386f2a32dfe1c26\",\n \"score\": \"0.5112985\",\n \"text\": \"def set_logger(l):\\r\\n logger = l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d195061b1b616d9c54ce75dd89461483\",\n \"score\": \"0.510295\",\n \"text\": \"def variable_key_args(** kwargs):\\n for key, value in kwargs.items():\\n print(key, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c414403c0ff5f4c5ded2cb10be57703d\",\n \"score\": \"0.5098381\",\n \"text\": \"def _shrink_kwargs(op, kwargs):\\n if hasattr(op, '_apply'): #NNOp\\n op = op._apply\\n elif isinstance(op, functionable): # functionable\\n op = op.function\\n elif not isinstance(op, types.FunctionType): # callable object\\n op = op.__call__\\n spec = inspect.getargspec(op)\\n keywords = {i: j for i, j in kwargs.iteritems()\\n if spec.keywords is not None or i in spec.args}\\n return keywords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79db2d61ce9f3a170d003d89793bbe53\",\n \"score\": \"0.50756115\",\n \"text\": \"def __init__(self, logger, level):\\n self.logger = logger\\n self.level = level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79db2d61ce9f3a170d003d89793bbe53\",\n \"score\": \"0.50756115\",\n \"text\": \"def __init__(self, logger, level):\\n self.logger = logger\\n self.level = level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79db2d61ce9f3a170d003d89793bbe53\",\n \"score\": \"0.50756115\",\n \"text\": \"def __init__(self, logger, level):\\n self.logger = logger\\n self.level = level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c0390cc1fe01553f2ef1878ca4e3abf\",\n \"score\": \"0.50606006\",\n \"text\": \"def getLogger(name=None, project='unknown', version='unknown'):\\n # NOTE(dhellmann): To maintain backwards compatibility with the\\n # old oslo namespace package logger configurations, and to make it\\n # possible to control all oslo logging with one logger node, we\\n # replace \\\"oslo_\\\" with \\\"oslo.\\\" so that modules under the new\\n # non-namespaced packages get loggers as though they are.\\n if name and name.startswith('oslo_'):\\n name = 'oslo.' + name[5:]\\n if name not in _loggers:\\n _loggers[name] = KeywordArgumentAdapter(logging.getLogger(name),\\n {'project': project,\\n 'version': version})\\n return _loggers[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29880aa1eddae96feb26781695ab653f\",\n \"score\": \"0.5058516\",\n \"text\": \"def _check_kwargs(self, kwargs: Dict[str, Any]) -> None:\\n if self._strict_kwargs_check:\\n if len(kwargs) > 0:\\n raise ValueError(\\\"Unused arguments: {}\\\".format(kwargs))\\n else:\\n logger = logging.getLogger(__name__)\\n for k, v in kwargs.items():\\n logger.warning(\\\"[PathManager] {}={} argument ignored\\\".format(k, v))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd8a83f527e761f211e8b7daee045bad\",\n \"score\": \"0.5045541\",\n \"text\": \"def extract_uniform_args_kwargs(f, *args, ignore=None, **kwargs):\\n if ignore is None:\\n ignore = set()\\n\\n sig = signature(f)\\n name_kind = {p.name: p.kind for p in sig.parameters.values()}\\n\\n bound_args = sig.bind(*args, **kwargs)\\n bound_args.apply_defaults()\\n\\n # Possible argument types:\\n #\\n # KEYWORD_ONLY\\n # POSITIONAL_ONLY\\n # POSITIONAL_OR_KEYWORD\\n # VAR_KEYWORD\\n # VAR_POSITIONAL\\n #\\n # Accumulate POSITIONAL_ONLY, POSITIONAL_OR_KEYWORD, and VAR_POSITIONAL in the\\n # order given in `arguments`.\\n new_args = []\\n pos_kind = (\\n Parameter.POSITIONAL_ONLY,\\n Parameter.POSITIONAL_OR_KEYWORD,\\n Parameter.VAR_POSITIONAL,\\n )\\n for name, value in bound_args.arguments.items():\\n if name in ignore:\\n continue\\n if name_kind[name] not in pos_kind:\\n break\\n if name_kind[name] == Parameter.VAR_POSITIONAL:\\n new_args.extend(value)\\n else:\\n new_args.append(value)\\n\\n # Accumulate KEYWORD_ONLY and VAR_KEYWORD in the\\n # order given in `arguments`.\\n new_kwargs = {}\\n kw_kind = (Parameter.KEYWORD_ONLY, Parameter.VAR_KEYWORD)\\n for name, value in bound_args.arguments.items():\\n if name in ignore:\\n continue\\n if name_kind[name] in pos_kind:\\n continue\\n assert name_kind[name] in kw_kind\\n if name_kind[name] == Parameter.VAR_KEYWORD:\\n new_kwargs.update(value)\\n else:\\n new_kwargs[name] = value\\n\\n return new_args, new_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b1f81f647d1e3526994eff75bf1f209\",\n \"score\": \"0.5042938\",\n \"text\": \"def kw_only(*args):\\n def decorator(func):\\n argspec = getfullargspec(func)\\n kw_args = (argspec.args[-len(argspec.defaults):]\\n if argspec.defaults else [])\\n for kw in args:\\n if kw not in kw_args:\\n raise Exception(\\n \\\"Invalid kw-only annotation of argument {0} for \\\"\\n \\\"function {1}\\\".format(kw, func.__name__))\\n func.kw_only = dict((kw, argspec.defaults[kw_args.index(kw)])\\n for kw in args)\\n return func\\n return decorator\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1a11c9a99ad15c8eab44830163708b5\",\n \"score\": \"0.50429076\",\n \"text\": \"def log(self, msg: Union[str, Dict[str, Any]], **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5442a91661206ef036d083b0b4d4457c\",\n \"score\": \"0.50379974\",\n \"text\": \"def _set_kw_xx(self, **kwargs):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"446236b951bc3a3788e601746a965dd9\",\n \"score\": \"0.5035791\",\n \"text\": \"def customize_log(log=None):\\n def arghandler(args=None, levelnum=None, prefix=None):\\n largs = list(args)\\n slargs = str(largs[0]).split('\\\\n')\\n for line in slargs:\\n if prefix:\\n line = prefix + line\\n if len(line) == 0:\\n log.log(levelnum, '')\\n else:\\n log.log(levelnum, line)\\n\\n def ccritical(*args):\\n arghandler(args, ll['CRITICAL'], 'CRITICAL - ' )\\n def cerror(*args):\\n arghandler(args, ll['ERROR'], 'ERROR - ')\\n def cwarning(*args):\\n arghandler(args, ll['WARNING'], 'WARNING - ')\\n def cstatus(*args):\\n arghandler(args, ll['STATUS'])\\n def cstdinfo(*args):\\n arghandler(args, ll['STDINFO'])\\n def cinfo(*args):\\n arghandler(args, ll['INFO'])\\n def cfullinfo(*args):\\n arghandler(args, ll['FULLINFO'])\\n def cdebug(*args):\\n arghandler(args, ll['DEBUG'], 'DEBUG - ')\\n\\n setattr(log, 'critical', ccritical)\\n setattr(log, 'error', cerror)\\n setattr(log, 'warning', cwarning)\\n setattr(log, 'status', cstatus)\\n setattr(log, 'stdinfo', cstdinfo)\\n setattr(log, 'info', cinfo)\\n setattr(log, 'fullinfo', cfullinfo)\\n setattr(log, 'debug', cdebug)\\n\\n add_filter(DuplicateWarningFilter, log)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef570160ec0c0a4251bc788e6930a56c\",\n \"score\": \"0.501676\",\n \"text\": \"def __init__(self, logger, level):\\n self.logger = logger\\n self.level = level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40b5f26c1088fd68d525feefa347080c\",\n \"score\": \"0.5012756\",\n \"text\": \"def _create_config_from_args(self, args, kwargs):\\n if not self.has_fn and args:\\n raise Exception(\\\"Builders with fn=None only accept keyword arguments\\\")\\n ba = self.fn_signature.bind(*args, **kwargs)\\n ba.apply_defaults()\\n a = ba.arguments\\n if self.fn_argspec.varkw:\\n kwargs = a.pop(self.fn_argspec.varkw, {})\\n a.update(kwargs)\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f74d4895552f91e92113c0dcf6d4f994\",\n \"score\": \"0.500969\",\n \"text\": \"def kwmapper (*args):\\n\\tkw = dict()\\n\\tfor d in args:\\n\\t\\tif isinstance(d, dict):\\n\\t\\t\\tkw.update(d)\\n\\t\\telif hasattr(d, '__dict__'):\\n\\t\\t\\tfor k in d.__dict__:\\n\\t\\t\\t\\tif k.startswith('_'):\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tkw[k] = getattr(d, k)\\n\\t\\telse:\\n\\t\\t\\traise TypeError('can not map: {0}'.format(type(d)))\\n\\treturn kw\",\n \"title\": \"\"\n },\n {\n \"docid\": \"671c21db249e5fb6af7914fb9a13a691\",\n \"score\": \"0.50074905\",\n \"text\": \"def _create_arguments_dictionary(parameter_extractors, environment, context):\\n arguments = {}\\n for parameter_extractor in parameter_extractors:\\n arguments.update(parameter_extractor(environment, context))\\n return arguments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46e93fbb5f89fe4fa67b77cd468eefa2\",\n \"score\": \"0.5002678\",\n \"text\": \"def __init__(self, logger):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab28d1a7351d6324a347f15809ebc5e9\",\n \"score\": \"0.49996233\",\n \"text\": \"def kwargs(self):\\n return self._kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab28d1a7351d6324a347f15809ebc5e9\",\n \"score\": \"0.49996233\",\n \"text\": \"def kwargs(self):\\n return self._kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e570b831fb2ebeb557fa851d3dc564bf\",\n \"score\": \"0.49982288\",\n \"text\": \"def debug_args(argd, **kwargs):\\n debug('Arguments:')\\n for name, info in kwargs.items():\\n debug(\\n '{:<12} ({:<12}) = {!r}'.format(\\n name,\\n info['flag'],\\n info.get('parsed', argd[info['flag']]),\\n ),\\n align=True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62dae295d526f9864a4a2f157c7d48ff\",\n \"score\": \"0.49975967\",\n \"text\": \"def log_debug(*args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d0b849e66b177594753c5bd404e40d2\",\n \"score\": \"0.49943867\",\n \"text\": \"def process_logging(log_bucket, log_object_prefix):\\n clear_log_bucket = log_bucket == user_request_args_factory.CLEAR\\n clear_log_object_prefix = log_object_prefix == user_request_args_factory.CLEAR\\n if clear_log_bucket and clear_log_object_prefix:\\n return user_request_args_factory.CLEAR\\n\\n logging_config = {}\\n if log_bucket and not clear_log_bucket:\\n logging_config['TargetBucket'] = log_bucket\\n if log_object_prefix and not clear_log_object_prefix:\\n logging_config['TargetPrefix'] = log_object_prefix\\n return {'LoggingEnabled': logging_config}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb5388c1e31985a2ba5ec4018e0018cb\",\n \"score\": \"0.4986229\",\n \"text\": \"def get_log_params(config):\\n params = {}\\n for sec in config:\\n for k, v in config[sec].items():\\n params[sec + \\\"/\\\" + k] = v\\n return params\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63dd1a80648dac6ff83d6cb9d76a4c1f\",\n \"score\": \"0.49819893\",\n \"text\": \"def S(**kwargs):\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e41baad21e632880909963f1cfbdda3\",\n \"score\": \"0.49803057\",\n \"text\": \"def __init__(self, logger, level):\\n self.logger = logger\\n self.level = level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e11d2b097da2d3e29a88f89a31f62e9\",\n \"score\": \"0.4975983\",\n \"text\": \"def _arbitrary_three_xr_func_args_keys(ds, da=None, other_da=None, **kwargs):\\n return ds, da, other_da, kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e78a186cfa773bbf39ed2be5531b758\",\n \"score\": \"0.49728063\",\n \"text\": \"def get_alg_kwargs(alg_cls: Type[Algorithm]) -> Dict[str, Any]:\\n return _get_alg_settings(alg_cls)['kwargs']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14e4d3b2556345d387ba7e30af26b8fb\",\n \"score\": \"0.49725124\",\n \"text\": \"def log_flags(FLAGS):\\n param_list = []\\n #for k, v in FLAGS.__flags.items():\\n for k in FLAGS:\\n v = FLAGS[k].value\\n param_list.append(\\\"'--{}': {}\\\".format(k, v))\\n logging.info('Parameters:\\\\n' + '\\\\n'.join(param_list))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94da3235f1406c9cb82734c95de4f828\",\n \"score\": \"0.4964443\",\n \"text\": \"def __wrap_log_fnc(*args,**kwargs):\\n # print(namespace.verbosity, args[0])\\n if (namespace.verbosity <= lvl and n_args == 1) or (n_args > 1 and namespace.verbosity <= args[0]):\\n if args and len(args) > 0:\\n if(n_args<1):\\n # if the wrapped method accepts no arg\\n # shouldn't happen\\n return func(**kwargs)\\n if(n_args==1):\\n # if the wrapped method only accepts 1 arg\\n return func(stringify(args),**kwargs)\\n else:\\n # print(namespace.verbosity, lvl, n_args, args[0])\\n # if the wrapped method accepts many arg\\n first_set = args[0:n_args-1]\\n second_set = args[n_args-1:]\\n # print(\\\"Logger.log was explicitly called\\\")\\n args = tuple([a for a in first_set]+[stringify(second_set)])\\n if len(args) == 1:\\n args = args[0]\\n return func(*args,**kwargs)\\n else:\\n return func(*args,**kwargs)\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35dd5854af16d0a6c0dd19e0033d01b4\",\n \"score\": \"0.49508324\",\n \"text\": \"def add_argument_kwargs(self):\\n kwargs = {}\\n\\n if self.description is not None:\\n kwargs['help'] = self.description\\n if self.parser is not None:\\n kwargs['type'] = self.parser\\n if self.choices is not None:\\n kwargs['choices'] = self.choices\\n if self.nargs is not None:\\n kwargs['nargs'] = self.nargs\\n\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec15c584ab249003a5cf555fc80db52a\",\n \"score\": \"0.49498397\",\n \"text\": \"def log_dict(trainer, to_log, is_param):\\n try:\\n if is_param:\\n trainer.logger.log_hyperparams(to_log)\\n else:\\n trainer.logger.log_metrics(to_log)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5412215a366d74129e9d5741529cdbba\",\n \"score\": \"0.49432275\",\n \"text\": \"def get_filter_form_kwargs(self, **kwargs):\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"050ed6ed333cd8d789679418e4531253\",\n \"score\": \"0.49391744\",\n \"text\": \"def _get_method_params(self):\\n caller = sys._getframe(2)\\n var_names = list(caller.f_code.co_varnames)\\n caller_locals = caller.f_locals\\n\\n var_names.remove('self')\\n kwargs = {key: value for key, value in caller_locals.items()\\n if key in var_names and value is not None}\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd043a1689626709003929fa605daaef\",\n \"score\": \"0.49377203\",\n \"text\": \"def _extract_csv_writer_kwargs(self):\\n kwargs = {}\\n\\n if getattr(self.args, 'line_numbers', None):\\n kwargs['line_numbers'] = True\\n\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72eb66c655a182e99b912902150c4b46\",\n \"score\": \"0.49311846\",\n \"text\": \"def handle_kwargs(func, defaults, input_kwargs):\\n # Get arguments that appear in function signature.\\n args, _, _, kwarg_defaults, _, _, _ = inspect.getfullargspec(unwrap_py2(func))\\n # Add defaults\\n kwargs = { k : v for (k,v) in defaults.items() }\\n # Override those that appear in args\\n kwargs.update({ k : v for (k,v) in input_kwargs.items() if k in args })\\n\\n return kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef10b1483638e9633badc3dd4b2aa4ee\",\n \"score\": \"0.49281985\",\n \"text\": \"def set_and_reflect(self, *args, **kwargs):\\n self.kwargs = kwargs\\n return args\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b86734738408f392c7cc03e3116d2023\",\n \"score\": \"0.492707\",\n \"text\": \"def get_logger(*name: str) -> LogGetter:\\n return LogGetter(*name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f333ee8a5af6bd9dc02b5f8b34beb16\",\n \"score\": \"0.49238986\",\n \"text\": \"def get_arg_docs():\\n return {\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fa2902d03cb25cea98e676b78424b1a\",\n \"score\": \"0.49200174\",\n \"text\": \"def kwargs(self):\\n return {kw: next(arg) for kw, arg in self._kwargs.items()}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f75df3bdfca627b7bd15037d2b8523ae\",\n \"score\": \"0.49176526\",\n \"text\": \"def filter_args(self, f, kwargs):\\n known = set(inspect.signature(f).parameters)\\n return {name: kwargs[name] for name in kwargs if name in known}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71ba6c6a8dd7b9ac90d0631b54e7c1ea\",\n \"score\": \"0.49163133\",\n \"text\": \"def _log_args(self, args):\\n for name, value in sorted(args.items()):\\n input_data = self.ij.params_dict.get(name)\\n if input_data is None and name not in self.default_args:\\n # if name is not in install.json and not a default arg log a warning\\n if name not in ['tcex_testing_context']: # values that should not cause a warning\\n self.log.data('run', 'input', f'{name}: unknown arg.', 'warning')\\n elif input_data is not None and input_data.get('encrypt') is True:\\n self.log.data('run', 'input', f'{name}: ***')\\n else:\\n self.log.data('run', 'input', f'{name}: {value}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2914e5365090572ee05245a807d0e798\",\n \"score\": \"0.49159974\",\n \"text\": \"def configure_logger(name: str | None, level: str) \\\\\\n -> Tuple[logging.Logger, int]:\\n logger = logging.getLogger(name)\\n level_ = getattr(logging, level.upper())\\n logger.setLevel(level_)\\n return logger, level\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5337717f0a4f3cc6d6dc390e0625fe51\",\n \"score\": \"0.490873\",\n \"text\": \"def inject(func, kwargs):\\n args = []\\n for name in inspect.getargspec(func).args:\\n args.append(kwargs.get(name))\\n return func(*args)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1693,"cells":{"query_id":{"kind":"string","value":"e8b27759e021dbf9f09cef998af40765"},"query":{"kind":"string","value":"Load a doc for a given sbid"},"positive_passages":{"kind":"list like","value":[{"docid":"6d8d2a37f31a63f95ece7b9f226370ec","score":"0.7412022","text":"def load_doc(sbid):\n data_path = os.path.join(base_dir, sbid, 'data.json')\n doc = json.loads(open(data_path).read())\n try:\n del(doc['_attachments'])\n del(doc['_rev'])\n except KeyError:\n # delete the item only if it exists\n pass\n return doc","title":""}],"string":"[\n {\n \"docid\": \"6d8d2a37f31a63f95ece7b9f226370ec\",\n \"score\": \"0.7412022\",\n \"text\": \"def load_doc(sbid):\\n data_path = os.path.join(base_dir, sbid, 'data.json')\\n doc = json.loads(open(data_path).read())\\n try:\\n del(doc['_attachments'])\\n del(doc['_rev'])\\n except KeyError:\\n # delete the item only if it exists\\n pass\\n return doc\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d07dff9a9e2a95a7dd8b26d6437b7032","score":"0.6116128","text":"def get_document(self, docid):\n return self.backend.get_document(docid)","title":""},{"docid":"3ccd0641fd2c91275191ac035c3efc67","score":"0.59131324","text":"def get_document_by_id(self, id):\n raise NotImplementedError()","title":""},{"docid":"45bb6d6f57cebebab0bd5a60b6facb1a","score":"0.5780434","text":"def load(self, cname, oid):\n check_oid(oid)\n docbytes = self.__ejdb.load(cname, oid)\n if docbytes is None:\n return None\n return bson.parse_bytes(docbytes)","title":""},{"docid":"8dcf93dee753f62c3c16b7e7f53dcd5f","score":"0.57688785","text":"def getDocumentsForWordId(wordid):","title":""},{"docid":"a0916893c8688a16227e709ef7828cda","score":"0.57070434","text":"def get(self, doc_id, document_class=None):\n doc = self._index.get(doc_id)\n document_class = document_class or self.document_class\n if doc and document_class:\n return construct_document(document_class, doc)\n return doc","title":""},{"docid":"0534d00187a7c31b0ffb73972c6ad26e","score":"0.56429005","text":"def get_doc(path,text_id):\n\tdocs = json_to_docs(path)\n\tfor doc in docs:\n\t\tif doc['text_id']==text_id:\n\t\t\treturn doc","title":""},{"docid":"90fdf91740f44ad54ba14faa93574c6e","score":"0.5626155","text":"def get_doc_for_docid(dbname, colname, docid, url=cc.URL) :\n r = request('%s/%s/%s/%s'%(url,dbname,colname,docid))\n return r.json()","title":""},{"docid":"614a4dd8e10441cf50e23808252a4040","score":"0.56003904","text":"def document(self, document_id: Optional[str] = None) -> DocumentReference:","title":""},{"docid":"88b72b8c729db95896ace8d6bd5742b5","score":"0.5555827","text":"def document_view(\n docid):\n encrypted = TimedJSONWebSignatureSerializer(current_app.config['SECRET_KEY'],\n 60 * 30) # 60 secs by 30 mins\n token = encrypted.dumps({'docid': docid}).decode('utf-8') # encode user id\n doc = File.query.get(docid)\n return render_template(\n \"document.html\",\n token=token,\n doc_name=doc.title,\n transcript=doc.transcript)","title":""},{"docid":"e9f09ec1c356757000b2c40f48d5f60e","score":"0.55219954","text":"def __setDoc(self, doc_id: str = \"http://isi.edu/default-ns/projects\"):\n doc = self.etk.create_document({}, doc_id=doc_id)\n # bind prefixes\n doc.kg.bind(\"wikibase\", \"http://wikiba.se/ontology#\")\n doc.kg.bind(\"wd\", \"http://www.wikidata.org/entity/\")\n doc.kg.bind(\"wdt\", \"http://www.wikidata.org/prop/direct/\")\n doc.kg.bind(\"wdtn\", \"http://www.wikidata.org/prop/direct-normalized/\")\n doc.kg.bind(\"wdno\", \"http://www.wikidata.org/prop/novalue/\")\n doc.kg.bind(\"wds\", \"http://www.wikidata.org/entity/statement/\")\n doc.kg.bind(\"wdv\", \"http://www.wikidata.org/value/\")\n doc.kg.bind(\"wdref\", \"http://www.wikidata.org/reference/\")\n doc.kg.bind(\"p\", \"http://www.wikidata.org/prop/\")\n doc.kg.bind(\"pr\", \"http://www.wikidata.org/prop/reference/\")\n doc.kg.bind(\"prv\", \"http://www.wikidata.org/prop/reference/value/\")\n doc.kg.bind(\n \"prn\", \"http://www.wikidata.org/prop/reference/value-normalized/\"\n )\n doc.kg.bind(\"ps\", \"http://www.wikidata.org/prop/statement/\")\n doc.kg.bind(\"psv\", \"http://www.wikidata.org/prop/statement/value/\")\n doc.kg.bind(\n \"psn\", \"http://www.wikidata.org/prop/statement/value-normalized/\"\n )\n doc.kg.bind(\"pq\", \"http://www.wikidata.org/prop/qualifier/\")\n doc.kg.bind(\"pqv\", \"http://www.wikidata.org/prop/qualifier/value/\")\n doc.kg.bind(\n \"pqn\", \"http://www.wikidata.org/prop/qualifier/value-normalized/\"\n )\n doc.kg.bind(\"skos\", \"http://www.w3.org/2004/02/skos/core#\")\n doc.kg.bind(\"prov\", \"http://www.w3.org/ns/prov#\")\n doc.kg.bind(\"schema\", \"http://schema.org/\")\n return doc","title":""},{"docid":"2955c98e10622aaca9e562ae93a9c5bf","score":"0.5503802","text":"def extract_doc(occi_id, db_data):\n for data in db_data:\n if data['OCCI_ID'] == occi_id:\n logger.debug(\"Document \" + occi_id + \" is found\")\n return data['Doc']\n logger.error(\"Document \" + occi_id + \"couldn't be found\")\n return None","title":""},{"docid":"e1573b229ed16a09135716ee1e2f900c","score":"0.548856","text":"def load(self, project_sfid):\n raise NotImplementedError()","title":""},{"docid":"5bf9575da7f69206182a9bde14ef7c68","score":"0.5483516","text":"def get_docs_by_bnid(db, bnid):\n rows = db.view('igsb_qc/all', key=bnid, descending=True)\n for row in rows:\n pp.pprint(row['value'])\n print row['value']['_id']\n return rows","title":""},{"docid":"61a08f115840ad4b92a761b927bffb54","score":"0.5469454","text":"def pull_doc(self, uid):\n pass","title":""},{"docid":"2b5aefab5e62b1221b106bb41d5243cd","score":"0.5441727","text":"def index_doc(self, docid, ob):\n if self.state == ID_STATE_OFF:\n return\n self.index(docid, ob)","title":""},{"docid":"30ce4d96ff767379793a23fa3b3eaf2e","score":"0.54225594","text":"def init_doc() -> None:\n bk_settings.simple_ids.set_value(False)\n doc = Document()\n set_curdoc(doc)\n doc.hold()\n doc._session_context = lambda: MockSessionContext(document=doc)\n state.curdoc = doc","title":""},{"docid":"ebfe419b39e31b849651044ee2d61f2d","score":"0.5413659","text":"def getDoc(self, id: str) -> Optional[Doc]:\n if id in self.index:\n return self.index[id]\n else:\n return None","title":""},{"docid":"d53d8bf82e0196c6cc450a35bb5fe3cc","score":"0.5412984","text":"def document(self, source, id):\n raise NotImplementedError()","title":""},{"docid":"10276dc67ba7fcd5962fef0457ffcbc8","score":"0.5405826","text":"def link_sti_document(self, build, obj):\n from objectsapi.XMLstringHandler.XMLtaxonomyUtilities import SKOSconcepts\n\n concept_utilities = SKOSconcepts(build=self.build)\n pref_label = obj.find(\"skos:preflabel\").string\n\n print(\"<<<<<<<<<<<<<<< LINK STI DOCUMENT prefLabel: \" + pref_label)\n\n doc = self.build.mongod.base.find_one({\"skos:prefLabel\": pref_label})\n\n if doc is None:\n id_ = self.store_sti_document(obj, obj.find(\"nt2:code\").string)\n doc = self.build.mongod.base.find_one({\"_id\": id_})\n\n try:\n build.append_link_to_mongodoc(doc, \"schema:provider\", self.provider, \"base\")\n except BaseException:\n pass\n\n if obj.find_all('skos:broader') is not None:\n for broad in obj.find_all('skos:broader'):\n c = [s for s in broad[\"rdf:resource\"] if s.isdigit()]\n code = str.join('', c)\n cc = self.find_concept(code)\n print(\"broad >>>> \" + code)\n concept_utilities.store_broader(obj, cc)\n\n if obj.find_all('skos:narrower') is not None:\n for narrow in obj.find_all('skos:narrower'):\n c = [s for s in narrow[\"rdf:resource\"] if s.isdigit()]\n code = str.join('', c)\n print(\"narrow >>>> \" + code)\n if code == '225' or code == '226':\n continue\n cc = self.find_concept(code)\n concept_utilities.store_narrower(obj, cc)","title":""},{"docid":"30b9a6682049361d19dbe3c489922e20","score":"0.5399206","text":"def getDocById(self, colId, docId, nrOfTranscripts=None):\r\n self._assertLoggedIn()\r\n myReq = self.sREQ_collection_fulldoc % (colId,docId)\r\n params = self._buidlParamsDic(nrOfTranscripts=None)\r\n resp = self._GET(myReq, params=params,accept=\"application/json\")\r\n resp.raise_for_status()\r\n\r\n #we get some json serialized data\r\n return json.loads(resp.text)","title":""},{"docid":"2f00e0ab8be29d5920c477571a6b008e","score":"0.5355247","text":"def documentdetail(request, docid):\n\tdocument = get_object_or_404(Document, pk=docid)\n\tif not request.user.has_perm('documents.view_document', obj=document):\n\t\treturn HttpResponse(loader.render_to_string('401.html',\n\t\t\tRequestContext(request, {'error_message':\n\t\t\t\t_(\"You are not allowed to view this document.\")})), status=401)\n\n\tlicense_agreement = getRecourseLicenseAgreement (document)\n\t\n\treturn render_to_response(\"documents/docinfo.html\", RequestContext(request, {\n\t\t'permissions_json': json.dumps(_perms_info(document, DOCUMENT_LEV_NAMES)),\n\t\t'document': document,\n\t\t'imgtypes': imgtypes,\n \"license_agreement\" : license_agreement,\n \"html\" : html,\n\t}))","title":""},{"docid":"44c04cd2dfb3f4544007de580cca58f1","score":"0.5351927","text":"def removeDocument(docid):","title":""},{"docid":"7191ef32165be5680749afee4f7b4cee","score":"0.5338648","text":"def retrieve_contents(doc_id):\n creds = None\n # The file token.pickle stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n if os.path.exists('token.pickle'):\n with open('token.pickle', 'rb') as token:\n creds = pickle.load(token)\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n 'credentials.json', SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open('token.pickle', 'wb') as token:\n pickle.dump(creds, token)\n\n service = build('docs', 'v1', credentials=creds)\n\n\n doc = service.documents().get(documentId=doc_id).execute()\n doc_content = doc.get('body').get('content')\n s = read_strucutural_elements(doc_content)\n\n if DEBUG:\n print(s)\n return s","title":""},{"docid":"abf7aec46cc7c43fdcae8b904acff14e","score":"0.5337387","text":"def get_specific_listed_doc(self, doc_id: int) -> Document:\n for doc in self.docs:\n if doc.document_id == doc_id:\n return doc\n else:\n raise ValueError(\"Document ID provided ({}) is not in the list of documents\".format(doc_id))","title":""},{"docid":"9e7ad6c1c92072e75ae3059d94094fe8","score":"0.5325918","text":"def load_doc(filename):\n\tfile = open(filename, 'r')\n\ttext = file.read()\n\tfile.close()\n\treturn text","title":""},{"docid":"72d9c5e3562efaa8a81d3e90fb5f93bd","score":"0.5317124","text":"def __request_solrxml(self, sbid):\n SHOW_FUNCTION = '_design/scielobooks/_show/solr_xml'\n request_url = '/'.join([self.__db_uri, self.__db_name, SHOW_FUNCTION, sbid])\n\n return urllib2.urlopen(request_url).read()","title":""},{"docid":"b5b317f90286bdab37c5186a689f1b60","score":"0.527629","text":"def indoc():\n return to_one(\"data/ideological_books/doc.txt\")","title":""},{"docid":"2c04fc1947a60e97a3275a2920ba44ce","score":"0.52638984","text":"def document(self, *document_path) -> DocumentReference:","title":""},{"docid":"52d56d6e25473655af3ccf1ac5f90aa3","score":"0.52608144","text":"def addDocument(url):\n if not url:\n print \"stocazzo\"\n from teapot.boris import docloader\n print docloader.add_document_to_fuseki(url)","title":""},{"docid":"51fc67515362a287ae13cad24319262f","score":"0.52499306","text":"def get_document_by_id(asset_id, document_id, file_ext):\n follower = current_app.config['follower']\n\n mime_type = \"\"\n first_chunk = None\n\n iterator = iter(follower.request_sap_document(asset_id, document_id))\n\n try:\n mime_type, first_chunk = next(iterator)\n except AssetUnknown:\n logger.warning('T2 - Asset %s no longer known, ignoring', asset_id)\n raise NotFound()\n except T2ResponseException as ex:\n if ex.reason == T2ReqFailureReason.REQ_UNHANDLED:\n logger.error('T2 request not handled by provider')\n elif ex.reason == T2ReqFailureReason.RESOURCE_UNKNOWN:\n logger.error('Data not available for given request and asset')\n raise NotFound()\n else:\n logger.error('T2 failed - reason: %s', ex.reason)\n raise InternalServerError({\"code\": \"failed requesting document\", \"description\": str(ex)}, status_code=500)\n except T2Unavailable as ex:\n msg = 'T2 functionality not enabled in follower'\n logger.critical(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(ex)}, status_code=500)\n except T2Timeout as ex:\n msg = 'T2 request timed out'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(ex)}, status_code=500)\n except ShutdownRequested as ex:\n msg = 'The follower is shutting down'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(ex)}, status_code=500)\n except NotConnected as ex:\n msg = 'The follower temporarily lost connection to the T2 providers'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(ex)}, status_code=500)\n except Exception as ex:\n msg = 'T2 failure fetching sap document'\n raise InternalServerError({\"code\": msg, \"description\": str(ex)}, status_code=500)\n\n @stream_with_context\n def generate():\n try:\n yield first_chunk\n for _, next_chunk in iterator:\n yield next_chunk\n except T2Timeout as ex:\n msg = 'T2 request timed out'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(ex)}, status_code=500)\n except ShutdownRequested as ex:\n msg = 'The follower is shutting down'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(ex)}, status_code=500)\n except NotConnected as ex:\n msg = 'The follower temporarily lost connection to the T2 providers'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(ex)}, status_code=500)\n except Exception as ex:\n msg = 'T2 failure fetching sap document'\n raise InternalServerError({\"code\": msg, \"description\": str(ex)}, status_code=500)\n\n try:\n response = Response(generate(), mimetype=mime_type)\n response.headers['Content-Disposition'] = 'attachment; filename=\"{}.{}\"'.format(document_id, file_ext)\n return response\n except Exception:\n logger.exception(\"get document by id failed\")\n raise","title":""},{"docid":"b20524f055c8c20c3c7b713f7958a70a","score":"0.52317876","text":"def open_document(self, docType, documentID):\n if docType == 'query':\n try:\n self.view.SetSelection(self.documentsOpen[documentID].page)\n except KeyError:\n document = self.profile.load_doc(documentID)\n self.documentsOpen[document.documentID] = selectpanel.QueryController(self.view, document, self.profile)","title":""},{"docid":"db67b49cfc1b4f2d8692c45e7fb8ef69","score":"0.51832986","text":"def loader(db, bids=None, use_journals=True, use_monographs=True):\n # TODO: add filter by issue\n # TODO: decide on force parsing on already parsed stuff: problem of disambiguations\n\n logging.basicConfig(filename=\"reference_parsing/logs/loader.log\", level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n # Initialize counters and data structures\n data = list() # a list of documents with all data for every page (both annotated and not)\n\n # check boundaries of loading\n if not bids or len(bids) == 0:\n if use_journals and use_monographs:\n bids = list(set([x[\"bid\"] for x in db.documents.find()]))\n elif use_journals:\n bids = list(set([x[\"bid\"] for x in db.documents.find() if x[\"type\"] == \"journal_issue\"]))\n elif use_monographs:\n bids = list(set([x[\"bid\"] for x in db.documents.find() if x[\"type\"] == \"monograph\"]))\n logger.info('Bids list established.')\n logger.info('Number of bids: %d'%len(bids))\n\n for bid in bids[:how_many]:\n # Build query for identifiers\n query = {\"bid\": bid}\n\n for doc in db.documents.find(query,no_cursor_timeout=True):\n #TODO: note that this check should be done in metadata.\n if \"marked_as_removed\" in doc and doc[\"marked_as_removed\"]:\n logger.info(str(doc[\"_id\"]) + \" Marked as removed, SKIPPING\")\n continue\n doc_type = doc[\"type\"]\n doc_number = \"\"\n if doc_type == \"journal_issue\" and \"number\" in doc.keys():\n doc_number = doc[\"number\"]\n doc_id = doc[\"_id\"]\n proc_doc = db.processing.find_one({\"bid\":bid,\"number\":doc_number})\n if not proc_doc or proc_doc[\"is_parsed\"] or not proc_doc[\"is_ingested_ocr\"]:\n logger.warning(str(doc[\"_id\"]) + \" Missing, no text yet or already parsed, SKIPPING\")\n continue\n logger.info(str(doc[\"_id\"])+\" OK\")\n pages = OrderedDict()\n\n for page in db.pages.find({\"_id\": {\"$in\": doc[\"pages\"]}}):\n page_number = int(page[\"single_page_file_number\"])\n pages[page_number] = {\"offsets\":list(),\"page_id\":\"\",\"page_mongo_id\":\"\",\"single_page_file_number\":page_number}\n page_id = bid+\"-\"+doc_number+\"-page-\"+page[\"filename\"].split(\"-\")[-1].split(\".\")[0]\n page_mongo_id = page[\"_id\"]\n container = list()\n if page[\"is_annotated\"]:\n logger.warning(page_id + \" Page annotated!! CHECK, SKIPPING\")\n continue\n # load text\n # create a reverse index\n offsets = dict()\n for line in page[\"lines\"]:\n line_number = line[\"line_number\"]\n if line_number is None:\n continue\n for token in line[\"tokens\"]:\n italics = False\n bold = False\n size = \"\"\n if \"features\" in token.keys():\n for feature in token[\"features\"]:\n feat = feature[\"feature\"]\n value = feature[\"value\"]\n if \"font-weight\" == feat:\n if value == \"bold\":\n bold = True\n if \"font-style\" == feat:\n if value == \"italics\":\n italics = True\n if \"font-size\" == feat:\n if \"small\" in value:\n size = \"small\"\n elif \"medium\" in value:\n size = \"medium\"\n elif \"large\" in value:\n size = \"large\"\n offsets.update({token[\"offset_start\"]: {\"surface\": token[\"surface\"],\n \"position\": token[\"token_number\"],\n \"end\": token[\"offset_end\"], \"line\": line_number,\n \"italics\": italics, \"bold\": bold, \"size\": size,\n \"bid\": bid,\n \"general_category\": \"\", \"specific_category\": \"\",\n \"beginend\": \"o\", \"taggedbe\": \"o\"}})\n offsets = OrderedDict(sorted(offsets.items(), key=lambda key_value: key_value[0]))\n\n # store meta\n for start,token in offsets.items():\n new_token = ((token[\"surface\"],start,token[\"end\"],token[\"position\"],token[\"line\"],token[\"bid\"]),(token[\"italics\"],token[\"bold\"],token[\"size\"]))\n container.append(new_token)\n # store annotated page in pertinent article\n pages[page_number][\"offsets\"] = container\n pages[page_number][\"page_id\"] = page_id\n pages[page_number][\"page_mongo_id\"] = page_mongo_id\n doc_data = {\"doc_mongo_id\":doc_id,\"doc_type\":doc_type,\"pages\":pages,\"bid\":bid,\"doc_number\":doc_number}\n data.append(doc_data)\n\n return data","title":""},{"docid":"17fedd119cf9e2453ad0f04b6b3c5cbf","score":"0.51598805","text":"def get_doc(doc_id):\n if not doc_id:\n abort(404)\n\n _file = UserDoc.query.get_or_404(doc_id)\n\n file_name = _file.filename\n # people who have access to the paper, administrators of this conference,\n # authors, reviewers.\n if _file.uploader_id != current_user.id:\n return abort(403)\n else:\n try:\n file_extension = file_name.split('.')[-1]\n _name = 'not_submitted'\n if _file.paper_id:\n _name = str(_file.paper_id)\n return send_file(\n current_app.config['PDF_URL'] + file_name,\n attachment_filename=_file.paper.conference.short_name +\n '_paper_' + _name + '.' + file_extension,\n as_attachment=True)\n else:\n flash('Paper doesn\\'t exist',\n 'error')\n return redirect(url_for('main.dashboard'))\n except IOError:\n flash('Download failed, please contact supporting assistant',\n 'error')\n return redirect(url_for('main.dashboard'))","title":""},{"docid":"c51dc90a5e1dadf5c278085b5c7a9d6e","score":"0.5152505","text":"def read(id):","title":""},{"docid":"a82f5766cbd94fd44bd8a6af177d1c59","score":"0.5105094","text":"def get_document(name, v):","title":""},{"docid":"548920c9727cf85e6187f2403a41a13c","score":"0.5097045","text":"def read_doc(self, doc_id, rev_id=None, datastore_name=None, object_type=None):\n ds, datastore_name = self._get_datastore(datastore_name)\n if not rev_id:\n try:\n status, cas, doc = ds.get(doc_id)\n except MemcachedError as e:\n raise NotFound('Object with id %s could not be read. Exception: %s' % (doc_id, e.message))\n if doc is None:\n raise NotFound('Object with id %s does not exist.' % str(doc_id))\n else:\n ## review\n doc = json.loads(doc)\n doc['_rev'] = cas\n doc['_id'] = str(doc['_id']) #doc_id\n else:\n raise NotImplementedError()\n return doc","title":""},{"docid":"53e5813a1314433c447ea5fca418426a","score":"0.5097007","text":"def get_data_for_id(dbname, dataid, url=cc.URL) :\n r = request('%s/%s/gridfs/%s'%(url,dbname,dataid))\n logger.debug('get_data_for_docid:'\\\n +'\\n r.status_code: %s\\n r.headers: %s\\n r.encoding: %s\\n r.content: %s...\\n' % \n (str(r.status_code), str(r.headers), str(r.encoding), str(r.content[:50])))\n return r.content","title":""},{"docid":"cd8e371307e8c24b1e8b43b1c7e2029a","score":"0.5088892","text":"def getDocByIdAsXml(self, colId, docId, nrOfTranscripts=None, bParse=True):\r\n self._assertLoggedIn()\r\n myReq = self.sREQ_collection_fulldoc_xml % (colId,docId)\r\n params = self._buidlParamsDic(nrOfTranscripts=nrOfTranscripts)\r\n resp = self._GET(myReq, params=params)\r\n resp.raise_for_status()\r\n \r\n #we should get some json serialized data \r\n if bParse:\r\n return self._xmlParseDoc(resp.text)\r\n else: \r\n return resp.text","title":""},{"docid":"a383d9a61291cdbac73c38068c438881","score":"0.5071902","text":"def ext_text(doc_id):\n\n doc = Document.get(Document.id==doc_id)\n\n if doc.syllabus.text:\n\n return Document_Text.create(\n text=doc.syllabus.text,\n document=doc\n )","title":""},{"docid":"116012e173cb4ec9c5cb081683c21d58","score":"0.5056275","text":"def get_talend_document_by_id(asset_id, document_label, document_name):\n\n follower = current_app.config['follower']\n\n mime_type = \"\"\n first_chunk = None\n\n iterator = iter(follower.request_talend_document(asset_id, document_label, document_name))\n\n try:\n mime_type, first_chunk = next(iterator)\n except AssetUnknown:\n msg = 'T2 - Asset {} no longer known, ignoring'.format(asset_id)\n logger.warning(msg)\n raise NotFound()\n except T2ResponseException as ex:\n # ex.reason is T2ReqFailureReason - see code documentation for what they mean\n if ex.reason == T2ReqFailureReason.REQ_UNHANDLED:\n logger.error('T2 request not handled by provider')\n # equivalent to HTTP 404\n elif ex.reason == T2ReqFailureReason.RESOURCE_UNKNOWN:\n logger.error('Data not available for given request and asset')\n raise NotFound()\n else:\n logger.error('T2 failed - reason: %s', ex.reason)\n\n raise InternalServerError({\"code\": \"failed requesting document\", \"description\": str(\n ex)}, status_code=500)\n except T2Unavailable as ex:\n msg = 'T2 functionality not enabled in follower'\n logger.critical(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(\n ex)}, status_code=500)\n except T2Timeout as ex:\n msg = 'T2 request timed out'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(\n ex)}, status_code=500)\n except ShutdownRequested as ex:\n msg = 'The follower is shutting down'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(\n ex)}, status_code=500)\n except NotConnected as ex:\n msg = 'The follower temporarily lost connection to the T2 providers'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(\n ex)}, status_code=500)\n except Exception as ex:\n msg = 'T2 failure fetching document'\n raise InternalServerError({\"code\": msg, \"description\": str(\n ex)}, status_code=500)\n\n @stream_with_context\n def generate():\n try:\n yield first_chunk\n for _, next_chunk in iterator:\n yield next_chunk\n except T2Timeout as ex:\n msg = 'T2 request timed out'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(\n ex)}, status_code=500)\n except ShutdownRequested as ex:\n msg = 'The follower is shutting down'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(\n ex)}, status_code=500)\n except NotConnected as ex:\n msg = 'The follower temporarily lost connection to the T2 providers'\n logger.error(msg)\n raise InternalServerError({\"code\": msg, \"description\": str(\n ex)}, status_code=500)\n except Exception as ex:\n msg = 'T2 failure fetching document'\n raise InternalServerError({\"code\": msg, \"description\": str(\n ex)}, status_code=500)\n\n try:\n response = Response(generate(), mimetype=mime_type)\n response.headers['Content-Disposition'] = 'attachment; filename=\"{}\"'.format(\n document_name)\n return response\n except Exception:\n logger.exception(\"get document by id failed\")\n raise","title":""},{"docid":"e0d5c5d13b707fba6ba06680ad04f85b","score":"0.5052802","text":"def load(self, gerrit_id):\n raise NotImplementedError()","title":""},{"docid":"15cc8e1f511309a236ca4108a4efe681","score":"0.5048177","text":"def get_document_by_id(self, doc_id):\n c = self.db.cursor()\n row = c.execute(\"SELECT text, opening_text, auxiliary_text, categories, headings, wiki_text, popularity_score, num_incoming_links FROM articles WHERE title=?\", (doc_id,)).fetchone()\n if row is None:\n return None\n return WikipediaArticle(self, doc_id,\n row[0], # text\n row[1], # opening_text\n json.loads(row[2]), # auxiliary_text\n json.loads(row[3]), # categories\n json.loads(row[4]), # headings\n row[5], # wiki_text\n row[6], # popularity_score\n row[7], # num_incoming_links\n )","title":""},{"docid":"da143a8f257f66b44a630c9ed2d55004","score":"0.5043891","text":"def loadDocument(self, filename, password=''):\n self.doc = Poppler.Document.load(filename, password, password)\n self.doc.setRenderHint(Poppler.Document.TextAntialiasing | Poppler.Document.TextHinting |\n Poppler.Document.Antialiasing | 0x00000020 )","title":""},{"docid":"fc20f6ee4064fd62ea0260bb202ca0da","score":"0.5043508","text":"def get_doc(self, uid):\n\n doc = None\n sql = \"select uid,name,location,kind FROM document WHERE uid = ?\"\n c = self.conn.cursor()\n c.execute(sql, (uid,))\n row = c.fetchone()\n if row:\n doc = Document(self, row[0], row[1], row[2], row[3])\n c.close()\n return doc","title":""},{"docid":"048bc6d6e17d2ee6950f021e28c5003c","score":"0.50360346","text":"def load(cls, backend_id):\n # Circular import\n from corehq.apps.sms.util import get_available_backends\n backend_classes = get_available_backends()\n backend = cls.get(backend_id)\n if backend.doc_type not in backend_classes:\n raise Exception(\"Unexpected backend doc_type found '%s' for backend '%s'\" % (backend.doc_type, backend._id))\n else:\n return backend_classes[backend.doc_type].wrap(backend.to_json())","title":""},{"docid":"b3c41b3997847918a3caa93fe7db055f","score":"0.50352436","text":"def docid(self):\n return self.id.split('.')[0]","title":""},{"docid":"a3825600281da6a01504f94bc3edbbc1","score":"0.5026529","text":"def checkDoc(self, **kw):\n if self.getParam('DOC') == 1:\n self.usid = threading.Thread(target = self.saveParams, args=())\n self.usid.daemon = True\n self.usid.start()\n else:\n return","title":""},{"docid":"72f0e6f0b19a74302f7c4eac9ccc7c95","score":"0.50209934","text":"def get_data_for_docid(dbname, colname, docid, url=cc.URL) :\n doc = get_doc_for_docid(dbname, colname, docid, url)\n logger.debug('get_data_for_docid: %s' % str(doc))\n return get_data_for_doc(dbname, colname, doc, url)","title":""},{"docid":"e66a848a769bfc971658b64b531a7eca","score":"0.50203437","text":"def docno_from_docid(self, docid):\n return self.docid_to_docno[docid]","title":""},{"docid":"83cbea05535c2f0652aea91428a3f2c5","score":"0.5017536","text":"def run(cls: DocumentType, doc: Document) -> None:\n doc.download().get_info().add_to_library()","title":""},{"docid":"a58ad7bc7f14856ecdc37621d5ecc5f1","score":"0.49980134","text":"def load_object(collection, id):\n path = object_path(collection, id)\n return load_object_at_path(path)","title":""},{"docid":"ceafaeb35c777cb8a8fbdc63030636a0","score":"0.49966866","text":"def doc():\n build_docs()","title":""},{"docid":"dbbefd5d640007e4e49a7001535f906b","score":"0.4994027","text":"def get_document_by_id(self, object_id):\n return self.model_class.objects.get(id=object_id)","title":""},{"docid":"8ec4b273f2a28f237a6c6036c1736cc6","score":"0.49901178","text":"def load(self):\n with open(self.disk_file, 'rb') as f:\n res = pickle.load(f)\n self.terms = res[\"terms\"]\n self.normalised_doc_length = res[\"normalised_doc_length\"]\n self.num_of_docs = res[\"num_of_docs\"]\n f.close()","title":""},{"docid":"9f93e2c463fecaf5bb84df0a8cf51629","score":"0.49885288","text":"def open_doc(self,name):\n if(self.database.__contains__(name)):\n doc = self.server.__getitem__(name)\n else:\n self.database.create(name)\n doc = self.server.__getitem__(name)\n\n return doc","title":""},{"docid":"5f7b3ff99e8e672b46105b91b5ca236f","score":"0.49759012","text":"def get_data_for_doc(dbname, colname, doc, url=cc.URL) :\n logger.debug('get_data_for_doc: %s', str(doc))\n idd = doc.get('id_data', None)\n if idd is None :\n logger.debug(\"get_data_for_doc: key 'id_data' is missing in selected document...\")\n return None\n\n r2 = request('%s/%s/gridfs/%s'%(url,dbname,idd))\n s = r2.content\n\n return mu.object_from_data_string(s, doc)","title":""},{"docid":"5707a5033bc76c797131abe9dfa9ea2e","score":"0.49750397","text":"def getWordIdsForDocId(docid):","title":""},{"docid":"d3e1632cd20ed53eaf05eb28681494e2","score":"0.49744937","text":"def load(self, oid):\n if not self.find(oid):\n return None\n object_path = self._object_path(oid)\n logger = logging.getLogger('war.database')\n logger.debug(CF('Loading object %s').dark_gray, object_path)\n with open(object_path, 'rb') as file:\n decompressed = zlib.decompress(file.read())\n obj = pickle.loads(decompressed)\n return obj","title":""},{"docid":"20bfb5e1fab8ff3e2911735c36db484c","score":"0.49615327","text":"def load(self):\n if self.doc_path == None:\n raise TypeError(\"Missing doc_path to load object.\")\n file = gzip.GzipFile(self.doc_path, 'rb')\n buffer = bytes()\n count = 0\n while True:\n data = file.read()\n if data == b'':\n break\n buffer += data\n object = pickle.loads(buffer)\n file.close()\n self._update_self(object)","title":""},{"docid":"ca94601aae278d691175e467e0a0b00f","score":"0.49535155","text":"def insertDocument(docid, WidList):","title":""},{"docid":"9009d9d15dbe0b8655e11e9b7f9d2abd","score":"0.4941772","text":"def load():","title":""},{"docid":"8105b786f96d08b0b7e1c7989841f5fe","score":"0.49398786","text":"def open(self, url):\r\n cache = self.cache()\r\n id = self.mangle(url, 'document')\r\n d = cache.get(id)\r\n if d is None:\r\n d = self.download(url)\r\n cache.put(id, d)\r\n self.plugins.document.parsed(url=url, document=d.root())\r\n return d","title":""},{"docid":"4d000e220288d544e40130d6de9438a0","score":"0.49336287","text":"def load_book_ids():\n pass","title":""},{"docid":"dd73975462c66e414fdeefc585c97f2d","score":"0.49317914","text":"def get_document_iri(doc_id):\n obj = \"document/%s\" % doc_id\n return \"<%s/%s>\" % (GRAPH, obj)","title":""},{"docid":"e960957d622380307711025b6b3ecdfe","score":"0.49234998","text":"def _get_distrubuted_idf(self, fname):\n dis_idf = None\n with open(fname, \"rb\") as f:\n dis_idf = pickle.load(f)\n return dis_idf","title":""},{"docid":"fecdf2538d2166fe7dbfd3721646a480","score":"0.49205577","text":"def get_doc(environ, start_response):\n\n args = environ['app.url_args']\n if args:\n doc_id = escape(args[0])\n username = escape(args[1])\n key = escape(args[2])\n\n if check_credentials(username, key):\n doc_db = dbm.open('doc', 'c')\n if doc_id in doc_db.keys():\n start_response('200 OK',\n [('Content-Type', 'application/json')])\n json_doc = {'doc_id': doc_id, 'doc_text': doc_db[doc_id]}\n return [json.dumps(json_doc)]\n else:\n json_doc = {'status': False,\n 'message': 'Document does not exist.'}\n else:\n json_doc = {'status': False, 'message': 'Missing data in request.'}\n\n start_response('400 BAD DATA', [('Content-Type', 'application/json')])\n return [json.dumps(json_doc)]","title":""},{"docid":"3f2327dd03f7a23dd130ba38adb7aa67","score":"0.4919811","text":"def docs(docket_id, filter_fn=None):\n # Use a list for consistent ordering, which is useful for caching\n params = [('api_key', REGS_GOV_KEY), ('dktid', docket_id), ('rpp', 1000),\n ('sb', 'docId'), ('so', 'ASC'), ('dct', 'N+PR+FR+O+SR')]\n results = http_client().get(REGS_GOV_DOC_API, params=params).json()\n if results.get('error'):\n logger.warning(\"Error retrieving data from regs.gov: %s\",\n results['error'].get('message'))\n for doc_dict in results.get('documents', []):\n if filter_fn is None or filter_fn(doc_dict):\n yield RegsGovDoc(doc_dict['documentId'], doc_dict['title'])","title":""},{"docid":"d9eb391051693198e5ca96029018b08d","score":"0.4910611","text":"def read_doc(path): # El parametro es la ruta donde se encuentra almacenado el documento, seguido del nombre del mismo\n document = pd.read_csv(path, error_bad_lines=False)\n document.head()\n\n columns = ['Supervision','Texto', 'ID']\n document = document[columns]\n document = document[pd.notnull(document['Texto'])]\n document.columns = ['Supervision', 'Texto', 'ID']\n\n return document","title":""},{"docid":"89d8cb39afe33f54966c6a106e761223","score":"0.49031883","text":"def __get_doc_by_domian_name(self):\n index_url = self.main_doc('a:first').attr('href')\n return papa.get_doc(self.domian_name + index_url)","title":""},{"docid":"b9868fc1418dd01fe2d46430a69933b0","score":"0.4892366","text":"def load(organism_id: str) -> SeqRecord:\n if not path.exists(path.join(\"data\", f\"{organism_id}.pkl.gz\")):\n with Entrez.efetch(db=\"nucleotide\", rettype=\"gb\", id=organism_id) as handle:\n record = SeqIO.read(handle, \"gb\")\n with open(path.join(\"data\", f\"{organism_id}.pkl.gz\"), \"wb\") as f:\n pickle.dump(record, f)\n else:\n with open(path.join(\"data\", f\"{organism_id}.pkl.gz\"), \"rb\") as f:\n record = pickle.load(f)\n\n return record","title":""},{"docid":"12174ad2392aa3bbb79f162d757fc710","score":"0.4889138","text":"def load(loadfile, docList):\n docList.clear()\n try:\n f = open(loadfile, 'rb')\n reader = csv.DictReader(f)\n for row in reader:\n docList[int(row['docId'])] = int(row['docLength'])\n finally:\n f.close()","title":""},{"docid":"5e57e83801ccd25584e83c5ac1d15c11","score":"0.4879617","text":"def setHead(self, doc_or_id):\n if isinstance(doc_or_id, basestring):\n self.head = doc_or_id\n try:\n doc = db.get(doc_or_id)\n except (db.BadKeyError, db.BadRequestError):\n # library.createnewdoc runs this inside a transaction\n # and updating doc and trunk at the same time will throw\n # an exception (different entity groups).\n # the caller will take care of updating the title in that case.\n return\n elif isinstance(doc_or_id, DocModel):\n doc = doc_or_id\n self.head = str(doc.key())\n else:\n # Unexpected input type. Ignore.\n return\n if isinstance(doc, DocModel):\n self.title = doc.title\n Subscription.notifyChange(self)","title":""},{"docid":"3f9b007b10241a1e19d170c7fb2baec7","score":"0.4853038","text":"def loadBookmark(ned, info, *args):\n\n pass","title":""},{"docid":"852904d3a9d7c41cadf1b8cc95f38256","score":"0.48505253","text":"def get_dso_docs(doc_oid, query_args) :\n\n\tprint()\n\tprint(\"-+- \"*40)\n\tlog.debug( \"... get_dso_docs \" )\n\n\tdso_doc_collection\t= db_dict_by_type['dso_doc']\n\n\tquery \t= build_first_term_query(doc_oid, query_args)\n\tlog.debug('query : \\n%s', pformat(query) ) \n\n\t# results = dso_doc_collection.find({'oid_dso' : doc_oid })\n\tcursor \t= dso_doc_collection.find(query)\n\n\tresults = list(cursor)\n\n\treturn results","title":""},{"docid":"c45952dc393a8f22bd59a9e96d61edf8","score":"0.48396093","text":"def retrieve_title(doc_id):\n creds = None\n # The file token.pickle stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n if os.path.exists('token.pickle'):\n with open('token.pickle', 'rb') as token:\n creds = pickle.load(token)\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n 'credentials.json', SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open('token.pickle', 'wb') as token:\n pickle.dump(creds, token)\n\n service = build('docs', 'v1', credentials=creds)\n\n\n doc = service.documents().get(documentId=doc_id).execute()\n doc_content = doc.get('title')\n if DEBUG:\n print(doc_content)\n\n return doc_content","title":""},{"docid":"179645d225bd76faa65677ac6c9f6f8c","score":"0.48380253","text":"def document(\n self, document_id: str = None\n ) -> async_document.AsyncDocumentReference:\n return super(AsyncCollectionReference, self).document(document_id)","title":""},{"docid":"7cd6a8bed607866679a3fb1ef774e3b0","score":"0.48290414","text":"def set_document_file_id(self, document_file_id):\n raise NotImplementedError()","title":""},{"docid":"8bee32bae419963963e0cf7e32dbb700","score":"0.48207775","text":"def openToolBOSDocumentation( fileName, parent=None ):\n Any.requireIsTextNonEmpty( fileName )\n\n logging.info( 'opening online documentation' )\n\n url = ToolBOSConf.getConfigOption( 'documentationURL_dir' ) + \\\n fileName + '.html'\n\n browser = WebBrowser( 'Documentation', parent )\n browser.open( url )","title":""},{"docid":"d8b6303d0308d86f7f71c50cecb135d1","score":"0.4819432","text":"def load_doc_chunks(path: str):\n ret = []\n if os.path.exists(path):\n with open(path, 'rb') as fr:\n try:\n while True: # until EOF\n ret.append(pickle.load(fr))\n except:\n return ret","title":""},{"docid":"c9b5be57379ef8f965fc7ee94bb05a47","score":"0.48114958","text":"def _doc_id(self, doc_slug: str, version: str) -> str:\n if not doc_slug:\n # Not all docs have a parent doc, in case we get no slug\n # we just return an empty string.\n return \"\"\n\n README_API_KEY = os.getenv(\"README_API_KEY\")\n if not README_API_KEY:\n warnings.warn(\"README_API_KEY env var is not set, using a placeholder doc ID\")\n return \"fake-doc-id\"\n\n headers = create_headers(version)\n res = requests.get(f\"https://dash.readme.com/api/v1/docs/{doc_slug}\", headers=headers, timeout=60)\n if not res.ok:\n sys.exit(f\"Error requesting {doc_slug} doc for version {version}\")\n\n return res.json()[\"id\"]","title":""},{"docid":"e659b40136dca60bec3fa0bda344d0cd","score":"0.4811252","text":"def load_user(id):\n\n try:\n user = User.get(id=id)\n except User.NoDocumentFound:\n return None\n else:\n return user","title":""},{"docid":"7ba0dd6ee1f011088271d57dd6c5c4d3","score":"0.48111725","text":"def load(self, id, level=AccessType.ADMIN, user=None, objectId=True,\n force=False, fields=None, exc=False):\n doc = Model.load(self, id=id, objectId=objectId, fields=fields, exc=exc)\n\n if not force and doc is not None:\n self.model('folder').load(doc['folderId'], level, user, objectId,\n force, fields)\n\n return doc","title":""},{"docid":"1a88e5aad653a12351c6b41c70e845d8","score":"0.4806092","text":"def get(self, index, doc_type, id):\n path = self._make_path([index, doc_type, id])\n response = self._send_request('GET', path)\n return response","title":""},{"docid":"36034d720dc43861ddd9b4a436e4acc3","score":"0.4805629","text":"def load_from_db(self):\n logger.info('loading state from mongo')\n ddoc = self.ddoc_get()\n blob = self.mongo_handle.get_attachment(ddoc, name='pkl')\n return cPickle.loads(blob)","title":""},{"docid":"c24ae408dd18d90a8a63c50b24d15b90","score":"0.47940406","text":"def load(astergui, file_name, url=''):\n return Study(astergui, file_name=file_name, url=url)","title":""},{"docid":"2327195497ef23f0bd7dc3c1e29d8656","score":"0.4790033","text":"def update_doc(doc):\n pass","title":""},{"docid":"3090388b0c7634bcb7411b8d4e2865c4","score":"0.4787972","text":"def loads(self):\n #id dictinories\n #id -- word\n self.word_2_id = load_vocab(self.vocab_filename) \n self.tag_2_id = load_vocab(self.tag_filename)\n if self.use_chars:\n\t self.char_2_id = load_vocab(self.char_filename)\n\t self.nchars = len(self.char_2_id)\n \n \n #load number of unique words present\n self.nwords = len(self.word_2_id)\n self.ntags = len(self.tag_2_id)\n \n #load embeddings \n self.word_embeddings = (get_glove_vectors(filename=self.vocab_embedding_filename)\n if self.use_pretrained else None)\n # self.tag_embeddings = get_glove_vectors(filename=self.tag_embedding_filename)\n \n #processing function that will map word str to id \n self.process_words = get_processing_word(vocab_words=self.word_2_id, vocab_chars=self.char_2_id,chars=self.use_chars)\n self.process_tags = get_processing_word(vocab_words=self.word_2_id, vocab_chars=self.char_2_id,chars=False)","title":""},{"docid":"a5b8ef5c52535a6f885e51ea96856de6","score":"0.47878912","text":"def test_get_doc1(self):\n doc = self.es_client.get_document(12345, index_name=TEST_ES_INDEX)\n assert not doc, \"Got {}\".format(doc)","title":""},{"docid":"f287f7f982b41765f8ceeb3a4cbf4045","score":"0.47874275","text":"def find_document(document_id: DocumentID) -> Optional[Document]:\n document = DbDocument.query.get(document_id)\n\n if document is None:\n return None\n\n return _db_entity_to_document(document)","title":""},{"docid":"c77b5438c14b6ea7d7d97a6d4df7e3fc","score":"0.47831517","text":"def load(self, fp, name=None, headers=False, **kwargs):\n return self.document(fp, name=name, headers=headers, **kwargs)","title":""},{"docid":"a78a9212e53f6cab90bab33ca73c64cd","score":"0.47792616","text":"def findById(cls, lobbyId):\n return cls.query.filter_by(lobbyId=lobbyId).first()","title":""},{"docid":"5530339761264873a286655e1db1c32b","score":"0.47767955","text":"def __init__(self,inputFileName):\n self.doc = open(inputFileName).read()","title":""},{"docid":"f98434b3a1082e08939e4f425898a491","score":"0.47672322","text":"def read(self, fmerge):\n if not isfile(fmerge):\n raise IOError(\"File doesn't exist: {}\".format(fmerge))\n self.fmerge = fmerge\n gidx, tokendict = 0, {}\n with open(fmerge, 'r') as fin:\n for line in fin:\n line = line.strip()\n if len(line) == 0:\n continue\n tok = self._parseline(line)\n tokendict[gidx] = tok\n gidx += 1\n # Get EDUs from tokendict\n edudict = self._recoveredus(tokendict)\n doc = Doc()\n doc.tokendict = tokendict\n doc.edudict = edudict\n return doc","title":""},{"docid":"5ed2b5abe6195e62e19e1718b73e8465","score":"0.47506806","text":"def loadBooks(filename):\n booksfile = cf.data_dir + filename\n return model.addBooks(booksfile)","title":""},{"docid":"4c1bab03279269670882ff5ad49f8253","score":"0.47495303","text":"def getDocumentsForWordIds(wordidlist):","title":""},{"docid":"12e27f3da47f5b29946b0e9bb802a37c","score":"0.47417745","text":"def main(bib_file):\n bib_file = os.environ[\"BUILD_WORKING_DIRECTORY\"] + \"/\" + bib_file\n with open(bib_file, \"r\") as bibtex_file:\n bib_database = bibtexparser.load(bibtex_file)\n\n # [name].bib -> [name].dblp.bib\n out_file = open(bib_file[:-4] + \".dblp.bib\", \"w\")\n\n for i, entry in enumerate(bib_database.entries):\n print(\"Entry:\", i + 1, \"/\", len(bib_database.entries))\n print(\"Title:\", entry.get(\"title\", \"[None]\"))\n print(\"Author:\", entry.get(\"author\", \"[None]\"))\n bibtex_id = entry[\"ID\"]\n results = search_dblp(entry[\"title\"])\n\n if not results.empty:\n result = select_row(results)\n\n if results.empty or result is None:\n out_file.write(\"\\n% COULD NOT FIND \" + entry[\"ID\"]\n + \": \" + entry.get(\"title\", \"[None]\")\n + \" by \" + entry.get(\"author\", \"[None]\") + \"} \\n\")\n continue\n\n bibtex_url = f\"https://dblp.uni-trier.de/rec/{result.Id}.bib?param=1\"\n bibtex_entry = urllib.request.urlopen(bibtex_url).read().decode(\"utf-8\")\n bibtex_entry = set_id(bibtex_entry, bibtex_id)\n print(bibtex_entry)\n out_file.write(bibtex_entry)\n out_file.close()","title":""},{"docid":"20794679b7433231d81c254095fa834d","score":"0.47389236","text":"def retrieve_document(document_id: str) -> Response:\n try:\n result = SearchSession.get_document(document_id) # type: ignore\n except index.IndexConnectionError as ex:\n raise BadGateway(\n \"There was a problem connecting to the search index. This is \"\n \"quite likely a transient issue, so please try your search \"\n \"again. If this problem persists, please report it to \"\n \"help@arxiv.org.\"\n ) from ex\n except index.QueryError as ex:\n raise BadRequest(\n \"There was a problem executing your query. Please try a \"\n \"different query. If this problem persists, please report it to \"\n \"help@arxiv.org.\"\n ) from ex\n except index.DocumentNotFound as ex:\n logger.error(\"DocumentNotFound: %s\", ex)\n raise NotFound(f\"Could not find a paper with id {document_id}\") from ex\n return {\"document\": result}, HTTPStatus.OK, {}","title":""},{"docid":"e5092e782acd63a759bd84321bab9e50","score":"0.47386977","text":"def load(self, what, loaded=None):\n id_list = []\n\n if isinstance(loaded, list):\n loaded_dict = {(x.id if isinstance(x, couchdb.client.Row) else x['_id']): (x.doc if isinstance(x, couchdb.client.Row) else x) for x in loaded}\n else:\n loaded_dict = loaded or {}\n\n #if not isinstance(what, (list, couchdb.client.ViewResults)):\n if not isinstance(what, list):\n load_list = [what]\n else:\n load_list = what\n\n for item in load_list:\n #print \"item\", item\n if isinstance(item, basestring):\n id_list.append(item)\n elif isinstance(item, couchdb.client.Row):\n id_list.append(item.id)\n\n if hasattr(item, 'doc'):\n loaded_dict[item.id] = item.doc\n\n elif isinstance(item, dict):\n id_list.append(item['_id'])\n\n if len(item) > 2:\n loaded_dict[item['_id']] = item\n\n if not isinstance(what, list):\n #print \"what\", what\n return [self._load(_id, loaded_dict) for _id in id_list][0]\n else:\n #print \"id_list\", id_list\n return [self._load(_id, loaded_dict) for _id in id_list]","title":""},{"docid":"705663e1f547e4d3599fc44cc94b5737","score":"0.47383815","text":"def load_script(self):\n try:\n self.raw_text = docx2txt.process(self.script_filepath)\n except Exception as err:\n raise RuntimeError(\"Script failed to load with the following error: \", err)","title":""},{"docid":"5b4abc6348810686700d3e2371d8cd0b","score":"0.47383443","text":"def show_doc(doc_id):\n \n doc = crud.get_doc_by_doc_id(doc_id)\n authors = crud.get_author_by_doc_id(doc_id)\n img_urls = crud.get_image_url_by_doc_id(doc_id)\n\n session['doc_id'] = doc_id\n\n img_url_list = []\n for img_url in img_urls:\n img_url_list.append(img_url.url)\n\n author_list = []\n for author in authors:\n author_list.append(author.name)\n\n return jsonify({\n 'doc': doc, \n 'authors': \" \".join(author_list), \n 'img_urls': img_url_list\n })","title":""}],"string":"[\n {\n \"docid\": \"d07dff9a9e2a95a7dd8b26d6437b7032\",\n \"score\": \"0.6116128\",\n \"text\": \"def get_document(self, docid):\\n return self.backend.get_document(docid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ccd0641fd2c91275191ac035c3efc67\",\n \"score\": \"0.59131324\",\n \"text\": \"def get_document_by_id(self, id):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45bb6d6f57cebebab0bd5a60b6facb1a\",\n \"score\": \"0.5780434\",\n \"text\": \"def load(self, cname, oid):\\n check_oid(oid)\\n docbytes = self.__ejdb.load(cname, oid)\\n if docbytes is None:\\n return None\\n return bson.parse_bytes(docbytes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dcf93dee753f62c3c16b7e7f53dcd5f\",\n \"score\": \"0.57688785\",\n \"text\": \"def getDocumentsForWordId(wordid):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0916893c8688a16227e709ef7828cda\",\n \"score\": \"0.57070434\",\n \"text\": \"def get(self, doc_id, document_class=None):\\n doc = self._index.get(doc_id)\\n document_class = document_class or self.document_class\\n if doc and document_class:\\n return construct_document(document_class, doc)\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0534d00187a7c31b0ffb73972c6ad26e\",\n \"score\": \"0.56429005\",\n \"text\": \"def get_doc(path,text_id):\\n\\tdocs = json_to_docs(path)\\n\\tfor doc in docs:\\n\\t\\tif doc['text_id']==text_id:\\n\\t\\t\\treturn doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90fdf91740f44ad54ba14faa93574c6e\",\n \"score\": \"0.5626155\",\n \"text\": \"def get_doc_for_docid(dbname, colname, docid, url=cc.URL) :\\n r = request('%s/%s/%s/%s'%(url,dbname,colname,docid))\\n return r.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614a4dd8e10441cf50e23808252a4040\",\n \"score\": \"0.56003904\",\n \"text\": \"def document(self, document_id: Optional[str] = None) -> DocumentReference:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88b72b8c729db95896ace8d6bd5742b5\",\n \"score\": \"0.5555827\",\n \"text\": \"def document_view(\\n docid):\\n encrypted = TimedJSONWebSignatureSerializer(current_app.config['SECRET_KEY'],\\n 60 * 30) # 60 secs by 30 mins\\n token = encrypted.dumps({'docid': docid}).decode('utf-8') # encode user id\\n doc = File.query.get(docid)\\n return render_template(\\n \\\"document.html\\\",\\n token=token,\\n doc_name=doc.title,\\n transcript=doc.transcript)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9f09ec1c356757000b2c40f48d5f60e\",\n \"score\": \"0.55219954\",\n \"text\": \"def __setDoc(self, doc_id: str = \\\"http://isi.edu/default-ns/projects\\\"):\\n doc = self.etk.create_document({}, doc_id=doc_id)\\n # bind prefixes\\n doc.kg.bind(\\\"wikibase\\\", \\\"http://wikiba.se/ontology#\\\")\\n doc.kg.bind(\\\"wd\\\", \\\"http://www.wikidata.org/entity/\\\")\\n doc.kg.bind(\\\"wdt\\\", \\\"http://www.wikidata.org/prop/direct/\\\")\\n doc.kg.bind(\\\"wdtn\\\", \\\"http://www.wikidata.org/prop/direct-normalized/\\\")\\n doc.kg.bind(\\\"wdno\\\", \\\"http://www.wikidata.org/prop/novalue/\\\")\\n doc.kg.bind(\\\"wds\\\", \\\"http://www.wikidata.org/entity/statement/\\\")\\n doc.kg.bind(\\\"wdv\\\", \\\"http://www.wikidata.org/value/\\\")\\n doc.kg.bind(\\\"wdref\\\", \\\"http://www.wikidata.org/reference/\\\")\\n doc.kg.bind(\\\"p\\\", \\\"http://www.wikidata.org/prop/\\\")\\n doc.kg.bind(\\\"pr\\\", \\\"http://www.wikidata.org/prop/reference/\\\")\\n doc.kg.bind(\\\"prv\\\", \\\"http://www.wikidata.org/prop/reference/value/\\\")\\n doc.kg.bind(\\n \\\"prn\\\", \\\"http://www.wikidata.org/prop/reference/value-normalized/\\\"\\n )\\n doc.kg.bind(\\\"ps\\\", \\\"http://www.wikidata.org/prop/statement/\\\")\\n doc.kg.bind(\\\"psv\\\", \\\"http://www.wikidata.org/prop/statement/value/\\\")\\n doc.kg.bind(\\n \\\"psn\\\", \\\"http://www.wikidata.org/prop/statement/value-normalized/\\\"\\n )\\n doc.kg.bind(\\\"pq\\\", \\\"http://www.wikidata.org/prop/qualifier/\\\")\\n doc.kg.bind(\\\"pqv\\\", \\\"http://www.wikidata.org/prop/qualifier/value/\\\")\\n doc.kg.bind(\\n \\\"pqn\\\", \\\"http://www.wikidata.org/prop/qualifier/value-normalized/\\\"\\n )\\n doc.kg.bind(\\\"skos\\\", \\\"http://www.w3.org/2004/02/skos/core#\\\")\\n doc.kg.bind(\\\"prov\\\", \\\"http://www.w3.org/ns/prov#\\\")\\n doc.kg.bind(\\\"schema\\\", \\\"http://schema.org/\\\")\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2955c98e10622aaca9e562ae93a9c5bf\",\n \"score\": \"0.5503802\",\n \"text\": \"def extract_doc(occi_id, db_data):\\n for data in db_data:\\n if data['OCCI_ID'] == occi_id:\\n logger.debug(\\\"Document \\\" + occi_id + \\\" is found\\\")\\n return data['Doc']\\n logger.error(\\\"Document \\\" + occi_id + \\\"couldn't be found\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1573b229ed16a09135716ee1e2f900c\",\n \"score\": \"0.548856\",\n \"text\": \"def load(self, project_sfid):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf9575da7f69206182a9bde14ef7c68\",\n \"score\": \"0.5483516\",\n \"text\": \"def get_docs_by_bnid(db, bnid):\\n rows = db.view('igsb_qc/all', key=bnid, descending=True)\\n for row in rows:\\n pp.pprint(row['value'])\\n print row['value']['_id']\\n return rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61a08f115840ad4b92a761b927bffb54\",\n \"score\": \"0.5469454\",\n \"text\": \"def pull_doc(self, uid):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b5aefab5e62b1221b106bb41d5243cd\",\n \"score\": \"0.5441727\",\n \"text\": \"def index_doc(self, docid, ob):\\n if self.state == ID_STATE_OFF:\\n return\\n self.index(docid, ob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30ce4d96ff767379793a23fa3b3eaf2e\",\n \"score\": \"0.54225594\",\n \"text\": \"def init_doc() -> None:\\n bk_settings.simple_ids.set_value(False)\\n doc = Document()\\n set_curdoc(doc)\\n doc.hold()\\n doc._session_context = lambda: MockSessionContext(document=doc)\\n state.curdoc = doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebfe419b39e31b849651044ee2d61f2d\",\n \"score\": \"0.5413659\",\n \"text\": \"def getDoc(self, id: str) -> Optional[Doc]:\\n if id in self.index:\\n return self.index[id]\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d53d8bf82e0196c6cc450a35bb5fe3cc\",\n \"score\": \"0.5412984\",\n \"text\": \"def document(self, source, id):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10276dc67ba7fcd5962fef0457ffcbc8\",\n \"score\": \"0.5405826\",\n \"text\": \"def link_sti_document(self, build, obj):\\n from objectsapi.XMLstringHandler.XMLtaxonomyUtilities import SKOSconcepts\\n\\n concept_utilities = SKOSconcepts(build=self.build)\\n pref_label = obj.find(\\\"skos:preflabel\\\").string\\n\\n print(\\\"<<<<<<<<<<<<<<< LINK STI DOCUMENT prefLabel: \\\" + pref_label)\\n\\n doc = self.build.mongod.base.find_one({\\\"skos:prefLabel\\\": pref_label})\\n\\n if doc is None:\\n id_ = self.store_sti_document(obj, obj.find(\\\"nt2:code\\\").string)\\n doc = self.build.mongod.base.find_one({\\\"_id\\\": id_})\\n\\n try:\\n build.append_link_to_mongodoc(doc, \\\"schema:provider\\\", self.provider, \\\"base\\\")\\n except BaseException:\\n pass\\n\\n if obj.find_all('skos:broader') is not None:\\n for broad in obj.find_all('skos:broader'):\\n c = [s for s in broad[\\\"rdf:resource\\\"] if s.isdigit()]\\n code = str.join('', c)\\n cc = self.find_concept(code)\\n print(\\\"broad >>>> \\\" + code)\\n concept_utilities.store_broader(obj, cc)\\n\\n if obj.find_all('skos:narrower') is not None:\\n for narrow in obj.find_all('skos:narrower'):\\n c = [s for s in narrow[\\\"rdf:resource\\\"] if s.isdigit()]\\n code = str.join('', c)\\n print(\\\"narrow >>>> \\\" + code)\\n if code == '225' or code == '226':\\n continue\\n cc = self.find_concept(code)\\n concept_utilities.store_narrower(obj, cc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b9a6682049361d19dbe3c489922e20\",\n \"score\": \"0.5399206\",\n \"text\": \"def getDocById(self, colId, docId, nrOfTranscripts=None):\\r\\n self._assertLoggedIn()\\r\\n myReq = self.sREQ_collection_fulldoc % (colId,docId)\\r\\n params = self._buidlParamsDic(nrOfTranscripts=None)\\r\\n resp = self._GET(myReq, params=params,accept=\\\"application/json\\\")\\r\\n resp.raise_for_status()\\r\\n\\r\\n #we get some json serialized data\\r\\n return json.loads(resp.text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f00e0ab8be29d5920c477571a6b008e\",\n \"score\": \"0.5355247\",\n \"text\": \"def documentdetail(request, docid):\\n\\tdocument = get_object_or_404(Document, pk=docid)\\n\\tif not request.user.has_perm('documents.view_document', obj=document):\\n\\t\\treturn HttpResponse(loader.render_to_string('401.html',\\n\\t\\t\\tRequestContext(request, {'error_message':\\n\\t\\t\\t\\t_(\\\"You are not allowed to view this document.\\\")})), status=401)\\n\\n\\tlicense_agreement = getRecourseLicenseAgreement (document)\\n\\t\\n\\treturn render_to_response(\\\"documents/docinfo.html\\\", RequestContext(request, {\\n\\t\\t'permissions_json': json.dumps(_perms_info(document, DOCUMENT_LEV_NAMES)),\\n\\t\\t'document': document,\\n\\t\\t'imgtypes': imgtypes,\\n \\\"license_agreement\\\" : license_agreement,\\n \\\"html\\\" : html,\\n\\t}))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c04cd2dfb3f4544007de580cca58f1\",\n \"score\": \"0.5351927\",\n \"text\": \"def removeDocument(docid):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7191ef32165be5680749afee4f7b4cee\",\n \"score\": \"0.5338648\",\n \"text\": \"def retrieve_contents(doc_id):\\n creds = None\\n # The file token.pickle stores the user's access and refresh tokens, and is\\n # created automatically when the authorization flow completes for the first\\n # time.\\n if os.path.exists('token.pickle'):\\n with open('token.pickle', 'rb') as token:\\n creds = pickle.load(token)\\n # If there are no (valid) credentials available, let the user log in.\\n if not creds or not creds.valid:\\n if creds and creds.expired and creds.refresh_token:\\n creds.refresh(Request())\\n else:\\n flow = InstalledAppFlow.from_client_secrets_file(\\n 'credentials.json', SCOPES)\\n creds = flow.run_local_server(port=0)\\n # Save the credentials for the next run\\n with open('token.pickle', 'wb') as token:\\n pickle.dump(creds, token)\\n\\n service = build('docs', 'v1', credentials=creds)\\n\\n\\n doc = service.documents().get(documentId=doc_id).execute()\\n doc_content = doc.get('body').get('content')\\n s = read_strucutural_elements(doc_content)\\n\\n if DEBUG:\\n print(s)\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abf7aec46cc7c43fdcae8b904acff14e\",\n \"score\": \"0.5337387\",\n \"text\": \"def get_specific_listed_doc(self, doc_id: int) -> Document:\\n for doc in self.docs:\\n if doc.document_id == doc_id:\\n return doc\\n else:\\n raise ValueError(\\\"Document ID provided ({}) is not in the list of documents\\\".format(doc_id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e7ad6c1c92072e75ae3059d94094fe8\",\n \"score\": \"0.5325918\",\n \"text\": \"def load_doc(filename):\\n\\tfile = open(filename, 'r')\\n\\ttext = file.read()\\n\\tfile.close()\\n\\treturn text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d9c5e3562efaa8a81d3e90fb5f93bd\",\n \"score\": \"0.5317124\",\n \"text\": \"def __request_solrxml(self, sbid):\\n SHOW_FUNCTION = '_design/scielobooks/_show/solr_xml'\\n request_url = '/'.join([self.__db_uri, self.__db_name, SHOW_FUNCTION, sbid])\\n\\n return urllib2.urlopen(request_url).read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5b317f90286bdab37c5186a689f1b60\",\n \"score\": \"0.527629\",\n \"text\": \"def indoc():\\n return to_one(\\\"data/ideological_books/doc.txt\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c04fc1947a60e97a3275a2920ba44ce\",\n \"score\": \"0.52638984\",\n \"text\": \"def document(self, *document_path) -> DocumentReference:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d56d6e25473655af3ccf1ac5f90aa3\",\n \"score\": \"0.52608144\",\n \"text\": \"def addDocument(url):\\n if not url:\\n print \\\"stocazzo\\\"\\n from teapot.boris import docloader\\n print docloader.add_document_to_fuseki(url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51fc67515362a287ae13cad24319262f\",\n \"score\": \"0.52499306\",\n \"text\": \"def get_document_by_id(asset_id, document_id, file_ext):\\n follower = current_app.config['follower']\\n\\n mime_type = \\\"\\\"\\n first_chunk = None\\n\\n iterator = iter(follower.request_sap_document(asset_id, document_id))\\n\\n try:\\n mime_type, first_chunk = next(iterator)\\n except AssetUnknown:\\n logger.warning('T2 - Asset %s no longer known, ignoring', asset_id)\\n raise NotFound()\\n except T2ResponseException as ex:\\n if ex.reason == T2ReqFailureReason.REQ_UNHANDLED:\\n logger.error('T2 request not handled by provider')\\n elif ex.reason == T2ReqFailureReason.RESOURCE_UNKNOWN:\\n logger.error('Data not available for given request and asset')\\n raise NotFound()\\n else:\\n logger.error('T2 failed - reason: %s', ex.reason)\\n raise InternalServerError({\\\"code\\\": \\\"failed requesting document\\\", \\\"description\\\": str(ex)}, status_code=500)\\n except T2Unavailable as ex:\\n msg = 'T2 functionality not enabled in follower'\\n logger.critical(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(ex)}, status_code=500)\\n except T2Timeout as ex:\\n msg = 'T2 request timed out'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(ex)}, status_code=500)\\n except ShutdownRequested as ex:\\n msg = 'The follower is shutting down'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(ex)}, status_code=500)\\n except NotConnected as ex:\\n msg = 'The follower temporarily lost connection to the T2 providers'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(ex)}, status_code=500)\\n except Exception as ex:\\n msg = 'T2 failure fetching sap document'\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(ex)}, status_code=500)\\n\\n @stream_with_context\\n def generate():\\n try:\\n yield first_chunk\\n for _, next_chunk in iterator:\\n yield next_chunk\\n except T2Timeout as ex:\\n msg = 'T2 request timed out'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(ex)}, status_code=500)\\n except ShutdownRequested as ex:\\n msg = 'The follower is shutting down'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(ex)}, status_code=500)\\n except NotConnected as ex:\\n msg = 'The follower temporarily lost connection to the T2 providers'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(ex)}, status_code=500)\\n except Exception as ex:\\n msg = 'T2 failure fetching sap document'\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(ex)}, status_code=500)\\n\\n try:\\n response = Response(generate(), mimetype=mime_type)\\n response.headers['Content-Disposition'] = 'attachment; filename=\\\"{}.{}\\\"'.format(document_id, file_ext)\\n return response\\n except Exception:\\n logger.exception(\\\"get document by id failed\\\")\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b20524f055c8c20c3c7b713f7958a70a\",\n \"score\": \"0.52317876\",\n \"text\": \"def open_document(self, docType, documentID):\\n if docType == 'query':\\n try:\\n self.view.SetSelection(self.documentsOpen[documentID].page)\\n except KeyError:\\n document = self.profile.load_doc(documentID)\\n self.documentsOpen[document.documentID] = selectpanel.QueryController(self.view, document, self.profile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db67b49cfc1b4f2d8692c45e7fb8ef69\",\n \"score\": \"0.51832986\",\n \"text\": \"def loader(db, bids=None, use_journals=True, use_monographs=True):\\n # TODO: add filter by issue\\n # TODO: decide on force parsing on already parsed stuff: problem of disambiguations\\n\\n logging.basicConfig(filename=\\\"reference_parsing/logs/loader.log\\\", level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n # Initialize counters and data structures\\n data = list() # a list of documents with all data for every page (both annotated and not)\\n\\n # check boundaries of loading\\n if not bids or len(bids) == 0:\\n if use_journals and use_monographs:\\n bids = list(set([x[\\\"bid\\\"] for x in db.documents.find()]))\\n elif use_journals:\\n bids = list(set([x[\\\"bid\\\"] for x in db.documents.find() if x[\\\"type\\\"] == \\\"journal_issue\\\"]))\\n elif use_monographs:\\n bids = list(set([x[\\\"bid\\\"] for x in db.documents.find() if x[\\\"type\\\"] == \\\"monograph\\\"]))\\n logger.info('Bids list established.')\\n logger.info('Number of bids: %d'%len(bids))\\n\\n for bid in bids[:how_many]:\\n # Build query for identifiers\\n query = {\\\"bid\\\": bid}\\n\\n for doc in db.documents.find(query,no_cursor_timeout=True):\\n #TODO: note that this check should be done in metadata.\\n if \\\"marked_as_removed\\\" in doc and doc[\\\"marked_as_removed\\\"]:\\n logger.info(str(doc[\\\"_id\\\"]) + \\\" Marked as removed, SKIPPING\\\")\\n continue\\n doc_type = doc[\\\"type\\\"]\\n doc_number = \\\"\\\"\\n if doc_type == \\\"journal_issue\\\" and \\\"number\\\" in doc.keys():\\n doc_number = doc[\\\"number\\\"]\\n doc_id = doc[\\\"_id\\\"]\\n proc_doc = db.processing.find_one({\\\"bid\\\":bid,\\\"number\\\":doc_number})\\n if not proc_doc or proc_doc[\\\"is_parsed\\\"] or not proc_doc[\\\"is_ingested_ocr\\\"]:\\n logger.warning(str(doc[\\\"_id\\\"]) + \\\" Missing, no text yet or already parsed, SKIPPING\\\")\\n continue\\n logger.info(str(doc[\\\"_id\\\"])+\\\" OK\\\")\\n pages = OrderedDict()\\n\\n for page in db.pages.find({\\\"_id\\\": {\\\"$in\\\": doc[\\\"pages\\\"]}}):\\n page_number = int(page[\\\"single_page_file_number\\\"])\\n pages[page_number] = {\\\"offsets\\\":list(),\\\"page_id\\\":\\\"\\\",\\\"page_mongo_id\\\":\\\"\\\",\\\"single_page_file_number\\\":page_number}\\n page_id = bid+\\\"-\\\"+doc_number+\\\"-page-\\\"+page[\\\"filename\\\"].split(\\\"-\\\")[-1].split(\\\".\\\")[0]\\n page_mongo_id = page[\\\"_id\\\"]\\n container = list()\\n if page[\\\"is_annotated\\\"]:\\n logger.warning(page_id + \\\" Page annotated!! CHECK, SKIPPING\\\")\\n continue\\n # load text\\n # create a reverse index\\n offsets = dict()\\n for line in page[\\\"lines\\\"]:\\n line_number = line[\\\"line_number\\\"]\\n if line_number is None:\\n continue\\n for token in line[\\\"tokens\\\"]:\\n italics = False\\n bold = False\\n size = \\\"\\\"\\n if \\\"features\\\" in token.keys():\\n for feature in token[\\\"features\\\"]:\\n feat = feature[\\\"feature\\\"]\\n value = feature[\\\"value\\\"]\\n if \\\"font-weight\\\" == feat:\\n if value == \\\"bold\\\":\\n bold = True\\n if \\\"font-style\\\" == feat:\\n if value == \\\"italics\\\":\\n italics = True\\n if \\\"font-size\\\" == feat:\\n if \\\"small\\\" in value:\\n size = \\\"small\\\"\\n elif \\\"medium\\\" in value:\\n size = \\\"medium\\\"\\n elif \\\"large\\\" in value:\\n size = \\\"large\\\"\\n offsets.update({token[\\\"offset_start\\\"]: {\\\"surface\\\": token[\\\"surface\\\"],\\n \\\"position\\\": token[\\\"token_number\\\"],\\n \\\"end\\\": token[\\\"offset_end\\\"], \\\"line\\\": line_number,\\n \\\"italics\\\": italics, \\\"bold\\\": bold, \\\"size\\\": size,\\n \\\"bid\\\": bid,\\n \\\"general_category\\\": \\\"\\\", \\\"specific_category\\\": \\\"\\\",\\n \\\"beginend\\\": \\\"o\\\", \\\"taggedbe\\\": \\\"o\\\"}})\\n offsets = OrderedDict(sorted(offsets.items(), key=lambda key_value: key_value[0]))\\n\\n # store meta\\n for start,token in offsets.items():\\n new_token = ((token[\\\"surface\\\"],start,token[\\\"end\\\"],token[\\\"position\\\"],token[\\\"line\\\"],token[\\\"bid\\\"]),(token[\\\"italics\\\"],token[\\\"bold\\\"],token[\\\"size\\\"]))\\n container.append(new_token)\\n # store annotated page in pertinent article\\n pages[page_number][\\\"offsets\\\"] = container\\n pages[page_number][\\\"page_id\\\"] = page_id\\n pages[page_number][\\\"page_mongo_id\\\"] = page_mongo_id\\n doc_data = {\\\"doc_mongo_id\\\":doc_id,\\\"doc_type\\\":doc_type,\\\"pages\\\":pages,\\\"bid\\\":bid,\\\"doc_number\\\":doc_number}\\n data.append(doc_data)\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17fedd119cf9e2453ad0f04b6b3c5cbf\",\n \"score\": \"0.51598805\",\n \"text\": \"def get_doc(doc_id):\\n if not doc_id:\\n abort(404)\\n\\n _file = UserDoc.query.get_or_404(doc_id)\\n\\n file_name = _file.filename\\n # people who have access to the paper, administrators of this conference,\\n # authors, reviewers.\\n if _file.uploader_id != current_user.id:\\n return abort(403)\\n else:\\n try:\\n file_extension = file_name.split('.')[-1]\\n _name = 'not_submitted'\\n if _file.paper_id:\\n _name = str(_file.paper_id)\\n return send_file(\\n current_app.config['PDF_URL'] + file_name,\\n attachment_filename=_file.paper.conference.short_name +\\n '_paper_' + _name + '.' + file_extension,\\n as_attachment=True)\\n else:\\n flash('Paper doesn\\\\'t exist',\\n 'error')\\n return redirect(url_for('main.dashboard'))\\n except IOError:\\n flash('Download failed, please contact supporting assistant',\\n 'error')\\n return redirect(url_for('main.dashboard'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c51dc90a5e1dadf5c278085b5c7a9d6e\",\n \"score\": \"0.5152505\",\n \"text\": \"def read(id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a82f5766cbd94fd44bd8a6af177d1c59\",\n \"score\": \"0.5105094\",\n \"text\": \"def get_document(name, v):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"548920c9727cf85e6187f2403a41a13c\",\n \"score\": \"0.5097045\",\n \"text\": \"def read_doc(self, doc_id, rev_id=None, datastore_name=None, object_type=None):\\n ds, datastore_name = self._get_datastore(datastore_name)\\n if not rev_id:\\n try:\\n status, cas, doc = ds.get(doc_id)\\n except MemcachedError as e:\\n raise NotFound('Object with id %s could not be read. Exception: %s' % (doc_id, e.message))\\n if doc is None:\\n raise NotFound('Object with id %s does not exist.' % str(doc_id))\\n else:\\n ## review\\n doc = json.loads(doc)\\n doc['_rev'] = cas\\n doc['_id'] = str(doc['_id']) #doc_id\\n else:\\n raise NotImplementedError()\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53e5813a1314433c447ea5fca418426a\",\n \"score\": \"0.5097007\",\n \"text\": \"def get_data_for_id(dbname, dataid, url=cc.URL) :\\n r = request('%s/%s/gridfs/%s'%(url,dbname,dataid))\\n logger.debug('get_data_for_docid:'\\\\\\n +'\\\\n r.status_code: %s\\\\n r.headers: %s\\\\n r.encoding: %s\\\\n r.content: %s...\\\\n' % \\n (str(r.status_code), str(r.headers), str(r.encoding), str(r.content[:50])))\\n return r.content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd8e371307e8c24b1e8b43b1c7e2029a\",\n \"score\": \"0.5088892\",\n \"text\": \"def getDocByIdAsXml(self, colId, docId, nrOfTranscripts=None, bParse=True):\\r\\n self._assertLoggedIn()\\r\\n myReq = self.sREQ_collection_fulldoc_xml % (colId,docId)\\r\\n params = self._buidlParamsDic(nrOfTranscripts=nrOfTranscripts)\\r\\n resp = self._GET(myReq, params=params)\\r\\n resp.raise_for_status()\\r\\n \\r\\n #we should get some json serialized data \\r\\n if bParse:\\r\\n return self._xmlParseDoc(resp.text)\\r\\n else: \\r\\n return resp.text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a383d9a61291cdbac73c38068c438881\",\n \"score\": \"0.5071902\",\n \"text\": \"def ext_text(doc_id):\\n\\n doc = Document.get(Document.id==doc_id)\\n\\n if doc.syllabus.text:\\n\\n return Document_Text.create(\\n text=doc.syllabus.text,\\n document=doc\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"116012e173cb4ec9c5cb081683c21d58\",\n \"score\": \"0.5056275\",\n \"text\": \"def get_talend_document_by_id(asset_id, document_label, document_name):\\n\\n follower = current_app.config['follower']\\n\\n mime_type = \\\"\\\"\\n first_chunk = None\\n\\n iterator = iter(follower.request_talend_document(asset_id, document_label, document_name))\\n\\n try:\\n mime_type, first_chunk = next(iterator)\\n except AssetUnknown:\\n msg = 'T2 - Asset {} no longer known, ignoring'.format(asset_id)\\n logger.warning(msg)\\n raise NotFound()\\n except T2ResponseException as ex:\\n # ex.reason is T2ReqFailureReason - see code documentation for what they mean\\n if ex.reason == T2ReqFailureReason.REQ_UNHANDLED:\\n logger.error('T2 request not handled by provider')\\n # equivalent to HTTP 404\\n elif ex.reason == T2ReqFailureReason.RESOURCE_UNKNOWN:\\n logger.error('Data not available for given request and asset')\\n raise NotFound()\\n else:\\n logger.error('T2 failed - reason: %s', ex.reason)\\n\\n raise InternalServerError({\\\"code\\\": \\\"failed requesting document\\\", \\\"description\\\": str(\\n ex)}, status_code=500)\\n except T2Unavailable as ex:\\n msg = 'T2 functionality not enabled in follower'\\n logger.critical(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(\\n ex)}, status_code=500)\\n except T2Timeout as ex:\\n msg = 'T2 request timed out'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(\\n ex)}, status_code=500)\\n except ShutdownRequested as ex:\\n msg = 'The follower is shutting down'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(\\n ex)}, status_code=500)\\n except NotConnected as ex:\\n msg = 'The follower temporarily lost connection to the T2 providers'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(\\n ex)}, status_code=500)\\n except Exception as ex:\\n msg = 'T2 failure fetching document'\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(\\n ex)}, status_code=500)\\n\\n @stream_with_context\\n def generate():\\n try:\\n yield first_chunk\\n for _, next_chunk in iterator:\\n yield next_chunk\\n except T2Timeout as ex:\\n msg = 'T2 request timed out'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(\\n ex)}, status_code=500)\\n except ShutdownRequested as ex:\\n msg = 'The follower is shutting down'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(\\n ex)}, status_code=500)\\n except NotConnected as ex:\\n msg = 'The follower temporarily lost connection to the T2 providers'\\n logger.error(msg)\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(\\n ex)}, status_code=500)\\n except Exception as ex:\\n msg = 'T2 failure fetching document'\\n raise InternalServerError({\\\"code\\\": msg, \\\"description\\\": str(\\n ex)}, status_code=500)\\n\\n try:\\n response = Response(generate(), mimetype=mime_type)\\n response.headers['Content-Disposition'] = 'attachment; filename=\\\"{}\\\"'.format(\\n document_name)\\n return response\\n except Exception:\\n logger.exception(\\\"get document by id failed\\\")\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0d5c5d13b707fba6ba06680ad04f85b\",\n \"score\": \"0.5052802\",\n \"text\": \"def load(self, gerrit_id):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15cc8e1f511309a236ca4108a4efe681\",\n \"score\": \"0.5048177\",\n \"text\": \"def get_document_by_id(self, doc_id):\\n c = self.db.cursor()\\n row = c.execute(\\\"SELECT text, opening_text, auxiliary_text, categories, headings, wiki_text, popularity_score, num_incoming_links FROM articles WHERE title=?\\\", (doc_id,)).fetchone()\\n if row is None:\\n return None\\n return WikipediaArticle(self, doc_id,\\n row[0], # text\\n row[1], # opening_text\\n json.loads(row[2]), # auxiliary_text\\n json.loads(row[3]), # categories\\n json.loads(row[4]), # headings\\n row[5], # wiki_text\\n row[6], # popularity_score\\n row[7], # num_incoming_links\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da143a8f257f66b44a630c9ed2d55004\",\n \"score\": \"0.5043891\",\n \"text\": \"def loadDocument(self, filename, password=''):\\n self.doc = Poppler.Document.load(filename, password, password)\\n self.doc.setRenderHint(Poppler.Document.TextAntialiasing | Poppler.Document.TextHinting |\\n Poppler.Document.Antialiasing | 0x00000020 )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc20f6ee4064fd62ea0260bb202ca0da\",\n \"score\": \"0.5043508\",\n \"text\": \"def get_doc(self, uid):\\n\\n doc = None\\n sql = \\\"select uid,name,location,kind FROM document WHERE uid = ?\\\"\\n c = self.conn.cursor()\\n c.execute(sql, (uid,))\\n row = c.fetchone()\\n if row:\\n doc = Document(self, row[0], row[1], row[2], row[3])\\n c.close()\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"048bc6d6e17d2ee6950f021e28c5003c\",\n \"score\": \"0.50360346\",\n \"text\": \"def load(cls, backend_id):\\n # Circular import\\n from corehq.apps.sms.util import get_available_backends\\n backend_classes = get_available_backends()\\n backend = cls.get(backend_id)\\n if backend.doc_type not in backend_classes:\\n raise Exception(\\\"Unexpected backend doc_type found '%s' for backend '%s'\\\" % (backend.doc_type, backend._id))\\n else:\\n return backend_classes[backend.doc_type].wrap(backend.to_json())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3c41b3997847918a3caa93fe7db055f\",\n \"score\": \"0.50352436\",\n \"text\": \"def docid(self):\\n return self.id.split('.')[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3825600281da6a01504f94bc3edbbc1\",\n \"score\": \"0.5026529\",\n \"text\": \"def checkDoc(self, **kw):\\n if self.getParam('DOC') == 1:\\n self.usid = threading.Thread(target = self.saveParams, args=())\\n self.usid.daemon = True\\n self.usid.start()\\n else:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72f0e6f0b19a74302f7c4eac9ccc7c95\",\n \"score\": \"0.50209934\",\n \"text\": \"def get_data_for_docid(dbname, colname, docid, url=cc.URL) :\\n doc = get_doc_for_docid(dbname, colname, docid, url)\\n logger.debug('get_data_for_docid: %s' % str(doc))\\n return get_data_for_doc(dbname, colname, doc, url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e66a848a769bfc971658b64b531a7eca\",\n \"score\": \"0.50203437\",\n \"text\": \"def docno_from_docid(self, docid):\\n return self.docid_to_docno[docid]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83cbea05535c2f0652aea91428a3f2c5\",\n \"score\": \"0.5017536\",\n \"text\": \"def run(cls: DocumentType, doc: Document) -> None:\\n doc.download().get_info().add_to_library()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a58ad7bc7f14856ecdc37621d5ecc5f1\",\n \"score\": \"0.49980134\",\n \"text\": \"def load_object(collection, id):\\n path = object_path(collection, id)\\n return load_object_at_path(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceafaeb35c777cb8a8fbdc63030636a0\",\n \"score\": \"0.49966866\",\n \"text\": \"def doc():\\n build_docs()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbbefd5d640007e4e49a7001535f906b\",\n \"score\": \"0.4994027\",\n \"text\": \"def get_document_by_id(self, object_id):\\n return self.model_class.objects.get(id=object_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ec4b273f2a28f237a6c6036c1736cc6\",\n \"score\": \"0.49901178\",\n \"text\": \"def load(self):\\n with open(self.disk_file, 'rb') as f:\\n res = pickle.load(f)\\n self.terms = res[\\\"terms\\\"]\\n self.normalised_doc_length = res[\\\"normalised_doc_length\\\"]\\n self.num_of_docs = res[\\\"num_of_docs\\\"]\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f93e2c463fecaf5bb84df0a8cf51629\",\n \"score\": \"0.49885288\",\n \"text\": \"def open_doc(self,name):\\n if(self.database.__contains__(name)):\\n doc = self.server.__getitem__(name)\\n else:\\n self.database.create(name)\\n doc = self.server.__getitem__(name)\\n\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f7b3ff99e8e672b46105b91b5ca236f\",\n \"score\": \"0.49759012\",\n \"text\": \"def get_data_for_doc(dbname, colname, doc, url=cc.URL) :\\n logger.debug('get_data_for_doc: %s', str(doc))\\n idd = doc.get('id_data', None)\\n if idd is None :\\n logger.debug(\\\"get_data_for_doc: key 'id_data' is missing in selected document...\\\")\\n return None\\n\\n r2 = request('%s/%s/gridfs/%s'%(url,dbname,idd))\\n s = r2.content\\n\\n return mu.object_from_data_string(s, doc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5707a5033bc76c797131abe9dfa9ea2e\",\n \"score\": \"0.49750397\",\n \"text\": \"def getWordIdsForDocId(docid):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3e1632cd20ed53eaf05eb28681494e2\",\n \"score\": \"0.49744937\",\n \"text\": \"def load(self, oid):\\n if not self.find(oid):\\n return None\\n object_path = self._object_path(oid)\\n logger = logging.getLogger('war.database')\\n logger.debug(CF('Loading object %s').dark_gray, object_path)\\n with open(object_path, 'rb') as file:\\n decompressed = zlib.decompress(file.read())\\n obj = pickle.loads(decompressed)\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20bfb5e1fab8ff3e2911735c36db484c\",\n \"score\": \"0.49615327\",\n \"text\": \"def load(self):\\n if self.doc_path == None:\\n raise TypeError(\\\"Missing doc_path to load object.\\\")\\n file = gzip.GzipFile(self.doc_path, 'rb')\\n buffer = bytes()\\n count = 0\\n while True:\\n data = file.read()\\n if data == b'':\\n break\\n buffer += data\\n object = pickle.loads(buffer)\\n file.close()\\n self._update_self(object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca94601aae278d691175e467e0a0b00f\",\n \"score\": \"0.49535155\",\n \"text\": \"def insertDocument(docid, WidList):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9009d9d15dbe0b8655e11e9b7f9d2abd\",\n \"score\": \"0.4941772\",\n \"text\": \"def load():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8105b786f96d08b0b7e1c7989841f5fe\",\n \"score\": \"0.49398786\",\n \"text\": \"def open(self, url):\\r\\n cache = self.cache()\\r\\n id = self.mangle(url, 'document')\\r\\n d = cache.get(id)\\r\\n if d is None:\\r\\n d = self.download(url)\\r\\n cache.put(id, d)\\r\\n self.plugins.document.parsed(url=url, document=d.root())\\r\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d000e220288d544e40130d6de9438a0\",\n \"score\": \"0.49336287\",\n \"text\": \"def load_book_ids():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd73975462c66e414fdeefc585c97f2d\",\n \"score\": \"0.49317914\",\n \"text\": \"def get_document_iri(doc_id):\\n obj = \\\"document/%s\\\" % doc_id\\n return \\\"<%s/%s>\\\" % (GRAPH, obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e960957d622380307711025b6b3ecdfe\",\n \"score\": \"0.49234998\",\n \"text\": \"def _get_distrubuted_idf(self, fname):\\n dis_idf = None\\n with open(fname, \\\"rb\\\") as f:\\n dis_idf = pickle.load(f)\\n return dis_idf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fecdf2538d2166fe7dbfd3721646a480\",\n \"score\": \"0.49205577\",\n \"text\": \"def get_doc(environ, start_response):\\n\\n args = environ['app.url_args']\\n if args:\\n doc_id = escape(args[0])\\n username = escape(args[1])\\n key = escape(args[2])\\n\\n if check_credentials(username, key):\\n doc_db = dbm.open('doc', 'c')\\n if doc_id in doc_db.keys():\\n start_response('200 OK',\\n [('Content-Type', 'application/json')])\\n json_doc = {'doc_id': doc_id, 'doc_text': doc_db[doc_id]}\\n return [json.dumps(json_doc)]\\n else:\\n json_doc = {'status': False,\\n 'message': 'Document does not exist.'}\\n else:\\n json_doc = {'status': False, 'message': 'Missing data in request.'}\\n\\n start_response('400 BAD DATA', [('Content-Type', 'application/json')])\\n return [json.dumps(json_doc)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f2327dd03f7a23dd130ba38adb7aa67\",\n \"score\": \"0.4919811\",\n \"text\": \"def docs(docket_id, filter_fn=None):\\n # Use a list for consistent ordering, which is useful for caching\\n params = [('api_key', REGS_GOV_KEY), ('dktid', docket_id), ('rpp', 1000),\\n ('sb', 'docId'), ('so', 'ASC'), ('dct', 'N+PR+FR+O+SR')]\\n results = http_client().get(REGS_GOV_DOC_API, params=params).json()\\n if results.get('error'):\\n logger.warning(\\\"Error retrieving data from regs.gov: %s\\\",\\n results['error'].get('message'))\\n for doc_dict in results.get('documents', []):\\n if filter_fn is None or filter_fn(doc_dict):\\n yield RegsGovDoc(doc_dict['documentId'], doc_dict['title'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9eb391051693198e5ca96029018b08d\",\n \"score\": \"0.4910611\",\n \"text\": \"def read_doc(path): # El parametro es la ruta donde se encuentra almacenado el documento, seguido del nombre del mismo\\n document = pd.read_csv(path, error_bad_lines=False)\\n document.head()\\n\\n columns = ['Supervision','Texto', 'ID']\\n document = document[columns]\\n document = document[pd.notnull(document['Texto'])]\\n document.columns = ['Supervision', 'Texto', 'ID']\\n\\n return document\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89d8cb39afe33f54966c6a106e761223\",\n \"score\": \"0.49031883\",\n \"text\": \"def __get_doc_by_domian_name(self):\\n index_url = self.main_doc('a:first').attr('href')\\n return papa.get_doc(self.domian_name + index_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9868fc1418dd01fe2d46430a69933b0\",\n \"score\": \"0.4892366\",\n \"text\": \"def load(organism_id: str) -> SeqRecord:\\n if not path.exists(path.join(\\\"data\\\", f\\\"{organism_id}.pkl.gz\\\")):\\n with Entrez.efetch(db=\\\"nucleotide\\\", rettype=\\\"gb\\\", id=organism_id) as handle:\\n record = SeqIO.read(handle, \\\"gb\\\")\\n with open(path.join(\\\"data\\\", f\\\"{organism_id}.pkl.gz\\\"), \\\"wb\\\") as f:\\n pickle.dump(record, f)\\n else:\\n with open(path.join(\\\"data\\\", f\\\"{organism_id}.pkl.gz\\\"), \\\"rb\\\") as f:\\n record = pickle.load(f)\\n\\n return record\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12174ad2392aa3bbb79f162d757fc710\",\n \"score\": \"0.4889138\",\n \"text\": \"def load(loadfile, docList):\\n docList.clear()\\n try:\\n f = open(loadfile, 'rb')\\n reader = csv.DictReader(f)\\n for row in reader:\\n docList[int(row['docId'])] = int(row['docLength'])\\n finally:\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e57e83801ccd25584e83c5ac1d15c11\",\n \"score\": \"0.4879617\",\n \"text\": \"def setHead(self, doc_or_id):\\n if isinstance(doc_or_id, basestring):\\n self.head = doc_or_id\\n try:\\n doc = db.get(doc_or_id)\\n except (db.BadKeyError, db.BadRequestError):\\n # library.createnewdoc runs this inside a transaction\\n # and updating doc and trunk at the same time will throw\\n # an exception (different entity groups).\\n # the caller will take care of updating the title in that case.\\n return\\n elif isinstance(doc_or_id, DocModel):\\n doc = doc_or_id\\n self.head = str(doc.key())\\n else:\\n # Unexpected input type. Ignore.\\n return\\n if isinstance(doc, DocModel):\\n self.title = doc.title\\n Subscription.notifyChange(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9b007b10241a1e19d170c7fb2baec7\",\n \"score\": \"0.4853038\",\n \"text\": \"def loadBookmark(ned, info, *args):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"852904d3a9d7c41cadf1b8cc95f38256\",\n \"score\": \"0.48505253\",\n \"text\": \"def get_dso_docs(doc_oid, query_args) :\\n\\n\\tprint()\\n\\tprint(\\\"-+- \\\"*40)\\n\\tlog.debug( \\\"... get_dso_docs \\\" )\\n\\n\\tdso_doc_collection\\t= db_dict_by_type['dso_doc']\\n\\n\\tquery \\t= build_first_term_query(doc_oid, query_args)\\n\\tlog.debug('query : \\\\n%s', pformat(query) ) \\n\\n\\t# results = dso_doc_collection.find({'oid_dso' : doc_oid })\\n\\tcursor \\t= dso_doc_collection.find(query)\\n\\n\\tresults = list(cursor)\\n\\n\\treturn results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45952dc393a8f22bd59a9e96d61edf8\",\n \"score\": \"0.48396093\",\n \"text\": \"def retrieve_title(doc_id):\\n creds = None\\n # The file token.pickle stores the user's access and refresh tokens, and is\\n # created automatically when the authorization flow completes for the first\\n # time.\\n if os.path.exists('token.pickle'):\\n with open('token.pickle', 'rb') as token:\\n creds = pickle.load(token)\\n # If there are no (valid) credentials available, let the user log in.\\n if not creds or not creds.valid:\\n if creds and creds.expired and creds.refresh_token:\\n creds.refresh(Request())\\n else:\\n flow = InstalledAppFlow.from_client_secrets_file(\\n 'credentials.json', SCOPES)\\n creds = flow.run_local_server(port=0)\\n # Save the credentials for the next run\\n with open('token.pickle', 'wb') as token:\\n pickle.dump(creds, token)\\n\\n service = build('docs', 'v1', credentials=creds)\\n\\n\\n doc = service.documents().get(documentId=doc_id).execute()\\n doc_content = doc.get('title')\\n if DEBUG:\\n print(doc_content)\\n\\n return doc_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"179645d225bd76faa65677ac6c9f6f8c\",\n \"score\": \"0.48380253\",\n \"text\": \"def document(\\n self, document_id: str = None\\n ) -> async_document.AsyncDocumentReference:\\n return super(AsyncCollectionReference, self).document(document_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd6a8bed607866679a3fb1ef774e3b0\",\n \"score\": \"0.48290414\",\n \"text\": \"def set_document_file_id(self, document_file_id):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bee32bae419963963e0cf7e32dbb700\",\n \"score\": \"0.48207775\",\n \"text\": \"def openToolBOSDocumentation( fileName, parent=None ):\\n Any.requireIsTextNonEmpty( fileName )\\n\\n logging.info( 'opening online documentation' )\\n\\n url = ToolBOSConf.getConfigOption( 'documentationURL_dir' ) + \\\\\\n fileName + '.html'\\n\\n browser = WebBrowser( 'Documentation', parent )\\n browser.open( url )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8b6303d0308d86f7f71c50cecb135d1\",\n \"score\": \"0.4819432\",\n \"text\": \"def load_doc_chunks(path: str):\\n ret = []\\n if os.path.exists(path):\\n with open(path, 'rb') as fr:\\n try:\\n while True: # until EOF\\n ret.append(pickle.load(fr))\\n except:\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9b5be57379ef8f965fc7ee94bb05a47\",\n \"score\": \"0.48114958\",\n \"text\": \"def _doc_id(self, doc_slug: str, version: str) -> str:\\n if not doc_slug:\\n # Not all docs have a parent doc, in case we get no slug\\n # we just return an empty string.\\n return \\\"\\\"\\n\\n README_API_KEY = os.getenv(\\\"README_API_KEY\\\")\\n if not README_API_KEY:\\n warnings.warn(\\\"README_API_KEY env var is not set, using a placeholder doc ID\\\")\\n return \\\"fake-doc-id\\\"\\n\\n headers = create_headers(version)\\n res = requests.get(f\\\"https://dash.readme.com/api/v1/docs/{doc_slug}\\\", headers=headers, timeout=60)\\n if not res.ok:\\n sys.exit(f\\\"Error requesting {doc_slug} doc for version {version}\\\")\\n\\n return res.json()[\\\"id\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e659b40136dca60bec3fa0bda344d0cd\",\n \"score\": \"0.4811252\",\n \"text\": \"def load_user(id):\\n\\n try:\\n user = User.get(id=id)\\n except User.NoDocumentFound:\\n return None\\n else:\\n return user\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ba0dd6ee1f011088271d57dd6c5c4d3\",\n \"score\": \"0.48111725\",\n \"text\": \"def load(self, id, level=AccessType.ADMIN, user=None, objectId=True,\\n force=False, fields=None, exc=False):\\n doc = Model.load(self, id=id, objectId=objectId, fields=fields, exc=exc)\\n\\n if not force and doc is not None:\\n self.model('folder').load(doc['folderId'], level, user, objectId,\\n force, fields)\\n\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a88e5aad653a12351c6b41c70e845d8\",\n \"score\": \"0.4806092\",\n \"text\": \"def get(self, index, doc_type, id):\\n path = self._make_path([index, doc_type, id])\\n response = self._send_request('GET', path)\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36034d720dc43861ddd9b4a436e4acc3\",\n \"score\": \"0.4805629\",\n \"text\": \"def load_from_db(self):\\n logger.info('loading state from mongo')\\n ddoc = self.ddoc_get()\\n blob = self.mongo_handle.get_attachment(ddoc, name='pkl')\\n return cPickle.loads(blob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c24ae408dd18d90a8a63c50b24d15b90\",\n \"score\": \"0.47940406\",\n \"text\": \"def load(astergui, file_name, url=''):\\n return Study(astergui, file_name=file_name, url=url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2327195497ef23f0bd7dc3c1e29d8656\",\n \"score\": \"0.4790033\",\n \"text\": \"def update_doc(doc):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3090388b0c7634bcb7411b8d4e2865c4\",\n \"score\": \"0.4787972\",\n \"text\": \"def loads(self):\\n #id dictinories\\n #id -- word\\n self.word_2_id = load_vocab(self.vocab_filename) \\n self.tag_2_id = load_vocab(self.tag_filename)\\n if self.use_chars:\\n\\t self.char_2_id = load_vocab(self.char_filename)\\n\\t self.nchars = len(self.char_2_id)\\n \\n \\n #load number of unique words present\\n self.nwords = len(self.word_2_id)\\n self.ntags = len(self.tag_2_id)\\n \\n #load embeddings \\n self.word_embeddings = (get_glove_vectors(filename=self.vocab_embedding_filename)\\n if self.use_pretrained else None)\\n # self.tag_embeddings = get_glove_vectors(filename=self.tag_embedding_filename)\\n \\n #processing function that will map word str to id \\n self.process_words = get_processing_word(vocab_words=self.word_2_id, vocab_chars=self.char_2_id,chars=self.use_chars)\\n self.process_tags = get_processing_word(vocab_words=self.word_2_id, vocab_chars=self.char_2_id,chars=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5b8ef5c52535a6f885e51ea96856de6\",\n \"score\": \"0.47878912\",\n \"text\": \"def test_get_doc1(self):\\n doc = self.es_client.get_document(12345, index_name=TEST_ES_INDEX)\\n assert not doc, \\\"Got {}\\\".format(doc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f287f7f982b41765f8ceeb3a4cbf4045\",\n \"score\": \"0.47874275\",\n \"text\": \"def find_document(document_id: DocumentID) -> Optional[Document]:\\n document = DbDocument.query.get(document_id)\\n\\n if document is None:\\n return None\\n\\n return _db_entity_to_document(document)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c77b5438c14b6ea7d7d97a6d4df7e3fc\",\n \"score\": \"0.47831517\",\n \"text\": \"def load(self, fp, name=None, headers=False, **kwargs):\\n return self.document(fp, name=name, headers=headers, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a78a9212e53f6cab90bab33ca73c64cd\",\n \"score\": \"0.47792616\",\n \"text\": \"def findById(cls, lobbyId):\\n return cls.query.filter_by(lobbyId=lobbyId).first()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5530339761264873a286655e1db1c32b\",\n \"score\": \"0.47767955\",\n \"text\": \"def __init__(self,inputFileName):\\n self.doc = open(inputFileName).read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f98434b3a1082e08939e4f425898a491\",\n \"score\": \"0.47672322\",\n \"text\": \"def read(self, fmerge):\\n if not isfile(fmerge):\\n raise IOError(\\\"File doesn't exist: {}\\\".format(fmerge))\\n self.fmerge = fmerge\\n gidx, tokendict = 0, {}\\n with open(fmerge, 'r') as fin:\\n for line in fin:\\n line = line.strip()\\n if len(line) == 0:\\n continue\\n tok = self._parseline(line)\\n tokendict[gidx] = tok\\n gidx += 1\\n # Get EDUs from tokendict\\n edudict = self._recoveredus(tokendict)\\n doc = Doc()\\n doc.tokendict = tokendict\\n doc.edudict = edudict\\n return doc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed2b5abe6195e62e19e1718b73e8465\",\n \"score\": \"0.47506806\",\n \"text\": \"def loadBooks(filename):\\n booksfile = cf.data_dir + filename\\n return model.addBooks(booksfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1bab03279269670882ff5ad49f8253\",\n \"score\": \"0.47495303\",\n \"text\": \"def getDocumentsForWordIds(wordidlist):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12e27f3da47f5b29946b0e9bb802a37c\",\n \"score\": \"0.47417745\",\n \"text\": \"def main(bib_file):\\n bib_file = os.environ[\\\"BUILD_WORKING_DIRECTORY\\\"] + \\\"/\\\" + bib_file\\n with open(bib_file, \\\"r\\\") as bibtex_file:\\n bib_database = bibtexparser.load(bibtex_file)\\n\\n # [name].bib -> [name].dblp.bib\\n out_file = open(bib_file[:-4] + \\\".dblp.bib\\\", \\\"w\\\")\\n\\n for i, entry in enumerate(bib_database.entries):\\n print(\\\"Entry:\\\", i + 1, \\\"/\\\", len(bib_database.entries))\\n print(\\\"Title:\\\", entry.get(\\\"title\\\", \\\"[None]\\\"))\\n print(\\\"Author:\\\", entry.get(\\\"author\\\", \\\"[None]\\\"))\\n bibtex_id = entry[\\\"ID\\\"]\\n results = search_dblp(entry[\\\"title\\\"])\\n\\n if not results.empty:\\n result = select_row(results)\\n\\n if results.empty or result is None:\\n out_file.write(\\\"\\\\n% COULD NOT FIND \\\" + entry[\\\"ID\\\"]\\n + \\\": \\\" + entry.get(\\\"title\\\", \\\"[None]\\\")\\n + \\\" by \\\" + entry.get(\\\"author\\\", \\\"[None]\\\") + \\\"} \\\\n\\\")\\n continue\\n\\n bibtex_url = f\\\"https://dblp.uni-trier.de/rec/{result.Id}.bib?param=1\\\"\\n bibtex_entry = urllib.request.urlopen(bibtex_url).read().decode(\\\"utf-8\\\")\\n bibtex_entry = set_id(bibtex_entry, bibtex_id)\\n print(bibtex_entry)\\n out_file.write(bibtex_entry)\\n out_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20794679b7433231d81c254095fa834d\",\n \"score\": \"0.47389236\",\n \"text\": \"def retrieve_document(document_id: str) -> Response:\\n try:\\n result = SearchSession.get_document(document_id) # type: ignore\\n except index.IndexConnectionError as ex:\\n raise BadGateway(\\n \\\"There was a problem connecting to the search index. This is \\\"\\n \\\"quite likely a transient issue, so please try your search \\\"\\n \\\"again. If this problem persists, please report it to \\\"\\n \\\"help@arxiv.org.\\\"\\n ) from ex\\n except index.QueryError as ex:\\n raise BadRequest(\\n \\\"There was a problem executing your query. Please try a \\\"\\n \\\"different query. If this problem persists, please report it to \\\"\\n \\\"help@arxiv.org.\\\"\\n ) from ex\\n except index.DocumentNotFound as ex:\\n logger.error(\\\"DocumentNotFound: %s\\\", ex)\\n raise NotFound(f\\\"Could not find a paper with id {document_id}\\\") from ex\\n return {\\\"document\\\": result}, HTTPStatus.OK, {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5092e782acd63a759bd84321bab9e50\",\n \"score\": \"0.47386977\",\n \"text\": \"def load(self, what, loaded=None):\\n id_list = []\\n\\n if isinstance(loaded, list):\\n loaded_dict = {(x.id if isinstance(x, couchdb.client.Row) else x['_id']): (x.doc if isinstance(x, couchdb.client.Row) else x) for x in loaded}\\n else:\\n loaded_dict = loaded or {}\\n\\n #if not isinstance(what, (list, couchdb.client.ViewResults)):\\n if not isinstance(what, list):\\n load_list = [what]\\n else:\\n load_list = what\\n\\n for item in load_list:\\n #print \\\"item\\\", item\\n if isinstance(item, basestring):\\n id_list.append(item)\\n elif isinstance(item, couchdb.client.Row):\\n id_list.append(item.id)\\n\\n if hasattr(item, 'doc'):\\n loaded_dict[item.id] = item.doc\\n\\n elif isinstance(item, dict):\\n id_list.append(item['_id'])\\n\\n if len(item) > 2:\\n loaded_dict[item['_id']] = item\\n\\n if not isinstance(what, list):\\n #print \\\"what\\\", what\\n return [self._load(_id, loaded_dict) for _id in id_list][0]\\n else:\\n #print \\\"id_list\\\", id_list\\n return [self._load(_id, loaded_dict) for _id in id_list]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"705663e1f547e4d3599fc44cc94b5737\",\n \"score\": \"0.47383815\",\n \"text\": \"def load_script(self):\\n try:\\n self.raw_text = docx2txt.process(self.script_filepath)\\n except Exception as err:\\n raise RuntimeError(\\\"Script failed to load with the following error: \\\", err)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b4abc6348810686700d3e2371d8cd0b\",\n \"score\": \"0.47383443\",\n \"text\": \"def show_doc(doc_id):\\n \\n doc = crud.get_doc_by_doc_id(doc_id)\\n authors = crud.get_author_by_doc_id(doc_id)\\n img_urls = crud.get_image_url_by_doc_id(doc_id)\\n\\n session['doc_id'] = doc_id\\n\\n img_url_list = []\\n for img_url in img_urls:\\n img_url_list.append(img_url.url)\\n\\n author_list = []\\n for author in authors:\\n author_list.append(author.name)\\n\\n return jsonify({\\n 'doc': doc, \\n 'authors': \\\" \\\".join(author_list), \\n 'img_urls': img_url_list\\n })\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1694,"cells":{"query_id":{"kind":"string","value":"0911e9e71f69f1b97436a7108347a172"},"query":{"kind":"string","value":"Identify the persons by disambiguation. Apply Bipartite Graph Matching. Take list of matrices and for each consecutive pair of them, get the trajectory of people. Tracks of people over the frames."},"positive_passages":{"kind":"list like","value":[{"docid":"fa30220e3af254543409af6460daed89","score":"0.666316","text":"def disambiguatePersons(self):\n track_labels = []\n start = True\n # Iterate over the frame pose matrices\n for i, poses in enumerate(self.vid_persons):\n \n if poses.shape == (0,): # no person in frame\n track_labels.append([])\n start = True\n continue","title":""}],"string":"[\n {\n \"docid\": \"fa30220e3af254543409af6460daed89\",\n \"score\": \"0.666316\",\n \"text\": \"def disambiguatePersons(self):\\n track_labels = []\\n start = True\\n # Iterate over the frame pose matrices\\n for i, poses in enumerate(self.vid_persons):\\n \\n if poses.shape == (0,): # no person in frame\\n track_labels.append([])\\n start = True\\n continue\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"49950be6e0f4806ad2611217e0a64591","score":"0.5999887","text":"def _extractmatches(keyfaces):\n key, faces = keyfaces\n \n # no match found\n if len(faces) == 1:\n start, end, isgt = key\n seg1, segmap, sid, hack1, segbodies = faces[0]\n bodymatches = []\n if hack1:\n for label, body in segmap.items():\n bodymatches.append(((body, isgt), [(label, sid, True)]))\n for body in segbodies:\n bodymatches.append(((body, isgt), [(body, sid, True)]))\n\n return bodymatches\n assert(len(faces) == 2)\n\n start, end, isgt = key\n seg1, segmap, sid, hack1, segbodies = faces[0]\n seg2, segmap2, sid2, hack2, segbodies2 = faces[1]\n\n seg1 = seg1.flatten()\n seg2 = seg2.flatten()\n \n seg1seg2 = numpy.column_stack((seg1, seg2))\n unique_pairs = numpy.unique(seg1seg2, axis=0)\n\n bodymatches = []\n\n for val in unique_pairs:\n if val[0] == 0 or val[1] == 0:\n continue\n \n mapped1 = val[0]\n if mapped1 in segmap:\n mapped1 = segmap[mapped1]\n mapped2 = val[1]\n if mapped2 in segmap2:\n mapped2 = segmap2[mapped2]\n\n if mapped1 == mapped2:\n bodymatches.append(((mapped1, isgt), [((val[0], sid), (val[1], sid2))]))\n\n\n # hack: send all bodies that have new labels\n # assume 1) disjoint bodies will always include implicit identity mapping\n # and 2) each subvolume will be represented at least 6 times\n if hack1:\n for label, body in segmap.items():\n bodymatches.append(((body, isgt), [(label, sid, True)]))\n for body in segbodies:\n bodymatches.append(((body, isgt), [(body, sid, True)]))\n \n if hack2:\n for label, body in segmap2.items():\n bodymatches.append(((body, isgt), [(label, sid2, True)]))\n for body in segbodies2:\n bodymatches.append(((body, isgt), [(body, sid2, True)]))\n\n return bodymatches","title":""},{"docid":"45e8322f2048482b2134c9821139f3b9","score":"0.56178755","text":"def MatchFeatures(self, descFrame):","title":""},{"docid":"0facab35af28030efdb89c42584e52f6","score":"0.5616024","text":"def find_connected_joints(paf, joints_list_per_type, num_inter_points=10):\n connnected_limbs = []\n limb_inter_coord = np.empty((4, num_inter_points), dtype=np.intp) # store info of interpoints x,y coords paf, id of paf correspond limbs\n for limb_type in range(NUM_LIMBS):\n # print(LIMBS[limb_type])\n joints_src = joints_list_per_type[LIMBS[limb_type][0]]\n joints_dst = joints_list_per_type[LIMBS[limb_type][1]]\n # print('JOINT SRC', joints_src)\n # print('JOINT DST', joints_dst)\n if len(joints_src)==0 or len(joints_dst)==0: # if limb type has no candidate (peak)\n connnected_limbs.append([])\n else:\n connection_candidate = []\n # id of paf in output, 2 id: src point and dst point\n limb_inter_coord[2, :] = PAF_XY_COORDS_PER_LIMB[limb_type][0]\n limb_inter_coord[3, :] = PAF_XY_COORDS_PER_LIMB[limb_type][1]\n for i, joint_src in enumerate(joints_src):\n for j, joint_dst in enumerate(joints_dst):\n limb_dir = joint_dst[:2] - joint_src[:2] # direction vector\n limb_dist = np.sqrt(np.sum(limb_dir**2)) + 1e-8\n limb_dir = limb_dir / limb_dist # normalize\n limb_inter_coord[1, :] = np.round(np.linspace(\n joint_src[0], joint_dst[0], num=num_inter_points\n )) # interpoint x coord\n limb_inter_coord[0, :] = np.round(np.linspace(\n joint_src[1], joint_dst[1], num=num_inter_points\n )) # interpoint y coord\n inter_paf = paf[limb_inter_coord[0, :],\n limb_inter_coord[1, :],\n limb_inter_coord[2:4, :]].T\n\n score_inter_pts = np.dot(inter_paf, limb_dir) # score \n score = score_inter_pts.mean()\n # print(score)\n score_penalizing_long_dist = score_inter_pts.mean()+min(0.5*paf.shape[0]/limb_dist-1, 0) # penalty long distance\n criteria1 = (np.count_nonzero(score_inter_pts>CONFIDENT_SCORE_LIMB) > 0.6*num_inter_points) # no. score > threshold at least 60%\n # print(criteria1)\n criteria2 = (score_penalizing_long_dist>-10)\n if criteria1:# check criteria, criteria2???!!\n connection_candidate.append(\n [i, j, score,\n score+joint_src[2]+joint_dst[2]]\n )\n\n connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True) # sorted by score \n # for con in connection_candidate:\n # print('CONNECTION CANDIDATE', con)\n connections = np.empty((0, 5), dtype=np.int)\n max_connections = min(len(joints_src), len(joints_dst))\n for potential_connection in connection_candidate:\n i, j, s = potential_connection[:3]\n # [joint_src_id, joint_dst_id, limb_score, joint_src_index, joint_dst_index]\n if i not in connections[:, 3] and j not in connections[:, 4]:\n connections = np.vstack(\n [connections, [joints_src[i][3], joints_dst[j][3], s, i, j]]\n )\n if len(connections) >= max_connections:\n break\n # print('CONNECTION', connections)\n\n connnected_limbs.append(connections)\n return connnected_limbs","title":""},{"docid":"c7ad0c43d043c34576b37d237f47dfbe","score":"0.5493029","text":"def read_interaction_file_mat(Human_HighQuality):\r\n \r\n with open(Human_HighQuality, \"r\") as interaction_file:\r\n lines_num_int = int(interaction_file.readline())\r\n data_str = interaction_file.readlines()\r\n prot_data_list = []\r\n prot_graph_dict = {}\r\n\r\n for line in data_str:\r\n prot1_exist = False\r\n prot2_exist = False\r\n\r\n # This loop includes elements in a list, pass only once (when the list is empty), initialize the first\r\n # protein\r\n if prot_data_list == []: # Checks if list is empty (not mandatory though)\r\n prot_data_list = [(line.split()[0])] # Adds the value into the list\r\n prot_graph_dict[(line.split()[0])] = list() # Declaration of the key without value (value is a list)\r\n prot_graph_dict[(line.split()[0])].append((line.split())[1]) # Adds the value as second element\r\n\r\n # When the function will screen the list, it will match twice only instead of passing at each loops (not\r\n # mandatory but it processes faster than passing every loops)\r\n for elt in prot_data_list:\r\n if line.split()[0] == elt or line.split()[1] == elt:\r\n if (line.split()[0]) == elt:\r\n prot1_exist = True\r\n prot_graph_dict[(line.split()[0])].append((line.split())[1])\r\n if (line.split()[1]) == elt:\r\n prot2_exist = True\r\n prot_graph_dict[(line.split()[1])].append((line.split())[0])\r\n\r\n if prot2_exist == False:\r\n prot_data_list.append((line.split()[1]))\r\n prot_graph_dict[(line.split()[1])] = list()\r\n prot_graph_dict[(line.split()[1])].append((line.split())[0])\r\n\r\n if prot1_exist == False:\r\n prot_data_list.append((line.split()[0]))\r\n prot_graph_dict[(line.split()[0])] = list()\r\n prot_graph_dict[(line.split()[0])].append((line.split())[1])\r\n\r\n prot_data_list.sort() # Sorts in alphabetic order in order to find the proteins' order (the array returns the\r\n # number of proteins positioned as the second element only.\r\n lines_for_mat_dict = {} # Dictionary initialization\r\n prot_interaction_mat = np.empty((len(prot_data_list), len(prot_data_list)), int) # Creation of an empty array\r\n # which defines dimensions based on proteins lists length (number of columns, number of lines)\r\n\r\n # This loops compares the protein list from the top to the bottom. It shifts at each case of one dimension, a\r\n # key is declared in the dictionary for each dimension. A key is declared at each loop. At the end of the loop,\r\n # we retrieve each key and all values associated to a dimension.\r\n for i, elt in enumerate(prot_data_list):\r\n lines_for_mat_dict[i] = list() # Dictionary declaration which will be used to create the array, it browses\r\n # all the proteins list\r\n print(\"entry in comparison\", elt) # From 0 to 27276, declares the dictionary key\r\n\r\n for j, el in enumerate(prot_data_list): # For each line, browses all columns, from left to right\r\n compare_list = [prot_graph_dict[el]] # Gives a comparison by list from protein in the dictionary of\r\n # interaction\r\n for k, e in enumerate(compare_list): # compares from top to bottom, left to right by shifting case by\r\n # case\r\n if el == e:\r\n compare_boo = True # For each case, tests if there is a correspondence between the protein of\r\n # the column and the protein of the line\r\n break # If a correspondence is found, the loop breaks (prevents from loop running until the end\r\n # of the list, so if it founds a corresponding protein and it is not the last one, then it will\r\n # return false and won't count it\r\n else:\r\n compare_boo = False # Returns true if there is an interaction or false if there isn't\r\n if compare_boo == True: # Key created before is inserted in the array\r\n lines_for_mat_dict[i].append(1) # Adds the correspondence to the right column if it returns true\r\n else:\r\n lines_for_mat_dict[i].append(0) # Adds the protein to the left column if it returns false\r\n\r\n # prot_interact_mat is the variable name, i is the dimension where i want to add it,\r\n # [lines_for_mat_dict[i]] is the dictionary line with value i, axis=0 is the direction where we want to add i\r\n np.insert(prot_interaction_mat, i, [lines_for_mat_dict[i]], axis=0)\r\n\r\n return prot_interaction_mat","title":""},{"docid":"2f6de02f2a94850e9fc826985ee6642f","score":"0.54444426","text":"def mapMatch(track, segments, decayconstantNet = 30, decayConstantEu = 10, maxDist = 50, addfullpath = True):\n #Make sure passed in parameters are floats\n decayconstantNet = float(decayconstantNet)\n decayConstantEu = float(decayConstantEu)\n maxDist= float(maxDist)\n\n #gest start time\n start_time = time.time()\n\n #this array stores, for each point in a track, probability distributions over segments, together with the (most probable) predecessor segment taking into account a network distance\n V = [{}]\n\n #get track points, build network graph (graph, endpoints, lengths) and get segment info from arcpy\n\n points = getTrackPoints(track, segments)\n print(\"These are the points\")\n # print(points)\n # points=[]\n\n # point1 = arcpy.Point(51.467057004358743, 5.474460367112933)\n # ptGeometry1 = arcpy.PointGeometry(point1)\n\n # point2 = arcpy.Point(51.465963331817377, 5.473848990808071)\n # ptGeometry2 = arcpy.PointGeometry(point2)\n # points.append(ptGeometry1)\n # points.append(ptGeometry2)\n\n\n\n r = getSegmentInfo(segments)\n endpoints = r[0]\n lengths = r[1]\n graph = getNetworkGraph(segments,lengths)\n pathnodes = [] #set of pathnodes to prevent loops\n\n #init first point\n sc = getSegmentCandidates(points[0], segments, decayConstantEu, maxDist)\n for s in sc:\n V[0][s] = {\"prob\": sc[s], \"prev\": None, \"path\": [], \"pathnodes\":[]}\n # Run Viterbi when t > 0\n for t in range(1, len(points)):\n V.append({})\n #Store previous segment candidates\n lastsc = sc\n #Get segment candidates and their a-priori probabilities (based on Euclidean distance for current point t)\n sc = getSegmentCandidates(points[t], segments, decayConstantEu, maxDist)\n print(\"Printing the sc\")\n # print(sc)\n for s in sc:\n max_tr_prob = 0\n prev_ss = None\n path = []\n for prev_s in lastsc:\n #determine the highest network transition probability from previous candidates to s and get the corresponding network path\n # print(\"this i the line\")\n pathnodes = V[t-1][prev_s][\"pathnodes\"][-10:]\n # print(pathnodes)\n n = getNetworkTransP(prev_s, s, graph, endpoints, lengths, pathnodes, decayconstantNet)\n np = n[0] #This is the network transition probability\n tr_prob = V[t-1][prev_s][\"prob\"]*np\n #this selects the most probable predecessor candidate and the path to it\n if tr_prob > max_tr_prob:\n max_tr_prob = tr_prob\n prev_ss = prev_s\n path = n[1]\n if n[2] != None:\n pathnodes.append(n[2])\n #The final probability of a candidate is the product of a-priori and network transitional probability\n max_prob = sc[s] * max_tr_prob\n V[t][s] = {\"prob\": max_prob, \"prev\": prev_ss, \"path\": path, \"pathnodes\":pathnodes}\n\n #Now max standardize all p-values to prevent running out of digits\n maxv = max(value[\"prob\"] for value in V[t].values())\n maxv = (1 if maxv == 0 else maxv)\n for s in V[t].keys():\n V[t][s][\"prob\"]=V[t][s][\"prob\"]/maxv\n # print(\"Printing V\")\n # print(V)\n intertime1 = time.time()\n print(\"--- Viterbi forward: %s seconds ---\" % (intertime1 - start_time))\n #print V\n\n #opt is the result: a list of (matched) segments [s1, s2, s3,...] in the exact order of the point track: [p1, p2, p3,...]\n opt = []\n\n # get the highest probability at the end of the track\n max_prob = max(value[\"prob\"] for value in V[-1].values())\n previous = None\n if max_prob == 0:\n print(\" probabilities fall to zero (network distances in data are too large, try increasing network decay parameter)\")\n\n # Get most probable ending state and its backtrack\n for st, data in V[-1].items():\n if data[\"prob\"] == max_prob:\n opt.append(st)\n previous = st\n break\n## print \" previous: \"+str(previous)\n## print \" max_prob: \"+str(max_prob)\n## print \" V -1: \"+str(V[-1].items())\n\n # Follow the backtrack till the first observation to fish out most probable states and corresponding paths\n for t in range(len(V) - 2, -1, -1):\n #Get the subpath between last and most probable previous segment and add it to the resulting path\n path = V[t + 1][previous][\"path\"]\n opt[0:0] =(path if path !=None else [])\n #Insert the previous segment\n opt.insert(0, V[t + 1][previous][\"prev\"])\n previous = V[t + 1][previous][\"prev\"]\n intertime2 = time.time()\n print(\"--- Viterbi backtracking: %s seconds ---\" % (intertime2 - intertime1))\n\n #Clean the path (remove double segments and crossings) (only in full path option)\n print( \"path length before cleaning :\" +str(len(opt)))\n opt = cleanPath(opt, endpoints)\n intertime3 = time.time()\n print(\"--- Path cleaning: %s seconds ---\" % (intertime3 - intertime2))\n print( \"final length: \"+str(len(opt)))\n pointstr= [str(g.firstPoint.X)+' '+str(g.firstPoint.Y) for g in points]\n optstr= [str(i) for i in opt]\n # print( 'The path for points ['+' '.join(pointstr)+'] is: ')\n # print( '[' + ' '.join(optstr) + '] with highest probability of %s' % max_prob)\n\n #If only a single segment candidate should be returned for each point:\n if addfullpath == False:\n opt = getpointMatches(points,opt)\n optstr= [str(i) for i in opt]\n # print( \"Individual point matches: \"+'[' + ' '.join(optstr) + ']')\n intertime4 = time.time()\n print(\"--- Picking point matches: %s seconds ---\" % (intertime4 - intertime3))\n\n return opt","title":""},{"docid":"f722e61dcf88afd49c382cc5bebbbf38","score":"0.54296696","text":"def sample_from_strata(\n strata, bates_param=1, latin=\"none\", matching_init=\"approx\", full_output=False\n):\n\n def bipartite_match(graph, matching=None):\n \"\"\"Find maximum cardinality matching of a bipartite graph (U, V, E).\n\n The input format is a dictionary mapping members of U to a list\n of their neighbors in V. The output is a triple (M, A, B) where M\n is a dictionary mapping members of V to their matches in U, A is\n the part of the maximum independent set in U, and B is the part of\n the MIS in V. The same object may occur in both U and V, and is\n treated as two distinct vertices if this happens.\n\n Adapted from\n https://github.com/ActiveState/code/tree/master/recipes/Python/123641_HopcroftKarp_bipartite_matching\n Hopcroft-Karp bipartite max-cardinality matching and max independent set\n David Eppstein, UC Irvine, 27 Apr 2002\n\n \"\"\"\n if not matching:\n # initialize greedy matching (redundant, but faster than full search)\n matching = {}\n for u in graph:\n for v in graph[u]:\n if v not in matching:\n matching[v] = u\n break\n while True:\n # structure residual graph into layers\n # pred[u] gives the neighbor in the previous layer for u in U\n # preds[v] gives a list of neighbors in the previous layer for v in V\n # unmatched gives a list of unmatched vertices in final layer of V,\n # and is also used as a flag value for pred[u] when u is in the first layer\n preds = {}\n unmatched = []\n pred = dict([(u, unmatched) for u in graph])\n for v in matching:\n del pred[matching[v]]\n layer = list(pred)\n # repeatedly extend layering structure by another pair of layers\n while layer and not unmatched:\n new_layer = {}\n for u in layer:\n for v in graph[u]:\n if v not in preds:\n new_layer.setdefault(v, []).append(u)\n layer = []\n for v in new_layer:\n preds[v] = new_layer[v]\n if v in matching:\n layer.append(matching[v])\n pred[matching[v]] = v\n else:\n unmatched.append(v)\n # did we finish layering without finding any alternating paths?\n if not unmatched:\n unlayered = {}\n for u in graph:\n for v in graph[u]:\n if v not in preds:\n unlayered[v] = None\n return matching, list(pred), list(unlayered)\n\n # recursively search backward through layers to find alternating paths\n # recursion returns true if found path, false otherwise\n def recurse(v):\n if v in preds:\n L = preds[v]\n del preds[v]\n for u in L:\n if u in pred:\n pu = pred[u]\n del pred[u]\n if pu is unmatched or recurse(pu):\n matching[v] = u\n return 1\n return 0\n\n for v in unmatched:\n recurse(v)\n\n # sanity checks & initialization\n assert len(strata) > 0\n assert bates_param > 0\n assert latin != \"matching\" or matching_init in (\n \"approx\",\n \"greedy-det\",\n \"greedy-rand\",\n )\n num_points = len(strata)\n dimension = len(strata[0][0])\n points = np.empty((num_points, dimension))\n rand_uni = random.uniform\n error_indices = set()\n if latin == \"none\":\n for dim_index in range(dimension):\n for j, stratum in enumerate(strata):\n low, high = stratum[0][dim_index], stratum[1][dim_index]\n if np.isinf(bates_param):\n points[j][dim_index] = (low + high) * 0.5\n elif bates_param == 1:\n points[j][dim_index] = rand_uni(low, high)\n else:\n uniform_sum = sum(rand_uni(low, high) for _ in range(bates_param))\n points[j][dim_index] = uniform_sum / bates_param\n else:\n bin_size = 1.0 / num_points\n for dim_index in range(dimension):\n\n def cog_key(strat_idx):\n \"\"\"Two times the center of gravity.\"\"\"\n stratum = strata[strat_idx]\n return stratum[0][dim_index] + stratum[1][dim_index]\n\n strat_indices = np.random.permutation(num_points).tolist()\n strat_indices.sort(key=cog_key)\n bin_indices = list(range(num_points))\n if latin == \"approx\":\n # strat_indices has already been sorted above\n pass\n elif latin == \"matching\":\n dim_graph = {}\n matching = {}\n for bin_idx, strat_idx in zip(bin_indices, strat_indices):\n stratum = strata[strat_idx]\n low, high = stratum[0][dim_index], stratum[1][dim_index]\n low_bin = int(low / bin_size)\n high_bin = int(np.ceil(high / bin_size))\n avail_bins = np.arange(low_bin, high_bin)\n if matching_init == \"approx\" and bin_idx in avail_bins:\n matching[bin_idx] = strat_idx\n elif matching_init == \"greedy-rand\":\n np.random.shuffle(avail_bins)\n dim_graph[strat_idx] = avail_bins\n if len(matching) < num_points:\n matching, max_ind_set, _ = bipartite_match(dim_graph, matching)\n assert len(max_ind_set) == 0\n bin_indices = matching.keys()\n strat_indices = matching.values()\n else:\n raise Exception(\"unknown latin hypercube option \" + str(latin))\n for bin_idx, strat_idx in zip(bin_indices, strat_indices):\n stratum = strata[strat_idx]\n low, high = stratum[0][dim_index], stratum[1][dim_index]\n low2, high2 = bin_idx * bin_size, (bin_idx + 1) * bin_size\n low, high = max(low, low2), min(high, high2)\n if not high >= low:\n error_indices.add(strat_idx)\n # revert to uniform sampling of the stratum in this dim\n low, high = stratum[0][dim_index], stratum[1][dim_index]\n if np.isinf(bates_param):\n points[strat_idx][dim_index] = (low + high) * 0.5\n elif bates_param == 1:\n points[strat_idx][dim_index] = rand_uni(low, high)\n else:\n uniform_sum = sum(rand_uni(low, high) for _ in range(bates_param))\n points[strat_idx][dim_index] = uniform_sum / bates_param\n if full_output:\n return points, error_indices\n else:\n return points","title":""},{"docid":"d9954a9ceaf8e1245d46c315a8e0f581","score":"0.53942454","text":"def get_objects(matchings, coords,snap_threshold = 30, frames_lost_lim = 20):\n active_objs = []\n inactive_objs = []\n \n # initialize with all objects found in first frame\n for i,row in enumerate(coords[0]):\n obj = {\n 'current': (row[0],row[1]),\n 'all': [], # keeps track of all positions of the object\n 'obj_id': i, # position in coords list\n 'fsld': 0, # frames since last detected\n 'first_frame': 0 # frame in which object is first detected\n }\n obj['all'].append(obj['current']) \n active_objs.append(obj)\n \n # for one set of matchings between frames - this loop will run (# frames -1) times\n # f is the frame number\n # frame_set is the 1 x objects numopy array of matchings from frame f to frame f+1\n for f, frame_set in enumerate(matchings):\n move_to_inactive = []\n matched_in_next = [] # keeps track of which objects from next frame are already dealt with\n \n # first, deal with all existing objects (each loop deals with one object)\n # o is the objecobjs[4]t index\n # obj is the object \n for o, obj in enumerate(active_objs):\n matched = False\n obj_id_next = int(frame_set[obj['obj_id']]) # get the index of object in next frame or -1 if not matched in next frame\n # first deals with problem of reverse indexing, second verifies there is a match\n if obj['obj_id'] != -1 and obj_id_next != -1: # object has a match\n obj['obj_id'] = obj_id_next\n obj['current'] = (coords[f+1][obj_id_next,0],coords[f+1][obj_id_next,1])\n obj['all'].append(obj['current'])\n obj['fsld'] = 0\n \n matched_in_next.append(obj_id_next)\n matched = True\n \n else: # object does not have a certain match in next frame\n # search for match among detatched objects\n for j, row in enumerate(coords[f+1]):\n if j not in matched_in_next: # not already matched to object in previous frame\n # calculate distance to current obj\n distance = np.sqrt((obj['current'][0]-row[0])**2 + (obj['current'][1]-row[1])**2)\n if distance < snap_threshold: # close enough to call a match\n obj['obj_id'] = j\n obj['current'] = (row[0],row[1])\n obj['all'].append(obj['current'])\n obj['fsld'] = 0\n \n matched_in_next.append(j)\n matched = True\n break\n # if no match at all\n if not matched:\n obj['obj_id'] = -1\n obj['all'].append(obj['current']) # don't update location at all\n obj['fsld'] += 1\n \n if obj['fsld'] > frames_lost_lim:\n move_to_inactive.append(o)\n \n # now, deal with objects found only in second frame - each row is one object \n for k, row in enumerate(coords[f+1]):\n # if row was matched in previous frame, the object has already been dealt with\n if k not in matched_in_next:\n # create a new object\n new_obj = {\n 'current': (row[0],row[1]),\n 'all': [], # keeps track of all positions of the object\n 'obj_id': k, # position in coords list\n 'fsld': 0, # frames since last detected\n 'first_frame': f # frame in which object is first detected\n }\n new_obj['all'].append(new_obj['current']) \n active_objs.append(new_obj)\n \n # lastly, move all objects in move_to_inactive\n move_to_inactive.sort()\n move_to_inactive.reverse()\n for idx in move_to_inactive:\n inactive_objs.append(active_objs[idx])\n del active_objs[idx]\n \n return active_objs + inactive_objs","title":""},{"docid":"4f3dcd1c3d8e2651053fd23254674024","score":"0.535804","text":"def fiducialsAlignment(MF,\n src,\n dst):\n\n def get_distance(x_spots,y_spots,xe,ye,n_neighbor):\n \"\"\"Measure the euclidean distance between each point of an array to its n nearest neighbor in a second array using\n kd-tree algorithm.\n\n Args:\n x_spots (list): X coordinates of the array to query (1D).\n y_spots (list): Y coordinates of the array to query (1D).\n xe (list): X coordinates of the array to index (1D).\n ye (list): Y coordinates of the array to index(1D).\n n_neighbor (int): The number of nearest neighbor to consider.\n\n Returns:\n distances (list): Distances of the indexed points n nearest queried neighbors (2D).\n\n \"\"\"\n data = list(zip(xe.ravel(), ye.ravel()))\n tree = spatial.KDTree(data)\n return tree.query(list(zip(x_spots.ravel(), y_spots.ravel())),n_neighbor)\n\n def err_func(params, preX, preY, postX, postY):\n \"\"\"Error function passed in the optimizer. Transforms coordinates of target frame and returns the mean nearest neighbor\n distance to the 1st frame fiducials.\n\n Args:\n params (list): Array of coordinate transformation function:\n [Translation in X(float), Translation in Y(float), rotation(float)] (1D).\n preX (list): X coordinates of the 1st frame fiducials (1D).\n preY (list): Y coordinates of the 1st frame fiducials (1D).\n postX (list): X coordinates of the target frame fiducials (1D).\n postY (list): Y coordinates of the target frame fiducials (1D).\n\n Returns:\n mean_distances (): Mean N nearest neighbor distance to the 1st frame fiducials.\n\n \"\"\"\n transX, transY, rot = params\n transformed = transform(postX, postY, transX, transY, rot)\n distances = np.array(get_distance(transformed[:, 0], transformed[:, 1], preX, preY, 1)[0])\n return np.mean(distances)\n\n preX, preY = np.load(dst)\n postX, postY = np.load(src)\n\n n_features = 2000\n post_den = int(np.round(np.shape(postX)[0] / n_features))\n postX_redu = postX[::post_den]#reduces optimizer computation time\n #TODO --> need to evaluate impact on alignment precision\n postY_redu = postY[::post_den]\n # print('post features length = {}'.format(np.shape(postX_redu)))\n\n pre_den = int(np.round(np.shape(preX)[0] / n_features))\n preX_redu = preX[::pre_den]\n preY_redu = preY[::pre_den]\n # print('pre features length = {}'.format(np.shape(preX_redu)))\n\n minF = basinhopping(err_func, x0=(0, 0, 0 ),\n niter=1, T=1.0, stepsize=10,\n minimizer_kwargs={'args': ((preX_redu, preY_redu, postX_redu, postY_redu))},\n take_step=None, accept_test=None, callback=None, interval=50, disp=True,\n niter_success=1)\n # print(minF)\n\n # #Case of 180deg rotation:\n # deg = 180\n # rad = -deg / 180. * np.pi\n # transformed = transform(postX_redu, postY_redu, 0, 0, rad)\n #\n # minF_rot = basinhopping(err_func, x0=((np.mean(postX) - np.mean(transformed[:, 0])), (np.mean(postY) - np.mean(transformed[:, 1])), rad),\n # niter=1, T=1.0, stepsize=10,\n # minimizer_kwargs={'args': ((preX_redu, preY_redu, postX_redu, postY_redu))},\n # take_step=None, accept_test=None, callback=None, interval=50, disp=True,\n # niter_success=1)\n # print(minF_rot)\n # if minF.fun < minF_rot.fun:\n\n transX = minF.x[0]\n transY = minF.x[1]\n rot = minF.x[2]\n # scale = minF.x[3]\n # print('rot = 0 deg')\n # else:\n # transX = minF_rot.x[0]\n # transY = minF_rot.x[1]\n # rot = minF_rot.x[2]\n # # scale = minF.x[3]\n # print('rot = 180 deg')\n if dst == MF + 'Analysis/Fiducials/timelapseXYpenmarks.npy':\n np.save(MF + 'Analysis//Fiducials/optimized_params_timelapse.npy', [transX, transY, rot])\n else:\n np.save(MF + 'Analysis//Fiducials/optimized_params.npy', [transX, transY, rot])\n transformed = transform(postX, postY, transX, transY, rot)\n plt.figure()\n plt.scatter(transformed[:, 0], transformed[:, 1],1)\n plt.scatter(preX, preY, 1, 'r')\n plt.axis('equal')\n plt.savefig(MF + 'Analysis//Fiducials/surfRegResults.png', dpi = 500)\n plt.close('all')","title":""},{"docid":"b61e83c26abac3cb891bac653e668521","score":"0.5316389","text":"def run(self):\n\n print('>> Generating pose predictions using sparse-to-dense matching...')\n output = []\n tqdm_bar = tqdm(enumerate(self._ranks.T), total=self._ranks.shape[1],\n unit='images', leave=True)\n for i, rank in tqdm_bar:\n\n # Compute the query dense hypercolumn\n query_image = self._dataset.data['query_image_names'][i]\n if query_image not in self._filename_to_intrinsics:\n continue\n query_dense_hypercolumn, _ = self._network.compute_hypercolumn(\n [query_image], to_cpu=False, resize=True)\n channels, width, height = query_dense_hypercolumn.shape[1:]\n query_dense_hypercolumn = query_dense_hypercolumn.squeeze().view(\n (channels, -1))\n predictions = []\n\n for j in rank[:self._top_N]:\n\n # Compute dense reference hypercolumns\n nearest_neighbor = self._dataset.data['reference_image_names'][j]\n local_reconstruction = \\\n self._filename_to_local_reconstruction[nearest_neighbor]\n reference_sparse_hypercolumns, cell_size = \\\n self._compute_sparse_reference_hypercolumn(\n nearest_neighbor, local_reconstruction)\n\n # Perform exhaustive search\n matches_2D, mask = exhaustive_search.exhaustive_search(\n query_dense_hypercolumn,\n reference_sparse_hypercolumns,\n Image.open(nearest_neighbor).size[::-1],\n [width, height],\n cell_size)\n\n # Solve PnP\n points_2D = np.reshape(\n matches_2D.cpu().numpy()[mask], (-1, 1, 2))\n points_3D = np.reshape(\n local_reconstruction.points_3D[mask], (-1, 1, 3))\n distortion_coefficients = \\\n local_reconstruction.distortion_coefficients\n intrinsics = local_reconstruction.intrinsics\n prediction = solve_pnp.solve_pnp(\n points_2D=points_2D,\n points_3D=points_3D,\n intrinsics=intrinsics,\n distortion_coefficients=distortion_coefficients,\n reference_filename=nearest_neighbor,\n reference_2D_points=local_reconstruction.points_2D[mask],\n reference_keypoints=None)\n\n # If PnP failed, fall back to nearest-neighbor prediction\n if not prediction.success:\n prediction = self._nearest_neighbor_prediction(\n nearest_neighbor)\n if prediction:\n predictions.append(prediction)\n else:\n predictions.append(prediction)\n\n if len(predictions):\n export, best_prediction = self._choose_best_prediction(\n predictions, query_image)\n if self._log_images:\n if np.ndim(np.squeeze(best_prediction.query_inliers)):\n self._plot_inliers(\n left_image_path=query_image,\n right_image_path=best_prediction.reference_filename,\n left_keypoints=keypoint_association.kpt_to_cv2(\n best_prediction.query_inliers),\n right_keypoints=keypoint_association.kpt_to_cv2(\n best_prediction.reference_inliers),\n matches=[(i, i) for i in range(best_prediction.num_inliers)],\n title='Sparse-to-Dense Correspondences',\n export_filename=self._dataset.output_converter(query_image))\n\n plot_correspondences.plot_image_retrieval(\n left_image_path=query_image,\n right_image_path=best_prediction.reference_filename,\n title='Best match',\n export_filename=self._dataset.output_converter(query_image))\n\n output.append(export)\n tqdm_bar.set_description(\n \"[{} inliers]\".format(best_prediction.num_inliers))\n tqdm_bar.refresh()\n\n return output","title":""},{"docid":"b5bb49d3e4b6b95777f9331f5340af70","score":"0.53086776","text":"def match_all_by_similarity(self):\n self.prepare()\n beacon_tags=np.unique([ds.beacon_id.item() for ds in self.all_detects])\n\n # Filter down to rx that actually have some pings\n dss=[ds for ds in self.all_detects if ds.dims['index']]\n print(f\"{len(dss)} rx datasets (including time resets)\")\n while len(dss)>1:\n Ndet=len(dss)\n\n tag_counts=[ [ (ds.tag.values==b).sum() for b in beacon_tags ]\n for ds in dss ]\n\n best_match=[0,None]\n for ds_ai in range(Ndet):\n for ds_bi in range(ds_ai+1,Ndet):\n # dss can contain sequential instances of the same stations,\n # before/after a clock reset. Would like to ignore cases where station\n # is the same. Not that simple. Only the original datasets\n # have station.\n # If it's not ignored here, then we risk match_sequences failing\n # below, and then we won't know how to proceed.\n # Instead, ignore cases where the times do not significantly overlap.\n # Still not great...\n common_start=max(dss[ds_ai].tnum.values[0], dss[ds_bi].tnum.values[0])\n common_end = min(dss[ds_ai].tnum.values[-1],dss[ds_bi].tnum.values[-1])\n t_overlap=common_end-common_start\n # don't process if less than 10 minutes. Note that big clock shifts\n # like an hour will screw this up, but none of this process can deal\n # with errors that large.\n if t_overlap<600.0: \n continue\n \n a_tag_counts=tag_counts[ds_ai]\n b_tag_counts=tag_counts[ds_bi]\n # calculate a max possible number of matches\n ab_tag_counts=[ min(a,b) for a,b in zip(a_tag_counts,b_tag_counts)]\n sim=np.sum(ab_tag_counts)\n if sim>best_match[0]:\n best_match=[sim,(ds_ai,ds_bi)]\n if best_match[0]==0:\n break\n ai,bi=best_match[1]\n print(f\"Will match ai={ai} bi={bi} with best possible length of {best_match[0]}\")\n ab=self.match_sequences(dss[ai], dss[bi])\n dss=dss[:ai] + dss[ai+1:bi] + dss[bi+1:]\n dss.append(ab)\n\n # Scan the remaining datasets, and hope that exactly one has merged results.\n merged_dss=[ds for ds in dss if ds.dims.get('rx',0)>0 ]\n if len(merged_dss)==0:\n return None\n elif len(merged_dss)>1:\n # Probably an indication of a bug. It *could* be real, in which case\n # the easiest solution is to return the one with the most total pings,\n # but that would discard the information in the other connected components.\n raise Exception(\"Multiple connected components of rx. Not ready for that\")\n else:\n ds_result=merged_dss[0]\n self.add_beacon_list(ds_result) \n return ds_result","title":""},{"docid":"d5ad42bee895907630e371df99fd5e21","score":"0.5243194","text":"def matching(args, weight_data, repre_seqs, curr_file_index, raw_index, raw_data):\n\n print(\"Step 6. Matching, start matching with original data\", weight_data.shape)\n\n # calculate the distances among weighted data and representative list,\n # find the nearest cluster for each sequence data\n\n distance_matrix = cdist(weight_data, repre_seqs)\n min_index_list = np.argmin(distance_matrix, axis=1)\n valid_min_list = distance_matrix[np.arange(len(min_index_list)), min_index_list] < args.threshold\n\n clu_array = np.array([min_index_list[i] if x else -1 for i, x in enumerate(valid_min_list)])\n\n clu_result = np.array(np.vstack([raw_index, clu_array]).T)\n\n # get the mismatched data with its index\n if -1 in clu_array:\n mismatch_index = np.where(clu_array == -1)[0] # mismatched sequence indexes\n else:\n mismatch_index = []\n new_raw_index = [raw_index[i] for i in mismatch_index]\n mismatch_data = np.array([weight_data[i] for i in mismatch_index])\n\n # choose whether to save the matched clusters into files, set in run.py. Although multiprocessing is applied,\n # This costs a lot, turn it off if not needed. False by default\n if args.save_file:\n curr_file_index = save_matching(args, raw_data, clu_array, curr_file_index, raw_index)\n return mismatch_index, mismatch_data, curr_file_index, new_raw_index, clu_result","title":""},{"docid":"24e23238b3ca1914e9b964da2b9dccab","score":"0.52187335","text":"def createM(keypointsVideo1,keypointsVideo2,numberOfFramesVideo1,numberOfFramesVideo2):\n\t#X=np.array([[1, 4], [3, 4], [5, 4], [7, 4]])\n\t#X=np.array([[1, 2, 3], [2, 4, 5], [3, 5, 6], [4, 7, 8]])\n\t\n\t#for each in keypointsVideo2:\n\t#\tprint each['frame']\n\t#\tlist.append(each['kp'])\n\tisFirst=1\n\tfor eachFeature in keypointsVideo2:\n\t\t#print eachFeature['desc']\n\t\tif isFirst is 1:\n\t\t\tX=np.array(eachFeature['desc'])\n\t\t\tisFirst=0\n\t\telse:\n\t\t\tX=np.vstack((X,np.array(eachFeature['desc'])))\n\t#print X\n\t\t\n\tprint \"Done some work\"\t\n\t\n\ttree = KDTree(X, leaf_size=2,metric='euclidean')\n\tmatchMatrix=[]\n\tfor each in keypointsVideo1:\n\t\tdistance, indices = tree.query(each['desc'], k=len(X))\n\t\t#print keypointsVideo2[indices[0][0]]\n\t\tmatch={}\n\t\tmatch['frame1']=each['frame']\n\t\tmatch['frame2']=keypointsVideo2[indices[0][0]]['frame']\n\t\tmatch['kp1']=each['kp']\n\t\tmatch['kp2']=keypointsVideo2[indices[0][0]]['kp']\n\t\tmatchMatrix.append(match)\n\t\t#print \"Yep\"\n\t#print matchMatrix\n\treturn matchMatrix","title":""},{"docid":"ab06a464a7a88ac0f41ae4c412c9761e","score":"0.5214311","text":"def forward(self, bottom, top):\n\n try:\n assert len(bottom) == self._num_anchors + 2\n assert len(top) == 1 or len(top) == 4\n\n detections_data = np.array(bottom[0].data)\n batch_detections = self._read_detections(detections_data, MATCHED_RECORD_SIZE,\n self._translate_matched_prediction,\n self._valid_action_ids, self._min_conf)\n\n centers = np.array(bottom[1].data)\n\n self._embeddings = []\n for i in range(self._num_anchors):\n self._embeddings.append(np.array(bottom[i + 2].data))\n\n all_candidates = []\n total_num_pairs = 0\n total_num_overlapped = 0\n total_num_incorrect = 0\n for item_id in batch_detections:\n detections = batch_detections[item_id]\n\n for i, _ in enumerate(detections):\n anchor_det = detections[i]\n for j in range(i + 1, len(detections)):\n ref_det = detections[j]\n\n # exclude same class predictions\n if anchor_det.action == ref_det.action:\n continue\n\n overlap = self._iou(anchor_det, ref_det)\n if overlap < self._min_overlap:\n continue\n\n total_num_overlapped += 1\n\n anchor_embed = self._embeddings[anchor_det.anchor][anchor_det.item, :,\n anchor_det.y, anchor_det.x]\n ref_embed = self._embeddings[ref_det.anchor][ref_det.item, :, ref_det.y, ref_det.x]\n\n anchor_center_distances =\\\n (1.0 - np.matmul(centers, anchor_embed.reshape([-1, 1]))).reshape([-1])\n ref_center_distances =\\\n (1.0 - np.matmul(centers, ref_embed.reshape([-1, 1]))).reshape([-1])\n\n anchor_action = np.argmin(anchor_center_distances)\n ref_action = np.argmin(ref_center_distances)\n\n # exclude well-separated predictions\n if anchor_action != ref_action:\n continue\n\n # exclude predictions with both incorrect labels\n if anchor_action != anchor_det.action and ref_action != ref_det.action:\n continue\n\n total_num_incorrect += 1\n\n embed_dist = 1.0 - np.sum(anchor_embed * ref_embed)\n\n if anchor_action != anchor_det.action:\n loss = self._margin + anchor_center_distances[anchor_det.action] - embed_dist\n if loss > 0.0:\n all_candidates.append((loss, embed_dist, anchor_det, ref_det))\n total_num_pairs += 1\n\n if ref_action != ref_det.action:\n loss = self._margin + ref_center_distances[ref_det.action] - embed_dist\n if loss > 0.0:\n all_candidates.append((loss, embed_dist, ref_det, anchor_det))\n total_num_pairs += 1\n\n if len(all_candidates) == 0:\n self._candidates = []\n\n top[0].data[...] = 0.0\n if len(top) == 4:\n top[1].data[...] = 0.0\n top[2].data[...] = 0.0\n top[3].data[...] = 0.0\n else:\n self._candidates = all_candidates\n\n loss = np.sum([tup[0] for tup in self._candidates]) / float(len(self._candidates))\n top[0].data[...] = loss\n\n if len(top) == 4:\n top[1].data[...] = np.median([tup[1] for tup in self._candidates])\n top[2].data[...] = float(len(self._candidates)) / float(total_num_pairs)\n top[3].data[...] = float(total_num_incorrect) / float(total_num_overlapped)\n except Exception:\n LOG('SplitLossLayer forward pass exception: {}'.format(traceback.format_exc()))\n exit()","title":""},{"docid":"5a9e5bb0b865f03c99b3982f7cdfd797","score":"0.5212836","text":"def test_find_matching_pairs():\n labelmap_0 = torch.tensor(\n [\n [0,0,0,0,0,0,0,0],\n [4,4,0,0,0,0,4,4],\n [4,3,2,2,2,2,3,4],\n [4,2,2,2,2,2,2,4]\n ])\n\n labelmap_1 = torch.tensor(\n [\n [1,1,1,1,0,0,0,0],\n [1,1,1,1,2,2,2,4],\n [4,4,4,4,2,2,2,4],\n [4,4,4,3,2,2,2,4]\n ])\n labelmap_2 = torch.tensor(\n [\n [4,4,4,4,4,4,4,4],\n [4,4,4,4,4,4,4,4],\n [4,4,4,4,4,4,4,4],\n [4,4,4,4,4,4,4,4]\n ])\n\n Y = torch.stack([labelmap_0, labelmap_1, labelmap_2])\n assert Y.shape == (3,4,8)\n \n a_pair_info = torch.tensor(\n [\n [0,1,1], # pos\n [2,1,4], # neg\n [1,1,7], # pos\n [0,2,2] # neg\n ])\n b_pair_info = torch.tensor(\n [\n [2,3,7], # pos\n [0,1,4], # neg\n [2,3,0], # pos\n [1,3,3] # neg\n ])\n pos_pair_info, neg_pair_info = find_matching_pairs(Y, a_pair_info, b_pair_info)\n gt_pos_pair_info = torch.tensor(\n [\n [0, 1, 1, 2, 3, 7], # pos pairs\n [1, 1, 7, 2, 3, 0]\n ])\n assert torch.allclose(pos_pair_info, gt_pos_pair_info)\n gt_neg_pair_info = torch.tensor(\n [\n [2, 1, 4, 0, 1, 4], # neg pairs\n [0, 2, 2, 1, 3, 3]\n ])\n assert torch.allclose(neg_pair_info, gt_neg_pair_info)","title":""},{"docid":"6b89f542738eca8e62e5fbb082a938c4","score":"0.52080387","text":"def compute_proj_xform(matches,features1,features2,image1,image2):\n \n proj_xform = np.zeros((3,3))\n threeSamplesIn1 = np.zeros((3,3))\n threeSamplesIn2 = np.zeros((3,3))\n rnd = 10000\n maxVote = 0\n for n in range(rnd):\n correspondences = []\n samplesIndex = random.sample(range(0, len(matches) - 1), 4)\n tmp1 = matches[samplesIndex[0]]\n tmp2 = matches[samplesIndex[1]]\n tmp3 = matches[samplesIndex[2]]\n tmp4 = matches[samplesIndex[3]]\n x11 = features1[tmp1[0]][1]\n y11 = features1[tmp1[0]][0]\n x21 = features1[tmp2[0]][1]\n y21 = features1[tmp2[0]][0]\n x31 = features1[tmp3[0]][1]\n y31 = features1[tmp3[0]][0]\n x41 = features1[tmp4[0]][1]\n y41 = features1[tmp4[0]][0]\n x12 = features2[tmp1[1]][1]\n y12 = features2[tmp1[1]][0]\n x22 = features2[tmp2[1]][1]\n y22 = features2[tmp2[1]][0]\n x32 = features2[tmp3[1]][1]\n y32 = features2[tmp3[1]][0]\n x42 = features2[tmp4[1]][1]\n y42 = features2[tmp4[1]][0]\n correspondences.append([x11, y11, x12, y12])\n correspondences.append([x21, y21, x22, y22])\n correspondences.append([x31, y31, x32, y32])\n correspondences.append([x41, y41, x42, y42])\n aList = []\n for corr in correspondences:\n p1 = np.matrix([corr[0], corr[1], 1])\n p2 = np.matrix([corr[2], corr[3], 1])\n a2 = [0, 0, 0, -p2.item(2) * p1.item(0), -p2.item(2) * p1.item(1), -p2.item(2) * p1.item(2),\n p2.item(1) * p1.item(0), p2.item(1) * p1.item(1), p2.item(1) * p1.item(2)]\n a1 = [-p2.item(2) * p1.item(0), -p2.item(2) * p1.item(1), -p2.item(2) * p1.item(2), 0, 0, 0,\n p2.item(0) * p1.item(0), p2.item(0) * p1.item(1), p2.item(0) * p1.item(2)]\n aList.append(a1)\n aList.append(a2)\n A = np.matrix(aList)\n w, v = np.linalg.eig(np.dot(A.T, A))\n h = v.T[np.argmin(w)]\n h = np.reshape(h, (3, 3))\n h = (1 / h.item(8)) * h\n count = 0\n for pairs in matches:\n x11Test = features1[pairs[0]][1]\n y11Test = features1[pairs[0]][0]\n x12Test = features2[pairs[1]][1]\n y12Test = features2[pairs[1]][0]\n den = h[2,0]*x11Test+h[2,1]*y11Test+h[2,2]\n tmp1 = ((h[0,0]*x11Test+h[0,1]*y11Test+h[0,2])/den-x12Test)**2\n tmp2 = ((h[1,0]*x11Test+h[1,1]*y11Test+h[1,2])/den-y12Test)**2\n if(tmp1<1 and tmp2<1): count+=1\n if (count > maxVote):\n maxVote = count\n proj_xform = h\n return proj_xform","title":""},{"docid":"e19a7823117d0d2474258c0e579300fe","score":"0.52023745","text":"def __initial_match(candidate_list: (np.ndarray, np.generic), min_pts=2, t=50, criterion='distance'):\n # TODO group matching for non-grouped user\n # 1 : dbscan algorithm + gps based movement vector alignment -> clear!\n # 2 : acceleration -> let's discuss\n assert isinstance(candidate_list, (np.ndarray, np.generic))\n num_of_data, num_time_steps, _ = candidate_list.shape\n X = np.array([candidate_list[i, num_time_steps - 1, :] for i in range(num_of_data)])\n rads = np.radians(X) # [N,2]\n # Clustering with gps-data of 1-time step.\n # 'haversine' do clustering using distance transformed from (lat, long)\n clusterer = hdbscan.HDBSCAN(min_cluster_size=min_pts, min_samples=2, metric='haversine')\n labels = clusterer.fit_predict(rads)\n print('Before trajectory clustering, labels are ', labels)\n n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n groups = []\n for ulb in range(n_clusters_):\n groups.append([])\n for i, lb in enumerate(labels):\n if lb == -1:\n continue\n groups[lb].append(i)\n total_n_clusters = n_clusters_\n # Group refinement considering user's trajectory\n for nc in range(n_clusters_):\n group_member_mask = (labels == nc)\n group_members = candidate_list[group_member_mask]\n pdist = tdist.pdist(group_members.transpose([0, 2, 1]), metric=\"sspd\",type_d=\"spherical\")\n Z = fc.linkage(pdist, method=\"ward\")\n sub_labels = sch.fcluster(Z, t, criterion=criterion) - 1\n unique_sub_labels = len(set(sub_labels))\n if unique_sub_labels == 1:\n continue\n for ad in range(unique_sub_labels - 1):\n groups.append([])\n member_indices = list(compress(range(len(group_member_mask)), group_member_mask))\n for sb in range(unique_sub_labels):\n sub_group_mask = (sub_labels == sb)\n sub_member_indices = list(compress(range(len(sub_group_mask)), sub_group_mask))\n #Noise case\n if len(sub_member_indices) == 1:\n groups[nc].remove(member_indices[sub_member_indices[0]])\n labels[member_indices[sub_member_indices[0]]] = -1\n continue\n for m in range(len(sub_member_indices)):\n # remove from wrong group\n groups[nc].remove(member_indices[sub_member_indices[m]])\n # add to refined group\n groups[total_n_clusters].append(member_indices[sub_member_indices[m]])\n labels[member_indices[sub_member_indices[m]]] = total_n_clusters\n total_n_clusters += 1\n print('After trajectory clustering, labels are ', labels)\n return groups.copy()","title":""},{"docid":"1bdc36e06c37d5d8b07e469b650b6385","score":"0.52014744","text":"def match_gious(threshold, truths, priors, variances, labels, loc_t, conf_t, idx):\n # jaccard index\n loc_t[idx] = box_utils.point_form(priors)\n # print(\"truths:\", truths.shape)\n overlaps = box_utils.jaccard(\n truths,\n box_utils.point_form(priors)\n )\n # (Bipartite Matching)\n # [1,num_objects] best prior for each ground truth\n best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)\n # [1,num_priors] best ground truth for each prior\n best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)\n best_truth_idx.squeeze_(0)\n best_truth_overlap.squeeze_(0)\n best_prior_idx.squeeze_(1)\n best_prior_overlap.squeeze_(1)\n best_truth_overlap.index_fill_(0, best_prior_idx, 2) # ensure best prior\n # TODO refactor: index best_prior_idx with long tensor\n # ensure every gt matches with its prior of max overlap\n for j in range(best_prior_idx.size(0)):\n best_truth_idx[best_prior_idx[j]] = j\n matches = truths[best_truth_idx] # Shape: [num_priors,4]\n # print(\"labels.shape:\", labels.shape) # torch.Size([3000, 1])\n # conf = labels[best_truth_idx] + 1 # Shape: [num_priors]\n conf = labels[best_truth_idx]\n conf[best_truth_overlap < threshold] = 0 # label as background\n loc_t[idx] = matches # [num_priors,4] encoded offsets to learn\n conf_t[idx] = conf # [num_priors] top class label for each prior","title":""},{"docid":"3cb58bfd85d53cec6362dc4b75850f6c","score":"0.5185468","text":"def images_detection(self, imgs, orig_dim_list):\n args = self.tracker_opt\n _CUDA = True\n if args:\n if args.gpus[0] < 0:\n _CUDA = False\n if not self.model:\n self.load_model()\n \n \n activated_starcks = []\n refind_stracks = []\n lost_stracks = []\n removed_stracks = []\n\n ''' Step 1: Network forward, get detections & embeddings'''\n with torch.no_grad():\n imgs = imgs.to(args.device) if args else imgs.cuda()\n pred = self.model(imgs)\n\n if len(pred) > 0:\n dets = non_max_suppression(pred, self.confidence, self.nms_thres)\n\n output_stracks = []\n for image_i in range(len(imgs)):\n self.frame_id += 1\n if dets[image_i] is not None:\n det_i = scale_coords(self.img_size, dets[image_i], orig_dim_list[image_i])\n '''Detections'''\n detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f.numpy(), 30) for\n (tlbrs, f) in zip(det_i[:, :5], det_i[:, -self.emb_dim:])]\n else:\n detections = []\n\n\n ''' Add newly detected tracklets to tracked_stracks'''\n unconfirmed = []\n tracked_stracks = [] # type: list[STrack]\n for track in self.tracked_stracks:\n if not track.is_activated:\n unconfirmed.append(track)\n else:\n tracked_stracks.append(track)\n\n ''' Step 2: First association, with embedding'''\n strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)\n # Predict the current location with KF\n for strack in strack_pool:\n strack.predict()\n\n dists = matching.embedding_distance(strack_pool, detections)\n dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections)\n matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7)\n\n for itracked, idet in matches:\n track = strack_pool[itracked]\n det = detections[idet]\n if track.state == TrackState.Tracked:\n track.update(detections[idet], self.frame_id)\n activated_starcks.append(track)\n else:\n track.re_activate(det, self.frame_id, new_id=False)\n refind_stracks.append(track)\n\n ''' Step 3: Second association, with IOU'''\n detections = [detections[i] for i in u_detection]\n r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state==TrackState.Tracked ]\n dists = matching.iou_distance(r_tracked_stracks, detections)\n matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)\n \n for itracked, idet in matches:\n track = r_tracked_stracks[itracked]\n det = detections[idet]\n if track.state == TrackState.Tracked:\n track.update(det, self.frame_id)\n activated_starcks.append(track)\n else:\n track.re_activate(det, self.frame_id, new_id=False)\n refind_stracks.append(track)\n\n for it in u_track:\n track = r_tracked_stracks[it]\n if not track.state == TrackState.Lost:\n track.mark_lost()\n lost_stracks.append(track)\n\n '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''\n detections = [detections[i] for i in u_detection]\n dists = matching.iou_distance(unconfirmed, detections)\n matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)\n for itracked, idet in matches:\n unconfirmed[itracked].update(detections[idet], self.frame_id)\n activated_starcks.append(unconfirmed[itracked])\n for it in u_unconfirmed:\n track = unconfirmed[it]\n track.mark_removed()\n removed_stracks.append(track)\n\n \"\"\" Step 4: Init new stracks\"\"\"\n for inew in u_detection:\n track = detections[inew]\n if track.score < self.confidence:\n continue\n track.activate(self.kalman_filter, self.frame_id)\n activated_starcks.append(track)\n\n \"\"\" Step 5: Update state\"\"\"\n for track in self.lost_stracks:\n if self.frame_id - track.end_frame > self.max_time_lost:\n track.mark_removed()\n removed_stracks.append(track)\n\n self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]\n self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)\n self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)\n # self.lost_stracks = [t for t in self.lost_stracks if t.state == TrackState.Lost] # type: list[STrack]\n self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)\n self.lost_stracks.extend(lost_stracks)\n self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)\n self.removed_stracks.extend(removed_stracks)\n self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)\n\n if self.tracker_opt.debug:\n logger.debug('===========Frame {}=========='.format(self.frame_id))\n logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks]))\n logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks]))\n logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))\n logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))\n\n # Add tracks to outputs\n for t in self.tracked_stracks:\n tlwh = t.tlwh\n tid = t.track_id\n tlbr = t.tlbr\n ts = t.score\n if tlwh[2] * tlwh[3] > self.tracker_opt.min_box_area:\n res = torch.tensor([image_i, tlbr[0], tlbr[1], tlbr[2], tlbr[3], ts, tid])\n output_stracks.append(res)\n\n if len(output_stracks) == 0:\n return 0\n \n return torch.stack(output_stracks)","title":""},{"docid":"ffd874fe1dbc69c18e457e6bf818166b","score":"0.51813614","text":"def motifMatchingBiopython(combinationList,pwmList,coordDict,pwmLocation,genomeList,tempLocation,fpr=0.01,pseudocounts=0.0,precision=10**4,color=\"black\"):\n \n # Reading PWM\n pwmDict = dict()\n for pwmName in pwmList: pwmDict[pwmName] = readPwmFile(pwmLocation+pwmName+\".pwm\",\"/\".join(tempLocation.split(\"/\")[:-1])+\"/\",pseudocounts)\n\n # Evaluating thresholds\n pwmThresholdDict = dict()\n for pwmName in pwmList:\n sd = Motif.ScoreDistribution(pwmDict[pwmName],precision=precision)\n pwmThresholdDict[pwmName] = sd.threshold_fpr(fpr)\n\n # Reading genome\n genomeDict = genome.readFastaFiles(genomeList)\n\n # Creating chromosome list\n chrList = constants.getChromList(reference=[coordDict])\n # Removing chrX, chrY and chrM\n # TODO Stop removing these chromosomes\n #chrListT = []\n #for e in chrList:\n # if(e not in [\"chrX\", \"chrY\", \"chrM\"]): chrListT.append(e)\n #chrList = chrListT\n\n # Evaluating bed additionals\n if(color == \"green\"): color = \"0,130,0\"\n elif(color == \"red\"): color = \"130,0,0\"\n elif(color == \"black\"): color = \"0,0,0\"\n\n # Create combinations dictionary keys\n combKeys = []\n for c in combinationList:\n for b in [\",\".join(e) for e in itertools.combinations(pwmList,c)]: combKeys.append(b)\n\n # Iterating on chromosomes\n mpbsDict = dict([(e,dict()) for e in pwmDict.keys()])\n statDict = dict([(e,[0,0]) for e in combKeys]) # Left is evidence / Right is not evidence\n geneDict = dict([(e,[]) for e in combKeys])\n maxDict = dict([(e,-99.0) for e in pwmDict.keys()])\n ct=0\n for chrName in chrList:\n\n # Reading genome\n if(chrName not in genomeDict.keys()): continue\n sequence = genomeDict[chrName]\n\n # Iterating on coordinate dictionary\n for e in mpbsDict.keys(): mpbsDict[e][chrName] = []\n for coord in coordDict[chrName]:\n ct=ct+1\n #print \"region\", ct\n # Getting current sequence based on coordinates\n currSeq = sequence[coord[0]:coord[1]]\n\n # Keeping track of the factors found in this coordinate\n flagMotifs = dict([(e,False) for e in pwmDict.keys()])\n\n # Iterating on PWMs\n for pwmName in pwmDict.keys():\n pwmLen = len(pwmDict[pwmName])\n for pos, score in pwmDict[pwmName].search_pwm(currSeq,threshold=pwmThresholdDict[pwmName]):\n if(score > maxDict[pwmName]): maxDict[pwmName] = score\n if(pos >= 0): mpbsDict[pwmName][chrName].append([pos+coord[0],pos+coord[0]+pwmLen,pwmName,score,\"+\",pos+coord[0],pos+coord[0]+pwmLen,color])\n else: mpbsDict[pwmName][chrName].append([-pos+coord[0],-pos+coord[0]+pwmLen,pwmName,score,\"-\",-pos+coord[0],-pos+coord[0]+pwmLen,color])\n flagMotifs[pwmName] = True\n \n # Updating statistic counts and genes\n motifsFoundList = [k for k in pwmList if flagMotifs[k]]\n motifsFoundKeys = []\n motifsNotFoundKeys = [e for e in combKeys]\n for c in combinationList:\n for b in [\",\".join(e) for e in itertools.combinations(motifsFoundList,c)]:\n motifsFoundKeys.append(b)\n motifsNotFoundKeys.remove(b)\n for k in motifsFoundKeys:\n statDict[k][0] += 1\n for e in coord[2].split(\":\"): geneDict[k].append(e)\n for k in motifsNotFoundKeys:\n statDict[k][1] += 1\n\n # Update scores - new scores are within [0,1000]\n for pwmName in pwmDict.keys():\n for chrName in mpbsDict[pwmName].keys():\n for e in mpbsDict[pwmName][chrName]:\n e[3] = int(1000*(e[3]-pwmThresholdDict[pwmName])/(maxDict[pwmName]-pwmThresholdDict[pwmName]))\n\n # Remove repetitive genes from geneList\n for k in geneDict.keys(): geneDict[k] = list(set(geneDict[k]))\n \n return mpbsDict, statDict, geneDict","title":""},{"docid":"c232c199c57defa2a0277ca0a2f290c0","score":"0.51793605","text":"def generate_matches(reference_field,data_field,params):\n referencelist = find_rivermouth_points(reference_field,params)\n rmouthlist = find_rivermouth_points(data_field,params)\n candidatepairs = generate_candidate_pairs(referencelist, rmouthlist)\n conflicts, conflict_free_pairs = \\\n ConflictChecker.check_pair_sets_for_conflicts(candidatepairs)\n pairs_from_resolved_conflicts,pairs_from_unresolved_conflicts =\\\n ConflictResolver.resolve_conflicts(conflicts,params)\n conflict_free_pairs.extend(pairs_from_resolved_conflicts)\n return conflict_free_pairs, pairs_from_unresolved_conflicts","title":""},{"docid":"ea5de3d933eb15c10089a093f7a71e58","score":"0.5161643","text":"def question4():\n\n p = 'GGCGCGGTGGCTCACGCCTGTAAT'\n\n #number of mistmatches = 2, so we need to split into 3 (2+1)\n mistmatches_allowed = 2\n num_segments_required = mistmatches_allowed + 1\n k_mer_size = 8\n pattern_size = 24\n\n reads, qualities = readFastq('chr1.GRCh38.excerpt.fasta')\n consolidated_read = ''.join([read for read in reads])\n\n index = Index(consolidated_read, k_mer_size)\n p_segments = [ p[i*k_mer_size:i*k_mer_size+k_mer_size] for i in range(num_segments_required) ] # = ['GGCGCGGT', 'GGCTCACG', 'CCTGTAAT']\n #print p_segments\n\n hits_per_segment = {} # not used\n hit_lists = []\n i = 0\n index_hits = 0\n for segment in p_segments:\n hits = index.query(segment)\n index_hits += 1\n if len(hits) > 0:\n #print segment, hits\n hits_per_segment[segment] = hits\n hit_lists.append(set([hit - i*k_mer_size for hit in hits])) # we keep the starting point of the pattern in our results set for easy comparison (verification means equality)\n # and we store them in set for easy equality check by means of intersecting the sets.