List[Metamodel]\n return cls._metamodelById.values()","def list_models(self, sort: bool = True, limit: int | None = None) -> Iterator[ExecutableModelSpace]:\n return self._strategy.list_models(sort=sort, limit=limit)","def ListModels(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def list_models(self):\n \n # Interpret the request data based on the expected row and column structure\n row_template = ['strData']\n col_headers = ['search_pattern']\n \n # Create a Pandas Data Frame for the request data\n self.request_df = utils.request_df(self.request, row_template, col_headers)\n \n # Get the list of models based on the search pattern\n search_pattern = self.request_df.loc[0, 'search_pattern']\n \n # If the search pattern is empty default to all models\n if not search_pattern.strip():\n search_pattern = '*'\n \n # Get the list of models as a string\n models = \"\\n\".join([str(p).split(\"\\\\\")[-1] for p in list(pathlib.Path(self.path).glob(search_pattern))])\n \n # Prepare the output\n self.response = pd.Series(models)\n \n # Finally send the response\n return self.response","def models(self):\n return self._base.classes","def get_models(make):\n api_url = 'https://api.edmunds.com/api/vehicle/v2/{}/models?fmt=json&api_key={}'\\\n .format(make, API_KEY)\n r = requests.get(api_url).json()\n all_models = [model['name'] for model in r['models']]\n return all_models","def input_models():\n return [\n PDBFile(\n Path(golden_data, \"protdna_complex_1.pdb\"),\n path=golden_data,\n score=42.0,\n restr_fname=Path(golden_data, \"example_ambig_1.tbl\")\n ),\n PDBFile(\n Path(golden_data, \"protdna_complex_2.pdb\"),\n path=golden_data,\n score=28.0,\n restr_fname=Path(golden_data, \"example_ambig_2.tbl\")\n )]","def test_list_models():\n model_names = find_model_files()\n listed_model_names = list_available_nagl_models()\n assert listed_model_names == model_names","def list_available_models(cls) -> 'List[PretrainedModelInfo]':\n # recursively walk the subclasses to generate pretrained model info\n list_of_models = model_utils.resolve_subclass_pretrained_model_info(cls)\n return list_of_models","def GetModels(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","def list_available_models(cls) -> List[PretrainedModelInfo]:\n list_of_models = []\n\n # en-US, ARPABET-based\n model = PretrainedModelInfo(\n pretrained_model_name=\"tts_en_radtts_aligner\",\n location=\"https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_radtts_aligner/versions/ARPABET_1.11.0/files/Aligner.nemo\",\n description=\"This model is trained on LJSpeech sampled at 22050Hz with and can be used to align text and audio.\",\n class_=cls,\n )\n list_of_models.append(model)\n\n # en-US, IPA-based\n model = PretrainedModelInfo(\n pretrained_model_name=\"tts_en_radtts_aligner_ipa\",\n location=\"https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_radtts_aligner/versions/IPA_1.13.0/files/Aligner.nemo\",\n description=\"This model is trained on LJSpeech sampled at 22050Hz with and can be used to align text and audio.\",\n class_=cls,\n )\n list_of_models.append(model)\n\n return list_of_models","def test_listModel(self):\n\t\tmodelOptions = {}\n\t\twith open(os.path.join(os.path.dirname(cancerscope.get_models.__file__), 'resources/scope_files.txt'), 'r') as f:\n\t\t\tfor line in f:\n\t\t\t\tif line.strip()!= '':\n\t\t\t\t\tmodelname, url, expectedFile, expectedmd5 = line.strip().split('\\t')\n\t\t\t\t\tmodelOptions[modelname] = (url, expectedFile, expectedmd5)\n\t\n\t\tassert len(modelOptions.keys()) == 5","def list():\n project_root = get_project_root()\n config_file = os.path.join(project_root, CONFIG_DIR, CONFIG_FILE)\n if os.path.exists(config_file):\n kwargs = load_yaml_file(config_file)\n if PACKAGE_INDEX_KEY in kwargs:\n packages = load_yaml_url(kwargs[PACKAGE_INDEX_KEY])\n # Should update and look in .carme/config\n else:\n packages = load_yaml_url(PACKAGE_INDEX)\n\n ruamel.yaml.dump(packages, sys.stdout, Dumper=ruamel.yaml.RoundTripDumper)","def get_seo_models():\n seo_models = []\n for model_name in getattr(settings, setting_name_seo_models, ()):\n if \".\" in model_name:\n # TODO: Test this block\n app_label, model_name = model_name.split(\".\", 1)\n model = models.get_model(app_label, model_name)\n if model:\n seo_models.append(model)\n else:\n app = models.get_app(model_name)\n if app:\n seo_models.extend(models.get_models(app))\n\n return seo_models","def iter_models(self):\n return iter(self.model_list)","def models_from(module):\n return {model.__name__.lower(): model for model in vars(module).values() if is_model(model)}","def import_data(self):\n self.models = []\n for o in self.loader.load():\n klass = self.type_for(o)\n if hasattr(klass, \"from_api\"):\n self.models.append(klass.from_api(o))\n else:\n self.models.append(klass(o))\n return self.models","def ListModels(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\n raise NotImplementedError()","def opt_get_all_models_rest_api():\n return retrieve_all_models()","def test_get_models_returns_models(fc: fetcher.Fetcher):\n ml = fc.get_models()\n assert isinstance(ml, list)\n assert isinstance(ml[0], models.LookmlModel)","def handle(self, *args, **options):\n app_labels = [app.split('.')[-1] for app in settings.INSTALLED_APPS]\n if not args:\n args = app_labels\n for app in args:\n if app not in app_labels:\n print \"%s is not a valid application\" % app\n continue\n\n app_module = get_app(app_label=app, emptyOK=True)\n if app_module is None:\n continue\n\n print \"Models of %s:\" % app\n for model in get_models(app_module):\n print \" - %s has %d entries\" % (\n model.__name__,\n model.objects.count()\n )","def show_database_structure(self):\n self.analyze()\n items = []\n for model in get_models():\n names = []\n # for f, m in model._meta.get_fields_with_model():\n for f in model._meta.concrete_fields:\n names.append(f.name)\n items.append(\n \"{0} : {1}\".format(fmn(model), ', '.join(names)))\n\n items = sorted(items)\n return rstgen.ul(items)","def packages(self):\n return []","def list(self):\n return [os.splitext(el)[0] for el in\n os.listdir(str(self.model_dir))]","def models_list(request):\n projects = Project.objects.filter(models=1)\n return render(request, 'screenshower/app/models_list.html', {'projects': projects})","def list_available_models(cls) -> 'List[PretrainedModelInfo]':\n list_of_models = []\n for subclass in cls.__subclasses__():\n subclass_models = subclass.list_available_models()\n if subclass_models is not None and len(subclass_models) > 0:\n list_of_models.extend(subclass_models)\n return list_of_models","def list_available_models(cls) -> 'List[PretrainedModelInfo]':\n list_of_models = []\n for subclass in cls.__subclasses__():\n subclass_models = subclass.list_available_models()\n if subclass_models is not None and len(subclass_models) > 0:\n list_of_models.extend(subclass_models)\n return list_of_models","def list_available_models(cls) -> 'List[PretrainedModelInfo]':\n list_of_models = []\n for subclass in cls.__subclasses__():\n subclass_models = subclass.list_available_models()\n if subclass_models is not None and len(subclass_models) > 0:\n list_of_models.extend(subclass_models)\n return list_of_models","def list_available_models(cls) -> 'List[PretrainedModelInfo]':\n list_of_models = []\n for subclass in cls.__subclasses__():\n subclass_models = subclass.list_available_models()\n if subclass_models is not None and len(subclass_models) > 0:\n list_of_models.extend(subclass_models)\n return list_of_models","def ListModels(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def ListModels(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def get_all_models() -> list:\n\n conn = database_connect()\n if(conn is None):\n return None\n cur = conn.cursor()\n\n try:\n # SQL statement and execute\n sql = \"\"\"SELECT manufacturer, description, modelnumber, weight\n FROM Model\"\"\"\n cur.execute(sql, ())\n\n # Attempt to fetch first row\n result = cur.fetchall()\n\n if result == None:\n cur.close()\n conn.close()\n return []\n\n models = []\n for row in result:\n models.append(\n [row[0], row[1], row[2], row[3]]\n )\n\n cur.close()\n conn.close()\n return models\n except Exception as e:\n print(\"fff\")\n print(e)\n # If login failed, return None\n cur.close()\n conn.close()\n return []","def load_models():\n vectorizer = ModelStorage.objects.all().values_list(\"vectorizer\", flat = True)[0]\n classifier = ModelStorage.objects.all().values_list(\"classifier\", flat = True)[0]\n\n return vectorizer, classifier","def list_packages(self):\n for tag, pkg in PACKAGES.iteritems():\n print \"{tag} - {label}\".format(tag=tag, label=pkg['label'])","def supported_models(cls):\n \n models = []\n \n for subclass in cls.__subclasses__():\n models+=subclass.supported_models()\n return models","def get_models(self, model_names, callback):\n models = self.get_collection('_model')\n models.find(spec={'_id': {'$in': tuple(model_names)}}).to_list(\n callback=callback)","def test_get_model_names():\n\n names = Instafilter.get_models()\n assert isinstance(names, list)\n assert len(names) > 1","def get_models_for_make(self, make):\n return self.get('vehicles/GetModelsForMake/{}'.format(make))","def pilots_from_models(self):\n models_list = [f for f in os.scandir(self.models_path)]\n pilot_list = []\n for d in models_list:\n last_modified = datetime.fromtimestamp(d.stat().st_mtime)\n pilot = KerasPositionalCategorical(d.path, name=d.name, last_modified=last_modified)\n pilot_list.append(pilot)\n\n print (pilot_list)\n return pilot_list","def load_models(appname):\n return import_module('.models', appname)","def get_model_specs(self):\n raise NotImplementedError()","def import_all():\n\n # count the number of files loaded\n count = 0\n\n # get model name\n model_name_list = [model for data_models in settings.OBJECT_DATA_MODELS\n for model in data_models]\n\n model_name_list += [model for model in settings.OTHER_DATA_MODELS]\n\n # import models one by one\n for model_name in model_name_list:\n import_model(model_name)\n\n # import localized strings\n import_localized_strings(settings.LANGUAGE_CODE)","def setup_models(self):\n pass","def fetch_ml_model_info() -> ApiResponse:\n return _api_response(settings.ML_MODELS)","def packages():","def describe_model_package(ModelPackageName=None):\n pass","def list(self, project_id):\n endpoint = \"/project/{}/model\".format(project_id)\n return self._get(endpoint, _ModelSchema(many=True))","def list_spss_models(self, fields=['model_name']):\n\t\tmodels = dbop.get_models(self, model_type='SPSS Predictive Model', fields=fields)\n\t\treturn models","async def list(request):\n dict_answer = {'models': [item[1]+' '+item[0]+str(item[2:]) for item in models_db],\n 'datasets': [conv_time(d.stat().st_atime)+' '+str(d.name) for d in Path('data/datasets/').glob('*')],\n }\n return web.json_response(dict_answer)","def get(self, *args):\n return _libsbml.ListOfModelDefinitions_get(self, *args)","def download_all_models() -> None:\n model_keys = ModelInfo.get_all_models()\n for model_key in model_keys:\n download_model(model_key)","def register_models(self, app_label, *models):\n for model in models:\n # Store as 'name: model' pair in a dictionary\n # in the app_models dictionary\n model_name = model._meta.model_name\n model_dict = self.app_models.setdefault(app_label, SortedDict())\n if model_name in model_dict:\n # The same model may be imported via different paths (e.g.\n # appname.models and project.appname.models). We use the source\n # filename as a means to detect identity.\n fname1 = os.path.abspath(upath(\n sys.modules[model.__module__].__file__))\n fname2 = os.path.abspath(upath(\n sys.modules[model_dict[model_name].__module__].__file__))\n # Since the filename extension could be .py the first time and\n # .pyc or .pyo the second time, ignore the extension when\n # comparing.\n if os.path.splitext(fname1)[0] == os.path.splitext(fname2)[0]:\n continue\n model_dict[model_name] = model\n self._get_models_cache.clear()","def fetch_models(self):\n if self.model_pool is None:\n print(\"Please train a model first.\", file=STDE)\n EXIT(1)\n else:\n return [copy.deepcopy(m.steps[-1][-1]) for m in self.model_pool]","def load_models(self, models, iteration = None):\n print(\"NOT IMPLEMENTED YET\")","def test_get_models(self):\n models = get_models()\n self.assertTrue(len(models) > 6)\n self.assertIn((\"csvimport.Item\", \"csvimport.Item\"), models)","def models(self) -> list[AbstractModel]:\n return self._models","def import_all_model_modules():\r\n import brokerage.model\r\n # ensure that these imports don't get auto-deleted! they have side effects.\r\n brokerage.model","async def list_models(\n list_models_request: ListModels,\n token: str = Depends(oauth2_scheme),\n):\n try:\n logging.info(\"Calling /gcp/automl/list_models endpoint\")\n logging.debug(f\"Request: {list_models_request}\")\n if decodeJWT(token=token):\n response = ManageModelController().list_model_controller(\n request=list_models_request\n )\n return response\n else:\n raise HTTPException(\n status_code=status.HTTP_401_UNAUTHORIZED,\n detail=\"Invalid access token\",\n headers={\"WWW-Authenticate\": \"Bearer\"},\n )\n except Exception as error:\n logging.error(f\"Error in /gcp/automl/list_models endpoint: {error}\")\n raise error","def models(self) -> t.List[Model]:\n _models: t.List[Model] = [\n item for item in self._deployables if isinstance(item, Model)\n ]\n return _models","def load_models(model_name=\"\", path=\"\", read_grains=False, **kwargs):\n models = pc.load_models('{0}{1}'.format(path, model_name), read_grains=read_grains, **kwargs)\n models_return = []\n for m in models:\n if m.n_zones > 1:\n models_return.append(m)\n else:\n # log failed model\n pass\n return models_return","def list_models():\n\n cmd = dict()\n cmd[\"type_\"] = \"list_models\"\n cmd[\"name_\"] = \"\"\n \n s = comm.send_and_receive_socket(cmd)\n\n msg = comm.recv_string(s)\n\n if msg != \"Success!\":\n raise Exception(msg)\n \n json_str = comm.recv_string(s) \n \n s.close() \n\n return json.loads(json_str)","def _pc_load_models(model_name = None, mod_list = None, n_sample = None, verbose = False, **kwargs):\n\n if model_name is not None:\n mod_list = glob.glob(model_name + '*.out')\n if mod_list is None or mod_list == []:\n pc.log_.error('No model found', calling='load models')\n return None\n if n_sample is not None:\n if n_sample > len(mod_list):\n pc.log_.error('less models {0:d} than n_sample {1:d}'.format(len(mod_list), n_sample),\n calling='load models')\n return None\n mod_list = random.sample(mod_list, n_sample)\n m = []\n for outfile in mod_list:\n if outfile[-4::] == '.out':\n model_name = outfile[0:-4]\n else:\n model_name = outfile\n try:\n cm = pc.CloudyModel(model_name, verbose=0, **kwargs)\n if not cm.aborted:\n m.append(cm)\n if verbose:\n print('{0} model read'.format(outfile[0:-4]))\n except:\n pass\n pc.log_.message('{0} models read'.format(len(m)), calling='load_models')\n return m","def all_meta_types(self, interfaces=None):\n mts = super(ZenModelBase,self).all_meta_types(interfaces)\n if self.sub_meta_types:\n mts = filter(lambda mt: mt['name'] in self.sub_meta_types, mts)\n return mts","def get_available_entities_models():\n return ['concat', 'bahdanau', 'luong']","def main(args):\n metafiles = []\n verbose = args.verbose\n\n if (args.metalist is not None):\n for listfile in args.metalist:\n metafiles.extend(addmeta.list_from_file(listfile))\n\n if (args.metafiles is not None):\n metafiles.extend(args.metafiles)\n\n if verbose: print(\"metafiles: \",\" \".join([str(f) for f in metafiles]))\n\n addmeta.find_and_add_meta(args.files, metafiles)"],"string":"[\n \"def _get_models_from_metafile(dir: str):\\n meta_indexes = load(osp.join(dir, 'model-index.yml'))\\n for meta_path in meta_indexes['Import']:\\n # meta_path example: mmcls/.mim/configs/conformer/metafile.yml\\n meta_path = osp.join(dir, meta_path)\\n metainfo = load(meta_path)\\n yield from metainfo['Models']\",\n \"def get_models():\\n all_models = gfile.Glob(os.path.join(MODELS_DIR, '*.meta'))\\n model_filenames = [os.path.basename(m) for m in all_models]\\n model_numbers_names = sorted([\\n (shipname.detect_model_num(m), shipname.detect_model_name(m))\\n for m in model_filenames])\\n return model_numbers_names\",\n \"def get_model_list():\\n with open(os.path.join(MODELS_FOLDER, \\\"models.json\\\"), \\\"r\\\") as model_file:\\n model_list = json.load(model_file)\\n return model_list\",\n \"def peak_all_models(self) -> List:\\n models = list(self.meta.name)\\n print(models)\\n return models\",\n \"def list_models(self, fields=['model_name']):\\n\\t\\tmodels = dbop.get_models(self, model_type='all', fields=fields)\\n\\t\\treturn models\",\n \"def models(self):\\n return self.config.models()\",\n \"def models(self):\\n models = []\\n for bundle in self.bundles.values():\\n models.extend(list(bundle.models.values()))\\n\\n return models\",\n \"def ListModels(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"def list_models():\\n\\tclsmembers = inspect.getmembers(sys.modules[__name__], inspect.isclass)\\n\\tverif = lambda cls: 'Verified: {0}'.format(cls[1]().verified)\\n\\tfit_models = [ (cls[0], verif(cls)) for cls in clsmembers if cls[1].__bases__[0] == core.FitModel ]\\n\\treturn fit_models\",\n \"def models():\\n return list(alg2module.keys())\",\n \"def list_supported_models() -> Sequence[str]:\\r\\n return list(_MODELS)\",\n \"def get_models(self, app_name):\\n try:\\n models = list(apps.get_app_config(app_name).get_models())\\n return models\\n except:\\n raise LookupError(f\\\"this is no such app {app_name}\\\")\",\n \"def list_models(SortBy=None, SortOrder=None, NextToken=None, MaxResults=None, NameContains=None, CreationTimeBefore=None, CreationTimeAfter=None):\\n pass\",\n \"def models() -> list[str]:\\n return list(models_url.keys())\",\n \"def get_data_models(models_file):\\n list_models = []\\n model = []\\n pos_numeric = [] # Position of numeric fields\\n info_keys = [] # Info. about keys\\n re_field = re.compile('\\\\s+\\\\w+\\\\s*=\\\\s*models\\\\.') # Line with field name\\n re_class = re.compile('\\\\s+class ') # For Admin and Meta\\n re_def = re.compile('\\\\s+def ')\\n is_new_model = False\\n\\n for line in open(models_file):\\n # The models start with 'class'\\n if not is_new_model and line.startswith('class'):\\n model_name = line.replace('class','').split('(')[0].strip()\\n model.append(model_name)\\n is_new_model = True\\n elif is_new_model:\\n if re_field.match(line):\\n field_name = line.split('=')[0].strip()\\n model.append(field_name)\\n\\n if 'models.DecimalField' in line or 'models.IntegerField' in line:\\n pos_numeric.append(len(model)-2) # Discard model name.\\n elif 'models.ForeignKey' in line:\\n key_name = line.split('(')[-1].strip().strip(')')\\n position = len(model)-2 # Discard model name.\\n info_keys.append(':')\\n info_keys.append(str(position) + ',')\\n info_keys.append(key_name)\\n # It is supposed that models in localization has at the end:\\n # ('class Meta', 'class Admin', or some 'def')\\n elif re_class.match(line) or re_def.match(line):\\n if pos_numeric:\\n pos_num2str = '#'\\n for num in pos_numeric:\\n pos_num2str += str(num)\\n model.append(pos_num2str)\\n model.append(':N') # To detect the numeric field.\\n pos_numeric = []\\n if info_keys:\\n all_keys = \\\"\\\"\\n for key in info_keys:\\n all_keys += key\\n model.append(all_keys)\\n model.append(':K') # To detect fastly some key.\\n info_keys = []\\n list_models.append(model)\\n model = []\\n is_new_model = False\\n\\n return list_models\",\n \"def availablemodels(self):\\n return self.__models.keys()\",\n \"def list_cmd(ctx):\\n client = ctx.obj['CLIENT']\\n models = client.list_models()\\n\\n x = PrettyTable()\\n x.field_names = [\\\"Name\\\",\\\"Tag\\\",\\\"Created\\\"]\\n for m in models:\\n x.add_row([m[\\\"name\\\"],m[\\\"tag\\\"],m[\\\"uploaded_at\\\"]])\\n print(x)\",\n \"def get_available_models():\\n modelpath = os.path.join(os.path.dirname(__file__), \\\"train\\\", \\\"model\\\")\\n models = sorted(item.name.replace(\\\".py\\\", \\\"\\\").replace(\\\"_\\\", \\\"-\\\")\\n for item in os.scandir(modelpath)\\n if not item.name.startswith(\\\"_\\\")\\n and item.name.endswith(\\\".py\\\"))\\n return models\",\n \"def list_model_packages(CreationTimeAfter=None, CreationTimeBefore=None, MaxResults=None, NameContains=None, NextToken=None, SortBy=None, SortOrder=None):\\n pass\",\n \"def _get_model_list(self):\\n model_filelist = os.listdir(os.path.join(self.pack_file_path, self.model_dir))\\n for i in model_filelist:\\n model_file = os.path.join(self.model_dir, i)\\n self.filelist.append(model_file)\",\n \"def get_models(self):\\n return [Doc(system_object) for system_object in self._get_documents()]\",\n \"def _get_models(self, req, is_detail):\\n context = req.environ['meteos.context']\\n\\n search_opts = {}\\n search_opts.update(req.GET)\\n\\n # Remove keys that are not related to model attrs\\n search_opts.pop('limit', None)\\n search_opts.pop('offset', None)\\n sort_key = search_opts.pop('sort_key', 'created_at')\\n sort_dir = search_opts.pop('sort_dir', 'desc')\\n\\n models = self.engine_api.get_all_models(\\n context, search_opts=search_opts, sort_key=sort_key,\\n sort_dir=sort_dir)\\n\\n limited_list = common.limited(models, req)\\n\\n if is_detail:\\n models = self._view_builder.detail_list(req, limited_list)\\n else:\\n models = self._view_builder.summary_list(req, limited_list)\\n return models\",\n \"def _get_model_list(self):\\n self.model_arch_file = os.path.join(self.model_dir, self.model_name + '.prototxt')\\n self.model_weight_file = os.path.join(self.model_dir, self.model_name + '.caffemodel')\\n self.filelist.append(self.model_arch_file)\\n self.filelist.append(self.model_weight_file)\",\n \"def models(self):\\r\\n return self.get_field('model')\",\n \"def models(self):\\r\\n return self.get_field('model')\",\n \"def get_object_models(self):\\n parser = WorldParser(self.world_fpath)\\n return parser.models\",\n \"def list_dashdb_models(self, fields=['model_name']):\\n\\t\\tmodels = dbop.get_models(self, model_type='DashDB In-database Model', fields=fields)\\n\\t\\treturn models\",\n \"def get_models(self):\\n self.load()\\n return self._models\",\n \"def index(self, req):\\n return self._get_models(req, is_detail=False)\",\n \"def generate_model_list():\\n\\t\\n\\tmodels = [\\n\\t\\tapi.v1.models.job.Job,\\n\\t]\\n\\treturn models\",\n \"def get_supported_models(self):\\n # type: () -> list\\n return [model for model in self.__MODELS]\",\n \"def models(self):\\n \\n # Call the Razor RESTful API to get a list of models\\n headers = {'content-type': 'application/json'}\\n r = requests.get(self.url + '/model', headers=headers)\\n\\n # Check the status code and return appropriately\\n if r.status_code == 200:\\n return json.loads(r.content)\\n else:\\n return 'Error in request, exited with status code:' + str(r.status_code)\",\n \"def get_models():\\n\\n from lmfit.models import lmfit_models\\n models = lmfit_models\\n if 'Expression' in models:\\n del models['Expression']\\n if 'Gaussian-2D' in models:\\n del models['Gaussian-2D']\\n\\n filenames = set()\\n\\n models_path = pkg_resources.resource_filename('nexpy.api.frills',\\n 'models')\\n sys.path.append(models_path)\\n for file_ in os.listdir(models_path):\\n name, ext = os.path.splitext(file_)\\n if name != '__init__' and ext.startswith('.py'):\\n filenames.add(name)\\n\\n private_path = os.path.join(os.path.expanduser('~'), '.nexpy', 'models')\\n if os.path.isdir(private_path):\\n sys.path.append(private_path)\\n for file_ in os.listdir(private_path):\\n name, ext = os.path.splitext(file_)\\n if name != '__init__' and ext.startswith('.py'):\\n filenames.add(name)\\n\\n for name in sorted(filenames):\\n try:\\n module = importlib.import_module(name)\\n models.update(dict((n.strip('Model'), m)\\n for n, m in inspect.getmembers(module,\\n inspect.isclass)\\n if issubclass(m, Model) and n != 'Model'))\\n except ImportError:\\n pass\\n\\n return models\",\n \"def models(self, type, version, file='all.txt'):\\n return '{base}/{type}/{version}/lib/{file}'.format(\\n base=self.config['models'], type=type, version=version, file=file)\",\n \"def list_models(\\n architecture: Optional[str] = typer.Option(None, '-n', '--name', help='Model architecture name'),\\n framework: Optional[Framework] = typer.Option(None, '-fw', '--framework', case_sensitive=False,\\n help='Framework'),\\n engine: Optional[Engine] = typer.Option(None, '-e', '--engine', case_sensitive=False, help='Serving engine'),\\n version: Optional[int] = typer.Option(None, '-v', '--version', help='Version'),\\n list_all: Optional[bool] = typer.Option(\\n False,\\n '-a', '--all', is_flag=True,\\n help='Display queried models. otherwise, only partial result will be shown.'\\n ),\\n):\\n\\n payload = remove_dict_null(\\n {'architecture': architecture, 'framework': framework, 'engine': engine, 'version': version}\\n )\\n with requests.get(f'{app_settings.api_v1_prefix}/model', params=payload) as r:\\n model_list = r.json()\\n model_view([MLModel.parse_obj(model) for model in model_list], list_all=list_all)\",\n \"def metamodels(cls):\\n #type: () -> List[Metamodel]\\n return cls._metamodelById.values()\",\n \"def list_models(self, sort: bool = True, limit: int | None = None) -> Iterator[ExecutableModelSpace]:\\n return self._strategy.list_models(sort=sort, limit=limit)\",\n \"def ListModels(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def list_models(self):\\n \\n # Interpret the request data based on the expected row and column structure\\n row_template = ['strData']\\n col_headers = ['search_pattern']\\n \\n # Create a Pandas Data Frame for the request data\\n self.request_df = utils.request_df(self.request, row_template, col_headers)\\n \\n # Get the list of models based on the search pattern\\n search_pattern = self.request_df.loc[0, 'search_pattern']\\n \\n # If the search pattern is empty default to all models\\n if not search_pattern.strip():\\n search_pattern = '*'\\n \\n # Get the list of models as a string\\n models = \\\"\\\\n\\\".join([str(p).split(\\\"\\\\\\\\\\\")[-1] for p in list(pathlib.Path(self.path).glob(search_pattern))])\\n \\n # Prepare the output\\n self.response = pd.Series(models)\\n \\n # Finally send the response\\n return self.response\",\n \"def models(self):\\n return self._base.classes\",\n \"def get_models(make):\\n api_url = 'https://api.edmunds.com/api/vehicle/v2/{}/models?fmt=json&api_key={}'\\\\\\n .format(make, API_KEY)\\n r = requests.get(api_url).json()\\n all_models = [model['name'] for model in r['models']]\\n return all_models\",\n \"def input_models():\\n return [\\n PDBFile(\\n Path(golden_data, \\\"protdna_complex_1.pdb\\\"),\\n path=golden_data,\\n score=42.0,\\n restr_fname=Path(golden_data, \\\"example_ambig_1.tbl\\\")\\n ),\\n PDBFile(\\n Path(golden_data, \\\"protdna_complex_2.pdb\\\"),\\n path=golden_data,\\n score=28.0,\\n restr_fname=Path(golden_data, \\\"example_ambig_2.tbl\\\")\\n )]\",\n \"def test_list_models():\\n model_names = find_model_files()\\n listed_model_names = list_available_nagl_models()\\n assert listed_model_names == model_names\",\n \"def list_available_models(cls) -> 'List[PretrainedModelInfo]':\\n # recursively walk the subclasses to generate pretrained model info\\n list_of_models = model_utils.resolve_subclass_pretrained_model_info(cls)\\n return list_of_models\",\n \"def GetModels(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"def list_available_models(cls) -> List[PretrainedModelInfo]:\\n list_of_models = []\\n\\n # en-US, ARPABET-based\\n model = PretrainedModelInfo(\\n pretrained_model_name=\\\"tts_en_radtts_aligner\\\",\\n location=\\\"https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_radtts_aligner/versions/ARPABET_1.11.0/files/Aligner.nemo\\\",\\n description=\\\"This model is trained on LJSpeech sampled at 22050Hz with and can be used to align text and audio.\\\",\\n class_=cls,\\n )\\n list_of_models.append(model)\\n\\n # en-US, IPA-based\\n model = PretrainedModelInfo(\\n pretrained_model_name=\\\"tts_en_radtts_aligner_ipa\\\",\\n location=\\\"https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_radtts_aligner/versions/IPA_1.13.0/files/Aligner.nemo\\\",\\n description=\\\"This model is trained on LJSpeech sampled at 22050Hz with and can be used to align text and audio.\\\",\\n class_=cls,\\n )\\n list_of_models.append(model)\\n\\n return list_of_models\",\n \"def test_listModel(self):\\n\\t\\tmodelOptions = {}\\n\\t\\twith open(os.path.join(os.path.dirname(cancerscope.get_models.__file__), 'resources/scope_files.txt'), 'r') as f:\\n\\t\\t\\tfor line in f:\\n\\t\\t\\t\\tif line.strip()!= '':\\n\\t\\t\\t\\t\\tmodelname, url, expectedFile, expectedmd5 = line.strip().split('\\\\t')\\n\\t\\t\\t\\t\\tmodelOptions[modelname] = (url, expectedFile, expectedmd5)\\n\\t\\n\\t\\tassert len(modelOptions.keys()) == 5\",\n \"def list():\\n project_root = get_project_root()\\n config_file = os.path.join(project_root, CONFIG_DIR, CONFIG_FILE)\\n if os.path.exists(config_file):\\n kwargs = load_yaml_file(config_file)\\n if PACKAGE_INDEX_KEY in kwargs:\\n packages = load_yaml_url(kwargs[PACKAGE_INDEX_KEY])\\n # Should update and look in .carme/config\\n else:\\n packages = load_yaml_url(PACKAGE_INDEX)\\n\\n ruamel.yaml.dump(packages, sys.stdout, Dumper=ruamel.yaml.RoundTripDumper)\",\n \"def get_seo_models():\\n seo_models = []\\n for model_name in getattr(settings, setting_name_seo_models, ()):\\n if \\\".\\\" in model_name:\\n # TODO: Test this block\\n app_label, model_name = model_name.split(\\\".\\\", 1)\\n model = models.get_model(app_label, model_name)\\n if model:\\n seo_models.append(model)\\n else:\\n app = models.get_app(model_name)\\n if app:\\n seo_models.extend(models.get_models(app))\\n\\n return seo_models\",\n \"def iter_models(self):\\n return iter(self.model_list)\",\n \"def models_from(module):\\n return {model.__name__.lower(): model for model in vars(module).values() if is_model(model)}\",\n \"def import_data(self):\\n self.models = []\\n for o in self.loader.load():\\n klass = self.type_for(o)\\n if hasattr(klass, \\\"from_api\\\"):\\n self.models.append(klass.from_api(o))\\n else:\\n self.models.append(klass(o))\\n return self.models\",\n \"def ListModels(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\\n raise NotImplementedError()\",\n \"def opt_get_all_models_rest_api():\\n return retrieve_all_models()\",\n \"def test_get_models_returns_models(fc: fetcher.Fetcher):\\n ml = fc.get_models()\\n assert isinstance(ml, list)\\n assert isinstance(ml[0], models.LookmlModel)\",\n \"def handle(self, *args, **options):\\n app_labels = [app.split('.')[-1] for app in settings.INSTALLED_APPS]\\n if not args:\\n args = app_labels\\n for app in args:\\n if app not in app_labels:\\n print \\\"%s is not a valid application\\\" % app\\n continue\\n\\n app_module = get_app(app_label=app, emptyOK=True)\\n if app_module is None:\\n continue\\n\\n print \\\"Models of %s:\\\" % app\\n for model in get_models(app_module):\\n print \\\" - %s has %d entries\\\" % (\\n model.__name__,\\n model.objects.count()\\n )\",\n \"def show_database_structure(self):\\n self.analyze()\\n items = []\\n for model in get_models():\\n names = []\\n # for f, m in model._meta.get_fields_with_model():\\n for f in model._meta.concrete_fields:\\n names.append(f.name)\\n items.append(\\n \\\"{0} : {1}\\\".format(fmn(model), ', '.join(names)))\\n\\n items = sorted(items)\\n return rstgen.ul(items)\",\n \"def packages(self):\\n return []\",\n \"def list(self):\\n return [os.splitext(el)[0] for el in\\n os.listdir(str(self.model_dir))]\",\n \"def models_list(request):\\n projects = Project.objects.filter(models=1)\\n return render(request, 'screenshower/app/models_list.html', {'projects': projects})\",\n \"def list_available_models(cls) -> 'List[PretrainedModelInfo]':\\n list_of_models = []\\n for subclass in cls.__subclasses__():\\n subclass_models = subclass.list_available_models()\\n if subclass_models is not None and len(subclass_models) > 0:\\n list_of_models.extend(subclass_models)\\n return list_of_models\",\n \"def list_available_models(cls) -> 'List[PretrainedModelInfo]':\\n list_of_models = []\\n for subclass in cls.__subclasses__():\\n subclass_models = subclass.list_available_models()\\n if subclass_models is not None and len(subclass_models) > 0:\\n list_of_models.extend(subclass_models)\\n return list_of_models\",\n \"def list_available_models(cls) -> 'List[PretrainedModelInfo]':\\n list_of_models = []\\n for subclass in cls.__subclasses__():\\n subclass_models = subclass.list_available_models()\\n if subclass_models is not None and len(subclass_models) > 0:\\n list_of_models.extend(subclass_models)\\n return list_of_models\",\n \"def list_available_models(cls) -> 'List[PretrainedModelInfo]':\\n list_of_models = []\\n for subclass in cls.__subclasses__():\\n subclass_models = subclass.list_available_models()\\n if subclass_models is not None and len(subclass_models) > 0:\\n list_of_models.extend(subclass_models)\\n return list_of_models\",\n \"def ListModels(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def ListModels(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def get_all_models() -> list:\\n\\n conn = database_connect()\\n if(conn is None):\\n return None\\n cur = conn.cursor()\\n\\n try:\\n # SQL statement and execute\\n sql = \\\"\\\"\\\"SELECT manufacturer, description, modelnumber, weight\\n FROM Model\\\"\\\"\\\"\\n cur.execute(sql, ())\\n\\n # Attempt to fetch first row\\n result = cur.fetchall()\\n\\n if result == None:\\n cur.close()\\n conn.close()\\n return []\\n\\n models = []\\n for row in result:\\n models.append(\\n [row[0], row[1], row[2], row[3]]\\n )\\n\\n cur.close()\\n conn.close()\\n return models\\n except Exception as e:\\n print(\\\"fff\\\")\\n print(e)\\n # If login failed, return None\\n cur.close()\\n conn.close()\\n return []\",\n \"def load_models():\\n vectorizer = ModelStorage.objects.all().values_list(\\\"vectorizer\\\", flat = True)[0]\\n classifier = ModelStorage.objects.all().values_list(\\\"classifier\\\", flat = True)[0]\\n\\n return vectorizer, classifier\",\n \"def list_packages(self):\\n for tag, pkg in PACKAGES.iteritems():\\n print \\\"{tag} - {label}\\\".format(tag=tag, label=pkg['label'])\",\n \"def supported_models(cls):\\n \\n models = []\\n \\n for subclass in cls.__subclasses__():\\n models+=subclass.supported_models()\\n return models\",\n \"def get_models(self, model_names, callback):\\n models = self.get_collection('_model')\\n models.find(spec={'_id': {'$in': tuple(model_names)}}).to_list(\\n callback=callback)\",\n \"def test_get_model_names():\\n\\n names = Instafilter.get_models()\\n assert isinstance(names, list)\\n assert len(names) > 1\",\n \"def get_models_for_make(self, make):\\n return self.get('vehicles/GetModelsForMake/{}'.format(make))\",\n \"def pilots_from_models(self):\\n models_list = [f for f in os.scandir(self.models_path)]\\n pilot_list = []\\n for d in models_list:\\n last_modified = datetime.fromtimestamp(d.stat().st_mtime)\\n pilot = KerasPositionalCategorical(d.path, name=d.name, last_modified=last_modified)\\n pilot_list.append(pilot)\\n\\n print (pilot_list)\\n return pilot_list\",\n \"def load_models(appname):\\n return import_module('.models', appname)\",\n \"def get_model_specs(self):\\n raise NotImplementedError()\",\n \"def import_all():\\n\\n # count the number of files loaded\\n count = 0\\n\\n # get model name\\n model_name_list = [model for data_models in settings.OBJECT_DATA_MODELS\\n for model in data_models]\\n\\n model_name_list += [model for model in settings.OTHER_DATA_MODELS]\\n\\n # import models one by one\\n for model_name in model_name_list:\\n import_model(model_name)\\n\\n # import localized strings\\n import_localized_strings(settings.LANGUAGE_CODE)\",\n \"def setup_models(self):\\n pass\",\n \"def fetch_ml_model_info() -> ApiResponse:\\n return _api_response(settings.ML_MODELS)\",\n \"def packages():\",\n \"def describe_model_package(ModelPackageName=None):\\n pass\",\n \"def list(self, project_id):\\n endpoint = \\\"/project/{}/model\\\".format(project_id)\\n return self._get(endpoint, _ModelSchema(many=True))\",\n \"def list_spss_models(self, fields=['model_name']):\\n\\t\\tmodels = dbop.get_models(self, model_type='SPSS Predictive Model', fields=fields)\\n\\t\\treturn models\",\n \"async def list(request):\\n dict_answer = {'models': [item[1]+' '+item[0]+str(item[2:]) for item in models_db],\\n 'datasets': [conv_time(d.stat().st_atime)+' '+str(d.name) for d in Path('data/datasets/').glob('*')],\\n }\\n return web.json_response(dict_answer)\",\n \"def get(self, *args):\\n return _libsbml.ListOfModelDefinitions_get(self, *args)\",\n \"def download_all_models() -> None:\\n model_keys = ModelInfo.get_all_models()\\n for model_key in model_keys:\\n download_model(model_key)\",\n \"def register_models(self, app_label, *models):\\n for model in models:\\n # Store as 'name: model' pair in a dictionary\\n # in the app_models dictionary\\n model_name = model._meta.model_name\\n model_dict = self.app_models.setdefault(app_label, SortedDict())\\n if model_name in model_dict:\\n # The same model may be imported via different paths (e.g.\\n # appname.models and project.appname.models). We use the source\\n # filename as a means to detect identity.\\n fname1 = os.path.abspath(upath(\\n sys.modules[model.__module__].__file__))\\n fname2 = os.path.abspath(upath(\\n sys.modules[model_dict[model_name].__module__].__file__))\\n # Since the filename extension could be .py the first time and\\n # .pyc or .pyo the second time, ignore the extension when\\n # comparing.\\n if os.path.splitext(fname1)[0] == os.path.splitext(fname2)[0]:\\n continue\\n model_dict[model_name] = model\\n self._get_models_cache.clear()\",\n \"def fetch_models(self):\\n if self.model_pool is None:\\n print(\\\"Please train a model first.\\\", file=STDE)\\n EXIT(1)\\n else:\\n return [copy.deepcopy(m.steps[-1][-1]) for m in self.model_pool]\",\n \"def load_models(self, models, iteration = None):\\n print(\\\"NOT IMPLEMENTED YET\\\")\",\n \"def test_get_models(self):\\n models = get_models()\\n self.assertTrue(len(models) > 6)\\n self.assertIn((\\\"csvimport.Item\\\", \\\"csvimport.Item\\\"), models)\",\n \"def models(self) -> list[AbstractModel]:\\n return self._models\",\n \"def import_all_model_modules():\\r\\n import brokerage.model\\r\\n # ensure that these imports don't get auto-deleted! they have side effects.\\r\\n brokerage.model\",\n \"async def list_models(\\n list_models_request: ListModels,\\n token: str = Depends(oauth2_scheme),\\n):\\n try:\\n logging.info(\\\"Calling /gcp/automl/list_models endpoint\\\")\\n logging.debug(f\\\"Request: {list_models_request}\\\")\\n if decodeJWT(token=token):\\n response = ManageModelController().list_model_controller(\\n request=list_models_request\\n )\\n return response\\n else:\\n raise HTTPException(\\n status_code=status.HTTP_401_UNAUTHORIZED,\\n detail=\\\"Invalid access token\\\",\\n headers={\\\"WWW-Authenticate\\\": \\\"Bearer\\\"},\\n )\\n except Exception as error:\\n logging.error(f\\\"Error in /gcp/automl/list_models endpoint: {error}\\\")\\n raise error\",\n \"def models(self) -> t.List[Model]:\\n _models: t.List[Model] = [\\n item for item in self._deployables if isinstance(item, Model)\\n ]\\n return _models\",\n \"def load_models(model_name=\\\"\\\", path=\\\"\\\", read_grains=False, **kwargs):\\n models = pc.load_models('{0}{1}'.format(path, model_name), read_grains=read_grains, **kwargs)\\n models_return = []\\n for m in models:\\n if m.n_zones > 1:\\n models_return.append(m)\\n else:\\n # log failed model\\n pass\\n return models_return\",\n \"def list_models():\\n\\n cmd = dict()\\n cmd[\\\"type_\\\"] = \\\"list_models\\\"\\n cmd[\\\"name_\\\"] = \\\"\\\"\\n \\n s = comm.send_and_receive_socket(cmd)\\n\\n msg = comm.recv_string(s)\\n\\n if msg != \\\"Success!\\\":\\n raise Exception(msg)\\n \\n json_str = comm.recv_string(s) \\n \\n s.close() \\n\\n return json.loads(json_str)\",\n \"def _pc_load_models(model_name = None, mod_list = None, n_sample = None, verbose = False, **kwargs):\\n\\n if model_name is not None:\\n mod_list = glob.glob(model_name + '*.out')\\n if mod_list is None or mod_list == []:\\n pc.log_.error('No model found', calling='load models')\\n return None\\n if n_sample is not None:\\n if n_sample > len(mod_list):\\n pc.log_.error('less models {0:d} than n_sample {1:d}'.format(len(mod_list), n_sample),\\n calling='load models')\\n return None\\n mod_list = random.sample(mod_list, n_sample)\\n m = []\\n for outfile in mod_list:\\n if outfile[-4::] == '.out':\\n model_name = outfile[0:-4]\\n else:\\n model_name = outfile\\n try:\\n cm = pc.CloudyModel(model_name, verbose=0, **kwargs)\\n if not cm.aborted:\\n m.append(cm)\\n if verbose:\\n print('{0} model read'.format(outfile[0:-4]))\\n except:\\n pass\\n pc.log_.message('{0} models read'.format(len(m)), calling='load_models')\\n return m\",\n \"def all_meta_types(self, interfaces=None):\\n mts = super(ZenModelBase,self).all_meta_types(interfaces)\\n if self.sub_meta_types:\\n mts = filter(lambda mt: mt['name'] in self.sub_meta_types, mts)\\n return mts\",\n \"def get_available_entities_models():\\n return ['concat', 'bahdanau', 'luong']\",\n \"def main(args):\\n metafiles = []\\n verbose = args.verbose\\n\\n if (args.metalist is not None):\\n for listfile in args.metalist:\\n metafiles.extend(addmeta.list_from_file(listfile))\\n\\n if (args.metafiles is not None):\\n metafiles.extend(args.metafiles)\\n\\n if verbose: print(\\\"metafiles: \\\",\\\" \\\".join([str(f) for f in metafiles]))\\n\\n addmeta.find_and_add_meta(args.files, metafiles)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72860724","0.6853126","0.6804148","0.6785551","0.6757944","0.66406256","0.6595081","0.65213096","0.6515239","0.64433444","0.6440567","0.6412613","0.6402561","0.63477504","0.63247734","0.63229364","0.6300605","0.6296517","0.6291255","0.6263","0.62552387","0.6249753","0.6240447","0.6235324","0.6235324","0.62267435","0.6181433","0.6172824","0.61347264","0.6113558","0.6111847","0.6110484","0.6108828","0.6090097","0.60416293","0.60407746","0.6025083","0.60026705","0.5984845","0.59761995","0.59671706","0.5916463","0.59131354","0.58722323","0.5866884","0.58529854","0.5831142","0.5775301","0.57679003","0.57482237","0.5743707","0.5737127","0.5734907","0.5730246","0.5725898","0.57196367","0.571154","0.57054615","0.57035404","0.5698952","0.5688712","0.5688712","0.5688712","0.5688712","0.56846625","0.56846625","0.5677021","0.5675175","0.5657499","0.56439644","0.56391114","0.5616057","0.56034136","0.5586566","0.5576216","0.5568727","0.5566716","0.55525947","0.55521065","0.55474055","0.55321056","0.5528529","0.55163884","0.5515434","0.5496587","0.5485726","0.5484987","0.5481138","0.5475758","0.54544526","0.5453564","0.54479426","0.54386914","0.54278827","0.5425028","0.54230255","0.54198366","0.54059356","0.539768","0.53624547"],"string":"[\n \"0.72860724\",\n \"0.6853126\",\n \"0.6804148\",\n \"0.6785551\",\n \"0.6757944\",\n \"0.66406256\",\n \"0.6595081\",\n \"0.65213096\",\n \"0.6515239\",\n \"0.64433444\",\n \"0.6440567\",\n \"0.6412613\",\n \"0.6402561\",\n \"0.63477504\",\n \"0.63247734\",\n \"0.63229364\",\n \"0.6300605\",\n \"0.6296517\",\n \"0.6291255\",\n \"0.6263\",\n \"0.62552387\",\n \"0.6249753\",\n \"0.6240447\",\n \"0.6235324\",\n \"0.6235324\",\n \"0.62267435\",\n \"0.6181433\",\n \"0.6172824\",\n \"0.61347264\",\n \"0.6113558\",\n \"0.6111847\",\n \"0.6110484\",\n \"0.6108828\",\n \"0.6090097\",\n \"0.60416293\",\n \"0.60407746\",\n \"0.6025083\",\n \"0.60026705\",\n \"0.5984845\",\n \"0.59761995\",\n \"0.59671706\",\n \"0.5916463\",\n \"0.59131354\",\n \"0.58722323\",\n \"0.5866884\",\n \"0.58529854\",\n \"0.5831142\",\n \"0.5775301\",\n \"0.57679003\",\n \"0.57482237\",\n \"0.5743707\",\n \"0.5737127\",\n \"0.5734907\",\n \"0.5730246\",\n \"0.5725898\",\n \"0.57196367\",\n \"0.571154\",\n \"0.57054615\",\n \"0.57035404\",\n \"0.5698952\",\n \"0.5688712\",\n \"0.5688712\",\n \"0.5688712\",\n \"0.5688712\",\n \"0.56846625\",\n \"0.56846625\",\n \"0.5677021\",\n \"0.5675175\",\n \"0.5657499\",\n \"0.56439644\",\n \"0.56391114\",\n \"0.5616057\",\n \"0.56034136\",\n \"0.5586566\",\n \"0.5576216\",\n \"0.5568727\",\n \"0.5566716\",\n \"0.55525947\",\n \"0.55521065\",\n \"0.55474055\",\n \"0.55321056\",\n \"0.5528529\",\n \"0.55163884\",\n \"0.5515434\",\n \"0.5496587\",\n \"0.5485726\",\n \"0.5484987\",\n \"0.5481138\",\n \"0.5475758\",\n \"0.54544526\",\n \"0.5453564\",\n \"0.54479426\",\n \"0.54386914\",\n \"0.54278827\",\n \"0.5425028\",\n \"0.54230255\",\n \"0.54198366\",\n \"0.54059356\",\n \"0.539768\",\n \"0.53624547\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70225334"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11082,"cells":{"query":{"kind":"string","value":"By this save method we create user and make a relationship with contact model and secoud depend on conatc type."},"document":{"kind":"string","value":"def save(self,commit=True):\n instance = super(ClientSignupForm, self).save(commit=False)\n if commit:\n instance.username = self.cleaned_data['email']\n instance.is_active = False\n instance.save()\n contact = Client.objects.create(first_name=self.cleaned_data['first_name'],last_name=self.cleaned_data['last_name'],\n user_role=self.cleaned_data['user_role'],\n department=self.cleaned_data['department'],\n user=instance)\n return instance"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def save_model(self, request, obj, form, change):\n if not change:\n if form.is_valid():\n user = form.save()\n user.identity = Users.SUPERVISOR\n user.set_password(form.data.get('password'))\n user.iCode = InviteCls.encode_invite_code(user.id)\n user.save()\n UserExtra.objects.create(uid=user)\n UserBase.objects.create(\n uid=user,\n phone=user.username\n )\n UserBusiness.objects.create(uid=user)\n else:\n super().save_model(request, obj, form, change)","def save_model(self, request, obj, form, change):\n if not change:\n if form.is_valid():\n user = form.save()\n user.identity = Users.SALESMAN\n user.set_password(form.data.get('password'))\n user.iCode = InviteCls.encode_invite_code(user.id)\n user.save()\n UserExtra.objects.create(uid=user)\n UserBase.objects.create(\n uid=user,\n phone=user.username\n )\n leader = Team.objects.get(id=form.data.get('team')).leader\n inviter_queryset = InviteRelationManager.objects.filter(invitee=leader)\n if inviter_queryset.exists():\n inviter_obj = inviter_queryset.first()\n superior = f'{inviter_obj.superior}|{leader.id}'\n else:\n superior = f'{leader.id}'\n InviteRelationManager.objects.create(inviter=leader, invitee=user, level=1, superior=superior)\n UserBusiness.objects.create(uid=user)\n super().save_model(request, obj, form, change)","def save(self, commit=True):\n user = super(UserCreationForm, self).save(commit=False)\n user.set_password(self.cleaned_data['password1'])\n\n user.save()\n\n # Making user profile and assigning to CPCESU\n # CPCESU\n #group = Organization.objects.get(name='Colorado Plateau')\n\n # New profile with group\n profile = UserProfile(user=user, first_name=self.cleaned_data.get('first_name'),\n last_name=self.cleaned_data.get('last_name'))\n profile.save()\n\n return user","def users_create():","def create(self,contact: Contact) -> bool:\n try:\n contact_new=ContactSet(name=contact.name,birthdate=contact.birthdate\n ,contact_type=contact.contact_type, description=contact.description, phone=contact.phone)\n db.session.add(contact_new)\n db.session.commit()\n return True\n except Exception as ex:\n app.logger.error('Error creating a new Contact. {}'.format(ex))\n return False","def save_model(self, request, obj, form, change):\n obj.propietario = request.user\n obj.save()","def save_model(self, request, obj, form, change):\n if not change:\n obj.creator = request.user\n obj.save()","def forwards(self, orm):\n \n location_type = orm['mooi.Location']\n if len(location_type.objects.all()) == 0:\n l = location_type(city=\"Lethbridge\", country=\"Canada\")\n l.save()\n else:\n l = location_type.objects.all()[0]\n \n \n user_type = orm['auth.User']\n user = user_type(username=self.suUsername, email=self.suEmail, password=self.suPassword)\n user.first_name = self.suFirstName\n user.last_name = self.suLastName\n user.is_superuser = True\n user.save()\n \n profile_type = orm['mooi.Profile']\n userProfile = profile_type(user=user, location=l, phone=self.suPhone)\n userProfile.location = l\n userProfile.phone = self.suPhone\n userProfile.save()","def perform_create(self, serializer):\n serializer.save(using=UserConf.db)","def commit(self):\n\t\t#firstly, get all variables and values of this model\n\t\tcontent = self.__dict__.copy() \n\t\t#if '_rev' is one of the variables of this model instance,\n\t\t#it means this user is retrived from database. \n\t\t#We are actually going to update the model document in database\n\t\t#instead of creating a new user document.\n\t\tres = dbop.update_create_user_in_database(self._id, content) \n\t\tself._id = res['id']\n\t\tself._rev = res['rev']","def save(self)->None:\n database.cursor.execute(\n \"INSERT INTO users(firstname,lastname,othernames,email,phone,username,password,role) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) RETURNING id\", (\n self.first_name,\n self.last_name,\n self.other_name,\n self.email,\n self.phone_number,\n self.user_name,\n self.password,\n self.is_admin\n ))\n super().save()","def _create_user(self, email, mobile_number, password, **extra_fields):\n\n print('model number')\n print(mobile_number)\n \n user = self.model(email=email,mobile_number = mobile_number, **extra_fields)\n user.set_password(password)\n \n user.save(using=self._db)\n return user","def save_user_ref(sender, created, instance, **_):\n if created:\n UserExtend.objects.create(user=instance)\n UserSettings.objects.create(user=instance)","def save_user(self):\n db.session.add(self)\n db.session.commit()","def create_user(self, phone_number, type, password, is_staff):\n return self.__create_user(phone_number, type, password, is_staff, False, False)","def add_contact_to_db(self):\n self.init_db(self._testing)\n\n # make sure that the object is not in the db\n assert self.uid == \"\"\n\n self._insert_row_into_db(Contact.table_name, Contact.columns, self.values)\n\n # update this objects uid\n self.uid = self._get_id_of_last_row(Contact.table_name)","def save(self, commit=True):\n\t\tprint('save django.user ')\n\t\tprint(self.cleaned_data)\n\t\tusr = User.objects.create_user(self.cleaned_data['username'], self.cleaned_data['email'], self.cleaned_data['pass1'])\n\t\tkuser = users.models.KUser()\n\t\tkuser.user = usr\n\t\tkuser.realName = self.cleaned_data['realName']\n\t\tkuser.job = self.cleaned_data['job']\n\t\tkuser.privilege = self.cleaned_data['privilege']\n\t\tkuser.employeeId = self.cleaned_data['employeeId']\n\t\tkuser.isManager = self.cleaned_data['isManager']\n\t\tkuser.gender = self.cleaned_data['gender']\n\t\tprint('create kuser:')\n\t\tprint(kuser)\n\n\t\tif commit:\n\t\t\tkuser.save()\n\t\treturn kuser","def on_user_create(self, user):","def perform_create(self,serializer):\n serializer.save(user_profile=self.request.user)","def perform_create(self,serializer):\n serializer.save(user_profile=self.request.user)","def createProfile(self):\n if self.profile:\n return\n from soc.modules.gsoc.models.profile import GSoCProfile\n user = self.createUser()\n properties = {'link_id': user.link_id, 'student_info': None, 'user': user,\n 'parent': user, 'scope': self.program, 'status': 'active'}\n self.profile = seeder_logic.seed(GSoCProfile, properties)","def save_to_db(self):\n # update\n if self.user_db:\n self.db.session.query(UserDB).filter(UserDB.login == self.params['login']).\\\n update({'access_token': self.params['access_token'],\n# 'social_net': self.params['social_net'] or 'social_net',\n 'profile_url': self.params.get('profile_url', None),\n 'fio': self.params.get('name', None),\n 'email': self.params.get('email', None)},\n synchronize_session='fetch')\n self.db.commit()\n log.debug('Updated social user: %s', self.params['login'],)\n # create\n else:\n user = UserDB(self.params['login'],\n self.params['email'],\n fio = self.params['name'],\n avatar = '',\n access_token = self.params['access_token'],\n social_net = self.params['social_net'],\n profile_url = self.params['link']\n )\n\n self.db.create(user)\n log.debug('Social user <%s> created', self.params['login'])\n return {'success': True}","def perform_create(self, serializer): # this method runs everytime a POST method is called\n serializer.save(user_profile=self.request.user)","def save(self, *args, **kwargs):\n create = self.id is None\n if create:\n self.username = u'lc_' + self.city.lower()\n # Give the user a randomly generated password\n password = generate_random_string()\n self.password = password\n # Compose the email to send to the LC\n context = {\n 'lc': self,\n 'password': password,\n }\n subject = render_to_string(\n 'registration/lc_register_email_subject.txt')\n subject = subject.strip()\n message = render_to_string(\n 'registration/lc_register_email_text.txt',\n context)\n send_mail(subject,\n message,\n settings.DEFAULT_FROM_EMAIL,\n [self.email])\n # Delegate upward to the parent classes\n super(LocalCommittee, self).save(*args, **kwargs)","def save(self):\n data = self.cleaned_data\n # Como este metodo no nos sirve para nada por eso tenemos que sacarlo,\n # se saca ya que solo es con el proposito de tener una contraseña con\n # su respectiva confirmacion. el modelo User no tiene ese campo por eso\n # se lo saca con el metodo *pop*\n data.pop('password_confirmation')\n # Los asteriscos lo que hacen es enviar la estructura desvaratada\n user = User.objects.create_user(**data)\n profile = Profile(user=user)\n profile.save()","def save(self):\n # First save the parent form and get the user.\n new_user = super(SignupFormExtra, self).save()\n\n # Get the profile, the `save` method above creates a profile for each\n # user because it calls the manager method `create_user`.\n # See: https://github.com/django-userena-ce/django-userena-ce/blob/master/userena/managers.py#L65\n profile = new_user.my_profile\n profile.gender = self.cleaned_data['gender']\n profile.education = self.cleaned_data['education']\n profile.birthday = self.cleaned_data['birthday']\n profile.annual_income = self.cleaned_data['annual_income']\n profile.save()\n\n # Userena expects to get the new user from this form, so return the new\n # user.\n return new_user","def _create(cls, model_class, *args, **kwargs):\n manager = cls._get_manager(model_class)\n # The default would use ``manager.create(*args, **kwargs)``\n return manager.create_user(*args, **kwargs)","def _create_user(self, telephone,username, password, email=None, **kwargs):\n if not username:\n raise ValueError('请输入用户名')\n if not telephone:\n raise ValueError('请输入手机号')\n if not password:\n raise ValueError('请输入密码')\n email = self.normalize_email(email)\n username = self.model.normalize_username(username)\n user = self.model(telephone=telephone, username=username, email=email, **kwargs)\n user.set_password(password)\n user.save()\n return user","def post(self):\r\n return create_user(request)","def save(self, commit=False):\n mail_result = self.send_email()\n if mail_result:\n self.instance.is_admin_notified = True\n\n contact = super().save(commit=commit)\n\n return contact","def crud_user(request):\n if request.POST['action'] == 'EDIT':\n username = request.POST['username']\n tel = request.POST['tel']\n mobile = request.POST['mobile']\n office = request.POST['office']\n num = request.POST['num']\n user = IMPUser.objects.get(username = username)\n user.tel = tel\n user.mobile = mobile\n user.office = office\n user.num = num\n user.save()\n return redirect(urlresolvers.reverse(\"address_page\"))\n #print(request.POST['tel'])\n #username = request.POST['username']\n #password = request.POST['password']\n #password1 = request.POST['password1']\n #tel = request.POST['tel']\n #try:\n # #if password.equals(password1)\n # User.objects.all().get(username = username) \n # return render(request, \"account/sign_up.html\", context={\"errmsg\":\"Duplicated User!\"})\n #except:\n # print(\"NO USER!\")\n # IMPUser.objects.create_user(username = username, password = password, tel=tel)\n return redirect(urlresolvers.reverse(\"sign_in\"))","def save(self, *args, **kwargs):\n self.clean()\n try:\n if self.email:\n if self.id:\n LOG.debug('trying save method of consumer %s' % (self.id))\n Consumer.objects.exclude(id=self.id).get(email=self.email)\n else:\n Consumer.objects.get(email=self.email)\n error_message = 'Cannot create user with this email.'\n LOG.error('Consumer %s already exists' % (self.email))\n raise ValidationError(_(error_message))\n except Consumer.DoesNotExist:\n pass\n if len(self.username) > 30:\n self.username = generate_guid()[:30]\n self.email_hash = generate_email_hash(self.email)\n self.get_geolocation_id()\n super(Consumer, self).save(*args, **kwargs)\n return self","def create(self,validated_data):\n user_obj = User.objects.create(**validated_data)\n return user_obj","def save(self, *args, **kwargs):\n create = self.id is None\n # Strip out the user keyword argument, since the super save method\n # does not expect it.\n user = None\n if 'user' in kwargs:\n user = kwargs.pop('user')\n super(Member, self).save(*args, **kwargs)\n # Only register if the object is not being updated\n if create:\n self._register(user=user)\n else:\n # User and UserProfile already exist so save them too\n self.userprofile.save()\n self.userprofile.user.save()","def save(self):\n # First save the parent form and get the user.\n new_user = super(SignupFormExtra, self).save()\n\n new_user.first_name = self.cleaned_data['first_name']\n new_user.last_name = self.cleaned_data['last_name']\n new_user.save()\n\n # Userena expects to get the new user from this form, so return the new\n # user.\n return new_user","def perform_create(self, serializer):\n print(self.request.user)\n serializer.save(user_profile=self.request.user)","def mutate(parent, info, user_details):\n\n user = User()\n user.name = user_details.name\n user.address = user_details.address\n user.phone_number = user_details.phone_number\n user.sex = user_details.sex\n\n user.save()\n\n return user","def perform_create(self, serializer):\n serializer.save(user_profile = self.request.user)","def perform_create(self, serializer):\n serializer.save(user_profile=self.request.user)","def perform_create(self, serializer):\n serializer.save(user_profile=self.request.user)","def perform_create(self, serializer):\n serializer.save(user_profile=self.request.user)","def perform_create(self, serializer):\n serializer.save(user_profile=self.request.user)","def perform_create(self, serializer):\n serializer.save(user_profile=self.request.user)","def perform_create(self, serializer):\n serializer.save(user_profile=self.request.user)","def _create_user(self, FirstName,LastName, EmailId, MobileNo, password=None, **extra_fields):\n if not (FirstName and LastName):\n raise ValueError(\"The user's Name must be set\")\n if not EmailId:\n raise ValueError('The given EmailId must be set')\n if not password:\n raise ValueError('The given password must be set')\n if not MobileNo:\n raise ValueError('The given mobile must be set')\n EmailId = self.normalize_email(EmailId)\n user = self.model(FirstName =FirstName, LastName =LastName ,EmailId=EmailId, MobileNo=MobileNo, **extra_fields)\n user.set_password(password)\n user.save(using=self._db)\n return user","def save(self, postValues):\n\n params = postValues.copy()\n cursor = connection.cursor()\n record = None\n\n if(params.get('id')):\n record = WebUsers.objects.get(pk=params.get('id'))\n\n record.status = True\n if 'false' == params.get('status'):\n record.status = False\n else:\n email = params.get('user', '')\n password = params.get('pass', '')\n\n #first thing to check is if the email already exists\n hasher = PasswordHasher()\n pwd = hasher.user_hash_password(password, 15)\n\n try :\n WebUsers.objects.get(mail=email)#get the account using the email\n #if this email already exists, inform the user of that and advice them accordingly\n raise CriticalError({'message' : \"The email '\" + email + \"' already exists in the system. Please use the email to login into the platform or request for a new password if you have forgotten it.\"})\n except WebUsers.DoesNotExist:\n record = WebUsers( name=email,\n pass_field=pwd,\n mail=email,\n access=0,\n login=0,\n status=1,\n picture=0,\n uuid='',\n created=time.time())\n \n # TODO\n # You can initialize common modules here\n # params['can_use_admin'] = 'false' \n params['can_use_admin'] = 'true'\n \n record.save()\n\n try:\n #print(record.__dict__)\n user_detail = WebUserDetail.objects.get(user_id=record.uid)\n \n user_detail.can_use_admin = False\n if 'true' == params.get('can_use_admin'):\n user_detail.can_use_admin = True\n except WebUserDetail.DoesNotExist:\n #create the user detail information\n #print(\"NO Detail\")\n user_detail = WebUserDetail(user_id=record.uid, can_use_admin=True, description = '',\n alert_frequency='never', full_name=email)\n #user_detail = WebUserDetail(user_id=record.uid, can_use_admin=False, description = '',\n # alert_frequency='never', full_name=email)\n #print(user_detail.__dict__)\n \n user_detail.save()\n\n cursor.execute(\"DELETE FROM user_module_perm WHERE system_user_id = \" + str(record.uid))\n \n if 'true' == params.get('can_use_admin'):\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (1, record.uid))\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (2, record.uid))\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (3, record.uid))\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (4, record.uid))\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (5, record.uid))\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (6, record.uid))\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (7, record.uid))\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (8, record.uid))\n else:\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (8, record.uid))\n\n cursor.close()\n \n connection.connection.commit()\n \n return record","def save_object(self, data):\n return Contact(**data)","def save(self, request):\n user = get_user_model()()\n cleaned_data = self.get_cleaned_data()\n email = cleaned_data.get('email')\n nickname = cleaned_data.get('nickname')\n\n user.email = email\n user.nickname = nickname\n\n if 'password1' in cleaned_data:\n user.set_password(cleaned_data[\"password1\"])\n else:\n user.set_unusable_password()\n\n user.save()\n\n return user","def create(self, validated_data):\n user_data = validated_data.pop('user')\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\n businessuser, created = BusinessUser.objects.update_or_create(user=user,\n company_name=validated_data.pop('company_name'), \n reg=validated_data.pop('reg'), \n vat=validated_data.pop('vat'), )\n return businessuser","def post(self):\n post_data = request.get_json()\n\n # decode token and check if expired\n token = post_data.get('odoo_contact_token')\n odoo_contact_id, expiration_date = decode_token(token)\n\n if datetime.now() > expiration_date:\n return {\n \"error_id\": \"alumni_register_link_expired_error\",\n \"message\": \"Unauthorized: Registration link is expired.\"\n }, 401\n\n # check if such odoo user exists\n filter_list = []\n filter_list.append(['id', '=', odoo_contact_id])\n from app.controllers.odoo_controller import OdooController\n try:\n contacts_number = OdooController.count_number_of_odoo_contacts_by_filter_list(filter_list)\n except OdooIsDeadError as err:\n abort(503, err, error_id='odoo_connection_error')\n\n if contacts_number == 0:\n return {\n \"error_id\": \"odoo_contact_not_found_error\",\n \"message\": \"Odoo contact not found.\"\n }, 404\n\n # create alumni user\n from app.controllers.alumni_controller import AlumniController\n post_data.update({'odoo_contact_id': odoo_contact_id})\n response = AlumniController.create_alumni_user(post_data)\n\n \n if response[1] == 201:\n # delete record in alumni invite status\n from app.controllers.alumni_invite_status_controller import AlumniInviteStatusController\n AlumniInviteStatusController.delete_invite_status_record(odoo_contact_id)\n\n # send email for confirmation\n receiver_email = response[0]['email']\n alumni_uuid = response[0]['alumni_uuid']\n send_confirmation_email(receiver_email, alumni_uuid)\n\n return response","def add_user(first_name,last_name,email,password,typeOfUser):\n user=User.objects.create(first_name=first_name,last_name=last_name,email=email,password=password,role=typeOfUser)\n return user","def signup(self, request, user):\n user.first_name = self.cleaned_data['first_name']\n user.last_name = self.cleaned_data['last_name']\n user.save()\n\n return user","def _create_user(self, email_or_phone, password, is_staff, is_superuser, **extra_fields):\n if not email_or_phone:\n raise ValueError('The given email_or_phone must be set')\n\n if \"@\" in email_or_phone:\n username, email, phone = (email_or_phone, email_or_phone, \"\")\n else:\n username, email, phone = (email_or_phone, \"\", email_or_phone)\n\n now = timezone.now()\n extra_fields.setdefault('is_staff', True)\n is_active = extra_fields.pop(\"is_active\", True)\n user = self.model(username=username, email=email,\n mobile=phone,\n is_staff=is_staff,\n is_active=is_active,\n is_superuser=is_superuser,\n\n date_joined=now,\n **extra_fields\n )\n user.set_password(password)\n user.save(using=self._db)\n return user","def create_user(self) -> None:\n # update when the account was created\n self.account_created = datetime.now().date()\n self.insert_to_db()\n log(f\"An account for User:{self.id} has been created.\")","def validate_and_save_to_db(self, user, msg=None, accept_tos=True):\n # self._autocomplete_tos()\n\n self._autocomplete_email(user.email)\n self._autocomplete_phone()\n self._doctor_names()\n\n if self.validate():\n # get user's default info and either create new info or get the same info instance\n first_umi = UserMessageInfo.query.filter_by(user=user, default=True).first()\n umi = UserMessageInfo.first_or_create(user.id, **self.data_dict())\n\n #umi = first_umi if (first_umi and first_umi.accept_tos is None) \\\n # else UserMessageInfo.first_or_create(user.id, **self.data_dict())\n\n # check if the new user info differs from the newly submitted info and adjust appropriately\n if first_umi.id is not umi.id:\n first_umi.default = False\n umi.default = True\n if msg is not None:\n msg.user_message_info = umi\n\n # save form data to models\n for field, val in self.data.iteritems():\n try:\n setattr(umi, field, str(val))\n except:\n return False, 'error'\n\n # form is valid so user accepted tos at this time\n umi.accept_tos = datetime.datetime.now() if accept_tos else None\n\n # if everything succeeded then we commit and return True\n db.session.commit()\n\n if umi.determine_district(force=True) is None:\n return False, 'district_error'\n\n return True, 'success'\n else:\n return False, 'invalid_form_error'","def _create_user(self, phone_number, password, **extra_fields):\n\n if not phone_number:\n raise ValueError(\"Phone number is missing\")\n\n user = self.model(phone_number=phone_number, **extra_fields)\n user.set_password(password)\n\n user.save(using=self._db)\n\n # Create a Profile object for the user\n profile = Profile(user=user)\n profile.save()\n\n # Create a Cart object for the user\n cart = Cart(user=user)\n cart.save()\n\n # Create a UserAddress object for the user\n address = UserAddress(user=user)\n address.save()\n\n return user","def save(self, user, **kwargs):\n corporate_membership = super(CorpMembForm, self).save(commit=False)\n corporate_membership.corp_app = self.corp_app\n creator_owner = user\n\n if not self.instance.pk:\n mode = 'add'\n else:\n mode = 'edit'\n\n if mode == 'add':\n anonymous_creator = kwargs.get('creator', None)\n if anonymous_creator:\n corporate_membership.anonymous_creator = anonymous_creator\n if not isinstance(creator_owner, User):\n # if anonymous is creating the corporate membership\n # temporarily use the first admin, the creator will be assigned \n # back to the real user on approval\n tmp_user = User.objects.filter(is_staff=1, is_active=1)[0]\n creator_owner = tmp_user\n\n corporate_membership.creator = creator_owner\n corporate_membership.creator_username = creator_owner.username\n\n if not user.profile.is_superuser:\n corporate_membership.status = 1\n corporate_membership.status_detail = 'pending'\n corporate_membership.join_dt = datetime.now()\n\n corporate_membership.owner = creator_owner\n corporate_membership.owner_username = creator_owner.username\n\n # calculate the expiration dt\n corporate_membership.save()\n for field_obj in self.field_objs:\n if (not field_obj.field_name) and field_obj.field_type not in [\n 'section_break', 'page_break']:\n field_key = \"field_%s\" % field_obj.id\n value = self.cleaned_data[field_key]\n if value and self.fields[field_key].widget.needs_multipart_form:\n if not type(value) is unicode:\n value = fs.save(join(\"forms\",\n str(uuid4()),\n value.name),\n value)\n # if the value is a list convert is to a comma delimited string\n if isinstance(value, list):\n value = ','.join(value)\n if not value:\n value = ''\n\n if hasattr(field_obj, 'entry') and field_obj.entry:\n field_obj.entry.value = value\n field_obj.entry.save()\n else:\n corporate_membership.fields.create(field_id=field_obj.id,\n value=value)\n\n # update authorized domain if needed\n if self.corp_app.authentication_method == 'email':\n update_auth_domains(corporate_membership,\n self.cleaned_data['authorized_domains'])\n\n return corporate_membership","def createDeveloper(self):\n self.createUser()\n self.user.is_developer = True\n self.user.put()","def save(self):\n new_user = RegistrationProfile.objects.create_inactive_user(username=self.cleaned_data['username'],\n password=self.cleaned_data['password1'],\n email=self.cleaned_data['email'],\n firstname=self.cleaned_data['first_name'],\n lastname=self.cleaned_data['last_name'],\n agree=self.cleaned_data['tos'])\n return new_user","def save_model(self, request, obj, form, change):\n if not change:\n obj.author = request.user\n obj.save()","def form_valid(self, form):\n print self.kwargs['pk']\n self.object = form.save(commit=False)\n self.object.contact = Contact.objects.get(pk=self.kwargs['pk'])\n self.object.status = 'Abierto'\n self.object.save()\n user_full = UserFullName.objects.get(id=self.request.user.id)\n notes = 'Hola tienes un nuevo caso asignado'\n CaseTrack.objects.create(user_from=user_full, user_to=self.object.assigned, notes=notes, case=self.object)\n return super(ModelFormMixin, self).form_valid(form)","def _saveUser(self, cursor):\n user_tablename = \"_users_\"\n if not self.exists(user_tablename, cursor):\n sql = f\"CREATE TABLE {user_tablename}(id INTEGER PRIMARY KEY, user_code TEXT, firstname TEXT, lastname TEXT)\"\n cursor.executescript(sql)\n sql = f\" INSERT INTO {user_tablename}(id,user_code,firstname,lastname)\"\n sql += f\" SELECT {self.client_session.userCompanyId},?,?,? \"\n sql += f\" WHERE NOT EXISTS( SELECT 1 FROM {user_tablename} WHERE id = {self.client_session.userCompanyId} )\"\n params = (self.current_user.username, self.client_session.userFirstName,\n self.client_session.userLastName)\n cursor.execute(sql, params)","def create_user(self, username, email, password=None, is_staff =False, is_superuser = False):\n print(\"create_user:\", \"creating .....\")\n if not email:\n raise ValueError('Users must have an email address')\n\n # get prefix \n try:\n db = select_write_db(model_name=self.model._meta.model_name)\n prefix = db.get_prefix \n except:\n uuid3 = uuid.uuid3(uuid.NAMESPACE_DNS,settings.USER_INIT_DATABASE)\n prefix = str(uuid3)[:8]\n \n # create uuidn\n uuidn = prefix + \"-\" + str(uuid.uuid4())[9:] \n\n user = self.model(\n username = username, \n email = self.normalize_email(email),\n nid = str(uuidn),\n )\n\n user.set_password(password)\n user.staff = is_staff\n user.admin = is_superuser\n\n if settings.SHARDING_USER_MODEL:\n user.save(using=str(db.get_name))\n db.count = db.count + 1\n db.save()\n else:\n user.save(using=self._db)\n return user","def perform_create(self, serializer):\n if serializer.instance is None:\n profile = Profile.objects.get(user=self.request.user)\n #print profile\n serializer.save(owner=profile)","def perform_create(self, serializer):\r\n serializer.save(user_type=\"SPEAKER\")","def save(self, *args, **kwargs):\n\n if self.id:\n firstcreation = False\n else:\n firstcreation = True\n\n #common save functionality for all models\n self._save_base()\n self.save_default(firstcreation)\n super(ComicSiteModel,self).save()","def save(self):\n data = self.cleaned_data\n data.pop('password_confirmation')\n user = User.objects.create_user(**data)\n profile = Profile(user=user)\n profile.save()","def createUser(self):\n if self.user:\n return self.user\n from soc.models.user import User\n from soc.modules.seeder.logic.providers.user import CurrentUserProvider\n properties = {'account': CurrentUserProvider(),\n 'status': 'valid', 'is_developer': self.dev_test}\n self.user = seeder_logic.seed(User, properties=properties)\n return self.user","def mutate(self, info, **kwargs):\n email = kwargs.get('email')\n password = kwargs.get('password')\n complete_name = kwargs.get('complete_name')\n user = get_user_model()(email=email)\n user.set_password(password)\n user.is_service = True\n user.save()\n service = Service.objects.create(user=user)\n service = Service(\n complete_name=complete_name,\n email=email,\n user=user,\n )\n service.save()\n\n return CreateService(service=service)","def save(self):\n payload = self.context['payload']\n user = User.objects.get(username=payload['user'])\n user.is_verified = True\n user.save()","def save(self):\n payload = self.context['payload']\n user = User.objects.get(username=payload['user'])\n user.is_verified = True\n user.save()","def save(self, *args, **kwargs):\n self.entity_type = \"Charity\"\n super().save(*args, **kwargs)","def save(self, *args, **kwargs):\n self.entity_type = \"Person\"\n super().save(*args, **kwargs)","def user_post_save(sender, instance, created, **kwargs):\n\t\tif created == True:\n\t\t\tup = UserProfile()\n\t\t\tup.user = instance\n\t\t\tup.save()","def signup(self, request, user):\n pass","def save(self, *args, **kwargs):\n\n kwargs[\"commit\"] = False\n user = super(JOSProfileForm, self).save(*args, **kwargs)\n\n try:\n profile = get_profile_for_user(user)\n profile_form = self.get_profile_fields_form()\n profile_form(self.data, self.files, instance=profile).save()\n except ProfileNotConfigured:\n pass\n\n return user","def perform_create(self, serializer):\n if self.request.data.get('user_type', None) == 'employee':\n serializer.save(is_staff=False)\n else:\n serializer.save()","def create_or_update_contact(customer, entity):\n\tname = frappe.db.get_value('Contact', { 'entity_id': entity.get('entity_id') })\n\tif not name:\n\t\tcontact = frappe.new_doc('Contact')\n\telse:\n\t\tcontact = frappe.get_doc(\"Contact\", name)\n\n\tif not entity.get('firstname'):\n\t\treturn\n\t\n\tcontact.first_name = entity.get('firstname')\n\tcontact.last_name = entity.get('lastname')\n\tcontact.customer = customer.name\n\tcontact.customer_name = customer.customer_name\n\tcontact.entity_id = entity.get('entity_id')\n\tcontact.email_id = entity.get('email')\n\tcontact.save(ignore_permissions=True)","def _create_user(self, username, email, password, phone, **extra_fields):\n\n username = self.model.normalize_username(username)\n user = self.model(username=username, email=email, phone=phone, **extra_fields) # using email_id instead of email\n user.set_password(password)\n user.save(using=self._db)\n return user","def save(self, commit=True):\n\n company = super(CompanyForm, self).save(commit=False)\n company.caffe = self._caffe\n if commit:\n company.save()\n\n return company","def save_user(self):\n\n User.user_list.append(self)","def create_user(self, request):\n if User.query(User.name == request.user_name).get():\n raise endpoints.ConflictException(\n 'A User with that name already exists!')\n #By adding wins, it added it to the create_user input #api page.\n wins = defaults['wins']\n user = User(name=request.user_name, email=request.email, wins = wins)\n #user.put() sends the user info that is ndb\n user.put()\n\n for key,val in sorted(craft.items()):\n outmessage =(\"{} : Can be make with {}\".format(key, val))\n return StringMessage(message='User {} created!'.format(\n outmessage))\n #This just returns a message for response at bottom of API\n #screen.","def create(self, data):\n # Make User\n username = data['email'].split(\"@\")[0]\n user = User.objects.create_user(**data, username=username, is_verified=False, is_client=True)\n Profile.objects.create(user=user)\n send_confirmation_email.delay(user_pk=user.pk)\n return user","def create_user(self):\n User.objects.create_user('test', 'testing@test.com', 'testing')","def save_model(self, request, obj, form, change):\n try:\n obj.prescription = self.prescription\n except AttributeError:\n pass\n if not obj.pk:\n obj.creator = request.user\n obj.modifier = request.user\n\n obj.save()\n\n # If can_delete is set, allow the user to delete this object.\n if self.can_delete:\n opts = self.opts\n group = Group.objects.get(name='Users')\n perm = get_permission_codename('delete', opts)\n assign_perm(\"%s.%s\" % (opts.app_label, perm), group, obj)","def create(self, validated_data):\n\n user_data = {\n \"username\" : validated_data.get(\"username\"),\n \"email\" : validated_data.get(\"email\"),\n \"password\" : validated_data.get(\"password\")\n }\n user = User.objects.create_user(**user_data)\n user.save()\n\n account_data = {\n \"phone\" : validated_data.get(\"phone\"),\n \"type\" : validated_data.get(\"type\"),\n \"lat\" : validated_data.get(\"lat\"),\n \"lang\" : validated_data.get(\"lang\"),\n \"center_point\" : validated_data.get(\"center_point\")\n }\n account = Account(user = user, **account_data)\n account.save()\n\n return user","def create_profile(sender, instance, signal, created, **kwargs):\n \n from phylocommons.models import UserProfile\n \n if created:\n UserProfile(user = instance).save()","def create(self,validated_data):\n\n user = models.User.object.create_user(\n email = validated_data['email'],\n full_name = validated_data['full_name'],\n phone = validated_data['phone'],\n password = validated_data['password']\n )\n\n #user.set_password(validated_data['password'])\n user.save()\n return user","def create_user(user, first_name, last_name, major, bio):\n return userAccount.objects.create(user=user, first_name=first_name, last_name=last_name, major=major, bio=bio)","def create(self, request, *args, **kwargs):\n print(\"request data is %s\" % (request.data))\n parent = request.data.get('parent', None)\n valid_object_list = list()\n for key in request.data.get('to_save', {}):\n print(\"key is %s\" % key)\n if key != \"parent\":\n raw_data = request.data.get('to_save', None).get(key, None)\n print(\"raw data is %s\" % raw_data)\n raw_data['password'] = \"22023801@aA\"\n serializer = DiscoUserModelSerializer(data=raw_data)\n student_id = request.data.get('to_save', None).get(key, None).get('roll_number', None)\n if serializer.is_valid():\n if StudentDetail.objects.filter(roll_number=student_id).exists():\n return Response({'Student ID': \"Student Id %s already exists\" % student_id},\n status=status.HTTP_400_BAD_REQUEST)\n elif student_id is None:\n return Response({'Student Id' \"Student Id is required to have in data\"}, status=status.HTTP_400_BAD_REQUEST)\n else:\n valid_object_list.append({\n 'roll_number': student_id,\n 'student_type': request.data.get('to_save', None).get(key, None).get('student_type', None),\n 'serializer': serializer})\n else:\n return Response(serializer.errors,\n status=status.HTTP_400_BAD_REQUEST)\n else:\n pass\n \n # once buck validate is done we have the data\n # in desired_format to create the\n for valid_object in valid_object_list:\n print(\"valid object is %s \\n\" % valid_object)\n user = valid_object['serializer'].save()\n # create student_details object\n StudentDetail.objects.create(roll_number=valid_object['roll_number'],\n student_type=valid_object['student_type'],\n user=user)\n\n # update the to update users here\n for user_id in request.data.get('to_update', {}):\n raw_data = request.data.get('to_update', None).get(user_id, None)\n print(\"raw data is %s\" % raw_data)\n serializer = DiscoUserModelSerializer(DiscoUser.objects.get(id=user_id), data=raw_data, partial=True)\n if serializer.is_valid():\n serializer.save()\n StudentDetail.objects.filter(user__id=user_id).update(student_type=raw_data['student_type'],\n roll_number=raw_data['roll_number'])\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n # finally get all the childrens of the parent\n # and send their data as reponse back.\n user_data_serializer = UserAccountDashboardPostRequestSerializer(DiscoUser.objects.filter(parent__id=parent), many=True)\n return Response(user_data_serializer.data, status=status.HTTP_201_CREATED)","def post(self):\n return Contacts().create_one(request.get_json())","def _create_user(self, username, email, persona_id, nombre_completo, password, is_staff, is_superuser,\n **kwargs):\n now = timezone.now()\n if not email:\n raise ValueError(_('El email debe ser proporcionado'))\n email = self.normalize_email(email)\n user = self.model(\n username=username,\n persona_id=persona_id,\n nombre_completo=nombre_completo,\n email=email,\n is_staff=is_staff,\n is_active=True,\n is_superuser=is_superuser,\n last_login=now,\n fecha_registro=now,\n **kwargs\n )\n user.set_password(password)\n user.save(using=self._db)\n return user","def create_custom_user(sender, instance, signal, created, **kwargs):\n from gpsfun.main.User.models import GPSFunUser\n if created:\n GPSFunUser.objects.create(user=instance)\n instance.gpsfunuser.save()","def perform_create(self, serializer):\n serializer.save(creator=self.request.user)","def do_user_create():\n target = User(\n request.form['gender'],\n request.form['first_name'],\n request.form['name'],\n request.form['mail'],\n request.form['meter_id'],\n request.form['group_id'],\n secrets.token_hex(33))\n target.set_role(request.form['role'])\n target.nick = request.form['nick']\n db.session.add(target)\n db.session.commit()\n return user_list(\"Created user \" + target.name)","def create(self, validated_data):\n user_data = validated_data.pop('user')\n user = UserSerializer.create(\n UserSerializer(), validated_data=user_data)\n student, created = Profile.objects.update_or_create(\n user=user, contact_number=validated_data.pop('contact_number'), meter_id=validated_data.pop('meter_id'))\n return student","def perform_create(self,serializer):\n serializer.save(user_id=self.request.user)","def create_user(self, email, password, first_name, last_name, phone, credits=0, \n driver=False, drivers_license=None, profile_picture=None, number_of_rides=0, years_of_experience=0):\n password_hash = make_password_hash(email, password)\n return UserModel(email=email, password=password_hash, first_name=first_name, last_name=last_name, \n phone=phone, credits=credits, driver=driver, drivers_license=drivers_license, \n profile_picture=profile_picture, number_of_rides=number_of_rides, \n years_of_experience=years_of_experience)","def create_contact(contact, party_type, party):\n\tcontact = contact\t.split(\" \")\n\n\tcontact = frappe.get_doc({\n\t\t\"doctype\":\"Contact\",\n\t\t\"first_name\":contact[0],\n\t\t\"last_name\": len(contact) > 1 and contact[1] or \"\"\n\t})\n\tcontact.append('links', dict(link_doctype=party_type, link_name=party))\n\tcontact.insert()","def users_page(request):\n if request.method == 'POST':\n user = request.user\n form = CompetenceForm(request.POST)\n\n if form.is_valid():\n form.instance.person = request.user\n form.save()\n # return redirect('user-page')\n # competence = Competence.objects.create_competence(user, form.title_of_competence, form.level_of_competence)\n else:\n form = CompetenceForm()\n\n return render(request, 'core/user-page.html', {'form': form})"],"string":"[\n \"def save_model(self, request, obj, form, change):\\n if not change:\\n if form.is_valid():\\n user = form.save()\\n user.identity = Users.SUPERVISOR\\n user.set_password(form.data.get('password'))\\n user.iCode = InviteCls.encode_invite_code(user.id)\\n user.save()\\n UserExtra.objects.create(uid=user)\\n UserBase.objects.create(\\n uid=user,\\n phone=user.username\\n )\\n UserBusiness.objects.create(uid=user)\\n else:\\n super().save_model(request, obj, form, change)\",\n \"def save_model(self, request, obj, form, change):\\n if not change:\\n if form.is_valid():\\n user = form.save()\\n user.identity = Users.SALESMAN\\n user.set_password(form.data.get('password'))\\n user.iCode = InviteCls.encode_invite_code(user.id)\\n user.save()\\n UserExtra.objects.create(uid=user)\\n UserBase.objects.create(\\n uid=user,\\n phone=user.username\\n )\\n leader = Team.objects.get(id=form.data.get('team')).leader\\n inviter_queryset = InviteRelationManager.objects.filter(invitee=leader)\\n if inviter_queryset.exists():\\n inviter_obj = inviter_queryset.first()\\n superior = f'{inviter_obj.superior}|{leader.id}'\\n else:\\n superior = f'{leader.id}'\\n InviteRelationManager.objects.create(inviter=leader, invitee=user, level=1, superior=superior)\\n UserBusiness.objects.create(uid=user)\\n super().save_model(request, obj, form, change)\",\n \"def save(self, commit=True):\\n user = super(UserCreationForm, self).save(commit=False)\\n user.set_password(self.cleaned_data['password1'])\\n\\n user.save()\\n\\n # Making user profile and assigning to CPCESU\\n # CPCESU\\n #group = Organization.objects.get(name='Colorado Plateau')\\n\\n # New profile with group\\n profile = UserProfile(user=user, first_name=self.cleaned_data.get('first_name'),\\n last_name=self.cleaned_data.get('last_name'))\\n profile.save()\\n\\n return user\",\n \"def users_create():\",\n \"def create(self,contact: Contact) -> bool:\\n try:\\n contact_new=ContactSet(name=contact.name,birthdate=contact.birthdate\\n ,contact_type=contact.contact_type, description=contact.description, phone=contact.phone)\\n db.session.add(contact_new)\\n db.session.commit()\\n return True\\n except Exception as ex:\\n app.logger.error('Error creating a new Contact. {}'.format(ex))\\n return False\",\n \"def save_model(self, request, obj, form, change):\\n obj.propietario = request.user\\n obj.save()\",\n \"def save_model(self, request, obj, form, change):\\n if not change:\\n obj.creator = request.user\\n obj.save()\",\n \"def forwards(self, orm):\\n \\n location_type = orm['mooi.Location']\\n if len(location_type.objects.all()) == 0:\\n l = location_type(city=\\\"Lethbridge\\\", country=\\\"Canada\\\")\\n l.save()\\n else:\\n l = location_type.objects.all()[0]\\n \\n \\n user_type = orm['auth.User']\\n user = user_type(username=self.suUsername, email=self.suEmail, password=self.suPassword)\\n user.first_name = self.suFirstName\\n user.last_name = self.suLastName\\n user.is_superuser = True\\n user.save()\\n \\n profile_type = orm['mooi.Profile']\\n userProfile = profile_type(user=user, location=l, phone=self.suPhone)\\n userProfile.location = l\\n userProfile.phone = self.suPhone\\n userProfile.save()\",\n \"def perform_create(self, serializer):\\n serializer.save(using=UserConf.db)\",\n \"def commit(self):\\n\\t\\t#firstly, get all variables and values of this model\\n\\t\\tcontent = self.__dict__.copy() \\n\\t\\t#if '_rev' is one of the variables of this model instance,\\n\\t\\t#it means this user is retrived from database. \\n\\t\\t#We are actually going to update the model document in database\\n\\t\\t#instead of creating a new user document.\\n\\t\\tres = dbop.update_create_user_in_database(self._id, content) \\n\\t\\tself._id = res['id']\\n\\t\\tself._rev = res['rev']\",\n \"def save(self)->None:\\n database.cursor.execute(\\n \\\"INSERT INTO users(firstname,lastname,othernames,email,phone,username,password,role) VALUES (%s,%s,%s,%s,%s,%s,%s,%s) RETURNING id\\\", (\\n self.first_name,\\n self.last_name,\\n self.other_name,\\n self.email,\\n self.phone_number,\\n self.user_name,\\n self.password,\\n self.is_admin\\n ))\\n super().save()\",\n \"def _create_user(self, email, mobile_number, password, **extra_fields):\\n\\n print('model number')\\n print(mobile_number)\\n \\n user = self.model(email=email,mobile_number = mobile_number, **extra_fields)\\n user.set_password(password)\\n \\n user.save(using=self._db)\\n return user\",\n \"def save_user_ref(sender, created, instance, **_):\\n if created:\\n UserExtend.objects.create(user=instance)\\n UserSettings.objects.create(user=instance)\",\n \"def save_user(self):\\n db.session.add(self)\\n db.session.commit()\",\n \"def create_user(self, phone_number, type, password, is_staff):\\n return self.__create_user(phone_number, type, password, is_staff, False, False)\",\n \"def add_contact_to_db(self):\\n self.init_db(self._testing)\\n\\n # make sure that the object is not in the db\\n assert self.uid == \\\"\\\"\\n\\n self._insert_row_into_db(Contact.table_name, Contact.columns, self.values)\\n\\n # update this objects uid\\n self.uid = self._get_id_of_last_row(Contact.table_name)\",\n \"def save(self, commit=True):\\n\\t\\tprint('save django.user ')\\n\\t\\tprint(self.cleaned_data)\\n\\t\\tusr = User.objects.create_user(self.cleaned_data['username'], self.cleaned_data['email'], self.cleaned_data['pass1'])\\n\\t\\tkuser = users.models.KUser()\\n\\t\\tkuser.user = usr\\n\\t\\tkuser.realName = self.cleaned_data['realName']\\n\\t\\tkuser.job = self.cleaned_data['job']\\n\\t\\tkuser.privilege = self.cleaned_data['privilege']\\n\\t\\tkuser.employeeId = self.cleaned_data['employeeId']\\n\\t\\tkuser.isManager = self.cleaned_data['isManager']\\n\\t\\tkuser.gender = self.cleaned_data['gender']\\n\\t\\tprint('create kuser:')\\n\\t\\tprint(kuser)\\n\\n\\t\\tif commit:\\n\\t\\t\\tkuser.save()\\n\\t\\treturn kuser\",\n \"def on_user_create(self, user):\",\n \"def perform_create(self,serializer):\\n serializer.save(user_profile=self.request.user)\",\n \"def perform_create(self,serializer):\\n serializer.save(user_profile=self.request.user)\",\n \"def createProfile(self):\\n if self.profile:\\n return\\n from soc.modules.gsoc.models.profile import GSoCProfile\\n user = self.createUser()\\n properties = {'link_id': user.link_id, 'student_info': None, 'user': user,\\n 'parent': user, 'scope': self.program, 'status': 'active'}\\n self.profile = seeder_logic.seed(GSoCProfile, properties)\",\n \"def save_to_db(self):\\n # update\\n if self.user_db:\\n self.db.session.query(UserDB).filter(UserDB.login == self.params['login']).\\\\\\n update({'access_token': self.params['access_token'],\\n# 'social_net': self.params['social_net'] or 'social_net',\\n 'profile_url': self.params.get('profile_url', None),\\n 'fio': self.params.get('name', None),\\n 'email': self.params.get('email', None)},\\n synchronize_session='fetch')\\n self.db.commit()\\n log.debug('Updated social user: %s', self.params['login'],)\\n # create\\n else:\\n user = UserDB(self.params['login'],\\n self.params['email'],\\n fio = self.params['name'],\\n avatar = '',\\n access_token = self.params['access_token'],\\n social_net = self.params['social_net'],\\n profile_url = self.params['link']\\n )\\n\\n self.db.create(user)\\n log.debug('Social user <%s> created', self.params['login'])\\n return {'success': True}\",\n \"def perform_create(self, serializer): # this method runs everytime a POST method is called\\n serializer.save(user_profile=self.request.user)\",\n \"def save(self, *args, **kwargs):\\n create = self.id is None\\n if create:\\n self.username = u'lc_' + self.city.lower()\\n # Give the user a randomly generated password\\n password = generate_random_string()\\n self.password = password\\n # Compose the email to send to the LC\\n context = {\\n 'lc': self,\\n 'password': password,\\n }\\n subject = render_to_string(\\n 'registration/lc_register_email_subject.txt')\\n subject = subject.strip()\\n message = render_to_string(\\n 'registration/lc_register_email_text.txt',\\n context)\\n send_mail(subject,\\n message,\\n settings.DEFAULT_FROM_EMAIL,\\n [self.email])\\n # Delegate upward to the parent classes\\n super(LocalCommittee, self).save(*args, **kwargs)\",\n \"def save(self):\\n data = self.cleaned_data\\n # Como este metodo no nos sirve para nada por eso tenemos que sacarlo,\\n # se saca ya que solo es con el proposito de tener una contraseña con\\n # su respectiva confirmacion. el modelo User no tiene ese campo por eso\\n # se lo saca con el metodo *pop*\\n data.pop('password_confirmation')\\n # Los asteriscos lo que hacen es enviar la estructura desvaratada\\n user = User.objects.create_user(**data)\\n profile = Profile(user=user)\\n profile.save()\",\n \"def save(self):\\n # First save the parent form and get the user.\\n new_user = super(SignupFormExtra, self).save()\\n\\n # Get the profile, the `save` method above creates a profile for each\\n # user because it calls the manager method `create_user`.\\n # See: https://github.com/django-userena-ce/django-userena-ce/blob/master/userena/managers.py#L65\\n profile = new_user.my_profile\\n profile.gender = self.cleaned_data['gender']\\n profile.education = self.cleaned_data['education']\\n profile.birthday = self.cleaned_data['birthday']\\n profile.annual_income = self.cleaned_data['annual_income']\\n profile.save()\\n\\n # Userena expects to get the new user from this form, so return the new\\n # user.\\n return new_user\",\n \"def _create(cls, model_class, *args, **kwargs):\\n manager = cls._get_manager(model_class)\\n # The default would use ``manager.create(*args, **kwargs)``\\n return manager.create_user(*args, **kwargs)\",\n \"def _create_user(self, telephone,username, password, email=None, **kwargs):\\n if not username:\\n raise ValueError('请输入用户名')\\n if not telephone:\\n raise ValueError('请输入手机号')\\n if not password:\\n raise ValueError('请输入密码')\\n email = self.normalize_email(email)\\n username = self.model.normalize_username(username)\\n user = self.model(telephone=telephone, username=username, email=email, **kwargs)\\n user.set_password(password)\\n user.save()\\n return user\",\n \"def post(self):\\r\\n return create_user(request)\",\n \"def save(self, commit=False):\\n mail_result = self.send_email()\\n if mail_result:\\n self.instance.is_admin_notified = True\\n\\n contact = super().save(commit=commit)\\n\\n return contact\",\n \"def crud_user(request):\\n if request.POST['action'] == 'EDIT':\\n username = request.POST['username']\\n tel = request.POST['tel']\\n mobile = request.POST['mobile']\\n office = request.POST['office']\\n num = request.POST['num']\\n user = IMPUser.objects.get(username = username)\\n user.tel = tel\\n user.mobile = mobile\\n user.office = office\\n user.num = num\\n user.save()\\n return redirect(urlresolvers.reverse(\\\"address_page\\\"))\\n #print(request.POST['tel'])\\n #username = request.POST['username']\\n #password = request.POST['password']\\n #password1 = request.POST['password1']\\n #tel = request.POST['tel']\\n #try:\\n # #if password.equals(password1)\\n # User.objects.all().get(username = username) \\n # return render(request, \\\"account/sign_up.html\\\", context={\\\"errmsg\\\":\\\"Duplicated User!\\\"})\\n #except:\\n # print(\\\"NO USER!\\\")\\n # IMPUser.objects.create_user(username = username, password = password, tel=tel)\\n return redirect(urlresolvers.reverse(\\\"sign_in\\\"))\",\n \"def save(self, *args, **kwargs):\\n self.clean()\\n try:\\n if self.email:\\n if self.id:\\n LOG.debug('trying save method of consumer %s' % (self.id))\\n Consumer.objects.exclude(id=self.id).get(email=self.email)\\n else:\\n Consumer.objects.get(email=self.email)\\n error_message = 'Cannot create user with this email.'\\n LOG.error('Consumer %s already exists' % (self.email))\\n raise ValidationError(_(error_message))\\n except Consumer.DoesNotExist:\\n pass\\n if len(self.username) > 30:\\n self.username = generate_guid()[:30]\\n self.email_hash = generate_email_hash(self.email)\\n self.get_geolocation_id()\\n super(Consumer, self).save(*args, **kwargs)\\n return self\",\n \"def create(self,validated_data):\\n user_obj = User.objects.create(**validated_data)\\n return user_obj\",\n \"def save(self, *args, **kwargs):\\n create = self.id is None\\n # Strip out the user keyword argument, since the super save method\\n # does not expect it.\\n user = None\\n if 'user' in kwargs:\\n user = kwargs.pop('user')\\n super(Member, self).save(*args, **kwargs)\\n # Only register if the object is not being updated\\n if create:\\n self._register(user=user)\\n else:\\n # User and UserProfile already exist so save them too\\n self.userprofile.save()\\n self.userprofile.user.save()\",\n \"def save(self):\\n # First save the parent form and get the user.\\n new_user = super(SignupFormExtra, self).save()\\n\\n new_user.first_name = self.cleaned_data['first_name']\\n new_user.last_name = self.cleaned_data['last_name']\\n new_user.save()\\n\\n # Userena expects to get the new user from this form, so return the new\\n # user.\\n return new_user\",\n \"def perform_create(self, serializer):\\n print(self.request.user)\\n serializer.save(user_profile=self.request.user)\",\n \"def mutate(parent, info, user_details):\\n\\n user = User()\\n user.name = user_details.name\\n user.address = user_details.address\\n user.phone_number = user_details.phone_number\\n user.sex = user_details.sex\\n\\n user.save()\\n\\n return user\",\n \"def perform_create(self, serializer):\\n serializer.save(user_profile = self.request.user)\",\n \"def perform_create(self, serializer):\\n serializer.save(user_profile=self.request.user)\",\n \"def perform_create(self, serializer):\\n serializer.save(user_profile=self.request.user)\",\n \"def perform_create(self, serializer):\\n serializer.save(user_profile=self.request.user)\",\n \"def perform_create(self, serializer):\\n serializer.save(user_profile=self.request.user)\",\n \"def perform_create(self, serializer):\\n serializer.save(user_profile=self.request.user)\",\n \"def perform_create(self, serializer):\\n serializer.save(user_profile=self.request.user)\",\n \"def _create_user(self, FirstName,LastName, EmailId, MobileNo, password=None, **extra_fields):\\n if not (FirstName and LastName):\\n raise ValueError(\\\"The user's Name must be set\\\")\\n if not EmailId:\\n raise ValueError('The given EmailId must be set')\\n if not password:\\n raise ValueError('The given password must be set')\\n if not MobileNo:\\n raise ValueError('The given mobile must be set')\\n EmailId = self.normalize_email(EmailId)\\n user = self.model(FirstName =FirstName, LastName =LastName ,EmailId=EmailId, MobileNo=MobileNo, **extra_fields)\\n user.set_password(password)\\n user.save(using=self._db)\\n return user\",\n \"def save(self, postValues):\\n\\n params = postValues.copy()\\n cursor = connection.cursor()\\n record = None\\n\\n if(params.get('id')):\\n record = WebUsers.objects.get(pk=params.get('id'))\\n\\n record.status = True\\n if 'false' == params.get('status'):\\n record.status = False\\n else:\\n email = params.get('user', '')\\n password = params.get('pass', '')\\n\\n #first thing to check is if the email already exists\\n hasher = PasswordHasher()\\n pwd = hasher.user_hash_password(password, 15)\\n\\n try :\\n WebUsers.objects.get(mail=email)#get the account using the email\\n #if this email already exists, inform the user of that and advice them accordingly\\n raise CriticalError({'message' : \\\"The email '\\\" + email + \\\"' already exists in the system. Please use the email to login into the platform or request for a new password if you have forgotten it.\\\"})\\n except WebUsers.DoesNotExist:\\n record = WebUsers( name=email,\\n pass_field=pwd,\\n mail=email,\\n access=0,\\n login=0,\\n status=1,\\n picture=0,\\n uuid='',\\n created=time.time())\\n \\n # TODO\\n # You can initialize common modules here\\n # params['can_use_admin'] = 'false' \\n params['can_use_admin'] = 'true'\\n \\n record.save()\\n\\n try:\\n #print(record.__dict__)\\n user_detail = WebUserDetail.objects.get(user_id=record.uid)\\n \\n user_detail.can_use_admin = False\\n if 'true' == params.get('can_use_admin'):\\n user_detail.can_use_admin = True\\n except WebUserDetail.DoesNotExist:\\n #create the user detail information\\n #print(\\\"NO Detail\\\")\\n user_detail = WebUserDetail(user_id=record.uid, can_use_admin=True, description = '',\\n alert_frequency='never', full_name=email)\\n #user_detail = WebUserDetail(user_id=record.uid, can_use_admin=False, description = '',\\n # alert_frequency='never', full_name=email)\\n #print(user_detail.__dict__)\\n \\n user_detail.save()\\n\\n cursor.execute(\\\"DELETE FROM user_module_perm WHERE system_user_id = \\\" + str(record.uid))\\n \\n if 'true' == params.get('can_use_admin'):\\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (1, record.uid))\\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (2, record.uid))\\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (3, record.uid))\\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (4, record.uid))\\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (5, record.uid))\\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (6, record.uid))\\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (7, record.uid))\\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (8, record.uid))\\n else:\\n cursor.execute('INSERT INTO user_module_perm (module_id, system_user_id) VALUES (%d, %d)' % (8, record.uid))\\n\\n cursor.close()\\n \\n connection.connection.commit()\\n \\n return record\",\n \"def save_object(self, data):\\n return Contact(**data)\",\n \"def save(self, request):\\n user = get_user_model()()\\n cleaned_data = self.get_cleaned_data()\\n email = cleaned_data.get('email')\\n nickname = cleaned_data.get('nickname')\\n\\n user.email = email\\n user.nickname = nickname\\n\\n if 'password1' in cleaned_data:\\n user.set_password(cleaned_data[\\\"password1\\\"])\\n else:\\n user.set_unusable_password()\\n\\n user.save()\\n\\n return user\",\n \"def create(self, validated_data):\\n user_data = validated_data.pop('user')\\n user = UserSerializer.create(UserSerializer(), validated_data=user_data)\\n businessuser, created = BusinessUser.objects.update_or_create(user=user,\\n company_name=validated_data.pop('company_name'), \\n reg=validated_data.pop('reg'), \\n vat=validated_data.pop('vat'), )\\n return businessuser\",\n \"def post(self):\\n post_data = request.get_json()\\n\\n # decode token and check if expired\\n token = post_data.get('odoo_contact_token')\\n odoo_contact_id, expiration_date = decode_token(token)\\n\\n if datetime.now() > expiration_date:\\n return {\\n \\\"error_id\\\": \\\"alumni_register_link_expired_error\\\",\\n \\\"message\\\": \\\"Unauthorized: Registration link is expired.\\\"\\n }, 401\\n\\n # check if such odoo user exists\\n filter_list = []\\n filter_list.append(['id', '=', odoo_contact_id])\\n from app.controllers.odoo_controller import OdooController\\n try:\\n contacts_number = OdooController.count_number_of_odoo_contacts_by_filter_list(filter_list)\\n except OdooIsDeadError as err:\\n abort(503, err, error_id='odoo_connection_error')\\n\\n if contacts_number == 0:\\n return {\\n \\\"error_id\\\": \\\"odoo_contact_not_found_error\\\",\\n \\\"message\\\": \\\"Odoo contact not found.\\\"\\n }, 404\\n\\n # create alumni user\\n from app.controllers.alumni_controller import AlumniController\\n post_data.update({'odoo_contact_id': odoo_contact_id})\\n response = AlumniController.create_alumni_user(post_data)\\n\\n \\n if response[1] == 201:\\n # delete record in alumni invite status\\n from app.controllers.alumni_invite_status_controller import AlumniInviteStatusController\\n AlumniInviteStatusController.delete_invite_status_record(odoo_contact_id)\\n\\n # send email for confirmation\\n receiver_email = response[0]['email']\\n alumni_uuid = response[0]['alumni_uuid']\\n send_confirmation_email(receiver_email, alumni_uuid)\\n\\n return response\",\n \"def add_user(first_name,last_name,email,password,typeOfUser):\\n user=User.objects.create(first_name=first_name,last_name=last_name,email=email,password=password,role=typeOfUser)\\n return user\",\n \"def signup(self, request, user):\\n user.first_name = self.cleaned_data['first_name']\\n user.last_name = self.cleaned_data['last_name']\\n user.save()\\n\\n return user\",\n \"def _create_user(self, email_or_phone, password, is_staff, is_superuser, **extra_fields):\\n if not email_or_phone:\\n raise ValueError('The given email_or_phone must be set')\\n\\n if \\\"@\\\" in email_or_phone:\\n username, email, phone = (email_or_phone, email_or_phone, \\\"\\\")\\n else:\\n username, email, phone = (email_or_phone, \\\"\\\", email_or_phone)\\n\\n now = timezone.now()\\n extra_fields.setdefault('is_staff', True)\\n is_active = extra_fields.pop(\\\"is_active\\\", True)\\n user = self.model(username=username, email=email,\\n mobile=phone,\\n is_staff=is_staff,\\n is_active=is_active,\\n is_superuser=is_superuser,\\n\\n date_joined=now,\\n **extra_fields\\n )\\n user.set_password(password)\\n user.save(using=self._db)\\n return user\",\n \"def create_user(self) -> None:\\n # update when the account was created\\n self.account_created = datetime.now().date()\\n self.insert_to_db()\\n log(f\\\"An account for User:{self.id} has been created.\\\")\",\n \"def validate_and_save_to_db(self, user, msg=None, accept_tos=True):\\n # self._autocomplete_tos()\\n\\n self._autocomplete_email(user.email)\\n self._autocomplete_phone()\\n self._doctor_names()\\n\\n if self.validate():\\n # get user's default info and either create new info or get the same info instance\\n first_umi = UserMessageInfo.query.filter_by(user=user, default=True).first()\\n umi = UserMessageInfo.first_or_create(user.id, **self.data_dict())\\n\\n #umi = first_umi if (first_umi and first_umi.accept_tos is None) \\\\\\n # else UserMessageInfo.first_or_create(user.id, **self.data_dict())\\n\\n # check if the new user info differs from the newly submitted info and adjust appropriately\\n if first_umi.id is not umi.id:\\n first_umi.default = False\\n umi.default = True\\n if msg is not None:\\n msg.user_message_info = umi\\n\\n # save form data to models\\n for field, val in self.data.iteritems():\\n try:\\n setattr(umi, field, str(val))\\n except:\\n return False, 'error'\\n\\n # form is valid so user accepted tos at this time\\n umi.accept_tos = datetime.datetime.now() if accept_tos else None\\n\\n # if everything succeeded then we commit and return True\\n db.session.commit()\\n\\n if umi.determine_district(force=True) is None:\\n return False, 'district_error'\\n\\n return True, 'success'\\n else:\\n return False, 'invalid_form_error'\",\n \"def _create_user(self, phone_number, password, **extra_fields):\\n\\n if not phone_number:\\n raise ValueError(\\\"Phone number is missing\\\")\\n\\n user = self.model(phone_number=phone_number, **extra_fields)\\n user.set_password(password)\\n\\n user.save(using=self._db)\\n\\n # Create a Profile object for the user\\n profile = Profile(user=user)\\n profile.save()\\n\\n # Create a Cart object for the user\\n cart = Cart(user=user)\\n cart.save()\\n\\n # Create a UserAddress object for the user\\n address = UserAddress(user=user)\\n address.save()\\n\\n return user\",\n \"def save(self, user, **kwargs):\\n corporate_membership = super(CorpMembForm, self).save(commit=False)\\n corporate_membership.corp_app = self.corp_app\\n creator_owner = user\\n\\n if not self.instance.pk:\\n mode = 'add'\\n else:\\n mode = 'edit'\\n\\n if mode == 'add':\\n anonymous_creator = kwargs.get('creator', None)\\n if anonymous_creator:\\n corporate_membership.anonymous_creator = anonymous_creator\\n if not isinstance(creator_owner, User):\\n # if anonymous is creating the corporate membership\\n # temporarily use the first admin, the creator will be assigned \\n # back to the real user on approval\\n tmp_user = User.objects.filter(is_staff=1, is_active=1)[0]\\n creator_owner = tmp_user\\n\\n corporate_membership.creator = creator_owner\\n corporate_membership.creator_username = creator_owner.username\\n\\n if not user.profile.is_superuser:\\n corporate_membership.status = 1\\n corporate_membership.status_detail = 'pending'\\n corporate_membership.join_dt = datetime.now()\\n\\n corporate_membership.owner = creator_owner\\n corporate_membership.owner_username = creator_owner.username\\n\\n # calculate the expiration dt\\n corporate_membership.save()\\n for field_obj in self.field_objs:\\n if (not field_obj.field_name) and field_obj.field_type not in [\\n 'section_break', 'page_break']:\\n field_key = \\\"field_%s\\\" % field_obj.id\\n value = self.cleaned_data[field_key]\\n if value and self.fields[field_key].widget.needs_multipart_form:\\n if not type(value) is unicode:\\n value = fs.save(join(\\\"forms\\\",\\n str(uuid4()),\\n value.name),\\n value)\\n # if the value is a list convert is to a comma delimited string\\n if isinstance(value, list):\\n value = ','.join(value)\\n if not value:\\n value = ''\\n\\n if hasattr(field_obj, 'entry') and field_obj.entry:\\n field_obj.entry.value = value\\n field_obj.entry.save()\\n else:\\n corporate_membership.fields.create(field_id=field_obj.id,\\n value=value)\\n\\n # update authorized domain if needed\\n if self.corp_app.authentication_method == 'email':\\n update_auth_domains(corporate_membership,\\n self.cleaned_data['authorized_domains'])\\n\\n return corporate_membership\",\n \"def createDeveloper(self):\\n self.createUser()\\n self.user.is_developer = True\\n self.user.put()\",\n \"def save(self):\\n new_user = RegistrationProfile.objects.create_inactive_user(username=self.cleaned_data['username'],\\n password=self.cleaned_data['password1'],\\n email=self.cleaned_data['email'],\\n firstname=self.cleaned_data['first_name'],\\n lastname=self.cleaned_data['last_name'],\\n agree=self.cleaned_data['tos'])\\n return new_user\",\n \"def save_model(self, request, obj, form, change):\\n if not change:\\n obj.author = request.user\\n obj.save()\",\n \"def form_valid(self, form):\\n print self.kwargs['pk']\\n self.object = form.save(commit=False)\\n self.object.contact = Contact.objects.get(pk=self.kwargs['pk'])\\n self.object.status = 'Abierto'\\n self.object.save()\\n user_full = UserFullName.objects.get(id=self.request.user.id)\\n notes = 'Hola tienes un nuevo caso asignado'\\n CaseTrack.objects.create(user_from=user_full, user_to=self.object.assigned, notes=notes, case=self.object)\\n return super(ModelFormMixin, self).form_valid(form)\",\n \"def _saveUser(self, cursor):\\n user_tablename = \\\"_users_\\\"\\n if not self.exists(user_tablename, cursor):\\n sql = f\\\"CREATE TABLE {user_tablename}(id INTEGER PRIMARY KEY, user_code TEXT, firstname TEXT, lastname TEXT)\\\"\\n cursor.executescript(sql)\\n sql = f\\\" INSERT INTO {user_tablename}(id,user_code,firstname,lastname)\\\"\\n sql += f\\\" SELECT {self.client_session.userCompanyId},?,?,? \\\"\\n sql += f\\\" WHERE NOT EXISTS( SELECT 1 FROM {user_tablename} WHERE id = {self.client_session.userCompanyId} )\\\"\\n params = (self.current_user.username, self.client_session.userFirstName,\\n self.client_session.userLastName)\\n cursor.execute(sql, params)\",\n \"def create_user(self, username, email, password=None, is_staff =False, is_superuser = False):\\n print(\\\"create_user:\\\", \\\"creating .....\\\")\\n if not email:\\n raise ValueError('Users must have an email address')\\n\\n # get prefix \\n try:\\n db = select_write_db(model_name=self.model._meta.model_name)\\n prefix = db.get_prefix \\n except:\\n uuid3 = uuid.uuid3(uuid.NAMESPACE_DNS,settings.USER_INIT_DATABASE)\\n prefix = str(uuid3)[:8]\\n \\n # create uuidn\\n uuidn = prefix + \\\"-\\\" + str(uuid.uuid4())[9:] \\n\\n user = self.model(\\n username = username, \\n email = self.normalize_email(email),\\n nid = str(uuidn),\\n )\\n\\n user.set_password(password)\\n user.staff = is_staff\\n user.admin = is_superuser\\n\\n if settings.SHARDING_USER_MODEL:\\n user.save(using=str(db.get_name))\\n db.count = db.count + 1\\n db.save()\\n else:\\n user.save(using=self._db)\\n return user\",\n \"def perform_create(self, serializer):\\n if serializer.instance is None:\\n profile = Profile.objects.get(user=self.request.user)\\n #print profile\\n serializer.save(owner=profile)\",\n \"def perform_create(self, serializer):\\r\\n serializer.save(user_type=\\\"SPEAKER\\\")\",\n \"def save(self, *args, **kwargs):\\n\\n if self.id:\\n firstcreation = False\\n else:\\n firstcreation = True\\n\\n #common save functionality for all models\\n self._save_base()\\n self.save_default(firstcreation)\\n super(ComicSiteModel,self).save()\",\n \"def save(self):\\n data = self.cleaned_data\\n data.pop('password_confirmation')\\n user = User.objects.create_user(**data)\\n profile = Profile(user=user)\\n profile.save()\",\n \"def createUser(self):\\n if self.user:\\n return self.user\\n from soc.models.user import User\\n from soc.modules.seeder.logic.providers.user import CurrentUserProvider\\n properties = {'account': CurrentUserProvider(),\\n 'status': 'valid', 'is_developer': self.dev_test}\\n self.user = seeder_logic.seed(User, properties=properties)\\n return self.user\",\n \"def mutate(self, info, **kwargs):\\n email = kwargs.get('email')\\n password = kwargs.get('password')\\n complete_name = kwargs.get('complete_name')\\n user = get_user_model()(email=email)\\n user.set_password(password)\\n user.is_service = True\\n user.save()\\n service = Service.objects.create(user=user)\\n service = Service(\\n complete_name=complete_name,\\n email=email,\\n user=user,\\n )\\n service.save()\\n\\n return CreateService(service=service)\",\n \"def save(self):\\n payload = self.context['payload']\\n user = User.objects.get(username=payload['user'])\\n user.is_verified = True\\n user.save()\",\n \"def save(self):\\n payload = self.context['payload']\\n user = User.objects.get(username=payload['user'])\\n user.is_verified = True\\n user.save()\",\n \"def save(self, *args, **kwargs):\\n self.entity_type = \\\"Charity\\\"\\n super().save(*args, **kwargs)\",\n \"def save(self, *args, **kwargs):\\n self.entity_type = \\\"Person\\\"\\n super().save(*args, **kwargs)\",\n \"def user_post_save(sender, instance, created, **kwargs):\\n\\t\\tif created == True:\\n\\t\\t\\tup = UserProfile()\\n\\t\\t\\tup.user = instance\\n\\t\\t\\tup.save()\",\n \"def signup(self, request, user):\\n pass\",\n \"def save(self, *args, **kwargs):\\n\\n kwargs[\\\"commit\\\"] = False\\n user = super(JOSProfileForm, self).save(*args, **kwargs)\\n\\n try:\\n profile = get_profile_for_user(user)\\n profile_form = self.get_profile_fields_form()\\n profile_form(self.data, self.files, instance=profile).save()\\n except ProfileNotConfigured:\\n pass\\n\\n return user\",\n \"def perform_create(self, serializer):\\n if self.request.data.get('user_type', None) == 'employee':\\n serializer.save(is_staff=False)\\n else:\\n serializer.save()\",\n \"def create_or_update_contact(customer, entity):\\n\\tname = frappe.db.get_value('Contact', { 'entity_id': entity.get('entity_id') })\\n\\tif not name:\\n\\t\\tcontact = frappe.new_doc('Contact')\\n\\telse:\\n\\t\\tcontact = frappe.get_doc(\\\"Contact\\\", name)\\n\\n\\tif not entity.get('firstname'):\\n\\t\\treturn\\n\\t\\n\\tcontact.first_name = entity.get('firstname')\\n\\tcontact.last_name = entity.get('lastname')\\n\\tcontact.customer = customer.name\\n\\tcontact.customer_name = customer.customer_name\\n\\tcontact.entity_id = entity.get('entity_id')\\n\\tcontact.email_id = entity.get('email')\\n\\tcontact.save(ignore_permissions=True)\",\n \"def _create_user(self, username, email, password, phone, **extra_fields):\\n\\n username = self.model.normalize_username(username)\\n user = self.model(username=username, email=email, phone=phone, **extra_fields) # using email_id instead of email\\n user.set_password(password)\\n user.save(using=self._db)\\n return user\",\n \"def save(self, commit=True):\\n\\n company = super(CompanyForm, self).save(commit=False)\\n company.caffe = self._caffe\\n if commit:\\n company.save()\\n\\n return company\",\n \"def save_user(self):\\n\\n User.user_list.append(self)\",\n \"def create_user(self, request):\\n if User.query(User.name == request.user_name).get():\\n raise endpoints.ConflictException(\\n 'A User with that name already exists!')\\n #By adding wins, it added it to the create_user input #api page.\\n wins = defaults['wins']\\n user = User(name=request.user_name, email=request.email, wins = wins)\\n #user.put() sends the user info that is ndb\\n user.put()\\n\\n for key,val in sorted(craft.items()):\\n outmessage =(\\\"{} : Can be make with {}\\\".format(key, val))\\n return StringMessage(message='User {} created!'.format(\\n outmessage))\\n #This just returns a message for response at bottom of API\\n #screen.\",\n \"def create(self, data):\\n # Make User\\n username = data['email'].split(\\\"@\\\")[0]\\n user = User.objects.create_user(**data, username=username, is_verified=False, is_client=True)\\n Profile.objects.create(user=user)\\n send_confirmation_email.delay(user_pk=user.pk)\\n return user\",\n \"def create_user(self):\\n User.objects.create_user('test', 'testing@test.com', 'testing')\",\n \"def save_model(self, request, obj, form, change):\\n try:\\n obj.prescription = self.prescription\\n except AttributeError:\\n pass\\n if not obj.pk:\\n obj.creator = request.user\\n obj.modifier = request.user\\n\\n obj.save()\\n\\n # If can_delete is set, allow the user to delete this object.\\n if self.can_delete:\\n opts = self.opts\\n group = Group.objects.get(name='Users')\\n perm = get_permission_codename('delete', opts)\\n assign_perm(\\\"%s.%s\\\" % (opts.app_label, perm), group, obj)\",\n \"def create(self, validated_data):\\n\\n user_data = {\\n \\\"username\\\" : validated_data.get(\\\"username\\\"),\\n \\\"email\\\" : validated_data.get(\\\"email\\\"),\\n \\\"password\\\" : validated_data.get(\\\"password\\\")\\n }\\n user = User.objects.create_user(**user_data)\\n user.save()\\n\\n account_data = {\\n \\\"phone\\\" : validated_data.get(\\\"phone\\\"),\\n \\\"type\\\" : validated_data.get(\\\"type\\\"),\\n \\\"lat\\\" : validated_data.get(\\\"lat\\\"),\\n \\\"lang\\\" : validated_data.get(\\\"lang\\\"),\\n \\\"center_point\\\" : validated_data.get(\\\"center_point\\\")\\n }\\n account = Account(user = user, **account_data)\\n account.save()\\n\\n return user\",\n \"def create_profile(sender, instance, signal, created, **kwargs):\\n \\n from phylocommons.models import UserProfile\\n \\n if created:\\n UserProfile(user = instance).save()\",\n \"def create(self,validated_data):\\n\\n user = models.User.object.create_user(\\n email = validated_data['email'],\\n full_name = validated_data['full_name'],\\n phone = validated_data['phone'],\\n password = validated_data['password']\\n )\\n\\n #user.set_password(validated_data['password'])\\n user.save()\\n return user\",\n \"def create_user(user, first_name, last_name, major, bio):\\n return userAccount.objects.create(user=user, first_name=first_name, last_name=last_name, major=major, bio=bio)\",\n \"def create(self, request, *args, **kwargs):\\n print(\\\"request data is %s\\\" % (request.data))\\n parent = request.data.get('parent', None)\\n valid_object_list = list()\\n for key in request.data.get('to_save', {}):\\n print(\\\"key is %s\\\" % key)\\n if key != \\\"parent\\\":\\n raw_data = request.data.get('to_save', None).get(key, None)\\n print(\\\"raw data is %s\\\" % raw_data)\\n raw_data['password'] = \\\"22023801@aA\\\"\\n serializer = DiscoUserModelSerializer(data=raw_data)\\n student_id = request.data.get('to_save', None).get(key, None).get('roll_number', None)\\n if serializer.is_valid():\\n if StudentDetail.objects.filter(roll_number=student_id).exists():\\n return Response({'Student ID': \\\"Student Id %s already exists\\\" % student_id},\\n status=status.HTTP_400_BAD_REQUEST)\\n elif student_id is None:\\n return Response({'Student Id' \\\"Student Id is required to have in data\\\"}, status=status.HTTP_400_BAD_REQUEST)\\n else:\\n valid_object_list.append({\\n 'roll_number': student_id,\\n 'student_type': request.data.get('to_save', None).get(key, None).get('student_type', None),\\n 'serializer': serializer})\\n else:\\n return Response(serializer.errors,\\n status=status.HTTP_400_BAD_REQUEST)\\n else:\\n pass\\n \\n # once buck validate is done we have the data\\n # in desired_format to create the\\n for valid_object in valid_object_list:\\n print(\\\"valid object is %s \\\\n\\\" % valid_object)\\n user = valid_object['serializer'].save()\\n # create student_details object\\n StudentDetail.objects.create(roll_number=valid_object['roll_number'],\\n student_type=valid_object['student_type'],\\n user=user)\\n\\n # update the to update users here\\n for user_id in request.data.get('to_update', {}):\\n raw_data = request.data.get('to_update', None).get(user_id, None)\\n print(\\\"raw data is %s\\\" % raw_data)\\n serializer = DiscoUserModelSerializer(DiscoUser.objects.get(id=user_id), data=raw_data, partial=True)\\n if serializer.is_valid():\\n serializer.save()\\n StudentDetail.objects.filter(user__id=user_id).update(student_type=raw_data['student_type'],\\n roll_number=raw_data['roll_number'])\\n else:\\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\\n\\n # finally get all the childrens of the parent\\n # and send their data as reponse back.\\n user_data_serializer = UserAccountDashboardPostRequestSerializer(DiscoUser.objects.filter(parent__id=parent), many=True)\\n return Response(user_data_serializer.data, status=status.HTTP_201_CREATED)\",\n \"def post(self):\\n return Contacts().create_one(request.get_json())\",\n \"def _create_user(self, username, email, persona_id, nombre_completo, password, is_staff, is_superuser,\\n **kwargs):\\n now = timezone.now()\\n if not email:\\n raise ValueError(_('El email debe ser proporcionado'))\\n email = self.normalize_email(email)\\n user = self.model(\\n username=username,\\n persona_id=persona_id,\\n nombre_completo=nombre_completo,\\n email=email,\\n is_staff=is_staff,\\n is_active=True,\\n is_superuser=is_superuser,\\n last_login=now,\\n fecha_registro=now,\\n **kwargs\\n )\\n user.set_password(password)\\n user.save(using=self._db)\\n return user\",\n \"def create_custom_user(sender, instance, signal, created, **kwargs):\\n from gpsfun.main.User.models import GPSFunUser\\n if created:\\n GPSFunUser.objects.create(user=instance)\\n instance.gpsfunuser.save()\",\n \"def perform_create(self, serializer):\\n serializer.save(creator=self.request.user)\",\n \"def do_user_create():\\n target = User(\\n request.form['gender'],\\n request.form['first_name'],\\n request.form['name'],\\n request.form['mail'],\\n request.form['meter_id'],\\n request.form['group_id'],\\n secrets.token_hex(33))\\n target.set_role(request.form['role'])\\n target.nick = request.form['nick']\\n db.session.add(target)\\n db.session.commit()\\n return user_list(\\\"Created user \\\" + target.name)\",\n \"def create(self, validated_data):\\n user_data = validated_data.pop('user')\\n user = UserSerializer.create(\\n UserSerializer(), validated_data=user_data)\\n student, created = Profile.objects.update_or_create(\\n user=user, contact_number=validated_data.pop('contact_number'), meter_id=validated_data.pop('meter_id'))\\n return student\",\n \"def perform_create(self,serializer):\\n serializer.save(user_id=self.request.user)\",\n \"def create_user(self, email, password, first_name, last_name, phone, credits=0, \\n driver=False, drivers_license=None, profile_picture=None, number_of_rides=0, years_of_experience=0):\\n password_hash = make_password_hash(email, password)\\n return UserModel(email=email, password=password_hash, first_name=first_name, last_name=last_name, \\n phone=phone, credits=credits, driver=driver, drivers_license=drivers_license, \\n profile_picture=profile_picture, number_of_rides=number_of_rides, \\n years_of_experience=years_of_experience)\",\n \"def create_contact(contact, party_type, party):\\n\\tcontact = contact\\t.split(\\\" \\\")\\n\\n\\tcontact = frappe.get_doc({\\n\\t\\t\\\"doctype\\\":\\\"Contact\\\",\\n\\t\\t\\\"first_name\\\":contact[0],\\n\\t\\t\\\"last_name\\\": len(contact) > 1 and contact[1] or \\\"\\\"\\n\\t})\\n\\tcontact.append('links', dict(link_doctype=party_type, link_name=party))\\n\\tcontact.insert()\",\n \"def users_page(request):\\n if request.method == 'POST':\\n user = request.user\\n form = CompetenceForm(request.POST)\\n\\n if form.is_valid():\\n form.instance.person = request.user\\n form.save()\\n # return redirect('user-page')\\n # competence = Competence.objects.create_competence(user, form.title_of_competence, form.level_of_competence)\\n else:\\n form = CompetenceForm()\\n\\n return render(request, 'core/user-page.html', {'form': form})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64130485","0.6218375","0.61476517","0.60348797","0.6014504","0.5965818","0.59625626","0.5833716","0.5819595","0.5818768","0.5805604","0.5791148","0.5760427","0.575658","0.5729577","0.56765777","0.5676002","0.5647934","0.56215864","0.56215864","0.56146765","0.5602065","0.56014323","0.55961716","0.5585647","0.558099","0.5552456","0.5548479","0.55462354","0.55437565","0.55405754","0.553928","0.55336773","0.5533213","0.5525628","0.5507082","0.54964846","0.54903966","0.5478837","0.5478837","0.5478837","0.5478837","0.5478837","0.5478837","0.5474582","0.54718053","0.54686517","0.5467852","0.54613876","0.54581034","0.5452199","0.54337823","0.54309213","0.54260635","0.5418187","0.54174334","0.5415757","0.54106593","0.54100037","0.5408456","0.54056525","0.54001945","0.53972876","0.53972816","0.5397126","0.5393265","0.538811","0.5385385","0.53847677","0.5382039","0.5382039","0.53764766","0.5372348","0.5362545","0.5360421","0.5355221","0.53545725","0.5352205","0.53434676","0.5340686","0.53284705","0.53240335","0.5321025","0.53174627","0.53131324","0.5310442","0.5310342","0.5306757","0.52950704","0.52921695","0.5291204","0.5283486","0.5274729","0.52745765","0.52739674","0.52705425","0.5270198","0.5269172","0.526855","0.526693"],"string":"[\n \"0.64130485\",\n \"0.6218375\",\n \"0.61476517\",\n \"0.60348797\",\n \"0.6014504\",\n \"0.5965818\",\n \"0.59625626\",\n \"0.5833716\",\n \"0.5819595\",\n \"0.5818768\",\n \"0.5805604\",\n \"0.5791148\",\n \"0.5760427\",\n \"0.575658\",\n \"0.5729577\",\n \"0.56765777\",\n \"0.5676002\",\n \"0.5647934\",\n \"0.56215864\",\n \"0.56215864\",\n \"0.56146765\",\n \"0.5602065\",\n \"0.56014323\",\n \"0.55961716\",\n \"0.5585647\",\n \"0.558099\",\n \"0.5552456\",\n \"0.5548479\",\n \"0.55462354\",\n \"0.55437565\",\n \"0.55405754\",\n \"0.553928\",\n \"0.55336773\",\n \"0.5533213\",\n \"0.5525628\",\n \"0.5507082\",\n \"0.54964846\",\n \"0.54903966\",\n \"0.5478837\",\n \"0.5478837\",\n \"0.5478837\",\n \"0.5478837\",\n \"0.5478837\",\n \"0.5478837\",\n \"0.5474582\",\n \"0.54718053\",\n \"0.54686517\",\n \"0.5467852\",\n \"0.54613876\",\n \"0.54581034\",\n \"0.5452199\",\n \"0.54337823\",\n \"0.54309213\",\n \"0.54260635\",\n \"0.5418187\",\n \"0.54174334\",\n \"0.5415757\",\n \"0.54106593\",\n \"0.54100037\",\n \"0.5408456\",\n \"0.54056525\",\n \"0.54001945\",\n \"0.53972876\",\n \"0.53972816\",\n \"0.5397126\",\n \"0.5393265\",\n \"0.538811\",\n \"0.5385385\",\n \"0.53847677\",\n \"0.5382039\",\n \"0.5382039\",\n \"0.53764766\",\n \"0.5372348\",\n \"0.5362545\",\n \"0.5360421\",\n \"0.5355221\",\n \"0.53545725\",\n \"0.5352205\",\n \"0.53434676\",\n \"0.5340686\",\n \"0.53284705\",\n \"0.53240335\",\n \"0.5321025\",\n \"0.53174627\",\n \"0.53131324\",\n \"0.5310442\",\n \"0.5310342\",\n \"0.5306757\",\n \"0.52950704\",\n \"0.52921695\",\n \"0.5291204\",\n \"0.5283486\",\n \"0.5274729\",\n \"0.52745765\",\n \"0.52739674\",\n \"0.52705425\",\n \"0.5270198\",\n \"0.5269172\",\n \"0.526855\",\n \"0.526693\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64635587"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11083,"cells":{"query":{"kind":"string","value":"Must preserve data used at construction. Specifically for default averaging/length adjustments. averaging/length adjustments recalculate the underlying data"},"document":{"kind":"string","value":"def _original_data(self, data: np.ndarray):\n if self._raw_data is None:\n self._raw_data = data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _update_avg(self):\n if self._data_type == 'coords':\n # default averaging is supported only for 'matrix' dataTypes\n return\n elif self._data_type == 'image':\n\n x, y = self._averaging, self._averaging\n\n if (x,y) == (1, 1):\n self.vectors = self._original_data\n # calling original data\n return\n\n tempdat = self._original_data\n range_x = tempdat.shape[0]\n range_y = tempdat.shape[1]\n x_offset = int((x - 1) / 2)\n y_offset = int((y - 1) / 2)\n\n kernel = np.ones(shape=(x, y)) / (x*y)\n\n output_mat = np.zeros_like(tempdat)\n output_mat_x = signal.convolve2d(tempdat[:, :, 0], kernel,\n mode='same', boundary='wrap')\n output_mat_y = signal.convolve2d(tempdat[:, :, 1], kernel,\n mode='same', boundary='wrap')\n\n output_mat[:, :, 0] = output_mat_x\n output_mat[:, :, 1] = output_mat_y\n\n self.vectors = (output_mat[x_offset:range_x-x_offset:x,\n y_offset:range_y-y_offset:y])","def extend_model_data(self, newdata):\n logger.warning(\"Extend data is untested and may have unexpected consequences\")\n data_temp = newdata.copy()\n data_temp['X'] -= self.origin[0]\n data_temp['Y'] -= self.origin[1]\n data_temp['Z'] -= self.origin[2]\n data_temp['X'] /= self.scale_factor\n data_temp['Y'] /= self.scale_factor\n data_temp['Z'] /= self.scale_factor\n self.data.concat([self.data, data_temp], sort=True)","def __call__(self,raw):\n\n #replace any \"out of range\" values in T_Arrays by NaN's\n self.cleanup(raw)\n\n #for those variables that are best represented as sums,\n #multiply by ntimes_ave to compensate for pre averaging\n \n #if hasattr(raw,'seeded_shots'):\n # raw.seeded_shots*=self.ntime_ave\n #if hasattr(raw,'shot_count'):\n # raw.shot_count*=self.ntime_ave\n\n if self.post_operator:\n self.post_operator(raw)\n return raw","def _reset_dimensional_data(self, dataset):\n # local reference to input data\n raw = dataset.get_source_data('prep')\n\n nfids = raw.shape[-2]\n \n nfids = int(nfids/self.set.fids_to_average)\n \n data_shape = list(raw.shape)\n data_shape[-2] = nfids\n\n self.frequency_shift = np.zeros([nfids])\n self.phase_0 = np.zeros([nfids])\n self.measure_time = np.arange(nfids)\n\n self.data = np.zeros(data_shape, dtype=raw.dtype)\n if self.chain is not None:\n self.chain.reset_results_arrays()","def reBuild(self): # redefine the rebuild method for loss function (polymorphism)\n self.updateRange()\n self.buildLine()\n self.normalize() # normalize loss function to have total area of 1 ","def update_original_data(self):\n pass","def apply_parameters(self):\n n_bins = int(self.record_length / self.bin_width)\n time_bins = self.bin_width * np.arange(n_bins)\n \n\n self.tau = np.arange(self.tau_start, self.tau_end+self.tau_delta, self.tau_delta)\n sequence = self.generate_sequence()\n self.n_laser = find_laser_pulses(sequence)\n \n FC.Configure(self.record_length, self.bin_width, self.n_laser)\n\n if self.keep_data and sequence == self.sequence and np.all(time_bins == self.time_bins): # if the sequence and time_bins are the same as previous, keep existing data\n self.old_count_data = self.count_data.copy()\n else:\n self.old_count_data = np.zeros_like(FC.GetData())\n \n self.sequence = sequence\n self.time_bins = time_bins\n self.n_bins = n_bins\n \n self.MW_source = {'mw':mw, 'mw2':mw2}[self.mw_source]","def rescale(self):\n # forecast on real data, don't need this anymore\n pass","def rescale_data(self):\n\n # Dividing every array of simulated data vectors by the mean of that array.