\n i += 1\n #print hits_per_segment\n #print hit_lists\n\n \n results = []\n for i in range(len(hit_lists)):\n # WARN: this bit here only works for numbr of mismatches = 2 as we intersect two sets for any values of mistmatches_allowed, it would lead to invalid results otherwise\n intersect = hit_lists[i].intersection(hit_lists[(i+1)%len(hit_lists)])\n if len(intersect) > 0 : \n for item in intersect:\n results.append(item)\n print len(results)\n return sorted(results), index_hits","title":""},{"docid":"f58ec4d1266f87d8bfd7b39e71822f39","score":"0.5160608","text":"def match_insertions(identifiedBp, maxDist, max_hit_end_dist = 200, seqList = None, filterByDist = False, filterByEndDist = False):\n \n # filtering by seqList allows different paramerts to be applied for matching different sets of breakpoints\n # for example, breakpoints that matched to transposon sequences should be processed differently than \n # breakpoints that matched insertion sequences\n if seqList is not None:\n # select just the breakpoints with BLAST hits from the list of sequences from seqName\n # for example, if seqName is 'Cth_transposons.fa', just match transposons\n transpDf = identifiedBp[identifiedBp['BLAST source'].isin(seqList)]\n else:\n transpDf = identifiedBp # don't filter breakpoints\n \n # check to see if transpDf is empty\n if len(transpDf) == 0:\n print('No breakpoints supplied to match_insertions. '\n 'This will generate a runtime error when you try to merge 2 empty dataframes. '\n 'This error can be ignored')\n \n # split transposons into left and right groups and only keep the relevant columns\n # list of columns to keep\n good_cols = ['Strain','Sample', 'Chromosome', \n 'Hit Name', 'Seq Name', 'StartReg', \n 'EndReg', 'readFrac', \n 'BP distance', 'Hit end distance', 'e-value', 'BLAST source']\n transpDfLeft = transpDf.loc[transpDf['Name'] == 'Left breakpoint', :].reindex(columns = good_cols)\n transpDfRight = transpDf.loc[transpDf['Name'] == 'Right breakpoint', :].reindex(columns = good_cols)\n \n # do an inner join of left and right sets of breakpoints\n # match all breakpoints with the same name and chromosome\n lr = pd.merge(transpDfLeft, transpDfRight, how='inner', on=['Strain','Sample','Chromosome', 'Hit Name'], suffixes=(' L', ' R'))\n \n # filter by distance\n # choose left and right breakpoint pairs based on how close they are\n # this is useful for transposons, \n # its only important for targetd mutations if there are multiple identical insertions in the same strain\n if filterByDist:\n # transposons have a small negative distance\n lr['absDist'] = lr['EndReg R'] - lr['StartReg L']\n lr['absDist'] = lr['absDist'].abs()\n correctDist = (lr['absDist'] < maxDist)\n lr = lr.loc[correctDist, :]\n # if there are multiple matches, try to choose the best one (or at least get rid of some bad ones)\n # select all rows that match the smallest distance\n minDistIdx = lr.groupby('Seq Name L')['absDist'].transform(min) == lr['absDist']\n lr = lr.loc[minDistIdx, :] \n \n # filter by end distance (i.e. the distance between where the breakpoint starts matching and \n # the start of the sequence). Real insertion sequences have breakpoints that match only at the\n # ends (i.e. Hit end distance is close to 0). \n # This is particularly important for merodiploid insertions, where internal matches are likely\n # This is not important for other types of mutations\n if filterByEndDist:\n correctRDist = (lr['Hit end distance R'].abs() < max_hit_end_dist)\n correctLDist = (lr['Hit end distance L'].abs() < max_hit_end_dist)\n lr = lr.loc[correctRDist & correctLDist, :]\n # if there are several matches for a given breakpoint, choose the one with the shortest end distances\n # allEndDist is a composite of the left and right BP distances and hit end distances\n # a low score means that the breakpoint starts matching the target sequence close to the chromosomal junction and close to the \n # end of the insertion sequence\n lr['allEndDist'] = lr['BP distance L'].abs() + lr['BP distance R'].abs() + lr['Hit end distance L'].abs() + lr['Hit end distance R'].abs()\n # select rows where 'allEndDist' is equal to the minimum 'allEndDist' value in the group\n minAllDistIdx = lr.groupby('Seq Name L')['allEndDist'].transform(min) == lr['allEndDist']\n lr = lr.loc[minAllDistIdx, :]\n \n result = lr.copy()\n ## Clean up dataframe\n # add new columns as necessary\n result['readFrac'] = (result['readFrac L'] + result['readFrac R']) / 2\n result['Type'] = 'Insertion'\n result['Source'] = 'match_insertion_' + result['BLAST source L'].str[:5] # add first 6 letters of seq list to distinguish origins \n \n # rename columns\n # for deletions, the StartReg coordinate will always be lower than the EndReg coordinate\n # for insertions, the StartReg coordinate will be greater than the EndReg coordinate\n result.rename(columns = {'Hit Name':'Description',\n 'Seq Name L':'lBpId',\n 'Seq Name R':'rBpId',\n 'EndReg R': 'StartReg',\n 'StartReg L':'EndReg'\n }, inplace=True)\n \n \n # fix chromosome spaces\n result['Chromosome'] = result['Chromosome'].apply(fixChromosomeSpaces)\n \n # select only the desired columns for the final output\n print('selecting columns for final output')\n result = result.reindex(columns = ['Strain','Sample','Chromosome','StartReg','EndReg','lBpId','rBpId','readFrac','Type','Description','Source'])\n \n # figure out which breakpoints weren't matched\n # make a list of the values in the lBpId and rBpId columns\n matchedBpList = sorted(result['lBpId'].astype(int).tolist() + result['rBpId'].astype(int).tolist())\n # match those against the original list\n matchedBooleanSeries = identifiedBp['Seq Name'].astype(int).isin(matchedBpList)\n # find the original breakpoints whose breakpoint IDs are not in the matchedBpList\n remainingBp = identifiedBp.loc[~matchedBooleanSeries, :]\n \n return (result, remainingBp)","title":""},{"docid":"30966cdd7569cdfb252969be9b3b49c7","score":"0.5156748","text":"def matchFrame(self, frame1, frame2):\n matcher = cv2.BFMatcher(cv2.NORM_HAMMING)\n matches = matcher.knnMatch(frame1.descriptors, frame2.descriptors, k=2)\n ratio = 0.65\n match_idx1, match_idx2 = [], []\n for m1, m2 in matches:\n if (m1.distance < ratio*m2.distance) and (m1.distance < 32):\n if m1.queryIdx not in match_idx1 and m1.trainIdx not in match_idx2:\n match_idx1.append(m1.queryIdx)\n match_idx2.append(m1.trainIdx)\n\n # We need at least 8 matches for calculating Fundamental matrix\n assert(len(match_idx1) >= 8)\n assert(len(match_idx2) >= 8)\n\n assert(len(set(match_idx1)) == len(match_idx1))\n assert(len(set(match_idx2)) == len(match_idx2))\n\n match_idx1, match_idx2 = np.asarray(match_idx1), np.asarray(match_idx2)\n\n frame1_pts_un = frame1.keypoints_un[match_idx1, :]\n frame2_pts_un = frame2.keypoints_un[match_idx2, :]\n\n F, inlier_mask = cv2.findFundamentalMat(\n frame1_pts_un, frame2_pts_un, cv2.RANSAC, 0.1, 0.99)\n E = self.K.T @ F @ self.K\n inlier_mask = inlier_mask.astype(bool).squeeze()\n print(\"Matches: matches = {}, inliers = {}\".format(\n len(matches), inlier_mask.shape[0]))\n print(\"Fundamental matrix: \\n{}\\n\".format(F))\n\n _, R, t, _ = cv2.recoverPose(E, frame1_pts_un, frame2_pts_un, self.K)\n pose = np.eye(4)\n pose[0:3, 0:3] = R\n pose[0:3, 3] = t.squeeze()\n return pose, match_idx1[inlier_mask], match_idx2[inlier_mask]","title":""},{"docid":"073dd7513139c733d2b481f13d3f4c5c","score":"0.5154203","text":"def find_recommendation(cls, sentiment_filename,\n nodelist_filename,\n direct_friend_filename,\n edgelist_filename,\n the_user):\n \n \n NUM_MOVIES = 86\n DEGREE_THRESHOLD = 50\n LIKE = 0\n DISLIKE = 1\n\n df_list = []\n df2idx = {}\n idf2idx = {}\n \n file_ = open(direct_friend_filename, 'r') \n count = 0\n for each in file_.readlines():\n temp = each.strip()\n df_list.append([temp, 0])\n df2idx[temp] = count\n count += 1\n num_df = count \n file_.close()\n\n count = 0 \n file_ = open(nodelist_filename, 'r')\n for each in file_.readlines():\n user = each.strip()\n if (user in df2idx): continue \n idf2idx[user] = count\n count += 1\n num_idf = count\n file_.close()\n\n # create a num_idf x num_df matrix populated w/ [0,1] in each cell\n train_mat = [[[0,1] for col in range(num_df)] for row in range(num_idf)] \n\n ## fill out train_matrix\n graph_edges = nx.read_edgelist(path=edgelist_filename, delimiter='\\t').edges()\n for edge in graph_edges:\n ## edge = (idf, df)\n if (edge[1] in df2idx and edge[0] not in df2idx):\n train_mat[idf2idx[edge[0]]][df2idx[edge[1]]] = [1, 0]\n df_list[df2idx[edge[1]]][1] += 1\n graph_edges = [] # clear memory\n\n ## update the row of the central user\n tuser_idx = idf2idx[the_user]\n num_like = 0\n for col in range(num_df):\n if (df_list[col][1] >= DEGREE_THRESHOLD):\n train_mat[tuser_idx][col] = [1,0]\n num_like += 1\n else :\n train_mat[tuser_idx][col] = [0,1]\n\n ## update prob(class = like)\n prob_class = 2*[0]\n prob_class[LIKE] = float(num_like)/float(num_df)\n prob_class[DISLIKE] = 1 - prob_class[LIKE] \n\n ## update prob[feature index i][like/dislike][class like/dislike]\n ## = proprotoin of likes/dislike in row i (i.e.[1,0]) given class = like/dislike\n EPSILON = 0.0000000000000001\n prob = [[[EPSILON,EPSILON] for i in range(2)] for row in range(num_idf)]\n \n num_dislike = num_df - num_like\n CLASS_LIKE = FEATURE_LIKE = 0\n CLASS_DIS = FEATURE_DIS = 1\n \n for row in range(num_idf):\n if (row == tuser_idx): continue\n nll = sum([1 for j in range(num_df)\n if train_mat[tuser_idx][j][LIKE] == 1 and train_mat[row][j][LIKE] == 1])\n prob[row][FEATURE_LIKE][CLASS_LIKE] = float(nll+1)/float(num_like+2)\n prob[row][FEATURE_DIS][CLASS_LIKE] = float(num_like-nll+1)/float(num_like+2)\n\n nld = sum([1 for j in range(num_df)\n if train_mat[tuser_idx][j][DISLIKE] == 1 and train_mat[row][j][LIKE] == 1])\n prob[row][FEATURE_LIKE][CLASS_DIS] = float(nld+1)/float(num_dislike+2)\n prob[row][FEATURE_DIS][CLASS_DIS] = float(num_dislike-nld+1)/float(num_dislike+2)\n\n train_mat = [] # clear memory\n\n\n ## update the prediction_mat\n pred_mat = [[[0,0] for col in range(NUM_MOVIES)] for row in range(num_idf)]\n\n sentiment = json.loads(codecs.open(sentiment_filename,\n 'r', 'utf8', 'replace').read())\n for status in sentiment['data']:\n if (status['name'] in df2idx): continue\n if (status['polarity'] >= 2):\n pred_mat[idf2idx[status['name']]][status['no']][LIKE] = 1\n else : pred_mat[idf2idx[status['name']]][status['no']][DISLIKE] = 1\n\n ## compute the probabilities\n rec_list = {}\n for col in range(NUM_MOVIES):\n log_class_like = math.log(prob_class[LIKE])\n log_class_dis = math.log(prob_class[DISLIKE])\n for row in range(num_idf):\n # row 2*i\n if (pred_mat[row][col][LIKE] == 1): # 'like' is present\n log_class_like += math.log(prob[row][FEATURE_LIKE][CLASS_LIKE])\n log_class_dis += math.log(prob[row][FEATURE_LIKE][CLASS_DIS])\n else :\n log_class_like += math.log(prob[row][FEATURE_DIS][CLASS_LIKE])\n log_class_dis += math.log(prob[row][FEATURE_DIS][CLASS_DIS])\n \n # row 2*i + 1\n if (pred_mat[row][col][DISLIKE] == 1): # 'dislike' is present \n log_class_like += math.log(prob[row][FEATURE_DIS][CLASS_LIKE])\n log_class_dis += math.log(prob[row][FEATURE_DIS][CLASS_DIS])\n else :\n log_class_like += math.log(prob[row][FEATURE_LIKE][CLASS_LIKE])\n log_class_dis += math.log(prob[row][FEATURE_LIKE][CLASS_DIS])\n \n if (log_class_like > log_class_dis):\n rec_list[cls.movies[col]] = log_class_dis/log_class_like\n\n print \"Recommended List\"\n for each in reversed(sorted(rec_list.iteritems(), key=operator.itemgetter(1))):\n print \"%s \\t %0.5f\" % (each[0], each[1])","title":""},{"docid":"e9edb3e95c32d07b05bcff6f6948f6f6","score":"0.514272","text":"def get_patent_patient_matrix(patient_info_df):\n related_features = ['patient_id', 'infected_by']\n patient_info_df = patient_info_df[related_features]\n infection_rel = patient_info_df[~patient_info_df['infected_by'].isna()]\n uids = []\n vids = []\n for i in range(infection_rel.shape[0]):\n pid = infection_rel.iloc[i, 0]\n # Get code of patient instead of patient_id.\n u = infection_rel[infection_rel['patient_id'] == pid]['patient_id'].cat.codes.values[0]\n infected_by_pids = [int(i) for i in infection_rel.iloc[i, 1].split(',')]\n for ibpid in infected_by_pids:\n if len(patient_info_df[patient_info_df['patient_id'] == ibpid]) != 0:\n # Check existance of user in the dataset.\n # Get code of patient.\n v = patient_info_df[patient_info_df['patient_id'] == ibpid]['patient_id'].cat.codes.values[0]\n uids.append(u)\n vids.append(v)\n \n # Test the number of relationship.\n assert len(uids) == len(vids) == 1340\n\n # Adjacency matrix for patient - infected by - patient relationship.\n p_infected_by_p = sp.coo_matrix((np.ones(len(uids)), (uids, vids)),\n shape=(patient_info_df.shape[0], patient_info_df.shape[0]))\n return p_infected_by_p","title":""},{"docid":"128ab8f4d552ce9a0939445d630f509f","score":"0.51354086","text":"def match(image1,image2):\n im1, keypoints1, descriptors1 = ReadKeys(image1)\n im2, keypoints2, descriptors2 = ReadKeys(image2)\n #\n # REPLACE THIS CODE WITH YOUR SOLUTION (ASSIGNMENT 5, QUESTION 3)\n #\n #Generate five random matches (for testing purposes)\n\n # initialize values\n print 'Debug - Setp 1: Initilizing values'\n matched_pairs = []\n descriptor_lengthOne = len(descriptors1)\n descriptor_lengthTwo = len(descriptors2)\n threshold = 0.7\n\n # loop through each item in descriptors1\n print 'Debug - Setp 2: Loop through descriptors'\n for x in range(descriptor_lengthOne):\n \n # initlaize dot product array\n dotproduct_arr = []\n\n # loop through each item in descriptors2\n # print 'Debug - Setp 3: Calculating dot products'\n for y in range (descriptor_lengthTwo):\n\n # compute the dot product and insert into the array\n dotproduct = np.dot(descriptors1[x], descriptors2[y])\n dotproduct_arr.append(dotproduct)\n\n # initilize arrays for angles\n angle_arr = []\n\n # loop through the array of dot products we just calculated\n # print 'Debug - Setp 4: Calculating angles'\n for dotpos in range (len(dotproduct_arr)):\n\n # compute the angle theta (cos theta = a dot b over mag(a)mag(b))\n angle_arr.append(math.acos(dotproduct_arr[dotpos]))\n\n # sort the angle array we calcauted above and grab the two smallest val\n # print 'Debug - Setp 5: Sorting angle array'\n sorted_angle_arr = sorted(angle_arr)\n best_angle = sorted_angle_arr[0]\n runnerup_angle = sorted_angle_arr[1]\n\n # compute the ratio and initalize index\n angle_ratio = best_angle/runnerup_angle\n index = 0\n\n # if the ratio is less than the threshold, we proceed\n # print 'Debug - Setp 6: Checking threshold'\n if (angle_ratio <= threshold):\n \n # calculate the index and insert pair into match_pairs and print out\n index = angle_arr.index(best_angle)\n matched_pairs.append([keypoints1[x], keypoints2[index]])\n\n print 'Debug - Setp 7: End of MP'\n \n # initalize values that is used in RANSAC\n delta_orientation_between_keypoints = 20 # plus or minus (+/-)\n delta_of_scale = 0.8\n matched_pairs_len = len(matched_pairs)\n ransac_all = []\n\n # do it 10 times\n for i in range(10):\n ransac_current = []\n mp_pos = np.random.randint(matched_pairs_len)\n random_matchpairs = matched_pairs[mp_pos]\n\n print 'Debug - Setp 8: Cal delata s/o'\n # calculate change in scale and orientation\n scale_change = abs(random_matchpairs[0][2] - random_matchpairs[1][2])\n orientation_change = random_matchpairs[0][3] - random_matchpairs[1][3]\n\n # loop through all the matched pairs and check for consistancy\n print 'Debug - Setp 9: looping mp for consistancy check'\n for m in range(matched_pairs_len):\n mp = matched_pairs[m]\n orientation_change_compare = mp[0][3] - mp[1][3]\n diff_in_angle = abs(math.degrees(orientation_change) + 360 - math.degrees(orientation_change_compare) + 360) % 180\n\n # if the change in angle fits our specfication and check for consistancy\n # print 'Debug - Setp 10: angle check'\n if (diff_in_angle <= delta_orientation_between_keypoints):\n\n # then we compare scale change and consistancy\n # print 'Debug - Setp 11: cal scale'\n scale_change_compare = abs(mp[0][2] - mp[1][2])\n scale_change_upper = max(scale_change, scale_change_compare)\n scale_change_lower = min(scale_change, scale_change_compare)\n scale_with_deltaval = scale_change_upper * delta_of_scale\n\n # if it is consistant enough, we add it to the current array\n # print 'Debug - Setp 12: scale check'\n if (scale_with_deltaval <= scale_change_lower):\n ransac_current.append(mp)\n\n # consistancy check failed (scale), skip this item\n else:\n continue;\n # consistancy check failed (angle), skip item\n else:\n continue;\n # compare the length of array and only keep the largest\n print 'Debug - Setp 13: length check'\n if (len(ransac_current) > len(ransac_all)):\n ransac_all = ransac_current;\n #\n # END OF SECTION OF CODE TO REPLACE\n #\n im3 = DisplayMatches(im1, im2, ransac_all)\n return im3","title":""},{"docid":"3c636a25bc0e9c84c0b6f7f14a94c65b","score":"0.5135033","text":"def map_identical_molecules(trj,selection_list,bead_label_list,*args,**kwargs):\n index_list = []\n resSeq_list = []\n label_list = []\n\n internal_indices_list = [] \n\n if len(selection_list) != len(bead_label_list):\n raise ValueError(\"Error in map_identical_molecules, must submit selection list and bead label list of same length\")\n\n for sel in selection_list:\n internal_indices = trj.top.select(\"(resSeq == 1) and (%s)\"%sel)\n if len(internal_indices)==0:\n raise ValueError(\"Error in map_identical_molecules, selection string '%s' produced an empty list of atom indices\"%sel)\n internal_indices_list.append(internal_indices)\n\n start_index = 0\n resSeq = 1\n for r in trj.top.residues:\n for bead_idx, internal_indices in enumerate(internal_indices_list):\n system_indices = internal_indices + start_index\n index_list.append(system_indices) \n resSeq_list.append(resSeq)\n label_list.append(bead_label_list[bead_idx])\n resSeq = resSeq+1\n start_index = start_index + r.n_atoms\n\n return cg_by_index(trj, index_list, label_list, *args, **kwargs)","title":""},{"docid":"ff66018483f9ebdd27e8cf779927eeb1","score":"0.5126581","text":"def check_medlist_batch(variables):\n \n# 1. read in matching dictionary (drug->CID) - we keep all drugs in dictionary, then batch\n# 2. read in AE profile lists (ie, panels of AE's we care about)\n# 3. read in CID AE list- do not need to keep in memory\n# 4. map all pt meds to CID's - let complist be a dictionary\n# 5. parse all patients\n# 6. spit out file\n\n# 1. read in drug->CID dict\n\n# map to CID for lookup\n\n matcher_dict={}\n backmatch_dict={}\n matcheddruglist=[]\n \n # format: row0=brand, row1=generic, row3=CID\n # note we currently only match brand!\n with gzip.open(FNAME_MED_TO_CID) as gzfile:\n medpairs=csv.reader(gzfile,delimiter='\\t') \n for row in medpairs:\n\n# first match generic name\n gname=row[1].upper().split(\" \")[0]\n if (not gname in matcher_dict) and (not gname in matcheddruglist) :\n matcher_dict[gname]= row[3]\n if not backmatch_dict.has_key(row[3]): #necessary to prevent duplicate backmatches\n backmatch_dict[row[3]]=gname\n matcheddruglist.append(gname) #this is here to prevent duplicate entries\n# then match brand name \n bname=row[0].upper().split(\" \")[0]\n if (not bname in matcher_dict) and (not bname in matcheddruglist) :\n matcher_dict[bname]= row[3]\n # backmatch_dict[row[3]]=row[1].upper() ----> backmatch only for generics\n matcheddruglist.append(bname) #this is here to prevent duplicate entries\n\n print(\"loaded drug match list\")\n# 2. read in panels of AE's\n\n# aelist will be a dictionary, rather than a plain list as in single-patient version\n #key[drug]->list of AE concept ID's\n aedict={}\n aedict['Psychiatric']= load_aefilelist(\"CNS_psychiatric.txt\") \n aedict['Cognition']=load_aefilelist(\"CNS_cognition.txt\")\n aedict['Other_neurologic']=load_aefilelist(\"CNS_other_neurologic.txt\")\n aedict['Fall_risk']=load_aefilelist(\"CNS_fall_risk.txt\") \n\n print(\"loaded AE panels\")\n# 3. read in CID AE list\n# read in AE megafile - it's gzipped...\n property_dict={}\n parseddict={} # this prevents duplicate AEs per drug\n with gzip.open(FNAME_CID_TO_AE) as gzfile:\n drug_properties=csv.reader(gzfile,delimiter='\\t')\n for row in drug_properties:\n drugname=row[0].upper().split(\" \")[0]\n aename=row[11]\n if not(parseddict.has_key(drugname)): parseddict[drugname]={}\n if not(parseddict[drugname].has_key(aename)):\n if (row[9]==\"PT\" and not(row[5]==\"placebo\")) and not drugname=='' :\n parseddict[drugname][aename]=1\n # need to reassign frequencies (exact) to categories\n #postmarketing, rare, infrequent, frequent, or exact #\n freqnum=parse_frequency(row[6])\n if variables['Option_1']==0 and freqnum>0: freqnum=.1 # treats any ae as freq=.1\n \n # next section modified from single-patient version:\n # loop through aelist and see if the ae here is in our list. \n # if it is, increment the drug-ae-burden-item for that aelist\n \n for key in aedict: # loop through AE panels\n if aename in aedict[key]: #if ae is in a panel,\n if property_dict.has_key(drugname):\n if property_dict[drugname].has_key(key):\n property_dict[drugname][key]=property_dict[drugname][key]+freqnum\n else : property_dict[drugname][key]=freqnum\n else : \n property_dict[drugname]={}\n property_dict[drugname][key]=freqnum\n print(\"loaded AE list\") \n#4. now read in list of patients and meds. format: pt_id drug_list (tab-separated)\n# read by making everything into a list, pop first item from list and call it pt_id, assign remaining list to it.\n\n# files from Victor - column 1 (0-index) is name, col 0 is #, col 3 is high-level term\n pt_drugdict={} \n\n pt_drugfile=csv.reader(open(PATIENT_FILE),delimiter=',')\n for row in pt_drugfile:\n rowlist=row\n pt_id=rowlist.pop(0)\n rowlist=[element.upper() for element in rowlist]\n pt_drugdict[pt_id]=rowlist\n\n# now loop through patients - for each patient, make p450 list, use it to parse AE's\n pt_output={}\n pt_output_nop450={}\n for ptname in pt_drugdict:\n p450_inhibitors,p450_inducers,p450_substrates,multiplier=map_p450(pt_drugdict[ptname],1)\n # match meds to cid's for AE lookup\n matchedcid=[]\n for med in pt_drugdict[ptname]:\n if matcher_dict.has_key(med.upper()):\n matchedcid.append(matcher_dict[med.upper()]) \n \n # loop by drug, then by ae category - make one dict with p450, another without\n for cid in matchedcid:\n for aecat in aedict:\n if property_dict.has_key(cid):\n if property_dict[cid].has_key(aecat):\n if not pt_output.has_key(ptname): \n pt_output[ptname]={}\n pt_output_nop450[ptname]={}\n if pt_output[ptname].has_key(aecat):\n pt_output[ptname][aecat]=pt_output[ptname][aecat]+(property_dict[cid][aecat]*multiplier[backmatch_dict[cid]])\n pt_output_nop450[ptname][aecat]=pt_output[ptname][aecat]+(property_dict[cid][aecat])\n else : \n pt_output[ptname][aecat]=property_dict[cid][aecat]*multiplier[backmatch_dict[cid]]\n pt_output_nop450[ptname][aecat]=property_dict[cid][aecat]\n print(\"matched patients\") \n \n# now return results \n filename=PATIENT_FILE_OUTPUT\n if variables['Option_1']==0: filename=filename+\"_nofreq\"\n plink_file=file(filename+\".tsv\",\"w\")\n plink_file.write(\"Pt_id\")\n for aecats in aedict:\n plink_file.write(\"\\t\"+aecats)\n for pt_name in pt_output:\n plink_file.write(\"\\n\"+pt_name)\n for aecats in aedict:\n if pt_output[pt_name].has_key(aecats) : plink_file.write(\"\\t\"+str(pt_output[pt_name][aecats]))\n else : plink_file.write(\"\\t\"+'0')\n plink_file.close()\n\n# and again with nop450 \n plink_file=file(filename+\"_nop450\"+\".tsv\",\"w\")\n plink_file.write(\"Pt_id\")\n for aecats in aedict:\n plink_file.write(\"\\t\"+aecats)\n for pt_name in pt_output_nop450:\n plink_file.write(\"\\n\"+pt_name)\n for aecats in aedict:\n if pt_output_nop450[pt_name].has_key(aecats) : plink_file.write(\"\\t\"+str(pt_output_nop450[pt_name][aecats]))\n else : plink_file.write(\"\\t\"+'0')\n plink_file.close()\n \n return {\n 'pts_written': len(pt_output),\n 'ae_categories': len(aedict)\n }","title":""},{"docid":"936ee509aaa01fb7dbff95673f17dc6f","score":"0.511288","text":"def find_joints(heatmap, thresh):\n joints_per_type = []\n joints_id = 0\n for jid in range(NUM_JOINTS):\n joint_heatmap = heatmap[:, :, jid]\n peaks_coord = find_peaks(joint_heatmap, thresh)\n peaks = np.zeros((len(peaks_coord), 4)) ### x, y, score, id_peak\n for i, peak in enumerate(peaks_coord):\n x, y = peak\n peak_score = joint_heatmap[y, x]\n peaks[i, :] = (x, y, peak_score, joints_id)\n joints_id += 1\n joints_per_type.append(peaks)\n return joints_per_type","title":""},{"docid":"17dceb9324e64316783f82a6776f2a40","score":"0.5111084","text":"def findLRmatch( mesIn, X, nNeighMaxIn=5, stepIn=10 ):\n HEIGHT_VIS = 700\n N_NEIGH = nNeighMaxIn # neighbour comparison\n\n # pMod = man.TSNE(n_components=2, random_state=0)\n # X = pre.StandardScaler().fit_transform( X )\n # Xp = pMod.fit_transform(X)\n Xp = X\n\n # absolute path\n imgDir = mesIn.imgDir\n # GT\n gtFr = mesIn.gtFr\n\n # Sides\n gtLFr = gtFr[gtFr['side']=='l'].reset_index(drop=True)\n gtRFr = gtFr[gtFr['side']=='r'].reset_index(drop=True)\n XpL = Xp[(gtFr['side']=='l'),:]\n XpR = Xp[(gtFr['side']=='r'),:]\n\n # fit nearest neighbor model\n nbrsMod = nei.NearestNeighbors(n_neighbors=N_NEIGH, algorithm='ball_tree').fit(XpL)\n # find closest image fomr the right eye set\n distVec, indDstVec = nbrsMod.kneighbors(XpR)\n \n # init a list of number of neighbours and correctly identified pairs based on the neighbours\n nnLst = []\n ratioFoundLst = []\n # for an increasing number of neighbours\n for curNN in range(1,nNeighMaxIn+1,stepIn):\n foundCnt = 0\n for i in range(len(gtRFr)):\n # find left target image from right\n refPiD = gtRFr.iloc[i].pID\n lImgId = gtLFr[gtLFr['pID']==refPiD].index.values[0]\n # check if found\n found = (lImgId in indDstVec[i,:curNN])\n if found:\n foundCnt += 1\n \n ratioFound = (foundCnt*1.0)/len(gtRFr)\n \n # add to lists\n ratioFoundLst.append(ratioFound)\n nnLst.append(curNN)\n # print progress\n print (curNN, 'of', nNeighMaxIn)\n \n \n \n return (np.array(nnLst),np.array(ratioFoundLst))","title":""},{"docid":"fb4d8232df3b9c3332e01c902274719a","score":"0.5095707","text":"def matches(dataset, pairs, path=_default_path):\n match_file = ''.join([\"m50_\", str(2*pairs), \"_\", str(2*pairs), \"_0.txt\"]) \n match_file = join(path, dataset, match_file)\n \n print pairs, \"pairs each (matching/non_matching) from\", match_file\n \n return pairings(match_file)","title":""},{"docid":"1bc63afdf77b1398ad448c70cab16699","score":"0.5079945","text":"def matching(self, reconJ, gt_data, exeno, lowpass=True):\n # self.fcnt += 1\n if self.segini: # new segement/movement start\n self.segini = False\n # self.Ani_idx = self.aniorder[exeno][self.Ani_idx]\n if len(self.order[self.oidx]) == 1:\n self.gt_idx = self.order[self.oidx][0]\n self.idxlist.append(self.gt_idx)\n if len(self.seqlist_reg) == 0: # build sequence list\n self.seqlist_reg = reconJ\n # print self.seqlist_reg.shape\n self.seqlist_reg = self.seqlist_reg.reshape(-1, 21)\n self.seqlist = self.seqlist_reg\n else:\n self.seqlist_reg = np.vstack([self.seqlist_reg, reconJ])\n self.seqlist_gf = gf(self.seqlist_reg, 3, axis=0)\n if not lowpass:\n self.seqlist = self.seqlist_reg\n else:\n self.seqlist = self.seqlist_gf\n if not self.deflag: # Not yet decreasing\n if np.mod(self.seqlist.shape[0]-self.presv_size-1, 10) == 0:\n # check every 10 frames\n if len(self.order[self.oidx]) > 1:\n if self.seqlist.shape[0] > 1:\n result = self.clip(self.seqlist, exeno)\n if result != []:\n endidx = result[0]\n if self.seqlist[endidx, 7] < 150:\n minidx = 2\n else:\n minidx = 3\n self.gt_idx = minidx\n self.idxlist.append(self.gt_idx)\n self.evalstr = 'well done'\n self.seg_update(endidx)\n else:\n test_data_p = self.seqlist + np.atleast_2d((gt_data[self.gt_idx][0, :]-self.seqlist[0, :]))\n self.dist_p, _ = fastdtw(gt_data[self.gt_idx], test_data_p, self.jweight, dist=self.wt_euclidean)\n if (self.seqlist.shape[0] == 1+self.presv_size): # new movement initail setting\n self.dpfirst, _ = fastdtw(gt_data[self.gt_idx], test_data_p[:2], self.jweight, dist=self.wt_euclidean)\n print('dpfirst is : %f' % self.dpfirst)\n else:\n print('de diff is :%f' % (self.dpfirst - self.dist_p))\n if (self.dpfirst - self.dist_p) > self.decTh:\n print('=========')\n print('deflag on')\n print('=========')\n self.deflag = True\n self.distp_prev = self.dist_p\n else: # already start decreasing\n test_data_p = self.seqlist + np.atleast_2d((gt_data[self.gt_idx][0, :] - self.seqlist[0, :]))\n self.dist_p, path_p = fastdtw(gt_data[self.gt_idx], test_data_p, self.jweight, dist=self.wt_euclidean)\n if self.chk_flag: # in check global min status\n self.cnt += 1\n if self.dist_p < self.distp_cmp: # find smaller value\n self.cnt = 1\n self.distp_cmp = self.dist_p\n self.idx_cmp = self.seqlist.shape[0]\n print(' ==== reset ====')\n elif self.cnt == self.srchfw:\n self.evalstr = 'Well done'\n self.chk_flag = False\n tgrad = 0\n for ii in xrange(self.seqlist.shape[1]): # maybe can include jweight\n tgrad += (np.gradient(gf(self.seqlist[:, ii], 1))**2)*self.jweight[ii]\n tgrad = tgrad**0.5\n endidx = np.argmin(tgrad[self.idx_cmp-self.srchbw:self.idx_cmp+self.srchfw-1])\\\n + (self.idx_cmp-self.srchbw)\n self.seg_update(endidx)\n else:\n if (self.dist_p - self.distp_prev) > 0: # turning point\n print (' ============== large ====================')\n self.distp_cmp = self.distp_prev\n self.idx_cmp = self.seqlist.shape[0]\n self.chk_flag = True\n self.distp_prev = self.dist_p","title":""},{"docid":"c357386465d12fb5a5098e896e742508","score":"0.50787693","text":"def match_movies_with_wikipedia_abstracts(\n df_movies_metadata: DataFrame,\n df_wikipedia_abstracts: DataFrame\n) -> DataFrame:\n df_movies_with_wikipedia_abstracts = df_movies_metadata.join(\n df_wikipedia_abstracts,\n df_movies_metadata[f'{w.TITLE}_clean'] == df_wikipedia_abstracts[f'{w.TITLE}_clean'],\n how='left'\n ).drop(df_wikipedia_abstracts[w.TITLE]).drop(df_wikipedia_abstracts[f'{w.TITLE}_clean'])\n\n matches_by_movie = df_movies_with_wikipedia_abstracts \\\n .groupBy(df_movies_metadata.columns) \\\n .agg(f.count(w.URL).alias('count_urls'))\n\n df_movies_single_match = df_movies_with_wikipedia_abstracts.join(\n matches_by_movie.filter('count_urls = 1').drop('count_urls'),\n df_movies_metadata.columns,\n )\n\n df_movies_multiple_matches = df_movies_with_wikipedia_abstracts.join(\n matches_by_movie.filter('count_urls > 1').drop('count_urls'),\n df_movies_metadata.columns\n )\n\n df_movies_multiple_matches_best_match = find_best_link_for_movie(df_movies_multiple_matches)\n\n return df_movies_single_match.unionByName(df_movies_multiple_matches_best_match).select(\n m.TITLE, m.BUDGET, m.YEAR, m.REVENUE, m.RATING, m.RATIO, m.GENRES, m.PRODUCTION_COMPANIES, w.URL, w.ABSTRACT\n ).dropDuplicates(subset=[m.TITLE, m.BUDGET, m.YEAR, m.REVENUE, m.RATING, m.RATIO, m.GENRES, m.PRODUCTION_COMPANIES])","title":""},{"docid":"34f8fa566b1cc0fb6f747363084d172d","score":"0.5069949","text":"def forward_pass_entire_dataset(list_of_demonstrations, net_name, camera):\n\tnet = CNNFeatureExtractor(net_name)\n\t\n\ttotal = len(list_of_demonstrations) \n\ti = 1\n\tfor video in list_of_demonstrations:\n\t\tprint \"-------------------- \" + str(i) +\"--out-of-\"+ str(total)+\"-----------------\"\n\t\tPATH_TO_ANNOTATION = constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER + video + \"_\" + camera + \".p\"\n\t\tPATH_TO_DATA = constants.PATH_TO_DATA + constants.NEW_FRAMES_FOLDER + video + \"_\" + camera + \"/\"\n\t\tget_cnn_features_pickle_dump(net, video + \"_\" + camera, PATH_TO_DATA, PATH_TO_ANNOTATION, net_name)\n\t\ti += 1","title":""},{"docid":"2f7f7900cada825ca3523012c12d1b4c","score":"0.5053165","text":"def search_for_intermediary_tinyexon(self,similaritymatrix=None):\n finding_new = True\n startat = 0\n while finding_new:\n for pos in range(startat,len(self)):\n sg = self.codingblockgraphs[pos]\n prev = None\n if pos >= 1: prev = self.codingblockgraphs[pos-1]\n \n # confirm that organism sets in both CBGs are identical (no missing organisms / partial CBGs)\n if prev and not prev.organism_set().symmetric_difference(sg.organism_set()):\n\n # make DonorSiteCollectionGraph and AcceptorSiteCollectionGraph, NO projected sites included yet!!!\n sg.harvest_elegiable_acceptor_sites(projected_acceptors={},prev=prev)\n prev.harvest_elegiable_donor_sites(projected_donors={},next=sg)\n\n tinyexonsg = find_intermediary_codingblockgraph_with_tinyexon(\n prev,sg,input=self.input,\n similaritymatrix=similaritymatrix)\n if tinyexonsg:\n # insert into genestructure graphs at the correct position\n self.codingblockgraphs.insert( pos, tinyexonsg[0] )\n \n # set startat pointer to current point in the list genestructure_graphs\n # all previous sg's are scanned for tinyexons\n startat = pos+1\n break\n else:\n finding_new = False","title":""},{"docid":"3d0b7c6e34a418557034747435ceffdf","score":"0.50507283","text":"def get_dinucleotide_substitutions(ids, sequence_list, motifs):\n\n outputs = []\n\n motif_dint_count = collections.defaultdict(lambda: 0)\n motif_dint_sub = collections.defaultdict(lambda: 0)\n non_motif_dint_count = collections.defaultdict(lambda: 0)\n non_motif_dint_sub = collections.defaultdict(lambda: 0)\n\n if len(ids):\n for i, id in enumerate(ids):\n gen.print_parallel_status(i, ids)\n\n sequences = sequence_list[id][0]\n human = sequences[0]\n mac = sequences[1]\n # get predicted hits to motifs and chunk to get motifs\n hits = sequence_overlap_indicies(human, motifs)\n non_hits = [i for i in range(len(human)) if i not in hits]\n chunked_hits = chunk_overlaps(hits)\n chunked_non_hits = chunk_overlaps(non_hits)\n # get the motif sequences\n hit_human_motifs = [\"\".join([human[i] for i in chunk]) for chunk in chunked_hits]\n hit_mac_motifs = [\"\".join([mac[i] for i in chunk]) for chunk in chunked_hits]\n non_hit_human_motifs = [\"\".join([human[i] for i in chunk]) for chunk in chunked_non_hits]\n non_hit_mac_motifs = [\"\".join([mac[i] for i in chunk]) for chunk in chunked_non_hits]\n\n # for each motif hit in the hit motifs, go through each position,\n # get the dinucleotide and see whether it is different\n for i, hit in enumerate(hit_human_motifs):\n human_motif = hit\n mac_motif = hit_mac_motifs[i]\n\n for pos in range(len(human_motif)-1):\n if human_motif[pos] in nucleotides and mac_motif[pos] in nucleotides:\n motif_dint_count[human_motif[pos:pos+2]] += 1\n if human_motif[pos:pos+2] != mac_motif[pos:pos+2]:\n motif_dint_sub[human_motif[pos:pos+2]] += 1\n # for each non hit in the non hit motifs, go through each position,\n # get the dinucleotide and see whether it is different\n for i, non_hit in enumerate(non_hit_human_motifs):\n human_motif = non_hit\n mac_motif = non_hit_mac_motifs[i]\n for pos in range(len(human_motif)-1):\n if human_motif[pos] in nucleotides and mac_motif[pos] in nucleotides:\n non_motif_dint_count[human_motif[pos:pos+2]] += 1\n if human_motif[pos:pos+2] != mac_motif[pos:pos+2]:\n non_motif_dint_sub[human_motif[pos:pos+2]] += 1\n\n #unpickle\n motif_dint_count = unpickle(motif_dint_count)\n motif_dint_sub = unpickle(motif_dint_sub)\n non_motif_dint_count = unpickle(non_motif_dint_count)\n non_motif_dint_sub = unpickle(non_motif_dint_sub)\n output = [motif_dint_count, motif_dint_sub, non_motif_dint_count, non_motif_dint_sub]\n return output","title":""},{"docid":"4c1a75a2ef81c75f2c7fa8851ec5e717","score":"0.5046244","text":"def occurencexmatrix(repgenarray, dictpark, dictcont, outfile):\n #GenomeID1/GenomeID2\n #Set of all the known couples\n #Set of couples in each sample","title":""},{"docid":"6d83e52cbeffd8c3d8fa549fd5a57074","score":"0.50410557","text":"def console_label(deduper: dedupe.api.ActiveMatching) -> None: # noqa: C901\n\n confirmed_matches = []\n\n finished = False\n use_previous = False\n fields = unique(field.field for field in deduper.data_model.primary_fields)\n\n buffer_len = 1 # Max number of previous operations\n examples_buffer: List[Tuple[Any, Literal[\"match\", \"distinct\", \"uncertain\"]]] = []\n uncertain_pairs: List[Any] = []\n\n # don't re-use items that are confirmed with a mapping\n mapped_items = set()\n\n while not finished:\n if use_previous:\n record_pair, _ = examples_buffer.pop(0)\n use_previous = False\n else:\n try:\n if not uncertain_pairs:\n uncertain_pairs = deduper.uncertain_pairs()\n while True:\n record_pair = uncertain_pairs.pop()\n if (\n len(\n set([x[\"uri\"] for x in record_pair]).intersection(\n mapped_items\n )\n )\n > 0\n ):\n examples_buffer.insert(0, (record_pair, \"distinct\"))\n # TODO: do i need to process these?\n else:\n break\n except IndexError:\n break\n\n n_match = len(deduper.training_pairs[\"match\"]) + sum(\n label == \"match\" for _, label in examples_buffer\n )\n n_distinct = len(deduper.training_pairs[\"distinct\"]) + sum(\n label == \"distinct\" for _, label in examples_buffer\n )\n\n for pair in record_pair:\n for field in fields:\n line = \"%s : %s\" % (field, pair[field])\n print(line, file=sys.stderr)\n print(file=sys.stderr)\n\n print(\n \"{0}/10 positive, {1}/10 negative\".format(n_match, n_distinct),\n file=sys.stderr,\n )\n print(\n Fore.YELLOW + \"Do these records refer to the same thing?\" + Style.RESET_ALL,\n file=sys.stderr,\n )\n\n valid_response = False\n user_input = \"\"\n while not valid_response:\n if examples_buffer:\n prompt = \"(y)es / (n)o / (u)nsure / (f)inished / (p)revious\"\n valid_responses = {\"y\", \"n\", \"u\", \"f\", \"p\"}\n else:\n prompt = \"(y)es / (n)o / (u)nsure / (f)inished\"\n valid_responses = {\"y\", \"n\", \"u\", \"f\"}\n\n print(Fore.YELLOW + prompt + Style.RESET_ALL, file=sys.stderr)\n user_input = input()\n if user_input in valid_responses:\n valid_response = True\n\n if user_input == \"y\":\n examples_buffer.insert(0, (record_pair, \"match\"))\n mapped_items.add(record_pair[0][\"uri\"])\n mapped_items.add(record_pair[1][\"uri\"])\n confirmed_matches.append((record_pair[0][\"uri\"], record_pair[1][\"uri\"]))\n # deduper.mark_pairs({'match': record_pair})\n elif user_input == \"n\":\n examples_buffer.insert(0, (record_pair, \"distinct\"))\n elif user_input == \"u\":\n examples_buffer.insert(0, (record_pair, \"uncertain\"))\n elif user_input == \"f\":\n print(Fore.GREEN + \"Finished labeling\" + Style.RESET_ALL, file=sys.stderr)\n finished = True\n elif user_input == \"p\":\n use_previous = True\n uncertain_pairs.append(record_pair)\n\n if len(examples_buffer) > buffer_len:\n record_pair, label = examples_buffer.pop()\n if label in {\"distinct\", \"match\"}:\n\n examples: TrainingData\n examples = {\"distinct\": [], \"match\": []}\n examples[label].append(record_pair) # type: ignore\n deduper.mark_pairs(examples)\n\n for record_pair, label in examples_buffer:\n if label in [\"distinct\", \"match\"]:\n\n examples: TrainingData\n examples = {\"distinct\": [], \"match\": []}\n examples[label].append(record_pair) # type: ignore\n deduper.mark_pairs(examples)\n return confirmed_matches","title":""},{"docid":"3493745fb9db9a93123a641d078d5146","score":"0.50409645","text":"def match(img, patlst):\n\t# Mida de la imatge\n\timg_size = (len(img[0]), len(img))\n\n\t# ((int) valor de la imatge, (float) coincidence)\n\tbest_match = (-1, 0.0)\n\n\tfor i, pattern in enumerate(patlst):\n\t\tpattern = (\"1\", pattern[1])\n\n\t\tdebug(\"img_size \" + str(img_size))\n\t\tpattern_size = (get_w(pattern), get_h(pattern))\n\t\tdebug(\"pattern_size \" + str(pattern_size))\n\t\t\n\t\tif img_size[1] >= pattern_size[1]:\n\t\t\timg = tranf.scale((\"1\", img), pattern_size[1])[1]\n\t\t\timg_size = (get_w((\"\", img)), get_h((\"\", img)))\n\t\t\t\n\t\telse:\n\t\t\tpattern = tranf.scale(pattern, img_size[1])[1]\n\t\t\tpattern_size = (get_w((\"1\", pattern)), get_h((\"1\", pattern)))\n\n\t\tif img_size[0] >= pattern_size[0]:\n\t\t\tscroll = img_size[0] - pattern_size[0]\n\t\t\t\n\t\t\tbig_size = img_size[0]\n\t\t\tsmall_size = pattern_size[0]\n\t\t\t\n\t\t\tbig_image = img\n\t\t\tif isinstance(pattern, tuple):\n\t\t\t\tsmall_image = pattern[1]\n\t\t\telse:\n\t\t\t\tsmall_image = pattern\t\n\t\telse:\n\t\t\tscroll = pattern_size[0] - img_size[0]\n\n\t\t\tbig_size = pattern_size[0]\n\t\t\tsmall_size = img_size[0]\n\t\t\tif isinstance(pattern, tuple):\n\t\t\t\tbig_image = pattern[1]\n\t\t\telse:\n\t\t\t\tbig_image = pattern\n\t\t\tsmall_image = img\n\n\t\tdebug(\"Scroll: \" + str(scroll))\n\t\t\n\t\t# Es desa la nova mida\n\t\tdebug(\"pattern_size_scalated \" + str(pattern_size))\n\t\tlast_coincidence = -1\n\t\t\n\t\tfor position in range(scroll+1):\n\t\t\tcroped_img = split.image_slice_vertical(big_image, position, small_size + position)\n\t\t\tcoincidence = compare_image(croped_img, small_image)\n\t\t\tif coincidence > last_coincidence:\n\t\t\t\tlast_coincidence = coincidence\n\t\t\n\t\tif best_match[1] < last_coincidence:\n\t\t\tbest_match = (i, last_coincidence)\n\n\tdebug(\"El best_match es \" + str(best_match))\n\treturn best_match[0] if best_match[1] >= 0.6 else -1","title":""},{"docid":"e9b4c614290f9634313d81ef028cc56b","score":"0.50314224","text":"def match ( self , segm , tree ):\n\n# print ( 'comparing' , segm )\n\n if len(self.indx) == 0: return 0 # no matches if FSA is empty\n\n if len(segm) == 0: return 0 # string is empty\n\n lim = bound(segm) # get text limit for matching\n\n mtl = 0 # accumulated match length\n mtls = 0 # saved final match length\n\n state = 0 # set to mandatory initial state for FSA\n\n stk = [ ] # for tracking possible multiple matches\n\n ls = self.indx[state] # for state 0!\n ix = 0 # index into current possible transitions\n sg = segm[:lim] # text subsegment for matching\n# print ( 'initial sg=' , sg )\n# print ( len(ls) , 'transitions from state 0' )\n capd = False if len(sg) == 0 else ellyChar.isUpperCaseLetter(sg[0])\n\n while True: # run FSA to find all possible matches\n# print ( 'state=' , state )\n# print ( 'count=' , mtl , 'matched so far' )\n# print ( 'links=' , len(ls) , 'ix=' , ix )\n nls = len(ls) # how many links from current state\n\n if ix == nls: # if none, then must back up\n if len(stk) == 0: break\n r = stk.pop() # restore match status\n# print ( 'pop r= [' , r[0] , r[1][0].shortcode() , ']' )\n state = r[0] # FSA state\n ls = r[1] # remaining links to check\n sg = r[2] # input string\n mtl = r[3] # total match length\n ix = 0\n# print ( 'pop sg=' , sg )\n continue\n\n# print ( 'substring to match, sg=' , sg , 'nls=' , nls )\n m = 0\n while ix < nls:\n lk = ls[ix] # get next link at current state\n ix += 1 # and increment link index\n# print ( '@' , state , 'lk= [' , str(lk), ']' , 'ix=' , ix )\n# print ( 'patn=' , lk.patn )\n po = lk.patn[0]\n if po == '\\x00': # do state change without matching?\n m = 0 # no match length\n elif po != ellyWildcard.cEND:\n# print ( 'po=' , po )\n bds = ellyWildcard.match(lk.patn,sg)\n# print ( 'bds=' , bds )\n if bds == None: continue\n m = bds[0] # get match length, ignore wildcard bindings\n elif (len(sg) > 0 and\n (ellyChar.isLetterOrDigit(sg[0]) or sg[0] == ellyChar.PRME)):\n# print ( 'unmatched solitary $' )\n continue\n else:\n# print ( 'matched solitary $, state=' , state )\n m = 0\n\n# print ( 'm=' , m )\n\n if lk.nxts < 0: # final state?\n if lk.nxts == -2: m = 0 # last part of match not counted\n# print ( 'state=' , state , str(lk) )\n# print ( 'flags=' , lk.synf , '/' , lk.semf )\n if tree.addLiteralPhraseWithSemantics(\n lk.catg,lk.synf,lk.semf,lk.bias,cap=capd): # make phrase\n ml = mtl + m\n if mtls < ml: mtls = ml\n# print ( 'success!' , 'mtls=' , mtls )\n tree.lastph.lens = mtls # save its length\n# print ( 'match state=' , state , 'length=' , mtls )\n\n# print ( 'ix=' , ix , 'nls=' , nls )\n if ix < nls: # any links not yet checked?\n r = [ state , ls[ix:] , sg , mtl ]\n# print ( 'saved r= ' , state , end=' ' )\n# print ( [ x.shortcode() for x in ls[ix:] ] )\n stk.append(r) # if not, save info for later continuation\n\n mtl += m # update match length\n break # leave loop at this state, go to next state\n else:\n# print ( 'no matches' )\n continue # all patterns exhausted for state\n\n ix = 0\n sg = sg[m:] # move up in text input\n state = lk.nxts # next state\n if state < 0:\n ls = [ ]\n else:\n ls = self.indx[state]\n# print ( 'sg=' , sg )\n# print ( 'state=' , state )\n# print ( 'len(ls)=' , len(ls) )\n\n# print ( 'mtls=' , mtls )\n return mtls","title":""},{"docid":"67e324997aea4c7521bbea0f9f8685e8","score":"0.50052834","text":"def match(desc1,desc2):\n \n desc1 = array([d/linalg.norm(d) for d in desc1])\n desc2 = array([d/linalg.norm(d) for d in desc2])\n \n dist_ratio = 0.6\n desc1_size = desc1.shape\n \n matchscores = zeros((desc1_size[0],1))\n desc2t = desc2.T #precompute matrix transpose\n for i in range(desc1_size[0]):\n dotprods = dot(desc1[i,:],desc2t) #vector of dot products\n dotprods = 0.9999*dotprods\n #inverse cosine and sort, return index for features in second image\n indx = argsort(arccos(dotprods))\n \n #check if nearest neighbor has angle less than dist_ratio times 2nd\n if arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\n matchscores[i] = int(indx[0])\n\n return matchscores","title":""},{"docid":"8cab4431b79b80069cf3860f349031d7","score":"0.49845678","text":"def ut_relocalization():\n obj = SequenceManager(\"../../dataset/basketball/basketball_anno.mat\",\n \"../../dataset/basketball/images\",\n \"../../dataset/basketball/basketball_ground_truth.mat\",\n \"../../dataset/basketball/objects_basketball.mat\")\n img1 = 0\n img2 = 390\n\n gray1 = obj.get_image_gray(img1)\n gray2 = obj.get_image_gray(img2)\n\n pose1 = obj.get_ptz(img1)\n pose2 = obj.get_ptz(img2)\n\n mask1 = obj.get_bounding_box_mask(img1)\n mask2 = obj.get_bounding_box_mask(img2)\n\n camera = obj.get_camera(0)\n\n keyframe1 = KeyFrame(gray1, img1, camera.camera_center, camera.base_rotation,\n camera.principal_point[0], camera.principal_point[1], pose1[0], pose1[1], pose1[2])\n keyframe2 = KeyFrame(gray2, img2, camera.camera_center, camera.base_rotation,\n camera.principal_point[0], camera.principal_point[1], pose2[0], pose2[1], pose2[2])\n\n kp1, des1 = detect_compute_sift(gray1, 100)\n after_removed_index1 = keypoints_masking(kp1, mask1)\n kp1 = list(np.array(kp1)[after_removed_index1])\n des1 = des1[after_removed_index1]\n\n kp2, des2 = detect_compute_sift(gray2, 100)\n after_removed_index2 = keypoints_masking(kp2, mask2)\n kp2 = list(np.array(kp2)[after_removed_index2])\n des2 = des2[after_removed_index2]\n\n keyframe1.feature_pts = kp1\n keyframe1.feature_des = des1\n\n keyframe2.feature_pts = kp2\n keyframe2.feature_des = des2\n\n kp1_inlier, index1, kp2_inlier, index2 = match_sift_features(kp1, des1, kp2, des2)\n\n cv.imshow(\"test\",\n draw_matches(obj.get_image(img1), obj.get_image(img2), kp1_inlier, kp2_inlier))\n cv.waitKey(0)\n\n map = Map()\n\n \"\"\"first frame\"\"\"\n for i in range(len(kp1)):\n theta, phi = TransFunction.from_image_to_ray(\n obj.u, obj.v, pose1[2], pose1[0], pose1[1], kp1[i].pt[0], kp1[i].pt[1])\n\n map.global_ray.append(np.array([theta, phi]))\n\n keyframe1.landmark_index = np.array([i for i in range(len(kp1))])\n\n \"\"\"second frame\"\"\"\n keyframe2.landmark_index = np.ndarray([len(kp2)], dtype=np.int32)\n for i in range(len(kp2_inlier)):\n keyframe2.landmark_index[index2[i]] = index1[i]\n\n kp2_outlier_index = list(set([i for i in range(len(des2))]) - set(index2))\n\n for i in range(len(kp2_outlier_index)):\n theta, phi = TransFunction.from_image_to_ray(\n obj.u, obj.v, pose2[2], pose2[0], pose2[1], kp2[kp2_outlier_index[i]].pt[0],\n kp2[kp2_outlier_index[i]].pt[1])\n map.global_ray.append(np.array([theta, phi]))\n\n keyframe2.landmark_index[kp2_outlier_index[i]] = len(map.global_ray) - 1\n\n map.keyframe_list.append(keyframe1)\n map.keyframe_list.append(keyframe2)\n\n pose_test = obj.get_ptz(142)\n\n optimized = relocalization_camera(map=map, img=obj.get_image_gray(142), pose=np.array([20, -16, 3000]))\n\n print(pose_test)\n print(optimized.x)","title":""},{"docid":"ef9dec0e2cf081547efb326dcf34e95a","score":"0.4971874","text":"def discover_movie(movie_merge):\n '''\n # load data tables\n movie = pd.read_csv(\"movies_metadata.csv\")\n credit = pd.read_csv(\"credits.csv\")\n keyword = pd.read_csv(\"keywords.csv\")\n\n # deal with NAs in movie \n movie = movie.drop(movie.index[19730])\n movie = movie.drop(movie.index[29502])\n movie = movie.drop(movie.index[35585])\n pd.to_numeric(movie['id'])\n\n # select a sample of data\n movie = movie.head(5000)\n\n # genre columns\n movie['genres'] = movie['genres'].fillna('[]').apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\n\n # transform the data type of id column\n keyword['id'] = keyword['id'].astype('int')\n credit['id'] = credit['id'].astype('int')\n movie['id'] = movie['id'].astype('int')\n\n # merge movie, keyword, credit\n movie_merge = movie.merge(keyword, on='id')\n movie_merge = movie_merge.merge(credit, on='id')\n\n # for the credits table:\n # we only take directors from crew\n # and pick the top2 actors from cast\n movie_merge['cast'] = movie_merge['cast'].apply(literal_eval)\n movie_merge['crew'] = movie_merge['crew'].apply(literal_eval)\n movie_merge['keywords'] = movie_merge['keywords'].apply(literal_eval)\n movie_merge['cast_size'] = movie_merge['cast'].apply(lambda x: len(x))\n movie_merge['crew_size'] = movie_merge['crew'].apply(lambda x: len(x))\n\n # extract director from crew\n def get_director(input):\n \"\"\"read crew table\n\n Args:\n param1 (dictionary): crew table\n\n Returns:\n the director name \n \"\"\"\n for item in input:\n if (item['job'] == 'Director'):\n return item['name']\n return np.nan\n\n\n movie_merge['director'] = movie_merge['crew'].apply(get_director)\n\n # extract actors from cast\n movie_merge['cast'] = movie_merge['cast'].apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\n movie_merge['cast'] = movie_merge['cast'].apply(lambda x: x[:2] if len(x) >=2 else x)\n\n # deal with keywords\n movie_merge['keywords'] = movie_merge['keywords'].apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\n\n # tranform actors into lower case\n movie_merge['cast'] = movie_merge['cast'].apply(lambda x: [str.lower(i.replace(\" \", \"\")) for i in x])\n\n # transform directors into lower case\n movie_merge['director'] = movie_merge['director'].astype('str').apply(lambda x: str.lower(x.replace(\" \", \"\")))\n movie_merge['director'] = movie_merge['director'].apply(lambda x: [x,x, x])\n\n # count the frequency of each keywords\n s = movie_merge.apply(lambda x: pd.Series(x['keywords']),axis=1).stack().reset_index(level=1, drop=True)\n s.name = 'keyword'\n s = s.value_counts()\n\n # As i am doing frequency count, the keyword with frequency as 1 should be dropped.\n s = s[s>1]\n\n # we have to make sure singular and plural forms of a word can be recognized as one single word\n stemmer = SnowballStemmer('english')\n\n\n # filter keywords\n def filter_keywords(x):\n \"\"\"extract keywords\n\n Args:\n param1 (String): keywords string\n\n Returns:\n a list of keywords\n \"\"\"\n words = []\n for item in x:\n if item in s:\n words.append(item)\n return words\n\n\n\n movie_merge['keywords'] = movie_merge['keywords'].apply(filter_keywords)\n movie_merge['keywords'] = movie_merge['keywords'].apply(lambda x: [stemmer.stem(i) for i in x])\n movie_merge['keywords'] = movie_merge['keywords'].apply(lambda x: [str.lower(i.replace(\" \", \"\")) for i in x])\n \n movie_merge['criteria'] = movie_merge['keywords'] + movie_merge['cast'] + movie_merge['director'] + movie_merge['genres']\n movie_merge['criteria'] = movie_merge['criteria'].apply(lambda x: ' '.join(x))\n '''\n count = CountVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\n count_matrix = count.fit_transform(movie_merge['criteria'])\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\n\n titles = movie_merge['title']\n indices = pd.Series(movie_merge.index, index=movie_merge['title'])\n\n return (indices, cosine_sim, titles)","title":""},{"docid":"6dc0e6fa158e19b3e9e32d2f1d380eb7","score":"0.49574938","text":"def irides_position_form_video(frame):\n \n frame_equalized = clahe(frame)\n gray = cv2.cvtColor(frame_equalized, cv2.COLOR_BGR2GRAY)\n faces = detector(gray)\n\n for i, face in enumerate(faces):\n landmarks = predictor(frame_equalized, face)\n landmarks = face_utils.shape_to_np(landmarks)\n yield irides_position(gray, landmarks)","title":""},{"docid":"7907dacae3cade511d7d68166012db79","score":"0.4954953","text":"def forward(self):\n start_time = time()\n #self.out_pathes = self.netG(self.input_G) # coordinates encoding\n f1,self.out_pathes1,self.path_long1 = self.netG.side_branch1 (self.input_G) # coordinates encoding\n \n f2,self.out_pathes2,self.path_long2 = self.netG.side_branch2 (self.input_G) # coordinates encoding\n f3,self.out_pathes3,self.path_long3 = self.netG.side_branch3 (self.input_G) # coordinates encoding\n self.out_pathes0 =self.netG. fuse_forward( f1,f2,f3)\n test_time_point = time()\n print (\" pair to path time [%f] \" % ( test_time_point - start_time))\n self.out_pathes = [self.out_pathes0,self.out_pathes1,self.out_pathes2,self.out_pathes3]\n\n\n\n\n #self.fake_B= rendering.layers_visualized_integer_encodeing (self.out_pathes3,Resample_size) \n #self.fake_B_1_hot = rendering.layers_visualized_OneHot_encodeing (self.out_pathes3,Resample_size) \n #self.fake_B = self.netG(self.real_A) # G(A)","title":""},{"docid":"391c7e1b193a5743a20e9426d82f9a57","score":"0.49282202","text":"def query(read, kmer_dict, protein_dict, max_mismatch = 4, l = 4):\n\n #will throw AssertionError if we can't make >max_mismatch partitions of size l\n '''\n Min # of partitions needed for pigeonhole = (l*max_mismatch+1)\n Min read length = min # partitions * 3\n '''\n min_rl = 3*(l*max_mismatch + 1)\n assert len(read)>= min_rl, f'Please enter a read of length {min_rl} or higher'\n\n # get amino acid sequence by six frame translation\n prot_translations = six_frame_translation(read)\n # Result stored as a dict of tuples (id, offset)->edit_distance\n occurrences = {}\n seen = set()\n\n for protein in prot_translations:\n for i in range(len(protein) - l + 1): # for all lmers\n\n # for each l-mer\n part_off = i\n part = protein[part_off:part_off+l]\n\n if part not in kmer_dict:\n continue\n\n # hits is a list of [(protein_id, offset_in_protein)]\n hits = kmer_dict[part]\n\n for (id, offset) in hits:\n t = protein_dict[id]\n\n if len(t) == 0:\n continue\n\n # left edge of T to include in DP matrix\n lf = max(0, offset - part_off - max_mismatch)\n\n # right edge of T to include in DP matrix\n rt = min(len(t), offset - part_off + len(protein) + max_mismatch)\n\n # check if we have seen this substring before\n if (id, lf, rt) in seen:\n continue\n\n seen.add((id, lf, rt))\n\n # get min edit distance, and start offset of traceback\n start_off, min_ed = dp_edit(protein, t[lf:rt])\n start_off += lf # get actual offset in original protein\n\n if min_ed <= max_mismatch:\n occurrences[(id, start_off)] = min_ed\n\n return occurrences","title":""},{"docid":"dd01556447063d790924277bd8da214e","score":"0.48788846","text":"def gen_bone_data():\n for dataset in datasets:\n for set in sets:\n print(dataset, set)\n data = np.load('../data/{}/{}_data_joint.npy'.format(dataset, set))\n N, C, T, V, M = data.shape\n fp_sp = open_memmap(\n '../data/{}/{}_data_bone.npy'.format(dataset, set),\n dtype='float32',\n mode='w+',\n shape=(N, 3, T, len(directed_edges), M))\n\n for edge_id, (source_node, target_node) in tqdm(enumerate(directed_edges), total=len(directed_edges)):\n # Assign bones to be joint1 - joint2, the pairs are pre-determined and hardcoded\n # There also happens to be 25 bones\n fp_sp[:, :, :, edge_id, :] = data[:, :, :, source_node, :] - data[:, :, :, target_node, :]","title":""},{"docid":"5de2c84fd6bb9bbab05c5b6bd83eff75","score":"0.48778585","text":"def matching_ztf(self, obsGAIA, listImg, dist, gaia_sources, my_path = '/Users/melissa/Phd/',\n ffcq = \"F_000762_zg_c06_q2\"):\n \n count = 0\n fail = []\n \n for i in range(len(listImg)):\n #try:\n count += 1\n fImg = fits.open(path[i])\n dataImg = fImg[1].data\n dataImgH = fImg[0].header\n obsmjdImg = dataImgH[\"OBSMJD\"]\n #print(obsmjdImg)\n fieldImg = dataImgH[\"FIELDID\"] \n filterImg = dataImgH[\"FILTERID\"]\n \n raImg, decImg = dataImg[\"ra\"], dataImg[\"dec\"]\n X, Y = dataImg[\"xpos\"], dataImg[\"ypos\"]\n fluxImg, errfluxImg = dataImg[\"flux\"], dataImg[\"sigflux\"]\n magImg, errmagImg = dataImg['mag']+dataImgH[\"MAGZP\"], dataImg['sigmag']\n \n obsImg = SkyCoord(ra=raImg*u.degree, dec=decImg*u.degree)\n idx, d2dSN, d3dSN = obsImg.match_to_catalog_sky(obsGAIA)\n d2dSNarcsec = d2dSN.to(\"arcsec\").value\n flagin = (d2dSNarcsec<dist)\n d2dflag = d2dSNarcsec[flagin]\n idx_Img = np.arange(len(idx))[flagin]\n idx_source = idx[flagin]\n \n ra_sources = [gaia_sources['RA_ICRS'][i] for i in idx_source]\n dec_sources = [gaia_sources['DE_ICRS'][i] for i in idx_source]\n \n for k,j,l in zip(idx_Img, ra_sources, dec_sources):\n \n dat_2 = {\"ra\": [raImg[k]], \"dec\": [decImg[k]], \"X\": [X[k]], \n \"Y\": [Y[k]], \"flux\": [fluxImg[k]], \"err_f\": [errfluxImg[k]], \n \"mag\": [magImg[k]], \"err_m\": [errmagImg[k]], \"filter_id\": filterImg, \"src\": path[i]}\n \n data_2 = pd.DataFrame(data=dat_2)\n \n path_source = my_path/\"ZTF/stars_info/\"+ffcq+\"/source_\"+str(j)+\"_\"+str(l)+\".csv\"\n \n test_file = pathi.exists(path_source)\n if test_file == True:\n data_1 = pd.read_csv(path_source)\n data_1.drop(data_1.columns[data_1.columns.str.contains('unnamed',case = False)],axis = 1, inplace = True)\n new = data_1.append(data_2)\n new.to_csv(path_source)\n print('existed', i, path_source)\n if test_file == False:\n print('new', i, path_source)\n data_new = data_2\n data_new.to_csv(path_source)\n \n #except:\n # fail.append(path[i])","title":""},{"docid":"87f65f06a1bbc078812a0dec1623e31a","score":"0.48695722","text":"def matching(imgs, detect=sift_detector()):\n kpts, descs = [], []\n start = time.time()\n for i, img in enumerate(imgs):\n logging.debug(f\"Processing image #{i+1}\")\n\n kp_, des = detect(img)\n cent = np.array([img.shape[1], img.shape[0]]) / 2\n kpts.append(np.float32([kp.pt - cent for kp in kp_]))\n descs.append(des)\n logging.info(f\"Extracted keypoints, time: {time.time() - start}\")\n\n # match all possible pairs\n matches, n_imgs = defaultdict(dict), len(imgs)\n start = time.time()\n for src in range(n_imgs):\n for dst in range(src+1, n_imgs):\n logging.debug(f\"Matching {src+1}-{dst+1}\")\n match, hom = _match_hom(\n kpts[src], kpts[dst], descs[src], descs[dst])\n if hom is None:\n continue\n\n matches[src][dst] = (match, hom)\n matches[dst][src] = _reverse(match, hom)\n logging.info(f\"Matched features, time: {time.time() - start}\")\n\n return np.array(kpts, dtype=np.object), np.array(matches, dtype=np.object)","title":""},{"docid":"0c315b0af7073ccc6025161ed235c86a","score":"0.48590472","text":"def infer_pose_each_part(factor_graph_list):\n num_body_parts = len(factor_graph_list)\n\n # Generate the intial sample to start Gibbs Sampling\n inferred_pose = generate_initial_pose_matrix(num_body_parts)\n\n # Generate nodes which will be selected for Gibbs Sampling\n node_indices_array = np.ndarray.tolist(np.random.randint(0, 24, size=num_samples))\n\n # Start Gibbs Sampling over Factor Graph\n for node_index in node_indices_array:\n node_to_update = factor_graph_list[node_index]\n\n # The below variables mean, the combined distribution of the node which we \n # sample from\n resulting_mean = node_to_update.unary_pot_mean\n resulting_cov = node_to_update.unary_pot_cov\n\n # Building a resulting distribution by combining all the distributions\n for neighbor_index in node_to_update.neighbors:\n # We find parameters of joint distribution of current node and neighbor\n pairwise_mean = node_to_update.pairwise_pots_mean[neighbor_index]\n pairwise_cov = node_to_update.pairwise_pots_cov[neighbor_index]\n\n # Finding value of conditioned variable (neighbor)\n pose = get_node_val_pose_only(neighbor_index, inferred_pose)\n\n\n # Conditioning on the pairwise potentials to get resulting distribution\n cond_mean, cond_cov = condition_on_pairwise_potential(node_index, neighbor_index\n , pairwise_mean, pairwise_cov, pose)\n\n resulting_mean, resulting_cov = multiply_two_multivariate_normal_distributions_pdfs(\n cond_mean, resulting_mean, cond_cov, resulting_cov)\n\n # Perform Sampling on the resulting distribution\n pose_curr_node = np.random.multivariate_normal(mean=resulting_mean, cov=resulting_cov)\n \n # Updating the value in the overall pose matrix\n inferred_pose[node_to_update.node_index] = pose_curr_node\n\n return inferred_pose","title":""},{"docid":"65ccc97e854b31375e198f08d9bd16a5","score":"0.48581877","text":"def map_nopt(orflist, theta_contact=2, theta_dist=10, randomized=False):\n\n noptDF = pd.DataFrame(columns=['ORF', 'substrate', 'nopt', 'Cnopt', 'CC'])\n counter = 0\n\n for i in orflist:\n substr = structures[structures['ORF']==i]['substrate'].item()\n current_cm = CONTMAT[i]\n\n # create directed graph, direction in sequence order\n cm2 = nx.DiGraph()\n for e in current_cm.edges():\n e1 = e[0]\n e2 = e[1]\n if e1 < e2:\n cm2.add_edge(e1,e2)\n else:\n cm2.add_edge(e2,e1)\n\n ddist_cm2 = dict( cm2.in_degree() ) \n degin = []\n for k in ddist_cm2.keys():\n if ddist_cm2[k] > theta_contact:\n degin.append(k-1) # this is a fix for python indexing!\n\n if substr == '-':\n current_substr = 0\n elif substr == '+':\n current_substr = 1\n\n if i in rna_seqs.keys():\n rna_seq = rna_seqs[i].seq\n L = int( np.floor( len(rna_seq) / 3. ) )\n profile = np.zeros(( L ))\n\n for pos in range(L):\n start = 3*int(pos)\n end = start + 3\n current_codon = rna_seq[start:end]\n if randomized: \n current_codon = rand_syn(current_codon)\n if current_codon in nopt:\n profile[pos] = 1\n\n # check for cluster,w=window_size, theta=threshold\n w = 4\n theta = 3\n cluster_profile = np.zeros(( L ))\n for pos in range(L-w):\n current_window = profile[pos:(pos+w)]\n if np.sum(current_window) >= theta:\n cluster_profile[pos] = 1\n\n CC = 0 \n current_contacts = np.array(degin)\n current_clusters = np.where(cluster_profile == 1)[0]\n for pos in current_clusters:\n current_dists = ( current_contacts - 35 ) - pos\n current_dists = current_dists[current_dists >= 0]\n if any( current_dists < theta_dist ):\n CC += 1\n \n noptDF.loc[len(noptDF)] = (i, current_substr, np.sum(profile)/len(profile), np.sum(cluster_profile)/len(profile), CC )\n\n\n return noptDF","title":""},{"docid":"f340dbcf784b5aa736f7d73f6d311a08","score":"0.48565173","text":"def FindJaccardMatches_NoOvercountBigrams(self, text1,text2,\n name1='Text1',name2='Text2'):\n shinMatches=[]\n for t1Sent in text1:\n text1shins=MakeShingles(t1Sent,self.shingleSize,goodTags=self.goodTags,\n stops=self.stops)\n for t2Sent in text2:\n text2shins=MakeShingles(t2Sent,self.shingleSize,\n goodTags=self.goodTags,stops=self.stops)\n compared=MatchShinglesNoOvercountBigrams(text1shins,\n text2shins,bigrams=bgs,\n thresh=self.matchThreshold)\n \n if compared[0]==True:\n \n copywords=[[w for w,t,l in t1Sent if l in compared[1]],\n [w for w,t,l in t2Sent if l in compared[1]]]\n \n shinMatches.append(([w for w,t,l in t1Sent],\n [w for w,t,l in t2Sent],copywords))\n \n return(MatcherOutput(shinMatches,name1,name2))","title":""},{"docid":"ccfc656049536173b7358cbcc910631d","score":"0.4850419","text":"def solution(dimensions, your_position, guard_position, distance):\n #1. Initialize variables\n origin, dims, your_pos, guard_pos, grid_dims = initialize_variables(dimensions, your_position, guard_position, distance)\n\n #2. Create the grid of mirrored landscapes with dims equal to (1 + grid_dims.x*2, 1 + grid_dims.y*2)\n x_landscape = deque()\n x_landscape.append(Landscape(Person(False, your_pos, your_pos), Person(True, guard_pos, your_pos), origin, dims))\n\n for i in range(grid_dims.x): \n ls = x_landscape[-1].reflect('east', original_dims=dims, shooter=your_pos)\n x_landscape.append(ls)\n\n for i in range(grid_dims.x):\n ls = x_landscape[0].reflect('west', original_dims=dims, shooter=your_pos)\n x_landscape.appendleft(ls)\n \n grid = deque()\n grid.append(list(x_landscape))\n\n for i in range(grid_dims.y):\n grid.append(map(lambda ls: ls.reflect('north', original_dims=dims, shooter=your_pos), grid[-1]))\n \n for i in range(grid_dims.y):\n grid.appendleft(map(lambda ls: ls.reflect('south', original_dims=dims, shooter=your_pos), grid[0]))\n\n grid = [landscape for x_landscape in list(grid) for landscape in x_landscape] #flatten to 1D list\n\n #3. Extract the People from the landscapes\n people = [landscape.guard for landscape in grid] + [landscape.you for landscape in grid]\n\n #4. Filter People by distance and sort by distance\n people = filter(lambda p: p.in_range(distance), people)\n people.sort(key=lambda p: p.dist)\n\n #5. Count valid shots\n shots = 0\n invalid_bearings = set()\n for person in people:\n if person.is_enemy and person.bear not in invalid_bearings:\n shots += 1\n invalid_bearings.add(person.bear)\n\n return shots","title":""},{"docid":"2f7e07a79233181890966b823c1d9e43","score":"0.48479083","text":"def make_adj_matrix1(group) :\n n = len(group)\n adj_matrix = []\n for i in xrange(n):\n adj_matrix.append([])\n for j in xrange(n):\n adj_matrix[i].append(0)\n\n #For each abstract in extracted abstract set:\n for z in xrange(len(abstracts)) :\n for i in xrange(n) :\n if abstracts[z].AbstractText.rfind( group[i].name ) != -1 :\n group[i].Freq += 1\n group[i].abstract_indexes.append(abstracts[z].id)\n for j in xrange(n) :\n if ( abstracts[z].AbstractText.rfind( group[j].name ) != -1 ) and (i != j) :\n adj_matrix[i][j]+=1\n group[j].Freq += 1\n #continue\n\n return adj_matrix #, GroupA, GroupB","title":""},{"docid":"09a6c3988e9017a4694f21fc39690d0b","score":"0.4842541","text":"def get_activity_cliffs(DataFrame, augmentation_factor, dataset_name = None):\n \n paired = pair(DataFrame, 1)\n paired.columns = ['SMILES_1', 'SMILES_2','similarity']\n paired.columns = ['SMILES_1', 'SMILES_2','similarity']\n paired = paired.merge(DataFrame, left_on = 'SMILES_1', right_on = 'SMILES', how = 'left')\n paired = paired.merge(DataFrame, left_on = 'SMILES_2', right_on = 'SMILES', how = 'left')\n paired['potency_difference'] = abs(paired['pXC50_x'] - paired['pXC50_y'])\n \n pool = mp.Pool(processes = 6)\n paired['fp_1'] = [pool.apply(calculate_circular_fps, args=(x,)) for x in paired['SMILES_1']]\n paired['fp_2'] = [pool.apply(calculate_circular_fps, args=(x,)) for x in paired['SMILES_2']]\n paired['Tanimoto'] = paired.apply(lambda x: calculate_bit_similarity(x.fp_1, x.fp_2), axis = 1)\n paired.drop(columns = ['SMILES_x','Activity_x', 'SMILES_y', 'Activity_y'], inplace = True)\n paired.columns = ['SMILES_1','SMILES_2','similarity','pXC50_1','pXC50_2','potency_difference','fp_1','fp_2','Tanimoto']\n \n paired.to_csv('/projects/../../PythonNotebooks/density_scatter/datasets/' + str(dataset_name) + '.csv')\n \n return paired","title":""},{"docid":"993968a266097d43755cd7201538861e","score":"0.4836572","text":"def labeld_unlabeled_similarities(*, sim_thereshold, \n x_unlabeled, X_unlabeled, X_labeled, y): \n sim_dict = {}\n timenow = time()\n for text, features in zip(x_unlabeled, X_unlabeled):\n sim_dict[text] = [(y[i], jaccard_sim(obs, features)) for i, obs in enumerate(X_labeled)]\n\n print('Calculating all Jaccard similarities took {:.2f} seconds'.format(time() - timenow))\n \n # filtering out entries with similarity below threshold\n timenow = time()\n sim_dict_filtered = {}\n for k, v in sim_dict.items():\n sim_dict_filtered[k] = [(label, sim) for (label, sim) in v if sim >= sim_thereshold]\n # remove labels that don't meet the criteria\n if sim_dict_filtered[k] == []: del sim_dict_filtered[k] \n print('Removing entries with Jaccard sim lower than the threshold took {:.2f} seconds'.\n format(time() - timenow))\n \n # return only the top result\n sim_result_list = []\n for k, v in sim_dict_filtered.items():\n for label, sim in v:\n # keep the entry with the highest similarity\n top_sim = 0\n if sim > top_sim:\n top_label, top_sim = label, sim\n sim_result_list.append([k, top_label, top_sim])\n \n return sim_result_list, sim_dict_filtered, sim_dict","title":""},{"docid":"7381551231eb17c62d358a5a10ee9d8c","score":"0.48355132","text":"def frame_processing(frame):\n bread = cv2.imread('bread.jpg') \n doge = cv2.imread('doge.png') \n\n frame = imutils.resize(frame, width=min(2256, frame.shape[1])) \n\n #detect regions with faces\n faces = face_cascade.detectMultiScale(frame, \n scaleFactor=1.1, \n minSize=(200,200), \n maxSize=(1500,1500),\n minNeighbors=1\n )\n\n #detect regions with walkers\n (walkers, _) = hog.detectMultiScale(frame, \n\t\t\t\t\twinStride=(4, 4), \n padding=(64, 64), \n scale=1.20,\n finalThreshold=0.6,\n hitThreshold=0.5\n ) \n\n #replace faces with doge face \n for (x, y, w, h) in faces:\n frame[y : y + h, x : x + w] = cv2.resize(doge, (w, h))\n\n #replace pedestrians with doge bread\n for (x, y, w, h) in walkers:\n frame[y : y + h, x : x + w] = cv2.resize(bread, (w, h))\n\n return frame","title":""},{"docid":"5d19ea0a4fac8c9186b8fa22ed36beb0","score":"0.4834399","text":"def test_match(fname):\n\t# global options\n\topts = {'daophot': {'r_psf': 20, 'r_fit': 15, 'fwhm': 3.5, 'psf_model': 5.00}, 'photo': {}, 'allstar': {}, 'allframe': {}, 'misc': {'stacknum': 1, 'counts_limit': 15000, 'number_limit': 400, 'minpsf': 35, 'sigma_psf': 4.5, 'sigma_all': 3.5, 'min_frame': 1, 'min_frac': 0.1, 'enough_frame': 2, 'max_sigma': 10, 'degfree': 6, 'r_match': 15, 'repeat_2': 3, 'repeat_1': 30}}\n\t\n\t# cleanup\n\t#cleanup_files(fname, (\"log\", \"mch\", \"mag1\", \"raw1\", \"mch1\", \"cor1\"))\n\t#cleanup_files(fname, (\"mtr\", \"mag2\", \"raw2\", \"mch2\", \"cor2\"))\n\t\n\t# initialize names\n\tname = os.path.splitext(fname)[0]\n\tdr = os.path.split(fname)[0]\n\tofname = os.path.basename(name)\n\t\n\t# compile catalog list\n\tcatalogs = glob.glob(dr+\"/*.als\")\n\tcatalogs = sorted(catalogs)\n\toldindex=[ i for i, x in enumerate(catalogs) if \"r_1\" in x][0]\n\tcatalogs.insert(0, catalogs.pop(oldindex))\n\tcatalogs = [os.path.basename(x) for x in catalogs]\n\tnfiles = len(catalogs)\n\t\n\t## run daomatch \n\t#match = Daomatch(opts)\n\t#match(fname, catalogs)\n\t\n\t## run daomaster\n\t#master = Daomaster(opts)\n\t#master(fname, nfiles, index=1, idtrans='n')\n\t\n\t## run allframe\n\t#aframe = Allframe(opts)\n\t#aframe(fname, index=1)\n\t\n\t## first move to targeted directory\n\t#origwd = os.getcwd()\n\t#os.chdir(dr)\n\t\n\t## get & sort catalogs\n\t#catalogs = glob.glob(\"*.alf\")\n\t#catalogs = sorted(catalogs)\n\t#oldindex=[ i for i, x in enumerate(catalogs) if \"r_1\" in x][0]\n\t#catalogs.insert(0, catalogs.pop(oldindex))\n\t#nfiles = len(catalogs)\n\t\n\t## change back directory\n\t#os.chdir(origwd)\n\t\n\t## run daomatch \n\t#match = Daomatch(opts)\n\t#match(fname, catalogs)\n\t\n\t## run daomaster\n\t#master = Daomaster(opts)\n\t#master(fname, nfiles, index=2, idtrans='y')\n\t\n\t# final catalog\n\tlines_startwith(name+\".raw2\", \" \", fout=name+\"_cs.temp\")\n\tlines_startwith(name+\".raw2\", \" \", fout=name+\"_mag.temp\", toggle=False, skip=2)\n\t\n\thmerge_files((name+\"_mag.temp\", name+\"_cs.temp\"), fout=name+\".cat\")","title":""},{"docid":"1cb162fb0cccfff2a8efa7734c8149ef","score":"0.4833147","text":"def tri_subjects(dir_data, logger=None, f_mids='mid.csv'):\n\n # family directories\n dirs_fid, fid_list = load_fids(dir_data)\n\n print(\"{} families are being processed\".format(len(fid_list)))\n # Load MID LUT for all FIDs.\n df_mids = db.load_mids(dirs_fid, f_csv=f_mids)\n\n # # Load relationship matrices for all FIDs.\n # df_relationships = load_relationship_matrices(dirs_fid, f_csv=f_rel_matrix)\n fms = []\n fmd = []\n kind = 'parents-child'\n for i, fid in enumerate(fid_list):\n print(fid)\n # ids = [i for i, s in enumerate(genders) if 'Male' in s]\n # rel_mat = db.parse_relationship_matrices(df_mids[i])\n rel_mat = db.parse_relationship_matrices(df_mids[i])\n genders = list(df_mids[i].Gender)\n\n # indices of matrix containing 4 or 1; that the matrix is inversed across the diagonal\n # mat_ids = np.where(rel_mat == 1) and np.where(rel_mat.T == 1), np.where(rel_mat == 1) and np.where(\n # rel_mat.T == 4)\n\n c_ids = np.where(rel_mat == 1)\n p_ids = np.where(rel_mat == 4)\n\n if len(list(c_ids)) == 0:\n logger.warn(\"No pair of parents for child in {}.\".format(fid))\n # print(\"Two parents are not present for child\")\n continue\n\n if len(set(p_ids[1]).__xor__(set(c_ids[0]))) or len(set(c_ids[1]).__xor__(set(p_ids[0]))):\n logger.error(\"Unmatched pair in {}.\".format(fid))\n # print(\"Unmatched pair\")\n continue\n ch_ids = [(p1, p2) for p1, p2 in zip(list(c_ids[0]), list(c_ids[1]))]\n\n cp_pairs = group_child_parents(ch_ids)\n\n for cid, pids in cp_pairs.items():\n # pars = rows[np.where(cols == cc)]\n if len(pids) != 2:\n if len(pids) > 2:\n logger.error(\"{} parents in {}. {}\".format(len(pids), fid, pids))\n continue\n # warn.warn(\"Three parents\")\n else:\n continue\n try:\n p_genders = [genders[pids[0][1]], genders[pids[1][1]]]\n\n except IndexError:\n print()\n if \"m\" in p_genders[0] and \"f\" in p_genders[1]:\n pars_ids = (pids[0][1] + 1, pids[1][1] + 1)\n elif \"m\" in p_genders[1] and \"f\" in p_genders[0]:\n pars_ids = pids[1][1] + 1, pids[0][1] + 1\n else:\n logger.error(\"Parents of same gender in {}. {}\".format(fid, pids))\n continue\n # warn.warn(\"Parents are of same gender for \", fid)\n\n cmid = \"{}/MID{}\".format(fid, cid + 1)\n fmid = \"{}/MID{}\".format(fid, pars_ids[0])\n mmid = \"{}/MID{}\".format(fid, pars_ids[1])\n\n if \"m\" in genders[cid]:\n fms.append((fmid, mmid, cmid))\n else:\n fmd.append((fmid, mmid, cmid))\n\n return fmd, fms","title":""},{"docid":"a48e67920a10f54b8133b2fb86479282","score":"0.48310778","text":"def match_features(im1_features, im2_features):\n\n # TODO: Your implementation here! See block comments and the project webpage for instructions\n\n # These are placeholders - replace with your matches and confidences!\n num_freatures = min(len(im1_features), len(im2_features))\n matches = np.zeros((num_freatures, 2))\n confidences = np.ones(num_freatures)\n threshold = .8\n\n # print(num_freatures)\n # print(len(im2_features))\n # print(im1_features)\n for i in range (1,num_freatures):\n distances = np.zeros(( num_freatures ,1))\n\n for j in range (1,num_freatures):\n distances[j][0] = np.linalg.norm(im1_features[i] - im2_features[j])\n\n # distances = distances[np.logical_not(np.isnan(distances))]\n distances = distances.flatten()\n distances = np.nan_to_num(distances)\n distances_sorted = np.sort(distances)\n distances_index = distances.argsort()\n # print(i)\n\n # print(distances)\n # print(distances_index)\n\n NNDR = distances_sorted[-2]/distances_sorted[-1]\n print(NNDR)\n # print(distances_sorted)\n if NNDR < .95 :\n matches[i] = [ i , distances_index[-1] ]\n confidences[i] = 1/NNDR\n # print(confidences)\n indx = np.where(confidences > 0)\n # # print(indx)\n confidences = confidences[indx]\n confidences = np.sort(confidences)\n confidences_index = confidences.argsort()\n # #print(confidences_index)\n matches = matches[confidences_index]\n #print(confidences)\n #print(matches)\n return matches, confidences","title":""},{"docid":"4b51c929d979a284f8a5088b7c1fbefe","score":"0.4824405","text":"def getRep(rgbImg, multiple=False):\r\n\r\n global profiler, prev_bbs, real_bbs\r\n\r\n profiler.inform_transmission_time_start(MEASURE_TYPE.LOCATE)\r\n if multiple:\r\n if prev_bbs and len(prev_bbs) != 0 and SERVER_FACE_SEARCH_OPTIMIZE: # face search optimization\r\n threads = []\r\n bbs = []\r\n for prev_bb in prev_bbs:\r\n get_face_boxes(extend_boundary_box(rgbImg, prev_bb), prev_bb, bbs)\r\n # threads.append(Thread(target=get_face_boxes, args=(extend_boundary_box(rgbImg, prev_bb), prev_bb, bbs)))\r\n\r\n for t in threads: t.start()\r\n for t in threads: t.join()\r\n\r\n else:\r\n bbs = [bb for bb in align.getAllFaceBoundingBoxes(rgbImg)]\r\n\r\n else:\r\n bb1 = align.getLargestFaceBoundingBox(rgbImg)\r\n bbs = [bb1]\r\n\r\n\r\n if real_bbs and len(real_bbs) > len(bbs):\r\n prev_bbs = real_bbs\r\n real_bbs = None\r\n else:\r\n prev_bbs = bbs\r\n\r\n profiler.inform_transmission_time_stop(MEASURE_TYPE.LOCATE)\r\n\r\n if len(bbs) == 0 or (not multiple and bb1 is None):\r\n print(\"Unable to find a face\")\r\n return []\r\n\r\n profiler.inform_transmission_time_start(MEASURE_TYPE.CLASSIFY)\r\n reps = []\r\n threads = []\r\n aligned_faces = []\r\n\r\n for bb in bbs:\r\n align_faces(rgbImg, bb, aligned_faces)\r\n # threads.append(Thread(target=align_faces, args=(rgbImg, bb, aligned_faces)))\r\n\r\n for t in threads: t.start()\r\n for t in threads: t.join()\r\n\r\n for bb, aligned_face in aligned_faces:\r\n rep = forward_to_net(aligned_face)\r\n reps.append((bb.center().x, rep, bb, aligned_face))\r\n\r\n profiler.inform_transmission_time_stop(MEASURE_TYPE.CLASSIFY)\r\n\r\n sreps = sorted(reps, key=lambda x: x[0])\r\n return sreps","title":""},{"docid":"ef19ecbef41a2e44ac739a061b6514bd","score":"0.48226717","text":"def track_SORT(coords_list,mod_err=1,meas_err=1,state_err=100,fsld_max = 60):\n\n active_objs = []\n inactive_objs = []\n keep_classes = False\n if len(coords_list[0][0]) == 5:\n keep_classes = True\n \n # initialize with all objects found in first frame\n for i,row in enumerate(coords_list[0]):\n if keep_classes:\n cls = row[4]\n row = row[:4]\n obj = KF_Object(row,0,mod_err,meas_err,state_err)\n obj.all.append(obj.get_coords())\n obj.tags.append(1)\n if keep_classes:\n obj.cls = cls\n active_objs.append(obj)\n\n # loop through all frames\n for frame_num in range(1,len(coords_list)):\n \n print(\"On frame {} of {}\".format(frame_num,len(coords_list)))\n \n # predict new locations of all objects\n # look at next set of detected objects\n # match\n # for all matches, update Kalman filter\n # for all detached objects, update fsld and delete if too high\n # for all unmatched objects, intialize new object\n \n # 1. predict new locations of all objects x_k | x_k-1\n for obj in active_objs:\n obj.predict()\n \n # 2. look at next set of detected objects - all objects are included in this even if detached\n # convert into numpy array - where row i corresponds to object i in active_objs\n locations = np.zeros([len(active_objs),4])\n for i,obj in enumerate(active_objs):\n locations[i,:] = obj.get_coords()\n \n # 3. match - these arrays are both N x 4 but last two columns will be ignored \n # remove matches with IOU below threshold (i.e. too far apart)\n second = coords_list[frame_num]\n if keep_classes:\n classes = second[:,4]\n second = second[:,:4]\n matches = match_hungarian(locations,second,iou_cutoff = 0.2) \n #matches = match_greedy(locations,second)\n \n # traverse object list\n move_to_inactive = []\n for i in range(0,len(active_objs)):\n obj = active_objs[i]\n \n # update fsld and delete if too high\n if matches[i] == -1:\n obj.fsld += 1\n obj.all.append(obj.get_coords())\n obj.tags.append(0) # indicates object not detected in this frame\n if obj.fsld > fsld_max or (obj.fsld > 0 and len(obj.all)< 3):\n move_to_inactive.append(i)\n \n \n else: # object was matched \n # update Kalman filter\n measure_coords = second[matches[i]]\n obj.update(measure_coords)\n obj.fsld = 0\n obj.all.append(obj.get_coords())\n obj.tags.append(1) # indicates object detected in this frame\n\n # for all unmatched objects, intialize new object\n for j in range(0,len(second)):\n if j not in matches:\n new_obj = KF_Object(second[j],frame_num,mod_err,meas_err,state_err)\n new_obj.all.append(new_obj.get_coords())\n if keep_classes:\n new_obj.cls = classes[j]\n new_obj.tags.append(1) # indicates object detected in this frame\n active_objs.append(new_obj)\n\n \n # move all necessary objects to inactive list\n move_to_inactive.sort()\n move_to_inactive.reverse()\n for idx in move_to_inactive:\n inactive_objs.append(active_objs[idx])\n del active_objs[idx]\n \n \n objs = active_objs + inactive_objs\n \n final_objs = []\n # create final point array\n points_array = np.zeros([len(coords_list),len(objs)*2])-1\n for j in range(0,len(objs)):\n obj = objs[j]\n \n \n hit_ratio = sum(obj.tags)/len(obj.tags)\n # only keep object that were found at least 30% of the time\n if hit_ratio > 0.4:\n final_objs.append(obj)\n first_frame = int(obj.first_frame)\n for i in range(0,len(obj.all)):\n points_array[i+first_frame,j*2] = obj.all[i][0]\n points_array[i+first_frame,(j*2)+1] = obj.all[i][1]\n \n return final_objs, points_array","title":""},{"docid":"31ff577e3055b8834035d93a9491a62d","score":"0.4812476","text":"def inference(self,sequence,mode):\n #Method 1: Greedy Decoding\n if mode.lower()=='greedy':\n tag_sequence = []\n tag_penult = '*'\n tag_prev = '*'\n for idx,word in enumerate(sequence.split(' ')):\n scores = []\n for tag in self.tag_set:\n scores.append(self.get_q(tag_penult,tag_prev,tag)*self.get_e(word,tag,idx))\n final_tag = self.tag_set[np.argmax(scores)]\n tag_sequence.append(final_tag)\n tag_penult = tag_prev\n tag_prev = final_tag\n\n\n return tag_sequence\n\n #Method 2: Beam Search\n elif mode.lower()=='beam':\n k=1\n sequence = sequence.split()\n best_sequences = [['*','*']]\n for idx,word in enumerate(sequence):\n scores = {}\n for item in best_sequences:\n item = list(item)\n tag_penult = item[-2]\n tag_prev = item[-1]\n for tag in self.tag_set:\n score = self.get_q(tag_penult,tag_prev,tag)*self.get_e(word,tag,idx)\n item.append(tag)\n scores[tuple(item)] = score\n item.pop()\n topk_scores = sorted(scores.items(), key=lambda x: x[1], reverse = True)\n best_sequences = list(zip(*topk_scores)[0][:k])\n\n best_sequence = best_sequences[0][2:]\n\n return best_sequence\n\n #Method 3: Viterbi Algorithm\n elif mode.lower()=='viterbi':\n tag_sequence= []\n path = {}\n path['*','*'] = []\n pi_func = defaultdict(float)\n pi_func[(0, \"*\", '*')] = 0.0\n\n # v = Tag current, u = Tag previous, w = Tag penult\n sequence = sequence.split(\" \")\n n = len(sequence)\n\n for k in range(1,n+1):\n temp_path = {}\n for u in self.possible_tags(k-1):\n for v in self.possible_tags(k):\n max_tag = \"\"\n max_score = float(\"-Inf\")\n for w in self.possible_tags(k - 2):\n score = pi_func.get((k-1, w, u),float('-Inf'))*self.get_q(w,u,v)*self.get_e(sequence[k-1],v,k-1)\n if score > max_score:\n max_score = score\n max_tag = w\n pi_func[(k, u, v)] = max_score\n temp_path[u,v] = path[max_tag,u] + [v]\n path = temp_path\n\n prob,umax,vmax = max([(pi_func.get((n,u,v))*self.get_q(u,v,'.'),u,v) for u in self.possible_tags(n-1) for v in self.possible_tags(n)])\n\n return path[umax,vmax]","title":""},{"docid":"e130d94f6ef02526fe47125be3163b1d","score":"0.48111892","text":"def recognitionAndSEO(self): #New implementation\n \n chainList = []\n recogSEO = []\n if len(self.closedNeighborhood) > 1:\n for row, col in self.closedNeighborhood.iteritems():\n candidate = []\n tempOrderedNeighbors = []\n for c in col:\n tempOrderedNeighbors.append(self.closedNeighborhood[c])\n \n tempOrderedNeighbors = sorted(tempOrderedNeighbors, key=lambda l: (len(l), l))\n \n for i in range(len(tempOrderedNeighbors)):\n if i+1 < len(tempOrderedNeighbors):\n v1 = tempOrderedNeighbors[i]\n v2 = tempOrderedNeighbors[i+1]\n if set(v1).issubset(set(v2)):\n if v1 not in candidate:\n v1Keys = [key for (key, value) in self.closedNeighborhood.items() if value == v1]\n for v1k in v1Keys:\n if v1k not in candidate:\n candidate.append(v1k)\n if v2 not in candidate:\n v2Keys = [key for (key, value) in self.closedNeighborhood.items() if value == v2]\n for v2k in v2Keys:\n if v2k not in candidate:\n candidate.append(v2k)\n else:\n candidate = []\n break\n if candidate and candidate not in chainList:\n chainList.append(candidate)\n elif len(self.closedNeighborhood) == 1:\n chainList.append(self.closedNeighborhood.keys())\n \n if chainList:\n chainList = sorted(chainList, key=lambda l: (len(l), l))\n #print(sorted(list2, key=lambda l: (len(l), l)))\n vertexToBeRemoved = chainList[0][0]\n print \"Chains: \"+str(chainList,)+\" and vertex to be removed \"+str(vertexToBeRemoved)\n self.recognitionSEO.append(vertexToBeRemoved)\n \n if self.closedNeighborhood:\n self.updateClosedNeighbors(vertexToBeRemoved)\n self.recognitionAndSEO()","title":""},{"docid":"067c9320a3bf25cc14323353d30ec8b9","score":"0.4805177","text":"def _get_correspondences(\n self,\n alignments,\n idxA,\n idxB,\n heuristic = 'distance'\n ):\n reg_dist = {}\n\n if idxA != idxB and heuristic == 'distance':\n for almA,almB,scoreAB in alignments:\n \n score = self._distance_score(almA,almB,scoreAB,self.score_dict)\n if score <= self.pairwise_threshold:\n for a,b in zip(almA,almB):\n try:\n reg_dist[a,b] += 1.0\n except KeyError:\n reg_dist[a,b] = 1.0\n \n # a new attempt, we use a preprocessing and cognate clustering with\n # simple sca and then only consider those words found by the rough\n # algorithm for the calculation of sound correspondences\n elif heuristic == 'sca':\n for i,(almA,almB,scoreAB) in enumerate(alignments):\n x,y = self._pairs[idxA,idxB][i]\n # remember that we excluded pairs from the calc which occur\n # more than ones, i.e. identical strings, so we follow the same\n # procedure here\n if x > 0 and y > 0:\n x = self.cogs[self.idxd[x]]\n y = self.cogs[self.idxd[y]]\n if x == y:\n for a,b in zip(almA,almB):\n try:\n reg_dist[a,b] += 1.0\n except:\n reg_dist[a,b] = 1.0\n\n elif idxA == idxB:\n counter = 0\n for almA,almB,scoreAB in alignments:\n \n score = self._distance_score(almA,almB,scoreAB,self.score_dict)\n\n if score == 0:\n for a,b in zip(almA,almB):\n try:\n reg_dist[a,b] += 1.0\n except KeyError:\n reg_dist[a,b] = 1.0\n counter += 1\n\n # change gap notation\n for a,b in list(reg_dist.keys()):\n \n # change for gap positions\n if a == '-':\n reg_dist[str(idxA)+'.X.-',b] = reg_dist[a,b]\n elif b == '-':\n reg_dist[a,str(idxB)+'.X.-'] = reg_dist[a,b]\n\n return reg_dist","title":""},{"docid":"1b23915f2a99f510faf96ec2bdb96513","score":"0.48001367","text":"def write_movie(experiment,name,simple_trajectories,predictions,correspondances):\n \n path = os.path.join(\"..\",\"data\",\"microglia\",name)\n colors = ['green','red','blue','pink','yellow']\n if not os.path.isdir(path):\n os.mkdir(path)\n #Separate each cell from each trajectory\n cell_list=[]\n for i in range(241):\n cell_list.append([])\n #Copy the frames in new directory. these frames will be modified by the loop\n frame = m.open_frame(experiment.path,i+1)\n cv2.imwrite(os.path.join(path,str(i+1)+\".png\"),frame)\n \n for i,(index_traj,index_cell) in enumerate(correspondances):\n traj = simple_trajectories[index_traj][1]\n cell=traj.cells[index_cell]\n pred = predictions[i]\n cell_list[cell.frame_number].append((cell,pred))\n n_pred = np.max(predictions)+1\n for frame_nr,cells in enumerate(cell_list):\n print \"processing frame nr\",frame_nr+1\n frame = m.open_frame(path,frame_nr+1)\n body = m.open_frame(experiment.body_path,frame_nr+1)\n arm = m.open_frame(experiment.arm_path,frame_nr+1)\n mask = np.zeros((frame.shape[0],frame.shape[1],n_pred),dtype=np.uint8)\n out = np.zeros((frame.shape[0],frame.shape[1],3),dtype=np.uint8)\n for cell,pred in cells:\n \n mask[:,:,pred] += (body==cell.body+1).astype(np.uint8)\n for elt in cell.arms:\n mask[:,:,pred]+=(arm==elt+1).astype(np.uint8)\n mask*=255\n for i in range(n_pred):\n out+= m.cv_overlay_mask2image(mask[:,:,i],frame,color=colors[i])/n_pred\n cv2.imwrite(os.path.join(path,str(frame_nr+1)+\".png\"),out)","title":""},{"docid":"3dbecd1c352c21d5a0dbd4fe95a3e996","score":"0.47992152","text":"def match(desc1, desc2):\n\n desc1 = array([d / linalg.norm(d) for d in desc1])\n desc2 = array([d / linalg.norm(d) for d in desc2])\n\n dist_ratio = 0.6\n desc1_size = desc1.shape\n\n matchscores = zeros((desc1_size[0]), 'int')\n desc2t = desc2.T # precompute matrix transpose\n for i in range(desc1_size[0]):\n dotprods = dot(desc1[i, :], desc2t) # vector of dot products\n dotprods = 0.9999 * dotprods\n # inverse cosine and sort, return index for features in second image\n indx = argsort(arccos(dotprods))\n\n # check if nearest neighbor has angle less than dist_ratio times 2nd\n if arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\n matchscores[i] = int(indx[0])\n\n return matchscores","title":""},{"docid":"e46484b450ba90ad25ccb87f883467df","score":"0.47991565","text":"def FindJaccardMatches(self, text1,text2,name1='Text1',name2='Text2'):\n \n shinMatches=[]\n for t1Sent in text1:\n text1shins=MakeShingles(t1Sent,self.shingleSize,goodTags=self.goodTags,\n stops=self.stops)\n for t2Sent in text2:\n text2shins=MakeShingles(t2Sent,self.shingleSize,\n goodTags=self.goodTags,stops=self.stops)\n compared=MatchShinglesNoOvercountBigrams(text1shins,\n text2shins,bigrams=self.bigrams,\n thresh=self.matchThreshold)\n \n if compared[0]==True:\n \n copywords=[[w for w,t,l in t1Sent if l in compared[1]],\n [w for w,t,l in t2Sent if l in compared[1]]]\n \n shinMatches.append(([w for w,t,l in t1Sent],\n [w for w,t,l in t2Sent],copywords))\n \n return(MatcherOutput(shinMatches,name1,name2))","title":""},{"docid":"b8389207bdee99cd2168bf15e85f1b30","score":"0.47881946","text":"def match_features(feature_coords1,feature_coords2,image1,image2):\n matches = list()\n ncc_descriptor1 = ncc_descriptor(image1, feature_coords1) \n ncc_descriptor2 = ncc_descriptor(image2, feature_coords2)\n num_descriptors1 = np.shape(ncc_descriptor1)[2]\n num_descriptors2 = np.shape(ncc_descriptor2)[2]\n potential_match = np.array([])\n\n for x in range(0, num_descriptors1):\n index = -1\n max_dist = -1\n for y in range(0, num_descriptors2):\n ncc = compute_ncc(ncc_descriptor1, ncc_descriptor2, x, y)\n if ncc > max_dist:\n max_dist = ncc\n index = y\n if index > -1:\n match = [x, index]\n potential_match = np.append(potential_match, match)\n for x in range(0, num_descriptors2):\n index = -1\n max_dist = -1 \n for y in range(0, num_descriptors1):\n ncc = compute_ncc(ncc_descriptor1, ncc_descriptor2, y, x)\n if ncc > max_dist:\n max_dist = ncc\n index = y\n if index > -1:\n union = -1\n \n for z in range(0, len(potential_match)):\n if potential_match[z] == x:\n union = z\n if union > -1:\n match = [index, x]\n matches.append(match)\n return matches","title":""},{"docid":"b1e957dd17434837c40018b185d653dc","score":"0.47775978","text":"def who_is_it(targetImgDir, imagePath, file, db, resizeShape, model):\n # print(\"who_is_it: \", db[\"B1_2i.png\"])\n\n ## Step 1: Compute the target \"encoding\" for the image. Use img_to_encoding() see example above. ## (≈ 1 line)\n encoding = img_to_encoding(targetImgDir+file, resizeShape, model)\n\n ## Step 2: Find the closest encoding ##\n # Initialize \"min_dist\" to a large value, say 100 (≈1 line)\n min_dist = 100\n\n similarityArr = []\n # Loop over the database dictionary's names and encodings.\n for name in db:\n # if name == file:\n # continue\n\n # Compute L2 distance between the target \"encoding\" and the current \"emb\" from the db. (≈ 1 line)\n dist = np.linalg.norm(encoding - db[name])\n\n dists = {\"name\": name, \"dist\": dist}\n similarityArr.append(dists)\n\n # If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines)\n # if dist < min_dist:\n # min_dist = dist\n # identity = name\n\n\n def dist(s):\n return s['dist']\n\n similarityArr = sorted(similarityArr, key = dist)\n\n # print(\"similarityArr: \", similarityArr)\n # picNum = len(similarityArr)\n\n plt.figure(num=1, figsize=(10, 10))\n\n for key in range(10):\n plt.subplot(5, 2, key+1)\n plt.title('distance: ' + str(similarityArr[key][\"dist\"]))\n plt.imshow(Image.open(imagePath + similarityArr[key][\"name\"]))\n\n plt.show()\n\n # if min_dist > 0.7:\n # print(\"Not in the database.\")","title":""},{"docid":"34d57734cd027fa87d872f125b163800","score":"0.4771992","text":"def produce_pairs(folder, ment, pair):\n\n print \"Producing Pairs data frame\"\n #Initialize DF for all results\n column_titles_all = [\"PMID\", \"Paragraph\", \"Sentence\", \"Char_Start_x\", \"Char_End_x\",\n \"Entity_name_x\", \"TaxID\", \"SerialNo_x\", \"Char_Start_y\", \"Char_End_y\",\n \"Entity_name_y\", \"SerialNo_y\", \"Text\"]\n results_all = pd.DataFrame(columns=column_titles_all)\n column_titles_nec = [\"Entity_name_x\", \"SerialNo_x\", \"Entity_name_y\", \"SerialNo_y\", \"Text\"]\n results_nec = pd.DataFrame(columns=column_titles_nec)\n\n #Go over all the files in the specified folder\n for filename in glob.glob(os.path.join(folder, '*tsv.gz')):\n try:\n texts = open_tsv(filename)\n pair = pair[[\"SerialNo1\",\n \"SerialNo2\",\n \"Final_Score\"]].sort_values(['SerialNo1', 'SerialNo2'])\n ment = ment.sort_values('PMID')\n common_j = find_common(texts, ment)\n\n shared_entities = ment[ment[\"PMID\"].isin(common_j)]\n shared_entities = shared_entities.sort_values('SerialNo')\n\n # extract only the common pairs with their respective entries\n ser_no_ents = shared_entities[\"SerialNo\"].tolist()\n temp_s1_values = pair[pair[\"SerialNo1\"].isin(ser_no_ents)]\n temp_s2_list = temp_s1_values[\"SerialNo2\"].tolist()\n both_entries = shared_entities[shared_entities[\"SerialNo\"].isin(temp_s2_list)]\n # temp_both_list = both_entries[\"SerialNo\"].tolist()\n # both_pairs = temp_s1_values[temp_s1_values[\"SerialNo1\"].isin(temp_both_list) &\n # temp_s1_values[\"SerialNo2\"].isin(temp_both_list)]\n both_entries = both_entries.sort_values('PMID')\n\n #Get True Pairs\n true_pairs = extract_true_pairs(both_entries)\n\n #Add senteces\n\n pairs_with_sentences = add_sentences(texts, true_pairs)\n only_nec = pairs_with_sentences[[\"Entity_name_x\", \"SerialNo_x\", \"Entity_name_y\",\n \"SerialNo_y\", \"Text\"]]\n\n results_all = results_all.append(pairs_with_sentences, ignore_index=True)\n results_nec = results_nec.append(only_nec, ignore_index=True)\n print \"Done with \", filename[54:]\n\n except (KeyError, UnicodeDecodeError, StopIteration):\n\n print \"Something went wrong with \", filename[54:], \"\\n\", \"Ignoring it!\"\n\n #Append to results DF\n\n\n results_all[[\"PMID\", \"Paragraph\", \"Sentence\", \"Char_Start_x\", \"Char_End_x\", \"TaxID\",\n \"SerialNo_x\", \"Char_Start_y\", \"Char_End_y\", \"SerialNo_y\"]] = results_all[[\"PMID\", \"Paragraph\", \"Sentence\", \"Char_Start_x\", \"Char_End_x\", \"TaxID\", \"SerialNo_x\", \"Char_Start_y\", \"Char_End_y\", \"SerialNo_y\"]].astype(int)\n results_nec[[\"SerialNo_x\", \"SerialNo_y\"]] = results_nec[[\"SerialNo_x\", \"SerialNo_y\"]].astype(int)\n\n results_all.to_csv('Pairs_With_Sentences_Full.tsv', sep='\\t', index=False, header=False)\n results_nec.to_csv('Pairs_With_Sentences_Only_nec.tsv', sep='\\t', index=False, header=False)\n print \"\\n\", \"DONE with Pairs data frame production!\", \"\\n\"\n\n return results_nec","title":""},{"docid":"a8b388152bb05366bab79b50f3e16166","score":"0.4766146","text":"def needleman_wunsch_matrix(seq1, seq2, win=0):\n \n match = 1 # match score\n mismatch = -1 # mismatch penalty\n indel = -1 # indel penalty\n \n n = len(seq1)\n m = len(seq2)\n s = np.zeros( (n+1, m+1) ) - 100 # DP matrix (large negative is default)\n s[0,0]=0\n ptr = np.zeros( (n+1, m+1), dtype=int ) # matrix of pointers\n \n ##### INITIALIZE SCORING MATRIX (base case) #####\n \n for i in range(1, n+1) :\n s[i,0] = indel * i\n for j in range(1, m+1):\n s[0,j] = indel * j\n \n ########## INITIALIZE TRACEBACK MATRIX ##########\n \n # Tag first row by LEFT, indicating initial \"-\"s\n ptr[0,1:] = LEFT\n \n # Tag first column by UP, indicating initial \"-\"s\n ptr[1:,0] = UP\n \n #####################################################\n\n\n for i in range(1,n+1):\n jrange = range(max(1,i-win),min(i+win,m+1)) if win else range(1,m+1)\n for j in jrange:\n # match\n if seq1[i-1] == seq2[j-1]:\n s[i,j] = s[i-1,j-1] + match\n ptr[i,j] = DIAG\n # mismatch\n else :\n s[i,j] = s[i-1,j-1] + mismatch\n ptr[i,j] = DIAG\n # indel penalty\n if s[i-1,j] + indel > s[i,j] :\n s[i,j] = s[i-1,j] + indel\n ptr[i,j] = UP\n # indel penalty\n if s[i, j-1] + indel > s[i,j]:\n s[i,j] = s[i, j-1] + indel\n ptr[i,j] = LEFT\n \n return s, ptr","title":""},{"docid":"ddcbf6bfe83f9849a81984831b511e96","score":"0.47493348","text":"def _extractfaces(label_pairs):\n \n key, (subvolume, gtmap, segmap, gtvol, segvol) = label_pairs\n\n # extract unique bodies not remapped\n allgt = set(numpy.unique(gtvol))\n allseg = set(numpy.unique(segvol))\n\n gtmapbodies = set()\n for key2, body in gtmap.items():\n gtmapbodies.add(key2)\n segmapbodies = set()\n for key2, body in segmap.items():\n segmapbodies.add(key2)\n allgt = allgt.difference(gtmapbodies)\n if 0 in allgt:\n allgt.remove(0)\n allseg = allseg.difference(segmapbodies)\n if 0 in allseg:\n allseg.remove(0)\n \"\"\"\n if 0 in allseg:\n allseg.remove(0)\n if 0 in allgt:\n allgt.remove(0)\n \"\"\"\n\n zmax,ymax,xmax = gtvol.shape\n start = (subvolume.box.z1, subvolume.box.y1, subvolume.box.x1)\n\n mappedfaces = []\n\n # grab 6 faces for gt\n slicex0 = gtvol[:,:,0]\n slicexmax = gtvol[:,:,xmax-1]\n\n slicey0 = gtvol[:,0,:]\n sliceymax = gtvol[:,ymax-1,:]\n \n slicez0 = gtvol[0,:,:]\n slicezmax = gtvol[zmax-1,:,:]\n\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+ymax, start[2]+1), True), \n [(slicex0, gtmap, key, True, allgt)] ))\n mappedfaces.append(( ((start[0], start[1], start[2]+xmax),\n (start[0]+zmax, start[1]+ymax, start[2]+xmax+1), True), \n [(slicexmax, gtmap, key, False, set())] ))\n \n mappedfaces.append(( (start, (start[0]+zmax, start[1]+1, start[2]+xmax), True), \n [(slicey0, gtmap, key, False, set())] ))\n mappedfaces.append(( ((start[0], start[1]+ymax, start[2]),\n (start[0]+zmax, start[1]+ymax+1, start[2]+xmax), True), \n [(sliceymax, gtmap, key, False, set())] ))\n \n mappedfaces.append(( (start, (start[0]+1, start[1]+ymax, start[2]+xmax), True), \n [(slicez0, gtmap, key, False, set())] ))\n mappedfaces.append(( ((start[0]+zmax, start[1], start[2]),\n (start[0]+zmax+1, start[1]+ymax, start[2]+xmax), True), \n [(slicezmax, gtmap, key, False, set())] ))\n\n # grab 6 faces for seg\n segslicex0 = segvol[:,:,0]\n segslicexmax = segvol[:,:,xmax-1]\n\n segslicey0 = segvol[:,0,:]\n segsliceymax = segvol[:,ymax-1,:]\n \n segslicez0 = segvol[0,:,:]\n segslicezmax = segvol[zmax-1,:,:]\n\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+ymax, start[2]+1), False), \n [(segslicex0, segmap, key, True, allseg)] ))\n mappedfaces.append(( ((start[0], start[1], start[2]+xmax),\n (start[0]+zmax, start[1]+ymax, start[2]+xmax+1), False), \n [(segslicexmax, segmap, key, False, set())] ))\n \n mappedfaces.append(( (start, (start[0]+zmax, start[1]+1, start[2]+xmax), False), \n [(segslicey0, segmap, key, False, set())] ))\n mappedfaces.append(( ((start[0], start[1]+ymax, start[2]),\n (start[0]+zmax, start[1]+ymax+1, start[2]+xmax), False), \n [(segsliceymax, segmap, key, False, set())] ))\n \n mappedfaces.append(( (start, (start[0]+1, start[1]+ymax, start[2]+xmax), False), \n [(segslicez0, segmap, key, False, set())] ))\n mappedfaces.append(( ((start[0]+zmax, start[1], start[2]),\n (start[0]+zmax+1, start[1]+ymax, start[2]+xmax), False), \n [(segslicezmax, segmap, key, False, set())] ))\n \n return mappedfaces","title":""},{"docid":"177af38d4c0e94ca5a76b045683aa071","score":"0.474284","text":"def match_transform(self, e):\n\n logger = logging.getLogger('petr_log.PETRgraph')\n\n def recurse(pdict, event, a2v={}, v2a={}):\n '''\n Parameters\n -----------\n a2v: dictionary\n actor to variable mapping\n\n v2a: dictionary\n variable to actor mapping\n\n '''\n logger.debug(\"recurse entry..\")\n #print(pdict)\n\n\n path = pdict\n if isinstance(pdict, list):\n # transfromation pattern is found\n line = pdict[1]\n path = pdict[0]\n verb = utilities.convert_code(path[2])[0] if not path[\n 2] == \"Q\" else utilities.convert_code(v2a[\"Q\"])[0]\n #verb = utilities.convert_code(verb)\n if isinstance(v2a[path[1]], tuple):\n results = []\n for item in v2a[path[1]]:\n results.append((list(v2a[path[0]]), item, verb))\n logger.debug(\"line:\" + line)\n return results, line\n logger.debug(\"line:\" + line)\n return [(list(v2a[path[0]]), v2a[path[1]], verb)], line\n\n if isinstance(event, tuple):\n if e[2] in [None, '---']:\n return False\n\n if event[2] in [None, '---']:\n return False\n # print(event)\n actor = None if not event[0] else tuple(event[0])\n # print(actor)\n eventcode = utilities.convert_code(event[2])[0]\n # , eventcode - eventcode % 0x10,eventcode - eventcode % 0x100, eventcode - eventcode % 0x1000]\n codelist = [eventcode]\n # print(eventcode)\n # print(pdict)\n # print(codelist)\n masks = [a for a in codelist if a in pdict]\n\n # print(masks)\n logger.debug(\"actor:\")\n logger.debug(actor)\n\n logger.debug(\"masks:\")\n logger.debug(masks)\n\n if masks:\n # print(masks)\n path = pdict[masks[0]]\n elif -1 in pdict:\n v2a[\"Q\"] = event[2]\n path = pdict[-1]\n # print(path)\n else:\n #print(\"nothing is found\")\n return False\n else:\n actor = event\n if event == []:\n actor = \".\"\n\n logger.debug(\"actor:\")\n logger.debug(actor)\n\n if isinstance(actor,list):\n actor = actor[0]\n event = actor\n\n if actor in a2v:\n #print(\"in a2v\")\n actor = a2v[actor]\n\n if not actor:\n actor = \".\"\n\n logger.debug(\"actor:\")\n logger.debug(actor)\n \n #print(actor != '_')\n #print(actor == '_')\n if actor in path:\n #print(\"actor in path\")\n #print(path[actor])\n if actor == \".\":\n return recurse(path[actor], \".\", a2v, v2a)\n else:\n return recurse(path[actor], event[1], a2v, v2a)\n elif actor != '_':\n #print(\"entry\")\n for var in sorted(path.keys())[::-1]:\n #print(var)\n if var in v2a:\n continue\n if var != '.':\n v2a[var] = actor\n a2v[actor] = var\n\n #print(\"v2a:\")\n #print(v2a)\n #print(\"a2v:\")\n #print(a2v)\n #print(\"event1:\")\n #print(event)\n if event:\n return recurse(path[var], event[1], a2v, v2a)\n \n\n\n #logger.debug(\"no transformation is present\")\n\n return False\n\n logger.debug(\"match_transform entry...\")\n t = recurse(PETRglobals.VerbDict['transformations'], e)\n\n try:\n logger.debug(e)\n\n t = recurse(PETRglobals.VerbDict['transformations'], e)\n if t:\n logger.debug(\"transformation is present:\")\n logger.debug(\"t:\")\n logger.debug(t)\n transfered = []\n for event in t[0]:\n tevent = (event[0],[event[1]],utilities.convert_code(event[2], 0))\n transfered.append(tevent)\n\n return transfered\n else:\n logger.debug(\"no transformation is present:\")\n #c = utilities.convert_code(e[1][2])[0]\n # print(c)\n #print(16 ** 3)\n #print(c / (16 ** 3))\n # not e[1][2] / (16 ** 3):\n if e[0] and e[2] and isinstance(e[1], tuple) and e[1][0] and e[1][2] and e[0] != e[1][0]:\n\n logger.debug(utilities.convert_code(e[2])[0])\n logger.debug(e[2])\n\n logger.debug(\"the event is of the form: a ( b . Q ) P\")\n if isinstance(e[1][0], list):\n results = []\n for item in e[1][0]:\n code_combined = utilities.combine_code(utilities.convert_code(e[2])[\n 0], utilities.convert_code(e[1][2])[0])\n #target = []\n # target.append(item)\n event = (e[0], [item], utilities.convert_code(\n code_combined, 0))\n logger.debug(event)\n results.append(event)\n return results\n\n code_combined = utilities.combine_code(utilities.convert_code(e[2])[\n 0], utilities.convert_code(e[1][2])[0])\n event = (e[0], [e[1][0]],\n utilities.convert_code(code_combined, 0))\n logger.debug(event)\n return [event]\n #'''\n elif e[0] and isinstance(e[1], tuple) and e[1][0] and e[1][2] and e[0] == e[1][0] and e[1][1]:\n\n logger.debug(\"the event is of the form: a ( a b Q ) P\")\n if e[2] in [None, '---']:\n code_combined = utilities.convert_code(e[1][2])[0]\n else:\n code_combined = utilities.combine_code(utilities.convert_code(e[2])[\n 0], utilities.convert_code(e[1][2])[0])\n event = (e[0], e[1][1], utilities.convert_code(\n code_combined, 0))\n logger.debug(event)\n\n return [event]\n '''\n elif e[0] and isinstance(e[1], tuple) and not e[1][0] and e[1][2] and e[1][1]:\n logger.debug(\"the event is of the form: a ( [] b Q ) P\")\n logger.debug(e[2])\n\n code_combineds = []\n for code in e[2].split(\":\"):\n print(code)\n if code in [None, '---','']:\n #code_combineds.append(utilities.convert_code(e[1][2])[0])\n code_combineds.append('')\n else:\n code_combineds.append(utilities.combine_code(utilities.convert_code(code)[\n 0], utilities.convert_code(e[1][2])[0]))\n\n print(code_combineds)\n for code in code_combineds:\n c = utilities.convert_code(code, 0)\n print(c)\n\n \n if e[2] in [None, '---']:\n code_combined = utilities.convert_code(e[1][2])[0]\n else:\n code_combined = utilities.combine_code(utilities.convert_code(e[2])[\n 0], utilities.convert_code(e[1][2])[0])\n event = (e[0], e[1][1], utilities.convert_code(\n code_combined, 0))\n logger.debug(event)\n\n return[event]\n '''\n\n except Exception as ex:\n pass # print(ex)\n return [e]","title":""},{"docid":"0ad68691eed9268ffc5acd0b58cc7bc7","score":"0.47421414","text":"def enumerate_matches(tnums_a,tnums_b,tags_a,tags_b,\n tagn_interval,\n max_shift=20.0,max_drift=0.005,max_delta=0.500,\n verbose=False,max_matches=1):\n visited={}\n full_matches=[]\n next_a=next_b=0\n matches=[]\n \n # When trying to find a bad ping, keep track of how far the match\n # got. This might be a little generous, in the sense that these\n # are tracked separately, and there could be one set of matches\n # that gets further in a, and a separate set that gets further\n # in b. maybe that's ok, depending on how these values get used.\n # 2019-12-03: HERE so far this has tracked the last matched ping, and\n # on failure started axing successive pings.\n # This is problematic because we may consider many ping after the\n # last match, and much later consider a pair that fails.\n # tempting to track the greatest next_a/next_b while building the\n # longest match. but the search can go quite far (up to max shift)\n # on either side without having to advance the other side, such\n # that we may see very far beyond the real culprit.\n # Solution probably to make test smarter, and allow it to return\n # potential bad apples.\n max_match_a=-1\n max_match_b=-1\n\n # New approach to finding bad apples.\n # Keep the test result from the longest set of matches\n longest_fail=None\n max_matched=0\n longest_match=None\n \n stack=[] # tuples of next_a,next_b,matches\n \n def do_test(next_a,next_b,matches):\n return test_matches(next_a,next_b,matches,tnums_a,tnums_b,tags_a,tags_b,\n tagn_interval=tagn_interval,\n max_shift=max_shift,max_drift=max_drift,max_delta=max_delta)\n\n stack.append( (next_a,next_b,matches) )\n\n while stack:\n next_a,next_b,matches = stack.pop()\n\n # Check to see if this has already been tried\n key=(next_a,next_b,tuple(matches))\n if key in visited: continue\n visited[key]=1\n\n if verbose:\n print(f\"match next_a={next_a} next_b={next_b} matches={matches}\")\n\n test_result=do_test(next_a,next_b,matches)\n if test_result is not True:\n if len(matches)>max_matched:\n max_matched=len(matches)\n longest_fail=test_result\n longest_match=matches\n elif len(matches)==max_matched:\n longest_fail=( list(set( longest_fail[0]+test_result[0])), \n list(set( longest_fail[1]+test_result[1])) )\n continue\n \n if len(matches):\n max_match_a=max(max_match_a,matches[-1][0])\n max_match_b=max(max_match_b,matches[-1][1])\n \n if (next_a==len(tnums_a)) and (next_b==len(tnums_b)):\n # termination condition\n if len(matches)>0:\n full_matches.append(matches)\n print(\"MATCH\")\n if len(full_matches)>=max_matches:\n break\n continue\n elif (next_a==len(tnums_a)):\n # finish out b\n stack.append( (next_a,len(tnums_b),matches) )\n elif (next_b==len(tnums_b)):\n # finish out a\n stack.append( (len(tnums_a),next_b,matches) )\n else:\n # This ordering will prefer chasing down matches before\n # trying to drop things. Hopefully that means that we'll\n # find the non-empty match first, and could potentially stop\n # early if that is sufficient.\n if tnums_a[next_a]<tnums_b[next_b] + max_shift:\n # drop which ever one is earlier. doesn't work in general, but I'm not\n # explicitly including shifts yet\n stack.append( (next_a+1,next_b,matches) )\n if tnums_b[next_b]<tnums_a[next_a] + max_shift:\n stack.append( (next_a,next_b+1,matches) )\n\n # is it possible to declare the next two a match?\n if (tags_a[next_a]==tags_b[next_b]):\n time_diff=tnums_a[next_a]-tnums_b[next_b]\n if (np.abs(time_diff)<max_shift):\n # sure, try a match\n stack.append( (next_a+1,next_b+1,matches + [(next_a,next_b)]) )\n else:\n # Tags match, but time diff is too big for max_shift\n pass\n\n return full_matches,max_match_a,max_match_b,longest_fail","title":""},{"docid":"f709fecc08cff4e41691daca8e018aff","score":"0.47405988","text":"def translate_all_frames(self):\n nucleotides_sequences = self.group_environment()\n total = 0\n for entry in nucleotides_sequences:\n total += nucleotides_sequences[entry]\n if total != len(self.reads):\n Error.LogicError(\"Genomic_environment: translate_all_frames: Total is different\" +\n \" than depth\")\n frames = []\n for i in range(0, 6):\n new_frame = {}\n frames.append(new_frame)\n for entry in nucleotides_sequences:\n #Kinda fuzzy... we must find the amino acid in all frames.\n #We will have a list of frame where each fram is a dict stored with aa.\n if len(entry) != 5:\n Error.error(\"Length of sequence: %s is not 5\" % entry)\n #I dont see a way to avoid hardcoding :(\n #Frame 1\n print(entry)\n print(entry[0:3])\n #print(len(entry[0:3]))\n codon = Seq(entry[0:3], IUPAC.unambiguous_dna)\n aa = str(codon.translate(table=11))\n if not aa in frames[0]:\n frames[0][aa] = 0\n frames[0][aa] += nucleotides_sequences[entry]\n #frame 2\n print(entry[1:4])\n codon = Seq(entry[1:4], IUPAC.unambiguous_dna)\n aa = str(codon.translate(table=11))\n if not aa in frames[1]:\n frames[1][aa] = 0\n frames[1][aa] += nucleotides_sequences[entry]\n #Frame 3\n print(entry[2:5])\n codon = Seq(entry[2:5], IUPAC.unambiguous_dna)\n aa = str(codon.translate(table=11))\n if not aa in frames[2]:\n frames[2][aa] = 0\n frames[2][aa] += nucleotides_sequences[entry]\n\n reverse_entry = entry[::-1]\n\n #Frame 4 (reverse starting from right)\n print(reverse_entry[0:3])\n codon = Seq(reverse_entry[0:3], IUPAC.unambiguous_dna)\n aa = str(codon.translate(table=11))\n if not aa in frames[3]:\n frames[3][aa] = 0\n frames[3][aa] += nucleotides_sequences[entry]\n #Frame 5\n print(reverse_entry[1:4])\n codon = Seq(reverse_entry[1:4], IUPAC.unambiguous_dna)\n aa = str(codon.translate(table=11))\n if not aa in frames[4]:\n frames[4][aa] = 0\n frames[4][aa] += nucleotides_sequences[entry]\n #Frame 6\n print(reverse_entry[2:5])\n codon = Seq(reverse_entry[2:5], IUPAC.unambiguous_dna)\n aa = str(codon.translate(table=11))\n if not aa in frames[5]:\n frames[5][aa] = 0\n frames[5][aa] += nucleotides_sequences[entry]\n ##We output this in a cute way\n header = \"Contig\\tSNP position(contig)\\tFrame\\tAmino acid\\tProportion\\n\"\n print(header),\n for frame in range(0, 6):\n frame_number = str(frame+1)\n for amino_acid in frames[frame]:\n line = \"\"\n line = self.contig+\"\\t\"+str(self.position)+'\\t'+frame_number+\"\\t\"+amino_acid+\"\\t\"+str(float(frames[frame][amino_acid]/total))\n print(line)","title":""},{"docid":"c2ef0bf81e86ceb32d036182c38b37f0","score":"0.47225416","text":"def drawMatches(img1, kp1, img2, kp2, matches):\n\n # Create a new output image that concatenates the two images together\n # (a.k.a) a montage\n rows1 = img1.shape[0]\n cols1 = img1.shape[1]\n rows2 = img2.shape[0]\n cols2 = img2.shape[1]\n\n out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8')\n\n # Place the first image to the left\n out[:rows1,:cols1,:] = np.dstack([img1, img1, img1])\n\n # Place the next image to the right of it\n out[:rows2,cols1:cols1+cols2,:] = np.dstack([img2, img2, img2])\n\n # For each pair of points we have between both images\n # draw circles, then connect a line between them\n for mat in matches:\n\n # Get the matching keypoints for each of the images\n img1_idx = mat.queryIdx\n img2_idx = mat.trainIdx\n\n # x - columns\n # y - rows\n (x1,y1) = kp1[img1_idx].pt\n (x2,y2) = kp2[img2_idx].pt\n\n # Draw a small circle at both co-ordinates\n # radius 4\n # colour blue\n # thickness = 1\n cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1)\n cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1)\n\n # Draw a line in between the two points\n # thickness = 1\n # colour blue\n cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1)\n\n\n # Show the image\n cv2.imshow('Matched Features', out)\n cv2.waitKey(0)\n cv2.destroyAllWindows()","title":""},{"docid":"2dd5749e4acd726858c73dd0c80dd8d7","score":"0.47193238","text":"def disambiguate_entities(entity_candidates, w2v):\n disambiguation_graph = nx.DiGraph()\n embeddings = w2v.get_vectors([entity for (index, surface_form) in entity_candidates\n for entity in entity_candidates.get((index, surface_form))])\n\n for (index, surface_form) in entity_candidates:\n graph = nx.DiGraph()\n nodes = filter_candidates((index, surface_form), entity_candidates, embeddings)\n graph.add_nodes_from(nodes)\n entity_candidates[(index, surface_form)] = graph\n disambiguation_graph = nx.compose(disambiguation_graph, entity_candidates.get((index, surface_form)))\n\n return disambiguation_graph, entity_candidates","title":""},{"docid":"00d3807a2d1fcc96b12a84573130c0b9","score":"0.47176337","text":"def execute(self, views: list) -> list:\n n_samp = len(views[0])\n\n labels = np.zeros((n_samp, n_samp), dtype=int)\n\n for i in range(0, len(views)):\n\n l = views[i]\n res = [[int(x == y) for y in l] for x in l]\n res = np.matrix(res)\n labels = labels + res\n\n # in_place=False does not work, we have to edit the matrix in place\n # labels = np.fill_diagonal(labels, 0, in_place=False)\n np.fill_diagonal(labels, 0)\n return labels","title":""},{"docid":"176ae036d4ab18b6922caf78ef8a438a","score":"0.47169954","text":"def user_alignment(alignment_file_path,\n thresh=0.9):\n\n alignment_files = glob.glob(alignment_file_path + '/Tng_an_userAlignment_*.json')\n\n alignment_data = []\n for fn in alignment_files:\n with open(fn, 'r') as f:\n for line in f:\n alignment_data.append(json.loads(line))\n \n df = pd.DataFrame(alignment_data)\n #only include scores above .9\n df = df[df['score'] > thresh]\n\n platforms = [c for c in df.columns if c != 'score']\n\n dfs = []\n for i, p1 in enumerate(platforms):\n for j, p2 in enumerate(platforms[i:]):\n if p2 != p1:\n df_map = df[[p1, p2, 'score']].dropna()\n df_map.columns = ['username1', 'username2', 'score']\n\n # if username has multiple matches keep the highest score\n df_map = df_map.sort_values('score', ascending=False)\n df_map = df_map.drop_duplicates(subset=['username2'])\n df_map = df_map.drop_duplicates(subset=['username1'])\n\n df_map = df_map.drop('score', axis=1)\n df_map = df_map[df_map['username1'] != df_map['username2']]\n\n dfs.append(df_map)\n\n edge_df = pd.concat(dfs, axis=0)\n edge_df = edge_df.drop_duplicates()\n\n G = nx.from_pandas_edgelist(edge_df, source='username1', target='username2')\n\n components = nx.connected_components(G)\n username_map = {}\n for comp in components:\n comp = list(comp)\n for i in range(len(comp) - 1):\n username_map[comp[i + 1]] = comp[0]\n\n return (username_map)","title":""},{"docid":"4b8d6f98557bd369e6190bcc06acb5fb","score":"0.47135895","text":"def match_traj_parallelized_general(Start_index,End_index):\n trajectory_file='~/UP_analysis/Eastman_analysis/Analysis/CAM5_trajectories_v2.dms' #trajectory file is already specified in this case\n # open table with trajectory times and location\n Traj_table=pandas.read_table(trajectory_file,delim_whitespace=True) \n Traj_table_array=np.array(Traj_table[0:])\n \n # For regridding - take grid from GPCPv1pt2\n f_gpcp=cdm.open('~/Obs_datasets/GPCP_PDF/GPCPv1pt2_PREC_pdf.nc')\n obs_freq_pdf=f_gpcp('PRECFREQPDF')\n gpcp_grid=obs_freq_pdf.getGrid()\n \n \n f_log=open(\"\".join([\"log_trajv4_\",str(Start_index),\"_\",str(End_index),\".txt\"]),\"w+\")\n #length_traj_table=Traj_table_array.shape[0]\n length_traj_table=End_index-Start_index\n Output_table=np.zeros((length_traj_table,29)) # Create table to output data\n Output_table[:,:]=np.nan # Set all data to nan\n Output_table_noanom=np.zeros((length_traj_table,29)) # Create table to output data\n Output_table_noanom[:,:]=np.nan # Set all data to nan\n start_index=Start_index\n for i in np.arange(start_index,End_index): #loop from start to end index\n #print i\n Output_table[i-start_index,0]=int(Traj_table_array[i,0]) #write the traj number\n Output_table_noanom[i-start_index,0]=int(Traj_table_array[i,0]) #write the traj number\n #In a log file, keep track of what trajectories get analyzed\n f_log=open(\"\".join([\"log_trajv4_\",str(Start_index),\"_\",str(End_index),\".txt\"]),\"a\")\n f_log.write(\"\".join([str(int(Traj_table_array[i,0])),\" \\n\"]))\n f_log.close()\n for j in np.arange(7): #loop across the 7 instances along trajectory\n #Retrieve days after 2008-12-31 and time in UTC\n Day0=Traj_table_array[i,2+j]\n Time0=Traj_table_array[i,9+j]\n # Add exception when Time is 24 hrs = 0 hrs the next day\n if Time0>=24:\n Day0=Traj_table_array[i,2+j]+1\n relday0=cdu.cdtime.reltime(Day0,\"days since 2008-12-31\")\n comptime0=cdu.cdtime.r2c(relday0)\n str_comptime0month=str(comptime0.month)\n str_comptime0day=str(comptime0.day)\n if ~(Day0>0):\n continue #Skip cases where the date or time is NaN\n if comptime0.month<10: #Add zero to single digit months\n str_comptime0month=''.join(['0',str(comptime0.month)])\n if comptime0.day<10: #Add zero to single digit days\n str_comptime0day=''.join(['0',str(comptime0.day)])\n str_day=''.join(['2009-',str_comptime0month,'-',str_comptime0day])\n #Decide which file to access based on time of day\n if Time0<6:\n str_time0='00000'\n time_index0=np.floor(Time0)\n if Time0>=6 and Time0<12:\n str_time0='21600'\n time_index0=np.floor(Time0)-6\n if Time0>=12 and Time0<18:\n str_time0='43200'\n time_index0=np.floor(Time0)-12\n if Time0>=18 and Time0<24:\n str_time0='64800'\n time_index0=np.floor(Time0)-18\n if Time0>=24:\n str_time0='00000'\n time_index0=0\n str_time=''.join([str_day,'-',str_time0])\n filelocation='/global/cscratch1/sd/terai/UP_analysis/Eastman_analysis/CAM5_1deg_run2/Processed/'\n fileprefix='longcam5I_L30_20081001_0Z_f09_g16_828.cam.h1.'\n # Access the dataset using cdms2 tools\n f_CloudTop=cdm.open(''.join([filelocation,'CloudTopv4_',fileprefix,str_time,'.nc']))\n time_index0=int(time_index0) #Convert any decimals to integer to index\n lat0=Traj_table_array[i,16+j] #Locate the latitude from the trajectory table\n lon0=Traj_table_array[i,23+j]\n # Take the time slice and box with 5 x 5 deg. around interested area\n LWP_grid=f_CloudTop('TGCLDLWP',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\n CLDTOT_grid=f_CloudTop('CLDTOT',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\n CltopZ3_grid=f_CloudTop('CltopZ3',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\n CltopSPNC_grid=f_CloudTop('CltopSPNC',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\n # Regrid the data to 1x1 deg boxes using the grid from GPCP (see above)\n LWP_regridded=LWP_grid.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\n CLDTOT_regridded=CLDTOT_grid.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\n CltopZ3_regridded=CltopZ3_grid.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\n CltopSPNC_regridded=CltopSPNC_grid.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\n # Take the value from the grid box that lies within 1deg box around the point\n LWP=LWP_regridded(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\n CLDTOT=CLDTOT_regridded(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\n CltopZ3=CltopZ3_regridded(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\n CltopSPNC=CltopSPNC_regridded(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\n LWP_array=np.array(cdu.averager(LWP,axis='xyt'))\n CLDTOT_array=np.array(cdu.averager(CLDTOT,axis='xyt'))\n CltopZ3_array=np.array(cdu.averager(CltopZ3,axis='xyt'))\n CltopSPNC_array=np.array(cdu.averager(CltopSPNC,axis='xyt'))\n # Set all very small values of zeros to NaNs\n if LWP_array<0.0001:\n LWP_array=np.nan\n if CLDTOT_array<0.0001:\n CLDTOT_array=np.nan\n if CltopZ3_array<0.0001:\n CltopZ3_array=np.nan\n if CltopSPNC_array<0.0001:\n CltopSPNC_array=np.nan\n \n # ******** Repeat the steps above for the 100-day mean data\n f_CloudTop_100dm=cdm.open(''.join([filelocation,'AVG100days_CloudTopv4_',fileprefix,str_time,'.nc']))\n # Take the time slice and box with 5 x 5 deg. around interested area\n LWP_grid_100dm=f_CloudTop_100dm('TGCLDLWP',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\n CLDTOT_grid_100dm=f_CloudTop_100dm('CLDTOT',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\n CltopZ3_grid_100dm=f_CloudTop_100dm('CltopZ3',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\n CltopSPNC_grid_100dm=f_CloudTop_100dm('CltopSPNC',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\n # Regrid the data to 1x1 deg boxes using the grid from GPCP (see above)\n LWP_regridded_100dm=LWP_grid_100dm.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\n CLDTOT_regridded_100dm=CLDTOT_grid_100dm.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\n CltopZ3_regridded_100dm=CltopZ3_grid_100dm.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\n CltopSPNC_regridded_100dm=CltopSPNC_grid_100dm.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\n # Take the value from the grid box that lies within 1deg box around the point\n LWP_100dm=LWP_regridded_100dm(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\n CLDTOT_100dm=CLDTOT_regridded_100dm(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\n CltopZ3_100dm=CltopZ3_regridded_100dm(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\n CltopSPNC_100dm=CltopSPNC_regridded_100dm(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\n LWP_array_100dm=np.array(cdu.averager(LWP_100dm,axis='xyt'))\n CLDTOT_array_100dm=np.array(cdu.averager(CLDTOT_100dm,axis='xyt'))\n CltopZ3_array_100dm=np.array(cdu.averager(CltopZ3_100dm,axis='xyt'))\n CltopSPNC_array_100dm=np.array(cdu.averager(CltopSPNC_100dm,axis='xyt'))\n # **************************************************************\n\n # Enter values into the new output table\n Output_table[i-start_index,j+1]=CLDTOT_array-CLDTOT_array_100dm\n Output_table[i-start_index,j+8]=LWP_array-LWP_array_100dm\n Output_table[i-start_index,j+15]=CltopZ3_array-CltopZ3_array_100dm\n Output_table[i-start_index,j+22]=CltopSPNC_array-CltopSPNC_array_100dm\n \n # Enter values into the new output table\n Output_table_noanom[i-start_index,j+1]=CLDTOT_array\n Output_table_noanom[i-start_index,j+8]=LWP_array\n Output_table_noanom[i-start_index,j+15]=CltopZ3_array\n Output_table_noanom[i-start_index,j+22]=CltopSPNC_array\n np.set_printoptions(precision=5)\n # Save the table into a text file with 6 significant digits\n np.savetxt(''.join(['/global/homes/t/terai/UP_analysis/Eastman_analysis/Analysis/CAM5_trajectoryv4_CLDTOT_LWP_Cltop_Nc_',str(Start_index),'_',str(End_index),'.txt']),Output_table,delimiter=', ',fmt='%.5e')\n np.savetxt(''.join(['/global/homes/t/terai/UP_analysis/Eastman_analysis/Analysis/CAM5_trajectoryv4_raw_CLDTOT_LWP_Cltop_Nc_',str(Start_index),'_',str(End_index),'.txt']),Output_table_noanom,delimiter=', ',fmt='%.5e')","title":""},{"docid":"421edd7ec4e5a80c99393bb1dcaf9fca","score":"0.4713398","text":"def local_align(path, match=1, mism=-1, gap=-1):\n with open(path, \"r\") as fasta: # read sequences from file\n lines = fasta.readlines()\n A = lines[1].strip()\n B = lines[3].strip()\n matrix = [[0] for i in range(len(B)+1)] # making matrix with scores\n matrix[0] = [0 for i in range(len(A)+1)]\n for a in range(1, len(B)+1): # filling the matrix\n for i in range(1, len(A)+1):\n if A[i-1] == B[a-1]:\n matrix[a].append(max(matrix[a - 1][i] + gap, matrix[a][i - 1] + gap, matrix[a - 1][i - 1] + match))\n else:\n matrix[a].append(max(matrix[a - 1][i] + gap, matrix[a][i - 1] + gap, matrix[a - 1][i - 1] + mism))\n if matrix[a][i] < 0:\n matrix[a][i] = 0\n n = 0 # score\n a = 0\n i = 0\n maxn = min(matrix[-1])\n alignA = []\n alignB = []\n maxalignA = []\n maxalignB = []\n \"\"\" for e in range(len(B)): # Doing global alignment from and to every possible coordinate\n for b in range(len(A)):\n for c in range(1, len(A)):\n for d in range(1, len(B)):\n a = e\n i = b\n if c <= i or d <= a:\n continue\n n = 0\n while a < d and i < c: # Going through the matrix with highest score on the way\n if matrix[a + 1][i + 1] >= matrix[a][i + 1] and matrix[a + 1][i + 1] >= matrix[a + 1][i]:\n alignB.append(B[a])\n alignA.append(A[i])\n n = n + matrix[a + 1][i + 1]\n a += 1\n i += 1\n elif matrix[a + 1][i] >= matrix[a + 1][i + 1] and matrix[a + 1][i] >= matrix[a][i + 1]:\n alignB.append(B[a])\n alignA.append(\"-\")\n n = n + matrix[a + 1][i]\n a += 1\n elif matrix[a][i + 1] >= matrix[a + 1][i + 1] and matrix[a][i + 1] >= matrix[a + 1][i]:\n alignB.append(\"-\")\n alignA.append(A[i])\n n = n + matrix[a][i + 1]\n i += 1\n if a == d: # way from the edge to the corner\n while i < c:\n alignB.append(\"-\")\n alignA.append(A[i])\n n = n + matrix[a][i + 1]\n i += 1\n elif i == c:\n while a < d:\n alignB.append(B[a])\n alignA.append(\"-\")\n n = n + matrix[a + 1][i]\n a += 1\n if n > maxn: # choose max score\n maxn = n\n maxalignA = \"\".join(alignA)\n maxalignB = \"\".join(alignB)\n alignA.clear()\n alignB.clear()\"\"\"\n while a < len(B) and i < len(A): # Going through the matrix with highest score on the way\n if matrix[a][i + 1] >= matrix[a + 1][i + 1] and matrix[a][i + 1] >= matrix[a + 1][i]:\n alignB.append(\"-\")\n alignA.append(A[i])\n n = n + matrix[a][i + 1]\n i += 1\n elif matrix[a + 1][i + 1] >= matrix[a + 1][i] and matrix[a + 1][i + 1] >= matrix[a][i + 1]:\n alignB.append(B[a])\n alignA.append(A[i])\n n = n + matrix[a + 1][i + 1]\n a += 1\n i += 1\n elif matrix[a + 1][i] >= matrix[a + 1][i + 1] and matrix[a + 1][i] >= matrix[a][i + 1]:\n alignB.append(B[a])\n alignA.append(\"-\")\n n = n + matrix[a + 1][i]\n a += 1\n print(matrix)\n if a == len(B): # way from the edge to the corner\n while i < len(A):\n alignB.append(\"-\")\n alignA.append(A[i])\n n = n + matrix[a][i + 1]\n i += 1\n elif i == len(A):\n while a < len(B):\n alignB.append(B[a])\n alignA.append(\"-\")\n n = n + matrix[a + 1][i]\n a += 1\n alignA.append(\"\\n\") # joining to sequences into one\n align = \"\".join(alignA + alignB)\n return n, align","title":""},{"docid":"4f3d3761cef027ea40120cd02ef70763","score":"0.47080684","text":"def get_deduplicated_triples(triples_fname, target_dedup, person_dedup):\n\ttt = pd.read_table(triples_fname, sep=',', header=0)\n\tprint 'Read input triples: {}'.format(tt.shape),\n\ttt = tt.dropna()\n\tprint '.. After dropping NAs, #input triples: {}'.format(tt.shape)\n\ty = tt['human_relevance']\n\ttt = tt[['sub', 'obj']].to_dict(orient='list')\n\ttriples = zip(tt['sub'], tt['obj'])\n\n\t# de-duplicate target: professions/nationalities\n\ttarget_all = pd.read_table(target_dedup, sep=',', header=0)\n\ttarget_all = target_all[['cup_node_name', 'node_name']]\n\ttarget_all.index = target_all['cup_node_name']\n\ttarget_all = target_all.drop('cup_node_name', axis=1).to_dict()['node_name']\n\ttriples = [(t[0], target_all[t[1]]) if t[1] in target_all else t for t in triples]\n\n\t# de-duplicate persons\n\tpersons_all = pd.read_table(person_dedup, sep=',', header=0)\n\tpersons_all = persons_all[['cup_node_name', 'node_name']]\n\tpersons_all.index = persons_all['cup_node_name']\n\tpersons_all = persons_all.drop('cup_node_name', axis=1).to_dict()['node_name']\n\ttriples = [(persons_all[t[0]], t[1]) if t[0] in persons_all else t for t in triples]\n\tprint '=> Deduplicated triples: {}'.format(len(triples))\n\treturn triples, y","title":""},{"docid":"cd68f0a8b75ce94e3e5a265baffe8142","score":"0.4703198","text":"def drawMatches(img1, kp1, img2, kp2, matches):\n\n # Create a new output image that concatenates the two images together\n # (a.k.a) a montage\n rows1 = img1.shape[0]\n cols1 = img1.shape[1]\n rows2 = img2.shape[0]\n cols2 = img2.shape[1]\n\n out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8')\n\n # Place the first image to the left\n out[:rows1,:cols1] = np.dstack([img1, img1, img1])\n\n # Place the next image to the right of it\n out[:rows2,cols1:] = np.dstack([img2, img2, img2])\n\n # For each pair of points we have between both images\n # draw circles, then connect a line between them\n for mat in matches:\n\n # Get the matching keypoints for each of the images\n img1_idx = mat.queryIdx\n img2_idx = mat.trainIdx\n\n # x - columns\n # y - rows\n (x1,y1) = kp1[img1_idx].pt\n (x2,y2) = kp2[img2_idx].pt\n\n # Draw a small circle at both co-ordinates\n # radius 4\n # colour blue\n # thickness = 1\n cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1) \n cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1)\n\n # Draw a line in between the two points\n # thickness = 1\n # colour blue\n cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1)\n\n\n # Show the image\n #cv2.imshow('Matched Features', out)\n #cv2.waitKey(0)\n #cv2.destroyWindow('Matched Features')\n\n # Also return the image if you'd like a copy\n return out","title":""},{"docid":"586a602d028419eee80c1315fdd0edef","score":"0.47029915","text":"def Following(annot, anchor, container=None, cnt=3): \n # Get the following annotations\n def followingAQ(rec,cnt):\n # Sort the following annotations (limit the number of results to the cnt)\n srecs = sorted(rec[1], key=lambda x: (-1 if x.LstartOffset == None else x.LstartOffset))[0:cnt]\n \n # We can extract the key from the any 'right' entry in sorted recs (we will use the first one)\n key = Row(docId = srecs[0].RdocId,\n annotSet = srecs[0].RannotSet,\n annotType = srecs[0].RannotType,\n startOffset = int(srecs[0].RstartOffset),\n endOffset = int(srecs[0].RendOffset),\n annotId = int(srecs[0].RannotId),\n properties = srecs[0].Rproperties)\n\n # Construct the array \n values = []\n for rec in srecs:\n if rec.LdocId != None:\n values.append(Row(docId = rec.LdocId,\n annotSet = rec.LannotSet,\n annotType = rec.LannotType,\n startOffset = int(rec.LstartOffset),\n endOffset = int(rec.LendOffset),\n annotId = int(rec.LannotId),\n properties = rec.Lproperties)) \n\n return(key,values)\n \n \n # Get the following 'contained' annotations\n def followingContainedAQ(rec):\n if rec.CdocId == None:\n return (rec.annot,[])\n else:\n values = []\n for entry in rec.annots:\n if (entry.startOffset >= rec.CstartOffset) and (entry.endOffset <= rec.CendOffset):\n values.append(entry)\n return (rec.annot,values) \n \n l = annot.select(\"annotId\",\"annotSet\",\"annotType\",\"docId\",\"endOffset\",\"properties\",\"startOffset\").toDF(\"LannotId\",\"LannotSet\",\"LannotType\",\"LdocId\",\"LendOffset\",\"Lproperties\",\"LstartOffset\")\n r = anchor.select(\"annotId\",\"annotSet\",\"annotType\",\"docId\",\"endOffset\",\"properties\",\"startOffset\").toDF(\"RannotId\",\"RannotSet\",\"RannotType\",\"RdocId\",\"RendOffset\",\"Rproperties\",\"RstartOffset\")\n # Group on the anchor annotation\n results = l.join(r, \n (col(\"LdocId\") == col(\"RdocId\")) & \n (col(\"LstartOffset\") >= col(\"RendOffset\")), \n \"rightouter\") \\\n .rdd \\\n .groupBy(lambda x: (x[\"RdocId\"],x[\"RstartOffset\"],x[\"RendOffset\"])) \\\n .map(lambda rec: followingAQ(rec,cnt))\n\n results = spark.createDataFrame(results.map(lambda x: x),AQSchemaList())\n \n if (container != None) and (not(container.rdd.isEmpty())):\n c = container.select(\"annotId\",\"annotSet\",\"annotType\",\"docId\",\"endOffset\",\"properties\",\"startOffset\").toDF(\"CannotId\",\"CannotSet\",\"CannotType\",\"CdocId\",\"CendOffset\",\"Cproperties\",\"CstartOffset\")\n cResults = results.join(c, \n (col(\"annot.docId\") == col(\"CdocId\")) & \n (col(\"annot.startOffset\") >= col(\"CstartOffset\")) & \n (col(\"annot.endOffset\") <= col(\"CendOffset\")), \n \"leftouter\") \\\n .rdd \\\n .map(lambda rec: followingContainedAQ(rec))\n\n # Need to drop duplicates\n return spark.createDataFrame(cResults.map(lambda x: x),AQSchemaList())\n \n else:\n return results","title":""},{"docid":"c10d64495be8daa3049ef69dff59bd17","score":"0.47024792","text":"def _predict_joint_anchors_from_heatmap(self, heatmap):\n predictions = []\n\n for part_id, name in enumerate(c.ELEMENTS):\n map_ori = heatmap[:, :, part_id]\n smoothed_heatmap = filters.gaussian_filter(map_ori, sigma=3)\n\n map_left = np.zeros(smoothed_heatmap.shape)\n map_left[1:, :] = smoothed_heatmap[:-1, :]\n\n map_right = np.zeros(smoothed_heatmap.shape)\n map_right[:-1, :] = smoothed_heatmap[1:, :]\n\n map_up = np.zeros(smoothed_heatmap.shape)\n map_up[:, 1:] = smoothed_heatmap[:, :-1]\n\n map_down = np.zeros(smoothed_heatmap.shape)\n map_down[:, :-1] = smoothed_heatmap[:, 1:]\n\n # Determine local maxima in the heatmap fo find estimated locations\n # for each joint\n peaks = np.logical_and.reduce(\n (smoothed_heatmap >= map_left,\n smoothed_heatmap >= map_right,\n smoothed_heatmap >= map_up,\n smoothed_heatmap >= map_down,\n smoothed_heatmap > c.JOINT_CONFIDENCE_THRESHOLD)\n )\n\n peak_coordinates = np.vstack(np.nonzero(peaks))[[1, 0]]\n predictions.append(peak_coordinates)\n\n return predictions","title":""},{"docid":"e7165d7e4c5119a725a304cd8576828b","score":"0.46984684","text":"def run(self):\n\n person_count = 0\n num_buses = int(len(self.pedestrians)/8.0)\n print(num_buses)\n # sort pedestrians according to time\n ped_idx = np.argsort([pedestrian.depart for pedestrian in self.pedestrians])\n pedestrians_sorted = np.array(self.pedestrians)[ped_idx]\n\n for bus_index in range(num_buses):\n bus_id = f'bus_{bus_index}'\n bus_pedestrians = pedestrians_sorted[bus_index::num_buses]\n\n # add vehicle\n try:\n traci.vehicle.add(vehID=bus_id, typeID=\"BUS_L\", routeID=\"\", depart=bus_pedestrians[0].depart + 240.0, departPos=0,\n departSpeed=0, departLane=0, personCapacity=8)\n except traci.exceptions.TraCIException as err:\n print(\"TraCIException: {0}\".format(err))\n except:\n print(\"Unexpected error:\", sys.exc_info()[0])\n\n\n\n # concatenate all paths of pedestrians\n person_paths = []\n person_depart_times = []\n for person in bus_pedestrians:\n person_count += 1 # number of passengers not transported at the end\n person_path = ((person.edge_from, person.position_from), (person.edge_to, person.position_to))\n person_paths.append(person_path)\n person_depart_times.append(person.depart)\n\n # set the route for the vehicle\n route = [self.bus_depot_start_edge]\n for (edge_from, position_from), (edge_to, position_to) in person_paths:\n route = route + list(\n traci.simulation.findRoute(fromEdge=route[-1], toEdge=edge_from).edges)[1:] \\\n + list(traci.simulation.findRoute(fromEdge=edge_from, toEdge=edge_to).edges)[1:]\n traci.vehicle.setRoute(bus_id, route)\n traci.vehicle.setStop(vehID=bus_id, edgeID=edge_from, pos=position_from, laneIndex=0,\n duration=500, flags=tc.STOP_DEFAULT)\n traci.vehicle.setStop(vehID=bus_id, edgeID=edge_to, pos=position_to, laneIndex=0,\n duration=500, flags=tc.STOP_DEFAULT)\n\n traci.vehicle.subscribe('bus_0', (tc.VAR_ROAD_ID, tc.VAR_LANEPOSITION, tc.VAR_POSITION, tc.VAR_NEXT_STOPS))\n loss = len(self.pedestrians) - person_count\n print(\"loss\", loss)\n\n step = 0\n while step <= self.simulation_steps:\n traci.simulationStep()\n if self.sleep_time > 0:\n sleep(self.sleep_time)\n step += 1\n # print(traci.vehicle.getSubscriptionResults('bus_0'))\n\n traci.close()","title":""},{"docid":"f32441d151c319624fd3002c9d2a1333","score":"0.46980944","text":"def match(self, patterns):\n string = self.string\n n = len(string)\n results = []\n for pattern in patterns:\n top = 0\n btm = n-1\n for c in reversed(pattern):\n if top > btm:\n break\n top = self.first_occ[c][0] + self.ranks[c][top]\n btm = self.first_occ[c][0] + self.ranks[c][btm+1] - 1\n results.append([self.sa[i] for i in range(top, btm+1)])\n #results.append(btm-top+1)\n return results","title":""},{"docid":"1514d35740a5aea9522b3c71a1d92043","score":"0.4695781","text":"def anatomical_connectivity_matrix(track_file, label_file, t1_file,\n diffusion_file, trf_file, outdir,\n symmetric=True):\n # Load the dataset\n label_image = nibabel.load(label_file)\n label_array = label_image.get_data()\n label_shape = label_image.get_shape()\n tractogram = Tractogram(track_file)\n affine = flirt2aff(trf_file, diffusion_file, t1_file)\n\n # Check the validity of the label array\n kind = label_array.dtype.kind\n label_positive = (\n (kind == \"u\") or ((kind == \"i\") and (label_array.min() >= 0)))\n if not (label_positive and label_array.ndim == 3):\n raise ValueError(\"Label array must be a 3d integer array with \"\n \"non-negative label values.\")\n\n # To compute the connectivity matrix we consider only the first and last\n # point of each track\n tractogram.apply_affine(affine)\n endpoints = tractogram.endpoints().astype(int)\n pointsx, pointsy, pointsz = endpoints.T\n\n # Get labels associted to track end points\n endlabels = label_array[pointsx, pointsy, pointsz]\n if symmetric:\n endlabels.sort(axis=0)\n matrix = ndbincount(endlabels)\n if symmetric:\n matrix = numpy.maximum(matrix, matrix.T)\n\n # Remove the connectivity associated to the background\n matrix = matrix[1:, 1:]\n\n # Compute the fiber density map\n density_map = tractogram.density(shape=label_shape)\n\n # Save the resulting connectivity matrix and density map\n proba_file = os.path.join(outdir, \"det_paths.nii.gz\")\n density_image = nibabel.Nifti1Image(density_map, label_image.get_affine())\n nibabel.save(density_image, proba_file)\n network_file = os.path.join(outdir, \"det_network_matrix\")\n numpy.savetxt(network_file, matrix)\n\n return proba_file, network_file","title":""},{"docid":"7cae5aae6422fd182dd0e0a640c4f237","score":"0.46938214","text":"def reconstruct(b, matching):\n h_match, w_match = matching.shape[0], matching.shape[1]\n result = np.zeros([h_match, w_match, b.shape[2]])\n for y in range(h_match):\n for x in range(w_match):\n result[y, x] = b[matching[y, x, 0], matching[y, x, 1]]\n return result","title":""},{"docid":"86dc5922d0260006cf10a2b20decdb42","score":"0.46935165","text":"def load_images_names_and_joints():\n training_indices = loadmat('data/FLIC-full/tr_plus_indices.mat')\n training_indices = training_indices['tr_plus_indices'].flatten()\n\n examples = loadmat('data/FLIC-full/examples.mat')\n examples = examples['examples'][0]\n joint_ids = ['lsho', 'lelb', 'lwri', 'rsho', 'relb', 'rwri', 'lhip',\n 'lkne', 'lank', 'rhip', 'rkne', 'rank', 'leye', 'reye',\n 'lear', 'rear', 'nose', 'msho', 'mhip', 'mear', 'mtorso',\n 'mluarm', 'mruarm', 'mllarm', 'mrlarm', 'mluleg', 'mruleg',\n 'mllleg', 'mrlleg']\n\n fp_train = open('data/FLIC-full/train_joints.csv', 'w')\n fp_test = open('data/FLIC-full/test_joints.csv', 'w')\n for i, example in enumerate(examples):\n joint = example[2].T\n joint = dict(zip(joint_ids, joint))\n fname = example[3][0]\n joint = get_joint_list(joint)\n msg = '{},{}'.format(fname, ','.join([str(j) for j in joint.tolist()]))\n\n if i in training_indices:\n print(msg, file=fp_train)\n else:\n print(msg, file=fp_test)","title":""},{"docid":"f2f9e11f694f08872a216bb038151701","score":"0.46840385","text":"def forward(self, samples, matches, anchors, refs):\n # TODO(zhreshold): batch_pick, take multiple elements?\n # refs [B, M, 4], anchors [B, N, 4], samples [B, N], matches [B, N]\n # refs [B, M, 4] -> reshape [B, 1, M, 4] -> repeat [B, N, M, 4]\n n, m = matches.shape[1], refs.shape[1]\n matches.clamp_(0).unsqueeze_(2)\n ref_boxes = refs.unsqueeze(1).repeat(1, n, 1, 1)\n # refs [B, N, M, 4] -> 4 * [B, N, M]\n ref_boxes = torch.split(ref_boxes, 1, -1)\n # refs 4 * [B, N, M] -> pick from matches [B, N, 1] -> concat to [B, N, 4]\n ref_boxes = torch.cat([torch.gather(ref_boxes[i].squeeze(-1), 2, matches) \\\n for i in range(4)], dim=2)\n # transform based on x, y, w, h\n # g [B, N, 4], a [B, N, 4] -> codecs [B, N, 4]\n g = self.corner_to_center(ref_boxes)\n a = self.corner_to_center(anchors)\n t0 = ((g[0] - a[0]) / a[2] - self._means[0]) / self._stds[0]\n t1 = ((g[1] - a[1]) / a[3] - self._means[1]) / self._stds[1]\n t2 = (torch.log(g[2] / a[2]) - self._means[2]) / self._stds[2]\n t3 = (torch.log(g[3] / a[3]) - self._means[3]) / self._stds[3]\n codecs = torch.cat([t0, t1, t2, t3], dim=2)\n # samples [B, N] -> [B, N, 1] -> [B, N, 4] -> boolean\n temp = samples.unsqueeze(2).repeat(1, 1, 4) > 0.5\n # fill targets and masks [B, N, 4]\n targets = torch.where(temp, codecs, torch.zeros_like(codecs))\n masks = torch.where(temp, torch.ones_like(temp), torch.zeros_like(temp))\n return targets, masks","title":""},{"docid":"8bca85da125ac8b86c218545380618da","score":"0.4682914","text":"def drawMatches(img1, kp1, img2, kp2, matches):\n\n # Create a new output image that concatenates the two images together\n # (a.k.a) a montage\n rows1 = img1.shape[0]\n cols1 = img1.shape[1]\n rows2 = img2.shape[0]\n cols2 = img2.shape[1]\n\n out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8')\n\n # Place the first image to the left\n out[:rows1,:cols1] = np.dstack([img1, img1, img1])\n\n # Place the next image to the right of it\n out[:rows2,cols1:] = np.dstack([img2, img2, img2])\n\n # For each pair of points we have between both images\n # draw circles, then connect a line between them\n for m in matches:\n\n # Get the matching keypoints for each of the images\n img1_idx = m.queryIdx\n img2_idx = m.trainIdx\n\n # x - columns\n # y - rows\n (x1,y1) = kp1[img1_idx].pt\n (x2,y2) = kp2[img2_idx].pt\n\n # Draw a small circle at both co-ordinates\n # radius 4\n # colour blue\n # thickness = 1\n cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1) \n cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1)\n\n # Draw a line in between the two points\n # thickness = 1\n # colour blue\n # cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1)\n\n\n # Show the image\n cv2.imshow('Matched Features', out)\n cv2.waitKey(0)\n cv2.destroyWindow('Matched Features')\n\n # Also return the image if you'd like a copy\n return out","title":""},{"docid":"5d9a9a9a2290e0680750a9c2f55ccb3a","score":"0.46730903","text":"def matching(reference, *args, **kwargs):\n matched_datasets = df_match(reference, *args, dropna=True,\n dropduplicates=True, **kwargs)\n\n if type(matched_datasets) != tuple:\n matched_datasets = [matched_datasets]\n\n matched_data = pd.DataFrame(reference)\n\n for match in matched_datasets:\n match = match.drop(['distance', 'index'], axis=1)\n matched_data = matched_data.join(match)\n\n return matched_data.dropna()","title":""},{"docid":"284bba5c2d32b0ca2476a898f69caab7","score":"0.46729293","text":"def process_data_for_MTB(data):\n # Maximum number of sentences sharing the same entity pair.\n # This parameter is set for limit the bias towards popular \n # entity pairs which have many sentences. Of cource, you can\n # change this parameter, but in our expriment, we use 8.\n max_num = 8 \n\n # We change the original data's format. The ent_data is \n # a python dict of which key is `head_id#tail_id` and value\n # is sentences which hold this same entity pair.\n ent_data = defaultdict(list)\n for key in data.keys():\n for sentence in data[key]:\n if filter_sentence(sentence):\n continue\n head = sentence[\"h\"][\"id\"]\n tail = sentence[\"t\"][\"id\"]\n ent_data[head + \"#\" + tail].append(sentence)\n\n ll = 0\n list_data = []\n entpair2scope = {}\n for key in ent_data.keys():\n if len(ent_data[key]) < 2:\n continue\n list_data.extend(ent_data[key][0:max_num])\n entpair2scope[key] = [ll, len(list_data)]\n ll = len(list_data)\n\n # We will pre-generate `hard` nagative samples. The entpair2negpair\n # is a python dict of which key is `head_id#tail_id`. And the value of the dict\n # is the same format as key, but head_id or tail_id is different(only one id is \n # different). \n entpair2negpair = defaultdict(list)\n entpairs = list(entpair2scope.keys())\n entpairs.sort(key=lambda a: a.split(\"#\")[0])\n for i in range(len(entpairs)):\n head = entpairs[i].split(\"#\")[0]\n for j in range(i+1, len(entpairs)):\n if entpairs[j].split(\"#\")[0] != head:\n break\n entpair2negpair[entpairs[i]].append(entpairs[j])\n\n entpairs.sort(key=lambda a: a.split(\"#\")[1])\n for i in range(len(entpairs)):\n tail = entpairs[i].split(\"#\")[1]\n for j in range(i+1, len(entpairs)):\n if entpairs[j].split(\"#\")[1] != tail:\n break\n entpair2negpair[entpairs[i]].append(entpairs[j])\n\n if not os.path.exists(\"../data/MTB\"):\n os.mkdir(\"../data/MTB\")\n json.dump(entpair2negpair, open(\"../data/MTB/entpair2negpair.json\",\"w\"))\n json.dump(entpair2scope, open(\"../data/MTB/entpair2scope.json\", \"w\"))\n json.dump(list_data, open(\"../data/MTB/mtbdata.json\", \"w\"))","title":""},{"docid":"ee4ef46368ab5215299c4eca9237b92d","score":"0.46673894","text":"def updateAssociations(self, npreceding=30):\n N = min(npreceding, len(self.frames)-1)\n minDists = np.inf\n minIdx = 0\n for i in range(len(self.frames[-1].people)):\n distsi = 0.0\n if len(self.frames[-1].people) < 2:\n continue\n x = self.frames[-1].people[i]['pose_keypoints_2d']\n for j in range(N):\n if len(self.frames[-2-j].people) == 0:\n continue\n y = self.frames[-2-j].people[0]['pose_keypoints_2d']\n idx = (np.sum(x, 1) > 0)*(np.sum(y, 1) > 0)\n dists = np.sqrt(np.sum((x[idx, :] - y[idx, :])**2, 1))\n distsi += np.mean(dists)\n if distsi < minDists:\n minDists = distsi\n minIdx = i\n if minIdx > 0:\n p = self.frames[-1].people[0]\n self.frames[-1].people[0] = self.frames[-1].people[minIdx]\n self.frames[-1].people[minIdx] = p","title":""},{"docid":"7730955d39577e91a747679b0f162b6a","score":"0.46647954","text":"def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):\n if(len(trackers)==0):\n return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\n\n iou_matrix = iou_batch(detections, trackers)\n\n if min(iou_matrix.shape) > 0:\n a = (iou_matrix > iou_threshold).astype(np.int32)\n if a.sum(1).max() == 1 and a.sum(0).max() == 1:\n matched_indices = np.stack(np.where(a), axis=1)\n else:\n matched_indices = linear_assignment(-iou_matrix)\n else:\n matched_indices = np.empty(shape=(0,2))\n\n unmatched_detections = []\n for d, det in enumerate(detections):\n if(d not in matched_indices[:,0]):\n unmatched_detections.append(d)\n unmatched_trackers = []\n for t, trk in enumerate(trackers):\n if(t not in matched_indices[:,1]):\n unmatched_trackers.append(t)\n\n #filter out matched with low IOU\n matches = []\n for m in matched_indices:\n if(iou_matrix[m[0], m[1]]<iou_threshold):\n unmatched_detections.append(m[0])\n unmatched_trackers.append(m[1])\n else:\n matches.append(m.reshape(1,2))\n \n if(len(matches)==0):\n matches = np.empty((0,2),dtype=int)\n else:\n matches = np.concatenate(matches,axis=0)\n\n return matches, np.array(unmatched_detections), np.array(unmatched_trackers)","title":""},{"docid":"602fc140a4234217b6d1c0250185e2f3","score":"0.46600485","text":"def RunHydroAnalysis(psf, dcd, psf_info, close_lipid,close_prot,outname,segprot,segmemb,pdb\\\n ,first_frame,last_frame,skip,libpath):\n\n prot_cand_atom, memb_cand_atom = readCandidates(psf_info.lipids,libpath)\n atomid_cand_prot, atomid_cand_memb = HydroCandidateSelection(psf,segprot, segmemb, prot_cand_atom, memb_cand_atom)\n prot_candidates, memb_candidates = GetCandidate(psf_info,close_lipid,close_prot,atomid_cand_prot, atomid_cand_memb)\n prot_candidates = common.convertIntToStr(prot_candidates)\n memb_candidates = common.convertIntToStr(memb_candidates)\n\n\n\n univers = mda.Universe(psf, dcd)\n\n counting = {}\n nbFrame = 0\n\n output_avg = open(\"%s_average_hydrophobic_contact_per_frame.csv\"%outname,\"w\")\n output_avg.write(\"#Residue,AVG_nb_of_contact\\n\")\n\n output_raw = open(\"%s_hydrophobic_contact_raw_data.csv\"%outname,\"w\")\n output_raw .write(\"#Frame,ProtResidue,MembRes\\n\")\n\n frame_to_read = first_frame\n\n for ts in univers.trajectory:\n if ts.frame == frame_to_read and frame_to_read < last_frame:\n nbFrame += 1\n sel_memb = \"index %s\" % (\" \".join(memb_candidates))\n memb = univers.select_atoms(sel_memb)\n sel_prot = \"index %s\" % (\" \".join(prot_candidates))\n prot = univers.select_atoms(sel_prot)\n dist_mat = mda.analysis.distances.distance_array(prot.positions, memb.positions)\n\n conctacts =[]\n\n #Contact will be fill with list of 2 elements: [atomid in prot,atomid in memb]\n for i in range(dist_mat.shape[0]):\n for j in range(dist_mat.shape[1]):\n if dist_mat[i,j] < 3:\n conctacts.append([prot_candidates[i],memb_candidates[j]])\n\n\n #parse contact and write the raw data\n for i in range(len(conctacts)):\n residues_prot = common.obtainResInfo(int(conctacts[i][0]), psf_info.NbAtomPerProtRes)\n residues_lipids = common.obtainResInfo(int(conctacts[i][1]), psf_info.NbAtomPerLipRes)\n\n if residues_prot not in counting:\n counting[residues_prot] = 1\n\n else:\n counting[residues_prot] += 1\n\n output_raw.write(\"%i,%s,%s\\n\"%(ts.frame,residues_prot,residues_lipids))\n\n frame_to_read += skip\n\n\n\n #Get the average\n for residue in counting.keys():\n counting[residue] = float(counting[residue])/nbFrame\n\n output_avg.write(\"%s,%f\\n\"%(residue,counting[residue]))\n\n for residue in psf_info.protresid:\n if str(residue) not in counting.keys():\n counting[str(residue)] = 0\n\n if pdb != 'None':\n common.Mapped(pdb,counting,segprot,outname,\"average_hydrophobic_contact_per_frame\")\n\n\n output_avg.close()\n output_raw.close()","title":""},{"docid":"8d78d553e6ec2ecc9422c0de6dc75c58","score":"0.46582937","text":"def group_limbs_same_idcard(connected_limbs, joint_list):\n idcard_to_joint_assoc = []\n for limb_type in range(NUM_LIMBS):\n joint_src_type, joint_dst_type = LIMBS[limb_type]\n for limb_info in connected_limbs[limb_type]:\n idcard_assoc_idx = []\n # find joints current in list\n # print('CURRENT IDCARD', idcard_to_joint_assoc)\n for idcard, idcard_limbs in enumerate(idcard_to_joint_assoc):\n # print('IDCARD LIMBS', idcard_limbs)\n # print('LIMB INFO', limb_info)\n if idcard_limbs[joint_src_type]==limb_info[0] or \\\n idcard_limbs[joint_dst_type]==limb_info[1]: # if already in list\n # print('INDEX', idcard)\n idcard_assoc_idx.append(idcard) # append index to check later\n # print('IDCARD ASSOC IDX', idcard_assoc_idx)\n if len(idcard_assoc_idx)==1: # found 1 joints\n idcard_limbs = idcard_to_joint_assoc[idcard_assoc_idx[0]]\n if idcard_limbs[joint_dst_type]!=limb_info[1]: # add to list\n idcard_limbs[joint_src_type] = limb_info[0]\n idcard_limbs[joint_dst_type] = limb_info[1]\n # update info: score, num joints in limbs\n idcard_limbs[-1] += 1\n idcard_limbs[-2] += joint_list[\n limb_info[1].astype(int), 2\n ] + limb_info[2]\n \n elif len(idcard_assoc_idx)==2: # found 2 joints, merge them\n idcard1_limbs = idcard_to_joint_assoc[idcard_assoc_idx[0]]\n idcard2_limbs = idcard_to_joint_assoc[idcard_assoc_idx[1]]\n membership = ((idcard1_limbs>=0)&(idcard2_limbs>=0))[:-2]\n # print(idcard1_limbs>=0, idcard2_limbs>=0)\n # print(membership)\n # print('IDCARD 1', idcard1_limbs)\n # print('IDCARD 2', idcard2_limbs)\n if not membership.any(): # if 2 idcard has no connect joint, merge them\n idcard1_limbs[:-2] += (idcard2_limbs[:-2]+1) # update joint connected\n idcard1_limbs[-2:] += idcard2_limbs[-2:] # add score\n idcard1_limbs[-2] += limb_info[2] # overall score\n idcard_to_joint_assoc.pop(idcard_assoc_idx[1])\n else: # same with len==1 above\n # print('CASE')\n if np.sum((idcard1_limbs>=0)[:-2]) < np.sum((idcard2_limbs>=0)[:-2]):\n idcard1_limbs = idcard2_limbs\n idcard_to_joint_assoc.pop(idcard_assoc_idx[0])\n else:\n idcard_to_joint_assoc.pop(idcard_assoc_idx[1])\n idcard1_limbs[joint_src_type] = limb_info[0]\n idcard1_limbs[joint_dst_type] = limb_info[1]\n idcard1_limbs[-1] += 1\n idcard1_limbs[-2] += joint_list[limb_info[1].astype(int), 2] + limb_info[2]\n else: # if not in list, create new idcard\n row = -1*np.ones(NUM_LIMBS+2)\n row[joint_src_type] = limb_info[0]\n row[joint_dst_type] = limb_info[1]\n row[-1] = 2\n row[-2] = sum(joint_list[limb_info[:2].astype(int), 2]) + limb_info[2]\n idcard_to_joint_assoc.append(row)\n\n # delete idcard has very few limbs\n idcard_to_delete = []\n for idcard_id, idcard_info in enumerate(idcard_to_joint_assoc):\n if idcard_info[-1] < MINIMUM_NUM_JOINT or idcard_info[-2]/idcard_info[-1] < 0.2: # not enough 4 corner\n idcard_to_delete.append(idcard_id) \n for index in idcard_to_delete[::-1]:\n idcard_to_joint_assoc.pop(index)\n return np.array(idcard_to_joint_assoc)","title":""},{"docid":"a0dbf52d7d2aa68c5741181b26763b16","score":"0.46542156","text":"def play_match(self):\n\n\t\t# To save time, try to find mantra lineups already calculated\n\t\ttry:\n\t\t\tlineup1 = dbf.db_select(\n\t\t\t\t\ttable='mantra_lineups',\n\t\t\t\t\tcolumns=[f'day_{self.day}'],\n\t\t\t\t\twhere=f'team_name=\"{self.team1.name}\"')[0].split(', ')\n\t\t\tmalus1 = int(lineup1[0])\n\t\t\tlineup1 = [tuple(el.split(':')) for el in lineup1[1:]]\n\n\t\t\tlineup2 = dbf.db_select(\n\t\t\t\t\ttable='mantra_lineups',\n\t\t\t\t\tcolumns=[f'day_{self.day}'],\n\t\t\t\t\twhere=f'team_name=\"{self.team2.name}\"')[0].split(', ')\n\t\t\tmalus2 = int(lineup2[0])\n\t\t\tlineup2 = [tuple(el.split(':')) for el in lineup2[1:]]\n\n\t\t\t# malus1 = sum([1 for nm, rl in lineup1 if '*' in rl])\n\t\t\t# malus2 = sum([1 for nm, rl in lineup2 if '*' in rl])\n\n\t\t\tlineup1 = [nm for nm, rl in lineup1]\n\t\t\tlineup2 = [nm for nm, rl in lineup2]\n\n\t\t# if not found, calculate them with mantra algorithm\n\t\texcept IndexError:\n\t\t\tlineup1, _, malus1 = mf.mantra(day=self.day,\n\t\t\t fantateam=self.team1.name,\n\t\t\t starting_players=10)\n\t\t\tlineup2, _, malus2 = mf.mantra(day=self.day,\n\t\t\t fantateam=self.team2.name,\n\t\t\t starting_players=10)\n\n\t\tself.update_fantateams_data(lineup1, lineup2, malus1, malus2)","title":""}],"string":"[\n {\n \"docid\": \"49950be6e0f4806ad2611217e0a64591\",\n \"score\": \"0.5999887\",\n \"text\": \"def _extractmatches(keyfaces):\\n key, faces = keyfaces\\n \\n # no match found\\n if len(faces) == 1:\\n start, end, isgt = key\\n seg1, segmap, sid, hack1, segbodies = faces[0]\\n bodymatches = []\\n if hack1:\\n for label, body in segmap.items():\\n bodymatches.append(((body, isgt), [(label, sid, True)]))\\n for body in segbodies:\\n bodymatches.append(((body, isgt), [(body, sid, True)]))\\n\\n return bodymatches\\n assert(len(faces) == 2)\\n\\n start, end, isgt = key\\n seg1, segmap, sid, hack1, segbodies = faces[0]\\n seg2, segmap2, sid2, hack2, segbodies2 = faces[1]\\n\\n seg1 = seg1.flatten()\\n seg2 = seg2.flatten()\\n \\n seg1seg2 = numpy.column_stack((seg1, seg2))\\n unique_pairs = numpy.unique(seg1seg2, axis=0)\\n\\n bodymatches = []\\n\\n for val in unique_pairs:\\n if val[0] == 0 or val[1] == 0:\\n continue\\n \\n mapped1 = val[0]\\n if mapped1 in segmap:\\n mapped1 = segmap[mapped1]\\n mapped2 = val[1]\\n if mapped2 in segmap2:\\n mapped2 = segmap2[mapped2]\\n\\n if mapped1 == mapped2:\\n bodymatches.append(((mapped1, isgt), [((val[0], sid), (val[1], sid2))]))\\n\\n\\n # hack: send all bodies that have new labels\\n # assume 1) disjoint bodies will always include implicit identity mapping\\n # and 2) each subvolume will be represented at least 6 times\\n if hack1:\\n for label, body in segmap.items():\\n bodymatches.append(((body, isgt), [(label, sid, True)]))\\n for body in segbodies:\\n bodymatches.append(((body, isgt), [(body, sid, True)]))\\n \\n if hack2:\\n for label, body in segmap2.items():\\n bodymatches.append(((body, isgt), [(label, sid2, True)]))\\n for body in segbodies2:\\n bodymatches.append(((body, isgt), [(body, sid2, True)]))\\n\\n return bodymatches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45e8322f2048482b2134c9821139f3b9\",\n \"score\": \"0.56178755\",\n \"text\": \"def MatchFeatures(self, descFrame):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0facab35af28030efdb89c42584e52f6\",\n \"score\": \"0.5616024\",\n \"text\": \"def find_connected_joints(paf, joints_list_per_type, num_inter_points=10):\\n connnected_limbs = []\\n limb_inter_coord = np.empty((4, num_inter_points), dtype=np.intp) # store info of interpoints x,y coords paf, id of paf correspond limbs\\n for limb_type in range(NUM_LIMBS):\\n # print(LIMBS[limb_type])\\n joints_src = joints_list_per_type[LIMBS[limb_type][0]]\\n joints_dst = joints_list_per_type[LIMBS[limb_type][1]]\\n # print('JOINT SRC', joints_src)\\n # print('JOINT DST', joints_dst)\\n if len(joints_src)==0 or len(joints_dst)==0: # if limb type has no candidate (peak)\\n connnected_limbs.append([])\\n else:\\n connection_candidate = []\\n # id of paf in output, 2 id: src point and dst point\\n limb_inter_coord[2, :] = PAF_XY_COORDS_PER_LIMB[limb_type][0]\\n limb_inter_coord[3, :] = PAF_XY_COORDS_PER_LIMB[limb_type][1]\\n for i, joint_src in enumerate(joints_src):\\n for j, joint_dst in enumerate(joints_dst):\\n limb_dir = joint_dst[:2] - joint_src[:2] # direction vector\\n limb_dist = np.sqrt(np.sum(limb_dir**2)) + 1e-8\\n limb_dir = limb_dir / limb_dist # normalize\\n limb_inter_coord[1, :] = np.round(np.linspace(\\n joint_src[0], joint_dst[0], num=num_inter_points\\n )) # interpoint x coord\\n limb_inter_coord[0, :] = np.round(np.linspace(\\n joint_src[1], joint_dst[1], num=num_inter_points\\n )) # interpoint y coord\\n inter_paf = paf[limb_inter_coord[0, :],\\n limb_inter_coord[1, :],\\n limb_inter_coord[2:4, :]].T\\n\\n score_inter_pts = np.dot(inter_paf, limb_dir) # score \\n score = score_inter_pts.mean()\\n # print(score)\\n score_penalizing_long_dist = score_inter_pts.mean()+min(0.5*paf.shape[0]/limb_dist-1, 0) # penalty long distance\\n criteria1 = (np.count_nonzero(score_inter_pts>CONFIDENT_SCORE_LIMB) > 0.6*num_inter_points) # no. score > threshold at least 60%\\n # print(criteria1)\\n criteria2 = (score_penalizing_long_dist>-10)\\n if criteria1:# check criteria, criteria2???!!\\n connection_candidate.append(\\n [i, j, score,\\n score+joint_src[2]+joint_dst[2]]\\n )\\n\\n connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True) # sorted by score \\n # for con in connection_candidate:\\n # print('CONNECTION CANDIDATE', con)\\n connections = np.empty((0, 5), dtype=np.int)\\n max_connections = min(len(joints_src), len(joints_dst))\\n for potential_connection in connection_candidate:\\n i, j, s = potential_connection[:3]\\n # [joint_src_id, joint_dst_id, limb_score, joint_src_index, joint_dst_index]\\n if i not in connections[:, 3] and j not in connections[:, 4]:\\n connections = np.vstack(\\n [connections, [joints_src[i][3], joints_dst[j][3], s, i, j]]\\n )\\n if len(connections) >= max_connections:\\n break\\n # print('CONNECTION', connections)\\n\\n connnected_limbs.append(connections)\\n return connnected_limbs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7ad0c43d043c34576b37d237f47dfbe\",\n \"score\": \"0.5493029\",\n \"text\": \"def read_interaction_file_mat(Human_HighQuality):\\r\\n \\r\\n with open(Human_HighQuality, \\\"r\\\") as interaction_file:\\r\\n lines_num_int = int(interaction_file.readline())\\r\\n data_str = interaction_file.readlines()\\r\\n prot_data_list = []\\r\\n prot_graph_dict = {}\\r\\n\\r\\n for line in data_str:\\r\\n prot1_exist = False\\r\\n prot2_exist = False\\r\\n\\r\\n # This loop includes elements in a list, pass only once (when the list is empty), initialize the first\\r\\n # protein\\r\\n if prot_data_list == []: # Checks if list is empty (not mandatory though)\\r\\n prot_data_list = [(line.split()[0])] # Adds the value into the list\\r\\n prot_graph_dict[(line.split()[0])] = list() # Declaration of the key without value (value is a list)\\r\\n prot_graph_dict[(line.split()[0])].append((line.split())[1]) # Adds the value as second element\\r\\n\\r\\n # When the function will screen the list, it will match twice only instead of passing at each loops (not\\r\\n # mandatory but it processes faster than passing every loops)\\r\\n for elt in prot_data_list:\\r\\n if line.split()[0] == elt or line.split()[1] == elt:\\r\\n if (line.split()[0]) == elt:\\r\\n prot1_exist = True\\r\\n prot_graph_dict[(line.split()[0])].append((line.split())[1])\\r\\n if (line.split()[1]) == elt:\\r\\n prot2_exist = True\\r\\n prot_graph_dict[(line.split()[1])].append((line.split())[0])\\r\\n\\r\\n if prot2_exist == False:\\r\\n prot_data_list.append((line.split()[1]))\\r\\n prot_graph_dict[(line.split()[1])] = list()\\r\\n prot_graph_dict[(line.split()[1])].append((line.split())[0])\\r\\n\\r\\n if prot1_exist == False:\\r\\n prot_data_list.append((line.split()[0]))\\r\\n prot_graph_dict[(line.split()[0])] = list()\\r\\n prot_graph_dict[(line.split()[0])].append((line.split())[1])\\r\\n\\r\\n prot_data_list.sort() # Sorts in alphabetic order in order to find the proteins' order (the array returns the\\r\\n # number of proteins positioned as the second element only.\\r\\n lines_for_mat_dict = {} # Dictionary initialization\\r\\n prot_interaction_mat = np.empty((len(prot_data_list), len(prot_data_list)), int) # Creation of an empty array\\r\\n # which defines dimensions based on proteins lists length (number of columns, number of lines)\\r\\n\\r\\n # This loops compares the protein list from the top to the bottom. It shifts at each case of one dimension, a\\r\\n # key is declared in the dictionary for each dimension. A key is declared at each loop. At the end of the loop,\\r\\n # we retrieve each key and all values associated to a dimension.\\r\\n for i, elt in enumerate(prot_data_list):\\r\\n lines_for_mat_dict[i] = list() # Dictionary declaration which will be used to create the array, it browses\\r\\n # all the proteins list\\r\\n print(\\\"entry in comparison\\\", elt) # From 0 to 27276, declares the dictionary key\\r\\n\\r\\n for j, el in enumerate(prot_data_list): # For each line, browses all columns, from left to right\\r\\n compare_list = [prot_graph_dict[el]] # Gives a comparison by list from protein in the dictionary of\\r\\n # interaction\\r\\n for k, e in enumerate(compare_list): # compares from top to bottom, left to right by shifting case by\\r\\n # case\\r\\n if el == e:\\r\\n compare_boo = True # For each case, tests if there is a correspondence between the protein of\\r\\n # the column and the protein of the line\\r\\n break # If a correspondence is found, the loop breaks (prevents from loop running until the end\\r\\n # of the list, so if it founds a corresponding protein and it is not the last one, then it will\\r\\n # return false and won't count it\\r\\n else:\\r\\n compare_boo = False # Returns true if there is an interaction or false if there isn't\\r\\n if compare_boo == True: # Key created before is inserted in the array\\r\\n lines_for_mat_dict[i].append(1) # Adds the correspondence to the right column if it returns true\\r\\n else:\\r\\n lines_for_mat_dict[i].append(0) # Adds the protein to the left column if it returns false\\r\\n\\r\\n # prot_interact_mat is the variable name, i is the dimension where i want to add it,\\r\\n # [lines_for_mat_dict[i]] is the dictionary line with value i, axis=0 is the direction where we want to add i\\r\\n np.insert(prot_interaction_mat, i, [lines_for_mat_dict[i]], axis=0)\\r\\n\\r\\n return prot_interaction_mat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f6de02f2a94850e9fc826985ee6642f\",\n \"score\": \"0.54444426\",\n \"text\": \"def mapMatch(track, segments, decayconstantNet = 30, decayConstantEu = 10, maxDist = 50, addfullpath = True):\\n #Make sure passed in parameters are floats\\n decayconstantNet = float(decayconstantNet)\\n decayConstantEu = float(decayConstantEu)\\n maxDist= float(maxDist)\\n\\n #gest start time\\n start_time = time.time()\\n\\n #this array stores, for each point in a track, probability distributions over segments, together with the (most probable) predecessor segment taking into account a network distance\\n V = [{}]\\n\\n #get track points, build network graph (graph, endpoints, lengths) and get segment info from arcpy\\n\\n points = getTrackPoints(track, segments)\\n print(\\\"These are the points\\\")\\n # print(points)\\n # points=[]\\n\\n # point1 = arcpy.Point(51.467057004358743, 5.474460367112933)\\n # ptGeometry1 = arcpy.PointGeometry(point1)\\n\\n # point2 = arcpy.Point(51.465963331817377, 5.473848990808071)\\n # ptGeometry2 = arcpy.PointGeometry(point2)\\n # points.append(ptGeometry1)\\n # points.append(ptGeometry2)\\n\\n\\n\\n r = getSegmentInfo(segments)\\n endpoints = r[0]\\n lengths = r[1]\\n graph = getNetworkGraph(segments,lengths)\\n pathnodes = [] #set of pathnodes to prevent loops\\n\\n #init first point\\n sc = getSegmentCandidates(points[0], segments, decayConstantEu, maxDist)\\n for s in sc:\\n V[0][s] = {\\\"prob\\\": sc[s], \\\"prev\\\": None, \\\"path\\\": [], \\\"pathnodes\\\":[]}\\n # Run Viterbi when t > 0\\n for t in range(1, len(points)):\\n V.append({})\\n #Store previous segment candidates\\n lastsc = sc\\n #Get segment candidates and their a-priori probabilities (based on Euclidean distance for current point t)\\n sc = getSegmentCandidates(points[t], segments, decayConstantEu, maxDist)\\n print(\\\"Printing the sc\\\")\\n # print(sc)\\n for s in sc:\\n max_tr_prob = 0\\n prev_ss = None\\n path = []\\n for prev_s in lastsc:\\n #determine the highest network transition probability from previous candidates to s and get the corresponding network path\\n # print(\\\"this i the line\\\")\\n pathnodes = V[t-1][prev_s][\\\"pathnodes\\\"][-10:]\\n # print(pathnodes)\\n n = getNetworkTransP(prev_s, s, graph, endpoints, lengths, pathnodes, decayconstantNet)\\n np = n[0] #This is the network transition probability\\n tr_prob = V[t-1][prev_s][\\\"prob\\\"]*np\\n #this selects the most probable predecessor candidate and the path to it\\n if tr_prob > max_tr_prob:\\n max_tr_prob = tr_prob\\n prev_ss = prev_s\\n path = n[1]\\n if n[2] != None:\\n pathnodes.append(n[2])\\n #The final probability of a candidate is the product of a-priori and network transitional probability\\n max_prob = sc[s] * max_tr_prob\\n V[t][s] = {\\\"prob\\\": max_prob, \\\"prev\\\": prev_ss, \\\"path\\\": path, \\\"pathnodes\\\":pathnodes}\\n\\n #Now max standardize all p-values to prevent running out of digits\\n maxv = max(value[\\\"prob\\\"] for value in V[t].values())\\n maxv = (1 if maxv == 0 else maxv)\\n for s in V[t].keys():\\n V[t][s][\\\"prob\\\"]=V[t][s][\\\"prob\\\"]/maxv\\n # print(\\\"Printing V\\\")\\n # print(V)\\n intertime1 = time.time()\\n print(\\\"--- Viterbi forward: %s seconds ---\\\" % (intertime1 - start_time))\\n #print V\\n\\n #opt is the result: a list of (matched) segments [s1, s2, s3,...] in the exact order of the point track: [p1, p2, p3,...]\\n opt = []\\n\\n # get the highest probability at the end of the track\\n max_prob = max(value[\\\"prob\\\"] for value in V[-1].values())\\n previous = None\\n if max_prob == 0:\\n print(\\\" probabilities fall to zero (network distances in data are too large, try increasing network decay parameter)\\\")\\n\\n # Get most probable ending state and its backtrack\\n for st, data in V[-1].items():\\n if data[\\\"prob\\\"] == max_prob:\\n opt.append(st)\\n previous = st\\n break\\n## print \\\" previous: \\\"+str(previous)\\n## print \\\" max_prob: \\\"+str(max_prob)\\n## print \\\" V -1: \\\"+str(V[-1].items())\\n\\n # Follow the backtrack till the first observation to fish out most probable states and corresponding paths\\n for t in range(len(V) - 2, -1, -1):\\n #Get the subpath between last and most probable previous segment and add it to the resulting path\\n path = V[t + 1][previous][\\\"path\\\"]\\n opt[0:0] =(path if path !=None else [])\\n #Insert the previous segment\\n opt.insert(0, V[t + 1][previous][\\\"prev\\\"])\\n previous = V[t + 1][previous][\\\"prev\\\"]\\n intertime2 = time.time()\\n print(\\\"--- Viterbi backtracking: %s seconds ---\\\" % (intertime2 - intertime1))\\n\\n #Clean the path (remove double segments and crossings) (only in full path option)\\n print( \\\"path length before cleaning :\\\" +str(len(opt)))\\n opt = cleanPath(opt, endpoints)\\n intertime3 = time.time()\\n print(\\\"--- Path cleaning: %s seconds ---\\\" % (intertime3 - intertime2))\\n print( \\\"final length: \\\"+str(len(opt)))\\n pointstr= [str(g.firstPoint.X)+' '+str(g.firstPoint.Y) for g in points]\\n optstr= [str(i) for i in opt]\\n # print( 'The path for points ['+' '.join(pointstr)+'] is: ')\\n # print( '[' + ' '.join(optstr) + '] with highest probability of %s' % max_prob)\\n\\n #If only a single segment candidate should be returned for each point:\\n if addfullpath == False:\\n opt = getpointMatches(points,opt)\\n optstr= [str(i) for i in opt]\\n # print( \\\"Individual point matches: \\\"+'[' + ' '.join(optstr) + ']')\\n intertime4 = time.time()\\n print(\\\"--- Picking point matches: %s seconds ---\\\" % (intertime4 - intertime3))\\n\\n return opt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f722e61dcf88afd49c382cc5bebbbf38\",\n \"score\": \"0.54296696\",\n \"text\": \"def sample_from_strata(\\n strata, bates_param=1, latin=\\\"none\\\", matching_init=\\\"approx\\\", full_output=False\\n):\\n\\n def bipartite_match(graph, matching=None):\\n \\\"\\\"\\\"Find maximum cardinality matching of a bipartite graph (U, V, E).\\n\\n The input format is a dictionary mapping members of U to a list\\n of their neighbors in V. The output is a triple (M, A, B) where M\\n is a dictionary mapping members of V to their matches in U, A is\\n the part of the maximum independent set in U, and B is the part of\\n the MIS in V. The same object may occur in both U and V, and is\\n treated as two distinct vertices if this happens.\\n\\n Adapted from\\n https://github.com/ActiveState/code/tree/master/recipes/Python/123641_HopcroftKarp_bipartite_matching\\n Hopcroft-Karp bipartite max-cardinality matching and max independent set\\n David Eppstein, UC Irvine, 27 Apr 2002\\n\\n \\\"\\\"\\\"\\n if not matching:\\n # initialize greedy matching (redundant, but faster than full search)\\n matching = {}\\n for u in graph:\\n for v in graph[u]:\\n if v not in matching:\\n matching[v] = u\\n break\\n while True:\\n # structure residual graph into layers\\n # pred[u] gives the neighbor in the previous layer for u in U\\n # preds[v] gives a list of neighbors in the previous layer for v in V\\n # unmatched gives a list of unmatched vertices in final layer of V,\\n # and is also used as a flag value for pred[u] when u is in the first layer\\n preds = {}\\n unmatched = []\\n pred = dict([(u, unmatched) for u in graph])\\n for v in matching:\\n del pred[matching[v]]\\n layer = list(pred)\\n # repeatedly extend layering structure by another pair of layers\\n while layer and not unmatched:\\n new_layer = {}\\n for u in layer:\\n for v in graph[u]:\\n if v not in preds:\\n new_layer.setdefault(v, []).append(u)\\n layer = []\\n for v in new_layer:\\n preds[v] = new_layer[v]\\n if v in matching:\\n layer.append(matching[v])\\n pred[matching[v]] = v\\n else:\\n unmatched.append(v)\\n # did we finish layering without finding any alternating paths?\\n if not unmatched:\\n unlayered = {}\\n for u in graph:\\n for v in graph[u]:\\n if v not in preds:\\n unlayered[v] = None\\n return matching, list(pred), list(unlayered)\\n\\n # recursively search backward through layers to find alternating paths\\n # recursion returns true if found path, false otherwise\\n def recurse(v):\\n if v in preds:\\n L = preds[v]\\n del preds[v]\\n for u in L:\\n if u in pred:\\n pu = pred[u]\\n del pred[u]\\n if pu is unmatched or recurse(pu):\\n matching[v] = u\\n return 1\\n return 0\\n\\n for v in unmatched:\\n recurse(v)\\n\\n # sanity checks & initialization\\n assert len(strata) > 0\\n assert bates_param > 0\\n assert latin != \\\"matching\\\" or matching_init in (\\n \\\"approx\\\",\\n \\\"greedy-det\\\",\\n \\\"greedy-rand\\\",\\n )\\n num_points = len(strata)\\n dimension = len(strata[0][0])\\n points = np.empty((num_points, dimension))\\n rand_uni = random.uniform\\n error_indices = set()\\n if latin == \\\"none\\\":\\n for dim_index in range(dimension):\\n for j, stratum in enumerate(strata):\\n low, high = stratum[0][dim_index], stratum[1][dim_index]\\n if np.isinf(bates_param):\\n points[j][dim_index] = (low + high) * 0.5\\n elif bates_param == 1:\\n points[j][dim_index] = rand_uni(low, high)\\n else:\\n uniform_sum = sum(rand_uni(low, high) for _ in range(bates_param))\\n points[j][dim_index] = uniform_sum / bates_param\\n else:\\n bin_size = 1.0 / num_points\\n for dim_index in range(dimension):\\n\\n def cog_key(strat_idx):\\n \\\"\\\"\\\"Two times the center of gravity.\\\"\\\"\\\"\\n stratum = strata[strat_idx]\\n return stratum[0][dim_index] + stratum[1][dim_index]\\n\\n strat_indices = np.random.permutation(num_points).tolist()\\n strat_indices.sort(key=cog_key)\\n bin_indices = list(range(num_points))\\n if latin == \\\"approx\\\":\\n # strat_indices has already been sorted above\\n pass\\n elif latin == \\\"matching\\\":\\n dim_graph = {}\\n matching = {}\\n for bin_idx, strat_idx in zip(bin_indices, strat_indices):\\n stratum = strata[strat_idx]\\n low, high = stratum[0][dim_index], stratum[1][dim_index]\\n low_bin = int(low / bin_size)\\n high_bin = int(np.ceil(high / bin_size))\\n avail_bins = np.arange(low_bin, high_bin)\\n if matching_init == \\\"approx\\\" and bin_idx in avail_bins:\\n matching[bin_idx] = strat_idx\\n elif matching_init == \\\"greedy-rand\\\":\\n np.random.shuffle(avail_bins)\\n dim_graph[strat_idx] = avail_bins\\n if len(matching) < num_points:\\n matching, max_ind_set, _ = bipartite_match(dim_graph, matching)\\n assert len(max_ind_set) == 0\\n bin_indices = matching.keys()\\n strat_indices = matching.values()\\n else:\\n raise Exception(\\\"unknown latin hypercube option \\\" + str(latin))\\n for bin_idx, strat_idx in zip(bin_indices, strat_indices):\\n stratum = strata[strat_idx]\\n low, high = stratum[0][dim_index], stratum[1][dim_index]\\n low2, high2 = bin_idx * bin_size, (bin_idx + 1) * bin_size\\n low, high = max(low, low2), min(high, high2)\\n if not high >= low:\\n error_indices.add(strat_idx)\\n # revert to uniform sampling of the stratum in this dim\\n low, high = stratum[0][dim_index], stratum[1][dim_index]\\n if np.isinf(bates_param):\\n points[strat_idx][dim_index] = (low + high) * 0.5\\n elif bates_param == 1:\\n points[strat_idx][dim_index] = rand_uni(low, high)\\n else:\\n uniform_sum = sum(rand_uni(low, high) for _ in range(bates_param))\\n points[strat_idx][dim_index] = uniform_sum / bates_param\\n if full_output:\\n return points, error_indices\\n else:\\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9954a9ceaf8e1245d46c315a8e0f581\",\n \"score\": \"0.53942454\",\n \"text\": \"def get_objects(matchings, coords,snap_threshold = 30, frames_lost_lim = 20):\\n active_objs = []\\n inactive_objs = []\\n \\n # initialize with all objects found in first frame\\n for i,row in enumerate(coords[0]):\\n obj = {\\n 'current': (row[0],row[1]),\\n 'all': [], # keeps track of all positions of the object\\n 'obj_id': i, # position in coords list\\n 'fsld': 0, # frames since last detected\\n 'first_frame': 0 # frame in which object is first detected\\n }\\n obj['all'].append(obj['current']) \\n active_objs.append(obj)\\n \\n # for one set of matchings between frames - this loop will run (# frames -1) times\\n # f is the frame number\\n # frame_set is the 1 x objects numopy array of matchings from frame f to frame f+1\\n for f, frame_set in enumerate(matchings):\\n move_to_inactive = []\\n matched_in_next = [] # keeps track of which objects from next frame are already dealt with\\n \\n # first, deal with all existing objects (each loop deals with one object)\\n # o is the objecobjs[4]t index\\n # obj is the object \\n for o, obj in enumerate(active_objs):\\n matched = False\\n obj_id_next = int(frame_set[obj['obj_id']]) # get the index of object in next frame or -1 if not matched in next frame\\n # first deals with problem of reverse indexing, second verifies there is a match\\n if obj['obj_id'] != -1 and obj_id_next != -1: # object has a match\\n obj['obj_id'] = obj_id_next\\n obj['current'] = (coords[f+1][obj_id_next,0],coords[f+1][obj_id_next,1])\\n obj['all'].append(obj['current'])\\n obj['fsld'] = 0\\n \\n matched_in_next.append(obj_id_next)\\n matched = True\\n \\n else: # object does not have a certain match in next frame\\n # search for match among detatched objects\\n for j, row in enumerate(coords[f+1]):\\n if j not in matched_in_next: # not already matched to object in previous frame\\n # calculate distance to current obj\\n distance = np.sqrt((obj['current'][0]-row[0])**2 + (obj['current'][1]-row[1])**2)\\n if distance < snap_threshold: # close enough to call a match\\n obj['obj_id'] = j\\n obj['current'] = (row[0],row[1])\\n obj['all'].append(obj['current'])\\n obj['fsld'] = 0\\n \\n matched_in_next.append(j)\\n matched = True\\n break\\n # if no match at all\\n if not matched:\\n obj['obj_id'] = -1\\n obj['all'].append(obj['current']) # don't update location at all\\n obj['fsld'] += 1\\n \\n if obj['fsld'] > frames_lost_lim:\\n move_to_inactive.append(o)\\n \\n # now, deal with objects found only in second frame - each row is one object \\n for k, row in enumerate(coords[f+1]):\\n # if row was matched in previous frame, the object has already been dealt with\\n if k not in matched_in_next:\\n # create a new object\\n new_obj = {\\n 'current': (row[0],row[1]),\\n 'all': [], # keeps track of all positions of the object\\n 'obj_id': k, # position in coords list\\n 'fsld': 0, # frames since last detected\\n 'first_frame': f # frame in which object is first detected\\n }\\n new_obj['all'].append(new_obj['current']) \\n active_objs.append(new_obj)\\n \\n # lastly, move all objects in move_to_inactive\\n move_to_inactive.sort()\\n move_to_inactive.reverse()\\n for idx in move_to_inactive:\\n inactive_objs.append(active_objs[idx])\\n del active_objs[idx]\\n \\n return active_objs + inactive_objs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3dcd1c3d8e2651053fd23254674024\",\n \"score\": \"0.535804\",\n \"text\": \"def fiducialsAlignment(MF,\\n src,\\n dst):\\n\\n def get_distance(x_spots,y_spots,xe,ye,n_neighbor):\\n \\\"\\\"\\\"Measure the euclidean distance between each point of an array to its n nearest neighbor in a second array using\\n kd-tree algorithm.\\n\\n Args:\\n x_spots (list): X coordinates of the array to query (1D).\\n y_spots (list): Y coordinates of the array to query (1D).\\n xe (list): X coordinates of the array to index (1D).\\n ye (list): Y coordinates of the array to index(1D).\\n n_neighbor (int): The number of nearest neighbor to consider.\\n\\n Returns:\\n distances (list): Distances of the indexed points n nearest queried neighbors (2D).\\n\\n \\\"\\\"\\\"\\n data = list(zip(xe.ravel(), ye.ravel()))\\n tree = spatial.KDTree(data)\\n return tree.query(list(zip(x_spots.ravel(), y_spots.ravel())),n_neighbor)\\n\\n def err_func(params, preX, preY, postX, postY):\\n \\\"\\\"\\\"Error function passed in the optimizer. Transforms coordinates of target frame and returns the mean nearest neighbor\\n distance to the 1st frame fiducials.\\n\\n Args:\\n params (list): Array of coordinate transformation function:\\n [Translation in X(float), Translation in Y(float), rotation(float)] (1D).\\n preX (list): X coordinates of the 1st frame fiducials (1D).\\n preY (list): Y coordinates of the 1st frame fiducials (1D).\\n postX (list): X coordinates of the target frame fiducials (1D).\\n postY (list): Y coordinates of the target frame fiducials (1D).\\n\\n Returns:\\n mean_distances (): Mean N nearest neighbor distance to the 1st frame fiducials.\\n\\n \\\"\\\"\\\"\\n transX, transY, rot = params\\n transformed = transform(postX, postY, transX, transY, rot)\\n distances = np.array(get_distance(transformed[:, 0], transformed[:, 1], preX, preY, 1)[0])\\n return np.mean(distances)\\n\\n preX, preY = np.load(dst)\\n postX, postY = np.load(src)\\n\\n n_features = 2000\\n post_den = int(np.round(np.shape(postX)[0] / n_features))\\n postX_redu = postX[::post_den]#reduces optimizer computation time\\n #TODO --> need to evaluate impact on alignment precision\\n postY_redu = postY[::post_den]\\n # print('post features length = {}'.format(np.shape(postX_redu)))\\n\\n pre_den = int(np.round(np.shape(preX)[0] / n_features))\\n preX_redu = preX[::pre_den]\\n preY_redu = preY[::pre_den]\\n # print('pre features length = {}'.format(np.shape(preX_redu)))\\n\\n minF = basinhopping(err_func, x0=(0, 0, 0 ),\\n niter=1, T=1.0, stepsize=10,\\n minimizer_kwargs={'args': ((preX_redu, preY_redu, postX_redu, postY_redu))},\\n take_step=None, accept_test=None, callback=None, interval=50, disp=True,\\n niter_success=1)\\n # print(minF)\\n\\n # #Case of 180deg rotation:\\n # deg = 180\\n # rad = -deg / 180. * np.pi\\n # transformed = transform(postX_redu, postY_redu, 0, 0, rad)\\n #\\n # minF_rot = basinhopping(err_func, x0=((np.mean(postX) - np.mean(transformed[:, 0])), (np.mean(postY) - np.mean(transformed[:, 1])), rad),\\n # niter=1, T=1.0, stepsize=10,\\n # minimizer_kwargs={'args': ((preX_redu, preY_redu, postX_redu, postY_redu))},\\n # take_step=None, accept_test=None, callback=None, interval=50, disp=True,\\n # niter_success=1)\\n # print(minF_rot)\\n # if minF.fun < minF_rot.fun:\\n\\n transX = minF.x[0]\\n transY = minF.x[1]\\n rot = minF.x[2]\\n # scale = minF.x[3]\\n # print('rot = 0 deg')\\n # else:\\n # transX = minF_rot.x[0]\\n # transY = minF_rot.x[1]\\n # rot = minF_rot.x[2]\\n # # scale = minF.x[3]\\n # print('rot = 180 deg')\\n if dst == MF + 'Analysis/Fiducials/timelapseXYpenmarks.npy':\\n np.save(MF + 'Analysis//Fiducials/optimized_params_timelapse.npy', [transX, transY, rot])\\n else:\\n np.save(MF + 'Analysis//Fiducials/optimized_params.npy', [transX, transY, rot])\\n transformed = transform(postX, postY, transX, transY, rot)\\n plt.figure()\\n plt.scatter(transformed[:, 0], transformed[:, 1],1)\\n plt.scatter(preX, preY, 1, 'r')\\n plt.axis('equal')\\n plt.savefig(MF + 'Analysis//Fiducials/surfRegResults.png', dpi = 500)\\n plt.close('all')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b61e83c26abac3cb891bac653e668521\",\n \"score\": \"0.5316389\",\n \"text\": \"def run(self):\\n\\n print('>> Generating pose predictions using sparse-to-dense matching...')\\n output = []\\n tqdm_bar = tqdm(enumerate(self._ranks.T), total=self._ranks.shape[1],\\n unit='images', leave=True)\\n for i, rank in tqdm_bar:\\n\\n # Compute the query dense hypercolumn\\n query_image = self._dataset.data['query_image_names'][i]\\n if query_image not in self._filename_to_intrinsics:\\n continue\\n query_dense_hypercolumn, _ = self._network.compute_hypercolumn(\\n [query_image], to_cpu=False, resize=True)\\n channels, width, height = query_dense_hypercolumn.shape[1:]\\n query_dense_hypercolumn = query_dense_hypercolumn.squeeze().view(\\n (channels, -1))\\n predictions = []\\n\\n for j in rank[:self._top_N]:\\n\\n # Compute dense reference hypercolumns\\n nearest_neighbor = self._dataset.data['reference_image_names'][j]\\n local_reconstruction = \\\\\\n self._filename_to_local_reconstruction[nearest_neighbor]\\n reference_sparse_hypercolumns, cell_size = \\\\\\n self._compute_sparse_reference_hypercolumn(\\n nearest_neighbor, local_reconstruction)\\n\\n # Perform exhaustive search\\n matches_2D, mask = exhaustive_search.exhaustive_search(\\n query_dense_hypercolumn,\\n reference_sparse_hypercolumns,\\n Image.open(nearest_neighbor).size[::-1],\\n [width, height],\\n cell_size)\\n\\n # Solve PnP\\n points_2D = np.reshape(\\n matches_2D.cpu().numpy()[mask], (-1, 1, 2))\\n points_3D = np.reshape(\\n local_reconstruction.points_3D[mask], (-1, 1, 3))\\n distortion_coefficients = \\\\\\n local_reconstruction.distortion_coefficients\\n intrinsics = local_reconstruction.intrinsics\\n prediction = solve_pnp.solve_pnp(\\n points_2D=points_2D,\\n points_3D=points_3D,\\n intrinsics=intrinsics,\\n distortion_coefficients=distortion_coefficients,\\n reference_filename=nearest_neighbor,\\n reference_2D_points=local_reconstruction.points_2D[mask],\\n reference_keypoints=None)\\n\\n # If PnP failed, fall back to nearest-neighbor prediction\\n if not prediction.success:\\n prediction = self._nearest_neighbor_prediction(\\n nearest_neighbor)\\n if prediction:\\n predictions.append(prediction)\\n else:\\n predictions.append(prediction)\\n\\n if len(predictions):\\n export, best_prediction = self._choose_best_prediction(\\n predictions, query_image)\\n if self._log_images:\\n if np.ndim(np.squeeze(best_prediction.query_inliers)):\\n self._plot_inliers(\\n left_image_path=query_image,\\n right_image_path=best_prediction.reference_filename,\\n left_keypoints=keypoint_association.kpt_to_cv2(\\n best_prediction.query_inliers),\\n right_keypoints=keypoint_association.kpt_to_cv2(\\n best_prediction.reference_inliers),\\n matches=[(i, i) for i in range(best_prediction.num_inliers)],\\n title='Sparse-to-Dense Correspondences',\\n export_filename=self._dataset.output_converter(query_image))\\n\\n plot_correspondences.plot_image_retrieval(\\n left_image_path=query_image,\\n right_image_path=best_prediction.reference_filename,\\n title='Best match',\\n export_filename=self._dataset.output_converter(query_image))\\n\\n output.append(export)\\n tqdm_bar.set_description(\\n \\\"[{} inliers]\\\".format(best_prediction.num_inliers))\\n tqdm_bar.refresh()\\n\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5bb49d3e4b6b95777f9331f5340af70\",\n \"score\": \"0.53086776\",\n \"text\": \"def match_all_by_similarity(self):\\n self.prepare()\\n beacon_tags=np.unique([ds.beacon_id.item() for ds in self.all_detects])\\n\\n # Filter down to rx that actually have some pings\\n dss=[ds for ds in self.all_detects if ds.dims['index']]\\n print(f\\\"{len(dss)} rx datasets (including time resets)\\\")\\n while len(dss)>1:\\n Ndet=len(dss)\\n\\n tag_counts=[ [ (ds.tag.values==b).sum() for b in beacon_tags ]\\n for ds in dss ]\\n\\n best_match=[0,None]\\n for ds_ai in range(Ndet):\\n for ds_bi in range(ds_ai+1,Ndet):\\n # dss can contain sequential instances of the same stations,\\n # before/after a clock reset. Would like to ignore cases where station\\n # is the same. Not that simple. Only the original datasets\\n # have station.\\n # If it's not ignored here, then we risk match_sequences failing\\n # below, and then we won't know how to proceed.\\n # Instead, ignore cases where the times do not significantly overlap.\\n # Still not great...\\n common_start=max(dss[ds_ai].tnum.values[0], dss[ds_bi].tnum.values[0])\\n common_end = min(dss[ds_ai].tnum.values[-1],dss[ds_bi].tnum.values[-1])\\n t_overlap=common_end-common_start\\n # don't process if less than 10 minutes. Note that big clock shifts\\n # like an hour will screw this up, but none of this process can deal\\n # with errors that large.\\n if t_overlap<600.0: \\n continue\\n \\n a_tag_counts=tag_counts[ds_ai]\\n b_tag_counts=tag_counts[ds_bi]\\n # calculate a max possible number of matches\\n ab_tag_counts=[ min(a,b) for a,b in zip(a_tag_counts,b_tag_counts)]\\n sim=np.sum(ab_tag_counts)\\n if sim>best_match[0]:\\n best_match=[sim,(ds_ai,ds_bi)]\\n if best_match[0]==0:\\n break\\n ai,bi=best_match[1]\\n print(f\\\"Will match ai={ai} bi={bi} with best possible length of {best_match[0]}\\\")\\n ab=self.match_sequences(dss[ai], dss[bi])\\n dss=dss[:ai] + dss[ai+1:bi] + dss[bi+1:]\\n dss.append(ab)\\n\\n # Scan the remaining datasets, and hope that exactly one has merged results.\\n merged_dss=[ds for ds in dss if ds.dims.get('rx',0)>0 ]\\n if len(merged_dss)==0:\\n return None\\n elif len(merged_dss)>1:\\n # Probably an indication of a bug. It *could* be real, in which case\\n # the easiest solution is to return the one with the most total pings,\\n # but that would discard the information in the other connected components.\\n raise Exception(\\\"Multiple connected components of rx. Not ready for that\\\")\\n else:\\n ds_result=merged_dss[0]\\n self.add_beacon_list(ds_result) \\n return ds_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ad42bee895907630e371df99fd5e21\",\n \"score\": \"0.5243194\",\n \"text\": \"def matching(args, weight_data, repre_seqs, curr_file_index, raw_index, raw_data):\\n\\n print(\\\"Step 6. Matching, start matching with original data\\\", weight_data.shape)\\n\\n # calculate the distances among weighted data and representative list,\\n # find the nearest cluster for each sequence data\\n\\n distance_matrix = cdist(weight_data, repre_seqs)\\n min_index_list = np.argmin(distance_matrix, axis=1)\\n valid_min_list = distance_matrix[np.arange(len(min_index_list)), min_index_list] < args.threshold\\n\\n clu_array = np.array([min_index_list[i] if x else -1 for i, x in enumerate(valid_min_list)])\\n\\n clu_result = np.array(np.vstack([raw_index, clu_array]).T)\\n\\n # get the mismatched data with its index\\n if -1 in clu_array:\\n mismatch_index = np.where(clu_array == -1)[0] # mismatched sequence indexes\\n else:\\n mismatch_index = []\\n new_raw_index = [raw_index[i] for i in mismatch_index]\\n mismatch_data = np.array([weight_data[i] for i in mismatch_index])\\n\\n # choose whether to save the matched clusters into files, set in run.py. Although multiprocessing is applied,\\n # This costs a lot, turn it off if not needed. False by default\\n if args.save_file:\\n curr_file_index = save_matching(args, raw_data, clu_array, curr_file_index, raw_index)\\n return mismatch_index, mismatch_data, curr_file_index, new_raw_index, clu_result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e23238b3ca1914e9b964da2b9dccab\",\n \"score\": \"0.52187335\",\n \"text\": \"def createM(keypointsVideo1,keypointsVideo2,numberOfFramesVideo1,numberOfFramesVideo2):\\n\\t#X=np.array([[1, 4], [3, 4], [5, 4], [7, 4]])\\n\\t#X=np.array([[1, 2, 3], [2, 4, 5], [3, 5, 6], [4, 7, 8]])\\n\\t\\n\\t#for each in keypointsVideo2:\\n\\t#\\tprint each['frame']\\n\\t#\\tlist.append(each['kp'])\\n\\tisFirst=1\\n\\tfor eachFeature in keypointsVideo2:\\n\\t\\t#print eachFeature['desc']\\n\\t\\tif isFirst is 1:\\n\\t\\t\\tX=np.array(eachFeature['desc'])\\n\\t\\t\\tisFirst=0\\n\\t\\telse:\\n\\t\\t\\tX=np.vstack((X,np.array(eachFeature['desc'])))\\n\\t#print X\\n\\t\\t\\n\\tprint \\\"Done some work\\\"\\t\\n\\t\\n\\ttree = KDTree(X, leaf_size=2,metric='euclidean')\\n\\tmatchMatrix=[]\\n\\tfor each in keypointsVideo1:\\n\\t\\tdistance, indices = tree.query(each['desc'], k=len(X))\\n\\t\\t#print keypointsVideo2[indices[0][0]]\\n\\t\\tmatch={}\\n\\t\\tmatch['frame1']=each['frame']\\n\\t\\tmatch['frame2']=keypointsVideo2[indices[0][0]]['frame']\\n\\t\\tmatch['kp1']=each['kp']\\n\\t\\tmatch['kp2']=keypointsVideo2[indices[0][0]]['kp']\\n\\t\\tmatchMatrix.append(match)\\n\\t\\t#print \\\"Yep\\\"\\n\\t#print matchMatrix\\n\\treturn matchMatrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab06a464a7a88ac0f41ae4c412c9761e\",\n \"score\": \"0.5214311\",\n \"text\": \"def forward(self, bottom, top):\\n\\n try:\\n assert len(bottom) == self._num_anchors + 2\\n assert len(top) == 1 or len(top) == 4\\n\\n detections_data = np.array(bottom[0].data)\\n batch_detections = self._read_detections(detections_data, MATCHED_RECORD_SIZE,\\n self._translate_matched_prediction,\\n self._valid_action_ids, self._min_conf)\\n\\n centers = np.array(bottom[1].data)\\n\\n self._embeddings = []\\n for i in range(self._num_anchors):\\n self._embeddings.append(np.array(bottom[i + 2].data))\\n\\n all_candidates = []\\n total_num_pairs = 0\\n total_num_overlapped = 0\\n total_num_incorrect = 0\\n for item_id in batch_detections:\\n detections = batch_detections[item_id]\\n\\n for i, _ in enumerate(detections):\\n anchor_det = detections[i]\\n for j in range(i + 1, len(detections)):\\n ref_det = detections[j]\\n\\n # exclude same class predictions\\n if anchor_det.action == ref_det.action:\\n continue\\n\\n overlap = self._iou(anchor_det, ref_det)\\n if overlap < self._min_overlap:\\n continue\\n\\n total_num_overlapped += 1\\n\\n anchor_embed = self._embeddings[anchor_det.anchor][anchor_det.item, :,\\n anchor_det.y, anchor_det.x]\\n ref_embed = self._embeddings[ref_det.anchor][ref_det.item, :, ref_det.y, ref_det.x]\\n\\n anchor_center_distances =\\\\\\n (1.0 - np.matmul(centers, anchor_embed.reshape([-1, 1]))).reshape([-1])\\n ref_center_distances =\\\\\\n (1.0 - np.matmul(centers, ref_embed.reshape([-1, 1]))).reshape([-1])\\n\\n anchor_action = np.argmin(anchor_center_distances)\\n ref_action = np.argmin(ref_center_distances)\\n\\n # exclude well-separated predictions\\n if anchor_action != ref_action:\\n continue\\n\\n # exclude predictions with both incorrect labels\\n if anchor_action != anchor_det.action and ref_action != ref_det.action:\\n continue\\n\\n total_num_incorrect += 1\\n\\n embed_dist = 1.0 - np.sum(anchor_embed * ref_embed)\\n\\n if anchor_action != anchor_det.action:\\n loss = self._margin + anchor_center_distances[anchor_det.action] - embed_dist\\n if loss > 0.0:\\n all_candidates.append((loss, embed_dist, anchor_det, ref_det))\\n total_num_pairs += 1\\n\\n if ref_action != ref_det.action:\\n loss = self._margin + ref_center_distances[ref_det.action] - embed_dist\\n if loss > 0.0:\\n all_candidates.append((loss, embed_dist, ref_det, anchor_det))\\n total_num_pairs += 1\\n\\n if len(all_candidates) == 0:\\n self._candidates = []\\n\\n top[0].data[...] = 0.0\\n if len(top) == 4:\\n top[1].data[...] = 0.0\\n top[2].data[...] = 0.0\\n top[3].data[...] = 0.0\\n else:\\n self._candidates = all_candidates\\n\\n loss = np.sum([tup[0] for tup in self._candidates]) / float(len(self._candidates))\\n top[0].data[...] = loss\\n\\n if len(top) == 4:\\n top[1].data[...] = np.median([tup[1] for tup in self._candidates])\\n top[2].data[...] = float(len(self._candidates)) / float(total_num_pairs)\\n top[3].data[...] = float(total_num_incorrect) / float(total_num_overlapped)\\n except Exception:\\n LOG('SplitLossLayer forward pass exception: {}'.format(traceback.format_exc()))\\n exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a9e5bb0b865f03c99b3982f7cdfd797\",\n \"score\": \"0.5212836\",\n \"text\": \"def test_find_matching_pairs():\\n labelmap_0 = torch.tensor(\\n [\\n [0,0,0,0,0,0,0,0],\\n [4,4,0,0,0,0,4,4],\\n [4,3,2,2,2,2,3,4],\\n [4,2,2,2,2,2,2,4]\\n ])\\n\\n labelmap_1 = torch.tensor(\\n [\\n [1,1,1,1,0,0,0,0],\\n [1,1,1,1,2,2,2,4],\\n [4,4,4,4,2,2,2,4],\\n [4,4,4,3,2,2,2,4]\\n ])\\n labelmap_2 = torch.tensor(\\n [\\n [4,4,4,4,4,4,4,4],\\n [4,4,4,4,4,4,4,4],\\n [4,4,4,4,4,4,4,4],\\n [4,4,4,4,4,4,4,4]\\n ])\\n\\n Y = torch.stack([labelmap_0, labelmap_1, labelmap_2])\\n assert Y.shape == (3,4,8)\\n \\n a_pair_info = torch.tensor(\\n [\\n [0,1,1], # pos\\n [2,1,4], # neg\\n [1,1,7], # pos\\n [0,2,2] # neg\\n ])\\n b_pair_info = torch.tensor(\\n [\\n [2,3,7], # pos\\n [0,1,4], # neg\\n [2,3,0], # pos\\n [1,3,3] # neg\\n ])\\n pos_pair_info, neg_pair_info = find_matching_pairs(Y, a_pair_info, b_pair_info)\\n gt_pos_pair_info = torch.tensor(\\n [\\n [0, 1, 1, 2, 3, 7], # pos pairs\\n [1, 1, 7, 2, 3, 0]\\n ])\\n assert torch.allclose(pos_pair_info, gt_pos_pair_info)\\n gt_neg_pair_info = torch.tensor(\\n [\\n [2, 1, 4, 0, 1, 4], # neg pairs\\n [0, 2, 2, 1, 3, 3]\\n ])\\n assert torch.allclose(neg_pair_info, gt_neg_pair_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b89f542738eca8e62e5fbb082a938c4\",\n \"score\": \"0.52080387\",\n \"text\": \"def compute_proj_xform(matches,features1,features2,image1,image2):\\n \\n proj_xform = np.zeros((3,3))\\n threeSamplesIn1 = np.zeros((3,3))\\n threeSamplesIn2 = np.zeros((3,3))\\n rnd = 10000\\n maxVote = 0\\n for n in range(rnd):\\n correspondences = []\\n samplesIndex = random.sample(range(0, len(matches) - 1), 4)\\n tmp1 = matches[samplesIndex[0]]\\n tmp2 = matches[samplesIndex[1]]\\n tmp3 = matches[samplesIndex[2]]\\n tmp4 = matches[samplesIndex[3]]\\n x11 = features1[tmp1[0]][1]\\n y11 = features1[tmp1[0]][0]\\n x21 = features1[tmp2[0]][1]\\n y21 = features1[tmp2[0]][0]\\n x31 = features1[tmp3[0]][1]\\n y31 = features1[tmp3[0]][0]\\n x41 = features1[tmp4[0]][1]\\n y41 = features1[tmp4[0]][0]\\n x12 = features2[tmp1[1]][1]\\n y12 = features2[tmp1[1]][0]\\n x22 = features2[tmp2[1]][1]\\n y22 = features2[tmp2[1]][0]\\n x32 = features2[tmp3[1]][1]\\n y32 = features2[tmp3[1]][0]\\n x42 = features2[tmp4[1]][1]\\n y42 = features2[tmp4[1]][0]\\n correspondences.append([x11, y11, x12, y12])\\n correspondences.append([x21, y21, x22, y22])\\n correspondences.append([x31, y31, x32, y32])\\n correspondences.append([x41, y41, x42, y42])\\n aList = []\\n for corr in correspondences:\\n p1 = np.matrix([corr[0], corr[1], 1])\\n p2 = np.matrix([corr[2], corr[3], 1])\\n a2 = [0, 0, 0, -p2.item(2) * p1.item(0), -p2.item(2) * p1.item(1), -p2.item(2) * p1.item(2),\\n p2.item(1) * p1.item(0), p2.item(1) * p1.item(1), p2.item(1) * p1.item(2)]\\n a1 = [-p2.item(2) * p1.item(0), -p2.item(2) * p1.item(1), -p2.item(2) * p1.item(2), 0, 0, 0,\\n p2.item(0) * p1.item(0), p2.item(0) * p1.item(1), p2.item(0) * p1.item(2)]\\n aList.append(a1)\\n aList.append(a2)\\n A = np.matrix(aList)\\n w, v = np.linalg.eig(np.dot(A.T, A))\\n h = v.T[np.argmin(w)]\\n h = np.reshape(h, (3, 3))\\n h = (1 / h.item(8)) * h\\n count = 0\\n for pairs in matches:\\n x11Test = features1[pairs[0]][1]\\n y11Test = features1[pairs[0]][0]\\n x12Test = features2[pairs[1]][1]\\n y12Test = features2[pairs[1]][0]\\n den = h[2,0]*x11Test+h[2,1]*y11Test+h[2,2]\\n tmp1 = ((h[0,0]*x11Test+h[0,1]*y11Test+h[0,2])/den-x12Test)**2\\n tmp2 = ((h[1,0]*x11Test+h[1,1]*y11Test+h[1,2])/den-y12Test)**2\\n if(tmp1<1 and tmp2<1): count+=1\\n if (count > maxVote):\\n maxVote = count\\n proj_xform = h\\n return proj_xform\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e19a7823117d0d2474258c0e579300fe\",\n \"score\": \"0.52023745\",\n \"text\": \"def __initial_match(candidate_list: (np.ndarray, np.generic), min_pts=2, t=50, criterion='distance'):\\n # TODO group matching for non-grouped user\\n # 1 : dbscan algorithm + gps based movement vector alignment -> clear!\\n # 2 : acceleration -> let's discuss\\n assert isinstance(candidate_list, (np.ndarray, np.generic))\\n num_of_data, num_time_steps, _ = candidate_list.shape\\n X = np.array([candidate_list[i, num_time_steps - 1, :] for i in range(num_of_data)])\\n rads = np.radians(X) # [N,2]\\n # Clustering with gps-data of 1-time step.\\n # 'haversine' do clustering using distance transformed from (lat, long)\\n clusterer = hdbscan.HDBSCAN(min_cluster_size=min_pts, min_samples=2, metric='haversine')\\n labels = clusterer.fit_predict(rads)\\n print('Before trajectory clustering, labels are ', labels)\\n n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\\n groups = []\\n for ulb in range(n_clusters_):\\n groups.append([])\\n for i, lb in enumerate(labels):\\n if lb == -1:\\n continue\\n groups[lb].append(i)\\n total_n_clusters = n_clusters_\\n # Group refinement considering user's trajectory\\n for nc in range(n_clusters_):\\n group_member_mask = (labels == nc)\\n group_members = candidate_list[group_member_mask]\\n pdist = tdist.pdist(group_members.transpose([0, 2, 1]), metric=\\\"sspd\\\",type_d=\\\"spherical\\\")\\n Z = fc.linkage(pdist, method=\\\"ward\\\")\\n sub_labels = sch.fcluster(Z, t, criterion=criterion) - 1\\n unique_sub_labels = len(set(sub_labels))\\n if unique_sub_labels == 1:\\n continue\\n for ad in range(unique_sub_labels - 1):\\n groups.append([])\\n member_indices = list(compress(range(len(group_member_mask)), group_member_mask))\\n for sb in range(unique_sub_labels):\\n sub_group_mask = (sub_labels == sb)\\n sub_member_indices = list(compress(range(len(sub_group_mask)), sub_group_mask))\\n #Noise case\\n if len(sub_member_indices) == 1:\\n groups[nc].remove(member_indices[sub_member_indices[0]])\\n labels[member_indices[sub_member_indices[0]]] = -1\\n continue\\n for m in range(len(sub_member_indices)):\\n # remove from wrong group\\n groups[nc].remove(member_indices[sub_member_indices[m]])\\n # add to refined group\\n groups[total_n_clusters].append(member_indices[sub_member_indices[m]])\\n labels[member_indices[sub_member_indices[m]]] = total_n_clusters\\n total_n_clusters += 1\\n print('After trajectory clustering, labels are ', labels)\\n return groups.copy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bdc36e06c37d5d8b07e469b650b6385\",\n \"score\": \"0.52014744\",\n \"text\": \"def match_gious(threshold, truths, priors, variances, labels, loc_t, conf_t, idx):\\n # jaccard index\\n loc_t[idx] = box_utils.point_form(priors)\\n # print(\\\"truths:\\\", truths.shape)\\n overlaps = box_utils.jaccard(\\n truths,\\n box_utils.point_form(priors)\\n )\\n # (Bipartite Matching)\\n # [1,num_objects] best prior for each ground truth\\n best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)\\n # [1,num_priors] best ground truth for each prior\\n best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)\\n best_truth_idx.squeeze_(0)\\n best_truth_overlap.squeeze_(0)\\n best_prior_idx.squeeze_(1)\\n best_prior_overlap.squeeze_(1)\\n best_truth_overlap.index_fill_(0, best_prior_idx, 2) # ensure best prior\\n # TODO refactor: index best_prior_idx with long tensor\\n # ensure every gt matches with its prior of max overlap\\n for j in range(best_prior_idx.size(0)):\\n best_truth_idx[best_prior_idx[j]] = j\\n matches = truths[best_truth_idx] # Shape: [num_priors,4]\\n # print(\\\"labels.shape:\\\", labels.shape) # torch.Size([3000, 1])\\n # conf = labels[best_truth_idx] + 1 # Shape: [num_priors]\\n conf = labels[best_truth_idx]\\n conf[best_truth_overlap < threshold] = 0 # label as background\\n loc_t[idx] = matches # [num_priors,4] encoded offsets to learn\\n conf_t[idx] = conf # [num_priors] top class label for each prior\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cb58bfd85d53cec6362dc4b75850f6c\",\n \"score\": \"0.5185468\",\n \"text\": \"def images_detection(self, imgs, orig_dim_list):\\n args = self.tracker_opt\\n _CUDA = True\\n if args:\\n if args.gpus[0] < 0:\\n _CUDA = False\\n if not self.model:\\n self.load_model()\\n \\n \\n activated_starcks = []\\n refind_stracks = []\\n lost_stracks = []\\n removed_stracks = []\\n\\n ''' Step 1: Network forward, get detections & embeddings'''\\n with torch.no_grad():\\n imgs = imgs.to(args.device) if args else imgs.cuda()\\n pred = self.model(imgs)\\n\\n if len(pred) > 0:\\n dets = non_max_suppression(pred, self.confidence, self.nms_thres)\\n\\n output_stracks = []\\n for image_i in range(len(imgs)):\\n self.frame_id += 1\\n if dets[image_i] is not None:\\n det_i = scale_coords(self.img_size, dets[image_i], orig_dim_list[image_i])\\n '''Detections'''\\n detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f.numpy(), 30) for\\n (tlbrs, f) in zip(det_i[:, :5], det_i[:, -self.emb_dim:])]\\n else:\\n detections = []\\n\\n\\n ''' Add newly detected tracklets to tracked_stracks'''\\n unconfirmed = []\\n tracked_stracks = [] # type: list[STrack]\\n for track in self.tracked_stracks:\\n if not track.is_activated:\\n unconfirmed.append(track)\\n else:\\n tracked_stracks.append(track)\\n\\n ''' Step 2: First association, with embedding'''\\n strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)\\n # Predict the current location with KF\\n for strack in strack_pool:\\n strack.predict()\\n\\n dists = matching.embedding_distance(strack_pool, detections)\\n dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections)\\n matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7)\\n\\n for itracked, idet in matches:\\n track = strack_pool[itracked]\\n det = detections[idet]\\n if track.state == TrackState.Tracked:\\n track.update(detections[idet], self.frame_id)\\n activated_starcks.append(track)\\n else:\\n track.re_activate(det, self.frame_id, new_id=False)\\n refind_stracks.append(track)\\n\\n ''' Step 3: Second association, with IOU'''\\n detections = [detections[i] for i in u_detection]\\n r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state==TrackState.Tracked ]\\n dists = matching.iou_distance(r_tracked_stracks, detections)\\n matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5)\\n \\n for itracked, idet in matches:\\n track = r_tracked_stracks[itracked]\\n det = detections[idet]\\n if track.state == TrackState.Tracked:\\n track.update(det, self.frame_id)\\n activated_starcks.append(track)\\n else:\\n track.re_activate(det, self.frame_id, new_id=False)\\n refind_stracks.append(track)\\n\\n for it in u_track:\\n track = r_tracked_stracks[it]\\n if not track.state == TrackState.Lost:\\n track.mark_lost()\\n lost_stracks.append(track)\\n\\n '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''\\n detections = [detections[i] for i in u_detection]\\n dists = matching.iou_distance(unconfirmed, detections)\\n matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)\\n for itracked, idet in matches:\\n unconfirmed[itracked].update(detections[idet], self.frame_id)\\n activated_starcks.append(unconfirmed[itracked])\\n for it in u_unconfirmed:\\n track = unconfirmed[it]\\n track.mark_removed()\\n removed_stracks.append(track)\\n\\n \\\"\\\"\\\" Step 4: Init new stracks\\\"\\\"\\\"\\n for inew in u_detection:\\n track = detections[inew]\\n if track.score < self.confidence:\\n continue\\n track.activate(self.kalman_filter, self.frame_id)\\n activated_starcks.append(track)\\n\\n \\\"\\\"\\\" Step 5: Update state\\\"\\\"\\\"\\n for track in self.lost_stracks:\\n if self.frame_id - track.end_frame > self.max_time_lost:\\n track.mark_removed()\\n removed_stracks.append(track)\\n\\n self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]\\n self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)\\n self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)\\n # self.lost_stracks = [t for t in self.lost_stracks if t.state == TrackState.Lost] # type: list[STrack]\\n self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)\\n self.lost_stracks.extend(lost_stracks)\\n self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)\\n self.removed_stracks.extend(removed_stracks)\\n self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)\\n\\n if self.tracker_opt.debug:\\n logger.debug('===========Frame {}=========='.format(self.frame_id))\\n logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks]))\\n logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks]))\\n logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks]))\\n logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks]))\\n\\n # Add tracks to outputs\\n for t in self.tracked_stracks:\\n tlwh = t.tlwh\\n tid = t.track_id\\n tlbr = t.tlbr\\n ts = t.score\\n if tlwh[2] * tlwh[3] > self.tracker_opt.min_box_area:\\n res = torch.tensor([image_i, tlbr[0], tlbr[1], tlbr[2], tlbr[3], ts, tid])\\n output_stracks.append(res)\\n\\n if len(output_stracks) == 0:\\n return 0\\n \\n return torch.stack(output_stracks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd874fe1dbc69c18e457e6bf818166b\",\n \"score\": \"0.51813614\",\n \"text\": \"def motifMatchingBiopython(combinationList,pwmList,coordDict,pwmLocation,genomeList,tempLocation,fpr=0.01,pseudocounts=0.0,precision=10**4,color=\\\"black\\\"):\\n \\n # Reading PWM\\n pwmDict = dict()\\n for pwmName in pwmList: pwmDict[pwmName] = readPwmFile(pwmLocation+pwmName+\\\".pwm\\\",\\\"/\\\".join(tempLocation.split(\\\"/\\\")[:-1])+\\\"/\\\",pseudocounts)\\n\\n # Evaluating thresholds\\n pwmThresholdDict = dict()\\n for pwmName in pwmList:\\n sd = Motif.ScoreDistribution(pwmDict[pwmName],precision=precision)\\n pwmThresholdDict[pwmName] = sd.threshold_fpr(fpr)\\n\\n # Reading genome\\n genomeDict = genome.readFastaFiles(genomeList)\\n\\n # Creating chromosome list\\n chrList = constants.getChromList(reference=[coordDict])\\n # Removing chrX, chrY and chrM\\n # TODO Stop removing these chromosomes\\n #chrListT = []\\n #for e in chrList:\\n # if(e not in [\\\"chrX\\\", \\\"chrY\\\", \\\"chrM\\\"]): chrListT.append(e)\\n #chrList = chrListT\\n\\n # Evaluating bed additionals\\n if(color == \\\"green\\\"): color = \\\"0,130,0\\\"\\n elif(color == \\\"red\\\"): color = \\\"130,0,0\\\"\\n elif(color == \\\"black\\\"): color = \\\"0,0,0\\\"\\n\\n # Create combinations dictionary keys\\n combKeys = []\\n for c in combinationList:\\n for b in [\\\",\\\".join(e) for e in itertools.combinations(pwmList,c)]: combKeys.append(b)\\n\\n # Iterating on chromosomes\\n mpbsDict = dict([(e,dict()) for e in pwmDict.keys()])\\n statDict = dict([(e,[0,0]) for e in combKeys]) # Left is evidence / Right is not evidence\\n geneDict = dict([(e,[]) for e in combKeys])\\n maxDict = dict([(e,-99.0) for e in pwmDict.keys()])\\n ct=0\\n for chrName in chrList:\\n\\n # Reading genome\\n if(chrName not in genomeDict.keys()): continue\\n sequence = genomeDict[chrName]\\n\\n # Iterating on coordinate dictionary\\n for e in mpbsDict.keys(): mpbsDict[e][chrName] = []\\n for coord in coordDict[chrName]:\\n ct=ct+1\\n #print \\\"region\\\", ct\\n # Getting current sequence based on coordinates\\n currSeq = sequence[coord[0]:coord[1]]\\n\\n # Keeping track of the factors found in this coordinate\\n flagMotifs = dict([(e,False) for e in pwmDict.keys()])\\n\\n # Iterating on PWMs\\n for pwmName in pwmDict.keys():\\n pwmLen = len(pwmDict[pwmName])\\n for pos, score in pwmDict[pwmName].search_pwm(currSeq,threshold=pwmThresholdDict[pwmName]):\\n if(score > maxDict[pwmName]): maxDict[pwmName] = score\\n if(pos >= 0): mpbsDict[pwmName][chrName].append([pos+coord[0],pos+coord[0]+pwmLen,pwmName,score,\\\"+\\\",pos+coord[0],pos+coord[0]+pwmLen,color])\\n else: mpbsDict[pwmName][chrName].append([-pos+coord[0],-pos+coord[0]+pwmLen,pwmName,score,\\\"-\\\",-pos+coord[0],-pos+coord[0]+pwmLen,color])\\n flagMotifs[pwmName] = True\\n \\n # Updating statistic counts and genes\\n motifsFoundList = [k for k in pwmList if flagMotifs[k]]\\n motifsFoundKeys = []\\n motifsNotFoundKeys = [e for e in combKeys]\\n for c in combinationList:\\n for b in [\\\",\\\".join(e) for e in itertools.combinations(motifsFoundList,c)]:\\n motifsFoundKeys.append(b)\\n motifsNotFoundKeys.remove(b)\\n for k in motifsFoundKeys:\\n statDict[k][0] += 1\\n for e in coord[2].split(\\\":\\\"): geneDict[k].append(e)\\n for k in motifsNotFoundKeys:\\n statDict[k][1] += 1\\n\\n # Update scores - new scores are within [0,1000]\\n for pwmName in pwmDict.keys():\\n for chrName in mpbsDict[pwmName].keys():\\n for e in mpbsDict[pwmName][chrName]:\\n e[3] = int(1000*(e[3]-pwmThresholdDict[pwmName])/(maxDict[pwmName]-pwmThresholdDict[pwmName]))\\n\\n # Remove repetitive genes from geneList\\n for k in geneDict.keys(): geneDict[k] = list(set(geneDict[k]))\\n \\n return mpbsDict, statDict, geneDict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c232c199c57defa2a0277ca0a2f290c0\",\n \"score\": \"0.51793605\",\n \"text\": \"def generate_matches(reference_field,data_field,params):\\n referencelist = find_rivermouth_points(reference_field,params)\\n rmouthlist = find_rivermouth_points(data_field,params)\\n candidatepairs = generate_candidate_pairs(referencelist, rmouthlist)\\n conflicts, conflict_free_pairs = \\\\\\n ConflictChecker.check_pair_sets_for_conflicts(candidatepairs)\\n pairs_from_resolved_conflicts,pairs_from_unresolved_conflicts =\\\\\\n ConflictResolver.resolve_conflicts(conflicts,params)\\n conflict_free_pairs.extend(pairs_from_resolved_conflicts)\\n return conflict_free_pairs, pairs_from_unresolved_conflicts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea5de3d933eb15c10089a093f7a71e58\",\n \"score\": \"0.5161643\",\n \"text\": \"def question4():\\n\\n p = 'GGCGCGGTGGCTCACGCCTGTAAT'\\n\\n #number of mistmatches = 2, so we need to split into 3 (2+1)\\n mistmatches_allowed = 2\\n num_segments_required = mistmatches_allowed + 1\\n k_mer_size = 8\\n pattern_size = 24\\n\\n reads, qualities = readFastq('chr1.GRCh38.excerpt.fasta')\\n consolidated_read = ''.join([read for read in reads])\\n\\n index = Index(consolidated_read, k_mer_size)\\n p_segments = [ p[i*k_mer_size:i*k_mer_size+k_mer_size] for i in range(num_segments_required) ] # = ['GGCGCGGT', 'GGCTCACG', 'CCTGTAAT']\\n #print p_segments\\n\\n hits_per_segment = {} # not used\\n hit_lists = []\\n i = 0\\n index_hits = 0\\n for segment in p_segments:\\n hits = index.query(segment)\\n index_hits += 1\\n if len(hits) > 0:\\n #print segment, hits\\n hits_per_segment[segment] = hits\\n hit_lists.append(set([hit - i*k_mer_size for hit in hits])) # we keep the starting point of the pattern in our results set for easy comparison (verification means equality)\\n # and we store them in set for easy equality check by means of intersecting the sets.\\n i += 1\\n #print hits_per_segment\\n #print hit_lists\\n\\n \\n results = []\\n for i in range(len(hit_lists)):\\n # WARN: this bit here only works for numbr of mismatches = 2 as we intersect two sets for any values of mistmatches_allowed, it would lead to invalid results otherwise\\n intersect = hit_lists[i].intersection(hit_lists[(i+1)%len(hit_lists)])\\n if len(intersect) > 0 : \\n for item in intersect:\\n results.append(item)\\n print len(results)\\n return sorted(results), index_hits\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58ec4d1266f87d8bfd7b39e71822f39\",\n \"score\": \"0.5160608\",\n \"text\": \"def match_insertions(identifiedBp, maxDist, max_hit_end_dist = 200, seqList = None, filterByDist = False, filterByEndDist = False):\\n \\n # filtering by seqList allows different paramerts to be applied for matching different sets of breakpoints\\n # for example, breakpoints that matched to transposon sequences should be processed differently than \\n # breakpoints that matched insertion sequences\\n if seqList is not None:\\n # select just the breakpoints with BLAST hits from the list of sequences from seqName\\n # for example, if seqName is 'Cth_transposons.fa', just match transposons\\n transpDf = identifiedBp[identifiedBp['BLAST source'].isin(seqList)]\\n else:\\n transpDf = identifiedBp # don't filter breakpoints\\n \\n # check to see if transpDf is empty\\n if len(transpDf) == 0:\\n print('No breakpoints supplied to match_insertions. '\\n 'This will generate a runtime error when you try to merge 2 empty dataframes. '\\n 'This error can be ignored')\\n \\n # split transposons into left and right groups and only keep the relevant columns\\n # list of columns to keep\\n good_cols = ['Strain','Sample', 'Chromosome', \\n 'Hit Name', 'Seq Name', 'StartReg', \\n 'EndReg', 'readFrac', \\n 'BP distance', 'Hit end distance', 'e-value', 'BLAST source']\\n transpDfLeft = transpDf.loc[transpDf['Name'] == 'Left breakpoint', :].reindex(columns = good_cols)\\n transpDfRight = transpDf.loc[transpDf['Name'] == 'Right breakpoint', :].reindex(columns = good_cols)\\n \\n # do an inner join of left and right sets of breakpoints\\n # match all breakpoints with the same name and chromosome\\n lr = pd.merge(transpDfLeft, transpDfRight, how='inner', on=['Strain','Sample','Chromosome', 'Hit Name'], suffixes=(' L', ' R'))\\n \\n # filter by distance\\n # choose left and right breakpoint pairs based on how close they are\\n # this is useful for transposons, \\n # its only important for targetd mutations if there are multiple identical insertions in the same strain\\n if filterByDist:\\n # transposons have a small negative distance\\n lr['absDist'] = lr['EndReg R'] - lr['StartReg L']\\n lr['absDist'] = lr['absDist'].abs()\\n correctDist = (lr['absDist'] < maxDist)\\n lr = lr.loc[correctDist, :]\\n # if there are multiple matches, try to choose the best one (or at least get rid of some bad ones)\\n # select all rows that match the smallest distance\\n minDistIdx = lr.groupby('Seq Name L')['absDist'].transform(min) == lr['absDist']\\n lr = lr.loc[minDistIdx, :] \\n \\n # filter by end distance (i.e. the distance between where the breakpoint starts matching and \\n # the start of the sequence). Real insertion sequences have breakpoints that match only at the\\n # ends (i.e. Hit end distance is close to 0). \\n # This is particularly important for merodiploid insertions, where internal matches are likely\\n # This is not important for other types of mutations\\n if filterByEndDist:\\n correctRDist = (lr['Hit end distance R'].abs() < max_hit_end_dist)\\n correctLDist = (lr['Hit end distance L'].abs() < max_hit_end_dist)\\n lr = lr.loc[correctRDist & correctLDist, :]\\n # if there are several matches for a given breakpoint, choose the one with the shortest end distances\\n # allEndDist is a composite of the left and right BP distances and hit end distances\\n # a low score means that the breakpoint starts matching the target sequence close to the chromosomal junction and close to the \\n # end of the insertion sequence\\n lr['allEndDist'] = lr['BP distance L'].abs() + lr['BP distance R'].abs() + lr['Hit end distance L'].abs() + lr['Hit end distance R'].abs()\\n # select rows where 'allEndDist' is equal to the minimum 'allEndDist' value in the group\\n minAllDistIdx = lr.groupby('Seq Name L')['allEndDist'].transform(min) == lr['allEndDist']\\n lr = lr.loc[minAllDistIdx, :]\\n \\n result = lr.copy()\\n ## Clean up dataframe\\n # add new columns as necessary\\n result['readFrac'] = (result['readFrac L'] + result['readFrac R']) / 2\\n result['Type'] = 'Insertion'\\n result['Source'] = 'match_insertion_' + result['BLAST source L'].str[:5] # add first 6 letters of seq list to distinguish origins \\n \\n # rename columns\\n # for deletions, the StartReg coordinate will always be lower than the EndReg coordinate\\n # for insertions, the StartReg coordinate will be greater than the EndReg coordinate\\n result.rename(columns = {'Hit Name':'Description',\\n 'Seq Name L':'lBpId',\\n 'Seq Name R':'rBpId',\\n 'EndReg R': 'StartReg',\\n 'StartReg L':'EndReg'\\n }, inplace=True)\\n \\n \\n # fix chromosome spaces\\n result['Chromosome'] = result['Chromosome'].apply(fixChromosomeSpaces)\\n \\n # select only the desired columns for the final output\\n print('selecting columns for final output')\\n result = result.reindex(columns = ['Strain','Sample','Chromosome','StartReg','EndReg','lBpId','rBpId','readFrac','Type','Description','Source'])\\n \\n # figure out which breakpoints weren't matched\\n # make a list of the values in the lBpId and rBpId columns\\n matchedBpList = sorted(result['lBpId'].astype(int).tolist() + result['rBpId'].astype(int).tolist())\\n # match those against the original list\\n matchedBooleanSeries = identifiedBp['Seq Name'].astype(int).isin(matchedBpList)\\n # find the original breakpoints whose breakpoint IDs are not in the matchedBpList\\n remainingBp = identifiedBp.loc[~matchedBooleanSeries, :]\\n \\n return (result, remainingBp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30966cdd7569cdfb252969be9b3b49c7\",\n \"score\": \"0.5156748\",\n \"text\": \"def matchFrame(self, frame1, frame2):\\n matcher = cv2.BFMatcher(cv2.NORM_HAMMING)\\n matches = matcher.knnMatch(frame1.descriptors, frame2.descriptors, k=2)\\n ratio = 0.65\\n match_idx1, match_idx2 = [], []\\n for m1, m2 in matches:\\n if (m1.distance < ratio*m2.distance) and (m1.distance < 32):\\n if m1.queryIdx not in match_idx1 and m1.trainIdx not in match_idx2:\\n match_idx1.append(m1.queryIdx)\\n match_idx2.append(m1.trainIdx)\\n\\n # We need at least 8 matches for calculating Fundamental matrix\\n assert(len(match_idx1) >= 8)\\n assert(len(match_idx2) >= 8)\\n\\n assert(len(set(match_idx1)) == len(match_idx1))\\n assert(len(set(match_idx2)) == len(match_idx2))\\n\\n match_idx1, match_idx2 = np.asarray(match_idx1), np.asarray(match_idx2)\\n\\n frame1_pts_un = frame1.keypoints_un[match_idx1, :]\\n frame2_pts_un = frame2.keypoints_un[match_idx2, :]\\n\\n F, inlier_mask = cv2.findFundamentalMat(\\n frame1_pts_un, frame2_pts_un, cv2.RANSAC, 0.1, 0.99)\\n E = self.K.T @ F @ self.K\\n inlier_mask = inlier_mask.astype(bool).squeeze()\\n print(\\\"Matches: matches = {}, inliers = {}\\\".format(\\n len(matches), inlier_mask.shape[0]))\\n print(\\\"Fundamental matrix: \\\\n{}\\\\n\\\".format(F))\\n\\n _, R, t, _ = cv2.recoverPose(E, frame1_pts_un, frame2_pts_un, self.K)\\n pose = np.eye(4)\\n pose[0:3, 0:3] = R\\n pose[0:3, 3] = t.squeeze()\\n return pose, match_idx1[inlier_mask], match_idx2[inlier_mask]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"073dd7513139c733d2b481f13d3f4c5c\",\n \"score\": \"0.5154203\",\n \"text\": \"def find_recommendation(cls, sentiment_filename,\\n nodelist_filename,\\n direct_friend_filename,\\n edgelist_filename,\\n the_user):\\n \\n \\n NUM_MOVIES = 86\\n DEGREE_THRESHOLD = 50\\n LIKE = 0\\n DISLIKE = 1\\n\\n df_list = []\\n df2idx = {}\\n idf2idx = {}\\n \\n file_ = open(direct_friend_filename, 'r') \\n count = 0\\n for each in file_.readlines():\\n temp = each.strip()\\n df_list.append([temp, 0])\\n df2idx[temp] = count\\n count += 1\\n num_df = count \\n file_.close()\\n\\n count = 0 \\n file_ = open(nodelist_filename, 'r')\\n for each in file_.readlines():\\n user = each.strip()\\n if (user in df2idx): continue \\n idf2idx[user] = count\\n count += 1\\n num_idf = count\\n file_.close()\\n\\n # create a num_idf x num_df matrix populated w/ [0,1] in each cell\\n train_mat = [[[0,1] for col in range(num_df)] for row in range(num_idf)] \\n\\n ## fill out train_matrix\\n graph_edges = nx.read_edgelist(path=edgelist_filename, delimiter='\\\\t').edges()\\n for edge in graph_edges:\\n ## edge = (idf, df)\\n if (edge[1] in df2idx and edge[0] not in df2idx):\\n train_mat[idf2idx[edge[0]]][df2idx[edge[1]]] = [1, 0]\\n df_list[df2idx[edge[1]]][1] += 1\\n graph_edges = [] # clear memory\\n\\n ## update the row of the central user\\n tuser_idx = idf2idx[the_user]\\n num_like = 0\\n for col in range(num_df):\\n if (df_list[col][1] >= DEGREE_THRESHOLD):\\n train_mat[tuser_idx][col] = [1,0]\\n num_like += 1\\n else :\\n train_mat[tuser_idx][col] = [0,1]\\n\\n ## update prob(class = like)\\n prob_class = 2*[0]\\n prob_class[LIKE] = float(num_like)/float(num_df)\\n prob_class[DISLIKE] = 1 - prob_class[LIKE] \\n\\n ## update prob[feature index i][like/dislike][class like/dislike]\\n ## = proprotoin of likes/dislike in row i (i.e.[1,0]) given class = like/dislike\\n EPSILON = 0.0000000000000001\\n prob = [[[EPSILON,EPSILON] for i in range(2)] for row in range(num_idf)]\\n \\n num_dislike = num_df - num_like\\n CLASS_LIKE = FEATURE_LIKE = 0\\n CLASS_DIS = FEATURE_DIS = 1\\n \\n for row in range(num_idf):\\n if (row == tuser_idx): continue\\n nll = sum([1 for j in range(num_df)\\n if train_mat[tuser_idx][j][LIKE] == 1 and train_mat[row][j][LIKE] == 1])\\n prob[row][FEATURE_LIKE][CLASS_LIKE] = float(nll+1)/float(num_like+2)\\n prob[row][FEATURE_DIS][CLASS_LIKE] = float(num_like-nll+1)/float(num_like+2)\\n\\n nld = sum([1 for j in range(num_df)\\n if train_mat[tuser_idx][j][DISLIKE] == 1 and train_mat[row][j][LIKE] == 1])\\n prob[row][FEATURE_LIKE][CLASS_DIS] = float(nld+1)/float(num_dislike+2)\\n prob[row][FEATURE_DIS][CLASS_DIS] = float(num_dislike-nld+1)/float(num_dislike+2)\\n\\n train_mat = [] # clear memory\\n\\n\\n ## update the prediction_mat\\n pred_mat = [[[0,0] for col in range(NUM_MOVIES)] for row in range(num_idf)]\\n\\n sentiment = json.loads(codecs.open(sentiment_filename,\\n 'r', 'utf8', 'replace').read())\\n for status in sentiment['data']:\\n if (status['name'] in df2idx): continue\\n if (status['polarity'] >= 2):\\n pred_mat[idf2idx[status['name']]][status['no']][LIKE] = 1\\n else : pred_mat[idf2idx[status['name']]][status['no']][DISLIKE] = 1\\n\\n ## compute the probabilities\\n rec_list = {}\\n for col in range(NUM_MOVIES):\\n log_class_like = math.log(prob_class[LIKE])\\n log_class_dis = math.log(prob_class[DISLIKE])\\n for row in range(num_idf):\\n # row 2*i\\n if (pred_mat[row][col][LIKE] == 1): # 'like' is present\\n log_class_like += math.log(prob[row][FEATURE_LIKE][CLASS_LIKE])\\n log_class_dis += math.log(prob[row][FEATURE_LIKE][CLASS_DIS])\\n else :\\n log_class_like += math.log(prob[row][FEATURE_DIS][CLASS_LIKE])\\n log_class_dis += math.log(prob[row][FEATURE_DIS][CLASS_DIS])\\n \\n # row 2*i + 1\\n if (pred_mat[row][col][DISLIKE] == 1): # 'dislike' is present \\n log_class_like += math.log(prob[row][FEATURE_DIS][CLASS_LIKE])\\n log_class_dis += math.log(prob[row][FEATURE_DIS][CLASS_DIS])\\n else :\\n log_class_like += math.log(prob[row][FEATURE_LIKE][CLASS_LIKE])\\n log_class_dis += math.log(prob[row][FEATURE_LIKE][CLASS_DIS])\\n \\n if (log_class_like > log_class_dis):\\n rec_list[cls.movies[col]] = log_class_dis/log_class_like\\n\\n print \\\"Recommended List\\\"\\n for each in reversed(sorted(rec_list.iteritems(), key=operator.itemgetter(1))):\\n print \\\"%s \\\\t %0.5f\\\" % (each[0], each[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9edb3e95c32d07b05bcff6f6948f6f6\",\n \"score\": \"0.514272\",\n \"text\": \"def get_patent_patient_matrix(patient_info_df):\\n related_features = ['patient_id', 'infected_by']\\n patient_info_df = patient_info_df[related_features]\\n infection_rel = patient_info_df[~patient_info_df['infected_by'].isna()]\\n uids = []\\n vids = []\\n for i in range(infection_rel.shape[0]):\\n pid = infection_rel.iloc[i, 0]\\n # Get code of patient instead of patient_id.\\n u = infection_rel[infection_rel['patient_id'] == pid]['patient_id'].cat.codes.values[0]\\n infected_by_pids = [int(i) for i in infection_rel.iloc[i, 1].split(',')]\\n for ibpid in infected_by_pids:\\n if len(patient_info_df[patient_info_df['patient_id'] == ibpid]) != 0:\\n # Check existance of user in the dataset.\\n # Get code of patient.\\n v = patient_info_df[patient_info_df['patient_id'] == ibpid]['patient_id'].cat.codes.values[0]\\n uids.append(u)\\n vids.append(v)\\n \\n # Test the number of relationship.\\n assert len(uids) == len(vids) == 1340\\n\\n # Adjacency matrix for patient - infected by - patient relationship.\\n p_infected_by_p = sp.coo_matrix((np.ones(len(uids)), (uids, vids)),\\n shape=(patient_info_df.shape[0], patient_info_df.shape[0]))\\n return p_infected_by_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128ab8f4d552ce9a0939445d630f509f\",\n \"score\": \"0.51354086\",\n \"text\": \"def match(image1,image2):\\n im1, keypoints1, descriptors1 = ReadKeys(image1)\\n im2, keypoints2, descriptors2 = ReadKeys(image2)\\n #\\n # REPLACE THIS CODE WITH YOUR SOLUTION (ASSIGNMENT 5, QUESTION 3)\\n #\\n #Generate five random matches (for testing purposes)\\n\\n # initialize values\\n print 'Debug - Setp 1: Initilizing values'\\n matched_pairs = []\\n descriptor_lengthOne = len(descriptors1)\\n descriptor_lengthTwo = len(descriptors2)\\n threshold = 0.7\\n\\n # loop through each item in descriptors1\\n print 'Debug - Setp 2: Loop through descriptors'\\n for x in range(descriptor_lengthOne):\\n \\n # initlaize dot product array\\n dotproduct_arr = []\\n\\n # loop through each item in descriptors2\\n # print 'Debug - Setp 3: Calculating dot products'\\n for y in range (descriptor_lengthTwo):\\n\\n # compute the dot product and insert into the array\\n dotproduct = np.dot(descriptors1[x], descriptors2[y])\\n dotproduct_arr.append(dotproduct)\\n\\n # initilize arrays for angles\\n angle_arr = []\\n\\n # loop through the array of dot products we just calculated\\n # print 'Debug - Setp 4: Calculating angles'\\n for dotpos in range (len(dotproduct_arr)):\\n\\n # compute the angle theta (cos theta = a dot b over mag(a)mag(b))\\n angle_arr.append(math.acos(dotproduct_arr[dotpos]))\\n\\n # sort the angle array we calcauted above and grab the two smallest val\\n # print 'Debug - Setp 5: Sorting angle array'\\n sorted_angle_arr = sorted(angle_arr)\\n best_angle = sorted_angle_arr[0]\\n runnerup_angle = sorted_angle_arr[1]\\n\\n # compute the ratio and initalize index\\n angle_ratio = best_angle/runnerup_angle\\n index = 0\\n\\n # if the ratio is less than the threshold, we proceed\\n # print 'Debug - Setp 6: Checking threshold'\\n if (angle_ratio <= threshold):\\n \\n # calculate the index and insert pair into match_pairs and print out\\n index = angle_arr.index(best_angle)\\n matched_pairs.append([keypoints1[x], keypoints2[index]])\\n\\n print 'Debug - Setp 7: End of MP'\\n \\n # initalize values that is used in RANSAC\\n delta_orientation_between_keypoints = 20 # plus or minus (+/-)\\n delta_of_scale = 0.8\\n matched_pairs_len = len(matched_pairs)\\n ransac_all = []\\n\\n # do it 10 times\\n for i in range(10):\\n ransac_current = []\\n mp_pos = np.random.randint(matched_pairs_len)\\n random_matchpairs = matched_pairs[mp_pos]\\n\\n print 'Debug - Setp 8: Cal delata s/o'\\n # calculate change in scale and orientation\\n scale_change = abs(random_matchpairs[0][2] - random_matchpairs[1][2])\\n orientation_change = random_matchpairs[0][3] - random_matchpairs[1][3]\\n\\n # loop through all the matched pairs and check for consistancy\\n print 'Debug - Setp 9: looping mp for consistancy check'\\n for m in range(matched_pairs_len):\\n mp = matched_pairs[m]\\n orientation_change_compare = mp[0][3] - mp[1][3]\\n diff_in_angle = abs(math.degrees(orientation_change) + 360 - math.degrees(orientation_change_compare) + 360) % 180\\n\\n # if the change in angle fits our specfication and check for consistancy\\n # print 'Debug - Setp 10: angle check'\\n if (diff_in_angle <= delta_orientation_between_keypoints):\\n\\n # then we compare scale change and consistancy\\n # print 'Debug - Setp 11: cal scale'\\n scale_change_compare = abs(mp[0][2] - mp[1][2])\\n scale_change_upper = max(scale_change, scale_change_compare)\\n scale_change_lower = min(scale_change, scale_change_compare)\\n scale_with_deltaval = scale_change_upper * delta_of_scale\\n\\n # if it is consistant enough, we add it to the current array\\n # print 'Debug - Setp 12: scale check'\\n if (scale_with_deltaval <= scale_change_lower):\\n ransac_current.append(mp)\\n\\n # consistancy check failed (scale), skip this item\\n else:\\n continue;\\n # consistancy check failed (angle), skip item\\n else:\\n continue;\\n # compare the length of array and only keep the largest\\n print 'Debug - Setp 13: length check'\\n if (len(ransac_current) > len(ransac_all)):\\n ransac_all = ransac_current;\\n #\\n # END OF SECTION OF CODE TO REPLACE\\n #\\n im3 = DisplayMatches(im1, im2, ransac_all)\\n return im3\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c636a25bc0e9c84c0b6f7f14a94c65b\",\n \"score\": \"0.5135033\",\n \"text\": \"def map_identical_molecules(trj,selection_list,bead_label_list,*args,**kwargs):\\n index_list = []\\n resSeq_list = []\\n label_list = []\\n\\n internal_indices_list = [] \\n\\n if len(selection_list) != len(bead_label_list):\\n raise ValueError(\\\"Error in map_identical_molecules, must submit selection list and bead label list of same length\\\")\\n\\n for sel in selection_list:\\n internal_indices = trj.top.select(\\\"(resSeq == 1) and (%s)\\\"%sel)\\n if len(internal_indices)==0:\\n raise ValueError(\\\"Error in map_identical_molecules, selection string '%s' produced an empty list of atom indices\\\"%sel)\\n internal_indices_list.append(internal_indices)\\n\\n start_index = 0\\n resSeq = 1\\n for r in trj.top.residues:\\n for bead_idx, internal_indices in enumerate(internal_indices_list):\\n system_indices = internal_indices + start_index\\n index_list.append(system_indices) \\n resSeq_list.append(resSeq)\\n label_list.append(bead_label_list[bead_idx])\\n resSeq = resSeq+1\\n start_index = start_index + r.n_atoms\\n\\n return cg_by_index(trj, index_list, label_list, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff66018483f9ebdd27e8cf779927eeb1\",\n \"score\": \"0.5126581\",\n \"text\": \"def check_medlist_batch(variables):\\n \\n# 1. read in matching dictionary (drug->CID) - we keep all drugs in dictionary, then batch\\n# 2. read in AE profile lists (ie, panels of AE's we care about)\\n# 3. read in CID AE list- do not need to keep in memory\\n# 4. map all pt meds to CID's - let complist be a dictionary\\n# 5. parse all patients\\n# 6. spit out file\\n\\n# 1. read in drug->CID dict\\n\\n# map to CID for lookup\\n\\n matcher_dict={}\\n backmatch_dict={}\\n matcheddruglist=[]\\n \\n # format: row0=brand, row1=generic, row3=CID\\n # note we currently only match brand!\\n with gzip.open(FNAME_MED_TO_CID) as gzfile:\\n medpairs=csv.reader(gzfile,delimiter='\\\\t') \\n for row in medpairs:\\n\\n# first match generic name\\n gname=row[1].upper().split(\\\" \\\")[0]\\n if (not gname in matcher_dict) and (not gname in matcheddruglist) :\\n matcher_dict[gname]= row[3]\\n if not backmatch_dict.has_key(row[3]): #necessary to prevent duplicate backmatches\\n backmatch_dict[row[3]]=gname\\n matcheddruglist.append(gname) #this is here to prevent duplicate entries\\n# then match brand name \\n bname=row[0].upper().split(\\\" \\\")[0]\\n if (not bname in matcher_dict) and (not bname in matcheddruglist) :\\n matcher_dict[bname]= row[3]\\n # backmatch_dict[row[3]]=row[1].upper() ----> backmatch only for generics\\n matcheddruglist.append(bname) #this is here to prevent duplicate entries\\n\\n print(\\\"loaded drug match list\\\")\\n# 2. read in panels of AE's\\n\\n# aelist will be a dictionary, rather than a plain list as in single-patient version\\n #key[drug]->list of AE concept ID's\\n aedict={}\\n aedict['Psychiatric']= load_aefilelist(\\\"CNS_psychiatric.txt\\\") \\n aedict['Cognition']=load_aefilelist(\\\"CNS_cognition.txt\\\")\\n aedict['Other_neurologic']=load_aefilelist(\\\"CNS_other_neurologic.txt\\\")\\n aedict['Fall_risk']=load_aefilelist(\\\"CNS_fall_risk.txt\\\") \\n\\n print(\\\"loaded AE panels\\\")\\n# 3. read in CID AE list\\n# read in AE megafile - it's gzipped...\\n property_dict={}\\n parseddict={} # this prevents duplicate AEs per drug\\n with gzip.open(FNAME_CID_TO_AE) as gzfile:\\n drug_properties=csv.reader(gzfile,delimiter='\\\\t')\\n for row in drug_properties:\\n drugname=row[0].upper().split(\\\" \\\")[0]\\n aename=row[11]\\n if not(parseddict.has_key(drugname)): parseddict[drugname]={}\\n if not(parseddict[drugname].has_key(aename)):\\n if (row[9]==\\\"PT\\\" and not(row[5]==\\\"placebo\\\")) and not drugname=='' :\\n parseddict[drugname][aename]=1\\n # need to reassign frequencies (exact) to categories\\n #postmarketing, rare, infrequent, frequent, or exact #\\n freqnum=parse_frequency(row[6])\\n if variables['Option_1']==0 and freqnum>0: freqnum=.1 # treats any ae as freq=.1\\n \\n # next section modified from single-patient version:\\n # loop through aelist and see if the ae here is in our list. \\n # if it is, increment the drug-ae-burden-item for that aelist\\n \\n for key in aedict: # loop through AE panels\\n if aename in aedict[key]: #if ae is in a panel,\\n if property_dict.has_key(drugname):\\n if property_dict[drugname].has_key(key):\\n property_dict[drugname][key]=property_dict[drugname][key]+freqnum\\n else : property_dict[drugname][key]=freqnum\\n else : \\n property_dict[drugname]={}\\n property_dict[drugname][key]=freqnum\\n print(\\\"loaded AE list\\\") \\n#4. now read in list of patients and meds. format: pt_id drug_list (tab-separated)\\n# read by making everything into a list, pop first item from list and call it pt_id, assign remaining list to it.\\n\\n# files from Victor - column 1 (0-index) is name, col 0 is #, col 3 is high-level term\\n pt_drugdict={} \\n\\n pt_drugfile=csv.reader(open(PATIENT_FILE),delimiter=',')\\n for row in pt_drugfile:\\n rowlist=row\\n pt_id=rowlist.pop(0)\\n rowlist=[element.upper() for element in rowlist]\\n pt_drugdict[pt_id]=rowlist\\n\\n# now loop through patients - for each patient, make p450 list, use it to parse AE's\\n pt_output={}\\n pt_output_nop450={}\\n for ptname in pt_drugdict:\\n p450_inhibitors,p450_inducers,p450_substrates,multiplier=map_p450(pt_drugdict[ptname],1)\\n # match meds to cid's for AE lookup\\n matchedcid=[]\\n for med in pt_drugdict[ptname]:\\n if matcher_dict.has_key(med.upper()):\\n matchedcid.append(matcher_dict[med.upper()]) \\n \\n # loop by drug, then by ae category - make one dict with p450, another without\\n for cid in matchedcid:\\n for aecat in aedict:\\n if property_dict.has_key(cid):\\n if property_dict[cid].has_key(aecat):\\n if not pt_output.has_key(ptname): \\n pt_output[ptname]={}\\n pt_output_nop450[ptname]={}\\n if pt_output[ptname].has_key(aecat):\\n pt_output[ptname][aecat]=pt_output[ptname][aecat]+(property_dict[cid][aecat]*multiplier[backmatch_dict[cid]])\\n pt_output_nop450[ptname][aecat]=pt_output[ptname][aecat]+(property_dict[cid][aecat])\\n else : \\n pt_output[ptname][aecat]=property_dict[cid][aecat]*multiplier[backmatch_dict[cid]]\\n pt_output_nop450[ptname][aecat]=property_dict[cid][aecat]\\n print(\\\"matched patients\\\") \\n \\n# now return results \\n filename=PATIENT_FILE_OUTPUT\\n if variables['Option_1']==0: filename=filename+\\\"_nofreq\\\"\\n plink_file=file(filename+\\\".tsv\\\",\\\"w\\\")\\n plink_file.write(\\\"Pt_id\\\")\\n for aecats in aedict:\\n plink_file.write(\\\"\\\\t\\\"+aecats)\\n for pt_name in pt_output:\\n plink_file.write(\\\"\\\\n\\\"+pt_name)\\n for aecats in aedict:\\n if pt_output[pt_name].has_key(aecats) : plink_file.write(\\\"\\\\t\\\"+str(pt_output[pt_name][aecats]))\\n else : plink_file.write(\\\"\\\\t\\\"+'0')\\n plink_file.close()\\n\\n# and again with nop450 \\n plink_file=file(filename+\\\"_nop450\\\"+\\\".tsv\\\",\\\"w\\\")\\n plink_file.write(\\\"Pt_id\\\")\\n for aecats in aedict:\\n plink_file.write(\\\"\\\\t\\\"+aecats)\\n for pt_name in pt_output_nop450:\\n plink_file.write(\\\"\\\\n\\\"+pt_name)\\n for aecats in aedict:\\n if pt_output_nop450[pt_name].has_key(aecats) : plink_file.write(\\\"\\\\t\\\"+str(pt_output_nop450[pt_name][aecats]))\\n else : plink_file.write(\\\"\\\\t\\\"+'0')\\n plink_file.close()\\n \\n return {\\n 'pts_written': len(pt_output),\\n 'ae_categories': len(aedict)\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"936ee509aaa01fb7dbff95673f17dc6f\",\n \"score\": \"0.511288\",\n \"text\": \"def find_joints(heatmap, thresh):\\n joints_per_type = []\\n joints_id = 0\\n for jid in range(NUM_JOINTS):\\n joint_heatmap = heatmap[:, :, jid]\\n peaks_coord = find_peaks(joint_heatmap, thresh)\\n peaks = np.zeros((len(peaks_coord), 4)) ### x, y, score, id_peak\\n for i, peak in enumerate(peaks_coord):\\n x, y = peak\\n peak_score = joint_heatmap[y, x]\\n peaks[i, :] = (x, y, peak_score, joints_id)\\n joints_id += 1\\n joints_per_type.append(peaks)\\n return joints_per_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17dceb9324e64316783f82a6776f2a40\",\n \"score\": \"0.5111084\",\n \"text\": \"def findLRmatch( mesIn, X, nNeighMaxIn=5, stepIn=10 ):\\n HEIGHT_VIS = 700\\n N_NEIGH = nNeighMaxIn # neighbour comparison\\n\\n # pMod = man.TSNE(n_components=2, random_state=0)\\n # X = pre.StandardScaler().fit_transform( X )\\n # Xp = pMod.fit_transform(X)\\n Xp = X\\n\\n # absolute path\\n imgDir = mesIn.imgDir\\n # GT\\n gtFr = mesIn.gtFr\\n\\n # Sides\\n gtLFr = gtFr[gtFr['side']=='l'].reset_index(drop=True)\\n gtRFr = gtFr[gtFr['side']=='r'].reset_index(drop=True)\\n XpL = Xp[(gtFr['side']=='l'),:]\\n XpR = Xp[(gtFr['side']=='r'),:]\\n\\n # fit nearest neighbor model\\n nbrsMod = nei.NearestNeighbors(n_neighbors=N_NEIGH, algorithm='ball_tree').fit(XpL)\\n # find closest image fomr the right eye set\\n distVec, indDstVec = nbrsMod.kneighbors(XpR)\\n \\n # init a list of number of neighbours and correctly identified pairs based on the neighbours\\n nnLst = []\\n ratioFoundLst = []\\n # for an increasing number of neighbours\\n for curNN in range(1,nNeighMaxIn+1,stepIn):\\n foundCnt = 0\\n for i in range(len(gtRFr)):\\n # find left target image from right\\n refPiD = gtRFr.iloc[i].pID\\n lImgId = gtLFr[gtLFr['pID']==refPiD].index.values[0]\\n # check if found\\n found = (lImgId in indDstVec[i,:curNN])\\n if found:\\n foundCnt += 1\\n \\n ratioFound = (foundCnt*1.0)/len(gtRFr)\\n \\n # add to lists\\n ratioFoundLst.append(ratioFound)\\n nnLst.append(curNN)\\n # print progress\\n print (curNN, 'of', nNeighMaxIn)\\n \\n \\n \\n return (np.array(nnLst),np.array(ratioFoundLst))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb4d8232df3b9c3332e01c902274719a\",\n \"score\": \"0.5095707\",\n \"text\": \"def matches(dataset, pairs, path=_default_path):\\n match_file = ''.join([\\\"m50_\\\", str(2*pairs), \\\"_\\\", str(2*pairs), \\\"_0.txt\\\"]) \\n match_file = join(path, dataset, match_file)\\n \\n print pairs, \\\"pairs each (matching/non_matching) from\\\", match_file\\n \\n return pairings(match_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bc63afdf77b1398ad448c70cab16699\",\n \"score\": \"0.5079945\",\n \"text\": \"def matching(self, reconJ, gt_data, exeno, lowpass=True):\\n # self.fcnt += 1\\n if self.segini: # new segement/movement start\\n self.segini = False\\n # self.Ani_idx = self.aniorder[exeno][self.Ani_idx]\\n if len(self.order[self.oidx]) == 1:\\n self.gt_idx = self.order[self.oidx][0]\\n self.idxlist.append(self.gt_idx)\\n if len(self.seqlist_reg) == 0: # build sequence list\\n self.seqlist_reg = reconJ\\n # print self.seqlist_reg.shape\\n self.seqlist_reg = self.seqlist_reg.reshape(-1, 21)\\n self.seqlist = self.seqlist_reg\\n else:\\n self.seqlist_reg = np.vstack([self.seqlist_reg, reconJ])\\n self.seqlist_gf = gf(self.seqlist_reg, 3, axis=0)\\n if not lowpass:\\n self.seqlist = self.seqlist_reg\\n else:\\n self.seqlist = self.seqlist_gf\\n if not self.deflag: # Not yet decreasing\\n if np.mod(self.seqlist.shape[0]-self.presv_size-1, 10) == 0:\\n # check every 10 frames\\n if len(self.order[self.oidx]) > 1:\\n if self.seqlist.shape[0] > 1:\\n result = self.clip(self.seqlist, exeno)\\n if result != []:\\n endidx = result[0]\\n if self.seqlist[endidx, 7] < 150:\\n minidx = 2\\n else:\\n minidx = 3\\n self.gt_idx = minidx\\n self.idxlist.append(self.gt_idx)\\n self.evalstr = 'well done'\\n self.seg_update(endidx)\\n else:\\n test_data_p = self.seqlist + np.atleast_2d((gt_data[self.gt_idx][0, :]-self.seqlist[0, :]))\\n self.dist_p, _ = fastdtw(gt_data[self.gt_idx], test_data_p, self.jweight, dist=self.wt_euclidean)\\n if (self.seqlist.shape[0] == 1+self.presv_size): # new movement initail setting\\n self.dpfirst, _ = fastdtw(gt_data[self.gt_idx], test_data_p[:2], self.jweight, dist=self.wt_euclidean)\\n print('dpfirst is : %f' % self.dpfirst)\\n else:\\n print('de diff is :%f' % (self.dpfirst - self.dist_p))\\n if (self.dpfirst - self.dist_p) > self.decTh:\\n print('=========')\\n print('deflag on')\\n print('=========')\\n self.deflag = True\\n self.distp_prev = self.dist_p\\n else: # already start decreasing\\n test_data_p = self.seqlist + np.atleast_2d((gt_data[self.gt_idx][0, :] - self.seqlist[0, :]))\\n self.dist_p, path_p = fastdtw(gt_data[self.gt_idx], test_data_p, self.jweight, dist=self.wt_euclidean)\\n if self.chk_flag: # in check global min status\\n self.cnt += 1\\n if self.dist_p < self.distp_cmp: # find smaller value\\n self.cnt = 1\\n self.distp_cmp = self.dist_p\\n self.idx_cmp = self.seqlist.shape[0]\\n print(' ==== reset ====')\\n elif self.cnt == self.srchfw:\\n self.evalstr = 'Well done'\\n self.chk_flag = False\\n tgrad = 0\\n for ii in xrange(self.seqlist.shape[1]): # maybe can include jweight\\n tgrad += (np.gradient(gf(self.seqlist[:, ii], 1))**2)*self.jweight[ii]\\n tgrad = tgrad**0.5\\n endidx = np.argmin(tgrad[self.idx_cmp-self.srchbw:self.idx_cmp+self.srchfw-1])\\\\\\n + (self.idx_cmp-self.srchbw)\\n self.seg_update(endidx)\\n else:\\n if (self.dist_p - self.distp_prev) > 0: # turning point\\n print (' ============== large ====================')\\n self.distp_cmp = self.distp_prev\\n self.idx_cmp = self.seqlist.shape[0]\\n self.chk_flag = True\\n self.distp_prev = self.dist_p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c357386465d12fb5a5098e896e742508\",\n \"score\": \"0.50787693\",\n \"text\": \"def match_movies_with_wikipedia_abstracts(\\n df_movies_metadata: DataFrame,\\n df_wikipedia_abstracts: DataFrame\\n) -> DataFrame:\\n df_movies_with_wikipedia_abstracts = df_movies_metadata.join(\\n df_wikipedia_abstracts,\\n df_movies_metadata[f'{w.TITLE}_clean'] == df_wikipedia_abstracts[f'{w.TITLE}_clean'],\\n how='left'\\n ).drop(df_wikipedia_abstracts[w.TITLE]).drop(df_wikipedia_abstracts[f'{w.TITLE}_clean'])\\n\\n matches_by_movie = df_movies_with_wikipedia_abstracts \\\\\\n .groupBy(df_movies_metadata.columns) \\\\\\n .agg(f.count(w.URL).alias('count_urls'))\\n\\n df_movies_single_match = df_movies_with_wikipedia_abstracts.join(\\n matches_by_movie.filter('count_urls = 1').drop('count_urls'),\\n df_movies_metadata.columns,\\n )\\n\\n df_movies_multiple_matches = df_movies_with_wikipedia_abstracts.join(\\n matches_by_movie.filter('count_urls > 1').drop('count_urls'),\\n df_movies_metadata.columns\\n )\\n\\n df_movies_multiple_matches_best_match = find_best_link_for_movie(df_movies_multiple_matches)\\n\\n return df_movies_single_match.unionByName(df_movies_multiple_matches_best_match).select(\\n m.TITLE, m.BUDGET, m.YEAR, m.REVENUE, m.RATING, m.RATIO, m.GENRES, m.PRODUCTION_COMPANIES, w.URL, w.ABSTRACT\\n ).dropDuplicates(subset=[m.TITLE, m.BUDGET, m.YEAR, m.REVENUE, m.RATING, m.RATIO, m.GENRES, m.PRODUCTION_COMPANIES])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34f8fa566b1cc0fb6f747363084d172d\",\n \"score\": \"0.5069949\",\n \"text\": \"def forward_pass_entire_dataset(list_of_demonstrations, net_name, camera):\\n\\tnet = CNNFeatureExtractor(net_name)\\n\\t\\n\\ttotal = len(list_of_demonstrations) \\n\\ti = 1\\n\\tfor video in list_of_demonstrations:\\n\\t\\tprint \\\"-------------------- \\\" + str(i) +\\\"--out-of-\\\"+ str(total)+\\\"-----------------\\\"\\n\\t\\tPATH_TO_ANNOTATION = constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER + video + \\\"_\\\" + camera + \\\".p\\\"\\n\\t\\tPATH_TO_DATA = constants.PATH_TO_DATA + constants.NEW_FRAMES_FOLDER + video + \\\"_\\\" + camera + \\\"/\\\"\\n\\t\\tget_cnn_features_pickle_dump(net, video + \\\"_\\\" + camera, PATH_TO_DATA, PATH_TO_ANNOTATION, net_name)\\n\\t\\ti += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f7f7900cada825ca3523012c12d1b4c\",\n \"score\": \"0.5053165\",\n \"text\": \"def search_for_intermediary_tinyexon(self,similaritymatrix=None):\\n finding_new = True\\n startat = 0\\n while finding_new:\\n for pos in range(startat,len(self)):\\n sg = self.codingblockgraphs[pos]\\n prev = None\\n if pos >= 1: prev = self.codingblockgraphs[pos-1]\\n \\n # confirm that organism sets in both CBGs are identical (no missing organisms / partial CBGs)\\n if prev and not prev.organism_set().symmetric_difference(sg.organism_set()):\\n\\n # make DonorSiteCollectionGraph and AcceptorSiteCollectionGraph, NO projected sites included yet!!!\\n sg.harvest_elegiable_acceptor_sites(projected_acceptors={},prev=prev)\\n prev.harvest_elegiable_donor_sites(projected_donors={},next=sg)\\n\\n tinyexonsg = find_intermediary_codingblockgraph_with_tinyexon(\\n prev,sg,input=self.input,\\n similaritymatrix=similaritymatrix)\\n if tinyexonsg:\\n # insert into genestructure graphs at the correct position\\n self.codingblockgraphs.insert( pos, tinyexonsg[0] )\\n \\n # set startat pointer to current point in the list genestructure_graphs\\n # all previous sg's are scanned for tinyexons\\n startat = pos+1\\n break\\n else:\\n finding_new = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d0b7c6e34a418557034747435ceffdf\",\n \"score\": \"0.50507283\",\n \"text\": \"def get_dinucleotide_substitutions(ids, sequence_list, motifs):\\n\\n outputs = []\\n\\n motif_dint_count = collections.defaultdict(lambda: 0)\\n motif_dint_sub = collections.defaultdict(lambda: 0)\\n non_motif_dint_count = collections.defaultdict(lambda: 0)\\n non_motif_dint_sub = collections.defaultdict(lambda: 0)\\n\\n if len(ids):\\n for i, id in enumerate(ids):\\n gen.print_parallel_status(i, ids)\\n\\n sequences = sequence_list[id][0]\\n human = sequences[0]\\n mac = sequences[1]\\n # get predicted hits to motifs and chunk to get motifs\\n hits = sequence_overlap_indicies(human, motifs)\\n non_hits = [i for i in range(len(human)) if i not in hits]\\n chunked_hits = chunk_overlaps(hits)\\n chunked_non_hits = chunk_overlaps(non_hits)\\n # get the motif sequences\\n hit_human_motifs = [\\\"\\\".join([human[i] for i in chunk]) for chunk in chunked_hits]\\n hit_mac_motifs = [\\\"\\\".join([mac[i] for i in chunk]) for chunk in chunked_hits]\\n non_hit_human_motifs = [\\\"\\\".join([human[i] for i in chunk]) for chunk in chunked_non_hits]\\n non_hit_mac_motifs = [\\\"\\\".join([mac[i] for i in chunk]) for chunk in chunked_non_hits]\\n\\n # for each motif hit in the hit motifs, go through each position,\\n # get the dinucleotide and see whether it is different\\n for i, hit in enumerate(hit_human_motifs):\\n human_motif = hit\\n mac_motif = hit_mac_motifs[i]\\n\\n for pos in range(len(human_motif)-1):\\n if human_motif[pos] in nucleotides and mac_motif[pos] in nucleotides:\\n motif_dint_count[human_motif[pos:pos+2]] += 1\\n if human_motif[pos:pos+2] != mac_motif[pos:pos+2]:\\n motif_dint_sub[human_motif[pos:pos+2]] += 1\\n # for each non hit in the non hit motifs, go through each position,\\n # get the dinucleotide and see whether it is different\\n for i, non_hit in enumerate(non_hit_human_motifs):\\n human_motif = non_hit\\n mac_motif = non_hit_mac_motifs[i]\\n for pos in range(len(human_motif)-1):\\n if human_motif[pos] in nucleotides and mac_motif[pos] in nucleotides:\\n non_motif_dint_count[human_motif[pos:pos+2]] += 1\\n if human_motif[pos:pos+2] != mac_motif[pos:pos+2]:\\n non_motif_dint_sub[human_motif[pos:pos+2]] += 1\\n\\n #unpickle\\n motif_dint_count = unpickle(motif_dint_count)\\n motif_dint_sub = unpickle(motif_dint_sub)\\n non_motif_dint_count = unpickle(non_motif_dint_count)\\n non_motif_dint_sub = unpickle(non_motif_dint_sub)\\n output = [motif_dint_count, motif_dint_sub, non_motif_dint_count, non_motif_dint_sub]\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c1a75a2ef81c75f2c7fa8851ec5e717\",\n \"score\": \"0.5046244\",\n \"text\": \"def occurencexmatrix(repgenarray, dictpark, dictcont, outfile):\\n #GenomeID1/GenomeID2\\n #Set of all the known couples\\n #Set of couples in each sample\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d83e52cbeffd8c3d8fa549fd5a57074\",\n \"score\": \"0.50410557\",\n \"text\": \"def console_label(deduper: dedupe.api.ActiveMatching) -> None: # noqa: C901\\n\\n confirmed_matches = []\\n\\n finished = False\\n use_previous = False\\n fields = unique(field.field for field in deduper.data_model.primary_fields)\\n\\n buffer_len = 1 # Max number of previous operations\\n examples_buffer: List[Tuple[Any, Literal[\\\"match\\\", \\\"distinct\\\", \\\"uncertain\\\"]]] = []\\n uncertain_pairs: List[Any] = []\\n\\n # don't re-use items that are confirmed with a mapping\\n mapped_items = set()\\n\\n while not finished:\\n if use_previous:\\n record_pair, _ = examples_buffer.pop(0)\\n use_previous = False\\n else:\\n try:\\n if not uncertain_pairs:\\n uncertain_pairs = deduper.uncertain_pairs()\\n while True:\\n record_pair = uncertain_pairs.pop()\\n if (\\n len(\\n set([x[\\\"uri\\\"] for x in record_pair]).intersection(\\n mapped_items\\n )\\n )\\n > 0\\n ):\\n examples_buffer.insert(0, (record_pair, \\\"distinct\\\"))\\n # TODO: do i need to process these?\\n else:\\n break\\n except IndexError:\\n break\\n\\n n_match = len(deduper.training_pairs[\\\"match\\\"]) + sum(\\n label == \\\"match\\\" for _, label in examples_buffer\\n )\\n n_distinct = len(deduper.training_pairs[\\\"distinct\\\"]) + sum(\\n label == \\\"distinct\\\" for _, label in examples_buffer\\n )\\n\\n for pair in record_pair:\\n for field in fields:\\n line = \\\"%s : %s\\\" % (field, pair[field])\\n print(line, file=sys.stderr)\\n print(file=sys.stderr)\\n\\n print(\\n \\\"{0}/10 positive, {1}/10 negative\\\".format(n_match, n_distinct),\\n file=sys.stderr,\\n )\\n print(\\n Fore.YELLOW + \\\"Do these records refer to the same thing?\\\" + Style.RESET_ALL,\\n file=sys.stderr,\\n )\\n\\n valid_response = False\\n user_input = \\\"\\\"\\n while not valid_response:\\n if examples_buffer:\\n prompt = \\\"(y)es / (n)o / (u)nsure / (f)inished / (p)revious\\\"\\n valid_responses = {\\\"y\\\", \\\"n\\\", \\\"u\\\", \\\"f\\\", \\\"p\\\"}\\n else:\\n prompt = \\\"(y)es / (n)o / (u)nsure / (f)inished\\\"\\n valid_responses = {\\\"y\\\", \\\"n\\\", \\\"u\\\", \\\"f\\\"}\\n\\n print(Fore.YELLOW + prompt + Style.RESET_ALL, file=sys.stderr)\\n user_input = input()\\n if user_input in valid_responses:\\n valid_response = True\\n\\n if user_input == \\\"y\\\":\\n examples_buffer.insert(0, (record_pair, \\\"match\\\"))\\n mapped_items.add(record_pair[0][\\\"uri\\\"])\\n mapped_items.add(record_pair[1][\\\"uri\\\"])\\n confirmed_matches.append((record_pair[0][\\\"uri\\\"], record_pair[1][\\\"uri\\\"]))\\n # deduper.mark_pairs({'match': record_pair})\\n elif user_input == \\\"n\\\":\\n examples_buffer.insert(0, (record_pair, \\\"distinct\\\"))\\n elif user_input == \\\"u\\\":\\n examples_buffer.insert(0, (record_pair, \\\"uncertain\\\"))\\n elif user_input == \\\"f\\\":\\n print(Fore.GREEN + \\\"Finished labeling\\\" + Style.RESET_ALL, file=sys.stderr)\\n finished = True\\n elif user_input == \\\"p\\\":\\n use_previous = True\\n uncertain_pairs.append(record_pair)\\n\\n if len(examples_buffer) > buffer_len:\\n record_pair, label = examples_buffer.pop()\\n if label in {\\\"distinct\\\", \\\"match\\\"}:\\n\\n examples: TrainingData\\n examples = {\\\"distinct\\\": [], \\\"match\\\": []}\\n examples[label].append(record_pair) # type: ignore\\n deduper.mark_pairs(examples)\\n\\n for record_pair, label in examples_buffer:\\n if label in [\\\"distinct\\\", \\\"match\\\"]:\\n\\n examples: TrainingData\\n examples = {\\\"distinct\\\": [], \\\"match\\\": []}\\n examples[label].append(record_pair) # type: ignore\\n deduper.mark_pairs(examples)\\n return confirmed_matches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3493745fb9db9a93123a641d078d5146\",\n \"score\": \"0.50409645\",\n \"text\": \"def match(img, patlst):\\n\\t# Mida de la imatge\\n\\timg_size = (len(img[0]), len(img))\\n\\n\\t# ((int) valor de la imatge, (float) coincidence)\\n\\tbest_match = (-1, 0.0)\\n\\n\\tfor i, pattern in enumerate(patlst):\\n\\t\\tpattern = (\\\"1\\\", pattern[1])\\n\\n\\t\\tdebug(\\\"img_size \\\" + str(img_size))\\n\\t\\tpattern_size = (get_w(pattern), get_h(pattern))\\n\\t\\tdebug(\\\"pattern_size \\\" + str(pattern_size))\\n\\t\\t\\n\\t\\tif img_size[1] >= pattern_size[1]:\\n\\t\\t\\timg = tranf.scale((\\\"1\\\", img), pattern_size[1])[1]\\n\\t\\t\\timg_size = (get_w((\\\"\\\", img)), get_h((\\\"\\\", img)))\\n\\t\\t\\t\\n\\t\\telse:\\n\\t\\t\\tpattern = tranf.scale(pattern, img_size[1])[1]\\n\\t\\t\\tpattern_size = (get_w((\\\"1\\\", pattern)), get_h((\\\"1\\\", pattern)))\\n\\n\\t\\tif img_size[0] >= pattern_size[0]:\\n\\t\\t\\tscroll = img_size[0] - pattern_size[0]\\n\\t\\t\\t\\n\\t\\t\\tbig_size = img_size[0]\\n\\t\\t\\tsmall_size = pattern_size[0]\\n\\t\\t\\t\\n\\t\\t\\tbig_image = img\\n\\t\\t\\tif isinstance(pattern, tuple):\\n\\t\\t\\t\\tsmall_image = pattern[1]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tsmall_image = pattern\\t\\n\\t\\telse:\\n\\t\\t\\tscroll = pattern_size[0] - img_size[0]\\n\\n\\t\\t\\tbig_size = pattern_size[0]\\n\\t\\t\\tsmall_size = img_size[0]\\n\\t\\t\\tif isinstance(pattern, tuple):\\n\\t\\t\\t\\tbig_image = pattern[1]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbig_image = pattern\\n\\t\\t\\tsmall_image = img\\n\\n\\t\\tdebug(\\\"Scroll: \\\" + str(scroll))\\n\\t\\t\\n\\t\\t# Es desa la nova mida\\n\\t\\tdebug(\\\"pattern_size_scalated \\\" + str(pattern_size))\\n\\t\\tlast_coincidence = -1\\n\\t\\t\\n\\t\\tfor position in range(scroll+1):\\n\\t\\t\\tcroped_img = split.image_slice_vertical(big_image, position, small_size + position)\\n\\t\\t\\tcoincidence = compare_image(croped_img, small_image)\\n\\t\\t\\tif coincidence > last_coincidence:\\n\\t\\t\\t\\tlast_coincidence = coincidence\\n\\t\\t\\n\\t\\tif best_match[1] < last_coincidence:\\n\\t\\t\\tbest_match = (i, last_coincidence)\\n\\n\\tdebug(\\\"El best_match es \\\" + str(best_match))\\n\\treturn best_match[0] if best_match[1] >= 0.6 else -1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9b4c614290f9634313d81ef028cc56b\",\n \"score\": \"0.50314224\",\n \"text\": \"def match ( self , segm , tree ):\\n\\n# print ( 'comparing' , segm )\\n\\n if len(self.indx) == 0: return 0 # no matches if FSA is empty\\n\\n if len(segm) == 0: return 0 # string is empty\\n\\n lim = bound(segm) # get text limit for matching\\n\\n mtl = 0 # accumulated match length\\n mtls = 0 # saved final match length\\n\\n state = 0 # set to mandatory initial state for FSA\\n\\n stk = [ ] # for tracking possible multiple matches\\n\\n ls = self.indx[state] # for state 0!\\n ix = 0 # index into current possible transitions\\n sg = segm[:lim] # text subsegment for matching\\n# print ( 'initial sg=' , sg )\\n# print ( len(ls) , 'transitions from state 0' )\\n capd = False if len(sg) == 0 else ellyChar.isUpperCaseLetter(sg[0])\\n\\n while True: # run FSA to find all possible matches\\n# print ( 'state=' , state )\\n# print ( 'count=' , mtl , 'matched so far' )\\n# print ( 'links=' , len(ls) , 'ix=' , ix )\\n nls = len(ls) # how many links from current state\\n\\n if ix == nls: # if none, then must back up\\n if len(stk) == 0: break\\n r = stk.pop() # restore match status\\n# print ( 'pop r= [' , r[0] , r[1][0].shortcode() , ']' )\\n state = r[0] # FSA state\\n ls = r[1] # remaining links to check\\n sg = r[2] # input string\\n mtl = r[3] # total match length\\n ix = 0\\n# print ( 'pop sg=' , sg )\\n continue\\n\\n# print ( 'substring to match, sg=' , sg , 'nls=' , nls )\\n m = 0\\n while ix < nls:\\n lk = ls[ix] # get next link at current state\\n ix += 1 # and increment link index\\n# print ( '@' , state , 'lk= [' , str(lk), ']' , 'ix=' , ix )\\n# print ( 'patn=' , lk.patn )\\n po = lk.patn[0]\\n if po == '\\\\x00': # do state change without matching?\\n m = 0 # no match length\\n elif po != ellyWildcard.cEND:\\n# print ( 'po=' , po )\\n bds = ellyWildcard.match(lk.patn,sg)\\n# print ( 'bds=' , bds )\\n if bds == None: continue\\n m = bds[0] # get match length, ignore wildcard bindings\\n elif (len(sg) > 0 and\\n (ellyChar.isLetterOrDigit(sg[0]) or sg[0] == ellyChar.PRME)):\\n# print ( 'unmatched solitary $' )\\n continue\\n else:\\n# print ( 'matched solitary $, state=' , state )\\n m = 0\\n\\n# print ( 'm=' , m )\\n\\n if lk.nxts < 0: # final state?\\n if lk.nxts == -2: m = 0 # last part of match not counted\\n# print ( 'state=' , state , str(lk) )\\n# print ( 'flags=' , lk.synf , '/' , lk.semf )\\n if tree.addLiteralPhraseWithSemantics(\\n lk.catg,lk.synf,lk.semf,lk.bias,cap=capd): # make phrase\\n ml = mtl + m\\n if mtls < ml: mtls = ml\\n# print ( 'success!' , 'mtls=' , mtls )\\n tree.lastph.lens = mtls # save its length\\n# print ( 'match state=' , state , 'length=' , mtls )\\n\\n# print ( 'ix=' , ix , 'nls=' , nls )\\n if ix < nls: # any links not yet checked?\\n r = [ state , ls[ix:] , sg , mtl ]\\n# print ( 'saved r= ' , state , end=' ' )\\n# print ( [ x.shortcode() for x in ls[ix:] ] )\\n stk.append(r) # if not, save info for later continuation\\n\\n mtl += m # update match length\\n break # leave loop at this state, go to next state\\n else:\\n# print ( 'no matches' )\\n continue # all patterns exhausted for state\\n\\n ix = 0\\n sg = sg[m:] # move up in text input\\n state = lk.nxts # next state\\n if state < 0:\\n ls = [ ]\\n else:\\n ls = self.indx[state]\\n# print ( 'sg=' , sg )\\n# print ( 'state=' , state )\\n# print ( 'len(ls)=' , len(ls) )\\n\\n# print ( 'mtls=' , mtls )\\n return mtls\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67e324997aea4c7521bbea0f9f8685e8\",\n \"score\": \"0.50052834\",\n \"text\": \"def match(desc1,desc2):\\n \\n desc1 = array([d/linalg.norm(d) for d in desc1])\\n desc2 = array([d/linalg.norm(d) for d in desc2])\\n \\n dist_ratio = 0.6\\n desc1_size = desc1.shape\\n \\n matchscores = zeros((desc1_size[0],1))\\n desc2t = desc2.T #precompute matrix transpose\\n for i in range(desc1_size[0]):\\n dotprods = dot(desc1[i,:],desc2t) #vector of dot products\\n dotprods = 0.9999*dotprods\\n #inverse cosine and sort, return index for features in second image\\n indx = argsort(arccos(dotprods))\\n \\n #check if nearest neighbor has angle less than dist_ratio times 2nd\\n if arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\\n matchscores[i] = int(indx[0])\\n\\n return matchscores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cab4431b79b80069cf3860f349031d7\",\n \"score\": \"0.49845678\",\n \"text\": \"def ut_relocalization():\\n obj = SequenceManager(\\\"../../dataset/basketball/basketball_anno.mat\\\",\\n \\\"../../dataset/basketball/images\\\",\\n \\\"../../dataset/basketball/basketball_ground_truth.mat\\\",\\n \\\"../../dataset/basketball/objects_basketball.mat\\\")\\n img1 = 0\\n img2 = 390\\n\\n gray1 = obj.get_image_gray(img1)\\n gray2 = obj.get_image_gray(img2)\\n\\n pose1 = obj.get_ptz(img1)\\n pose2 = obj.get_ptz(img2)\\n\\n mask1 = obj.get_bounding_box_mask(img1)\\n mask2 = obj.get_bounding_box_mask(img2)\\n\\n camera = obj.get_camera(0)\\n\\n keyframe1 = KeyFrame(gray1, img1, camera.camera_center, camera.base_rotation,\\n camera.principal_point[0], camera.principal_point[1], pose1[0], pose1[1], pose1[2])\\n keyframe2 = KeyFrame(gray2, img2, camera.camera_center, camera.base_rotation,\\n camera.principal_point[0], camera.principal_point[1], pose2[0], pose2[1], pose2[2])\\n\\n kp1, des1 = detect_compute_sift(gray1, 100)\\n after_removed_index1 = keypoints_masking(kp1, mask1)\\n kp1 = list(np.array(kp1)[after_removed_index1])\\n des1 = des1[after_removed_index1]\\n\\n kp2, des2 = detect_compute_sift(gray2, 100)\\n after_removed_index2 = keypoints_masking(kp2, mask2)\\n kp2 = list(np.array(kp2)[after_removed_index2])\\n des2 = des2[after_removed_index2]\\n\\n keyframe1.feature_pts = kp1\\n keyframe1.feature_des = des1\\n\\n keyframe2.feature_pts = kp2\\n keyframe2.feature_des = des2\\n\\n kp1_inlier, index1, kp2_inlier, index2 = match_sift_features(kp1, des1, kp2, des2)\\n\\n cv.imshow(\\\"test\\\",\\n draw_matches(obj.get_image(img1), obj.get_image(img2), kp1_inlier, kp2_inlier))\\n cv.waitKey(0)\\n\\n map = Map()\\n\\n \\\"\\\"\\\"first frame\\\"\\\"\\\"\\n for i in range(len(kp1)):\\n theta, phi = TransFunction.from_image_to_ray(\\n obj.u, obj.v, pose1[2], pose1[0], pose1[1], kp1[i].pt[0], kp1[i].pt[1])\\n\\n map.global_ray.append(np.array([theta, phi]))\\n\\n keyframe1.landmark_index = np.array([i for i in range(len(kp1))])\\n\\n \\\"\\\"\\\"second frame\\\"\\\"\\\"\\n keyframe2.landmark_index = np.ndarray([len(kp2)], dtype=np.int32)\\n for i in range(len(kp2_inlier)):\\n keyframe2.landmark_index[index2[i]] = index1[i]\\n\\n kp2_outlier_index = list(set([i for i in range(len(des2))]) - set(index2))\\n\\n for i in range(len(kp2_outlier_index)):\\n theta, phi = TransFunction.from_image_to_ray(\\n obj.u, obj.v, pose2[2], pose2[0], pose2[1], kp2[kp2_outlier_index[i]].pt[0],\\n kp2[kp2_outlier_index[i]].pt[1])\\n map.global_ray.append(np.array([theta, phi]))\\n\\n keyframe2.landmark_index[kp2_outlier_index[i]] = len(map.global_ray) - 1\\n\\n map.keyframe_list.append(keyframe1)\\n map.keyframe_list.append(keyframe2)\\n\\n pose_test = obj.get_ptz(142)\\n\\n optimized = relocalization_camera(map=map, img=obj.get_image_gray(142), pose=np.array([20, -16, 3000]))\\n\\n print(pose_test)\\n print(optimized.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef9dec0e2cf081547efb326dcf34e95a\",\n \"score\": \"0.4971874\",\n \"text\": \"def discover_movie(movie_merge):\\n '''\\n # load data tables\\n movie = pd.read_csv(\\\"movies_metadata.csv\\\")\\n credit = pd.read_csv(\\\"credits.csv\\\")\\n keyword = pd.read_csv(\\\"keywords.csv\\\")\\n\\n # deal with NAs in movie \\n movie = movie.drop(movie.index[19730])\\n movie = movie.drop(movie.index[29502])\\n movie = movie.drop(movie.index[35585])\\n pd.to_numeric(movie['id'])\\n\\n # select a sample of data\\n movie = movie.head(5000)\\n\\n # genre columns\\n movie['genres'] = movie['genres'].fillna('[]').apply(literal_eval).apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\\n\\n # transform the data type of id column\\n keyword['id'] = keyword['id'].astype('int')\\n credit['id'] = credit['id'].astype('int')\\n movie['id'] = movie['id'].astype('int')\\n\\n # merge movie, keyword, credit\\n movie_merge = movie.merge(keyword, on='id')\\n movie_merge = movie_merge.merge(credit, on='id')\\n\\n # for the credits table:\\n # we only take directors from crew\\n # and pick the top2 actors from cast\\n movie_merge['cast'] = movie_merge['cast'].apply(literal_eval)\\n movie_merge['crew'] = movie_merge['crew'].apply(literal_eval)\\n movie_merge['keywords'] = movie_merge['keywords'].apply(literal_eval)\\n movie_merge['cast_size'] = movie_merge['cast'].apply(lambda x: len(x))\\n movie_merge['crew_size'] = movie_merge['crew'].apply(lambda x: len(x))\\n\\n # extract director from crew\\n def get_director(input):\\n \\\"\\\"\\\"read crew table\\n\\n Args:\\n param1 (dictionary): crew table\\n\\n Returns:\\n the director name \\n \\\"\\\"\\\"\\n for item in input:\\n if (item['job'] == 'Director'):\\n return item['name']\\n return np.nan\\n\\n\\n movie_merge['director'] = movie_merge['crew'].apply(get_director)\\n\\n # extract actors from cast\\n movie_merge['cast'] = movie_merge['cast'].apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\\n movie_merge['cast'] = movie_merge['cast'].apply(lambda x: x[:2] if len(x) >=2 else x)\\n\\n # deal with keywords\\n movie_merge['keywords'] = movie_merge['keywords'].apply(lambda x: [i['name'] for i in x] if isinstance(x, list) else [])\\n\\n # tranform actors into lower case\\n movie_merge['cast'] = movie_merge['cast'].apply(lambda x: [str.lower(i.replace(\\\" \\\", \\\"\\\")) for i in x])\\n\\n # transform directors into lower case\\n movie_merge['director'] = movie_merge['director'].astype('str').apply(lambda x: str.lower(x.replace(\\\" \\\", \\\"\\\")))\\n movie_merge['director'] = movie_merge['director'].apply(lambda x: [x,x, x])\\n\\n # count the frequency of each keywords\\n s = movie_merge.apply(lambda x: pd.Series(x['keywords']),axis=1).stack().reset_index(level=1, drop=True)\\n s.name = 'keyword'\\n s = s.value_counts()\\n\\n # As i am doing frequency count, the keyword with frequency as 1 should be dropped.\\n s = s[s>1]\\n\\n # we have to make sure singular and plural forms of a word can be recognized as one single word\\n stemmer = SnowballStemmer('english')\\n\\n\\n # filter keywords\\n def filter_keywords(x):\\n \\\"\\\"\\\"extract keywords\\n\\n Args:\\n param1 (String): keywords string\\n\\n Returns:\\n a list of keywords\\n \\\"\\\"\\\"\\n words = []\\n for item in x:\\n if item in s:\\n words.append(item)\\n return words\\n\\n\\n\\n movie_merge['keywords'] = movie_merge['keywords'].apply(filter_keywords)\\n movie_merge['keywords'] = movie_merge['keywords'].apply(lambda x: [stemmer.stem(i) for i in x])\\n movie_merge['keywords'] = movie_merge['keywords'].apply(lambda x: [str.lower(i.replace(\\\" \\\", \\\"\\\")) for i in x])\\n \\n movie_merge['criteria'] = movie_merge['keywords'] + movie_merge['cast'] + movie_merge['director'] + movie_merge['genres']\\n movie_merge['criteria'] = movie_merge['criteria'].apply(lambda x: ' '.join(x))\\n '''\\n count = CountVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\\n count_matrix = count.fit_transform(movie_merge['criteria'])\\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\\n\\n titles = movie_merge['title']\\n indices = pd.Series(movie_merge.index, index=movie_merge['title'])\\n\\n return (indices, cosine_sim, titles)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6dc0e6fa158e19b3e9e32d2f1d380eb7\",\n \"score\": \"0.49574938\",\n \"text\": \"def irides_position_form_video(frame):\\n \\n frame_equalized = clahe(frame)\\n gray = cv2.cvtColor(frame_equalized, cv2.COLOR_BGR2GRAY)\\n faces = detector(gray)\\n\\n for i, face in enumerate(faces):\\n landmarks = predictor(frame_equalized, face)\\n landmarks = face_utils.shape_to_np(landmarks)\\n yield irides_position(gray, landmarks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7907dacae3cade511d7d68166012db79\",\n \"score\": \"0.4954953\",\n \"text\": \"def forward(self):\\n start_time = time()\\n #self.out_pathes = self.netG(self.input_G) # coordinates encoding\\n f1,self.out_pathes1,self.path_long1 = self.netG.side_branch1 (self.input_G) # coordinates encoding\\n \\n f2,self.out_pathes2,self.path_long2 = self.netG.side_branch2 (self.input_G) # coordinates encoding\\n f3,self.out_pathes3,self.path_long3 = self.netG.side_branch3 (self.input_G) # coordinates encoding\\n self.out_pathes0 =self.netG. fuse_forward( f1,f2,f3)\\n test_time_point = time()\\n print (\\\" pair to path time [%f] \\\" % ( test_time_point - start_time))\\n self.out_pathes = [self.out_pathes0,self.out_pathes1,self.out_pathes2,self.out_pathes3]\\n\\n\\n\\n\\n #self.fake_B= rendering.layers_visualized_integer_encodeing (self.out_pathes3,Resample_size) \\n #self.fake_B_1_hot = rendering.layers_visualized_OneHot_encodeing (self.out_pathes3,Resample_size) \\n #self.fake_B = self.netG(self.real_A) # G(A)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"391c7e1b193a5743a20e9426d82f9a57\",\n \"score\": \"0.49282202\",\n \"text\": \"def query(read, kmer_dict, protein_dict, max_mismatch = 4, l = 4):\\n\\n #will throw AssertionError if we can't make >max_mismatch partitions of size l\\n '''\\n Min # of partitions needed for pigeonhole = (l*max_mismatch+1)\\n Min read length = min # partitions * 3\\n '''\\n min_rl = 3*(l*max_mismatch + 1)\\n assert len(read)>= min_rl, f'Please enter a read of length {min_rl} or higher'\\n\\n # get amino acid sequence by six frame translation\\n prot_translations = six_frame_translation(read)\\n # Result stored as a dict of tuples (id, offset)->edit_distance\\n occurrences = {}\\n seen = set()\\n\\n for protein in prot_translations:\\n for i in range(len(protein) - l + 1): # for all lmers\\n\\n # for each l-mer\\n part_off = i\\n part = protein[part_off:part_off+l]\\n\\n if part not in kmer_dict:\\n continue\\n\\n # hits is a list of [(protein_id, offset_in_protein)]\\n hits = kmer_dict[part]\\n\\n for (id, offset) in hits:\\n t = protein_dict[id]\\n\\n if len(t) == 0:\\n continue\\n\\n # left edge of T to include in DP matrix\\n lf = max(0, offset - part_off - max_mismatch)\\n\\n # right edge of T to include in DP matrix\\n rt = min(len(t), offset - part_off + len(protein) + max_mismatch)\\n\\n # check if we have seen this substring before\\n if (id, lf, rt) in seen:\\n continue\\n\\n seen.add((id, lf, rt))\\n\\n # get min edit distance, and start offset of traceback\\n start_off, min_ed = dp_edit(protein, t[lf:rt])\\n start_off += lf # get actual offset in original protein\\n\\n if min_ed <= max_mismatch:\\n occurrences[(id, start_off)] = min_ed\\n\\n return occurrences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd01556447063d790924277bd8da214e\",\n \"score\": \"0.48788846\",\n \"text\": \"def gen_bone_data():\\n for dataset in datasets:\\n for set in sets:\\n print(dataset, set)\\n data = np.load('../data/{}/{}_data_joint.npy'.format(dataset, set))\\n N, C, T, V, M = data.shape\\n fp_sp = open_memmap(\\n '../data/{}/{}_data_bone.npy'.format(dataset, set),\\n dtype='float32',\\n mode='w+',\\n shape=(N, 3, T, len(directed_edges), M))\\n\\n for edge_id, (source_node, target_node) in tqdm(enumerate(directed_edges), total=len(directed_edges)):\\n # Assign bones to be joint1 - joint2, the pairs are pre-determined and hardcoded\\n # There also happens to be 25 bones\\n fp_sp[:, :, :, edge_id, :] = data[:, :, :, source_node, :] - data[:, :, :, target_node, :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de2c84fd6bb9bbab05c5b6bd83eff75\",\n \"score\": \"0.48778585\",\n \"text\": \"def matching_ztf(self, obsGAIA, listImg, dist, gaia_sources, my_path = '/Users/melissa/Phd/',\\n ffcq = \\\"F_000762_zg_c06_q2\\\"):\\n \\n count = 0\\n fail = []\\n \\n for i in range(len(listImg)):\\n #try:\\n count += 1\\n fImg = fits.open(path[i])\\n dataImg = fImg[1].data\\n dataImgH = fImg[0].header\\n obsmjdImg = dataImgH[\\\"OBSMJD\\\"]\\n #print(obsmjdImg)\\n fieldImg = dataImgH[\\\"FIELDID\\\"] \\n filterImg = dataImgH[\\\"FILTERID\\\"]\\n \\n raImg, decImg = dataImg[\\\"ra\\\"], dataImg[\\\"dec\\\"]\\n X, Y = dataImg[\\\"xpos\\\"], dataImg[\\\"ypos\\\"]\\n fluxImg, errfluxImg = dataImg[\\\"flux\\\"], dataImg[\\\"sigflux\\\"]\\n magImg, errmagImg = dataImg['mag']+dataImgH[\\\"MAGZP\\\"], dataImg['sigmag']\\n \\n obsImg = SkyCoord(ra=raImg*u.degree, dec=decImg*u.degree)\\n idx, d2dSN, d3dSN = obsImg.match_to_catalog_sky(obsGAIA)\\n d2dSNarcsec = d2dSN.to(\\\"arcsec\\\").value\\n flagin = (d2dSNarcsec<dist)\\n d2dflag = d2dSNarcsec[flagin]\\n idx_Img = np.arange(len(idx))[flagin]\\n idx_source = idx[flagin]\\n \\n ra_sources = [gaia_sources['RA_ICRS'][i] for i in idx_source]\\n dec_sources = [gaia_sources['DE_ICRS'][i] for i in idx_source]\\n \\n for k,j,l in zip(idx_Img, ra_sources, dec_sources):\\n \\n dat_2 = {\\\"ra\\\": [raImg[k]], \\\"dec\\\": [decImg[k]], \\\"X\\\": [X[k]], \\n \\\"Y\\\": [Y[k]], \\\"flux\\\": [fluxImg[k]], \\\"err_f\\\": [errfluxImg[k]], \\n \\\"mag\\\": [magImg[k]], \\\"err_m\\\": [errmagImg[k]], \\\"filter_id\\\": filterImg, \\\"src\\\": path[i]}\\n \\n data_2 = pd.DataFrame(data=dat_2)\\n \\n path_source = my_path/\\\"ZTF/stars_info/\\\"+ffcq+\\\"/source_\\\"+str(j)+\\\"_\\\"+str(l)+\\\".csv\\\"\\n \\n test_file = pathi.exists(path_source)\\n if test_file == True:\\n data_1 = pd.read_csv(path_source)\\n data_1.drop(data_1.columns[data_1.columns.str.contains('unnamed',case = False)],axis = 1, inplace = True)\\n new = data_1.append(data_2)\\n new.to_csv(path_source)\\n print('existed', i, path_source)\\n if test_file == False:\\n print('new', i, path_source)\\n data_new = data_2\\n data_new.to_csv(path_source)\\n \\n #except:\\n # fail.append(path[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87f65f06a1bbc078812a0dec1623e31a\",\n \"score\": \"0.48695722\",\n \"text\": \"def matching(imgs, detect=sift_detector()):\\n kpts, descs = [], []\\n start = time.time()\\n for i, img in enumerate(imgs):\\n logging.debug(f\\\"Processing image #{i+1}\\\")\\n\\n kp_, des = detect(img)\\n cent = np.array([img.shape[1], img.shape[0]]) / 2\\n kpts.append(np.float32([kp.pt - cent for kp in kp_]))\\n descs.append(des)\\n logging.info(f\\\"Extracted keypoints, time: {time.time() - start}\\\")\\n\\n # match all possible pairs\\n matches, n_imgs = defaultdict(dict), len(imgs)\\n start = time.time()\\n for src in range(n_imgs):\\n for dst in range(src+1, n_imgs):\\n logging.debug(f\\\"Matching {src+1}-{dst+1}\\\")\\n match, hom = _match_hom(\\n kpts[src], kpts[dst], descs[src], descs[dst])\\n if hom is None:\\n continue\\n\\n matches[src][dst] = (match, hom)\\n matches[dst][src] = _reverse(match, hom)\\n logging.info(f\\\"Matched features, time: {time.time() - start}\\\")\\n\\n return np.array(kpts, dtype=np.object), np.array(matches, dtype=np.object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c315b0af7073ccc6025161ed235c86a\",\n \"score\": \"0.48590472\",\n \"text\": \"def infer_pose_each_part(factor_graph_list):\\n num_body_parts = len(factor_graph_list)\\n\\n # Generate the intial sample to start Gibbs Sampling\\n inferred_pose = generate_initial_pose_matrix(num_body_parts)\\n\\n # Generate nodes which will be selected for Gibbs Sampling\\n node_indices_array = np.ndarray.tolist(np.random.randint(0, 24, size=num_samples))\\n\\n # Start Gibbs Sampling over Factor Graph\\n for node_index in node_indices_array:\\n node_to_update = factor_graph_list[node_index]\\n\\n # The below variables mean, the combined distribution of the node which we \\n # sample from\\n resulting_mean = node_to_update.unary_pot_mean\\n resulting_cov = node_to_update.unary_pot_cov\\n\\n # Building a resulting distribution by combining all the distributions\\n for neighbor_index in node_to_update.neighbors:\\n # We find parameters of joint distribution of current node and neighbor\\n pairwise_mean = node_to_update.pairwise_pots_mean[neighbor_index]\\n pairwise_cov = node_to_update.pairwise_pots_cov[neighbor_index]\\n\\n # Finding value of conditioned variable (neighbor)\\n pose = get_node_val_pose_only(neighbor_index, inferred_pose)\\n\\n\\n # Conditioning on the pairwise potentials to get resulting distribution\\n cond_mean, cond_cov = condition_on_pairwise_potential(node_index, neighbor_index\\n , pairwise_mean, pairwise_cov, pose)\\n\\n resulting_mean, resulting_cov = multiply_two_multivariate_normal_distributions_pdfs(\\n cond_mean, resulting_mean, cond_cov, resulting_cov)\\n\\n # Perform Sampling on the resulting distribution\\n pose_curr_node = np.random.multivariate_normal(mean=resulting_mean, cov=resulting_cov)\\n \\n # Updating the value in the overall pose matrix\\n inferred_pose[node_to_update.node_index] = pose_curr_node\\n\\n return inferred_pose\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ccc97e854b31375e198f08d9bd16a5\",\n \"score\": \"0.48581877\",\n \"text\": \"def map_nopt(orflist, theta_contact=2, theta_dist=10, randomized=False):\\n\\n noptDF = pd.DataFrame(columns=['ORF', 'substrate', 'nopt', 'Cnopt', 'CC'])\\n counter = 0\\n\\n for i in orflist:\\n substr = structures[structures['ORF']==i]['substrate'].item()\\n current_cm = CONTMAT[i]\\n\\n # create directed graph, direction in sequence order\\n cm2 = nx.DiGraph()\\n for e in current_cm.edges():\\n e1 = e[0]\\n e2 = e[1]\\n if e1 < e2:\\n cm2.add_edge(e1,e2)\\n else:\\n cm2.add_edge(e2,e1)\\n\\n ddist_cm2 = dict( cm2.in_degree() ) \\n degin = []\\n for k in ddist_cm2.keys():\\n if ddist_cm2[k] > theta_contact:\\n degin.append(k-1) # this is a fix for python indexing!\\n\\n if substr == '-':\\n current_substr = 0\\n elif substr == '+':\\n current_substr = 1\\n\\n if i in rna_seqs.keys():\\n rna_seq = rna_seqs[i].seq\\n L = int( np.floor( len(rna_seq) / 3. ) )\\n profile = np.zeros(( L ))\\n\\n for pos in range(L):\\n start = 3*int(pos)\\n end = start + 3\\n current_codon = rna_seq[start:end]\\n if randomized: \\n current_codon = rand_syn(current_codon)\\n if current_codon in nopt:\\n profile[pos] = 1\\n\\n # check for cluster,w=window_size, theta=threshold\\n w = 4\\n theta = 3\\n cluster_profile = np.zeros(( L ))\\n for pos in range(L-w):\\n current_window = profile[pos:(pos+w)]\\n if np.sum(current_window) >= theta:\\n cluster_profile[pos] = 1\\n\\n CC = 0 \\n current_contacts = np.array(degin)\\n current_clusters = np.where(cluster_profile == 1)[0]\\n for pos in current_clusters:\\n current_dists = ( current_contacts - 35 ) - pos\\n current_dists = current_dists[current_dists >= 0]\\n if any( current_dists < theta_dist ):\\n CC += 1\\n \\n noptDF.loc[len(noptDF)] = (i, current_substr, np.sum(profile)/len(profile), np.sum(cluster_profile)/len(profile), CC )\\n\\n\\n return noptDF\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f340dbcf784b5aa736f7d73f6d311a08\",\n \"score\": \"0.48565173\",\n \"text\": \"def FindJaccardMatches_NoOvercountBigrams(self, text1,text2,\\n name1='Text1',name2='Text2'):\\n shinMatches=[]\\n for t1Sent in text1:\\n text1shins=MakeShingles(t1Sent,self.shingleSize,goodTags=self.goodTags,\\n stops=self.stops)\\n for t2Sent in text2:\\n text2shins=MakeShingles(t2Sent,self.shingleSize,\\n goodTags=self.goodTags,stops=self.stops)\\n compared=MatchShinglesNoOvercountBigrams(text1shins,\\n text2shins,bigrams=bgs,\\n thresh=self.matchThreshold)\\n \\n if compared[0]==True:\\n \\n copywords=[[w for w,t,l in t1Sent if l in compared[1]],\\n [w for w,t,l in t2Sent if l in compared[1]]]\\n \\n shinMatches.append(([w for w,t,l in t1Sent],\\n [w for w,t,l in t2Sent],copywords))\\n \\n return(MatcherOutput(shinMatches,name1,name2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccfc656049536173b7358cbcc910631d\",\n \"score\": \"0.4850419\",\n \"text\": \"def solution(dimensions, your_position, guard_position, distance):\\n #1. Initialize variables\\n origin, dims, your_pos, guard_pos, grid_dims = initialize_variables(dimensions, your_position, guard_position, distance)\\n\\n #2. Create the grid of mirrored landscapes with dims equal to (1 + grid_dims.x*2, 1 + grid_dims.y*2)\\n x_landscape = deque()\\n x_landscape.append(Landscape(Person(False, your_pos, your_pos), Person(True, guard_pos, your_pos), origin, dims))\\n\\n for i in range(grid_dims.x): \\n ls = x_landscape[-1].reflect('east', original_dims=dims, shooter=your_pos)\\n x_landscape.append(ls)\\n\\n for i in range(grid_dims.x):\\n ls = x_landscape[0].reflect('west', original_dims=dims, shooter=your_pos)\\n x_landscape.appendleft(ls)\\n \\n grid = deque()\\n grid.append(list(x_landscape))\\n\\n for i in range(grid_dims.y):\\n grid.append(map(lambda ls: ls.reflect('north', original_dims=dims, shooter=your_pos), grid[-1]))\\n \\n for i in range(grid_dims.y):\\n grid.appendleft(map(lambda ls: ls.reflect('south', original_dims=dims, shooter=your_pos), grid[0]))\\n\\n grid = [landscape for x_landscape in list(grid) for landscape in x_landscape] #flatten to 1D list\\n\\n #3. Extract the People from the landscapes\\n people = [landscape.guard for landscape in grid] + [landscape.you for landscape in grid]\\n\\n #4. Filter People by distance and sort by distance\\n people = filter(lambda p: p.in_range(distance), people)\\n people.sort(key=lambda p: p.dist)\\n\\n #5. Count valid shots\\n shots = 0\\n invalid_bearings = set()\\n for person in people:\\n if person.is_enemy and person.bear not in invalid_bearings:\\n shots += 1\\n invalid_bearings.add(person.bear)\\n\\n return shots\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f7e07a79233181890966b823c1d9e43\",\n \"score\": \"0.48479083\",\n \"text\": \"def make_adj_matrix1(group) :\\n n = len(group)\\n adj_matrix = []\\n for i in xrange(n):\\n adj_matrix.append([])\\n for j in xrange(n):\\n adj_matrix[i].append(0)\\n\\n #For each abstract in extracted abstract set:\\n for z in xrange(len(abstracts)) :\\n for i in xrange(n) :\\n if abstracts[z].AbstractText.rfind( group[i].name ) != -1 :\\n group[i].Freq += 1\\n group[i].abstract_indexes.append(abstracts[z].id)\\n for j in xrange(n) :\\n if ( abstracts[z].AbstractText.rfind( group[j].name ) != -1 ) and (i != j) :\\n adj_matrix[i][j]+=1\\n group[j].Freq += 1\\n #continue\\n\\n return adj_matrix #, GroupA, GroupB\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09a6c3988e9017a4694f21fc39690d0b\",\n \"score\": \"0.4842541\",\n \"text\": \"def get_activity_cliffs(DataFrame, augmentation_factor, dataset_name = None):\\n \\n paired = pair(DataFrame, 1)\\n paired.columns = ['SMILES_1', 'SMILES_2','similarity']\\n paired.columns = ['SMILES_1', 'SMILES_2','similarity']\\n paired = paired.merge(DataFrame, left_on = 'SMILES_1', right_on = 'SMILES', how = 'left')\\n paired = paired.merge(DataFrame, left_on = 'SMILES_2', right_on = 'SMILES', how = 'left')\\n paired['potency_difference'] = abs(paired['pXC50_x'] - paired['pXC50_y'])\\n \\n pool = mp.Pool(processes = 6)\\n paired['fp_1'] = [pool.apply(calculate_circular_fps, args=(x,)) for x in paired['SMILES_1']]\\n paired['fp_2'] = [pool.apply(calculate_circular_fps, args=(x,)) for x in paired['SMILES_2']]\\n paired['Tanimoto'] = paired.apply(lambda x: calculate_bit_similarity(x.fp_1, x.fp_2), axis = 1)\\n paired.drop(columns = ['SMILES_x','Activity_x', 'SMILES_y', 'Activity_y'], inplace = True)\\n paired.columns = ['SMILES_1','SMILES_2','similarity','pXC50_1','pXC50_2','potency_difference','fp_1','fp_2','Tanimoto']\\n \\n paired.to_csv('/projects/../../PythonNotebooks/density_scatter/datasets/' + str(dataset_name) + '.csv')\\n \\n return paired\",\n \"title\": \"\"\n },\n {\n \"docid\": \"993968a266097d43755cd7201538861e\",\n \"score\": \"0.4836572\",\n \"text\": \"def labeld_unlabeled_similarities(*, sim_thereshold, \\n x_unlabeled, X_unlabeled, X_labeled, y): \\n sim_dict = {}\\n timenow = time()\\n for text, features in zip(x_unlabeled, X_unlabeled):\\n sim_dict[text] = [(y[i], jaccard_sim(obs, features)) for i, obs in enumerate(X_labeled)]\\n\\n print('Calculating all Jaccard similarities took {:.2f} seconds'.format(time() - timenow))\\n \\n # filtering out entries with similarity below threshold\\n timenow = time()\\n sim_dict_filtered = {}\\n for k, v in sim_dict.items():\\n sim_dict_filtered[k] = [(label, sim) for (label, sim) in v if sim >= sim_thereshold]\\n # remove labels that don't meet the criteria\\n if sim_dict_filtered[k] == []: del sim_dict_filtered[k] \\n print('Removing entries with Jaccard sim lower than the threshold took {:.2f} seconds'.\\n format(time() - timenow))\\n \\n # return only the top result\\n sim_result_list = []\\n for k, v in sim_dict_filtered.items():\\n for label, sim in v:\\n # keep the entry with the highest similarity\\n top_sim = 0\\n if sim > top_sim:\\n top_label, top_sim = label, sim\\n sim_result_list.append([k, top_label, top_sim])\\n \\n return sim_result_list, sim_dict_filtered, sim_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7381551231eb17c62d358a5a10ee9d8c\",\n \"score\": \"0.48355132\",\n \"text\": \"def frame_processing(frame):\\n bread = cv2.imread('bread.jpg') \\n doge = cv2.imread('doge.png') \\n\\n frame = imutils.resize(frame, width=min(2256, frame.shape[1])) \\n\\n #detect regions with faces\\n faces = face_cascade.detectMultiScale(frame, \\n scaleFactor=1.1, \\n minSize=(200,200), \\n maxSize=(1500,1500),\\n minNeighbors=1\\n )\\n\\n #detect regions with walkers\\n (walkers, _) = hog.detectMultiScale(frame, \\n\\t\\t\\t\\t\\twinStride=(4, 4), \\n padding=(64, 64), \\n scale=1.20,\\n finalThreshold=0.6,\\n hitThreshold=0.5\\n ) \\n\\n #replace faces with doge face \\n for (x, y, w, h) in faces:\\n frame[y : y + h, x : x + w] = cv2.resize(doge, (w, h))\\n\\n #replace pedestrians with doge bread\\n for (x, y, w, h) in walkers:\\n frame[y : y + h, x : x + w] = cv2.resize(bread, (w, h))\\n\\n return frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d19ea0a4fac8c9186b8fa22ed36beb0\",\n \"score\": \"0.4834399\",\n \"text\": \"def test_match(fname):\\n\\t# global options\\n\\topts = {'daophot': {'r_psf': 20, 'r_fit': 15, 'fwhm': 3.5, 'psf_model': 5.00}, 'photo': {}, 'allstar': {}, 'allframe': {}, 'misc': {'stacknum': 1, 'counts_limit': 15000, 'number_limit': 400, 'minpsf': 35, 'sigma_psf': 4.5, 'sigma_all': 3.5, 'min_frame': 1, 'min_frac': 0.1, 'enough_frame': 2, 'max_sigma': 10, 'degfree': 6, 'r_match': 15, 'repeat_2': 3, 'repeat_1': 30}}\\n\\t\\n\\t# cleanup\\n\\t#cleanup_files(fname, (\\\"log\\\", \\\"mch\\\", \\\"mag1\\\", \\\"raw1\\\", \\\"mch1\\\", \\\"cor1\\\"))\\n\\t#cleanup_files(fname, (\\\"mtr\\\", \\\"mag2\\\", \\\"raw2\\\", \\\"mch2\\\", \\\"cor2\\\"))\\n\\t\\n\\t# initialize names\\n\\tname = os.path.splitext(fname)[0]\\n\\tdr = os.path.split(fname)[0]\\n\\tofname = os.path.basename(name)\\n\\t\\n\\t# compile catalog list\\n\\tcatalogs = glob.glob(dr+\\\"/*.als\\\")\\n\\tcatalogs = sorted(catalogs)\\n\\toldindex=[ i for i, x in enumerate(catalogs) if \\\"r_1\\\" in x][0]\\n\\tcatalogs.insert(0, catalogs.pop(oldindex))\\n\\tcatalogs = [os.path.basename(x) for x in catalogs]\\n\\tnfiles = len(catalogs)\\n\\t\\n\\t## run daomatch \\n\\t#match = Daomatch(opts)\\n\\t#match(fname, catalogs)\\n\\t\\n\\t## run daomaster\\n\\t#master = Daomaster(opts)\\n\\t#master(fname, nfiles, index=1, idtrans='n')\\n\\t\\n\\t## run allframe\\n\\t#aframe = Allframe(opts)\\n\\t#aframe(fname, index=1)\\n\\t\\n\\t## first move to targeted directory\\n\\t#origwd = os.getcwd()\\n\\t#os.chdir(dr)\\n\\t\\n\\t## get & sort catalogs\\n\\t#catalogs = glob.glob(\\\"*.alf\\\")\\n\\t#catalogs = sorted(catalogs)\\n\\t#oldindex=[ i for i, x in enumerate(catalogs) if \\\"r_1\\\" in x][0]\\n\\t#catalogs.insert(0, catalogs.pop(oldindex))\\n\\t#nfiles = len(catalogs)\\n\\t\\n\\t## change back directory\\n\\t#os.chdir(origwd)\\n\\t\\n\\t## run daomatch \\n\\t#match = Daomatch(opts)\\n\\t#match(fname, catalogs)\\n\\t\\n\\t## run daomaster\\n\\t#master = Daomaster(opts)\\n\\t#master(fname, nfiles, index=2, idtrans='y')\\n\\t\\n\\t# final catalog\\n\\tlines_startwith(name+\\\".raw2\\\", \\\" \\\", fout=name+\\\"_cs.temp\\\")\\n\\tlines_startwith(name+\\\".raw2\\\", \\\" \\\", fout=name+\\\"_mag.temp\\\", toggle=False, skip=2)\\n\\t\\n\\thmerge_files((name+\\\"_mag.temp\\\", name+\\\"_cs.temp\\\"), fout=name+\\\".cat\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cb162fb0cccfff2a8efa7734c8149ef\",\n \"score\": \"0.4833147\",\n \"text\": \"def tri_subjects(dir_data, logger=None, f_mids='mid.csv'):\\n\\n # family directories\\n dirs_fid, fid_list = load_fids(dir_data)\\n\\n print(\\\"{} families are being processed\\\".format(len(fid_list)))\\n # Load MID LUT for all FIDs.\\n df_mids = db.load_mids(dirs_fid, f_csv=f_mids)\\n\\n # # Load relationship matrices for all FIDs.\\n # df_relationships = load_relationship_matrices(dirs_fid, f_csv=f_rel_matrix)\\n fms = []\\n fmd = []\\n kind = 'parents-child'\\n for i, fid in enumerate(fid_list):\\n print(fid)\\n # ids = [i for i, s in enumerate(genders) if 'Male' in s]\\n # rel_mat = db.parse_relationship_matrices(df_mids[i])\\n rel_mat = db.parse_relationship_matrices(df_mids[i])\\n genders = list(df_mids[i].Gender)\\n\\n # indices of matrix containing 4 or 1; that the matrix is inversed across the diagonal\\n # mat_ids = np.where(rel_mat == 1) and np.where(rel_mat.T == 1), np.where(rel_mat == 1) and np.where(\\n # rel_mat.T == 4)\\n\\n c_ids = np.where(rel_mat == 1)\\n p_ids = np.where(rel_mat == 4)\\n\\n if len(list(c_ids)) == 0:\\n logger.warn(\\\"No pair of parents for child in {}.\\\".format(fid))\\n # print(\\\"Two parents are not present for child\\\")\\n continue\\n\\n if len(set(p_ids[1]).__xor__(set(c_ids[0]))) or len(set(c_ids[1]).__xor__(set(p_ids[0]))):\\n logger.error(\\\"Unmatched pair in {}.\\\".format(fid))\\n # print(\\\"Unmatched pair\\\")\\n continue\\n ch_ids = [(p1, p2) for p1, p2 in zip(list(c_ids[0]), list(c_ids[1]))]\\n\\n cp_pairs = group_child_parents(ch_ids)\\n\\n for cid, pids in cp_pairs.items():\\n # pars = rows[np.where(cols == cc)]\\n if len(pids) != 2:\\n if len(pids) > 2:\\n logger.error(\\\"{} parents in {}. {}\\\".format(len(pids), fid, pids))\\n continue\\n # warn.warn(\\\"Three parents\\\")\\n else:\\n continue\\n try:\\n p_genders = [genders[pids[0][1]], genders[pids[1][1]]]\\n\\n except IndexError:\\n print()\\n if \\\"m\\\" in p_genders[0] and \\\"f\\\" in p_genders[1]:\\n pars_ids = (pids[0][1] + 1, pids[1][1] + 1)\\n elif \\\"m\\\" in p_genders[1] and \\\"f\\\" in p_genders[0]:\\n pars_ids = pids[1][1] + 1, pids[0][1] + 1\\n else:\\n logger.error(\\\"Parents of same gender in {}. {}\\\".format(fid, pids))\\n continue\\n # warn.warn(\\\"Parents are of same gender for \\\", fid)\\n\\n cmid = \\\"{}/MID{}\\\".format(fid, cid + 1)\\n fmid = \\\"{}/MID{}\\\".format(fid, pars_ids[0])\\n mmid = \\\"{}/MID{}\\\".format(fid, pars_ids[1])\\n\\n if \\\"m\\\" in genders[cid]:\\n fms.append((fmid, mmid, cmid))\\n else:\\n fmd.append((fmid, mmid, cmid))\\n\\n return fmd, fms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a48e67920a10f54b8133b2fb86479282\",\n \"score\": \"0.48310778\",\n \"text\": \"def match_features(im1_features, im2_features):\\n\\n # TODO: Your implementation here! See block comments and the project webpage for instructions\\n\\n # These are placeholders - replace with your matches and confidences!\\n num_freatures = min(len(im1_features), len(im2_features))\\n matches = np.zeros((num_freatures, 2))\\n confidences = np.ones(num_freatures)\\n threshold = .8\\n\\n # print(num_freatures)\\n # print(len(im2_features))\\n # print(im1_features)\\n for i in range (1,num_freatures):\\n distances = np.zeros(( num_freatures ,1))\\n\\n for j in range (1,num_freatures):\\n distances[j][0] = np.linalg.norm(im1_features[i] - im2_features[j])\\n\\n # distances = distances[np.logical_not(np.isnan(distances))]\\n distances = distances.flatten()\\n distances = np.nan_to_num(distances)\\n distances_sorted = np.sort(distances)\\n distances_index = distances.argsort()\\n # print(i)\\n\\n # print(distances)\\n # print(distances_index)\\n\\n NNDR = distances_sorted[-2]/distances_sorted[-1]\\n print(NNDR)\\n # print(distances_sorted)\\n if NNDR < .95 :\\n matches[i] = [ i , distances_index[-1] ]\\n confidences[i] = 1/NNDR\\n # print(confidences)\\n indx = np.where(confidences > 0)\\n # # print(indx)\\n confidences = confidences[indx]\\n confidences = np.sort(confidences)\\n confidences_index = confidences.argsort()\\n # #print(confidences_index)\\n matches = matches[confidences_index]\\n #print(confidences)\\n #print(matches)\\n return matches, confidences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b51c929d979a284f8a5088b7c1fbefe\",\n \"score\": \"0.4824405\",\n \"text\": \"def getRep(rgbImg, multiple=False):\\r\\n\\r\\n global profiler, prev_bbs, real_bbs\\r\\n\\r\\n profiler.inform_transmission_time_start(MEASURE_TYPE.LOCATE)\\r\\n if multiple:\\r\\n if prev_bbs and len(prev_bbs) != 0 and SERVER_FACE_SEARCH_OPTIMIZE: # face search optimization\\r\\n threads = []\\r\\n bbs = []\\r\\n for prev_bb in prev_bbs:\\r\\n get_face_boxes(extend_boundary_box(rgbImg, prev_bb), prev_bb, bbs)\\r\\n # threads.append(Thread(target=get_face_boxes, args=(extend_boundary_box(rgbImg, prev_bb), prev_bb, bbs)))\\r\\n\\r\\n for t in threads: t.start()\\r\\n for t in threads: t.join()\\r\\n\\r\\n else:\\r\\n bbs = [bb for bb in align.getAllFaceBoundingBoxes(rgbImg)]\\r\\n\\r\\n else:\\r\\n bb1 = align.getLargestFaceBoundingBox(rgbImg)\\r\\n bbs = [bb1]\\r\\n\\r\\n\\r\\n if real_bbs and len(real_bbs) > len(bbs):\\r\\n prev_bbs = real_bbs\\r\\n real_bbs = None\\r\\n else:\\r\\n prev_bbs = bbs\\r\\n\\r\\n profiler.inform_transmission_time_stop(MEASURE_TYPE.LOCATE)\\r\\n\\r\\n if len(bbs) == 0 or (not multiple and bb1 is None):\\r\\n print(\\\"Unable to find a face\\\")\\r\\n return []\\r\\n\\r\\n profiler.inform_transmission_time_start(MEASURE_TYPE.CLASSIFY)\\r\\n reps = []\\r\\n threads = []\\r\\n aligned_faces = []\\r\\n\\r\\n for bb in bbs:\\r\\n align_faces(rgbImg, bb, aligned_faces)\\r\\n # threads.append(Thread(target=align_faces, args=(rgbImg, bb, aligned_faces)))\\r\\n\\r\\n for t in threads: t.start()\\r\\n for t in threads: t.join()\\r\\n\\r\\n for bb, aligned_face in aligned_faces:\\r\\n rep = forward_to_net(aligned_face)\\r\\n reps.append((bb.center().x, rep, bb, aligned_face))\\r\\n\\r\\n profiler.inform_transmission_time_stop(MEASURE_TYPE.CLASSIFY)\\r\\n\\r\\n sreps = sorted(reps, key=lambda x: x[0])\\r\\n return sreps\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef19ecbef41a2e44ac739a061b6514bd\",\n \"score\": \"0.48226717\",\n \"text\": \"def track_SORT(coords_list,mod_err=1,meas_err=1,state_err=100,fsld_max = 60):\\n\\n active_objs = []\\n inactive_objs = []\\n keep_classes = False\\n if len(coords_list[0][0]) == 5:\\n keep_classes = True\\n \\n # initialize with all objects found in first frame\\n for i,row in enumerate(coords_list[0]):\\n if keep_classes:\\n cls = row[4]\\n row = row[:4]\\n obj = KF_Object(row,0,mod_err,meas_err,state_err)\\n obj.all.append(obj.get_coords())\\n obj.tags.append(1)\\n if keep_classes:\\n obj.cls = cls\\n active_objs.append(obj)\\n\\n # loop through all frames\\n for frame_num in range(1,len(coords_list)):\\n \\n print(\\\"On frame {} of {}\\\".format(frame_num,len(coords_list)))\\n \\n # predict new locations of all objects\\n # look at next set of detected objects\\n # match\\n # for all matches, update Kalman filter\\n # for all detached objects, update fsld and delete if too high\\n # for all unmatched objects, intialize new object\\n \\n # 1. predict new locations of all objects x_k | x_k-1\\n for obj in active_objs:\\n obj.predict()\\n \\n # 2. look at next set of detected objects - all objects are included in this even if detached\\n # convert into numpy array - where row i corresponds to object i in active_objs\\n locations = np.zeros([len(active_objs),4])\\n for i,obj in enumerate(active_objs):\\n locations[i,:] = obj.get_coords()\\n \\n # 3. match - these arrays are both N x 4 but last two columns will be ignored \\n # remove matches with IOU below threshold (i.e. too far apart)\\n second = coords_list[frame_num]\\n if keep_classes:\\n classes = second[:,4]\\n second = second[:,:4]\\n matches = match_hungarian(locations,second,iou_cutoff = 0.2) \\n #matches = match_greedy(locations,second)\\n \\n # traverse object list\\n move_to_inactive = []\\n for i in range(0,len(active_objs)):\\n obj = active_objs[i]\\n \\n # update fsld and delete if too high\\n if matches[i] == -1:\\n obj.fsld += 1\\n obj.all.append(obj.get_coords())\\n obj.tags.append(0) # indicates object not detected in this frame\\n if obj.fsld > fsld_max or (obj.fsld > 0 and len(obj.all)< 3):\\n move_to_inactive.append(i)\\n \\n \\n else: # object was matched \\n # update Kalman filter\\n measure_coords = second[matches[i]]\\n obj.update(measure_coords)\\n obj.fsld = 0\\n obj.all.append(obj.get_coords())\\n obj.tags.append(1) # indicates object detected in this frame\\n\\n # for all unmatched objects, intialize new object\\n for j in range(0,len(second)):\\n if j not in matches:\\n new_obj = KF_Object(second[j],frame_num,mod_err,meas_err,state_err)\\n new_obj.all.append(new_obj.get_coords())\\n if keep_classes:\\n new_obj.cls = classes[j]\\n new_obj.tags.append(1) # indicates object detected in this frame\\n active_objs.append(new_obj)\\n\\n \\n # move all necessary objects to inactive list\\n move_to_inactive.sort()\\n move_to_inactive.reverse()\\n for idx in move_to_inactive:\\n inactive_objs.append(active_objs[idx])\\n del active_objs[idx]\\n \\n \\n objs = active_objs + inactive_objs\\n \\n final_objs = []\\n # create final point array\\n points_array = np.zeros([len(coords_list),len(objs)*2])-1\\n for j in range(0,len(objs)):\\n obj = objs[j]\\n \\n \\n hit_ratio = sum(obj.tags)/len(obj.tags)\\n # only keep object that were found at least 30% of the time\\n if hit_ratio > 0.4:\\n final_objs.append(obj)\\n first_frame = int(obj.first_frame)\\n for i in range(0,len(obj.all)):\\n points_array[i+first_frame,j*2] = obj.all[i][0]\\n points_array[i+first_frame,(j*2)+1] = obj.all[i][1]\\n \\n return final_objs, points_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31ff577e3055b8834035d93a9491a62d\",\n \"score\": \"0.4812476\",\n \"text\": \"def inference(self,sequence,mode):\\n #Method 1: Greedy Decoding\\n if mode.lower()=='greedy':\\n tag_sequence = []\\n tag_penult = '*'\\n tag_prev = '*'\\n for idx,word in enumerate(sequence.split(' ')):\\n scores = []\\n for tag in self.tag_set:\\n scores.append(self.get_q(tag_penult,tag_prev,tag)*self.get_e(word,tag,idx))\\n final_tag = self.tag_set[np.argmax(scores)]\\n tag_sequence.append(final_tag)\\n tag_penult = tag_prev\\n tag_prev = final_tag\\n\\n\\n return tag_sequence\\n\\n #Method 2: Beam Search\\n elif mode.lower()=='beam':\\n k=1\\n sequence = sequence.split()\\n best_sequences = [['*','*']]\\n for idx,word in enumerate(sequence):\\n scores = {}\\n for item in best_sequences:\\n item = list(item)\\n tag_penult = item[-2]\\n tag_prev = item[-1]\\n for tag in self.tag_set:\\n score = self.get_q(tag_penult,tag_prev,tag)*self.get_e(word,tag,idx)\\n item.append(tag)\\n scores[tuple(item)] = score\\n item.pop()\\n topk_scores = sorted(scores.items(), key=lambda x: x[1], reverse = True)\\n best_sequences = list(zip(*topk_scores)[0][:k])\\n\\n best_sequence = best_sequences[0][2:]\\n\\n return best_sequence\\n\\n #Method 3: Viterbi Algorithm\\n elif mode.lower()=='viterbi':\\n tag_sequence= []\\n path = {}\\n path['*','*'] = []\\n pi_func = defaultdict(float)\\n pi_func[(0, \\\"*\\\", '*')] = 0.0\\n\\n # v = Tag current, u = Tag previous, w = Tag penult\\n sequence = sequence.split(\\\" \\\")\\n n = len(sequence)\\n\\n for k in range(1,n+1):\\n temp_path = {}\\n for u in self.possible_tags(k-1):\\n for v in self.possible_tags(k):\\n max_tag = \\\"\\\"\\n max_score = float(\\\"-Inf\\\")\\n for w in self.possible_tags(k - 2):\\n score = pi_func.get((k-1, w, u),float('-Inf'))*self.get_q(w,u,v)*self.get_e(sequence[k-1],v,k-1)\\n if score > max_score:\\n max_score = score\\n max_tag = w\\n pi_func[(k, u, v)] = max_score\\n temp_path[u,v] = path[max_tag,u] + [v]\\n path = temp_path\\n\\n prob,umax,vmax = max([(pi_func.get((n,u,v))*self.get_q(u,v,'.'),u,v) for u in self.possible_tags(n-1) for v in self.possible_tags(n)])\\n\\n return path[umax,vmax]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e130d94f6ef02526fe47125be3163b1d\",\n \"score\": \"0.48111892\",\n \"text\": \"def recognitionAndSEO(self): #New implementation\\n \\n chainList = []\\n recogSEO = []\\n if len(self.closedNeighborhood) > 1:\\n for row, col in self.closedNeighborhood.iteritems():\\n candidate = []\\n tempOrderedNeighbors = []\\n for c in col:\\n tempOrderedNeighbors.append(self.closedNeighborhood[c])\\n \\n tempOrderedNeighbors = sorted(tempOrderedNeighbors, key=lambda l: (len(l), l))\\n \\n for i in range(len(tempOrderedNeighbors)):\\n if i+1 < len(tempOrderedNeighbors):\\n v1 = tempOrderedNeighbors[i]\\n v2 = tempOrderedNeighbors[i+1]\\n if set(v1).issubset(set(v2)):\\n if v1 not in candidate:\\n v1Keys = [key for (key, value) in self.closedNeighborhood.items() if value == v1]\\n for v1k in v1Keys:\\n if v1k not in candidate:\\n candidate.append(v1k)\\n if v2 not in candidate:\\n v2Keys = [key for (key, value) in self.closedNeighborhood.items() if value == v2]\\n for v2k in v2Keys:\\n if v2k not in candidate:\\n candidate.append(v2k)\\n else:\\n candidate = []\\n break\\n if candidate and candidate not in chainList:\\n chainList.append(candidate)\\n elif len(self.closedNeighborhood) == 1:\\n chainList.append(self.closedNeighborhood.keys())\\n \\n if chainList:\\n chainList = sorted(chainList, key=lambda l: (len(l), l))\\n #print(sorted(list2, key=lambda l: (len(l), l)))\\n vertexToBeRemoved = chainList[0][0]\\n print \\\"Chains: \\\"+str(chainList,)+\\\" and vertex to be removed \\\"+str(vertexToBeRemoved)\\n self.recognitionSEO.append(vertexToBeRemoved)\\n \\n if self.closedNeighborhood:\\n self.updateClosedNeighbors(vertexToBeRemoved)\\n self.recognitionAndSEO()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"067c9320a3bf25cc14323353d30ec8b9\",\n \"score\": \"0.4805177\",\n \"text\": \"def _get_correspondences(\\n self,\\n alignments,\\n idxA,\\n idxB,\\n heuristic = 'distance'\\n ):\\n reg_dist = {}\\n\\n if idxA != idxB and heuristic == 'distance':\\n for almA,almB,scoreAB in alignments:\\n \\n score = self._distance_score(almA,almB,scoreAB,self.score_dict)\\n if score <= self.pairwise_threshold:\\n for a,b in zip(almA,almB):\\n try:\\n reg_dist[a,b] += 1.0\\n except KeyError:\\n reg_dist[a,b] = 1.0\\n \\n # a new attempt, we use a preprocessing and cognate clustering with\\n # simple sca and then only consider those words found by the rough\\n # algorithm for the calculation of sound correspondences\\n elif heuristic == 'sca':\\n for i,(almA,almB,scoreAB) in enumerate(alignments):\\n x,y = self._pairs[idxA,idxB][i]\\n # remember that we excluded pairs from the calc which occur\\n # more than ones, i.e. identical strings, so we follow the same\\n # procedure here\\n if x > 0 and y > 0:\\n x = self.cogs[self.idxd[x]]\\n y = self.cogs[self.idxd[y]]\\n if x == y:\\n for a,b in zip(almA,almB):\\n try:\\n reg_dist[a,b] += 1.0\\n except:\\n reg_dist[a,b] = 1.0\\n\\n elif idxA == idxB:\\n counter = 0\\n for almA,almB,scoreAB in alignments:\\n \\n score = self._distance_score(almA,almB,scoreAB,self.score_dict)\\n\\n if score == 0:\\n for a,b in zip(almA,almB):\\n try:\\n reg_dist[a,b] += 1.0\\n except KeyError:\\n reg_dist[a,b] = 1.0\\n counter += 1\\n\\n # change gap notation\\n for a,b in list(reg_dist.keys()):\\n \\n # change for gap positions\\n if a == '-':\\n reg_dist[str(idxA)+'.X.-',b] = reg_dist[a,b]\\n elif b == '-':\\n reg_dist[a,str(idxB)+'.X.-'] = reg_dist[a,b]\\n\\n return reg_dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b23915f2a99f510faf96ec2bdb96513\",\n \"score\": \"0.48001367\",\n \"text\": \"def write_movie(experiment,name,simple_trajectories,predictions,correspondances):\\n \\n path = os.path.join(\\\"..\\\",\\\"data\\\",\\\"microglia\\\",name)\\n colors = ['green','red','blue','pink','yellow']\\n if not os.path.isdir(path):\\n os.mkdir(path)\\n #Separate each cell from each trajectory\\n cell_list=[]\\n for i in range(241):\\n cell_list.append([])\\n #Copy the frames in new directory. these frames will be modified by the loop\\n frame = m.open_frame(experiment.path,i+1)\\n cv2.imwrite(os.path.join(path,str(i+1)+\\\".png\\\"),frame)\\n \\n for i,(index_traj,index_cell) in enumerate(correspondances):\\n traj = simple_trajectories[index_traj][1]\\n cell=traj.cells[index_cell]\\n pred = predictions[i]\\n cell_list[cell.frame_number].append((cell,pred))\\n n_pred = np.max(predictions)+1\\n for frame_nr,cells in enumerate(cell_list):\\n print \\\"processing frame nr\\\",frame_nr+1\\n frame = m.open_frame(path,frame_nr+1)\\n body = m.open_frame(experiment.body_path,frame_nr+1)\\n arm = m.open_frame(experiment.arm_path,frame_nr+1)\\n mask = np.zeros((frame.shape[0],frame.shape[1],n_pred),dtype=np.uint8)\\n out = np.zeros((frame.shape[0],frame.shape[1],3),dtype=np.uint8)\\n for cell,pred in cells:\\n \\n mask[:,:,pred] += (body==cell.body+1).astype(np.uint8)\\n for elt in cell.arms:\\n mask[:,:,pred]+=(arm==elt+1).astype(np.uint8)\\n mask*=255\\n for i in range(n_pred):\\n out+= m.cv_overlay_mask2image(mask[:,:,i],frame,color=colors[i])/n_pred\\n cv2.imwrite(os.path.join(path,str(frame_nr+1)+\\\".png\\\"),out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dbecd1c352c21d5a0dbd4fe95a3e996\",\n \"score\": \"0.47992152\",\n \"text\": \"def match(desc1, desc2):\\n\\n desc1 = array([d / linalg.norm(d) for d in desc1])\\n desc2 = array([d / linalg.norm(d) for d in desc2])\\n\\n dist_ratio = 0.6\\n desc1_size = desc1.shape\\n\\n matchscores = zeros((desc1_size[0]), 'int')\\n desc2t = desc2.T # precompute matrix transpose\\n for i in range(desc1_size[0]):\\n dotprods = dot(desc1[i, :], desc2t) # vector of dot products\\n dotprods = 0.9999 * dotprods\\n # inverse cosine and sort, return index for features in second image\\n indx = argsort(arccos(dotprods))\\n\\n # check if nearest neighbor has angle less than dist_ratio times 2nd\\n if arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:\\n matchscores[i] = int(indx[0])\\n\\n return matchscores\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e46484b450ba90ad25ccb87f883467df\",\n \"score\": \"0.47991565\",\n \"text\": \"def FindJaccardMatches(self, text1,text2,name1='Text1',name2='Text2'):\\n \\n shinMatches=[]\\n for t1Sent in text1:\\n text1shins=MakeShingles(t1Sent,self.shingleSize,goodTags=self.goodTags,\\n stops=self.stops)\\n for t2Sent in text2:\\n text2shins=MakeShingles(t2Sent,self.shingleSize,\\n goodTags=self.goodTags,stops=self.stops)\\n compared=MatchShinglesNoOvercountBigrams(text1shins,\\n text2shins,bigrams=self.bigrams,\\n thresh=self.matchThreshold)\\n \\n if compared[0]==True:\\n \\n copywords=[[w for w,t,l in t1Sent if l in compared[1]],\\n [w for w,t,l in t2Sent if l in compared[1]]]\\n \\n shinMatches.append(([w for w,t,l in t1Sent],\\n [w for w,t,l in t2Sent],copywords))\\n \\n return(MatcherOutput(shinMatches,name1,name2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8389207bdee99cd2168bf15e85f1b30\",\n \"score\": \"0.47881946\",\n \"text\": \"def match_features(feature_coords1,feature_coords2,image1,image2):\\n matches = list()\\n ncc_descriptor1 = ncc_descriptor(image1, feature_coords1) \\n ncc_descriptor2 = ncc_descriptor(image2, feature_coords2)\\n num_descriptors1 = np.shape(ncc_descriptor1)[2]\\n num_descriptors2 = np.shape(ncc_descriptor2)[2]\\n potential_match = np.array([])\\n\\n for x in range(0, num_descriptors1):\\n index = -1\\n max_dist = -1\\n for y in range(0, num_descriptors2):\\n ncc = compute_ncc(ncc_descriptor1, ncc_descriptor2, x, y)\\n if ncc > max_dist:\\n max_dist = ncc\\n index = y\\n if index > -1:\\n match = [x, index]\\n potential_match = np.append(potential_match, match)\\n for x in range(0, num_descriptors2):\\n index = -1\\n max_dist = -1 \\n for y in range(0, num_descriptors1):\\n ncc = compute_ncc(ncc_descriptor1, ncc_descriptor2, y, x)\\n if ncc > max_dist:\\n max_dist = ncc\\n index = y\\n if index > -1:\\n union = -1\\n \\n for z in range(0, len(potential_match)):\\n if potential_match[z] == x:\\n union = z\\n if union > -1:\\n match = [index, x]\\n matches.append(match)\\n return matches\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e957dd17434837c40018b185d653dc\",\n \"score\": \"0.47775978\",\n \"text\": \"def who_is_it(targetImgDir, imagePath, file, db, resizeShape, model):\\n # print(\\\"who_is_it: \\\", db[\\\"B1_2i.png\\\"])\\n\\n ## Step 1: Compute the target \\\"encoding\\\" for the image. Use img_to_encoding() see example above. ## (≈ 1 line)\\n encoding = img_to_encoding(targetImgDir+file, resizeShape, model)\\n\\n ## Step 2: Find the closest encoding ##\\n # Initialize \\\"min_dist\\\" to a large value, say 100 (≈1 line)\\n min_dist = 100\\n\\n similarityArr = []\\n # Loop over the database dictionary's names and encodings.\\n for name in db:\\n # if name == file:\\n # continue\\n\\n # Compute L2 distance between the target \\\"encoding\\\" and the current \\\"emb\\\" from the db. (≈ 1 line)\\n dist = np.linalg.norm(encoding - db[name])\\n\\n dists = {\\\"name\\\": name, \\\"dist\\\": dist}\\n similarityArr.append(dists)\\n\\n # If this distance is less than the min_dist, then set min_dist to dist, and identity to name. (≈ 3 lines)\\n # if dist < min_dist:\\n # min_dist = dist\\n # identity = name\\n\\n\\n def dist(s):\\n return s['dist']\\n\\n similarityArr = sorted(similarityArr, key = dist)\\n\\n # print(\\\"similarityArr: \\\", similarityArr)\\n # picNum = len(similarityArr)\\n\\n plt.figure(num=1, figsize=(10, 10))\\n\\n for key in range(10):\\n plt.subplot(5, 2, key+1)\\n plt.title('distance: ' + str(similarityArr[key][\\\"dist\\\"]))\\n plt.imshow(Image.open(imagePath + similarityArr[key][\\\"name\\\"]))\\n\\n plt.show()\\n\\n # if min_dist > 0.7:\\n # print(\\\"Not in the database.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34d57734cd027fa87d872f125b163800\",\n \"score\": \"0.4771992\",\n \"text\": \"def produce_pairs(folder, ment, pair):\\n\\n print \\\"Producing Pairs data frame\\\"\\n #Initialize DF for all results\\n column_titles_all = [\\\"PMID\\\", \\\"Paragraph\\\", \\\"Sentence\\\", \\\"Char_Start_x\\\", \\\"Char_End_x\\\",\\n \\\"Entity_name_x\\\", \\\"TaxID\\\", \\\"SerialNo_x\\\", \\\"Char_Start_y\\\", \\\"Char_End_y\\\",\\n \\\"Entity_name_y\\\", \\\"SerialNo_y\\\", \\\"Text\\\"]\\n results_all = pd.DataFrame(columns=column_titles_all)\\n column_titles_nec = [\\\"Entity_name_x\\\", \\\"SerialNo_x\\\", \\\"Entity_name_y\\\", \\\"SerialNo_y\\\", \\\"Text\\\"]\\n results_nec = pd.DataFrame(columns=column_titles_nec)\\n\\n #Go over all the files in the specified folder\\n for filename in glob.glob(os.path.join(folder, '*tsv.gz')):\\n try:\\n texts = open_tsv(filename)\\n pair = pair[[\\\"SerialNo1\\\",\\n \\\"SerialNo2\\\",\\n \\\"Final_Score\\\"]].sort_values(['SerialNo1', 'SerialNo2'])\\n ment = ment.sort_values('PMID')\\n common_j = find_common(texts, ment)\\n\\n shared_entities = ment[ment[\\\"PMID\\\"].isin(common_j)]\\n shared_entities = shared_entities.sort_values('SerialNo')\\n\\n # extract only the common pairs with their respective entries\\n ser_no_ents = shared_entities[\\\"SerialNo\\\"].tolist()\\n temp_s1_values = pair[pair[\\\"SerialNo1\\\"].isin(ser_no_ents)]\\n temp_s2_list = temp_s1_values[\\\"SerialNo2\\\"].tolist()\\n both_entries = shared_entities[shared_entities[\\\"SerialNo\\\"].isin(temp_s2_list)]\\n # temp_both_list = both_entries[\\\"SerialNo\\\"].tolist()\\n # both_pairs = temp_s1_values[temp_s1_values[\\\"SerialNo1\\\"].isin(temp_both_list) &\\n # temp_s1_values[\\\"SerialNo2\\\"].isin(temp_both_list)]\\n both_entries = both_entries.sort_values('PMID')\\n\\n #Get True Pairs\\n true_pairs = extract_true_pairs(both_entries)\\n\\n #Add senteces\\n\\n pairs_with_sentences = add_sentences(texts, true_pairs)\\n only_nec = pairs_with_sentences[[\\\"Entity_name_x\\\", \\\"SerialNo_x\\\", \\\"Entity_name_y\\\",\\n \\\"SerialNo_y\\\", \\\"Text\\\"]]\\n\\n results_all = results_all.append(pairs_with_sentences, ignore_index=True)\\n results_nec = results_nec.append(only_nec, ignore_index=True)\\n print \\\"Done with \\\", filename[54:]\\n\\n except (KeyError, UnicodeDecodeError, StopIteration):\\n\\n print \\\"Something went wrong with \\\", filename[54:], \\\"\\\\n\\\", \\\"Ignoring it!\\\"\\n\\n #Append to results DF\\n\\n\\n results_all[[\\\"PMID\\\", \\\"Paragraph\\\", \\\"Sentence\\\", \\\"Char_Start_x\\\", \\\"Char_End_x\\\", \\\"TaxID\\\",\\n \\\"SerialNo_x\\\", \\\"Char_Start_y\\\", \\\"Char_End_y\\\", \\\"SerialNo_y\\\"]] = results_all[[\\\"PMID\\\", \\\"Paragraph\\\", \\\"Sentence\\\", \\\"Char_Start_x\\\", \\\"Char_End_x\\\", \\\"TaxID\\\", \\\"SerialNo_x\\\", \\\"Char_Start_y\\\", \\\"Char_End_y\\\", \\\"SerialNo_y\\\"]].astype(int)\\n results_nec[[\\\"SerialNo_x\\\", \\\"SerialNo_y\\\"]] = results_nec[[\\\"SerialNo_x\\\", \\\"SerialNo_y\\\"]].astype(int)\\n\\n results_all.to_csv('Pairs_With_Sentences_Full.tsv', sep='\\\\t', index=False, header=False)\\n results_nec.to_csv('Pairs_With_Sentences_Only_nec.tsv', sep='\\\\t', index=False, header=False)\\n print \\\"\\\\n\\\", \\\"DONE with Pairs data frame production!\\\", \\\"\\\\n\\\"\\n\\n return results_nec\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8b388152bb05366bab79b50f3e16166\",\n \"score\": \"0.4766146\",\n \"text\": \"def needleman_wunsch_matrix(seq1, seq2, win=0):\\n \\n match = 1 # match score\\n mismatch = -1 # mismatch penalty\\n indel = -1 # indel penalty\\n \\n n = len(seq1)\\n m = len(seq2)\\n s = np.zeros( (n+1, m+1) ) - 100 # DP matrix (large negative is default)\\n s[0,0]=0\\n ptr = np.zeros( (n+1, m+1), dtype=int ) # matrix of pointers\\n \\n ##### INITIALIZE SCORING MATRIX (base case) #####\\n \\n for i in range(1, n+1) :\\n s[i,0] = indel * i\\n for j in range(1, m+1):\\n s[0,j] = indel * j\\n \\n ########## INITIALIZE TRACEBACK MATRIX ##########\\n \\n # Tag first row by LEFT, indicating initial \\\"-\\\"s\\n ptr[0,1:] = LEFT\\n \\n # Tag first column by UP, indicating initial \\\"-\\\"s\\n ptr[1:,0] = UP\\n \\n #####################################################\\n\\n\\n for i in range(1,n+1):\\n jrange = range(max(1,i-win),min(i+win,m+1)) if win else range(1,m+1)\\n for j in jrange:\\n # match\\n if seq1[i-1] == seq2[j-1]:\\n s[i,j] = s[i-1,j-1] + match\\n ptr[i,j] = DIAG\\n # mismatch\\n else :\\n s[i,j] = s[i-1,j-1] + mismatch\\n ptr[i,j] = DIAG\\n # indel penalty\\n if s[i-1,j] + indel > s[i,j] :\\n s[i,j] = s[i-1,j] + indel\\n ptr[i,j] = UP\\n # indel penalty\\n if s[i, j-1] + indel > s[i,j]:\\n s[i,j] = s[i, j-1] + indel\\n ptr[i,j] = LEFT\\n \\n return s, ptr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddcbf6bfe83f9849a81984831b511e96\",\n \"score\": \"0.47493348\",\n \"text\": \"def _extractfaces(label_pairs):\\n \\n key, (subvolume, gtmap, segmap, gtvol, segvol) = label_pairs\\n\\n # extract unique bodies not remapped\\n allgt = set(numpy.unique(gtvol))\\n allseg = set(numpy.unique(segvol))\\n\\n gtmapbodies = set()\\n for key2, body in gtmap.items():\\n gtmapbodies.add(key2)\\n segmapbodies = set()\\n for key2, body in segmap.items():\\n segmapbodies.add(key2)\\n allgt = allgt.difference(gtmapbodies)\\n if 0 in allgt:\\n allgt.remove(0)\\n allseg = allseg.difference(segmapbodies)\\n if 0 in allseg:\\n allseg.remove(0)\\n \\\"\\\"\\\"\\n if 0 in allseg:\\n allseg.remove(0)\\n if 0 in allgt:\\n allgt.remove(0)\\n \\\"\\\"\\\"\\n\\n zmax,ymax,xmax = gtvol.shape\\n start = (subvolume.box.z1, subvolume.box.y1, subvolume.box.x1)\\n\\n mappedfaces = []\\n\\n # grab 6 faces for gt\\n slicex0 = gtvol[:,:,0]\\n slicexmax = gtvol[:,:,xmax-1]\\n\\n slicey0 = gtvol[:,0,:]\\n sliceymax = gtvol[:,ymax-1,:]\\n \\n slicez0 = gtvol[0,:,:]\\n slicezmax = gtvol[zmax-1,:,:]\\n\\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+ymax, start[2]+1), True), \\n [(slicex0, gtmap, key, True, allgt)] ))\\n mappedfaces.append(( ((start[0], start[1], start[2]+xmax),\\n (start[0]+zmax, start[1]+ymax, start[2]+xmax+1), True), \\n [(slicexmax, gtmap, key, False, set())] ))\\n \\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+1, start[2]+xmax), True), \\n [(slicey0, gtmap, key, False, set())] ))\\n mappedfaces.append(( ((start[0], start[1]+ymax, start[2]),\\n (start[0]+zmax, start[1]+ymax+1, start[2]+xmax), True), \\n [(sliceymax, gtmap, key, False, set())] ))\\n \\n mappedfaces.append(( (start, (start[0]+1, start[1]+ymax, start[2]+xmax), True), \\n [(slicez0, gtmap, key, False, set())] ))\\n mappedfaces.append(( ((start[0]+zmax, start[1], start[2]),\\n (start[0]+zmax+1, start[1]+ymax, start[2]+xmax), True), \\n [(slicezmax, gtmap, key, False, set())] ))\\n\\n # grab 6 faces for seg\\n segslicex0 = segvol[:,:,0]\\n segslicexmax = segvol[:,:,xmax-1]\\n\\n segslicey0 = segvol[:,0,:]\\n segsliceymax = segvol[:,ymax-1,:]\\n \\n segslicez0 = segvol[0,:,:]\\n segslicezmax = segvol[zmax-1,:,:]\\n\\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+ymax, start[2]+1), False), \\n [(segslicex0, segmap, key, True, allseg)] ))\\n mappedfaces.append(( ((start[0], start[1], start[2]+xmax),\\n (start[0]+zmax, start[1]+ymax, start[2]+xmax+1), False), \\n [(segslicexmax, segmap, key, False, set())] ))\\n \\n mappedfaces.append(( (start, (start[0]+zmax, start[1]+1, start[2]+xmax), False), \\n [(segslicey0, segmap, key, False, set())] ))\\n mappedfaces.append(( ((start[0], start[1]+ymax, start[2]),\\n (start[0]+zmax, start[1]+ymax+1, start[2]+xmax), False), \\n [(segsliceymax, segmap, key, False, set())] ))\\n \\n mappedfaces.append(( (start, (start[0]+1, start[1]+ymax, start[2]+xmax), False), \\n [(segslicez0, segmap, key, False, set())] ))\\n mappedfaces.append(( ((start[0]+zmax, start[1], start[2]),\\n (start[0]+zmax+1, start[1]+ymax, start[2]+xmax), False), \\n [(segslicezmax, segmap, key, False, set())] ))\\n \\n return mappedfaces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"177af38d4c0e94ca5a76b045683aa071\",\n \"score\": \"0.474284\",\n \"text\": \"def match_transform(self, e):\\n\\n logger = logging.getLogger('petr_log.PETRgraph')\\n\\n def recurse(pdict, event, a2v={}, v2a={}):\\n '''\\n Parameters\\n -----------\\n a2v: dictionary\\n actor to variable mapping\\n\\n v2a: dictionary\\n variable to actor mapping\\n\\n '''\\n logger.debug(\\\"recurse entry..\\\")\\n #print(pdict)\\n\\n\\n path = pdict\\n if isinstance(pdict, list):\\n # transfromation pattern is found\\n line = pdict[1]\\n path = pdict[0]\\n verb = utilities.convert_code(path[2])[0] if not path[\\n 2] == \\\"Q\\\" else utilities.convert_code(v2a[\\\"Q\\\"])[0]\\n #verb = utilities.convert_code(verb)\\n if isinstance(v2a[path[1]], tuple):\\n results = []\\n for item in v2a[path[1]]:\\n results.append((list(v2a[path[0]]), item, verb))\\n logger.debug(\\\"line:\\\" + line)\\n return results, line\\n logger.debug(\\\"line:\\\" + line)\\n return [(list(v2a[path[0]]), v2a[path[1]], verb)], line\\n\\n if isinstance(event, tuple):\\n if e[2] in [None, '---']:\\n return False\\n\\n if event[2] in [None, '---']:\\n return False\\n # print(event)\\n actor = None if not event[0] else tuple(event[0])\\n # print(actor)\\n eventcode = utilities.convert_code(event[2])[0]\\n # , eventcode - eventcode % 0x10,eventcode - eventcode % 0x100, eventcode - eventcode % 0x1000]\\n codelist = [eventcode]\\n # print(eventcode)\\n # print(pdict)\\n # print(codelist)\\n masks = [a for a in codelist if a in pdict]\\n\\n # print(masks)\\n logger.debug(\\\"actor:\\\")\\n logger.debug(actor)\\n\\n logger.debug(\\\"masks:\\\")\\n logger.debug(masks)\\n\\n if masks:\\n # print(masks)\\n path = pdict[masks[0]]\\n elif -1 in pdict:\\n v2a[\\\"Q\\\"] = event[2]\\n path = pdict[-1]\\n # print(path)\\n else:\\n #print(\\\"nothing is found\\\")\\n return False\\n else:\\n actor = event\\n if event == []:\\n actor = \\\".\\\"\\n\\n logger.debug(\\\"actor:\\\")\\n logger.debug(actor)\\n\\n if isinstance(actor,list):\\n actor = actor[0]\\n event = actor\\n\\n if actor in a2v:\\n #print(\\\"in a2v\\\")\\n actor = a2v[actor]\\n\\n if not actor:\\n actor = \\\".\\\"\\n\\n logger.debug(\\\"actor:\\\")\\n logger.debug(actor)\\n \\n #print(actor != '_')\\n #print(actor == '_')\\n if actor in path:\\n #print(\\\"actor in path\\\")\\n #print(path[actor])\\n if actor == \\\".\\\":\\n return recurse(path[actor], \\\".\\\", a2v, v2a)\\n else:\\n return recurse(path[actor], event[1], a2v, v2a)\\n elif actor != '_':\\n #print(\\\"entry\\\")\\n for var in sorted(path.keys())[::-1]:\\n #print(var)\\n if var in v2a:\\n continue\\n if var != '.':\\n v2a[var] = actor\\n a2v[actor] = var\\n\\n #print(\\\"v2a:\\\")\\n #print(v2a)\\n #print(\\\"a2v:\\\")\\n #print(a2v)\\n #print(\\\"event1:\\\")\\n #print(event)\\n if event:\\n return recurse(path[var], event[1], a2v, v2a)\\n \\n\\n\\n #logger.debug(\\\"no transformation is present\\\")\\n\\n return False\\n\\n logger.debug(\\\"match_transform entry...\\\")\\n t = recurse(PETRglobals.VerbDict['transformations'], e)\\n\\n try:\\n logger.debug(e)\\n\\n t = recurse(PETRglobals.VerbDict['transformations'], e)\\n if t:\\n logger.debug(\\\"transformation is present:\\\")\\n logger.debug(\\\"t:\\\")\\n logger.debug(t)\\n transfered = []\\n for event in t[0]:\\n tevent = (event[0],[event[1]],utilities.convert_code(event[2], 0))\\n transfered.append(tevent)\\n\\n return transfered\\n else:\\n logger.debug(\\\"no transformation is present:\\\")\\n #c = utilities.convert_code(e[1][2])[0]\\n # print(c)\\n #print(16 ** 3)\\n #print(c / (16 ** 3))\\n # not e[1][2] / (16 ** 3):\\n if e[0] and e[2] and isinstance(e[1], tuple) and e[1][0] and e[1][2] and e[0] != e[1][0]:\\n\\n logger.debug(utilities.convert_code(e[2])[0])\\n logger.debug(e[2])\\n\\n logger.debug(\\\"the event is of the form: a ( b . Q ) P\\\")\\n if isinstance(e[1][0], list):\\n results = []\\n for item in e[1][0]:\\n code_combined = utilities.combine_code(utilities.convert_code(e[2])[\\n 0], utilities.convert_code(e[1][2])[0])\\n #target = []\\n # target.append(item)\\n event = (e[0], [item], utilities.convert_code(\\n code_combined, 0))\\n logger.debug(event)\\n results.append(event)\\n return results\\n\\n code_combined = utilities.combine_code(utilities.convert_code(e[2])[\\n 0], utilities.convert_code(e[1][2])[0])\\n event = (e[0], [e[1][0]],\\n utilities.convert_code(code_combined, 0))\\n logger.debug(event)\\n return [event]\\n #'''\\n elif e[0] and isinstance(e[1], tuple) and e[1][0] and e[1][2] and e[0] == e[1][0] and e[1][1]:\\n\\n logger.debug(\\\"the event is of the form: a ( a b Q ) P\\\")\\n if e[2] in [None, '---']:\\n code_combined = utilities.convert_code(e[1][2])[0]\\n else:\\n code_combined = utilities.combine_code(utilities.convert_code(e[2])[\\n 0], utilities.convert_code(e[1][2])[0])\\n event = (e[0], e[1][1], utilities.convert_code(\\n code_combined, 0))\\n logger.debug(event)\\n\\n return [event]\\n '''\\n elif e[0] and isinstance(e[1], tuple) and not e[1][0] and e[1][2] and e[1][1]:\\n logger.debug(\\\"the event is of the form: a ( [] b Q ) P\\\")\\n logger.debug(e[2])\\n\\n code_combineds = []\\n for code in e[2].split(\\\":\\\"):\\n print(code)\\n if code in [None, '---','']:\\n #code_combineds.append(utilities.convert_code(e[1][2])[0])\\n code_combineds.append('')\\n else:\\n code_combineds.append(utilities.combine_code(utilities.convert_code(code)[\\n 0], utilities.convert_code(e[1][2])[0]))\\n\\n print(code_combineds)\\n for code in code_combineds:\\n c = utilities.convert_code(code, 0)\\n print(c)\\n\\n \\n if e[2] in [None, '---']:\\n code_combined = utilities.convert_code(e[1][2])[0]\\n else:\\n code_combined = utilities.combine_code(utilities.convert_code(e[2])[\\n 0], utilities.convert_code(e[1][2])[0])\\n event = (e[0], e[1][1], utilities.convert_code(\\n code_combined, 0))\\n logger.debug(event)\\n\\n return[event]\\n '''\\n\\n except Exception as ex:\\n pass # print(ex)\\n return [e]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ad68691eed9268ffc5acd0b58cc7bc7\",\n \"score\": \"0.47421414\",\n \"text\": \"def enumerate_matches(tnums_a,tnums_b,tags_a,tags_b,\\n tagn_interval,\\n max_shift=20.0,max_drift=0.005,max_delta=0.500,\\n verbose=False,max_matches=1):\\n visited={}\\n full_matches=[]\\n next_a=next_b=0\\n matches=[]\\n \\n # When trying to find a bad ping, keep track of how far the match\\n # got. This might be a little generous, in the sense that these\\n # are tracked separately, and there could be one set of matches\\n # that gets further in a, and a separate set that gets further\\n # in b. maybe that's ok, depending on how these values get used.\\n # 2019-12-03: HERE so far this has tracked the last matched ping, and\\n # on failure started axing successive pings.\\n # This is problematic because we may consider many ping after the\\n # last match, and much later consider a pair that fails.\\n # tempting to track the greatest next_a/next_b while building the\\n # longest match. but the search can go quite far (up to max shift)\\n # on either side without having to advance the other side, such\\n # that we may see very far beyond the real culprit.\\n # Solution probably to make test smarter, and allow it to return\\n # potential bad apples.\\n max_match_a=-1\\n max_match_b=-1\\n\\n # New approach to finding bad apples.\\n # Keep the test result from the longest set of matches\\n longest_fail=None\\n max_matched=0\\n longest_match=None\\n \\n stack=[] # tuples of next_a,next_b,matches\\n \\n def do_test(next_a,next_b,matches):\\n return test_matches(next_a,next_b,matches,tnums_a,tnums_b,tags_a,tags_b,\\n tagn_interval=tagn_interval,\\n max_shift=max_shift,max_drift=max_drift,max_delta=max_delta)\\n\\n stack.append( (next_a,next_b,matches) )\\n\\n while stack:\\n next_a,next_b,matches = stack.pop()\\n\\n # Check to see if this has already been tried\\n key=(next_a,next_b,tuple(matches))\\n if key in visited: continue\\n visited[key]=1\\n\\n if verbose:\\n print(f\\\"match next_a={next_a} next_b={next_b} matches={matches}\\\")\\n\\n test_result=do_test(next_a,next_b,matches)\\n if test_result is not True:\\n if len(matches)>max_matched:\\n max_matched=len(matches)\\n longest_fail=test_result\\n longest_match=matches\\n elif len(matches)==max_matched:\\n longest_fail=( list(set( longest_fail[0]+test_result[0])), \\n list(set( longest_fail[1]+test_result[1])) )\\n continue\\n \\n if len(matches):\\n max_match_a=max(max_match_a,matches[-1][0])\\n max_match_b=max(max_match_b,matches[-1][1])\\n \\n if (next_a==len(tnums_a)) and (next_b==len(tnums_b)):\\n # termination condition\\n if len(matches)>0:\\n full_matches.append(matches)\\n print(\\\"MATCH\\\")\\n if len(full_matches)>=max_matches:\\n break\\n continue\\n elif (next_a==len(tnums_a)):\\n # finish out b\\n stack.append( (next_a,len(tnums_b),matches) )\\n elif (next_b==len(tnums_b)):\\n # finish out a\\n stack.append( (len(tnums_a),next_b,matches) )\\n else:\\n # This ordering will prefer chasing down matches before\\n # trying to drop things. Hopefully that means that we'll\\n # find the non-empty match first, and could potentially stop\\n # early if that is sufficient.\\n if tnums_a[next_a]<tnums_b[next_b] + max_shift:\\n # drop which ever one is earlier. doesn't work in general, but I'm not\\n # explicitly including shifts yet\\n stack.append( (next_a+1,next_b,matches) )\\n if tnums_b[next_b]<tnums_a[next_a] + max_shift:\\n stack.append( (next_a,next_b+1,matches) )\\n\\n # is it possible to declare the next two a match?\\n if (tags_a[next_a]==tags_b[next_b]):\\n time_diff=tnums_a[next_a]-tnums_b[next_b]\\n if (np.abs(time_diff)<max_shift):\\n # sure, try a match\\n stack.append( (next_a+1,next_b+1,matches + [(next_a,next_b)]) )\\n else:\\n # Tags match, but time diff is too big for max_shift\\n pass\\n\\n return full_matches,max_match_a,max_match_b,longest_fail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f709fecc08cff4e41691daca8e018aff\",\n \"score\": \"0.47405988\",\n \"text\": \"def translate_all_frames(self):\\n nucleotides_sequences = self.group_environment()\\n total = 0\\n for entry in nucleotides_sequences:\\n total += nucleotides_sequences[entry]\\n if total != len(self.reads):\\n Error.LogicError(\\\"Genomic_environment: translate_all_frames: Total is different\\\" +\\n \\\" than depth\\\")\\n frames = []\\n for i in range(0, 6):\\n new_frame = {}\\n frames.append(new_frame)\\n for entry in nucleotides_sequences:\\n #Kinda fuzzy... we must find the amino acid in all frames.\\n #We will have a list of frame where each fram is a dict stored with aa.\\n if len(entry) != 5:\\n Error.error(\\\"Length of sequence: %s is not 5\\\" % entry)\\n #I dont see a way to avoid hardcoding :(\\n #Frame 1\\n print(entry)\\n print(entry[0:3])\\n #print(len(entry[0:3]))\\n codon = Seq(entry[0:3], IUPAC.unambiguous_dna)\\n aa = str(codon.translate(table=11))\\n if not aa in frames[0]:\\n frames[0][aa] = 0\\n frames[0][aa] += nucleotides_sequences[entry]\\n #frame 2\\n print(entry[1:4])\\n codon = Seq(entry[1:4], IUPAC.unambiguous_dna)\\n aa = str(codon.translate(table=11))\\n if not aa in frames[1]:\\n frames[1][aa] = 0\\n frames[1][aa] += nucleotides_sequences[entry]\\n #Frame 3\\n print(entry[2:5])\\n codon = Seq(entry[2:5], IUPAC.unambiguous_dna)\\n aa = str(codon.translate(table=11))\\n if not aa in frames[2]:\\n frames[2][aa] = 0\\n frames[2][aa] += nucleotides_sequences[entry]\\n\\n reverse_entry = entry[::-1]\\n\\n #Frame 4 (reverse starting from right)\\n print(reverse_entry[0:3])\\n codon = Seq(reverse_entry[0:3], IUPAC.unambiguous_dna)\\n aa = str(codon.translate(table=11))\\n if not aa in frames[3]:\\n frames[3][aa] = 0\\n frames[3][aa] += nucleotides_sequences[entry]\\n #Frame 5\\n print(reverse_entry[1:4])\\n codon = Seq(reverse_entry[1:4], IUPAC.unambiguous_dna)\\n aa = str(codon.translate(table=11))\\n if not aa in frames[4]:\\n frames[4][aa] = 0\\n frames[4][aa] += nucleotides_sequences[entry]\\n #Frame 6\\n print(reverse_entry[2:5])\\n codon = Seq(reverse_entry[2:5], IUPAC.unambiguous_dna)\\n aa = str(codon.translate(table=11))\\n if not aa in frames[5]:\\n frames[5][aa] = 0\\n frames[5][aa] += nucleotides_sequences[entry]\\n ##We output this in a cute way\\n header = \\\"Contig\\\\tSNP position(contig)\\\\tFrame\\\\tAmino acid\\\\tProportion\\\\n\\\"\\n print(header),\\n for frame in range(0, 6):\\n frame_number = str(frame+1)\\n for amino_acid in frames[frame]:\\n line = \\\"\\\"\\n line = self.contig+\\\"\\\\t\\\"+str(self.position)+'\\\\t'+frame_number+\\\"\\\\t\\\"+amino_acid+\\\"\\\\t\\\"+str(float(frames[frame][amino_acid]/total))\\n print(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2ef0bf81e86ceb32d036182c38b37f0\",\n \"score\": \"0.47225416\",\n \"text\": \"def drawMatches(img1, kp1, img2, kp2, matches):\\n\\n # Create a new output image that concatenates the two images together\\n # (a.k.a) a montage\\n rows1 = img1.shape[0]\\n cols1 = img1.shape[1]\\n rows2 = img2.shape[0]\\n cols2 = img2.shape[1]\\n\\n out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8')\\n\\n # Place the first image to the left\\n out[:rows1,:cols1,:] = np.dstack([img1, img1, img1])\\n\\n # Place the next image to the right of it\\n out[:rows2,cols1:cols1+cols2,:] = np.dstack([img2, img2, img2])\\n\\n # For each pair of points we have between both images\\n # draw circles, then connect a line between them\\n for mat in matches:\\n\\n # Get the matching keypoints for each of the images\\n img1_idx = mat.queryIdx\\n img2_idx = mat.trainIdx\\n\\n # x - columns\\n # y - rows\\n (x1,y1) = kp1[img1_idx].pt\\n (x2,y2) = kp2[img2_idx].pt\\n\\n # Draw a small circle at both co-ordinates\\n # radius 4\\n # colour blue\\n # thickness = 1\\n cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1)\\n cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1)\\n\\n # Draw a line in between the two points\\n # thickness = 1\\n # colour blue\\n cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1)\\n\\n\\n # Show the image\\n cv2.imshow('Matched Features', out)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd5749e4acd726858c73dd0c80dd8d7\",\n \"score\": \"0.47193238\",\n \"text\": \"def disambiguate_entities(entity_candidates, w2v):\\n disambiguation_graph = nx.DiGraph()\\n embeddings = w2v.get_vectors([entity for (index, surface_form) in entity_candidates\\n for entity in entity_candidates.get((index, surface_form))])\\n\\n for (index, surface_form) in entity_candidates:\\n graph = nx.DiGraph()\\n nodes = filter_candidates((index, surface_form), entity_candidates, embeddings)\\n graph.add_nodes_from(nodes)\\n entity_candidates[(index, surface_form)] = graph\\n disambiguation_graph = nx.compose(disambiguation_graph, entity_candidates.get((index, surface_form)))\\n\\n return disambiguation_graph, entity_candidates\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00d3807a2d1fcc96b12a84573130c0b9\",\n \"score\": \"0.47176337\",\n \"text\": \"def execute(self, views: list) -> list:\\n n_samp = len(views[0])\\n\\n labels = np.zeros((n_samp, n_samp), dtype=int)\\n\\n for i in range(0, len(views)):\\n\\n l = views[i]\\n res = [[int(x == y) for y in l] for x in l]\\n res = np.matrix(res)\\n labels = labels + res\\n\\n # in_place=False does not work, we have to edit the matrix in place\\n # labels = np.fill_diagonal(labels, 0, in_place=False)\\n np.fill_diagonal(labels, 0)\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"176ae036d4ab18b6922caf78ef8a438a\",\n \"score\": \"0.47169954\",\n \"text\": \"def user_alignment(alignment_file_path,\\n thresh=0.9):\\n\\n alignment_files = glob.glob(alignment_file_path + '/Tng_an_userAlignment_*.json')\\n\\n alignment_data = []\\n for fn in alignment_files:\\n with open(fn, 'r') as f:\\n for line in f:\\n alignment_data.append(json.loads(line))\\n \\n df = pd.DataFrame(alignment_data)\\n #only include scores above .9\\n df = df[df['score'] > thresh]\\n\\n platforms = [c for c in df.columns if c != 'score']\\n\\n dfs = []\\n for i, p1 in enumerate(platforms):\\n for j, p2 in enumerate(platforms[i:]):\\n if p2 != p1:\\n df_map = df[[p1, p2, 'score']].dropna()\\n df_map.columns = ['username1', 'username2', 'score']\\n\\n # if username has multiple matches keep the highest score\\n df_map = df_map.sort_values('score', ascending=False)\\n df_map = df_map.drop_duplicates(subset=['username2'])\\n df_map = df_map.drop_duplicates(subset=['username1'])\\n\\n df_map = df_map.drop('score', axis=1)\\n df_map = df_map[df_map['username1'] != df_map['username2']]\\n\\n dfs.append(df_map)\\n\\n edge_df = pd.concat(dfs, axis=0)\\n edge_df = edge_df.drop_duplicates()\\n\\n G = nx.from_pandas_edgelist(edge_df, source='username1', target='username2')\\n\\n components = nx.connected_components(G)\\n username_map = {}\\n for comp in components:\\n comp = list(comp)\\n for i in range(len(comp) - 1):\\n username_map[comp[i + 1]] = comp[0]\\n\\n return (username_map)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b8d6f98557bd369e6190bcc06acb5fb\",\n \"score\": \"0.47135895\",\n \"text\": \"def match_traj_parallelized_general(Start_index,End_index):\\n trajectory_file='~/UP_analysis/Eastman_analysis/Analysis/CAM5_trajectories_v2.dms' #trajectory file is already specified in this case\\n # open table with trajectory times and location\\n Traj_table=pandas.read_table(trajectory_file,delim_whitespace=True) \\n Traj_table_array=np.array(Traj_table[0:])\\n \\n # For regridding - take grid from GPCPv1pt2\\n f_gpcp=cdm.open('~/Obs_datasets/GPCP_PDF/GPCPv1pt2_PREC_pdf.nc')\\n obs_freq_pdf=f_gpcp('PRECFREQPDF')\\n gpcp_grid=obs_freq_pdf.getGrid()\\n \\n \\n f_log=open(\\\"\\\".join([\\\"log_trajv4_\\\",str(Start_index),\\\"_\\\",str(End_index),\\\".txt\\\"]),\\\"w+\\\")\\n #length_traj_table=Traj_table_array.shape[0]\\n length_traj_table=End_index-Start_index\\n Output_table=np.zeros((length_traj_table,29)) # Create table to output data\\n Output_table[:,:]=np.nan # Set all data to nan\\n Output_table_noanom=np.zeros((length_traj_table,29)) # Create table to output data\\n Output_table_noanom[:,:]=np.nan # Set all data to nan\\n start_index=Start_index\\n for i in np.arange(start_index,End_index): #loop from start to end index\\n #print i\\n Output_table[i-start_index,0]=int(Traj_table_array[i,0]) #write the traj number\\n Output_table_noanom[i-start_index,0]=int(Traj_table_array[i,0]) #write the traj number\\n #In a log file, keep track of what trajectories get analyzed\\n f_log=open(\\\"\\\".join([\\\"log_trajv4_\\\",str(Start_index),\\\"_\\\",str(End_index),\\\".txt\\\"]),\\\"a\\\")\\n f_log.write(\\\"\\\".join([str(int(Traj_table_array[i,0])),\\\" \\\\n\\\"]))\\n f_log.close()\\n for j in np.arange(7): #loop across the 7 instances along trajectory\\n #Retrieve days after 2008-12-31 and time in UTC\\n Day0=Traj_table_array[i,2+j]\\n Time0=Traj_table_array[i,9+j]\\n # Add exception when Time is 24 hrs = 0 hrs the next day\\n if Time0>=24:\\n Day0=Traj_table_array[i,2+j]+1\\n relday0=cdu.cdtime.reltime(Day0,\\\"days since 2008-12-31\\\")\\n comptime0=cdu.cdtime.r2c(relday0)\\n str_comptime0month=str(comptime0.month)\\n str_comptime0day=str(comptime0.day)\\n if ~(Day0>0):\\n continue #Skip cases where the date or time is NaN\\n if comptime0.month<10: #Add zero to single digit months\\n str_comptime0month=''.join(['0',str(comptime0.month)])\\n if comptime0.day<10: #Add zero to single digit days\\n str_comptime0day=''.join(['0',str(comptime0.day)])\\n str_day=''.join(['2009-',str_comptime0month,'-',str_comptime0day])\\n #Decide which file to access based on time of day\\n if Time0<6:\\n str_time0='00000'\\n time_index0=np.floor(Time0)\\n if Time0>=6 and Time0<12:\\n str_time0='21600'\\n time_index0=np.floor(Time0)-6\\n if Time0>=12 and Time0<18:\\n str_time0='43200'\\n time_index0=np.floor(Time0)-12\\n if Time0>=18 and Time0<24:\\n str_time0='64800'\\n time_index0=np.floor(Time0)-18\\n if Time0>=24:\\n str_time0='00000'\\n time_index0=0\\n str_time=''.join([str_day,'-',str_time0])\\n filelocation='/global/cscratch1/sd/terai/UP_analysis/Eastman_analysis/CAM5_1deg_run2/Processed/'\\n fileprefix='longcam5I_L30_20081001_0Z_f09_g16_828.cam.h1.'