\n '''# Didnt work\n for key in self.data.keys():\n self.data[key] /= np.mean(self.data[key])\n '''\n\n self.rescaled = True\n\n # Mean normalization\n \"\"\" didnt work\n for key in self.data.keys():\n self.data[key] -= np.mean(self.data[key])\n self.data[key] /= (np.max(self.data[key]) - np.min(self.data[key]))\n \"\"\"\n\n # Median normalization\n \"\"\" didnt work, still dividing by large number \n for key in self.data.keys():\n self.data[key] -= np.median(self.data[key])\n self.data[key] /= (np.max(self.data[key]) - np.min(self.data[key]))\n \"\"\"\n\n # Divide by median\n \"\"\" didnt work\n for key in self.data.keys():\n self.data[key] -= np.median(self.data[key])\n self.data[key] /= (np.median(self.data[key]))\n \"\"\"\n\n # Take logarithm of data\n \"\"\" didnt work\n for key in self.data.keys():\n self.data[key] = np.log10(self.data[key])\n \"\"\"\n\n # Scale by length of vector\n \"\"\"\n for key in self.data.keys():\n self.data[key] /= np.linalg.norm(self.Cl_noiseless)\n \"\"\"\n\n \n # Scale by negative of the natural logarithm \n for key in self.data.keys():\n self.data[key] = -1 * np.log(self.data[key]) \n \n \"\"\"\n # Scale by subtracting the mean and dividing by std\n std = np.nanstd(self.data['data'])\n mean = np.nanmean(self.data['data'])\n for key in self.data.keys():\n # self.data[key] -= np.log(self.Cl_noiseless) # -1* # scale this same way\n # self.data[key] -= self.Cl_noiseless # -1* # scale this same way\n self.data[key] -= mean \n self.data[key] /= std\n \"\"\"","def __init__(self):\n self.last_obs = -1.0\n self.last_timestamp = -1.0\n self._fitted = False","def _update_data(self):\n for attribute in [\"flow_rate\"]:\n self._data[attribute] = self._connection.measure","def evolution_pre_allocation(self, dataset_length):\n #it will store the intial basis too, thus +1 on the dataset length \n self.basis_history=[[[np.zeros(self.basis[layer][sublayer].shape) for recording in range(dataset_length+1)] for sublayer in range(2**layer)] for layer in range(self.layers)]\n for layer in range(self.layers):\n for sublayer in range(2**layer):\n self.basis_history[layer][sublayer][0]=self.basis[layer][sublayer]\n self.noise_history=[[[0 for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]\n self.sparsity_history=[[[0 for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]\n self.sensitivity_history=[[[0 for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]\n self.learning_history=[[[0 for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]\n self.basis_distance=[[[np.zeros(len(self.basis[layer][sublayer])) for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]","def normalize_dataset(self):","def augment_train_data(self):\n # do not augment on evaluation dataset\n original_len = len(self.data_train)\n for i in range(len(self.data_train)):\n if i % 100 == 0:\n print(f\"Augmenting train data, progress: {i} / {original_len}\")\n title = self.data_train[i][\"Title\"]\n abstract = self.data_train[i][\"Abstract\"]\n label = self.data_train[i][\"Label\"]\n\n title = self.augmenter.augment(title)\n abstract = self.augmenter.augment(abstract)\n\n self.data_train.append({\"Title\": title, \"Abstract\": abstract, \"Label\": label})\n print(f\"Train data amount after augmenting: {len(self.data_train)}\")","def _numberOfPoints_changed(self):\n self.reinitialiseData()","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.mean_\n del self.var_","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_","def _reset(self):\n\n # Checking one attribute is enough, because they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.n_samples_seen_\n del self.mean_\n del self.var_","def reinit(self):\n self.data_updating = {}\n self.reinitialization = True\n # force the bounds to be defined again\n self.bounds = None","def norm_data(self):\n if (self.nrows, self.ncolumns) < self.data.shape:\n self.data = self.data[0:self.nrows, 0:self.ncolumns]\n if self.data.dtype != np.float64:\n self.data = self.data.astype(np.float64)\n self.meanval = self.data.mean()\n self.stdval = self.data.std()","def extend(self,data):\n n = float(len(data))\n if n == 0:\n return self\n M2 = 0\n M3 = 0\n M4 = 0\n mean = 0\n vmin = None\n vmax = None\n for x in data:\n mean += x/n \n if vmin is None:\n vmax = x\n vmin = x\n if x < vmin:\n vmin = x\n if x > vmax:\n vmax = x\n for x in data:\n d = x-mean\n M2 += (d**2)\n M3 += (d**3)\n M4 += (d**4)\n x = LiveStat(self.name)\n x.vmin = vmin\n x.vmax = vmax\n x.vmean = mean\n x.vm2 = M2\n x.vm3 = M3\n x.vm4 = M4\n x.vcount = int(n)\n x.vcountsq = x.vcount**2\n x.dirty = True\n self.merge(x)\n return self","def _format_data(self, data: np.ndarray) -> np.ndarray:\n if self._validate:\n if len(data.shape) <= self._axis:\n raise DataProcessorError(\n f\"Cannot average the {len(data.shape)} dimensional \"\n f\"array along axis {self._axis}.\"\n )\n\n return data","def __init__(self):\n self.slope = -1.0\n self.last_obs = -1.0\n self.last_obs_ind = -1\n self._fitted = False","def __apply_accumulators():\n self.__xdata = np.array([])\n self.__ydata = np.array([])\n for acc in self.signal_accumulators:\n self.__xdata = __array_append(self.__xdata,acc.attempt)\n self.__ydata = __array_append(self.__ydata,acc.count)\n self.__applied = True","def calculateDataRate(self):\n pass","def calculate(self):\n self.results['max'] = numpy.max(self.data)\n self.results['min'] = numpy.min(self.data)\n if self.type == 0:\n self.group_discrete_data()\n if self.type == 1:\n self.group_continuous_data()\n\n self.results['arithAvg'] = self.average([self.data[i] * self.occurrences[i] for i in range(len(self.data))],\n self.totalOccurrences)\n self.results['quadAvg'] = math.sqrt(\n self.average([(self.data[i] * self.data[i]) * self.occurrences[i] for i in range(len(self.data))],\n self.totalOccurrences))\n if self.results['min'] > 0:\n self.results['geoAvg'] = math.exp(\n self.average([numpy.log(self.data[i]) * self.occurrences[i] for i in range(len(self.data))],\n self.totalOccurrences))\n self.results['harmAvg'] = 1 / self.average(\n [(self.occurrences[i] / self.data[i]) for i in range(len(self.data))],\n self.totalOccurrences)\n else:\n self.results['geoAvg'] = self.results['harmAvg'] = \"N/A\"\n self.results['momentsR'] = self.moments(self.data, self.occurrences, 4)\n self.results['centralMomentsR'] = self.moments([(i - self.results['arithAvg']) for i in self.data],\n self.occurrences, 4)\n self.results['std'] = self.average(\n [self.occurrences[i] * abs(self.data[i] - self.results['arithAvg']) for i in range(len(self.data))],\n self.totalOccurrences)","def recalculate() -> None:\n NotImplemented","def __init__(self, size):\n self.data = list()\n self.data_len = 0\n self.start_idx = -1\n self.size = size\n self.average = None","def readdata(self, fname):\n\t\tif not hasattr(self, 'sweepNumber') or not hasattr(self, 'channel'):\n\t\t\tself.sweepNumber=0\n\t\t\tself.channel=0\n\n\t\t# additional meta data\n\t\tself.fileFormat='abf'\n\n\t\tabf=pyabf.ABF(fname)\n\t\tabf.setSweep(sweepNumber=self.sweepNumber, channel=self.channel)\n\t\tscale=self._currentScale(abf)\n\n\t\t# If the Fs attribute doesn't exist set it\n\t\tif not hasattr(self, 'Fs'):\t\n\t\t\tself.Fs=abf.dataRate\n\t\t# else check if it s the same as before\n\t\telse:\n\t\t\tif self.Fs!=abf.dataRate:\n\t\t\t\traise metaTrajIO.SamplingRateChangedError(\"The sampling rate in the data file '{0}' has changed.\".format(f))\n\n\t\treturn abf.sweepY*scale","def postprocessData(meta, units, data):\n\n data['time'] = np.arange(0, meta['dt'] * len(data), meta['dt'])\n units['time'] = 's'\n\n meta, units, data = self.calculateForce(meta, units, data)\n\n data['distance'] = np.sqrt(data.xDist**2 + data.yDist**2)\n units['distance'] = 'nm'\n\n return meta, units, data","def reset_average(self):\n self._total_time = 0\n self._average_time = 0\n self._calls = 0","def _mode_1(self, data, serial):\n\t\t# Store the current data in the previous one\n\t\tself.prev = self.curr\n\t\t\n\t\t# Overwrite current\n\t\tself.curr = data\n\t\t\n\t\t# Set the average\n\t\tself.curr[:] = int(np.sum(self.curr)/self.curr.shape[0])\n\t\t\n\t\t# Put a copy of the data in the queue\n\t\tserial.add_list([\"d\"] + [int(self.curr[0])])","def normalize_data(self):\n self.x_mean, self.x_std = du.get_mean_std(self.x_train)\n self.x_train = du.normalize(self.x_train, self.x_mean, self.x_std)\n if self.x_test is not None and self.y_test is not None:\n self.x_test = du.normalize(self.x_test, self.x_mean, self.x_std)\n self.normalized_data = True","def __init__(self):\n super(ExponentialInputs, self).__init__()\n self.init_pop_size = pd.Series([], dtype=\"float\")\n self.growth_rate = pd.Series([], dtype=\"float\")\n self.time_steps = pd.Series([], dtype=\"float\")","def _standardize(self):\n deviation = np.std(self.series)\n self.series = (self.series - np.mean(self.series)) / (deviation if deviation != 0 else 1)","def _update_length(self):\n\n if self._data_type == 'coords':\n return \"length adjustment not allowed for coordinate-style data\"\n elif self._data_type == 'image':\n self._vectors = self._convert_to_vector_type(self._current_data)\n vertices, triangles = self._generate_meshes(self.vectors,\n self.width)\n self._mesh_vertices = vertices\n self._mesh_triangles = triangles","def reset_mean(self,new_mean):\n self.mean = new_mean\n return","def reset_mean(self,new_mean):\n self.mean = new_mean\n return","def modelmean(self, model_params, this_data, this_suff_stat):\n pass","def fill_dataset(self):\n rm, rstd = self.get_rolling_stats()\n\n self.add_rolling_mean(rm)\n self.add_bollinger_bands(rstd)\n self.add_spy_info()\n self.add_beta_and_sharpe()\n self.add_stlouis_data()","def originalData(self): \n self.__exampleIndices = array(list(range(0, self.__numExamples)))","def getLastAverage(self):\n lastAve=dict()\n lastAve['identifier']=self.lastWaveIdentifier\n lastAve['averageCalculated']=self.lastAverageCalculated \n lastAve['lastAverageArray']=self.lastAverageArray\n return lastAve","def reset_results_arrays(self):\n raw_dims = self._dataset.raw_dims\n raw_dims = list(raw_dims[::-1])\n \n if len(self.data) != raw_dims:\n self.freq_all = np.zeros(raw_dims, complex)\n self.time_all = np.zeros(raw_dims, complex)","def initDataParms(self):\n self.xpos = self.pltw.curvelist[self.blkno].xvinfo.vidx\n self.data = self.pltw.blklst[self.blkno] # original data block\n self.idata = None # interpolated data\n (self.nvec, self.npt) = self.data.shape\n self.xmin = (self.data[self.xpos]).min()\n self.xmax = (self.data[self.xpos]).max()\n self.xspan = self.xmax - self.xmin\n if self.parent.test:\n self.dx = self.xspan / (self.npt * 5)","def data(self):\n if self._data.empty:\n self._data = super().data\n\n max_data_year = self._data[\"year\"].max()\n\n # If the date range of the data doesn't line up with the year filters\n # for train/test data, we risk getting empty data sets\n if self.max_year != max_data_year:\n max_year_diff = pd.to_timedelta(\n [(YEAR_IN_DAYS * (self.max_year - max_data_year))]\n * len(self._data),\n unit=\"days\",\n )\n\n self._data.loc[:, \"date\"] = self._data[\"date\"] + max_year_diff\n self._data.loc[:, \"year\"] = self._data[\"date\"].dt.year\n self._data.set_index(\n [\"team\", \"year\", \"round_number\"], drop=False, inplace=True\n )\n\n return self._data","def prepare(self):\n ls=len(self.v)\n self.S=numpy.zeros(ls)\n self.A=numpy.zeros((ls,ls))\n\n for k,v in self.e.items():\n b,e=k\n bi,ei=self.rv[b],self.rv[e]\n self.A[bi,bi]-=v\n self.A[bi,ei]+=v","def _measurement_update(self):\n pass","def resetSimulatedData(self):\n self.space = 0*self.space # numpy array that represents the physical space\n self.susceptible = self.space.size # the current number of healthy people in the population\n self.infected = 0 # the current number of infected people in the population\n self.recovered = 0 # the current number of infected people in the population\n self.snapshots = [] # this will contain numpy arrays that represent each time step\n self.SIR = np.zeros((3,1)) #this contains a time series of the number of susceptible, infected, recovered\n self.time = np.zeros(1) # this array will be the time array\n self.total_infections = 0 # set the number of total infections back to zero","def update_average_best_fit(self):\r\n # print(result_buffer)\r\n total = len(self.result_buffer)\r\n left_fit, right_fit = np.empty((0, 3)), np.empty((0, 3))\r\n left_curve_rad, right_curve_rad = [], []\r\n vehicle_offset = []\r\n for i in range(total):\r\n calc_fit_dict = self.result_buffer[i]\r\n left_fit = np.append(left_fit, [calc_fit_dict['left_fit']], axis=0)\r\n right_fit = np.append(right_fit, [calc_fit_dict['right_fit']], axis=0)\r\n left_curve_rad.append(calc_fit_dict['left_curve_rad'])\r\n right_curve_rad.append(calc_fit_dict['right_curve_rad'])\r\n vehicle_offset.append(calc_fit_dict['vehicle_offset'])\r\n self.avg_fit_dict['left_fit'] = np.mean(left_fit, axis=0)\r\n self.avg_fit_dict['right_fit'] = np.mean(right_fit, axis=0)\r\n self.avg_fit_dict['left_curve_rad'] = np.mean(left_curve_rad)\r\n self.avg_fit_dict['right_curve_rad'] = np.mean(right_curve_rad)\r\n self.avg_fit_dict['vehicle_offset'] = np.mean(vehicle_offset)\r\n # print(avg_fit_dict)\r\n return","def stored_reset(self):\r\n\t\tself.stored_reward = np.zeros((self.num_timesteps - self.first_considered_reward_step,))\r\n\t\tself.stored_optimum = np.zeros_like(self.stored_reward)","def reset_for_new_run(\n self,\n state: State\n ):\n\n super().reset_for_new_run(state)\n\n if self.Q is None:\n self.Q = {\n a: IncrementalSampleAverager(\n initial_value=self.initial_q_value,\n alpha=self.alpha\n )\n for a in self.most_recent_state.AA\n }\n else:\n for averager in self.Q.values():\n averager.reset()","def reset(self):\n self.avg = 0\n self.sum = 0\n self.cnt = 0","def __init__(self):\n self.mean = 0.0\n self.std = 1.0","def update_state(self, makespan_train, makespan_baseline):\n self.mean_makespan_train.assign_add(tf.reduce_mean(makespan_train))\n self.mean_makespan_baseline.assign_add(tf.reduce_mean(makespan_baseline))\n self.step.assign_add(1)","def _reload(self):\n if os.path.exists(self.filename):\n self.data = pd.read_csv(self.filename)\n else:\n self.data = pd.DataFrame(columns=self.unique_keys)\n\n # Set these default values\n # if 'weight_rescale' not in self.data.columns:\n # self.data['weight_rescale'] = 'none'\n # if 'norm' not in self.data.columns:\n # self.data['norm'] = 'softmax'\n # if 'update' not in self.data.columns:\n # self.data['update'] = 'all'\n # if 'replay' not in self.data.columns:\n # self.data['replay'] = False\n if 'debug' not in self.data.columns:\n self.data['debug'] = False\n\n # if 'tie' not in self.data.columns:\n # self.data['tie'] = False\n\n if 'update_length' not in self.data.columns:\n self.data['update_length'] = 0\n # for key in self.unique_keys:\n # self.data[key] = np.nan\n # Remaining set to None\n # for k in self.check_keys:\n # if k not in self.data.columns:\n # self.data[k] = None","def normalize(self, attr_name): # DONE\n self.data[attr_name] = (self.data[attr_name] - self.data[attr_name].mean()) / self.data[attr_name].std()","def update(self, observed, axis):\n\n # -------------------------------------------------------------------------\n # YOUR CODE GOES HERE\n #\n total = 0\n \n # zero out irrelevant data and calculate total for observed sample\n for each in self._table:\n if (axis == 1 and each[1] != observed) or (axis == 0 and each[0] != observed):\n self._table[each] = 0\n else:\n total += self._table[each]\n \n # normalize the revelant data\n for each in self._table:\n if (axis == 1 and each[1] == observed) or (axis == 0 and each[0] == observed):\n self._table[each] = self._table[each] / total\n\n #\n # END OF YOUR CODE\n # ------------------------------------------------------------------------- ","def rebuild(self):\n self.from_samples(self.samples)","def reset_data_recorder(self):\n\n self.t_values = []\n self.x_values = []\n self.tau_values = []","def _update_data_range(self):\r\n self._h_min = np.min(self.h)\r\n self._h_max = np.max(self.h)\r\n self._hr_min = np.min(self.hr)\r\n self._hr_max = np.max(self.hr)\r\n self._m_min = np.nanmin(self.m)\r\n self._m_max = np.nanmax(self.m)\r\n\r\n if self.temperature is None or np.all(np.isnan(self.temperature)):\r\n self._T_min = np.nan\r\n self._T_max = np.nan\r\n else:\r\n self._T_min = np.nanmin(self.temperature)\r\n self._T_max = np.nanmax(self.temperature)\r\n\r\n return","def copy(self, old):\n self.h = old.h\n self.L_h = old.L_h\n\n self.d = np.arange(1,self.L_h+1)\n\n self.it = old.it\n self.N_first = old.N_first\n self.la = old.la\n self.a = old.a\n self.e = np.copy(old.e)\n self.e2 = old.e2\n\n self.P = old.P\n self.alpha_g = np.copy(old.alpha_g)\n self.A = np.copy(old.A)\n self.sigma2 = old.sigma2\n self.mu = np.copy(old.mu)\n self.R = np.copy(old.R)\n\n self.b = np.copy(old.mu)\n self.w = np.copy(old.w)\n self.pie = np.copy(old.pie)\n self.pi = np.copy(old.pi)\n self.p = np.copy(old.p)\n\n self.mu_pad = np.copy(old.mu_pad)\n self.M_mu = np.copy(old.M_mu)\n self.R_pad = np.copy(old.R_pad)\n #self.M_R = np.copy(old.M_R)\n\n self.half_pie_var = np.copy(old.half_pie_var)\n self.half_pie_var_pad = np.copy(old.half_pie_var_pad)\n self.M_half_pie_var_pad = np.copy(old.M_half_pie_var_pad)\n self.pie_var = np.copy(old.pie_var)\n\n self.rev_A = np.copy(old.rev_A)\n\n self.LP = old.LP\n self.LP_list = old.LP_list\n self.la_list = old.la_list\n self.a_list = old.a_list\n self.sigma2_list = old.sigma2_list\n self.A_list = old.A_list","def normalize(self):\n d = learning_utils.convert_data_to_2d(self._data)\n d = learning_utils.normalize_2d(d)\n self._data = learning_utils.convert_data_to_1d(d)","def reinitialiseData(self):\n if self.arrayPlotData is not None:\n self.currentPosition = 0\n self.xs = scipy.linspace(0.0, self.numberOfPoints*self.resolution, self.numberOfPoints)\n self.cursorXS = self.getCurrentPositionArray()\n self.cursorVertical = scipy.array([self.verticalLimit,0.0])\n self.arrayPlotData.set_data(\"xs\",self.xs)\n self.array0 = scipy.zeros(self.numberOfPoints)\n self.array1 = scipy.zeros(self.numberOfPoints)\n self.array2 = scipy.zeros(self.numberOfPoints)\n self.array3 = scipy.zeros(self.numberOfPoints)\n self.array4 = scipy.zeros(self.numberOfPoints)\n self.array5 = scipy.zeros(self.numberOfPoints)\n self.array6 = scipy.zeros(self.numberOfPoints)\n self.array7 = scipy.zeros(self.numberOfPoints)\n self.channels = [self.array0,self.array1,self.array2,self.array3,\n self.array4,self.array5,self.array6,self.array7]\n self.updateArrayPlotData()","def reconstruct(self,data):\n k,d = self.w.shape\n return dot(data,self.w)+self.mean.reshape(1,d)","def calculateAverage(self): \n if not self.lastTransferAverage: \n size=[0,0,0,0]\n for i in range(0,4):\n if self.lastTransferredChannel & (1 << i):\n size[i]=self.lastNbrSamplesPerSeg\n self.lastAverageArray = [zeros(size[0]),zeros(size[1]),zeros(size[2]),zeros(size[3])]\n nbrSamp=self.lastNbrSamplesPerSeg\n for i in range(0,4):\n if self.lastTransferredChannel & (1 << i):\n nbrSeg=self.lastNbrSegmentsArray[i]\n for j in range (0,nbrSamp):\n for k in range(0,nbrSeg): \n self.lastAverageArray[i][j]+=self.lastWaveformArray[i][k*nbrSamp+j]\n self.lastAverageArray[i][j]/=nbrSeg\n self.lastAverageCalculated=True\n else: print \"NOn averaged data are not available\"","def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0","def _initialize_attributes(self):\n height, width = self.image.shape[:2]\n\n self.confidence = (1 - self.mask).astype(float)\n self.data = np.zeros([height, width])\n\n self.working_image = np.copy(self.image)\n self.working_mask = np.copy(self.mask)","def new_entry_update(cls, summary):\n totaltimes = [x.totaltime for x in summary.entries]\n total = sum(totaltimes, timedelta())\n average = total / len(totaltimes)\n summary.total_time = total\n summary.daily_average = average","def process(self, data):\n # get truncation parameters from config or container defaults\n self._get_params(type(data))\n\n if self.weight_dataset is None:\n self.weight_dataset = [None] * len(self.dataset)\n\n for dset, wgt in zip(self.dataset, self.weight_dataset):\n old_shape = data[dset].local_shape\n val = np.ndarray.reshape(data[dset][:], data[dset][:].size)\n if wgt is None:\n if np.iscomplexobj(data[dset]):\n data[dset][:].real = bit_truncate_fixed(\n val.real, self.fixed_precision\n ).reshape(old_shape)\n data[dset][:].imag = bit_truncate_fixed(\n val.imag, self.fixed_precision\n ).reshape(old_shape)\n else:\n data[dset][:] = bit_truncate_fixed(\n val, self.fixed_precision\n ).reshape(old_shape)\n else:\n if data[dset][:].shape != data[wgt][:].shape:\n raise pipeline.PipelineRuntimeError(\n \"Dataset and weight arrays must have same shape ({} != {})\".format(\n data[dset].shape, data[wgt].shape\n )\n )\n invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size)\n if np.iscomplexobj(data[dset]):\n data[dset][:].real = bit_truncate_weights(\n val.real,\n invvar * 2.0 / self.variance_increase,\n self.fixed_precision,\n ).reshape(old_shape)\n data[dset][:].imag = bit_truncate_weights(\n val.imag,\n invvar * 2.0 / self.variance_increase,\n self.fixed_precision,\n ).reshape(old_shape)\n else:\n data[dset][:] = bit_truncate_weights(\n val, invvar / self.variance_increase, self.fixed_precision\n ).reshape(old_shape)\n\n return data","def __init__(self):\n\n\n self.dtype = np.dtype([\n ('fault_flags', np.uint32),\n ('raw_x', np.int16),\n ('raw_y', np.int16),\n ('raw_z', np.int16),\n ('accel_x', np.float32),\n ('accel_y', np.float32),\n ('accel_z', np.float32), \n ('pitch', np.float32),\n ('roll', np.float32), \n ])\n \n self._accel_indices = [0, 4, 5, 6]\n \n self.data = np.array([(0, 0.1, 12, 1234, 0.12345678901234, 0.12345678901234, 0.12345678901234, 0.12345678901234, 0.12345678901234)], dtype=self.dtype)\n self.data['fault_flags'] = 0\n self.data['raw_x'] = 0.1\n self.data['raw_y'] = 12\n self.data['raw_z'] = 31929\n self.data['accel_x'] = 0.12345678901234\n self.data['accel_y'] = 0.23456789012345\n self.data['accel_z'] = 0.34567890123456\n self.data['pitch'] = 0.1000\n self.data['roll'] = 0.2000\n\n\n #print len(self.data.tostring(order=\"C\"))","def reset(self) -> np.array:\n raise NotImplementedError","def reset_results_arrays(self):\n raw_dim0 = self._dataset.raw_dims[0]\n \n if len(self.data) != raw_dim0:\n self.freq_current = np.zeros(raw_dim0, complex)","def _reconstruct(self, num_samples=None):","def reset(self):\n self.sum = [0.] * len(self.topk)\n self.data_num = 0\n self.pfm = [0.] * len(self.topk)","def take_one_averaged(self):\n self.na.set_center_frequency(6.160574e9)\n self.na.set_span(10e6)\n self.na.set_power(-5, 1)\n self.na.set_ifbw(1e3)\n\n self.na.set_query_timeout(40e3)\n set_format = self.na.set_format('polar')\n print \"set_format returned: \", set_format\n self.na.set_trigger_source(\"manual\")\n self.na.set_averages(10)\n self.na.set_trigger_average_mode()\n\n self.na.clear_averages(channel=1)\n self.na.trigger_single(channel=1)\n fpts, xs, ys = self.na.read_data()\n #\n plt.figure()\n plt.plot(fpts, xs)\n plt.plot(fpts, ys)\n plt.show()","def reset(self):\n super().reset()\n self.m_n = 1\n self.m_num_errors = 0\n self.m_d = 0\n self.m_lastd = 0\n self.m_mean = 0.0\n self.m_std_temp = 0.0\n self.m_m2s_max = 0.0\n self.estimation = 0.0","def __init__(self, df):\n self.original_data = df\n self.preprocessed_data = pd.DataFrame()","def __post_init__(self):\n assert self.data.shape == self.theory.shape","def reinit(self):\n # parameter's current index back to start value\n self._currentindex = np.argmin(np.abs(self.values - self._startval))\n\n # reinitializing proposed next index\n self._proposednextindex = None\n\n # reinitializing parameter's samples\n self.samples[...] = 0.0\n self.nsample = 0","def renorm(self):\n self.data -= self.data.min()\n self.data /= self.data.max()\n return self","def __init__(self, matlab_data, params: RatDay_Preprocessing_Parameters) -> None:\n self.params = params\n print(\"Reformating data\")\n self.raw_data = self.reformat_data(matlab_data)\n print(\"Cleaning data\")\n self.data = self.clean_recording_data(self.raw_data)\n print(\"Calculating run periods\")\n self.velocity_info = self.calculate_velocity_info()\n print(\"Calculating place fields\")\n np.random.seed(0)\n self.place_field_data = self.calculate_place_fields()\n print(\"DONE\")","def Resize(self):\n\n self.history_length = int( round( self.owner['time_span']/self.owner['sample_speed']))\n self.FreshStart()","def _data_rate_default(self):\n raise NotImplementedError(\"Must be implemented by subclass.\")","def __adjust(self, *args):\n return \"adjust\"","def force_averaging(self):\n return self._force_averaging","def compute(self):\n # get the values in widgets\n if self.first:\n self.first = False\n type = self.type\n nfilter = self.nfilter\n order = self.order\n npass = self.npass\n else:\n type = self.typeComboBox.currentIndex()\n nfilter = int(str(self.filterComboBox.currentText()))\n order = self.orderSpinBox.value()\n npass = self.nbpassSpinBox.value()\n self.data[2] = self.data[1] # Restore initial values\n for i in range(npass):\n if type == 0:\n self.data[2] = MovAver(self.data[2], nfilter)\n else:\n self.data[2] = signal.savgol_filter(self.data[2],\n window_length = nfilter,\n polyorder = order)\n self.data[3] = self.data[2] - self.data[1] - self.diffshift\n self.type = type\n self.nfilter = nfilter\n self.npass = npass\n if type == 1:\n self.order = order\n self.updatePlot()","def reset(self):\n self.ref_value = 0.0\n self._average = 0.0\n self.num_samples = 0","def process(self, data):\n return data - np.mean(data)","def __init__(self, data):\n\n self.xs = np.array(sorted(data))\n self.N = float(len(self.xs))\n self.ys = np.arange(1, self.N + 1) / self.N","def update(self) -> np.ndarray:\r\n raise NotImplementedError(\"`update` method is not implemented!\")","def __init__(self, total_time, *args, **kwargs):\n super(AdjustingFSNN, self).__init__(*args, **kwargs)\n\n self._adjustment_distribution = [\n (.05, 32, .6),\n (.15, 64, .75),\n (.70, 1024, .98),\n (.10, 4096, .996)\n ]\n self._start_time = None\n self._next_adjustment_index = 0\n\n self._adjustments = [\n (0,\n self._adjustment_distribution[0][1],\n self._adjustment_distribution[0][2])]\n\n time_alloted_so_far = total_time * self._adjustment_distribution[0][0]\n idx = 1\n while idx < len(self._adjustment_distribution):\n self._adjustments.append(\n (time_alloted_so_far,\n self._adjustment_distribution[idx][1],\n self._adjustment_distribution[idx][2]))\n this_allotment = self._adjustment_distribution[idx][0]\n time_alloted_so_far += total_time * this_allotment\n idx += 1","def _compute_new_data(self, desvars, responses, objectives, constraints, metadata):\n # type: (dict, dict, dict, dict, dict) -> np.ndarray\n raise NotImplementedError","def unscale_data(self, data):\n return (data + self.mean)*self.std","def transform(self, data):\n data -= self.mean\n if 0.0 in self.std:\n self.std = np.where(self.std == 0.0, 1.0, self.std)\n data /= self.std\n return data","def update(self, data):\n if self.mode == 'image':\n data = self.preprocess(data)\n self.main_object.set_data(data)\n\n vmin, vmax = self._parse_vrange(data)\n self.main_object.set_clim([vmin, vmax])\n\n if self.mode == 'histogram':\n raise NotImplementedError(\"Updating layer data is not in supported in 'histogram' mode. \")\n\n if self.mode == 'curve':\n x_data, y_data = self.preprocess(data)\n self.main_object.set_data(x_data, y_data)\n self.update_lims()\n\n if self.mode == 'loss':\n raise NotImplementedError(\"Updating layer data is not in supported in 'loss' mode. \")","def reset(self,prepare=True):\n if self.lowmem:\n self.load(self.filename,prepare=prepare)\n else:\n self.data = np.copy(self.data_orig)\n self.weights = np.copy(self.weights_orig)\n self.durations = self.getSubintinfo('TSUBINT')\n #if prepare:\n # self.scrunch()","def _update_raw_data(params):\n from scipy.signal import filtfilt\n start = params['t_start']\n stop = params['raw'].time_as_index(start + params['duration'])[0]\n start = params['raw'].time_as_index(start)[0]\n data_picks = _pick_data_channels(params['raw'].info)\n data, times = params['raw'][:, start:stop]\n if params['projector'] is not None:\n data = np.dot(params['projector'], data)\n # remove DC\n if params['remove_dc'] is True:\n data -= np.mean(data, axis=1)[:, np.newaxis]\n if params['ba'] is not None:\n data[data_picks] = filtfilt(params['ba'][0], params['ba'][1],\n data[data_picks], axis=1, padlen=0)\n # scale\n for di in range(data.shape[0]):\n data[di] /= params['scalings'][params['types'][di]]\n # stim channels should be hard limited\n if params['types'][di] == 'stim':\n norm = float(max(data[di]))\n data[di] /= norm if norm > 0 else 1.\n # clip\n if params['clipping'] == 'transparent':\n data[np.logical_or(data > 1, data < -1)] = np.nan\n elif params['clipping'] == 'clamp':\n data = np.clip(data, -1, 1, data)\n params['data'] = data\n params['times'] = times","def initial_values(self):\n y = self._y\n trend = self.trend\n seasonal = self.seasonal\n seasoning = self.seasoning\n trending = self.trending\n m = self.seasonal_periods\n l0 = self._l0\n b0 = self._b0\n if seasoning:\n l0 = y[np.arange(self.nobs) % m == 0].mean() if l0 is None else l0\n if b0 is None and trending:\n lead, lag = y[m:m + m], y[:m]\n if trend == 'mul':\n b0 = np.exp((np.log(lead.mean()) - np.log(lag.mean())) / m)\n else:\n b0 = ((lead - lag) / m).mean()\n s0 = list(y[:m] / l0) if seasonal == 'mul' else list(y[:m] - l0)\n elif trending:\n l0 = y[0] if l0 is None else l0\n if b0 is None:\n b0 = y[1] / y[0] if trend == 'mul' else y[1] - y[0]\n s0 = []\n else:\n if l0 is None:\n l0 = y[0]\n b0 = None\n s0 = []\n\n return l0, b0, s0","def set_normalization(self, dataloader):\n mean = 0\n square = 0\n for (data_in, _) in dataloader:\n mean += data_in.mean()\n square += data_in.pow(2).mean()\n\n mean /= len(dataloader)\n square /= len(dataloader)\n std = np.sqrt(square - mean ** 2)\n\n # The input data should be roughly normally distributed after\n # passing through net_fixed.\n self.scale_in.bias.data.fill_(- mean / std)\n self.scale_in.weight.data.fill_(1 / std)"],"string":"[\n \"def _update_avg(self):\\n if self._data_type == 'coords':\\n # default averaging is supported only for 'matrix' dataTypes\\n return\\n elif self._data_type == 'image':\\n\\n x, y = self._averaging, self._averaging\\n\\n if (x,y) == (1, 1):\\n self.vectors = self._original_data\\n # calling original data\\n return\\n\\n tempdat = self._original_data\\n range_x = tempdat.shape[0]\\n range_y = tempdat.shape[1]\\n x_offset = int((x - 1) / 2)\\n y_offset = int((y - 1) / 2)\\n\\n kernel = np.ones(shape=(x, y)) / (x*y)\\n\\n output_mat = np.zeros_like(tempdat)\\n output_mat_x = signal.convolve2d(tempdat[:, :, 0], kernel,\\n mode='same', boundary='wrap')\\n output_mat_y = signal.convolve2d(tempdat[:, :, 1], kernel,\\n mode='same', boundary='wrap')\\n\\n output_mat[:, :, 0] = output_mat_x\\n output_mat[:, :, 1] = output_mat_y\\n\\n self.vectors = (output_mat[x_offset:range_x-x_offset:x,\\n y_offset:range_y-y_offset:y])\",\n \"def extend_model_data(self, newdata):\\n logger.warning(\\\"Extend data is untested and may have unexpected consequences\\\")\\n data_temp = newdata.copy()\\n data_temp['X'] -= self.origin[0]\\n data_temp['Y'] -= self.origin[1]\\n data_temp['Z'] -= self.origin[2]\\n data_temp['X'] /= self.scale_factor\\n data_temp['Y'] /= self.scale_factor\\n data_temp['Z'] /= self.scale_factor\\n self.data.concat([self.data, data_temp], sort=True)\",\n \"def __call__(self,raw):\\n\\n #replace any \\\"out of range\\\" values in T_Arrays by NaN's\\n self.cleanup(raw)\\n\\n #for those variables that are best represented as sums,\\n #multiply by ntimes_ave to compensate for pre averaging\\n \\n #if hasattr(raw,'seeded_shots'):\\n # raw.seeded_shots*=self.ntime_ave\\n #if hasattr(raw,'shot_count'):\\n # raw.shot_count*=self.ntime_ave\\n\\n if self.post_operator:\\n self.post_operator(raw)\\n return raw\",\n \"def _reset_dimensional_data(self, dataset):\\n # local reference to input data\\n raw = dataset.get_source_data('prep')\\n\\n nfids = raw.shape[-2]\\n \\n nfids = int(nfids/self.set.fids_to_average)\\n \\n data_shape = list(raw.shape)\\n data_shape[-2] = nfids\\n\\n self.frequency_shift = np.zeros([nfids])\\n self.phase_0 = np.zeros([nfids])\\n self.measure_time = np.arange(nfids)\\n\\n self.data = np.zeros(data_shape, dtype=raw.dtype)\\n if self.chain is not None:\\n self.chain.reset_results_arrays()\",\n \"def reBuild(self): # redefine the rebuild method for loss function (polymorphism)\\n self.updateRange()\\n self.buildLine()\\n self.normalize() # normalize loss function to have total area of 1 \",\n \"def update_original_data(self):\\n pass\",\n \"def apply_parameters(self):\\n n_bins = int(self.record_length / self.bin_width)\\n time_bins = self.bin_width * np.arange(n_bins)\\n \\n\\n self.tau = np.arange(self.tau_start, self.tau_end+self.tau_delta, self.tau_delta)\\n sequence = self.generate_sequence()\\n self.n_laser = find_laser_pulses(sequence)\\n \\n FC.Configure(self.record_length, self.bin_width, self.n_laser)\\n\\n if self.keep_data and sequence == self.sequence and np.all(time_bins == self.time_bins): # if the sequence and time_bins are the same as previous, keep existing data\\n self.old_count_data = self.count_data.copy()\\n else:\\n self.old_count_data = np.zeros_like(FC.GetData())\\n \\n self.sequence = sequence\\n self.time_bins = time_bins\\n self.n_bins = n_bins\\n \\n self.MW_source = {'mw':mw, 'mw2':mw2}[self.mw_source]\",\n \"def rescale(self):\\n # forecast on real data, don't need this anymore\\n pass\",\n \"def rescale_data(self):\\n\\n # Dividing every array of simulated data vectors by the mean of that array.\\n '''# Didnt work\\n for key in self.data.keys():\\n self.data[key] /= np.mean(self.data[key])\\n '''\\n\\n self.rescaled = True\\n\\n # Mean normalization\\n \\\"\\\"\\\" didnt work\\n for key in self.data.keys():\\n self.data[key] -= np.mean(self.data[key])\\n self.data[key] /= (np.max(self.data[key]) - np.min(self.data[key]))\\n \\\"\\\"\\\"\\n\\n # Median normalization\\n \\\"\\\"\\\" didnt work, still dividing by large number \\n for key in self.data.keys():\\n self.data[key] -= np.median(self.data[key])\\n self.data[key] /= (np.max(self.data[key]) - np.min(self.data[key]))\\n \\\"\\\"\\\"\\n\\n # Divide by median\\n \\\"\\\"\\\" didnt work\\n for key in self.data.keys():\\n self.data[key] -= np.median(self.data[key])\\n self.data[key] /= (np.median(self.data[key]))\\n \\\"\\\"\\\"\\n\\n # Take logarithm of data\\n \\\"\\\"\\\" didnt work\\n for key in self.data.keys():\\n self.data[key] = np.log10(self.data[key])\\n \\\"\\\"\\\"\\n\\n # Scale by length of vector\\n \\\"\\\"\\\"\\n for key in self.data.keys():\\n self.data[key] /= np.linalg.norm(self.Cl_noiseless)\\n \\\"\\\"\\\"\\n\\n \\n # Scale by negative of the natural logarithm \\n for key in self.data.keys():\\n self.data[key] = -1 * np.log(self.data[key]) \\n \\n \\\"\\\"\\\"\\n # Scale by subtracting the mean and dividing by std\\n std = np.nanstd(self.data['data'])\\n mean = np.nanmean(self.data['data'])\\n for key in self.data.keys():\\n # self.data[key] -= np.log(self.Cl_noiseless) # -1* # scale this same way\\n # self.data[key] -= self.Cl_noiseless # -1* # scale this same way\\n self.data[key] -= mean \\n self.data[key] /= std\\n \\\"\\\"\\\"\",\n \"def __init__(self):\\n self.last_obs = -1.0\\n self.last_timestamp = -1.0\\n self._fitted = False\",\n \"def _update_data(self):\\n for attribute in [\\\"flow_rate\\\"]:\\n self._data[attribute] = self._connection.measure\",\n \"def evolution_pre_allocation(self, dataset_length):\\n #it will store the intial basis too, thus +1 on the dataset length \\n self.basis_history=[[[np.zeros(self.basis[layer][sublayer].shape) for recording in range(dataset_length+1)] for sublayer in range(2**layer)] for layer in range(self.layers)]\\n for layer in range(self.layers):\\n for sublayer in range(2**layer):\\n self.basis_history[layer][sublayer][0]=self.basis[layer][sublayer]\\n self.noise_history=[[[0 for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]\\n self.sparsity_history=[[[0 for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]\\n self.sensitivity_history=[[[0 for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]\\n self.learning_history=[[[0 for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]\\n self.basis_distance=[[[np.zeros(len(self.basis[layer][sublayer])) for recording in range(dataset_length)] for sublayer in range(2**layer)] for layer in range(self.layers)]\",\n \"def normalize_dataset(self):\",\n \"def augment_train_data(self):\\n # do not augment on evaluation dataset\\n original_len = len(self.data_train)\\n for i in range(len(self.data_train)):\\n if i % 100 == 0:\\n print(f\\\"Augmenting train data, progress: {i} / {original_len}\\\")\\n title = self.data_train[i][\\\"Title\\\"]\\n abstract = self.data_train[i][\\\"Abstract\\\"]\\n label = self.data_train[i][\\\"Label\\\"]\\n\\n title = self.augmenter.augment(title)\\n abstract = self.augmenter.augment(abstract)\\n\\n self.data_train.append({\\\"Title\\\": title, \\\"Abstract\\\": abstract, \\\"Label\\\": label})\\n print(f\\\"Train data amount after augmenting: {len(self.data_train)}\\\")\",\n \"def _numberOfPoints_changed(self):\\n self.reinitialiseData()\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.mean_\\n del self.var_\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, because they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.n_samples_seen_\\n del self.mean_\\n del self.var_\",\n \"def reinit(self):\\n self.data_updating = {}\\n self.reinitialization = True\\n # force the bounds to be defined again\\n self.bounds = None\",\n \"def norm_data(self):\\n if (self.nrows, self.ncolumns) < self.data.shape:\\n self.data = self.data[0:self.nrows, 0:self.ncolumns]\\n if self.data.dtype != np.float64:\\n self.data = self.data.astype(np.float64)\\n self.meanval = self.data.mean()\\n self.stdval = self.data.std()\",\n \"def extend(self,data):\\n n = float(len(data))\\n if n == 0:\\n return self\\n M2 = 0\\n M3 = 0\\n M4 = 0\\n mean = 0\\n vmin = None\\n vmax = None\\n for x in data:\\n mean += x/n \\n if vmin is None:\\n vmax = x\\n vmin = x\\n if x < vmin:\\n vmin = x\\n if x > vmax:\\n vmax = x\\n for x in data:\\n d = x-mean\\n M2 += (d**2)\\n M3 += (d**3)\\n M4 += (d**4)\\n x = LiveStat(self.name)\\n x.vmin = vmin\\n x.vmax = vmax\\n x.vmean = mean\\n x.vm2 = M2\\n x.vm3 = M3\\n x.vm4 = M4\\n x.vcount = int(n)\\n x.vcountsq = x.vcount**2\\n x.dirty = True\\n self.merge(x)\\n return self\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n if self._validate:\\n if len(data.shape) <= self._axis:\\n raise DataProcessorError(\\n f\\\"Cannot average the {len(data.shape)} dimensional \\\"\\n f\\\"array along axis {self._axis}.\\\"\\n )\\n\\n return data\",\n \"def __init__(self):\\n self.slope = -1.0\\n self.last_obs = -1.0\\n self.last_obs_ind = -1\\n self._fitted = False\",\n \"def __apply_accumulators():\\n self.__xdata = np.array([])\\n self.__ydata = np.array([])\\n for acc in self.signal_accumulators:\\n self.__xdata = __array_append(self.__xdata,acc.attempt)\\n self.__ydata = __array_append(self.__ydata,acc.count)\\n self.__applied = True\",\n \"def calculateDataRate(self):\\n pass\",\n \"def calculate(self):\\n self.results['max'] = numpy.max(self.data)\\n self.results['min'] = numpy.min(self.data)\\n if self.type == 0:\\n self.group_discrete_data()\\n if self.type == 1:\\n self.group_continuous_data()\\n\\n self.results['arithAvg'] = self.average([self.data[i] * self.occurrences[i] for i in range(len(self.data))],\\n self.totalOccurrences)\\n self.results['quadAvg'] = math.sqrt(\\n self.average([(self.data[i] * self.data[i]) * self.occurrences[i] for i in range(len(self.data))],\\n self.totalOccurrences))\\n if self.results['min'] > 0:\\n self.results['geoAvg'] = math.exp(\\n self.average([numpy.log(self.data[i]) * self.occurrences[i] for i in range(len(self.data))],\\n self.totalOccurrences))\\n self.results['harmAvg'] = 1 / self.average(\\n [(self.occurrences[i] / self.data[i]) for i in range(len(self.data))],\\n self.totalOccurrences)\\n else:\\n self.results['geoAvg'] = self.results['harmAvg'] = \\\"N/A\\\"\\n self.results['momentsR'] = self.moments(self.data, self.occurrences, 4)\\n self.results['centralMomentsR'] = self.moments([(i - self.results['arithAvg']) for i in self.data],\\n self.occurrences, 4)\\n self.results['std'] = self.average(\\n [self.occurrences[i] * abs(self.data[i] - self.results['arithAvg']) for i in range(len(self.data))],\\n self.totalOccurrences)\",\n \"def recalculate() -> None:\\n NotImplemented\",\n \"def __init__(self, size):\\n self.data = list()\\n self.data_len = 0\\n self.start_idx = -1\\n self.size = size\\n self.average = None\",\n \"def readdata(self, fname):\\n\\t\\tif not hasattr(self, 'sweepNumber') or not hasattr(self, 'channel'):\\n\\t\\t\\tself.sweepNumber=0\\n\\t\\t\\tself.channel=0\\n\\n\\t\\t# additional meta data\\n\\t\\tself.fileFormat='abf'\\n\\n\\t\\tabf=pyabf.ABF(fname)\\n\\t\\tabf.setSweep(sweepNumber=self.sweepNumber, channel=self.channel)\\n\\t\\tscale=self._currentScale(abf)\\n\\n\\t\\t# If the Fs attribute doesn't exist set it\\n\\t\\tif not hasattr(self, 'Fs'):\\t\\n\\t\\t\\tself.Fs=abf.dataRate\\n\\t\\t# else check if it s the same as before\\n\\t\\telse:\\n\\t\\t\\tif self.Fs!=abf.dataRate:\\n\\t\\t\\t\\traise metaTrajIO.SamplingRateChangedError(\\\"The sampling rate in the data file '{0}' has changed.\\\".format(f))\\n\\n\\t\\treturn abf.sweepY*scale\",\n \"def postprocessData(meta, units, data):\\n\\n data['time'] = np.arange(0, meta['dt'] * len(data), meta['dt'])\\n units['time'] = 's'\\n\\n meta, units, data = self.calculateForce(meta, units, data)\\n\\n data['distance'] = np.sqrt(data.xDist**2 + data.yDist**2)\\n units['distance'] = 'nm'\\n\\n return meta, units, data\",\n \"def reset_average(self):\\n self._total_time = 0\\n self._average_time = 0\\n self._calls = 0\",\n \"def _mode_1(self, data, serial):\\n\\t\\t# Store the current data in the previous one\\n\\t\\tself.prev = self.curr\\n\\t\\t\\n\\t\\t# Overwrite current\\n\\t\\tself.curr = data\\n\\t\\t\\n\\t\\t# Set the average\\n\\t\\tself.curr[:] = int(np.sum(self.curr)/self.curr.shape[0])\\n\\t\\t\\n\\t\\t# Put a copy of the data in the queue\\n\\t\\tserial.add_list([\\\"d\\\"] + [int(self.curr[0])])\",\n \"def normalize_data(self):\\n self.x_mean, self.x_std = du.get_mean_std(self.x_train)\\n self.x_train = du.normalize(self.x_train, self.x_mean, self.x_std)\\n if self.x_test is not None and self.y_test is not None:\\n self.x_test = du.normalize(self.x_test, self.x_mean, self.x_std)\\n self.normalized_data = True\",\n \"def __init__(self):\\n super(ExponentialInputs, self).__init__()\\n self.init_pop_size = pd.Series([], dtype=\\\"float\\\")\\n self.growth_rate = pd.Series([], dtype=\\\"float\\\")\\n self.time_steps = pd.Series([], dtype=\\\"float\\\")\",\n \"def _standardize(self):\\n deviation = np.std(self.series)\\n self.series = (self.series - np.mean(self.series)) / (deviation if deviation != 0 else 1)\",\n \"def _update_length(self):\\n\\n if self._data_type == 'coords':\\n return \\\"length adjustment not allowed for coordinate-style data\\\"\\n elif self._data_type == 'image':\\n self._vectors = self._convert_to_vector_type(self._current_data)\\n vertices, triangles = self._generate_meshes(self.vectors,\\n self.width)\\n self._mesh_vertices = vertices\\n self._mesh_triangles = triangles\",\n \"def reset_mean(self,new_mean):\\n self.mean = new_mean\\n return\",\n \"def reset_mean(self,new_mean):\\n self.mean = new_mean\\n return\",\n \"def modelmean(self, model_params, this_data, this_suff_stat):\\n pass\",\n \"def fill_dataset(self):\\n rm, rstd = self.get_rolling_stats()\\n\\n self.add_rolling_mean(rm)\\n self.add_bollinger_bands(rstd)\\n self.add_spy_info()\\n self.add_beta_and_sharpe()\\n self.add_stlouis_data()\",\n \"def originalData(self): \\n self.__exampleIndices = array(list(range(0, self.__numExamples)))\",\n \"def getLastAverage(self):\\n lastAve=dict()\\n lastAve['identifier']=self.lastWaveIdentifier\\n lastAve['averageCalculated']=self.lastAverageCalculated \\n lastAve['lastAverageArray']=self.lastAverageArray\\n return lastAve\",\n \"def reset_results_arrays(self):\\n raw_dims = self._dataset.raw_dims\\n raw_dims = list(raw_dims[::-1])\\n \\n if len(self.data) != raw_dims:\\n self.freq_all = np.zeros(raw_dims, complex)\\n self.time_all = np.zeros(raw_dims, complex)\",\n \"def initDataParms(self):\\n self.xpos = self.pltw.curvelist[self.blkno].xvinfo.vidx\\n self.data = self.pltw.blklst[self.blkno] # original data block\\n self.idata = None # interpolated data\\n (self.nvec, self.npt) = self.data.shape\\n self.xmin = (self.data[self.xpos]).min()\\n self.xmax = (self.data[self.xpos]).max()\\n self.xspan = self.xmax - self.xmin\\n if self.parent.test:\\n self.dx = self.xspan / (self.npt * 5)\",\n \"def data(self):\\n if self._data.empty:\\n self._data = super().data\\n\\n max_data_year = self._data[\\\"year\\\"].max()\\n\\n # If the date range of the data doesn't line up with the year filters\\n # for train/test data, we risk getting empty data sets\\n if self.max_year != max_data_year:\\n max_year_diff = pd.to_timedelta(\\n [(YEAR_IN_DAYS * (self.max_year - max_data_year))]\\n * len(self._data),\\n unit=\\\"days\\\",\\n )\\n\\n self._data.loc[:, \\\"date\\\"] = self._data[\\\"date\\\"] + max_year_diff\\n self._data.loc[:, \\\"year\\\"] = self._data[\\\"date\\\"].dt.year\\n self._data.set_index(\\n [\\\"team\\\", \\\"year\\\", \\\"round_number\\\"], drop=False, inplace=True\\n )\\n\\n return self._data\",\n \"def prepare(self):\\n ls=len(self.v)\\n self.S=numpy.zeros(ls)\\n self.A=numpy.zeros((ls,ls))\\n\\n for k,v in self.e.items():\\n b,e=k\\n bi,ei=self.rv[b],self.rv[e]\\n self.A[bi,bi]-=v\\n self.A[bi,ei]+=v\",\n \"def _measurement_update(self):\\n pass\",\n \"def resetSimulatedData(self):\\n self.space = 0*self.space # numpy array that represents the physical space\\n self.susceptible = self.space.size # the current number of healthy people in the population\\n self.infected = 0 # the current number of infected people in the population\\n self.recovered = 0 # the current number of infected people in the population\\n self.snapshots = [] # this will contain numpy arrays that represent each time step\\n self.SIR = np.zeros((3,1)) #this contains a time series of the number of susceptible, infected, recovered\\n self.time = np.zeros(1) # this array will be the time array\\n self.total_infections = 0 # set the number of total infections back to zero\",\n \"def update_average_best_fit(self):\\r\\n # print(result_buffer)\\r\\n total = len(self.result_buffer)\\r\\n left_fit, right_fit = np.empty((0, 3)), np.empty((0, 3))\\r\\n left_curve_rad, right_curve_rad = [], []\\r\\n vehicle_offset = []\\r\\n for i in range(total):\\r\\n calc_fit_dict = self.result_buffer[i]\\r\\n left_fit = np.append(left_fit, [calc_fit_dict['left_fit']], axis=0)\\r\\n right_fit = np.append(right_fit, [calc_fit_dict['right_fit']], axis=0)\\r\\n left_curve_rad.append(calc_fit_dict['left_curve_rad'])\\r\\n right_curve_rad.append(calc_fit_dict['right_curve_rad'])\\r\\n vehicle_offset.append(calc_fit_dict['vehicle_offset'])\\r\\n self.avg_fit_dict['left_fit'] = np.mean(left_fit, axis=0)\\r\\n self.avg_fit_dict['right_fit'] = np.mean(right_fit, axis=0)\\r\\n self.avg_fit_dict['left_curve_rad'] = np.mean(left_curve_rad)\\r\\n self.avg_fit_dict['right_curve_rad'] = np.mean(right_curve_rad)\\r\\n self.avg_fit_dict['vehicle_offset'] = np.mean(vehicle_offset)\\r\\n # print(avg_fit_dict)\\r\\n return\",\n \"def stored_reset(self):\\r\\n\\t\\tself.stored_reward = np.zeros((self.num_timesteps - self.first_considered_reward_step,))\\r\\n\\t\\tself.stored_optimum = np.zeros_like(self.stored_reward)\",\n \"def reset_for_new_run(\\n self,\\n state: State\\n ):\\n\\n super().reset_for_new_run(state)\\n\\n if self.Q is None:\\n self.Q = {\\n a: IncrementalSampleAverager(\\n initial_value=self.initial_q_value,\\n alpha=self.alpha\\n )\\n for a in self.most_recent_state.AA\\n }\\n else:\\n for averager in self.Q.values():\\n averager.reset()\",\n \"def reset(self):\\n self.avg = 0\\n self.sum = 0\\n self.cnt = 0\",\n \"def __init__(self):\\n self.mean = 0.0\\n self.std = 1.0\",\n \"def update_state(self, makespan_train, makespan_baseline):\\n self.mean_makespan_train.assign_add(tf.reduce_mean(makespan_train))\\n self.mean_makespan_baseline.assign_add(tf.reduce_mean(makespan_baseline))\\n self.step.assign_add(1)\",\n \"def _reload(self):\\n if os.path.exists(self.filename):\\n self.data = pd.read_csv(self.filename)\\n else:\\n self.data = pd.DataFrame(columns=self.unique_keys)\\n\\n # Set these default values\\n # if 'weight_rescale' not in self.data.columns:\\n # self.data['weight_rescale'] = 'none'\\n # if 'norm' not in self.data.columns:\\n # self.data['norm'] = 'softmax'\\n # if 'update' not in self.data.columns:\\n # self.data['update'] = 'all'\\n # if 'replay' not in self.data.columns:\\n # self.data['replay'] = False\\n if 'debug' not in self.data.columns:\\n self.data['debug'] = False\\n\\n # if 'tie' not in self.data.columns:\\n # self.data['tie'] = False\\n\\n if 'update_length' not in self.data.columns:\\n self.data['update_length'] = 0\\n # for key in self.unique_keys:\\n # self.data[key] = np.nan\\n # Remaining set to None\\n # for k in self.check_keys:\\n # if k not in self.data.columns:\\n # self.data[k] = None\",\n \"def normalize(self, attr_name): # DONE\\n self.data[attr_name] = (self.data[attr_name] - self.data[attr_name].mean()) / self.data[attr_name].std()\",\n \"def update(self, observed, axis):\\n\\n # -------------------------------------------------------------------------\\n # YOUR CODE GOES HERE\\n #\\n total = 0\\n \\n # zero out irrelevant data and calculate total for observed sample\\n for each in self._table:\\n if (axis == 1 and each[1] != observed) or (axis == 0 and each[0] != observed):\\n self._table[each] = 0\\n else:\\n total += self._table[each]\\n \\n # normalize the revelant data\\n for each in self._table:\\n if (axis == 1 and each[1] == observed) or (axis == 0 and each[0] == observed):\\n self._table[each] = self._table[each] / total\\n\\n #\\n # END OF YOUR CODE\\n # ------------------------------------------------------------------------- \",\n \"def rebuild(self):\\n self.from_samples(self.samples)\",\n \"def reset_data_recorder(self):\\n\\n self.t_values = []\\n self.x_values = []\\n self.tau_values = []\",\n \"def _update_data_range(self):\\r\\n self._h_min = np.min(self.h)\\r\\n self._h_max = np.max(self.h)\\r\\n self._hr_min = np.min(self.hr)\\r\\n self._hr_max = np.max(self.hr)\\r\\n self._m_min = np.nanmin(self.m)\\r\\n self._m_max = np.nanmax(self.m)\\r\\n\\r\\n if self.temperature is None or np.all(np.isnan(self.temperature)):\\r\\n self._T_min = np.nan\\r\\n self._T_max = np.nan\\r\\n else:\\r\\n self._T_min = np.nanmin(self.temperature)\\r\\n self._T_max = np.nanmax(self.temperature)\\r\\n\\r\\n return\",\n \"def copy(self, old):\\n self.h = old.h\\n self.L_h = old.L_h\\n\\n self.d = np.arange(1,self.L_h+1)\\n\\n self.it = old.it\\n self.N_first = old.N_first\\n self.la = old.la\\n self.a = old.a\\n self.e = np.copy(old.e)\\n self.e2 = old.e2\\n\\n self.P = old.P\\n self.alpha_g = np.copy(old.alpha_g)\\n self.A = np.copy(old.A)\\n self.sigma2 = old.sigma2\\n self.mu = np.copy(old.mu)\\n self.R = np.copy(old.R)\\n\\n self.b = np.copy(old.mu)\\n self.w = np.copy(old.w)\\n self.pie = np.copy(old.pie)\\n self.pi = np.copy(old.pi)\\n self.p = np.copy(old.p)\\n\\n self.mu_pad = np.copy(old.mu_pad)\\n self.M_mu = np.copy(old.M_mu)\\n self.R_pad = np.copy(old.R_pad)\\n #self.M_R = np.copy(old.M_R)\\n\\n self.half_pie_var = np.copy(old.half_pie_var)\\n self.half_pie_var_pad = np.copy(old.half_pie_var_pad)\\n self.M_half_pie_var_pad = np.copy(old.M_half_pie_var_pad)\\n self.pie_var = np.copy(old.pie_var)\\n\\n self.rev_A = np.copy(old.rev_A)\\n\\n self.LP = old.LP\\n self.LP_list = old.LP_list\\n self.la_list = old.la_list\\n self.a_list = old.a_list\\n self.sigma2_list = old.sigma2_list\\n self.A_list = old.A_list\",\n \"def normalize(self):\\n d = learning_utils.convert_data_to_2d(self._data)\\n d = learning_utils.normalize_2d(d)\\n self._data = learning_utils.convert_data_to_1d(d)\",\n \"def reinitialiseData(self):\\n if self.arrayPlotData is not None:\\n self.currentPosition = 0\\n self.xs = scipy.linspace(0.0, self.numberOfPoints*self.resolution, self.numberOfPoints)\\n self.cursorXS = self.getCurrentPositionArray()\\n self.cursorVertical = scipy.array([self.verticalLimit,0.0])\\n self.arrayPlotData.set_data(\\\"xs\\\",self.xs)\\n self.array0 = scipy.zeros(self.numberOfPoints)\\n self.array1 = scipy.zeros(self.numberOfPoints)\\n self.array2 = scipy.zeros(self.numberOfPoints)\\n self.array3 = scipy.zeros(self.numberOfPoints)\\n self.array4 = scipy.zeros(self.numberOfPoints)\\n self.array5 = scipy.zeros(self.numberOfPoints)\\n self.array6 = scipy.zeros(self.numberOfPoints)\\n self.array7 = scipy.zeros(self.numberOfPoints)\\n self.channels = [self.array0,self.array1,self.array2,self.array3,\\n self.array4,self.array5,self.array6,self.array7]\\n self.updateArrayPlotData()\",\n \"def reconstruct(self,data):\\n k,d = self.w.shape\\n return dot(data,self.w)+self.mean.reshape(1,d)\",\n \"def calculateAverage(self): \\n if not self.lastTransferAverage: \\n size=[0,0,0,0]\\n for i in range(0,4):\\n if self.lastTransferredChannel & (1 << i):\\n size[i]=self.lastNbrSamplesPerSeg\\n self.lastAverageArray = [zeros(size[0]),zeros(size[1]),zeros(size[2]),zeros(size[3])]\\n nbrSamp=self.lastNbrSamplesPerSeg\\n for i in range(0,4):\\n if self.lastTransferredChannel & (1 << i):\\n nbrSeg=self.lastNbrSegmentsArray[i]\\n for j in range (0,nbrSamp):\\n for k in range(0,nbrSeg): \\n self.lastAverageArray[i][j]+=self.lastWaveformArray[i][k*nbrSamp+j]\\n self.lastAverageArray[i][j]/=nbrSeg\\n self.lastAverageCalculated=True\\n else: print \\\"NOn averaged data are not available\\\"\",\n \"def reset(self):\\n self.val = 0\\n self.avg = 0\\n self.sum = 0\\n self.count = 0\",\n \"def _initialize_attributes(self):\\n height, width = self.image.shape[:2]\\n\\n self.confidence = (1 - self.mask).astype(float)\\n self.data = np.zeros([height, width])\\n\\n self.working_image = np.copy(self.image)\\n self.working_mask = np.copy(self.mask)\",\n \"def new_entry_update(cls, summary):\\n totaltimes = [x.totaltime for x in summary.entries]\\n total = sum(totaltimes, timedelta())\\n average = total / len(totaltimes)\\n summary.total_time = total\\n summary.daily_average = average\",\n \"def process(self, data):\\n # get truncation parameters from config or container defaults\\n self._get_params(type(data))\\n\\n if self.weight_dataset is None:\\n self.weight_dataset = [None] * len(self.dataset)\\n\\n for dset, wgt in zip(self.dataset, self.weight_dataset):\\n old_shape = data[dset].local_shape\\n val = np.ndarray.reshape(data[dset][:], data[dset][:].size)\\n if wgt is None:\\n if np.iscomplexobj(data[dset]):\\n data[dset][:].real = bit_truncate_fixed(\\n val.real, self.fixed_precision\\n ).reshape(old_shape)\\n data[dset][:].imag = bit_truncate_fixed(\\n val.imag, self.fixed_precision\\n ).reshape(old_shape)\\n else:\\n data[dset][:] = bit_truncate_fixed(\\n val, self.fixed_precision\\n ).reshape(old_shape)\\n else:\\n if data[dset][:].shape != data[wgt][:].shape:\\n raise pipeline.PipelineRuntimeError(\\n \\\"Dataset and weight arrays must have same shape ({} != {})\\\".format(\\n data[dset].shape, data[wgt].shape\\n )\\n )\\n invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size)\\n if np.iscomplexobj(data[dset]):\\n data[dset][:].real = bit_truncate_weights(\\n val.real,\\n invvar * 2.0 / self.variance_increase,\\n self.fixed_precision,\\n ).reshape(old_shape)\\n data[dset][:].imag = bit_truncate_weights(\\n val.imag,\\n invvar * 2.0 / self.variance_increase,\\n self.fixed_precision,\\n ).reshape(old_shape)\\n else:\\n data[dset][:] = bit_truncate_weights(\\n val, invvar / self.variance_increase, self.fixed_precision\\n ).reshape(old_shape)\\n\\n return data\",\n \"def __init__(self):\\n\\n\\n self.dtype = np.dtype([\\n ('fault_flags', np.uint32),\\n ('raw_x', np.int16),\\n ('raw_y', np.int16),\\n ('raw_z', np.int16),\\n ('accel_x', np.float32),\\n ('accel_y', np.float32),\\n ('accel_z', np.float32), \\n ('pitch', np.float32),\\n ('roll', np.float32), \\n ])\\n \\n self._accel_indices = [0, 4, 5, 6]\\n \\n self.data = np.array([(0, 0.1, 12, 1234, 0.12345678901234, 0.12345678901234, 0.12345678901234, 0.12345678901234, 0.12345678901234)], dtype=self.dtype)\\n self.data['fault_flags'] = 0\\n self.data['raw_x'] = 0.1\\n self.data['raw_y'] = 12\\n self.data['raw_z'] = 31929\\n self.data['accel_x'] = 0.12345678901234\\n self.data['accel_y'] = 0.23456789012345\\n self.data['accel_z'] = 0.34567890123456\\n self.data['pitch'] = 0.1000\\n self.data['roll'] = 0.2000\\n\\n\\n #print len(self.data.tostring(order=\\\"C\\\"))\",\n \"def reset(self) -> np.array:\\n raise NotImplementedError\",\n \"def reset_results_arrays(self):\\n raw_dim0 = self._dataset.raw_dims[0]\\n \\n if len(self.data) != raw_dim0:\\n self.freq_current = np.zeros(raw_dim0, complex)\",\n \"def _reconstruct(self, num_samples=None):\",\n \"def reset(self):\\n self.sum = [0.] * len(self.topk)\\n self.data_num = 0\\n self.pfm = [0.] * len(self.topk)\",\n \"def take_one_averaged(self):\\n self.na.set_center_frequency(6.160574e9)\\n self.na.set_span(10e6)\\n self.na.set_power(-5, 1)\\n self.na.set_ifbw(1e3)\\n\\n self.na.set_query_timeout(40e3)\\n set_format = self.na.set_format('polar')\\n print \\\"set_format returned: \\\", set_format\\n self.na.set_trigger_source(\\\"manual\\\")\\n self.na.set_averages(10)\\n self.na.set_trigger_average_mode()\\n\\n self.na.clear_averages(channel=1)\\n self.na.trigger_single(channel=1)\\n fpts, xs, ys = self.na.read_data()\\n #\\n plt.figure()\\n plt.plot(fpts, xs)\\n plt.plot(fpts, ys)\\n plt.show()\",\n \"def reset(self):\\n super().reset()\\n self.m_n = 1\\n self.m_num_errors = 0\\n self.m_d = 0\\n self.m_lastd = 0\\n self.m_mean = 0.0\\n self.m_std_temp = 0.0\\n self.m_m2s_max = 0.0\\n self.estimation = 0.0\",\n \"def __init__(self, df):\\n self.original_data = df\\n self.preprocessed_data = pd.DataFrame()\",\n \"def __post_init__(self):\\n assert self.data.shape == self.theory.shape\",\n \"def reinit(self):\\n # parameter's current index back to start value\\n self._currentindex = np.argmin(np.abs(self.values - self._startval))\\n\\n # reinitializing proposed next index\\n self._proposednextindex = None\\n\\n # reinitializing parameter's samples\\n self.samples[...] = 0.0\\n self.nsample = 0\",\n \"def renorm(self):\\n self.data -= self.data.min()\\n self.data /= self.data.max()\\n return self\",\n \"def __init__(self, matlab_data, params: RatDay_Preprocessing_Parameters) -> None:\\n self.params = params\\n print(\\\"Reformating data\\\")\\n self.raw_data = self.reformat_data(matlab_data)\\n print(\\\"Cleaning data\\\")\\n self.data = self.clean_recording_data(self.raw_data)\\n print(\\\"Calculating run periods\\\")\\n self.velocity_info = self.calculate_velocity_info()\\n print(\\\"Calculating place fields\\\")\\n np.random.seed(0)\\n self.place_field_data = self.calculate_place_fields()\\n print(\\\"DONE\\\")\",\n \"def Resize(self):\\n\\n self.history_length = int( round( self.owner['time_span']/self.owner['sample_speed']))\\n self.FreshStart()\",\n \"def _data_rate_default(self):\\n raise NotImplementedError(\\\"Must be implemented by subclass.\\\")\",\n \"def __adjust(self, *args):\\n return \\\"adjust\\\"\",\n \"def force_averaging(self):\\n return self._force_averaging\",\n \"def compute(self):\\n # get the values in widgets\\n if self.first:\\n self.first = False\\n type = self.type\\n nfilter = self.nfilter\\n order = self.order\\n npass = self.npass\\n else:\\n type = self.typeComboBox.currentIndex()\\n nfilter = int(str(self.filterComboBox.currentText()))\\n order = self.orderSpinBox.value()\\n npass = self.nbpassSpinBox.value()\\n self.data[2] = self.data[1] # Restore initial values\\n for i in range(npass):\\n if type == 0:\\n self.data[2] = MovAver(self.data[2], nfilter)\\n else:\\n self.data[2] = signal.savgol_filter(self.data[2],\\n window_length = nfilter,\\n polyorder = order)\\n self.data[3] = self.data[2] - self.data[1] - self.diffshift\\n self.type = type\\n self.nfilter = nfilter\\n self.npass = npass\\n if type == 1:\\n self.order = order\\n self.updatePlot()\",\n \"def reset(self):\\n self.ref_value = 0.0\\n self._average = 0.0\\n self.num_samples = 0\",\n \"def process(self, data):\\n return data - np.mean(data)\",\n \"def __init__(self, data):\\n\\n self.xs = np.array(sorted(data))\\n self.N = float(len(self.xs))\\n self.ys = np.arange(1, self.N + 1) / self.N\",\n \"def update(self) -> np.ndarray:\\r\\n raise NotImplementedError(\\\"`update` method is not implemented!\\\")\",\n \"def __init__(self, total_time, *args, **kwargs):\\n super(AdjustingFSNN, self).__init__(*args, **kwargs)\\n\\n self._adjustment_distribution = [\\n (.05, 32, .6),\\n (.15, 64, .75),\\n (.70, 1024, .98),\\n (.10, 4096, .996)\\n ]\\n self._start_time = None\\n self._next_adjustment_index = 0\\n\\n self._adjustments = [\\n (0,\\n self._adjustment_distribution[0][1],\\n self._adjustment_distribution[0][2])]\\n\\n time_alloted_so_far = total_time * self._adjustment_distribution[0][0]\\n idx = 1\\n while idx < len(self._adjustment_distribution):\\n self._adjustments.append(\\n (time_alloted_so_far,\\n self._adjustment_distribution[idx][1],\\n self._adjustment_distribution[idx][2]))\\n this_allotment = self._adjustment_distribution[idx][0]\\n time_alloted_so_far += total_time * this_allotment\\n idx += 1\",\n \"def _compute_new_data(self, desvars, responses, objectives, constraints, metadata):\\n # type: (dict, dict, dict, dict, dict) -> np.ndarray\\n raise NotImplementedError\",\n \"def unscale_data(self, data):\\n return (data + self.mean)*self.std\",\n \"def transform(self, data):\\n data -= self.mean\\n if 0.0 in self.std:\\n self.std = np.where(self.std == 0.0, 1.0, self.std)\\n data /= self.std\\n return data\",\n \"def update(self, data):\\n if self.mode == 'image':\\n data = self.preprocess(data)\\n self.main_object.set_data(data)\\n\\n vmin, vmax = self._parse_vrange(data)\\n self.main_object.set_clim([vmin, vmax])\\n\\n if self.mode == 'histogram':\\n raise NotImplementedError(\\\"Updating layer data is not in supported in 'histogram' mode. \\\")\\n\\n if self.mode == 'curve':\\n x_data, y_data = self.preprocess(data)\\n self.main_object.set_data(x_data, y_data)\\n self.update_lims()\\n\\n if self.mode == 'loss':\\n raise NotImplementedError(\\\"Updating layer data is not in supported in 'loss' mode. \\\")\",\n \"def reset(self,prepare=True):\\n if self.lowmem:\\n self.load(self.filename,prepare=prepare)\\n else:\\n self.data = np.copy(self.data_orig)\\n self.weights = np.copy(self.weights_orig)\\n self.durations = self.getSubintinfo('TSUBINT')\\n #if prepare:\\n # self.scrunch()\",\n \"def _update_raw_data(params):\\n from scipy.signal import filtfilt\\n start = params['t_start']\\n stop = params['raw'].time_as_index(start + params['duration'])[0]\\n start = params['raw'].time_as_index(start)[0]\\n data_picks = _pick_data_channels(params['raw'].info)\\n data, times = params['raw'][:, start:stop]\\n if params['projector'] is not None:\\n data = np.dot(params['projector'], data)\\n # remove DC\\n if params['remove_dc'] is True:\\n data -= np.mean(data, axis=1)[:, np.newaxis]\\n if params['ba'] is not None:\\n data[data_picks] = filtfilt(params['ba'][0], params['ba'][1],\\n data[data_picks], axis=1, padlen=0)\\n # scale\\n for di in range(data.shape[0]):\\n data[di] /= params['scalings'][params['types'][di]]\\n # stim channels should be hard limited\\n if params['types'][di] == 'stim':\\n norm = float(max(data[di]))\\n data[di] /= norm if norm > 0 else 1.\\n # clip\\n if params['clipping'] == 'transparent':\\n data[np.logical_or(data > 1, data < -1)] = np.nan\\n elif params['clipping'] == 'clamp':\\n data = np.clip(data, -1, 1, data)\\n params['data'] = data\\n params['times'] = times\",\n \"def initial_values(self):\\n y = self._y\\n trend = self.trend\\n seasonal = self.seasonal\\n seasoning = self.seasoning\\n trending = self.trending\\n m = self.seasonal_periods\\n l0 = self._l0\\n b0 = self._b0\\n if seasoning:\\n l0 = y[np.arange(self.nobs) % m == 0].mean() if l0 is None else l0\\n if b0 is None and trending:\\n lead, lag = y[m:m + m], y[:m]\\n if trend == 'mul':\\n b0 = np.exp((np.log(lead.mean()) - np.log(lag.mean())) / m)\\n else:\\n b0 = ((lead - lag) / m).mean()\\n s0 = list(y[:m] / l0) if seasonal == 'mul' else list(y[:m] - l0)\\n elif trending:\\n l0 = y[0] if l0 is None else l0\\n if b0 is None:\\n b0 = y[1] / y[0] if trend == 'mul' else y[1] - y[0]\\n s0 = []\\n else:\\n if l0 is None:\\n l0 = y[0]\\n b0 = None\\n s0 = []\\n\\n return l0, b0, s0\",\n \"def set_normalization(self, dataloader):\\n mean = 0\\n square = 0\\n for (data_in, _) in dataloader:\\n mean += data_in.mean()\\n square += data_in.pow(2).mean()\\n\\n mean /= len(dataloader)\\n square /= len(dataloader)\\n std = np.sqrt(square - mean ** 2)\\n\\n # The input data should be roughly normally distributed after\\n # passing through net_fixed.\\n self.scale_in.bias.data.fill_(- mean / std)\\n self.scale_in.weight.data.fill_(1 / std)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60771775","0.5992512","0.5975735","0.5974339","0.58970016","0.5867703","0.58252496","0.58230865","0.58149874","0.57894266","0.5773829","0.5726125","0.5725895","0.570828","0.56946415","0.56483364","0.56139916","0.56139916","0.5607044","0.5600587","0.55963033","0.55920905","0.5582612","0.5579361","0.55742526","0.5564162","0.5557394","0.55548453","0.5535021","0.55293477","0.5523192","0.55056876","0.5499174","0.548968","0.5477518","0.5443166","0.54399365","0.54393846","0.54393846","0.54367536","0.54304236","0.5417016","0.5414098","0.5407009","0.5401339","0.5384272","0.53825945","0.53794795","0.5371984","0.5370848","0.5369808","0.5362136","0.53582627","0.53549135","0.535198","0.53416926","0.53413755","0.5333011","0.53296614","0.53228897","0.53219104","0.5316748","0.53160113","0.5315672","0.5315385","0.53108156","0.52979434","0.5290341","0.52849185","0.5282454","0.527862","0.52715725","0.52691305","0.52650857","0.5262745","0.5260897","0.52578217","0.5251552","0.5251023","0.52438265","0.5240015","0.52391315","0.52336013","0.5232967","0.5230532","0.522285","0.52211815","0.5216346","0.52156276","0.52128845","0.52125025","0.52109","0.52080005","0.5202108","0.5195656","0.5194835","0.51943403","0.5189432","0.5189039","0.518749"],"string":"[\n \"0.60771775\",\n \"0.5992512\",\n \"0.5975735\",\n \"0.5974339\",\n \"0.58970016\",\n \"0.5867703\",\n \"0.58252496\",\n \"0.58230865\",\n \"0.58149874\",\n \"0.57894266\",\n \"0.5773829\",\n \"0.5726125\",\n \"0.5725895\",\n \"0.570828\",\n \"0.56946415\",\n \"0.56483364\",\n \"0.56139916\",\n \"0.56139916\",\n \"0.5607044\",\n \"0.5600587\",\n \"0.55963033\",\n \"0.55920905\",\n \"0.5582612\",\n \"0.5579361\",\n \"0.55742526\",\n \"0.5564162\",\n \"0.5557394\",\n \"0.55548453\",\n \"0.5535021\",\n \"0.55293477\",\n \"0.5523192\",\n \"0.55056876\",\n \"0.5499174\",\n \"0.548968\",\n \"0.5477518\",\n \"0.5443166\",\n \"0.54399365\",\n \"0.54393846\",\n \"0.54393846\",\n \"0.54367536\",\n \"0.54304236\",\n \"0.5417016\",\n \"0.5414098\",\n \"0.5407009\",\n \"0.5401339\",\n \"0.5384272\",\n \"0.53825945\",\n \"0.53794795\",\n \"0.5371984\",\n \"0.5370848\",\n \"0.5369808\",\n \"0.5362136\",\n \"0.53582627\",\n \"0.53549135\",\n \"0.535198\",\n \"0.53416926\",\n \"0.53413755\",\n \"0.5333011\",\n \"0.53296614\",\n \"0.53228897\",\n \"0.53219104\",\n \"0.5316748\",\n \"0.53160113\",\n \"0.5315672\",\n \"0.5315385\",\n \"0.53108156\",\n \"0.52979434\",\n \"0.5290341\",\n \"0.52849185\",\n \"0.5282454\",\n \"0.527862\",\n \"0.52715725\",\n \"0.52691305\",\n \"0.52650857\",\n \"0.5262745\",\n \"0.5260897\",\n \"0.52578217\",\n \"0.5251552\",\n \"0.5251023\",\n \"0.52438265\",\n \"0.5240015\",\n \"0.52391315\",\n \"0.52336013\",\n \"0.5232967\",\n \"0.5230532\",\n \"0.522285\",\n \"0.52211815\",\n \"0.5216346\",\n \"0.52156276\",\n \"0.52128845\",\n \"0.52125025\",\n \"0.52109\",\n \"0.52080005\",\n \"0.5202108\",\n \"0.5195656\",\n \"0.5194835\",\n \"0.51943403\",\n \"0.5189432\",\n \"0.5189039\",\n \"0.518749\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60240406"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11084,"cells":{"query":{"kind":"string","value":"Check on input data for proper shape and dtype"},"document":{"kind":"string","value":"def _convert_to_vector_type(self, vectors):\n if vectors.shape[-1] == 4 and vectors.ndim == 2:\n coord_list = self._convert_coords_to_coordinates(vectors)\n self._data_type = self._data_types[1]\n\n elif vectors.shape[-1] == 2 and vectors.ndim == 3:\n coord_list = self._convert_image_to_coordinates(vectors)\n self._data_type = self._data_types[0]\n\n else:\n raise TypeError(\n \"Vector data of shape %s is not supported\" %\n str(vectors.shape))\n\n return coord_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _check_shape(placeholder_shape, data_shape):\n\n return True","def check_dims(self, data):\n if np.ndim(data) != 2:\n raise ValueError('Input data must be a two dimensional numpy array. '\n 'Data received has shape (%g, %g).' % data.shape)","def check_data_shape(self, data_shape):\n if not len(data_shape) == 2:\n raise ValueError('data_shape should have length 2')\n if not data_shape[0] == 1:\n raise ValueError('This iterator expects inputs to have 1 channels.')","def _validate_input(self, data: Union[np.ndarray, pd.DataFrame, pd.Series],\n expected_dim: int, inference: bool = False) -> np.ndarray:\n allowed_types = (\n np.ndarray,\n pd.core.frame.DataFrame,\n pd.core.frame.Series\n )\n\n if type(data) not in allowed_types:\n raise TypeError('Supported input types: np.ndarray, '\n 'pd.core.frame.DataFrame, pd.core.frame.Series got'\n ' {}'.format(type(data)))\n\n if isinstance(data, pd.DataFrame) or isinstance(data, pd.Series):\n data = data.values\n\n if data.size == 0:\n raise ValueError('Empty array passed to fit() or predict()')\n\n if data.ndim > expected_dim:\n raise ValueError('Data with incorrect number of dimensions '\n 'passed to fit() or predict(). Max dim is '\n '{}, got {}'.format(expected_dim, data.ndim))\n\n if not np.issubdtype(data.dtype, np.number):\n raise ValueError('Non numeric value found in data')\n\n if not np.isfinite(data).all():\n raise ValueError('Data contains nan or inf')\n\n if inference:\n # additional checks on prediction time\n if not self._fitted:\n raise ValueError('Fit the model first.')\n\n if self._ndim == 2 and data.shape[-1] != self._shape[-1]:\n raise ValueError('Number of features does not match'\n ' data model was trained on. Expected'\n ' {}, got {}'\n .format(self._shape[-1], data.shape[-1]))\n\n return data","def _validate_dimensionality(self):\r\n\r\n if self.time.ndim != 1:\r\n raise ValueError(\"time array must be one-dimensional\")\r\n npoints = self.data.shape[-1]\r\n if npoints != len(self.time):\r\n raise ValueError(\"mismatch of time and data dimensions\")","def _isvalid(self, data):\n if data is None:\n return False\n elif isinstance(data, (list,tuple)):\n if len(data) <= 0:\n return False\n else:\n return True\n elif isinstance(data, (np.ndarray)):\n if data.size <= 0:\n return False\n else:\n return True\n elif not data:\n return False\n else:\n return True","def check_input_dimension(self, data):\n if len(data[0]) != self.input_dimension:\n raise ValueError(\"Received {} features, expected {}.\".format(self.input_dimension, len(data[0])))","def _type_check(data):\n if data.__class__.__name__ != \"Matrix3\":\n return False\n return True","def validate_ndarray(ndarray, expected_dtypes, expected_dimentions, name):\n\tvalid_dtype_assertion(expected_dtypes, ndarray.dtype, name)\n\tvalid_ndim_assertion(expected_dimentions, ndarray.ndim, name)","def _check_input_for_asarray(array_like):\n if isinstance(array_like, (Tensor, list, tuple, int, float, bool, onp.ndarray)):\n return True\n raise TypeError(\"input data must be `int`, `float`, `bool`, `Tensor`, `list`, `tuple`\" + \\\n f\"or numpy.ndarray, but got {type(array_like)}\")","def _handle_input_data(data):\n data = np.asarray(data)\n if np.ndim(data) == 1:\n d_rows = 1\n d_cols = len(data)\n data = data.reshape((1, data.shape[0]))\n elif np.ndim(data) == 2:\n d_rows = data.shape[0]\n d_cols = data.shape[1]\n else:\n raise ValueError(\"Incorrect dimensionality of data. Must be <= 2\")\n return data, d_rows, d_cols","def _check_data(data):\n if not (data.dtype == _np.float32 and data.flags.c_contiguous):\n raise ValueError('supplied data must be float32 and C contiguous')\n if data.ndim == 2:\n num_frames, channels = data.shape\n elif data.ndim == 1:\n num_frames, channels = data.size, 1\n else:\n raise ValueError('rank > 2 not supported')\n return num_frames, channels","def _verify_integrity(self):\n if len(self.data.shape) != 1:\n raise ValueError(\n \"Data array must be one dimensional \"\n \"(is {})\".format(len(self.data.shape))\n )\n\n if len(self.shape.shape) != 2:\n raise ValueError(\n \"Shape array must be two dimensional \"\n \"(is {})\".format(len(self.shape.shape))\n )\n\n shape_size, data_size = self._cumsum[-1], self.data.size\n\n if not shape_size == data_size:\n raise ValueError(\n \"Size of data ({data_size}) does not match that \"\n \"of the given shapes ({shape_size}).\".format(\n data_size=data_size, shape_size=shape_size\n )\n )","def __check_signal(self, signal):\n if not(isinstance(signal, np.ndarray)):\n raise TypeError()\n if len(signal.shape) != 1:\n raise TypeError()\n if not(hasattr(self, 'dim')):\n self.dim = signal.shape[0]\n else:\n if signal.shape[0] != self.dim:\n raise TypeError()","def verify_transformed(self, data):\n data_dim = data.shape[-1]\n if data_dim != self.dimension:\n error(\n \"{} result dimension {} does not match the prescribed input dimension {}\"\n .format(self.name, data_dim, self.dimension))\n nans, _ = np.where(np.isnan(data))\n if np.size(nans) != 0:\n error(\"{} result contains nan elements in :{}\".format(\n self.name, nans))","def _validate_input_data(self):\n\n if type(self.data) in [np.ndarray, da.Array]:\n if not self.data.dtype.names:\n raise ValueError('QuadTree: numpy array provided for data, but no names were found, array must be a structured array')\n if 'x' not in self.data.dtype.names or 'y' not in self.data.dtype.names:\n raise ValueError('QuadTree: numpy structured array provided for data, but \"x\" or \"y\" not found in variable names')\n self.layernames = [self.rev_layer_lookup[var] for var in self.data.dtype.names if var in ['z', 'tvu']]\n elif type(self.data) == xr.Dataset:\n if 'x' not in self.data:\n raise ValueError('QuadTree: xarray Dataset provided for data, but \"x\" or \"y\" not found in variable names')\n if len(self.data.dims) > 1:\n raise ValueError('QuadTree: xarray Dataset provided for data, but found multiple dimensions, must be one dimensional: {}'.format(self.data.dims))\n self.layernames = [self.rev_layer_lookup[var] for var in self.data if var in ['z', 'tvu']]\n self._convert_dataset() # internally we just convert xarray dataset to numpy for ease of use\n else:\n raise ValueError('QuadTree: numpy structured array or dask array with \"x\" and \"y\" as variable must be provided')","def _verify_data(inputs, targets):\n check_value_type('inputs', inputs, Tensor)\n if len(inputs.shape) != 4:\n raise ValueError(f'Argument inputs must be 4D Tensor, but got {len(inputs.shape)}D Tensor.')\n check_value_type('targets', targets, (Tensor, int, tuple, list))\n if isinstance(targets, Tensor):\n if len(targets.shape) > 2:\n raise ValueError('Dimension invalid. If `targets` is a Tensor, it should be 0D, 1D or 2D. '\n 'But got {}D.'.format(len(targets.shape)))\n if targets.shape and len(targets) != len(inputs):\n raise ValueError(\n 'If `targets` is a 2D, 1D Tensor, it should have the same length as inputs {}. But got {}.'.format(\n len(inputs), len(targets)))","def _check_shape(self, X):\n return all([X.shape[i] == self.train_shape[i] for i in range(2)])","def checkDataShape(self, data):\n if data.shape[0] > 1:\n msg.notifyMessage(\"Looks like you did not open a single data frame. \"\n \"Automated calibration only works with single frame data.\")\n return None\n else:\n return data[0]","def do_grid_check(self,):\n self.ydim, self.xdim = self.data_fcst.shape \n if self.data_obs.shape != (self.ydim,self.xdim):\n raise FormatError(\"Obs and forecast data not same size.\")\n return","def __check_signal(self, signal: np.ndarray):\n # if signal is a list, convert it to nparray\n if isinstance(signal, list):\n signal = np.array(signal)\n # if signal is still not nparray, raise error\n if not (isinstance(signal, np.ndarray)):\n print(\"1\") \n # unknown type of input signal\n raise TypeError()\n # if signal is not a vector of shape(nFeature, )\n if len(signal.shape) != 1:\n print(\"2\") \n # input signal has to be a vector\n raise TypeError()\n # set self.dim\n self.dim = signal.shape[0]\n # if self still doesn't has the attribute 'dim', set it.\n if not (hasattr(self, 'dim')):\n self.dim = signal.shape[0]\n else:\n # if dim of signal doesn't match self.dim, raise error\n if signal.shape[0] != self.dim:\n print(\"3\")\n raise TypeError()\n return signal","def _check_data(data, dset_attrs):\n dtype = dset_attrs['dtype']\n float_to_int = (np.issubdtype(dtype, np.integer)\n and np.issubdtype(data.dtype, np.floating))\n if float_to_int:\n attrs = dset_attrs['attrs']\n scale_factor = [c for c in attrs if 'scale_factor' in c][0]\n scale_factor = attrs[scale_factor]\n\n # apply scale factor and dtype\n data = np.multiply(data, scale_factor)\n if np.issubdtype(dtype, np.integer):\n data = np.round(data)\n\n data = data.astype(dtype)\n\n return data","def _check_shape(input_shape):\n msg = ('Input to FlattenAxis must be 5D with dimensions: '\n '(n_observations, n_spatial_0, n_spatial_1, n_temporal, '\n 'n_features), but received shape: {}'.format(input_shape))\n assert len(input_shape) == 5, msg","def can_insert(data):\n if not isinstance(data, np.ndarray):\n return False\n if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:\n return False\n return np.issubdtype(data.dtype, np.number)","def _check_dtype(input_dtype):\n\n product_version = tbe_platform.cce_conf.get_soc_spec(\"SOC_VERSION\")\n if product_version in (\"Hi3796CV300ES\"):\n if input_dtype == \"float32\":\n raise RuntimeError(\"float32 is not support in ES\")\n util.check_dtype_rule(input_dtype, (\"float16\",))\n else:\n util.check_dtype_rule(input_dtype, (\"float16\", \"float32\",))","def do_type_checking(self, node):\r\n\r\n if not isinstance(node.inputs[0].type, CudaNdarrayType):\r\n raise NotImplementedError()","def _check_dimensions(self) -> None:\n dims = (self.y_dim, self.x_dim)\n da = self._obj[self.vars[0]] if isinstance(self._obj, xr.Dataset) else self._obj\n extra_dims = [dim for dim in da.dims if dim not in dims]\n if len(extra_dims) == 1:\n dims = tuple(extra_dims) + dims\n self.set_attrs(dim0=extra_dims[0])\n elif len(extra_dims) == 0:\n self._obj.coords[GEO_MAP_COORD].attrs.pop(\"dim0\", None)\n elif len(extra_dims) > 1:\n raise ValueError(\"Only 2D and 3D data arrays supported.\")\n if isinstance(self._obj, xr.Dataset):\n check = np.all([self._obj[name].dims == dims for name in self.vars])\n else:\n check = self._obj.dims == dims\n if check == False:\n raise ValueError(\n f\"Invalid dimension order ({da.dims}). \"\n f\"You can use `obj.transpose({dims}) to reorder your dimensions.\"\n )","def check_type(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\n\tassert type(X) == xr.DataArray, 'XCast requires a dataset to be of type \"Xarray.DataArray\"'","def _shape_check(self, X, y):\n if not len(y.shape) > 1:\n raise RuntimeError(\"The shape of y is incorrect.\")\n if y.shape != X.shape[:-1]:\n raise RuntimeError(\"X and y must have the same number of \" +\n \"samples and microstructure shape.\")\n if X.shape[-1] != 3:\n raise RuntimeError(\"X must have 3 continuous local states \" +\n \"(euler angles)\")","def f_supports(self, data):\n dtype = type(data)\n if dtype is tuple or dtype is list:\n\n # Parameters cannot handle empty tuples\n if len(data) == 0:\n return False\n\n old_type = None\n\n # Check if the data in the tuple is homogeneous\n for item in data:\n if not type(item) in pypetconstants.PARAMETER_SUPPORTED_DATA:\n return False\n if not old_type is None and old_type != type(item):\n return False\n old_type = type(item)\n return True\n\n elif dtype is np.ndarray or dtype is np.matrix:\n\n if data.size == 0:\n return False # Parameters cannot handle empty arrays and matrices\n\n # Numpy has many string types that depend on the length of the string,\n # We allow all of them\n dtype = data.dtype\n if np.issubdtype(dtype, str):\n dtype = str\n\n return dtype in pypetconstants.PARAMETER_SUPPORTED_DATA","def test_data(self):\n\n self.assertIsInstance(self.image.data, np.ndarray)","def _match_shape_to_data_tensor(self,\n data: 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor]',\n target_type: Literal['X', 'Y']\n ) -> 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor]':\n target_shape = getattr(self, f'{target_type}_dims')\n if data.shape == target_shape:\n data = data[None]\n if data.shape[1:] != target_shape:\n raise ValueError((f'Input `{target_type}` has shape {data.shape[1:]}'\n f' but training data has shape {target_shape}'))\n return data","def _check_shape(input_shape):\n msg = ('Input to SpatialExpansion must be 4D with dimensions: '\n '(n_observations, n_spatial_0, n_spatial_1, n_features), '\n 'but received shape: {}'.format(input_shape))\n assert len(input_shape) == 4, msg","def check_input_shape(self, op, block):\n\n ipt_name = op.input(op.input_names[0])\n ipt_shape = block.var(ipt_name).shape\n for i in ipt_shape:\n if i < 0:\n warning_msg = (\n f\"Input {ipt_name}(shape={ipt_shape}) has unkown dimension shapes. \"\n f\"Specifying static values may improve performance\"\n )\n warnings.warn(warning_msg)","def check_data_type_column_data(X):\n if type(X) is not numpy.ndarray:\n raise TypeError(\"X should be type numpy.ndarray\")\n\n if len(X.shape) == 2 and X.shape[1] > 1:\n raise TypeError(\"X should have a single column.\")","def f_supports(self, data):\n dtype = type(data)\n if dtype is tuple or dtype is list and len(data) == 0:\n return True # ArrayParameter does support empty tuples\n elif dtype is np.ndarray and data.size == 0 and data.ndim == 1:\n return True # ArrayParameter supports empty numpy arrays\n else:\n return super(ArrayParameter, self).f_supports(data)","def _check_dataset(self, dataset):\n if not isinstance(dataset, Dataset):\n raise ValueError('wrong training_set or validation_set are not instances of the nn.Dataset class')\n\n if dataset.inputs.shape[1] != self.arch[0]:\n raise ValueError('dataset inputs shape is inconsistent with number of network input nodes.')\n\n if dataset.targets.shape[1] != self.arch[-1]:\n raise ValueError('dataset targets shape is inconsistent with number of network output nodes.')","def correct_type(data):\n\n if all(data[col].dtypes == data.dtypes[0] for col in data.columns):\n if all(data[col].isnull().sum() == 0 for col in data.columns):\n print('All columns have values of the correct type.')\n else:\n print('Bad result.')","def _format_data(self, data: np.ndarray) -> np.ndarray:\n if self._validate:\n if len(data.shape) <= 1:\n raise DataProcessorError(\n \"The data should be an array with at least two dimensions.\"\n )\n\n return data","def _check_inputvalues(self):\n # Check x, y and z are int or float dtypes\n # ie do not contain any unusable values like strings\n if not (self.x.dtype in [np.int, np.float]):\n raise TypeError(\"Route input 'x' must be either int or float dtypes\")\n\n if not (self.y.dtype in [np.int, np.float]):\n raise TypeError(\"Route input 'x' must be either int or float dtypes\")\n\n # Performs checks on z if not empty\n if self.z is not None:\n for v in self.z.values():\n if not (v.dtype in [np.int, np.float]):\n raise TypeError(\"Route input 'x' must be either int or float dtypes\")","def check_param(self):\n check_tuple = (\"float16\", \"float32\", \"int32\")\n check_shape(self.shape_x, param_name=\"x\")\n check_shape(self.shape_indices, param_name=\"indices\")\n check_shape(self.shape_v, param_name=\"v\")\n check_dtype(self.dtype_x, check_tuple, param_name=\"x\")\n check_dtype(self.dtype_indices, (\"int32\",), param_name=\"indices\")\n check_dtype(self.dtype_v, check_tuple, param_name=\"v\")\n if len(self.shape_x) != len(self.shape_v):\n raise RuntimeError(\"The number of dimension x must\"\n \" be same as dimension v\")\n\n if self.shape_v[0] != self.shape_indices[0]:\n raise RuntimeError(\"The length of rank 0 of tensor v must\"\n \" be the same as length of indices\")\n\n if len(self.shape_indices) != 1:\n raise RuntimeError(\"The length of indices only support 1\")\n for i in range(1, len(self.shape_v)):\n if self.shape_x[i] != self.shape_v[i]:\n if not self.check_special():\n raise RuntimeError(\"The length of each rank of tensor x\"\n \" must be the same as length of\"\n \" each or next rank of tensor v\")","def _check_shape(self, obj, expected_shape):\n if self.shape != expected_shape:\n raise ValueError(\n '%s expects internal signal %s to be %s, but it is %s' % (\n obj, self.name, Shaped(expected_shape).describe_shape(),\n self.describe_shape()))","def check_input_shapes(*args):\n\n # Collect the shapes of the inputs\n shapes = set()\n\n # DESIGN NOTES - currently allow:\n # - scalars,\n # - 0 dim ndarrays (also scalars but packaged differently)\n # - 1 dim ndarrays with only a single value\n\n for val in args:\n if isinstance(val, np.ndarray):\n # Note that 0-dim ndarrays (which are scalars) pass through as do\n # one dimensional arrays with a single value (also a scalar)\n if not(val.ndim == 0 or val.shape == (1,)):\n shapes.add(val.shape)\n # elif isinstance(val, Series):\n # # Note that 0-dim ndarrays (which are scalars) pass through\n # if val.ndim > 0:\n # shapes.add(val.shape)\n elif val is None or isinstance(val, (float, int, np.generic)):\n pass # No need to track scalars and optional values pass None\n else:\n raise ValueError(f'Unexpected input to check_input_shapes: {type(val)}')\n\n # shapes can be an empty set (all scalars) or contain one common shape\n # otherwise raise an error\n if len(shapes) > 1:\n raise ValueError('Inputs contain arrays of different shapes.')\n\n if len(shapes) == 1:\n return shapes.pop()\n\n return 1","def validate_dataset(self):\n pass","def _check_input_len(self, data):\n data_len = len(data[0])\n if self._input_len != data_len:\n msg = 'Received %d features, expected %d.' % (data_len,\n self._input_len)\n raise ValueError(msg)","def _check_dtype(self):\n\n # assert valid dtype\n if self.dtype not in PRIMITIVE_TYPES:\n raise ValueError(\"Type '{}' is invalid. Following types are \"\n \"allowed: {}\"\n .format(self.dtype, PRIMITIVE_TYPES.keys()))\n\n # assert valid dtypes for values\n allowed_types = PRIMITIVE_TYPES[self.dtype]\n\n for value in self.values:\n if not isinstance(value, allowed_types):\n raise TypeError(\"Column '{}' has invalud value '{}' with \"\n \"invalid type '{}'. Allowed types are: {}.\"\n .format(self.name,\n value,\n type(value),\n allowed_types))","def check_shape(self):\r\n if np.array(self.img).shape != (1536, 2048, 3):\r\n raise BadShape","def _check_shape(input_shape):\n msg = ('Input to SpatioTemporalExpansion must be 5D with dimensions: '\n '(n_observations, n_spatial_0, n_spatial_1, n_temporal, '\n 'n_features), but received shape: {}'.format(input_shape))\n assert len(input_shape) == 5, msg","def validate_shape_and_dtype(self):\n if self.rgb.dtype != tf.float32:\n raise ValueError(\"Expected float32 rgb!\")\n if len(self.rgb.shape) != 4:\n raise ValueError(f\"Expected (B, H, W, C), got {self.rgb.shape}\")\n _, _, _, channels = self.rgb.shape.as_list()\n if channels != 3:\n raise ValueError(f\"Expected 3 rgb channels, got shape {self.rgb.shape}\")","def check_input_data(self, align=True):\n return self.check_data_list(self.inputs, align)","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 raise(ValueError, '{0}.shape must equal initial_heading.shape'.format(item))","def validate_data_format(\n Xs: List[Tensor], Ys: List[Tensor], Yvars: List[Tensor], metric_names: List[str]\n) -> None:\n if len({len(Xs), len(Ys), len(Yvars), len(metric_names)}) > 1:\n raise ValueError( # pragma: no cover\n \"Lengths of Xs, Ys, Yvars, and metric_names must match. Your \"\n f\"inputs have lengths {len(Xs)}, {len(Ys)}, {len(Yvars)}, and \"\n f\"{len(metric_names)}, respectively.\"\n )","def _validate_from_plain(data: Sequence[Sequence],\n columns: Sequence[str],\n dtypes: Sequence[str],\n row_wise: bool):\n\n if row_wise:\n # assert equal number of elements across rows\n row_lenghts = {len(row) for row in data}\n if len(row_lenghts) > 1:\n raise ValueError(\"Input data has varying number of values per \"\n \"row. Please check provided input data\")\n\n # assert equal number of columns and elements per row\n row_lenghts.add(len(columns))\n if len(row_lenghts) > 1:\n raise ValueError(\n \"Number of columns has to equal the number of \"\n \"values per row. Please check column names and \"\n \"provided input data.\")\n\n # assert equal number of dtypes and elements per row\n row_lenghts.add(len(dtypes))\n if len(row_lenghts) > 1:\n raise ValueError(\"Number of dtypes has to equal the number of \"\n \"values per row. Please check dtypes and \"\n \"provided input data.\")\n\n else:\n # assert equal number of elements across columns\n col_lengths = {len(col) for col in data}\n if len(col_lengths) > 1:\n raise ValueError(\"Input data has varying number of values per \"\n \"columns. Please check provided input data\")\n\n # assert equal number of columns in data, column names and dtypes\n col_count = len(columns)\n if col_count != len(data):\n raise ValueError(\"Input data and column names have different \"\n \"amount of columns. Please check provided \"\n \"input data\")\n\n if col_count != len(dtypes):\n raise ValueError(\"Input data and dtypes have different \"\n \"amount of columns. Please check provided \"\n \"input data\")","def _check_uvh5_dtype(dtype):\n if not isinstance(dtype, np.dtype):\n raise ValueError(\"dtype in a uvh5 file must be a numpy dtype\")\n if \"r\" not in dtype.names or \"i\" not in dtype.names:\n raise ValueError(\n \"dtype must be a compound datatype with an 'r' field and an 'i' field\"\n )\n rkind = dtype[\"r\"].kind\n ikind = dtype[\"i\"].kind\n if rkind != ikind:\n raise ValueError(\n \"dtype must have the same kind ('i4', 'r8', etc.) for both real \"\n \"and imaginary fields\"\n )\n return","def check_data(network, data_set):\n data_input, data_targets = data_set\n assert len(data_input[0]) == network.num_inputs, \\\n \"ERROR: input size varies from the defined input setting\"\n assert len(data_targets[0]) == network.layers[-1][0], \\\n \"ERROR: output size varies from the defined output setting\"\n\n inputs = np.array([data_point for data_point in data_input])\n targets = np.array([data_target for data_target in data_targets])\n\n return inputs, targets","def enforce_2D(self,):\n for data in (self.data_obs,self.data_fcst):\n shp = data.shape\n if len(shp) == 2:\n pass \n elif len(shp) == 3:\n if shp[0] == 0:\n data = data[0,:,:]\n elif len(shp) == 4:\n if (shp[0] == 0) and (shp[1] == 0):\n data = data[0,0,:,:]\n else:\n raise FormatError(\"Data needs to be 2D.\")\n return","def _is_DataArrays(data):\n if isinstance(data, (Dataset, DataArray)):\n return True\n if isinstance(data, Mapping):\n for da in data.values():\n if not isinstance(da, DataArray):\n raise TypeError(\"Please provide List/Mapping of DataArrays\")\n return True\n if isinstance(data, Iterable):\n for da in data:\n if not isinstance(da, DataArray):\n return False\n # raise TypeError(\"Please provide List/Mapping of DataArrays\")\n return True\n return False","def _isscalar(x):\n return np.isscalar(x) or hasattr(x, \"shape\") and x.shape == ()","def checkTrainData(cls, data):\n\n if data == None or len(data) == 0:\n raise Exception(\"No data\")\n\n if type(data[0]) != tuple:\n raise Exception(\"Not a list of tuples\")\n\n if len(data[0]) != 2 and type(data[0][0]) != str and type(data[0][1]) != list:\n raise Exception(\"Not a tuple of (String, [data])\")\n\n length = len(data[0][1])\n\n for tup in data:\n if len(tup) != 2 and type(tup[0]) != str and type(tup[1]) != list:\n raise Exception(\"Not a tuple of (String, [data])\")\n\n if len(tup[1]) != length:\n raise Exception(\"Not all elements have the same amount of data\")","def _format_data(self, data: np.ndarray) -> np.ndarray:\n if self._validate:\n if len(data.shape) <= self._axis:\n raise DataProcessorError(\n f\"Cannot average the {len(data.shape)} dimensional \"\n f\"array along axis {self._axis}.\"\n )\n\n return data","def _validate_dtype():\n\n test_array = _spsparse.random(5, 5, density=0.5, format=\"csc\", dtype=np.float32, random_state=50)\n test_comparison = test_array.A\n\n csc_ref, precision_flag = _create_mkl_sparse(test_array)\n\n try:\n csr_ref = _convert_to_csr(csc_ref)\n final_array = _export_mkl(csr_ref, precision_flag)\n if not np.allclose(test_comparison, final_array.A):\n raise ValueError(\"Match failed after matrix conversion\")\n _destroy_mkl_handle(csr_ref)\n finally:\n _destroy_mkl_handle(csc_ref)","def _check_dtype_array(array, dtype):\n # enlist the dtype expected\n if isinstance(dtype, type):\n dtype = [dtype]\n\n # check the dtype of the array\n for dtype_expected in dtype:\n if array.dtype == dtype_expected:\n return\n raise TypeError(\"{0} is not supported yet. Use one of those dtypes \"\n \"instead: {1}.\".format(array.dtype, dtype))","def validate_input(self):\n self._validate_limits_cols_prefixed()\n self._validate_fillna_cols_prefixed()\n self._validate_ratio_input()","def _check_type(title, data_y):\n if(title not in data_conf):\n data_conf[title] = {}\n if('data_type' not in data_conf[title]):\n if(isinstance(data_y, numpy.ndarray)):\n # Special handling for data plots\n if(len(data_y.shape) == 1) or (len(data_y.shape) == 2 and data_y.shape[0] == 2):\n data_conf[title]['data_type'] = 'vector'\n # Images with more longer first dimension\n elif(len(data_y.shape) == 2):\n data_conf[title]['data_type'] = 'image'\n else:\n raise ValueError((\"%dD data not supported; shape=%s\" %\n (len(data_y.shape), data_y.shape)))\n\n else:\n data_conf[title]['data_type'] = 'scalar'\n if(ipc.mpi.is_slave()):\n ipc.mpi.send('__data_conf__', data_conf)","def safe_check(dicts, kernel_name):\n x_shape = dicts[0].get(\"shape\")\n x_dtype = dicts[0].get(\"dtype\").lower()\n rois_shape = dicts[1].get(\"shape\")\n rois_dtype = dicts[1].get(\"dtype\").lower()\n\n y_dtype = dicts[3].get(\"dtype\").lower()\n y_shape = dicts[3].get(\"shape\")\n\n profile = tik.Dprofile()\n tik_name_check = tbe_platform.cce_conf.get_soc_spec(\"SOC_VERSION\")\n if tik_name_check in (\"Ascend310\", \"Ascend910\", \"Hi3796CV300ES\", \"Hi3796CV300CS\"):\n util.check_dtype_rule(x_dtype, (\"float16\",))\n util.check_dtype_rule(rois_dtype, (\"float16\",))\n else:\n util.check_dtype_rule(x_dtype, (\"float16\", \"float32\"))\n util.check_dtype_rule(rois_dtype, (\"float16\", \"float32\"))\n\n if x_dtype != rois_dtype or x_dtype != y_dtype:\n raise RuntimeError(\"dtype in x, rois and y must be equal\")\n\n util.check_shape_rule(x_shape, min_dim=5, max_dim=5)\n util.check_tensor_shape_size(x_shape)\n util.check_shape_rule(rois_shape, min_dim=3, max_dim=3)\n util.check_tensor_shape_size(rois_shape)\n util.check_shape_rule(y_shape, min_dim=5, max_dim=5)\n util.check_tensor_shape_size(y_shape)\n\n roi_max_num = rois_shape[2]\n if roi_max_num > 6000 or roi_max_num % 16 != 0:\n raise ValueError(\"the dim 2 of rois_shape is illegal\")\n\n util.check_kernel_name(kernel_name)","def dimension_check():\n print(\"### DIMENSION CHECK ###\")\n print(X.shape,\n y.shape,\n X_train.shape,\n y_train.shape,\n X_test.shape,\n y_test.shape,\n weights.shape)\n print(\"### END ###\")","def _check_input_args(scale, shape, dtype):\n if tf.as_dtype(dtype) not in (tf.int32, tf.int64):\n raise ValueError(\n f'Only tf.int32 and tf.int64 are supported. Found dtype `{dtype}`.')\n\n checks = [\n tf.compat.v1.assert_non_negative(scale),\n tf.compat.v1.assert_integer(scale)\n ]\n with tf.control_dependencies(checks):\n return tf.identity(scale), shape, dtype","def _is_number(data):\n return len(data) and np.issubdtype(_to_ndarray(data).dtype, np.number)","def test_shape(self, varname, varshape, ndim): \n if len(varshape) != ndim:\n raise ShapeError('Shape=%s. Expected %i-D array for %s' %\n (repr(varshape), ndim, varname))","def is_array(a):\n try:\n shape = a.shape\n return len(shape) >= 1\n except AttributeError:\n return False","def _check_input_params(input_params):\n\n in_dtype, dst_dtype, perm = input_params\n\n if in_dtype != dst_dtype:\n error_manager_vector.raise_err_inputs_dtype_not_equal(\"transpose_d\", \"in_dtype\",\n \"dst_dtype\", in_dtype, dst_dtype)\n\n if len(perm) < 2 or len(perm) > 3:\n perm_rule = \"the perm length should be in range (2,3)\"\n error_manager_vector.raise_err_check_params_rules(\"transpose_d\", perm_rule,\n \"perm\", len(perm))\n\n if in_dtype not in TYPE_FLOAT_LIST:\n error_detail = \"only support dtype float32 now\"\n error_manager_vector.raise_err_two_input_dtype_invalid(\"transpose_d\", \"in_dtype\",\n \"dst_dtype\", error_detail)","def _check_binary_data(data):\n if not np.array_equal(data, data.astype(bool)):\n raise ValueError(\n \"This mechanism works with binary data, \"\n \"but input is not binary.\")","def test_with_valid_input(self):\n for dataset_type in ['regular', 'raw', 'REGULAR', 'RAW']:\n try:\n check_dataset_type(dataset_type)\n except ValueError:\n self.fail(\"Dataset {0} should be valid\".format(dataset_type))","def check_dimensions(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\n\tassert 4 <= len(X.dims) <= 5, 'XCast requires a dataset to be 4-Dimensional'\n\tassert x_lat_dim in X.dims, 'XCast requires a dataset_lat_dim to be a dimension on X'\n\tassert x_lon_dim in X.dims, 'XCast requires a dataset_lon_dim to be a dimension on X'\n\tassert x_sample_dim in X.dims, 'XCast requires a dataset_sample_dim to be a dimension on X'\n\tassert x_feature_dim in X.dims, 'XCast requires a dataset_feature_dim to be a dimension on X'","def is_valid(self, dataset):\n pass","def _match_shape_to_data(self,\n data: 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor, Any, List[Any]]',\n target_type: Literal['X', 'Y']\n ) -> 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor, List[Any]]':\n if self._is_tensor(data):\n return self._match_shape_to_data_tensor(data, target_type)\n return self._match_shape_to_data_any(data)","def _check_input_timeseries(x: np.ndarray) -> np.ndarray:\n if not isinstance(x, np.ndarray):\n raise ValueError(\"The input time series must be a numpy array.\")\n if x.ndim <= 0 or x.ndim >= 4:\n raise ValueError(\n \"The input time series must have more than 0 dimensions and\"\n \"less than 4 dimensions.\"\n )\n if x.ndim == 3:\n return x[0]\n return x","def _verify_audio_data(audio_data):\n if audio_data is None:\n return None\n\n if isinstance(audio_data, list):\n audio_data = np.array(audio_data)\n\n if not isinstance(audio_data, np.ndarray):\n raise ValueError('Type of audio_data must be of type np.ndarray!')\n\n if not np.isfinite(audio_data).all():\n raise ValueError('Not all values of audio_data are finite!')\n\n if audio_data.ndim > 1 \\\n and audio_data.shape[constants.CHAN_INDEX] > audio_data.shape[constants.LEN_INDEX]:\n warnings.warn('audio_data is not as we expect it. Transposing signal...')\n audio_data = audio_data.T\n\n if audio_data.ndim > 2:\n raise ValueError('audio_data cannot have more than 2 dimensions!')\n\n if audio_data.ndim < 2:\n audio_data = np.expand_dims(audio_data, axis=constants.CHAN_INDEX)\n\n return audio_data","def _check_and_transform_input(self, data):\n if isinstance(data, list):\n if np.array(data).shape == (len(data),):\n if len(data) == 1:\n data = np.array(data).reshape(1, 1)\n data = np.array(data).reshape(len(data), 1)\n else:\n data = np.concatenate(data).reshape(len(data), -1)\n else:\n raise TypeError('Input data should be of type list, but found type {}'.format(type(data)))\n\n return data","def can_insert(data):\n types = (float, complex, int, np.long)\n if isinstance(data, types) and not isinstance(data, bool):\n return True\n elif isinstance(data, np.number):\n return data.dtype.char not in UNSUPPORTED_NUMERIC_TYPE_CODES","def check_consistent(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\n\tassert X.shape[list(X.dims).index(x_lat_dim)] == len(X.coords[x_lat_dim].values), \"XCast requires a dataset's x_lat_dim coordinate to be the same length as its x_lat_dim dimension\"\n\tassert X.shape[list(X.dims).index(x_lon_dim)] == len(X.coords[x_lon_dim].values), \"XCast requires a dataset's x_lon_dim coordinate to be the same length as its x_lon_dim dimension\"\n\tassert X.shape[list(X.dims).index(x_sample_dim)] == len(X.coords[x_sample_dim].values), \"XCast requires a dataset's x_sample_dim coordinate to be the same length as its x_sample_dim dimension\"\n\tassert X.shape[list(X.dims).index(x_feature_dim)] == len(X.coords[x_feature_dim].values), \"XCast requires a dataset's x_feature_dim coordinate to be the same length as its x_feature_dim dimension\"","def valid_dtype_assertion(expected_dtypes, actual_dtype, name):\n\tassert (actual_dtype in expected_dtypes), \"Invalid dtype of {} should be {}\".format(name, str(expected_dtypes))","def __verify_arguments(self):\n if len(self.__pointer_data) == 0:\n raise ValueError(\n \"Input data is empty (size: '%d').\" % len(self.__pointer_data)\n )\n\n if self.__number_clusters <= 0:\n raise ValueError(\n \"Amount of cluster (current value: '%d') for allocation should be greater than 0.\"\n % self.__number_clusters\n )\n\n if self.__numlocal < 0:\n raise ValueError(\n \"Local minima (current value: '%d') should be greater or equal to 0.\"\n % self.__numlocal\n )\n\n if self.__maxneighbor < 0:\n raise ValueError(\n \"Maximum number of neighbors (current value: '%d') should be greater or \"\n \"equal to 0.\" % self.__maxneighbor\n )","def __verify_arguments(self):\r\n if len(self.__pointer_data) == 0:\r\n raise ValueError(\"Input data is empty (size: '%d').\" % len(self.__pointer_data))\r\n\r\n if self.__number_clusters <= 0:\r\n raise ValueError(\"Amount of cluster (current value: '%d') for allocation should be greater than 0.\" %\r\n self.__number_clusters)\r\n\r\n if self.__numlocal < 0:\r\n raise ValueError(\"Local minima (current value: '%d') should be greater or equal to 0.\" % self.__numlocal)\r\n\r\n if self.__maxneighbor < 0:\r\n raise ValueError(\"Maximum number of neighbors (current value: '%d') should be greater or \"\r\n \"equal to 0.\" % self.__maxneighbor)","def _check_input(self, X):\n symbols = np.concatenate(X)\n if len(symbols) == 1: # not enough data\n raise ValueError(\"expected at least 1 observation \"\n \"but none found.\")\n elif (symbols < 0).any(): # contains negative integers\n raise ValueError(\"expected non-negative features \"\n \"for each observation.\")\n elif X.shape[1] > 1: # contains to many features\n raise ValueError(\"expected only 1 feature but got {0} \"\n \"for each observation.\".format(X.shape[1]))\n else:\n return True","def check_input_dims(df: pd.DataFrame,\n input_check_flag: str,\n input_file_name: str = None,\n raise_err: bool = True\n ) -> bool:\n messages = []\n file_name = input_file_name or input_check_flag\n target_dims = INPUT_CHECKS[input_check_flag]\n if df.shape[0] != target_dims[0]:\n messages.append(f\"Incorrect number of rows in {file_name}: Should be \"\n f\"{target_dims[0]} but found {df.shape[0]}\")\n if df.shape[1] != target_dims[1]:\n messages.append(f\"Incorrect number of columns in {file_name}: Should \"\n f\"be {target_dims[1]} but found {df.shape[1]}\")\n if len(messages) > 0:\n if raise_err:\n raise ValueError(\"::\".join(messages))\n else:\n warnings.warn(\"::\".join(messages))\n return False\n return True","def _check_data_shape_to_num_outputs(preds: Tensor, target: Tensor, num_outputs: int) ->None:\n if preds.ndim > 2 or target.ndim > 2:\n raise ValueError(f'Expected both predictions and target to be either 1- or 2-dimensional tensors, but got {target.ndim} and {preds.ndim}.')\n if num_outputs == 1 and preds.ndim != 1 or num_outputs > 1 and num_outputs != preds.shape[1]:\n raise ValueError(f'Expected argument `num_outputs` to match the second dimension of input, but got {num_outputs} and {preds.shape[1]}.')","def is_valid(self) -> bool:\r\n try:\r\n self.shape\r\n return True\r\n except ValueError:\r\n return False","def _check_dtype(ds_in, dset_attrs):\n dtype = dset_attrs['dtype']\n attrs = dset_attrs['attrs']\n if ds_in.dtype.name != dtype:\n msg = ('Source dtype ({}) does not match specified dtype ({}), '\n .format(ds_in.dtype, dtype))\n logger.warning(msg)\n warn(msg)\n float_to_int = (np.issubdtype(ds_in.dtype, np.floating)\n and np.issubdtype(dtype, np.integer))\n int_to_float = (np.issubdtype(ds_in.dtype, np.integer)\n and np.issubdtype(dtype, np.floating))\n if float_to_int:\n if not any(c for c in attrs if 'scale_factor' in c):\n msg = ('Cannot downscale from {} to {} without a '\n 'scale_factor!'.format(ds_in.dtype, dtype))\n logger.error(msg)\n raise RuntimeError(msg)\n else:\n msg = 'Converting {} to {}'.format(ds_in.dtype, dtype)\n logger.warning(msg)\n warn(msg)\n elif int_to_float:\n msg = ('Cannot scale up an {} to a {}'\n .format(ds_in.dtype, dtype))\n logger.error(msg)\n raise RuntimeError(msg)\n elif not np.issubdtype(dtype, ds_in.dtype):\n msg = ('Output dtype ({}) has greater precision than input '\n 'dtype ({}), using input dtype'\n .format(dtype, ds_in.dtype))\n logger.warning(msg)\n warn(msg)\n\n dset_attrs['dtype'] = ds_in.dtype\n\n return dset_attrs","def _format_data(self, data: np.ndarray) -> np.ndarray:\n\n self._n_shots = len(data[0])\n self._n_circuits = len(data)\n\n if self._validate:\n if data.shape[:2] != (self._n_circuits, self._n_shots):\n raise DataProcessorError(\n f\"The datum given to {self.__class__.__name__} does not convert \"\n \"of an array with dimension (number of circuit, number of shots).\"\n )\n\n return data","def _is_all_input_shape_generalize(input_shape_tuple):\n for elem in input_shape_tuple:\n if not is_shape_unknown(elem.shape):\n return False\n return True","def do_type_checking(self, node):\r\n\r\n if not isinstance(node.inputs[0].type, GpuArrayType):\r\n raise NotImplementedError()","def check_transformations(*args):\n assert args[0].shape == (21,21)\n assert args[0].dtype == np.float64\n if len(args) == 2:\n assert args[1].shape == (2,2)\n assert args[1].dtype == np.float64","def test_shapes(self):\n\n # Creates a raw layer\n self.validator.adata.raw = self.validator.adata\n self.validator.adata.raw.var.drop(\"feature_is_filtered\", axis=1, inplace=True)\n self.validator.adata.X = examples.adata_non_raw.X.copy()\n self.validator.adata.uns[\"X_normalization\"] = \"CPM\"\n\n # remove one gene\n self.validator.adata = self.validator.adata[:, 1:]\n self.validator.validate_adata()\n self.assertEqual(\n self.validator.errors,\n [\"ERROR: Number of genes in X (3) is different than raw.X (4).\"],\n )","def _format_data(self, data: np.ndarray) -> np.ndarray:\n if self._validate:\n if not all(0.0 <= p <= 1.0 for p in data):\n raise DataProcessorError(\n f\"Input data for node {self.__class__.__name__} is not likely probability.\"\n )\n\n return data","def shape_for_keras(data):\n raise NotImplementedError","def test_types(self):\n \n self.assertIsInstance(self.tx_data_in, numpy.ndarray)\n self.assertIsInstance(self.circuit_simulation, bool)\n self.assertIsInstance(self.bypass, bool)\n \n pass","def _check_data_valid(self):\n\n is_valid = (sum(~np.isnan(self.data).flatten()) > 0 and self.data.flatten().sum() != 0)\n if not is_valid:\n raise FITSException(f\"No data in {self.survey}\")","def test_shape_error_1():\n dmd = DMD()\n with raises(ValueError):\n dmd.fit(X=sample_data, Y=sample_data[:, 1:])","def __check_is_xarray(self, data):\n if type(data) is xr.core.dataarray.DataArray or \\\n type(data) is xr.core.dataarray.Dataset:\n\n return True\n else:\n msg = \"Variable {data} is not an xarray DataArray/Dataset\"\n raise Exception(msg)","def format_write_data(data, mydtype):\n try:\n len(data) # Raise TypeError if no len\n dshape = data.shape\n except TypeError:\n # Data has no length\n data = np.asarray(data, dtype=mydtype)\n dshape = data.shape\n\n except AttributeError:\n # Data is not a numpy array\n data = np.asarray(data, dtype=mydtype)\n dshape = data.shape\n\n # Force at least 1 column\n if get_shape_columns(dshape) == 0:\n data = np.reshape(data, (-1, 1))\n dshape = data.shape\n\n return data, dshape"],"string":"[\n \"def _check_shape(placeholder_shape, data_shape):\\n\\n return True\",\n \"def check_dims(self, data):\\n if np.ndim(data) != 2:\\n raise ValueError('Input data must be a two dimensional numpy array. '\\n 'Data received has shape (%g, %g).' % data.shape)\",\n \"def check_data_shape(self, data_shape):\\n if not len(data_shape) == 2:\\n raise ValueError('data_shape should have length 2')\\n if not data_shape[0] == 1:\\n raise ValueError('This iterator expects inputs to have 1 channels.')\",\n \"def _validate_input(self, data: Union[np.ndarray, pd.DataFrame, pd.Series],\\n expected_dim: int, inference: bool = False) -> np.ndarray:\\n allowed_types = (\\n np.ndarray,\\n pd.core.frame.DataFrame,\\n pd.core.frame.Series\\n )\\n\\n if type(data) not in allowed_types:\\n raise TypeError('Supported input types: np.ndarray, '\\n 'pd.core.frame.DataFrame, pd.core.frame.Series got'\\n ' {}'.format(type(data)))\\n\\n if isinstance(data, pd.DataFrame) or isinstance(data, pd.Series):\\n data = data.values\\n\\n if data.size == 0:\\n raise ValueError('Empty array passed to fit() or predict()')\\n\\n if data.ndim > expected_dim:\\n raise ValueError('Data with incorrect number of dimensions '\\n 'passed to fit() or predict(). Max dim is '\\n '{}, got {}'.format(expected_dim, data.ndim))\\n\\n if not np.issubdtype(data.dtype, np.number):\\n raise ValueError('Non numeric value found in data')\\n\\n if not np.isfinite(data).all():\\n raise ValueError('Data contains nan or inf')\\n\\n if inference:\\n # additional checks on prediction time\\n if not self._fitted:\\n raise ValueError('Fit the model first.')\\n\\n if self._ndim == 2 and data.shape[-1] != self._shape[-1]:\\n raise ValueError('Number of features does not match'\\n ' data model was trained on. Expected'\\n ' {}, got {}'\\n .format(self._shape[-1], data.shape[-1]))\\n\\n return data\",\n \"def _validate_dimensionality(self):\\r\\n\\r\\n if self.time.ndim != 1:\\r\\n raise ValueError(\\\"time array must be one-dimensional\\\")\\r\\n npoints = self.data.shape[-1]\\r\\n if npoints != len(self.time):\\r\\n raise ValueError(\\\"mismatch of time and data dimensions\\\")\",\n \"def _isvalid(self, data):\\n if data is None:\\n return False\\n elif isinstance(data, (list,tuple)):\\n if len(data) <= 0:\\n return False\\n else:\\n return True\\n elif isinstance(data, (np.ndarray)):\\n if data.size <= 0:\\n return False\\n else:\\n return True\\n elif not data:\\n return False\\n else:\\n return True\",\n \"def check_input_dimension(self, data):\\n if len(data[0]) != self.input_dimension:\\n raise ValueError(\\\"Received {} features, expected {}.\\\".format(self.input_dimension, len(data[0])))\",\n \"def _type_check(data):\\n if data.__class__.__name__ != \\\"Matrix3\\\":\\n return False\\n return True\",\n \"def validate_ndarray(ndarray, expected_dtypes, expected_dimentions, name):\\n\\tvalid_dtype_assertion(expected_dtypes, ndarray.dtype, name)\\n\\tvalid_ndim_assertion(expected_dimentions, ndarray.ndim, name)\",\n \"def _check_input_for_asarray(array_like):\\n if isinstance(array_like, (Tensor, list, tuple, int, float, bool, onp.ndarray)):\\n return True\\n raise TypeError(\\\"input data must be `int`, `float`, `bool`, `Tensor`, `list`, `tuple`\\\" + \\\\\\n f\\\"or numpy.ndarray, but got {type(array_like)}\\\")\",\n \"def _handle_input_data(data):\\n data = np.asarray(data)\\n if np.ndim(data) == 1:\\n d_rows = 1\\n d_cols = len(data)\\n data = data.reshape((1, data.shape[0]))\\n elif np.ndim(data) == 2:\\n d_rows = data.shape[0]\\n d_cols = data.shape[1]\\n else:\\n raise ValueError(\\\"Incorrect dimensionality of data. Must be <= 2\\\")\\n return data, d_rows, d_cols\",\n \"def _check_data(data):\\n if not (data.dtype == _np.float32 and data.flags.c_contiguous):\\n raise ValueError('supplied data must be float32 and C contiguous')\\n if data.ndim == 2:\\n num_frames, channels = data.shape\\n elif data.ndim == 1:\\n num_frames, channels = data.size, 1\\n else:\\n raise ValueError('rank > 2 not supported')\\n return num_frames, channels\",\n \"def _verify_integrity(self):\\n if len(self.data.shape) != 1:\\n raise ValueError(\\n \\\"Data array must be one dimensional \\\"\\n \\\"(is {})\\\".format(len(self.data.shape))\\n )\\n\\n if len(self.shape.shape) != 2:\\n raise ValueError(\\n \\\"Shape array must be two dimensional \\\"\\n \\\"(is {})\\\".format(len(self.shape.shape))\\n )\\n\\n shape_size, data_size = self._cumsum[-1], self.data.size\\n\\n if not shape_size == data_size:\\n raise ValueError(\\n \\\"Size of data ({data_size}) does not match that \\\"\\n \\\"of the given shapes ({shape_size}).\\\".format(\\n data_size=data_size, shape_size=shape_size\\n )\\n )\",\n \"def __check_signal(self, signal):\\n if not(isinstance(signal, np.ndarray)):\\n raise TypeError()\\n if len(signal.shape) != 1:\\n raise TypeError()\\n if not(hasattr(self, 'dim')):\\n self.dim = signal.shape[0]\\n else:\\n if signal.shape[0] != self.dim:\\n raise TypeError()\",\n \"def verify_transformed(self, data):\\n data_dim = data.shape[-1]\\n if data_dim != self.dimension:\\n error(\\n \\\"{} result dimension {} does not match the prescribed input dimension {}\\\"\\n .format(self.name, data_dim, self.dimension))\\n nans, _ = np.where(np.isnan(data))\\n if np.size(nans) != 0:\\n error(\\\"{} result contains nan elements in :{}\\\".format(\\n self.name, nans))\",\n \"def _validate_input_data(self):\\n\\n if type(self.data) in [np.ndarray, da.Array]:\\n if not self.data.dtype.names:\\n raise ValueError('QuadTree: numpy array provided for data, but no names were found, array must be a structured array')\\n if 'x' not in self.data.dtype.names or 'y' not in self.data.dtype.names:\\n raise ValueError('QuadTree: numpy structured array provided for data, but \\\"x\\\" or \\\"y\\\" not found in variable names')\\n self.layernames = [self.rev_layer_lookup[var] for var in self.data.dtype.names if var in ['z', 'tvu']]\\n elif type(self.data) == xr.Dataset:\\n if 'x' not in self.data:\\n raise ValueError('QuadTree: xarray Dataset provided for data, but \\\"x\\\" or \\\"y\\\" not found in variable names')\\n if len(self.data.dims) > 1:\\n raise ValueError('QuadTree: xarray Dataset provided for data, but found multiple dimensions, must be one dimensional: {}'.format(self.data.dims))\\n self.layernames = [self.rev_layer_lookup[var] for var in self.data if var in ['z', 'tvu']]\\n self._convert_dataset() # internally we just convert xarray dataset to numpy for ease of use\\n else:\\n raise ValueError('QuadTree: numpy structured array or dask array with \\\"x\\\" and \\\"y\\\" as variable must be provided')\",\n \"def _verify_data(inputs, targets):\\n check_value_type('inputs', inputs, Tensor)\\n if len(inputs.shape) != 4:\\n raise ValueError(f'Argument inputs must be 4D Tensor, but got {len(inputs.shape)}D Tensor.')\\n check_value_type('targets', targets, (Tensor, int, tuple, list))\\n if isinstance(targets, Tensor):\\n if len(targets.shape) > 2:\\n raise ValueError('Dimension invalid. If `targets` is a Tensor, it should be 0D, 1D or 2D. '\\n 'But got {}D.'.format(len(targets.shape)))\\n if targets.shape and len(targets) != len(inputs):\\n raise ValueError(\\n 'If `targets` is a 2D, 1D Tensor, it should have the same length as inputs {}. But got {}.'.format(\\n len(inputs), len(targets)))\",\n \"def _check_shape(self, X):\\n return all([X.shape[i] == self.train_shape[i] for i in range(2)])\",\n \"def checkDataShape(self, data):\\n if data.shape[0] > 1:\\n msg.notifyMessage(\\\"Looks like you did not open a single data frame. \\\"\\n \\\"Automated calibration only works with single frame data.\\\")\\n return None\\n else:\\n return data[0]\",\n \"def do_grid_check(self,):\\n self.ydim, self.xdim = self.data_fcst.shape \\n if self.data_obs.shape != (self.ydim,self.xdim):\\n raise FormatError(\\\"Obs and forecast data not same size.\\\")\\n return\",\n \"def __check_signal(self, signal: np.ndarray):\\n # if signal is a list, convert it to nparray\\n if isinstance(signal, list):\\n signal = np.array(signal)\\n # if signal is still not nparray, raise error\\n if not (isinstance(signal, np.ndarray)):\\n print(\\\"1\\\") \\n # unknown type of input signal\\n raise TypeError()\\n # if signal is not a vector of shape(nFeature, )\\n if len(signal.shape) != 1:\\n print(\\\"2\\\") \\n # input signal has to be a vector\\n raise TypeError()\\n # set self.dim\\n self.dim = signal.shape[0]\\n # if self still doesn't has the attribute 'dim', set it.\\n if not (hasattr(self, 'dim')):\\n self.dim = signal.shape[0]\\n else:\\n # if dim of signal doesn't match self.dim, raise error\\n if signal.shape[0] != self.dim:\\n print(\\\"3\\\")\\n raise TypeError()\\n return signal\",\n \"def _check_data(data, dset_attrs):\\n dtype = dset_attrs['dtype']\\n float_to_int = (np.issubdtype(dtype, np.integer)\\n and np.issubdtype(data.dtype, np.floating))\\n if float_to_int:\\n attrs = dset_attrs['attrs']\\n scale_factor = [c for c in attrs if 'scale_factor' in c][0]\\n scale_factor = attrs[scale_factor]\\n\\n # apply scale factor and dtype\\n data = np.multiply(data, scale_factor)\\n if np.issubdtype(dtype, np.integer):\\n data = np.round(data)\\n\\n data = data.astype(dtype)\\n\\n return data\",\n \"def _check_shape(input_shape):\\n msg = ('Input to FlattenAxis must be 5D with dimensions: '\\n '(n_observations, n_spatial_0, n_spatial_1, n_temporal, '\\n 'n_features), but received shape: {}'.format(input_shape))\\n assert len(input_shape) == 5, msg\",\n \"def can_insert(data):\\n if not isinstance(data, np.ndarray):\\n return False\\n if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:\\n return False\\n return np.issubdtype(data.dtype, np.number)\",\n \"def _check_dtype(input_dtype):\\n\\n product_version = tbe_platform.cce_conf.get_soc_spec(\\\"SOC_VERSION\\\")\\n if product_version in (\\\"Hi3796CV300ES\\\"):\\n if input_dtype == \\\"float32\\\":\\n raise RuntimeError(\\\"float32 is not support in ES\\\")\\n util.check_dtype_rule(input_dtype, (\\\"float16\\\",))\\n else:\\n util.check_dtype_rule(input_dtype, (\\\"float16\\\", \\\"float32\\\",))\",\n \"def do_type_checking(self, node):\\r\\n\\r\\n if not isinstance(node.inputs[0].type, CudaNdarrayType):\\r\\n raise NotImplementedError()\",\n \"def _check_dimensions(self) -> None:\\n dims = (self.y_dim, self.x_dim)\\n da = self._obj[self.vars[0]] if isinstance(self._obj, xr.Dataset) else self._obj\\n extra_dims = [dim for dim in da.dims if dim not in dims]\\n if len(extra_dims) == 1:\\n dims = tuple(extra_dims) + dims\\n self.set_attrs(dim0=extra_dims[0])\\n elif len(extra_dims) == 0:\\n self._obj.coords[GEO_MAP_COORD].attrs.pop(\\\"dim0\\\", None)\\n elif len(extra_dims) > 1:\\n raise ValueError(\\\"Only 2D and 3D data arrays supported.\\\")\\n if isinstance(self._obj, xr.Dataset):\\n check = np.all([self._obj[name].dims == dims for name in self.vars])\\n else:\\n check = self._obj.dims == dims\\n if check == False:\\n raise ValueError(\\n f\\\"Invalid dimension order ({da.dims}). \\\"\\n f\\\"You can use `obj.transpose({dims}) to reorder your dimensions.\\\"\\n )\",\n \"def check_type(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\\n\\tassert type(X) == xr.DataArray, 'XCast requires a dataset to be of type \\\"Xarray.DataArray\\\"'\",\n \"def _shape_check(self, X, y):\\n if not len(y.shape) > 1:\\n raise RuntimeError(\\\"The shape of y is incorrect.\\\")\\n if y.shape != X.shape[:-1]:\\n raise RuntimeError(\\\"X and y must have the same number of \\\" +\\n \\\"samples and microstructure shape.\\\")\\n if X.shape[-1] != 3:\\n raise RuntimeError(\\\"X must have 3 continuous local states \\\" +\\n \\\"(euler angles)\\\")\",\n \"def f_supports(self, data):\\n dtype = type(data)\\n if dtype is tuple or dtype is list:\\n\\n # Parameters cannot handle empty tuples\\n if len(data) == 0:\\n return False\\n\\n old_type = None\\n\\n # Check if the data in the tuple is homogeneous\\n for item in data:\\n if not type(item) in pypetconstants.PARAMETER_SUPPORTED_DATA:\\n return False\\n if not old_type is None and old_type != type(item):\\n return False\\n old_type = type(item)\\n return True\\n\\n elif dtype is np.ndarray or dtype is np.matrix:\\n\\n if data.size == 0:\\n return False # Parameters cannot handle empty arrays and matrices\\n\\n # Numpy has many string types that depend on the length of the string,\\n # We allow all of them\\n dtype = data.dtype\\n if np.issubdtype(dtype, str):\\n dtype = str\\n\\n return dtype in pypetconstants.PARAMETER_SUPPORTED_DATA\",\n \"def test_data(self):\\n\\n self.assertIsInstance(self.image.data, np.ndarray)\",\n \"def _match_shape_to_data_tensor(self,\\n data: 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor]',\\n target_type: Literal['X', 'Y']\\n ) -> 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor]':\\n target_shape = getattr(self, f'{target_type}_dims')\\n if data.shape == target_shape:\\n data = data[None]\\n if data.shape[1:] != target_shape:\\n raise ValueError((f'Input `{target_type}` has shape {data.shape[1:]}'\\n f' but training data has shape {target_shape}'))\\n return data\",\n \"def _check_shape(input_shape):\\n msg = ('Input to SpatialExpansion must be 4D with dimensions: '\\n '(n_observations, n_spatial_0, n_spatial_1, n_features), '\\n 'but received shape: {}'.format(input_shape))\\n assert len(input_shape) == 4, msg\",\n \"def check_input_shape(self, op, block):\\n\\n ipt_name = op.input(op.input_names[0])\\n ipt_shape = block.var(ipt_name).shape\\n for i in ipt_shape:\\n if i < 0:\\n warning_msg = (\\n f\\\"Input {ipt_name}(shape={ipt_shape}) has unkown dimension shapes. \\\"\\n f\\\"Specifying static values may improve performance\\\"\\n )\\n warnings.warn(warning_msg)\",\n \"def check_data_type_column_data(X):\\n if type(X) is not numpy.ndarray:\\n raise TypeError(\\\"X should be type numpy.ndarray\\\")\\n\\n if len(X.shape) == 2 and X.shape[1] > 1:\\n raise TypeError(\\\"X should have a single column.\\\")\",\n \"def f_supports(self, data):\\n dtype = type(data)\\n if dtype is tuple or dtype is list and len(data) == 0:\\n return True # ArrayParameter does support empty tuples\\n elif dtype is np.ndarray and data.size == 0 and data.ndim == 1:\\n return True # ArrayParameter supports empty numpy arrays\\n else:\\n return super(ArrayParameter, self).f_supports(data)\",\n \"def _check_dataset(self, dataset):\\n if not isinstance(dataset, Dataset):\\n raise ValueError('wrong training_set or validation_set are not instances of the nn.Dataset class')\\n\\n if dataset.inputs.shape[1] != self.arch[0]:\\n raise ValueError('dataset inputs shape is inconsistent with number of network input nodes.')\\n\\n if dataset.targets.shape[1] != self.arch[-1]:\\n raise ValueError('dataset targets shape is inconsistent with number of network output nodes.')\",\n \"def correct_type(data):\\n\\n if all(data[col].dtypes == data.dtypes[0] for col in data.columns):\\n if all(data[col].isnull().sum() == 0 for col in data.columns):\\n print('All columns have values of the correct type.')\\n else:\\n print('Bad result.')\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n if self._validate:\\n if len(data.shape) <= 1:\\n raise DataProcessorError(\\n \\\"The data should be an array with at least two dimensions.\\\"\\n )\\n\\n return data\",\n \"def _check_inputvalues(self):\\n # Check x, y and z are int or float dtypes\\n # ie do not contain any unusable values like strings\\n if not (self.x.dtype in [np.int, np.float]):\\n raise TypeError(\\\"Route input 'x' must be either int or float dtypes\\\")\\n\\n if not (self.y.dtype in [np.int, np.float]):\\n raise TypeError(\\\"Route input 'x' must be either int or float dtypes\\\")\\n\\n # Performs checks on z if not empty\\n if self.z is not None:\\n for v in self.z.values():\\n if not (v.dtype in [np.int, np.float]):\\n raise TypeError(\\\"Route input 'x' must be either int or float dtypes\\\")\",\n \"def check_param(self):\\n check_tuple = (\\\"float16\\\", \\\"float32\\\", \\\"int32\\\")\\n check_shape(self.shape_x, param_name=\\\"x\\\")\\n check_shape(self.shape_indices, param_name=\\\"indices\\\")\\n check_shape(self.shape_v, param_name=\\\"v\\\")\\n check_dtype(self.dtype_x, check_tuple, param_name=\\\"x\\\")\\n check_dtype(self.dtype_indices, (\\\"int32\\\",), param_name=\\\"indices\\\")\\n check_dtype(self.dtype_v, check_tuple, param_name=\\\"v\\\")\\n if len(self.shape_x) != len(self.shape_v):\\n raise RuntimeError(\\\"The number of dimension x must\\\"\\n \\\" be same as dimension v\\\")\\n\\n if self.shape_v[0] != self.shape_indices[0]:\\n raise RuntimeError(\\\"The length of rank 0 of tensor v must\\\"\\n \\\" be the same as length of indices\\\")\\n\\n if len(self.shape_indices) != 1:\\n raise RuntimeError(\\\"The length of indices only support 1\\\")\\n for i in range(1, len(self.shape_v)):\\n if self.shape_x[i] != self.shape_v[i]:\\n if not self.check_special():\\n raise RuntimeError(\\\"The length of each rank of tensor x\\\"\\n \\\" must be the same as length of\\\"\\n \\\" each or next rank of tensor v\\\")\",\n \"def _check_shape(self, obj, expected_shape):\\n if self.shape != expected_shape:\\n raise ValueError(\\n '%s expects internal signal %s to be %s, but it is %s' % (\\n obj, self.name, Shaped(expected_shape).describe_shape(),\\n self.describe_shape()))\",\n \"def check_input_shapes(*args):\\n\\n # Collect the shapes of the inputs\\n shapes = set()\\n\\n # DESIGN NOTES - currently allow:\\n # - scalars,\\n # - 0 dim ndarrays (also scalars but packaged differently)\\n # - 1 dim ndarrays with only a single value\\n\\n for val in args:\\n if isinstance(val, np.ndarray):\\n # Note that 0-dim ndarrays (which are scalars) pass through as do\\n # one dimensional arrays with a single value (also a scalar)\\n if not(val.ndim == 0 or val.shape == (1,)):\\n shapes.add(val.shape)\\n # elif isinstance(val, Series):\\n # # Note that 0-dim ndarrays (which are scalars) pass through\\n # if val.ndim > 0:\\n # shapes.add(val.shape)\\n elif val is None or isinstance(val, (float, int, np.generic)):\\n pass # No need to track scalars and optional values pass None\\n else:\\n raise ValueError(f'Unexpected input to check_input_shapes: {type(val)}')\\n\\n # shapes can be an empty set (all scalars) or contain one common shape\\n # otherwise raise an error\\n if len(shapes) > 1:\\n raise ValueError('Inputs contain arrays of different shapes.')\\n\\n if len(shapes) == 1:\\n return shapes.pop()\\n\\n return 1\",\n \"def validate_dataset(self):\\n pass\",\n \"def _check_input_len(self, data):\\n data_len = len(data[0])\\n if self._input_len != data_len:\\n msg = 'Received %d features, expected %d.' % (data_len,\\n self._input_len)\\n raise ValueError(msg)\",\n \"def _check_dtype(self):\\n\\n # assert valid dtype\\n if self.dtype not in PRIMITIVE_TYPES:\\n raise ValueError(\\\"Type '{}' is invalid. Following types are \\\"\\n \\\"allowed: {}\\\"\\n .format(self.dtype, PRIMITIVE_TYPES.keys()))\\n\\n # assert valid dtypes for values\\n allowed_types = PRIMITIVE_TYPES[self.dtype]\\n\\n for value in self.values:\\n if not isinstance(value, allowed_types):\\n raise TypeError(\\\"Column '{}' has invalud value '{}' with \\\"\\n \\\"invalid type '{}'. Allowed types are: {}.\\\"\\n .format(self.name,\\n value,\\n type(value),\\n allowed_types))\",\n \"def check_shape(self):\\r\\n if np.array(self.img).shape != (1536, 2048, 3):\\r\\n raise BadShape\",\n \"def _check_shape(input_shape):\\n msg = ('Input to SpatioTemporalExpansion must be 5D with dimensions: '\\n '(n_observations, n_spatial_0, n_spatial_1, n_temporal, '\\n 'n_features), but received shape: {}'.format(input_shape))\\n assert len(input_shape) == 5, msg\",\n \"def validate_shape_and_dtype(self):\\n if self.rgb.dtype != tf.float32:\\n raise ValueError(\\\"Expected float32 rgb!\\\")\\n if len(self.rgb.shape) != 4:\\n raise ValueError(f\\\"Expected (B, H, W, C), got {self.rgb.shape}\\\")\\n _, _, _, channels = self.rgb.shape.as_list()\\n if channels != 3:\\n raise ValueError(f\\\"Expected 3 rgb channels, got shape {self.rgb.shape}\\\")\",\n \"def check_input_data(self, align=True):\\n return self.check_data_list(self.inputs, align)\",\n \"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 raise(ValueError, '{0}.shape must equal initial_heading.shape'.format(item))\",\n \"def validate_data_format(\\n Xs: List[Tensor], Ys: List[Tensor], Yvars: List[Tensor], metric_names: List[str]\\n) -> None:\\n if len({len(Xs), len(Ys), len(Yvars), len(metric_names)}) > 1:\\n raise ValueError( # pragma: no cover\\n \\\"Lengths of Xs, Ys, Yvars, and metric_names must match. Your \\\"\\n f\\\"inputs have lengths {len(Xs)}, {len(Ys)}, {len(Yvars)}, and \\\"\\n f\\\"{len(metric_names)}, respectively.\\\"\\n )\",\n \"def _validate_from_plain(data: Sequence[Sequence],\\n columns: Sequence[str],\\n dtypes: Sequence[str],\\n row_wise: bool):\\n\\n if row_wise:\\n # assert equal number of elements across rows\\n row_lenghts = {len(row) for row in data}\\n if len(row_lenghts) > 1:\\n raise ValueError(\\\"Input data has varying number of values per \\\"\\n \\\"row. Please check provided input data\\\")\\n\\n # assert equal number of columns and elements per row\\n row_lenghts.add(len(columns))\\n if len(row_lenghts) > 1:\\n raise ValueError(\\n \\\"Number of columns has to equal the number of \\\"\\n \\\"values per row. Please check column names and \\\"\\n \\\"provided input data.\\\")\\n\\n # assert equal number of dtypes and elements per row\\n row_lenghts.add(len(dtypes))\\n if len(row_lenghts) > 1:\\n raise ValueError(\\\"Number of dtypes has to equal the number of \\\"\\n \\\"values per row. Please check dtypes and \\\"\\n \\\"provided input data.\\\")\\n\\n else:\\n # assert equal number of elements across columns\\n col_lengths = {len(col) for col in data}\\n if len(col_lengths) > 1:\\n raise ValueError(\\\"Input data has varying number of values per \\\"\\n \\\"columns. Please check provided input data\\\")\\n\\n # assert equal number of columns in data, column names and dtypes\\n col_count = len(columns)\\n if col_count != len(data):\\n raise ValueError(\\\"Input data and column names have different \\\"\\n \\\"amount of columns. Please check provided \\\"\\n \\\"input data\\\")\\n\\n if col_count != len(dtypes):\\n raise ValueError(\\\"Input data and dtypes have different \\\"\\n \\\"amount of columns. Please check provided \\\"\\n \\\"input data\\\")\",\n \"def _check_uvh5_dtype(dtype):\\n if not isinstance(dtype, np.dtype):\\n raise ValueError(\\\"dtype in a uvh5 file must be a numpy dtype\\\")\\n if \\\"r\\\" not in dtype.names or \\\"i\\\" not in dtype.names:\\n raise ValueError(\\n \\\"dtype must be a compound datatype with an 'r' field and an 'i' field\\\"\\n )\\n rkind = dtype[\\\"r\\\"].kind\\n ikind = dtype[\\\"i\\\"].kind\\n if rkind != ikind:\\n raise ValueError(\\n \\\"dtype must have the same kind ('i4', 'r8', etc.) for both real \\\"\\n \\\"and imaginary fields\\\"\\n )\\n return\",\n \"def check_data(network, data_set):\\n data_input, data_targets = data_set\\n assert len(data_input[0]) == network.num_inputs, \\\\\\n \\\"ERROR: input size varies from the defined input setting\\\"\\n assert len(data_targets[0]) == network.layers[-1][0], \\\\\\n \\\"ERROR: output size varies from the defined output setting\\\"\\n\\n inputs = np.array([data_point for data_point in data_input])\\n targets = np.array([data_target for data_target in data_targets])\\n\\n return inputs, targets\",\n \"def enforce_2D(self,):\\n for data in (self.data_obs,self.data_fcst):\\n shp = data.shape\\n if len(shp) == 2:\\n pass \\n elif len(shp) == 3:\\n if shp[0] == 0:\\n data = data[0,:,:]\\n elif len(shp) == 4:\\n if (shp[0] == 0) and (shp[1] == 0):\\n data = data[0,0,:,:]\\n else:\\n raise FormatError(\\\"Data needs to be 2D.\\\")\\n return\",\n \"def _is_DataArrays(data):\\n if isinstance(data, (Dataset, DataArray)):\\n return True\\n if isinstance(data, Mapping):\\n for da in data.values():\\n if not isinstance(da, DataArray):\\n raise TypeError(\\\"Please provide List/Mapping of DataArrays\\\")\\n return True\\n if isinstance(data, Iterable):\\n for da in data:\\n if not isinstance(da, DataArray):\\n return False\\n # raise TypeError(\\\"Please provide List/Mapping of DataArrays\\\")\\n return True\\n return False\",\n \"def _isscalar(x):\\n return np.isscalar(x) or hasattr(x, \\\"shape\\\") and x.shape == ()\",\n \"def checkTrainData(cls, data):\\n\\n if data == None or len(data) == 0:\\n raise Exception(\\\"No data\\\")\\n\\n if type(data[0]) != tuple:\\n raise Exception(\\\"Not a list of tuples\\\")\\n\\n if len(data[0]) != 2 and type(data[0][0]) != str and type(data[0][1]) != list:\\n raise Exception(\\\"Not a tuple of (String, [data])\\\")\\n\\n length = len(data[0][1])\\n\\n for tup in data:\\n if len(tup) != 2 and type(tup[0]) != str and type(tup[1]) != list:\\n raise Exception(\\\"Not a tuple of (String, [data])\\\")\\n\\n if len(tup[1]) != length:\\n raise Exception(\\\"Not all elements have the same amount of data\\\")\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n if self._validate:\\n if len(data.shape) <= self._axis:\\n raise DataProcessorError(\\n f\\\"Cannot average the {len(data.shape)} dimensional \\\"\\n f\\\"array along axis {self._axis}.\\\"\\n )\\n\\n return data\",\n \"def _validate_dtype():\\n\\n test_array = _spsparse.random(5, 5, density=0.5, format=\\\"csc\\\", dtype=np.float32, random_state=50)\\n test_comparison = test_array.A\\n\\n csc_ref, precision_flag = _create_mkl_sparse(test_array)\\n\\n try:\\n csr_ref = _convert_to_csr(csc_ref)\\n final_array = _export_mkl(csr_ref, precision_flag)\\n if not np.allclose(test_comparison, final_array.A):\\n raise ValueError(\\\"Match failed after matrix conversion\\\")\\n _destroy_mkl_handle(csr_ref)\\n finally:\\n _destroy_mkl_handle(csc_ref)\",\n \"def _check_dtype_array(array, dtype):\\n # enlist the dtype expected\\n if isinstance(dtype, type):\\n dtype = [dtype]\\n\\n # check the dtype of the array\\n for dtype_expected in dtype:\\n if array.dtype == dtype_expected:\\n return\\n raise TypeError(\\\"{0} is not supported yet. Use one of those dtypes \\\"\\n \\\"instead: {1}.\\\".format(array.dtype, dtype))\",\n \"def validate_input(self):\\n self._validate_limits_cols_prefixed()\\n self._validate_fillna_cols_prefixed()\\n self._validate_ratio_input()\",\n \"def _check_type(title, data_y):\\n if(title not in data_conf):\\n data_conf[title] = {}\\n if('data_type' not in data_conf[title]):\\n if(isinstance(data_y, numpy.ndarray)):\\n # Special handling for data plots\\n if(len(data_y.shape) == 1) or (len(data_y.shape) == 2 and data_y.shape[0] == 2):\\n data_conf[title]['data_type'] = 'vector'\\n # Images with more longer first dimension\\n elif(len(data_y.shape) == 2):\\n data_conf[title]['data_type'] = 'image'\\n else:\\n raise ValueError((\\\"%dD data not supported; shape=%s\\\" %\\n (len(data_y.shape), data_y.shape)))\\n\\n else:\\n data_conf[title]['data_type'] = 'scalar'\\n if(ipc.mpi.is_slave()):\\n ipc.mpi.send('__data_conf__', data_conf)\",\n \"def safe_check(dicts, kernel_name):\\n x_shape = dicts[0].get(\\\"shape\\\")\\n x_dtype = dicts[0].get(\\\"dtype\\\").lower()\\n rois_shape = dicts[1].get(\\\"shape\\\")\\n rois_dtype = dicts[1].get(\\\"dtype\\\").lower()\\n\\n y_dtype = dicts[3].get(\\\"dtype\\\").lower()\\n y_shape = dicts[3].get(\\\"shape\\\")\\n\\n profile = tik.Dprofile()\\n tik_name_check = tbe_platform.cce_conf.get_soc_spec(\\\"SOC_VERSION\\\")\\n if tik_name_check in (\\\"Ascend310\\\", \\\"Ascend910\\\", \\\"Hi3796CV300ES\\\", \\\"Hi3796CV300CS\\\"):\\n util.check_dtype_rule(x_dtype, (\\\"float16\\\",))\\n util.check_dtype_rule(rois_dtype, (\\\"float16\\\",))\\n else:\\n util.check_dtype_rule(x_dtype, (\\\"float16\\\", \\\"float32\\\"))\\n util.check_dtype_rule(rois_dtype, (\\\"float16\\\", \\\"float32\\\"))\\n\\n if x_dtype != rois_dtype or x_dtype != y_dtype:\\n raise RuntimeError(\\\"dtype in x, rois and y must be equal\\\")\\n\\n util.check_shape_rule(x_shape, min_dim=5, max_dim=5)\\n util.check_tensor_shape_size(x_shape)\\n util.check_shape_rule(rois_shape, min_dim=3, max_dim=3)\\n util.check_tensor_shape_size(rois_shape)\\n util.check_shape_rule(y_shape, min_dim=5, max_dim=5)\\n util.check_tensor_shape_size(y_shape)\\n\\n roi_max_num = rois_shape[2]\\n if roi_max_num > 6000 or roi_max_num % 16 != 0:\\n raise ValueError(\\\"the dim 2 of rois_shape is illegal\\\")\\n\\n util.check_kernel_name(kernel_name)\",\n \"def dimension_check():\\n print(\\\"### DIMENSION CHECK ###\\\")\\n print(X.shape,\\n y.shape,\\n X_train.shape,\\n y_train.shape,\\n X_test.shape,\\n y_test.shape,\\n weights.shape)\\n print(\\\"### END ###\\\")\",\n \"def _check_input_args(scale, shape, dtype):\\n if tf.as_dtype(dtype) not in (tf.int32, tf.int64):\\n raise ValueError(\\n f'Only tf.int32 and tf.int64 are supported. Found dtype `{dtype}`.')\\n\\n checks = [\\n tf.compat.v1.assert_non_negative(scale),\\n tf.compat.v1.assert_integer(scale)\\n ]\\n with tf.control_dependencies(checks):\\n return tf.identity(scale), shape, dtype\",\n \"def _is_number(data):\\n return len(data) and np.issubdtype(_to_ndarray(data).dtype, np.number)\",\n \"def test_shape(self, varname, varshape, ndim): \\n if len(varshape) != ndim:\\n raise ShapeError('Shape=%s. Expected %i-D array for %s' %\\n (repr(varshape), ndim, varname))\",\n \"def is_array(a):\\n try:\\n shape = a.shape\\n return len(shape) >= 1\\n except AttributeError:\\n return False\",\n \"def _check_input_params(input_params):\\n\\n in_dtype, dst_dtype, perm = input_params\\n\\n if in_dtype != dst_dtype:\\n error_manager_vector.raise_err_inputs_dtype_not_equal(\\\"transpose_d\\\", \\\"in_dtype\\\",\\n \\\"dst_dtype\\\", in_dtype, dst_dtype)\\n\\n if len(perm) < 2 or len(perm) > 3:\\n perm_rule = \\\"the perm length should be in range (2,3)\\\"\\n error_manager_vector.raise_err_check_params_rules(\\\"transpose_d\\\", perm_rule,\\n \\\"perm\\\", len(perm))\\n\\n if in_dtype not in TYPE_FLOAT_LIST:\\n error_detail = \\\"only support dtype float32 now\\\"\\n error_manager_vector.raise_err_two_input_dtype_invalid(\\\"transpose_d\\\", \\\"in_dtype\\\",\\n \\\"dst_dtype\\\", error_detail)\",\n \"def _check_binary_data(data):\\n if not np.array_equal(data, data.astype(bool)):\\n raise ValueError(\\n \\\"This mechanism works with binary data, \\\"\\n \\\"but input is not binary.\\\")\",\n \"def test_with_valid_input(self):\\n for dataset_type in ['regular', 'raw', 'REGULAR', 'RAW']:\\n try:\\n check_dataset_type(dataset_type)\\n except ValueError:\\n self.fail(\\\"Dataset {0} should be valid\\\".format(dataset_type))\",\n \"def check_dimensions(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\\n\\tassert 4 <= len(X.dims) <= 5, 'XCast requires a dataset to be 4-Dimensional'\\n\\tassert x_lat_dim in X.dims, 'XCast requires a dataset_lat_dim to be a dimension on X'\\n\\tassert x_lon_dim in X.dims, 'XCast requires a dataset_lon_dim to be a dimension on X'\\n\\tassert x_sample_dim in X.dims, 'XCast requires a dataset_sample_dim to be a dimension on X'\\n\\tassert x_feature_dim in X.dims, 'XCast requires a dataset_feature_dim to be a dimension on X'\",\n \"def is_valid(self, dataset):\\n pass\",\n \"def _match_shape_to_data(self,\\n data: 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor, Any, List[Any]]',\\n target_type: Literal['X', 'Y']\\n ) -> 'Union[np.ndarray, tensorflow.Tensor, torch.Tensor, List[Any]]':\\n if self._is_tensor(data):\\n return self._match_shape_to_data_tensor(data, target_type)\\n return self._match_shape_to_data_any(data)\",\n \"def _check_input_timeseries(x: np.ndarray) -> np.ndarray:\\n if not isinstance(x, np.ndarray):\\n raise ValueError(\\\"The input time series must be a numpy array.\\\")\\n if x.ndim <= 0 or x.ndim >= 4:\\n raise ValueError(\\n \\\"The input time series must have more than 0 dimensions and\\\"\\n \\\"less than 4 dimensions.\\\"\\n )\\n if x.ndim == 3:\\n return x[0]\\n return x\",\n \"def _verify_audio_data(audio_data):\\n if audio_data is None:\\n return None\\n\\n if isinstance(audio_data, list):\\n audio_data = np.array(audio_data)\\n\\n if not isinstance(audio_data, np.ndarray):\\n raise ValueError('Type of audio_data must be of type np.ndarray!')\\n\\n if not np.isfinite(audio_data).all():\\n raise ValueError('Not all values of audio_data are finite!')\\n\\n if audio_data.ndim > 1 \\\\\\n and audio_data.shape[constants.CHAN_INDEX] > audio_data.shape[constants.LEN_INDEX]:\\n warnings.warn('audio_data is not as we expect it. Transposing signal...')\\n audio_data = audio_data.T\\n\\n if audio_data.ndim > 2:\\n raise ValueError('audio_data cannot have more than 2 dimensions!')\\n\\n if audio_data.ndim < 2:\\n audio_data = np.expand_dims(audio_data, axis=constants.CHAN_INDEX)\\n\\n return audio_data\",\n \"def _check_and_transform_input(self, data):\\n if isinstance(data, list):\\n if np.array(data).shape == (len(data),):\\n if len(data) == 1:\\n data = np.array(data).reshape(1, 1)\\n data = np.array(data).reshape(len(data), 1)\\n else:\\n data = np.concatenate(data).reshape(len(data), -1)\\n else:\\n raise TypeError('Input data should be of type list, but found type {}'.format(type(data)))\\n\\n return data\",\n \"def can_insert(data):\\n types = (float, complex, int, np.long)\\n if isinstance(data, types) and not isinstance(data, bool):\\n return True\\n elif isinstance(data, np.number):\\n return data.dtype.char not in UNSUPPORTED_NUMERIC_TYPE_CODES\",\n \"def check_consistent(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\\n\\tassert X.shape[list(X.dims).index(x_lat_dim)] == len(X.coords[x_lat_dim].values), \\\"XCast requires a dataset's x_lat_dim coordinate to be the same length as its x_lat_dim dimension\\\"\\n\\tassert X.shape[list(X.dims).index(x_lon_dim)] == len(X.coords[x_lon_dim].values), \\\"XCast requires a dataset's x_lon_dim coordinate to be the same length as its x_lon_dim dimension\\\"\\n\\tassert X.shape[list(X.dims).index(x_sample_dim)] == len(X.coords[x_sample_dim].values), \\\"XCast requires a dataset's x_sample_dim coordinate to be the same length as its x_sample_dim dimension\\\"\\n\\tassert X.shape[list(X.dims).index(x_feature_dim)] == len(X.coords[x_feature_dim].values), \\\"XCast requires a dataset's x_feature_dim coordinate to be the same length as its x_feature_dim dimension\\\"\",\n \"def valid_dtype_assertion(expected_dtypes, actual_dtype, name):\\n\\tassert (actual_dtype in expected_dtypes), \\\"Invalid dtype of {} should be {}\\\".format(name, str(expected_dtypes))\",\n \"def __verify_arguments(self):\\n if len(self.__pointer_data) == 0:\\n raise ValueError(\\n \\\"Input data is empty (size: '%d').\\\" % len(self.__pointer_data)\\n )\\n\\n if self.__number_clusters <= 0:\\n raise ValueError(\\n \\\"Amount of cluster (current value: '%d') for allocation should be greater than 0.\\\"\\n % self.__number_clusters\\n )\\n\\n if self.__numlocal < 0:\\n raise ValueError(\\n \\\"Local minima (current value: '%d') should be greater or equal to 0.\\\"\\n % self.__numlocal\\n )\\n\\n if self.__maxneighbor < 0:\\n raise ValueError(\\n \\\"Maximum number of neighbors (current value: '%d') should be greater or \\\"\\n \\\"equal to 0.\\\" % self.__maxneighbor\\n )\",\n \"def __verify_arguments(self):\\r\\n if len(self.__pointer_data) == 0:\\r\\n raise ValueError(\\\"Input data is empty (size: '%d').\\\" % len(self.__pointer_data))\\r\\n\\r\\n if self.__number_clusters <= 0:\\r\\n raise ValueError(\\\"Amount of cluster (current value: '%d') for allocation should be greater than 0.\\\" %\\r\\n self.__number_clusters)\\r\\n\\r\\n if self.__numlocal < 0:\\r\\n raise ValueError(\\\"Local minima (current value: '%d') should be greater or equal to 0.\\\" % self.__numlocal)\\r\\n\\r\\n if self.__maxneighbor < 0:\\r\\n raise ValueError(\\\"Maximum number of neighbors (current value: '%d') should be greater or \\\"\\r\\n \\\"equal to 0.\\\" % self.__maxneighbor)\",\n \"def _check_input(self, X):\\n symbols = np.concatenate(X)\\n if len(symbols) == 1: # not enough data\\n raise ValueError(\\\"expected at least 1 observation \\\"\\n \\\"but none found.\\\")\\n elif (symbols < 0).any(): # contains negative integers\\n raise ValueError(\\\"expected non-negative features \\\"\\n \\\"for each observation.\\\")\\n elif X.shape[1] > 1: # contains to many features\\n raise ValueError(\\\"expected only 1 feature but got {0} \\\"\\n \\\"for each observation.\\\".format(X.shape[1]))\\n else:\\n return True\",\n \"def check_input_dims(df: pd.DataFrame,\\n input_check_flag: str,\\n input_file_name: str = None,\\n raise_err: bool = True\\n ) -> bool:\\n messages = []\\n file_name = input_file_name or input_check_flag\\n target_dims = INPUT_CHECKS[input_check_flag]\\n if df.shape[0] != target_dims[0]:\\n messages.append(f\\\"Incorrect number of rows in {file_name}: Should be \\\"\\n f\\\"{target_dims[0]} but found {df.shape[0]}\\\")\\n if df.shape[1] != target_dims[1]:\\n messages.append(f\\\"Incorrect number of columns in {file_name}: Should \\\"\\n f\\\"be {target_dims[1]} but found {df.shape[1]}\\\")\\n if len(messages) > 0:\\n if raise_err:\\n raise ValueError(\\\"::\\\".join(messages))\\n else:\\n warnings.warn(\\\"::\\\".join(messages))\\n return False\\n return True\",\n \"def _check_data_shape_to_num_outputs(preds: Tensor, target: Tensor, num_outputs: int) ->None:\\n if preds.ndim > 2 or target.ndim > 2:\\n raise ValueError(f'Expected both predictions and target to be either 1- or 2-dimensional tensors, but got {target.ndim} and {preds.ndim}.')\\n if num_outputs == 1 and preds.ndim != 1 or num_outputs > 1 and num_outputs != preds.shape[1]:\\n raise ValueError(f'Expected argument `num_outputs` to match the second dimension of input, but got {num_outputs} and {preds.shape[1]}.')\",\n \"def is_valid(self) -> bool:\\r\\n try:\\r\\n self.shape\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def _check_dtype(ds_in, dset_attrs):\\n dtype = dset_attrs['dtype']\\n attrs = dset_attrs['attrs']\\n if ds_in.dtype.name != dtype:\\n msg = ('Source dtype ({}) does not match specified dtype ({}), '\\n .format(ds_in.dtype, dtype))\\n logger.warning(msg)\\n warn(msg)\\n float_to_int = (np.issubdtype(ds_in.dtype, np.floating)\\n and np.issubdtype(dtype, np.integer))\\n int_to_float = (np.issubdtype(ds_in.dtype, np.integer)\\n and np.issubdtype(dtype, np.floating))\\n if float_to_int:\\n if not any(c for c in attrs if 'scale_factor' in c):\\n msg = ('Cannot downscale from {} to {} without a '\\n 'scale_factor!'.format(ds_in.dtype, dtype))\\n logger.error(msg)\\n raise RuntimeError(msg)\\n else:\\n msg = 'Converting {} to {}'.format(ds_in.dtype, dtype)\\n logger.warning(msg)\\n warn(msg)\\n elif int_to_float:\\n msg = ('Cannot scale up an {} to a {}'\\n .format(ds_in.dtype, dtype))\\n logger.error(msg)\\n raise RuntimeError(msg)\\n elif not np.issubdtype(dtype, ds_in.dtype):\\n msg = ('Output dtype ({}) has greater precision than input '\\n 'dtype ({}), using input dtype'\\n .format(dtype, ds_in.dtype))\\n logger.warning(msg)\\n warn(msg)\\n\\n dset_attrs['dtype'] = ds_in.dtype\\n\\n return dset_attrs\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n\\n self._n_shots = len(data[0])\\n self._n_circuits = len(data)\\n\\n if self._validate:\\n if data.shape[:2] != (self._n_circuits, self._n_shots):\\n raise DataProcessorError(\\n f\\\"The datum given to {self.__class__.__name__} does not convert \\\"\\n \\\"of an array with dimension (number of circuit, number of shots).\\\"\\n )\\n\\n return data\",\n \"def _is_all_input_shape_generalize(input_shape_tuple):\\n for elem in input_shape_tuple:\\n if not is_shape_unknown(elem.shape):\\n return False\\n return True\",\n \"def do_type_checking(self, node):\\r\\n\\r\\n if not isinstance(node.inputs[0].type, GpuArrayType):\\r\\n raise NotImplementedError()\",\n \"def check_transformations(*args):\\n assert args[0].shape == (21,21)\\n assert args[0].dtype == np.float64\\n if len(args) == 2:\\n assert args[1].shape == (2,2)\\n assert args[1].dtype == np.float64\",\n \"def test_shapes(self):\\n\\n # Creates a raw layer\\n self.validator.adata.raw = self.validator.adata\\n self.validator.adata.raw.var.drop(\\\"feature_is_filtered\\\", axis=1, inplace=True)\\n self.validator.adata.X = examples.adata_non_raw.X.copy()\\n self.validator.adata.uns[\\\"X_normalization\\\"] = \\\"CPM\\\"\\n\\n # remove one gene\\n self.validator.adata = self.validator.adata[:, 1:]\\n self.validator.validate_adata()\\n self.assertEqual(\\n self.validator.errors,\\n [\\\"ERROR: Number of genes in X (3) is different than raw.X (4).\\\"],\\n )\",\n \"def _format_data(self, data: np.ndarray) -> np.ndarray:\\n if self._validate:\\n if not all(0.0 <= p <= 1.0 for p in data):\\n raise DataProcessorError(\\n f\\\"Input data for node {self.__class__.__name__} is not likely probability.\\\"\\n )\\n\\n return data\",\n \"def shape_for_keras(data):\\n raise NotImplementedError\",\n \"def test_types(self):\\n \\n self.assertIsInstance(self.tx_data_in, numpy.ndarray)\\n self.assertIsInstance(self.circuit_simulation, bool)\\n self.assertIsInstance(self.bypass, bool)\\n \\n pass\",\n \"def _check_data_valid(self):\\n\\n is_valid = (sum(~np.isnan(self.data).flatten()) > 0 and self.data.flatten().sum() != 0)\\n if not is_valid:\\n raise FITSException(f\\\"No data in {self.survey}\\\")\",\n \"def test_shape_error_1():\\n dmd = DMD()\\n with raises(ValueError):\\n dmd.fit(X=sample_data, Y=sample_data[:, 1:])\",\n \"def __check_is_xarray(self, data):\\n if type(data) is xr.core.dataarray.DataArray or \\\\\\n type(data) is xr.core.dataarray.Dataset:\\n\\n return True\\n else:\\n msg = \\\"Variable {data} is not an xarray DataArray/Dataset\\\"\\n raise Exception(msg)\",\n \"def format_write_data(data, mydtype):\\n try:\\n len(data) # Raise TypeError if no len\\n dshape = data.shape\\n except TypeError:\\n # Data has no length\\n data = np.asarray(data, dtype=mydtype)\\n dshape = data.shape\\n\\n except AttributeError:\\n # Data is not a numpy array\\n data = np.asarray(data, dtype=mydtype)\\n dshape = data.shape\\n\\n # Force at least 1 column\\n if get_shape_columns(dshape) == 0:\\n data = np.reshape(data, (-1, 1))\\n dshape = data.shape\\n\\n return data, dshape\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7713703","0.76080585","0.74882436","0.7184162","0.71101797","0.70855606","0.7055508","0.7006338","0.6966901","0.6945867","0.6939449","0.69268495","0.69028056","0.68751085","0.6828708","0.6807963","0.67849463","0.6784447","0.6716908","0.67096233","0.67094177","0.6700148","0.66811097","0.6670719","0.66406024","0.6638477","0.66117084","0.6595777","0.65909564","0.65880984","0.65779454","0.65595174","0.6556262","0.6544166","0.65417325","0.6536021","0.65285575","0.6516484","0.6507289","0.6506482","0.65019053","0.6480836","0.64709044","0.645996","0.64590746","0.6449729","0.6448992","0.6428189","0.6409779","0.6404297","0.63954216","0.63929385","0.6373214","0.63680255","0.6363229","0.6349051","0.63443923","0.6342032","0.6340207","0.63345164","0.63265455","0.6319997","0.63088894","0.6308059","0.62932223","0.627305","0.62722516","0.62667805","0.62528646","0.62518495","0.6248274","0.6246848","0.62463284","0.6239068","0.6236658","0.623636","0.62330663","0.6223936","0.6220219","0.62188625","0.621849","0.621362","0.6211216","0.6207546","0.620015","0.6185268","0.61814076","0.6162964","0.6161531","0.6159995","0.6154817","0.61492324","0.6146982","0.6141181","0.6140213","0.61341083","0.613377","0.6132081","0.6131201","0.61302984","0.61180204"],"string":"[\n \"0.7713703\",\n \"0.76080585\",\n \"0.74882436\",\n \"0.7184162\",\n \"0.71101797\",\n \"0.70855606\",\n \"0.7055508\",\n \"0.7006338\",\n \"0.6966901\",\n \"0.6945867\",\n \"0.6939449\",\n \"0.69268495\",\n \"0.69028056\",\n \"0.68751085\",\n \"0.6828708\",\n \"0.6807963\",\n \"0.67849463\",\n \"0.6784447\",\n \"0.6716908\",\n \"0.67096233\",\n \"0.67094177\",\n \"0.6700148\",\n \"0.66811097\",\n \"0.6670719\",\n \"0.66406024\",\n \"0.6638477\",\n \"0.66117084\",\n \"0.6595777\",\n \"0.65909564\",\n \"0.65880984\",\n \"0.65779454\",\n \"0.65595174\",\n \"0.6556262\",\n \"0.6544166\",\n \"0.65417325\",\n \"0.6536021\",\n \"0.65285575\",\n \"0.6516484\",\n \"0.6507289\",\n \"0.6506482\",\n \"0.65019053\",\n \"0.6480836\",\n \"0.64709044\",\n \"0.645996\",\n \"0.64590746\",\n \"0.6449729\",\n \"0.6448992\",\n \"0.6428189\",\n \"0.6409779\",\n \"0.6404297\",\n \"0.63954216\",\n \"0.63929385\",\n \"0.6373214\",\n \"0.63680255\",\n \"0.6363229\",\n \"0.6349051\",\n \"0.63443923\",\n \"0.6342032\",\n \"0.6340207\",\n \"0.63345164\",\n \"0.63265455\",\n \"0.6319997\",\n \"0.63088894\",\n \"0.6308059\",\n \"0.62932223\",\n \"0.627305\",\n \"0.62722516\",\n \"0.62667805\",\n \"0.62528646\",\n \"0.62518495\",\n \"0.6248274\",\n \"0.6246848\",\n \"0.62463284\",\n \"0.6239068\",\n \"0.6236658\",\n \"0.623636\",\n \"0.62330663\",\n \"0.6223936\",\n \"0.6220219\",\n \"0.62188625\",\n \"0.621849\",\n \"0.621362\",\n \"0.6211216\",\n \"0.6207546\",\n \"0.620015\",\n \"0.6185268\",\n \"0.61814076\",\n \"0.6162964\",\n \"0.6161531\",\n \"0.6159995\",\n \"0.6154817\",\n \"0.61492324\",\n \"0.6146982\",\n \"0.6141181\",\n \"0.6140213\",\n \"0.61341083\",\n \"0.613377\",\n \"0.6132081\",\n \"0.6131201\",\n \"0.61302984\",\n \"0.61180204\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11085,"cells":{"query":{"kind":"string","value":"To convert an imagelike array with elements (yproj, xproj) into a position list of coordinates Every pixel position (n, m) results in two output coordinates of (N,2)"},"document":{"kind":"string","value":"def _convert_image_to_coordinates(self, vect) -> np.ndarray:\n xdim = vect.shape[0]\n ydim = vect.shape[1]\n\n # stride is used during averaging and length adjustment\n stride_x, stride_y = self._averaging, self._averaging\n\n # create empty vector of necessary shape\n # every \"pixel\" has 2 coordinates\n pos = np.empty((2 * xdim * ydim, 2), dtype=np.float32)\n\n # create coordinate spacing for x-y\n # double the num of elements by doubling x sampling\n xspace = np.linspace(0, stride_x*xdim, 2 * xdim, endpoint=False)\n yspace = np.linspace(0, stride_y*ydim, ydim, endpoint=False)\n xv, yv = np.meshgrid(xspace, yspace)\n\n # assign coordinates (pos) to all pixels\n pos[:, 0] = xv.flatten()\n pos[:, 1] = yv.flatten()\n\n # pixel midpoints are the first x-values of positions\n midpt = np.zeros((xdim * ydim, 2), dtype=np.float32)\n midpt[:, 0] = pos[0::2, 0]+(stride_x-1)/2\n midpt[:, 1] = pos[0::2, 1]+(stride_y-1)/2\n\n # rotate coordinates about midpoint to represent angle and length\n pos[0::2, 0] = midpt[:, 0] - (stride_x / 2) * (self._length/2) * \\\n vect.reshape((xdim*ydim, 2))[:, 0]\n pos[0::2, 1] = midpt[:, 1] - (stride_y / 2) * (self._length/2) * \\\n vect.reshape((xdim*ydim, 2))[:, 1]\n pos[1::2, 0] = midpt[:, 0] + (stride_x / 2) * (self._length/2) * \\\n vect.reshape((xdim*ydim, 2))[:, 0]\n pos[1::2, 1] = midpt[:, 1] + (stride_y / 2) * (self._length/2) * \\\n vect.reshape((xdim*ydim, 2))[:, 1]\n\n return pos"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def proj_coords(coords, proj_in, proj_out): \n return [proj_coord(coord, proj_in, proj_out) for coord in coords]","def coordinates_to_imgpts(x, y):\n pts = np.array([np.flipud(np.transpose(np.vstack([x, y])))])\n return pts","def get_pix_coordinates(pts, proj_mat, w, h):\n points_t = np.ones(shape=(4, pts.shape[1]))\n points_t[:3, :] = pts\n pixels = project_points_img(points_t, proj_mat, w, h)\n return pixels","def pixel_to_coords(self, x, y):\n rx, ry = self.size\n nx = (x / rx - 0.5) * self.scale + self.center[0]\n ny = ((ry - y) / ry - 0.5) * self.scale + self.center[1]\n nz = self.center[2]\n return [nx, ny, nz]","def projectBack(points, proj):\n\n mpoints = MultiPoint(points)\n project = partial(\n pyproj.transform,\n proj,\n pyproj.Proj(proj='latlong', datum='WGS84'))\n gmpoints = transform(project, mpoints)\n coords = []\n for point in gmpoints.geoms:\n x, y = point.coords[0]\n coords.append((x, y))\n coords = np.array(coords)\n return coords","def calcul_xy_array(img_x, img_y, tile_x, tile_y):\n array = []\n\n modu_x = img_x % tile_x\n modu_y = img_y % tile_y\n div_x = img_x // tile_x\n div_y = img_y // tile_y\n current_x = 0\n current_y = 0\n\n for i in range(div_y):\n for j in range(div_x):\n array.append((current_x, current_y))\n current_x += tile_x\n if modu_x:\n array.append((img_x - tile_x, current_y))\n current_y += tile_y\n current_x = 0\n\n if modu_y:\n current_y = img_y - tile_y\n for j in range(div_x):\n array.append((current_x, current_y))\n current_x += tile_x\n if modu_x:\n array.append((img_x - tile_x, current_y))\n\n return array","def reproject_coordinates(coordinates, inproj, outproj, flat=False):\n if flat:\n return np.array([transform(inproj, outproj, coord[0], coord[1]) for coord in coordinates]).flatten()\n return [list(transform(inproj, outproj, coord[0], coord[1])) for coord in coordinates]","def flatten_pixcoord(image):\n hdr = pyfits.getheader(image)\n naxis1 = hdr['naxis1']\n naxis2 = hdr['naxis2']\n pixcoord = np.mgrid[1:naxis1+1,1:naxis2+1]\n pixcoord = pixcoord.swapaxes(0,1).swapaxes(1,2)\n pixcoord = pixcoord.ravel().reshape(naxis1*naxis2, 2)\n return pixcoord","def ReprojectCoords(coords,src_srs,tgt_srs):\n trans_coords = []\n transform = osr.CoordinateTransformation( src_srs, tgt_srs)\n for x,y in coords:\n x,y,z = transform.TransformPoint(x,y)\n trans_coords.append([x,y])\n return trans_coords","def _convert_pix_coords(self):\n if self.region_type in ['polygon', 'line']:\n # have to special-case polygon in the phys coord case\n # b/c can't typecheck when iterating as in sky coord case\n coords = [PixCoord(self.coord[0::2], self.coord[1::2])]\n else:\n temp = [_.value for _ in self.coord]\n coord = PixCoord(temp[0], temp[1])\n coords = [coord] + temp[2:]\n\n # The angle remains as a quantity object.\n # Modulus check makes sure that it works for ellipse/rectangle annulus\n if self.region_type in ['ellipse', 'rectangle'] and len(coords) % 2 == 0:\n coords[-1] = self.coord[-1]\n\n return coords","def toNativePixelCoordinates(self, x, y, proj=None, roundResults=True):\n if proj is None:\n proj = self.projection\n # convert to the native projection\n inProj = self._proj4Proj(proj)\n outProj = self._proj4Proj(self.getProj4String())\n px, py = pyproj.Transformer.from_proj(inProj, outProj, always_xy=True).transform(x, y)\n # convert to native pixel coordinates\n gt = self._getGeoTransform()\n d = gt[2] * gt[4] - gt[1] * gt[5]\n x = (gt[0] * gt[5] - gt[2] * gt[3] - gt[5] * px + gt[2] * py) / d\n y = (gt[1] * gt[3] - gt[0] * gt[4] + gt[4] * px - gt[1] * py) / d\n if roundResults:\n x = int(round(x))\n y = int(round(y))\n return x, y","def pixel2coords(self, x, y):\n xoff, a, b, yoff, d, e = self.geotransform()\n\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n return (xp, yp)","def image_to_points(numpy_image):\r\n res = []\r\n for i in range(numpy_image.shape[0]):\r\n for j in range(numpy_image.shape[1]):\r\n if numpy_image[i,j]==0:\r\n res.append([i,j])\r\n return res","def _pixel_coordinates(nx, ny, mode):\n if mode == \"center\":\n x = np.linspace(0.5, nx - 0.5, num=nx)\n y = np.linspace(0.5, ny - 0.5, num=ny)\n\n if mode == \"edge\":\n x = np.linspace(0, nx, num=nx + 1)\n y = np.linspace(0, ny, num=ny + 1)\n\n X, Y = np.meshgrid(x, y)\n coordinates = np.stack((X, Y), axis=-1)\n\n return coordinates","def camera_pixels_to_camera_coords(left_pt, right_pt, nparrays=False):\n assert len(left_pt) == len(right_pt) == 2\n disparity = np.linalg.norm( np.array(left_pt) - np.array(right_pt) )\n (xx,yy,zz) = STEREO_MODEL.projectPixelTo3d( (left_pt[0],left_pt[1]), disparity )\n if nparrays:\n return np.array([xx,yy,zz])\n else:\n return [xx,yy,zz]","def Reproject(x, y, in_grid = 4326, out_grid = 32737):\n \n inProj = Proj(init='epsg:'+str(in_grid))\n outProj = Proj(init='epsg:'+str(out_grid))\n \n \n x2,y2 = transform(inProj,outProj,x,y)\n \n return x2, y2","def positions(r):\n\n X = []\n Y = []\n\n leftx = -r*(nx - 1) / 2\n topy = -r*(ny - 1) / 2\n\n for i in range(0, nx):\n for j in range(0, ny):\n X.append(leftx + r * i)\n Y.append(topy + r * j)\n\n return (X, Y)","def world2Pixel(geoMatrix, x, y):\r\n ulX = geoMatrix[0]\r\n ulY = geoMatrix[3]\r\n xDist = geoMatrix[1]\r\n yDist = geoMatrix[5]\r\n rtnX = geoMatrix[2]\r\n rtnY = geoMatrix[4]\r\n # pixel = int((x - ulX) / xDist)\r\n # line = int((ulY - y) / xDist)\r\n # Floor for x and ceiling for y seems to produce the best looking output\r\n #\t(for one test case, may want to change later to np.round?)\r\n pixx = np.round((x - ulX) / xDist, decimals=0).astype(np.int)\r\n pixy = np.round((ulY - y) / xDist, decimals=0).astype(np.int)\r\n\r\n return pixx, pixy","def get_scanner_xy(points, proj):\n\n # Find the pixel corresponding to (x=0,y=0)\n res_x = proj.projector.res_x # 5 pixels / m, 1 px = 20 cm\n res_y = proj.projector.res_y # 5 pixels / m , 1 px = 20 cm\n\n min_x, min_y, min_z = points.min(0)\n\n # the first coordinate is associated to the row coordinate of the image\n y0 = int(np.floor((0 - min_y) * res_y).astype(np.int))\n # the second coordinate is associated to the column coordinate of the image\n x0 = int(np.floor((0 - min_x) * res_x).astype(np.int))\n\n return x0, y0","def make_maps_of_2x1_pix_coordinates (sp) : \n x_rhs = np.arange(sp.colsh)*sp.pixs + sp.pixw - sp.pixsh\n x_rhs[0] = sp.pixwh # set x-coordinate of the wide pixel \n x_arr = np.hstack([-x_rhs[::-1],x_rhs])\n\n y_arr = np.arange(sp.rows) * sp.pixs\n y_arr -= y_arr[-1]/2 # move origin to the center of array\n\n sp.x_map2x1, sp.y_map2x1 = np.meshgrid(x_arr, y_arr)","def cart_coords2d(self):\r\n\r\n mgx, mgy = self.meshup2d()\r\n coords = np.column_stack((mgx.flatten(), mgy.flatten()))\r\n\r\n return coords","def get_coordinate_lists(self, crs=None):\n x, y = self.vertices.vectors()[:2]\n if crs is not None and (crs != self.crs):\n x, y = _reproject((x,y), self.crs, crs)\n return x, y","def display_image_coords_to_canvas_coords(display_image_yx_coords):\n\n return [(yx[1], yx[0]) for yx in display_image_yx_coords]","def Pixel2World(geoMatrix, x, y):\r\n ulX = geoMatrix[0]\r\n ulY = geoMatrix[3]\r\n xdist = geoMatrix[1]\r\n ydist = geoMatrix[5]\r\n coorX = (ulX + (x * xdist))\r\n coorY = (ulY + (y * ydist))\r\n return (coorX, coorY)","def coordinates(self):\n return np.array([[f.x, f.y] for f in self])","def convert_coords(self):\n if self.coordsys in ['image', 'physical']:\n coords = self._convert_pix_coords()\n else:\n coords = self._convert_sky_coords()\n\n if self.region_type == 'line':\n coords = [coords[0][0], coords[0][1]]\n\n if self.region_type == 'text':\n coords.append(self.meta['text'])\n\n return coords","def xy(self):\n xcol = self.xx.reshape(-1, 1)\n ycol = self.yy.reshape(-1, 1)\n return np.column_stack([xcol, ycol])","def make_coordinates(image, line_params):\n slope, intercept = line_params\n y1 = image.shape[0]\n y2 = int(y1 * (3/5))\n x1 = int((y1 - intercept)/ slope)\n x2 = int((y2 - intercept)/ slope)\n return np.array([x1, y1, x2, y2])","def proj_coord(coord, proj_in, proj_out):\n x, y = coord\n return pyproj.transform(proj_in, proj_out, x, y)","def _binary_image_to_grid_points(binary_image_matrix):\n\n return numpy.where(binary_image_matrix)","def grid_coords(self):\n return [(x, y) for y in range(self.height) for x in range(self.width)]","def im2pos_coordinates(pix_x, pix_z):\n # x_lim = [-0.85, 0.86]\n # z_lim = [-1.22, 0.47]\n x_lim = [-0.365, 0.365]\n z_lim = [-0.95, -0.24]\n x = x_lim[1] - (x_lim[1] - x_lim[0]) * pix_x/127\n z = z_lim[1] - (z_lim[1] - z_lim[0]) * pix_z/127\n return x, z","def pose_2d_pts(self,image):\n '''\n image- rgb image \n return:-\n pts - list of 2d pose landmarks as img coords\n image- rgb image on which the 2d pose landmarks are drawn\n ''' \n pts=[]\n imgRGB=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\n results=pose.process(imgRGB)\n if results.pose_landmarks:\n mpDraw.draw_landmarks(image,results.pose_landmarks,mpPose.POSE_CONNECTIONS)\n for id,lm in enumerate(results.pose_landmarks.landmark):\n h,w,c=image.shape\n imgx,imgy=int(lm.x*w),int(lm.y*h)\n \n pts.append((imgx,imgy)) \n return pts,image","def pos2im_coordinates(x, z):\n # x_lim = [-0.85, 0.86]\n # z_lim = [-1.22, 0.47]\n x_lim = [-0.365, 0.365]\n z_lim = [-0.95, -0.24]\n\n pix_x = int(127 * (x_lim[1] - x) / (x_lim[1] - x_lim[0]))\n pix_z = int(127 * (z_lim[1] - z) / (z_lim[1] - z_lim[0]))\n return pix_x, pix_z","def world2Pixel(geoMatrix, x, y):\r\n ulX = geoMatrix[0]\r\n ulY = geoMatrix[3]\r\n xDist = geoMatrix[1]\r\n yDist = geoMatrix[5]\r\n rtnX = geoMatrix[2]\r\n rtnY = geoMatrix[4]\r\n pixel = int((x - ulX) / xDist)\r\n line = int((ulY - y) / xDist)\r\n return (pixel, line)","def get_all_coordinates(self):\n coordinates = []\n\n for relative_coordinate in self.shape:\n co = [self.coordinate[0] + relative_coordinate[0], self.coordinate[1] + relative_coordinate[1]]\n coordinates.append(co)\n return coordinates","def decimated_image_coords_to_display_image_coords(self, decimated_image_yx_cords):\n\n scale_factor = self.compute_display_scale_factor(self.canvas_decimated_image)\n return [(coord[0]*scale_factor, coord[1]*scale_factor) for coord in decimated_image_yx_cords]","def coordinates(self, mask):\n y,x = mask.nonzero()\n return list(zip(x,y))","def proj(self,lon,lat):\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\n x[x > 1e29] = None\n y[y > 1e29] = None\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\n return x, y","def cam2pixel_torch(cam_coords, proj):\n batch, _, height, width = cam_coords.shape\n cam_coords = torch.reshape(cam_coords, [batch, 4, -1])\n unnormalized_pixel_coords = torch.matmul(proj, cam_coords)\n xy_u = unnormalized_pixel_coords[:, 0:2, :]\n z_u = unnormalized_pixel_coords[:, 2:3, :]\n\n pixel_coords = xy_u / (z_u + 1e-10)\n pixel_coords = torch.reshape(pixel_coords, [batch, 2, height, width])\n return pixel_coords.permute([0, 2, 3, 1])","def xyFromROI(aROIPoints):\n pt1 = aROIPoints[0]\n pt2 = aROIPoints[1]\n x0, y0 = pt1\n x1, y1 = pt2\n return x0, y0, x1, y1","def _pixel_coord_np(width, height):\n x = np.linspace(0, width - 1, width).astype(np.int32)\n y = np.linspace(0, height - 1, height).astype(np.int32)\n [x, y] = np.meshgrid(x, y)\n return np.vstack((x.flatten(), y.flatten(), np.ones_like(x.flatten())))","def get_coord(depth, pose, intrinsics_color_inv):\n img_height, img_width = depth.shape[0], depth.shape[1]\n mask = np.ones_like(depth)\n mask[depth==0] = 0\n mask = np.reshape(mask, (img_height, img_width,1))\n x = np.linspace(0, img_width-1, img_width)\n y = np.linspace(0, img_height-1, img_height)\n xx, yy = np.meshgrid(x, y)\n xx = np.reshape(xx, (1, -1))\n yy = np.reshape(yy, (1, -1))\n ones = np.ones_like(xx)\n pcoord = np.concatenate((xx, yy, ones), axis=0)\n depth = np.reshape(depth, (1, img_height*img_width))\n ccoord = np.dot(intrinsics_color_inv, pcoord) * depth\n ccoord = np.concatenate((ccoord, ones), axis=0)\n scoord = np.dot(pose, ccoord)\n scoord = np.swapaxes(scoord,0,1)\n scoord = scoord[:,0:3]\n scoord = np.reshape(scoord, (img_height, img_width,3))\n scoord = scoord * mask\n mask = np.reshape(mask, (img_height, img_width))\n return scoord, mask","def to_coords(self, px, py):\n if px not in range(self.SIZE**2) or py not in range(self.SIZE**2):\n raise IndexError\n return (px // self.SIZE, py // self.SIZE,\n px % self.SIZE, py % self.SIZE)","def _image_to_point_space(pixel_coordinates: np.array, boundary_radius: int, resolution: int) -> np.array:\n pix_origin = np.array([resolution / 2, resolution / 2])\n return np.array((pixel_coordinates - pix_origin) * (2 * boundary_radius) / resolution)","def get_coords_2d(obj, P):\n bbox3 = get_coords_3d(obj)\n # Rl = obj.Rl\n # height = obj.height\n # width = obj.width\n # length = obj.length\n # x = obj.x\n # y = obj.y\n # z = obj.z\n # bbox = np.array([[-length, 0, -width],\n # [-length, 0, width],\n # [ length, 0, width],\n # [ length, 0, -width],\n # [-length, -2*height, -width],\n # [-length, -2*height, width],\n # [ length, -2*height, width],\n # [ length, -2*height, -width]])/2\n # if abs(z) < 1e-6: z = np.sign(z) * 1e-6\n # M = rotationM([0, 1, 0], Rl + math.atan(x/z))\n # bbox = np.transpose(np.dot(M, np.transpose(bbox)))\n # bbox3 = bbox + np.array([x, y, z])\n bbox2 = project_pts3_to_image(bbox3, P)\n return bbox2","def make_xarray_coords(y, x, crs):\n if crs.is_geographic:\n y_attrs, x_attrs = COORD_DEFS['latitude'], COORD_DEFS['longitude']\n elif crs.is_projected:\n crs_osr = crs2osr(crs)\n units = crs_osr.GetLinearUnitsName()\n y_attrs, x_attrs = COORD_DEFS['y'], COORD_DEFS['x']\n y_attrs['units'], x_attrs['units'] = units, units\n\n y = xr.Variable(('y', ), y, attrs=y_attrs)\n x = xr.Variable(('x', ), x, attrs=x_attrs)\n\n return y, x","def getYesPoints(pshapes, proj, dx, nmax, touch_center=True):\n\n mxmin = 9e10\n mxmax = -9e10\n mymin = 9e10\n mymax = -9e10\n for pshape in pshapes:\n pxmin, pymin, pxmax, pymax = pshape.bounds\n if pxmin < mxmin:\n mxmin = pxmin\n if pxmax > mxmax:\n mxmax = pxmax\n if pymin < mymin:\n mymin = pymin\n if pymax > mymax:\n mymax = pymax\n\n if not touch_center:\n geodict = GeoDict.createDictFromBox(mxmin, mxmax, mymin, mymax, dx, dx)\n img = rasterizeShapes(pshapes, geodict)\n #now get the numpy array of x/y coordinates where covgrid == 1\n idx = np.where(img == 1)[0]\n x, y = np.unravel_index(idx, (geodict.ny, geodict.nx))\n yespoints = list(zip(x.flatten(), y.flatten()))\n nrows = geodict.ny\n ncols = geodict.nx\n xvar = np.arange(geodict.xmin, geodict.xmax+geodict.dx, geodict.dx)\n yvar = np.arange(geodict.ymin, geodict.ymax+geodict.dy, geodict.dy)\n else:\n xvar = np.arange(mxmin, mxmax+dx, dx)\n yvar = np.arange(mymin, mymax+dx, dx)\n ncols = len(xvar)\n nrows = len(yvar)\n if nmax is not None:\n if ncols*nrows > nmax:\n aspect = ncols/nrows\n ncols = np.sqrt(nmax*aspect)\n nrows = nmax/ncols\n ncols = int(ncols)\n nrows = int(nrows)\n #re-calculate dx here...\n tdx = (mxmax-mxmin)/ncols\n tdy = (mymax-mymin)/nrows\n dx = np.max(tdx, tdy)\n xvar = np.arange(mxmin, mxmax+dx, dx)\n yvar = np.arange(mymin, mymax+dx, dx)\n\n #Get the \"yes\" points to sample from\n yespoints = []\n idx = []\n shapeidx = 0\n if pshapes[0].type == 'Polygon':\n #loop over shapes, projecting each one, then get the sample points\n for pshape in pshapes:\n if not shapeidx % 1000:\n print('Searching polygon %i of %i' % (shapeidx, len(pshapes)))\n shapeidx += 1\n pxmin, pymin, pxmax, pymax = pshape.bounds\n leftcol = np.where((pxmin - xvar) >= 0)[0].argmax()\n rightcol = np.where((xvar - pxmax) >= 0)[0][0]\n bottomrow = np.where((pymin - yvar) >= 0)[0].argmax()\n toprow = np.where((yvar - pymax) >= 0)[0][0]\n xp = np.arange(xvar[leftcol], xvar[rightcol]+dx, dx)\n yp = np.arange(yvar[bottomrow], yvar[toprow]+dx, dx)\n xmesh, ymesh = np.meshgrid(xp, yp)\n xy = list(zip(xmesh.flatten(), ymesh.flatten()))\n for point in xy:\n ix = np.where(xvar == point[0])[0][0]\n iy = np.where(yvar == point[1])[0][0]\n if pshape.contains(Point(point)):\n yespoints.append(point)\n idx.append(np.ravel_multi_index((iy, ix), (nrows, ncols), mode='raise', order='C'))\n else:\n yespoints = []\n for pshape in pshapes:\n yespoints.append(pshape.coords[0])\n\n return (np.array(yespoints), nrows, ncols, xvar, yvar, idx)","def coords2D(self):\n return (self.x, self.y)","def pixels_to_latlon(x_pos, y_pos):\n radius = 489.5\n x_pos_from_center = -1*(512.5 - x_pos)\n angle_1 = np.rad2deg(np.arccos(x_pos_from_center/radius))\n\n y_pos_from_center = -1*(512.5 - y_pos)\n angle_2 = np.rad2deg(np.arccos(y_pos_from_center/radius)) \n\n return -1*angle_1, angle_2","def project_points_img(points, proj_mat, width, height):\n pixels = proj_mat.dot(points)\n pixels = np.divide(pixels[:2, :], pixels[2, :]).transpose().astype(np.int)\n\n # Remove pixels that are outside the image\n pixels[:, 0] = np.clip(pixels[:, 0], 0, width)\n pixels[:, 1] = np.clip(pixels[:, 1], 0, height)\n # mask_x = (pixels[:, 0] < width) & (pixels[:, 0] > 0)\n # mask_y = (pixels[:, 1] < height) & (pixels[:, 1] > 0)\n\n # # Return the pixels and points that are inside the image\n # pixels = pixels[mask_x & mask_y]\n return pixels","def positions_to_coords(self, positions):\n return [self.to_coords(px, py) for (px, py) in positions]","def projective_transform(self, x):\n\n x = np.asarray(x)\n # Assume no intensity column\n x0, y0, z0 = x\n\n # Camera coors to pixel coors\n u = ((x0 / z0) * self.f) + (self.sensor_size[0] // 2)\n v = ((y0 / z0) * self.f) + (self.sensor_size[1] // 2)\n\n u_min = np.min(u)\n v_min = np.min(v)\n\n n = len(u)\n u_list = []\n v_list = []\n if self.error_on_oob:\n for i in range(n):\n if (u[i] >= u_min and u[i] <= self.sensor_size[0] and v[i] >= v_min and v[i] <= self.sensor_size[1]):\n u_list.append(u[i])\n v_list.append(v[i])\n else:\n raise OutOfSensorBoundsError(\"Projected coordinate was outside the sensor\")\n else:\n for i in range(n):\n u_list.append(u[i])\n v_list.append(v[i])\n\n u = np.asarray(u_list)\n v = np.asarray(v_list)\n\n return np.vstack((u, v))","def get_roi_coords(self):\n return [group.get_roi_coords() for group in self.roi_groups]","def pointToWorld( nImageX, nImageY, rDepth, rMaxX = 320, rMaxY = 240, rFieldOfViewX = 60, rFieldOfViewY = 40 ):\n # convert to [-0.5,0.5]\n rCenteredX = ( nImageX / rMaxX ) - 0.5;\n rCenteredY = ( nImageY / rMaxY ) - 0.5;","def make_simple_coords():\n \n x = np.array([144, 124, 97, 165, 114, 60, 165, 0, 76, 50, 147])\n y = np.array([ 0, 3, 21, 28, 34, 38, 51, 54, 58, 56, 61])\n coords = np.vstack((x,y)).T\n return coords","def _to_image_coords(self, boxes, height, width):\n box_coords = np.zeros_like(boxes)\n box_coords[:, 0] = boxes[:, 0] * height\n box_coords[:, 1] = boxes[:, 1] * width\n box_coords[:, 2] = boxes[:, 2] * height\n box_coords[:, 3] = boxes[:, 3] * width\n \n return box_coords","def get_MultiPolyLists_xy(mpoly):\n # Get the x or y coordinates\n x = []\n y = []\n if isinstance(mpoly,Polygon):\n mpoly = [mpoly]\n for poly in mpoly: # the polygon objects return arrays, it's important they be lists or Bokeh fails\n exterior_coords_x = poly.exterior.coords.xy[0].tolist();\n interior_coords_x = []\n exterior_coords_y = poly.exterior.coords.xy[1].tolist();\n interior_coords_y = []\n\n for interior in poly.interiors:\n if isinstance(interior.coords.xy[0],list):\n interior_coords_x += [interior.coords.xy[0]];\n interior_coords_y += [interior.coords.xy[1]];\n else:\n interior_coords_x += [interior.coords.xy[0].tolist()];\n interior_coords_y += [interior.coords.xy[1].tolist()];\n x.append([exterior_coords_x, *interior_coords_x])\n y.append([exterior_coords_y, *interior_coords_y])\n return (x,y)","def get_roi_coords(self):\n return [roi.get_coords() for roi in self.rois]","def convertFrom1Dto2D(coord, num_cols):\n y = int(np.floor(coord/num_cols))\n x = coord % num_cols\n return (y,x)","def _pixel_to_map(coordinates, geotransform):\n coordinates_map = np.empty(coordinates.shape)\n coordinates_map[..., 0] = (\n geotransform[0]\n + geotransform[1] * coordinates[..., 0]\n + geotransform[2] * coordinates[..., 1]\n )\n coordinates_map[..., 1] = (\n geotransform[3]\n + geotransform[4] * coordinates[..., 0]\n + geotransform[5] * coordinates[..., 1]\n )\n return coordinates_map","def project_onto_image(self, points_in_camera_frame: ARRAY_LIKE, image: int = 0,\n temperature: Real = 0) -> np.ndarray:\n\n _, __, picture_locations = self.get_projections(points_in_camera_frame, image, temperature=temperature)\n\n return picture_locations","def homo_project(pixel_coords,homo_mat):\n b, _, h, w = pixel_coords.size()\n pixel_coords_flat = pixel_coords.reshape(b, 3, -1) # [B, 3, H*W]\n pcoords = homo_mat@pixel_coords_flat\n X = pcoords[:, 0]\n Y = pcoords[:, 1]\n Z = pcoords[:, 2].clamp(min=1e-3)\n\n X_norm = 2*(X / Z)/(w-1) - 1 # Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]\n Y_norm = 2*(Y / Z)/(h-1) - 1 # Idem [B, H*W]\n\n pixel_coords_n = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]\n return pixel_coords_n.reshape(b,h,w,2)","def pixel2coord(self, x, y):\n # NEED TO CHANGE TO USE INVERSE TRANSFORM COEFFS\n # partly taken from Sean Gillies \"affine.py\"\n a,b,c,d,e,f = self.coordspace_transform\n det = a*e - b*d\n idet = 1 / float(det)\n ra = e * idet\n rb = -b * idet\n rd = -d * idet\n re = a * idet\n newx = (x*ra + y*rb + (-c*ra - f*rb) )\n newy = (x*rd + y*re + (-c*rd - f*re) )\n return newx,newy","def read_pts_from_grid(jparams):\n print(\"=== Reading points from grid ===\")\n input_data = rasterio.open(jparams[\"input-file\"])\n nodata_value = input_data.nodata\n raw_data = input_data.read()\n ncols = input_data.width\n nrows = input_data.height\n shape = input_data.shape\n PixelSizeX = input_data.transform[0]\n PixelSizeY = -input_data.transform[4]\n #transform_matrix = input_data.transform\n #print(raw_data[0])\n #lower_left_corner = transform_matrix * (0,input_data.height)\n # outlist = []\n # row_cnt = 0\n # for rev_row in reversed(range(0,nrows)):\n # for col in range(0,ncols):\n # z=raw_data[0][row_cnt][col]\n # if z!=nodata_value:\n # outlist.append([(rev_row+0.5)*PixelSizeY, (col+0.5)*PixelSizeX, z])\n # row_cnt+=1\n # print(len(raw_data[0][1]))\n # print(len(np.array(generate_raster_points(nrows, ncols, raw_data, nodata_value, PixelSizeX, PixelSizeY))))\n return np.array(generate_raster_points(nrows, ncols, raw_data, nodata_value, PixelSizeX, PixelSizeY))\n # Tip: the most efficient implementation of this function does not use any loops. Use numpy functions instead.","def _getitem_2d(self, pos):\n if isinstance(pos, list):\n if isinstance(pos[0], (int, np.integer)): # single point\n return self[pos[0], pos[1]]\n else:\n return [self[p] for p in pos]\n elif isinstance(pos, np.ndarray):\n if isinstance(pos[0], (int, np.integer)): # single point\n return np.array(self[pos[0], pos[1]])\n else:\n return np.array([self[p] for p in pos])\n # compute coordinates from OneDimGrids of its master.\n x = self.master.x[pos[0]]\n y = self.master.y[pos[1]]\n # TODO: Refactor the following code to avoid the use of double for loops and list comprehensions.\n if (not isinstance(x, np.ndarray)) and (not isinstance(y, np.ndarray)): # x and y are scalars.\n return np.array([x, y])\n if not isinstance(x, np.ndarray): # x is a scalar.\n return np.array([np.array([x, _y]) for _y in y])\n elif not isinstance(y, np.ndarray): # y is a scalar.\n return np.array([np.array([_x, y]) for _x in x])\n else:\n xy = []\n for _x in x: # vectorize this operation.\n row = []\n for _y in y:\n row.append(np.array([_x, _y]))\n xy.append(np.array(row))\n return np.array(xy)","def map_coordinates(self,geometry):\n\t\tg = self.geomatrix\n\t\tdef project_coord(x,y,z=None):\n\t\t\tx = g[0] + g[1] * x + g[2] * y\n\t\t\ty = g[3] + g[4] * x + g[5] * y\n\t\t\tif z is None:\n\t\t\t\treturn x,y\n\t\t\telse:\n\t\t\t\treturn x,y,z\n\t\treturn transform(project_coord, geometry)","def condense_coords(matches):\n x = []\n y = []\n for m in matches:\n x += m['matches']['p'][0]\n x += m['matches']['q'][0]\n y += m['matches']['p'][1]\n y += m['matches']['q'][1]\n coords = np.transpose(np.vstack((np.array(x), np.array(y))))\n return coords","def reproject_coordinates_batch(aX_in, aY_in, spatial_reference_source, spatial_reference_target=None):\n #Reproject a list of x,y coordinates. \n\n if spatial_reference_target is not None:\n\n pass\n else:\n spatial_reference_target = osr.SpatialReference()\n spatial_reference_target.ImportFromEPSG(4326)\n \n pass\n\n \n if int(osgeo.__version__[0]) >= 3:\n # GDAL 3 changes axis order: https://github.com/OSGeo/gdal/issues/1546\n \n spatial_reference_source.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\n spatial_reference_target.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\n\n \n pTransform = osr.CoordinateTransformation( spatial_reference_source, spatial_reference_target)\n\n npoint = len(aX_in)\n x_new=list()\n y_new=list()\n for i in range(npoint):\n x0 = aX_in[i]\n y0 = aY_in[i]\n \n x1,y1, z = pTransform.TransformPoint( x0,y0)\n\n x_new.append(x1)\n y_new.append(y1)\n \n return x_new,y_new","def get_pixel_list(img):\n orig_shape = img.shape # Remember the original shape of the img.\n # Store the img as a x by z array (z being the length of the colour space)\n # Essentially just a list of pixels.\n\n if len(img.shape) == 3:\n img = img.reshape(img.shape[0] * img.shape[1], img.shape[2])\n elif len(img.shape) == 2:\n img = img.reshape(img.shape[0] * img.shape[1],)\n return orig_shape, img","def cam2pixel(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):\n b, _, h, w = cam_coords.size()\n cam_coords_flat = cam_coords.view(b, 3, -1) # [B, 3, H*W]\n if proj_c2p_rot is not None:\n pcoords = proj_c2p_rot.bmm(cam_coords_flat)\n else:\n pcoords = cam_coords_flat\n\n if proj_c2p_tr is not None:\n pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]\n X = pcoords[:, 0]\n Y = pcoords[:, 1]\n Z = pcoords[:, 2].clamp(min=1e-8)\n\n X_norm = 2 * (X / Z) / (w - 1) - 1 # Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]\n Y_norm = 2 * (Y / Z) / (h - 1) - 1 # Idem [B, H*W]\n if padding_mode == 'zeros':\n X_mask = ((X_norm > 1) + (X_norm < -1)).detach()\n X_norm[X_mask] = 2 # make sure that no point in warped image is a combinaison of im and gray\n Y_mask = ((Y_norm > 1) + (Y_norm < -1)).detach()\n Y_norm[Y_mask] = 2\n\n pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]\n return pixel_coords.view(b, h, w, 2)","def _compute_ij_images_xarray_numpy(src_geo_coding: GridMapping,\n output_geom: GridMapping,\n uv_delta: float) -> np.ndarray:\n dst_width = output_geom.width\n dst_height = output_geom.height\n dst_shape = 2, dst_height, dst_width\n dst_src_ij_images = np.full(dst_shape, np.nan, dtype=np.float64)\n dst_x_offset = output_geom.x_min\n dst_y_offset = output_geom.y_min \\\n if output_geom.is_j_axis_up else output_geom.y_max\n dst_x_scale = output_geom.x_res\n dst_y_scale = output_geom.y_res \\\n if output_geom.is_j_axis_up else -output_geom.y_res\n # TODO: GridMapping: this will cause out-of-memory problems!\n x_values, y_values = src_geo_coding.xy_coords.values\n _compute_ij_images_numpy_parallel(x_values,\n y_values,\n 0,\n 0,\n dst_src_ij_images,\n dst_x_offset,\n dst_y_offset,\n dst_x_scale,\n dst_y_scale,\n uv_delta)\n return dst_src_ij_images","def imshow2array(imx,imy):\n # NOTE: would be more efficient to not reconstruct 'tr' transform object every time...\n # NOTE: different order comp to 'extent' keyword in imshow\n bbox_in = Bbox.from_extents([x1, y1, x2, y2])\n # NOTE: lower left corner always -0.5,-0.5 by deafult with imshow\n bbox_out = Bbox.from_bounds(-0.5, -0.5, zi.shape[1], zi.shape[0])\n # transform from data coordinate into image coordinate.\n tr = BboxTransform(bbox_in, bbox_out)\n arrXY = tr.transform_point((imx, imy)).astype(int)\n\n return arrXY","def box2pts(x1: int, y1: int, x2: int, y2: int) -> np.ndarray:\n return np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]])","def array_coords(shape=(1, 1)):\n indexes = np.arange(np.product(shape))\n coords = [indexes % shape[1], indexes // shape[1]]\n return np.transpose(coords).astype('float64')","def GeneratePointsImg(self, n, ppa):\n x = np.linspace(0,self.camera.sensorSize,n)+ppa[0]\n y = np.linspace(0,self.camera.sensorSize,n)+ppa[1]\n\n return np.meshgrid(x, y)","def pixelToProjection(self, x, y, level=None):\n if level is None:\n level = self.levels - 1\n if not self.projection:\n x *= 2 ** (self.levels - 1 - level)\n y *= 2 ** (self.levels - 1 - level)\n gt = self._getGeoTransform()\n px = gt[0] + gt[1] * x + gt[2] * y\n py = gt[3] + gt[4] * x + gt[5] * y\n return px, py\n xScale = 2 ** level * self.tileWidth\n yScale = 2 ** level * self.tileHeight\n x = x / xScale - 0.5\n y = 0.5 - y / yScale\n x = x * self.unitsAcrossLevel0 + self.projectionOrigin[0]\n y = y * self.unitsAcrossLevel0 + self.projectionOrigin[1]\n return x, y","def coord_vecs(self):\n return [np.linspace(x0, x1, nx) for x0, x1, nx in zip(self.mins, self.maxs, self.shape)]","def coordinates(self):\n return np.array([self.x, self.y])","def coords(self):\n return np.column_stack((self.x_coord_list, self.y_coord_list, self.z_coord_list))","def to_image_coords(boxes, height, width):\n box_coords = np.zeros_like(boxes)\n box_coords[:, 0] = boxes[:, 0] * height\n box_coords[:, 1] = boxes[:, 1] * width\n box_coords[:, 2] = boxes[:, 2] * height\n box_coords[:, 3] = boxes[:, 3] * width\n \n return box_coords","def map_grid_loc_to_pixel((grid, x, y), panel_dimensions = bm_panel_dimensions, xc = 17.25, yc = 630, run = 17.25):\n x_offset = 0\n for panel_index, panel_dim in panel_dimensions.iteritems():\n if panel_index < grid:\n width, height = panel_dim\n x_offset += width*xc\n xp, yp = xc + x*run + x_offset, yc - y*run\n return (xp, yp)","def image_coordinates(self, temp):\n iy = np.array((temp.y[:,None]-self.extent[2])/self.spacing[1],dtype=np.int64)\n ix = np.array((temp.x[None,:]-self.extent[0])/self.spacing[0],dtype=np.int64)\n return (iy,ix)","def _getitem_2d(self, pos):\n # If pos contains multiple coordinates (or objects), convert recursively.\n if isinstance(pos, list):\n if isinstance(pos[0], (int, np.integer)): # It's actually a single coordinate.\n return self[pos[0], pos[1]]\n else:\n return [self[p] for p in pos]\n elif isinstance(pos, np.ndarray):\n if isinstance(pos[0], (int, np.integer)): # It's actually a single coordinate.\n return np.array(self[pos[0], pos[1]])\n else:\n return np.array([self[p] for p in pos])\n # If pos contains only one physical object, convert its bounding box to abstract coordinates\n if (pos.__class__.__name__ == 'PhysicalObject') or (issubclass(pos.__class__, laygo2.object.PhysicalObject)):\n return self.bbox(pos)\n # If pos contains only one coordinate, convert it to abstract grid.\n m = self.master.x == pos[0]\n n = self.master.y == pos[1]\n # refactor the following code to avoid the use of double for-loops and list comprehensions.\n if (not isinstance(m, np.ndarray)) and (not isinstance(n, np.ndarray)): # x and y are scalars.\n return np.array([m, n])\n if not isinstance(m, np.ndarray): # x is a scalar.\n return np.array([np.array([m, _n]) for _n in n])\n elif not isinstance(n, np.ndarray): # y is a scalar.\n return np.array([np.array([_m, n]) for _m in m])\n else:\n mn = []\n for _m in m: # vectorize this operation.\n row = []\n for _n in n:\n row.append(np.array([_m, _n]))\n mn.append(np.array(row))\n return np.array(mn)","def pixel2coord(x, y,a,b,xoff,yoff,d,e):\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n return(xp, yp)","def ij_coordinates(self):\n\n x = np.arange(self.nx)\n y = np.arange(self.ny)\n return np.meshgrid(x, y)","def convertView2Geo(self, x, y):\n\n # x_pix is from left map edge, y_pix from top map edge\n x_pix = x + self.view_offset_x\n y_pix = y + self.view_offset_y\n\n lon = self.map_llon + x_pix/self.ppd_x\n lat = self.map_tlat - y_pix/self.ppd_y\n\n return (lon, lat)","def xprojection(self):\n return self.image.sum(axis=0)","def convert_coords(self, coords):\n xPos = int(int(coords[0]) / 8)\n yPos = int(coords[1])\n zPos = int(int(coords[2]) / 8)\n return list(xPos, yPos, zPos)","def get_coordinates(self):\n return np.array([(n.x, n.y) for n in self.nodes])","def coordinate_matrix(n):\n xcoordinates = np.zeros((n,n))\n xcoordinates = xcoordinates + np.arange(0,worksize) #broadcasting trick\n ycoordinates = xcoordinates.T\n return np.array([ycoordinates,xcoordinates])","def mgrid_to_points(mgrid):\n points = np.empty(shape=(np.prod(mgrid[0].shape), len(mgrid)),\n dtype=mgrid[0].dtype)\n for i in range(len(mgrid)):\n points[:, i] = mgrid[i].ravel()\n return points","def transform_coords(x, y, w, h, nw, nh):\r\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)","def transform(o):\n\t\t\tp = o.pos() - self.pos()\n\t\t\tx = cosa * p.x + sina * p.y\n\t\t\ty = -sina * p.x + cosa * p.y\n\t\t\treturn tuple(px_to_mm(x,y))","def pos_to_pixel(intersection_pos, scale = 1, pixels_per_meter = PIXELS_PER_METER, world_offset = (0,0), offset = (0,0)):\n x = scale * pixels_per_meter * (intersection_pos[0] - world_offset[0])\n y = scale * pixels_per_meter * (intersection_pos[1] - world_offset[1])\n return [int(x - offset[0]), int(y - offset[1])]","def grid_coordinates(points: np.array, dtype = np.uint16) -> np.array:\n xmin = np.min(points[:, 0])\n xmax = np.max(points[:, 0]) + 1\n ymin = np.min(points[:, 1])\n ymax = np.max(points[:, 1]) + 1\n return np.asarray([(x, y) for y in range(ymin, ymax)\n for x in range(xmin, xmax)], dtype = dtype)","def _project_2d(self, activations: np.ndarray) -> List[np.ndarray]:\n projections = [[] for _ in range(self.n_components)]\n for activation in activations:\n flat = activation.reshape(activation.shape[0], -1).transpose()\n flat = flat - flat.mean(axis=0)\n U, S, VT = np.linalg.svd(flat, full_matrices=True)\n for i in range(self.n_components):\n component_i = flat @ VT[i, :]\n component_i = component_i.reshape(activation.shape[1:])\n projections[i].append(component_i)\n return [np.array(elem, dtype=np.float32) for elem in projections]","def to_pose_2d(np_trajectory):\n arr = []\n for i in range(len(np_trajectory[0])):\n pose = msgs.Pose2D(np_trajectory[0,i], np_trajectory[1,i], 0.)\n arr.append(pose)\n return arr","def row_to_pixel(row, image_shape, columns=None):\n start_y, start_x = latlon_to_pixel(row[\"s_lat\"],\n row[\"s_lng\"], image_shape)\n end_y, end_x = latlon_to_pixel(row[\"e_lat\"],\n row[\"e_lng\"], image_shape)\n xy = (start_x, start_y, end_x, end_y)\n return xy","def test_coords():\n x = np.array([8, 67, 79, 10, 52, 53, 98, 34, 15, 58], dtype=float)\n y = np.array([24, 87, 48, 94, 98, 66, 14, 24, 60, 16], dtype=float)\n\n return x, y"],"string":"[\n \"def proj_coords(coords, proj_in, proj_out): \\n return [proj_coord(coord, proj_in, proj_out) for coord in coords]\",\n \"def coordinates_to_imgpts(x, y):\\n pts = np.array([np.flipud(np.transpose(np.vstack([x, y])))])\\n return pts\",\n \"def get_pix_coordinates(pts, proj_mat, w, h):\\n points_t = np.ones(shape=(4, pts.shape[1]))\\n points_t[:3, :] = pts\\n pixels = project_points_img(points_t, proj_mat, w, h)\\n return pixels\",\n \"def pixel_to_coords(self, x, y):\\n rx, ry = self.size\\n nx = (x / rx - 0.5) * self.scale + self.center[0]\\n ny = ((ry - y) / ry - 0.5) * self.scale + self.center[1]\\n nz = self.center[2]\\n return [nx, ny, nz]\",\n \"def projectBack(points, proj):\\n\\n mpoints = MultiPoint(points)\\n project = partial(\\n pyproj.transform,\\n proj,\\n pyproj.Proj(proj='latlong', datum='WGS84'))\\n gmpoints = transform(project, mpoints)\\n coords = []\\n for point in gmpoints.geoms:\\n x, y = point.coords[0]\\n coords.append((x, y))\\n coords = np.array(coords)\\n return coords\",\n \"def calcul_xy_array(img_x, img_y, tile_x, tile_y):\\n array = []\\n\\n modu_x = img_x % tile_x\\n modu_y = img_y % tile_y\\n div_x = img_x // tile_x\\n div_y = img_y // tile_y\\n current_x = 0\\n current_y = 0\\n\\n for i in range(div_y):\\n for j in range(div_x):\\n array.append((current_x, current_y))\\n current_x += tile_x\\n if modu_x:\\n array.append((img_x - tile_x, current_y))\\n current_y += tile_y\\n current_x = 0\\n\\n if modu_y:\\n current_y = img_y - tile_y\\n for j in range(div_x):\\n array.append((current_x, current_y))\\n current_x += tile_x\\n if modu_x:\\n array.append((img_x - tile_x, current_y))\\n\\n return array\",\n \"def reproject_coordinates(coordinates, inproj, outproj, flat=False):\\n if flat:\\n return np.array([transform(inproj, outproj, coord[0], coord[1]) for coord in coordinates]).flatten()\\n return [list(transform(inproj, outproj, coord[0], coord[1])) for coord in coordinates]\",\n \"def flatten_pixcoord(image):\\n hdr = pyfits.getheader(image)\\n naxis1 = hdr['naxis1']\\n naxis2 = hdr['naxis2']\\n pixcoord = np.mgrid[1:naxis1+1,1:naxis2+1]\\n pixcoord = pixcoord.swapaxes(0,1).swapaxes(1,2)\\n pixcoord = pixcoord.ravel().reshape(naxis1*naxis2, 2)\\n return pixcoord\",\n \"def ReprojectCoords(coords,src_srs,tgt_srs):\\n trans_coords = []\\n transform = osr.CoordinateTransformation( src_srs, tgt_srs)\\n for x,y in coords:\\n x,y,z = transform.TransformPoint(x,y)\\n trans_coords.append([x,y])\\n return trans_coords\",\n \"def _convert_pix_coords(self):\\n if self.region_type in ['polygon', 'line']:\\n # have to special-case polygon in the phys coord case\\n # b/c can't typecheck when iterating as in sky coord case\\n coords = [PixCoord(self.coord[0::2], self.coord[1::2])]\\n else:\\n temp = [_.value for _ in self.coord]\\n coord = PixCoord(temp[0], temp[1])\\n coords = [coord] + temp[2:]\\n\\n # The angle remains as a quantity object.\\n # Modulus check makes sure that it works for ellipse/rectangle annulus\\n if self.region_type in ['ellipse', 'rectangle'] and len(coords) % 2 == 0:\\n coords[-1] = self.coord[-1]\\n\\n return coords\",\n \"def toNativePixelCoordinates(self, x, y, proj=None, roundResults=True):\\n if proj is None:\\n proj = self.projection\\n # convert to the native projection\\n inProj = self._proj4Proj(proj)\\n outProj = self._proj4Proj(self.getProj4String())\\n px, py = pyproj.Transformer.from_proj(inProj, outProj, always_xy=True).transform(x, y)\\n # convert to native pixel coordinates\\n gt = self._getGeoTransform()\\n d = gt[2] * gt[4] - gt[1] * gt[5]\\n x = (gt[0] * gt[5] - gt[2] * gt[3] - gt[5] * px + gt[2] * py) / d\\n y = (gt[1] * gt[3] - gt[0] * gt[4] + gt[4] * px - gt[1] * py) / d\\n if roundResults:\\n x = int(round(x))\\n y = int(round(y))\\n return x, y\",\n \"def pixel2coords(self, x, y):\\n xoff, a, b, yoff, d, e = self.geotransform()\\n\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n return (xp, yp)\",\n \"def image_to_points(numpy_image):\\r\\n res = []\\r\\n for i in range(numpy_image.shape[0]):\\r\\n for j in range(numpy_image.shape[1]):\\r\\n if numpy_image[i,j]==0:\\r\\n res.append([i,j])\\r\\n return res\",\n \"def _pixel_coordinates(nx, ny, mode):\\n if mode == \\\"center\\\":\\n x = np.linspace(0.5, nx - 0.5, num=nx)\\n y = np.linspace(0.5, ny - 0.5, num=ny)\\n\\n if mode == \\\"edge\\\":\\n x = np.linspace(0, nx, num=nx + 1)\\n y = np.linspace(0, ny, num=ny + 1)\\n\\n X, Y = np.meshgrid(x, y)\\n coordinates = np.stack((X, Y), axis=-1)\\n\\n return coordinates\",\n \"def camera_pixels_to_camera_coords(left_pt, right_pt, nparrays=False):\\n assert len(left_pt) == len(right_pt) == 2\\n disparity = np.linalg.norm( np.array(left_pt) - np.array(right_pt) )\\n (xx,yy,zz) = STEREO_MODEL.projectPixelTo3d( (left_pt[0],left_pt[1]), disparity )\\n if nparrays:\\n return np.array([xx,yy,zz])\\n else:\\n return [xx,yy,zz]\",\n \"def Reproject(x, y, in_grid = 4326, out_grid = 32737):\\n \\n inProj = Proj(init='epsg:'+str(in_grid))\\n outProj = Proj(init='epsg:'+str(out_grid))\\n \\n \\n x2,y2 = transform(inProj,outProj,x,y)\\n \\n return x2, y2\",\n \"def positions(r):\\n\\n X = []\\n Y = []\\n\\n leftx = -r*(nx - 1) / 2\\n topy = -r*(ny - 1) / 2\\n\\n for i in range(0, nx):\\n for j in range(0, ny):\\n X.append(leftx + r * i)\\n Y.append(topy + r * j)\\n\\n return (X, Y)\",\n \"def world2Pixel(geoMatrix, x, y):\\r\\n ulX = geoMatrix[0]\\r\\n ulY = geoMatrix[3]\\r\\n xDist = geoMatrix[1]\\r\\n yDist = geoMatrix[5]\\r\\n rtnX = geoMatrix[2]\\r\\n rtnY = geoMatrix[4]\\r\\n # pixel = int((x - ulX) / xDist)\\r\\n # line = int((ulY - y) / xDist)\\r\\n # Floor for x and ceiling for y seems to produce the best looking output\\r\\n #\\t(for one test case, may want to change later to np.round?)\\r\\n pixx = np.round((x - ulX) / xDist, decimals=0).astype(np.int)\\r\\n pixy = np.round((ulY - y) / xDist, decimals=0).astype(np.int)\\r\\n\\r\\n return pixx, pixy\",\n \"def get_scanner_xy(points, proj):\\n\\n # Find the pixel corresponding to (x=0,y=0)\\n res_x = proj.projector.res_x # 5 pixels / m, 1 px = 20 cm\\n res_y = proj.projector.res_y # 5 pixels / m , 1 px = 20 cm\\n\\n min_x, min_y, min_z = points.min(0)\\n\\n # the first coordinate is associated to the row coordinate of the image\\n y0 = int(np.floor((0 - min_y) * res_y).astype(np.int))\\n # the second coordinate is associated to the column coordinate of the image\\n x0 = int(np.floor((0 - min_x) * res_x).astype(np.int))\\n\\n return x0, y0\",\n \"def make_maps_of_2x1_pix_coordinates (sp) : \\n x_rhs = np.arange(sp.colsh)*sp.pixs + sp.pixw - sp.pixsh\\n x_rhs[0] = sp.pixwh # set x-coordinate of the wide pixel \\n x_arr = np.hstack([-x_rhs[::-1],x_rhs])\\n\\n y_arr = np.arange(sp.rows) * sp.pixs\\n y_arr -= y_arr[-1]/2 # move origin to the center of array\\n\\n sp.x_map2x1, sp.y_map2x1 = np.meshgrid(x_arr, y_arr)\",\n \"def cart_coords2d(self):\\r\\n\\r\\n mgx, mgy = self.meshup2d()\\r\\n coords = np.column_stack((mgx.flatten(), mgy.flatten()))\\r\\n\\r\\n return coords\",\n \"def get_coordinate_lists(self, crs=None):\\n x, y = self.vertices.vectors()[:2]\\n if crs is not None and (crs != self.crs):\\n x, y = _reproject((x,y), self.crs, crs)\\n return x, y\",\n \"def display_image_coords_to_canvas_coords(display_image_yx_coords):\\n\\n return [(yx[1], yx[0]) for yx in display_image_yx_coords]\",\n \"def Pixel2World(geoMatrix, x, y):\\r\\n ulX = geoMatrix[0]\\r\\n ulY = geoMatrix[3]\\r\\n xdist = geoMatrix[1]\\r\\n ydist = geoMatrix[5]\\r\\n coorX = (ulX + (x * xdist))\\r\\n coorY = (ulY + (y * ydist))\\r\\n return (coorX, coorY)\",\n \"def coordinates(self):\\n return np.array([[f.x, f.y] for f in self])\",\n \"def convert_coords(self):\\n if self.coordsys in ['image', 'physical']:\\n coords = self._convert_pix_coords()\\n else:\\n coords = self._convert_sky_coords()\\n\\n if self.region_type == 'line':\\n coords = [coords[0][0], coords[0][1]]\\n\\n if self.region_type == 'text':\\n coords.append(self.meta['text'])\\n\\n return coords\",\n \"def xy(self):\\n xcol = self.xx.reshape(-1, 1)\\n ycol = self.yy.reshape(-1, 1)\\n return np.column_stack([xcol, ycol])\",\n \"def make_coordinates(image, line_params):\\n slope, intercept = line_params\\n y1 = image.shape[0]\\n y2 = int(y1 * (3/5))\\n x1 = int((y1 - intercept)/ slope)\\n x2 = int((y2 - intercept)/ slope)\\n return np.array([x1, y1, x2, y2])\",\n \"def proj_coord(coord, proj_in, proj_out):\\n x, y = coord\\n return pyproj.transform(proj_in, proj_out, x, y)\",\n \"def _binary_image_to_grid_points(binary_image_matrix):\\n\\n return numpy.where(binary_image_matrix)\",\n \"def grid_coords(self):\\n return [(x, y) for y in range(self.height) for x in range(self.width)]\",\n \"def im2pos_coordinates(pix_x, pix_z):\\n # x_lim = [-0.85, 0.86]\\n # z_lim = [-1.22, 0.47]\\n x_lim = [-0.365, 0.365]\\n z_lim = [-0.95, -0.24]\\n x = x_lim[1] - (x_lim[1] - x_lim[0]) * pix_x/127\\n z = z_lim[1] - (z_lim[1] - z_lim[0]) * pix_z/127\\n return x, z\",\n \"def pose_2d_pts(self,image):\\n '''\\n image- rgb image \\n return:-\\n pts - list of 2d pose landmarks as img coords\\n image- rgb image on which the 2d pose landmarks are drawn\\n ''' \\n pts=[]\\n imgRGB=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\\n results=pose.process(imgRGB)\\n if results.pose_landmarks:\\n mpDraw.draw_landmarks(image,results.pose_landmarks,mpPose.POSE_CONNECTIONS)\\n for id,lm in enumerate(results.pose_landmarks.landmark):\\n h,w,c=image.shape\\n imgx,imgy=int(lm.x*w),int(lm.y*h)\\n \\n pts.append((imgx,imgy)) \\n return pts,image\",\n \"def pos2im_coordinates(x, z):\\n # x_lim = [-0.85, 0.86]\\n # z_lim = [-1.22, 0.47]\\n x_lim = [-0.365, 0.365]\\n z_lim = [-0.95, -0.24]\\n\\n pix_x = int(127 * (x_lim[1] - x) / (x_lim[1] - x_lim[0]))\\n pix_z = int(127 * (z_lim[1] - z) / (z_lim[1] - z_lim[0]))\\n return pix_x, pix_z\",\n \"def world2Pixel(geoMatrix, x, y):\\r\\n ulX = geoMatrix[0]\\r\\n ulY = geoMatrix[3]\\r\\n xDist = geoMatrix[1]\\r\\n yDist = geoMatrix[5]\\r\\n rtnX = geoMatrix[2]\\r\\n rtnY = geoMatrix[4]\\r\\n pixel = int((x - ulX) / xDist)\\r\\n line = int((ulY - y) / xDist)\\r\\n return (pixel, line)\",\n \"def get_all_coordinates(self):\\n coordinates = []\\n\\n for relative_coordinate in self.shape:\\n co = [self.coordinate[0] + relative_coordinate[0], self.coordinate[1] + relative_coordinate[1]]\\n coordinates.append(co)\\n return coordinates\",\n \"def decimated_image_coords_to_display_image_coords(self, decimated_image_yx_cords):\\n\\n scale_factor = self.compute_display_scale_factor(self.canvas_decimated_image)\\n return [(coord[0]*scale_factor, coord[1]*scale_factor) for coord in decimated_image_yx_cords]\",\n \"def coordinates(self, mask):\\n y,x = mask.nonzero()\\n return list(zip(x,y))\",\n \"def proj(self,lon,lat):\\n x, y = self(np.atleast_1d(lon),np.atleast_1d(lat))\\n x[x > 1e29] = None\\n y[y > 1e29] = None\\n #return np.ma.array(x,mask=x>1e2),np.ma.array(y,mask=y>1e2)\\n return x, y\",\n \"def cam2pixel_torch(cam_coords, proj):\\n batch, _, height, width = cam_coords.shape\\n cam_coords = torch.reshape(cam_coords, [batch, 4, -1])\\n unnormalized_pixel_coords = torch.matmul(proj, cam_coords)\\n xy_u = unnormalized_pixel_coords[:, 0:2, :]\\n z_u = unnormalized_pixel_coords[:, 2:3, :]\\n\\n pixel_coords = xy_u / (z_u + 1e-10)\\n pixel_coords = torch.reshape(pixel_coords, [batch, 2, height, width])\\n return pixel_coords.permute([0, 2, 3, 1])\",\n \"def xyFromROI(aROIPoints):\\n pt1 = aROIPoints[0]\\n pt2 = aROIPoints[1]\\n x0, y0 = pt1\\n x1, y1 = pt2\\n return x0, y0, x1, y1\",\n \"def _pixel_coord_np(width, height):\\n x = np.linspace(0, width - 1, width).astype(np.int32)\\n y = np.linspace(0, height - 1, height).astype(np.int32)\\n [x, y] = np.meshgrid(x, y)\\n return np.vstack((x.flatten(), y.flatten(), np.ones_like(x.flatten())))\",\n \"def get_coord(depth, pose, intrinsics_color_inv):\\n img_height, img_width = depth.shape[0], depth.shape[1]\\n mask = np.ones_like(depth)\\n mask[depth==0] = 0\\n mask = np.reshape(mask, (img_height, img_width,1))\\n x = np.linspace(0, img_width-1, img_width)\\n y = np.linspace(0, img_height-1, img_height)\\n xx, yy = np.meshgrid(x, y)\\n xx = np.reshape(xx, (1, -1))\\n yy = np.reshape(yy, (1, -1))\\n ones = np.ones_like(xx)\\n pcoord = np.concatenate((xx, yy, ones), axis=0)\\n depth = np.reshape(depth, (1, img_height*img_width))\\n ccoord = np.dot(intrinsics_color_inv, pcoord) * depth\\n ccoord = np.concatenate((ccoord, ones), axis=0)\\n scoord = np.dot(pose, ccoord)\\n scoord = np.swapaxes(scoord,0,1)\\n scoord = scoord[:,0:3]\\n scoord = np.reshape(scoord, (img_height, img_width,3))\\n scoord = scoord * mask\\n mask = np.reshape(mask, (img_height, img_width))\\n return scoord, mask\",\n \"def to_coords(self, px, py):\\n if px not in range(self.SIZE**2) or py not in range(self.SIZE**2):\\n raise IndexError\\n return (px // self.SIZE, py // self.SIZE,\\n px % self.SIZE, py % self.SIZE)\",\n \"def _image_to_point_space(pixel_coordinates: np.array, boundary_radius: int, resolution: int) -> np.array:\\n pix_origin = np.array([resolution / 2, resolution / 2])\\n return np.array((pixel_coordinates - pix_origin) * (2 * boundary_radius) / resolution)\",\n \"def get_coords_2d(obj, P):\\n bbox3 = get_coords_3d(obj)\\n # Rl = obj.Rl\\n # height = obj.height\\n # width = obj.width\\n # length = obj.length\\n # x = obj.x\\n # y = obj.y\\n # z = obj.z\\n # bbox = np.array([[-length, 0, -width],\\n # [-length, 0, width],\\n # [ length, 0, width],\\n # [ length, 0, -width],\\n # [-length, -2*height, -width],\\n # [-length, -2*height, width],\\n # [ length, -2*height, width],\\n # [ length, -2*height, -width]])/2\\n # if abs(z) < 1e-6: z = np.sign(z) * 1e-6\\n # M = rotationM([0, 1, 0], Rl + math.atan(x/z))\\n # bbox = np.transpose(np.dot(M, np.transpose(bbox)))\\n # bbox3 = bbox + np.array([x, y, z])\\n bbox2 = project_pts3_to_image(bbox3, P)\\n return bbox2\",\n \"def make_xarray_coords(y, x, crs):\\n if crs.is_geographic:\\n y_attrs, x_attrs = COORD_DEFS['latitude'], COORD_DEFS['longitude']\\n elif crs.is_projected:\\n crs_osr = crs2osr(crs)\\n units = crs_osr.GetLinearUnitsName()\\n y_attrs, x_attrs = COORD_DEFS['y'], COORD_DEFS['x']\\n y_attrs['units'], x_attrs['units'] = units, units\\n\\n y = xr.Variable(('y', ), y, attrs=y_attrs)\\n x = xr.Variable(('x', ), x, attrs=x_attrs)\\n\\n return y, x\",\n \"def getYesPoints(pshapes, proj, dx, nmax, touch_center=True):\\n\\n mxmin = 9e10\\n mxmax = -9e10\\n mymin = 9e10\\n mymax = -9e10\\n for pshape in pshapes:\\n pxmin, pymin, pxmax, pymax = pshape.bounds\\n if pxmin < mxmin:\\n mxmin = pxmin\\n if pxmax > mxmax:\\n mxmax = pxmax\\n if pymin < mymin:\\n mymin = pymin\\n if pymax > mymax:\\n mymax = pymax\\n\\n if not touch_center:\\n geodict = GeoDict.createDictFromBox(mxmin, mxmax, mymin, mymax, dx, dx)\\n img = rasterizeShapes(pshapes, geodict)\\n #now get the numpy array of x/y coordinates where covgrid == 1\\n idx = np.where(img == 1)[0]\\n x, y = np.unravel_index(idx, (geodict.ny, geodict.nx))\\n yespoints = list(zip(x.flatten(), y.flatten()))\\n nrows = geodict.ny\\n ncols = geodict.nx\\n xvar = np.arange(geodict.xmin, geodict.xmax+geodict.dx, geodict.dx)\\n yvar = np.arange(geodict.ymin, geodict.ymax+geodict.dy, geodict.dy)\\n else:\\n xvar = np.arange(mxmin, mxmax+dx, dx)\\n yvar = np.arange(mymin, mymax+dx, dx)\\n ncols = len(xvar)\\n nrows = len(yvar)\\n if nmax is not None:\\n if ncols*nrows > nmax:\\n aspect = ncols/nrows\\n ncols = np.sqrt(nmax*aspect)\\n nrows = nmax/ncols\\n ncols = int(ncols)\\n nrows = int(nrows)\\n #re-calculate dx here...\\n tdx = (mxmax-mxmin)/ncols\\n tdy = (mymax-mymin)/nrows\\n dx = np.max(tdx, tdy)\\n xvar = np.arange(mxmin, mxmax+dx, dx)\\n yvar = np.arange(mymin, mymax+dx, dx)\\n\\n #Get the \\\"yes\\\" points to sample from\\n yespoints = []\\n idx = []\\n shapeidx = 0\\n if pshapes[0].type == 'Polygon':\\n #loop over shapes, projecting each one, then get the sample points\\n for pshape in pshapes:\\n if not shapeidx % 1000:\\n print('Searching polygon %i of %i' % (shapeidx, len(pshapes)))\\n shapeidx += 1\\n pxmin, pymin, pxmax, pymax = pshape.bounds\\n leftcol = np.where((pxmin - xvar) >= 0)[0].argmax()\\n rightcol = np.where((xvar - pxmax) >= 0)[0][0]\\n bottomrow = np.where((pymin - yvar) >= 0)[0].argmax()\\n toprow = np.where((yvar - pymax) >= 0)[0][0]\\n xp = np.arange(xvar[leftcol], xvar[rightcol]+dx, dx)\\n yp = np.arange(yvar[bottomrow], yvar[toprow]+dx, dx)\\n xmesh, ymesh = np.meshgrid(xp, yp)\\n xy = list(zip(xmesh.flatten(), ymesh.flatten()))\\n for point in xy:\\n ix = np.where(xvar == point[0])[0][0]\\n iy = np.where(yvar == point[1])[0][0]\\n if pshape.contains(Point(point)):\\n yespoints.append(point)\\n idx.append(np.ravel_multi_index((iy, ix), (nrows, ncols), mode='raise', order='C'))\\n else:\\n yespoints = []\\n for pshape in pshapes:\\n yespoints.append(pshape.coords[0])\\n\\n return (np.array(yespoints), nrows, ncols, xvar, yvar, idx)\",\n \"def coords2D(self):\\n return (self.x, self.y)\",\n \"def pixels_to_latlon(x_pos, y_pos):\\n radius = 489.5\\n x_pos_from_center = -1*(512.5 - x_pos)\\n angle_1 = np.rad2deg(np.arccos(x_pos_from_center/radius))\\n\\n y_pos_from_center = -1*(512.5 - y_pos)\\n angle_2 = np.rad2deg(np.arccos(y_pos_from_center/radius)) \\n\\n return -1*angle_1, angle_2\",\n \"def project_points_img(points, proj_mat, width, height):\\n pixels = proj_mat.dot(points)\\n pixels = np.divide(pixels[:2, :], pixels[2, :]).transpose().astype(np.int)\\n\\n # Remove pixels that are outside the image\\n pixels[:, 0] = np.clip(pixels[:, 0], 0, width)\\n pixels[:, 1] = np.clip(pixels[:, 1], 0, height)\\n # mask_x = (pixels[:, 0] < width) & (pixels[:, 0] > 0)\\n # mask_y = (pixels[:, 1] < height) & (pixels[:, 1] > 0)\\n\\n # # Return the pixels and points that are inside the image\\n # pixels = pixels[mask_x & mask_y]\\n return pixels\",\n \"def positions_to_coords(self, positions):\\n return [self.to_coords(px, py) for (px, py) in positions]\",\n \"def projective_transform(self, x):\\n\\n x = np.asarray(x)\\n # Assume no intensity column\\n x0, y0, z0 = x\\n\\n # Camera coors to pixel coors\\n u = ((x0 / z0) * self.f) + (self.sensor_size[0] // 2)\\n v = ((y0 / z0) * self.f) + (self.sensor_size[1] // 2)\\n\\n u_min = np.min(u)\\n v_min = np.min(v)\\n\\n n = len(u)\\n u_list = []\\n v_list = []\\n if self.error_on_oob:\\n for i in range(n):\\n if (u[i] >= u_min and u[i] <= self.sensor_size[0] and v[i] >= v_min and v[i] <= self.sensor_size[1]):\\n u_list.append(u[i])\\n v_list.append(v[i])\\n else:\\n raise OutOfSensorBoundsError(\\\"Projected coordinate was outside the sensor\\\")\\n else:\\n for i in range(n):\\n u_list.append(u[i])\\n v_list.append(v[i])\\n\\n u = np.asarray(u_list)\\n v = np.asarray(v_list)\\n\\n return np.vstack((u, v))\",\n \"def get_roi_coords(self):\\n return [group.get_roi_coords() for group in self.roi_groups]\",\n \"def pointToWorld( nImageX, nImageY, rDepth, rMaxX = 320, rMaxY = 240, rFieldOfViewX = 60, rFieldOfViewY = 40 ):\\n # convert to [-0.5,0.5]\\n rCenteredX = ( nImageX / rMaxX ) - 0.5;\\n rCenteredY = ( nImageY / rMaxY ) - 0.5;\",\n \"def make_simple_coords():\\n \\n x = np.array([144, 124, 97, 165, 114, 60, 165, 0, 76, 50, 147])\\n y = np.array([ 0, 3, 21, 28, 34, 38, 51, 54, 58, 56, 61])\\n coords = np.vstack((x,y)).T\\n return coords\",\n \"def _to_image_coords(self, boxes, height, width):\\n box_coords = np.zeros_like(boxes)\\n box_coords[:, 0] = boxes[:, 0] * height\\n box_coords[:, 1] = boxes[:, 1] * width\\n box_coords[:, 2] = boxes[:, 2] * height\\n box_coords[:, 3] = boxes[:, 3] * width\\n \\n return box_coords\",\n \"def get_MultiPolyLists_xy(mpoly):\\n # Get the x or y coordinates\\n x = []\\n y = []\\n if isinstance(mpoly,Polygon):\\n mpoly = [mpoly]\\n for poly in mpoly: # the polygon objects return arrays, it's important they be lists or Bokeh fails\\n exterior_coords_x = poly.exterior.coords.xy[0].tolist();\\n interior_coords_x = []\\n exterior_coords_y = poly.exterior.coords.xy[1].tolist();\\n interior_coords_y = []\\n\\n for interior in poly.interiors:\\n if isinstance(interior.coords.xy[0],list):\\n interior_coords_x += [interior.coords.xy[0]];\\n interior_coords_y += [interior.coords.xy[1]];\\n else:\\n interior_coords_x += [interior.coords.xy[0].tolist()];\\n interior_coords_y += [interior.coords.xy[1].tolist()];\\n x.append([exterior_coords_x, *interior_coords_x])\\n y.append([exterior_coords_y, *interior_coords_y])\\n return (x,y)\",\n \"def get_roi_coords(self):\\n return [roi.get_coords() for roi in self.rois]\",\n \"def convertFrom1Dto2D(coord, num_cols):\\n y = int(np.floor(coord/num_cols))\\n x = coord % num_cols\\n return (y,x)\",\n \"def _pixel_to_map(coordinates, geotransform):\\n coordinates_map = np.empty(coordinates.shape)\\n coordinates_map[..., 0] = (\\n geotransform[0]\\n + geotransform[1] * coordinates[..., 0]\\n + geotransform[2] * coordinates[..., 1]\\n )\\n coordinates_map[..., 1] = (\\n geotransform[3]\\n + geotransform[4] * coordinates[..., 0]\\n + geotransform[5] * coordinates[..., 1]\\n )\\n return coordinates_map\",\n \"def project_onto_image(self, points_in_camera_frame: ARRAY_LIKE, image: int = 0,\\n temperature: Real = 0) -> np.ndarray:\\n\\n _, __, picture_locations = self.get_projections(points_in_camera_frame, image, temperature=temperature)\\n\\n return picture_locations\",\n \"def homo_project(pixel_coords,homo_mat):\\n b, _, h, w = pixel_coords.size()\\n pixel_coords_flat = pixel_coords.reshape(b, 3, -1) # [B, 3, H*W]\\n pcoords = homo_mat@pixel_coords_flat\\n X = pcoords[:, 0]\\n Y = pcoords[:, 1]\\n Z = pcoords[:, 2].clamp(min=1e-3)\\n\\n X_norm = 2*(X / Z)/(w-1) - 1 # Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]\\n Y_norm = 2*(Y / Z)/(h-1) - 1 # Idem [B, H*W]\\n\\n pixel_coords_n = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]\\n return pixel_coords_n.reshape(b,h,w,2)\",\n \"def pixel2coord(self, x, y):\\n # NEED TO CHANGE TO USE INVERSE TRANSFORM COEFFS\\n # partly taken from Sean Gillies \\\"affine.py\\\"\\n a,b,c,d,e,f = self.coordspace_transform\\n det = a*e - b*d\\n idet = 1 / float(det)\\n ra = e * idet\\n rb = -b * idet\\n rd = -d * idet\\n re = a * idet\\n newx = (x*ra + y*rb + (-c*ra - f*rb) )\\n newy = (x*rd + y*re + (-c*rd - f*re) )\\n return newx,newy\",\n \"def read_pts_from_grid(jparams):\\n print(\\\"=== Reading points from grid ===\\\")\\n input_data = rasterio.open(jparams[\\\"input-file\\\"])\\n nodata_value = input_data.nodata\\n raw_data = input_data.read()\\n ncols = input_data.width\\n nrows = input_data.height\\n shape = input_data.shape\\n PixelSizeX = input_data.transform[0]\\n PixelSizeY = -input_data.transform[4]\\n #transform_matrix = input_data.transform\\n #print(raw_data[0])\\n #lower_left_corner = transform_matrix * (0,input_data.height)\\n # outlist = []\\n # row_cnt = 0\\n # for rev_row in reversed(range(0,nrows)):\\n # for col in range(0,ncols):\\n # z=raw_data[0][row_cnt][col]\\n # if z!=nodata_value:\\n # outlist.append([(rev_row+0.5)*PixelSizeY, (col+0.5)*PixelSizeX, z])\\n # row_cnt+=1\\n # print(len(raw_data[0][1]))\\n # print(len(np.array(generate_raster_points(nrows, ncols, raw_data, nodata_value, PixelSizeX, PixelSizeY))))\\n return np.array(generate_raster_points(nrows, ncols, raw_data, nodata_value, PixelSizeX, PixelSizeY))\\n # Tip: the most efficient implementation of this function does not use any loops. Use numpy functions instead.\",\n \"def _getitem_2d(self, pos):\\n if isinstance(pos, list):\\n if isinstance(pos[0], (int, np.integer)): # single point\\n return self[pos[0], pos[1]]\\n else:\\n return [self[p] for p in pos]\\n elif isinstance(pos, np.ndarray):\\n if isinstance(pos[0], (int, np.integer)): # single point\\n return np.array(self[pos[0], pos[1]])\\n else:\\n return np.array([self[p] for p in pos])\\n # compute coordinates from OneDimGrids of its master.\\n x = self.master.x[pos[0]]\\n y = self.master.y[pos[1]]\\n # TODO: Refactor the following code to avoid the use of double for loops and list comprehensions.\\n if (not isinstance(x, np.ndarray)) and (not isinstance(y, np.ndarray)): # x and y are scalars.\\n return np.array([x, y])\\n if not isinstance(x, np.ndarray): # x is a scalar.\\n return np.array([np.array([x, _y]) for _y in y])\\n elif not isinstance(y, np.ndarray): # y is a scalar.\\n return np.array([np.array([_x, y]) for _x in x])\\n else:\\n xy = []\\n for _x in x: # vectorize this operation.\\n row = []\\n for _y in y:\\n row.append(np.array([_x, _y]))\\n xy.append(np.array(row))\\n return np.array(xy)\",\n \"def map_coordinates(self,geometry):\\n\\t\\tg = self.geomatrix\\n\\t\\tdef project_coord(x,y,z=None):\\n\\t\\t\\tx = g[0] + g[1] * x + g[2] * y\\n\\t\\t\\ty = g[3] + g[4] * x + g[5] * y\\n\\t\\t\\tif z is None:\\n\\t\\t\\t\\treturn x,y\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn x,y,z\\n\\t\\treturn transform(project_coord, geometry)\",\n \"def condense_coords(matches):\\n x = []\\n y = []\\n for m in matches:\\n x += m['matches']['p'][0]\\n x += m['matches']['q'][0]\\n y += m['matches']['p'][1]\\n y += m['matches']['q'][1]\\n coords = np.transpose(np.vstack((np.array(x), np.array(y))))\\n return coords\",\n \"def reproject_coordinates_batch(aX_in, aY_in, spatial_reference_source, spatial_reference_target=None):\\n #Reproject a list of x,y coordinates. \\n\\n if spatial_reference_target is not None:\\n\\n pass\\n else:\\n spatial_reference_target = osr.SpatialReference()\\n spatial_reference_target.ImportFromEPSG(4326)\\n \\n pass\\n\\n \\n if int(osgeo.__version__[0]) >= 3:\\n # GDAL 3 changes axis order: https://github.com/OSGeo/gdal/issues/1546\\n \\n spatial_reference_source.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\\n spatial_reference_target.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\\n\\n \\n pTransform = osr.CoordinateTransformation( spatial_reference_source, spatial_reference_target)\\n\\n npoint = len(aX_in)\\n x_new=list()\\n y_new=list()\\n for i in range(npoint):\\n x0 = aX_in[i]\\n y0 = aY_in[i]\\n \\n x1,y1, z = pTransform.TransformPoint( x0,y0)\\n\\n x_new.append(x1)\\n y_new.append(y1)\\n \\n return x_new,y_new\",\n \"def get_pixel_list(img):\\n orig_shape = img.shape # Remember the original shape of the img.\\n # Store the img as a x by z array (z being the length of the colour space)\\n # Essentially just a list of pixels.\\n\\n if len(img.shape) == 3:\\n img = img.reshape(img.shape[0] * img.shape[1], img.shape[2])\\n elif len(img.shape) == 2:\\n img = img.reshape(img.shape[0] * img.shape[1],)\\n return orig_shape, img\",\n \"def cam2pixel(cam_coords, proj_c2p_rot, proj_c2p_tr, padding_mode):\\n b, _, h, w = cam_coords.size()\\n cam_coords_flat = cam_coords.view(b, 3, -1) # [B, 3, H*W]\\n if proj_c2p_rot is not None:\\n pcoords = proj_c2p_rot.bmm(cam_coords_flat)\\n else:\\n pcoords = cam_coords_flat\\n\\n if proj_c2p_tr is not None:\\n pcoords = pcoords + proj_c2p_tr # [B, 3, H*W]\\n X = pcoords[:, 0]\\n Y = pcoords[:, 1]\\n Z = pcoords[:, 2].clamp(min=1e-8)\\n\\n X_norm = 2 * (X / Z) / (w - 1) - 1 # Normalized, -1 if on extreme left, 1 if on extreme right (x = w-1) [B, H*W]\\n Y_norm = 2 * (Y / Z) / (h - 1) - 1 # Idem [B, H*W]\\n if padding_mode == 'zeros':\\n X_mask = ((X_norm > 1) + (X_norm < -1)).detach()\\n X_norm[X_mask] = 2 # make sure that no point in warped image is a combinaison of im and gray\\n Y_mask = ((Y_norm > 1) + (Y_norm < -1)).detach()\\n Y_norm[Y_mask] = 2\\n\\n pixel_coords = torch.stack([X_norm, Y_norm], dim=2) # [B, H*W, 2]\\n return pixel_coords.view(b, h, w, 2)\",\n \"def _compute_ij_images_xarray_numpy(src_geo_coding: GridMapping,\\n output_geom: GridMapping,\\n uv_delta: float) -> np.ndarray:\\n dst_width = output_geom.width\\n dst_height = output_geom.height\\n dst_shape = 2, dst_height, dst_width\\n dst_src_ij_images = np.full(dst_shape, np.nan, dtype=np.float64)\\n dst_x_offset = output_geom.x_min\\n dst_y_offset = output_geom.y_min \\\\\\n if output_geom.is_j_axis_up else output_geom.y_max\\n dst_x_scale = output_geom.x_res\\n dst_y_scale = output_geom.y_res \\\\\\n if output_geom.is_j_axis_up else -output_geom.y_res\\n # TODO: GridMapping: this will cause out-of-memory problems!\\n x_values, y_values = src_geo_coding.xy_coords.values\\n _compute_ij_images_numpy_parallel(x_values,\\n y_values,\\n 0,\\n 0,\\n dst_src_ij_images,\\n dst_x_offset,\\n dst_y_offset,\\n dst_x_scale,\\n dst_y_scale,\\n uv_delta)\\n return dst_src_ij_images\",\n \"def imshow2array(imx,imy):\\n # NOTE: would be more efficient to not reconstruct 'tr' transform object every time...\\n # NOTE: different order comp to 'extent' keyword in imshow\\n bbox_in = Bbox.from_extents([x1, y1, x2, y2])\\n # NOTE: lower left corner always -0.5,-0.5 by deafult with imshow\\n bbox_out = Bbox.from_bounds(-0.5, -0.5, zi.shape[1], zi.shape[0])\\n # transform from data coordinate into image coordinate.\\n tr = BboxTransform(bbox_in, bbox_out)\\n arrXY = tr.transform_point((imx, imy)).astype(int)\\n\\n return arrXY\",\n \"def box2pts(x1: int, y1: int, x2: int, y2: int) -> np.ndarray:\\n return np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]])\",\n \"def array_coords(shape=(1, 1)):\\n indexes = np.arange(np.product(shape))\\n coords = [indexes % shape[1], indexes // shape[1]]\\n return np.transpose(coords).astype('float64')\",\n \"def GeneratePointsImg(self, n, ppa):\\n x = np.linspace(0,self.camera.sensorSize,n)+ppa[0]\\n y = np.linspace(0,self.camera.sensorSize,n)+ppa[1]\\n\\n return np.meshgrid(x, y)\",\n \"def pixelToProjection(self, x, y, level=None):\\n if level is None:\\n level = self.levels - 1\\n if not self.projection:\\n x *= 2 ** (self.levels - 1 - level)\\n y *= 2 ** (self.levels - 1 - level)\\n gt = self._getGeoTransform()\\n px = gt[0] + gt[1] * x + gt[2] * y\\n py = gt[3] + gt[4] * x + gt[5] * y\\n return px, py\\n xScale = 2 ** level * self.tileWidth\\n yScale = 2 ** level * self.tileHeight\\n x = x / xScale - 0.5\\n y = 0.5 - y / yScale\\n x = x * self.unitsAcrossLevel0 + self.projectionOrigin[0]\\n y = y * self.unitsAcrossLevel0 + self.projectionOrigin[1]\\n return x, y\",\n \"def coord_vecs(self):\\n return [np.linspace(x0, x1, nx) for x0, x1, nx in zip(self.mins, self.maxs, self.shape)]\",\n \"def coordinates(self):\\n return np.array([self.x, self.y])\",\n \"def coords(self):\\n return np.column_stack((self.x_coord_list, self.y_coord_list, self.z_coord_list))\",\n \"def to_image_coords(boxes, height, width):\\n box_coords = np.zeros_like(boxes)\\n box_coords[:, 0] = boxes[:, 0] * height\\n box_coords[:, 1] = boxes[:, 1] * width\\n box_coords[:, 2] = boxes[:, 2] * height\\n box_coords[:, 3] = boxes[:, 3] * width\\n \\n return box_coords\",\n \"def map_grid_loc_to_pixel((grid, x, y), panel_dimensions = bm_panel_dimensions, xc = 17.25, yc = 630, run = 17.25):\\n x_offset = 0\\n for panel_index, panel_dim in panel_dimensions.iteritems():\\n if panel_index < grid:\\n width, height = panel_dim\\n x_offset += width*xc\\n xp, yp = xc + x*run + x_offset, yc - y*run\\n return (xp, yp)\",\n \"def image_coordinates(self, temp):\\n iy = np.array((temp.y[:,None]-self.extent[2])/self.spacing[1],dtype=np.int64)\\n ix = np.array((temp.x[None,:]-self.extent[0])/self.spacing[0],dtype=np.int64)\\n return (iy,ix)\",\n \"def _getitem_2d(self, pos):\\n # If pos contains multiple coordinates (or objects), convert recursively.\\n if isinstance(pos, list):\\n if isinstance(pos[0], (int, np.integer)): # It's actually a single coordinate.\\n return self[pos[0], pos[1]]\\n else:\\n return [self[p] for p in pos]\\n elif isinstance(pos, np.ndarray):\\n if isinstance(pos[0], (int, np.integer)): # It's actually a single coordinate.\\n return np.array(self[pos[0], pos[1]])\\n else:\\n return np.array([self[p] for p in pos])\\n # If pos contains only one physical object, convert its bounding box to abstract coordinates\\n if (pos.__class__.__name__ == 'PhysicalObject') or (issubclass(pos.__class__, laygo2.object.PhysicalObject)):\\n return self.bbox(pos)\\n # If pos contains only one coordinate, convert it to abstract grid.\\n m = self.master.x == pos[0]\\n n = self.master.y == pos[1]\\n # refactor the following code to avoid the use of double for-loops and list comprehensions.\\n if (not isinstance(m, np.ndarray)) and (not isinstance(n, np.ndarray)): # x and y are scalars.\\n return np.array([m, n])\\n if not isinstance(m, np.ndarray): # x is a scalar.\\n return np.array([np.array([m, _n]) for _n in n])\\n elif not isinstance(n, np.ndarray): # y is a scalar.\\n return np.array([np.array([_m, n]) for _m in m])\\n else:\\n mn = []\\n for _m in m: # vectorize this operation.\\n row = []\\n for _n in n:\\n row.append(np.array([_m, _n]))\\n mn.append(np.array(row))\\n return np.array(mn)\",\n \"def pixel2coord(x, y,a,b,xoff,yoff,d,e):\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n return(xp, yp)\",\n \"def ij_coordinates(self):\\n\\n x = np.arange(self.nx)\\n y = np.arange(self.ny)\\n return np.meshgrid(x, y)\",\n \"def convertView2Geo(self, x, y):\\n\\n # x_pix is from left map edge, y_pix from top map edge\\n x_pix = x + self.view_offset_x\\n y_pix = y + self.view_offset_y\\n\\n lon = self.map_llon + x_pix/self.ppd_x\\n lat = self.map_tlat - y_pix/self.ppd_y\\n\\n return (lon, lat)\",\n \"def xprojection(self):\\n return self.image.sum(axis=0)\",\n \"def convert_coords(self, coords):\\n xPos = int(int(coords[0]) / 8)\\n yPos = int(coords[1])\\n zPos = int(int(coords[2]) / 8)\\n return list(xPos, yPos, zPos)\",\n \"def get_coordinates(self):\\n return np.array([(n.x, n.y) for n in self.nodes])\",\n \"def coordinate_matrix(n):\\n xcoordinates = np.zeros((n,n))\\n xcoordinates = xcoordinates + np.arange(0,worksize) #broadcasting trick\\n ycoordinates = xcoordinates.T\\n return np.array([ycoordinates,xcoordinates])\",\n \"def mgrid_to_points(mgrid):\\n points = np.empty(shape=(np.prod(mgrid[0].shape), len(mgrid)),\\n dtype=mgrid[0].dtype)\\n for i in range(len(mgrid)):\\n points[:, i] = mgrid[i].ravel()\\n return points\",\n \"def transform_coords(x, y, w, h, nw, nh):\\r\\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)\",\n \"def transform(o):\\n\\t\\t\\tp = o.pos() - self.pos()\\n\\t\\t\\tx = cosa * p.x + sina * p.y\\n\\t\\t\\ty = -sina * p.x + cosa * p.y\\n\\t\\t\\treturn tuple(px_to_mm(x,y))\",\n \"def pos_to_pixel(intersection_pos, scale = 1, pixels_per_meter = PIXELS_PER_METER, world_offset = (0,0), offset = (0,0)):\\n x = scale * pixels_per_meter * (intersection_pos[0] - world_offset[0])\\n y = scale * pixels_per_meter * (intersection_pos[1] - world_offset[1])\\n return [int(x - offset[0]), int(y - offset[1])]\",\n \"def grid_coordinates(points: np.array, dtype = np.uint16) -> np.array:\\n xmin = np.min(points[:, 0])\\n xmax = np.max(points[:, 0]) + 1\\n ymin = np.min(points[:, 1])\\n ymax = np.max(points[:, 1]) + 1\\n return np.asarray([(x, y) for y in range(ymin, ymax)\\n for x in range(xmin, xmax)], dtype = dtype)\",\n \"def _project_2d(self, activations: np.ndarray) -> List[np.ndarray]:\\n projections = [[] for _ in range(self.n_components)]\\n for activation in activations:\\n flat = activation.reshape(activation.shape[0], -1).transpose()\\n flat = flat - flat.mean(axis=0)\\n U, S, VT = np.linalg.svd(flat, full_matrices=True)\\n for i in range(self.n_components):\\n component_i = flat @ VT[i, :]\\n component_i = component_i.reshape(activation.shape[1:])\\n projections[i].append(component_i)\\n return [np.array(elem, dtype=np.float32) for elem in projections]\",\n \"def to_pose_2d(np_trajectory):\\n arr = []\\n for i in range(len(np_trajectory[0])):\\n pose = msgs.Pose2D(np_trajectory[0,i], np_trajectory[1,i], 0.)\\n arr.append(pose)\\n return arr\",\n \"def row_to_pixel(row, image_shape, columns=None):\\n start_y, start_x = latlon_to_pixel(row[\\\"s_lat\\\"],\\n row[\\\"s_lng\\\"], image_shape)\\n end_y, end_x = latlon_to_pixel(row[\\\"e_lat\\\"],\\n row[\\\"e_lng\\\"], image_shape)\\n xy = (start_x, start_y, end_x, end_y)\\n return xy\",\n \"def test_coords():\\n x = np.array([8, 67, 79, 10, 52, 53, 98, 34, 15, 58], dtype=float)\\n y = np.array([24, 87, 48, 94, 98, 66, 14, 24, 60, 16], dtype=float)\\n\\n return x, y\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71438277","0.68676543","0.68333745","0.6638456","0.66094863","0.65278304","0.6524649","0.6480489","0.64521027","0.63857985","0.63771254","0.636936","0.6328122","0.6313248","0.62968314","0.62734854","0.62452483","0.6242931","0.6173009","0.6140261","0.6133866","0.6122962","0.610952","0.6091143","0.6076117","0.60584366","0.6007372","0.6001273","0.59882593","0.59576243","0.5951416","0.5946682","0.594088","0.59272814","0.5924271","0.5911583","0.59084725","0.5908314","0.5907796","0.5897837","0.58975947","0.5891373","0.5873559","0.58639014","0.5863778","0.58601546","0.5848406","0.5848317","0.58476734","0.5846395","0.58443016","0.5832456","0.58218694","0.5820891","0.5819956","0.5819276","0.581524","0.5807063","0.58037263","0.5801649","0.58016104","0.5799853","0.5796693","0.5788912","0.57839274","0.57828516","0.57770115","0.5769301","0.57663363","0.57557845","0.5748934","0.5742434","0.5741667","0.574011","0.57392496","0.57298064","0.5729138","0.5728453","0.57233137","0.5721333","0.5721126","0.5720897","0.57097256","0.5701425","0.5699307","0.56980085","0.5697924","0.5694963","0.5679183","0.5678218","0.56754243","0.56744385","0.56731546","0.5670677","0.56650186","0.5650361","0.5649563","0.56372315","0.56295604","0.5625961"],"string":"[\n \"0.71438277\",\n \"0.68676543\",\n \"0.68333745\",\n \"0.6638456\",\n \"0.66094863\",\n \"0.65278304\",\n \"0.6524649\",\n \"0.6480489\",\n \"0.64521027\",\n \"0.63857985\",\n \"0.63771254\",\n \"0.636936\",\n \"0.6328122\",\n \"0.6313248\",\n \"0.62968314\",\n \"0.62734854\",\n \"0.62452483\",\n \"0.6242931\",\n \"0.6173009\",\n \"0.6140261\",\n \"0.6133866\",\n \"0.6122962\",\n \"0.610952\",\n \"0.6091143\",\n \"0.6076117\",\n \"0.60584366\",\n \"0.6007372\",\n \"0.6001273\",\n \"0.59882593\",\n \"0.59576243\",\n \"0.5951416\",\n \"0.5946682\",\n \"0.594088\",\n \"0.59272814\",\n \"0.5924271\",\n \"0.5911583\",\n \"0.59084725\",\n \"0.5908314\",\n \"0.5907796\",\n \"0.5897837\",\n \"0.58975947\",\n \"0.5891373\",\n \"0.5873559\",\n \"0.58639014\",\n \"0.5863778\",\n \"0.58601546\",\n \"0.5848406\",\n \"0.5848317\",\n \"0.58476734\",\n \"0.5846395\",\n \"0.58443016\",\n \"0.5832456\",\n \"0.58218694\",\n \"0.5820891\",\n \"0.5819956\",\n \"0.5819276\",\n \"0.581524\",\n \"0.5807063\",\n \"0.58037263\",\n \"0.5801649\",\n \"0.58016104\",\n \"0.5799853\",\n \"0.5796693\",\n \"0.5788912\",\n \"0.57839274\",\n \"0.57828516\",\n \"0.57770115\",\n \"0.5769301\",\n \"0.57663363\",\n \"0.57557845\",\n \"0.5748934\",\n \"0.5742434\",\n \"0.5741667\",\n \"0.574011\",\n \"0.57392496\",\n \"0.57298064\",\n \"0.5729138\",\n \"0.5728453\",\n \"0.57233137\",\n \"0.5721333\",\n \"0.5721126\",\n \"0.5720897\",\n \"0.57097256\",\n \"0.5701425\",\n \"0.5699307\",\n \"0.56980085\",\n \"0.5697924\",\n \"0.5694963\",\n \"0.5679183\",\n \"0.5678218\",\n \"0.56754243\",\n \"0.56744385\",\n \"0.56731546\",\n \"0.5670677\",\n \"0.56650186\",\n \"0.5650361\",\n \"0.5649563\",\n \"0.56372315\",\n \"0.56295604\",\n \"0.5625961\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6165102"},"document_rank":{"kind":"string","value":"19"}}},{"rowIdx":11086,"cells":{"query":{"kind":"string","value":"To convert a list of coordinates of shape (ycenter, xcenter, yproj, xproj) into a list of coordinates Input coordinate of (N,4) becomes two output coordinates of (N,2)"},"document":{"kind":"string","value":"def _convert_coords_to_coordinates(self, vect) -> np.ndarray:\n # create empty vector of necessary shape\n # one coordinate for each endpoint of the vector\n pos = np.empty((2 * len(vect), 2), dtype=np.float32)\n\n # create pairs of points\n pos[0::2, 0] = vect[:, 0]\n pos[1::2, 0] = vect[:, 0]\n pos[0::2, 1] = vect[:, 1]\n pos[1::2, 1] = vect[:, 1]\n\n # adjust second of each pair according to x-y projection\n pos[1::2, 0] += vect[:, 2]\n pos[1::2, 1] += vect[:, 3]\n\n return pos"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def proj_coords(coords, proj_in, proj_out): \n return [proj_coord(coord, proj_in, proj_out) for coord in coords]","def get_coordinate_lists(self, crs=None):\n x, y = self.vertices.vectors()[:2]\n if crs is not None and (crs != self.crs):\n x, y = _reproject((x,y), self.crs, crs)\n return x, y","def reproject_coordinates(coordinates, inproj, outproj, flat=False):\n if flat:\n return np.array([transform(inproj, outproj, coord[0], coord[1]) for coord in coordinates]).flatten()\n return [list(transform(inproj, outproj, coord[0], coord[1])) for coord in coordinates]","def ReprojectCoords(coords,src_srs,tgt_srs):\n trans_coords = []\n transform = osr.CoordinateTransformation( src_srs, tgt_srs)\n for x,y in coords:\n x,y,z = transform.TransformPoint(x,y)\n trans_coords.append([x,y])\n return trans_coords","def m4_make_middle_shape_points_list(tx, ty, m4_info, SEN_info):\n \"\"\"\n 1 Get information from m1_info & SEN_info\n \"\"\"\n x_m4 = m4_info[0]\n y_m4 = m4_info[1]\n z_m = m4_info[2]\n\n m4_points = m4_info[3]\n\n m4_p0 = m4_points[0]\n m4_p1 = m4_points[1]\n m4_p2 = m4_points[2]\n m4_p3 = m4_points[3]\n\n w_sen = SEN_info[0]\n n_w_sen = SEN_info[1]\n h_sen = SEN_info[2]\n t_sen = SEN_info[3]\n u_n = SEN_info[4]\n l_n = SEN_info[5]\n set = SEN_info[6]\n u_offset = SEN_info[7]\n l_offset = SEN_info[8]\n\n \"\"\"\n 2 Make lists.\n middle_shape_upper_left_row list\n middle_shape_upper_right_row list\n\n middle_shape_lower_left_row list\n middle_shape_lower_right_row list\n \"\"\"\n # upper side\n middle_shape_upper_left_row = []\n middle_shape_upper_right_row = []\n\n for i in range(u_n - 1, -1, -1):\n # left row\n ix = tx - (x_m4 - t_sen)\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p3, p4, p1, p2]\n middle_shape_upper_left_row.extend((left_points))\n\n for i in range(u_n):\n # right row\n ix = tx - t_sen\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p1, p2, p3, p4]\n middle_shape_upper_right_row.extend(right_points)\n\n # lower side\n middle_shape_lower_left_row = []\n middle_shape_lower_right_row = []\n\n for i in range(l_n):\n # left row\n ix = tx - (x_m4 - t_sen)\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p3, p4, p1, p2]\n middle_shape_lower_left_row.extend((left_points))\n\n for i in range(l_n - 1, -1, -1):\n # right row\n ix = tx - t_sen\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p1, p2, p3, p4]\n middle_shape_lower_right_row.extend(right_points)\n\n middle_shape_upper = [middle_shape_upper_left_row, middle_shape_upper_right_row]\n middle_shape_lower = [middle_shape_lower_left_row, middle_shape_lower_right_row]\n\n return middle_shape_upper, middle_shape_lower","def m4_make_upper_shape_points_list(tx, ty, m4_info, SEN_info):\n \"\"\"\n 1 Get information from m4_info & SEN_info\n \"\"\"\n x_m4 = m4_info[0]\n y_m4 = m4_info[1]\n z_m = m4_info[2]\n\n m4_points = m4_info[3]\n\n m4_p0 = m4_points[0]\n m4_p1 = m4_points[1]\n m4_p2 = m4_points[2]\n m4_p3 = m4_points[3]\n\n w_sen = SEN_info[0]\n n_w_sen = SEN_info[1]\n h_sen = SEN_info[2]\n t_sen = SEN_info[3]\n u_n = SEN_info[4]\n l_n = SEN_info[5]\n set = SEN_info[6]\n u_offset = SEN_info[7]\n l_offset = SEN_info[8]\n\n \"\"\"\n 2 Make lists.\n upper_shape_upper_left_row list\n upper_shape_upper_right_row list\n\n upper_shape_lower_left_row list\n upper_shape_lower_right_row list\n \"\"\"\n # upper side\n upper_shape_upper_left_row = []\n upper_shape_upper_right_row = []\n\n for i in range(u_n - 1, -1, -1):\n # left row\n ix = tx - (x_m4 - t_sen)\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p8, p7, p6, p5, p4, p3, p2, p1]\n upper_shape_upper_left_row.extend((left_points))\n\n for i in range(u_n):\n # right row\n ix = tx - t_sen\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p4, p3, p2, p1, p8, p7, p6, p5]\n upper_shape_upper_right_row.extend(right_points)\n\n # lower side\n upper_shape_lower_left_row = []\n upper_shape_lower_right_row = []\n\n for i in range(l_n):\n # left row\n ix = tx - (x_m4 - t_sen)\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p8, p7, p6, p5, p4, p3, p2, p1]\n upper_shape_lower_left_row.extend((left_points))\n\n for i in range(l_n - 1, -1, -1):\n # right row\n ix = tx - t_sen\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p4, p3, p2, p1, p8, p7, p6, p5]\n upper_shape_lower_right_row.extend(right_points)\n\n upper_shape_upper = [upper_shape_upper_left_row, upper_shape_upper_right_row]\n upper_shape_lower = [upper_shape_lower_left_row, upper_shape_lower_right_row]\n\n return upper_shape_upper, upper_shape_lower","def get_all_coordinates(self):\n coordinates = []\n\n for relative_coordinate in self.shape:\n co = [self.coordinate[0] + relative_coordinate[0], self.coordinate[1] + relative_coordinate[1]]\n coordinates.append(co)\n return coordinates","def convert_shapely_points_to_tuples(list_of_points) -> list:\n return [(p.x, p.y) for p in list_of_points]","def transforming_coordinates(self, coordinates_lists, transform): \n \n transformed_coordinates_lists = []\n for coordinates_list in coordinates_lists:\n transformed_coordinates_list = []\n for coordinate in coordinates_list:\n coordinate = tuple(coordinate)\n transformed_coordinate = list(transform(coordinate[0], coordinate[1]))\n transformed_coordinates_list.append(transformed_coordinate)\n transformed_coordinates_lists.append(transformed_coordinates_list)\n \n \n return transformed_coordinates_lists","def transform_coords(x, y, w, h, nw, nh):\r\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)","def makeToCoordinates(fromCoords, Uframe, Vframe, scale):\n\n out = []\n\n for e in fromCoords:\n x = e[0]\n y = e[1]\n toX = Uframe[x][y]\n toY = Vframe[x][y]\n out.append((int(round(x+toX*scale)),int(round(y+toY*scale))))\n\n return out","def m4_make_lower_shape_points_list(tx, ty, m4_info, SEN_info):\n \"\"\"\n 1 Get information from m4_info & SEN_info\n \"\"\"\n x_m4 = m4_info[0]\n y_m4 = m4_info[1]\n z_m = m4_info[2]\n\n m4_points = m4_info[3]\n\n m4_p0 = m4_points[0]\n m4_p1 = m4_points[1]\n m4_p2 = m4_points[2]\n m4_p3 = m4_points[3]\n\n w_sen = SEN_info[0]\n n_w_sen = SEN_info[1]\n h_sen = SEN_info[2]\n t_sen = SEN_info[3]\n u_n = SEN_info[4]\n l_n = SEN_info[5]\n set = SEN_info[6]\n u_offset = SEN_info[7]\n l_offset = SEN_info[8]\n\n \"\"\"\n 2 Make lists.\n lower_shape_upper_left_row list\n lower_shape_upper_right_row list\n\n lower_shape_lower_left_row list\n lower_shape_lower_right_row list\n \"\"\"\n # upper side\n lower_shape_upper_left_row = []\n lower_shape_upper_right_row = []\n\n for i in range(u_n - 1, -1, -1):\n # left row\n ix = tx - (x_m4 - t_sen)\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p5, p6, p7, p8, p1, p2, p3, p4]\n lower_shape_upper_left_row.extend((left_points))\n\n for i in range(u_n):\n # right row\n ix = tx - t_sen\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p1, p2, p3, p4, p5, p6, p7, p8]\n lower_shape_upper_right_row.extend(right_points)\n\n # lower side\n lower_shape_lower_left_row = []\n lower_shape_lower_right_row = []\n\n for i in range(l_n):\n # left row\n ix = tx - (x_m4 - t_sen)\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p5, p6, p7, p8, p1, p2, p3, p4]\n lower_shape_lower_left_row.extend((left_points))\n\n for i in range(l_n - 1, -1, -1):\n # right row\n ix = tx - t_sen\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p1, p2, p3, p4, p5, p6, p7, p8]\n lower_shape_lower_right_row.extend(right_points)\n\n lower_shape_upper = [lower_shape_upper_left_row, lower_shape_upper_right_row]\n lower_shape_lower = [lower_shape_lower_left_row, lower_shape_lower_right_row]\n\n return lower_shape_upper, lower_shape_lower","def CreateReleasePoints(points_on_longitude, points_on_latitude, grids):\n \n ReleasePointsLon = []\n ReleasePointsLat = []\n \n GridsCW_array = np.asarray(grids[['min_lon', 'min_lat', 'max_lon', 'max_lat']])\n \n for i in range(len(GridsCW_array)):\n \n lon_space = np.linspace(GridsCW_array[i,0], GridsCW_array[i,2], num = points_on_longitude+2 )\n lat_space = np.linspace(GridsCW_array[i,1], GridsCW_array[i,3], num = points_on_latitude+2 )\n \n \n lon_space_cor = lon_space[1:-1]\n lat_space_cor = lat_space[1:-1]\n \n for j in lon_space_cor:\n for k in lat_space_cor:\n \n ReleasePointsLon.append(j)\n ReleasePointsLat.append(k)\n \n return ReleasePointsLon, ReleasePointsLat","def reproject_coordinates_batch(aX_in, aY_in, spatial_reference_source, spatial_reference_target=None):\n #Reproject a list of x,y coordinates. \n\n if spatial_reference_target is not None:\n\n pass\n else:\n spatial_reference_target = osr.SpatialReference()\n spatial_reference_target.ImportFromEPSG(4326)\n \n pass\n\n \n if int(osgeo.__version__[0]) >= 3:\n # GDAL 3 changes axis order: https://github.com/OSGeo/gdal/issues/1546\n \n spatial_reference_source.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\n spatial_reference_target.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\n\n \n pTransform = osr.CoordinateTransformation( spatial_reference_source, spatial_reference_target)\n\n npoint = len(aX_in)\n x_new=list()\n y_new=list()\n for i in range(npoint):\n x0 = aX_in[i]\n y0 = aY_in[i]\n \n x1,y1, z = pTransform.TransformPoint( x0,y0)\n\n x_new.append(x1)\n y_new.append(y1)\n \n return x_new,y_new","def Reproject(x, y, in_grid = 4326, out_grid = 32737):\n \n inProj = Proj(init='epsg:'+str(in_grid))\n outProj = Proj(init='epsg:'+str(out_grid))\n \n \n x2,y2 = transform(inProj,outProj,x,y)\n \n return x2, y2","def m2_m3_make_upper_shape_points_list(dx, dy, m_info, SEN_info):\n \"\"\"\n 1 Get information from m_info & SEN_info.\n \"\"\"\n x_m = m_info[0]\n y_m = m_info[1]\n z_m = m_info[2]\n\n m_points = m_info[3]\n\n m_p0 = m_points[0]\n m_p1 = m_points[1]\n m_p2 = m_points[2]\n m_p3 = m_points[3]\n\n w_sen = SEN_info[0]\n n_w_sen = SEN_info[1]\n h_sen = SEN_info[2]\n t_sen = SEN_info[3]\n l_n = SEN_info[4]\n r_n = SEN_info[5]\n set = SEN_info[6]\n l_offset = SEN_info[7]\n r_offset = SEN_info[8]\n\n \"\"\"\n 2 Make lists.\n upper_shape_left_upper_row list\n upper_shape_left_lower_row list\n\n upper_shape_right_upper_row list\n upper_shape_right_lower_row list\n \"\"\"\n # Leftside\n upper_shape_left_upper_row = []\n upper_shape_left_lower_row = []\n\n for i in range(l_n):\n # upper row\n ix = i * l_offset + set\n iy = y_m - t_sen + dy\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n upper_points = [p4, p3, p2, p1, p8, p7, p6, p5]\n upper_shape_left_upper_row.extend((upper_points))\n\n for i in range(l_n - 1, -1, -1):\n # lower row\n ix = i * l_offset + set\n iy = t_sen + dy\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n lower_points = [p8, p7, p6, p5, p4, p3, p2, p1]\n upper_shape_left_lower_row.extend(lower_points)\n\n # Rightside\n upper_shape_right_upper_row = []\n upper_shape_right_lower_row = []\n\n for i in range(r_n):\n # upper row\n ix = x_m - i * r_offset - set\n iy = y_m - t_sen + dy\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n upper_points = [p5, p6, p7, p8, p1, p2, p3, p4]\n upper_shape_right_upper_row.extend((upper_points))\n\n for i in range(r_n - 1, -1, -1):\n # lower row\n ix = x_m - i * r_offset - set\n iy = t_sen + dy\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n lower_points = [p1, p2, p3, p4, p5, p6, p7, p8]\n upper_shape_right_lower_row.extend(lower_points)\n\n upper_shape_left = [upper_shape_left_upper_row, upper_shape_left_lower_row]\n upper_shape_right = [upper_shape_right_upper_row, upper_shape_right_lower_row]\n\n return upper_shape_left, upper_shape_right","def m1_make_middle_shape_points_list(tx, ty, m1_info, SEN_info):\n \"\"\"\n 1 Get information from m1_info & SEN_info\n \"\"\"\n x_m1 = m1_info[0]\n y_m1 = m1_info[1]\n z_m = m1_info[2]\n\n m1_points = m1_info[3]\n\n m1_p0 = m1_points[0]\n m1_p1 = m1_points[1]\n m1_p2 = m1_points[2]\n m1_p3 = m1_points[3]\n\n w_sen = SEN_info[0]\n n_w_sen = SEN_info[1]\n h_sen = SEN_info[2]\n t_sen = SEN_info[3]\n u_n = SEN_info[4]\n l_n = SEN_info[5]\n set = SEN_info[6]\n u_offset = SEN_info[7]\n l_offset = SEN_info[8]\n\n \"\"\"\n 2 Make lists.\n middle_shape_upper_left_row list\n middle_shape_upper_right_row list\n\n middle_shape_lower_left_row list\n middle_shape_lower_right_row list\n \"\"\"\n # upper side\n middle_shape_upper_left_row = []\n middle_shape_upper_right_row = []\n\n for i in range(u_n):\n # left row\n ix = tx + t_sen\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p2, p1, p4, p3]\n middle_shape_upper_left_row.extend((left_points))\n\n for i in range(u_n - 1, -1, -1):\n # right row\n ix = tx + (x_m1 - t_sen)\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p4, p3, p2, p1]\n middle_shape_upper_right_row.extend(right_points)\n\n # lower side\n middle_shape_lower_left_row = []\n middle_shape_lower_right_row = []\n\n for i in range(l_n - 1, -1, -1):\n # left row\n ix = tx + t_sen\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p2, p1, p4, p3]\n middle_shape_lower_left_row.extend((left_points))\n\n for i in range(l_n):\n # right row\n ix = tx + (x_m1 - t_sen)\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p4, p3, p2, p1]\n middle_shape_lower_right_row.extend(right_points)\n\n middle_shape_upper = [middle_shape_upper_left_row, middle_shape_upper_right_row]\n middle_shape_lower = [middle_shape_lower_left_row, middle_shape_lower_right_row]\n\n return middle_shape_upper, middle_shape_lower","def transform_pnt(pnt, inEPSG, outEPSG):\r\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\r\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\r\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\r\n\r\n return pyproj.transform(inProj, outProj, x1, y1)","def projectBack(points, proj):\n\n mpoints = MultiPoint(points)\n project = partial(\n pyproj.transform,\n proj,\n pyproj.Proj(proj='latlong', datum='WGS84'))\n gmpoints = transform(project, mpoints)\n coords = []\n for point in gmpoints.geoms:\n x, y = point.coords[0]\n coords.append((x, y))\n coords = np.array(coords)\n return coords","def coord_vecs(self):\n return [np.linspace(x0, x1, nx) for x0, x1, nx in zip(self.mins, self.maxs, self.shape)]","def m1_make_upper_shape_points_list(tx, ty, m1_info, SEN_info):\n \"\"\"\n 1 Get information from m1_info & SEN_info\n \"\"\"\n x_m1 = m1_info[0]\n y_m1 = m1_info[1]\n z_m = m1_info[2]\n\n m1_points = m1_info[3]\n\n m1_p0 = m1_points[0]\n m1_p1 = m1_points[1]\n m1_p2 = m1_points[2]\n m1_p3 = m1_points[3]\n\n w_sen = SEN_info[0]\n n_w_sen = SEN_info[1]\n h_sen = SEN_info[2]\n t_sen = SEN_info[3]\n u_n = SEN_info[4]\n l_n = SEN_info[5]\n set = SEN_info[6]\n u_offset = SEN_info[7]\n l_offset = SEN_info[8]\n\n \"\"\"\n 2 Make lists.\n upper_shape_upper_left_row list\n upper_shape_upper_right_row list\n\n upper_shape_lower_left_row list\n upper_shape_lower_right_row list\n \"\"\"\n # upper side\n upper_shape_upper_left_row = []\n upper_shape_upper_right_row = []\n\n for i in range(u_n):\n # left row\n ix = tx + t_sen\n iy = ty + (i * u_offset + set) + 10 # have to \"+\" something now its magic number\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p1, p2, p3, p4, p5, p6, p7, p8]\n upper_shape_upper_left_row.extend((left_points))\n\n for i in range(u_n - 1, -1, -1):\n # right row\n ix = tx + (x_m1 - t_sen)\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p5, p6, p7, p8, p1, p2, p3, p4]\n upper_shape_upper_right_row.extend(right_points)\n\n # lower side\n upper_shape_lower_left_row = []\n upper_shape_lower_right_row = []\n\n for i in range(l_n -1, -1, -1):\n # left row\n ix = tx + t_sen\n iy = ty - (i * l_offset + set) - 10 # have to \"-\" something now its magic number\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p1, p2, p3, p4, p5, p6, p7, p8]\n upper_shape_lower_left_row.extend((left_points))\n\n for i in range(l_n):\n # right row\n ix = tx + (x_m1 - t_sen)\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p5, p6, p7, p8, p1, p2, p3, p4]\n upper_shape_lower_right_row.extend(right_points)\n\n upper_shape_upper = [upper_shape_upper_left_row, upper_shape_upper_right_row]\n upper_shape_lower = [upper_shape_lower_left_row, upper_shape_lower_right_row]\n\n return upper_shape_upper, upper_shape_lower","def trans_to_coordinates(T, pts):\n p = []\n for i in range(len(pts)):\n \n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\n p_a = np.matmul(T, p_b).tolist()\n p.append(p_a[0:3])\n\n return p","def trans_to_coordinates(T, pts):\n p = []\n for i in range(len(pts)):\n \n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\n p_a = np.matmul(T, p_b).tolist()\n p.append(p_a[0:3])\n\n return p","def m2_m3_make_lower_shape_points_list(dx, dy, m_info, SEN_info):\n \"\"\"\n 1 Get information from m_info & SEN_info.\n \"\"\"\n x_m = m_info[0]\n y_m = m_info[1]\n z_m = m_info[2]\n\n m_points = m_info[3]\n\n m_p0 = m_points[0]\n m_p1 = m_points[1]\n m_p2 = m_points[2]\n m_p3 = m_points[3]\n\n w_sen = SEN_info[0]\n n_w_sen = SEN_info[1]\n h_sen = SEN_info[2]\n t_sen = SEN_info[3]\n l_n = SEN_info[4]\n r_n = SEN_info[5]\n set = SEN_info[6]\n l_offset = SEN_info[7]\n r_offset = SEN_info[8]\n\n \"\"\"\n 2 Make lists.\n lower_shape_left_upper_row list\n lower_shape_left_lower_row list\n\n lower_shape_right_upper_row list\n lower_shape_right_lower_row list\n \"\"\"\n # Leftside\n lower_shape_left_upper_row = []\n lower_shape_left_lower_row = []\n\n for i in range(l_n):\n # upper row\n ix = i * l_offset + set\n iy = y_m - t_sen + dy\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n upper_points = [p1, p2, p3, p4, p5, p6, p7, p8]\n lower_shape_left_upper_row.extend((upper_points))\n\n for i in range(l_n - 1, -1, -1):\n # lower row\n ix = i * l_offset + set\n iy = t_sen + dy\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n lower_points = [p5, p6, p7, p8, p1, p2, p3, p4]\n lower_shape_left_lower_row.extend(lower_points)\n\n # Rightside\n lower_shape_right_upper_row = []\n lower_shape_right_lower_row = []\n\n for i in range(r_n):\n # upper row\n ix = x_m - i * r_offset - set\n iy = y_m - t_sen + dy\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n upper_points = [p8, p7, p6, p5, p4, p3, p2, p1]\n lower_shape_right_upper_row.extend((upper_points))\n\n for i in range(r_n - 1, -1, -1):\n # lower row\n ix = x_m - i * r_offset - set\n iy = t_sen + dy\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n lower_points = [p4, p3, p2, p1, p8, p7, p6, p5]\n lower_shape_right_lower_row.extend(lower_points)\n\n lower_shape_left = [lower_shape_left_upper_row, lower_shape_left_lower_row]\n lower_shape_right = [lower_shape_right_upper_row, lower_shape_right_lower_row]\n\n return lower_shape_left, lower_shape_right","def extractCoords(self):\n if not self.rank:\n logging.info('Extracting atomic poitions')\n\n # Extract coordinates from liggghts\n self.lmp.command('variable x atom x')\n x = Rxn.lmp.extract_variable(\"x\", \"group1\", 1)\n\n self.lmp.command('variable y atom y')\n y = Rxn.lmp.extract_variable(\"y\", \"group1\", 1)\n\n self.lmp.command('variable z atom z')\n z = Rxn.lmp.extract_variable(\"z\", \"group1\", 1)\n\n coords = np.zeros((self.lmp.get_natoms(),3))\n\n for i in range(self.lmp.get_natoms()):\n coords[i,:] = x[i], y[i], z[i]\n\n self.lmp.command('variable x delete')\n self.lmp.command('variable y delete')\n self.lmp.command('variable z delete')\n\n return coords","def _convert_pix_coords(self):\n if self.region_type in ['polygon', 'line']:\n # have to special-case polygon in the phys coord case\n # b/c can't typecheck when iterating as in sky coord case\n coords = [PixCoord(self.coord[0::2], self.coord[1::2])]\n else:\n temp = [_.value for _ in self.coord]\n coord = PixCoord(temp[0], temp[1])\n coords = [coord] + temp[2:]\n\n # The angle remains as a quantity object.\n # Modulus check makes sure that it works for ellipse/rectangle annulus\n if self.region_type in ['ellipse', 'rectangle'] and len(coords) % 2 == 0:\n coords[-1] = self.coord[-1]\n\n return coords","def coordinates(self):\n return np.array([[f.x, f.y] for f in self])","def coordinates_of(self, coord_index_list):\n return [self.transformed_coords[i] for i in coord_index_list]","def getYesPoints(pshapes, proj, dx, nmax, touch_center=True):\n\n mxmin = 9e10\n mxmax = -9e10\n mymin = 9e10\n mymax = -9e10\n for pshape in pshapes:\n pxmin, pymin, pxmax, pymax = pshape.bounds\n if pxmin < mxmin:\n mxmin = pxmin\n if pxmax > mxmax:\n mxmax = pxmax\n if pymin < mymin:\n mymin = pymin\n if pymax > mymax:\n mymax = pymax\n\n if not touch_center:\n geodict = GeoDict.createDictFromBox(mxmin, mxmax, mymin, mymax, dx, dx)\n img = rasterizeShapes(pshapes, geodict)\n #now get the numpy array of x/y coordinates where covgrid == 1\n idx = np.where(img == 1)[0]\n x, y = np.unravel_index(idx, (geodict.ny, geodict.nx))\n yespoints = list(zip(x.flatten(), y.flatten()))\n nrows = geodict.ny\n ncols = geodict.nx\n xvar = np.arange(geodict.xmin, geodict.xmax+geodict.dx, geodict.dx)\n yvar = np.arange(geodict.ymin, geodict.ymax+geodict.dy, geodict.dy)\n else:\n xvar = np.arange(mxmin, mxmax+dx, dx)\n yvar = np.arange(mymin, mymax+dx, dx)\n ncols = len(xvar)\n nrows = len(yvar)\n if nmax is not None:\n if ncols*nrows > nmax:\n aspect = ncols/nrows\n ncols = np.sqrt(nmax*aspect)\n nrows = nmax/ncols\n ncols = int(ncols)\n nrows = int(nrows)\n #re-calculate dx here...\n tdx = (mxmax-mxmin)/ncols\n tdy = (mymax-mymin)/nrows\n dx = np.max(tdx, tdy)\n xvar = np.arange(mxmin, mxmax+dx, dx)\n yvar = np.arange(mymin, mymax+dx, dx)\n\n #Get the \"yes\" points to sample from\n yespoints = []\n idx = []\n shapeidx = 0\n if pshapes[0].type == 'Polygon':\n #loop over shapes, projecting each one, then get the sample points\n for pshape in pshapes:\n if not shapeidx % 1000:\n print('Searching polygon %i of %i' % (shapeidx, len(pshapes)))\n shapeidx += 1\n pxmin, pymin, pxmax, pymax = pshape.bounds\n leftcol = np.where((pxmin - xvar) >= 0)[0].argmax()\n rightcol = np.where((xvar - pxmax) >= 0)[0][0]\n bottomrow = np.where((pymin - yvar) >= 0)[0].argmax()\n toprow = np.where((yvar - pymax) >= 0)[0][0]\n xp = np.arange(xvar[leftcol], xvar[rightcol]+dx, dx)\n yp = np.arange(yvar[bottomrow], yvar[toprow]+dx, dx)\n xmesh, ymesh = np.meshgrid(xp, yp)\n xy = list(zip(xmesh.flatten(), ymesh.flatten()))\n for point in xy:\n ix = np.where(xvar == point[0])[0][0]\n iy = np.where(yvar == point[1])[0][0]\n if pshape.contains(Point(point)):\n yespoints.append(point)\n idx.append(np.ravel_multi_index((iy, ix), (nrows, ncols), mode='raise', order='C'))\n else:\n yespoints = []\n for pshape in pshapes:\n yespoints.append(pshape.coords[0])\n\n return (np.array(yespoints), nrows, ncols, xvar, yvar, idx)","def convert_coords(self):\n if self.coordsys in ['image', 'physical']:\n coords = self._convert_pix_coords()\n else:\n coords = self._convert_sky_coords()\n\n if self.region_type == 'line':\n coords = [coords[0][0], coords[0][1]]\n\n if self.region_type == 'text':\n coords.append(self.meta['text'])\n\n return coords","def convert_coords(self, coords):\n xPos = int(int(coords[0]) / 8)\n yPos = int(coords[1])\n zPos = int(int(coords[2]) / 8)\n return list(xPos, yPos, zPos)","def get_coords_2d(obj, P):\n bbox3 = get_coords_3d(obj)\n # Rl = obj.Rl\n # height = obj.height\n # width = obj.width\n # length = obj.length\n # x = obj.x\n # y = obj.y\n # z = obj.z\n # bbox = np.array([[-length, 0, -width],\n # [-length, 0, width],\n # [ length, 0, width],\n # [ length, 0, -width],\n # [-length, -2*height, -width],\n # [-length, -2*height, width],\n # [ length, -2*height, width],\n # [ length, -2*height, -width]])/2\n # if abs(z) < 1e-6: z = np.sign(z) * 1e-6\n # M = rotationM([0, 1, 0], Rl + math.atan(x/z))\n # bbox = np.transpose(np.dot(M, np.transpose(bbox)))\n # bbox3 = bbox + np.array([x, y, z])\n bbox2 = project_pts3_to_image(bbox3, P)\n return bbox2","def m1_make_lower_shape_points_list(tx, ty, m1_info, SEN_info):\n \"\"\"\n 1 Get information from m1_info & SEN_info\n \"\"\"\n x_m1 = m1_info[0]\n y_m1 = m1_info[1]\n z_m = m1_info[2]\n\n m1_points = m1_info[3]\n\n m1_p0 = m1_points[0]\n m1_p1 = m1_points[1]\n m1_p2 = m1_points[2]\n m1_p3 = m1_points[3]\n\n w_sen = SEN_info[0]\n n_w_sen = SEN_info[1]\n h_sen = SEN_info[2]\n t_sen = SEN_info[3]\n u_n = SEN_info[4]\n l_n = SEN_info[5]\n set = SEN_info[6]\n u_offset = SEN_info[7]\n l_offset = SEN_info[8]\n\n \"\"\"\n 2 Make lists.