\\n # Access the dataset using cdms2 tools\\n f_CloudTop=cdm.open(''.join([filelocation,'CloudTopv4_',fileprefix,str_time,'.nc']))\\n time_index0=int(time_index0) #Convert any decimals to integer to index\\n lat0=Traj_table_array[i,16+j] #Locate the latitude from the trajectory table\\n lon0=Traj_table_array[i,23+j]\\n # Take the time slice and box with 5 x 5 deg. around interested area\\n LWP_grid=f_CloudTop('TGCLDLWP',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\\n CLDTOT_grid=f_CloudTop('CLDTOT',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\\n CltopZ3_grid=f_CloudTop('CltopZ3',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\\n CltopSPNC_grid=f_CloudTop('CltopSPNC',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\\n # Regrid the data to 1x1 deg boxes using the grid from GPCP (see above)\\n LWP_regridded=LWP_grid.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\\n CLDTOT_regridded=CLDTOT_grid.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\\n CltopZ3_regridded=CltopZ3_grid.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\\n CltopSPNC_regridded=CltopSPNC_grid.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\\n # Take the value from the grid box that lies within 1deg box around the point\\n LWP=LWP_regridded(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\\n CLDTOT=CLDTOT_regridded(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\\n CltopZ3=CltopZ3_regridded(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\\n CltopSPNC=CltopSPNC_regridded(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\\n LWP_array=np.array(cdu.averager(LWP,axis='xyt'))\\n CLDTOT_array=np.array(cdu.averager(CLDTOT,axis='xyt'))\\n CltopZ3_array=np.array(cdu.averager(CltopZ3,axis='xyt'))\\n CltopSPNC_array=np.array(cdu.averager(CltopSPNC,axis='xyt'))\\n # Set all very small values of zeros to NaNs\\n if LWP_array<0.0001:\\n LWP_array=np.nan\\n if CLDTOT_array<0.0001:\\n CLDTOT_array=np.nan\\n if CltopZ3_array<0.0001:\\n CltopZ3_array=np.nan\\n if CltopSPNC_array<0.0001:\\n CltopSPNC_array=np.nan\\n \\n # ******** Repeat the steps above for the 100-day mean data\\n f_CloudTop_100dm=cdm.open(''.join([filelocation,'AVG100days_CloudTopv4_',fileprefix,str_time,'.nc']))\\n # Take the time slice and box with 5 x 5 deg. around interested area\\n LWP_grid_100dm=f_CloudTop_100dm('TGCLDLWP',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\\n CLDTOT_grid_100dm=f_CloudTop_100dm('CLDTOT',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\\n CltopZ3_grid_100dm=f_CloudTop_100dm('CltopZ3',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\\n CltopSPNC_grid_100dm=f_CloudTop_100dm('CltopSPNC',time=slice(time_index0,time_index0+1),lat=(lat0-5,lat0+5),lon=(lon0-5,lon0+5))\\n # Regrid the data to 1x1 deg boxes using the grid from GPCP (see above)\\n LWP_regridded_100dm=LWP_grid_100dm.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\\n CLDTOT_regridded_100dm=CLDTOT_grid_100dm.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\\n CltopZ3_regridded_100dm=CltopZ3_grid_100dm.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\\n CltopSPNC_regridded_100dm=CltopSPNC_grid_100dm.regrid(gpcp_grid,regridTool='esmf',regridMethod='bilinear')\\n # Take the value from the grid box that lies within 1deg box around the point\\n LWP_100dm=LWP_regridded_100dm(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\\n CLDTOT_100dm=CLDTOT_regridded_100dm(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\\n CltopZ3_100dm=CltopZ3_regridded_100dm(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\\n CltopSPNC_100dm=CltopSPNC_regridded_100dm(lat=(lat0-0.5,lat0+0.5),lon=(lon0-0.5,lon0+0.5))\\n LWP_array_100dm=np.array(cdu.averager(LWP_100dm,axis='xyt'))\\n CLDTOT_array_100dm=np.array(cdu.averager(CLDTOT_100dm,axis='xyt'))\\n CltopZ3_array_100dm=np.array(cdu.averager(CltopZ3_100dm,axis='xyt'))\\n CltopSPNC_array_100dm=np.array(cdu.averager(CltopSPNC_100dm,axis='xyt'))\\n # **************************************************************\\n\\n # Enter values into the new output table\\n Output_table[i-start_index,j+1]=CLDTOT_array-CLDTOT_array_100dm\\n Output_table[i-start_index,j+8]=LWP_array-LWP_array_100dm\\n Output_table[i-start_index,j+15]=CltopZ3_array-CltopZ3_array_100dm\\n Output_table[i-start_index,j+22]=CltopSPNC_array-CltopSPNC_array_100dm\\n \\n # Enter values into the new output table\\n Output_table_noanom[i-start_index,j+1]=CLDTOT_array\\n Output_table_noanom[i-start_index,j+8]=LWP_array\\n Output_table_noanom[i-start_index,j+15]=CltopZ3_array\\n Output_table_noanom[i-start_index,j+22]=CltopSPNC_array\\n np.set_printoptions(precision=5)\\n # Save the table into a text file with 6 significant digits\\n np.savetxt(''.join(['/global/homes/t/terai/UP_analysis/Eastman_analysis/Analysis/CAM5_trajectoryv4_CLDTOT_LWP_Cltop_Nc_',str(Start_index),'_',str(End_index),'.txt']),Output_table,delimiter=', ',fmt='%.5e')\\n np.savetxt(''.join(['/global/homes/t/terai/UP_analysis/Eastman_analysis/Analysis/CAM5_trajectoryv4_raw_CLDTOT_LWP_Cltop_Nc_',str(Start_index),'_',str(End_index),'.txt']),Output_table_noanom,delimiter=', ',fmt='%.5e')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"421edd7ec4e5a80c99393bb1dcaf9fca\",\n \"score\": \"0.4713398\",\n \"text\": \"def local_align(path, match=1, mism=-1, gap=-1):\\n with open(path, \\\"r\\\") as fasta: # read sequences from file\\n lines = fasta.readlines()\\n A = lines[1].strip()\\n B = lines[3].strip()\\n matrix = [[0] for i in range(len(B)+1)] # making matrix with scores\\n matrix[0] = [0 for i in range(len(A)+1)]\\n for a in range(1, len(B)+1): # filling the matrix\\n for i in range(1, len(A)+1):\\n if A[i-1] == B[a-1]:\\n matrix[a].append(max(matrix[a - 1][i] + gap, matrix[a][i - 1] + gap, matrix[a - 1][i - 1] + match))\\n else:\\n matrix[a].append(max(matrix[a - 1][i] + gap, matrix[a][i - 1] + gap, matrix[a - 1][i - 1] + mism))\\n if matrix[a][i] < 0:\\n matrix[a][i] = 0\\n n = 0 # score\\n a = 0\\n i = 0\\n maxn = min(matrix[-1])\\n alignA = []\\n alignB = []\\n maxalignA = []\\n maxalignB = []\\n \\\"\\\"\\\" for e in range(len(B)): # Doing global alignment from and to every possible coordinate\\n for b in range(len(A)):\\n for c in range(1, len(A)):\\n for d in range(1, len(B)):\\n a = e\\n i = b\\n if c <= i or d <= a:\\n continue\\n n = 0\\n while a < d and i < c: # Going through the matrix with highest score on the way\\n if matrix[a + 1][i + 1] >= matrix[a][i + 1] and matrix[a + 1][i + 1] >= matrix[a + 1][i]:\\n alignB.append(B[a])\\n alignA.append(A[i])\\n n = n + matrix[a + 1][i + 1]\\n a += 1\\n i += 1\\n elif matrix[a + 1][i] >= matrix[a + 1][i + 1] and matrix[a + 1][i] >= matrix[a][i + 1]:\\n alignB.append(B[a])\\n alignA.append(\\\"-\\\")\\n n = n + matrix[a + 1][i]\\n a += 1\\n elif matrix[a][i + 1] >= matrix[a + 1][i + 1] and matrix[a][i + 1] >= matrix[a + 1][i]:\\n alignB.append(\\\"-\\\")\\n alignA.append(A[i])\\n n = n + matrix[a][i + 1]\\n i += 1\\n if a == d: # way from the edge to the corner\\n while i < c:\\n alignB.append(\\\"-\\\")\\n alignA.append(A[i])\\n n = n + matrix[a][i + 1]\\n i += 1\\n elif i == c:\\n while a < d:\\n alignB.append(B[a])\\n alignA.append(\\\"-\\\")\\n n = n + matrix[a + 1][i]\\n a += 1\\n if n > maxn: # choose max score\\n maxn = n\\n maxalignA = \\\"\\\".join(alignA)\\n maxalignB = \\\"\\\".join(alignB)\\n alignA.clear()\\n alignB.clear()\\\"\\\"\\\"\\n while a < len(B) and i < len(A): # Going through the matrix with highest score on the way\\n if matrix[a][i + 1] >= matrix[a + 1][i + 1] and matrix[a][i + 1] >= matrix[a + 1][i]:\\n alignB.append(\\\"-\\\")\\n alignA.append(A[i])\\n n = n + matrix[a][i + 1]\\n i += 1\\n elif matrix[a + 1][i + 1] >= matrix[a + 1][i] and matrix[a + 1][i + 1] >= matrix[a][i + 1]:\\n alignB.append(B[a])\\n alignA.append(A[i])\\n n = n + matrix[a + 1][i + 1]\\n a += 1\\n i += 1\\n elif matrix[a + 1][i] >= matrix[a + 1][i + 1] and matrix[a + 1][i] >= matrix[a][i + 1]:\\n alignB.append(B[a])\\n alignA.append(\\\"-\\\")\\n n = n + matrix[a + 1][i]\\n a += 1\\n print(matrix)\\n if a == len(B): # way from the edge to the corner\\n while i < len(A):\\n alignB.append(\\\"-\\\")\\n alignA.append(A[i])\\n n = n + matrix[a][i + 1]\\n i += 1\\n elif i == len(A):\\n while a < len(B):\\n alignB.append(B[a])\\n alignA.append(\\\"-\\\")\\n n = n + matrix[a + 1][i]\\n a += 1\\n alignA.append(\\\"\\\\n\\\") # joining to sequences into one\\n align = \\\"\\\".join(alignA + alignB)\\n return n, align\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3d3761cef027ea40120cd02ef70763\",\n \"score\": \"0.47080684\",\n \"text\": \"def get_deduplicated_triples(triples_fname, target_dedup, person_dedup):\\n\\ttt = pd.read_table(triples_fname, sep=',', header=0)\\n\\tprint 'Read input triples: {}'.format(tt.shape),\\n\\ttt = tt.dropna()\\n\\tprint '.. After dropping NAs, #input triples: {}'.format(tt.shape)\\n\\ty = tt['human_relevance']\\n\\ttt = tt[['sub', 'obj']].to_dict(orient='list')\\n\\ttriples = zip(tt['sub'], tt['obj'])\\n\\n\\t# de-duplicate target: professions/nationalities\\n\\ttarget_all = pd.read_table(target_dedup, sep=',', header=0)\\n\\ttarget_all = target_all[['cup_node_name', 'node_name']]\\n\\ttarget_all.index = target_all['cup_node_name']\\n\\ttarget_all = target_all.drop('cup_node_name', axis=1).to_dict()['node_name']\\n\\ttriples = [(t[0], target_all[t[1]]) if t[1] in target_all else t for t in triples]\\n\\n\\t# de-duplicate persons\\n\\tpersons_all = pd.read_table(person_dedup, sep=',', header=0)\\n\\tpersons_all = persons_all[['cup_node_name', 'node_name']]\\n\\tpersons_all.index = persons_all['cup_node_name']\\n\\tpersons_all = persons_all.drop('cup_node_name', axis=1).to_dict()['node_name']\\n\\ttriples = [(persons_all[t[0]], t[1]) if t[0] in persons_all else t for t in triples]\\n\\tprint '=> Deduplicated triples: {}'.format(len(triples))\\n\\treturn triples, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd68f0a8b75ce94e3e5a265baffe8142\",\n \"score\": \"0.4703198\",\n \"text\": \"def drawMatches(img1, kp1, img2, kp2, matches):\\n\\n # Create a new output image that concatenates the two images together\\n # (a.k.a) a montage\\n rows1 = img1.shape[0]\\n cols1 = img1.shape[1]\\n rows2 = img2.shape[0]\\n cols2 = img2.shape[1]\\n\\n out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8')\\n\\n # Place the first image to the left\\n out[:rows1,:cols1] = np.dstack([img1, img1, img1])\\n\\n # Place the next image to the right of it\\n out[:rows2,cols1:] = np.dstack([img2, img2, img2])\\n\\n # For each pair of points we have between both images\\n # draw circles, then connect a line between them\\n for mat in matches:\\n\\n # Get the matching keypoints for each of the images\\n img1_idx = mat.queryIdx\\n img2_idx = mat.trainIdx\\n\\n # x - columns\\n # y - rows\\n (x1,y1) = kp1[img1_idx].pt\\n (x2,y2) = kp2[img2_idx].pt\\n\\n # Draw a small circle at both co-ordinates\\n # radius 4\\n # colour blue\\n # thickness = 1\\n cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1) \\n cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1)\\n\\n # Draw a line in between the two points\\n # thickness = 1\\n # colour blue\\n cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1)\\n\\n\\n # Show the image\\n #cv2.imshow('Matched Features', out)\\n #cv2.waitKey(0)\\n #cv2.destroyWindow('Matched Features')\\n\\n # Also return the image if you'd like a copy\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"586a602d028419eee80c1315fdd0edef\",\n \"score\": \"0.47029915\",\n \"text\": \"def Following(annot, anchor, container=None, cnt=3): \\n # Get the following annotations\\n def followingAQ(rec,cnt):\\n # Sort the following annotations (limit the number of results to the cnt)\\n srecs = sorted(rec[1], key=lambda x: (-1 if x.LstartOffset == None else x.LstartOffset))[0:cnt]\\n \\n # We can extract the key from the any 'right' entry in sorted recs (we will use the first one)\\n key = Row(docId = srecs[0].RdocId,\\n annotSet = srecs[0].RannotSet,\\n annotType = srecs[0].RannotType,\\n startOffset = int(srecs[0].RstartOffset),\\n endOffset = int(srecs[0].RendOffset),\\n annotId = int(srecs[0].RannotId),\\n properties = srecs[0].Rproperties)\\n\\n # Construct the array \\n values = []\\n for rec in srecs:\\n if rec.LdocId != None:\\n values.append(Row(docId = rec.LdocId,\\n annotSet = rec.LannotSet,\\n annotType = rec.LannotType,\\n startOffset = int(rec.LstartOffset),\\n endOffset = int(rec.LendOffset),\\n annotId = int(rec.LannotId),\\n properties = rec.Lproperties)) \\n\\n return(key,values)\\n \\n \\n # Get the following 'contained' annotations\\n def followingContainedAQ(rec):\\n if rec.CdocId == None:\\n return (rec.annot,[])\\n else:\\n values = []\\n for entry in rec.annots:\\n if (entry.startOffset >= rec.CstartOffset) and (entry.endOffset <= rec.CendOffset):\\n values.append(entry)\\n return (rec.annot,values) \\n \\n l = annot.select(\\\"annotId\\\",\\\"annotSet\\\",\\\"annotType\\\",\\\"docId\\\",\\\"endOffset\\\",\\\"properties\\\",\\\"startOffset\\\").toDF(\\\"LannotId\\\",\\\"LannotSet\\\",\\\"LannotType\\\",\\\"LdocId\\\",\\\"LendOffset\\\",\\\"Lproperties\\\",\\\"LstartOffset\\\")\\n r = anchor.select(\\\"annotId\\\",\\\"annotSet\\\",\\\"annotType\\\",\\\"docId\\\",\\\"endOffset\\\",\\\"properties\\\",\\\"startOffset\\\").toDF(\\\"RannotId\\\",\\\"RannotSet\\\",\\\"RannotType\\\",\\\"RdocId\\\",\\\"RendOffset\\\",\\\"Rproperties\\\",\\\"RstartOffset\\\")\\n # Group on the anchor annotation\\n results = l.join(r, \\n (col(\\\"LdocId\\\") == col(\\\"RdocId\\\")) & \\n (col(\\\"LstartOffset\\\") >= col(\\\"RendOffset\\\")), \\n \\\"rightouter\\\") \\\\\\n .rdd \\\\\\n .groupBy(lambda x: (x[\\\"RdocId\\\"],x[\\\"RstartOffset\\\"],x[\\\"RendOffset\\\"])) \\\\\\n .map(lambda rec: followingAQ(rec,cnt))\\n\\n results = spark.createDataFrame(results.map(lambda x: x),AQSchemaList())\\n \\n if (container != None) and (not(container.rdd.isEmpty())):\\n c = container.select(\\\"annotId\\\",\\\"annotSet\\\",\\\"annotType\\\",\\\"docId\\\",\\\"endOffset\\\",\\\"properties\\\",\\\"startOffset\\\").toDF(\\\"CannotId\\\",\\\"CannotSet\\\",\\\"CannotType\\\",\\\"CdocId\\\",\\\"CendOffset\\\",\\\"Cproperties\\\",\\\"CstartOffset\\\")\\n cResults = results.join(c, \\n (col(\\\"annot.docId\\\") == col(\\\"CdocId\\\")) & \\n (col(\\\"annot.startOffset\\\") >= col(\\\"CstartOffset\\\")) & \\n (col(\\\"annot.endOffset\\\") <= col(\\\"CendOffset\\\")), \\n \\\"leftouter\\\") \\\\\\n .rdd \\\\\\n .map(lambda rec: followingContainedAQ(rec))\\n\\n # Need to drop duplicates\\n return spark.createDataFrame(cResults.map(lambda x: x),AQSchemaList())\\n \\n else:\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c10d64495be8daa3049ef69dff59bd17\",\n \"score\": \"0.47024792\",\n \"text\": \"def _predict_joint_anchors_from_heatmap(self, heatmap):\\n predictions = []\\n\\n for part_id, name in enumerate(c.ELEMENTS):\\n map_ori = heatmap[:, :, part_id]\\n smoothed_heatmap = filters.gaussian_filter(map_ori, sigma=3)\\n\\n map_left = np.zeros(smoothed_heatmap.shape)\\n map_left[1:, :] = smoothed_heatmap[:-1, :]\\n\\n map_right = np.zeros(smoothed_heatmap.shape)\\n map_right[:-1, :] = smoothed_heatmap[1:, :]\\n\\n map_up = np.zeros(smoothed_heatmap.shape)\\n map_up[:, 1:] = smoothed_heatmap[:, :-1]\\n\\n map_down = np.zeros(smoothed_heatmap.shape)\\n map_down[:, :-1] = smoothed_heatmap[:, 1:]\\n\\n # Determine local maxima in the heatmap fo find estimated locations\\n # for each joint\\n peaks = np.logical_and.reduce(\\n (smoothed_heatmap >= map_left,\\n smoothed_heatmap >= map_right,\\n smoothed_heatmap >= map_up,\\n smoothed_heatmap >= map_down,\\n smoothed_heatmap > c.JOINT_CONFIDENCE_THRESHOLD)\\n )\\n\\n peak_coordinates = np.vstack(np.nonzero(peaks))[[1, 0]]\\n predictions.append(peak_coordinates)\\n\\n return predictions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7165d7e4c5119a725a304cd8576828b\",\n \"score\": \"0.46984684\",\n \"text\": \"def run(self):\\n\\n person_count = 0\\n num_buses = int(len(self.pedestrians)/8.0)\\n print(num_buses)\\n # sort pedestrians according to time\\n ped_idx = np.argsort([pedestrian.depart for pedestrian in self.pedestrians])\\n pedestrians_sorted = np.array(self.pedestrians)[ped_idx]\\n\\n for bus_index in range(num_buses):\\n bus_id = f'bus_{bus_index}'\\n bus_pedestrians = pedestrians_sorted[bus_index::num_buses]\\n\\n # add vehicle\\n try:\\n traci.vehicle.add(vehID=bus_id, typeID=\\\"BUS_L\\\", routeID=\\\"\\\", depart=bus_pedestrians[0].depart + 240.0, departPos=0,\\n departSpeed=0, departLane=0, personCapacity=8)\\n except traci.exceptions.TraCIException as err:\\n print(\\\"TraCIException: {0}\\\".format(err))\\n except:\\n print(\\\"Unexpected error:\\\", sys.exc_info()[0])\\n\\n\\n\\n # concatenate all paths of pedestrians\\n person_paths = []\\n person_depart_times = []\\n for person in bus_pedestrians:\\n person_count += 1 # number of passengers not transported at the end\\n person_path = ((person.edge_from, person.position_from), (person.edge_to, person.position_to))\\n person_paths.append(person_path)\\n person_depart_times.append(person.depart)\\n\\n # set the route for the vehicle\\n route = [self.bus_depot_start_edge]\\n for (edge_from, position_from), (edge_to, position_to) in person_paths:\\n route = route + list(\\n traci.simulation.findRoute(fromEdge=route[-1], toEdge=edge_from).edges)[1:] \\\\\\n + list(traci.simulation.findRoute(fromEdge=edge_from, toEdge=edge_to).edges)[1:]\\n traci.vehicle.setRoute(bus_id, route)\\n traci.vehicle.setStop(vehID=bus_id, edgeID=edge_from, pos=position_from, laneIndex=0,\\n duration=500, flags=tc.STOP_DEFAULT)\\n traci.vehicle.setStop(vehID=bus_id, edgeID=edge_to, pos=position_to, laneIndex=0,\\n duration=500, flags=tc.STOP_DEFAULT)\\n\\n traci.vehicle.subscribe('bus_0', (tc.VAR_ROAD_ID, tc.VAR_LANEPOSITION, tc.VAR_POSITION, tc.VAR_NEXT_STOPS))\\n loss = len(self.pedestrians) - person_count\\n print(\\\"loss\\\", loss)\\n\\n step = 0\\n while step <= self.simulation_steps:\\n traci.simulationStep()\\n if self.sleep_time > 0:\\n sleep(self.sleep_time)\\n step += 1\\n # print(traci.vehicle.getSubscriptionResults('bus_0'))\\n\\n traci.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32441d151c319624fd3002c9d2a1333\",\n \"score\": \"0.46980944\",\n \"text\": \"def match(self, patterns):\\n string = self.string\\n n = len(string)\\n results = []\\n for pattern in patterns:\\n top = 0\\n btm = n-1\\n for c in reversed(pattern):\\n if top > btm:\\n break\\n top = self.first_occ[c][0] + self.ranks[c][top]\\n btm = self.first_occ[c][0] + self.ranks[c][btm+1] - 1\\n results.append([self.sa[i] for i in range(top, btm+1)])\\n #results.append(btm-top+1)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1514d35740a5aea9522b3c71a1d92043\",\n \"score\": \"0.4695781\",\n \"text\": \"def anatomical_connectivity_matrix(track_file, label_file, t1_file,\\n diffusion_file, trf_file, outdir,\\n symmetric=True):\\n # Load the dataset\\n label_image = nibabel.load(label_file)\\n label_array = label_image.get_data()\\n label_shape = label_image.get_shape()\\n tractogram = Tractogram(track_file)\\n affine = flirt2aff(trf_file, diffusion_file, t1_file)\\n\\n # Check the validity of the label array\\n kind = label_array.dtype.kind\\n label_positive = (\\n (kind == \\\"u\\\") or ((kind == \\\"i\\\") and (label_array.min() >= 0)))\\n if not (label_positive and label_array.ndim == 3):\\n raise ValueError(\\\"Label array must be a 3d integer array with \\\"\\n \\\"non-negative label values.\\\")\\n\\n # To compute the connectivity matrix we consider only the first and last\\n # point of each track\\n tractogram.apply_affine(affine)\\n endpoints = tractogram.endpoints().astype(int)\\n pointsx, pointsy, pointsz = endpoints.T\\n\\n # Get labels associted to track end points\\n endlabels = label_array[pointsx, pointsy, pointsz]\\n if symmetric:\\n endlabels.sort(axis=0)\\n matrix = ndbincount(endlabels)\\n if symmetric:\\n matrix = numpy.maximum(matrix, matrix.T)\\n\\n # Remove the connectivity associated to the background\\n matrix = matrix[1:, 1:]\\n\\n # Compute the fiber density map\\n density_map = tractogram.density(shape=label_shape)\\n\\n # Save the resulting connectivity matrix and density map\\n proba_file = os.path.join(outdir, \\\"det_paths.nii.gz\\\")\\n density_image = nibabel.Nifti1Image(density_map, label_image.get_affine())\\n nibabel.save(density_image, proba_file)\\n network_file = os.path.join(outdir, \\\"det_network_matrix\\\")\\n numpy.savetxt(network_file, matrix)\\n\\n return proba_file, network_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cae5aae6422fd182dd0e0a640c4f237\",\n \"score\": \"0.46938214\",\n \"text\": \"def reconstruct(b, matching):\\n h_match, w_match = matching.shape[0], matching.shape[1]\\n result = np.zeros([h_match, w_match, b.shape[2]])\\n for y in range(h_match):\\n for x in range(w_match):\\n result[y, x] = b[matching[y, x, 0], matching[y, x, 1]]\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86dc5922d0260006cf10a2b20decdb42\",\n \"score\": \"0.46935165\",\n \"text\": \"def load_images_names_and_joints():\\n training_indices = loadmat('data/FLIC-full/tr_plus_indices.mat')\\n training_indices = training_indices['tr_plus_indices'].flatten()\\n\\n examples = loadmat('data/FLIC-full/examples.mat')\\n examples = examples['examples'][0]\\n joint_ids = ['lsho', 'lelb', 'lwri', 'rsho', 'relb', 'rwri', 'lhip',\\n 'lkne', 'lank', 'rhip', 'rkne', 'rank', 'leye', 'reye',\\n 'lear', 'rear', 'nose', 'msho', 'mhip', 'mear', 'mtorso',\\n 'mluarm', 'mruarm', 'mllarm', 'mrlarm', 'mluleg', 'mruleg',\\n 'mllleg', 'mrlleg']\\n\\n fp_train = open('data/FLIC-full/train_joints.csv', 'w')\\n fp_test = open('data/FLIC-full/test_joints.csv', 'w')\\n for i, example in enumerate(examples):\\n joint = example[2].T\\n joint = dict(zip(joint_ids, joint))\\n fname = example[3][0]\\n joint = get_joint_list(joint)\\n msg = '{},{}'.format(fname, ','.join([str(j) for j in joint.tolist()]))\\n\\n if i in training_indices:\\n print(msg, file=fp_train)\\n else:\\n print(msg, file=fp_test)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2f9e11f694f08872a216bb038151701\",\n \"score\": \"0.46840385\",\n \"text\": \"def forward(self, samples, matches, anchors, refs):\\n # TODO(zhreshold): batch_pick, take multiple elements?\\n # refs [B, M, 4], anchors [B, N, 4], samples [B, N], matches [B, N]\\n # refs [B, M, 4] -> reshape [B, 1, M, 4] -> repeat [B, N, M, 4]\\n n, m = matches.shape[1], refs.shape[1]\\n matches.clamp_(0).unsqueeze_(2)\\n ref_boxes = refs.unsqueeze(1).repeat(1, n, 1, 1)\\n # refs [B, N, M, 4] -> 4 * [B, N, M]\\n ref_boxes = torch.split(ref_boxes, 1, -1)\\n # refs 4 * [B, N, M] -> pick from matches [B, N, 1] -> concat to [B, N, 4]\\n ref_boxes = torch.cat([torch.gather(ref_boxes[i].squeeze(-1), 2, matches) \\\\\\n for i in range(4)], dim=2)\\n # transform based on x, y, w, h\\n # g [B, N, 4], a [B, N, 4] -> codecs [B, N, 4]\\n g = self.corner_to_center(ref_boxes)\\n a = self.corner_to_center(anchors)\\n t0 = ((g[0] - a[0]) / a[2] - self._means[0]) / self._stds[0]\\n t1 = ((g[1] - a[1]) / a[3] - self._means[1]) / self._stds[1]\\n t2 = (torch.log(g[2] / a[2]) - self._means[2]) / self._stds[2]\\n t3 = (torch.log(g[3] / a[3]) - self._means[3]) / self._stds[3]\\n codecs = torch.cat([t0, t1, t2, t3], dim=2)\\n # samples [B, N] -> [B, N, 1] -> [B, N, 4] -> boolean\\n temp = samples.unsqueeze(2).repeat(1, 1, 4) > 0.5\\n # fill targets and masks [B, N, 4]\\n targets = torch.where(temp, codecs, torch.zeros_like(codecs))\\n masks = torch.where(temp, torch.ones_like(temp), torch.zeros_like(temp))\\n return targets, masks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bca85da125ac8b86c218545380618da\",\n \"score\": \"0.4682914\",\n \"text\": \"def drawMatches(img1, kp1, img2, kp2, matches):\\n\\n # Create a new output image that concatenates the two images together\\n # (a.k.a) a montage\\n rows1 = img1.shape[0]\\n cols1 = img1.shape[1]\\n rows2 = img2.shape[0]\\n cols2 = img2.shape[1]\\n\\n out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8')\\n\\n # Place the first image to the left\\n out[:rows1,:cols1] = np.dstack([img1, img1, img1])\\n\\n # Place the next image to the right of it\\n out[:rows2,cols1:] = np.dstack([img2, img2, img2])\\n\\n # For each pair of points we have between both images\\n # draw circles, then connect a line between them\\n for m in matches:\\n\\n # Get the matching keypoints for each of the images\\n img1_idx = m.queryIdx\\n img2_idx = m.trainIdx\\n\\n # x - columns\\n # y - rows\\n (x1,y1) = kp1[img1_idx].pt\\n (x2,y2) = kp2[img2_idx].pt\\n\\n # Draw a small circle at both co-ordinates\\n # radius 4\\n # colour blue\\n # thickness = 1\\n cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1) \\n cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1)\\n\\n # Draw a line in between the two points\\n # thickness = 1\\n # colour blue\\n # cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1)\\n\\n\\n # Show the image\\n cv2.imshow('Matched Features', out)\\n cv2.waitKey(0)\\n cv2.destroyWindow('Matched Features')\\n\\n # Also return the image if you'd like a copy\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d9a9a9a2290e0680750a9c2f55ccb3a\",\n \"score\": \"0.46730903\",\n \"text\": \"def matching(reference, *args, **kwargs):\\n matched_datasets = df_match(reference, *args, dropna=True,\\n dropduplicates=True, **kwargs)\\n\\n if type(matched_datasets) != tuple:\\n matched_datasets = [matched_datasets]\\n\\n matched_data = pd.DataFrame(reference)\\n\\n for match in matched_datasets:\\n match = match.drop(['distance', 'index'], axis=1)\\n matched_data = matched_data.join(match)\\n\\n return matched_data.dropna()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"284bba5c2d32b0ca2476a898f69caab7\",\n \"score\": \"0.46729293\",\n \"text\": \"def process_data_for_MTB(data):\\n # Maximum number of sentences sharing the same entity pair.\\n # This parameter is set for limit the bias towards popular \\n # entity pairs which have many sentences. Of cource, you can\\n # change this parameter, but in our expriment, we use 8.\\n max_num = 8 \\n\\n # We change the original data's format. The ent_data is \\n # a python dict of which key is `head_id#tail_id` and value\\n # is sentences which hold this same entity pair.\\n ent_data = defaultdict(list)\\n for key in data.keys():\\n for sentence in data[key]:\\n if filter_sentence(sentence):\\n continue\\n head = sentence[\\\"h\\\"][\\\"id\\\"]\\n tail = sentence[\\\"t\\\"][\\\"id\\\"]\\n ent_data[head + \\\"#\\\" + tail].append(sentence)\\n\\n ll = 0\\n list_data = []\\n entpair2scope = {}\\n for key in ent_data.keys():\\n if len(ent_data[key]) < 2:\\n continue\\n list_data.extend(ent_data[key][0:max_num])\\n entpair2scope[key] = [ll, len(list_data)]\\n ll = len(list_data)\\n\\n # We will pre-generate `hard` nagative samples. The entpair2negpair\\n # is a python dict of which key is `head_id#tail_id`. And the value of the dict\\n # is the same format as key, but head_id or tail_id is different(only one id is \\n # different). \\n entpair2negpair = defaultdict(list)\\n entpairs = list(entpair2scope.keys())\\n entpairs.sort(key=lambda a: a.split(\\\"#\\\")[0])\\n for i in range(len(entpairs)):\\n head = entpairs[i].split(\\\"#\\\")[0]\\n for j in range(i+1, len(entpairs)):\\n if entpairs[j].split(\\\"#\\\")[0] != head:\\n break\\n entpair2negpair[entpairs[i]].append(entpairs[j])\\n\\n entpairs.sort(key=lambda a: a.split(\\\"#\\\")[1])\\n for i in range(len(entpairs)):\\n tail = entpairs[i].split(\\\"#\\\")[1]\\n for j in range(i+1, len(entpairs)):\\n if entpairs[j].split(\\\"#\\\")[1] != tail:\\n break\\n entpair2negpair[entpairs[i]].append(entpairs[j])\\n\\n if not os.path.exists(\\\"../data/MTB\\\"):\\n os.mkdir(\\\"../data/MTB\\\")\\n json.dump(entpair2negpair, open(\\\"../data/MTB/entpair2negpair.json\\\",\\\"w\\\"))\\n json.dump(entpair2scope, open(\\\"../data/MTB/entpair2scope.json\\\", \\\"w\\\"))\\n json.dump(list_data, open(\\\"../data/MTB/mtbdata.json\\\", \\\"w\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee4ef46368ab5215299c4eca9237b92d\",\n \"score\": \"0.46673894\",\n \"text\": \"def updateAssociations(self, npreceding=30):\\n N = min(npreceding, len(self.frames)-1)\\n minDists = np.inf\\n minIdx = 0\\n for i in range(len(self.frames[-1].people)):\\n distsi = 0.0\\n if len(self.frames[-1].people) < 2:\\n continue\\n x = self.frames[-1].people[i]['pose_keypoints_2d']\\n for j in range(N):\\n if len(self.frames[-2-j].people) == 0:\\n continue\\n y = self.frames[-2-j].people[0]['pose_keypoints_2d']\\n idx = (np.sum(x, 1) > 0)*(np.sum(y, 1) > 0)\\n dists = np.sqrt(np.sum((x[idx, :] - y[idx, :])**2, 1))\\n distsi += np.mean(dists)\\n if distsi < minDists:\\n minDists = distsi\\n minIdx = i\\n if minIdx > 0:\\n p = self.frames[-1].people[0]\\n self.frames[-1].people[0] = self.frames[-1].people[minIdx]\\n self.frames[-1].people[minIdx] = p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7730955d39577e91a747679b0f162b6a\",\n \"score\": \"0.46647954\",\n \"text\": \"def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3):\\n if(len(trackers)==0):\\n return np.empty((0,2),dtype=int), np.arange(len(detections)), np.empty((0,5),dtype=int)\\n\\n iou_matrix = iou_batch(detections, trackers)\\n\\n if min(iou_matrix.shape) > 0:\\n a = (iou_matrix > iou_threshold).astype(np.int32)\\n if a.sum(1).max() == 1 and a.sum(0).max() == 1:\\n matched_indices = np.stack(np.where(a), axis=1)\\n else:\\n matched_indices = linear_assignment(-iou_matrix)\\n else:\\n matched_indices = np.empty(shape=(0,2))\\n\\n unmatched_detections = []\\n for d, det in enumerate(detections):\\n if(d not in matched_indices[:,0]):\\n unmatched_detections.append(d)\\n unmatched_trackers = []\\n for t, trk in enumerate(trackers):\\n if(t not in matched_indices[:,1]):\\n unmatched_trackers.append(t)\\n\\n #filter out matched with low IOU\\n matches = []\\n for m in matched_indices:\\n if(iou_matrix[m[0], m[1]]<iou_threshold):\\n unmatched_detections.append(m[0])\\n unmatched_trackers.append(m[1])\\n else:\\n matches.append(m.reshape(1,2))\\n \\n if(len(matches)==0):\\n matches = np.empty((0,2),dtype=int)\\n else:\\n matches = np.concatenate(matches,axis=0)\\n\\n return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"602fc140a4234217b6d1c0250185e2f3\",\n \"score\": \"0.46600485\",\n \"text\": \"def RunHydroAnalysis(psf, dcd, psf_info, close_lipid,close_prot,outname,segprot,segmemb,pdb\\\\\\n ,first_frame,last_frame,skip,libpath):\\n\\n prot_cand_atom, memb_cand_atom = readCandidates(psf_info.lipids,libpath)\\n atomid_cand_prot, atomid_cand_memb = HydroCandidateSelection(psf,segprot, segmemb, prot_cand_atom, memb_cand_atom)\\n prot_candidates, memb_candidates = GetCandidate(psf_info,close_lipid,close_prot,atomid_cand_prot, atomid_cand_memb)\\n prot_candidates = common.convertIntToStr(prot_candidates)\\n memb_candidates = common.convertIntToStr(memb_candidates)\\n\\n\\n\\n univers = mda.Universe(psf, dcd)\\n\\n counting = {}\\n nbFrame = 0\\n\\n output_avg = open(\\\"%s_average_hydrophobic_contact_per_frame.csv\\\"%outname,\\\"w\\\")\\n output_avg.write(\\\"#Residue,AVG_nb_of_contact\\\\n\\\")\\n\\n output_raw = open(\\\"%s_hydrophobic_contact_raw_data.csv\\\"%outname,\\\"w\\\")\\n output_raw .write(\\\"#Frame,ProtResidue,MembRes\\\\n\\\")\\n\\n frame_to_read = first_frame\\n\\n for ts in univers.trajectory:\\n if ts.frame == frame_to_read and frame_to_read < last_frame:\\n nbFrame += 1\\n sel_memb = \\\"index %s\\\" % (\\\" \\\".join(memb_candidates))\\n memb = univers.select_atoms(sel_memb)\\n sel_prot = \\\"index %s\\\" % (\\\" \\\".join(prot_candidates))\\n prot = univers.select_atoms(sel_prot)\\n dist_mat = mda.analysis.distances.distance_array(prot.positions, memb.positions)\\n\\n conctacts =[]\\n\\n #Contact will be fill with list of 2 elements: [atomid in prot,atomid in memb]\\n for i in range(dist_mat.shape[0]):\\n for j in range(dist_mat.shape[1]):\\n if dist_mat[i,j] < 3:\\n conctacts.append([prot_candidates[i],memb_candidates[j]])\\n\\n\\n #parse contact and write the raw data\\n for i in range(len(conctacts)):\\n residues_prot = common.obtainResInfo(int(conctacts[i][0]), psf_info.NbAtomPerProtRes)\\n residues_lipids = common.obtainResInfo(int(conctacts[i][1]), psf_info.NbAtomPerLipRes)\\n\\n if residues_prot not in counting:\\n counting[residues_prot] = 1\\n\\n else:\\n counting[residues_prot] += 1\\n\\n output_raw.write(\\\"%i,%s,%s\\\\n\\\"%(ts.frame,residues_prot,residues_lipids))\\n\\n frame_to_read += skip\\n\\n\\n\\n #Get the average\\n for residue in counting.keys():\\n counting[residue] = float(counting[residue])/nbFrame\\n\\n output_avg.write(\\\"%s,%f\\\\n\\\"%(residue,counting[residue]))\\n\\n for residue in psf_info.protresid:\\n if str(residue) not in counting.keys():\\n counting[str(residue)] = 0\\n\\n if pdb != 'None':\\n common.Mapped(pdb,counting,segprot,outname,\\\"average_hydrophobic_contact_per_frame\\\")\\n\\n\\n output_avg.close()\\n output_raw.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d78d553e6ec2ecc9422c0de6dc75c58\",\n \"score\": \"0.46582937\",\n \"text\": \"def group_limbs_same_idcard(connected_limbs, joint_list):\\n idcard_to_joint_assoc = []\\n for limb_type in range(NUM_LIMBS):\\n joint_src_type, joint_dst_type = LIMBS[limb_type]\\n for limb_info in connected_limbs[limb_type]:\\n idcard_assoc_idx = []\\n # find joints current in list\\n # print('CURRENT IDCARD', idcard_to_joint_assoc)\\n for idcard, idcard_limbs in enumerate(idcard_to_joint_assoc):\\n # print('IDCARD LIMBS', idcard_limbs)\\n # print('LIMB INFO', limb_info)\\n if idcard_limbs[joint_src_type]==limb_info[0] or \\\\\\n idcard_limbs[joint_dst_type]==limb_info[1]: # if already in list\\n # print('INDEX', idcard)\\n idcard_assoc_idx.append(idcard) # append index to check later\\n # print('IDCARD ASSOC IDX', idcard_assoc_idx)\\n if len(idcard_assoc_idx)==1: # found 1 joints\\n idcard_limbs = idcard_to_joint_assoc[idcard_assoc_idx[0]]\\n if idcard_limbs[joint_dst_type]!=limb_info[1]: # add to list\\n idcard_limbs[joint_src_type] = limb_info[0]\\n idcard_limbs[joint_dst_type] = limb_info[1]\\n # update info: score, num joints in limbs\\n idcard_limbs[-1] += 1\\n idcard_limbs[-2] += joint_list[\\n limb_info[1].astype(int), 2\\n ] + limb_info[2]\\n \\n elif len(idcard_assoc_idx)==2: # found 2 joints, merge them\\n idcard1_limbs = idcard_to_joint_assoc[idcard_assoc_idx[0]]\\n idcard2_limbs = idcard_to_joint_assoc[idcard_assoc_idx[1]]\\n membership = ((idcard1_limbs>=0)&(idcard2_limbs>=0))[:-2]\\n # print(idcard1_limbs>=0, idcard2_limbs>=0)\\n # print(membership)\\n # print('IDCARD 1', idcard1_limbs)\\n # print('IDCARD 2', idcard2_limbs)\\n if not membership.any(): # if 2 idcard has no connect joint, merge them\\n idcard1_limbs[:-2] += (idcard2_limbs[:-2]+1) # update joint connected\\n idcard1_limbs[-2:] += idcard2_limbs[-2:] # add score\\n idcard1_limbs[-2] += limb_info[2] # overall score\\n idcard_to_joint_assoc.pop(idcard_assoc_idx[1])\\n else: # same with len==1 above\\n # print('CASE')\\n if np.sum((idcard1_limbs>=0)[:-2]) < np.sum((idcard2_limbs>=0)[:-2]):\\n idcard1_limbs = idcard2_limbs\\n idcard_to_joint_assoc.pop(idcard_assoc_idx[0])\\n else:\\n idcard_to_joint_assoc.pop(idcard_assoc_idx[1])\\n idcard1_limbs[joint_src_type] = limb_info[0]\\n idcard1_limbs[joint_dst_type] = limb_info[1]\\n idcard1_limbs[-1] += 1\\n idcard1_limbs[-2] += joint_list[limb_info[1].astype(int), 2] + limb_info[2]\\n else: # if not in list, create new idcard\\n row = -1*np.ones(NUM_LIMBS+2)\\n row[joint_src_type] = limb_info[0]\\n row[joint_dst_type] = limb_info[1]\\n row[-1] = 2\\n row[-2] = sum(joint_list[limb_info[:2].astype(int), 2]) + limb_info[2]\\n idcard_to_joint_assoc.append(row)\\n\\n # delete idcard has very few limbs\\n idcard_to_delete = []\\n for idcard_id, idcard_info in enumerate(idcard_to_joint_assoc):\\n if idcard_info[-1] < MINIMUM_NUM_JOINT or idcard_info[-2]/idcard_info[-1] < 0.2: # not enough 4 corner\\n idcard_to_delete.append(idcard_id) \\n for index in idcard_to_delete[::-1]:\\n idcard_to_joint_assoc.pop(index)\\n return np.array(idcard_to_joint_assoc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0dbf52d7d2aa68c5741181b26763b16\",\n \"score\": \"0.46542156\",\n \"text\": \"def play_match(self):\\n\\n\\t\\t# To save time, try to find mantra lineups already calculated\\n\\t\\ttry:\\n\\t\\t\\tlineup1 = dbf.db_select(\\n\\t\\t\\t\\t\\ttable='mantra_lineups',\\n\\t\\t\\t\\t\\tcolumns=[f'day_{self.day}'],\\n\\t\\t\\t\\t\\twhere=f'team_name=\\\"{self.team1.name}\\\"')[0].split(', ')\\n\\t\\t\\tmalus1 = int(lineup1[0])\\n\\t\\t\\tlineup1 = [tuple(el.split(':')) for el in lineup1[1:]]\\n\\n\\t\\t\\tlineup2 = dbf.db_select(\\n\\t\\t\\t\\t\\ttable='mantra_lineups',\\n\\t\\t\\t\\t\\tcolumns=[f'day_{self.day}'],\\n\\t\\t\\t\\t\\twhere=f'team_name=\\\"{self.team2.name}\\\"')[0].split(', ')\\n\\t\\t\\tmalus2 = int(lineup2[0])\\n\\t\\t\\tlineup2 = [tuple(el.split(':')) for el in lineup2[1:]]\\n\\n\\t\\t\\t# malus1 = sum([1 for nm, rl in lineup1 if '*' in rl])\\n\\t\\t\\t# malus2 = sum([1 for nm, rl in lineup2 if '*' in rl])\\n\\n\\t\\t\\tlineup1 = [nm for nm, rl in lineup1]\\n\\t\\t\\tlineup2 = [nm for nm, rl in lineup2]\\n\\n\\t\\t# if not found, calculate them with mantra algorithm\\n\\t\\texcept IndexError:\\n\\t\\t\\tlineup1, _, malus1 = mf.mantra(day=self.day,\\n\\t\\t\\t fantateam=self.team1.name,\\n\\t\\t\\t starting_players=10)\\n\\t\\t\\tlineup2, _, malus2 = mf.mantra(day=self.day,\\n\\t\\t\\t fantateam=self.team2.name,\\n\\t\\t\\t starting_players=10)\\n\\n\\t\\tself.update_fantateams_data(lineup1, lineup2, malus1, malus2)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1695,"cells":{"query_id":{"kind":"string","value":"f0f7919b90c422c4f92ba926d87b1354"},"query":{"kind":"string","value":"Persist results of block b."},"positive_passages":{"kind":"list like","value":[{"docid":"6cc70a39c64b0f6b2e3631f1e6463026","score":"0.71250165","text":"def persist_result(b, psco_name=''):\n from storage_model.block import Block\n bl = Block()\n bl.block = b\n bl.make_persistent(psco_name)","title":""}],"string":"[\n {\n \"docid\": \"6cc70a39c64b0f6b2e3631f1e6463026\",\n \"score\": \"0.71250165\",\n \"text\": \"def persist_result(b, psco_name=''):\\n from storage_model.block import Block\\n bl = Block()\\n bl.block = b\\n bl.make_persistent(psco_name)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"bb23a3e42525e164153054d51237f6b4","score":"0.5830644","text":"def _store_result(self, total_count, open_result, values, data):\n [self._result] = values\n self._push(None)","title":""},{"docid":"8aab13632aba6065c760c498de19e80b","score":"0.5778085","text":"def store_results(self):\n pass","title":""},{"docid":"60814a5360a1a84320d7b75bf80c5574","score":"0.57654816","text":"def callback(result):\n #print 'Got call back with %s' % (result,)\n d, i, j = result\n s[j,i] = s[i,j] = d\n self.save(infork=1)","title":""},{"docid":"94a0e0c00a42c4f39b31caf4ae34d83f","score":"0.5729467","text":"def storeState(self):\r\n\r\n self.storeStandardResults()\r\n self.recsigmaA.append(self.sigmaA.copy())\r\n self.recalphaA.append(self.alphaA.copy())","title":""},{"docid":"0f6693817bf29153a20b71f46b2de07b","score":"0.5527896","text":"def store_result(self):\n\n pass","title":""},{"docid":"516a0baf23fdf1fe8b153ff8fe042214","score":"0.5510886","text":"def save_block(self):\n\n # If no dump file is specified, do not store any data in state_results.\n if self.options.dump_filename is None:\n return\n\n cuda.memcpy_dtoh(self._rng_state, self._gpu_rng_state)\n for i in range(0, self.num_vars):\n cuda.memcpy_dtoh(self.vec[i], self._gpu_vec[i])\n\n if self.options.output_mode == 'path':\n self.state_results.append((self.sim_t, copy.deepcopy(self.vec), self.vec_nx.copy(),\n self._rng_state.copy()))\n else:\n self.state_results.append((self.sim_t, copy.deepcopy(self.vec), self.vec_nx.copy(),\n self._rng_state.copy(), self.start_t, copy.deepcopy(self.vec_start),\n self.vec_start_nx.copy()))","title":""},{"docid":"b5213d094c78c4dcdf3c435d65678e73","score":"0.5439924","text":"def _append(self, block):\n serialized_block = encode(block)\n self.head = self.object_store.add(serialized_block)\n self._cache[self.head] = block","title":""},{"docid":"ecffc5eca71c8dbad5deaceac9a8e9bb","score":"0.5425643","text":"def _save_processed_blocks(self, block_range):\n block_flags = [{\n \"id\": block,\n \"name\": \"events_extracted\",\n \"value\": 1\n } for block in range(*block_range)]\n self.client.bulk_index(index=self.indices[\"block_flag\"], docs=block_flags)","title":""},{"docid":"9e141fb124a3d44162d0e2f49dfa414b","score":"0.5394522","text":"def notify(self, block):\n self.block_store.append(block)\n\n if len(self.block_store) == self._blocks_per_chunk:\n self._difficulty = self.recalculate_difficulty()\n self.block_store = []","title":""},{"docid":"1b315f5bc784d609449e1b030d0c6487","score":"0.5384338","text":"def save_results(self):\r\n pass","title":""},{"docid":"3c46e75e7ad7a22b55d86b7fc77088c1","score":"0.5382038","text":"def save_txresults(self, tx_results: list, new_block_hash: str):\n Logger.debug(f'save_txresults:{tx_results}', LOG_BLOCK)\n\n # write transaction result with batch\n with self.db.create_write_batch() as wb:\n for tx_result in tx_results:\n # key from transaction hash\n key = DbPrefix.TXRESULT + bytes.fromhex(tx_result.get('txHash'))\n\n # get value from transaction result\n tx_result['blockHash'] = new_block_hash\n value = json.dumps(tx_result).encode()\n\n self.db.write_batch(write_batch=wb, key=key, value=value)","title":""},{"docid":"95130d44fafabf0f65c579e8df6c3f6f","score":"0.51690006","text":"def hard_copy_returned(self):\n for rec in self:\n if rec.state == 'done':\n rec.write({\n 'state': 'returned',\n 'returned_by': self.env.uid,\n 'returned_date': datetime.datetime.now().strftime(\"%Y-%m-%d\")\n })","title":""},{"docid":"8563dd5c1457196452875bd49a38f824","score":"0.5161495","text":"def save(self):\n state = (self.last_block, self.addresses)\n pickle.dump(state, open(self.filepath, \"wb\"))\n print(self.addresses.__len__())","title":""},{"docid":"4815b7423161d1718424c347bd6edb4b","score":"0.51102716","text":"def put(self, block):\n raise TypeError('not implemented')","title":""},{"docid":"da76d0ab169320200a15a5025e1e28f0","score":"0.5095671","text":"def _add_block(self, block):\n if len(self._block_hash) >= self._backtrack_limit:\n self._block_hash.popleft()\n self._block_height.popleft()\n\n with self._lock:\n self._block_hash.append(block.block_hash)\n self._block_height.append(block.height)\n\n # Process record into balance\n self._balance_processor.add_block(block)","title":""},{"docid":"4f6d9a453b27547810703ac1d34b66f5","score":"0.5062092","text":"def store_completed(self, store_completed):\n\n self._store_completed = store_completed","title":""},{"docid":"94827d90b07f2553dda841383c5f4f99","score":"0.50238407","text":"def stored(self):\n self.storage_count += 1","title":""},{"docid":"c619208a846e1e6f533fbccf5fb262c0","score":"0.50149727","text":"def store_after_invalidate_test(self):\n self.cache.store(self.prediction)\n self.cache.invalidate()\n self.cache.store(self.prediction)","title":""},{"docid":"f23d0dbf9fc439da3dec455d3d076222","score":"0.5007939","text":"def _finishExecuting(self, result):\n self._currentBlock = None\n self._checkNextBlock()","title":""},{"docid":"f1a46ed6740bed53c8e79dfb8cf07558","score":"0.4996419","text":"def flush(self):\n super().flush()\n self.persistenceCounter = 0\n self.basicStatPP = None","title":""},{"docid":"1ded39e047f6c1c77a5bce69b6d694fb","score":"0.49933785","text":"def save_block(self, block: dict):\n block_hash = block['hash']\n block_height = block['height']\n # save block\n self.db.put(DbPrefix.BLOCK + bytes.fromhex(block_hash), json.dumps(block).encode())\n\n # save block height/hash for block query request\n self.db.put(DbPrefix.BLOCK_INDEX + block_height.to_bytes(DEFAULT_BYTE_SIZE, DATA_BYTE_ORDER),\n bytes.fromhex(block_hash))\n\n Logger.debug(f'save block : block_height:{block_height}, block_hash: {block_hash}, block: {block}', LOG_BLOCK)","title":""},{"docid":"9bb8b4e2878c2b0421825a53bad933b4","score":"0.49447474","text":"def _integrate_into_store(self):\n with connection.cursor() as cursor:\n self._dequeuing_delete_rmcb_records(cursor)\n self._dequeuing_delete_buffered_records(cursor)\n current_id = InstanceIDModel.get_current_instance_and_increment_counter()\n self._dequeuing_merge_conflict_buffer(cursor, current_id)\n self._dequeuing_merge_conflict_rmcb(cursor)\n self._dequeuing_update_rmcs_last_saved_by(cursor, current_id)\n self._dequeuing_delete_mc_rmcb(cursor)\n self._dequeuing_delete_mc_buffer(cursor)\n self._dequeuing_insert_remaining_buffer(cursor)\n self._dequeuing_insert_remaining_rmcb(cursor)\n self._dequeuing_delete_remaining_rmcb(cursor)\n self._dequeuing_delete_remaining_buffer(cursor)","title":""},{"docid":"d0d9bcc5ea89a69a445ed15885162306","score":"0.49373809","text":"def sav(self):\n self.bak = self.acc","title":""},{"docid":"2c0837f1afe35a452a7537114194cd2b","score":"0.493308","text":"def persist_data(self, df):\n self.storage.store_results(df)","title":""},{"docid":"fc832fae1bc5fb5a0de52cb5c6538b33","score":"0.4929122","text":"def write_back(self, block_id):\n\n self.parent.write(block_id)","title":""},{"docid":"2d5adfcb3f2c088cc8555359f9f49724","score":"0.49286047","text":"def endLoad(self):\n if self.currentTxn:\n if self.distortionCount == 0:\n self.appendTxn(self.currentTxn)\n else:\n self.compromisedTxns.append(self.currentTxn)\n self.distortionCount = 0\n self.currentTxn = None","title":""},{"docid":"624e33ae7971d87a42d8e5263d78e689","score":"0.49024948","text":"def checkpoint(self):\n self._iterator, self._checkpoint = tee(self._iterator)","title":""},{"docid":"c16d34fc52beb1dbd5652bfe01eeda0f","score":"0.49001044","text":"def test_setting_result_calls_persist(self):\n from furious.async import Async\n\n result = \"here be the results.\"\n\n persistence_engine = mock.Mock()\n\n job = Async(target=dir, persist_result=True)\n\n # Manually set the persistence_engine so that the Async doesn't try to\n # reload the mock persistence_engine.\n job._persistence_engine = persistence_engine\n\n job.executing = True\n job.result = result\n\n persistence_engine.store_async_result.assert_called_once_with(job.id,\n result)","title":""},{"docid":"d33f4b1aaea016ef1978ad34474a6c00","score":"0.48894456","text":"def on_snapshot(self, asks: Sequence[List], bids: Sequence[List]):\n for price, size in asks:\n self.asks[float(price)] = float(size)\n\n for price, size in bids:\n self.bids[float(price)] = float(size)","title":""},{"docid":"2618adeb4c89c4eff421fb0b365b79d7","score":"0.48851195","text":"def dump_results(self, scores: Mapping[BaseEntity, Tuple]) -> None:\n self.result = pickle.dumps(scores)\n self.completed = True","title":""},{"docid":"da560691764241c09a94bf5ca6198172","score":"0.48743635","text":"def save_result(self, item, spider, task):\n for link, img in item.items():\n result = ResultsItem()\n result['task'] = task\n result['link'] = link\n result['img'] = img\n result['rank'] = self.items_processed[spider.name]\n result.save()\n upload_photo_to_aws.delay(img, str(spider.user_pk), spider.search_phrase[0])","title":""},{"docid":"7ae57f0e59c8534770fd210821e28b11","score":"0.4844505","text":"def flushBlockList(self):\n self.blocks = []","title":""},{"docid":"51119e7e2842f6f47ec9769304425e99","score":"0.48391396","text":"def writeResults(self, result):\n result.done()","title":""},{"docid":"94b1922be031f0f9e43133b8ca1346c9","score":"0.4838126","text":"def sync_memory(self):\n latest_brain_version = load_item_from_dynamo()\n updated_brain_version = (write_dict_to_s3('ts-chat-bot',\n 'brains/word_graph.json',\n self.word_graph.graph),\n write_dict_to_s3('ts-chat-bot',\n 'brains/answer_trie.json',\n self.answer_trie.trie))\n write_item_to_dynamo(updated_brain_version, latest_brain_version)\n delete_file_in_s3('ts-chat-bot',\n 'brains/word_graph.json',\n latest_brain_version[0])\n delete_file_in_s3('ts-chat-bot',\n 'brains/answer_trie.json',\n latest_brain_version[1])","title":""},{"docid":"d17aa57736f51e51b228ec7963de2774","score":"0.48349193","text":"def cache(self, result):\n with open(self._filename, \"w\") as file_:\n try:\n pkl.dump(result, file_)\n except Exception as err:\n LOGGER.error(\"Could not dump object!\\n\\t%s\", err.message)","title":""},{"docid":"7a68967efac2f3ae929db5773cf788bf","score":"0.48338112","text":"def save_results(self, save_path):\n self.fcst.to_pickle(save_path)","title":""},{"docid":"cb9a13bbd26f8855a8a0a9ba09d80592","score":"0.48152733","text":"def write_block( self, from_block, data ):\n\t\traise NotImplementedError('zut alors!')","title":""},{"docid":"9c506e03385d2a1a6fbb9caddd879db9","score":"0.48140597","text":"def f2b(self):\n for passenger in self.passengers:\n passenger.block = passenger.id","title":""},{"docid":"61463616bcb88fe79db46b64d24d8be7","score":"0.4797915","text":"def cache(self, roi, result):\n with h5py.File(self._filename, \"a\") as file_:\n dataset = self._get_dataset(file_)\n slice_ = roi.toSlice()\n dataset[slice_] = result","title":""},{"docid":"b8f5f719b6d0e281f505c6cb82d270bc","score":"0.47818726","text":"def storeState(self):\r\n\r\n self.record_actions[self.t] = self.action\r\n self.record_reward_values[self.t] = self.reward_value","title":""},{"docid":"b9b94db4f1f3da0aa339384d8467f993","score":"0.47742218","text":"def __save(self):\n self.__saved_record = copy.deepcopy(self.record)\n self.__saved_counter = copy.deepcopy(self.counter)\n self.__saved_objects = copy.deepcopy(self.current_objects)","title":""},{"docid":"5a5cc484c3a9f755bc81d31f7d11cc02","score":"0.47738168","text":"def backup(self):","title":""},{"docid":"ce6ca70147423ebd4aeee6986c1159cd","score":"0.4772042","text":"def store_run_results(model, a_results, rep):\n \n col = 0\n \n for i in model.get_results().items():\n #df_results.loc[rep][i[0]] = i[1]\n a_results[rep][col] = i[1]\n col += 1","title":""},{"docid":"86495ba7ec6fad37452662ebbd648507","score":"0.47719678","text":"def handle_relation(self, astra_block):\n if type(astra_block) == AstraBatchBlock:\n for astra_block_temp in self.blocks:\n if type(astra_block_temp) == AstraShuffleBlock:\n astra_block_temp.core.set_batches(astra_block.batches)\n return\n\n if type(astra_block) == AstraJobBlock:\n self.input_string = self.replace_block([astra_block])\n return","title":""},{"docid":"41b8f1397a1041d2085bfdb0632f83d6","score":"0.47415376","text":"def _blocks_data_handler(self, block_data, socket):\n self._block_data_lock.acquire()\n for block in block_data:\n self._quantcoin.store_block(Block.from_json(block))\n self._block_data_lock.release()","title":""},{"docid":"48a1e4017450b8b0a0dc8ca2e651258a","score":"0.47386348","text":"def blockstorage(self):\n return self._bs # TODO: is this necessary","title":""},{"docid":"6fd7dca980c87ec7ea6b237b13cca2ac","score":"0.47367123","text":"def persist_progress(self, collection):\n for obj in collection:\n yield obj\n self.update(obj.key)","title":""},{"docid":"359356ccbb7f56732de2050d5e18b91d","score":"0.47267163","text":"def save_result(ctx, results):\n\n if type(results) == Result:\n results = [results]\n\n ses = ctx.obj['session']\n for result in results:\n ses.add(result)\n try:\n ses.flush()\n except IntegrityError:\n click.echo(\"Incompatible result: typename:%s name:%s with %d arrays:\" % \\\n (result.typename, result.name, len(result.data)))\n for typ, nam, arr in result.triplets():\n click.echo(\"\\tarray typename:%s name:%s shape:%s\" % (typ, nam, arr.shape))\n #click.echo(\"Parameters:\")\n #click.echo(pprint.pformat(result.params))\n raise\n ses.commit()\n click.echo('id:%d typename:%s name:%s narrays:%d' % (result.id, result.typename, result.name, len(result.data)))\n return","title":""},{"docid":"581c4e2b125cbd2a332ef0259c5e8dbf","score":"0.472436","text":"def write_data_slice(self, partial_result):\n self.store_data_chunk(\"data\", partial_result, grow_dimension=0, close_file=False)","title":""},{"docid":"3434fa20a9e1978836dee9d6cfa86ae0","score":"0.47236443","text":"def write_results(self):\n for (name, step) in self.steps.items():\n self.snapshot(step)\n self.fh.close()","title":""},{"docid":"a1fadc35fa2262b93eaa574afdb6f08a","score":"0.47232848","text":"def _setResult(self, result):\n if self._result is not _NO_RESULT:\n raise AlreadyCalledError()\n\n self._result = result","title":""},{"docid":"8e134f16791fbeae9b66fdfeb805380c","score":"0.47203913","text":"def store(self, envid, obs_1, obs_2):\n assert self.ptr[envid].item() < self.block_size # buffer has to have room so you can store\n ptr = self.ptr[envid].item()+ envid * self.block_size\n self.obs_buf_1[ptr].copy_(obs_1)\n self.obs_buf_2[ptr].copy_(obs_2)\n # self.act_buf[ptr].copy_(act)\n # self.rst_buf[ptr].copy_(result)\n \n self.ptr[envid] += 1","title":""},{"docid":"01414db0a879db27d36437d603eb866f","score":"0.47022763","text":"def test_write_pickle(bucket_results):\n c1 = CountCache()\n c1[1900, 'token', 'POS', 1] = 1\n\n path = c1.flush(bucket_results.path)\n\n with open(path, 'rb') as fh:\n c2 = pickle.load(fh)\n\n assert c2[1900, 'token', 'POS', 1] == 1","title":""},{"docid":"0ea1ed13a450af065602189257f6170d","score":"0.46937618","text":"def store_existing_test(self):\n self.cache.store(self.prediction)\n self.cache.store(self.prediction)","title":""},{"docid":"732dec33d179ad76ebc03452ee2a31fc","score":"0.46787602","text":"def test_setting_result_does_not_call_persist(self):\n from furious.async import Async\n\n result = \"here be the results.\"\n\n persistence_engine = mock.Mock()\n\n job = Async(target=dir)\n\n # Manually set the persistence_engine so that the Async doesn't try to\n # reload the mock persistence_engine.\n job._persistence_engine = persistence_engine\n\n job.executing = True\n job.result = result\n\n self.assertEqual(persistence_engine.store_async_result.call_count, 0)","title":""},{"docid":"37440cae2c20faddac6eeb2b521b2aa3","score":"0.46737978","text":"def test_persist_result_helper(self):\n wrapper = AnalysisResultWrapper().save()\n analysis_result = AnalysisResultMeta(sample_similarity=wrapper).save()\n sample_similarity = create_mvp_sample_similarity()\n\n persist_result_helper(sample_similarity,\n analysis_result.uuid,\n 'sample_similarity')\n analysis_result.reload()\n self.assertIn('sample_similarity', analysis_result)\n wrapper = getattr(analysis_result, 'sample_similarity').fetch()\n self.assertIn('status', wrapper)\n self.assertEqual('S', wrapper.status)","title":""},{"docid":"be2f01b47695d30ab4ea5c81d88828fe","score":"0.4673275","text":"async def commit_data(self, result: Dict, namespace: str, hostname: str,\n boot_timestamp: int):\n records = []\n key = f'{namespace}.{hostname}'\n prev_res = self.previous_results.get(key, None)\n last_device_session = self._node_boot_timestamps.get(key, 0)\n # No data previously polled with this service from the the\n # current namespace and hostname. Check if the datastore contains some\n # information about\n filter_df = f\"hostname.str.match('{hostname}') and \" \\\n f\"namespace.str.match('{namespace}')\"\n if prev_res is None:\n df = self._db_access.read(\n self.schema_table._table,\n 'pandas',\n start_time='',\n end_time='',\n columns=self.schema_table.fields,\n view='latest',\n key_fields=self.schema_table.key_fields(),\n add_filter=filter_df,\n hostname=[hostname],\n namespace=[namespace]).query('active')\n # Get the latest device session we have written with this service\n if not df.empty:\n # If we are dealing with old data then the value of\n # deviceSession will be unset, if this is the case, set the\n # value to 0 so that we force write. We need to check only\n # the first value of deviceSession as it is equal for all the\n # latest record, as in case of reboot we rewrite everything.\n if not df['deviceSession'].iloc[:1].isnull().any():\n last_device_session = df['deviceSession'].iloc[0] or 0\n else:\n last_device_session = 0\n\n self._node_boot_timestamps[key] = int(last_device_session)\n\n prev_res = df.to_dict('records')\n self.previous_results[key] = prev_res\n\n if result or prev_res:\n # Check whether there has been a node reboot and in this case\n # write everything\n write_all = abs(boot_timestamp -\n last_device_session) > TIME_DRIFT_TOLERANCE\n if write_all:\n last_device_session = int(boot_timestamp)\n self._node_boot_timestamps[key] = boot_timestamp\n\n adds, dels = self.get_diff(prev_res, result, write_all)\n if adds or dels:\n self.previous_results[key] = copy.deepcopy(result)\n for entry in adds:\n entry['deviceSession'] = last_device_session\n records.append(entry)\n # make sure to use the same timestamp for all the deleted\n # records. If the result is not empty, use that to get the\n # timestamp. If the result is empty, use the current timestamp\n if result:\n del_ts = result[0]['timestamp']\n else:\n del_ts = int(datetime.now(\n tz=timezone.utc).timestamp() * 1000)\n\n for entry in dels:\n if entry.get(\"active\", True):\n # If there's already an entry marked as deleted\n # No point in adding one more\n entry.update({\n \"active\": False,\n \"timestamp\": del_ts\n })\n records.append(entry)\n\n self._post_work_to_writer(records)","title":""},{"docid":"d5747936655711f84b79ab94b1f6092f","score":"0.46716902","text":"def dump( self ):\n for b in self.blocks:\n b.dump()","title":""},{"docid":"18d7a57aea22e71362b2d73f4f56873d","score":"0.46701097","text":"def store(self):\n picklefile = file(self.cache_filename, 'wb')\n stamp = datetime.now()\n pickle.dump((stamp, self.addrbk), picklefile)","title":""},{"docid":"84ac232a01f62dea04ffb27633f525a1","score":"0.46680924","text":"def callback(result):\n d, i, j = result\n self.dists[j,i] = self.dists[i,j] = d","title":""},{"docid":"f5d74018dd3556cd844bf9f5a25098ac","score":"0.4657807","text":"def collect_result(stub):\n logging.debug(\"#### collect_result ####\")\n arr_B = stub.fetch_result_from_peer(NIL)\n peer_B_data = [x.item for x in arr_B]\n # end connection of process A\n stub.end_connection(NIL)\n return peer_B_data\n #raise NotImplementedError(\"Method not implemented\")","title":""},{"docid":"5e7da9a8bda5b64d0a39a484be772ef3","score":"0.46565598","text":"def _backup_and_load_cache(self):\n for group in self.optimizer.param_groups:\n for p in group['params']:\n param_state = self.state[p]\n param_state['backup_params'] = torch.zeros_like(p.data)\n param_state['backup_params'].copy_(p.data)\n p.data.copy_(param_state['cached_params'])","title":""},{"docid":"d11e65b2d5e2d52583b7f28ab1dda24f","score":"0.46543387","text":"def set_result(self, result):\n ...","title":""},{"docid":"7a2f71e17c14bdc277d2bac8ef0289d6","score":"0.46539396","text":"def store(self):\n self.Cb = self.C.copy()\n self.Vb = self.V.copy()\n self.Vcb = self.Vc.copy()\n if self.en_direct:\n self.Mb = self.M.copy()\n self.Mcb = self.Mc.copy()\n if self.en_bias:\n self.db = self.d.copy()\n self.dcb = self.dc.copy()\n self.hidden.store()","title":""},{"docid":"f653dbb1e8db8d57bd5ca2017aa8e677","score":"0.46529964","text":"def endLoad(self):\n if self.currentTxn:\n if self.currentTxn.hasEndProbe:\n self.appendTxn(self.currentTxn)\n else:\n self.compromisedTxns.append(self.currentTxn)\n self.currentTxn = None","title":""},{"docid":"e701bd02f2f63b61a48902fb8282fbd8","score":"0.4648478","text":"def last_block(self, block: 'Block'):\n with self._lock:\n self._last_block = block","title":""},{"docid":"a2171e8d26660a4b2f7cf7d51e24cddb","score":"0.46372822","text":"def store_resultSet(self, session, rset):\n raise NotImplementedError","title":""},{"docid":"90e0225d60b41842ecb34f78f86217fd","score":"0.46336642","text":"def next_block( **kw ):\n global snapshots_dir, state_engine\n\n if snapshots_dir is None:\n snapshots_dir = tempfile.mkdtemp(prefix='zonefilemanage-test-databases-')\n\n\n # Flush all transactions and reset state engine\n kw['next_block_upcall']()\n kw['sync_virtualchain_upcall']()","title":""},{"docid":"6c2341f28f20ff4ddbd4a2799155e8a0","score":"0.46315885","text":"def save(self):\n\n # Don't do real update if doing batch update\n if not self._batch_acquire(False):\n return\n\n try:\n is_last_exit = (self._batch_counter == 1)\n if is_last_exit:\n self.storage.save(self.data)\n finally:\n self._batch_release()","title":""},{"docid":"cd7bc01e0ffdad22948a6563464824e7","score":"0.463055","text":"def db_save( block_id, filename, db_state=None ):\n print \"\\nreference implementation of db_save\\n\"\n return True","title":""},{"docid":"f14ce5d3b139b9752487a12f55d946ae","score":"0.46303406","text":"def _commit(self) ->None:\n exponential_average_factor = 0.0\n self.num_batches_tracked += 1\n if self.momentum is None:\n exponential_average_factor = 1.0 / float(self.num_batches_tracked)\n else:\n exponential_average_factor = self.momentum\n mean = self.sum / self.counter\n var = self.sum_squares / self.counter - mean ** 2\n m = exponential_average_factor\n self.running_mean *= 1 - m\n self.running_mean += mean * m\n self.running_var *= 1 - m\n self.running_var += var * m\n self.sum.zero_()\n self.sum_squares.zero_()\n self.counter = 0\n self.tracked = 0","title":""},{"docid":"ffae898dd305af167f7030c54923a2ec","score":"0.46296242","text":"def save(self):\n if len(self._cache_list) > 0:\n dump_cache(self.cache_file, self._cache_list)\n if self._last_cache is not None:\n dump_cache(self.last_file, (self._last_cache,))\n\n self.conn.commit()","title":""},{"docid":"42164cca24570777f65788d18fa49eee","score":"0.46158135","text":"def commit(self): \n\t\tif not self.trans:\n\t\t\t# If no open transaction; then nothing to do\n\t\t\tprint \"NO TRANSACTION\"\n\t\t\treturn\n\t\n\t\tself.trans.append(self.latest_trans)\n\t\t# Going through list of transaction from oldest, as we want database\n\t\t# to have latest value for any variable\n\t\tfor t in self.trans:\n\t\t\tprint \"DEBUG: \", t\n\t\t\tfor k, v in t.iteritems():\n\t\t\t\tself.database[k] = v\n\t\t\n\t\tself.value_counter = {}\n\t\tself.latest_trans = {}\n\t\tself.no_trans = True","title":""},{"docid":"02d2421dd6dd3880c53787277cde9033","score":"0.46091604","text":"def store_output(self):\n # call store methods\n if self.job_settings.get('disable_output', False):\n return\n\n self.store_configs()\n self.store_metadata()\n self.store_dataframes()\n self.store_objects()\n self.store_images()","title":""},{"docid":"fddf14cce7a3bc116e31bec6425957db","score":"0.46043563","text":"def store_in_memory(self, program, register):\n ast_tree.Number(self.memory_localisation).generate_code(program, 'A')\n program.code.append(f'STORE {register} #store into memory {self.name}')\n program.line_no += 1","title":""},{"docid":"a825ffad20753dc1e331d70da21ac58e","score":"0.46028402","text":"def flush(self):","title":""},{"docid":"a825ffad20753dc1e331d70da21ac58e","score":"0.46028402","text":"def flush(self):","title":""},{"docid":"9bb8651575bb7c89713380a4bad5928c","score":"0.45999917","text":"def commit(self):\n self.pre_commit()\n current_block = self._block\n self.chain._append(current_block)\n self._block = self._make_next_block()\n return current_block","title":""},{"docid":"ce1cc1d12dcb8ff4baaf12c7c167d458","score":"0.45990166","text":"def commit(self):","title":""},{"docid":"ce1cc1d12dcb8ff4baaf12c7c167d458","score":"0.45990166","text":"def commit(self):","title":""},{"docid":"ccc7dd10f3d2b54c3969fbd28cca05e2","score":"0.45974588","text":"def write_to(self, xblock, value):\r\n self.__set__(xblock, value)","title":""},{"docid":"6607799c663d1f43d04026b3f2e8650f","score":"0.45966214","text":"def StoreBlock(self, request, context):\n lock = threading.Lock()\n lock.acquire()\n try:\n self.myBlockStore[request.hash] = request.data.decode()\n finally:\n lock.release()\n return SurfStoreBasic_pb2.Empty()","title":""},{"docid":"a65b97b28aa145d5c3225712ec025012","score":"0.45957798","text":"def store(self):\n if self.needs_update:\n self.syn.store(self.obj)\n self.needs_update = False","title":""},{"docid":"30531419cbad8859f8267d368abcea83","score":"0.45952567","text":"def retrieve_block(self, address, callee, callee_name):\n if self.debug_info is True:\n print(\"[Cache] Retrieving missing block...\")\n block = self.bus.communicate(\"cache\", callee, callee_name, \"memory, give block\", address)\n self.insert([address, block])","title":""},{"docid":"0d42df3af65d09e0be8e716ad8000cb7","score":"0.45931914","text":"def save_result(self, result):\n self._monitor.save_task_result(self.name, result)","title":""},{"docid":"0786abb47193f4ddff4eb67452d06192","score":"0.45832354","text":"def endLoad(self):\n if self.currentTxn:\n self.compromisedTxns.append(self.currentTxn)\n self.currentTxn = None","title":""},{"docid":"3a871932f2ddc8f549e533e08c9f3e5f","score":"0.45737946","text":"def saveb(self, key, content, wantold=False):\n pass","title":""},{"docid":"5b650fcb5f5679b77723decbc64077b8","score":"0.45656642","text":"def save_result(self):\n with open(self.outfile, 'w') as fp:\n pickle.dump(self.result, fp)\n logging.debug('Wrote results to file `%s`', self.outfile)","title":""},{"docid":"1c8c96c07863e37fa7a64c1d6f113f36","score":"0.45637867","text":"def sync(self):","title":""},{"docid":"658ee39c9f08ef7fb9f5d3d6ebb43676","score":"0.4559463","text":"def commit(self):\n self.connection.bgsave()","title":""},{"docid":"ddb96ccd22a8132556d944d75f9cee1f","score":"0.4547768","text":"def FillFromDBSBuffer(self, blockInfo):\n # Blocks loaded out of the buffer should\n # have both a creation time, and should\n # be in the buffer (duh)\n self.startTime = blockInfo.get('creation_date')\n self.inBuff = True\n\n if 'status' in blockInfo:\n self.status = blockInfo['status']\n if self.status == \"Pending\":\n self.data['block']['open_for_writing'] = 0\n\n del blockInfo['status']\n\n for key in blockInfo:\n self.data['block'][key] = blockInfo.get(key)","title":""},{"docid":"da39803d89c10403fa863b2fa1f74fd1","score":"0.45475605","text":"def handle_write(self, address, proc):\n # Data request\n # See if our data is in the cache\n data = self.is_cached(address, proc)\n (tag, index, _) = self.decode(address)\n # Update statistics\n self.addr_access[address][proc] = 1\n self.buffer_retire(proc)\n if data:\n # Cached. Write to cache\n # We write data to cache here. This is not a real operation as no\n # data is transferred and the block tag is already in the cache.\n # Check if we need to write lower level memory too.\n if self.full_verbose:\n print \"Writing to data in cache\"\n # Update statistics\n shared = 0\n modified = 0\n for i in range(0, self.num_cpu):\n try:\n idx = self.cache[i][index].index((tag, State.S))\n # Found shared in other\n shared += 1\n except ValueError:\n pass\n try:\n idx = self.cache[i][index].index((tag, State.M))\n # Found modified in other\n modified += 1\n except ValueError:\n pass\n if (shared == 1 and modified == 0) or \\\n (shared == 0 and modified == 1):\n self.priv_access += 1\n else:\n self.shared_read_write += 1\n # Need to invalidate the data for other caches\n # Actual snooping takes place here\n # Either modified or shared in our cache\n try:\n our_idx = self.cache[proc][index].index((tag, State.M))\n if self.full_verbose:\n print \"Cache modified\"\n self.stat_write_hit[proc] += 1\n except ValueError:\n pass\n try:\n our_idx = self.cache[proc][index].index((tag, State.S))\n if self.full_verbose:\n print \"Cache shared\"\n # Write to shared is a miss\n self.stat_write_miss[proc] += 1\n except ValueError:\n pass\n (_, flag) = self.cache[proc][index][our_idx]\n if flag == State.S:\n for i in range(0, self.num_cpu):\n if i != proc:\n try:\n # Found it modified in another's cache, need to\n # get him to commit\n idx = self.cache[i][index].index((tag, State.M))\n if self.full_verbose:\n print \"Snooping: found modified tag in \\\n processor's %d cache, committing and \\\n invalidating\" % i\n if not self.write_back:\n self.write_ram(address, proc)\n # Invalidate this cache for now\n self.invalidations[proc] += 1\n self.cache[i][index][idx] = (tag, State.I)\n except ValueError:\n pass\n try:\n # Found it shared in another's cache, invalidate\n idx = self.cache[i][index].index((tag, State.S))\n if self.full_verbose:\n print \"Snooping: found shared tag in \\\n processor's %d cache, invalidating\" % i\n self.invalidations[proc] += 1\n self.cache[i][index][idx] = (tag, State.I)\n except ValueError:\n pass\n # Update processor self.counter\n self.counter[proc] += BUS\n if self.full_verbose:\n print \"Updating counter - bus transaction\"\n # Set our state to modified\n self.cache[proc][index][our_idx] = (tag, State.M)\n if not self.write_back:\n self.write_ram(address, proc)\n self.prefetch(address, proc)\n else:\n # Not cached or invalidated\n # Check other caches first\n for i in range(0, self.num_cpu):\n if i != proc:\n try:\n # Found it modified in another's cache, need to get him\n # to commit\n idx = self.cache[i][index].index((tag, State.M))\n if self.full_verbose:\n print \"Snooping: found modified tag in processor's \\\n %d cache, committing and invalidating\" % i\n if not self.write_back:\n self.write_ram(address, proc)\n # Write to cache and invalidate\n self.invalidations[proc] += 1\n self.cache[i][index][idx] = (tag, State.I)\n except ValueError:\n pass\n try:\n # Found it shared in another's cache, invalidate\n idx = self.cache[i][index].index((tag, State.S))\n if self.full_verbose:\n print \"Snooping: found shared tag in processor's %d \\\n cache, invalidating\" % i\n self.invalidations[proc] += 1\n self.cache[i][index][idx] = (tag, State.I)\n except ValueError:\n pass\n # Update processor self.counter\n self.counter[proc] += BUS\n if self.full_verbose:\n print \"Updating counter - bus transaction\"\n # Check if we need to add the missed data to the cache.\n if self.write_allocate:\n self.write_cache(address, proc, State.M)\n else:\n self.write_ram(address, proc)\n self.stat_write_miss[proc] += 1\n self.prefetch(address, proc)","title":""},{"docid":"5f8e5d65a9a6f0aa23a69dff3647134b","score":"0.4544314","text":"def flush(self):\n\n self.save()","title":""},{"docid":"81522cb64192fc8a66ab77c82d275762","score":"0.4542956","text":"def store(self) -> None:\n for storer_cls in self.storer_cls_set:\n name = getattr(storer_cls, \"__name__\", None)\n logger.info(f'Storing result with \"{name}\"...')\n\n storer = storer_cls(*self.args, **self.kwargs)\n storer.store()","title":""},{"docid":"2632b22711495311ab0a880e9c0c42a8","score":"0.45422256","text":"def store(self):\r\n # Get modified track fields.\r\n track_updates = {}\r\n for key in self.item_keys:\r\n if key in self._dirty:\r\n track_updates[key] = self[key]\r\n\r\n with self._db.transaction():\r\n super(Album, self).store()\r\n if track_updates:\r\n for item in self.items():\r\n for key, value in track_updates.items():\r\n item[key] = value\r\n item.store()","title":""},{"docid":"5b57a18fa04c1fa34aaeb8e8241f4926","score":"0.45394748","text":"def result(self, result):\n\n self._result = result","title":""},{"docid":"5b57a18fa04c1fa34aaeb8e8241f4926","score":"0.45394748","text":"def result(self, result):\n\n self._result = result","title":""},{"docid":"fbc2114218beb56a9446e58b16914319","score":"0.45392713","text":"def save_data():","title":""},{"docid":"cbe92b3eb24ce0253ca810d27724d2b7","score":"0.45391983","text":"def transaction(self):","title":""},{"docid":"44598be25c72b8eea9586d27013a3f48","score":"0.4537693","text":"def append_to_memory(self, transistion):\n self.memory.append(transistion)","title":""}],"string":"[\n {\n \"docid\": \"bb23a3e42525e164153054d51237f6b4\",\n \"score\": \"0.5830644\",\n \"text\": \"def _store_result(self, total_count, open_result, values, data):\\n [self._result] = values\\n self._push(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aab13632aba6065c760c498de19e80b\",\n \"score\": \"0.5778085\",\n \"text\": \"def store_results(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60814a5360a1a84320d7b75bf80c5574\",\n \"score\": \"0.57654816\",\n \"text\": \"def callback(result):\\n #print 'Got call back with %s' % (result,)\\n d, i, j = result\\n s[j,i] = s[i,j] = d\\n self.save(infork=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94a0e0c00a42c4f39b31caf4ae34d83f\",\n \"score\": \"0.5729467\",\n \"text\": \"def storeState(self):\\r\\n\\r\\n self.storeStandardResults()\\r\\n self.recsigmaA.append(self.sigmaA.copy())\\r\\n self.recalphaA.append(self.alphaA.copy())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f6693817bf29153a20b71f46b2de07b\",\n \"score\": \"0.5527896\",\n \"text\": \"def store_result(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"516a0baf23fdf1fe8b153ff8fe042214\",\n \"score\": \"0.5510886\",\n \"text\": \"def save_block(self):\\n\\n # If no dump file is specified, do not store any data in state_results.\\n if self.options.dump_filename is None:\\n return\\n\\n cuda.memcpy_dtoh(self._rng_state, self._gpu_rng_state)\\n for i in range(0, self.num_vars):\\n cuda.memcpy_dtoh(self.vec[i], self._gpu_vec[i])\\n\\n if self.options.output_mode == 'path':\\n self.state_results.append((self.sim_t, copy.deepcopy(self.vec), self.vec_nx.copy(),\\n self._rng_state.copy()))\\n else:\\n self.state_results.append((self.sim_t, copy.deepcopy(self.vec), self.vec_nx.copy(),\\n self._rng_state.copy(), self.start_t, copy.deepcopy(self.vec_start),\\n self.vec_start_nx.copy()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5213d094c78c4dcdf3c435d65678e73\",\n \"score\": \"0.5439924\",\n \"text\": \"def _append(self, block):\\n serialized_block = encode(block)\\n self.head = self.object_store.add(serialized_block)\\n self._cache[self.head] = block\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecffc5eca71c8dbad5deaceac9a8e9bb\",\n \"score\": \"0.5425643\",\n \"text\": \"def _save_processed_blocks(self, block_range):\\n block_flags = [{\\n \\\"id\\\": block,\\n \\\"name\\\": \\\"events_extracted\\\",\\n \\\"value\\\": 1\\n } for block in range(*block_range)]\\n self.client.bulk_index(index=self.indices[\\\"block_flag\\\"], docs=block_flags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e141fb124a3d44162d0e2f49dfa414b\",\n \"score\": \"0.5394522\",\n \"text\": \"def notify(self, block):\\n self.block_store.append(block)\\n\\n if len(self.block_store) == self._blocks_per_chunk:\\n self._difficulty = self.recalculate_difficulty()\\n self.block_store = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b315f5bc784d609449e1b030d0c6487\",\n \"score\": \"0.5384338\",\n \"text\": \"def save_results(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c46e75e7ad7a22b55d86b7fc77088c1\",\n \"score\": \"0.5382038\",\n \"text\": \"def save_txresults(self, tx_results: list, new_block_hash: str):\\n Logger.debug(f'save_txresults:{tx_results}', LOG_BLOCK)\\n\\n # write transaction result with batch\\n with self.db.create_write_batch() as wb:\\n for tx_result in tx_results:\\n # key from transaction hash\\n key = DbPrefix.TXRESULT + bytes.fromhex(tx_result.get('txHash'))\\n\\n # get value from transaction result\\n tx_result['blockHash'] = new_block_hash\\n value = json.dumps(tx_result).encode()\\n\\n self.db.write_batch(write_batch=wb, key=key, value=value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95130d44fafabf0f65c579e8df6c3f6f\",\n \"score\": \"0.51690006\",\n \"text\": \"def hard_copy_returned(self):\\n for rec in self:\\n if rec.state == 'done':\\n rec.write({\\n 'state': 'returned',\\n 'returned_by': self.env.uid,\\n 'returned_date': datetime.datetime.now().strftime(\\\"%Y-%m-%d\\\")\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8563dd5c1457196452875bd49a38f824\",\n \"score\": \"0.5161495\",\n \"text\": \"def save(self):\\n state = (self.last_block, self.addresses)\\n pickle.dump(state, open(self.filepath, \\\"wb\\\"))\\n print(self.addresses.__len__())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4815b7423161d1718424c347bd6edb4b\",\n \"score\": \"0.51102716\",\n \"text\": \"def put(self, block):\\n raise TypeError('not implemented')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da76d0ab169320200a15a5025e1e28f0\",\n \"score\": \"0.5095671\",\n \"text\": \"def _add_block(self, block):\\n if len(self._block_hash) >= self._backtrack_limit:\\n self._block_hash.popleft()\\n self._block_height.popleft()\\n\\n with self._lock:\\n self._block_hash.append(block.block_hash)\\n self._block_height.append(block.height)\\n\\n # Process record into balance\\n self._balance_processor.add_block(block)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f6d9a453b27547810703ac1d34b66f5\",\n \"score\": \"0.5062092\",\n \"text\": \"def store_completed(self, store_completed):\\n\\n self._store_completed = store_completed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94827d90b07f2553dda841383c5f4f99\",\n \"score\": \"0.50238407\",\n \"text\": \"def stored(self):\\n self.storage_count += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c619208a846e1e6f533fbccf5fb262c0\",\n \"score\": \"0.50149727\",\n \"text\": \"def store_after_invalidate_test(self):\\n self.cache.store(self.prediction)\\n self.cache.invalidate()\\n self.cache.store(self.prediction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f23d0dbf9fc439da3dec455d3d076222\",\n \"score\": \"0.5007939\",\n \"text\": \"def _finishExecuting(self, result):\\n self._currentBlock = None\\n self._checkNextBlock()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1a46ed6740bed53c8e79dfb8cf07558\",\n \"score\": \"0.4996419\",\n \"text\": \"def flush(self):\\n super().flush()\\n self.persistenceCounter = 0\\n self.basicStatPP = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ded39e047f6c1c77a5bce69b6d694fb\",\n \"score\": \"0.49933785\",\n \"text\": \"def save_block(self, block: dict):\\n block_hash = block['hash']\\n block_height = block['height']\\n # save block\\n self.db.put(DbPrefix.BLOCK + bytes.fromhex(block_hash), json.dumps(block).encode())\\n\\n # save block height/hash for block query request\\n self.db.put(DbPrefix.BLOCK_INDEX + block_height.to_bytes(DEFAULT_BYTE_SIZE, DATA_BYTE_ORDER),\\n bytes.fromhex(block_hash))\\n\\n Logger.debug(f'save block : block_height:{block_height}, block_hash: {block_hash}, block: {block}', LOG_BLOCK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb8b4e2878c2b0421825a53bad933b4\",\n \"score\": \"0.49447474\",\n \"text\": \"def _integrate_into_store(self):\\n with connection.cursor() as cursor:\\n self._dequeuing_delete_rmcb_records(cursor)\\n self._dequeuing_delete_buffered_records(cursor)\\n current_id = InstanceIDModel.get_current_instance_and_increment_counter()\\n self._dequeuing_merge_conflict_buffer(cursor, current_id)\\n self._dequeuing_merge_conflict_rmcb(cursor)\\n self._dequeuing_update_rmcs_last_saved_by(cursor, current_id)\\n self._dequeuing_delete_mc_rmcb(cursor)\\n self._dequeuing_delete_mc_buffer(cursor)\\n self._dequeuing_insert_remaining_buffer(cursor)\\n self._dequeuing_insert_remaining_rmcb(cursor)\\n self._dequeuing_delete_remaining_rmcb(cursor)\\n self._dequeuing_delete_remaining_buffer(cursor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0d9bcc5ea89a69a445ed15885162306\",\n \"score\": \"0.49373809\",\n \"text\": \"def sav(self):\\n self.bak = self.acc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c0837f1afe35a452a7537114194cd2b\",\n \"score\": \"0.493308\",\n \"text\": \"def persist_data(self, df):\\n self.storage.store_results(df)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc832fae1bc5fb5a0de52cb5c6538b33\",\n \"score\": \"0.4929122\",\n \"text\": \"def write_back(self, block_id):\\n\\n self.parent.write(block_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d5adfcb3f2c088cc8555359f9f49724\",\n \"score\": \"0.49286047\",\n \"text\": \"def endLoad(self):\\n if self.currentTxn:\\n if self.distortionCount == 0:\\n self.appendTxn(self.currentTxn)\\n else:\\n self.compromisedTxns.append(self.currentTxn)\\n self.distortionCount = 0\\n self.currentTxn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"624e33ae7971d87a42d8e5263d78e689\",\n \"score\": \"0.49024948\",\n \"text\": \"def checkpoint(self):\\n self._iterator, self._checkpoint = tee(self._iterator)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c16d34fc52beb1dbd5652bfe01eeda0f\",\n \"score\": \"0.49001044\",\n \"text\": \"def test_setting_result_calls_persist(self):\\n from furious.async import Async\\n\\n result = \\\"here be the results.\\\"\\n\\n persistence_engine = mock.Mock()\\n\\n job = Async(target=dir, persist_result=True)\\n\\n # Manually set the persistence_engine so that the Async doesn't try to\\n # reload the mock persistence_engine.\\n job._persistence_engine = persistence_engine\\n\\n job.executing = True\\n job.result = result\\n\\n persistence_engine.store_async_result.assert_called_once_with(job.id,\\n result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d33f4b1aaea016ef1978ad34474a6c00\",\n \"score\": \"0.48894456\",\n \"text\": \"def on_snapshot(self, asks: Sequence[List], bids: Sequence[List]):\\n for price, size in asks:\\n self.asks[float(price)] = float(size)\\n\\n for price, size in bids:\\n self.bids[float(price)] = float(size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2618adeb4c89c4eff421fb0b365b79d7\",\n \"score\": \"0.48851195\",\n \"text\": \"def dump_results(self, scores: Mapping[BaseEntity, Tuple]) -> None:\\n self.result = pickle.dumps(scores)\\n self.completed = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da560691764241c09a94bf5ca6198172\",\n \"score\": \"0.48743635\",\n \"text\": \"def save_result(self, item, spider, task):\\n for link, img in item.items():\\n result = ResultsItem()\\n result['task'] = task\\n result['link'] = link\\n result['img'] = img\\n result['rank'] = self.items_processed[spider.name]\\n result.save()\\n upload_photo_to_aws.delay(img, str(spider.user_pk), spider.search_phrase[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ae57f0e59c8534770fd210821e28b11\",\n \"score\": \"0.4844505\",\n \"text\": \"def flushBlockList(self):\\n self.blocks = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51119e7e2842f6f47ec9769304425e99\",\n \"score\": \"0.48391396\",\n \"text\": \"def writeResults(self, result):\\n result.done()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b1922be031f0f9e43133b8ca1346c9\",\n \"score\": \"0.4838126\",\n \"text\": \"def sync_memory(self):\\n latest_brain_version = load_item_from_dynamo()\\n updated_brain_version = (write_dict_to_s3('ts-chat-bot',\\n 'brains/word_graph.json',\\n self.word_graph.graph),\\n write_dict_to_s3('ts-chat-bot',\\n 'brains/answer_trie.json',\\n self.answer_trie.trie))\\n write_item_to_dynamo(updated_brain_version, latest_brain_version)\\n delete_file_in_s3('ts-chat-bot',\\n 'brains/word_graph.json',\\n latest_brain_version[0])\\n delete_file_in_s3('ts-chat-bot',\\n 'brains/answer_trie.json',\\n latest_brain_version[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d17aa57736f51e51b228ec7963de2774\",\n \"score\": \"0.48349193\",\n \"text\": \"def cache(self, result):\\n with open(self._filename, \\\"w\\\") as file_:\\n try:\\n pkl.dump(result, file_)\\n except Exception as err:\\n LOGGER.error(\\\"Could not dump object!\\\\n\\\\t%s\\\", err.message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a68967efac2f3ae929db5773cf788bf\",\n \"score\": \"0.48338112\",\n \"text\": \"def save_results(self, save_path):\\n self.fcst.to_pickle(save_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb9a13bbd26f8855a8a0a9ba09d80592\",\n \"score\": \"0.48152733\",\n \"text\": \"def write_block( self, from_block, data ):\\n\\t\\traise NotImplementedError('zut alors!')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c506e03385d2a1a6fbb9caddd879db9\",\n \"score\": \"0.48140597\",\n \"text\": \"def f2b(self):\\n for passenger in self.passengers:\\n passenger.block = passenger.id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61463616bcb88fe79db46b64d24d8be7\",\n \"score\": \"0.4797915\",\n \"text\": \"def cache(self, roi, result):\\n with h5py.File(self._filename, \\\"a\\\") as file_:\\n dataset = self._get_dataset(file_)\\n slice_ = roi.toSlice()\\n dataset[slice_] = result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8f5f719b6d0e281f505c6cb82d270bc\",\n \"score\": \"0.47818726\",\n \"text\": \"def storeState(self):\\r\\n\\r\\n self.record_actions[self.t] = self.action\\r\\n self.record_reward_values[self.t] = self.reward_value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9b94db4f1f3da0aa339384d8467f993\",\n \"score\": \"0.47742218\",\n \"text\": \"def __save(self):\\n self.__saved_record = copy.deepcopy(self.record)\\n self.__saved_counter = copy.deepcopy(self.counter)\\n self.__saved_objects = copy.deepcopy(self.current_objects)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a5cc484c3a9f755bc81d31f7d11cc02\",\n \"score\": \"0.47738168\",\n \"text\": \"def backup(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce6ca70147423ebd4aeee6986c1159cd\",\n \"score\": \"0.4772042\",\n \"text\": \"def store_run_results(model, a_results, rep):\\n \\n col = 0\\n \\n for i in model.get_results().items():\\n #df_results.loc[rep][i[0]] = i[1]\\n a_results[rep][col] = i[1]\\n col += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86495ba7ec6fad37452662ebbd648507\",\n \"score\": \"0.47719678\",\n \"text\": \"def handle_relation(self, astra_block):\\n if type(astra_block) == AstraBatchBlock:\\n for astra_block_temp in self.blocks:\\n if type(astra_block_temp) == AstraShuffleBlock:\\n astra_block_temp.core.set_batches(astra_block.batches)\\n return\\n\\n if type(astra_block) == AstraJobBlock:\\n self.input_string = self.replace_block([astra_block])\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41b8f1397a1041d2085bfdb0632f83d6\",\n \"score\": \"0.47415376\",\n \"text\": \"def _blocks_data_handler(self, block_data, socket):\\n self._block_data_lock.acquire()\\n for block in block_data:\\n self._quantcoin.store_block(Block.from_json(block))\\n self._block_data_lock.release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48a1e4017450b8b0a0dc8ca2e651258a\",\n \"score\": \"0.47386348\",\n \"text\": \"def blockstorage(self):\\n return self._bs # TODO: is this necessary\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd7dca980c87ec7ea6b237b13cca2ac\",\n \"score\": \"0.47367123\",\n \"text\": \"def persist_progress(self, collection):\\n for obj in collection:\\n yield obj\\n self.update(obj.key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"359356ccbb7f56732de2050d5e18b91d\",\n \"score\": \"0.47267163\",\n \"text\": \"def save_result(ctx, results):\\n\\n if type(results) == Result:\\n results = [results]\\n\\n ses = ctx.obj['session']\\n for result in results:\\n ses.add(result)\\n try:\\n ses.flush()\\n except IntegrityError:\\n click.echo(\\\"Incompatible result: typename:%s name:%s with %d arrays:\\\" % \\\\\\n (result.typename, result.name, len(result.data)))\\n for typ, nam, arr in result.triplets():\\n click.echo(\\\"\\\\tarray typename:%s name:%s shape:%s\\\" % (typ, nam, arr.shape))\\n #click.echo(\\\"Parameters:\\\")\\n #click.echo(pprint.pformat(result.params))\\n raise\\n ses.commit()\\n click.echo('id:%d typename:%s name:%s narrays:%d' % (result.id, result.typename, result.name, len(result.data)))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"581c4e2b125cbd2a332ef0259c5e8dbf\",\n \"score\": \"0.472436\",\n \"text\": \"def write_data_slice(self, partial_result):\\n self.store_data_chunk(\\\"data\\\", partial_result, grow_dimension=0, close_file=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3434fa20a9e1978836dee9d6cfa86ae0\",\n \"score\": \"0.47236443\",\n \"text\": \"def write_results(self):\\n for (name, step) in self.steps.items():\\n self.snapshot(step)\\n self.fh.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1fadc35fa2262b93eaa574afdb6f08a\",\n \"score\": \"0.47232848\",\n \"text\": \"def _setResult(self, result):\\n if self._result is not _NO_RESULT:\\n raise AlreadyCalledError()\\n\\n self._result = result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e134f16791fbeae9b66fdfeb805380c\",\n \"score\": \"0.47203913\",\n \"text\": \"def store(self, envid, obs_1, obs_2):\\n assert self.ptr[envid].item() < self.block_size # buffer has to have room so you can store\\n ptr = self.ptr[envid].item()+ envid * self.block_size\\n self.obs_buf_1[ptr].copy_(obs_1)\\n self.obs_buf_2[ptr].copy_(obs_2)\\n # self.act_buf[ptr].copy_(act)\\n # self.rst_buf[ptr].copy_(result)\\n \\n self.ptr[envid] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01414db0a879db27d36437d603eb866f\",\n \"score\": \"0.47022763\",\n \"text\": \"def test_write_pickle(bucket_results):\\n c1 = CountCache()\\n c1[1900, 'token', 'POS', 1] = 1\\n\\n path = c1.flush(bucket_results.path)\\n\\n with open(path, 'rb') as fh:\\n c2 = pickle.load(fh)\\n\\n assert c2[1900, 'token', 'POS', 1] == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea1ed13a450af065602189257f6170d\",\n \"score\": \"0.46937618\",\n \"text\": \"def store_existing_test(self):\\n self.cache.store(self.prediction)\\n self.cache.store(self.prediction)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732dec33d179ad76ebc03452ee2a31fc\",\n \"score\": \"0.46787602\",\n \"text\": \"def test_setting_result_does_not_call_persist(self):\\n from furious.async import Async\\n\\n result = \\\"here be the results.\\\"\\n\\n persistence_engine = mock.Mock()\\n\\n job = Async(target=dir)\\n\\n # Manually set the persistence_engine so that the Async doesn't try to\\n # reload the mock persistence_engine.\\n job._persistence_engine = persistence_engine\\n\\n job.executing = True\\n job.result = result\\n\\n self.assertEqual(persistence_engine.store_async_result.call_count, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37440cae2c20faddac6eeb2b521b2aa3\",\n \"score\": \"0.46737978\",\n \"text\": \"def test_persist_result_helper(self):\\n wrapper = AnalysisResultWrapper().save()\\n analysis_result = AnalysisResultMeta(sample_similarity=wrapper).save()\\n sample_similarity = create_mvp_sample_similarity()\\n\\n persist_result_helper(sample_similarity,\\n analysis_result.uuid,\\n 'sample_similarity')\\n analysis_result.reload()\\n self.assertIn('sample_similarity', analysis_result)\\n wrapper = getattr(analysis_result, 'sample_similarity').fetch()\\n self.assertIn('status', wrapper)\\n self.assertEqual('S', wrapper.status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2f01b47695d30ab4ea5c81d88828fe\",\n \"score\": \"0.4673275\",\n \"text\": \"async def commit_data(self, result: Dict, namespace: str, hostname: str,\\n boot_timestamp: int):\\n records = []\\n key = f'{namespace}.{hostname}'\\n prev_res = self.previous_results.get(key, None)\\n last_device_session = self._node_boot_timestamps.get(key, 0)\\n # No data previously polled with this service from the the\\n # current namespace and hostname. Check if the datastore contains some\\n # information about\\n filter_df = f\\\"hostname.str.match('{hostname}') and \\\" \\\\\\n f\\\"namespace.str.match('{namespace}')\\\"\\n if prev_res is None:\\n df = self._db_access.read(\\n self.schema_table._table,\\n 'pandas',\\n start_time='',\\n end_time='',\\n columns=self.schema_table.fields,\\n view='latest',\\n key_fields=self.schema_table.key_fields(),\\n add_filter=filter_df,\\n hostname=[hostname],\\n namespace=[namespace]).query('active')\\n # Get the latest device session we have written with this service\\n if not df.empty:\\n # If we are dealing with old data then the value of\\n # deviceSession will be unset, if this is the case, set the\\n # value to 0 so that we force write. We need to check only\\n # the first value of deviceSession as it is equal for all the\\n # latest record, as in case of reboot we rewrite everything.\\n if not df['deviceSession'].iloc[:1].isnull().any():\\n last_device_session = df['deviceSession'].iloc[0] or 0\\n else:\\n last_device_session = 0\\n\\n self._node_boot_timestamps[key] = int(last_device_session)\\n\\n prev_res = df.to_dict('records')\\n self.previous_results[key] = prev_res\\n\\n if result or prev_res:\\n # Check whether there has been a node reboot and in this case\\n # write everything\\n write_all = abs(boot_timestamp -\\n last_device_session) > TIME_DRIFT_TOLERANCE\\n if write_all:\\n last_device_session = int(boot_timestamp)\\n self._node_boot_timestamps[key] = boot_timestamp\\n\\n adds, dels = self.get_diff(prev_res, result, write_all)\\n if adds or dels:\\n self.previous_results[key] = copy.deepcopy(result)\\n for entry in adds:\\n entry['deviceSession'] = last_device_session\\n records.append(entry)\\n # make sure to use the same timestamp for all the deleted\\n # records. If the result is not empty, use that to get the\\n # timestamp. If the result is empty, use the current timestamp\\n if result:\\n del_ts = result[0]['timestamp']\\n else:\\n del_ts = int(datetime.now(\\n tz=timezone.utc).timestamp() * 1000)\\n\\n for entry in dels:\\n if entry.get(\\\"active\\\", True):\\n # If there's already an entry marked as deleted\\n # No point in adding one more\\n entry.update({\\n \\\"active\\\": False,\\n \\\"timestamp\\\": del_ts\\n })\\n records.append(entry)\\n\\n self._post_work_to_writer(records)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5747936655711f84b79ab94b1f6092f\",\n \"score\": \"0.46716902\",\n \"text\": \"def dump( self ):\\n for b in self.blocks:\\n b.dump()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18d7a57aea22e71362b2d73f4f56873d\",\n \"score\": \"0.46701097\",\n \"text\": \"def store(self):\\n picklefile = file(self.cache_filename, 'wb')\\n stamp = datetime.now()\\n pickle.dump((stamp, self.addrbk), picklefile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ac232a01f62dea04ffb27633f525a1\",\n \"score\": \"0.46680924\",\n \"text\": \"def callback(result):\\n d, i, j = result\\n self.dists[j,i] = self.dists[i,j] = d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5d74018dd3556cd844bf9f5a25098ac\",\n \"score\": \"0.4657807\",\n \"text\": \"def collect_result(stub):\\n logging.debug(\\\"#### collect_result ####\\\")\\n arr_B = stub.fetch_result_from_peer(NIL)\\n peer_B_data = [x.item for x in arr_B]\\n # end connection of process A\\n stub.end_connection(NIL)\\n return peer_B_data\\n #raise NotImplementedError(\\\"Method not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e7da9a8bda5b64d0a39a484be772ef3\",\n \"score\": \"0.46565598\",\n \"text\": \"def _backup_and_load_cache(self):\\n for group in self.optimizer.param_groups:\\n for p in group['params']:\\n param_state = self.state[p]\\n param_state['backup_params'] = torch.zeros_like(p.data)\\n param_state['backup_params'].copy_(p.data)\\n p.data.copy_(param_state['cached_params'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d11e65b2d5e2d52583b7f28ab1dda24f\",\n \"score\": \"0.46543387\",\n \"text\": \"def set_result(self, result):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a2f71e17c14bdc277d2bac8ef0289d6\",\n \"score\": \"0.46539396\",\n \"text\": \"def store(self):\\n self.Cb = self.C.copy()\\n self.Vb = self.V.copy()\\n self.Vcb = self.Vc.copy()\\n if self.en_direct:\\n self.Mb = self.M.copy()\\n self.Mcb = self.Mc.copy()\\n if self.en_bias:\\n self.db = self.d.copy()\\n self.dcb = self.dc.copy()\\n self.hidden.store()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f653dbb1e8db8d57bd5ca2017aa8e677\",\n \"score\": \"0.46529964\",\n \"text\": \"def endLoad(self):\\n if self.currentTxn:\\n if self.currentTxn.hasEndProbe:\\n self.appendTxn(self.currentTxn)\\n else:\\n self.compromisedTxns.append(self.currentTxn)\\n self.currentTxn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e701bd02f2f63b61a48902fb8282fbd8\",\n \"score\": \"0.4648478\",\n \"text\": \"def last_block(self, block: 'Block'):\\n with self._lock:\\n self._last_block = block\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2171e8d26660a4b2f7cf7d51e24cddb\",\n \"score\": \"0.46372822\",\n \"text\": \"def store_resultSet(self, session, rset):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90e0225d60b41842ecb34f78f86217fd\",\n \"score\": \"0.46336642\",\n \"text\": \"def next_block( **kw ):\\n global snapshots_dir, state_engine\\n\\n if snapshots_dir is None:\\n snapshots_dir = tempfile.mkdtemp(prefix='zonefilemanage-test-databases-')\\n\\n\\n # Flush all transactions and reset state engine\\n kw['next_block_upcall']()\\n kw['sync_virtualchain_upcall']()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c2341f28f20ff4ddbd4a2799155e8a0\",\n \"score\": \"0.46315885\",\n \"text\": \"def save(self):\\n\\n # Don't do real update if doing batch update\\n if not self._batch_acquire(False):\\n return\\n\\n try:\\n is_last_exit = (self._batch_counter == 1)\\n if is_last_exit:\\n self.storage.save(self.data)\\n finally:\\n self._batch_release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd7bc01e0ffdad22948a6563464824e7\",\n \"score\": \"0.463055\",\n \"text\": \"def db_save( block_id, filename, db_state=None ):\\n print \\\"\\\\nreference implementation of db_save\\\\n\\\"\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f14ce5d3b139b9752487a12f55d946ae\",\n \"score\": \"0.46303406\",\n \"text\": \"def _commit(self) ->None:\\n exponential_average_factor = 0.0\\n self.num_batches_tracked += 1\\n if self.momentum is None:\\n exponential_average_factor = 1.0 / float(self.num_batches_tracked)\\n else:\\n exponential_average_factor = self.momentum\\n mean = self.sum / self.counter\\n var = self.sum_squares / self.counter - mean ** 2\\n m = exponential_average_factor\\n self.running_mean *= 1 - m\\n self.running_mean += mean * m\\n self.running_var *= 1 - m\\n self.running_var += var * m\\n self.sum.zero_()\\n self.sum_squares.zero_()\\n self.counter = 0\\n self.tracked = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffae898dd305af167f7030c54923a2ec\",\n \"score\": \"0.46296242\",\n \"text\": \"def save(self):\\n if len(self._cache_list) > 0:\\n dump_cache(self.cache_file, self._cache_list)\\n if self._last_cache is not None:\\n dump_cache(self.last_file, (self._last_cache,))\\n\\n self.conn.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42164cca24570777f65788d18fa49eee\",\n \"score\": \"0.46158135\",\n \"text\": \"def commit(self): \\n\\t\\tif not self.trans:\\n\\t\\t\\t# If no open transaction; then nothing to do\\n\\t\\t\\tprint \\\"NO TRANSACTION\\\"\\n\\t\\t\\treturn\\n\\t\\n\\t\\tself.trans.append(self.latest_trans)\\n\\t\\t# Going through list of transaction from oldest, as we want database\\n\\t\\t# to have latest value for any variable\\n\\t\\tfor t in self.trans:\\n\\t\\t\\tprint \\\"DEBUG: \\\", t\\n\\t\\t\\tfor k, v in t.iteritems():\\n\\t\\t\\t\\tself.database[k] = v\\n\\t\\t\\n\\t\\tself.value_counter = {}\\n\\t\\tself.latest_trans = {}\\n\\t\\tself.no_trans = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d2421dd6dd3880c53787277cde9033\",\n \"score\": \"0.46091604\",\n \"text\": \"def store_output(self):\\n # call store methods\\n if self.job_settings.get('disable_output', False):\\n return\\n\\n self.store_configs()\\n self.store_metadata()\\n self.store_dataframes()\\n self.store_objects()\\n self.store_images()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fddf14cce7a3bc116e31bec6425957db\",\n \"score\": \"0.46043563\",\n \"text\": \"def store_in_memory(self, program, register):\\n ast_tree.Number(self.memory_localisation).generate_code(program, 'A')\\n program.code.append(f'STORE {register} #store into memory {self.name}')\\n program.line_no += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a825ffad20753dc1e331d70da21ac58e\",\n \"score\": \"0.46028402\",\n \"text\": \"def flush(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a825ffad20753dc1e331d70da21ac58e\",\n \"score\": \"0.46028402\",\n \"text\": \"def flush(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb8651575bb7c89713380a4bad5928c\",\n \"score\": \"0.45999917\",\n \"text\": \"def commit(self):\\n self.pre_commit()\\n current_block = self._block\\n self.chain._append(current_block)\\n self._block = self._make_next_block()\\n return current_block\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce1cc1d12dcb8ff4baaf12c7c167d458\",\n \"score\": \"0.45990166\",\n \"text\": \"def commit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce1cc1d12dcb8ff4baaf12c7c167d458\",\n \"score\": \"0.45990166\",\n \"text\": \"def commit(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccc7dd10f3d2b54c3969fbd28cca05e2\",\n \"score\": \"0.45974588\",\n \"text\": \"def write_to(self, xblock, value):\\r\\n self.__set__(xblock, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6607799c663d1f43d04026b3f2e8650f\",\n \"score\": \"0.45966214\",\n \"text\": \"def StoreBlock(self, request, context):\\n lock = threading.Lock()\\n lock.acquire()\\n try:\\n self.myBlockStore[request.hash] = request.data.decode()\\n finally:\\n lock.release()\\n return SurfStoreBasic_pb2.Empty()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a65b97b28aa145d5c3225712ec025012\",\n \"score\": \"0.45957798\",\n \"text\": \"def store(self):\\n if self.needs_update:\\n self.syn.store(self.obj)\\n self.needs_update = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30531419cbad8859f8267d368abcea83\",\n \"score\": \"0.45952567\",\n \"text\": \"def retrieve_block(self, address, callee, callee_name):\\n if self.debug_info is True:\\n print(\\\"[Cache] Retrieving missing block...\\\")\\n block = self.bus.communicate(\\\"cache\\\", callee, callee_name, \\\"memory, give block\\\", address)\\n self.insert([address, block])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d42df3af65d09e0be8e716ad8000cb7\",\n \"score\": \"0.45931914\",\n \"text\": \"def save_result(self, result):\\n self._monitor.save_task_result(self.name, result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0786abb47193f4ddff4eb67452d06192\",\n \"score\": \"0.45832354\",\n \"text\": \"def endLoad(self):\\n if self.currentTxn:\\n self.compromisedTxns.append(self.currentTxn)\\n self.currentTxn = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a871932f2ddc8f549e533e08c9f3e5f\",\n \"score\": \"0.45737946\",\n \"text\": \"def saveb(self, key, content, wantold=False):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b650fcb5f5679b77723decbc64077b8\",\n \"score\": \"0.45656642\",\n \"text\": \"def save_result(self):\\n with open(self.outfile, 'w') as fp:\\n pickle.dump(self.result, fp)\\n logging.debug('Wrote results to file `%s`', self.outfile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c8c96c07863e37fa7a64c1d6f113f36\",\n \"score\": \"0.45637867\",\n \"text\": \"def sync(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"658ee39c9f08ef7fb9f5d3d6ebb43676\",\n \"score\": \"0.4559463\",\n \"text\": \"def commit(self):\\n self.connection.bgsave()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddb96ccd22a8132556d944d75f9cee1f\",\n \"score\": \"0.4547768\",\n \"text\": \"def FillFromDBSBuffer(self, blockInfo):\\n # Blocks loaded out of the buffer should\\n # have both a creation time, and should\\n # be in the buffer (duh)\\n self.startTime = blockInfo.get('creation_date')\\n self.inBuff = True\\n\\n if 'status' in blockInfo:\\n self.status = blockInfo['status']\\n if self.status == \\\"Pending\\\":\\n self.data['block']['open_for_writing'] = 0\\n\\n del blockInfo['status']\\n\\n for key in blockInfo:\\n self.data['block'][key] = blockInfo.get(key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da39803d89c10403fa863b2fa1f74fd1\",\n \"score\": \"0.45475605\",\n \"text\": \"def handle_write(self, address, proc):\\n # Data request\\n # See if our data is in the cache\\n data = self.is_cached(address, proc)\\n (tag, index, _) = self.decode(address)\\n # Update statistics\\n self.addr_access[address][proc] = 1\\n self.buffer_retire(proc)\\n if data:\\n # Cached. Write to cache\\n # We write data to cache here. This is not a real operation as no\\n # data is transferred and the block tag is already in the cache.\\n # Check if we need to write lower level memory too.\\n if self.full_verbose:\\n print \\\"Writing to data in cache\\\"\\n # Update statistics\\n shared = 0\\n modified = 0\\n for i in range(0, self.num_cpu):\\n try:\\n idx = self.cache[i][index].index((tag, State.S))\\n # Found shared in other\\n shared += 1\\n except ValueError:\\n pass\\n try:\\n idx = self.cache[i][index].index((tag, State.M))\\n # Found modified in other\\n modified += 1\\n except ValueError:\\n pass\\n if (shared == 1 and modified == 0) or \\\\\\n (shared == 0 and modified == 1):\\n self.priv_access += 1\\n else:\\n self.shared_read_write += 1\\n # Need to invalidate the data for other caches\\n # Actual snooping takes place here\\n # Either modified or shared in our cache\\n try:\\n our_idx = self.cache[proc][index].index((tag, State.M))\\n if self.full_verbose:\\n print \\\"Cache modified\\\"\\n self.stat_write_hit[proc] += 1\\n except ValueError:\\n pass\\n try:\\n our_idx = self.cache[proc][index].index((tag, State.S))\\n if self.full_verbose:\\n print \\\"Cache shared\\\"\\n # Write to shared is a miss\\n self.stat_write_miss[proc] += 1\\n except ValueError:\\n pass\\n (_, flag) = self.cache[proc][index][our_idx]\\n if flag == State.S:\\n for i in range(0, self.num_cpu):\\n if i != proc:\\n try:\\n # Found it modified in another's cache, need to\\n # get him to commit\\n idx = self.cache[i][index].index((tag, State.M))\\n if self.full_verbose:\\n print \\\"Snooping: found modified tag in \\\\\\n processor's %d cache, committing and \\\\\\n invalidating\\\" % i\\n if not self.write_back:\\n self.write_ram(address, proc)\\n # Invalidate this cache for now\\n self.invalidations[proc] += 1\\n self.cache[i][index][idx] = (tag, State.I)\\n except ValueError:\\n pass\\n try:\\n # Found it shared in another's cache, invalidate\\n idx = self.cache[i][index].index((tag, State.S))\\n if self.full_verbose:\\n print \\\"Snooping: found shared tag in \\\\\\n processor's %d cache, invalidating\\\" % i\\n self.invalidations[proc] += 1\\n self.cache[i][index][idx] = (tag, State.I)\\n except ValueError:\\n pass\\n # Update processor self.counter\\n self.counter[proc] += BUS\\n if self.full_verbose:\\n print \\\"Updating counter - bus transaction\\\"\\n # Set our state to modified\\n self.cache[proc][index][our_idx] = (tag, State.M)\\n if not self.write_back:\\n self.write_ram(address, proc)\\n self.prefetch(address, proc)\\n else:\\n # Not cached or invalidated\\n # Check other caches first\\n for i in range(0, self.num_cpu):\\n if i != proc:\\n try:\\n # Found it modified in another's cache, need to get him\\n # to commit\\n idx = self.cache[i][index].index((tag, State.M))\\n if self.full_verbose:\\n print \\\"Snooping: found modified tag in processor's \\\\\\n %d cache, committing and invalidating\\\" % i\\n if not self.write_back:\\n self.write_ram(address, proc)\\n # Write to cache and invalidate\\n self.invalidations[proc] += 1\\n self.cache[i][index][idx] = (tag, State.I)\\n except ValueError:\\n pass\\n try:\\n # Found it shared in another's cache, invalidate\\n idx = self.cache[i][index].index((tag, State.S))\\n if self.full_verbose:\\n print \\\"Snooping: found shared tag in processor's %d \\\\\\n cache, invalidating\\\" % i\\n self.invalidations[proc] += 1\\n self.cache[i][index][idx] = (tag, State.I)\\n except ValueError:\\n pass\\n # Update processor self.counter\\n self.counter[proc] += BUS\\n if self.full_verbose:\\n print \\\"Updating counter - bus transaction\\\"\\n # Check if we need to add the missed data to the cache.\\n if self.write_allocate:\\n self.write_cache(address, proc, State.M)\\n else:\\n self.write_ram(address, proc)\\n self.stat_write_miss[proc] += 1\\n self.prefetch(address, proc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f8e5d65a9a6f0aa23a69dff3647134b\",\n \"score\": \"0.4544314\",\n \"text\": \"def flush(self):\\n\\n self.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81522cb64192fc8a66ab77c82d275762\",\n \"score\": \"0.4542956\",\n \"text\": \"def store(self) -> None:\\n for storer_cls in self.storer_cls_set:\\n name = getattr(storer_cls, \\\"__name__\\\", None)\\n logger.info(f'Storing result with \\\"{name}\\\"...')\\n\\n storer = storer_cls(*self.args, **self.kwargs)\\n storer.store()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2632b22711495311ab0a880e9c0c42a8\",\n \"score\": \"0.45422256\",\n \"text\": \"def store(self):\\r\\n # Get modified track fields.\\r\\n track_updates = {}\\r\\n for key in self.item_keys:\\r\\n if key in self._dirty:\\r\\n track_updates[key] = self[key]\\r\\n\\r\\n with self._db.transaction():\\r\\n super(Album, self).store()\\r\\n if track_updates:\\r\\n for item in self.items():\\r\\n for key, value in track_updates.items():\\r\\n item[key] = value\\r\\n item.store()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b57a18fa04c1fa34aaeb8e8241f4926\",\n \"score\": \"0.45394748\",\n \"text\": \"def result(self, result):\\n\\n self._result = result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b57a18fa04c1fa34aaeb8e8241f4926\",\n \"score\": \"0.45394748\",\n \"text\": \"def result(self, result):\\n\\n self._result = result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbc2114218beb56a9446e58b16914319\",\n \"score\": \"0.45392713\",\n \"text\": \"def save_data():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbe92b3eb24ce0253ca810d27724d2b7\",\n \"score\": \"0.45391983\",\n \"text\": \"def transaction(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44598be25c72b8eea9586d27013a3f48\",\n \"score\": \"0.4537693\",\n \"text\": \"def append_to_memory(self, transistion):\\n self.memory.append(transistion)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1696,"cells":{"query_id":{"kind":"string","value":"ede51f1a4b660f32c1c46cac49914b10"},"query":{"kind":"string","value":"Verify creating and deleting subnet with allocation pools"},"positive_passages":{"kind":"list like","value":[{"docid":"5a24538a9d1351937a182e14b0edc031","score":"0.82713765","text":"def test_create_delete_subnet_with_allocation_pools(self):\n self._create_verify_delete_subnet(\n **self.subnet_dict(['allocation_pools']))","title":""}],"string":"[\n {\n \"docid\": \"5a24538a9d1351937a182e14b0edc031\",\n \"score\": \"0.82713765\",\n \"text\": \"def test_create_delete_subnet_with_allocation_pools(self):\\n self._create_verify_delete_subnet(\\n **self.subnet_dict(['allocation_pools']))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"5e577dc0af595ea0742a1c4a8e599307","score":"0.8004876","text":"def test_create_delete_subnet_with_gw_and_allocation_pools(self):\n self._create_verify_delete_subnet(**self.subnet_dict(\n ['gateway', 'allocation_pools']))","title":""},{"docid":"ff2c39b96c05d197145766bd5ea6e126","score":"0.7607366","text":"def test_bulk_create_delete_subnet(self):\n networks = [self.create_network(), self.create_network()]\n # Creates 2 subnets in one request\n cidrs = [subnet_cidr\n for subnet_cidr in self.cidr.subnet(self.mask_bits)]\n\n names = [data_utils.rand_name('subnet-') for i in range(len(networks))]\n subnets_list = []\n for i in range(len(names)):\n p1 = {\n 'network_id': networks[i]['id'],\n 'cidr': str(cidrs[(i)]),\n 'name': names[i],\n 'ip_version': self._ip_version\n }\n subnets_list.append(p1)\n del subnets_list[1]['name']\n body = self.subnets_client.create_bulk_subnets(subnets=subnets_list)\n created_subnets = body['subnets']\n self.addCleanup(self._delete_subnets, created_subnets)\n # Asserting that the subnets are found in the list after creation\n body = self.subnets_client.list_subnets()\n subnets_list = [subnet['id'] for subnet in body['subnets']]\n for n in created_subnets:\n self.assertIsNotNone(n['id'])\n self.assertIn(n['id'], subnets_list)","title":""},{"docid":"cbff5b4cc78deb4cdd68b993e0665598","score":"0.74533916","text":"def test_create_delete_subnet_without_gateway(self):\n self._create_verify_delete_subnet()","title":""},{"docid":"bf0b97ebb02e60c844c8ad37bbba7cff","score":"0.7061275","text":"def test_create_delete_stateless_subnet_with_ports(self):\n self._test_delete_subnet_with_ports(\"dhcpv6-stateless\")","title":""},{"docid":"c339e2e69ec460e49e0aa5a3f01d2656","score":"0.695529","text":"def test_create_delete_subnet_with_gw(self):\n self._create_verify_delete_subnet(\n **self.subnet_dict(['gateway']))","title":""},{"docid":"f3d082d04fca6ee612509a592768caea","score":"0.6871421","text":"def test_create_delete_subnet_all_attributes(self):\n self._create_verify_delete_subnet(\n enable_dhcp=True,\n **self.subnet_dict(['gateway', 'host_routes', 'dns_nameservers']))","title":""},{"docid":"336ecf8f444525ebb67b5d021f0af21f","score":"0.6783448","text":"def test_create_update_delete_network_subnet(self):\n # Create a network\n network = self.create_network()\n self.addCleanup(test_utils.call_and_ignore_notfound_exc,\n self.networks_client.delete_network, network['id'])\n net_id = network['id']\n self.assertEqual('ACTIVE', network['status'])\n # Verify network update\n new_name = \"New_network\"\n body = self.networks_client.update_network(net_id, name=new_name)\n updated_net = body['network']\n self.assertEqual(updated_net['name'], new_name)\n # Find a cidr that is not in use yet and create a subnet with it\n subnet = self.create_subnet(network)\n subnet_id = subnet['id']\n # Verify subnet update\n new_name = \"New_subnet\"\n body = self.subnets_client.update_subnet(subnet_id, name=new_name)\n updated_subnet = body['subnet']\n self.assertEqual(updated_subnet['name'], new_name)\n # Verify network delete\n self.networks_client.delete_network(network['id'])","title":""},{"docid":"33ad0a52e9dba9609ada33c48b87c0ed","score":"0.6766238","text":"def test_create_delete_subnet_with_dhcp_enabled(self):\n self._create_verify_delete_subnet(enable_dhcp=True)","title":""},{"docid":"03a8d3bd4d8e8f5ce93c7c708f275fa3","score":"0.67158526","text":"def test_delete_network_with_subnet(self):\n # Creates a network\n network = self.create_network()\n net_id = network['id']\n self.addCleanup(test_utils.call_and_ignore_notfound_exc,\n self.networks_client.delete_network, network['id'])\n\n # Find a cidr that is not in use yet and create a subnet with it\n subnet = self.create_subnet(network)\n subnet_id = subnet['id']\n\n # Delete network while the subnet still exists\n self.networks_client.delete_network(net_id)\n\n # Verify that the subnet got automatically deleted.\n self.assertRaises(lib_exc.NotFound, self.subnets_client.show_subnet,\n subnet_id)","title":""},{"docid":"f1499e8d08cc21e8e3f0631e3366c3d1","score":"0.6703337","text":"def test_create_delete_subnet_with_host_routes_and_dns_nameservers(self):\n self._create_verify_delete_subnet(\n **self.subnet_dict(['host_routes', 'dns_nameservers']))","title":""},{"docid":"83020724141c2677935ed0feaf505399","score":"0.6689397","text":"def test_create_delete_slaac_subnet_with_ports(self):\n self._test_delete_subnet_with_ports(\"slaac\")","title":""},{"docid":"4e029294b61e4e72d50a0b036aeabc7f","score":"0.66316277","text":"def test_create_delete_subnet_with_gw(self):\n net = netaddr.IPNetwork(CONF.network.project_network_v6_cidr)\n gateway = str(netaddr.IPAddress(net.first + 2))\n network = self.create_network()\n subnet = self.create_subnet(network, gateway)\n # Verifies Subnet GW in IPv6\n self.assertEqual(subnet['gateway_ip'], gateway)","title":""},{"docid":"c60359df2dfc0a58b93c5955a787e3f2","score":"0.6626505","text":"def validate_subnet_operation(session, object_uuid, operation, data):\n if operation in ('create', 'update'):\n network_id = data['network_id']\n # Check for pending or processing network operations\n if _check_for_pending_or_processing_ops(session, network_id):\n return False\n elif operation == 'delete':\n # TODO(asomya):Check for pending port operations\n pass\n\n return True","title":""},{"docid":"d57d71ebc97f85ba4da55350e8509d77","score":"0.6586373","text":"def _test_delete_subnet_with_ports(self, mode):\n slaac_network = self.create_network()\n subnet_slaac = self.create_subnet(slaac_network,\n **{'ipv6_ra_mode': mode,\n 'ipv6_address_mode': mode})\n port = self.create_port(slaac_network)\n self.assertIsNotNone(port['fixed_ips'][0]['ip_address'])\n self.subnets_client.delete_subnet(subnet_slaac['id'])\n subnets = self.subnets_client.list_subnets()\n subnet_ids = [subnet['id'] for subnet in subnets['subnets']]\n self.assertNotIn(subnet_slaac['id'], subnet_ids,\n \"Subnet wasn't deleted\")\n self.assertRaisesRegex(\n lib_exc.Conflict,\n \"There are one or more ports still in use on the network\",\n self.networks_client.delete_network,\n slaac_network['id'])","title":""},{"docid":"55474683fed3f459910a6d9f9919a28d","score":"0.6573536","text":"def _create_pool(self):\n subnets = self.network_client.list_subnets()\n for subnet in subnets['subnets']:\n if subnet['tenant_id'] == self.tenant_id:\n self.subnets.append(subnet)\n pool = super(TestLoadBalancerBasic, self)._create_pool(\n 'ROUND_ROBIN',\n 'HTTP',\n subnet['id'])\n self.pools.append(pool)\n break\n self.assertTrue(self.pools)","title":""},{"docid":"485bcd21533f335b59e210bc83b4dc58","score":"0.63405085","text":"def test_remove_bulk_pool_nodes_to_single_non_existant_pool_id_1(self):\n server_id = text_type(uuid4())\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0])]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(201, response.code)\n pool_id = text_type(uuid4())\n node_and_pool_ids = [(server_id, pool_id)]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"DELETE\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(\"Load Balancer Pool {0} does not exist\".format(pool_id),\n resp_json[\"errors\"][0])","title":""},{"docid":"2d02c262a0b56167b9a8fbaed801ab46","score":"0.63148034","text":"def test_create_delete_subnet_with_v6_attributes_stateless(self):\n self._create_verify_delete_subnet(\n ipv6_ra_mode='dhcpv6-stateless',\n ipv6_address_mode='dhcpv6-stateless')","title":""},{"docid":"8640a4e95d6b29507b16e055944e2fc8","score":"0.6307388","text":"def test_create_list_subnet_with_no_gw64_one_network(self):\n network = self.create_network()\n ipv6_gateway = self.subnet_dict(['gateway'])['gateway']\n subnet1 = self.create_subnet(network,\n ip_version=6,\n gateway=ipv6_gateway)\n self.assertEqual(netaddr.IPNetwork(subnet1['cidr']).version, 6,\n 'The created subnet is not IPv6')\n subnet2 = self.create_subnet(network,\n gateway=None,\n ip_version=4)\n self.assertEqual(netaddr.IPNetwork(subnet2['cidr']).version, 4,\n 'The created subnet is not IPv4')\n # Verifies Subnet GW is set in IPv6\n self.assertEqual(subnet1['gateway_ip'], ipv6_gateway)\n # Verifies Subnet GW is None in IPv4\n self.assertIsNone(subnet2['gateway_ip'])\n # Verifies all 2 subnets in the same network\n body = self.subnets_client.list_subnets()\n subnets = [sub['id'] for sub in body['subnets']\n if sub['network_id'] == network['id']]\n test_subnet_ids = [sub['id'] for sub in (subnet1, subnet2)]\n self.assertCountEqual(subnets,\n test_subnet_ids,\n 'Subnet are not in the same network')","title":""},{"docid":"c61d8d1c695d17240dc6d0c9e2700396","score":"0.63036966","text":"def test_create_delete_subnet_with_default_gw(self):\n net = netaddr.IPNetwork(CONF.network.project_network_v6_cidr)\n gateway_ip = str(netaddr.IPAddress(net.first + 1))\n network = self.create_network()\n subnet = self.create_subnet(network)\n # Verifies Subnet GW in IPv6\n self.assertEqual(subnet['gateway_ip'], gateway_ip)","title":""},{"docid":"3a4705d6936107eca9889be5265e2842","score":"0.62991136","text":"def test_remove_bulk_pool_nodes_to_single_non_existant_pool_id_2(self):\n pool_id = text_type(uuid4())\n node_and_pool_ids = [(text_type(uuid4()), pool_id),\n (text_type(uuid4()), pool_id)]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"DELETE\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(\"Load Balancer Pool {0} does not exist\".format(pool_id),\n resp_json[\"errors\"][0])","title":""},{"docid":"9fa1fba9dcb5b6640dc8467ef55c3542","score":"0.62906045","text":"def test_remove_bulk_pool_nodes_to_multiple_non_existant_pool_ids(self):\n node_and_pool_ids = [(text_type(uuid4()), text_type(uuid4())),\n (text_type(uuid4()), text_type(uuid4()))]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(len(resp_json['errors']), 2)","title":""},{"docid":"7107cfac605549e1f48e0b10f9aab2ee","score":"0.6240493","text":"def test_remove_bulk_pool_nodes_given_a_non_existing_node(self):\n server_id = text_type(uuid4())\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0])]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(201, response.code)\n response, _ = self.successResultOf(self.request_with_content(\n b\"DELETE\", \"/load_balancer_pools/nodes\",\n body=json.dumps(add_data).encode(\"utf-8\")))\n self.assertEqual(204, response.code)\n\n # get the node count for the lb pool\n _, list_json = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n\n response, resp_json = self.successResultOf(self.request_with_content(\n b\"DELETE\", \"/load_balancer_pools/nodes\",\n body=json.dumps(add_data).encode(\"utf-8\")))\n self.assertEqual(409, response.code)\n resp = json.loads(resp_json.decode(\"utf-8\"))\n self.assertEqual(\"Cloud Server {0} is not a member of \"\n \"Load Balancer Pool {1}\".format(server_id, self.get_lb_ids()[0][0]),\n resp[\"errors\"][0])\n\n # ensure no other nodes were deleted from the pool\n _, list_json_again = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n self.assertEqual(len(list_json_again), len(list_json))","title":""},{"docid":"17e8046dad3e5966f0c68d7c1248fcbb","score":"0.6232873","text":"def test_remove_bulk_pool_nodes_multiple_errors(self):\n server_id = text_type(uuid4())\n random_pool_id = text_type(uuid4())\n\n # get the node count for the lb pool\n _, list_json = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0]),\n (server_id, random_pool_id)]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(\"Load Balancer Pool {0} does not exist\".format(random_pool_id),\n resp_json[\"errors\"][0])\n\n # ensure no nodes were deleted from the pool\n _, list_json_again = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n self.assertEqual(len(list_json_again), len(list_json))","title":""},{"docid":"1f23b537c0637ceb5e32cb2685fbb0f4","score":"0.6175446","text":"def test_bulk_create_delete_network(self):\n # Creates 2 networks in one request\n network_list = [{'name': data_utils.rand_name('network-')},\n {'name': data_utils.rand_name('network-')}]\n body = self.networks_client.create_bulk_networks(networks=network_list)\n created_networks = body['networks']\n self.addCleanup(self._delete_networks, created_networks)\n # Asserting that the networks are found in the list after creation\n body = self.networks_client.list_networks()\n networks_list = [network['id'] for network in body['networks']]\n for n in created_networks:\n self.assertIsNotNone(n['id'])\n self.assertIn(n['id'], networks_list)","title":""},{"docid":"a7634246859f5589aa80eda0008ee964","score":"0.6169449","text":"def delete_subnet(self, context, subnet_id):","title":""},{"docid":"7d6f210309901571abb721b82c0ae386","score":"0.6149471","text":"def test_create_delete_subnet_with_v6_attributes_stateful(self):\n self._create_verify_delete_subnet(\n gateway=self._subnet_data[self._ip_version]['gateway'],\n ipv6_ra_mode='dhcpv6-stateful',\n ipv6_address_mode='dhcpv6-stateful')","title":""},{"docid":"512a656b44fcd1956a250dc37e906e32","score":"0.61132514","text":"def test_list_subnets(self):\n body = self.subnets_client.list_subnets()\n subnets = [subnet['id'] for subnet in body['subnets']\n if subnet['id'] == self.subnet['id']]\n self.assertNotEmpty(subnets, \"Created subnet not found in the list\")","title":""},{"docid":"a35f2746f75bcb9461e94da1b2b26cb4","score":"0.6110581","text":"def test_add_bulk_pool_nodes_to_single_non_existant_pool_id_2(self):\n self.helper.clock.advance(50)\n pool_id = text_type(uuid4())\n node_and_pool_ids = [(text_type(uuid4()), pool_id),\n (text_type(uuid4()), pool_id)]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(\"Load Balancer Pool {0} does not exist\".format(pool_id),\n resp_json[\"errors\"][0])\n self.assertEqual(\"Load Balancer Pool {0} does not exist\".format(pool_id),\n resp_json[\"errors\"][1])","title":""},{"docid":"8a13ca2c70339729a626dc62fbe18c46","score":"0.6096396","text":"def test_remove_bulk_pool_nodes_success(self):\n server_data = self._get_add_nodes_json()\n\n # add first\n resp, body = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=server_data))\n\n # ensure the node has been added\n _, list_json = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n self.assertEqual(1, len(list_json))\n\n # delete\n response, _ = self.successResultOf(self.request_with_content(\n b\"DELETE\", \"/load_balancer_pools/nodes\",\n body=json.dumps(server_data).encode(\"utf-8\")))\n self.assertEqual(204, response.code)\n\n # ensure there are 0\n _, list_json = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n self.assertEqual(0, len(list_json))","title":""},{"docid":"6c56a280bf8cc0cff070664985fabadc","score":"0.6007462","text":"def test_add_bulk_pool_nodes_to_multiple_non_existant_pool_ids(self):\n self.helper.clock.advance(50)\n node_and_pool_ids = [(text_type(uuid4()), text_type(uuid4())),\n (text_type(uuid4()), text_type(uuid4()))]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(len(resp_json['errors']), 2)","title":""},{"docid":"47b47eb7dec24f3d320221d5385611c5","score":"0.60043937","text":"def test_remove_bulk_pool_nodes_when_pool_id_is_non_uuid(self):\n node_and_pool_ids = [(text_type(uuid4()), 122)]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"DELETE\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(400, response.code)","title":""},{"docid":"55e2aa9e1052702c1c6dab94e844fd34","score":"0.5994194","text":"def test_different_regions_same_tenant_different_pools(self):\n self.rcv3 = RackConnectV3(regions=[\"ORD\", \"DFW\"])\n self.helper = APIMockHelper(self, [self.rcv3])\n lb_ids = self.get_lb_ids()\n self.assertEqual(1, len(lb_ids[0]))\n self.assertEqual(1, len(lb_ids[1]))\n self.assertNotEqual(set(lb_ids[0]), set(lb_ids[1]))","title":""},{"docid":"6996a240319d8c560c8b9418e7055bf2","score":"0.59820086","text":"def test_add_bulk_pool_nodes_to_single_non_existant_pool_id_1(self):\n self.helper.clock.advance(50)\n pool_id = text_type(uuid4())\n node_and_pool_ids = [(text_type(uuid4()), pool_id)]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(\"Load Balancer Pool {0} does not exist\".format(pool_id),\n resp_json[\"errors\"][0])","title":""},{"docid":"f064673d944b0dacd5010ac62715abbb","score":"0.59779304","text":"def test_too_many_addresses(self):\n admin_context = context.get_admin_context()\n network = db.project_get_network(admin_context, self.projects[0].id)\n num_available_ips = db.network_count_available_ips(admin_context,\n network['id'])\n addresses = []\n instance_ids = []\n for i in range(num_available_ips):\n instance_ref = self._create_instance(0)\n instance_ids.append(instance_ref['id'])\n address = self._create_address(0, instance_ref['id'])\n addresses.append(address)\n\n ip_count = db.network_count_available_ips(context.get_admin_context(),\n network['id'])\n self.assertEqual(ip_count, 0)\n self.assertRaises(db.NoMoreAddresses,\n self.network.allocate_fixed_ip,\n self.context,\n 'foo')\n\n for i in range(num_available_ips):\n self.network.deallocate_fixed_ip(self.context, addresses[i])\n db.instance_destroy(context.get_admin_context(), instance_ids[i])\n ip_count = db.network_count_available_ips(context.get_admin_context(),\n network['id'])\n self.assertEqual(ip_count, num_available_ips)","title":""},{"docid":"60a01b5e78abcb5debf8e67fa4ccde6d","score":"0.59712166","text":"def test_add_bulk_pool_nodes_multiple_errors(self):\n self.helper.clock.advance(50)\n\n # add node to a valid lb pool\n server_id = text_type(uuid4())\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0])]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(201, response.code)\n\n # get the node count for the lb pool\n _, list_json = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n\n # re-add the node to a valid lb pool id as well to a non-existant pool_id\n node_and_pool_ids.append((server_id, text_type(uuid4())))\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(len(resp_json[\"errors\"]), 2)\n\n # ensure no new nodes were added to the pool\n _, list_json_again = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n self.assertEqual(len(list_json_again), len(list_json))","title":""},{"docid":"fc7931f782b317a080c28e8f742a7cea","score":"0.59557337","text":"def _create_subnets(self, engine, data):\n networks = db_utils.get_table(engine, 'networks')\n subnets = db_utils.get_table(engine, 'subnets')\n pools = db_utils.get_table(engine, 'ipallocationpools')\n allocations = db_utils.get_table(engine, 'ipallocations')\n\n for cidr in data:\n ip_version = (constants.IP_VERSION_6 if ':' in cidr else\n constants.IP_VERSION_4)\n # Save generated id in incoming dict to simplify validations\n network_id = uuidutils.generate_uuid()\n network_dict = dict(\n id=network_id,\n standard_attr_id=self._gen_attr_id(engine, 'networks'))\n engine.execute(networks.insert().values(network_dict))\n\n data[cidr]['id'] = uuidutils.generate_uuid()\n subnet_dict = dict(id=data[cidr]['id'],\n cidr=cidr,\n ip_version=ip_version,\n standard_attr_id=self._gen_attr_id(engine,\n 'subnets'))\n engine.execute(subnets.insert().values(subnet_dict))\n\n if data[cidr].get('pools'):\n for pool in data[cidr]['pools']:\n pool_dict = dict(id=uuidutils.generate_uuid(),\n first_ip=pool['first_ip'],\n last_ip=pool['last_ip'],\n subnet_id=data[cidr]['id'])\n engine.execute(pools.insert().values(pool_dict))\n\n if data[cidr].get('allocations'):\n for ip in data[cidr]['allocations']:\n ip_dict = dict(ip_address=ip,\n subnet_id=data[cidr]['id'],\n network_id=network_id)\n engine.execute(allocations.insert().values(ip_dict))","title":""},{"docid":"368b714d1c842845153e7b71ff039ed3","score":"0.59212273","text":"def test_create_disk_pool(self):\n try:\n #\n # create volume\n volume_name_a = self.volume_name_prefix + uuid.uuid4().hex\n cmd = [self.oci_iscsi_config, 'create',\n '--size', self.volume_size,\n '--volume-name', volume_name_a,\n '--attach-volume']\n\n create_volume_data = _run_step('Create Volume', cmd)\n self.assertIn('created', str(create_volume_data), 'Failed to create volume.')\n\n time.sleep(self.waittime)\n volume_data = self._get_iscsi_show(all_vol=False)\n _show_res('Volume data', volume_data)\n time.sleep(self.waittime)\n #\n # create pool\n pool_name_a = self.pool_name_prefix + uuid.uuid4().hex\n this_disk = '/dev/' + self._get_volume_data('name', volume_name_a, 'device')\n cmd = [self.oci_kvm_path, 'create-pool',\n '--disk', this_disk,\n '--name', pool_name_a]\n create_pool_data = _run_step('Create Pool', cmd)\n self.assertIn('successfully', create_pool_data, 'Failed to create pool.')\n time.sleep(self.waittime)\n #\n # list pool\n cmd = [self.oci_kvm_path, 'list-pool',\n '--output-mode', 'table']\n list_pool_data = _run_step('List Pool', cmd)\n # delete pool\n # using virsh commands for now till delete-pool is implemented.\n virsh_path = '/bin/virsh'\n cmd = [virsh_path, 'pool-destroy', pool_name_a]\n destroy_output = _run_step('Destroy pool by virsh', cmd)\n self.asssertIn('destroyed', destroy_output, 'Failed to destroy pool.')\n #\n cmd = [virsh_path, 'pool-delete', pool_name_a]\n delete_output = _run_step('Delete pool by virsh', cmd)\n self.assertIn('deleted', delete_output, 'Failed to delete pool.')\n #\n cmd = [virsh_path, 'pool-undefine', pool_name_a]\n undefine_output = _run_step('Undefine pool by virsh', cmd)\n self.assertIn('undefined', undefine_output, 'Failed to undefine pool.')\n #\n # detach volume\n this_iqn = self._get_volume_data('name', volume_name_a, 'iqn')\n cmd = [self.oci_iscsi_config, 'detach', '--iqns', this_iqn]\n detach_volume_data = _run_step('Detach volume', cmd)\n self.assertIn('detached', detach_volume_data, 'Failed to detach volume.')\n #\n # destroy volume\n this_ocid = self._get_volume_data('name', volume_name_a, 'ocid')\n cmd = [self.oci_iscsi_config, 'destroy', '--ocids', this_ocid, '--yes']\n destroy_volume_data = _run_step('Destroy volume', cmd)\n self.assertIn('destroyed', destroy_volume_data, 'Failed to destroy volume.')\n time.sleep(self.waittime)\n except Exception as e:\n self.fail('Execution of create disk pool has failed: %s' % str(e))","title":""},{"docid":"7d23c2f08a4d021ab828bd4031076097","score":"0.5921016","text":"def test_network_unavailable_on_ec2_instance_in_rollback_mode():","title":""},{"docid":"f3a822037b5ea72734264cce8793a3ca","score":"0.5916754","text":"def test_delete_ip_association(self):\n pass","title":""},{"docid":"d4e44b875ddebf49b2441d73bc2ec7b0","score":"0.5898968","text":"def create_subnet(self, server_name, network_address, network_mask, n_reserved_ips, allowed_ips):\n sql_query = \"INSERT INTO subnets (serverID, server_ip, network_address, network_mask, n_reserved_ips, allowed_ips ) VALUES ( %s, %s, %s, %s, %s, %s );\"\n \n try:\n server_ip = str(ipaddress.IPv4Network(network_address + \"/\" + str(network_mask))[1])\n sql_data = (server_name, server_ip, network_address, network_mask, n_reserved_ips, allowed_ips,)\n\n logging.debug(server_ip)\n self.cursor.execute(sql_query, sql_data)\n self.db_connection.commit()\n except (Exception, psycopg2.DatabaseError) as error:\n self.db_connection.rollback()\n logging.error(f\"Could not add subnet for {server_name}: %s\", error)\n return False\n else:\n logging.debug(f\"Successfully added subnet: {network_address}/{network_mask}.\")\n return True","title":""},{"docid":"354e8b289356d0b5da9a3938813d8e1c","score":"0.5876439","text":"def test_network_has_subnet(yaml_file):\n\n with open(yaml_file) as fh:\n yml = yaml.load(fh)\n\n networks = []\n\n for k, v in yml.get(\"resources\", {}).items():\n if not has_properties(v) or v.get(\"type\") not in [\"OS::Neutron::Net\"]:\n continue\n # need to check if contrail networks also require subnet\n # and it is defined the same as neutron networks\n # if v.get(\"type\") not in NETWORK_RESOURCE_TYPES:\n networks.append(k)\n\n for k, v in yml.get(\"resources\", {}).items():\n network_prop = v.get(\"properties\", {}).get(\"network\", {}).get(\"get_resource\")\n if (\n not has_properties(v)\n and v.get(\"type\") != \"OS::Neutron::Subnet\"\n and not network_prop\n ):\n continue\n x = 0\n for network in networks:\n if network == network_prop:\n networks.pop(x)\n break\n x += 1\n\n assert not networks, \"Networks detected without subnet {}\".format(networks)","title":""},{"docid":"f32b9fbe89a990b719596fe2711c778e","score":"0.5862595","text":"def create_subnets_subnet_pool(self, subnets_subnet_pool, groupnet, subnet, **kwargs):\n\n all_params = ['subnets_subnet_pool', 'groupnet', 'subnet', 'force']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method create_subnets_subnet_pool\" % key\n )\n params[key] = val\n del params['kwargs']\n\n # verify the required parameter 'subnets_subnet_pool' is set\n if ('subnets_subnet_pool' not in params) or (params['subnets_subnet_pool'] is None):\n raise ValueError(\"Missing the required parameter `subnets_subnet_pool` when calling `create_subnets_subnet_pool`\")\n # verify the required parameter 'groupnet' is set\n if ('groupnet' not in params) or (params['groupnet'] is None):\n raise ValueError(\"Missing the required parameter `groupnet` when calling `create_subnets_subnet_pool`\")\n # verify the required parameter 'subnet' is set\n if ('subnet' not in params) or (params['subnet'] is None):\n raise ValueError(\"Missing the required parameter `subnet` when calling `create_subnets_subnet_pool`\")\n\n\n resource_path = '/platform/3/network/groupnets/{Groupnet}/subnets/{Subnet}/pools'.replace('{format}', 'json')\n path_params = {}\n if 'groupnet' in params:\n path_params['Groupnet'] = params['groupnet']\n if 'subnet' in params:\n path_params['Subnet'] = params['subnet']\n\n query_params = {}\n if 'force' in params:\n query_params['force'] = params['force']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n if 'subnets_subnet_pool' in params:\n body_params = params['subnets_subnet_pool']\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['application/json'])\n\n # Authentication setting\n auth_settings = ['basic_auth']\n\n response = self.api_client.call_api(resource_path, 'POST',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='CreateResponse',\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","title":""},{"docid":"cdded764cfc9b009c1e4a5562ae56691","score":"0.5851271","text":"def test_del_has_dependent(self):\n entry = self._create_many_entries(\n 'user_hw_admin@domain.com', 1)[0][0]\n\n dep_subnet = models.Subnet(\n name=\"some_subnet\",\n zone=entry['name'],\n address=\"192.168.0.0/24\",\n modifier=\"user_hw_admin@domain.com\",\n desc=\"\",\n vlan=1801,\n project=self._db_entries['Project'][0]['name'],\n gateway=None,\n dns_1=None,\n dns_2=None,\n owner=\"user_hw_admin@domain.com\"\n )\n self._test_del_has_dependent(\n 'user_hw_admin@domain.com', entry['id'], dep_subnet)","title":""},{"docid":"f73953859766055a783a0fdeb50c7cee","score":"0.5829061","text":"def safe_create_network(network_name, subnet):\n\n\n try:\n # REVIEW this is pretty fragile, depends on the runtime error string\n # format in avi_rest_lib.delete_over_rest\n # Maybe better to just call avi_rest_lib.delete('network',\n # network_name, force=True) and check the\n # response_code directly\n rest.delete('network', name=network_name, check_status_code=False, force=True)\n except:\n pass\n network_lib.network_create(network_name, subnet)","title":""},{"docid":"0a76462c99e3c223d77785e726b31263","score":"0.5816983","text":"def test_add_bulk_pool_nodes_for_an_existing_node(self):\n self.helper.clock.advance(50)\n server_id = text_type(uuid4())\n\n # add node to lb_pool\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0])]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(201, response.code)\n\n # re-adding the node\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(\"Cloud Server {0} is already a member of \"\n \"Load Balancer Pool {1}\".format(server_id, self.get_lb_ids()[0][0]),\n resp_json[\"errors\"][0])","title":""},{"docid":"7042746695ebc64b88c658e98c5afcf2","score":"0.5750156","text":"def ex_delete_subnet(self, subnet):\n return super(Ec2Adapter, self).ex_delete_subnet(subnet)","title":""},{"docid":"14b0b99a50c60ab11615244414f3ca7e","score":"0.57478267","text":"def aws_subnet_no_ip_space_check(cache: dict, session, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:\n ec2 = session.client(\"ec2\")\n iso8601Time = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()\n for vpcs in describe_vpcs(cache, session):\n vpcId = vpcs[\"VpcId\"]\n # Get subnets for the VPC\n for snet in ec2.describe_subnets(Filters=[{'Name': 'vpc-id','Values': [vpcId]}])[\"Subnets\"]:\n # B64 encode all of the details for the Asset\n assetJson = json.dumps(snet,default=str).encode(\"utf-8\")\n assetB64 = base64.b64encode(assetJson)\n snetArn = snet[\"SubnetArn\"]\n snetId = snet[\"SubnetId\"] \n if int(snet[\"AvailableIpAddressCount\"]) <= 1:\n # This is a failing check\n finding = {\n \"SchemaVersion\": \"2018-10-08\",\n \"Id\": f\"{snetArn}/subnet-no-remaining-ip-space-check\",\n \"ProductArn\": f\"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default\",\n \"GeneratorId\": f\"{snetArn}/subnet-no-remaining-ip-space-check\",\n \"AwsAccountId\": awsAccountId,\n \"Types\": [\"Software and Configuration Checks/AWS Security Best Practices\"],\n \"FirstObservedAt\": iso8601Time,\n \"CreatedAt\": iso8601Time,\n \"UpdatedAt\": iso8601Time,\n \"Severity\": {\"Label\": \"MEDIUM\"},\n \"Confidence\": 99,\n \"Title\": \"[VPC.4] Amazon Virtual Private Cloud (VPC) subnets should be monitored for available IP address space\",\n \"Description\": f\"Amazon Virtual Private Cloud (VPC) subnet {snetId} does not have any available IP address space, consider terminating unncessary workloads, adding a Secondary CIDR to the parent VPC, or expanding CIDR capacity to avoid availability losses. Refer to the remediation instructions if this configuration is not intended.\",\n \"Remediation\": {\n \"Recommendation\": {\n \"Text\": \"For information on IP addressing refer to the IP Addressing in your VPC section of the Amazon Virtual Private Cloud User Guide\",\n \"Url\": \"https://docs.aws.amazon.com/vpc/latest/userguide/vpc-ip-addressing.html\"\n }\n },\n \"ProductFields\": {\n \"ProductName\": \"ElectricEye\",\n \"Provider\": \"AWS\",\n \"ProviderType\": \"CSP\",\n \"ProviderAccountId\": awsAccountId,\n \"AssetRegion\": awsRegion,\n \"AssetDetails\": assetB64,\n \"AssetClass\": \"Networking\",\n \"AssetService\": \"Amazon Virtual Private Cloud\",\n \"AssetComponent\": \"Subnet\"\n },\n \"Resources\": [\n {\n \"Type\": \"AwsEc2Subnet\",\n \"Id\": snetArn,\n \"Partition\": awsPartition,\n \"Region\": awsRegion,\n \"Details\": {\n \"Other\": {\n \"VpcId\": vpcId,\n \"SubnetId\": snetId\n }\n }\n }\n ],\n \"Compliance\": {\n \"Status\": \"FAILED\",\n \"RelatedRequirements\": [\n \"NIST CSF V1.1 ID.BE-5\",\n \"NIST CSF V1.1 PR.DS-4\",\n \"NIST CSF V1.1 PR.PT-5\",\n \"NIST SP 800-53 Rev. 4 AU-4\",\n \"NIST SP 800-53 Rev. 4 CP-2\",\n \"NIST SP 800-53 Rev. 4 CP-7\",\n \"NIST SP 800-53 Rev. 4 CP-8\",\n \"NIST SP 800-53 Rev. 4 CP-11\",\n \"NIST SP 800-53 Rev. 4 CP-13\",\n \"NIST SP 800-53 Rev. 4 PL-8\",\n \"NIST SP 800-53 Rev. 4 SA-14\",\n \"NIST SP 800-53 Rev. 4 SC-5\",\n \"NIST SP 800-53 Rev. 4 SC-6\",\n \"AICPA TSC CC3.1\",\n \"AICPA TSC A1.1\",\n \"AICPA TSC A1.2\",\n \"ISO 27001:2013 A.11.1.4\",\n \"ISO 27001:2013 A.12.3.1\",\n \"ISO 27001:2013 A.17.1.1\",\n \"ISO 27001:2013 A.17.1.2\",\n \"ISO 27001:2013 A.17.2.1\"\n ]\n },\n \"Workflow\": {\"Status\": \"NEW\"},\n \"RecordState\": \"ACTIVE\"\n }\n yield finding\n else:\n finding = {\n \"SchemaVersion\": \"2018-10-08\",\n \"Id\": f\"{snetArn}/subnet-no-remaining-ip-space-check\",\n \"ProductArn\": f\"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default\",\n \"GeneratorId\": f\"{snetArn}/subnet-no-remaining-ip-space-check\",\n \"AwsAccountId\": awsAccountId,\n \"Types\": [\"Software and Configuration Checks/AWS Security Best Practices\"],\n \"FirstObservedAt\": iso8601Time,\n \"CreatedAt\": iso8601Time,\n \"UpdatedAt\": iso8601Time,\n \"Severity\": {\"Label\": \"INFORMATIONAL\"},\n \"Confidence\": 99,\n \"Title\": \"[VPC.4] Amazon Virtual Private Cloud (VPC) subnets should be monitored for available IP address space\",\n \"Description\": f\"Amazon Virtual Private Cloud (VPC) subnet {snetId} does have available IP address space.\",\n \"Remediation\": {\n \"Recommendation\": {\n \"Text\": \"For information on IP addressing refer to the IP Addressing in your VPC section of the Amazon Virtual Private Cloud User Guide\",\n \"Url\": \"https://docs.aws.amazon.com/vpc/latest/userguide/vpc-ip-addressing.html\"\n }\n },\n \"ProductFields\": {\n \"ProductName\": \"ElectricEye\",\n \"Provider\": \"AWS\",\n \"ProviderType\": \"CSP\",\n \"ProviderAccountId\": awsAccountId,\n \"AssetRegion\": awsRegion,\n \"AssetDetails\": assetB64,\n \"AssetClass\": \"Networking\",\n \"AssetService\": \"Amazon Virtual Private Cloud\",\n \"AssetComponent\": \"Subnet\"\n },\n \"Resources\": [\n {\n \"Type\": \"AwsEc2Subnet\",\n \"Id\": snetArn,\n \"Partition\": awsPartition,\n \"Region\": awsRegion,\n \"Details\": {\n \"Other\": {\n \"VpcId\": vpcId,\n \"SubnetId\": snetId\n }\n }\n }\n ],\n \"Compliance\": {\n \"Status\": \"PASSED\",\n \"RelatedRequirements\": [\n \"NIST CSF V1.1 ID.BE-5\",\n \"NIST CSF V1.1 PR.DS-4\",\n \"NIST CSF V1.1 PR.PT-5\",\n \"NIST SP 800-53 Rev. 4 AU-4\",\n \"NIST SP 800-53 Rev. 4 CP-2\",\n \"NIST SP 800-53 Rev. 4 CP-7\",\n \"NIST SP 800-53 Rev. 4 CP-8\",\n \"NIST SP 800-53 Rev. 4 CP-11\",\n \"NIST SP 800-53 Rev. 4 CP-13\",\n \"NIST SP 800-53 Rev. 4 PL-8\",\n \"NIST SP 800-53 Rev. 4 SA-14\",\n \"NIST SP 800-53 Rev. 4 SC-5\",\n \"NIST SP 800-53 Rev. 4 SC-6\",\n \"AICPA TSC CC3.1\",\n \"AICPA TSC A1.1\",\n \"AICPA TSC A1.2\",\n \"ISO 27001:2013 A.11.1.4\",\n \"ISO 27001:2013 A.12.3.1\",\n \"ISO 27001:2013 A.17.1.1\",\n \"ISO 27001:2013 A.17.1.2\",\n \"ISO 27001:2013 A.17.2.1\"\n ]\n },\n \"Workflow\": {\"Status\": \"RESOLVED\"},\n \"RecordState\": \"ARCHIVED\"\n }\n yield finding","title":""},{"docid":"388557b556c60844c7eb05563cd11b9c","score":"0.57184243","text":"def test_add_bulk_pool_nodes_errors_with_no_node_added(self):\n self.helper.clock.advance(50)\n # get the node count for the lb pool\n _, list_json = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n server_id = text_type(uuid4())\n random_pool_id = text_type(uuid4())\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0]),\n (server_id, random_pool_id)]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(409, response.code)\n self.assertEqual(\"Load Balancer Pool {0} does not exist\".format(random_pool_id),\n resp_json[\"errors\"][0])\n # ensure no new nodes were added to the pool\n _, list_json_again = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n self.assertEqual(len(list_json_again), len(list_json))","title":""},{"docid":"6d2d84ec48d9a383f1a4de4dbe113eee","score":"0.5709778","text":"def test_ips_are_reused(self):\n address = self._create_address(0)\n self.network.deallocate_fixed_ip(self.context, address)\n\n address2 = self._create_address(0)\n self.assertEqual(address, address2)\n\n self.network.deallocate_fixed_ip(self.context, address2)","title":""},{"docid":"1d625c8e4f5741279ba1782126606f34","score":"0.57035017","text":"def pool_add(pool_name, option, devices):\n if pool_name.startswith(TEST_PREF):\n exec_command([STRATIS_CLI, \"pool\", option, pool_name] + devices)\n blockdevs = StratisCli.blockdev_list()\n for b in devices:\n assert b in blockdevs\n assert blockdevs[b][\"POOL_NAME\"] == pool_name","title":""},{"docid":"eb7e3690d72351025787dacc7d22c7d8","score":"0.5683978","text":"def update_allocation(module, allocations):\n net_name = module.params['net_name']\n hostname = module.params['hostname']\n cidr = module.params['cidr']\n allocation_pool_start = module.params['allocation_pool_start']\n allocation_pool_end = module.params['allocation_pool_end']\n network = netaddr.IPNetwork(cidr)\n result = {\n 'changed': False,\n }\n object_name = \"%s_ips\" % net_name\n net_allocations = allocations.setdefault(object_name, {})\n invalid_allocations = {hn: ip for hn, ip in net_allocations.items()\n if netaddr.IPAddress(ip) not in network}\n if invalid_allocations:\n module.fail_json(msg=\"Found invalid existing allocations in network %s: %s\" %\n (network,\n \", \".join(\"%s: %s\" % (hn, ip)\n for hn, ip in invalid_allocations.items())))\n if hostname not in net_allocations:\n result['changed'] = True\n allocated_ips = netaddr.IPSet(net_allocations.values())\n if allocation_pool_start and allocation_pool_end:\n allocation_pool = netaddr.IPRange(allocation_pool_start, allocation_pool_end)\n allocation_pool = netaddr.IPSet(allocation_pool)\n else:\n allocation_pool = netaddr.IPSet([network])\n if network.prefixlen != 32:\n reserved_ips = [network.network, network.broadcast]\n allocation_pool -= netaddr.IPSet(reserved_ips)\n free_ips = allocation_pool - allocated_ips\n for free_cidr in free_ips.iter_cidrs():\n ip = free_cidr[0]\n break\n else:\n module.fail_json(msg=\"No unallocated IP addresses for %s in %s\" % (hostname, net_name))\n free_ips.remove(ip)\n net_allocations[hostname] = str(ip)\n result['ip'] = net_allocations[hostname]\n return result","title":""},{"docid":"177fc48007b264d99fa7e6589daef17a","score":"0.5683613","text":"def delete_subnets_subnet_pool(self, subnets_subnet_pool_id, groupnet, subnet, **kwargs):\n\n all_params = ['subnets_subnet_pool_id', 'groupnet', 'subnet']\n all_params.append('callback')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method delete_subnets_subnet_pool\" % key\n )\n params[key] = val\n del params['kwargs']\n\n # verify the required parameter 'subnets_subnet_pool_id' is set\n if ('subnets_subnet_pool_id' not in params) or (params['subnets_subnet_pool_id'] is None):\n raise ValueError(\"Missing the required parameter `subnets_subnet_pool_id` when calling `delete_subnets_subnet_pool`\")\n # verify the required parameter 'groupnet' is set\n if ('groupnet' not in params) or (params['groupnet'] is None):\n raise ValueError(\"Missing the required parameter `groupnet` when calling `delete_subnets_subnet_pool`\")\n # verify the required parameter 'subnet' is set\n if ('subnet' not in params) or (params['subnet'] is None):\n raise ValueError(\"Missing the required parameter `subnet` when calling `delete_subnets_subnet_pool`\")\n\n\n resource_path = '/platform/3/network/groupnets/{Groupnet}/subnets/{Subnet}/pools/{SubnetsSubnetPoolId}'.replace('{format}', 'json')\n path_params = {}\n if 'subnets_subnet_pool_id' in params:\n path_params['SubnetsSubnetPoolId'] = params['subnets_subnet_pool_id']\n if 'groupnet' in params:\n path_params['Groupnet'] = params['groupnet']\n if 'subnet' in params:\n path_params['Subnet'] = params['subnet']\n\n query_params = {}\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['application/json'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['application/json'])\n\n # Authentication setting\n auth_settings = ['basic_auth']\n\n response = self.api_client.call_api(resource_path, 'DELETE',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type=None,\n auth_settings=auth_settings,\n callback=params.get('callback'))\n return response","title":""},{"docid":"1d3279b200bbd1ad84f0995a205f5bc2","score":"0.5667308","text":"def test_create_delete_subnet_with_v6_attributes_slaac(self):\n self._create_verify_delete_subnet(\n ipv6_ra_mode='slaac',\n ipv6_address_mode='slaac')","title":""},{"docid":"396193b2f449a5fca6e91fb0abadba78","score":"0.5650636","text":"def test_add_bulk_pool_nodes_when_pool_id_is_non_uuid(self):\n self.helper.clock.advance(50)\n node_and_pool_ids = [(text_type(uuid4()), 122),\n (text_type(uuid4()), text_type(uuid4()))]\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(400, response.code)","title":""},{"docid":"d81864f735aadcf8ef6d8caafa58fcbc","score":"0.5648901","text":"def test_task_id_create_in_delete_one_ipv4_error(self):\n\n pass","title":""},{"docid":"185daf38d1343c75225e1c4f71356987","score":"0.5605987","text":"def test_add_pool(rml_pool_store: Store):\n # GIVEN a valid customer and a valid application_version\n customer: Customer = rml_pool_store.get_customers()[0]\n application = rml_pool_store.get_application_by_tag(tag=\"RMLP05R800\")\n app_version = (\n rml_pool_store._get_query(table=ApplicationVersion)\n .filter(ApplicationVersion.application_id == application.id)\n .first()\n )\n\n # WHEN adding a new pool\n new_pool = rml_pool_store.add_pool(\n customer=customer,\n name=\"pool2\",\n order=\"123456\",\n ordered=dt.now(),\n application_version=app_version,\n )\n\n rml_pool_store.session.add(new_pool)\n rml_pool_store.session.commit()\n # THEN the new pool should have no_invoice = False\n pool = rml_pool_store.get_pool_by_entry_id(entry_id=2)\n assert pool.no_invoice is False","title":""},{"docid":"ee4b1e513b912e7c1ea0db54d1210401","score":"0.55956775","text":"def test_createSharedNetwork_usedVlan2(self):\n\n # Steps,\n # 1. create an Admin account\n # 2. create a shared NetworkOffering\n # 3. enable the network offering\n # 4. listPhysicalNetworks\n # - vlan = guest VLAN range = 10-90 (say)\n # 5. createNetwork\n # - name = mysharednetwork, displaytext = mysharednetwork\n # - vlan = any vlan beyond 10-90 (123 for eg)\n # - networkofferingid = <mysharedoffering>\n # - gw = 172.16.15.1, startip = 172.16.15.2 , endip = 172.16.15.200\n # - netmask=255.255.255.0\n # - scope = all\n # 6. createNetwork again with same VLAN but different IP ranges and\n # gateway\n # 7. delete admin account\n # Validations,\n # 1. listAccounts state=enabled returns your account\n # 2. listNetworkOfferings - name=mysharedoffering , should list\n # offering in disabled state\n # 3. listNetworkOfferings - name=mysharedoffering, should list\n # enabled offering\n # 4. listPhysicalNetworks should return at least one active\n # physical network\n # 5. network creation should PASS\n # 6. network creation should FAIL since VLAN is already used by\n # previously created network\n\n # Create admin account\n self.admin_account = Account.create(\n self.api_client,\n self.testdata[\"account\"],\n admin=True,\n domainid=self.domain.id\n )\n self.cleanup_accounts.append(self.admin_account)\n\n # verify that the account got created with state enabled\n list_accounts_response = Account.list(\n self.api_client,\n id=self.admin_account.id,\n listall=True\n )\n self.assertEqual(\n isinstance(list_accounts_response, list),\n True,\n \"listAccounts returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_accounts_response),\n 0,\n \"listAccounts returned empty list.\"\n )\n self.assertEqual(\n list_accounts_response[0].state,\n \"enabled\",\n \"The admin account created is not enabled.\"\n )\n\n self.debug(\"Admin account created: %s\" % self.admin_account.id)\n\n physical_network, shared_vlan = get_free_vlan(\n self.api_client, self.zone.id)\n if shared_vlan is None:\n self.fail(\"Failed to get free vlan id for shared network\")\n\n self.debug(\"Physical Network found: %s\" % physical_network.id)\n\n self.testdata[\"shared_network_offering\"][\"specifyVlan\"] = \"True\"\n self.testdata[\"shared_network_offering\"][\"specifyIpRanges\"] = \"True\"\n\n # Create Network Offering\n self.shared_network_offering = NetworkOffering.create(\n self.api_client,\n self.testdata[\"shared_network_offering\"],\n conservemode=False\n )\n\n # Verify that the network offering got created\n list_network_offerings_response = NetworkOffering.list(\n self.api_client,\n id=self.shared_network_offering.id\n )\n self.assertEqual(\n isinstance(list_network_offerings_response, list),\n True,\n \"listNetworkOfferings returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_network_offerings_response),\n 0,\n \"listNetworkOfferings returned empty list.\"\n )\n self.assertEqual(\n list_network_offerings_response[0].state,\n \"Disabled\",\n \"The network offering created should be bydefault disabled.\"\n )\n\n self.debug(\n \"Shared Network Offering created: %s\" %\n self.shared_network_offering.id)\n\n # Update network offering state from disabled to enabled.\n NetworkOffering.update(\n self.shared_network_offering,\n self.api_client,\n id=self.shared_network_offering.id,\n state=\"enabled\"\n )\n\n # Verify that the state of the network offering is updated\n list_network_offerings_response = NetworkOffering.list(\n self.api_client,\n id=self.shared_network_offering.id\n )\n self.assertEqual(\n isinstance(list_network_offerings_response, list),\n True,\n \"listNetworkOfferings returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_network_offerings_response),\n 0,\n \"listNetworkOfferings returned empty list.\"\n )\n self.assertEqual(\n list_network_offerings_response[0].state,\n \"Enabled\",\n \"The network offering state should get updated to Enabled.\"\n )\n\n # create network using the shared network offering created\n self.testdata[\"shared_network\"][\"acltype\"] = \"Domain\"\n self.testdata[\"shared_network\"][\n \"networkofferingid\"] = self.shared_network_offering.id\n self.testdata[\"shared_network\"][\n \"physicalnetworkid\"] = physical_network.id\n self.testdata[\"shared_network\"][\"vlan\"] = shared_vlan\n self.debug(\n \"Creating a shared network in non-cloudstack VLAN %s\" %\n shared_vlan)\n self.network = Network.create(\n self.api_client,\n self.testdata[\"shared_network\"],\n networkofferingid=self.shared_network_offering.id,\n zoneid=self.zone.id,\n )\n\n self.cleanup_networks.append(self.network)\n\n list_networks_response = Network.list(\n self.api_client,\n id=self.network.id\n )\n self.assertEqual(\n isinstance(list_networks_response, list),\n True,\n \"listNetworks returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_networks_response),\n 0,\n \"listNetworks returned empty list.\"\n )\n self.assertEqual(\n list_networks_response[0].specifyipranges,\n True,\n \"The network is created with ip range but the flag is\\\n set to False.\")\n\n self.debug(\"Network created: %s\" % self.network.id)\n\n shared_network_subnet_number = random.randrange(1, 254)\n\n self.testdata[\"shared_network\"][\"gateway\"] = \"172.16.\" + \\\n str(shared_network_subnet_number) + \".1\"\n self.testdata[\"shared_network\"][\"startip\"] = \"172.16.\" + \\\n str(shared_network_subnet_number) + \".2\"\n self.testdata[\"shared_network\"][\"endip\"] = \"172.16.\" + \\\n str(shared_network_subnet_number) + \".20\"\n\n self.testdata[\"shared_network\"][\"acltype\"] = \"domain\"\n self.testdata[\"shared_network\"][\n \"networkofferingid\"] = self.shared_network_offering.id\n self.testdata[\"shared_network\"][\n \"physicalnetworkid\"] = physical_network.id\n\n try:\n self.network1 = Network.create(\n self.api_client,\n self.testdata[\"shared_network\"],\n networkofferingid=self.shared_network_offering.id,\n zoneid=self.zone.id,\n )\n self.cleanup_networks.append(self.network1)\n self.fail(\n \"Network got created with used vlan id, which is invalid\")\n except Exception as e:\n self.debug(\n \"Network creation failed because the valn id being used by\\\n another network. Exception: %s\" % e)","title":""},{"docid":"04d7cb27cba857720f66ccd66a01b628","score":"0.55905604","text":"def _validate_pool(self, session, lb_id, pool_id, listener_protocol):\n db_pool = self.repositories.pool.get(\n session, load_balancer_id=lb_id, id=pool_id)\n if not db_pool:\n raise exceptions.NotFound(\n resource=data_models.Pool._name(), id=pool_id)\n self._validate_protocol(listener_protocol, db_pool.protocol)","title":""},{"docid":"cddd70d604bdfab8fdf2af9c89f45db4","score":"0.55753607","text":"def test_create_delete_network_in_datacenter(self):\n testflow.step(\"Log in as user.\")\n common.login_as_user()\n\n for net in config.NETWORK_NAMES[:2]:\n assert networks.add_network(\n False,\n name=net,\n data_center=config.DC_NAME[0]\n )","title":""},{"docid":"6d6a248c732a2eae12d6dd51b3fbef67","score":"0.55617326","text":"def _validate_pools_exist(self):\n pool_list = self._get_directory_list('/')\n for pool in self.pools:\n if pool not in pool_list:\n msg = '%s is not exist in backend storage.' % pool\n LOG.error(msg)\n raise exception.InvalidInput(reason=msg)","title":""},{"docid":"daa9420ebf07ddcc0ee8fba49ad0219a","score":"0.5544308","text":"def _validate_task_pools(self, *, dagbag: DagBag, session: Session = NEW_SESSION):\n from airflow.models.pool import Pool\n\n def check_pools(dag):\n task_pools = {task.pool for task in dag.tasks}\n nonexistent_pools = task_pools - pools\n if nonexistent_pools:\n return f\"Dag '{dag.dag_id}' references non-existent pools: {sorted(nonexistent_pools)!r}\"\n\n pools = {p.pool for p in Pool.get_pools(session)}\n for dag in dagbag.dags.values():\n message = check_pools(dag)\n if message:\n self.dag_warnings.add(DagWarning(dag.dag_id, DagWarningType.NONEXISTENT_POOL, message))\n for subdag in dag.subdags:\n message = check_pools(subdag)\n if message:\n self.dag_warnings.add(DagWarning(subdag.dag_id, DagWarningType.NONEXISTENT_POOL, message))","title":""},{"docid":"e86e452b74b1eceae60e5d6a3432060c","score":"0.5539687","text":"def _check_networks(self):\n\n seen_nets = self.admin_manager.networks_client.list_networks()\n seen_names = [n['name'] for n in seen_nets['networks']]\n seen_ids = [n['id'] for n in seen_nets['networks']]\n self.assertIn(self.network['name'], seen_names)\n self.assertIn(self.network['id'], seen_ids)\n\n seen_subnets = self.admin_manager.subnets_client.list_subnets()\n seen_net_ids = [n['network_id'] for n in seen_subnets['subnets']]\n seen_subnet_ids = [n['id'] for n in seen_subnets['subnets']]\n self.assertIn(self.network['id'], seen_net_ids)\n self.assertIn(self.subnet['id'], seen_subnet_ids)","title":""},{"docid":"1f1b8c39308955cbb21f87b867911fcd","score":"0.5529731","text":"def test_network_unavailable_on_ec2_instance():","title":""},{"docid":"668711c7277837562d041da240d574ab","score":"0.5526954","text":"def test_10_network_services_VPC_DeleteAllMultiplePF(self):\n\n network_1 = self.create_network(self.services[\"network_offering\"])\n network_2 = self.create_network(self.services[\"network_offering_no_lb\"], '10.1.2.1')\n vm_1 = self.deployvm_in_network(network_1)\n vm_2 = self.deployvm_in_network(network_1)\n vm_3 = self.deployvm_in_network(network_2)\n vm_4 = self.deployvm_in_network(network_2)\n public_ip_1 = self.acquire_publicip(network_1)\n public_ip_2 = self.acquire_publicip(network_1)\n nat_rule1 = self.create_natrule_for_services(vm_1, public_ip_1, network_1)\n nat_rule2 = self.create_natrule_for_services(vm_2, public_ip_2, network_1)\n http_rule1 = self.create_natrule_for_services(vm_1, public_ip_1, network_1, self.services[\"http_rule\"])\n http_rule2 = self.create_natrule_for_services(vm_2, public_ip_2, network_1, self.services[\"http_rule\"])\n public_ip_3 = self.acquire_publicip(network_2)\n public_ip_4 = self.acquire_publicip(network_2)\n nat_rule3 = self.create_natrule_for_services(vm_3, public_ip_3, network_2)\n nat_rule4 = self.create_natrule_for_services(vm_4, public_ip_4, network_2)\n http_rule3 = self.create_natrule_for_services(vm_3, public_ip_3, network_2, self.services[\"http_rule\"])\n http_rule4 = self.create_natrule_for_services(vm_4, public_ip_4, network_2, self.services[\"http_rule\"])\n self.check_ssh_into_vm(vm_1, public_ip_1, testnegative=False)\n self.check_ssh_into_vm(vm_2, public_ip_2, testnegative=False)\n self.check_ssh_into_vm(vm_3, public_ip_3, testnegative=False)\n self.check_ssh_into_vm(vm_4, public_ip_4, testnegative=False)\n self.check_wget_from_vm(vm_1, public_ip_1, testnegative=False)\n self.check_wget_from_vm(vm_2, public_ip_2, testnegative=False)\n self.check_wget_from_vm(vm_3, public_ip_3, testnegative=False)\n self.check_wget_from_vm(vm_4, public_ip_4, testnegative=False)\n nat_rule1.delete(self.apiclient)\n self.cleanup.remove(nat_rule1)\n nat_rule2.delete(self.apiclient)\n self.cleanup.remove(nat_rule2)\n nat_rule3.delete(self.apiclient)\n self.cleanup.remove(nat_rule3)\n nat_rule4.delete(self.apiclient)\n self.cleanup.remove(nat_rule4)\n http_rule1.delete(self.apiclient)\n self.cleanup.remove(http_rule1)\n http_rule2.delete(self.apiclient)\n self.cleanup.remove(http_rule2)\n http_rule3.delete(self.apiclient)\n self.cleanup.remove(http_rule3)\n http_rule4.delete(self.apiclient)\n self.cleanup.remove(http_rule4)\n self.check_ssh_into_vm(vm_1, public_ip_1, testnegative=True)\n self.check_ssh_into_vm(vm_2, public_ip_2, testnegative=True)\n self.check_ssh_into_vm(vm_3, public_ip_3, testnegative=True)\n self.check_ssh_into_vm(vm_4, public_ip_4, testnegative=True)\n self.check_wget_from_vm(vm_1, public_ip_1, testnegative=True,\n isVmAccessible=False, network=network_1)\n self.check_wget_from_vm(vm_2, public_ip_2, testnegative=True,\n isVmAccessible=False, network=network_1)\n self.check_wget_from_vm(vm_3, public_ip_3, testnegative=True,\n isVmAccessible=False, network=network_2)\n self.check_wget_from_vm(vm_4, public_ip_4, testnegative=True,\n isVmAccessible=False, network=network_2)\n return","title":""},{"docid":"9e701da995c64bd12c17456f4b7086fa","score":"0.5478033","text":"def _check_constraints(\n self, random_subnet: SupportRandomSubnet) -> Tuple[bool, Dict]:\n is_pass, results = check_subnet_resources(\n model=self.model,\n subnet=random_subnet,\n estimator=self.estimator,\n constraints_range=self.constraints_range)\n\n return is_pass, results","title":""},{"docid":"8e30c9c14122a807f746026fc2e6e045","score":"0.5462444","text":"def test_createSharedNetwork_All(self):\n\n # Steps,\n # 1. create an Admin Account - admin-XABU1\n # 2. listPhysicalNetworks in available zone\n # 3. createNetworkOffering:\n # - name = \"MySharedOffering\"\n # - guestiptype=\"shared\"\n # - services = {Dns, Dhcp, UserData}\n # - conservemode = false\n # - specifyVlan = true\n # - specifyIpRanges = true\n # 4. Create network offering - updateNetworkOffering - state=Enabled\n # 5. createNetwork\n # - name = mysharednetwork, displaytext = mysharednetwork\n # - vlan = 123 (say)\n # - networkofferingid = <mysharedoffering>\n # - gw = 172.16.15.1, startip = 172.16.15.2 , endip = 172.16.15.200\n # - netmask=255.255.255.0\n # - scope = all\n # 6. create User account - user-ASJDK\n # 7. deployVirtualMachine in this account and in admin account &\n # within networkid = <mysharednetwork>\n # 8. delete the admin account and the user account\n # Validations,\n # 1. listAccounts name=admin-XABU1, state=enabled returns account\n # 2. listPhysicalNetworks should return at least one active\n # physical network\n # 3. listNetworkOfferings - name=mysharedoffering , should list\n # offering in disabled state\n # 4. listNetworkOfferings - name=mysharedoffering, should list\n # enabled offering\n # 5. listNetworks - name = mysharednetwork should list the\n # successfully created network, verify the guestIp ranges and\n # CIDR are as given in the createNetwork call\n # 6. No checks reqd\n # 7. a. listVirtualMachines should show both VMs in running state\n # in the user account and the admin account\n # b. VM's IPs should be in the range of the shared network ip ranges\n\n # Create admin account\n self.admin_account = Account.create(\n self.api_client,\n self.testdata[\"account\"],\n admin=True,\n domainid=self.domain.id\n )\n\n self.cleanup_accounts.append(self.admin_account)\n\n # verify that the account got created with state enabled\n list_accounts_response = Account.list(\n self.api_client,\n id=self.admin_account.id,\n listall=True\n )\n self.assertEqual(\n isinstance(list_accounts_response, list),\n True,\n \"listAccounts returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_accounts_response),\n 0,\n \"listAccounts returned empty list.