\n lower_shape_upper_left_row list\n lower_shape_upper_right_row list\n\n lower_shape_lower_left_row list\n lower_shape_lower_right_row list\n \"\"\"\n # upper side\n lower_shape_upper_left_row = []\n lower_shape_upper_right_row = []\n\n for i in range(u_n):\n # left row\n ix = tx + t_sen\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p4, p3, p2, p1, p8, p7, p6, p5]\n lower_shape_upper_left_row.extend((left_points))\n\n for i in range(u_n - 1, -1, -1):\n # right row\n ix = tx + (x_m1 - t_sen)\n iy = ty + (i * u_offset + set) + 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p8, p7, p6, p5, p4, p3, p2, p1]\n lower_shape_upper_right_row.extend(right_points)\n\n # lower side\n lower_shape_lower_left_row = []\n lower_shape_lower_right_row = []\n\n for i in range(l_n - 1, -1, -1):\n # left row\n ix = tx + t_sen\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n left_points = [p4, p3, p2, p1, p8, p7, p6, p5]\n lower_shape_lower_left_row.extend((left_points))\n\n for i in range(l_n):\n # right row\n ix = tx + (x_m1 - t_sen)\n iy = ty - (i * l_offset + set) - 10\n\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\n right_points = [p8, p7, p6, p5, p4, p3, p2, p1]\n lower_shape_lower_right_row.extend(right_points)\n\n lower_shape_upper = [lower_shape_upper_left_row, lower_shape_upper_right_row]\n lower_shape_lower = [lower_shape_lower_left_row, lower_shape_lower_right_row]\n\n return lower_shape_upper, lower_shape_lower","def coords(self):\n return np.column_stack((self.x_coord_list, self.y_coord_list, self.z_coord_list))","def pristine_coords_to_objects(list_of_coords):\n list_of_objects = []\n for element in range(len(list_of_coords)):\n list_of_objects.append(Atom(element, \"CX\", \"GGG\", element, list_of_coords[element][0], list_of_coords[element][1], list_of_coords[element][2]))\n return list_of_objects","def make_simple_coords():\n \n x = np.array([144, 124, 97, 165, 114, 60, 165, 0, 76, 50, 147])\n y = np.array([ 0, 3, 21, 28, 34, 38, 51, 54, 58, 56, 61])\n coords = np.vstack((x,y)).T\n return coords","def positions_to_coords(self, positions):\n return [self.to_coords(px, py) for (px, py) in positions]","def affine_pts(pts):\n if ndim == 2:\n for x, y in pts:\n xp = a * x + b * y + xoff\n yp = d * x + e * y + yoff\n yield (xp, yp)\n elif ndim == 3:\n for x, y, z in pts:\n xp = a * x + b * y + c * z + xoff\n yp = d * x + e * y + f * z + yoff\n zp = g * x + h * y + i * z + zoff\n yield (xp, yp, zp)","def reconstructXY(self, inputs):\n return (self.reconstructX(inputs),\n self.reconstructY(inputs))","def convert_to_cartesian(grid: List[Tuple[float, float]], radius: float = 1.0) -> List[Tuple[float, float, float]]:\n\n # conversion radians -> degrees\n r2d = 180.0 / np.pi\n\n # calculate x/y/z coordinates, assuming r=1\n return [\n (\n radius * np.cos(lat / r2d) * np.cos(lon / r2d),\n radius * np.cos(lat / r2d) * np.sin(lon / r2d),\n radius * np.sin(lat / r2d),\n )\n for lon, lat in grid\n ]","def CreateTargetGeoMap(latS, latN, lonW, lonE, latlen, lonlen):\n\n lat_grid = np.linspace(latS, latN, latlen)\n lon_grid = np.linspace(lonW, lonE, lonlen)\n\n return lat_grid,lon_grid","def lonlat(n_lon: int, n_lat: int) -> List[Tuple[float, float]]:\n grid = []\n for lon in np.linspace(0, 360.0 - 360.0 / n_lon, n_lon):\n for lat in np.linspace(-90, 90, n_lat):\n grid.append((lon, lat))\n return grid","def get_MultiPolyLists_xy(mpoly):\n # Get the x or y coordinates\n x = []\n y = []\n if isinstance(mpoly,Polygon):\n mpoly = [mpoly]\n for poly in mpoly: # the polygon objects return arrays, it's important they be lists or Bokeh fails\n exterior_coords_x = poly.exterior.coords.xy[0].tolist();\n interior_coords_x = []\n exterior_coords_y = poly.exterior.coords.xy[1].tolist();\n interior_coords_y = []\n\n for interior in poly.interiors:\n if isinstance(interior.coords.xy[0],list):\n interior_coords_x += [interior.coords.xy[0]];\n interior_coords_y += [interior.coords.xy[1]];\n else:\n interior_coords_x += [interior.coords.xy[0].tolist()];\n interior_coords_y += [interior.coords.xy[1].tolist()];\n x.append([exterior_coords_x, *interior_coords_x])\n y.append([exterior_coords_y, *interior_coords_y])\n return (x,y)","def makeFromCoorinates(ncols, nrows, frame_width, frame_height):\n\n dx = frame_width/float(ncols)\n dy = frame_height/float(nrows)\n out = []\n x = int(round(dx/2.0))\n\n for c in xrange(ncols):\n y = int(round(dy/2.0))\n for r in xrange(nrows):\n out.append((int(round(x)),int(round(y))))\n y += dy\n\n x += dx\n return out","def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\n\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\n return (-1,-1)\n\n lat_step = abs(max_lat-min_lat)/rows\n lon_step = abs(max_lon-min_lon)/cols\n\n lat_spot = int((max_lat-lat)/lat_step)\n lon_spot = int((lon-min_lon)/lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)","def condense_coords(matches):\n x = []\n y = []\n for m in matches:\n x += m['matches']['p'][0]\n x += m['matches']['q'][0]\n y += m['matches']['p'][1]\n y += m['matches']['q'][1]\n coords = np.transpose(np.vstack((np.array(x), np.array(y))))\n return coords","def camera_pixels_to_camera_coords(left_pt, right_pt, nparrays=False):\n assert len(left_pt) == len(right_pt) == 2\n disparity = np.linalg.norm( np.array(left_pt) - np.array(right_pt) )\n (xx,yy,zz) = STEREO_MODEL.projectPixelTo3d( (left_pt[0],left_pt[1]), disparity )\n if nparrays:\n return np.array([xx,yy,zz])\n else:\n return [xx,yy,zz]","def createGrid(nx, ny, include_center = False):\n direction = 0\n positions = []\n if (nx > 1) or (ny > 1):\n half_x = int(nx/2)\n half_y = int(ny/2)\n for i in range(-half_y, half_y+1):\n for j in range(-half_x, half_x+1):\n if ((i==0) and (j==0)) and not include_center:\n continue\n else:\n if ((direction%2)==0):\n positions.append([j,i])\n else:\n positions.append([-j,i])\n direction += 1\n return positions","def array_coords(shape=(1, 1)):\n indexes = np.arange(np.product(shape))\n coords = [indexes % shape[1], indexes // shape[1]]\n return np.transpose(coords).astype('float64')","def feature_coords(features):\n coords_list = []\n for feature in features:\n coord_start = feature.location.nofuzzy_start\n coord_end = feature.location.nofuzzy_end\n coord_pair = (coord_start, coord_end)\n coords_list.append(coord_pair)\n ## consider adding some info to the log\n return coords_list","def cart_coords2d(self):\r\n\r\n mgx, mgy = self.meshup2d()\r\n coords = np.column_stack((mgx.flatten(), mgy.flatten()))\r\n\r\n return coords","def convert_coordinates(self, coordinates):\n return np.array(zip(*self.basemap(*zip(*coordinates))))","def transform_coordinates(coords):\n # WGS 84 reference coordinate system parameters\n A = 6378.137 # major axis [km]\n E2 = 6.69437999014e-3 # eccentricity squared\n\n coords = prepare_coords(coords)\n\n # convert to radiants\n lat_rad = np.radians(coords[:, 0])\n lon_rad = np.radians(coords[:, 1])\n\n # convert to cartesian coordinates\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\n z = r_n * (1 - E2) * np.sin(lat_rad)\n\n return np.column_stack((x, y, z))","def RegularPolygonPoints(n,c):\n coord = []\n for i in range(n):\n x = m.cos(2*m.pi*i/n)+c[0]\n y = m.sin(2*m.pi*i/n)+c[1]\n coord.append([x,y])\n return(coord)","def convert_input(coords: Union[npt.NDArray, List]) -> Tuple[npt.NDArray, ...]:\n if isinstance(coords, np.ndarray):\n assert coords.ndim == 2, \"'coords' must be a (n, 2) array\"\n assert coords.shape[1] == 2, \"'coords' must be a (n, 2) array\"\n x = coords[:, 0]\n y = coords[:, 1]\n elif isinstance(coords, list):\n x = np.array([point[0] for point in coords])\n y = np.array([point[1] for point in coords])\n else:\n raise Exception(\"Parameter 'coords' is an unsupported type: \" + str(type(coords)))\n return x, y","def build_coord(norm, d, pts):\n # Compute the origin as the mean point of the points, and this point has to be on the plane\n \n n = len(pts) \n x_total = 0\n y_total = 0\n z_total = 0\n \n for i in range(n):\n x_total += pts[i][0]\n y_total += pts[i][1]\n z_total += pts[i][2]\n\n x_o = x_total * 1.0 / n\n y_o = y_total * 1.0 / n\n z_o = z_total * 1.0 / n\n p_o = [x_o, y_o, z_o]\n \n # Choose p be the projection of a vector in the z-axis to the plane\n # If the plane is not perpendicular to the z-axis\n if ((norm[2] != 1) and (norm[2] != -1)): \n # Choose a point\n o_z = [x_o, y_o, z_o + 1]\n \n [[x_p, y_p, z_p]] = proj_to_plane(norm, d, [o_z])\n \n dist = np.linalg.norm([x_p - x_o, y_p - y_o, z_p - z_o])\n\n x_c = (x_p - x_o) * 1.0 / dist \n y_c = (y_p - y_o) * 1.0 / dist\n z_c = (z_p - z_o) * 1.0 / dist\n # Thus we have unit vector in x direction\n e_y = [x_c, y_c, z_c]\n #Compute the unit vector in y direction\n e_x = np.cross(e_y, norm).tolist()\n else:\n e_x = [1, 0, 0]\n e_y = [0, 1, 0]\n \n return [e_x, e_y, norm] , p_o","def ll_coordinates(self):\n\n x, y = self.xy_coordinates\n proj_out = check_crs('EPSG:4326')\n\n return transform_proj(self.proj, proj_out, x, y)","def generate_coordinates(coords):\n x = coords.reshape(-1, 1).repeat(1, len(coords) * len(coords)).flatten()\n y = coords.reshape(-1, 1).repeat(1, len(coords)).flatten().repeat(len(coords))\n z = coords.reshape(-1, 1).flatten().repeat(len(coords)*len(coords))\n\n return x, y, z","def convert_to_xywh(pts):\n boxes = []\n for pt1, pt2 in pts:\n x = pt1[0]\n y = pt1[1]\n w = pt2[0] - pt1[0]\n h = pt2[1] - pt1[1]\n boxes.append([x, y, w, h])\n return boxes","def convertLatLon(latCell, lonCell):\n cell_lats = np.array([])\n cell_lons = np.array([])\n for lat in latCell:\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \n for lon in lonCell:\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \n\n return cell_lats, cell_lons","def make_xarray_coords(y, x, crs):\n if crs.is_geographic:\n y_attrs, x_attrs = COORD_DEFS['latitude'], COORD_DEFS['longitude']\n elif crs.is_projected:\n crs_osr = crs2osr(crs)\n units = crs_osr.GetLinearUnitsName()\n y_attrs, x_attrs = COORD_DEFS['y'], COORD_DEFS['x']\n y_attrs['units'], x_attrs['units'] = units, units\n\n y = xr.Variable(('y', ), y, attrs=y_attrs)\n x = xr.Variable(('x', ), x, attrs=x_attrs)\n\n return y, x","def latlon_2_grid(x, y, z, origin):\n new_y = (y - origin[1]) * 111111\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\n return new_x, new_y, z","def to_coords(self, px, py):\n if px not in range(self.SIZE**2) or py not in range(self.SIZE**2):\n raise IndexError\n return (px // self.SIZE, py // self.SIZE,\n px % self.SIZE, py % self.SIZE)","def pixel_to_coords(self, x, y):\n rx, ry = self.size\n nx = (x / rx - 0.5) * self.scale + self.center[0]\n ny = ((ry - y) / ry - 0.5) * self.scale + self.center[1]\n nz = self.center[2]\n return [nx, ny, nz]","def gen_center_points(x_res, y_res, dim):\n center_points = []\n\n for x in range(math.floor(dim[0] / x_res)):\n for y in range(math.floor(dim[1] / y_res)):\n x = (x + 1) * x_res\n y = (y + 1) * y_res\n center_points.append((x, y))\n\n return center_points","def coordinates_to_locations(coordinates):\n np_coords = np.array(coordinates)\n longs, lats = transform(Proj(init=EPSG_IN), Proj(init=EPSG_OUT), np_coords[:, 0], np_coords[:, 1])\n length = len(lats)\n result = []\n for i in range(length):\n loc = Location(lats[i], longs[i])\n raw_location = extract_raw_simple_coordinates(coordinates[i])\n loc.set_raw_coordinates_simplified(raw_location[0],raw_location[1])\n result.append(loc)\n\n return result","def format_coords(self, coordinates):\n\n coords = []\n for i in range(0, len(coordinates), 3):\n coords.append(tuple(coordinates[i:i+3]))\n\n return coords","def get_city_base(city_map = CHICAGO_NEIGHBORHOOD):\n shapes = shp.Reader(city_map).shapeRecords()\n x,y = [],[]\n for shape in shapes:\n inner_x,inner_y = list(zip(*shape.shape.points))\n x.append(inner_x)\n y.append(inner_y)\n x_flat = [item for sublist in x for item in sublist]\n y_flat = [item for sublist in y for item in sublist]\n return x_flat,y_flat","def build_coord(norm, d, pts):\n # Compute the origin as the mean point of the points, and this point has to be on the plane\n \n n = len(pts)\n x_total = 0\n y_total = 0\n z_total = 0\n \n for i in range(n):\n x_total += pts[i][0]\n y_total += pts[i][1]\n z_total += pts[i][2]\n\n x_o = x_total * 1.0 / n\n y_o = y_total * 1.0 / n\n z_o = z_total * 1.0 / n\n p_o = [x_o, y_o, z_o]\n \n # Choose p be the projection of a vector in the z-axis to the plane\n # If the plane is not perpendicular to the z-axis\n if ((norm[2] != 1) and (norm[2] != -1)): \n # Choose a point\n o_z = [x_o, y_o, z_o + 1]\n \n [[x_p, y_p, z_p]] = proj_to_plane(norm, d, [o_z])\n \n dist = np.linalg.norm([x_p - x_o, y_p - y_o, z_p - z_o])\n\n x_c = (x_p - x_o) * 1.0 / dist \n y_c = (y_p - y_o) * 1.0 / dist\n z_c = (z_p - z_o) * 1.0 / dist\n # Thus we have unit vector in x direction\n e_y = [x_c, y_c, z_c]\n #Compute the unit vector in y direction\n e_x = np.cross(e_y, norm).tolist()\n else:\n e_x = [1, 0, 0]\n e_y = [0, 1, 0]\n \n return [e_x, e_y, norm] , p_o","def transform_obj_list(object_list,M,M2 = None):\n \n for i, obj in enumerate(object_list):\n points = obj.all\n num_points = len(points)\n \n \n # add third row\n ones = np.ones([num_points,1])\n points3d = np.concatenate((points,ones),1)\n points3d = points3d[:,[0,1,4]]\n # transform points\n tf_points3d = np.transpose(np.matmul(M,np.transpose(points3d)))\n \n # condense to two-dimensional coordinates\n tf_points = np.zeros([num_points,2])\n tf_points[:,0] = tf_points3d[:,0]/tf_points3d[:,2]\n tf_points[:,1] = tf_points3d[:,1]/tf_points3d[:,2]\n \n object_list[i].all_world = tf_points\n \n if M2 is not None:\n tf_points3d = np.transpose(np.matmul(M2,np.transpose(points3d)))\n \n # condense to two-dimensional coordinates\n tf_points = np.zeros([num_points,2])\n tf_points[:,0] = tf_points3d[:,0]/tf_points3d[:,2]\n tf_points[:,1] = tf_points3d[:,1]/tf_points3d[:,2]\n \n object_list[i].all_gps = tf_points\n \n return object_list","def project_long_lag_coord_into_cartesian(list_lat_long):\n # setup your projections\n # crs_wgs = proj.Proj(init='epsg:4326') # assuming you're using WGS84 geographic\n crs_wgs = proj.Proj(init='epsg:3857') # assuming you're using WGS3857 geographic\n crs_bng = proj.Proj(init='epsg:2039') # use a locally appropriate projected CRS\n\n return [(proj.transform(crs_wgs, crs_bng, lat_long[LONGITUDE], lat_long[LATITUDE])) for lat_long in list_lat_long]","def getProjectedShapes(shapes, xmin, xmax, ymin, ymax):\n latmiddle = ymin + (ymax-ymin)/2.0\n lonmiddle = xmin + (xmax-xmin)/2.0\n projstr = '+proj=ortho +datum=WGS84 +lat_0=%.4f +lon_0=%.4f +x_0=0.0 +y_0=0.0' % (latmiddle, lonmiddle)\n proj = pyproj.Proj(projparams=projstr)\n project = partial(\n pyproj.transform,\n pyproj.Proj(proj='latlong', datum='WGS84'),\n proj)\n\n pshapes = []\n for tshape in shapes:\n if tshape['geometry']['type'] == 'Polygon':\n pshapegeo = shape(tshape['geometry'])\n else:\n pshapegeo = shape(tshape['geometry'])\n pshape = transform(project, pshapegeo)\n pshapes.append(pshape) # assuming here that these are simple polygons\n\n return (pshapes, proj)","def transform(self,points):\n new_points = []\n for p in points:\n new_coordinates=p.coordinates\n new_coordinates = [(new_coordinates[i] - self.min_coordinate[i]) /\n (self.max_coordinate[i]-self.min_coordinate[i]) for i in range(len(p.coordinates))]\n new_points.append(Point(p.name, new_coordinates, p.label))\n return new_points","def ll2xyz(lon_pt,lat_pt):\n\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\n zPt = np.sin(lat_pt)\n return [xPt,yPt,zPt]","def grid_coords(self):\n return [(x, y) for y in range(self.height) for x in range(self.width)]","def coordinate_pairs(lat_axis, lon_axis):\n \n lon_mesh, lat_mesh = numpy.meshgrid(lon_axis, lat_axis) # This is the correct order\n \n return lat_mesh.flatten(), lon_mesh.flatten()","def coordinates_to_imgpts(x, y):\n pts = np.array([np.flipud(np.transpose(np.vstack([x, y])))])\n return pts","def cylinders_base_to_pixelcoord(cylinder_list: list, volume_shape: (int, int, int)):\n cylinders_px_list = []\n for point1, point2, radius in cylinder_list:\n p1 = phantom_base.scale_point_to_pixelcoord(point1, volume_shape)\n p2 = phantom_base.scale_point_to_pixelcoord(point2, volume_shape)\n r = phantom_base.scale_radius_to_pixelcoord(radius, volume_shape)\n cylinders_px_list.append((p1, p2, r))\n return cylinders_px_list","def coordinates(self, mask):\n y,x = mask.nonzero()\n return list(zip(x,y))","def getProjections(self): \n x, y, z = self.XYZCoordinate\n origin = self.SkeletonPoints[0]\n self.coorOrigin = origin\n self.XYProjections = [GeometryToolBox.projected_point(p, origin, x, y) for p in self.SkeletonPoints]\n self.XZProjections = [GeometryToolBox.projected_point(p, origin, x, z) for p in self.SkeletonPoints]","def get_geom_center(coordlist):\n return sum(coordlist) / len(coordlist)","def positions(r):\n\n X = []\n Y = []\n\n leftx = -r*(nx - 1) / 2\n topy = -r*(ny - 1) / 2\n\n for i in range(0, nx):\n for j in range(0, ny):\n X.append(leftx + r * i)\n Y.append(topy + r * j)\n\n return (X, Y)","def _convert_sky_coords(self):\n parsed_angles = [(x, y)\n for x, y in zip(self.coord[:-1:2], self.coord[1::2])\n if (isinstance(x, coordinates.Angle) and isinstance(y, coordinates.Angle))\n ]\n frame = coordinates.frame_transform_graph.lookup_name(self.coordsys)\n\n lon, lat = zip(*parsed_angles)\n if hasattr(lon, '__len__') and hasattr(lat, '__len__') and len(lon) == 1 and len(lat) == 1:\n # force entries to be scalar if they are length-1\n lon, lat = u.Quantity(lon[0]), u.Quantity(lat[0])\n else:\n # otherwise, they are vector quantities\n lon, lat = u.Quantity(lon), u.Quantity(lat)\n sphcoords = coordinates.UnitSphericalRepresentation(lon, lat)\n coords = [SkyCoord(frame(sphcoords))]\n\n if self.region_type != 'polygon':\n coords += self.coord[len(coords * 2):]\n\n return coords","def generateBallGCoordinateList():\r\n l=[]\r\n # x 6 & 21\r\n for i in range(2,27):\r\n l.append((6,i))\r\n l.append((21,i))\r\n \r\n for i in range(1,27):\r\n # y 1\r\n if i not in [13,14]:\r\n l.append((i,1))\r\n # y 5 & 29\r\n if i not in [6,21]:\r\n l.append((i,5))\r\n l.append((i,29))\r\n # y 8 & 26 & 20 & 23\r\n if i not in [6,13,14,21]:\r\n l.append((i,20))\r\n if i not in [1,4,5,22,23,26]:\r\n l.append((i,23))\r\n if i not in [7,8,19,20]:\r\n l.append((i,8))\r\n l.append((i,26))\r\n # y 2-4\r\n for i in range(2,5):\r\n if i!=3:\r\n l.append((1,i))\r\n l.append((26,i))\r\n l.append((12,i))\r\n l.append((15,i))\r\n # y 6 % 7\r\n for i in [1,9,18,26]:\r\n l.append((i,6))\r\n l.append((i,7))\r\n # y 21 &22\r\n for i in [1,12,15,26]:\r\n l.append((i,21))\r\n l.append((i,22))\r\n # y 24 & 25\r\n for i in [3,9,18,24]:\r\n l.append((i,24))\r\n l.append((i,25))\r\n # y 27 & 28\r\n for i in [1,12,15,26]:\r\n l.append((i,27))\r\n l.append((i,28))\r\n return l","def map_coordinates(self,geometry):\n\t\tg = self.geomatrix\n\t\tdef project_coord(x,y,z=None):\n\t\t\tx = g[0] + g[1] * x + g[2] * y\n\t\t\ty = g[3] + g[4] * x + g[5] * y\n\t\t\tif z is None:\n\t\t\t\treturn x,y\n\t\t\telse:\n\t\t\t\treturn x,y,z\n\t\treturn transform(project_coord, geometry)","def coordinates(self):","def center_point_to_coordinates(bbox):\n y, x, h, w = tf.split(bbox, 4, -1)\n h2, w2 = tf.cast(h / 2, h.dtype), tf.cast(w / 2, w.dtype)\n return tf.concat([y - h2, x - w2, y + h2, x + w2], -1)","def make_xy(N, origin=None):\r\n if type(N) == int :\r\n ny, nx = N, N\r\n elif len(N) == 2 :\r\n ny, nx = N \r\n elif len(N) == 1 and (type(N) == tuple or type(N) == list):\r\n ny = N[0] \r\n nx = 0\r\n if ny != 0 and nx != 0 :\r\n x, y = np.meshgrid(np.arange(nx), np.arange(ny))\r\n if (origin is None) or (origin == 'centre') :\r\n origin_x, origin_y = nx // 2 - 1, ny // 2 - 1\r\n x -= origin_x\r\n y -= origin_y\r\n elif origin[0] == 0 and (origin[1] == 0):\r\n x = ((x + nx // 2 - 1) % nx) - (nx // 2 - 1)\r\n y = ((y + ny // 2 - 1) % ny) - (ny // 2 - 1)\r\n return x, y\r\n elif ny != 0 and nx == 0 :\r\n y = np.arange(ny)\r\n if (origin is None) or (origin == 'centre') :\r\n origin_y = ny // 2 - 1\r\n y -= origin_y\r\n elif origin[0] == 0 :\r\n y = ((y + ny // 2 - 1) % ny) - (ny // 2 - 1)\r\n return y","def test_xyz_to_coords_list(self):\n xyz_dict = {'symbols': ('O', 'N', 'C', 'H', 'H'),\n 'isotopes': (16, 14, 12, 1, 1),\n 'coords': ((1.1746411, -0.15309781, 0.0),\n (0.06304988, 0.35149648, 0.0),\n (-1.12708952, -0.11333971, 0.0),\n (-1.93800144, 0.60171738, 0.0),\n (-1.29769464, -1.18742971, 0.0))}\n coords = converter.xyz_to_coords_list(xyz_dict)\n expected_coords = [[1.1746411, -0.15309781, 0.0],\n [0.06304988, 0.35149648, 0.0],\n [-1.12708952, -0.11333971, 0.0],\n [-1.93800144, 0.60171738, 0.0],\n [-1.29769464, -1.18742971, 0.0]]\n self.assertEqual(coords, expected_coords)","def cartesian_to_geographical(coordinate_triples):\n if len(coordinate_triples.shape) == 1:\n x = coordinate_triples[0]\n y = coordinate_triples[1]\n z = coordinate_triples[2]\n elif len(coordinate_triples.shape) == 2:\n assert coordinate_triples.shape[1] == 3\n x = coordinate_triples[:, 0]\n y = coordinate_triples[:, 1]\n z = coordinate_triples[:, 2]\n radius = np.sqrt(x**2 + y**2 + z**2)\n longitudes = np.arctan2(y, x)\n latitudes = np.arcsin(z/radius)\n return (latitudes, longitudes)","def _project_2d(self, activations: np.ndarray) -> List[np.ndarray]:\n projections = [[] for _ in range(self.n_components)]\n for activation in activations:\n flat = activation.reshape(activation.shape[0], -1).transpose()\n flat = flat - flat.mean(axis=0)\n U, S, VT = np.linalg.svd(flat, full_matrices=True)\n for i in range(self.n_components):\n component_i = flat @ VT[i, :]\n component_i = component_i.reshape(activation.shape[1:])\n projections[i].append(component_i)\n return [np.array(elem, dtype=np.float32) for elem in projections]","def _pixel_coordinates(nx, ny, mode):\n if mode == \"center\":\n x = np.linspace(0.5, nx - 0.5, num=nx)\n y = np.linspace(0.5, ny - 0.5, num=ny)\n\n if mode == \"edge\":\n x = np.linspace(0, nx, num=nx + 1)\n y = np.linspace(0, ny, num=ny + 1)\n\n X, Y = np.meshgrid(x, y)\n coordinates = np.stack((X, Y), axis=-1)\n\n return coordinates","def get_coordinates(bounding_box, bounding_box_type=''):\n coordinates = []\n if bounding_box_type == \"box\":\n coordinates.append([bounding_box[1], bounding_box[0]])\n coordinates.append([bounding_box[1], bounding_box[2]])\n coordinates.append([bounding_box[3], bounding_box[2]])\n coordinates.append([bounding_box[3], bounding_box[0]])\n coordinates.append([bounding_box[1], bounding_box[0]])\n return coordinates","def vxl2mni(coordlist):\n mni_list = []\n for item in coordlist:\n temp = (90 - 2 * item[0],\n -126 + 2 * item[1],\n -72 + 2 * item[2])\n mni_list.append(temp)\n return mni_list","def convert(points):\n distance = []\n for i in points:\n x = int(i[0])\n y = int(i[1])\n distance.append([x,y])\n return distance","def find_obstacle_loc(self, obstacle_list):\n\n x_obst = []\n y_obst = []\n #x_obst_append = x_obst.append\n #y_obst_append = y_obst.append\n locs = []\n\n for x in obstacle_list:\n if x < self.width:\n x_obst.append(x*self.resolution + self.resolution/2)\n else:\n x_obst.append((x % self.width)*self.resolution + self.resolution/2)\n\n for y in obstacle_list:\n y_obst.append((y/self.width)*self.resolution + self.resolution/2)\n\n locs = map(lambda x: x, zip(x_obst, y_obst))\n\n return(locs)","def get_coords(self):\n return [self.x,self.y,self.w,self.h]","def to_point_form(boxes):\n center_x, center_y = boxes[:, 0], boxes[:, 1]\n width, height = boxes[:, 2], boxes[:, 3]\n x_min = center_x - (width / 2.)\n x_max = center_x + (width / 2.)\n y_min = center_y - (height / 2.)\n y_max = center_y + (height / 2.)\n return np.concatenate([x_min[:, None], y_min[:, None],\n x_max[:, None], y_max[:, None]], axis=1)","def get_node_coordinates(nodes) :\r\n\r\n coords = [] #The list of coordinates\r\n\r\n for node in nodes :\r\n coords.append(node.coords)\r\n\r\n return coords","def project_pos(lats, lons, map_center = False):\n pos = [(lat,-lon) for lat,lon in zip(lats,lons)]\n if map_center:\n loncenter = map_center[0]\n latcenter = map_center[1]\n else:\n loncenter = np.mean(list(lats.values()))\n latcenter = -1* np.mean(list(lons.values()))\n local_azimuthal_projection = \"+proj=aeqd +R=6371000 +units=m +lat_0={} +lon_0={}\".format(latcenter, loncenter)\n # Use transformer: https://gis.stackexchange.com/questions/127427/transforming-shapely-polygon-and-multipolygon-objects\n wgs84_to_aeqd = pyproj.Transformer.from_proj(\n pyproj.Proj(\"+proj=longlat +datum=WGS84 +no_defs\"),\n pyproj.Proj(local_azimuthal_projection))\n pos_transformed = [(ops.transform(wgs84_to_aeqd.transform, Point(latlon)).coords)[0] for latlon in pos]\n return pos_transformed, (loncenter,latcenter)"],"string":"[\n \"def proj_coords(coords, proj_in, proj_out): \\n return [proj_coord(coord, proj_in, proj_out) for coord in coords]\",\n \"def get_coordinate_lists(self, crs=None):\\n x, y = self.vertices.vectors()[:2]\\n if crs is not None and (crs != self.crs):\\n x, y = _reproject((x,y), self.crs, crs)\\n return x, y\",\n \"def reproject_coordinates(coordinates, inproj, outproj, flat=False):\\n if flat:\\n return np.array([transform(inproj, outproj, coord[0], coord[1]) for coord in coordinates]).flatten()\\n return [list(transform(inproj, outproj, coord[0], coord[1])) for coord in coordinates]\",\n \"def ReprojectCoords(coords,src_srs,tgt_srs):\\n trans_coords = []\\n transform = osr.CoordinateTransformation( src_srs, tgt_srs)\\n for x,y in coords:\\n x,y,z = transform.TransformPoint(x,y)\\n trans_coords.append([x,y])\\n return trans_coords\",\n \"def m4_make_middle_shape_points_list(tx, ty, m4_info, SEN_info):\\n \\\"\\\"\\\"\\n 1 Get information from m1_info & SEN_info\\n \\\"\\\"\\\"\\n x_m4 = m4_info[0]\\n y_m4 = m4_info[1]\\n z_m = m4_info[2]\\n\\n m4_points = m4_info[3]\\n\\n m4_p0 = m4_points[0]\\n m4_p1 = m4_points[1]\\n m4_p2 = m4_points[2]\\n m4_p3 = m4_points[3]\\n\\n w_sen = SEN_info[0]\\n n_w_sen = SEN_info[1]\\n h_sen = SEN_info[2]\\n t_sen = SEN_info[3]\\n u_n = SEN_info[4]\\n l_n = SEN_info[5]\\n set = SEN_info[6]\\n u_offset = SEN_info[7]\\n l_offset = SEN_info[8]\\n\\n \\\"\\\"\\\"\\n 2 Make lists.\\n middle_shape_upper_left_row list\\n middle_shape_upper_right_row list\\n\\n middle_shape_lower_left_row list\\n middle_shape_lower_right_row list\\n \\\"\\\"\\\"\\n # upper side\\n middle_shape_upper_left_row = []\\n middle_shape_upper_right_row = []\\n\\n for i in range(u_n - 1, -1, -1):\\n # left row\\n ix = tx - (x_m4 - t_sen)\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p3, p4, p1, p2]\\n middle_shape_upper_left_row.extend((left_points))\\n\\n for i in range(u_n):\\n # right row\\n ix = tx - t_sen\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p1, p2, p3, p4]\\n middle_shape_upper_right_row.extend(right_points)\\n\\n # lower side\\n middle_shape_lower_left_row = []\\n middle_shape_lower_right_row = []\\n\\n for i in range(l_n):\\n # left row\\n ix = tx - (x_m4 - t_sen)\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p3, p4, p1, p2]\\n middle_shape_lower_left_row.extend((left_points))\\n\\n for i in range(l_n - 1, -1, -1):\\n # right row\\n ix = tx - t_sen\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p1, p2, p3, p4]\\n middle_shape_lower_right_row.extend(right_points)\\n\\n middle_shape_upper = [middle_shape_upper_left_row, middle_shape_upper_right_row]\\n middle_shape_lower = [middle_shape_lower_left_row, middle_shape_lower_right_row]\\n\\n return middle_shape_upper, middle_shape_lower\",\n \"def m4_make_upper_shape_points_list(tx, ty, m4_info, SEN_info):\\n \\\"\\\"\\\"\\n 1 Get information from m4_info & SEN_info\\n \\\"\\\"\\\"\\n x_m4 = m4_info[0]\\n y_m4 = m4_info[1]\\n z_m = m4_info[2]\\n\\n m4_points = m4_info[3]\\n\\n m4_p0 = m4_points[0]\\n m4_p1 = m4_points[1]\\n m4_p2 = m4_points[2]\\n m4_p3 = m4_points[3]\\n\\n w_sen = SEN_info[0]\\n n_w_sen = SEN_info[1]\\n h_sen = SEN_info[2]\\n t_sen = SEN_info[3]\\n u_n = SEN_info[4]\\n l_n = SEN_info[5]\\n set = SEN_info[6]\\n u_offset = SEN_info[7]\\n l_offset = SEN_info[8]\\n\\n \\\"\\\"\\\"\\n 2 Make lists.\\n upper_shape_upper_left_row list\\n upper_shape_upper_right_row list\\n\\n upper_shape_lower_left_row list\\n upper_shape_lower_right_row list\\n \\\"\\\"\\\"\\n # upper side\\n upper_shape_upper_left_row = []\\n upper_shape_upper_right_row = []\\n\\n for i in range(u_n - 1, -1, -1):\\n # left row\\n ix = tx - (x_m4 - t_sen)\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p8, p7, p6, p5, p4, p3, p2, p1]\\n upper_shape_upper_left_row.extend((left_points))\\n\\n for i in range(u_n):\\n # right row\\n ix = tx - t_sen\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p4, p3, p2, p1, p8, p7, p6, p5]\\n upper_shape_upper_right_row.extend(right_points)\\n\\n # lower side\\n upper_shape_lower_left_row = []\\n upper_shape_lower_right_row = []\\n\\n for i in range(l_n):\\n # left row\\n ix = tx - (x_m4 - t_sen)\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p8, p7, p6, p5, p4, p3, p2, p1]\\n upper_shape_lower_left_row.extend((left_points))\\n\\n for i in range(l_n - 1, -1, -1):\\n # right row\\n ix = tx - t_sen\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p4, p3, p2, p1, p8, p7, p6, p5]\\n upper_shape_lower_right_row.extend(right_points)\\n\\n upper_shape_upper = [upper_shape_upper_left_row, upper_shape_upper_right_row]\\n upper_shape_lower = [upper_shape_lower_left_row, upper_shape_lower_right_row]\\n\\n return upper_shape_upper, upper_shape_lower\",\n \"def get_all_coordinates(self):\\n coordinates = []\\n\\n for relative_coordinate in self.shape:\\n co = [self.coordinate[0] + relative_coordinate[0], self.coordinate[1] + relative_coordinate[1]]\\n coordinates.append(co)\\n return coordinates\",\n \"def convert_shapely_points_to_tuples(list_of_points) -> list:\\n return [(p.x, p.y) for p in list_of_points]\",\n \"def transforming_coordinates(self, coordinates_lists, transform): \\n \\n transformed_coordinates_lists = []\\n for coordinates_list in coordinates_lists:\\n transformed_coordinates_list = []\\n for coordinate in coordinates_list:\\n coordinate = tuple(coordinate)\\n transformed_coordinate = list(transform(coordinate[0], coordinate[1]))\\n transformed_coordinates_list.append(transformed_coordinate)\\n transformed_coordinates_lists.append(transformed_coordinates_list)\\n \\n \\n return transformed_coordinates_lists\",\n \"def transform_coords(x, y, w, h, nw, nh):\\r\\n return ((((x / w) - 0.5) * nw), (((h - y) / h) - 0.5) * nh)\",\n \"def makeToCoordinates(fromCoords, Uframe, Vframe, scale):\\n\\n out = []\\n\\n for e in fromCoords:\\n x = e[0]\\n y = e[1]\\n toX = Uframe[x][y]\\n toY = Vframe[x][y]\\n out.append((int(round(x+toX*scale)),int(round(y+toY*scale))))\\n\\n return out\",\n \"def m4_make_lower_shape_points_list(tx, ty, m4_info, SEN_info):\\n \\\"\\\"\\\"\\n 1 Get information from m4_info & SEN_info\\n \\\"\\\"\\\"\\n x_m4 = m4_info[0]\\n y_m4 = m4_info[1]\\n z_m = m4_info[2]\\n\\n m4_points = m4_info[3]\\n\\n m4_p0 = m4_points[0]\\n m4_p1 = m4_points[1]\\n m4_p2 = m4_points[2]\\n m4_p3 = m4_points[3]\\n\\n w_sen = SEN_info[0]\\n n_w_sen = SEN_info[1]\\n h_sen = SEN_info[2]\\n t_sen = SEN_info[3]\\n u_n = SEN_info[4]\\n l_n = SEN_info[5]\\n set = SEN_info[6]\\n u_offset = SEN_info[7]\\n l_offset = SEN_info[8]\\n\\n \\\"\\\"\\\"\\n 2 Make lists.\\n lower_shape_upper_left_row list\\n lower_shape_upper_right_row list\\n\\n lower_shape_lower_left_row list\\n lower_shape_lower_right_row list\\n \\\"\\\"\\\"\\n # upper side\\n lower_shape_upper_left_row = []\\n lower_shape_upper_right_row = []\\n\\n for i in range(u_n - 1, -1, -1):\\n # left row\\n ix = tx - (x_m4 - t_sen)\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p5, p6, p7, p8, p1, p2, p3, p4]\\n lower_shape_upper_left_row.extend((left_points))\\n\\n for i in range(u_n):\\n # right row\\n ix = tx - t_sen\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p1, p2, p3, p4, p5, p6, p7, p8]\\n lower_shape_upper_right_row.extend(right_points)\\n\\n # lower side\\n lower_shape_lower_left_row = []\\n lower_shape_lower_right_row = []\\n\\n for i in range(l_n):\\n # left row\\n ix = tx - (x_m4 - t_sen)\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p5, p6, p7, p8, p1, p2, p3, p4]\\n lower_shape_lower_left_row.extend((left_points))\\n\\n for i in range(l_n - 1, -1, -1):\\n # right row\\n ix = tx - t_sen\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p1, p2, p3, p4, p5, p6, p7, p8]\\n lower_shape_lower_right_row.extend(right_points)\\n\\n lower_shape_upper = [lower_shape_upper_left_row, lower_shape_upper_right_row]\\n lower_shape_lower = [lower_shape_lower_left_row, lower_shape_lower_right_row]\\n\\n return lower_shape_upper, lower_shape_lower\",\n \"def CreateReleasePoints(points_on_longitude, points_on_latitude, grids):\\n \\n ReleasePointsLon = []\\n ReleasePointsLat = []\\n \\n GridsCW_array = np.asarray(grids[['min_lon', 'min_lat', 'max_lon', 'max_lat']])\\n \\n for i in range(len(GridsCW_array)):\\n \\n lon_space = np.linspace(GridsCW_array[i,0], GridsCW_array[i,2], num = points_on_longitude+2 )\\n lat_space = np.linspace(GridsCW_array[i,1], GridsCW_array[i,3], num = points_on_latitude+2 )\\n \\n \\n lon_space_cor = lon_space[1:-1]\\n lat_space_cor = lat_space[1:-1]\\n \\n for j in lon_space_cor:\\n for k in lat_space_cor:\\n \\n ReleasePointsLon.append(j)\\n ReleasePointsLat.append(k)\\n \\n return ReleasePointsLon, ReleasePointsLat\",\n \"def reproject_coordinates_batch(aX_in, aY_in, spatial_reference_source, spatial_reference_target=None):\\n #Reproject a list of x,y coordinates. \\n\\n if spatial_reference_target is not None:\\n\\n pass\\n else:\\n spatial_reference_target = osr.SpatialReference()\\n spatial_reference_target.ImportFromEPSG(4326)\\n \\n pass\\n\\n \\n if int(osgeo.__version__[0]) >= 3:\\n # GDAL 3 changes axis order: https://github.com/OSGeo/gdal/issues/1546\\n \\n spatial_reference_source.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\\n spatial_reference_target.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\\n\\n \\n pTransform = osr.CoordinateTransformation( spatial_reference_source, spatial_reference_target)\\n\\n npoint = len(aX_in)\\n x_new=list()\\n y_new=list()\\n for i in range(npoint):\\n x0 = aX_in[i]\\n y0 = aY_in[i]\\n \\n x1,y1, z = pTransform.TransformPoint( x0,y0)\\n\\n x_new.append(x1)\\n y_new.append(y1)\\n \\n return x_new,y_new\",\n \"def Reproject(x, y, in_grid = 4326, out_grid = 32737):\\n \\n inProj = Proj(init='epsg:'+str(in_grid))\\n outProj = Proj(init='epsg:'+str(out_grid))\\n \\n \\n x2,y2 = transform(inProj,outProj,x,y)\\n \\n return x2, y2\",\n \"def m2_m3_make_upper_shape_points_list(dx, dy, m_info, SEN_info):\\n \\\"\\\"\\\"\\n 1 Get information from m_info & SEN_info.\\n \\\"\\\"\\\"\\n x_m = m_info[0]\\n y_m = m_info[1]\\n z_m = m_info[2]\\n\\n m_points = m_info[3]\\n\\n m_p0 = m_points[0]\\n m_p1 = m_points[1]\\n m_p2 = m_points[2]\\n m_p3 = m_points[3]\\n\\n w_sen = SEN_info[0]\\n n_w_sen = SEN_info[1]\\n h_sen = SEN_info[2]\\n t_sen = SEN_info[3]\\n l_n = SEN_info[4]\\n r_n = SEN_info[5]\\n set = SEN_info[6]\\n l_offset = SEN_info[7]\\n r_offset = SEN_info[8]\\n\\n \\\"\\\"\\\"\\n 2 Make lists.\\n upper_shape_left_upper_row list\\n upper_shape_left_lower_row list\\n\\n upper_shape_right_upper_row list\\n upper_shape_right_lower_row list\\n \\\"\\\"\\\"\\n # Leftside\\n upper_shape_left_upper_row = []\\n upper_shape_left_lower_row = []\\n\\n for i in range(l_n):\\n # upper row\\n ix = i * l_offset + set\\n iy = y_m - t_sen + dy\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n upper_points = [p4, p3, p2, p1, p8, p7, p6, p5]\\n upper_shape_left_upper_row.extend((upper_points))\\n\\n for i in range(l_n - 1, -1, -1):\\n # lower row\\n ix = i * l_offset + set\\n iy = t_sen + dy\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n lower_points = [p8, p7, p6, p5, p4, p3, p2, p1]\\n upper_shape_left_lower_row.extend(lower_points)\\n\\n # Rightside\\n upper_shape_right_upper_row = []\\n upper_shape_right_lower_row = []\\n\\n for i in range(r_n):\\n # upper row\\n ix = x_m - i * r_offset - set\\n iy = y_m - t_sen + dy\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n upper_points = [p5, p6, p7, p8, p1, p2, p3, p4]\\n upper_shape_right_upper_row.extend((upper_points))\\n\\n for i in range(r_n - 1, -1, -1):\\n # lower row\\n ix = x_m - i * r_offset - set\\n iy = t_sen + dy\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n lower_points = [p1, p2, p3, p4, p5, p6, p7, p8]\\n upper_shape_right_lower_row.extend(lower_points)\\n\\n upper_shape_left = [upper_shape_left_upper_row, upper_shape_left_lower_row]\\n upper_shape_right = [upper_shape_right_upper_row, upper_shape_right_lower_row]\\n\\n return upper_shape_left, upper_shape_right\",\n \"def m1_make_middle_shape_points_list(tx, ty, m1_info, SEN_info):\\n \\\"\\\"\\\"\\n 1 Get information from m1_info & SEN_info\\n \\\"\\\"\\\"\\n x_m1 = m1_info[0]\\n y_m1 = m1_info[1]\\n z_m = m1_info[2]\\n\\n m1_points = m1_info[3]\\n\\n m1_p0 = m1_points[0]\\n m1_p1 = m1_points[1]\\n m1_p2 = m1_points[2]\\n m1_p3 = m1_points[3]\\n\\n w_sen = SEN_info[0]\\n n_w_sen = SEN_info[1]\\n h_sen = SEN_info[2]\\n t_sen = SEN_info[3]\\n u_n = SEN_info[4]\\n l_n = SEN_info[5]\\n set = SEN_info[6]\\n u_offset = SEN_info[7]\\n l_offset = SEN_info[8]\\n\\n \\\"\\\"\\\"\\n 2 Make lists.\\n middle_shape_upper_left_row list\\n middle_shape_upper_right_row list\\n\\n middle_shape_lower_left_row list\\n middle_shape_lower_right_row list\\n \\\"\\\"\\\"\\n # upper side\\n middle_shape_upper_left_row = []\\n middle_shape_upper_right_row = []\\n\\n for i in range(u_n):\\n # left row\\n ix = tx + t_sen\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p2, p1, p4, p3]\\n middle_shape_upper_left_row.extend((left_points))\\n\\n for i in range(u_n - 1, -1, -1):\\n # right row\\n ix = tx + (x_m1 - t_sen)\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p4, p3, p2, p1]\\n middle_shape_upper_right_row.extend(right_points)\\n\\n # lower side\\n middle_shape_lower_left_row = []\\n middle_shape_lower_right_row = []\\n\\n for i in range(l_n - 1, -1, -1):\\n # left row\\n ix = tx + t_sen\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p2, p1, p4, p3]\\n middle_shape_lower_left_row.extend((left_points))\\n\\n for i in range(l_n):\\n # right row\\n ix = tx + (x_m1 - t_sen)\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4 = Y_middle_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p4, p3, p2, p1]\\n middle_shape_lower_right_row.extend(right_points)\\n\\n middle_shape_upper = [middle_shape_upper_left_row, middle_shape_upper_right_row]\\n middle_shape_lower = [middle_shape_lower_left_row, middle_shape_lower_right_row]\\n\\n return middle_shape_upper, middle_shape_lower\",\n \"def transform_pnt(pnt, inEPSG, outEPSG):\\r\\n x1, y1 = pnt[0], pnt[1] #added benefit of checking pnt format\\r\\n inProj = pyproj.Proj(init='epsg:{}'.format(inEPSG))\\r\\n outProj = pyproj.Proj(init='epsg:{}'.format(outEPSG))\\r\\n\\r\\n return pyproj.transform(inProj, outProj, x1, y1)\",\n \"def projectBack(points, proj):\\n\\n mpoints = MultiPoint(points)\\n project = partial(\\n pyproj.transform,\\n proj,\\n pyproj.Proj(proj='latlong', datum='WGS84'))\\n gmpoints = transform(project, mpoints)\\n coords = []\\n for point in gmpoints.geoms:\\n x, y = point.coords[0]\\n coords.append((x, y))\\n coords = np.array(coords)\\n return coords\",\n \"def coord_vecs(self):\\n return [np.linspace(x0, x1, nx) for x0, x1, nx in zip(self.mins, self.maxs, self.shape)]\",\n \"def m1_make_upper_shape_points_list(tx, ty, m1_info, SEN_info):\\n \\\"\\\"\\\"\\n 1 Get information from m1_info & SEN_info\\n \\\"\\\"\\\"\\n x_m1 = m1_info[0]\\n y_m1 = m1_info[1]\\n z_m = m1_info[2]\\n\\n m1_points = m1_info[3]\\n\\n m1_p0 = m1_points[0]\\n m1_p1 = m1_points[1]\\n m1_p2 = m1_points[2]\\n m1_p3 = m1_points[3]\\n\\n w_sen = SEN_info[0]\\n n_w_sen = SEN_info[1]\\n h_sen = SEN_info[2]\\n t_sen = SEN_info[3]\\n u_n = SEN_info[4]\\n l_n = SEN_info[5]\\n set = SEN_info[6]\\n u_offset = SEN_info[7]\\n l_offset = SEN_info[8]\\n\\n \\\"\\\"\\\"\\n 2 Make lists.\\n upper_shape_upper_left_row list\\n upper_shape_upper_right_row list\\n\\n upper_shape_lower_left_row list\\n upper_shape_lower_right_row list\\n \\\"\\\"\\\"\\n # upper side\\n upper_shape_upper_left_row = []\\n upper_shape_upper_right_row = []\\n\\n for i in range(u_n):\\n # left row\\n ix = tx + t_sen\\n iy = ty + (i * u_offset + set) + 10 # have to \\\"+\\\" something now its magic number\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p1, p2, p3, p4, p5, p6, p7, p8]\\n upper_shape_upper_left_row.extend((left_points))\\n\\n for i in range(u_n - 1, -1, -1):\\n # right row\\n ix = tx + (x_m1 - t_sen)\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p5, p6, p7, p8, p1, p2, p3, p4]\\n upper_shape_upper_right_row.extend(right_points)\\n\\n # lower side\\n upper_shape_lower_left_row = []\\n upper_shape_lower_right_row = []\\n\\n for i in range(l_n -1, -1, -1):\\n # left row\\n ix = tx + t_sen\\n iy = ty - (i * l_offset + set) - 10 # have to \\\"-\\\" something now its magic number\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p1, p2, p3, p4, p5, p6, p7, p8]\\n upper_shape_lower_left_row.extend((left_points))\\n\\n for i in range(l_n):\\n # right row\\n ix = tx + (x_m1 - t_sen)\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_upper_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p5, p6, p7, p8, p1, p2, p3, p4]\\n upper_shape_lower_right_row.extend(right_points)\\n\\n upper_shape_upper = [upper_shape_upper_left_row, upper_shape_upper_right_row]\\n upper_shape_lower = [upper_shape_lower_left_row, upper_shape_lower_right_row]\\n\\n return upper_shape_upper, upper_shape_lower\",\n \"def trans_to_coordinates(T, pts):\\n p = []\\n for i in range(len(pts)):\\n \\n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\\n p_a = np.matmul(T, p_b).tolist()\\n p.append(p_a[0:3])\\n\\n return p\",\n \"def trans_to_coordinates(T, pts):\\n p = []\\n for i in range(len(pts)):\\n \\n p_b = [pts[i][0], pts[i][1], pts[i][2], 1]\\n p_a = np.matmul(T, p_b).tolist()\\n p.append(p_a[0:3])\\n\\n return p\",\n \"def m2_m3_make_lower_shape_points_list(dx, dy, m_info, SEN_info):\\n \\\"\\\"\\\"\\n 1 Get information from m_info & SEN_info.\\n \\\"\\\"\\\"\\n x_m = m_info[0]\\n y_m = m_info[1]\\n z_m = m_info[2]\\n\\n m_points = m_info[3]\\n\\n m_p0 = m_points[0]\\n m_p1 = m_points[1]\\n m_p2 = m_points[2]\\n m_p3 = m_points[3]\\n\\n w_sen = SEN_info[0]\\n n_w_sen = SEN_info[1]\\n h_sen = SEN_info[2]\\n t_sen = SEN_info[3]\\n l_n = SEN_info[4]\\n r_n = SEN_info[5]\\n set = SEN_info[6]\\n l_offset = SEN_info[7]\\n r_offset = SEN_info[8]\\n\\n \\\"\\\"\\\"\\n 2 Make lists.\\n lower_shape_left_upper_row list\\n lower_shape_left_lower_row list\\n\\n lower_shape_right_upper_row list\\n lower_shape_right_lower_row list\\n \\\"\\\"\\\"\\n # Leftside\\n lower_shape_left_upper_row = []\\n lower_shape_left_lower_row = []\\n\\n for i in range(l_n):\\n # upper row\\n ix = i * l_offset + set\\n iy = y_m - t_sen + dy\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n upper_points = [p1, p2, p3, p4, p5, p6, p7, p8]\\n lower_shape_left_upper_row.extend((upper_points))\\n\\n for i in range(l_n - 1, -1, -1):\\n # lower row\\n ix = i * l_offset + set\\n iy = t_sen + dy\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n lower_points = [p5, p6, p7, p8, p1, p2, p3, p4]\\n lower_shape_left_lower_row.extend(lower_points)\\n\\n # Rightside\\n lower_shape_right_upper_row = []\\n lower_shape_right_lower_row = []\\n\\n for i in range(r_n):\\n # upper row\\n ix = x_m - i * r_offset - set\\n iy = y_m - t_sen + dy\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n upper_points = [p8, p7, p6, p5, p4, p3, p2, p1]\\n lower_shape_right_upper_row.extend((upper_points))\\n\\n for i in range(r_n - 1, -1, -1):\\n # lower row\\n ix = x_m - i * r_offset - set\\n iy = t_sen + dy\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = X_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n lower_points = [p4, p3, p2, p1, p8, p7, p6, p5]\\n lower_shape_right_lower_row.extend(lower_points)\\n\\n lower_shape_left = [lower_shape_left_upper_row, lower_shape_left_lower_row]\\n lower_shape_right = [lower_shape_right_upper_row, lower_shape_right_lower_row]\\n\\n return lower_shape_left, lower_shape_right\",\n \"def extractCoords(self):\\n if not self.rank:\\n logging.info('Extracting atomic poitions')\\n\\n # Extract coordinates from liggghts\\n self.lmp.command('variable x atom x')\\n x = Rxn.lmp.extract_variable(\\\"x\\\", \\\"group1\\\", 1)\\n\\n self.lmp.command('variable y atom y')\\n y = Rxn.lmp.extract_variable(\\\"y\\\", \\\"group1\\\", 1)\\n\\n self.lmp.command('variable z atom z')\\n z = Rxn.lmp.extract_variable(\\\"z\\\", \\\"group1\\\", 1)\\n\\n coords = np.zeros((self.lmp.get_natoms(),3))\\n\\n for i in range(self.lmp.get_natoms()):\\n coords[i,:] = x[i], y[i], z[i]\\n\\n self.lmp.command('variable x delete')\\n self.lmp.command('variable y delete')\\n self.lmp.command('variable z delete')\\n\\n return coords\",\n \"def _convert_pix_coords(self):\\n if self.region_type in ['polygon', 'line']:\\n # have to special-case polygon in the phys coord case\\n # b/c can't typecheck when iterating as in sky coord case\\n coords = [PixCoord(self.coord[0::2], self.coord[1::2])]\\n else:\\n temp = [_.value for _ in self.coord]\\n coord = PixCoord(temp[0], temp[1])\\n coords = [coord] + temp[2:]\\n\\n # The angle remains as a quantity object.\\n # Modulus check makes sure that it works for ellipse/rectangle annulus\\n if self.region_type in ['ellipse', 'rectangle'] and len(coords) % 2 == 0:\\n coords[-1] = self.coord[-1]\\n\\n return coords\",\n \"def coordinates(self):\\n return np.array([[f.x, f.y] for f in self])\",\n \"def coordinates_of(self, coord_index_list):\\n return [self.transformed_coords[i] for i in coord_index_list]\",\n \"def getYesPoints(pshapes, proj, dx, nmax, touch_center=True):\\n\\n mxmin = 9e10\\n mxmax = -9e10\\n mymin = 9e10\\n mymax = -9e10\\n for pshape in pshapes:\\n pxmin, pymin, pxmax, pymax = pshape.bounds\\n if pxmin < mxmin:\\n mxmin = pxmin\\n if pxmax > mxmax:\\n mxmax = pxmax\\n if pymin < mymin:\\n mymin = pymin\\n if pymax > mymax:\\n mymax = pymax\\n\\n if not touch_center:\\n geodict = GeoDict.createDictFromBox(mxmin, mxmax, mymin, mymax, dx, dx)\\n img = rasterizeShapes(pshapes, geodict)\\n #now get the numpy array of x/y coordinates where covgrid == 1\\n idx = np.where(img == 1)[0]\\n x, y = np.unravel_index(idx, (geodict.ny, geodict.nx))\\n yespoints = list(zip(x.flatten(), y.flatten()))\\n nrows = geodict.ny\\n ncols = geodict.nx\\n xvar = np.arange(geodict.xmin, geodict.xmax+geodict.dx, geodict.dx)\\n yvar = np.arange(geodict.ymin, geodict.ymax+geodict.dy, geodict.dy)\\n else:\\n xvar = np.arange(mxmin, mxmax+dx, dx)\\n yvar = np.arange(mymin, mymax+dx, dx)\\n ncols = len(xvar)\\n nrows = len(yvar)\\n if nmax is not None:\\n if ncols*nrows > nmax:\\n aspect = ncols/nrows\\n ncols = np.sqrt(nmax*aspect)\\n nrows = nmax/ncols\\n ncols = int(ncols)\\n nrows = int(nrows)\\n #re-calculate dx here...\\n tdx = (mxmax-mxmin)/ncols\\n tdy = (mymax-mymin)/nrows\\n dx = np.max(tdx, tdy)\\n xvar = np.arange(mxmin, mxmax+dx, dx)\\n yvar = np.arange(mymin, mymax+dx, dx)\\n\\n #Get the \\\"yes\\\" points to sample from\\n yespoints = []\\n idx = []\\n shapeidx = 0\\n if pshapes[0].type == 'Polygon':\\n #loop over shapes, projecting each one, then get the sample points\\n for pshape in pshapes:\\n if not shapeidx % 1000:\\n print('Searching polygon %i of %i' % (shapeidx, len(pshapes)))\\n shapeidx += 1\\n pxmin, pymin, pxmax, pymax = pshape.bounds\\n leftcol = np.where((pxmin - xvar) >= 0)[0].argmax()\\n rightcol = np.where((xvar - pxmax) >= 0)[0][0]\\n bottomrow = np.where((pymin - yvar) >= 0)[0].argmax()\\n toprow = np.where((yvar - pymax) >= 0)[0][0]\\n xp = np.arange(xvar[leftcol], xvar[rightcol]+dx, dx)\\n yp = np.arange(yvar[bottomrow], yvar[toprow]+dx, dx)\\n xmesh, ymesh = np.meshgrid(xp, yp)\\n xy = list(zip(xmesh.flatten(), ymesh.flatten()))\\n for point in xy:\\n ix = np.where(xvar == point[0])[0][0]\\n iy = np.where(yvar == point[1])[0][0]\\n if pshape.contains(Point(point)):\\n yespoints.append(point)\\n idx.append(np.ravel_multi_index((iy, ix), (nrows, ncols), mode='raise', order='C'))\\n else:\\n yespoints = []\\n for pshape in pshapes:\\n yespoints.append(pshape.coords[0])\\n\\n return (np.array(yespoints), nrows, ncols, xvar, yvar, idx)\",\n \"def convert_coords(self):\\n if self.coordsys in ['image', 'physical']:\\n coords = self._convert_pix_coords()\\n else:\\n coords = self._convert_sky_coords()\\n\\n if self.region_type == 'line':\\n coords = [coords[0][0], coords[0][1]]\\n\\n if self.region_type == 'text':\\n coords.append(self.meta['text'])\\n\\n return coords\",\n \"def convert_coords(self, coords):\\n xPos = int(int(coords[0]) / 8)\\n yPos = int(coords[1])\\n zPos = int(int(coords[2]) / 8)\\n return list(xPos, yPos, zPos)\",\n \"def get_coords_2d(obj, P):\\n bbox3 = get_coords_3d(obj)\\n # Rl = obj.Rl\\n # height = obj.height\\n # width = obj.width\\n # length = obj.length\\n # x = obj.x\\n # y = obj.y\\n # z = obj.z\\n # bbox = np.array([[-length, 0, -width],\\n # [-length, 0, width],\\n # [ length, 0, width],\\n # [ length, 0, -width],\\n # [-length, -2*height, -width],\\n # [-length, -2*height, width],\\n # [ length, -2*height, width],\\n # [ length, -2*height, -width]])/2\\n # if abs(z) < 1e-6: z = np.sign(z) * 1e-6\\n # M = rotationM([0, 1, 0], Rl + math.atan(x/z))\\n # bbox = np.transpose(np.dot(M, np.transpose(bbox)))\\n # bbox3 = bbox + np.array([x, y, z])\\n bbox2 = project_pts3_to_image(bbox3, P)\\n return bbox2\",\n \"def m1_make_lower_shape_points_list(tx, ty, m1_info, SEN_info):\\n \\\"\\\"\\\"\\n 1 Get information from m1_info & SEN_info\\n \\\"\\\"\\\"\\n x_m1 = m1_info[0]\\n y_m1 = m1_info[1]\\n z_m = m1_info[2]\\n\\n m1_points = m1_info[3]\\n\\n m1_p0 = m1_points[0]\\n m1_p1 = m1_points[1]\\n m1_p2 = m1_points[2]\\n m1_p3 = m1_points[3]\\n\\n w_sen = SEN_info[0]\\n n_w_sen = SEN_info[1]\\n h_sen = SEN_info[2]\\n t_sen = SEN_info[3]\\n u_n = SEN_info[4]\\n l_n = SEN_info[5]\\n set = SEN_info[6]\\n u_offset = SEN_info[7]\\n l_offset = SEN_info[8]\\n\\n \\\"\\\"\\\"\\n 2 Make lists.\\n lower_shape_upper_left_row list\\n lower_shape_upper_right_row list\\n\\n lower_shape_lower_left_row list\\n lower_shape_lower_right_row list\\n \\\"\\\"\\\"\\n # upper side\\n lower_shape_upper_left_row = []\\n lower_shape_upper_right_row = []\\n\\n for i in range(u_n):\\n # left row\\n ix = tx + t_sen\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p4, p3, p2, p1, p8, p7, p6, p5]\\n lower_shape_upper_left_row.extend((left_points))\\n\\n for i in range(u_n - 1, -1, -1):\\n # right row\\n ix = tx + (x_m1 - t_sen)\\n iy = ty + (i * u_offset + set) + 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p8, p7, p6, p5, p4, p3, p2, p1]\\n lower_shape_upper_right_row.extend(right_points)\\n\\n # lower side\\n lower_shape_lower_left_row = []\\n lower_shape_lower_right_row = []\\n\\n for i in range(l_n - 1, -1, -1):\\n # left row\\n ix = tx + t_sen\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n left_points = [p4, p3, p2, p1, p8, p7, p6, p5]\\n lower_shape_lower_left_row.extend((left_points))\\n\\n for i in range(l_n):\\n # right row\\n ix = tx + (x_m1 - t_sen)\\n iy = ty - (i * l_offset + set) - 10\\n\\n p0, p1, p2, p3, p4, p5, p6, p7, p8 = Y_lower_shape_points(ix, iy, w_sen, t_sen, n_w_sen)\\n right_points = [p8, p7, p6, p5, p4, p3, p2, p1]\\n lower_shape_lower_right_row.extend(right_points)\\n\\n lower_shape_upper = [lower_shape_upper_left_row, lower_shape_upper_right_row]\\n lower_shape_lower = [lower_shape_lower_left_row, lower_shape_lower_right_row]\\n\\n return lower_shape_upper, lower_shape_lower\",\n \"def coords(self):\\n return np.column_stack((self.x_coord_list, self.y_coord_list, self.z_coord_list))\",\n \"def pristine_coords_to_objects(list_of_coords):\\n list_of_objects = []\\n for element in range(len(list_of_coords)):\\n list_of_objects.append(Atom(element, \\\"CX\\\", \\\"GGG\\\", element, list_of_coords[element][0], list_of_coords[element][1], list_of_coords[element][2]))\\n return list_of_objects\",\n \"def make_simple_coords():\\n \\n x = np.array([144, 124, 97, 165, 114, 60, 165, 0, 76, 50, 147])\\n y = np.array([ 0, 3, 21, 28, 34, 38, 51, 54, 58, 56, 61])\\n coords = np.vstack((x,y)).T\\n return coords\",\n \"def positions_to_coords(self, positions):\\n return [self.to_coords(px, py) for (px, py) in positions]\",\n \"def affine_pts(pts):\\n if ndim == 2:\\n for x, y in pts:\\n xp = a * x + b * y + xoff\\n yp = d * x + e * y + yoff\\n yield (xp, yp)\\n elif ndim == 3:\\n for x, y, z in pts:\\n xp = a * x + b * y + c * z + xoff\\n yp = d * x + e * y + f * z + yoff\\n zp = g * x + h * y + i * z + zoff\\n yield (xp, yp, zp)\",\n \"def reconstructXY(self, inputs):\\n return (self.reconstructX(inputs),\\n self.reconstructY(inputs))\",\n \"def convert_to_cartesian(grid: List[Tuple[float, float]], radius: float = 1.0) -> List[Tuple[float, float, float]]:\\n\\n # conversion radians -> degrees\\n r2d = 180.0 / np.pi\\n\\n # calculate x/y/z coordinates, assuming r=1\\n return [\\n (\\n radius * np.cos(lat / r2d) * np.cos(lon / r2d),\\n radius * np.cos(lat / r2d) * np.sin(lon / r2d),\\n radius * np.sin(lat / r2d),\\n )\\n for lon, lat in grid\\n ]\",\n \"def CreateTargetGeoMap(latS, latN, lonW, lonE, latlen, lonlen):\\n\\n lat_grid = np.linspace(latS, latN, latlen)\\n lon_grid = np.linspace(lonW, lonE, lonlen)\\n\\n return lat_grid,lon_grid\",\n \"def lonlat(n_lon: int, n_lat: int) -> List[Tuple[float, float]]:\\n grid = []\\n for lon in np.linspace(0, 360.0 - 360.0 / n_lon, n_lon):\\n for lat in np.linspace(-90, 90, n_lat):\\n grid.append((lon, lat))\\n return grid\",\n \"def get_MultiPolyLists_xy(mpoly):\\n # Get the x or y coordinates\\n x = []\\n y = []\\n if isinstance(mpoly,Polygon):\\n mpoly = [mpoly]\\n for poly in mpoly: # the polygon objects return arrays, it's important they be lists or Bokeh fails\\n exterior_coords_x = poly.exterior.coords.xy[0].tolist();\\n interior_coords_x = []\\n exterior_coords_y = poly.exterior.coords.xy[1].tolist();\\n interior_coords_y = []\\n\\n for interior in poly.interiors:\\n if isinstance(interior.coords.xy[0],list):\\n interior_coords_x += [interior.coords.xy[0]];\\n interior_coords_y += [interior.coords.xy[1]];\\n else:\\n interior_coords_x += [interior.coords.xy[0].tolist()];\\n interior_coords_y += [interior.coords.xy[1].tolist()];\\n x.append([exterior_coords_x, *interior_coords_x])\\n y.append([exterior_coords_y, *interior_coords_y])\\n return (x,y)\",\n \"def makeFromCoorinates(ncols, nrows, frame_width, frame_height):\\n\\n dx = frame_width/float(ncols)\\n dy = frame_height/float(nrows)\\n out = []\\n x = int(round(dx/2.0))\\n\\n for c in xrange(ncols):\\n y = int(round(dy/2.0))\\n for r in xrange(nrows):\\n out.append((int(round(x)),int(round(y))))\\n y += dy\\n\\n x += dx\\n return out\",\n \"def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\\n\\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\\n return (-1,-1)\\n\\n lat_step = abs(max_lat-min_lat)/rows\\n lon_step = abs(max_lon-min_lon)/cols\\n\\n lat_spot = int((max_lat-lat)/lat_step)\\n lon_spot = int((lon-min_lon)/lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\",\n \"def condense_coords(matches):\\n x = []\\n y = []\\n for m in matches:\\n x += m['matches']['p'][0]\\n x += m['matches']['q'][0]\\n y += m['matches']['p'][1]\\n y += m['matches']['q'][1]\\n coords = np.transpose(np.vstack((np.array(x), np.array(y))))\\n return coords\",\n \"def camera_pixels_to_camera_coords(left_pt, right_pt, nparrays=False):\\n assert len(left_pt) == len(right_pt) == 2\\n disparity = np.linalg.norm( np.array(left_pt) - np.array(right_pt) )\\n (xx,yy,zz) = STEREO_MODEL.projectPixelTo3d( (left_pt[0],left_pt[1]), disparity )\\n if nparrays:\\n return np.array([xx,yy,zz])\\n else:\\n return [xx,yy,zz]\",\n \"def createGrid(nx, ny, include_center = False):\\n direction = 0\\n positions = []\\n if (nx > 1) or (ny > 1):\\n half_x = int(nx/2)\\n half_y = int(ny/2)\\n for i in range(-half_y, half_y+1):\\n for j in range(-half_x, half_x+1):\\n if ((i==0) and (j==0)) and not include_center:\\n continue\\n else:\\n if ((direction%2)==0):\\n positions.append([j,i])\\n else:\\n positions.append([-j,i])\\n direction += 1\\n return positions\",\n \"def array_coords(shape=(1, 1)):\\n indexes = np.arange(np.product(shape))\\n coords = [indexes % shape[1], indexes // shape[1]]\\n return np.transpose(coords).astype('float64')\",\n \"def feature_coords(features):\\n coords_list = []\\n for feature in features:\\n coord_start = feature.location.nofuzzy_start\\n coord_end = feature.location.nofuzzy_end\\n coord_pair = (coord_start, coord_end)\\n coords_list.append(coord_pair)\\n ## consider adding some info to the log\\n return coords_list\",\n \"def cart_coords2d(self):\\r\\n\\r\\n mgx, mgy = self.meshup2d()\\r\\n coords = np.column_stack((mgx.flatten(), mgy.flatten()))\\r\\n\\r\\n return coords\",\n \"def convert_coordinates(self, coordinates):\\n return np.array(zip(*self.basemap(*zip(*coordinates))))\",\n \"def transform_coordinates(coords):\\n # WGS 84 reference coordinate system parameters\\n A = 6378.137 # major axis [km]\\n E2 = 6.69437999014e-3 # eccentricity squared\\n\\n coords = prepare_coords(coords)\\n\\n # convert to radiants\\n lat_rad = np.radians(coords[:, 0])\\n lon_rad = np.radians(coords[:, 1])\\n\\n # convert to cartesian coordinates\\n r_n = A / (np.sqrt(1 - E2 * (np.sin(lat_rad) ** 2)))\\n x = r_n * np.cos(lat_rad) * np.cos(lon_rad)\\n y = r_n * np.cos(lat_rad) * np.sin(lon_rad)\\n z = r_n * (1 - E2) * np.sin(lat_rad)\\n\\n return np.column_stack((x, y, z))\",\n \"def RegularPolygonPoints(n,c):\\n coord = []\\n for i in range(n):\\n x = m.cos(2*m.pi*i/n)+c[0]\\n y = m.sin(2*m.pi*i/n)+c[1]\\n coord.append([x,y])\\n return(coord)\",\n \"def convert_input(coords: Union[npt.NDArray, List]) -> Tuple[npt.NDArray, ...]:\\n if isinstance(coords, np.ndarray):\\n assert coords.ndim == 2, \\\"'coords' must be a (n, 2) array\\\"\\n assert coords.shape[1] == 2, \\\"'coords' must be a (n, 2) array\\\"\\n x = coords[:, 0]\\n y = coords[:, 1]\\n elif isinstance(coords, list):\\n x = np.array([point[0] for point in coords])\\n y = np.array([point[1] for point in coords])\\n else:\\n raise Exception(\\\"Parameter 'coords' is an unsupported type: \\\" + str(type(coords)))\\n return x, y\",\n \"def build_coord(norm, d, pts):\\n # Compute the origin as the mean point of the points, and this point has to be on the plane\\n \\n n = len(pts) \\n x_total = 0\\n y_total = 0\\n z_total = 0\\n \\n for i in range(n):\\n x_total += pts[i][0]\\n y_total += pts[i][1]\\n z_total += pts[i][2]\\n\\n x_o = x_total * 1.0 / n\\n y_o = y_total * 1.0 / n\\n z_o = z_total * 1.0 / n\\n p_o = [x_o, y_o, z_o]\\n \\n # Choose p be the projection of a vector in the z-axis to the plane\\n # If the plane is not perpendicular to the z-axis\\n if ((norm[2] != 1) and (norm[2] != -1)): \\n # Choose a point\\n o_z = [x_o, y_o, z_o + 1]\\n \\n [[x_p, y_p, z_p]] = proj_to_plane(norm, d, [o_z])\\n \\n dist = np.linalg.norm([x_p - x_o, y_p - y_o, z_p - z_o])\\n\\n x_c = (x_p - x_o) * 1.0 / dist \\n y_c = (y_p - y_o) * 1.0 / dist\\n z_c = (z_p - z_o) * 1.0 / dist\\n # Thus we have unit vector in x direction\\n e_y = [x_c, y_c, z_c]\\n #Compute the unit vector in y direction\\n e_x = np.cross(e_y, norm).tolist()\\n else:\\n e_x = [1, 0, 0]\\n e_y = [0, 1, 0]\\n \\n return [e_x, e_y, norm] , p_o\",\n \"def ll_coordinates(self):\\n\\n x, y = self.xy_coordinates\\n proj_out = check_crs('EPSG:4326')\\n\\n return transform_proj(self.proj, proj_out, x, y)\",\n \"def generate_coordinates(coords):\\n x = coords.reshape(-1, 1).repeat(1, len(coords) * len(coords)).flatten()\\n y = coords.reshape(-1, 1).repeat(1, len(coords)).flatten().repeat(len(coords))\\n z = coords.reshape(-1, 1).flatten().repeat(len(coords)*len(coords))\\n\\n return x, y, z\",\n \"def convert_to_xywh(pts):\\n boxes = []\\n for pt1, pt2 in pts:\\n x = pt1[0]\\n y = pt1[1]\\n w = pt2[0] - pt1[0]\\n h = pt2[1] - pt1[1]\\n boxes.append([x, y, w, h])\\n return boxes\",\n \"def convertLatLon(latCell, lonCell):\\n cell_lats = np.array([])\\n cell_lons = np.array([])\\n for lat in latCell:\\n cell_lats = np.append(cell_lats, lat * (180 / np.pi)) \\n for lon in lonCell:\\n cell_lons = np.append(cell_lons, lon * (180 / np.pi)) \\n\\n return cell_lats, cell_lons\",\n \"def make_xarray_coords(y, x, crs):\\n if crs.is_geographic:\\n y_attrs, x_attrs = COORD_DEFS['latitude'], COORD_DEFS['longitude']\\n elif crs.is_projected:\\n crs_osr = crs2osr(crs)\\n units = crs_osr.GetLinearUnitsName()\\n y_attrs, x_attrs = COORD_DEFS['y'], COORD_DEFS['x']\\n y_attrs['units'], x_attrs['units'] = units, units\\n\\n y = xr.Variable(('y', ), y, attrs=y_attrs)\\n x = xr.Variable(('x', ), x, attrs=x_attrs)\\n\\n return y, x\",\n \"def latlon_2_grid(x, y, z, origin):\\n new_y = (y - origin[1]) * 111111\\n new_x = (x - origin[0]) * (111111 * np.cos(origin[1] * (np.pi/180)))\\n return new_x, new_y, z\",\n \"def to_coords(self, px, py):\\n if px not in range(self.SIZE**2) or py not in range(self.SIZE**2):\\n raise IndexError\\n return (px // self.SIZE, py // self.SIZE,\\n px % self.SIZE, py % self.SIZE)\",\n \"def pixel_to_coords(self, x, y):\\n rx, ry = self.size\\n nx = (x / rx - 0.5) * self.scale + self.center[0]\\n ny = ((ry - y) / ry - 0.5) * self.scale + self.center[1]\\n nz = self.center[2]\\n return [nx, ny, nz]\",\n \"def gen_center_points(x_res, y_res, dim):\\n center_points = []\\n\\n for x in range(math.floor(dim[0] / x_res)):\\n for y in range(math.floor(dim[1] / y_res)):\\n x = (x + 1) * x_res\\n y = (y + 1) * y_res\\n center_points.append((x, y))\\n\\n return center_points\",\n \"def coordinates_to_locations(coordinates):\\n np_coords = np.array(coordinates)\\n longs, lats = transform(Proj(init=EPSG_IN), Proj(init=EPSG_OUT), np_coords[:, 0], np_coords[:, 1])\\n length = len(lats)\\n result = []\\n for i in range(length):\\n loc = Location(lats[i], longs[i])\\n raw_location = extract_raw_simple_coordinates(coordinates[i])\\n loc.set_raw_coordinates_simplified(raw_location[0],raw_location[1])\\n result.append(loc)\\n\\n return result\",\n \"def format_coords(self, coordinates):\\n\\n coords = []\\n for i in range(0, len(coordinates), 3):\\n coords.append(tuple(coordinates[i:i+3]))\\n\\n return coords\",\n \"def get_city_base(city_map = CHICAGO_NEIGHBORHOOD):\\n shapes = shp.Reader(city_map).shapeRecords()\\n x,y = [],[]\\n for shape in shapes:\\n inner_x,inner_y = list(zip(*shape.shape.points))\\n x.append(inner_x)\\n y.append(inner_y)\\n x_flat = [item for sublist in x for item in sublist]\\n y_flat = [item for sublist in y for item in sublist]\\n return x_flat,y_flat\",\n \"def build_coord(norm, d, pts):\\n # Compute the origin as the mean point of the points, and this point has to be on the plane\\n \\n n = len(pts)\\n x_total = 0\\n y_total = 0\\n z_total = 0\\n \\n for i in range(n):\\n x_total += pts[i][0]\\n y_total += pts[i][1]\\n z_total += pts[i][2]\\n\\n x_o = x_total * 1.0 / n\\n y_o = y_total * 1.0 / n\\n z_o = z_total * 1.0 / n\\n p_o = [x_o, y_o, z_o]\\n \\n # Choose p be the projection of a vector in the z-axis to the plane\\n # If the plane is not perpendicular to the z-axis\\n if ((norm[2] != 1) and (norm[2] != -1)): \\n # Choose a point\\n o_z = [x_o, y_o, z_o + 1]\\n \\n [[x_p, y_p, z_p]] = proj_to_plane(norm, d, [o_z])\\n \\n dist = np.linalg.norm([x_p - x_o, y_p - y_o, z_p - z_o])\\n\\n x_c = (x_p - x_o) * 1.0 / dist \\n y_c = (y_p - y_o) * 1.0 / dist\\n z_c = (z_p - z_o) * 1.0 / dist\\n # Thus we have unit vector in x direction\\n e_y = [x_c, y_c, z_c]\\n #Compute the unit vector in y direction\\n e_x = np.cross(e_y, norm).tolist()\\n else:\\n e_x = [1, 0, 0]\\n e_y = [0, 1, 0]\\n \\n return [e_x, e_y, norm] , p_o\",\n \"def transform_obj_list(object_list,M,M2 = None):\\n \\n for i, obj in enumerate(object_list):\\n points = obj.all\\n num_points = len(points)\\n \\n \\n # add third row\\n ones = np.ones([num_points,1])\\n points3d = np.concatenate((points,ones),1)\\n points3d = points3d[:,[0,1,4]]\\n # transform points\\n tf_points3d = np.transpose(np.matmul(M,np.transpose(points3d)))\\n \\n # condense to two-dimensional coordinates\\n tf_points = np.zeros([num_points,2])\\n tf_points[:,0] = tf_points3d[:,0]/tf_points3d[:,2]\\n tf_points[:,1] = tf_points3d[:,1]/tf_points3d[:,2]\\n \\n object_list[i].all_world = tf_points\\n \\n if M2 is not None:\\n tf_points3d = np.transpose(np.matmul(M2,np.transpose(points3d)))\\n \\n # condense to two-dimensional coordinates\\n tf_points = np.zeros([num_points,2])\\n tf_points[:,0] = tf_points3d[:,0]/tf_points3d[:,2]\\n tf_points[:,1] = tf_points3d[:,1]/tf_points3d[:,2]\\n \\n object_list[i].all_gps = tf_points\\n \\n return object_list\",\n \"def project_long_lag_coord_into_cartesian(list_lat_long):\\n # setup your projections\\n # crs_wgs = proj.Proj(init='epsg:4326') # assuming you're using WGS84 geographic\\n crs_wgs = proj.Proj(init='epsg:3857') # assuming you're using WGS3857 geographic\\n crs_bng = proj.Proj(init='epsg:2039') # use a locally appropriate projected CRS\\n\\n return [(proj.transform(crs_wgs, crs_bng, lat_long[LONGITUDE], lat_long[LATITUDE])) for lat_long in list_lat_long]\",\n \"def getProjectedShapes(shapes, xmin, xmax, ymin, ymax):\\n latmiddle = ymin + (ymax-ymin)/2.0\\n lonmiddle = xmin + (xmax-xmin)/2.0\\n projstr = '+proj=ortho +datum=WGS84 +lat_0=%.4f +lon_0=%.4f +x_0=0.0 +y_0=0.0' % (latmiddle, lonmiddle)\\n proj = pyproj.Proj(projparams=projstr)\\n project = partial(\\n pyproj.transform,\\n pyproj.Proj(proj='latlong', datum='WGS84'),\\n proj)\\n\\n pshapes = []\\n for tshape in shapes:\\n if tshape['geometry']['type'] == 'Polygon':\\n pshapegeo = shape(tshape['geometry'])\\n else:\\n pshapegeo = shape(tshape['geometry'])\\n pshape = transform(project, pshapegeo)\\n pshapes.append(pshape) # assuming here that these are simple polygons\\n\\n return (pshapes, proj)\",\n \"def transform(self,points):\\n new_points = []\\n for p in points:\\n new_coordinates=p.coordinates\\n new_coordinates = [(new_coordinates[i] - self.min_coordinate[i]) /\\n (self.max_coordinate[i]-self.min_coordinate[i]) for i in range(len(p.coordinates))]\\n new_points.append(Point(p.name, new_coordinates, p.label))\\n return new_points\",\n \"def ll2xyz(lon_pt,lat_pt):\\n\\n xPt = np.cos(lat_pt) * np.cos(lon_pt)\\n yPt = np.cos(lat_pt) * np.sin(lon_pt)\\n zPt = np.sin(lat_pt)\\n return [xPt,yPt,zPt]\",\n \"def grid_coords(self):\\n return [(x, y) for y in range(self.height) for x in range(self.width)]\",\n \"def coordinate_pairs(lat_axis, lon_axis):\\n \\n lon_mesh, lat_mesh = numpy.meshgrid(lon_axis, lat_axis) # This is the correct order\\n \\n return lat_mesh.flatten(), lon_mesh.flatten()\",\n \"def coordinates_to_imgpts(x, y):\\n pts = np.array([np.flipud(np.transpose(np.vstack([x, y])))])\\n return pts\",\n \"def cylinders_base_to_pixelcoord(cylinder_list: list, volume_shape: (int, int, int)):\\n cylinders_px_list = []\\n for point1, point2, radius in cylinder_list:\\n p1 = phantom_base.scale_point_to_pixelcoord(point1, volume_shape)\\n p2 = phantom_base.scale_point_to_pixelcoord(point2, volume_shape)\\n r = phantom_base.scale_radius_to_pixelcoord(radius, volume_shape)\\n cylinders_px_list.append((p1, p2, r))\\n return cylinders_px_list\",\n \"def coordinates(self, mask):\\n y,x = mask.nonzero()\\n return list(zip(x,y))\",\n \"def getProjections(self): \\n x, y, z = self.XYZCoordinate\\n origin = self.SkeletonPoints[0]\\n self.coorOrigin = origin\\n self.XYProjections = [GeometryToolBox.projected_point(p, origin, x, y) for p in self.SkeletonPoints]\\n self.XZProjections = [GeometryToolBox.projected_point(p, origin, x, z) for p in self.SkeletonPoints]\",\n \"def get_geom_center(coordlist):\\n return sum(coordlist) / len(coordlist)\",\n \"def positions(r):\\n\\n X = []\\n Y = []\\n\\n leftx = -r*(nx - 1) / 2\\n topy = -r*(ny - 1) / 2\\n\\n for i in range(0, nx):\\n for j in range(0, ny):\\n X.append(leftx + r * i)\\n Y.append(topy + r * j)\\n\\n return (X, Y)\",\n \"def _convert_sky_coords(self):\\n parsed_angles = [(x, y)\\n for x, y in zip(self.coord[:-1:2], self.coord[1::2])\\n if (isinstance(x, coordinates.Angle) and isinstance(y, coordinates.Angle))\\n ]\\n frame = coordinates.frame_transform_graph.lookup_name(self.coordsys)\\n\\n lon, lat = zip(*parsed_angles)\\n if hasattr(lon, '__len__') and hasattr(lat, '__len__') and len(lon) == 1 and len(lat) == 1:\\n # force entries to be scalar if they are length-1\\n lon, lat = u.Quantity(lon[0]), u.Quantity(lat[0])\\n else:\\n # otherwise, they are vector quantities\\n lon, lat = u.Quantity(lon), u.Quantity(lat)\\n sphcoords = coordinates.UnitSphericalRepresentation(lon, lat)\\n coords = [SkyCoord(frame(sphcoords))]\\n\\n if self.region_type != 'polygon':\\n coords += self.coord[len(coords * 2):]\\n\\n return coords\",\n \"def generateBallGCoordinateList():\\r\\n l=[]\\r\\n # x 6 & 21\\r\\n for i in range(2,27):\\r\\n l.append((6,i))\\r\\n l.append((21,i))\\r\\n \\r\\n for i in range(1,27):\\r\\n # y 1\\r\\n if i not in [13,14]:\\r\\n l.append((i,1))\\r\\n # y 5 & 29\\r\\n if i not in [6,21]:\\r\\n l.append((i,5))\\r\\n l.append((i,29))\\r\\n # y 8 & 26 & 20 & 23\\r\\n if i not in [6,13,14,21]:\\r\\n l.append((i,20))\\r\\n if i not in [1,4,5,22,23,26]:\\r\\n l.append((i,23))\\r\\n if i not in [7,8,19,20]:\\r\\n l.append((i,8))\\r\\n l.append((i,26))\\r\\n # y 2-4\\r\\n for i in range(2,5):\\r\\n if i!=3:\\r\\n l.append((1,i))\\r\\n l.append((26,i))\\r\\n l.append((12,i))\\r\\n l.append((15,i))\\r\\n # y 6 % 7\\r\\n for i in [1,9,18,26]:\\r\\n l.append((i,6))\\r\\n l.append((i,7))\\r\\n # y 21 &22\\r\\n for i in [1,12,15,26]:\\r\\n l.append((i,21))\\r\\n l.append((i,22))\\r\\n # y 24 & 25\\r\\n for i in [3,9,18,24]:\\r\\n l.append((i,24))\\r\\n l.append((i,25))\\r\\n # y 27 & 28\\r\\n for i in [1,12,15,26]:\\r\\n l.append((i,27))\\r\\n l.append((i,28))\\r\\n return l\",\n \"def map_coordinates(self,geometry):\\n\\t\\tg = self.geomatrix\\n\\t\\tdef project_coord(x,y,z=None):\\n\\t\\t\\tx = g[0] + g[1] * x + g[2] * y\\n\\t\\t\\ty = g[3] + g[4] * x + g[5] * y\\n\\t\\t\\tif z is None:\\n\\t\\t\\t\\treturn x,y\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn x,y,z\\n\\t\\treturn transform(project_coord, geometry)\",\n \"def coordinates(self):\",\n \"def center_point_to_coordinates(bbox):\\n y, x, h, w = tf.split(bbox, 4, -1)\\n h2, w2 = tf.cast(h / 2, h.dtype), tf.cast(w / 2, w.dtype)\\n return tf.concat([y - h2, x - w2, y + h2, x + w2], -1)\",\n \"def make_xy(N, origin=None):\\r\\n if type(N) == int :\\r\\n ny, nx = N, N\\r\\n elif len(N) == 2 :\\r\\n ny, nx = N \\r\\n elif len(N) == 1 and (type(N) == tuple or type(N) == list):\\r\\n ny = N[0] \\r\\n nx = 0\\r\\n if ny != 0 and nx != 0 :\\r\\n x, y = np.meshgrid(np.arange(nx), np.arange(ny))\\r\\n if (origin is None) or (origin == 'centre') :\\r\\n origin_x, origin_y = nx // 2 - 1, ny // 2 - 1\\r\\n x -= origin_x\\r\\n y -= origin_y\\r\\n elif origin[0] == 0 and (origin[1] == 0):\\r\\n x = ((x + nx // 2 - 1) % nx) - (nx // 2 - 1)\\r\\n y = ((y + ny // 2 - 1) % ny) - (ny // 2 - 1)\\r\\n return x, y\\r\\n elif ny != 0 and nx == 0 :\\r\\n y = np.arange(ny)\\r\\n if (origin is None) or (origin == 'centre') :\\r\\n origin_y = ny // 2 - 1\\r\\n y -= origin_y\\r\\n elif origin[0] == 0 :\\r\\n y = ((y + ny // 2 - 1) % ny) - (ny // 2 - 1)\\r\\n return y\",\n \"def test_xyz_to_coords_list(self):\\n xyz_dict = {'symbols': ('O', 'N', 'C', 'H', 'H'),\\n 'isotopes': (16, 14, 12, 1, 1),\\n 'coords': ((1.1746411, -0.15309781, 0.0),\\n (0.06304988, 0.35149648, 0.0),\\n (-1.12708952, -0.11333971, 0.0),\\n (-1.93800144, 0.60171738, 0.0),\\n (-1.29769464, -1.18742971, 0.0))}\\n coords = converter.xyz_to_coords_list(xyz_dict)\\n expected_coords = [[1.1746411, -0.15309781, 0.0],\\n [0.06304988, 0.35149648, 0.0],\\n [-1.12708952, -0.11333971, 0.0],\\n [-1.93800144, 0.60171738, 0.0],\\n [-1.29769464, -1.18742971, 0.0]]\\n self.assertEqual(coords, expected_coords)\",\n \"def cartesian_to_geographical(coordinate_triples):\\n if len(coordinate_triples.shape) == 1:\\n x = coordinate_triples[0]\\n y = coordinate_triples[1]\\n z = coordinate_triples[2]\\n elif len(coordinate_triples.shape) == 2:\\n assert coordinate_triples.shape[1] == 3\\n x = coordinate_triples[:, 0]\\n y = coordinate_triples[:, 1]\\n z = coordinate_triples[:, 2]\\n radius = np.sqrt(x**2 + y**2 + z**2)\\n longitudes = np.arctan2(y, x)\\n latitudes = np.arcsin(z/radius)\\n return (latitudes, longitudes)\",\n \"def _project_2d(self, activations: np.ndarray) -> List[np.ndarray]:\\n projections = [[] for _ in range(self.n_components)]\\n for activation in activations:\\n flat = activation.reshape(activation.shape[0], -1).transpose()\\n flat = flat - flat.mean(axis=0)\\n U, S, VT = np.linalg.svd(flat, full_matrices=True)\\n for i in range(self.n_components):\\n component_i = flat @ VT[i, :]\\n component_i = component_i.reshape(activation.shape[1:])\\n projections[i].append(component_i)\\n return [np.array(elem, dtype=np.float32) for elem in projections]\",\n \"def _pixel_coordinates(nx, ny, mode):\\n if mode == \\\"center\\\":\\n x = np.linspace(0.5, nx - 0.5, num=nx)\\n y = np.linspace(0.5, ny - 0.5, num=ny)\\n\\n if mode == \\\"edge\\\":\\n x = np.linspace(0, nx, num=nx + 1)\\n y = np.linspace(0, ny, num=ny + 1)\\n\\n X, Y = np.meshgrid(x, y)\\n coordinates = np.stack((X, Y), axis=-1)\\n\\n return coordinates\",\n \"def get_coordinates(bounding_box, bounding_box_type=''):\\n coordinates = []\\n if bounding_box_type == \\\"box\\\":\\n coordinates.append([bounding_box[1], bounding_box[0]])\\n coordinates.append([bounding_box[1], bounding_box[2]])\\n coordinates.append([bounding_box[3], bounding_box[2]])\\n coordinates.append([bounding_box[3], bounding_box[0]])\\n coordinates.append([bounding_box[1], bounding_box[0]])\\n return coordinates\",\n \"def vxl2mni(coordlist):\\n mni_list = []\\n for item in coordlist:\\n temp = (90 - 2 * item[0],\\n -126 + 2 * item[1],\\n -72 + 2 * item[2])\\n mni_list.append(temp)\\n return mni_list\",\n \"def convert(points):\\n distance = []\\n for i in points:\\n x = int(i[0])\\n y = int(i[1])\\n distance.append([x,y])\\n return distance\",\n \"def find_obstacle_loc(self, obstacle_list):\\n\\n x_obst = []\\n y_obst = []\\n #x_obst_append = x_obst.append\\n #y_obst_append = y_obst.append\\n locs = []\\n\\n for x in obstacle_list:\\n if x < self.width:\\n x_obst.append(x*self.resolution + self.resolution/2)\\n else:\\n x_obst.append((x % self.width)*self.resolution + self.resolution/2)\\n\\n for y in obstacle_list:\\n y_obst.append((y/self.width)*self.resolution + self.resolution/2)\\n\\n locs = map(lambda x: x, zip(x_obst, y_obst))\\n\\n return(locs)\",\n \"def get_coords(self):\\n return [self.x,self.y,self.w,self.h]\",\n \"def to_point_form(boxes):\\n center_x, center_y = boxes[:, 0], boxes[:, 1]\\n width, height = boxes[:, 2], boxes[:, 3]\\n x_min = center_x - (width / 2.)\\n x_max = center_x + (width / 2.)\\n y_min = center_y - (height / 2.)\\n y_max = center_y + (height / 2.)\\n return np.concatenate([x_min[:, None], y_min[:, None],\\n x_max[:, None], y_max[:, None]], axis=1)\",\n \"def get_node_coordinates(nodes) :\\r\\n\\r\\n coords = [] #The list of coordinates\\r\\n\\r\\n for node in nodes :\\r\\n coords.append(node.coords)\\r\\n\\r\\n return coords\",\n \"def project_pos(lats, lons, map_center = False):\\n pos = [(lat,-lon) for lat,lon in zip(lats,lons)]\\n if map_center:\\n loncenter = map_center[0]\\n latcenter = map_center[1]\\n else:\\n loncenter = np.mean(list(lats.values()))\\n latcenter = -1* np.mean(list(lons.values()))\\n local_azimuthal_projection = \\\"+proj=aeqd +R=6371000 +units=m +lat_0={} +lon_0={}\\\".format(latcenter, loncenter)\\n # Use transformer: https://gis.stackexchange.com/questions/127427/transforming-shapely-polygon-and-multipolygon-objects\\n wgs84_to_aeqd = pyproj.Transformer.from_proj(\\n pyproj.Proj(\\\"+proj=longlat +datum=WGS84 +no_defs\\\"),\\n pyproj.Proj(local_azimuthal_projection))\\n pos_transformed = [(ops.transform(wgs84_to_aeqd.transform, Point(latlon)).coords)[0] for latlon in pos]\\n return pos_transformed, (loncenter,latcenter)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7183775","0.6618891","0.6581095","0.652824","0.64622325","0.6417996","0.63773674","0.6365129","0.63329947","0.63210684","0.6282502","0.626608","0.6254573","0.6216185","0.61284137","0.61090726","0.6102427","0.6072899","0.60686827","0.6067336","0.6059041","0.5985969","0.5985969","0.5983431","0.59789747","0.5978391","0.59718406","0.5962131","0.59602845","0.5959649","0.5945613","0.5942448","0.5936995","0.5928471","0.59270704","0.59225976","0.5920995","0.591684","0.5908751","0.5908134","0.5904989","0.5892201","0.58918476","0.5888472","0.5859997","0.58585054","0.5834202","0.5833408","0.5829178","0.58013463","0.5795409","0.5792342","0.5790839","0.5783882","0.57808524","0.57700515","0.5763813","0.5761219","0.5751151","0.57466006","0.5746249","0.57419413","0.57398385","0.573525","0.57316107","0.5730136","0.57299197","0.5729061","0.5723181","0.5723158","0.572252","0.5721863","0.57206184","0.5716418","0.5707316","0.56937975","0.5689146","0.56757563","0.5674854","0.5666532","0.5665539","0.5661647","0.56538105","0.5650991","0.5643106","0.56382364","0.5634803","0.5634473","0.5627912","0.56196225","0.5611759","0.56075317","0.5603086","0.56025666","0.560113","0.5592616","0.5582682","0.55806166","0.55726033","0.5569078"],"string":"[\n \"0.7183775\",\n \"0.6618891\",\n \"0.6581095\",\n \"0.652824\",\n \"0.64622325\",\n \"0.6417996\",\n \"0.63773674\",\n \"0.6365129\",\n \"0.63329947\",\n \"0.63210684\",\n \"0.6282502\",\n \"0.626608\",\n \"0.6254573\",\n \"0.6216185\",\n \"0.61284137\",\n \"0.61090726\",\n \"0.6102427\",\n \"0.6072899\",\n \"0.60686827\",\n \"0.6067336\",\n \"0.6059041\",\n \"0.5985969\",\n \"0.5985969\",\n \"0.5983431\",\n \"0.59789747\",\n \"0.5978391\",\n \"0.59718406\",\n \"0.5962131\",\n \"0.59602845\",\n \"0.5959649\",\n \"0.5945613\",\n \"0.5942448\",\n \"0.5936995\",\n \"0.5928471\",\n \"0.59270704\",\n \"0.59225976\",\n \"0.5920995\",\n \"0.591684\",\n \"0.5908751\",\n \"0.5908134\",\n \"0.5904989\",\n \"0.5892201\",\n \"0.58918476\",\n \"0.5888472\",\n \"0.5859997\",\n \"0.58585054\",\n \"0.5834202\",\n \"0.5833408\",\n \"0.5829178\",\n \"0.58013463\",\n \"0.5795409\",\n \"0.5792342\",\n \"0.5790839\",\n \"0.5783882\",\n \"0.57808524\",\n \"0.57700515\",\n \"0.5763813\",\n \"0.5761219\",\n \"0.5751151\",\n \"0.57466006\",\n \"0.5746249\",\n \"0.57419413\",\n \"0.57398385\",\n \"0.573525\",\n \"0.57316107\",\n \"0.5730136\",\n \"0.57299197\",\n \"0.5729061\",\n \"0.5723181\",\n \"0.5723158\",\n \"0.572252\",\n \"0.5721863\",\n \"0.57206184\",\n \"0.5716418\",\n \"0.5707316\",\n \"0.56937975\",\n \"0.5689146\",\n \"0.56757563\",\n \"0.5674854\",\n \"0.5666532\",\n \"0.5665539\",\n \"0.5661647\",\n \"0.56538105\",\n \"0.5650991\",\n \"0.5643106\",\n \"0.56382364\",\n \"0.5634803\",\n \"0.5634473\",\n \"0.5627912\",\n \"0.56196225\",\n \"0.5611759\",\n \"0.56075317\",\n \"0.5603086\",\n \"0.56025666\",\n \"0.560113\",\n \"0.5592616\",\n \"0.5582682\",\n \"0.55806166\",\n \"0.55726033\",\n \"0.5569078\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5870924"},"document_rank":{"kind":"string","value":"44"}}},{"rowIdx":11087,"cells":{"query":{"kind":"string","value":"Calculates an average vector over a kernel"},"document":{"kind":"string","value":"def averaging(self, value: int):\n self._averaging = value\n\n self.events.averaging()\n self._update_avg()\n\n self.refresh()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def avg(u: np.ndarray, v: np.ndarray) -> np.ndarray:\n \n return (u + v) / 2.0","def kernels_bayes_average(g_y, w):\n return np.dot(g_y, w)","def get_input_vector(img):\n parts = partition_matrix(blurmap(img), 10)\n return numpy.array([numpy.mean(part) for part in parts],\n dtype=numpy.float32)","def generate_avg_vector(self, data):\r\n doc=nlp(data)\r\n data_vector = [token.vector for token in doc]\r\n mean_vector = np.mean(data_vector, axis=0)\r\n return mean_vector","def avg(vector):\n if len(vector) == 0:\n return 0\n return sum(vector) / len(vector)","def average(img, size=3):\n \n size = int(size)\n kernel = np.ones((size,size)) / float(size**2)\n\n return ndi.convolve(img,kernel)","def entity_average(hidden_output, e_mask):\n e_mask_unsqueeze = e_mask.unsqueeze(1) # [b, 1, j-i+1]\n length_tensor = (e_mask != 0).sum(\n dim=1).unsqueeze(1) # [batch_size, 1]\n\n # [b, 1, j-i+1] * [b, j-i+1, dim] = [b, 1, dim] -> [b, dim]\n sum_vector = torch.bmm(e_mask_unsqueeze.float(),\n hidden_output).squeeze(1)\n avg_vector = sum_vector.float() / length_tensor.float() # broadcasting\n return avg_vector","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def entity_average(hidden_output, e_mask):\n\n e_mask_unsqueeze = e_mask.unsqueeze(1) # [b, 1, j-i+1]\n length_tensor = (e_mask != 0).sum(dim=1).unsqueeze(1) # [batch_size, 1]\n\n # [b, 1, j-i+1] * [b, j-i+1, dim] = [b, 1, dim] -> [b, dim]\n sum_vector = torch.bmm(e_mask_unsqueeze.float(), hidden_output).squeeze(1)\n avg_vector = sum_vector.float() / length_tensor.float() # broadcasting\n return avg_vector","def apply(self, grid, xs, ys):\n\n\t # Cropping the kernel according to the pixel grid.\n\t\tmodel = self.model[ys, xs]\n\n\t\tconvolution = grid * model\n\n\t\t# Adding up all the kernel values\n\t\tdivisor = max(np.sum(model, axis=(0,1)), 1)\n\n\t\tavg = np.sum(convolution, axis = (0,1)) / divisor\n\t\t\n\t\treturn int(avg)","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1 / n, vector_sum(vectors))","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1 / n, vector_sum(vectors))","def compute_average(vec_list):\r\n return np.sum(vec_list, axis = 0)/len(vec_list)","def average_frobenius(kernels, num_runs):\n norms = []\n for i in range(len(kernels)): \n norms.append(tf.norm(kernels[i], 'euclidean'))\n \n return np.mean(norms)","def average(data):\n return np.average(data)","def _kernel(self, bw, X, x):\n return (1.0 / np.sqrt(2 * np.pi) / bw) * np.exp(\n -((X - x) ** 2) / (bw ** 2 * 2.0)\n )","def calc_mean(sig):\n # m = mean(sig)\n return np.mean(sig)","def average(cls, vectors):\n return cls.sum(vectors) / len(vectors)","def vector_mean(vectors: List[Vector]) -> Vector:\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def vector_mean(vectors: List[Vector]) -> Vector:\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def d_mean(x, y):\n return (x + y) / 2","def average(self):\n s = self.sum()\n flat_shape = self.flatten_shape(self.shape)\n num_of_elements = fct.reduce(opr.mul, flat_shape, 1)\n average = s / num_of_elements\n return average","def vector_mean(vectors: List[Vector]) -> Vector:\n n = len(vectors)\n\n return scalar_multiply(1/n, vector_sum(vectors))","def _compute_mean(index, M, R, rake):\r\n mean = (a1[index] + _compute_linear_magnitude_term(index, M) + _compute_quadratic_magnitude_term(index, M) +\r\n _compute_logarithmic_distance_term(index, M, R) + _compute_faulting_style_term(index, rake))\r\n\r\n return mean","def _compute_mean(index, M, R, rake):\r\n mean = (a1[index] + _compute_linear_magnitude_term(index, M) + _compute_quadratic_magnitude_term(index, M) +\r\n _compute_logarithmic_distance_term(index, M, R) + _compute_faulting_style_term(index, rake))\r\n\r\n return mean","def _get_average(self):\n norm = 1.0\n for pos, idx in enumerate(self.idx):\n norm *= (self.high[pos] - self.low[pos])\n return 1.0/norm","def avgX(self):\n return np.mean(self.getx())","def manual_mean(arr):\n my_sum = 0\n for i in range(0, arr.shape[0]):\n for j in range(0, arr.shape[1]):\n my_sum += arr[i,j]\n return my_sum / arr.size","def vector_weighted_average(vf, weights):\n weights_sum = weights.sum()\n y_average = (vf[:,:,0] * weights).sum() / weights_sum\n x_average = (vf[:,:,1] * weights).sum() / weights_sum\n return np.array([y_average, x_average])","def avg(x, y):\n return (x + y)/2","def average_H(eigval_col, eigvec_col):\n nH = len(eigvec_col)\n dimen = eigval_col.shape[1]\n H_avg = np.zeros((dimen, dimen))\n for iH in range(nH):\n H = (eigvec_col[iH] * eigval_col[iH][np.newaxis, :]) @ eigvec_col[iH].T\n H_avg += H\n H_avg /= nH\n eva_avg, evc_avg = np.linalg.eigh(H_avg)\n return H_avg, eva_avg, evc_avg","def mean(v):\n return sum(v)/len(v)","def get_channel_average_from_batch(batch):\n # YOUR CODE HERE\n return( mx.nd.mean(batch, axis=1, exclude=True) )\n raise NotImplementedError()","def boxcar_average(D,Y,N):\n boxY = np.convolve(Y, np.ones((N,))/N, mode='valid')\n edge = (len(Y)-len(boxY))/2 \n boxD = D[math.floor(edge):-math.ceil(edge)]\n \n return (boxD, boxY)","def average_vectors(vectors_list):\n return np.mean(vectors_list, axis=0)","def test_gauss_kernel():\n\n gauss = gauss_kernel(2, 5)\n\n assert gauss.shape == (5, 5)\n assert gauss[2, 2] == 0.039788735772973836","def _update_avg(self):\n if self._data_type == 'coords':\n # default averaging is supported only for 'matrix' dataTypes\n return\n elif self._data_type == 'image':\n\n x, y = self._averaging, self._averaging\n\n if (x,y) == (1, 1):\n self.vectors = self._original_data\n # calling original data\n return\n\n tempdat = self._original_data\n range_x = tempdat.shape[0]\n range_y = tempdat.shape[1]\n x_offset = int((x - 1) / 2)\n y_offset = int((y - 1) / 2)\n\n kernel = np.ones(shape=(x, y)) / (x*y)\n\n output_mat = np.zeros_like(tempdat)\n output_mat_x = signal.convolve2d(tempdat[:, :, 0], kernel,\n mode='same', boundary='wrap')\n output_mat_y = signal.convolve2d(tempdat[:, :, 1], kernel,\n mode='same', boundary='wrap')\n\n output_mat[:, :, 0] = output_mat_x\n output_mat[:, :, 1] = output_mat_y\n\n self.vectors = (output_mat[x_offset:range_x-x_offset:x,\n y_offset:range_y-y_offset:y])","def _kernel(self, point, observation, bandwidth):\n denom = bandwidth * ((2*math.pi)**.5) \n num = math.exp(-0.5 * ((point-observation)/bandwidth)**2)\n return num/denom","def class_average(X):\n\t# compute average row vector\n\tmean_vector = np.mean(X, axis = 0)\n\treturn(mean_vector)","def makeGaussianKernel(sigma: float) -> np.ndarray:\n\n # Your code here.\n kernel_size = 8*sigma+1\n kernel = np.zeros([kernel_size,kernel_size], dtype=float)\n center = kernel_size//2\n \n \n s = 2*(sigma**2)\n sum_val = 0\n for i in range(0,kernel_size):\n for j in range(0,kernel_size):\n x = i-center\n y = j-center\n kernel[i,j] = np.exp(-(x**2+y**2) / s)\n sum_val += kernel[i,j]\n #/(np.pi * s)\n sum_val = 1/sum_val\n print(\"here is the kernel\", kernel*sum_val)\n return kernel*sum_val","def calccalmean(self,blk):\n calind=self.getcalind(blk)\n x=self.spec[calind,:]\n return np.nanmean(x,axis=0)","def compute(self, node, input_vals):\r\n assert len(input_vals) == 1\r\n if node.const_attr!=None:\r\n return np.array(np.mean(input_vals[0], node.const_attr))\r\n else:\r\n return np.array(np.mean(input_vals[0]))","def call(self, inputs, mask=None):\n weights = K.expand_dims(inputs[0])\n vectors = inputs[1]\n wtd_vectors = weights * vectors\n wtd_avg = K.sum(wtd_vectors, axis=-2)\n return wtd_avg","def calculate_posterior_mean_var(self, context_arm_grid):\n # all points played so far\n data = self.played_points_hist[:self.t]\n # kernels between all possible context-arms and the previous rounds\n kernel_vectors = self.kernel_fn(context_arm_grid, data, self.best_ard_params)\n # kernel matrix of data\n kernel_matrix = self.kernel_fn(data, data, self.best_ard_params)\n c_matrix = kernel_matrix + (self.noise_sigma ** 2) * np.eye(data.shape[0])\n c_matrix_inv = np.linalg.inv(c_matrix)\n mu_ests_vector = np.matmul(kernel_vectors, np.matmul(c_matrix_inv, self.rews_hist[:self.t])) # mean estimation\n sigma_ests_first_term = np.diag(self.kernel_fn(context_arm_grid, context_arm_grid, self.best_ard_params))\n sigma_ests_second_term = np.diag(np.matmul(kernel_vectors, np.matmul(c_matrix_inv, kernel_vectors.T)))\n sigma_ests_vector = sigma_ests_first_term - sigma_ests_second_term # variance estimation\n return mu_ests_vector, sigma_ests_vector","def average(self):\n return np.mean(self.buf[:self._size], axis=0)","def conv_helper(fragment, kernel):\r\n\r\n f_row, f_col = fragment.shape\r\n k_row, k_col = kernel.shape \r\n result = 0.0\r\n for row in range(f_row):\r\n for col in range(f_col):\r\n result += fragment[row,col] * kernel[row,col]\r\n return result","def _global_avg_pool(x):\n with tf.name_scope('global_avg_pool'):\n assert x.get_shape().ndims == 4\n return tf.reduce_mean(x, [1, 2])","def normalise_kernel(K_temp):\n\n nb_item = K_temp.shape[0]\n K_norm = np.zeros((nb_item, nb_item))\n for i in range(nb_item):\n for j in range(i, nb_item):\n K_norm[i, j] = K_temp[i, j] / math.sqrt(K_temp[i, i] * K_temp[j, j])\n K_norm[j, i] = K_norm[i, j]\n\n return K_norm","def mean(self):\n return np.average(self.particles, weights=self.weights, axis=0)","def forward_avg(array_in):\n return (array_in[:-1] + array_in[1:]) * 0.5","def compute_means(runtimes):\n# tmp = runtimes[kernel_name]\n tmp_ = [ (int(key), float(np.mean(val)))\n for key, val in runtimes.iteritems()\n ]\n return sort_fst(tmp_)","def aver_and_var(self):\n # assert not self.is_empty\n\n for axis in range(3):\n c1, c2 = self.bounds[axis]\n w = self.n_pix_partial[axis]\n aver = np.average(np.arange(c1, c2), weights=w)\n var = np.average(np.arange(c1, c2)**2, weights=w) - aver ** 2 # D = E(X^2) - (EX)^2\n yield aver, var","def _ave(self):\n return np.asarray(np.mean(self.model_estim.x, axis=0)).flatten()","def get_average_image_from_batch(batch):\n # YOUR CODE HERE\n return( mx.nd.mean(batch, axis=0) )\n raise NotImplementedError()","def _ave(self):\n\n return np.asarray(np.mean(self.model_estim.x, axis=0)).flatten()","def mean(arr) -> float:\n return sum(arr) / len(arr)","def ensemble_mean(self):\n return self.mean(dim='mem')","def get_mean(self):\r\n for i in range(1,len(self.data[0])):\r\n self.prom.append(np.mean(self.data[:,i]))","def mean(self):\n return self.vmean","def m_average_fun(self, dx=df.dx):\n\n mx = df.assemble(\n self.material._Ms_dg * df.dot(self._m, df.Constant([1, 0, 0])) * dx)\n my = df.assemble(\n self.material._Ms_dg * df.dot(self._m, df.Constant([0, 1, 0])) * dx)\n mz = df.assemble(\n self.material._Ms_dg * df.dot(self._m, df.Constant([0, 0, 1])) * dx)\n volume = df.assemble(self.material._Ms_dg * dx)\n\n return np.array([mx, my, mz]) / volume","def _kernel(self, x, y, t):\n return (self.C / (2 * np.pi * self.sigma_x * self.sigma_y * t)) * \\\n tf.exp(- self.beta * t - (tf.square(x)/tf.square(self.sigma_x) + tf.square(y)/tf.square(self.sigma_y)) / (2*t))","def Kernel(x, y):\n\n Result = (np.dot(x_train[x, :], x_train[y, :])+1)**5 # Polynomial\n #Result = (np.dot(x_train[x, :], x_train[y, :])+1) # Linear\n #Gaussian\n \"\"\"\n sigma = 1\n if np.ndim(x_train[x, :]) == 1 and np.ndim(x_train[y, :]) == 1:\n Result = np.exp(- (np.linalg.norm(x_train[x, :] - x_train[y, :], 2)) ** 2 / (2 * sigma ** 2))\n elif (np.ndim(x_train[x, :]) > 1 and np.ndim(x_train[y, :]) == 1) or (np.ndim(x_train[x, :]) == 1 and np.ndim(x_train[y, :]) > 1):\n Result = np.exp(- (np.linalg.norm(x_train[x, :] - x_train[y, :], 2, axis=1) ** 2) / (2 * sigma ** 2))\n elif np.ndim(x_train[x, :]) > 1 and np.ndim(x_train[y, :]) > 1:\n Result = np.exp(- (np.linalg.norm(x[:, np.newaxis] - y[np.newaxis, :], 2, axis=2) ** 2) / (2 * sigma ** 2))\n \"\"\"\n return Result","def metric_average(val, name, hvd):\n tensor = val.clone().detach()\n avg_tensor = hvd.allreduce(tensor, name=name)\n return avg_tensor.item()","def AllFreAverageV(self,):\n \t\tv_array = self.data\n \t\taaverage_v = np.average(v_array[:,1])\n \t\tprint('Whole frequency average group velocity:\\nVw=',aaverage_v/1000,'km/s')\n \t\treturn","def mean(vals):","def Kernel1(x, y):\n # Result = (np.dot(x, y.T)+1)**5\n Result = (x @ y.T + 1)**5\n # Sum = DotProduct(x, y)\n # Result = (Sum+1)**5\n \n return Result","def avg(a,b):\r\n return (a+b)/2","def dp_mean( data_vect, epsilon=1.0, delta=0.1 ):\n\n import numpy as np\n\n if ( any(data_vect < 0.0) or any(data_vect > 1.0) ):\n print('ERROR: Input vector should have bounded entries in [0,1].')\n return\n elif epsilon < 0.0:\n print('ERROR: Epsilon should be positive.')\n return\n elif delta < 0.0 or delta > 1.0:\n print('ERROR: Delta should be bounded in [0,1].')\n return\n else:\n\n n = len(data_vect)\n f = np.mean(data_vect)\n if delta == 0:\n noise = np.random.laplace(loc = 0, scale = 1/float(n*epsilon), size = (1,1))\n else:\n sigma = (1.0/(n*epsilon))*np.sqrt(2*np.log(1.25/delta))\n noise = np.random.normal(0.0, sigma, 1)\n f += noise\n\n return f","def running_mean(x, N):\n return np.convolve(x, np.ones((N,))/N, mode='valid')","def get_fiber_density_average():\n return Global_Module.global_fiber_density_with_average","def get_avg(self) -> float:\n if self._cur_elem_count < 1:\n return 0\n self._mtx.acquire()\n avg = self._sum / float(self._cur_elem_count)\n self._mtx.release()\n return avg","def _calc_dim_average(vectors_array):\n array = np.array(vectors_array)\n threshold = 0.001\n res = array.sum(axis=0, dtype='float32')\n valid_count = (array > threshold).sum(axis=0)\n valid_count[valid_count == 0] = 1\n res /= valid_count\n return res","def mean(X, shape):\n return np.real(X[0, 0]) / (shape[0] * shape[1])","def spatial_avg(self, input_layer):\n return tf.reduce_mean(input_layer, [2, 3], name='spatial_avg')","def average(self):\n return self.summation() / self.count()","def gate_average(recurrent_net, sample_input):\n length = sample_input.size\n score, select = np.zeros(length), np.zeros(length)\n for i in range(length):\n select[i], score[i] = recurrent_net.activate([sample_input[i]])\n select = sigmoid(select)\n return np.sum(select * score) / np.sum(select)","def computeMeans(X, idx, K):\n\tm, n = X.shape\n\tcentroids = np.zeros((K, n))\n\tcount = np.zeros(K)\n\n\tfor j in range(m):\n\t\tcentroids[int(idx[j])] += X[j]\n\n\tfor i in range(m):\n\t\tcount[int(idx[i])] += 1\n\n\treturn centroids / np.tile(count.reshape((K, 1)), n)","def compute(self): \r\n hist = self.confusion_matrix\r\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\r\n mean_iu = np.nanmean(iu)\r\n return mean_iu","def _compute_mean(self, C, mag, rjb, rake):\n mean = (C['a1'] +\n self._compute_linear_magnitude_term(C, mag) +\n self._compute_quadratic_magnitude_term(C, mag) +\n self._compute_logarithmic_distance_term(C, mag, rjb) +\n self._compute_faulting_style_term(C, rake))\n\n return mean","def gaussian_process_pointwise_variance(kernel, pred_samples, train_samples,\n nugget = 0):\n K_train = kernel(train_samples.T)\n # add small number to diagonal to ensure covariance matrix is\n # positive definite\n ntrain_samples = train_samples.shape[1]\n K_train[np.arange(ntrain_samples), np.arange(ntrain_samples)] += nugget\n k_pred = kernel(train_samples.T, pred_samples.T)\n L = np.linalg.cholesky(K_train)\n tmp = solve_triangular(L, k_pred, lower=True)\n variance = kernel.diag(pred_samples.T) - np.sum(tmp*tmp, axis=0)\n return variance","def gaussian_kernel(windowX, windowY, sigma):\n X,Y = createKernalWindowRanges(windowX, windowY, increment)\n \n gKernel = gaussianNormalised(X, 0, sigma) * gaussianNormalised(Y, 0, sigma)\n gSum = np.sum(np.abs(gKernel))\n \n if gSum == 0:\n print \"Warning gaussian_kernel:: Not normalising by sum of values, as sum = \" + str(gSum)\n return (gKernel)\n else:\n return (gKernel / gSum)","def apply_kernel(x, h, out=None):\n if out is None:\n out = np.zeros(x.shape[1])\n else:\n out.fill(0)\n\n for ind in range(len(h)):\n out += np.convolve(h[ind], x[ind])[:len(out)]\n\n return out","def build_mean_vector(X, Y_range):\r\n C = Y_range.size -1\r\n M = np.zeros((X.shape[0], C))\r\n for c in xrange(C):\r\n Xc = get_block_col(X, c, Y_range)\r\n M[:, c] = np.mean(Xc, axis=1)\r\n return M","def global_average(x, batch_lengths):\n\n # Loop over the clouds of the batch\n averaged_features = []\n i0 = 0\n for b_i, length in enumerate(batch_lengths):\n\n # Average features for each batch cloud\n averaged_features.append(torch.mean(x[i0:i0 + length], dim=0))\n\n # Increment for next cloud\n i0 += length\n\n # Average features in each batch\n return torch.stack(averaged_features)","def avg(arr):\n return sum(arr) / float(len(arr))","def ageometricmean (inarray,dimension=None,keepdims=0):\r\n inarray = N.array(inarray,N.float_)\r\n if dimension == None:\r\n inarray = N.ravel(inarray)\r\n size = len(inarray)\r\n mult = N.power(inarray,1.0/size)\r\n mult = N.multiply.reduce(mult)\r\n elif type(dimension) in [IntType,FloatType]:\r\n size = inarray.shape[dimension]\r\n mult = N.power(inarray,1.0/size)\r\n mult = N.multiply.reduce(mult,dimension)\r\n if keepdims == 1:\r\n shp = list(inarray.shape)\r\n shp[dimension] = 1\r\n sum = N.reshape(sum,shp)\r\n else: # must be a SEQUENCE of dims to average over\r\n dims = list(dimension)\r\n dims.sort()\r\n dims.reverse()\r\n size = N.array(N.multiply.reduce(N.take(inarray.shape,dims)),N.float_)\r\n mult = N.power(inarray,1.0/size)\r\n for dim in dims:\r\n mult = N.multiply.reduce(mult,dim)\r\n if keepdims == 1:\r\n shp = list(inarray.shape)\r\n for dim in dims:\r\n shp[dim] = 1\r\n mult = N.reshape(mult,shp)\r\n return mult","def _mean_attentive_vectors(self, x2, cosine_matrix):\n # (batch_size, x1_timesteps, x2_timesteps, 1)\n expanded_cosine_matrix = K.expand_dims(cosine_matrix, axis=-1)\n # (batch_size, 1, x2_timesteps, embedding_size)\n x2 = K.expand_dims(x2, axis=1)\n # (batch_size, x1_timesteps, embedding_size)\n weighted_sum = K.sum(expanded_cosine_matrix * x2, axis=2)\n # (batch_size, x1_timesteps, 1)\n sum_cosine = K.expand_dims(K.sum(cosine_matrix, axis=-1) + self.epsilon, axis=-1)\n # (batch_size, x1_timesteps, embedding_size)\n attentive_vector = weighted_sum / sum_cosine\n return attentive_vector","def mean(self):\n return self.k * self.theta","def evse(self, data, *args, **kwargs):\n darr = np.array(data)\n d = darr if len(darr.shape) == 1 else darr[0] / darr[1]\n return (d - self.evs(darr, *args, **kwargs))**2","def _mask_and_avg(values, padding_mask):\n\n dec_lens = tf.reduce_sum(padding_mask, axis=1) # shape batch_size. float32\n values_per_step = [v * padding_mask[:,dec_step] for dec_step,v in enumerate(values)]\n values_per_ex = sum(values_per_step)/dec_lens # shape (batch_size); normalized value for each batch member\n return tf.reduce_mean(values_per_ex) # overall average","def average(self, start, end):\n return self.integrate(start, end) / (end - start)","def har_mean(array):\n return ((sum([1/x for x in array]))**(-1))*len(array)","def _kernel_norm(self, vector, bandwidth):\n\n vector = (vector.T / vector.sum(1)).T\n vector = np.abs(self._kernel_method(vector, bandwidth))\n vector = (vector.T / vector.sum(1)).T\n return vector","def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):\n\n return _apply(_crank8.mean, _crank16.mean, image, selem, out=out,\n mask=mask, shift_x=shift_x, shift_y=shift_y)","def center_and_normalise_kernel(K_temp):\n\n K_temp = KernelCenterer().fit_transform(K_temp)\n nb_item = K_temp.shape[0]\n K_norm = np.zeros((nb_item, nb_item))\n for i in range(nb_item):\n for j in range(i, nb_item):\n K_norm[i, j] = K_temp[i, j] / math.sqrt(K_temp[i, i] * K_temp[j, j])\n K_norm[j, i] = K_norm[i, j]\n\n return K_norm","def gaus_kernel_calc(kernel_size):\n base_gaus_binom = np.array([[1], [1]])\n kernel = base_gaus_binom\n\n if kernel_size == 1:\n # If the kernel size is 1 we need a 2d array that keeps the image the same.\n kernel = np.array([[1]])\n kernel = scipy.signal.convolve2d(kernel, kernel.transpose())\n return kernel\n\n for i in range(kernel_size - 2):\n kernel = scipy.signal.convolve2d(kernel, base_gaus_binom)\n\n kernel = scipy.signal.convolve2d(kernel, kernel.transpose())\n return kernel/kernel.sum()","def mean(x):\n return sum(x) / len(x)","def mean(self):\n mean = sum(self.data)/self.size\n return mean","def average(self):\n return (self.current + self.last) / 2.0"],"string":"[\n \"def avg(u: np.ndarray, v: np.ndarray) -> np.ndarray:\\n \\n return (u + v) / 2.0\",\n \"def kernels_bayes_average(g_y, w):\\n return np.dot(g_y, w)\",\n \"def get_input_vector(img):\\n parts = partition_matrix(blurmap(img), 10)\\n return numpy.array([numpy.mean(part) for part in parts],\\n dtype=numpy.float32)\",\n \"def generate_avg_vector(self, data):\\r\\n doc=nlp(data)\\r\\n data_vector = [token.vector for token in doc]\\r\\n mean_vector = np.mean(data_vector, axis=0)\\r\\n return mean_vector\",\n \"def avg(vector):\\n if len(vector) == 0:\\n return 0\\n return sum(vector) / len(vector)\",\n \"def average(img, size=3):\\n \\n size = int(size)\\n kernel = np.ones((size,size)) / float(size**2)\\n\\n return ndi.convolve(img,kernel)\",\n \"def entity_average(hidden_output, e_mask):\\n e_mask_unsqueeze = e_mask.unsqueeze(1) # [b, 1, j-i+1]\\n length_tensor = (e_mask != 0).sum(\\n dim=1).unsqueeze(1) # [batch_size, 1]\\n\\n # [b, 1, j-i+1] * [b, j-i+1, dim] = [b, 1, dim] -> [b, dim]\\n sum_vector = torch.bmm(e_mask_unsqueeze.float(),\\n hidden_output).squeeze(1)\\n avg_vector = sum_vector.float() / length_tensor.float() # broadcasting\\n return avg_vector\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def entity_average(hidden_output, e_mask):\\n\\n e_mask_unsqueeze = e_mask.unsqueeze(1) # [b, 1, j-i+1]\\n length_tensor = (e_mask != 0).sum(dim=1).unsqueeze(1) # [batch_size, 1]\\n\\n # [b, 1, j-i+1] * [b, j-i+1, dim] = [b, 1, dim] -> [b, dim]\\n sum_vector = torch.bmm(e_mask_unsqueeze.float(), hidden_output).squeeze(1)\\n avg_vector = sum_vector.float() / length_tensor.float() # broadcasting\\n return avg_vector\",\n \"def apply(self, grid, xs, ys):\\n\\n\\t # Cropping the kernel according to the pixel grid.\\n\\t\\tmodel = self.model[ys, xs]\\n\\n\\t\\tconvolution = grid * model\\n\\n\\t\\t# Adding up all the kernel values\\n\\t\\tdivisor = max(np.sum(model, axis=(0,1)), 1)\\n\\n\\t\\tavg = np.sum(convolution, axis = (0,1)) / divisor\\n\\t\\t\\n\\t\\treturn int(avg)\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1 / n, vector_sum(vectors))\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1 / n, vector_sum(vectors))\",\n \"def compute_average(vec_list):\\r\\n return np.sum(vec_list, axis = 0)/len(vec_list)\",\n \"def average_frobenius(kernels, num_runs):\\n norms = []\\n for i in range(len(kernels)): \\n norms.append(tf.norm(kernels[i], 'euclidean'))\\n \\n return np.mean(norms)\",\n \"def average(data):\\n return np.average(data)\",\n \"def _kernel(self, bw, X, x):\\n return (1.0 / np.sqrt(2 * np.pi) / bw) * np.exp(\\n -((X - x) ** 2) / (bw ** 2 * 2.0)\\n )\",\n \"def calc_mean(sig):\\n # m = mean(sig)\\n return np.mean(sig)\",\n \"def average(cls, vectors):\\n return cls.sum(vectors) / len(vectors)\",\n \"def vector_mean(vectors: List[Vector]) -> Vector:\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def vector_mean(vectors: List[Vector]) -> Vector:\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def d_mean(x, y):\\n return (x + y) / 2\",\n \"def average(self):\\n s = self.sum()\\n flat_shape = self.flatten_shape(self.shape)\\n num_of_elements = fct.reduce(opr.mul, flat_shape, 1)\\n average = s / num_of_elements\\n return average\",\n \"def vector_mean(vectors: List[Vector]) -> Vector:\\n n = len(vectors)\\n\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def _compute_mean(index, M, R, rake):\\r\\n mean = (a1[index] + _compute_linear_magnitude_term(index, M) + _compute_quadratic_magnitude_term(index, M) +\\r\\n _compute_logarithmic_distance_term(index, M, R) + _compute_faulting_style_term(index, rake))\\r\\n\\r\\n return mean\",\n \"def _compute_mean(index, M, R, rake):\\r\\n mean = (a1[index] + _compute_linear_magnitude_term(index, M) + _compute_quadratic_magnitude_term(index, M) +\\r\\n _compute_logarithmic_distance_term(index, M, R) + _compute_faulting_style_term(index, rake))\\r\\n\\r\\n return mean\",\n \"def _get_average(self):\\n norm = 1.0\\n for pos, idx in enumerate(self.idx):\\n norm *= (self.high[pos] - self.low[pos])\\n return 1.0/norm\",\n \"def avgX(self):\\n return np.mean(self.getx())\",\n \"def manual_mean(arr):\\n my_sum = 0\\n for i in range(0, arr.shape[0]):\\n for j in range(0, arr.shape[1]):\\n my_sum += arr[i,j]\\n return my_sum / arr.size\",\n \"def vector_weighted_average(vf, weights):\\n weights_sum = weights.sum()\\n y_average = (vf[:,:,0] * weights).sum() / weights_sum\\n x_average = (vf[:,:,1] * weights).sum() / weights_sum\\n return np.array([y_average, x_average])\",\n \"def avg(x, y):\\n return (x + y)/2\",\n \"def average_H(eigval_col, eigvec_col):\\n nH = len(eigvec_col)\\n dimen = eigval_col.shape[1]\\n H_avg = np.zeros((dimen, dimen))\\n for iH in range(nH):\\n H = (eigvec_col[iH] * eigval_col[iH][np.newaxis, :]) @ eigvec_col[iH].T\\n H_avg += H\\n H_avg /= nH\\n eva_avg, evc_avg = np.linalg.eigh(H_avg)\\n return H_avg, eva_avg, evc_avg\",\n \"def mean(v):\\n return sum(v)/len(v)\",\n \"def get_channel_average_from_batch(batch):\\n # YOUR CODE HERE\\n return( mx.nd.mean(batch, axis=1, exclude=True) )\\n raise NotImplementedError()\",\n \"def boxcar_average(D,Y,N):\\n boxY = np.convolve(Y, np.ones((N,))/N, mode='valid')\\n edge = (len(Y)-len(boxY))/2 \\n boxD = D[math.floor(edge):-math.ceil(edge)]\\n \\n return (boxD, boxY)\",\n \"def average_vectors(vectors_list):\\n return np.mean(vectors_list, axis=0)\",\n \"def test_gauss_kernel():\\n\\n gauss = gauss_kernel(2, 5)\\n\\n assert gauss.shape == (5, 5)\\n assert gauss[2, 2] == 0.039788735772973836\",\n \"def _update_avg(self):\\n if self._data_type == 'coords':\\n # default averaging is supported only for 'matrix' dataTypes\\n return\\n elif self._data_type == 'image':\\n\\n x, y = self._averaging, self._averaging\\n\\n if (x,y) == (1, 1):\\n self.vectors = self._original_data\\n # calling original data\\n return\\n\\n tempdat = self._original_data\\n range_x = tempdat.shape[0]\\n range_y = tempdat.shape[1]\\n x_offset = int((x - 1) / 2)\\n y_offset = int((y - 1) / 2)\\n\\n kernel = np.ones(shape=(x, y)) / (x*y)\\n\\n output_mat = np.zeros_like(tempdat)\\n output_mat_x = signal.convolve2d(tempdat[:, :, 0], kernel,\\n mode='same', boundary='wrap')\\n output_mat_y = signal.convolve2d(tempdat[:, :, 1], kernel,\\n mode='same', boundary='wrap')\\n\\n output_mat[:, :, 0] = output_mat_x\\n output_mat[:, :, 1] = output_mat_y\\n\\n self.vectors = (output_mat[x_offset:range_x-x_offset:x,\\n y_offset:range_y-y_offset:y])\",\n \"def _kernel(self, point, observation, bandwidth):\\n denom = bandwidth * ((2*math.pi)**.5) \\n num = math.exp(-0.5 * ((point-observation)/bandwidth)**2)\\n return num/denom\",\n \"def class_average(X):\\n\\t# compute average row vector\\n\\tmean_vector = np.mean(X, axis = 0)\\n\\treturn(mean_vector)\",\n \"def makeGaussianKernel(sigma: float) -> np.ndarray:\\n\\n # Your code here.\\n kernel_size = 8*sigma+1\\n kernel = np.zeros([kernel_size,kernel_size], dtype=float)\\n center = kernel_size//2\\n \\n \\n s = 2*(sigma**2)\\n sum_val = 0\\n for i in range(0,kernel_size):\\n for j in range(0,kernel_size):\\n x = i-center\\n y = j-center\\n kernel[i,j] = np.exp(-(x**2+y**2) / s)\\n sum_val += kernel[i,j]\\n #/(np.pi * s)\\n sum_val = 1/sum_val\\n print(\\\"here is the kernel\\\", kernel*sum_val)\\n return kernel*sum_val\",\n \"def calccalmean(self,blk):\\n calind=self.getcalind(blk)\\n x=self.spec[calind,:]\\n return np.nanmean(x,axis=0)\",\n \"def compute(self, node, input_vals):\\r\\n assert len(input_vals) == 1\\r\\n if node.const_attr!=None:\\r\\n return np.array(np.mean(input_vals[0], node.const_attr))\\r\\n else:\\r\\n return np.array(np.mean(input_vals[0]))\",\n \"def call(self, inputs, mask=None):\\n weights = K.expand_dims(inputs[0])\\n vectors = inputs[1]\\n wtd_vectors = weights * vectors\\n wtd_avg = K.sum(wtd_vectors, axis=-2)\\n return wtd_avg\",\n \"def calculate_posterior_mean_var(self, context_arm_grid):\\n # all points played so far\\n data = self.played_points_hist[:self.t]\\n # kernels between all possible context-arms and the previous rounds\\n kernel_vectors = self.kernel_fn(context_arm_grid, data, self.best_ard_params)\\n # kernel matrix of data\\n kernel_matrix = self.kernel_fn(data, data, self.best_ard_params)\\n c_matrix = kernel_matrix + (self.noise_sigma ** 2) * np.eye(data.shape[0])\\n c_matrix_inv = np.linalg.inv(c_matrix)\\n mu_ests_vector = np.matmul(kernel_vectors, np.matmul(c_matrix_inv, self.rews_hist[:self.t])) # mean estimation\\n sigma_ests_first_term = np.diag(self.kernel_fn(context_arm_grid, context_arm_grid, self.best_ard_params))\\n sigma_ests_second_term = np.diag(np.matmul(kernel_vectors, np.matmul(c_matrix_inv, kernel_vectors.T)))\\n sigma_ests_vector = sigma_ests_first_term - sigma_ests_second_term # variance estimation\\n return mu_ests_vector, sigma_ests_vector\",\n \"def average(self):\\n return np.mean(self.buf[:self._size], axis=0)\",\n \"def conv_helper(fragment, kernel):\\r\\n\\r\\n f_row, f_col = fragment.shape\\r\\n k_row, k_col = kernel.shape \\r\\n result = 0.0\\r\\n for row in range(f_row):\\r\\n for col in range(f_col):\\r\\n result += fragment[row,col] * kernel[row,col]\\r\\n return result\",\n \"def _global_avg_pool(x):\\n with tf.name_scope('global_avg_pool'):\\n assert x.get_shape().ndims == 4\\n return tf.reduce_mean(x, [1, 2])\",\n \"def normalise_kernel(K_temp):\\n\\n nb_item = K_temp.shape[0]\\n K_norm = np.zeros((nb_item, nb_item))\\n for i in range(nb_item):\\n for j in range(i, nb_item):\\n K_norm[i, j] = K_temp[i, j] / math.sqrt(K_temp[i, i] * K_temp[j, j])\\n K_norm[j, i] = K_norm[i, j]\\n\\n return K_norm\",\n \"def mean(self):\\n return np.average(self.particles, weights=self.weights, axis=0)\",\n \"def forward_avg(array_in):\\n return (array_in[:-1] + array_in[1:]) * 0.5\",\n \"def compute_means(runtimes):\\n# tmp = runtimes[kernel_name]\\n tmp_ = [ (int(key), float(np.mean(val)))\\n for key, val in runtimes.iteritems()\\n ]\\n return sort_fst(tmp_)\",\n \"def aver_and_var(self):\\n # assert not self.is_empty\\n\\n for axis in range(3):\\n c1, c2 = self.bounds[axis]\\n w = self.n_pix_partial[axis]\\n aver = np.average(np.arange(c1, c2), weights=w)\\n var = np.average(np.arange(c1, c2)**2, weights=w) - aver ** 2 # D = E(X^2) - (EX)^2\\n yield aver, var\",\n \"def _ave(self):\\n return np.asarray(np.mean(self.model_estim.x, axis=0)).flatten()\",\n \"def get_average_image_from_batch(batch):\\n # YOUR CODE HERE\\n return( mx.nd.mean(batch, axis=0) )\\n raise NotImplementedError()\",\n \"def _ave(self):\\n\\n return np.asarray(np.mean(self.model_estim.x, axis=0)).flatten()\",\n \"def mean(arr) -> float:\\n return sum(arr) / len(arr)\",\n \"def ensemble_mean(self):\\n return self.mean(dim='mem')\",\n \"def get_mean(self):\\r\\n for i in range(1,len(self.data[0])):\\r\\n self.prom.append(np.mean(self.data[:,i]))\",\n \"def mean(self):\\n return self.vmean\",\n \"def m_average_fun(self, dx=df.dx):\\n\\n mx = df.assemble(\\n self.material._Ms_dg * df.dot(self._m, df.Constant([1, 0, 0])) * dx)\\n my = df.assemble(\\n self.material._Ms_dg * df.dot(self._m, df.Constant([0, 1, 0])) * dx)\\n mz = df.assemble(\\n self.material._Ms_dg * df.dot(self._m, df.Constant([0, 0, 1])) * dx)\\n volume = df.assemble(self.material._Ms_dg * dx)\\n\\n return np.array([mx, my, mz]) / volume\",\n \"def _kernel(self, x, y, t):\\n return (self.C / (2 * np.pi * self.sigma_x * self.sigma_y * t)) * \\\\\\n tf.exp(- self.beta * t - (tf.square(x)/tf.square(self.sigma_x) + tf.square(y)/tf.square(self.sigma_y)) / (2*t))\",\n \"def Kernel(x, y):\\n\\n Result = (np.dot(x_train[x, :], x_train[y, :])+1)**5 # Polynomial\\n #Result = (np.dot(x_train[x, :], x_train[y, :])+1) # Linear\\n #Gaussian\\n \\\"\\\"\\\"\\n sigma = 1\\n if np.ndim(x_train[x, :]) == 1 and np.ndim(x_train[y, :]) == 1:\\n Result = np.exp(- (np.linalg.norm(x_train[x, :] - x_train[y, :], 2)) ** 2 / (2 * sigma ** 2))\\n elif (np.ndim(x_train[x, :]) > 1 and np.ndim(x_train[y, :]) == 1) or (np.ndim(x_train[x, :]) == 1 and np.ndim(x_train[y, :]) > 1):\\n Result = np.exp(- (np.linalg.norm(x_train[x, :] - x_train[y, :], 2, axis=1) ** 2) / (2 * sigma ** 2))\\n elif np.ndim(x_train[x, :]) > 1 and np.ndim(x_train[y, :]) > 1:\\n Result = np.exp(- (np.linalg.norm(x[:, np.newaxis] - y[np.newaxis, :], 2, axis=2) ** 2) / (2 * sigma ** 2))\\n \\\"\\\"\\\"\\n return Result\",\n \"def metric_average(val, name, hvd):\\n tensor = val.clone().detach()\\n avg_tensor = hvd.allreduce(tensor, name=name)\\n return avg_tensor.item()\",\n \"def AllFreAverageV(self,):\\n \\t\\tv_array = self.data\\n \\t\\taaverage_v = np.average(v_array[:,1])\\n \\t\\tprint('Whole frequency average group velocity:\\\\nVw=',aaverage_v/1000,'km/s')\\n \\t\\treturn\",\n \"def mean(vals):\",\n \"def Kernel1(x, y):\\n # Result = (np.dot(x, y.T)+1)**5\\n Result = (x @ y.T + 1)**5\\n # Sum = DotProduct(x, y)\\n # Result = (Sum+1)**5\\n \\n return Result\",\n \"def avg(a,b):\\r\\n return (a+b)/2\",\n \"def dp_mean( data_vect, epsilon=1.0, delta=0.1 ):\\n\\n import numpy as np\\n\\n if ( any(data_vect < 0.0) or any(data_vect > 1.0) ):\\n print('ERROR: Input vector should have bounded entries in [0,1].')\\n return\\n elif epsilon < 0.0:\\n print('ERROR: Epsilon should be positive.')\\n return\\n elif delta < 0.0 or delta > 1.0:\\n print('ERROR: Delta should be bounded in [0,1].')\\n return\\n else:\\n\\n n = len(data_vect)\\n f = np.mean(data_vect)\\n if delta == 0:\\n noise = np.random.laplace(loc = 0, scale = 1/float(n*epsilon), size = (1,1))\\n else:\\n sigma = (1.0/(n*epsilon))*np.sqrt(2*np.log(1.25/delta))\\n noise = np.random.normal(0.0, sigma, 1)\\n f += noise\\n\\n return f\",\n \"def running_mean(x, N):\\n return np.convolve(x, np.ones((N,))/N, mode='valid')\",\n \"def get_fiber_density_average():\\n return Global_Module.global_fiber_density_with_average\",\n \"def get_avg(self) -> float:\\n if self._cur_elem_count < 1:\\n return 0\\n self._mtx.acquire()\\n avg = self._sum / float(self._cur_elem_count)\\n self._mtx.release()\\n return avg\",\n \"def _calc_dim_average(vectors_array):\\n array = np.array(vectors_array)\\n threshold = 0.001\\n res = array.sum(axis=0, dtype='float32')\\n valid_count = (array > threshold).sum(axis=0)\\n valid_count[valid_count == 0] = 1\\n res /= valid_count\\n return res\",\n \"def mean(X, shape):\\n return np.real(X[0, 0]) / (shape[0] * shape[1])\",\n \"def spatial_avg(self, input_layer):\\n return tf.reduce_mean(input_layer, [2, 3], name='spatial_avg')\",\n \"def average(self):\\n return self.summation() / self.count()\",\n \"def gate_average(recurrent_net, sample_input):\\n length = sample_input.size\\n score, select = np.zeros(length), np.zeros(length)\\n for i in range(length):\\n select[i], score[i] = recurrent_net.activate([sample_input[i]])\\n select = sigmoid(select)\\n return np.sum(select * score) / np.sum(select)\",\n \"def computeMeans(X, idx, K):\\n\\tm, n = X.shape\\n\\tcentroids = np.zeros((K, n))\\n\\tcount = np.zeros(K)\\n\\n\\tfor j in range(m):\\n\\t\\tcentroids[int(idx[j])] += X[j]\\n\\n\\tfor i in range(m):\\n\\t\\tcount[int(idx[i])] += 1\\n\\n\\treturn centroids / np.tile(count.reshape((K, 1)), n)\",\n \"def compute(self): \\r\\n hist = self.confusion_matrix\\r\\n iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))\\r\\n mean_iu = np.nanmean(iu)\\r\\n return mean_iu\",\n \"def _compute_mean(self, C, mag, rjb, rake):\\n mean = (C['a1'] +\\n self._compute_linear_magnitude_term(C, mag) +\\n self._compute_quadratic_magnitude_term(C, mag) +\\n self._compute_logarithmic_distance_term(C, mag, rjb) +\\n self._compute_faulting_style_term(C, rake))\\n\\n return mean\",\n \"def gaussian_process_pointwise_variance(kernel, pred_samples, train_samples,\\n nugget = 0):\\n K_train = kernel(train_samples.T)\\n # add small number to diagonal to ensure covariance matrix is\\n # positive definite\\n ntrain_samples = train_samples.shape[1]\\n K_train[np.arange(ntrain_samples), np.arange(ntrain_samples)] += nugget\\n k_pred = kernel(train_samples.T, pred_samples.T)\\n L = np.linalg.cholesky(K_train)\\n tmp = solve_triangular(L, k_pred, lower=True)\\n variance = kernel.diag(pred_samples.T) - np.sum(tmp*tmp, axis=0)\\n return variance\",\n \"def gaussian_kernel(windowX, windowY, sigma):\\n X,Y = createKernalWindowRanges(windowX, windowY, increment)\\n \\n gKernel = gaussianNormalised(X, 0, sigma) * gaussianNormalised(Y, 0, sigma)\\n gSum = np.sum(np.abs(gKernel))\\n \\n if gSum == 0:\\n print \\\"Warning gaussian_kernel:: Not normalising by sum of values, as sum = \\\" + str(gSum)\\n return (gKernel)\\n else:\\n return (gKernel / gSum)\",\n \"def apply_kernel(x, h, out=None):\\n if out is None:\\n out = np.zeros(x.shape[1])\\n else:\\n out.fill(0)\\n\\n for ind in range(len(h)):\\n out += np.convolve(h[ind], x[ind])[:len(out)]\\n\\n return out\",\n \"def build_mean_vector(X, Y_range):\\r\\n C = Y_range.size -1\\r\\n M = np.zeros((X.shape[0], C))\\r\\n for c in xrange(C):\\r\\n Xc = get_block_col(X, c, Y_range)\\r\\n M[:, c] = np.mean(Xc, axis=1)\\r\\n return M\",\n \"def global_average(x, batch_lengths):\\n\\n # Loop over the clouds of the batch\\n averaged_features = []\\n i0 = 0\\n for b_i, length in enumerate(batch_lengths):\\n\\n # Average features for each batch cloud\\n averaged_features.append(torch.mean(x[i0:i0 + length], dim=0))\\n\\n # Increment for next cloud\\n i0 += length\\n\\n # Average features in each batch\\n return torch.stack(averaged_features)\",\n \"def avg(arr):\\n return sum(arr) / float(len(arr))\",\n \"def ageometricmean (inarray,dimension=None,keepdims=0):\\r\\n inarray = N.array(inarray,N.float_)\\r\\n if dimension == None:\\r\\n inarray = N.ravel(inarray)\\r\\n size = len(inarray)\\r\\n mult = N.power(inarray,1.0/size)\\r\\n mult = N.multiply.reduce(mult)\\r\\n elif type(dimension) in [IntType,FloatType]:\\r\\n size = inarray.shape[dimension]\\r\\n mult = N.power(inarray,1.0/size)\\r\\n mult = N.multiply.reduce(mult,dimension)\\r\\n if keepdims == 1:\\r\\n shp = list(inarray.shape)\\r\\n shp[dimension] = 1\\r\\n sum = N.reshape(sum,shp)\\r\\n else: # must be a SEQUENCE of dims to average over\\r\\n dims = list(dimension)\\r\\n dims.sort()\\r\\n dims.reverse()\\r\\n size = N.array(N.multiply.reduce(N.take(inarray.shape,dims)),N.float_)\\r\\n mult = N.power(inarray,1.0/size)\\r\\n for dim in dims:\\r\\n mult = N.multiply.reduce(mult,dim)\\r\\n if keepdims == 1:\\r\\n shp = list(inarray.shape)\\r\\n for dim in dims:\\r\\n shp[dim] = 1\\r\\n mult = N.reshape(mult,shp)\\r\\n return mult\",\n \"def _mean_attentive_vectors(self, x2, cosine_matrix):\\n # (batch_size, x1_timesteps, x2_timesteps, 1)\\n expanded_cosine_matrix = K.expand_dims(cosine_matrix, axis=-1)\\n # (batch_size, 1, x2_timesteps, embedding_size)\\n x2 = K.expand_dims(x2, axis=1)\\n # (batch_size, x1_timesteps, embedding_size)\\n weighted_sum = K.sum(expanded_cosine_matrix * x2, axis=2)\\n # (batch_size, x1_timesteps, 1)\\n sum_cosine = K.expand_dims(K.sum(cosine_matrix, axis=-1) + self.epsilon, axis=-1)\\n # (batch_size, x1_timesteps, embedding_size)\\n attentive_vector = weighted_sum / sum_cosine\\n return attentive_vector\",\n \"def mean(self):\\n return self.k * self.theta\",\n \"def evse(self, data, *args, **kwargs):\\n darr = np.array(data)\\n d = darr if len(darr.shape) == 1 else darr[0] / darr[1]\\n return (d - self.evs(darr, *args, **kwargs))**2\",\n \"def _mask_and_avg(values, padding_mask):\\n\\n dec_lens = tf.reduce_sum(padding_mask, axis=1) # shape batch_size. float32\\n values_per_step = [v * padding_mask[:,dec_step] for dec_step,v in enumerate(values)]\\n values_per_ex = sum(values_per_step)/dec_lens # shape (batch_size); normalized value for each batch member\\n return tf.reduce_mean(values_per_ex) # overall average\",\n \"def average(self, start, end):\\n return self.integrate(start, end) / (end - start)\",\n \"def har_mean(array):\\n return ((sum([1/x for x in array]))**(-1))*len(array)\",\n \"def _kernel_norm(self, vector, bandwidth):\\n\\n vector = (vector.T / vector.sum(1)).T\\n vector = np.abs(self._kernel_method(vector, bandwidth))\\n vector = (vector.T / vector.sum(1)).T\\n return vector\",\n \"def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):\\n\\n return _apply(_crank8.mean, _crank16.mean, image, selem, out=out,\\n mask=mask, shift_x=shift_x, shift_y=shift_y)\",\n \"def center_and_normalise_kernel(K_temp):\\n\\n K_temp = KernelCenterer().fit_transform(K_temp)\\n nb_item = K_temp.shape[0]\\n K_norm = np.zeros((nb_item, nb_item))\\n for i in range(nb_item):\\n for j in range(i, nb_item):\\n K_norm[i, j] = K_temp[i, j] / math.sqrt(K_temp[i, i] * K_temp[j, j])\\n K_norm[j, i] = K_norm[i, j]\\n\\n return K_norm\",\n \"def gaus_kernel_calc(kernel_size):\\n base_gaus_binom = np.array([[1], [1]])\\n kernel = base_gaus_binom\\n\\n if kernel_size == 1:\\n # If the kernel size is 1 we need a 2d array that keeps the image the same.\\n kernel = np.array([[1]])\\n kernel = scipy.signal.convolve2d(kernel, kernel.transpose())\\n return kernel\\n\\n for i in range(kernel_size - 2):\\n kernel = scipy.signal.convolve2d(kernel, base_gaus_binom)\\n\\n kernel = scipy.signal.convolve2d(kernel, kernel.transpose())\\n return kernel/kernel.sum()\",\n \"def mean(x):\\n return sum(x) / len(x)\",\n \"def mean(self):\\n mean = sum(self.data)/self.size\\n return mean\",\n \"def average(self):\\n return (self.current + self.last) / 2.0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6827808","0.65671843","0.6564396","0.654793","0.6526386","0.63922465","0.63613415","0.6356005","0.6356005","0.6356005","0.63499135","0.63343555","0.63088894","0.63088894","0.62725526","0.6247251","0.62032187","0.6195959","0.6177069","0.61551315","0.6155042","0.6155042","0.61340576","0.61209583","0.6117589","0.6024189","0.6024189","0.6016315","0.59935945","0.59836805","0.5978209","0.5966772","0.5951255","0.59366125","0.59365404","0.5933317","0.59308624","0.593069","0.59253806","0.5920956","0.5917906","0.59118","0.5911175","0.5893702","0.58861595","0.5874456","0.5853233","0.5841351","0.5839249","0.5838047","0.5836277","0.5834204","0.5832931","0.5826961","0.5826456","0.5821625","0.5818958","0.5812076","0.5804608","0.5795622","0.57955307","0.5795483","0.579259","0.57870245","0.57752955","0.5762294","0.57563287","0.5749913","0.5746568","0.57460743","0.5745297","0.5742599","0.57299584","0.57207465","0.57180274","0.5716924","0.5714547","0.5708037","0.57071334","0.57066804","0.570641","0.5701317","0.5699871","0.5696642","0.56937206","0.56805676","0.5677478","0.56767404","0.56542504","0.5652868","0.5643067","0.56377184","0.5619286","0.56173456","0.5613244","0.5610203","0.5601576","0.5594533","0.5593851","0.55872697","0.55859125"],"string":"[\n \"0.6827808\",\n \"0.65671843\",\n \"0.6564396\",\n \"0.654793\",\n \"0.6526386\",\n \"0.63922465\",\n \"0.63613415\",\n \"0.6356005\",\n \"0.6356005\",\n \"0.6356005\",\n \"0.63499135\",\n \"0.63343555\",\n \"0.63088894\",\n \"0.63088894\",\n \"0.62725526\",\n \"0.6247251\",\n \"0.62032187\",\n \"0.6195959\",\n \"0.6177069\",\n \"0.61551315\",\n \"0.6155042\",\n \"0.6155042\",\n \"0.61340576\",\n \"0.61209583\",\n \"0.6117589\",\n \"0.6024189\",\n \"0.6024189\",\n \"0.6016315\",\n \"0.59935945\",\n \"0.59836805\",\n \"0.5978209\",\n \"0.5966772\",\n \"0.5951255\",\n \"0.59366125\",\n \"0.59365404\",\n \"0.5933317\",\n \"0.59308624\",\n \"0.593069\",\n \"0.59253806\",\n \"0.5920956\",\n \"0.5917906\",\n \"0.59118\",\n \"0.5911175\",\n \"0.5893702\",\n \"0.58861595\",\n \"0.5874456\",\n \"0.5853233\",\n \"0.5841351\",\n \"0.5839249\",\n \"0.5838047\",\n \"0.5836277\",\n \"0.5834204\",\n \"0.5832931\",\n \"0.5826961\",\n \"0.5826456\",\n \"0.5821625\",\n \"0.5818958\",\n \"0.5812076\",\n \"0.5804608\",\n \"0.5795622\",\n \"0.57955307\",\n \"0.5795483\",\n \"0.579259\",\n \"0.57870245\",\n \"0.57752955\",\n \"0.5762294\",\n \"0.57563287\",\n \"0.5749913\",\n \"0.5746568\",\n \"0.57460743\",\n \"0.5745297\",\n \"0.5742599\",\n \"0.57299584\",\n \"0.57207465\",\n \"0.57180274\",\n \"0.5716924\",\n \"0.5714547\",\n \"0.5708037\",\n \"0.57071334\",\n \"0.57066804\",\n \"0.570641\",\n \"0.5701317\",\n \"0.5699871\",\n \"0.5696642\",\n \"0.56937206\",\n \"0.56805676\",\n \"0.5677478\",\n \"0.56767404\",\n \"0.56542504\",\n \"0.5652868\",\n \"0.5643067\",\n \"0.56377184\",\n \"0.5619286\",\n \"0.56173456\",\n \"0.5613244\",\n \"0.5610203\",\n \"0.5601576\",\n \"0.5594533\",\n \"0.5593851\",\n \"0.55872697\",\n \"0.55859125\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11088,"cells":{"query":{"kind":"string","value":"Method for calculating average Implemented ONLY for imagelike vector data"},"document":{"kind":"string","value":"def _update_avg(self):\n if self._data_type == 'coords':\n # default averaging is supported only for 'matrix' dataTypes\n return\n elif self._data_type == 'image':\n\n x, y = self._averaging, self._averaging\n\n if (x,y) == (1, 1):\n self.vectors = self._original_data\n # calling original data\n return\n\n tempdat = self._original_data\n range_x = tempdat.shape[0]\n range_y = tempdat.shape[1]\n x_offset = int((x - 1) / 2)\n y_offset = int((y - 1) / 2)\n\n kernel = np.ones(shape=(x, y)) / (x*y)\n\n output_mat = np.zeros_like(tempdat)\n output_mat_x = signal.convolve2d(tempdat[:, :, 0], kernel,\n mode='same', boundary='wrap')\n output_mat_y = signal.convolve2d(tempdat[:, :, 1], kernel,\n mode='same', boundary='wrap')\n\n output_mat[:, :, 0] = output_mat_x\n output_mat[:, :, 1] = output_mat_y\n\n self.vectors = (output_mat[x_offset:range_x-x_offset:x,\n y_offset:range_y-y_offset:y])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def average(data):\n return np.average(data)","def avg(vector):\n if len(vector) == 0:\n return 0\n return sum(vector) / len(vector)","def _calc_avg_img(self, data: Union[Sequence[np.ndarray],\n Sequence[Sequence[np.ndarray]]]\n ) -> np.ndarray:\n summed = None\n cnt = 0\n for seq in data:\n if isinstance(seq, np.ndarray) and seq.ndim == 2:\n # seq is a single image, turn it into a sequence\n seq = [seq]\n\n for img in seq:\n # Sequence of image sequences\n norm_img = self._normalize_image(img)\n if summed is None:\n summed = norm_img\n else:\n summed += norm_img\n cnt += 1\n\n ret = summed / cnt\n return ret","def _image_average(self, images):\n image_data = [\n image.normalize().data for image in images\n # Workaround: skip partial volcano images at the edges\n if image.data.shape[0] == image.data.shape[1]\n ]\n return np.rint(\n np.mean(image_data, axis=0)\n ).astype(np.uint8)","def avg(u: np.ndarray, v: np.ndarray) -> np.ndarray:\n \n return (u + v) / 2.0","def average(cls, vectors):\n return cls.sum(vectors) / len(vectors)","def average_image(im):\n color_vector = [int(x) for x in ImageStat.Stat(im).mean]\n return color_vector","def compute_average(vec_list):\r\n return np.sum(vec_list, axis = 0)/len(vec_list)","def generate_avg_vector(self, data):\r\n doc=nlp(data)\r\n data_vector = [token.vector for token in doc]\r\n mean_vector = np.mean(data_vector, axis=0)\r\n return mean_vector","def average(self):\n s = self.sum()\n flat_shape = self.flatten_shape(self.shape)\n num_of_elements = fct.reduce(opr.mul, flat_shape, 1)\n average = s / num_of_elements\n return average","def getAverageL(image):\n # get image as numpy array \n im = np.array(image) \n \n # get shape \n w,h = im.shape \n \n # get average \n return np.average(im.reshape(w*h))","def average_vectors(vectors_list):\n return np.mean(vectors_list, axis=0)","def get_average_image_from_batch(batch):\n # YOUR CODE HERE\n return( mx.nd.mean(batch, axis=0) )\n raise NotImplementedError()","def average(img, size=3):\n \n size = int(size)\n kernel = np.ones((size,size)) / float(size**2)\n\n return ndi.convolve(img,kernel)","def average_tac(img):\n return img.sum()/img.size","def imageAvg(img1, img2):\n return myimg.imageAvg(img1.tolist(), img2.tolist())","def class_average(X):\n\t# compute average row vector\n\tmean_vector = np.mean(X, axis = 0)\n\treturn(mean_vector)","def average(self):\n return np.mean(self.buf[:self._size], axis=0)","def compute_average( imlist): \n\t# open first image and make into array of type float \n\taverageim = array( Image.open( imlist[ 0]), 'f') \n\tfor imname in imlist[ 1:]: \t\t\n\t\taverageim = averageim+array( Image.open( imname))\n\taverageim = averageim/len( imlist) \n\t# return average as uint8 \n\t#return array( averageim, 'uint8')\n\treturn averageim","def vector_weighted_average(vf, weights):\n weights_sum = weights.sum()\n y_average = (vf[:,:,0] * weights).sum() / weights_sum\n x_average = (vf[:,:,1] * weights).sum() / weights_sum\n return np.array([y_average, x_average])","def preprocess(self, img):\n return img - np.mean(img)","def image_roi_average(img3d,roi):\n AVGs=np.empty(len(img3d))\n for frame in range(len(img3d)):\n values=np.empty(len(roi['area']))\n for i,pair in enumerate(roi['area']):\n values[i]=img3d[frame,pair[1],pair[0]]\n AVGs[frame]=np.average(values)\n #print(\"WARNING: LOWPASSING\")\n #AVGs=lowpass(AVGs,10)\n return AVGs","def calcAverage(dat):\n return sum(dat)/len(dat)","def _average_data(self):\n # TODO: This function looks inefficient - can it be improved?\n output = np.zeros([self.nints_file, self.ngroups_file,\n self.rows, self.columns], dtype=self.data.dtype)\n count = np.zeros([self.nints_file, self.ngroups_file, 1, 1],\n dtype=self.data.dtype)\n\n for intg in range(0, self.nints):\n intg_file = intg // self.intavg\n for grp in range(0, self.ngroups):\n grp_file = grp // self.grpavg\n output[intg_file, grp_file, :, :] += self.data[intg, grp, :, :]\n count[intg_file, grp_file, 0, 0] += 1.0\n\n # Avoid divide by zero.\n iszero = np.where(count < 1.0)\n if iszero:\n count[iszero] = 1.0\n output = output / count\n\n del count \n return output","def average(data):\r\n sum =0\r\n for i in data:\r\n sum+=i\r\n return sum/len(data)","def get_average(data):\n average = sum(data) / len(data)\n\n return average","def get_input_vector(img):\n parts = partition_matrix(blurmap(img), 10)\n return numpy.array([numpy.mean(part) for part in parts],\n dtype=numpy.float32)","def _calc_dim_average(vectors_array):\n array = np.array(vectors_array)\n threshold = 0.001\n res = array.sum(axis=0, dtype='float32')\n valid_count = (array > threshold).sum(axis=0)\n valid_count[valid_count == 0] = 1\n res /= valid_count\n return res","def _get_average(self):\n norm = 1.0\n for pos, idx in enumerate(self.idx):\n norm *= (self.high[pos] - self.low[pos])\n return 1.0/norm","def color_averages(img):\n return np.average(img, axis = (0, 1))","def aver_and_var(self):\n # assert not self.is_empty\n\n for axis in range(3):\n c1, c2 = self.bounds[axis]\n w = self.n_pix_partial[axis]\n aver = np.average(np.arange(c1, c2), weights=w)\n var = np.average(np.arange(c1, c2)**2, weights=w) - aver ** 2 # D = E(X^2) - (EX)^2\n yield aver, var","def genre_average(genre_vectors):\n array = [vector for vector in genre_vectors]\n return np.average(array, axis=0)","def manual_mean(arr):\n my_sum = 0\n for i in range(0, arr.shape[0]):\n for j in range(0, arr.shape[1]):\n my_sum += arr[i,j]\n return my_sum / arr.size","def calculate_average_image(self,imlist):\n\t\t\n\t\tN=len(imlist)\n\t\t\n\t\tif self.mode == 'RGB':\n\t\t\tw,h,c=imlist[0].shape\n\t\t\tarr=np.zeros((h,w,3),theano.config.floatX)\n\t\telse:\n\t\t\tw,h=imlist[0].shape\t\t\n\t\t\tarr=np.zeros((h,w),theano.config.floatX)\n\n\t\tfor im in imlist:\n\t\t\timarr=np.array(im,dtype=theano.config.floatX)\n\t\t\ttry:\n\t\t\t\tarr=arr+imarr/N\n\t\t\texcept Exception, e:\n\t\t\t\tprint e\n\t\t\t\n\t\tarr=np.array(np.round(arr),dtype=np.uint8)\n\t\t#arr=np.array(np.round(arr),dtype=theano.config.floatX)\n\t\t#average_image=Image.fromarray(arr,mode=\"RGB\")\n\t\taverage_image=Image.fromarray(arr,mode=self.mode)\n\n\t\treturn average_image","def calculateAverage(self): \n if not self.lastTransferAverage: \n size=[0,0,0,0]\n for i in range(0,4):\n if self.lastTransferredChannel & (1 << i):\n size[i]=self.lastNbrSamplesPerSeg\n self.lastAverageArray = [zeros(size[0]),zeros(size[1]),zeros(size[2]),zeros(size[3])]\n nbrSamp=self.lastNbrSamplesPerSeg\n for i in range(0,4):\n if self.lastTransferredChannel & (1 << i):\n nbrSeg=self.lastNbrSegmentsArray[i]\n for j in range (0,nbrSamp):\n for k in range(0,nbrSeg): \n self.lastAverageArray[i][j]+=self.lastWaveformArray[i][k*nbrSamp+j]\n self.lastAverageArray[i][j]/=nbrSeg\n self.lastAverageCalculated=True\n else: print \"NOn averaged data are not available\"","def aziAvg(data):\n\n size = data.shape[0]\n avg = numpy.empty(size/2, dtype=\"float\")\n for i in range(size/2):\n ring = circle(i+1, size) - circle(i, size)\n avg[i] = (ring*data).sum()/(ring.sum())\n\n return avg","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def _mean(images):\n try:\n total = next(images)\n except StopIteration:\n print(\"No images found. Use 0.\")\n return 0.\n num = 1\n for image in images:\n total += image\n mean = total / num\n if np.ndim(mean) == 3:\n mean = np.mean(mean, axis=0)\n return mean","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1 / n, vector_sum(vectors))","def vector_mean(vectors):\n n = len(vectors)\n return scalar_multiply(1 / n, vector_sum(vectors))","def avg(arr):\n return sum(arr) / float(len(arr))","def get_mean(self):\r\n for i in range(1,len(self.data[0])):\r\n self.prom.append(np.mean(self.data[:,i]))","def vector_mean(vectors: List[Vector]) -> Vector:\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def vector_mean(vectors: List[Vector]) -> Vector:\n n = len(vectors)\n return scalar_multiply(1/n, vector_sum(vectors))","def mean(v):\n return sum(v)/len(v)","def compute_average(imlist):\n # open first image and make into array of type float\n averageim = np.array(Image.open(imlist[0]), 'f')\n for imname in imlist[1:]:\n try:\n averageim += np.array(Image.open(imname))\n except:\n print(imname + '...skipped')\n averageim /= len(imlist)\n # return average as uint8\n return np.array(averageim, 'uint8')","def average(self):\n return self.summation() / self.count()","def _mask_and_avg(values, padding_mask):\n\tdec_lens = torch.sum(padding_mask,dim=1)\n\tlosses = torch.stack(values, dim=1)\n\tlosses = losses * padding_mask\n\tvalues_per_ex = torch.sum(losses, dim=1)/dec_lens\n\treturn torch.sum(values_per_ex)","def vector_mean(vectors: List[Vector]) -> Vector:\n n = len(vectors)\n\n return scalar_multiply(1/n, vector_sum(vectors))","def sum_img(img_array):\n sum_img = 0\n counter= 0\n for n in range(len(img_array)):\n sum_img += img_array[n]\n counter += 1\n sum_img = np.asarray(sum_img)\n avg_img = sum_img / (counter)\n return sum_img, avg_img","def entity_average(hidden_output, e_mask):\n\n e_mask_unsqueeze = e_mask.unsqueeze(1) # [b, 1, j-i+1]\n length_tensor = (e_mask != 0).sum(dim=1).unsqueeze(1) # [batch_size, 1]\n\n # [b, 1, j-i+1] * [b, j-i+1, dim] = [b, 1, dim] -> [b, dim]\n sum_vector = torch.bmm(e_mask_unsqueeze.float(), hidden_output).squeeze(1)\n avg_vector = sum_vector.float() / length_tensor.float() # broadcasting\n return avg_vector","def average_H(eigval_col, eigvec_col):\n nH = len(eigvec_col)\n dimen = eigval_col.shape[1]\n H_avg = np.zeros((dimen, dimen))\n for iH in range(nH):\n H = (eigvec_col[iH] * eigval_col[iH][np.newaxis, :]) @ eigvec_col[iH].T\n H_avg += H\n H_avg /= nH\n eva_avg, evc_avg = np.linalg.eigh(H_avg)\n return H_avg, eva_avg, evc_avg","def get_average(self, *dims):\n p = self.get_points(*dims)\n ret = np.average(p, axis=0)\n if ret.size == 1:\n return ret[0]\n return ret","def avgX(self):\n return np.mean(self.getx())","def mean(self):\n return self.vmean","def amean (inarray,dimension=None,keepdims=0):\r\n if inarray.dtype in [N.int_, N.short,N.ubyte]:\r\n inarray = inarray.astype(N.float_)\r\n if dimension == None:\r\n inarray = N.ravel(inarray)\r\n sum = N.add.reduce(inarray)\r\n denom = float(len(inarray))\r\n elif type(dimension) in [IntType,FloatType]:\r\n sum = asum(inarray,dimension)\r\n denom = float(inarray.shape[dimension])\r\n if keepdims == 1:\r\n shp = list(inarray.shape)\r\n shp[dimension] = 1\r\n sum = N.reshape(sum,shp)\r\n else: # must be a TUPLE of dims to average over\r\n dims = list(dimension)\r\n dims.sort()\r\n dims.reverse()\r\n sum = inarray *1.0\r\n for dim in dims:\r\n sum = N.add.reduce(sum,dim)\r\n denom = N.array(N.multiply.reduce(N.take(inarray.shape,dims)),N.float_)\r\n if keepdims == 1:\r\n shp = list(inarray.shape)\r\n for dim in dims:\r\n shp[dim] = 1\r\n sum = N.reshape(sum,shp)\r\n return sum/denom","def average(values):\n\treturn sum(values)/len(values)","def txt2avg_vector(self, txt, is_html):\n vectors = self.txt2vectors(txt, is_html=is_html)\n vectors_sum = next(vectors, None)\n if vectors_sum is None:\n return None\n count = 1.0\n for v in vectors:\n count += 1\n vectors_sum = np.add(vectors_sum, v)\n\n # calculate the average vector and replace +infy and -inf with numeric values\n avg_vector = np.nan_to_num(vectors_sum / count)\n return avg_vector","def _mean(items):\n return sum(items) / len(items)","def entity_average(hidden_output, e_mask):\n e_mask_unsqueeze = e_mask.unsqueeze(1) # [b, 1, j-i+1]\n length_tensor = (e_mask != 0).sum(\n dim=1).unsqueeze(1) # [batch_size, 1]\n\n # [b, 1, j-i+1] * [b, j-i+1, dim] = [b, 1, dim] -> [b, dim]\n sum_vector = torch.bmm(e_mask_unsqueeze.float(),\n hidden_output).squeeze(1)\n avg_vector = sum_vector.float() / length_tensor.float() # broadcasting\n return avg_vector","def avg(x, y):\n return (x + y)/2","def meanVal(img):\n\tmean = cv2.mean(img)\n\tif img is None:\n\t\tprint \"ERROR: MeanValue: Sent in None-Type Object\"\n\t\treturn -1\n\tif len(img.shape) == 3:\n\t\treturn (mean[0], mean[1], mean[2])\n\telif len(img.shape) == 2:\n\t\treturn (mean[0])\n\telse:\n\t\treturn mean","def average(self):\n return (self.current + self.last) / 2.0","def AllFreAverageV(self,):\n \t\tv_array = self.data\n \t\taaverage_v = np.average(v_array[:,1])\n \t\tprint('Whole frequency average group velocity:\\nVw=',aaverage_v/1000,'km/s')\n \t\treturn","def boxcar_average(D,Y,N):\n boxY = np.convolve(Y, np.ones((N,))/N, mode='valid')\n edge = (len(Y)-len(boxY))/2 \n boxD = D[math.floor(edge):-math.ceil(edge)]\n \n return (boxD, boxY)","def processImage(imgs):\r\n imgs = imgs.astype(np.float32)\r\n for i, img in enumerate(imgs):\r\n m = img.mean()\r\n s = img.std()\r\n imgs[i] = (img - m) / s\r\n return imgs","def getAverageColor(img):\n avgColorPerRow = np.average(img, axis=0)\n avgColor = np.average(avgColorPerRow, axis=0)\n return avgColor","def get_mean(self):\n self.meanval = np.mean(self.adulist)","def get_channel_average_from_batch(batch):\n # YOUR CODE HERE\n return( mx.nd.mean(batch, axis=1, exclude=True) )\n raise NotImplementedError()","def metric_average(val, name, hvd):\n tensor = val.clone().detach()\n avg_tensor = hvd.allreduce(tensor, name=name)\n return avg_tensor.item()","def average(array):\n unique_vals = set(array)\n return sum(unique_vals) / len(unique_vals)\n\n \n # your code goes here","def average(x, y):\n #helper function for get_accuracy\n average = (x+y)/2 \n return average","def mean(self):\r\n return np.mean(self.data_array)","def get_average(array):\n total = sum(array)\n count = len(array)\n average = total / count\n return average","def ComputeAveY(data):\n NBINSY=data.shape[0]\n NBINSX=data.shape[1]\n the_averY=np.zeros(NBINSX)\n the_y=np.zeros(NBINSY)\n for ix in np.arange(NBINSX):\n the_ysum=np.sum(data[:,ix])\n for iy in np.arange(NBINSY):\n the_y[iy]=iy*data[iy,ix]\n if(the_ysum>0):\n med=np.sum(the_y)/the_ysum\n the_averY[ix]=med\n return the_averY","def average(values):\n return sum(values) / len(values)","def average(values):\n return sum(values) / len(values)","def average_image_value(surface):\n return sum(\n [pixels[0] for pixels in get_pixels(surface)]\n ) // len(get_pixels(surface))","def process(self, data):\n return data - np.mean(data)","def get_average():\n args = json.loads(request.get_json())\n vector = args['vector']\n result = arithmetic.average_vectors(vector)\n return json.dumps({'result': result})","def forward_avg(array_in):\n return (array_in[:-1] + array_in[1:]) * 0.5","def get_mean_image(data):\n\n\tno_of_images = len(data)\n\tmean_im = np.zeros((28, 28))\n\tfor i in xrange(no_of_images):\n\t\tmean_im = mean_im + data[i, 0:28, 0:28]\n\n\tmean_im = mean_im / no_of_images\n\treturn mean_im","def per_img_demean(img):\n assert (len(img.size()) == 3 and img.size(0) == 3) # 1 RGB image, tensor\n mean = img.sum(dim=1, keepdim=True).sum(dim=2, keepdim=True) / (img.size(1) * img.size(2))\n return img - mean # expands","def _mask_and_avg(values, padding_mask):\n\n dec_lens = tf.reduce_sum(padding_mask, axis=1) # shape batch_size. float32\n values_per_step = [v * padding_mask[:,dec_step] for dec_step,v in enumerate(values)]\n values_per_ex = sum(values_per_step)/dec_lens # shape (batch_size); normalized value for each batch member\n return tf.reduce_mean(values_per_ex) # overall average","def averageImage(dir): \n total_image = np.zeros(shape=(496, 512))\n for subdir, dirs, files in os.walk(dir):\n # print(subdir, files)\n # try:\n \n for file in files:\n img_path = os.path.join(subdir, file)\n img = np.asarray(Image.open(img_path))\n \n delta_height = img.shape[0] - total_image.shape[0]\n delta_width = img.shape[1] - total_image.shape[1]\n \n \n delta_left = delta_width//2 if not delta_width%2 else (delta_width//2+1)\n delta_right = delta_width//2\n delta_top = delta_height//2 if not delta_height%2 else (delta_height//2+1)\n delta_bottom = delta_height//2\n \n # print(img.shape, delta_top, delta_bottom, delta_left, delta_right)\n total_image = np.add(total_image, img[delta_top: (img.shape[0]-delta_bottom), delta_left:(img.shape[1]-delta_right)])\n # except:\n # total_image = np.zeros(shape=(496, 768))\n # for file in files:\n # img_path = os.path.join(subdir, file)\n # total_image = np.add(total_image, np.asarray(Image.open(img_path)))\n \n # if total_image.shape == (496, 768):\n # total_image = total_image[:, 128:(768-127)]\n return total_image/len(files)","def mean_average_position():\n pass","def mean(arr) -> float:\n return sum(arr) / len(arr)","def m_average_fun(self, dx=df.dx):\n\n mx = df.assemble(\n self.material._Ms_dg * df.dot(self._m, df.Constant([1, 0, 0])) * dx)\n my = df.assemble(\n self.material._Ms_dg * df.dot(self._m, df.Constant([0, 1, 0])) * dx)\n mz = df.assemble(\n self.material._Ms_dg * df.dot(self._m, df.Constant([0, 0, 1])) * dx)\n volume = df.assemble(self.material._Ms_dg * dx)\n\n return np.array([mx, my, mz]) / volume","def compute_average(image_list: list) -> array:\n\n # open first image and make into array of type float\n average_image = array(Image.open(image_list[0]), 'f')\n\n for image_file in image_list[1:]:\n try:\n average_image += array(Image.open(image_file))\n except:\n print(\"{0}...skipped\", image_file)\n average_image /= len(image_list)\n\n # return average as uint8\n return array(average_image, 'uint8')","def calc_mean(sig):\n # m = mean(sig)\n return np.mean(sig)","def make_flat_avg(images, out):\n image = Image(avg_images(images, out))\n image.normalise()\n return out","def mean(self):\n return self.data.mean(axis=-1, keepdims=True)","def mean(data):\n n = len(data)\n return sum(data)/float(n)","def avg(values):\n return sum(values) / float(len(values))","def dp_mean( data_vect, epsilon=1.0, delta=0.1 ):\n\n import numpy as np\n\n if ( any(data_vect < 0.0) or any(data_vect > 1.0) ):\n print('ERROR: Input vector should have bounded entries in [0,1].')