\"\n )\n self.assertEqual(\n list_accounts_response[0].state,\n \"enabled\",\n \"The admin account created is not enabled.\"\n )\n\n self.debug(\"Admin type account created: %s\" % self.admin_account.name)\n\n # Create an user account\n self.user_account = Account.create(\n self.api_client,\n self.testdata[\"account\"],\n admin=False,\n domainid=self.domain.id\n )\n\n self.cleanup_accounts.append(self.user_account)\n\n # verify that the account got created with state enabled\n list_accounts_response = Account.list(\n self.api_client,\n id=self.user_account.id,\n listall=True\n )\n self.assertEqual(\n isinstance(list_accounts_response, list),\n True,\n \"listAccounts returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_accounts_response),\n 0,\n \"listAccounts returned empty list.\"\n )\n self.assertEqual(\n list_accounts_response[0].state,\n \"enabled\",\n \"The user account created is not enabled.\"\n )\n\n self.debug(\"User type account created: %s\" % self.user_account.name)\n\n physical_network, shared_vlan = get_free_vlan(\n self.api_client, self.zone.id)\n if shared_vlan is None:\n self.fail(\"Failed to get free vlan id for shared network\")\n\n self.debug(\"Physical network found: %s\" % physical_network.id)\n\n self.testdata[\"shared_network_offering\"][\"specifyVlan\"] = \"True\"\n self.testdata[\"shared_network_offering\"][\"specifyIpRanges\"] = \"True\"\n\n # Create Network Offering\n self.shared_network_offering = NetworkOffering.create(\n self.api_client,\n self.testdata[\"shared_network_offering\"],\n conservemode=False\n )\n\n # Verify that the network offering got created\n list_network_offerings_response = NetworkOffering.list(\n self.api_client,\n id=self.shared_network_offering.id\n )\n self.assertEqual(\n isinstance(list_network_offerings_response, list),\n True,\n \"listNetworkOfferings returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_network_offerings_response),\n 0,\n \"listNetworkOfferings returned empty list.\"\n )\n self.assertEqual(\n list_network_offerings_response[0].state,\n \"Disabled\",\n \"The network offering created should be bydefault disabled.\"\n )\n\n self.debug(\n \"Shared Network offering created: %s\" %\n self.shared_network_offering.id)\n\n # Update network offering state from disabled to enabled.\n NetworkOffering.update(\n self.shared_network_offering,\n self.api_client,\n id=self.shared_network_offering.id,\n state=\"enabled\"\n )\n\n # Verify that the state of the network offering is updated\n list_network_offerings_response = NetworkOffering.list(\n self.api_client,\n id=self.shared_network_offering.id\n )\n self.assertEqual(\n isinstance(list_network_offerings_response, list),\n True,\n \"listNetworkOfferings returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_network_offerings_response),\n 0,\n \"listNetworkOfferings returned empty list.\"\n )\n self.assertEqual(\n list_network_offerings_response[0].state,\n \"Enabled\",\n \"The network offering state should get updated to Enabled.\"\n )\n\n # create network using the shared network offering created\n self.testdata[\"shared_network\"][\"acltype\"] = \"Domain\"\n self.testdata[\"shared_network\"][\n \"networkofferingid\"] = self.shared_network_offering.id\n self.testdata[\"shared_network\"][\n \"physicalnetworkid\"] = physical_network.id\n self.testdata[\"shared_network\"][\"vlan\"] = shared_vlan\n\n self.network = Network.create(\n self.api_client,\n self.testdata[\"shared_network\"],\n networkofferingid=self.shared_network_offering.id,\n zoneid=self.zone.id,\n )\n\n self.cleanup_networks.append(self.network)\n\n list_networks_response = Network.list(\n self.api_client,\n id=self.network.id\n )\n self.assertEqual(\n isinstance(list_networks_response, list),\n True,\n \"listNetworks returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_networks_response),\n 0,\n \"listNetworks returned empty list.\"\n )\n self.assertEqual(\n list_networks_response[0].specifyipranges,\n True,\n \"The network is created with ip range but the flag is\\\n set to False.\")\n\n self.debug(\n \"Shared Network created for scope domain: %s\" %\n self.network.id)\n\n self.admin_account_virtual_machine = VirtualMachine.create(\n self.api_client,\n self.testdata[\"virtual_machine\"],\n networkids=self.network.id,\n serviceofferingid=self.service_offering.id\n )\n\n self.cleanup_vms.append(self.admin_account_virtual_machine)\n\n vms = VirtualMachine.list(\n self.api_client,\n id=self.admin_account_virtual_machine.id,\n listall=True\n )\n self.assertEqual(\n isinstance(vms, list),\n True,\n \"listVirtualMachines returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(vms),\n 0,\n \"listVirtualMachines returned empty list.\"\n )\n\n self.debug(\n \"Virtual Machine created: %s\" %\n self.admin_account_virtual_machine.id)\n\n ip_range = list(\n netaddr.iter_iprange(\n str(\n self.testdata[\"shared_network\"][\"startip\"]), str(\n self.testdata[\"shared_network\"][\"endip\"])))\n if netaddr.IPAddress(str(vms[0].nic[0].ipaddress)) not in ip_range:\n self.fail(\n \"Virtual machine ip should be from the ip range assigned to\\\n network created.\")\n\n self.user_account_virtual_machine = VirtualMachine.create(\n self.api_client,\n self.testdata[\"virtual_machine\"],\n accountid=self.user_account.name,\n domainid=self.user_account.domainid,\n serviceofferingid=self.service_offering.id,\n networkids=self.network.id\n )\n vms = VirtualMachine.list(\n self.api_client,\n id=self.user_account_virtual_machine.id,\n listall=True\n )\n self.assertEqual(\n isinstance(vms, list),\n True,\n \"listVirtualMachines returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(vms),\n 0,\n \"listVirtualMachines returned empty list.\"\n )\n\n self.debug(\n \"Virtual Machine created: %s\" %\n self.user_account_virtual_machine.id)\n\n ip_range = list(\n netaddr.iter_iprange(\n str(\n self.testdata[\"shared_network\"][\"startip\"]), str(\n self.testdata[\"shared_network\"][\"endip\"])))\n if netaddr.IPAddress(str(vms[0].nic[0].ipaddress)) not in ip_range:\n self.fail(\n \"Virtual machine ip should be from the ip range assigned to\\\n network created.\")","title":""},{"docid":"6f0b397f2d7ec67e812cb4c62f63915b","score":"0.54589295","text":"def test_add_bulk_pool_nodes_success_response(self):\n self.helper.clock.advance(50)\n add_data = self._get_add_nodes_json()\n response, resp_json = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(201, response.code)\n self._check_added_nodes_result(50, add_data, resp_json)","title":""},{"docid":"5625fec6c2e5dbd44a4e985375d67d55","score":"0.54575235","text":"def validate_network_operation(session, object_uuid, operation, data):\n return True","title":""},{"docid":"b3bd04e57d8ea45e3863da206b6bc2a6","score":"0.54552305","text":"def delete_subnet(compute, project, region, name):\n return compute.subnetworks().delete(project=project, region=region,\n subnetwork=name).execute()","title":""},{"docid":"7028ef5173fb70fd4403dccb688c000b","score":"0.54527587","text":"def test_09_network_services_VPC_StopDeleteAllMultiplePF(self):\n\n network_1 = self.create_network(self.services[\"network_offering\"])\n network_2 = self.create_network(self.services[\"network_offering_no_lb\"], '10.1.2.1')\n vm_1 = self.deployvm_in_network(network_1)\n vm_2 = self.deployvm_in_network(network_1)\n vm_3 = self.deployvm_in_network(network_2)\n vm_4 = self.deployvm_in_network(network_2)\n public_ip_1 = self.acquire_publicip(network_1)\n public_ip_2 = self.acquire_publicip(network_1)\n nat_rule1 = self.create_natrule_for_services(vm_1, public_ip_1, network_1)\n nat_rule2 = self.create_natrule_for_services(vm_2, public_ip_2, network_1)\n http_rule1 = self.create_natrule_for_services(vm_1, public_ip_1, network_1, self.services[\"http_rule\"])\n http_rule2 = self.create_natrule_for_services(vm_2, public_ip_2, network_1, self.services[\"http_rule\"])\n public_ip_3 = self.acquire_publicip(network_2)\n public_ip_4 = self.acquire_publicip(network_2)\n nat_rule3 = self.create_natrule_for_services(vm_3, public_ip_3, network_2)\n nat_rule4 = self.create_natrule_for_services(vm_4, public_ip_4, network_2)\n http_rule3 = self.create_natrule_for_services(vm_3, public_ip_3, network_2, self.services[\"http_rule\"])\n http_rule4 = self.create_natrule_for_services(vm_4, public_ip_4, network_2, self.services[\"http_rule\"])\n self.check_ssh_into_vm(vm_1, public_ip_1, testnegative=False)\n self.check_ssh_into_vm(vm_2, public_ip_2, testnegative=False)\n self.check_ssh_into_vm(vm_3, public_ip_3, testnegative=False)\n self.check_ssh_into_vm(vm_4, public_ip_4, testnegative=False)\n self.check_wget_from_vm(vm_1, public_ip_1, testnegative=False)\n self.check_wget_from_vm(vm_2, public_ip_2, testnegative=False)\n self.check_wget_from_vm(vm_3, public_ip_3, testnegative=False)\n self.check_wget_from_vm(vm_4, public_ip_4, testnegative=False)\n router = self.stop_vpcrouter()\n nat_rule1.delete(self.apiclient)\n self.cleanup.remove(nat_rule1)\n nat_rule2.delete(self.apiclient)\n self.cleanup.remove(nat_rule2)\n nat_rule3.delete(self.apiclient)\n self.cleanup.remove(nat_rule3)\n nat_rule4.delete(self.apiclient)\n self.cleanup.remove(nat_rule4)\n http_rule1.delete(self.apiclient)\n self.cleanup.remove(http_rule1)\n http_rule2.delete(self.apiclient)\n self.cleanup.remove(http_rule2)\n http_rule3.delete(self.apiclient)\n self.cleanup.remove(http_rule3)\n http_rule4.delete(self.apiclient)\n self.cleanup.remove(http_rule4)\n self.start_vpcrouter(router)\n self.check_ssh_into_vm(vm_1, public_ip_1, testnegative=True)\n self.check_ssh_into_vm(vm_2, public_ip_2, testnegative=True)\n self.check_ssh_into_vm(vm_3, public_ip_3, testnegative=True)\n self.check_ssh_into_vm(vm_4, public_ip_4, testnegative=True)\n self.check_wget_from_vm(vm_1, public_ip_1, testnegative=True,\n isVmAccessible=False, network=network_1)\n self.check_wget_from_vm(vm_2, public_ip_2, testnegative=True,\n isVmAccessible=False, network=network_1)\n self.check_wget_from_vm(vm_3, public_ip_3, testnegative=True,\n isVmAccessible=False, network=network_2)\n self.check_wget_from_vm(vm_4, public_ip_4, testnegative=True,\n isVmAccessible=False, network=network_2)\n return","title":""},{"docid":"b6e65452dc2538a52350f0f72efb782b","score":"0.5449026","text":"def create_one_subnet_pool(attrs=None):\n attrs = attrs or {}\n\n # Set default attributes.\n subnet_pool_attrs = {\n 'id': 'subnet-pool-id-' + uuid.uuid4().hex,\n 'name': 'subnet-pool-name-' + uuid.uuid4().hex,\n 'prefixes': ['10.0.0.0/24', '10.1.0.0/24'],\n 'default_prefixlen': '8',\n 'address_scope_id': 'address-scope-id-' + uuid.uuid4().hex,\n 'project_id': 'project-id-' + uuid.uuid4().hex,\n 'is_default': False,\n 'shared': False,\n 'max_prefixlen': '32',\n 'min_prefixlen': '8',\n 'default_quota': None,\n 'ip_version': '4',\n 'description': 'subnet-pool-description-' + uuid.uuid4().hex,\n 'tags': [],\n 'location': 'MUNCHMUNCHMUNCH',\n }\n\n # Overwrite default attributes.\n subnet_pool_attrs.update(attrs)\n\n subnet_pool = fakes.FakeResource(\n info=copy.deepcopy(subnet_pool_attrs), loaded=True\n )\n\n # Set attributes with special mapping in OpenStack SDK.\n subnet_pool.default_prefix_length = subnet_pool_attrs[\n 'default_prefixlen'\n ]\n subnet_pool.is_shared = subnet_pool_attrs['shared']\n subnet_pool.maximum_prefix_length = subnet_pool_attrs['max_prefixlen']\n subnet_pool.minimum_prefix_length = subnet_pool_attrs['min_prefixlen']\n\n return subnet_pool","title":""},{"docid":"6586992dee6870dd7c960cd41c83f07f","score":"0.54485893","text":"def test_create_network(self):\n try:\n #\n # create vnic\n vnic_name_b = self.vnic_name_prefix + uuid.uuid4().hex[:6]\n cmd = [self.oci_network_path, 'attach-vnic',\n '--name', vnic_name_b]\n print(cmd)\n create_vnic_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\n self.assertIn('Creating', str(create_vnic_data), 'attach vnic failed')\n _show_res('Create VNIC', create_vnic_data)\n time.sleep(self.waittime)\n cmd = [self.oci_network_path, 'show',\n '--details',\n '--output-mode', 'table']\n print(cmd)\n vnic_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\n _show_res('Vnic Data', vnic_data)\n #\n # create kvm bridge\n kvm_network_name = self.kvm_net_prefix + uuid.uuid4().hex[:6]\n kvm_net_ip = self._get_vnic_data('vnic', vnic_name_b, 'ipaddress')\n cmd = [self.oci_kvm_path, 'create-network',\n '--network-name', kvm_network_name,\n '--net', kvm_net_ip,\n '--ip-bridge', self.ip_bridge,\n '--ip-start', self.ip_start,\n '--ip-end', self.ip_end,\n '--ip-prefix', self.ip_prefix]\n print(cmd)\n create_kvm_bridge_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\n _show_res('Create KVM network', create_kvm_bridge_data)\n time.sleep(self.waittime)\n #\n # show\n cmd = [self.oci_network_path, 'show',\n '--details',\n '--output-mode', 'text']\n print(cmd)\n show_vnic_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\n _show_res('Show network data', show_vnic_data)\n #\n # delete kvm bridge\n cmd = [self.oci_kvm_path, 'delete-network',\n '--network-name', kvm_network_name,\n '--yes']\n print(cmd)\n delete_kvm_bridge_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\n _show_res('Delete KVM network', delete_kvm_bridge_data)\n time.sleep(self.waittime)\n #\n # delete vnic\n cmd = [self.oci_network_path, 'detach-vnic',\n '--ip-address', kvm_net_ip]\n print(cmd)\n delete_vnic_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\n _show_res('Delete VNIC', delete_vnic_data)\n except Exception as e:\n self.fail('Execution of create kvm network has failed: %s' % str(e))","title":""},{"docid":"b9f4ad28054ff7c05b09fb3cc18595a0","score":"0.5436422","text":"def test_bulk_create_delete_port(self):\n networks = [self.create_network(), self.create_network()]\n # Creates 2 ports in one request\n names = [data_utils.rand_name('port-') for i in range(len(networks))]\n port_list = []\n state = [True, False]\n for i in range(len(names)):\n p1 = {\n 'network_id': networks[i]['id'],\n 'name': names[i],\n 'admin_state_up': state[i],\n }\n port_list.append(p1)\n del port_list[1]['name']\n body = self.ports_client.create_bulk_ports(ports=port_list)\n created_ports = body['ports']\n self.addCleanup(self._delete_ports, created_ports)\n # Asserting that the ports are found in the list after creation\n body = self.ports_client.list_ports()\n ports_list = [port['id'] for port in body['ports']]\n for n in created_ports:\n self.assertIsNotNone(n['id'])\n self.assertIn(n['id'], ports_list)","title":""},{"docid":"5d87ebf9527a82afadb0a043e6f92f73","score":"0.54313594","text":"def test_show_subnet(self):\n body = self.subnets_client.show_subnet(self.subnet['id'])\n subnet = body['subnet']\n self.assertNotEmpty(subnet, \"Subnet returned has no fields\")\n for key in ['id', 'cidr']:\n self.assertIn(key, subnet)\n self.assertEqual(subnet[key], self.subnet[key])","title":""},{"docid":"d036b4d48dd42603a7e3178f12a3fc81","score":"0.5422875","text":"def run(**kw):\n rbd_obj = initial_rbd_config(**kw)\n if rbd_obj:\n log.info(\"Executing test on Replication pool\")\n if rbd_clone_delete_parent_image(\n rbd_obj.get(\"rbd_reppool\"), \"rep_pool_config\", **kw\n ):\n return 1\n log.info(\"Executing test on EC pool\")\n if rbd_clone_delete_parent_image(\n rbd_obj.get(\"rbd_ecpool\"), \"ec_pool_config\", **kw\n ):\n return 1\n return 0","title":""},{"docid":"35c19dd09ac48a7100fe88f06a744b4d","score":"0.5421959","text":"def test_delete_two_cidr_success(self):\n\n # Does post request\n response = self.client.delete(\n '/api/v3/cidr/4;6/',\n content_type='application/json')\n\n self.compare_status(200, response.status_code)\n\n # Does get request\n response = self.client.get(\n '/api/v3/cidr/4;6/',\n content_type='application/json')\n\n self.compare_status(400, response.status_code)","title":""},{"docid":"e181f9c3ae6e5101f1b32c6e87075a47","score":"0.54202074","text":"def test_create_long_network_LINUX_11442(self):\n try:\n #\n # create vnic\n vnic_name_b = self.vnic_name_prefix + uuid.uuid4().hex\n cmd = [self.oci_network_path, 'attach-vnic',\n '--name', vnic_name_b]\n create_vnic_data = _run_step('Create VNIC', cmd)\n self.assertIn('Creating', create_vnic_data, 'attach vnic failed')\n #\n # show\n time.sleep(self.waittime)\n cmd = [self.oci_network_path, 'show',\n '--details',\n '--output-mode', 'table']\n vnic_data = _run_step('VNIC data', cmd)\n #\n # create kvm bridge\n kvm_network_name = self.kvm_net_prefix + uuid.uuid4().hex\n kvm_net_ip = self._get_vnic_data('vnic', vnic_name_b, 'ipaddress')\n cmd = [self.oci_kvm_path, 'create-network',\n '--network-name', kvm_network_name,\n '--net', kvm_net_ip,\n '--ip-bridge', self.ip_bridge,\n '--ip-start', self.ip_start,\n '--ip-end', self.ip_end,\n '--ip-prefix', self.ip_prefix]\n create_kvm_bridge_data = _run_step('Create KVM network', cmd)\n self.assertIn('characters', create_kvm_bridge_data, '%s should have failed' % cmd)\n time.sleep(self.waittime)\n except Exception as e:\n self.fail('Execution of create kvm network has failed: %s' % str(e))\n finally:\n # clean up\n #\n # delete kvm bridge\n try:\n cmd = [self.oci_kvm_path, 'delete-network',\n '--network-name', kvm_network_name,\n '--yes']\n delete_kvm_bridge_data = _run_step('Delete KVM network', cmd)\n time.sleep(self.waittime)\n except Exception as e:\n pass\n #\n # delete vnic\n try:\n cmd = [self.oci_network_path, 'detach-vnic',\n '--ip-address', kvm_net_ip]\n delete_vnic_data = _run_step('Delete VNIC', cmd)\n except Exception as e:\n pass","title":""},{"docid":"c39e400c1d16c9c261343a564559ba96","score":"0.53987324","text":"def cleanup_pools():\n\n for pool in StoragePool.all():\n if not _is_test_name(pool):\n continue\n pool.delete()\n\n assert not [p for p in StoragePool.all() if _is_test_name(p)]","title":""},{"docid":"d51a2c1f67116b3b9bee633c68593a92","score":"0.53894895","text":"def test_createSharedNetwork_usedVlan(self):\n\n # Steps,\n # 1. create an Admin account\n # 2. create a shared NetworkOffering\n # 3. enable the network offering\n # 4. listPhysicalNetworks\n # - vlan = guest VLAN range = 10-90 (say)\n # 5. createNetwork\n # - name = mysharednetwork, displaytext = mysharednetwork\n # - vlan = any vlan between 10-90\n # - networkofferingid = <mysharedoffering>\n # - gw = 172.16.15.1, startip = 172.16.15.2 , endip = 172.16.15.200\n # - netmask=255.255.255.0\n # - scope = all\n # 6. delete admin account\n # Validations,\n # 1. listAccounts state=enabled returns your account\n # 2. listNetworkOfferings - name=mysharedoffering , should list\n # offering in disabled state\n # 3. listNetworkOfferings - name=mysharedoffering, should list\n # enabled offering\n # 4. listPhysicalNetworks should return at least one active\n # physical network\n # 5. network creation should FAIL since VLAN is used for\n # guest networks\n\n # Create admin account\n self.admin_account = Account.create(\n self.api_client,\n self.testdata[\"account\"],\n admin=True,\n domainid=self.domain.id\n )\n\n self.cleanup_accounts.append(self.admin_account)\n\n # verify that the account got created with state enabled\n list_accounts_response = Account.list(\n self.api_client,\n id=self.admin_account.id,\n listall=True\n )\n self.assertEqual(\n isinstance(list_accounts_response, list),\n True,\n \"listAccounts returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_accounts_response),\n 0,\n \"listAccounts returned empty list.\"\n )\n self.assertEqual(\n list_accounts_response[0].state,\n \"enabled\",\n \"The admin account created is not enabled.\"\n )\n\n self.debug(\"Domain admin account created: %s\" % self.admin_account.id)\n\n physical_network, shared_vlan = get_free_vlan(\n self.api_client, self.zone.id)\n if shared_vlan is None:\n self.fail(\"Failed to get free vlan id for shared network\")\n\n self.testdata[\"shared_network_offering\"][\"specifyVlan\"] = \"True\"\n self.testdata[\"shared_network_offering\"][\"specifyIpRanges\"] = \"True\"\n\n # Create Network Offering\n self.shared_network_offering = NetworkOffering.create(\n self.api_client,\n self.testdata[\"shared_network_offering\"],\n conservemode=False\n )\n\n # Verify that the network offering got created\n list_network_offerings_response = NetworkOffering.list(\n self.api_client,\n id=self.shared_network_offering.id\n )\n self.assertEqual(\n isinstance(list_network_offerings_response, list),\n True,\n \"listNetworkOfferings returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_network_offerings_response),\n 0,\n \"listNetworkOfferings returned empty list.\"\n )\n self.assertEqual(\n list_network_offerings_response[0].state,\n \"Disabled\",\n \"The network offering created should be bydefault disabled.\"\n )\n\n self.debug(\n \"Shared Network Offering created: %s\" %\n self.shared_network_offering.id)\n\n # Update network offering state from disabled to enabled.\n NetworkOffering.update(\n self.shared_network_offering,\n self.api_client,\n id=self.shared_network_offering.id,\n state=\"enabled\"\n )\n\n # Verify that the state of the network offering is updated\n list_network_offerings_response = NetworkOffering.list(\n self.api_client,\n id=self.shared_network_offering.id\n )\n self.assertEqual(\n isinstance(list_network_offerings_response, list),\n True,\n \"listNetworkOfferings returned invalid object in response.\"\n )\n self.assertNotEqual(\n len(list_network_offerings_response),\n 0,\n \"listNetworkOfferings returned empty list.\"\n )\n self.assertEqual(\n list_network_offerings_response[0].state,\n \"Enabled\",\n \"The network offering state should get updated to Enabled.\"\n )\n\n # create network using the shared network offering created\n self.testdata[\"shared_network\"][\"vlan\"] = str.split(\n str(physical_network.vlan), \"-\")[0]\n self.testdata[\"shared_network\"][\"acltype\"] = \"domain\"\n self.testdata[\"shared_network\"][\n \"networkofferingid\"] = self.shared_network_offering.id\n self.testdata[\"shared_network\"][\n \"physicalnetworkid\"] = physical_network.id\n self.testdata[\"shared_network\"][\"vlan\"] = shared_vlan\n\n try:\n self.network = Network.create(\n self.api_client,\n self.testdata[\"shared_network\"],\n networkofferingid=self.shared_network_offering.id,\n zoneid=self.zone.id,\n )\n self.fail(\n \"Network created with used vlan %s id, which is invalid\" %\n shared_vlan)\n except Exception as e:\n self.debug(\n \"Network creation failed because the valn id being used by\\\n another network. Exception: %s\" % e)","title":""},{"docid":"cf8514f24de179e4e1e075cb7e1a0513","score":"0.5379174","text":"def subnet_save(context, subnet):\n\n return IMPL.subnet_save(context, subnet)","title":""},{"docid":"4adff772cbcb58977cacc9caf0074431","score":"0.5376052","text":"def test_add_bulk_pool_nodes_then_list(self):\n self.helper.clock.advance(50)\n add_data = self._get_add_nodes_json()\n add_response, _ = self.successResultOf(self.json_request(\n b\"POST\", \"/load_balancer_pools/nodes\", body=add_data))\n self.assertEqual(201, add_response.code)\n\n _, list_json = self.successResultOf(self.json_request(\n b\"GET\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id)))\n self._check_added_nodes_result(50, add_data, list_json)","title":""},{"docid":"f9679461750e4b5c9aaf929456417549","score":"0.53745955","text":"def build_share_network_subnet_create_check(self, request, result):\n context = request.environ['manila.context']\n view = {\n 'compatible': result['compatible'],\n }\n if context.is_admin:\n view['hosts_check_result'] = result['hosts_check_result']\n return view","title":""},{"docid":"9ebe37663cfbfbff0b1b180934cda230","score":"0.53626186","text":"def test_new_sc_new_rbd_pool_e2e_wl(\n self,\n storageclass_factory,\n amq_factory_fixture,\n pgsql_factory_fixture,\n replica,\n compression,\n ):\n interface_type = constants.CEPHBLOCKPOOL\n sc_obj = storageclass_factory(\n interface=interface_type,\n new_rbd_pool=True,\n replica=replica,\n compression=compression,\n )\n bg_handler = flowtest.BackgroundOps()\n executor_run_bg_ios_ops = ThreadPoolExecutor(max_workers=5)\n self.amq, self.threads = amq_factory_fixture(sc_name=sc_obj.name)\n\n pgsql_workload = executor_run_bg_ios_ops.submit(\n bg_handler.handler,\n pgsql_factory_fixture,\n replicas=1,\n clients=1,\n transactions=100,\n timeout=100,\n sc_name=sc_obj.name,\n iterations=1,\n )\n bg_handler = flowtest.BackgroundOps()\n bg_ops = [pgsql_workload]\n bg_handler.wait_for_bg_operations(bg_ops, timeout=3600)\n # AMQ Validate the results\n log.info(\"Validate message run completely\")\n for thread in self.threads:\n thread.result(timeout=1800)\n\n cluster_used_space = get_percent_used_capacity()\n log.info(\n f\" Cluster used percentage space with replica size {replica}, \"\n f\"compression mode {compression}={cluster_used_space}\"\n )","title":""},{"docid":"214993c99b340fc887244d7f71842a44","score":"0.53611493","text":"def test_delete_two_cidr_inexistent_error(self):\n\n # Does post request\n response = self.client.delete(\n '/api/v3/cidr/1000;1001/',\n content_type='application/json')\n\n # Tests code returned\n self.compare_status(400, response.status_code)","title":""},{"docid":"7c79e409bc62223a8eb43cecbfd2d9d4","score":"0.5360599","text":"def create_subnets(ctx, from_file):\n subnet = {}\n subnet['name'] = \"10.230.0.0\"\n subnet['network'] = \"10.230.0.0\"\n subnet['mask'] = \"255.255.255.252\"\n subnet['gateway'] = \"10.230.0.1\"\n subnet['dns_primary'] = \"10.230.0.1\"\n subnet['ipam'] = \"DHCP\"\n #subnet['domain_ids'] = [2649, 2701, 2702,]\n #subnet['dhcp_ids'] = 8\n #subnet['tftp_id'] = 8\n #subnet['dns_id'] = 8\n subnet['boot_mode'] = \"DHCP\"\n\n # Not using this, because I can't get it to associate with a domain.\n subnet['domain_ids'] = [2649,]\n\n from pprint import pprint\n pprint(subnet); \n output = make_request(subnet_endpoint, data=subnet, content_json=True, request_type='post')\n print output","title":""},{"docid":"54a56beff2c4cb7704ff5d503b15eb0d","score":"0.5358008","text":"def test_create_bigip_member(pool, bigip_members):\n member_cfg = bigip_members[0]\n\n pp(bigip_members)\n pp(member_cfg)\n # pdb.set_trace()\n member = IcrPoolMember(\n pool=pool,\n **member_cfg\n )\n\n assert member\n\n # Test data\n assert member.data\n assert member.data['name'] == \"192.168.200.2:80\"\n assert member.data['ratio'] == 1\n assert member.data['connectionLimit'] == 0\n assert member.data['priorityGroup'] == 0\n assert member.data['session'] == \"user-enabled\"\n assert not member.data['description']","title":""},{"docid":"74db01edcdfe42846460369453b7f5c9","score":"0.5356684","text":"def test_pool_size() -> None:\n assert len(Match._registry) == 0\n match = Match()\n match.create_deck(card_set)\n match.put_in_pool()\n assert len(Match._registry) == 1\n Match.perform_housekeeping()\n assert len(Match._registry) == 0","title":""},{"docid":"b9cb9784cbc22c925ff93e3619115eca","score":"0.5350776","text":"def check_overlapping_subnets(config):\n if is_calico_flavor(config[\"flavor\"]):\n subnet_info = {\n \"cluster_svc_subnet\": config[\"net_config\"][\"cluster_svc_subnet\"]\n }\n else:\n subnet_info = {\n \"node_svc_subnet\": config[\"net_config\"][\"node_svc_subnet\"]\n }\n\n counter = 0\n\n if not isinstance(config[\"net_config\"][\"pod_subnet\"], list):\n subnet_info[-1] = config[\"net_config\"][\"pod_subnet\"]\n else:\n pod_subnets = []\n for pod_subnet in config[\"net_config\"][\"pod_subnet\"]:\n pod_subnets.append(pod_subnet)\n for pod_subnet in pod_subnets:\n subnet_info[counter] = pod_subnet\n counter += 1\n\n if not isinstance(config[\"net_config\"][\"node_subnet\"], list):\n subnet_info[-1] = config[\"net_config\"][\"node_subnet\"]\n else:\n node_subnets = []\n for node_subnet in config[\"net_config\"][\"node_subnet\"]:\n node_subnets.append(node_subnet)\n # counter = 0\n for node_subnet in node_subnets:\n subnet_info[counter] = node_subnet\n counter += 1\n\n if not isinstance(config[\"net_config\"][\"extern_dynamic\"], list):\n subnet_info[-1] = config[\"net_config\"][\"extern_dynamic\"]\n else:\n extern_dynamics = []\n for extern_dynamic in config[\"net_config\"][\"extern_dynamic\"]:\n extern_dynamics.append(extern_dynamic)\n # counter = 0\n for extern_dynamic in extern_dynamics:\n subnet_info[counter] = extern_dynamic\n counter += 1\n\n # Don't have extern_static field set for OpenShift flavors\n if not is_calico_flavor(config[\"flavor\"]) and config[\"net_config\"][\"extern_static\"]:\n if not isinstance(config[\"net_config\"][\"extern_static\"], list):\n subnet_info[-1] = config[\"net_config\"][\"extern_static\"]\n else:\n extern_statics = []\n for subnet in config[\"net_config\"][\"extern_static\"]:\n extern_statics.append(subnet)\n # counter = 0\n for subnet in extern_statics:\n subnet_info[counter] = subnet\n counter += 1\n\n for sub1, sub2 in combinations(subnet_info.values(), r=2):\n # Checking if sub1 and sub2 are IPv4 or IPv6\n rtr1, _ = sub1.split(\"/\")\n ip1 = ipaddress.ip_address(rtr1)\n rtr2, _ = sub2.split(\"/\")\n ip2 = ipaddress.ip_address(rtr2)\n if ip1.version == 4 and ip2.version == 4:\n net1, net2 = ipaddress.IPv4Network(sub1, strict=False), ipaddress.IPv4Network(sub2, strict=False)\n elif ip1.version == 6 and ip2.version == 6:\n net1, net2 = ipaddress.IPv6Network(sub1, strict=False), ipaddress.IPv6Network(sub2, strict=False)\n else:\n continue\n out = net1.overlaps(net2)\n if out:\n return False\n return True","title":""},{"docid":"40e606dc5eaf62489aaadca4c80b4985","score":"0.5350735","text":"def create_network(request, id_vlan=\"0\", sf_number='0', sf_name='0', sf_environment='0', sf_nettype='0',\n sf_subnet='0', sf_ipversion='0', sf_network='0', sf_iexact='0', sf_acl='0'):\n try:\n if request.method == 'POST':\n\n form = CreateForm(request.POST)\n\n # Get user\n auth = AuthSession(request.session)\n client = auth.get_clientFactory()\n\n networks_activated = False\n networks_was_activated = True\n\n equipments_ipv4 = list()\n equipments_ipv6 = list()\n\n if form.is_valid():\n\n # If vlan with parameter id_vlan don't exist,\n # VlanNaoExisteError exception will be called\n vlan = client.create_vlan().get(id_vlan)\n\n environment = client.create_ambiente().buscar_por_id(\n vlan['vlan'][\"ambiente\"]).get(\"ambiente\")\n\n # All ids to be activated\n ids = split_to_array(form.cleaned_data['ids_create'])\n\n for id in ids:\n value = id.split('-')\n id_net = value[0]\n network_type = value[1]\n\n if network_type == 'v4':\n net = client.create_network().get_network_ipv4(id_net)\n else:\n net = client.create_network().get_network_ipv6(id_net)\n\n if net['network']['active'] == 'True':\n networks_activated = True\n else:\n networks_was_activated = False\n\n if network_type == 'v4':\n equipments_ipv4.extend(\n list_equipment_by_network_ip4(client, id_net))\n\n client.create_api_network_ipv4().deploy(id_net)\n else:\n equipments_ipv6.extend(\n list_equipment_by_network_ip6(client, id_net))\n\n client.create_api_network_ipv6().deploy(id_net)\n\n apply_acl_for_network_v4(\n request, client, equipments_ipv4, vlan, environment)\n\n apply_acl_for_network_v6(\n request, client, equipments_ipv6, vlan, environment)\n\n if networks_activated is True:\n messages.add_message(\n request, messages.ERROR, network_ip_messages.get(\"networks_activated\"))\n\n if networks_was_activated is False:\n messages.add_message(\n request, messages.SUCCESS, network_ip_messages.get(\"net_create_success\"))\n\n else:\n vlan = client.create_vlan().get(id_vlan)\n if vlan['vlan']['ativada'] == 'True':\n messages.add_message(\n request, messages.ERROR, error_messages.get(\"vlan_select_one\"))\n else:\n messages.add_message(\n request, messages.ERROR, error_messages.get(\"select_one\"))\n\n except VlanNaoExisteError as e:\n logger.error(e)\n return redirect('vlan.search.list')\n except Exception as e:\n logger.error(e)\n messages.add_message(request, messages.ERROR, e)\n\n return HttpResponseRedirect(reverse('vlan.list.by.id', args=[id_vlan, sf_number, sf_name, sf_environment,\n sf_nettype, sf_subnet, sf_ipversion, sf_network,\n sf_iexact, sf_acl]))","title":""},{"docid":"efe615f554c62783c14229c1c5562eae","score":"0.53500634","text":"def test_allocate_deallocate_fixed_ip(self):\n address = self._create_address(0)\n self.assertTrue(self._is_allocated_in_project(address,\n self.projects[0].id))\n self._deallocate_address(0, address)\n\n # check if the fixed ip address is really deallocated\n self.assertFalse(self._is_allocated_in_project(address,\n self.projects[0].id))","title":""},{"docid":"e295299d3324063d91ca6e2e1cd09b14","score":"0.5342462","text":"def test_add_ip_association(self):\n pass","title":""},{"docid":"17ad1d9e6f99bb975644f274cca799fd","score":"0.53379923","text":"def remove_network(request, id_vlan=\"0\", sf_number='0', sf_name='0', sf_environment='0', sf_nettype='0',\n sf_subnet='0', sf_ipversion='0', sf_network='0', sf_iexact='0', sf_acl='0'):\n try:\n if request.method == 'POST':\n\n form = RemoveForm(request.POST)\n\n # Get user\n auth = AuthSession(request.session)\n client = auth.get_clientFactory()\n\n networks_activated = False\n networks_was_activated = True\n\n equipments_ipv4 = list()\n equipments_ipv6 = list()\n\n if form.is_valid():\n\n # If vlan with parameter id_vlan don't exist,\n # VlanNaoExisteError exception will be called\n vlan = client.create_vlan().get(id_vlan)\n\n environment = client.create_ambiente().buscar_por_id(\n vlan['vlan'][\"ambiente\"]).get(\"ambiente\")\n\n # All ids to be activated\n ids = split_to_array(form.cleaned_data['ids_remove'])\n\n for id in ids:\n value = id.split('-')\n id_net = value[0]\n network_type = value[1]\n\n if network_type == 'v4':\n net = client.create_network().get_network_ipv4(id_net)\n else:\n net = client.create_network().get_network_ipv6(id_net)\n\n if net['network']['active'] == 'True':\n networks_activated = True\n else:\n networks_was_activated = False\n\n if network_type == 'v4':\n equipments_ipv4.extend(\n list_equipment_by_network_ip4(client, id_net))\n\n client.create_api_network_ipv4().undeploy(id_net)\n else:\n equipments_ipv6.extend(\n list_equipment_by_network_ip6(client, id_net))\n\n client.create_api_network_ipv6().undeploy(id_net)\n\n if networks_activated is False:\n messages.add_message(\n request, messages.ERROR, network_ip_messages.get(\"networks_deactivated\"))\n\n if networks_was_activated is True:\n messages.add_message(\n request, messages.SUCCESS, network_ip_messages.get(\"net_remove_success\"))\n\n else:\n vlan = client.create_vlan().get(id_vlan)\n if vlan['vlan']['ativada'] == 'False':\n messages.add_message(\n request, messages.ERROR, error_messages.get(\"vlan_select_one\"))\n else:\n messages.add_message(\n request, messages.ERROR, error_messages.get(\"select_one\"))\n\n except VlanNaoExisteError as e:\n logger.error(e)\n return redirect('vlan.search.list')\n except Exception as e:\n logger.error(e)\n messages.add_message(request, messages.ERROR, e)\n\n return HttpResponseRedirect(reverse('vlan.list.by.id', args=[id_vlan, sf_number, sf_name, sf_environment,\n sf_nettype, sf_subnet, sf_ipversion, sf_network,\n sf_iexact, sf_acl]))","title":""},{"docid":"7491790095274acba070e25927ad2ae3","score":"0.53265524","text":"def create_subnet(compute, project, region, name, ipcidr, network_link):\n body = {\n 'privateIpGoogleAccess': False,\n 'name': name,\n 'ipCidrRange': ipcidr,\n 'network': network_link\n }\n return compute.subnetworks().insert(project=project, region=region,\n body=body).execute()","title":""},{"docid":"1bde213e4e0e1fa81737ebc968241571","score":"0.5283236","text":"def net_request(service, svc_xpath, username,\n pool_name, allocation_name, cidrmask, invert_cidr=False):\n template = ncs.template.Template(service)\n vars = ncs.template.Variables()\n vars.add(\"POOL\", pool_name)\n vars.add(\"ALLOCATIONID\", allocation_name)\n vars.add(\"USERNAME\", username)\n vars.add(\"SERVICE\", svc_xpath)\n vars.add(\"SUBNET_SIZE\", cidrmask)\n vars.add(\"INVERT\", \"true\" if invert_cidr else \"\")\n template.apply('resource-manager-ipaddress-allocation', vars)","title":""},{"docid":"3052816c8979ac986ca2023e3188f464","score":"0.52786934","text":"def test_delete_cidr_by_env_success(self):\n\n # Does post request\n response = self.client.delete(\n '/api/v3/cidr/environment/1/',\n content_type='application/json')\n\n self.compare_status(200, response.status_code)\n\n # Does get request\n response = self.client.get(\n '/api/v3/cidr/2;3/',\n content_type='application/json')\n\n self.compare_status(400, response.status_code)","title":""},{"docid":"44fbec08576245729da9ff729a6b4cbd","score":"0.52769566","text":"def test_add_pool_node_unimplemented(self):\n response, content = self.successResultOf(self.request_with_content(\n b\"POST\", \"/load_balancer_pools/{0}/nodes\".format(self.pool_id),\n body=json.dumps({\n \"cloud_server\": {\"id\": \"d95ae0c4-6ab8-4873-b82f-f8433840cff2\"}\n }).encode(\"utf-8\")))\n self.assertEqual(501, response.code)","title":""},{"docid":"66f8c7df8d01f01cd4e88d3bca90c08f","score":"0.5272971","text":"def test_db_constraints(self):\n self.assertEqual(self._inspre('1.3.0.0/16', 'reservation'), True, 'Unable to insert prefix 1.3.0.0/16')\n self.assertRaises(nipap.nipap.NipapDuplicateError, self._inspre, '1.3.0.0/16', 'reservation') # Duplicate prefix detection not working\n self.assertEqual(self._inspre('1.3.3.0/24', 'reservation'), True)\n self.assertEqual(self._inspre('1.3.3.0/27', 'assignment'), True)\n self.assertEqual(self._inspre('1.3.3.0/32', 'host'), True)\n self.assertEqual(self._inspre('1.3.3.1/32', 'host'), True)\n self.assertRaises(nipap.nipap.NipapValueError, self._inspre, '1.3.3.2/31', 'host') # do not allow /31 as type 'host'\n self.assertRaises(nipap.nipap.NipapValueError, self._inspre, '1.3.3.3/32', 'assignment') # Able to create assignment within assignment - we should not\n self.assertRaises(nipap.nipap.NipapValueError, self._delpre, '1.3.3.0/27') # Able to delete assignment containing hosts - we should not\n self.assertRaises(nipap.nipap.NipapValueError, self._updpre, '1.3.3.0/24', 'assignment')","title":""},{"docid":"3e871bd14b0bf67e09b17f1de72704f6","score":"0.526662","text":"def test_remove_pool_node_unimplemented(self):\n response, content = self.successResultOf(self.request_with_content(\n b\"DELETE\", \"/load_balancer_pools/{0}/nodes/1\".format(self.pool_id)\n ))\n self.assertEqual(501, response.code)","title":""},{"docid":"9c7b0697df7038e1e5d7ea3766bc63ed","score":"0.526566","text":"def verifyExistingTxnMempool(self, n, addrsbch, name=\"hub\", count=4):\n if (name in n.clientName):\n # validate txns in mempool from other clients\n mp = [ x.getmempoolinfo()[\"size\"] for x in self.nodes]\n print(\"memory pools are %s\" % str(mp))\n txn_count = count-1\n assert mp == [txn_count, txn_count, txn_count, txn_count], \"transaction was not relayed to all nodes %s\" % str(mp)\n\n # only pick up hub addr to use the generate interface which requires public key\n coinbase0 = [x for x in addrsbch if len(x.split(':'))==1]\n pubkey = n.validateaddress(coinbase0[0])['pubkey']\n # generate enough blocks so that hub client node has a balance\n n.generate(200, pubkey) # hub need blocks and public addr\n self.sync_blocks()\n\n # after mined block and mempool should be cleared\n mp = [ x.getmempoolinfo()[\"size\"] for x in self.nodes]\n print(\"memory pools are %s\" % str(mp))\n assert mp == [0, 0, 0, 0], \"transaction was not relayed to all nodes %s\" % str(mp)","title":""},{"docid":"b744d833285a68d2578efb90a2c9a8c2","score":"0.52650785","text":"def test_create_pool_member(pool, bigip_members):\n member_cfg = bigip_members[0]\n\n member = PoolMember(\n pool=pool,\n **member_cfg\n )\n\n assert member\n assert member._pool\n\n # Test data\n assert member.data\n assert member.data['name'] == \"192.168.200.2:80\"\n assert member.data['ratio'] == 1\n assert member.data['connectionLimit'] == 0\n assert member.data['priorityGroup'] == 0\n assert member.data['session'] == \"user-enabled\"\n assert not member.data['description']","title":""}],"string":"[\n {\n \"docid\": \"5e577dc0af595ea0742a1c4a8e599307\",\n \"score\": \"0.8004876\",\n \"text\": \"def test_create_delete_subnet_with_gw_and_allocation_pools(self):\\n self._create_verify_delete_subnet(**self.subnet_dict(\\n ['gateway', 'allocation_pools']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff2c39b96c05d197145766bd5ea6e126\",\n \"score\": \"0.7607366\",\n \"text\": \"def test_bulk_create_delete_subnet(self):\\n networks = [self.create_network(), self.create_network()]\\n # Creates 2 subnets in one request\\n cidrs = [subnet_cidr\\n for subnet_cidr in self.cidr.subnet(self.mask_bits)]\\n\\n names = [data_utils.rand_name('subnet-') for i in range(len(networks))]\\n subnets_list = []\\n for i in range(len(names)):\\n p1 = {\\n 'network_id': networks[i]['id'],\\n 'cidr': str(cidrs[(i)]),\\n 'name': names[i],\\n 'ip_version': self._ip_version\\n }\\n subnets_list.append(p1)\\n del subnets_list[1]['name']\\n body = self.subnets_client.create_bulk_subnets(subnets=subnets_list)\\n created_subnets = body['subnets']\\n self.addCleanup(self._delete_subnets, created_subnets)\\n # Asserting that the subnets are found in the list after creation\\n body = self.subnets_client.list_subnets()\\n subnets_list = [subnet['id'] for subnet in body['subnets']]\\n for n in created_subnets:\\n self.assertIsNotNone(n['id'])\\n self.assertIn(n['id'], subnets_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbff5b4cc78deb4cdd68b993e0665598\",\n \"score\": \"0.74533916\",\n \"text\": \"def test_create_delete_subnet_without_gateway(self):\\n self._create_verify_delete_subnet()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf0b97ebb02e60c844c8ad37bbba7cff\",\n \"score\": \"0.7061275\",\n \"text\": \"def test_create_delete_stateless_subnet_with_ports(self):\\n self._test_delete_subnet_with_ports(\\\"dhcpv6-stateless\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c339e2e69ec460e49e0aa5a3f01d2656\",\n \"score\": \"0.695529\",\n \"text\": \"def test_create_delete_subnet_with_gw(self):\\n self._create_verify_delete_subnet(\\n **self.subnet_dict(['gateway']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3d082d04fca6ee612509a592768caea\",\n \"score\": \"0.6871421\",\n \"text\": \"def test_create_delete_subnet_all_attributes(self):\\n self._create_verify_delete_subnet(\\n enable_dhcp=True,\\n **self.subnet_dict(['gateway', 'host_routes', 'dns_nameservers']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"336ecf8f444525ebb67b5d021f0af21f\",\n \"score\": \"0.6783448\",\n \"text\": \"def test_create_update_delete_network_subnet(self):\\n # Create a network\\n network = self.create_network()\\n self.addCleanup(test_utils.call_and_ignore_notfound_exc,\\n self.networks_client.delete_network, network['id'])\\n net_id = network['id']\\n self.assertEqual('ACTIVE', network['status'])\\n # Verify network update\\n new_name = \\\"New_network\\\"\\n body = self.networks_client.update_network(net_id, name=new_name)\\n updated_net = body['network']\\n self.assertEqual(updated_net['name'], new_name)\\n # Find a cidr that is not in use yet and create a subnet with it\\n subnet = self.create_subnet(network)\\n subnet_id = subnet['id']\\n # Verify subnet update\\n new_name = \\\"New_subnet\\\"\\n body = self.subnets_client.update_subnet(subnet_id, name=new_name)\\n updated_subnet = body['subnet']\\n self.assertEqual(updated_subnet['name'], new_name)\\n # Verify network delete\\n self.networks_client.delete_network(network['id'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33ad0a52e9dba9609ada33c48b87c0ed\",\n \"score\": \"0.6766238\",\n \"text\": \"def test_create_delete_subnet_with_dhcp_enabled(self):\\n self._create_verify_delete_subnet(enable_dhcp=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03a8d3bd4d8e8f5ce93c7c708f275fa3\",\n \"score\": \"0.67158526\",\n \"text\": \"def test_delete_network_with_subnet(self):\\n # Creates a network\\n network = self.create_network()\\n net_id = network['id']\\n self.addCleanup(test_utils.call_and_ignore_notfound_exc,\\n self.networks_client.delete_network, network['id'])\\n\\n # Find a cidr that is not in use yet and create a subnet with it\\n subnet = self.create_subnet(network)\\n subnet_id = subnet['id']\\n\\n # Delete network while the subnet still exists\\n self.networks_client.delete_network(net_id)\\n\\n # Verify that the subnet got automatically deleted.\\n self.assertRaises(lib_exc.NotFound, self.subnets_client.show_subnet,\\n subnet_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1499e8d08cc21e8e3f0631e3366c3d1\",\n \"score\": \"0.6703337\",\n \"text\": \"def test_create_delete_subnet_with_host_routes_and_dns_nameservers(self):\\n self._create_verify_delete_subnet(\\n **self.subnet_dict(['host_routes', 'dns_nameservers']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83020724141c2677935ed0feaf505399\",\n \"score\": \"0.6689397\",\n \"text\": \"def test_create_delete_slaac_subnet_with_ports(self):\\n self._test_delete_subnet_with_ports(\\\"slaac\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e029294b61e4e72d50a0b036aeabc7f\",\n \"score\": \"0.66316277\",\n \"text\": \"def test_create_delete_subnet_with_gw(self):\\n net = netaddr.IPNetwork(CONF.network.project_network_v6_cidr)\\n gateway = str(netaddr.IPAddress(net.first + 2))\\n network = self.create_network()\\n subnet = self.create_subnet(network, gateway)\\n # Verifies Subnet GW in IPv6\\n self.assertEqual(subnet['gateway_ip'], gateway)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c60359df2dfc0a58b93c5955a787e3f2\",\n \"score\": \"0.6626505\",\n \"text\": \"def validate_subnet_operation(session, object_uuid, operation, data):\\n if operation in ('create', 'update'):\\n network_id = data['network_id']\\n # Check for pending or processing network operations\\n if _check_for_pending_or_processing_ops(session, network_id):\\n return False\\n elif operation == 'delete':\\n # TODO(asomya):Check for pending port operations\\n pass\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d57d71ebc97f85ba4da55350e8509d77\",\n \"score\": \"0.6586373\",\n \"text\": \"def _test_delete_subnet_with_ports(self, mode):\\n slaac_network = self.create_network()\\n subnet_slaac = self.create_subnet(slaac_network,\\n **{'ipv6_ra_mode': mode,\\n 'ipv6_address_mode': mode})\\n port = self.create_port(slaac_network)\\n self.assertIsNotNone(port['fixed_ips'][0]['ip_address'])\\n self.subnets_client.delete_subnet(subnet_slaac['id'])\\n subnets = self.subnets_client.list_subnets()\\n subnet_ids = [subnet['id'] for subnet in subnets['subnets']]\\n self.assertNotIn(subnet_slaac['id'], subnet_ids,\\n \\\"Subnet wasn't deleted\\\")\\n self.assertRaisesRegex(\\n lib_exc.Conflict,\\n \\\"There are one or more ports still in use on the network\\\",\\n self.networks_client.delete_network,\\n slaac_network['id'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55474683fed3f459910a6d9f9919a28d\",\n \"score\": \"0.6573536\",\n \"text\": \"def _create_pool(self):\\n subnets = self.network_client.list_subnets()\\n for subnet in subnets['subnets']:\\n if subnet['tenant_id'] == self.tenant_id:\\n self.subnets.append(subnet)\\n pool = super(TestLoadBalancerBasic, self)._create_pool(\\n 'ROUND_ROBIN',\\n 'HTTP',\\n subnet['id'])\\n self.pools.append(pool)\\n break\\n self.assertTrue(self.pools)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"485bcd21533f335b59e210bc83b4dc58\",\n \"score\": \"0.63405085\",\n \"text\": \"def test_remove_bulk_pool_nodes_to_single_non_existant_pool_id_1(self):\\n server_id = text_type(uuid4())\\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0])]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(201, response.code)\\n pool_id = text_type(uuid4())\\n node_and_pool_ids = [(server_id, pool_id)]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"DELETE\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(\\\"Load Balancer Pool {0} does not exist\\\".format(pool_id),\\n resp_json[\\\"errors\\\"][0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d02c262a0b56167b9a8fbaed801ab46\",\n \"score\": \"0.63148034\",\n \"text\": \"def test_create_delete_subnet_with_v6_attributes_stateless(self):\\n self._create_verify_delete_subnet(\\n ipv6_ra_mode='dhcpv6-stateless',\\n ipv6_address_mode='dhcpv6-stateless')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8640a4e95d6b29507b16e055944e2fc8\",\n \"score\": \"0.6307388\",\n \"text\": \"def test_create_list_subnet_with_no_gw64_one_network(self):\\n network = self.create_network()\\n ipv6_gateway = self.subnet_dict(['gateway'])['gateway']\\n subnet1 = self.create_subnet(network,\\n ip_version=6,\\n gateway=ipv6_gateway)\\n self.assertEqual(netaddr.IPNetwork(subnet1['cidr']).version, 6,\\n 'The created subnet is not IPv6')\\n subnet2 = self.create_subnet(network,\\n gateway=None,\\n ip_version=4)\\n self.assertEqual(netaddr.IPNetwork(subnet2['cidr']).version, 4,\\n 'The created subnet is not IPv4')\\n # Verifies Subnet GW is set in IPv6\\n self.assertEqual(subnet1['gateway_ip'], ipv6_gateway)\\n # Verifies Subnet GW is None in IPv4\\n self.assertIsNone(subnet2['gateway_ip'])\\n # Verifies all 2 subnets in the same network\\n body = self.subnets_client.list_subnets()\\n subnets = [sub['id'] for sub in body['subnets']\\n if sub['network_id'] == network['id']]\\n test_subnet_ids = [sub['id'] for sub in (subnet1, subnet2)]\\n self.assertCountEqual(subnets,\\n test_subnet_ids,\\n 'Subnet are not in the same network')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61d8d1c695d17240dc6d0c9e2700396\",\n \"score\": \"0.63036966\",\n \"text\": \"def test_create_delete_subnet_with_default_gw(self):\\n net = netaddr.IPNetwork(CONF.network.project_network_v6_cidr)\\n gateway_ip = str(netaddr.IPAddress(net.first + 1))\\n network = self.create_network()\\n subnet = self.create_subnet(network)\\n # Verifies Subnet GW in IPv6\\n self.assertEqual(subnet['gateway_ip'], gateway_ip)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a4705d6936107eca9889be5265e2842\",\n \"score\": \"0.62991136\",\n \"text\": \"def test_remove_bulk_pool_nodes_to_single_non_existant_pool_id_2(self):\\n pool_id = text_type(uuid4())\\n node_and_pool_ids = [(text_type(uuid4()), pool_id),\\n (text_type(uuid4()), pool_id)]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"DELETE\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(\\\"Load Balancer Pool {0} does not exist\\\".format(pool_id),\\n resp_json[\\\"errors\\\"][0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fa1fba9dcb5b6640dc8467ef55c3542\",\n \"score\": \"0.62906045\",\n \"text\": \"def test_remove_bulk_pool_nodes_to_multiple_non_existant_pool_ids(self):\\n node_and_pool_ids = [(text_type(uuid4()), text_type(uuid4())),\\n (text_type(uuid4()), text_type(uuid4()))]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(len(resp_json['errors']), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7107cfac605549e1f48e0b10f9aab2ee\",\n \"score\": \"0.6240493\",\n \"text\": \"def test_remove_bulk_pool_nodes_given_a_non_existing_node(self):\\n server_id = text_type(uuid4())\\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0])]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(201, response.code)\\n response, _ = self.successResultOf(self.request_with_content(\\n b\\\"DELETE\\\", \\\"/load_balancer_pools/nodes\\\",\\n body=json.dumps(add_data).encode(\\\"utf-8\\\")))\\n self.assertEqual(204, response.code)\\n\\n # get the node count for the lb pool\\n _, list_json = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n\\n response, resp_json = self.successResultOf(self.request_with_content(\\n b\\\"DELETE\\\", \\\"/load_balancer_pools/nodes\\\",\\n body=json.dumps(add_data).encode(\\\"utf-8\\\")))\\n self.assertEqual(409, response.code)\\n resp = json.loads(resp_json.decode(\\\"utf-8\\\"))\\n self.assertEqual(\\\"Cloud Server {0} is not a member of \\\"\\n \\\"Load Balancer Pool {1}\\\".format(server_id, self.get_lb_ids()[0][0]),\\n resp[\\\"errors\\\"][0])\\n\\n # ensure no other nodes were deleted from the pool\\n _, list_json_again = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n self.assertEqual(len(list_json_again), len(list_json))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17e8046dad3e5966f0c68d7c1248fcbb\",\n \"score\": \"0.6232873\",\n \"text\": \"def test_remove_bulk_pool_nodes_multiple_errors(self):\\n server_id = text_type(uuid4())\\n random_pool_id = text_type(uuid4())\\n\\n # get the node count for the lb pool\\n _, list_json = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n\\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0]),\\n (server_id, random_pool_id)]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(\\\"Load Balancer Pool {0} does not exist\\\".format(random_pool_id),\\n resp_json[\\\"errors\\\"][0])\\n\\n # ensure no nodes were deleted from the pool\\n _, list_json_again = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n self.assertEqual(len(list_json_again), len(list_json))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f23b537c0637ceb5e32cb2685fbb0f4\",\n \"score\": \"0.6175446\",\n \"text\": \"def test_bulk_create_delete_network(self):\\n # Creates 2 networks in one request\\n network_list = [{'name': data_utils.rand_name('network-')},\\n {'name': data_utils.rand_name('network-')}]\\n body = self.networks_client.create_bulk_networks(networks=network_list)\\n created_networks = body['networks']\\n self.addCleanup(self._delete_networks, created_networks)\\n # Asserting that the networks are found in the list after creation\\n body = self.networks_client.list_networks()\\n networks_list = [network['id'] for network in body['networks']]\\n for n in created_networks:\\n self.assertIsNotNone(n['id'])\\n self.assertIn(n['id'], networks_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7634246859f5589aa80eda0008ee964\",\n \"score\": \"0.6169449\",\n \"text\": \"def delete_subnet(self, context, subnet_id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d6f210309901571abb721b82c0ae386\",\n \"score\": \"0.6149471\",\n \"text\": \"def test_create_delete_subnet_with_v6_attributes_stateful(self):\\n self._create_verify_delete_subnet(\\n gateway=self._subnet_data[self._ip_version]['gateway'],\\n ipv6_ra_mode='dhcpv6-stateful',\\n ipv6_address_mode='dhcpv6-stateful')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"512a656b44fcd1956a250dc37e906e32\",\n \"score\": \"0.61132514\",\n \"text\": \"def test_list_subnets(self):\\n body = self.subnets_client.list_subnets()\\n subnets = [subnet['id'] for subnet in body['subnets']\\n if subnet['id'] == self.subnet['id']]\\n self.assertNotEmpty(subnets, \\\"Created subnet not found in the list\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35f2746f75bcb9461e94da1b2b26cb4\",\n \"score\": \"0.6110581\",\n \"text\": \"def test_add_bulk_pool_nodes_to_single_non_existant_pool_id_2(self):\\n self.helper.clock.advance(50)\\n pool_id = text_type(uuid4())\\n node_and_pool_ids = [(text_type(uuid4()), pool_id),\\n (text_type(uuid4()), pool_id)]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(\\\"Load Balancer Pool {0} does not exist\\\".format(pool_id),\\n resp_json[\\\"errors\\\"][0])\\n self.assertEqual(\\\"Load Balancer Pool {0} does not exist\\\".format(pool_id),\\n resp_json[\\\"errors\\\"][1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a13ca2c70339729a626dc62fbe18c46\",\n \"score\": \"0.6096396\",\n \"text\": \"def test_remove_bulk_pool_nodes_success(self):\\n server_data = self._get_add_nodes_json()\\n\\n # add first\\n resp, body = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=server_data))\\n\\n # ensure the node has been added\\n _, list_json = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n self.assertEqual(1, len(list_json))\\n\\n # delete\\n response, _ = self.successResultOf(self.request_with_content(\\n b\\\"DELETE\\\", \\\"/load_balancer_pools/nodes\\\",\\n body=json.dumps(server_data).encode(\\\"utf-8\\\")))\\n self.assertEqual(204, response.code)\\n\\n # ensure there are 0\\n _, list_json = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n self.assertEqual(0, len(list_json))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c56a280bf8cc0cff070664985fabadc\",\n \"score\": \"0.6007462\",\n \"text\": \"def test_add_bulk_pool_nodes_to_multiple_non_existant_pool_ids(self):\\n self.helper.clock.advance(50)\\n node_and_pool_ids = [(text_type(uuid4()), text_type(uuid4())),\\n (text_type(uuid4()), text_type(uuid4()))]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(len(resp_json['errors']), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47b47eb7dec24f3d320221d5385611c5\",\n \"score\": \"0.60043937\",\n \"text\": \"def test_remove_bulk_pool_nodes_when_pool_id_is_non_uuid(self):\\n node_and_pool_ids = [(text_type(uuid4()), 122)]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"DELETE\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(400, response.code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55e2aa9e1052702c1c6dab94e844fd34\",\n \"score\": \"0.5994194\",\n \"text\": \"def test_different_regions_same_tenant_different_pools(self):\\n self.rcv3 = RackConnectV3(regions=[\\\"ORD\\\", \\\"DFW\\\"])\\n self.helper = APIMockHelper(self, [self.rcv3])\\n lb_ids = self.get_lb_ids()\\n self.assertEqual(1, len(lb_ids[0]))\\n self.assertEqual(1, len(lb_ids[1]))\\n self.assertNotEqual(set(lb_ids[0]), set(lb_ids[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6996a240319d8c560c8b9418e7055bf2\",\n \"score\": \"0.59820086\",\n \"text\": \"def test_add_bulk_pool_nodes_to_single_non_existant_pool_id_1(self):\\n self.helper.clock.advance(50)\\n pool_id = text_type(uuid4())\\n node_and_pool_ids = [(text_type(uuid4()), pool_id)]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(\\\"Load Balancer Pool {0} does not exist\\\".format(pool_id),\\n resp_json[\\\"errors\\\"][0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f064673d944b0dacd5010ac62715abbb\",\n \"score\": \"0.59779304\",\n \"text\": \"def test_too_many_addresses(self):\\n admin_context = context.get_admin_context()\\n network = db.project_get_network(admin_context, self.projects[0].id)\\n num_available_ips = db.network_count_available_ips(admin_context,\\n network['id'])\\n addresses = []\\n instance_ids = []\\n for i in range(num_available_ips):\\n instance_ref = self._create_instance(0)\\n instance_ids.append(instance_ref['id'])\\n address = self._create_address(0, instance_ref['id'])\\n addresses.append(address)\\n\\n ip_count = db.network_count_available_ips(context.get_admin_context(),\\n network['id'])\\n self.assertEqual(ip_count, 0)\\n self.assertRaises(db.NoMoreAddresses,\\n self.network.allocate_fixed_ip,\\n self.context,\\n 'foo')\\n\\n for i in range(num_available_ips):\\n self.network.deallocate_fixed_ip(self.context, addresses[i])\\n db.instance_destroy(context.get_admin_context(), instance_ids[i])\\n ip_count = db.network_count_available_ips(context.get_admin_context(),\\n network['id'])\\n self.assertEqual(ip_count, num_available_ips)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a01b5e78abcb5debf8e67fa4ccde6d\",\n \"score\": \"0.59712166\",\n \"text\": \"def test_add_bulk_pool_nodes_multiple_errors(self):\\n self.helper.clock.advance(50)\\n\\n # add node to a valid lb pool\\n server_id = text_type(uuid4())\\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0])]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(201, response.code)\\n\\n # get the node count for the lb pool\\n _, list_json = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n\\n # re-add the node to a valid lb pool id as well to a non-existant pool_id\\n node_and_pool_ids.append((server_id, text_type(uuid4())))\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(len(resp_json[\\\"errors\\\"]), 2)\\n\\n # ensure no new nodes were added to the pool\\n _, list_json_again = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n self.assertEqual(len(list_json_again), len(list_json))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc7931f782b317a080c28e8f742a7cea\",\n \"score\": \"0.59557337\",\n \"text\": \"def _create_subnets(self, engine, data):\\n networks = db_utils.get_table(engine, 'networks')\\n subnets = db_utils.get_table(engine, 'subnets')\\n pools = db_utils.get_table(engine, 'ipallocationpools')\\n allocations = db_utils.get_table(engine, 'ipallocations')\\n\\n for cidr in data:\\n ip_version = (constants.IP_VERSION_6 if ':' in cidr else\\n constants.IP_VERSION_4)\\n # Save generated id in incoming dict to simplify validations\\n network_id = uuidutils.generate_uuid()\\n network_dict = dict(\\n id=network_id,\\n standard_attr_id=self._gen_attr_id(engine, 'networks'))\\n engine.execute(networks.insert().values(network_dict))\\n\\n data[cidr]['id'] = uuidutils.generate_uuid()\\n subnet_dict = dict(id=data[cidr]['id'],\\n cidr=cidr,\\n ip_version=ip_version,\\n standard_attr_id=self._gen_attr_id(engine,\\n 'subnets'))\\n engine.execute(subnets.insert().values(subnet_dict))\\n\\n if data[cidr].get('pools'):\\n for pool in data[cidr]['pools']:\\n pool_dict = dict(id=uuidutils.generate_uuid(),\\n first_ip=pool['first_ip'],\\n last_ip=pool['last_ip'],\\n subnet_id=data[cidr]['id'])\\n engine.execute(pools.insert().values(pool_dict))\\n\\n if data[cidr].get('allocations'):\\n for ip in data[cidr]['allocations']:\\n ip_dict = dict(ip_address=ip,\\n subnet_id=data[cidr]['id'],\\n network_id=network_id)\\n engine.execute(allocations.insert().values(ip_dict))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"368b714d1c842845153e7b71ff039ed3\",\n \"score\": \"0.59212273\",\n \"text\": \"def test_create_disk_pool(self):\\n try:\\n #\\n # create volume\\n volume_name_a = self.volume_name_prefix + uuid.uuid4().hex\\n cmd = [self.oci_iscsi_config, 'create',\\n '--size', self.volume_size,\\n '--volume-name', volume_name_a,\\n '--attach-volume']\\n\\n create_volume_data = _run_step('Create Volume', cmd)\\n self.assertIn('created', str(create_volume_data), 'Failed to create volume.')\\n\\n time.sleep(self.waittime)\\n volume_data = self._get_iscsi_show(all_vol=False)\\n _show_res('Volume data', volume_data)\\n time.sleep(self.waittime)\\n #\\n # create pool\\n pool_name_a = self.pool_name_prefix + uuid.uuid4().hex\\n this_disk = '/dev/' + self._get_volume_data('name', volume_name_a, 'device')\\n cmd = [self.oci_kvm_path, 'create-pool',\\n '--disk', this_disk,\\n '--name', pool_name_a]\\n create_pool_data = _run_step('Create Pool', cmd)\\n self.assertIn('successfully', create_pool_data, 'Failed to create pool.')\\n time.sleep(self.waittime)\\n #\\n # list pool\\n cmd = [self.oci_kvm_path, 'list-pool',\\n '--output-mode', 'table']\\n list_pool_data = _run_step('List Pool', cmd)\\n # delete pool\\n # using virsh commands for now till delete-pool is implemented.\\n virsh_path = '/bin/virsh'\\n cmd = [virsh_path, 'pool-destroy', pool_name_a]\\n destroy_output = _run_step('Destroy pool by virsh', cmd)\\n self.asssertIn('destroyed', destroy_output, 'Failed to destroy pool.')\\n #\\n cmd = [virsh_path, 'pool-delete', pool_name_a]\\n delete_output = _run_step('Delete pool by virsh', cmd)\\n self.assertIn('deleted', delete_output, 'Failed to delete pool.')\\n #\\n cmd = [virsh_path, 'pool-undefine', pool_name_a]\\n undefine_output = _run_step('Undefine pool by virsh', cmd)\\n self.assertIn('undefined', undefine_output, 'Failed to undefine pool.')\\n #\\n # detach volume\\n this_iqn = self._get_volume_data('name', volume_name_a, 'iqn')\\n cmd = [self.oci_iscsi_config, 'detach', '--iqns', this_iqn]\\n detach_volume_data = _run_step('Detach volume', cmd)\\n self.assertIn('detached', detach_volume_data, 'Failed to detach volume.')\\n #\\n # destroy volume\\n this_ocid = self._get_volume_data('name', volume_name_a, 'ocid')\\n cmd = [self.oci_iscsi_config, 'destroy', '--ocids', this_ocid, '--yes']\\n destroy_volume_data = _run_step('Destroy volume', cmd)\\n self.assertIn('destroyed', destroy_volume_data, 'Failed to destroy volume.')\\n time.sleep(self.waittime)\\n except Exception as e:\\n self.fail('Execution of create disk pool has failed: %s' % str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d23c2f08a4d021ab828bd4031076097\",\n \"score\": \"0.5921016\",\n \"text\": \"def test_network_unavailable_on_ec2_instance_in_rollback_mode():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3a822037b5ea72734264cce8793a3ca\",\n \"score\": \"0.5916754\",\n \"text\": \"def test_delete_ip_association(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4e44b875ddebf49b2441d73bc2ec7b0\",\n \"score\": \"0.5898968\",\n \"text\": \"def create_subnet(self, server_name, network_address, network_mask, n_reserved_ips, allowed_ips):\\n sql_query = \\\"INSERT INTO subnets (serverID, server_ip, network_address, network_mask, n_reserved_ips, allowed_ips ) VALUES ( %s, %s, %s, %s, %s, %s );\\\"\\n \\n try:\\n server_ip = str(ipaddress.IPv4Network(network_address + \\\"/\\\" + str(network_mask))[1])\\n sql_data = (server_name, server_ip, network_address, network_mask, n_reserved_ips, allowed_ips,)\\n\\n logging.debug(server_ip)\\n self.cursor.execute(sql_query, sql_data)\\n self.db_connection.commit()\\n except (Exception, psycopg2.DatabaseError) as error:\\n self.db_connection.rollback()\\n logging.error(f\\\"Could not add subnet for {server_name}: %s\\\", error)\\n return False\\n else:\\n logging.debug(f\\\"Successfully added subnet: {network_address}/{network_mask}.\\\")\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"354e8b289356d0b5da9a3938813d8e1c\",\n \"score\": \"0.5876439\",\n \"text\": \"def test_network_has_subnet(yaml_file):\\n\\n with open(yaml_file) as fh:\\n yml = yaml.load(fh)\\n\\n networks = []\\n\\n for k, v in yml.get(\\\"resources\\\", {}).items():\\n if not has_properties(v) or v.get(\\\"type\\\") not in [\\\"OS::Neutron::Net\\\"]:\\n continue\\n # need to check if contrail networks also require subnet\\n # and it is defined the same as neutron networks\\n # if v.get(\\\"type\\\") not in NETWORK_RESOURCE_TYPES:\\n networks.append(k)\\n\\n for k, v in yml.get(\\\"resources\\\", {}).items():\\n network_prop = v.get(\\\"properties\\\", {}).get(\\\"network\\\", {}).get(\\\"get_resource\\\")\\n if (\\n not has_properties(v)\\n and v.get(\\\"type\\\") != \\\"OS::Neutron::Subnet\\\"\\n and not network_prop\\n ):\\n continue\\n x = 0\\n for network in networks:\\n if network == network_prop:\\n networks.pop(x)\\n break\\n x += 1\\n\\n assert not networks, \\\"Networks detected without subnet {}\\\".format(networks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f32b9fbe89a990b719596fe2711c778e\",\n \"score\": \"0.5862595\",\n \"text\": \"def create_subnets_subnet_pool(self, subnets_subnet_pool, groupnet, subnet, **kwargs):\\n\\n all_params = ['subnets_subnet_pool', 'groupnet', 'subnet', 'force']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method create_subnets_subnet_pool\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n # verify the required parameter 'subnets_subnet_pool' is set\\n if ('subnets_subnet_pool' not in params) or (params['subnets_subnet_pool'] is None):\\n raise ValueError(\\\"Missing the required parameter `subnets_subnet_pool` when calling `create_subnets_subnet_pool`\\\")\\n # verify the required parameter 'groupnet' is set\\n if ('groupnet' not in params) or (params['groupnet'] is None):\\n raise ValueError(\\\"Missing the required parameter `groupnet` when calling `create_subnets_subnet_pool`\\\")\\n # verify the required parameter 'subnet' is set\\n if ('subnet' not in params) or (params['subnet'] is None):\\n raise ValueError(\\\"Missing the required parameter `subnet` when calling `create_subnets_subnet_pool`\\\")\\n\\n\\n resource_path = '/platform/3/network/groupnets/{Groupnet}/subnets/{Subnet}/pools'.replace('{format}', 'json')\\n path_params = {}\\n if 'groupnet' in params:\\n path_params['Groupnet'] = params['groupnet']\\n if 'subnet' in params:\\n path_params['Subnet'] = params['subnet']\\n\\n query_params = {}\\n if 'force' in params:\\n query_params['force'] = params['force']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n if 'subnets_subnet_pool' in params:\\n body_params = params['subnets_subnet_pool']\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['application/json'])\\n\\n # Authentication setting\\n auth_settings = ['basic_auth']\\n\\n response = self.api_client.call_api(resource_path, 'POST',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='CreateResponse',\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cdded764cfc9b009c1e4a5562ae56691\",\n \"score\": \"0.5851271\",\n \"text\": \"def test_del_has_dependent(self):\\n entry = self._create_many_entries(\\n 'user_hw_admin@domain.com', 1)[0][0]\\n\\n dep_subnet = models.Subnet(\\n name=\\\"some_subnet\\\",\\n zone=entry['name'],\\n address=\\\"192.168.0.0/24\\\",\\n modifier=\\\"user_hw_admin@domain.com\\\",\\n desc=\\\"\\\",\\n vlan=1801,\\n project=self._db_entries['Project'][0]['name'],\\n gateway=None,\\n dns_1=None,\\n dns_2=None,\\n owner=\\\"user_hw_admin@domain.com\\\"\\n )\\n self._test_del_has_dependent(\\n 'user_hw_admin@domain.com', entry['id'], dep_subnet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f73953859766055a783a0fdeb50c7cee\",\n \"score\": \"0.5829061\",\n \"text\": \"def safe_create_network(network_name, subnet):\\n\\n\\n try:\\n # REVIEW this is pretty fragile, depends on the runtime error string\\n # format in avi_rest_lib.delete_over_rest\\n # Maybe better to just call avi_rest_lib.delete('network',\\n # network_name, force=True) and check the\\n # response_code directly\\n rest.delete('network', name=network_name, check_status_code=False, force=True)\\n except:\\n pass\\n network_lib.network_create(network_name, subnet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a76462c99e3c223d77785e726b31263\",\n \"score\": \"0.5816983\",\n \"text\": \"def test_add_bulk_pool_nodes_for_an_existing_node(self):\\n self.helper.clock.advance(50)\\n server_id = text_type(uuid4())\\n\\n # add node to lb_pool\\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0])]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(201, response.code)\\n\\n # re-adding the node\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(\\\"Cloud Server {0} is already a member of \\\"\\n \\\"Load Balancer Pool {1}\\\".format(server_id, self.get_lb_ids()[0][0]),\\n resp_json[\\\"errors\\\"][0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7042746695ebc64b88c658e98c5afcf2\",\n \"score\": \"0.5750156\",\n \"text\": \"def ex_delete_subnet(self, subnet):\\n return super(Ec2Adapter, self).ex_delete_subnet(subnet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14b0b99a50c60ab11615244414f3ca7e\",\n \"score\": \"0.57478267\",\n \"text\": \"def aws_subnet_no_ip_space_check(cache: dict, session, awsAccountId: str, awsRegion: str, awsPartition: str) -> dict:\\n ec2 = session.client(\\\"ec2\\\")\\n iso8601Time = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()\\n for vpcs in describe_vpcs(cache, session):\\n vpcId = vpcs[\\\"VpcId\\\"]\\n # Get subnets for the VPC\\n for snet in ec2.describe_subnets(Filters=[{'Name': 'vpc-id','Values': [vpcId]}])[\\\"Subnets\\\"]:\\n # B64 encode all of the details for the Asset\\n assetJson = json.dumps(snet,default=str).encode(\\\"utf-8\\\")\\n assetB64 = base64.b64encode(assetJson)\\n snetArn = snet[\\\"SubnetArn\\\"]\\n snetId = snet[\\\"SubnetId\\\"] \\n if int(snet[\\\"AvailableIpAddressCount\\\"]) <= 1:\\n # This is a failing check\\n finding = {\\n \\\"SchemaVersion\\\": \\\"2018-10-08\\\",\\n \\\"Id\\\": f\\\"{snetArn}/subnet-no-remaining-ip-space-check\\\",\\n \\\"ProductArn\\\": f\\\"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default\\\",\\n \\\"GeneratorId\\\": f\\\"{snetArn}/subnet-no-remaining-ip-space-check\\\",\\n \\\"AwsAccountId\\\": awsAccountId,\\n \\\"Types\\\": [\\\"Software and Configuration Checks/AWS Security Best Practices\\\"],\\n \\\"FirstObservedAt\\\": iso8601Time,\\n \\\"CreatedAt\\\": iso8601Time,\\n \\\"UpdatedAt\\\": iso8601Time,\\n \\\"Severity\\\": {\\\"Label\\\": \\\"MEDIUM\\\"},\\n \\\"Confidence\\\": 99,\\n \\\"Title\\\": \\\"[VPC.4] Amazon Virtual Private Cloud (VPC) subnets should be monitored for available IP address space\\\",\\n \\\"Description\\\": f\\\"Amazon Virtual Private Cloud (VPC) subnet {snetId} does not have any available IP address space, consider terminating unncessary workloads, adding a Secondary CIDR to the parent VPC, or expanding CIDR capacity to avoid availability losses. Refer to the remediation instructions if this configuration is not intended.\\\",\\n \\\"Remediation\\\": {\\n \\\"Recommendation\\\": {\\n \\\"Text\\\": \\\"For information on IP addressing refer to the IP Addressing in your VPC section of the Amazon Virtual Private Cloud User Guide\\\",\\n \\\"Url\\\": \\\"https://docs.aws.amazon.com/vpc/latest/userguide/vpc-ip-addressing.html\\\"\\n }\\n },\\n \\\"ProductFields\\\": {\\n \\\"ProductName\\\": \\\"ElectricEye\\\",\\n \\\"Provider\\\": \\\"AWS\\\",\\n \\\"ProviderType\\\": \\\"CSP\\\",\\n \\\"ProviderAccountId\\\": awsAccountId,\\n \\\"AssetRegion\\\": awsRegion,\\n \\\"AssetDetails\\\": assetB64,\\n \\\"AssetClass\\\": \\\"Networking\\\",\\n \\\"AssetService\\\": \\\"Amazon Virtual Private Cloud\\\",\\n \\\"AssetComponent\\\": \\\"Subnet\\\"\\n },\\n \\\"Resources\\\": [\\n {\\n \\\"Type\\\": \\\"AwsEc2Subnet\\\",\\n \\\"Id\\\": snetArn,\\n \\\"Partition\\\": awsPartition,\\n \\\"Region\\\": awsRegion,\\n \\\"Details\\\": {\\n \\\"Other\\\": {\\n \\\"VpcId\\\": vpcId,\\n \\\"SubnetId\\\": snetId\\n }\\n }\\n }\\n ],\\n \\\"Compliance\\\": {\\n \\\"Status\\\": \\\"FAILED\\\",\\n \\\"RelatedRequirements\\\": [\\n \\\"NIST CSF V1.1 ID.BE-5\\\",\\n \\\"NIST CSF V1.1 PR.DS-4\\\",\\n \\\"NIST CSF V1.1 PR.PT-5\\\",\\n \\\"NIST SP 800-53 Rev. 4 AU-4\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-2\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-7\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-8\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-11\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-13\\\",\\n \\\"NIST SP 800-53 Rev. 4 PL-8\\\",\\n \\\"NIST SP 800-53 Rev. 4 SA-14\\\",\\n \\\"NIST SP 800-53 Rev. 4 SC-5\\\",\\n \\\"NIST SP 800-53 Rev. 4 SC-6\\\",\\n \\\"AICPA TSC CC3.1\\\",\\n \\\"AICPA TSC A1.1\\\",\\n \\\"AICPA TSC A1.2\\\",\\n \\\"ISO 27001:2013 A.11.1.4\\\",\\n \\\"ISO 27001:2013 A.12.3.1\\\",\\n \\\"ISO 27001:2013 A.17.1.1\\\",\\n \\\"ISO 27001:2013 A.17.1.2\\\",\\n \\\"ISO 27001:2013 A.17.2.1\\\"\\n ]\\n },\\n \\\"Workflow\\\": {\\\"Status\\\": \\\"NEW\\\"},\\n \\\"RecordState\\\": \\\"ACTIVE\\\"\\n }\\n yield finding\\n else:\\n finding = {\\n \\\"SchemaVersion\\\": \\\"2018-10-08\\\",\\n \\\"Id\\\": f\\\"{snetArn}/subnet-no-remaining-ip-space-check\\\",\\n \\\"ProductArn\\\": f\\\"arn:{awsPartition}:securityhub:{awsRegion}:{awsAccountId}:product/{awsAccountId}/default\\\",\\n \\\"GeneratorId\\\": f\\\"{snetArn}/subnet-no-remaining-ip-space-check\\\",\\n \\\"AwsAccountId\\\": awsAccountId,\\n \\\"Types\\\": [\\\"Software and Configuration Checks/AWS Security Best Practices\\\"],\\n \\\"FirstObservedAt\\\": iso8601Time,\\n \\\"CreatedAt\\\": iso8601Time,\\n \\\"UpdatedAt\\\": iso8601Time,\\n \\\"Severity\\\": {\\\"Label\\\": \\\"INFORMATIONAL\\\"},\\n \\\"Confidence\\\": 99,\\n \\\"Title\\\": \\\"[VPC.4] Amazon Virtual Private Cloud (VPC) subnets should be monitored for available IP address space\\\",\\n \\\"Description\\\": f\\\"Amazon Virtual Private Cloud (VPC) subnet {snetId} does have available IP address space.\\\",\\n \\\"Remediation\\\": {\\n \\\"Recommendation\\\": {\\n \\\"Text\\\": \\\"For information on IP addressing refer to the IP Addressing in your VPC section of the Amazon Virtual Private Cloud User Guide\\\",\\n \\\"Url\\\": \\\"https://docs.aws.amazon.com/vpc/latest/userguide/vpc-ip-addressing.html\\\"\\n }\\n },\\n \\\"ProductFields\\\": {\\n \\\"ProductName\\\": \\\"ElectricEye\\\",\\n \\\"Provider\\\": \\\"AWS\\\",\\n \\\"ProviderType\\\": \\\"CSP\\\",\\n \\\"ProviderAccountId\\\": awsAccountId,\\n \\\"AssetRegion\\\": awsRegion,\\n \\\"AssetDetails\\\": assetB64,\\n \\\"AssetClass\\\": \\\"Networking\\\",\\n \\\"AssetService\\\": \\\"Amazon Virtual Private Cloud\\\",\\n \\\"AssetComponent\\\": \\\"Subnet\\\"\\n },\\n \\\"Resources\\\": [\\n {\\n \\\"Type\\\": \\\"AwsEc2Subnet\\\",\\n \\\"Id\\\": snetArn,\\n \\\"Partition\\\": awsPartition,\\n \\\"Region\\\": awsRegion,\\n \\\"Details\\\": {\\n \\\"Other\\\": {\\n \\\"VpcId\\\": vpcId,\\n \\\"SubnetId\\\": snetId\\n }\\n }\\n }\\n ],\\n \\\"Compliance\\\": {\\n \\\"Status\\\": \\\"PASSED\\\",\\n \\\"RelatedRequirements\\\": [\\n \\\"NIST CSF V1.1 ID.BE-5\\\",\\n \\\"NIST CSF V1.1 PR.DS-4\\\",\\n \\\"NIST CSF V1.1 PR.PT-5\\\",\\n \\\"NIST SP 800-53 Rev. 4 AU-4\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-2\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-7\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-8\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-11\\\",\\n \\\"NIST SP 800-53 Rev. 4 CP-13\\\",\\n \\\"NIST SP 800-53 Rev. 4 PL-8\\\",\\n \\\"NIST SP 800-53 Rev. 4 SA-14\\\",\\n \\\"NIST SP 800-53 Rev. 4 SC-5\\\",\\n \\\"NIST SP 800-53 Rev. 4 SC-6\\\",\\n \\\"AICPA TSC CC3.1\\\",\\n \\\"AICPA TSC A1.1\\\",\\n \\\"AICPA TSC A1.2\\\",\\n \\\"ISO 27001:2013 A.11.1.4\\\",\\n \\\"ISO 27001:2013 A.12.3.1\\\",\\n \\\"ISO 27001:2013 A.17.1.1\\\",\\n \\\"ISO 27001:2013 A.17.1.2\\\",\\n \\\"ISO 27001:2013 A.17.2.1\\\"\\n ]\\n },\\n \\\"Workflow\\\": {\\\"Status\\\": \\\"RESOLVED\\\"},\\n \\\"RecordState\\\": \\\"ARCHIVED\\\"\\n }\\n yield finding\",\n \"title\": \"\"\n },\n {\n \"docid\": \"388557b556c60844c7eb05563cd11b9c\",\n \"score\": \"0.57184243\",\n \"text\": \"def test_add_bulk_pool_nodes_errors_with_no_node_added(self):\\n self.helper.clock.advance(50)\\n # get the node count for the lb pool\\n _, list_json = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n server_id = text_type(uuid4())\\n random_pool_id = text_type(uuid4())\\n node_and_pool_ids = [(server_id, self.get_lb_ids()[0][0]),\\n (server_id, random_pool_id)]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(409, response.code)\\n self.assertEqual(\\\"Load Balancer Pool {0} does not exist\\\".format(random_pool_id),\\n resp_json[\\\"errors\\\"][0])\\n # ensure no new nodes were added to the pool\\n _, list_json_again = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n self.assertEqual(len(list_json_again), len(list_json))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d2d84ec48d9a383f1a4de4dbe113eee\",\n \"score\": \"0.5709778\",\n \"text\": \"def test_ips_are_reused(self):\\n address = self._create_address(0)\\n self.network.deallocate_fixed_ip(self.context, address)\\n\\n address2 = self._create_address(0)\\n self.assertEqual(address, address2)\\n\\n self.network.deallocate_fixed_ip(self.context, address2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d625c8e4f5741279ba1782126606f34\",\n \"score\": \"0.57035017\",\n \"text\": \"def pool_add(pool_name, option, devices):\\n if pool_name.startswith(TEST_PREF):\\n exec_command([STRATIS_CLI, \\\"pool\\\", option, pool_name] + devices)\\n blockdevs = StratisCli.blockdev_list()\\n for b in devices:\\n assert b in blockdevs\\n assert blockdevs[b][\\\"POOL_NAME\\\"] == pool_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb7e3690d72351025787dacc7d22c7d8\",\n \"score\": \"0.5683978\",\n \"text\": \"def update_allocation(module, allocations):\\n net_name = module.params['net_name']\\n hostname = module.params['hostname']\\n cidr = module.params['cidr']\\n allocation_pool_start = module.params['allocation_pool_start']\\n allocation_pool_end = module.params['allocation_pool_end']\\n network = netaddr.IPNetwork(cidr)\\n result = {\\n 'changed': False,\\n }\\n object_name = \\\"%s_ips\\\" % net_name\\n net_allocations = allocations.setdefault(object_name, {})\\n invalid_allocations = {hn: ip for hn, ip in net_allocations.items()\\n if netaddr.IPAddress(ip) not in network}\\n if invalid_allocations:\\n module.fail_json(msg=\\\"Found invalid existing allocations in network %s: %s\\\" %\\n (network,\\n \\\", \\\".join(\\\"%s: %s\\\" % (hn, ip)\\n for hn, ip in invalid_allocations.items())))\\n if hostname not in net_allocations:\\n result['changed'] = True\\n allocated_ips = netaddr.IPSet(net_allocations.values())\\n if allocation_pool_start and allocation_pool_end:\\n allocation_pool = netaddr.IPRange(allocation_pool_start, allocation_pool_end)\\n allocation_pool = netaddr.IPSet(allocation_pool)\\n else:\\n allocation_pool = netaddr.IPSet([network])\\n if network.prefixlen != 32:\\n reserved_ips = [network.network, network.broadcast]\\n allocation_pool -= netaddr.IPSet(reserved_ips)\\n free_ips = allocation_pool - allocated_ips\\n for free_cidr in free_ips.iter_cidrs():\\n ip = free_cidr[0]\\n break\\n else:\\n module.fail_json(msg=\\\"No unallocated IP addresses for %s in %s\\\" % (hostname, net_name))\\n free_ips.remove(ip)\\n net_allocations[hostname] = str(ip)\\n result['ip'] = net_allocations[hostname]\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"177fc48007b264d99fa7e6589daef17a\",\n \"score\": \"0.5683613\",\n \"text\": \"def delete_subnets_subnet_pool(self, subnets_subnet_pool_id, groupnet, subnet, **kwargs):\\n\\n all_params = ['subnets_subnet_pool_id', 'groupnet', 'subnet']\\n all_params.append('callback')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method delete_subnets_subnet_pool\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n # verify the required parameter 'subnets_subnet_pool_id' is set\\n if ('subnets_subnet_pool_id' not in params) or (params['subnets_subnet_pool_id'] is None):\\n raise ValueError(\\\"Missing the required parameter `subnets_subnet_pool_id` when calling `delete_subnets_subnet_pool`\\\")\\n # verify the required parameter 'groupnet' is set\\n if ('groupnet' not in params) or (params['groupnet'] is None):\\n raise ValueError(\\\"Missing the required parameter `groupnet` when calling `delete_subnets_subnet_pool`\\\")\\n # verify the required parameter 'subnet' is set\\n if ('subnet' not in params) or (params['subnet'] is None):\\n raise ValueError(\\\"Missing the required parameter `subnet` when calling `delete_subnets_subnet_pool`\\\")\\n\\n\\n resource_path = '/platform/3/network/groupnets/{Groupnet}/subnets/{Subnet}/pools/{SubnetsSubnetPoolId}'.replace('{format}', 'json')\\n path_params = {}\\n if 'subnets_subnet_pool_id' in params:\\n path_params['SubnetsSubnetPoolId'] = params['subnets_subnet_pool_id']\\n if 'groupnet' in params:\\n path_params['Groupnet'] = params['groupnet']\\n if 'subnet' in params:\\n path_params['Subnet'] = params['subnet']\\n\\n query_params = {}\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['application/json'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['application/json'])\\n\\n # Authentication setting\\n auth_settings = ['basic_auth']\\n\\n response = self.api_client.call_api(resource_path, 'DELETE',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type=None,\\n auth_settings=auth_settings,\\n callback=params.get('callback'))\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d3279b200bbd1ad84f0995a205f5bc2\",\n \"score\": \"0.5667308\",\n \"text\": \"def test_create_delete_subnet_with_v6_attributes_slaac(self):\\n self._create_verify_delete_subnet(\\n ipv6_ra_mode='slaac',\\n ipv6_address_mode='slaac')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"396193b2f449a5fca6e91fb0abadba78\",\n \"score\": \"0.5650636\",\n \"text\": \"def test_add_bulk_pool_nodes_when_pool_id_is_non_uuid(self):\\n self.helper.clock.advance(50)\\n node_and_pool_ids = [(text_type(uuid4()), 122),\\n (text_type(uuid4()), text_type(uuid4()))]\\n add_data = self._get_custom_add_nodes_json(node_and_pool_ids)\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(400, response.code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d81864f735aadcf8ef6d8caafa58fcbc\",\n \"score\": \"0.5648901\",\n \"text\": \"def test_task_id_create_in_delete_one_ipv4_error(self):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"185daf38d1343c75225e1c4f71356987\",\n \"score\": \"0.5605987\",\n \"text\": \"def test_add_pool(rml_pool_store: Store):\\n # GIVEN a valid customer and a valid application_version\\n customer: Customer = rml_pool_store.get_customers()[0]\\n application = rml_pool_store.get_application_by_tag(tag=\\\"RMLP05R800\\\")\\n app_version = (\\n rml_pool_store._get_query(table=ApplicationVersion)\\n .filter(ApplicationVersion.application_id == application.id)\\n .first()\\n )\\n\\n # WHEN adding a new pool\\n new_pool = rml_pool_store.add_pool(\\n customer=customer,\\n name=\\\"pool2\\\",\\n order=\\\"123456\\\",\\n ordered=dt.now(),\\n application_version=app_version,\\n )\\n\\n rml_pool_store.session.add(new_pool)\\n rml_pool_store.session.commit()\\n # THEN the new pool should have no_invoice = False\\n pool = rml_pool_store.get_pool_by_entry_id(entry_id=2)\\n assert pool.no_invoice is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee4b1e513b912e7c1ea0db54d1210401\",\n \"score\": \"0.55956775\",\n \"text\": \"def test_createSharedNetwork_usedVlan2(self):\\n\\n # Steps,\\n # 1. create an Admin account\\n # 2. create a shared NetworkOffering\\n # 3. enable the network offering\\n # 4. listPhysicalNetworks\\n # - vlan = guest VLAN range = 10-90 (say)\\n # 5. createNetwork\\n # - name = mysharednetwork, displaytext = mysharednetwork\\n # - vlan = any vlan beyond 10-90 (123 for eg)\\n # - networkofferingid = <mysharedoffering>\\n # - gw = 172.16.15.1, startip = 172.16.15.2 , endip = 172.16.15.200\\n # - netmask=255.255.255.0\\n # - scope = all\\n # 6. createNetwork again with same VLAN but different IP ranges and\\n # gateway\\n # 7. delete admin account\\n # Validations,\\n # 1. listAccounts state=enabled returns your account\\n # 2. listNetworkOfferings - name=mysharedoffering , should list\\n # offering in disabled state\\n # 3. listNetworkOfferings - name=mysharedoffering, should list\\n # enabled offering\\n # 4. listPhysicalNetworks should return at least one active\\n # physical network\\n # 5. network creation should PASS\\n # 6. network creation should FAIL since VLAN is already used by\\n # previously created network\\n\\n # Create admin account\\n self.admin_account = Account.create(\\n self.api_client,\\n self.testdata[\\\"account\\\"],\\n admin=True,\\n domainid=self.domain.id\\n )\\n self.cleanup_accounts.append(self.admin_account)\\n\\n # verify that the account got created with state enabled\\n list_accounts_response = Account.list(\\n self.api_client,\\n id=self.admin_account.id,\\n listall=True\\n )\\n self.assertEqual(\\n isinstance(list_accounts_response, list),\\n True,\\n \\\"listAccounts returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_accounts_response),\\n 0,\\n \\\"listAccounts returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_accounts_response[0].state,\\n \\\"enabled\\\",\\n \\\"The admin account created is not enabled.\\\"\\n )\\n\\n self.debug(\\\"Admin account created: %s\\\" % self.admin_account.id)\\n\\n physical_network, shared_vlan = get_free_vlan(\\n self.api_client, self.zone.id)\\n if shared_vlan is None:\\n self.fail(\\\"Failed to get free vlan id for shared network\\\")\\n\\n self.debug(\\\"Physical Network found: %s\\\" % physical_network.id)\\n\\n self.testdata[\\\"shared_network_offering\\\"][\\\"specifyVlan\\\"] = \\\"True\\\"\\n self.testdata[\\\"shared_network_offering\\\"][\\\"specifyIpRanges\\\"] = \\\"True\\\"\\n\\n # Create Network Offering\\n self.shared_network_offering = NetworkOffering.create(\\n self.api_client,\\n self.testdata[\\\"shared_network_offering\\\"],\\n conservemode=False\\n )\\n\\n # Verify that the network offering got created\\n list_network_offerings_response = NetworkOffering.list(\\n self.api_client,\\n id=self.shared_network_offering.id\\n )\\n self.assertEqual(\\n isinstance(list_network_offerings_response, list),\\n True,\\n \\\"listNetworkOfferings returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_network_offerings_response),\\n 0,\\n \\\"listNetworkOfferings returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_network_offerings_response[0].state,\\n \\\"Disabled\\\",\\n \\\"The network offering created should be bydefault disabled.\\\"\\n )\\n\\n self.debug(\\n \\\"Shared Network Offering created: %s\\\" %\\n self.shared_network_offering.id)\\n\\n # Update network offering state from disabled to enabled.\\n NetworkOffering.update(\\n self.shared_network_offering,\\n self.api_client,\\n id=self.shared_network_offering.id,\\n state=\\\"enabled\\\"\\n )\\n\\n # Verify that the state of the network offering is updated\\n list_network_offerings_response = NetworkOffering.list(\\n self.api_client,\\n id=self.shared_network_offering.id\\n )\\n self.assertEqual(\\n isinstance(list_network_offerings_response, list),\\n True,\\n \\\"listNetworkOfferings returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_network_offerings_response),\\n 0,\\n \\\"listNetworkOfferings returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_network_offerings_response[0].state,\\n \\\"Enabled\\\",\\n \\\"The network offering state should get updated to Enabled.\\\"\\n )\\n\\n # create network using the shared network offering created\\n self.testdata[\\\"shared_network\\\"][\\\"acltype\\\"] = \\\"Domain\\\"\\n self.testdata[\\\"shared_network\\\"][\\n \\\"networkofferingid\\\"] = self.shared_network_offering.id\\n self.testdata[\\\"shared_network\\\"][\\n \\\"physicalnetworkid\\\"] = physical_network.id\\n self.testdata[\\\"shared_network\\\"][\\\"vlan\\\"] = shared_vlan\\n self.debug(\\n \\\"Creating a shared network in non-cloudstack VLAN %s\\\" %\\n shared_vlan)\\n self.network = Network.create(\\n self.api_client,\\n self.testdata[\\\"shared_network\\\"],\\n networkofferingid=self.shared_network_offering.id,\\n zoneid=self.zone.id,\\n )\\n\\n self.cleanup_networks.append(self.network)\\n\\n list_networks_response = Network.list(\\n self.api_client,\\n id=self.network.id\\n )\\n self.assertEqual(\\n isinstance(list_networks_response, list),\\n True,\\n \\\"listNetworks returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_networks_response),\\n 0,\\n \\\"listNetworks returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_networks_response[0].specifyipranges,\\n True,\\n \\\"The network is created with ip range but the flag is\\\\\\n set to False.\\\")\\n\\n self.debug(\\\"Network created: %s\\\" % self.network.id)\\n\\n shared_network_subnet_number = random.randrange(1, 254)\\n\\n self.testdata[\\\"shared_network\\\"][\\\"gateway\\\"] = \\\"172.16.\\\" + \\\\\\n str(shared_network_subnet_number) + \\\".1\\\"\\n self.testdata[\\\"shared_network\\\"][\\\"startip\\\"] = \\\"172.16.\\\" + \\\\\\n str(shared_network_subnet_number) + \\\".2\\\"\\n self.testdata[\\\"shared_network\\\"][\\\"endip\\\"] = \\\"172.16.\\\" + \\\\\\n str(shared_network_subnet_number) + \\\".20\\\"\\n\\n self.testdata[\\\"shared_network\\\"][\\\"acltype\\\"] = \\\"domain\\\"\\n self.testdata[\\\"shared_network\\\"][\\n \\\"networkofferingid\\\"] = self.shared_network_offering.id\\n self.testdata[\\\"shared_network\\\"][\\n \\\"physicalnetworkid\\\"] = physical_network.id\\n\\n try:\\n self.network1 = Network.create(\\n self.api_client,\\n self.testdata[\\\"shared_network\\\"],\\n networkofferingid=self.shared_network_offering.id,\\n zoneid=self.zone.id,\\n )\\n self.cleanup_networks.append(self.network1)\\n self.fail(\\n \\\"Network got created with used vlan id, which is invalid\\\")\\n except Exception as e:\\n self.debug(\\n \\\"Network creation failed because the valn id being used by\\\\\\n another network. Exception: %s\\\" % e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04d7cb27cba857720f66ccd66a01b628\",\n \"score\": \"0.55905604\",\n \"text\": \"def _validate_pool(self, session, lb_id, pool_id, listener_protocol):\\n db_pool = self.repositories.pool.get(\\n session, load_balancer_id=lb_id, id=pool_id)\\n if not db_pool:\\n raise exceptions.NotFound(\\n resource=data_models.Pool._name(), id=pool_id)\\n self._validate_protocol(listener_protocol, db_pool.protocol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cddd70d604bdfab8fdf2af9c89f45db4\",\n \"score\": \"0.55753607\",\n \"text\": \"def test_create_delete_network_in_datacenter(self):\\n testflow.step(\\\"Log in as user.\\\")\\n common.login_as_user()\\n\\n for net in config.NETWORK_NAMES[:2]:\\n assert networks.add_network(\\n False,\\n name=net,\\n data_center=config.DC_NAME[0]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d6a248c732a2eae12d6dd51b3fbef67\",\n \"score\": \"0.55617326\",\n \"text\": \"def _validate_pools_exist(self):\\n pool_list = self._get_directory_list('/')\\n for pool in self.pools:\\n if pool not in pool_list:\\n msg = '%s is not exist in backend storage.' % pool\\n LOG.error(msg)\\n raise exception.InvalidInput(reason=msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa9420ebf07ddcc0ee8fba49ad0219a\",\n \"score\": \"0.5544308\",\n \"text\": \"def _validate_task_pools(self, *, dagbag: DagBag, session: Session = NEW_SESSION):\\n from airflow.models.pool import Pool\\n\\n def check_pools(dag):\\n task_pools = {task.pool for task in dag.tasks}\\n nonexistent_pools = task_pools - pools\\n if nonexistent_pools:\\n return f\\\"Dag '{dag.dag_id}' references non-existent pools: {sorted(nonexistent_pools)!r}\\\"\\n\\n pools = {p.pool for p in Pool.get_pools(session)}\\n for dag in dagbag.dags.values():\\n message = check_pools(dag)\\n if message:\\n self.dag_warnings.add(DagWarning(dag.dag_id, DagWarningType.NONEXISTENT_POOL, message))\\n for subdag in dag.subdags:\\n message = check_pools(subdag)\\n if message:\\n self.dag_warnings.add(DagWarning(subdag.dag_id, DagWarningType.NONEXISTENT_POOL, message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e86e452b74b1eceae60e5d6a3432060c\",\n \"score\": \"0.5539687\",\n \"text\": \"def _check_networks(self):\\n\\n seen_nets = self.admin_manager.networks_client.list_networks()\\n seen_names = [n['name'] for n in seen_nets['networks']]\\n seen_ids = [n['id'] for n in seen_nets['networks']]\\n self.assertIn(self.network['name'], seen_names)\\n self.assertIn(self.network['id'], seen_ids)\\n\\n seen_subnets = self.admin_manager.subnets_client.list_subnets()\\n seen_net_ids = [n['network_id'] for n in seen_subnets['subnets']]\\n seen_subnet_ids = [n['id'] for n in seen_subnets['subnets']]\\n self.assertIn(self.network['id'], seen_net_ids)\\n self.assertIn(self.subnet['id'], seen_subnet_ids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f1b8c39308955cbb21f87b867911fcd\",\n \"score\": \"0.5529731\",\n \"text\": \"def test_network_unavailable_on_ec2_instance():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"668711c7277837562d041da240d574ab\",\n \"score\": \"0.5526954\",\n \"text\": \"def test_10_network_services_VPC_DeleteAllMultiplePF(self):\\n\\n network_1 = self.create_network(self.services[\\\"network_offering\\\"])\\n network_2 = self.create_network(self.services[\\\"network_offering_no_lb\\\"], '10.1.2.1')\\n vm_1 = self.deployvm_in_network(network_1)\\n vm_2 = self.deployvm_in_network(network_1)\\n vm_3 = self.deployvm_in_network(network_2)\\n vm_4 = self.deployvm_in_network(network_2)\\n public_ip_1 = self.acquire_publicip(network_1)\\n public_ip_2 = self.acquire_publicip(network_1)\\n nat_rule1 = self.create_natrule_for_services(vm_1, public_ip_1, network_1)\\n nat_rule2 = self.create_natrule_for_services(vm_2, public_ip_2, network_1)\\n http_rule1 = self.create_natrule_for_services(vm_1, public_ip_1, network_1, self.services[\\\"http_rule\\\"])\\n http_rule2 = self.create_natrule_for_services(vm_2, public_ip_2, network_1, self.services[\\\"http_rule\\\"])\\n public_ip_3 = self.acquire_publicip(network_2)\\n public_ip_4 = self.acquire_publicip(network_2)\\n nat_rule3 = self.create_natrule_for_services(vm_3, public_ip_3, network_2)\\n nat_rule4 = self.create_natrule_for_services(vm_4, public_ip_4, network_2)\\n http_rule3 = self.create_natrule_for_services(vm_3, public_ip_3, network_2, self.services[\\\"http_rule\\\"])\\n http_rule4 = self.create_natrule_for_services(vm_4, public_ip_4, network_2, self.services[\\\"http_rule\\\"])\\n self.check_ssh_into_vm(vm_1, public_ip_1, testnegative=False)\\n self.check_ssh_into_vm(vm_2, public_ip_2, testnegative=False)\\n self.check_ssh_into_vm(vm_3, public_ip_3, testnegative=False)\\n self.check_ssh_into_vm(vm_4, public_ip_4, testnegative=False)\\n self.check_wget_from_vm(vm_1, public_ip_1, testnegative=False)\\n self.check_wget_from_vm(vm_2, public_ip_2, testnegative=False)\\n self.check_wget_from_vm(vm_3, public_ip_3, testnegative=False)\\n self.check_wget_from_vm(vm_4, public_ip_4, testnegative=False)\\n nat_rule1.delete(self.apiclient)\\n self.cleanup.remove(nat_rule1)\\n nat_rule2.delete(self.apiclient)\\n self.cleanup.remove(nat_rule2)\\n nat_rule3.delete(self.apiclient)\\n self.cleanup.remove(nat_rule3)\\n nat_rule4.delete(self.apiclient)\\n self.cleanup.remove(nat_rule4)\\n http_rule1.delete(self.apiclient)\\n self.cleanup.remove(http_rule1)\\n http_rule2.delete(self.apiclient)\\n self.cleanup.remove(http_rule2)\\n http_rule3.delete(self.apiclient)\\n self.cleanup.remove(http_rule3)\\n http_rule4.delete(self.apiclient)\\n self.cleanup.remove(http_rule4)\\n self.check_ssh_into_vm(vm_1, public_ip_1, testnegative=True)\\n self.check_ssh_into_vm(vm_2, public_ip_2, testnegative=True)\\n self.check_ssh_into_vm(vm_3, public_ip_3, testnegative=True)\\n self.check_ssh_into_vm(vm_4, public_ip_4, testnegative=True)\\n self.check_wget_from_vm(vm_1, public_ip_1, testnegative=True,\\n isVmAccessible=False, network=network_1)\\n self.check_wget_from_vm(vm_2, public_ip_2, testnegative=True,\\n isVmAccessible=False, network=network_1)\\n self.check_wget_from_vm(vm_3, public_ip_3, testnegative=True,\\n isVmAccessible=False, network=network_2)\\n self.check_wget_from_vm(vm_4, public_ip_4, testnegative=True,\\n isVmAccessible=False, network=network_2)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e701da995c64bd12c17456f4b7086fa\",\n \"score\": \"0.5478033\",\n \"text\": \"def _check_constraints(\\n self, random_subnet: SupportRandomSubnet) -> Tuple[bool, Dict]:\\n is_pass, results = check_subnet_resources(\\n model=self.model,\\n subnet=random_subnet,\\n estimator=self.estimator,\\n constraints_range=self.constraints_range)\\n\\n return is_pass, results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e30c9c14122a807f746026fc2e6e045\",\n \"score\": \"0.5462444\",\n \"text\": \"def test_createSharedNetwork_All(self):\\n\\n # Steps,\\n # 1. create an Admin Account - admin-XABU1\\n # 2. listPhysicalNetworks in available zone\\n # 3. createNetworkOffering:\\n # - name = \\\"MySharedOffering\\\"\\n # - guestiptype=\\\"shared\\\"\\n # - services = {Dns, Dhcp, UserData}\\n # - conservemode = false\\n # - specifyVlan = true\\n # - specifyIpRanges = true\\n # 4. Create network offering - updateNetworkOffering - state=Enabled\\n # 5. createNetwork\\n # - name = mysharednetwork, displaytext = mysharednetwork\\n # - vlan = 123 (say)\\n # - networkofferingid = <mysharedoffering>\\n # - gw = 172.16.15.1, startip = 172.16.15.2 , endip = 172.16.15.200\\n # - netmask=255.255.255.0\\n # - scope = all\\n # 6. create User account - user-ASJDK\\n # 7. deployVirtualMachine in this account and in admin account &\\n # within networkid = <mysharednetwork>\\n # 8. delete the admin account and the user account\\n # Validations,\\n # 1. listAccounts name=admin-XABU1, state=enabled returns account\\n # 2. listPhysicalNetworks should return at least one active\\n # physical network\\n # 3. listNetworkOfferings - name=mysharedoffering , should list\\n # offering in disabled state\\n # 4. listNetworkOfferings - name=mysharedoffering, should list\\n # enabled offering\\n # 5. listNetworks - name = mysharednetwork should list the\\n # successfully created network, verify the guestIp ranges and\\n # CIDR are as given in the createNetwork call\\n # 6. No checks reqd\\n # 7. a. listVirtualMachines should show both VMs in running state\\n # in the user account and the admin account\\n # b. VM's IPs should be in the range of the shared network ip ranges\\n\\n # Create admin account\\n self.admin_account = Account.create(\\n self.api_client,\\n self.testdata[\\\"account\\\"],\\n admin=True,\\n domainid=self.domain.id\\n )\\n\\n self.cleanup_accounts.append(self.admin_account)\\n\\n # verify that the account got created with state enabled\\n list_accounts_response = Account.list(\\n self.api_client,\\n id=self.admin_account.id,\\n listall=True\\n )\\n self.assertEqual(\\n isinstance(list_accounts_response, list),\\n True,\\n \\\"listAccounts returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_accounts_response),\\n 0,\\n \\\"listAccounts returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_accounts_response[0].state,\\n \\\"enabled\\\",\\n \\\"The admin account created is not enabled.\\\"\\n )\\n\\n self.debug(\\\"Admin type account created: %s\\\" % self.admin_account.name)\\n\\n # Create an user account\\n self.user_account = Account.create(\\n self.api_client,\\n self.testdata[\\\"account\\\"],\\n admin=False,\\n domainid=self.domain.id\\n )\\n\\n self.cleanup_accounts.append(self.user_account)\\n\\n # verify that the account got created with state enabled\\n list_accounts_response = Account.list(\\n self.api_client,\\n id=self.user_account.id,\\n listall=True\\n )\\n self.assertEqual(\\n isinstance(list_accounts_response, list),\\n True,\\n \\\"listAccounts returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_accounts_response),\\n 0,\\n \\\"listAccounts returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_accounts_response[0].state,\\n \\\"enabled\\\",\\n \\\"The user account created is not enabled.\\\"\\n )\\n\\n self.debug(\\\"User type account created: %s\\\" % self.user_account.name)\\n\\n physical_network, shared_vlan = get_free_vlan(\\n self.api_client, self.zone.id)\\n if shared_vlan is None:\\n self.fail(\\\"Failed to get free vlan id for shared network\\\")\\n\\n self.debug(\\\"Physical network found: %s\\\" % physical_network.id)\\n\\n self.testdata[\\\"shared_network_offering\\\"][\\\"specifyVlan\\\"] = \\\"True\\\"\\n self.testdata[\\\"shared_network_offering\\\"][\\\"specifyIpRanges\\\"] = \\\"True\\\"\\n\\n # Create Network Offering\\n self.shared_network_offering = NetworkOffering.create(\\n self.api_client,\\n self.testdata[\\\"shared_network_offering\\\"],\\n conservemode=False\\n )\\n\\n # Verify that the network offering got created\\n list_network_offerings_response = NetworkOffering.list(\\n self.api_client,\\n id=self.shared_network_offering.id\\n )\\n self.assertEqual(\\n isinstance(list_network_offerings_response, list),\\n True,\\n \\\"listNetworkOfferings returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_network_offerings_response),\\n 0,\\n \\\"listNetworkOfferings returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_network_offerings_response[0].state,\\n \\\"Disabled\\\",\\n \\\"The network offering created should be bydefault disabled.\\\"\\n )\\n\\n self.debug(\\n \\\"Shared Network offering created: %s\\\" %\\n self.shared_network_offering.id)\\n\\n # Update network offering state from disabled to enabled.\\n NetworkOffering.update(\\n self.shared_network_offering,\\n self.api_client,\\n id=self.shared_network_offering.id,\\n state=\\\"enabled\\\"\\n )\\n\\n # Verify that the state of the network offering is updated\\n list_network_offerings_response = NetworkOffering.list(\\n self.api_client,\\n id=self.shared_network_offering.id\\n )\\n self.assertEqual(\\n isinstance(list_network_offerings_response, list),\\n True,\\n \\\"listNetworkOfferings returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_network_offerings_response),\\n 0,\\n \\\"listNetworkOfferings returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_network_offerings_response[0].state,\\n \\\"Enabled\\\",\\n \\\"The network offering state should get updated to Enabled.\\\"\\n )\\n\\n # create network using the shared network offering created\\n self.testdata[\\\"shared_network\\\"][\\\"acltype\\\"] = \\\"Domain\\\"\\n self.testdata[\\\"shared_network\\\"][\\n \\\"networkofferingid\\\"] = self.shared_network_offering.id\\n self.testdata[\\\"shared_network\\\"][\\n \\\"physicalnetworkid\\\"] = physical_network.id\\n self.testdata[\\\"shared_network\\\"][\\\"vlan\\\"] = shared_vlan\\n\\n self.network = Network.create(\\n self.api_client,\\n self.testdata[\\\"shared_network\\\"],\\n networkofferingid=self.shared_network_offering.id,\\n zoneid=self.zone.id,\\n )\\n\\n self.cleanup_networks.append(self.network)\\n\\n list_networks_response = Network.list(\\n self.api_client,\\n id=self.network.id\\n )\\n self.assertEqual(\\n isinstance(list_networks_response, list),\\n True,\\n \\\"listNetworks returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_networks_response),\\n 0,\\n \\\"listNetworks returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_networks_response[0].specifyipranges,\\n True,\\n \\\"The network is created with ip range but the flag is\\\\\\n set to False.\\\")\\n\\n self.debug(\\n \\\"Shared Network created for scope domain: %s\\\" %\\n self.network.id)\\n\\n self.admin_account_virtual_machine = VirtualMachine.create(\\n self.api_client,\\n self.testdata[\\\"virtual_machine\\\"],\\n networkids=self.network.id,\\n serviceofferingid=self.service_offering.id\\n )\\n\\n self.cleanup_vms.append(self.admin_account_virtual_machine)\\n\\n vms = VirtualMachine.list(\\n self.api_client,\\n id=self.admin_account_virtual_machine.id,\\n listall=True\\n )\\n self.assertEqual(\\n isinstance(vms, list),\\n True,\\n \\\"listVirtualMachines returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(vms),\\n 0,\\n \\\"listVirtualMachines returned empty list.\\\"\\n )\\n\\n self.debug(\\n \\\"Virtual Machine created: %s\\\" %\\n self.admin_account_virtual_machine.id)\\n\\n ip_range = list(\\n netaddr.iter_iprange(\\n str(\\n self.testdata[\\\"shared_network\\\"][\\\"startip\\\"]), str(\\n self.testdata[\\\"shared_network\\\"][\\\"endip\\\"])))\\n if netaddr.IPAddress(str(vms[0].nic[0].ipaddress)) not in ip_range:\\n self.fail(\\n \\\"Virtual machine ip should be from the ip range assigned to\\\\\\n network created.\\\")\\n\\n self.user_account_virtual_machine = VirtualMachine.create(\\n self.api_client,\\n self.testdata[\\\"virtual_machine\\\"],\\n accountid=self.user_account.name,\\n domainid=self.user_account.domainid,\\n serviceofferingid=self.service_offering.id,\\n networkids=self.network.id\\n )\\n vms = VirtualMachine.list(\\n self.api_client,\\n id=self.user_account_virtual_machine.id,\\n listall=True\\n )\\n self.assertEqual(\\n isinstance(vms, list),\\n True,\\n \\\"listVirtualMachines returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(vms),\\n 0,\\n \\\"listVirtualMachines returned empty list.\\\"\\n )\\n\\n self.debug(\\n \\\"Virtual Machine created: %s\\\" %\\n self.user_account_virtual_machine.id)\\n\\n ip_range = list(\\n netaddr.iter_iprange(\\n str(\\n self.testdata[\\\"shared_network\\\"][\\\"startip\\\"]), str(\\n self.testdata[\\\"shared_network\\\"][\\\"endip\\\"])))\\n if netaddr.IPAddress(str(vms[0].nic[0].ipaddress)) not in ip_range:\\n self.fail(\\n \\\"Virtual machine ip should be from the ip range assigned to\\\\\\n network created.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f0b397f2d7ec67e812cb4c62f63915b\",\n \"score\": \"0.54589295\",\n \"text\": \"def test_add_bulk_pool_nodes_success_response(self):\\n self.helper.clock.advance(50)\\n add_data = self._get_add_nodes_json()\\n response, resp_json = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(201, response.code)\\n self._check_added_nodes_result(50, add_data, resp_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5625fec6c2e5dbd44a4e985375d67d55\",\n \"score\": \"0.54575235\",\n \"text\": \"def validate_network_operation(session, object_uuid, operation, data):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3bd04e57d8ea45e3863da206b6bc2a6\",\n \"score\": \"0.54552305\",\n \"text\": \"def delete_subnet(compute, project, region, name):\\n return compute.subnetworks().delete(project=project, region=region,\\n subnetwork=name).execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7028ef5173fb70fd4403dccb688c000b\",\n \"score\": \"0.54527587\",\n \"text\": \"def test_09_network_services_VPC_StopDeleteAllMultiplePF(self):\\n\\n network_1 = self.create_network(self.services[\\\"network_offering\\\"])\\n network_2 = self.create_network(self.services[\\\"network_offering_no_lb\\\"], '10.1.2.1')\\n vm_1 = self.deployvm_in_network(network_1)\\n vm_2 = self.deployvm_in_network(network_1)\\n vm_3 = self.deployvm_in_network(network_2)\\n vm_4 = self.deployvm_in_network(network_2)\\n public_ip_1 = self.acquire_publicip(network_1)\\n public_ip_2 = self.acquire_publicip(network_1)\\n nat_rule1 = self.create_natrule_for_services(vm_1, public_ip_1, network_1)\\n nat_rule2 = self.create_natrule_for_services(vm_2, public_ip_2, network_1)\\n http_rule1 = self.create_natrule_for_services(vm_1, public_ip_1, network_1, self.services[\\\"http_rule\\\"])\\n http_rule2 = self.create_natrule_for_services(vm_2, public_ip_2, network_1, self.services[\\\"http_rule\\\"])\\n public_ip_3 = self.acquire_publicip(network_2)\\n public_ip_4 = self.acquire_publicip(network_2)\\n nat_rule3 = self.create_natrule_for_services(vm_3, public_ip_3, network_2)\\n nat_rule4 = self.create_natrule_for_services(vm_4, public_ip_4, network_2)\\n http_rule3 = self.create_natrule_for_services(vm_3, public_ip_3, network_2, self.services[\\\"http_rule\\\"])\\n http_rule4 = self.create_natrule_for_services(vm_4, public_ip_4, network_2, self.services[\\\"http_rule\\\"])\\n self.check_ssh_into_vm(vm_1, public_ip_1, testnegative=False)\\n self.check_ssh_into_vm(vm_2, public_ip_2, testnegative=False)\\n self.check_ssh_into_vm(vm_3, public_ip_3, testnegative=False)\\n self.check_ssh_into_vm(vm_4, public_ip_4, testnegative=False)\\n self.check_wget_from_vm(vm_1, public_ip_1, testnegative=False)\\n self.check_wget_from_vm(vm_2, public_ip_2, testnegative=False)\\n self.check_wget_from_vm(vm_3, public_ip_3, testnegative=False)\\n self.check_wget_from_vm(vm_4, public_ip_4, testnegative=False)\\n router = self.stop_vpcrouter()\\n nat_rule1.delete(self.apiclient)\\n self.cleanup.remove(nat_rule1)\\n nat_rule2.delete(self.apiclient)\\n self.cleanup.remove(nat_rule2)\\n nat_rule3.delete(self.apiclient)\\n self.cleanup.remove(nat_rule3)\\n nat_rule4.delete(self.apiclient)\\n self.cleanup.remove(nat_rule4)\\n http_rule1.delete(self.apiclient)\\n self.cleanup.remove(http_rule1)\\n http_rule2.delete(self.apiclient)\\n self.cleanup.remove(http_rule2)\\n http_rule3.delete(self.apiclient)\\n self.cleanup.remove(http_rule3)\\n http_rule4.delete(self.apiclient)\\n self.cleanup.remove(http_rule4)\\n self.start_vpcrouter(router)\\n self.check_ssh_into_vm(vm_1, public_ip_1, testnegative=True)\\n self.check_ssh_into_vm(vm_2, public_ip_2, testnegative=True)\\n self.check_ssh_into_vm(vm_3, public_ip_3, testnegative=True)\\n self.check_ssh_into_vm(vm_4, public_ip_4, testnegative=True)\\n self.check_wget_from_vm(vm_1, public_ip_1, testnegative=True,\\n isVmAccessible=False, network=network_1)\\n self.check_wget_from_vm(vm_2, public_ip_2, testnegative=True,\\n isVmAccessible=False, network=network_1)\\n self.check_wget_from_vm(vm_3, public_ip_3, testnegative=True,\\n isVmAccessible=False, network=network_2)\\n self.check_wget_from_vm(vm_4, public_ip_4, testnegative=True,\\n isVmAccessible=False, network=network_2)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6e65452dc2538a52350f0f72efb782b\",\n \"score\": \"0.5449026\",\n \"text\": \"def create_one_subnet_pool(attrs=None):\\n attrs = attrs or {}\\n\\n # Set default attributes.\\n subnet_pool_attrs = {\\n 'id': 'subnet-pool-id-' + uuid.uuid4().hex,\\n 'name': 'subnet-pool-name-' + uuid.uuid4().hex,\\n 'prefixes': ['10.0.0.0/24', '10.1.0.0/24'],\\n 'default_prefixlen': '8',\\n 'address_scope_id': 'address-scope-id-' + uuid.uuid4().hex,\\n 'project_id': 'project-id-' + uuid.uuid4().hex,\\n 'is_default': False,\\n 'shared': False,\\n 'max_prefixlen': '32',\\n 'min_prefixlen': '8',\\n 'default_quota': None,\\n 'ip_version': '4',\\n 'description': 'subnet-pool-description-' + uuid.uuid4().hex,\\n 'tags': [],\\n 'location': 'MUNCHMUNCHMUNCH',\\n }\\n\\n # Overwrite default attributes.\\n subnet_pool_attrs.update(attrs)\\n\\n subnet_pool = fakes.FakeResource(\\n info=copy.deepcopy(subnet_pool_attrs), loaded=True\\n )\\n\\n # Set attributes with special mapping in OpenStack SDK.\\n subnet_pool.default_prefix_length = subnet_pool_attrs[\\n 'default_prefixlen'\\n ]\\n subnet_pool.is_shared = subnet_pool_attrs['shared']\\n subnet_pool.maximum_prefix_length = subnet_pool_attrs['max_prefixlen']\\n subnet_pool.minimum_prefix_length = subnet_pool_attrs['min_prefixlen']\\n\\n return subnet_pool\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6586992dee6870dd7c960cd41c83f07f\",\n \"score\": \"0.54485893\",\n \"text\": \"def test_create_network(self):\\n try:\\n #\\n # create vnic\\n vnic_name_b = self.vnic_name_prefix + uuid.uuid4().hex[:6]\\n cmd = [self.oci_network_path, 'attach-vnic',\\n '--name', vnic_name_b]\\n print(cmd)\\n create_vnic_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\\n self.assertIn('Creating', str(create_vnic_data), 'attach vnic failed')\\n _show_res('Create VNIC', create_vnic_data)\\n time.sleep(self.waittime)\\n cmd = [self.oci_network_path, 'show',\\n '--details',\\n '--output-mode', 'table']\\n print(cmd)\\n vnic_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\\n _show_res('Vnic Data', vnic_data)\\n #\\n # create kvm bridge\\n kvm_network_name = self.kvm_net_prefix + uuid.uuid4().hex[:6]\\n kvm_net_ip = self._get_vnic_data('vnic', vnic_name_b, 'ipaddress')\\n cmd = [self.oci_kvm_path, 'create-network',\\n '--network-name', kvm_network_name,\\n '--net', kvm_net_ip,\\n '--ip-bridge', self.ip_bridge,\\n '--ip-start', self.ip_start,\\n '--ip-end', self.ip_end,\\n '--ip-prefix', self.ip_prefix]\\n print(cmd)\\n create_kvm_bridge_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\\n _show_res('Create KVM network', create_kvm_bridge_data)\\n time.sleep(self.waittime)\\n #\\n # show\\n cmd = [self.oci_network_path, 'show',\\n '--details',\\n '--output-mode', 'text']\\n print(cmd)\\n show_vnic_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\\n _show_res('Show network data', show_vnic_data)\\n #\\n # delete kvm bridge\\n cmd = [self.oci_kvm_path, 'delete-network',\\n '--network-name', kvm_network_name,\\n '--yes']\\n print(cmd)\\n delete_kvm_bridge_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\\n _show_res('Delete KVM network', delete_kvm_bridge_data)\\n time.sleep(self.waittime)\\n #\\n # delete vnic\\n cmd = [self.oci_network_path, 'detach-vnic',\\n '--ip-address', kvm_net_ip]\\n print(cmd)\\n delete_vnic_data = subprocess.check_output(cmd).decode('utf-8').splitlines()\\n _show_res('Delete VNIC', delete_vnic_data)\\n except Exception as e:\\n self.fail('Execution of create kvm network has failed: %s' % str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f4ad28054ff7c05b09fb3cc18595a0\",\n \"score\": \"0.5436422\",\n \"text\": \"def test_bulk_create_delete_port(self):\\n networks = [self.create_network(), self.create_network()]\\n # Creates 2 ports in one request\\n names = [data_utils.rand_name('port-') for i in range(len(networks))]\\n port_list = []\\n state = [True, False]\\n for i in range(len(names)):\\n p1 = {\\n 'network_id': networks[i]['id'],\\n 'name': names[i],\\n 'admin_state_up': state[i],\\n }\\n port_list.append(p1)\\n del port_list[1]['name']\\n body = self.ports_client.create_bulk_ports(ports=port_list)\\n created_ports = body['ports']\\n self.addCleanup(self._delete_ports, created_ports)\\n # Asserting that the ports are found in the list after creation\\n body = self.ports_client.list_ports()\\n ports_list = [port['id'] for port in body['ports']]\\n for n in created_ports:\\n self.assertIsNotNone(n['id'])\\n self.assertIn(n['id'], ports_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d87ebf9527a82afadb0a043e6f92f73\",\n \"score\": \"0.54313594\",\n \"text\": \"def test_show_subnet(self):\\n body = self.subnets_client.show_subnet(self.subnet['id'])\\n subnet = body['subnet']\\n self.assertNotEmpty(subnet, \\\"Subnet returned has no fields\\\")\\n for key in ['id', 'cidr']:\\n self.assertIn(key, subnet)\\n self.assertEqual(subnet[key], self.subnet[key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d036b4d48dd42603a7e3178f12a3fc81\",\n \"score\": \"0.5422875\",\n \"text\": \"def run(**kw):\\n rbd_obj = initial_rbd_config(**kw)\\n if rbd_obj:\\n log.info(\\\"Executing test on Replication pool\\\")\\n if rbd_clone_delete_parent_image(\\n rbd_obj.get(\\\"rbd_reppool\\\"), \\\"rep_pool_config\\\", **kw\\n ):\\n return 1\\n log.info(\\\"Executing test on EC pool\\\")\\n if rbd_clone_delete_parent_image(\\n rbd_obj.get(\\\"rbd_ecpool\\\"), \\\"ec_pool_config\\\", **kw\\n ):\\n return 1\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35c19dd09ac48a7100fe88f06a744b4d\",\n \"score\": \"0.5421959\",\n \"text\": \"def test_delete_two_cidr_success(self):\\n\\n # Does post request\\n response = self.client.delete(\\n '/api/v3/cidr/4;6/',\\n content_type='application/json')\\n\\n self.compare_status(200, response.status_code)\\n\\n # Does get request\\n response = self.client.get(\\n '/api/v3/cidr/4;6/',\\n content_type='application/json')\\n\\n self.compare_status(400, response.status_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e181f9c3ae6e5101f1b32c6e87075a47\",\n \"score\": \"0.54202074\",\n \"text\": \"def test_create_long_network_LINUX_11442(self):\\n try:\\n #\\n # create vnic\\n vnic_name_b = self.vnic_name_prefix + uuid.uuid4().hex\\n cmd = [self.oci_network_path, 'attach-vnic',\\n '--name', vnic_name_b]\\n create_vnic_data = _run_step('Create VNIC', cmd)\\n self.assertIn('Creating', create_vnic_data, 'attach vnic failed')\\n #\\n # show\\n time.sleep(self.waittime)\\n cmd = [self.oci_network_path, 'show',\\n '--details',\\n '--output-mode', 'table']\\n vnic_data = _run_step('VNIC data', cmd)\\n #\\n # create kvm bridge\\n kvm_network_name = self.kvm_net_prefix + uuid.uuid4().hex\\n kvm_net_ip = self._get_vnic_data('vnic', vnic_name_b, 'ipaddress')\\n cmd = [self.oci_kvm_path, 'create-network',\\n '--network-name', kvm_network_name,\\n '--net', kvm_net_ip,\\n '--ip-bridge', self.ip_bridge,\\n '--ip-start', self.ip_start,\\n '--ip-end', self.ip_end,\\n '--ip-prefix', self.ip_prefix]\\n create_kvm_bridge_data = _run_step('Create KVM network', cmd)\\n self.assertIn('characters', create_kvm_bridge_data, '%s should have failed' % cmd)\\n time.sleep(self.waittime)\\n except Exception as e:\\n self.fail('Execution of create kvm network has failed: %s' % str(e))\\n finally:\\n # clean up\\n #\\n # delete kvm bridge\\n try:\\n cmd = [self.oci_kvm_path, 'delete-network',\\n '--network-name', kvm_network_name,\\n '--yes']\\n delete_kvm_bridge_data = _run_step('Delete KVM network', cmd)\\n time.sleep(self.waittime)\\n except Exception as e:\\n pass\\n #\\n # delete vnic\\n try:\\n cmd = [self.oci_network_path, 'detach-vnic',\\n '--ip-address', kvm_net_ip]\\n delete_vnic_data = _run_step('Delete VNIC', cmd)\\n except Exception as e:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39e400c1d16c9c261343a564559ba96\",\n \"score\": \"0.53987324\",\n \"text\": \"def cleanup_pools():\\n\\n for pool in StoragePool.all():\\n if not _is_test_name(pool):\\n continue\\n pool.delete()\\n\\n assert not [p for p in StoragePool.all() if _is_test_name(p)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d51a2c1f67116b3b9bee633c68593a92\",\n \"score\": \"0.53894895\",\n \"text\": \"def test_createSharedNetwork_usedVlan(self):\\n\\n # Steps,\\n # 1. create an Admin account\\n # 2. create a shared NetworkOffering\\n # 3. enable the network offering\\n # 4. listPhysicalNetworks\\n # - vlan = guest VLAN range = 10-90 (say)\\n # 5. createNetwork\\n # - name = mysharednetwork, displaytext = mysharednetwork\\n # - vlan = any vlan between 10-90\\n # - networkofferingid = <mysharedoffering>\\n # - gw = 172.16.15.1, startip = 172.16.15.2 , endip = 172.16.15.200\\n # - netmask=255.255.255.0\\n # - scope = all\\n # 6. delete admin account\\n # Validations,\\n # 1. listAccounts state=enabled returns your account\\n # 2. listNetworkOfferings - name=mysharedoffering , should list\\n # offering in disabled state\\n # 3. listNetworkOfferings - name=mysharedoffering, should list\\n # enabled offering\\n # 4. listPhysicalNetworks should return at least one active\\n # physical network\\n # 5. network creation should FAIL since VLAN is used for\\n # guest networks\\n\\n # Create admin account\\n self.admin_account = Account.create(\\n self.api_client,\\n self.testdata[\\\"account\\\"],\\n admin=True,\\n domainid=self.domain.id\\n )\\n\\n self.cleanup_accounts.append(self.admin_account)\\n\\n # verify that the account got created with state enabled\\n list_accounts_response = Account.list(\\n self.api_client,\\n id=self.admin_account.id,\\n listall=True\\n )\\n self.assertEqual(\\n isinstance(list_accounts_response, list),\\n True,\\n \\\"listAccounts returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_accounts_response),\\n 0,\\n \\\"listAccounts returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_accounts_response[0].state,\\n \\\"enabled\\\",\\n \\\"The admin account created is not enabled.\\\"\\n )\\n\\n self.debug(\\\"Domain admin account created: %s\\\" % self.admin_account.id)\\n\\n physical_network, shared_vlan = get_free_vlan(\\n self.api_client, self.zone.id)\\n if shared_vlan is None:\\n self.fail(\\\"Failed to get free vlan id for shared network\\\")\\n\\n self.testdata[\\\"shared_network_offering\\\"][\\\"specifyVlan\\\"] = \\\"True\\\"\\n self.testdata[\\\"shared_network_offering\\\"][\\\"specifyIpRanges\\\"] = \\\"True\\\"\\n\\n # Create Network Offering\\n self.shared_network_offering = NetworkOffering.create(\\n self.api_client,\\n self.testdata[\\\"shared_network_offering\\\"],\\n conservemode=False\\n )\\n\\n # Verify that the network offering got created\\n list_network_offerings_response = NetworkOffering.list(\\n self.api_client,\\n id=self.shared_network_offering.id\\n )\\n self.assertEqual(\\n isinstance(list_network_offerings_response, list),\\n True,\\n \\\"listNetworkOfferings returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_network_offerings_response),\\n 0,\\n \\\"listNetworkOfferings returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_network_offerings_response[0].state,\\n \\\"Disabled\\\",\\n \\\"The network offering created should be bydefault disabled.\\\"\\n )\\n\\n self.debug(\\n \\\"Shared Network Offering created: %s\\\" %\\n self.shared_network_offering.id)\\n\\n # Update network offering state from disabled to enabled.\\n NetworkOffering.update(\\n self.shared_network_offering,\\n self.api_client,\\n id=self.shared_network_offering.id,\\n state=\\\"enabled\\\"\\n )\\n\\n # Verify that the state of the network offering is updated\\n list_network_offerings_response = NetworkOffering.list(\\n self.api_client,\\n id=self.shared_network_offering.id\\n )\\n self.assertEqual(\\n isinstance(list_network_offerings_response, list),\\n True,\\n \\\"listNetworkOfferings returned invalid object in response.\\\"\\n )\\n self.assertNotEqual(\\n len(list_network_offerings_response),\\n 0,\\n \\\"listNetworkOfferings returned empty list.\\\"\\n )\\n self.assertEqual(\\n list_network_offerings_response[0].state,\\n \\\"Enabled\\\",\\n \\\"The network offering state should get updated to Enabled.\\\"\\n )\\n\\n # create network using the shared network offering created\\n self.testdata[\\\"shared_network\\\"][\\\"vlan\\\"] = str.split(\\n str(physical_network.vlan), \\\"-\\\")[0]\\n self.testdata[\\\"shared_network\\\"][\\\"acltype\\\"] = \\\"domain\\\"\\n self.testdata[\\\"shared_network\\\"][\\n \\\"networkofferingid\\\"] = self.shared_network_offering.id\\n self.testdata[\\\"shared_network\\\"][\\n \\\"physicalnetworkid\\\"] = physical_network.id\\n self.testdata[\\\"shared_network\\\"][\\\"vlan\\\"] = shared_vlan\\n\\n try:\\n self.network = Network.create(\\n self.api_client,\\n self.testdata[\\\"shared_network\\\"],\\n networkofferingid=self.shared_network_offering.id,\\n zoneid=self.zone.id,\\n )\\n self.fail(\\n \\\"Network created with used vlan %s id, which is invalid\\\" %\\n shared_vlan)\\n except Exception as e:\\n self.debug(\\n \\\"Network creation failed because the valn id being used by\\\\\\n another network. Exception: %s\\\" % e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf8514f24de179e4e1e075cb7e1a0513\",\n \"score\": \"0.5379174\",\n \"text\": \"def subnet_save(context, subnet):\\n\\n return IMPL.subnet_save(context, subnet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4adff772cbcb58977cacc9caf0074431\",\n \"score\": \"0.5376052\",\n \"text\": \"def test_add_bulk_pool_nodes_then_list(self):\\n self.helper.clock.advance(50)\\n add_data = self._get_add_nodes_json()\\n add_response, _ = self.successResultOf(self.json_request(\\n b\\\"POST\\\", \\\"/load_balancer_pools/nodes\\\", body=add_data))\\n self.assertEqual(201, add_response.code)\\n\\n _, list_json = self.successResultOf(self.json_request(\\n b\\\"GET\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id)))\\n self._check_added_nodes_result(50, add_data, list_json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9679461750e4b5c9aaf929456417549\",\n \"score\": \"0.53745955\",\n \"text\": \"def build_share_network_subnet_create_check(self, request, result):\\n context = request.environ['manila.context']\\n view = {\\n 'compatible': result['compatible'],\\n }\\n if context.is_admin:\\n view['hosts_check_result'] = result['hosts_check_result']\\n return view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ebe37663cfbfbff0b1b180934cda230\",\n \"score\": \"0.53626186\",\n \"text\": \"def test_new_sc_new_rbd_pool_e2e_wl(\\n self,\\n storageclass_factory,\\n amq_factory_fixture,\\n pgsql_factory_fixture,\\n replica,\\n compression,\\n ):\\n interface_type = constants.CEPHBLOCKPOOL\\n sc_obj = storageclass_factory(\\n interface=interface_type,\\n new_rbd_pool=True,\\n replica=replica,\\n compression=compression,\\n )\\n bg_handler = flowtest.BackgroundOps()\\n executor_run_bg_ios_ops = ThreadPoolExecutor(max_workers=5)\\n self.amq, self.threads = amq_factory_fixture(sc_name=sc_obj.name)\\n\\n pgsql_workload = executor_run_bg_ios_ops.submit(\\n bg_handler.handler,\\n pgsql_factory_fixture,\\n replicas=1,\\n clients=1,\\n transactions=100,\\n timeout=100,\\n sc_name=sc_obj.name,\\n iterations=1,\\n )\\n bg_handler = flowtest.BackgroundOps()\\n bg_ops = [pgsql_workload]\\n bg_handler.wait_for_bg_operations(bg_ops, timeout=3600)\\n # AMQ Validate the results\\n log.info(\\\"Validate message run completely\\\")\\n for thread in self.threads:\\n thread.result(timeout=1800)\\n\\n cluster_used_space = get_percent_used_capacity()\\n log.info(\\n f\\\" Cluster used percentage space with replica size {replica}, \\\"\\n f\\\"compression mode {compression}={cluster_used_space}\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"214993c99b340fc887244d7f71842a44\",\n \"score\": \"0.53611493\",\n \"text\": \"def test_delete_two_cidr_inexistent_error(self):\\n\\n # Does post request\\n response = self.client.delete(\\n '/api/v3/cidr/1000;1001/',\\n content_type='application/json')\\n\\n # Tests code returned\\n self.compare_status(400, response.status_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c79e409bc62223a8eb43cecbfd2d9d4\",\n \"score\": \"0.5360599\",\n \"text\": \"def create_subnets(ctx, from_file):\\n subnet = {}\\n subnet['name'] = \\\"10.230.0.0\\\"\\n subnet['network'] = \\\"10.230.0.0\\\"\\n subnet['mask'] = \\\"255.255.255.252\\\"\\n subnet['gateway'] = \\\"10.230.0.1\\\"\\n subnet['dns_primary'] = \\\"10.230.0.1\\\"\\n subnet['ipam'] = \\\"DHCP\\\"\\n #subnet['domain_ids'] = [2649, 2701, 2702,]\\n #subnet['dhcp_ids'] = 8\\n #subnet['tftp_id'] = 8\\n #subnet['dns_id'] = 8\\n subnet['boot_mode'] = \\\"DHCP\\\"\\n\\n # Not using this, because I can't get it to associate with a domain.\\n subnet['domain_ids'] = [2649,]\\n\\n from pprint import pprint\\n pprint(subnet); \\n output = make_request(subnet_endpoint, data=subnet, content_json=True, request_type='post')\\n print output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54a56beff2c4cb7704ff5d503b15eb0d\",\n \"score\": \"0.5358008\",\n \"text\": \"def test_create_bigip_member(pool, bigip_members):\\n member_cfg = bigip_members[0]\\n\\n pp(bigip_members)\\n pp(member_cfg)\\n # pdb.set_trace()\\n member = IcrPoolMember(\\n pool=pool,\\n **member_cfg\\n )\\n\\n assert member\\n\\n # Test data\\n assert member.data\\n assert member.data['name'] == \\\"192.168.200.2:80\\\"\\n assert member.data['ratio'] == 1\\n assert member.data['connectionLimit'] == 0\\n assert member.data['priorityGroup'] == 0\\n assert member.data['session'] == \\\"user-enabled\\\"\\n assert not member.data['description']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74db01edcdfe42846460369453b7f5c9\",\n \"score\": \"0.5356684\",\n \"text\": \"def test_pool_size() -> None:\\n assert len(Match._registry) == 0\\n match = Match()\\n match.create_deck(card_set)\\n match.put_in_pool()\\n assert len(Match._registry) == 1\\n Match.perform_housekeeping()\\n assert len(Match._registry) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9cb9784cbc22c925ff93e3619115eca\",\n \"score\": \"0.5350776\",\n \"text\": \"def check_overlapping_subnets(config):\\n if is_calico_flavor(config[\\\"flavor\\\"]):\\n subnet_info = {\\n \\\"cluster_svc_subnet\\\": config[\\\"net_config\\\"][\\\"cluster_svc_subnet\\\"]\\n }\\n else:\\n subnet_info = {\\n \\\"node_svc_subnet\\\": config[\\\"net_config\\\"][\\\"node_svc_subnet\\\"]\\n }\\n\\n counter = 0\\n\\n if not isinstance(config[\\\"net_config\\\"][\\\"pod_subnet\\\"], list):\\n subnet_info[-1] = config[\\\"net_config\\\"][\\\"pod_subnet\\\"]\\n else:\\n pod_subnets = []\\n for pod_subnet in config[\\\"net_config\\\"][\\\"pod_subnet\\\"]:\\n pod_subnets.append(pod_subnet)\\n for pod_subnet in pod_subnets:\\n subnet_info[counter] = pod_subnet\\n counter += 1\\n\\n if not isinstance(config[\\\"net_config\\\"][\\\"node_subnet\\\"], list):\\n subnet_info[-1] = config[\\\"net_config\\\"][\\\"node_subnet\\\"]\\n else:\\n node_subnets = []\\n for node_subnet in config[\\\"net_config\\\"][\\\"node_subnet\\\"]:\\n node_subnets.append(node_subnet)\\n # counter = 0\\n for node_subnet in node_subnets:\\n subnet_info[counter] = node_subnet\\n counter += 1\\n\\n if not isinstance(config[\\\"net_config\\\"][\\\"extern_dynamic\\\"], list):\\n subnet_info[-1] = config[\\\"net_config\\\"][\\\"extern_dynamic\\\"]\\n else:\\n extern_dynamics = []\\n for extern_dynamic in config[\\\"net_config\\\"][\\\"extern_dynamic\\\"]:\\n extern_dynamics.append(extern_dynamic)\\n # counter = 0\\n for extern_dynamic in extern_dynamics:\\n subnet_info[counter] = extern_dynamic\\n counter += 1\\n\\n # Don't have extern_static field set for OpenShift flavors\\n if not is_calico_flavor(config[\\\"flavor\\\"]) and config[\\\"net_config\\\"][\\\"extern_static\\\"]:\\n if not isinstance(config[\\\"net_config\\\"][\\\"extern_static\\\"], list):\\n subnet_info[-1] = config[\\\"net_config\\\"][\\\"extern_static\\\"]\\n else:\\n extern_statics = []\\n for subnet in config[\\\"net_config\\\"][\\\"extern_static\\\"]:\\n extern_statics.append(subnet)\\n # counter = 0\\n for subnet in extern_statics:\\n subnet_info[counter] = subnet\\n counter += 1\\n\\n for sub1, sub2 in combinations(subnet_info.values(), r=2):\\n # Checking if sub1 and sub2 are IPv4 or IPv6\\n rtr1, _ = sub1.split(\\\"/\\\")\\n ip1 = ipaddress.ip_address(rtr1)\\n rtr2, _ = sub2.split(\\\"/\\\")\\n ip2 = ipaddress.ip_address(rtr2)\\n if ip1.version == 4 and ip2.version == 4:\\n net1, net2 = ipaddress.IPv4Network(sub1, strict=False), ipaddress.IPv4Network(sub2, strict=False)\\n elif ip1.version == 6 and ip2.version == 6:\\n net1, net2 = ipaddress.IPv6Network(sub1, strict=False), ipaddress.IPv6Network(sub2, strict=False)\\n else:\\n continue\\n out = net1.overlaps(net2)\\n if out:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e606dc5eaf62489aaadca4c80b4985\",\n \"score\": \"0.5350735\",\n \"text\": \"def create_network(request, id_vlan=\\\"0\\\", sf_number='0', sf_name='0', sf_environment='0', sf_nettype='0',\\n sf_subnet='0', sf_ipversion='0', sf_network='0', sf_iexact='0', sf_acl='0'):\\n try:\\n if request.method == 'POST':\\n\\n form = CreateForm(request.POST)\\n\\n # Get user\\n auth = AuthSession(request.session)\\n client = auth.get_clientFactory()\\n\\n networks_activated = False\\n networks_was_activated = True\\n\\n equipments_ipv4 = list()\\n equipments_ipv6 = list()\\n\\n if form.is_valid():\\n\\n # If vlan with parameter id_vlan don't exist,\\n # VlanNaoExisteError exception will be called\\n vlan = client.create_vlan().get(id_vlan)\\n\\n environment = client.create_ambiente().buscar_por_id(\\n vlan['vlan'][\\\"ambiente\\\"]).get(\\\"ambiente\\\")\\n\\n # All ids to be activated\\n ids = split_to_array(form.cleaned_data['ids_create'])\\n\\n for id in ids:\\n value = id.split('-')\\n id_net = value[0]\\n network_type = value[1]\\n\\n if network_type == 'v4':\\n net = client.create_network().get_network_ipv4(id_net)\\n else:\\n net = client.create_network().get_network_ipv6(id_net)\\n\\n if net['network']['active'] == 'True':\\n networks_activated = True\\n else:\\n networks_was_activated = False\\n\\n if network_type == 'v4':\\n equipments_ipv4.extend(\\n list_equipment_by_network_ip4(client, id_net))\\n\\n client.create_api_network_ipv4().deploy(id_net)\\n else:\\n equipments_ipv6.extend(\\n list_equipment_by_network_ip6(client, id_net))\\n\\n client.create_api_network_ipv6().deploy(id_net)\\n\\n apply_acl_for_network_v4(\\n request, client, equipments_ipv4, vlan, environment)\\n\\n apply_acl_for_network_v6(\\n request, client, equipments_ipv6, vlan, environment)\\n\\n if networks_activated is True:\\n messages.add_message(\\n request, messages.ERROR, network_ip_messages.get(\\\"networks_activated\\\"))\\n\\n if networks_was_activated is False:\\n messages.add_message(\\n request, messages.SUCCESS, network_ip_messages.get(\\\"net_create_success\\\"))\\n\\n else:\\n vlan = client.create_vlan().get(id_vlan)\\n if vlan['vlan']['ativada'] == 'True':\\n messages.add_message(\\n request, messages.ERROR, error_messages.get(\\\"vlan_select_one\\\"))\\n else:\\n messages.add_message(\\n request, messages.ERROR, error_messages.get(\\\"select_one\\\"))\\n\\n except VlanNaoExisteError as e:\\n logger.error(e)\\n return redirect('vlan.search.list')\\n except Exception as e:\\n logger.error(e)\\n messages.add_message(request, messages.ERROR, e)\\n\\n return HttpResponseRedirect(reverse('vlan.list.by.id', args=[id_vlan, sf_number, sf_name, sf_environment,\\n sf_nettype, sf_subnet, sf_ipversion, sf_network,\\n sf_iexact, sf_acl]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efe615f554c62783c14229c1c5562eae\",\n \"score\": \"0.53500634\",\n \"text\": \"def test_allocate_deallocate_fixed_ip(self):\\n address = self._create_address(0)\\n self.assertTrue(self._is_allocated_in_project(address,\\n self.projects[0].id))\\n self._deallocate_address(0, address)\\n\\n # check if the fixed ip address is really deallocated\\n self.assertFalse(self._is_allocated_in_project(address,\\n self.projects[0].id))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e295299d3324063d91ca6e2e1cd09b14\",\n \"score\": \"0.5342462\",\n \"text\": \"def test_add_ip_association(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ad1d9e6f99bb975644f274cca799fd\",\n \"score\": \"0.53379923\",\n \"text\": \"def remove_network(request, id_vlan=\\\"0\\\", sf_number='0', sf_name='0', sf_environment='0', sf_nettype='0',\\n sf_subnet='0', sf_ipversion='0', sf_network='0', sf_iexact='0', sf_acl='0'):\\n try:\\n if request.method == 'POST':\\n\\n form = RemoveForm(request.POST)\\n\\n # Get user\\n auth = AuthSession(request.session)\\n client = auth.get_clientFactory()\\n\\n networks_activated = False\\n networks_was_activated = True\\n\\n equipments_ipv4 = list()\\n equipments_ipv6 = list()\\n\\n if form.is_valid():\\n\\n # If vlan with parameter id_vlan don't exist,\\n # VlanNaoExisteError exception will be called\\n vlan = client.create_vlan().get(id_vlan)\\n\\n environment = client.create_ambiente().buscar_por_id(\\n vlan['vlan'][\\\"ambiente\\\"]).get(\\\"ambiente\\\")\\n\\n # All ids to be activated\\n ids = split_to_array(form.cleaned_data['ids_remove'])\\n\\n for id in ids:\\n value = id.split('-')\\n id_net = value[0]\\n network_type = value[1]\\n\\n if network_type == 'v4':\\n net = client.create_network().get_network_ipv4(id_net)\\n else:\\n net = client.create_network().get_network_ipv6(id_net)\\n\\n if net['network']['active'] == 'True':\\n networks_activated = True\\n else:\\n networks_was_activated = False\\n\\n if network_type == 'v4':\\n equipments_ipv4.extend(\\n list_equipment_by_network_ip4(client, id_net))\\n\\n client.create_api_network_ipv4().undeploy(id_net)\\n else:\\n equipments_ipv6.extend(\\n list_equipment_by_network_ip6(client, id_net))\\n\\n client.create_api_network_ipv6().undeploy(id_net)\\n\\n if networks_activated is False:\\n messages.add_message(\\n request, messages.ERROR, network_ip_messages.get(\\\"networks_deactivated\\\"))\\n\\n if networks_was_activated is True:\\n messages.add_message(\\n request, messages.SUCCESS, network_ip_messages.get(\\\"net_remove_success\\\"))\\n\\n else:\\n vlan = client.create_vlan().get(id_vlan)\\n if vlan['vlan']['ativada'] == 'False':\\n messages.add_message(\\n request, messages.ERROR, error_messages.get(\\\"vlan_select_one\\\"))\\n else:\\n messages.add_message(\\n request, messages.ERROR, error_messages.get(\\\"select_one\\\"))\\n\\n except VlanNaoExisteError as e:\\n logger.error(e)\\n return redirect('vlan.search.list')\\n except Exception as e:\\n logger.error(e)\\n messages.add_message(request, messages.ERROR, e)\\n\\n return HttpResponseRedirect(reverse('vlan.list.by.id', args=[id_vlan, sf_number, sf_name, sf_environment,\\n sf_nettype, sf_subnet, sf_ipversion, sf_network,\\n sf_iexact, sf_acl]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7491790095274acba070e25927ad2ae3\",\n \"score\": \"0.53265524\",\n \"text\": \"def create_subnet(compute, project, region, name, ipcidr, network_link):\\n body = {\\n 'privateIpGoogleAccess': False,\\n 'name': name,\\n 'ipCidrRange': ipcidr,\\n 'network': network_link\\n }\\n return compute.subnetworks().insert(project=project, region=region,\\n body=body).execute()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bde213e4e0e1fa81737ebc968241571\",\n \"score\": \"0.5283236\",\n \"text\": \"def net_request(service, svc_xpath, username,\\n pool_name, allocation_name, cidrmask, invert_cidr=False):\\n template = ncs.template.Template(service)\\n vars = ncs.template.Variables()\\n vars.add(\\\"POOL\\\", pool_name)\\n vars.add(\\\"ALLOCATIONID\\\", allocation_name)\\n vars.add(\\\"USERNAME\\\", username)\\n vars.add(\\\"SERVICE\\\", svc_xpath)\\n vars.add(\\\"SUBNET_SIZE\\\", cidrmask)\\n vars.add(\\\"INVERT\\\", \\\"true\\\" if invert_cidr else \\\"\\\")\\n template.apply('resource-manager-ipaddress-allocation', vars)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3052816c8979ac986ca2023e3188f464\",\n \"score\": \"0.52786934\",\n \"text\": \"def test_delete_cidr_by_env_success(self):\\n\\n # Does post request\\n response = self.client.delete(\\n '/api/v3/cidr/environment/1/',\\n content_type='application/json')\\n\\n self.compare_status(200, response.status_code)\\n\\n # Does get request\\n response = self.client.get(\\n '/api/v3/cidr/2;3/',\\n content_type='application/json')\\n\\n self.compare_status(400, response.status_code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44fbec08576245729da9ff729a6b4cbd\",\n \"score\": \"0.52769566\",\n \"text\": \"def test_add_pool_node_unimplemented(self):\\n response, content = self.successResultOf(self.request_with_content(\\n b\\\"POST\\\", \\\"/load_balancer_pools/{0}/nodes\\\".format(self.pool_id),\\n body=json.dumps({\\n \\\"cloud_server\\\": {\\\"id\\\": \\\"d95ae0c4-6ab8-4873-b82f-f8433840cff2\\\"}\\n }).encode(\\\"utf-8\\\")))\\n self.assertEqual(501, response.code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66f8c7df8d01f01cd4e88d3bca90c08f\",\n \"score\": \"0.5272971\",\n \"text\": \"def test_db_constraints(self):\\n self.assertEqual(self._inspre('1.3.0.0/16', 'reservation'), True, 'Unable to insert prefix 1.3.0.0/16')\\n self.assertRaises(nipap.nipap.NipapDuplicateError, self._inspre, '1.3.0.0/16', 'reservation') # Duplicate prefix detection not working\\n self.assertEqual(self._inspre('1.3.3.0/24', 'reservation'), True)\\n self.assertEqual(self._inspre('1.3.3.0/27', 'assignment'), True)\\n self.assertEqual(self._inspre('1.3.3.0/32', 'host'), True)\\n self.assertEqual(self._inspre('1.3.3.1/32', 'host'), True)\\n self.assertRaises(nipap.nipap.NipapValueError, self._inspre, '1.3.3.2/31', 'host') # do not allow /31 as type 'host'\\n self.assertRaises(nipap.nipap.NipapValueError, self._inspre, '1.3.3.3/32', 'assignment') # Able to create assignment within assignment - we should not\\n self.assertRaises(nipap.nipap.NipapValueError, self._delpre, '1.3.3.0/27') # Able to delete assignment containing hosts - we should not\\n self.assertRaises(nipap.nipap.NipapValueError, self._updpre, '1.3.3.0/24', 'assignment')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e871bd14b0bf67e09b17f1de72704f6\",\n \"score\": \"0.526662\",\n \"text\": \"def test_remove_pool_node_unimplemented(self):\\n response, content = self.successResultOf(self.request_with_content(\\n b\\\"DELETE\\\", \\\"/load_balancer_pools/{0}/nodes/1\\\".format(self.pool_id)\\n ))\\n self.assertEqual(501, response.code)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c7b0697df7038e1e5d7ea3766bc63ed\",\n \"score\": \"0.526566\",\n \"text\": \"def verifyExistingTxnMempool(self, n, addrsbch, name=\\\"hub\\\", count=4):\\n if (name in n.clientName):\\n # validate txns in mempool from other clients\\n mp = [ x.getmempoolinfo()[\\\"size\\\"] for x in self.nodes]\\n print(\\\"memory pools are %s\\\" % str(mp))\\n txn_count = count-1\\n assert mp == [txn_count, txn_count, txn_count, txn_count], \\\"transaction was not relayed to all nodes %s\\\" % str(mp)\\n\\n # only pick up hub addr to use the generate interface which requires public key\\n coinbase0 = [x for x in addrsbch if len(x.split(':'))==1]\\n pubkey = n.validateaddress(coinbase0[0])['pubkey']\\n # generate enough blocks so that hub client node has a balance\\n n.generate(200, pubkey) # hub need blocks and public addr\\n self.sync_blocks()\\n\\n # after mined block and mempool should be cleared\\n mp = [ x.getmempoolinfo()[\\\"size\\\"] for x in self.nodes]\\n print(\\\"memory pools are %s\\\" % str(mp))\\n assert mp == [0, 0, 0, 0], \\\"transaction was not relayed to all nodes %s\\\" % str(mp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b744d833285a68d2578efb90a2c9a8c2\",\n \"score\": \"0.52650785\",\n \"text\": \"def test_create_pool_member(pool, bigip_members):\\n member_cfg = bigip_members[0]\\n\\n member = PoolMember(\\n pool=pool,\\n **member_cfg\\n )\\n\\n assert member\\n assert member._pool\\n\\n # Test data\\n assert member.data\\n assert member.data['name'] == \\\"192.168.200.2:80\\\"\\n assert member.data['ratio'] == 1\\n assert member.data['connectionLimit'] == 0\\n assert member.data['priorityGroup'] == 0\\n assert member.data['session'] == \\\"user-enabled\\\"\\n assert not member.data['description']\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1697,"cells":{"query_id":{"kind":"string","value":"eb29bbd417a4e579073ff643eec19a99"},"query":{"kind":"string","value":"Hints that we don't want a blank line before the next statement."},"positive_passages":{"kind":"list like","value":[{"docid":"d34dc89189a06ab496cc7a221d4ef6ef","score":"0.0","text":"def stick(self):\n self._in_group = True","title":""}],"string":"[\n {\n \"docid\": \"d34dc89189a06ab496cc7a221d4ef6ef\",\n \"score\": \"0.0\",\n \"text\": \"def stick(self):\\n self._in_group = True\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"6f698b7c22a9db644bfffee571ec3470","score":"0.68750113","text":"def emptyline(self):\n pass","title":""},{"docid":"1a326ba446e1570feeb998b5aa9c6b8e","score":"0.6862263","text":"def skip_over_next_blank(self):\n while self.readline().strip(): pass","title":""},{"docid":"593de671795a6c349c0831a1853bece5","score":"0.66916424","text":"def emptyline(self):\n return True","title":""},{"docid":"7c8003172bf0a229fd79533338c8b408","score":"0.66434944","text":"def emptyline(self):\n self.repeat_last_nonempty_command = is_step_mode()\n return super(DebugCmd, self).emptyline()","title":""},{"docid":"1825a1bc66ce657265b9c4f2a96a03c3","score":"0.639368","text":"def emptyline(self):\n return False","title":""},{"docid":"97583ca3f496a7a65e98e7313297f9f5","score":"0.6254386","text":"def test_indented_line_should_not_be_blank(self):\n pp = ProjectParser()\n line = \"\\ttest\"\n assert pp.is_blank(line) is False","title":""},{"docid":"842f75c12d7ea3cc9090e19a148e873f","score":"0.6202605","text":"def test_empty_multiline():\n Lines = ['','','Title', 'cmd part 3', 'info']\n assert multiline_start_check(Lines, 1, False) == False","title":""},{"docid":"28323cc493efb0efc6364bfc8cc57d16","score":"0.61700827","text":"def test_comments_not_multiline():\n Lines = ['',\n 'Remember: TAB completion works in many contexts, not just file names',\n 'or python names.', '']\n assert multiline_start_check(Lines, 1, True) == False","title":""},{"docid":"c0bdbafef2c51fdb26c68131750a9fd2","score":"0.6153704","text":"def eol():\n p('')","title":""},{"docid":"e7f3ea03b8eac461ee7b941b48b61f82","score":"0.61497724","text":"def spnl(self):\n self.match(re.compile(r'^ *(?:\\n *)?'))\n return 1","title":""},{"docid":"73c72df93371825ef550694cdd229660","score":"0.6110715","text":"def first_line(self):\n return ''","title":""},{"docid":"dc4b3588e702b5e03c7bff820daa2d42","score":"0.5999922","text":"def test_example_8_12__empty_separation_lines():\n\n text = dedent(\"\"\"\n >\n \n folded\n line \n \n next\n line \n * bullet\n \n * list\n * line \n \n last\n line\n \n # Comment\n \n \"\"\")[1:-1]\n \n expected = Docs( # :off\n Doc(\n Str(' folded line \\nnext line * bullet\\n * list * line \\nlast line\\n'),\n ),\n ) # :on\n \n nodes = parser.parse(text)\n print_nodes(nodes)\n \n assert nodes == expected","title":""},{"docid":"1694c3b29727991897e54074c647b921","score":"0.5997253","text":"def check_blank_lines(reporter, raw):\n\n lines = [(i, x) for (i, x) in enumerate(\n raw.strip().split('\\n')) if not x.strip()]\n reporter.check(not lines,\n None,\n 'Blank line(s) in header: {0}',\n ', '.join([\"{0}: {1}\".format(i, x.rstrip()) for (i, x) in lines]))","title":""},{"docid":"515b3dadadfd8fc4d38f54c016be44f9","score":"0.5981519","text":"def match_blank(self, line):\n return False","title":""},{"docid":"5db14a0dd620d1935c4bf36edbc9a912","score":"0.59137326","text":"def exhaust_till_comment_or_line_break(parser_state):\n return repeat_exhauster(parser_state, exhaust_non_comment_or_line_break)","title":""},{"docid":"f4392db6c0cc6ae7a3ffce78a62d0b8e","score":"0.5819575","text":"def __semicolon(self, stmt, no_newline = False):\n\t\tif stmt.__class__.__name__ not in self.NO_SEMICOLON:\n\t\t\tif no_newline:\n\t\t\t\tself.__write(\";\")\n\t\t\telse:\n\t\t\t\tself.__write(\";\\n\")","title":""},{"docid":"bfc4b544cd08af101ee62e5a8443f9a8","score":"0.58184564","text":"def test_no_text_before_record_definition(self, src):\n self._parse_fails(src, line_number=3)","title":""},{"docid":"f36d092bf13ec27cced1a82c0627170a","score":"0.5817698","text":"def exhaust_non_comment_or_line_break(parser_state):\n return exhaust_if(parser_state, is_character_non_comment_or_line_break)","title":""},{"docid":"7056c7045d85123e2e43cc7fdf204b53","score":"0.5797453","text":"def t_ANY_newline(self, token: lex.LexToken) -> None:\n token.lexer.lineno += len(token.value)\n token.lexer.begin('INITIAL')","title":""},{"docid":"f404ab083fea248f2b6e3e9083ebd39d","score":"0.5788991","text":"def skip_to_nonblank(self):\n pos = self._file.tell()\n while not self.readline().strip(): pos = self._file.tell()\n self._file.seek(pos)","title":""},{"docid":"93a318ff95f60cbfa26d8e816b4003a8","score":"0.57727695","text":"def emptyline(self):\n msg.info(\"No command entered. Type help or ? for command listing.\")","title":""},{"docid":"39fc6845bbaef8281ad343c861e7eb3f","score":"0.57686543","text":"def exhaust_line_break_all(parser_state):\n return repeat_exhauster(parser_state, exhaust_line_break)","title":""},{"docid":"c984b9d07bef33182693c0c8d0dbdf80","score":"0.57638854","text":"def test_blank_note_line():\n blank_note_line = \"* Sea-Bird SBE9 Data File:\\n*\\n* \\n# name 3 = depSM\\n# file_type = ascii\\n*END*\\n1\\n\"\n from seabird.utils import load_rule\n\n load_rule(blank_note_line)","title":""},{"docid":"ae30ab1c5171dfc5178443b763638f25","score":"0.57391065","text":"def trim_lines(self):\n # get rid of comments\n self.lines = [ln for ln in self.lines if ln[0] is not '#']\n # keep variable assigments (shell and environment)\n self.lines = [ln for ln in self.lines if\n ('=' in ln) or ('setenv' in ln)]","title":""},{"docid":"f2be955bab0fc7b829798394a1ffcbd0","score":"0.5723432","text":"def test_force_single_line_shouldnt_remove_preceding_comment_lines_issue_1296():\n test_input = \"\"\"\n# A comment\n# A comment\n\n# Oh no, I'm gone\nfrom moo import foo\n\"\"\"\n # assert isort.code(test_input) == test_input\n assert isort.code(test_input, force_single_line=True) == test_input","title":""},{"docid":"478f1b0b51cb2ca2d37686bc727b0dca","score":"0.57160175","text":"def _do_line_break(self, number_of_rows):\n code_block_1 = self._create_remove_empty_lines(self.lines[:number_of_rows], self.starter, self.ender)\n code_block_2 = self._create_remove_empty_lines(self.lines[number_of_rows:], self.starter, self.ender)\n return code_block_1, code_block_2","title":""},{"docid":"20955e6a288431a3b18a461c8a9e95f6","score":"0.571125","text":"def min_before_use_statements(tokens, curr_token_index, config):\n\n res = {\"min_newlines\": -1}\n\n if len(tokens) == curr_token_index + 1:\n return res\n\n #this line did not start use statements, but next one does\n if (tokens[curr_token_index] == \"SEMICOLON\" and not check_end_of_keyword(tokens, curr_token_index, \"T_USE\")\n and tokens[curr_token_index+1] == \"T_USE\"):\n res[\"min_newlines\"] = int(config[\"Blank Lines\"][\"Before 'Use' statements\"])\n\n return res","title":""},{"docid":"f9a4f4867b9a716d94de783e1653dc6c","score":"0.5702618","text":"def skip_to_blank(self):\n pos = self._file.tell()\n while self.readline().strip(): pos = self._file.tell()\n self._file.seek(pos)","title":""},{"docid":"9f49b0a2688f5c357a20dc6224f66d9a","score":"0.5667318","text":"def test_example_8_13__final_empty_lines():\n\n text = dedent(\"\"\"\n >\n folded\n line\n \n next\n line\n * bullet\n \n * list\n * line\n \n last\n line \n \n # Comment\n \n \"\"\")[1:-1]\n \n expected = Docs( # :off\n Doc(\n Str('folded line\\nnext line * bullet\\n * list * line\\nlast line\\n'),\n ),\n ) # :on\n \n nodes = parser.parse(text)\n print_nodes(nodes)\n \n assert nodes == expected","title":""},{"docid":"75fd26da21fec27f5d12667df440a750","score":"0.56659377","text":"def exhaust_line_break_and_white_space_all(parser_state):\n return chain_exhausters(parser_state, (exhaust_white_space_all, exhaust_line_break_all))","title":""},{"docid":"5250caf303ba501732582cf38a929a9f","score":"0.56654793","text":"def blank_metadata_line(line):\n return (not line) or (_BLANK_LINE.match(line) is not None)","title":""},{"docid":"55b6d9e4d08a1d663406e142ff2d5d9a","score":"0.5661903","text":"def parse_empty_statement(self):\n location = self.consume(';').loc\n return statements.Empty(location)","title":""},{"docid":"3c23a54293f3c58de6c0b818553bac39","score":"0.5661109","text":"def skip_inline(view, point):\n line_reg = view.line(point)\n line = view.substr(sublime.Region(line_reg.a, point)).strip()\n if line == \"\":\n return False\n else:\n return True","title":""},{"docid":"b121849ff089473265b568b40ddc62da","score":"0.56411207","text":"def _test_break(self):\n # At least 4 lines.\n if len(self.lines < 3):\n return False\n \n return True","title":""},{"docid":"a4bc9318a84a798652d131bceb240ae8","score":"0.5635953","text":"def test_parseEmptyLines(self):\n self._doHSPLTest(\"High-DoS-8.csv\", \"landscape1.xml\", 2, [\"TCP\"] * 5, [\"drop\"] * 5)\n self._doHSPLTest(\"High-DoS-8.csv\", \"landscape1.xml\", 2, [\"TCP\"] * 5, [\"limit\"] * 5)","title":""},{"docid":"13df4dfd1a1390700ec39fa259836d9f","score":"0.562965","text":"def advance():\n Parser.current_command = Parser.opened_file.readline()\n while Parser.current_command.startswith(Parser.EMPTY_LINE) or \\\n Parser.remove_whitespace(Parser.current_command).startswith(\n Parser.COMMENT) or \\\n Parser.current_command.startswith(Parser.EMPTY_LINE_2):\n Parser.current_command = Parser.opened_file.readline()\n Parser.current_command = Parser.remove_comments(Parser.current_command)\n Parser.current_command = Parser.remove_whitespace(Parser.current_command)","title":""},{"docid":"1d12902cc36f0cd8d941eca99be75d37","score":"0.56202453","text":"def test_blank_lined_removed_issue_1275():\n assert (\n isort.code(\n '''\"\"\"\nMy docstring\n\"\"\"\n\nfrom b import thing\nfrom a import other_thing\n'''\n )\n == '''\"\"\"\nMy docstring\n\"\"\"\n\nfrom a import other_thing\nfrom b import thing\n'''\n )","title":""},{"docid":"b448a02a1dbfab3a6c25bbb9587c1149","score":"0.5604572","text":"def simple_statement(self):\n\n if (first_statement := self.small_statement()) is not None:\n small_statements = [first_statement]\n\n self.register_revert()\n while self.revertable(\n self.consume_string(\";\") is not None\n and (statement := self.small_statement()) is not None\n ):\n small_statements.append(statement)\n\n self.consume_string(\";\")\n\n return small_statements if len(small_statements) > 1 else first_statement\n\n return None","title":""},{"docid":"c5a3ba90a1c9ef0aed394c1630fcc061","score":"0.56031424","text":"def testNotAtStart(self):\n self.check('GGGGFRRFRRFR',\n '----HHHHHHHH')","title":""},{"docid":"848e7a1baffc88f0f98a7c806c75f76f","score":"0.5600438","text":"def test_example_6_5__empty_lines():\n\n text = dedent(\"\"\"\n Folding:\n \"Empty line\n \n as a line feed\"\n Chomping: |\n Clipped empty lines\n \n \n \"\"\")[1:-1]\n \n expected = None\n \n nodes = parser.parse(text)\n print_nodes(nodes)\n \n assert nodes == expected","title":""},{"docid":"0b11d5957696550848f37ec0a3132146","score":"0.5590883","text":"def test_ignore_one_line_comments(self):\n self.filename = \"parser_tests/python_comments.txt\"\n self.run_parser()\n expected_keywords = []\n self.assertListEqual(expected_keywords, self.p.keywords)","title":""},{"docid":"c06de9f63b2b217d3a90b6fe8efcc96a","score":"0.5585234","text":"def is_during_line_pass(self) -> bool:","title":""},{"docid":"c2c16b180febbad581aeb252fa380a29","score":"0.5581325","text":"def pre_exec(self):\n lines = []\n if self.starts_dstep and not self.ends_dstep:\n lines.append(\"BEGIN_DSTEP()\")\n if self.starts_atomic and not self.ends_atomic:\n lines.append(\"BEGIN_ATOMIC()\")\n return len(lines) and \"; \".join(lines) or None","title":""},{"docid":"96e5a8441b95883fc5db29f936728069","score":"0.5578854","text":"def notCommentLine(label):\n return label == '' or label[0] != '.'","title":""},{"docid":"1b8d7ddbcfc2e9404962b5b68f97fbfb","score":"0.55779546","text":"def _skip_whitespace(self):\n\n while self.current_char is not None and self.current_char.isspace():\n self._advance()","title":""},{"docid":"d75adc73c49164c793e34f9bb6326711","score":"0.5577453","text":"def test_empty_comment_check_7():\n Lines = ['','cmd:', ' explained']\n assert comment_check(Lines,1) == False","title":""},{"docid":"eaf2999759ae853486c4133100d505cb","score":"0.55631554","text":"def skip(self,nn):\n\n logging.debug(\"Skipping these lines\")\n for ii in range(1, nn + 1, 1):\n logging.debug(self.inFile.readline())\n logging.debug(\" \")","title":""},{"docid":"81026edf2260cc2f12936def6f33029f","score":"0.55471396","text":"def check_for_block(self, thisLine):\n if thisLine[0:4] == \" \" and not self.state.inTable:\n self.isParagraph = False\n self.isBlock = True","title":""},{"docid":"93bda38a76fa65c1a8f09dedeb2c1955","score":"0.5542358","text":"def _next_statement(self,command,*args):\r\n self.next_statement()","title":""},{"docid":"4bd90d7713ea6e3450765b866bf146fe","score":"0.55352306","text":"def set_no_para_start_if_empty(self) -> None:\n self.__no_para_start_if_empty = True","title":""},{"docid":"4e086c0ef3182ad8989733f1cd14c4c5","score":"0.553173","text":"def do_EOF(self\n , line):\n line = (line, ) # silence pychecker\n return True","title":""},{"docid":"698811dc6f0ce3f092fd27a8c940d9d5","score":"0.5530603","text":"def _test_break(self):\n if self.row_count < 2:\n return False\n \n if self.line_count < 7:\n return False\n \n return True","title":""},{"docid":"fc12d094bee4f7b24acbffe81f0f144b","score":"0.55281615","text":"def min_after_use_statements(tokens, curr_token_index, config):\n\n res = {\"min_newlines\": -1}\n\n if len(tokens) == curr_token_index + 1:\n return res\n\n #this line did start use statements, but next one does not\n if (tokens[curr_token_index] == \"SEMICOLON\" and check_end_of_keyword(tokens, curr_token_index, \"T_USE\")\n and not tokens[curr_token_index+1] == \"T_USE\"):\n res[\"min_newlines\"] = int(config[\"Blank Lines\"][\"After 'Use' statements\"])\n\n return res","title":""},{"docid":"b1bd0840a2bc2a36e89a8e25528be86c","score":"0.5516863","text":"def test_trace__funky_first_line():\n with pytest.raises(SyntaxError):\n set_trace(helper_funky_first_line, trace_func)","title":""},{"docid":"61dc47e60ff48267ae0c8a2fd853a310","score":"0.55121017","text":"def test_multiline_start_check5():\n Lines = ['','obj?, obj?? : Get help, or more help for object (also works as'\n ,' ?obj, ??obj).', 'cmd part 3', 'info']\n assert multiline_start_check(Lines, 1, False) == True","title":""},{"docid":"17a59ffbcd37ae7e8487adeb11da480a","score":"0.5511949","text":"def _skip_whitespaces(self):\n while True:\n symbol = self._get_next_symbol()\n if symbol == None:\n break\n elif not symbol.isspace():\n self._back()\n break","title":""},{"docid":"03685c60fb0e6d234a641787101f0908","score":"0.55034363","text":"def test_example_7_9__single_quoted_lines():\n\n text = dedent(\"\"\"\n ' 1st non-empty \n \n 2nd non-empty \n 3rd non-empty '\n \n \"\"\")[1:-1]\n \n expected = Docs( # :off\n Doc(\n Str(' 1st non-empty \\n \\n 2nd non-empty \\n 3rd non-empty '),\n ),\n ) # :on\n \n nodes = parser.parse(text)\n print_nodes(nodes)\n \n assert nodes == expected","title":""},{"docid":"2a60c154da564e753d66b1d00c1f3da6","score":"0.5502023","text":"def test_example_8_5__chomping_trailing_lines():\n\n text = dedent(\"\"\"\n # Strip\n # Comments:\n strip: |-\n # text \n \n # Clip\n # comments:\n \n clip: |\n # text \n \n # Keep\n # comments:\n \n keep: |+\n # text \n \n # Trail\n # comments.\n \n \"\"\")[1:-1]\n \n expected = None\n \n nodes = parser.parse(text)\n print_nodes(nodes)\n \n assert nodes == expected","title":""},{"docid":"0624b0cf2d29dfe02e65c8527d7ecc27","score":"0.5496613","text":"def test_empty_comment_check_6():\n Lines = [':','', 'Heading']\n assert comment_check(Lines,1) == False","title":""},{"docid":"faf2b97e417cc2434289704fac1b94b0","score":"0.54769564","text":"def find_break_stmts(self):\n return []","title":""},{"docid":"a78251b1c2fd07e63ba6669f4a0cf08f","score":"0.5475196","text":"def at_start_of_line(self):\n return Lexer.column.fget(self) == 0 and Lexer.line.fget(self) == 1","title":""},{"docid":"37f2a866033e573c9b5b5e5b2e179dee","score":"0.54739666","text":"def ignore_comment(program, pos):\n while pos < len(program):\n if program[pos] == '\\n':\n break\n pos += 1\n return pos","title":""},{"docid":"2c4ec1d19e58251ee9d42857a8dc104e","score":"0.5469185","text":"def isIgnored(line):\n blankRe = re.compile(r'\\s*[\\r|\\n]$')\n m = blankRe.match(line)\n if m:\n return True\n return False","title":""},{"docid":"5dc8e7968348abb76e7204e52d7856f1","score":"0.5457542","text":"def no_para_start_if_empty(self) -> bool:\n return self.__no_para_start_if_empty","title":""},{"docid":"c7636fc7845753985ceec0a59c0c18aa","score":"0.5457234","text":"def test_blank_lined_removed_issue_1283():\n test_input = \"\"\"__version__ = \"0.58.1\"\n\nfrom starlette import status\n\"\"\"\n assert isort.code(test_input) == test_input","title":""},{"docid":"63c5b5e14a51984da3724f8bfddc5da5","score":"0.54545444","text":"def test_example_7_12__plain_lines():\n\n text = dedent(\"\"\"\n 1st non-empty \n \n 2nd non-empty \n 3rd non-empty\n \n \"\"\")[1:-1]\n \n expected = Docs( # :off\n Doc(\n Str('1st non-empty'),\n ),\n Doc(\n Str('2nd non-empty 3rd non-empty'),\n ),\n ) # :on\n \n nodes = parser.parse(text)\n print_nodes(nodes)\n \n assert nodes == expected","title":""},{"docid":"26b2c2543fa3030c124cb5c666c1eafa","score":"0.5447701","text":"def should_keep_line(line):\n return not should_ignore_line(line)","title":""},{"docid":"dcb61436b9aa04c2d1a73ec2350047a9","score":"0.5444827","text":"def test_empty_declaration():\n eq_(SqlStatement().sql(), '')","title":""},{"docid":"03cd8c24ddb95cd50b094f6a9d92ad4e","score":"0.5432485","text":"def test_multiline_start_check6():\n Lines = ['','obj?? : Get help, or more help for object (also works as'\n ,' ?obj, ??obj).', 'cmd part 3', 'info']\n assert multiline_start_check(Lines, 2, False) == False","title":""},{"docid":"9192085dd848a8e4b321c9691a058cc4","score":"0.5419167","text":"def _skip(self, amount):\n match = None\n has_linebreak = False\n if isinstance(amount, Match):\n match = amount.group()\n has_linebreak = '\\n' in match\n amount = len(match)\n self.buf = self.buf[amount:]\n if has_linebreak:\n self.line_num += match.count('\\n')\n self.column = len(match) - match.rindex('\\n')\n else:\n self.column += amount","title":""},{"docid":"c5ad2a835e1777eed98e4653f18c94d5","score":"0.54183304","text":"def min_before_method_body(tokens, curr_token_index, config):\n\n res = {\"min_newlines\": -1}\n\n if (check_after_function_declaration(tokens, curr_token_index, \"BRACKET_OPEN\")\n and check_in_block(tokens, curr_token_index, \"T_CLASS\", 2)):\n\n res[\"min_newlines\"] = int(config[\"Blank Lines\"][\"Before method body\"])\n\n return res","title":""},{"docid":"b21bbe96b0d9b4b0191c1bcdc716c7a3","score":"0.5414916","text":"def eat_blank_lines(mystr):\n blankregexp = re.compile(\"^[\\s]*$\")\n retstr = \"\"\n for line in mystr.splitlines():\n\tif not blankregexp.match(line):\n\t retstr += line + \"\\n\"\n return retstr","title":""},{"docid":"51e09c19c30029bd9d711d5d2d1deeb9","score":"0.5410105","text":"def small_statement(self):\n\n if (\n (statement := self.assignment_statement()) is not None\n or (statement := self.indentable_exprs()) is not None\n or (statement := self.pass_statement()) is not None\n or (statement := self.flow_statement()) is not None\n or (statement := self.import_statement()) is not None\n or (statement := self.global_statement()) is not None\n or (statement := self.nonlocal_statement()) is not None\n or (statement := self.assert_statement()) is not None\n ):\n return statement\n\n return None","title":""},{"docid":"94f9b69fa09a5d1192452174e4d230ae","score":"0.5409066","text":"def acknowledge_linebreak_consumed(self):\n self._break_required = False","title":""},{"docid":"84be9af1086835cbd4bd2e9f4ee46357","score":"0.5407795","text":"def _check_empty_line(fh):\n line = next(fh, None)\n if line is None:\n raise OrdResFormatError(\"Reached end of file while looking for blank \"\n \"line separating sections.\")\n\n if line.strip():\n raise OrdResFormatError(\"Expected an empty line.\")","title":""},{"docid":"7a6ca317ee850419bda443390d92a2fc","score":"0.5407715","text":"def test_entries_cannot_have_leading_whitespace():\n\n cfg = \" key = cannot be indented\"\n error = 'test.cfg: 1: expected \"key = value\" entry\\n' \"on this line: \" + cfg\n with pytest.raises(SyntaxError, match=error):\n list(run_cfg_test(cfg))","title":""},{"docid":"00111e8335cd9ad9f144177eee42fbb9","score":"0.54055476","text":"def process_whitespace(self) -> Token:\n ws = Token.whitespace.search(self.code, pos=self.index)\n self.index = ws.end()\n if ws.group(2) is not None:\n self.line += 1\n return Token(\"NEWLINE\", self.line, self.index)\n return self.next()","title":""},{"docid":"567a74ca90f7fb913d6a5b44917cd8bd","score":"0.54033405","text":"def test_is_blank_if_blank_cars(self):\n pp = ProjectParser()\n line = \" \\t \\n\"\n assert pp.is_blank(line) is True","title":""},{"docid":"943f4bad15252e7e4c1ff0765252b5ad","score":"0.5402925","text":"def test_no_settings_before_records(self, src):\n self._parse_fails(src, line_number=2)","title":""},{"docid":"b50b28b53f6e572d22bd31fd2d765df1","score":"0.53929025","text":"def test_multiline_start_check4():\n Lines = ['cmd: info','Test cmd: exlpained','next cmd: exlpained', 'cmd part 3', 'info']\n assert multiline_start_check(Lines, 1, False) == False","title":""},{"docid":"216b14a64f920fe69123273d1ddda8e8","score":"0.53918934","text":"def test_tab_2226_tblastn_005_comments_false(self):\n tab_file = get_file(\"tab_2226_tblastn_005.txt\")\n exc_msg = (\n \"Encountered unexpected character '#' at the beginning of a line. \"\n \"Set comments=True if the file is a commented file.\"\n )\n qresults = parse(tab_file, FMT)\n with self.assertRaises(ValueError, msg=exc_msg):\n next(qresults)","title":""}],"string":"[\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f698b7c22a9db644bfffee571ec3470\",\n \"score\": \"0.68750113\",\n \"text\": \"def emptyline(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a326ba446e1570feeb998b5aa9c6b8e\",\n \"score\": \"0.6862263\",\n \"text\": \"def skip_over_next_blank(self):\\n while self.readline().strip(): pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"593de671795a6c349c0831a1853bece5\",\n \"score\": \"0.66916424\",\n \"text\": \"def emptyline(self):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c8003172bf0a229fd79533338c8b408\",\n \"score\": \"0.66434944\",\n \"text\": \"def emptyline(self):\\n self.repeat_last_nonempty_command = is_step_mode()\\n return super(DebugCmd, self).emptyline()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1825a1bc66ce657265b9c4f2a96a03c3\",\n \"score\": \"0.639368\",\n \"text\": \"def emptyline(self):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97583ca3f496a7a65e98e7313297f9f5\",\n \"score\": \"0.6254386\",\n \"text\": \"def test_indented_line_should_not_be_blank(self):\\n pp = ProjectParser()\\n line = \\\"\\\\ttest\\\"\\n assert pp.is_blank(line) is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"842f75c12d7ea3cc9090e19a148e873f\",\n \"score\": \"0.6202605\",\n \"text\": \"def test_empty_multiline():\\n Lines = ['','','Title', 'cmd part 3', 'info']\\n assert multiline_start_check(Lines, 1, False) == False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28323cc493efb0efc6364bfc8cc57d16\",\n \"score\": \"0.61700827\",\n \"text\": \"def test_comments_not_multiline():\\n Lines = ['',\\n 'Remember: TAB completion works in many contexts, not just file names',\\n 'or python names.', '']\\n assert multiline_start_check(Lines, 1, True) == False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0bdbafef2c51fdb26c68131750a9fd2\",\n \"score\": \"0.6153704\",\n \"text\": \"def eol():\\n p('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7f3ea03b8eac461ee7b941b48b61f82\",\n \"score\": \"0.61497724\",\n \"text\": \"def spnl(self):\\n self.match(re.compile(r'^ *(?:\\\\n *)?'))