\n return\n elif epsilon < 0.0:\n print('ERROR: Epsilon should be positive.')\n return\n elif delta < 0.0 or delta > 1.0:\n print('ERROR: Delta should be bounded in [0,1].')\n return\n else:\n\n n = len(data_vect)\n f = np.mean(data_vect)\n if delta == 0:\n noise = np.random.laplace(loc = 0, scale = 1/float(n*epsilon), size = (1,1))\n else:\n sigma = (1.0/(n*epsilon))*np.sqrt(2*np.log(1.25/delta))\n noise = np.random.normal(0.0, sigma, 1)\n f += noise\n\n return f","def calculate_average(array):\n result = 0\n for item in array:\n result += float(item)\n final_result = result/len(array)\n return final_result","def make_average(self, arr):\n\n if not self.degen:\n self.get_degen()\n\n nkpt, nband = arr.shape[-2:]\n \n for ikpt in range(nkpt):\n for group in self.degen[ikpt]:\n average = copy(arr[...,ikpt,group[0][1]])\n for ispin, iband in group[1:]:\n average += arr[...,ikpt,iband]\n \n average /= len(group)\n for ispin, iband in group:\n arr[...,ikpt,iband] = average\n \n return arr","def sim_avg(sim_mats):\n return np.array(sim_mats).mean(axis=0)"],"string":"[\n \"def average(data):\\n return np.average(data)\",\n \"def avg(vector):\\n if len(vector) == 0:\\n return 0\\n return sum(vector) / len(vector)\",\n \"def _calc_avg_img(self, data: Union[Sequence[np.ndarray],\\n Sequence[Sequence[np.ndarray]]]\\n ) -> np.ndarray:\\n summed = None\\n cnt = 0\\n for seq in data:\\n if isinstance(seq, np.ndarray) and seq.ndim == 2:\\n # seq is a single image, turn it into a sequence\\n seq = [seq]\\n\\n for img in seq:\\n # Sequence of image sequences\\n norm_img = self._normalize_image(img)\\n if summed is None:\\n summed = norm_img\\n else:\\n summed += norm_img\\n cnt += 1\\n\\n ret = summed / cnt\\n return ret\",\n \"def _image_average(self, images):\\n image_data = [\\n image.normalize().data for image in images\\n # Workaround: skip partial volcano images at the edges\\n if image.data.shape[0] == image.data.shape[1]\\n ]\\n return np.rint(\\n np.mean(image_data, axis=0)\\n ).astype(np.uint8)\",\n \"def avg(u: np.ndarray, v: np.ndarray) -> np.ndarray:\\n \\n return (u + v) / 2.0\",\n \"def average(cls, vectors):\\n return cls.sum(vectors) / len(vectors)\",\n \"def average_image(im):\\n color_vector = [int(x) for x in ImageStat.Stat(im).mean]\\n return color_vector\",\n \"def compute_average(vec_list):\\r\\n return np.sum(vec_list, axis = 0)/len(vec_list)\",\n \"def generate_avg_vector(self, data):\\r\\n doc=nlp(data)\\r\\n data_vector = [token.vector for token in doc]\\r\\n mean_vector = np.mean(data_vector, axis=0)\\r\\n return mean_vector\",\n \"def average(self):\\n s = self.sum()\\n flat_shape = self.flatten_shape(self.shape)\\n num_of_elements = fct.reduce(opr.mul, flat_shape, 1)\\n average = s / num_of_elements\\n return average\",\n \"def getAverageL(image):\\n # get image as numpy array \\n im = np.array(image) \\n \\n # get shape \\n w,h = im.shape \\n \\n # get average \\n return np.average(im.reshape(w*h))\",\n \"def average_vectors(vectors_list):\\n return np.mean(vectors_list, axis=0)\",\n \"def get_average_image_from_batch(batch):\\n # YOUR CODE HERE\\n return( mx.nd.mean(batch, axis=0) )\\n raise NotImplementedError()\",\n \"def average(img, size=3):\\n \\n size = int(size)\\n kernel = np.ones((size,size)) / float(size**2)\\n\\n return ndi.convolve(img,kernel)\",\n \"def average_tac(img):\\n return img.sum()/img.size\",\n \"def imageAvg(img1, img2):\\n return myimg.imageAvg(img1.tolist(), img2.tolist())\",\n \"def class_average(X):\\n\\t# compute average row vector\\n\\tmean_vector = np.mean(X, axis = 0)\\n\\treturn(mean_vector)\",\n \"def average(self):\\n return np.mean(self.buf[:self._size], axis=0)\",\n \"def compute_average( imlist): \\n\\t# open first image and make into array of type float \\n\\taverageim = array( Image.open( imlist[ 0]), 'f') \\n\\tfor imname in imlist[ 1:]: \\t\\t\\n\\t\\taverageim = averageim+array( Image.open( imname))\\n\\taverageim = averageim/len( imlist) \\n\\t# return average as uint8 \\n\\t#return array( averageim, 'uint8')\\n\\treturn averageim\",\n \"def vector_weighted_average(vf, weights):\\n weights_sum = weights.sum()\\n y_average = (vf[:,:,0] * weights).sum() / weights_sum\\n x_average = (vf[:,:,1] * weights).sum() / weights_sum\\n return np.array([y_average, x_average])\",\n \"def preprocess(self, img):\\n return img - np.mean(img)\",\n \"def image_roi_average(img3d,roi):\\n AVGs=np.empty(len(img3d))\\n for frame in range(len(img3d)):\\n values=np.empty(len(roi['area']))\\n for i,pair in enumerate(roi['area']):\\n values[i]=img3d[frame,pair[1],pair[0]]\\n AVGs[frame]=np.average(values)\\n #print(\\\"WARNING: LOWPASSING\\\")\\n #AVGs=lowpass(AVGs,10)\\n return AVGs\",\n \"def calcAverage(dat):\\n return sum(dat)/len(dat)\",\n \"def _average_data(self):\\n # TODO: This function looks inefficient - can it be improved?\\n output = np.zeros([self.nints_file, self.ngroups_file,\\n self.rows, self.columns], dtype=self.data.dtype)\\n count = np.zeros([self.nints_file, self.ngroups_file, 1, 1],\\n dtype=self.data.dtype)\\n\\n for intg in range(0, self.nints):\\n intg_file = intg // self.intavg\\n for grp in range(0, self.ngroups):\\n grp_file = grp // self.grpavg\\n output[intg_file, grp_file, :, :] += self.data[intg, grp, :, :]\\n count[intg_file, grp_file, 0, 0] += 1.0\\n\\n # Avoid divide by zero.\\n iszero = np.where(count < 1.0)\\n if iszero:\\n count[iszero] = 1.0\\n output = output / count\\n\\n del count \\n return output\",\n \"def average(data):\\r\\n sum =0\\r\\n for i in data:\\r\\n sum+=i\\r\\n return sum/len(data)\",\n \"def get_average(data):\\n average = sum(data) / len(data)\\n\\n return average\",\n \"def get_input_vector(img):\\n parts = partition_matrix(blurmap(img), 10)\\n return numpy.array([numpy.mean(part) for part in parts],\\n dtype=numpy.float32)\",\n \"def _calc_dim_average(vectors_array):\\n array = np.array(vectors_array)\\n threshold = 0.001\\n res = array.sum(axis=0, dtype='float32')\\n valid_count = (array > threshold).sum(axis=0)\\n valid_count[valid_count == 0] = 1\\n res /= valid_count\\n return res\",\n \"def _get_average(self):\\n norm = 1.0\\n for pos, idx in enumerate(self.idx):\\n norm *= (self.high[pos] - self.low[pos])\\n return 1.0/norm\",\n \"def color_averages(img):\\n return np.average(img, axis = (0, 1))\",\n \"def aver_and_var(self):\\n # assert not self.is_empty\\n\\n for axis in range(3):\\n c1, c2 = self.bounds[axis]\\n w = self.n_pix_partial[axis]\\n aver = np.average(np.arange(c1, c2), weights=w)\\n var = np.average(np.arange(c1, c2)**2, weights=w) - aver ** 2 # D = E(X^2) - (EX)^2\\n yield aver, var\",\n \"def genre_average(genre_vectors):\\n array = [vector for vector in genre_vectors]\\n return np.average(array, axis=0)\",\n \"def manual_mean(arr):\\n my_sum = 0\\n for i in range(0, arr.shape[0]):\\n for j in range(0, arr.shape[1]):\\n my_sum += arr[i,j]\\n return my_sum / arr.size\",\n \"def calculate_average_image(self,imlist):\\n\\t\\t\\n\\t\\tN=len(imlist)\\n\\t\\t\\n\\t\\tif self.mode == 'RGB':\\n\\t\\t\\tw,h,c=imlist[0].shape\\n\\t\\t\\tarr=np.zeros((h,w,3),theano.config.floatX)\\n\\t\\telse:\\n\\t\\t\\tw,h=imlist[0].shape\\t\\t\\n\\t\\t\\tarr=np.zeros((h,w),theano.config.floatX)\\n\\n\\t\\tfor im in imlist:\\n\\t\\t\\timarr=np.array(im,dtype=theano.config.floatX)\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tarr=arr+imarr/N\\n\\t\\t\\texcept Exception, e:\\n\\t\\t\\t\\tprint e\\n\\t\\t\\t\\n\\t\\tarr=np.array(np.round(arr),dtype=np.uint8)\\n\\t\\t#arr=np.array(np.round(arr),dtype=theano.config.floatX)\\n\\t\\t#average_image=Image.fromarray(arr,mode=\\\"RGB\\\")\\n\\t\\taverage_image=Image.fromarray(arr,mode=self.mode)\\n\\n\\t\\treturn average_image\",\n \"def calculateAverage(self): \\n if not self.lastTransferAverage: \\n size=[0,0,0,0]\\n for i in range(0,4):\\n if self.lastTransferredChannel & (1 << i):\\n size[i]=self.lastNbrSamplesPerSeg\\n self.lastAverageArray = [zeros(size[0]),zeros(size[1]),zeros(size[2]),zeros(size[3])]\\n nbrSamp=self.lastNbrSamplesPerSeg\\n for i in range(0,4):\\n if self.lastTransferredChannel & (1 << i):\\n nbrSeg=self.lastNbrSegmentsArray[i]\\n for j in range (0,nbrSamp):\\n for k in range(0,nbrSeg): \\n self.lastAverageArray[i][j]+=self.lastWaveformArray[i][k*nbrSamp+j]\\n self.lastAverageArray[i][j]/=nbrSeg\\n self.lastAverageCalculated=True\\n else: print \\\"NOn averaged data are not available\\\"\",\n \"def aziAvg(data):\\n\\n size = data.shape[0]\\n avg = numpy.empty(size/2, dtype=\\\"float\\\")\\n for i in range(size/2):\\n ring = circle(i+1, size) - circle(i, size)\\n avg[i] = (ring*data).sum()/(ring.sum())\\n\\n return avg\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def _mean(images):\\n try:\\n total = next(images)\\n except StopIteration:\\n print(\\\"No images found. Use 0.\\\")\\n return 0.\\n num = 1\\n for image in images:\\n total += image\\n mean = total / num\\n if np.ndim(mean) == 3:\\n mean = np.mean(mean, axis=0)\\n return mean\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1 / n, vector_sum(vectors))\",\n \"def vector_mean(vectors):\\n n = len(vectors)\\n return scalar_multiply(1 / n, vector_sum(vectors))\",\n \"def avg(arr):\\n return sum(arr) / float(len(arr))\",\n \"def get_mean(self):\\r\\n for i in range(1,len(self.data[0])):\\r\\n self.prom.append(np.mean(self.data[:,i]))\",\n \"def vector_mean(vectors: List[Vector]) -> Vector:\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def vector_mean(vectors: List[Vector]) -> Vector:\\n n = len(vectors)\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def mean(v):\\n return sum(v)/len(v)\",\n \"def compute_average(imlist):\\n # open first image and make into array of type float\\n averageim = np.array(Image.open(imlist[0]), 'f')\\n for imname in imlist[1:]:\\n try:\\n averageim += np.array(Image.open(imname))\\n except:\\n print(imname + '...skipped')\\n averageim /= len(imlist)\\n # return average as uint8\\n return np.array(averageim, 'uint8')\",\n \"def average(self):\\n return self.summation() / self.count()\",\n \"def _mask_and_avg(values, padding_mask):\\n\\tdec_lens = torch.sum(padding_mask,dim=1)\\n\\tlosses = torch.stack(values, dim=1)\\n\\tlosses = losses * padding_mask\\n\\tvalues_per_ex = torch.sum(losses, dim=1)/dec_lens\\n\\treturn torch.sum(values_per_ex)\",\n \"def vector_mean(vectors: List[Vector]) -> Vector:\\n n = len(vectors)\\n\\n return scalar_multiply(1/n, vector_sum(vectors))\",\n \"def sum_img(img_array):\\n sum_img = 0\\n counter= 0\\n for n in range(len(img_array)):\\n sum_img += img_array[n]\\n counter += 1\\n sum_img = np.asarray(sum_img)\\n avg_img = sum_img / (counter)\\n return sum_img, avg_img\",\n \"def entity_average(hidden_output, e_mask):\\n\\n e_mask_unsqueeze = e_mask.unsqueeze(1) # [b, 1, j-i+1]\\n length_tensor = (e_mask != 0).sum(dim=1).unsqueeze(1) # [batch_size, 1]\\n\\n # [b, 1, j-i+1] * [b, j-i+1, dim] = [b, 1, dim] -> [b, dim]\\n sum_vector = torch.bmm(e_mask_unsqueeze.float(), hidden_output).squeeze(1)\\n avg_vector = sum_vector.float() / length_tensor.float() # broadcasting\\n return avg_vector\",\n \"def average_H(eigval_col, eigvec_col):\\n nH = len(eigvec_col)\\n dimen = eigval_col.shape[1]\\n H_avg = np.zeros((dimen, dimen))\\n for iH in range(nH):\\n H = (eigvec_col[iH] * eigval_col[iH][np.newaxis, :]) @ eigvec_col[iH].T\\n H_avg += H\\n H_avg /= nH\\n eva_avg, evc_avg = np.linalg.eigh(H_avg)\\n return H_avg, eva_avg, evc_avg\",\n \"def get_average(self, *dims):\\n p = self.get_points(*dims)\\n ret = np.average(p, axis=0)\\n if ret.size == 1:\\n return ret[0]\\n return ret\",\n \"def avgX(self):\\n return np.mean(self.getx())\",\n \"def mean(self):\\n return self.vmean\",\n \"def amean (inarray,dimension=None,keepdims=0):\\r\\n if inarray.dtype in [N.int_, N.short,N.ubyte]:\\r\\n inarray = inarray.astype(N.float_)\\r\\n if dimension == None:\\r\\n inarray = N.ravel(inarray)\\r\\n sum = N.add.reduce(inarray)\\r\\n denom = float(len(inarray))\\r\\n elif type(dimension) in [IntType,FloatType]:\\r\\n sum = asum(inarray,dimension)\\r\\n denom = float(inarray.shape[dimension])\\r\\n if keepdims == 1:\\r\\n shp = list(inarray.shape)\\r\\n shp[dimension] = 1\\r\\n sum = N.reshape(sum,shp)\\r\\n else: # must be a TUPLE of dims to average over\\r\\n dims = list(dimension)\\r\\n dims.sort()\\r\\n dims.reverse()\\r\\n sum = inarray *1.0\\r\\n for dim in dims:\\r\\n sum = N.add.reduce(sum,dim)\\r\\n denom = N.array(N.multiply.reduce(N.take(inarray.shape,dims)),N.float_)\\r\\n if keepdims == 1:\\r\\n shp = list(inarray.shape)\\r\\n for dim in dims:\\r\\n shp[dim] = 1\\r\\n sum = N.reshape(sum,shp)\\r\\n return sum/denom\",\n \"def average(values):\\n\\treturn sum(values)/len(values)\",\n \"def txt2avg_vector(self, txt, is_html):\\n vectors = self.txt2vectors(txt, is_html=is_html)\\n vectors_sum = next(vectors, None)\\n if vectors_sum is None:\\n return None\\n count = 1.0\\n for v in vectors:\\n count += 1\\n vectors_sum = np.add(vectors_sum, v)\\n\\n # calculate the average vector and replace +infy and -inf with numeric values\\n avg_vector = np.nan_to_num(vectors_sum / count)\\n return avg_vector\",\n \"def _mean(items):\\n return sum(items) / len(items)\",\n \"def entity_average(hidden_output, e_mask):\\n e_mask_unsqueeze = e_mask.unsqueeze(1) # [b, 1, j-i+1]\\n length_tensor = (e_mask != 0).sum(\\n dim=1).unsqueeze(1) # [batch_size, 1]\\n\\n # [b, 1, j-i+1] * [b, j-i+1, dim] = [b, 1, dim] -> [b, dim]\\n sum_vector = torch.bmm(e_mask_unsqueeze.float(),\\n hidden_output).squeeze(1)\\n avg_vector = sum_vector.float() / length_tensor.float() # broadcasting\\n return avg_vector\",\n \"def avg(x, y):\\n return (x + y)/2\",\n \"def meanVal(img):\\n\\tmean = cv2.mean(img)\\n\\tif img is None:\\n\\t\\tprint \\\"ERROR: MeanValue: Sent in None-Type Object\\\"\\n\\t\\treturn -1\\n\\tif len(img.shape) == 3:\\n\\t\\treturn (mean[0], mean[1], mean[2])\\n\\telif len(img.shape) == 2:\\n\\t\\treturn (mean[0])\\n\\telse:\\n\\t\\treturn mean\",\n \"def average(self):\\n return (self.current + self.last) / 2.0\",\n \"def AllFreAverageV(self,):\\n \\t\\tv_array = self.data\\n \\t\\taaverage_v = np.average(v_array[:,1])\\n \\t\\tprint('Whole frequency average group velocity:\\\\nVw=',aaverage_v/1000,'km/s')\\n \\t\\treturn\",\n \"def boxcar_average(D,Y,N):\\n boxY = np.convolve(Y, np.ones((N,))/N, mode='valid')\\n edge = (len(Y)-len(boxY))/2 \\n boxD = D[math.floor(edge):-math.ceil(edge)]\\n \\n return (boxD, boxY)\",\n \"def processImage(imgs):\\r\\n imgs = imgs.astype(np.float32)\\r\\n for i, img in enumerate(imgs):\\r\\n m = img.mean()\\r\\n s = img.std()\\r\\n imgs[i] = (img - m) / s\\r\\n return imgs\",\n \"def getAverageColor(img):\\n avgColorPerRow = np.average(img, axis=0)\\n avgColor = np.average(avgColorPerRow, axis=0)\\n return avgColor\",\n \"def get_mean(self):\\n self.meanval = np.mean(self.adulist)\",\n \"def get_channel_average_from_batch(batch):\\n # YOUR CODE HERE\\n return( mx.nd.mean(batch, axis=1, exclude=True) )\\n raise NotImplementedError()\",\n \"def metric_average(val, name, hvd):\\n tensor = val.clone().detach()\\n avg_tensor = hvd.allreduce(tensor, name=name)\\n return avg_tensor.item()\",\n \"def average(array):\\n unique_vals = set(array)\\n return sum(unique_vals) / len(unique_vals)\\n\\n \\n # your code goes here\",\n \"def average(x, y):\\n #helper function for get_accuracy\\n average = (x+y)/2 \\n return average\",\n \"def mean(self):\\r\\n return np.mean(self.data_array)\",\n \"def get_average(array):\\n total = sum(array)\\n count = len(array)\\n average = total / count\\n return average\",\n \"def ComputeAveY(data):\\n NBINSY=data.shape[0]\\n NBINSX=data.shape[1]\\n the_averY=np.zeros(NBINSX)\\n the_y=np.zeros(NBINSY)\\n for ix in np.arange(NBINSX):\\n the_ysum=np.sum(data[:,ix])\\n for iy in np.arange(NBINSY):\\n the_y[iy]=iy*data[iy,ix]\\n if(the_ysum>0):\\n med=np.sum(the_y)/the_ysum\\n the_averY[ix]=med\\n return the_averY\",\n \"def average(values):\\n return sum(values) / len(values)\",\n \"def average(values):\\n return sum(values) / len(values)\",\n \"def average_image_value(surface):\\n return sum(\\n [pixels[0] for pixels in get_pixels(surface)]\\n ) // len(get_pixels(surface))\",\n \"def process(self, data):\\n return data - np.mean(data)\",\n \"def get_average():\\n args = json.loads(request.get_json())\\n vector = args['vector']\\n result = arithmetic.average_vectors(vector)\\n return json.dumps({'result': result})\",\n \"def forward_avg(array_in):\\n return (array_in[:-1] + array_in[1:]) * 0.5\",\n \"def get_mean_image(data):\\n\\n\\tno_of_images = len(data)\\n\\tmean_im = np.zeros((28, 28))\\n\\tfor i in xrange(no_of_images):\\n\\t\\tmean_im = mean_im + data[i, 0:28, 0:28]\\n\\n\\tmean_im = mean_im / no_of_images\\n\\treturn mean_im\",\n \"def per_img_demean(img):\\n assert (len(img.size()) == 3 and img.size(0) == 3) # 1 RGB image, tensor\\n mean = img.sum(dim=1, keepdim=True).sum(dim=2, keepdim=True) / (img.size(1) * img.size(2))\\n return img - mean # expands\",\n \"def _mask_and_avg(values, padding_mask):\\n\\n dec_lens = tf.reduce_sum(padding_mask, axis=1) # shape batch_size. float32\\n values_per_step = [v * padding_mask[:,dec_step] for dec_step,v in enumerate(values)]\\n values_per_ex = sum(values_per_step)/dec_lens # shape (batch_size); normalized value for each batch member\\n return tf.reduce_mean(values_per_ex) # overall average\",\n \"def averageImage(dir): \\n total_image = np.zeros(shape=(496, 512))\\n for subdir, dirs, files in os.walk(dir):\\n # print(subdir, files)\\n # try:\\n \\n for file in files:\\n img_path = os.path.join(subdir, file)\\n img = np.asarray(Image.open(img_path))\\n \\n delta_height = img.shape[0] - total_image.shape[0]\\n delta_width = img.shape[1] - total_image.shape[1]\\n \\n \\n delta_left = delta_width//2 if not delta_width%2 else (delta_width//2+1)\\n delta_right = delta_width//2\\n delta_top = delta_height//2 if not delta_height%2 else (delta_height//2+1)\\n delta_bottom = delta_height//2\\n \\n # print(img.shape, delta_top, delta_bottom, delta_left, delta_right)\\n total_image = np.add(total_image, img[delta_top: (img.shape[0]-delta_bottom), delta_left:(img.shape[1]-delta_right)])\\n # except:\\n # total_image = np.zeros(shape=(496, 768))\\n # for file in files:\\n # img_path = os.path.join(subdir, file)\\n # total_image = np.add(total_image, np.asarray(Image.open(img_path)))\\n \\n # if total_image.shape == (496, 768):\\n # total_image = total_image[:, 128:(768-127)]\\n return total_image/len(files)\",\n \"def mean_average_position():\\n pass\",\n \"def mean(arr) -> float:\\n return sum(arr) / len(arr)\",\n \"def m_average_fun(self, dx=df.dx):\\n\\n mx = df.assemble(\\n self.material._Ms_dg * df.dot(self._m, df.Constant([1, 0, 0])) * dx)\\n my = df.assemble(\\n self.material._Ms_dg * df.dot(self._m, df.Constant([0, 1, 0])) * dx)\\n mz = df.assemble(\\n self.material._Ms_dg * df.dot(self._m, df.Constant([0, 0, 1])) * dx)\\n volume = df.assemble(self.material._Ms_dg * dx)\\n\\n return np.array([mx, my, mz]) / volume\",\n \"def compute_average(image_list: list) -> array:\\n\\n # open first image and make into array of type float\\n average_image = array(Image.open(image_list[0]), 'f')\\n\\n for image_file in image_list[1:]:\\n try:\\n average_image += array(Image.open(image_file))\\n except:\\n print(\\\"{0}...skipped\\\", image_file)\\n average_image /= len(image_list)\\n\\n # return average as uint8\\n return array(average_image, 'uint8')\",\n \"def calc_mean(sig):\\n # m = mean(sig)\\n return np.mean(sig)\",\n \"def make_flat_avg(images, out):\\n image = Image(avg_images(images, out))\\n image.normalise()\\n return out\",\n \"def mean(self):\\n return self.data.mean(axis=-1, keepdims=True)\",\n \"def mean(data):\\n n = len(data)\\n return sum(data)/float(n)\",\n \"def avg(values):\\n return sum(values) / float(len(values))\",\n \"def dp_mean( data_vect, epsilon=1.0, delta=0.1 ):\\n\\n import numpy as np\\n\\n if ( any(data_vect < 0.0) or any(data_vect > 1.0) ):\\n print('ERROR: Input vector should have bounded entries in [0,1].')\\n return\\n elif epsilon < 0.0:\\n print('ERROR: Epsilon should be positive.')\\n return\\n elif delta < 0.0 or delta > 1.0:\\n print('ERROR: Delta should be bounded in [0,1].')\\n return\\n else:\\n\\n n = len(data_vect)\\n f = np.mean(data_vect)\\n if delta == 0:\\n noise = np.random.laplace(loc = 0, scale = 1/float(n*epsilon), size = (1,1))\\n else:\\n sigma = (1.0/(n*epsilon))*np.sqrt(2*np.log(1.25/delta))\\n noise = np.random.normal(0.0, sigma, 1)\\n f += noise\\n\\n return f\",\n \"def calculate_average(array):\\n result = 0\\n for item in array:\\n result += float(item)\\n final_result = result/len(array)\\n return final_result\",\n \"def make_average(self, arr):\\n\\n if not self.degen:\\n self.get_degen()\\n\\n nkpt, nband = arr.shape[-2:]\\n \\n for ikpt in range(nkpt):\\n for group in self.degen[ikpt]:\\n average = copy(arr[...,ikpt,group[0][1]])\\n for ispin, iband in group[1:]:\\n average += arr[...,ikpt,iband]\\n \\n average /= len(group)\\n for ispin, iband in group:\\n arr[...,ikpt,iband] = average\\n \\n return arr\",\n \"def sim_avg(sim_mats):\\n return np.array(sim_mats).mean(axis=0)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7302006","0.7168423","0.7156485","0.71019864","0.694467","0.6885104","0.6851713","0.6831221","0.6690518","0.6667299","0.65953726","0.65504265","0.64968616","0.6474742","0.64647216","0.6450318","0.6422171","0.6420864","0.64044243","0.63954943","0.6390917","0.6380681","0.6376033","0.6356576","0.6347184","0.63438064","0.63253045","0.6317144","0.63084507","0.62796944","0.6267553","0.62624776","0.6236099","0.62288547","0.62131643","0.62088376","0.61918","0.61918","0.61918","0.61864734","0.6181595","0.6181595","0.6157617","0.61127144","0.61044526","0.61044526","0.6103334","0.609946","0.60738707","0.6054595","0.6053837","0.6040823","0.60342854","0.60339624","0.60274667","0.60134345","0.6011919","0.60112923","0.6009869","0.6009649","0.600868","0.60035217","0.60034657","0.5997557","0.59836996","0.5956851","0.5954639","0.59501654","0.5949876","0.5948244","0.5945733","0.5942828","0.59371704","0.5934844","0.5933703","0.5932715","0.5932568","0.59321","0.59321","0.59316605","0.5931448","0.5930155","0.5927472","0.5914543","0.59071875","0.59036845","0.5893174","0.5891961","0.5882597","0.58780545","0.58685935","0.58602244","0.58600014","0.5859905","0.58422107","0.58390903","0.581798","0.58161837","0.58109415","0.5804992"],"string":"[\n \"0.7302006\",\n \"0.7168423\",\n \"0.7156485\",\n \"0.71019864\",\n \"0.694467\",\n \"0.6885104\",\n \"0.6851713\",\n \"0.6831221\",\n \"0.6690518\",\n \"0.6667299\",\n \"0.65953726\",\n \"0.65504265\",\n \"0.64968616\",\n \"0.6474742\",\n \"0.64647216\",\n \"0.6450318\",\n \"0.6422171\",\n \"0.6420864\",\n \"0.64044243\",\n \"0.63954943\",\n \"0.6390917\",\n \"0.6380681\",\n \"0.6376033\",\n \"0.6356576\",\n \"0.6347184\",\n \"0.63438064\",\n \"0.63253045\",\n \"0.6317144\",\n \"0.63084507\",\n \"0.62796944\",\n \"0.6267553\",\n \"0.62624776\",\n \"0.6236099\",\n \"0.62288547\",\n \"0.62131643\",\n \"0.62088376\",\n \"0.61918\",\n \"0.61918\",\n \"0.61918\",\n \"0.61864734\",\n \"0.6181595\",\n \"0.6181595\",\n \"0.6157617\",\n \"0.61127144\",\n \"0.61044526\",\n \"0.61044526\",\n \"0.6103334\",\n \"0.609946\",\n \"0.60738707\",\n \"0.6054595\",\n \"0.6053837\",\n \"0.6040823\",\n \"0.60342854\",\n \"0.60339624\",\n \"0.60274667\",\n \"0.60134345\",\n \"0.6011919\",\n \"0.60112923\",\n \"0.6009869\",\n \"0.6009649\",\n \"0.600868\",\n \"0.60035217\",\n \"0.60034657\",\n \"0.5997557\",\n \"0.59836996\",\n \"0.5956851\",\n \"0.5954639\",\n \"0.59501654\",\n \"0.5949876\",\n \"0.5948244\",\n \"0.5945733\",\n \"0.5942828\",\n \"0.59371704\",\n \"0.5934844\",\n \"0.5933703\",\n \"0.5932715\",\n \"0.5932568\",\n \"0.59321\",\n \"0.59321\",\n \"0.59316605\",\n \"0.5931448\",\n \"0.5930155\",\n \"0.5927472\",\n \"0.5914543\",\n \"0.59071875\",\n \"0.59036845\",\n \"0.5893174\",\n \"0.5891961\",\n \"0.5882597\",\n \"0.58780545\",\n \"0.58685935\",\n \"0.58602244\",\n \"0.58600014\",\n \"0.5859905\",\n \"0.58422107\",\n \"0.58390903\",\n \"0.581798\",\n \"0.58161837\",\n \"0.58109415\",\n \"0.5804992\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7117909"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":11089,"cells":{"query":{"kind":"string","value":"width of the line in pixels widths greater than 1px only guaranteed to work with \"agg\" method"},"document":{"kind":"string","value":"def width(self, width: Union[int, float]):\n self._width = width\n\n vertices, triangles = self._generate_meshes(self.vectors, self._width)\n self._mesh_vertices = vertices\n self._mesh_triangles = triangles\n\n self.events.width()\n\n self.refresh()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def line_width(et: pd.DataFrame, lw_by: Hashable):\n if lw_by is not None:\n return encodings.data_linewidth(et[lw_by], et[lw_by])\n return pd.Series([1] * len(et), name=\"lw\")","def get_line_width(self):\n return self.lwidth","def line_width(self, width=2.0):\n self._impl.line_width(width)","def width(self) -> int:","def width(self) -> int:","def innerWidth(self):\n raise NotImplementedError","def line_size(line, style):\n logical_extents = ffi.new('PangoRectangle *')\n pango.pango_layout_line_get_extents(line, ffi.NULL, logical_extents)\n width = units_to_double(logical_extents.width)\n height = units_to_double(logical_extents.height)\n ffi.release(logical_extents)\n if style['letter_spacing'] != 'normal':\n width += style['letter_spacing']\n return width, height","def _extra_width(self) -> int:\n width = 0\n if self.box and self.show_edge:\n width += 2\n if self.box:\n width += len(self.columns) - 1\n return width","def getWidth(self) -> int:\n ...","def line_width(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"line_width\")","def line_width(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"line_width\")","def line_width(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"line_width\")","def harfbuzz_width(lines, hb_font):\n\tout = []\n\tbuf = hb.buffer_create ()\n\tfor text in lines:\n\t\thb.buffer_clear_contents (buf)\n\t\thb.buffer_add_utf8 (buf, tobytes(text), 0, -1)\n\t\thb.buffer_guess_segment_properties (buf)\n\n\t\thb.shape (hb_font, buf, [])\n\n\t\tpositions = hb.buffer_get_glyph_positions (buf)\n\t\tw = sum(pos.x_advance for pos in positions)\n\t\tout.append(w)\n\treturn out","def get_width(self):\n return max(map(len, self.get_lines()))","def estimate_width(start, end, numlabels=None, char_width=None):\n return 0, 0","def width(self):\n\t\tpass","def width(self):\n return len(self.rows[0])","def set_line_width(self, val):\n self.lwidth = val","def row_width(width: int) -> int:\n # NOTE: Lines in BDF BITMAPs are always stored in multiples of 8 bits\n # (https://stackoverflow.com/a/37944252)\n return -((-width) // 8) * 8","def width(self, width):\n self.col += width","def linewidth(self, size: float):\n for line in self.ax.lines:\n line.set_linewidth(size)\n self.canvas.draw()","def _multiline_width(multiline_s, line_width_fn=len):\n return max(map(line_width_fn, re.split(\"[\\r\\n]\", multiline_s)))","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def getWidth(*args):","def line_width(self, line_width=None):\n\n if line_width is None:\n return self._line_width\n else:\n if not isinstance(line_width, int) and\\\n not isinstance(line_width, float):\n raise TypeError(\n \"line_width must be numeric, not '%s'\" % line_width\n )\n self._line_width = line_width","def _width_extraction_fn(cls):\n pass","def pulse_width(self) -> int:","def getWidth(self):\n return len(self._data[0])","def __button_line_width_clicked(self):\n val, okPressed = QInputDialog.getDouble(self, \"Set line width\",\"Value:\", self.line_thickness, 0, 100, 4)\n if okPressed:\n self.vis.change_line_width(val)\n self.line_thickness = val","def numberOfLines(self, widths, S):\n\n\t\t# create a storage for the number of lines and current line's width\n\t\tlineCount = 1\n\t\tlineWidth = 0\n\n\t\tfor c in S:\n\t\t\t# find the width of c by getting the index and retrieving the \n\t\t\t# width from the widths list\n\t\t\tcWidth = widths[ord(c) - ord('a')]\n\t\t\t# add width to current line's width\n\t\t\tlineWidth += cWidth\n\n\t\t\t# Check if current line's width exceeds the max\n\t\t\tif (lineWidth > 100):\n\t\t\t\t# add a new line\n\t\t\t\tlineCount += 1\n\t\t\t\t# add the current character's width to current line's width\n\t\t\t\tlineWidth = cWidth\n\n\n\t\t# return the results\n\t\treturn [lineCount, lineWidth]","def width(self):\n return self.col","def width(self):\n return abs(self.end[0] - self.start[0])","def get_column_width(self):\n return \"%.2f\\\\locallinewidth\" % self._col_width[self._cell_in_row-1]","def _width(self, array):\n return max([len(self._render(c)) for c in array ])","def widths(self):\n return self._widths","def get_grid_width(self):\r\n # replace with your code\r\n return self._width","def num_bytes_per_line(self, width):\n\n if width < 1:\n raise ValueError(\"Invalid bytes per line value (must be > 0)\")\n\n if width % HEX_TYPE_WIDTH[self._hex_format]:\n raise ValueError(\"Bytes per line must be a multiple of display format type\")\n\n self._num_bytes_per_line = width\n #self._refresh_view_settings()","def get_text_line_width(\n self, text: str, cap_height: float, font: \"FontFace\" = None\n ) -> float:\n # https://stackoverflow.com/questions/32555015/how-to-get-the-visual-length-of-a-text-string-in-python\n # https://stackoverflow.com/questions/4190667/how-to-get-width-of-a-truetype-font-character-in-1200ths-of-an-inch-with-python\n raise NotImplementedError","def calc_width(self):\n current_longest = 0\n\n for i in range(len(self.list_of_motif_objects)):\n current_object = self.list_of_motif_objects[i]\n if(len(current_object.sequence) > current_longest):\n current_longest = len(current_object.sequence)\n \n width_for_drawing = current_longest + 50 + 15 \n\n self.width = width_for_drawing + self.width_of_legend","def ink_width(self):\n if not self._rows:\n return 0\n # maximum row inkwidth\n return max(\n (len(_row) - _row.index(True) - list(reversed(_row)).index(True)) if True in _row else 0\n for _row in self._rows\n )","def width(self):\n if not self._rows:\n return 0\n return len(self._rows[0])","def get_grid_width(self):\n # replace with your code\n return 0","def get_grid_width(self):\n # replace with your code\n return self._width","def get_grid_width(self):\n # replace with your code\n return self._width","def min_width(self):\n ...","def embedcount(line):\r\n\r\n x_temp = line.count(BOX_CHAR['lu'])\r\n return self.defaults.get('size')-(4*x_temp)","def get_grid_width(self):\r\n # replace with your code\r\n return self.grid_width","def get_roi_line_len(self):\n return len(self.line_list)","def get_grid_width(self):\r\n # replace with your code\r\n return self._grid_width","def get_grid_width(self):\r\n # replace with your code\r\n return self._grid_width","def _label_width(text):\n width = 0\n for lineno, line in enumerate(text.split(u'\\n')):\n size = [_BIG_FONT, _SMALL_FONT][lineno > 0] # Cool idiom, huh?\n width = max(width, size * len(line))\n return width","def get_dimension_width(self):\n pass","def calculate_width(self):\n return self.endX - self.startX","def getWidth(self, l):\n\n return max(len(repr(s)) for s in l)","def browser_width(lines, W):\n\tout = []\n\tfor text in lines:\n\t\tw = 0\n\t\tfor c in text:\n\t\t\tw += W[ord(c)]\n\t\tout.append(w)\n\treturn out","def get_grid_width(self):\n # replace with your code\n return self.grid_width","def get_grid_width(self):\n # replace with your code\n return self.grid_width","def col_width_percent(self,column_no): \n return float(self.col_width(column_no)*100)/self.total_width()","def width(self):\n # type: () -> float\n return self._width","def lines(self, sizer, surface_width):\n if self.should_update(surface_width):\n del self._lines[:]\n for line in self.text:\n line_width, _ = sizer(line)\n if line_width >= surface_width:\n self._lines += split_line(line, sizer, surface_width)\n else:\n self._lines.append(line)\n self._last_width = surface_width\n return self._lines","def get_grid_width(self):\n # replace with your code\n return self._grid_width","def get_grid_width(self):\n # replace with your code\n return self._grid_width","def width(self):\n return np.array([f.width() for f in self])","def setLineWidth(w=1):\n dislin.linwid(w)","def longlines(x):\n return sum(len(plain(line)) // cw for line in x.split(\"\\n\"))","def getSegmentWidth(self):\n l = [segment.width for segment in self.segments]\n if l.count(l[0]) == len(l):\n return l[0]\n else:\n raise ValueError(\"The widths of the segments must be the same otherwise it makes no sense.\")","def lineShape(self,peakAngle,width,angle):\n x = abs(peakAngle - angle)/width\n if x == 0.0:\n return 1.0\n else:\n return (2*j1(x)/x)**2","def width(self):\n return (self.__width)","def onChartSeriesLineWidthChanged(self, name, width):\n series = self.series[name]\n self.pen[name].setWidth(width)\n series.setPen(self.pen[name])","def width(self):\n return _libsbml.Dimensions_width(self)","def _find_spacing(self, row, ordering, max_width):\n return max_width / (len(ordering[row]) + 1)","def get_widths(self) -> tuple:\n words_width = 0\n spaces_width = 0\n for part in self.line_parts:\n words_width += part.width\n spaces_width += part.spaces_width\n return words_width, spaces_width","def total_width(self): \n total = 0\n for i in range(self.no_of_columns()):\n total += self.col_width(i)\n return total","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self):\n return self.__width","def width(self) -> float:\n return self._width","def setPointWidth(self, width):\n for point in self.points:\n point.width = width","def width(self):\n xx = self.xx\n return max(xx) - min(xx)"],"string":"[\n \"def line_width(et: pd.DataFrame, lw_by: Hashable):\\n if lw_by is not None:\\n return encodings.data_linewidth(et[lw_by], et[lw_by])\\n return pd.Series([1] * len(et), name=\\\"lw\\\")\",\n \"def get_line_width(self):\\n return self.lwidth\",\n \"def line_width(self, width=2.0):\\n self._impl.line_width(width)\",\n \"def width(self) -> int:\",\n \"def width(self) -> int:\",\n \"def innerWidth(self):\\n raise NotImplementedError\",\n \"def line_size(line, style):\\n logical_extents = ffi.new('PangoRectangle *')\\n pango.pango_layout_line_get_extents(line, ffi.NULL, logical_extents)\\n width = units_to_double(logical_extents.width)\\n height = units_to_double(logical_extents.height)\\n ffi.release(logical_extents)\\n if style['letter_spacing'] != 'normal':\\n width += style['letter_spacing']\\n return width, height\",\n \"def _extra_width(self) -> int:\\n width = 0\\n if self.box and self.show_edge:\\n width += 2\\n if self.box:\\n width += len(self.columns) - 1\\n return width\",\n \"def getWidth(self) -> int:\\n ...\",\n \"def line_width(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"line_width\\\")\",\n \"def line_width(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"line_width\\\")\",\n \"def line_width(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"line_width\\\")\",\n \"def harfbuzz_width(lines, hb_font):\\n\\tout = []\\n\\tbuf = hb.buffer_create ()\\n\\tfor text in lines:\\n\\t\\thb.buffer_clear_contents (buf)\\n\\t\\thb.buffer_add_utf8 (buf, tobytes(text), 0, -1)\\n\\t\\thb.buffer_guess_segment_properties (buf)\\n\\n\\t\\thb.shape (hb_font, buf, [])\\n\\n\\t\\tpositions = hb.buffer_get_glyph_positions (buf)\\n\\t\\tw = sum(pos.x_advance for pos in positions)\\n\\t\\tout.append(w)\\n\\treturn out\",\n \"def get_width(self):\\n return max(map(len, self.get_lines()))\",\n \"def estimate_width(start, end, numlabels=None, char_width=None):\\n return 0, 0\",\n \"def width(self):\\n\\t\\tpass\",\n \"def width(self):\\n return len(self.rows[0])\",\n \"def set_line_width(self, val):\\n self.lwidth = val\",\n \"def row_width(width: int) -> int:\\n # NOTE: Lines in BDF BITMAPs are always stored in multiples of 8 bits\\n # (https://stackoverflow.com/a/37944252)\\n return -((-width) // 8) * 8\",\n \"def width(self, width):\\n self.col += width\",\n \"def linewidth(self, size: float):\\n for line in self.ax.lines:\\n line.set_linewidth(size)\\n self.canvas.draw()\",\n \"def _multiline_width(multiline_s, line_width_fn=len):\\n return max(map(line_width_fn, re.split(\\\"[\\\\r\\\\n]\\\", multiline_s)))\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def getWidth(*args):\",\n \"def line_width(self, line_width=None):\\n\\n if line_width is None:\\n return self._line_width\\n else:\\n if not isinstance(line_width, int) and\\\\\\n not isinstance(line_width, float):\\n raise TypeError(\\n \\\"line_width must be numeric, not '%s'\\\" % line_width\\n )\\n self._line_width = line_width\",\n \"def _width_extraction_fn(cls):\\n pass\",\n \"def pulse_width(self) -> int:\",\n \"def getWidth(self):\\n return len(self._data[0])\",\n \"def __button_line_width_clicked(self):\\n val, okPressed = QInputDialog.getDouble(self, \\\"Set line width\\\",\\\"Value:\\\", self.line_thickness, 0, 100, 4)\\n if okPressed:\\n self.vis.change_line_width(val)\\n self.line_thickness = val\",\n \"def numberOfLines(self, widths, S):\\n\\n\\t\\t# create a storage for the number of lines and current line's width\\n\\t\\tlineCount = 1\\n\\t\\tlineWidth = 0\\n\\n\\t\\tfor c in S:\\n\\t\\t\\t# find the width of c by getting the index and retrieving the \\n\\t\\t\\t# width from the widths list\\n\\t\\t\\tcWidth = widths[ord(c) - ord('a')]\\n\\t\\t\\t# add width to current line's width\\n\\t\\t\\tlineWidth += cWidth\\n\\n\\t\\t\\t# Check if current line's width exceeds the max\\n\\t\\t\\tif (lineWidth > 100):\\n\\t\\t\\t\\t# add a new line\\n\\t\\t\\t\\tlineCount += 1\\n\\t\\t\\t\\t# add the current character's width to current line's width\\n\\t\\t\\t\\tlineWidth = cWidth\\n\\n\\n\\t\\t# return the results\\n\\t\\treturn [lineCount, lineWidth]\",\n \"def width(self):\\n return self.col\",\n \"def width(self):\\n return abs(self.end[0] - self.start[0])\",\n \"def get_column_width(self):\\n return \\\"%.2f\\\\\\\\locallinewidth\\\" % self._col_width[self._cell_in_row-1]\",\n \"def _width(self, array):\\n return max([len(self._render(c)) for c in array ])\",\n \"def widths(self):\\n return self._widths\",\n \"def get_grid_width(self):\\r\\n # replace with your code\\r\\n return self._width\",\n \"def num_bytes_per_line(self, width):\\n\\n if width < 1:\\n raise ValueError(\\\"Invalid bytes per line value (must be > 0)\\\")\\n\\n if width % HEX_TYPE_WIDTH[self._hex_format]:\\n raise ValueError(\\\"Bytes per line must be a multiple of display format type\\\")\\n\\n self._num_bytes_per_line = width\\n #self._refresh_view_settings()\",\n \"def get_text_line_width(\\n self, text: str, cap_height: float, font: \\\"FontFace\\\" = None\\n ) -> float:\\n # https://stackoverflow.com/questions/32555015/how-to-get-the-visual-length-of-a-text-string-in-python\\n # https://stackoverflow.com/questions/4190667/how-to-get-width-of-a-truetype-font-character-in-1200ths-of-an-inch-with-python\\n raise NotImplementedError\",\n \"def calc_width(self):\\n current_longest = 0\\n\\n for i in range(len(self.list_of_motif_objects)):\\n current_object = self.list_of_motif_objects[i]\\n if(len(current_object.sequence) > current_longest):\\n current_longest = len(current_object.sequence)\\n \\n width_for_drawing = current_longest + 50 + 15 \\n\\n self.width = width_for_drawing + self.width_of_legend\",\n \"def ink_width(self):\\n if not self._rows:\\n return 0\\n # maximum row inkwidth\\n return max(\\n (len(_row) - _row.index(True) - list(reversed(_row)).index(True)) if True in _row else 0\\n for _row in self._rows\\n )\",\n \"def width(self):\\n if not self._rows:\\n return 0\\n return len(self._rows[0])\",\n \"def get_grid_width(self):\\n # replace with your code\\n return 0\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self._width\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self._width\",\n \"def min_width(self):\\n ...\",\n \"def embedcount(line):\\r\\n\\r\\n x_temp = line.count(BOX_CHAR['lu'])\\r\\n return self.defaults.get('size')-(4*x_temp)\",\n \"def get_grid_width(self):\\r\\n # replace with your code\\r\\n return self.grid_width\",\n \"def get_roi_line_len(self):\\n return len(self.line_list)\",\n \"def get_grid_width(self):\\r\\n # replace with your code\\r\\n return self._grid_width\",\n \"def get_grid_width(self):\\r\\n # replace with your code\\r\\n return self._grid_width\",\n \"def _label_width(text):\\n width = 0\\n for lineno, line in enumerate(text.split(u'\\\\n')):\\n size = [_BIG_FONT, _SMALL_FONT][lineno > 0] # Cool idiom, huh?\\n width = max(width, size * len(line))\\n return width\",\n \"def get_dimension_width(self):\\n pass\",\n \"def calculate_width(self):\\n return self.endX - self.startX\",\n \"def getWidth(self, l):\\n\\n return max(len(repr(s)) for s in l)\",\n \"def browser_width(lines, W):\\n\\tout = []\\n\\tfor text in lines:\\n\\t\\tw = 0\\n\\t\\tfor c in text:\\n\\t\\t\\tw += W[ord(c)]\\n\\t\\tout.append(w)\\n\\treturn out\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self.grid_width\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self.grid_width\",\n \"def col_width_percent(self,column_no): \\n return float(self.col_width(column_no)*100)/self.total_width()\",\n \"def width(self):\\n # type: () -> float\\n return self._width\",\n \"def lines(self, sizer, surface_width):\\n if self.should_update(surface_width):\\n del self._lines[:]\\n for line in self.text:\\n line_width, _ = sizer(line)\\n if line_width >= surface_width:\\n self._lines += split_line(line, sizer, surface_width)\\n else:\\n self._lines.append(line)\\n self._last_width = surface_width\\n return self._lines\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self._grid_width\",\n \"def get_grid_width(self):\\n # replace with your code\\n return self._grid_width\",\n \"def width(self):\\n return np.array([f.width() for f in self])\",\n \"def setLineWidth(w=1):\\n dislin.linwid(w)\",\n \"def longlines(x):\\n return sum(len(plain(line)) // cw for line in x.split(\\\"\\\\n\\\"))\",\n \"def getSegmentWidth(self):\\n l = [segment.width for segment in self.segments]\\n if l.count(l[0]) == len(l):\\n return l[0]\\n else:\\n raise ValueError(\\\"The widths of the segments must be the same otherwise it makes no sense.\\\")\",\n \"def lineShape(self,peakAngle,width,angle):\\n x = abs(peakAngle - angle)/width\\n if x == 0.0:\\n return 1.0\\n else:\\n return (2*j1(x)/x)**2\",\n \"def width(self):\\n return (self.__width)\",\n \"def onChartSeriesLineWidthChanged(self, name, width):\\n series = self.series[name]\\n self.pen[name].setWidth(width)\\n series.setPen(self.pen[name])\",\n \"def width(self):\\n return _libsbml.Dimensions_width(self)\",\n \"def _find_spacing(self, row, ordering, max_width):\\n return max_width / (len(ordering[row]) + 1)\",\n \"def get_widths(self) -> tuple:\\n words_width = 0\\n spaces_width = 0\\n for part in self.line_parts:\\n words_width += part.width\\n spaces_width += part.spaces_width\\n return words_width, spaces_width\",\n \"def total_width(self): \\n total = 0\\n for i in range(self.no_of_columns()):\\n total += self.col_width(i)\\n return total\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self):\\n return self.__width\",\n \"def width(self) -> float:\\n return self._width\",\n \"def setPointWidth(self, width):\\n for point in self.points:\\n point.width = width\",\n \"def width(self):\\n xx = self.xx\\n return max(xx) - min(xx)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67635524","0.64742523","0.6465246","0.639043","0.639043","0.61956435","0.6169401","0.61666083","0.6136135","0.6110745","0.6110745","0.6110745","0.602749","0.602131","0.5990465","0.5978512","0.59784377","0.59361607","0.5847792","0.5825186","0.5816536","0.581552","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5815408","0.5801515","0.5723041","0.5699779","0.5693649","0.56903434","0.5678318","0.56690246","0.5667807","0.56630874","0.56585056","0.5641188","0.56245285","0.56144726","0.55947834","0.55608606","0.5532762","0.5531219","0.5529545","0.55275714","0.55275714","0.5516387","0.54962724","0.54950404","0.5495038","0.54890114","0.54890114","0.5487308","0.5486709","0.545977","0.54494005","0.5439461","0.5434393","0.5434393","0.54283017","0.54232377","0.54200166","0.5418267","0.5418267","0.54157263","0.5408058","0.53964126","0.5394243","0.5390438","0.5385698","0.5379936","0.53655946","0.536526","0.5365003","0.5356932","0.5353738","0.5353738","0.5353738","0.5353738","0.5353738","0.5353738","0.5353738","0.5353738","0.5353738","0.5353738","0.5353738","0.5353738","0.53493273","0.5344043","0.5342297"],"string":"[\n \"0.67635524\",\n \"0.64742523\",\n \"0.6465246\",\n \"0.639043\",\n \"0.639043\",\n \"0.61956435\",\n \"0.6169401\",\n \"0.61666083\",\n \"0.6136135\",\n \"0.6110745\",\n \"0.6110745\",\n \"0.6110745\",\n \"0.602749\",\n \"0.602131\",\n \"0.5990465\",\n \"0.5978512\",\n \"0.59784377\",\n \"0.59361607\",\n \"0.5847792\",\n \"0.5825186\",\n \"0.5816536\",\n \"0.581552\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5815408\",\n \"0.5801515\",\n \"0.5723041\",\n \"0.5699779\",\n \"0.5693649\",\n \"0.56903434\",\n \"0.5678318\",\n \"0.56690246\",\n \"0.5667807\",\n \"0.56630874\",\n \"0.56585056\",\n \"0.5641188\",\n \"0.56245285\",\n \"0.56144726\",\n \"0.55947834\",\n \"0.55608606\",\n \"0.5532762\",\n \"0.5531219\",\n \"0.5529545\",\n \"0.55275714\",\n \"0.55275714\",\n \"0.5516387\",\n \"0.54962724\",\n \"0.54950404\",\n \"0.5495038\",\n \"0.54890114\",\n \"0.54890114\",\n \"0.5487308\",\n \"0.5486709\",\n \"0.545977\",\n \"0.54494005\",\n \"0.5439461\",\n \"0.5434393\",\n \"0.5434393\",\n \"0.54283017\",\n \"0.54232377\",\n \"0.54200166\",\n \"0.5418267\",\n \"0.5418267\",\n \"0.54157263\",\n \"0.5408058\",\n \"0.53964126\",\n \"0.5394243\",\n \"0.5390438\",\n \"0.5385698\",\n \"0.5379936\",\n \"0.53655946\",\n \"0.536526\",\n \"0.5365003\",\n \"0.5356932\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.5353738\",\n \"0.53493273\",\n \"0.5344043\",\n \"0.5342297\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11090,"cells":{"query":{"kind":"string","value":"Change the length of all lines"},"document":{"kind":"string","value":"def length(self, length: Union[int, float]):\n self._length = length\n self._update_length()\n self.events.length()\n\n self.refresh()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setLength(self, new_length):\n\n self.length = new_length","def set_max_lines(self, n):\n\t\tself._maxLines = n\n\t\tself._trunc_lines()","def set_line_width(self, val):\n self.lwidth = val","def change_tail_length(self, value):\n self.layer.tail_length = value","def longlines(x):\n return sum(len(plain(line)) // cw for line in x.split(\"\\n\"))","def set_length(self, ak_tpl: BKT, newLength: float): # -> None:\n ...","def shorten_lines(string, lineLen, maxLines):\n lines = string.split(\"\\n\")\n lineCount = 0\n newLines = \"\"\n for line in lines:\n length = int(len(line) / lineLen) if len(line) > lineLen else 1\n if len(line) > length * lineLen:\n length += 1\n lineCount += length\n if lineCount > maxLines:\n break\n newLines += \"{}\\n\".format(line)\n return newLines","def test_lineLength(self):\n failures = []\n for line in self.output:\n if not len(line) <= lineWidth:\n failures.append(len(line))\n if failures:\n self.fail(\"%d of %d lines were too long.\\n\"\n \"%d < %s\" % (len(failures), len(self.output),\n lineWidth, failures))","def set_length(self, length):\n if length < 0:\n raise AttributeError('length should be positive')\n self.progress_char_length = length","def __shortenEmptyLines(self):\n self.activeWindow().shortenEmptyLines()","def _getNewCodeLength(self):\n nb_lines = 0\n for line in self.body.splitlines():\n if not line.startswith(\"-\"):\n nb_lines += 1\n return nb_lines","async def gpt2_set_length(self, ctx, *, arg=None):\n print('Command gpt2_set_length triggered')\n if arg:\n try:\n i = int(arg)\n assert (i > 0) and (i < 1024)\n except ValueError or AssertionError:\n ctx.send(\"ERROR: Argument must be a positive integer number\")\n self.update_config(length=arg)\n else:\n await ctx.send(\"ERROR: Argument required\")","def length(self, length):\n\n self._length = length","def defaultRecordSizer(recordLines):\n size=0\n for line in recordLines:\n size+=len(line)\n return size","def _trunc_lines_append(self):\n\t\tp = self._edit.get_buffer()\n\t\tnLines = p.get_line_count()\n\t\twhile nLines > 0:\n\t\t\tif nLines <= self._maxLines +1:\n\t\t\t\tbreak\n\t\t\tstart = p.get_start_iter()\n\t\t\tend = p.get_start_iter()\n\t\t\tend.forward_line()\n\t\t\tp.delete(start, end)\n\t\t\tnLines = p.get_line_count()","def line_length(self, dLine = 0):\n return self.buffer.line_length(self.line + dLine)","def _getOldCodeLength(self):\n nb_lines = 0\n for line in self.body.splitlines():\n if not line.startswith(\"+\"):\n nb_lines += 1\n return nb_lines","def change_length(self, value):\n self.layer.length = value\n self.lengthSpinBox.clearFocus()\n self.setFocus()","def reset_line_count(self):\n self._line_count = 0","def inc_size(self):\r\n self.__length += 1","def __updateSize(self, *args):\n bottom, top = self.getRange()\n nbchar = max(len(str(bottom)), len(str(top)))\n font = self.font()\n font.setStyle(qt.QFont.StyleItalic)\n fontMetrics = qt.QFontMetrics(font)\n self.setMaximumWidth(\n fontMetrics.boundingRect('0' * (nbchar + 1)).width()\n )\n self.setMaxLength(nbchar)","def setNewLen(self):\n self.wordLen = randint(3, 31)","def write_line(self, text, **kwargs):\n\n style = kwargs.pop('style', None) or self.style_body\n\n row = kwargs.pop('row', None) or self.row\n col = kwargs.pop('col', None) or self.col\n\n length = kwargs.pop('length', None)\n\n sheet = self.get_sheet()\n to_python = self.format_data(text)\n sheet.write(row, col, to_python, style)\n\n if isinstance(to_python, str):\n if length is not None and 10 + len(to_python) > length:\n length = 10 + len(to_python)\n else:\n length = length or (10 + len(to_python))\n else:\n if length is not None and 2 * 6 > length:\n length = 2 * 6\n else:\n length = length or (2 * 6)\n\n sheet.set_column('{0}:{0}'.format(self.get_letter_by_index(col)), length)\n return length","def set_length(self, new_length):\n if(new_length == None):\n self._logger.write(\"Error! new_length cannot be a NoneType\")\n elif(type(new_length) != float):\n self._logger.write(\"Error! new_length must be of type float\")\n else:\n try:\n self._length = new_length\n except Exception as e:\n self._logger.write(\"Error! Could not set the new length:\\n %s\" % e)","def _trunc_lines(self):\n\t\tif self._appendMessages:\n\t\t\tself._trunc_lines_append()\n\t\telse:\n\t\t\tself._trunc_lines_prepend()","def same_len(txt, name_len):\n return '\\n'.join(txt + ([' '] * (name_len - len(txt))))","def test_long_line(tmp_path: Path, capsys) -> None:\n line_length: Final = 89\n docstring = '\"\"\"Docstring.\"\"\"\\n\\n'\n long_line = line_length * \"#\"\n run_linter(\n tmp_path,\n capsys,\n f\"{docstring}{long_line}\\n\",\n f\"line too long ({line_length} > 88 characters)\",\n )","def linewidth(self, size: float):\n for line in self.ax.lines:\n line.set_linewidth(size)\n self.canvas.draw()","def length_changed(self):\n\t\tprint \"length changed\"\n\t\tlength_cbox = self.ui.findChild(QWidget, \"length_cbox\")\t\n\t\tnew_length = length_cbox.currentText()\n\t\t# todo: calculate new values","def _fixHeaderLength(self):\n self.header.seek(0)\n lines = self.header.readlines()\n headlength = len(lines)\n lines[0] = wrapLine(\"NLHEAD_FFI\", self.annotation, self.delimiter, \"%d%s%d\\n\" % (headlength, self.delimiter, self.FFI))\n self.header = StringIO(\"\".join(lines))\n self.header.seek(0)","def updateTotalLines(self):\n self.viewer().TotalLinesChanged.emit( self.editor().lines() )","def _set_length(self, length):\n self.bottom.pos.y = self.top.pos.y + length","def update_total_length(self):\n self.total_length = len(bytes(self))","def lineLengthExceeded(self, line):\n segments = self._breakLineIntoSegments(line)\n for segment in segments:\n self.lineReceived(segment)","def set_auto_throats_length(self):\n\n for n1, n2 in self.graph.edges:\n self.graph[n1][n2]['length'] = self._compute_auto_throat_length(\n n1, n2)","def length(self):\n ...","def line_width(self, width=2.0):\n self._impl.line_width(width)","def setPacketLength(self):\n self.packetLength = len(self) - PRIMARY_HEADER_BYTE_SIZE - 1","def do_len(self, line):\n\t\tif isinstance(self.cl, Book):\n\t\t\tprint(len(self.cl.data))\n\t\telse:\n\t\t\tprint(\"To get number of contacts you need to open or create book\")","def make_line(self, char: str, count: int):\r\n return char * count","def len23(self, len): # -> None:\n ...","def change_tail_width(self, value):\n self.layer.tail_width = float(value) / 2.0","def _trunc_lines_prepend(self):\n\t\tp = self._edit.get_buffer()\n\t\tnLines = p.get_line_count()\n\t\twhile nLines > 0:\n\t\t\tif nLines <= self._maxLines +1:\n\t\t\t\tbreak\n\t\t\tend = p.get_end_iter()\n\t\t\tstart = p.get_end_iter()\n\t\t\tstart.backward_line()\n\t\t\tp.delete(start, end)\n\t\t\tnLines = p.get_line_count()","def estimate_lines(self):\r\n logger.debug(\"estimate Lines\")\r\n self.filesize = Path(self.fileName).stat().st_size\r\n text = self.textwnd.toPlainText()\r\n linetext = text.split(\"\\n\")[1] + \"\\\\r\\\\n\"\r\n self.linesize = len(linetext.encode('utf-8'))\r\n self.estimated_lines = self.filesize // self.linesize\r\n logger.debug(\"Estimate Lines: {}\".format(self.estimated_lines))\r\n self.statusBar.showMessage(f\"Estimated lines: {self.estimated_lines}\")","def __padlen(self,l):\n return Utils.padlen(l,self.com.granularity)","def line_counter(self, event=None):\n try:\n text_area = self.get_current()\n self.canvas.delete('all')\n i = text_area.index(\"@0,0\")\n while True:\n dline = text_area.dlineinfo(i)\n if dline is None: break\n y = dline[1]\n linenum = str(i).split(\".\")[0]\n self.canvas.create_text(10, y + 28, anchor=\"w\", text=linenum,\n font=self.lineFont, width=0)\n text_length = self.canvas.bbox('all') # returns a tuple in the form of (x1, y1, x2, y2)\n width = text_length[2] - text_length[0] # x2-x1\n self.canvas.config(width=width + 15)\n i = text_area.index(\"%s+1line\" % i)\n # print(self.cursor_pos.cget('pady'), self.statusbar_frame.cget('pady'), )\n except:\n self.canvas.delete('all')","def update(self, line):","def default_length(self) -> int:\r\n ...","def _refresh_width(self):\n self._width = curses.tigetnum('cols')\n self._writer = formatter.DumbWriter(self._output, maxcol=self._width)","def set_base_length_entry(self, base_length):\n self.entries[\"ent_base_length\"].delete(0, END)\n self.entries[\"ent_base_length\"].insert(\n 0, str(base_length))","def setLength(self, length):\n self.vector.norm = length","def num_bytes_per_line(self, width):\n\n if width < 1:\n raise ValueError(\"Invalid bytes per line value (must be > 0)\")\n\n if width % HEX_TYPE_WIDTH[self._hex_format]:\n raise ValueError(\"Bytes per line must be a multiple of display format type\")\n\n self._num_bytes_per_line = width\n #self._refresh_view_settings()","def keep_last_lines(self, num_lines):\n self.data = self.data[-num_lines:]","def cut_in_lines(self,line):\n limit_screen = 30 #caracteres que tiene de ancho la pantalla\n length = 0 #para comparar leineas\n res = ''\n\n for linea in line.split('\\n'):\n if length + len(linea) <= limit_screen:\n new_linea = linea\n length += len(new_linea)\n else:\n if len(linea) > limit_screen:\n linea = self.cut_in_words(linea)\n new_linea = '\\n' + linea\n length = len(new_linea) - 2 #-2 para no tener en cuenta el \\n\n res += new_linea\n return res","def setTickLength(major=24,minor=16):\n dislin.ticlen(major,minor)","def reset_lines(chat_lines):\n for line in xrange(24):\n chat_lines[line].setText(chat_lines[line+1].getText())\n chat_lines[line].setTextColor(chat_lines[line+1].getTextColor())\n chat_lines[24].setText(\"\")","def len12(self, len): # -> None:\n ...","def embedcount(line):\r\n\r\n x_temp = line.count(BOX_CHAR['lu'])\r\n return self.defaults.get('size')-(4*x_temp)","def _dig_line_count_changed(self, text):\n self._setup_table_digital()","def __len__(self):\n return len(self.spricht())","def _on_len_change(self, event=None):\n with self.layer.events.length.blocker():\n self.lengthSpinBox.setValue(self.layer.length)","def AdjustFontSize(self):\r\n self.sh.Columns(\"A\").Delete()","def _update_length(self, field, tag_id, value):\n # pylint: disable=unused-argument\n if tag_id not in {8, 9, 10}:\n self._message_length += len(field) + 1\n if self._message_length >= self._max_length:\n raise FIXLengthTooLongError(\n f'message too long: {self._message_length}')","def set_line_height(self, val=30):\n if val < 24:\n val = 24\n self._line_spacing = val - 24\n\n # The printer doesn't take into account the current text height\n # when setting line height, making this more akin to inter-line\n # spacing. Default line spacing is 30 (char height of 24, line\n # spacing of 6).\n self.write(self.ASCII_ESC, '3', val)","def output(self, minLength: int = 0, maxLength=None) -> str:\n tempLine = Textline(self._contents)\n if maxLength != None:\n if minLength > maxLength:\n return(\"Error: minLength must be less than maxLength\")\n elif len(self._contents) > maxLength:\n return(self._contents[:(maxLength-3)]+\"...\")\n elif len(self._contents) == maxLength:\n return(self._contents)\n else:\n return(tempLine.output(minLength))\n else:\n while len(tempLine.contents) < minLength:\n tempLine.contents = tempLine.contents + \" \"\n return(tempLine.contents)","def _output_padding_line(self):\n for i in range(self.num_new_columns):\n self._write_column(self.new_columns[i], '|')\n self.buf += ' '\n\n self._pad_horizontally(self.num_new_columns * 2)","def len(self):\n\t\t\n\t\treturn len(self.line)","def linewrap(width = None):\n wrapper = TextWrapper()\n wrapper.width = width or 251\n wrapper.replace_whitespace = True\n wrapper.break_long_words = False\n wrapper.break_on_hyphens = False\n return wrapper.wrap","def wordwrap():\n file = open(sys.argv[2])\n width = int(sys.argv[1]) \n line = file.readline()\n while line:\n line = line.strip()\n line1,line = wordwrap_on(line,width)\n print line1\n if line=='' :\n line = file.readline()","def test_longest_line_length():\n print('Testing longest_line_length')\n\n # Case given to test this problem\n assert_equals(13, hw1.longest_line_length('poem.txt'))\n\n # Additional case to test empty file check\n assert_equals(None, hw1.longest_line_length('test1.txt'))\n\n # Additional case to test basic function\n assert_equals(18, hw1.longest_line_length('test2.txt'))","def set_body_size(self, length: int) -> None:\n self._body = [Coord2D(0, 0) for _ in range(length)]\n self._tail_visited = set()\n self.record_tail_location()","def _extend(self, newlen: int) -> None:\n diff = newlen - len(self)\n if diff > 0:\n self.extend([0] * diff)","def _set_packet_len(self, packet_len):\n self._packet_len = packet_len","def n_lines(self):\n try: \n return self._n_lines\n except AttributeError:\n self._n_lines = len(self.lines())\n return self._n_lines","def set_part_length(self, seconds):\n self._part_length = seconds","def _overwrite(self, message):\n lines = message.split('\\n')\n\n # Append whitespace to match the line's length\n if self._last_messages_length is not None:\n for i, line in enumerate(lines):\n if self._last_messages_length > Helper.len_without_decoration(self._output.get_formatter(), line):\n lines[i] = line.ljust(self._last_messages_length, '\\x20')\n\n if self._should_overwrite:\n # move back to the beginning of the progress bar before redrawing it\n self._output.write('\\x0D')\n elif self._step > 0:\n # move to new line\n self._output.writeln('')\n\n if self._format_line_count:\n self._output.write('\\033[%dA' % self._format_line_count)\n\n self._output.write('\\n'.join(lines))\n\n self._last_messages_length = 0\n\n for line in lines:\n length = Helper.len_without_decoration(self._output.get_formatter(), line)\n if length > self._last_messages_length:\n self._last_messages_length = length","def __len__(self):\n nlines = self.get_endline() - self.get_startline() + 1\n if nlines < 0:\n nlines = 0\n return nlines","def __init__(self, lineLen):\n if lineLen < 10:\n raise ValueError('lineLen cannot be less than 10')\n self.len = lineLen\n self.currentSpace = lineLen\n self.beginningOfLine = True","def truncate_lines(str, num_lines, EOL=\"\\n\"):\n return EOL.join(str.split(EOL)[0:num_lines])","def truncate(line: str, limit: int) -> str:\n if len(line) > limit:\n return line[:limit]\n return line","def setTextHeight(h=36):\n dislin.height(h)","def set_size(self, new_size: int):\n self.__tab_size = new_size\n self.__check_interpreter()\n self.__vals = [0 for _ in range(self.__tab_size)]","def setGoalLength(self, length):\n assert isinstance(length, int)\n self.goal_length = length","def bad_underline_length(): # noqa: D416","def no_of_lines():\n return render_template(\"no_of_lines.html\", no_of_lines=no_of_lines())","def copy_all(text_len):\n global counter\n counter += 1\n return (text_len)","def to_width(multiline_string, width=79, **kwargs):\n return textwrap.fill(multiline_string, width, **kwargs)","def __init__(self, keep_last_n_lines=5) :\r\n self.contextLines_ = keep_last_n_lines\r\n self.data_ = CircularBuffer(1024)\r\n self.lineNumber_ = 1\r\n self.charNumber_ = 0","def insertNewlines(text, lineLength):\r\n lenCtr = 0\r\n this = []\r\n \r\n # convert entire text to a list of words\r\n words = text.split()\r\n \r\n # get a length of all the word\r\n for each in words[:]:\r\n lenCtr += len(each) + 1 # word length + whitespace\r\n this.append(each) # add word to this line\r\n words.remove(each) # remove word from remaining text\r\n print this,\r\n print words,\r\n print lenCtr\r\n if lenCtr >= lineLength:\r\n # insert \\n when lineLength is reached\r\n this.append('\\n')\r\n break\r\n # if itarable is exhausted, just return entire text\r\n else:\r\n return ' '.join(words)\r\n # remaining text\r\n text = ' '.join(words)\r\n return ' '.join(this) + insertNewlines(text, lineLength)","def OnChanged(self, evt):\n if self._line_num:\n # Adjust line number margin width to expand as needed when line\n # number width over fills the area.\n lines = self.GetLineCount()\n mwidth = self.GetTextExtent(str(lines))[0]\n\n adj = 8\n if wx.Platform == '__WXMAC__':\n adj = 2\n\n nwidth = max(15, mwidth + adj)\n if self.GetMarginWidth(NUM_MARGIN) != nwidth:\n self.SetMarginWidth(NUM_MARGIN, nwidth)\n\n wx.PostEvent(self.GetParent(), evt)\n ed_msg.PostMessage(ed_msg.EDMSG_UI_STC_CHANGED, context=self)","def __len__(self):\n return 30","def correct_length_of_all_items(self):\n if self.need_correct_length:\n if not is_power_of_2(self.max_length) and self.length_is_power_of_2:\n self.max_length = 2 ** next_power_of_2(self.max_length)\n for i in self.items:\n i.correct_length(self.max_length)\n self.need_correct_length = False","def __length_hint__(self):\n return 2","def update(self):\n self.entire_string = self.lines[-1]\n self.str_ = self.REX.search(self.entire_string)['curstr']\n self.str_len = len(self.str_)\n self.whitespace_len = len(self.entire_string) - len(self.str_)","def fix_length(msg, length):\n string = str(msg)\n if len(string) > length:\n return string[:length - 3] + \" ..\"\n else:\n return string.ljust(length)","def fill_with_spaces(line: string, width: int) -> string:\n size = len(line)\n spaces_left = width - size\n return line + (' ' * spaces_left)","def extendSequenceLength(self, timeLength):\n timeLength = self.secToStep(timeLength)\n self._addNewSwitch(timeLength,0,0)","def token_length(self, token_length):\n\n self._token_length = token_length","def long_repeat(line):\n # your code here\n max_len = 0\n this_len = 1\n if len(line) <= 1:\n return len(line)\n\n for i in range(0, len(line)-1):\n if line[i:i+1] == line[i+1:i+2]:\n this_len += 1\n else:\n if max_len < this_len:\n max_len = this_len\n this_len = 1\n\n if max_len < this_len:\n max_len = this_len\n\n return max_len","def _truncateLine(self, line):\n return line[: irc.MAX_COMMAND_LENGTH - len(self.delimiter)]"],"string":"[\n \"def setLength(self, new_length):\\n\\n self.length = new_length\",\n \"def set_max_lines(self, n):\\n\\t\\tself._maxLines = n\\n\\t\\tself._trunc_lines()\",\n \"def set_line_width(self, val):\\n self.lwidth = val\",\n \"def change_tail_length(self, value):\\n self.layer.tail_length = value\",\n \"def longlines(x):\\n return sum(len(plain(line)) // cw for line in x.split(\\\"\\\\n\\\"))\",\n \"def set_length(self, ak_tpl: BKT, newLength: float): # -> None:\\n ...\",\n \"def shorten_lines(string, lineLen, maxLines):\\n lines = string.split(\\\"\\\\n\\\")\\n lineCount = 0\\n newLines = \\\"\\\"\\n for line in lines:\\n length = int(len(line) / lineLen) if len(line) > lineLen else 1\\n if len(line) > length * lineLen:\\n length += 1\\n lineCount += length\\n if lineCount > maxLines:\\n break\\n newLines += \\\"{}\\\\n\\\".format(line)\\n return newLines\",\n \"def test_lineLength(self):\\n failures = []\\n for line in self.output:\\n if not len(line) <= lineWidth:\\n failures.append(len(line))\\n if failures:\\n self.fail(\\\"%d of %d lines were too long.\\\\n\\\"\\n \\\"%d < %s\\\" % (len(failures), len(self.output),\\n lineWidth, failures))\",\n \"def set_length(self, length):\\n if length < 0:\\n raise AttributeError('length should be positive')\\n self.progress_char_length = length\",\n \"def __shortenEmptyLines(self):\\n self.activeWindow().shortenEmptyLines()\",\n \"def _getNewCodeLength(self):\\n nb_lines = 0\\n for line in self.body.splitlines():\\n if not line.startswith(\\\"-\\\"):\\n nb_lines += 1\\n return nb_lines\",\n \"async def gpt2_set_length(self, ctx, *, arg=None):\\n print('Command gpt2_set_length triggered')\\n if arg:\\n try:\\n i = int(arg)\\n assert (i > 0) and (i < 1024)\\n except ValueError or AssertionError:\\n ctx.send(\\\"ERROR: Argument must be a positive integer number\\\")\\n self.update_config(length=arg)\\n else:\\n await ctx.send(\\\"ERROR: Argument required\\\")\",\n \"def length(self, length):\\n\\n self._length = length\",\n \"def defaultRecordSizer(recordLines):\\n size=0\\n for line in recordLines:\\n size+=len(line)\\n return size\",\n \"def _trunc_lines_append(self):\\n\\t\\tp = self._edit.get_buffer()\\n\\t\\tnLines = p.get_line_count()\\n\\t\\twhile nLines > 0:\\n\\t\\t\\tif nLines <= self._maxLines +1:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tstart = p.get_start_iter()\\n\\t\\t\\tend = p.get_start_iter()\\n\\t\\t\\tend.forward_line()\\n\\t\\t\\tp.delete(start, end)\\n\\t\\t\\tnLines = p.get_line_count()\",\n \"def line_length(self, dLine = 0):\\n return self.buffer.line_length(self.line + dLine)\",\n \"def _getOldCodeLength(self):\\n nb_lines = 0\\n for line in self.body.splitlines():\\n if not line.startswith(\\\"+\\\"):\\n nb_lines += 1\\n return nb_lines\",\n \"def change_length(self, value):\\n self.layer.length = value\\n self.lengthSpinBox.clearFocus()\\n self.setFocus()\",\n \"def reset_line_count(self):\\n self._line_count = 0\",\n \"def inc_size(self):\\r\\n self.__length += 1\",\n \"def __updateSize(self, *args):\\n bottom, top = self.getRange()\\n nbchar = max(len(str(bottom)), len(str(top)))\\n font = self.font()\\n font.setStyle(qt.QFont.StyleItalic)\\n fontMetrics = qt.QFontMetrics(font)\\n self.setMaximumWidth(\\n fontMetrics.boundingRect('0' * (nbchar + 1)).width()\\n )\\n self.setMaxLength(nbchar)\",\n \"def setNewLen(self):\\n self.wordLen = randint(3, 31)\",\n \"def write_line(self, text, **kwargs):\\n\\n style = kwargs.pop('style', None) or self.style_body\\n\\n row = kwargs.pop('row', None) or self.row\\n col = kwargs.pop('col', None) or self.col\\n\\n length = kwargs.pop('length', None)\\n\\n sheet = self.get_sheet()\\n to_python = self.format_data(text)\\n sheet.write(row, col, to_python, style)\\n\\n if isinstance(to_python, str):\\n if length is not None and 10 + len(to_python) > length:\\n length = 10 + len(to_python)\\n else:\\n length = length or (10 + len(to_python))\\n else:\\n if length is not None and 2 * 6 > length:\\n length = 2 * 6\\n else:\\n length = length or (2 * 6)\\n\\n sheet.set_column('{0}:{0}'.format(self.get_letter_by_index(col)), length)\\n return length\",\n \"def set_length(self, new_length):\\n if(new_length == None):\\n self._logger.write(\\\"Error! new_length cannot be a NoneType\\\")\\n elif(type(new_length) != float):\\n self._logger.write(\\\"Error! new_length must be of type float\\\")\\n else:\\n try:\\n self._length = new_length\\n except Exception as e:\\n self._logger.write(\\\"Error! Could not set the new length:\\\\n %s\\\" % e)\",\n \"def _trunc_lines(self):\\n\\t\\tif self._appendMessages:\\n\\t\\t\\tself._trunc_lines_append()\\n\\t\\telse:\\n\\t\\t\\tself._trunc_lines_prepend()\",\n \"def same_len(txt, name_len):\\n return '\\\\n'.join(txt + ([' '] * (name_len - len(txt))))\",\n \"def test_long_line(tmp_path: Path, capsys) -> None:\\n line_length: Final = 89\\n docstring = '\\\"\\\"\\\"Docstring.\\\"\\\"\\\"\\\\n\\\\n'\\n long_line = line_length * \\\"#\\\"\\n run_linter(\\n tmp_path,\\n capsys,\\n f\\\"{docstring}{long_line}\\\\n\\\",\\n f\\\"line too long ({line_length} > 88 characters)\\\",\\n )\",\n \"def linewidth(self, size: float):\\n for line in self.ax.lines:\\n line.set_linewidth(size)\\n self.canvas.draw()\",\n \"def length_changed(self):\\n\\t\\tprint \\\"length changed\\\"\\n\\t\\tlength_cbox = self.ui.findChild(QWidget, \\\"length_cbox\\\")\\t\\n\\t\\tnew_length = length_cbox.currentText()\\n\\t\\t# todo: calculate new values\",\n \"def _fixHeaderLength(self):\\n self.header.seek(0)\\n lines = self.header.readlines()\\n headlength = len(lines)\\n lines[0] = wrapLine(\\\"NLHEAD_FFI\\\", self.annotation, self.delimiter, \\\"%d%s%d\\\\n\\\" % (headlength, self.delimiter, self.FFI))\\n self.header = StringIO(\\\"\\\".join(lines))\\n self.header.seek(0)\",\n \"def updateTotalLines(self):\\n self.viewer().TotalLinesChanged.emit( self.editor().lines() )\",\n \"def _set_length(self, length):\\n self.bottom.pos.y = self.top.pos.y + length\",\n \"def update_total_length(self):\\n self.total_length = len(bytes(self))\",\n \"def lineLengthExceeded(self, line):\\n segments = self._breakLineIntoSegments(line)\\n for segment in segments:\\n self.lineReceived(segment)\",\n \"def set_auto_throats_length(self):\\n\\n for n1, n2 in self.graph.edges:\\n self.graph[n1][n2]['length'] = self._compute_auto_throat_length(\\n n1, n2)\",\n \"def length(self):\\n ...\",\n \"def line_width(self, width=2.0):\\n self._impl.line_width(width)\",\n \"def setPacketLength(self):\\n self.packetLength = len(self) - PRIMARY_HEADER_BYTE_SIZE - 1\",\n \"def do_len(self, line):\\n\\t\\tif isinstance(self.cl, Book):\\n\\t\\t\\tprint(len(self.cl.data))\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"To get number of contacts you need to open or create book\\\")\",\n \"def make_line(self, char: str, count: int):\\r\\n return char * count\",\n \"def len23(self, len): # -> None:\\n ...\",\n \"def change_tail_width(self, value):\\n self.layer.tail_width = float(value) / 2.0\",\n \"def _trunc_lines_prepend(self):\\n\\t\\tp = self._edit.get_buffer()\\n\\t\\tnLines = p.get_line_count()\\n\\t\\twhile nLines > 0:\\n\\t\\t\\tif nLines <= self._maxLines +1:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tend = p.get_end_iter()\\n\\t\\t\\tstart = p.get_end_iter()\\n\\t\\t\\tstart.backward_line()\\n\\t\\t\\tp.delete(start, end)\\n\\t\\t\\tnLines = p.get_line_count()\",\n \"def estimate_lines(self):\\r\\n logger.debug(\\\"estimate Lines\\\")\\r\\n self.filesize = Path(self.fileName).stat().st_size\\r\\n text = self.textwnd.toPlainText()\\r\\n linetext = text.split(\\\"\\\\n\\\")[1] + \\\"\\\\\\\\r\\\\\\\\n\\\"\\r\\n self.linesize = len(linetext.encode('utf-8'))\\r\\n self.estimated_lines = self.filesize // self.linesize\\r\\n logger.debug(\\\"Estimate Lines: {}\\\".format(self.estimated_lines))\\r\\n self.statusBar.showMessage(f\\\"Estimated lines: {self.estimated_lines}\\\")\",\n \"def __padlen(self,l):\\n return Utils.padlen(l,self.com.granularity)\",\n \"def line_counter(self, event=None):\\n try:\\n text_area = self.get_current()\\n self.canvas.delete('all')\\n i = text_area.index(\\\"@0,0\\\")\\n while True:\\n dline = text_area.dlineinfo(i)\\n if dline is None: break\\n y = dline[1]\\n linenum = str(i).split(\\\".\\\")[0]\\n self.canvas.create_text(10, y + 28, anchor=\\\"w\\\", text=linenum,\\n font=self.lineFont, width=0)\\n text_length = self.canvas.bbox('all') # returns a tuple in the form of (x1, y1, x2, y2)\\n width = text_length[2] - text_length[0] # x2-x1\\n self.canvas.config(width=width + 15)\\n i = text_area.index(\\\"%s+1line\\\" % i)\\n # print(self.cursor_pos.cget('pady'), self.statusbar_frame.cget('pady'), )\\n except:\\n self.canvas.delete('all')\",\n \"def update(self, line):\",\n \"def default_length(self) -> int:\\r\\n ...\",\n \"def _refresh_width(self):\\n self._width = curses.tigetnum('cols')\\n self._writer = formatter.DumbWriter(self._output, maxcol=self._width)\",\n \"def set_base_length_entry(self, base_length):\\n self.entries[\\\"ent_base_length\\\"].delete(0, END)\\n self.entries[\\\"ent_base_length\\\"].insert(\\n 0, str(base_length))\",\n \"def setLength(self, length):\\n self.vector.norm = length\",\n \"def num_bytes_per_line(self, width):\\n\\n if width < 1:\\n raise ValueError(\\\"Invalid bytes per line value (must be > 0)\\\")\\n\\n if width % HEX_TYPE_WIDTH[self._hex_format]:\\n raise ValueError(\\\"Bytes per line must be a multiple of display format type\\\")\\n\\n self._num_bytes_per_line = width\\n #self._refresh_view_settings()\",\n \"def keep_last_lines(self, num_lines):\\n self.data = self.data[-num_lines:]\",\n \"def cut_in_lines(self,line):\\n limit_screen = 30 #caracteres que tiene de ancho la pantalla\\n length = 0 #para comparar leineas\\n res = ''\\n\\n for linea in line.split('\\\\n'):\\n if length + len(linea) <= limit_screen:\\n new_linea = linea\\n length += len(new_linea)\\n else:\\n if len(linea) > limit_screen:\\n linea = self.cut_in_words(linea)\\n new_linea = '\\\\n' + linea\\n length = len(new_linea) - 2 #-2 para no tener en cuenta el \\\\n\\n res += new_linea\\n return res\",\n \"def setTickLength(major=24,minor=16):\\n dislin.ticlen(major,minor)\",\n \"def reset_lines(chat_lines):\\n for line in xrange(24):\\n chat_lines[line].setText(chat_lines[line+1].getText())\\n chat_lines[line].setTextColor(chat_lines[line+1].getTextColor())\\n chat_lines[24].setText(\\\"\\\")\",\n \"def len12(self, len): # -> None:\\n ...\",\n \"def embedcount(line):\\r\\n\\r\\n x_temp = line.count(BOX_CHAR['lu'])\\r\\n return self.defaults.get('size')-(4*x_temp)\",\n \"def _dig_line_count_changed(self, text):\\n self._setup_table_digital()\",\n \"def __len__(self):\\n return len(self.spricht())\",\n \"def _on_len_change(self, event=None):\\n with self.layer.events.length.blocker():\\n self.lengthSpinBox.setValue(self.layer.length)\",\n \"def AdjustFontSize(self):\\r\\n self.sh.Columns(\\\"A\\\").Delete()\",\n \"def _update_length(self, field, tag_id, value):\\n # pylint: disable=unused-argument\\n if tag_id not in {8, 9, 10}:\\n self._message_length += len(field) + 1\\n if self._message_length >= self._max_length:\\n raise FIXLengthTooLongError(\\n f'message too long: {self._message_length}')\",\n \"def set_line_height(self, val=30):\\n if val < 24:\\n val = 24\\n self._line_spacing = val - 24\\n\\n # The printer doesn't take into account the current text height\\n # when setting line height, making this more akin to inter-line\\n # spacing. Default line spacing is 30 (char height of 24, line\\n # spacing of 6).\\n self.write(self.ASCII_ESC, '3', val)\",\n \"def output(self, minLength: int = 0, maxLength=None) -> str:\\n tempLine = Textline(self._contents)\\n if maxLength != None:\\n if minLength > maxLength:\\n return(\\\"Error: minLength must be less than maxLength\\\")\\n elif len(self._contents) > maxLength:\\n return(self._contents[:(maxLength-3)]+\\\"...\\\")\\n elif len(self._contents) == maxLength:\\n return(self._contents)\\n else:\\n return(tempLine.output(minLength))\\n else:\\n while len(tempLine.contents) < minLength:\\n tempLine.contents = tempLine.contents + \\\" \\\"\\n return(tempLine.contents)\",\n \"def _output_padding_line(self):\\n for i in range(self.num_new_columns):\\n self._write_column(self.new_columns[i], '|')\\n self.buf += ' '\\n\\n self._pad_horizontally(self.num_new_columns * 2)\",\n \"def len(self):\\n\\t\\t\\n\\t\\treturn len(self.line)\",\n \"def linewrap(width = None):\\n wrapper = TextWrapper()\\n wrapper.width = width or 251\\n wrapper.replace_whitespace = True\\n wrapper.break_long_words = False\\n wrapper.break_on_hyphens = False\\n return wrapper.wrap\",\n \"def wordwrap():\\n file = open(sys.argv[2])\\n width = int(sys.argv[1]) \\n line = file.readline()\\n while line:\\n line = line.strip()\\n line1,line = wordwrap_on(line,width)\\n print line1\\n if line=='' :\\n line = file.readline()\",\n \"def test_longest_line_length():\\n print('Testing longest_line_length')\\n\\n # Case given to test this problem\\n assert_equals(13, hw1.longest_line_length('poem.txt'))\\n\\n # Additional case to test empty file check\\n assert_equals(None, hw1.longest_line_length('test1.txt'))\\n\\n # Additional case to test basic function\\n assert_equals(18, hw1.longest_line_length('test2.txt'))\",\n \"def set_body_size(self, length: int) -> None:\\n self._body = [Coord2D(0, 0) for _ in range(length)]\\n self._tail_visited = set()\\n self.record_tail_location()\",\n \"def _extend(self, newlen: int) -> None:\\n diff = newlen - len(self)\\n if diff > 0:\\n self.extend([0] * diff)\",\n \"def _set_packet_len(self, packet_len):\\n self._packet_len = packet_len\",\n \"def n_lines(self):\\n try: \\n return self._n_lines\\n except AttributeError:\\n self._n_lines = len(self.lines())\\n return self._n_lines\",\n \"def set_part_length(self, seconds):\\n self._part_length = seconds\",\n \"def _overwrite(self, message):\\n lines = message.split('\\\\n')\\n\\n # Append whitespace to match the line's length\\n if self._last_messages_length is not None:\\n for i, line in enumerate(lines):\\n if self._last_messages_length > Helper.len_without_decoration(self._output.get_formatter(), line):\\n lines[i] = line.ljust(self._last_messages_length, '\\\\x20')\\n\\n if self._should_overwrite:\\n # move back to the beginning of the progress bar before redrawing it\\n self._output.write('\\\\x0D')\\n elif self._step > 0:\\n # move to new line\\n self._output.writeln('')\\n\\n if self._format_line_count:\\n self._output.write('\\\\033[%dA' % self._format_line_count)\\n\\n self._output.write('\\\\n'.join(lines))\\n\\n self._last_messages_length = 0\\n\\n for line in lines:\\n length = Helper.len_without_decoration(self._output.get_formatter(), line)\\n if length > self._last_messages_length:\\n self._last_messages_length = length\",\n \"def __len__(self):\\n nlines = self.get_endline() - self.get_startline() + 1\\n if nlines < 0:\\n nlines = 0\\n return nlines\",\n \"def __init__(self, lineLen):\\n if lineLen < 10:\\n raise ValueError('lineLen cannot be less than 10')\\n self.len = lineLen\\n self.currentSpace = lineLen\\n self.beginningOfLine = True\",\n \"def truncate_lines(str, num_lines, EOL=\\\"\\\\n\\\"):\\n return EOL.join(str.split(EOL)[0:num_lines])\",\n \"def truncate(line: str, limit: int) -> str:\\n if len(line) > limit:\\n return line[:limit]\\n return line\",\n \"def setTextHeight(h=36):\\n dislin.height(h)\",\n \"def set_size(self, new_size: int):\\n self.__tab_size = new_size\\n self.__check_interpreter()\\n self.__vals = [0 for _ in range(self.__tab_size)]\",\n \"def setGoalLength(self, length):\\n assert isinstance(length, int)\\n self.goal_length = length\",\n \"def bad_underline_length(): # noqa: D416\",\n \"def no_of_lines():\\n return render_template(\\\"no_of_lines.html\\\", no_of_lines=no_of_lines())\",\n \"def copy_all(text_len):\\n global counter\\n counter += 1\\n return (text_len)\",\n \"def to_width(multiline_string, width=79, **kwargs):\\n return textwrap.fill(multiline_string, width, **kwargs)\",\n \"def __init__(self, keep_last_n_lines=5) :\\r\\n self.contextLines_ = keep_last_n_lines\\r\\n self.data_ = CircularBuffer(1024)\\r\\n self.lineNumber_ = 1\\r\\n self.charNumber_ = 0\",\n \"def insertNewlines(text, lineLength):\\r\\n lenCtr = 0\\r\\n this = []\\r\\n \\r\\n # convert entire text to a list of words\\r\\n words = text.split()\\r\\n \\r\\n # get a length of all the word\\r\\n for each in words[:]:\\r\\n lenCtr += len(each) + 1 # word length + whitespace\\r\\n this.append(each) # add word to this line\\r\\n words.remove(each) # remove word from remaining text\\r\\n print this,\\r\\n print words,\\r\\n print lenCtr\\r\\n if lenCtr >= lineLength:\\r\\n # insert \\\\n when lineLength is reached\\r\\n this.append('\\\\n')\\r\\n break\\r\\n # if itarable is exhausted, just return entire text\\r\\n else:\\r\\n return ' '.join(words)\\r\\n # remaining text\\r\\n text = ' '.join(words)\\r\\n return ' '.join(this) + insertNewlines(text, lineLength)\",\n \"def OnChanged(self, evt):\\n if self._line_num:\\n # Adjust line number margin width to expand as needed when line\\n # number width over fills the area.\\n lines = self.GetLineCount()\\n mwidth = self.GetTextExtent(str(lines))[0]\\n\\n adj = 8\\n if wx.Platform == '__WXMAC__':\\n adj = 2\\n\\n nwidth = max(15, mwidth + adj)\\n if self.GetMarginWidth(NUM_MARGIN) != nwidth:\\n self.SetMarginWidth(NUM_MARGIN, nwidth)\\n\\n wx.PostEvent(self.GetParent(), evt)\\n ed_msg.PostMessage(ed_msg.EDMSG_UI_STC_CHANGED, context=self)\",\n \"def __len__(self):\\n return 30\",\n \"def correct_length_of_all_items(self):\\n if self.need_correct_length:\\n if not is_power_of_2(self.max_length) and self.length_is_power_of_2:\\n self.max_length = 2 ** next_power_of_2(self.max_length)\\n for i in self.items:\\n i.correct_length(self.max_length)\\n self.need_correct_length = False\",\n \"def __length_hint__(self):\\n return 2\",\n \"def update(self):\\n self.entire_string = self.lines[-1]\\n self.str_ = self.REX.search(self.entire_string)['curstr']\\n self.str_len = len(self.str_)\\n self.whitespace_len = len(self.entire_string) - len(self.str_)\",\n \"def fix_length(msg, length):\\n string = str(msg)\\n if len(string) > length:\\n return string[:length - 3] + \\\" ..\\\"\\n else:\\n return string.ljust(length)\",\n \"def fill_with_spaces(line: string, width: int) -> string:\\n size = len(line)\\n spaces_left = width - size\\n return line + (' ' * spaces_left)\",\n \"def extendSequenceLength(self, timeLength):\\n timeLength = self.secToStep(timeLength)\\n self._addNewSwitch(timeLength,0,0)\",\n \"def token_length(self, token_length):\\n\\n self._token_length = token_length\",\n \"def long_repeat(line):\\n # your code here\\n max_len = 0\\n this_len = 1\\n if len(line) <= 1:\\n return len(line)\\n\\n for i in range(0, len(line)-1):\\n if line[i:i+1] == line[i+1:i+2]:\\n this_len += 1\\n else:\\n if max_len < this_len:\\n max_len = this_len\\n this_len = 1\\n\\n if max_len < this_len:\\n max_len = this_len\\n\\n return max_len\",\n \"def _truncateLine(self, line):\\n return line[: irc.MAX_COMMAND_LENGTH - len(self.delimiter)]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67900187","0.66235274","0.6572527","0.64778185","0.630202","0.62923074","0.62599736","0.62461704","0.62156516","0.6177423","0.6118437","0.6068556","0.6058712","0.60584587","0.60579914","0.5999391","0.5994094","0.5986776","0.59459186","0.5893659","0.58866584","0.5878015","0.58271325","0.58256114","0.5816628","0.5771301","0.5748398","0.57405794","0.5702476","0.5695587","0.5691902","0.5689695","0.5668656","0.5641838","0.5636051","0.5619915","0.5618561","0.5616685","0.5605904","0.55914974","0.558294","0.5569282","0.55540705","0.5547681","0.55466944","0.5544533","0.5542294","0.5541617","0.55403763","0.55318683","0.5530104","0.5526176","0.5521519","0.5516277","0.5514548","0.55133563","0.5496188","0.54835373","0.5481963","0.5480076","0.547547","0.5467732","0.5467612","0.5462525","0.5452323","0.5451494","0.544293","0.54405886","0.5436953","0.5432554","0.543145","0.5426933","0.54223114","0.54209954","0.5420905","0.5412546","0.54005843","0.5398546","0.539817","0.5386898","0.53827065","0.53818923","0.53660053","0.53638077","0.5362633","0.5356615","0.5349229","0.5342385","0.53307885","0.5327442","0.5324819","0.5323512","0.53187156","0.53122705","0.531029","0.5303333","0.5302908","0.5301856","0.52975774","0.5296524"],"string":"[\n \"0.67900187\",\n \"0.66235274\",\n \"0.6572527\",\n \"0.64778185\",\n \"0.630202\",\n \"0.62923074\",\n \"0.62599736\",\n \"0.62461704\",\n \"0.62156516\",\n \"0.6177423\",\n \"0.6118437\",\n \"0.6068556\",\n \"0.6058712\",\n \"0.60584587\",\n \"0.60579914\",\n \"0.5999391\",\n \"0.5994094\",\n \"0.5986776\",\n \"0.59459186\",\n \"0.5893659\",\n \"0.58866584\",\n \"0.5878015\",\n \"0.58271325\",\n \"0.58256114\",\n \"0.5816628\",\n \"0.5771301\",\n \"0.5748398\",\n \"0.57405794\",\n \"0.5702476\",\n \"0.5695587\",\n \"0.5691902\",\n \"0.5689695\",\n \"0.5668656\",\n \"0.5641838\",\n \"0.5636051\",\n \"0.5619915\",\n \"0.5618561\",\n \"0.5616685\",\n \"0.5605904\",\n \"0.55914974\",\n \"0.558294\",\n \"0.5569282\",\n \"0.55540705\",\n \"0.5547681\",\n \"0.55466944\",\n \"0.5544533\",\n \"0.5542294\",\n \"0.5541617\",\n \"0.55403763\",\n \"0.55318683\",\n \"0.5530104\",\n \"0.5526176\",\n \"0.5521519\",\n \"0.5516277\",\n \"0.5514548\",\n \"0.55133563\",\n \"0.5496188\",\n \"0.54835373\",\n \"0.5481963\",\n \"0.5480076\",\n \"0.547547\",\n \"0.5467732\",\n \"0.5467612\",\n \"0.5462525\",\n \"0.5452323\",\n \"0.5451494\",\n \"0.544293\",\n \"0.54405886\",\n \"0.5436953\",\n \"0.5432554\",\n \"0.543145\",\n \"0.5426933\",\n \"0.54223114\",\n \"0.54209954\",\n \"0.5420905\",\n \"0.5412546\",\n \"0.54005843\",\n \"0.5398546\",\n \"0.539817\",\n \"0.5386898\",\n \"0.53827065\",\n \"0.53818923\",\n \"0.53660053\",\n \"0.53638077\",\n \"0.5362633\",\n \"0.5356615\",\n \"0.5349229\",\n \"0.5342385\",\n \"0.53307885\",\n \"0.5327442\",\n \"0.5324819\",\n \"0.5323512\",\n \"0.53187156\",\n \"0.53122705\",\n \"0.531029\",\n \"0.5303333\",\n \"0.5302908\",\n \"0.5301856\",\n \"0.52975774\",\n \"0.5296524\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5355139"},"document_rank":{"kind":"string","value":"86"}}},{"rowIdx":11091,"cells":{"query":{"kind":"string","value":"Method for calculating vector lengths Implemented ONLY for imagelike vector data"},"document":{"kind":"string","value":"def _update_length(self):\n\n if self._data_type == 'coords':\n return \"length adjustment not allowed for coordinate-style data\"\n elif self._data_type == 'image':\n self._vectors = self._convert_to_vector_type(self._current_data)\n vertices, triangles = self._generate_meshes(self.vectors,\n self.width)\n self._mesh_vertices = vertices\n self._mesh_triangles = triangles"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def length_vector(v):\n return sqrt(dot_vectors(v, v))","def veclength(vec):\n vec = np.array(vec, copy=False).reshape(-1, 3)\n return np.sqrt(np.einsum('ij,ij->i', vec, vec))","def vec_len(x):\r\n \r\n length = math.sqrt(x[0]**2 + x[1]**2)\r\n return length","def get_vector_length(vector):\n return np.linalg.norm(vector)","def get_vector_length(v):\r\n v = as_tensor_variable(v)\r\n if v.ndim != 1:\r\n raise TypeError('argument must be symbolic vector')\r\n if v.type.broadcastable[0]:\r\n return 1\r\n if isinstance(v, gof.Constant) and v.type.ndim == 1:\r\n return len(v.data)\r\n if v.owner and isinstance(v.owner.op, theano.tensor.opt.MakeVector):\r\n return len(v.owner.inputs)\r\n if v.owner and isinstance(v.owner.op, Shape):\r\n return v.owner.inputs[0].type.ndim\r\n raise ValueError(\"length not known\")","def length(vec):\n return vec.dot(vec)**.5","def length(vec):\n\n return math.sqrt(dotproduct(vec, vec))","def vector_len( vector ):\n \n if not isinstance(vector, np.ndarray ):\n return len(vector)\n else:\n shape = vector.shape # shape is a tuple\n \n sl = len(shape)\n if sl == 0:\n return 0\n elif sl == 1:\n return shape[0]\n else: \n non_one_dims = [ s for s in shape if s > 1 ]\n non_one_dims_len = len(non_one_dims)\n if non_one_dims_len > 1:\n raise ValueError(\"Function vector_len: Not a vector provided, shape : %s\", shape)\n elif non_one_dims_len == 0:\n return 1\n else:\n return non_one_dims[0]","def _get_vector_size(self):\n if len(self):\n return len(self.values()[0])\n else:\n return 0","def length(vector):\n a, b, c = vector\n return math.sqrt(a ** 2 + b ** 2 + c ** 2)","def length(vec):\n return np.linalg.norm(vec)","def vectorLength(v1, v2=None):\n if v2 is None:\n v2 = v1\n return math.sqrt(dotProduct(v1, v2))","def len(self):\n return math.sqrt(self.v[0] * self.v[0] + self.v[1] * self.v[1])","def length(v: Vec2) -> float:\n return sqrt(v.x ** 2 + v.y ** 2)","def getLength(self):\n return self.vector.norm","def length(v):\n return math.sqrt(v[0]**2 + v[1]**2)","def get_length(data):\n return np.array([len(conv) for conv in data]).reshape(-1, 1)","def V_length(atoms):\n \n Vl = 0 # this is the variable we will store the sum of all the energies in\n N = len(atoms)\n for i in range(N):\n j = (i+1) % N\n length = norm(atoms.coords[i] - atoms.coords[j]) # norm computes the length of a vector\n \n Vl += (length - L0)**2\n \n return Vl","def get_vector_norm(v1):\n #sets the sum of all coordinates of vectors to 0\n sum = 0\n \n #updates the sum by multiplying the coordinate by itself and adding it into the variable sum\n for key in v1:\n sum += (v1[key])**2\n #find the square root of the sum \n vector_length = math.sqrt(sum)\n \n return vector_length","def __len__(self):\n return len(self._representation_vector)","def vector_length(self, x: float, y: float, z: float) -> float:\n A = 2.0 * (x * y * self.aga + x * z * self.bbe + y * z * self.cal)\n return sqrt(x ** 2 * self.asq + y ** 2 * self.bsq + z ** 2 * self.csq + A)","def __len__(self):\n num_x, num_y = self.conv_dims()\n return num_x * num_y","def computeDirLength(self):\n self.length , uv = dirAndLength(self.pointN, self.point1)\n self.unitv = uv\n return uv","def dim_from_concatenated_vector(v):\n return int(np.sqrt(v.shape[0] - 1))","def length(first):\n if isinstance(first,FreeCAD.Vector):\n return math.sqrt(first.x*first.x + first.y*first.y + first.z*first.z)","def Lengths(self):\n\n self.__do_essential_memebers_exist__()\n\n if self.element_type == \"line\":\n coords = self.points[self.elements[:,:2],:]\n lengths = np.linalg.norm(coords[:,1,:] - coords[:,0,:],axis=1)\n else:\n # self.GetEdges()\n # coords = self.points[self.all_edges,:]\n coords = self.points[self.elements[:,:2],:]\n lengths = np.linalg.norm(coords[:,1,:] - coords[:,0,:],axis=1)\n\n return lengths","def length_vector_numpy(vector):\n return np.linalg.norm(vector)","def dims(self):\n raise NotImplementedError('Please use Vector2Array or Vector3Array')","def boxLengths(self):\n try:\n # See if we have a cached version\n return self._boxLengths\n except AttributeError:\n pass\n if self.box_vectors is not None:\n # Get the lengths of each vector\n if u.is_quantity(self.box_vectors):\n # Unlikely -- each vector is like a quantity\n vecs = self.box_vectors.value_in_unit(u.nanometers)\n elif u.is_quantity(self.box_vectors[0]):\n # Assume all box vectors are quantities\n vecs = [x.value_in_unit(u.nanometers) for x in self.box_vectors]\n else:\n # Assume nanometers\n vecs = self.box_vectors\n a = sqrt(vecs[0][0]*vecs[0][0] + vecs[0][1]*vecs[0][1] +\n vecs[0][2]*vecs[0][2])\n b = sqrt(vecs[1][0]*vecs[1][0] + vecs[1][1]*vecs[1][1] +\n vecs[1][2]*vecs[1][2])\n c = sqrt(vecs[2][0]*vecs[2][0] + vecs[2][1]*vecs[2][1] +\n vecs[2][2]*vecs[2][2])\n self._boxLengths = (a, b, c) * u.nanometers\n return self._boxLengths\n return None","def _osLen(self):\n return int(np.ceil(self.minOverscan * self.sampleRate / self.downsample) * self.downsample)\n\n #osv = self.osVector\n #return np.ceil(np.linalg.norm(osv) / self.pixelWidth)","def length_vector_sqrd(v):\n return dot_vectors(v, v)","def dimensionality(self):\n if self.vector.shape is ():\n return 0\n if len(self.vector.shape) is 1:\n return 1\n _, dim = self.vector.shape\n return dim","def __len__(self):\n return self._vector.degree()","def __len__(self):\n return len(self.centroid_vector)","def length(self):\n return np.sqrt(np.sum(self**2, axis=1)).view(np.ndarray)","def compare_vectors(v1, v2):\n if len(v1) == len(v2):\n distance = 0\n for i in xrange(len(v1)):\n distance += (v1[i] - v2[i]) ** 2\n return distance\n else:\n print \"vector not match in dimensions\"","def vector_magnitude(v):\n\n v = np.atleast_2d(v)\n\n return np.sqrt((v**2).sum(axis=1))","def __len__(self):\n\n value_length = []\n for v in chain(self.values(), self.metainfo_values()):\n if isinstance(v, LabelData):\n value_length.append(v.label.shape[0])\n elif is_splitable_var(v):\n value_length.append(len(v))\n else:\n continue\n\n # NOTE: If length of values are not same or the current data sample\n # is empty, return length as 1\n if len(list(set(value_length))) != 1:\n return 1\n\n length = value_length[0]\n return length","def dims(self):\n return self.v.dims() # TODO: check (empty? etc)\n #return self.t.shape # TODO: check (empty? etc)\n # TODO: convert to tuple? here / in varset?","def length(self):\n return math.sqrt(self.x * self.x + self.y * self.y)","def length(self):\n return pyvista.Box(self.bounds).length","def __len__(self):\n _, timesteps, height, width = self.data.shape\n height //= self.size\n width //= self.size\n\n if self.subset == 'train':\n out = self.length\n elif self.subset == 'all':\n out = height * width\n else:\n out = (height // 2) * (width // 2)\n\n if not self.time:\n out *= timesteps\n\n return out","def __size_restriction_correct_vector_vector(self):\n\n strTestName = 'Vector size lower or equal to the size of a vector (correct)'\n RxCSObject = _RxCSobject()\n\n # Firstly, let us define a reference parameter\n RxCSObject.paramAddMan('vRefParameter1', 'Vector ref. parameter')\n RxCSObject.paramType('vRefParameter1', np.ndarray)\n\n # Now, let me define a Numpy vector\n RxCSObject.paramAddMan('parameter1', 'Numpy array 1D parameter')\n RxCSObject.paramType('parameter1', np.ndarray)\n RxCSObject.paramSizLE('parameter1', 'vRefParameter1', mul=3)\n\n RxCSObject.vRefParameter1 = np.array([0, 1, 0, 4])\n RxCSObject.parameter1 = np.random.randn(9)\n\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)","def length(xyz, along=False):\n xyz = np.asarray(xyz)\n if xyz.shape[0] < 2:\n if along:\n return np.array([0])\n return 0\n dists = np.sqrt((np.diff(xyz, axis=0) ** 2).sum(axis=1))\n if along:\n return np.cumsum(dists)\n return np.sum(dists)","def GetVoxelSize(vDataSet):\r\n nx = vDataSet.GetSizeX()\r\n ny = vDataSet.GetSizeY()\r\n nz = vDataSet.GetSizeZ()\r\n\r\n if nx > 0: nx = abs(vDataSet.GetExtendMaxX()-vDataSet.GetExtendMinX())/nx;\r\n if ny > 0: ny = abs(vDataSet.GetExtendMaxY()-vDataSet.GetExtendMinY())/ny;\r\n if nz > 0: nz = abs(vDataSet.GetExtendMaxZ()-vDataSet.GetExtendMinZ())/nz;\r\n\r\n return nx,ny,nz","def length(self, src_lengths: torch.LongTensor) -> torch.LongTensor:\n return src_lengths","def is_vector(self):\n return len(self.coeffs.shape[self.sdim:]) == 1","def length(self):\n\n return np.array([f.length() for f in self])","def test_normal_unit_length(self):\n neighborhood, pc = create_point_cloud_in_plane_and_neighborhood()\n normals = np.array(EigenValueVectorizeFeatureExtractor().extract(pc, neighborhood, None, None, None)[3:6])\n lengths = np.sum(normals * normals, axis=0)\n np.testing.assert_almost_equal(np.ones_like(lengths), lengths)","def length(self):\n return len(self.x)","def get_dimension_length(self):\n pass","def dimensions():","def length(self) -> npt.NDArray[np.float_]:\n return dist(*self.vertices)","def length(self):\n points = [Point(v, crs=self.crs) for v in self.vertices]\n distances = [a.distance(b) for a, b in zip(points[:-1], points[1:])]\n return sum(distances)","def GetOffsets(self) -> \"itkVectorContainerUCO2 const *\":\n return _itkScalarImageToRunLengthFeaturesFilterPython.itkScalarImageToRunLengthFeaturesFilterIUS2_GetOffsets(self)","def lengths(x: Any) -> IntOrIter:\n if is_scalar(x):\n return Array([1], dtype=numpy.int_)\n return Array([length(elem) for elem in x], dtype=numpy.int_)","def actual_size(v=(0, 0)):\n return v[0], v[1]","def GetOffsets(self) -> \"itkVectorContainerUCO2 const *\":\n return _itkScalarImageToRunLengthFeaturesFilterPython.itkScalarImageToRunLengthFeaturesFilterIF2_GetOffsets(self)","def length(self):\n return _lattice.length(self._accelerator.lattice)","def __len__(self):\n return len(self.focals)","def __getitem__(self, *args):\n return _uhd_swig.uhd_size_vector_t___getitem__(self, *args)","def compute_size(self):\n length = np.max(np.max(self.positions, axis=1) -\n np.min(self.positions, axis=1))\n return length + 2*self.get_radii().max()","def euclidian_length(self):\n\n if self.get_len() > 1:\n shape_length = 0\n last_x = self.x\n last_y = self.y\n scale = [0]\n for i in range(self.len - 2):\n x = np.array(self.x[i + 1])\n y = np.array(self.y[i + 1])\n last_x = np.array(self.x[i])\n last_y = np.array(self.y[i])\n shape_length += np.sqrt((x - last_x) ** 2 + (y - last_y) ** 2)\n scale.append(shape_length)\n return shape_length, scale\n\n else:\n return 0, [0]","def dimension(self):\r\n a = 0\r\n for x in self.faces():\r\n if (len(x) > a):\r\n a = len(x) \r\n return a-1","def length(self):\n return math.sqrt(self.x**2 + self.y**2 + self.z**2)","def calculate_length(self):\n raise NotImplementedError","def numel(self):\n return self.t.size","def element_size(self):\n vecs = (\n self.nodes[self.elements[:, :4], :][:, 1:, :]\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\n )\n return np.abs(np.linalg.det(vecs)) / 6","def __len__(self):\n return self.xyz.shape[0]","def __len__(self):\n return self.xyz.shape[0]","def get_length(self):\n return math.sqrt(self.x**2 + self.y**2)","def __len__(self):\n return len(self.kernels)","def GetOffsets(self) -> \"itkVectorContainerUCO2 const *\":\n return _itkScalarImageToRunLengthFeaturesFilterPython.itkScalarImageToRunLengthFeaturesFilterIUC2_GetOffsets(self)","def length(self):\n return int(np.sum([x.length for x in self.parameters]))","def lic_flow(vectors,len_pix=10):\n vectors = np.asarray(vectors)\n m,n,two = vectors.shape\n if two!=2:\n raise ValueError\n\n result = np.zeros((2*len_pix+1,m,n,2),dtype=np.int32) # FIXME: int16?\n center = len_pix\n result[center,:,:,0] = np.arange(m)[:,np.newaxis]\n result[center,:,:,1] = np.arange(n)[np.newaxis,:]\n\n for i in range(m):\n for j in range(n):\n y = i\n x = j\n fx = 0.5\n fy = 0.5\n for k in range(len_pix):\n vx, vy = vectors[y,x]\n print x, y, vx, vy\n if vx>=0:\n tx = (1-fx)/vx\n else:\n tx = -fx/vx\n if vy>=0:\n ty = (1-fy)/vy\n else:\n ty = -fy/vy\n if tx<ty:\n print \"x step\"\n if vx>0:\n x+=1\n fy+=vy*tx\n fx=0.\n else:\n x-=1\n fy+=vy*tx\n fx=1.\n else:\n print \"y step\"\n if vy>0:\n y+=1\n fx+=vx*ty\n fy=0.\n else:\n y-=1\n fx+=vx*ty\n fy=1.\n if x<0: x=0\n if y<0: y=0\n if x>=n: x=n-1\n if y>=m: y=m-1\n result[center+k+1,i,j,:] = y, x\n\n\n\n return result","def calc_vector_from_ndarray(data):\n return data[1:] - data[0:-1]","def getlen(self):\n if self.onlydiag():\n return self.lendiag()\n else:\n return len(self)","def length(sequence):\n #sequence = torch.from_numpy(sequence).type('torch.FloatTensor')\n if isinstance(sequence, np.ndarray):\n sequence = Variable(torch.from_numpy(sequence).type('torch.FloatTensor'))\n #print('type:',type(sequence))\n populated = torch.sign(torch.abs(sequence))\n length = torch.sum(populated, 1)\n length = length.type('torch.IntTensor')\n mask = torch.unsqueeze(populated, -1)\n mask = mask.type('torch.FloatTensor')\n return length, mask","def calculate_vector_magnitude(self, vector):\n magnitude_squared = 0\n for d in vector:\n magnitude_squared += pow(d, 2)\n \n magnitude = pow(magnitude_squared, .5)\n\n return magnitude","def EdgeLengths(self,which_edges='boundary'):\n\n assert self.points is not None\n assert self.element_type is not None\n\n\n lengths = None\n if which_edges == 'boundary':\n if self.edges is None:\n self.GetBoundaryEdges()\n\n edge_coords = self.points[self.edges[:,:2],:]\n lengths = np.linalg.norm(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)\n\n elif which_edges == 'all':\n if self.all_edges is None:\n self.GetEdges()\n\n edge_coords = self.points[self.all_edges[:,:2],:]\n lengths = np.linalg.norm(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)\n\n return lengths","def __size_restriction_incorrect_vector_vector(self):\n\n strTestName = 'Vector size lower than the size of a vector (incorrect)'\n RxCSObject = _RxCSobject()\n\n # Firstly, let us define a reference parameter\n RxCSObject.paramAddMan('vRefParameter1', 'Vector ref. parameter')\n RxCSObject.paramType('vRefParameter1', np.ndarray)\n\n # Now, let me define a Numpy vector\n RxCSObject.paramAddMan('parameter1', 'Numpy array 2D parameter')\n RxCSObject.paramType('parameter1', np.ndarray)\n RxCSObject.paramSizL('parameter1', 'vRefParameter1')\n\n RxCSObject.vRefParameter1 = np.array([0, 1, 0, 4])\n RxCSObject.parameter1 = np.random.randn(4)\n\n self.__parametersCheck_error(RxCSObject, SizeError, strTestName)","def __nonzero__(self):\n return _uhd_swig.uhd_size_vector_t___nonzero__(self)","def length(self):\n if not hasattr(self, '_length'):\n lengths = np.linalg.norm(np.diff(self.points, axis=0), axis=1)\n self._segment_lengths = lengths\n self._length = lengths.sum()\n return self._length","def length(x):\n if ispoint(x):\n # return pointlength(x):\n return 0.0\n elif isline(x):\n return linelength(x)\n elif isarc(x):\n return arclength(x)\n elif ispoly(x):\n return polylength(x)\n elif isgeomlist(x):\n l = 0.0\n for g in x:\n l += length(g)\n return l\n else:\n raise ValueError(\"inappropriate type for length(): \".format(x))","def length(sequence):\r\n populated = tf.sign(tf.abs(sequence))\r\n length = tf.cast(tf.reduce_sum(populated, axis=1), tf.int32)\r\n mask = tf.cast(tf.expand_dims(populated, -1), tf.float32)\r\n return length, mask","def ctrlpts_size_v(self):\n return self._control_points_size_v","def tensor_length(data):\n if hasattr(data, \"shape\"):\n return data.shape[0]\n else:\n try:\n length = len(data)\n except TypeError:\n length = 0\n return length","def magnitude_of_vector(v):\n return math.sqrt(sum_of_squares(v))","def len(self):\r\n return len(self.input), len(self.target)","def __len__(self):\n return np.size(self.A,0)","def dims(x):\n return len(x.shape)","def GetOffsets(self) -> \"itkVectorContainerUCO2 const *\":\n return _itkScalarImageToRunLengthFeaturesFilterPython.itkScalarImageToRunLengthFeaturesFilterISS2_GetOffsets(self)","def sizeIsVlen(self):\n return self.rank > 0 and 'vlen' in self.tags","def getVoxelSize(self):\n\t\treturn self.voxelsize","def nvar(self):\n return len(self.v)","def __len__(self):\n return self.width * self.height","def n_Vrepresentation(self):\n return len(self.Vrepresentation())","def length(self):\n return float(np.max([self.width(),self.height()]))","def iterator(self):\n return _uhd_swig.uhd_size_vector_t_iterator(self)","def calc_side_lengths(triangles):\n first_vec = [2, 0, 1]\n second_vec = [1, 2, 0]\n sides = triangles[:, first_vec] - triangles[:, second_vec]\n lengths = np.sqrt(np.sum(sides**2, axis=2))\n return lengths"],"string":"[\n \"def length_vector(v):\\n return sqrt(dot_vectors(v, v))\",\n \"def veclength(vec):\\n vec = np.array(vec, copy=False).reshape(-1, 3)\\n return np.sqrt(np.einsum('ij,ij->i', vec, vec))\",\n \"def vec_len(x):\\r\\n \\r\\n length = math.sqrt(x[0]**2 + x[1]**2)\\r\\n return length\",\n \"def get_vector_length(vector):\\n return np.linalg.norm(vector)\",\n \"def get_vector_length(v):\\r\\n v = as_tensor_variable(v)\\r\\n if v.ndim != 1:\\r\\n raise TypeError('argument must be symbolic vector')\\r\\n if v.type.broadcastable[0]:\\r\\n return 1\\r\\n if isinstance(v, gof.Constant) and v.type.ndim == 1:\\r\\n return len(v.data)\\r\\n if v.owner and isinstance(v.owner.op, theano.tensor.opt.MakeVector):\\r\\n return len(v.owner.inputs)\\r\\n if v.owner and isinstance(v.owner.op, Shape):\\r\\n return v.owner.inputs[0].type.ndim\\r\\n raise ValueError(\\\"length not known\\\")\",\n \"def length(vec):\\n return vec.dot(vec)**.5\",\n \"def length(vec):\\n\\n return math.sqrt(dotproduct(vec, vec))\",\n \"def vector_len( vector ):\\n \\n if not isinstance(vector, np.ndarray ):\\n return len(vector)\\n else:\\n shape = vector.shape # shape is a tuple\\n \\n sl = len(shape)\\n if sl == 0:\\n return 0\\n elif sl == 1:\\n return shape[0]\\n else: \\n non_one_dims = [ s for s in shape if s > 1 ]\\n non_one_dims_len = len(non_one_dims)\\n if non_one_dims_len > 1:\\n raise ValueError(\\\"Function vector_len: Not a vector provided, shape : %s\\\", shape)\\n elif non_one_dims_len == 0:\\n return 1\\n else:\\n return non_one_dims[0]\",\n \"def _get_vector_size(self):\\n if len(self):\\n return len(self.values()[0])\\n else:\\n return 0\",\n \"def length(vector):\\n a, b, c = vector\\n return math.sqrt(a ** 2 + b ** 2 + c ** 2)\",\n \"def length(vec):\\n return np.linalg.norm(vec)\",\n \"def vectorLength(v1, v2=None):\\n if v2 is None:\\n v2 = v1\\n return math.sqrt(dotProduct(v1, v2))\",\n \"def len(self):\\n return math.sqrt(self.v[0] * self.v[0] + self.v[1] * self.v[1])\",\n \"def length(v: Vec2) -> float:\\n return sqrt(v.x ** 2 + v.y ** 2)\",\n \"def getLength(self):\\n return self.vector.norm\",\n \"def length(v):\\n return math.sqrt(v[0]**2 + v[1]**2)\",\n \"def get_length(data):\\n return np.array([len(conv) for conv in data]).reshape(-1, 1)\",\n \"def V_length(atoms):\\n \\n Vl = 0 # this is the variable we will store the sum of all the energies in\\n N = len(atoms)\\n for i in range(N):\\n j = (i+1) % N\\n length = norm(atoms.coords[i] - atoms.coords[j]) # norm computes the length of a vector\\n \\n Vl += (length - L0)**2\\n \\n return Vl\",\n \"def get_vector_norm(v1):\\n #sets the sum of all coordinates of vectors to 0\\n sum = 0\\n \\n #updates the sum by multiplying the coordinate by itself and adding it into the variable sum\\n for key in v1:\\n sum += (v1[key])**2\\n #find the square root of the sum \\n vector_length = math.sqrt(sum)\\n \\n return vector_length\",\n \"def __len__(self):\\n return len(self._representation_vector)\",\n \"def vector_length(self, x: float, y: float, z: float) -> float:\\n A = 2.0 * (x * y * self.aga + x * z * self.bbe + y * z * self.cal)\\n return sqrt(x ** 2 * self.asq + y ** 2 * self.bsq + z ** 2 * self.csq + A)\",\n \"def __len__(self):\\n num_x, num_y = self.conv_dims()\\n return num_x * num_y\",\n \"def computeDirLength(self):\\n self.length , uv = dirAndLength(self.pointN, self.point1)\\n self.unitv = uv\\n return uv\",\n \"def dim_from_concatenated_vector(v):\\n return int(np.sqrt(v.shape[0] - 1))\",\n \"def length(first):\\n if isinstance(first,FreeCAD.Vector):\\n return math.sqrt(first.x*first.x + first.y*first.y + first.z*first.z)\",\n \"def Lengths(self):\\n\\n self.__do_essential_memebers_exist__()\\n\\n if self.element_type == \\\"line\\\":\\n coords = self.points[self.elements[:,:2],:]\\n lengths = np.linalg.norm(coords[:,1,:] - coords[:,0,:],axis=1)\\n else:\\n # self.GetEdges()\\n # coords = self.points[self.all_edges,:]\\n coords = self.points[self.elements[:,:2],:]\\n lengths = np.linalg.norm(coords[:,1,:] - coords[:,0,:],axis=1)\\n\\n return lengths\",\n \"def length_vector_numpy(vector):\\n return np.linalg.norm(vector)\",\n \"def dims(self):\\n raise NotImplementedError('Please use Vector2Array or Vector3Array')\",\n \"def boxLengths(self):\\n try:\\n # See if we have a cached version\\n return self._boxLengths\\n except AttributeError:\\n pass\\n if self.box_vectors is not None:\\n # Get the lengths of each vector\\n if u.is_quantity(self.box_vectors):\\n # Unlikely -- each vector is like a quantity\\n vecs = self.box_vectors.value_in_unit(u.nanometers)\\n elif u.is_quantity(self.box_vectors[0]):\\n # Assume all box vectors are quantities\\n vecs = [x.value_in_unit(u.nanometers) for x in self.box_vectors]\\n else:\\n # Assume nanometers\\n vecs = self.box_vectors\\n a = sqrt(vecs[0][0]*vecs[0][0] + vecs[0][1]*vecs[0][1] +\\n vecs[0][2]*vecs[0][2])\\n b = sqrt(vecs[1][0]*vecs[1][0] + vecs[1][1]*vecs[1][1] +\\n vecs[1][2]*vecs[1][2])\\n c = sqrt(vecs[2][0]*vecs[2][0] + vecs[2][1]*vecs[2][1] +\\n vecs[2][2]*vecs[2][2])\\n self._boxLengths = (a, b, c) * u.nanometers\\n return self._boxLengths\\n return None\",\n \"def _osLen(self):\\n return int(np.ceil(self.minOverscan * self.sampleRate / self.downsample) * self.downsample)\\n\\n #osv = self.osVector\\n #return np.ceil(np.linalg.norm(osv) / self.pixelWidth)\",\n \"def length_vector_sqrd(v):\\n return dot_vectors(v, v)\",\n \"def dimensionality(self):\\n if self.vector.shape is ():\\n return 0\\n if len(self.vector.shape) is 1:\\n return 1\\n _, dim = self.vector.shape\\n return dim\",\n \"def __len__(self):\\n return self._vector.degree()\",\n \"def __len__(self):\\n return len(self.centroid_vector)\",\n \"def length(self):\\n return np.sqrt(np.sum(self**2, axis=1)).view(np.ndarray)\",\n \"def compare_vectors(v1, v2):\\n if len(v1) == len(v2):\\n distance = 0\\n for i in xrange(len(v1)):\\n distance += (v1[i] - v2[i]) ** 2\\n return distance\\n else:\\n print \\\"vector not match in dimensions\\\"\",\n \"def vector_magnitude(v):\\n\\n v = np.atleast_2d(v)\\n\\n return np.sqrt((v**2).sum(axis=1))\",\n \"def __len__(self):\\n\\n value_length = []\\n for v in chain(self.values(), self.metainfo_values()):\\n if isinstance(v, LabelData):\\n value_length.append(v.label.shape[0])\\n elif is_splitable_var(v):\\n value_length.append(len(v))\\n else:\\n continue\\n\\n # NOTE: If length of values are not same or the current data sample\\n # is empty, return length as 1\\n if len(list(set(value_length))) != 1:\\n return 1\\n\\n length = value_length[0]\\n return length\",\n \"def dims(self):\\n return self.v.dims() # TODO: check (empty? etc)\\n #return self.t.shape # TODO: check (empty? etc)\\n # TODO: convert to tuple? here / in varset?\",\n \"def length(self):\\n return math.sqrt(self.x * self.x + self.y * self.y)\",\n \"def length(self):\\n return pyvista.Box(self.bounds).length\",\n \"def __len__(self):\\n _, timesteps, height, width = self.data.shape\\n height //= self.size\\n width //= self.size\\n\\n if self.subset == 'train':\\n out = self.length\\n elif self.subset == 'all':\\n out = height * width\\n else:\\n out = (height // 2) * (width // 2)\\n\\n if not self.time:\\n out *= timesteps\\n\\n return out\",\n \"def __size_restriction_correct_vector_vector(self):\\n\\n strTestName = 'Vector size lower or equal to the size of a vector (correct)'\\n RxCSObject = _RxCSobject()\\n\\n # Firstly, let us define a reference parameter\\n RxCSObject.paramAddMan('vRefParameter1', 'Vector ref. parameter')\\n RxCSObject.paramType('vRefParameter1', np.ndarray)\\n\\n # Now, let me define a Numpy vector\\n RxCSObject.paramAddMan('parameter1', 'Numpy array 1D parameter')\\n RxCSObject.paramType('parameter1', np.ndarray)\\n RxCSObject.paramSizLE('parameter1', 'vRefParameter1', mul=3)\\n\\n RxCSObject.vRefParameter1 = np.array([0, 1, 0, 4])\\n RxCSObject.parameter1 = np.random.randn(9)\\n\\n self.__parametersCheck_error(RxCSObject, 'correct', strTestName)\",\n \"def length(xyz, along=False):\\n xyz = np.asarray(xyz)\\n if xyz.shape[0] < 2:\\n if along:\\n return np.array([0])\\n return 0\\n dists = np.sqrt((np.diff(xyz, axis=0) ** 2).sum(axis=1))\\n if along:\\n return np.cumsum(dists)\\n return np.sum(dists)\",\n \"def GetVoxelSize(vDataSet):\\r\\n nx = vDataSet.GetSizeX()\\r\\n ny = vDataSet.GetSizeY()\\r\\n nz = vDataSet.GetSizeZ()\\r\\n\\r\\n if nx > 0: nx = abs(vDataSet.GetExtendMaxX()-vDataSet.GetExtendMinX())/nx;\\r\\n if ny > 0: ny = abs(vDataSet.GetExtendMaxY()-vDataSet.GetExtendMinY())/ny;\\r\\n if nz > 0: nz = abs(vDataSet.GetExtendMaxZ()-vDataSet.GetExtendMinZ())/nz;\\r\\n\\r\\n return nx,ny,nz\",\n \"def length(self, src_lengths: torch.LongTensor) -> torch.LongTensor:\\n return src_lengths\",\n \"def is_vector(self):\\n return len(self.coeffs.shape[self.sdim:]) == 1\",\n \"def length(self):\\n\\n return np.array([f.length() for f in self])\",\n \"def test_normal_unit_length(self):\\n neighborhood, pc = create_point_cloud_in_plane_and_neighborhood()\\n normals = np.array(EigenValueVectorizeFeatureExtractor().extract(pc, neighborhood, None, None, None)[3:6])\\n lengths = np.sum(normals * normals, axis=0)\\n np.testing.assert_almost_equal(np.ones_like(lengths), lengths)\",\n \"def length(self):\\n return len(self.x)\",\n \"def get_dimension_length(self):\\n pass\",\n \"def dimensions():\",\n \"def length(self) -> npt.NDArray[np.float_]:\\n return dist(*self.vertices)\",\n \"def length(self):\\n points = [Point(v, crs=self.crs) for v in self.vertices]\\n distances = [a.distance(b) for a, b in zip(points[:-1], points[1:])]\\n return sum(distances)\",\n \"def GetOffsets(self) -> \\\"itkVectorContainerUCO2 const *\\\":\\n return _itkScalarImageToRunLengthFeaturesFilterPython.itkScalarImageToRunLengthFeaturesFilterIUS2_GetOffsets(self)\",\n \"def lengths(x: Any) -> IntOrIter:\\n if is_scalar(x):\\n return Array([1], dtype=numpy.int_)\\n return Array([length(elem) for elem in x], dtype=numpy.int_)\",\n \"def actual_size(v=(0, 0)):\\n return v[0], v[1]\",\n \"def GetOffsets(self) -> \\\"itkVectorContainerUCO2 const *\\\":\\n return _itkScalarImageToRunLengthFeaturesFilterPython.itkScalarImageToRunLengthFeaturesFilterIF2_GetOffsets(self)\",\n \"def length(self):\\n return _lattice.length(self._accelerator.lattice)\",\n \"def __len__(self):\\n return len(self.focals)\",\n \"def __getitem__(self, *args):\\n return _uhd_swig.uhd_size_vector_t___getitem__(self, *args)\",\n \"def compute_size(self):\\n length = np.max(np.max(self.positions, axis=1) -\\n np.min(self.positions, axis=1))\\n return length + 2*self.get_radii().max()\",\n \"def euclidian_length(self):\\n\\n if self.get_len() > 1:\\n shape_length = 0\\n last_x = self.x\\n last_y = self.y\\n scale = [0]\\n for i in range(self.len - 2):\\n x = np.array(self.x[i + 1])\\n y = np.array(self.y[i + 1])\\n last_x = np.array(self.x[i])\\n last_y = np.array(self.y[i])\\n shape_length += np.sqrt((x - last_x) ** 2 + (y - last_y) ** 2)\\n scale.append(shape_length)\\n return shape_length, scale\\n\\n else:\\n return 0, [0]\",\n \"def dimension(self):\\r\\n a = 0\\r\\n for x in self.faces():\\r\\n if (len(x) > a):\\r\\n a = len(x) \\r\\n return a-1\",\n \"def length(self):\\n return math.sqrt(self.x**2 + self.y**2 + self.z**2)\",\n \"def calculate_length(self):\\n raise NotImplementedError\",\n \"def numel(self):\\n return self.t.size\",\n \"def element_size(self):\\n vecs = (\\n self.nodes[self.elements[:, :4], :][:, 1:, :]\\n - self.nodes[self.elements[:, :4], :][:, 0, None, :]\\n )\\n return np.abs(np.linalg.det(vecs)) / 6\",\n \"def __len__(self):\\n return self.xyz.shape[0]\",\n \"def __len__(self):\\n return self.xyz.shape[0]\",\n \"def get_length(self):\\n return math.sqrt(self.x**2 + self.y**2)\",\n \"def __len__(self):\\n return len(self.kernels)\",\n \"def GetOffsets(self) -> \\\"itkVectorContainerUCO2 const *\\\":\\n return _itkScalarImageToRunLengthFeaturesFilterPython.itkScalarImageToRunLengthFeaturesFilterIUC2_GetOffsets(self)\",\n \"def length(self):\\n return int(np.sum([x.length for x in self.parameters]))\",\n \"def lic_flow(vectors,len_pix=10):\\n vectors = np.asarray(vectors)\\n m,n,two = vectors.shape\\n if two!=2:\\n raise ValueError\\n\\n result = np.zeros((2*len_pix+1,m,n,2),dtype=np.int32) # FIXME: int16?\\n center = len_pix\\n result[center,:,:,0] = np.arange(m)[:,np.newaxis]\\n result[center,:,:,1] = np.arange(n)[np.newaxis,:]\\n\\n for i in range(m):\\n for j in range(n):\\n y = i\\n x = j\\n fx = 0.5\\n fy = 0.5\\n for k in range(len_pix):\\n vx, vy = vectors[y,x]\\n print x, y, vx, vy\\n if vx>=0:\\n tx = (1-fx)/vx\\n else:\\n tx = -fx/vx\\n if vy>=0:\\n ty = (1-fy)/vy\\n else:\\n ty = -fy/vy\\n if tx<ty:\\n print \\\"x step\\\"\\n if vx>0:\\n x+=1\\n fy+=vy*tx\\n fx=0.\\n else:\\n x-=1\\n fy+=vy*tx\\n fx=1.\\n else:\\n print \\\"y step\\\"\\n if vy>0:\\n y+=1\\n fx+=vx*ty\\n fy=0.\\n else:\\n y-=1\\n fx+=vx*ty\\n fy=1.\\n if x<0: x=0\\n if y<0: y=0\\n if x>=n: x=n-1\\n if y>=m: y=m-1\\n result[center+k+1,i,j,:] = y, x\\n\\n\\n\\n return result\",\n \"def calc_vector_from_ndarray(data):\\n return data[1:] - data[0:-1]\",\n \"def getlen(self):\\n if self.onlydiag():\\n return self.lendiag()\\n else:\\n return len(self)\",\n \"def length(sequence):\\n #sequence = torch.from_numpy(sequence).type('torch.FloatTensor')\\n if isinstance(sequence, np.ndarray):\\n sequence = Variable(torch.from_numpy(sequence).type('torch.FloatTensor'))\\n #print('type:',type(sequence))\\n populated = torch.sign(torch.abs(sequence))\\n length = torch.sum(populated, 1)\\n length = length.type('torch.IntTensor')\\n mask = torch.unsqueeze(populated, -1)\\n mask = mask.type('torch.FloatTensor')\\n return length, mask\",\n \"def calculate_vector_magnitude(self, vector):\\n magnitude_squared = 0\\n for d in vector:\\n magnitude_squared += pow(d, 2)\\n \\n magnitude = pow(magnitude_squared, .5)\\n\\n return magnitude\",\n \"def EdgeLengths(self,which_edges='boundary'):\\n\\n assert self.points is not None\\n assert self.element_type is not None\\n\\n\\n lengths = None\\n if which_edges == 'boundary':\\n if self.edges is None:\\n self.GetBoundaryEdges()\\n\\n edge_coords = self.points[self.edges[:,:2],:]\\n lengths = np.linalg.norm(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)\\n\\n elif which_edges == 'all':\\n if self.all_edges is None:\\n self.GetEdges()\\n\\n edge_coords = self.points[self.all_edges[:,:2],:]\\n lengths = np.linalg.norm(edge_coords[:,1,:] - edge_coords[:,0,:],axis=1)\\n\\n return lengths\",\n \"def __size_restriction_incorrect_vector_vector(self):\\n\\n strTestName = 'Vector size lower than the size of a vector (incorrect)'\\n RxCSObject = _RxCSobject()\\n\\n # Firstly, let us define a reference parameter\\n RxCSObject.paramAddMan('vRefParameter1', 'Vector ref. parameter')\\n RxCSObject.paramType('vRefParameter1', np.ndarray)\\n\\n # Now, let me define a Numpy vector\\n RxCSObject.paramAddMan('parameter1', 'Numpy array 2D parameter')\\n RxCSObject.paramType('parameter1', np.ndarray)\\n RxCSObject.paramSizL('parameter1', 'vRefParameter1')\\n\\n RxCSObject.vRefParameter1 = np.array([0, 1, 0, 4])\\n RxCSObject.parameter1 = np.random.randn(4)\\n\\n self.__parametersCheck_error(RxCSObject, SizeError, strTestName)\",\n \"def __nonzero__(self):\\n return _uhd_swig.uhd_size_vector_t___nonzero__(self)\",\n \"def length(self):\\n if not hasattr(self, '_length'):\\n lengths = np.linalg.norm(np.diff(self.points, axis=0), axis=1)\\n self._segment_lengths = lengths\\n self._length = lengths.sum()\\n return self._length\",\n \"def length(x):\\n if ispoint(x):\\n # return pointlength(x):\\n return 0.0\\n elif isline(x):\\n return linelength(x)\\n elif isarc(x):\\n return arclength(x)\\n elif ispoly(x):\\n return polylength(x)\\n elif isgeomlist(x):\\n l = 0.0\\n for g in x:\\n l += length(g)\\n return l\\n else:\\n raise ValueError(\\\"inappropriate type for length(): \\\".format(x))\",\n \"def length(sequence):\\r\\n populated = tf.sign(tf.abs(sequence))\\r\\n length = tf.cast(tf.reduce_sum(populated, axis=1), tf.int32)\\r\\n mask = tf.cast(tf.expand_dims(populated, -1), tf.float32)\\r\\n return length, mask\",\n \"def ctrlpts_size_v(self):\\n return self._control_points_size_v\",\n \"def tensor_length(data):\\n if hasattr(data, \\\"shape\\\"):\\n return data.shape[0]\\n else:\\n try:\\n length = len(data)\\n except TypeError:\\n length = 0\\n return length\",\n \"def magnitude_of_vector(v):\\n return math.sqrt(sum_of_squares(v))\",\n \"def len(self):\\r\\n return len(self.input), len(self.target)\",\n \"def __len__(self):\\n return np.size(self.A,0)\",\n \"def dims(x):\\n return len(x.shape)\",\n \"def GetOffsets(self) -> \\\"itkVectorContainerUCO2 const *\\\":\\n return _itkScalarImageToRunLengthFeaturesFilterPython.itkScalarImageToRunLengthFeaturesFilterISS2_GetOffsets(self)\",\n \"def sizeIsVlen(self):\\n return self.rank > 0 and 'vlen' in self.tags\",\n \"def getVoxelSize(self):\\n\\t\\treturn self.voxelsize\",\n \"def nvar(self):\\n return len(self.v)\",\n \"def __len__(self):\\n return self.width * self.height\",\n \"def n_Vrepresentation(self):\\n return len(self.Vrepresentation())\",\n \"def length(self):\\n return float(np.max([self.width(),self.height()]))\",\n \"def iterator(self):\\n return _uhd_swig.uhd_size_vector_t_iterator(self)\",\n \"def calc_side_lengths(triangles):\\n first_vec = [2, 0, 1]\\n second_vec = [1, 2, 0]\\n sides = triangles[:, first_vec] - triangles[:, second_vec]\\n lengths = np.sqrt(np.sum(sides**2, axis=2))\\n return lengths\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69771564","0.6968557","0.69287777","0.68751884","0.68681043","0.6843323","0.67979896","0.6776573","0.6688724","0.66632444","0.6648538","0.6634671","0.6530027","0.65078795","0.64724904","0.6456348","0.64492345","0.6435357","0.6399516","0.6285586","0.6256722","0.62542474","0.62085414","0.6180795","0.6169659","0.6160211","0.614695","0.60813665","0.6048629","0.60371864","0.6035968","0.59935355","0.5990655","0.5987508","0.596237","0.59143895","0.5871774","0.58678365","0.5866588","0.58438","0.5825138","0.57906765","0.5775352","0.5756066","0.5741538","0.57391334","0.5729895","0.5718553","0.5705447","0.569199","0.5679924","0.56688863","0.56617653","0.56556296","0.5650871","0.5649652","0.56469834","0.56419253","0.5640859","0.5640715","0.5636958","0.5606277","0.5604594","0.5603683","0.56028974","0.55974734","0.5595854","0.558643","0.5573967","0.5573967","0.5573828","0.5560463","0.5559241","0.5553074","0.5552745","0.55343616","0.55278003","0.5519809","0.5515053","0.5514038","0.55118084","0.5506242","0.55059856","0.5503239","0.5491713","0.54906565","0.54897624","0.54876685","0.5487289","0.5482749","0.5481248","0.5481023","0.5479292","0.54784435","0.54770696","0.5467394","0.5463746","0.5462363","0.54585046","0.54576504"],"string":"[\n \"0.69771564\",\n \"0.6968557\",\n \"0.69287777\",\n \"0.68751884\",\n \"0.68681043\",\n \"0.6843323\",\n \"0.67979896\",\n \"0.6776573\",\n \"0.6688724\",\n \"0.66632444\",\n \"0.6648538\",\n \"0.6634671\",\n \"0.6530027\",\n \"0.65078795\",\n \"0.64724904\",\n \"0.6456348\",\n \"0.64492345\",\n \"0.6435357\",\n \"0.6399516\",\n \"0.6285586\",\n \"0.6256722\",\n \"0.62542474\",\n \"0.62085414\",\n \"0.6180795\",\n \"0.6169659\",\n \"0.6160211\",\n \"0.614695\",\n \"0.60813665\",\n \"0.6048629\",\n \"0.60371864\",\n \"0.6035968\",\n \"0.59935355\",\n \"0.5990655\",\n \"0.5987508\",\n \"0.596237\",\n \"0.59143895\",\n \"0.5871774\",\n \"0.58678365\",\n \"0.5866588\",\n \"0.58438\",\n \"0.5825138\",\n \"0.57906765\",\n \"0.5775352\",\n \"0.5756066\",\n \"0.5741538\",\n \"0.57391334\",\n \"0.5729895\",\n \"0.5718553\",\n \"0.5705447\",\n \"0.569199\",\n \"0.5679924\",\n \"0.56688863\",\n \"0.56617653\",\n \"0.56556296\",\n \"0.5650871\",\n \"0.5649652\",\n \"0.56469834\",\n \"0.56419253\",\n \"0.5640859\",\n \"0.5640715\",\n \"0.5636958\",\n \"0.5606277\",\n \"0.5604594\",\n \"0.5603683\",\n \"0.56028974\",\n \"0.55974734\",\n \"0.5595854\",\n \"0.558643\",\n \"0.5573967\",\n \"0.5573967\",\n \"0.5573828\",\n \"0.5560463\",\n \"0.5559241\",\n \"0.5553074\",\n \"0.5552745\",\n \"0.55343616\",\n \"0.55278003\",\n \"0.5519809\",\n \"0.5515053\",\n \"0.5514038\",\n \"0.55118084\",\n \"0.5506242\",\n \"0.55059856\",\n \"0.5503239\",\n \"0.5491713\",\n \"0.54906565\",\n \"0.54897624\",\n \"0.54876685\",\n \"0.5487289\",\n \"0.5482749\",\n \"0.5481248\",\n \"0.5481023\",\n \"0.5479292\",\n \"0.54784435\",\n \"0.54770696\",\n \"0.5467394\",\n \"0.5463746\",\n \"0.5462363\",\n \"0.54585046\",\n \"0.54576504\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5496276"},"document_rank":{"kind":"string","value":"84"}}},{"rowIdx":11092,"cells":{"query":{"kind":"string","value":"Fully refresh the underlying visual."},"document":{"kind":"string","value":"def _refresh(self):\n self._need_display_update = True\n self._update()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def redraw(self):\n self.vispy_viewer.canvas.update()","def refresh(self):\n\t\tself.win.refresh()\n\t\tfor c in self.components:\n\t\t\tc.refresh()","def refresh(self):\n self.getWindow().getDecorView().postInvalidate()","def update_visualization(self) -> None:\n pass","def redraw(self):\r\n self.c.update()","def redraw(self):\n self.vispy_widget.canvas.update()","def update(self):\n self.redraw()\n self._changed = False","def update(self):\n self.redraw()\n self._changed = False","def refresh(self):\n self.Refresh()","def refresh(self) -> None:\n self.screen.refresh()","def redraw(self):\n raise NotImplementedError()","def refresh(self):\n\n self.ax.relim()\n self.ax.autoscale_view()\n self.canvas.draw()","def update_figure(self):\n\n self.draw()","def redraw(self) -> None:\n self.canvas.draw_idle()\n self.Refresh()","def _update(self):\n if self._need_display_update:\n self._need_display_update = False\n\n self._set_view_slice(self.viewer.dims.indices)\n\n if self._need_visual_update:\n self._need_visual_update = False\n self._node.update()","def update_visualizer(self):\n if self.visualizer:\n if self.frame_count == 2:\n self.visualizer.add_geometry(self.vis_points)\n self.visualizer.update_geometry(self.vis_points)\n self.visualizer.poll_events()\n self.visualizer.update_renderer()\n time.sleep(0.001)\n self.frame_count += 1","def Refresh(self):\n pass","def redraw(self):\n self._create()","def refresh(self):\n self.__refresh()","def plot_refresh():\n figure.canvas.draw()","def repaint(self):\n pass","def refresh_screen(self):","def refresh_HDV(self):\n self.canvas.draw()\n self.dicom_navigation.parent.dicom_right_window.top_info.canvas_HDV.draw()","def refresh(self):\n\n for w in self.windows.values():\n w.refresh()","def redraw_figures(self):\n pass","def redraw_figures(self):\n pass","def redraw_viz():\n\tglobal g_last_draw\n\tif (rospy.Time.now().to_sec() > (refresh_rate + g_last_draw)):\n\t\tg_last_draw = rospy.Time.now().to_sec()\n\t\t# redraw imu box\n\t\tdoDraw()","def redraw(self, **kwargs):\n #src_dict = self.data_sources\n #self.remove_sources(src_dict.keys())\n self.renderers = {}\n #self.renderers = {}\n self.figure = self.draw_figure(**kwargs)\n #self.add_sources(src_dict)\n # todo does the old figure linger on?