\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c72df93371825ef550694cdd229660\",\n \"score\": \"0.6110715\",\n \"text\": \"def first_line(self):\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc4b3588e702b5e03c7bff820daa2d42\",\n \"score\": \"0.5999922\",\n \"text\": \"def test_example_8_12__empty_separation_lines():\\n\\n text = dedent(\\\"\\\"\\\"\\n >\\n \\n folded\\n line \\n \\n next\\n line \\n * bullet\\n \\n * list\\n * line \\n \\n last\\n line\\n \\n # Comment\\n \\n \\\"\\\"\\\")[1:-1]\\n \\n expected = Docs( # :off\\n Doc(\\n Str(' folded line \\\\nnext line * bullet\\\\n * list * line \\\\nlast line\\\\n'),\\n ),\\n ) # :on\\n \\n nodes = parser.parse(text)\\n print_nodes(nodes)\\n \\n assert nodes == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1694c3b29727991897e54074c647b921\",\n \"score\": \"0.5997253\",\n \"text\": \"def check_blank_lines(reporter, raw):\\n\\n lines = [(i, x) for (i, x) in enumerate(\\n raw.strip().split('\\\\n')) if not x.strip()]\\n reporter.check(not lines,\\n None,\\n 'Blank line(s) in header: {0}',\\n ', '.join([\\\"{0}: {1}\\\".format(i, x.rstrip()) for (i, x) in lines]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"515b3dadadfd8fc4d38f54c016be44f9\",\n \"score\": \"0.5981519\",\n \"text\": \"def match_blank(self, line):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5db14a0dd620d1935c4bf36edbc9a912\",\n \"score\": \"0.59137326\",\n \"text\": \"def exhaust_till_comment_or_line_break(parser_state):\\n return repeat_exhauster(parser_state, exhaust_non_comment_or_line_break)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4392db6c0cc6ae7a3ffce78a62d0b8e\",\n \"score\": \"0.5819575\",\n \"text\": \"def __semicolon(self, stmt, no_newline = False):\\n\\t\\tif stmt.__class__.__name__ not in self.NO_SEMICOLON:\\n\\t\\t\\tif no_newline:\\n\\t\\t\\t\\tself.__write(\\\";\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.__write(\\\";\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc4b544cd08af101ee62e5a8443f9a8\",\n \"score\": \"0.58184564\",\n \"text\": \"def test_no_text_before_record_definition(self, src):\\n self._parse_fails(src, line_number=3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f36d092bf13ec27cced1a82c0627170a\",\n \"score\": \"0.5817698\",\n \"text\": \"def exhaust_non_comment_or_line_break(parser_state):\\n return exhaust_if(parser_state, is_character_non_comment_or_line_break)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7056c7045d85123e2e43cc7fdf204b53\",\n \"score\": \"0.5797453\",\n \"text\": \"def t_ANY_newline(self, token: lex.LexToken) -> None:\\n token.lexer.lineno += len(token.value)\\n token.lexer.begin('INITIAL')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f404ab083fea248f2b6e3e9083ebd39d\",\n \"score\": \"0.5788991\",\n \"text\": \"def skip_to_nonblank(self):\\n pos = self._file.tell()\\n while not self.readline().strip(): pos = self._file.tell()\\n self._file.seek(pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93a318ff95f60cbfa26d8e816b4003a8\",\n \"score\": \"0.57727695\",\n \"text\": \"def emptyline(self):\\n msg.info(\\\"No command entered. Type help or ? for command listing.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39fc6845bbaef8281ad343c861e7eb3f\",\n \"score\": \"0.57686543\",\n \"text\": \"def exhaust_line_break_all(parser_state):\\n return repeat_exhauster(parser_state, exhaust_line_break)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c984b9d07bef33182693c0c8d0dbdf80\",\n \"score\": \"0.57638854\",\n \"text\": \"def test_blank_note_line():\\n blank_note_line = \\\"* Sea-Bird SBE9 Data File:\\\\n*\\\\n* \\\\n# name 3 = depSM\\\\n# file_type = ascii\\\\n*END*\\\\n1\\\\n\\\"\\n from seabird.utils import load_rule\\n\\n load_rule(blank_note_line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae30ab1c5171dfc5178443b763638f25\",\n \"score\": \"0.57391065\",\n \"text\": \"def trim_lines(self):\\n # get rid of comments\\n self.lines = [ln for ln in self.lines if ln[0] is not '#']\\n # keep variable assigments (shell and environment)\\n self.lines = [ln for ln in self.lines if\\n ('=' in ln) or ('setenv' in ln)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2be955bab0fc7b829798394a1ffcbd0\",\n \"score\": \"0.5723432\",\n \"text\": \"def test_force_single_line_shouldnt_remove_preceding_comment_lines_issue_1296():\\n test_input = \\\"\\\"\\\"\\n# A comment\\n# A comment\\n\\n# Oh no, I'm gone\\nfrom moo import foo\\n\\\"\\\"\\\"\\n # assert isort.code(test_input) == test_input\\n assert isort.code(test_input, force_single_line=True) == test_input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"478f1b0b51cb2ca2d37686bc727b0dca\",\n \"score\": \"0.57160175\",\n \"text\": \"def _do_line_break(self, number_of_rows):\\n code_block_1 = self._create_remove_empty_lines(self.lines[:number_of_rows], self.starter, self.ender)\\n code_block_2 = self._create_remove_empty_lines(self.lines[number_of_rows:], self.starter, self.ender)\\n return code_block_1, code_block_2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20955e6a288431a3b18a461c8a9e95f6\",\n \"score\": \"0.571125\",\n \"text\": \"def min_before_use_statements(tokens, curr_token_index, config):\\n\\n res = {\\\"min_newlines\\\": -1}\\n\\n if len(tokens) == curr_token_index + 1:\\n return res\\n\\n #this line did not start use statements, but next one does\\n if (tokens[curr_token_index] == \\\"SEMICOLON\\\" and not check_end_of_keyword(tokens, curr_token_index, \\\"T_USE\\\")\\n and tokens[curr_token_index+1] == \\\"T_USE\\\"):\\n res[\\\"min_newlines\\\"] = int(config[\\\"Blank Lines\\\"][\\\"Before 'Use' statements\\\"])\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9a4f4867b9a716d94de783e1653dc6c\",\n \"score\": \"0.5702618\",\n \"text\": \"def skip_to_blank(self):\\n pos = self._file.tell()\\n while self.readline().strip(): pos = self._file.tell()\\n self._file.seek(pos)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f49b0a2688f5c357a20dc6224f66d9a\",\n \"score\": \"0.5667318\",\n \"text\": \"def test_example_8_13__final_empty_lines():\\n\\n text = dedent(\\\"\\\"\\\"\\n >\\n folded\\n line\\n \\n next\\n line\\n * bullet\\n \\n * list\\n * line\\n \\n last\\n line \\n \\n # Comment\\n \\n \\\"\\\"\\\")[1:-1]\\n \\n expected = Docs( # :off\\n Doc(\\n Str('folded line\\\\nnext line * bullet\\\\n * list * line\\\\nlast line\\\\n'),\\n ),\\n ) # :on\\n \\n nodes = parser.parse(text)\\n print_nodes(nodes)\\n \\n assert nodes == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75fd26da21fec27f5d12667df440a750\",\n \"score\": \"0.56659377\",\n \"text\": \"def exhaust_line_break_and_white_space_all(parser_state):\\n return chain_exhausters(parser_state, (exhaust_white_space_all, exhaust_line_break_all))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5250caf303ba501732582cf38a929a9f\",\n \"score\": \"0.56654793\",\n \"text\": \"def blank_metadata_line(line):\\n return (not line) or (_BLANK_LINE.match(line) is not None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55b6d9e4d08a1d663406e142ff2d5d9a\",\n \"score\": \"0.5661903\",\n \"text\": \"def parse_empty_statement(self):\\n location = self.consume(';').loc\\n return statements.Empty(location)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c23a54293f3c58de6c0b818553bac39\",\n \"score\": \"0.5661109\",\n \"text\": \"def skip_inline(view, point):\\n line_reg = view.line(point)\\n line = view.substr(sublime.Region(line_reg.a, point)).strip()\\n if line == \\\"\\\":\\n return False\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b121849ff089473265b568b40ddc62da\",\n \"score\": \"0.56411207\",\n \"text\": \"def _test_break(self):\\n # At least 4 lines.\\n if len(self.lines < 3):\\n return False\\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4bc9318a84a798652d131bceb240ae8\",\n \"score\": \"0.5635953\",\n \"text\": \"def test_parseEmptyLines(self):\\n self._doHSPLTest(\\\"High-DoS-8.csv\\\", \\\"landscape1.xml\\\", 2, [\\\"TCP\\\"] * 5, [\\\"drop\\\"] * 5)\\n self._doHSPLTest(\\\"High-DoS-8.csv\\\", \\\"landscape1.xml\\\", 2, [\\\"TCP\\\"] * 5, [\\\"limit\\\"] * 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13df4dfd1a1390700ec39fa259836d9f\",\n \"score\": \"0.562965\",\n \"text\": \"def advance():\\n Parser.current_command = Parser.opened_file.readline()\\n while Parser.current_command.startswith(Parser.EMPTY_LINE) or \\\\\\n Parser.remove_whitespace(Parser.current_command).startswith(\\n Parser.COMMENT) or \\\\\\n Parser.current_command.startswith(Parser.EMPTY_LINE_2):\\n Parser.current_command = Parser.opened_file.readline()\\n Parser.current_command = Parser.remove_comments(Parser.current_command)\\n Parser.current_command = Parser.remove_whitespace(Parser.current_command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d12902cc36f0cd8d941eca99be75d37\",\n \"score\": \"0.56202453\",\n \"text\": \"def test_blank_lined_removed_issue_1275():\\n assert (\\n isort.code(\\n '''\\\"\\\"\\\"\\nMy docstring\\n\\\"\\\"\\\"\\n\\nfrom b import thing\\nfrom a import other_thing\\n'''\\n )\\n == '''\\\"\\\"\\\"\\nMy docstring\\n\\\"\\\"\\\"\\n\\nfrom a import other_thing\\nfrom b import thing\\n'''\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b448a02a1dbfab3a6c25bbb9587c1149\",\n \"score\": \"0.5604572\",\n \"text\": \"def simple_statement(self):\\n\\n if (first_statement := self.small_statement()) is not None:\\n small_statements = [first_statement]\\n\\n self.register_revert()\\n while self.revertable(\\n self.consume_string(\\\";\\\") is not None\\n and (statement := self.small_statement()) is not None\\n ):\\n small_statements.append(statement)\\n\\n self.consume_string(\\\";\\\")\\n\\n return small_statements if len(small_statements) > 1 else first_statement\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5a3ba90a1c9ef0aed394c1630fcc061\",\n \"score\": \"0.56031424\",\n \"text\": \"def testNotAtStart(self):\\n self.check('GGGGFRRFRRFR',\\n '----HHHHHHHH')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"848e7a1baffc88f0f98a7c806c75f76f\",\n \"score\": \"0.5600438\",\n \"text\": \"def test_example_6_5__empty_lines():\\n\\n text = dedent(\\\"\\\"\\\"\\n Folding:\\n \\\"Empty line\\n \\n as a line feed\\\"\\n Chomping: |\\n Clipped empty lines\\n \\n \\n \\\"\\\"\\\")[1:-1]\\n \\n expected = None\\n \\n nodes = parser.parse(text)\\n print_nodes(nodes)\\n \\n assert nodes == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b11d5957696550848f37ec0a3132146\",\n \"score\": \"0.5590883\",\n \"text\": \"def test_ignore_one_line_comments(self):\\n self.filename = \\\"parser_tests/python_comments.txt\\\"\\n self.run_parser()\\n expected_keywords = []\\n self.assertListEqual(expected_keywords, self.p.keywords)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c06de9f63b2b217d3a90b6fe8efcc96a\",\n \"score\": \"0.5585234\",\n \"text\": \"def is_during_line_pass(self) -> bool:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2c16b180febbad581aeb252fa380a29\",\n \"score\": \"0.5581325\",\n \"text\": \"def pre_exec(self):\\n lines = []\\n if self.starts_dstep and not self.ends_dstep:\\n lines.append(\\\"BEGIN_DSTEP()\\\")\\n if self.starts_atomic and not self.ends_atomic:\\n lines.append(\\\"BEGIN_ATOMIC()\\\")\\n return len(lines) and \\\"; \\\".join(lines) or None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96e5a8441b95883fc5db29f936728069\",\n \"score\": \"0.5578854\",\n \"text\": \"def notCommentLine(label):\\n return label == '' or label[0] != '.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b8d7ddbcfc2e9404962b5b68f97fbfb\",\n \"score\": \"0.55779546\",\n \"text\": \"def _skip_whitespace(self):\\n\\n while self.current_char is not None and self.current_char.isspace():\\n self._advance()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75adc73c49164c793e34f9bb6326711\",\n \"score\": \"0.5577453\",\n \"text\": \"def test_empty_comment_check_7():\\n Lines = ['','cmd:', ' explained']\\n assert comment_check(Lines,1) == False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaf2999759ae853486c4133100d505cb\",\n \"score\": \"0.55631554\",\n \"text\": \"def skip(self,nn):\\n\\n logging.debug(\\\"Skipping these lines\\\")\\n for ii in range(1, nn + 1, 1):\\n logging.debug(self.inFile.readline())\\n logging.debug(\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81026edf2260cc2f12936def6f33029f\",\n \"score\": \"0.55471396\",\n \"text\": \"def check_for_block(self, thisLine):\\n if thisLine[0:4] == \\\" \\\" and not self.state.inTable:\\n self.isParagraph = False\\n self.isBlock = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93bda38a76fa65c1a8f09dedeb2c1955\",\n \"score\": \"0.5542358\",\n \"text\": \"def _next_statement(self,command,*args):\\r\\n self.next_statement()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bd90d7713ea6e3450765b866bf146fe\",\n \"score\": \"0.55352306\",\n \"text\": \"def set_no_para_start_if_empty(self) -> None:\\n self.__no_para_start_if_empty = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e086c0ef3182ad8989733f1cd14c4c5\",\n \"score\": \"0.553173\",\n \"text\": \"def do_EOF(self\\n , line):\\n line = (line, ) # silence pychecker\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"698811dc6f0ce3f092fd27a8c940d9d5\",\n \"score\": \"0.5530603\",\n \"text\": \"def _test_break(self):\\n if self.row_count < 2:\\n return False\\n \\n if self.line_count < 7:\\n return False\\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc12d094bee4f7b24acbffe81f0f144b\",\n \"score\": \"0.55281615\",\n \"text\": \"def min_after_use_statements(tokens, curr_token_index, config):\\n\\n res = {\\\"min_newlines\\\": -1}\\n\\n if len(tokens) == curr_token_index + 1:\\n return res\\n\\n #this line did start use statements, but next one does not\\n if (tokens[curr_token_index] == \\\"SEMICOLON\\\" and check_end_of_keyword(tokens, curr_token_index, \\\"T_USE\\\")\\n and not tokens[curr_token_index+1] == \\\"T_USE\\\"):\\n res[\\\"min_newlines\\\"] = int(config[\\\"Blank Lines\\\"][\\\"After 'Use' statements\\\"])\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1bd0840a2bc2a36e89a8e25528be86c\",\n \"score\": \"0.5516863\",\n \"text\": \"def test_trace__funky_first_line():\\n with pytest.raises(SyntaxError):\\n set_trace(helper_funky_first_line, trace_func)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61dc47e60ff48267ae0c8a2fd853a310\",\n \"score\": \"0.55121017\",\n \"text\": \"def test_multiline_start_check5():\\n Lines = ['','obj?, obj?? : Get help, or more help for object (also works as'\\n ,' ?obj, ??obj).', 'cmd part 3', 'info']\\n assert multiline_start_check(Lines, 1, False) == True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17a59ffbcd37ae7e8487adeb11da480a\",\n \"score\": \"0.5511949\",\n \"text\": \"def _skip_whitespaces(self):\\n while True:\\n symbol = self._get_next_symbol()\\n if symbol == None:\\n break\\n elif not symbol.isspace():\\n self._back()\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03685c60fb0e6d234a641787101f0908\",\n \"score\": \"0.55034363\",\n \"text\": \"def test_example_7_9__single_quoted_lines():\\n\\n text = dedent(\\\"\\\"\\\"\\n ' 1st non-empty \\n \\n 2nd non-empty \\n 3rd non-empty '\\n \\n \\\"\\\"\\\")[1:-1]\\n \\n expected = Docs( # :off\\n Doc(\\n Str(' 1st non-empty \\\\n \\\\n 2nd non-empty \\\\n 3rd non-empty '),\\n ),\\n ) # :on\\n \\n nodes = parser.parse(text)\\n print_nodes(nodes)\\n \\n assert nodes == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a60c154da564e753d66b1d00c1f3da6\",\n \"score\": \"0.5502023\",\n \"text\": \"def test_example_8_5__chomping_trailing_lines():\\n\\n text = dedent(\\\"\\\"\\\"\\n # Strip\\n # Comments:\\n strip: |-\\n # text \\n \\n # Clip\\n # comments:\\n \\n clip: |\\n # text \\n \\n # Keep\\n # comments:\\n \\n keep: |+\\n # text \\n \\n # Trail\\n # comments.\\n \\n \\\"\\\"\\\")[1:-1]\\n \\n expected = None\\n \\n nodes = parser.parse(text)\\n print_nodes(nodes)\\n \\n assert nodes == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0624b0cf2d29dfe02e65c8527d7ecc27\",\n \"score\": \"0.5496613\",\n \"text\": \"def test_empty_comment_check_6():\\n Lines = [':','', 'Heading']\\n assert comment_check(Lines,1) == False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faf2b97e417cc2434289704fac1b94b0\",\n \"score\": \"0.54769564\",\n \"text\": \"def find_break_stmts(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a78251b1c2fd07e63ba6669f4a0cf08f\",\n \"score\": \"0.5475196\",\n \"text\": \"def at_start_of_line(self):\\n return Lexer.column.fget(self) == 0 and Lexer.line.fget(self) == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37f2a866033e573c9b5b5e5b2e179dee\",\n \"score\": \"0.54739666\",\n \"text\": \"def ignore_comment(program, pos):\\n while pos < len(program):\\n if program[pos] == '\\\\n':\\n break\\n pos += 1\\n return pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c4ec1d19e58251ee9d42857a8dc104e\",\n \"score\": \"0.5469185\",\n \"text\": \"def isIgnored(line):\\n blankRe = re.compile(r'\\\\s*[\\\\r|\\\\n]$')\\n m = blankRe.match(line)\\n if m:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dc8e7968348abb76e7204e52d7856f1\",\n \"score\": \"0.5457542\",\n \"text\": \"def no_para_start_if_empty(self) -> bool:\\n return self.__no_para_start_if_empty\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7636fc7845753985ceec0a59c0c18aa\",\n \"score\": \"0.5457234\",\n \"text\": \"def test_blank_lined_removed_issue_1283():\\n test_input = \\\"\\\"\\\"__version__ = \\\"0.58.1\\\"\\n\\nfrom starlette import status\\n\\\"\\\"\\\"\\n assert isort.code(test_input) == test_input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63c5b5e14a51984da3724f8bfddc5da5\",\n \"score\": \"0.54545444\",\n \"text\": \"def test_example_7_12__plain_lines():\\n\\n text = dedent(\\\"\\\"\\\"\\n 1st non-empty \\n \\n 2nd non-empty \\n 3rd non-empty\\n \\n \\\"\\\"\\\")[1:-1]\\n \\n expected = Docs( # :off\\n Doc(\\n Str('1st non-empty'),\\n ),\\n Doc(\\n Str('2nd non-empty 3rd non-empty'),\\n ),\\n ) # :on\\n \\n nodes = parser.parse(text)\\n print_nodes(nodes)\\n \\n assert nodes == expected\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26b2c2543fa3030c124cb5c666c1eafa\",\n \"score\": \"0.5447701\",\n \"text\": \"def should_keep_line(line):\\n return not should_ignore_line(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb61436b9aa04c2d1a73ec2350047a9\",\n \"score\": \"0.5444827\",\n \"text\": \"def test_empty_declaration():\\n eq_(SqlStatement().sql(), '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03cd8c24ddb95cd50b094f6a9d92ad4e\",\n \"score\": \"0.5432485\",\n \"text\": \"def test_multiline_start_check6():\\n Lines = ['','obj?? : Get help, or more help for object (also works as'\\n ,' ?obj, ??obj).', 'cmd part 3', 'info']\\n assert multiline_start_check(Lines, 2, False) == False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9192085dd848a8e4b321c9691a058cc4\",\n \"score\": \"0.5419167\",\n \"text\": \"def _skip(self, amount):\\n match = None\\n has_linebreak = False\\n if isinstance(amount, Match):\\n match = amount.group()\\n has_linebreak = '\\\\n' in match\\n amount = len(match)\\n self.buf = self.buf[amount:]\\n if has_linebreak:\\n self.line_num += match.count('\\\\n')\\n self.column = len(match) - match.rindex('\\\\n')\\n else:\\n self.column += amount\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5ad2a835e1777eed98e4653f18c94d5\",\n \"score\": \"0.54183304\",\n \"text\": \"def min_before_method_body(tokens, curr_token_index, config):\\n\\n res = {\\\"min_newlines\\\": -1}\\n\\n if (check_after_function_declaration(tokens, curr_token_index, \\\"BRACKET_OPEN\\\")\\n and check_in_block(tokens, curr_token_index, \\\"T_CLASS\\\", 2)):\\n\\n res[\\\"min_newlines\\\"] = int(config[\\\"Blank Lines\\\"][\\\"Before method body\\\"])\\n\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b21bbe96b0d9b4b0191c1bcdc716c7a3\",\n \"score\": \"0.5414916\",\n \"text\": \"def eat_blank_lines(mystr):\\n blankregexp = re.compile(\\\"^[\\\\s]*$\\\")\\n retstr = \\\"\\\"\\n for line in mystr.splitlines():\\n\\tif not blankregexp.match(line):\\n\\t retstr += line + \\\"\\\\n\\\"\\n return retstr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e09c19c30029bd9d711d5d2d1deeb9\",\n \"score\": \"0.5410105\",\n \"text\": \"def small_statement(self):\\n\\n if (\\n (statement := self.assignment_statement()) is not None\\n or (statement := self.indentable_exprs()) is not None\\n or (statement := self.pass_statement()) is not None\\n or (statement := self.flow_statement()) is not None\\n or (statement := self.import_statement()) is not None\\n or (statement := self.global_statement()) is not None\\n or (statement := self.nonlocal_statement()) is not None\\n or (statement := self.assert_statement()) is not None\\n ):\\n return statement\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94f9b69fa09a5d1192452174e4d230ae\",\n \"score\": \"0.5409066\",\n \"text\": \"def acknowledge_linebreak_consumed(self):\\n self._break_required = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84be9af1086835cbd4bd2e9f4ee46357\",\n \"score\": \"0.5407795\",\n \"text\": \"def _check_empty_line(fh):\\n line = next(fh, None)\\n if line is None:\\n raise OrdResFormatError(\\\"Reached end of file while looking for blank \\\"\\n \\\"line separating sections.\\\")\\n\\n if line.strip():\\n raise OrdResFormatError(\\\"Expected an empty line.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6ca317ee850419bda443390d92a2fc\",\n \"score\": \"0.5407715\",\n \"text\": \"def test_entries_cannot_have_leading_whitespace():\\n\\n cfg = \\\" key = cannot be indented\\\"\\n error = 'test.cfg: 1: expected \\\"key = value\\\" entry\\\\n' \\\"on this line: \\\" + cfg\\n with pytest.raises(SyntaxError, match=error):\\n list(run_cfg_test(cfg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00111e8335cd9ad9f144177eee42fbb9\",\n \"score\": \"0.54055476\",\n \"text\": \"def process_whitespace(self) -> Token:\\n ws = Token.whitespace.search(self.code, pos=self.index)\\n self.index = ws.end()\\n if ws.group(2) is not None:\\n self.line += 1\\n return Token(\\\"NEWLINE\\\", self.line, self.index)\\n return self.next()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"567a74ca90f7fb913d6a5b44917cd8bd\",\n \"score\": \"0.54033405\",\n \"text\": \"def test_is_blank_if_blank_cars(self):\\n pp = ProjectParser()\\n line = \\\" \\\\t \\\\n\\\"\\n assert pp.is_blank(line) is True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"943f4bad15252e7e4c1ff0765252b5ad\",\n \"score\": \"0.5402925\",\n \"text\": \"def test_no_settings_before_records(self, src):\\n self._parse_fails(src, line_number=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b50b28b53f6e572d22bd31fd2d765df1\",\n \"score\": \"0.53929025\",\n \"text\": \"def test_multiline_start_check4():\\n Lines = ['cmd: info','Test cmd: exlpained','next cmd: exlpained', 'cmd part 3', 'info']\\n assert multiline_start_check(Lines, 1, False) == False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"216b14a64f920fe69123273d1ddda8e8\",\n \"score\": \"0.53918934\",\n \"text\": \"def test_tab_2226_tblastn_005_comments_false(self):\\n tab_file = get_file(\\\"tab_2226_tblastn_005.txt\\\")\\n exc_msg = (\\n \\\"Encountered unexpected character '#' at the beginning of a line. \\\"\\n \\\"Set comments=True if the file is a commented file.\\\"\\n )\\n qresults = parse(tab_file, FMT)\\n with self.assertRaises(ValueError, msg=exc_msg):\\n next(qresults)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1698,"cells":{"query_id":{"kind":"string","value":"a93ae90d531f7014b808bb4520c8ef6a"},"query":{"kind":"string","value":"split ecg to make 2d matrix of the form beats x points. bad qrst complexes are replaced by average"},"positive_passages":{"kind":"list like","value":[{"docid":"b99ece4bdabcd6f14afd3e1e6ad713d0","score":"0.5666693","text":"def makeMat(ecg,qrsonsets,qrsflags):\n \n #convert to millivolts\n #ecg = ecg*1000 # do this later in the calling script if reqd\n \n #Get mean cycle length\n rrintervals = qrsonsets[1:]-qrsonsets[:-1]\n meanrr = int(scipy.mean(rrintervals))\n \n # do we have one rr after last qrsonset?\n # am not changing qrsflags, but last qrsflag will become unpaired\n if qrsonsets[-1] + meanrr > len(ecg):\n qrsonsets = qrsonsets[:-1]\n \n #segment into a 2d matrix\n Nbeats = len(qrsonsets)\n ecgmat = scipy.zeros((Nbeats,meanrr))\n \n #for l in range(leads):\n for i in range(Nbeats):\n if qrsflags[i] == 1:\n ecgmat[i,:] = ecg[qrsonsets[i]:qrsonsets[i]+meanrr]\n \n #get mean qrst and insert where qrsflag is 0\n \n #for l in range(leads):\n meanqrst = scipy.zeros(meanrr)\n sumqrst = scipy.zeros(meanrr)\n \n for i in range(meanrr):\n sumqrst[i] += scipy.sum(ecgmat[:,i])\n meanqrst = sumqrst/scipy.sum(qrsflags)\n \n # if there is atleast one good qrs, replace bad by avg good\n if sum(qrsflags) > 0:\n for i in range(Nbeats):\n if qrsflags[i] == 0:\n #print meanqrst.shape\n ecgmat[i,:] = meanqrst[:]\n \n return (ecgmat,meanqrst)","title":""}],"string":"[\n {\n \"docid\": \"b99ece4bdabcd6f14afd3e1e6ad713d0\",\n \"score\": \"0.5666693\",\n \"text\": \"def makeMat(ecg,qrsonsets,qrsflags):\\n \\n #convert to millivolts\\n #ecg = ecg*1000 # do this later in the calling script if reqd\\n \\n #Get mean cycle length\\n rrintervals = qrsonsets[1:]-qrsonsets[:-1]\\n meanrr = int(scipy.mean(rrintervals))\\n \\n # do we have one rr after last qrsonset?\\n # am not changing qrsflags, but last qrsflag will become unpaired\\n if qrsonsets[-1] + meanrr > len(ecg):\\n qrsonsets = qrsonsets[:-1]\\n \\n #segment into a 2d matrix\\n Nbeats = len(qrsonsets)\\n ecgmat = scipy.zeros((Nbeats,meanrr))\\n \\n #for l in range(leads):\\n for i in range(Nbeats):\\n if qrsflags[i] == 1:\\n ecgmat[i,:] = ecg[qrsonsets[i]:qrsonsets[i]+meanrr]\\n \\n #get mean qrst and insert where qrsflag is 0\\n \\n #for l in range(leads):\\n meanqrst = scipy.zeros(meanrr)\\n sumqrst = scipy.zeros(meanrr)\\n \\n for i in range(meanrr):\\n sumqrst[i] += scipy.sum(ecgmat[:,i])\\n meanqrst = sumqrst/scipy.sum(qrsflags)\\n \\n # if there is atleast one good qrs, replace bad by avg good\\n if sum(qrsflags) > 0:\\n for i in range(Nbeats):\\n if qrsflags[i] == 0:\\n #print meanqrst.shape\\n ecgmat[i,:] = meanqrst[:]\\n \\n return (ecgmat,meanqrst)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"d243886f471226abb337be55d4f06252","score":"0.58604336","text":"def G_ee(basis,molecule,P,ee):\n\n # Size of the basis set\n K = basis.K\n\n G = np.zeros((K,K))\n\n for i in range(K):\n for j in range(K):\n for k in range(K):\n for l in range(K):\n G[i,j] += P[k,l] * (ee[i,j,k,l] - 0.5 * ee[i,l,k,j])\n\n return G","title":""},{"docid":"35799d87a6e6302646fe6bc15d7cb9a7","score":"0.5726206","text":"def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.K = np.zeros((16,16))\n self.f = np.zeros(16)\n self.M_small = np.zeros((8,8))\n self.M = np.zeros((16,16))\n self.S = np.zeros((8,6))\n self.E = np.zeros((8,6))\n\n # Quadrature like ANSYS or ABAQUS:\n g = np.sqrt(3/5)\n w = 5/9\n w0 = 8/9\n self.gauss_points = ((-g, -g, w*w), ( g, -g, w*w ), ( g, g, w*w),\n (-g, g, w*w), ( 0, -g, w0*w ), ( g, 0, w*w0),\n ( 0, g, w0*w), (-g, 0, w*w0), ( 0, 0, w0*w0))\n\n # a little bit dirty but correct. Comes from sympy file.\n self.extrapolation_points = np.array(\n [[ 5*sqrt(15)/18 + 10/9, 5/18, -5*sqrt(15)/18 + 10/9,\n 5/18, -5/9 - sqrt(15)/9, -5/9 + sqrt(15)/9,\n -5/9 + sqrt(15)/9, -5/9 - sqrt(15)/9, 4/9],\n [5/18, 5*sqrt(15)/18 + 10/9, 5/18, -5*sqrt(15)/18 + 10/9,\n -5/9 - sqrt(15)/9, -5/9 - sqrt(15)/9, -5/9 + sqrt(15)/9,\n -5/9 + sqrt(15)/9, 4/9],\n [-5*sqrt(15)/18 + 10/9, 5/18, 5*sqrt(15)/18 + 10/9, 5/18,\n -5/9 + sqrt(15)/9, -5/9 - sqrt(15)/9, -5/9 - sqrt(15)/9,\n -5/9 + sqrt(15)/9, 4/9],\n [ 5/18, -5*sqrt(15)/18 + 10/9, 5/18, 5*sqrt(15)/18 + 10/9,\n -5/9 + sqrt(15)/9, -5/9 + sqrt(15)/9, -5/9 - sqrt(15)/9,\n -5/9 - sqrt(15)/9, 4/9],\n [ 0, 0, 0, 0, sqrt(15)/6 + 5/6, 0, -sqrt(15)/6 + 5/6, 0, -2/3],\n [0, 0, 0, 0, 0, sqrt(15)/6 + 5/6, 0, -sqrt(15)/6 + 5/6, -2/3],\n [ 0, 0, 0, 0, -sqrt(15)/6 + 5/6, 0, sqrt(15)/6 + 5/6, 0, -2/3],\n [ 0, 0, 0, 0, 0, -sqrt(15)/6 + 5/6, 0, sqrt(15)/6 + 5/6, -2/3]])","title":""},{"docid":"0a860194041436d2c6039f28502341bd","score":"0.55689144","text":"def gaussian2D(grid,cenX,cenY,amp,widX,widY):\n x = grid[0]\n y = grid[1]\n return(np.matrix([ [amp*np.exp(-(x[i]-cenX)**2/(2*widX**2))*np.exp(-(y[j]-cenY)**2/(2*widY**2)) for j in range(len(y))] for i in range(len(x))]))","title":""},{"docid":"23573828795f15e79a74ab7d7d0a815f","score":"0.55412376","text":"def pre_process_plot(self,eeg):\n eeg = np.array(eeg)\n [fil,col] = eeg.shape\n eeg_processed = np.zeros([fil,col])\n for i in range(fil):\n data = eeg[i,:] * 2.23517444553071e-02\n data = data - np.mean(data)\n data = lfilter(self.b_n,self.a_n, data)\n data = lfilter(self.b,self.a,data) \n eeg_processed[i,:] = data\n return(eeg_processed)","title":""},{"docid":"b40431c8374a16dc9b8e23314146261d","score":"0.5504659","text":"def echelonMat(ech, *args):\n if len(args) == 2: # matrice augmentée\n A = np.array(args[0]).astype(float)\n m = A.shape[0]\n n = A.shape[1]\n b = args[1]\n if type(b[0]) == list:\n b = np.array(b).astype(float)\n A = np.concatenate((A, b), axis=1)\n else:\n b = [b[i] for i in range(m)]\n A = [A[i] + [b[i]] for i in range(0, m)]\n else: # matrice coeff\n A = np.array(args[0]).astype(float)\n m = A.shape[0]\n n = A.shape[1]\n\n if ech == 'E': # Echelonnée\n Mat = np.array(A)\n Mat = Mat.astype(float) # in case the array in int instead of float.\n numPivot = 0\n for i in range(len(Mat)):\n j = i\n while all(abs(Mat[j:, i]) < 1e-15) and j != len(Mat[0, :]) - 1: # if column (or rest of) is 0, take next\n j += 1\n if j == len(Mat[0, :]) - 1:\n if len(Mat[0, :]) > j:\n Mat[i + 1:len(Mat), :] = 0\n break\n if abs(Mat[i, j]) < 1e-15:\n Mat[i, j] = 0\n zero = abs(Mat[i:, j]) < 1e-15\n M = echZero(zero, Mat[i:, :])\n Mat[i:, :] = M\n Mat = Ealpha(Mat, i, 1 / Mat[i, j]) # usually Mat[i,j]!=0\n for k in range(i + 1, len(A)):\n Mat = Eijalpha(Mat, k, i, -Mat[k, j])\n # Mat[k,:]=[0 if abs(Mat[k,l])<1e-15 else Mat[k,l] for l in range(len(MatCoeff[0,:]))]\n numPivot += 1\n Mat[abs(Mat) < 1e-15] = 0\n\n print(\"La matrice est sous la forme échelonnée\")\n if len(args) == 2:\n printEquMatrices([A[:, :n], Mat[:, :n]], [A[:, n:], Mat[:, n:]])\n else:\n printEquMatrices([A, Mat])\n\n elif ech == 'ER': # Echelonnée réduite\n Mat = np.array(A)\n Mat = Mat.astype(float) # in case the array in int instead of float.\n numPivot = 0\n for i in range(len(Mat)):\n j = i\n while all(abs(Mat[j:, i]) < 1e-15) and j != len(\n Mat[0, :]) - 1: # if column (or rest of) is zero, take next column\n j += 1\n if j == len(Mat[0, :]) - 1:\n # ADD ZERO LINES BELOW!!!!!!\n if len(Mat[0, :]) > j:\n Mat[i + 1:len(Mat), :] = 0\n break\n if abs(Mat[i, j]) < 1e-15:\n Mat[i, j] = 0\n zero = abs(Mat[i:, j]) < 1e-15\n M = echZero(zero, Mat[i:, :])\n Mat[i:, :] = M\n Mat = Ealpha(Mat, i, 1 / Mat[i, j]) # normalement Mat[i,j]!=0\n for k in range(i + 1, len(A)):\n Mat = Eijalpha(Mat, k, i, -Mat[k, j])\n # Mat[k,:]=[0 if abs(Mat[k,l])<1e-15 else Mat[k,l] for l in range(len(MatCoeff[0,:]))]\n numPivot += 1\n Mat[abs(Mat) < 1e-15] = 0\n\n print(\"La matrice est sous la forme échelonnée\")\n if len(args) == 2:\n printEquMatrices([A[:, :n], Mat[:, :n]], [A[:, n:], Mat[:, n:]])\n else:\n printEquMatrices([np.asmatrix(A), np.asmatrix(Mat)])\n\n Mat = np.array(Mat)\n i = (len(Mat) - 1)\n while i >= 1:\n while all(\n abs(Mat[i, :len(Mat[0]) - 1]) < 1e-15) and i != 0: # if ligne (or rest of) is zero, take next ligne\n i -= 1\n # we have a lign with one non-nul element\n j = i # we can start at pos ij at least the pivot is there\n if abs(Mat[i, j]) < 1e-15: # if element Aij=0 take next one --> find pivot\n j += 1\n # Aij!=0 and Aij==1 if echelonMat worked\n for k in range(i): # put zeros above pivot (which is 1 now)\n Mat = Eijalpha(Mat, k, i, -Mat[k, j])\n i -= 1\n\n print(\"La matrice est sous la forme échelonnée réduite\")\n if len(args) == 2:\n printEquMatrices([A[:, :n], Mat[:, :n]], [A[:, n:], Mat[:, n:]])\n else:\n printEquMatrices([A, Mat])\n\n else:\n print(f\"Méthode d'échelonnage non reconnue {ech}. Méthodes disponibles: 'E' (pour la forme échelonnée standard)\"\n f\", 'ER' (pour la forme échelonnée réduite))\")\n\n return np.asmatrix(Mat)","title":""},{"docid":"112093be2c1a28af2f0a55c77e755ee6","score":"0.54873","text":"def g(x):\r\n return 5. - x[:, 1] - .5 * x[:, 0] ** 2.","title":""},{"docid":"661eb6c4894412204ec69176d9303cc6","score":"0.54661554","text":"def flatten_composition(EXX):\n E=EXX[:,0]\n a=EXX[:,1]\n b=EXX[:,2]\n\n xNi=1+a-b\n xAl=1-a\n xVa=b\n\n x=xNi/(xNi+xAl)\n E=E/(xNi+xAl)\n\n return np.array((E,x)).T","title":""},{"docid":"673752aa7550f2a624633cad2bb919cf","score":"0.54626995","text":"def smooth(self, xform, errors, e_binning, cz_binning):\n\n sumw2 = np.square(errors)\n\n # First, let's check if we have entire coszen values with zero, this is for example\n # the case for all nutau CC below 3.5 GeV\n\n non_zero_idx = 0\n while np.sum(xform[non_zero_idx, :]) == 0:\n non_zero_idx += 1\n\n # cut away these all-zero regions for now\n truncated_xform = xform[non_zero_idx:, :]\n truncated_sumw2 = sumw2[non_zero_idx:, :]\n truncated_e_binning = e_binning[non_zero_idx:]\n\n # Now lets extend that array at both energy ends\n # by about 10% of bins\n num_extension_bins_lower = int(np.floor(0.1*len(e_binning)))\n num_extension_bins_upper = 0 \n assert e_binning.is_lin or e_binning.is_log, 'Do not know how to extend arbitrary binning'\n\n # what will new bin edges be?\n bin_edges = truncated_e_binning.bin_edges.m\n if e_binning.is_log:\n bin_edges = np.log10(bin_edges)\n bin_edges = list(bin_edges)\n lower_edges = []\n upper_edges = []\n delta = bin_edges[1] - bin_edges[0]\n for i in range(num_extension_bins_lower):\n lower_edges.append(bin_edges[0] - (i+1)*delta)\n for i in range(num_extension_bins_upper):\n upper_edges.append(bin_edges[-1] + (i+1)*delta)\n new_edges = np.array(lower_edges[::-1] + bin_edges + upper_edges)\n if e_binning.is_log:\n new_edges = np.power(10, new_edges)\n\n extended_binning = OneDimBinning('true_energy', bin_edges=new_edges, is_lin=e_binning.is_lin, is_log=e_binning.is_log)\n\n # also extend that arrays\n # We do that by point-reflecting the values\n # so an array like [0 1 2 3 4 ...] will become [-3 -2 -1 0 1 2 3 4 ...]\n\n #if non_zero_idx == 0:\n lower_bit = 2*truncated_xform[0, :] - np.flipud(truncated_xform[1:num_extension_bins_lower+1, :])\n #else:\n # lower_bit = - np.flipud(truncated_xform[1:num_extension_bins+1,:])\n upper_bit = 2*truncated_xform[-1, :] - np.flipud(truncated_xform[-num_extension_bins_upper-1:-1, :])\n extended_xform = np.concatenate((lower_bit, truncated_xform, upper_bit))\n\n # also handle the errors (which simply add up in quadrature)\n #if non_zero_idx == 0:\n lower_bit = truncated_sumw2[0, :] + np.flipud(truncated_sumw2[1:num_extension_bins_lower+1, :])\n #else:\n # lower_bit = np.flipud(truncated_sumw2[1:num_extension_bins+1,:])\n upper_bit = truncated_sumw2[-1, :] + np.flipud(truncated_sumw2[-num_extension_bins_upper-1:-1, :])\n extended_sumw2 = np.concatenate((lower_bit, truncated_sumw2, upper_bit))\n\n # what's the stat. situation here?\n rel_error = np.square(errors)/xform\n rel_error = np.median(rel_error[xform != 0])\n logging.debug('Relative errors are ~ %.5f', rel_error)\n # how many zero bins?\n zero_fraction = np.count_nonzero(truncated_xform == 0)/float(truncated_xform.size)\n logging.debug('zero fraction is %.3f', zero_fraction)\n\n\n # now use gaussian smoothing on those\n # some black magic sigma values\n sigma_e = xform.shape[0] * np.sqrt(zero_fraction) * 0.02\n sigma_cz = xform.shape[1] * np.sqrt(zero_fraction) * 0.05\n sigma1 = (0, sigma_cz)\n sigma2 = (sigma_e, 0)\n smooth_extended_xform = ndimage.filters.gaussian_filter(extended_xform, sigma1, mode='reflect')\n smooth_extended_sumw2 = ndimage.filters.gaussian_filter(extended_sumw2, sigma1, mode='reflect')\n smooth_extended_xform = ndimage.filters.gaussian_filter(smooth_extended_xform, sigma2, mode='nearest')\n smooth_extended_sumw2 = ndimage.filters.gaussian_filter(smooth_extended_sumw2, sigma2, mode='nearest')\n smooth_extended_errors = np.sqrt(smooth_extended_sumw2)\n\n # for low stats, smooth less....otherwise leave factor alone\n smooth_corr_factor = max(1., (rel_error * 10000))\n\n # now spline smooth\n new_xform, _ = spline_smooth(array=smooth_extended_xform,\n spline_binning=extended_binning,\n eval_binning=e_binning,\n axis=0,\n smooth_factor=self.params.aeff_e_smooth_factor.value/smooth_corr_factor,\n k=3,\n errors=smooth_extended_errors)\n\n final_xform, _ = spline_smooth(array=new_xform,\n spline_binning=cz_binning,\n eval_binning=cz_binning,\n axis=1,\n smooth_factor=self.params.aeff_cz_smooth_factor.value/smooth_corr_factor,\n k=3,\n errors=None)\n\n # the final array has the right shape again, because we evaluated the splines only\n # on the real binning\n\n # don't forget to zero out the zero bins again\n final_xform[:non_zero_idx, :] *= 0\n\n # clip unphysical (negative) values\n return final_xform.clip(0)","title":""},{"docid":"6d09d8d698f70e2f136685ca97d750d6","score":"0.5376346","text":"def reconstruct_plot_solution(U, xfaces):\n Nrec = 10 # Number of reconstruction points in each cell.\n N = U.shape[0]\n p = U.shape[1] - 1\n s = U.shape[2]\n\n # Create array of x at specified points.\n # We will calculate * u * at these points\n xrec = np.zeros((N, Nrec))\n for k in range(N):\n xrec[k, :] = np.linspace(xfaces[k], xfaces[k + 1], Nrec)\n\n print(xrec.shape)\n \n # Calculate u at each point.\n u = np.zeros((N, Nrec, s))\n for k in range(N):\n for i in range(Nrec):\n eps = (xrec[k, i] - xfaces[k]) / (xfaces[k + 1] - xfaces[k]) * 2 - 1\n for m in range(s):\n for j in range(p + 1):\n u[k, i, m] += U[k, j, m] * calc_phi(eps, p, j)\n\n\n u[:, :, 2] = (GAMMA - 1) * (u[:, :, 2] - u[:, :, 1]**2 / u[:, :, 0] / 2)\n u[:, :, 1] = u[:, :, 1] / u[:, :, 0]\n \n # Plot it.\n import matplotlib.pyplot as plt\n fig, ax = plt.subplots(3, 1, figsize=(11, 8.5))\n for m in range(s):\n for k in range(N):\n ax[m].plot(xrec[k, :], u[k, :, m])\n\n ax[0].set_ylabel(r'$\\rho$')\n ax[1].set_ylabel('$u$')\n ax[2].set_ylabel('$p$')\n ax[2].set_xlabel('$x$')\n \n plt.show()\n return xrec, u","title":""},{"docid":"e3a1dd270269901800fcff413c080b7e","score":"0.5370001","text":"def decompose_gate_matrix(operationMx):\n\n op_evals, op_evecs = _np.linalg.eig(_np.asarray(operationMx))\n # fp_eigenvec = None\n # aor_eval = None; aor_eigenvec = None\n # ra_eval = None; ra1_eigenvec = None; ra2_eigenvec = None\n\n TOL = 1e-4 # 1e-7\n\n unit_eval_indices = [i for (i, ev) in enumerate(op_evals) if abs(ev - 1.0) < TOL]\n #unit_eval_indices = [ i for (i,ev) in enumerate(op_evals) if ev > (1.0-TOL) ]\n\n conjpair_eval_indices = []\n for (i, ev) in enumerate(op_evals):\n if i in unit_eval_indices: continue # don't include the unit eigenvalues in the conjugate pair count\n # don't include existing conjugate pairs\n if any([(i in conjpair) for conjpair in conjpair_eval_indices]): continue\n for (j, ev2) in enumerate(op_evals[i + 1:]):\n if abs(ev - _np.conjugate(ev2)) < TOL:\n conjpair_eval_indices.append((i, j + (i + 1)))\n break # don't pair i-th eigenvalue with any other (pairs should be disjoint)\n\n real_eval_indices = [] # indices of real eigenvalues that are not units or a part of any conjugate pair\n complex_eval_indices = [] # indices of complex eigenvalues that are not units or a part of any conjugate pair\n for (i, ev) in enumerate(op_evals):\n if i in unit_eval_indices: continue # don't include the unit eigenvalues\n if any([(i in conjpair) for conjpair in conjpair_eval_indices]): continue # don't include the conjugate pairs\n if abs(ev.imag) < TOL: real_eval_indices.append(i)\n else: complex_eval_indices.append(i)\n\n #if len(real_eval_indices + unit_eval_indices) > 0:\n # max_real_eval = max([ op_evals[i] for i in real_eval_indices + unit_eval_indices])\n # min_real_eval = min([ op_evals[i] for i in real_eval_indices + unit_eval_indices])\n #else:\n # max_real_eval = _np.nan\n # min_real_eval = _np.nan\n #\n #fixed_points = [ op_evecs[:,i] for i in unit_eval_indices ]\n #real_eval_axes = [ op_evecs[:,i] for i in real_eval_indices ]\n #conjpair_eval_axes = [ (op_evecs[:,i],op_evecs[:,j]) for (i,j) in conjpair_eval_indices ]\n #\n #ret = { }\n\n nQubits = _np.log2(operationMx.shape[0]) / 2\n if nQubits == 1:\n #print \"DEBUG: 1 qubit decomp --------------------------\"\n #print \" --> evals = \", op_evals\n #print \" --> unit eval indices = \", unit_eval_indices\n #print \" --> conj eval indices = \", conjpair_eval_indices\n #print \" --> unpaired real eval indices = \", real_eval_indices\n\n #Special case: if have two conjugate pairs, check if one (or both) are real\n # and break the one with the largest (real) value into two unpaired real evals.\n if len(conjpair_eval_indices) == 2:\n iToBreak = None\n if abs(_np.imag(op_evals[conjpair_eval_indices[0][0]])) < TOL and \\\n abs(_np.imag(op_evals[conjpair_eval_indices[1][0]])) < TOL:\n iToBreak = _np.argmax([_np.real(conjpair_eval_indices[0][0]), _np.real(conjpair_eval_indices[1][0])])\n elif abs(_np.imag(op_evals[conjpair_eval_indices[0][0]])) < TOL: iToBreak = 0\n elif abs(_np.imag(op_evals[conjpair_eval_indices[1][0]])) < TOL: iToBreak = 1\n\n if iToBreak is not None:\n real_eval_indices.append(conjpair_eval_indices[iToBreak][0])\n real_eval_indices.append(conjpair_eval_indices[iToBreak][1])\n del conjpair_eval_indices[iToBreak]\n\n #Find eigenvector corresponding to fixed point (or closest we can get). This\n # should be a unit eigenvalue with identity eigenvector.\n if len(unit_eval_indices) > 0:\n #Find linear least squares solution within possibly degenerate unit-eigenvalue eigenspace\n # of eigenvector closest to identity density mx (the desired fixed point), then orthogonalize\n # the remaining eigenvectors w.r.t this one.\n A = _np.take(op_evecs, unit_eval_indices, axis=1)\n b = _np.array([[1], [0], [0], [0]], 'd') # identity density mx\n x = _np.dot(_np.linalg.pinv(_np.dot(A.T, A)), _np.dot(A.T, b))\n fixedPtVec = _np.dot(A, x) # fixedPtVec / _np.linalg.norm(fixedPtVec)\n fixedPtVec = fixedPtVec[:, 0]\n\n iLargestContrib = _np.argmax(_np.abs(x)) # index of gate eigenvector which contributed the most\n for ii, i in enumerate(unit_eval_indices):\n if ii == iLargestContrib:\n op_evecs[:, i] = fixedPtVec\n iFixedPt = i\n else:\n op_evecs[:, i] = op_evecs[:, i] - _np.vdot(fixedPtVec, op_evecs[:, i]) * fixedPtVec\n for jj, j in enumerate(unit_eval_indices[:ii]):\n if jj == iLargestContrib: continue\n op_evecs[:, i] = op_evecs[:, i] - _np.vdot(op_evecs[:, j], op_evecs[:, i]) * op_evecs[:, j]\n op_evecs[:, i] /= _np.linalg.norm(op_evecs[:, i])\n\n elif len(real_eval_indices) > 0:\n # just take eigenvector corresponding to the largest real eigenvalue?\n #iFixedPt = real_eval_indices[ _np.argmax( [ op_evals[i] for i in real_eval_indices ] ) ]\n\n # ...OR take eigenvector corresponding to a real unpaired eigenvalue closest to identity:\n idmx = _np.array([[1], [0], [0], [0]], 'd') # identity density mx\n iFixedPt = real_eval_indices[_np.argmin([_np.linalg.norm(op_evecs[i] - idmx) for i in real_eval_indices])]\n\n else:\n #No unit or real eigenvalues => two complex conjugate pairs or unpaired complex evals --> bail out\n return {'isValid': False, 'isUnitary': False, 'msg': \"All evals are complex.\"}\n\n #Find eigenvector corresponding to axis of rotation: find the *largest* unpaired real/unit eval\n indsToConsider = (unit_eval_indices + real_eval_indices)[:]\n del indsToConsider[indsToConsider.index(iFixedPt)] # don't consider fixed pt evec\n\n if len(indsToConsider) > 0:\n iRotAxis = indsToConsider[_np.argmax([op_evals[i] for i in indsToConsider])]\n else:\n #No unit or real eigenvalues => an unpaired complex eval --> bail out\n return {'isValid': False, 'isUnitary': False, 'msg': \"Unpaired complex eval.\"}\n\n #There are only 2 eigenvalues left -- hopefully a conjugate pair giving rotation\n inds = list(range(4))\n del inds[inds.index(iFixedPt)]\n del inds[inds.index(iRotAxis)]\n if abs(op_evals[inds[0]] - _np.conjugate(op_evals[inds[1]])) < TOL:\n iConjPair1, iConjPair2 = inds\n else:\n return {'isValid': False, 'isUnitary': False, 'msg': \"No conjugate pair for rotn.\"}\n\n return {'isValid': True,\n 'isUnitary': bool(len(unit_eval_indices) >= 2),\n 'fixed point': op_evecs[:, iFixedPt],\n 'axis of rotation': op_evecs[:, iRotAxis],\n 'rotating axis 1': op_evecs[:, iConjPair1],\n 'rotating axis 2': op_evecs[:, iConjPair2],\n 'decay of diagonal rotation terms': 1.0 - abs(op_evals[iRotAxis]),\n 'decay of off diagonal rotation terms': 1.0 - abs(op_evals[iConjPair1]),\n 'pi rotations': _np.angle(op_evals[iConjPair1]) / _np.pi,\n 'msg': \"Success\"}\n\n else:\n return {'isValid': False,\n 'isUnitary': False,\n 'msg': \"Unsupported number of qubits: %d\" % nQubits}","title":""},{"docid":"dc649914d8fe100550a97db0409a8af8","score":"0.535696","text":"def stress_gausspoint(xp, Vp, sig, mpe, xn):\n \n # Calcular las coordenas del punto de gauss - Centro del elemento\n x = (xn[0, 0] + xn[1, 0])/2 # equivalente a shi=0\n y = (xn[3, 1] + xn[0, 1])/2 # equivalente a eta=0\n \n sigq = np.zeros(3) # vector donde se guardan los esfuerzos en el punto de gauss\n LS_Ma = np.zeros((3,3)) # MAtriz con los coefcientes del sistema de Ec de minimos cuadrados\n \n # llenado la Ma de minimos cuadrados\n LS_Ma[0,0] = np.sum(Vp[mpe-1])\n LS_Ma[0,1] = np.sum(np.multiply(xp[mpe-1,0], Vp[mpe-1]))\n LS_Ma[0,2] = np.sum(np.multiply(xp[mpe-1,1], Vp[mpe-1]))\n LS_Ma[1,0] = LS_Ma[0,1]\n LS_Ma[1,1] = np.sum(np.multiply(np.multiply(xp[mpe-1,0], xp[mpe-1,0]), Vp[mpe-1]))\n LS_Ma[1,2] = np.sum(np.multiply(np.multiply(xp[mpe-1,0], xp[mpe-1,1]), Vp[mpe-1]))\n LS_Ma[2,0] = LS_Ma[0,2]\n LS_Ma[2,1] = LS_Ma[1,2]\n LS_Ma[2,2] = np.sum(np.multiply(np.multiply(xp[mpe-1,1], xp[mpe-1,1]), Vp[mpe-1]))\n \n # ciclo en las 3 componentes de esfuerzo\n for i in range(3):\n LS_Ve = np.zeros(3)\n LS_Ve[0] = np.sum(np.multiply(sig[mpe-1,i], Vp[mpe-1]))\n LS_Ve[1] = np.sum(np.multiply(np.multiply(xp[mpe-1,0], sig[mpe-1,i]), Vp[mpe-1]))\n LS_Ve[2] = np.sum(np.multiply(np.multiply(xp[mpe-1,1], sig[mpe-1,i]), Vp[mpe-1]))\n \n # solucionado el sistema de ecuaciones de minimos cuadrados\n alpha = np.linalg.solve(LS_Ma, LS_Ve)\n # Calculando el esfuerzo correspondiente en el punto de gauss\n sigq[i] = alpha[0] + alpha[1]*x + alpha[2]*y\n \n return sigq","title":""},{"docid":"15579181d3fee56aee6b95b28c7dbe03","score":"0.53551936","text":"def greedyEI(self):\n ymax=np.max(self.y[self.tt])\n nz=10\n zq=np.linspace(-2,2,nz)\n pz=norm.pdf(zq)\n pz=pz/np.sum(pz)\n EI=np.zeros((self.n,nz))\n EI[self.tt,:]=0\n zquad=np.zeros(self.n)\n zquad[self.tu+self.tt]=self.z[self.tu+self.tt]\n SIG_z_pos=self.SIG_z[np.ix_(self.uu,self.uu)]-np.matmul(self.SIG_z[np.ix_(self.uu,self.tu+self.tt)],np.linalg.solve(self.SIG_z[np.ix_(self.tu+self.tt,self.tu+self.tt)],self.SIG_z[np.ix_(self.tu+self.tt,self.uu)]))\n sig_z_pos=np.diag(SIG_z_pos)**0.5\n mu_z_pos=np.matmul(self.SIG_z[np.ix_(self.uu,self.tu+self.tt)],np.linalg.solve(self.SIG_z[np.ix_(self.tu+self.tt,self.tu+self.tt)],self.z[self.tu+self.tt])) \n for i in range(nz):\n zquad[self.uu]=mu_z_pos+zq[i]*sig_z_pos \n Dz=euclidean_distances(zquad.reshape(-1,1),zquad.reshape(-1,1),squared=True)\n SIG_y=self.ay**2*np.exp(-self.Dx*self.lyx**2/2-Dz*self.lyz**2/2)+self.by**2*np.identity(self.n)\n SIG_y_pos=SIG_y[np.ix_(self.uu+self.tu,self.uu+self.tu)]-np.matmul(SIG_y[np.ix_(self.uu+self.tu,self.tt)],np.linalg.solve(SIG_y[np.ix_(self.tt,self.tt)],SIG_y[np.ix_(self.tt,self.uu+self.tu)]))\n sig_y_pos=np.diag(SIG_y_pos)**0.5\n mu_y_pos=np.matmul(SIG_y[np.ix_(self.uu+self.tu,self.tt)],np.linalg.solve(SIG_y[np.ix_(self.tt,self.tt)],self.y[self.tt])) \n EI[self.uu+self.tu,i]=(mu_y_pos-ymax)*norm.cdf(np.divide(mu_y_pos-ymax,sig_y_pos))+sig_y_pos*norm.pdf(np.divide(mu_y_pos-ymax,sig_y_pos))\n alpha=np.matmul(EI,pz)\n \"\"\" greedy controller \"\"\"\n uua=[i for i in self.uu if i not in self.tz]\n tua=[i for i in self.tu if i not in self.ty]\n iuu=uua[np.argmax(alpha[uua])]\n if len(tua)>0:\n itu=tua[np.argmax(alpha[tua])]\n r1=np.dot(pz,np.maximum(EI[itu],EI[iuu]))/(self.cz+self.cy) \n r2=alpha[itu]/self.cy\n if r2>r1:\n return itu\n return iuu","title":""},{"docid":"4b96d303e07b3b525f3ea9d6e2f33435","score":"0.5351081","text":"def calculate_A_Bush_XY(self,xo,df_vcmax):\n \n alphaS,vcmax1,vcmax2,vcmax3,vcmax4,TP1,TP2,TP3,TP4,gm0,jmax1,jmax2,jmax3,jmax4 = xo\n vcmaxs=[vcmax1,vcmax2,vcmax3,vcmax4]\n tps=[TP1,TP2,TP3,TP4]\n jmaxs=[jmax1,jmax2,jmax3,jmax4]\n \n kmc = 267\n kmo = 164\n\n sco = 3.259;\n alphaT = 0;\n alphaG = 0;\n m=0;\n R = 0;\n # gm0 = 0\n replicates = df_vcmax['Replicate'].unique()\n A_mod_arr = np.array([])\n for replicate in replicates:\n df_vcmax_r=df_vcmax[df_vcmax['Replicate']==replicate];\n ci = df_vcmax_r['Ci'].values.astype(float)\n i_inc = df_vcmax_r['Iinc'].values.astype(float)\n theta = df_vcmax_r['Theta'].values.astype(float) \n O = df_vcmax_r['O'].values.astype(float)\n rd = df_vcmax_r['Rd'].values.astype(float)\n #jmax = df_vcmax_r['Jmax'].values.astype(float)\n \n k2LL = df_vcmax_r['k2LL'].values.astype(float) \n vcmax = vcmaxs[replicate-1]\n jmax = jmaxs[replicate-1]\n TP = tps[replicate-1]\n #gm0 = gms[replicate-1]\n # Rubisco-limited part\n\n gamma_st = 0.5*O/sco;\n gamma_x = gamma_st*(1-alphaG+2*alphaT);\n \n \n # Rubisco-limited part;\n KMCMO = kmc*(1+O/kmo);\n X1R = vcmax;\n X2R = KMCMO;\n \n PR = gm0*(X2R+gamma_x)+(X1R-rd)*R;\n QR2 = (ci-gamma_x)*X1R-(ci+X2R)*rd;\n QR = (X2R+gamma_x*(1-m))*(X1R-rd);\n AAR = X2R+gamma_x*(1-m)+R*(ci+X2R);\n BBR = m*(rd*X2R+gamma_x*X1R)-QR-PR*(ci+X2R)-R*QR2;\n CCR = -m*(rd*X2R+gamma_x*X1R)*(X1R-rd)+PR*QR2;\n AR = (-BBR-(BBR**2-4.*AAR*CCR)**0.5)/(2.*AAR);\n \n # Electron transport limited part;\n \n BB = k2LL*i_inc + jmax;\n J = (BB-(BB**2-4*theta*jmax*k2LL*i_inc)**0.5)/(2*theta);\n X1R = J/4;\n X2R = (1+2*alphaG-alphaT+alphaS)*2.*gamma_st;\n PR = gm0*(X2R+gamma_x)+(X1R-rd)*R;\n QR2 = (ci-gamma_x)*X1R-(ci+X2R)*rd;\n QR = (X2R+gamma_x*(1-m))*(X1R-rd);\n AAR = X2R+gamma_x*(1-m)+R*(ci+X2R);\n BBR = m*(rd*X2R+gamma_x*X1R)-QR-PR*(ci+X2R)-R*QR2;\n CCR = -m*(rd*X2R+gamma_x*X1R)*(X1R-rd)+PR*QR2;\n AJ = (-BBR-(BBR**2-4.*AAR*CCR)**0.5)/(2.*AAR);\n \n # TPU limited part; \n X1R = 3*TP;\n X2R = -(1+3*alphaG+6*alphaT+4*alphaS)*gamma_st;\n PR = gm0*(X2R+gamma_x)+(X1R-rd)*R;\n QR2 = (ci-gamma_x)*X1R-(ci+X2R)*rd;\n QR = (X2R+gamma_x*(1-m))*(X1R-rd);\n AAR = X2R+gamma_x*(1-m)+R*(ci+X2R);\n BBR = m*(rd*X2R+gamma_x*X1R)-QR-PR*(ci+X2R)-R*QR2;\n CCR = -m*(rd*X2R+gamma_x*X1R)*(X1R-rd)+PR*QR2;\n \n #NOTE: + sign in front of SQRT part;\n bacT = BBR**2-4*AAR*CCR;\n AP = (-BBR + (np.maximum(0.00001,BBR**2-4*AAR*CCR))**0.5)/(2*AAR);\n \n #--- Finding the Ci transition point from Aj to Ap;\n CCTS = np.maximum((1.0001+3*alphaG+6*alphaT+4*alphaS)*gamma_x, (24*gamma_x*TP+(1+3*alphaG+6*alphaT+4*alphaS)*gamma_x*J)/(J-12*TP));\n GMTS = gm0+R*3*TP/(CCTS-(1+3*alphaG+6*alphaT+4*alphaS)*gamma_x);\n AATS = (CCTS-(1-m)*gamma_x)*3*TP/(CCTS-(1+3*alphaG+6*alphaT+4*alphaS)*gamma_x);\n CITS = CCTS + (AATS-(1-m)*rd)/GMTS;\n\n #-- Model for net photosynthesis rate;\n if ci.all()<CITS.all() or bacT.all()<0:\n A_mod = np.minimum(AR,AJ)\n else:\n A1 = np.minimum(AR,AJ)\n A_mod = np.minimum(A1,AP)\n \n A_mod_arr = np.append(A_mod_arr,A_mod)\n \n return A_mod_arr","title":""},{"docid":"25c245b16fa923a4ef77735094353ff2","score":"0.5335676","text":"def g(x):\n return 5.0 - x[:, 1] - 0.5 * x[:, 0] ** 2.0","title":""},{"docid":"44fc6ce255a5b2294841b08f270ec105","score":"0.5328571","text":"def craig(self, x, size=0.3):\n \n sigma = []\n s0 = np.zeros(x.shape[-1]).tolist()\n self.x = x \n self.size = int(len(x)*size)\n gammas = np.zeros(self.size)\n \n print(\"Finding coreset...\")\n for i in range(self.size): \n e, L = np.inf, np.inf\n \n if sigma != []:\n S = x[sigma].tolist()\n else:\n S = []\n \n for j in range(len(x)): \n if j not in sigma:\n \n s_ = np.stack(S + [x[j], s0])\n l = 0 \n for k in range(len(x)):\n l += min(np.std(s_ - x[k], 1))\n# l = np.sum(np.std(x - x[j], -1))\n \n if l < L:\n L = l\n e = j\n \n sigma.append(e)\n \n self.sigma = np.stack(sigma)\n print(\"Calculating gammas...\")\n \n for i in range(len(x)):\n gammas[np.argmin(np.std(x[sigma] - x[i], 1))] += 1\n \n self.gammas = gammas\n \n return self.sigma, self.gammas","title":""},{"docid":"3a2124f26ab0674210ef2dff595cd34e","score":"0.5328473","text":"def cell_volumes(self):\n X = self.X\n Y = self.Y\n Z = self.Z\n a = [np.zeros(shape=(self.IMAX,self.JMAX,self.KMAX)) for _ in range(9)]\n # face csi=const \n for k in range(1,self.KMAX):\n for j in range(1,self.JMAX):\n for i in range(self.IMAX): # csi-const\n dx1 = X[i,j,k-1] - X[i,j-1,k]\n dy1 = Y[i,j,k-1] - Y[i,j-1,k]\n dz1 = Z[i,j,k-1] - Z[i,j-1,k]\n\n dx2 = X[i,j,k] - X[i,j-1,k-1]\n dy2 = Y[i,j,k] - Y[i,j-1,k-1]\n dz2 = Z[i,j,k] - Z[i,j-1,k-1]\n\n ax = dy1*dz2 - dz1*dy2\n ay = dz1*dx2 - dx1*dz2\n az = dx1*dy2 - dy1*dx2\n\n a[0][i,j,k] = ax*0.5\n a[1][i,j,k] = ay*0.5\n a[2][i,j,k] = az*0.5\n\n \n for k in range(1,self.KMAX):\n for j in range(self.JMAX): # face eta=const \n for i in range(1,self.IMAX):\n dx1 = X[i,j,k] - X[i-1,j,k-1]\n dy1 = Y[i,j,k] - Y[i-1,j,k-1]\n dz1 = Z[i,j,k] - Z[i-1,j,k-1]\n\n dx2 = X[i,j,k-1] - X[i-1,j,k]\n dy2 = Y[i,j,k-1] - Y[i-1,j,k]\n dz2 = Z[i,j,k-1] - Z[i-1,j,k]\n\n ax = dy1*dz2 - dz1*dy2\n ay = dz1*dx2 - dx1*dz2\n az = dx1*dy2 - dy1*dx2\n\n a[3][i,j,k] = ax*0.5\n a[4][i,j,k] = ay*0.5\n a[5][i,j,k] = az*0.5\n\n # face zit=const \n for k in range(self.KMAX): # zit=const\n for j in range(1,self.JMAX): \n for i in range(1,self.IMAX):\n dx1 = X[i,j,k] - X[i-1,j-1,k]\n dy1 = Y[i,j,k] - Y[i-1,j-1,k]\n dz1 = Z[i,j,k] - Z[i-1,j-1,k]\n\n dx2 = X[i-1,j,k] - X[i,j-1,k]\n dy2 = Y[i-1,j,k] - Y[i,j-1,k]\n dz2 = Z[i-1,j,k] - Z[i,j-1,k]\n\n ax = dy1*dz2 - dz1*dy2\n ay = dz1*dx2 - dx1*dz2\n az = dx1*dy2 - dy1*dx2\n\n a[6][i,j,k] = ax*0.5\n a[7][i,j,k] = ay*0.5\n a[8][i,j,k] = az*0.5\n\n cf = np.zeros(shape=(6,3))\n v = np.zeros(shape=(self.IMAX,self.JMAX,self.KMAX))\n \n for k in trange(1,self.KMAX,desc='Calculating the volumes'):\n for j in range(1,self.JMAX): \n for i in range(1,self.IMAX):\n cf[0,0] = X[i-1,j-1,k-1] + X[i-1,j-1,k] + X[i-1,j,k-1] + X[i-1,j,k]\n cf[0,1] = Y[i-1,j-1,k-1] + Y[i-1,j-1,k] + Y[i-1,j,k-1] + Y[i-1,j,k]\n cf[0,2] = Z[i-1,j-1,k-1] + Z[i-1,j-1,k] + Z[i-1,j,k-1] + Z[i-1,j,k]\n cf[1,0] = X[i,j-1,k-1] + X[i,j-1,k] + X[i,j,k-1] + X[i,j,k]\n cf[1,1] = Y[i,j-1,k-1] + Y[i,j-1,k] + Y[i,j,k-1] + Y[i,j,k]\n cf[1,2] = Z[i,j-1,k-1] + Z[i,j-1,k] + Z[i,j,k-1] + Z[i,j,k]\n cf[2,0] = X[i-1,j-1,k-1] + X[i-1,j-1,k] + X[i,j-1,k-1] + X[i,j-1,k]\n cf[2,1] = Y[i-1,j-1,k-1] + Y[i-1,j-1,k] + Y[i,j-1,k-1] + Y[i,j-1,k]\n cf[2,2] = Z[i-1,j-1,k-1] + Z[i-1,j-1,k] + Z[i,j-1,k-1] + Z[i,j-1,k]\n cf[3,0] = X[i-1,j,k-1] + X[i-1,j,k] + X[i,j,k-1] + X[i,j,k]\n cf[3,1] = Y[i-1,j,k-1] + Y[i-1,j,k] + Y[i,j,k-1] + Y[i,j,k]\n cf[3,2] = Z[i-1,j,k-1] + Z[i-1,j,k] + Z[i,j,k-1] + Z[i,j,k]\n cf[4,0] = X[i-1,j-1,k-1] + X[i-1,j,k-1] + X[i,j-1,k-1] + X[i,j,k-1]\n cf[4,1] = Y[i-1,j-1,k-1] + Y[i-1,j,k-1] + Y[i,j-1,k-1] + Y[i,j,k-1]\n cf[4,2] = Z[i-1,j-1,k-1] + Z[i-1,j,k-1] + Z[i,j-1,k-1] + Z[i,j,k-1]\n cf[5,0] = X[i-1,j-1,k] + X[i-1,j,k] + X[i,j-1,k] + X[i,j,k]\n cf[5,1] = Y[i-1,j-1,k] + Y[i-1,j,k] + Y[i,j-1,k] + Y[i,j,k]\n cf[5,2] = Z[i-1,j-1,k] + Z[i-1,j,k] + Z[i,j-1,k] + Z[i,j,k]\n\n vol12=0\n for n in range(0,2): # n = 0,1\n for l in range(0,3): # l = 0,1,2\n vol12 += math.pow(-1,n+1) *( \n +cf[n,l]*a[l][i-1+n,j,k]\n +cf[2+n,l]*a[3+l][i,j-1+n,k]\n +cf[4+n,l]*a[6+l][i,j,k-1+n])\n v[i,j,k]= vol12/12\n return v","title":""},{"docid":"92c604d0324a1e9f6ef30e00339a2002","score":"0.532701","text":"def partition_energy(self):\n\n\n self.E.Ef_nuc = self.molE.Enuc # Sum of nuclear fragments is just nuclear of molecule\n self.E.Ef_ext = 0.0\n self.E.Ef_kin = 0.0\n self.E.Ef_vxc = 0.0\n self.E.Ef_vha = 0.0\n self.E.Ef_ext = 0.0\n\n self.E.Ep_ext = 0.0\n #devwarn Will only work for diatomics for now\n # External nuclear potential\n # self.E.Ep_ext += np.sum( (self.frags[0].da_frac + self.frags[0].db_frac) * self.frags[1].V.Vnm )\n # self.E.Ep_ext += np.sum( (self.frags[1].da_frac + self.frags[1].db_frac) * self.frags[0].V.Vnm )\n\n # Kinetic\n for ifrag in self.frags:\n self.E.Ef_ext += ifrag.E.Eext # External nuclear potential\n self.E.Ef_kin += ifrag.E.Ekin # Kinetic\n self.E.Ef_vxc += ifrag.E.Evxc # XC\n self.E.Ef_vha += ifrag.E.Evha # Hartree\n\n self.frags_ekin = np.sum( self.Tnm0.np * (self.dfa + self.dfb) )\n self.frags_eha = np.einsum('pq,pq->', (self.inverter.va), self.dfa, optimize=True)\n self.frags_ext = np.sum( self.Vnm0.np * (self.dfa + self.dfb) )\n #self.frags_exc\n\n # Components of Ep\n self.E.Ep_kin = self.frags_ekin - self.E.Ef_kin\n self.E.Ep_vxc = self.frags_exc - self.E.Ef_vxc\n self.E.Ep_vha = self.frags_eha - self.E.Ef_vha\n self.E.Ep_ext = self.frags_ext - self.E.Ef_ext\n # self.E.Ep_kin = self.molE.Ekin - self.E.Ef_kin\n # self.E.Ep_vxc = self.molE.Evxc - self.E.Ef_vxc\n # self.E.Ep_vha = self.molE.Evha - self.E.Ef_vha\n # self.E.Ep_ext = self.molE.Eext - self.E.Ef_ext\n\n # Ep and Ef\n self.E.Ep = self.E.Ep_kin + self.E.Ep_vha + self.E.Ep_vxc + self.E.Ep_ext\n self.E.Ef = self.frags[0].E.Etot + self.frags[1].E.Etot","title":""},{"docid":"3f804d20ecd80bfff95563f20da4ab0f","score":"0.5308593","text":"def computeDecomposition(self):\n self.computeBoundingBox()\n # self.polygon.E = [self.polygon.E[i] for i in [1, 7, 3, 5]] \\\n # + [self.polygon.E[i] for i in range(len(self.polygon.E)) if i not in [1, 7, 3, 5]]\n random.shuffle(self.polygon.E)\n for lineSegment in self.polygon.E:\n self.insertLinesegment(lineSegment)\n # self.T.visualize()\n # self.T.visualize_graph()","title":""},{"docid":"8f152fe9457ed7397db6d9f46ad03554","score":"0.53022593","text":"def _get_hybrid_stack(x0, y0,final_q, final_a, final_var, im_args):\r\n X_lg,Y_lg,_lg_fac_x,_lg_fac_y, shape = im_args\r\n X_use = X_lg - (x0 + _lg_fac_x)\r\n Y_use = Y_lg - (y0 + _lg_fac_y)\r\n stack_full = np.zeros((X_use.shape[0],X_use.shape[1], len(final_q)))\r\n num_g = final_q.shape[0]\r\n\r\n for k in range(num_g):\r\n q,a,var = final_q[k],final_a[k],final_var[k]\r\n use_gauss = (var*q*q > 25.)\r\n R2_use = np.square(X_use) + np.square(Y_use)/(q*q)\r\n\r\n for i in range(X_use.shape[0]):\r\n for j in range(X_use.shape[1]):\r\n #If outside 5sigma then keep as 0\r\n if R2_use[i,j]/var > 25.:\r\n continue\r\n elif use_gauss:\r\n #If sigma>5 no benefit to using erf so go with quicker simple calc\r\n stack_full[i,j,k] = a / (2*np.pi*var * q) * np.exp( -1.*(X_use[i,j]*X_use[i,j] + Y_use[i,j]*Y_use[i,j]/(q*q) )/ (2*var) )\r\n else:\r\n c_x = 1./(np.sqrt(2*var))\r\n c_y = 1./(np.sqrt(2*var)*q)\r\n stack_full[i,j,k] = a/4 *( ( _erf_approx(c_x*(X_use[i,j]-0.5)) - _erf_approx(c_x*(X_use[i,j]+0.5)) )* ( _erf_approx(c_y*(Y_use[i,j] -0.5)) - _erf_approx(c_y*(Y_use[i,j]+0.5)) ) )\r\n\r\n return stack_full","title":""},{"docid":"5a490c555bf647cf0a8b5ef651f4cf65","score":"0.5285301","text":"def exo4():\n Ablock = []\n for i in 1: p:\n for j in 1: p:\n for k in 1: n:\n Z = zeros(n, n, n)\n Z((1: p) + (i-1)*p, (1: p) + (j-1)*p, k) = 1\n Ablock(end + 1, : ) = Z(: )'\n imageplot(Ablock)","title":""},{"docid":"2a8b8a8299bf03aab7c6a75db36ed6ca","score":"0.52766126","text":"def __scater_fill__(gdt_alpha, mx_gdt_rh, mx_gdt_rw, mx_gdt_x, mx_gdt_y, k, ctx):\n assert gdt_alpha.shape[1] ==1, gdts.shape\n n = gdt_alpha.shape[0]\n alpha = gdt_alpha\n rh, rw = [mx_gdt_rh, mx_gdt_rw]\n x_offset, y_offset = [mx_gdt_x, mx_gdt_y]\n\n\n # s1: grid...\n ## do computations in a small scale as much as poosible...\n x=np.arange(0,k)*1./k -0.5 + 1./(2*k) # align center to origin for rotation(-.5) and, each node represents a integritied block(+ 1/(2k))\n X,Y = np.meshgrid(x,x)\n x_grid, y_grid = [ np.reshape(_,(1,k**2)) for _ in [X,Y] ]\n # repeat to same shape[0] of gdt...\n x_grid, y_grid = [ mx.nd.array(np.repeat(_, n, axis=0), ctx) for _ in [x_grid, y_grid] ]\n\n ## s2: scale...\n x_grid, y_grid = [mx.nd.broadcast_mul(grid, 2*r) for grid,r in zip([x_grid, y_grid], [rh, rw]) ]\n\n ## s3: rotate...\n # do it in a seperate definition\n x_grid, y_grid = mx_rotate(x_grid, y_grid, alpha)\n\n ## s4: shift...\n# assert 0, (x_grid.shape, x_offset.shape)\n x_grid, y_grid = [ mx.nd.broadcast_add(_ , offset) for\\\n _, offset in zip([x_grid, y_grid], [x_offset, y_offset] ) ]\n\n return x_grid, y_grid","title":""},{"docid":"b9e7c9ba875065f0afaee104bb54b13a","score":"0.52637494","text":"def compute_edges_dxdy(I):\n #I = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)\n\n# Gaussian filter\n I = I.astype(np.float32) / 255.\n sigma = 2\n size = 9\n\n x_points = np.arange(-(size - 1) // 2, (size - 1) // 2 + 1, 1)\n y_points = x_points[::-1]\n xs, ys = np.meshgrid(x_points, y_points)\n kernel = 1 / (2 * np.pi * sigma ** 2) * np.exp(- (xs ** 2 + ys** 2) / (2 * sigma ** 2)) * (-ys / sigma ** 2)\n kernel = kernel/kernel.sum()\n\n dx = np.zeros(I.shape)\n dy = np.zeros(I.shape)\n mag = np.zeros(I.shape)\n for channel in range(3):\n dx[:, :, channel] = signal.convolve2d(I[:, :, channel], kernel, mode='same', boundary='symm')\n dy[:, :, channel] = signal.convolve2d(I[:, :, channel], kernel.T, mode='same', boundary='symm')\n mag[:, :, channel] = np.sqrt(dx[:, :, channel] ** 2 + dy[:, :, channel] ** 2)\n mag[:, :, channel] = mag[:, :, channel] / np.max(mag[:, :, channel])\n # dx = signal.convolve2d(I, kernel, mode='same', boundary='symm')\n # dy = signal.convolve2d(I, kernel.T, mode='same', boundary='symm')\n mag = np.sqrt(np.sum(mag ** 2, axis=2))\n #mag = np.sqrt(dx ** 2 + dy ** 2)\n mag = mag / np.max(mag)\n\n dx = dx.sum(axis=2)\n dy = dy.sum(axis=2)\n\n #Non-maximum suppression\n height,width= mag.shape\n for i in range(1, height - 1):\n for j in range(1, width - 1):\n grade = np.sqrt(dx[i][j] ** 2 + dy[i][j] ** 2)\n if mag[i][j] <= 0 or mag_check(mag, [i + 1 * dx[i][j] / grade, j + 1 * dx[i][j] / grade], [i, j]) or mag_check(mag, [i - 1 * dx[i][j] / grade, j - 1 * dx[i][j] / grade], [i, j]):\n mag[i][j] = 0\n\n\n mag = mag * 255\n mag = np.clip(mag, 0, 255)\n mag = mag.astype(np.uint8)\n return mag","title":""},{"docid":"25453b2263570fba5e90c08a9c7fef92","score":"0.5258398","text":"def GERPFunc (x, *p)->ndarray:\n amp = p[0]\n total_width = p[1]\n start_pos = p[2]\n edge_width = p[3]\n peak_width = edge_width*2\n edge_sigma = p[4]\n\n flat_start = start_pos +edge_width\n flat_width = total_width -peak_width\n\n flat_end = flat_start +flat_width\n\n\n raising_edge_pars = [ amp, edge_sigma, flat_start ]\n gaussUp = where( x<flat_start, gaussianFunc(x, *raising_edge_pars), 0. )\n falling_edge_pars = [ amp, edge_sigma, flat_end ]\n gaussDn = where( x>flat_end, gaussianFunc(x, *falling_edge_pars),0. )\n\n flat_pars = [ amp, flat_width, flat_start]\n rect_pulse = rectPulseFunc( x, *flat_pars )\n return gaussUp +rect_pulse +gaussDn","title":""},{"docid":"82969ce9ab561f65623b31f2b3c28937","score":"0.5249008","text":"def shapfunc_gauss1(xn):\n \n nnodes = len(xn[:,0]) # obtener el numero de nodos del elemento para determinar si es trangular o cudrilateral\n assert (nnodes == 3 or nnodes == 4), \"Hay un error en el mallado\"\n \n Ng = np.zeros((3, len(xn)))\n if nnodes == 3:\n # Elemento triangular lineal\n x1, x2, x3 = xn[0, 0], xn[1, 0], xn[2, 0]\n y1, y2, y3 = xn[0, 1], xn[1, 1], xn[2, 1]\n A = 1/2*(x1*y2 - x1*y3 + x2*y3 - x2*y1 + x3*y1 - x3*y2)\n # -- funciones de forma ---\n Ng[0, 0] = 1 / 3\n Ng[0, 1] = 1 / 3\n Ng[0, 2] = 1 / 3\n # -- Dervidas en x de las funcioens de forma\n Ng[1, 0] = 1/(2*A)*(y2 - y3)\n Ng[1, 1] = 1/(2*A)*(y3 - y1)\n Ng[1, 2] = 1/(2*A)*(y1 - y2)\n # -- Derivadas en y de las funciones de forma\n Ng[2, 0] = 1/(2*A)*(x3 - x2)\n Ng[2, 1] = 1/(2*A)*(x1 - x3)\n Ng[2, 2] = 1/(2*A)*(x2 - x1)\n else:\n # elemento cuadrilateral bilineal\n x1, x2, x3, x4 = xn[0, 0], xn[1, 0], xn[2, 0], xn[3, 0]\n y1, y2, y3, y4 = xn[0, 1], xn[1, 1], xn[2, 1], xn[3, 1]\n a = 1/2 * (x2 - x1)\n b = 1/2 * (y4 - y1)\n x = (x1 + x2)/2 # equivalente a shi=0\n y = (y4 + y1)/2 # equivalente a eta=0\n \n # -- funciones de forma ---\n Ng[0, 0] = 1 / (4*a*b) * (x - x2) * (y - y4)\n Ng[0, 1] = -1 / (4*a*b) * (x - x1) * (y - y3)\n Ng[0, 2] = 1 / (4*a*b) * (x - x4) * (y - y2)\n Ng[0, 3] = -1 / (4*a*b) * (x - x3) * (y - y1)\n # -- Dervidas en x de las funcioens de forma\n Ng[1, 0] = 1 / (4*a*b) * (y - y4)\n Ng[1, 1] = -1 / (4*a*b) * (y - y3)\n Ng[1, 2] = 1 / (4*a*b) * (y - y2)\n Ng[1, 3] = -1 / (4*a*b) * (y - y1)\n # -- Derivadas en y de las funciones de forma\n Ng[2, 0] = 1 / (4*a*b) * (x - x2)\n Ng[2, 1] = -1 / (4*a*b) * (x - x1)\n Ng[2, 2] = 1 / (4*a*b) * (x - x4)\n Ng[2, 3] = -1 / (4*a*b) * (x - x3)\n \n return Ng","title":""},{"docid":"695f72bbd74514b203a43d4d4520180a","score":"0.5248518","text":"def f_gauss2d2cA(p, Xg, Yg, Xr, Yr, Arr):\n #Ag,Ar, x0, y0, s, bG, bR, b_x, b_y = p\n #return A*scipy.exp(-((X-x0)**2 + (Y - y0)**2)/(2*s**2)) + b + b_x*X + b_y*Y\n #genGaussInArray(Arr[:,:,0], Xr,Yr,Ar,x0,y0,s,bR,b_x,b_y)\n #r.strides = r.strides #Really dodgy hack to get around something which numpy is not doing right ....\n\n #genGaussInArray(Arr[:,:,1],Xg,Yg,Ag,x0,y0,s,bG,b_x,b_y)\n #genSplitGaussInArray(Arr,Xg, Yg,Xr,Yr,Ag, Ar,x0,y0,s,bG,b_x,b_y)\n genSplitGaussInArrayPVec(p,Xg, Yg,Xr,Yr,Arr)\n #g.strides = g.strides #Really dodgy hack to get around something which numpy is not doing right ....\n\n return Arr #numpy.concatenate((g.reshape(g.shape + (1,)),r.reshape(g.shape + (1,))), 2)","title":""},{"docid":"aa6f04ac75363c3c696c91ce7f1f9676","score":"0.5246236","text":"def __call__(self, x, incumbent=0, *args, **kwargs):\n x = np.atleast_2d(x)\n\n a_ES = np.empty((x.shape[0], self.n_samples_y))\n\n # Evaluate mean and covariance of GP at all representer points and\n # points x where MRS will be evaluated\n f_mean_all, f_cov_all = \\\n self.model.gp.predict(np.vstack((self.X_candidate, x)),return_cov=True)\n\n \n #self.model.gp.predict(np.vstack((self.X_candidate, x)),return_cov=True)\n f_mean = f_mean_all[:self.n_candidates]\n f_cov = f_cov_all[:self.n_candidates, :self.n_candidates]\n\n # Iterate over all x[i] at which we will evaluate the acquisition \n # function (often x.shape[0]=1)\n for i in range(self.n_candidates, self.n_candidates+x.shape[0]):\n # Simulate change of covariance (f_cov_delta) for a sample at x[i], \n # which actually would not depend on the observed value y[i]\n f_cov_query = f_cov_all[[i]][:, [i]]\n f_cov_cross = f_cov_all[:self.n_candidates, [i]]\n f_cov_query_inv = np.linalg.inv(f_cov_query)\n f_cov_delta = -np.dot(np.dot(f_cov_cross, f_cov_query_inv),\n f_cov_cross.T)\n\n # precompute samples from GP posterior for non-modified mean\n f_mean=np.ravel(f_mean)\n\n f_samples = np.random.RandomState(self.rng_seed).multivariate_normal(\n f_mean, f_cov + f_cov_delta, self.n_gp_samples).T\n\n # adapt for different outcomes y_i[j] of the query at x[i]\n for j in range(self.n_samples_y): \n # \"sample\" outcomes y_i[j] (more specifically where on the \n # Gaussian distribution over y_i[j] we would end up)\n y_delta = np.sqrt(f_cov_query + self.model.gp.alpha)[:, 0] \\\n * self.percent_points[j]\n # Compute change in GP mean at representer points\n f_mean_delta = f_cov_cross.dot(f_cov_query_inv).dot(y_delta)\n\n # Adapt samples to changes in GP posterior mean\n f_samples_j = f_samples + f_mean_delta[:, np.newaxis]\n # Count frequency of the candidates being the optima in the samples\n p_max = np.bincount(np.argmax(f_samples_j, 0),\n minlength=f_mean.shape[0]) \\\n / float(self.n_gp_samples)\n # Determing entropy of distr. p_max and compare to base entropy\n a_ES[i - self.n_candidates, j] = \\\n self.base_entropy - entropy(p_max)\n\n # Average entropy change over the different assumed outcomes y_i[j]\n return a_ES.mean(1)","title":""},{"docid":"3e995038eb672ef15b11bcfa1a2cb1c5","score":"0.52152836","text":"def _cut_geometries(geom, start_x, start_y, end_x, end_y, kcu_array):\n\n cut_geometries = np.zeros((len(start_x),), dtype=DT_VARIABLE)\n start_points = MultiPoint(list(zip(start_x, start_y)))\n end_points = MultiPoint(list(zip(end_x, end_y)))\n\n for piece in range(len(cut_geometries)):\n kcu = kcu_array[piece]\n if kcu == 0:\n start_pnt = geom.interpolate(geom.project(start_points[piece]))\n end_pnt = geom.interpolate(geom.project(end_points[piece]))\n else:\n start_pnt = start_points[piece]\n end_pnt = end_points[piece]\n start_distance = round(geom.project(start_pnt), 3)\n end_distance = round(geom.project(end_pnt), 3)\n\n # Don't use the z-coordinate\n if geom.has_z:\n coords = [(x, y) for x, y, z in geom.coords]\n else:\n coords = list(geom.coords)\n start_set = False\n # no additional calc points\n if start_distance <= 0.0 and end_distance >= geom.length:\n linestring = np.array(\n [(start_pnt.x, start_pnt.y)]\n + coords[1:-1]\n + [(end_pnt.x, end_pnt.y)]\n )\n # \"F\" means to flatten in column-major (Fortran- style) order\n cut_geometries[piece] = linestring.flatten(\"F\")\n\n for i, p in enumerate(coords):\n # dist to vertex\n pd = round(geom.project(Point(p)), 3)\n\n # should not happen but sometimes drawing direction does not\n # correspond with start and endpoints, so flip them\n if start_distance > end_distance:\n # This is not safe!!!!!!\n # TODO: check if this actually can happen\n start_distance, end_distance = end_distance, start_distance\n start_points, end_points = end_points, start_points\n\n # check for start point\n if (pd == start_distance) and not start_set:\n start_pnt = []\n start_i = i\n start_set = True\n # should not happen but in case pd (point) and first vertex\n # do not have the same position move pd back\n elif (pd > start_distance) and not start_set:\n start_pnt = [(start_pnt.x, start_pnt.y)]\n start_i = i\n start_set = True\n if pd >= end_distance:\n linestring = np.array(\n start_pnt + coords[start_i:i] + [(end_pnt.x, end_pnt.y)]\n )\n # \"F\" means to flatten in column-major (Fortran- style)\n # order\n cut_geometries[piece] = linestring.flatten(\"F\")\n break\n\n return cut_geometries","title":""},{"docid":"deb2267b18d294393eb60b0184821600","score":"0.51865214","text":"def gauss_xmat(filename, natoms): \n full = io.read_file(filename)\n nmodes = 3*natoms-6 \n lines = full.split('X matrix')[1].split('Resonance')[0]\n lines = lines.split('\\n')\n del lines[0]\n del lines[-1]\n \n xmat = np.zeros((nmodes, nmodes))\n rangemod = 1\n if nmodes%5 == 0:\n rangemod = 0\n marker = 0\n\n for m in range(0, nmodes/5+rangemod):\n length = nmodes - m * 5 \n a= np.array( lines[marker+1:marker+length+1])\n for i in range(length):\n for j in range(0, len(a[i].split())-1):\n xmat[m*5 + i, m*5 + j] = a[i].split()[j+1]\n xmat[m*5 + j, m*5 + i] = a[i].split()[j+1]\n marker += length+1\n\n return xmat","title":""},{"docid":"a042232809fe53a9fa749d7fbe7e7b75","score":"0.5185479","text":"def drawECG(self, start=0, leads=range(12), savefilename=None):\n# if self.data.shape[1] < 12:\n# raise ValueError, 'Less than 12 leads available'\n# if self.data.shape[0] / self.samplingrate < 10:\n# raise ValueError, 'Less than 10 seconds of data available'\n\n data = self.data[:, leads]\n\n ################################################################################\n #\n # Draw the background \n #\n ################################################################################\n\n #time scale - 1 ms/px (1mm = 40 px at 25 mm/s)\n #amp scale - 2.5 mcV/px (1mm = 40 px at 100 mcv/mm) \n #image width = 10 seconds = 10000 px\n #image height = 4 strips\n #strip height = 34 mm (3.4 mV)\n \n onemm = 40 * int(self.samplingrate / 1000)\n onesec = self.samplingrate\n onemv = (400 * self.samplingrate) / (1000 * 1000) # correct for microV\n lenecg = 10*onesec\n htecg = 136*onemm\n\n #Linethicknesses\n thickbgwidth = 0.4\n thinbgwidth = 0.1\n ecgwidth = 0.8\n\n ecgfig = pylab.figure()\n #thick horizontal lines\n for horiz in range(0,htecg,5*onemm):\n pylab.plot([0,lenecg],[horiz,horiz],'r',linewidth=thickbgwidth)\n\n #thick vertical lines\n for vert in range(0,lenecg,5*onemm):\n pylab.plot([vert,vert],[0,htecg],'r',linewidth=thickbgwidth)\n\n #thin horizontal lines\n for horiz in range(0,htecg,onemm):\n pylab.plot([0,lenecg],[horiz,horiz],'r',linewidth=thinbgwidth)\n\n #thin vertical lines\n for vert in range(0,lenecg,onemm):\n pylab.plot([vert,vert],[0,htecg],'r',linewidth=thinbgwidth)\n \n ################################################################################\n #\n # Draw the ECG \n #\n ################################################################################\n startplot = 0\n stripcenter = 17 #in mm\n striplength = int(62.5*onemm) # in px (2.5 seconds)\n rhythmlead = 1\n\n horizcenters = (((scipy.array([0,-1,-2,-3]))*2*stripcenter) - 17) *onemm\n\n #prepare data\n for lead in range(12):\n #center horizontally\n data[:,lead] -= scipy.mean(data[:,lead])\n #rescale \n data *= onemv \n\n #column 1\n for lead in range(3):\n pylab.plot(range(striplength),\\\n data[startplot:striplength+startplot,lead]-horizcenters[3-lead],\\\n 'k',linewidth = ecgwidth)\n\n #column 2\n for lead in range(3,6):\n pylab.plot(range(striplength,2*striplength),\\\n data[striplength+startplot:striplength*2+startplot,lead]-horizcenters[6-lead],\\\n 'k',linewidth = ecgwidth)\n \n #column 3\n for lead in range(6,9):\n pylab.plot(range(2*striplength,3*striplength),\\\n data[striplength*2+startplot:striplength*3+startplot,lead]-horizcenters[9-lead],\\\n 'k',linewidth = ecgwidth)\n\n #column 4\n for lead in range(9,12):\n pylab.plot(range(3*striplength,4*striplength),\\\n data[striplength*3+startplot:striplength*4+startplot,lead]-horizcenters[12-lead],\\\n 'k',linewidth = ecgwidth)\n\n #rhythm strip\n pylab.plot(range(4*striplength),\\\n data[startplot:4*striplength,rhythmlead]-horizcenters[0],\\\n 'k',linewidth = ecgwidth)\n\n ################################################################################\n #\n # Labels\n #\n ################################################################################\n labels = ['I','II','III','aVR','aVL','aVF','V1','V2','V3','V4','V5','V6']\n xoffset = 20\n yoffset = -250\n labelx = [xoffset]*3 + [xoffset+striplength]*3 +\\\n [xoffset+2*striplength]*3 + [xoffset+3*striplength]*3\n labely = list(yoffset - horizcenters[3:0:-1])*4\n\n for labelct in range(12):\n pylab.text(labelx[labelct],labely[labelct],labels[labelct],fontsize=8)\n\n pylab.text(labelx[0],yoffset-horizcenters[0],labels[rhythmlead],fontsize=8)\n\n #pylab.axis('off')\n pylab.setp(pylab.gca(),xticklabels=[])\n pylab.setp(pylab.gca(),yticklabels=[])\n pylab.axis([0,lenecg,0,htecg])\n \n if not savefilename:\n pylab.show()\n else:\n pylab.savefig(savefilename, dpi=300)\n\n #if possible, crop with imagemagick\n try:\n commands.getoutput(\"mogrify -trim '%s'\" %(savefilename))\n except:\n pass\n\n # clear the figure\n # Otherwise subsequent plots overlap\n ecgfig.clf()","title":""},{"docid":"5d81b57f53c669b05c8a0964c0b320d5","score":"0.517849","text":"def gauss1(s):\n\n size = s * 3\n \n x = y = np.arange(-size, size + 1)\n \n ker_x = np.exp(-(x**2 / float(s)))\n return ker_x / ker_x.sum() \n #ker_y = np.array([[s] for s in ker_x])","title":""},{"docid":"d25fae2f7ff20ffb84ce68accc411cf0","score":"0.51747596","text":"def ElGamal_Bob(xQa, yQa, xM, yM):\n xC1, yC1 = fastAdding(k, xG, yG)\n xkQa, ykQa = fastAdding(k, xQa, yQa)\n xC2, yC2 = ecPQaddition(xkQa, ykQa, xM, yM)\n return [(xC1, yC1), (xC2, yC2)]","title":""},{"docid":"e665180984ecdb16bf5aef4664bda822","score":"0.5172982","text":"def calculateExteriorElementBoundaryCoefficients(self,ebqe):\n pass","title":""},{"docid":"1534ca07567b247c4421f8e588a36bb4","score":"0.51671344","text":"def test_points():\n with get_test_data('interpolation_test_grid.npz') as fobj:\n data = np.load(fobj)\n return np.stack([data['xg'].reshape(-1), data['yg'].reshape(-1)], axis=1)","title":""},{"docid":"825dadc7a5d2ef1a1f6aa52306f3eb5a","score":"0.5150982","text":"def TwoGroupDiffusionEigenvalue(R,I,D1,D2,Sig_r1,Sig_r2, nu_Sigf1, nu_Sigf2,Sig_s12, geometry,epsilon = 1.0e-8):\n #create the grid\n Delta_r, centers, edges = create_grid(R,I)\n M11 = np.zeros((I+1,I+1))\n M21 = np.zeros((I+1,I+1))\n M22 = np.zeros((I+1,I+1))\n P11 = np.zeros((I+1,I+1))\n P12 = np.zeros((I+1,I+1))\n #define surface areas and volumes\n assert( (geometry==0) or (geometry == 1) or (geometry == 2))\n if (geometry == 0):\n #in slab it's 1 everywhere except at the left edge\n S = 0.0*edges+1\n S[0] = 0.0 #to enforce Refl BC\n #in slab its dr\n V = 0.0*centers + Delta_r\n elif (geometry == 1):\n #in cylinder it is 2 pi r\n S = 2.0*np.pi*edges\n #in cylinder its pi (r^2 - r^2)\n V = np.pi*( edges[1:(I+1)]**2 \n - edges[0:I]**2 )\n elif (geometry == 2):\n #in sphere it is 4 pi r^2\n S = 4.0*np.pi*edges**2\n #in sphere its 4/3 pi (r^3 - r^3)\n V = 4.0/3.0*np.pi*( edges[1:(I+1)]**3\n - edges[0:I]**3 )\n \n #Set up BC at R\n M11[I,I] = 1.0\n M11[I,I-1] = 1.0\n M22[I,I] = 1.0\n M22[I,I-1] = 1.0\n \n #fill in rest of matrix\n for i in range(I):\n r = centers[i]\n M11[i,i] = (0.5/(Delta_r * V[i])*((D1(r)+D1(r+Delta_r))*S[i+1]) +\n Sig_r1(r))\n M22[i,i] = (0.5/(Delta_r * V[i])*((D2(r)+D2(r+Delta_r))*S[i+1]) +\n Sig_r2(r))\n M21[i,i] = -Sig_s12(r)\n P11[i,i] = nu_Sigf1(r)\n P12[i,i] = nu_Sigf2(r)\n if (i>0):\n M11[i,i-1] = -0.5*(D1(r)+D1(r-Delta_r))/(Delta_r * V[i])*S[i] \n M11[i,i] += 0.5/(Delta_r * V[i])*((D1(r)+D1(r-Delta_r))*S[i])\n M22[i,i-1] = -0.5*(D2(r)+D2(r-Delta_r))/(Delta_r * V[i])*S[i] \n M22[i,i] += 0.5/(Delta_r * V[i])*((D2(r)+D2(r-Delta_r))*S[i])\n M11[i,i+1] = -0.5*(D1(r)+D1(r+Delta_r))/(Delta_r * V[i])*S[i+1]\n M22[i,i+1] = -0.5*(D2(r)+D2(r+Delta_r))/(Delta_r * V[i])*S[i+1]\n \n #find eigenvalue\n l,phi1,phi2 = inversePowerBlock(M11,M21,M22,P11,P12,epsilon)\n k = 1.0/l\n #remove last element of phi because it is outside the domain\n phi1 = phi1[0:I]\n phi2 = phi2[0:I]\n return k, phi1, phi2, centers","title":""},{"docid":"4f13dafd0c9b6833f89ac644930b22bf","score":"0.514944","text":"def quick_method(aves, sq, fde, xint=5000, altitude=7000,station_requirement=6,c0 = 3.0e8,\n mindist = 300000):\n center = (np.mean(aves[:,0]), np.mean(aves[:,1]), np.mean(aves[:,2]))\n geo = GeographicSystem()\n mapp = MapProjection\n projl = MapProjection(projection='laea', lat_0=center[0], lon_0=center[1])\n\n lat, lon, alt = aves[:,:3].T\n stations_ecef = np.array(geo.toECEF(lon, lat, alt)).T\n\n center_ecef = np.array(geo.toECEF(center[1],center[0],center[2]))\n ordered_threshs = aves[:,-1]\n\n check = projl.fromECEF(stations_ecef[:,0],stations_ecef[:,1],stations_ecef[:,2])\n xmin = np.min(check[0]) - mindist\n xmax = np.max(check[0]) + mindist\n ymin = np.min(check[1]) - mindist\n ymax = np.max(check[1]) + mindist\n alts = np.array([altitude])\n initial_points = np.array(np.meshgrid(np.arange(xmin,xmax+xint,xint),\n np.arange(ymin,ymax+xint,xint), alts))\n\n x,y,z=initial_points.reshape((3,int(np.size(initial_points)/3)))\n points2 = np.array(projl.toECEF(x,y,z)).T\n\n xp,yp,zp = points2.T\n lonp,latp,zp = geo.fromECEF(xp,yp,zp)\n latp = latp.reshape(np.shape(initial_points)[1],np.shape(initial_points)[2])\n lonp = lonp.reshape(np.shape(initial_points)[1],np.shape(initial_points)[2])\n\n tanp_all = []\n for i in range(len(aves[:,0])): \n tanp_all = tanp_all + [TangentPlaneCartesianSystem(aves[i,0],aves[i,1],aves[i,2])]\n\n masking2 = np.ma.empty((np.shape(stations_ecef)[0],np.shape(x)[0]))\n dt, ran = travel_time(points2, stations_ecef, c=c0, get_r=True)\n selection = np.sum(ran<320000,axis=0) >0\n for i in range(len(stations_ecef[:,0])):\n masking2[i,selection] = tanp_all[i].toLocal(points2[selection].T)[2]>0\n \n ran = np.ma.masked_where(masking2==0, ran)\n ran = np.ma.masked_where(ran>=320000, ran)\n\n mins = min_power((10**(ordered_threshs/10.))*1e-3,ran)\n np.ma.set_fill_value(mins, 999)\n req_power = np.partition(mins.filled(), station_requirement-1,axis=0)[station_requirement-1]\n\n sde = np.zeros_like(req_power)\n for i in range(len(sq[0])-1):\n selects = (req_power >= sq[1,i]) & (req_power < sq[1,i+1])\n sde[selects] = 100-sq[0,i+1]\n \n selects = (req_power < sq[1,0]) \n sde[selects] = 100\n sde = (sde.T.reshape(np.shape(initial_points[0,:,:,0])))\n\n fde_a = np.zeros_like(sde)\n xs = 1000./np.arange(10,1000,1.) # Theoretical source detection efficiency that corresponds with fde\n\n selects = sde == 100. # Put into the next lowest or equivalent flash DE from given source DE\n fde_a[selects] = 100.\n for i in range(len(xs)-1):\n selects = (sde >= xs[1+i]) & (sde < xs[i])\n fde_a[selects] = fde[i]\n\n req_power = (req_power.T.reshape(np.shape(initial_points[0,:,:,0])))\n\n return latp,lonp,sde,fde_a,req_power","title":""},{"docid":"35a7818774a0cdbfefd49bffa6212037","score":"0.5138041","text":"def stiff_matrix(self):\n\n p = self.p\n assert p >= 1, 'the polynomial-order should have p >= 1 '\n\n mesh = self.mesh\n node = mesh.entity('node')\n\n NC = mesh.number_of_cells()\n edge = mesh.entity('edge')\n edge2cell = mesh.ds.edge_to_cell()\n edgeArea = mesh.edge_length()\n nm = mesh.edge_normal()\n # # (NE,2). The length of the normal-vector isn't 1, is the length of corresponding edge.\n\n isInEdge = (edge2cell[:, 0] != edge2cell[:, 1]) # the bool vars, to get the inner edges\n\n qf = GaussLegendreQuadrature(p + 1) # the integral points on edges (1D)\n bcs, ws = qf.quadpts, qf.weights # bcs.shape: (NQ,2); ws.shape: (NQ,)\n ps = np.einsum('ij, kjm->ikm', bcs, node[edge]) # ps.shape: (NQ,NE,2), NE is the number of edges\n\n gphi0 = self.grad_basis(ps, index=edge2cell[:, 0])\n # # gphi0.shape: (NQ,NInE,ldof,2), NInE is the number of interior edges, lodf is the number of local DOFs\n # # gphi0 is the grad-value of the cell basis functions on the one-side of the corresponding edges.\n gphi1 = self.grad_basis(ps[:, isInEdge, :], index=edge2cell[isInEdge, 1])\n\n # # using the divergence-theorem to get the\n S0 = np.einsum('i, ijkm, ijpm->jpk', ws, gphi0, gphi0) # (NE,ldof,ldof)\n b = node[edge[:, 0]] - self.cellbarycenter[edge2cell[:, 0]] # (NE,2)\n S0 = np.einsum('ij, ij, ikm->ikm', b, nm, S0) # (NE,ldof,ldof)\n\n S1 = np.einsum('i, ijkm, ijpm->jpk', ws, gphi1, gphi1) # (NInE,ldof,ldof)\n b = node[edge[isInEdge, 0]] - self.cellbarycenter[edge2cell[isInEdge, 1]] # (NInE,2)\n S1 = np.einsum('ij, ij, ikm->ikm', b, -nm[isInEdge], S1) # (NInE,ldof,ldof)\n\n ldof = self.number_of_local_dofs()\n S = np.zeros((NC, ldof, ldof), dtype=np.float)\n np.add.at(S, edge2cell[:, 0], S0)\n np.add.at(S, edge2cell[isInEdge, 1], S1)\n\n multiIndex = self.dof.multiIndex\n q = np.sum(multiIndex, axis=1) - 1 # here, we used the grad-basis to get stiff-matrix, so we need to -1\n qq = q + q.reshape(-1, 1) + 2\n qq[0, 0] = 1\n # # note that this is the special case, since we want to compute the \\int_T \\nabla u\\cdot \\nabla v,\n # # this needs to minus 1 in the 'q', so qq[0,0] is 0, moreover, S[:, 0, :] == S[:, :, 0] is 0-values,\n # # so we set qq[0, 0] = 1 which doesn't affect the result of S /= qq.\n\n S /= qq\n\n # --- get row and col --- #\n row, col = self.global_dof_location(np.arange(NC), np.arange(NC))\n\n gdof = self.number_of_global_dofs()\n S = csr_matrix((S.flat, (row.flat, col.flat)), shape=(gdof, gdof))\n\n return S","title":""},{"docid":"0ab9615fcafef5996e89987536fc36b4","score":"0.513694","text":"def _m_int(self, X, u, t=0):\n X_mat = X.reshape(-1, 2)\n t = self.material.thickness\n rho = self.material.rho\n\n self.M_small *= 0\n\n for xi, eta, w in self.gauss_points:\n N = np.array([ [(-eta + 1)*(-xi + 1)*(-eta - xi - 1)/4],\n [ (-eta + 1)*(xi + 1)*(-eta + xi - 1)/4],\n [ (eta + 1)*(xi + 1)*(eta + xi - 1)/4],\n [ (eta + 1)*(-xi + 1)*(eta - xi - 1)/4],\n [ (-eta + 1)*(-xi**2 + 1)/2],\n [ (-eta**2 + 1)*(xi + 1)/2],\n [ (eta + 1)*(-xi**2 + 1)/2],\n [ (-eta**2 + 1)*(-xi + 1)/2]])\n\n dN_dxi = np.array([\n [-(eta - 1)*(eta + 2*xi)/4, -(2*eta + xi)*(xi - 1)/4],\n [ (eta - 1)*(eta - 2*xi)/4, (2*eta - xi)*(xi + 1)/4],\n [ (eta + 1)*(eta + 2*xi)/4, (2*eta + xi)*(xi + 1)/4],\n [-(eta + 1)*(eta - 2*xi)/4, -(2*eta - xi)*(xi - 1)/4],\n [ xi*(eta - 1), xi**2/2 - 1/2],\n [ -eta**2/2 + 1/2, -eta*(xi + 1)],\n [ -xi*(eta + 1), -xi**2/2 + 1/2],\n [ eta**2/2 - 1/2, eta*(xi - 1)]])\n dX_dxi = X_mat.T @ dN_dxi\n det = dX_dxi[0,0]*dX_dxi[1,1] - dX_dxi[1,0]*dX_dxi[0,1]\n self.M_small += N @ N.T * det * rho * t * w\n\n self.M = scatter_matrix(self.M_small, 2)\n return self.M","title":""},{"docid":"dc05f238c7f453e0ca4428a0f8a6e0fa","score":"0.5122469","text":"def split_ecg_segments(ecg_data, start_minute=5):\n # How many usable 2-minute interval chunks are usable (rounded down) (Basically available sample points starting from the 5th-minute mark)\n # ISSUE: THE '300' USED IN HERE REFLECTS WHEN SAMPLING FREQUENCY IS 360 (THUS 1 SECOND) BUT DOES NOT PROPERLY REFLECT IF \n # SAMPLING FREQUENCY IS 1024 WHERE 300 DOES NOT ANYMORE REFLECT THE 5TH MINUTE AS 1024 IS IS AROUND 2.88 SECONDS\n usable_chunks = int(((ecg_data.shape[0] - 300) / 120))\n # chunk size for the noised and clean segments\n segment_size = int( (usable_chunks/2) * 120 )\n # According to MIT-BIH, two minute intervals of noised and clean data will be generated by the NST script. This function assigns a \n # contiguous version of those intervals in the variables below that will be returned\n noised_seg = np.zeros( shape=(segment_size, ecg_data.shape[1]) )\n clean_seg = np.zeros( shape=(segment_size, ecg_data.shape[1]) )\n # counters\n ecg_chunk_start = 60 * start_minute\n noised_chunk_start = 0\n clean_chunk_start = 0\n \n for i in range(usable_chunks):\n if i % 2 == 0:\n noised_seg[ noised_chunk_start:(noised_chunk_start+120) ] = ecg_data[ ecg_chunk_start:ecg_chunk_start+120 ]\n noised_chunk_start = noised_chunk_start+120\n else:\n clean_seg[ clean_chunk_start:(clean_chunk_start+120) ] = ecg_data[ ecg_chunk_start:ecg_chunk_start+120 ]\n clean_chunk_start = clean_chunk_start+120\n ecg_chunk_start = ecg_chunk_start+120\n\n noised_seg = noised_seg.reshape( (noised_seg.shape[0], noised_seg.shape[1], 1) )\n clean_seg = clean_seg.reshape( (clean_seg.shape[0], clean_seg.shape[1], 1) )\n\n return noised_seg, clean_seg","title":""},{"docid":"25ad3aef06e3c86162829164322a3d2d","score":"0.509761","text":"def Xcalc(self):\n print 'Calculation of concentration ... \\n\\n';\n self.TabX = self.TabExt[::]; #Copy doesn't work !!!!\n self.nTot = sum(self.M);\n for el in self.El:\n for i in range(self.nC):\n x = 0.0;\n for j in range(self.nM):\n if self.TabX[i][1 + j] == el: x += self.M[j];\n x = x / float(self.nTot);\n self.TabX[i][1 + self.nM + self.El.index(el)] = round(x, 3);\n if x != 0: self.TabX[i][1 + self.nM + self.nEl] += 10 + self.El.index(el);\n if self.ModeVerbose == 'YES': print np.array(self.TabX), '\\n\\n';","title":""},{"docid":"30067b751f2f3518bfdb941760437637","score":"0.5095768","text":"def remesh(boxes):","title":""},{"docid":"35bbe4ae9de6feceed4af01d7135b6bf","score":"0.5093147","text":"def g_xyz_equat_sun_at_other_mean_equinox (jde, T) :\n # we use the special matrix to transform J2000 Fk5\n xyz = g_xyz_equat_sun_j2000(jde)\n # once were J2000 fk5, we can apply the normal precesion matrix to precess\n T1 = 0 # we are in J2000\n T2 = T\n return prec_mtx_equat(T1,T2).dot(xyz)","title":""},{"docid":"20396f550b205b0854850359572cfec6","score":"0.50858396","text":"def __init__(self, *args, **kwargs):\n\n '''Basic geometric parameters'''\n assert len(args) == 7\n self.Cr = args[0] # Chord length at root\n self.Cm = args[1] # Chord length at mid\n self.Ct = args[2] # Chord length at tip\n self.Bm = args[3] # Width of the inner part\n self.Bt = args[4] # Half span\n self.Am = args[5] # Mean sweep-back of the inner part\n self.At = args[6] # Mean sweep-back at tip.\n\n '''Calculate pivots on each curve'''\n front_pnt = np.zeros((3, 3))\n tail_pnt = np.zeros((3, 3))\n tail_pnt[0][0] = self.Cr\n front_pnt[1][0] = self.Bm * math.tan(math.radians(self.Am))\n front_pnt[1][2] = self.Bm\n tail_pnt[1][0] = front_pnt[1][0] + self.Cm\n tail_pnt[1][2] = self.Bm\n front_pnt[2][0] = front_pnt[1][0] + (self.Bt - self.Bm) * math.tan(math.radians(self.At))\n front_pnt[2][2] = self.Bt\n tail_pnt[2][0] = front_pnt[2][0] + self.Ct\n tail_pnt[2][2] = self.Bt\n\n '''Build interpolated functions'''\n self.u = np.array([0, self.Bm / self.Bt, 1.0])\n self.zw = np.array([0, self.Bm, self.Bt])\n self.xf = make_interp_spline(self.u, front_pnt[:, 0], 3, bc_type=([(1, 0)], [(2, 0)]))\n self.xt = make_interp_spline(self.u, tail_pnt[:, 0], 3, bc_type=([(1, 0)], [(2, 0)]))","title":""},{"docid":"62db15950a352c97e755403898e86244","score":"0.5078494","text":"def __split_ligature(self, cc: np.ndarray) -> List[np.ndarray]:\n\n # deskew letter(s)\n cc = self.__deskew(cc)\n\n # Smooth edges\n bin_sm = sig.medfilt(cc, 5)\n\n # calculate euclidean distance transform\n edt = nd.distance_transform_edt(bin_sm)\n\n # calculate vertical projection profile of edt\n vpp = np.sum(edt, axis=0)\n\n # find valleys\n valls = sig.argrelextrema(vpp, np.less_equal, order=int(self.stroke_width * 2.5))[0]\n l = int(len(vpp) * 0.25)\n r = int(len(vpp) * 0.75)\n valls = valls[valls > l]\n valls = valls[valls < r]\n\n # create mask\n mask = np.zeros_like(vpp)\n mask[valls] = 1\n\n # combine close points\n mask = nd.binary_closing(mask)\n val_labs, n_labs = nd.label(mask)\n\n split_at = []\n\n for n in range(1, n_labs + 1):\n # find x coords of adjacent splits\n region = np.argwhere(val_labs == n)\n\n if np.count_nonzero(region) > 1:\n # combine adjacents split points into one point\n new_split = int(np.mean(region))\n split_at.append(new_split)\n else:\n # if there's only one split point, just return the split point\n split_at.append(region[0])\n\n # split image into chars\n chars = []\n if len(split_at) > 0:\n split_at = [0] + split_at + [len(vpp)]\n for i in range(len(split_at) - 1):\n split_l = int(split_at[i])\n split_r = int(split_at[i + 1])\n char = np.fliplr(bin_sm[:, split_l:split_r])\n chars.append(char)\n else:\n chars = [np.fliplr(bin_sm)]\n\n return chars","title":""},{"docid":"b75124fbe55198868177c04d63495880","score":"0.5076858","text":"def calc_multipole(npoles, imskb, kptrange, bdrange, eqp, newen):\n import numpy as np\n from multipole import fit_multipole_const #, write_f_as_sum_of_poles\n print \" ### ================== ###\" \n print \" ### Multipole fit ###\" \n print \" Number of poles:\", npoles \n omegampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,npoles))\n ampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,npoles))\n for ik in kptrange:\n for ib in bdrange:\n if eqp[ik,ib] > newen[-npoles]:\n omegampole[ik,ib] = omegampole[ik,ib-1]\n ampole[ik,ib] = ampole[ik,ib-1]\n print \" Eqp beyond available energy range. Values from lower band are taken.\" \n continue\n else:\n ibeff = bdrange[0] + ib - 1\n print \" ik, ib\", ik, ib \n # Here we take the curve starting from eqp and then we invert it\n # so as to have it defined on the positive x axis\n # and so that the positive direction is in the \n # increasing direction of the array index\n if eqp[ik,ib] <= 0:\n en3 = newen[newen<=eqp[ik,ib]] # So as to avoid negative omegampole\n im3 = abs(imskb[ik,ib][newen<=eqp[ik,ib]]/np.pi) # This is what should be fitted\n else:\n en3 = newen[newen>eqp[ik,ib]] # So as to avoid negative omegampole\n im3 = abs(imskb[ik,ib][newen<=eqp[ik,ib]]/np.pi) # This is what should be fitted\n if en3.size == 0:\n print \n print \" WARNING: QP energy is outside of given energy range!\\n\"+\\\n \" This state will be skipped!\\n\"+\\\n \"You might want to modify enmin/enmax.\" \n print \" eqp[ik,ib], newen[-1]\", eqp[ik,ib] , newen[-1] \n continue\n en3 = en3 - eqp[ik,ib]\n if eqp[ik,ib] <= 0:\n en3 = -en3[::-1] \n im3 = im3[::-1]\n omegai, lambdai, deltai = fit_multipole_const(en3,im3,npoles)\n # HERE WE MUST CHECK THAT THE NUMBER OF POLES \n # IS NOT BIGGER THAN THE NUMBER OF POINTS THAT HAS TO BE FITTED\n if npoles > omegai.size:\n omegampole[ik,ib][:omegai.size] = omegai \n ampole[ik,ib][:omegai.size] = np.true_divide(lambdai,(np.square(omegai)))\n print \n print \" WARNING: npoles used (\"+str(npoles)+\") is larger\"+\\\n \" than x data array (\"+str(omegai.size)+\").\"\n print \" Reduce npoles. You are wasting resources!!!\" \n else:\n omegampole[ik,ib] = omegai \n ampole[ik,ib] = np.true_divide(lambdai,(np.square(omegai)))\n print \" Integral test. Compare \\int\\Sigma and \\sum_j^N\\lambda_j.\" \n print \" 1/pi*\\int\\Sigma =\", np.trapz(im3,en3) \n print \" \\sum_j^N\\lambda_j =\", np.sum(lambdai) \n # Writing out a_j e omega_j\n print \" ### Writing out a_j and omega_j...\" \n outname = \"a_j_np\"+str(npoles)+\".dat\"\n outfile = open(outname,'w')\n outname = \"omega_j_np\"+str(npoles)+\".dat\"\n outfile2 = open(outname,'w')\n for ipole in xrange(npoles):\n for ik in range(imskb[:,0,0].size):\n for ib in range(imskb[0,:,0].size):\n outfile.write(\"%10.5f\" % (ampole[ik,ib,ipole]))\n outfile2.write(\"%10.5f\" % (omegampole[ik,ib,ipole]))\n outfile.write(\"\\n\")\n outfile2.write(\"\\n\")\n outfile.write(\"\\n\")\n outfile2.write(\"\\n\")\n outfile.close()\n outfile2.close()\n return omegampole, ampole","title":""},{"docid":"65e39070b97a395f5449b727bd55d382","score":"0.50663656","text":"def gridarr_get( self ):\n\t return self.ma_grid\n\n \tdef __add__( self, cg ):\n \t \"\"\"\n \t \tAdds two grids together, returning the result as a new\n \t \tgrid. For each grid cell (which contains a spectrum)\n \t \ta new spectrum is created which is the sum of the \n \t \tconstituent spectra.\n \t \"\"\"\n \t if self.ma_grid.shape != cg.ma_grid.shape: self.fatal( 'Operands' )\n \t C_add = C_ggrid( self.ma_grid, self.macs_grid[0, 0] )\n \t C_add.ma_grid = self.ma_grid + cg.ma_grid\n \t b_setFromArray = True\n \t C_add.internals_sync( b_setFromArray )\n#\t for row in np.arange(0, self.m_rows):\n#\t\tfor col in np.arange(0, self.m_cols):\n#\t\t spectrum_self = self.macs_grid[row, col]\n#\t\t spectrum_cg = cg.macs_grid[row, col]\n#\t\t C_add.macs_grid[row, col] = spectrum_self + spectrum_cg\n#\t\t C_add.spectrumDefaults_set(row, col)\n \t return C_add","title":""},{"docid":"e8b40d6054c11ebeddb7b3e74d799ca9","score":"0.506236","text":"def test_em_2d_gauss_newton():\n\n fname = get_fnames('t1_coronal_slice')\n nslices = 1\n b = 0.1\n m = 4\n\n image = np.load(fname)\n moving, static = get_warped_stacked_image(image, nslices, b, m)\n\n # Configure the metric\n smooth = 5.0\n inner_iter = 20\n q_levels = 256\n double_gradient = False\n iter_type = 'gauss_newton'\n metric = metrics.EMMetric(\n 2, smooth, inner_iter, q_levels, double_gradient, iter_type)\n\n # Configure and run the Optimizer\n level_iters = [40, 20, 10]\n optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)\n optimizer.verbosity = VerbosityLevels.DEBUG\n mapping = optimizer.optimize(static, moving, None)\n m = optimizer.get_map()\n assert_equal(mapping, m)\n\n warped = mapping.transform(moving)\n starting_energy = np.sum((static - moving)**2)\n final_energy = np.sum((static - warped)**2)\n reduced = 1.0 - final_energy / starting_energy\n\n assert(reduced > 0.9)","title":""},{"docid":"2e0c78aac585fb9fc010ce95ba338c9f","score":"0.50496846","text":"def generate_eg_matrix(self, binary=False):\n\n if not hasattr(self, 'event_map'):\n self.event_map = self.events.reset_index(drop=False)['index']\n\n inv_event_map = pd.Series(self.event_map.index, index=self.event_map)\n\n # Make a sparse EG matrix\n rows = []\n cols = []\n data = []\n for ix, edge in self.eg_edges.iterrows():\n rows.append(inv_event_map[edge.source])\n cols.append(inv_event_map[edge.target])\n data.append(edge.delta)\n if binary:\n data = [1 for d in data]\n self.eg_matrix = csc_matrix((data, (rows, cols)),\n shape=(self.M, self.M),\n dtype=int)\n return self.eg_matrix","title":""},{"docid":"a2778618620e029a5a570ba853cd322d","score":"0.5044396","text":"def factorize(self):\t\n\t\tdef separate_positive(m):\n\t\t\treturn (np.abs(m) + m)/2.0 \n\t\t\n\t\tdef separate_negative(m):\n\t\t\treturn (np.abs(m) - m)/2.0 \n\t\t\t\n\t\tXtX = np.dot(self.data[:,:].T, self.data[:,:])\n\t\tXtX_pos = separate_positive(XtX)\n\t\tXtX_neg = separate_negative(XtX)\n\t\t\t\t\t\t\t\n\t\t# iterate over W and H\n\t\tfor i in xrange(self._niter):\n\t\t\t# update H\n\t\t\tH_x_WT = np.dot(self.H.T, self.G.T)\n\t\t\t\t\n\t\t\tXtX_neg_x_W = np.dot(XtX_neg, self.G)\n\t\t\tXtX_pos_x_W = np.dot(XtX_pos, self.G)\n\t\t\n\t\t\tha = XtX_pos_x_W + np.dot(H_x_WT, XtX_neg_x_W)\n\t\t\thb = XtX_neg_x_W + np.dot(H_x_WT, XtX_pos_x_W) + 10**-9\n\t\t\tself.H = (self.H.T*np.sqrt(ha/hb)).T\n\t\t\n\t\t\t# update W\t\t\t\n\t\t\tif self._compW:\n\t\t\t\tHT_x_H = np.dot(self.H, self.H.T)\n\t\t\t\twa = np.dot(XtX_pos, self.H.T) + np.dot(XtX_neg_x_W, HT_x_H)\n\t\t\t\twb = np.dot(XtX_neg, self.H.T) + np.dot(XtX_pos_x_W, HT_x_H) + 10**-9\n\t\t\t\n\t\t\t\tself.G *= np.sqrt(wa/wb)\n\t\t\t\tself.W = np.dot(self.data[:,:], self.G)\n\t\t\t\t\t\t\t\t\n\t\t\tself.ferr[i] = self.frobenius_norm()\n\n\t\t\tself._print_cur_status('iteration ' + str(i+1) + '/' + str(self._niter) + ' Fro:' + str(self.ferr[i]))\n\n\t\t\tif i > 1:\n\t\t\t\tif self.converged(i):\t\t\t\t\t\n\t\t\t\t\tself.ferr = self.ferr[:i]\t\t\t\t\t\n\t\t\t\t\tbreak","title":""},{"docid":"61a234ea1210f5d0ec74db4c67c3e748","score":"0.5043434","text":"def _calculate_interpolation_matrix(self, X):\n G = np.zeros((len(X), self.hidden_shape))\n for data_point_arg, data_point in enumerate(X):\n for center_arg, center in enumerate(self.centers):\n G[data_point_arg, center_arg] = self._kernel_function(center, data_point)\n return G","title":""},{"docid":"1055f639ee65bf6c8dfea2be1a17e830","score":"0.5042965","text":"def __interpolateSpline__(self):\n # Get x and y values for all supplied points\n x = self.source[:, 0]\n y = self.source[:, 1]\n mdim = len(x)\n h = [x[i+1] - x[i] for i in range(0, mdim - 1)]\n # Initialize de matrix\n Ab = np.zeros((3, mdim))\n # Construct the Ab banded matrix and B vector\n Ab[1, 0] = 1 # A[0, 0] = 1\n B = [0]\n for i in range(1, mdim-1):\n Ab[2, i - 1] = h[i - 1] # A[i, i - 1] = h[i - 1]\n Ab[1, i] = 2*(h[i] + h[i - 1]) # A[i, i] = 2*(h[i] + h[i - 1])\n Ab[0, i + 1] = h[i] # A[i, i + 1] = h[i]\n B.append(3*((y[i + 1] - y[i])/(h[i]) - (y[i] - y[i - 1])/(h[i-1])))\n Ab[1, mdim - 1] = 1 # A[-1, -1] = 1\n B.append(0)\n # Solve the system for c coefficients\n c = linalg.solve_banded((1, 1), Ab, B, True, True)\n # Calculate other coefficients\n b = [((y[i+1] - y[i])/h[i] - h[i]*(2*c[i]+c[i+1])/3)\n for i in range(0, mdim-1)]\n d = [(c[i+1] - c[i])/(3*h[i]) for i in range(0, mdim - 1)]\n # Store coefficients\n self.__splineCoefficients__ = np.array([y[0:-1], b, c[0:-1], d])","title":""},{"docid":"1a7d0036cd951bdea8ba85d38403b61d","score":"0.503697","text":"def collapse(self, e):\n vnew = e.vnew\n\n p2 = e.v1.p; p3 = e.v2.p\n q1 = zeros(4); q2 = e.v1.q; q3 = e.v2.q; q4 = zeros(4)\n S1 = zeros([2,2]); S2 = e.v1.S; S3 = e.v2.S; S4 = zeros([2,2])\n\n a = zeros(4)\n if e.v1.e1 is not None:\n p1 = e.v1.e1.v1.p\n q1 = e.v1.e1.v1.q\n S1 = e.v1.e1.v1.S\n a1 = barycentricWeights(p1,p2,p3,vnew.p)\n a[:3] = a[:3] + a1\n vnew.e1 = e.v1.e1\n vnew.e1.v2 = vnew\n vnew.e1.dirty = True\n if e.v2.e2 is not None:\n p4 = e.v2.e2.v2.p\n q4 = e.v2.e2.v2.q\n S4 = e.v2.e2.v2.S\n a2 = barycentricWeights(p2,p3,p4,vnew.p)\n a[1:] = a[1:] + a2\n vnew.e2 = e.v2.e2\n vnew.e2.v1 = vnew\n vnew.e2.dirty = True\n q = a[0]*q1 + a[1]*q2 + a[2]*q3 + a[3]*q3\n S = a[0]*S1 + a[1]*S2 + a[2]*S3 + a[3]*S3\n if a[0]!=0 and a[-1]!=0:\n q = 0.5*q\n S = 0.5*S\n\n if S[0,0] < 0 or S[1,1] < 0:\n print \"Trace:\",S.trace()\n print \"Sum a1:\",a1.sum()\n print \"Sum a2:\",a1.sum()\n print \"a1:\",a1.T\n print \"a2:\",a2.T\n print \"p1:\",p1.T\n print \"p2:\",p2.T\n print \"p3:\",p3.T\n print \"p4:\",p4.T\n print \"p5:\",vnew.p.T\n print \"l1:\",linalg.norm(p1-p2)\n print \"l2:\",linalg.norm(p2-p3)\n print \"l3:\",linalg.norm(p3-p4)\n print \"S1:\\n\",S1\n print \"S2:\\n\",S2\n print \"S3:\\n\",S3\n print \"S4:\\n\",S4\n print \"S:\\n\",S\n\n vnew.q = q/linalg.norm(q)\n vnew.S = S\n\n\n #print \"\\nV: \",len(self.V)\n #for vi in self.V: print vi\n #print \"\\nE: \",len(self.E)\n #for ei in self.E: print ei\n print \"insert: \",vnew\n print \"delete: \",e\n self.V.append(vnew)\n self.V.remove(e.v1)\n self.V.remove(e.v2)\n self.E.remove(e)","title":""},{"docid":"508f2d8821ec0ccd8dc647af87429c86","score":"0.50351197","text":"def ecg_segment(self):\n\n \n import numpy as np\n from scipy.signal import resample\n from .misc import epochs_create, epochs_to_df\n\n\n def _ecg_segment_window( heart_rate=None, rpeaks=None, sampling_rate=1000, desired_length=None):\n\n # Extract heart rate\n if heart_rate is not None:\n heart_rate = np.mean(heart_rate)\n if rpeaks is not None:\n self.rate = self.signal_rate(self.rpeaks, desired_length=desired_length)\n heart_rate = np.mean(self.rate)\n\n # Modulator\n m = heart_rate / 60\n\n # Window\n epochs_start = -0.35 / m\n epochs_end = 0.5 / m\n\n # Adjust for high heart rates\n if heart_rate >= 80:\n c = 0.1\n epochs_start = epochs_start - c\n epochs_end = epochs_end + c\n\n return epochs_start, epochs_end\n\n epochs_start, epochs_end = _ecg_segment_window(rpeaks=self.rpeaks, sampling_rate=self.fs, desired_length=self.sig_len)\n\n heartbeats = epochs_create(self.data, self.rpeaks, sampling_rate=self.fs, epochs_start=epochs_start, epochs_end=epochs_end )\n\n\n heartbeats_plot = epochs_to_df(heartbeats)\n heartbeats_pivoted = heartbeats_plot.pivot(index=\"Time\", columns=\"Label\", values=\"Signal\")\n\n beats=[]\n for i in range(heartbeats_pivoted.shape[1]):\n x = heartbeats_pivoted[heartbeats_pivoted.columns[i]]\n beats.append(resample(x, 100, t=None, axis=0, window=None, domain='time'))\n \n beats = np.array(beats)\n\n self.ecg_templates = beats","title":""},{"docid":"a0a6cd653160ac56a43f4630a743969c","score":"0.5027593","text":"def compute_disagg_matrix(job_ctxt, site, poe, result_dir):\n lat_bin_lims = job_ctxt[job_cfg.LAT_BIN_LIMITS]\n lon_bin_lims = job_ctxt[job_cfg.LON_BIN_LIMITS]\n mag_bin_lims = job_ctxt[job_cfg.MAG_BIN_LIMITS]\n eps_bin_lims = job_ctxt[job_cfg.EPS_BIN_LIMITS]\n\n jd = list_to_jdouble_array\n\n disagg_calc = java.jclass('DisaggregationCalculator')(\n jd(lat_bin_lims), jd(lon_bin_lims),\n jd(mag_bin_lims), jd(eps_bin_lims))\n\n cache = java.jclass('KVS')(\n config.get('kvs', 'host'),\n int(config.get('kvs', 'port')))\n\n erf = general.generate_erf(job_ctxt.job_id, cache)\n gmpe_map = general.generate_gmpe_map(job_ctxt.job_id, cache)\n general.set_gmpe_params(gmpe_map, job_ctxt.params)\n\n imls = general.get_iml_list(job_ctxt['INTENSITY_MEASURE_LEVELS'],\n job_ctxt['INTENSITY_MEASURE_TYPE'])\n\n site_model = general.get_site_model(job_ctxt.oq_job.id)\n\n if site_model is not None:\n sm_data = general.get_closest_site_model_data(site_model, site)\n vs30_type = sm_data.vs30_type.capitalize()\n vs30 = sm_data.vs30\n z1pt0 = sm_data.z1pt0\n z2pt5 = sm_data.z2pt5\n else:\n jp = job_ctxt.oq_job_profile\n\n vs30_type = jp.vs30_type.capitalize()\n vs30 = jp.reference_vs30_value\n z1pt0 = jp.depth_to_1pt_0km_per_sec\n z2pt5 = jp.reference_depth_to_2pt5km_per_sec_param\n\n matrix_result = _compute_matrix(\n disagg_calc, site.latitude, site.longitude, erf, gmpe_map, poe, imls,\n vs30_type, vs30, z1pt0, z2pt5)\n\n matrix_path = save_5d_matrix_to_h5(result_dir,\n numpy.array(matrix_result.getMatrix()))\n\n return (matrix_result.getGMV(), matrix_path)","title":""},{"docid":"5b2a71d26eef485527d479270542a9a8","score":"0.5024965","text":"def compute_edges_dxdy(I): #part4\n I = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)\n I = I.astype(np.float32)/255.\n I = cv2.GaussianBlur(I, (7,7),2)\n dx = signal.convolve2d(I,np.array([[-1,0, 1],[-4,0,4],[-1,0,1]]),mode='same', boundary = 'symm')\n dy = signal.convolve2d(I,np.array([[-1,0, 1],[-4,0,4],[-1,0,1]]).T,mode='same', boundary = 'symm')\n \n\n I = np.hypot(dx, dy) \n I = I / I.max() * 255\n \n angle = np.arctan2(dy, dx)\n \n Z = np.zeros((I.shape))\n\n angle = angle * 180. / np.pi\n angle[angle < 0] += 180\n\n angle_mod = angle % 180\n\n for i in range(I.shape[0] - 1):\n for j in range(I.shape[1] - 1):\n q = 0\n r = 0\n \n if (0 <= angle_mod[i,j] < 45):\n alpha = np.abs(np.tan(angle_mod[i,j]))\n q = (alpha * I[i + 1, j + 1]) + ((1 - alpha) * I[i, j + 1])\n r = (alpha * I[i - 1, j - 1]) + ((1 - alpha) * I[i, j - 1]) \n \n elif (45 <= angle_mod[i,j] < 90):\n alpha = np.abs(1./np.tan(angle_mod[i,j]))\n q = (alpha * I[i + 1, j + 1]) + ((1 - alpha) * I[i + 1, j])\n r = (alpha * I[i - 1, j - 1]) + ((1 - alpha) * I[i - 1, j])\n \n elif (90 <= angle_mod[i,j] < 135):\n alpha = np.abs(1./np.tan(angle_mod[i,j]))\n q = (alpha * I[i + 1, j - 1]) + ((1 - alpha) * I[i + 1, j])\n r = (alpha * I[i - 1, j + 1]) + ((1 - alpha) * I[i - 1, j])\n \n elif (135 <= angle_mod[i,j] < 180):\n alpha = np.abs(np.tan(angle_mod[i,j]))\n q = (alpha * I[i + 1, j - 1]) + ((1 - alpha) * I[i, j - 1])\n r = (alpha * I[i - 1, j + 1]) + ((1 - alpha) * I[i, j + 1])\n\n if (I[i,j] >= q) and (I[i,j] >= r):\n Z[i,j] = I[i,j]\n else:\n Z[i,j] = 0\n \n #normalize it\n mag = ((Z - np.min(Z)) / np.max(Z - np.min(Z))) * 255.0\n mag = np.clip(mag, 0, 255)\n mag = mag.astype(np.uint8)\n\n return mag","title":""},{"docid":"1ef202c8f08786252bdb52510d48a1af","score":"0.5016774","text":"def seird(\n x, t, a1, a2, a3, a4\n ): # pylint: disable=unused-argument, too-many-arguments\n return np.array(\n [\n -a1 * x[0] * x[2] / population,\n a1 * x[0] * x[2] / population - a2 * x[1],\n a2 * x[1] - a3 * x[2] - a4 * x[2],\n a3 * x[2],\n a4 * x[2],\n ]\n )","title":""},{"docid":"fddce028219a070efcb4b65323b03c32","score":"0.50156116","text":"def _coefficients(self):\n # Grid indices corresponding to the corners of the cell ie x1, y1, z1\n indices1 = tuple(sympy.symbols('%s1' % d) for d in self.grid.dimensions)\n indices2 = tuple(sympy.symbols('%s2' % d) for d in self.grid.dimensions)\n # 1, x1, y1, z1, x1*y1, ...\n indices = list(powerset(indices1))\n indices[0] = (1,)\n point_sym = list(powerset(self._point_symbols))\n point_sym[0] = (1,)\n # 1, px. py, pz, px*py, ...\n A = []\n ref_A = [np.prod(ind) for ind in indices]\n # Create the matrix with the same increment order as the point increment\n for i in self._point_increments:\n # substitute x1 by x2 if increment in that dimension\n subs = dict((indices1[d], indices2[d] if i[d] == 1 else indices1[d])\n for d in range(len(i)))\n A += [[1] + [a.subs(subs) for a in ref_A[1:]]]\n\n A = sympy.Matrix(A)\n # Coordinate values of the sparse point\n p = sympy.Matrix([[np.prod(ind)] for ind in point_sym])\n\n # reference cell x1:0, x2:h_x\n left = dict((a, 0) for a in indices1)\n right = dict((b, dim.spacing) for b, dim in zip(indices2, self.grid.dimensions))\n reference_cell = {**left, **right}\n # Substitute in interpolation matrix\n A = A.subs(reference_cell)\n return A.inv().T * p","title":""},{"docid":"371f10e7a498ea2bae052537bc57ef6a","score":"0.501462","text":"def find_contour(x):\n global intersect_point_x1\n global intersect_point_x2\n print('X is : ', x)\n origin_energy = kr_model.predict_single(x) # The energy level at saddle point\n #origin_energy = -6562\n print('Origin energy is : ', origin_energy)\n egienvalue = np.linalg.eig(H(x))[0] \n egienvector = np.linalg.eig(H(x))[1][0] if egienvalue[0] < 0 else np.linalg.eig(H(x))[1][1] # the egienvector we want corresponding to negtive egienvalue\n x1, x2 = x + 0.5*egienvector, x - 0.5*egienvector # x1, x2 are points deviate a little from the origin saddle point. We want to draw contour of basin from x1 and x2.\n \n intersect_point_x1, intersect_point_x2 = x1, x2 # when we start from x1 and follow some trace to construct contour, it should overlap with the starting point when finishing the contour.\n inter_x1_energy, inter_x2_energy = kr_model.predict_single(intersec_point_x1), kr_model.predict_single(intersec_point_x2)\n slt = 0.1\n i = 0\n #trace_x1, trace_x2 = [], []\n \"\"\"\n while(not (kr_model.predict_single(x1) - origin_energy) < 1): \n #gradient desending\n if kr_model.predict_single(x1) > origin_energy:\n x1 -= slt * normalize(G(x1).reshape(1,-1))[0]\n else:\n x1 += slt * normalize(G(x1).reshape(1,-1))[0]\n print('Now you should be closer to origin energy : ', kr_model.predict_single(x1))\n \"\"\"\n while(i < 1500): # you can modify the value of i.\n i += 1\n tagent = np.array([G(x1)[1], -G(x1)[0]]) #tagent is perpendicular/orthonal to gradient\n x1 = x1 + slt * normalize(tagent.reshape(1,-1))[0] # follow the tagent to construct contour\n while(not (kr_model.predict_single(x1) - inter_x1_energy) < 0.5): # If it deviates from contour\n #gradient desending to return to desired energy level\n if kr_model.predict_single(x1) > inter_x1_energy:\n x1 -= slt * normalize(G(x1).reshape(1,-1))[0]\n else:\n x1 += slt * normalize(G(x1).reshape(1,-1))[0]\n #print('Now you should be closer to origin energy : ', kr_model.predict_single(x1))\n trace_x1.append(x1)\n #print('Cooool! Add a point in contour : ', x1)\n if math.hypot(x1[0] - intersect_point_x1[0],x1[1] - intersect_point_x1[1]) < 0.1: # If we are close enought to the starting point, we assume we finish constructing contour\n if i > 10: # do not break at first.\n break\n \"\"\"\n #This is for the other contour line\n while(not (kr_model.predict_single(x2) - origin_energy) < 1): \n #gradient desending\n if kr_model.predict_single(x2) > origin_energy:\n x2 -= slt * normalize(G(x2).reshape(1,-1))[0]\n else:\n x2 += slt * normalize(G(x2).reshape(1,-1))[0]\n print('Now you should be closer to origin energy : ', kr_model.predict_single(x2))\n \"\"\"\n i = 0\n while(i < 1500): # might go back to the first point!!\n i += 1\n tagent = np.array([G(x2)[1], -G(x2)[0]]) #orthonal to gradient\n x2 = x2 + slt * normalize(tagent.reshape(1,-1))[0]\n while(not (kr_model.predict_single(x2) - inter_x2_energy) < 0.5):\n #gradient desending\n if kr_model.predict_single(x2) > inter_x2_energy:\n x2 -= slt * normalize(G(x2).reshape(1,-1))[0]\n else:\n x2 += slt * normalize(G(x2).reshape(1,-1))[0]\n #print('Now you should be closer to origin energy : ', kr_model.predict_single(x2))\n trace_x2.append(x2)\n #print('Cooool! Add a point in contour : ', x2)\n if math.hypot(x2[0] - intersect_point_x2[0],x2[1] - intersect_point_x2[1]) < 0.1:\n if i > 10:\n break","title":""},{"docid":"4674dbb623e5507f0af63f6be0701814","score":"0.501428","text":"def q2ba():\r\n \r\n # Nested list to store the final results as [[J(0,0), ... J(0,20)], \r\n # [J(1,0), ... J(1,20)], [J(2,0), ... J(2,20)]] produced by J(m,x)\r\n bessel_arr = [[], [], []] \r\n \r\n # Nested list to store the final results in the same format as bassel_arr\r\n # produced by scipy.special.jv\r\n jv_arr = [[], [], []]\r\n \r\n for m in range(3): # for J_0, J_1, J_2\r\n for x in range(21): # for x = 0, 1, ..., 20\r\n # evaluate Bessel function using function J code above\r\n bessel_arr[m].append(J(m,x))\r\n \r\n # evlauate function using scipy.special.jv function\r\n jv_arr[m].append(jv(m,x))\r\n \r\n \r\n # x array from 0 to 20 to be used for the plot\r\n x_arr = np.arange(21) \r\n \r\n # Plot Bessel function for m = 0,1,2 and x = 0,1,...,20 in the same plot\r\n # evaluated using J(m,x) function coded above\r\n plt.figure()\r\n plt.plot(x_arr, bessel_arr[0], label=\"J_0\") # Plot J_0 against x\r\n plt.plot(x_arr, bessel_arr[1], label=\"J_1\") # Plot J_1 against x\r\n plt.plot(x_arr, bessel_arr[2], label=\"J_2\") # Plot J_2 against x\r\n plt.xlabel(\"x\")\r\n plt.ylabel(\"J_m(x)\")\r\n plt.title(\"Bessel functions as a function of x using Simpson's rule\")\r\n plt.legend()\r\n plt.savefig(\"q2b_simp.png\")\r\n \r\n # Plot Bessel function for m = 0,1,2 and x = 0,1,...,20 in the same plot\r\n # evaluated using scipy.special.jv function\r\n plt.figure()\r\n plt.plot(x_arr, jv_arr[0], label=\"J_0\") # Plot J_0 against x\r\n plt.plot(x_arr, jv_arr[1], label=\"J_1\") # Plot J_1 against x\r\n plt.plot(x_arr, jv_arr[2], label=\"J_2\") # Plot J_2 against x\r\n plt.xlabel(\"x\")\r\n plt.ylabel(\"scipy.special.jv(x)\")\r\n plt.title(\"Bessel functions as a function of x using scipy.special.jv\")\r\n plt.legend()\r\n plt.savefig(\"q2b_jv.png\")","title":""},{"docid":"6ea6c832e69614c338b0019d67eef870","score":"0.50096685","text":"def e_erp(t0,t1,g):\n\n n0 = len(t0)\n n1 = len(t1)\n C=np.zeros((n0+1,n1+1))\n\n t0_float = []\n t1_float = []\n\n k = 0\n while(k < n0):\n pos1 = t0[k]\n #pos2 = t1[k]\n x1, y1 = pos1.split(\",\")\n #x2, y2 = pos2.split(\",\")\n coord1 = [(float(x1), float(y1))]\n #coord2 = [(float(x2), float(y2))]\n t0_float.append(coord1)\n #t1_float.append(coord2)\n k = k + 1\n\n k = 0\n while(k < n1):\n #pos1 = t0[k]\n pos2 = t1[k]\n #x1, y1 = pos1.split(\",\")\n x2, y2 = pos2.split(\",\")\n #coord1 = [(float(x1), float(y1))]\n coord2 = [(float(x2), float(y2))]\n #t0_float.append(coord1)\n t1_float.append(coord2)\n k = k + 1\n \n #print(str(len(t0)) + \", \" + str(len(t1)))\n\n C[1:,0]=sum(map(lambda x : abs(euc_dist(g,x)),t0_float))\n C[0,1:]=sum(map(lambda y : abs(euc_dist(g,y)),t1_float))\n for i in np.arange(n0)+1:\n for j in np.arange(n1)+1:\n derp0 = C[i-1,j] + euc_dist(t0_float[i-1],g)\n derp1 = C[i,j-1] + euc_dist(g,t1_float[j-1])\n derp01 = C[i-1,j-1] + euc_dist(t0_float[i-1],t1_float[j-1])\n C[i,j] = min(derp0,derp1,derp01)\n erp = C[n0,n1]\n return erp","title":""},{"docid":"a5d3d3f71cd416e6dbef731d7b0c47ab","score":"0.50093824","text":"def gsmear(xd,yd,sigma,ngwidth=3):\n dx= xd[1]-xd[0]\n sgm= sigma*dx\n pref= dx/(np.sqrt(2.0*np.pi)*sgm)\n ndat= len(xd)\n nwidth = int(sigma*ngwidth)\n #nwidth = ndat\n gdat= np.zeros(ndat,dtype=float)\n expfact = 1.0/sgm**2/2\n for ix in range(ndat):\n #gdat[ix]= yd[ix]\n for jx in range(-nwidth+1,nwidth):\n kx= ix+jx\n if kx < 0:\n kx = abs(kx)\n elif kx >= ndat:\n kx = 2*ndat -kx -1\n gdat[ix] += yd[kx]*pref*np.exp(-(dx*jx)**2*expfact)\n #gdat /= 2\n return gdat","title":""},{"docid":"2dbb8f0c996cc1346e99bcb387046ebb","score":"0.5004536","text":"def multilateration( ar\n , br, bc_x\n , cr, cc_x, cc_y\n , dr, dc_x, dc_y, dc_z\n , e = None, x = None, y = None, z = None\n , e_solution_num = 1\n ):\n\t# These formuae were worked out in Maxima\n\tx_p1 = (br-ar)/bc_x\n\tx_p0 = -(br*br-bc_x*bc_x-ar*ar)/(2.0*bc_x)\n\ty_p1 = -(cc_x*x_p1-cr+ar)/cc_y\n\ty_p0 = -(2.0*cc_x*x_p0+cr*cr-cc_y*cc_y-cc_x*cc_x-ar*ar)/(2.0*cc_y)\n\tz_p1 = -(dc_y*y_p1+dc_x*x_p1-dr+ar)/dc_z\n\tz_p0 = -(2.0*dc_y*y_p0+2.0*dc_x*x_p0+dr*dr-dc_z*dc_z-dc_y*dc_y-dc_x*dc_x-ar*ar) \\\n\t /(2.0*dc_z)\n\t\n\tif e is None:\n\t\tif e_solution_num == 1:\n\t\t\te = -(sqrt((-y_p0*y_p0-x_p0*x_p0+ar*ar)*z_p1*z_p1 \\\n\t\t\t +(2.0*y_p0*y_p1+2.0*x_p0*x_p1+2.0*ar)*z_p0*z_p1 \\\n\t\t\t +(-y_p1*y_p1-x_p1*x_p1+1.0)*z_p0*z_p0+(ar*ar-x_p0*x_p0)*y_p1*y_p1 \\\n\t\t\t +(2.0*x_p0*x_p1+2.0*ar)*y_p0*y_p1+(1.0-x_p1*x_p1)*y_p0*y_p0 \\\n\t\t\t +ar*ar*x_p1*x_p1+2.0*ar*x_p0*x_p1+x_p0*x_p0) \\\n\t\t\t +z_p0*z_p1+y_p0*y_p1+x_p0*x_p1+ar) \\\n\t\t\t /(z_p1*z_p1+y_p1*y_p1+x_p1*x_p1-1.0)\n\t\telif e_solution_num == 2:\n\t\t\te = (sqrt((-y_p0*y_p0-x_p0*x_p0+ar*ar)*z_p1*z_p1 \\\n\t\t\t +(2.0*y_p0*y_p1+2.0*x_p0*x_p1+2.0*ar)*z_p0*z_p1 \\\n\t\t\t +(-y_p1*y_p1-x_p1*x_p1+1.0)*z_p0*z_p0+(ar*ar-x_p0*x_p0)*y_p1*y_p1 \\\n\t\t\t +(2.0*x_p0*x_p1+2.0*ar)*y_p0*y_p1+(1.0-x_p1*x_p1)*y_p0*y_p0+ar*ar*x_p1*x_p1 \\\n\t\t\t +2.0*ar*x_p0*x_p1+x_p0*x_p0) \\\n\t\t\t -z_p0*z_p1-y_p0*y_p1-x_p0*x_p1-ar) \\\n\t\t\t /(z_p1*z_p1+y_p1*y_p1+x_p1*x_p1-1.0)\n\tif x is None:\n\t\tx = e*x_p1+x_p0\n\tif y is None:\n\t\ty = e*y_p1+y_p0\n\tif z is None:\n\t\tz = e*z_p1+z_p0\n\t\n\treturn (e, x, y, z)","title":""},{"docid":"0f99579d846b40836d79a37cc12ca6f7","score":"0.5002701","text":"def monteCarlo( f_xy, g_xy, xmin, xmax, ymin, ymax, n):\n # create n random points in x and y\n a_xran = np.random.uniform( xmin, xmax, n)\n a_yran = np.random.uniform( ymin, ymax, n)\n ########### solve using for loop: A_om, f_mean###########\n f_fct_mean = 0\n num_inside = 0 # number of points with x,y; g(x,y) >= 0\n for i in range( n): # x loop\n for j in range( n): # y loop\n if g_xy( a_xran[i], a_yran[j]) >= 0:\n num_inside += 1\n f_fct_mean += f_xy( a_xran[i], a_yran[j])\n if (num_inside != 0):\n f_fct_mean /= num_inside\n # ############ vectorized version ###########\n # m_xran, m_yran = np.meshgrid( a_xran, a_yran)\n # sel = g_xy( m_xran, m_yran) >= 0\n # num_inside = sel.sum()\n # print( f_fct_mean)\n # f_fct_mean = np.mean( f_xy( m_xran[sel], m_yran[sel]))\n # print( f_fct_mean)\n\n # last two lines are the same for loop and vectorized solutions:\n # area of domain is approximate by q*Ar, where q is fraction of points in domain, and Ar is area of rectangle\n f_Aom = num_inside/float(n**2) * (xmax-xmin)*(ymax-ymin)\n return f_Aom*f_fct_mean","title":""},{"docid":"55412bfc636935d13ae20330a241745e","score":"0.4998856","text":"def edge_integral(self,direction,boundary,patch,flux,test=False):\r\n if direction==0:\r\n if boundary==0:\r\n global_indeces=self.patch_data.patch_global_2D(patch)[0,:]\r\n local_indeces=self.patch_data.local_IEN(patch,1)[0,:]\r\n elif boundary==1:\r\n global_indeces=self.patch_data.patch_global_2D(patch)[-1,:]\r\n local_indeces=self.patch_data.local_IEN(patch,1)[-1,:]\r\n\r\n else:\r\n if boundary==0:\r\n global_indeces=self.patch_data.patch_global_2D(patch)[:,0]\r\n local_indeces=self.patch_data.local_IEN(patch,1)[:,0]\r\n elif boundary==1:\r\n global_indeces=self.patch_data.patch_global_2D(patch)[:,-1]\r\n local_indeces=self.patch_data.local_IEN(patch,1)[:,-1]\r\n \r\n# print(global_indeces,local_indeces)\r\n \r\n pts,wts=np.polynomial.legendre.leggauss(np.max(self.order)+1) \r\n# print(pts)\r\n \r\n for ele_num in range(self.number_elements[patch,direction]):\r\n if direction==0 and boundary==0:\r\n ele=ele_num\r\n elif direction==0 and boundary==1:\r\n max_ele=np.prod(self.number_elements[patch,:])-1\r\n ele=max_ele-self.number_elements[patch,direction]+ele_num+1\r\n elif direction==1 and boundary==0:\r\n ele=(ele_num)* self.number_elements[patch,0] \r\n elif direction==1 and boundary==1:\r\n ele=(ele_num+1)* self.number_elements[patch,0]-1 \r\n \r\n# for int_pt in range(len(pts)):\r\n# \r\n## print(ele,self.number_elements[patch,direction])\r\n# \r\n# if direction==0:\r\n# if boundary==0:\r\n# xi,eta=self.parent_to_para(ele,pts[int_pt],-1,patch)\r\n# elif boundary==1:\r\n# xi,eta=self.parent_to_para(ele,pts[int_pt],1,patch)\r\n# elif direction==1:\r\n# if boundary==0:\r\n# xi,eta=self.parent_to_para(ele,-1,pts[int_pt],patch)\r\n# elif boundary==1:\r\n# xi,eta=self.parent_to_para(ele,1,pts[int_pt],patch)\r\n \r\n \r\n# print(ele,self.number_elements[patch,direction])\r\n filler=np.ones(len(pts))*1\r\n if direction==0:\r\n if boundary==0:\r\n xi,eta=self.parent_to_para(ele,pts,-filler,patch)\r\n elif boundary==1:\r\n xi,eta=self.parent_to_para(ele,pts,filler,patch)\r\n elif direction==1:\r\n if boundary==0:\r\n xi,eta=self.parent_to_para(ele,-filler,pts,patch)\r\n elif boundary==1:\r\n xi,eta=self.parent_to_para(ele,filler,pts,patch)\r\n \"\"\"\r\n Finding Jacobians\r\n \"\"\"\r\n R,dR_edge=self.NURBS_bases(xi,eta,patch,'all')\r\n \r\n edge_control_points=self.B[global_indeces]\r\n \r\n dR_edge=dR_edge[local_indeces,direction,:]\r\n R=R[:,local_indeces]\r\n \r\n# xpts=self.B[global_indeces,0]\r\n# fflux=lambda x: .1*np.sin(np.pi*x/800)\r\n fflux=lambda x: flux\r\n# flux=fflux(xpts)\r\n xi_locs,eta_locs=self.parent_to_para(ele_num,pts,np.ones(len(pts)),0)\r\n x_locs=xi_locs*800\r\n for lvl in range(len(pts)):\r\n dx_d=np.sum(dR_edge[:,lvl]*edge_control_points[:,0])\r\n dy_d=np.sum(dR_edge[:,lvl]*edge_control_points[:,1])\r\n \r\n detJ=np.sqrt(dx_d**2+dy_d**2)*(1./self.number_elements[patch,direction]/2.)\r\n # print(detJ)\r\n # if ele_num==3:\r\n # print(integrals,R)\r\n self.F[global_indeces]+=fflux(x_locs[lvl])*R[lvl,:]*detJ*wts[lvl]\r\n \r\n if test==True:\r\n return detJ,self.F,R,dR_edge","title":""},{"docid":"20cd9215635cc8caf5cfe0d328cd777e","score":"0.49963087","text":"def c2e(component='cube', ngrain=100, w0=10, ifig=1, mode='contour'):\n import numpy as np\n from .euler import euler\n from . import upf\n comp = component.lower()\n print(comp)\n if comp=='cube':\n hkl=[1,0,0]\n uvw=[0,1,0]\n elif comp=='copper':\n hkl=[1,1,2]\n uvw=[-1,-1,1]\n elif comp=='goss':\n hkl=[1,1,0]\n uvw=[0,0,1]\n elif comp=='rbrass':\n hkl=[-1,1,0]\n uvw=[1,1,1]\n\n print('hkl',hkl)\n print('uvw',uvw,'\\n')\n\n # mat0: trasformation matrix that converts\n # sample axes to crystal ones [ca<-sa]\n mat0 = miller2mat(hkl=list(map(round, hkl)), uvw=list(map(round, uvw)))\n\n print('mat0')\n for i in range(len(mat0)):\n for j in range(len(mat0[i])):\n print('%5.1f '%mat0[i][j], end=' ')\n print('')\n\n # Mirror reflection by RD and TD in the sample coordinate system.\n # mat0[ca<-sa]\n RD, TD = orthogonal()\n mat1 = np.dot(mat0, RD)\n mat2 = np.dot(mat0, TD)\n mat3 = np.dot(np.dot(mat0, RD), TD)\n print('mat1')\n for i in range(len(mat1)):\n for j in range(len(mat1[i])):\n print('%5.1f '%mat1[i][j], end=' ')\n print('')\n print('mat2')\n for i in range(len(mat2)):\n for j in range(len(mat2[i])):\n print('%5.1f '%mat2[i][j], end=' ')\n print('')\n print('mat3')\n for i in range(len(mat3)):\n for j in range(len(mat3[i])):\n print('%5.1f '%mat3[i][j], end=' ')\n print('')\n\n\n ang0 = euler(a=mat0, echo=False)\n ang1 = euler(a=mat1, echo=False)\n ang2 = euler(a=mat2, echo=False)\n ang3 = euler(a=mat3, echo=False)\n\n angs = [ang0, ang1, ang2, ang3]\n\n for i in range(len(angs)):\n for j in iter(angs[i]):\n print('%5.1f '%j, end=' ')\n print('')\n\n xtal = []\n for i in range(4):\n phi1, p, phi2 = angs[i]\n myx = textur(p1=phi1,p=p, p2=phi2, w0=w0, ngrain = ngrain/4,\n filename='dum', iplot=False)\n\n for j in range(len(myx.gr)):\n xtal.append(myx.gr[j])\n\n mypf = upf.polefigure(grains=xtal,csym='cubic')\n mypf.pf(pole=[[1,0,0],[1,1,0],[1,1,1]], mode=mode, ifig=ifig)\n\n ## Write\n gr2f(gr=xtal, header='module: %s, w0: %i'%('ce2', w0),\n filename='%i%s%i_%s.cmb'%(len(xtal), component, w0, 'ce2'))\n return xtal","title":""},{"docid":"333ba44707fb9a5875673075633240bb","score":"0.49866527","text":"def part_1e(x, y):\n six_x = np.arange(50, dtype=int) # creates a vector of length 50\n six_x = np.full_like(six_x, 6)\n six_y = 2*six_x + 10 + np.random.normal(0, 1, six_x.size) # [1050x1]\n\n x = np.append(x, six_x)\n y = np.append(y, six_y)\n\n fig4 = plt.figure()\n plt.plot(x, y, \".\", label=\"sample points\")\n plt.plot(six_x, six_y, 'g.', label=\"added points\")\n plt.plot(x, 2*x, \"r\", label=\"original function\")\n plt.title(\"Part E: Pertubation\")\n plt.legend()\n\n return x, y, six_x, six_y","title":""},{"docid":"0f413a5c948836517eaa6c0cf72ab844","score":"0.4985353","text":"def OperatorX_XBasis(self):\t\n\t\treturn np.diag( self.X_Range() ).astype(np.complex128)","title":""},{"docid":"a8403970a0b0f514fa6528a6dc2da025","score":"0.49753568","text":"def Q_I_metric(point,g_function):\n g_matrix=g_function(point)\n dimension=len(point)\n\n Q_matrix = np.zeros([dimension, dimension])\n\n Q_matrix=np.matrix(g_matrix)*np.matrix(g_matrix.transpose())\n\n#Q_matrix=np.add(Q_matrix,0.0000001*np.random.rand(dimension,dimension))\n\n Q_I_matrix = linalg.inv(Q_matrix)\n return Q_I_matrix","title":""},{"docid":"5b6c15a462cda005989f02e0f5d75644","score":"0.49742237","text":"def ecdf_1D(data):\r\n # Number of data points: n\r\n n = len(data)\r\n\r\n # x-data for the ECDF: x\r\n x = np.sort(data)\r\n\r\n # y-data for the ECDF: y\r\n y = np.arange(1, len(x)+1) / n\r\n\r\n return x, y","title":""},{"docid":"515c8210c1d29b26a330c2721b4b39bf","score":"0.496816","text":"def focus_field_debye_gauss(shape,units,lam,NAs, sig = 1./np.sqrt(2), n_integration_steps = 200):\n\n #p = OCLProgram(absPath(\"kernels/psf_debye.cl\"),build_options = str(\"-I %s -D INT_STEPS=%s\"%(absPath(\".\"),n_integration_steps)))\n p = OCLProgram(absPath(\"kernels/psf_debye.cl\"),\n build_options = [\"-I\",absPath(\"kernels\"),\"-D\",\"INT_STEPS=%s\"%n_integration_steps])\n\n\n assert (sig>0)\n\n Nx0, Ny0, Nz0 = shape\n dx, dy, dz = units\n\n alphas = np.arcsin(np.array(NAs))\n\n Nx = (Nx0+1)/2\n Ny = (Ny0+1)/2\n Nz = (Nz0+1)/2\n\n u_g = OCLArray.empty((Nz,Ny,Nx),np.float32)\n ex_g = OCLArray.empty(u_g.shape,np.complex64)\n ey_g = OCLArray.empty(u_g.shape,np.complex64)\n ez_g = OCLArray.empty(u_g.shape,np.complex64)\n\n alpha_g = OCLArray.from_array(alphas.astype(np.float32))\n\n t = time.time()\n\n p.run_kernel(\"debye_wolf_gauss\",u_g.shape[::-1],None,\n ex_g.data,ey_g.data,ez_g.data, u_g.data,\n np.float32(1.),np.float32(0.),\n np.float32(0),np.float32(dx*Nx),\n np.float32(0),np.float32(dy*Ny),\n np.float32(0),np.float32(dz*Nz),\n np.float32(lam),\n np.float32(sig),\n alpha_g.data, np.int32(len(alphas)))\n\n u = u_g.get()\n ex = ex_g.get()\n ey = ey_g.get()\n ez = ez_g.get()\n\n print \"time in secs:\" , time.time()-t\n\n u_all = np.empty((Nz0,Ny0,Nx0),np.float32)\n ex_all = np.empty((Nz0,Ny0,Nx0),np.complex64)\n ey_all = np.empty((Nz0,Ny0,Nx0),np.complex64)\n ez_all = np.empty((Nz0,Ny0,Nx0),np.complex64)\n\n sx = [slice(0,Nx),slice(Nx0-Nx0/2,Nx0)]\n sy = [slice(0,Ny),slice(Ny0-Ny0/2,Ny0)]\n sz = [slice(0,Nz),slice(Nz0-Nz0/2,Nz0)]\n\n sx = [slice(0,Nx),slice(Nx0-Nx,Nx0)]\n sy = [slice(0,Ny),slice(Ny0-Ny,Ny0)]\n sz = [slice(0,Nz),slice(Nz0-Nz,Nz0)]\n\n for i,j,k in itertools.product([0,1],[0,1],[0,1]):\n u_all[sz[1-i],sy[1-j],sx[1-k]] = u[::(-1)**i,::(-1)**j,::(-1)**k]\n ex_all[sz[1-i],sy[1-j],sx[1-k]] = ex[::(-1)**i,::(-1)**j,::(-1)**k]\n ey_all[sz[1-i],sy[1-j],sx[1-k]] = ey[::(-1)**i,::(-1)**j,::(-1)**k]\n ez_all[sz[1-i],sy[1-j],sx[1-k]] = ez[::(-1)**i,::(-1)**j,::(-1)**k]\n\n\n return u_all, ex_all, ey_all, ez_all","title":""},{"docid":"f876c6fcbf9a7396de99f22776282633","score":"0.49596038","text":"def __resample_edges_of_triangle(self,simplices,indC,x=None,nDivide=10): \n # handle optional arguments (sampling along CA and CB)\n if x is None:\n x = np.linspace(0,1,nDivide,endpoint=True)\n else:\n x = np.asarray(x); \n nDivide=x.size;\n # get indices of points ABC as shown above (C is isolated point)\n nTriangles = simplices.shape[0]; \n ind_triangle = np.arange(nTriangles)\n C = simplices[ind_triangle,(indC)%3];\n A = simplices[ind_triangle,(indC+1)%3];\n B = simplices[ind_triangle,(indC-1)%3];\n # create dense sampling along C->B and C->A in domain space\n CA = np.outer(1-x,self.domain[C]) + np.outer(x,self.domain[A]);\n CB = np.outer(1-x,self.domain[C]) + np.outer(x,self.domain[B]);\n # map sampling on CA and CB to image space \n domain_points= np.hstack((CA,CB)).reshape(nDivide,2,nTriangles,2);\n image_points = self.mapping(domain_points.reshape(-1,2)).reshape(nDivide,2,nTriangles,2);\n return A,B,C,domain_points,image_points;","title":""},{"docid":"41c1720e9527bb3ee9bcca581be9c7a3","score":"0.49587786","text":"def assemble(self,test=False):\r\n f_term=self.f\r\n k_term=self.k\r\n nelem=self.number_elements\r\n \r\n \r\n if np.array_equal(self.patch_data.order[1],np.ones(2)*-1)==0:\r\n number_patches=len(nelem)\r\n else:\r\n number_patches=1\r\n \r\n number_global_bases=len(self.B) #number global basis functions\r\n \r\n \"\"\"\r\n Integration points. May need to add additional points for log funcitons\r\n \"\"\"\r\n eta_loc,xi_loc,wl=Ippre.compute_gauss_points_and_weights(np.max(self.order)+1,2) \r\n\r\n \"\"\"\r\n Iniitializing enriched matrices, as indicated by addition of terms to number of standard bases\r\n \"\"\"\r\n self.K_en=np.zeros((number_global_bases+2,number_global_bases+2))\r\n self.F_en=np.zeros(number_global_bases+2)\r\n \r\n \"\"\"\r\n Initializing un-enriched matriices for comparison\r\n \"\"\"\r\n self.K=np.zeros((number_global_bases,number_global_bases))\r\n self.F=np.zeros(number_global_bases)\r\n \r\n #Looping through patches\r\n for aa in range(number_patches):\r\n \r\n #Find patch data and control net specific to each patch\r\n if number_patches!=1:\r\n Global_IEN_patch=self.patch_data.Global_IEN[self.patch_data.Global_IEN_length[aa,0]:self.patch_data.Global_IEN_length[aa,1]+1,:]\r\n else:\r\n Global_IEN_patch=self.patch_data.Global_IEN\r\n \r\n IEN_patch=self.patch_data.local_IEN_array[aa]\r\n control_point_ref=self.patch_data.patch_global_2D(aa).flatten()\r\n B_patch=self.B[control_point_ref]\r\n \r\n #Loop over elements in patch\r\n for bb in range(np.prod(nelem[aa])): \r\n \r\n #Finds basis functions supported within element\r\n if len(Global_IEN_patch.shape)!=1:\r\n global_basis_functions=Global_IEN_patch[bb,:]\r\n else:\r\n global_basis_functions=Global_IEN_patch\r\n\r\n global_basis_functions=global_basis_functions[0:np.prod(self.order[aa]+1)]\r\n \r\n \r\n idx_grid = np.ix_(global_basis_functions, global_basis_functions)\r\n \r\n#\r\n# \"\"\"\r\n# Additional code for enrichment\r\n# \"\"\"\r\n# if bb==0 and np.prod(self.number_elements[0])==1:\r\n# global_basis_functions_en=range(np.prod(self.order[aa]+1)+2)\r\n# \r\n# elif bb==range(np.prod(nelem[aa]))[-1]:\r\n# global_basis_functions_en=np.hstack((global_basis_functions,len(self.B)+1)) #Adds last basis function\r\n# \r\n# elif bb==0:\r\n# global_basis_functions_en=np.hstack((global_basis_functions,len(self.B))) #Adds second to last basis function\r\n# \r\n# else:\r\n# global_basis_functions_en=global_basis_functions[:]\r\n# \r\n# idx_grid_en=np.ix_(global_basis_functions_en, global_basis_functions_en)\r\n# \r\n# \r\n# return idx_grid_en\r\n\r\n\r\n \"\"\"\r\n Solves mapping and NURBS bases at various integration points. Puts results into 2 or 3D array\r\n \"\"\"\r\n \r\n \"\"\"\r\n Mappings to x,y space\r\n \"\"\"\r\n detJ,R,dR_dx=self.mappings(bb,xi_loc,eta_loc,aa)\r\n \r\n \"\"\"\r\n x and y for xi and eta on element bb in patch aa\r\n \"\"\"\r\n# xfun=np.zeros(len(xi_loc)); yfun=np.zeros(len(xi_loc))\r\n# print(R,B_patch,xfun)\r\n# for lvl in range(len(xi_loc)):\r\n# xfun[lvl]=np.sum(R[lvl,:]*B_patch[:,0])\r\n# yfun[lvl]=np.sum(R[lvl,:]*B_patch[:,1])\r\n \r\n xfun=np.einsum('ij,j->i',R,B_patch[:,0])\r\n yfun=np.einsum('ij,j->i',R,B_patch[:,1])\r\n \"\"\"\r\n Material modulus and forcing terms\r\n \"\"\"\r\n# k=np.zeros(len(xi_loc)); f=np.zeros(len(xi_loc))\r\n# for lvl in range(len(xi_loc)):\r\n# k[lvl]=k_term(xfun[lvl],yfun[lvl])\r\n# f[lvl]=f_term(xfun[lvl],yfun[lvl])\r\n k=k_term(xfun,yfun)\r\n f=f_term(xfun,yfun)\r\n \r\n \"\"\"\r\n Selecting NURBS bases and their derivatives supported on element\r\n \"\"\"\r\n R=R[:,IEN_patch[bb,:]] #in reverse order now. IEN_patch may be single row\r\n dR_dx=dR_dx[IEN_patch[bb,:],:,:]\r\n \r\n# R=R[::-1]\r\n# dR_dx=np.flipud(dR_dx)\r\n \"\"\"\r\n Making grid for both non enriched and enriched meshes\r\n \"\"\"\r\n dRdx_grid=np.zeros((len(dR_dx[:,0,0]),len(dR_dx[:,0,0]),len(xi_loc)))\r\n dRdy_grid=np.zeros((len(dR_dx[:,0,0]),len(dR_dx[:,0,0]),len(xi_loc)))\r\n \r\n# for lvl in range(len(xi_loc)):\r\n#\r\n# dRdx_grid[:,:,lvl]=np.einsum('i,j',dR_dx[:,0,lvl],dR_dx[:,0,lvl])\r\n# dRdy_grid[:,:,lvl]=np.einsum('i,j',dR_dx[:,1,lvl],dR_dx[:,1,lvl])\r\n dRdx_grid=np.einsum('ik,jk->ijk',dR_dx[:,0,:],dR_dx[:,0,:]) \r\n dRdy_grid=np.einsum('ik,jk->ijk',dR_dx[:,1,:],dR_dx[:,1,:]) \r\n \"\"\"\r\n Calculating pertinent entries in K and F\r\n \"\"\"\r\n# return dRdy_grid_modified.shape,dRdy_grid_modified.shape,idx_grid_en\r\n# for lvl in range(len(xi_loc)):\r\n# self.K[idx_grid]+=k[lvl]*(dRdx_grid[:,:,lvl]+dRdy_grid[:,:,lvl])*detJ[lvl]*wl[lvl]\r\n# self.F[global_basis_functions]+=f[lvl]*R[lvl,:]*detJ[lvl]*wl[lvl]\r\n self.K[idx_grid]+=np.einsum('ijk,k->ij',(dRdx_grid[:,:,:]+dRdy_grid[:,:,:]),k*detJ*wl)\r\n self.F[global_basis_functions]+=np.einsum('ji,j->i',R,f*detJ*wl)\r\n \r\n if test==True:\r\n return self.K,detJ","title":""},{"docid":"292833471b3b380101fbccacc94e6d9c","score":"0.49501646","text":"def sgd(self):\r\n for i, j, r in self.samples:\r\n # Computer prediction and error\r\n prediction = self.get_rating(i, j)\r\n e = (r - prediction)\r\n\r\n # Update biases\r\n self.b_u[i] += self.alpha * (e - self.beta * self.b_u[i])\r\n self.b_i[j] += self.alpha * (e - self.beta * self.b_i[j])\r\n\r\n # Create copy of row of P since we need to update it but use older values for update on Q\r\n P_i = self.P[i, :][:]\r\n\r\n # Update user and item latent feature matrices\r\n self.P[i, :] += self.alpha * (e * self.Q[j, :] - self.beta * self.P[i, :])\r\n self.Q[j, :] += self.alpha * (e * P_i - self.beta * self.Q[j, :])","title":""},{"docid":"f81b6635d3492655457599a8d31bc209","score":"0.4949912","text":"def test_hodge_star(self):\n regular_cases = []\n #Unit line segment\n regular_cases.append((matrix([[0],[1]]),matrix([[0,1]]),[1,1],[0.5,1]))\n #One regular triangle, edge length 1\n regular_cases.append((matrix([[0,0],[1,0],[0.5,sqrt(3.0)/2.0]]),matrix([[0,1,2]]), [1, 1, sqrt(3.0)/4.0],\\\n [sqrt(3.0)/12.0,sqrt(3.0)/6.0,1]))\n # One regular tet, edge length sqrt(2)\n regular_cases.append((matrix([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0]]),matrix([[0,1,2,3]]),\\\n [1, sqrt(2.0),sqrt(3.0/4.0), 1.0/3.0],[1.0/12.0,sqrt(2.0)/12.0,sqrt(3.0/36.0),1]))\n \n for v,e,primal,dual in regular_cases:\n sc = simplicial_complex((v,e))\n for dim in range(sc.complex_dimension() + 1):\n (n,m) = sc[dim].star.shape\n for i in range(n): \n assert_almost_equal( sc[dim].star[i,i], dual[dim]/primal[dim] )\n \n \n multi_cases = []\n #two line segments\n multi_cases.append((matrix([[0],[1],[2]]),matrix([[0,1],[1,2]]), \\\n [([0],0.5),([1],1.0),([2],0.5),([0,1],1),([1,2],1)]))\n #three line segments\n multi_cases.append((matrix([[0],[1],[2],[3]]),matrix([[0,1],[1,2],[2,3]]), \\\n [([0],0.5),([1],1.0),([2],1),([3],0.5),([0,1],1),([1,2],1),([2,3],1)]))\n #two triangles\n multi_cases.append((matrix([[0,0],[1.0,0],[0.5,sqrt(3.0)/2.0],[1.5,sqrt(3.0)/2.0]]), \\\n matrix([[0,1,2],[1,3,2]]), \\\n [([0],sqrt(3.0)/12.0),([1],2*sqrt(3.0)/12.0),([2],2*sqrt(3.0)/12.0), ([3],sqrt(3.0)/12.0), \\\n ([0,1],sqrt(3.0)/6.0),([0,2],sqrt(3.0)/6.0),([1,2],2*sqrt(3.0)/6.0),([1,3],sqrt(3.0)/6.0), \\\n ([2,3],sqrt(3.0)/6.0),([0,1,2],4.0/sqrt(3.0)),([1,2,3],4.0/sqrt(3.0))]))\n #three triangles\n multi_cases.append((matrix([[0,0],[1.0,0],[0.5,sqrt(3.0)/2.0],[1.5,sqrt(3.0)/2.0],[2,0]]), \\\n matrix([[0,1,2],[1,3,2],[1,4,3]]), \\\n [([0],sqrt(3.0)/12.0),([1],3*sqrt(3.0)/12.0),([2],2*sqrt(3.0)/12.0), ([3],2*sqrt(3.0)/12.0), \\\n ([4],sqrt(3.0)/12.0),([0,1],sqrt(3.0)/6.0),([0,2],sqrt(3.0)/6.0),([1,2],2*sqrt(3.0)/6.0),\n ([1,3],2*sqrt(3.0)/6.0), ([1,4],sqrt(3.0)/6.0),([3,4],sqrt(3.0)/6.0),\\\n ([2,3],sqrt(3.0)/6.0),([0,1,2],4.0/sqrt(3.0)),([1,2,3],4.0/sqrt(3.0)),([1,4,3],4.0/sqrt(3.0))]))\n \n \n for v,e,t_list in multi_cases: \n sc = simplicial_complex((v,e))\n for s,ratio in t_list:\n data = sc[len(s)-1]\n index = data.simplex_to_index[simplex(s)]\n assert_almost_equal(ratio,data.star[index,index])\n \n \n #use the same multi_cases, but add dimensions and translate the vertices \n #TODO rotate also\n random.seed(0)\n for v,e,t_list in multi_cases:\n \n (rows,cols) = v.shape\n v = concatenate([v,zeros((rows,4))],1) \n v += rand(1,cols +4) #translate\n \n sc = simplicial_complex((v,e))\n for s,ratio in t_list:\n data = sc[len(s)-1]\n index = data.simplex_to_index[simplex(s)]\n assert_almost_equal(ratio,data.star[index,index])\n \n #Test sign of dual star, check that: ** = -1 ^(k (n-k))\n for v,e,t_list in multi_cases:\n sc = simplicial_complex((v,e))\n for dim,data in enumerate(sc):\n n = sc.complex_dimension()\n k = dim\n assert_almost_equal((data.star * data.star_inv).todense(), \\\n ((-1)**(k*(n-k))*sparse.identity(data.num_simplices)).todense())","title":""},{"docid":"64f8a496b0e19fa419122af14c1af901","score":"0.49495548","text":"def _gcn(X, flag=0, scale=55.):\r\n if flag == 0:\r\n print(\"1\")\r\n mean = np.mean(X, axis=1)\r\n X = X - mean[:, np.newaxis]\r\n contrast = np.sqrt(10. + (X**2).sum(axis=1)) / scale\r\n contrast[contrast < 1e-8] = 1.\r\n X = X / contrast[:, np.newaxis]\r\n else:\r\n print(\"3\")\r\n X = X.reshape([-1, 32, 32, 3])\r\n mu = np.mean(X, axis=(1, 2)).reshape([-1, 1, 1, 3])\r\n std = np.std(X, axis=(1, 2)).reshape([-1, 1, 1, 3])\r\n X = X - mu\r\n X = X / std\r\n X = X.reshape([-1, 3072])\r\n return X","title":""},{"docid":"64f8a496b0e19fa419122af14c1af901","score":"0.49495548","text":"def _gcn(X, flag=0, scale=55.):\r\n if flag == 0:\r\n print(\"1\")\r\n mean = np.mean(X, axis=1)\r\n X = X - mean[:, np.newaxis]\r\n contrast = np.sqrt(10. + (X**2).sum(axis=1)) / scale\r\n contrast[contrast < 1e-8] = 1.\r\n X = X / contrast[:, np.newaxis]\r\n else:\r\n print(\"3\")\r\n X = X.reshape([-1, 32, 32, 3])\r\n mu = np.mean(X, axis=(1, 2)).reshape([-1, 1, 1, 3])\r\n std = np.std(X, axis=(1, 2)).reshape([-1, 1, 1, 3])\r\n X = X - mu\r\n X = X / std\r\n X = X.reshape([-1, 3072])\r\n return X","title":""},{"docid":"ae9f5817f2a539f10c4bfd2ca7b97c7f","score":"0.4948728","text":"def CMB_E_and_B_modes():\n res = 512\n frame = 50\n param = [1,1]\n\n res = 512\n N = np.array([res,res], dtype=np.int32)\n xsize = 1 * np.pi / 180\n size2d = np.array([xsize,xsize])\n Delta = size2d/N\n print \"Delta\", Delta\n Deltal = cmbtools.Delta2l(Delta, N)\n print \"Deltal\", Deltal\n\n for axis in axes:\n for sim in sims:\n beta = {'High_512':'B02', 'Mid_512':'B2', 'Low_512':'B20'}[sim] \n at = access_thing(sim,res,frame,param[0],param[1], machine=machine)\n plotloc = at.get_plot_location_QU2EB()\n \n # Get frbs\n Q = np.array(at.get_fits_array('Q%s_n0-%04d_p-%d'%(axis,param[0],param[1]), axis))\n U = np.array(at.get_fits_array('U%s_n0-%04d_p-%d'%(axis,param[0],param[1]), axis))\n\n print \"Q\\n\", Q \n print \"U\\n\", U\n print \"Q.shape\", Q.shape\n print \"U.shape\", U.shape\n\n # 2d fourier transforms of Q and U\n #Qharm = cmbtools.map2harm(Q, Delta)\n #Uharm = cmbtools.map2harm(U, Delta)\n if not Q.flags['C_CONTIGUOUS']:\n Q = np.ascontiguousarray(Q)\n if not U.flags['C_CONTIGUOUS']:\n U = np.ascontiguousarray(U)\n Qharm = np.fft.fft2(Q)\n Uharm = np.fft.fft2(U)\n\n print \"Qharm\\n\", Qharm\n print \"Uharm\\n\", Uharm\n \n # Decompose stokes into E and B modes\n Eharm, Bharm = cmbtools.QU2EB(Qharm, Uharm, Deltal)\n\n print \"Eharm\\n\", Eharm\n print \"Bharm\\n\", Bharm\n \n # 2d inverse fourier transforms of E and B to bring them back into real space\n #E = cmbtools.harm2map(Eharm, Delta)\n #B = cmbtools.harm2map(Bharm, Delta)\n E = np.fft.ifft2(Eharm)\n B = np.fft.ifft2(Bharm)\n\n print \"E\\n\", E\n print \"B\\n\", B\n\n # Compute coeffictients for the harmonics\n deltalbins=2.5\n lmax = 100\n lbins = np.arange(0,lmax,deltalbins)\n\n ClE = cmbtools.harm2cl(Eharm, Deltal, lbins)\n ClB = cmbtools.harm2cl(Bharm, Deltal, lbins)\n print \"ClE/ClB\", ClE/ClB\n\n plt.clf()\n plt.imshow(Q,interpolation='nearest')\n plt.title('Q')\n plt.colorbar()\n plt.savefig('p49_Q_%s_%s'%(axis, beta))\n \n plt.clf()\n plt.imshow(U,interpolation='nearest')\n plt.title('U')\n plt.colorbar()\n plt.savefig('p49_U_%s_%s'%(axis, beta))\n\n plt.clf()\n plt.imshow(E,interpolation='nearest')\n plt.title('E')\n plt.colorbar()\n plt.savefig('p49_E_%s_%s'%(axis, beta))\n \n plt.clf()\n plt.imshow(B,interpolation='nearest')\n plt.title('B')\n plt.colorbar()\n plt.savefig('p49_B_%s_%s'%(axis, beta))","title":""},{"docid":"61ef1aeec43563daef9c058778788f24","score":"0.49442142","text":"def calc_sf_c_serial(vardct, hartree, pdos, eqp, imeqp, newen, allkb):\n print \" calc_sf_c_serial :: \" \n import numpy as np;\n wtk = np.array(vardct['wtk'])\n hartree = np.array(hartree)\n pdos = np.array(pdos)\n minkpt = int(vardct['minkpt'])\n maxkpt = int(vardct['maxkpt'])\n nkpt = maxkpt - minkpt + 1\n minband = int(vardct['minband'])\n maxband = int(vardct['maxband'])\n nband = maxband - minband + 1\n bdgw = map(int, vardct['sig_bdgw'])\n bdrange = range(minband-bdgw[0],maxband-bdgw[0]+1)\n kptrange = range(minkpt - 1, maxkpt)\n #print \"kptrange, bdrange \", kptrange, bdrange \n newdx = 0.005\n enmin = float(vardct['enmin'])\n enmax = float(vardct['enmax'])\n #if enmin < en[0] and enmax >= en[-1]: \n # newen = np.arange(en[0],en[-1],newdx)\n #elif enmin < en[0]: \n # newen = np.arange(en[0],enmax,newdx)\n #elif enmax >= en[-1] : \n # newen = np.arange(enmin,en[-1],newdx)\n #else : \n # newen = np.arange(enmin,enmax,newdx)\n npoles = int(vardct['npoles'])\n extinf = int(vardct['extinf'])\n penergy = int(vardct['penergy'])\n #allkb = [spfkb,reskb, rdenkb, imskb]\n reskb = allkb[1]\n imskb = allkb[3]\n if npoles==999: # same omega_p for every state, with the intensity calculated integrating Im(Sigma)\n omega_p = float(vardct['omega_p'])\n #omegampole = np.ones((nkpt,nband))*omega_p\n #ampole = np.zeros((nkpt,nband))\n omegampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size))*omega_p\n ampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size))\n #for ik in range(nkpt):\n #for ik in kptrange:\n #for ib in range(nband):\n #for ib in bdrange:\n for ik in range(imskb[:,0,0].size):\n for ib in range(imskb[0,:,0].size):\n print \" ik, ib\", ik, ib \n #interpims = interp1d(en, ims[ik,ib], kind = 'linear', axis = -1)\n #if eqp[ik,ib]<=efermi:\n if eqp[ik,ib]<=0:\n tmpen = newen[imskb[ik,ib]>=0]\n tmpim = imskb[ik,ib,imskb[ik,ib]>=0]\n else:\n tmpen = newen[imskb[ik,ib]<0]\n tmpim = imskb[ik,ib,ims[ik,ib]<0]\n ampole[ik,ib] = abs(np.trapz(tmpim,tmpen))/np.pi\n print \" 1/pi*\\int\\Sigma =\", ampole[ik,ib] \n # Workaround correction for small energy plasmons\n ampole[ik,ib] = ampole[ik,ib]/(abs(tmpen[-1]-tmpen[0]))*omega_p\n# # Workaround for small energy plasmons\n# if eqp[ik,ib]<=efermi:\n# tmpim = tmpim[tmpen>=eqp[ik,ib]-2.5]\n# tmpen = tmpen[tmpen>=eqp[ik,ib]-2.5]\n# else:\n# tmpim = tmpim[tmpen <eqp[ik,ib]+2.5]\n# tmpen = tmpen[tmpen <eqp[ik,ib]+2.5]\n# ampole[ik,ib] = np.trapz(tmpim,tmpen)/np.pi\n #ampole = ampole/omega_p**2\n #ampole[ik,ib] = np.trapz(en[ims[ik,ib]>=0],ims[ik,ib,ims[ik,ib]>=0])/np.pi\n elif npoles != 0:\n from multipole import fit_multipole, fit_multipole_const, getdata_file #, write_f_as_sum_of_poles\n print \" ### ================== ###\" \n print \" ### Multipole fit ###\" \n print \" Number of poles:\", npoles \n #omegampole = np.zeros((nkpt,nband,npoles))\n #ampole = np.zeros((nkpt,nband,npoles))\n omegampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,npoles))\n ampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,npoles))\n #for ik in range(nkpt):\n # ikeff=minkpt+ik-1\n #bdrange = vardct['bdrange']\n #kptrange = vardct['kptrange']\n #print \"kptrange, bdrange \", kptrange, bdrange \n for ik in kptrange:\n for ib in bdrange:\n #for ik in range(imskb[:,0,0].size):\n #for ib in range(nband):\n # for ib in range(imskb[0,:,0].size):\n if eqp[ik,ib] > newen[-npoles]:\n #if eqp[ik,ib] > newen[-1]:\n omegampole[ik,ib] = omegampole[ik,ib-1]\n ampole[ik,ib] = ampole[ik,ib-1]\n print \" Eqp beyond available energy range. Values from lower band are taken.\" \n continue\n else:\n ibeff=minband+ib-1\n print \" ik, ib\", ik, ib \n #interpims = interp1d(en, ims[ik,ib], kind = 'linear', axis = -1)\n #print newen.shape, imskb.shape \n interpims = interp1d(newen, imskb[ik,ib], kind = 'linear', axis = -1)\n # Here we take the curve starting from eqp and then we invert it\n # so as to have it defined on the positive x axis\n # and so that the positive direction is in the \n # increasing direction of the array index\n #if eqp[ik,ib] <= efermi:\n if eqp[ik,ib] <= 0:\n #en3 = en[en<=eqp[ik,ib]] # So as to avoid negative omegampole\n #en3 = newen[newen<=eqp[ik,ib]] # So as to avoid negative omegampole\n en3 = newen[newen<0.] # So as to avoid negative omegampole\n else:\n en3 = newen[newen>eqp[ik,ib]] # So as to avoid negative omegampole\n #en3 = en[en>eqp[ik,ib]] # So as to avoid negative omegampole\n #en3 = en[en<=efermi]\n if en3.size == 0:\n print \n print \" WARNING: QP energy is outside of given energy range!\\n\"+\\\n \" This state will be skipped!\\n\"+\\\n \"You might want to modify enmin/enmax.\"\n print \" eqp[ik,ib], newen[-1]\", eqp[ik,ib] , newen[-1] \n continue\n im3 = abs(interpims(en3)/np.pi) # This is what should be fitted\n zcut = 3.0\n for i in range(en3.size):\n if en3[i]>(eqp[ik,ib]-zcut) and en3[i]<(eqp[ik,ib]+zcut):\n im3[i] = 0.\n #import matplotlib.pylab as plt\n #plt.plot(en3,im3,'-')\n #plt.show()\n en3 = en3 - eqp[ik,ib]\n if eqp[ik,ib] <= 0:\n en3 = -en3[::-1] \n im3 = im3[::-1]\n #### TESTING ###\n #print \"ik, ib, eqp[ik,ib], en3[0], en3[-1], newen[0], newen[-1]:\\n\", ik, ib, eqp[ik,ib], en3[0], en3[-1], newen[0], newen[-1] \n #import matplotlib.pylab as plt\n #plt.plot(newen, imskb[ik,ib]/np.pi,\"-\")\n #plt.plot(en3+eqp[ik,ib], im3,\"x\")\n #plt.show()\n #sys.exit()\n #### END TESTING ###\n omegai, lambdai, deltai = fit_multipole_const(en3,im3,npoles)\n plot_fit = int(vardct['plot_fit'])\n if plot_fit == 1:\n from multipole import write_f_as_sum_of_poles\n import matplotlib.pylab as plt\n import pylab\n plt.figure(2)\n eta = 0.5\n enlor, flor = write_f_as_sum_of_poles(en3, omegai, lambdai, deltai, eta)\n plt.plot(enlor, flor,\"-\",label=\"sum of poles, eta: \"+str(eta))\n plt.plot(en3,im3,\"-\",label=\"ImS(e-w)\")\n plt.plot(omegai,lambdai,\"go\", label = \"omegai, lambdai\")\n plt.plot(omegai,lambdai/deltai,\"ro\", label = \"omegai, lambdai/deltai\")\n plt.title(\"ik: \"+str(ik)+\", ib: \"+str(ib)+\", npoles: \"+str(npoles))\n plt.legend()\n pylab.savefig('imS_fit_np'+str(npoles)+'_ik'+str(ik)+'_ib'+str(ib)+'.pdf')\n plt.close()\n ## TESTING THE MULTIPOLE REPRESENTATION\n #from multipole import write_f_as_sum_of_poles\n #import matplotlib.pylab as plt\n #import pylab\n #eta = 0.01\n #for eta in [0.1]: #, 0.1, 0.5]:\n # for npoles in [1,10,20,100]:\n # omegai, lambdai, deltai = fit_multipole_const(en3,im3,npoles)\n # print \"ik, ib, eqp[ik,ib], en3[0], en3[-1], newen[0], newen[-1]:\\n\", ik, ib, eqp[ik,ib], en3[0], en3[-1], newen[0], newen[-1] \n # print omegai, lambdai, deltai \n # enlor, flor = write_f_as_sum_of_poles(en3, omegai, lambdai, deltai, eta)\n # plt.plot(enlor, flor,\"-\",label=\"sum of poles, eta: \"+str(eta))\n # plt.plot(en3,im3,\"-\",label=\"ImS(e-w)\")\n # plt.plot(omegai,lambdai,\"go\", label = \"omegai, lambdai\")\n # plt.plot(omegai,lambdai/deltai,\"ro\", label = \"omegai, lambdai/deltai\")\n # plt.title(\"ik: \"+str(ik)+\", ib: \"+str(ib)+\", npoles: \"+str(npoles))\n # plt.legend()\n # pylab.savefig('imS_test_np'+str(npoles)+'_ik'+str(ik)+'_ib'+str(ib)+'_eta'+str(eta)+'.pdf')\n # plt.show()\n #sys.exit()\n # END TESTING THE MULTIPOLE REPRESENTATION \n # HERE WE MUST CHECK THAT THE NUMBER OF POLES \n # IS NOT BIGGER THAN THE NUMBER OF POINTS THAT HAS TO BE FITTED\n if npoles > omegai.size:\n omegampole[ik,ib][:omegai.size] = omegai \n ampole[ik,ib][:omegai.size] = np.true_divide(lambdai,(np.square(omegai)))\n print \n print \" WARNING: npoles used (\"+str(npoles)+\") is larger\"+\\\n \" than poles x data array can give (\"+str(omegai.size)+\").\"\n #print \"WARNING: Reduce npoles. You are wasting resources!!!\" \n print \" Im(Sigma) will be interpolated to obtain the desired number of poles.\" \n current_size = omegai.size\n counter = 0\n while npoles > current_size:\n counter += 1\n print \n print \" WARNING: Arrays are too coarse.\" \n print \" npoles, omegai.size:\", npoles, omegai.size \n print \" Filling arrays with interpolated values...\" \n en1 = array_doublefill(en3)\n im1 = array_doublefill(im3)\n en3 = en1\n im3 = im1\n omegai, lambdai, deltai = fit_multipole_const(en1,im1,npoles)\n current_size = omegai.size\n if counter > 4:\n print 60*\"=\" \n print \" WARNING: You are trying too hard with too few points.\" \n print \" The array has been interpolated more than 4 times.\" \n print \" Maybe use less poles or calculate more points for Sigma?\" \n print 60*\"=\" \n # im1 = fit_double(im3)\n else:\n omegampole[ik,ib] = omegai \n ampole[ik,ib] = np.true_divide(lambdai,(np.square(omegai)))\n #ampole[ik,ib] = gi\n print \" Integral test. Compare \\int\\Sigma and \\sum_j^N\\lambda_j.\" \n print \" 1/pi*\\int\\Sigma =\", np.trapz(im3,en3) \n print \" \\sum_j^N\\lambda_j =\", np.sum(lambdai) \n #plt.plot(en3,im3,\"-\"); plt.plot(omegai,np.pi/2*gi*omegai/deltai,\"-o\")\n #e1,f1 = write_f_as_sum_of_poles(en3,omegai,gi,deltai,0)\n # Writing out a_j e omega_j\n print \" ### Writing out a_j and omega_j...\" \n outname = \"a_j_np\"+str(npoles)+\".dat\"\n outfile = open(outname,'w')\n outname2 = \"omega_j_np\"+str(npoles)+\".dat\"\n outfile2 = open(outname2,'w')\n for ipole in xrange(npoles):\n # for ik in kptrange:\n # #for ib in range(nband):\n # for ib in bdrange:\n for ik in range(imskb[:,0,0].size):\n for ib in range(imskb[0,:,0].size):\n outfile.write(\"%15.7e\" % (ampole[ik,ib,ipole]))\n outfile2.write(\"%15.7e\" % (omegampole[ik,ib,ipole]))\n #outfile.write(\"%10.5f\" % (ampole[ik,ib,ipole]))\n #outfile2.write(\"%10.5f\" % (omegampole[ik,ib,ipole]))\n outfile.write(\"\\n\")\n outfile2.write(\"\\n\")\n outfile.write(\"\\n\")\n outfile2.write(\"\\n\")\n outfile.close()\n outfile2.close()\n # Extrinsic and interference contribution\n if extinf == 1:\n origdir = vardct['origdir']\n extinfname = \"a_wp.\"+str(penergy)\n amp_exinf, w_extinf = calc_extinf_corrections(origdir,extinfname,ampole,omegampole)\n print \" ### Writing out a_j_extinf...\" \n outname = \"a_j_np\"+str(npoles)+\"_extinf.\"+str(penergy)\n outfile = open(outname,'w')\n for ipole in xrange(npoles):\n # for ik in kptrange:\n # for ib in bdrange:\n for ik in range(imskb[:,0,0].size):\n for ib in range(imskb[0,:,0].size):\n outfile.write(\"%10.5f\" % (amp_exinf[ik,ib,ipole]))\n outfile.write(\"\\n\")\n outfile.write(\"\\n\")\n outfile.close()\n else: # npoles == 0\n omegampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size))\n ampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size))\n #omegampole = np.zeros((nkpt,nband))\n #ampole = np.zeros((nkpt,nband))\n #elaps2 = time.time() - elaps1 - e0\n #cpu2 = time.clock() - cpu1 - c0\n #print elaps2, cpu2 \n #print str(\" Used time (elaps, cpu): %10.6e %10.6e\"% (elaps2, cpu2)) \n print \" Calculating multipole exponential A...\" \n dxexp=0.005 \n enexp = np.arange(enmin,enmax,dxexp)\n nenexp = np.size(enexp)\n ftot = np.zeros((np.size(enexp)),order='Fortran')\n nen = np.size(enexp)\n #sfkb_c = np.zeros((nkpt,nband,nenexp))\n sfkb_c = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,nenexp))\n ############################\n # With extrinsic effects ###\n if extinf == 1:\n from extmod_spf_mpole import f2py_calc_spf_mpole_extinf\n #for ik in range(nkpt):\n for ik in kptrange:\n ikeff = ik + 1\n #for ib in range(nband):\n for ib in bdrange:\n ibeff=bdgw[0]+ib\n print \" ik, ib, ikeff, ibeff\", ik, ib, ikeff, ibeff \n #prefac=np.exp(-np.sum(amp_exinf[ik,ib]))/np.pi*wtk[ik]*pdos[ib]*abs(imeqp[ik,ib])\n # Experimental fix for npoles dependence\n tmp = 1/np.pi*wtk[ik]*pdos[ib]*abs(imeqp[ik,ib])\n prefac=np.exp(-np.sum(ampole[ik,ib]))*tmp\n #prefac=np.exp(-tmp*np.trapz(imskb[ik,ib],enexp)/np.sum(omegai)*npoles)\n akb=amp_exinf[ik,ib] # This is a numpy array (slice)\n omegakb=omegampole[ik,ib] # This is a numpy array (slice)\n wkb=w_extinf[ik,ib] # This is a numpy array (slice)\n eqpkb=eqp[ik,ib]\n imkb=imeqp[ik,ib] # + w_extinf[ik,ib]/2 # extinf width added\n #tmpf = calc_spf_mpole(enexp,prefac,akb,omegakb,eqpkb,imkb,npoles,wkb)\n #ftot += tmpf\n if eqpkb < 0.0:\n pass\n #tmpf = calc_spf_mpole(enexp,prefac,akb,omegakb,eqpkb,imkb,npoles)\n else:\n print \" This state is empty! eqpkb ik ib:\",eqpkb, ikeff+1, ibeff+1 \n #print \"omegakb\", omegakb \n omegakb=-omegakb\n #print \"-omegakb\", omegakb \n tmpf = np.zeros((nenexp), order='Fortran')\n tmpf = f2py_calc_spf_mpole_extinf(tmpf,enexp,prefac,akb,omegakb,wkb,eqpkb,imkb) #,np.size(enexp),npoles)\n #outnamekb = \"spf_exp-k\"+str(\"%02d\"%(ikeff+1))+\"-b\"+str(\"%02d\"%(ibeff+1))+\"_np\"+str(npoles)+\"_extinf.\"+str(penergy)\n #outfilekb = open(outnamekb,'w')\n #for ien in xrange(nenexp):\n # outfilekb.write(\"%8.4f %12.8f\\n\" % (enexp[ien], tmpf[ien]))\n #outfilekb.close()\n sfkb_c[ik,ib] = tmpf\n ftot = ftot + tmpf\n else: # extinf == 0\n from extmod_spf_mpole import f2py_calc_spf_mpole\n #for ik in range(nkpt):\n #for ib in range(nband):\n for ik in kptrange:\n ikeff = ik + 1\n for ib in bdrange:\n ibeff = ib + 1\n print \" ik, ib, ikeff, ibeff\", ik, ib, ikeff, ibeff \n #prefac=np.exp(-np.sum(ampole[ik,ib]))/np.pi*wtk[ik]*pdos[ib]*abs(imeqp[ik,ib])\n # Experimental fix for npoles dependence\n tmp = 1/np.pi*wtk[ik]*pdos[ib]*abs(imeqp[ik,ib])\n prefac=np.exp(-np.sum(ampole[ik,ib]))*tmp\n #prefac=np.exp(-tmp*np.trapz(imskb[ik,ib],enexp)/np.sum(omegai)*npoles)\n print \"\\n === Normalization test === \" \n print \" Prefactor:\", np.exp(-np.sum(ampole[ik,ib])) \n print \" Exponent:\", np.sum(ampole[ik,ib]) \n print \" Exponent/npoles:\", np.sum(ampole[ik,ib])/npoles,\n print\n print\n akb=ampole[ik,ib] # This is a numpy array (slice)\n omegakb=omegampole[ik,ib] # This is a numpy array (slice)\n eqpkb=eqp[ik,ib]\n imkb=imeqp[ik,ib]\n #tmpf1 = calc_spf_mpole(enexp,prefac,akb,omegakb,eqpkb,imkb,npoles)\n #print nen, np.size(enexp) \n #tmpf = 0.0*tmpf\n if eqpkb < 0.0:\n pass\n else:\n print \" This state is empty! eqpkb ik ib:\",eqpkb, ikeff+1, ibeff+1 \n #print \"omegakb\", omegakb \n omegakb=-omegakb\n #print \"-omegakb\", omegakb \n tmpf = np.zeros((nenexp), order='Fortran')\n tmpf = f2py_calc_spf_mpole(tmpf,enexp,prefac,akb,omegakb,eqpkb,imkb) #,nen,npoles)\n #tmpf = calc_spf_mpole(enexp,prefac,akb,omegakb,eqpkb,imkb,npoles)\n #outnamekb = \"spf_exp-k\"+str(\"%02d\"%(ikeff+1))+\"-b\"+str(\"%02d\"%(ibeff+1))+\"_np\"+str(npoles)+\".\"+str(penergy)\n #outfilekb = open(outnamekb,'w')\n #for ien in xrange(nenexp):\n # outfilekb.write(\"%8.4f %12.8f\\n\" % (enexp[ien], tmpf[ien]))\n #outfilekb.close()\n sfkb_c[ik,ib] = tmpf\n ftot = ftot + tmpf\n #print ftot[0], tmpf[0] \n #elaps2 = time.time() - elaps1 - e0\n #cpu2 = time.clock() - cpu1 - c0\n #print elaps2, cpu2 \n #print str(\" Used time (elaps, cpu): %10.6e %10.6e\"% (elaps2, cpu2)) \n #print \" ### Writing out A(\\omega)_exp... \" \n #enexp = enexp-efermi\n write_sftot_c(vardct, enexp, ftot)\n print \" calc_sf_c_serial :: Done.\" \n return enexp, ftot, sfkb_c","title":""},{"docid":"57f52c063fa754865a066bcf71a1da85","score":"0.4941068","text":"def calc_spf_gw(minkpt,maxkpt,minband,maxband,wtk,pdos,en,enmin,enmax,res,ims,hartree):\n import numpy as np;\n nkpt = maxkpt-minkpt+1\n nband = maxband-minband+1\n newdx = 0.005\n if enmin < en[0] and enmax >= en[-1]: \n newen = np.arange(en[0],en[-1],newdx)\n elif enmin < en[0]: \n newen = np.arange(en[0],enmax,newdx)\n elif enmax >= en[-1] : \n newen = np.arange(enmin,en[-1],newdx)\n else : \n newen = np.arange(enmin,enmax,newdx)\n print \" ### Interpolation and calculation of A(\\omega)_GW... \"\n spftot = np.zeros((np.size(newen)));\n # Here we interpolate re and im sigma\n # for each band and k point\n for ik in xrange(nkpt):\n ikeff = minkpt+ik-1\n print \" k point = %02d \" % (ikeff+1)\n for ib in xrange(0,nband):\n ibeff = minband+ib-1\n interpres = interp1d(en, res[ik,ib], kind = 'linear', axis = -1)\n interpims = interp1d(en, ims[ik,ib], kind = 'linear', axis = -1)\n tmpres = interpres(newen)\n #redenom = newen + efermi - hartree[ik,ib] - interpres(newen)\n redenom = newen - hartree[ikeff,ibeff] - interpres(newen)\n #print \"ik ib minband maxband ibeff hartree[ik,ib]\", ik, ib, minband, maxband, ibeff, hartree[ik,ib]\n tmpim = interpims(newen)\n spfkb = wtk[ikeff] * pdos[ib] * abs(tmpim)/np.pi/(redenom**2 + tmpim**2)\n spftot += spfkb \n outnamekb = \"spf_gw-k\"+str(\"%02d\"%(ikeff+1))+\"-b\"+str(\"%02d\"%(ibeff+1))+\".dat\"\n outfilekb = open(outnamekb,'w')\n for ien in xrange(np.size(newen)) :\n outfilekb.write(\"%8.4f %12.8e %12.8e %12.8e %12.8e\\n\" % (newen[ien], spfkb[ien], redenom[ien], tmpres[ien], tmpim[ien]))\n outfilekb.close()\n return newen, spftot","title":""},{"docid":"6ea7f5f1a94c76f7f512e8494a0eedc4","score":"0.49386457","text":"def gaussianElimination(aMat): # for square matrix\n matrix = copy.deepcopy(aMat)\n num_row = len(matrix) # number of row\n ESP = 0.0000001\n for k in range(num_row): # forward elimination\n max_column_elem = abs(matrix[k][k])\n ir = k\n if k != num_row:\n for i in range(k+1, num_row):\n ab = abs(matrix[i][k])\n if ab >= max_column_elem:\n max_column_elem = ab\n ir = i # row number of max coefficient\n if max_column_elem < ESP:\n print(\"Can't resolve\")\n return []\n if ir != k: # swap row\n D = matrix[k]\n matrix[k] = matrix[ir]\n matrix[ir] = D\n for i in range(k+1, num_row):\n alfa = matrix[i][k] / matrix[k][k] # each element / max element\n for j in range(k, num_row+1):\n matrix[i][j] -= alfa * matrix[k][j] # subtract same column\n\n print(\"Forward\", matrix)\n B = np.zeros((num_row, 1))\n for i in range(num_row - 1, -1, -1): # backward elimination\n T = matrix[i][num_row] # last column\n for j in range(i + 1, num_row):\n T -= matrix[i][j] * matrix[j][num_row]\n matrix[i][num_row] = T / matrix[i][i]\n B[i][0] = matrix[i][num_row]\n return B","title":""},{"docid":"0e6be8944497c82ebf4ffdc6fd6f7bbc","score":"0.4938389","text":"def part_1b(x):\n sigma = 1 # standard deviation\n x_u = np.linspace(-12, 12, 48) # centers at -12 to 12 @ every .5 [48x1]\n\n h = np.zeros((len(x),len(x_u))) # [1000x48]\n for j in range(len(x_u)): # 1-48\n for i in range(len(x)): # 1-1000\n h[i,j] = math.exp((-abs(x[i]-x_u[j])**2)/sigma**2)\n\n fig2 = plt.figure()\n plt.plot(x_u, h.T)#,\":\")\n plt.title(\"Part B: Radial Basis Functions\")\n plt.xlabel(\"n = 48, -12 < n < 12\")\n plt.ylabel(\"axis=0, mu = 0, sigma = 1, size(x)\")\n plt.ylim(bottom = 0)\n plt.ylim(top = 1)\n # plt.legend()\n\n return h # [1000x48]","title":""},{"docid":"4d42cb31d49ae7c12252a0bb35386b7a","score":"0.493811","text":"def mult_arrays(a,b,p):\r\n ds = curve_points(a,b,p).drop('id', axis = 1)\r\n ds['point'] = ds[\"x\"].map(str) + \",\" + ds[\"y\"].map(str)\r\n n = len(ds)\r\n X = pd.DataFrame(index=range(n*n), columns=['x_X','y_X','point_X'])\r\n Y = pd.DataFrame(index=range(n*n), columns=['x_Y','y_Y','point_Y'])\r\n k = np.empty(n*n, dtype=int)\r\n for i,row1 in ds.iterrows():\r\n if i == n-1:\r\n xp, yp = None, None\r\n xq, yq = None, None\r\n else:\r\n xp, yp = tuple(map(int, row1['point'].split(',')))\r\n xq, yq = tuple(map(int, row1['point'].split(',')))\r\n for j in range(n):\r\n X.at[i * n + j,'x_X'],X.at[i * n + j,'y_X'] = xp,yp\r\n k[i * n + j] = j\r\n if j == 0: # x*0 = INF\r\n Y.at[i * n + j,'x_Y'],Y.at[i * n + j,'y_Y'] = None, None #INF is last entry in ds\r\n elif j == 1: # x*1 = x = y\r\n Y.at[i * n + j, 'x_Y'],Y.at[i * n + j, 'y_Y'] = X.at[i * n + j, 'x_X'], X.at[i * n + j, 'y_X']\r\n else:\r\n point = add(xp,yp,xq,yq,a,p)\r\n Y.at[i * n + j, 'x_Y'], Y.at[i * n + j, 'y_Y'] = point[0], point[1]\r\n xq, yq = point[0], point[1]\r\n return(X,k,Y)","title":""},{"docid":"d47687571571cbea324c67ce843fb166","score":"0.49304563","text":"def getInitialEggholderDataABO(num_points,max_time):\n lower_x = -512\n upper_x = 512\n t = getInitialInputData(num_points, 1, lower_x, max_time)\n X_ = getInitialInputData(num_points, 1, lower_x, upper_x)\n X = numpy.concatenate( (t,X_), axis=1 )\n y = ObjectiveFunctions.eggholder_bulk(X)\n return X, y","title":""},{"docid":"087eca8a9a97743c7adcd0213d00fad6","score":"0.4924656","text":"def interior_points(self):\n import itertools\n all_dims = [range(self.ghost_depth,self.shape[x]-self.ghost_depth) for x in range(0,self.dim)]\n for item in itertools.product(*all_dims):\n yield tuple(item)","title":""},{"docid":"62778447fa52bb04eb7c580943cbaa28","score":"0.49222958","text":"def culaDeviceZgglse(m, n, p, a, lda, b, ldb, c, d, x):\r\n \r\n raise NotImplementedError('CULA Dense required')","title":""},{"docid":"cf0895986443cb8a0b8cc8a4b6189e59","score":"0.4918374","text":"def E(q):\n\n # We're going to use a 2D python array at first.\n euler_array = [[0 for x in range(len(q[\"vertices\"]))] for y in range(len(q[\"vertices\"]))]\n for out_vertex in range(np.size(q[\"vertices\"])):\n for in_vertex in range(np.size(q[\"vertices\"])):\n # Set Kronecker symbol\n kronecker = 0\n if out_vertex == in_vertex:\n kronecker = 1\n\n # iterate through edges and find edges with appropriate head and tail.\n edges = [edge for edge in q[\"edges\"] if head(edge) == in_vertex and tail(edge) == out_vertex]\n euler_array[out_vertex][in_vertex] = kronecker - len(edges)\n\n return np.matrix(euler_array)","title":""},{"docid":"6d74ba649c9804fe8c7f85d64b1fb729","score":"0.4916504","text":"def scatter_points(self):\n return np.transpose(np.concatenate((np.ones(self.h_eigs.size) * self.pq, np.flatten(self.h_eigs))))","title":""},{"docid":"f18ce0ce7cb35898f5dd814cde105482","score":"0.49138215","text":"def create_matE_grandeur(grandeur, epsilon = 0.8, chemin_data = \"data/datasets/\",\\\n chemin_mat = \"data/matrices/\"):\n t1 = time.time();\n data = pd.read_csv(chemin_data+'dataset_'+grandeur+'.csv')\n if \"timestamp\" in data.columns.tolist():\n data = data.set_index(\"timestamp\");\n else:\n data = data.set_index(\"Unnamed: 0\")\n \n logging.debug(\"correlation_sax = %s columns = %s\",grandeur, data.columns.tolist())\n \n dict_similitude = create_dico_similarite(data, epsilon)\n supp_dict_similitude = supprimer_les_grands_distances(dict_similitude, 10);\n dico_gp = dict(); cols = data.columns.tolist(); \n df = pd.DataFrame(index = cols, columns = cols);\n for cle, dico_valeur in supp_dict_similitude.items():\n for cle_dico_valeur, valeur in dico_valeur.items():\n arete0 = cle.split(\"_\")[0].upper();\n arete1 = cle_dico_valeur.split(\"_\")[0].upper();\n df.loc[arete0, arete1] = valeur;\n df.loc[arete1, arete0] = valeur;\n \n df = df.apply(lambda x: (100-x)/100) \n df[df == 100] = 1\n for row, col in fct_aux.range_2d(cols):\n dico_gp[(row, col)] = df.loc[row, col]\n \n df.fillna(0, inplace = True)\n df.to_csv(chemin_mat+'matrice_adjacence_proba_'+grandeur+'.csv')\n logging.debug(\"correlation_sax: grandeur = %s termine en = %s\",grandeur, time.time()-t1)\n return dico_gp;","title":""},{"docid":"adfd5032d910bd5c733f0b138523625f","score":"0.4912536","text":"def gue_eigs(N = 10):\n A = np.random.randn(N, N) + 1j * np.random.randn(N, N) \n B = (2 ** -1) * (A + np.conj(A.T)) \n D = np.linalg.eigvals(B) \n D = np.real(D)\n return D","title":""},{"docid":"5dad920d00e6591a99a9e65602757fdd","score":"0.49088925","text":"def reduced(self):\r\n m = self.gauss()\r\n nr, nc = m.shape\r\n for i in range(nr-1, -1, -1):\r\n # print m.formated()\r\n d = float(m[i, i])\r\n for j in range(i):\r\n for k in range(nc-1, i-1, -1):\r\n m[j,k] -= m[i,k] * m[j, i] / d\r\n # print m.formated()\r\n m[i, i] /= d\r\n for j in range(nr, nc):\r\n m[i, j] /= d\r\n # print m.formated()\r\n return m","title":""},{"docid":"471ef3a8c0e3e6d73a318a6a0116f773","score":"0.49068865","text":"def get_eps_pmf(self):\r\n\t\treturn self.deagg_matrix.fold_axes([-6,-5,-4,-3,-1])","title":""},{"docid":"238188f2dd301d3a138f27cf98372359","score":"0.49057457","text":"def diamond(row, column, b_m, offset, size, ml):\n\n avg = []\n\n \n\n if row - size >= 0:\n avg.append(b_m[row-size,column])\n if column - size >= 0:\n avg.append(b_m[row,column-size])\n if row + size < ml:\n avg.append(b_m[row+size,column])\n if column + size < ml:\n avg.append(b_m[row,column+size])\n #print avg\n\n avgg = np.average(avg)\n\n # print 'avg', avgg\n # print 'offset', offset\n \n\n\n\n b_m[row,column] = avgg + offset\n # if x == 2 and y == 1:\n # b_m[x,y] = 0","title":""},{"docid":"aaa832b74a3489291502dfa8c4c05549","score":"0.49042434","text":"def getAccel(x, a):\n r3 = np.power(np.power(x[0], 2) + np.power(x[1], 2), 1.5)\n a[0] = -GM*x[0]/r3\n a[1] = -GM*x[1]/r3 \n return (x, a)","title":""},{"docid":"f58fe49531ffcdee70350e780b51b27a","score":"0.49038118","text":"def generate_calibration(single_pnt_cali_d=508,\n single_pnt_cali_ior=1.6,\n single_pnt_cali_int=1000,\n noise_level = 12,\n ior=1.5,\n dr=[100, 5000],\n no_pts=5000,\n no_cal_pts=50,\n plot=True,\n raise_error=True,\n test=False\n ):\n drum = np.array(dr)/1e3\n d, amp = mie.makeMie_diameter(diameterRangeInMikroMeter=drum,\n noOfdiameters=no_pts,\n IOR=ior)\n\n df = pd.DataFrame({'d': d, 'amp': amp})\n # a calibration function is created with no_cal_pts of calibration points. no_cal_pts is steadyly decreased until\n # the calibration function is bijective\n valid = False\n while not valid:\n no_cal_pts -= 1\n binedgs = np.logspace(np.log10(df.amp.min()), np.log10(df.amp.max()), no_cal_pts)\n binedgs[0] -= (binedgs[1] - binedgs[\n 0]) * 0.01 # this is to ensure the first point is not onthe edge ... for cut function used later\n\n mie_cal = df.groupby(pd.cut(df.amp, binedgs)).median()\n dfstd = df.groupby(pd.cut(df.amp, binedgs)).mad()\n\n mie_cal.index = mie_cal.d\n dfstd.index = mie_cal.d\n mie_cal['sigma_d'] = dfstd.d\n mie_cal.index.name = None\n mie_cal.sort_values('amp', axis=0, inplace=True)\n\n # check for bijectivity\n if not ((mie_cal.d.values[1:] - mie_cal.d.values[:-1]) < 0).sum():\n valid = True\n\n # final conditioning: um2nm, indexname, drop d\n # mie_cal.drop('d', axis=1, inplace=True)\n mie_cal.index.name = 'd_nm'\n mie_cal.index *= 1e3\n mie_cal.d *= 1e3\n mie_cal.sigma_d *= 1.e3\n cali_inst_pre = Calibration(mie_cal)\n\n # single point calibration\n ## solve calibration function ot get amp at calibration diameter\n ### first guess\n dt = mie_cal.index[abs(mie_cal.index - single_pnt_cali_d).argmin()]\n at = mie_cal.loc[dt, 'amp']\n\n # cali_inst_at_single_pnt_calid_d = cali_inst_pre.calibrationFunction(single_pnt_cali_d)\n\n ### solve\n cali_inst_at_single_pnt_calid_d = optimize.fsolve(lambda x: cali_inst_pre.calibrationFunction(x) - single_pnt_cali_d, at)\n\n ## scale for ior mismatch\n if ior == single_pnt_cali_ior:\n scale_ioradj = 1\n single_pnt_cali_int_pre = cali_inst_at_single_pnt_calid_d\n else:\n # single_pnt_cali_d = 500\n single_pnt_cali_d *= 1e-3\n dt, mt = mie.makeMie_diameter(diameterRangeInMikroMeter=[single_pnt_cali_d, single_pnt_cali_d + 1e-3],\n IOR=single_pnt_cali_ior,\n noOfdiameters=2)\n single_pnt_cali_int_pre = mt[0]\n\n scale_ioradj = cali_inst_at_single_pnt_calid_d / single_pnt_cali_int_pre\n\n ## scale for instrument calibration\n scale_instrument = single_pnt_cali_int / single_pnt_cali_int_pre\n\n ## total scale\n scale = scale_ioradj * scale_instrument\n\n mie_cal.loc[:,'amp'] *= scale\n\n if not isinstance(noise_level, type(None)):\n mie_cal.loc[:, 'amp'] += noise_level\n\n cali_inst = Calibration(mie_cal)\n\n if plot:\n f, a = plt.subplots()\n a.plot(df.d * 1e3, df.amp * scale, label='POPS resp.')\n cali_inst.plot(ax = a)\n # a.plot(ampm.index * 1e3, ampm.values * scale, label='POPS resp. smooth')\n # g, = a.plot(cali.index, cali.values, label='cali')\n # g.set_linestyle('')\n # g.set_marker('x')\n # g.set_markersize(10)\n # g.set_markeredgewidth(2)\n g, = a.plot(single_pnt_cali_d * 1e3, single_pnt_cali_int, label='single ptn cal')\n g.set_linestyle('')\n g.set_marker('x')\n g.set_markersize(10)\n g.set_markeredgewidth(2)\n g.set_label('single pt. cali.')\n # # st.plot(ax = a)\n # a.loglog()\n a.legend()\n # return dft\n # return cali_inst, a\n\n ##########\n # start of old mie calcultations\n old = False\n if old:\n dr = np.array(dr)/1000\n\n single_pnt_cali_d *= 1e-3\n # rr = dr / 2 / 1000\n\n d, amp = mie.makeMie_diameter(noOfdiameters=no_pts,\n diameterRangeInMikroMeter = dr,\n # radiusRangeInMikroMeter=rr,\n IOR=ior)\n ds = pd.Series(amp, d)\n\n cal_d = pd.Series(index=np.logspace(np.log10(dr[0]), np.log10(dr[1]), no_cal_pts + 2)[1:-1])\n\n if test:\n return cal_d\n\n # single point calibration\n if ior == single_pnt_cali_ior:\n ds_spc = ds\n else:\n d, amp = mie.makeMie_diameter(noOfdiameters=no_pts,\n diameterRangeInMikroMeter = dr,\n # radiusRangeInMikroMeter=rr,\n IOR=single_pnt_cali_ior)\n ds_spc = pd.Series(amp, d)\n\n # rolling, the issue here is that I am rolling over windows of diameter rather then windows of amp, which would be better\n ampm = ds.rolling(int(no_pts / no_cal_pts), center=True).mean()\n\n #\n cali = ampm.append(cal_d).sort_index().interpolate().reindex(cal_d.index)\n\n spc_point = ds_spc.append(pd.Series(index=[single_pnt_cali_d])).sort_index().interpolate().reindex(\n [single_pnt_cali_d]) # .values[0]\n scale = single_pnt_cali_int / spc_point.values[0]\n\n cali *= scale\n cali.index *= 1e3\n\n cali_inst_pre = pd.DataFrame(cali, columns=['amp'])\n cali_inst_pre['d'] = cali_inst_pre.index\n cali_inst_pre = Calibration(cali_inst_pre)\n\n if raise_error:\n ct = cali.values\n if (ct[1:] - ct[:-1]).min() < 0:\n raise ValueError(\n 'Clibration function is not bijective. usually decreasing the number of calibration points will help!')\n\n cal_fkt_test = cali_inst_pre.calibrationFunction(cali_inst_pre.data.amp.values)\n if not np.all(~np.isnan(cal_fkt_test)):\n raise ValueError(\n 'Clibration function is not bijective. usually decreasing the number of calibration points will help!')\n\n if plot:\n f, a = plt.subplots()\n a.plot(ds.index * 1e3, ds.values * scale, label='POPS resp.')\n a.plot(ampm.index * 1e3, ampm.values * scale, label='POPS resp. smooth')\n g, = a.plot(cali.index, cali.values, label='cali')\n g.set_linestyle('')\n g.set_marker('x')\n g.set_markersize(10)\n g.set_markeredgewidth(2)\n g, = a.plot(single_pnt_cali_d * 1e3, single_pnt_cali_int, label='single ptn cal')\n g.set_linestyle('')\n g.set_marker('o')\n g.set_markersize(10)\n g.set_markeredgewidth(2)\n # st.plot(ax = a)\n a.loglog()\n a.legend()\n return cali_inst_pre, a\n ###### end of old 2\n\n return cali_inst","title":""},{"docid":"722bdb8749ada409c0d6300f5a887e47","score":"0.4902075","text":"def _fit_reconstruction():\n\n h = self.encode(x)\n r = self.decode(h)\n\n dedr = -( x/r - (1 - x)/(1 - r) ) \n\n a = r*(1-r)\n b = h*(1-h)\n \n od = a * dedr\n oe = b * numpy.dot(od, self.W)\n\n gW = x[ :, :, None] * oe[ :, None, : ]\n\n return gW.mean(0), od.mean(0), oe.mean(0)","title":""},{"docid":"a8d92351e9ff611e6639f093fd27bcc4","score":"0.48936796","text":"def gaussian(self,c,sigma):\n d = 2*pi*sigma*sigma\n ax = exp(-power(self.neigx-c[0],2)/d)\n ay = exp(-power(self.neigy-c[1],2)/d)\n return outer(ax,ay) # the external product gives a matrix","title":""},{"docid":"f5e03b1e06233d546663e66c2b3886a7","score":"0.4893111","text":"def find_four_point_transform(src_points, dst_points):\n A = []\n for i in range(4):\n aui = dst_points[i][0]\n avi = dst_points[i][1]\n bui = src_points[i][0]\n bvi = src_points[i][1]\n A.append([bui, bvi, 1, 0, 0, 0, -bui * aui, -bvi * aui, -aui])\n A.append([0, 0, 0, bui, bvi, 1, -bui * avi, -bvi * avi, -avi])\n A = np.array(A)\n A=np.float32(A)\n #print A\n AT = np.transpose(A)\n M = np.dot(AT, A)\n val, vec = np.linalg.eig(M)\n index = 0\n mini = val[index]\n # print np.dot(M,(vec[:,index]))\n # print val[index]*vec[:,index]\n\n for i in range(vec.shape[0]):\n if (val[i] < mini):\n index = i\n mini = val[i]\n a = vec[:, index]\n # print a\n lst = []\n for b in range(3):\n lst.append([a[0 + 3 * b], a[1 + 3 * b], a[2 + 3 * b]])\n ans=np.array(lst)\n ans=ans/ans[2,2]\n return ans","title":""},{"docid":"643e2799a7f1aefb37a0ab267567dc5c","score":"0.48883772","text":"def gauss(s):\n\n size = s * 3\n x, y = np.meshgrid(np.arange(-size,size + 1), np.arange(-size,size + 1))\n kernel = np.exp(-(x**2 + y**2 / float(s)))\n kernel = kernel / kernel.sum()\n \n return x, y, kernel","title":""},{"docid":"06bd44a0bc916ac1768e80bea374da51","score":"0.4887511","text":"def accvec2(i, x, G, m, epsilon): #Added epsilon softening now\n x_i = x[i]\n x_j = remove_i(x, i)\n m_j = remove_i(m, i)\n \n #diff = x_j - x_i\n diff = x_j - x_i + epsilon\n \n mag3 = np.sum(diff**2, axis=1)**1.5\n result = G * np.sum(diff * (m_j / mag3)[:,np.newaxis], axis=0)\n return result","title":""},{"docid":"732611c88337413d191f55f1fad9d415","score":"0.4887131","text":"def forward(self, x, e, ij):\n xnew = torch.empty((x.shape[0], self.Wc.shape[0]), dtype=torch.float)\n for center in range(ij.shape[0]): # For all nodes in the graph\n nhbrs = ij[center, :, 0] # Neighbourhood around center\n xni = x[center, :] # Center vertex\n xnj = x[nhbrs].sum(dim=0) / nhbrs.numel() # Neighborhood (vertex feats)\n xej = e[center].sum(dim=0) / nhbrs.numel() # Neighborhood (edge feats)\n xout = self.Wc @ xni + self.Wn @ xnj + self.We @ xej\n xnew[center, :] = self.relu(xout)\n\n return xnew","title":""},{"docid":"f488687b93c925bf940143cf8c45a5cc","score":"0.4884402","text":"def computeEinc(self, kb, msh):\n if self.numPlws <=0:\n raise RuntimeError(\"No. of excitations (planewave) must be > 0\")\n\n numCells = len(msh)\n Einc = np.zeros((numCells,self.numPlws), dtype=np.complex)\n\n # start looping for each cell and plw\n for i in range(self.numPlws):\n cosPhi = np.cos(self.phiInc[i])\n sinPhi = np.sin(self.phiInc[i])\n for j in range(numCells):\n xc = msh.cells[j].x * cosPhi\n yc = msh.cells[j].y * sinPhi\n e0 = exp(-1j*kb*(xc + yc))\n Einc[j,i] =e0\n return Einc","title":""}],"string":"[\n {\n \"docid\": \"d243886f471226abb337be55d4f06252\",\n \"score\": \"0.58604336\",\n \"text\": \"def G_ee(basis,molecule,P,ee):\\n\\n # Size of the basis set\\n K = basis.K\\n\\n G = np.zeros((K,K))\\n\\n for i in range(K):\\n for j in range(K):\\n for k in range(K):\\n for l in range(K):\\n G[i,j] += P[k,l] * (ee[i,j,k,l] - 0.5 * ee[i,l,k,j])\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35799d87a6e6302646fe6bc15d7cb9a7\",\n \"score\": \"0.5726206\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.K = np.zeros((16,16))\\n self.f = np.zeros(16)\\n self.M_small = np.zeros((8,8))\\n self.M = np.zeros((16,16))\\n self.S = np.zeros((8,6))\\n self.E = np.zeros((8,6))\\n\\n # Quadrature like ANSYS or ABAQUS:\\n g = np.sqrt(3/5)\\n w = 5/9\\n w0 = 8/9\\n self.gauss_points = ((-g, -g, w*w), ( g, -g, w*w ), ( g, g, w*w),\\n (-g, g, w*w), ( 0, -g, w0*w ), ( g, 0, w*w0),\\n ( 0, g, w0*w), (-g, 0, w*w0), ( 0, 0, w0*w0))\\n\\n # a little bit dirty but correct. Comes from sympy file.\\n self.extrapolation_points = np.array(\\n [[ 5*sqrt(15)/18 + 10/9, 5/18, -5*sqrt(15)/18 + 10/9,\\n 5/18, -5/9 - sqrt(15)/9, -5/9 + sqrt(15)/9,\\n -5/9 + sqrt(15)/9, -5/9 - sqrt(15)/9, 4/9],\\n [5/18, 5*sqrt(15)/18 + 10/9, 5/18, -5*sqrt(15)/18 + 10/9,\\n -5/9 - sqrt(15)/9, -5/9 - sqrt(15)/9, -5/9 + sqrt(15)/9,\\n -5/9 + sqrt(15)/9, 4/9],\\n [-5*sqrt(15)/18 + 10/9, 5/18, 5*sqrt(15)/18 + 10/9, 5/18,\\n -5/9 + sqrt(15)/9, -5/9 - sqrt(15)/9, -5/9 - sqrt(15)/9,\\n -5/9 + sqrt(15)/9, 4/9],\\n [ 5/18, -5*sqrt(15)/18 + 10/9, 5/18, 5*sqrt(15)/18 + 10/9,\\n -5/9 + sqrt(15)/9, -5/9 + sqrt(15)/9, -5/9 - sqrt(15)/9,\\n -5/9 - sqrt(15)/9, 4/9],\\n [ 0, 0, 0, 0, sqrt(15)/6 + 5/6, 0, -sqrt(15)/6 + 5/6, 0, -2/3],\\n [0, 0, 0, 0, 0, sqrt(15)/6 + 5/6, 0, -sqrt(15)/6 + 5/6, -2/3],\\n [ 0, 0, 0, 0, -sqrt(15)/6 + 5/6, 0, sqrt(15)/6 + 5/6, 0, -2/3],\\n [ 0, 0, 0, 0, 0, -sqrt(15)/6 + 5/6, 0, sqrt(15)/6 + 5/6, -2/3]])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a860194041436d2c6039f28502341bd\",\n \"score\": \"0.55689144\",\n \"text\": \"def gaussian2D(grid,cenX,cenY,amp,widX,widY):\\n x = grid[0]\\n y = grid[1]\\n return(np.matrix([ [amp*np.exp(-(x[i]-cenX)**2/(2*widX**2))*np.exp(-(y[j]-cenY)**2/(2*widY**2)) for j in range(len(y))] for i in range(len(x))]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23573828795f15e79a74ab7d7d0a815f\",\n \"score\": \"0.55412376\",\n \"text\": \"def pre_process_plot(self,eeg):\\n eeg = np.array(eeg)\\n [fil,col] = eeg.shape\\n eeg_processed = np.zeros([fil,col])\\n for i in range(fil):\\n data = eeg[i,:] * 2.23517444553071e-02\\n data = data - np.mean(data)\\n data = lfilter(self.b_n,self.a_n, data)\\n data = lfilter(self.b,self.a,data) \\n eeg_processed[i,:] = data\\n return(eeg_processed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b40431c8374a16dc9b8e23314146261d\",\n \"score\": \"0.5504659\",\n \"text\": \"def echelonMat(ech, *args):\\n if len(args) == 2: # matrice augmentée\\n A = np.array(args[0]).astype(float)\\n m = A.shape[0]\\n n = A.shape[1]\\n b = args[1]\\n if type(b[0]) == list:\\n b = np.array(b).astype(float)\\n A = np.concatenate((A, b), axis=1)\\n else:\\n b = [b[i] for i in range(m)]\\n A = [A[i] + [b[i]] for i in range(0, m)]\\n else: # matrice coeff\\n A = np.array(args[0]).astype(float)\\n m = A.shape[0]\\n n = A.shape[1]\\n\\n if ech == 'E': # Echelonnée\\n Mat = np.array(A)\\n Mat = Mat.astype(float) # in case the array in int instead of float.\\n numPivot = 0\\n for i in range(len(Mat)):\\n j = i\\n while all(abs(Mat[j:, i]) < 1e-15) and j != len(Mat[0, :]) - 1: # if column (or rest of) is 0, take next\\n j += 1\\n if j == len(Mat[0, :]) - 1:\\n if len(Mat[0, :]) > j:\\n Mat[i + 1:len(Mat), :] = 0\\n break\\n if abs(Mat[i, j]) < 1e-15:\\n Mat[i, j] = 0\\n zero = abs(Mat[i:, j]) < 1e-15\\n M = echZero(zero, Mat[i:, :])\\n Mat[i:, :] = M\\n Mat = Ealpha(Mat, i, 1 / Mat[i, j]) # usually Mat[i,j]!=0\\n for k in range(i + 1, len(A)):\\n Mat = Eijalpha(Mat, k, i, -Mat[k, j])\\n # Mat[k,:]=[0 if abs(Mat[k,l])<1e-15 else Mat[k,l] for l in range(len(MatCoeff[0,:]))]\\n numPivot += 1\\n Mat[abs(Mat) < 1e-15] = 0\\n\\n print(\\\"La matrice est sous la forme échelonnée\\\")\\n if len(args) == 2:\\n printEquMatrices([A[:, :n], Mat[:, :n]], [A[:, n:], Mat[:, n:]])\\n else:\\n printEquMatrices([A, Mat])\\n\\n elif ech == 'ER': # Echelonnée réduite\\n Mat = np.array(A)\\n Mat = Mat.astype(float) # in case the array in int instead of float.\\n numPivot = 0\\n for i in range(len(Mat)):\\n j = i\\n while all(abs(Mat[j:, i]) < 1e-15) and j != len(\\n Mat[0, :]) - 1: # if column (or rest of) is zero, take next column\\n j += 1\\n if j == len(Mat[0, :]) - 1:\\n # ADD ZERO LINES BELOW!!!!!!\\n if len(Mat[0, :]) > j:\\n Mat[i + 1:len(Mat), :] = 0\\n break\\n if abs(Mat[i, j]) < 1e-15:\\n Mat[i, j] = 0\\n zero = abs(Mat[i:, j]) < 1e-15\\n M = echZero(zero, Mat[i:, :])\\n Mat[i:, :] = M\\n Mat = Ealpha(Mat, i, 1 / Mat[i, j]) # normalement Mat[i,j]!=0\\n for k in range(i + 1, len(A)):\\n Mat = Eijalpha(Mat, k, i, -Mat[k, j])\\n # Mat[k,:]=[0 if abs(Mat[k,l])<1e-15 else Mat[k,l] for l in range(len(MatCoeff[0,:]))]\\n numPivot += 1\\n Mat[abs(Mat) < 1e-15] = 0\\n\\n print(\\\"La matrice est sous la forme échelonnée\\\")\\n if len(args) == 2:\\n printEquMatrices([A[:, :n], Mat[:, :n]], [A[:, n:], Mat[:, n:]])\\n else:\\n printEquMatrices([np.asmatrix(A), np.asmatrix(Mat)])\\n\\n Mat = np.array(Mat)\\n i = (len(Mat) - 1)\\n while i >= 1:\\n while all(\\n abs(Mat[i, :len(Mat[0]) - 1]) < 1e-15) and i != 0: # if ligne (or rest of) is zero, take next ligne\\n i -= 1\\n # we have a lign with one non-nul element\\n j = i # we can start at pos ij at least the pivot is there\\n if abs(Mat[i, j]) < 1e-15: # if element Aij=0 take next one --> find pivot\\n j += 1\\n # Aij!=0 and Aij==1 if echelonMat worked\\n for k in range(i): # put zeros above pivot (which is 1 now)\\n Mat = Eijalpha(Mat, k, i, -Mat[k, j])\\n i -= 1\\n\\n print(\\\"La matrice est sous la forme échelonnée réduite\\\")\\n if len(args) == 2:\\n printEquMatrices([A[:, :n], Mat[:, :n]], [A[:, n:], Mat[:, n:]])\\n else:\\n printEquMatrices([A, Mat])\\n\\n else:\\n print(f\\\"Méthode d'échelonnage non reconnue {ech}. Méthodes disponibles: 'E' (pour la forme échelonnée standard)\\\"\\n f\\\", 'ER' (pour la forme échelonnée réduite))\\\")\\n\\n return np.asmatrix(Mat)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"112093be2c1a28af2f0a55c77e755ee6\",\n \"score\": \"0.54873\",\n \"text\": \"def g(x):\\r\\n return 5. - x[:, 1] - .5 * x[:, 0] ** 2.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"661eb6c4894412204ec69176d9303cc6\",\n \"score\": \"0.54661554\",\n \"text\": \"def flatten_composition(EXX):\\n E=EXX[:,0]\\n a=EXX[:,1]\\n b=EXX[:,2]\\n\\n xNi=1+a-b\\n xAl=1-a\\n xVa=b\\n\\n x=xNi/(xNi+xAl)\\n E=E/(xNi+xAl)\\n\\n return np.array((E,x)).T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"673752aa7550f2a624633cad2bb919cf\",\n \"score\": \"0.54626995\",\n \"text\": \"def smooth(self, xform, errors, e_binning, cz_binning):\\n\\n sumw2 = np.square(errors)\\n\\n # First, let's check if we have entire coszen values with zero, this is for example\\n # the case for all nutau CC below 3.5 GeV\\n\\n non_zero_idx = 0\\n while np.sum(xform[non_zero_idx, :]) == 0:\\n non_zero_idx += 1\\n\\n # cut away these all-zero regions for now\\n truncated_xform = xform[non_zero_idx:, :]\\n truncated_sumw2 = sumw2[non_zero_idx:, :]\\n truncated_e_binning = e_binning[non_zero_idx:]\\n\\n # Now lets extend that array at both energy ends\\n # by about 10% of bins\\n num_extension_bins_lower = int(np.floor(0.1*len(e_binning)))\\n num_extension_bins_upper = 0 \\n assert e_binning.is_lin or e_binning.is_log, 'Do not know how to extend arbitrary binning'\\n\\n # what will new bin edges be?\\n bin_edges = truncated_e_binning.bin_edges.m\\n if e_binning.is_log:\\n bin_edges = np.log10(bin_edges)\\n bin_edges = list(bin_edges)\\n lower_edges = []\\n upper_edges = []\\n delta = bin_edges[1] - bin_edges[0]\\n for i in range(num_extension_bins_lower):\\n lower_edges.append(bin_edges[0] - (i+1)*delta)\\n for i in range(num_extension_bins_upper):\\n upper_edges.append(bin_edges[-1] + (i+1)*delta)\\n new_edges = np.array(lower_edges[::-1] + bin_edges + upper_edges)\\n if e_binning.is_log:\\n new_edges = np.power(10, new_edges)\\n\\n extended_binning = OneDimBinning('true_energy', bin_edges=new_edges, is_lin=e_binning.is_lin, is_log=e_binning.is_log)\\n\\n # also extend that arrays\\n # We do that by point-reflecting the values\\n # so an array like [0 1 2 3 4 ...] will become [-3 -2 -1 0 1 2 3 4 ...]\\n\\n #if non_zero_idx == 0:\\n lower_bit = 2*truncated_xform[0, :] - np.flipud(truncated_xform[1:num_extension_bins_lower+1, :])\\n #else:\\n # lower_bit = - np.flipud(truncated_xform[1:num_extension_bins+1,:])\\n upper_bit = 2*truncated_xform[-1, :] - np.flipud(truncated_xform[-num_extension_bins_upper-1:-1, :])\\n extended_xform = np.concatenate((lower_bit, truncated_xform, upper_bit))\\n\\n # also handle the errors (which simply add up in quadrature)\\n #if non_zero_idx == 0:\\n lower_bit = truncated_sumw2[0, :] + np.flipud(truncated_sumw2[1:num_extension_bins_lower+1, :])\\n #else:\\n # lower_bit = np.flipud(truncated_sumw2[1:num_extension_bins+1,:])\\n upper_bit = truncated_sumw2[-1, :] + np.flipud(truncated_sumw2[-num_extension_bins_upper-1:-1, :])\\n extended_sumw2 = np.concatenate((lower_bit, truncated_sumw2, upper_bit))\\n\\n # what's the stat. situation here?\\n rel_error = np.square(errors)/xform\\n rel_error = np.median(rel_error[xform != 0])\\n logging.debug('Relative errors are ~ %.5f', rel_error)\\n # how many zero bins?\\n zero_fraction = np.count_nonzero(truncated_xform == 0)/float(truncated_xform.size)\\n logging.debug('zero fraction is %.3f', zero_fraction)\\n\\n\\n # now use gaussian smoothing on those\\n # some black magic sigma values\\n sigma_e = xform.shape[0] * np.sqrt(zero_fraction) * 0.02\\n sigma_cz = xform.shape[1] * np.sqrt(zero_fraction) * 0.05\\n sigma1 = (0, sigma_cz)\\n sigma2 = (sigma_e, 0)\\n smooth_extended_xform = ndimage.filters.gaussian_filter(extended_xform, sigma1, mode='reflect')\\n smooth_extended_sumw2 = ndimage.filters.gaussian_filter(extended_sumw2, sigma1, mode='reflect')\\n smooth_extended_xform = ndimage.filters.gaussian_filter(smooth_extended_xform, sigma2, mode='nearest')\\n smooth_extended_sumw2 = ndimage.filters.gaussian_filter(smooth_extended_sumw2, sigma2, mode='nearest')\\n smooth_extended_errors = np.sqrt(smooth_extended_sumw2)\\n\\n # for low stats, smooth less....otherwise leave factor alone\\n smooth_corr_factor = max(1., (rel_error * 10000))\\n\\n # now spline smooth\\n new_xform, _ = spline_smooth(array=smooth_extended_xform,\\n spline_binning=extended_binning,\\n eval_binning=e_binning,\\n axis=0,\\n smooth_factor=self.params.aeff_e_smooth_factor.value/smooth_corr_factor,\\n k=3,\\n errors=smooth_extended_errors)\\n\\n final_xform, _ = spline_smooth(array=new_xform,\\n spline_binning=cz_binning,\\n eval_binning=cz_binning,\\n axis=1,\\n smooth_factor=self.params.aeff_cz_smooth_factor.value/smooth_corr_factor,\\n k=3,\\n errors=None)\\n\\n # the final array has the right shape again, because we evaluated the splines only\\n # on the real binning\\n\\n # don't forget to zero out the zero bins again\\n final_xform[:non_zero_idx, :] *= 0\\n\\n # clip unphysical (negative) values\\n return final_xform.clip(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d09d8d698f70e2f136685ca97d750d6\",\n \"score\": \"0.5376346\",\n \"text\": \"def reconstruct_plot_solution(U, xfaces):\\n Nrec = 10 # Number of reconstruction points in each cell.\\n N = U.shape[0]\\n p = U.shape[1] - 1\\n s = U.shape[2]\\n\\n # Create array of x at specified points.\\n # We will calculate * u * at these points\\n xrec = np.zeros((N, Nrec))\\n for k in range(N):\\n xrec[k, :] = np.linspace(xfaces[k], xfaces[k + 1], Nrec)\\n\\n print(xrec.shape)\\n \\n # Calculate u at each point.\\n u = np.zeros((N, Nrec, s))\\n for k in range(N):\\n for i in range(Nrec):\\n eps = (xrec[k, i] - xfaces[k]) / (xfaces[k + 1] - xfaces[k]) * 2 - 1\\n for m in range(s):\\n for j in range(p + 1):\\n u[k, i, m] += U[k, j, m] * calc_phi(eps, p, j)\\n\\n\\n u[:, :, 2] = (GAMMA - 1) * (u[:, :, 2] - u[:, :, 1]**2 / u[:, :, 0] / 2)\\n u[:, :, 1] = u[:, :, 1] / u[:, :, 0]\\n \\n # Plot it.\\n import matplotlib.pyplot as plt\\n fig, ax = plt.subplots(3, 1, figsize=(11, 8.5))\\n for m in range(s):\\n for k in range(N):\\n ax[m].plot(xrec[k, :], u[k, :, m])\\n\\n ax[0].set_ylabel(r'$\\\\rho$')\\n ax[1].set_ylabel('$u$')\\n ax[2].set_ylabel('$p$')\\n ax[2].set_xlabel('$x$')\\n \\n plt.show()\\n return xrec, u\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3a1dd270269901800fcff413c080b7e\",\n \"score\": \"0.5370001\",\n \"text\": \"def decompose_gate_matrix(operationMx):\\n\\n op_evals, op_evecs = _np.linalg.eig(_np.asarray(operationMx))\\n # fp_eigenvec = None\\n # aor_eval = None; aor_eigenvec = None\\n # ra_eval = None; ra1_eigenvec = None; ra2_eigenvec = None\\n\\n TOL = 1e-4 # 1e-7\\n\\n unit_eval_indices = [i for (i, ev) in enumerate(op_evals) if abs(ev - 1.0) < TOL]\\n #unit_eval_indices = [ i for (i,ev) in enumerate(op_evals) if ev > (1.0-TOL) ]\\n\\n conjpair_eval_indices = []\\n for (i, ev) in enumerate(op_evals):\\n if i in unit_eval_indices: continue # don't include the unit eigenvalues in the conjugate pair count\\n # don't include existing conjugate pairs\\n if any([(i in conjpair) for conjpair in conjpair_eval_indices]): continue\\n for (j, ev2) in enumerate(op_evals[i + 1:]):\\n if abs(ev - _np.conjugate(ev2)) < TOL:\\n conjpair_eval_indices.append((i, j + (i + 1)))\\n break # don't pair i-th eigenvalue with any other (pairs should be disjoint)\\n\\n real_eval_indices = [] # indices of real eigenvalues that are not units or a part of any conjugate pair\\n complex_eval_indices = [] # indices of complex eigenvalues that are not units or a part of any conjugate pair\\n for (i, ev) in enumerate(op_evals):\\n if i in unit_eval_indices: continue # don't include the unit eigenvalues\\n if any([(i in conjpair) for conjpair in conjpair_eval_indices]): continue # don't include the conjugate pairs\\n if abs(ev.imag) < TOL: real_eval_indices.append(i)\\n else: complex_eval_indices.append(i)\\n\\n #if len(real_eval_indices + unit_eval_indices) > 0:\\n # max_real_eval = max([ op_evals[i] for i in real_eval_indices + unit_eval_indices])\\n # min_real_eval = min([ op_evals[i] for i in real_eval_indices + unit_eval_indices])\\n #else:\\n # max_real_eval = _np.nan\\n # min_real_eval = _np.nan\\n #\\n #fixed_points = [ op_evecs[:,i] for i in unit_eval_indices ]\\n #real_eval_axes = [ op_evecs[:,i] for i in real_eval_indices ]\\n #conjpair_eval_axes = [ (op_evecs[:,i],op_evecs[:,j]) for (i,j) in conjpair_eval_indices ]\\n #\\n #ret = { }\\n\\n nQubits = _np.log2(operationMx.shape[0]) / 2\\n if nQubits == 1:\\n #print \\\"DEBUG: 1 qubit decomp --------------------------\\\"\\n #print \\\" --> evals = \\\", op_evals\\n #print \\\" --> unit eval indices = \\\", unit_eval_indices\\n #print \\\" --> conj eval indices = \\\", conjpair_eval_indices\\n #print \\\" --> unpaired real eval indices = \\\", real_eval_indices\\n\\n #Special case: if have two conjugate pairs, check if one (or both) are real\\n # and break the one with the largest (real) value into two unpaired real evals.\\n if len(conjpair_eval_indices) == 2:\\n iToBreak = None\\n if abs(_np.imag(op_evals[conjpair_eval_indices[0][0]])) < TOL and \\\\\\n abs(_np.imag(op_evals[conjpair_eval_indices[1][0]])) < TOL:\\n iToBreak = _np.argmax([_np.real(conjpair_eval_indices[0][0]), _np.real(conjpair_eval_indices[1][0])])\\n elif abs(_np.imag(op_evals[conjpair_eval_indices[0][0]])) < TOL: iToBreak = 0\\n elif abs(_np.imag(op_evals[conjpair_eval_indices[1][0]])) < TOL: iToBreak = 1\\n\\n if iToBreak is not None:\\n real_eval_indices.append(conjpair_eval_indices[iToBreak][0])\\n real_eval_indices.append(conjpair_eval_indices[iToBreak][1])\\n del conjpair_eval_indices[iToBreak]\\n\\n #Find eigenvector corresponding to fixed point (or closest we can get). This\\n # should be a unit eigenvalue with identity eigenvector.\\n if len(unit_eval_indices) > 0:\\n #Find linear least squares solution within possibly degenerate unit-eigenvalue eigenspace\\n # of eigenvector closest to identity density mx (the desired fixed point), then orthogonalize\\n # the remaining eigenvectors w.r.t this one.\\n A = _np.take(op_evecs, unit_eval_indices, axis=1)\\n b = _np.array([[1], [0], [0], [0]], 'd') # identity density mx\\n x = _np.dot(_np.linalg.pinv(_np.dot(A.T, A)), _np.dot(A.T, b))\\n fixedPtVec = _np.dot(A, x) # fixedPtVec / _np.linalg.norm(fixedPtVec)\\n fixedPtVec = fixedPtVec[:, 0]\\n\\n iLargestContrib = _np.argmax(_np.abs(x)) # index of gate eigenvector which contributed the most\\n for ii, i in enumerate(unit_eval_indices):\\n if ii == iLargestContrib:\\n op_evecs[:, i] = fixedPtVec\\n iFixedPt = i\\n else:\\n op_evecs[:, i] = op_evecs[:, i] - _np.vdot(fixedPtVec, op_evecs[:, i]) * fixedPtVec\\n for jj, j in enumerate(unit_eval_indices[:ii]):\\n if jj == iLargestContrib: continue\\n op_evecs[:, i] = op_evecs[:, i] - _np.vdot(op_evecs[:, j], op_evecs[:, i]) * op_evecs[:, j]\\n op_evecs[:, i] /= _np.linalg.norm(op_evecs[:, i])\\n\\n elif len(real_eval_indices) > 0:\\n # just take eigenvector corresponding to the largest real eigenvalue?\\n #iFixedPt = real_eval_indices[ _np.argmax( [ op_evals[i] for i in real_eval_indices ] ) ]\\n\\n # ...OR take eigenvector corresponding to a real unpaired eigenvalue closest to identity:\\n idmx = _np.array([[1], [0], [0], [0]], 'd') # identity density mx\\n iFixedPt = real_eval_indices[_np.argmin([_np.linalg.norm(op_evecs[i] - idmx) for i in real_eval_indices])]\\n\\n else:\\n #No unit or real eigenvalues => two complex conjugate pairs or unpaired complex evals --> bail out\\n return {'isValid': False, 'isUnitary': False, 'msg': \\\"All evals are complex.\\\"}\\n\\n #Find eigenvector corresponding to axis of rotation: find the *largest* unpaired real/unit eval\\n indsToConsider = (unit_eval_indices + real_eval_indices)[:]\\n del indsToConsider[indsToConsider.index(iFixedPt)] # don't consider fixed pt evec\\n\\n if len(indsToConsider) > 0:\\n iRotAxis = indsToConsider[_np.argmax([op_evals[i] for i in indsToConsider])]\\n else:\\n #No unit or real eigenvalues => an unpaired complex eval --> bail out\\n return {'isValid': False, 'isUnitary': False, 'msg': \\\"Unpaired complex eval.\\\"}\\n\\n #There are only 2 eigenvalues left -- hopefully a conjugate pair giving rotation\\n inds = list(range(4))\\n del inds[inds.index(iFixedPt)]\\n del inds[inds.index(iRotAxis)]\\n if abs(op_evals[inds[0]] - _np.conjugate(op_evals[inds[1]])) < TOL:\\n iConjPair1, iConjPair2 = inds\\n else:\\n return {'isValid': False, 'isUnitary': False, 'msg': \\\"No conjugate pair for rotn.\\\"}\\n\\n return {'isValid': True,\\n 'isUnitary': bool(len(unit_eval_indices) >= 2),\\n 'fixed point': op_evecs[:, iFixedPt],\\n 'axis of rotation': op_evecs[:, iRotAxis],\\n 'rotating axis 1': op_evecs[:, iConjPair1],\\n 'rotating axis 2': op_evecs[:, iConjPair2],\\n 'decay of diagonal rotation terms': 1.0 - abs(op_evals[iRotAxis]),\\n 'decay of off diagonal rotation terms': 1.0 - abs(op_evals[iConjPair1]),\\n 'pi rotations': _np.angle(op_evals[iConjPair1]) / _np.pi,\\n 'msg': \\\"Success\\\"}\\n\\n else:\\n return {'isValid': False,\\n 'isUnitary': False,\\n 'msg': \\\"Unsupported number of qubits: %d\\\" % nQubits}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc649914d8fe100550a97db0409a8af8\",\n \"score\": \"0.535696\",\n \"text\": \"def stress_gausspoint(xp, Vp, sig, mpe, xn):\\n \\n # Calcular las coordenas del punto de gauss - Centro del elemento\\n x = (xn[0, 0] + xn[1, 0])/2 # equivalente a shi=0\\n y = (xn[3, 1] + xn[0, 1])/2 # equivalente a eta=0\\n \\n sigq = np.zeros(3) # vector donde se guardan los esfuerzos en el punto de gauss\\n LS_Ma = np.zeros((3,3)) # MAtriz con los coefcientes del sistema de Ec de minimos cuadrados\\n \\n # llenado la Ma de minimos cuadrados\\n LS_Ma[0,0] = np.sum(Vp[mpe-1])\\n LS_Ma[0,1] = np.sum(np.multiply(xp[mpe-1,0], Vp[mpe-1]))\\n LS_Ma[0,2] = np.sum(np.multiply(xp[mpe-1,1], Vp[mpe-1]))\\n LS_Ma[1,0] = LS_Ma[0,1]\\n LS_Ma[1,1] = np.sum(np.multiply(np.multiply(xp[mpe-1,0], xp[mpe-1,0]), Vp[mpe-1]))\\n LS_Ma[1,2] = np.sum(np.multiply(np.multiply(xp[mpe-1,0], xp[mpe-1,1]), Vp[mpe-1]))\\n LS_Ma[2,0] = LS_Ma[0,2]\\n LS_Ma[2,1] = LS_Ma[1,2]\\n LS_Ma[2,2] = np.sum(np.multiply(np.multiply(xp[mpe-1,1], xp[mpe-1,1]), Vp[mpe-1]))\\n \\n # ciclo en las 3 componentes de esfuerzo\\n for i in range(3):\\n LS_Ve = np.zeros(3)\\n LS_Ve[0] = np.sum(np.multiply(sig[mpe-1,i], Vp[mpe-1]))\\n LS_Ve[1] = np.sum(np.multiply(np.multiply(xp[mpe-1,0], sig[mpe-1,i]), Vp[mpe-1]))\\n LS_Ve[2] = np.sum(np.multiply(np.multiply(xp[mpe-1,1], sig[mpe-1,i]), Vp[mpe-1]))\\n \\n # solucionado el sistema de ecuaciones de minimos cuadrados\\n alpha = np.linalg.solve(LS_Ma, LS_Ve)\\n # Calculando el esfuerzo correspondiente en el punto de gauss\\n sigq[i] = alpha[0] + alpha[1]*x + alpha[2]*y\\n \\n return sigq\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15579181d3fee56aee6b95b28c7dbe03\",\n \"score\": \"0.53551936\",\n \"text\": \"def greedyEI(self):\\n ymax=np.max(self.y[self.tt])\\n nz=10\\n zq=np.linspace(-2,2,nz)\\n pz=norm.pdf(zq)\\n pz=pz/np.sum(pz)\\n EI=np.zeros((self.n,nz))\\n EI[self.tt,:]=0\\n zquad=np.zeros(self.n)\\n zquad[self.tu+self.tt]=self.z[self.tu+self.tt]\\n SIG_z_pos=self.SIG_z[np.ix_(self.uu,self.uu)]-np.matmul(self.SIG_z[np.ix_(self.uu,self.tu+self.tt)],np.linalg.solve(self.SIG_z[np.ix_(self.tu+self.tt,self.tu+self.tt)],self.SIG_z[np.ix_(self.tu+self.tt,self.uu)]))\\n sig_z_pos=np.diag(SIG_z_pos)**0.5\\n mu_z_pos=np.matmul(self.SIG_z[np.ix_(self.uu,self.tu+self.tt)],np.linalg.solve(self.SIG_z[np.ix_(self.tu+self.tt,self.tu+self.tt)],self.z[self.tu+self.tt])) \\n for i in range(nz):\\n zquad[self.uu]=mu_z_pos+zq[i]*sig_z_pos \\n Dz=euclidean_distances(zquad.reshape(-1,1),zquad.reshape(-1,1),squared=True)\\n SIG_y=self.ay**2*np.exp(-self.Dx*self.lyx**2/2-Dz*self.lyz**2/2)+self.by**2*np.identity(self.n)\\n SIG_y_pos=SIG_y[np.ix_(self.uu+self.tu,self.uu+self.tu)]-np.matmul(SIG_y[np.ix_(self.uu+self.tu,self.tt)],np.linalg.solve(SIG_y[np.ix_(self.tt,self.tt)],SIG_y[np.ix_(self.tt,self.uu+self.tu)]))\\n sig_y_pos=np.diag(SIG_y_pos)**0.5\\n mu_y_pos=np.matmul(SIG_y[np.ix_(self.uu+self.tu,self.tt)],np.linalg.solve(SIG_y[np.ix_(self.tt,self.tt)],self.y[self.tt])) \\n EI[self.uu+self.tu,i]=(mu_y_pos-ymax)*norm.cdf(np.divide(mu_y_pos-ymax,sig_y_pos))+sig_y_pos*norm.pdf(np.divide(mu_y_pos-ymax,sig_y_pos))\\n alpha=np.matmul(EI,pz)\\n \\\"\\\"\\\" greedy controller \\\"\\\"\\\"\\n uua=[i for i in self.uu if i not in self.tz]\\n tua=[i for i in self.tu if i not in self.ty]\\n iuu=uua[np.argmax(alpha[uua])]\\n if len(tua)>0:\\n itu=tua[np.argmax(alpha[tua])]\\n r1=np.dot(pz,np.maximum(EI[itu],EI[iuu]))/(self.cz+self.cy) \\n r2=alpha[itu]/self.cy\\n if r2>r1:\\n return itu\\n return iuu\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b96d303e07b3b525f3ea9d6e2f33435\",\n \"score\": \"0.5351081\",\n \"text\": \"def calculate_A_Bush_XY(self,xo,df_vcmax):\\n \\n alphaS,vcmax1,vcmax2,vcmax3,vcmax4,TP1,TP2,TP3,TP4,gm0,jmax1,jmax2,jmax3,jmax4 = xo\\n vcmaxs=[vcmax1,vcmax2,vcmax3,vcmax4]\\n tps=[TP1,TP2,TP3,TP4]\\n jmaxs=[jmax1,jmax2,jmax3,jmax4]\\n \\n kmc = 267\\n kmo = 164\\n\\n sco = 3.259;\\n alphaT = 0;\\n alphaG = 0;\\n m=0;\\n R = 0;\\n # gm0 = 0\\n replicates = df_vcmax['Replicate'].unique()\\n A_mod_arr = np.array([])\\n for replicate in replicates:\\n df_vcmax_r=df_vcmax[df_vcmax['Replicate']==replicate];\\n ci = df_vcmax_r['Ci'].values.astype(float)\\n i_inc = df_vcmax_r['Iinc'].values.astype(float)\\n theta = df_vcmax_r['Theta'].values.astype(float) \\n O = df_vcmax_r['O'].values.astype(float)\\n rd = df_vcmax_r['Rd'].values.astype(float)\\n #jmax = df_vcmax_r['Jmax'].values.astype(float)\\n \\n k2LL = df_vcmax_r['k2LL'].values.astype(float) \\n vcmax = vcmaxs[replicate-1]\\n jmax = jmaxs[replicate-1]\\n TP = tps[replicate-1]\\n #gm0 = gms[replicate-1]\\n # Rubisco-limited part\\n\\n gamma_st = 0.5*O/sco;\\n gamma_x = gamma_st*(1-alphaG+2*alphaT);\\n \\n \\n # Rubisco-limited part;\\n KMCMO = kmc*(1+O/kmo);\\n X1R = vcmax;\\n X2R = KMCMO;\\n \\n PR = gm0*(X2R+gamma_x)+(X1R-rd)*R;\\n QR2 = (ci-gamma_x)*X1R-(ci+X2R)*rd;\\n QR = (X2R+gamma_x*(1-m))*(X1R-rd);\\n AAR = X2R+gamma_x*(1-m)+R*(ci+X2R);\\n BBR = m*(rd*X2R+gamma_x*X1R)-QR-PR*(ci+X2R)-R*QR2;\\n CCR = -m*(rd*X2R+gamma_x*X1R)*(X1R-rd)+PR*QR2;\\n AR = (-BBR-(BBR**2-4.*AAR*CCR)**0.5)/(2.*AAR);\\n \\n # Electron transport limited part;\\n \\n BB = k2LL*i_inc + jmax;\\n J = (BB-(BB**2-4*theta*jmax*k2LL*i_inc)**0.5)/(2*theta);\\n X1R = J/4;\\n X2R = (1+2*alphaG-alphaT+alphaS)*2.*gamma_st;\\n PR = gm0*(X2R+gamma_x)+(X1R-rd)*R;\\n QR2 = (ci-gamma_x)*X1R-(ci+X2R)*rd;\\n QR = (X2R+gamma_x*(1-m))*(X1R-rd);\\n AAR = X2R+gamma_x*(1-m)+R*(ci+X2R);\\n BBR = m*(rd*X2R+gamma_x*X1R)-QR-PR*(ci+X2R)-R*QR2;\\n CCR = -m*(rd*X2R+gamma_x*X1R)*(X1R-rd)+PR*QR2;\\n AJ = (-BBR-(BBR**2-4.*AAR*CCR)**0.5)/(2.*AAR);\\n \\n # TPU limited part; \\n X1R = 3*TP;\\n X2R = -(1+3*alphaG+6*alphaT+4*alphaS)*gamma_st;\\n PR = gm0*(X2R+gamma_x)+(X1R-rd)*R;\\n QR2 = (ci-gamma_x)*X1R-(ci+X2R)*rd;\\n QR = (X2R+gamma_x*(1-m))*(X1R-rd);\\n AAR = X2R+gamma_x*(1-m)+R*(ci+X2R);\\n BBR = m*(rd*X2R+gamma_x*X1R)-QR-PR*(ci+X2R)-R*QR2;\\n CCR = -m*(rd*X2R+gamma_x*X1R)*(X1R-rd)+PR*QR2;\\n \\n #NOTE: + sign in front of SQRT part;\\n bacT = BBR**2-4*AAR*CCR;\\n AP = (-BBR + (np.maximum(0.00001,BBR**2-4*AAR*CCR))**0.5)/(2*AAR);\\n \\n #--- Finding the Ci transition point from Aj to Ap;\\n CCTS = np.maximum((1.0001+3*alphaG+6*alphaT+4*alphaS)*gamma_x, (24*gamma_x*TP+(1+3*alphaG+6*alphaT+4*alphaS)*gamma_x*J)/(J-12*TP));\\n GMTS = gm0+R*3*TP/(CCTS-(1+3*alphaG+6*alphaT+4*alphaS)*gamma_x);\\n AATS = (CCTS-(1-m)*gamma_x)*3*TP/(CCTS-(1+3*alphaG+6*alphaT+4*alphaS)*gamma_x);\\n CITS = CCTS + (AATS-(1-m)*rd)/GMTS;\\n\\n #-- Model for net photosynthesis rate;\\n if ci.all()<CITS.all() or bacT.all()<0:\\n A_mod = np.minimum(AR,AJ)\\n else:\\n A1 = np.minimum(AR,AJ)\\n A_mod = np.minimum(A1,AP)\\n \\n A_mod_arr = np.append(A_mod_arr,A_mod)\\n \\n return A_mod_arr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25c245b16fa923a4ef77735094353ff2\",\n \"score\": \"0.5335676\",\n \"text\": \"def g(x):\\n return 5.0 - x[:, 1] - 0.5 * x[:, 0] ** 2.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44fc6ce255a5b2294841b08f270ec105\",\n \"score\": \"0.5328571\",\n \"text\": \"def craig(self, x, size=0.3):\\n \\n sigma = []\\n s0 = np.zeros(x.shape[-1]).tolist()\\n self.x = x \\n self.size = int(len(x)*size)\\n gammas = np.zeros(self.size)\\n \\n print(\\\"Finding coreset...\\\")\\n for i in range(self.size): \\n e, L = np.inf, np.inf\\n \\n if sigma != []:\\n S = x[sigma].tolist()\\n else:\\n S = []\\n \\n for j in range(len(x)): \\n if j not in sigma:\\n \\n s_ = np.stack(S + [x[j], s0])\\n l = 0 \\n for k in range(len(x)):\\n l += min(np.std(s_ - x[k], 1))\\n# l = np.sum(np.std(x - x[j], -1))\\n \\n if l < L:\\n L = l\\n e = j\\n \\n sigma.append(e)\\n \\n self.sigma = np.stack(sigma)\\n print(\\\"Calculating gammas...\\\")\\n \\n for i in range(len(x)):\\n gammas[np.argmin(np.std(x[sigma] - x[i], 1))] += 1\\n \\n self.gammas = gammas\\n \\n return self.sigma, self.gammas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2124f26ab0674210ef2dff595cd34e\",\n \"score\": \"0.5328473\",\n \"text\": \"def cell_volumes(self):\\n X = self.X\\n Y = self.Y\\n Z = self.Z\\n a = [np.zeros(shape=(self.IMAX,self.JMAX,self.KMAX)) for _ in range(9)]\\n # face csi=const \\n for k in range(1,self.KMAX):\\n for j in range(1,self.JMAX):\\n for i in range(self.IMAX): # csi-const\\n dx1 = X[i,j,k-1] - X[i,j-1,k]\\n dy1 = Y[i,j,k-1] - Y[i,j-1,k]\\n dz1 = Z[i,j,k-1] - Z[i,j-1,k]\\n\\n dx2 = X[i,j,k] - X[i,j-1,k-1]\\n dy2 = Y[i,j,k] - Y[i,j-1,k-1]\\n dz2 = Z[i,j,k] - Z[i,j-1,k-1]\\n\\n ax = dy1*dz2 - dz1*dy2\\n ay = dz1*dx2 - dx1*dz2\\n az = dx1*dy2 - dy1*dx2\\n\\n a[0][i,j,k] = ax*0.5\\n a[1][i,j,k] = ay*0.5\\n a[2][i,j,k] = az*0.5\\n\\n \\n for k in range(1,self.KMAX):\\n for j in range(self.JMAX): # face eta=const \\n for i in range(1,self.IMAX):\\n dx1 = X[i,j,k] - X[i-1,j,k-1]\\n dy1 = Y[i,j,k] - Y[i-1,j,k-1]\\n dz1 = Z[i,j,k] - Z[i-1,j,k-1]\\n\\n dx2 = X[i,j,k-1] - X[i-1,j,k]\\n dy2 = Y[i,j,k-1] - Y[i-1,j,k]\\n dz2 = Z[i,j,k-1] - Z[i-1,j,k]\\n\\n ax = dy1*dz2 - dz1*dy2\\n ay = dz1*dx2 - dx1*dz2\\n az = dx1*dy2 - dy1*dx2\\n\\n a[3][i,j,k] = ax*0.5\\n a[4][i,j,k] = ay*0.5\\n a[5][i,j,k] = az*0.5\\n\\n # face zit=const \\n for k in range(self.KMAX): # zit=const\\n for j in range(1,self.JMAX): \\n for i in range(1,self.IMAX):\\n dx1 = X[i,j,k] - X[i-1,j-1,k]\\n dy1 = Y[i,j,k] - Y[i-1,j-1,k]\\n dz1 = Z[i,j,k] - Z[i-1,j-1,k]\\n\\n dx2 = X[i-1,j,k] - X[i,j-1,k]\\n dy2 = Y[i-1,j,k] - Y[i,j-1,k]\\n dz2 = Z[i-1,j,k] - Z[i,j-1,k]\\n\\n ax = dy1*dz2 - dz1*dy2\\n ay = dz1*dx2 - dx1*dz2\\n az = dx1*dy2 - dy1*dx2\\n\\n a[6][i,j,k] = ax*0.5\\n a[7][i,j,k] = ay*0.5\\n a[8][i,j,k] = az*0.5\\n\\n cf = np.zeros(shape=(6,3))\\n v = np.zeros(shape=(self.IMAX,self.JMAX,self.KMAX))\\n \\n for k in trange(1,self.KMAX,desc='Calculating the volumes'):\\n for j in range(1,self.JMAX): \\n for i in range(1,self.IMAX):\\n cf[0,0] = X[i-1,j-1,k-1] + X[i-1,j-1,k] + X[i-1,j,k-1] + X[i-1,j,k]\\n cf[0,1] = Y[i-1,j-1,k-1] + Y[i-1,j-1,k] + Y[i-1,j,k-1] + Y[i-1,j,k]\\n cf[0,2] = Z[i-1,j-1,k-1] + Z[i-1,j-1,k] + Z[i-1,j,k-1] + Z[i-1,j,k]\\n cf[1,0] = X[i,j-1,k-1] + X[i,j-1,k] + X[i,j,k-1] + X[i,j,k]\\n cf[1,1] = Y[i,j-1,k-1] + Y[i,j-1,k] + Y[i,j,k-1] + Y[i,j,k]\\n cf[1,2] = Z[i,j-1,k-1] + Z[i,j-1,k] + Z[i,j,k-1] + Z[i,j,k]\\n cf[2,0] = X[i-1,j-1,k-1] + X[i-1,j-1,k] + X[i,j-1,k-1] + X[i,j-1,k]\\n cf[2,1] = Y[i-1,j-1,k-1] + Y[i-1,j-1,k] + Y[i,j-1,k-1] + Y[i,j-1,k]\\n cf[2,2] = Z[i-1,j-1,k-1] + Z[i-1,j-1,k] + Z[i,j-1,k-1] + Z[i,j-1,k]\\n cf[3,0] = X[i-1,j,k-1] + X[i-1,j,k] + X[i,j,k-1] + X[i,j,k]\\n cf[3,1] = Y[i-1,j,k-1] + Y[i-1,j,k] + Y[i,j,k-1] + Y[i,j,k]\\n cf[3,2] = Z[i-1,j,k-1] + Z[i-1,j,k] + Z[i,j,k-1] + Z[i,j,k]\\n cf[4,0] = X[i-1,j-1,k-1] + X[i-1,j,k-1] + X[i,j-1,k-1] + X[i,j,k-1]\\n cf[4,1] = Y[i-1,j-1,k-1] + Y[i-1,j,k-1] + Y[i,j-1,k-1] + Y[i,j,k-1]\\n cf[4,2] = Z[i-1,j-1,k-1] + Z[i-1,j,k-1] + Z[i,j-1,k-1] + Z[i,j,k-1]\\n cf[5,0] = X[i-1,j-1,k] + X[i-1,j,k] + X[i,j-1,k] + X[i,j,k]\\n cf[5,1] = Y[i-1,j-1,k] + Y[i-1,j,k] + Y[i,j-1,k] + Y[i,j,k]\\n cf[5,2] = Z[i-1,j-1,k] + Z[i-1,j,k] + Z[i,j-1,k] + Z[i,j,k]\\n\\n vol12=0\\n for n in range(0,2): # n = 0,1\\n for l in range(0,3): # l = 0,1,2\\n vol12 += math.pow(-1,n+1) *( \\n +cf[n,l]*a[l][i-1+n,j,k]\\n +cf[2+n,l]*a[3+l][i,j-1+n,k]\\n +cf[4+n,l]*a[6+l][i,j,k-1+n])\\n v[i,j,k]= vol12/12\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92c604d0324a1e9f6ef30e00339a2002\",\n \"score\": \"0.532701\",\n \"text\": \"def partition_energy(self):\\n\\n\\n self.E.Ef_nuc = self.molE.Enuc # Sum of nuclear fragments is just nuclear of molecule\\n self.E.Ef_ext = 0.0\\n self.E.Ef_kin = 0.0\\n self.E.Ef_vxc = 0.0\\n self.E.Ef_vha = 0.0\\n self.E.Ef_ext = 0.0\\n\\n self.E.Ep_ext = 0.0\\n #devwarn Will only work for diatomics for now\\n # External nuclear potential\\n # self.E.Ep_ext += np.sum( (self.frags[0].da_frac + self.frags[0].db_frac) * self.frags[1].V.Vnm )\\n # self.E.Ep_ext += np.sum( (self.frags[1].da_frac + self.frags[1].db_frac) * self.frags[0].V.Vnm )\\n\\n # Kinetic\\n for ifrag in self.frags:\\n self.E.Ef_ext += ifrag.E.Eext # External nuclear potential\\n self.E.Ef_kin += ifrag.E.Ekin # Kinetic\\n self.E.Ef_vxc += ifrag.E.Evxc # XC\\n self.E.Ef_vha += ifrag.E.Evha # Hartree\\n\\n self.frags_ekin = np.sum( self.Tnm0.np * (self.dfa + self.dfb) )\\n self.frags_eha = np.einsum('pq,pq->', (self.inverter.va), self.dfa, optimize=True)\\n self.frags_ext = np.sum( self.Vnm0.np * (self.dfa + self.dfb) )\\n #self.frags_exc\\n\\n # Components of Ep\\n self.E.Ep_kin = self.frags_ekin - self.E.Ef_kin\\n self.E.Ep_vxc = self.frags_exc - self.E.Ef_vxc\\n self.E.Ep_vha = self.frags_eha - self.E.Ef_vha\\n self.E.Ep_ext = self.frags_ext - self.E.Ef_ext\\n # self.E.Ep_kin = self.molE.Ekin - self.E.Ef_kin\\n # self.E.Ep_vxc = self.molE.Evxc - self.E.Ef_vxc\\n # self.E.Ep_vha = self.molE.Evha - self.E.Ef_vha\\n # self.E.Ep_ext = self.molE.Eext - self.E.Ef_ext\\n\\n # Ep and Ef\\n self.E.Ep = self.E.Ep_kin + self.E.Ep_vha + self.E.Ep_vxc + self.E.Ep_ext\\n self.E.Ef = self.frags[0].E.Etot + self.frags[1].E.Etot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f804d20ecd80bfff95563f20da4ab0f\",\n \"score\": \"0.5308593\",\n \"text\": \"def computeDecomposition(self):\\n self.computeBoundingBox()\\n # self.polygon.E = [self.polygon.E[i] for i in [1, 7, 3, 5]] \\\\\\n # + [self.polygon.E[i] for i in range(len(self.polygon.E)) if i not in [1, 7, 3, 5]]\\n random.shuffle(self.polygon.E)\\n for lineSegment in self.polygon.E:\\n self.insertLinesegment(lineSegment)\\n # self.T.visualize()\\n # self.T.visualize_graph()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f152fe9457ed7397db6d9f46ad03554\",\n \"score\": \"0.53022593\",\n \"text\": \"def _get_hybrid_stack(x0, y0,final_q, final_a, final_var, im_args):\\r\\n X_lg,Y_lg,_lg_fac_x,_lg_fac_y, shape = im_args\\r\\n X_use = X_lg - (x0 + _lg_fac_x)\\r\\n Y_use = Y_lg - (y0 + _lg_fac_y)\\r\\n stack_full = np.zeros((X_use.shape[0],X_use.shape[1], len(final_q)))\\r\\n num_g = final_q.shape[0]\\r\\n\\r\\n for k in range(num_g):\\r\\n q,a,var = final_q[k],final_a[k],final_var[k]\\r\\n use_gauss = (var*q*q > 25.)\\r\\n R2_use = np.square(X_use) + np.square(Y_use)/(q*q)\\r\\n\\r\\n for i in range(X_use.shape[0]):\\r\\n for j in range(X_use.shape[1]):\\r\\n #If outside 5sigma then keep as 0\\r\\n if R2_use[i,j]/var > 25.:\\r\\n continue\\r\\n elif use_gauss:\\r\\n #If sigma>5 no benefit to using erf so go with quicker simple calc\\r\\n stack_full[i,j,k] = a / (2*np.pi*var * q) * np.exp( -1.*(X_use[i,j]*X_use[i,j] + Y_use[i,j]*Y_use[i,j]/(q*q) )/ (2*var) )\\r\\n else:\\r\\n c_x = 1./(np.sqrt(2*var))\\r\\n c_y = 1./(np.sqrt(2*var)*q)\\r\\n stack_full[i,j,k] = a/4 *( ( _erf_approx(c_x*(X_use[i,j]-0.5)) - _erf_approx(c_x*(X_use[i,j]+0.5)) )* ( _erf_approx(c_y*(Y_use[i,j] -0.5)) - _erf_approx(c_y*(Y_use[i,j]+0.5)) ) )\\r\\n\\r\\n return stack_full\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a490c555bf647cf0a8b5ef651f4cf65\",\n \"score\": \"0.5285301\",\n \"text\": \"def exo4():\\n Ablock = []\\n for i in 1: p:\\n for j in 1: p:\\n for k in 1: n:\\n Z = zeros(n, n, n)\\n Z((1: p) + (i-1)*p, (1: p) + (j-1)*p, k) = 1\\n Ablock(end + 1, : ) = Z(: )'\\n imageplot(Ablock)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a8b8a8299bf03aab7c6a75db36ed6ca\",\n \"score\": \"0.52766126\",\n \"text\": \"def __scater_fill__(gdt_alpha, mx_gdt_rh, mx_gdt_rw, mx_gdt_x, mx_gdt_y, k, ctx):\\n assert gdt_alpha.shape[1] ==1, gdts.shape\\n n = gdt_alpha.shape[0]\\n alpha = gdt_alpha\\n rh, rw = [mx_gdt_rh, mx_gdt_rw]\\n x_offset, y_offset = [mx_gdt_x, mx_gdt_y]\\n\\n\\n # s1: grid...\\n ## do computations in a small scale as much as poosible...\\n x=np.arange(0,k)*1./k -0.5 + 1./(2*k) # align center to origin for rotation(-.5) and, each node represents a integritied block(+ 1/(2k))\\n X,Y = np.meshgrid(x,x)\\n x_grid, y_grid = [ np.reshape(_,(1,k**2)) for _ in [X,Y] ]\\n # repeat to same shape[0] of gdt...\\n x_grid, y_grid = [ mx.nd.array(np.repeat(_, n, axis=0), ctx) for _ in [x_grid, y_grid] ]\\n\\n ## s2: scale...\\n x_grid, y_grid = [mx.nd.broadcast_mul(grid, 2*r) for grid,r in zip([x_grid, y_grid], [rh, rw]) ]\\n\\n ## s3: rotate...\\n # do it in a seperate definition\\n x_grid, y_grid = mx_rotate(x_grid, y_grid, alpha)\\n\\n ## s4: shift...\\n# assert 0, (x_grid.shape, x_offset.shape)\\n x_grid, y_grid = [ mx.nd.broadcast_add(_ , offset) for\\\\\\n _, offset in zip([x_grid, y_grid], [x_offset, y_offset] ) ]\\n\\n return x_grid, y_grid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9e7c9ba875065f0afaee104bb54b13a\",\n \"score\": \"0.52637494\",\n \"text\": \"def compute_edges_dxdy(I):\\n #I = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)\\n\\n# Gaussian filter\\n I = I.astype(np.float32) / 255.\\n sigma = 2\\n size = 9\\n\\n x_points = np.arange(-(size - 1) // 2, (size - 1) // 2 + 1, 1)\\n y_points = x_points[::-1]\\n xs, ys = np.meshgrid(x_points, y_points)\\n kernel = 1 / (2 * np.pi * sigma ** 2) * np.exp(- (xs ** 2 + ys** 2) / (2 * sigma ** 2)) * (-ys / sigma ** 2)\\n kernel = kernel/kernel.sum()\\n\\n dx = np.zeros(I.shape)\\n dy = np.zeros(I.shape)\\n mag = np.zeros(I.shape)\\n for channel in range(3):\\n dx[:, :, channel] = signal.convolve2d(I[:, :, channel], kernel, mode='same', boundary='symm')\\n dy[:, :, channel] = signal.convolve2d(I[:, :, channel], kernel.T, mode='same', boundary='symm')\\n mag[:, :, channel] = np.sqrt(dx[:, :, channel] ** 2 + dy[:, :, channel] ** 2)\\n mag[:, :, channel] = mag[:, :, channel] / np.max(mag[:, :, channel])\\n # dx = signal.convolve2d(I, kernel, mode='same', boundary='symm')\\n # dy = signal.convolve2d(I, kernel.T, mode='same', boundary='symm')\\n mag = np.sqrt(np.sum(mag ** 2, axis=2))\\n #mag = np.sqrt(dx ** 2 + dy ** 2)\\n mag = mag / np.max(mag)\\n\\n dx = dx.sum(axis=2)\\n dy = dy.sum(axis=2)\\n\\n #Non-maximum suppression\\n height,width= mag.shape\\n for i in range(1, height - 1):\\n for j in range(1, width - 1):\\n grade = np.sqrt(dx[i][j] ** 2 + dy[i][j] ** 2)\\n if mag[i][j] <= 0 or mag_check(mag, [i + 1 * dx[i][j] / grade, j + 1 * dx[i][j] / grade], [i, j]) or mag_check(mag, [i - 1 * dx[i][j] / grade, j - 1 * dx[i][j] / grade], [i, j]):\\n mag[i][j] = 0\\n\\n\\n mag = mag * 255\\n mag = np.clip(mag, 0, 255)\\n mag = mag.astype(np.uint8)\\n return mag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25453b2263570fba5e90c08a9c7fef92\",\n \"score\": \"0.5258398\",\n \"text\": \"def GERPFunc (x, *p)->ndarray:\\n amp = p[0]\\n total_width = p[1]\\n start_pos = p[2]\\n edge_width = p[3]\\n peak_width = edge_width*2\\n edge_sigma = p[4]\\n\\n flat_start = start_pos +edge_width\\n flat_width = total_width -peak_width\\n\\n flat_end = flat_start +flat_width\\n\\n\\n raising_edge_pars = [ amp, edge_sigma, flat_start ]\\n gaussUp = where( x<flat_start, gaussianFunc(x, *raising_edge_pars), 0. )\\n falling_edge_pars = [ amp, edge_sigma, flat_end ]\\n gaussDn = where( x>flat_end, gaussianFunc(x, *falling_edge_pars),0. )\\n\\n flat_pars = [ amp, flat_width, flat_start]\\n rect_pulse = rectPulseFunc( x, *flat_pars )\\n return gaussUp +rect_pulse +gaussDn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82969ce9ab561f65623b31f2b3c28937\",\n \"score\": \"0.5249008\",\n \"text\": \"def shapfunc_gauss1(xn):\\n \\n nnodes = len(xn[:,0]) # obtener el numero de nodos del elemento para determinar si es trangular o cudrilateral\\n assert (nnodes == 3 or nnodes == 4), \\\"Hay un error en el mallado\\\"\\n \\n Ng = np.zeros((3, len(xn)))\\n if nnodes == 3:\\n # Elemento triangular lineal\\n x1, x2, x3 = xn[0, 0], xn[1, 0], xn[2, 0]\\n y1, y2, y3 = xn[0, 1], xn[1, 1], xn[2, 1]\\n A = 1/2*(x1*y2 - x1*y3 + x2*y3 - x2*y1 + x3*y1 - x3*y2)\\n # -- funciones de forma ---\\n Ng[0, 0] = 1 / 3\\n Ng[0, 1] = 1 / 3\\n Ng[0, 2] = 1 / 3\\n # -- Dervidas en x de las funcioens de forma\\n Ng[1, 0] = 1/(2*A)*(y2 - y3)\\n Ng[1, 1] = 1/(2*A)*(y3 - y1)\\n Ng[1, 2] = 1/(2*A)*(y1 - y2)\\n # -- Derivadas en y de las funciones de forma\\n Ng[2, 0] = 1/(2*A)*(x3 - x2)\\n Ng[2, 1] = 1/(2*A)*(x1 - x3)\\n Ng[2, 2] = 1/(2*A)*(x2 - x1)\\n else:\\n # elemento cuadrilateral bilineal\\n x1, x2, x3, x4 = xn[0, 0], xn[1, 0], xn[2, 0], xn[3, 0]\\n y1, y2, y3, y4 = xn[0, 1], xn[1, 1], xn[2, 1], xn[3, 1]\\n a = 1/2 * (x2 - x1)\\n b = 1/2 * (y4 - y1)\\n x = (x1 + x2)/2 # equivalente a shi=0\\n y = (y4 + y1)/2 # equivalente a eta=0\\n \\n # -- funciones de forma ---\\n Ng[0, 0] = 1 / (4*a*b) * (x - x2) * (y - y4)\\n Ng[0, 1] = -1 / (4*a*b) * (x - x1) * (y - y3)\\n Ng[0, 2] = 1 / (4*a*b) * (x - x4) * (y - y2)\\n Ng[0, 3] = -1 / (4*a*b) * (x - x3) * (y - y1)\\n # -- Dervidas en x de las funcioens de forma\\n Ng[1, 0] = 1 / (4*a*b) * (y - y4)\\n Ng[1, 1] = -1 / (4*a*b) * (y - y3)\\n Ng[1, 2] = 1 / (4*a*b) * (y - y2)\\n Ng[1, 3] = -1 / (4*a*b) * (y - y1)\\n # -- Derivadas en y de las funciones de forma\\n Ng[2, 0] = 1 / (4*a*b) * (x - x2)\\n Ng[2, 1] = -1 / (4*a*b) * (x - x1)\\n Ng[2, 2] = 1 / (4*a*b) * (x - x4)\\n Ng[2, 3] = -1 / (4*a*b) * (x - x3)\\n \\n return Ng\",\n \"title\": \"\"\n },\n {\n \"docid\": \"695f72bbd74514b203a43d4d4520180a\",\n \"score\": \"0.5248518\",\n \"text\": \"def f_gauss2d2cA(p, Xg, Yg, Xr, Yr, Arr):\\n #Ag,Ar, x0, y0, s, bG, bR, b_x, b_y = p\\n #return A*scipy.exp(-((X-x0)**2 + (Y - y0)**2)/(2*s**2)) + b + b_x*X + b_y*Y\\n #genGaussInArray(Arr[:,:,0], Xr,Yr,Ar,x0,y0,s,bR,b_x,b_y)\\n #r.strides = r.strides #Really dodgy hack to get around something which numpy is not doing right ....\\n\\n #genGaussInArray(Arr[:,:,1],Xg,Yg,Ag,x0,y0,s,bG,b_x,b_y)\\n #genSplitGaussInArray(Arr,Xg, Yg,Xr,Yr,Ag, Ar,x0,y0,s,bG,b_x,b_y)\\n genSplitGaussInArrayPVec(p,Xg, Yg,Xr,Yr,Arr)\\n #g.strides = g.strides #Really dodgy hack to get around something which numpy is not doing right ....\\n\\n return Arr #numpy.concatenate((g.reshape(g.shape + (1,)),r.reshape(g.shape + (1,))), 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa6f04ac75363c3c696c91ce7f1f9676\",\n \"score\": \"0.5246236\",\n \"text\": \"def __call__(self, x, incumbent=0, *args, **kwargs):\\n x = np.atleast_2d(x)\\n\\n a_ES = np.empty((x.shape[0], self.n_samples_y))\\n\\n # Evaluate mean and covariance of GP at all representer points and\\n # points x where MRS will be evaluated\\n f_mean_all, f_cov_all = \\\\\\n self.model.gp.predict(np.vstack((self.X_candidate, x)),return_cov=True)\\n\\n \\n #self.model.gp.predict(np.vstack((self.X_candidate, x)),return_cov=True)\\n f_mean = f_mean_all[:self.n_candidates]\\n f_cov = f_cov_all[:self.n_candidates, :self.n_candidates]\\n\\n # Iterate over all x[i] at which we will evaluate the acquisition \\n # function (often x.shape[0]=1)\\n for i in range(self.n_candidates, self.n_candidates+x.shape[0]):\\n # Simulate change of covariance (f_cov_delta) for a sample at x[i], \\n # which actually would not depend on the observed value y[i]\\n f_cov_query = f_cov_all[[i]][:, [i]]\\n f_cov_cross = f_cov_all[:self.n_candidates, [i]]\\n f_cov_query_inv = np.linalg.inv(f_cov_query)\\n f_cov_delta = -np.dot(np.dot(f_cov_cross, f_cov_query_inv),\\n f_cov_cross.T)\\n\\n # precompute samples from GP posterior for non-modified mean\\n f_mean=np.ravel(f_mean)\\n\\n f_samples = np.random.RandomState(self.rng_seed).multivariate_normal(\\n f_mean, f_cov + f_cov_delta, self.n_gp_samples).T\\n\\n # adapt for different outcomes y_i[j] of the query at x[i]\\n for j in range(self.n_samples_y): \\n # \\\"sample\\\" outcomes y_i[j] (more specifically where on the \\n # Gaussian distribution over y_i[j] we would end up)\\n y_delta = np.sqrt(f_cov_query + self.model.gp.alpha)[:, 0] \\\\\\n * self.percent_points[j]\\n # Compute change in GP mean at representer points\\n f_mean_delta = f_cov_cross.dot(f_cov_query_inv).dot(y_delta)\\n\\n # Adapt samples to changes in GP posterior mean\\n f_samples_j = f_samples + f_mean_delta[:, np.newaxis]\\n # Count frequency of the candidates being the optima in the samples\\n p_max = np.bincount(np.argmax(f_samples_j, 0),\\n minlength=f_mean.shape[0]) \\\\\\n / float(self.n_gp_samples)\\n # Determing entropy of distr. p_max and compare to base entropy\\n a_ES[i - self.n_candidates, j] = \\\\\\n self.base_entropy - entropy(p_max)\\n\\n # Average entropy change over the different assumed outcomes y_i[j]\\n return a_ES.mean(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e995038eb672ef15b11bcfa1a2cb1c5\",\n \"score\": \"0.52152836\",\n \"text\": \"def _cut_geometries(geom, start_x, start_y, end_x, end_y, kcu_array):\\n\\n cut_geometries = np.zeros((len(start_x),), dtype=DT_VARIABLE)\\n start_points = MultiPoint(list(zip(start_x, start_y)))\\n end_points = MultiPoint(list(zip(end_x, end_y)))\\n\\n for piece in range(len(cut_geometries)):\\n kcu = kcu_array[piece]\\n if kcu == 0:\\n start_pnt = geom.interpolate(geom.project(start_points[piece]))\\n end_pnt = geom.interpolate(geom.project(end_points[piece]))\\n else:\\n start_pnt = start_points[piece]\\n end_pnt = end_points[piece]\\n start_distance = round(geom.project(start_pnt), 3)\\n end_distance = round(geom.project(end_pnt), 3)\\n\\n # Don't use the z-coordinate\\n if geom.has_z:\\n coords = [(x, y) for x, y, z in geom.coords]\\n else:\\n coords = list(geom.coords)\\n start_set = False\\n # no additional calc points\\n if start_distance <= 0.0 and end_distance >= geom.length:\\n linestring = np.array(\\n [(start_pnt.x, start_pnt.y)]\\n + coords[1:-1]\\n + [(end_pnt.x, end_pnt.y)]\\n )\\n # \\\"F\\\" means to flatten in column-major (Fortran- style) order\\n cut_geometries[piece] = linestring.flatten(\\\"F\\\")\\n\\n for i, p in enumerate(coords):\\n # dist to vertex\\n pd = round(geom.project(Point(p)), 3)\\n\\n # should not happen but sometimes drawing direction does not\\n # correspond with start and endpoints, so flip them\\n if start_distance > end_distance:\\n # This is not safe!!!!!!\\n # TODO: check if this actually can happen\\n start_distance, end_distance = end_distance, start_distance\\n start_points, end_points = end_points, start_points\\n\\n # check for start point\\n if (pd == start_distance) and not start_set:\\n start_pnt = []\\n start_i = i\\n start_set = True\\n # should not happen but in case pd (point) and first vertex\\n # do not have the same position move pd back\\n elif (pd > start_distance) and not start_set:\\n start_pnt = [(start_pnt.x, start_pnt.y)]\\n start_i = i\\n start_set = True\\n if pd >= end_distance:\\n linestring = np.array(\\n start_pnt + coords[start_i:i] + [(end_pnt.x, end_pnt.y)]\\n )\\n # \\\"F\\\" means to flatten in column-major (Fortran- style)\\n # order\\n cut_geometries[piece] = linestring.flatten(\\\"F\\\")\\n break\\n\\n return cut_geometries\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb2267b18d294393eb60b0184821600\",\n \"score\": \"0.51865214\",\n \"text\": \"def gauss_xmat(filename, natoms): \\n full = io.read_file(filename)\\n nmodes = 3*natoms-6 \\n lines = full.split('X matrix')[1].split('Resonance')[0]\\n lines = lines.split('\\\\n')\\n del lines[0]\\n del lines[-1]\\n \\n xmat = np.zeros((nmodes, nmodes))\\n rangemod = 1\\n if nmodes%5 == 0:\\n rangemod = 0\\n marker = 0\\n\\n for m in range(0, nmodes/5+rangemod):\\n length = nmodes - m * 5 \\n a= np.array( lines[marker+1:marker+length+1])\\n for i in range(length):\\n for j in range(0, len(a[i].split())-1):\\n xmat[m*5 + i, m*5 + j] = a[i].split()[j+1]\\n xmat[m*5 + j, m*5 + i] = a[i].split()[j+1]\\n marker += length+1\\n\\n return xmat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a042232809fe53a9fa749d7fbe7e7b75\",\n \"score\": \"0.5185479\",\n \"text\": \"def drawECG(self, start=0, leads=range(12), savefilename=None):\\n# if self.data.shape[1] < 12:\\n# raise ValueError, 'Less than 12 leads available'\\n# if self.data.shape[0] / self.samplingrate < 10:\\n# raise ValueError, 'Less than 10 seconds of data available'\\n\\n data = self.data[:, leads]\\n\\n ################################################################################\\n #\\n # Draw the background \\n #\\n ################################################################################\\n\\n #time scale - 1 ms/px (1mm = 40 px at 25 mm/s)\\n #amp scale - 2.5 mcV/px (1mm = 40 px at 100 mcv/mm) \\n #image width = 10 seconds = 10000 px\\n #image height = 4 strips\\n #strip height = 34 mm (3.4 mV)\\n \\n onemm = 40 * int(self.samplingrate / 1000)\\n onesec = self.samplingrate\\n onemv = (400 * self.samplingrate) / (1000 * 1000) # correct for microV\\n lenecg = 10*onesec\\n htecg = 136*onemm\\n\\n #Linethicknesses\\n thickbgwidth = 0.4\\n thinbgwidth = 0.1\\n ecgwidth = 0.8\\n\\n ecgfig = pylab.figure()\\n #thick horizontal lines\\n for horiz in range(0,htecg,5*onemm):\\n pylab.plot([0,lenecg],[horiz,horiz],'r',linewidth=thickbgwidth)\\n\\n #thick vertical lines\\n for vert in range(0,lenecg,5*onemm):\\n pylab.plot([vert,vert],[0,htecg],'r',linewidth=thickbgwidth)\\n\\n #thin horizontal lines\\n for horiz in range(0,htecg,onemm):\\n pylab.plot([0,lenecg],[horiz,horiz],'r',linewidth=thinbgwidth)\\n\\n #thin vertical lines\\n for vert in range(0,lenecg,onemm):\\n pylab.plot([vert,vert],[0,htecg],'r',linewidth=thinbgwidth)\\n \\n ################################################################################\\n #\\n # Draw the ECG \\n #\\n ################################################################################\\n startplot = 0\\n stripcenter = 17 #in mm\\n striplength = int(62.5*onemm) # in px (2.5 seconds)\\n rhythmlead = 1\\n\\n horizcenters = (((scipy.array([0,-1,-2,-3]))*2*stripcenter) - 17) *onemm\\n\\n #prepare data\\n for lead in range(12):\\n #center horizontally\\n data[:,lead] -= scipy.mean(data[:,lead])\\n #rescale \\n data *= onemv \\n\\n #column 1\\n for lead in range(3):\\n pylab.plot(range(striplength),\\\\\\n data[startplot:striplength+startplot,lead]-horizcenters[3-lead],\\\\\\n 'k',linewidth = ecgwidth)\\n\\n #column 2\\n for lead in range(3,6):\\n pylab.plot(range(striplength,2*striplength),\\\\\\n data[striplength+startplot:striplength*2+startplot,lead]-horizcenters[6-lead],\\\\\\n 'k',linewidth = ecgwidth)\\n \\n #column 3\\n for lead in range(6,9):\\n pylab.plot(range(2*striplength,3*striplength),\\\\\\n data[striplength*2+startplot:striplength*3+startplot,lead]-horizcenters[9-lead],\\\\\\n 'k',linewidth = ecgwidth)\\n\\n #column 4\\n for lead in range(9,12):\\n pylab.plot(range(3*striplength,4*striplength),\\\\\\n data[striplength*3+startplot:striplength*4+startplot,lead]-horizcenters[12-lead],\\\\\\n 'k',linewidth = ecgwidth)\\n\\n #rhythm strip\\n pylab.plot(range(4*striplength),\\\\\\n data[startplot:4*striplength,rhythmlead]-horizcenters[0],\\\\\\n 'k',linewidth = ecgwidth)\\n\\n ################################################################################\\n #\\n # Labels\\n #\\n ################################################################################\\n labels = ['I','II','III','aVR','aVL','aVF','V1','V2','V3','V4','V5','V6']\\n xoffset = 20\\n yoffset = -250\\n labelx = [xoffset]*3 + [xoffset+striplength]*3 +\\\\\\n [xoffset+2*striplength]*3 + [xoffset+3*striplength]*3\\n labely = list(yoffset - horizcenters[3:0:-1])*4\\n\\n for labelct in range(12):\\n pylab.text(labelx[labelct],labely[labelct],labels[labelct],fontsize=8)\\n\\n pylab.text(labelx[0],yoffset-horizcenters[0],labels[rhythmlead],fontsize=8)\\n\\n #pylab.axis('off')\\n pylab.setp(pylab.gca(),xticklabels=[])\\n pylab.setp(pylab.gca(),yticklabels=[])\\n pylab.axis([0,lenecg,0,htecg])\\n \\n if not savefilename:\\n pylab.show()\\n else:\\n pylab.savefig(savefilename, dpi=300)\\n\\n #if possible, crop with imagemagick\\n try:\\n commands.getoutput(\\\"mogrify -trim '%s'\\\" %(savefilename))\\n except:\\n pass\\n\\n # clear the figure\\n # Otherwise subsequent plots overlap\\n ecgfig.clf()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d81b57f53c669b05c8a0964c0b320d5\",\n \"score\": \"0.517849\",\n \"text\": \"def gauss1(s):\\n\\n size = s * 3\\n \\n x = y = np.arange(-size, size + 1)\\n \\n ker_x = np.exp(-(x**2 / float(s)))\\n return ker_x / ker_x.sum() \\n #ker_y = np.array([[s] for s in ker_x])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d25fae2f7ff20ffb84ce68accc411cf0\",\n \"score\": \"0.51747596\",\n \"text\": \"def ElGamal_Bob(xQa, yQa, xM, yM):\\n xC1, yC1 = fastAdding(k, xG, yG)\\n xkQa, ykQa = fastAdding(k, xQa, yQa)\\n xC2, yC2 = ecPQaddition(xkQa, ykQa, xM, yM)\\n return [(xC1, yC1), (xC2, yC2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e665180984ecdb16bf5aef4664bda822\",\n \"score\": \"0.5172982\",\n \"text\": \"def calculateExteriorElementBoundaryCoefficients(self,ebqe):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1534ca07567b247c4421f8e588a36bb4\",\n \"score\": \"0.51671344\",\n \"text\": \"def test_points():\\n with get_test_data('interpolation_test_grid.npz') as fobj:\\n data = np.load(fobj)\\n return np.stack([data['xg'].reshape(-1), data['yg'].reshape(-1)], axis=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825dadc7a5d2ef1a1f6aa52306f3eb5a\",\n \"score\": \"0.5150982\",\n \"text\": \"def TwoGroupDiffusionEigenvalue(R,I,D1,D2,Sig_r1,Sig_r2, nu_Sigf1, nu_Sigf2,Sig_s12, geometry,epsilon = 1.0e-8):\\n #create the grid\\n Delta_r, centers, edges = create_grid(R,I)\\n M11 = np.zeros((I+1,I+1))\\n M21 = np.zeros((I+1,I+1))\\n M22 = np.zeros((I+1,I+1))\\n P11 = np.zeros((I+1,I+1))\\n P12 = np.zeros((I+1,I+1))\\n #define surface areas and volumes\\n assert( (geometry==0) or (geometry == 1) or (geometry == 2))\\n if (geometry == 0):\\n #in slab it's 1 everywhere except at the left edge\\n S = 0.0*edges+1\\n S[0] = 0.0 #to enforce Refl BC\\n #in slab its dr\\n V = 0.0*centers + Delta_r\\n elif (geometry == 1):\\n #in cylinder it is 2 pi r\\n S = 2.0*np.pi*edges\\n #in cylinder its pi (r^2 - r^2)\\n V = np.pi*( edges[1:(I+1)]**2 \\n - edges[0:I]**2 )\\n elif (geometry == 2):\\n #in sphere it is 4 pi r^2\\n S = 4.0*np.pi*edges**2\\n #in sphere its 4/3 pi (r^3 - r^3)\\n V = 4.0/3.0*np.pi*( edges[1:(I+1)]**3\\n - edges[0:I]**3 )\\n \\n #Set up BC at R\\n M11[I,I] = 1.0\\n M11[I,I-1] = 1.0\\n M22[I,I] = 1.0\\n M22[I,I-1] = 1.0\\n \\n #fill in rest of matrix\\n for i in range(I):\\n r = centers[i]\\n M11[i,i] = (0.5/(Delta_r * V[i])*((D1(r)+D1(r+Delta_r))*S[i+1]) +\\n Sig_r1(r))\\n M22[i,i] = (0.5/(Delta_r * V[i])*((D2(r)+D2(r+Delta_r))*S[i+1]) +\\n Sig_r2(r))\\n M21[i,i] = -Sig_s12(r)\\n P11[i,i] = nu_Sigf1(r)\\n P12[i,i] = nu_Sigf2(r)\\n if (i>0):\\n M11[i,i-1] = -0.5*(D1(r)+D1(r-Delta_r))/(Delta_r * V[i])*S[i] \\n M11[i,i] += 0.5/(Delta_r * V[i])*((D1(r)+D1(r-Delta_r))*S[i])\\n M22[i,i-1] = -0.5*(D2(r)+D2(r-Delta_r))/(Delta_r * V[i])*S[i] \\n M22[i,i] += 0.5/(Delta_r * V[i])*((D2(r)+D2(r-Delta_r))*S[i])\\n M11[i,i+1] = -0.5*(D1(r)+D1(r+Delta_r))/(Delta_r * V[i])*S[i+1]\\n M22[i,i+1] = -0.5*(D2(r)+D2(r+Delta_r))/(Delta_r * V[i])*S[i+1]\\n \\n #find eigenvalue\\n l,phi1,phi2 = inversePowerBlock(M11,M21,M22,P11,P12,epsilon)\\n k = 1.0/l\\n #remove last element of phi because it is outside the domain\\n phi1 = phi1[0:I]\\n phi2 = phi2[0:I]\\n return k, phi1, phi2, centers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f13dafd0c9b6833f89ac644930b22bf\",\n \"score\": \"0.514944\",\n \"text\": \"def quick_method(aves, sq, fde, xint=5000, altitude=7000,station_requirement=6,c0 = 3.0e8,\\n mindist = 300000):\\n center = (np.mean(aves[:,0]), np.mean(aves[:,1]), np.mean(aves[:,2]))\\n geo = GeographicSystem()\\n mapp = MapProjection\\n projl = MapProjection(projection='laea', lat_0=center[0], lon_0=center[1])\\n\\n lat, lon, alt = aves[:,:3].T\\n stations_ecef = np.array(geo.toECEF(lon, lat, alt)).T\\n\\n center_ecef = np.array(geo.toECEF(center[1],center[0],center[2]))\\n ordered_threshs = aves[:,-1]\\n\\n check = projl.fromECEF(stations_ecef[:,0],stations_ecef[:,1],stations_ecef[:,2])\\n xmin = np.min(check[0]) - mindist\\n xmax = np.max(check[0]) + mindist\\n ymin = np.min(check[1]) - mindist\\n ymax = np.max(check[1]) + mindist\\n alts = np.array([altitude])\\n initial_points = np.array(np.meshgrid(np.arange(xmin,xmax+xint,xint),\\n np.arange(ymin,ymax+xint,xint), alts))\\n\\n x,y,z=initial_points.reshape((3,int(np.size(initial_points)/3)))\\n points2 = np.array(projl.toECEF(x,y,z)).T\\n\\n xp,yp,zp = points2.T\\n lonp,latp,zp = geo.fromECEF(xp,yp,zp)\\n latp = latp.reshape(np.shape(initial_points)[1],np.shape(initial_points)[2])\\n lonp = lonp.reshape(np.shape(initial_points)[1],np.shape(initial_points)[2])\\n\\n tanp_all = []\\n for i in range(len(aves[:,0])): \\n tanp_all = tanp_all + [TangentPlaneCartesianSystem(aves[i,0],aves[i,1],aves[i,2])]\\n\\n masking2 = np.ma.empty((np.shape(stations_ecef)[0],np.shape(x)[0]))\\n dt, ran = travel_time(points2, stations_ecef, c=c0, get_r=True)\\n selection = np.sum(ran<320000,axis=0) >0\\n for i in range(len(stations_ecef[:,0])):\\n masking2[i,selection] = tanp_all[i].toLocal(points2[selection].T)[2]>0\\n \\n ran = np.ma.masked_where(masking2==0, ran)\\n ran = np.ma.masked_where(ran>=320000, ran)\\n\\n mins = min_power((10**(ordered_threshs/10.))*1e-3,ran)\\n np.ma.set_fill_value(mins, 999)\\n req_power = np.partition(mins.filled(), station_requirement-1,axis=0)[station_requirement-1]\\n\\n sde = np.zeros_like(req_power)\\n for i in range(len(sq[0])-1):\\n selects = (req_power >= sq[1,i]) & (req_power < sq[1,i+1])\\n sde[selects] = 100-sq[0,i+1]\\n \\n selects = (req_power < sq[1,0]) \\n sde[selects] = 100\\n sde = (sde.T.reshape(np.shape(initial_points[0,:,:,0])))\\n\\n fde_a = np.zeros_like(sde)\\n xs = 1000./np.arange(10,1000,1.) # Theoretical source detection efficiency that corresponds with fde\\n\\n selects = sde == 100. # Put into the next lowest or equivalent flash DE from given source DE\\n fde_a[selects] = 100.\\n for i in range(len(xs)-1):\\n selects = (sde >= xs[1+i]) & (sde < xs[i])\\n fde_a[selects] = fde[i]\\n\\n req_power = (req_power.T.reshape(np.shape(initial_points[0,:,:,0])))\\n\\n return latp,lonp,sde,fde_a,req_power\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a7818774a0cdbfefd49bffa6212037\",\n \"score\": \"0.5138041\",\n \"text\": \"def stiff_matrix(self):\\n\\n p = self.p\\n assert p >= 1, 'the polynomial-order should have p >= 1 '\\n\\n mesh = self.mesh\\n node = mesh.entity('node')\\n\\n NC = mesh.number_of_cells()\\n edge = mesh.entity('edge')\\n edge2cell = mesh.ds.edge_to_cell()\\n edgeArea = mesh.edge_length()\\n nm = mesh.edge_normal()\\n # # (NE,2). The length of the normal-vector isn't 1, is the length of corresponding edge.\\n\\n isInEdge = (edge2cell[:, 0] != edge2cell[:, 1]) # the bool vars, to get the inner edges\\n\\n qf = GaussLegendreQuadrature(p + 1) # the integral points on edges (1D)\\n bcs, ws = qf.quadpts, qf.weights # bcs.shape: (NQ,2); ws.shape: (NQ,)\\n ps = np.einsum('ij, kjm->ikm', bcs, node[edge]) # ps.shape: (NQ,NE,2), NE is the number of edges\\n\\n gphi0 = self.grad_basis(ps, index=edge2cell[:, 0])\\n # # gphi0.shape: (NQ,NInE,ldof,2), NInE is the number of interior edges, lodf is the number of local DOFs\\n # # gphi0 is the grad-value of the cell basis functions on the one-side of the corresponding edges.\\n gphi1 = self.grad_basis(ps[:, isInEdge, :], index=edge2cell[isInEdge, 1])\\n\\n # # using the divergence-theorem to get the\\n S0 = np.einsum('i, ijkm, ijpm->jpk', ws, gphi0, gphi0) # (NE,ldof,ldof)\\n b = node[edge[:, 0]] - self.cellbarycenter[edge2cell[:, 0]] # (NE,2)\\n S0 = np.einsum('ij, ij, ikm->ikm', b, nm, S0) # (NE,ldof,ldof)\\n\\n S1 = np.einsum('i, ijkm, ijpm->jpk', ws, gphi1, gphi1) # (NInE,ldof,ldof)\\n b = node[edge[isInEdge, 0]] - self.cellbarycenter[edge2cell[isInEdge, 1]] # (NInE,2)\\n S1 = np.einsum('ij, ij, ikm->ikm', b, -nm[isInEdge], S1) # (NInE,ldof,ldof)\\n\\n ldof = self.number_of_local_dofs()\\n S = np.zeros((NC, ldof, ldof), dtype=np.float)\\n np.add.at(S, edge2cell[:, 0], S0)\\n np.add.at(S, edge2cell[isInEdge, 1], S1)\\n\\n multiIndex = self.dof.multiIndex\\n q = np.sum(multiIndex, axis=1) - 1 # here, we used the grad-basis to get stiff-matrix, so we need to -1\\n qq = q + q.reshape(-1, 1) + 2\\n qq[0, 0] = 1\\n # # note that this is the special case, since we want to compute the \\\\int_T \\\\nabla u\\\\cdot \\\\nabla v,\\n # # this needs to minus 1 in the 'q', so qq[0,0] is 0, moreover, S[:, 0, :] == S[:, :, 0] is 0-values,\\n # # so we set qq[0, 0] = 1 which doesn't affect the result of S /= qq.\\n\\n S /= qq\\n\\n # --- get row and col --- #\\n row, col = self.global_dof_location(np.arange(NC), np.arange(NC))\\n\\n gdof = self.number_of_global_dofs()\\n S = csr_matrix((S.flat, (row.flat, col.flat)), shape=(gdof, gdof))\\n\\n return S\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab9615fcafef5996e89987536fc36b4\",\n \"score\": \"0.513694\",\n \"text\": \"def _m_int(self, X, u, t=0):\\n X_mat = X.reshape(-1, 2)\\n t = self.material.thickness\\n rho = self.material.rho\\n\\n self.M_small *= 0\\n\\n for xi, eta, w in self.gauss_points:\\n N = np.array([ [(-eta + 1)*(-xi + 1)*(-eta - xi - 1)/4],\\n [ (-eta + 1)*(xi + 1)*(-eta + xi - 1)/4],\\n [ (eta + 1)*(xi + 1)*(eta + xi - 1)/4],\\n [ (eta + 1)*(-xi + 1)*(eta - xi - 1)/4],\\n [ (-eta + 1)*(-xi**2 + 1)/2],\\n [ (-eta**2 + 1)*(xi + 1)/2],\\n [ (eta + 1)*(-xi**2 + 1)/2],\\n [ (-eta**2 + 1)*(-xi + 1)/2]])\\n\\n dN_dxi = np.array([\\n [-(eta - 1)*(eta + 2*xi)/4, -(2*eta + xi)*(xi - 1)/4],\\n [ (eta - 1)*(eta - 2*xi)/4, (2*eta - xi)*(xi + 1)/4],\\n [ (eta + 1)*(eta + 2*xi)/4, (2*eta + xi)*(xi + 1)/4],\\n [-(eta + 1)*(eta - 2*xi)/4, -(2*eta - xi)*(xi - 1)/4],\\n [ xi*(eta - 1), xi**2/2 - 1/2],\\n [ -eta**2/2 + 1/2, -eta*(xi + 1)],\\n [ -xi*(eta + 1), -xi**2/2 + 1/2],\\n [ eta**2/2 - 1/2, eta*(xi - 1)]])\\n dX_dxi = X_mat.T @ dN_dxi\\n det = dX_dxi[0,0]*dX_dxi[1,1] - dX_dxi[1,0]*dX_dxi[0,1]\\n self.M_small += N @ N.T * det * rho * t * w\\n\\n self.M = scatter_matrix(self.M_small, 2)\\n return self.M\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc05f238c7f453e0ca4428a0f8a6e0fa\",\n \"score\": \"0.5122469\",\n \"text\": \"def split_ecg_segments(ecg_data, start_minute=5):\\n # How many usable 2-minute interval chunks are usable (rounded down) (Basically available sample points starting from the 5th-minute mark)\\n # ISSUE: THE '300' USED IN HERE REFLECTS WHEN SAMPLING FREQUENCY IS 360 (THUS 1 SECOND) BUT DOES NOT PROPERLY REFLECT IF \\n # SAMPLING FREQUENCY IS 1024 WHERE 300 DOES NOT ANYMORE REFLECT THE 5TH MINUTE AS 1024 IS IS AROUND 2.88 SECONDS\\n usable_chunks = int(((ecg_data.shape[0] - 300) / 120))\\n # chunk size for the noised and clean segments\\n segment_size = int( (usable_chunks/2) * 120 )\\n # According to MIT-BIH, two minute intervals of noised and clean data will be generated by the NST script. This function assigns a \\n # contiguous version of those intervals in the variables below that will be returned\\n noised_seg = np.zeros( shape=(segment_size, ecg_data.shape[1]) )\\n clean_seg = np.zeros( shape=(segment_size, ecg_data.shape[1]) )\\n # counters\\n ecg_chunk_start = 60 * start_minute\\n noised_chunk_start = 0\\n clean_chunk_start = 0\\n \\n for i in range(usable_chunks):\\n if i % 2 == 0:\\n noised_seg[ noised_chunk_start:(noised_chunk_start+120) ] = ecg_data[ ecg_chunk_start:ecg_chunk_start+120 ]\\n noised_chunk_start = noised_chunk_start+120\\n else:\\n clean_seg[ clean_chunk_start:(clean_chunk_start+120) ] = ecg_data[ ecg_chunk_start:ecg_chunk_start+120 ]\\n clean_chunk_start = clean_chunk_start+120\\n ecg_chunk_start = ecg_chunk_start+120\\n\\n noised_seg = noised_seg.reshape( (noised_seg.shape[0], noised_seg.shape[1], 1) )\\n clean_seg = clean_seg.reshape( (clean_seg.shape[0], clean_seg.shape[1], 1) )\\n\\n return noised_seg, clean_seg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25ad3aef06e3c86162829164322a3d2d\",\n \"score\": \"0.509761\",\n \"text\": \"def Xcalc(self):\\n print 'Calculation of concentration ... \\\\n\\\\n';\\n self.TabX = self.TabExt[::]; #Copy doesn't work !!!!\\n self.nTot = sum(self.M);\\n for el in self.El:\\n for i in range(self.nC):\\n x = 0.0;\\n for j in range(self.nM):\\n if self.TabX[i][1 + j] == el: x += self.M[j];\\n x = x / float(self.nTot);\\n self.TabX[i][1 + self.nM + self.El.index(el)] = round(x, 3);\\n if x != 0: self.TabX[i][1 + self.nM + self.nEl] += 10 + self.El.index(el);\\n if self.ModeVerbose == 'YES': print np.array(self.TabX), '\\\\n\\\\n';\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30067b751f2f3518bfdb941760437637\",\n \"score\": \"0.5095768\",\n \"text\": \"def remesh(boxes):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35bbe4ae9de6feceed4af01d7135b6bf\",\n \"score\": \"0.5093147\",\n \"text\": \"def g_xyz_equat_sun_at_other_mean_equinox (jde, T) :\\n # we use the special matrix to transform J2000 Fk5\\n xyz = g_xyz_equat_sun_j2000(jde)\\n # once were J2000 fk5, we can apply the normal precesion matrix to precess\\n T1 = 0 # we are in J2000\\n T2 = T\\n return prec_mtx_equat(T1,T2).dot(xyz)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20396f550b205b0854850359572cfec6\",\n \"score\": \"0.50858396\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n\\n '''Basic geometric parameters'''\\n assert len(args) == 7\\n self.Cr = args[0] # Chord length at root\\n self.Cm = args[1] # Chord length at mid\\n self.Ct = args[2] # Chord length at tip\\n self.Bm = args[3] # Width of the inner part\\n self.Bt = args[4] # Half span\\n self.Am = args[5] # Mean sweep-back of the inner part\\n self.At = args[6] # Mean sweep-back at tip.\\n\\n '''Calculate pivots on each curve'''\\n front_pnt = np.zeros((3, 3))\\n tail_pnt = np.zeros((3, 3))\\n tail_pnt[0][0] = self.Cr\\n front_pnt[1][0] = self.Bm * math.tan(math.radians(self.Am))\\n front_pnt[1][2] = self.Bm\\n tail_pnt[1][0] = front_pnt[1][0] + self.Cm\\n tail_pnt[1][2] = self.Bm\\n front_pnt[2][0] = front_pnt[1][0] + (self.Bt - self.Bm) * math.tan(math.radians(self.At))\\n front_pnt[2][2] = self.Bt\\n tail_pnt[2][0] = front_pnt[2][0] + self.Ct\\n tail_pnt[2][2] = self.Bt\\n\\n '''Build interpolated functions'''\\n self.u = np.array([0, self.Bm / self.Bt, 1.0])\\n self.zw = np.array([0, self.Bm, self.Bt])\\n self.xf = make_interp_spline(self.u, front_pnt[:, 0], 3, bc_type=([(1, 0)], [(2, 0)]))\\n self.xt = make_interp_spline(self.u, tail_pnt[:, 0], 3, bc_type=([(1, 0)], [(2, 0)]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62db15950a352c97e755403898e86244\",\n \"score\": \"0.5078494\",\n \"text\": \"def __split_ligature(self, cc: np.ndarray) -> List[np.ndarray]:\\n\\n # deskew letter(s)\\n cc = self.__deskew(cc)\\n\\n # Smooth edges\\n bin_sm = sig.medfilt(cc, 5)\\n\\n # calculate euclidean distance transform\\n edt = nd.distance_transform_edt(bin_sm)\\n\\n # calculate vertical projection profile of edt\\n vpp = np.sum(edt, axis=0)\\n\\n # find valleys\\n valls = sig.argrelextrema(vpp, np.less_equal, order=int(self.stroke_width * 2.5))[0]\\n l = int(len(vpp) * 0.25)\\n r = int(len(vpp) * 0.75)\\n valls = valls[valls > l]\\n valls = valls[valls < r]\\n\\n # create mask\\n mask = np.zeros_like(vpp)\\n mask[valls] = 1\\n\\n # combine close points\\n mask = nd.binary_closing(mask)\\n val_labs, n_labs = nd.label(mask)\\n\\n split_at = []\\n\\n for n in range(1, n_labs + 1):\\n # find x coords of adjacent splits\\n region = np.argwhere(val_labs == n)\\n\\n if np.count_nonzero(region) > 1:\\n # combine adjacents split points into one point\\n new_split = int(np.mean(region))\\n split_at.append(new_split)\\n else:\\n # if there's only one split point, just return the split point\\n split_at.append(region[0])\\n\\n # split image into chars\\n chars = []\\n if len(split_at) > 0:\\n split_at = [0] + split_at + [len(vpp)]\\n for i in range(len(split_at) - 1):\\n split_l = int(split_at[i])\\n split_r = int(split_at[i + 1])\\n char = np.fliplr(bin_sm[:, split_l:split_r])\\n chars.append(char)\\n else:\\n chars = [np.fliplr(bin_sm)]\\n\\n return chars\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b75124fbe55198868177c04d63495880\",\n \"score\": \"0.5076858\",\n \"text\": \"def calc_multipole(npoles, imskb, kptrange, bdrange, eqp, newen):\\n import numpy as np\\n from multipole import fit_multipole_const #, write_f_as_sum_of_poles\\n print \\\" ### ================== ###\\\" \\n print \\\" ### Multipole fit ###\\\" \\n print \\\" Number of poles:\\\", npoles \\n omegampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,npoles))\\n ampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,npoles))\\n for ik in kptrange:\\n for ib in bdrange:\\n if eqp[ik,ib] > newen[-npoles]:\\n omegampole[ik,ib] = omegampole[ik,ib-1]\\n ampole[ik,ib] = ampole[ik,ib-1]\\n print \\\" Eqp beyond available energy range. Values from lower band are taken.\\\" \\n continue\\n else:\\n ibeff = bdrange[0] + ib - 1\\n print \\\" ik, ib\\\", ik, ib \\n # Here we take the curve starting from eqp and then we invert it\\n # so as to have it defined on the positive x axis\\n # and so that the positive direction is in the \\n # increasing direction of the array index\\n if eqp[ik,ib] <= 0:\\n en3 = newen[newen<=eqp[ik,ib]] # So as to avoid negative omegampole\\n im3 = abs(imskb[ik,ib][newen<=eqp[ik,ib]]/np.pi) # This is what should be fitted\\n else:\\n en3 = newen[newen>eqp[ik,ib]] # So as to avoid negative omegampole\\n im3 = abs(imskb[ik,ib][newen<=eqp[ik,ib]]/np.pi) # This is what should be fitted\\n if en3.size == 0:\\n print \\n print \\\" WARNING: QP energy is outside of given energy range!\\\\n\\\"+\\\\\\n \\\" This state will be skipped!\\\\n\\\"+\\\\\\n \\\"You might want to modify enmin/enmax.\\\" \\n print \\\" eqp[ik,ib], newen[-1]\\\", eqp[ik,ib] , newen[-1] \\n continue\\n en3 = en3 - eqp[ik,ib]\\n if eqp[ik,ib] <= 0:\\n en3 = -en3[::-1] \\n im3 = im3[::-1]\\n omegai, lambdai, deltai = fit_multipole_const(en3,im3,npoles)\\n # HERE WE MUST CHECK THAT THE NUMBER OF POLES \\n # IS NOT BIGGER THAN THE NUMBER OF POINTS THAT HAS TO BE FITTED\\n if npoles > omegai.size:\\n omegampole[ik,ib][:omegai.size] = omegai \\n ampole[ik,ib][:omegai.size] = np.true_divide(lambdai,(np.square(omegai)))\\n print \\n print \\\" WARNING: npoles used (\\\"+str(npoles)+\\\") is larger\\\"+\\\\\\n \\\" than x data array (\\\"+str(omegai.size)+\\\").\\\"\\n print \\\" Reduce npoles. You are wasting resources!!!\\\" \\n else:\\n omegampole[ik,ib] = omegai \\n ampole[ik,ib] = np.true_divide(lambdai,(np.square(omegai)))\\n print \\\" Integral test. Compare \\\\int\\\\Sigma and \\\\sum_j^N\\\\lambda_j.\\\" \\n print \\\" 1/pi*\\\\int\\\\Sigma =\\\", np.trapz(im3,en3) \\n print \\\" \\\\sum_j^N\\\\lambda_j =\\\", np.sum(lambdai) \\n # Writing out a_j e omega_j\\n print \\\" ### Writing out a_j and omega_j...\\\" \\n outname = \\\"a_j_np\\\"+str(npoles)+\\\".dat\\\"\\n outfile = open(outname,'w')\\n outname = \\\"omega_j_np\\\"+str(npoles)+\\\".dat\\\"\\n outfile2 = open(outname,'w')\\n for ipole in xrange(npoles):\\n for ik in range(imskb[:,0,0].size):\\n for ib in range(imskb[0,:,0].size):\\n outfile.write(\\\"%10.5f\\\" % (ampole[ik,ib,ipole]))\\n outfile2.write(\\\"%10.5f\\\" % (omegampole[ik,ib,ipole]))\\n outfile.write(\\\"\\\\n\\\")\\n outfile2.write(\\\"\\\\n\\\")\\n outfile.write(\\\"\\\\n\\\")\\n outfile2.write(\\\"\\\\n\\\")\\n outfile.close()\\n outfile2.close()\\n return omegampole, ampole\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65e39070b97a395f5449b727bd55d382\",\n \"score\": \"0.50663656\",\n \"text\": \"def gridarr_get( self ):\\n\\t return self.ma_grid\\n\\n \\tdef __add__( self, cg ):\\n \\t \\\"\\\"\\\"\\n \\t \\tAdds two grids together, returning the result as a new\\n \\t \\tgrid. For each grid cell (which contains a spectrum)\\n \\t \\ta new spectrum is created which is the sum of the \\n \\t \\tconstituent spectra.\\n \\t \\\"\\\"\\\"\\n \\t if self.ma_grid.shape != cg.ma_grid.shape: self.fatal( 'Operands' )\\n \\t C_add = C_ggrid( self.ma_grid, self.macs_grid[0, 0] )\\n \\t C_add.ma_grid = self.ma_grid + cg.ma_grid\\n \\t b_setFromArray = True\\n \\t C_add.internals_sync( b_setFromArray )\\n#\\t for row in np.arange(0, self.m_rows):\\n#\\t\\tfor col in np.arange(0, self.m_cols):\\n#\\t\\t spectrum_self = self.macs_grid[row, col]\\n#\\t\\t spectrum_cg = cg.macs_grid[row, col]\\n#\\t\\t C_add.macs_grid[row, col] = spectrum_self + spectrum_cg\\n#\\t\\t C_add.spectrumDefaults_set(row, col)\\n \\t return C_add\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8b40d6054c11ebeddb7b3e74d799ca9\",\n \"score\": \"0.506236\",\n \"text\": \"def test_em_2d_gauss_newton():\\n\\n fname = get_fnames('t1_coronal_slice')\\n nslices = 1\\n b = 0.1\\n m = 4\\n\\n image = np.load(fname)\\n moving, static = get_warped_stacked_image(image, nslices, b, m)\\n\\n # Configure the metric\\n smooth = 5.0\\n inner_iter = 20\\n q_levels = 256\\n double_gradient = False\\n iter_type = 'gauss_newton'\\n metric = metrics.EMMetric(\\n 2, smooth, inner_iter, q_levels, double_gradient, iter_type)\\n\\n # Configure and run the Optimizer\\n level_iters = [40, 20, 10]\\n optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)\\n optimizer.verbosity = VerbosityLevels.DEBUG\\n mapping = optimizer.optimize(static, moving, None)\\n m = optimizer.get_map()\\n assert_equal(mapping, m)\\n\\n warped = mapping.transform(moving)\\n starting_energy = np.sum((static - moving)**2)\\n final_energy = np.sum((static - warped)**2)\\n reduced = 1.0 - final_energy / starting_energy\\n\\n assert(reduced > 0.9)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e0c78aac585fb9fc010ce95ba338c9f\",\n \"score\": \"0.50496846\",\n \"text\": \"def generate_eg_matrix(self, binary=False):\\n\\n if not hasattr(self, 'event_map'):\\n self.event_map = self.events.reset_index(drop=False)['index']\\n\\n inv_event_map = pd.Series(self.event_map.index, index=self.event_map)\\n\\n # Make a sparse EG matrix\\n rows = []\\n cols = []\\n data = []\\n for ix, edge in self.eg_edges.iterrows():\\n rows.append(inv_event_map[edge.source])\\n cols.append(inv_event_map[edge.target])\\n data.append(edge.delta)\\n if binary:\\n data = [1 for d in data]\\n self.eg_matrix = csc_matrix((data, (rows, cols)),\\n shape=(self.M, self.M),\\n dtype=int)\\n return self.eg_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2778618620e029a5a570ba853cd322d\",\n \"score\": \"0.5044396\",\n \"text\": \"def factorize(self):\\t\\n\\t\\tdef separate_positive(m):\\n\\t\\t\\treturn (np.abs(m) + m)/2.0 \\n\\t\\t\\n\\t\\tdef separate_negative(m):\\n\\t\\t\\treturn (np.abs(m) - m)/2.0 \\n\\t\\t\\t\\n\\t\\tXtX = np.dot(self.data[:,:].T, self.data[:,:])\\n\\t\\tXtX_pos = separate_positive(XtX)\\n\\t\\tXtX_neg = separate_negative(XtX)\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t# iterate over W and H\\n\\t\\tfor i in xrange(self._niter):\\n\\t\\t\\t# update H\\n\\t\\t\\tH_x_WT = np.dot(self.H.T, self.G.T)\\n\\t\\t\\t\\t\\n\\t\\t\\tXtX_neg_x_W = np.dot(XtX_neg, self.G)\\n\\t\\t\\tXtX_pos_x_W = np.dot(XtX_pos, self.G)\\n\\t\\t\\n\\t\\t\\tha = XtX_pos_x_W + np.dot(H_x_WT, XtX_neg_x_W)\\n\\t\\t\\thb = XtX_neg_x_W + np.dot(H_x_WT, XtX_pos_x_W) + 10**-9\\n\\t\\t\\tself.H = (self.H.T*np.sqrt(ha/hb)).T\\n\\t\\t\\n\\t\\t\\t# update W\\t\\t\\t\\n\\t\\t\\tif self._compW:\\n\\t\\t\\t\\tHT_x_H = np.dot(self.H, self.H.T)\\n\\t\\t\\t\\twa = np.dot(XtX_pos, self.H.T) + np.dot(XtX_neg_x_W, HT_x_H)\\n\\t\\t\\t\\twb = np.dot(XtX_neg, self.H.T) + np.dot(XtX_pos_x_W, HT_x_H) + 10**-9\\n\\t\\t\\t\\n\\t\\t\\t\\tself.G *= np.sqrt(wa/wb)\\n\\t\\t\\t\\tself.W = np.dot(self.data[:,:], self.G)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\tself.ferr[i] = self.frobenius_norm()\\n\\n\\t\\t\\tself._print_cur_status('iteration ' + str(i+1) + '/' + str(self._niter) + ' Fro:' + str(self.ferr[i]))\\n\\n\\t\\t\\tif i > 1:\\n\\t\\t\\t\\tif self.converged(i):\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tself.ferr = self.ferr[:i]\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tbreak\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61a234ea1210f5d0ec74db4c67c3e748\",\n \"score\": \"0.5043434\",\n \"text\": \"def _calculate_interpolation_matrix(self, X):\\n G = np.zeros((len(X), self.hidden_shape))\\n for data_point_arg, data_point in enumerate(X):\\n for center_arg, center in enumerate(self.centers):\\n G[data_point_arg, center_arg] = self._kernel_function(center, data_point)\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1055f639ee65bf6c8dfea2be1a17e830\",\n \"score\": \"0.5042965\",\n \"text\": \"def __interpolateSpline__(self):\\n # Get x and y values for all supplied points\\n x = self.source[:, 0]\\n y = self.source[:, 1]\\n mdim = len(x)\\n h = [x[i+1] - x[i] for i in range(0, mdim - 1)]\\n # Initialize de matrix\\n Ab = np.zeros((3, mdim))\\n # Construct the Ab banded matrix and B vector\\n Ab[1, 0] = 1 # A[0, 0] = 1\\n B = [0]\\n for i in range(1, mdim-1):\\n Ab[2, i - 1] = h[i - 1] # A[i, i - 1] = h[i - 1]\\n Ab[1, i] = 2*(h[i] + h[i - 1]) # A[i, i] = 2*(h[i] + h[i - 1])\\n Ab[0, i + 1] = h[i] # A[i, i + 1] = h[i]\\n B.append(3*((y[i + 1] - y[i])/(h[i]) - (y[i] - y[i - 1])/(h[i-1])))\\n Ab[1, mdim - 1] = 1 # A[-1, -1] = 1\\n B.append(0)\\n # Solve the system for c coefficients\\n c = linalg.solve_banded((1, 1), Ab, B, True, True)\\n # Calculate other coefficients\\n b = [((y[i+1] - y[i])/h[i] - h[i]*(2*c[i]+c[i+1])/3)\\n for i in range(0, mdim-1)]\\n d = [(c[i+1] - c[i])/(3*h[i]) for i in range(0, mdim - 1)]\\n # Store coefficients\\n self.__splineCoefficients__ = np.array([y[0:-1], b, c[0:-1], d])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a7d0036cd951bdea8ba85d38403b61d\",\n \"score\": \"0.503697\",\n \"text\": \"def collapse(self, e):\\n vnew = e.vnew\\n\\n p2 = e.v1.p; p3 = e.v2.p\\n q1 = zeros(4); q2 = e.v1.q; q3 = e.v2.q; q4 = zeros(4)\\n S1 = zeros([2,2]); S2 = e.v1.S; S3 = e.v2.S; S4 = zeros([2,2])\\n\\n a = zeros(4)\\n if e.v1.e1 is not None:\\n p1 = e.v1.e1.v1.p\\n q1 = e.v1.e1.v1.q\\n S1 = e.v1.e1.v1.S\\n a1 = barycentricWeights(p1,p2,p3,vnew.p)\\n a[:3] = a[:3] + a1\\n vnew.e1 = e.v1.e1\\n vnew.e1.v2 = vnew\\n vnew.e1.dirty = True\\n if e.v2.e2 is not None:\\n p4 = e.v2.e2.v2.p\\n q4 = e.v2.e2.v2.q\\n S4 = e.v2.e2.v2.S\\n a2 = barycentricWeights(p2,p3,p4,vnew.p)\\n a[1:] = a[1:] + a2\\n vnew.e2 = e.v2.e2\\n vnew.e2.v1 = vnew\\n vnew.e2.dirty = True\\n q = a[0]*q1 + a[1]*q2 + a[2]*q3 + a[3]*q3\\n S = a[0]*S1 + a[1]*S2 + a[2]*S3 + a[3]*S3\\n if a[0]!=0 and a[-1]!=0:\\n q = 0.5*q\\n S = 0.5*S\\n\\n if S[0,0] < 0 or S[1,1] < 0:\\n print \\\"Trace:\\\",S.trace()\\n print \\\"Sum a1:\\\",a1.sum()\\n print \\\"Sum a2:\\\",a1.sum()\\n print \\\"a1:\\\",a1.T\\n print \\\"a2:\\\",a2.T\\n print \\\"p1:\\\",p1.T\\n print \\\"p2:\\\",p2.T\\n print \\\"p3:\\\",p3.T\\n print \\\"p4:\\\",p4.T\\n print \\\"p5:\\\",vnew.p.T\\n print \\\"l1:\\\",linalg.norm(p1-p2)\\n print \\\"l2:\\\",linalg.norm(p2-p3)\\n print \\\"l3:\\\",linalg.norm(p3-p4)\\n print \\\"S1:\\\\n\\\",S1\\n print \\\"S2:\\\\n\\\",S2\\n print \\\"S3:\\\\n\\\",S3\\n print \\\"S4:\\\\n\\\",S4\\n print \\\"S:\\\\n\\\",S\\n\\n vnew.q = q/linalg.norm(q)\\n vnew.S = S\\n\\n\\n #print \\\"\\\\nV: \\\",len(self.V)\\n #for vi in self.V: print vi\\n #print \\\"\\\\nE: \\\",len(self.E)\\n #for ei in self.E: print ei\\n print \\\"insert: \\\",vnew\\n print \\\"delete: \\\",e\\n self.V.append(vnew)\\n self.V.remove(e.v1)\\n self.V.remove(e.v2)\\n self.E.remove(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"508f2d8821ec0ccd8dc647af87429c86\",\n \"score\": \"0.50351197\",\n \"text\": \"def ecg_segment(self):\\n\\n \\n import numpy as np\\n from scipy.signal import resample\\n from .misc import epochs_create, epochs_to_df\\n\\n\\n def _ecg_segment_window( heart_rate=None, rpeaks=None, sampling_rate=1000, desired_length=None):\\n\\n # Extract heart rate\\n if heart_rate is not None:\\n heart_rate = np.mean(heart_rate)\\n if rpeaks is not None:\\n self.rate = self.signal_rate(self.rpeaks, desired_length=desired_length)\\n heart_rate = np.mean(self.rate)\\n\\n # Modulator\\n m = heart_rate / 60\\n\\n # Window\\n epochs_start = -0.35 / m\\n epochs_end = 0.5 / m\\n\\n # Adjust for high heart rates\\n if heart_rate >= 80:\\n c = 0.1\\n epochs_start = epochs_start - c\\n epochs_end = epochs_end + c\\n\\n return epochs_start, epochs_end\\n\\n epochs_start, epochs_end = _ecg_segment_window(rpeaks=self.rpeaks, sampling_rate=self.fs, desired_length=self.sig_len)\\n\\n heartbeats = epochs_create(self.data, self.rpeaks, sampling_rate=self.fs, epochs_start=epochs_start, epochs_end=epochs_end )\\n\\n\\n heartbeats_plot = epochs_to_df(heartbeats)\\n heartbeats_pivoted = heartbeats_plot.pivot(index=\\\"Time\\\", columns=\\\"Label\\\", values=\\\"Signal\\\")\\n\\n beats=[]\\n for i in range(heartbeats_pivoted.shape[1]):\\n x = heartbeats_pivoted[heartbeats_pivoted.columns[i]]\\n beats.append(resample(x, 100, t=None, axis=0, window=None, domain='time'))\\n \\n beats = np.array(beats)\\n\\n self.ecg_templates = beats\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a6cd653160ac56a43f4630a743969c\",\n \"score\": \"0.5027593\",\n \"text\": \"def compute_disagg_matrix(job_ctxt, site, poe, result_dir):\\n lat_bin_lims = job_ctxt[job_cfg.LAT_BIN_LIMITS]\\n lon_bin_lims = job_ctxt[job_cfg.LON_BIN_LIMITS]\\n mag_bin_lims = job_ctxt[job_cfg.MAG_BIN_LIMITS]\\n eps_bin_lims = job_ctxt[job_cfg.EPS_BIN_LIMITS]\\n\\n jd = list_to_jdouble_array\\n\\n disagg_calc = java.jclass('DisaggregationCalculator')(\\n jd(lat_bin_lims), jd(lon_bin_lims),\\n jd(mag_bin_lims), jd(eps_bin_lims))\\n\\n cache = java.jclass('KVS')(\\n config.get('kvs', 'host'),\\n int(config.get('kvs', 'port')))\\n\\n erf = general.generate_erf(job_ctxt.job_id, cache)\\n gmpe_map = general.generate_gmpe_map(job_ctxt.job_id, cache)\\n general.set_gmpe_params(gmpe_map, job_ctxt.params)\\n\\n imls = general.get_iml_list(job_ctxt['INTENSITY_MEASURE_LEVELS'],\\n job_ctxt['INTENSITY_MEASURE_TYPE'])\\n\\n site_model = general.get_site_model(job_ctxt.oq_job.id)\\n\\n if site_model is not None:\\n sm_data = general.get_closest_site_model_data(site_model, site)\\n vs30_type = sm_data.vs30_type.capitalize()\\n vs30 = sm_data.vs30\\n z1pt0 = sm_data.z1pt0\\n z2pt5 = sm_data.z2pt5\\n else:\\n jp = job_ctxt.oq_job_profile\\n\\n vs30_type = jp.vs30_type.capitalize()\\n vs30 = jp.reference_vs30_value\\n z1pt0 = jp.depth_to_1pt_0km_per_sec\\n z2pt5 = jp.reference_depth_to_2pt5km_per_sec_param\\n\\n matrix_result = _compute_matrix(\\n disagg_calc, site.latitude, site.longitude, erf, gmpe_map, poe, imls,\\n vs30_type, vs30, z1pt0, z2pt5)\\n\\n matrix_path = save_5d_matrix_to_h5(result_dir,\\n numpy.array(matrix_result.getMatrix()))\\n\\n return (matrix_result.getGMV(), matrix_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b2a71d26eef485527d479270542a9a8\",\n \"score\": \"0.5024965\",\n \"text\": \"def compute_edges_dxdy(I): #part4\\n I = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)\\n I = I.astype(np.float32)/255.\\n I = cv2.GaussianBlur(I, (7,7),2)\\n dx = signal.convolve2d(I,np.array([[-1,0, 1],[-4,0,4],[-1,0,1]]),mode='same', boundary = 'symm')\\n dy = signal.convolve2d(I,np.array([[-1,0, 1],[-4,0,4],[-1,0,1]]).T,mode='same', boundary = 'symm')\\n \\n\\n I = np.hypot(dx, dy) \\n I = I / I.max() * 255\\n \\n angle = np.arctan2(dy, dx)\\n \\n Z = np.zeros((I.shape))\\n\\n angle = angle * 180. / np.pi\\n angle[angle < 0] += 180\\n\\n angle_mod = angle % 180\\n\\n for i in range(I.shape[0] - 1):\\n for j in range(I.shape[1] - 1):\\n q = 0\\n r = 0\\n \\n if (0 <= angle_mod[i,j] < 45):\\n alpha = np.abs(np.tan(angle_mod[i,j]))\\n q = (alpha * I[i + 1, j + 1]) + ((1 - alpha) * I[i, j + 1])\\n r = (alpha * I[i - 1, j - 1]) + ((1 - alpha) * I[i, j - 1]) \\n \\n elif (45 <= angle_mod[i,j] < 90):\\n alpha = np.abs(1./np.tan(angle_mod[i,j]))\\n q = (alpha * I[i + 1, j + 1]) + ((1 - alpha) * I[i + 1, j])\\n r = (alpha * I[i - 1, j - 1]) + ((1 - alpha) * I[i - 1, j])\\n \\n elif (90 <= angle_mod[i,j] < 135):\\n alpha = np.abs(1./np.tan(angle_mod[i,j]))\\n q = (alpha * I[i + 1, j - 1]) + ((1 - alpha) * I[i + 1, j])\\n r = (alpha * I[i - 1, j + 1]) + ((1 - alpha) * I[i - 1, j])\\n \\n elif (135 <= angle_mod[i,j] < 180):\\n alpha = np.abs(np.tan(angle_mod[i,j]))\\n q = (alpha * I[i + 1, j - 1]) + ((1 - alpha) * I[i, j - 1])\\n r = (alpha * I[i - 1, j + 1]) + ((1 - alpha) * I[i, j + 1])\\n\\n if (I[i,j] >= q) and (I[i,j] >= r):\\n Z[i,j] = I[i,j]\\n else:\\n Z[i,j] = 0\\n \\n #normalize it\\n mag = ((Z - np.min(Z)) / np.max(Z - np.min(Z))) * 255.0\\n mag = np.clip(mag, 0, 255)\\n mag = mag.astype(np.uint8)\\n\\n return mag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ef202c8f08786252bdb52510d48a1af\",\n \"score\": \"0.5016774\",\n \"text\": \"def seird(\\n x, t, a1, a2, a3, a4\\n ): # pylint: disable=unused-argument, too-many-arguments\\n return np.array(\\n [\\n -a1 * x[0] * x[2] / population,\\n a1 * x[0] * x[2] / population - a2 * x[1],\\n a2 * x[1] - a3 * x[2] - a4 * x[2],\\n a3 * x[2],\\n a4 * x[2],\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fddce028219a070efcb4b65323b03c32\",\n \"score\": \"0.50156116\",\n \"text\": \"def _coefficients(self):\\n # Grid indices corresponding to the corners of the cell ie x1, y1, z1\\n indices1 = tuple(sympy.symbols('%s1' % d) for d in self.grid.dimensions)\\n indices2 = tuple(sympy.symbols('%s2' % d) for d in self.grid.dimensions)\\n # 1, x1, y1, z1, x1*y1, ...\\n indices = list(powerset(indices1))\\n indices[0] = (1,)\\n point_sym = list(powerset(self._point_symbols))\\n point_sym[0] = (1,)\\n # 1, px. py, pz, px*py, ...\\n A = []\\n ref_A = [np.prod(ind) for ind in indices]\\n # Create the matrix with the same increment order as the point increment\\n for i in self._point_increments:\\n # substitute x1 by x2 if increment in that dimension\\n subs = dict((indices1[d], indices2[d] if i[d] == 1 else indices1[d])\\n for d in range(len(i)))\\n A += [[1] + [a.subs(subs) for a in ref_A[1:]]]\\n\\n A = sympy.Matrix(A)\\n # Coordinate values of the sparse point\\n p = sympy.Matrix([[np.prod(ind)] for ind in point_sym])\\n\\n # reference cell x1:0, x2:h_x\\n left = dict((a, 0) for a in indices1)\\n right = dict((b, dim.spacing) for b, dim in zip(indices2, self.grid.dimensions))\\n reference_cell = {**left, **right}\\n # Substitute in interpolation matrix\\n A = A.subs(reference_cell)\\n return A.inv().T * p\",\n \"title\": \"\"\n },\n {\n \"docid\": \"371f10e7a498ea2bae052537bc57ef6a\",\n \"score\": \"0.501462\",\n \"text\": \"def find_contour(x):\\n global intersect_point_x1\\n global intersect_point_x2\\n print('X is : ', x)\\n origin_energy = kr_model.predict_single(x) # The energy level at saddle point\\n #origin_energy = -6562\\n print('Origin energy is : ', origin_energy)\\n egienvalue = np.linalg.eig(H(x))[0] \\n egienvector = np.linalg.eig(H(x))[1][0] if egienvalue[0] < 0 else np.linalg.eig(H(x))[1][1] # the egienvector we want corresponding to negtive egienvalue\\n x1, x2 = x + 0.5*egienvector, x - 0.5*egienvector # x1, x2 are points deviate a little from the origin saddle point. We want to draw contour of basin from x1 and x2.\\n \\n intersect_point_x1, intersect_point_x2 = x1, x2 # when we start from x1 and follow some trace to construct contour, it should overlap with the starting point when finishing the contour.\\n inter_x1_energy, inter_x2_energy = kr_model.predict_single(intersec_point_x1), kr_model.predict_single(intersec_point_x2)\\n slt = 0.1\\n i = 0\\n #trace_x1, trace_x2 = [], []\\n \\\"\\\"\\\"\\n while(not (kr_model.predict_single(x1) - origin_energy) < 1): \\n #gradient desending\\n if kr_model.predict_single(x1) > origin_energy:\\n x1 -= slt * normalize(G(x1).reshape(1,-1))[0]\\n else:\\n x1 += slt * normalize(G(x1).reshape(1,-1))[0]\\n print('Now you should be closer to origin energy : ', kr_model.predict_single(x1))\\n \\\"\\\"\\\"\\n while(i < 1500): # you can modify the value of i.\\n i += 1\\n tagent = np.array([G(x1)[1], -G(x1)[0]]) #tagent is perpendicular/orthonal to gradient\\n x1 = x1 + slt * normalize(tagent.reshape(1,-1))[0] # follow the tagent to construct contour\\n while(not (kr_model.predict_single(x1) - inter_x1_energy) < 0.5): # If it deviates from contour\\n #gradient desending to return to desired energy level\\n if kr_model.predict_single(x1) > inter_x1_energy:\\n x1 -= slt * normalize(G(x1).reshape(1,-1))[0]\\n else:\\n x1 += slt * normalize(G(x1).reshape(1,-1))[0]\\n #print('Now you should be closer to origin energy : ', kr_model.predict_single(x1))\\n trace_x1.append(x1)\\n #print('Cooool! Add a point in contour : ', x1)\\n if math.hypot(x1[0] - intersect_point_x1[0],x1[1] - intersect_point_x1[1]) < 0.1: # If we are close enought to the starting point, we assume we finish constructing contour\\n if i > 10: # do not break at first.\\n break\\n \\\"\\\"\\\"\\n #This is for the other contour line\\n while(not (kr_model.predict_single(x2) - origin_energy) < 1): \\n #gradient desending\\n if kr_model.predict_single(x2) > origin_energy:\\n x2 -= slt * normalize(G(x2).reshape(1,-1))[0]\\n else:\\n x2 += slt * normalize(G(x2).reshape(1,-1))[0]\\n print('Now you should be closer to origin energy : ', kr_model.predict_single(x2))\\n \\\"\\\"\\\"\\n i = 0\\n while(i < 1500): # might go back to the first point!!\\n i += 1\\n tagent = np.array([G(x2)[1], -G(x2)[0]]) #orthonal to gradient\\n x2 = x2 + slt * normalize(tagent.reshape(1,-1))[0]\\n while(not (kr_model.predict_single(x2) - inter_x2_energy) < 0.5):\\n #gradient desending\\n if kr_model.predict_single(x2) > inter_x2_energy:\\n x2 -= slt * normalize(G(x2).reshape(1,-1))[0]\\n else:\\n x2 += slt * normalize(G(x2).reshape(1,-1))[0]\\n #print('Now you should be closer to origin energy : ', kr_model.predict_single(x2))\\n trace_x2.append(x2)\\n #print('Cooool! Add a point in contour : ', x2)\\n if math.hypot(x2[0] - intersect_point_x2[0],x2[1] - intersect_point_x2[1]) < 0.1:\\n if i > 10:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4674dbb623e5507f0af63f6be0701814\",\n \"score\": \"0.501428\",\n \"text\": \"def q2ba():\\r\\n \\r\\n # Nested list to store the final results as [[J(0,0), ... J(0,20)], \\r\\n # [J(1,0), ... J(1,20)], [J(2,0), ... J(2,20)]] produced by J(m,x)\\r\\n bessel_arr = [[], [], []] \\r\\n \\r\\n # Nested list to store the final results in the same format as bassel_arr\\r\\n # produced by scipy.special.jv\\r\\n jv_arr = [[], [], []]\\r\\n \\r\\n for m in range(3): # for J_0, J_1, J_2\\r\\n for x in range(21): # for x = 0, 1, ..., 20\\r\\n # evaluate Bessel function using function J code above\\r\\n bessel_arr[m].append(J(m,x))\\r\\n \\r\\n # evlauate function using scipy.special.jv function\\r\\n jv_arr[m].append(jv(m,x))\\r\\n \\r\\n \\r\\n # x array from 0 to 20 to be used for the plot\\r\\n x_arr = np.arange(21) \\r\\n \\r\\n # Plot Bessel function for m = 0,1,2 and x = 0,1,...,20 in the same plot\\r\\n # evaluated using J(m,x) function coded above\\r\\n plt.figure()\\r\\n plt.plot(x_arr, bessel_arr[0], label=\\\"J_0\\\") # Plot J_0 against x\\r\\n plt.plot(x_arr, bessel_arr[1], label=\\\"J_1\\\") # Plot J_1 against x\\r\\n plt.plot(x_arr, bessel_arr[2], label=\\\"J_2\\\") # Plot J_2 against x\\r\\n plt.xlabel(\\\"x\\\")\\r\\n plt.ylabel(\\\"J_m(x)\\\")\\r\\n plt.title(\\\"Bessel functions as a function of x using Simpson's rule\\\")\\r\\n plt.legend()\\r\\n plt.savefig(\\\"q2b_simp.png\\\")\\r\\n \\r\\n # Plot Bessel function for m = 0,1,2 and x = 0,1,...,20 in the same plot\\r\\n # evaluated using scipy.special.jv function\\r\\n plt.figure()\\r\\n plt.plot(x_arr, jv_arr[0], label=\\\"J_0\\\") # Plot J_0 against x\\r\\n plt.plot(x_arr, jv_arr[1], label=\\\"J_1\\\") # Plot J_1 against x\\r\\n plt.plot(x_arr, jv_arr[2], label=\\\"J_2\\\") # Plot J_2 against x\\r\\n plt.xlabel(\\\"x\\\")\\r\\n plt.ylabel(\\\"scipy.special.jv(x)\\\")\\r\\n plt.title(\\\"Bessel functions as a function of x using scipy.special.jv\\\")\\r\\n plt.legend()\\r\\n plt.savefig(\\\"q2b_jv.png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea6c832e69614c338b0019d67eef870\",\n \"score\": \"0.50096685\",\n \"text\": \"def e_erp(t0,t1,g):\\n\\n n0 = len(t0)\\n n1 = len(t1)\\n C=np.zeros((n0+1,n1+1))\\n\\n t0_float = []\\n t1_float = []\\n\\n k = 0\\n while(k < n0):\\n pos1 = t0[k]\\n #pos2 = t1[k]\\n x1, y1 = pos1.split(\\\",\\\")\\n #x2, y2 = pos2.split(\\\",\\\")\\n coord1 = [(float(x1), float(y1))]\\n #coord2 = [(float(x2), float(y2))]\\n t0_float.append(coord1)\\n #t1_float.append(coord2)\\n k = k + 1\\n\\n k = 0\\n while(k < n1):\\n #pos1 = t0[k]\\n pos2 = t1[k]\\n #x1, y1 = pos1.split(\\\",\\\")\\n x2, y2 = pos2.split(\\\",\\\")\\n #coord1 = [(float(x1), float(y1))]\\n coord2 = [(float(x2), float(y2))]\\n #t0_float.append(coord1)\\n t1_float.append(coord2)\\n k = k + 1\\n \\n #print(str(len(t0)) + \\\", \\\" + str(len(t1)))\\n\\n C[1:,0]=sum(map(lambda x : abs(euc_dist(g,x)),t0_float))\\n C[0,1:]=sum(map(lambda y : abs(euc_dist(g,y)),t1_float))\\n for i in np.arange(n0)+1:\\n for j in np.arange(n1)+1:\\n derp0 = C[i-1,j] + euc_dist(t0_float[i-1],g)\\n derp1 = C[i,j-1] + euc_dist(g,t1_float[j-1])\\n derp01 = C[i-1,j-1] + euc_dist(t0_float[i-1],t1_float[j-1])\\n C[i,j] = min(derp0,derp1,derp01)\\n erp = C[n0,n1]\\n return erp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d3d3f71cd416e6dbef731d7b0c47ab\",\n \"score\": \"0.50093824\",\n \"text\": \"def gsmear(xd,yd,sigma,ngwidth=3):\\n dx= xd[1]-xd[0]\\n sgm= sigma*dx\\n pref= dx/(np.sqrt(2.0*np.pi)*sgm)\\n ndat= len(xd)\\n nwidth = int(sigma*ngwidth)\\n #nwidth = ndat\\n gdat= np.zeros(ndat,dtype=float)\\n expfact = 1.0/sgm**2/2\\n for ix in range(ndat):\\n #gdat[ix]= yd[ix]\\n for jx in range(-nwidth+1,nwidth):\\n kx= ix+jx\\n if kx < 0:\\n kx = abs(kx)\\n elif kx >= ndat:\\n kx = 2*ndat -kx -1\\n gdat[ix] += yd[kx]*pref*np.exp(-(dx*jx)**2*expfact)\\n #gdat /= 2\\n return gdat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dbb8f0c996cc1346e99bcb387046ebb\",\n \"score\": \"0.5004536\",\n \"text\": \"def multilateration( ar\\n , br, bc_x\\n , cr, cc_x, cc_y\\n , dr, dc_x, dc_y, dc_z\\n , e = None, x = None, y = None, z = None\\n , e_solution_num = 1\\n ):\\n\\t# These formuae were worked out in Maxima\\n\\tx_p1 = (br-ar)/bc_x\\n\\tx_p0 = -(br*br-bc_x*bc_x-ar*ar)/(2.0*bc_x)\\n\\ty_p1 = -(cc_x*x_p1-cr+ar)/cc_y\\n\\ty_p0 = -(2.0*cc_x*x_p0+cr*cr-cc_y*cc_y-cc_x*cc_x-ar*ar)/(2.0*cc_y)\\n\\tz_p1 = -(dc_y*y_p1+dc_x*x_p1-dr+ar)/dc_z\\n\\tz_p0 = -(2.0*dc_y*y_p0+2.0*dc_x*x_p0+dr*dr-dc_z*dc_z-dc_y*dc_y-dc_x*dc_x-ar*ar) \\\\\\n\\t /(2.0*dc_z)\\n\\t\\n\\tif e is None:\\n\\t\\tif e_solution_num == 1:\\n\\t\\t\\te = -(sqrt((-y_p0*y_p0-x_p0*x_p0+ar*ar)*z_p1*z_p1 \\\\\\n\\t\\t\\t +(2.0*y_p0*y_p1+2.0*x_p0*x_p1+2.0*ar)*z_p0*z_p1 \\\\\\n\\t\\t\\t +(-y_p1*y_p1-x_p1*x_p1+1.0)*z_p0*z_p0+(ar*ar-x_p0*x_p0)*y_p1*y_p1 \\\\\\n\\t\\t\\t +(2.0*x_p0*x_p1+2.0*ar)*y_p0*y_p1+(1.0-x_p1*x_p1)*y_p0*y_p0 \\\\\\n\\t\\t\\t +ar*ar*x_p1*x_p1+2.0*ar*x_p0*x_p1+x_p0*x_p0) \\\\\\n\\t\\t\\t +z_p0*z_p1+y_p0*y_p1+x_p0*x_p1+ar) \\\\\\n\\t\\t\\t /(z_p1*z_p1+y_p1*y_p1+x_p1*x_p1-1.0)\\n\\t\\telif e_solution_num == 2:\\n\\t\\t\\te = (sqrt((-y_p0*y_p0-x_p0*x_p0+ar*ar)*z_p1*z_p1 \\\\\\n\\t\\t\\t +(2.0*y_p0*y_p1+2.0*x_p0*x_p1+2.0*ar)*z_p0*z_p1 \\\\\\n\\t\\t\\t +(-y_p1*y_p1-x_p1*x_p1+1.0)*z_p0*z_p0+(ar*ar-x_p0*x_p0)*y_p1*y_p1 \\\\\\n\\t\\t\\t +(2.0*x_p0*x_p1+2.0*ar)*y_p0*y_p1+(1.0-x_p1*x_p1)*y_p0*y_p0+ar*ar*x_p1*x_p1 \\\\\\n\\t\\t\\t +2.0*ar*x_p0*x_p1+x_p0*x_p0) \\\\\\n\\t\\t\\t -z_p0*z_p1-y_p0*y_p1-x_p0*x_p1-ar) \\\\\\n\\t\\t\\t /(z_p1*z_p1+y_p1*y_p1+x_p1*x_p1-1.0)\\n\\tif x is None:\\n\\t\\tx = e*x_p1+x_p0\\n\\tif y is None:\\n\\t\\ty = e*y_p1+y_p0\\n\\tif z is None:\\n\\t\\tz = e*z_p1+z_p0\\n\\t\\n\\treturn (e, x, y, z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f99579d846b40836d79a37cc12ca6f7\",\n \"score\": \"0.5002701\",\n \"text\": \"def monteCarlo( f_xy, g_xy, xmin, xmax, ymin, ymax, n):\\n # create n random points in x and y\\n a_xran = np.random.uniform( xmin, xmax, n)\\n a_yran = np.random.uniform( ymin, ymax, n)\\n ########### solve using for loop: A_om, f_mean###########\\n f_fct_mean = 0\\n num_inside = 0 # number of points with x,y; g(x,y) >= 0\\n for i in range( n): # x loop\\n for j in range( n): # y loop\\n if g_xy( a_xran[i], a_yran[j]) >= 0:\\n num_inside += 1\\n f_fct_mean += f_xy( a_xran[i], a_yran[j])\\n if (num_inside != 0):\\n f_fct_mean /= num_inside\\n # ############ vectorized version ###########\\n # m_xran, m_yran = np.meshgrid( a_xran, a_yran)\\n # sel = g_xy( m_xran, m_yran) >= 0\\n # num_inside = sel.sum()\\n # print( f_fct_mean)\\n # f_fct_mean = np.mean( f_xy( m_xran[sel], m_yran[sel]))\\n # print( f_fct_mean)\\n\\n # last two lines are the same for loop and vectorized solutions:\\n # area of domain is approximate by q*Ar, where q is fraction of points in domain, and Ar is area of rectangle\\n f_Aom = num_inside/float(n**2) * (xmax-xmin)*(ymax-ymin)\\n return f_Aom*f_fct_mean\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55412bfc636935d13ae20330a241745e\",\n \"score\": \"0.4998856\",\n \"text\": \"def edge_integral(self,direction,boundary,patch,flux,test=False):\\r\\n if direction==0:\\r\\n if boundary==0:\\r\\n global_indeces=self.patch_data.patch_global_2D(patch)[0,:]\\r\\n local_indeces=self.patch_data.local_IEN(patch,1)[0,:]\\r\\n elif boundary==1:\\r\\n global_indeces=self.patch_data.patch_global_2D(patch)[-1,:]\\r\\n local_indeces=self.patch_data.local_IEN(patch,1)[-1,:]\\r\\n\\r\\n else:\\r\\n if boundary==0:\\r\\n global_indeces=self.patch_data.patch_global_2D(patch)[:,0]\\r\\n local_indeces=self.patch_data.local_IEN(patch,1)[:,0]\\r\\n elif boundary==1:\\r\\n global_indeces=self.patch_data.patch_global_2D(patch)[:,-1]\\r\\n local_indeces=self.patch_data.local_IEN(patch,1)[:,-1]\\r\\n \\r\\n# print(global_indeces,local_indeces)\\r\\n \\r\\n pts,wts=np.polynomial.legendre.leggauss(np.max(self.order)+1) \\r\\n# print(pts)\\r\\n \\r\\n for ele_num in range(self.number_elements[patch,direction]):\\r\\n if direction==0 and boundary==0:\\r\\n ele=ele_num\\r\\n elif direction==0 and boundary==1:\\r\\n max_ele=np.prod(self.number_elements[patch,:])-1\\r\\n ele=max_ele-self.number_elements[patch,direction]+ele_num+1\\r\\n elif direction==1 and boundary==0:\\r\\n ele=(ele_num)* self.number_elements[patch,0] \\r\\n elif direction==1 and boundary==1:\\r\\n ele=(ele_num+1)* self.number_elements[patch,0]-1 \\r\\n \\r\\n# for int_pt in range(len(pts)):\\r\\n# \\r\\n## print(ele,self.number_elements[patch,direction])\\r\\n# \\r\\n# if direction==0:\\r\\n# if boundary==0:\\r\\n# xi,eta=self.parent_to_para(ele,pts[int_pt],-1,patch)\\r\\n# elif boundary==1:\\r\\n# xi,eta=self.parent_to_para(ele,pts[int_pt],1,patch)\\r\\n# elif direction==1:\\r\\n# if boundary==0:\\r\\n# xi,eta=self.parent_to_para(ele,-1,pts[int_pt],patch)\\r\\n# elif boundary==1:\\r\\n# xi,eta=self.parent_to_para(ele,1,pts[int_pt],patch)\\r\\n \\r\\n \\r\\n# print(ele,self.number_elements[patch,direction])\\r\\n filler=np.ones(len(pts))*1\\r\\n if direction==0:\\r\\n if boundary==0:\\r\\n xi,eta=self.parent_to_para(ele,pts,-filler,patch)\\r\\n elif boundary==1:\\r\\n xi,eta=self.parent_to_para(ele,pts,filler,patch)\\r\\n elif direction==1:\\r\\n if boundary==0:\\r\\n xi,eta=self.parent_to_para(ele,-filler,pts,patch)\\r\\n elif boundary==1:\\r\\n xi,eta=self.parent_to_para(ele,filler,pts,patch)\\r\\n \\\"\\\"\\\"\\r\\n Finding Jacobians\\r\\n \\\"\\\"\\\"\\r\\n R,dR_edge=self.NURBS_bases(xi,eta,patch,'all')\\r\\n \\r\\n edge_control_points=self.B[global_indeces]\\r\\n \\r\\n dR_edge=dR_edge[local_indeces,direction,:]\\r\\n R=R[:,local_indeces]\\r\\n \\r\\n# xpts=self.B[global_indeces,0]\\r\\n# fflux=lambda x: .1*np.sin(np.pi*x/800)\\r\\n fflux=lambda x: flux\\r\\n# flux=fflux(xpts)\\r\\n xi_locs,eta_locs=self.parent_to_para(ele_num,pts,np.ones(len(pts)),0)\\r\\n x_locs=xi_locs*800\\r\\n for lvl in range(len(pts)):\\r\\n dx_d=np.sum(dR_edge[:,lvl]*edge_control_points[:,0])\\r\\n dy_d=np.sum(dR_edge[:,lvl]*edge_control_points[:,1])\\r\\n \\r\\n detJ=np.sqrt(dx_d**2+dy_d**2)*(1./self.number_elements[patch,direction]/2.)\\r\\n # print(detJ)\\r\\n # if ele_num==3:\\r\\n # print(integrals,R)\\r\\n self.F[global_indeces]+=fflux(x_locs[lvl])*R[lvl,:]*detJ*wts[lvl]\\r\\n \\r\\n if test==True:\\r\\n return detJ,self.F,R,dR_edge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20cd9215635cc8caf5cfe0d328cd777e\",\n \"score\": \"0.49963087\",\n \"text\": \"def c2e(component='cube', ngrain=100, w0=10, ifig=1, mode='contour'):\\n import numpy as np\\n from .euler import euler\\n from . import upf\\n comp = component.lower()\\n print(comp)\\n if comp=='cube':\\n hkl=[1,0,0]\\n uvw=[0,1,0]\\n elif comp=='copper':\\n hkl=[1,1,2]\\n uvw=[-1,-1,1]\\n elif comp=='goss':\\n hkl=[1,1,0]\\n uvw=[0,0,1]\\n elif comp=='rbrass':\\n hkl=[-1,1,0]\\n uvw=[1,1,1]\\n\\n print('hkl',hkl)\\n print('uvw',uvw,'\\\\n')\\n\\n # mat0: trasformation matrix that converts\\n # sample axes to crystal ones [ca<-sa]\\n mat0 = miller2mat(hkl=list(map(round, hkl)), uvw=list(map(round, uvw)))\\n\\n print('mat0')\\n for i in range(len(mat0)):\\n for j in range(len(mat0[i])):\\n print('%5.1f '%mat0[i][j], end=' ')\\n print('')\\n\\n # Mirror reflection by RD and TD in the sample coordinate system.\\n # mat0[ca<-sa]\\n RD, TD = orthogonal()\\n mat1 = np.dot(mat0, RD)\\n mat2 = np.dot(mat0, TD)\\n mat3 = np.dot(np.dot(mat0, RD), TD)\\n print('mat1')\\n for i in range(len(mat1)):\\n for j in range(len(mat1[i])):\\n print('%5.1f '%mat1[i][j], end=' ')\\n print('')\\n print('mat2')\\n for i in range(len(mat2)):\\n for j in range(len(mat2[i])):\\n print('%5.1f '%mat2[i][j], end=' ')\\n print('')\\n print('mat3')\\n for i in range(len(mat3)):\\n for j in range(len(mat3[i])):\\n print('%5.1f '%mat3[i][j], end=' ')\\n print('')\\n\\n\\n ang0 = euler(a=mat0, echo=False)\\n ang1 = euler(a=mat1, echo=False)\\n ang2 = euler(a=mat2, echo=False)\\n ang3 = euler(a=mat3, echo=False)\\n\\n angs = [ang0, ang1, ang2, ang3]\\n\\n for i in range(len(angs)):\\n for j in iter(angs[i]):\\n print('%5.1f '%j, end=' ')\\n print('')\\n\\n xtal = []\\n for i in range(4):\\n phi1, p, phi2 = angs[i]\\n myx = textur(p1=phi1,p=p, p2=phi2, w0=w0, ngrain = ngrain/4,\\n filename='dum', iplot=False)\\n\\n for j in range(len(myx.gr)):\\n xtal.append(myx.gr[j])\\n\\n mypf = upf.polefigure(grains=xtal,csym='cubic')\\n mypf.pf(pole=[[1,0,0],[1,1,0],[1,1,1]], mode=mode, ifig=ifig)\\n\\n ## Write\\n gr2f(gr=xtal, header='module: %s, w0: %i'%('ce2', w0),\\n filename='%i%s%i_%s.cmb'%(len(xtal), component, w0, 'ce2'))\\n return xtal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"333ba44707fb9a5875673075633240bb\",\n \"score\": \"0.49866527\",\n \"text\": \"def part_1e(x, y):\\n six_x = np.arange(50, dtype=int) # creates a vector of length 50\\n six_x = np.full_like(six_x, 6)\\n six_y = 2*six_x + 10 + np.random.normal(0, 1, six_x.size) # [1050x1]\\n\\n x = np.append(x, six_x)\\n y = np.append(y, six_y)\\n\\n fig4 = plt.figure()\\n plt.plot(x, y, \\\".\\\", label=\\\"sample points\\\")\\n plt.plot(six_x, six_y, 'g.', label=\\\"added points\\\")\\n plt.plot(x, 2*x, \\\"r\\\", label=\\\"original function\\\")\\n plt.title(\\\"Part E: Pertubation\\\")\\n plt.legend()\\n\\n return x, y, six_x, six_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f413a5c948836517eaa6c0cf72ab844\",\n \"score\": \"0.4985353\",\n \"text\": \"def OperatorX_XBasis(self):\\t\\n\\t\\treturn np.diag( self.X_Range() ).astype(np.complex128)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8403970a0b0f514fa6528a6dc2da025\",\n \"score\": \"0.49753568\",\n \"text\": \"def Q_I_metric(point,g_function):\\n g_matrix=g_function(point)\\n dimension=len(point)\\n\\n Q_matrix = np.zeros([dimension, dimension])\\n\\n Q_matrix=np.matrix(g_matrix)*np.matrix(g_matrix.transpose())\\n\\n#Q_matrix=np.add(Q_matrix,0.0000001*np.random.rand(dimension,dimension))\\n\\n Q_I_matrix = linalg.inv(Q_matrix)\\n return Q_I_matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b6c15a462cda005989f02e0f5d75644\",\n \"score\": \"0.49742237\",\n \"text\": \"def ecdf_1D(data):\\r\\n # Number of data points: n\\r\\n n = len(data)\\r\\n\\r\\n # x-data for the ECDF: x\\r\\n x = np.sort(data)\\r\\n\\r\\n # y-data for the ECDF: y\\r\\n y = np.arange(1, len(x)+1) / n\\r\\n\\r\\n return x, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"515c8210c1d29b26a330c2721b4b39bf\",\n \"score\": \"0.496816\",\n \"text\": \"def focus_field_debye_gauss(shape,units,lam,NAs, sig = 1./np.sqrt(2), n_integration_steps = 200):\\n\\n #p = OCLProgram(absPath(\\\"kernels/psf_debye.cl\\\"),build_options = str(\\\"-I %s -D INT_STEPS=%s\\\"%(absPath(\\\".\\\"),n_integration_steps)))\\n p = OCLProgram(absPath(\\\"kernels/psf_debye.cl\\\"),\\n build_options = [\\\"-I\\\",absPath(\\\"kernels\\\"),\\\"-D\\\",\\\"INT_STEPS=%s\\\"%n_integration_steps])\\n\\n\\n assert (sig>0)\\n\\n Nx0, Ny0, Nz0 = shape\\n dx, dy, dz = units\\n\\n alphas = np.arcsin(np.array(NAs))\\n\\n Nx = (Nx0+1)/2\\n Ny = (Ny0+1)/2\\n Nz = (Nz0+1)/2\\n\\n u_g = OCLArray.empty((Nz,Ny,Nx),np.float32)\\n ex_g = OCLArray.empty(u_g.shape,np.complex64)\\n ey_g = OCLArray.empty(u_g.shape,np.complex64)\\n ez_g = OCLArray.empty(u_g.shape,np.complex64)\\n\\n alpha_g = OCLArray.from_array(alphas.astype(np.float32))\\n\\n t = time.time()\\n\\n p.run_kernel(\\\"debye_wolf_gauss\\\",u_g.shape[::-1],None,\\n ex_g.data,ey_g.data,ez_g.data, u_g.data,\\n np.float32(1.),np.float32(0.),\\n np.float32(0),np.float32(dx*Nx),\\n np.float32(0),np.float32(dy*Ny),\\n np.float32(0),np.float32(dz*Nz),\\n np.float32(lam),\\n np.float32(sig),\\n alpha_g.data, np.int32(len(alphas)))\\n\\n u = u_g.get()\\n ex = ex_g.get()\\n ey = ey_g.get()\\n ez = ez_g.get()\\n\\n print \\\"time in secs:\\\" , time.time()-t\\n\\n u_all = np.empty((Nz0,Ny0,Nx0),np.float32)\\n ex_all = np.empty((Nz0,Ny0,Nx0),np.complex64)\\n ey_all = np.empty((Nz0,Ny0,Nx0),np.complex64)\\n ez_all = np.empty((Nz0,Ny0,Nx0),np.complex64)\\n\\n sx = [slice(0,Nx),slice(Nx0-Nx0/2,Nx0)]\\n sy = [slice(0,Ny),slice(Ny0-Ny0/2,Ny0)]\\n sz = [slice(0,Nz),slice(Nz0-Nz0/2,Nz0)]\\n\\n sx = [slice(0,Nx),slice(Nx0-Nx,Nx0)]\\n sy = [slice(0,Ny),slice(Ny0-Ny,Ny0)]\\n sz = [slice(0,Nz),slice(Nz0-Nz,Nz0)]\\n\\n for i,j,k in itertools.product([0,1],[0,1],[0,1]):\\n u_all[sz[1-i],sy[1-j],sx[1-k]] = u[::(-1)**i,::(-1)**j,::(-1)**k]\\n ex_all[sz[1-i],sy[1-j],sx[1-k]] = ex[::(-1)**i,::(-1)**j,::(-1)**k]\\n ey_all[sz[1-i],sy[1-j],sx[1-k]] = ey[::(-1)**i,::(-1)**j,::(-1)**k]\\n ez_all[sz[1-i],sy[1-j],sx[1-k]] = ez[::(-1)**i,::(-1)**j,::(-1)**k]\\n\\n\\n return u_all, ex_all, ey_all, ez_all\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f876c6fcbf9a7396de99f22776282633\",\n \"score\": \"0.49596038\",\n \"text\": \"def __resample_edges_of_triangle(self,simplices,indC,x=None,nDivide=10): \\n # handle optional arguments (sampling along CA and CB)\\n if x is None:\\n x = np.linspace(0,1,nDivide,endpoint=True)\\n else:\\n x = np.asarray(x); \\n nDivide=x.size;\\n # get indices of points ABC as shown above (C is isolated point)\\n nTriangles = simplices.shape[0]; \\n ind_triangle = np.arange(nTriangles)\\n C = simplices[ind_triangle,(indC)%3];\\n A = simplices[ind_triangle,(indC+1)%3];\\n B = simplices[ind_triangle,(indC-1)%3];\\n # create dense sampling along C->B and C->A in domain space\\n CA = np.outer(1-x,self.domain[C]) + np.outer(x,self.domain[A]);\\n CB = np.outer(1-x,self.domain[C]) + np.outer(x,self.domain[B]);\\n # map sampling on CA and CB to image space \\n domain_points= np.hstack((CA,CB)).reshape(nDivide,2,nTriangles,2);\\n image_points = self.mapping(domain_points.reshape(-1,2)).reshape(nDivide,2,nTriangles,2);\\n return A,B,C,domain_points,image_points;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41c1720e9527bb3ee9bcca581be9c7a3\",\n \"score\": \"0.49587786\",\n \"text\": \"def assemble(self,test=False):\\r\\n f_term=self.f\\r\\n k_term=self.k\\r\\n nelem=self.number_elements\\r\\n \\r\\n \\r\\n if np.array_equal(self.patch_data.order[1],np.ones(2)*-1)==0:\\r\\n number_patches=len(nelem)\\r\\n else:\\r\\n number_patches=1\\r\\n \\r\\n number_global_bases=len(self.B) #number global basis functions\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n Integration points. May need to add additional points for log funcitons\\r\\n \\\"\\\"\\\"\\r\\n eta_loc,xi_loc,wl=Ippre.compute_gauss_points_and_weights(np.max(self.order)+1,2) \\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Iniitializing enriched matrices, as indicated by addition of terms to number of standard bases\\r\\n \\\"\\\"\\\"\\r\\n self.K_en=np.zeros((number_global_bases+2,number_global_bases+2))\\r\\n self.F_en=np.zeros(number_global_bases+2)\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n Initializing un-enriched matriices for comparison\\r\\n \\\"\\\"\\\"\\r\\n self.K=np.zeros((number_global_bases,number_global_bases))\\r\\n self.F=np.zeros(number_global_bases)\\r\\n \\r\\n #Looping through patches\\r\\n for aa in range(number_patches):\\r\\n \\r\\n #Find patch data and control net specific to each patch\\r\\n if number_patches!=1:\\r\\n Global_IEN_patch=self.patch_data.Global_IEN[self.patch_data.Global_IEN_length[aa,0]:self.patch_data.Global_IEN_length[aa,1]+1,:]\\r\\n else:\\r\\n Global_IEN_patch=self.patch_data.Global_IEN\\r\\n \\r\\n IEN_patch=self.patch_data.local_IEN_array[aa]\\r\\n control_point_ref=self.patch_data.patch_global_2D(aa).flatten()\\r\\n B_patch=self.B[control_point_ref]\\r\\n \\r\\n #Loop over elements in patch\\r\\n for bb in range(np.prod(nelem[aa])): \\r\\n \\r\\n #Finds basis functions supported within element\\r\\n if len(Global_IEN_patch.shape)!=1:\\r\\n global_basis_functions=Global_IEN_patch[bb,:]\\r\\n else:\\r\\n global_basis_functions=Global_IEN_patch\\r\\n\\r\\n global_basis_functions=global_basis_functions[0:np.prod(self.order[aa]+1)]\\r\\n \\r\\n \\r\\n idx_grid = np.ix_(global_basis_functions, global_basis_functions)\\r\\n \\r\\n#\\r\\n# \\\"\\\"\\\"\\r\\n# Additional code for enrichment\\r\\n# \\\"\\\"\\\"\\r\\n# if bb==0 and np.prod(self.number_elements[0])==1:\\r\\n# global_basis_functions_en=range(np.prod(self.order[aa]+1)+2)\\r\\n# \\r\\n# elif bb==range(np.prod(nelem[aa]))[-1]:\\r\\n# global_basis_functions_en=np.hstack((global_basis_functions,len(self.B)+1)) #Adds last basis function\\r\\n# \\r\\n# elif bb==0:\\r\\n# global_basis_functions_en=np.hstack((global_basis_functions,len(self.B))) #Adds second to last basis function\\r\\n# \\r\\n# else:\\r\\n# global_basis_functions_en=global_basis_functions[:]\\r\\n# \\r\\n# idx_grid_en=np.ix_(global_basis_functions_en, global_basis_functions_en)\\r\\n# \\r\\n# \\r\\n# return idx_grid_en\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Solves mapping and NURBS bases at various integration points. Puts results into 2 or 3D array\\r\\n \\\"\\\"\\\"\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n Mappings to x,y space\\r\\n \\\"\\\"\\\"\\r\\n detJ,R,dR_dx=self.mappings(bb,xi_loc,eta_loc,aa)\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n x and y for xi and eta on element bb in patch aa\\r\\n \\\"\\\"\\\"\\r\\n# xfun=np.zeros(len(xi_loc)); yfun=np.zeros(len(xi_loc))\\r\\n# print(R,B_patch,xfun)\\r\\n# for lvl in range(len(xi_loc)):\\r\\n# xfun[lvl]=np.sum(R[lvl,:]*B_patch[:,0])\\r\\n# yfun[lvl]=np.sum(R[lvl,:]*B_patch[:,1])\\r\\n \\r\\n xfun=np.einsum('ij,j->i',R,B_patch[:,0])\\r\\n yfun=np.einsum('ij,j->i',R,B_patch[:,1])\\r\\n \\\"\\\"\\\"\\r\\n Material modulus and forcing terms\\r\\n \\\"\\\"\\\"\\r\\n# k=np.zeros(len(xi_loc)); f=np.zeros(len(xi_loc))\\r\\n# for lvl in range(len(xi_loc)):\\r\\n# k[lvl]=k_term(xfun[lvl],yfun[lvl])\\r\\n# f[lvl]=f_term(xfun[lvl],yfun[lvl])\\r\\n k=k_term(xfun,yfun)\\r\\n f=f_term(xfun,yfun)\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n Selecting NURBS bases and their derivatives supported on element\\r\\n \\\"\\\"\\\"\\r\\n R=R[:,IEN_patch[bb,:]] #in reverse order now. IEN_patch may be single row\\r\\n dR_dx=dR_dx[IEN_patch[bb,:],:,:]\\r\\n \\r\\n# R=R[::-1]\\r\\n# dR_dx=np.flipud(dR_dx)\\r\\n \\\"\\\"\\\"\\r\\n Making grid for both non enriched and enriched meshes\\r\\n \\\"\\\"\\\"\\r\\n dRdx_grid=np.zeros((len(dR_dx[:,0,0]),len(dR_dx[:,0,0]),len(xi_loc)))\\r\\n dRdy_grid=np.zeros((len(dR_dx[:,0,0]),len(dR_dx[:,0,0]),len(xi_loc)))\\r\\n \\r\\n# for lvl in range(len(xi_loc)):\\r\\n#\\r\\n# dRdx_grid[:,:,lvl]=np.einsum('i,j',dR_dx[:,0,lvl],dR_dx[:,0,lvl])\\r\\n# dRdy_grid[:,:,lvl]=np.einsum('i,j',dR_dx[:,1,lvl],dR_dx[:,1,lvl])\\r\\n dRdx_grid=np.einsum('ik,jk->ijk',dR_dx[:,0,:],dR_dx[:,0,:]) \\r\\n dRdy_grid=np.einsum('ik,jk->ijk',dR_dx[:,1,:],dR_dx[:,1,:]) \\r\\n \\\"\\\"\\\"\\r\\n Calculating pertinent entries in K and F\\r\\n \\\"\\\"\\\"\\r\\n# return dRdy_grid_modified.shape,dRdy_grid_modified.shape,idx_grid_en\\r\\n# for lvl in range(len(xi_loc)):\\r\\n# self.K[idx_grid]+=k[lvl]*(dRdx_grid[:,:,lvl]+dRdy_grid[:,:,lvl])*detJ[lvl]*wl[lvl]\\r\\n# self.F[global_basis_functions]+=f[lvl]*R[lvl,:]*detJ[lvl]*wl[lvl]\\r\\n self.K[idx_grid]+=np.einsum('ijk,k->ij',(dRdx_grid[:,:,:]+dRdy_grid[:,:,:]),k*detJ*wl)\\r\\n self.F[global_basis_functions]+=np.einsum('ji,j->i',R,f*detJ*wl)\\r\\n \\r\\n if test==True:\\r\\n return self.K,detJ\",\n \"title\": \"\"\n },\n {\n \"docid\": \"292833471b3b380101fbccacc94e6d9c\",\n \"score\": \"0.49501646\",\n \"text\": \"def sgd(self):\\r\\n for i, j, r in self.samples:\\r\\n # Computer prediction and error\\r\\n prediction = self.get_rating(i, j)\\r\\n e = (r - prediction)\\r\\n\\r\\n # Update biases\\r\\n self.b_u[i] += self.alpha * (e - self.beta * self.b_u[i])\\r\\n self.b_i[j] += self.alpha * (e - self.beta * self.b_i[j])\\r\\n\\r\\n # Create copy of row of P since we need to update it but use older values for update on Q\\r\\n P_i = self.P[i, :][:]\\r\\n\\r\\n # Update user and item latent feature matrices\\r\\n self.P[i, :] += self.alpha * (e * self.Q[j, :] - self.beta * self.P[i, :])\\r\\n self.Q[j, :] += self.alpha * (e * P_i - self.beta * self.Q[j, :])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f81b6635d3492655457599a8d31bc209\",\n \"score\": \"0.4949912\",\n \"text\": \"def test_hodge_star(self):\\n regular_cases = []\\n #Unit line segment\\n regular_cases.append((matrix([[0],[1]]),matrix([[0,1]]),[1,1],[0.5,1]))\\n #One regular triangle, edge length 1\\n regular_cases.append((matrix([[0,0],[1,0],[0.5,sqrt(3.0)/2.0]]),matrix([[0,1,2]]), [1, 1, sqrt(3.0)/4.0],\\\\\\n [sqrt(3.0)/12.0,sqrt(3.0)/6.0,1]))\\n # One regular tet, edge length sqrt(2)\\n regular_cases.append((matrix([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0]]),matrix([[0,1,2,3]]),\\\\\\n [1, sqrt(2.0),sqrt(3.0/4.0), 1.0/3.0],[1.0/12.0,sqrt(2.0)/12.0,sqrt(3.0/36.0),1]))\\n \\n for v,e,primal,dual in regular_cases:\\n sc = simplicial_complex((v,e))\\n for dim in range(sc.complex_dimension() + 1):\\n (n,m) = sc[dim].star.shape\\n for i in range(n): \\n assert_almost_equal( sc[dim].star[i,i], dual[dim]/primal[dim] )\\n \\n \\n multi_cases = []\\n #two line segments\\n multi_cases.append((matrix([[0],[1],[2]]),matrix([[0,1],[1,2]]), \\\\\\n [([0],0.5),([1],1.0),([2],0.5),([0,1],1),([1,2],1)]))\\n #three line segments\\n multi_cases.append((matrix([[0],[1],[2],[3]]),matrix([[0,1],[1,2],[2,3]]), \\\\\\n [([0],0.5),([1],1.0),([2],1),([3],0.5),([0,1],1),([1,2],1),([2,3],1)]))\\n #two triangles\\n multi_cases.append((matrix([[0,0],[1.0,0],[0.5,sqrt(3.0)/2.0],[1.5,sqrt(3.0)/2.0]]), \\\\\\n matrix([[0,1,2],[1,3,2]]), \\\\\\n [([0],sqrt(3.0)/12.0),([1],2*sqrt(3.0)/12.0),([2],2*sqrt(3.0)/12.0), ([3],sqrt(3.0)/12.0), \\\\\\n ([0,1],sqrt(3.0)/6.0),([0,2],sqrt(3.0)/6.0),([1,2],2*sqrt(3.0)/6.0),([1,3],sqrt(3.0)/6.0), \\\\\\n ([2,3],sqrt(3.0)/6.0),([0,1,2],4.0/sqrt(3.0)),([1,2,3],4.0/sqrt(3.0))]))\\n #three triangles\\n multi_cases.append((matrix([[0,0],[1.0,0],[0.5,sqrt(3.0)/2.0],[1.5,sqrt(3.0)/2.0],[2,0]]), \\\\\\n matrix([[0,1,2],[1,3,2],[1,4,3]]), \\\\\\n [([0],sqrt(3.0)/12.0),([1],3*sqrt(3.0)/12.0),([2],2*sqrt(3.0)/12.0), ([3],2*sqrt(3.0)/12.0), \\\\\\n ([4],sqrt(3.0)/12.0),([0,1],sqrt(3.0)/6.0),([0,2],sqrt(3.0)/6.0),([1,2],2*sqrt(3.0)/6.0),\\n ([1,3],2*sqrt(3.0)/6.0), ([1,4],sqrt(3.0)/6.0),([3,4],sqrt(3.0)/6.0),\\\\\\n ([2,3],sqrt(3.0)/6.0),([0,1,2],4.0/sqrt(3.0)),([1,2,3],4.0/sqrt(3.0)),([1,4,3],4.0/sqrt(3.0))]))\\n \\n \\n for v,e,t_list in multi_cases: \\n sc = simplicial_complex((v,e))\\n for s,ratio in t_list:\\n data = sc[len(s)-1]\\n index = data.simplex_to_index[simplex(s)]\\n assert_almost_equal(ratio,data.star[index,index])\\n \\n \\n #use the same multi_cases, but add dimensions and translate the vertices \\n #TODO rotate also\\n random.seed(0)\\n for v,e,t_list in multi_cases:\\n \\n (rows,cols) = v.shape\\n v = concatenate([v,zeros((rows,4))],1) \\n v += rand(1,cols +4) #translate\\n \\n sc = simplicial_complex((v,e))\\n for s,ratio in t_list:\\n data = sc[len(s)-1]\\n index = data.simplex_to_index[simplex(s)]\\n assert_almost_equal(ratio,data.star[index,index])\\n \\n #Test sign of dual star, check that: ** = -1 ^(k (n-k))\\n for v,e,t_list in multi_cases:\\n sc = simplicial_complex((v,e))\\n for dim,data in enumerate(sc):\\n n = sc.complex_dimension()\\n k = dim\\n assert_almost_equal((data.star * data.star_inv).todense(), \\\\\\n ((-1)**(k*(n-k))*sparse.identity(data.num_simplices)).todense())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f8a496b0e19fa419122af14c1af901\",\n \"score\": \"0.49495548\",\n \"text\": \"def _gcn(X, flag=0, scale=55.):\\r\\n if flag == 0:\\r\\n print(\\\"1\\\")\\r\\n mean = np.mean(X, axis=1)\\r\\n X = X - mean[:, np.newaxis]\\r\\n contrast = np.sqrt(10. + (X**2).sum(axis=1)) / scale\\r\\n contrast[contrast < 1e-8] = 1.\\r\\n X = X / contrast[:, np.newaxis]\\r\\n else:\\r\\n print(\\\"3\\\")\\r\\n X = X.reshape([-1, 32, 32, 3])\\r\\n mu = np.mean(X, axis=(1, 2)).reshape([-1, 1, 1, 3])\\r\\n std = np.std(X, axis=(1, 2)).reshape([-1, 1, 1, 3])\\r\\n X = X - mu\\r\\n X = X / std\\r\\n X = X.reshape([-1, 3072])\\r\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f8a496b0e19fa419122af14c1af901\",\n \"score\": \"0.49495548\",\n \"text\": \"def _gcn(X, flag=0, scale=55.):\\r\\n if flag == 0:\\r\\n print(\\\"1\\\")\\r\\n mean = np.mean(X, axis=1)\\r\\n X = X - mean[:, np.newaxis]\\r\\n contrast = np.sqrt(10. + (X**2).sum(axis=1)) / scale\\r\\n contrast[contrast < 1e-8] = 1.\\r\\n X = X / contrast[:, np.newaxis]\\r\\n else:\\r\\n print(\\\"3\\\")\\r\\n X = X.reshape([-1, 32, 32, 3])\\r\\n mu = np.mean(X, axis=(1, 2)).reshape([-1, 1, 1, 3])\\r\\n std = np.std(X, axis=(1, 2)).reshape([-1, 1, 1, 3])\\r\\n X = X - mu\\r\\n X = X / std\\r\\n X = X.reshape([-1, 3072])\\r\\n return X\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9f5817f2a539f10c4bfd2ca7b97c7f\",\n \"score\": \"0.4948728\",\n \"text\": \"def CMB_E_and_B_modes():\\n res = 512\\n frame = 50\\n param = [1,1]\\n\\n res = 512\\n N = np.array([res,res], dtype=np.int32)\\n xsize = 1 * np.pi / 180\\n size2d = np.array([xsize,xsize])\\n Delta = size2d/N\\n print \\\"Delta\\\", Delta\\n Deltal = cmbtools.Delta2l(Delta, N)\\n print \\\"Deltal\\\", Deltal\\n\\n for axis in axes:\\n for sim in sims:\\n beta = {'High_512':'B02', 'Mid_512':'B2', 'Low_512':'B20'}[sim] \\n at = access_thing(sim,res,frame,param[0],param[1], machine=machine)\\n plotloc = at.get_plot_location_QU2EB()\\n \\n # Get frbs\\n Q = np.array(at.get_fits_array('Q%s_n0-%04d_p-%d'%(axis,param[0],param[1]), axis))\\n U = np.array(at.get_fits_array('U%s_n0-%04d_p-%d'%(axis,param[0],param[1]), axis))\\n\\n print \\\"Q\\\\n\\\", Q \\n print \\\"U\\\\n\\\", U\\n print \\\"Q.shape\\\", Q.shape\\n print \\\"U.shape\\\", U.shape\\n\\n # 2d fourier transforms of Q and U\\n #Qharm = cmbtools.map2harm(Q, Delta)\\n #Uharm = cmbtools.map2harm(U, Delta)\\n if not Q.flags['C_CONTIGUOUS']:\\n Q = np.ascontiguousarray(Q)\\n if not U.flags['C_CONTIGUOUS']:\\n U = np.ascontiguousarray(U)\\n Qharm = np.fft.fft2(Q)\\n Uharm = np.fft.fft2(U)\\n\\n print \\\"Qharm\\\\n\\\", Qharm\\n print \\\"Uharm\\\\n\\\", Uharm\\n \\n # Decompose stokes into E and B modes\\n Eharm, Bharm = cmbtools.QU2EB(Qharm, Uharm, Deltal)\\n\\n print \\\"Eharm\\\\n\\\", Eharm\\n print \\\"Bharm\\\\n\\\", Bharm\\n \\n # 2d inverse fourier transforms of E and B to bring them back into real space\\n #E = cmbtools.harm2map(Eharm, Delta)\\n #B = cmbtools.harm2map(Bharm, Delta)\\n E = np.fft.ifft2(Eharm)\\n B = np.fft.ifft2(Bharm)\\n\\n print \\\"E\\\\n\\\", E\\n print \\\"B\\\\n\\\", B\\n\\n # Compute coeffictients for the harmonics\\n deltalbins=2.5\\n lmax = 100\\n lbins = np.arange(0,lmax,deltalbins)\\n\\n ClE = cmbtools.harm2cl(Eharm, Deltal, lbins)\\n ClB = cmbtools.harm2cl(Bharm, Deltal, lbins)\\n print \\\"ClE/ClB\\\", ClE/ClB\\n\\n plt.clf()\\n plt.imshow(Q,interpolation='nearest')\\n plt.title('Q')\\n plt.colorbar()\\n plt.savefig('p49_Q_%s_%s'%(axis, beta))\\n \\n plt.clf()\\n plt.imshow(U,interpolation='nearest')\\n plt.title('U')\\n plt.colorbar()\\n plt.savefig('p49_U_%s_%s'%(axis, beta))\\n\\n plt.clf()\\n plt.imshow(E,interpolation='nearest')\\n plt.title('E')\\n plt.colorbar()\\n plt.savefig('p49_E_%s_%s'%(axis, beta))\\n \\n plt.clf()\\n plt.imshow(B,interpolation='nearest')\\n plt.title('B')\\n plt.colorbar()\\n plt.savefig('p49_B_%s_%s'%(axis, beta))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61ef1aeec43563daef9c058778788f24\",\n \"score\": \"0.49442142\",\n \"text\": \"def calc_sf_c_serial(vardct, hartree, pdos, eqp, imeqp, newen, allkb):\\n print \\\" calc_sf_c_serial :: \\\" \\n import numpy as np;\\n wtk = np.array(vardct['wtk'])\\n hartree = np.array(hartree)\\n pdos = np.array(pdos)\\n minkpt = int(vardct['minkpt'])\\n maxkpt = int(vardct['maxkpt'])\\n nkpt = maxkpt - minkpt + 1\\n minband = int(vardct['minband'])\\n maxband = int(vardct['maxband'])\\n nband = maxband - minband + 1\\n bdgw = map(int, vardct['sig_bdgw'])\\n bdrange = range(minband-bdgw[0],maxband-bdgw[0]+1)\\n kptrange = range(minkpt - 1, maxkpt)\\n #print \\\"kptrange, bdrange \\\", kptrange, bdrange \\n newdx = 0.005\\n enmin = float(vardct['enmin'])\\n enmax = float(vardct['enmax'])\\n #if enmin < en[0] and enmax >= en[-1]: \\n # newen = np.arange(en[0],en[-1],newdx)\\n #elif enmin < en[0]: \\n # newen = np.arange(en[0],enmax,newdx)\\n #elif enmax >= en[-1] : \\n # newen = np.arange(enmin,en[-1],newdx)\\n #else : \\n # newen = np.arange(enmin,enmax,newdx)\\n npoles = int(vardct['npoles'])\\n extinf = int(vardct['extinf'])\\n penergy = int(vardct['penergy'])\\n #allkb = [spfkb,reskb, rdenkb, imskb]\\n reskb = allkb[1]\\n imskb = allkb[3]\\n if npoles==999: # same omega_p for every state, with the intensity calculated integrating Im(Sigma)\\n omega_p = float(vardct['omega_p'])\\n #omegampole = np.ones((nkpt,nband))*omega_p\\n #ampole = np.zeros((nkpt,nband))\\n omegampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size))*omega_p\\n ampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size))\\n #for ik in range(nkpt):\\n #for ik in kptrange:\\n #for ib in range(nband):\\n #for ib in bdrange:\\n for ik in range(imskb[:,0,0].size):\\n for ib in range(imskb[0,:,0].size):\\n print \\\" ik, ib\\\", ik, ib \\n #interpims = interp1d(en, ims[ik,ib], kind = 'linear', axis = -1)\\n #if eqp[ik,ib]<=efermi:\\n if eqp[ik,ib]<=0:\\n tmpen = newen[imskb[ik,ib]>=0]\\n tmpim = imskb[ik,ib,imskb[ik,ib]>=0]\\n else:\\n tmpen = newen[imskb[ik,ib]<0]\\n tmpim = imskb[ik,ib,ims[ik,ib]<0]\\n ampole[ik,ib] = abs(np.trapz(tmpim,tmpen))/np.pi\\n print \\\" 1/pi*\\\\int\\\\Sigma =\\\", ampole[ik,ib] \\n # Workaround correction for small energy plasmons\\n ampole[ik,ib] = ampole[ik,ib]/(abs(tmpen[-1]-tmpen[0]))*omega_p\\n# # Workaround for small energy plasmons\\n# if eqp[ik,ib]<=efermi:\\n# tmpim = tmpim[tmpen>=eqp[ik,ib]-2.5]\\n# tmpen = tmpen[tmpen>=eqp[ik,ib]-2.5]\\n# else:\\n# tmpim = tmpim[tmpen <eqp[ik,ib]+2.5]\\n# tmpen = tmpen[tmpen <eqp[ik,ib]+2.5]\\n# ampole[ik,ib] = np.trapz(tmpim,tmpen)/np.pi\\n #ampole = ampole/omega_p**2\\n #ampole[ik,ib] = np.trapz(en[ims[ik,ib]>=0],ims[ik,ib,ims[ik,ib]>=0])/np.pi\\n elif npoles != 0:\\n from multipole import fit_multipole, fit_multipole_const, getdata_file #, write_f_as_sum_of_poles\\n print \\\" ### ================== ###\\\" \\n print \\\" ### Multipole fit ###\\\" \\n print \\\" Number of poles:\\\", npoles \\n #omegampole = np.zeros((nkpt,nband,npoles))\\n #ampole = np.zeros((nkpt,nband,npoles))\\n omegampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,npoles))\\n ampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,npoles))\\n #for ik in range(nkpt):\\n # ikeff=minkpt+ik-1\\n #bdrange = vardct['bdrange']\\n #kptrange = vardct['kptrange']\\n #print \\\"kptrange, bdrange \\\", kptrange, bdrange \\n for ik in kptrange:\\n for ib in bdrange:\\n #for ik in range(imskb[:,0,0].size):\\n #for ib in range(nband):\\n # for ib in range(imskb[0,:,0].size):\\n if eqp[ik,ib] > newen[-npoles]:\\n #if eqp[ik,ib] > newen[-1]:\\n omegampole[ik,ib] = omegampole[ik,ib-1]\\n ampole[ik,ib] = ampole[ik,ib-1]\\n print \\\" Eqp beyond available energy range. Values from lower band are taken.\\\" \\n continue\\n else:\\n ibeff=minband+ib-1\\n print \\\" ik, ib\\\", ik, ib \\n #interpims = interp1d(en, ims[ik,ib], kind = 'linear', axis = -1)\\n #print newen.shape, imskb.shape \\n interpims = interp1d(newen, imskb[ik,ib], kind = 'linear', axis = -1)\\n # Here we take the curve starting from eqp and then we invert it\\n # so as to have it defined on the positive x axis\\n # and so that the positive direction is in the \\n # increasing direction of the array index\\n #if eqp[ik,ib] <= efermi:\\n if eqp[ik,ib] <= 0:\\n #en3 = en[en<=eqp[ik,ib]] # So as to avoid negative omegampole\\n #en3 = newen[newen<=eqp[ik,ib]] # So as to avoid negative omegampole\\n en3 = newen[newen<0.] # So as to avoid negative omegampole\\n else:\\n en3 = newen[newen>eqp[ik,ib]] # So as to avoid negative omegampole\\n #en3 = en[en>eqp[ik,ib]] # So as to avoid negative omegampole\\n #en3 = en[en<=efermi]\\n if en3.size == 0:\\n print \\n print \\\" WARNING: QP energy is outside of given energy range!\\\\n\\\"+\\\\\\n \\\" This state will be skipped!\\\\n\\\"+\\\\\\n \\\"You might want to modify enmin/enmax.\\\"\\n print \\\" eqp[ik,ib], newen[-1]\\\", eqp[ik,ib] , newen[-1] \\n continue\\n im3 = abs(interpims(en3)/np.pi) # This is what should be fitted\\n zcut = 3.0\\n for i in range(en3.size):\\n if en3[i]>(eqp[ik,ib]-zcut) and en3[i]<(eqp[ik,ib]+zcut):\\n im3[i] = 0.\\n #import matplotlib.pylab as plt\\n #plt.plot(en3,im3,'-')\\n #plt.show()\\n en3 = en3 - eqp[ik,ib]\\n if eqp[ik,ib] <= 0:\\n en3 = -en3[::-1] \\n im3 = im3[::-1]\\n #### TESTING ###\\n #print \\\"ik, ib, eqp[ik,ib], en3[0], en3[-1], newen[0], newen[-1]:\\\\n\\\", ik, ib, eqp[ik,ib], en3[0], en3[-1], newen[0], newen[-1] \\n #import matplotlib.pylab as plt\\n #plt.plot(newen, imskb[ik,ib]/np.pi,\\\"-\\\")\\n #plt.plot(en3+eqp[ik,ib], im3,\\\"x\\\")\\n #plt.show()\\n #sys.exit()\\n #### END TESTING ###\\n omegai, lambdai, deltai = fit_multipole_const(en3,im3,npoles)\\n plot_fit = int(vardct['plot_fit'])\\n if plot_fit == 1:\\n from multipole import write_f_as_sum_of_poles\\n import matplotlib.pylab as plt\\n import pylab\\n plt.figure(2)\\n eta = 0.5\\n enlor, flor = write_f_as_sum_of_poles(en3, omegai, lambdai, deltai, eta)\\n plt.plot(enlor, flor,\\\"-\\\",label=\\\"sum of poles, eta: \\\"+str(eta))\\n plt.plot(en3,im3,\\\"-\\\",label=\\\"ImS(e-w)\\\")\\n plt.plot(omegai,lambdai,\\\"go\\\", label = \\\"omegai, lambdai\\\")\\n plt.plot(omegai,lambdai/deltai,\\\"ro\\\", label = \\\"omegai, lambdai/deltai\\\")\\n plt.title(\\\"ik: \\\"+str(ik)+\\\", ib: \\\"+str(ib)+\\\", npoles: \\\"+str(npoles))\\n plt.legend()\\n pylab.savefig('imS_fit_np'+str(npoles)+'_ik'+str(ik)+'_ib'+str(ib)+'.pdf')\\n plt.close()\\n ## TESTING THE MULTIPOLE REPRESENTATION\\n #from multipole import write_f_as_sum_of_poles\\n #import matplotlib.pylab as plt\\n #import pylab\\n #eta = 0.01\\n #for eta in [0.1]: #, 0.1, 0.5]:\\n # for npoles in [1,10,20,100]:\\n # omegai, lambdai, deltai = fit_multipole_const(en3,im3,npoles)\\n # print \\\"ik, ib, eqp[ik,ib], en3[0], en3[-1], newen[0], newen[-1]:\\\\n\\\", ik, ib, eqp[ik,ib], en3[0], en3[-1], newen[0], newen[-1] \\n # print omegai, lambdai, deltai \\n # enlor, flor = write_f_as_sum_of_poles(en3, omegai, lambdai, deltai, eta)\\n # plt.plot(enlor, flor,\\\"-\\\",label=\\\"sum of poles, eta: \\\"+str(eta))\\n # plt.plot(en3,im3,\\\"-\\\",label=\\\"ImS(e-w)\\\")\\n # plt.plot(omegai,lambdai,\\\"go\\\", label = \\\"omegai, lambdai\\\")\\n # plt.plot(omegai,lambdai/deltai,\\\"ro\\\", label = \\\"omegai, lambdai/deltai\\\")\\n # plt.title(\\\"ik: \\\"+str(ik)+\\\", ib: \\\"+str(ib)+\\\", npoles: \\\"+str(npoles))\\n # plt.legend()\\n # pylab.savefig('imS_test_np'+str(npoles)+'_ik'+str(ik)+'_ib'+str(ib)+'_eta'+str(eta)+'.pdf')\\n # plt.show()\\n #sys.exit()\\n # END TESTING THE MULTIPOLE REPRESENTATION \\n # HERE WE MUST CHECK THAT THE NUMBER OF POLES \\n # IS NOT BIGGER THAN THE NUMBER OF POINTS THAT HAS TO BE FITTED\\n if npoles > omegai.size:\\n omegampole[ik,ib][:omegai.size] = omegai \\n ampole[ik,ib][:omegai.size] = np.true_divide(lambdai,(np.square(omegai)))\\n print \\n print \\\" WARNING: npoles used (\\\"+str(npoles)+\\\") is larger\\\"+\\\\\\n \\\" than poles x data array can give (\\\"+str(omegai.size)+\\\").\\\"\\n #print \\\"WARNING: Reduce npoles. You are wasting resources!!!\\\" \\n print \\\" Im(Sigma) will be interpolated to obtain the desired number of poles.\\\" \\n current_size = omegai.size\\n counter = 0\\n while npoles > current_size:\\n counter += 1\\n print \\n print \\\" WARNING: Arrays are too coarse.\\\" \\n print \\\" npoles, omegai.size:\\\", npoles, omegai.size \\n print \\\" Filling arrays with interpolated values...\\\" \\n en1 = array_doublefill(en3)\\n im1 = array_doublefill(im3)\\n en3 = en1\\n im3 = im1\\n omegai, lambdai, deltai = fit_multipole_const(en1,im1,npoles)\\n current_size = omegai.size\\n if counter > 4:\\n print 60*\\\"=\\\" \\n print \\\" WARNING: You are trying too hard with too few points.\\\" \\n print \\\" The array has been interpolated more than 4 times.\\\" \\n print \\\" Maybe use less poles or calculate more points for Sigma?\\\" \\n print 60*\\\"=\\\" \\n # im1 = fit_double(im3)\\n else:\\n omegampole[ik,ib] = omegai \\n ampole[ik,ib] = np.true_divide(lambdai,(np.square(omegai)))\\n #ampole[ik,ib] = gi\\n print \\\" Integral test. Compare \\\\int\\\\Sigma and \\\\sum_j^N\\\\lambda_j.\\\" \\n print \\\" 1/pi*\\\\int\\\\Sigma =\\\", np.trapz(im3,en3) \\n print \\\" \\\\sum_j^N\\\\lambda_j =\\\", np.sum(lambdai) \\n #plt.plot(en3,im3,\\\"-\\\"); plt.plot(omegai,np.pi/2*gi*omegai/deltai,\\\"-o\\\")\\n #e1,f1 = write_f_as_sum_of_poles(en3,omegai,gi,deltai,0)\\n # Writing out a_j e omega_j\\n print \\\" ### Writing out a_j and omega_j...\\\" \\n outname = \\\"a_j_np\\\"+str(npoles)+\\\".dat\\\"\\n outfile = open(outname,'w')\\n outname2 = \\\"omega_j_np\\\"+str(npoles)+\\\".dat\\\"\\n outfile2 = open(outname2,'w')\\n for ipole in xrange(npoles):\\n # for ik in kptrange:\\n # #for ib in range(nband):\\n # for ib in bdrange:\\n for ik in range(imskb[:,0,0].size):\\n for ib in range(imskb[0,:,0].size):\\n outfile.write(\\\"%15.7e\\\" % (ampole[ik,ib,ipole]))\\n outfile2.write(\\\"%15.7e\\\" % (omegampole[ik,ib,ipole]))\\n #outfile.write(\\\"%10.5f\\\" % (ampole[ik,ib,ipole]))\\n #outfile2.write(\\\"%10.5f\\\" % (omegampole[ik,ib,ipole]))\\n outfile.write(\\\"\\\\n\\\")\\n outfile2.write(\\\"\\\\n\\\")\\n outfile.write(\\\"\\\\n\\\")\\n outfile2.write(\\\"\\\\n\\\")\\n outfile.close()\\n outfile2.close()\\n # Extrinsic and interference contribution\\n if extinf == 1:\\n origdir = vardct['origdir']\\n extinfname = \\\"a_wp.\\\"+str(penergy)\\n amp_exinf, w_extinf = calc_extinf_corrections(origdir,extinfname,ampole,omegampole)\\n print \\\" ### Writing out a_j_extinf...\\\" \\n outname = \\\"a_j_np\\\"+str(npoles)+\\\"_extinf.\\\"+str(penergy)\\n outfile = open(outname,'w')\\n for ipole in xrange(npoles):\\n # for ik in kptrange:\\n # for ib in bdrange:\\n for ik in range(imskb[:,0,0].size):\\n for ib in range(imskb[0,:,0].size):\\n outfile.write(\\\"%10.5f\\\" % (amp_exinf[ik,ib,ipole]))\\n outfile.write(\\\"\\\\n\\\")\\n outfile.write(\\\"\\\\n\\\")\\n outfile.close()\\n else: # npoles == 0\\n omegampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size))\\n ampole = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size))\\n #omegampole = np.zeros((nkpt,nband))\\n #ampole = np.zeros((nkpt,nband))\\n #elaps2 = time.time() - elaps1 - e0\\n #cpu2 = time.clock() - cpu1 - c0\\n #print elaps2, cpu2 \\n #print str(\\\" Used time (elaps, cpu): %10.6e %10.6e\\\"% (elaps2, cpu2)) \\n print \\\" Calculating multipole exponential A...\\\" \\n dxexp=0.005 \\n enexp = np.arange(enmin,enmax,dxexp)\\n nenexp = np.size(enexp)\\n ftot = np.zeros((np.size(enexp)),order='Fortran')\\n nen = np.size(enexp)\\n #sfkb_c = np.zeros((nkpt,nband,nenexp))\\n sfkb_c = np.zeros((imskb[:,0,0].size,imskb[0,:,0].size,nenexp))\\n ############################\\n # With extrinsic effects ###\\n if extinf == 1:\\n from extmod_spf_mpole import f2py_calc_spf_mpole_extinf\\n #for ik in range(nkpt):\\n for ik in kptrange:\\n ikeff = ik + 1\\n #for ib in range(nband):\\n for ib in bdrange:\\n ibeff=bdgw[0]+ib\\n print \\\" ik, ib, ikeff, ibeff\\\", ik, ib, ikeff, ibeff \\n #prefac=np.exp(-np.sum(amp_exinf[ik,ib]))/np.pi*wtk[ik]*pdos[ib]*abs(imeqp[ik,ib])\\n # Experimental fix for npoles dependence\\n tmp = 1/np.pi*wtk[ik]*pdos[ib]*abs(imeqp[ik,ib])\\n prefac=np.exp(-np.sum(ampole[ik,ib]))*tmp\\n #prefac=np.exp(-tmp*np.trapz(imskb[ik,ib],enexp)/np.sum(omegai)*npoles)\\n akb=amp_exinf[ik,ib] # This is a numpy array (slice)\\n omegakb=omegampole[ik,ib] # This is a numpy array (slice)\\n wkb=w_extinf[ik,ib] # This is a numpy array (slice)\\n eqpkb=eqp[ik,ib]\\n imkb=imeqp[ik,ib] # + w_extinf[ik,ib]/2 # extinf width added\\n #tmpf = calc_spf_mpole(enexp,prefac,akb,omegakb,eqpkb,imkb,npoles,wkb)\\n #ftot += tmpf\\n if eqpkb < 0.0:\\n pass\\n #tmpf = calc_spf_mpole(enexp,prefac,akb,omegakb,eqpkb,imkb,npoles)\\n else:\\n print \\\" This state is empty! eqpkb ik ib:\\\",eqpkb, ikeff+1, ibeff+1 \\n #print \\\"omegakb\\\", omegakb \\n omegakb=-omegakb\\n #print \\\"-omegakb\\\", omegakb \\n tmpf = np.zeros((nenexp), order='Fortran')\\n tmpf = f2py_calc_spf_mpole_extinf(tmpf,enexp,prefac,akb,omegakb,wkb,eqpkb,imkb) #,np.size(enexp),npoles)\\n #outnamekb = \\\"spf_exp-k\\\"+str(\\\"%02d\\\"%(ikeff+1))+\\\"-b\\\"+str(\\\"%02d\\\"%(ibeff+1))+\\\"_np\\\"+str(npoles)+\\\"_extinf.\\\"+str(penergy)\\n #outfilekb = open(outnamekb,'w')\\n #for ien in xrange(nenexp):\\n # outfilekb.write(\\\"%8.4f %12.8f\\\\n\\\" % (enexp[ien], tmpf[ien]))\\n #outfilekb.close()\\n sfkb_c[ik,ib] = tmpf\\n ftot = ftot + tmpf\\n else: # extinf == 0\\n from extmod_spf_mpole import f2py_calc_spf_mpole\\n #for ik in range(nkpt):\\n #for ib in range(nband):\\n for ik in kptrange:\\n ikeff = ik + 1\\n for ib in bdrange:\\n ibeff = ib + 1\\n print \\\" ik, ib, ikeff, ibeff\\\", ik, ib, ikeff, ibeff \\n #prefac=np.exp(-np.sum(ampole[ik,ib]))/np.pi*wtk[ik]*pdos[ib]*abs(imeqp[ik,ib])\\n # Experimental fix for npoles dependence\\n tmp = 1/np.pi*wtk[ik]*pdos[ib]*abs(imeqp[ik,ib])\\n prefac=np.exp(-np.sum(ampole[ik,ib]))*tmp\\n #prefac=np.exp(-tmp*np.trapz(imskb[ik,ib],enexp)/np.sum(omegai)*npoles)\\n print \\\"\\\\n === Normalization test === \\\" \\n print \\\" Prefactor:\\\", np.exp(-np.sum(ampole[ik,ib])) \\n print \\\" Exponent:\\\", np.sum(ampole[ik,ib]) \\n print \\\" Exponent/npoles:\\\", np.sum(ampole[ik,ib])/npoles,\\n print\\n print\\n akb=ampole[ik,ib] # This is a numpy array (slice)\\n omegakb=omegampole[ik,ib] # This is a numpy array (slice)\\n eqpkb=eqp[ik,ib]\\n imkb=imeqp[ik,ib]\\n #tmpf1 = calc_spf_mpole(enexp,prefac,akb,omegakb,eqpkb,imkb,npoles)\\n #print nen, np.size(enexp) \\n #tmpf = 0.0*tmpf\\n if eqpkb < 0.0:\\n pass\\n else:\\n print \\\" This state is empty! eqpkb ik ib:\\\",eqpkb, ikeff+1, ibeff+1 \\n #print \\\"omegakb\\\", omegakb \\n omegakb=-omegakb\\n #print \\\"-omegakb\\\", omegakb \\n tmpf = np.zeros((nenexp), order='Fortran')\\n tmpf = f2py_calc_spf_mpole(tmpf,enexp,prefac,akb,omegakb,eqpkb,imkb) #,nen,npoles)\\n #tmpf = calc_spf_mpole(enexp,prefac,akb,omegakb,eqpkb,imkb,npoles)\\n #outnamekb = \\\"spf_exp-k\\\"+str(\\\"%02d\\\"%(ikeff+1))+\\\"-b\\\"+str(\\\"%02d\\\"%(ibeff+1))+\\\"_np\\\"+str(npoles)+\\\".\\\"+str(penergy)\\n #outfilekb = open(outnamekb,'w')\\n #for ien in xrange(nenexp):\\n # outfilekb.write(\\\"%8.4f %12.8f\\\\n\\\" % (enexp[ien], tmpf[ien]))\\n #outfilekb.close()\\n sfkb_c[ik,ib] = tmpf\\n ftot = ftot + tmpf\\n #print ftot[0], tmpf[0] \\n #elaps2 = time.time() - elaps1 - e0\\n #cpu2 = time.clock() - cpu1 - c0\\n #print elaps2, cpu2 \\n #print str(\\\" Used time (elaps, cpu): %10.6e %10.6e\\\"% (elaps2, cpu2)) \\n #print \\\" ### Writing out A(\\\\omega)_exp... \\\" \\n #enexp = enexp-efermi\\n write_sftot_c(vardct, enexp, ftot)\\n print \\\" calc_sf_c_serial :: Done.\\\" \\n return enexp, ftot, sfkb_c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57f52c063fa754865a066bcf71a1da85\",\n \"score\": \"0.4941068\",\n \"text\": \"def calc_spf_gw(minkpt,maxkpt,minband,maxband,wtk,pdos,en,enmin,enmax,res,ims,hartree):\\n import numpy as np;\\n nkpt = maxkpt-minkpt+1\\n nband = maxband-minband+1\\n newdx = 0.005\\n if enmin < en[0] and enmax >= en[-1]: \\n newen = np.arange(en[0],en[-1],newdx)\\n elif enmin < en[0]: \\n newen = np.arange(en[0],enmax,newdx)\\n elif enmax >= en[-1] : \\n newen = np.arange(enmin,en[-1],newdx)\\n else : \\n newen = np.arange(enmin,enmax,newdx)\\n print \\\" ### Interpolation and calculation of A(\\\\omega)_GW... \\\"\\n spftot = np.zeros((np.size(newen)));\\n # Here we interpolate re and im sigma\\n # for each band and k point\\n for ik in xrange(nkpt):\\n ikeff = minkpt+ik-1\\n print \\\" k point = %02d \\\" % (ikeff+1)\\n for ib in xrange(0,nband):\\n ibeff = minband+ib-1\\n interpres = interp1d(en, res[ik,ib], kind = 'linear', axis = -1)\\n interpims = interp1d(en, ims[ik,ib], kind = 'linear', axis = -1)\\n tmpres = interpres(newen)\\n #redenom = newen + efermi - hartree[ik,ib] - interpres(newen)\\n redenom = newen - hartree[ikeff,ibeff] - interpres(newen)\\n #print \\\"ik ib minband maxband ibeff hartree[ik,ib]\\\", ik, ib, minband, maxband, ibeff, hartree[ik,ib]\\n tmpim = interpims(newen)\\n spfkb = wtk[ikeff] * pdos[ib] * abs(tmpim)/np.pi/(redenom**2 + tmpim**2)\\n spftot += spfkb \\n outnamekb = \\\"spf_gw-k\\\"+str(\\\"%02d\\\"%(ikeff+1))+\\\"-b\\\"+str(\\\"%02d\\\"%(ibeff+1))+\\\".dat\\\"\\n outfilekb = open(outnamekb,'w')\\n for ien in xrange(np.size(newen)) :\\n outfilekb.write(\\\"%8.4f %12.8e %12.8e %12.8e %12.8e\\\\n\\\" % (newen[ien], spfkb[ien], redenom[ien], tmpres[ien], tmpim[ien]))\\n outfilekb.close()\\n return newen, spftot\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea7f5f1a94c76f7f512e8494a0eedc4\",\n \"score\": \"0.49386457\",\n \"text\": \"def gaussianElimination(aMat): # for square matrix\\n matrix = copy.deepcopy(aMat)\\n num_row = len(matrix) # number of row\\n ESP = 0.0000001\\n for k in range(num_row): # forward elimination\\n max_column_elem = abs(matrix[k][k])\\n ir = k\\n if k != num_row:\\n for i in range(k+1, num_row):\\n ab = abs(matrix[i][k])\\n if ab >= max_column_elem:\\n max_column_elem = ab\\n ir = i # row number of max coefficient\\n if max_column_elem < ESP:\\n print(\\\"Can't resolve\\\")\\n return []\\n if ir != k: # swap row\\n D = matrix[k]\\n matrix[k] = matrix[ir]\\n matrix[ir] = D\\n for i in range(k+1, num_row):\\n alfa = matrix[i][k] / matrix[k][k] # each element / max element\\n for j in range(k, num_row+1):\\n matrix[i][j] -= alfa * matrix[k][j] # subtract same column\\n\\n print(\\\"Forward\\\", matrix)\\n B = np.zeros((num_row, 1))\\n for i in range(num_row - 1, -1, -1): # backward elimination\\n T = matrix[i][num_row] # last column\\n for j in range(i + 1, num_row):\\n T -= matrix[i][j] * matrix[j][num_row]\\n matrix[i][num_row] = T / matrix[i][i]\\n B[i][0] = matrix[i][num_row]\\n return B\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e6be8944497c82ebf4ffdc6fd6f7bbc\",\n \"score\": \"0.4938389\",\n \"text\": \"def part_1b(x):\\n sigma = 1 # standard deviation\\n x_u = np.linspace(-12, 12, 48) # centers at -12 to 12 @ every .5 [48x1]\\n\\n h = np.zeros((len(x),len(x_u))) # [1000x48]\\n for j in range(len(x_u)): # 1-48\\n for i in range(len(x)): # 1-1000\\n h[i,j] = math.exp((-abs(x[i]-x_u[j])**2)/sigma**2)\\n\\n fig2 = plt.figure()\\n plt.plot(x_u, h.T)#,\\\":\\\")\\n plt.title(\\\"Part B: Radial Basis Functions\\\")\\n plt.xlabel(\\\"n = 48, -12 < n < 12\\\")\\n plt.ylabel(\\\"axis=0, mu = 0, sigma = 1, size(x)\\\")\\n plt.ylim(bottom = 0)\\n plt.ylim(top = 1)\\n # plt.legend()\\n\\n return h # [1000x48]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d42cb31d49ae7c12252a0bb35386b7a\",\n \"score\": \"0.493811\",\n \"text\": \"def mult_arrays(a,b,p):\\r\\n ds = curve_points(a,b,p).drop('id', axis = 1)\\r\\n ds['point'] = ds[\\\"x\\\"].map(str) + \\\",\\\" + ds[\\\"y\\\"].map(str)\\r\\n n = len(ds)\\r\\n X = pd.DataFrame(index=range(n*n), columns=['x_X','y_X','point_X'])\\r\\n Y = pd.DataFrame(index=range(n*n), columns=['x_Y','y_Y','point_Y'])\\r\\n k = np.empty(n*n, dtype=int)\\r\\n for i,row1 in ds.iterrows():\\r\\n if i == n-1:\\r\\n xp, yp = None, None\\r\\n xq, yq = None, None\\r\\n else:\\r\\n xp, yp = tuple(map(int, row1['point'].split(',')))\\r\\n xq, yq = tuple(map(int, row1['point'].split(',')))\\r\\n for j in range(n):\\r\\n X.at[i * n + j,'x_X'],X.at[i * n + j,'y_X'] = xp,yp\\r\\n k[i * n + j] = j\\r\\n if j == 0: # x*0 = INF\\r\\n Y.at[i * n + j,'x_Y'],Y.at[i * n + j,'y_Y'] = None, None #INF is last entry in ds\\r\\n elif j == 1: # x*1 = x = y\\r\\n Y.at[i * n + j, 'x_Y'],Y.at[i * n + j, 'y_Y'] = X.at[i * n + j, 'x_X'], X.at[i * n + j, 'y_X']\\r\\n else:\\r\\n point = add(xp,yp,xq,yq,a,p)\\r\\n Y.at[i * n + j, 'x_Y'], Y.at[i * n + j, 'y_Y'] = point[0], point[1]\\r\\n xq, yq = point[0], point[1]\\r\\n return(X,k,Y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d47687571571cbea324c67ce843fb166\",\n \"score\": \"0.49304563\",\n \"text\": \"def getInitialEggholderDataABO(num_points,max_time):\\n lower_x = -512\\n upper_x = 512\\n t = getInitialInputData(num_points, 1, lower_x, max_time)\\n X_ = getInitialInputData(num_points, 1, lower_x, upper_x)\\n X = numpy.concatenate( (t,X_), axis=1 )\\n y = ObjectiveFunctions.eggholder_bulk(X)\\n return X, y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087eca8a9a97743c7adcd0213d00fad6\",\n \"score\": \"0.4924656\",\n \"text\": \"def interior_points(self):\\n import itertools\\n all_dims = [range(self.ghost_depth,self.shape[x]-self.ghost_depth) for x in range(0,self.dim)]\\n for item in itertools.product(*all_dims):\\n yield tuple(item)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62778447fa52bb04eb7c580943cbaa28\",\n \"score\": \"0.49222958\",\n \"text\": \"def culaDeviceZgglse(m, n, p, a, lda, b, ldb, c, d, x):\\r\\n \\r\\n raise NotImplementedError('CULA Dense required')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf0895986443cb8a0b8cc8a4b6189e59\",\n \"score\": \"0.4918374\",\n \"text\": \"def E(q):\\n\\n # We're going to use a 2D python array at first.\\n euler_array = [[0 for x in range(len(q[\\\"vertices\\\"]))] for y in range(len(q[\\\"vertices\\\"]))]\\n for out_vertex in range(np.size(q[\\\"vertices\\\"])):\\n for in_vertex in range(np.size(q[\\\"vertices\\\"])):\\n # Set Kronecker symbol\\n kronecker = 0\\n if out_vertex == in_vertex:\\n kronecker = 1\\n\\n # iterate through edges and find edges with appropriate head and tail.\\n edges = [edge for edge in q[\\\"edges\\\"] if head(edge) == in_vertex and tail(edge) == out_vertex]\\n euler_array[out_vertex][in_vertex] = kronecker - len(edges)\\n\\n return np.matrix(euler_array)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d74ba649c9804fe8c7f85d64b1fb729\",\n \"score\": \"0.4916504\",\n \"text\": \"def scatter_points(self):\\n return np.transpose(np.concatenate((np.ones(self.h_eigs.size) * self.pq, np.flatten(self.h_eigs))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f18ce0ce7cb35898f5dd814cde105482\",\n \"score\": \"0.49138215\",\n \"text\": \"def create_matE_grandeur(grandeur, epsilon = 0.8, chemin_data = \\\"data/datasets/\\\",\\\\\\n chemin_mat = \\\"data/matrices/\\\"):\\n t1 = time.time();\\n data = pd.read_csv(chemin_data+'dataset_'+grandeur+'.csv')\\n if \\\"timestamp\\\" in data.columns.tolist():\\n data = data.set_index(\\\"timestamp\\\");\\n else:\\n data = data.set_index(\\\"Unnamed: 0\\\")\\n \\n logging.debug(\\\"correlation_sax = %s columns = %s\\\",grandeur, data.columns.tolist())\\n \\n dict_similitude = create_dico_similarite(data, epsilon)\\n supp_dict_similitude = supprimer_les_grands_distances(dict_similitude, 10);\\n dico_gp = dict(); cols = data.columns.tolist(); \\n df = pd.DataFrame(index = cols, columns = cols);\\n for cle, dico_valeur in supp_dict_similitude.items():\\n for cle_dico_valeur, valeur in dico_valeur.items():\\n arete0 = cle.split(\\\"_\\\")[0].upper();\\n arete1 = cle_dico_valeur.split(\\\"_\\\")[0].upper();\\n df.loc[arete0, arete1] = valeur;\\n df.loc[arete1, arete0] = valeur;\\n \\n df = df.apply(lambda x: (100-x)/100) \\n df[df == 100] = 1\\n for row, col in fct_aux.range_2d(cols):\\n dico_gp[(row, col)] = df.loc[row, col]\\n \\n df.fillna(0, inplace = True)\\n df.to_csv(chemin_mat+'matrice_adjacence_proba_'+grandeur+'.csv')\\n logging.debug(\\\"correlation_sax: grandeur = %s termine en = %s\\\",grandeur, time.time()-t1)\\n return dico_gp;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"adfd5032d910bd5c733f0b138523625f\",\n \"score\": \"0.4912536\",\n \"text\": \"def gue_eigs(N = 10):\\n A = np.random.randn(N, N) + 1j * np.random.randn(N, N) \\n B = (2 ** -1) * (A + np.conj(A.T)) \\n D = np.linalg.eigvals(B) \\n D = np.real(D)\\n return D\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dad920d00e6591a99a9e65602757fdd\",\n \"score\": \"0.49088925\",\n \"text\": \"def reduced(self):\\r\\n m = self.gauss()\\r\\n nr, nc = m.shape\\r\\n for i in range(nr-1, -1, -1):\\r\\n # print m.formated()\\r\\n d = float(m[i, i])\\r\\n for j in range(i):\\r\\n for k in range(nc-1, i-1, -1):\\r\\n m[j,k] -= m[i,k] * m[j, i] / d\\r\\n # print m.formated()\\r\\n m[i, i] /= d\\r\\n for j in range(nr, nc):\\r\\n m[i, j] /= d\\r\\n # print m.formated()\\r\\n return m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"471ef3a8c0e3e6d73a318a6a0116f773\",\n \"score\": \"0.49068865\",\n \"text\": \"def get_eps_pmf(self):\\r\\n\\t\\treturn self.deagg_matrix.fold_axes([-6,-5,-4,-3,-1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"238188f2dd301d3a138f27cf98372359\",\n \"score\": \"0.49057457\",\n \"text\": \"def diamond(row, column, b_m, offset, size, ml):\\n\\n avg = []\\n\\n \\n\\n if row - size >= 0:\\n avg.append(b_m[row-size,column])\\n if column - size >= 0:\\n avg.append(b_m[row,column-size])\\n if row + size < ml:\\n avg.append(b_m[row+size,column])\\n if column + size < ml:\\n avg.append(b_m[row,column+size])\\n #print avg\\n\\n avgg = np.average(avg)\\n\\n # print 'avg', avgg\\n # print 'offset', offset\\n \\n\\n\\n\\n b_m[row,column] = avgg + offset\\n # if x == 2 and y == 1:\\n # b_m[x,y] = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaa832b74a3489291502dfa8c4c05549\",\n \"score\": \"0.49042434\",\n \"text\": \"def getAccel(x, a):\\n r3 = np.power(np.power(x[0], 2) + np.power(x[1], 2), 1.5)\\n a[0] = -GM*x[0]/r3\\n a[1] = -GM*x[1]/r3 \\n return (x, a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f58fe49531ffcdee70350e780b51b27a\",\n \"score\": \"0.49038118\",\n \"text\": \"def generate_calibration(single_pnt_cali_d=508,\\n single_pnt_cali_ior=1.6,\\n single_pnt_cali_int=1000,\\n noise_level = 12,\\n ior=1.5,\\n dr=[100, 5000],\\n no_pts=5000,\\n no_cal_pts=50,\\n plot=True,\\n raise_error=True,\\n test=False\\n ):\\n drum = np.array(dr)/1e3\\n d, amp = mie.makeMie_diameter(diameterRangeInMikroMeter=drum,\\n noOfdiameters=no_pts,\\n IOR=ior)\\n\\n df = pd.DataFrame({'d': d, 'amp': amp})\\n # a calibration function is created with no_cal_pts of calibration points. no_cal_pts is steadyly decreased until\\n # the calibration function is bijective\\n valid = False\\n while not valid:\\n no_cal_pts -= 1\\n binedgs = np.logspace(np.log10(df.amp.min()), np.log10(df.amp.max()), no_cal_pts)\\n binedgs[0] -= (binedgs[1] - binedgs[\\n 0]) * 0.01 # this is to ensure the first point is not onthe edge ... for cut function used later\\n\\n mie_cal = df.groupby(pd.cut(df.amp, binedgs)).median()\\n dfstd = df.groupby(pd.cut(df.amp, binedgs)).mad()\\n\\n mie_cal.index = mie_cal.d\\n dfstd.index = mie_cal.d\\n mie_cal['sigma_d'] = dfstd.d\\n mie_cal.index.name = None\\n mie_cal.sort_values('amp', axis=0, inplace=True)\\n\\n # check for bijectivity\\n if not ((mie_cal.d.values[1:] - mie_cal.d.values[:-1]) < 0).sum():\\n valid = True\\n\\n # final conditioning: um2nm, indexname, drop d\\n # mie_cal.drop('d', axis=1, inplace=True)\\n mie_cal.index.name = 'd_nm'\\n mie_cal.index *= 1e3\\n mie_cal.d *= 1e3\\n mie_cal.sigma_d *= 1.e3\\n cali_inst_pre = Calibration(mie_cal)\\n\\n # single point calibration\\n ## solve calibration function ot get amp at calibration diameter\\n ### first guess\\n dt = mie_cal.index[abs(mie_cal.index - single_pnt_cali_d).argmin()]\\n at = mie_cal.loc[dt, 'amp']\\n\\n # cali_inst_at_single_pnt_calid_d = cali_inst_pre.calibrationFunction(single_pnt_cali_d)\\n\\n ### solve\\n cali_inst_at_single_pnt_calid_d = optimize.fsolve(lambda x: cali_inst_pre.calibrationFunction(x) - single_pnt_cali_d, at)\\n\\n ## scale for ior mismatch\\n if ior == single_pnt_cali_ior:\\n scale_ioradj = 1\\n single_pnt_cali_int_pre = cali_inst_at_single_pnt_calid_d\\n else:\\n # single_pnt_cali_d = 500\\n single_pnt_cali_d *= 1e-3\\n dt, mt = mie.makeMie_diameter(diameterRangeInMikroMeter=[single_pnt_cali_d, single_pnt_cali_d + 1e-3],\\n IOR=single_pnt_cali_ior,\\n noOfdiameters=2)\\n single_pnt_cali_int_pre = mt[0]\\n\\n scale_ioradj = cali_inst_at_single_pnt_calid_d / single_pnt_cali_int_pre\\n\\n ## scale for instrument calibration\\n scale_instrument = single_pnt_cali_int / single_pnt_cali_int_pre\\n\\n ## total scale\\n scale = scale_ioradj * scale_instrument\\n\\n mie_cal.loc[:,'amp'] *= scale\\n\\n if not isinstance(noise_level, type(None)):\\n mie_cal.loc[:, 'amp'] += noise_level\\n\\n cali_inst = Calibration(mie_cal)\\n\\n if plot:\\n f, a = plt.subplots()\\n a.plot(df.d * 1e3, df.amp * scale, label='POPS resp.')\\n cali_inst.plot(ax = a)\\n # a.plot(ampm.index * 1e3, ampm.values * scale, label='POPS resp. smooth')\\n # g, = a.plot(cali.index, cali.values, label='cali')\\n # g.set_linestyle('')\\n # g.set_marker('x')\\n # g.set_markersize(10)\\n # g.set_markeredgewidth(2)\\n g, = a.plot(single_pnt_cali_d * 1e3, single_pnt_cali_int, label='single ptn cal')\\n g.set_linestyle('')\\n g.set_marker('x')\\n g.set_markersize(10)\\n g.set_markeredgewidth(2)\\n g.set_label('single pt. cali.')\\n # # st.plot(ax = a)\\n # a.loglog()\\n a.legend()\\n # return dft\\n # return cali_inst, a\\n\\n ##########\\n # start of old mie calcultations\\n old = False\\n if old:\\n dr = np.array(dr)/1000\\n\\n single_pnt_cali_d *= 1e-3\\n # rr = dr / 2 / 1000\\n\\n d, amp = mie.makeMie_diameter(noOfdiameters=no_pts,\\n diameterRangeInMikroMeter = dr,\\n # radiusRangeInMikroMeter=rr,\\n IOR=ior)\\n ds = pd.Series(amp, d)\\n\\n cal_d = pd.Series(index=np.logspace(np.log10(dr[0]), np.log10(dr[1]), no_cal_pts + 2)[1:-1])\\n\\n if test:\\n return cal_d\\n\\n # single point calibration\\n if ior == single_pnt_cali_ior:\\n ds_spc = ds\\n else:\\n d, amp = mie.makeMie_diameter(noOfdiameters=no_pts,\\n diameterRangeInMikroMeter = dr,\\n # radiusRangeInMikroMeter=rr,\\n IOR=single_pnt_cali_ior)\\n ds_spc = pd.Series(amp, d)\\n\\n # rolling, the issue here is that I am rolling over windows of diameter rather then windows of amp, which would be better\\n ampm = ds.rolling(int(no_pts / no_cal_pts), center=True).mean()\\n\\n #\\n cali = ampm.append(cal_d).sort_index().interpolate().reindex(cal_d.index)\\n\\n spc_point = ds_spc.append(pd.Series(index=[single_pnt_cali_d])).sort_index().interpolate().reindex(\\n [single_pnt_cali_d]) # .values[0]\\n scale = single_pnt_cali_int / spc_point.values[0]\\n\\n cali *= scale\\n cali.index *= 1e3\\n\\n cali_inst_pre = pd.DataFrame(cali, columns=['amp'])\\n cali_inst_pre['d'] = cali_inst_pre.index\\n cali_inst_pre = Calibration(cali_inst_pre)\\n\\n if raise_error:\\n ct = cali.values\\n if (ct[1:] - ct[:-1]).min() < 0:\\n raise ValueError(\\n 'Clibration function is not bijective. usually decreasing the number of calibration points will help!')\\n\\n cal_fkt_test = cali_inst_pre.calibrationFunction(cali_inst_pre.data.amp.values)\\n if not np.all(~np.isnan(cal_fkt_test)):\\n raise ValueError(\\n 'Clibration function is not bijective. usually decreasing the number of calibration points will help!')\\n\\n if plot:\\n f, a = plt.subplots()\\n a.plot(ds.index * 1e3, ds.values * scale, label='POPS resp.')\\n a.plot(ampm.index * 1e3, ampm.values * scale, label='POPS resp. smooth')\\n g, = a.plot(cali.index, cali.values, label='cali')\\n g.set_linestyle('')\\n g.set_marker('x')\\n g.set_markersize(10)\\n g.set_markeredgewidth(2)\\n g, = a.plot(single_pnt_cali_d * 1e3, single_pnt_cali_int, label='single ptn cal')\\n g.set_linestyle('')\\n g.set_marker('o')\\n g.set_markersize(10)\\n g.set_markeredgewidth(2)\\n # st.plot(ax = a)\\n a.loglog()\\n a.legend()\\n return cali_inst_pre, a\\n ###### end of old 2\\n\\n return cali_inst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"722bdb8749ada409c0d6300f5a887e47\",\n \"score\": \"0.4902075\",\n \"text\": \"def _fit_reconstruction():\\n\\n h = self.encode(x)\\n r = self.decode(h)\\n\\n dedr = -( x/r - (1 - x)/(1 - r) ) \\n\\n a = r*(1-r)\\n b = h*(1-h)\\n \\n od = a * dedr\\n oe = b * numpy.dot(od, self.W)\\n\\n gW = x[ :, :, None] * oe[ :, None, : ]\\n\\n return gW.mean(0), od.mean(0), oe.mean(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8d92351e9ff611e6639f093fd27bcc4\",\n \"score\": \"0.48936796\",\n \"text\": \"def gaussian(self,c,sigma):\\n d = 2*pi*sigma*sigma\\n ax = exp(-power(self.neigx-c[0],2)/d)\\n ay = exp(-power(self.neigy-c[1],2)/d)\\n return outer(ax,ay) # the external product gives a matrix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f5e03b1e06233d546663e66c2b3886a7\",\n \"score\": \"0.4893111\",\n \"text\": \"def find_four_point_transform(src_points, dst_points):\\n A = []\\n for i in range(4):\\n aui = dst_points[i][0]\\n avi = dst_points[i][1]\\n bui = src_points[i][0]\\n bvi = src_points[i][1]\\n A.append([bui, bvi, 1, 0, 0, 0, -bui * aui, -bvi * aui, -aui])\\n A.append([0, 0, 0, bui, bvi, 1, -bui * avi, -bvi * avi, -avi])\\n A = np.array(A)\\n A=np.float32(A)\\n #print A\\n AT = np.transpose(A)\\n M = np.dot(AT, A)\\n val, vec = np.linalg.eig(M)\\n index = 0\\n mini = val[index]\\n # print np.dot(M,(vec[:,index]))\\n # print val[index]*vec[:,index]\\n\\n for i in range(vec.shape[0]):\\n if (val[i] < mini):\\n index = i\\n mini = val[i]\\n a = vec[:, index]\\n # print a\\n lst = []\\n for b in range(3):\\n lst.append([a[0 + 3 * b], a[1 + 3 * b], a[2 + 3 * b]])\\n ans=np.array(lst)\\n ans=ans/ans[2,2]\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"643e2799a7f1aefb37a0ab267567dc5c\",\n \"score\": \"0.48883772\",\n \"text\": \"def gauss(s):\\n\\n size = s * 3\\n x, y = np.meshgrid(np.arange(-size,size + 1), np.arange(-size,size + 1))\\n kernel = np.exp(-(x**2 + y**2 / float(s)))\\n kernel = kernel / kernel.sum()\\n \\n return x, y, kernel\",\n \"title\": \"\"\n },\n {\n \"docid\": \"06bd44a0bc916ac1768e80bea374da51\",\n \"score\": \"0.4887511\",\n \"text\": \"def accvec2(i, x, G, m, epsilon): #Added epsilon softening now\\n x_i = x[i]\\n x_j = remove_i(x, i)\\n m_j = remove_i(m, i)\\n \\n #diff = x_j - x_i\\n diff = x_j - x_i + epsilon\\n \\n mag3 = np.sum(diff**2, axis=1)**1.5\\n result = G * np.sum(diff * (m_j / mag3)[:,np.newaxis], axis=0)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"732611c88337413d191f55f1fad9d415\",\n \"score\": \"0.4887131\",\n \"text\": \"def forward(self, x, e, ij):\\n xnew = torch.empty((x.shape[0], self.Wc.shape[0]), dtype=torch.float)\\n for center in range(ij.shape[0]): # For all nodes in the graph\\n nhbrs = ij[center, :, 0] # Neighbourhood around center\\n xni = x[center, :] # Center vertex\\n xnj = x[nhbrs].sum(dim=0) / nhbrs.numel() # Neighborhood (vertex feats)\\n xej = e[center].sum(dim=0) / nhbrs.numel() # Neighborhood (edge feats)\\n xout = self.Wc @ xni + self.Wn @ xnj + self.We @ xej\\n xnew[center, :] = self.relu(xout)\\n\\n return xnew\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f488687b93c925bf940143cf8c45a5cc\",\n \"score\": \"0.4884402\",\n \"text\": \"def computeEinc(self, kb, msh):\\n if self.numPlws <=0:\\n raise RuntimeError(\\\"No. of excitations (planewave) must be > 0\\\")\\n\\n numCells = len(msh)\\n Einc = np.zeros((numCells,self.numPlws), dtype=np.complex)\\n\\n # start looping for each cell and plw\\n for i in range(self.numPlws):\\n cosPhi = np.cos(self.phiInc[i])\\n sinPhi = np.sin(self.phiInc[i])\\n for j in range(numCells):\\n xc = msh.cells[j].x * cosPhi\\n yc = msh.cells[j].y * sinPhi\\n e0 = exp(-1j*kb*(xc + yc))\\n Einc[j,i] =e0\\n return Einc\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":1699,"cells":{"query_id":{"kind":"string","value":"1f8ff2e7af3dde7f64c880f4c1bb86f9"},"query":{"kind":"string","value":"Asserts tensor has expected shape. If tensor shape and expected_shape, are fully defined, assert they match. Otherwise, add assert op that will validate the shape when tensor is evaluated, and set shape on tensor."},"positive_passages":{"kind":"list like","value":[{"docid":"373c9a769ae38c46cd24b84d92a9126d","score":"0.7199602","text":"def with_shape(expected_shape, tensor):\n if isinstance(tensor, sparse_tensor.SparseTensor):\n raise ValueError('SparseTensor not supported.')\n\n # Shape type must be 1D int32.\n if tensor_util.is_tensor(expected_shape):\n if expected_shape.dtype.base_dtype != dtypes.int32:\n raise ValueError(\n 'Invalid dtype %s for shape %s expected of tensor %s.' % (\n expected_shape.dtype, expected_shape, tensor.name))\n if isinstance(expected_shape, (list, tuple)):\n if not expected_shape:\n expected_shape = np.asarray([], dtype=np.int32)\n else:\n np_expected_shape = np.asarray(expected_shape)\n expected_shape = (\n np.asarray(expected_shape, dtype=np.int32)\n if np_expected_shape.dtype == np.int64 else np_expected_shape)\n if isinstance(expected_shape, np.ndarray):\n if expected_shape.ndim > 1:\n raise ValueError(\n 'Invalid rank %s for shape %s expected of tensor %s.' % (\n expected_shape.ndim, expected_shape, tensor.name))\n if expected_shape.dtype != np.int32:\n raise ValueError(\n 'Invalid dtype %s for shape %s expected of tensor %s.' % (\n expected_shape.dtype, expected_shape, tensor.name))\n\n actual_shape = tensor.get_shape()\n\n if (not actual_shape.is_fully_defined()\n or tensor_util.is_tensor(expected_shape)):\n with ops.name_scope('%s/' % tensor.op.name, values=[tensor]):\n if (not tensor_util.is_tensor(expected_shape)\n and (len(expected_shape) < 1)):\n # TODO(irving): Remove scalar special case\n return array_ops.reshape(tensor, [])\n with ops.control_dependencies([_assert_shape_op(expected_shape, tensor)]):\n result = array_ops.identity(tensor)\n if not tensor_util.is_tensor(expected_shape):\n result.set_shape(expected_shape)\n return result\n\n if (not tensor_util.is_tensor(expected_shape) and\n not actual_shape.is_compatible_with(expected_shape)):\n if (len(expected_shape) < 1) and actual_shape.is_compatible_with([1]):\n # TODO(irving): Remove scalar special case.\n with ops.name_scope('%s/' % tensor.op.name, values=[tensor]):\n return array_ops.reshape(tensor, [])\n raise ValueError('Invalid shape for tensor %s, expected %s, got %s.' % (\n tensor.name, expected_shape, actual_shape))\n\n return tensor","title":""}],"string":"[\n {\n \"docid\": \"373c9a769ae38c46cd24b84d92a9126d\",\n \"score\": \"0.7199602\",\n \"text\": \"def with_shape(expected_shape, tensor):\\n if isinstance(tensor, sparse_tensor.SparseTensor):\\n raise ValueError('SparseTensor not supported.')\\n\\n # Shape type must be 1D int32.\\n if tensor_util.is_tensor(expected_shape):\\n if expected_shape.dtype.base_dtype != dtypes.int32:\\n raise ValueError(\\n 'Invalid dtype %s for shape %s expected of tensor %s.' % (\\n expected_shape.dtype, expected_shape, tensor.name))\\n if isinstance(expected_shape, (list, tuple)):\\n if not expected_shape:\\n expected_shape = np.asarray([], dtype=np.int32)\\n else:\\n np_expected_shape = np.asarray(expected_shape)\\n expected_shape = (\\n np.asarray(expected_shape, dtype=np.int32)\\n if np_expected_shape.dtype == np.int64 else np_expected_shape)\\n if isinstance(expected_shape, np.ndarray):\\n if expected_shape.ndim > 1:\\n raise ValueError(\\n 'Invalid rank %s for shape %s expected of tensor %s.' % (\\n expected_shape.ndim, expected_shape, tensor.name))\\n if expected_shape.dtype != np.int32:\\n raise ValueError(\\n 'Invalid dtype %s for shape %s expected of tensor %s.' % (\\n expected_shape.dtype, expected_shape, tensor.name))\\n\\n actual_shape = tensor.get_shape()\\n\\n if (not actual_shape.is_fully_defined()\\n or tensor_util.is_tensor(expected_shape)):\\n with ops.name_scope('%s/' % tensor.op.name, values=[tensor]):\\n if (not tensor_util.is_tensor(expected_shape)\\n and (len(expected_shape) < 1)):\\n # TODO(irving): Remove scalar special case\\n return array_ops.reshape(tensor, [])\\n with ops.control_dependencies([_assert_shape_op(expected_shape, tensor)]):\\n result = array_ops.identity(tensor)\\n if not tensor_util.is_tensor(expected_shape):\\n result.set_shape(expected_shape)\\n return result\\n\\n if (not tensor_util.is_tensor(expected_shape) and\\n not actual_shape.is_compatible_with(expected_shape)):\\n if (len(expected_shape) < 1) and actual_shape.is_compatible_with([1]):\\n # TODO(irving): Remove scalar special case.\\n with ops.name_scope('%s/' % tensor.op.name, values=[tensor]):\\n return array_ops.reshape(tensor, [])\\n raise ValueError('Invalid shape for tensor %s, expected %s, got %s.' % (\\n tensor.name, expected_shape, actual_shape))\\n\\n return tensor\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"81afcb0928fe5aa288d1028b6c28ecf6","score":"0.84670264","text":"def _assert_shape_op(expected_shape, actual_tensor):\n with ops.name_scope('assert_shape', values=[actual_tensor]) as scope:\n actual_shape = array_ops.shape(actual_tensor, name='actual')\n is_shape = _is_shape(expected_shape, actual_tensor, actual_shape)\n return control_flow_ops.Assert(\n is_shape, [\n 'Wrong shape for %s [expected] [actual].' % actual_tensor.name,\n expected_shape,\n actual_shape\n ], name=scope)","title":""},{"docid":"0e43df6b0c1ec9da65de5a686f0eccd6","score":"0.79265434","text":"def dynamic_assert_shape(tensor, shape, name=None):\n\n\tif global_args[\"use_assert\"]:\n\n\t\ttensor_shape = tf.shape(tensor)\n\t\ttensor_shape = tf.cast(tensor_shape, tf.int64)\n\t\t\n\t\texpected_shape = tf.convert_to_tensor(shape)\n\t\texpected_shape = tf.cast(expected_shape, tf.int64)\n\t\t\n\t\tt_name = \"tensor\" if tf.executing_eagerly() else tensor.name\n\n\t\tif isinstance(shape, list):\n\t\t\tassert len(tensor.shape) == len(shape), f\"Tensor shape {tensor_shape} and expected shape {expected_shape} have different lengths\"\n\n\t\tassert_op = tf.assert_equal(tensor_shape, expected_shape, message=f\"Asserting shape of {t_name}\", summarize=10, name=name)\n\n\t\twith tf.control_dependencies([assert_op]):\n\t\t\treturn tf.identity(tensor, name=\"dynamic_assert_shape\")\n\n\telse:\n\t\treturn tensor","title":""},{"docid":"933227132f7daf8cbd31c8194582a308","score":"0.72767884","text":"def with_same_shape(expected_tensor, tensor):\n with ops.name_scope('%s/' % tensor.op.name, values=[expected_tensor, tensor]):\n tensor_shape = expected_tensor.get_shape()\n expected_shape = (\n tensor_shape.as_list() if tensor_shape.is_fully_defined()\n else array_ops.shape(expected_tensor, name='expected_shape'))\n return with_shape(expected_shape, tensor)","title":""},{"docid":"0ecf7f8d7cd9a081d4872f0a80b86f5c","score":"0.7259211","text":"def assert_shape(variable, shape):\n variable.get_shape().assert_is_compatible_with(shape)","title":""},{"docid":"1b63504d2b42a7e8662e844118136dee","score":"0.7234409","text":"def _assert_shape(self, value: torch.Tensor, shape: tuple):\n if not isinstance(value, torch.Tensor):\n raise TypeError(\"value must be torch.Tensor. Got {}\".format(type(value)))\n if value.shape != shape:\n msg = \"Expected value to have shape {0}. Got {1} instead\"\n raise ValueError(msg.format(shape, value.shape))","title":""},{"docid":"70ebca2e6b9fc27d2a998ee3dad56fc7","score":"0.7224563","text":"def _is_shape(expected_shape, actual_tensor, actual_shape=None):\n with ops.name_scope('is_shape', values=[actual_tensor]) as scope:\n is_rank = _is_rank(array_ops.size(expected_shape), actual_tensor)\n if actual_shape is None:\n actual_shape = array_ops.shape(actual_tensor, name='actual')\n shape_equal = _all_equal(\n ops.convert_to_tensor(expected_shape, name='expected'),\n actual_shape)\n return math_ops.logical_and(is_rank, shape_equal, name=scope)","title":""},{"docid":"1d4757ab4e91305b2dc238042e0a7051","score":"0.6975526","text":"def check_tensor_shape(tensor_tf, target_shape):\n result = tf.constant(True)\n for i, target_length in enumerate(target_shape):\n if target_length:\n result = tf.logical_and(\n result,\n tf.equal(tf.constant(target_length), tf.shape(tensor_tf)[i]))\n return result","title":""},{"docid":"5e4fd5a095601ac593aa0c631cac648f","score":"0.69462264","text":"def test_unspecified_shape(self):\n\n tensor = tf.placeholder(dtype=tf.float32)\n\n shape = (9, 2, 3)\n data = np.ones(shape)\n rank = len(shape)\n dims = [utils.get_dimension(tensor, d) for d in range(rank)]\n\n with tf.Session() as sess:\n act_dims = sess.run(dims, {tensor: data})\n for act, exp in zip(act_dims, shape):\n self.assertEqual(act, exp)\n\n invalid_dim = utils.get_dimension(tensor, rank)\n with tf.Session() as sess:\n self.assertRaises(\n tf.errors.InvalidArgumentError,\n sess.run, invalid_dim, {tensor: data})","title":""},{"docid":"43c5981ecfc1a9a990f451d7e4b1057d","score":"0.6627683","text":"def _assert_shapes(ground_truth: np.ndarray, pred: np.ndarray, confidences: np.ndarray, avails: np.ndarray) -> None:\n assert len(pred.shape) == 3, f\"expected 3D (MxTxC) array for pred, got {pred.shape}\"\n num_modes, future_len, num_coords = pred.shape\n\n assert ground_truth.shape == (\n future_len,\n num_coords,\n ), f\"expected 2D (Time x Coords) array for gt, got {ground_truth.shape}\"\n assert confidences.shape == (num_modes,), f\"expected 1D (Modes) array for confidences, got {confidences.shape}\"\n assert np.allclose(np.sum(confidences), 1), \"confidences should sum to 1\"\n assert avails.shape == (future_len,), f\"expected 1D (Time) array for avails, got {avails.shape}\"\n # assert all data are valid\n assert np.isfinite(pred).all(), \"invalid value found in pred\"\n assert np.isfinite(ground_truth).all(), \"invalid value found in gt\"\n assert np.isfinite(confidences).all(), \"invalid value found in confidences\"\n assert np.isfinite(avails).all(), \"invalid value found in avails\"","title":""},{"docid":"2088e5ac45f93b6d496883da0e9153f6","score":"0.6626927","text":"def test_shapes(self, batch_size):\n if batch_size is None:\n init_fn = self._init_fn\n inference_fn = self._inference_fn\n else:\n init_fn = self._batch_init_fn\n inference_fn = self._batch_inference_fn\n\n input_size = 10\n inputs, side_info, _ = _get_dataset(input_size, batch_size)\n input_size = inputs.shape[-1]\n\n # Initialize network.