\n self.render_sources(self.data_sources)\n self.bk_pane.object = self.figure","def refresh(self):\n pass","def refresh(self):\n pass","def refresh(self):\n\n # Set Graphics scene\n self.setScene(QtGui.QGraphicsScene())\n self._connections = set()\n self._nodes = {}\n self._selection = set()\n self._manipulation_mode = 0\n self._selection_rect = None","def refresh(self) -> None:\n pass","def refresh(self) -> None:\n pass","def refresh(self) -> None:\n pass","def update_view(self, w: Wrapper) -> None:\n\n w.setStyleSheet(\"/* */\") # forces visual update","def relayout(self): \n\t\t#self.urmaswin.Layout()\n\t\t#wx.CallAfter(self.urmaswin.Layout)\n\t\t#wx.CallAfter(self.visualizer.OnSize)","def _refresh_screen(self):\n self.myscreen.refresh()\n self.box1.refresh()\n self.box2.refresh()","def display(self):\n\t\tself.imgDisplay.set_from_pixbuf(self.getVisible())\n\t\tgc.collect()","def _redraw_graph(self) -> None:\n self._clear_drawing()\n self.draw_graph()","def redraw(self):\n self._view.delete(tk.ALL)\n self._view.draw_entities(self._world.get_all_things())\n # calculate the health and score in every step\n max_hp = self._player.get_max_health()\n current_hp = self._player.get_health()\n # if player is invincible, don't change health\n self._statue.set_health(current_hp / max_hp, self._player.get_invincible())\n self._statue.set_score(self._player.get_score())","def _refresh_render(self):\n current_frame = self.frame\n self.frame = int(1E6)\n self.frame = current_frame","def refresh_self(self) -> None:\n self._logger.debug(\"running\")\n try:\n self.figure.canvas.draw()\n except Exception as e:\n self._logger.exception(\"issue with drawing canvas.\")\n self._logger.debug(\"done\")","def refresh():\n curve_editor.refresh()","def refresh_screen(self):\n stdscr = self.stdscr\n stdscr.refresh()","def refresh_canvas(self):\n self.canvas.delete('all')\n self.draw_handler(self)\n self.canvas.after(CANVAS[\"REFRESH_TIME\"], self.refresh_canvas)","def redraw(self):\n self.scene.redraw()\n self.SwapBuffers()","def refresh(self) :\n if not self.running:\n self.running = True\n self.strip.show()\n self.running = False\n self.refreshTimer.expired = True\n self.refreshTimer.isrunning = False","def XeprGUIrefresh(self):\n with self._lock:\n self._API.XeprRefreshGUI()","def drawChanges(self):\n self.draw(wait=False)\n draw(self.values,color='yellow',bbox=None,clear=False,shrink=self.shrink)","def update_plot():\n pass","def update(self):\r\n pygame.display.update()\r\n return","def update(self):\n self.grid.update()\n sleep(self.update_rate)","def refresh(self):\n raise NotImplementedError","def refresh(self):\n raise NotImplementedError","def refresh_view(self):\n # self.view_model.query_str = \"\"\n self.query_header.content.text = query_title_bar_text(self.shared_state)\n # self.view_model.update_results()\n self.layout.focus(self.query_window)","def redraw(self):\n dummy_figure = plt.figure()\n new_manager = dummy_figure.canvas.manager\n new_manager.canvas.figure = self.figure\n self.figure.set_canvas(new_manager.canvas)\n plt.show(block=False)","def updateVisualization(self):\n\t\tif self.visualization:\n\t\t\tif self.fixedVisualization:\n\t\t\t\tself.visualization.setFixedVisualization(self.fixedVisualization)\n\t\t\tif self.movingVisualization:\n\t\t\t\tself.visualization.setMovingVisualization(self.movingVisualization)\n\t\tself.multiRenderWidget.setVolumeVisualization(self.visualization)\n\t\tself.visualizationUpdated.emit(self.visualization)","def refresh(self):\n self.goto(self.starting_position)","def refresh_view(self):\n if self._step_number % 2 == 0:\n self._view.draw_enemies(self._game.enemies)\n self._view.draw_towers(self._game.towers)\n self._view.draw_obstacles(self._game.obstacles)","def refresh(self):\n raise NotImplementedError(\"To be implemented\")","def Refresh(self, erase=True, rect=None):\r\n\r\n self._main_win.Refresh(erase, rect)\r\n self._header_win.Refresh(erase, rect)","def _refresh(self):\r\n\r\n # Remove all the widgets from the layout\r\n self.__clear_layout()\r\n\r\n # Check if adding a top stretch is needed\r\n if self.top_stretch:\r\n self.__layout.addStretch()\r\n\r\n # Re-build layout from list\r\n for widget in self.widgets_list:\r\n self.__layout.addWidget(widget)\r\n\r\n # Check if adding a bottom stretch is needed\r\n if self.bottom_stretch:\r\n self.__layout.addStretch()","def redraw(self):\n bpy.context.scene.objects.active = bpy.context.scene.objects.active","def refresh(self):\n\n if self.eventBox.window != None:\n self.eventBox.window.set_cursor(None)\n self.enable_controls(False)\n threading.Thread(target=self.thread_refresh).start()","def updateDisplay(self):\n if self._displayPjt:\n self._displayPjt.updateim()\n if self._displayUsr:\n self._displayUsr.updateim()\n if self._displayVtk:\n self._displayVtk.updateim()","def show(self):\n self.root.update()\n self.root.deiconify()","async def _async_force_refresh_state(self):\n await self._shade.refresh()\n self._async_update_current_cover_position()\n self.async_write_ha_state()","def draw(self):\n\n for item in self.vis:\n item.undraw()\n self.render()\n for item in self.vis:\n item.draw(self.win)\n self.drawn = True","def _redraw_operation(self):\n try:\n self._profile()\n self._window.operate()\n except Exception as e:\n # Catch exceptions raised when executing the Use Case\n self._result = self._complete(exception=e)\n logger.debug(traceback.format_exc())\n self._record_current_time()\n self._check_if_completed()\n # self._window.process_events()","def force_update(self):\n self.old_selected_offset = -1\n self.old_top_offset = -1\n self.__update_display()\n self.__update_selection()","def visual_attr_changed(self):\n if self.component:\n self.component.invalidate_draw()\n self.component.request_redraw()\n else:\n self.invalidate_draw()\n self.request_redraw()","def _DoUpdateRedraw( self, hilite = True ):\n pass","def on_draw(self):\n self.clear()\n self.manager.draw()","def updateGraphics():\n _root.update_idletasks()","def RefreshButton(self, part):\r\n \r\n rect = wx.Rect(*part.rect)\r\n rect.Inflate(2, 2)\r\n self._frame.Refresh(True, rect)\r\n self._frame.Update()","def show( self ):\n if self.changed:\n self._update_ax() \n self.changed = False","def updateWidget(self):\n pass","def done_paint(self):\r\n curses.panel.update_panels()\r\n curses.doupdate()","def refresh_dialog(self):\n self._client.update_elements()","def refresh_svg_canvas(self):\n if self.ui.tabWidget.currentIndex() == 0:\n self.ui.svg_canvas.build_schematic()\n self.ui.svg_canvas.viewport().update()\n elif self.ui.tabWidget.currentIndex() in (1,2):\n self.ui.svg_canvas.build_pcb()\n self.ui.svg_canvas.viewport().update()\n else:\n raise Exception(\"Unknown view to draw\")","def refresh_plot(self):\n self.ax.relim() # recompute the data limits\n self.ax.autoscale_view() # automatic axis scaling\n self.fig.canvas.flush_events()","def draw(self):\n\n # Use update instead of update_idletasks because it works better\n # on some Windows machines.\n self.root.update()","def __refreshContent(self):\n self._window.clear()\n self.drawBorder()\n for i in range(self.__firstShownLine,\n self.__firstShownLine + self.height - 2):\n if self._focused and i == self.__selectedRow:\n self._window.attron(curses.A_BOLD)\n self.__printRow(i)\n self._window.attroff(curses.A_BOLD)","def build(self):\n self.rebuild = False\n self.redraw = True","def refresh(self):\n\t\tif len(self.curves) == 0: return\n\t\t\n\t\tmax_x = max((c[1][-1] for c in self.curves.itervalues()))\n\t\n\t\tstart_x = max_x - self.x_visible\n\t\t\n\t\tfor c, x, y in self.curves.itervalues():\n\t\t\twhile len(x) > 0 and x[0] < start_x:\n\t\t\t\tdel x[0]\n\t\t\t\tdel y[0]\n\n\t\tfor curve, x, y in self.curves.itervalues():\n\t\t\t# I guess this probably copies stuff, more\n\t\t\t# efficient way would of course to use a\n\t\t\t# ringbuffer, but I couldn't find a ready made\n\t\t\t# implementation and am too busy for doing things\n\t\t\t# right.\n\t\t\tcurve.setData(x, y)\n\t\t\n\t\t# The autoscale likes to hang on to integers without\n\t\t# this\n\t\tself.setAxisScale(self.xBottom, start_x, max_x)\n\t\tself.replot()","def _UpdatePlot( self ):\n self._BusyDoOp( self._UpdatePlotImpl )","def redraw(self):\n # enough to go to front, don't need to clear the line\n sys.stderr.write(self._FRONT)\n self.draw()","def redraw(self, state: EngineeringState) -> None:\n pass","def Redraw(self):\n print(\"EMPTY VIEW REDRAW\")","def update(self):\n self.m.update()","def redraw(self):\n offset = self.actual_row * self.row_size\n x = 5\n y = 5\n ind = 0\n self.scene.clear()\n for _ in range(self.column_size):\n for _ in range(self.row_size):\n if ind+offset < len(self.cards):\n self.draw_card(x, y, ind+offset)\n x += 90\n ind += 1\n x = 5\n y += 120","def test_visualisations_perform_visualisation_render_data_refresh_synchronously(self):\n pass","def repaint(self):\n self.screen.blit(self.source, (0, 0))\n self.lcd.draw(self.lcddraw)\n if self.drawmode & self.DRAW_CIRCLE:\n self.plot_circle()\n pygame.display.flip()","def __update(self):\n for b in self.__borders:\n b.redraw()\n\n for w in self.__allWins:\n w.refresh()","def refresh(self):\n self.fetch(False)","def update(self):\n self.backbone_module.update()\n mx.nd.waitall()","def update_figure(self):\n # if number of kinetics in model did not change\n # update just last lines\n if self.N_lines - 1 == len(self.model.spectra.keys()) * 2:\n self.dataplot.lines[-1].remove()\n self.dataplot.lines[-1].remove()\n self.draw_figure_first()\n # delete all and redraw\n else:\n n = int((self.N_lines - 1) / 2)\n for _ in range(n):\n self.dataplot.lines[-1].remove()\n self.dataplot.lines[-1].remove()\n self.draw_figure_total()\n\n self.dataplot.relim()\n\n self.dataplot.autoscale_view(True, True, True)\n\n self.draw()","def _relief_refresh(self, *_args):\n if self._relief_graphic_instructions.length():\n self.canvas.after.remove(self._relief_graphic_instructions)\n self._relief_graphic_instructions.clear()\n\n add = self._relief_graphic_instructions.add\n pos_size = self.x, self.y, self.width, self.height\n if self.relief_ellipse_inner_colors and self.relief_ellipse_inner_lines:\n self._relief_ellipse_inner_refresh(add, *self.relief_ellipse_inner_colors, *pos_size)\n if self.relief_ellipse_outer_colors and self.relief_ellipse_outer_lines:\n self._relief_ellipse_outer_refresh(add, *self.relief_ellipse_outer_colors, *pos_size)\n if self.relief_square_inner_colors and self.relief_square_inner_lines:\n self._relief_square_inner_refresh(add, *self.relief_square_inner_colors, *pos_size)\n if self.relief_square_outer_colors and self.relief_square_outer_lines:\n self._relief_square_outer_refresh(add, *self.relief_square_outer_colors, *pos_size)\n\n if self._relief_graphic_instructions.length():\n self.canvas.after.add(self._relief_graphic_instructions)","def refresh(self, data):\n for region in self.loaded_regions:\n self.regions[region].unload()\n super().clear()\n self.data = data\n self.loaded_regions = set()\n self.draw_visible_area()","def _Refresh(self):\n raise NotImplementedError"],"string":"[\n \"def redraw(self):\\n self.vispy_viewer.canvas.update()\",\n \"def refresh(self):\\n\\t\\tself.win.refresh()\\n\\t\\tfor c in self.components:\\n\\t\\t\\tc.refresh()\",\n \"def refresh(self):\\n self.getWindow().getDecorView().postInvalidate()\",\n \"def update_visualization(self) -> None:\\n pass\",\n \"def redraw(self):\\r\\n self.c.update()\",\n \"def redraw(self):\\n self.vispy_widget.canvas.update()\",\n \"def update(self):\\n self.redraw()\\n self._changed = False\",\n \"def update(self):\\n self.redraw()\\n self._changed = False\",\n \"def refresh(self):\\n self.Refresh()\",\n \"def refresh(self) -> None:\\n self.screen.refresh()\",\n \"def redraw(self):\\n raise NotImplementedError()\",\n \"def refresh(self):\\n\\n self.ax.relim()\\n self.ax.autoscale_view()\\n self.canvas.draw()\",\n \"def update_figure(self):\\n\\n self.draw()\",\n \"def redraw(self) -> None:\\n self.canvas.draw_idle()\\n self.Refresh()\",\n \"def _update(self):\\n if self._need_display_update:\\n self._need_display_update = False\\n\\n self._set_view_slice(self.viewer.dims.indices)\\n\\n if self._need_visual_update:\\n self._need_visual_update = False\\n self._node.update()\",\n \"def update_visualizer(self):\\n if self.visualizer:\\n if self.frame_count == 2:\\n self.visualizer.add_geometry(self.vis_points)\\n self.visualizer.update_geometry(self.vis_points)\\n self.visualizer.poll_events()\\n self.visualizer.update_renderer()\\n time.sleep(0.001)\\n self.frame_count += 1\",\n \"def Refresh(self):\\n pass\",\n \"def redraw(self):\\n self._create()\",\n \"def refresh(self):\\n self.__refresh()\",\n \"def plot_refresh():\\n figure.canvas.draw()\",\n \"def repaint(self):\\n pass\",\n \"def refresh_screen(self):\",\n \"def refresh_HDV(self):\\n self.canvas.draw()\\n self.dicom_navigation.parent.dicom_right_window.top_info.canvas_HDV.draw()\",\n \"def refresh(self):\\n\\n for w in self.windows.values():\\n w.refresh()\",\n \"def redraw_figures(self):\\n pass\",\n \"def redraw_figures(self):\\n pass\",\n \"def redraw_viz():\\n\\tglobal g_last_draw\\n\\tif (rospy.Time.now().to_sec() > (refresh_rate + g_last_draw)):\\n\\t\\tg_last_draw = rospy.Time.now().to_sec()\\n\\t\\t# redraw imu box\\n\\t\\tdoDraw()\",\n \"def redraw(self, **kwargs):\\n #src_dict = self.data_sources\\n #self.remove_sources(src_dict.keys())\\n self.renderers = {}\\n #self.renderers = {}\\n self.figure = self.draw_figure(**kwargs)\\n #self.add_sources(src_dict)\\n # todo does the old figure linger on?\\n self.render_sources(self.data_sources)\\n self.bk_pane.object = self.figure\",\n \"def refresh(self):\\n pass\",\n \"def refresh(self):\\n pass\",\n \"def refresh(self):\\n\\n # Set Graphics scene\\n self.setScene(QtGui.QGraphicsScene())\\n self._connections = set()\\n self._nodes = {}\\n self._selection = set()\\n self._manipulation_mode = 0\\n self._selection_rect = None\",\n \"def refresh(self) -> None:\\n pass\",\n \"def refresh(self) -> None:\\n pass\",\n \"def refresh(self) -> None:\\n pass\",\n \"def update_view(self, w: Wrapper) -> None:\\n\\n w.setStyleSheet(\\\"/* */\\\") # forces visual update\",\n \"def relayout(self): \\n\\t\\t#self.urmaswin.Layout()\\n\\t\\t#wx.CallAfter(self.urmaswin.Layout)\\n\\t\\t#wx.CallAfter(self.visualizer.OnSize)\",\n \"def _refresh_screen(self):\\n self.myscreen.refresh()\\n self.box1.refresh()\\n self.box2.refresh()\",\n \"def display(self):\\n\\t\\tself.imgDisplay.set_from_pixbuf(self.getVisible())\\n\\t\\tgc.collect()\",\n \"def _redraw_graph(self) -> None:\\n self._clear_drawing()\\n self.draw_graph()\",\n \"def redraw(self):\\n self._view.delete(tk.ALL)\\n self._view.draw_entities(self._world.get_all_things())\\n # calculate the health and score in every step\\n max_hp = self._player.get_max_health()\\n current_hp = self._player.get_health()\\n # if player is invincible, don't change health\\n self._statue.set_health(current_hp / max_hp, self._player.get_invincible())\\n self._statue.set_score(self._player.get_score())\",\n \"def _refresh_render(self):\\n current_frame = self.frame\\n self.frame = int(1E6)\\n self.frame = current_frame\",\n \"def refresh_self(self) -> None:\\n self._logger.debug(\\\"running\\\")\\n try:\\n self.figure.canvas.draw()\\n except Exception as e:\\n self._logger.exception(\\\"issue with drawing canvas.\\\")\\n self._logger.debug(\\\"done\\\")\",\n \"def refresh():\\n curve_editor.refresh()\",\n \"def refresh_screen(self):\\n stdscr = self.stdscr\\n stdscr.refresh()\",\n \"def refresh_canvas(self):\\n self.canvas.delete('all')\\n self.draw_handler(self)\\n self.canvas.after(CANVAS[\\\"REFRESH_TIME\\\"], self.refresh_canvas)\",\n \"def redraw(self):\\n self.scene.redraw()\\n self.SwapBuffers()\",\n \"def refresh(self) :\\n if not self.running:\\n self.running = True\\n self.strip.show()\\n self.running = False\\n self.refreshTimer.expired = True\\n self.refreshTimer.isrunning = False\",\n \"def XeprGUIrefresh(self):\\n with self._lock:\\n self._API.XeprRefreshGUI()\",\n \"def drawChanges(self):\\n self.draw(wait=False)\\n draw(self.values,color='yellow',bbox=None,clear=False,shrink=self.shrink)\",\n \"def update_plot():\\n pass\",\n \"def update(self):\\r\\n pygame.display.update()\\r\\n return\",\n \"def update(self):\\n self.grid.update()\\n sleep(self.update_rate)\",\n \"def refresh(self):\\n raise NotImplementedError\",\n \"def refresh(self):\\n raise NotImplementedError\",\n \"def refresh_view(self):\\n # self.view_model.query_str = \\\"\\\"\\n self.query_header.content.text = query_title_bar_text(self.shared_state)\\n # self.view_model.update_results()\\n self.layout.focus(self.query_window)\",\n \"def redraw(self):\\n dummy_figure = plt.figure()\\n new_manager = dummy_figure.canvas.manager\\n new_manager.canvas.figure = self.figure\\n self.figure.set_canvas(new_manager.canvas)\\n plt.show(block=False)\",\n \"def updateVisualization(self):\\n\\t\\tif self.visualization:\\n\\t\\t\\tif self.fixedVisualization:\\n\\t\\t\\t\\tself.visualization.setFixedVisualization(self.fixedVisualization)\\n\\t\\t\\tif self.movingVisualization:\\n\\t\\t\\t\\tself.visualization.setMovingVisualization(self.movingVisualization)\\n\\t\\tself.multiRenderWidget.setVolumeVisualization(self.visualization)\\n\\t\\tself.visualizationUpdated.emit(self.visualization)\",\n \"def refresh(self):\\n self.goto(self.starting_position)\",\n \"def refresh_view(self):\\n if self._step_number % 2 == 0:\\n self._view.draw_enemies(self._game.enemies)\\n self._view.draw_towers(self._game.towers)\\n self._view.draw_obstacles(self._game.obstacles)\",\n \"def refresh(self):\\n raise NotImplementedError(\\\"To be implemented\\\")\",\n \"def Refresh(self, erase=True, rect=None):\\r\\n\\r\\n self._main_win.Refresh(erase, rect)\\r\\n self._header_win.Refresh(erase, rect)\",\n \"def _refresh(self):\\r\\n\\r\\n # Remove all the widgets from the layout\\r\\n self.__clear_layout()\\r\\n\\r\\n # Check if adding a top stretch is needed\\r\\n if self.top_stretch:\\r\\n self.__layout.addStretch()\\r\\n\\r\\n # Re-build layout from list\\r\\n for widget in self.widgets_list:\\r\\n self.__layout.addWidget(widget)\\r\\n\\r\\n # Check if adding a bottom stretch is needed\\r\\n if self.bottom_stretch:\\r\\n self.__layout.addStretch()\",\n \"def redraw(self):\\n bpy.context.scene.objects.active = bpy.context.scene.objects.active\",\n \"def refresh(self):\\n\\n if self.eventBox.window != None:\\n self.eventBox.window.set_cursor(None)\\n self.enable_controls(False)\\n threading.Thread(target=self.thread_refresh).start()\",\n \"def updateDisplay(self):\\n if self._displayPjt:\\n self._displayPjt.updateim()\\n if self._displayUsr:\\n self._displayUsr.updateim()\\n if self._displayVtk:\\n self._displayVtk.updateim()\",\n \"def show(self):\\n self.root.update()\\n self.root.deiconify()\",\n \"async def _async_force_refresh_state(self):\\n await self._shade.refresh()\\n self._async_update_current_cover_position()\\n self.async_write_ha_state()\",\n \"def draw(self):\\n\\n for item in self.vis:\\n item.undraw()\\n self.render()\\n for item in self.vis:\\n item.draw(self.win)\\n self.drawn = True\",\n \"def _redraw_operation(self):\\n try:\\n self._profile()\\n self._window.operate()\\n except Exception as e:\\n # Catch exceptions raised when executing the Use Case\\n self._result = self._complete(exception=e)\\n logger.debug(traceback.format_exc())\\n self._record_current_time()\\n self._check_if_completed()\\n # self._window.process_events()\",\n \"def force_update(self):\\n self.old_selected_offset = -1\\n self.old_top_offset = -1\\n self.__update_display()\\n self.__update_selection()\",\n \"def visual_attr_changed(self):\\n if self.component:\\n self.component.invalidate_draw()\\n self.component.request_redraw()\\n else:\\n self.invalidate_draw()\\n self.request_redraw()\",\n \"def _DoUpdateRedraw( self, hilite = True ):\\n pass\",\n \"def on_draw(self):\\n self.clear()\\n self.manager.draw()\",\n \"def updateGraphics():\\n _root.update_idletasks()\",\n \"def RefreshButton(self, part):\\r\\n \\r\\n rect = wx.Rect(*part.rect)\\r\\n rect.Inflate(2, 2)\\r\\n self._frame.Refresh(True, rect)\\r\\n self._frame.Update()\",\n \"def show( self ):\\n if self.changed:\\n self._update_ax() \\n self.changed = False\",\n \"def updateWidget(self):\\n pass\",\n \"def done_paint(self):\\r\\n curses.panel.update_panels()\\r\\n curses.doupdate()\",\n \"def refresh_dialog(self):\\n self._client.update_elements()\",\n \"def refresh_svg_canvas(self):\\n if self.ui.tabWidget.currentIndex() == 0:\\n self.ui.svg_canvas.build_schematic()\\n self.ui.svg_canvas.viewport().update()\\n elif self.ui.tabWidget.currentIndex() in (1,2):\\n self.ui.svg_canvas.build_pcb()\\n self.ui.svg_canvas.viewport().update()\\n else:\\n raise Exception(\\\"Unknown view to draw\\\")\",\n \"def refresh_plot(self):\\n self.ax.relim() # recompute the data limits\\n self.ax.autoscale_view() # automatic axis scaling\\n self.fig.canvas.flush_events()\",\n \"def draw(self):\\n\\n # Use update instead of update_idletasks because it works better\\n # on some Windows machines.\\n self.root.update()\",\n \"def __refreshContent(self):\\n self._window.clear()\\n self.drawBorder()\\n for i in range(self.__firstShownLine,\\n self.__firstShownLine + self.height - 2):\\n if self._focused and i == self.__selectedRow:\\n self._window.attron(curses.A_BOLD)\\n self.__printRow(i)\\n self._window.attroff(curses.A_BOLD)\",\n \"def build(self):\\n self.rebuild = False\\n self.redraw = True\",\n \"def refresh(self):\\n\\t\\tif len(self.curves) == 0: return\\n\\t\\t\\n\\t\\tmax_x = max((c[1][-1] for c in self.curves.itervalues()))\\n\\t\\n\\t\\tstart_x = max_x - self.x_visible\\n\\t\\t\\n\\t\\tfor c, x, y in self.curves.itervalues():\\n\\t\\t\\twhile len(x) > 0 and x[0] < start_x:\\n\\t\\t\\t\\tdel x[0]\\n\\t\\t\\t\\tdel y[0]\\n\\n\\t\\tfor curve, x, y in self.curves.itervalues():\\n\\t\\t\\t# I guess this probably copies stuff, more\\n\\t\\t\\t# efficient way would of course to use a\\n\\t\\t\\t# ringbuffer, but I couldn't find a ready made\\n\\t\\t\\t# implementation and am too busy for doing things\\n\\t\\t\\t# right.\\n\\t\\t\\tcurve.setData(x, y)\\n\\t\\t\\n\\t\\t# The autoscale likes to hang on to integers without\\n\\t\\t# this\\n\\t\\tself.setAxisScale(self.xBottom, start_x, max_x)\\n\\t\\tself.replot()\",\n \"def _UpdatePlot( self ):\\n self._BusyDoOp( self._UpdatePlotImpl )\",\n \"def redraw(self):\\n # enough to go to front, don't need to clear the line\\n sys.stderr.write(self._FRONT)\\n self.draw()\",\n \"def redraw(self, state: EngineeringState) -> None:\\n pass\",\n \"def Redraw(self):\\n print(\\\"EMPTY VIEW REDRAW\\\")\",\n \"def update(self):\\n self.m.update()\",\n \"def redraw(self):\\n offset = self.actual_row * self.row_size\\n x = 5\\n y = 5\\n ind = 0\\n self.scene.clear()\\n for _ in range(self.column_size):\\n for _ in range(self.row_size):\\n if ind+offset < len(self.cards):\\n self.draw_card(x, y, ind+offset)\\n x += 90\\n ind += 1\\n x = 5\\n y += 120\",\n \"def test_visualisations_perform_visualisation_render_data_refresh_synchronously(self):\\n pass\",\n \"def repaint(self):\\n self.screen.blit(self.source, (0, 0))\\n self.lcd.draw(self.lcddraw)\\n if self.drawmode & self.DRAW_CIRCLE:\\n self.plot_circle()\\n pygame.display.flip()\",\n \"def __update(self):\\n for b in self.__borders:\\n b.redraw()\\n\\n for w in self.__allWins:\\n w.refresh()\",\n \"def refresh(self):\\n self.fetch(False)\",\n \"def update(self):\\n self.backbone_module.update()\\n mx.nd.waitall()\",\n \"def update_figure(self):\\n # if number of kinetics in model did not change\\n # update just last lines\\n if self.N_lines - 1 == len(self.model.spectra.keys()) * 2:\\n self.dataplot.lines[-1].remove()\\n self.dataplot.lines[-1].remove()\\n self.draw_figure_first()\\n # delete all and redraw\\n else:\\n n = int((self.N_lines - 1) / 2)\\n for _ in range(n):\\n self.dataplot.lines[-1].remove()\\n self.dataplot.lines[-1].remove()\\n self.draw_figure_total()\\n\\n self.dataplot.relim()\\n\\n self.dataplot.autoscale_view(True, True, True)\\n\\n self.draw()\",\n \"def _relief_refresh(self, *_args):\\n if self._relief_graphic_instructions.length():\\n self.canvas.after.remove(self._relief_graphic_instructions)\\n self._relief_graphic_instructions.clear()\\n\\n add = self._relief_graphic_instructions.add\\n pos_size = self.x, self.y, self.width, self.height\\n if self.relief_ellipse_inner_colors and self.relief_ellipse_inner_lines:\\n self._relief_ellipse_inner_refresh(add, *self.relief_ellipse_inner_colors, *pos_size)\\n if self.relief_ellipse_outer_colors and self.relief_ellipse_outer_lines:\\n self._relief_ellipse_outer_refresh(add, *self.relief_ellipse_outer_colors, *pos_size)\\n if self.relief_square_inner_colors and self.relief_square_inner_lines:\\n self._relief_square_inner_refresh(add, *self.relief_square_inner_colors, *pos_size)\\n if self.relief_square_outer_colors and self.relief_square_outer_lines:\\n self._relief_square_outer_refresh(add, *self.relief_square_outer_colors, *pos_size)\\n\\n if self._relief_graphic_instructions.length():\\n self.canvas.after.add(self._relief_graphic_instructions)\",\n \"def refresh(self, data):\\n for region in self.loaded_regions:\\n self.regions[region].unload()\\n super().clear()\\n self.data = data\\n self.loaded_regions = set()\\n self.draw_visible_area()\",\n \"def _Refresh(self):\\n raise NotImplementedError\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7797752","0.76777506","0.7624245","0.7607786","0.75885034","0.74661344","0.74655384","0.74655384","0.7456086","0.7437477","0.7407238","0.7366163","0.7329085","0.7314451","0.722998","0.7220857","0.7200671","0.7197712","0.7181533","0.71769416","0.7154286","0.70699596","0.7057484","0.7045358","0.70298964","0.70298964","0.7003218","0.698895","0.69641334","0.69641334","0.6883847","0.687029","0.687029","0.687029","0.68640804","0.68313795","0.679145","0.67645925","0.6755471","0.67544836","0.6750368","0.67337024","0.6698502","0.6698245","0.6696181","0.6693487","0.6691196","0.66826075","0.6674008","0.667171","0.6659224","0.66414917","0.6639015","0.6639015","0.66267926","0.6624927","0.66227245","0.66154903","0.66136557","0.6609104","0.6603454","0.66019523","0.6598592","0.65924853","0.6588567","0.65759826","0.65719473","0.65708095","0.655271","0.65472585","0.6544071","0.65207666","0.6514579","0.65071607","0.65047","0.64849126","0.64798325","0.6478178","0.64768565","0.64612573","0.64502436","0.6446285","0.64382","0.64303094","0.64176947","0.641021","0.64064837","0.6388016","0.63774264","0.63732314","0.635015","0.6336815","0.63318646","0.6320687","0.6316471","0.6315099","0.631264","0.63109547","0.6302592","0.6300748"],"string":"[\n \"0.7797752\",\n \"0.76777506\",\n \"0.7624245\",\n \"0.7607786\",\n \"0.75885034\",\n \"0.74661344\",\n \"0.74655384\",\n \"0.74655384\",\n \"0.7456086\",\n \"0.7437477\",\n \"0.7407238\",\n \"0.7366163\",\n \"0.7329085\",\n \"0.7314451\",\n \"0.722998\",\n \"0.7220857\",\n \"0.7200671\",\n \"0.7197712\",\n \"0.7181533\",\n \"0.71769416\",\n \"0.7154286\",\n \"0.70699596\",\n \"0.7057484\",\n \"0.7045358\",\n \"0.70298964\",\n \"0.70298964\",\n \"0.7003218\",\n \"0.698895\",\n \"0.69641334\",\n \"0.69641334\",\n \"0.6883847\",\n \"0.687029\",\n \"0.687029\",\n \"0.687029\",\n \"0.68640804\",\n \"0.68313795\",\n \"0.679145\",\n \"0.67645925\",\n \"0.6755471\",\n \"0.67544836\",\n \"0.6750368\",\n \"0.67337024\",\n \"0.6698502\",\n \"0.6698245\",\n \"0.6696181\",\n \"0.6693487\",\n \"0.6691196\",\n \"0.66826075\",\n \"0.6674008\",\n \"0.667171\",\n \"0.6659224\",\n \"0.66414917\",\n \"0.6639015\",\n \"0.6639015\",\n \"0.66267926\",\n \"0.6624927\",\n \"0.66227245\",\n \"0.66154903\",\n \"0.66136557\",\n \"0.6609104\",\n \"0.6603454\",\n \"0.66019523\",\n \"0.6598592\",\n \"0.65924853\",\n \"0.6588567\",\n \"0.65759826\",\n \"0.65719473\",\n \"0.65708095\",\n \"0.655271\",\n \"0.65472585\",\n \"0.6544071\",\n \"0.65207666\",\n \"0.6514579\",\n \"0.65071607\",\n \"0.65047\",\n \"0.64849126\",\n \"0.64798325\",\n \"0.6478178\",\n \"0.64768565\",\n \"0.64612573\",\n \"0.64502436\",\n \"0.6446285\",\n \"0.64382\",\n \"0.64303094\",\n \"0.64176947\",\n \"0.641021\",\n \"0.64064837\",\n \"0.6388016\",\n \"0.63774264\",\n \"0.63732314\",\n \"0.635015\",\n \"0.6336815\",\n \"0.63318646\",\n \"0.6320687\",\n \"0.6316471\",\n \"0.6315099\",\n \"0.631264\",\n \"0.63109547\",\n \"0.6302592\",\n \"0.6300748\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80525124"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11093,"cells":{"query":{"kind":"string","value":"Generates list of mesh vertices and triangles from a list of vectors"},"document":{"kind":"string","value":"def _generate_meshes(self, vectors, width):\n centers = np.repeat(vectors, 2, axis=0)\n offsets = segment_normal(vectors[::2, :], vectors[1::2, :])\n offsets = np.repeat(offsets, 4, axis=0)\n signs = np.ones((len(offsets), 2))\n signs[::2] = -1\n offsets = offsets*signs\n\n vertices = centers + width*offsets/2\n triangles = np.array([[2*i, 2*i+1, 2*i+2] if i % 2 == 0 else\n [2*i-1, 2*i, 2*i+1] for i in\n range(len(vectors))]).astype(np.uint32)\n\n return vertices, triangles"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def vertices(tri, vertex_list):\n dim = len(vertex_list[0])\n p = numpy.zeros((3, dim))\n for j in range(3):\n p[j] = vertex_list[tri[j]]\n return p","def get_vertices(self):\n vertices = []\n V = [[-self.base_vectors[:,n], self.base_vectors[:,n]] for n in range(self.base_vectors.shape[1])]\n combs = list(itertools.product(*V))\n for cb in combs:\n cb = np.sum(np.array(cb).T, axis=1, keepdims=True)\n vertices.append(self.base_vertices + cb)\n\n vertices = np.concatenate(vertices,axis=1)\n return vertices","def extract_triangles(mesh, materials_list):\n tri_list = []\n do_uv = bool(mesh.tessface_uv_textures)\n\n for mat in materials_list:\n for i, face in enumerate(mesh.tessfaces):\n f_v = face.vertices\n if mesh.materials[face.material_index].name != mat: continue\n\n uf = mesh.tessface_uv_textures.active.data[i] if do_uv else None\n\n fmt = 0\n if(do_uv): fmt = face.material_index\n\n if do_uv:\n f_uv = uf.uv\n\n if len(f_v) == 3:\n new_tri = tri_wrapper((f_v[0], f_v[1], f_v[2]), fmt)\n if (do_uv):\n new_tri.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[1]), uv_key(f_uv[2])\n else: new_tri.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\n tri_list.append(new_tri)\n\n else: # it's a quad\n new_tri = tri_wrapper((f_v[0], f_v[1], f_v[2]), fmt)\n new_tri_2 = tri_wrapper((f_v[0], f_v[2], f_v[3]), fmt)\n\n if (do_uv):\n new_tri.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[1]), uv_key(f_uv[2])\n new_tri_2.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[2]), uv_key(f_uv[3])\n else:\n new_tri.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\n new_tri_2.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\n\n tri_list.append(new_tri)\n tri_list.append(new_tri_2)\n\n return tri_list","def get_verts(ulist, vlist, func):\n verts = []\n for u in ulist:\n for v in vlist:\n verts.append(func(u, v))\n return verts","def simplex(v1, v2, v3):\n res = polyhedron([], [], [])\n p0 = res.addVertex(vertex(0, 0, 0))\n p1 = res.addVertex(vertex(v1))\n p2 = res.addVertex(vertex(v2))\n p3 = res.addVertex(vertex(v3))\n p12 = res.addVertex(vertex(v1 + v2))\n p13 = res.addVertex(vertex(v1 + v3))\n p23 = res.addVertex(vertex(v2 + v3))\n p123 = res.addVertex(vertex(v1 + v2 + v3))\n res.addEdge(p0, p1)\n res.addEdge(p0, p2)\n res.addEdge(p0, p3)\n res.addEdge(p1, p12)\n res.addEdge(p1, p13)\n res.addEdge(p2, p12)\n res.addEdge(p2, p23)\n res.addEdge(p3, p13)\n res.addEdge(p3, p23)\n res.addEdge(p1, p123)\n res.addEdge(p2, p123)\n res.addEdge(p3, p123)\n res.addFace([p0, p2, p12, p1])\n res.addFace([p0, p1, p13, p3])\n res.addFace([p0, p3, p23, p2])\n res.addFace([p1, p12, p123, p13])\n res.addFace([p2, p23, p123, p12])\n res.addFace([p3, p13, p123, p23])\n return res","def vertices(self):\n d = self.space_dimension()\n v = vector(ZZ, d)\n points = []\n for g in self.minimized_generators():\n for i in range(0,d):\n v[i] = g.coefficient(Variable(i))\n v_copy = copy.copy(v)\n v_copy.set_immutable()\n points.append(v_copy)\n return tuple(points)","def plotVects(vList, colors='k'):\n polyhedron([vertex(v) for v in vList] + [vertex(0, 0, 0)], [edge(v) for v in vList], []).plot(plotEdges=True)","def create_inner_tri(point, v1, v2, v3):\n return [(point, v1, v2), (point, v1, v3), (point, v2, v3)]","def get_faces(ulist, vlist):\n width = len(ulist)\n faces = []\n for i in range(len(ulist) - 1):\n for j in range(len(vlist) - 1):\n topleft = j * width + i\n topright = topleft + 1\n bottomleft = ((j + 1) * width) + i\n bottomright = bottomleft + 1\n one = [topleft, topright, bottomleft]\n two = [bottomleft, topright, bottomright]\n faces.append(one)\n faces.append(two)\n\n return faces","def split_triangles(mesh):\n triangles = np.asarray(mesh.triangles).copy()\n vertices = np.asarray(mesh.vertices).copy()\n\n triangles_3 = np.zeros_like(triangles)\n vertices_3 = np.zeros((len(triangles) * 3, 3), dtype=vertices.dtype)\n\n for index_triangle, t in enumerate(triangles):\n index_vertex = index_triangle * 3\n vertices_3[index_vertex] = vertices[t[0]]\n vertices_3[index_vertex + 1] = vertices[t[1]]\n vertices_3[index_vertex + 2] = vertices[t[2]]\n\n triangles_3[index_triangle] = np.arange(index_vertex, index_vertex + 3)\n\n mesh_return = deepcopy(mesh)\n mesh_return.triangles = o3d.utility.Vector3iVector(triangles_3)\n mesh_return.vertices = o3d.utility.Vector3dVector(vertices_3)\n mesh_return.triangle_normals = mesh.triangle_normals\n mesh_return.paint_uniform_color([0.5, 0.5, 0.5])\n return mesh_return","def create_triangles(list_of_points):\n # create the first two triangle using the create_two_init_triangles with\n # the first 4 points in the given list\n tri_list = create_two_init_triangles(list_of_points[0:FIRST_FOUR_POINTS])\n # run over the point list from the 5th point and on\n for i in range(FIRST_FOUR_POINTS, len(list_of_points)):\n # run on the existing triangles\n for j in range(0, len(tri_list)):\n # check if the point is inside the current triangle\n if is_point_inside_triangle(list_of_points[i], tri_list[j][0],\n tri_list[j][1], tri_list[j][2])[0]:\n # if the point is inside the current triangle, create 3 new\n # triangles using the old triangle vertexes and the new point\n # adding them to the triangle list instead of the triangle the\n # point was in\n tri_list[j:j+1] = create_inner_tri(list_of_points[i],\n tri_list[j][0],\n tri_list[j][1],\n tri_list[j][2])\n break\n return tri_list","def _generate_vertexes(self):\n # generate list of sets for each vms\n self._graph = [set() for _ in range(self._vm_count)]","def normals(t, v):\n n = numpy.zeros((len(t), 3))\n for i in range(0, len(t)):\n p = vertices(t[i], v)\n n[i] = triangle.normal(p)\n return n","def strang_mesh(filename):\n\n from math import pi\n from anuga.utilities.numerical_tools import anglediff\n\n\n fid = open(filename)\n points = [] # List of x, y coordinates\n triangles = [] # List of vertex ids as listed in the file\n\n for line in fid.readlines():\n fields = line.split()\n if len(fields) == 2:\n # we are reading vertex coordinates\n points.append([float(fields[0]), float(fields[1])])\n elif len(fields) == 3:\n # we are reading triangle point id's (format ae+b)\n triangles.append([int(float(fields[0]))-1,\n int(float(fields[1]))-1,\n int(float(fields[2]))-1])\n else:\n raise Excetion('wrong format in %s' % filename)\n\n elements = [] #Final list of elements\n\n for t in triangles:\n #Get vertex coordinates\n v0 = t[0]\n v1 = t[1]\n v2 = t[2]\n\n x0 = points[v0][0]\n y0 = points[v0][1]\n x1 = points[v1][0]\n y1 = points[v1][1]\n x2 = points[v2][0]\n y2 = points[v2][1]\n\n #Check that points are arranged in counter clock-wise order\n vec0 = [x1-x0, y1-y0]\n vec1 = [x2-x1, y2-y1]\n vec2 = [x0-x2, y0-y2]\n\n a0 = anglediff(vec1, vec0)\n a1 = anglediff(vec2, vec1)\n a2 = anglediff(vec0, vec2)\n\n if a0 < pi and a1 < pi and a2 < pi:\n elements.append([v0, v1, v2])\n else:\n elements.append([v0, v2, v1])\n\n return points, elements","def create_single_triangle_mesh():\n vertices = np.array(\n ((0, 0, 0), (1, 0, 0), (0, 1, 0)), dtype=np.float32)\n faces = np.array(((0, 1, 2),), dtype=np.int32)\n return vertices, faces","def faces_as_vertices(self) -> Iterable[List[Vec3]]:\n v = self.vertices\n for face in self.faces:\n yield [v[index] for index in face]","def vertexes(self):\n theta = self.orientation\n shifts = np.array([np.cos(theta), np.sin(theta)]) * self.a\n return self.coords + (shifts[:, None] * [-1, 1]).T","def getVectors(self):\n l = len(self.points)\n return [Vector.createFromTwoPoints(self.points[i % l], self.points[(i + 1) % l], \\\n color=self.side_color, width=self.side_width) for i in range(l)]","def get_sound_vertices(self):\n\n vals = np.sum(np.abs(self.base_vectors), axis=1, keepdims=True)\n V = [[v,-v] for v in vals]\n combs = list(itertools.product(*V))\n\n vertices = []\n for cb in combs:\n vertices.append(self.base_vertices+np.array(cb))\n vertices = np.concatenate(vertices, axis=1)\n return vertices","def get_vectors(nodes, mode=\"xform\"):\n for each in nodes:\n position = (0, 0, 0)\n\n if mode == \"xform\":\n position = cmds.xform(\n each,\n query=True,\n translation=True,\n worldSpace=True,\n )\n\n elif mode == \"pivot\":\n position = cmds.xform(\n each,\n query=True,\n translation=True,\n rotatePivot=True,\n worldSpace=True,\n )\n\n # when using xform on component like faces or edge, the returned value\n # will be a list of each vertices position, so we need to average that\n if len(position) > 3:\n vectors = [\n MVector(position[i : i + 3])\n for i in range(0, len(position), 3)\n ]\n result = MVector()\n for vector in vectors:\n result += vector\n position = result / len(vectors)\n\n yield MVector(position)","def create( vertices, faces, colour=(0.6,0.6,0.6) ):\r\n \r\n mesh = GLUtils.GLMesh()\r\n \r\n for vertex in vertices:\r\n mesh.addVertex( PyUtils.toPoint3d(vertex) )\r\n \r\n for face in faces:\r\n poly = GLUtils.GLIndexedPoly()\r\n for index in face:\r\n poly.addVertexIndex( index )\r\n mesh.addPoly(poly)\r\n \r\n try:\r\n mesh.setColour( *colour )\r\n except TypeError:\r\n mesh.setColour( *(colour + (1,)) )\r\n\r\n mesh.computeNormals()\r\n\r\n return mesh","def construct_convex_hull(vertices: Sequence[Point]) -> Polyhedron:\n coords = np.zeros((len(vertices),3))\n for i,vertex in enumerate(vertices):\n coords[i,:] = vertex.coordinates\n hull = qconvex(\"i\", coords)\n n_facets = int(hull[0])\n facets = []\n for facet_vertices_str in hull[1:]:\n facet_vertices_idx = [int(x) for x in facet_vertices_str.split(' ')]\n facet_vertices = [vertices[i] for i in facet_vertices_idx]\n facet = Facet([Contour.from_vertices(facet_vertices)])\n facets.append(facet)\n polyhedron = Polyhedron(facets)\n return polyhedron","def creaLados(vertices):\n\tlados = []\n\tfor i in range(len(vertices)-1):\n\t\tlados.append((i, i+1))\n\tlados.append((i+1,0))\n\treturn lados","def calculateMeshInv(mesh_face_vertices):\n mesh_inv = []\n for mesh in mesh_face_vertices:\n U = np.array([\n [mesh[0, 0], mesh[1, 0], mesh[2, 0]],\n [mesh[0, 1], mesh[1, 1], mesh[2, 1]],\n [1, 1, 1],\n ])\n mesh_inv.append(np.linalg.inv(U))\n return np.array(mesh_inv)","def vector(self):\n \n v_list = Householder.triangle_operation(self)[1]\n \n return(v_list)","def create_hull(vertices):\n dt = np.dtype([('vertex', np.float64, (2,)),\n ('length', np.float64),\n ('is_processed', bool)])\n\n hull = np.empty(len(vertices), dtype=dt)\n for i, v in enumerate(vertices):\n j = 0 if i == len(vertices)-1 else i+1\n hull[i] = (v, dist(v, vertices[j]), False)\n\n return np.rec.array(hull)","def cube_vertices(x, y, z, n):\r\n return [\r\n x-n,y+n,z-n, x-n,y+n,z+n, x+n,y+n,z+n, x+n,y+n,z-n, # top\r\n x-n,y-n,z-n, x+n,y-n,z-n, x+n,y-n,z+n, x-n,y-n,z+n, # bottom\r\n x-n,y-n,z-n, x-n,y-n,z+n, x-n,y+n,z+n, x-n,y+n,z-n, # left\r\n x+n,y-n,z+n, x+n,y-n,z-n, x+n,y+n,z-n, x+n,y+n,z+n, # right\r\n x-n,y-n,z+n, x+n,y-n,z+n, x+n,y+n,z+n, x-n,y+n,z+n, # front\r\n x+n,y-n,z-n, x-n,y-n,z-n, x-n,y+n,z-n, x+n,y+n,z-n, # back\r\n ]","def createMesh(width, height):\r\n mesh = [(x,y) for x in range(0, width+1) for y in range(0,height+1)]\r\n return mesh","def getVectors(self,graph):\n return [Vector.createFromTwoTuples(graph[i],graph[i+1]) for i in range(len(graph)-1)]","def get_mesh_boundary(triangles):\n # Create edges and sort each vertices on each edge.\n edge0 = triangles[:,0:2]\n edge1 = triangles[:,1:3]\n edge2 = triangles.take((0,2), axis=1)\n edges = np.concatenate((edge0, edge1, edge2), axis=0)\n edge_sort = np.sort(edges, axis=1)\n\n # Get unique edges that are only present once.\n (uniq, uniq_ids, counts) = np.unique(edge_sort, axis=0, return_index=True, return_counts=True)\n edge_inds = np.arange(edge_sort.shape[0], dtype=int)\n outer_edge_ids = edge_inds[np.in1d(edge_inds, uniq_ids[counts==1])]\n outer_edges = edge_sort[outer_edge_ids,:]\n num_outer_edges = outer_edges.shape[0]\n\n # Assume we need to close the polygon.\n num_outer_verts = num_outer_edges + 1\n\n # Loop over outer edges and use traversal method to get ordered vertices.\n v_start = outer_edges[0,0]\n v_end = outer_edges[0,1]\n vert_inds = -1*np.ones(num_outer_verts, dtype=int)\n vert_inds[0] = v_start\n vert_inds[1] = v_end\n vert_num = 2\n outer_edges[0,:] = -1\n for edge_num in range(1,num_outer_edges):\n edge_inds_next = np.where(outer_edges == v_end)\n if (edge_inds_next[0].shape[0] < 1):\n msg = \"Next edge not found for vertex %d\" % v_end\n raise ValueError(msg)\n edge_ind_next = edge_inds_next[0][0]\n vert_ind_next = 0\n if (edge_inds_next[1][0] == 0):\n vert_ind_next = 1\n vert_inds[vert_num] = outer_edges[edge_ind_next, vert_ind_next]\n outer_edges[edge_ind_next, :] = -1\n v_end = vert_inds[vert_num]\n vert_num += 1\n\n return vert_inds","def MeshPyTri(points,facets,*args,**kwargs):\n info = triangle.MeshInfo()\n info.set_points(points)\n info.set_facets(facets)\n\n return triangle.build(info,*args,**kwargs)","def triangle_facets(length=2.0, divisions=4):\n\n # Starting with a equilateral triangle.\n vertices = [\n (0, (math.sqrt(3) / 3.0) * length, 0.0),\n (-length / 2, -(math.sqrt(3) / 6.0) * length, 0.0),\n (length / 2, -(math.sqrt(3) / 6.0) * length, 0.0),\n ]\n\n # The facets should not be accumulated for all of them as the first\n # triangle will Z-fight with remaining triangles. The overall style is to\n # have 'holes' where there are triangles that aren't filled.\n\n facets = [(0, 1, 2)]\n new_triangles = facets\n\n for _ in range(divisions):\n facets = []\n for new_triangle in new_triangles:\n next_vertices, next_triangles = _divide(new_triangle, vertices)\n vertices.extend(next_vertices)\n facets.extend(next_triangles)\n\n new_triangles = facets\n\n return vertices, facets","def exportTriangles(self):\n # Filter out triangles with any vertex in the extended BBox\n return [(a-4,b-4,c-4)\n for (a,b,c) in self.triangles if a > 3 and b > 3 and c > 3]","def _create_vertex_list(self):\n raise NotImplementedError('_create_vertex_list must be defined in '\n 'order to use group or batch properties')","def _triangular_mesh_to_three_geometry(vertices, faces, vertex_colors=None):\n context = _js_builder.Js(mode=_js_builder.PERSISTENT)\n vertices = context.Float32Array.new_object(vertices.ravel().tolist())\n faces = context.Uint32Array.new_object(faces.ravel().tolist())\n geometry = context.THREE.BufferGeometry.new_object()\n geometry.addAttribute('position',\n context.THREE.BufferAttribute.new_object(vertices, 3))\n geometry.setIndex(context.THREE.BufferAttribute.new_object(faces, 1))\n geometry.computeVertexNormals()\n if vertex_colors is not None:\n vertex_colors = context.Float32Array.new_object(\n vertex_colors.ravel().tolist())\n geometry.addAttribute(\n 'color', context.THREE.BufferAttribute.new_object(vertex_colors, 3))\n\n return geometry","def MeshVtxAdjacentVtxs (strMesh, index, blnAbsolutConnections=False, blnCreate=False):\n \"\"\"custom function\"\"\"\n #-----------------------------------------------------------------------------------------------------------------------------------------\n def CullDuplicates(seq, idfun=None): \n # order preserving \n if idfun is None: \n def idfun(x): return x \n seen = {} \n result = [] \n for item in seq: \n marker = idfun(item) \n if marker in seen: continue \n seen[marker] = 1 \n result.append(item) \n return result\n #-----------------------------------------------------------------------------------------------------------------------------------------\n MeshVtxAdjacentVtxs = []\n if rs.IsMesh(strMesh)==False : \n print \"strMesh is not an mesh\"\n return None\n if type(index)==type(\"string\"):\n print \"index is not an integer\"\n return None\n if type(index)==type(0.1): index = int(index)\n\n arrVertices = rs.MeshVertices (strMesh)\n arrFaceVertices = rs.MeshFaceVertices(strMesh)\n\n intCount = 0\n arrAdjacentVtxs = []\n for arrFace in arrFaceVertices:\n blnIsAdjacent = False\n for arrVtxIndex in arrFace:\n if arrVtxIndex == index :\n blnIsAdjacent = True\n if blnIsAdjacent :\n if blnAbsolutConnections :\n if arrFace[2]==arrFace[3] :\n for arrVtxIndex in arrFace :\n if arrVtxIndex != index :\n arrAdjacentVtxs.append( arrVtxIndex)\n else :\n if index == arrFace[0] :\n arrAdjacentVtxs.append( arrFace[3] )\n arrAdjacentVtxs.append( arrFace[1] )\n elif index == arrFace[1] :\n arrAdjacentVtxs.append( arrFace[0] )\n arrAdjacentVtxs.append( arrFace[2] )\n elif index == arrFace[2] :\n arrAdjacentVtxs.append( arrFace[1] )\n arrAdjacentVtxs.append( arrFace[3] )\n elif index == arrFace(3) :\n arrAdjacentVtxs.append( arrFace[2] )\n arrAdjacentVtxs.append( arrFace[0] )\n else :\n for arrVtxIndex in arrFace :\n if arrVtxIndex != index :\n arrAdjacentVtxs.append( arrVtxIndex )\n if type(arrAdjacentVtxs) != type([]) : return None\n arrOrderAdjacentVtxs = CullDuplicates(arrAdjacentVtxs)\n if blnCreate :\n arrStrPts = []\n for arrVtxIndex in arrOrderAdjacentVtxs:\n rs.AddPoint ( arrVertices[arrVtxIndex] )\n arrStrPts.append( arrVertices[arrVtxIndex] )\n return arrStrPts\n else :\n return arrOrderAdjacentVtxs","def get_vertex_data(\n mesh: object,\n g: BinaryReader,\n v1: int,\n v2: int,\n v3: int,\n v4: int,\n n: int,\n verbose=False,\n):\n for i in range(v1):\n v_offset = g.tell()\n mesh.vertPosList.append(g.f(3))\n mesh.vertNormList.append(g.f(3))\n indice_offset = g.tell()\n if verbose:\n logger.debug({\n \"v1 v_offset\": v_offset,\n \"v1 indice_offset\": indice_offset,\n })\n mesh.skinIndiceList.append(g.B(4))\n mesh.skinWeightList.append([0, 0, 0, 1])\n\n for i in range(v2):\n v_offset = g.tell()\n mesh.vertPosList.append(g.f(3))\n mesh.vertNormList.append(g.f(3))\n indice_offset = g.tell()\n if verbose:\n logger.debug({\n \"v2 v_offset\": v_offset,\n \"v2 indice_offset\": indice_offset,\n })\n mesh.skinIndiceList.append(g.B(4))\n w1 = g.f(1)[0]\n w2 = 1.0 - w1\n mesh.skinWeightList.append([0, 0, w2, w1])\n\n for i in range(v3):\n v_offset = g.tell()\n mesh.vertPosList.append(g.f(3))\n mesh.vertNormList.append(g.f(3))\n indice_offset = g.tell()\n if verbose:\n logger.debug({\n \"v3 v_offset\": v_offset,\n \"v3 indice_offset\": indice_offset,\n })\n mesh.skinIndiceList.append(g.B(4))\n w1 = g.f(1)[0]\n w2 = g.f(1)[0]\n w3 = 1.0 - w1 - w2\n mesh.skinWeightList.append([0, w3, w2, w1])\n\n for i in range(v4):\n v_offset = g.tell()\n mesh.vertPosList.append(g.f(3))\n mesh.vertNormList.append(g.f(3))\n indice_offset = g.tell()\n if verbose:\n logger.debug({\n \"v4 v_offset\": v_offset,\n \"v4 indice_offset\": indice_offset,\n })\n mesh.skinIndiceList.append(g.B(4))\n w1 = g.f(1)[0]\n w2 = g.f(1)[0]\n w3 = g.f(1)[0]\n w4 = 1.0 - w1 - w2 - w3\n mesh.skinWeightList.append([w4, w3, w2, w1])","def exportTriangles(self):\n # Filter out triangles with any vertex in the extended BBox\n return [(a-4, b-4, c-4)\n for (a, b, c) in self.triangles if a > 3 and b > 3 and c > 3]","def polygonal(resolution, in_vertices, out_vertices_list = None):\n in_vertices = [Point(in_vertices[k,0],in_vertices[k,1]) for k in range(in_vertices.shape[0])] \n\n domain = mshr.Polygon(in_vertices) # https://bitbucket.org/fenics-project/mshr/wiki/API/Polygon\n # Create polygon defined by the given vertices. Vertices must be in counter-clockwise order and free of self-intersections.\n \n if(out_vertices_list is not None):\n for out_vertices in out_vertices_list:\n out_vertices = [Point(out_vertices[k,0],out_vertices[k,1]) for k in range(out_vertices.shape[0])]\n domain -= mshr.Polygon(out_vertices)\n \n mesh=mshr.generate_mesh(domain, resolution)\n\n # TODO : add refined mesh\n # if(refine_mesh):\n # d = mesh.topology().dim()\n \n # class To_refine(SubDomain):\n # def inside(self, x, on_boundary):\n # return x[1]<=0 and x[1]>= -l_mot/2-h_grid-l_vacuum/4\n\n # to_refine = To_refine()\n # marker = MeshFunction(\"bool\", mesh, d, False)\n # to_refine.mark(marker, True)\n # mesh = refine(mesh,marker)\n\n return mesh","def add_vertices(self, vertices: Iterable[\"Vertex\"]) -> Sequence[int]:\n start_index = len(self.vertices)\n self.vertices.extend(Vec3.generate(vertices))\n return tuple(range(start_index, len(self.vertices)))","def get_vectors(mols):\n vect_types = VectTypes()\n for mol in mols:\n if \".\" in mol.smiles:\n logger.debug(\"SKIPPING - FRAGMENT: %s\", mol.smiles)\n continue\n vectors = get_3d_vects_for_mol(mol.sdf_info)\n for vect_type in vectors:\n vect_choice = vect_types.translate_vect_types(vect_type)\n for vector in vectors[vect_type]:\n spl_vect = vector.split(\"__\")\n smiles = spl_vect[0]\n if len(spl_vect) > 1:\n vect_ind = int(spl_vect[1])\n else:\n vect_ind = 0\n new_vect = Vector.objects.get_or_create(\n smiles=smiles, cmpd_id=mol.cmpd_id, type=vect_choice\n )[0]\n create_vect_3d(mol, new_vect, vect_ind, vectors[vect_type][vector])","def getVectors(self, graph):\n return [Vector.createFromTwoTuples(graph[i], graph[i + 1]) for i in range(len(graph) - 1)]","def get_triangle(radius, xcenter, ycenter, numberOfVertices):\n\n\n angles = [0.0, (2. / 3.0) * math.pi, (4.0 / 3.0) * math.pi]\n\n vertices = []\n\n for angle in range(0, len(angles)):\n x = radius * math.cos(angles[angle]) + xcenter\n y = radius * math.sin(angles[angle]) + ycenter\n vertices.append(x) # append the x value to the vertex list\n vertices.append(y) # append the y value to the vertex list\n\n # convert the vertices list to pyGlet vertices format\n vertexList = pyglet.graphics.vertex_list(numberOfVertices, ('v2f', vertices))\n\n return vertexList","def create_square_triangle_mesh():\n vertices = np.array(\n ((0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0.5, 0.5, 0)),\n dtype=np.float32)\n faces = np.array(\n ((0, 1, 4), (1, 3, 4), (3, 2, 4), (2, 0, 4)), dtype=np.int32)\n return vertices, faces","def get_intermediate_triangles(source_triangles_list, target_triangles_list,\n alpha):\n # initiate the intermediate triangles list\n intermediate_tri_list = []\n # run over the lists of source triangles and target triangles\n for i in range(0, len(source_triangles_list)):\n # add to the list the intermediate triangle made with the method\n # create_intermediate_triangle\n intermediate_tri_list.append(create_intermediate_triangle\n (source_triangles_list[i],\n target_triangles_list[i], alpha))\n return intermediate_tri_list","def boundary_vertices(points: np.ndarray, triangles: np.ndarray) -> np.ndarray:\n tri = Triangulation(points[:, 0], points[:, 1], triangles)\n boundary_edges = set()\n for i, neighbors in enumerate(tri.neighbors):\n for k in range(3):\n if neighbors[k] == -1:\n boundary_edges.add((triangles[i, k], triangles[i, (k + 1) % 3]))\n edges = MultiLineString([points[edge, :] for edge in boundary_edges])\n polygons = list(polygonize(edges))\n assert len(polygons) == 1, polygons\n polygon = orient(polygons[0])\n points_list = [tuple(xy) for xy in points]\n indices = np.array([points_list.index(xy) for xy in polygon.exterior.coords])\n return indices[:-1]","def _uniform_refine_triangles(mesh):\n\n # get current cells and edges\n cells = mesh.cells\n edges = mesh.edges\n\n assert np.size(cells, axis=1) == 3\n\n # get number of current nodes\n n_nodes = np.size(mesh.nodes, axis=0)\n \n # first we create additional nodes as midpoints of the current edges\n midpoints = 0.5 * mesh.nodes.take(edges - 1, axis=0).sum(axis=1)\n\n # add them to the existing mesh nodes\n new_nodes = np.vstack([mesh.nodes, midpoints])\n \n # then we generate the indices of the newly created nodes\n #\n # their indices start at the current number of nodes (`n_nodes`) + 1\n # and end after additional `n_edges` nodes\n n_edges = np.size(edges, axis=0)\n new_node_indices = np.arange(n_nodes + 1, n_nodes + n_edges + 1, dtype=int)\n\n # refine elements\n #\n # for every element we need the indices of the edges at which the corresponding\n # new nodes are created\n indices_2_1 = mesh.topology.get_connectivity(2, 1, return_indices=True) - 1\n \n # next we augment the indices that define each element as if\n # they were defined by 6 nodes (including those of the edge midpoints)\n cells_ = np.hstack([cells, new_node_indices.take(indices_2_1, axis=0)])\n \n # now we can generate the four new elements for each existing one\n new_cells_1 = np.vstack([cells_[:,0], cells_[:,3], cells_[:,4]]).T\n new_cells_2 = np.vstack([cells_[:,1], cells_[:,3], cells_[:,5]]).T\n new_cells_3 = np.vstack([cells_[:,2], cells_[:,4], cells_[:,5]]).T\n new_cells_4 = np.vstack([cells_[:,3], cells_[:,4], cells_[:,5]]).T\n \n new_cells = np.vstack([new_cells_1, new_cells_2, new_cells_3, new_cells_4])\n\n return new_nodes, new_cells","def compute_normalvect(self):\n normvect = np.zeros((len(self.tri_pnts),3,3))\n zvec = np.array([0, 0, 1])\n for itri, tri in enumerate(self.tri_pnts):\n #import pdb; pdb.set_trace()\n tri0, tri1, tri2 = tri\n x1,y1 = self.points[tri1]-self.points[tri0]\n v1 = np.array([x1,y1,0])\n x2,y2 = self.points[tri2]-self.points[tri1]\n v2 = np.array([x2,y2,0])\n x3,y3 = self.points[tri0]-self.points[tri2]\n v3 = np.array([x3,y3,0])\n v1 = v1/np.linalg.norm(v1)\n v2 = v2/np.linalg.norm(v2)\n v3 = v3/np.linalg.norm(v3)\n #import pdb; pdb.set_trace()\n normvect[itri,:,:] = np.cross(v1,zvec), np.cross(v2,zvec), np.cross(v3,zvec)\n #import pdb; pdb.set_trace()\n return normvect","def cube_vertices(x, y, z, nx, ny=None, nz=None):\n if ny == None: ny = nx\n if nz == None: nz = nx\n return [\n x - nx, y + ny, z - nz, x - nx, y + ny, z + nz, x + nx, y + ny, z + nz, x + nx, y + ny, z - nz, # top\n x - nx, y - ny, z - nz, x + nx, y - ny, z - nz, x + nx, y - ny, z + nz, x - nx, y - ny, z + nz, # bottom\n x - nx, y - ny, z - nz, x - nx, y - ny, z + nz, x - nx, y + ny, z + nz, x - nx, y + ny, z - nz, # left\n x + nx, y - ny, z + nz, x + nx, y - ny, z - nz, x + nx, y + ny, z - nz, x + nx, y + ny, z + nz, # right\n x - nx, y - ny, z + nz, x + nx, y - ny, z + nz, x + nx, y + ny, z + nz, x - nx, y + ny, z + nz, # front\n x + nx, y - ny, z - nz, x - nx, y - ny, z - nz, x - nx, y + ny, z - nz, x + nx, y + ny, z - nz, # back\n ]","def trimesh_from_point_cloud(cloud):\n points = np.asarray(cloud)\n hull = scipy.spatial.ConvexHull(points)\n hull = scipy.spatial.ConvexHull(points[hull.vertices])\n ru.transforms.counterclockwise_hull(hull)\n vertices = hull.points\n faces = hull.simplices\n return vertices, faces","def non_rotated_vertices(self):\n v0 = [self.pos.x - self.width / 2, self.pos.y - self.height / 2]\n v1 = [self.pos.x + self.width / 2, self.pos.y - self.height / 2]\n v2 = [self.pos.x + self.width / 2, self.pos.y + self.height / 2]\n v3 = [self.pos.x - self.width / 2, self.pos.y + self.height / 2]\n return v0, v1, v2, v3","def vertices_tuple(self):\n vtuple = (*self.v0, *self.v1, *self.v2, *self.v3)\n return vtuple","def fromVertices(cls,\n xp0, yp0, zp0, xp1, yp1, zp1,\n xp2, yp2, zp2, xp3, yp3, zp3,\n origin,\n group_index=None,\n reference=None):\n if len(xp0) == len(yp0) == len(zp0) == len(xp1) == len(yp1) == \\\n len(zp1) == len(xp2) == len(yp2) == len(zp2) == len(xp3) == \\\n len(yp3) == len(zp3):\n pass\n else:\n raise ShakeLibException('All vectors specifying quadrilateral '\n 'vertices must have the same length.')\n\n nq = len(xp0)\n if group_index is not None:\n if len(group_index) != nq:\n raise Exception(\n \"group_index must have same length as vertices.\")\n else:\n group_index = np.array(range(nq))\n\n xp0 = np.array(xp0, dtype='d')\n yp0 = np.array(yp0, dtype='d')\n zp0 = np.array(zp0, dtype='d')\n xp1 = np.array(xp1, dtype='d')\n yp1 = np.array(yp1, dtype='d')\n zp1 = np.array(zp1, dtype='d')\n xp2 = np.array(xp2, dtype='d')\n yp2 = np.array(yp2, dtype='d')\n zp2 = np.array(zp2, dtype='d')\n xp3 = np.array(xp3, dtype='d')\n yp3 = np.array(yp3, dtype='d')\n zp3 = np.array(zp3, dtype='d')\n\n #----------------------------------------------------------------------\n # Create GeoJSON object\n #----------------------------------------------------------------------\n\n coords = []\n u_groups = np.unique(group_index)\n n_groups = len(u_groups)\n for i in range(n_groups):\n ind = np.where(u_groups[i] == group_index)[0]\n lons = np.concatenate(\n [xp0[ind[0]].reshape((1,)),\n xp1[ind],\n xp2[ind][::-1],\n xp3[ind][::-1][-1].reshape((1,)),\n xp0[ind[0]].reshape((1,))\n ])\n lats = np.concatenate(\n [yp0[ind[0]].reshape((1,)),\n yp1[ind],\n yp2[ind][::-1],\n yp3[ind][::-1][-1].reshape((1,)),\n yp0[ind[0]].reshape((1,))\n ])\n deps = np.concatenate(\n [zp0[ind[0]].reshape((1,)),\n zp1[ind],\n zp2[ind][::-1],\n zp3[ind][::-1][-1].reshape((1,)),\n zp0[ind[0]].reshape((1,))\n ])\n\n poly = []\n for lon, lat, dep in zip(lons, lats, deps):\n poly.append([lon, lat, dep])\n coords.append(poly)\n\n d = {\"type\": \"FeatureCollection\",\n \"metadata\": {},\n \"features\": [{\n \"type\": \"Feature\",\n \"properties\": {\n \"rupture type\": \"rupture extent\",\n \"reference\": reference,\n },\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [coords]\n }\n }]}\n\n # Add origin information to metadata\n odict = origin.__dict__\n for k, v in odict.items():\n if isinstance(v, HistoricTime):\n d['metadata'][k] = v.strftime('%Y-%m-%dT%H:%M:%SZ')\n else:\n d['metadata'][k] = v\n if hasattr(origin, 'id'):\n d['metadata']['eventid'] = origin.id\n\n return cls(d, origin)","def create_two_init_triangles(points):\n return [(points[0], points[1], points[2]),\n (points[0], points[2], points[3])]","def triangle_edges(length=2.0, divisions=4)):\n\n def _edges_from_triangle(triangle):\n a, b, c = triangle\n return [(a, b), (b, c), (c, a)]\n\n vertices, facets = triangle_facets(length, divisions)\n edges = []\n for facet in facets:\n edges.extend(_edges_from_triangle(facet))\n return vertices, edges","def yield_equilateral_triangles(cls):\n for i in range(1, 201):\n yield i-.5, i-.5, i-.5\n yield i, i, i","def triangular_affine_matrices(\n vertices: list,\n src_points: list,\n dest_points: list) -> np.matrix:\n\n ones = [1] * 3\n for tri_indices in vertices:\n src_tri = np.vstack((src_points[tri_indices, :].T, ones))\n dst_tri = np.vstack((dest_points[tri_indices, :].T, ones))\n # Inv computes inversion matrix\n mat = np.dot(src_tri, np.linalg.inv(dst_tri))[:2, :]\n yield mat","def add_subdivision(self):\n temp_sub_vertices = []\n for plane in (self.subdivision_list):\n current_mids = []\n mid_m_01 = Vec3d(0, 0, 0, 0)\n mid_m_12 = Vec3d(0, 0, 0, 0)\n mid_m_20 = Vec3d(0, 0, 0, 0)\n\n mid_m_01.x = (plane[0].x + plane[1].x) / 2\n mid_m_01.y = (plane[0].y + plane[1].y) / 2\n mid_m_01.z = (plane[0].z + plane[1].z) / 2\n mid_m_01.w = plane[0].w\n\n mid_m_12.x = (plane[1].x + plane[2].x) / 2\n mid_m_12.y = (plane[1].y + plane[2].y) / 2\n mid_m_12.z = (plane[1].z + plane[2].z) / 2\n mid_m_12.w = plane[1].w\n\n mid_m_20.x = (plane[2].x + plane[0].x) / 2\n mid_m_20.y = (plane[2].y + plane[0].y) / 2\n mid_m_20.z = (plane[2].z + plane[0].z) / 2\n mid_m_20.w = plane[2].w\n\n current_mids = [mid_m_01, mid_m_12, mid_m_20]\n temp_sub_vertices.append(current_mids)\n\n for index in range(len(current_mids)):\n v0 = Vec3d(0, 0, 0, 0)\n v1 = Vec3d(0, 0, 0, 0)\n v2 = Vec3d(0, 0, 0, 0)\n\n v0.x = plane[index].x\n v0.y = plane[index].y\n v0.z = plane[index].z\n\n v1.x = current_mids[index].x\n v1.y = current_mids[index].y\n v1.z = current_mids[index].z\n\n v2.x = current_mids[index - 1].x\n v2.y = current_mids[index - 1].y\n v2.z = current_mids[index - 1].z\n\n temp_sub_vertices.append([v0, v1, v2])\n\n self.subdivision_list = temp_sub_vertices","def get_verts(v_l, v_r):\n\n\t\tv_l = v_l%chain.length\n\t\tv_r = v_r%chain.length\n\n\t\tpoints = []\n\t\tcoords = list(chain.coords)\n\t\tif v_r > v_l:\n\n\t\t\tfor i in range(1, len(coords)):\n\t\t\t\n\t\t\t\tpd = LineString(coords[:i+1]).length\n\n\t\t\t\tif pd > v_l and pd < v_r:\n\t\t\t\t\tpoints.append(coords[i])\n\t\telse:\n\n\t\t\tfor i in range(1, len(coords)):\n\t\t\t\n\t\t\t\tpd = LineString(coords[:i+1]).length\n\n\t\t\t\tif pd > v_l:\n\t\t\t\t\tpoints.append(coords[i])\n\n\t\t\tfor i in range(1, len(coords)):\n\t\t\t\n\t\t\t\tpd = LineString(coords[:i+1]).length\n\n\t\t\t\tif pd < v_r:\n\t\t\t\t\tpoints.append(coords[i])\n\n\n\t\treturn points","def vertices(size):\n return set(range(size))","def populate_vertices(self, vertices_list):\n vertices = []\n for vertex in vertices_list:\n vertex_id = vertex[0]\n vertices.append(Vertex(vertex_id))\n self.vertices = vertices","def add_vertices(self, vertices_list):\n for vertex in vertices_list:\n self.add_vertex(vertex)","def vertify(data: list):\n assert len(data) == 4\n n = [float(d) for d in data]\n return np.array([[n[0], n[1]], [n[2], n[1]], [n[2], n[3]], [n[0], n[3]], [n[0], n[1]]])","def cube_vertices(x, y, z, n):\n #def cube_vertices(self):\n # \"\"\" Return the vertices of the cube at position x, y, z with size 2*n.\n #\n # \"\"\"\n # return [\n # x-n,y+n,z-n, x-n,y+n,z+n, x+n,y+n,z+n, x+n,y+n,z-n, # top\n # x-n,y-n,z-n, x+n,y-n,z-n, x+n,y-n,z+n, x-n,y-n,z+n, # bottom\n # x-n,y-n,z-n, x-n,y-n,z+n, x-n,y+n,z+n, x-n,y+n,z-n, # left\n # x+n,y-n,z+n, x+n,y-n,z-n, x+n,y+n,z-n, x+n,y+n,z+n, # right\n # x-n,y-n,z+n, x+n,y-n,z+n, x+n,y+n,z+n, x-n,y+n,z+n, # front\n # x+n,y-n,z-n, x-n,y-n,z-n, x-n,y+n,z-n, x+n,y+n,z-n, # back\n # ]\n return [\n x-n,y+n,z-n, x-n,y+n,z+n, x+n,y+n,z+n, x+n,y+n,z-n, # top\n x-n,y-n,z-n, x+n,y-n,z-n, x+n,y-n,z+n, x-n,y-n,z+n, # bottom\n x-n,y-n,z-n, x-n,y-n,z+n, x-n,y+n,z+n, x-n,y+n,z-n, # left\n x+n,y-n,z+n, x+n,y-n,z-n, x+n,y+n,z-n, x+n,y+n,z+n, # right\n x-n,y-n,z+n, x+n,y-n,z+n, x+n,y+n,z+n, x-n,y+n,z+n, # front\n x+n,y-n,z-n, x-n,y-n,z-n, x-n,y+n,z-n, x+n,y+n,z-n, # back\n ]","def calculate_box(vertices: [[float]]) -> [float]:\n x_coords = [x[0] for x in vertices]\n y_coords = [x[1] for x in vertices]\n z_coords = [x[2] for x in vertices]\n\n return [min(x_coords), min(y_coords), min(z_coords), max(x_coords), max(y_coords), max(z_coords)]","def get_all_object_triangles( filename, scale , translation=(0,0,0)):\n import warnings\n warnings.warn(\"@PendingDeprecationWarning\", PendingDeprecationWarning)\n vertexObjs = read_vertices_objects( filename )\n faceObjs = read_faces_objects( filename )\n\n r = []\n j = 0\n \n # Validation:\n vertices, faces = [],[]\n for obj in range(len(vertexObjs)):\n vertices += vertexObjs[obj]\n faces += faceObjs[obj]\n max_vertex_index = max([max(x) for x in faces])\n if len(vertices) != max_vertex_index:\n logging.warning( \"ParseWarning: A face's vertex index number is does not match the quantity of read vertices.\" )\n logging.warning( \"Qty of Vertices: \"+str(len(vertices))+\", Largest Face Index: \"+str(max_vertex_index) )\n\n # Parse as Tris:\n for obj in range(len(vertexObjs)):\n vertices = vertexObjs[obj]\n faces = faceObjs[obj]\n r.append([])\n c = 0\n for f in faces: # for every face\n for i in f: # for each index point in face\n c+=1\n try:\n # Get the face[i] vertex\n v = vertices[i-1]\n except IndexError as indErr:\n logging.warning(\"IndexError: Attempted to access index: \"+str(i-1)+\" in list of length: \"+str(len(vertices)))\n raise IndexError\n # Scale the face[i] vertex\n scV = [v[0]*scale, v[1]*scale, v[2]*scale]\n # Translate the scaled face vertex:\n t = translation\n tmpv = [scV[0]+t[0],scV[1]+t[1], scV[2]+t[2]]\n # Retain this vertex\n r[j].append(tmpv)\n # ---------------------\n if c % 3 == 0:\n j+=1\n r.append([])\n r = r[:len(r)-1] # remove the final empty list.\n\n checkShapeValidity( r )\n return r","def surface(func, umin=0, umax=2*np.pi, ucount=64, urepeat=1.0,\n vmin=0, vmax=2*np.pi, vcount=64, vrepeat=1.0):\n\n vtype = [('position', np.float32, 3),\n ('texcoord', np.float32, 2),\n ('normal', np.float32, 3)]\n itype = np.uint32\n\n # umin, umax, ucount = 0, 2*np.pi, 64\n # vmin, vmax, vcount = 0, 2*np.pi, 64\n\n vcount += 1\n ucount += 1\n n = vcount*ucount\n\n Un = np.repeat(np.linspace(0, 1, ucount, endpoint=True), vcount)\n Vn = np.tile (np.linspace(0, 1, vcount, endpoint=True), ucount)\n U = umin+Un*(umax-umin)\n V = vmin+Vn*(vmax-vmin)\n\n vertices = np.zeros(n, dtype=vtype)\n for i,(u,v) in enumerate(zip(U,V)):\n vertices[\"position\"][i] = func(u,v)\n\n vertices[\"texcoord\"][:,0] = Un*urepeat\n vertices[\"texcoord\"][:,1] = Vn*vrepeat\n\n indices = []\n for i in range(ucount-1):\n for j in range(vcount-1):\n indices.append(i*(vcount) + j )\n indices.append(i*(vcount) + j+1 )\n indices.append(i*(vcount) + j+vcount+1)\n indices.append(i*(vcount) + j+vcount )\n indices.append(i*(vcount) + j+vcount+1)\n indices.append(i*(vcount) + j )\n indices = np.array(indices, dtype=itype)\n vertices[\"normal\"] = normals(vertices[\"position\"],\n indices.reshape(len(indices)//3,3))\n\n return vertices.view(gloo.VertexBuffer), indices.view(gloo.IndexBuffer)","def hexgrid(self):\n n = self.n * 2\n vectors = []\n for u in range(-n, n+1):\n us = [u] * (2*n+1)\n if u < 0:\n vectors.extend(zip(us, range(-n-u, n+1), range(-n, n+u+1)))\n else:\n vectors.extend(zip(us, range(-n, n-u+1), range(-n+u, n+1)))\n return vectors","def VertNrmPairs(vertices, exportMatrix):\n entries = [None] * len(vertices)\n\n # putting them in pairs first, so that comparing is easier\n for i, v in enumerate(vertices):\n pos = exportMatrix @ v.co\n nrm = exportMatrix @ v.normal\n e = (Vector3(pos.x, pos.y, pos.z), Vector3(nrm.x, nrm.y, nrm.z))\n entries[i] = e\n\n return entries","def getVertexPositions(transform_name):\n positions = pm.xform('{}.vtx[*]'.format(transform_name), q=True, ws=True, t=True)\n return zip(positions[0::3], positions[1::3], positions[2::3])","def grab_40_vectors(x1,x2,y1,y2) -> list:\n\n if x1 > x2:\n x1,x2 = x2,x1\n if y1 > y2:\n y1,y2 = y2,y1\n\n height = abs(y2-y1)\n length = abs(x2-x1)\n\n height_delta = height/10\n length_delta = length/10\n\n left_vertical_edge = [(x1, y1+height_delta*i) for i in range(0,11)]\n # print(left_vertical_edge)\n right_vertical_edge = [(x2,y1+height_delta*i) for i in range(0,11)]\n # print(right_vertical_edge)\n bottom_horizontal_edge = [(x1+length_delta*i,y1) for i in range(0,11)]\n # print(bottom_horizontal_edge)\n top_horizontal_edge = [(x1+length_delta*i,y2) for i in range(0,11)]\n # print(top_horizontal_edge)\n\n all_vectors = left_vertical_edge+right_vertical_edge+bottom_horizontal_edge+top_horizontal_edge\n return(all_vectors)","def getNormals(vertA, vertB, vertC):\n xA = vertA[0]\n xB = vertB[0]\n xC = vertC[0]\n yA = vertA[1]\n yB = vertB[1]\n yC = vertC[1]\n zA = vertA[2]\n zB = vertB[2]\n zC = vertC[2]\n ABx = xB - xA\n ABy = yB - yA\n ABz = zB - zA\n BCx = xC - xB\n BCy = yC - yB\n BCz = zC - zB\n Nx = ABy * BCz - ABz * BCy\n Ny = ABz * BCx - ABx * BCz\n Nz = ABx * BCy - ABy * BCx\n VecMag = math.sqrt(Nx ** 2 + Ny ** 2 + Nz ** 2)\n Ni = Nx / VecMag\n Nj = Ny / VecMag\n Nk = Nz / VecMag\n return [Ni, Nj, Nk]","def _legacy_to_opengl(face_vertices_image, face_vertices_z, valid_faces=None):\n z = -face_vertices_z / (abs(face_vertices_z).max() + 1e-6)\n _face_vertices_image = face_vertices_image.reshape(*face_vertices_image.shape[:-3], -1, 2)\n pos = torch.stack([\n _face_vertices_image[..., 0],\n -_face_vertices_image[..., 1],\n z.reshape(*z.shape[:-2], -1)\n ], dim=-1)\n if valid_faces is None:\n pos = torch.nn.functional.pad(pos, (0, 1), value=1.)\n else:\n pad = (valid_faces.unsqueeze(-1) * 2. - 1.).expand(*valid_faces.shape, 3)\n pad = pad.reshape(*valid_faces.shape[:-1], -1, 1)\n pos = torch.cat([pos, pad], dim=-1)\n\n tri = torch.arange(pos.shape[-2], device=pos.device, dtype=torch.int).reshape(-1, 3)\n \n return pos, tri","def smooth_mesh(\n points: np.ndarray, triangles: np.ndarray, iterations: int\n) -> Tuple[np.ndarray, np.ndarray]:\n edges, _ = get_edges(triangles)\n n = points.shape[0]\n shape = (n, 2)\n boundary = boundary_vertices(points, triangles)\n for _ in range(iterations):\n num_neighbors = np.bincount(edges.ravel(), minlength=shape[0])\n new_points = np.zeros(shape)\n vals = points[edges[:, 1]].T\n new_points += np.array(\n [np.bincount(edges[:, 0], val, minlength=n) for val in vals]\n ).T\n vals = points[edges[:, 0]].T\n new_points += np.array(\n [np.bincount(edges[:, 1], val, minlength=n) for val in vals]\n ).T\n new_points /= num_neighbors[:, np.newaxis]\n # reset boundary points\n new_points[boundary] = points[boundary]\n points = new_points\n return points, triangles","def generate_vertex_normals(vertices, index, normalize_result=True):\n v1, v2, v3 = np.rollaxis(vertices[index], axis=-2)\n face_normals = generate_normals(v1, v2, v3, normalize_result=False)\n vertex_normals = np.zeros_like(vertices)\n for i in range(3):\n np.add.at(vertex_normals, index[..., i], face_normals)\n if normalize_result:\n vertex_normals = normalize(vertex_normals)\n return vertex_normals","def vec(self):\r\n\r\n xv = np.arange(self.dx / 2, self.lx, self.dx)\r\n yv = np.arange(-self.ly / 2 + self.dy / 2, self.ly / 2, self.dy)\r\n zv = np.arange(self.oz, self.lz + self.oz, self.dz)\r\n\r\n if self.ox != 0:\r\n xv = np.arange(self.ox, self.lx + self.ox, self.dx)\r\n yv = np.arange(self.oy, self.ly + self.oy, self.dy)\r\n zv = np.arange(self.oz, self.lz + self.oz, self.dz)\r\n\r\n return xv, yv, zv","def generate_mesh(\n poly_coords: np.ndarray,\n hole_coords: Optional[List[np.ndarray]] = None,\n min_points: Optional[int] = None,\n max_edge_length: Optional[float] = None,\n convex_hull: bool = False,\n boundary: Optional[np.ndarray] = None,\n preserve_boundary: bool = False,\n min_angle: float = 32.5,\n **kwargs,\n) -> Tuple[np.ndarray, np.ndarray]:\n poly_coords = ensure_unique(poly_coords)\n if hole_coords is None:\n hole_coords = []\n hole_coords = [ensure_unique(coords) for coords in hole_coords]\n # Facets is a shape (m, 2) array of edge indices.\n # coords[facets] is a shape (m, 2, 2) array of edge coordinates:\n # [(x0, y0), (x1, y1)]\n coords = np.concatenate([poly_coords] + hole_coords, axis=0)\n xmin = coords[:, 0].min()\n dx = np.ptp(coords[:, 0])\n ymin = coords[:, 1].min()\n dy = np.ptp(coords[:, 1])\n r0 = np.array([[xmin, ymin]]) + np.array([[dx, dy]]) / 2\n # Center the coordinates at (0, 0) to avoid floating point issues.\n coords = coords - r0\n indices = np.arange(len(poly_coords), dtype=int)\n if convex_hull:\n if boundary is not None:\n raise ValueError(\n \"Cannot have both boundary is not None and convex_hull = True.\"\n )\n facets = spatial.ConvexHull(coords).simplices\n else:\n if boundary is not None:\n boundary = list(map(tuple, ensure_unique(boundary - r0)))\n indices = [i for i in indices if tuple(coords[i]) in boundary]\n facets = np.array([indices, np.roll(indices, -1)]).T\n # Create facets for the holes.\n for hole in hole_coords:\n hole_indices = np.arange(\n indices[-1] + 1, indices[-1] + 1 + len(hole), dtype=int\n )\n hole_facets = np.array([hole_indices, np.roll(hole_indices, -1)]).T\n indices = np.concatenate([indices, hole_indices], axis=0)\n facets = np.concatenate([facets, hole_facets], axis=0)\n\n mesh_info = triangle.MeshInfo()\n mesh_info.set_points(coords)\n mesh_info.set_facets(facets)\n if hole_coords:\n # Triangle allows you to set holes by specifying a single point\n # that lies in each hole. Here we use the centroid of the hole.\n holes = [\n np.array(Polygon(hole).centroid.coords[0]) - r0.squeeze()\n for hole in hole_coords\n ]\n mesh_info.set_holes(holes)\n\n kwargs = kwargs.copy()\n kwargs[\"allow_boundary_steiner\"] = not preserve_boundary\n if \"min_angle\" not in kwargs:\n kwargs[\"min_angle\"] = min_angle\n\n mesh = triangle.build(mesh_info=mesh_info, **kwargs)\n points = np.array(mesh.points) + r0\n triangles = np.array(mesh.elements)\n if min_points is None and (max_edge_length is None or max_edge_length <= 0):\n return points, triangles\n\n kwargs[\"max_volume\"] = dx * dy / 100\n i = 1\n if min_points is None:\n min_points = 0\n if max_edge_length is None or max_edge_length <= 0:\n max_edge_length = np.inf\n max_length = get_edge_lengths(points, triangles).max()\n while (len(points) < min_points) or (max_length > max_edge_length):\n mesh = triangle.build(mesh_info=mesh_info, **kwargs)\n points = np.array(mesh.points) + r0\n triangles = np.array(mesh.elements)\n edges, is_boundary = get_edges(triangles)\n if preserve_boundary:\n # Only constrain the length of interior edges, i.e. edges not on the boundary.\n edges = edges[~is_boundary]\n edge_lengths = np.linalg.norm(np.diff(points[edges], axis=1), axis=2)\n max_length = edge_lengths.max()\n logger.debug(\n f\"Iteration {i}: Made mesh with {len(points)} points and \"\n f\"{len(triangles)} triangles with maximum interior edge length: \"\n f\"{max_length:.2e}. Target maximum edge length: {max_edge_length:.2e}.\"\n )\n if np.isfinite(max_edge_length):\n kwargs[\"max_volume\"] *= min(0.98, np.sqrt(max_edge_length / max_length))\n else:\n kwargs[\"max_volume\"] *= 0.98\n i += 1\n return points, triangles","def create_tetrahedron_vbo(arista=1.0):\n\n def ex(element):\n \"\"\"Exporta el elemento a una lista\"\"\"\n return element.export_to_list()\n\n # Se crean los puntos\n a = Point3(-0.5, -0.288675, -0.288675) * arista\n b = Point3(0.5, -0.288675, -0.288675) * arista\n c = Point3(0.0, 0.577350, -0.288675) * arista\n d = Point3(0.0, 0.0, 0.57735) * arista\n\n # Se crean las normales\n n1 = ex(normal3points(a, b, d))\n n2 = ex(normal3points(b, c, d))\n n3 = ex(normal3points(c, a, d))\n n4 = ex(normal3points(c, b, a))\n\n # Se crean las listas de puntos y normales en orden por triangulos, cada 3 puntos se forma una cara\n vertex_array = [ex(a), ex(b), ex(d), ex(b), ex(c), ex(d), ex(c), ex(a),\n ex(d), ex(a), ex(b), ex(c)]\n normal_array = [n1, n1, n1, n2, n2, n2, n3, n3, n3, n4, n4, n4]\n\n # Se retornan los vertex buffer object\n return VboObject(vbo.VBO(array(vertex_array, 'f')),\n vbo.VBO(array(normal_array, 'f')), len(vertex_array))","def list_plot3d_tuples(v, interpolation_type, texture, **kwds):\n from matplotlib import tri, delaunay\n import numpy\n import scipy\n from random import random\n from scipy import interpolate\n from .plot3d import plot3d\n\n if len(v)<3:\n raise ValueError(\"We need at least 3 points to perform the interpolation\")\n\n x = [float(p[0]) for p in v]\n y = [float(p[1]) for p in v]\n z = [float(p[2]) for p in v]\n\n # If the (x,y)-coordinates lie in a one-dimensional subspace, the\n # matplotlib Delaunay code segfaults. Therefore, we compute the\n # correlation of the x- and y-coordinates and add small random\n # noise to avoid the problem if needed.\n corr_matrix = numpy.corrcoef(x, y)\n if corr_matrix[0, 1] > 0.9 or corr_matrix[0, 1] < -0.9:\n ep = float(.000001)\n x = [float(p[0]) + random()*ep for p in v]\n y = [float(p[1]) + random()*ep for p in v]\n\n\n # If the list of data points has two points with the exact same\n # (x,y)-coordinate but different z-coordinates, then we sometimes\n # get segfaults. The following block checks for this and raises\n # an exception if this is the case.\n # We also remove duplicate points (which matplotlib can't handle).\n # Alternatively, the code in the if block above which adds random\n # error could be applied to perturb the points.\n drop_list = []\n nb_points = len(x)\n for i in range(nb_points):\n for j in range(i+1, nb_points):\n if x[i] == x[j] and y[i] == y[j]:\n if z[i] != z[j]:\n raise ValueError(\"Two points with same x,y coordinates and different z coordinates were given. Interpolation cannot handle this.\")\n elif z[i] == z[j]:\n drop_list.append(j)\n x = [x[i] for i in range(nb_points) if i not in drop_list]\n y = [y[i] for i in range(nb_points) if i not in drop_list]\n z = [z[i] for i in range(nb_points) if i not in drop_list]\n\n xmin = float(min(x))\n xmax = float(max(x))\n ymin = float(min(y))\n ymax = float(max(y))\n\n num_points = kwds['num_points'] if 'num_points' in kwds else int(4*numpy.sqrt(len(x)))\n #arbitrary choice - assuming more or less a nxn grid of points\n # x should have n^2 entries. We sample 4 times that many points.\n\n if interpolation_type == 'linear':\n T = tri.Triangulation(x, y)\n f = tri.LinearTriInterpolator(T, z)\n j = numpy.complex(0, 1)\n from .parametric_surface import ParametricSurface\n def g(x, y):\n z = f(x, y)\n return (x, y, z)\n G = ParametricSurface(g, (list(numpy.r_[xmin:xmax:num_points*j]), list(numpy.r_[ymin:ymax:num_points*j])), texture=texture, **kwds)\n G._set_extra_kwds(kwds)\n return G\n\n if interpolation_type == 'nn' or interpolation_type =='default':\n\n T=delaunay.Triangulation(x,y)\n f=T.nn_interpolator(z)\n f.default_value=0.0\n j=numpy.complex(0,1)\n vals=f[ymin:ymax:j*num_points,xmin:xmax:j*num_points]\n from .parametric_surface import ParametricSurface\n def g(x,y):\n i=round( (x-xmin)/(xmax-xmin)*(num_points-1) )\n j=round( (y-ymin)/(ymax-ymin)*(num_points-1) )\n z=vals[int(j),int(i)]\n return (x,y,z)\n G = ParametricSurface(g, (list(numpy.r_[xmin:xmax:num_points*j]), list(numpy.r_[ymin:ymax:num_points*j])), texture=texture, **kwds)\n G._set_extra_kwds(kwds)\n return G\n\n if interpolation_type == 'spline':\n from .plot3d import plot3d\n kx = kwds['kx'] if 'kx' in kwds else 3\n ky = kwds['ky'] if 'ky' in kwds else 3\n if 'degree' in kwds:\n kx = kwds['degree']\n ky = kwds['degree']\n s = kwds['smoothing'] if 'smoothing' in kwds else len(x)-numpy.sqrt(2*len(x))\n s = interpolate.bisplrep(x, y, z, [int(1)]*len(x), xmin, xmax, ymin, ymax, kx=kx, ky=ky, s=s)\n f = lambda x,y: interpolate.bisplev(x, y, s)\n return plot3d(f, (xmin, xmax), (ymin, ymax), texture=texture, plot_points=[num_points, num_points], **kwds)","def to_mesh(\n self,\n lims_x: array_like = (-1, 1),\n lims_y: array_like = (-1, 1),\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n a, b, c, d = self.cartesian()\n x_center, y_center = self.point[:2]\n\n values_x = x_center + lims_x\n values_y = y_center + lims_y\n\n X, Y = np.meshgrid(values_x, values_y)\n\n if c != 0:\n Z = -(a * X + b * Y + d) / c\n\n elif b != 0:\n Z = -(a * X + c * Y + d) / b\n X, Y, Z = X, Z, Y\n\n else:\n Z = -(b * X + c * Y + d) / a\n X, Y, Z = Z, X, Y\n\n return X, Y, Z","def triangles_list_to_triangles_strip(blender_mesh):\n # TODO: Fix changing of face orientation in some cases (see tests)\n edges_faces = {}\n current_strip = []\n strips = []\n joined_strips = []\n faces_indices = deque(p.index for p in blender_mesh.polygons)\n done_faces_indices = set()\n current_face_index = faces_indices.popleft()\n process_faces = True\n\n for polygon in blender_mesh.polygons:\n for edge in polygon.edge_keys:\n edges_faces.setdefault(edge, set()).add(polygon.index)\n\n while process_faces:\n current_face = blender_mesh.polygons[current_face_index]\n current_face_verts = current_face.vertices[:]\n strip_indices = [v for v in current_face_verts if v not in current_strip[-2:]]\n if current_strip:\n face_to_add = tuple(current_strip[-2:]) + tuple(strip_indices)\n if face_to_add != current_face_verts and face_to_add != tuple(reversed(current_face_verts)):\n # we arrived here because the current face shares and edge with the face in the strip\n # however, if we just add the verts, we would be changing the direction of the face\n # so we create a degenerate triangle before adding to it to the strip\n current_strip.append(current_strip[-2])\n current_strip.extend(strip_indices)\n done_faces_indices.add(current_face_index)\n\n next_face_index = None\n possible_face_indices = {}\n for edge in current_face.edge_keys:\n if edge not in edges_faces:\n continue\n checked_edge = {face_index: edge for face_index in edges_faces[edge]\n if face_index != current_face_index and face_index not in done_faces_indices}\n possible_face_indices.update(checked_edge)\n for face_index, edge in possible_face_indices.items():\n if not current_strip:\n next_face_index = face_index\n break\n elif edge == tuple(current_strip[-2:]) or edge == tuple(reversed(current_strip[-2:])):\n next_face_index = face_index\n break\n elif edge == (current_strip[-1], current_strip[-2]):\n if len(current_strip) % 2 != 0:\n # create a degenerate triangle to join them\n current_strip.append(current_strip[-2])\n next_face_index = face_index\n\n if next_face_index:\n faces_indices.remove(next_face_index)\n current_face_index = next_face_index\n else:\n strips.append(current_strip)\n current_strip = []\n try:\n current_face_index = faces_indices.popleft()\n except IndexError:\n process_faces = False\n\n prev_strip_len = 0\n # join strips with degenerate triangles\n for strip in strips:\n if not prev_strip_len:\n joined_strips.extend(strip)\n prev_strip_len = len(strip)\n elif prev_strip_len % 2 == 0:\n joined_strips.extend((joined_strips[-1], strip[0]))\n joined_strips.extend(strip)\n prev_strip_len = len(strip)\n else:\n joined_strips.extend((joined_strips[-1], strip[0], strip[0]))\n joined_strips.extend(strip)\n prev_strip_len = len(strip)\n\n return joined_strips","def vertices(self):\n\n if self._faces is None:\n if self._vertices is None:\n return None\n self.triangulate()\n return self._vertices","def subdivide_face(face: Sequence[Union[Vector, Vec2]], quads=True) -> Iterable[\n List[Vector]]:\n if len(face) < 3:\n raise ValueError('3 or more vertices required.')\n len_face = len(face)\n mid_pos = sum(face) / len_face\n subdiv_location = [face[i].lerp(face[(i + 1) % len_face]) for i in\n range(len_face)]\n\n for index, vertex in enumerate(face):\n if quads:\n yield vertex, subdiv_location[index], mid_pos, subdiv_location[\n index - 1]\n else:\n yield subdiv_location[index - 1], vertex, mid_pos\n yield vertex, subdiv_location[index], mid_pos","def edgify(vertices:list)->list:\n edges = []\n for k in range(0, len(vertices) - 1):\n edges.append([vertices[k], vertices[k + 1]])\n return edges","def get_triangles(self):\n location = TopLoc_Location()\n bt = BRep_Tool()\n facing = bt.Triangulation(self.topods_shape(), location)\n if facing == None:\n return [], []\n\n tab = facing.Nodes()\n tri = facing.Triangles()\n verts = []\n for i in range(1, facing.NbNodes() + 1):\n p = tab.Value(i).Transformed(location.Transformation())\n verts.append(np.array(list(p.Coord())))\n\n tris = []\n reversed = self.reversed()\n for i in range(1, facing.NbTriangles() + 1):\n # OCC triangle normals point in the surface normal\n # direction\n if reversed:\n index1, index3, index2 = tri.Value(i).Get()\n else:\n index1, index2, index3 = tri.Value(i).Get()\n\n tris.append([index1 - 1, index2 - 1, index3 - 1])\n\n return np.asarray(verts, dtype=np.float32), np.asarray(tris, dtype=np.int32)","def get_polygon_points(starting_point, vector_seq):\n \n res=[[]]\n res[0] = starting_point\n curr_point = starting_point\n\n i=1\n\n while i<len(vector_seq):\n if are_parallel(vector_seq[i],vector_seq[i-1]):\n tmp = vector_seq[i]\n vector_seq[i-1][0]=vector_seq[i-1][0]+ tmp[0]\n vector_seq[i-1][1]=vector_seq[i-1][1]+ tmp[1]\n vector_seq.pop(i)\n else:\n i=i+1\n \n for x in vector_seq:\n x_coord = curr_point[0]+x[0]\n y_coord = curr_point[1]+x[1]\n curr_point=[x_coord, y_coord]\n res.append(curr_point)\n\n return res","def make_vectors_output(coord_dict, add_vectors, num_coords, color, ids, display_name='trace', state='on'):\n # default parameters\n which_set = 0\n tip_fraction = 0.5\n arrow_colors = {'line_color': color, 'head_color': color}\n \n # header for section in kin file\n result = []\n result.append('@vectorlist {%s} dimension=%s %s' % (display_name, num_coords, state))\n \n # selecting the edges to draw in this batch\n edges = []\n for k, v in add_vectors['vectors'].iteritems():\n edges = edges + [(v[i][1],v[i+1][1]) for i in range(len(v[:-1])) if v[i][1] in ids]\n \n # creating \"vectors\"\n for edge in edges:\n id_fr, id_to = edge\n # extract the coords of each vertex\n pt_fr = coord_dict[id_fr][:num_coords]\n pt_to = coord_dict[id_to][:num_coords]\n\n # different tip color for each destination coords file\n #tip_color = kinemage_colors[which_set % len(kinemage_colors)]\n # plot a color 'tip' on the line (certain % of line length)\n # this gets the coords of the beginning of the 'tip'\n diffs = (pt_to-pt_fr) * (1-tip_fraction)\n middles = pt_fr + diffs\n # add a default-color line segment\n \n # modified to use user defined\n tip_color = arrow_colors['head_color']\n label_color = arrow_colors['line_color']\n \n result.append('%s %s' % \\\n (' '.join(map(str, pt_fr)),label_color))\n result.append('%s %s P' % \\\n (' '.join(map(str, middles)),label_color))\n # add the tip-colored line segment\n result.append('%s %s' % \\\n (' '.join(map(str, middles)), tip_color))\n result.append('%s %s P' % \\\n (' '.join(map(str, pt_to)), tip_color)) \n return result","def create_tetrahedron():\n obj = glGenLists(1)\n glNewList(obj, GL_COMPILE)\n glPushMatrix()\n try:\n glutSolidTetrahedron()\n except:\n if not _ERRS[5]:\n printGLError(\n \"la version actual de OpenGL no posee la funcion glutSolidTetrahedron\")\n _ERRS[5] = True\n glPopMatrix()\n glEndList()\n return obj","def subdivision(mesh):\n\t\n\t\n\t# 1. generate new nodes in the centre of quad\n\t# 1/4 o-------o 1/4 o: existing vertices\n\t# | | *: newly-generated vertices\n\t# | * |\n\t# | |\n\t# 1/4 o-------o 1/4\n\n\tnew_coor = mesh.give_nodes().give_coor()\n\t\n\tfor face_index in range(mesh.give_model_inf()[2]): \n\t\tnew_x, new_y, new_z = (0, 0, 0)\n\t\tfor vertex_index in range(4):\n\t\t\tmesh.give_faces()\n\t\t\tnode_index = mesh.give_faces().give_node_list(face_index)[vertex_index]\n\n\t\t\tnew_x += 0.25*mesh.give_nodes().give_coor(node_index)[0]\n\t\t\tnew_y += 0.25*mesh.give_nodes().give_coor(node_index)[1]\n\t\t\tnew_z += 0.25*mesh.give_nodes().give_coor(node_index)[2]\n\t\t\t\n\t\tnew_coor.append((new_x, new_y, new_z))\n\t\t\n\t# generating new nodes on the edge\n\t# figure out one edge is shared by how many surfaces\n\tedge_shared_by_faces_list = helper.find_edge_shared_by_which_faces(mesh.give_edges(), mesh.give_faces())\n\t\n\tfor edge_index in range(mesh.give_model_inf()[1]):\n\n\t\tnew_x, new_y, new_z = (0., 0., 0.)\n\t\t\n\t# 2. generate new node on boundary edge\n\t# o: existing vertices\n\t# 1/2 o---*---o 1/2 *: newly-generated vertices\n\t# \n\n\t\tnew_coor = mesh.give_nodes().give_coor()\n\t\tif len(edge_shared_by_faces_list[edge_index]) == 1:\t\n\t\t\tnew_x, new_y, new_z = (0., 0., 0.)\n\t\t\tfor vertex_index in range(2):\n\t\t\t\tthis_node = mesh.give_edges().give_node(edge_index)[vertex_index]\n\t\t\t\tnew_x += 0.5*mesh.give_nodes().give_coor()[this_node][0]\n\t\t\t\tnew_y += 0.5*mesh.give_nodes().give_coor()[this_node][1]\n\t\t\t\tnew_z += 0.5*mesh.give_nodes().give_coor()[this_node][2]\n\t\t\t\t\n\t\t\tnew_coor.append((new_x, new_y, new_z))\n\t\t\t\t\n\t# 3. generate new node on interior edge\n\t# 1/16 o-------o 1/16 o: existing vertices\n\t# | | *: newly-generated vertices\n\t# 3/8 o---*---o 3/8\n\t# | |\n\t# 1/16 o-------o 1/16\n\n\t\telse:\n\t\t\tnew_x, new_y, new_z = (0., 0., 0.)\n\t\t\tconsidered_node = []\n\t\t\tfor vertex_index in range(2):\n\t\t\t\tthis_node = mesh.give_edges().give_node(edge_index)[vertex_index]\n\t\t\t\tconsidered_node.append(this_node)\n\t\t\t\tnew_x += 3./8.*mesh.give_nodes().give_coor()[this_node][0]\n\t\t\t\tnew_y += 3./8.*mesh.give_nodes().give_coor()[this_node][1]\n\t\t\t\tnew_z += 3./8.*mesh.give_nodes().give_coor()[this_node][2]\n\t\t\t\n\t\t\t# faces contain this node\n\t\t\tpotential_node = []\n\t\t\tfor face_index in edge_shared_by_faces_list[edge_index]:\t\t\n\t\t\t\tfor vertex_index in range(4):\n\t\t\t\t\t\tpotential_node.append(mesh.give_faces().give_node_list(face_index)[vertex_index])\n\t\t\t\n\t\t\touter_node = []\n\t\t\tfor node in potential_node:\n\t\t\t\tif (node not in considered_node) & (node not in outer_node):\n\t\t\t\t\touter_node.append(node)\n\t\t\t\t\t\n\t\t\tfor vertex_index in outer_node:\n\t\t\t\tnew_x += 1./16.*mesh.give_nodes().give_coor()[vertex_index][0]\n\t\t\t\tnew_y += 1./16.*mesh.give_nodes().give_coor()[vertex_index][1]\n\t\t\t\tnew_z += 1./16.*mesh.give_nodes().give_coor()[vertex_index][2]\n\t\t\t\n\t\t\tnew_coor.append((new_x, new_y, new_z))\n\n\t# update the links of edges and surfaces\n\tnew_edge_list = []\n\tnew_face_list = []\n\tfor face_index in range(mesh.give_model_inf()[2]):\n\t\told_node0 = mesh.give_faces().give_node_list(face_index)[0]\n\t\told_node1 = mesh.give_faces().give_node_list(face_index)[1]\n\t\told_node2 = mesh.give_faces().give_node_list(face_index)[2]\n\t\told_node3 = mesh.give_faces().give_node_list(face_index)[3]\n\t\t\n\t\told_edge0 = mesh.give_faces().give_edge_list(face_index)[0]\n\t\told_edge1 = mesh.give_faces().give_edge_list(face_index)[1]\n\t\told_edge2 = mesh.give_faces().give_edge_list(face_index)[2]\n\t\told_edge3 = mesh.give_faces().give_edge_list(face_index)[3]\n\t\t\n\t\tnew_node4 = old_edge0 + mesh.give_model_inf()[0] + mesh.give_model_inf()[2] \n\t\tnew_node5 = old_edge1 + mesh.give_model_inf()[0] + mesh.give_model_inf()[2]\n\t\tnew_node6 = old_edge2 + mesh.give_model_inf()[0] + mesh.give_model_inf()[2]\n\t\tnew_node7 = old_edge3 + mesh.give_model_inf()[0] + mesh.give_model_inf()[2]\t\n\t\tnew_node8 = mesh.give_model_inf()[0] + face_index\n\t\t\n\t\tif helper.in_list((old_node0, new_node4), new_edge_list) == False: \n\t\t\tnew_edge_list.append((old_node0, new_node4))\n\t\tif helper.in_list((new_node4, new_node8), new_edge_list) == False: \n\t\t\tnew_edge_list.append((new_node4, new_node8))\n\t\tif helper.in_list((new_node8, new_node7), new_edge_list) == False: \n\t\t\tnew_edge_list.append((new_node8, new_node7))\n\t\tif helper.in_list((new_node7, old_node0), new_edge_list) == False: \n\t\t\tnew_edge_list.append((new_node7, old_node0))\n\t\tif helper.in_list((new_node4, old_node1), new_edge_list) == False: \n\t\t\tnew_edge_list.append((new_node4, old_node1))\n\t\tif helper.in_list((old_node1, new_node5), new_edge_list) == False: \n\t\t\tnew_edge_list.append((old_node1, new_node5))\n\t\tif helper.in_list((new_node5, new_node8), new_edge_list) == False: \n\t\t\tnew_edge_list.append((new_node5, new_node8))\n\t\tif helper.in_list((new_node7, old_node3), new_edge_list) == False: \n\t\t\tnew_edge_list.append((new_node7, old_node3))\n\t\tif helper.in_list((old_node3, new_node6), new_edge_list) == False: \n\t\t\tnew_edge_list.append((old_node3, new_node6))\n\t\tif helper.in_list((new_node6, new_node8), new_edge_list) == False: \n\t\t\tnew_edge_list.append((new_node6, new_node8))\n\t\tif helper.in_list((new_node6, old_node2), new_edge_list) == False: \n\t\t\tnew_edge_list.append((new_node6, old_node2))\n\t\tif helper.in_list((old_node2, new_node5), new_edge_list) == False: \n\t\t\tnew_edge_list.append((old_node2, new_node5))\n\t\n\t\tnew_face_list.append((old_node0, new_node4, new_node8, new_node7))\n\t\tnew_face_list.append((new_node4, old_node1, new_node5, new_node8))\n\t\tnew_face_list.append((new_node7, new_node8, new_node6, old_node3))\n\t\tnew_face_list.append((new_node8, new_node5, old_node2, new_node6))\n\t\t\n\tnew_edges = geo.Edge(new_edge_list)\n\t\n\tnew_faces = geo.Face(new_face_list, new_edges)\n\t\t\n\t# update existing nodes\t\n\tfor node_index in range(mesh.give_model_inf()[0]):\n\t\t\n\t\tring1, ring2 = helper.find_neighbour_node(new_edges, new_faces, node_index)\n\t\tvalence = helper.find_valence(node_index, new_faces) \n\t\t#: valence: the number of faces sharing on specific edge\n\n\t# 4. update existing corner vertex\n\t# 2/4 @---* 1/4 *: newly-generated vertices\n\t# | | @: existing vertices to be updated\n\t# 1/4 *---* 0 The higher mask values on neighbouring vertices, \n\t# the more likely a square mesh will be refined into a sphere.\n\t \n\t\tif valence == 1:\n\n\t\t\tnew_x, new_y, new_z = (0, 0, 0)\n\t\t\tprint\n\t\t\tfor node_in_ring1 in ring1:\n\t\t\t\tnew_x += 1./4.*mesh.give_nodes().give_coor()[node_in_ring1][0]\n\t\t\t\tnew_y += 1./4.*mesh.give_nodes().give_coor()[node_in_ring1][1]\n\t\t\t\tnew_z += 1./4.*mesh.give_nodes().give_coor()[node_in_ring1][2]\n\n\t\t\tfor node_in_ring2 in ring2:\n\t\t\t\tnew_x += 0.*mesh.give_nodes().give_coor()[node_in_ring2][0]\n\t\t\t\tnew_y += 0.*mesh.give_nodes().give_coor()[node_in_ring2][1]\n\t\t\t\tnew_z += 0.*mesh.give_nodes().give_coor()[node_in_ring2][2]\n\t\t\t\t\n\t\t\tnew_x += 2./4.*mesh.give_nodes().give_coor()[node_index][0]\n\t\t\tnew_y += 2./4.*mesh.give_nodes().give_coor()[node_index][1]\n\t\t\tnew_z += 2./4.*mesh.give_nodes().give_coor()[node_index][2]\n\n\t# 5. update existing boundary joint vertex\n\t# 3/4\n\t# 1/8 *---*---* 1/8 *: newly-generated vertices\n\t# | | | @: existing vertices to be updated\n\t# 0 *---*---* 0\n\n\t\telif valence == 2:\n\t\t\t\n\t\t\tnew_x, new_y, new_z = (0, 0, 0)\n\t\t\tfor node_in_ring1 in ring1:\n\t\t\t\tif helper.find_valence(node_in_ring1, new_faces) <= 2: \n\t\t\t\t\tnew_x += 1./8.*mesh.give_nodes().give_coor()[node_in_ring1][0]\n\t\t\t\t\tnew_y += 1./8.*mesh.give_nodes().give_coor()[node_in_ring1][1]\n\t\t\t\t\tnew_z += 1./8.*mesh.give_nodes().give_coor()[node_in_ring1][2]\n\t\t\t\t\t\n\t\t\tnew_x += 3./4.*mesh.give_nodes().give_coor()[node_index][0]\n\t\t\tnew_y += 3./4.*mesh.give_nodes().give_coor()[node_index][1]\n\t\t\tnew_z += 3./4.*mesh.give_nodes().give_coor()[node_index][2]\n\t\n\t# 6. update new node on interior edge\n\t# * r/k\n\t# /\\ b/k*\n\t# *__/ \\___ r/k\n\t# \\ \\ /¬¬/ *: newly-generated vertices: \n\t# \\ \\/ / b = 3/2/valence, r = 1/4/valence\n\t# *--@--* b/k\t @: existing vertices to be updated: 1-b-r\t\t\n\t# / /\\ \\\n\t# /__/ \\__\\\n\t# * \\ / * r/k\n\t# \\/\n\t\t\n\t\telse:\n\t\t\tnew_x, new_y, new_z = (0, 0, 0)\n\t\t\tbeta = 3./2./valence\n\t\t\tgamma = 1./4./valence\n\t\t\tfor node_in_ring1 in ring1:\n\t\t\t\tnew_x += beta/valence*mesh.give_nodes().give_coor()[node_in_ring1][0]\n\t\t\t\tnew_y += beta/valence*mesh.give_nodes().give_coor()[node_in_ring1][1]\n\t\t\t\tnew_z += beta/valence*mesh.give_nodes().give_coor()[node_in_ring1][2]\n\t\t\t\n\t\t\tfor node_in_ring2 in ring2:\n\t\t\t\tnew_x += gamma/valence*mesh.give_nodes().give_coor()[node_in_ring2][0]\n\t\t\t\tnew_y += gamma/valence*mesh.give_nodes().give_coor()[node_in_ring2][1]\n\t\t\t\tnew_z += gamma/valence*mesh.give_nodes().give_coor()[node_in_ring2][2]\n\t\t\t\n\t\t\tnew_x += (1. - beta - gamma)*mesh.give_nodes().give_coor()[node_index][0]\n\t\t\tnew_y += (1. - beta - gamma)*mesh.give_nodes().give_coor()[node_index][1]\n\t\t\tnew_z += (1. - beta - gamma)*mesh.give_nodes().give_coor()[node_index][2]\n\t\t\n\t\tnew_coor[node_index] = (new_x, new_y, new_z)\n\t\n\tnew_nodes = geo.Node(new_coor)\n\t\n\tmesh.update(new_nodes, new_edges, new_faces)\n\t\n\t# return new_mesh\n\treturn mesh","def remove_subdivison(self):\n temp_sub_vertices = []\n for index in range(0, len(self.subdivision_list) - 1, 4):\n v0 = Vec3d(0, 0, 0, 0)\n v1 = Vec3d(0, 0, 0, 0)\n v2 = Vec3d(0, 0, 0, 0)\n\n v0.x = self.subdivision_list[index + 1][0].x\n v0.y = self.subdivision_list[index + 1][0].y\n v0.z = self.subdivision_list[index + 1][0].z\n v0.w = self.subdivision_list[index + 1][0].w\n\n v1.x = self.subdivision_list[index + 2][0].x\n v1.y = self.subdivision_list[index + 2][0].y\n v1.z = self.subdivision_list[index + 2][0].z\n v1.w = self.subdivision_list[index + 2][0].w\n\n v2.x = self.subdivision_list[index + 3][0].x\n v2.y = self.subdivision_list[index + 3][0].y\n v2.z = self.subdivision_list[index + 3][0].z\n v2.w = self.subdivision_list[index + 3][0].w\n\n temp_sub_vertices.append([v0, v1, v2])\n\n self.subdivision_list = temp_sub_vertices","def generaCubo(self):\r\n #Use Panda predefined format for vertex coordinate only\r\n format = GeomVertexFormat.getV3()\r\n \r\n #Build Vertex data using the created format. Vertex will never change so I use Static attribute \r\n vdata = GeomVertexData('CuboData', format, Geom.UHStatic)\r\n \r\n #I will have to write vertex data so I create a writer for these data\r\n vertex = GeomVertexWriter(vdata, 'vertex')\r\n \r\n #I now use the writer to add vertex data\r\n vertex.addData3f(0, 0, 0)\r\n vertex.addData3f(1, 1, 1)\r\n vertex.addData3f(0, 1, 1)\r\n vertex.addData3f(0, 1, 0)\r\n vertex.addData3f(0, 0, 1)\r\n vertex.addData3f(1, 0, 0)\r\n vertex.addData3f(1, 0, 1)\r\n vertex.addData3f(1, 1, 0)\r\n \r\n #I now create 12 triangles\r\n prim = GeomTriangles(Geom.UHStatic)\r\n\r\n #and then I add vertex to them\r\n #Next time use addVertices(0,1,2) !!!\r\n prim.addVertex(7)\r\n prim.addVertex(0)\r\n prim.addVertex(5)\r\n prim.closePrimitive()\r\n \r\n prim.addVertex(3)\r\n prim.addVertex(0)\r\n prim.addVertex(7)\r\n prim.closePrimitive()\r\n \r\n prim.addVertex(2)\r\n prim.addVertex(6)\r\n prim.addVertex(4)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(1)\r\n prim.addVertex(6)\r\n prim.addVertex(2)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(7)\r\n prim.addVertex(2)\r\n prim.addVertex(3)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(1)\r\n prim.addVertex(2)\r\n prim.addVertex(7)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(3)\r\n prim.addVertex(4)\r\n prim.addVertex(0)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(2)\r\n prim.addVertex(4)\r\n prim.addVertex(3)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(0)\r\n prim.addVertex(6)\r\n prim.addVertex(5)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(4)\r\n prim.addVertex(6)\r\n prim.addVertex(0)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(5)\r\n prim.addVertex(1)\r\n prim.addVertex(7)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(6)\r\n prim.addVertex(1)\r\n prim.addVertex(5)\r\n prim.closePrimitive()\r\n\r\n #Create a Geom to bing vertex data to primitives\r\n geom = Geom(vdata)\r\n geom.addPrimitive(prim)\r\n\r\n #Create a node for the Geom in order to be able to render it\r\n node = GeomNode('gnode')\r\n node.addGeom(geom)\r\n\r\n #Adde the node to the scene graph == render it!\r\n nodePath = render.attachNewNode(node)\r\n \r\n #is this needed?\r\n nodePath.setPos( 0, 5, 0)\r\n \r\n self.camera.lookAt(nodePath)\r\n \r\n base.setBackgroundColor( .0, .0, .0 )\r\n \r\n taskMgr.add(self.SpinCameraTask, \"SpinCameraTask\")","def subdivide_ngons(faces: Iterable[Sequence[Union[Vector, Vec2]]]) -> Iterable[\n List[Vector]]:\n for face in faces:\n if len(face) < 5:\n yield face\n else:\n mid_pos = sum(face) / len(face)\n for index, vertex in enumerate(face):\n yield face[index - 1], vertex, mid_pos","def vertices(self):\n return self.pointlist","def generate(pts):\n cmds.polyCreateFacet(name=\"shirt\", p=points)\n cmds.polyTriangulate()\n cmds.polySubdivideFacet(dv=SUBDIVISIONS)\n cmds.polyTriangulate()","def edges_as_vertices(self) -> Iterable[Tuple[Vec3, Vec3]]:\n v = self.vertices\n for edge in self.edges:\n yield v[edge[0]], v[edge[1]]","def add_vectorlist(vectors):\n x, y, z = zip(*vectors)\n return sum(x), sum(y), sum(z)","def tessellate(self, divisions):\n theta_step = 360 / divisions\n phi_step = 180 / divisions\n radius = 1.0\n\n vertices = []\n elements = []\n\n # Calculate the vertices\n for theta in np.arange(0, 360, theta_step):\n for phi in np.arange(0, 180 + phi_step, phi_step):\n v = [math.sin(math.radians(theta)) * math.sin(math.radians(phi)),\n math.cos(math.radians(phi)),\n math.cos(math.radians(theta)) * math.sin(math.radians(phi))]\n vertices.append([radius * c for c in v])\n\n num_vertices = len(vertices)\n\n # Calculate the triangles\n for i in range(divisions):\n for j in range(divisions):\n elements.append((i * (divisions + 1) + j) % num_vertices)\n elements.append((i * (divisions + 1) + (j + 1)) % num_vertices)\n elements.append(((i + 1) * (divisions + 1) + (j + 1)) % num_vertices)\n elements.append(((i + 1) * (divisions + 1) + (j + 1)) % num_vertices)\n elements.append(((i + 1) * (divisions + 1) + j) % num_vertices)\n elements.append((i * (divisions + 1) + j) % num_vertices)\n\n vertices = [x for vertex in vertices for x in vertex]\n\n self.vertices = np.array(vertices, dtype=np.float32)\n self.normals = np.array(vertices, dtype=np.float32)\n self.elements = np.array(elements, dtype=np.uint16)","def make_lattice(min,max,lattice_vectors):\n xs = np.roll(np.arange(min[0],max[0]),max[0])\n ys = np.roll(np.arange(min[1],max[1]),max[1])\n lattice = np.dstack(np.meshgrid(xs,ys)).reshape(-1,2)\n lattice = np.matmul(lattice,lattice_vectors)\n return lattice","def build_mesh(self):\n vertices = []\n indices = []\n step = 10\n istep = (pi * 2) / float(step)\n for i in range(step):\n x = 350 + cos(istep * i) * 100\n y = 350 + sin(istep * i) * 100\n vertices.extend([x, y, 0, 0])\n indices.append(i)\n return Mesh(vertices=vertices, indices=indices)","def Triangle(points=None):\n if points is None:\n points = [[0, 0, 0], [1, 0, 0], [0.5, 0.5**0.5, 0]]\n\n if len(points) != 3:\n raise TypeError('Points must be given as length 3 np.ndarray or list')\n\n check_valid_vector(points[0], 'points[0]')\n check_valid_vector(points[1], 'points[1]')\n check_valid_vector(points[2], 'points[2]')\n\n cells = np.array([[3, 0, 1, 2]])\n return wrap(pyvista.PolyData(points, cells))"],"string":"[\n \"def vertices(tri, vertex_list):\\n dim = len(vertex_list[0])\\n p = numpy.zeros((3, dim))\\n for j in range(3):\\n p[j] = vertex_list[tri[j]]\\n return p\",\n \"def get_vertices(self):\\n vertices = []\\n V = [[-self.base_vectors[:,n], self.base_vectors[:,n]] for n in range(self.base_vectors.shape[1])]\\n combs = list(itertools.product(*V))\\n for cb in combs:\\n cb = np.sum(np.array(cb).T, axis=1, keepdims=True)\\n vertices.append(self.base_vertices + cb)\\n\\n vertices = np.concatenate(vertices,axis=1)\\n return vertices\",\n \"def extract_triangles(mesh, materials_list):\\n tri_list = []\\n do_uv = bool(mesh.tessface_uv_textures)\\n\\n for mat in materials_list:\\n for i, face in enumerate(mesh.tessfaces):\\n f_v = face.vertices\\n if mesh.materials[face.material_index].name != mat: continue\\n\\n uf = mesh.tessface_uv_textures.active.data[i] if do_uv else None\\n\\n fmt = 0\\n if(do_uv): fmt = face.material_index\\n\\n if do_uv:\\n f_uv = uf.uv\\n\\n if len(f_v) == 3:\\n new_tri = tri_wrapper((f_v[0], f_v[1], f_v[2]), fmt)\\n if (do_uv):\\n new_tri.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[1]), uv_key(f_uv[2])\\n else: new_tri.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\\n tri_list.append(new_tri)\\n\\n else: # it's a quad\\n new_tri = tri_wrapper((f_v[0], f_v[1], f_v[2]), fmt)\\n new_tri_2 = tri_wrapper((f_v[0], f_v[2], f_v[3]), fmt)\\n\\n if (do_uv):\\n new_tri.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[1]), uv_key(f_uv[2])\\n new_tri_2.faceuvs = uv_key(f_uv[0]), uv_key(f_uv[2]), uv_key(f_uv[3])\\n else:\\n new_tri.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\\n new_tri_2.faceuvs = uv_key((0.0,0.0)), uv_key((1.0,0.0)), uv_key((0.0,1.0))\\n\\n tri_list.append(new_tri)\\n tri_list.append(new_tri_2)\\n\\n return tri_list\",\n \"def get_verts(ulist, vlist, func):\\n verts = []\\n for u in ulist:\\n for v in vlist:\\n verts.append(func(u, v))\\n return verts\",\n \"def simplex(v1, v2, v3):\\n res = polyhedron([], [], [])\\n p0 = res.addVertex(vertex(0, 0, 0))\\n p1 = res.addVertex(vertex(v1))\\n p2 = res.addVertex(vertex(v2))\\n p3 = res.addVertex(vertex(v3))\\n p12 = res.addVertex(vertex(v1 + v2))\\n p13 = res.addVertex(vertex(v1 + v3))\\n p23 = res.addVertex(vertex(v2 + v3))\\n p123 = res.addVertex(vertex(v1 + v2 + v3))\\n res.addEdge(p0, p1)\\n res.addEdge(p0, p2)\\n res.addEdge(p0, p3)\\n res.addEdge(p1, p12)\\n res.addEdge(p1, p13)\\n res.addEdge(p2, p12)\\n res.addEdge(p2, p23)\\n res.addEdge(p3, p13)\\n res.addEdge(p3, p23)\\n res.addEdge(p1, p123)\\n res.addEdge(p2, p123)\\n res.addEdge(p3, p123)\\n res.addFace([p0, p2, p12, p1])\\n res.addFace([p0, p1, p13, p3])\\n res.addFace([p0, p3, p23, p2])\\n res.addFace([p1, p12, p123, p13])\\n res.addFace([p2, p23, p123, p12])\\n res.addFace([p3, p13, p123, p23])\\n return res\",\n \"def vertices(self):\\n d = self.space_dimension()\\n v = vector(ZZ, d)\\n points = []\\n for g in self.minimized_generators():\\n for i in range(0,d):\\n v[i] = g.coefficient(Variable(i))\\n v_copy = copy.copy(v)\\n v_copy.set_immutable()\\n points.append(v_copy)\\n return tuple(points)\",\n \"def plotVects(vList, colors='k'):\\n polyhedron([vertex(v) for v in vList] + [vertex(0, 0, 0)], [edge(v) for v in vList], []).plot(plotEdges=True)\",\n \"def create_inner_tri(point, v1, v2, v3):\\n return [(point, v1, v2), (point, v1, v3), (point, v2, v3)]\",\n \"def get_faces(ulist, vlist):\\n width = len(ulist)\\n faces = []\\n for i in range(len(ulist) - 1):\\n for j in range(len(vlist) - 1):\\n topleft = j * width + i\\n topright = topleft + 1\\n bottomleft = ((j + 1) * width) + i\\n bottomright = bottomleft + 1\\n one = [topleft, topright, bottomleft]\\n two = [bottomleft, topright, bottomright]\\n faces.append(one)\\n faces.append(two)\\n\\n return faces\",\n \"def split_triangles(mesh):\\n triangles = np.asarray(mesh.triangles).copy()\\n vertices = np.asarray(mesh.vertices).copy()\\n\\n triangles_3 = np.zeros_like(triangles)\\n vertices_3 = np.zeros((len(triangles) * 3, 3), dtype=vertices.dtype)\\n\\n for index_triangle, t in enumerate(triangles):\\n index_vertex = index_triangle * 3\\n vertices_3[index_vertex] = vertices[t[0]]\\n vertices_3[index_vertex + 1] = vertices[t[1]]\\n vertices_3[index_vertex + 2] = vertices[t[2]]\\n\\n triangles_3[index_triangle] = np.arange(index_vertex, index_vertex + 3)\\n\\n mesh_return = deepcopy(mesh)\\n mesh_return.triangles = o3d.utility.Vector3iVector(triangles_3)\\n mesh_return.vertices = o3d.utility.Vector3dVector(vertices_3)\\n mesh_return.triangle_normals = mesh.triangle_normals\\n mesh_return.paint_uniform_color([0.5, 0.5, 0.5])\\n return mesh_return\",\n \"def create_triangles(list_of_points):\\n # create the first two triangle using the create_two_init_triangles with\\n # the first 4 points in the given list\\n tri_list = create_two_init_triangles(list_of_points[0:FIRST_FOUR_POINTS])\\n # run over the point list from the 5th point and on\\n for i in range(FIRST_FOUR_POINTS, len(list_of_points)):\\n # run on the existing triangles\\n for j in range(0, len(tri_list)):\\n # check if the point is inside the current triangle\\n if is_point_inside_triangle(list_of_points[i], tri_list[j][0],\\n tri_list[j][1], tri_list[j][2])[0]:\\n # if the point is inside the current triangle, create 3 new\\n # triangles using the old triangle vertexes and the new point\\n # adding them to the triangle list instead of the triangle the\\n # point was in\\n tri_list[j:j+1] = create_inner_tri(list_of_points[i],\\n tri_list[j][0],\\n tri_list[j][1],\\n tri_list[j][2])\\n break\\n return tri_list\",\n \"def _generate_vertexes(self):\\n # generate list of sets for each vms\\n self._graph = [set() for _ in range(self._vm_count)]\",\n \"def normals(t, v):\\n n = numpy.zeros((len(t), 3))\\n for i in range(0, len(t)):\\n p = vertices(t[i], v)\\n n[i] = triangle.normal(p)\\n return n\",\n \"def strang_mesh(filename):\\n\\n from math import pi\\n from anuga.utilities.numerical_tools import anglediff\\n\\n\\n fid = open(filename)\\n points = [] # List of x, y coordinates\\n triangles = [] # List of vertex ids as listed in the file\\n\\n for line in fid.readlines():\\n fields = line.split()\\n if len(fields) == 2:\\n # we are reading vertex coordinates\\n points.append([float(fields[0]), float(fields[1])])\\n elif len(fields) == 3:\\n # we are reading triangle point id's (format ae+b)\\n triangles.append([int(float(fields[0]))-1,\\n int(float(fields[1]))-1,\\n int(float(fields[2]))-1])\\n else:\\n raise Excetion('wrong format in %s' % filename)\\n\\n elements = [] #Final list of elements\\n\\n for t in triangles:\\n #Get vertex coordinates\\n v0 = t[0]\\n v1 = t[1]\\n v2 = t[2]\\n\\n x0 = points[v0][0]\\n y0 = points[v0][1]\\n x1 = points[v1][0]\\n y1 = points[v1][1]\\n x2 = points[v2][0]\\n y2 = points[v2][1]\\n\\n #Check that points are arranged in counter clock-wise order\\n vec0 = [x1-x0, y1-y0]\\n vec1 = [x2-x1, y2-y1]\\n vec2 = [x0-x2, y0-y2]\\n\\n a0 = anglediff(vec1, vec0)\\n a1 = anglediff(vec2, vec1)\\n a2 = anglediff(vec0, vec2)\\n\\n if a0 < pi and a1 < pi and a2 < pi:\\n elements.append([v0, v1, v2])\\n else:\\n elements.append([v0, v2, v1])\\n\\n return points, elements\",\n \"def create_single_triangle_mesh():\\n vertices = np.array(\\n ((0, 0, 0), (1, 0, 0), (0, 1, 0)), dtype=np.float32)\\n faces = np.array(((0, 1, 2),), dtype=np.int32)\\n return vertices, faces\",\n \"def faces_as_vertices(self) -> Iterable[List[Vec3]]:\\n v = self.vertices\\n for face in self.faces:\\n yield [v[index] for index in face]\",\n \"def vertexes(self):\\n theta = self.orientation\\n shifts = np.array([np.cos(theta), np.sin(theta)]) * self.a\\n return self.coords + (shifts[:, None] * [-1, 1]).T\",\n \"def getVectors(self):\\n l = len(self.points)\\n return [Vector.createFromTwoPoints(self.points[i % l], self.points[(i + 1) % l], \\\\\\n color=self.side_color, width=self.side_width) for i in range(l)]\",\n \"def get_sound_vertices(self):\\n\\n vals = np.sum(np.abs(self.base_vectors), axis=1, keepdims=True)\\n V = [[v,-v] for v in vals]\\n combs = list(itertools.product(*V))\\n\\n vertices = []\\n for cb in combs:\\n vertices.append(self.base_vertices+np.array(cb))\\n vertices = np.concatenate(vertices, axis=1)\\n return vertices\",\n \"def get_vectors(nodes, mode=\\\"xform\\\"):\\n for each in nodes:\\n position = (0, 0, 0)\\n\\n if mode == \\\"xform\\\":\\n position = cmds.xform(\\n each,\\n query=True,\\n translation=True,\\n worldSpace=True,\\n )\\n\\n elif mode == \\\"pivot\\\":\\n position = cmds.xform(\\n each,\\n query=True,\\n translation=True,\\n rotatePivot=True,\\n worldSpace=True,\\n )\\n\\n # when using xform on component like faces or edge, the returned value\\n # will be a list of each vertices position, so we need to average that\\n if len(position) > 3:\\n vectors = [\\n MVector(position[i : i + 3])\\n for i in range(0, len(position), 3)\\n ]\\n result = MVector()\\n for vector in vectors:\\n result += vector\\n position = result / len(vectors)\\n\\n yield MVector(position)\",\n \"def create( vertices, faces, colour=(0.6,0.6,0.6) ):\\r\\n \\r\\n mesh = GLUtils.GLMesh()\\r\\n \\r\\n for vertex in vertices:\\r\\n mesh.addVertex( PyUtils.toPoint3d(vertex) )\\r\\n \\r\\n for face in faces:\\r\\n poly = GLUtils.GLIndexedPoly()\\r\\n for index in face:\\r\\n poly.addVertexIndex( index )\\r\\n mesh.addPoly(poly)\\r\\n \\r\\n try:\\r\\n mesh.setColour( *colour )\\r\\n except TypeError:\\r\\n mesh.setColour( *(colour + (1,)) )\\r\\n\\r\\n mesh.computeNormals()\\r\\n\\r\\n return mesh\",\n \"def construct_convex_hull(vertices: Sequence[Point]) -> Polyhedron:\\n coords = np.zeros((len(vertices),3))\\n for i,vertex in enumerate(vertices):\\n coords[i,:] = vertex.coordinates\\n hull = qconvex(\\\"i\\\", coords)\\n n_facets = int(hull[0])\\n facets = []\\n for facet_vertices_str in hull[1:]:\\n facet_vertices_idx = [int(x) for x in facet_vertices_str.split(' ')]\\n facet_vertices = [vertices[i] for i in facet_vertices_idx]\\n facet = Facet([Contour.from_vertices(facet_vertices)])\\n facets.append(facet)\\n polyhedron = Polyhedron(facets)\\n return polyhedron\",\n \"def creaLados(vertices):\\n\\tlados = []\\n\\tfor i in range(len(vertices)-1):\\n\\t\\tlados.append((i, i+1))\\n\\tlados.append((i+1,0))\\n\\treturn lados\",\n \"def calculateMeshInv(mesh_face_vertices):\\n mesh_inv = []\\n for mesh in mesh_face_vertices:\\n U = np.array([\\n [mesh[0, 0], mesh[1, 0], mesh[2, 0]],\\n [mesh[0, 1], mesh[1, 1], mesh[2, 1]],\\n [1, 1, 1],\\n ])\\n mesh_inv.append(np.linalg.inv(U))\\n return np.array(mesh_inv)\",\n \"def vector(self):\\n \\n v_list = Householder.triangle_operation(self)[1]\\n \\n return(v_list)\",\n \"def create_hull(vertices):\\n dt = np.dtype([('vertex', np.float64, (2,)),\\n ('length', np.float64),\\n ('is_processed', bool)])\\n\\n hull = np.empty(len(vertices), dtype=dt)\\n for i, v in enumerate(vertices):\\n j = 0 if i == len(vertices)-1 else i+1\\n hull[i] = (v, dist(v, vertices[j]), False)\\n\\n return np.rec.array(hull)\",\n \"def cube_vertices(x, y, z, n):\\r\\n return [\\r\\n x-n,y+n,z-n, x-n,y+n,z+n, x+n,y+n,z+n, x+n,y+n,z-n, # top\\r\\n x-n,y-n,z-n, x+n,y-n,z-n, x+n,y-n,z+n, x-n,y-n,z+n, # bottom\\r\\n x-n,y-n,z-n, x-n,y-n,z+n, x-n,y+n,z+n, x-n,y+n,z-n, # left\\r\\n x+n,y-n,z+n, x+n,y-n,z-n, x+n,y+n,z-n, x+n,y+n,z+n, # right\\r\\n x-n,y-n,z+n, x+n,y-n,z+n, x+n,y+n,z+n, x-n,y+n,z+n, # front\\r\\n x+n,y-n,z-n, x-n,y-n,z-n, x-n,y+n,z-n, x+n,y+n,z-n, # back\\r\\n ]\",\n \"def createMesh(width, height):\\r\\n mesh = [(x,y) for x in range(0, width+1) for y in range(0,height+1)]\\r\\n return mesh\",\n \"def getVectors(self,graph):\\n return [Vector.createFromTwoTuples(graph[i],graph[i+1]) for i in range(len(graph)-1)]\",\n \"def get_mesh_boundary(triangles):\\n # Create edges and sort each vertices on each edge.\\n edge0 = triangles[:,0:2]\\n edge1 = triangles[:,1:3]\\n edge2 = triangles.take((0,2), axis=1)\\n edges = np.concatenate((edge0, edge1, edge2), axis=0)\\n edge_sort = np.sort(edges, axis=1)\\n\\n # Get unique edges that are only present once.\\n (uniq, uniq_ids, counts) = np.unique(edge_sort, axis=0, return_index=True, return_counts=True)\\n edge_inds = np.arange(edge_sort.shape[0], dtype=int)\\n outer_edge_ids = edge_inds[np.in1d(edge_inds, uniq_ids[counts==1])]\\n outer_edges = edge_sort[outer_edge_ids,:]\\n num_outer_edges = outer_edges.shape[0]\\n\\n # Assume we need to close the polygon.\\n num_outer_verts = num_outer_edges + 1\\n\\n # Loop over outer edges and use traversal method to get ordered vertices.\\n v_start = outer_edges[0,0]\\n v_end = outer_edges[0,1]\\n vert_inds = -1*np.ones(num_outer_verts, dtype=int)\\n vert_inds[0] = v_start\\n vert_inds[1] = v_end\\n vert_num = 2\\n outer_edges[0,:] = -1\\n for edge_num in range(1,num_outer_edges):\\n edge_inds_next = np.where(outer_edges == v_end)\\n if (edge_inds_next[0].shape[0] < 1):\\n msg = \\\"Next edge not found for vertex %d\\\" % v_end\\n raise ValueError(msg)\\n edge_ind_next = edge_inds_next[0][0]\\n vert_ind_next = 0\\n if (edge_inds_next[1][0] == 0):\\n vert_ind_next = 1\\n vert_inds[vert_num] = outer_edges[edge_ind_next, vert_ind_next]\\n outer_edges[edge_ind_next, :] = -1\\n v_end = vert_inds[vert_num]\\n vert_num += 1\\n\\n return vert_inds\",\n \"def MeshPyTri(points,facets,*args,**kwargs):\\n info = triangle.MeshInfo()\\n info.set_points(points)\\n info.set_facets(facets)\\n\\n return triangle.build(info,*args,**kwargs)\",\n \"def triangle_facets(length=2.0, divisions=4):\\n\\n # Starting with a equilateral triangle.\\n vertices = [\\n (0, (math.sqrt(3) / 3.0) * length, 0.0),\\n (-length / 2, -(math.sqrt(3) / 6.0) * length, 0.0),\\n (length / 2, -(math.sqrt(3) / 6.0) * length, 0.0),\\n ]\\n\\n # The facets should not be accumulated for all of them as the first\\n # triangle will Z-fight with remaining triangles. The overall style is to\\n # have 'holes' where there are triangles that aren't filled.\\n\\n facets = [(0, 1, 2)]\\n new_triangles = facets\\n\\n for _ in range(divisions):\\n facets = []\\n for new_triangle in new_triangles:\\n next_vertices, next_triangles = _divide(new_triangle, vertices)\\n vertices.extend(next_vertices)\\n facets.extend(next_triangles)\\n\\n new_triangles = facets\\n\\n return vertices, facets\",\n \"def exportTriangles(self):\\n # Filter out triangles with any vertex in the extended BBox\\n return [(a-4,b-4,c-4)\\n for (a,b,c) in self.triangles if a > 3 and b > 3 and c > 3]\",\n \"def _create_vertex_list(self):\\n raise NotImplementedError('_create_vertex_list must be defined in '\\n 'order to use group or batch properties')\",\n \"def _triangular_mesh_to_three_geometry(vertices, faces, vertex_colors=None):\\n context = _js_builder.Js(mode=_js_builder.PERSISTENT)\\n vertices = context.Float32Array.new_object(vertices.ravel().tolist())\\n faces = context.Uint32Array.new_object(faces.ravel().tolist())\\n geometry = context.THREE.BufferGeometry.new_object()\\n geometry.addAttribute('position',\\n context.THREE.BufferAttribute.new_object(vertices, 3))\\n geometry.setIndex(context.THREE.BufferAttribute.new_object(faces, 1))\\n geometry.computeVertexNormals()\\n if vertex_colors is not None:\\n vertex_colors = context.Float32Array.new_object(\\n vertex_colors.ravel().tolist())\\n geometry.addAttribute(\\n 'color', context.THREE.BufferAttribute.new_object(vertex_colors, 3))\\n\\n return geometry\",\n \"def MeshVtxAdjacentVtxs (strMesh, index, blnAbsolutConnections=False, blnCreate=False):\\n \\\"\\\"\\\"custom function\\\"\\\"\\\"\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\n def CullDuplicates(seq, idfun=None): \\n # order preserving \\n if idfun is None: \\n def idfun(x): return x \\n seen = {} \\n result = [] \\n for item in seq: \\n marker = idfun(item) \\n if marker in seen: continue \\n seen[marker] = 1 \\n result.append(item) \\n return result\\n #-----------------------------------------------------------------------------------------------------------------------------------------\\n MeshVtxAdjacentVtxs = []\\n if rs.IsMesh(strMesh)==False : \\n print \\\"strMesh is not an mesh\\\"\\n return None\\n if type(index)==type(\\\"string\\\"):\\n print \\\"index is not an integer\\\"\\n return None\\n if type(index)==type(0.1): index = int(index)\\n\\n arrVertices = rs.MeshVertices (strMesh)\\n arrFaceVertices = rs.MeshFaceVertices(strMesh)\\n\\n intCount = 0\\n arrAdjacentVtxs = []\\n for arrFace in arrFaceVertices:\\n blnIsAdjacent = False\\n for arrVtxIndex in arrFace:\\n if arrVtxIndex == index :\\n blnIsAdjacent = True\\n if blnIsAdjacent :\\n if blnAbsolutConnections :\\n if arrFace[2]==arrFace[3] :\\n for arrVtxIndex in arrFace :\\n if arrVtxIndex != index :\\n arrAdjacentVtxs.append( arrVtxIndex)\\n else :\\n if index == arrFace[0] :\\n arrAdjacentVtxs.append( arrFace[3] )\\n arrAdjacentVtxs.append( arrFace[1] )\\n elif index == arrFace[1] :\\n arrAdjacentVtxs.append( arrFace[0] )\\n arrAdjacentVtxs.append( arrFace[2] )\\n elif index == arrFace[2] :\\n arrAdjacentVtxs.append( arrFace[1] )\\n arrAdjacentVtxs.append( arrFace[3] )\\n elif index == arrFace(3) :\\n arrAdjacentVtxs.append( arrFace[2] )\\n arrAdjacentVtxs.append( arrFace[0] )\\n else :\\n for arrVtxIndex in arrFace :\\n if arrVtxIndex != index :\\n arrAdjacentVtxs.append( arrVtxIndex )\\n if type(arrAdjacentVtxs) != type([]) : return None\\n arrOrderAdjacentVtxs = CullDuplicates(arrAdjacentVtxs)\\n if blnCreate :\\n arrStrPts = []\\n for arrVtxIndex in arrOrderAdjacentVtxs:\\n rs.AddPoint ( arrVertices[arrVtxIndex] )\\n arrStrPts.append( arrVertices[arrVtxIndex] )\\n return arrStrPts\\n else :\\n return arrOrderAdjacentVtxs\",\n \"def get_vertex_data(\\n mesh: object,\\n g: BinaryReader,\\n v1: int,\\n v2: int,\\n v3: int,\\n v4: int,\\n n: int,\\n verbose=False,\\n):\\n for i in range(v1):\\n v_offset = g.tell()\\n mesh.vertPosList.append(g.f(3))\\n mesh.vertNormList.append(g.f(3))\\n indice_offset = g.tell()\\n if verbose:\\n logger.debug({\\n \\\"v1 v_offset\\\": v_offset,\\n \\\"v1 indice_offset\\\": indice_offset,\\n })\\n mesh.skinIndiceList.append(g.B(4))\\n mesh.skinWeightList.append([0, 0, 0, 1])\\n\\n for i in range(v2):\\n v_offset = g.tell()\\n mesh.vertPosList.append(g.f(3))\\n mesh.vertNormList.append(g.f(3))\\n indice_offset = g.tell()\\n if verbose:\\n logger.debug({\\n \\\"v2 v_offset\\\": v_offset,\\n \\\"v2 indice_offset\\\": indice_offset,\\n })\\n mesh.skinIndiceList.append(g.B(4))\\n w1 = g.f(1)[0]\\n w2 = 1.0 - w1\\n mesh.skinWeightList.append([0, 0, w2, w1])\\n\\n for i in range(v3):\\n v_offset = g.tell()\\n mesh.vertPosList.append(g.f(3))\\n mesh.vertNormList.append(g.f(3))\\n indice_offset = g.tell()\\n if verbose:\\n logger.debug({\\n \\\"v3 v_offset\\\": v_offset,\\n \\\"v3 indice_offset\\\": indice_offset,\\n })\\n mesh.skinIndiceList.append(g.B(4))\\n w1 = g.f(1)[0]\\n w2 = g.f(1)[0]\\n w3 = 1.0 - w1 - w2\\n mesh.skinWeightList.append([0, w3, w2, w1])\\n\\n for i in range(v4):\\n v_offset = g.tell()\\n mesh.vertPosList.append(g.f(3))\\n mesh.vertNormList.append(g.f(3))\\n indice_offset = g.tell()\\n if verbose:\\n logger.debug({\\n \\\"v4 v_offset\\\": v_offset,\\n \\\"v4 indice_offset\\\": indice_offset,\\n })\\n mesh.skinIndiceList.append(g.B(4))\\n w1 = g.f(1)[0]\\n w2 = g.f(1)[0]\\n w3 = g.f(1)[0]\\n w4 = 1.0 - w1 - w2 - w3\\n mesh.skinWeightList.append([w4, w3, w2, w1])\",\n \"def exportTriangles(self):\\n # Filter out triangles with any vertex in the extended BBox\\n return [(a-4, b-4, c-4)\\n for (a, b, c) in self.triangles if a > 3 and b > 3 and c > 3]\",\n \"def polygonal(resolution, in_vertices, out_vertices_list = None):\\n in_vertices = [Point(in_vertices[k,0],in_vertices[k,1]) for k in range(in_vertices.shape[0])] \\n\\n domain = mshr.Polygon(in_vertices) # https://bitbucket.org/fenics-project/mshr/wiki/API/Polygon\\n # Create polygon defined by the given vertices. Vertices must be in counter-clockwise order and free of self-intersections.\\n \\n if(out_vertices_list is not None):\\n for out_vertices in out_vertices_list:\\n out_vertices = [Point(out_vertices[k,0],out_vertices[k,1]) for k in range(out_vertices.shape[0])]\\n domain -= mshr.Polygon(out_vertices)\\n \\n mesh=mshr.generate_mesh(domain, resolution)\\n\\n # TODO : add refined mesh\\n # if(refine_mesh):\\n # d = mesh.topology().dim()\\n \\n # class To_refine(SubDomain):\\n # def inside(self, x, on_boundary):\\n # return x[1]<=0 and x[1]>= -l_mot/2-h_grid-l_vacuum/4\\n\\n # to_refine = To_refine()\\n # marker = MeshFunction(\\\"bool\\\", mesh, d, False)\\n # to_refine.mark(marker, True)\\n # mesh = refine(mesh,marker)\\n\\n return mesh\",\n \"def add_vertices(self, vertices: Iterable[\\\"Vertex\\\"]) -> Sequence[int]:\\n start_index = len(self.vertices)\\n self.vertices.extend(Vec3.generate(vertices))\\n return tuple(range(start_index, len(self.vertices)))\",\n \"def get_vectors(mols):\\n vect_types = VectTypes()\\n for mol in mols:\\n if \\\".\\\" in mol.smiles:\\n logger.debug(\\\"SKIPPING - FRAGMENT: %s\\\", mol.smiles)\\n continue\\n vectors = get_3d_vects_for_mol(mol.sdf_info)\\n for vect_type in vectors:\\n vect_choice = vect_types.translate_vect_types(vect_type)\\n for vector in vectors[vect_type]:\\n spl_vect = vector.split(\\\"__\\\")\\n smiles = spl_vect[0]\\n if len(spl_vect) > 1:\\n vect_ind = int(spl_vect[1])\\n else:\\n vect_ind = 0\\n new_vect = Vector.objects.get_or_create(\\n smiles=smiles, cmpd_id=mol.cmpd_id, type=vect_choice\\n )[0]\\n create_vect_3d(mol, new_vect, vect_ind, vectors[vect_type][vector])\",\n \"def getVectors(self, graph):\\n return [Vector.createFromTwoTuples(graph[i], graph[i + 1]) for i in range(len(graph) - 1)]\",\n \"def get_triangle(radius, xcenter, ycenter, numberOfVertices):\\n\\n\\n angles = [0.0, (2. / 3.0) * math.pi, (4.0 / 3.0) * math.pi]\\n\\n vertices = []\\n\\n for angle in range(0, len(angles)):\\n x = radius * math.cos(angles[angle]) + xcenter\\n y = radius * math.sin(angles[angle]) + ycenter\\n vertices.append(x) # append the x value to the vertex list\\n vertices.append(y) # append the y value to the vertex list\\n\\n # convert the vertices list to pyGlet vertices format\\n vertexList = pyglet.graphics.vertex_list(numberOfVertices, ('v2f', vertices))\\n\\n return vertexList\",\n \"def create_square_triangle_mesh():\\n vertices = np.array(\\n ((0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0.5, 0.5, 0)),\\n dtype=np.float32)\\n faces = np.array(\\n ((0, 1, 4), (1, 3, 4), (3, 2, 4), (2, 0, 4)), dtype=np.int32)\\n return vertices, faces\",\n \"def get_intermediate_triangles(source_triangles_list, target_triangles_list,\\n alpha):\\n # initiate the intermediate triangles list\\n intermediate_tri_list = []\\n # run over the lists of source triangles and target triangles\\n for i in range(0, len(source_triangles_list)):\\n # add to the list the intermediate triangle made with the method\\n # create_intermediate_triangle\\n intermediate_tri_list.append(create_intermediate_triangle\\n (source_triangles_list[i],\\n target_triangles_list[i], alpha))\\n return intermediate_tri_list\",\n \"def boundary_vertices(points: np.ndarray, triangles: np.ndarray) -> np.ndarray:\\n tri = Triangulation(points[:, 0], points[:, 1], triangles)\\n boundary_edges = set()\\n for i, neighbors in enumerate(tri.neighbors):\\n for k in range(3):\\n if neighbors[k] == -1:\\n boundary_edges.add((triangles[i, k], triangles[i, (k + 1) % 3]))\\n edges = MultiLineString([points[edge, :] for edge in boundary_edges])\\n polygons = list(polygonize(edges))\\n assert len(polygons) == 1, polygons\\n polygon = orient(polygons[0])\\n points_list = [tuple(xy) for xy in points]\\n indices = np.array([points_list.index(xy) for xy in polygon.exterior.coords])\\n return indices[:-1]\",\n \"def _uniform_refine_triangles(mesh):\\n\\n # get current cells and edges\\n cells = mesh.cells\\n edges = mesh.edges\\n\\n assert np.size(cells, axis=1) == 3\\n\\n # get number of current nodes\\n n_nodes = np.size(mesh.nodes, axis=0)\\n \\n # first we create additional nodes as midpoints of the current edges\\n midpoints = 0.5 * mesh.nodes.take(edges - 1, axis=0).sum(axis=1)\\n\\n # add them to the existing mesh nodes\\n new_nodes = np.vstack([mesh.nodes, midpoints])\\n \\n # then we generate the indices of the newly created nodes\\n #\\n # their indices start at the current number of nodes (`n_nodes`) + 1\\n # and end after additional `n_edges` nodes\\n n_edges = np.size(edges, axis=0)\\n new_node_indices = np.arange(n_nodes + 1, n_nodes + n_edges + 1, dtype=int)\\n\\n # refine elements\\n #\\n # for every element we need the indices of the edges at which the corresponding\\n # new nodes are created\\n indices_2_1 = mesh.topology.get_connectivity(2, 1, return_indices=True) - 1\\n \\n # next we augment the indices that define each element as if\\n # they were defined by 6 nodes (including those of the edge midpoints)\\n cells_ = np.hstack([cells, new_node_indices.take(indices_2_1, axis=0)])\\n \\n # now we can generate the four new elements for each existing one\\n new_cells_1 = np.vstack([cells_[:,0], cells_[:,3], cells_[:,4]]).T\\n new_cells_2 = np.vstack([cells_[:,1], cells_[:,3], cells_[:,5]]).T\\n new_cells_3 = np.vstack([cells_[:,2], cells_[:,4], cells_[:,5]]).T\\n new_cells_4 = np.vstack([cells_[:,3], cells_[:,4], cells_[:,5]]).T\\n \\n new_cells = np.vstack([new_cells_1, new_cells_2, new_cells_3, new_cells_4])\\n\\n return new_nodes, new_cells\",\n \"def compute_normalvect(self):\\n normvect = np.zeros((len(self.tri_pnts),3,3))\\n zvec = np.array([0, 0, 1])\\n for itri, tri in enumerate(self.tri_pnts):\\n #import pdb; pdb.set_trace()\\n tri0, tri1, tri2 = tri\\n x1,y1 = self.points[tri1]-self.points[tri0]\\n v1 = np.array([x1,y1,0])\\n x2,y2 = self.points[tri2]-self.points[tri1]\\n v2 = np.array([x2,y2,0])\\n x3,y3 = self.points[tri0]-self.points[tri2]\\n v3 = np.array([x3,y3,0])\\n v1 = v1/np.linalg.norm(v1)\\n v2 = v2/np.linalg.norm(v2)\\n v3 = v3/np.linalg.norm(v3)\\n #import pdb; pdb.set_trace()\\n normvect[itri,:,:] = np.cross(v1,zvec), np.cross(v2,zvec), np.cross(v3,zvec)\\n #import pdb; pdb.set_trace()\\n return normvect\",\n \"def cube_vertices(x, y, z, nx, ny=None, nz=None):\\n if ny == None: ny = nx\\n if nz == None: nz = nx\\n return [\\n x - nx, y + ny, z - nz, x - nx, y + ny, z + nz, x + nx, y + ny, z + nz, x + nx, y + ny, z - nz, # top\\n x - nx, y - ny, z - nz, x + nx, y - ny, z - nz, x + nx, y - ny, z + nz, x - nx, y - ny, z + nz, # bottom\\n x - nx, y - ny, z - nz, x - nx, y - ny, z + nz, x - nx, y + ny, z + nz, x - nx, y + ny, z - nz, # left\\n x + nx, y - ny, z + nz, x + nx, y - ny, z - nz, x + nx, y + ny, z - nz, x + nx, y + ny, z + nz, # right\\n x - nx, y - ny, z + nz, x + nx, y - ny, z + nz, x + nx, y + ny, z + nz, x - nx, y + ny, z + nz, # front\\n x + nx, y - ny, z - nz, x - nx, y - ny, z - nz, x - nx, y + ny, z - nz, x + nx, y + ny, z - nz, # back\\n ]\",\n \"def trimesh_from_point_cloud(cloud):\\n points = np.asarray(cloud)\\n hull = scipy.spatial.ConvexHull(points)\\n hull = scipy.spatial.ConvexHull(points[hull.vertices])\\n ru.transforms.counterclockwise_hull(hull)\\n vertices = hull.points\\n faces = hull.simplices\\n return vertices, faces\",\n \"def non_rotated_vertices(self):\\n v0 = [self.pos.x - self.width / 2, self.pos.y - self.height / 2]\\n v1 = [self.pos.x + self.width / 2, self.pos.y - self.height / 2]\\n v2 = [self.pos.x + self.width / 2, self.pos.y + self.height / 2]\\n v3 = [self.pos.x - self.width / 2, self.pos.y + self.height / 2]\\n return v0, v1, v2, v3\",\n \"def vertices_tuple(self):\\n vtuple = (*self.v0, *self.v1, *self.v2, *self.v3)\\n return vtuple\",\n \"def fromVertices(cls,\\n xp0, yp0, zp0, xp1, yp1, zp1,\\n xp2, yp2, zp2, xp3, yp3, zp3,\\n origin,\\n group_index=None,\\n reference=None):\\n if len(xp0) == len(yp0) == len(zp0) == len(xp1) == len(yp1) == \\\\\\n len(zp1) == len(xp2) == len(yp2) == len(zp2) == len(xp3) == \\\\\\n len(yp3) == len(zp3):\\n pass\\n else:\\n raise ShakeLibException('All vectors specifying quadrilateral '\\n 'vertices must have the same length.')\\n\\n nq = len(xp0)\\n if group_index is not None:\\n if len(group_index) != nq:\\n raise Exception(\\n \\\"group_index must have same length as vertices.\\\")\\n else:\\n group_index = np.array(range(nq))\\n\\n xp0 = np.array(xp0, dtype='d')\\n yp0 = np.array(yp0, dtype='d')\\n zp0 = np.array(zp0, dtype='d')\\n xp1 = np.array(xp1, dtype='d')\\n yp1 = np.array(yp1, dtype='d')\\n zp1 = np.array(zp1, dtype='d')\\n xp2 = np.array(xp2, dtype='d')\\n yp2 = np.array(yp2, dtype='d')\\n zp2 = np.array(zp2, dtype='d')\\n xp3 = np.array(xp3, dtype='d')\\n yp3 = np.array(yp3, dtype='d')\\n zp3 = np.array(zp3, dtype='d')\\n\\n #----------------------------------------------------------------------\\n # Create GeoJSON object\\n #----------------------------------------------------------------------\\n\\n coords = []\\n u_groups = np.unique(group_index)\\n n_groups = len(u_groups)\\n for i in range(n_groups):\\n ind = np.where(u_groups[i] == group_index)[0]\\n lons = np.concatenate(\\n [xp0[ind[0]].reshape((1,)),\\n xp1[ind],\\n xp2[ind][::-1],\\n xp3[ind][::-1][-1].reshape((1,)),\\n xp0[ind[0]].reshape((1,))\\n ])\\n lats = np.concatenate(\\n [yp0[ind[0]].reshape((1,)),\\n yp1[ind],\\n yp2[ind][::-1],\\n yp3[ind][::-1][-1].reshape((1,)),\\n yp0[ind[0]].reshape((1,))\\n ])\\n deps = np.concatenate(\\n [zp0[ind[0]].reshape((1,)),\\n zp1[ind],\\n zp2[ind][::-1],\\n zp3[ind][::-1][-1].reshape((1,)),\\n zp0[ind[0]].reshape((1,))\\n ])\\n\\n poly = []\\n for lon, lat, dep in zip(lons, lats, deps):\\n poly.append([lon, lat, dep])\\n coords.append(poly)\\n\\n d = {\\\"type\\\": \\\"FeatureCollection\\\",\\n \\\"metadata\\\": {},\\n \\\"features\\\": [{\\n \\\"type\\\": \\\"Feature\\\",\\n \\\"properties\\\": {\\n \\\"rupture type\\\": \\\"rupture extent\\\",\\n \\\"reference\\\": reference,\\n },\\n \\\"geometry\\\": {\\n \\\"type\\\": \\\"MultiPolygon\\\",\\n \\\"coordinates\\\": [coords]\\n }\\n }]}\\n\\n # Add origin information to metadata\\n odict = origin.__dict__\\n for k, v in odict.items():\\n if isinstance(v, HistoricTime):\\n d['metadata'][k] = v.strftime('%Y-%m-%dT%H:%M:%SZ')\\n else:\\n d['metadata'][k] = v\\n if hasattr(origin, 'id'):\\n d['metadata']['eventid'] = origin.id\\n\\n return cls(d, origin)\",\n \"def create_two_init_triangles(points):\\n return [(points[0], points[1], points[2]),\\n (points[0], points[2], points[3])]\",\n \"def triangle_edges(length=2.0, divisions=4)):\\n\\n def _edges_from_triangle(triangle):\\n a, b, c = triangle\\n return [(a, b), (b, c), (c, a)]\\n\\n vertices, facets = triangle_facets(length, divisions)\\n edges = []\\n for facet in facets:\\n edges.extend(_edges_from_triangle(facet))\\n return vertices, edges\",\n \"def yield_equilateral_triangles(cls):\\n for i in range(1, 201):\\n yield i-.5, i-.5, i-.5\\n yield i, i, i\",\n \"def triangular_affine_matrices(\\n vertices: list,\\n src_points: list,\\n dest_points: list) -> np.matrix:\\n\\n ones = [1] * 3\\n for tri_indices in vertices:\\n src_tri = np.vstack((src_points[tri_indices, :].T, ones))\\n dst_tri = np.vstack((dest_points[tri_indices, :].T, ones))\\n # Inv computes inversion matrix\\n mat = np.dot(src_tri, np.linalg.inv(dst_tri))[:2, :]\\n yield mat\",\n \"def add_subdivision(self):\\n temp_sub_vertices = []\\n for plane in (self.subdivision_list):\\n current_mids = []\\n mid_m_01 = Vec3d(0, 0, 0, 0)\\n mid_m_12 = Vec3d(0, 0, 0, 0)\\n mid_m_20 = Vec3d(0, 0, 0, 0)\\n\\n mid_m_01.x = (plane[0].x + plane[1].x) / 2\\n mid_m_01.y = (plane[0].y + plane[1].y) / 2\\n mid_m_01.z = (plane[0].z + plane[1].z) / 2\\n mid_m_01.w = plane[0].w\\n\\n mid_m_12.x = (plane[1].x + plane[2].x) / 2\\n mid_m_12.y = (plane[1].y + plane[2].y) / 2\\n mid_m_12.z = (plane[1].z + plane[2].z) / 2\\n mid_m_12.w = plane[1].w\\n\\n mid_m_20.x = (plane[2].x + plane[0].x) / 2\\n mid_m_20.y = (plane[2].y + plane[0].y) / 2\\n mid_m_20.z = (plane[2].z + plane[0].z) / 2\\n mid_m_20.w = plane[2].w\\n\\n current_mids = [mid_m_01, mid_m_12, mid_m_20]\\n temp_sub_vertices.append(current_mids)\\n\\n for index in range(len(current_mids)):\\n v0 = Vec3d(0, 0, 0, 0)\\n v1 = Vec3d(0, 0, 0, 0)\\n v2 = Vec3d(0, 0, 0, 0)\\n\\n v0.x = plane[index].x\\n v0.y = plane[index].y\\n v0.z = plane[index].z\\n\\n v1.x = current_mids[index].x\\n v1.y = current_mids[index].y\\n v1.z = current_mids[index].z\\n\\n v2.x = current_mids[index - 1].x\\n v2.y = current_mids[index - 1].y\\n v2.z = current_mids[index - 1].z\\n\\n temp_sub_vertices.append([v0, v1, v2])\\n\\n self.subdivision_list = temp_sub_vertices\",\n \"def get_verts(v_l, v_r):\\n\\n\\t\\tv_l = v_l%chain.length\\n\\t\\tv_r = v_r%chain.length\\n\\n\\t\\tpoints = []\\n\\t\\tcoords = list(chain.coords)\\n\\t\\tif v_r > v_l:\\n\\n\\t\\t\\tfor i in range(1, len(coords)):\\n\\t\\t\\t\\n\\t\\t\\t\\tpd = LineString(coords[:i+1]).length\\n\\n\\t\\t\\t\\tif pd > v_l and pd < v_r:\\n\\t\\t\\t\\t\\tpoints.append(coords[i])\\n\\t\\telse:\\n\\n\\t\\t\\tfor i in range(1, len(coords)):\\n\\t\\t\\t\\n\\t\\t\\t\\tpd = LineString(coords[:i+1]).length\\n\\n\\t\\t\\t\\tif pd > v_l:\\n\\t\\t\\t\\t\\tpoints.append(coords[i])\\n\\n\\t\\t\\tfor i in range(1, len(coords)):\\n\\t\\t\\t\\n\\t\\t\\t\\tpd = LineString(coords[:i+1]).length\\n\\n\\t\\t\\t\\tif pd < v_r:\\n\\t\\t\\t\\t\\tpoints.append(coords[i])\\n\\n\\n\\t\\treturn points\",\n \"def vertices(size):\\n return set(range(size))\",\n \"def populate_vertices(self, vertices_list):\\n vertices = []\\n for vertex in vertices_list:\\n vertex_id = vertex[0]\\n vertices.append(Vertex(vertex_id))\\n self.vertices = vertices\",\n \"def add_vertices(self, vertices_list):\\n for vertex in vertices_list:\\n self.add_vertex(vertex)\",\n \"def vertify(data: list):\\n assert len(data) == 4\\n n = [float(d) for d in data]\\n return np.array([[n[0], n[1]], [n[2], n[1]], [n[2], n[3]], [n[0], n[3]], [n[0], n[1]]])\",\n \"def cube_vertices(x, y, z, n):\\n #def cube_vertices(self):\\n # \\\"\\\"\\\" Return the vertices of the cube at position x, y, z with size 2*n.\\n #\\n # \\\"\\\"\\\"\\n # return [\\n # x-n,y+n,z-n, x-n,y+n,z+n, x+n,y+n,z+n, x+n,y+n,z-n, # top\\n # x-n,y-n,z-n, x+n,y-n,z-n, x+n,y-n,z+n, x-n,y-n,z+n, # bottom\\n # x-n,y-n,z-n, x-n,y-n,z+n, x-n,y+n,z+n, x-n,y+n,z-n, # left\\n # x+n,y-n,z+n, x+n,y-n,z-n, x+n,y+n,z-n, x+n,y+n,z+n, # right\\n # x-n,y-n,z+n, x+n,y-n,z+n, x+n,y+n,z+n, x-n,y+n,z+n, # front\\n # x+n,y-n,z-n, x-n,y-n,z-n, x-n,y+n,z-n, x+n,y+n,z-n, # back\\n # ]\\n return [\\n x-n,y+n,z-n, x-n,y+n,z+n, x+n,y+n,z+n, x+n,y+n,z-n, # top\\n x-n,y-n,z-n, x+n,y-n,z-n, x+n,y-n,z+n, x-n,y-n,z+n, # bottom\\n x-n,y-n,z-n, x-n,y-n,z+n, x-n,y+n,z+n, x-n,y+n,z-n, # left\\n x+n,y-n,z+n, x+n,y-n,z-n, x+n,y+n,z-n, x+n,y+n,z+n, # right\\n x-n,y-n,z+n, x+n,y-n,z+n, x+n,y+n,z+n, x-n,y+n,z+n, # front\\n x+n,y-n,z-n, x-n,y-n,z-n, x-n,y+n,z-n, x+n,y+n,z-n, # back\\n ]\",\n \"def calculate_box(vertices: [[float]]) -> [float]:\\n x_coords = [x[0] for x in vertices]\\n y_coords = [x[1] for x in vertices]\\n z_coords = [x[2] for x in vertices]\\n\\n return [min(x_coords), min(y_coords), min(z_coords), max(x_coords), max(y_coords), max(z_coords)]\",\n \"def get_all_object_triangles( filename, scale , translation=(0,0,0)):\\n import warnings\\n warnings.warn(\\\"@PendingDeprecationWarning\\\", PendingDeprecationWarning)\\n vertexObjs = read_vertices_objects( filename )\\n faceObjs = read_faces_objects( filename )\\n\\n r = []\\n j = 0\\n \\n # Validation:\\n vertices, faces = [],[]\\n for obj in range(len(vertexObjs)):\\n vertices += vertexObjs[obj]\\n faces += faceObjs[obj]\\n max_vertex_index = max([max(x) for x in faces])\\n if len(vertices) != max_vertex_index:\\n logging.warning( \\\"ParseWarning: A face's vertex index number is does not match the quantity of read vertices.\\\" )\\n logging.warning( \\\"Qty of Vertices: \\\"+str(len(vertices))+\\\", Largest Face Index: \\\"+str(max_vertex_index) )\\n\\n # Parse as Tris:\\n for obj in range(len(vertexObjs)):\\n vertices = vertexObjs[obj]\\n faces = faceObjs[obj]\\n r.append([])\\n c = 0\\n for f in faces: # for every face\\n for i in f: # for each index point in face\\n c+=1\\n try:\\n # Get the face[i] vertex\\n v = vertices[i-1]\\n except IndexError as indErr:\\n logging.warning(\\\"IndexError: Attempted to access index: \\\"+str(i-1)+\\\" in list of length: \\\"+str(len(vertices)))\\n raise IndexError\\n # Scale the face[i] vertex\\n scV = [v[0]*scale, v[1]*scale, v[2]*scale]\\n # Translate the scaled face vertex:\\n t = translation\\n tmpv = [scV[0]+t[0],scV[1]+t[1], scV[2]+t[2]]\\n # Retain this vertex\\n r[j].append(tmpv)\\n # ---------------------\\n if c % 3 == 0:\\n j+=1\\n r.append([])\\n r = r[:len(r)-1] # remove the final empty list.\\n\\n checkShapeValidity( r )\\n return r\",\n \"def surface(func, umin=0, umax=2*np.pi, ucount=64, urepeat=1.0,\\n vmin=0, vmax=2*np.pi, vcount=64, vrepeat=1.0):\\n\\n vtype = [('position', np.float32, 3),\\n ('texcoord', np.float32, 2),\\n ('normal', np.float32, 3)]\\n itype = np.uint32\\n\\n # umin, umax, ucount = 0, 2*np.pi, 64\\n # vmin, vmax, vcount = 0, 2*np.pi, 64\\n\\n vcount += 1\\n ucount += 1\\n n = vcount*ucount\\n\\n Un = np.repeat(np.linspace(0, 1, ucount, endpoint=True), vcount)\\n Vn = np.tile (np.linspace(0, 1, vcount, endpoint=True), ucount)\\n U = umin+Un*(umax-umin)\\n V = vmin+Vn*(vmax-vmin)\\n\\n vertices = np.zeros(n, dtype=vtype)\\n for i,(u,v) in enumerate(zip(U,V)):\\n vertices[\\\"position\\\"][i] = func(u,v)\\n\\n vertices[\\\"texcoord\\\"][:,0] = Un*urepeat\\n vertices[\\\"texcoord\\\"][:,1] = Vn*vrepeat\\n\\n indices = []\\n for i in range(ucount-1):\\n for j in range(vcount-1):\\n indices.append(i*(vcount) + j )\\n indices.append(i*(vcount) + j+1 )\\n indices.append(i*(vcount) + j+vcount+1)\\n indices.append(i*(vcount) + j+vcount )\\n indices.append(i*(vcount) + j+vcount+1)\\n indices.append(i*(vcount) + j )\\n indices = np.array(indices, dtype=itype)\\n vertices[\\\"normal\\\"] = normals(vertices[\\\"position\\\"],\\n indices.reshape(len(indices)//3,3))\\n\\n return vertices.view(gloo.VertexBuffer), indices.view(gloo.IndexBuffer)\",\n \"def hexgrid(self):\\n n = self.n * 2\\n vectors = []\\n for u in range(-n, n+1):\\n us = [u] * (2*n+1)\\n if u < 0:\\n vectors.extend(zip(us, range(-n-u, n+1), range(-n, n+u+1)))\\n else:\\n vectors.extend(zip(us, range(-n, n-u+1), range(-n+u, n+1)))\\n return vectors\",\n \"def VertNrmPairs(vertices, exportMatrix):\\n entries = [None] * len(vertices)\\n\\n # putting them in pairs first, so that comparing is easier\\n for i, v in enumerate(vertices):\\n pos = exportMatrix @ v.co\\n nrm = exportMatrix @ v.normal\\n e = (Vector3(pos.x, pos.y, pos.z), Vector3(nrm.x, nrm.y, nrm.z))\\n entries[i] = e\\n\\n return entries\",\n \"def getVertexPositions(transform_name):\\n positions = pm.xform('{}.vtx[*]'.format(transform_name), q=True, ws=True, t=True)\\n return zip(positions[0::3], positions[1::3], positions[2::3])\",\n \"def grab_40_vectors(x1,x2,y1,y2) -> list:\\n\\n if x1 > x2:\\n x1,x2 = x2,x1\\n if y1 > y2:\\n y1,y2 = y2,y1\\n\\n height = abs(y2-y1)\\n length = abs(x2-x1)\\n\\n height_delta = height/10\\n length_delta = length/10\\n\\n left_vertical_edge = [(x1, y1+height_delta*i) for i in range(0,11)]\\n # print(left_vertical_edge)\\n right_vertical_edge = [(x2,y1+height_delta*i) for i in range(0,11)]\\n # print(right_vertical_edge)\\n bottom_horizontal_edge = [(x1+length_delta*i,y1) for i in range(0,11)]\\n # print(bottom_horizontal_edge)\\n top_horizontal_edge = [(x1+length_delta*i,y2) for i in range(0,11)]\\n # print(top_horizontal_edge)\\n\\n all_vectors = left_vertical_edge+right_vertical_edge+bottom_horizontal_edge+top_horizontal_edge\\n return(all_vectors)\",\n \"def getNormals(vertA, vertB, vertC):\\n xA = vertA[0]\\n xB = vertB[0]\\n xC = vertC[0]\\n yA = vertA[1]\\n yB = vertB[1]\\n yC = vertC[1]\\n zA = vertA[2]\\n zB = vertB[2]\\n zC = vertC[2]\\n ABx = xB - xA\\n ABy = yB - yA\\n ABz = zB - zA\\n BCx = xC - xB\\n BCy = yC - yB\\n BCz = zC - zB\\n Nx = ABy * BCz - ABz * BCy\\n Ny = ABz * BCx - ABx * BCz\\n Nz = ABx * BCy - ABy * BCx\\n VecMag = math.sqrt(Nx ** 2 + Ny ** 2 + Nz ** 2)\\n Ni = Nx / VecMag\\n Nj = Ny / VecMag\\n Nk = Nz / VecMag\\n return [Ni, Nj, Nk]\",\n \"def _legacy_to_opengl(face_vertices_image, face_vertices_z, valid_faces=None):\\n z = -face_vertices_z / (abs(face_vertices_z).max() + 1e-6)\\n _face_vertices_image = face_vertices_image.reshape(*face_vertices_image.shape[:-3], -1, 2)\\n pos = torch.stack([\\n _face_vertices_image[..., 0],\\n -_face_vertices_image[..., 1],\\n z.reshape(*z.shape[:-2], -1)\\n ], dim=-1)\\n if valid_faces is None:\\n pos = torch.nn.functional.pad(pos, (0, 1), value=1.)\\n else:\\n pad = (valid_faces.unsqueeze(-1) * 2. - 1.).expand(*valid_faces.shape, 3)\\n pad = pad.reshape(*valid_faces.shape[:-1], -1, 1)\\n pos = torch.cat([pos, pad], dim=-1)\\n\\n tri = torch.arange(pos.shape[-2], device=pos.device, dtype=torch.int).reshape(-1, 3)\\n \\n return pos, tri\",\n \"def smooth_mesh(\\n points: np.ndarray, triangles: np.ndarray, iterations: int\\n) -> Tuple[np.ndarray, np.ndarray]:\\n edges, _ = get_edges(triangles)\\n n = points.shape[0]\\n shape = (n, 2)\\n boundary = boundary_vertices(points, triangles)\\n for _ in range(iterations):\\n num_neighbors = np.bincount(edges.ravel(), minlength=shape[0])\\n new_points = np.zeros(shape)\\n vals = points[edges[:, 1]].T\\n new_points += np.array(\\n [np.bincount(edges[:, 0], val, minlength=n) for val in vals]\\n ).T\\n vals = points[edges[:, 0]].T\\n new_points += np.array(\\n [np.bincount(edges[:, 1], val, minlength=n) for val in vals]\\n ).T\\n new_points /= num_neighbors[:, np.newaxis]\\n # reset boundary points\\n new_points[boundary] = points[boundary]\\n points = new_points\\n return points, triangles\",\n \"def generate_vertex_normals(vertices, index, normalize_result=True):\\n v1, v2, v3 = np.rollaxis(vertices[index], axis=-2)\\n face_normals = generate_normals(v1, v2, v3, normalize_result=False)\\n vertex_normals = np.zeros_like(vertices)\\n for i in range(3):\\n np.add.at(vertex_normals, index[..., i], face_normals)\\n if normalize_result:\\n vertex_normals = normalize(vertex_normals)\\n return vertex_normals\",\n \"def vec(self):\\r\\n\\r\\n xv = np.arange(self.dx / 2, self.lx, self.dx)\\r\\n yv = np.arange(-self.ly / 2 + self.dy / 2, self.ly / 2, self.dy)\\r\\n zv = np.arange(self.oz, self.lz + self.oz, self.dz)\\r\\n\\r\\n if self.ox != 0:\\r\\n xv = np.arange(self.ox, self.lx + self.ox, self.dx)\\r\\n yv = np.arange(self.oy, self.ly + self.oy, self.dy)\\r\\n zv = np.arange(self.oz, self.lz + self.oz, self.dz)\\r\\n\\r\\n return xv, yv, zv\",\n \"def generate_mesh(\\n poly_coords: np.ndarray,\\n hole_coords: Optional[List[np.ndarray]] = None,\\n min_points: Optional[int] = None,\\n max_edge_length: Optional[float] = None,\\n convex_hull: bool = False,\\n boundary: Optional[np.ndarray] = None,\\n preserve_boundary: bool = False,\\n min_angle: float = 32.5,\\n **kwargs,\\n) -> Tuple[np.ndarray, np.ndarray]:\\n poly_coords = ensure_unique(poly_coords)\\n if hole_coords is None:\\n hole_coords = []\\n hole_coords = [ensure_unique(coords) for coords in hole_coords]\\n # Facets is a shape (m, 2) array of edge indices.\\n # coords[facets] is a shape (m, 2, 2) array of edge coordinates:\\n # [(x0, y0), (x1, y1)]\\n coords = np.concatenate([poly_coords] + hole_coords, axis=0)\\n xmin = coords[:, 0].min()\\n dx = np.ptp(coords[:, 0])\\n ymin = coords[:, 1].min()\\n dy = np.ptp(coords[:, 1])\\n r0 = np.array([[xmin, ymin]]) + np.array([[dx, dy]]) / 2\\n # Center the coordinates at (0, 0) to avoid floating point issues.\\n coords = coords - r0\\n indices = np.arange(len(poly_coords), dtype=int)\\n if convex_hull:\\n if boundary is not None:\\n raise ValueError(\\n \\\"Cannot have both boundary is not None and convex_hull = True.\\\"\\n )\\n facets = spatial.ConvexHull(coords).simplices\\n else:\\n if boundary is not None:\\n boundary = list(map(tuple, ensure_unique(boundary - r0)))\\n indices = [i for i in indices if tuple(coords[i]) in boundary]\\n facets = np.array([indices, np.roll(indices, -1)]).T\\n # Create facets for the holes.\\n for hole in hole_coords:\\n hole_indices = np.arange(\\n indices[-1] + 1, indices[-1] + 1 + len(hole), dtype=int\\n )\\n hole_facets = np.array([hole_indices, np.roll(hole_indices, -1)]).T\\n indices = np.concatenate([indices, hole_indices], axis=0)\\n facets = np.concatenate([facets, hole_facets], axis=0)\\n\\n mesh_info = triangle.MeshInfo()\\n mesh_info.set_points(coords)\\n mesh_info.set_facets(facets)\\n if hole_coords:\\n # Triangle allows you to set holes by specifying a single point\\n # that lies in each hole. Here we use the centroid of the hole.\\n holes = [\\n np.array(Polygon(hole).centroid.coords[0]) - r0.squeeze()\\n for hole in hole_coords\\n ]\\n mesh_info.set_holes(holes)\\n\\n kwargs = kwargs.copy()\\n kwargs[\\\"allow_boundary_steiner\\\"] = not preserve_boundary\\n if \\\"min_angle\\\" not in kwargs:\\n kwargs[\\\"min_angle\\\"] = min_angle\\n\\n mesh = triangle.build(mesh_info=mesh_info, **kwargs)\\n points = np.array(mesh.points) + r0\\n triangles = np.array(mesh.elements)\\n if min_points is None and (max_edge_length is None or max_edge_length <= 0):\\n return points, triangles\\n\\n kwargs[\\\"max_volume\\\"] = dx * dy / 100\\n i = 1\\n if min_points is None:\\n min_points = 0\\n if max_edge_length is None or max_edge_length <= 0:\\n max_edge_length = np.inf\\n max_length = get_edge_lengths(points, triangles).max()\\n while (len(points) < min_points) or (max_length > max_edge_length):\\n mesh = triangle.build(mesh_info=mesh_info, **kwargs)\\n points = np.array(mesh.points) + r0\\n triangles = np.array(mesh.elements)\\n edges, is_boundary = get_edges(triangles)\\n if preserve_boundary:\\n # Only constrain the length of interior edges, i.e. edges not on the boundary.\\n edges = edges[~is_boundary]\\n edge_lengths = np.linalg.norm(np.diff(points[edges], axis=1), axis=2)\\n max_length = edge_lengths.max()\\n logger.debug(\\n f\\\"Iteration {i}: Made mesh with {len(points)} points and \\\"\\n f\\\"{len(triangles)} triangles with maximum interior edge length: \\\"\\n f\\\"{max_length:.2e}. Target maximum edge length: {max_edge_length:.2e}.\\\"\\n )\\n if np.isfinite(max_edge_length):\\n kwargs[\\\"max_volume\\\"] *= min(0.98, np.sqrt(max_edge_length / max_length))\\n else:\\n kwargs[\\\"max_volume\\\"] *= 0.98\\n i += 1\\n return points, triangles\",\n \"def create_tetrahedron_vbo(arista=1.0):\\n\\n def ex(element):\\n \\\"\\\"\\\"Exporta el elemento a una lista\\\"\\\"\\\"\\n return element.export_to_list()\\n\\n # Se crean los puntos\\n a = Point3(-0.5, -0.288675, -0.288675) * arista\\n b = Point3(0.5, -0.288675, -0.288675) * arista\\n c = Point3(0.0, 0.577350, -0.288675) * arista\\n d = Point3(0.0, 0.0, 0.57735) * arista\\n\\n # Se crean las normales\\n n1 = ex(normal3points(a, b, d))\\n n2 = ex(normal3points(b, c, d))\\n n3 = ex(normal3points(c, a, d))\\n n4 = ex(normal3points(c, b, a))\\n\\n # Se crean las listas de puntos y normales en orden por triangulos, cada 3 puntos se forma una cara\\n vertex_array = [ex(a), ex(b), ex(d), ex(b), ex(c), ex(d), ex(c), ex(a),\\n ex(d), ex(a), ex(b), ex(c)]\\n normal_array = [n1, n1, n1, n2, n2, n2, n3, n3, n3, n4, n4, n4]\\n\\n # Se retornan los vertex buffer object\\n return VboObject(vbo.VBO(array(vertex_array, 'f')),\\n vbo.VBO(array(normal_array, 'f')), len(vertex_array))\",\n \"def list_plot3d_tuples(v, interpolation_type, texture, **kwds):\\n from matplotlib import tri, delaunay\\n import numpy\\n import scipy\\n from random import random\\n from scipy import interpolate\\n from .plot3d import plot3d\\n\\n if len(v)<3:\\n raise ValueError(\\\"We need at least 3 points to perform the interpolation\\\")\\n\\n x = [float(p[0]) for p in v]\\n y = [float(p[1]) for p in v]\\n z = [float(p[2]) for p in v]\\n\\n # If the (x,y)-coordinates lie in a one-dimensional subspace, the\\n # matplotlib Delaunay code segfaults. Therefore, we compute the\\n # correlation of the x- and y-coordinates and add small random\\n # noise to avoid the problem if needed.\\n corr_matrix = numpy.corrcoef(x, y)\\n if corr_matrix[0, 1] > 0.9 or corr_matrix[0, 1] < -0.9:\\n ep = float(.000001)\\n x = [float(p[0]) + random()*ep for p in v]\\n y = [float(p[1]) + random()*ep for p in v]\\n\\n\\n # If the list of data points has two points with the exact same\\n # (x,y)-coordinate but different z-coordinates, then we sometimes\\n # get segfaults. The following block checks for this and raises\\n # an exception if this is the case.\\n # We also remove duplicate points (which matplotlib can't handle).\\n # Alternatively, the code in the if block above which adds random\\n # error could be applied to perturb the points.\\n drop_list = []\\n nb_points = len(x)\\n for i in range(nb_points):\\n for j in range(i+1, nb_points):\\n if x[i] == x[j] and y[i] == y[j]:\\n if z[i] != z[j]:\\n raise ValueError(\\\"Two points with same x,y coordinates and different z coordinates were given. Interpolation cannot handle this.\\\")\\n elif z[i] == z[j]:\\n drop_list.append(j)\\n x = [x[i] for i in range(nb_points) if i not in drop_list]\\n y = [y[i] for i in range(nb_points) if i not in drop_list]\\n z = [z[i] for i in range(nb_points) if i not in drop_list]\\n\\n xmin = float(min(x))\\n xmax = float(max(x))\\n ymin = float(min(y))\\n ymax = float(max(y))\\n\\n num_points = kwds['num_points'] if 'num_points' in kwds else int(4*numpy.sqrt(len(x)))\\n #arbitrary choice - assuming more or less a nxn grid of points\\n # x should have n^2 entries. We sample 4 times that many points.\\n\\n if interpolation_type == 'linear':\\n T = tri.Triangulation(x, y)\\n f = tri.LinearTriInterpolator(T, z)\\n j = numpy.complex(0, 1)\\n from .parametric_surface import ParametricSurface\\n def g(x, y):\\n z = f(x, y)\\n return (x, y, z)\\n G = ParametricSurface(g, (list(numpy.r_[xmin:xmax:num_points*j]), list(numpy.r_[ymin:ymax:num_points*j])), texture=texture, **kwds)\\n G._set_extra_kwds(kwds)\\n return G\\n\\n if interpolation_type == 'nn' or interpolation_type =='default':\\n\\n T=delaunay.Triangulation(x,y)\\n f=T.nn_interpolator(z)\\n f.default_value=0.0\\n j=numpy.complex(0,1)\\n vals=f[ymin:ymax:j*num_points,xmin:xmax:j*num_points]\\n from .parametric_surface import ParametricSurface\\n def g(x,y):\\n i=round( (x-xmin)/(xmax-xmin)*(num_points-1) )\\n j=round( (y-ymin)/(ymax-ymin)*(num_points-1) )\\n z=vals[int(j),int(i)]\\n return (x,y,z)\\n G = ParametricSurface(g, (list(numpy.r_[xmin:xmax:num_points*j]), list(numpy.r_[ymin:ymax:num_points*j])), texture=texture, **kwds)\\n G._set_extra_kwds(kwds)\\n return G\\n\\n if interpolation_type == 'spline':\\n from .plot3d import plot3d\\n kx = kwds['kx'] if 'kx' in kwds else 3\\n ky = kwds['ky'] if 'ky' in kwds else 3\\n if 'degree' in kwds:\\n kx = kwds['degree']\\n ky = kwds['degree']\\n s = kwds['smoothing'] if 'smoothing' in kwds else len(x)-numpy.sqrt(2*len(x))\\n s = interpolate.bisplrep(x, y, z, [int(1)]*len(x), xmin, xmax, ymin, ymax, kx=kx, ky=ky, s=s)\\n f = lambda x,y: interpolate.bisplev(x, y, s)\\n return plot3d(f, (xmin, xmax), (ymin, ymax), texture=texture, plot_points=[num_points, num_points], **kwds)\",\n \"def to_mesh(\\n self,\\n lims_x: array_like = (-1, 1),\\n lims_y: array_like = (-1, 1),\\n ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n a, b, c, d = self.cartesian()\\n x_center, y_center = self.point[:2]\\n\\n values_x = x_center + lims_x\\n values_y = y_center + lims_y\\n\\n X, Y = np.meshgrid(values_x, values_y)\\n\\n if c != 0:\\n Z = -(a * X + b * Y + d) / c\\n\\n elif b != 0:\\n Z = -(a * X + c * Y + d) / b\\n X, Y, Z = X, Z, Y\\n\\n else:\\n Z = -(b * X + c * Y + d) / a\\n X, Y, Z = Z, X, Y\\n\\n return X, Y, Z\",\n \"def triangles_list_to_triangles_strip(blender_mesh):\\n # TODO: Fix changing of face orientation in some cases (see tests)\\n edges_faces = {}\\n current_strip = []\\n strips = []\\n joined_strips = []\\n faces_indices = deque(p.index for p in blender_mesh.polygons)\\n done_faces_indices = set()\\n current_face_index = faces_indices.popleft()\\n process_faces = True\\n\\n for polygon in blender_mesh.polygons:\\n for edge in polygon.edge_keys:\\n edges_faces.setdefault(edge, set()).add(polygon.index)\\n\\n while process_faces:\\n current_face = blender_mesh.polygons[current_face_index]\\n current_face_verts = current_face.vertices[:]\\n strip_indices = [v for v in current_face_verts if v not in current_strip[-2:]]\\n if current_strip:\\n face_to_add = tuple(current_strip[-2:]) + tuple(strip_indices)\\n if face_to_add != current_face_verts and face_to_add != tuple(reversed(current_face_verts)):\\n # we arrived here because the current face shares and edge with the face in the strip\\n # however, if we just add the verts, we would be changing the direction of the face\\n # so we create a degenerate triangle before adding to it to the strip\\n current_strip.append(current_strip[-2])\\n current_strip.extend(strip_indices)\\n done_faces_indices.add(current_face_index)\\n\\n next_face_index = None\\n possible_face_indices = {}\\n for edge in current_face.edge_keys:\\n if edge not in edges_faces:\\n continue\\n checked_edge = {face_index: edge for face_index in edges_faces[edge]\\n if face_index != current_face_index and face_index not in done_faces_indices}\\n possible_face_indices.update(checked_edge)\\n for face_index, edge in possible_face_indices.items():\\n if not current_strip:\\n next_face_index = face_index\\n break\\n elif edge == tuple(current_strip[-2:]) or edge == tuple(reversed(current_strip[-2:])):\\n next_face_index = face_index\\n break\\n elif edge == (current_strip[-1], current_strip[-2]):\\n if len(current_strip) % 2 != 0:\\n # create a degenerate triangle to join them\\n current_strip.append(current_strip[-2])\\n next_face_index = face_index\\n\\n if next_face_index:\\n faces_indices.remove(next_face_index)\\n current_face_index = next_face_index\\n else:\\n strips.append(current_strip)\\n current_strip = []\\n try:\\n current_face_index = faces_indices.popleft()\\n except IndexError:\\n process_faces = False\\n\\n prev_strip_len = 0\\n # join strips with degenerate triangles\\n for strip in strips:\\n if not prev_strip_len:\\n joined_strips.extend(strip)\\n prev_strip_len = len(strip)\\n elif prev_strip_len % 2 == 0:\\n joined_strips.extend((joined_strips[-1], strip[0]))\\n joined_strips.extend(strip)\\n prev_strip_len = len(strip)\\n else:\\n joined_strips.extend((joined_strips[-1], strip[0], strip[0]))\\n joined_strips.extend(strip)\\n prev_strip_len = len(strip)\\n\\n return joined_strips\",\n \"def vertices(self):\\n\\n if self._faces is None:\\n if self._vertices is None:\\n return None\\n self.triangulate()\\n return self._vertices\",\n \"def subdivide_face(face: Sequence[Union[Vector, Vec2]], quads=True) -> Iterable[\\n List[Vector]]:\\n if len(face) < 3:\\n raise ValueError('3 or more vertices required.')\\n len_face = len(face)\\n mid_pos = sum(face) / len_face\\n subdiv_location = [face[i].lerp(face[(i + 1) % len_face]) for i in\\n range(len_face)]\\n\\n for index, vertex in enumerate(face):\\n if quads:\\n yield vertex, subdiv_location[index], mid_pos, subdiv_location[\\n index - 1]\\n else:\\n yield subdiv_location[index - 1], vertex, mid_pos\\n yield vertex, subdiv_location[index], mid_pos\",\n \"def edgify(vertices:list)->list:\\n edges = []\\n for k in range(0, len(vertices) - 1):\\n edges.append([vertices[k], vertices[k + 1]])\\n return edges\",\n \"def get_triangles(self):\\n location = TopLoc_Location()\\n bt = BRep_Tool()\\n facing = bt.Triangulation(self.topods_shape(), location)\\n if facing == None:\\n return [], []\\n\\n tab = facing.Nodes()\\n tri = facing.Triangles()\\n verts = []\\n for i in range(1, facing.NbNodes() + 1):\\n p = tab.Value(i).Transformed(location.Transformation())\\n verts.append(np.array(list(p.Coord())))\\n\\n tris = []\\n reversed = self.reversed()\\n for i in range(1, facing.NbTriangles() + 1):\\n # OCC triangle normals point in the surface normal\\n # direction\\n if reversed:\\n index1, index3, index2 = tri.Value(i).Get()\\n else:\\n index1, index2, index3 = tri.Value(i).Get()\\n\\n tris.append([index1 - 1, index2 - 1, index3 - 1])\\n\\n return np.asarray(verts, dtype=np.float32), np.asarray(tris, dtype=np.int32)\",\n \"def get_polygon_points(starting_point, vector_seq):\\n \\n res=[[]]\\n res[0] = starting_point\\n curr_point = starting_point\\n\\n i=1\\n\\n while i<len(vector_seq):\\n if are_parallel(vector_seq[i],vector_seq[i-1]):\\n tmp = vector_seq[i]\\n vector_seq[i-1][0]=vector_seq[i-1][0]+ tmp[0]\\n vector_seq[i-1][1]=vector_seq[i-1][1]+ tmp[1]\\n vector_seq.pop(i)\\n else:\\n i=i+1\\n \\n for x in vector_seq:\\n x_coord = curr_point[0]+x[0]\\n y_coord = curr_point[1]+x[1]\\n curr_point=[x_coord, y_coord]\\n res.append(curr_point)\\n\\n return res\",\n \"def make_vectors_output(coord_dict, add_vectors, num_coords, color, ids, display_name='trace', state='on'):\\n # default parameters\\n which_set = 0\\n tip_fraction = 0.5\\n arrow_colors = {'line_color': color, 'head_color': color}\\n \\n # header for section in kin file\\n result = []\\n result.append('@vectorlist {%s} dimension=%s %s' % (display_name, num_coords, state))\\n \\n # selecting the edges to draw in this batch\\n edges = []\\n for k, v in add_vectors['vectors'].iteritems():\\n edges = edges + [(v[i][1],v[i+1][1]) for i in range(len(v[:-1])) if v[i][1] in ids]\\n \\n # creating \\\"vectors\\\"\\n for edge in edges:\\n id_fr, id_to = edge\\n # extract the coords of each vertex\\n pt_fr = coord_dict[id_fr][:num_coords]\\n pt_to = coord_dict[id_to][:num_coords]\\n\\n # different tip color for each destination coords file\\n #tip_color = kinemage_colors[which_set % len(kinemage_colors)]\\n # plot a color 'tip' on the line (certain % of line length)\\n # this gets the coords of the beginning of the 'tip'\\n diffs = (pt_to-pt_fr) * (1-tip_fraction)\\n middles = pt_fr + diffs\\n # add a default-color line segment\\n \\n # modified to use user defined\\n tip_color = arrow_colors['head_color']\\n label_color = arrow_colors['line_color']\\n \\n result.append('%s %s' % \\\\\\n (' '.join(map(str, pt_fr)),label_color))\\n result.append('%s %s P' % \\\\\\n (' '.join(map(str, middles)),label_color))\\n # add the tip-colored line segment\\n result.append('%s %s' % \\\\\\n (' '.join(map(str, middles)), tip_color))\\n result.append('%s %s P' % \\\\\\n (' '.join(map(str, pt_to)), tip_color)) \\n return result\",\n \"def create_tetrahedron():\\n obj = glGenLists(1)\\n glNewList(obj, GL_COMPILE)\\n glPushMatrix()\\n try:\\n glutSolidTetrahedron()\\n except:\\n if not _ERRS[5]:\\n printGLError(\\n \\\"la version actual de OpenGL no posee la funcion glutSolidTetrahedron\\\")\\n _ERRS[5] = True\\n glPopMatrix()\\n glEndList()\\n return obj\",\n \"def subdivision(mesh):\\n\\t\\n\\t\\n\\t# 1. generate new nodes in the centre of quad\\n\\t# 1/4 o-------o 1/4 o: existing vertices\\n\\t# | | *: newly-generated vertices\\n\\t# | * |\\n\\t# | |\\n\\t# 1/4 o-------o 1/4\\n\\n\\tnew_coor = mesh.give_nodes().give_coor()\\n\\t\\n\\tfor face_index in range(mesh.give_model_inf()[2]): \\n\\t\\tnew_x, new_y, new_z = (0, 0, 0)\\n\\t\\tfor vertex_index in range(4):\\n\\t\\t\\tmesh.give_faces()\\n\\t\\t\\tnode_index = mesh.give_faces().give_node_list(face_index)[vertex_index]\\n\\n\\t\\t\\tnew_x += 0.25*mesh.give_nodes().give_coor(node_index)[0]\\n\\t\\t\\tnew_y += 0.25*mesh.give_nodes().give_coor(node_index)[1]\\n\\t\\t\\tnew_z += 0.25*mesh.give_nodes().give_coor(node_index)[2]\\n\\t\\t\\t\\n\\t\\tnew_coor.append((new_x, new_y, new_z))\\n\\t\\t\\n\\t# generating new nodes on the edge\\n\\t# figure out one edge is shared by how many surfaces\\n\\tedge_shared_by_faces_list = helper.find_edge_shared_by_which_faces(mesh.give_edges(), mesh.give_faces())\\n\\t\\n\\tfor edge_index in range(mesh.give_model_inf()[1]):\\n\\n\\t\\tnew_x, new_y, new_z = (0., 0., 0.)\\n\\t\\t\\n\\t# 2. generate new node on boundary edge\\n\\t# o: existing vertices\\n\\t# 1/2 o---*---o 1/2 *: newly-generated vertices\\n\\t# \\n\\n\\t\\tnew_coor = mesh.give_nodes().give_coor()\\n\\t\\tif len(edge_shared_by_faces_list[edge_index]) == 1:\\t\\n\\t\\t\\tnew_x, new_y, new_z = (0., 0., 0.)\\n\\t\\t\\tfor vertex_index in range(2):\\n\\t\\t\\t\\tthis_node = mesh.give_edges().give_node(edge_index)[vertex_index]\\n\\t\\t\\t\\tnew_x += 0.5*mesh.give_nodes().give_coor()[this_node][0]\\n\\t\\t\\t\\tnew_y += 0.5*mesh.give_nodes().give_coor()[this_node][1]\\n\\t\\t\\t\\tnew_z += 0.5*mesh.give_nodes().give_coor()[this_node][2]\\n\\t\\t\\t\\t\\n\\t\\t\\tnew_coor.append((new_x, new_y, new_z))\\n\\t\\t\\t\\t\\n\\t# 3. generate new node on interior edge\\n\\t# 1/16 o-------o 1/16 o: existing vertices\\n\\t# | | *: newly-generated vertices\\n\\t# 3/8 o---*---o 3/8\\n\\t# | |\\n\\t# 1/16 o-------o 1/16\\n\\n\\t\\telse:\\n\\t\\t\\tnew_x, new_y, new_z = (0., 0., 0.)\\n\\t\\t\\tconsidered_node = []\\n\\t\\t\\tfor vertex_index in range(2):\\n\\t\\t\\t\\tthis_node = mesh.give_edges().give_node(edge_index)[vertex_index]\\n\\t\\t\\t\\tconsidered_node.append(this_node)\\n\\t\\t\\t\\tnew_x += 3./8.*mesh.give_nodes().give_coor()[this_node][0]\\n\\t\\t\\t\\tnew_y += 3./8.*mesh.give_nodes().give_coor()[this_node][1]\\n\\t\\t\\t\\tnew_z += 3./8.*mesh.give_nodes().give_coor()[this_node][2]\\n\\t\\t\\t\\n\\t\\t\\t# faces contain this node\\n\\t\\t\\tpotential_node = []\\n\\t\\t\\tfor face_index in edge_shared_by_faces_list[edge_index]:\\t\\t\\n\\t\\t\\t\\tfor vertex_index in range(4):\\n\\t\\t\\t\\t\\t\\tpotential_node.append(mesh.give_faces().give_node_list(face_index)[vertex_index])\\n\\t\\t\\t\\n\\t\\t\\touter_node = []\\n\\t\\t\\tfor node in potential_node:\\n\\t\\t\\t\\tif (node not in considered_node) & (node not in outer_node):\\n\\t\\t\\t\\t\\touter_node.append(node)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\tfor vertex_index in outer_node:\\n\\t\\t\\t\\tnew_x += 1./16.*mesh.give_nodes().give_coor()[vertex_index][0]\\n\\t\\t\\t\\tnew_y += 1./16.*mesh.give_nodes().give_coor()[vertex_index][1]\\n\\t\\t\\t\\tnew_z += 1./16.*mesh.give_nodes().give_coor()[vertex_index][2]\\n\\t\\t\\t\\n\\t\\t\\tnew_coor.append((new_x, new_y, new_z))\\n\\n\\t# update the links of edges and surfaces\\n\\tnew_edge_list = []\\n\\tnew_face_list = []\\n\\tfor face_index in range(mesh.give_model_inf()[2]):\\n\\t\\told_node0 = mesh.give_faces().give_node_list(face_index)[0]\\n\\t\\told_node1 = mesh.give_faces().give_node_list(face_index)[1]\\n\\t\\told_node2 = mesh.give_faces().give_node_list(face_index)[2]\\n\\t\\told_node3 = mesh.give_faces().give_node_list(face_index)[3]\\n\\t\\t\\n\\t\\told_edge0 = mesh.give_faces().give_edge_list(face_index)[0]\\n\\t\\told_edge1 = mesh.give_faces().give_edge_list(face_index)[1]\\n\\t\\told_edge2 = mesh.give_faces().give_edge_list(face_index)[2]\\n\\t\\told_edge3 = mesh.give_faces().give_edge_list(face_index)[3]\\n\\t\\t\\n\\t\\tnew_node4 = old_edge0 + mesh.give_model_inf()[0] + mesh.give_model_inf()[2] \\n\\t\\tnew_node5 = old_edge1 + mesh.give_model_inf()[0] + mesh.give_model_inf()[2]\\n\\t\\tnew_node6 = old_edge2 + mesh.give_model_inf()[0] + mesh.give_model_inf()[2]\\n\\t\\tnew_node7 = old_edge3 + mesh.give_model_inf()[0] + mesh.give_model_inf()[2]\\t\\n\\t\\tnew_node8 = mesh.give_model_inf()[0] + face_index\\n\\t\\t\\n\\t\\tif helper.in_list((old_node0, new_node4), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((old_node0, new_node4))\\n\\t\\tif helper.in_list((new_node4, new_node8), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((new_node4, new_node8))\\n\\t\\tif helper.in_list((new_node8, new_node7), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((new_node8, new_node7))\\n\\t\\tif helper.in_list((new_node7, old_node0), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((new_node7, old_node0))\\n\\t\\tif helper.in_list((new_node4, old_node1), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((new_node4, old_node1))\\n\\t\\tif helper.in_list((old_node1, new_node5), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((old_node1, new_node5))\\n\\t\\tif helper.in_list((new_node5, new_node8), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((new_node5, new_node8))\\n\\t\\tif helper.in_list((new_node7, old_node3), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((new_node7, old_node3))\\n\\t\\tif helper.in_list((old_node3, new_node6), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((old_node3, new_node6))\\n\\t\\tif helper.in_list((new_node6, new_node8), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((new_node6, new_node8))\\n\\t\\tif helper.in_list((new_node6, old_node2), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((new_node6, old_node2))\\n\\t\\tif helper.in_list((old_node2, new_node5), new_edge_list) == False: \\n\\t\\t\\tnew_edge_list.append((old_node2, new_node5))\\n\\t\\n\\t\\tnew_face_list.append((old_node0, new_node4, new_node8, new_node7))\\n\\t\\tnew_face_list.append((new_node4, old_node1, new_node5, new_node8))\\n\\t\\tnew_face_list.append((new_node7, new_node8, new_node6, old_node3))\\n\\t\\tnew_face_list.append((new_node8, new_node5, old_node2, new_node6))\\n\\t\\t\\n\\tnew_edges = geo.Edge(new_edge_list)\\n\\t\\n\\tnew_faces = geo.Face(new_face_list, new_edges)\\n\\t\\t\\n\\t# update existing nodes\\t\\n\\tfor node_index in range(mesh.give_model_inf()[0]):\\n\\t\\t\\n\\t\\tring1, ring2 = helper.find_neighbour_node(new_edges, new_faces, node_index)\\n\\t\\tvalence = helper.find_valence(node_index, new_faces) \\n\\t\\t#: valence: the number of faces sharing on specific edge\\n\\n\\t# 4. update existing corner vertex\\n\\t# 2/4 @---* 1/4 *: newly-generated vertices\\n\\t# | | @: existing vertices to be updated\\n\\t# 1/4 *---* 0 The higher mask values on neighbouring vertices, \\n\\t# the more likely a square mesh will be refined into a sphere.\\n\\t \\n\\t\\tif valence == 1:\\n\\n\\t\\t\\tnew_x, new_y, new_z = (0, 0, 0)\\n\\t\\t\\tprint\\n\\t\\t\\tfor node_in_ring1 in ring1:\\n\\t\\t\\t\\tnew_x += 1./4.*mesh.give_nodes().give_coor()[node_in_ring1][0]\\n\\t\\t\\t\\tnew_y += 1./4.*mesh.give_nodes().give_coor()[node_in_ring1][1]\\n\\t\\t\\t\\tnew_z += 1./4.*mesh.give_nodes().give_coor()[node_in_ring1][2]\\n\\n\\t\\t\\tfor node_in_ring2 in ring2:\\n\\t\\t\\t\\tnew_x += 0.*mesh.give_nodes().give_coor()[node_in_ring2][0]\\n\\t\\t\\t\\tnew_y += 0.*mesh.give_nodes().give_coor()[node_in_ring2][1]\\n\\t\\t\\t\\tnew_z += 0.*mesh.give_nodes().give_coor()[node_in_ring2][2]\\n\\t\\t\\t\\t\\n\\t\\t\\tnew_x += 2./4.*mesh.give_nodes().give_coor()[node_index][0]\\n\\t\\t\\tnew_y += 2./4.*mesh.give_nodes().give_coor()[node_index][1]\\n\\t\\t\\tnew_z += 2./4.*mesh.give_nodes().give_coor()[node_index][2]\\n\\n\\t# 5. update existing boundary joint vertex\\n\\t# 3/4\\n\\t# 1/8 *---*---* 1/8 *: newly-generated vertices\\n\\t# | | | @: existing vertices to be updated\\n\\t# 0 *---*---* 0\\n\\n\\t\\telif valence == 2:\\n\\t\\t\\t\\n\\t\\t\\tnew_x, new_y, new_z = (0, 0, 0)\\n\\t\\t\\tfor node_in_ring1 in ring1:\\n\\t\\t\\t\\tif helper.find_valence(node_in_ring1, new_faces) <= 2: \\n\\t\\t\\t\\t\\tnew_x += 1./8.*mesh.give_nodes().give_coor()[node_in_ring1][0]\\n\\t\\t\\t\\t\\tnew_y += 1./8.*mesh.give_nodes().give_coor()[node_in_ring1][1]\\n\\t\\t\\t\\t\\tnew_z += 1./8.*mesh.give_nodes().give_coor()[node_in_ring1][2]\\n\\t\\t\\t\\t\\t\\n\\t\\t\\tnew_x += 3./4.*mesh.give_nodes().give_coor()[node_index][0]\\n\\t\\t\\tnew_y += 3./4.*mesh.give_nodes().give_coor()[node_index][1]\\n\\t\\t\\tnew_z += 3./4.*mesh.give_nodes().give_coor()[node_index][2]\\n\\t\\n\\t# 6. update new node on interior edge\\n\\t# * r/k\\n\\t# /\\\\ b/k*\\n\\t# *__/ \\\\___ r/k\\n\\t# \\\\ \\\\ /¬¬/ *: newly-generated vertices: \\n\\t# \\\\ \\\\/ / b = 3/2/valence, r = 1/4/valence\\n\\t# *--@--* b/k\\t @: existing vertices to be updated: 1-b-r\\t\\t\\n\\t# / /\\\\ \\\\\\n\\t# /__/ \\\\__\\\\\\n\\t# * \\\\ / * r/k\\n\\t# \\\\/\\n\\t\\t\\n\\t\\telse:\\n\\t\\t\\tnew_x, new_y, new_z = (0, 0, 0)\\n\\t\\t\\tbeta = 3./2./valence\\n\\t\\t\\tgamma = 1./4./valence\\n\\t\\t\\tfor node_in_ring1 in ring1:\\n\\t\\t\\t\\tnew_x += beta/valence*mesh.give_nodes().give_coor()[node_in_ring1][0]\\n\\t\\t\\t\\tnew_y += beta/valence*mesh.give_nodes().give_coor()[node_in_ring1][1]\\n\\t\\t\\t\\tnew_z += beta/valence*mesh.give_nodes().give_coor()[node_in_ring1][2]\\n\\t\\t\\t\\n\\t\\t\\tfor node_in_ring2 in ring2:\\n\\t\\t\\t\\tnew_x += gamma/valence*mesh.give_nodes().give_coor()[node_in_ring2][0]\\n\\t\\t\\t\\tnew_y += gamma/valence*mesh.give_nodes().give_coor()[node_in_ring2][1]\\n\\t\\t\\t\\tnew_z += gamma/valence*mesh.give_nodes().give_coor()[node_in_ring2][2]\\n\\t\\t\\t\\n\\t\\t\\tnew_x += (1. - beta - gamma)*mesh.give_nodes().give_coor()[node_index][0]\\n\\t\\t\\tnew_y += (1. - beta - gamma)*mesh.give_nodes().give_coor()[node_index][1]\\n\\t\\t\\tnew_z += (1. - beta - gamma)*mesh.give_nodes().give_coor()[node_index][2]\\n\\t\\t\\n\\t\\tnew_coor[node_index] = (new_x, new_y, new_z)\\n\\t\\n\\tnew_nodes = geo.Node(new_coor)\\n\\t\\n\\tmesh.update(new_nodes, new_edges, new_faces)\\n\\t\\n\\t# return new_mesh\\n\\treturn mesh\",\n \"def remove_subdivison(self):\\n temp_sub_vertices = []\\n for index in range(0, len(self.subdivision_list) - 1, 4):\\n v0 = Vec3d(0, 0, 0, 0)\\n v1 = Vec3d(0, 0, 0, 0)\\n v2 = Vec3d(0, 0, 0, 0)\\n\\n v0.x = self.subdivision_list[index + 1][0].x\\n v0.y = self.subdivision_list[index + 1][0].y\\n v0.z = self.subdivision_list[index + 1][0].z\\n v0.w = self.subdivision_list[index + 1][0].w\\n\\n v1.x = self.subdivision_list[index + 2][0].x\\n v1.y = self.subdivision_list[index + 2][0].y\\n v1.z = self.subdivision_list[index + 2][0].z\\n v1.w = self.subdivision_list[index + 2][0].w\\n\\n v2.x = self.subdivision_list[index + 3][0].x\\n v2.y = self.subdivision_list[index + 3][0].y\\n v2.z = self.subdivision_list[index + 3][0].z\\n v2.w = self.subdivision_list[index + 3][0].w\\n\\n temp_sub_vertices.append([v0, v1, v2])\\n\\n self.subdivision_list = temp_sub_vertices\",\n \"def generaCubo(self):\\r\\n #Use Panda predefined format for vertex coordinate only\\r\\n format = GeomVertexFormat.getV3()\\r\\n \\r\\n #Build Vertex data using the created format. Vertex will never change so I use Static attribute \\r\\n vdata = GeomVertexData('CuboData', format, Geom.UHStatic)\\r\\n \\r\\n #I will have to write vertex data so I create a writer for these data\\r\\n vertex = GeomVertexWriter(vdata, 'vertex')\\r\\n \\r\\n #I now use the writer to add vertex data\\r\\n vertex.addData3f(0, 0, 0)\\r\\n vertex.addData3f(1, 1, 1)\\r\\n vertex.addData3f(0, 1, 1)\\r\\n vertex.addData3f(0, 1, 0)\\r\\n vertex.addData3f(0, 0, 1)\\r\\n vertex.addData3f(1, 0, 0)\\r\\n vertex.addData3f(1, 0, 1)\\r\\n vertex.addData3f(1, 1, 0)\\r\\n \\r\\n #I now create 12 triangles\\r\\n prim = GeomTriangles(Geom.UHStatic)\\r\\n\\r\\n #and then I add vertex to them\\r\\n #Next time use addVertices(0,1,2) !!!\\r\\n prim.addVertex(7)\\r\\n prim.addVertex(0)\\r\\n prim.addVertex(5)\\r\\n prim.closePrimitive()\\r\\n \\r\\n prim.addVertex(3)\\r\\n prim.addVertex(0)\\r\\n prim.addVertex(7)\\r\\n prim.closePrimitive()\\r\\n \\r\\n prim.addVertex(2)\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(4)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(1)\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(2)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(7)\\r\\n prim.addVertex(2)\\r\\n prim.addVertex(3)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(1)\\r\\n prim.addVertex(2)\\r\\n prim.addVertex(7)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(3)\\r\\n prim.addVertex(4)\\r\\n prim.addVertex(0)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(2)\\r\\n prim.addVertex(4)\\r\\n prim.addVertex(3)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(0)\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(5)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(4)\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(0)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(5)\\r\\n prim.addVertex(1)\\r\\n prim.addVertex(7)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(1)\\r\\n prim.addVertex(5)\\r\\n prim.closePrimitive()\\r\\n\\r\\n #Create a Geom to bing vertex data to primitives\\r\\n geom = Geom(vdata)\\r\\n geom.addPrimitive(prim)\\r\\n\\r\\n #Create a node for the Geom in order to be able to render it\\r\\n node = GeomNode('gnode')\\r\\n node.addGeom(geom)\\r\\n\\r\\n #Adde the node to the scene graph == render it!\\r\\n nodePath = render.attachNewNode(node)\\r\\n \\r\\n #is this needed?\\r\\n nodePath.setPos( 0, 5, 0)\\r\\n \\r\\n self.camera.lookAt(nodePath)\\r\\n \\r\\n base.setBackgroundColor( .0, .0, .0 )\\r\\n \\r\\n taskMgr.add(self.SpinCameraTask, \\\"SpinCameraTask\\\")\",\n \"def subdivide_ngons(faces: Iterable[Sequence[Union[Vector, Vec2]]]) -> Iterable[\\n List[Vector]]:\\n for face in faces:\\n if len(face) < 5:\\n yield face\\n else:\\n mid_pos = sum(face) / len(face)\\n for index, vertex in enumerate(face):\\n yield face[index - 1], vertex, mid_pos\",\n \"def vertices(self):\\n return self.pointlist\",\n \"def generate(pts):\\n cmds.polyCreateFacet(name=\\\"shirt\\\", p=points)\\n cmds.polyTriangulate()\\n cmds.polySubdivideFacet(dv=SUBDIVISIONS)\\n cmds.polyTriangulate()\",\n \"def edges_as_vertices(self) -> Iterable[Tuple[Vec3, Vec3]]:\\n v = self.vertices\\n for edge in self.edges:\\n yield v[edge[0]], v[edge[1]]\",\n \"def add_vectorlist(vectors):\\n x, y, z = zip(*vectors)\\n return sum(x), sum(y), sum(z)\",\n \"def tessellate(self, divisions):\\n theta_step = 360 / divisions\\n phi_step = 180 / divisions\\n radius = 1.0\\n\\n vertices = []\\n elements = []\\n\\n # Calculate the vertices\\n for theta in np.arange(0, 360, theta_step):\\n for phi in np.arange(0, 180 + phi_step, phi_step):\\n v = [math.sin(math.radians(theta)) * math.sin(math.radians(phi)),\\n math.cos(math.radians(phi)),\\n math.cos(math.radians(theta)) * math.sin(math.radians(phi))]\\n vertices.append([radius * c for c in v])\\n\\n num_vertices = len(vertices)\\n\\n # Calculate the triangles\\n for i in range(divisions):\\n for j in range(divisions):\\n elements.append((i * (divisions + 1) + j) % num_vertices)\\n elements.append((i * (divisions + 1) + (j + 1)) % num_vertices)\\n elements.append(((i + 1) * (divisions + 1) + (j + 1)) % num_vertices)\\n elements.append(((i + 1) * (divisions + 1) + (j + 1)) % num_vertices)\\n elements.append(((i + 1) * (divisions + 1) + j) % num_vertices)\\n elements.append((i * (divisions + 1) + j) % num_vertices)\\n\\n vertices = [x for vertex in vertices for x in vertex]\\n\\n self.vertices = np.array(vertices, dtype=np.float32)\\n self.normals = np.array(vertices, dtype=np.float32)\\n self.elements = np.array(elements, dtype=np.uint16)\",\n \"def make_lattice(min,max,lattice_vectors):\\n xs = np.roll(np.arange(min[0],max[0]),max[0])\\n ys = np.roll(np.arange(min[1],max[1]),max[1])\\n lattice = np.dstack(np.meshgrid(xs,ys)).reshape(-1,2)\\n lattice = np.matmul(lattice,lattice_vectors)\\n return lattice\",\n \"def build_mesh(self):\\n vertices = []\\n indices = []\\n step = 10\\n istep = (pi * 2) / float(step)\\n for i in range(step):\\n x = 350 + cos(istep * i) * 100\\n y = 350 + sin(istep * i) * 100\\n vertices.extend([x, y, 0, 0])\\n indices.append(i)\\n return Mesh(vertices=vertices, indices=indices)\",\n \"def Triangle(points=None):\\n if points is None:\\n points = [[0, 0, 0], [1, 0, 0], [0.5, 0.5**0.5, 0]]\\n\\n if len(points) != 3:\\n raise TypeError('Points must be given as length 3 np.ndarray or list')\\n\\n check_valid_vector(points[0], 'points[0]')\\n check_valid_vector(points[1], 'points[1]')\\n check_valid_vector(points[2], 'points[2]')\\n\\n cells = np.array([[3, 0, 1, 2]])\\n return wrap(pyvista.PolyData(points, cells))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70674706","0.67971975","0.6688014","0.6595674","0.65747494","0.65282524","0.6382806","0.63815814","0.63469017","0.623005","0.62253344","0.6019658","0.60096925","0.60038024","0.6003564","0.59902227","0.59812725","0.5978787","0.59492654","0.59366006","0.5893783","0.5866787","0.58515954","0.58126247","0.580837","0.5798436","0.57900834","0.5790011","0.5785579","0.575019","0.574828","0.57439923","0.5724061","0.5721582","0.57147306","0.5710672","0.56865627","0.5684418","0.56829274","0.5680604","0.56742257","0.56491965","0.5648689","0.5633076","0.56276035","0.5621256","0.56181705","0.5608196","0.5602313","0.5599313","0.55836016","0.5582194","0.55819005","0.5580308","0.55725014","0.55709046","0.5562355","0.5560977","0.55548686","0.5553095","0.554923","0.5548568","0.55419123","0.55364716","0.55329466","0.5527376","0.5516976","0.55154985","0.55114657","0.5494781","0.5476514","0.547149","0.546739","0.5464832","0.5458176","0.5452491","0.544253","0.5436965","0.54343146","0.5428233","0.5423319","0.54027927","0.53915787","0.53896433","0.5384246","0.53814024","0.5378658","0.53781015","0.5369808","0.53630364","0.5362597","0.536168","0.5357614","0.5357451","0.53525","0.5351646","0.5350733","0.53454226","0.53347385","0.53326577"],"string":"[\n \"0.70674706\",\n \"0.67971975\",\n \"0.6688014\",\n \"0.6595674\",\n \"0.65747494\",\n \"0.65282524\",\n \"0.6382806\",\n \"0.63815814\",\n \"0.63469017\",\n \"0.623005\",\n \"0.62253344\",\n \"0.6019658\",\n \"0.60096925\",\n \"0.60038024\",\n \"0.6003564\",\n \"0.59902227\",\n \"0.59812725\",\n \"0.5978787\",\n \"0.59492654\",\n \"0.59366006\",\n \"0.5893783\",\n \"0.5866787\",\n \"0.58515954\",\n \"0.58126247\",\n \"0.580837\",\n \"0.5798436\",\n \"0.57900834\",\n \"0.5790011\",\n \"0.5785579\",\n \"0.575019\",\n \"0.574828\",\n \"0.57439923\",\n \"0.5724061\",\n \"0.5721582\",\n \"0.57147306\",\n \"0.5710672\",\n \"0.56865627\",\n \"0.5684418\",\n \"0.56829274\",\n \"0.5680604\",\n \"0.56742257\",\n \"0.56491965\",\n \"0.5648689\",\n \"0.5633076\",\n \"0.56276035\",\n \"0.5621256\",\n \"0.56181705\",\n \"0.5608196\",\n \"0.5602313\",\n \"0.5599313\",\n \"0.55836016\",\n \"0.5582194\",\n \"0.55819005\",\n \"0.5580308\",\n \"0.55725014\",\n \"0.55709046\",\n \"0.5562355\",\n \"0.5560977\",\n \"0.55548686\",\n \"0.5553095\",\n \"0.554923\",\n \"0.5548568\",\n \"0.55419123\",\n \"0.55364716\",\n \"0.55329466\",\n \"0.5527376\",\n \"0.5516976\",\n \"0.55154985\",\n \"0.55114657\",\n \"0.5494781\",\n \"0.5476514\",\n \"0.547149\",\n \"0.546739\",\n \"0.5464832\",\n \"0.5458176\",\n \"0.5452491\",\n \"0.544253\",\n \"0.5436965\",\n \"0.54343146\",\n \"0.5428233\",\n \"0.5423319\",\n \"0.54027927\",\n \"0.53915787\",\n \"0.53896433\",\n \"0.5384246\",\n \"0.53814024\",\n \"0.5378658\",\n \"0.53781015\",\n \"0.5369808\",\n \"0.53630364\",\n \"0.5362597\",\n \"0.536168\",\n \"0.5357614\",\n \"0.5357451\",\n \"0.53525\",\n \"0.5351646\",\n \"0.5350733\",\n \"0.53454226\",\n \"0.53347385\",\n \"0.53326577\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6878562"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11094,"cells":{"query":{"kind":"string","value":"Update the underlying visual."},"document":{"kind":"string","value":"def _update(self):\n if self._need_display_update:\n self._need_display_update = False\n\n self._set_view_slice(self.viewer.dims.indices)\n\n if self._need_visual_update:\n self._need_visual_update = False\n self._node.update()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_visualization(self) -> None:\n pass","def update(self):\n self.redraw()\n self._changed = False","def update(self):\n self.redraw()\n self._changed = False","def update_figure(self):\n\n self.draw()","def redraw(self):\n self.vispy_viewer.canvas.update()","def redraw(self):\n self.vispy_widget.canvas.update()","def updateVisualization(self):\n\t\tif self.visualization:\n\t\t\tif self.fixedVisualization:\n\t\t\t\tself.visualization.setFixedVisualization(self.fixedVisualization)\n\t\t\tif self.movingVisualization:\n\t\t\t\tself.visualization.setMovingVisualization(self.movingVisualization)\n\t\tself.multiRenderWidget.setVolumeVisualization(self.visualization)\n\t\tself.visualizationUpdated.emit(self.visualization)","def update_visualizer(self):\n if self.visualizer:\n if self.frame_count == 2:\n self.visualizer.add_geometry(self.vis_points)\n self.visualizer.update_geometry(self.vis_points)\n self.visualizer.poll_events()\n self.visualizer.update_renderer()\n time.sleep(0.001)\n self.frame_count += 1","def update_view(self, w: Wrapper) -> None:\n\n w.setStyleSheet(\"/* */\") # forces visual update","def redraw(self):\r\n self.c.update()","def _update_visual(self):\n\n # Check if the options are opened\n if self._is_opened:\n\n self._option_border.set_color(self.border_press)\n\n else:\n\n self._option_border.set_color((0, 0, 0, 0))","def updateDisplay(self):\n if self._displayPjt:\n self._displayPjt.updateim()\n if self._displayUsr:\n self._displayUsr.updateim()\n if self._displayVtk:\n self._displayVtk.updateim()","def update_plot():\n pass","def _refresh(self):\n self._need_display_update = True\n self._update()","def visual_attr_changed(self):\n if self.component:\n self.component.invalidate_draw()\n self.component.request_redraw()\n else:\n self.invalidate_draw()\n self.request_redraw()","def redraw(self):\n raise NotImplementedError()","def drawChanges(self):\n self.draw(wait=False)\n draw(self.values,color='yellow',bbox=None,clear=False,shrink=self.shrink)","def updateWidget(self):\n pass","def update(self):\n if (not self.ax.get_visible() or\n self.ax.figure._get_renderer() is None):\n return\n if self.useblit:\n if self.background is not None:\n self.canvas.restore_region(self.background)\n else:\n self.update_background(None)\n # We need to draw all artists, which are not included in the\n # background, therefore we also draw self._get_animated_artists()\n # and we make sure that we respect z_order\n artists = sorted(self.artists + self._get_animated_artists(),\n key=lambda a: a.get_zorder())\n for artist in artists:\n self.ax.draw_artist(artist)\n self.canvas.blit(self.ax.bbox)\n else:\n self.canvas.draw_idle()","def update(self):\r\n pygame.display.update()\r\n return","def _update_screen(self) -> None:\n # setup the source image with an alpha channel\n alpha = 255 * np.ones_like(self.image[..., 0:1])\n image = np.concatenate([self._image, alpha], axis=-1).astype(np.uint8)\n # setup the super pixel segmentations\n super_pixels = np.zeros_like(self.image)\n super_pixels = mark_boundaries(\n super_pixels,\n self._super_pixel_segments,\n self._super_pixel_color\n )\n # concatenate the first channel of sup as the alpha channel\n super_pixels = [super_pixels, super_pixels[..., 0:1]]\n super_pixels = np.concatenate(super_pixels, axis=-1).astype(np.uint8)\n # setup the segmentation image with an alpha channel scaled by the\n # opacity parameter of the application [0, 9]\n intensity = 255 * (self._opacity / 9)\n intensity = intensity * np.ones_like(self._segmentation[..., 0:1])\n segmentation = np.concatenate([self._segmentation, intensity], axis=-1)\n segmentation = segmentation.astype(np.uint8)\n # send the images to the window\n self._view.show([image, segmentation, super_pixels])","def show( self ):\n if self.changed:\n self._update_ax() \n self.changed = False","def draw(self):\n\n for item in self.vis:\n item.undraw()\n self.render()\n for item in self.vis:\n item.draw(self.win)\n self.drawn = True","def redraw(self):\n self._create()","def draw(self):\n\n # Use update instead of update_idletasks because it works better\n # on some Windows machines.\n self.root.update()","def repaint(self):\n pass","def updateGraphics():\n _root.update_idletasks()","def update_visuals(self):\n\n result, data = self.dev.grab_pipe()\n if not result:\n log.critical(\"Problem grabbing pipe\")\n\n if self.live_updates == True:\n self.update_graph(data)\n self.curve_render += 1\n self.update_image(data)\n self.check_image(self.curve_render)\n\n self.update_fps()\n self.data_timer.start(0)","def redraw(self) -> None:\n self.canvas.draw_idle()\n self.Refresh()","def refresh(self):\n self.getWindow().getDecorView().postInvalidate()","def _update_plot(self):\n\n self.T_ex[:-1] = self.T_ex[1:]\n self.T_ex[-1] = self.ensemble.T_ex\n self.plot_T_ex[0].set_ydata(self.T_ex)\n self.T_kin[:-1] = self.T_kin[1:]\n self.T_kin[-1] = self.ensemble.T_kin\n self.plot_T_kin[0].set_ydata(self.T_kin)\n self.canvas.draw()\n\n renderer = self.canvas.get_renderer()\n raw_data = renderer.tostring_rgb()\n surf = pygame.image.fromstring(raw_data,\n (self.plot_width, self.disp_height),\n \"RGB\")\n self.game_display.blit(surf, (self.disp_width, 0))","def update(self):\n self.m.update()","def refresh(self):\n\n self.ax.relim()\n self.ax.autoscale_view()\n self.canvas.draw()","def redraw_viz():\n\tglobal g_last_draw\n\tif (rospy.Time.now().to_sec() > (refresh_rate + g_last_draw)):\n\t\tg_last_draw = rospy.Time.now().to_sec()\n\t\t# redraw imu box\n\t\tdoDraw()","def update(self):\n self.grid.update()\n sleep(self.update_rate)","def refresh_HDV(self):\n self.canvas.draw()\n self.dicom_navigation.parent.dicom_right_window.top_info.canvas_HDV.draw()","def redraw(self, **kwargs):\n #src_dict = self.data_sources\n #self.remove_sources(src_dict.keys())\n self.renderers = {}\n #self.renderers = {}\n self.figure = self.draw_figure(**kwargs)\n #self.add_sources(src_dict)\n # todo does the old figure linger on?\n self.render_sources(self.data_sources)\n self.bk_pane.object = self.figure","def _DoUpdateRedraw( self, hilite = True ):\n pass","def update_info(self):\n self.execution_status_widget.update()\n self.execution_info_widget.update()\n self.cluster_widget.update() # update the cluster info even if it is not being displayed\n self.details.original_widget.update()","def update(self):\n self.__redrawChat()\n self.__redrawUsers()\n self.__redrawChannels()\n self.__update()","def redraw_figures(self):\n pass","def redraw_figures(self):\n pass","def _update_display(self, loop=True):\n\n sensors_data = self.get_sensors_data()\n\n if self.current_item.entity_type is WeatherEntityType.TEMPERATURE:\n pixels = self.current_item.show_pixels(sensors_data[0])\n elif self.current_item.entity_type is WeatherEntityType.HUMIDITY:\n pixels = self.current_item.show_pixels(sensors_data[2])\n else:\n pixels = self.current_item.show_pixels(sensors_data[3])\n\n self._sense_hat.set_rotation(self.current_style.rotation)\n self._sense_hat.set_pixels(pixels)\n\n if loop:\n self._update_timer = self._start_timer(Config.UPDATE_INTERVAL, self._update_display)","def update(self, dt):\n self.gui_manager.update(dt)","def update_rect(self):\n self._update_vertex_list()","def __update(self):\n for b in self.__borders:\n b.redraw()\n\n for w in self.__allWins:\n w.refresh()","def _update(self):\n\n if self.rgb:\n self._canvas['bg'] = tks.color_funcs.rgb_to_hex_string(self.rgb)\n self._text['text'] = self._color_info_text()\n else:\n self._canvas['bg'] = self._blank_label_color\n self._text['text'] = ''","def _DoUpdatePlot( self, wd, ht ):\n self.ax.grid(\n True, 'both', 'both',\n\tcolor = '#c8c8c8', linestyle = ':', linewidth = 1\n\t)","def redraw(self):\n self._view.delete(tk.ALL)\n self._view.draw_entities(self._world.get_all_things())\n # calculate the health and score in every step\n max_hp = self._player.get_max_health()\n current_hp = self._player.get_health()\n # if player is invincible, don't change health\n self._statue.set_health(current_hp / max_hp, self._player.get_invincible())\n self._statue.set_score(self._player.get_score())","def update(self):\n check_key_press = lambda key: self._window.was_key_pressed(key)\n frame = self._current_image.copy()\n\n for key, event in self._key_events.items():\n if check_key_press(key):\n event()\n\n for annotation in self._annotations:\n annotation.draw(frame)\n\n if self._annotation_in_progress is not None:\n self._annotation_in_progress.draw(frame)\n\n self.show_controls(frame)\n\n self._window.draw(frame)\n return not self._window.should_quit","def relayout(self): \n\t\t#self.urmaswin.Layout()\n\t\t#wx.CallAfter(self.urmaswin.Layout)\n\t\t#wx.CallAfter(self.visualizer.OnSize)","def display(self):\n\t\tself.imgDisplay.set_from_pixbuf(self.getVisible())\n\t\tgc.collect()","def plot_refresh():\n figure.canvas.draw()","def update_output_window(self):\n\n self.output_window.refresh(self.coordinate_dict['output_y'], 0, 0,\n self.coordinate_dict['output_x'] + 1,\n self.full_height_out - 1,\n self.full_width_out - 1)","def visualise(self):\n self.w = VisualizeSetupBox(self.master, self._df)\n self.master.wait_window(self.w.top)","def visualise(self, obj):\n self.clear()\n self.draw(obj)\n self.show()","def UpdateUI(self):\n # colors & font\n self.SetBackgroundColour(wx.GetApp().settings.bg_color)\n self.SetForegroundColour(wx.GetApp().settings.fg_color)\n self.SetFont(wx.GetApp().settings.text_font)\n\n # apply new (or not) 'wx' values to content.\n p = self.FindWindow(\"content\")\n if p is not None:\n p.SetBackgroundColour(wx.GetApp().settings.bg_color)\n p.SetForegroundColour(wx.GetApp().settings.fg_color)\n p.SetFont(wx.GetApp().settings.text_font)\n\n # apply new (or not) 'wx' values to header.\n p = self.FindWindow(\"header\")\n if p is not None:\n p.SetBackgroundColour(wx.GetApp().settings.header_bg_color)\n p.SetForegroundColour(wx.GetApp().settings.header_fg_color)\n p.SetFont(wx.GetApp().settings.header_text_font)\n\n # apply new (or not) 'wx' values to actions.\n p = self.FindWindow(\"actions\")\n if p is not None:\n p.SetBackgroundColour(wx.GetApp().settings.action_bg_color)\n p.SetForegroundColour(wx.GetApp().settings.action_fg_color)\n p.SetFont(wx.GetApp().settings.action_text_font)\n\n self.Refresh()","def done_paint(self):\r\n curses.panel.update_panels()\r\n curses.doupdate()","def refresh(self):\n\t\tself.win.refresh()\n\t\tfor c in self.components:\n\t\t\tc.refresh()","def _update(self):\n screen = self.screen\n if screen._tracing == 0:\n return\n elif screen._tracing == 1:\n self._update_data()\n self._drawturtle()\n screen._update() # TurtleScreenBase\n screen._delay(screen._delayvalue) # TurtleScreenBase\n else:\n self._update_data()\n if screen._updatecounter == 0:\n for t in screen.turtles():\n t._drawturtle()\n screen._update()","def transform_view(self):\n translation = self.get_translation()\n scale = self.get_scaling()\n # update all non static visuals\n for visual in self.paint_manager.get_visuals():\n if not visual.get('is_static', False):\n self.set_data(visual=visual['name'], \n scale=scale, translation=translation)","def _UpdatePlotImpl( self ):\n if self.ax is not None:\n self.axline = None\n self.cursorLine = \\\n self.cursorLine2 = None\n\n# self.ax.clear()\n# if hasattr( self, 'ax2' ) and self.ax2 is not None:\n# self.ax2.clear()\n self.fig.clear()\n self._InitAxes()\n\n#\t\t-- Scale fonts\n#\t\t--\n wd, ht = self.GetClientSize()\n label_font_size = 14\n tick_font_size = 12\n self.titleFontSize = 16\n if 'wxMac' not in wx.PlatformInfo and wd < 800:\n\tdecr = (800 - wd) / 50.0\n\tlabel_font_size -= decr\n\ttick_font_size -= decr\n\tself.titleFontSize -= decr\n\n# self.ax.grid(\n# True, 'both', 'both',\n#\t color = '#c8c8c8', linestyle = ':', linewidth = 1\n#\t )\n self._DoUpdatePlot( wd, ht )\n self._DoUpdateRedraw()\n self.canvas.draw()\n #end if","def update_rect(self):\n self._update_tiles()","def draw(self):\n self.write_image()\n self.update()","def _update_plot(self, *args):\n # type: (dict, dict, dict, dict, dict) -> None\n if len(args) != 5 and not any([isinstance(arg, dict) for arg in args]):\n raise ValueError('Illegal arguments for _update_plot of %s' % self.__name__)\n desvars, responses, objectives, constraints, metadata = args\n\n data = self._compute_new_data(desvars, responses, objectives, constraints, metadata)\n self.cs[:, self.iter] = data[:]\n self.quad.set_array(self.cs.ravel())\n self.ax.set_xlim([-.5, self.iter+.5])\n self.iter += 1","def updateScreen(self) -> None:\n\n # fill game display black\n self.surface.fill(Colors.Black)\n\n # draw players and ball\n self.drawImageOnSurface(self.player_one)\n self.drawImageOnSurface(self.player_two)\n self.drawImageOnSurface(self.ball)\n\n # draw all the spacer images\n for image in self.spacers:\n self.drawImageOnSurface(image)\n\n # draw scores and format the scores in byte representation\n self.drawTextOnSurface(format(self._score[0], \"04b\"),\n (Configuration.windowWidth / 4, Configuration.windowHeight / 2), Colors.ByteGreen,\n font=self.font)\n self.drawTextOnSurface(format(self._score[1], \"04b\"),\n (3 * Configuration.windowWidth / 4, Configuration.windowHeight / 2), Colors.ByteGreen,\n font=self.font)\n\n super().updateScreen() # call the parent method to update the screen","def viewUpdate(self):\n # Update Capture\n imgtk = self.model.capture\n self.updateImage(self.view.lmain, imgtk)\n # Update Stitch \n imgtk = self.model.stitch\n self.updateImage(self.view.rmain, imgtk)\n self.view.dist.set(self.model.dist)","def update(self):\n tracing = self._tracing\n self._tracing = True\n for t in self.turtles():\n t._update_data()\n t._drawturtle()\n self._tracing = tracing\n self._update()","def update_plot_preview(self):\n\n min_val = self.range_slider.first_position\n max_val = self.range_slider.second_position\n img_data = self.normalize_histogram(min_val, max_val)\n new_hist = self.calc_histogram(img_data)\n\n self.hist_canvas.axes.clear()\n self.hist_canvas.axes.bar(range(256), self.original_hist, color='b', alpha=0.7)\n self.hist_canvas.axes.bar(range(256), new_hist, color='g', alpha=0.7)\n self.hist_canvas.draw()\n\n self.current_img_data = img_data","def updateDraw(self):\r\n self.delConns()\r\n self.delTags()\r\n self.drawConns()\r\n self.drawTags()","def _update_screen(self):\n self.screen.fill(self.bg_colour)\n\n if not self.waiting:\n self._check_cells()\n self._update_cells()\n for row in self.cells:\n for cell in row:\n cell.draw_cell()\n \n pygame.display.flip()","def update(self):\n # Find only unmasked data :\n xyz, sData, sColor, _ = self._select_unmasked()\n # xyz, sData, sColor = self.xyz, self.sData, self.sColor\n\n # Render as cloud points :\n if xyz.size:\n self.mesh.visible = True\n self.mesh.set_data(xyz, edge_color=self.edgecolor, size=sData,\n face_color=sColor, scaling=self.scaling,\n edge_width=self.edgewidth, symbol=self.symbol)\n # self.mesh.transform = self.transform\n self.mesh.update()\n else:\n self.mesh.visible = False","def redraw(self):\n self.scene.redraw()\n self.SwapBuffers()","def _update_screen(self):\n self.screen.fill(self.settings.bg_color)\n self.ship.blitme()\n for bullet in self.bullets.sprites():\n bullet.draw_bullet()\n self.aliens.draw(self.screen)\n\n # Draw score information\n self.scoreboard.show_score()\n\n if not self.stats.game_active:\n self.play_button.draw_button()","def _update_current_graph(self, **kwargs):\n\n self.current_graph.redraw()","def update(self):\n self.rect.x += self.change_x\n self.rect.y += self.change_y","def update(self):\n self.rect.x += self.change_x\n self.rect.y += self.change_y","def _update_screen(self):\n\t\tself.screen.fill(self.settings.bg_color)\n\t\tself.pigeon.blitme()\n\t\tfor dropping in self.droppings.sprites():\n\t\t\tdropping.draw_dropping()\n\t\tself.autos.draw(self.screen)\n\n\t\t# Draw the score information.\n\t\tself.sb.show_score()\n\n\t\t# Draw the play button if the game is inactive.\n\t\tif not self.stats.game_active:\n\t\t\tself.play_button.draw_button()\n\n\t\t# Make the most recently drawn screen visible.\n\t\tpygame.display.flip()","def update(self):\n self.rect = (self.x, self.y, self.width, self.height)","def Update(self):\n\n # note this function should be overloaded; this line is simply\n # indicating that the overloaded function should normally call\n # UpdateViews before returning.\n self.UpdateViews()","def update(self):\n self.backbone_module.update()\n mx.nd.waitall()","def _redraw(self):\n\n\t\tif not self._has_gui:\n\t\t\treturn\n\n\t\trows, _ = os.popen('stty size', 'r').read().split()\n\t\tself._gui_size = int(rows)\n\n\t\tif self._gui_output is None or len(self._gui_output) != self._gui_size:\n\t\t\tself._gui_output = [[\"\" for _ in range(0, self._gui_size)] for _ in range(0, self._gui_size)]\n\n\t\tself._update_output()\n\n\t\tos.system(\"clear\")\n\n\t\t# pylint: disable-msg=C0103; (Invalid variable names x, y)\n\t\t# We draw from top left (0,49) to bottom right (49,0)\n\t\tfor y in range(self._gui_size - 1, -1, -1):\n\t\t\tline_output = \"\"\n\t\t\tfor x in range(0, self._gui_size):\n\t\t\t\tline_output += self._gui_output[x][y] + \" \"\n\t\t\tprint(line_output)","def _update_output(self):\n\n\t\tscale = min(self.get_height(), float(self.get_height()) / self._gui_size)\n\n\t\t# pylint: disable-msg=C0103; (Invalid variable names x, y)\n\t\tfor x in range(0, self._gui_size):\n\t\t\tfor y in range(0, self._gui_size):\n\t\t\t\tself._gui_output[x][y] = self._get_output_letter(\n\t\t\t\t\tself._background[int(x * scale)][int(y * scale)])\n\n\t\tfor name, turtle in self._turtles.items():\n\t\t\ttrt_x = int(turtle.pos.x / scale)\n\t\t\ttrt_y = int(turtle.pos.y / scale)\n\t\t\tfor x in range(max(trt_x-1, 0), min(trt_x+2, self._gui_size)):\n\t\t\t\tfor y in range(max(trt_y-1, 0), min(trt_y+2, self._gui_size)):\n\t\t\t\t\tself._gui_output[x][y] = \" \"\n\n\t\t\tself._gui_output[trt_x][trt_y] = str(self._turtles.keys().index(name) + 1)","def update(self):\n cv2.imshow(self.window_name, self.map.get_crop())","def update_image(self, surface):\n self.ui_widget.update_image(surface=surface)","def updatePlot(self):\n self.axes.clear()\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='blue')\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='red')\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='gray')\n self.canvas.draw()","def draw(self):\n self.figure.show()\n self.figure.canvas.draw()","def redraw(self):\n dummy_figure = plt.figure()\n new_manager = dummy_figure.canvas.manager\n new_manager.canvas.figure = self.figure\n self.figure.set_canvas(new_manager.canvas)\n plt.show(block=False)","def _update_display(self, game, action):\n canvas_coord = self._havannah_coord_to_canvas_coord(action.coord)\n canvas_color = self._havannah_color_to_canvas_color(action.color)\n self._draw_hex(canvas_coord, canvas_color)","def updatePlot(self):\n self.axes.clear()\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\n self.canvas.draw()","def updatePlot(self):\n self.axes.clear()\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\n self.canvas.draw()","def updatePlot(self):\n self.axes.clear()\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\n self.canvas.draw()","def updatePlot(self):\n self.axes.clear()\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\n self.canvas.draw()","def updatePlot(self):\n self.axes.clear()\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\n self.canvas.draw()","def update_current_image(self):\n\n rect = (0, 0, self.variables.canvas_width, self.variables.canvas_height)\n if self.variables.canvas_image_object is not None:\n self.variables.canvas_image_object.update_canvas_display_image_from_canvas_rect(rect)\n self.set_image_from_numpy_array(self.variables.canvas_image_object.display_image)\n self.update()","def refresh_svg_canvas(self):\n if self.ui.tabWidget.currentIndex() == 0:\n self.ui.svg_canvas.build_schematic()\n self.ui.svg_canvas.viewport().update()\n elif self.ui.tabWidget.currentIndex() in (1,2):\n self.ui.svg_canvas.build_pcb()\n self.ui.svg_canvas.viewport().update()\n else:\n raise Exception(\"Unknown view to draw\")","def draw(self):\n self.figure.canvas.draw_idle()","def refresh_view(self):\n if self._step_number % 2 == 0:\n self._view.draw_enemies(self._game.enemies)\n self._view.draw_towers(self._game.towers)\n self._view.draw_obstacles(self._game.obstacles)","def _update_moved(self):\n self._RAS_textbox.setPlainText('{:.2f}, {:.2f}, {:.2f}'.format(\n *self._ras))\n self._VOX_textbox.setPlainText('{:3d}, {:3d}, {:3d}'.format(\n *self._current_slice))\n self._intensity_label.setText('intensity = {:.2f}'.format(\n self._base_data[tuple(self._current_slice)]))","def _update(self):\n self.cv.update()"],"string":"[\n \"def update_visualization(self) -> None:\\n pass\",\n \"def update(self):\\n self.redraw()\\n self._changed = False\",\n \"def update(self):\\n self.redraw()\\n self._changed = False\",\n \"def update_figure(self):\\n\\n self.draw()\",\n \"def redraw(self):\\n self.vispy_viewer.canvas.update()\",\n \"def redraw(self):\\n self.vispy_widget.canvas.update()\",\n \"def updateVisualization(self):\\n\\t\\tif self.visualization:\\n\\t\\t\\tif self.fixedVisualization:\\n\\t\\t\\t\\tself.visualization.setFixedVisualization(self.fixedVisualization)\\n\\t\\t\\tif self.movingVisualization:\\n\\t\\t\\t\\tself.visualization.setMovingVisualization(self.movingVisualization)\\n\\t\\tself.multiRenderWidget.setVolumeVisualization(self.visualization)\\n\\t\\tself.visualizationUpdated.emit(self.visualization)\",\n \"def update_visualizer(self):\\n if self.visualizer:\\n if self.frame_count == 2:\\n self.visualizer.add_geometry(self.vis_points)\\n self.visualizer.update_geometry(self.vis_points)\\n self.visualizer.poll_events()\\n self.visualizer.update_renderer()\\n time.sleep(0.001)\\n self.frame_count += 1\",\n \"def update_view(self, w: Wrapper) -> None:\\n\\n w.setStyleSheet(\\\"/* */\\\") # forces visual update\",\n \"def redraw(self):\\r\\n self.c.update()\",\n \"def _update_visual(self):\\n\\n # Check if the options are opened\\n if self._is_opened:\\n\\n self._option_border.set_color(self.border_press)\\n\\n else:\\n\\n self._option_border.set_color((0, 0, 0, 0))\",\n \"def updateDisplay(self):\\n if self._displayPjt:\\n self._displayPjt.updateim()\\n if self._displayUsr:\\n self._displayUsr.updateim()\\n if self._displayVtk:\\n self._displayVtk.updateim()\",\n \"def update_plot():\\n pass\",\n \"def _refresh(self):\\n self._need_display_update = True\\n self._update()\",\n \"def visual_attr_changed(self):\\n if self.component:\\n self.component.invalidate_draw()\\n self.component.request_redraw()\\n else:\\n self.invalidate_draw()\\n self.request_redraw()\",\n \"def redraw(self):\\n raise NotImplementedError()\",\n \"def drawChanges(self):\\n self.draw(wait=False)\\n draw(self.values,color='yellow',bbox=None,clear=False,shrink=self.shrink)\",\n \"def updateWidget(self):\\n pass\",\n \"def update(self):\\n if (not self.ax.get_visible() or\\n self.ax.figure._get_renderer() is None):\\n return\\n if self.useblit:\\n if self.background is not None:\\n self.canvas.restore_region(self.background)\\n else:\\n self.update_background(None)\\n # We need to draw all artists, which are not included in the\\n # background, therefore we also draw self._get_animated_artists()\\n # and we make sure that we respect z_order\\n artists = sorted(self.artists + self._get_animated_artists(),\\n key=lambda a: a.get_zorder())\\n for artist in artists:\\n self.ax.draw_artist(artist)\\n self.canvas.blit(self.ax.bbox)\\n else:\\n self.canvas.draw_idle()\",\n \"def update(self):\\r\\n pygame.display.update()\\r\\n return\",\n \"def _update_screen(self) -> None:\\n # setup the source image with an alpha channel\\n alpha = 255 * np.ones_like(self.image[..., 0:1])\\n image = np.concatenate([self._image, alpha], axis=-1).astype(np.uint8)\\n # setup the super pixel segmentations\\n super_pixels = np.zeros_like(self.image)\\n super_pixels = mark_boundaries(\\n super_pixels,\\n self._super_pixel_segments,\\n self._super_pixel_color\\n )\\n # concatenate the first channel of sup as the alpha channel\\n super_pixels = [super_pixels, super_pixels[..., 0:1]]\\n super_pixels = np.concatenate(super_pixels, axis=-1).astype(np.uint8)\\n # setup the segmentation image with an alpha channel scaled by the\\n # opacity parameter of the application [0, 9]\\n intensity = 255 * (self._opacity / 9)\\n intensity = intensity * np.ones_like(self._segmentation[..., 0:1])\\n segmentation = np.concatenate([self._segmentation, intensity], axis=-1)\\n segmentation = segmentation.astype(np.uint8)\\n # send the images to the window\\n self._view.show([image, segmentation, super_pixels])\",\n \"def show( self ):\\n if self.changed:\\n self._update_ax() \\n self.changed = False\",\n \"def draw(self):\\n\\n for item in self.vis:\\n item.undraw()\\n self.render()\\n for item in self.vis:\\n item.draw(self.win)\\n self.drawn = True\",\n \"def redraw(self):\\n self._create()\",\n \"def draw(self):\\n\\n # Use update instead of update_idletasks because it works better\\n # on some Windows machines.\\n self.root.update()\",\n \"def repaint(self):\\n pass\",\n \"def updateGraphics():\\n _root.update_idletasks()\",\n \"def update_visuals(self):\\n\\n result, data = self.dev.grab_pipe()\\n if not result:\\n log.critical(\\\"Problem grabbing pipe\\\")\\n\\n if self.live_updates == True:\\n self.update_graph(data)\\n self.curve_render += 1\\n self.update_image(data)\\n self.check_image(self.curve_render)\\n\\n self.update_fps()\\n self.data_timer.start(0)\",\n \"def redraw(self) -> None:\\n self.canvas.draw_idle()\\n self.Refresh()\",\n \"def refresh(self):\\n self.getWindow().getDecorView().postInvalidate()\",\n \"def _update_plot(self):\\n\\n self.T_ex[:-1] = self.T_ex[1:]\\n self.T_ex[-1] = self.ensemble.T_ex\\n self.plot_T_ex[0].set_ydata(self.T_ex)\\n self.T_kin[:-1] = self.T_kin[1:]\\n self.T_kin[-1] = self.ensemble.T_kin\\n self.plot_T_kin[0].set_ydata(self.T_kin)\\n self.canvas.draw()\\n\\n renderer = self.canvas.get_renderer()\\n raw_data = renderer.tostring_rgb()\\n surf = pygame.image.fromstring(raw_data,\\n (self.plot_width, self.disp_height),\\n \\\"RGB\\\")\\n self.game_display.blit(surf, (self.disp_width, 0))\",\n \"def update(self):\\n self.m.update()\",\n \"def refresh(self):\\n\\n self.ax.relim()\\n self.ax.autoscale_view()\\n self.canvas.draw()\",\n \"def redraw_viz():\\n\\tglobal g_last_draw\\n\\tif (rospy.Time.now().to_sec() > (refresh_rate + g_last_draw)):\\n\\t\\tg_last_draw = rospy.Time.now().to_sec()\\n\\t\\t# redraw imu box\\n\\t\\tdoDraw()\",\n \"def update(self):\\n self.grid.update()\\n sleep(self.update_rate)\",\n \"def refresh_HDV(self):\\n self.canvas.draw()\\n self.dicom_navigation.parent.dicom_right_window.top_info.canvas_HDV.draw()\",\n \"def redraw(self, **kwargs):\\n #src_dict = self.data_sources\\n #self.remove_sources(src_dict.keys())\\n self.renderers = {}\\n #self.renderers = {}\\n self.figure = self.draw_figure(**kwargs)\\n #self.add_sources(src_dict)\\n # todo does the old figure linger on?\\n self.render_sources(self.data_sources)\\n self.bk_pane.object = self.figure\",\n \"def _DoUpdateRedraw( self, hilite = True ):\\n pass\",\n \"def update_info(self):\\n self.execution_status_widget.update()\\n self.execution_info_widget.update()\\n self.cluster_widget.update() # update the cluster info even if it is not being displayed\\n self.details.original_widget.update()\",\n \"def update(self):\\n self.__redrawChat()\\n self.__redrawUsers()\\n self.__redrawChannels()\\n self.__update()\",\n \"def redraw_figures(self):\\n pass\",\n \"def redraw_figures(self):\\n pass\",\n \"def _update_display(self, loop=True):\\n\\n sensors_data = self.get_sensors_data()\\n\\n if self.current_item.entity_type is WeatherEntityType.TEMPERATURE:\\n pixels = self.current_item.show_pixels(sensors_data[0])\\n elif self.current_item.entity_type is WeatherEntityType.HUMIDITY:\\n pixels = self.current_item.show_pixels(sensors_data[2])\\n else:\\n pixels = self.current_item.show_pixels(sensors_data[3])\\n\\n self._sense_hat.set_rotation(self.current_style.rotation)\\n self._sense_hat.set_pixels(pixels)\\n\\n if loop:\\n self._update_timer = self._start_timer(Config.UPDATE_INTERVAL, self._update_display)\",\n \"def update(self, dt):\\n self.gui_manager.update(dt)\",\n \"def update_rect(self):\\n self._update_vertex_list()\",\n \"def __update(self):\\n for b in self.__borders:\\n b.redraw()\\n\\n for w in self.__allWins:\\n w.refresh()\",\n \"def _update(self):\\n\\n if self.rgb:\\n self._canvas['bg'] = tks.color_funcs.rgb_to_hex_string(self.rgb)\\n self._text['text'] = self._color_info_text()\\n else:\\n self._canvas['bg'] = self._blank_label_color\\n self._text['text'] = ''\",\n \"def _DoUpdatePlot( self, wd, ht ):\\n self.ax.grid(\\n True, 'both', 'both',\\n\\tcolor = '#c8c8c8', linestyle = ':', linewidth = 1\\n\\t)\",\n \"def redraw(self):\\n self._view.delete(tk.ALL)\\n self._view.draw_entities(self._world.get_all_things())\\n # calculate the health and score in every step\\n max_hp = self._player.get_max_health()\\n current_hp = self._player.get_health()\\n # if player is invincible, don't change health\\n self._statue.set_health(current_hp / max_hp, self._player.get_invincible())\\n self._statue.set_score(self._player.get_score())\",\n \"def update(self):\\n check_key_press = lambda key: self._window.was_key_pressed(key)\\n frame = self._current_image.copy()\\n\\n for key, event in self._key_events.items():\\n if check_key_press(key):\\n event()\\n\\n for annotation in self._annotations:\\n annotation.draw(frame)\\n\\n if self._annotation_in_progress is not None:\\n self._annotation_in_progress.draw(frame)\\n\\n self.show_controls(frame)\\n\\n self._window.draw(frame)\\n return not self._window.should_quit\",\n \"def relayout(self): \\n\\t\\t#self.urmaswin.Layout()\\n\\t\\t#wx.CallAfter(self.urmaswin.Layout)\\n\\t\\t#wx.CallAfter(self.visualizer.OnSize)\",\n \"def display(self):\\n\\t\\tself.imgDisplay.set_from_pixbuf(self.getVisible())\\n\\t\\tgc.collect()\",\n \"def plot_refresh():\\n figure.canvas.draw()\",\n \"def update_output_window(self):\\n\\n self.output_window.refresh(self.coordinate_dict['output_y'], 0, 0,\\n self.coordinate_dict['output_x'] + 1,\\n self.full_height_out - 1,\\n self.full_width_out - 1)\",\n \"def visualise(self):\\n self.w = VisualizeSetupBox(self.master, self._df)\\n self.master.wait_window(self.w.top)\",\n \"def visualise(self, obj):\\n self.clear()\\n self.draw(obj)\\n self.show()\",\n \"def UpdateUI(self):\\n # colors & font\\n self.SetBackgroundColour(wx.GetApp().settings.bg_color)\\n self.SetForegroundColour(wx.GetApp().settings.fg_color)\\n self.SetFont(wx.GetApp().settings.text_font)\\n\\n # apply new (or not) 'wx' values to content.\\n p = self.FindWindow(\\\"content\\\")\\n if p is not None:\\n p.SetBackgroundColour(wx.GetApp().settings.bg_color)\\n p.SetForegroundColour(wx.GetApp().settings.fg_color)\\n p.SetFont(wx.GetApp().settings.text_font)\\n\\n # apply new (or not) 'wx' values to header.\\n p = self.FindWindow(\\\"header\\\")\\n if p is not None:\\n p.SetBackgroundColour(wx.GetApp().settings.header_bg_color)\\n p.SetForegroundColour(wx.GetApp().settings.header_fg_color)\\n p.SetFont(wx.GetApp().settings.header_text_font)\\n\\n # apply new (or not) 'wx' values to actions.\\n p = self.FindWindow(\\\"actions\\\")\\n if p is not None:\\n p.SetBackgroundColour(wx.GetApp().settings.action_bg_color)\\n p.SetForegroundColour(wx.GetApp().settings.action_fg_color)\\n p.SetFont(wx.GetApp().settings.action_text_font)\\n\\n self.Refresh()\",\n \"def done_paint(self):\\r\\n curses.panel.update_panels()\\r\\n curses.doupdate()\",\n \"def refresh(self):\\n\\t\\tself.win.refresh()\\n\\t\\tfor c in self.components:\\n\\t\\t\\tc.refresh()\",\n \"def _update(self):\\n screen = self.screen\\n if screen._tracing == 0:\\n return\\n elif screen._tracing == 1:\\n self._update_data()\\n self._drawturtle()\\n screen._update() # TurtleScreenBase\\n screen._delay(screen._delayvalue) # TurtleScreenBase\\n else:\\n self._update_data()\\n if screen._updatecounter == 0:\\n for t in screen.turtles():\\n t._drawturtle()\\n screen._update()\",\n \"def transform_view(self):\\n translation = self.get_translation()\\n scale = self.get_scaling()\\n # update all non static visuals\\n for visual in self.paint_manager.get_visuals():\\n if not visual.get('is_static', False):\\n self.set_data(visual=visual['name'], \\n scale=scale, translation=translation)\",\n \"def _UpdatePlotImpl( self ):\\n if self.ax is not None:\\n self.axline = None\\n self.cursorLine = \\\\\\n self.cursorLine2 = None\\n\\n# self.ax.clear()\\n# if hasattr( self, 'ax2' ) and self.ax2 is not None:\\n# self.ax2.clear()\\n self.fig.clear()\\n self._InitAxes()\\n\\n#\\t\\t-- Scale fonts\\n#\\t\\t--\\n wd, ht = self.GetClientSize()\\n label_font_size = 14\\n tick_font_size = 12\\n self.titleFontSize = 16\\n if 'wxMac' not in wx.PlatformInfo and wd < 800:\\n\\tdecr = (800 - wd) / 50.0\\n\\tlabel_font_size -= decr\\n\\ttick_font_size -= decr\\n\\tself.titleFontSize -= decr\\n\\n# self.ax.grid(\\n# True, 'both', 'both',\\n#\\t color = '#c8c8c8', linestyle = ':', linewidth = 1\\n#\\t )\\n self._DoUpdatePlot( wd, ht )\\n self._DoUpdateRedraw()\\n self.canvas.draw()\\n #end if\",\n \"def update_rect(self):\\n self._update_tiles()\",\n \"def draw(self):\\n self.write_image()\\n self.update()\",\n \"def _update_plot(self, *args):\\n # type: (dict, dict, dict, dict, dict) -> None\\n if len(args) != 5 and not any([isinstance(arg, dict) for arg in args]):\\n raise ValueError('Illegal arguments for _update_plot of %s' % self.__name__)\\n desvars, responses, objectives, constraints, metadata = args\\n\\n data = self._compute_new_data(desvars, responses, objectives, constraints, metadata)\\n self.cs[:, self.iter] = data[:]\\n self.quad.set_array(self.cs.ravel())\\n self.ax.set_xlim([-.5, self.iter+.5])\\n self.iter += 1\",\n \"def updateScreen(self) -> None:\\n\\n # fill game display black\\n self.surface.fill(Colors.Black)\\n\\n # draw players and ball\\n self.drawImageOnSurface(self.player_one)\\n self.drawImageOnSurface(self.player_two)\\n self.drawImageOnSurface(self.ball)\\n\\n # draw all the spacer images\\n for image in self.spacers:\\n self.drawImageOnSurface(image)\\n\\n # draw scores and format the scores in byte representation\\n self.drawTextOnSurface(format(self._score[0], \\\"04b\\\"),\\n (Configuration.windowWidth / 4, Configuration.windowHeight / 2), Colors.ByteGreen,\\n font=self.font)\\n self.drawTextOnSurface(format(self._score[1], \\\"04b\\\"),\\n (3 * Configuration.windowWidth / 4, Configuration.windowHeight / 2), Colors.ByteGreen,\\n font=self.font)\\n\\n super().updateScreen() # call the parent method to update the screen\",\n \"def viewUpdate(self):\\n # Update Capture\\n imgtk = self.model.capture\\n self.updateImage(self.view.lmain, imgtk)\\n # Update Stitch \\n imgtk = self.model.stitch\\n self.updateImage(self.view.rmain, imgtk)\\n self.view.dist.set(self.model.dist)\",\n \"def update(self):\\n tracing = self._tracing\\n self._tracing = True\\n for t in self.turtles():\\n t._update_data()\\n t._drawturtle()\\n self._tracing = tracing\\n self._update()\",\n \"def update_plot_preview(self):\\n\\n min_val = self.range_slider.first_position\\n max_val = self.range_slider.second_position\\n img_data = self.normalize_histogram(min_val, max_val)\\n new_hist = self.calc_histogram(img_data)\\n\\n self.hist_canvas.axes.clear()\\n self.hist_canvas.axes.bar(range(256), self.original_hist, color='b', alpha=0.7)\\n self.hist_canvas.axes.bar(range(256), new_hist, color='g', alpha=0.7)\\n self.hist_canvas.draw()\\n\\n self.current_img_data = img_data\",\n \"def updateDraw(self):\\r\\n self.delConns()\\r\\n self.delTags()\\r\\n self.drawConns()\\r\\n self.drawTags()\",\n \"def _update_screen(self):\\n self.screen.fill(self.bg_colour)\\n\\n if not self.waiting:\\n self._check_cells()\\n self._update_cells()\\n for row in self.cells:\\n for cell in row:\\n cell.draw_cell()\\n \\n pygame.display.flip()\",\n \"def update(self):\\n # Find only unmasked data :\\n xyz, sData, sColor, _ = self._select_unmasked()\\n # xyz, sData, sColor = self.xyz, self.sData, self.sColor\\n\\n # Render as cloud points :\\n if xyz.size:\\n self.mesh.visible = True\\n self.mesh.set_data(xyz, edge_color=self.edgecolor, size=sData,\\n face_color=sColor, scaling=self.scaling,\\n edge_width=self.edgewidth, symbol=self.symbol)\\n # self.mesh.transform = self.transform\\n self.mesh.update()\\n else:\\n self.mesh.visible = False\",\n \"def redraw(self):\\n self.scene.redraw()\\n self.SwapBuffers()\",\n \"def _update_screen(self):\\n self.screen.fill(self.settings.bg_color)\\n self.ship.blitme()\\n for bullet in self.bullets.sprites():\\n bullet.draw_bullet()\\n self.aliens.draw(self.screen)\\n\\n # Draw score information\\n self.scoreboard.show_score()\\n\\n if not self.stats.game_active:\\n self.play_button.draw_button()\",\n \"def _update_current_graph(self, **kwargs):\\n\\n self.current_graph.redraw()\",\n \"def update(self):\\n self.rect.x += self.change_x\\n self.rect.y += self.change_y\",\n \"def update(self):\\n self.rect.x += self.change_x\\n self.rect.y += self.change_y\",\n \"def _update_screen(self):\\n\\t\\tself.screen.fill(self.settings.bg_color)\\n\\t\\tself.pigeon.blitme()\\n\\t\\tfor dropping in self.droppings.sprites():\\n\\t\\t\\tdropping.draw_dropping()\\n\\t\\tself.autos.draw(self.screen)\\n\\n\\t\\t# Draw the score information.\\n\\t\\tself.sb.show_score()\\n\\n\\t\\t# Draw the play button if the game is inactive.\\n\\t\\tif not self.stats.game_active:\\n\\t\\t\\tself.play_button.draw_button()\\n\\n\\t\\t# Make the most recently drawn screen visible.\\n\\t\\tpygame.display.flip()\",\n \"def update(self):\\n self.rect = (self.x, self.y, self.width, self.height)\",\n \"def Update(self):\\n\\n # note this function should be overloaded; this line is simply\\n # indicating that the overloaded function should normally call\\n # UpdateViews before returning.\\n self.UpdateViews()\",\n \"def update(self):\\n self.backbone_module.update()\\n mx.nd.waitall()\",\n \"def _redraw(self):\\n\\n\\t\\tif not self._has_gui:\\n\\t\\t\\treturn\\n\\n\\t\\trows, _ = os.popen('stty size', 'r').read().split()\\n\\t\\tself._gui_size = int(rows)\\n\\n\\t\\tif self._gui_output is None or len(self._gui_output) != self._gui_size:\\n\\t\\t\\tself._gui_output = [[\\\"\\\" for _ in range(0, self._gui_size)] for _ in range(0, self._gui_size)]\\n\\n\\t\\tself._update_output()\\n\\n\\t\\tos.system(\\\"clear\\\")\\n\\n\\t\\t# pylint: disable-msg=C0103; (Invalid variable names x, y)\\n\\t\\t# We draw from top left (0,49) to bottom right (49,0)\\n\\t\\tfor y in range(self._gui_size - 1, -1, -1):\\n\\t\\t\\tline_output = \\\"\\\"\\n\\t\\t\\tfor x in range(0, self._gui_size):\\n\\t\\t\\t\\tline_output += self._gui_output[x][y] + \\\" \\\"\\n\\t\\t\\tprint(line_output)\",\n \"def _update_output(self):\\n\\n\\t\\tscale = min(self.get_height(), float(self.get_height()) / self._gui_size)\\n\\n\\t\\t# pylint: disable-msg=C0103; (Invalid variable names x, y)\\n\\t\\tfor x in range(0, self._gui_size):\\n\\t\\t\\tfor y in range(0, self._gui_size):\\n\\t\\t\\t\\tself._gui_output[x][y] = self._get_output_letter(\\n\\t\\t\\t\\t\\tself._background[int(x * scale)][int(y * scale)])\\n\\n\\t\\tfor name, turtle in self._turtles.items():\\n\\t\\t\\ttrt_x = int(turtle.pos.x / scale)\\n\\t\\t\\ttrt_y = int(turtle.pos.y / scale)\\n\\t\\t\\tfor x in range(max(trt_x-1, 0), min(trt_x+2, self._gui_size)):\\n\\t\\t\\t\\tfor y in range(max(trt_y-1, 0), min(trt_y+2, self._gui_size)):\\n\\t\\t\\t\\t\\tself._gui_output[x][y] = \\\" \\\"\\n\\n\\t\\t\\tself._gui_output[trt_x][trt_y] = str(self._turtles.keys().index(name) + 1)\",\n \"def update(self):\\n cv2.imshow(self.window_name, self.map.get_crop())\",\n \"def update_image(self, surface):\\n self.ui_widget.update_image(surface=surface)\",\n \"def updatePlot(self):\\n self.axes.clear()\\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='blue')\\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='red')\\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='gray')\\n self.canvas.draw()\",\n \"def draw(self):\\n self.figure.show()\\n self.figure.canvas.draw()\",\n \"def redraw(self):\\n dummy_figure = plt.figure()\\n new_manager = dummy_figure.canvas.manager\\n new_manager.canvas.figure = self.figure\\n self.figure.set_canvas(new_manager.canvas)\\n plt.show(block=False)\",\n \"def _update_display(self, game, action):\\n canvas_coord = self._havannah_coord_to_canvas_coord(action.coord)\\n canvas_color = self._havannah_color_to_canvas_color(action.color)\\n self._draw_hex(canvas_coord, canvas_color)\",\n \"def updatePlot(self):\\n self.axes.clear()\\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\\n self.canvas.draw()\",\n \"def updatePlot(self):\\n self.axes.clear()\\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\\n self.canvas.draw()\",\n \"def updatePlot(self):\\n self.axes.clear()\\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\\n self.canvas.draw()\",\n \"def updatePlot(self):\\n self.axes.clear()\\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\\n self.canvas.draw()\",\n \"def updatePlot(self):\\n self.axes.clear()\\n self.axes.plot(self.data[0], self.data[1], linestyle='-', color='gray')\\n self.axes.plot(self.data[0], self.data[2], linestyle='-', color='blue')\\n self.axes.plot(self.data[0], self.data[3], linestyle='-', color='darkgreen')\\n self.canvas.draw()\",\n \"def update_current_image(self):\\n\\n rect = (0, 0, self.variables.canvas_width, self.variables.canvas_height)\\n if self.variables.canvas_image_object is not None:\\n self.variables.canvas_image_object.update_canvas_display_image_from_canvas_rect(rect)\\n self.set_image_from_numpy_array(self.variables.canvas_image_object.display_image)\\n self.update()\",\n \"def refresh_svg_canvas(self):\\n if self.ui.tabWidget.currentIndex() == 0:\\n self.ui.svg_canvas.build_schematic()\\n self.ui.svg_canvas.viewport().update()\\n elif self.ui.tabWidget.currentIndex() in (1,2):\\n self.ui.svg_canvas.build_pcb()\\n self.ui.svg_canvas.viewport().update()\\n else:\\n raise Exception(\\\"Unknown view to draw\\\")\",\n \"def draw(self):\\n self.figure.canvas.draw_idle()\",\n \"def refresh_view(self):\\n if self._step_number % 2 == 0:\\n self._view.draw_enemies(self._game.enemies)\\n self._view.draw_towers(self._game.towers)\\n self._view.draw_obstacles(self._game.obstacles)\",\n \"def _update_moved(self):\\n self._RAS_textbox.setPlainText('{:.2f}, {:.2f}, {:.2f}'.format(\\n *self._ras))\\n self._VOX_textbox.setPlainText('{:3d}, {:3d}, {:3d}'.format(\\n *self._current_slice))\\n self._intensity_label.setText('intensity = {:.2f}'.format(\\n self._base_data[tuple(self._current_slice)]))\",\n \"def _update(self):\\n self.cv.update()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80167747","0.767551","0.767551","0.7650333","0.7572356","0.7398728","0.735658","0.73552585","0.7344015","0.7229653","0.71321124","0.7061494","0.70430195","0.7035277","0.70231766","0.69140226","0.68243873","0.6794981","0.67679983","0.6736001","0.6723893","0.67235345","0.6708468","0.66621554","0.6647754","0.6608382","0.6535725","0.65202296","0.65187967","0.6515924","0.65002674","0.6493028","0.647825","0.6435215","0.6425775","0.64236015","0.63995653","0.6395728","0.6375719","0.63627464","0.6357176","0.6357176","0.6338704","0.63307345","0.6325092","0.6304869","0.62891066","0.62817943","0.6271338","0.62537986","0.6239169","0.6233556","0.6223152","0.62226313","0.6219013","0.62132525","0.6188235","0.6186998","0.61865807","0.61854476","0.61841327","0.61805737","0.61789274","0.6175896","0.6175611","0.61709046","0.616927","0.61682904","0.61662126","0.61532754","0.61480045","0.61420774","0.6130784","0.6125804","0.6122612","0.611851","0.611851","0.6106999","0.6104948","0.6104027","0.6099099","0.60990757","0.6098676","0.609427","0.60912544","0.6074762","0.60744816","0.60722655","0.60718334","0.6057083","0.6057083","0.6057083","0.6057083","0.6057083","0.60522574","0.60465586","0.60464334","0.6044894","0.603919","0.6037908"],"string":"[\n \"0.80167747\",\n \"0.767551\",\n \"0.767551\",\n \"0.7650333\",\n \"0.7572356\",\n \"0.7398728\",\n \"0.735658\",\n \"0.73552585\",\n \"0.7344015\",\n \"0.7229653\",\n \"0.71321124\",\n \"0.7061494\",\n \"0.70430195\",\n \"0.7035277\",\n \"0.70231766\",\n \"0.69140226\",\n \"0.68243873\",\n \"0.6794981\",\n \"0.67679983\",\n \"0.6736001\",\n \"0.6723893\",\n \"0.67235345\",\n \"0.6708468\",\n \"0.66621554\",\n \"0.6647754\",\n \"0.6608382\",\n \"0.6535725\",\n \"0.65202296\",\n \"0.65187967\",\n \"0.6515924\",\n \"0.65002674\",\n \"0.6493028\",\n \"0.647825\",\n \"0.6435215\",\n \"0.6425775\",\n \"0.64236015\",\n \"0.63995653\",\n \"0.6395728\",\n \"0.6375719\",\n \"0.63627464\",\n \"0.6357176\",\n \"0.6357176\",\n \"0.6338704\",\n \"0.63307345\",\n \"0.6325092\",\n \"0.6304869\",\n \"0.62891066\",\n \"0.62817943\",\n \"0.6271338\",\n \"0.62537986\",\n \"0.6239169\",\n \"0.6233556\",\n \"0.6223152\",\n \"0.62226313\",\n \"0.6219013\",\n \"0.62132525\",\n \"0.6188235\",\n \"0.6186998\",\n \"0.61865807\",\n \"0.61854476\",\n \"0.61841327\",\n \"0.61805737\",\n \"0.61789274\",\n \"0.6175896\",\n \"0.6175611\",\n \"0.61709046\",\n \"0.616927\",\n \"0.61682904\",\n \"0.61662126\",\n \"0.61532754\",\n \"0.61480045\",\n \"0.61420774\",\n \"0.6130784\",\n \"0.6125804\",\n \"0.6122612\",\n \"0.611851\",\n \"0.611851\",\n \"0.6106999\",\n \"0.6104948\",\n \"0.6104027\",\n \"0.6099099\",\n \"0.60990757\",\n \"0.6098676\",\n \"0.609427\",\n \"0.60912544\",\n \"0.6074762\",\n \"0.60744816\",\n \"0.60722655\",\n \"0.60718334\",\n \"0.6057083\",\n \"0.6057083\",\n \"0.6057083\",\n \"0.6057083\",\n \"0.6057083\",\n \"0.60522574\",\n \"0.60465586\",\n \"0.60464334\",\n \"0.6044894\",\n \"0.603919\",\n \"0.6037908\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72630036"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":11095,"cells":{"query":{"kind":"string","value":"Sets the view given the indices to slice with."},"document":{"kind":"string","value":"def _set_view_slice(self, indices):\n\n vertices = self._mesh_vertices\n faces = self._mesh_triangles\n\n if len(faces) == 0:\n self._node.set_data(vertices=None, faces=None)\n else:\n self._node.set_data(vertices=vertices[:, ::-1], faces=faces,\n color=self.color)\n\n self._need_visual_update = True\n self._update()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self, indices: Tuple[int, ...], slices: Tuple[slice, ...] = (slice(0, 0, 0),)):\n self.indices = indices\n self.slices = slices","def _set_neighs_slice(self, key):\n ## Condition to use slice type\n self._constant_neighs = True\n self.ks = range(1) if self.ks is None else self.ks\n ## Possible options\n if key is None:\n self.idxs = slice(0, self._n, 1)\n elif isinstance(key, slice):\n start = 0 if key.start is None else key.start\n stop = self._n if key.stop is None else key.stop\n stop = self._n if key.stop > 10*16 else key.stop\n step = 1 if key.step is None else key.step\n self.idxs = slice(start, stop, step)\n elif type(key) in inttypes:\n self.idxs = slice(0, key, 1)\n elif type(key) == tuple:\n self.idxs = slice(key[0], key[1], 1)\n self._setted = True","def _setSubset(self, label, indices):\n\n for label in FLAG_ALIASES.get(label, [label]):\n self._subsets[label] = indices","def _set_view_slice(self):\n nd = self.dims.not_displayed\n\n if self.multichannel:\n # if multichannel need to keep the final axis fixed during the\n # transpose. The index of the final axis depends on how many\n # axes are displayed.\n order = self.dims.displayed_order + (self.dims.ndisplay,)\n else:\n order = self.dims.displayed_order\n\n # Slice thumbnail\n indices = np.array(self.dims.indices)\n downsampled = indices[nd] / self.level_downsamples[-1, nd]\n downsampled = np.round(downsampled.astype(float)).astype(int)\n downsampled = np.clip(downsampled, 0, self.level_shapes[-1, nd] - 1)\n indices[nd] = downsampled\n\n image = np.asarray(self.data[-1][tuple(indices)]).transpose(order)\n\n if self.multichannel and image.dtype.kind == 'f':\n self._data_thumbnail = np.clip(image, 0, 1)\n else:\n self._data_thumbnail = image\n\n # Slice currently viewed level\n indices = np.array(self.dims.indices)\n level = self.data_level\n downsampled = indices[nd] / self.level_downsamples[level, nd]\n downsampled = np.round(downsampled.astype(float)).astype(int)\n downsampled = np.clip(downsampled, 0, self.level_shapes[level, nd] - 1)\n indices[nd] = downsampled\n\n disp_shape = self.level_shapes[level, self.dims.displayed]\n scale = np.ones(self.ndim)\n for d in self.dims.displayed:\n scale[d] = self.level_downsamples[self.data_level][d]\n self._scale = scale\n self.events.scale()\n\n if np.any(disp_shape > self._max_tile_shape):\n for d in self.dims.displayed:\n indices[d] = slice(\n self._top_left[d],\n self._top_left[d] + self._max_tile_shape,\n 1,\n )\n self.translate = self._top_left * self.scale\n else:\n self.translate = [0] * self.ndim\n\n image = np.asarray(self.data[level][tuple(indices)]).transpose(order)\n\n if self.multichannel and image.dtype.kind == 'f':\n self._data_view = np.clip(image, 0, 1)\n else:\n self._data_view = image\n\n self._update_thumbnail()\n self._update_coordinates()\n self.events.set_data()","def ResetSliceViews(self):\n num = slicer.mrmlScene.GetNumberOfNodesByClass('vtkMRMLSliceCompositeNode')\n for i in range(num):\n sliceViewer = slicer.mrmlScene.GetNthNodeByClass(i, 'vtkMRMLSliceCompositeNode')\n sliceViewer.SetBackgroundVolumeID(None)\n sliceViewer.SetForegroundVolumeID(None)","def set_subset(self):\r\n if self._random_subset:\r\n perm = torch.randperm(len(self._indices))\r\n self._subset = self._indices[perm][:self._subset_size]\r\n else:\r\n self._subset = torch.Tensor(self._indices[:self._subset_size])","def __setitem__(self, index, value):\n if isinstance(index, types.SliceType):\n keys = self._main._sequence[index]\n if len(keys) != len(value):\n raise ValueError('attempt to assign sequence of size %s '\n 'to slice of size %s' % (len(name), len(keys)))\n # FIXME: efficiency? Would be better to calculate the indexes\n # directly from the slice object\n # NOTE: the new keys can collide with existing keys (or even\n # contain duplicates) - these will overwrite\n for key, val in zip(keys, value):\n self._main[key] = val\n else:\n self._main[self._main._sequence[index]] = value","def set_preds_as_viewed(preds):\n predictions.set_preds_as_viewed(preds)","def SetViewParameters(ref, args, request):\n del ref # unused\n\n if not args.view:\n request.table.view = None\n\n return request","def test_slice_setslice_forbidden(self):\n global setVal\n class foo:\n def __setslice__(self, i, j, value):\n global setVal\n setVal = i, j, value\n def __setitem__(self, index, value):\n global setVal\n setVal = index, value\n\n foo()[::] = 23\n self.assertEqual(setVal, (slice(None, None, None), 23))\n foo()[::None] = 23\n self.assertEqual(setVal, (slice(None, None, None), 23))","def setPosition(self, position, view) -> None:\n ...","def views(self, views):\n\n self._views = views","def index_update(tensor, indices, values):\n tensor[indices] = values\n return tensor","def test_write_slices(self):\n dt = np.dtype('(3,)i')\n\n data1 = np.ones((2,), dtype=dt)\n data2 = np.ones((4,5), dtype=dt)\n\n dset = self.f.create_dataset('x', (10,9,11), dtype=dt)\n\n dset[0,0,2:4] = data1\n self.assertArrayEqual(dset[0,0,2:4], data1)\n\n dset[3, 1:5, 6:11] = data2\n self.assertArrayEqual(dset[3, 1:5, 6:11], data2)","def set_indexes(self, indexes):\n if not isinstance(indexes, list) or not all(isinstance(i, int) for i in indexes):\n raise ValueError(\"The indexes should be a list and all its elements should be int\")\n self._indexes = indexes\n return self","def SetDataSlice(vDataSet,arr,aIndexZ,aIndexC,aIndexT):\r\n nx = vDataSet.GetSizeX()\r\n ny = vDataSet.GetSizeY()\r\n nz = vDataSet.GetSizeZ()\r\n dtype = GetType(vDataSet)\r\n\r\n if DEBUG:\r\n print(\"SetDataVolume\")\r\n print(\"vDataSet:\",(nz,ny,nx),GetType(vDataSet))\r\n print(arr.shape)\r\n print(arr.dtype)\r\n print(aIndexC)\r\n print(aIndexT)\r\n\r\n #Make sure the data is in range and convert the array\r\n s = arr\r\n if dtype != arr.dtype:\r\n miset,maset = GetTotalRange(vDataSet)\r\n arr[arr<miset]=miset\r\n arr[arr>maset]=maset\r\n s = arr.astype(dtype)\r\n\r\n s = s.swapaxes(0,1)\r\n if dtype == np.uint8:\r\n SetData = vDataSet.SetDataSliceBytes\r\n elif dtype == np.uint16:\r\n SetData = vDataSet.SetDataSliceShorts\r\n elif dtype == np.float32:\r\n SetData = vDataSet.SetDataSliceFloat32\r\n\r\n SetData(s,aIndexZ,aIndexC,aIndexT)\r\n #vDataSet.SetChannelRange(aIndexC,miset,maset)\r","def set_view(self, index):\n view = index\n self.reset_scroll_area()\n self.clearSelection()\n\n if view == self.SURFACE_VIEW:\n self.toggle_surface_mode()\n self.view = \"Surface View\"\n elif view == self.BORDER_VIEW:\n self.toggle_border_mode()\n self.view = \"Border View\"\n elif view == self.GEOM_VIEW:\n if self.show_geom() == \"Canceled\":\n self.canceled = True\n else:\n self.view = \"Geom View\"\n elif view == self.MESH_VIEW:\n if self.show_mesh() == \"Canceled\":\n self.canceled = True\n else:\n self.view = \"Mesh View\"","def slice(self, start_date, end_date = None):\n\n if end_date is None:\n end_date = self.series.index[-1]\n self.series = self.series.loc[start_date:end_date]","def subset_from_indices(self, indices):\n return self.extract_inds(indices)","def _read_index_slice(self, *args, **kwargs): # real signature unknown\n pass","def slice(self,*Indices):\n \n Ind = tuple(Indices)\n\n\n try:\n \n OutShape=shape((1*self[(0,)*Dimension])[Indices])\n except:\n raise IndexError(\"Wrong format for indices\")\n \n Out = BZO(shape=OutShape)\n \n for Ind in self.IndList():\n\n Out[Ind]=array(self[Ind][Indices])\n \n Out.CleanUp()\n \n return Out","def index_points(points, idx):\n device = points.device\n B = points.shape[0]\n view_shape = list(idx.shape)\n view_shape[1:] = [1] * (len(view_shape) - 1)\n repeat_shape = list(idx.shape)\n repeat_shape[0] = 1\n batch_indices = torch.arange(B, dtype=torch.long).view(view_shape).repeat(repeat_shape)\n new_points = points[batch_indices, idx, :]\n return new_points","def slice(self, num_slices, slice_index):\n return ClippedDataset(CachedGenotypeDataset(self.basename, self.vector_names,\n _ceiling_partition(len(self), num_slices),\n self.sample_id),\n num_slices=num_slices, slice_index=slice_index)","def direct_set(self, neighs, sp_relative_pos=None):\n self.idxs = neighs\n self.sp_relative_pos = sp_relative_pos\n self.assert_goodness()","def set_index(self, index):\n self.index = index","def setReference(self, updatedIndices):\n # self.colors[:] = [self.colors[i] for i in updatedIndices]\n self.cellData[:] = [self.cellData[i] for i in updatedIndices]","def _index_dset(dset, indices):\n # get dset and arr shape\n dset_shape = dset.shape\n arr_shape = _get_dset_shape(dset, indices)\n\n # create empty array of dset dtype\n arr = np.empty(arr_shape, dtype=dset.dtype)\n\n # get arr and dset indices for each dimension in indices\n dset_indices = []\n arr_indices = []\n for i, dset_inds in enumerate(indices):\n if isinstance(dset_inds, (int, np.integer)):\n # this dimension is len 1, so slice is fine\n arr_indices.append([slice(None)])\n dset_indices.append([[dset_inds]])\n\n elif isinstance(dset_inds, slice):\n # this dimension is just a slice, so slice is fine\n arr_indices.append([slice(None)])\n dset_indices.append([dset_inds])\n\n elif isinstance(dset_inds, list):\n if isinstance(dset_inds[0], (int, np.integer)):\n # this is a list of integers, append slice\n arr_indices.append([slice(None)])\n dset_indices.append([dset_inds])\n elif isinstance(dset_inds[0], slice):\n # this is a list of slices, need list of slice lens\n slens = [_get_slice_len(s, dset_shape[i]) for s in dset_inds]\n ssums = [sum(slens[:j]) for j in range(len(slens))]\n arr_inds = [slice(s, s + l) for s, l in zip(ssums, slens)]\n arr_indices.append(arr_inds)\n dset_indices.append(dset_inds)\n\n # iterate over each of the 4 axes and fill the array\n for blt_arr, blt_dset in zip(arr_indices[0], dset_indices[0]):\n for spw_arr, spw_dset in zip(arr_indices[1], dset_indices[1]):\n for freq_arr, freq_dset in zip(arr_indices[2], dset_indices[2]):\n for pol_arr, pol_dset in zip(arr_indices[3], dset_indices[3]):\n # index dset and assign to arr\n arr[blt_arr, spw_arr, freq_arr, pol_arr] = dset[\n blt_dset, spw_dset, freq_dset, pol_dset\n ]\n\n return arr","def view_list(self, view_list):\n\n self._view_list = view_list","def get_slice(x, indices):\n return x[indices]","def set_next_slice(self, start_idx, end_idx, strip_slice: bool = False):\n si, ei = int(start_idx), int(end_idx)\n if strip_slice:\n si, ei = _strip_slice_of_string(self.string, si, ei)\n self._next_slice = (si, ei)","def __setitem__(self, index, name):\n if isinstance(index, types.SliceType):\n # FIXME: efficiency?\n # check length is the same\n indexes = range(len(self._main._sequence))[index]\n if len(indexes) != len(name):\n raise ValueError('attempt to assign sequence of size %s '\n 'to slice of size %s' % (len(name), len(indexes)))\n # check they are the same keys\n # FIXME: Use set\n old_keys = self._main._sequence[index]\n new_keys = list(name)\n old_keys.sort()\n new_keys.sort()\n if old_keys != new_keys:\n raise KeyError('Keylist is not the same as current keylist.')\n orig_vals = [self._main[k] for k in name]\n del self._main[index]\n vals = zip(indexes, name, orig_vals)\n vals.sort()\n for i, k, v in vals:\n if self._main.strict and k in self._main:\n raise ValueError('slice assignment must be from '\n 'unique keys')\n self._main.insert(i, k, v)\n else:\n raise ValueError('Cannot assign to keys')","def slice_parameters(self, slice_parameters: SliceParamsIm):\n\n self._slice_parameters = slice_parameters","def set_index(self, nIndex):\n\t\tcall_sdk_function('PrlVmDev_SetIndex', self.handle, nIndex)","def setViews(self,batchSize):\n assert batchSize <= self.maxBatch, \"Batch size exceeds max batch\"\n self.hActs = [H.get_col_slice(0,batchSize) for H in self.hActs_M]\n self.hActsFor = self.hActsFor_M.get_col_slice(0,batchSize)\n self.hActsBack = self.hActsBack_M.get_col_slice(0,batchSize)\n self.probs = self.probs_M.get_col_slice(0,batchSize)\n self.rowVec = self.rowVec_M.get_col_slice(0,batchSize)\n if self.train:\n self.deltasC = self.deltasC_M.get_col_slice(0,batchSize)\n self.deltasOut = self.deltasOut_M.get_col_slice(0,batchSize)\n self.deltasIn = self.deltasIn_M.get_col_slice(0,batchSize)\n self.tmpGrad = self.tmpGrad_M.get_col_slice(0,batchSize)\n if self.temporalLayer > 0:\n self.tmpGradFor = self.tmpGrad # reuse tmpGrad mem\n self.tmpGradBack = self.tmpGradBack_M.get_col_slice(0,batchSize)\n self.deltasFor = self.deltasFor_M.get_col_slice(0,batchSize)\n self.deltasBack = self.deltasBack_M.get_col_slice(0,batchSize)","def __setitem__(self, index, item):\n if isinstance(index, types.SliceType):\n # NOTE: item must be an iterable (list of tuples)\n self._main[index] = OrderedDict(item)\n else:\n # FIXME: Does this raise a sensible error?\n orig = self._main.keys[index]\n key, value = item\n if self._main.strict and key in self and (key != orig):\n raise ValueError('slice assignment must be from '\n 'unique keys')\n # delete the current one\n del self._main[self._main._sequence[index]]\n self._main.insert(index, key, value)","def set_slices(self, start=0.0, end=1.0, step=None, num=None):\n\n if step is None:\n s = (end - start) / float(num)\n self._slices = frange(start, end, s)\n elif num is None:\n self._slices = frange(start, end, step)\n else:\n raise RuntimeError()\n\n LOG.info('Num slices: %d', len(self._slices))\n LOG.info('Slices: %s', self._slices)","def __setitem__(self, index, item):\n # type: (int, Any) -> None\n items = self._refs(item) if isinstance(index, slice) else self.ref(item)\n return list.__setitem__(self, index, items)","def setPositionKey(self, time, index, value, id, view) -> None:\n ...","def set(self, **kwargs):\n for ax in self.axes.flat:\n ax.set(**kwargs)\n return self","def set(self, index, data):\n self.data[index] = data","def update_mask(self, indices):\n\n indices = indices.view(self.batch_size, -1)\n updated_mask = torch.zeros_like(self.mask.squeeze(-1)).scatter_(1, indices, 1)\n\n return updated_mask.unsqueeze(-1)","def index(self, *index):\n # .index() resets\n s = self._clone()\n if not index:\n s._index = None\n else:\n s._index = (self._index or []) + list(index)\n return s","def _set_parameter_access(self, idx=0):\n raise NotImplementedError(\"Should have implemented this.\")","def __setitem__(self, index, value):\n if index == Ellipsis:\n index = tuple(self.dim*[slice(None)])\n\n if len(index) < self.dim:\n # --- Add extra dims to index if needed\n index = list(index)\n for i in range(len(index), self.dim):\n index.append(slice(None))\n index = tuple(index)\n\n if self.dim == 2:\n return self._setitem2d(index, value)\n elif self.dim == 3:\n return self._setitem3d(index, value)","def setSpecificIndices(\n self,\n indicesOfFreeBetas,\n indicesOfFixedBetas,\n indicesOfRandomVariables,\n indicesOfDraws,\n ):\n for e in self.children:\n e.setSpecificIndices(\n indicesOfFreeBetas,\n indicesOfFixedBetas,\n indicesOfRandomVariables,\n indicesOfDraws,\n )","def set_xviews(self, *args):\n self.xview(*args)\n self.tablecolheader.xview(*args)\n self.redrawVisible()\n return","def subset(dataset, article_indices):\n indices = []\n for a_id in article_indices:\n indices += dataset.get_article_indices(a_id)\n return Subset(dataset, indices)","def setNSlices(self,n):\n assert(n> 0)\n self._c_param.lee_richards_n_slices = n","def set(self, idx_in, vals, fill=False):\n o = np.broadcast_arrays(vals, *idx_in)\n vals = np.ravel(o[0])\n\n # TODO: Determine whether new vs. existing indices are being\n # addressed, in the latter case we only need to update data\n # array\n\n vals = np.array(vals, ndmin=1)\n idx_flat_in, msk_in = self._to_flat_index(idx_in)\n vals = np.asanyarray(vals, dtype=self.data.dtype)\n idx, data = merge_sparse_arrays(\n idx_flat_in, vals, self.idx, self.data, fill=fill\n )\n\n # Remove elements equal to fill value\n msk = data != self._fill_value\n idx = idx[msk]\n data = data[msk]\n self._idx = idx\n self._data = data\n # idx, msk = find_in_array(idx_flat_in, self.idx)\n # self._data[idx[msk]] = vals[msk]","def _set_index(self):\n self.index = 0\n # If offset is negative, target window might start before 0\n self.index = -min(0, self._get_target_index())","def from_coords_index(self, from_coords_index):\n\n self._from_coords_index = from_coords_index","def __getitem__(self, i):\n new_data = super().__getitem__(i)\n if isinstance(i, slice):\n new_data = self.__class__(new_data)\n new_data.global_settings = copy.copy(self.global_settings)\n return new_data","def test_transform(self):\n shape = (3, 4, 5)\n index = (0, 2, 1)\n t = View(shape=shape, index=index)\n a = numpy.zeros(shape)\n a[index] = 2\n assert t.transform(a) == 2","def exercise_indexes():\n print(exercise_indexes.__doc__)\n print(\"The indexes of 'data' are:\", data.index)\n print(data, \"\\n\")\n print(\"Changing the indexes of 'data'\")\n print(data.reindex([2, 0, 1]), \"\\n\")\n print(\"Changing the indexes of 'data' randomly\")\n print(data.reindex(np.random.permutation(data.index)))","def __setitem__(self, key, val):\n if isinstance(key, types.SliceType):\n if not isinstance(val, OrderedDict):\n # FIXME: allow a list of tuples?\n raise TypeError('slice assignment requires an OrderedDict')\n keys = self._sequence[key]\n # NOTE: Could use ``range(*key.indices(len(self._sequence)))``\n indexes = range(len(self._sequence))[key]\n if key.step is None:\n # NOTE: new slice may not be the same size as the one being\n # overwritten !\n # NOTE: What is the algorithm for an impossible slice?\n # e.g. d[5:3]\n pos = key.start or 0\n del self[key]\n newkeys = val.keys()\n for k in newkeys:\n if k in self:\n if self.strict:\n raise ValueError('slice assignment must be from '\n 'unique keys')\n else:\n # NOTE: This removes duplicate keys *first*\n # so start position might have changed?\n del self[k]\n self._sequence = (self._sequence[:pos] + newkeys +\n self._sequence[pos:])\n dict.update(self, val)\n else:\n # extended slice - length of new slice must be the same\n # as the one being replaced\n if len(keys) != len(val):\n raise ValueError('attempt to assign sequence of size %s '\n 'to extended slice of size %s' % (len(val), len(keys)))\n # FIXME: efficiency?\n del self[key]\n item_list = zip(indexes, val.items())\n # smallest indexes first - higher indexes not guaranteed to\n # exist\n item_list.sort()\n for pos, (newkey, newval) in item_list:\n if self.strict and newkey in self:\n raise ValueError('slice assignment must be from unique'\n ' keys')\n self.insert(pos, newkey, newval)\n else:\n if key not in self:\n self._sequence.append(key)\n dict.__setitem__(self, key, val)","def show_slices(self, slices):\n fig, axes = plt.subplots(1, len(slices))\n for i, slice in enumerate(slices):\n axes[i].imshow(slice.T, cmap=\"gray\", origin=\"lower\")","def _init_sorted_slice(self, *args, **kwargs): # real signature unknown\n pass","def view_id(self, view_id):\n\n self._view_id = view_id","def set_custom_data(self, index_from, data):\r\n self.unif[index_from:(index_from + len(data))] = data","def test_write_slices(setup_teardown_file):\n f = setup_teardown_file[3]\n\n dt = np.dtype('(3,)i')\n\n data1 = np.ones((2, ), dtype=dt)\n data2 = np.ones((4, 5), dtype=dt)\n\n dset = f.create_dataset('x', (10, 9, 11), dtype=dt)\n\n dset[0, 0, 2:4] = data1\n assert np.array_equal(dset[0, 0, 2:4], data1)\n\n dset[3, 1:5, 6:11] = data2\n assert np.array_equal(dset[3, 1:5, 6:11], data2)","def refresh_view_attrs(self, rv, index, data):\r\n self.index = index\r\n return super(SelectableLabel, self).refresh_view_attrs(rv, index, data)","def refresh_view_attrs(self, rv, index, data):\n self.index = index\n return super(SelectableLabel, self).refresh_view_attrs(\n rv, index, data)","def __setitem__(self, *args):\n return _uhd_swig.range_vector_t___setitem__(self, *args)","def __setitem__(\n self,\n index: Union[int, slice],\n value: Union[_RowPrototype, Iterable[_RowPrototype]],\n ) -> None:\n if isinstance(index, slice):\n self._rows[index] = [D2TXTRow(self, row) for row in value]\n else:\n self._rows[index] = D2TXTRow(self, value)","def setVariableIndices(self, indicesOfVariables):\n if self.name in indicesOfVariables:\n self.variableId = indicesOfVariables[self.name]","def set_at_index(self, index: int, value: object) -> None:\n self.data[index] = value","def apply_selection(self, rv, index, is_selected):\r\n self.selected = is_selected","def apply_selection(self, rv, index, is_selected):\n self.selected = is_selected","def select_at_indexes(indexes, tensor):\n dim = len(indexes.shape)\n assert indexes.shape == tensor.shape[:dim]\n num = indexes.numel()\n t_flat = tensor.view((num,) + tensor.shape[dim:])\n s_flat = t_flat[torch.arange(num), indexes.view(-1)]\n return s_flat.view(tensor.shape[:dim] + tensor.shape[dim + 1:])","def refresh_view_attrs(self, rv, index, data):\n self.index = index\n return super(SelectableButton, self).refresh_view_attrs(rv, index, data)","def _set_window_time(slices, times):\n t_idx_ = [t[-1] for t in slices]\n return times[t_idx_]","def setDrawIndex(self, idsOfDraws):\n if self.name in idsOfDraws:\n self.drawId = idsOfDraws[self.name]\n else:\n error_msg = (\n f'No id is available for draw {self.name}. '\n f'List of available indices: '\n f'{[n for n, i in idsOfDraws.items()]}'\n )\n raise excep.biogemeError(error_msg)","def setVariableIndices(self, indicesOfVariables):\n for e in self.children:\n e.setVariableIndices(indicesOfVariables)","def slice(self, s=0, e=0):\r\n self.p = self.p[s:e]\r\n self.r = self.r[s:e]\r\n self.alpha = self.alpha[s:e]\r\n self.v = self.v[s:e]\r\n self.w = self.w[s:e]\r\n self.a = self.a[s:e]","def show_slices(slices):\n fig, axes = plt.subplots(1, len(slices))\n for i, slice in enumerate(slices):\n axes[i].imshow(slice.T, cmap=\"gray\", origin=\"lower\")","def _idxs_postformat_array(self):\n self.idxs = np.array(self.idxs)","def _slice(tensor, size, i):\n return tensor[:, i * size : (i + 1) * size]","def __setitem__(self, inds, value):\n i, j = inds\n self.array[i][j] = value","def index(self, index):\n\n self._index = index","def show_slices(slices):\n fig, axes = plt.subplots(1, len(slices))\n for i, slice in enumerate(slices):\n axes[i].imshow(slice.T, cmap=\"gray\", origin=\"lower\")","def slice(self, begin, end):\n sliced = self._collection.slice(begin, end)\n return self.set_collection(sliced)","def __setitem__(self, key, value):\n # Both row index and columns given\n if isinstance(key, tuple):\n index, column = key\n index = self._slice_index(index) if isinstance(index, slice) else index\n return self.set(indexes=index, columns=column, values=value)\n # Row indexed with slice, all columns\n elif isinstance(key, slice):\n return self.set(indexes=self._slice_index(key), values=value)\n # Single row\n else:\n return self.set(indexes=key, values=value)","def _set_selection(self, new_sel_index):\r\n if new_sel_index >= 0 and new_sel_index <= len(self.points) -1:\r\n iid = self._tree.get_children()[new_sel_index]\r\n self._tree.selection_set(iid)","def setSpecificIndices(\n self,\n indicesOfFreeBetas,\n indicesOfFixedBetas,\n indicesOfRandomVariables,\n indicesOfDraws,\n ):\n if self.name in indicesOfDraws:\n self.drawId = indicesOfDraws[self.name]\n else:\n error_msg = (\n f'No index is available for draw {self.drawType}.'\n f' Known types of draws: {indicesOfDraws.keys()}'\n )\n raise excep.biogemeError(error_msg)","def __init__(self, *args, **kwargs):\n self.__class__._init_slicer(self, *args, **kwargs)","def _slice_index(self, slicer):\n start = self.index_location(slicer.start) if slicer.start is not None else 0\n end = self.index_location(slicer.stop) if slicer.stop is not None else self.size\n return list(range(start, end))","def copy_run_slice(self, run_idx, target_file_path, target_grp_path,\n run_slice=None, mode='x'):\n\n assert mode in ['w', 'w-', 'x', 'r+'], \"must be opened in write mode\"\n\n if run_slice is not None:\n assert run_slice[1] >= run_slice[0], \"Must be a contiguous slice\"\n\n # get a list of the frames to use\n slice_frames = list(range(*run_slice))\n\n\n # we manually construct an HDF5 wrapper and copy the groups over\n new_h5 = h5py.File(target_file_path, mode=mode, libver=H5PY_LIBVER)\n\n # flush the datasets buffers\n self.h5.flush()\n new_h5.flush()\n\n # get the run group we are interested in\n run_grp = self.run(run_idx)\n\n # slice the datasets in the run and set them in the new file\n if run_slice is not None:\n\n # initialize the group for the run\n new_run_grp = new_h5.require_group(target_grp_path)\n\n\n # copy the init walkers group\n self.h5.copy(run_grp[INIT_WALKERS], new_run_grp,\n name=INIT_WALKERS)\n\n # copy the decision group\n self.h5.copy(run_grp[DECISION], new_run_grp,\n name=DECISION)\n\n\n # create the trajectories group\n new_trajs_grp = new_run_grp.require_group(TRAJECTORIES)\n\n # slice the trajectories and copy them\n for traj_idx in run_grp[TRAJECTORIES]:\n\n traj_grp = run_grp[TRAJECTORIES][traj_idx]\n\n traj_id = \"{}/{}\".format(TRAJECTORIES, traj_idx)\n\n new_traj_grp = new_trajs_grp.require_group(str(traj_idx))\n\n for field_name in _iter_field_paths(run_grp[traj_id]):\n field_path = \"{}/{}\".format(traj_id, field_name)\n\n data = self.get_traj_field(run_idx, traj_idx, field_name,\n frames=slice_frames)\n\n # if it is a sparse field we need to create the\n # dataset differently\n if field_name in self.sparse_fields:\n\n # create a group for the field\n new_field_grp = new_traj_grp.require_group(field_name)\n\n # slice the _sparse_idxs from the original\n # dataset that are between the slice\n cycle_idxs = self.traj(run_idx, traj_idx)[field_name]['_sparse_idxs'][:]\n\n sparse_idx_idxs = np.argwhere(np.logical_and(\n cycle_idxs[:] >= run_slice[0], cycle_idxs[:] < run_slice[1]\n )).flatten().tolist()\n\n # the cycle idxs there is data for\n sliced_cycle_idxs = cycle_idxs[sparse_idx_idxs]\n\n # get the data for these cycles\n field_data = data[sliced_cycle_idxs]\n\n # get the information on compression,\n # chunking, and filters and use it when we set\n # the new data\n field_data_dset = traj_grp[field_name]['data']\n data_dset_kwargs = {\n 'chunks' : field_data_dset.chunks,\n 'compression' : field_data_dset.compression,\n 'compression_opts' : field_data_dset.compression_opts,\n 'shuffle' : field_data_dset.shuffle,\n 'fletcher32' : field_data_dset.fletcher32,\n }\n\n # and for the sparse idxs although it is probably overkill\n field_idxs_dset = traj_grp[field_name]['_sparse_idxs']\n idxs_dset_kwargs = {\n 'chunks' : field_idxs_dset.chunks,\n 'compression' : field_idxs_dset.compression,\n 'compression_opts' : field_idxs_dset.compression_opts,\n 'shuffle' : field_idxs_dset.shuffle,\n 'fletcher32' : field_idxs_dset.fletcher32,\n }\n\n # then create the datasets\n new_field_grp.create_dataset('_sparse_idxs',\n data=sliced_cycle_idxs,\n **idxs_dset_kwargs)\n new_field_grp.create_dataset('data',\n data=field_data,\n **data_dset_kwargs)\n\n else:\n\n # get the information on compression,\n # chunking, and filters and use it when we set\n # the new data\n field_dset = traj_grp[field_name]\n\n # since we are slicing we want to make sure\n # that the chunks are smaller than the\n # slices. Normally chunks are (1, ...) for a\n # field, but may not be for observables\n # (perhaps they should but thats for another issue)\n chunks = (1, *field_dset.chunks[1:])\n\n dset_kwargs = {\n 'chunks' : chunks,\n 'compression' : field_dset.compression,\n 'compression_opts' : field_dset.compression_opts,\n 'shuffle' : field_dset.shuffle,\n 'fletcher32' : field_dset.fletcher32,\n }\n\n # require the dataset first to automatically build\n # subpaths for compound fields if necessary\n dset = new_traj_grp.require_dataset(field_name,\n data.shape, data.dtype,\n **dset_kwargs)\n\n # then set the data depending on whether it is\n # sparse or not\n dset[:] = data\n\n # then do it for the records\n for rec_grp_name, rec_fields in self.record_fields.items():\n\n rec_grp = run_grp[rec_grp_name]\n\n # if this is a contiguous record we can skip the cycle\n # indices to record indices conversion that is\n # necessary for sporadic records\n if self._is_sporadic_records(rec_grp_name):\n\n cycle_idxs = rec_grp[CYCLE_IDXS][:]\n\n # get dataset info\n cycle_idxs_dset = rec_grp[CYCLE_IDXS]\n\n # we use autochunk, because I can't figure out how\n # the chunks are set and I can't reuse them\n idxs_dset_kwargs = {\n 'chunks' : True,\n # 'chunks' : cycle_idxs_dset.chunks,\n 'compression' : cycle_idxs_dset.compression,\n 'compression_opts' : cycle_idxs_dset.compression_opts,\n 'shuffle' : cycle_idxs_dset.shuffle,\n 'fletcher32' : cycle_idxs_dset.fletcher32,\n }\n\n # get the indices of the records we are interested in\n record_idxs = np.argwhere(np.logical_and(\n cycle_idxs >= run_slice[0], cycle_idxs < run_slice[1]\n )).flatten().tolist()\n\n # set the cycle indices in the new run group\n new_recgrp_cycle_idxs_path = '{}/{}/_cycle_idxs'.format(target_grp_path,\n rec_grp_name)\n cycle_data = cycle_idxs[record_idxs]\n\n cycle_dset = new_h5.require_dataset(new_recgrp_cycle_idxs_path,\n cycle_data.shape, cycle_data.dtype,\n **idxs_dset_kwargs)\n cycle_dset[:] = cycle_data\n\n # if contiguous just set the record indices as the\n # range between the slice\n else:\n record_idxs = list(range(run_slice[0], run_slice[1]))\n\n # then for each rec_field slice those and set them in the new file\n for rec_field in rec_fields:\n\n field_dset = rec_grp[rec_field]\n\n # get dataset info\n field_dset_kwargs = {\n 'chunks' : True,\n # 'chunks' : field_dset.chunks,\n 'compression' : field_dset.compression,\n 'compression_opts' : field_dset.compression_opts,\n 'shuffle' : field_dset.shuffle,\n 'fletcher32' : field_dset.fletcher32,\n }\n\n\n rec_field_path = \"{}/{}\".format(rec_grp_name, rec_field)\n new_recfield_grp_path = '{}/{}'.format(target_grp_path, rec_field_path)\n\n # if it is a variable length dtype make the dtype\n # that for the dataset and we also slice the\n # dataset differently\n vlen_type = h5py.check_dtype(vlen=field_dset.dtype)\n if vlen_type is not None:\n\n dtype = h5py.special_dtype(vlen=vlen_type)\n\n else:\n dtype = field_dset.dtype\n\n\n\n # if there are no records don't attempt to add them\n # get the shape\n shape = (len(record_idxs), *field_dset.shape[1:])\n\n new_field_dset = new_h5.require_dataset(new_recfield_grp_path,\n shape, dtype,\n **field_dset_kwargs)\n\n # if there aren't records just don't do anything,\n # and if there are get them and add them\n if len(record_idxs) > 0:\n rec_data = field_dset[record_idxs]\n\n # if it is a variable length data type we have\n # to do it 1 by 1\n if vlen_type is not None:\n for i, vlen_rec in enumerate(rec_data):\n new_field_dset[i] = rec_data[i]\n # otherwise just set it all at once\n else:\n new_field_dset[:] = rec_data\n\n # just copy the whole thing over, since this will probably be\n # more efficient\n else:\n\n # split off the last bit of the target path, for copying we\n # need it's parent group but not it to exist\n target_grp_path_basename = target_grp_path.split('/')[-1]\n target_grp_path_prefix = target_grp_path.split('/')[:-1]\n\n new_run_prefix_grp = self.h5.require_group(target_grp_path_prefix)\n\n # copy the whole thing\n self.h5.copy(run_grp, new_run_prefix_grp,\n name=target_grp_path_basename)\n\n # flush the datasets buffers\n self.h5.flush()\n new_h5.flush()\n\n return new_h5","def setColorIndex(idx):\n dislin.setclr(idx)","def set_index_ub(self, param, length):\n if tik.Dprofile().get_product_name() in (MINI, CLOUD, HISI_ES):\n sum_mask_ub = self.instance.Tensor(self.dtype, (16,),\n name=\"sum_mask_ub\",\n scope=tik.scope_ubuf)\n work_tensor_ub = self.instance.Tensor(self.dtype, (16,),\n name=\"work_tensor_ub\",\n scope=tik.scope_ubuf)\n self.instance.vec_reduce_add(self.mask, sum_mask_ub, param['reduce_mask_ub'], work_tensor_ub, 1, 8)\n\n mask_scalar = self.instance.Scalar(\"uint16\", name=\"mask_scalar\")\n mask_scalar.set_as(sum_mask_ub[0])\n with self.instance.if_scope(mask_scalar != 0):\n with self.instance.if_scope(param['count'] < PRE_NMS_TOPN):\n with self.instance.for_range(0, length) as mask_index:\n param['index_offset'].set_as(param['index_offset'] + 1)\n with self.instance.if_scope(param['count'] < PRE_NMS_TOPN):\n mask_scalar.set_as(param['reduce_mask_ub'][mask_index])\n\n # 1 fp16 == 15360 uint16\n with self.instance.if_scope(mask_scalar == 15360):\n param['index_ub'][param['count']].set_as(\n param['index_offset'])\n param['count'].set_as(param['count'] + 1)\n with self.instance.else_scope():\n param['index_offset'].set_as(param['index_offset'] + length)","def initDataView(self,student_cols):\n self.notebook.initStudentsView(student_cols)\n #self.dataView.initStudentsView(student_cols)","def set_selected_index(self, index: int) -> None:\n self._selected_index = index","def set_data_subset(self, subset):\n self.data_subset = subset","def region_setup(self, slices, ipa_regions):\n self.ipa_regions = ipa_regions\n self.slices = slices","def _at_index(data, indices, keepdim=None, padding=np.nan):\n if not (keepdim is None or keepdim in ['data', 'index']):\n raise TypeError('unexpected argument keepdim={}'.format(keepdim))\n\n data = np.asarray(data)\n indices = np.asarray(indices)\n i = indices[indices < data.size]\n\n if keepdim is None:\n return data[i]\n elif keepdim == 'data':\n res = np.full(data.size, padding)\n res[i] = data[i]\n return res\n elif keepdim == 'index':\n res = np.full(indices.size, padding)\n if i.size !=0:\n res[0:indices.size-1] = data[i]\n return res","def setDataSelection(self, selection):\n pass","def setSel(selected: List[int], table_widget: QTableWidget):\n table_widget.setSelectionMode(QAbstractItemView.MultiSelection)\n for i in selected:\n table_widget.selectRow(i)","def slice(self, start=None, end=None, inplace=False):\n if inplace:\n self.data = self.data[start:end]\n else:\n cpy = self.copy()\n\n cpy.data = cpy.data[start:end]\n\n return cpy\n return","def _fix_slice(self, inputs, new_attr):\n begin = new_attr.get('begin')\n end = new_attr.get('end')\n axes = new_attr.get('axis', tuple(range(len(begin))))\n slice_op = mx.sym.slice_axis(inputs[0], axis=axes[0], begin=begin[0], end=end[0])\n if len(axes) > 1:\n for i, axis in enumerate(axes):\n slice_op = mx.sym.slice_axis(slice_op, axis=axis, begin=begin[i], end=end[i])\n return slice_op","def _selected_indices(self, subset):\n # We want the DataFrame to be indexed the same way its values array is\n ftr = self.frametracks.reset_index(drop=True)\n if subset is not None:\n ftr['tmpindex'] = ftr.index.values\n ftr = ftr.set_index('particle').reindex(subset).set_index('tmpindex')\n if self.autoclip:\n # Boundaries are computed for the whole system\n xmin = self.frametracks.x.min() + self.nncutoff\n xmax = self.frametracks.x.max() - self.nncutoff\n ymin = self.frametracks.y.min() + self.nncutoff\n ymax = self.frametracks.y.max() - self.nncutoff\n r = ftr.index[ (ftr.x > xmin) & (ftr.x < xmax) & \\\n (ftr.y > ymin) & (ftr.y < ymax) ].values.astype(int)\n else:\n r = ftr.index.values.astype(int)\n if self.fast:\n return np.random.permutation(r)[:int(len(r) / 10)]\n else:\n return r","def __setitem__(self, index, value):\n if isinstance(index, int):\n self.data.iloc[index] = value\n elif isinstance(index, str):\n self.data[index] = value\n elif (\n isinstance(index, tuple)\n and len(index) == 2\n and index[1] in self.data.columns\n ):\n self.data.loc[index] = value\n else:\n assert isinstance(index, slice) or len(index) > 0\n self.data[index] = value"],"string":"[\n \"def __init__(self, indices: Tuple[int, ...], slices: Tuple[slice, ...] = (slice(0, 0, 0),)):\\n self.indices = indices\\n self.slices = slices\",\n \"def _set_neighs_slice(self, key):\\n ## Condition to use slice type\\n self._constant_neighs = True\\n self.ks = range(1) if self.ks is None else self.ks\\n ## Possible options\\n if key is None:\\n self.idxs = slice(0, self._n, 1)\\n elif isinstance(key, slice):\\n start = 0 if key.start is None else key.start\\n stop = self._n if key.stop is None else key.stop\\n stop = self._n if key.stop > 10*16 else key.stop\\n step = 1 if key.step is None else key.step\\n self.idxs = slice(start, stop, step)\\n elif type(key) in inttypes:\\n self.idxs = slice(0, key, 1)\\n elif type(key) == tuple:\\n self.idxs = slice(key[0], key[1], 1)\\n self._setted = True\",\n \"def _setSubset(self, label, indices):\\n\\n for label in FLAG_ALIASES.get(label, [label]):\\n self._subsets[label] = indices\",\n \"def _set_view_slice(self):\\n nd = self.dims.not_displayed\\n\\n if self.multichannel:\\n # if multichannel need to keep the final axis fixed during the\\n # transpose. The index of the final axis depends on how many\\n # axes are displayed.