\n gln_params, gln_state = init_fn(next(self._rng), inputs, side_info)\n\n # Test shapes of parameters layer-wise.\n layer_input_size = input_size\n for layer_idx, output_size in enumerate(self._output_sizes):\n name = \"{}/~/{}_layer_{}\".format(self._name, self._name, layer_idx)\n weights = gln_params[name][\"weights\"]\n expected_shape = (output_size, 2**self._context_dim, layer_input_size + 1)\n self.assertEqual(weights.shape, expected_shape)\n\n layer_input_size = output_size\n\n # Test shape of output.\n output_size = sum(self._output_sizes)\n predictions, _ = inference_fn(gln_params, gln_state, inputs, side_info)\n expected_shape = (batch_size, output_size) if batch_size else (output_size,)\n self.assertEqual(predictions.shape, expected_shape)","title":""},{"docid":"218355b9d52278f08cf28ae953093285","score":"0.6610708","text":"def check_shape(value, expected_shape, name=''):\n if value.shape != tuple(expected_shape):\n raise ValueError('Shape mismatch %s: Expected %s, got %s' %\n (name, str(expected_shape), str(value.shape)))","title":""},{"docid":"d2c95b1de8298f2b8f8a4b92ca592c50","score":"0.6519247","text":"def _check_dims_equal(shape_x, shape):\n if shape_x[0] != shape[0] or shape_x[1] != shape[1] or shape_x[4] != \\\n shape[4]:\n check_rule(\"{} and {}\".format(shape_x, shape),\n \"The dimensions N, C1, C0 of shape_x\"\\\n \"and shape must be equal\",\n \"shape_x and shape\")\n if shape[2] != 1 or shape[3] != 1:\n check_rule(\"{} and {}\".format(shape[2], shape[3]),\n \"Dimension H,W must be 1\",\n \"H,W\")","title":""},{"docid":"03cede142c33ceaf928d6d2b1cebc6df","score":"0.6361819","text":"def test_shape(self):\n result = self._class(self._query)\n self.assertSequenceEqual(self._shape, result.shape)","title":""},{"docid":"a3c29f9863aa1297227f71ed40349159","score":"0.6302741","text":"def _check_shape(shape, param_name=\"\"):\n check_shape(shape, min_rank=5, max_rank=5, param_name=param_name)","title":""},{"docid":"5668eae78277b27bb6fde74df6dbbf1a","score":"0.623263","text":"def test_shape(self, n_layers, n_wires, expected_shape):\n\n shape = qml.SimplifiedTwoDesign.shape(n_layers, n_wires)\n assert shape == expected_shape","title":""},{"docid":"ccb7567d64cc9a07b93b1c67bdf575fb","score":"0.6202713","text":"def assert_tensor_equal(self, first, second, message=None):\n shape_message = 'The provided tensors do not have the same shape'\n if message is None:\n shape_message += '.'\n message = 'The tensors are not equal: \\n' + str(first) + '\\n' \\\n + str(second)\n else:\n shape_message += ': ' + message\n self.assertEqual(\n tf.convert_to_tensor(first).shape,\n tf.convert_to_tensor(second).shape,\n shape_message)\n self.assertTrue(tf.reduce_all(first == second), message)","title":""},{"docid":"056b0ea58054a2606ff62407b3ff2cb6","score":"0.61289614","text":"def assert_layout(node, min_size, expected_size, expected_layout):\n assert (\n node.layout.min_width,\n node.layout.min_height,\n ) == min_size, (\n f\"minimum size {node.layout.min_width}x{node.layout.min_height} \"\n f\"doesn't match expected {min_size[0]}x{min_size[1]}\"\n )\n\n assert (\n node.layout.width,\n node.layout.height,\n ) == expected_size, (\n f\"final size {node.layout.width}x{node.layout.height} \"\n f\"doesn't match expected {expected_size[0]}x{expected_size[1]}\"\n )\n _assert_layout(node, expected_layout)","title":""},{"docid":"934d91dc25b44ad0b9395b15a31f905f","score":"0.6106852","text":"def _is_valid_shape(shape):\n\n x_size, y_size = shape\n return x_size == y_size","title":""},{"docid":"e9428fe6363e336d36d877c968360f13","score":"0.59467024","text":"def is_compatible(shape: Tuple[int, ...],\n expected_shape: ShapeDef,\n dim_dict: Optional[NamedDimMap] = None) -> bool:\n if dim_dict is None:\n dim_dict = {}\n\n has_ellipsis = any(isinstance(s, str) and s.endswith('...') for s in expected_shape)\n if not has_ellipsis and len(shape) != len(expected_shape):\n return False\n if len(shape) < len(expected_shape) - int(has_ellipsis):\n return False\n\n s_ind = 0\n for e in expected_shape:\n exp_dim = dim_dict.get(cast(str, e), e)\n\n if exp_dim == -1:\n pass\n elif isinstance(exp_dim, int):\n if exp_dim != shape[s_ind]:\n return False\n elif isinstance(exp_dim, tuple):\n if exp_dim != shape[s_ind:s_ind + len(exp_dim)]:\n return False\n s_ind += len(exp_dim) - 1\n elif isinstance(exp_dim, str):\n if exp_dim.endswith('...'):\n diff = len(shape) - len(expected_shape)\n if exp_dim != '...': # named variadic dimensions\n dim_dict[exp_dim] = shape[s_ind:s_ind + diff + 1]\n s_ind += diff\n else:\n dim_dict[exp_dim] = shape[s_ind]\n\n s_ind += 1\n\n return s_ind >= len(shape) # false when last named variadic dimensions don't match","title":""},{"docid":"072335d29adc4c85f0309cfc5b61acdf","score":"0.5938297","text":"def check_shape(v, shape, field):\n vshape = np.shape(np.asarray(v))\n if vshape != shape:\n raise ValueError(\n \"Wrong array shape for {}! Should be {}, not {}\".format(\n field, shape, vshape\n )\n )","title":""},{"docid":"746779e92eb2308be3fd0bf411ffb74b","score":"0.59308136","text":"def test_default(self):\n exp_dim_0, exp_dim_1, exp_dim_2 = 9, 2, 3\n tensor = tf.placeholder(dtype=tf.float32, shape=[None, exp_dim_1, exp_dim_2])\n data = np.ones((exp_dim_0, exp_dim_1, exp_dim_2))\n\n dim_0 = utils.get_dimension(tensor, 0)\n dim_1 = utils.get_dimension(tensor, 1, ensure_tensor=True)\n dim_2 = utils.get_dimension(tensor, 2)\n\n # dim_0 and dim_1 are tensors and must be evaluated.\n with tf.Session() as sess:\n act_dim_0, act_dim_1 = sess.run([dim_0, dim_1], {tensor: data})\n self.assertEqual(exp_dim_0, act_dim_0)\n self.assertEqual(exp_dim_1, act_dim_1)\n\n # dim_2 is an integer and is equal to the expected value.\n self.assertEqual(int, type(dim_2))\n self.assertEqual(exp_dim_2, dim_2)","title":""},{"docid":"50b65b55ab6f87eb605155b2fa5f63a4","score":"0.5909947","text":"def _check_format_shape(data_format, shape):\n\n check_format(data_format, (\"NCHW\",), param_name=\"x\")\n\n check_shape(shape, min_rank=4, max_rank=4, param_name=\"x\")","title":""},{"docid":"77b0653074169c83c61ac97c048cab0b","score":"0.5895246","text":"def _check_shape(ref_shape, value_shape):\n if operator.ne(list(ref_shape), list(value_shape)):\n raise RuntimeError(\"ref and value must have the same shape !\")\n\n util.check_shape_rule(ref_shape)\n util.check_shape_size(ref_shape, SHAPE_SIZE_LIMIT)","title":""},{"docid":"c8c5bbcff79158bd5f111395117c0b15","score":"0.58944434","text":"def test_batch_shape() -> None:\n print(batch_v.shape)\n print(batch_w.shape)\n print(batch_a.shape)\n print(batch_tdc_label.shape)\n print(batch_cmc_label.shape)\n assert batch_v.shape == torch.Size([32, 12, 128, 128])\n assert batch_w.shape == torch.Size([32, 12, 128, 128])\n # TODO Find correct shape\n # assert batch_a.shape == torch.Size([])\n assert batch_tdc_label.shape == torch.Size([32, 1])\n assert batch_cmc_label.shape == torch.Size([32, 1])","title":""},{"docid":"12dbe8ac469ddad7edb269384d6d73b3","score":"0.58773565","text":"def _check_shape(param, param_shape, name):\n param = np.array(param)\n if param.shape != param_shape:\n raise ValueError(\n \"The parameter '%s' should have the shape of %s, but got %s\"\n % (name, param_shape, param.shape)\n )","title":""},{"docid":"8ba1725c26a4296f43b45cb7299d6bd4","score":"0.5871598","text":"def test_shapes(repeat, error_func):\n set_random_seed_from_args(\"test_shapes\", repeat)\n output_dim, act_funcs = _get_output_dim_act_funcs(error_func)\n n, x, t, N_D = get_random_network_inputs_targets(\n output_dim=output_dim,\n act_funcs=act_funcs,\n )\n y = n.forward_prop(x)\n assert error_func(y, t).shape == (1, N_D)\n assert error_func.dEdy(y, t).shape == (n.output_dim, N_D)\n assert error_func.d2Edy2(y, t).shape == (n.output_dim, n.output_dim, N_D)","title":""},{"docid":"77cb56618b6e91e0305e947501b31820","score":"0.58645195","text":"def _check_shape(param, param_shape, name):\n param = np.array(param)\n if param.shape != param_shape:\n raise ValueError(\"The parameter '%s' should have the shape of %s, \"\n \"but got %s\" % (name, param_shape, param.shape))","title":""},{"docid":"9a699c59659988d2e9935270cbe05280","score":"0.5834905","text":"def check_shape(self, input_shape=None):\n\n\n # check for empty __data possibilities:\n if len(self.__data) == 0:\n return True\n if self.__current_shape == ():\n return True\n\n # check for shape consistency\n if input_shape and input_shape != self.__current_shape:\n return False\n\n return True","title":""},{"docid":"a270b0b1b2c74913c9737d171ea2ce5b","score":"0.5826753","text":"def check_dimension(X, expected_shape=\"n_trials, n_channels, n_times\"):\n ndim = len(expected_shape.split(','))\n if X.ndim != ndim:\n raise ValueError(\"Expected shape ({}) but got input signal with \"\n \"ndim={} and shape {}\".format(\n expected_shape, X.ndim, X.shape))\n\n return X.shape","title":""},{"docid":"5aadc331483da2dc66baa3a62778a959","score":"0.57907325","text":"def _get_batch_size(self, tensor, expected_shape, expected_size):\n try:\n size = self._size(tensor)\n ndim = qml.math.ndim(tensor)\n if ndim > len(expected_shape) or size > expected_size:\n return size // expected_size\n\n except (ValueError, tf.errors.OperatorNotAllowedInGraphError) as err:\n # This except clause covers the usage of tf.function, which is not compatible\n # with `DefaultQubit._get_batch_size`\n if not qml.math.is_abstract(tensor):\n raise err\n\n return None","title":""},{"docid":"dafa7f2449798d78303c1a80662654f8","score":"0.5776042","text":"def validate(self, shape_inference=False) -> bool:\n ...","title":""},{"docid":"ffa5c868b045ff87a4b53d1bd25f8a4d","score":"0.5747235","text":"def _check_shape(logits_shape, label_shape, prim_name=None):\n validator.check('logits_shape', logits_shape, 'label_shape', label_shape, prim_name=prim_name)","title":""},{"docid":"379a3f32db58467cc5f486d5009c11fc","score":"0.5737433","text":"def assert_rank(tensor, expected_rank, name=None):\n if name is None:\n name = tensor.name\n\n expected_rank_dict = {}\n if isinstance(expected_rank, six.integer_types):\n expected_rank_dict[expected_rank] = True\n else:\n for x in expected_rank:\n expected_rank_dict[x] = True\n\n actual_rank = tensor.shape.ndims\n if actual_rank not in expected_rank_dict:\n scope_name = tf.get_variable_scope().name\n raise ValueError(\n \"For the tensor `%s` in scope `%s`, the actual rank \"\n \"`%d` (shape = %s) is not equal to the expected rank `%s`\" %\n (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))","title":""},{"docid":"fe3f59e54bfbbb4ea15f1c2385994bfc","score":"0.57328933","text":"def check_valid_shape(value, shape, cuba_key):\n # FIXME: cuba.yml uses [1] to mean a single value with no shape\n from simphony.core.keywords import KEYWORDS\n\n keyword = KEYWORDS[without_cuba_prefix(cuba_key)]\n\n expected_shape = []\n if list(shape) != [1]:\n expected_shape += list(shape)\n\n if list(keyword.shape) != [1]:\n expected_shape += list(keyword.shape)\n\n if expected_shape == []:\n expected_shape = [1]\n\n value_shape = numpy.asarray(value).shape or [1]\n\n if len(expected_shape) != len(value_shape):\n msg_fmt = (\"Value shape of {value_shape}, \"\n \"not comply with shape: {expected_shape}. \"\n \"len(expected_shape) = {len_expected}, \"\n \"len(value_shape) = {len_value}\")\n error_message = msg_fmt.format(value_shape=value_shape,\n expected_shape=expected_shape,\n len_expected=len(expected_shape),\n len_value=len(value_shape))\n raise ValueError(error_message)\n\n for s1, s2 in zip(expected_shape, value_shape):\n if s1 is None:\n continue\n if s1 != s2:\n raise ValueError((\"Incongruent shapes.\"\n \"Value shape of {value_shape}, \"\n \"not compliant with expected shape: \"\n \"{expected_shape}. \").format(\n expected_shape=expected_shape,\n value_shape=value_shape))","title":""},{"docid":"96d961d2da83b9d761ae31781b6c396d","score":"0.57142156","text":"def _check_shape(shape):\n if not isinstance(shape, Shape):\n raise TypeError(\"shape must be of type ngmix.Shape\")","title":""},{"docid":"9486d73b129e4f12483ff11c4bb9a7b7","score":"0.57122266","text":"def _check_shape(self, shape, ndims):\n\n if len(shape) < ndims:\n raise ValueError(\"Too few dimensions for texture\")\n elif len(shape) > ndims+1:\n raise ValueError(\"Too many dimensions for texture\")\n elif len(shape) == ndims:\n shape = list(shape) + [1,]\n elif len(shape) == ndims+1:\n if shape[-1] > 4:\n raise ValueError(\"Too many channels for texture\")\n return shape","title":""},{"docid":"e9c1892b6ce1015ce702b9064c87039e","score":"0.5696964","text":"def shape_inference_with_inputs(graph_def, sess, feed_dict):\n neuron_nodes = get_neuron_nodes(graph_def)\n tensor_name_map = {}\n for node in neuron_nodes:\n for port, name in enumerate(node.attr[knOutputNames].list.s):\n tensor_name_map['{}:{}'.format(node.name, port)] = name.decode()\n need_shape = []\n for node in neuron_nodes:\n input_shapes = node.attr[knInputShapes].list.shape\n output_names = node.attr[knOutputNames].list.s\n output_shapes = node.attr[knOutputShapes].list.shape\n for name, shape_proto in zip(node.input, input_shapes):\n if not TensorShape(shape_proto).is_fully_defined():\n if ':' not in name:\n name = '{}:0'.format(name)\n need_shape.append(tensor_name_map.get(name, name))\n for name, shape_proto in zip(output_names, output_shapes):\n if not TensorShape(shape_proto).is_fully_defined():\n need_shape.append(name.decode())\n need_shape = [sess.graph.get_tensor_by_name(name) for name in need_shape]\n need_shape_infer = []\n for tensor in need_shape:\n if sess.graph.is_fetchable(tensor.op):\n need_shape_infer.append(tensor)\n else:\n logging.warning('cannot infer shape for tensor {}; it is recommended '\n 'to provide its shape in shape_feed_dict'.format(tensor))\n if need_shape_infer:\n tensors_repr = reprlib.Repr()\n tensors_repr.maxlist = 8\n tensors_repr.maxother = 80\n ts_repr_str = tensors_repr.repr(need_shape_infer)\n logging.warning('running inference to find shape for {} ({} tensors)'\n .format(ts_repr_str, len(need_shape_infer)))\n need_shape_infer_np = sess.run(need_shape_infer, feed_dict)\n inferred_shapes = {ts.name: TensorShape(ts_np.shape) for ts, ts_np in zip(need_shape_infer, need_shape_infer_np)}\n for node in neuron_nodes:\n input_shapes = node.attr[knInputShapes].list.shape\n output_names = node.attr[knOutputNames].list.s\n output_shapes = node.attr[knOutputShapes].list.shape\n for idx, (name, shape_proto) in enumerate(zip(node.input, input_shapes)):\n if not TensorShape(shape_proto).is_fully_defined():\n if ':' not in name:\n name = '{}:0'.format(name)\n name = tensor_name_map.get(name, name)\n input_shapes[idx].CopyFrom(inferred_shapes[name].as_proto())\n for idx, (name, shape_proto) in enumerate(zip(output_names, output_shapes)):\n if not TensorShape(shape_proto).is_fully_defined():\n output_shapes[idx].CopyFrom(inferred_shapes[name.decode()].as_proto())\n return graph_def","title":""},{"docid":"1595ac14afb0d5155868794cee69b202","score":"0.5689258","text":"def _tensor_shape(tensor):\n return tensor.get_shape()\n #return getattr(tensor, '_shape_val')\n #return getattr(tensor, '_keras_shape')","title":""},{"docid":"4d5c658bd36926906930687775ed366a","score":"0.5677638","text":"def assert_dimension(x, y):\n assert_vector(x)\n assert_vector(y)\n assert sum(i for i in x.shape if i > 1) == sum(i for i in y.shape if i > 1)","title":""},{"docid":"7919db19a1a3bf74767850927cf5b395","score":"0.56697375","text":"def check_label_shapes(labels, preds, shape=False):\n if not shape:\n label_shape, pred_shape = len(labels), len(preds)\n else:\n label_shape, pred_shape = labels.shape, preds.shape\n\n if label_shape != pred_shape:\n raise ValueError(\"Shape of labels {} does not match shape of predictions {}\".format(label_shape, pred_shape))","title":""},{"docid":"445fad91f22d38656968eb339cb9d364","score":"0.56657517","text":"def check_param(self):\n if scipy.ndim(self.param['initial_heading'].shape) > 1:\n raise(ValueError, 'initial_heading must have ndim=1')\n\n equal_shape_list = ['x_start_position','y_start_position','flight_speed','release_time']\n for item in equal_shape_list:\n if self.param[item].shape != self.param['initial_heading'].shape:\n print(item)\n print(self.param[item].shape,self.param['initial_heading'].shape)\n raise(ValueError, '{0}.shape must equal initial_heading.shape'.format(item))","title":""},{"docid":"445fad91f22d38656968eb339cb9d364","score":"0.56657517","text":"def check_param(self):\n if scipy.ndim(self.param['initial_heading'].shape) > 1:\n raise(ValueError, 'initial_heading must have ndim=1')\n\n equal_shape_list = ['x_start_position','y_start_position','flight_speed','release_time']\n for item in equal_shape_list:\n if self.param[item].shape != self.param['initial_heading'].shape:\n print(item)\n print(self.param[item].shape,self.param['initial_heading'].shape)\n raise(ValueError, '{0}.shape must equal initial_heading.shape'.format(item))","title":""},{"docid":"c925c3b00022931283d3985c2423c8d7","score":"0.5655158","text":"def boudary_check(self):\r\n assert len(self.sub_dims) == len(self.tensor_shape)\r\n assert len(self.tensor_shape) == len(self.tensor_start_idx)\r\n\r\n for shape_n, sub_dim_n, idx_n in zip(self.tensor_shape, self.sub_dims,\r\n self.tensor_start_idx):\r\n assert sub_dim_n + idx_n <= shape_n\r\n assert idx_n >= 0","title":""},{"docid":"4d78e969f0ad0b585a19f4e86df0de6e","score":"0.56477827","text":"def test_input_shape_and_dtype(\n lstm_model: LSTMModel,\n batch_prev_tkids: torch.Tensor,\n):\n\n try:\n lstm_model = lstm_model.eval()\n lstm_model.pred(batch_prev_tkids)\n except Exception:\n assert False","title":""},{"docid":"9502120d1c84229b45d2858db078b23d","score":"0.5645347","text":"def _check_reduced_shape_valid(ori_shape, reduced_shape, axis, cls_name, arg_name1, arg_name2):\n validator.check_reduce_shape(ori_shape, reduced_shape, axis, cls_name, arg_name1, arg_name2)","title":""},{"docid":"04f38c121576a7046d6cd97aa7822700","score":"0.5634547","text":"def assert_rank(tensor, expected_rank, name=None):\r\n if name is None:\r\n name = tensor.name\r\n\r\n expected_rank_dict = {}\r\n if isinstance(expected_rank, six.integer_types):\r\n expected_rank_dict[expected_rank] = True\r\n else:\r\n for x in expected_rank:\r\n expected_rank_dict[x] = True\r\n\r\n actual_rank = tensor.shape.ndims\r\n if actual_rank not in expected_rank_dict:\r\n scope_name = tf.get_variable_scope().name\r\n raise ValueError(\r\n \"For the tensor `%s` in scope `%s`, the actual rank \"\r\n \"`%d` (shape = %s) is not equal to the expected rank `%s`\" %\r\n (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))","title":""},{"docid":"77362f353a95e85246ecde86278883a3","score":"0.5626419","text":"def set_tensor_shape(tensor, tensor_shape):\n # NOTE: That SOUND LIKE IN PLACE HERE ?\n tensor.set_shape(tensor_shape)\n return tensor","title":""},{"docid":"dffd27e0cdc643c42380bd777a629497","score":"0.5617799","text":"def _check_shapes(in_shape, out_shape):\n try:\n iter(in_shape)\n iter(out_shape)\n except:\n raise TypeError(\"Shape values must be iterables.\")\n if len(in_shape) not in (2, 3, 4):\n raise ValueError(\"Shapes must be of length 2, 3 or 4 (c, x, [y], [z]).\")\n # must be same dimensionality\n if len(in_shape) != len(out_shape):\n raise ValueError(\"Input and output shapes must be of the same number of dimensions.\")\n # must all be > 0\n if any(map(lambda x : x < 1, in_shape)) or any(map(lambda x : x < 1, out_shape)):\n raise ValueError(\"Input and output shapes must consist of entries > 0\")\n \n return True","title":""},{"docid":"979f9b616eb4427aa8cd51ca0f33d5c0","score":"0.5609291","text":"def testSampleShape(self):\n self.assertEqual((self.nC, self.nX, self.nSamples), self.samples.shape)","title":""},{"docid":"9c24d7af8598c74a54d391db99c11011","score":"0.5601215","text":"def check_shapes(self, *args, **kwargs):\r\n pass","title":""},{"docid":"e5aa24576b848a0e9cca40461aff962c","score":"0.5582732","text":"def _verify_compatible_image_shapes(img1, img2):\n shape1 = img1.get_shape().with_rank_at_least(3)\n shape2 = img2.get_shape().with_rank_at_least(3)\n shape1[-3:].assert_is_compatible_with(shape2[-3:])\n\n if shape1.ndims is not None and shape2.ndims is not None:\n for dim1, dim2 in zip(reversed(shape1.dims[:-3]),\n reversed(shape2.dims[:-3])):\n if not (dim1 == 1 or dim2 == 1 or dim1.is_compatible_with(dim2)):\n raise ValueError('Two images are not compatible: %s and %s' % (shape1, shape2))\n\n shape1, shape2 = array_ops.shape_n([img1, img2])\n\n checks = []\n checks.append(control_flow_ops.Assert(\n math_ops.greater_equal(array_ops.size(shape1), 3),\n [shape1, shape2], summarize=10))\n checks.append(control_flow_ops.Assert(\n math_ops.reduce_all(math_ops.equal(shape1[-3:], shape2[-3:])),\n [shape1, shape2], summarize=10))\n return shape1, shape2, checks","title":""},{"docid":"0997e72833e15a168dd48a77d5e152ec","score":"0.55487347","text":"def test_none_dim(self):\n tensor = tf.placeholder(dtype=tf.float32, shape=[9, 23])\n self.assertRaises(TypeError, utils.get_dimension, tensor, None)","title":""},{"docid":"f7114af889dcfcdad0c758c7115453b2","score":"0.554674","text":"def test_shape_simple(self):\n assert_dtype_not_equal(np.dtype('f8'), np.dtype(('f8', (1,))))\n assert_dtype_not_equal(np.dtype(('f8', (1,))), np.dtype(('f8', (1, 1))))\n assert_dtype_not_equal(np.dtype(('f4', (3, 2))), np.dtype(('f4', (2, 3))))","title":""},{"docid":"d4339d9f990909f379101a8cee1d1870","score":"0.55400586","text":"def _validate_shape_key(self, data_shape: Dict[str, Tuple[int, ...]]):\n assert (sorted(list(data_shape.keys())) == sorted(self.types))","title":""},{"docid":"f870f7fc45637c40cbabe806506282db","score":"0.5507647","text":"def _shape_tensor(shape):\n if isinstance(shape, (tuple, list)) and not shape:\n dtype = dtypes.int64\n else:\n dtype = None\n return ops.convert_to_tensor(shape, dtype=dtype, name=\"shape\")","title":""},{"docid":"662713ba7ed7439619e45af6f5a5262a","score":"0.5494885","text":"def validate_shapes(self) -> None:\n\n raise NotImplementedError","title":""},{"docid":"f3c315d86dcbd0331a86e969e511986f","score":"0.54915494","text":"def _check_input_shapes_forward(self, query: Tensor, mask: Tensor,\n values: Tensor) -> None:\n assert query.shape[0] == values.shape[0] == mask.shape[0]\n assert query.shape[1] == 1 == mask.shape[1]\n assert query.shape[2] == self.query_layer.in_features\n assert values.shape[2] == self.key_layer.in_features\n assert mask.shape[2] == values.shape[1]","title":""},{"docid":"aba28ef84530c72a8a5e748ff60b4ed7","score":"0.5476381","text":"def _get_shape_invariants(tensor):\r\n if isinstance(tensor, tf.TensorArray):\r\n shape = None\r\n else:\r\n shape = tensor.shape.as_list()\r\n for i in range(1, len(shape) - 1):\r\n shape[i] = None\r\n return tf.TensorShape(shape)","title":""},{"docid":"b8954b934ff21b295cd86088f5bc88b1","score":"0.5455332","text":"def runTest(self):\n self.assertEqual(self.db.get_input_shape(), (10, 3))","title":""},{"docid":"c2686a1c97bd5415892ced230577290c","score":"0.54496855","text":"def _check_input_shapes_forward(self, query: Tensor, mask: Tensor,\n values: Tensor) -> None:\n assert query.shape[0] == values.shape[0] == mask.shape[0]\n assert query.shape[1] == 1 == mask.shape[1]\n assert query.shape[2] == self.key_layer.out_features\n assert values.shape[2] == self.key_layer.in_features\n assert mask.shape[2] == values.shape[1]","title":""},{"docid":"abd1f213121dfc7e34cdd7b09355c914","score":"0.54447067","text":"def test_output_shape():\n assert sm.summary(toy_data).shape[1] <= toy_data.shape[1]","title":""},{"docid":"a171c1d7862ba7de3ca6535b3fb34f9c","score":"0.544112","text":"def test_output_shapes_and_dtypes(self):\n # asserting the test is correctly defined:\n assert len(self.expected_output_shapes) == len(\n self.expected_output_dtypes), \"Ill-defined test.\"\n\n # computing output tensors:\n actual_output_tensors = self.layer(**self.forward_propagation_kwargs)\n\n # when only one single output tensor is returned:\n if is_tensor(actual_output_tensors):\n actual_output_tensors = [actual_output_tensors]\n\n # ensuring the expected number of outputs is returned:\n self.assertEqual(len(self.expected_output_shapes),\n len(actual_output_tensors))\n\n # checking the shape and data type of each output tensor:\n for i, actual_output in enumerate(actual_output_tensors):\n\n # shape:\n\n subtest_name = \"shape - output {n}\".format(n=i)\n # extracting the actual and expected shapes of the output tensor:\n actual_shape = actual_output.shape\n expected_shape = self.expected_output_shapes[i]\n # checking the shape:\n with self.subTest(subtest_name):\n self.assertEqual(actual_shape, expected_shape)\n\n # data type:\n\n subtest_name = \"dtype - output {n}\".format(n=i)\n # extracting the actual and expected data types of the output\n # tensor:\n actual_dtype = actual_output.dtype\n expected_dtype = self.expected_output_dtypes[i]\n # checking the data type:\n with self.subTest(subtest_name):\n self.assertEqual(actual_dtype, expected_dtype)","title":""},{"docid":"edd68a244eea7a291412dc5721a34015","score":"0.54209","text":"def test_shape_functions(self):\n self.setup()\n\n self.assertEqual(\n self.X.shape[1],\n self.test_object.get_dimensionality(),\n \"get_dimensionality should return the number of columns in X.\",\n )\n self.assertEqual(\n self.X.shape[0],\n self.test_object.get_sample_size(),\n \"get_sample_size should return the number of rows in X.\",\n )\n self.assertEqual(\n 121,\n self.test_object.get_sample_size((1599, 1189)),\n \"get_sample_size should return the number of \"\n + \"rows in the specified partition.\",\n )","title":""},{"docid":"95a2d89c326983d2ac79403690f5c5b5","score":"0.54074335","text":"def test_input_shape_mismatch(\n k: pn.randprocs.covfuncs.CovarianceFunction, shape: ShapeType\n):\n\n if k.input_ndim > 0:\n input_shape = shape + tuple(dim + 1 for dim in k.input_shape)\n\n # `CovarianceFunction.linop`\n with pytest.raises(ValueError):\n k.linop(np.zeros(input_shape))\n\n with pytest.raises(ValueError):\n k.linop(np.ones(input_shape), np.zeros(shape + k.input_shape))\n\n with pytest.raises(ValueError):\n k.linop(np.ones(shape + k.input_shape), np.zeros(input_shape))\n\n # `CovarianceFunction.matrix`\n with pytest.raises(ValueError):\n k.matrix(np.zeros(input_shape))\n\n with pytest.raises(ValueError):\n k.matrix(np.ones(input_shape), np.zeros(shape + k.input_shape))\n\n with pytest.raises(ValueError):\n k.matrix(np.ones(shape + k.input_shape), np.zeros(input_shape))","title":""},{"docid":"2c3894c37e0a3c69d6d939d9bb4f61ec","score":"0.5396281","text":"def is_shape(df, shape):\n try:\n check = np.all(np.equal(df.shape, shape) | (np.equal(shape, [-1, -1]) |\n np.equal(shape, [None, None])))\n assert check\n except AssertionError as e:\n msg = (\"Expected shape: {}\\n\"\n \"\\t\\tActual shape: {}\".format(shape, df.shape))\n e.args = (msg,)\n raise\n return df","title":""},{"docid":"df7ffa3b0e660f4ddc75a5e5ac7923b1","score":"0.53939897","text":"def checkDims(var, varname, parentShape):\n assert (np.isscalar(var)) or (np.all(np.equal(np.array(var).shape,parentShape))), varname+\" dimensions not understood.\"","title":""},{"docid":"a68dcb5b8a00059b5d6de158ed38cc72","score":"0.5390994","text":"def validate_qid_shape(state: np.ndarray,\n qid_shape: Optional[Tuple[int, ...]]) -> Tuple[int, ...]:\n size = state.size\n if qid_shape is None:\n qid_shape = (2,) * (size.bit_length() - 1)\n if size != np.prod(qid_shape, dtype=int):\n raise ValueError(\n 'state.size ({}) is not a power of two or is not a product of the '\n 'qid shape {!r}.'.format(size, qid_shape))\n return qid_shape","title":""},{"docid":"582c1ca5853d05777662d3966825f232","score":"0.5370118","text":"def test_dimension_out_of_bound(self):\n tensor = tf.placeholder(dtype=tf.float32, shape=[9, 23])\n self.assertRaises(IndexError, utils.get_dimension, tensor, 2)\n for i in range(-len(tensor.shape), len(tensor.shape)-1):\n self.assertIsNotNone(utils.get_dimension(tensor, i))\n self.assertRaises(IndexError, utils.get_dimension, tensor, -3)","title":""},{"docid":"2d295fba067bfcd625f3a7142e7f0e48","score":"0.536972","text":"def tensor_shapes_helper(self, resnet_size, with_gpu=False):\n def reshape(shape):\n \"\"\"Returns the expected dimensions depending on if a\n GPU is being used.\n \"\"\"\n # If a GPU is used for the test, the shape is returned (already in NCHW\n # form). When GPU is not used, the shape is converted to NHWC.\n if with_gpu:\n return shape\n return shape[0], shape[2], shape[3], shape[1]\n\n graph = tf.Graph()\n\n with graph.as_default(), self.test_session(\n use_gpu=with_gpu, force_gpu=with_gpu):\n model = imagenet_main.ImagenetModel(\n resnet_size,\n data_format='channels_first' if with_gpu else 'channels_last')\n inputs = tf.random_uniform([1, 224, 224, 3])\n output = model(inputs, training=True)\n\n initial_conv = graph.get_tensor_by_name('initial_conv:0')\n max_pool = graph.get_tensor_by_name('initial_max_pool:0')\n block_layer1 = graph.get_tensor_by_name('block_layer1:0')\n block_layer2 = graph.get_tensor_by_name('block_layer2:0')\n block_layer3 = graph.get_tensor_by_name('block_layer3:0')\n block_layer4 = graph.get_tensor_by_name('block_layer4:0')\n avg_pool = graph.get_tensor_by_name('final_avg_pool:0')\n dense = graph.get_tensor_by_name('final_dense:0')\n\n self.assertAllEqual(initial_conv.shape, reshape((1, 64, 112, 112)))\n self.assertAllEqual(max_pool.shape, reshape((1, 64, 56, 56)))\n\n # The number of channels after each block depends on whether we're\n # using the building_block or the bottleneck_block.\n if resnet_size < 50:\n self.assertAllEqual(block_layer1.shape, reshape((1, 64, 56, 56)))\n self.assertAllEqual(block_layer2.shape, reshape((1, 128, 28, 28)))\n self.assertAllEqual(block_layer3.shape, reshape((1, 256, 14, 14)))\n self.assertAllEqual(block_layer4.shape, reshape((1, 512, 7, 7)))\n self.assertAllEqual(avg_pool.shape, reshape((1, 512, 1, 1)))\n else:\n self.assertAllEqual(block_layer1.shape, reshape((1, 256, 56, 56)))\n self.assertAllEqual(block_layer2.shape, reshape((1, 512, 28, 28)))\n self.assertAllEqual(block_layer3.shape, reshape((1, 1024, 14, 14)))\n self.assertAllEqual(block_layer4.shape, reshape((1, 2048, 7, 7)))\n self.assertAllEqual(avg_pool.shape, reshape((1, 2048, 1, 1)))\n\n self.assertAllEqual(dense.shape, (1, _LABEL_CLASSES))\n self.assertAllEqual(output.shape, (1, _LABEL_CLASSES))","title":""},{"docid":"a98210894a56fce3a4c76067ca450189","score":"0.5363492","text":"def verify_shapes(self, input_sets: np.ndarray, target_output: Optional[np.ndarray]=None) -> None:\n if input_sets.shape[1] != self._input_feature_count:\n raise ValueError(\n \"input doesn't have the right feature count - \" +\n str(input_sets.shape) + \" \" + str(self._layers[0].weights.shape)\n )\n if target_output is not None:\n if input_sets.shape[0] != target_output.shape[0]:\n raise ValueError(\"input and output sizes don't match - shapes \" +\n str(input_sets.shape) + \" \" + str(target_output.shape))\n if target_output.shape[1] != self._layers[-1].neuron_count:\n raise ValueError(\"target_output shape doesn't match output layer - \" +\n str(target_output.shape[1]))","title":""},{"docid":"e830f2d1c9b23c5b8e6d412ba3e9690e","score":"0.5355854","text":"def check_shape(self, weight: List[np.ndarray]) -> None:\n\n invalid_shape = False\n if len(weight) != len(self.layers) - 1:\n invalid_shape = True\n\n for i in range(len(self.layers)-1):\n w = weight[i]\n if np.shape(w) != (self.layers[i], self.layers[i+1]):\n invalid_shape = True\n break\n\n if invalid_shape:\n raise ValueError(\"Invalid size for weight. The size of weight should be the same as self.layers\")","title":""},{"docid":"c921632fefd45063ebfdcf92f6989d2b","score":"0.5343877","text":"def validate(self, dataset_output_shape):\n raise NotImplementedError","title":""},{"docid":"a0c4f311b10f0d82b0560db65f846ad2","score":"0.5327925","text":"def test_shapes(self):\n n_atoms = 5\n n_feat = 10\n batch_size = 3\n max_deg = 10\n min_deg = 0\n topology = GraphTopology(n_feat)\n\n # Degrees from 1 to max_deg inclusive \n # TODO(rbharath): Should this be 0 to max_deg inclusive?\n deg_adj_lists_placeholders = topology.get_deg_adjacency_lists_placeholders()\n assert len(deg_adj_lists_placeholders) == max_deg\n for ind, deg_adj_list in enumerate(deg_adj_lists_placeholders):\n deg = ind + 1\n # Should have shape (?, deg)\n assert deg_adj_list.get_shape()[1] == deg\n\n # Shape of atom_features should be (?, n_feat)\n atom_features = topology.get_atom_features_placeholder()\n assert atom_features.get_shape()[1] == n_feat\n\n # Shape of deg_slice placeholder should be (max_deg+1-min_deg, 2)\n deg_slice = topology.get_deg_slice_placeholder()\n print(\"deg_slice.get_shape()\")\n print(deg_slice.get_shape())\n assert deg_slice.get_shape() == (max_deg + 1 - min_deg, 2)","title":""},{"docid":"2cdb351522dc0e269c37cb16b723780c","score":"0.5325467","text":"def test_shape_equal(self):\n assert_dtype_equal(np.dtype('f8'), np.dtype(('f8', tuple())))\n assert_dtype_equal(np.dtype('f8'), np.dtype(('f8', 1)))\n assert_dtype_equal(np.dtype((int, 2)), np.dtype((int, (2,))))\n assert_dtype_equal(np.dtype(('<f4', (3, 2))), np.dtype(('<f4', (3, 2))))\n d = ([('a', 'f4', (1, 2)), ('b', 'f8', (3, 1))], (3, 2))\n assert_dtype_equal(np.dtype(d), np.dtype(d))","title":""},{"docid":"fa64187a1d7297572bba6eaf8ce61547","score":"0.532174","text":"def test_inference(self):\n image1 = np.random.randn(256, 256, 3).astype('float32')\n image2 = np.random.randn(256, 256, 3).astype('float32')\n uflow = UFlow()\n flow = uflow.infer(image1, image2)\n correct_shape = np.equal(flow.shape, [256, 256, 2]).all()\n self.assertTrue(correct_shape)","title":""},{"docid":"bee2f10786f93a50f1ba6411014efc1c","score":"0.53059673","text":"def _check_axes_shape(self, axes, axes_num=None, layout=None, figsize=(8.0, 6.0)):\r\n\r\n # derived from pandas.tests.test_graphics\r\n visible_axes = self._flatten_visible(axes)\r\n\r\n if axes_num is not None:\r\n self.assertEqual(len(visible_axes), axes_num)\r\n for ax in visible_axes:\r\n # check something drawn on visible axes\r\n self.assertTrue(len(ax.get_children()) > 0)\r\n\r\n if layout is not None:\r\n result = self._get_axes_layout(plotting._flatten(axes))\r\n self.assertEqual(result, layout)\r\n\r\n if figsize is not None:\r\n self.assert_numpy_array_equal(np.round(visible_axes[0].figure.get_size_inches()),\r\n np.array(figsize))","title":""},{"docid":"5a4a9e71225ab02b2ef2f262d713a402","score":"0.53033155","text":"def _check_shapes_input_forward(\n self,\n trg_embed: Tensor,\n encoder_output: Tensor,\n encoder_hidden: Tensor,\n src_mask: Tensor,\n hidden: Tensor = None,\n prev_att_vector: Tensor = None,\n ) -> None:\n assert len(encoder_output.shape) == 3\n if encoder_hidden is not None:\n assert len(encoder_hidden.shape) == 2\n assert encoder_hidden.shape[-1] == encoder_output.shape[-1]\n assert src_mask.shape[1] == 1\n assert src_mask.shape[0] == encoder_output.shape[0]\n assert src_mask.shape[2] == encoder_output.shape[1]\n assert trg_embed.shape[0] == encoder_output.shape[0]\n assert trg_embed.shape[2] == self.emb_size\n if hidden is not None:\n if isinstance(hidden, tuple): # for lstm\n hidden = hidden[0]\n assert hidden.shape[1] == encoder_output.shape[0]\n assert hidden.shape[2] == self.hidden_size\n if prev_att_vector is not None:\n assert prev_att_vector.shape[0] == encoder_output.shape[0]\n assert prev_att_vector.shape[2] == self.hidden_size\n assert prev_att_vector.shape[1] == 1","title":""},{"docid":"ca83036c1154f83a447cf73a6bf4a81b","score":"0.5303013","text":"def validate_specs(action_spec):\n flat_action_spec = tf.nest.flatten(action_spec)\n if len(flat_action_spec) > 1:\n raise ValueError(\n 'Network only supports action_specs with a single action.')\n\n if flat_action_spec[0].shape not in [(), (1, )]:\n raise ValueError(\n 'Network only supports action_specs with shape in [(), (1,)])')","title":""},{"docid":"14f855eb19ea7f422e0699234f3d80a0","score":"0.5296996","text":"def test_len_signature(self):\n x1 = np.array([1, 2, 3])\n x2 = np.array([10, 20, 30])\n inputs0 = ()\n inputs1 = x1 # NOT in a tuple\n inputs2 = (x1, x2)\n\n sig0 = shapes.signature(inputs0)\n sig1 = shapes.signature(inputs1)\n sig2 = shapes.signature(inputs2)\n\n # pylint: disable=g-generic-assert\n self.assertEqual(len(sig0), 0)\n self.assertEqual(len(sig1), 1)\n self.assertEqual(len(sig2), 2)\n # pylint: enable=g-generic-assert","title":""},{"docid":"8fe6ef892452b19eee89cfa5b4bff6fb","score":"0.52899265","text":"def _verify_data(inputs, targets):\n check_value_type('inputs', inputs, ms.Tensor)\n if len(inputs.shape) != 4:\n raise ValueError('Argument inputs must be 4D Tensor')\n check_value_type('targets', targets, (ms.Tensor, int))\n if isinstance(targets, ms.Tensor):\n if len(targets.shape) > 1 or (len(targets.shape) == 1 and len(targets) != len(inputs)):\n raise ValueError('Argument targets must be a 1D or 0D Tensor. If it is a 1D Tensor, '\n 'it should have the same length as inputs.')\n elif inputs.shape[0] != 1:\n raise ValueError('If targets have type of int, batch_size of inputs should equals 1. Receive batch_size {}'\n .format(inputs.shape[0]))","title":""},{"docid":"aa67651e10dab8cf7fe0de4f7598064f","score":"0.5278164","text":"def _check_conv_lstm_input(\n conv_lstm_input_shape,\n data_input_shape,\n n_batch_like_dims):\n shapes_match = all(\n [data_input_shape[i + n_batch_like_dims] == conv_lstm_input_shape[i]\n for i in range(len(conv_lstm_input_shape))])\n if not shapes_match:\n raise ValueError(\"RNN core input_shape does not match data input shape!\")","title":""},{"docid":"d97ff1a1bfd4322a6e13fd8a8764b119","score":"0.52741975","text":"def _shape(tensor):\n return tensor.get_shape().as_list()","title":""},{"docid":"b8c03a59ed32be8e7e0cba155c28b11a","score":"0.5272752","text":"def build(self, input_shape):\n super(ExpectationValue, self).build(input_shape)\n input_shape = tensor_shape.TensorShape(input_shape).as_list()\n if None in input_shape:\n assert ((input_shape[0] == None)) & (\n input_shape.count(None) == 1), \"Only the first dimension can be None, found {}\".format(\n input_shape)\n elif (input_shape[0] > 1):\n assert all([input_shape[i] == 2 for i in range(1, len(input_shape))]), \\\n \"Input vector needs to have shape (x, 2, 2,...,2), found {}\".format(input_shape)\n else:\n assert (input_shape[0] == 1) & (all([input_shape[i] == 2 for i in range(1, len(input_shape))])), \\\n \"Input vector needs to have shape (1, 2, 2,...,2), found {}\".format(input_shape)","title":""},{"docid":"4a1e942620af79c347d6b7277dcda9fa","score":"0.52723396","text":"def _assert_lengths(encoder_size, decoder_size, encoder_inputs, decoder_inputs, decoder_masks):\n if len(encoder_inputs) != encoder_size:\n raise ValueError(\"Encoder length must be equal to the one in bucket,\"\n \" %d != %d.\" % (len(encoder_inputs), encoder_size))\n if len(decoder_inputs) != decoder_size:\n raise ValueError(\"Decoder length must be equal to the one in bucket,\"\n \" %d != %d.\" % (len(decoder_inputs), decoder_size))\n if len(decoder_masks) != decoder_size:\n raise ValueError(\"Weights length must be equal to the one in bucket,\"\n \" %d != %d.\" % (len(decoder_masks), decoder_size))","title":""},{"docid":"02ca990f590b109d50022f4588a5629b","score":"0.5253316","text":"def test_analytical_expectation_no_circuits(self, op_and_sim):\n op = op_and_sim[0]\n sim = op_and_sim[1]\n\n circuit_batch = tf.raw_ops.Empty(shape=(0,), dtype=tf.string)\n empty_params = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.float32)\n empty_ops = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.string)\n\n op_exp = op(circuit_batch, [], empty_params, empty_ops).numpy()\n cirq_exp = batch_util.batch_calculate_expectation([], [], [[]], sim)\n self.assertEqual(op_exp.shape, cirq_exp.shape)","title":""},{"docid":"489e7d3a246a077a57a251bf40995e3f","score":"0.5252905","text":"def test_cnn_model_shape(self):\n data = tf.random.normal([32, 186, 1])\n model = create_cnn_model()\n logits = model(data)\n self.assertEqual(logits.shape, [32, 5])","title":""},{"docid":"37830d7ef3cf955f5c7212f004cdfbef","score":"0.5237124","text":"def test_sample_shape_scalar_arguments(self):\n test_cases = [\n {\"sample_shape\": (), \"expected_shape\": (1,)},\n {\"sample_shape\": (100,), \"expected_shape\": (100, 1)},\n {\"sample_shape\": (100, 10), \"expected_shape\": (100, 10, 1)},\n {\"sample_shape\": (1, 100), \"expected_shape\": (1, 100, 1)},\n ]\n for case in test_cases:\n samples = Bernoulli(0.5).sample(self.rng_key, case[\"sample_shape\"])\n self.assertEqual(samples.shape, case[\"expected_shape\"])","title":""},{"docid":"37cb26ee23244f2ddd111102eb781542","score":"0.5232785","text":"def _check_weights(weight_shape, label_shape, prim_name=None):\n msg_prefix = f'For \\'{prim_name}\\', the' if prim_name else \"The\"\n if weight_shape != label_shape:\n raise ValueError(f\"{msg_prefix} weight_shape[0] must be equal to label_shape[1], \"\n f\"but got weight_shape[0]: {weight_shape} and label_shape[1]: {label_shape}.\")","title":""},{"docid":"124ac8705e72e1e0e2e0f8e1e5232d34","score":"0.522652","text":"def test_model_shape(\n self,\n text_length,\n n_mentions,\n n_linked_mentions,\n no_entity_attention,\n ):\n\n config = copy.deepcopy(self.config)\n config['model_config']['encoder_config'][\n 'no_entity_attention'] = no_entity_attention\n config = ml_collections.FrozenConfigDict(self.config)\n model_config = config.model_config\n encoder_config = model_config.encoder_config\n\n max_length = model_config.encoder_config.max_length\n preprocess_fn = eae_task.EaETask.make_preprocess_fn(config)\n collater_fn = eae_task.EaETask.make_collater_fn(config)\n\n raw_example = test_utils.gen_mention_pretraining_sample(\n text_length, n_mentions, n_linked_mentions, max_length=max_length)\n processed_example = preprocess_fn(raw_example)\n batch = {\n key: np.tile(value, (config.per_device_batch_size, 1))\n for key, value in processed_example.items()\n }\n batch = collater_fn(batch)\n batch = jax.tree_map(np.asarray, batch)\n\n model = eae_encoder.EaEEncoder(**model_config.encoder_config)\n init_rng = jax.random.PRNGKey(0)\n (encoded_output, loss_helpers, _), _ = model.init_with_output(\n init_rng,\n batch,\n deterministic=True,\n method=model.forward,\n )\n\n self.assertEqual(encoded_output.shape,\n (config.per_device_batch_size, encoder_config.max_length,\n encoder_config.hidden_size))\n\n self.assertEqual(loss_helpers['target_mention_encodings'].shape,\n (config.max_mention_targets * config.per_device_batch_size,\n encoder_config.entity_dim))","title":""},{"docid":"b7780f377c8b13706510f2a0c0f82c34","score":"0.52256244","text":"def test_SegmentationEvaluator_inconsistent_shapes(\n num_classes: int, ground_truth: np.ndarray, model_prediction: np.ndarray\n) -> None:\n\n se = SegmentationEvaluator(num_classes)\n\n with pytest.raises(ValueError):\n se.evaluate(ground_truth, model_prediction, model_name=\"MockModel\")","title":""},{"docid":"269b341260b95f40d7b502a206bcecbd","score":"0.5224693","text":"def _check_shapes(self):\n assert len(self.documents) == self.D\n assert self.n_d.shape == (self.D,)\n\n assert self.q_tau.G.shape == (self.W,)\n assert self.G_pi.shape == (self.K,)\n\n assert len(self.q_z) == self.D\n for q_zd, document in zip(self.q_z, self.documents):\n assert q_zd.shape == (len(document), self.K)\n\n assert self.E_s_dk.shape == (self.D, self.K)\n assert self.E_t_kw.shape == (self.K, self.W)\n assert self.E_log_eta.shape == (self.D,)\n assert self.E_log_xi.shape == (self.K,)\n\n self.counts._check_shape(self)","title":""},{"docid":"04799db4aa0ba0da35e85ac376b01f13","score":"0.5219822","text":"def test_none_tensor(self):\n self.assertRaises(TypeError, utils.get_dimension, None, 0)","title":""},{"docid":"65255eed24ce51cb50402e7a81338acc","score":"0.5212433","text":"def shape(tensor):\n return tuple([d.value for d in tensor.get_shape()])","title":""},{"docid":"4814a90640b3899396b6ccc69a0ba0dd","score":"0.5210279","text":"def test_output_shape2():\n assert sm.summary(toy_all_na).shape[1] <= toy_all_na.shape[1]","title":""},{"docid":"65234ca5cedd42fb5e88edba1b7311b7","score":"0.5204047","text":"def shapes(draw, min_ndims=0, max_ndims=3, min_lastdimsize=1, max_side=None):\n rank = draw(hps.integers(min_value=min_ndims, max_value=max_ndims))\n shape = tf.TensorShape(None).with_rank(rank)\n if rank > 0:\n\n def resize_lastdim(x):\n return x[:-1] + (max(x[-1], min_lastdimsize),)\n\n if max_side is None:\n # Apparently we can't pass an explicit None to the Hypothesis strategy?\n shps = hpnp.array_shapes(min_dims=rank, max_dims=rank)\n else:\n shps = hpnp.array_shapes(min_dims=rank, max_dims=rank, max_side=max_side)\n shape = draw(shps.map(resize_lastdim).map(tf.TensorShape))\n return shape","title":""},{"docid":"dabe00531939d6f95970738d6808cecb","score":"0.52039474","text":"def test_tuple_dims(self, dims):\n value = random_unitary(dims)\n self.assertIsInstance(value, Operator)\n self.assertTrue(value.is_unitary())\n self.assertEqual(value.input_dims(), dims)\n self.assertEqual(value.output_dims(), dims)","title":""},{"docid":"4e7adfee56dd55641322a9a8b35dc878","score":"0.5203696","text":"def check_shape(ar: np.array):\n if not len(ar.shape) == 2:\n raise ValueError(f\"Unexpected error converting {ar} to np.array\")","title":""},{"docid":"4e7adfee56dd55641322a9a8b35dc878","score":"0.5203696","text":"def check_shape(ar: np.array):\n if not len(ar.shape) == 2:\n raise ValueError(f\"Unexpected error converting {ar} to np.array\")","title":""},{"docid":"e7f46b69dcd92b7c826ae67f5f72c60e","score":"0.5176949","text":"def test_shape_monster(self):\n assert_dtype_not_equal(\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\n np.dtype(([('a', 'f4', (1, 2)), ('b', 'f8', (1, 3))], (2, 2))))\n assert_dtype_not_equal(\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'i8', (1, 3))], (2, 2))))\n assert_dtype_not_equal(\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\n np.dtype(([('e', 'f8', (1, 3)), ('d', 'f4', (2, 1))], (2, 2))))\n assert_dtype_not_equal(\n np.dtype(([('a', [('a', 'i4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\n np.dtype(([('a', [('a', 'u4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))))","title":""},{"docid":"9f619527e664b8a01e112b95b35c4d5d","score":"0.5157394","text":"def test_1d_inputs(FlowClass):\n with pytest.raises(ValueError) as excinfo:\n FlowClass(1, 2, 2, 2)\n assert \"requires at least 2 dimensions\" in str(excinfo.value)","title":""}],"string":"[\n {\n \"docid\": \"81afcb0928fe5aa288d1028b6c28ecf6\",\n \"score\": \"0.84670264\",\n \"text\": \"def _assert_shape_op(expected_shape, actual_tensor):\\n with ops.name_scope('assert_shape', values=[actual_tensor]) as scope:\\n actual_shape = array_ops.shape(actual_tensor, name='actual')\\n is_shape = _is_shape(expected_shape, actual_tensor, actual_shape)\\n return control_flow_ops.Assert(\\n is_shape, [\\n 'Wrong shape for %s [expected] [actual].' % actual_tensor.name,\\n expected_shape,\\n actual_shape\\n ], name=scope)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e43df6b0c1ec9da65de5a686f0eccd6\",\n \"score\": \"0.79265434\",\n \"text\": \"def dynamic_assert_shape(tensor, shape, name=None):\\n\\n\\tif global_args[\\\"use_assert\\\"]:\\n\\n\\t\\ttensor_shape = tf.shape(tensor)\\n\\t\\ttensor_shape = tf.cast(tensor_shape, tf.int64)\\n\\t\\t\\n\\t\\texpected_shape = tf.convert_to_tensor(shape)\\n\\t\\texpected_shape = tf.cast(expected_shape, tf.int64)\\n\\t\\t\\n\\t\\tt_name = \\\"tensor\\\" if tf.executing_eagerly() else tensor.name\\n\\n\\t\\tif isinstance(shape, list):\\n\\t\\t\\tassert len(tensor.shape) == len(shape), f\\\"Tensor shape {tensor_shape} and expected shape {expected_shape} have different lengths\\\"\\n\\n\\t\\tassert_op = tf.assert_equal(tensor_shape, expected_shape, message=f\\\"Asserting shape of {t_name}\\\", summarize=10, name=name)\\n\\n\\t\\twith tf.control_dependencies([assert_op]):\\n\\t\\t\\treturn tf.identity(tensor, name=\\\"dynamic_assert_shape\\\")\\n\\n\\telse:\\n\\t\\treturn tensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"933227132f7daf8cbd31c8194582a308\",\n \"score\": \"0.72767884\",\n \"text\": \"def with_same_shape(expected_tensor, tensor):\\n with ops.name_scope('%s/' % tensor.op.name, values=[expected_tensor, tensor]):\\n tensor_shape = expected_tensor.get_shape()\\n expected_shape = (\\n tensor_shape.as_list() if tensor_shape.is_fully_defined()\\n else array_ops.shape(expected_tensor, name='expected_shape'))\\n return with_shape(expected_shape, tensor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ecf7f8d7cd9a081d4872f0a80b86f5c\",\n \"score\": \"0.7259211\",\n \"text\": \"def assert_shape(variable, shape):\\n variable.get_shape().assert_is_compatible_with(shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b63504d2b42a7e8662e844118136dee\",\n \"score\": \"0.7234409\",\n \"text\": \"def _assert_shape(self, value: torch.Tensor, shape: tuple):\\n if not isinstance(value, torch.Tensor):\\n raise TypeError(\\\"value must be torch.Tensor. Got {}\\\".format(type(value)))\\n if value.shape != shape:\\n msg = \\\"Expected value to have shape {0}. Got {1} instead\\\"\\n raise ValueError(msg.format(shape, value.shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70ebca2e6b9fc27d2a998ee3dad56fc7\",\n \"score\": \"0.7224563\",\n \"text\": \"def _is_shape(expected_shape, actual_tensor, actual_shape=None):\\n with ops.name_scope('is_shape', values=[actual_tensor]) as scope:\\n is_rank = _is_rank(array_ops.size(expected_shape), actual_tensor)\\n if actual_shape is None:\\n actual_shape = array_ops.shape(actual_tensor, name='actual')\\n shape_equal = _all_equal(\\n ops.convert_to_tensor(expected_shape, name='expected'),\\n actual_shape)\\n return math_ops.logical_and(is_rank, shape_equal, name=scope)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d4757ab4e91305b2dc238042e0a7051\",\n \"score\": \"0.6975526\",\n \"text\": \"def check_tensor_shape(tensor_tf, target_shape):\\n result = tf.constant(True)\\n for i, target_length in enumerate(target_shape):\\n if target_length:\\n result = tf.logical_and(\\n result,\\n tf.equal(tf.constant(target_length), tf.shape(tensor_tf)[i]))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e4fd5a095601ac593aa0c631cac648f\",\n \"score\": \"0.69462264\",\n \"text\": \"def test_unspecified_shape(self):\\n\\n tensor = tf.placeholder(dtype=tf.float32)\\n\\n shape = (9, 2, 3)\\n data = np.ones(shape)\\n rank = len(shape)\\n dims = [utils.get_dimension(tensor, d) for d in range(rank)]\\n\\n with tf.Session() as sess:\\n act_dims = sess.run(dims, {tensor: data})\\n for act, exp in zip(act_dims, shape):\\n self.assertEqual(act, exp)\\n\\n invalid_dim = utils.get_dimension(tensor, rank)\\n with tf.Session() as sess:\\n self.assertRaises(\\n tf.errors.InvalidArgumentError,\\n sess.run, invalid_dim, {tensor: data})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43c5981ecfc1a9a990f451d7e4b1057d\",\n \"score\": \"0.6627683\",\n \"text\": \"def _assert_shapes(ground_truth: np.ndarray, pred: np.ndarray, confidences: np.ndarray, avails: np.ndarray) -> None:\\n assert len(pred.shape) == 3, f\\\"expected 3D (MxTxC) array for pred, got {pred.shape}\\\"\\n num_modes, future_len, num_coords = pred.shape\\n\\n assert ground_truth.shape == (\\n future_len,\\n num_coords,\\n ), f\\\"expected 2D (Time x Coords) array for gt, got {ground_truth.shape}\\\"\\n assert confidences.shape == (num_modes,), f\\\"expected 1D (Modes) array for confidences, got {confidences.shape}\\\"\\n assert np.allclose(np.sum(confidences), 1), \\\"confidences should sum to 1\\\"\\n assert avails.shape == (future_len,), f\\\"expected 1D (Time) array for avails, got {avails.shape}\\\"\\n # assert all data are valid\\n assert np.isfinite(pred).all(), \\\"invalid value found in pred\\\"\\n assert np.isfinite(ground_truth).all(), \\\"invalid value found in gt\\\"\\n assert np.isfinite(confidences).all(), \\\"invalid value found in confidences\\\"\\n assert np.isfinite(avails).all(), \\\"invalid value found in avails\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2088e5ac45f93b6d496883da0e9153f6\",\n \"score\": \"0.6626927\",\n \"text\": \"def test_shapes(self, batch_size):\\n if batch_size is None:\\n init_fn = self._init_fn\\n inference_fn = self._inference_fn\\n else:\\n init_fn = self._batch_init_fn\\n inference_fn = self._batch_inference_fn\\n\\n input_size = 10\\n inputs, side_info, _ = _get_dataset(input_size, batch_size)\\n input_size = inputs.shape[-1]\\n\\n # Initialize network.\\n gln_params, gln_state = init_fn(next(self._rng), inputs, side_info)\\n\\n # Test shapes of parameters layer-wise.\\n layer_input_size = input_size\\n for layer_idx, output_size in enumerate(self._output_sizes):\\n name = \\\"{}/~/{}_layer_{}\\\".format(self._name, self._name, layer_idx)\\n weights = gln_params[name][\\\"weights\\\"]\\n expected_shape = (output_size, 2**self._context_dim, layer_input_size + 1)\\n self.assertEqual(weights.shape, expected_shape)\\n\\n layer_input_size = output_size\\n\\n # Test shape of output.\\n output_size = sum(self._output_sizes)\\n predictions, _ = inference_fn(gln_params, gln_state, inputs, side_info)\\n expected_shape = (batch_size, output_size) if batch_size else (output_size,)\\n self.assertEqual(predictions.shape, expected_shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"218355b9d52278f08cf28ae953093285\",\n \"score\": \"0.6610708\",\n \"text\": \"def check_shape(value, expected_shape, name=''):\\n if value.shape != tuple(expected_shape):\\n raise ValueError('Shape mismatch %s: Expected %s, got %s' %\\n (name, str(expected_shape), str(value.shape)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2c95b1de8298f2b8f8a4b92ca592c50\",\n \"score\": \"0.6519247\",\n \"text\": \"def _check_dims_equal(shape_x, shape):\\n if shape_x[0] != shape[0] or shape_x[1] != shape[1] or shape_x[4] != \\\\\\n shape[4]:\\n check_rule(\\\"{} and {}\\\".format(shape_x, shape),\\n \\\"The dimensions N, C1, C0 of shape_x\\\"\\\\\\n \\\"and shape must be equal\\\",\\n \\\"shape_x and shape\\\")\\n if shape[2] != 1 or shape[3] != 1:\\n check_rule(\\\"{} and {}\\\".format(shape[2], shape[3]),\\n \\\"Dimension H,W must be 1\\\",\\n \\\"H,W\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03cede142c33ceaf928d6d2b1cebc6df\",\n \"score\": \"0.6361819\",\n \"text\": \"def test_shape(self):\\n result = self._class(self._query)\\n self.assertSequenceEqual(self._shape, result.shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3c29f9863aa1297227f71ed40349159\",\n \"score\": \"0.6302741\",\n \"text\": \"def _check_shape(shape, param_name=\\\"\\\"):\\n check_shape(shape, min_rank=5, max_rank=5, param_name=param_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5668eae78277b27bb6fde74df6dbbf1a\",\n \"score\": \"0.623263\",\n \"text\": \"def test_shape(self, n_layers, n_wires, expected_shape):\\n\\n shape = qml.SimplifiedTwoDesign.shape(n_layers, n_wires)\\n assert shape == expected_shape\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccb7567d64cc9a07b93b1c67bdf575fb\",\n \"score\": \"0.6202713\",\n \"text\": \"def assert_tensor_equal(self, first, second, message=None):\\n shape_message = 'The provided tensors do not have the same shape'\\n if message is None:\\n shape_message += '.'\\n message = 'The tensors are not equal: \\\\n' + str(first) + '\\\\n' \\\\\\n + str(second)\\n else:\\n shape_message += ': ' + message\\n self.assertEqual(\\n tf.convert_to_tensor(first).shape,\\n tf.convert_to_tensor(second).shape,\\n shape_message)\\n self.assertTrue(tf.reduce_all(first == second), message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"056b0ea58054a2606ff62407b3ff2cb6\",\n \"score\": \"0.61289614\",\n \"text\": \"def assert_layout(node, min_size, expected_size, expected_layout):\\n assert (\\n node.layout.min_width,\\n node.layout.min_height,\\n ) == min_size, (\\n f\\\"minimum size {node.layout.min_width}x{node.layout.min_height} \\\"\\n f\\\"doesn't match expected {min_size[0]}x{min_size[1]}\\\"\\n )\\n\\n assert (\\n node.layout.width,\\n node.layout.height,\\n ) == expected_size, (\\n f\\\"final size {node.layout.width}x{node.layout.height} \\\"\\n f\\\"doesn't match expected {expected_size[0]}x{expected_size[1]}\\\"\\n )\\n _assert_layout(node, expected_layout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"934d91dc25b44ad0b9395b15a31f905f\",\n \"score\": \"0.6106852\",\n \"text\": \"def _is_valid_shape(shape):\\n\\n x_size, y_size = shape\\n return x_size == y_size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9428fe6363e336d36d877c968360f13\",\n \"score\": \"0.59467024\",\n \"text\": \"def is_compatible(shape: Tuple[int, ...],\\n expected_shape: ShapeDef,\\n dim_dict: Optional[NamedDimMap] = None) -> bool:\\n if dim_dict is None:\\n dim_dict = {}\\n\\n has_ellipsis = any(isinstance(s, str) and s.endswith('...') for s in expected_shape)\\n if not has_ellipsis and len(shape) != len(expected_shape):\\n return False\\n if len(shape) < len(expected_shape) - int(has_ellipsis):\\n return False\\n\\n s_ind = 0\\n for e in expected_shape:\\n exp_dim = dim_dict.get(cast(str, e), e)\\n\\n if exp_dim == -1:\\n pass\\n elif isinstance(exp_dim, int):\\n if exp_dim != shape[s_ind]:\\n return False\\n elif isinstance(exp_dim, tuple):\\n if exp_dim != shape[s_ind:s_ind + len(exp_dim)]:\\n return False\\n s_ind += len(exp_dim) - 1\\n elif isinstance(exp_dim, str):\\n if exp_dim.endswith('...'):\\n diff = len(shape) - len(expected_shape)\\n if exp_dim != '...': # named variadic dimensions\\n dim_dict[exp_dim] = shape[s_ind:s_ind + diff + 1]\\n s_ind += diff\\n else:\\n dim_dict[exp_dim] = shape[s_ind]\\n\\n s_ind += 1\\n\\n return s_ind >= len(shape) # false when last named variadic dimensions don't match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"072335d29adc4c85f0309cfc5b61acdf\",\n \"score\": \"0.5938297\",\n \"text\": \"def check_shape(v, shape, field):\\n vshape = np.shape(np.asarray(v))\\n if vshape != shape:\\n raise ValueError(\\n \\\"Wrong array shape for {}! Should be {}, not {}\\\".format(\\n field, shape, vshape\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746779e92eb2308be3fd0bf411ffb74b\",\n \"score\": \"0.59308136\",\n \"text\": \"def test_default(self):\\n exp_dim_0, exp_dim_1, exp_dim_2 = 9, 2, 3\\n tensor = tf.placeholder(dtype=tf.float32, shape=[None, exp_dim_1, exp_dim_2])\\n data = np.ones((exp_dim_0, exp_dim_1, exp_dim_2))\\n\\n dim_0 = utils.get_dimension(tensor, 0)\\n dim_1 = utils.get_dimension(tensor, 1, ensure_tensor=True)\\n dim_2 = utils.get_dimension(tensor, 2)\\n\\n # dim_0 and dim_1 are tensors and must be evaluated.\\n with tf.Session() as sess:\\n act_dim_0, act_dim_1 = sess.run([dim_0, dim_1], {tensor: data})\\n self.assertEqual(exp_dim_0, act_dim_0)\\n self.assertEqual(exp_dim_1, act_dim_1)\\n\\n # dim_2 is an integer and is equal to the expected value.\\n self.assertEqual(int, type(dim_2))\\n self.assertEqual(exp_dim_2, dim_2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50b65b55ab6f87eb605155b2fa5f63a4\",\n \"score\": \"0.5909947\",\n \"text\": \"def _check_format_shape(data_format, shape):\\n\\n check_format(data_format, (\\\"NCHW\\\",), param_name=\\\"x\\\")\\n\\n check_shape(shape, min_rank=4, max_rank=4, param_name=\\\"x\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77b0653074169c83c61ac97c048cab0b\",\n \"score\": \"0.5895246\",\n \"text\": \"def _check_shape(ref_shape, value_shape):\\n if operator.ne(list(ref_shape), list(value_shape)):\\n raise RuntimeError(\\\"ref and value must have the same shape !\\\")\\n\\n util.check_shape_rule(ref_shape)\\n util.check_shape_size(ref_shape, SHAPE_SIZE_LIMIT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8c5bbcff79158bd5f111395117c0b15\",\n \"score\": \"0.58944434\",\n \"text\": \"def test_batch_shape() -> None:\\n print(batch_v.shape)\\n print(batch_w.shape)\\n print(batch_a.shape)\\n print(batch_tdc_label.shape)\\n print(batch_cmc_label.shape)\\n assert batch_v.shape == torch.Size([32, 12, 128, 128])\\n assert batch_w.shape == torch.Size([32, 12, 128, 128])\\n # TODO Find correct shape\\n # assert batch_a.shape == torch.Size([])\\n assert batch_tdc_label.shape == torch.Size([32, 1])\\n assert batch_cmc_label.shape == torch.Size([32, 1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12dbe8ac469ddad7edb269384d6d73b3\",\n \"score\": \"0.58773565\",\n \"text\": \"def _check_shape(param, param_shape, name):\\n param = np.array(param)\\n if param.shape != param_shape:\\n raise ValueError(\\n \\\"The parameter '%s' should have the shape of %s, but got %s\\\"\\n % (name, param_shape, param.shape)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba1725c26a4296f43b45cb7299d6bd4\",\n \"score\": \"0.5871598\",\n \"text\": \"def test_shapes(repeat, error_func):\\n set_random_seed_from_args(\\\"test_shapes\\\", repeat)\\n output_dim, act_funcs = _get_output_dim_act_funcs(error_func)\\n n, x, t, N_D = get_random_network_inputs_targets(\\n output_dim=output_dim,\\n act_funcs=act_funcs,\\n )\\n y = n.forward_prop(x)\\n assert error_func(y, t).shape == (1, N_D)\\n assert error_func.dEdy(y, t).shape == (n.output_dim, N_D)\\n assert error_func.d2Edy2(y, t).shape == (n.output_dim, n.output_dim, N_D)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77cb56618b6e91e0305e947501b31820\",\n \"score\": \"0.58645195\",\n \"text\": \"def _check_shape(param, param_shape, name):\\n param = np.array(param)\\n if param.shape != param_shape:\\n raise ValueError(\\\"The parameter '%s' should have the shape of %s, \\\"\\n \\\"but got %s\\\" % (name, param_shape, param.shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a699c59659988d2e9935270cbe05280\",\n \"score\": \"0.5834905\",\n \"text\": \"def check_shape(self, input_shape=None):\\n\\n\\n # check for empty __data possibilities:\\n if len(self.__data) == 0:\\n return True\\n if self.__current_shape == ():\\n return True\\n\\n # check for shape consistency\\n if input_shape and input_shape != self.__current_shape:\\n return False\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a270b0b1b2c74913c9737d171ea2ce5b\",\n \"score\": \"0.5826753\",\n \"text\": \"def check_dimension(X, expected_shape=\\\"n_trials, n_channels, n_times\\\"):\\n ndim = len(expected_shape.split(','))\\n if X.ndim != ndim:\\n raise ValueError(\\\"Expected shape ({}) but got input signal with \\\"\\n \\\"ndim={} and shape {}\\\".format(\\n expected_shape, X.ndim, X.shape))\\n\\n return X.shape\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aadc331483da2dc66baa3a62778a959\",\n \"score\": \"0.57907325\",\n \"text\": \"def _get_batch_size(self, tensor, expected_shape, expected_size):\\n try:\\n size = self._size(tensor)\\n ndim = qml.math.ndim(tensor)\\n if ndim > len(expected_shape) or size > expected_size:\\n return size // expected_size\\n\\n except (ValueError, tf.errors.OperatorNotAllowedInGraphError) as err:\\n # This except clause covers the usage of tf.function, which is not compatible\\n # with `DefaultQubit._get_batch_size`\\n if not qml.math.is_abstract(tensor):\\n raise err\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dafa7f2449798d78303c1a80662654f8\",\n \"score\": \"0.5776042\",\n \"text\": \"def validate(self, shape_inference=False) -> bool:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffa5c868b045ff87a4b53d1bd25f8a4d\",\n \"score\": \"0.5747235\",\n \"text\": \"def _check_shape(logits_shape, label_shape, prim_name=None):\\n validator.check('logits_shape', logits_shape, 'label_shape', label_shape, prim_name=prim_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"379a3f32db58467cc5f486d5009c11fc\",\n \"score\": \"0.5737433\",\n \"text\": \"def assert_rank(tensor, expected_rank, name=None):\\n if name is None:\\n name = tensor.name\\n\\n expected_rank_dict = {}\\n if isinstance(expected_rank, six.integer_types):\\n expected_rank_dict[expected_rank] = True\\n else:\\n for x in expected_rank:\\n expected_rank_dict[x] = True\\n\\n actual_rank = tensor.shape.ndims\\n if actual_rank not in expected_rank_dict:\\n scope_name = tf.get_variable_scope().name\\n raise ValueError(\\n \\\"For the tensor `%s` in scope `%s`, the actual rank \\\"\\n \\\"`%d` (shape = %s) is not equal to the expected rank `%s`\\\" %\\n (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe3f59e54bfbbb4ea15f1c2385994bfc\",\n \"score\": \"0.57328933\",\n \"text\": \"def check_valid_shape(value, shape, cuba_key):\\n # FIXME: cuba.yml uses [1] to mean a single value with no shape\\n from simphony.core.keywords import KEYWORDS\\n\\n keyword = KEYWORDS[without_cuba_prefix(cuba_key)]\\n\\n expected_shape = []\\n if list(shape) != [1]:\\n expected_shape += list(shape)\\n\\n if list(keyword.shape) != [1]:\\n expected_shape += list(keyword.shape)\\n\\n if expected_shape == []:\\n expected_shape = [1]\\n\\n value_shape = numpy.asarray(value).shape or [1]\\n\\n if len(expected_shape) != len(value_shape):\\n msg_fmt = (\\\"Value shape of {value_shape}, \\\"\\n \\\"not comply with shape: {expected_shape}. \\\"\\n \\\"len(expected_shape) = {len_expected}, \\\"\\n \\\"len(value_shape) = {len_value}\\\")\\n error_message = msg_fmt.format(value_shape=value_shape,\\n expected_shape=expected_shape,\\n len_expected=len(expected_shape),\\n len_value=len(value_shape))\\n raise ValueError(error_message)\\n\\n for s1, s2 in zip(expected_shape, value_shape):\\n if s1 is None:\\n continue\\n if s1 != s2:\\n raise ValueError((\\\"Incongruent shapes.\\\"\\n \\\"Value shape of {value_shape}, \\\"\\n \\\"not compliant with expected shape: \\\"\\n \\\"{expected_shape}. \\\").format(\\n expected_shape=expected_shape,\\n value_shape=value_shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96d961d2da83b9d761ae31781b6c396d\",\n \"score\": \"0.57142156\",\n \"text\": \"def _check_shape(shape):\\n if not isinstance(shape, Shape):\\n raise TypeError(\\\"shape must be of type ngmix.Shape\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9486d73b129e4f12483ff11c4bb9a7b7\",\n \"score\": \"0.57122266\",\n \"text\": \"def _check_shape(self, shape, ndims):\\n\\n if len(shape) < ndims:\\n raise ValueError(\\\"Too few dimensions for texture\\\")\\n elif len(shape) > ndims+1:\\n raise ValueError(\\\"Too many dimensions for texture\\\")\\n elif len(shape) == ndims:\\n shape = list(shape) + [1,]\\n elif len(shape) == ndims+1:\\n if shape[-1] > 4:\\n raise ValueError(\\\"Too many channels for texture\\\")\\n return shape\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9c1892b6ce1015ce702b9064c87039e\",\n \"score\": \"0.5696964\",\n \"text\": \"def shape_inference_with_inputs(graph_def, sess, feed_dict):\\n neuron_nodes = get_neuron_nodes(graph_def)\\n tensor_name_map = {}\\n for node in neuron_nodes:\\n for port, name in enumerate(node.attr[knOutputNames].list.s):\\n tensor_name_map['{}:{}'.format(node.name, port)] = name.decode()\\n need_shape = []\\n for node in neuron_nodes:\\n input_shapes = node.attr[knInputShapes].list.shape\\n output_names = node.attr[knOutputNames].list.s\\n output_shapes = node.attr[knOutputShapes].list.shape\\n for name, shape_proto in zip(node.input, input_shapes):\\n if not TensorShape(shape_proto).is_fully_defined():\\n if ':' not in name:\\n name = '{}:0'.format(name)\\n need_shape.append(tensor_name_map.get(name, name))\\n for name, shape_proto in zip(output_names, output_shapes):\\n if not TensorShape(shape_proto).is_fully_defined():\\n need_shape.append(name.decode())\\n need_shape = [sess.graph.get_tensor_by_name(name) for name in need_shape]\\n need_shape_infer = []\\n for tensor in need_shape:\\n if sess.graph.is_fetchable(tensor.op):\\n need_shape_infer.append(tensor)\\n else:\\n logging.warning('cannot infer shape for tensor {}; it is recommended '\\n 'to provide its shape in shape_feed_dict'.format(tensor))\\n if need_shape_infer:\\n tensors_repr = reprlib.Repr()\\n tensors_repr.maxlist = 8\\n tensors_repr.maxother = 80\\n ts_repr_str = tensors_repr.repr(need_shape_infer)\\n logging.warning('running inference to find shape for {} ({} tensors)'\\n .format(ts_repr_str, len(need_shape_infer)))\\n need_shape_infer_np = sess.run(need_shape_infer, feed_dict)\\n inferred_shapes = {ts.name: TensorShape(ts_np.shape) for ts, ts_np in zip(need_shape_infer, need_shape_infer_np)}\\n for node in neuron_nodes:\\n input_shapes = node.attr[knInputShapes].list.shape\\n output_names = node.attr[knOutputNames].list.s\\n output_shapes = node.attr[knOutputShapes].list.shape\\n for idx, (name, shape_proto) in enumerate(zip(node.input, input_shapes)):\\n if not TensorShape(shape_proto).is_fully_defined():\\n if ':' not in name:\\n name = '{}:0'.format(name)\\n name = tensor_name_map.get(name, name)\\n input_shapes[idx].CopyFrom(inferred_shapes[name].as_proto())\\n for idx, (name, shape_proto) in enumerate(zip(output_names, output_shapes)):\\n if not TensorShape(shape_proto).is_fully_defined():\\n output_shapes[idx].CopyFrom(inferred_shapes[name.decode()].as_proto())\\n return graph_def\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1595ac14afb0d5155868794cee69b202\",\n \"score\": \"0.5689258\",\n \"text\": \"def _tensor_shape(tensor):\\n return tensor.get_shape()\\n #return getattr(tensor, '_shape_val')\\n #return getattr(tensor, '_keras_shape')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d5c658bd36926906930687775ed366a\",\n \"score\": \"0.5677638\",\n \"text\": \"def assert_dimension(x, y):\\n assert_vector(x)\\n assert_vector(y)\\n assert sum(i for i in x.shape if i > 1) == sum(i for i in y.shape if i > 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7919db19a1a3bf74767850927cf5b395\",\n \"score\": \"0.56697375\",\n \"text\": \"def check_label_shapes(labels, preds, shape=False):\\n if not shape:\\n label_shape, pred_shape = len(labels), len(preds)\\n else:\\n label_shape, pred_shape = labels.shape, preds.shape\\n\\n if label_shape != pred_shape:\\n raise ValueError(\\\"Shape of labels {} does not match shape of predictions {}\\\".format(label_shape, pred_shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445fad91f22d38656968eb339cb9d364\",\n \"score\": \"0.56657517\",\n \"text\": \"def check_param(self):\\n if scipy.ndim(self.param['initial_heading'].shape) > 1:\\n raise(ValueError, 'initial_heading must have ndim=1')\\n\\n equal_shape_list = ['x_start_position','y_start_position','flight_speed','release_time']\\n for item in equal_shape_list:\\n if self.param[item].shape != self.param['initial_heading'].shape:\\n print(item)\\n print(self.param[item].shape,self.param['initial_heading'].shape)\\n raise(ValueError, '{0}.shape must equal initial_heading.shape'.format(item))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"445fad91f22d38656968eb339cb9d364\",\n \"score\": \"0.56657517\",\n \"text\": \"def check_param(self):\\n if scipy.ndim(self.param['initial_heading'].shape) > 1:\\n raise(ValueError, 'initial_heading must have ndim=1')\\n\\n equal_shape_list = ['x_start_position','y_start_position','flight_speed','release_time']\\n for item in equal_shape_list:\\n if self.param[item].shape != self.param['initial_heading'].shape:\\n print(item)\\n print(self.param[item].shape,self.param['initial_heading'].shape)\\n raise(ValueError, '{0}.shape must equal initial_heading.shape'.format(item))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c925c3b00022931283d3985c2423c8d7\",\n \"score\": \"0.5655158\",\n \"text\": \"def boudary_check(self):\\r\\n assert len(self.sub_dims) == len(self.tensor_shape)\\r\\n assert len(self.tensor_shape) == len(self.tensor_start_idx)\\r\\n\\r\\n for shape_n, sub_dim_n, idx_n in zip(self.tensor_shape, self.sub_dims,\\r\\n self.tensor_start_idx):\\r\\n assert sub_dim_n + idx_n <= shape_n\\r\\n assert idx_n >= 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d78e969f0ad0b585a19f4e86df0de6e\",\n \"score\": \"0.56477827\",\n \"text\": \"def test_input_shape_and_dtype(\\n lstm_model: LSTMModel,\\n batch_prev_tkids: torch.Tensor,\\n):\\n\\n try:\\n lstm_model = lstm_model.eval()\\n lstm_model.pred(batch_prev_tkids)\\n except Exception:\\n assert False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9502120d1c84229b45d2858db078b23d\",\n \"score\": \"0.5645347\",\n \"text\": \"def _check_reduced_shape_valid(ori_shape, reduced_shape, axis, cls_name, arg_name1, arg_name2):\\n validator.check_reduce_shape(ori_shape, reduced_shape, axis, cls_name, arg_name1, arg_name2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04f38c121576a7046d6cd97aa7822700\",\n \"score\": \"0.5634547\",\n \"text\": \"def assert_rank(tensor, expected_rank, name=None):\\r\\n if name is None:\\r\\n name = tensor.name\\r\\n\\r\\n expected_rank_dict = {}\\r\\n if isinstance(expected_rank, six.integer_types):\\r\\n expected_rank_dict[expected_rank] = True\\r\\n else:\\r\\n for x in expected_rank:\\r\\n expected_rank_dict[x] = True\\r\\n\\r\\n actual_rank = tensor.shape.ndims\\r\\n if actual_rank not in expected_rank_dict:\\r\\n scope_name = tf.get_variable_scope().name\\r\\n raise ValueError(\\r\\n \\\"For the tensor `%s` in scope `%s`, the actual rank \\\"\\r\\n \\\"`%d` (shape = %s) is not equal to the expected rank `%s`\\\" %\\r\\n (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77362f353a95e85246ecde86278883a3\",\n \"score\": \"0.5626419\",\n \"text\": \"def set_tensor_shape(tensor, tensor_shape):\\n # NOTE: That SOUND LIKE IN PLACE HERE ?\\n tensor.set_shape(tensor_shape)\\n return tensor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dffd27e0cdc643c42380bd777a629497\",\n \"score\": \"0.5617799\",\n \"text\": \"def _check_shapes(in_shape, out_shape):\\n try:\\n iter(in_shape)\\n iter(out_shape)\\n except:\\n raise TypeError(\\\"Shape values must be iterables.\\\")\\n if len(in_shape) not in (2, 3, 4):\\n raise ValueError(\\\"Shapes must be of length 2, 3 or 4 (c, x, [y], [z]).\\\")\\n # must be same dimensionality\\n if len(in_shape) != len(out_shape):\\n raise ValueError(\\\"Input and output shapes must be of the same number of dimensions.\\\")\\n # must all be > 0\\n if any(map(lambda x : x < 1, in_shape)) or any(map(lambda x : x < 1, out_shape)):\\n raise ValueError(\\\"Input and output shapes must consist of entries > 0\\\")\\n \\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"979f9b616eb4427aa8cd51ca0f33d5c0\",\n \"score\": \"0.5609291\",\n \"text\": \"def testSampleShape(self):\\n self.assertEqual((self.nC, self.nX, self.nSamples), self.samples.shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c24d7af8598c74a54d391db99c11011\",\n \"score\": \"0.5601215\",\n \"text\": \"def check_shapes(self, *args, **kwargs):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5aa24576b848a0e9cca40461aff962c\",\n \"score\": \"0.5582732\",\n \"text\": \"def _verify_compatible_image_shapes(img1, img2):\\n shape1 = img1.get_shape().with_rank_at_least(3)\\n shape2 = img2.get_shape().with_rank_at_least(3)\\n shape1[-3:].assert_is_compatible_with(shape2[-3:])\\n\\n if shape1.ndims is not None and shape2.ndims is not None:\\n for dim1, dim2 in zip(reversed(shape1.dims[:-3]),\\n reversed(shape2.dims[:-3])):\\n if not (dim1 == 1 or dim2 == 1 or dim1.is_compatible_with(dim2)):\\n raise ValueError('Two images are not compatible: %s and %s' % (shape1, shape2))\\n\\n shape1, shape2 = array_ops.shape_n([img1, img2])\\n\\n checks = []\\n checks.append(control_flow_ops.Assert(\\n math_ops.greater_equal(array_ops.size(shape1), 3),\\n [shape1, shape2], summarize=10))\\n checks.append(control_flow_ops.Assert(\\n math_ops.reduce_all(math_ops.equal(shape1[-3:], shape2[-3:])),\\n [shape1, shape2], summarize=10))\\n return shape1, shape2, checks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0997e72833e15a168dd48a77d5e152ec\",\n \"score\": \"0.55487347\",\n \"text\": \"def test_none_dim(self):\\n tensor = tf.placeholder(dtype=tf.float32, shape=[9, 23])\\n self.assertRaises(TypeError, utils.get_dimension, tensor, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7114af889dcfcdad0c758c7115453b2\",\n \"score\": \"0.554674\",\n \"text\": \"def test_shape_simple(self):\\n assert_dtype_not_equal(np.dtype('f8'), np.dtype(('f8', (1,))))\\n assert_dtype_not_equal(np.dtype(('f8', (1,))), np.dtype(('f8', (1, 1))))\\n assert_dtype_not_equal(np.dtype(('f4', (3, 2))), np.dtype(('f4', (2, 3))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4339d9f990909f379101a8cee1d1870\",\n \"score\": \"0.55400586\",\n \"text\": \"def _validate_shape_key(self, data_shape: Dict[str, Tuple[int, ...]]):\\n assert (sorted(list(data_shape.keys())) == sorted(self.types))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f870f7fc45637c40cbabe806506282db\",\n \"score\": \"0.5507647\",\n \"text\": \"def _shape_tensor(shape):\\n if isinstance(shape, (tuple, list)) and not shape:\\n dtype = dtypes.int64\\n else:\\n dtype = None\\n return ops.convert_to_tensor(shape, dtype=dtype, name=\\\"shape\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"662713ba7ed7439619e45af6f5a5262a\",\n \"score\": \"0.5494885\",\n \"text\": \"def validate_shapes(self) -> None:\\n\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c315d86dcbd0331a86e969e511986f\",\n \"score\": \"0.54915494\",\n \"text\": \"def _check_input_shapes_forward(self, query: Tensor, mask: Tensor,\\n values: Tensor) -> None:\\n assert query.shape[0] == values.shape[0] == mask.shape[0]\\n assert query.shape[1] == 1 == mask.shape[1]\\n assert query.shape[2] == self.query_layer.in_features\\n assert values.shape[2] == self.key_layer.in_features\\n assert mask.shape[2] == values.shape[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba28ef84530c72a8a5e748ff60b4ed7\",\n \"score\": \"0.5476381\",\n \"text\": \"def _get_shape_invariants(tensor):\\r\\n if isinstance(tensor, tf.TensorArray):\\r\\n shape = None\\r\\n else:\\r\\n shape = tensor.shape.as_list()\\r\\n for i in range(1, len(shape) - 1):\\r\\n shape[i] = None\\r\\n return tf.TensorShape(shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8954b934ff21b295cd86088f5bc88b1\",\n \"score\": \"0.5455332\",\n \"text\": \"def runTest(self):\\n self.assertEqual(self.db.get_input_shape(), (10, 3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2686a1c97bd5415892ced230577290c\",\n \"score\": \"0.54496855\",\n \"text\": \"def _check_input_shapes_forward(self, query: Tensor, mask: Tensor,\\n values: Tensor) -> None:\\n assert query.shape[0] == values.shape[0] == mask.shape[0]\\n assert query.shape[1] == 1 == mask.shape[1]\\n assert query.shape[2] == self.key_layer.out_features\\n assert values.shape[2] == self.key_layer.in_features\\n assert mask.shape[2] == values.shape[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abd1f213121dfc7e34cdd7b09355c914\",\n \"score\": \"0.54447067\",\n \"text\": \"def test_output_shape():\\n assert sm.summary(toy_data).shape[1] <= toy_data.shape[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a171c1d7862ba7de3ca6535b3fb34f9c\",\n \"score\": \"0.544112\",\n \"text\": \"def test_output_shapes_and_dtypes(self):\\n # asserting the test is correctly defined:\\n assert len(self.expected_output_shapes) == len(\\n self.expected_output_dtypes), \\\"Ill-defined test.\\\"\\n\\n # computing output tensors:\\n actual_output_tensors = self.layer(**self.forward_propagation_kwargs)\\n\\n # when only one single output tensor is returned:\\n if is_tensor(actual_output_tensors):\\n actual_output_tensors = [actual_output_tensors]\\n\\n # ensuring the expected number of outputs is returned:\\n self.assertEqual(len(self.expected_output_shapes),\\n len(actual_output_tensors))\\n\\n # checking the shape and data type of each output tensor:\\n for i, actual_output in enumerate(actual_output_tensors):\\n\\n # shape:\\n\\n subtest_name = \\\"shape - output {n}\\\".format(n=i)\\n # extracting the actual and expected shapes of the output tensor:\\n actual_shape = actual_output.shape\\n expected_shape = self.expected_output_shapes[i]\\n # checking the shape:\\n with self.subTest(subtest_name):\\n self.assertEqual(actual_shape, expected_shape)\\n\\n # data type:\\n\\n subtest_name = \\\"dtype - output {n}\\\".format(n=i)\\n # extracting the actual and expected data types of the output\\n # tensor:\\n actual_dtype = actual_output.dtype\\n expected_dtype = self.expected_output_dtypes[i]\\n # checking the data type:\\n with self.subTest(subtest_name):\\n self.assertEqual(actual_dtype, expected_dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edd68a244eea7a291412dc5721a34015\",\n \"score\": \"0.54209\",\n \"text\": \"def test_shape_functions(self):\\n self.setup()\\n\\n self.assertEqual(\\n self.X.shape[1],\\n self.test_object.get_dimensionality(),\\n \\\"get_dimensionality should return the number of columns in X.\\\",\\n )\\n self.assertEqual(\\n self.X.shape[0],\\n self.test_object.get_sample_size(),\\n \\\"get_sample_size should return the number of rows in X.\\\",\\n )\\n self.assertEqual(\\n 121,\\n self.test_object.get_sample_size((1599, 1189)),\\n \\\"get_sample_size should return the number of \\\"\\n + \\\"rows in the specified partition.\\\",\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95a2d89c326983d2ac79403690f5c5b5\",\n \"score\": \"0.54074335\",\n \"text\": \"def test_input_shape_mismatch(\\n k: pn.randprocs.covfuncs.CovarianceFunction, shape: ShapeType\\n):\\n\\n if k.input_ndim > 0:\\n input_shape = shape + tuple(dim + 1 for dim in k.input_shape)\\n\\n # `CovarianceFunction.linop`\\n with pytest.raises(ValueError):\\n k.linop(np.zeros(input_shape))\\n\\n with pytest.raises(ValueError):\\n k.linop(np.ones(input_shape), np.zeros(shape + k.input_shape))\\n\\n with pytest.raises(ValueError):\\n k.linop(np.ones(shape + k.input_shape), np.zeros(input_shape))\\n\\n # `CovarianceFunction.matrix`\\n with pytest.raises(ValueError):\\n k.matrix(np.zeros(input_shape))\\n\\n with pytest.raises(ValueError):\\n k.matrix(np.ones(input_shape), np.zeros(shape + k.input_shape))\\n\\n with pytest.raises(ValueError):\\n k.matrix(np.ones(shape + k.input_shape), np.zeros(input_shape))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c3894c37e0a3c69d6d939d9bb4f61ec\",\n \"score\": \"0.5396281\",\n \"text\": \"def is_shape(df, shape):\\n try:\\n check = np.all(np.equal(df.shape, shape) | (np.equal(shape, [-1, -1]) |\\n np.equal(shape, [None, None])))\\n assert check\\n except AssertionError as e:\\n msg = (\\\"Expected shape: {}\\\\n\\\"\\n \\\"\\\\t\\\\tActual shape: {}\\\".format(shape, df.shape))\\n e.args = (msg,)\\n raise\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df7ffa3b0e660f4ddc75a5e5ac7923b1\",\n \"score\": \"0.53939897\",\n \"text\": \"def checkDims(var, varname, parentShape):\\n assert (np.isscalar(var)) or (np.all(np.equal(np.array(var).shape,parentShape))), varname+\\\" dimensions not understood.\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a68dcb5b8a00059b5d6de158ed38cc72\",\n \"score\": \"0.5390994\",\n \"text\": \"def validate_qid_shape(state: np.ndarray,\\n qid_shape: Optional[Tuple[int, ...]]) -> Tuple[int, ...]:\\n size = state.size\\n if qid_shape is None:\\n qid_shape = (2,) * (size.bit_length() - 1)\\n if size != np.prod(qid_shape, dtype=int):\\n raise ValueError(\\n 'state.size ({}) is not a power of two or is not a product of the '\\n 'qid shape {!r}.'.format(size, qid_shape))\\n return qid_shape\",\n \"title\": \"\"\n },\n {\n \"docid\": \"582c1ca5853d05777662d3966825f232\",\n \"score\": \"0.5370118\",\n \"text\": \"def test_dimension_out_of_bound(self):\\n tensor = tf.placeholder(dtype=tf.float32, shape=[9, 23])\\n self.assertRaises(IndexError, utils.get_dimension, tensor, 2)\\n for i in range(-len(tensor.shape), len(tensor.shape)-1):\\n self.assertIsNotNone(utils.get_dimension(tensor, i))\\n self.assertRaises(IndexError, utils.get_dimension, tensor, -3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d295fba067bfcd625f3a7142e7f0e48\",\n \"score\": \"0.536972\",\n \"text\": \"def tensor_shapes_helper(self, resnet_size, with_gpu=False):\\n def reshape(shape):\\n \\\"\\\"\\\"Returns the expected dimensions depending on if a\\n GPU is being used.\\n \\\"\\\"\\\"\\n # If a GPU is used for the test, the shape is returned (already in NCHW\\n # form). When GPU is not used, the shape is converted to NHWC.\\n if with_gpu:\\n return shape\\n return shape[0], shape[2], shape[3], shape[1]\\n\\n graph = tf.Graph()\\n\\n with graph.as_default(), self.test_session(\\n use_gpu=with_gpu, force_gpu=with_gpu):\\n model = imagenet_main.ImagenetModel(\\n resnet_size,\\n data_format='channels_first' if with_gpu else 'channels_last')\\n inputs = tf.random_uniform([1, 224, 224, 3])\\n output = model(inputs, training=True)\\n\\n initial_conv = graph.get_tensor_by_name('initial_conv:0')\\n max_pool = graph.get_tensor_by_name('initial_max_pool:0')\\n block_layer1 = graph.get_tensor_by_name('block_layer1:0')\\n block_layer2 = graph.get_tensor_by_name('block_layer2:0')\\n block_layer3 = graph.get_tensor_by_name('block_layer3:0')\\n block_layer4 = graph.get_tensor_by_name('block_layer4:0')\\n avg_pool = graph.get_tensor_by_name('final_avg_pool:0')\\n dense = graph.get_tensor_by_name('final_dense:0')\\n\\n self.assertAllEqual(initial_conv.shape, reshape((1, 64, 112, 112)))\\n self.assertAllEqual(max_pool.shape, reshape((1, 64, 56, 56)))\\n\\n # The number of channels after each block depends on whether we're\\n # using the building_block or the bottleneck_block.\\n if resnet_size < 50:\\n self.assertAllEqual(block_layer1.shape, reshape((1, 64, 56, 56)))\\n self.assertAllEqual(block_layer2.shape, reshape((1, 128, 28, 28)))\\n self.assertAllEqual(block_layer3.shape, reshape((1, 256, 14, 14)))\\n self.assertAllEqual(block_layer4.shape, reshape((1, 512, 7, 7)))\\n self.assertAllEqual(avg_pool.shape, reshape((1, 512, 1, 1)))\\n else:\\n self.assertAllEqual(block_layer1.shape, reshape((1, 256, 56, 56)))\\n self.assertAllEqual(block_layer2.shape, reshape((1, 512, 28, 28)))\\n self.assertAllEqual(block_layer3.shape, reshape((1, 1024, 14, 14)))\\n self.assertAllEqual(block_layer4.shape, reshape((1, 2048, 7, 7)))\\n self.assertAllEqual(avg_pool.shape, reshape((1, 2048, 1, 1)))\\n\\n self.assertAllEqual(dense.shape, (1, _LABEL_CLASSES))\\n self.assertAllEqual(output.shape, (1, _LABEL_CLASSES))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a98210894a56fce3a4c76067ca450189\",\n \"score\": \"0.5363492\",\n \"text\": \"def verify_shapes(self, input_sets: np.ndarray, target_output: Optional[np.ndarray]=None) -> None:\\n if input_sets.shape[1] != self._input_feature_count:\\n raise ValueError(\\n \\\"input doesn't have the right feature count - \\\" +\\n str(input_sets.shape) + \\\" \\\" + str(self._layers[0].weights.shape)\\n )\\n if target_output is not None:\\n if input_sets.shape[0] != target_output.shape[0]:\\n raise ValueError(\\\"input and output sizes don't match - shapes \\\" +\\n str(input_sets.shape) + \\\" \\\" + str(target_output.shape))\\n if target_output.shape[1] != self._layers[-1].neuron_count:\\n raise ValueError(\\\"target_output shape doesn't match output layer - \\\" +\\n str(target_output.shape[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e830f2d1c9b23c5b8e6d412ba3e9690e\",\n \"score\": \"0.5355854\",\n \"text\": \"def check_shape(self, weight: List[np.ndarray]) -> None:\\n\\n invalid_shape = False\\n if len(weight) != len(self.layers) - 1:\\n invalid_shape = True\\n\\n for i in range(len(self.layers)-1):\\n w = weight[i]\\n if np.shape(w) != (self.layers[i], self.layers[i+1]):\\n invalid_shape = True\\n break\\n\\n if invalid_shape:\\n raise ValueError(\\\"Invalid size for weight. The size of weight should be the same as self.layers\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c921632fefd45063ebfdcf92f6989d2b\",\n \"score\": \"0.5343877\",\n \"text\": \"def validate(self, dataset_output_shape):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c4f311b10f0d82b0560db65f846ad2\",\n \"score\": \"0.5327925\",\n \"text\": \"def test_shapes(self):\\n n_atoms = 5\\n n_feat = 10\\n batch_size = 3\\n max_deg = 10\\n min_deg = 0\\n topology = GraphTopology(n_feat)\\n\\n # Degrees from 1 to max_deg inclusive \\n # TODO(rbharath): Should this be 0 to max_deg inclusive?\\n deg_adj_lists_placeholders = topology.get_deg_adjacency_lists_placeholders()\\n assert len(deg_adj_lists_placeholders) == max_deg\\n for ind, deg_adj_list in enumerate(deg_adj_lists_placeholders):\\n deg = ind + 1\\n # Should have shape (?, deg)\\n assert deg_adj_list.get_shape()[1] == deg\\n\\n # Shape of atom_features should be (?, n_feat)\\n atom_features = topology.get_atom_features_placeholder()\\n assert atom_features.get_shape()[1] == n_feat\\n\\n # Shape of deg_slice placeholder should be (max_deg+1-min_deg, 2)\\n deg_slice = topology.get_deg_slice_placeholder()\\n print(\\\"deg_slice.get_shape()\\\")\\n print(deg_slice.get_shape())\\n assert deg_slice.get_shape() == (max_deg + 1 - min_deg, 2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cdb351522dc0e269c37cb16b723780c\",\n \"score\": \"0.5325467\",\n \"text\": \"def test_shape_equal(self):\\n assert_dtype_equal(np.dtype('f8'), np.dtype(('f8', tuple())))\\n assert_dtype_equal(np.dtype('f8'), np.dtype(('f8', 1)))\\n assert_dtype_equal(np.dtype((int, 2)), np.dtype((int, (2,))))\\n assert_dtype_equal(np.dtype(('<f4', (3, 2))), np.dtype(('<f4', (3, 2))))\\n d = ([('a', 'f4', (1, 2)), ('b', 'f8', (3, 1))], (3, 2))\\n assert_dtype_equal(np.dtype(d), np.dtype(d))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa64187a1d7297572bba6eaf8ce61547\",\n \"score\": \"0.532174\",\n \"text\": \"def test_inference(self):\\n image1 = np.random.randn(256, 256, 3).astype('float32')\\n image2 = np.random.randn(256, 256, 3).astype('float32')\\n uflow = UFlow()\\n flow = uflow.infer(image1, image2)\\n correct_shape = np.equal(flow.shape, [256, 256, 2]).all()\\n self.assertTrue(correct_shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee2f10786f93a50f1ba6411014efc1c\",\n \"score\": \"0.53059673\",\n \"text\": \"def _check_axes_shape(self, axes, axes_num=None, layout=None, figsize=(8.0, 6.0)):\\r\\n\\r\\n # derived from pandas.tests.test_graphics\\r\\n visible_axes = self._flatten_visible(axes)\\r\\n\\r\\n if axes_num is not None:\\r\\n self.assertEqual(len(visible_axes), axes_num)\\r\\n for ax in visible_axes:\\r\\n # check something drawn on visible axes\\r\\n self.assertTrue(len(ax.get_children()) > 0)\\r\\n\\r\\n if layout is not None:\\r\\n result = self._get_axes_layout(plotting._flatten(axes))\\r\\n self.assertEqual(result, layout)\\r\\n\\r\\n if figsize is not None:\\r\\n self.assert_numpy_array_equal(np.round(visible_axes[0].figure.get_size_inches()),\\r\\n np.array(figsize))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a4a9e71225ab02b2ef2f262d713a402\",\n \"score\": \"0.53033155\",\n \"text\": \"def _check_shapes_input_forward(\\n self,\\n trg_embed: Tensor,\\n encoder_output: Tensor,\\n encoder_hidden: Tensor,\\n src_mask: Tensor,\\n hidden: Tensor = None,\\n prev_att_vector: Tensor = None,\\n ) -> None:\\n assert len(encoder_output.shape) == 3\\n if encoder_hidden is not None:\\n assert len(encoder_hidden.shape) == 2\\n assert encoder_hidden.shape[-1] == encoder_output.shape[-1]\\n assert src_mask.shape[1] == 1\\n assert src_mask.shape[0] == encoder_output.shape[0]\\n assert src_mask.shape[2] == encoder_output.shape[1]\\n assert trg_embed.shape[0] == encoder_output.shape[0]\\n assert trg_embed.shape[2] == self.emb_size\\n if hidden is not None:\\n if isinstance(hidden, tuple): # for lstm\\n hidden = hidden[0]\\n assert hidden.shape[1] == encoder_output.shape[0]\\n assert hidden.shape[2] == self.hidden_size\\n if prev_att_vector is not None:\\n assert prev_att_vector.shape[0] == encoder_output.shape[0]\\n assert prev_att_vector.shape[2] == self.hidden_size\\n assert prev_att_vector.shape[1] == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca83036c1154f83a447cf73a6bf4a81b\",\n \"score\": \"0.5303013\",\n \"text\": \"def validate_specs(action_spec):\\n flat_action_spec = tf.nest.flatten(action_spec)\\n if len(flat_action_spec) > 1:\\n raise ValueError(\\n 'Network only supports action_specs with a single action.')\\n\\n if flat_action_spec[0].shape not in [(), (1, )]:\\n raise ValueError(\\n 'Network only supports action_specs with shape in [(), (1,)])')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14f855eb19ea7f422e0699234f3d80a0\",\n \"score\": \"0.5296996\",\n \"text\": \"def test_len_signature(self):\\n x1 = np.array([1, 2, 3])\\n x2 = np.array([10, 20, 30])\\n inputs0 = ()\\n inputs1 = x1 # NOT in a tuple\\n inputs2 = (x1, x2)\\n\\n sig0 = shapes.signature(inputs0)\\n sig1 = shapes.signature(inputs1)\\n sig2 = shapes.signature(inputs2)\\n\\n # pylint: disable=g-generic-assert\\n self.assertEqual(len(sig0), 0)\\n self.assertEqual(len(sig1), 1)\\n self.assertEqual(len(sig2), 2)\\n # pylint: enable=g-generic-assert\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe6ef892452b19eee89cfa5b4bff6fb\",\n \"score\": \"0.52899265\",\n \"text\": \"def _verify_data(inputs, targets):\\n check_value_type('inputs', inputs, ms.Tensor)\\n if len(inputs.shape) != 4:\\n raise ValueError('Argument inputs must be 4D Tensor')\\n check_value_type('targets', targets, (ms.Tensor, int))\\n if isinstance(targets, ms.Tensor):\\n if len(targets.shape) > 1 or (len(targets.shape) == 1 and len(targets) != len(inputs)):\\n raise ValueError('Argument targets must be a 1D or 0D Tensor. If it is a 1D Tensor, '\\n 'it should have the same length as inputs.')\\n elif inputs.shape[0] != 1:\\n raise ValueError('If targets have type of int, batch_size of inputs should equals 1. Receive batch_size {}'\\n .format(inputs.shape[0]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa67651e10dab8cf7fe0de4f7598064f\",\n \"score\": \"0.5278164\",\n \"text\": \"def _check_conv_lstm_input(\\n conv_lstm_input_shape,\\n data_input_shape,\\n n_batch_like_dims):\\n shapes_match = all(\\n [data_input_shape[i + n_batch_like_dims] == conv_lstm_input_shape[i]\\n for i in range(len(conv_lstm_input_shape))])\\n if not shapes_match:\\n raise ValueError(\\\"RNN core input_shape does not match data input shape!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97ff1a1bfd4322a6e13fd8a8764b119\",\n \"score\": \"0.52741975\",\n \"text\": \"def _shape(tensor):\\n return tensor.get_shape().as_list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8c03a59ed32be8e7e0cba155c28b11a\",\n \"score\": \"0.5272752\",\n \"text\": \"def build(self, input_shape):\\n super(ExpectationValue, self).build(input_shape)\\n input_shape = tensor_shape.TensorShape(input_shape).as_list()\\n if None in input_shape:\\n assert ((input_shape[0] == None)) & (\\n input_shape.count(None) == 1), \\\"Only the first dimension can be None, found {}\\\".format(\\n input_shape)\\n elif (input_shape[0] > 1):\\n assert all([input_shape[i] == 2 for i in range(1, len(input_shape))]), \\\\\\n \\\"Input vector needs to have shape (x, 2, 2,...,2), found {}\\\".format(input_shape)\\n else:\\n assert (input_shape[0] == 1) & (all([input_shape[i] == 2 for i in range(1, len(input_shape))])), \\\\\\n \\\"Input vector needs to have shape (1, 2, 2,...,2), found {}\\\".format(input_shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a1e942620af79c347d6b7277dcda9fa\",\n \"score\": \"0.52723396\",\n \"text\": \"def _assert_lengths(encoder_size, decoder_size, encoder_inputs, decoder_inputs, decoder_masks):\\n if len(encoder_inputs) != encoder_size:\\n raise ValueError(\\\"Encoder length must be equal to the one in bucket,\\\"\\n \\\" %d != %d.\\\" % (len(encoder_inputs), encoder_size))\\n if len(decoder_inputs) != decoder_size:\\n raise ValueError(\\\"Decoder length must be equal to the one in bucket,\\\"\\n \\\" %d != %d.\\\" % (len(decoder_inputs), decoder_size))\\n if len(decoder_masks) != decoder_size:\\n raise ValueError(\\\"Weights length must be equal to the one in bucket,\\\"\\n \\\" %d != %d.\\\" % (len(decoder_masks), decoder_size))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02ca990f590b109d50022f4588a5629b\",\n \"score\": \"0.5253316\",\n \"text\": \"def test_analytical_expectation_no_circuits(self, op_and_sim):\\n op = op_and_sim[0]\\n sim = op_and_sim[1]\\n\\n circuit_batch = tf.raw_ops.Empty(shape=(0,), dtype=tf.string)\\n empty_params = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.float32)\\n empty_ops = tf.raw_ops.Empty(shape=(0, 0), dtype=tf.string)\\n\\n op_exp = op(circuit_batch, [], empty_params, empty_ops).numpy()\\n cirq_exp = batch_util.batch_calculate_expectation([], [], [[]], sim)\\n self.assertEqual(op_exp.shape, cirq_exp.shape)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489e7d3a246a077a57a251bf40995e3f\",\n \"score\": \"0.5252905\",\n \"text\": \"def test_cnn_model_shape(self):\\n data = tf.random.normal([32, 186, 1])\\n model = create_cnn_model()\\n logits = model(data)\\n self.assertEqual(logits.shape, [32, 5])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37830d7ef3cf955f5c7212f004cdfbef\",\n \"score\": \"0.5237124\",\n \"text\": \"def test_sample_shape_scalar_arguments(self):\\n test_cases = [\\n {\\\"sample_shape\\\": (), \\\"expected_shape\\\": (1,)},\\n {\\\"sample_shape\\\": (100,), \\\"expected_shape\\\": (100, 1)},\\n {\\\"sample_shape\\\": (100, 10), \\\"expected_shape\\\": (100, 10, 1)},\\n {\\\"sample_shape\\\": (1, 100), \\\"expected_shape\\\": (1, 100, 1)},\\n ]\\n for case in test_cases:\\n samples = Bernoulli(0.5).sample(self.rng_key, case[\\\"sample_shape\\\"])\\n self.assertEqual(samples.shape, case[\\\"expected_shape\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37cb26ee23244f2ddd111102eb781542\",\n \"score\": \"0.5232785\",\n \"text\": \"def _check_weights(weight_shape, label_shape, prim_name=None):\\n msg_prefix = f'For \\\\'{prim_name}\\\\', the' if prim_name else \\\"The\\\"\\n if weight_shape != label_shape:\\n raise ValueError(f\\\"{msg_prefix} weight_shape[0] must be equal to label_shape[1], \\\"\\n f\\\"but got weight_shape[0]: {weight_shape} and label_shape[1]: {label_shape}.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"124ac8705e72e1e0e2e0f8e1e5232d34\",\n \"score\": \"0.522652\",\n \"text\": \"def test_model_shape(\\n self,\\n text_length,\\n n_mentions,\\n n_linked_mentions,\\n no_entity_attention,\\n ):\\n\\n config = copy.deepcopy(self.config)\\n config['model_config']['encoder_config'][\\n 'no_entity_attention'] = no_entity_attention\\n config = ml_collections.FrozenConfigDict(self.config)\\n model_config = config.model_config\\n encoder_config = model_config.encoder_config\\n\\n max_length = model_config.encoder_config.max_length\\n preprocess_fn = eae_task.EaETask.make_preprocess_fn(config)\\n collater_fn = eae_task.EaETask.make_collater_fn(config)\\n\\n raw_example = test_utils.gen_mention_pretraining_sample(\\n text_length, n_mentions, n_linked_mentions, max_length=max_length)\\n processed_example = preprocess_fn(raw_example)\\n batch = {\\n key: np.tile(value, (config.per_device_batch_size, 1))\\n for key, value in processed_example.items()\\n }\\n batch = collater_fn(batch)\\n batch = jax.tree_map(np.asarray, batch)\\n\\n model = eae_encoder.EaEEncoder(**model_config.encoder_config)\\n init_rng = jax.random.PRNGKey(0)\\n (encoded_output, loss_helpers, _), _ = model.init_with_output(\\n init_rng,\\n batch,\\n deterministic=True,\\n method=model.forward,\\n )\\n\\n self.assertEqual(encoded_output.shape,\\n (config.per_device_batch_size, encoder_config.max_length,\\n encoder_config.hidden_size))\\n\\n self.assertEqual(loss_helpers['target_mention_encodings'].shape,\\n (config.max_mention_targets * config.per_device_batch_size,\\n encoder_config.entity_dim))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7780f377c8b13706510f2a0c0f82c34\",\n \"score\": \"0.52256244\",\n \"text\": \"def test_SegmentationEvaluator_inconsistent_shapes(\\n num_classes: int, ground_truth: np.ndarray, model_prediction: np.ndarray\\n) -> None:\\n\\n se = SegmentationEvaluator(num_classes)\\n\\n with pytest.raises(ValueError):\\n se.evaluate(ground_truth, model_prediction, model_name=\\\"MockModel\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"269b341260b95f40d7b502a206bcecbd\",\n \"score\": \"0.5224693\",\n \"text\": \"def _check_shapes(self):\\n assert len(self.documents) == self.D\\n assert self.n_d.shape == (self.D,)\\n\\n assert self.q_tau.G.shape == (self.W,)\\n assert self.G_pi.shape == (self.K,)\\n\\n assert len(self.q_z) == self.D\\n for q_zd, document in zip(self.q_z, self.documents):\\n assert q_zd.shape == (len(document), self.K)\\n\\n assert self.E_s_dk.shape == (self.D, self.K)\\n assert self.E_t_kw.shape == (self.K, self.W)\\n assert self.E_log_eta.shape == (self.D,)\\n assert self.E_log_xi.shape == (self.K,)\\n\\n self.counts._check_shape(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04799db4aa0ba0da35e85ac376b01f13\",\n \"score\": \"0.5219822\",\n \"text\": \"def test_none_tensor(self):\\n self.assertRaises(TypeError, utils.get_dimension, None, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65255eed24ce51cb50402e7a81338acc\",\n \"score\": \"0.5212433\",\n \"text\": \"def shape(tensor):\\n return tuple([d.value for d in tensor.get_shape()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4814a90640b3899396b6ccc69a0ba0dd\",\n \"score\": \"0.5210279\",\n \"text\": \"def test_output_shape2():\\n assert sm.summary(toy_all_na).shape[1] <= toy_all_na.shape[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65234ca5cedd42fb5e88edba1b7311b7\",\n \"score\": \"0.5204047\",\n \"text\": \"def shapes(draw, min_ndims=0, max_ndims=3, min_lastdimsize=1, max_side=None):\\n rank = draw(hps.integers(min_value=min_ndims, max_value=max_ndims))\\n shape = tf.TensorShape(None).with_rank(rank)\\n if rank > 0:\\n\\n def resize_lastdim(x):\\n return x[:-1] + (max(x[-1], min_lastdimsize),)\\n\\n if max_side is None:\\n # Apparently we can't pass an explicit None to the Hypothesis strategy?\\n shps = hpnp.array_shapes(min_dims=rank, max_dims=rank)\\n else:\\n shps = hpnp.array_shapes(min_dims=rank, max_dims=rank, max_side=max_side)\\n shape = draw(shps.map(resize_lastdim).map(tf.TensorShape))\\n return shape\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dabe00531939d6f95970738d6808cecb\",\n \"score\": \"0.52039474\",\n \"text\": \"def test_tuple_dims(self, dims):\\n value = random_unitary(dims)\\n self.assertIsInstance(value, Operator)\\n self.assertTrue(value.is_unitary())\\n self.assertEqual(value.input_dims(), dims)\\n self.assertEqual(value.output_dims(), dims)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e7adfee56dd55641322a9a8b35dc878\",\n \"score\": \"0.5203696\",\n \"text\": \"def check_shape(ar: np.array):\\n if not len(ar.shape) == 2:\\n raise ValueError(f\\\"Unexpected error converting {ar} to np.array\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e7adfee56dd55641322a9a8b35dc878\",\n \"score\": \"0.5203696\",\n \"text\": \"def check_shape(ar: np.array):\\n if not len(ar.shape) == 2:\\n raise ValueError(f\\\"Unexpected error converting {ar} to np.array\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7f46b69dcd92b7c826ae67f5f72c60e\",\n \"score\": \"0.5176949\",\n \"text\": \"def test_shape_monster(self):\\n assert_dtype_not_equal(\\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\\n np.dtype(([('a', 'f4', (1, 2)), ('b', 'f8', (1, 3))], (2, 2))))\\n assert_dtype_not_equal(\\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'i8', (1, 3))], (2, 2))))\\n assert_dtype_not_equal(\\n np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\\n np.dtype(([('e', 'f8', (1, 3)), ('d', 'f4', (2, 1))], (2, 2))))\\n assert_dtype_not_equal(\\n np.dtype(([('a', [('a', 'i4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),\\n np.dtype(([('a', [('a', 'u4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f619527e664b8a01e112b95b35c4d5d\",\n \"score\": \"0.5157394\",\n \"text\": \"def test_1d_inputs(FlowClass):\\n with pytest.raises(ValueError) as excinfo:\\n FlowClass(1, 2, 2, 2)\\n assert \\\"requires at least 2 dimensions\\\" in str(excinfo.value)\",\n \"title\": \"\"\n 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query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
a1d5a9c378188368121aa3f333ecd191 | Annualize investment for this component assuming a fixed life span after which the component is replaced by the same. | [
{
"docid": "ed88cabca559f2ef6a0fc63ae6baa6f4",
"score": "0.67393124",
"text": "def annualize_investment(self, i):\n return 0",
"title": ""
}
] | [
{
"docid": "072f1455f03907f4d0b6e5f1c60675e0",
"score": "0.55601597",
"text": "def test_patch_investment_value(self):\n pass",
"title": ""
},
{
"docid": "9b2c1455ad018834e0ff383945397384",
"score": "0.5409791",
"text": "def testAggregateCorrectly_single(self):\n\n #Crea... |
1072070d27b8ffb539250e3be26b1945 | Sets the type of this OneOfCustomFluidBCPressure. | [
{
"docid": "30a86a01466cc929f4e080adeb548b24",
"score": "0.60116017",
"text": "def type(self, type):\n if self.local_vars_configuration.client_side_validation and type is None: # noqa: E501\n raise ValueError(\"Invalid value for `type`, must not be `None`\") # noqa: E501\n\n s... | [
{
"docid": "872d4d4a0cbc8d66a1e5170cf9204937",
"score": "0.699939",
"text": "def setType(self, type_):\n raise NotImplementedError(self)",
"title": ""
},
{
"docid": "a649b534947a904e113362ba5e80e814",
"score": "0.6868857",
"text": "def setType(self, type):\n self.__type... |
b83341db609ce8136c9d53118eb1a2db | Tests setting new object with key | [
{
"docid": "0a3d4c2e1a6dbeef913b959a3ada09ff",
"score": "0.0",
"text": "def test_new(self):\n pass",
"title": ""
}
] | [
{
"docid": "ef36253045ef5dc33403621a30a8569c",
"score": "0.7352714",
"text": "def test_set_into_existing(hash_test):\n assert hash_test.set('sup', 'bro') == {'sup': 'bro'}",
"title": ""
},
{
"docid": "638326fb2445778e498edf4553d491e1",
"score": "0.7140362",
"text": "def test_set_k... |
07716a92882ade315e082239e7a7ac3a | Returns and removes the last item for the list, which is also the top item in the stack. The runtime here is constant time, because all it does is index to the last item of the list. | [
{
"docid": "dc088678b98c9ab9619fa837b60ac02e",
"score": "0.65494514",
"text": "def pop(self):\n if self.items:\n return self.items.pop()\n \n return None",
"title": ""
}
] | [
{
"docid": "3caf8c698b44fefd5d2bd9ebe528caf4",
"score": "0.808521",
"text": "def pop_back(self):\n\t\t# runtime O(1) removing last element doesnt have effect on other elements\n\t\tself.size -= 1\n\t\treturn self.list.pop()",
"title": ""
},
{
"docid": "c946419c3bc9863bd1d9be7d07be386c",
... |
5f54f032bb746c8eccf706ba0adf00ff | Additional logging at high verbosity. | [
{
"docid": "e5e40b484c225af405582f14ed2f415d",
"score": "0.0",
"text": "def test_verbose_matching(self):\n\n match = GeometryStore.objects.create(label='Test Project')\n stdout, stderr = self.call_command(verbosity=2)\n stdout = stdout.read()\n self.assertIn(\n 'At... | [
{
"docid": "e9bf6c5f59bf23aedee4cc905ae08377",
"score": "0.76229805",
"text": "def verbosity(self, verbosity: str) -> None:",
"title": ""
},
{
"docid": "ca250f8ab4db6e42d5d66780a56a03a3",
"score": "0.7373726",
"text": "def setup_other_logging(self, args, verbose=False, silent=False, ... |
52b385f82d598c4966f69ce79a9368eb | Train the Deep Neural Network with Layers of LSTM and Dense | [
{
"docid": "27b6cbfcd48426a56aa38a14b95f9b96",
"score": "0.6677843",
"text": "def train_Model(self,X_train,y_train):\n model = Sequential()\n #Layes of LSTM along with Batch Normalization and dropouts\n model.add(LSTM(64, activation='relu', input_shape=(X_train.shape[1], X_train.sha... | [
{
"docid": "4ee27108906da1a7038afdfb7db417bf",
"score": "0.7474031",
"text": "def train_lstm_model(features_train, labels_train, train_opt):\n # create class weight for imbalanced classes\n class_weight = create_class_weight(Counter(labels_train))\n\n # one hot encode the output variable\n c... |
a87592c0a1f251ffe167771e498e176a | Show deserialized yaml objects as json | [
{
"docid": "ca579afa8bfa59a4acb8dddd1d505b88",
"score": "0.0",
"text": "def show(tokens, implementations, samples):\n for sample in samples:\n show_outcome(sample, implementations, tokens=tokens)",
"title": ""
}
] | [
{
"docid": "5ff19beda9e3dc99b78d5b2e8535bb62",
"score": "0.6889602",
"text": "def yaml(self, obj):\n return yaml.dump(obj, indent=2)",
"title": ""
},
{
"docid": "0bdf9282c12f07df80e30400677cef42",
"score": "0.6864921",
"text": "def dumps(obj):\n return yaml.dump(obj)",
... |
baf89afc01c06e06c59db7f960b365ad | Checks if the EMail type Object's query property matches the general format of an email If the query matches fully, the Object's state property is changed to True. If the query matches the general format at first, but doesn't match the required prefix format, a ValueError is raised with a fitting message If the query d... | [
{
"docid": "dc96a85eb4dcb474b5da1cbf6b7e1f19",
"score": "0.6491934",
"text": "def isQueryType(self) -> None:\n if re.match(r'[a-zA-Z0-9._-]+@[\\w.-]+', self.query):\n count = 0\n atSignIndex = self.query.find(\"@\")\n prefix = self.query[0:atSignIndex]\n ... | [
{
"docid": "f49ad204b00929aba89e915e72f89310",
"score": "0.50224423",
"text": "def test_email_raw(self):\n self.assertIsInstance(self.email.raw, Message)",
"title": ""
},
{
"docid": "696777fc9063d945392a285ec955cbf6",
"score": "0.4977578",
"text": "def _checkformat(self, topic... |
ca117a2b8ee289fc3feb561f9b2761cb | Leave one case with a comment, plus two empty cases to code manually. | [
{
"docid": "77186788f15b5cb01c1a0326033be580",
"score": "0.5725164",
"text": "def to_case_bodies(self) -> List[CaseBody]:\n case_containing_comment = \"//TODO: {clean_line};\\n\".format(clean_line=self.clean_line)\n blank_case_1 = \"//This case intentionally left blank;\"\n blank_ca... | [
{
"docid": "68ffeb1c0863474fe67ff4891c99734f",
"score": "0.64037967",
"text": "def example():\n '''\n this is also a type of multi line comment\n by this example we are now cleared what single and mutiple line comment is.\n '''\n return 1",
"title": ""
},
{
"docid": "f84f6507e... |
72dede1bf3e2391570d1bc0944879777 | Validate the value passed in is a method or function. | [
{
"docid": "54a07a92f9ab3d325eae131906d32231",
"score": "0.65965426",
"text": "def _validate_callback(self, callback):\r\n if (callback is not None and\r\n not utils.is_callable(callback)):\r\n raise TypeError(\"Callback should be a function or method, is %s\",\r\n ... | [
{
"docid": "9dd35315575d3ac767b491c8aed79efa",
"score": "0.71747696",
"text": "def validate(self, value):\n super().validate(value)\n transformed_value = self.value\n if not callable(transformed_value):\n raise ValidationError(\"Value %(value)s is not a callable.\", param... |
535070a27b2717509f95e5f3505ae922 | Takes folder name as input and reformats files and saves them into trajectories_as_arrays as | [
{
"docid": "46fa1ad8cfdfa5ff9575a6e693c7bebf",
"score": "0.68550557",
"text": "def folder_reformatter(folder_name, folder_save_location= \"trajectories_as_arrays\", plot_trajectories=False):\r\n # file_list = list(reversed(os.listdir(folder_name)))\r\n file_list = os.listdir(folder_name)\r\n fo... | [
{
"docid": "79f908af9b166fa0853a3a3dd0af52b1",
"score": "0.6129415",
"text": "def preprocessfolder(self):\n imgs, _ = getFilesAndHdf(str(self.in_directory.text()))\n self.img_list = sorted(imgs)\n self.updateImageGroups()",
"title": ""
},
{
"docid": "513494fddab22762fbce... |
93453b0f1ab761401a7424d4fea5494e | Wrapper function to catch exceptions in functions | [
{
"docid": "b74458788cd6e592636fb2f7e01db75b",
"score": "0.73409563",
"text": "def exception(function):\n\n @functools.wraps(function)\n def wrapper(*args, **kwargs):\n try:\n return function(*args, **kwargs)\n except Exception as e:\n import traceback\n ... | [
{
"docid": "a6f37b37e017e5d5235d9d54f94e6403",
"score": "0.790852",
"text": "def catch(fn, *args, **kw):\n try:\n return fn(*args, **kw)\n except:\n #print(\"catch: caught exception!\")\n return",
"title": ""
},
{
"docid": "3bff01b60606860bb030ddf8eb8cf27f",
"score": "0.779941... |
d8f71f39f3546df5a48cc4d184f1dcd4 | Calculate the Idzorek omega matrix by taking into account usersupplied confidences in the views. | [
{
"docid": "86f10905d6b0ef587a01e657b733cd89",
"score": "0.7267532",
"text": "def _calculate_idzorek_omega(covariance, view_confidences, pick_matrix):\n\n view_confidences = np.array(np.reshape(view_confidences, (1, covariance.shape[0])))\n alpha = (1 - view_confidences) / view_confidences... | [
{
"docid": "641a4ecb3ac788414cb261d6677a1b03",
"score": "0.63104594",
"text": "def _calculate_omega(self, covariance, tau, pick_matrix, view_confidences, omega_method):\n\n if omega_method == 'prior_variance':\n omega = pick_matrix.dot((tau * covariance).dot(pick_matrix.T))\n el... |
d3fd9360d977a970e21935009af48f43 | Deletes AWS resources used by the demo. | [
{
"docid": "62389e4ca3e1b60139e2fbe7d9719b47",
"score": "0.70848787",
"text": "def cleanup(self):\n self.demo_resources.cleanup()",
"title": ""
}
] | [
{
"docid": "972f95f2959f6decd98dd447115ac2f5",
"score": "0.67874366",
"text": "def teardown_s3():\n s3 = boto3.resource('s3')\n bucket = s3.Bucket(CAMD_S3_BUCKET)\n bucket.objects.filter(Prefix=\"proto-dft-2/runs/Si\").delete()\n bucket.objects.filter(Prefix=\"proto-dft-2/submit/Si\").delete... |
036e467bb9d026eb31995dd134ef8c0a | Tests correct behaviour of AgentData.validate_trans(). | [
{
"docid": "488d8cffea7b369248a6a1036a8e5839",
"score": "0.7768848",
"text": "def test_positive_validate_trans(self):\n agent = Agent()\n tree = Tree()\n generator = tree_test.Generator()\n genesis = generator.gen_genesis()\n tree.add(genesis.get_identifier(), deepcopy... | [
{
"docid": "0bc392ab9c7b1a716a5bb615b4c51f5f",
"score": "0.7115385",
"text": "def test_negative_validate_trans_1(self):\n agent = Agent()\n tree = Tree()\n generator = tree_test.Generator()\n genesis = generator.gen_genesis()\n tree.add(genesis.get_identifier(), deepco... |
a2a7028798eabae5b2f98956a5a428dd | Get point loading due to axial force as heavside for axial force diagrams | [
{
"docid": "5ee3c5a2ed9c45ef5bbfaca8e2d87634",
"score": "0.5364127",
"text": "def ax_singularity(self, x):\n return _h(self._location, x)*self.get_point_loading()",
"title": ""
}
] | [
{
"docid": "7aba0bb55b2a7df4cb076717df0ee1be",
"score": "0.63613003",
"text": "def v_get_point_loading(self, mom_bal=False):\n return self._reaction_vert*(self._location if mom_bal else 1)",
"title": ""
},
{
"docid": "9bf26284dfc25e4a35555f5551f4185c",
"score": "0.6351065",
"t... |
62b19a105ee3feb4f0a143b34d0e6264 | Smooths a given image with a gaussian kernel with widths given as sigmas. This smoothing can be used to mimic charge diffusion within the CCD. The default values are from Table 82 of CCD_273_Euclid_secification_1.0.130812.pdf converted to sigmas (FWHM / (2sqrt(2ln2)) and rounded up to the second decimal. | [
{
"docid": "52d2bd1457532ac6d94e36c21e777b40",
"score": "0.67235106",
"text": "def smoothingWithChargeDiffusion(self, image, sigma=(0.32, 0.32)):\n return ndimage.filters.gaussian_filter(image, sigma)",
"title": ""
}
] | [
{
"docid": "94ef8d8eefc2e7a85259a8caf3b6470d",
"score": "0.7369736",
"text": "def calc_gaussian(img, sigma=3):\n blurFilter = sitk.SmoothingRecursiveGaussianImageFilter()\n blurFilter.SetSigma(sigma)\n imgSmooth = blurFilter.Execute(img)\n return imgSmooth",
"title": ""
},
{
"doc... |
d06c87d46961dda91f4000e4f7098dca | We only want to keep the smallest k elements in max heap | [
{
"docid": "ace66780b7068ff05261ad1fb9c304cf",
"score": "0.0",
"text": "def add_element(self, element: int, end: int = -1):\n if element < self.arr[0]:\n self.arr[0] = element\n self.heapify(0, end)",
"title": ""
}
] | [
{
"docid": "1857a079b2f58622838f9e22110f0ff7",
"score": "0.7386581",
"text": "def get_static_top_k(nums, k):\n if len(nums) <= k:\n return nums\n min_h = MinHeap(nums[:k], k) # one time use\n min_h.build_heap()\n # compare data with heap top\n for n in range(k, len(nums)):\n ... |
13ababd9f38e4a5b7e561e0b4271185a | Opens a color picker and sets point color for the plot if a color is not specified | [
{
"docid": "afa5f4df8b7ea114ff5c75f92ccfc8c4",
"score": "0.5313986",
"text": "def set_isopleth_2_color(self, color=None):\n if not color:\n initial_color = self.isopleth_2_color_preview.palette().window().color()\n color = QColorDialog.getColor(initial_color).name()\n\n ... | [
{
"docid": "54e7485b4e104092fbdb698b7ef604a0",
"score": "0.6972183",
"text": "def set_point_color(self, color=None):\n if not color:\n initial_color = self.point_color_preview.palette().window().color()\n color = QColorDialog.getColor(initial_color).name()\n\n style =... |
ef8a52bdae12a6ac4938c2e83caec730 | Get the extension point URL based on a method instance | [
{
"docid": "811f9a3da5396af738f5b356e07f06fc",
"score": "0.8212115",
"text": "def _get_extension_point_url_from_method(domain, category, plugin_method):\n name = getattr(plugin_method, \"__name__\", None) or str(id(plugin_method))\n return \"{}/{}/{}\".format(domain, category, name).replace(\"//\"... | [
{
"docid": "2307f7b249e1992727f39ee67e8fcadd",
"score": "0.65730506",
"text": "def get_url(self):\n raise NotImplementedError",
"title": ""
},
{
"docid": "1e4323b2a02f8bb72ea12c070c0523f4",
"score": "0.643839",
"text": "def _get_url_extension(self):\n return ''",
"t... |
ec44e9316b422f01495aea3db5d0d7e7 | Shows a small widget that counts the cheers and offers a link to cheers more | [
{
"docid": "d2e43b23e486083c0885aa37f119c349",
"score": "0.6571422",
"text": "def show_cheers_widget(context, content):\n \n container = Cheers.objects.get_container(content)\n cheers = Cheers.objects.get_for_object(content).count()\n\n if context['request'].user.is_authenticated():\n ... | [
{
"docid": "5b16dda87344f8df349eb7d5f9c9bd67",
"score": "0.6046909",
"text": "def show_count_goods_in_wishlist(user):\n if user.is_authenticated():\n try:\n count = user.wishlist.goods.count()\n return \"<span id='wish_number'>\" + str(count) + \"</span>\"\n except... |
940d1b8a17ff81df583ac617b2ad1d17 | Use the jsonplaceholder API to add a fake todo. | [
{
"docid": "2e7a09d9413f62928d5d7fa02441e649",
"score": "0.6715809",
"text": "def request_todo():\n response = requests.post(\n \"https://jsonplaceholder.typicode.com/todos\",\n data = {\n 'title': \"laundry\",\n 'userId': 1\n }\n )\n\n if not response... | [
{
"docid": "a08f660ecae0f916d38bb18b7ce345e8",
"score": "0.66141295",
"text": "def post(self, id=None):\n data = self.todo_list_parser.parse_args()\n user = get_jwt_identity()\n public.create_todo_for_user(data[\"name\"], user)\n return {}",
"title": ""
},
{
"doci... |
9d7e17a82caf04f86602e993a381ff27 | Check link status of the ports. | [
{
"docid": "ee269d649d3b305fbaac5c50ccba5242",
"score": "0.8408618",
"text": "def check_ports(self, status=True):\n # RRC not support link speed change\n if self.kdriver == \"fm10k\":\n return\n\n for port in self.ports:\n out = self.tester.send_expect(\n ... | [
{
"docid": "2ef339ec34af11ddfeb71823e0d58949",
"score": "0.74812603",
"text": "def link_state(self, *args):\n\n try:\n interface = self.parse_portnumber(*args)\n self.execute(f'show int status {interface}')\n answer = self.__conn.response.split('\\n')\n ... |
6e54a8eca9e4180926415d486ca1f740 | Verifies that frame identified by ``locator`` contains ``text``. See the `Locating elements` section for details about the locator syntax. See `Page Should Contain` for an explanation about the ``loglevel`` argument. | [
{
"docid": "714c40aee4f537ef3fee94ba26b34fc3",
"score": "0.85518944",
"text": "def frame_should_contain(\n self, locator: Union[WebElement, str], text: str, loglevel: str = \"TRACE\"\n ):\n if not self._frame_contains(locator, text):\n self.log_source(loglevel)\n r... | [
{
"docid": "ca452c4bc7c156401f6a4263b46ba2b5",
"score": "0.7412529",
"text": "def current_frame_should_contain(self, text: str, loglevel: str = \"TRACE\"):\n if not self.is_text_present(text):\n self.log_source(loglevel)\n raise AssertionError(\n f\"Frame shou... |
4eb0b034bb9d1dcf9b517325f826a365 | Gets the full value (all bits) from the Fctl field. | [
{
"docid": "e374a7ba5f5ba767a2e0810738308ac4",
"score": "0.63612956",
"text": "def fctl(self,):\n return self._fctl",
"title": ""
}
] | [
{
"docid": "b3c30f5c9fc9ae6a6784e721e7631336",
"score": "0.63656574",
"text": "def raw_val(self):\r\n return self.msg_val >> self.bit_start",
"title": ""
},
{
"docid": "9fd14b6d4ea516241d100e0437f8d56c",
"score": "0.62198126",
"text": "def get_raw(self):\n value = self.... |
f78a87ac168d79e800d9089de3221bfc | Display the geographic map. | [
{
"docid": "b1de97ed75a0a70594e5f330171169df",
"score": "0.0",
"text": "def map(self,var,y1=None,y2=None,x1=None,x2=None,y='I',x='J',loc=[],\\\n m=None,p='cyl',edge='n',face='y',intp='n',cbar='y',ref=None,**kwargs):\n nc=self.nc\n dims=self.dims\n dimkeys=self.dimkeys\n \n bmsp... | [
{
"docid": "6288591c2420b1551b0762768914cda4",
"score": "0.75788015",
"text": "def display_map(self):\n print(self.map)",
"title": ""
},
{
"docid": "cf57fb48b5bf3a6b0da2afff1ad61ab1",
"score": "0.7310399",
"text": "def show_map(self, name):\n g = geocoder.osm(name)\n ... |
afd710d6e63b3ce9114bd5b71ef13b55 | r""" sub/pub registration Create a subscription. The subscription becomes part of the federate and is destroyed when the federate is freed so there are no separate free functions for subscriptions and publications. | [
{
"docid": "9c656d3e6a3c7568ae38a914670f4924",
"score": "0.0",
"text": "def helicsFederateRegisterSubscription(fed: \"helics_federate\", key: \"char const *\", units: \"char const *\") -> \"helics_input\":\n return _helics.helicsFederateRegisterSubscription(fed, key, units)",
"title": ""
}
] | [
{
"docid": "1809d7dd2ce08c8e421be7ae0d2d031d",
"score": "0.799498",
"text": "def create_subscription():\n pass",
"title": ""
},
{
"docid": "290bb296d352a8553287332b46bcb2cc",
"score": "0.7299079",
"text": "def create_subscription():\n subscriber = pubsub_v1.SubscriberClient()\n... |
b6717fddd5dc12d4f7d26459c810a82d | Rename a menu item | [
{
"docid": "ad82d3e09405d379790ce7c18d7a60a5",
"score": "0.72674495",
"text": "def RenameMenu(self, MenuItemString=defaultNamedNotOptArg, NewName=defaultNamedNotOptArg):\n\t\treturn self._oleobj_.InvokeTypes(6, LCID, 1, (24, 0), ((8, 1), (8, 1)),MenuItemString\n\t\t\t, NewName)",
"title": ""
}
] | [
{
"docid": "e4070dbf12de343d946415b89420539e",
"score": "0.70504844",
"text": "def renameDialog(self):\n item = self.itemOfContextMenu\n newName, ok = QInputDialog.getText(self.views()[0], self.sender().text(),\n \"Enter new name of \" + item.itemT... |
d87bdfd38f12e72ca63811f13c7988bf | Setter method for config, mapped from YANG variable /mpls/signaling_protocols/rsvp_te/interface_attributes/interface/config (container) | [
{
"docid": "7947316147ea0b17b1df53fefac1ef00",
"score": "0.79970694",
"text": "def _set_config(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_config_openconfig_mpls_igp__mpls_signaling_protocols_rsvp_te_interface_attributes_interf... | [
{
"docid": "2ab4817b5a6c17b20fc22fdab5329cfb",
"score": "0.8041246",
"text": "def _set_config(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=yc_config_openconfig_mpls__mpls_signaling_protocols_rsvp_te_interface_attributes_interface_c... |
41eec308ed7d459c1e85fea52ba54ebf | For `print` and `pprint` | [
{
"docid": "bff2052ac2ba06621cc51030f2133214",
"score": "0.0",
"text": "def __repr__(self):\n return self.to_str()",
"title": ""
}
] | [
{
"docid": "6400e853207eb06fb5f0764d2fd40c08",
"score": "0.7634721",
"text": "def __special_print(self):\n pass",
"title": ""
},
{
"docid": "3fc3020430a216ca8555aca00f1b1d6c",
"score": "0.74984014",
"text": "def print(*args, **kwargs): # known special case of print\n pass",... |
2642d6f90c9cbf6babb31277a1c8f50d | Converts a token (str) in an id using the vocab. | [
{
"docid": "58c9afb30ab42882f134ae6da1db5f53",
"score": "0.8012835",
"text": "def _convert_token_to_id(self, token):\n return self.vocab.get(token, self.vocab.get(self.unk_token))",
"title": ""
}
] | [
{
"docid": "a247f0841dd5145445a241a1bb0b66f2",
"score": "0.8083753",
"text": "def convert_token_to_id(self, token):\n return self.vocab[token]",
"title": ""
},
{
"docid": "0afb07ec120009bde5fcd4306251584e",
"score": "0.7667727",
"text": "def convert_id_to_token(self, id_: int)... |
d13dc7fde480a06e47995b264c6ad3cb | View for deleting an existing Contact object. Retrieves the Contact object and deletes it. | [
{
"docid": "80f4f55163ef61f081fa3e763d7e0828",
"score": "0.7051108",
"text": "def delete(request, listing_id):\n user_id = request.user.id\n listing_id = listing_id\n contact = Contact.objects.get(listing_id=listing_id, user_id=user_id)\n contact.delete()\n # Directs user to dashboard if ... | [
{
"docid": "4100e1b3a32b650904cdbae7d244ed04",
"score": "0.7768654",
"text": "def delete_contact(request, id):\n contact = Contact.objects.get(pk=id)\n contact.delete()\n return JsonResponse({'success': True})",
"title": ""
},
{
"docid": "68c4ad4c75fc7d17ae02de041d699c15",
"scor... |
fcf66e350d11c77456a9999c0f06784c | drawing single rate figure | [
{
"docid": "b77b93ecb18e8e442d22179b3eac678b",
"score": "0.60216314",
"text": "def __viz_rate(self, precoder: str, alpha: float, mode: str, color: str, marker: str):\n rows = 16\n cols = 8\n power_db = np.array([10 * np.log10(p_temp) for p_temp in power_range])\n if precoder ... | [
{
"docid": "c6aaa2b25d65f5b6c2c0414e398b40e1",
"score": "0.6754473",
"text": "def drawing() -> None:",
"title": ""
},
{
"docid": "f943f411fbf7b24eae5940bc2b9a266c",
"score": "0.6675388",
"text": "def draw( self ):\n pass",
"title": ""
},
{
"docid": "cb1d6b2102c44e4... |
5ddf5c501f1f2fe13d68b6b1d2cd9af5 | Renders the template for user management, passing forward all users | [
{
"docid": "4486f8b3ee5a4bdaac7d698c3ae6fc47",
"score": "0.7548799",
"text": "def manage_users():\n if not verify_login(url_for('manage_users'), True):\n return redirect('login')\n\n return render_template('Site/manage_users.html', user=session, title='User Management', css='manage_users.cs... | [
{
"docid": "a61619db8a1b7d1492be3ee68d6b639b",
"score": "0.768411",
"text": "def user():\n return render_template(\"user.html\")",
"title": ""
},
{
"docid": "877b2d12ce8a50687c4f64453fb8668c",
"score": "0.7255453",
"text": "def user():\n return render_template(\"user.html\", us... |
797d1eeaba0a56a56e39a89f0a8f8501 | gets all the possible moves that can be made by white pieces on the board returns a list of these moves in the form (id, (i, j)) | [
{
"docid": "e46570c47b177b7151920b462f9ce900",
"score": "0.74544895",
"text": "def get_all_moves_w(self):\n all_moves = list()\n\n # loop through all pieces\n for piece in self.whites.values():\n\n # loop through moves for each piece and add to list\n for move ... | [
{
"docid": "83262dc4583a9d25441ca4e0965de8c3",
"score": "0.81470615",
"text": "def possibleMoves(self):\n moves = []\n for row in range(len(self.board)):\n for col in range(len(self.board[row])):\n turn = self.board[row][col][0]\n piece = self.board... |
5218d72562896a3b73945ca10c13f9d5 | Basic linear regression 'model' for use with ODR | [
{
"docid": "fcc9d567c27592e89fedaee9668a2a46",
"score": "0.0",
"text": "def f(p, x):\n\treturn (p[0] * x) + p[1]",
"title": ""
}
] | [
{
"docid": "1808d0a75c0b5ecdb4e518b76faccd47",
"score": "0.75374883",
"text": "def _liner_regression_model():\r\n reg = linear_model.LinearRegression()\r\n reg.fit(wines[3][:, np.newaxis], wines[5])\r\n print(reg.coef_)\r\n plt.scatter(wines[3], wines[5])\r\n plt.show()",
"title": ""
... |
49963c8fe86ccc3e6e035b6d2d4764cd | Trains model and generates graphs. | [
{
"docid": "ca12baae486ca1f1034edf22c2e62805",
"score": "0.0",
"text": "def training_phase(self) -> None:\n print(\"* Setting up training job.\", flush=True)\n self.train_dataloader = self.get_dataloader(hdf_path=self.train_h5_path,\n data... | [
{
"docid": "18487819a1bee50ed5592899c326623d",
"score": "0.6983044",
"text": "def run(self):\n # initialize\n self.initialize()\n\n # model\n self.train()",
"title": ""
},
{
"docid": "729d4935cec53b11067ba0d682bfa901",
"score": "0.6971355",
"text": "def st... |
59dd2c0cc43a35a4714728514b1f82fb | Reads Json File into memory | [
{
"docid": "cdcea52a1923175dbd72e1c605e241c6",
"score": "0.0",
"text": "def read_json_file(path_to_file):\n with open(path_to_file) as p:\n # Read file\n df = pd.read_json(p)\n # Remove outdated versions\n df = df[df[2] > CONF.ignore_version_min]\n df = df[df[2] < C... | [
{
"docid": "d4c6ea6f2e57f9d83a8e20db663625c6",
"score": "0.80215484",
"text": "def json_reader(self):\n fn = self.fn\n if os.path.isfile(fn) and os.path.getsize(fn) > 0:\n with open(fn, 'rt') as ff:\n return json.load(ff)",
"title": ""
},
{
"docid": "0... |
0b3d9defa1f9a870e865f9379f04c32c | render static map with all map features that were added to map before | [
{
"docid": "5c7736ad9d3098e5dd5d61867879f491",
"score": "0.0",
"text": "async def render(self, zoom: Optional[int] = None, center: Optional[Tuple[float, float]] = None) -> Image:\n\n if not self.lines and not self.markers and not self.polygons and not (center and zoom):\n raise Runtime... | [
{
"docid": "2aba0deaf061ccb789440dde516ca413",
"score": "0.66984594",
"text": "def make_map(self):",
"title": ""
},
{
"docid": "e531918031e7fd3ebe28f005dd336ee4",
"score": "0.6492546",
"text": "def render_map(self):\n cent_x = (self.map_width / 2) + 3\n cent_y = (self.... |
b95f166da26990e6e0a569a356bc1e3a | Serie on a site. | [
{
"docid": "fc5c5a436a6ac1abeb96a78bfb2c8892",
"score": "0.0",
"text": "def test_base_01(self):\n s = sitehydro.Sitehydro(code='A0445810', libelle='Le Rhône à Marseille')\n g = 'Q'\n t = 16\n o = obshydro.Observations(\n obshydro.Observation('2012-10-03 06:00', 33)... | [
{
"docid": "cf596b356d7fbe2e3ca2b14f34f14b87",
"score": "0.5957596",
"text": "def getSite():",
"title": ""
},
{
"docid": "729b53be2114614c394e7de90f6cc3d1",
"score": "0.5566644",
"text": "def on_site(data):\n\treturn data",
"title": ""
},
{
"docid": "7c5356b343a03f668a8a6... |
36df3bf95d8e7a9344a2496070236251 | Your extreme 2048 evaluation function (question 5). | [
{
"docid": "79c5b69cf10a38e1926299165b75eeb8",
"score": "0.55875546",
"text": "def better_evaluation_function(current_game_state):\n Weights= [1,10,15]\n\n board = current_game_state.board\n max_tile = current_game_state.max_tile\n emptyCells = current_game_state.get_empty_tiles()[0].size\n\... | [
{
"docid": "b5c1d5d053826bebf5aa991412177189",
"score": "0.62113535",
"text": "def betterEvaluationFunction(gameState):\n return betterEvaluationFunction_bestButSlower(gameState)",
"title": ""
},
{
"docid": "80b0ef018e5ab06c48139bea3909eef5",
"score": "0.5929156",
"text": "def probl... |
01e4e2952b97720cc5d44088ccd07f2f | Upload an asset to Hangar51 | [
{
"docid": "e5e378f588f24ff53abe15b53d01b7d7",
"score": "0.0",
"text": "def create(cls, client, file, name=None, expire=None, secure=False):\n return cls(\n client,\n client(\n 'put',\n f'assets',\n files={'file': file},\n ... | [
{
"docid": "437025a0ea46080597aae093421805bc",
"score": "0.68052816",
"text": "def upload(args):\n asset = asset_utils.Asset(args.asset_name,\n asset_utils.MultiStore(gsutil=args.gsutil))\n asset.upload_new_version(args.target_dir, commit=args.commit,\n ... |
1a832616cb6b06708dfe06313fca72e0 | Returns the amount of production the tile produces | [
{
"docid": "6a42bb33e0766afa656bf732d4134a74",
"score": "0.0",
"text": "def getProduction(self):\n return self._production",
"title": ""
}
] | [
{
"docid": "58da72d9708ea165b7cc80487bbcbcd4",
"score": "0.71903956",
"text": "def getNumTiles(self):\n return self.height*self.width \n # TODO: Your code goes here",
"title": ""
},
{
"docid": "4891df995f4acb7df2ae9203bae8ad18",
"score": "0.7129132",
"text": "def getNum... |
a34b1fba110096252a79289ce019e213 | Closes down the connection to Gmail. | [
{
"docid": "2fef5efd7e1dd7bff50d7a4a481838f6",
"score": "0.61492366",
"text": "def close(self) -> bool:\n if not self.gmail.close():\n raise ControllerCloseError()\n else:\n return True",
"title": ""
}
] | [
{
"docid": "5104d31bbd7cab0a6da16165b8e44bc3",
"score": "0.69253117",
"text": "def close(self):\n self.close_imap()\n self.close_smtp()",
"title": ""
},
{
"docid": "51abb427412e2e71c3b2f18686fa1520",
"score": "0.67812467",
"text": "def close(self):\n self.imap.cl... |
95d92eade0c13c442c8cf261369b36ac | Read a csv file and import the attributes. | [
{
"docid": "f63a35f99ecdf151a6624f8f9ecdc07f",
"score": "0.6875641",
"text": "def from_csv(filename, sep=\";\"):\n with open(filename, \"r\") as f:\n for line in f.readlines():\n key, val = line.strip().split(sep)\n setattr(self, key, val)",
"title": "... | [
{
"docid": "18273c8be73e04f6a56ac07ac4ba5df8",
"score": "0.726499",
"text": "def import_from_csv(self, file_name):\n with open(file_name) as csvfile:\n reader = csv.reader(csvfile, delimiter=',')\n next(reader, None)\n for row in reader:\n self.add_... |
dbdc5fd2d3f41bfb0bd7d2641ffc9013 | __next__(PixelIterator self) > double | [
{
"docid": "92674ae7f4f7e9705f417d4a941eabf8",
"score": "0.81508714",
"text": "def __next__(self):\r\n return _ilwisobjects.PixelIterator___next__(self)",
"title": ""
}
] | [
{
"docid": "ca16689992ffc5b9f7c7ae7768cc29c9",
"score": "0.81843203",
"text": "def __float__(self):\r\n return _ilwisobjects.PixelIterator___float__(self)",
"title": ""
},
{
"docid": "5825166d4d566fb4be1932786e79ad94",
"score": "0.74169964",
"text": "def __int__(self):\r\n ... |
4d4b69001315667607b1f7faa42b93d7 | Return the smallest and largest eigenvalue of hermitian operator ``A``. | [
{
"docid": "be15624cfe423eae6eb95a64bac60cec",
"score": "0.5767281",
"text": "def bound_spectrum(A, backend='auto', **kwargs):\n el_min = eigvalsh(A, k=1, which='SA', backend=backend, **kwargs)[0]\n el_max = eigvalsh(A, k=1, which='LA', backend=backend, **kwargs)[0]\n return el_min, el_max",
... | [
{
"docid": "2b1b3da2611f3d4c65c4525d3af722fa",
"score": "0.6762377",
"text": "def eigsh(A: Tensor,\n largest: bool = True,\n m: int = 20,\n max_iter: int = 10000,\n tol: float = 1e-5\n ) -> Tuple[Tensor, Tensor, int]:\n e, v, n_iter = Eigsh.apply(A, larges... |
a4ee6faead96f35a3b1b16184b361e13 | str> str some_cat is string inputted category ledg_list is a list of dictionaries representing transactions returns string transactions from dictionary where category matches parameters presented | [
{
"docid": "1c31535c876d29367da1c7861e3c9e84",
"score": "0.58800286",
"text": "def print_by_cat(some_cat, ledg_list):\n val_list = []\n for transaction in ledg_list:\n if transaction[CATEGORY_COL] == some_cat:\n for key in transaction:\n if key == AMOUNT_COL:\n ... | [
{
"docid": "7db5287fed0a62e8502975fdf3899a87",
"score": "0.5633938",
"text": "def combine_category(category):\n\n\tcategory_filter = []\n\n\tfor c in category:\n\t\tif c == \"sex\":\n\t\t\tcategory_filter.extend((\"SEX OFFENSES, NON FORCIBLE\", \"SEX OFFENSES, FORCIBLE\",\n\t\t\t\t\t\t\t\t\t\"PROSTITUIO... |
328b92bde75dadfe209108158be68dec | Handles the dropping of the dragged connection | [
{
"docid": "1f2bf1c01672169d8d0ecc19b68c05a0",
"score": "0.61582077",
"text": "def _drop(self, position): # pylint: disable=unused-argument\n if not self._playing:\n return\n\n if self._game.is_resolving():\n return\n\n self._grid_view.clear_dragged_connection... | [
{
"docid": "8766a2cd58f3ce147a6d4ea91ea6590c",
"score": "0.7067113",
"text": "def after_drop(self, target, connection, **kw):",
"title": ""
},
{
"docid": "6f3319ea51c67fb1d1095f968bcc72d6",
"score": "0.6980423",
"text": "def hook_drop(self):\n widget = self.widget\n wid... |
bd2f05cd580045979c2412703f64ee52 | Test restore_file_version raises 404 error | [
{
"docid": "c4c327fe6c722803ef647d67251ca282",
"score": "0.81118536",
"text": "def test_restore_file_version_fails_with_404_exception(self) -> None:\n\n URL.API_BASE_URL = \"http://test.com\"\n url = \"{}/v1/files/{}/versions/{}/restore\".format(\n URL.API_BASE_URL, self.file_id... | [
{
"docid": "d61331df523c92996c20317a014e9cbf",
"score": "0.7169947",
"text": "def test_restore_file_version_succeeds(self) -> None:\n\n URL.API_BASE_URL = \"http://test.com\"\n url = \"{}/v1/files/{}/versions/{}/restore\".format(\n URL.API_BASE_URL, self.file_id, self.version_id... |
285755df5d7f67c6059001aa63fc4002 | Prints and returns the number of errors relating to objects within arrays lacking `id` fields. | [
{
"docid": "87316cb585a31940d1a5895ff7149f60",
"score": "0.553918",
"text": "def validate_object_id(*args):\n exceptions = {\n 'changes', # deprecated\n 'records', # uses `ocid` not `id`\n '0', # linked releases\n }\n\n required_id_exceptions = {\n # 2.0 fixes.\n ... | [
{
"docid": "e401fc12a143e3bc1b8f74ee30a903f3",
"score": "0.60479474",
"text": "def display_errors():\n for e in parable.errors:\n write(\"\\n\" + e, COLOR_ERROR)\n return len(parable.errors)",
"title": ""
},
{
"docid": "80be0c10a7e0e124d7f61e9367081f85",
"score": "0.60005736... |
76278f0e5efc26b02b96ab25f8229df1 | Returns the attack power of your army | [
{
"docid": "24e4a5710af35b78f21d14c0706dd9c7",
"score": "0.8111584",
"text": "def get_attack_power(county, army):\n strength = 0\n for name, amount in army.items():\n strength += county.armies.values()[name].attack * amount\n strength *= uniform(0.85, 1.15)\n return int(strength)",
... | [
{
"docid": "e60dba37e35027f7496d1eaef118f31c",
"score": "0.7620182",
"text": "def attack_power(self, params):\n return self.polity.attack_power(params)",
"title": ""
},
{
"docid": "78330785932fa22be6e4b9e27e1030cd",
"score": "0.728332",
"text": "def attack_power(self, params):... |
752bfb8f156c2e4c52797ac6ddd1e564 | Return the username for further use. | [
{
"docid": "00e03a92d0d1b352e150f26119f313d9",
"score": "0.87460893",
"text": "def get_username(self):\n return self._username",
"title": ""
}
] | [
{
"docid": "32ce8f3d9b2d89f3ff088323f3c6e6d6",
"score": "0.8789279",
"text": "def get_username(self):\n return self.username",
"title": ""
},
{
"docid": "b9f3d48e59dd013ff027376c19cf5463",
"score": "0.86927414",
"text": "def username(self) -> str:\n return pulumi.get(se... |
3baa3c732ef9d18f9a5305271ca48a56 | Computes the evaluation mask. | [
{
"docid": "acbc7f6b3daba332facb5f50cb5a46dc",
"score": "0.6564162",
"text": "def computeEvaluationMask(maskDIR, resolution, level):\n #slide = openslide.open_slide(maskDIR)\n #dims = slide.level_dimensions[level]\n #pixelarray = np.zeros(dims[0] * dims[1], dtype='uint')\n #pixelarray = np.a... | [
{
"docid": "4a46143edac361dbfc09e3c6ae223b01",
"score": "0.6575322",
"text": "def apply_mask(self, mask: torch.Tensor) -> None:",
"title": ""
},
{
"docid": "7ae3c375a662ab9d38f9ae79b30cb0e3",
"score": "0.65126675",
"text": "def calc_mask(self, layer, config):\n raise NotImplem... |
7adfcbd2a184d6e5c05c1413ad13bdf4 | Work around python reserved word | [
{
"docid": "ab07dd336111b6d444888dc26bd4aedf",
"score": "0.0",
"text": "def ws_import(x):\n getattr(ws, \"import\")(x)",
"title": ""
}
] | [
{
"docid": "d779d8cf4f5b99294984cb5fb4e5fcdf",
"score": "0.661349",
"text": "def _name_exsit(self):",
"title": ""
},
{
"docid": "f6ca2f39abcd0667a25be60b540c11fc",
"score": "0.649878",
"text": "def check_reserved_keyword(self, name):\r\n for backend in self.check_reserved:\r\n... |
aa5bad694ee4262475ec322683c3ebca | Given a list of paths to CSV metric files, returns a dictionary containing the global maximum for each metric key | [
{
"docid": "6d32dca4c94d56e0ec3c06bc1d7c06d0",
"score": "0.72449195",
"text": "def findMaxima(inpaths, core):\n maxima = {}\n readers = {}\n infiles = {}\n for ind, p in enumerate(inpaths):\n infile = open(p, 'r')\n infiles[p] = infile\n readers[p] = DictRead... | [
{
"docid": "4a9a0fac8bedc4f1a6429c56d0b68a2a",
"score": "0.5977436",
"text": "def get_max_yaxis(filename):\n max_master = -1\n max_no_master = -1\n with open(filename) as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n value = float(row[\"value\"])\n ... |
5c26ab72c9a537b0f09d4240cafafe2b | Adds a product to the db access only for admin | [
{
"docid": "c6d4a9588073a83c47a35c6412e5308e",
"score": "0.6830448",
"text": "def add_product():\n # Checks if user is in session\n if \"user\" in session:\n # Checks if user is admin\n if session[\"user\"] == \"admin\".lower():\n if request.method == \"POST\":\n ... | [
{
"docid": "1061fca0fdb1996a72462df588ee9065",
"score": "0.73962516",
"text": "def add_product():\n\n check_admin()\n add_product = True\n\n form = ProductForm()\n if form.validate_on_submit():\n product = Products(\n name=form.name.data,\n description=form.descr... |
8aaa38d8bca476576a3131617ce995cf | Unique event id used by calendars | [
{
"docid": "c403bff3fb4d933f1bfd206491488043",
"score": "0.0",
"text": "def cal_guid(self):\n return \"mtg\" + str(self.id) + \"@lnldb\"",
"title": ""
}
] | [
{
"docid": "78d08c2588e7581c4c9ace3fffe653a0",
"score": "0.78009397",
"text": "def EventID(self) -> int:",
"title": ""
},
{
"docid": "0c53035e738c1972090844622e47386c",
"score": "0.77761203",
"text": "def event_id(cls) -> str:\n return jsii.sget(cls, \"eventId\")",
"title"... |
c84c6f086d512304fa5d8c87585128d6 | Set the given binding as the active one. | [
{
"docid": "75341077ed9254acdb1f3b58629af968",
"score": "0.734818",
"text": "def set(self, binding):\n if not isinstance(binding, Bindings):\n # here, we assume that binding is a class, so we take the first\n # existing binding that is an instance of this class\n ... | [
{
"docid": "011cacc27b248b4ce131045f712b8819",
"score": "0.792457",
"text": "def set_binding(self, binding):\n self.binding = binding",
"title": ""
},
{
"docid": "78bbcdcd31ff234e252f29c2aa99c353",
"score": "0.65545285",
"text": "def setBinding(self, name, value):\n if ... |
c30c667d5c5acfd97a1ef3c96272db95 | Create a module from spec. | [
{
"docid": "5cc8b8b1a3720e02264fccaecee78c40",
"score": "0.78956467",
"text": "def create_module(self, spec):\n if spec.name in sys.modules:\n # NOTE: Original name was already loaded, no need for going through all of importlib\n return sys.modules[spec.name]\n return... | [
{
"docid": "ed48e74b84544028ce9264f387690eba",
"score": "0.8555461",
"text": "def create_module(cls, spec):\n return None",
"title": ""
},
{
"docid": "ed48e74b84544028ce9264f387690eba",
"score": "0.8555461",
"text": "def create_module(cls, spec):\n return None",
"ti... |
9c579ee58ffb6223b9fb2c25c3ce72c5 | Returns a new address of the custom currency or an existing one if the waves_address is already associated to an address of the custom cryptocurrency. | [
{
"docid": "f41b358d49817ab1f5d163f58ffa8550",
"score": "0.6344724",
"text": "def create_address(self, waves_address: str) -> str:\n pass",
"title": ""
}
] | [
{
"docid": "d858fdd21e034f2ef86e40ace1983a4b",
"score": "0.60368794",
"text": "def save(self,\n force_insert=False,\n force_update=False,\n using=None,\n update_fields=None):\n self.address_str = \"{}:{}\".format(self.coin.symbol, self.address)\n ... |
a2ea01cceefd4fefdf12e838a468986b | Check that a generated cell is valid on the board. | [
{
"docid": "7f8942579202c333a920584560995ae4",
"score": "0.67646253",
"text": "def isValidCell(cell):\n assert(type(cell) == tuple)\n if cell[0] < 0 or cell[1] < 0: return False\n if cell[0] > 11 or cell[1] > 11: return False\n if cell[0] < 6 and cell[1] > 5: return False\... | [
{
"docid": "676a43fd6e31baadcd7271a8ea4f829a",
"score": "0.71884423",
"text": "def check_valid(cell: str) -> bool:\r\n global user_sequence\r\n return cell not in user_sequence",
"title": ""
},
{
"docid": "9f411eb37deddc80476fb1d7e657da59",
"score": "0.7176858",
"text": "def va... |
8c762669aac2bf002e9d0ebbf40e3d96 | Test parallel feature fetch vector and return a file | [
{
"docid": "70d4a68abae269b4c8caafec9da0ea86",
"score": "0.7135391",
"text": "def test_bqparallelfeature_fetch_2():\n filename = 'bqparallelfeature_fetch_2.h5'\n path = os.path.join(results_location, filename)\n PF=ParallelFeature()\n PF.set_thread_num(2)\n PF.set_chunk_size(5)\n filen... | [
{
"docid": "5860a95e8ac72ce55a34838e7ccfd876",
"score": "0.7470921",
"text": "def test_bqparallelfeature_fetchvector_1():\n PF=ParallelFeature()\n PF.set_thread_num(2)\n PF.set_chunk_size(5)\n feature_vectors = PF.fetch_vector(bqsession, 'SimpleTestFeature', resource_list)",
"title": ""
... |
a938a21caf56eaceb7e5feec8346c651 | get a bucket object, gc | [
{
"docid": "5577f1f5f369e82ba39244911c5daa7a",
"score": "0.73369956",
"text": "def _get_bucket_gc(self, storage_bucket):\n client = gc.Client(project=self.gc_project_name)\n bucket = client.bucket(storage_bucket)\n if not bucket.exists():\n raise DataCloudError('sync up: ... | [
{
"docid": "122723d39081f1efc4f682af4696cc92",
"score": "0.6894451",
"text": "def get_bucket(self):\n raise NotImplementedError()",
"title": ""
},
{
"docid": "98a1f7a64bc2eb8398c35d2bdfd2ae87",
"score": "0.67157984",
"text": "def _gcs_object(self, path):\n bucket_name, blob... |
08d7629f6b7f6c8b964a363946f21f61 | >>> a = Link(1, Link(2, Link(3))) >>> b = reverse(a) >>> b Link(3, Link(2, Link(1))) >>> a Link(1, Link(2, Link(3))) | [
{
"docid": "c1d7655c8dbcdf83f67c6c1880e51550",
"score": "0.73522156",
"text": "def reverse(lst):\n rev = Link.empty\n while lst is not Link.empty:\n rev = Link(lst.first, rev)\n lst = lst.rest\n return rev",
"title": ""
}
] | [
{
"docid": "754c30f123c8966abcf8cc515faa1da6",
"score": "0.75776565",
"text": "def reverse(self):\n reverse = Link.empty\n while self is not Link.empty:\n reverse = Link(self.first, reverse)\n self = self.rest\n return reverse",
"title": ""
},
{
"do... |
0f3e2405c3155d56cfec9c2af6116240 | Take name string, give back security group ID. To get around VPC's API being stupid. | [
{
"docid": "afe3481db4dc4f46fe24a53d3b04c401",
"score": "0.7532722",
"text": "def get_security_group_id(self, name):\n # Memoize entire list of groups\n if not hasattr(self, '_security_groups'):\n self._security_groups = {}\n for group in self.get_all_security_groups(... | [
{
"docid": "e592471444ff87ca1469506859a90787",
"score": "0.75126034",
"text": "def get_secgroup_id(kwargs=None, call=None):\n if call == \"action\":\n raise SaltCloudSystemExit(\n \"The get_secgroup_id function must be called with -f or --function.\"\n )\n\n if kwargs is N... |
60ea7da3974f1023b2ee423d0b3a4c8d | Draw GO as a DAG | [
{
"docid": "12cac323d199a866919b9d5ffc6b2bfc",
"score": "0.6420478",
"text": "def draw_GO(self):\n nx.draw(self.Ontology)\n plt.show()",
"title": ""
}
] | [
{
"docid": "ffedb5c528864cc0eb748fe488e4ccea",
"score": "0.7305107",
"text": "def draw_dag(self):\n return nx.draw(self.graph, labelled=True)",
"title": ""
},
{
"docid": "a8a4d8ecd0afefb18f9a6bbd89555450",
"score": "0.6930505",
"text": "def makeDotGraph(self) -> str:\n ... |
3094f7e4637cdc5fe59ceefaa65a5198 | Find component with componentType specified types and property key hast value | [
{
"docid": "664414399cf9a693786a9d8f0656e235",
"score": "0.63733405",
"text": "def findValue(self, key: str, searchValue: Any, componentType: Optional[QWidget.__class__] = None) -> List[QWidget]:\n lst = list()\n for component in self.findKey(key, componentType):\n value = compo... | [
{
"docid": "9eb39ff9195599ecc4a8f6bd6a42f00f",
"score": "0.6614395",
"text": "def findKey(self, key: str, componentType: Optional[QWidget.__class__] = None) -> List[QWidget]:\n\n if not isinstance(key, str):\n print(\"Property key typeError: {!r}\".format(key.__class__.__name__))\n ... |
730408ed2c8f7f1da6a8babc8c53cfc9 | Get the current audio status | [
{
"docid": "10ebacf1356eff7924df86560ca26b88",
"score": "0.8365147",
"text": "async def get_audio_status(self):\n await self.request(EP_GET_AUDIO_STATUS)\n return {} if self.last_response is None else self.last_response.get('payload')",
"title": ""
}
] | [
{
"docid": "eec5ad075a9497899d8872fe4bbb1ed8",
"score": "0.717519",
"text": "def audioTrack(self):\n audio = self.__setting['audio']\n code = getClientLanguage()\n if code in audio:\n return audio[code]\n return 0",
"title": ""
},
{
"docid": "b4feeab7c5... |
884430a0909d760db377c9498b33f682 | Helper command to italicalize text | [
{
"docid": "c23a9db612a1b62317217505331427b2",
"score": "0.54463166",
"text": "async def cmd_italics(self, message, content, leftover_args):\n content = ' '.join([content, *leftover_args])\n return await self._check_bot(message, \"*{}*\".format(content))",
"title": ""
}
] | [
{
"docid": "39221d5d2f76a8dd3feef4404db1445c",
"score": "0.684061",
"text": "def _text_transformation(text):",
"title": ""
},
{
"docid": "67528e8072b69d8b7d95672ab4eb63cc",
"score": "0.6472909",
"text": "def itex(self, text):\n if itex2mml:\n try:\n t... |
4c2d55e98090af004c3e5c7fa53e0491 | Get 'protocol.Data_Pool' instance for all pools. | [
{
"docid": "4b7048b9bcc1531778030fb295109810",
"score": "0.5296444",
"text": "def storage_pools(node):\n\tif node.conn is None:\n\t\traise StorageError(_('Error listing pools at \"%(uri)s\": %(error)s'), uri=node.pd.uri, error='no connection')\n\ttry:\n\t\tpools = []\n\t\tfor name in timeout(node.conn.l... | [
{
"docid": "21f619874ee3d30d602925fb877823f0",
"score": "0.696777",
"text": "def _pooldata(self):\n return next(\n (pool for pool in self.coordinator.data if pool[\"id\"] == self._poolid),\n None,\n )",
"title": ""
},
{
"docid": "1f38ac6e613c151b874ddf2ad7... |
cb01d8f179b96afe456c4374ba00c084 | Setter method for times_enabled, mapped from YANG variable /mpls_state/times_enabled (uint32) | [
{
"docid": "f3d5d37c63d62fd6ee442a490118d304",
"score": "0.8622464",
"text": "def _set_times_enabled(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, in... | [
{
"docid": "ca2c91c265b62974f208a2229e7eaaf7",
"score": "0.6474298",
"text": "def _get_times_enabled(self):\n return self.__times_enabled",
"title": ""
},
{
"docid": "bb6e7db1e1f56d93a73cad871b6c2e2f",
"score": "0.58690506",
"text": "def _set_transit_lsps_enabled(self, v, load=Fal... |
fe035d829c552549a539f537efab9262 | Emit a record. The record is translated to OTLP format, and then sent across the pipeline. | [
{
"docid": "1c96e07c20eae7c6bb89f3bb6ea3b55d",
"score": "0.7012174",
"text": "def emit(self, record: logging.LogRecord) -> None:\n self._log_emitter.emit(self._translate(record))",
"title": ""
}
] | [
{
"docid": "d35408cfec0b6ff72aeeda9906fae37b",
"score": "0.76543707",
"text": "def emit(self, record):\n\n pass",
"title": ""
},
{
"docid": "4fcb41e745bc2849ef5355704b347341",
"score": "0.76331604",
"text": "def emit(self, record):\n pass",
"title": ""
},
{
... |
8dfce375e6ed156a70383611e0499a25 | add work logs from list | [
{
"docid": "40d677023c64a6275d9fc492b4a9c7ef",
"score": "0.76735646",
"text": "def add_work_logs(list_dict=worklogs):\n for worklog in list_dict:\n try:\n Worklog.create(worker_name=worklog['worker_name'],\n task_name=worklog['task_name'],\n ... | [
{
"docid": "7cfece95eff671c2df92744a07cd0138",
"score": "0.6894855",
"text": "def append_logs(self, logs: List[Dict[str, Any]]) -> None:\n\n raise NotImplementedError",
"title": ""
},
{
"docid": "a1796a43d9c88e9e9e91a4ac3d4083f6",
"score": "0.6539237",
"text": "def test_log_li... |
5339215e19cc8d68841aa88014416c61 | drop(self, noun) consumes a World and a noun, removes the noun from the players inventory if noun exists in Player's inventory and mutates noun by adding noun to Room's contents. | [
{
"docid": "b133d149b23bb2ff56c52a195b042ede",
"score": "0.80960494",
"text": "def drop(self, noun):\n for things in self.player.inventory:\n if noun == things.name:\n self.player.inventory.remove(things)\n self.player.location.contents.append(things)\n ... | [
{
"docid": "f7ec4dbcc13ed912996edd6451514158",
"score": "0.76989484",
"text": "def drop(self, verb, noun, inoun = None):\n objNoun = None\n objInoun = None\n self.moved = False\n\n if noun in self.inventory:\n objNoun = self.inventory[noun]\n if inoun:\n... |
343d2860196b19ce3084cd41f48cff08 | image = jpg, jpeg, png On success returns ``mglib.path.PagePath`` instance. | [
{
"docid": "f33e34470cc5a4dc08950830b714c41c",
"score": "0.52879614",
"text": "def ocr_page_image(\n doc_path,\n page_num,\n lang,\n **kwargs\n):\n logger.debug(\"OCR image (jpeg, jpg, png) document\")\n\n page_path = PagePath(\n document_path=doc_path,\n page_num=page_nu... | [
{
"docid": "1836a08dc9f468691afa711b35b0118f",
"score": "0.63259786",
"text": "def open_image_label(page_path, img_path, label_path):\n page = PrimaPage(page_path)\n p_img_open = page_img_opener(page)\n img = p_img_open(img_path)\n l_img = p_img_open(label_path)\n return img, l_img",
... |
155d8b341ec361d98c586dab33045e70 | Extract layer, region, module number and position from a unique half module identifier. | [
{
"docid": "222d3258e5dc71dc20c25c2a11449233",
"score": "0.77563006",
"text": "def locateTTHalfModule(halfModuleId):\n position = halfModuleId[-1:]\n uLayer = halfModuleId.split('Region')[0]\n region, moduleNum = halfModuleId.split('Region')[1].split('Module')\n return uLayer, region, int(mo... | [
{
"docid": "9a4ed24cee1d705b2da57b78137b0493",
"score": "0.65242213",
"text": "def locateTTModule(moduleId):\n uLayer = moduleId.split('Region')[0]\n region, moduleNum = moduleId.split('Region')[1].split('Module')\n return uLayer, region, int(moduleNum)",
"title": ""
},
{
"docid": "... |
a180806d61024f0350df46a9bd274ae8 | Given a list of ExternalWorkUnit, makes sure that all ExternalExtensibleBase have materialized tracked objects. Then returns the wrapped WorkUnits. | [
{
"docid": "7234f0836ae26093c1c422bec307c429",
"score": "0.7181121",
"text": "def _unwrap_ext_work_units(self, ext_work_units):\n\n external_work_units_grouped_by_class = defaultdict(list)\n external_work_units_grouped_by_global_id = defaultdict(list)\n\n for ext_work_unit in ext_wo... | [
{
"docid": "cfe144b4a80d516769a57d2159701103",
"score": "0.72182626",
"text": "def _sync_work_units(self, work_units):\n\n if not work_units:\n return work_units\n\n # 1. Batch query for existing work units by (workflow_provider, external_work_unit_id)\n existing = list(W... |
33593f0e78201eba2947500129adce71 | A test to check that rank finds index of a key in sorted order. | [
{
"docid": "60eab464fc87ca845690c8ea8a664d75",
"score": "0.7065371",
"text": "def test_rank_negative():\n keys = list(range(-100, 100))\n d = OrderedTreeDict((key, None) for key in keys)\n assert all(list(\n keys[d.rank(k)] == k for k in keys\n ))",
"title": ""... | [
{
"docid": "89a15ef69961bdfc7cf40c95814aa0b9",
"score": "0.7637563",
"text": "def test_rank():\n keys = list(range(100))\n d = OrderedTreeDict((key, None) for key in keys)\n assert all(list(\n keys[d.rank(k)] == k for k in keys\n ))",
"title": ""
},
{
"... |
988f504f229ab3b4a5f2b97b190eef4c | The bits whose parity stores the parity of the bits 0 .. `index`. | [
{
"docid": "cc3b88ae371ff7610cf6db652f3a46c7",
"score": "0.7036098",
"text": "def _parity_set(index):\n indices = set()\n\n # For bit manipulation we need to count from 1 rather than 0\n index += 1\n\n while index > 0:\n indices.add(index - 1)\n # Remove least significant one f... | [
{
"docid": "d0d714be8d59dfb6c9cf2bf613e5cfdc",
"score": "0.70778936",
"text": "def parity(n):\n # bin(n) returns 0b.... \n # bin(n)[2:] trims \"ob\"\n return sum(int(x) for x in bin(n)[2:]) % 2",
"title": ""
},
{
"docid": "5db780d213b678e431fa2da2ac6c604a",
"score": "0.70349866",
... |
85e99bef792e9ff5155cd81d3c20f826 | In each job, the methods are executed with the same dataset split and their results are put in an array. | [
{
"docid": "b609984317ade71cbbf1be7340f999ea",
"score": "0.0",
"text": "def validate(analysis, dataset_manager, pixel_methods):\n results = []\n train, verify = dataset_manager.get_data_splits()\n if analysis is True:\n data_analysis(train)\n for pixel_method in pixel_methods:\n ... | [
{
"docid": "2c75164bb06e2fe93a8887b597c49827",
"score": "0.6873918",
"text": "def batch_run(self) -> None:",
"title": ""
},
{
"docid": "93e4fb49c095b7635f67d8e053f02338",
"score": "0.65535104",
"text": "def run_parallel(self, **kwargs):\n\n duts=kwargs.get('duts') \n me... |
31ece964885d2dce60226c5e8cdbc050 | Emit ``CREATE TYPE`` for this | [
{
"docid": "4deeed2a3eb4a6af3b3697f5038a011a",
"score": "0.0",
"text": "def create(self, bind=None, checkfirst=True):\n if not bind.dialect.supports_native_enum:\n return\n\n super().create(bind, checkfirst=checkfirst)",
"title": ""
}
] | [
{
"docid": "f6f886f23fbe426eefa80c709ee61745",
"score": "0.66751873",
"text": "def create(self):\n return CreateDatatypes([self])[0]",
"title": ""
},
{
"docid": "4e275bd469fadcc00e9677156359e143",
"score": "0.66318345",
"text": "def create_type(self, create_type):\n\n s... |
b91cdbd5e0f55cb1591ad023618cb050 | Add a morph.DFMRotate object or create a new one. If no argument is given, a new instance of morph.DFMRotate will be created. | [
{
"docid": "8abe78c38d9fcefb8cf2294aa8859ef7",
"score": "0.0",
"text": "def add_rotate(rotate_obj):",
"title": ""
}
] | [
{
"docid": "976dffd0871d2fa44491dc8d42c37db7",
"score": "0.5145458",
"text": "def MorphParamCreateDFMRotate(name, rotate_args, entities, bounds, autobounds):",
"title": ""
},
{
"docid": "fe8b8d14829630d2c7752af44f9745ef",
"score": "0.502624",
"text": "def add(self) -> None:\n\n ... |
8e915c9bed9fe88cb18c1dc152baa1e4 | Generacion de hinchas de River | [
{
"docid": "043b0f1bef0ba62cfa505cba8d4eeee7",
"score": "0.0",
"text": "def barra_brava_river(viajes, lugares_bote, mutex):\n while viajes < 20:\n time.sleep(random.randrange(0, 5))\n hr = threading.Thread(target=hincha_river, args=(lugares_bote, mutex,))\n hr.start()\n # ... | [
{
"docid": "0cc41407da05b64dc4ef2d3a5f2701d2",
"score": "0.67497545",
"text": "def hincha_river():",
"title": ""
},
{
"docid": "3b0bb52fa9521499ef508c6bb7079ab1",
"score": "0.60572916",
"text": "def genemap(self):\n\t\treturn [('W1',8, 'Weighted'), ('L1',8, 'Weighted'), ('R1',8, 'Wei... |
4c3f1e80e1a13647cfac3dc3f583becc | Test for searching and finding through the children of a node with location specified. | [
{
"docid": "040f8892cd1ebc22e86bd7fd0ef294be",
"score": "0.5892507",
"text": "def test_find_node(self):\n # Assign\n node2 = Node(\"Child node 2\")\n node3 = Node(\"Child node 3\")\n node2.add_or_replace_child(node=node3, index=2)\n\n node1 = Node(\"Child node 1\")\n ... | [
{
"docid": "402682c267e585a31521634972681bfe",
"score": "0.63392574",
"text": "def getChildren(node, *args, **kwargs):",
"title": ""
},
{
"docid": "47f5e66c1cce9bfeecaeb53fb73a0493",
"score": "0.63026905",
"text": "def findChildren(self, name='None', attrs='{}', recursive='True', tex... |
faf67ca2c6b38c34528540e8f97c1612 | Checks that _ViewerApplet doesn't crash on the given model file. | [
{
"docid": "b1121d0ebf7609c6e9b520ac4d1b5a01",
"score": "0.57598865",
"text": "def _check_viewer_applet_on_model(self, resource):\n dut = mut.Meldis()\n lcm = dut._lcm\n diagram = self._make_diagram(\n resource=resource,\n visualizer_params=DrakeVisualizerParam... | [
{
"docid": "6b58e04ec2a8a0bd02e23457960f62ba",
"score": "0.61653244",
"text": "def test_viewer_applet_plain_meshes(self):\n self._check_viewer_applet_on_model(\n \"drake/manipulation/models/iiwa_description/urdf/\"\n \"iiwa14_no_collision.urdf\")",
"title": ""
},
{
... |
4589f6947d22d318bcdf8a791f92482b | compute boundary of disjoint union of polygons, return union polygon(s) and list of boundary pieces in the same order as corresponding input polygons | [
{
"docid": "1257ff00729ac91d555243cdc1ab5e4f",
"score": "0.83428013",
"text": "def compute_disjoint_union(*polygons):\n # note: the outputs that are holes are correctly returned in clockwise\n # i.e. opposite direction. this can be identified with the is_a_hole\n # function below.\n\n # dete... | [
{
"docid": "6c3d5b45fb28b128783dbaf7b86098fd",
"score": "0.6554626",
"text": "def polygon_iou(list1, list2):\n polygon_points1 = np.array(list1).reshape(4, 2)\n poly1 = Polygon(polygon_points1).convex_hull\n polygon_points2 = np.array(list2).reshape(4, 2)\n poly2 = Polygon(polygon_points2).c... |
db7e74bcfd026b96bad79174d35983aa | create a new branch that merges the clusters of two or more branches that have different clusters | [
{
"docid": "3d8533a6bbc0b74951d479f93c58ce05",
"score": "0.6773576",
"text": "def create_merge_branch(self, cid, merge_child, occluded_parents, fathers, line_num, point_list):\n\n saved_parent_nodes = []\n\n for oparent in occluded_parents:\n\n parent_node = [pnode for pnode in ... | [
{
"docid": "a3c052576b04bd128432dd0a90f4608b",
"score": "0.6381184",
"text": "def mergeBranchToCluster(self, mcid, cluster_id, branch_id, close=1):\n\n node_list = Graph.cluster_dict[cluster_id][branch_id]\n # make all the cluster_id's the same\n for node in node_list:\n ... |
3755e75101ba7ee74b0543b79d5cc561 | Return list of GET parameters that should be removed from querystring | [
{
"docid": "d53a7ac1ac0903af71c3a29c647c9a20",
"score": "0.7678245",
"text": "def get_querystring_parameter_to_remove(self):\n return self.del_query_parameters + [self.sort_parameter]",
"title": ""
}
] | [
{
"docid": "6d9cb29b157f9a0901208652d3c8ceb7",
"score": "0.7658783",
"text": "def remove_from_query(context, *args, **kwargs):\n query_params = []\n # go through current query params..\n for key, value_list in context[\"request\"].GET._iterlists():\n # skip keys mentioned in the args\n ... |
05820bf0cda8ff6e6985ae5b64e56305 | used to print fraction | [
{
"docid": "a7ef7522c1dba33096776029f106df1d",
"score": "0.67656446",
"text": "def __str__(self):\n x = self.numerator / self.denominator\n if x != 0:\n return \"%s and %s / %s\" % (x, self.numerator % self.denominator, self.denominator)\n else:\n return \"%s /... | [
{
"docid": "e8e765c12dee572b44efd64798c47853",
"score": "0.7505799",
"text": "def printFrac(self, num, den):\n # Wrap LaTeX in \\frac tag\n code = r\"\\frac{%s}{%s}\" % (num, den)\n\n # Append to editor\n self.ui.text_equation.append(code)",
"title": ""
},
{
"doci... |
26bf712cdea56b737a3a82b2da71dbc5 | Control the normals of an object. This command works on faces or polygonal objects. | [
{
"docid": "e9126d584ef78d5ce88a4c8db5fd9b97",
"score": "0.52289",
"text": "def polyNormal(*args, **kwargs):\n\n pass",
"title": ""
}
] | [
{
"docid": "1bbb05d3a41115c7ef5c1abf20d11d98",
"score": "0.73524904",
"text": "def setNormals(self):\n self.Normals[0] = normalize(cross(map(operator.sub, self.V1[0], self.V1[2]), map(operator.sub, self.V1[0], self.V1[1])))\n self.Normals[1] = normalize(cross(map(operator.sub, self.V1[0], ... |
f049ba5d88464a89918b015e73f5e21b | Return the string representation of the model. | [
{
"docid": "0f283634439620e9ecd215d87804ec00",
"score": "0.0",
"text": "def to_str(self):\n return pprint.pformat(self.to_dict())",
"title": ""
}
] | [
{
"docid": "0be70d3e77e77041a9ddcf1c23fe823d",
"score": "0.848826",
"text": "def __str__(self):\n\t\treturn str(self.__model)",
"title": ""
},
{
"docid": "0fbfd148e937420716433d412dc3b6bf",
"score": "0.8422646",
"text": "def __str__(self):\n\n return str(self.model)",
"tit... |
83be23f920b87d1cbb6264dd415dccd4 | Return keyword arguments bound to given logger | [
{
"docid": "ec185db085a952fee750b32b67b30585",
"score": "0.75169855",
"text": "def bound_log_kwargs(log):\n f = log.msg\n kwargs_list = []\n while True:\n try:\n kwargs_list.append(f.keywords)\n except AttributeError:\n break\n else:\n f = f... | [
{
"docid": "2e798378f8e489aba849418d7a681020",
"score": "0.6344701",
"text": "def parseKwargs(cls, config_stats, logger=None):\n kwargs = {}\n kwargs.update(config_stats)\n kwargs.pop('type',None)\n return kwargs",
"title": ""
},
{
"docid": "09192833047de487d28bfa... |
e8b27759e021dbf9f09cef998af40765 | Load a doc for a given sbid | [
{
"docid": "6d8d2a37f31a63f95ece7b9f226370ec",
"score": "0.7412022",
"text": "def load_doc(sbid):\n data_path = os.path.join(base_dir, sbid, 'data.json')\n doc = json.loads(open(data_path).read())\n try:\n del(doc['_attachments'])\n del(doc['_rev'])\n except KeyError:\n ... | [
{
"docid": "d07dff9a9e2a95a7dd8b26d6437b7032",
"score": "0.6116128",
"text": "def get_document(self, docid):\n return self.backend.get_document(docid)",
"title": ""
},
{
"docid": "3ccd0641fd2c91275191ac035c3efc67",
"score": "0.59131324",
"text": "def get_document_by_id(self, i... |
0911e9e71f69f1b97436a7108347a172 | Identify the persons by disambiguation. Apply Bipartite Graph Matching. Take list of matrices and for each consecutive pair of them, get the trajectory of people. Tracks of people over the frames. | [
{
"docid": "fa30220e3af254543409af6460daed89",
"score": "0.666316",
"text": "def disambiguatePersons(self):\n track_labels = []\n start = True\n # Iterate over the frame pose matrices\n for i, poses in enumerate(self.vid_persons):\n \n if poses.shape == ... | [
{
"docid": "49950be6e0f4806ad2611217e0a64591",
"score": "0.5999887",
"text": "def _extractmatches(keyfaces):\n key, faces = keyfaces\n \n # no match found\n if len(faces) == 1:\n start, end, isgt = key\n seg1, ... |
f0f7919b90c422c4f92ba926d87b1354 | Persist results of block b. | [
{
"docid": "6cc70a39c64b0f6b2e3631f1e6463026",
"score": "0.71250165",
"text": "def persist_result(b, psco_name=''):\n from storage_model.block import Block\n bl = Block()\n bl.block = b\n bl.make_persistent(psco_name)",
"title": ""
}
] | [
{
"docid": "bb23a3e42525e164153054d51237f6b4",
"score": "0.5830644",
"text": "def _store_result(self, total_count, open_result, values, data):\n [self._result] = values\n self._push(None)",
"title": ""
},
{
"docid": "8aab13632aba6065c760c498de19e80b",
"score": "0.5778085",
"tex... |
ede51f1a4b660f32c1c46cac49914b10 | Verify creating and deleting subnet with allocation pools | [
{
"docid": "5a24538a9d1351937a182e14b0edc031",
"score": "0.82713765",
"text": "def test_create_delete_subnet_with_allocation_pools(self):\n self._create_verify_delete_subnet(\n **self.subnet_dict(['allocation_pools']))",
"title": ""
}
] | [
{
"docid": "5e577dc0af595ea0742a1c4a8e599307",
"score": "0.8004876",
"text": "def test_create_delete_subnet_with_gw_and_allocation_pools(self):\n self._create_verify_delete_subnet(**self.subnet_dict(\n ['gateway', 'allocation_pools']))",
"title": ""
},
{
"docid": "ff2c39b96... |
eb29bbd417a4e579073ff643eec19a99 | Hints that we don't want a blank line before the next statement. | [
{
"docid": "d34dc89189a06ab496cc7a221d4ef6ef",
"score": "0.0",
"text": "def stick(self):\n self._in_group = True",
"title": ""
}
] | [
{
"docid": "6f698b7c22a9db644bfffee571ec3470",
"score": "0.68750113",
"text": "def emptyline(self):\n pass",
"title": ""
},
{
"docid": "6f698b7c22a9db644bfffee571ec3470",
"score": "0.68750113",
"text": "def emptyline(self):\n pass",
"title": ""
},
{
"docid":... |
a93ae90d531f7014b808bb4520c8ef6a | split ecg to make 2d matrix of the form beats x points. bad qrst complexes are replaced by average | [
{
"docid": "b99ece4bdabcd6f14afd3e1e6ad713d0",
"score": "0.5666693",
"text": "def makeMat(ecg,qrsonsets,qrsflags):\n \n #convert to millivolts\n #ecg = ecg*1000 # do this later in the calling script if reqd\n \n #Get mean cycle length\n rrintervals = qrsonsets[1:]-qrsonsets[:-1]... | [
{
"docid": "d243886f471226abb337be55d4f06252",
"score": "0.58604336",
"text": "def G_ee(basis,molecule,P,ee):\n\n # Size of the basis set\n K = basis.K\n\n G = np.zeros((K,K))\n\n for i in range(K):\n for j in range(K):\n for k in range(K):\n for l in range(K... |
1f8ff2e7af3dde7f64c880f4c1bb86f9 | Asserts tensor has expected shape. If tensor shape and expected_shape, are fully defined, assert they match. Otherwise, add assert op that will validate the shape when tensor is evaluated, and set shape on tensor. | [
{
"docid": "373c9a769ae38c46cd24b84d92a9126d",
"score": "0.7199602",
"text": "def with_shape(expected_shape, tensor):\n if isinstance(tensor, sparse_tensor.SparseTensor):\n raise ValueError('SparseTensor not supported.')\n\n # Shape type must be 1D int32.\n if tensor_util.is_tensor(expected_shape)... | [
{
"docid": "81afcb0928fe5aa288d1028b6c28ecf6",
"score": "0.84670264",
"text": "def _assert_shape_op(expected_shape, actual_tensor):\n with ops.name_scope('assert_shape', values=[actual_tensor]) as scope:\n actual_shape = array_ops.shape(actual_tensor, name='actual')\n is_shape = _is_shape(expecte... |