\\n order = self.dims.displayed_order + (self.dims.ndisplay,)\\n else:\\n order = self.dims.displayed_order\\n\\n # Slice thumbnail\\n indices = np.array(self.dims.indices)\\n downsampled = indices[nd] / self.level_downsamples[-1, nd]\\n downsampled = np.round(downsampled.astype(float)).astype(int)\\n downsampled = np.clip(downsampled, 0, self.level_shapes[-1, nd] - 1)\\n indices[nd] = downsampled\\n\\n image = np.asarray(self.data[-1][tuple(indices)]).transpose(order)\\n\\n if self.multichannel and image.dtype.kind == 'f':\\n self._data_thumbnail = np.clip(image, 0, 1)\\n else:\\n self._data_thumbnail = image\\n\\n # Slice currently viewed level\\n indices = np.array(self.dims.indices)\\n level = self.data_level\\n downsampled = indices[nd] / self.level_downsamples[level, nd]\\n downsampled = np.round(downsampled.astype(float)).astype(int)\\n downsampled = np.clip(downsampled, 0, self.level_shapes[level, nd] - 1)\\n indices[nd] = downsampled\\n\\n disp_shape = self.level_shapes[level, self.dims.displayed]\\n scale = np.ones(self.ndim)\\n for d in self.dims.displayed:\\n scale[d] = self.level_downsamples[self.data_level][d]\\n self._scale = scale\\n self.events.scale()\\n\\n if np.any(disp_shape > self._max_tile_shape):\\n for d in self.dims.displayed:\\n indices[d] = slice(\\n self._top_left[d],\\n self._top_left[d] + self._max_tile_shape,\\n 1,\\n )\\n self.translate = self._top_left * self.scale\\n else:\\n self.translate = [0] * self.ndim\\n\\n image = np.asarray(self.data[level][tuple(indices)]).transpose(order)\\n\\n if self.multichannel and image.dtype.kind == 'f':\\n self._data_view = np.clip(image, 0, 1)\\n else:\\n self._data_view = image\\n\\n self._update_thumbnail()\\n self._update_coordinates()\\n self.events.set_data()\",\n \"def ResetSliceViews(self):\\n num = slicer.mrmlScene.GetNumberOfNodesByClass('vtkMRMLSliceCompositeNode')\\n for i in range(num):\\n sliceViewer = slicer.mrmlScene.GetNthNodeByClass(i, 'vtkMRMLSliceCompositeNode')\\n sliceViewer.SetBackgroundVolumeID(None)\\n sliceViewer.SetForegroundVolumeID(None)\",\n \"def set_subset(self):\\r\\n if self._random_subset:\\r\\n perm = torch.randperm(len(self._indices))\\r\\n self._subset = self._indices[perm][:self._subset_size]\\r\\n else:\\r\\n self._subset = torch.Tensor(self._indices[:self._subset_size])\",\n \"def __setitem__(self, index, value):\\n if isinstance(index, types.SliceType):\\n keys = self._main._sequence[index]\\n if len(keys) != len(value):\\n raise ValueError('attempt to assign sequence of size %s '\\n 'to slice of size %s' % (len(name), len(keys)))\\n # FIXME: efficiency? Would be better to calculate the indexes\\n # directly from the slice object\\n # NOTE: the new keys can collide with existing keys (or even\\n # contain duplicates) - these will overwrite\\n for key, val in zip(keys, value):\\n self._main[key] = val\\n else:\\n self._main[self._main._sequence[index]] = value\",\n \"def set_preds_as_viewed(preds):\\n predictions.set_preds_as_viewed(preds)\",\n \"def SetViewParameters(ref, args, request):\\n del ref # unused\\n\\n if not args.view:\\n request.table.view = None\\n\\n return request\",\n \"def test_slice_setslice_forbidden(self):\\n global setVal\\n class foo:\\n def __setslice__(self, i, j, value):\\n global setVal\\n setVal = i, j, value\\n def __setitem__(self, index, value):\\n global setVal\\n setVal = index, value\\n\\n foo()[::] = 23\\n self.assertEqual(setVal, (slice(None, None, None), 23))\\n foo()[::None] = 23\\n self.assertEqual(setVal, (slice(None, None, None), 23))\",\n \"def setPosition(self, position, view) -> None:\\n ...\",\n \"def views(self, views):\\n\\n self._views = views\",\n \"def index_update(tensor, indices, values):\\n tensor[indices] = values\\n return tensor\",\n \"def test_write_slices(self):\\n dt = np.dtype('(3,)i')\\n\\n data1 = np.ones((2,), dtype=dt)\\n data2 = np.ones((4,5), dtype=dt)\\n\\n dset = self.f.create_dataset('x', (10,9,11), dtype=dt)\\n\\n dset[0,0,2:4] = data1\\n self.assertArrayEqual(dset[0,0,2:4], data1)\\n\\n dset[3, 1:5, 6:11] = data2\\n self.assertArrayEqual(dset[3, 1:5, 6:11], data2)\",\n \"def set_indexes(self, indexes):\\n if not isinstance(indexes, list) or not all(isinstance(i, int) for i in indexes):\\n raise ValueError(\\\"The indexes should be a list and all its elements should be int\\\")\\n self._indexes = indexes\\n return self\",\n \"def SetDataSlice(vDataSet,arr,aIndexZ,aIndexC,aIndexT):\\r\\n nx = vDataSet.GetSizeX()\\r\\n ny = vDataSet.GetSizeY()\\r\\n nz = vDataSet.GetSizeZ()\\r\\n dtype = GetType(vDataSet)\\r\\n\\r\\n if DEBUG:\\r\\n print(\\\"SetDataVolume\\\")\\r\\n print(\\\"vDataSet:\\\",(nz,ny,nx),GetType(vDataSet))\\r\\n print(arr.shape)\\r\\n print(arr.dtype)\\r\\n print(aIndexC)\\r\\n print(aIndexT)\\r\\n\\r\\n #Make sure the data is in range and convert the array\\r\\n s = arr\\r\\n if dtype != arr.dtype:\\r\\n miset,maset = GetTotalRange(vDataSet)\\r\\n arr[arr<miset]=miset\\r\\n arr[arr>maset]=maset\\r\\n s = arr.astype(dtype)\\r\\n\\r\\n s = s.swapaxes(0,1)\\r\\n if dtype == np.uint8:\\r\\n SetData = vDataSet.SetDataSliceBytes\\r\\n elif dtype == np.uint16:\\r\\n SetData = vDataSet.SetDataSliceShorts\\r\\n elif dtype == np.float32:\\r\\n SetData = vDataSet.SetDataSliceFloat32\\r\\n\\r\\n SetData(s,aIndexZ,aIndexC,aIndexT)\\r\\n #vDataSet.SetChannelRange(aIndexC,miset,maset)\\r\",\n \"def set_view(self, index):\\n view = index\\n self.reset_scroll_area()\\n self.clearSelection()\\n\\n if view == self.SURFACE_VIEW:\\n self.toggle_surface_mode()\\n self.view = \\\"Surface View\\\"\\n elif view == self.BORDER_VIEW:\\n self.toggle_border_mode()\\n self.view = \\\"Border View\\\"\\n elif view == self.GEOM_VIEW:\\n if self.show_geom() == \\\"Canceled\\\":\\n self.canceled = True\\n else:\\n self.view = \\\"Geom View\\\"\\n elif view == self.MESH_VIEW:\\n if self.show_mesh() == \\\"Canceled\\\":\\n self.canceled = True\\n else:\\n self.view = \\\"Mesh View\\\"\",\n \"def slice(self, start_date, end_date = None):\\n\\n if end_date is None:\\n end_date = self.series.index[-1]\\n self.series = self.series.loc[start_date:end_date]\",\n \"def subset_from_indices(self, indices):\\n return self.extract_inds(indices)\",\n \"def _read_index_slice(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def slice(self,*Indices):\\n \\n Ind = tuple(Indices)\\n\\n\\n try:\\n \\n OutShape=shape((1*self[(0,)*Dimension])[Indices])\\n except:\\n raise IndexError(\\\"Wrong format for indices\\\")\\n \\n Out = BZO(shape=OutShape)\\n \\n for Ind in self.IndList():\\n\\n Out[Ind]=array(self[Ind][Indices])\\n \\n Out.CleanUp()\\n \\n return Out\",\n \"def index_points(points, idx):\\n device = points.device\\n B = points.shape[0]\\n view_shape = list(idx.shape)\\n view_shape[1:] = [1] * (len(view_shape) - 1)\\n repeat_shape = list(idx.shape)\\n repeat_shape[0] = 1\\n batch_indices = torch.arange(B, dtype=torch.long).view(view_shape).repeat(repeat_shape)\\n new_points = points[batch_indices, idx, :]\\n return new_points\",\n \"def slice(self, num_slices, slice_index):\\n return ClippedDataset(CachedGenotypeDataset(self.basename, self.vector_names,\\n _ceiling_partition(len(self), num_slices),\\n self.sample_id),\\n num_slices=num_slices, slice_index=slice_index)\",\n \"def direct_set(self, neighs, sp_relative_pos=None):\\n self.idxs = neighs\\n self.sp_relative_pos = sp_relative_pos\\n self.assert_goodness()\",\n \"def set_index(self, index):\\n self.index = index\",\n \"def setReference(self, updatedIndices):\\n # self.colors[:] = [self.colors[i] for i in updatedIndices]\\n self.cellData[:] = [self.cellData[i] for i in updatedIndices]\",\n \"def _index_dset(dset, indices):\\n # get dset and arr shape\\n dset_shape = dset.shape\\n arr_shape = _get_dset_shape(dset, indices)\\n\\n # create empty array of dset dtype\\n arr = np.empty(arr_shape, dtype=dset.dtype)\\n\\n # get arr and dset indices for each dimension in indices\\n dset_indices = []\\n arr_indices = []\\n for i, dset_inds in enumerate(indices):\\n if isinstance(dset_inds, (int, np.integer)):\\n # this dimension is len 1, so slice is fine\\n arr_indices.append([slice(None)])\\n dset_indices.append([[dset_inds]])\\n\\n elif isinstance(dset_inds, slice):\\n # this dimension is just a slice, so slice is fine\\n arr_indices.append([slice(None)])\\n dset_indices.append([dset_inds])\\n\\n elif isinstance(dset_inds, list):\\n if isinstance(dset_inds[0], (int, np.integer)):\\n # this is a list of integers, append slice\\n arr_indices.append([slice(None)])\\n dset_indices.append([dset_inds])\\n elif isinstance(dset_inds[0], slice):\\n # this is a list of slices, need list of slice lens\\n slens = [_get_slice_len(s, dset_shape[i]) for s in dset_inds]\\n ssums = [sum(slens[:j]) for j in range(len(slens))]\\n arr_inds = [slice(s, s + l) for s, l in zip(ssums, slens)]\\n arr_indices.append(arr_inds)\\n dset_indices.append(dset_inds)\\n\\n # iterate over each of the 4 axes and fill the array\\n for blt_arr, blt_dset in zip(arr_indices[0], dset_indices[0]):\\n for spw_arr, spw_dset in zip(arr_indices[1], dset_indices[1]):\\n for freq_arr, freq_dset in zip(arr_indices[2], dset_indices[2]):\\n for pol_arr, pol_dset in zip(arr_indices[3], dset_indices[3]):\\n # index dset and assign to arr\\n arr[blt_arr, spw_arr, freq_arr, pol_arr] = dset[\\n blt_dset, spw_dset, freq_dset, pol_dset\\n ]\\n\\n return arr\",\n \"def view_list(self, view_list):\\n\\n self._view_list = view_list\",\n \"def get_slice(x, indices):\\n return x[indices]\",\n \"def set_next_slice(self, start_idx, end_idx, strip_slice: bool = False):\\n si, ei = int(start_idx), int(end_idx)\\n if strip_slice:\\n si, ei = _strip_slice_of_string(self.string, si, ei)\\n self._next_slice = (si, ei)\",\n \"def __setitem__(self, index, name):\\n if isinstance(index, types.SliceType):\\n # FIXME: efficiency?\\n # check length is the same\\n indexes = range(len(self._main._sequence))[index]\\n if len(indexes) != len(name):\\n raise ValueError('attempt to assign sequence of size %s '\\n 'to slice of size %s' % (len(name), len(indexes)))\\n # check they are the same keys\\n # FIXME: Use set\\n old_keys = self._main._sequence[index]\\n new_keys = list(name)\\n old_keys.sort()\\n new_keys.sort()\\n if old_keys != new_keys:\\n raise KeyError('Keylist is not the same as current keylist.')\\n orig_vals = [self._main[k] for k in name]\\n del self._main[index]\\n vals = zip(indexes, name, orig_vals)\\n vals.sort()\\n for i, k, v in vals:\\n if self._main.strict and k in self._main:\\n raise ValueError('slice assignment must be from '\\n 'unique keys')\\n self._main.insert(i, k, v)\\n else:\\n raise ValueError('Cannot assign to keys')\",\n \"def slice_parameters(self, slice_parameters: SliceParamsIm):\\n\\n self._slice_parameters = slice_parameters\",\n \"def set_index(self, nIndex):\\n\\t\\tcall_sdk_function('PrlVmDev_SetIndex', self.handle, nIndex)\",\n \"def setViews(self,batchSize):\\n assert batchSize <= self.maxBatch, \\\"Batch size exceeds max batch\\\"\\n self.hActs = [H.get_col_slice(0,batchSize) for H in self.hActs_M]\\n self.hActsFor = self.hActsFor_M.get_col_slice(0,batchSize)\\n self.hActsBack = self.hActsBack_M.get_col_slice(0,batchSize)\\n self.probs = self.probs_M.get_col_slice(0,batchSize)\\n self.rowVec = self.rowVec_M.get_col_slice(0,batchSize)\\n if self.train:\\n self.deltasC = self.deltasC_M.get_col_slice(0,batchSize)\\n self.deltasOut = self.deltasOut_M.get_col_slice(0,batchSize)\\n self.deltasIn = self.deltasIn_M.get_col_slice(0,batchSize)\\n self.tmpGrad = self.tmpGrad_M.get_col_slice(0,batchSize)\\n if self.temporalLayer > 0:\\n self.tmpGradFor = self.tmpGrad # reuse tmpGrad mem\\n self.tmpGradBack = self.tmpGradBack_M.get_col_slice(0,batchSize)\\n self.deltasFor = self.deltasFor_M.get_col_slice(0,batchSize)\\n self.deltasBack = self.deltasBack_M.get_col_slice(0,batchSize)\",\n \"def __setitem__(self, index, item):\\n if isinstance(index, types.SliceType):\\n # NOTE: item must be an iterable (list of tuples)\\n self._main[index] = OrderedDict(item)\\n else:\\n # FIXME: Does this raise a sensible error?\\n orig = self._main.keys[index]\\n key, value = item\\n if self._main.strict and key in self and (key != orig):\\n raise ValueError('slice assignment must be from '\\n 'unique keys')\\n # delete the current one\\n del self._main[self._main._sequence[index]]\\n self._main.insert(index, key, value)\",\n \"def set_slices(self, start=0.0, end=1.0, step=None, num=None):\\n\\n if step is None:\\n s = (end - start) / float(num)\\n self._slices = frange(start, end, s)\\n elif num is None:\\n self._slices = frange(start, end, step)\\n else:\\n raise RuntimeError()\\n\\n LOG.info('Num slices: %d', len(self._slices))\\n LOG.info('Slices: %s', self._slices)\",\n \"def __setitem__(self, index, item):\\n # type: (int, Any) -> None\\n items = self._refs(item) if isinstance(index, slice) else self.ref(item)\\n return list.__setitem__(self, index, items)\",\n \"def setPositionKey(self, time, index, value, id, view) -> None:\\n ...\",\n \"def set(self, **kwargs):\\n for ax in self.axes.flat:\\n ax.set(**kwargs)\\n return self\",\n \"def set(self, index, data):\\n self.data[index] = data\",\n \"def update_mask(self, indices):\\n\\n indices = indices.view(self.batch_size, -1)\\n updated_mask = torch.zeros_like(self.mask.squeeze(-1)).scatter_(1, indices, 1)\\n\\n return updated_mask.unsqueeze(-1)\",\n \"def index(self, *index):\\n # .index() resets\\n s = self._clone()\\n if not index:\\n s._index = None\\n else:\\n s._index = (self._index or []) + list(index)\\n return s\",\n \"def _set_parameter_access(self, idx=0):\\n raise NotImplementedError(\\\"Should have implemented this.\\\")\",\n \"def __setitem__(self, index, value):\\n if index == Ellipsis:\\n index = tuple(self.dim*[slice(None)])\\n\\n if len(index) < self.dim:\\n # --- Add extra dims to index if needed\\n index = list(index)\\n for i in range(len(index), self.dim):\\n index.append(slice(None))\\n index = tuple(index)\\n\\n if self.dim == 2:\\n return self._setitem2d(index, value)\\n elif self.dim == 3:\\n return self._setitem3d(index, value)\",\n \"def setSpecificIndices(\\n self,\\n indicesOfFreeBetas,\\n indicesOfFixedBetas,\\n indicesOfRandomVariables,\\n indicesOfDraws,\\n ):\\n for e in self.children:\\n e.setSpecificIndices(\\n indicesOfFreeBetas,\\n indicesOfFixedBetas,\\n indicesOfRandomVariables,\\n indicesOfDraws,\\n )\",\n \"def set_xviews(self, *args):\\n self.xview(*args)\\n self.tablecolheader.xview(*args)\\n self.redrawVisible()\\n return\",\n \"def subset(dataset, article_indices):\\n indices = []\\n for a_id in article_indices:\\n indices += dataset.get_article_indices(a_id)\\n return Subset(dataset, indices)\",\n \"def setNSlices(self,n):\\n assert(n> 0)\\n self._c_param.lee_richards_n_slices = n\",\n \"def set(self, idx_in, vals, fill=False):\\n o = np.broadcast_arrays(vals, *idx_in)\\n vals = np.ravel(o[0])\\n\\n # TODO: Determine whether new vs. existing indices are being\\n # addressed, in the latter case we only need to update data\\n # array\\n\\n vals = np.array(vals, ndmin=1)\\n idx_flat_in, msk_in = self._to_flat_index(idx_in)\\n vals = np.asanyarray(vals, dtype=self.data.dtype)\\n idx, data = merge_sparse_arrays(\\n idx_flat_in, vals, self.idx, self.data, fill=fill\\n )\\n\\n # Remove elements equal to fill value\\n msk = data != self._fill_value\\n idx = idx[msk]\\n data = data[msk]\\n self._idx = idx\\n self._data = data\\n # idx, msk = find_in_array(idx_flat_in, self.idx)\\n # self._data[idx[msk]] = vals[msk]\",\n \"def _set_index(self):\\n self.index = 0\\n # If offset is negative, target window might start before 0\\n self.index = -min(0, self._get_target_index())\",\n \"def from_coords_index(self, from_coords_index):\\n\\n self._from_coords_index = from_coords_index\",\n \"def __getitem__(self, i):\\n new_data = super().__getitem__(i)\\n if isinstance(i, slice):\\n new_data = self.__class__(new_data)\\n new_data.global_settings = copy.copy(self.global_settings)\\n return new_data\",\n \"def test_transform(self):\\n shape = (3, 4, 5)\\n index = (0, 2, 1)\\n t = View(shape=shape, index=index)\\n a = numpy.zeros(shape)\\n a[index] = 2\\n assert t.transform(a) == 2\",\n \"def exercise_indexes():\\n print(exercise_indexes.__doc__)\\n print(\\\"The indexes of 'data' are:\\\", data.index)\\n print(data, \\\"\\\\n\\\")\\n print(\\\"Changing the indexes of 'data'\\\")\\n print(data.reindex([2, 0, 1]), \\\"\\\\n\\\")\\n print(\\\"Changing the indexes of 'data' randomly\\\")\\n print(data.reindex(np.random.permutation(data.index)))\",\n \"def __setitem__(self, key, val):\\n if isinstance(key, types.SliceType):\\n if not isinstance(val, OrderedDict):\\n # FIXME: allow a list of tuples?\\n raise TypeError('slice assignment requires an OrderedDict')\\n keys = self._sequence[key]\\n # NOTE: Could use ``range(*key.indices(len(self._sequence)))``\\n indexes = range(len(self._sequence))[key]\\n if key.step is None:\\n # NOTE: new slice may not be the same size as the one being\\n # overwritten !\\n # NOTE: What is the algorithm for an impossible slice?\\n # e.g. d[5:3]\\n pos = key.start or 0\\n del self[key]\\n newkeys = val.keys()\\n for k in newkeys:\\n if k in self:\\n if self.strict:\\n raise ValueError('slice assignment must be from '\\n 'unique keys')\\n else:\\n # NOTE: This removes duplicate keys *first*\\n # so start position might have changed?\\n del self[k]\\n self._sequence = (self._sequence[:pos] + newkeys +\\n self._sequence[pos:])\\n dict.update(self, val)\\n else:\\n # extended slice - length of new slice must be the same\\n # as the one being replaced\\n if len(keys) != len(val):\\n raise ValueError('attempt to assign sequence of size %s '\\n 'to extended slice of size %s' % (len(val), len(keys)))\\n # FIXME: efficiency?\\n del self[key]\\n item_list = zip(indexes, val.items())\\n # smallest indexes first - higher indexes not guaranteed to\\n # exist\\n item_list.sort()\\n for pos, (newkey, newval) in item_list:\\n if self.strict and newkey in self:\\n raise ValueError('slice assignment must be from unique'\\n ' keys')\\n self.insert(pos, newkey, newval)\\n else:\\n if key not in self:\\n self._sequence.append(key)\\n dict.__setitem__(self, key, val)\",\n \"def show_slices(self, slices):\\n fig, axes = plt.subplots(1, len(slices))\\n for i, slice in enumerate(slices):\\n axes[i].imshow(slice.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def _init_sorted_slice(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"def view_id(self, view_id):\\n\\n self._view_id = view_id\",\n \"def set_custom_data(self, index_from, data):\\r\\n self.unif[index_from:(index_from + len(data))] = data\",\n \"def test_write_slices(setup_teardown_file):\\n f = setup_teardown_file[3]\\n\\n dt = np.dtype('(3,)i')\\n\\n data1 = np.ones((2, ), dtype=dt)\\n data2 = np.ones((4, 5), dtype=dt)\\n\\n dset = f.create_dataset('x', (10, 9, 11), dtype=dt)\\n\\n dset[0, 0, 2:4] = data1\\n assert np.array_equal(dset[0, 0, 2:4], data1)\\n\\n dset[3, 1:5, 6:11] = data2\\n assert np.array_equal(dset[3, 1:5, 6:11], data2)\",\n \"def refresh_view_attrs(self, rv, index, data):\\r\\n self.index = index\\r\\n return super(SelectableLabel, self).refresh_view_attrs(rv, index, data)\",\n \"def refresh_view_attrs(self, rv, index, data):\\n self.index = index\\n return super(SelectableLabel, self).refresh_view_attrs(\\n rv, index, data)\",\n \"def __setitem__(self, *args):\\n return _uhd_swig.range_vector_t___setitem__(self, *args)\",\n \"def __setitem__(\\n self,\\n index: Union[int, slice],\\n value: Union[_RowPrototype, Iterable[_RowPrototype]],\\n ) -> None:\\n if isinstance(index, slice):\\n self._rows[index] = [D2TXTRow(self, row) for row in value]\\n else:\\n self._rows[index] = D2TXTRow(self, value)\",\n \"def setVariableIndices(self, indicesOfVariables):\\n if self.name in indicesOfVariables:\\n self.variableId = indicesOfVariables[self.name]\",\n \"def set_at_index(self, index: int, value: object) -> None:\\n self.data[index] = value\",\n \"def apply_selection(self, rv, index, is_selected):\\r\\n self.selected = is_selected\",\n \"def apply_selection(self, rv, index, is_selected):\\n self.selected = is_selected\",\n \"def select_at_indexes(indexes, tensor):\\n dim = len(indexes.shape)\\n assert indexes.shape == tensor.shape[:dim]\\n num = indexes.numel()\\n t_flat = tensor.view((num,) + tensor.shape[dim:])\\n s_flat = t_flat[torch.arange(num), indexes.view(-1)]\\n return s_flat.view(tensor.shape[:dim] + tensor.shape[dim + 1:])\",\n \"def refresh_view_attrs(self, rv, index, data):\\n self.index = index\\n return super(SelectableButton, self).refresh_view_attrs(rv, index, data)\",\n \"def _set_window_time(slices, times):\\n t_idx_ = [t[-1] for t in slices]\\n return times[t_idx_]\",\n \"def setDrawIndex(self, idsOfDraws):\\n if self.name in idsOfDraws:\\n self.drawId = idsOfDraws[self.name]\\n else:\\n error_msg = (\\n f'No id is available for draw {self.name}. '\\n f'List of available indices: '\\n f'{[n for n, i in idsOfDraws.items()]}'\\n )\\n raise excep.biogemeError(error_msg)\",\n \"def setVariableIndices(self, indicesOfVariables):\\n for e in self.children:\\n e.setVariableIndices(indicesOfVariables)\",\n \"def slice(self, s=0, e=0):\\r\\n self.p = self.p[s:e]\\r\\n self.r = self.r[s:e]\\r\\n self.alpha = self.alpha[s:e]\\r\\n self.v = self.v[s:e]\\r\\n self.w = self.w[s:e]\\r\\n self.a = self.a[s:e]\",\n \"def show_slices(slices):\\n fig, axes = plt.subplots(1, len(slices))\\n for i, slice in enumerate(slices):\\n axes[i].imshow(slice.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def _idxs_postformat_array(self):\\n self.idxs = np.array(self.idxs)\",\n \"def _slice(tensor, size, i):\\n return tensor[:, i * size : (i + 1) * size]\",\n \"def __setitem__(self, inds, value):\\n i, j = inds\\n self.array[i][j] = value\",\n \"def index(self, index):\\n\\n self._index = index\",\n \"def show_slices(slices):\\n fig, axes = plt.subplots(1, len(slices))\\n for i, slice in enumerate(slices):\\n axes[i].imshow(slice.T, cmap=\\\"gray\\\", origin=\\\"lower\\\")\",\n \"def slice(self, begin, end):\\n sliced = self._collection.slice(begin, end)\\n return self.set_collection(sliced)\",\n \"def __setitem__(self, key, value):\\n # Both row index and columns given\\n if isinstance(key, tuple):\\n index, column = key\\n index = self._slice_index(index) if isinstance(index, slice) else index\\n return self.set(indexes=index, columns=column, values=value)\\n # Row indexed with slice, all columns\\n elif isinstance(key, slice):\\n return self.set(indexes=self._slice_index(key), values=value)\\n # Single row\\n else:\\n return self.set(indexes=key, values=value)\",\n \"def _set_selection(self, new_sel_index):\\r\\n if new_sel_index >= 0 and new_sel_index <= len(self.points) -1:\\r\\n iid = self._tree.get_children()[new_sel_index]\\r\\n self._tree.selection_set(iid)\",\n \"def setSpecificIndices(\\n self,\\n indicesOfFreeBetas,\\n indicesOfFixedBetas,\\n indicesOfRandomVariables,\\n indicesOfDraws,\\n ):\\n if self.name in indicesOfDraws:\\n self.drawId = indicesOfDraws[self.name]\\n else:\\n error_msg = (\\n f'No index is available for draw {self.drawType}.'\\n f' Known types of draws: {indicesOfDraws.keys()}'\\n )\\n raise excep.biogemeError(error_msg)\",\n \"def __init__(self, *args, **kwargs):\\n self.__class__._init_slicer(self, *args, **kwargs)\",\n \"def _slice_index(self, slicer):\\n start = self.index_location(slicer.start) if slicer.start is not None else 0\\n end = self.index_location(slicer.stop) if slicer.stop is not None else self.size\\n return list(range(start, end))\",\n \"def copy_run_slice(self, run_idx, target_file_path, target_grp_path,\\n run_slice=None, mode='x'):\\n\\n assert mode in ['w', 'w-', 'x', 'r+'], \\\"must be opened in write mode\\\"\\n\\n if run_slice is not None:\\n assert run_slice[1] >= run_slice[0], \\\"Must be a contiguous slice\\\"\\n\\n # get a list of the frames to use\\n slice_frames = list(range(*run_slice))\\n\\n\\n # we manually construct an HDF5 wrapper and copy the groups over\\n new_h5 = h5py.File(target_file_path, mode=mode, libver=H5PY_LIBVER)\\n\\n # flush the datasets buffers\\n self.h5.flush()\\n new_h5.flush()\\n\\n # get the run group we are interested in\\n run_grp = self.run(run_idx)\\n\\n # slice the datasets in the run and set them in the new file\\n if run_slice is not None:\\n\\n # initialize the group for the run\\n new_run_grp = new_h5.require_group(target_grp_path)\\n\\n\\n # copy the init walkers group\\n self.h5.copy(run_grp[INIT_WALKERS], new_run_grp,\\n name=INIT_WALKERS)\\n\\n # copy the decision group\\n self.h5.copy(run_grp[DECISION], new_run_grp,\\n name=DECISION)\\n\\n\\n # create the trajectories group\\n new_trajs_grp = new_run_grp.require_group(TRAJECTORIES)\\n\\n # slice the trajectories and copy them\\n for traj_idx in run_grp[TRAJECTORIES]:\\n\\n traj_grp = run_grp[TRAJECTORIES][traj_idx]\\n\\n traj_id = \\\"{}/{}\\\".format(TRAJECTORIES, traj_idx)\\n\\n new_traj_grp = new_trajs_grp.require_group(str(traj_idx))\\n\\n for field_name in _iter_field_paths(run_grp[traj_id]):\\n field_path = \\\"{}/{}\\\".format(traj_id, field_name)\\n\\n data = self.get_traj_field(run_idx, traj_idx, field_name,\\n frames=slice_frames)\\n\\n # if it is a sparse field we need to create the\\n # dataset differently\\n if field_name in self.sparse_fields:\\n\\n # create a group for the field\\n new_field_grp = new_traj_grp.require_group(field_name)\\n\\n # slice the _sparse_idxs from the original\\n # dataset that are between the slice\\n cycle_idxs = self.traj(run_idx, traj_idx)[field_name]['_sparse_idxs'][:]\\n\\n sparse_idx_idxs = np.argwhere(np.logical_and(\\n cycle_idxs[:] >= run_slice[0], cycle_idxs[:] < run_slice[1]\\n )).flatten().tolist()\\n\\n # the cycle idxs there is data for\\n sliced_cycle_idxs = cycle_idxs[sparse_idx_idxs]\\n\\n # get the data for these cycles\\n field_data = data[sliced_cycle_idxs]\\n\\n # get the information on compression,\\n # chunking, and filters and use it when we set\\n # the new data\\n field_data_dset = traj_grp[field_name]['data']\\n data_dset_kwargs = {\\n 'chunks' : field_data_dset.chunks,\\n 'compression' : field_data_dset.compression,\\n 'compression_opts' : field_data_dset.compression_opts,\\n 'shuffle' : field_data_dset.shuffle,\\n 'fletcher32' : field_data_dset.fletcher32,\\n }\\n\\n # and for the sparse idxs although it is probably overkill\\n field_idxs_dset = traj_grp[field_name]['_sparse_idxs']\\n idxs_dset_kwargs = {\\n 'chunks' : field_idxs_dset.chunks,\\n 'compression' : field_idxs_dset.compression,\\n 'compression_opts' : field_idxs_dset.compression_opts,\\n 'shuffle' : field_idxs_dset.shuffle,\\n 'fletcher32' : field_idxs_dset.fletcher32,\\n }\\n\\n # then create the datasets\\n new_field_grp.create_dataset('_sparse_idxs',\\n data=sliced_cycle_idxs,\\n **idxs_dset_kwargs)\\n new_field_grp.create_dataset('data',\\n data=field_data,\\n **data_dset_kwargs)\\n\\n else:\\n\\n # get the information on compression,\\n # chunking, and filters and use it when we set\\n # the new data\\n field_dset = traj_grp[field_name]\\n\\n # since we are slicing we want to make sure\\n # that the chunks are smaller than the\\n # slices. Normally chunks are (1, ...) for a\\n # field, but may not be for observables\\n # (perhaps they should but thats for another issue)\\n chunks = (1, *field_dset.chunks[1:])\\n\\n dset_kwargs = {\\n 'chunks' : chunks,\\n 'compression' : field_dset.compression,\\n 'compression_opts' : field_dset.compression_opts,\\n 'shuffle' : field_dset.shuffle,\\n 'fletcher32' : field_dset.fletcher32,\\n }\\n\\n # require the dataset first to automatically build\\n # subpaths for compound fields if necessary\\n dset = new_traj_grp.require_dataset(field_name,\\n data.shape, data.dtype,\\n **dset_kwargs)\\n\\n # then set the data depending on whether it is\\n # sparse or not\\n dset[:] = data\\n\\n # then do it for the records\\n for rec_grp_name, rec_fields in self.record_fields.items():\\n\\n rec_grp = run_grp[rec_grp_name]\\n\\n # if this is a contiguous record we can skip the cycle\\n # indices to record indices conversion that is\\n # necessary for sporadic records\\n if self._is_sporadic_records(rec_grp_name):\\n\\n cycle_idxs = rec_grp[CYCLE_IDXS][:]\\n\\n # get dataset info\\n cycle_idxs_dset = rec_grp[CYCLE_IDXS]\\n\\n # we use autochunk, because I can't figure out how\\n # the chunks are set and I can't reuse them\\n idxs_dset_kwargs = {\\n 'chunks' : True,\\n # 'chunks' : cycle_idxs_dset.chunks,\\n 'compression' : cycle_idxs_dset.compression,\\n 'compression_opts' : cycle_idxs_dset.compression_opts,\\n 'shuffle' : cycle_idxs_dset.shuffle,\\n 'fletcher32' : cycle_idxs_dset.fletcher32,\\n }\\n\\n # get the indices of the records we are interested in\\n record_idxs = np.argwhere(np.logical_and(\\n cycle_idxs >= run_slice[0], cycle_idxs < run_slice[1]\\n )).flatten().tolist()\\n\\n # set the cycle indices in the new run group\\n new_recgrp_cycle_idxs_path = '{}/{}/_cycle_idxs'.format(target_grp_path,\\n rec_grp_name)\\n cycle_data = cycle_idxs[record_idxs]\\n\\n cycle_dset = new_h5.require_dataset(new_recgrp_cycle_idxs_path,\\n cycle_data.shape, cycle_data.dtype,\\n **idxs_dset_kwargs)\\n cycle_dset[:] = cycle_data\\n\\n # if contiguous just set the record indices as the\\n # range between the slice\\n else:\\n record_idxs = list(range(run_slice[0], run_slice[1]))\\n\\n # then for each rec_field slice those and set them in the new file\\n for rec_field in rec_fields:\\n\\n field_dset = rec_grp[rec_field]\\n\\n # get dataset info\\n field_dset_kwargs = {\\n 'chunks' : True,\\n # 'chunks' : field_dset.chunks,\\n 'compression' : field_dset.compression,\\n 'compression_opts' : field_dset.compression_opts,\\n 'shuffle' : field_dset.shuffle,\\n 'fletcher32' : field_dset.fletcher32,\\n }\\n\\n\\n rec_field_path = \\\"{}/{}\\\".format(rec_grp_name, rec_field)\\n new_recfield_grp_path = '{}/{}'.format(target_grp_path, rec_field_path)\\n\\n # if it is a variable length dtype make the dtype\\n # that for the dataset and we also slice the\\n # dataset differently\\n vlen_type = h5py.check_dtype(vlen=field_dset.dtype)\\n if vlen_type is not None:\\n\\n dtype = h5py.special_dtype(vlen=vlen_type)\\n\\n else:\\n dtype = field_dset.dtype\\n\\n\\n\\n # if there are no records don't attempt to add them\\n # get the shape\\n shape = (len(record_idxs), *field_dset.shape[1:])\\n\\n new_field_dset = new_h5.require_dataset(new_recfield_grp_path,\\n shape, dtype,\\n **field_dset_kwargs)\\n\\n # if there aren't records just don't do anything,\\n # and if there are get them and add them\\n if len(record_idxs) > 0:\\n rec_data = field_dset[record_idxs]\\n\\n # if it is a variable length data type we have\\n # to do it 1 by 1\\n if vlen_type is not None:\\n for i, vlen_rec in enumerate(rec_data):\\n new_field_dset[i] = rec_data[i]\\n # otherwise just set it all at once\\n else:\\n new_field_dset[:] = rec_data\\n\\n # just copy the whole thing over, since this will probably be\\n # more efficient\\n else:\\n\\n # split off the last bit of the target path, for copying we\\n # need it's parent group but not it to exist\\n target_grp_path_basename = target_grp_path.split('/')[-1]\\n target_grp_path_prefix = target_grp_path.split('/')[:-1]\\n\\n new_run_prefix_grp = self.h5.require_group(target_grp_path_prefix)\\n\\n # copy the whole thing\\n self.h5.copy(run_grp, new_run_prefix_grp,\\n name=target_grp_path_basename)\\n\\n # flush the datasets buffers\\n self.h5.flush()\\n new_h5.flush()\\n\\n return new_h5\",\n \"def setColorIndex(idx):\\n dislin.setclr(idx)\",\n \"def set_index_ub(self, param, length):\\n if tik.Dprofile().get_product_name() in (MINI, CLOUD, HISI_ES):\\n sum_mask_ub = self.instance.Tensor(self.dtype, (16,),\\n name=\\\"sum_mask_ub\\\",\\n scope=tik.scope_ubuf)\\n work_tensor_ub = self.instance.Tensor(self.dtype, (16,),\\n name=\\\"work_tensor_ub\\\",\\n scope=tik.scope_ubuf)\\n self.instance.vec_reduce_add(self.mask, sum_mask_ub, param['reduce_mask_ub'], work_tensor_ub, 1, 8)\\n\\n mask_scalar = self.instance.Scalar(\\\"uint16\\\", name=\\\"mask_scalar\\\")\\n mask_scalar.set_as(sum_mask_ub[0])\\n with self.instance.if_scope(mask_scalar != 0):\\n with self.instance.if_scope(param['count'] < PRE_NMS_TOPN):\\n with self.instance.for_range(0, length) as mask_index:\\n param['index_offset'].set_as(param['index_offset'] + 1)\\n with self.instance.if_scope(param['count'] < PRE_NMS_TOPN):\\n mask_scalar.set_as(param['reduce_mask_ub'][mask_index])\\n\\n # 1 fp16 == 15360 uint16\\n with self.instance.if_scope(mask_scalar == 15360):\\n param['index_ub'][param['count']].set_as(\\n param['index_offset'])\\n param['count'].set_as(param['count'] + 1)\\n with self.instance.else_scope():\\n param['index_offset'].set_as(param['index_offset'] + length)\",\n \"def initDataView(self,student_cols):\\n self.notebook.initStudentsView(student_cols)\\n #self.dataView.initStudentsView(student_cols)\",\n \"def set_selected_index(self, index: int) -> None:\\n self._selected_index = index\",\n \"def set_data_subset(self, subset):\\n self.data_subset = subset\",\n \"def region_setup(self, slices, ipa_regions):\\n self.ipa_regions = ipa_regions\\n self.slices = slices\",\n \"def _at_index(data, indices, keepdim=None, padding=np.nan):\\n if not (keepdim is None or keepdim in ['data', 'index']):\\n raise TypeError('unexpected argument keepdim={}'.format(keepdim))\\n\\n data = np.asarray(data)\\n indices = np.asarray(indices)\\n i = indices[indices < data.size]\\n\\n if keepdim is None:\\n return data[i]\\n elif keepdim == 'data':\\n res = np.full(data.size, padding)\\n res[i] = data[i]\\n return res\\n elif keepdim == 'index':\\n res = np.full(indices.size, padding)\\n if i.size !=0:\\n res[0:indices.size-1] = data[i]\\n return res\",\n \"def setDataSelection(self, selection):\\n pass\",\n \"def setSel(selected: List[int], table_widget: QTableWidget):\\n table_widget.setSelectionMode(QAbstractItemView.MultiSelection)\\n for i in selected:\\n table_widget.selectRow(i)\",\n \"def slice(self, start=None, end=None, inplace=False):\\n if inplace:\\n self.data = self.data[start:end]\\n else:\\n cpy = self.copy()\\n\\n cpy.data = cpy.data[start:end]\\n\\n return cpy\\n return\",\n \"def _fix_slice(self, inputs, new_attr):\\n begin = new_attr.get('begin')\\n end = new_attr.get('end')\\n axes = new_attr.get('axis', tuple(range(len(begin))))\\n slice_op = mx.sym.slice_axis(inputs[0], axis=axes[0], begin=begin[0], end=end[0])\\n if len(axes) > 1:\\n for i, axis in enumerate(axes):\\n slice_op = mx.sym.slice_axis(slice_op, axis=axis, begin=begin[i], end=end[i])\\n return slice_op\",\n \"def _selected_indices(self, subset):\\n # We want the DataFrame to be indexed the same way its values array is\\n ftr = self.frametracks.reset_index(drop=True)\\n if subset is not None:\\n ftr['tmpindex'] = ftr.index.values\\n ftr = ftr.set_index('particle').reindex(subset).set_index('tmpindex')\\n if self.autoclip:\\n # Boundaries are computed for the whole system\\n xmin = self.frametracks.x.min() + self.nncutoff\\n xmax = self.frametracks.x.max() - self.nncutoff\\n ymin = self.frametracks.y.min() + self.nncutoff\\n ymax = self.frametracks.y.max() - self.nncutoff\\n r = ftr.index[ (ftr.x > xmin) & (ftr.x < xmax) & \\\\\\n (ftr.y > ymin) & (ftr.y < ymax) ].values.astype(int)\\n else:\\n r = ftr.index.values.astype(int)\\n if self.fast:\\n return np.random.permutation(r)[:int(len(r) / 10)]\\n else:\\n return r\",\n \"def __setitem__(self, index, value):\\n if isinstance(index, int):\\n self.data.iloc[index] = value\\n elif isinstance(index, str):\\n self.data[index] = value\\n elif (\\n isinstance(index, tuple)\\n and len(index) == 2\\n and index[1] in self.data.columns\\n ):\\n self.data.loc[index] = value\\n else:\\n assert isinstance(index, slice) or len(index) > 0\\n self.data[index] = value\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62833464","0.61686224","0.6001683","0.58963263","0.5595757","0.5592054","0.5561091","0.55347645","0.5515838","0.55009526","0.5443674","0.5426867","0.5424508","0.53574157","0.52848315","0.52843475","0.5270382","0.5265261","0.52636987","0.5232416","0.52287483","0.5218011","0.52006","0.5191687","0.5168884","0.51576096","0.5157263","0.51524496","0.51509464","0.5142711","0.51330626","0.51321983","0.5130272","0.5129168","0.510425","0.5094242","0.5084387","0.507619","0.5073183","0.5058179","0.5053745","0.504681","0.5041993","0.50049454","0.49875817","0.4985264","0.49814788","0.49725112","0.4964899","0.49490634","0.49462718","0.49241176","0.488895","0.4882336","0.48816442","0.48816353","0.48746964","0.48705342","0.48697907","0.48648486","0.48612314","0.4860018","0.48554966","0.48375222","0.48347342","0.48266643","0.48264134","0.48217028","0.48137844","0.48105124","0.48033848","0.4802846","0.4801662","0.4800275","0.479146","0.4789232","0.4787341","0.47798526","0.47673938","0.47656357","0.47634012","0.47629058","0.4760772","0.4759898","0.47490904","0.47378734","0.47199416","0.47174084","0.471164","0.47109258","0.47068185","0.47022876","0.47015762","0.47013974","0.46979427","0.46968082","0.46912643","0.46864772","0.46855593","0.4683211"],"string":"[\n \"0.62833464\",\n \"0.61686224\",\n \"0.6001683\",\n \"0.58963263\",\n \"0.5595757\",\n \"0.5592054\",\n \"0.5561091\",\n \"0.55347645\",\n \"0.5515838\",\n \"0.55009526\",\n \"0.5443674\",\n \"0.5426867\",\n \"0.5424508\",\n \"0.53574157\",\n \"0.52848315\",\n \"0.52843475\",\n \"0.5270382\",\n \"0.5265261\",\n \"0.52636987\",\n \"0.5232416\",\n \"0.52287483\",\n \"0.5218011\",\n \"0.52006\",\n \"0.5191687\",\n \"0.5168884\",\n \"0.51576096\",\n \"0.5157263\",\n \"0.51524496\",\n \"0.51509464\",\n \"0.5142711\",\n \"0.51330626\",\n \"0.51321983\",\n \"0.5130272\",\n \"0.5129168\",\n \"0.510425\",\n \"0.5094242\",\n \"0.5084387\",\n \"0.507619\",\n \"0.5073183\",\n \"0.5058179\",\n \"0.5053745\",\n \"0.504681\",\n \"0.5041993\",\n \"0.50049454\",\n \"0.49875817\",\n \"0.4985264\",\n \"0.49814788\",\n \"0.49725112\",\n \"0.4964899\",\n \"0.49490634\",\n \"0.49462718\",\n \"0.49241176\",\n \"0.488895\",\n \"0.4882336\",\n \"0.48816442\",\n \"0.48816353\",\n \"0.48746964\",\n 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zed.uchicago.edu Utilities for spatio temporal analysis zed.uchicago.edu Given location tile boundaries and type category filter, creates the corresponding timeseries as a pandas DataFrame"},"document":{"kind":"string","value":"def getTS(self,_types=None,tile=None):\n\n assert(self._END is not None)\n TS_NAME = ('#'.join(str(x) for x in tile))+\"#\"+stringify(_types)\n\n lat_ = tile[0:2]\n lon_ = tile[2:4]\n\n if self._value_limits is None:\n df = self._logdf[self._columns]\\\n .loc[self._logdf[self._EVENT].isin(_types)]\\\n .sort_values(by=self._DATE).dropna()\n else:\n df = self._logdf[self._columns]\\\n .loc[self._logdf[self._EVENT]\\\n .between(self._value_limits[0],\n self._value_limits[1])]\\\n .sort_values(by=self._DATE).dropna()\n\n df = df.loc[(df[self._coord1] > lat_[0])\n & (df[self._coord1] <= lat_[1])\n & (df[self._coord2] > lon_[0])\n & (df[self._coord2] <= lon_[1])]\n df.index = df[self._DATE]\n df=df[[self._EVENT]]\n\n ts = [df.loc[self._trng[i]:self._trng[i + 1]].size for i in\n np.arange(self._trng.size - 1)]\n\n return pd.DataFrame(ts, columns=[TS_NAME],\n index=self._trng[:-1]).transpose()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def filter(df: pd.DataFrame = pd.DataFrame()):\n if df.empty:\n df = read()\n print('Filtering data...')\n df = df.dropna()\n df2 = pd.DataFrame()\n df2['Longitude'] = df['Longitude']\n df2['Latitude'] = df['Latitude']\n df2['Month'] = df['Date'].dt.strftime('%m').astype(int)\n df2['Day'] = df['Date'].dt.strftime('%d').astype(int)\n df2['Day_of_Week'] = df['Day_of_Week']\n df2['Time'] = np.array([t.timestamp() for t in df['Time']]) - df['Time'].min().timestamp()\n df2['Weather_Conditions'] = df['Weather_Conditions']\n return pd.get_dummies(df2)","def extract_tt_by_periods(ttri, periods, start_time, end_time, filters):\n logger = getLogger(__name__)\n # sess = conn.get_session()\n das = {}\n all_wz_features = {}\n all_wz_laneconfigs = {}\n\n # collecting daily data\n for prd in periods:\n logger.debug('>>>> retrieving data for %s' % prd.get_date_string())\n year = prd.start_date.year\n sdate = prd.start_date\n edate = prd.end_date\n if year not in das:\n da_tt = tt.TravelTimeDataAccess(year)\n da_tt_wz = tt_workzone.TTWorkZoneDataAccess(year)\n da_tt_wz_feature = wz_feature.WZFeatureDataAccess()\n da_tt_wz_lncfg = wz_laneconfig.WZLaneConfigDataAccess()\n da_tt_weather = tt_weather.TTWeatherDataAccess(year)\n da_tt_snowmgmt = tt_snowmgmt.TTSnowManagementDataAccess(year)\n da_tt_incident = tt_incident.TTIncidentDataAccess(year)\n da_tt_specialevent = tt_specialevent.TTSpecialeventDataAccess(year)\n das[year] = (\n da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\n da_tt_specialevent)\n\n (da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\n da_tt_specialevent) = das[year]\n\n # traveltimes = da_tt.list_by_period(ttri.id, self.prd)\n weathers = da_tt_weather.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.WeatherInfo] \"\"\"\n workzones = da_tt_wz.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.WorkZoneInfo] \"\"\"\n incidents = da_tt_incident.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.IncidentInfo] \"\"\"\n snowmgmts = da_tt_snowmgmt.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.SnowManagementInfo] \"\"\"\n specialevents = da_tt_specialevent.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.SpecialEventInfo] \"\"\"\n traveltimes = da_tt.list_by_period(ttri.id, prd)\n \"\"\":type: list[pyticas_tetres.ttrms_types.TravelTimeInfo] \"\"\"\n\n if not any(weathers):\n logger.debug('>>>> end of retrieving data for %s (no weather data)' % prd.get_date_string())\n continue\n\n extras = {\n 'weathers': {_tt.id: [] for _tt in traveltimes},\n 'workzones': {_tt.id: [] for _tt in traveltimes},\n 'incidents': {_tt.id: [] for _tt in traveltimes},\n 'specialevents': {_tt.id: [] for _tt in traveltimes},\n 'snowmgmts': {_tt.id: [] for _tt in traveltimes},\n }\n \"\"\":type: dict[str, dict[int, list]]\"\"\"\n\n _put_to_bucket(ttri, weathers, extras['weathers'], da_tt_weather, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, workzones, extras['workzones'], da_tt_wz, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, incidents, extras['incidents'], da_tt_incident, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, snowmgmts, extras['snowmgmts'], da_tt_snowmgmt, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, specialevents, extras['specialevents'], da_tt_specialevent, year, all_wz_features, all_wz_laneconfigs, das)\n\n for tti in traveltimes:\n _tt_weathers = extras['weathers'][tti.id]\n extdata = ExtData(tti,\n _tt_weathers[0] if _tt_weathers else None,\n extras['incidents'][tti.id],\n extras['workzones'][tti.id],\n extras['specialevents'][tti.id],\n extras['snowmgmts'][tti.id])\n\n if start_time <= tti.str2datetime(tti.time).time() <= end_time:\n for ef in filters:\n try:\n ef.check(extdata)\n except Exception as ex:\n tb.traceback(ex)\n logger.debug('>>>> end of retrieving data for %s (error occured 1)' % prd.get_date_string())\n continue\n else:\n for ef in filters:\n try:\n ef.check_outofrange(extdata)\n except Exception as ex:\n tb.traceback(ex)\n logger.debug('>>>> end of retrieving data for %s (error occured 2)' % prd.get_date_string())\n continue\n\n del extras\n logger.debug('>>>> end of retrieving data for %s' % prd.get_date_string())\n\n # sess.close()","def clean_and_save_timeseries(df):\n drop_columns = ['Lat', \n 'Long', \n 'Province/State']\n\n df.drop(columns=drop_columns, inplace = True)\n \n df_grouped = df.groupby(['Country/Region'], as_index=False).sum()\n df_grouped = df_grouped.set_index('Country/Region').transpose()\n df_grouped.reset_index(level=0, inplace=True)\n df_grouped.rename(columns={'index': 'Date'}, inplace=True)\n df_grouped['Date'] = pd.to_datetime(df_grouped['Date'])\n\n df_grouped.to_csv('../data/worldwide_timeseries.csv', index=False)","def get_time_series_at_location(data, lat, lon, feature):\n\n ts = data.sel(lat=lat, lon=lon, method='nearest', drop=True).to_series()\n index = ts.index.get_level_values('time')\n values = ts.values\n\n return pd.DataFrame({'Date': index.values, feature: values})","def zonal_statistics(wrksppath, timestamp, region, model):\n logging.info('\\nDoing Zonal Statistics on ' + region)\n # Define app workspace and sub-paths\n resampleds = os.path.join(wrksppath, region, model + '_GeoTIFFs_resampled')\n shp_path = os.path.join(wrksppath, region, 'shapefiles', 'ffgs_' + region + '.shp')\n\n stat_file = os.path.join(wrksppath, region, model + 'results.csv')\n\n # check that there are resampled tiffs to do zonal statistics on\n if not os.path.exists(resampleds):\n logging.info('There are no resampled tiffs to do zonal statistics on. Skipping Zonal Statistics')\n return\n\n # List all Resampled GeoTIFFs\n files = os.listdir(resampleds)\n files = [tif for tif in files if tif.endswith('.tif')]\n files.sort()\n\n # do zonal statistics for each resampled tiff file and put it in the stats dataframe\n stats_df = pd.DataFrame()\n for i in range(len(files)):\n logging.info('starting zonal statistics for ' + files[i])\n ras_path = os.path.join(resampleds, files[i])\n stats = rasterstats.zonal_stats(\n shp_path,\n ras_path,\n stats=['count', 'max', 'mean'],\n geojson_out=True\n )\n\n timestep = files[i][:10]\n\n # for each stat that you get out, write it to the dataframe\n logging.info('writing the statistics for this file to the dataframe')\n for j in range(len(stats)):\n\n temp_data = stats[j]['properties']\n temp_data.update({'Forecast Timestamp': timestamp})\n temp_data.update({'Timestep': timestep})\n\n temp_df = pd.DataFrame([temp_data])\n stats_df = stats_df.append(temp_df, ignore_index=True)\n\n # write the resulting dataframe to a csv\n logging.info('\\ndone with zonal statistics, rounding values, writing to a csv file')\n stats_df = stats_df.round({'max': 1, 'mean': 1})\n stats_df.to_csv(stat_file, index=False)\n\n # delete the resampled tiffs now that we dont need them\n logging.info('deleting the resampled tiffs directory')\n shutil.rmtree(resampleds)\n\n return","def time_of_visit_bins(df):\n mask_8_5 = (df[\"admit_hour_code\"] >= 8) & (df[\"admit_hour_code\"] < 17)\n mask_5_11 = (df[\"admit_hour_code\"] >= 17) & (df[\"admit_hour_code\"] < 23)\n mask_11_8 = (df[\"admit_hour_code\"] >= 23) | (df[\"admit_hour_code\"] < 8)\n\n df[\"hour_category\"] = np.where(mask_8_5, \"Open Hours\", \"Unknown\")\n df[\"hour_category\"] = np.where(mask_5_11, \"Evening\", df[\"hour_category\"])\n df[\"hour_category\"] = np.where(mask_11_8, \"Overnight\", df[\"hour_category\"])\n\n return df","def prep_data():\n # read data\n df = pd.read_csv(\"data/SF-crime-data_2016.csv\")\n\n # convert to datetime and extract hour\n df['datetime'] = pd.to_datetime(df[[\"Date\",\"Time\"]].apply(lambda x: x[0].split()[0] +\" \"+x[1], axis=1), format=\"%m/%d/%Y %H:%M\")\n df['hour'] = df['datetime'].dt.hour \n df.dropna(inplace=True)\n \n # filter out top 4 crimes\n top_4_crimes = df['Category'].value_counts()[:6].index.to_list()\n top_4_crimes\n top_4_crimes.remove(\"NON-CRIMINAL\")\n top_4_crimes.remove(\"OTHER OFFENSES\")\n\n return df[df[\"Category\"].isin(top_4_crimes)]","def get_post_df(temp_post_df):\n \n cat_dfs = []\n for num in np.arange(1080/2,0,-30):\n # making > null i.e keeping <=\n temp_post_df.loc[temp_post_df['event_time'] > int(num), 'event_time'] = np.nan\n for col in ['event_name', 'specialty', 'plan_type']:\n cat_df = temp_post_df.groupby([\"id\", col]).agg({\"event_time\": 'count'}).unstack(level=col)\n cat_df = cat_df/num\n cat_df.columns = ['__'.join(['normChange', col, name, str(int(num))]) for name in cat_df.columns.droplevel()]\n cat_dfs.append(cat_df) \n post_df = pd.concat(cat_dfs, axis = 1)\n return post_df.fillna(0)","def hotmap(datetime, room, query):\n\n #IMPORTANT: this query returns M-F values\n\n # get a df with date and count values, for a given room and date\n week_counts = pd.read_sql_query(query.format(date=datetime, room=room),\n connectDB())\n\n headcount = list(week_counts['count'])\n headcount = [linear_predictor(x) for x in headcount] # predict on all supplied values\n\n bins = list(week_counts['count'])\n # bins = [tertiary_classifier(x) for x in bins] # predict on all supplied values\n tert_dictionary = {'Empty': 0, 'Medium': 0.5, 'High': 1} # map to int as only ints can go in heatmap\n # bins = [tert_dictionary[x] for x in bins] # map with listcomp\n\n # 5 * 9 = 45, so all elements need to have 45 values. Days and hours are just multipled out.\n data = {'days': ['fri']*9 + ['thu']*9 + ['wed']*9 + ['tue']*9 + ['mon']*9,\n 'occupancy': headcount,\n 'hours': ['9', '10', '11', '12', '13', '14', '15', '16', '17']*5}\n\n week_counts['headcount'] = week_counts['count'].apply(linear_predictor)\n week_counts['bins'] = week_counts['count'].apply(tertiary_classifier)\n\n source = ColumnDataSource(ColumnDataSource.from_df(week_counts))\n\n\n # hover = HoverTool()\n #\n # tooltips = [(\"headcount\", \"@headcount\")]\n #\n # tools = [hover]\n\n # colors = ['#1abc9c', '#ec583a', '#474e5d'] # set colors; currently not shading on continuous features\n\n hm = HeatMap(data, x='hours', y='days', values='occupancy',\n title='Occupancy in room over the week'.format(room), stat=None,\n palette=palette, tools=None,\n hover_tool=True)\n\n legend = Legend(location=(0, -30))\n\n hm.add_layout(legend, 'right')\n\n all_plots = gridplot([[hm]])\n\n return all_plots","def load_data(city, month, day):\n\n if city == 'all':\n df = pd.read_csv(CITY_DATA['chicago'])\n df['city'] = 'chicago'\n ny = pd.read_csv(CITY_DATA['new york city'])\n ny['city'] = 'new york city'\n df = df.append(ny,sort = True)\n wa = pd.read_csv(CITY_DATA['washington'])\n wa['city'] = 'washington'\n df = df.append(wa,sort = True)\n else:\n df = pd.read_csv(CITY_DATA[city])\n df['city'] = CITY_DATA[city]\n\n df['Start Time'] = pd.to_datetime(df['Start Time']) #converts Start Time to datetime\n df['End Time'] = pd.to_datetime(df['End Time']) #converts End Time to datetime\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.weekday_name\n df['hour'] = df['Start Time'].dt.hour\n #print(df) #DataFrame\n\n #filter by month\n if month != 'all':\n month_name = ['january', 'february', 'march', 'april', 'may', 'june','july','august','september','october','november','december']\n month_num = month_name.index(month) + 1\n df = df[df['month'] == month_num]\n #filter by day\n if day != 'all':\n df = df[df['day_of_week'] == day.title()]\n\n recs = df['Start Time'].count()\n\n return df, recs","def manipulate_data(ds, var, predef_clim, predef_trnd, trn_yrs, all_yrs, \n apply_latw=True, apply_detrending=True, dropna=True):\n\n \n if((var=='SD')|(var=='sd')|(var=='snowc')): \n ds[var] = ds[var].where(ds[var]>=0, other=0.0)\n ds[var] = ds[var].where(ds[var]==0, other=1.0)\n #ds[var].values = Gauss_filter(ds[var].values, (0,3,3))\n \n \"\"\"\n if((var=='hgt')|(var=='z')|(var=='GPT')):\n months = ds.time.to_index().month; ssn_ends = (months==2)|(months==5)|(months==8)|(months==11)\n ds = ds.sel(time=ssn_ends)\n else: \n ds = ds.resample(time='3M').mean()\n \"\"\"\n \n ds = ds.resample(time='3M').mean()\n\n ds = ds.sel(time=slice(str(all_yrs[0])+'-01-01', str(all_yrs[-1])+'-12-31')) \n \n try: \n clim = predef_clim\n ds = ds.groupby('time.season') - clim\n print('Predefined climatology used')\n except:\n clim = ds.sel(time=slice(str(trn_yrs[0])+'-01-01', str(trn_yrs[-1])+'-12-31')).groupby('time.season').mean('time')\n ds = ds.groupby('time.season') - clim\n print('Climatology calculated from data')\n \n if(apply_latw): ds[var].values = lat_weighting(ds[var].values, \n ds.lat, ds.lon)\n if(dropna):\n ds = ds.stack(gridcell=('lat', 'lon')).dropna(dim='gridcell',how='any')\n else: \n ds = ds.stack(gridcell=('lat', 'lon')).fillna(0)\n \n \n trend_models = { }\n if(apply_detrending): \n ds = ds.load()\n for ssn in ('DJF', 'MAM', 'JJA', 'SON'):\n #ssn_idx = ds['time.season'] == ssn\n \n trn_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], trn_yrs))\n all_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], all_yrs))\n \n trn_x = np.array(ds.time[trn_idx].values.tolist()).reshape(-1,1)\n all_x = np.array(ds.time[all_idx].values.tolist()).reshape(-1,1)\n try:\n trend = predef_trnd[ssn].predict(all_x)\n trend_models[ssn] = predef_trnd[ssn]\n print('Predefined trend model used')\n except:\n #_, trend_model = define_trends(ds[var][trn_idx], trn_x)\n _, trend_model = define_trends(ds[var][all_idx], all_x)\n trend = trend_model.predict(all_x)\n trend_models[ssn] = trend_model\n print('Trends calculated from data')\n \n ds[var][all_idx] = ds[var][all_idx] - trend\n \n\n \n return ds, clim, trend_models","def warren_time_series(lat, lon, dates, variable='snow_depth'):\n\n my_func = {'snow_depth': snow_depth,\n 'swe': swe}\n\n #if not all([isinstance(d, dt.datetime) for d in dates]):\n # print ('Expects datetime objects')\n\n # If lat, lon are vectors, generate 2d grids\n # Need to add code to make sure x and y are DataArrays\n if (count_dims(lat) == 1):\n x, y = np.meshgrid(lon, lat)\n else:\n x, y = lon, lat\n \n if dates.size == 1:\n cube = my_func[variable](x,y,dates.dt.month)\n da = xr.DataArray(cube,\n coords={'lat': x, 'lon': y},\n dims=['lat', 'lon'])\n else:\n cube = [my_func[variable](x, y, m) for m in dates.dt.month.values]\n da = xr.concat(cube, dim='time')\n da['time'] = dates\n\n return da","def compliance_time_series(county, core_path , patterns_path, backfill = False, GEOID_type = 'CBG'):\n #Load data about places and patterns\n county_places = pd.read_csv(core_path+'places-'+ county +'.csv', index_col='safegraph_place_id') \n \n pattern_dates = [x[5:9] for x in sorted(os.listdir(patterns_path+'main-file-'+ county +'/'))]\n w = 0\n next_date = pattern_dates[w]\n #Create visitor table\n place_cts = pd.DataFrame() #First two months don't have weekly patterns, use empty data.frame\n\n #List files for social distancing metrics in that county\n months_path = '../social_distancing/social_dist_'+ county +'/'\n month_list = sorted(os.listdir(months_path))\n os.makedirs( '../stats/time_series/', exist_ok = True)\n #metrics dictionary to be filled looping through every day\n if not os.path.isfile('../stats/time_series/metrics_{}_CT.csv'.format(county)) or backfill:\n metrics = pd.DataFrame()\n existing_dates = []\n else:\n metrics = pd.read_csv('../stats/time_series/metrics_{}_CT.csv'.format(county), dtype = {'origin_census_tract':str})\n #dates already processed\n existing_dates = metrics['date'].unique() #series of unique dates\n\n #Initialize columns of new data frame\n if GEOID_type == 'CBG':\n columns= {'date':[], 'origin_census_block_group':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'total_expected_contacts':[], 'places_visited':[], 'cbgs_visited':[]}\n if GEOID_type == 'CT':\n columns= {'date':[], 'origin_census_tract':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'places_visited':[]} \n changed = False\n for month in month_list:\n #Loop through every day\n day_list = sorted(os.listdir(months_path + month))\n for day in day_list: \n date_name = month + '-' + day\n print(date_name)\n if month+'-'+day == next_date:\n w = w+1\n if w < len(pattern_dates):\n next_date = pattern_dates[w]\n if date_name in existing_dates:\n continue\n print(\"--changing to next patterns file\")\n county_patterns = pd.read_csv(patterns_path + 'main-file-{}/2020-{}-weekly-patterns.csv.gz'.format(county, date_name),\n index_col='safegraph_place_id')\n norm_factors = pd.read_csv(\n '../social_distancing/normalization/'+'normalization_{}.csv'.format(county),\n dtype={'origin_census_block_group':str})\n norm_factors.set_index('origin_census_block_group', drop =True, inplace=True)\n\n\n #Establish prior for hourly distribution of visits at the top_category level\n county_patterns= county_patterns.join(county_places[['top_category','sub_category']], how='inner')\n restaurants = county_patterns['top_category'] == 'Restaurants and Other Eating Places'\n county_patterns.loc[restaurants, 'top_category'] = county_patterns.loc[restaurants, 'sub_category']\n norm_factor = norm_factors.loc[norm_factors.date == date_name].norm_factor\n\n prior_dict = {}\n for category in county_patterns.top_category.value_counts().index:\n places_in_cat = county_patterns['top_category'] == category\n dirich_samples = [np.array(json.loads(x)) for x in county_patterns.loc[places_in_cat, 'visits_by_each_hour'] ] \n prior_dict[category] = dirichlet.getInitAlphas(dirich_samples)\n if GEOID_type == 'CBG':\n place_cbgs = ccc.place_cbg_contacts_table(\n county_patterns,\n prior_dict,\n norm_factor,\n GEOID_type)\n place_cbgs = place_cbgs.loc[place_cbgs['expected_contacts']>1]\n place_cbgs = place_cbgs.join(county_places[['location_name','latitude','longitude']], how='inner')\n place_cbgs.reset_index(inplace=True, drop=False)\n place_cbgs.set_index('origin_census_block_group', inplace=True, drop=True)\n if GEOID_type == 'CT':\n place_cts = ccc.place_cbg_contacts_table(\n county_patterns,\n prior_dict,\n norm_factor,\n GEOID_type)\n place_cts = place_cts.join(county_places[['location_name','latitude','longitude']], how='inner')\n place_cts.reset_index(inplace=True, drop=False)\n place_cts.set_index('origin_census_tract', inplace=True, drop=True)\n print('--computed bipartite contact network')\n\n if date_name in existing_dates:\n continue\n\n\n file_name = os.listdir(months_path+ '/' + month+'/'+day)[0]\n data_soc_dist = pd.read_csv(months_path+ '/' + month+'/'+day+'/'+file_name, dtype={'origin_census_block_group':str})\n data_soc_dist['median_distance_traveled_from_home'] = [0 if math.isnan(x) else x for x in data_soc_dist['distance_traveled_from_home']] #NOT NEEDED?\n \n if GEOID_type == 'CBG':\n [update_metrics_columns(row, date_name, place_cbgs, county_places, columns) for i, row in data_soc_dist.iterrows()]\n else:\n ####CREATE FUNCTION THAT AGGREGATES TO CENSUS TRACT\n data_soc_dist['origin_census_tract'] = [x[:-1] for x in data_soc_dist['origin_census_block_group']]\n ct_data_soc_dist = data_soc_dist.groupby('origin_census_tract').apply(ddd.aggregate_to_ct).reset_index()\n [update_metrics_columns_CT(row, date_name, place_cts, county_places, columns) for i, row in ct_data_soc_dist.iterrows()]\n changed = True\n\n print(\"--merging rows from new dates\")\n new_metrics = pd.DataFrame.from_dict(columns, orient='index').transpose()\n\n metrics = pd.concat([metrics, new_metrics], ignore_index=True)\n if changed:\n if GEOID_type == 'CT':\n metrics.to_csv('../stats/time_series/metrics_{}_CT.csv'.format(county),index=False)\n else:\n metrics.to_csv('../stats/time_series/metrics_{}_CBG.csv'.format(county),index=False)\n print(\"--finished updating time series for {}\".format(county))\n return(0)","def load_data(city, time_filter, month, day):\n print(\"\\nLoading data and preprocessing...\")\n start_time = time.time()\n df = pd.read_csv(CITY_DATA[city])\n\n #Create columns for the dataframe for convinience in calculations\n df['start_year'] = [int(value[0:4]) for value in df[\"Start Time\"]]\n df['start_month'] = [int(value[5:7]) for value in df[\"Start Time\"]]\n df['start_day'] = [int(value[8:10]) for value in df[\"Start Time\"]]\n df['start_hour'] = [int(value[11:13]) for value in df[\"Start Time\"]]\n df['start_min'] = [int(value[14:16]) for value in df[\"Start Time\"]]\n df['start_sec'] = [int(value[17:20]) for value in df[\"Start Time\"]]\n\n df['end_year'] = [int(value[0:4]) for value in df[\"End Time\"]]\n df['end_month'] = [int(value[5:7]) for value in df[\"End Time\"]]\n df['end_day'] = [int(value[8:10]) for value in df[\"End Time\"]]\n df['end_hour'] = [int(value[11:13]) for value in df[\"End Time\"]]\n df['end_min'] = [int(value[14:16]) for value in df[\"End Time\"]]\n df['end_sec'] = [int(value[17:20]) for value in df[\"End Time\"]]\n\n #Create a new column for the dataframe containing the day of the week\n day_of_week = []\n for index, row in df.iterrows():\n day_of_week.append( dt.date(row[\"start_year\"], row[\"start_month\"], row[\"start_day\"]).weekday())\n df['day_of_week'] = day_of_week\n\n #Apply the required filters and return the dataframe\n if time_filter == 'none':\n load_data_print(start_time)\n return df\n elif time_filter == 'month':\n df = df[(df['start_month']==MONTH[month])]\n load_data_print(start_time)\n return df\n elif time_filter == 'day':\n df = df[(df['day_of_week']==WEEKDAY[day])]\n load_data_print(start_time)\n return df\n else:\n print(\"\\nERROR on viable_unput_time value\")\n return 1","def filter_data(city, data_filter, Month, Day):\r\n directory = 'C:/Users/The Presence/Documents/Personal Document/Personal Development/Data Science and Analytics/Udacity (Python Course)/Labs Tasks-20200924T221854Z-001/Lab Results/Capstone Project/bikeshare-2/' + city \r\n df = pd.read_csv(directory)\r\n df_1 = df.copy() \r\n df['Start Time'] = pd.to_datetime(df['Start Time'])\r\n df['Months'] = df['Start Time'].dt.month_name(locale='English')\r\n df['Days'] = df['Start Time'].dt.day_name(locale='English')\r\n df['Hours'] = df['Start Time'].dt.hour\r\n if data_filter == 'None':\r\n df = df\r\n elif data_filter == 'Month':\r\n spef_mon = Month\r\n df_month = df[df.Months == spef_mon]\r\n df = df_month\r\n elif data_filter == 'Day':\r\n spef_day = Day\r\n df_day = df[df.Days == spef_day]\r\n df = df_day\r\n elif data_filter == 'Both':\r\n spef_mon, spef_day = Month, Day\r\n df_month = df[df.Months == spef_mon]\r\n df_month_day = df_month[df_month.Days == spef_day]\r\n df = df_month_day\r\n return(df, df_1)","def __init__(self, location, type, log_df = TWEET_HISTORY_DF):\n self.location = location\n self.type = type\n self.lat = c.LOCATIONS[self.location]['lat']\n self.lon = c.LOCATIONS[self.location]['lon']\n self.sunsetwx_response = py_sunsetwx.get_quality(self.lat, self.lon, self.type)\n self.log_df = log_df \n \n ## Find the time of the sunrise/sunset\n #### Lookup civil time of \"dawn\" if sunrise, \"dusk\" if sunset\n if type == 'sunrise':\n self.dawn_dusk = 'dawn'\n elif type == 'sunset':\n self.dawn_dusk = 'dusk'\n self.utc_time = pytz.utc.localize(datetime.strptime(self.sunsetwx_response['features'][0]['properties'][self.dawn_dusk]['civil'], '%Y-%m-%dT%H:%M:%SZ'))\n self.time_converted = self.utc_time.astimezone(pytz.timezone(c.LOCATIONS[location]['timezone']))","def prepare_for_influxdb(df):\n df = df.drop(columns=\"landkreis\", errors=\"ignore\") # prevent name collision in get_ags()\n df = get_ags(df)\n df[\"time\"] = df.apply(lambda x: 1000000000*int(datetime.timestamp((pd.to_datetime(x[\"timestamp\"])))), 1)\n df[\"measurement\"] = \"hystreet\"\n df[\"origin\"] = \"https://hystreet.com\"\n df = df.rename(columns={\n 'station_id': '_id',\n 'pedestrians_count': 'pedestrian_count',\n 'state': 'bundesland'\n })\n df['ags'] = pd.to_numeric(df['ags'])\n # import pdb; pdb.set_trace()\n return df","def streaming_weather_data(**kwargs):\n df = weather_data(['San Francisco'])\n df['time'] = [pd.Timestamp.now()]\n return df.set_index('time')","def create_table(f, geoinfo):\n bounds_cols = xb_points + yb_points\n df = pd.read_csv(f, delimiter=\";\", index_col=\"INDEX_RC\")\n df[duration_name] = parse_duration_level(f)\n df = df.join(geoinfo[[\"X_CENT_GEO\", \"Y_CENT_GEO\", \"Col\", \"Row\"]])\n df = df.rename(columns={\"Col\": x, \"Row\": y, \"X_CENT_GEO\": lon, \"Y_CENT_GEO\": lat})\n return df","def flat_ftr_hotspot(cities, perm_time_ranges=None, hourly=True, hotspot_types='loubar'):\n if isinstance(hotspot_types, str):\n hotspot_types = [hotspot_types]\n elif not isinstance(hotspot_types, (list, tuple)):\n raise ValueError('hotspot_types should be one type of str, list or tuple')\n\n if perm_time_ranges is None:\n perm_time_ranges = ['all_day', 'home_hour', 'work_hour']\n elif isinstance(perm_time_ranges, str):\n if perm_time_ranges not in ['all_day', 'home_hour', 'work_hour']:\n raise ValueError('perm_time_ranges should be one of [all_day, home_hour, work_hour]')\n perm_time_ranges = [perm_time_ranges]\n elif not isinstance(perm_time_ranges, (list, tuple)):\n raise ValueError('perm_time_ranges should be one type of None, str, list or tuple')\n\n flat_ftrs = []\n for i, city in enumerate(cities):\n # print(i, city['admin_id'])\n flat_ftr = {city['admin_lvl']: city['admin_id']}\n for hs_type in hotspot_types:\n features = city['features'][f'hs-{hs_type}']\n # add permanent hotspots indices\n for fname, values in features.items():\n for time_range in perm_time_ranges:\n flat_ftr[f'{hs_type}-{fname}-{time_range}'] = values[time_range]\n # add hourly hotspots indices\n if hourly:\n for item in values['hourly']:\n hour = item['hour']\n value = item['value']\n flat_ftr[f'{hs_type}-{fname}-hour{hour}'] = value\n flat_ftrs.append(flat_ftr)\n return flat_ftrs","def filter_dataframe(df, start_date_dt, end_date_dt):\n\n dff = df \n # df[\n # (df[\"timestamp\"].dt.date >= dt.date(start_date_dt.year, start_date_dt.month, start_date_dt.day))\n # & (df[\"timestamp\"].dt.date <= dt.date(end_date_dt.year, end_date_dt.month, end_date_dt.day))\n # ]\n # if (lat_min != -90) or (lat_max != 90):\n # dff = dff[\n # (dff[\"lat\"] >= lat_min)\n # & (dff[\"lat\"] <= lat_max)\n # ]\n # if (lon_min != -90) or (lon_max != 90):\n # dff = dff[\n # (dff[\"lon\"] >= lon_min)\n # & (dff[\"lon\"] <= lon_max)\n # ]\n\n return dff","def filter_meteo_data(self, startdate, enddate):\n self.all_meteo_data.columns.values[0]='Datum-tijd'\n self.all_meteo_data['datetime']=pd.to_datetime(self.all_meteo_data['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\n self.all_meteo_data.drop(['Datum-tijd'],axis=1, inplace=True)\n mask = (self.all_meteo_data['datetime'] > startdate) & (self.all_meteo_data['datetime'] <= enddate)\n meteodata = self.all_meteo_data.loc[mask].copy()\n meteodata.set_index('datetime',inplace=True)\n return meteodata","def test_plt_mag_time():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n # create the arrays per filter and readout pattern\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\n for i, val in enumerate(max_val_box):\n if '140' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(val)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(val)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif '110' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(val)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(val)\n nrsrapidd6_clear.append(np.NaN)\n else:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(val)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(val)\n # add to the bokeh data structure\n ta.source.data[\"nrsrapid_f140x\"] = nrsrapid_f140x\n ta.source.data[\"nrsrapid_f110w\"] = nrsrapid_f110w\n ta.source.data[\"nrsrapid_clear\"] = nrsrapid_clear\n ta.source.data[\"nrsrapidd6_f140x\"] = nrsrapidd6_f140x\n ta.source.data[\"nrsrapidd6_f110w\"] = nrsrapidd6_f110w\n ta.source.data[\"nrsrapidd6_clear\"] = nrsrapidd6_clear\n result = ta.plt_mag_time()\n\n assert bokeh_plot_type == type(result)","def select_features(dfp):\n df = dfp[['5', '7', '12']].copy() \n df.columns=['type', 'duration','location']\n for col in df.columns:\n strings = df[col].unique()\n if col != \"location\":\n mapper = dict( zip(strings, range(len(strings))) )\n df[col].replace(mapper, inplace=True)\n else:\n df[col] = df[col].str.contains(\"LONDON\").astype(int)\n return df","def pull_traffic():\n ga_conn = initialize_analyticsreporting()\n\n device_traffic = get_report(\n ga_conn=ga_conn,\n dimensions=[{'name': 'ga:deviceCategory'},{'name': 'ga:yearMonth'},])\n device_traffic = melt_ga(device_traffic,\n a='Traffic',\n c='Device',\n d='deviceCategory')\n\n geo_traffic = get_report(\n ga_conn=ga_conn,\n dimensions=[{'name': 'ga:country'},{'name': 'ga:yearMonth'},])\n bucketed_countries = geo_traffic.apply(lambda row: bucket_countries(row), axis=1)\n geo_traffic['country'] = bucketed_countries\n geo_traffic = geo_traffic.groupby(['yearMonth','country'])\\\n .sum()\\\n .reset_index()\n geo_traffic = melt_ga(geo_traffic,\n a='Traffic',\n c='Geography',\n d='country')\n\n channel_traffic = get_report(\n ga_conn=ga_conn,\n dimensions=[{'name': 'ga:channelGrouping'},{'name': 'ga:yearMonth'},])\n bucketed_channels = channel_traffic.apply(lambda row: bucket_channels(row), axis=1)\n channel_traffic['channelGrouping'] = bucketed_channels\n channel_traffic = channel_traffic.groupby(['yearMonth','channelGrouping'])\\\n .sum()\\\n .reset_index()\n channel_traffic = melt_ga(channel_traffic,\n a='Traffic',\n c='Channel',\n d='channelGrouping')\n df_out = pd.concat([device_traffic, geo_traffic, channel_traffic])\n return df_out","def load_data(city, month, day):\n df = pd.read_csv(CITY_DATA[city])\n #convert Start Time to datetime for filtering\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n #add columns for filters to match on\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.weekday_name\n df['hour'] = df['Start Time'].dt.hour\n\n #filter by month and day if not all\n #limited index options to available date range in the data set\n if month != 'all':\n months = ['january', 'february', 'march', 'april', 'may', 'june']\n month_index = 1 + months.index(month)\n\n # filter by month to create the new dataframe\n df = df.loc[df['month'] == month_index]\n\n if day != 'all':\n # filter by day of week to create the new dataframe\n df = df.loc[df['day_of_week'] == day.title()]\n\n return df","def filtering(self, Z):\n coord_x_est = [0]\n coord_y_est = [0]\n time_est = [Z[['time']].iloc[0][0]]\n for i in range(1, len(Z.x)):\n new_dt = Z[['time']].iloc[i][0] - time_est[-1]\n while (new_dt > 150):\n new_state = [coord_x_est[-1], coord_y_est[-1], time_est[-1]]\n new_state = self._restore(new_state)\n coord_x_est.append(new_state[0])\n coord_y_est.append(new_state[1])\n time_est.append(new_state[2])\n new_dt -= 100\n\n# self.configure(new_dt)\n self._predict()\n mes = Z[['x', 'y']].iloc[i].to_numpy()\n self._update(np.resize(mes, (2, 1)))\n # save for latter plotting\n coord_x_est.append(self.x[0])\n coord_y_est.append(self.x[4])\n time_est.append(int(Z[['time']].iloc[i]))\n\n return pd.DataFrame({'X_f': coord_x_est,\n 'Y_f': coord_y_est,\n 'time': time_est\n })","def _tseries_from_nifti_helper(coords, data, TR, filter, normalize, average):\r\n if coords is not None:\r\n out_data = np.asarray(data[coords[0], coords[1], coords[2]])\r\n else:\r\n out_data = data\r\n\r\n tseries = ts.TimeSeries(out_data, sampling_interval=TR)\r\n\r\n if filter is not None:\r\n if filter['method'] not in ('boxcar', 'fourier', 'fir', 'iir'):\r\n e_s = \"Filter method %s is not recognized\" % filter['method']\r\n raise ValueError(e_s)\r\n else:\r\n #Construct the key-word arguments to FilterAnalyzer:\r\n kwargs = dict(lb=filter.get('lb', 0),\r\n ub=filter.get('ub', None),\r\n boxcar_iterations=filter.get('boxcar_iterations', 2),\r\n filt_order=filter.get('filt_order', 64),\r\n gpass=filter.get('gpass', 1),\r\n gstop=filter.get('gstop', 60),\r\n iir_ftype=filter.get('iir_ftype', 'ellip'),\r\n fir_win=filter.get('fir_win', 'hamming'))\r\n\r\n F = tsa.FilterAnalyzer(tseries, **kwargs)\r\n\r\n if filter['method'] == 'boxcar':\r\n tseries = F.filtered_boxcar\r\n elif filter['method'] == 'fourier':\r\n tseries = F.filtered_fourier\r\n elif filter['method'] == 'fir':\r\n tseries = F.fir\r\n elif filter['method'] == 'iir':\r\n tseries = F.iir\r\n\r\n if normalize == 'percent':\r\n tseries = tsa.NormalizationAnalyzer(tseries).percent_change\r\n elif normalize == 'zscore':\r\n tseries = tsa.NormalizationAnalyzer(tseries).z_score\r\n\r\n if average:\r\n if coords is None:\r\n tseries.data = np.mean(np.reshape(tseries.data,\r\n (np.array(tseries.shape[:-1]).prod(),\r\n tseries.shape[-1])),0)\r\n else:\r\n tseries.data = np.mean(tseries.data, 0)\r\n\r\n return tseries","def load_data(city, month, day):\n df = pd.read_csv(CITY_DATA[city])\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n#changed 'weekday_name' to just 'weekday' which outputs the weekday as integer\n # extract month, day of week and hour from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n df['dow'] = df['Start Time'].dt.weekday\n df['hour'] = df['Start Time'].dt.hour\n\n # filter by month if applicable\n if month != 'all':\n # use the index of the months list to get the corresponding int\n month = months.index(month) + 1\n # filter by month to create the new dataframe\n df = df[df['month'] == month]\n\n# problem with the 'day'-filter, if a day (not 'all') is applied, the output is not right\n # filter by day of week if applicable\n if day != 'all':\n\n # filter by day of week to create the new dataframe\n day = days.index(day) + 1\n df = df[df['dow'] == day]\n\n return df","def create_dataframe():\n df = pd.read_csv(\"data/311-calls.csv\", parse_dates=[\"created\"])\n df[\"created\"] = df[\"created\"].dt.date\n df.drop(columns=[\"incident_zip\"], inplace=True)\n num_complaints = df[\"complaint_type\"].value_counts()\n to_remove = num_complaints[num_complaints <= 30].index\n df.replace(to_remove, np.nan, inplace=True)\n return df","def prep_data(df, datatype):\n\n start = time.time()\n if datatype == \"training\":\n df = drop_cols(df, \"gross_bookings_usd\")\n df = add_category(df)\n df = get_train_data(df)\n\n df = stats(df)\n print(\"(1/6 - train only) add categories and downsample train data: \", np.round((time.time() - start)*1000 / 60, 2), \"min\")\n\n start = time.time()\n df = competitors(df)\n print(\"(2/6) competitors: \", np.round((time.time() - start) / 60, 2), \"min\")\n\n start = time.time()\n df = seasonality(df)\n print(\"(3/6) seasons: \", np.round((time.time() - start)*1000 / 60, 2), \"min\")\n\n start = time.time()\n df = missing_values(df)\n print(\"(4/6) missing values: \", np.round((time.time() - start)*1000 / 60, 2), \"min\")\n\n start = time.time()\n df = price_star_diff(df)\n print(\"(5/6) price and star difference: \", np.round((time.time() - start)*1000 / 60, 2), \"min\")\n\n # print(\"er zijn nog zoveel nans(2): \", df.isnull().sum().sum())\n # pd.set_option('display.max_rows', None)\n # print(df.isnull().sum())\n\n\n start = time.time()\n df = scale(df)\n print(\"(6/6) scaling: \", np.round((time.time() - start) / 60, 2), \"min\")\n\n start = time.time()\n df = categorical(df)\n print(\"(6/6) categorical transformation: \", np.round((time.time() - start) / 60, 2), \"min\")\n\n return df","def outliers_solid_tsds():\n timestamp1 = np.arange(np.datetime64(\"2021-01-01\"), np.datetime64(\"2021-02-10\"))\n target1 = [np.sin(i) for i in range(len(timestamp1))]\n target1[10] += 10\n\n timestamp2 = np.arange(np.datetime64(\"2021-01-01\"), np.datetime64(\"2021-02-10\"))\n target2 = [np.sin(i) for i in range(len(timestamp2))]\n target2[8] += 8\n target2[15] = 2\n target2[26] -= 12\n\n df1 = pd.DataFrame({\"timestamp\": timestamp1, \"target\": target1, \"segment\": \"1\"})\n df2 = pd.DataFrame({\"timestamp\": timestamp2, \"target\": target2, \"segment\": \"2\"})\n\n df = pd.concat([df1, df2], ignore_index=True)\n\n df = df.pivot(index=\"timestamp\", columns=\"segment\")\n df = df.reorder_levels([1, 0], axis=1)\n df = df.sort_index(axis=1)\n df.columns.names = [\"segment\", \"feature\"]\n tsds = TSDataset(df, \"1d\")\n return tsds","def create_dt(is_train=True):\n train = pd.read_csv(\"/Users/milesklingenberg/Documents/UWMSBA/590/Data/train_house-1.csv\", dtype=CAT_DTYPES)\n train = pd.DataFrame(train)\n for col, col_dtype in CAT_DTYPES.items():\n if col_dtype == \"category\":\n train[col] = train[col].astype('category').cat.codes\n\n #drop_columns = ['']\n #was going to drop columns\n\n dt = train\n\n return dt","def shapely_tileset(processed_query,min_ovp = 0,max_ovp = 1,\n n_neg = None,buffer = 0):\n types, xx, yy, qual, tags = [],[],[],[],[]\n z = processed_query['zoom']\n for elem in processed_query['elements']:\n for tile in elem['tiles']:\n qq = tile[1]\n if qq >= min_ovp and qq <= max_ovp:\n x,y,_ = find_tile_coords(tile[0],z)\n xx.append(x)\n yy.append(y)\n qual.append(tile[1])\n tags.append(json.dumps(elem['tags']))\n types.append(elem['type'])\n \n pos_df = pd.DataFrame({\n 'z': z, 'x' : xx, 'y': yy, \n 'entity': types,\n 'overlap': qual,'tags': tags,\n 'placename': processed_query['query_info']['placename']\n }) \\\n .drop_duplicates(subset = ['x','y']) \\\n .sort_values(by = ['x','y'])\n if n_neg is None: n_neg = pos_df.shape[0]\n negt = sample_complement(pos_df['x'],pos_df['y'],n_neg,buffer)\n neg_df = pd.DataFrame({'z': z,'x': negt[0],'y': negt[1]}) \\\n .sort_values(by = ['x','y'])\n return { \n 'positive': add_latlon(pos_df),\n 'negative': add_latlon(neg_df)\n }","def process_underway():\n uway_path = CMORE_BULA_path + \"underway/bu1uw2.dat\"\n df = pd.read_csv(\n uway_path,\n delim_whitespace=True,\n names=[\n \"year\",\n \"doy\",\n \"hour\",\n \"minute\",\n \"lat\",\n \"lon\",\n \"salinity\",\n \"temperature\",\n \"chloropigment\",\n \"quality_flags\",\n ],\n )\n # builds time out of year, doy,hour,min\n df[\"time\"] = pd.to_datetime(\n (df[\"year\"]).astype(str)\n + (df[\"doy\"]).astype(str)\n + (df[\"hour\"].astype(str))\n + (df[\"minute\"].astype(str)),\n format=\"%Y%j%H%M\",\n )\n # split flags into variable flag columns\n df[\"salinity_flag\"] = (\n df[\"quality_flags\"].astype(str).str.split(\"\", expand=True).iloc[:, [1]]\n )\n df[\"temperature_flag\"] = (\n df[\"quality_flags\"].astype(str).str.split(\"\", expand=True).iloc[:, [2]]\n )\n df[\"chloropigment_flag\"] = (\n df[\"quality_flags\"].astype(str).str.split(\"\", expand=True).iloc[:, [3]]\n )\n # removes old cols and reorders\n df.drop([\"year\", \"doy\", \"hour\", \"minute\", \"quality_flags\"], axis=1, inplace=True)\n df = df[\n [\n \"time\",\n \"lat\",\n \"lon\",\n \"salinity\",\n \"temperature\",\n \"chloropigment\",\n \"salinity_flag\",\n \"temperature_flag\",\n \"chloropigment_flag\",\n ]\n ]\n return df","def make_dataset_for_time_series(date_key):\n by_date_key = time_series_data[time_series_data['date_key'] == date_key]\n by_date_key.sort_values('datetime', inplace=True)\n return ColumnDataSource(by_date_key), ColumnDataSource(by_date_key.interpolate('slinear'))","def make_time_series(rics: list, fields: list, start_date: str, end_date: str, name: str) -> None:\n df = get_time_series(rics, fields, start_date, end_date)\n df.dropna(inplace=True)\n to_csv(df, name, time_series=True)","def read_and_prepare_dataframe(start_date='1980-01-01'):\n \n # Read the dataset and rename 'dt' to 'Date'\n df = pd.read_csv('Data/GlobalLandTemperaturesByCountry.csv', parse_dates=['dt'])\n df.rename(columns={'dt':'Date'}, inplace=True)\n \n # Filter for Canada\n df = df[df['Country']=='Canada']\n \n # Filter out data prior to start date\n df = df[df['Date'] >= start_date]\n \n # To ensure data is sorted\n df = df.sort_values('Date')\n \n # Set index to Date and return the final dataframe\n return df.set_index('Date')","def get_time_series(self, tile, year, loc, px, py, cols=1, rows=1, step=1, save_dir='txt/'):\n\n # keys = ['sza', 'saa', 'vza', 'vaa', 'qa', 'b01', 'b02', 'b03', 'b04', 'b05', 'b06', 'b07']\n\n timeBefore = time.clock()\n\n f_list = np.array(glob.glob(loc + '%s_%d_*_1km.nc' % (tile, year)))\n\n # if there are no files for this year\n if f_list.shape[0] == 0:\n print 'Data for year %d and tile %s not found' % (year, tile)\n return -1\n\n # get DOYs from file name\n doys0 = np.sort([int(s.split('_')[4]) for s in f_list])\n # print 'doys0', doys0\n\n # arrange DOYs according to step. I.e. we assume that...\n # step = 7\n doys = np.zeros(doys0.shape).astype(int)\n doys_real = np.zeros(doys0.shape).astype(int)\n\n for jj, ii in enumerate(xrange(0, doys0.shape[0], step)):\n # doys = np.append(doys, doys0[ii:ii+7])\n doys[ii:ii+step] = jj + 1#doys0[ii]\n doys_real[ii:ii+step] = doys0[ii]\n\n ind = np.argsort([int(s.split('_')[4]) for s in f_list])\n f_list = f_list[ind]\n\n # print 'loc:', loc\n # print 'tile:', tile\n # print 'year:', year\n # print 'f_list:', f_list[0]\n\n output = {}\n for f in f_list:\n print 'f:', f\n try:\n ds = nc.Dataset(f)\n except:\n print 'something wrong with %s' % f\n continue\n\n # if a dataset is empty\n # i.e. if it has less than 2 bands\n # i.e. vza, vaa, sza, saa, proj, geo, qa, b1, b2\n if len(ds.variables.keys()) < 9:\n print 'Dataset exists but empty:'\n print 'loc:', loc\n print 'tile:', tile\n print 'year:', year\n print 'f_list:', f_list[0]\n print ''\n # return -1\n else:\n break\n if f == f_list[-1]:\n print 'all datasets are empty'\n return -1\n\n for key in ds.variables.keys():\n if len(ds.variables[key].shape) == 2:\n # output[key] = np.zeros((f_list.shape[0], ds.variables[key].shape[0], ds.variables[key].shape[1]))\n output[key] = np.zeros((f_list.shape[0], cols, rows))\n if len(ds.variables[key].shape) == 1:\n output[key] = np.zeros(ds.variables[key].shape[0]).astype(str)\n\n for i, fname in enumerate(f_list):\n #print fname\n ds = nc.Dataset(fname)\n for key in ds.variables.keys():\n if len(ds.variables[key].shape) == 2:\n\n try:\n # print ds.variables[key][px:px+cols, py:py+rows]\n output[key][i, :, :] = ds.variables[key][px:px+cols, py:py+rows]\n except:\n print 'something wrong in output[%s][%d, :, :]' % (key, i)\n print 'output:', output[key][i, :, :].shape\n print 'ds.variables:', ds.variables[key][px:px+cols, py:py+rows].shape\n\n if len(ds.variables[key].shape) == 1:\n output[key][:] = ds.variables[key][:]\n\n # print 'output.keys:', output.keys()\n\n QA_OK = np.array([8, 72, 136, 200, 1288, 2056, 2120, 2184, 2248])\n qa_passer = np.logical_or.reduce([output['qa'] == x for x in QA_OK])\n\n for b in [1,2,3,4,5,7]:\n\n qa_passer[output[\"b0%d\" % b] <= 0] = 0.\n qa_passer[output[\"b0%d\" % b] >= 10000] = 0.\n\n # if, for this pixel, we have just a few observations we don't need them\n if np.sum(qa_passer) < 2:\n qa_passer[:] = 0\n\n output['qa_passer'] = qa_passer\n [bin(b) for b in QA_OK]\n\n #doys = np.array([int(g.GetRasterBand(b+1).GetMetadata()['DoY']) for b in xrange(g.RasterCount)])\n #years = np.array([int(g.GetRasterBand(b + 1).GetMetadata()['Year']) for b in xrange(g.RasterCount)])\n output['doys'] = doys\n output['doys_real'] = np.unique(doys_real)\n output['years'] = np.ones(doys.shape) * year\n\n output[\"sza\"] = output[\"sza\"] / 100.\n output[\"saa\"] = output[\"saa\"] / 100.\n output[\"vza\"] = output[\"vza\"] / 100.\n output[\"vaa\"] = output[\"vaa\"] / 100.\n output['b01'] = output['b01'] / 10000.\n output['b02'] = output['b02'] / 10000.\n output['b03'] = output['b03'] / 10000.\n output['b04'] = output['b04'] / 10000.\n output['b05'] = output['b05'] / 10000.\n output['b06'] = output['b06'] / 10000.\n output['b07'] = output['b07'] / 10000.\n\n # print 'qa_passer:', output['qa_passer']\n #print output['b01']\n\n #pkl.dump(output, open(f_out, 'wb'))\n\n timeAfter = time.clock()\n elapsed_time = timeAfter - timeBefore\n print 'read time series time (s): ', elapsed_time\n\n print 'Read MODIS for year %d data is done' % year\n return output","def temperature_range_analysis(temp_df, dt_from, dt_to):\n df = copy.deepcopy(temp_df)\n\n df_unique_sensors = df[[\"sensor_id\", \"name\"]].drop_duplicates([\"sensor_id\", \"name\"])\n\n sensor_ids = df_unique_sensors[\"sensor_id\"].tolist()\n sensor_names = df_unique_sensors[\"name\"].tolist()\n\n # extracting date from datetime\n df[\"date\"] = pd.to_datetime(df[\"timestamp\"].dt.date)\n\n # Reseting index\n df.sort_values(by=[\"timestamp\"], ascending=True).reset_index(inplace=True)\n\n data_by_sensor_id = {}\n for sensor_name, sensor_id in zip(sensor_names, sensor_ids):\n df_sensor = df[df[\"sensor_id\"] == sensor_id]\n # grouping data by date-hour and sensor id\n sensor_grp = df_sensor.groupby(\n by=[\n df_sensor.timestamp.map(\n lambda x: \"%04d-%02d-%02d-%02d\" % (x.year, x.month, x.day, x.hour)\n ),\n \"date\",\n ]\n )\n\n # estimating hourly temperature mean values\n sensor_grp_temp = sensor_grp[\"temperature\"].mean().reset_index()\n\n try:\n bins = TEMP_BINS[SENSOR_CATEORIES[sensor_id]]\n except KeyError:\n logging.error(\n f\"Don't know how to categorise or bin sensor {sensor_id} \"\n \"in the dashboard.\"\n )\n continue\n # binning temperature values\n sensor_grp_temp[\"temp_bin\"] = pd.cut(sensor_grp_temp[\"temperature\"], bins)\n\n # converting bins to str\n sensor_grp_temp[\"temp_bin\"] = sensor_grp_temp[\"temp_bin\"].astype(str)\n\n # get bin counts for each sensor-day combination\n sensor_grp_date = sensor_grp_temp.groupby(by=[\"date\", \"temp_bin\"])\n\n sensor_cnt = sensor_grp_date[\"temperature\"].count().reset_index()\n sensor_cnt.rename(columns={\"temperature\": \"temp_cnt\"}, inplace=True)\n\n # Adding missing date/temp_bin combos\n bins_list, df_list = resample(sensor_cnt, bins, dt_from, dt_to)\n\n data_by_sensor_id[sensor_id] = {\n \"name\": sensor_name,\n \"bins\": bins_list,\n \"data\": [\n {\n \"date\": df[\"date\"].dt.strftime(\"%Y-%m-%d\").to_list(),\n \"count\": df[\"temp_cnt\"].to_list(),\n }\n for df in df_list\n ],\n }\n return len(data_by_sensor_id.keys()), json.dumps(data_by_sensor_id)","def get_cut_level_dataframe(start, end, level):\n entries = get_cut_dataframe(start, end)\n data = entries.copy()\n break_point = break_level(start, end, level)\n if len(break_point) != 0:\n data['datetime'] = data['to'].map(ts2date)\n\n ind = 0\n new_row = 0\n for index, row in entries.iterrows():\n if row['to'] == break_point[ind]:\n ind += 1\n elif row['from'] < break_point[ind] < row['to']:\n row = entries.iloc[[index]].copy()\n row.loc[index, 'delta'] = abs(break_point[ind]-row.loc[index, 'from'])\n row.loc[index, 'to'] = break_point[ind]\n row.loc[index, 'datetime'] = ts2date(row.loc[index, 'from'])\n new_ind = index + new_row\n data.loc[new_ind, 'delta'] = abs(break_point[ind]-data.loc[new_ind, 'to'])\n data.loc[new_ind, 'from'] = break_point[ind]\n data = pd.concat([data[:new_ind], row, data[new_ind:]])\n data = data.reset_index(drop=True)\n new_row += 1\n ind += 1\n if ind >= len(break_point):\n break\n\n data['datetime'] = data['from'].map(ts2datetime)\n data['date_agg'] = data['from'].map(lambda x: ts2str_level(x, level))\n return data","def load_data(city, month, day):\n #create the DataFrame\n #I'll be honest, I was struggling with this bit of code so I searched the internet and found what I needed to get started.\n df = pd.read_csv(CITY_DATA[city])\n\n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n # extract month and day of week and hour from Start Time to create new columns. New columns are needed for filtering.\n df['month'] = df['Start Time'].dt.month\n df['day'] = df['Start Time'].dt.day\n df['hour'] = df['Start Time'].dt.hour\n\n # filter by month if applicable\n if month != 'all':\n month = MONTHS.index(month) + 1\n df = df[ df['month'] == month ]\n\n # filter by day of week if applicable\n if day != 'all':\n # filter by day of week to create the new dataframe\n df = df[ df['day'] == day.title()]\n\n return df","def load_data(city, month, day, filters):\n print('\\n\\n*****************LOADING DATA*****************')\n start_time = time.time()\n\n dataframe = pd.read_csv(CITY_DATA[city])\n print('City: ', city)\n print('Total data points found: ', len(dataframe))\n\n # Changing start time to datetime format\n dataframe['Start Time'] = pd.to_datetime(dataframe['Start Time'])\n dataframe['Day'] = dataframe['Start Time'].dt.weekday\n dataframe['Month'] = dataframe['Start Time'].dt.month\n dataframe['Hour'] = dataframe['Start Time'].dt.hour\n\n # Displaying statistics for whole data\n if filters == 'Month':\n popular_month, count_popular_month = common_month(dataframe)\n print('Most popular month for travelling: ', popular_month)\n print('Counts: ', count_popular_month)\n\n elif filters == 'Day':\n popular_day, count_popular_day = common_day(dataframe)\n print('Most popular day for travelling: ', get_day_name(popular_day))\n print('Counts: ', count_popular_day)\n\n elif filters == 'Both':\n popular_month, count_popular_month = common_month(dataframe)\n popular_day, count_popular_day = common_day(dataframe)\n print('\\nMost popular month for travelling: ', popular_month)\n print('Counts: ', count_popular_month)\n print('\\nMost popular day for travelling: ', get_day_name(popular_day))\n print('Counts: ', count_popular_day)\n\n print(\"\\nThis took {} seconds.\".format(time.time() - start_time))\n print('----------------------------------------------')\n\n print('\\n\\n***************APPLYING FILTERS***************')\n start_time = time.time()\n months = ['January', 'February', 'March', 'April', 'May', 'June']\n\n if filters == 'Month':\n print('Filter:\\n Month = ', month.title())\n dataframe = dataframe[dataframe['Month'] == months.index(month) + 1]\n elif filters == 'Day':\n print('Filter: Day = ', day)\n dataframe = dataframe[dataframe['Day'] == get_day_number(day)]\n elif filters == 'Both':\n print('Filter:\\n Month = {}\\n Day = {}'.format(month.title(), day))\n dataframe = dataframe[dataframe['Month'] == months.index(month) + 1]\n dataframe = dataframe[dataframe['Day'] == get_day_number(day)]\n else:\n print('Filter: ', filters)\n\n print('Total data points after applying filter: ', len(dataframe))\n print(\"\\nThis took {} seconds.\".format(time.time() - start_time))\n print('----------------------------------------------')\n\n return dataframe","def load_data(city, month, day):\n # Read csv file for city\n df = pd.read_csv(city)\n ## Make sure numbers/dates/etc. are treated appropriately for our needs\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n df['End Time'] = pd.to_datetime(df['End Time'])\n #df[['Start Time', 'End Time']] = df[['Start Time', 'End Time']].apply(pd.to_datetime)\n # Filter dates/times where applicable\n ## Define tuples for months/days so we can index as numerals\n month_tuple = ('january', 'february', 'march', 'april', 'may', 'june')\n day_tuple = ('monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday')\n if month != '':\n #Index months as integers; in this case we need to add 1 to each to convert them\n month_int = month_tuple.index(str(month.lower()))+1\n #Create new columns indicating the start and end months respectively\n #This preserves the original data while still allowing us to filter\n df['Start Month'] = df['Start Time'].dt.month\n df['End Month'] = df['End Time'].dt.month\n df = df[df['Start Month']== month_int]\n return df\n elif day != '':\n #Index days as integers\n day_int = day_tuple.index(str(day.lower()))\n #Create new columns indicating the start and end weekdays respectively\n #This preserves the original data while still allowing us to filter\n df['Start Weekday'] = df['Start Time'].dt.dayofweek\n df['End Weekday'] = df['End Time'].dt.dayofweek\n df = df[df['Start Weekday']== day_int]\n return df\n else:\n return df","def load_data(city, month, day):\n df = pd.read_csv(CITY_DATA [city])\n \n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n \n # extract month and day of week from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n df['hour'] = df['Start Time'].dt.hour\n df['day_of_week'] = df['Start Time'].dt.weekday_name\n \n # Do the filter below\n # no filter is applied\n if month == 0 and day == 0:\n return df\n # only filter by day\n elif month == 0:\n df = df[df['day_of_week']==day]\n # only filter by month\n elif day == 0:\n df = df[df['month']== month]\n else:\n df = df[df['day_of_week']==day]\n df = df[df['month']== month]\n \n return df","def get_category_timetable(categ_ids, start_dt, end_dt, detail_level='event', tz=utc, from_categ=None, grouped=True,\n includible=lambda item: True):\n day_start = start_dt.astimezone(utc)\n day_end = end_dt.astimezone(utc)\n dates_overlap = lambda t: (t.start_dt >= day_start) & (t.start_dt <= day_end)\n\n items = defaultdict(lambda: defaultdict(list))\n\n # first of all, query TimetableEntries/events that fall within\n # specified range of dates (and category set)\n events = _query_events(categ_ids, day_start, day_end)\n if from_categ:\n events = events.filter(Event.is_visible_in(from_categ.id))\n for eid, tt_start_dt in events:\n if tt_start_dt:\n items[eid][tt_start_dt.astimezone(tz).date()].append(tt_start_dt)\n else:\n items[eid] = None\n\n # then, retrieve detailed information about the events\n event_ids = set(items)\n query = (Event.query\n .filter(Event.id.in_(event_ids))\n .options(subqueryload(Event.person_links).joinedload(EventPersonLink.person),\n joinedload(Event.own_room).noload('owner'),\n joinedload(Event.own_venue),\n joinedload(Event.category).undefer('effective_icon_data'),\n undefer('effective_protection_mode')))\n scheduled_events = defaultdict(list)\n ongoing_events = []\n events = []\n for e in query:\n if not includible(e):\n continue\n if grouped:\n local_start_dt = e.start_dt.astimezone(tz).date()\n local_end_dt = e.end_dt.astimezone(tz).date()\n if items[e.id] is None:\n # if there is no TimetableEntry, this means the event has not timetable on that interval\n for day in iterdays(max(start_dt.date(), local_start_dt), min(end_dt.date(), local_end_dt)):\n # if the event starts on this date, we've got a time slot\n if day.date() == local_start_dt:\n scheduled_events[day.date()].append((e.start_dt, e))\n else:\n ongoing_events.append(e)\n else:\n for start_d, start_dts in items[e.id].items():\n scheduled_events[start_d].append((start_dts[0], e))\n else:\n events.append(e)\n\n # result['events'][date(...)] -> [(datetime(....), Event(...))]\n # result[event_id]['contribs'][date(...)] -> [(TimetableEntry(...), Contribution(...))]\n # result['ongoing_events'] = [Event(...)]\n if grouped:\n result = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))\n else:\n result = defaultdict(lambda: defaultdict(list))\n\n result.update({\n 'events': scheduled_events if grouped else events,\n 'ongoing_events': ongoing_events\n })\n\n # according to detail level, ask for extra information from the DB\n if detail_level != 'event':\n query = _query_blocks(event_ids, dates_overlap, detail_level)\n if grouped:\n for b in query:\n start_date = b.timetable_entry.start_dt.astimezone(tz).date()\n result[b.session.event_id]['blocks'][start_date].append((b.timetable_entry, b))\n else:\n for b in query:\n result[b.session.event_id]['blocks'].append(b)\n\n if detail_level == 'contribution':\n query = (Contribution.query\n .filter(Contribution.event_id.in_(event_ids),\n dates_overlap(TimetableEntry),\n ~Contribution.is_deleted)\n .options(contains_eager(Contribution.timetable_entry),\n joinedload(Contribution.person_links))\n .join(TimetableEntry))\n if grouped:\n for c in query:\n start_date = c.timetable_entry.start_dt.astimezone(tz).date()\n result[c.event_id]['contribs'][start_date].append((c.timetable_entry, c))\n else:\n for c in query:\n result[c.event_id]['contributions'].append(c)\n\n query = (Break.query\n .filter(TimetableEntry.event_id.in_(event_ids), dates_overlap(TimetableEntry))\n .options(contains_eager(Break.timetable_entry))\n .join(TimetableEntry))\n if grouped:\n for b in query:\n start_date = b.timetable_entry.start_dt.astimezone(tz).date()\n result[b.timetable_entry.event_id]['breaks'][start_date].append((b.timetable_entry, b))\n else:\n for b in query:\n result[b.timetable_entry.event_id]['breaks'].append(b)\n return result","def get_meter_data_for_time_slice(apt_no, start_time, end_time):\n if apt_no in ['102A', 102]:\n apt_no = '102A'\n\n logger.debug(\"sMap: Getting meter data for %s between %s and %s\", apt_no, start_time, end_time)\n\n query = (\"select data in ('\" + str(start_time) + \"','\" + str(end_time) + \"') \"\n \"limit 200000 \"\n \"where Metadata/LoadLocation/FlatNumber ='\" + str(apt_no) + \"' and \"\n \"Metadata/Extra/PhysicalParameter='Power'\")\n\n r = requests.post(url, data=query)\n # logger.debug (\"%s\",r)\n payload = r.json()\n # logger.debug(\"Payload:%s\", payload)\n\n if apt_no in ['102A', 102]:\n apt_no = 102\n meters = retrieve_meter_info(apt_no)\n logger.debug(\"Meters: %s\", meters)\n\n streams = []\n meter_type = []\n l_meters = range(0, len(meters))\n for i in l_meters:\n uuid = payload[i]['uuid']\n\n # Get meter type based on uuid\n for meter in meters:\n if meter['uuid'] == uuid:\n m_type = meter['type']\n # logger.debug (uuid, m_type)\n\n meter_type.append(m_type)\n streams.append(np.array(payload[i]['Readings']))\n # logger.debug(\"Streams: %s\", streams)\n\n if len(streams[0]) > 0:\n\n df = [pd.DataFrame({'time': readings[:, 0] / 1000, 'power': readings[:, 1],\n 'type': [meter_type[i]] * len(readings)},\n columns=['time', 'power', 'type']) for i, readings in enumerate(streams)]\n else:\n df = []\n\n return df","def get_traffic_sensor_df(sensorURI: str, fromTime: str, toTime: str, resampleFreq: str = None, remove_outliers=False):\n values = [\"count\", \"sumSpeed\"]\n result = None\n for v in values:\n # data = query_ensor(sensorURI, fromTime, toTime, v)\n data = multiday_query(sensorURI, fromTime, toTime, v)\n df = pd.DataFrame(data, columns=[\"measuredTime\", v])\n df[\"measuredTime\"] = pd.to_datetime(df[\"measuredTime\"])\n df.index = df[\"measuredTime\"]\n del df[\"measuredTime\"]\n if remove_outliers:\n z_scores = np.abs(stats.zscore(df))\n print(f\"Removed outliers: {df.size - df[(z_scores < 3).all(axis=1)].size}\")\n df = df[(z_scores < 3).all(axis=1)]\n if resampleFreq is not None:\n df = df.resample(resampleFreq).sum()\n if result is not None:\n result = pd.merge_ordered(result, df, left_on=\"measuredTime\", right_on=\"measuredTime\")\n result.index = result[\"measuredTime\"]\n del result[\"measuredTime\"]\n else:\n result = df\n # avg speed\n result[\"avgSpeed\"] = result[\"sumSpeed\"] / result[\"count\"]\n result.loc[~np.isfinite(result[\"avgSpeed\"]), \"avgSpeed\"] = np.nan\n result[\"avgSpeed\"] = result[\"avgSpeed\"].interpolate()\n return result","def load_data(city, month, day):\n #load the data of the specified city in a dataframe:\n df = pd.read_csv(CITY_DATA[city])\n\n #convert the type of data in 'Start Time' column to datetime:\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n #create new columns required to calculate time_stats:\n df['month'] = df['Start Time'].dt.month\n df['weekday'] = df['Start Time'].dt.day_name()\n df['hour'] = df['Start Time'].dt.hour\n\n #unless user input is all, filter by month:\n if month != 'all':\n month = months.index(month) + 1 #get the index of the month\n df = df[df['month'] == month]\n\n #uless user input is all, filter by weekday:\n if day != 'all':\n df = df[df['weekday'] == day.title()]\n\n\n return df.set_index(pd.Series([i for i in range(df.shape[0])])) #reset the indices of the filterd df","def get_moorings_urls(varname=None, site=None, featuretype=None, fileversion=None, datacategory=None, realtime=None, timestart=None, timeend=None, filterout=None):\n\n WEBROOT = 'http://thredds.aodn.org.au/thredds/dodsC/'\n\n if realtime:\n if realtime.lower() == \"yes\":\n url = \"http://geoserver-123.aodn.org.au/geoserver/ows?typeName=moorings_all_map&SERVICE=WFS&REQUEST=GetFeature&VERSION=1.0.0&outputFormat=csv&CQL_FILTER=(realtime=TRUE)\"\n elif realtime.lower() == \"no\":\n url = \"http://geoserver-123.aodn.org.au/geoserver/ows?typeName=moorings_all_map&SERVICE=WFS&REQUEST=GetFeature&VERSION=1.0.0&outputFormat=csv&CQL_FILTER=(realtime=FALSE)\"\n else:\n raise ValueError('ERROR: realtime %s is not valid' % realtime)\n else:\n url = \"http://geoserver-123.aodn.org.au/geoserver/ows?typeName=moorings_all_map&SERVICE=WFS&REQUEST=GetFeature&VERSION=1.0.0&outputFormat=csv\"\n\n df = pd.read_csv(url)\n df = df.sort_values(by='time_coverage_start')\n criteria_all = df.url != None\n\n if varname:\n separator = ', '\n varnames_all = set(separator.join(list(df.variables)).split(', '))\n if varname in varnames_all:\n criteria_all = criteria_all & df.variables.str.contains('.*\\\\b'+varname+'\\\\b.*', regex=True)\n else:\n raise ValueError('ERROR: %s not a valid variable name' % varname)\n\n if site:\n site_all = list(df.site_code.unique())\n if site in site_all:\n criteria_all = criteria_all & df.site_code.str.contains(site, regex=False)\n else:\n raise ValueError('ERROR: %s is not a valid site code' % site)\n\n if featuretype:\n if featuretype in [\"timeseries\", \"profile\", \"timeseriesprofile\"]:\n criteria_all = criteria_all & (df.feature_type.str.lower() == featuretype.lower())\n else:\n raise ValueError('ERROR: %s is not a valid feature type' % featuretype)\n\n if datacategory:\n datacategory_all = list(df.data_category.str.lower().unique())\n if datacategory.lower() in datacategory_all:\n criteria_all = criteria_all & (df.data_category.str.lower() == datacategory.lower())\n else:\n raise ValueError('ERROR: %s is not a valid data category' % datacategory)\n\n if fileversion is not None:\n if fileversion in [0, 1, 2]:\n criteria_all = criteria_all & (df.file_version == fileversion)\n else:\n raise ValueError('ERROR: %s is not a valid file version' % fileversion)\n\n if timestart:\n try:\n criteria_all = criteria_all & (pd.to_datetime(df.time_coverage_end) >= datetime.strptime(timestart, '%Y-%m-%d'))\n except ValueError:\n raise ValueError('ERROR: invalid start date.')\n\n if timeend:\n try:\n criteria_all = criteria_all & (pd.to_datetime(df.time_coverage_start) <= datetime.strptime(timeend, '%Y-%m-%d'))\n except ValueError:\n raise ValueError('ERROR: invalid end date.')\n\n if filterout is not None:\n for keyword in filterout:\n criteria_all = criteria_all & (~df.url.str.contains(keyword, regex=True))\n\n\n return list(WEBROOT + df.url[criteria_all])","def get_weather_data(lat='40.761440',lng='-73.981806'):\r\n key ='********************************'\r\n x = pd.DataFrame()\r\n unix_now = int((dt.datetime.now()- dt.datetime(1970,1,1)).total_seconds())\r\n for time in range(unix_now-86400, unix_now+604800, 86400):\r\n rsp = rq.get('https://api.darksky.net/forecast/{}/{},{},{}'.format(key, lat, lng, time))\r\n rsp_json = json.loads(rsp.text)\r\n row = json_normalize(rsp_json[\"daily\"]['data'])\r\n x = x.append(row)\r\n \r\n x = x[['icon','apparentTemperatureHigh','apparentTemperatureLow','cloudCover','humidity','precipProbability',\r\n 'pressure','visibility','windBearing','windGust','windSpeed']].reset_index(drop=True)\r\n return x","def get_outdoor_data(temp_dir,site):\n if site == 'berk':\n files_od = glob(join(temp_dir,'outdoor','20*.xlsx'))\n elif site == 'bus':\n files_od = glob(join(temp_dir,'outdoor','Busara*.csv'))\n else:\n raise NameError(site)\n\n dfs = []\n for f in files_od:\n if site == 'berk':\n this_df = pd.read_excel(f,sheet_name=0,usecols='B:D',index_col=0,parse_dates=True, header=1)\n elif site == 'bus':\n this_df = pd.read_csv(f,usecols=[0,1,2],index_col=0,parse_dates=True,header=2)\n \n # drop missing values that prevented conversion to float type\n if this_df.iloc[:,0].dtype != np.float64:\n this_df = this_df[this_df.iloc[:,0] != ' ']\n this_df = this_df.astype(np.float64)\n\n # correct for weird timezones in berkeley datalogger\n this_df = correct_tz(this_df,site)\n \n this_df.columns = ['T','RH']\n this_df.index.name = 'time'\n\n # convert to celsius\n this_df['T'] = (this_df['T'] - 32) * 5/9\n dfs.append(this_df)\n \n df_od = pd.concat(dfs)\n\n # drop duplicated measurements\n df_od = df_od[~df_od.index.duplicated(keep='last')].sort_index()\n \n # separate out into daily min,mean,max\n groups = df_od.groupby(df_od.index.date)\n dfs_od = {'all':df_od,\n 'min': groups.min(),\n 'mean': groups.mean(),\n 'max': groups.max()}\n \n for i in ['min','mean','max']:\n # remove first and last day to ignore days where we did not get full recording\n dfs_od[i] = dfs_od[i].iloc[1:-1,:]\n \n # name index so that we can merge onto multiIndex'd dataframe\n dfs_od[i].index.name = 'date'\n \n return dfs_od","def convert_data (data_taxi,density):\n \n n_trips = len(data_taxi)\n \n min_longitude = min(min(list(data_taxi.loc[:,'pickup_longitude'])),\n min(list(data_taxi.loc[:,'dropoff_longitude'])))\n max_longitude = max(max(list(data_taxi.loc[:,'pickup_longitude'])),\n max(list(data_taxi.loc[:,'dropoff_longitude'])))\n min_latitude = min(min(list(data_taxi.loc[:,'pickup_latitude'])),\n min(list(data_taxi.loc[:,'dropoff_latitude'])))\n max_latitude = max(max(list(data_taxi.loc[:,'pickup_latitude'])),\n max(list(data_taxi.loc[:,'dropoff_latitude'])))\n \n e_longitude = max_longitude - min_longitude\n \n e_latitude = max_latitude - min_latitude\n \n scale =np.sqrt( n_trips/( e_longitude* e_latitude * density) )\n\n taxis = []\n \n for i in range(n_trips):\n selected_taxi = data_taxi.iloc[i]\n departure = [int((selected_taxi.pickup_longitude - min_longitude) * scale),\n int((selected_taxi.pickup_latitude - min_latitude) * scale),\n ]\n \n arrival = [\n int((selected_taxi.dropoff_longitude - min_longitude) * scale),\n int((selected_taxi.dropoff_latitude - min_latitude) * scale)]\n \n taxis.append(taxi(departure,arrival,departure))\n return taxis,int(scale*(e_latitude))+1,int(scale*(e_longitude))+1","def create_dataframe(euctr_cond):\n def f(x):\n d = {}\n d['number_of_countries'] = x.eudract_number_with_country.nunique()\n d['min_end_date'] = x.date_of_the_global_end_of_the_trial.min()\n d['max_end_date'] = x.date_of_the_global_end_of_the_trial.max()\n d['comp_date'] = np.where(pd.notnull(x.date_of_the_global_end_of_the_trial),1,0).sum()\n d['has_results'] = x.trial_results.sum()\n d['includes_pip'] = x.trial_is_part_of_a_paediatric_investigation_plan.sum()\n d['single_blind'] = x.trial_single_blind.sum()\n d['not_single_blind'] = x.not_single_blind.sum()\n d['rare_disease'] = x.trial_condition_being_studied_is_a_rare_disease.sum()\n d['not_rare_disease'] = x.not_rare_disease.sum()\n d['rare_disease_blank'] = x.rare_disease_blank.sum()\n d['completed'] = np.where(x.end_of_trial_status == 'Completed', 1, 0).sum()\n d['ongoing'] = np.where((x.end_of_trial_status == 'Ongoing') | (x.end_of_trial_status == 'Restarted'), 1, 0).sum()\n d['terminated'] = np.where(x.end_of_trial_status == 'Prematurely Ended', 1, 0).sum()\n d['suspended'] = np.where((x.end_of_trial_status == 'Temporarily Halted') | (x.end_of_trial_status == 'Suspended by CA'), 1, 0).sum()\n d['other_status'] = np.where((x.end_of_trial_status == 'Not Authorised') | (x.end_of_trial_status == 'Prohibited by CA'), 1, 0).sum()\n d['no_status'] = np.where(pd.isnull(x.end_of_trial_status),1,0).sum()\n d['phase_1'] = x.trial_human_pharmacology_phase_i.sum()\n d['phase_2'] = x.trial_therapeutic_exploratory_phase_ii.sum()\n d['phase_3'] = x.trial_therapeutic_confirmatory_phase_iii.sum()\n d['phase_4'] = x.trial_therapeutic_use_phase_iv.sum()\n d['bioequivalence'] = x.trial_bioequivalence_study.sum()\n d['not_bioequivalence'] = x.not_bioequivalence_study.sum()\n d['healthy_volunteers'] = x.subject_healthy_volunteers.sum()\n d['not_healthy_volunteers'] = x.not_healthy_volunteers.sum()\n d['full_title'] = x.full_title.astype('str').min()\n d['abbreviated_title'] = x.abbreviated_title.astype('str').max()\n d['non_eu'] = x.non_eu.sum()\n return pd.Series(d)\n\n return euctr_cond.groupby('eudract_number').apply(f).reset_index()","def use_ground_truth_time_slice(gt, df_p, df_l, app):\n\n gt = gt.reset_index(drop=True)\n gt['start_time'] = gt.start_time / 1000\n gt['end_time'] = gt.end_time / 1000\n gt['pred_location'] = np.zeros(len(gt))\n gt['true_location'] = gt.wlabel\n gt['true_appliance'] = gt.slabel\n\n gt['type'] = np.zeros(len(gt))\n gt['magnitude'] = np.zeros(len(gt))\n gt['phase'] = np.zeros(len(gt))\n\n print \"\\n\"\n # Label lights and fans as \"light\" type and magnitude as the fall magnitude of the time slice\n for i in gt.index:\n appl = gt.ix[i]['slabel']\n etype = ''\n\n if appl in ['Fan', 'Light']:\n gt.ix[i, 'type'] = etype = \"light\"\n else:\n gt.ix[i, 'type'] = etype = \"power\"\n\n # Get index from the main light/power data stream\n fall_time = gt.ix[i]['end_time']\n logger.debug(\"Fall_time:: %s (%s)\", dt.datetime.fromtimestamp(fall_time), fall_time)\n if etype == \"light\":\n idx = 0\n try:\n idx = np.where(df_l.time == fall_time)[0][0]\n except IndexError, e:\n print \"-----Index not found--------\", e\n continue\n\n edge = get_magnitude_metadata(df_l, idx, \"lightpower\", lwinmin)\n if edge:\n gt.ix[i, 'magnitude'] = math.fabs(edge['magnitude'])\n print i, \"Light:: Index\", idx, \"Magnitude::\", math.fabs(edge['magnitude'])\n else:\n logger.debug(\"--------Edge not found---------\")\n continue\n\n # Get phase of the edge\n gt.ix[i, 'phase'] = get_phase(idx, df_l, \"light\")\n # print \"LIGHT:: Index\", idx, \"magnitude\", magnitude\n else:\n idx = 0\n try:\n idx = np.where(df_p.time == fall_time)[0][0]\n except IndexError, e:\n print \"-----Index not found--------\", e\n continue\n\n edge = get_magnitude_metadata(df_p, idx, \"power\", pwinmin)\n if edge:\n gt.ix[i, 'magnitude'] = math.fabs(edge['magnitude'])\n print i, \"Power:: Index\", idx, \"Magnitude::\", math.fabs(edge['magnitude'])\n else:\n logger.debug(\"--------Edge not found---------\")\n continue\n\n # Get phase of the edge\n gt.ix[i, 'phase'] = get_phase(idx, df_p, \"power\")\n\n # print \"POWER:: Index\", idx, \"magnitude\", magnitude\n\n gt.pop('wlabel')\n gt.pop('slabel')\n\n if app in [4, 7]:\n gt = gt[(gt.phase != 0) & (gt.magnitude > lthresmin)]\n else:\n gt = gt[gt.magnitude > lthresmin]\n\n gt['act_start_time'] = [dt.datetime.fromtimestamp(i)\n for i in gt['start_time']]\n gt['act_end_time'] = [dt.datetime.fromtimestamp(i)\n for i in gt['end_time']]\n gt = gt.sort(['act_start_time'])\n\n print \"\\nGround Truth Time slices:\\n\", gt\n\n return gt","def time_features(s: pd.Series, result: Optional[pd.DataFrame] = None) -> pd.DataFrame:\n if result is None:\n result = pd.DataFrame(s, copy=False)\n index = cast(pd.DatetimeIndex, s.index)\n\n result[\"hour\"] = index.hour\n result[\"minute\"] = index.minute\n result[\"second\"] = index.second\n result[\"milliseconds\"] = index.microsecond / 1000\n result[\"quarterhour\"] = index.minute // 15 + 1\n result[\"hourofweek\"] = index.dayofweek * 24 + index.hour\n result[\"daytime\"] = _map(index.hour, _DAYTIME_MAP)\n\n # No vectorized methods for these:\n dst = np.zeros(len(result))\n utcoffset = np.zeros(len(result))\n for i, dt in enumerate(index):\n try:\n dst[i] = dt.dst().total_seconds()\n utcoffset[i] = dt.utcoffset().total_seconds()\n except AttributeError:\n # No timezone\n dst[i] = np.nan\n utcoffset[i] = np.nan\n result[\"dst\"] = dst\n result[\"utcoffset\"] = utcoffset\n return result","def dropoff_features_fn(df, ts_column, start_date, end_date):\n df = filter_df_by_ts(\n df, ts_column, start_date, end_date\n )\n dropoffzip_features = (\n df.groupBy(\"dropoff_zip\", window(\"tpep_dropoff_datetime\", \"30 minute\"))\n .agg(count(\"*\").alias(\"count_trips_window_30m_dropoff_zip\"))\n .select(\n col(\"dropoff_zip\").alias(\"zip\"),\n unix_timestamp(col(\"window.end\")).alias(\"ts\").cast(IntegerType()),\n partition_id(to_timestamp(col(\"window.end\"))).alias(\"yyyy_mm\"),\n col(\"count_trips_window_30m_dropoff_zip\").cast(IntegerType()),\n is_weekend(col(\"window.end\")).alias(\"dropoff_is_weekend\"),\n )\n )\n return dropoffzip_features","def get_date_features(gt_ids=[], gt_masks=None, gt_shifts=None, first_year=None):\n # If particular arguments aren't lists, replace with repeating iterators\n if not isinstance(gt_masks, list):\n gt_masks = itertools.repeat(gt_masks)\n if not isinstance(gt_shifts, list):\n gt_shifts = itertools.repeat(gt_shifts)\n\n # Add each ground truth feature to dataframe\n df = None\n for gt_id, gt_mask, gt_shift in zip(gt_ids, gt_masks, gt_shifts):\n print \"Getting {}_shift{}\".format(gt_id, gt_shift)\n t = time.time()\n # Load ground truth data\n gt = get_ground_truth(gt_id, gt_mask, gt_shift)\n # Discard years prior to first_year\n gt = year_slice(gt, first_year = first_year)\n # If lat, lon columns exist, pivot to wide format\n if 'lat' in gt.columns and 'lon' in gt.columns:\n if gt_shift == None:\n measurement_variable = get_measurement_variable(gt_id)\n else:\n measurement_variable = get_measurement_variable(gt_id)+'_shift'+str(gt_shift)\n gt = pd.pivot_table(gt, values=measurement_variable, index='start_date',\n columns=['lat', 'lon']).reset_index()\n gt = pd.DataFrame(gt.to_records())\n gt.drop(\"index\", axis=1, inplace=True)\n # Rename columns to start_date and precip_(27.0,261.0), etc.\n gt.rename(columns={gt.columns[0]: 'start_date'}, inplace=True)\n gt.rename(columns=lambda x: x.replace('(',\n measurement_variable +\n '_('), inplace=True)\n # Use outer merge to include union of start_date values across all features\n # combinations across all features\n df = df_merge(df, gt, on=\"start_date\")\n print \"Elapsed: {}s\".format(time.time() - t)\n\n return df","def create_sample_dataframe():\n ax_readings = []\n ay_readings = []\n az_readings = []\n mx_readings = []\n my_readings = []\n mz_readings = []\n gx_readings = []\n gy_readings = []\n gz_readings = []\n activity_list = [LABELS_NAMES[0] for _ in range(SEGMENT_TIME_SIZE)]\n\n\n for _ in range(SEGMENT_TIME_SIZE):\n ax_readings.append(random.uniform(-10,10))\n ay_readings.append(random.uniform(-10,10))\n az_readings.append(random.uniform(-10,10))\n mx_readings.append(random.uniform(-10,10))\n my_readings.append(random.uniform(-10,10))\n mz_readings.append(random.uniform(-10,10))\n gx_readings.append(random.uniform(-10,10))\n gy_readings.append(random.uniform(-10,10))\n gz_readings.append(random.uniform(-10,10))\n\n data_dict = {\n COLUMN_NAMES[0]: activity_list, COLUMN_NAMES[1]: ax_readings,\n COLUMN_NAMES[2]: ay_readings, COLUMN_NAMES[3]: az_readings,\n COLUMN_NAMES[4]: gx_readings, COLUMN_NAMES[5]: gy_readings,\n COLUMN_NAMES[6]: gz_readings, COLUMN_NAMES[7]: mx_readings,\n COLUMN_NAMES[8]: my_readings, COLUMN_NAMES[9]: mz_readings\n }\n\n df = pd.DataFrame(data=data_dict)\n return df","def get_weather_timeseries_2017():\n weather_df = pd.read_csv(\"src/kumpula-weather-2017.csv\")\n\n # -1 value in columns \"Precipitation amount (mm)\" and \"Snow depth (cm)\" mean\n # that there was no absolutely no rain or snow that day, whereas 0 can mean\n # a little of either. Let's convert the -1 values to 0 to make the dataset\n # more logical to read.\n weather_df.loc[weather_df[\"Precipitation amount (mm)\"] == -1,\n \"Precipitation amount (mm)\"] = 0\n weather_df.loc[weather_df[\"Snow depth (cm)\"] == -1, \"Snow depth (cm)\"] = 0\n\n # Create datetime index\n weather_df[\"Month\"] = weather_df[\"m\"]\n weather_df[\"Day\"] = weather_df[\"d\"]\n weather_df[\"Date\"] = pd.to_datetime(weather_df[[\"Year\", \"Month\", \"Day\"]])\n\n # Reindex dataset\n weather_df = weather_df.set_index(\"Date\")\n\n # Drop redundant columns\n weather_df.drop([\"Time\", \"Time zone\", \"m\", \"d\", \"Year\",\n \"Month\", \"Day\"], axis=\"columns\", inplace=True)\n\n return weather_df","def _create_ts_features(df, tscol):\r\n df = copy.deepcopy(df)\r\n dt_adds = []\r\n try:\r\n df[tscol+'_hour'] = df[tscol].dt.hour.fillna(0).astype(int)\r\n df[tscol+'_minute'] = df[tscol].dt.minute.fillna(0).astype(int)\r\n dt_adds.append(tscol+'_hour')\r\n dt_adds.append(tscol+'_minute')\r\n except:\r\n print(' Error in creating hour-second derived features. Continuing...')\r\n try:\r\n df[tscol+'_dayofweek'] = df[tscol].dt.dayofweek.fillna(0).astype(int)\r\n dt_adds.append(tscol+'_dayofweek')\r\n if tscol+'_hour' in dt_adds:\r\n DAYS = dict(zip(range(7),['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']))\r\n df[tscol+'_dayofweek'] = df[tscol+'_dayofweek'].map(DAYS)\r\n df.loc[:,tscol+'_dayofweek_hour_cross'] = df[tscol+'_dayofweek'] +\" \"+ df[tscol+'_hour'].astype(str)\r\n dt_adds.append(tscol+'_dayofweek_hour_cross')\r\n df[tscol+'_quarter'] = df[tscol].dt.quarter.fillna(0).astype(int)\r\n dt_adds.append(tscol+'_quarter')\r\n df[tscol+'_month'] = df[tscol].dt.month.fillna(0).astype(int)\r\n MONTHS = dict(zip(range(1,13),['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul',\r\n 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']))\r\n df[tscol+'_month'] = df[tscol+'_month'].map(MONTHS)\r\n dt_adds.append(tscol+'_month')\r\n #### Add some features for months ########################################\r\n festives = ['Oct','Nov','Dec']\r\n name_col = tscol+\"_is_festive\"\r\n df[name_col] = 0\r\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in festives else 0).values\r\n df[name_col].fillna(0,inplace=True)\r\n dt_adds.append(name_col)\r\n summer = ['Jun','Jul','Aug']\r\n name_col = tscol+\"_is_summer\"\r\n df[name_col] = 0\r\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in summer else 0).values\r\n df[name_col].fillna(0,inplace=True)\r\n dt_adds.append(name_col)\r\n winter = ['Dec','Jan','Feb']\r\n name_col = tscol+\"_is_winter\"\r\n df[name_col] = 0\r\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in winter else 0).values\r\n df[name_col].fillna(0,inplace=True)\r\n dt_adds.append(name_col)\r\n cold = ['Oct','Nov','Dec','Jan','Feb','Mar']\r\n name_col = tscol+\"_is_cold\"\r\n df[name_col] = 0\r\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in cold else 0).values\r\n df[name_col].fillna(0,inplace=True)\r\n dt_adds.append(name_col)\r\n warm = ['Apr','May','Jun','Jul','Aug','Sep']\r\n name_col = tscol+\"_is_warm\"\r\n df[name_col] = 0\r\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in warm else 0).values\r\n df[name_col].fillna(0,inplace=True)\r\n dt_adds.append(name_col)\r\n #########################################################################\r\n if tscol+'_dayofweek' in dt_adds:\r\n df.loc[:,tscol+'_month_dayofweek_cross'] = df[tscol+'_month'] +\" \"+ df[tscol+'_dayofweek']\r\n dt_adds.append(tscol+'_month_dayofweek_cross')\r\n df[tscol+'_year'] = df[tscol].dt.year.fillna(0).astype(int)\r\n dt_adds.append(tscol+'_year')\r\n today = date.today()\r\n df[tscol+'_age_in_years'] = today.year - df[tscol].dt.year.fillna(0).astype(int)\r\n dt_adds.append(tscol+'_age_in_years')\r\n df[tscol+'_dayofyear'] = df[tscol].dt.dayofyear.fillna(0).astype(int)\r\n dt_adds.append(tscol+'_dayofyear')\r\n df[tscol+'_dayofmonth'] = df[tscol].dt.day.fillna(0).astype(int)\r\n dt_adds.append(tscol+'_dayofmonth')\r\n df[tscol+'_weekofyear'] = df[tscol].dt.weekofyear.fillna(0).astype(int)\r\n dt_adds.append(tscol+'_weekofyear')\r\n weekends = (df[tscol+'_dayofweek'] == 'Sat') | (df[tscol+'_dayofweek'] == 'Sun')\r\n df[tscol+'_typeofday'] = 'weekday'\r\n df.loc[weekends, tscol+'_typeofday'] = 'weekend'\r\n dt_adds.append(tscol+'_typeofday')\r\n if tscol+'_typeofday' in dt_adds:\r\n df.loc[:,tscol+'_month_typeofday_cross'] = df[tscol+'_month'] +\" \"+ df[tscol+'_typeofday']\r\n dt_adds.append(tscol+'_month_typeofday_cross')\r\n except:\r\n print(' Error in creating date time derived features. Continuing...')\r\n print(' created %d columns from time series %s column' %(len(dt_adds),tscol))\r\n return df, dt_adds","def load_data(city, month, day):\n ##task0.4 now the user choices are all ready and saved, start preparing your data \n ## first, load the data of the selected city using the pd.read_csv command for the user specified city \n df = pd.read_csv(CITY_DATA[city])\n ## read the column start time as a date, to start extracting information from it\n df['Start Time']=pd.to_datetime(df['Start Time'])\n ##define a new column\"month\" as a function from start time, which reads the month from the date\n df['month'] = df['Start Time'].dt.month\n ## extract the day name from the start time, append a new column\"day_of_week\" to the dataframe \n df['day_of_week'] = df['Start Time'].dt.day_name()\n ## extract the hour of the start time and save to a new variable\"hour\" \n df['hour'] = df['Start Time'].dt.hour\n \n ## now, the panel data is complete with variables of interest created\n ## time to consider user filters on month and day!!\n ## for the month, if user choice is all, no changes to dataset\n ## if user specifies a month,filter the dataset column\"month\" with the selected value\n if month !=\"all\":\n df=(df[df['month']==int(month)])\n \n ## for the day column, if user doesnt choose a specific day and select\"all\", no change to df\n ## if the user chooses a day, filter the \"day_of_week\" column for the specified day\n if day !=\"All\":\n df=(df[df['day_of_week']==day])\n \n ## print the first five rows of data to confirm filters are applied\n ## return the data frame to start calculating statistics\n #print(df.head())\n return df","def load_data(city, month, day):\n\n data = pd.read_csv(\"{}.csv\".format(city))\n data.drop(data.columns[0], axis = 1, inplace = True) #dropping this strange column\n\n data['Start Time'] = pd.to_datetime(data['Start Time'], format='%Y-%m-%d %H:%M:%S')\n data['End Time'] = pd.to_datetime(data['End Time'], format='%Y-%m-%d %H:%M:%S')\n\n data['weekday'] = data['Start Time'].dt.dayofweek #0 - monday\n data['month'] = data['Start Time'].dt.month #1 - january\n data['hour'] = data['Start Time'].dt.hour # 1 - hour 1\n\n day_dict = {\"Mon\":0, \"Tue\":1, \"Wed\":2, \"Thu\":3, \"Fry\":4, \"Sat\":5, \"Sun\":6}\n\n month_dict = {\"Jan\":1, \"Feb\":2, \"Mar\":3, \"Apr\":4, \"May\":5, \"Jun\":6}\n\n if month == 'all_months' and day != 'all_days': # filter just by day\n day = day_dict.get(day)\n df = data[data['weekday'] == day]\n elif day == 'all_days' and month != 'all_months': # filter just by month\n month = month_dict.get(month)\n df = data[data['month'] == month]\n elif day == 'all_days' and month == 'all_months': # no filters\n df = data\n else: # filter both by day and month\n day = day_dict.get(day)\n month = month_dict.get(month)\n df = data[(data['weekday']== day) & (data['month']==month)]\n return df","def create_features(energy_data, label=None):\n energy_data['date'] = energy_data.index\n energy_data['hour'] = energy_data['Datetime'].dt.hour\n energy_data['dayofweek'] = energy_data['Datetime'].dt.dayofweek\n energy_data['month'] = energy_data['Datetime'].dt.month\n energy_data['quarter'] = energy_data['Datetime'].dt.quarter\n energy_data['year'] = energy_data['Datetime'].dt.year\n energy_data['dayofyear'] = energy_data['Datetime'].dt.dayofyear\n energy_data['dayofmonth'] = energy_data['Datetime'].dt.day\n energy_data['weekofyear'] = energy_data['Datetime'].dt.weekofyear\n energy_data['pjme_2_hrs_lag'] = energy_data['PJME_MW'].shift(2)\n energy_data['pjme_4_hrs_lag'] = energy_data['PJME_MW'].shift(4)\n energy_data['pjme_8_hrs_lag'] = energy_data['PJME_MW'].shift(8)\n energy_data['pjme_12_hrs_lag'] = energy_data['PJME_MW'].shift(12)\n energy_data['pjme_24_hrs_lag'] = energy_data['PJME_MW'].shift(24)\n energy_data['pjme_4_hrs_mean'] = energy_data['PJME_MW'].rolling(window=4).mean()\n energy_data['pjme_8_hrs_mean'] = energy_data['PJME_MW'].rolling(window=8).mean()\n energy_data['pjme_12_hrs_mean'] = energy_data['PJME_MW'].rolling(window=12).mean()\n energy_data['pjme_24_hrs_mean'] = energy_data['PJME_MW'].rolling(window=24).mean()\n energy_data['pjme_4_hrs_std'] = energy_data['PJME_MW'].rolling(window=4).std()\n energy_data['pjme_8_hrs_std'] = energy_data['PJME_MW'].rolling(window=8).std()\n energy_data['pjme_12_hrs_std'] = energy_data['PJME_MW'].rolling(window=12).std()\n energy_data['pjme_24_hrs_std'] = energy_data['PJME_MW'].rolling(window=24).std()\n energy_data['pjme_4_hrs_max'] = energy_data['PJME_MW'].rolling(window=4).max()\n energy_data['pjme_8_hrs_max'] = energy_data['PJME_MW'].rolling(window=8).max()\n energy_data['pjme_12_hrs_max'] = energy_data['PJME_MW'].rolling(window=12).max()\n energy_data['pjme_24_hrs_max'] = energy_data['PJME_MW'].rolling(window=24).max()\n energy_data['pjme_4_hrs_min'] = energy_data['PJME_MW'].rolling(window=4).min()\n energy_data['pjme_8_hrs_min'] = energy_data['PJME_MW'].rolling(window=8).min()\n energy_data['pjme_12_hrs_min'] = energy_data['PJME_MW'].rolling(window=12).min()\n energy_data['pjme_24_hrs_min'] = energy_data['PJME_MW'].rolling(window=24).min()\n\n features = energy_data[['hour', 'dayofweek', 'quarter', 'month', 'year',\n 'dayofyear', 'dayofmonth', 'weekofyear', 'pjme_2_hrs_lag', 'pjme_4_hrs_lag',\n 'pjme_8_hrs_lag', 'pjme_12_hrs_lag', 'pjme_24_hrs_lag', 'pjme_4_hrs_mean',\n \"pjme_8_hrs_mean\", \"pjme_12_hrs_mean\", \"pjme_24_hrs_mean\", \"pjme_4_hrs_std\",\n \"pjme_8_hrs_std\", \"pjme_12_hrs_std\", \"pjme_24_hrs_std\",\n \"pjme_4_hrs_max\", \"pjme_8_hrs_max\", \"pjme_12_hrs_max\", \"pjme_24_hrs_max\",\n \"pjme_4_hrs_min\", \"pjme_8_hrs_min\", \"pjme_12_hrs_min\", \"pjme_24_hrs_min\"]]\n if label:\n label = energy_data[label]\n return features, label\n return features","def make_time_features(ts, index=None, epoch=None, epoch_span=None):\n # input validation\n try:\n if len(ts) == 1:\n _singleton = True\n elif len(ts) > 1:\n _singleton = False\n elif len(ts) < 1:\n raise ValueError(\"must pass non-empty iterable of timestamps\")\n except TypeError:\n return make_time_features([ts], index=index, epoch=epoch, epoch_span=epoch_span)\n\n if not isinstance(ts, pd.DatetimeIndex):\n ts = pd.Series(0, index=ts).index\n if not isinstance(ts, pd.DatetimeIndex):\n raise ValueError(\"must pass non-empty iterable of timestamps\")\n\n if index is None:\n index = pd.RangeIndex(len(ts))\n if epoch is None:\n epoch = min(ts)\n if epoch_span is None:\n epoch_span = float((end - epoch).total_seconds())\n\n time_features = {}\n start = min(ts)\n end = max(ts)\n\n # Major US holidays\n NewYearsDay = pd.tseries.holiday.Holiday('New Years Day', month=1, day=1)\n MemorialDay = pd.tseries.holiday.Holiday('Memorial Day', month=6, day=1, offset=pd.DateOffset(weekday=MO(-1)))\n IndependenceDay = pd.tseries.holiday.Holiday('Independence Day', month=7, day=4)\n LaborDay = pd.tseries.holiday.Holiday('Labor Day', month=9, day=1, offset=pd.DateOffset(weekday=MO(1)))\n ThanksgivingDay = pd.tseries.holiday.Holiday('Thanksgiving Day', month=11, day=1, offset=pd.DateOffset(weekday=TH(4)))\n ChristmasDay = pd.tseries.holiday.Holiday('Christmas Day', month=12, day=25)\n holidays = \\\n NewYearsDay.dates(start.date(), end.date()).tolist() +\\\n MemorialDay.dates(start.date(), end.date()).tolist() +\\\n IndependenceDay.dates(start.date(), end.date()).tolist() +\\\n LaborDay.dates(start.date(), end.date()).tolist() +\\\n ThanksgivingDay.dates(start.date(), end.date()).tolist() +\\\n ChristmasDay.dates(start.date(), end.date()).tolist()\n holidays = set([h.date() for h in holidays])\n\n # projections onto unit circle\n time_features['day_cos'] = np.cos((ts.hour * 3600 + ts.minute * 60 + ts.second) * 2 * np.pi / 86400.)\n time_features['day_sin'] = np.sin((ts.hour * 3600 + ts.minute * 60 + ts.second) * 2 * np.pi / 86400.)\n time_features['week_cos'] = np.cos(ts.dayofweek * 2 * np.pi / 7.)\n time_features['week_sin'] = np.sin(ts.dayofweek * 2 * np.pi / 7.)\n time_features['year_cos'] = np.cos(ts.dayofyear * 2 * np.pi / 365.)\n time_features['year_sin'] = np.sin(ts.dayofyear * 2 * np.pi / 365.)\n # linear march through time\n time_features['epoch'] = (ts - epoch).total_seconds() / epoch_span\n # workday indicator\n time_features['workday'] = [int(weekday < 5 and date not in holidays) for weekday, date in zip(ts.weekday, ts.date)]\n\n if _singleton:\n return {k: v[0] for k, v in time_features.iteritems()}\n else:\n return pd.DataFrame(time_features, index=index)","def get_canada(data_path: str, filter_: Union[dict, bool] = True,\n fixes: bool = False) -> None:\n dfs = [] # we will append dfs for cases, deaths, recovered here\n # URL for API call to get Province-level timeseries data starting on Jan 22 2020\n url_template = 'https://api.opencovid.ca/timeseries?stat=%s&loc=prov&date=01-22-2020'\n for kind in ['cases', 'mortality', 'recovered']:\n url_path = url_template % kind # Create the full data URL\n with urllib.request.urlopen(url_path) as url:\n data = json.loads(url.read().decode())\n source = pd.json_normalize(data[kind])\n if kind == 'cases':\n source.drop('cases', axis=1, inplace=True) # removing this column so\n # we can index into date on all 3 dfs at same position\n source.rename(columns={source.columns[1]: \"date\" }, inplace=True)\n dfs.append(source)\n cases = dfs[0]\n deaths = dfs[1]\n recovered = dfs[2]\n # combine dfs\n df_rawtemp = cases.merge(recovered, on=['date', 'province'], how='outer')\n df_raw = df_rawtemp.merge(deaths, on=['date', 'province'], how='outer')\n df_raw.fillna(0, inplace=True)\n\n provinces = ['Alberta', 'BC', 'Manitoba', 'New Brunswick', 'NL',\n 'Nova Scotia', 'Nunavut', 'NWT', 'Ontario', 'PEI', 'Quebec',\n 'Saskatchewan', 'Yukon']\n\n # Export timeseries data for each province\n for province in tqdm(provinces, desc='Canadian Provinces'):\n source = df_raw[df_raw['province'] == province] # Only the given province\n df = pd.DataFrame(columns=['dates2','cum_cases', 'cum_deaths',\n 'cum_recover', 'new_cases',\n 'new_deaths', 'new_recover',\n 'new_uninfected'])\n df['dates2'] = source['date'].apply(fix_canada_dates) # Convert date format\n df['cum_cases'] = source['cumulative_cases'].values\n df['cum_deaths'] = source['cumulative_deaths'].values\n df['cum_recover'] = source['cumulative_recovered'].values\n\n df[['new_cases', 'new_deaths', 'new_recover']] = \\\n df[['cum_cases', 'cum_deaths', 'cum_recover']].diff()\n df['new_uninfected'] = df['new_recover'] + df['new_deaths']\n\n try:\n population = get_population_count(data_path, 'CA_' + province)\n df['population'] = population\n except:\n pass\n\n df.sort_values(by=['dates2'], inplace=True) # sort by datetime obj before converting to string\n df['dates2'] = pd.to_datetime(df['dates2']).dt.strftime('%m/%d/%y') # convert dates to string\n df = df.set_index('dates2').fillna(0).astype(int) # Fill NaN with 0 and convert to int\n df.to_csv(data_path / ('covidtimeseries_CA_%s.csv' % province))","def filter_plot(mode, country, continent, start_date, end_date, options):\n # Default is World mode\n chart_data = world_daywise_df\n map_data = countries_daywise_df\n print(country, continent)\n if mode == SelectionMode.Continents.value:\n #Continents mode\n if not isinstance(continent, list):\n continent = [continent]\n\n chart_data = continents_daywise_df[continents_daywise_df['WHO Region'].isin(continent)]\n map_data = map_data[map_data['WHO Region'].isin(continent)]\n elif mode == SelectionMode.Countries.value:\n # Countries mode\n if not isinstance(country, list):\n country = [country]\n\n chart_data = countries_daywise_df[countries_daywise_df['Country/Region'].isin(country)]\n map_data = chart_data\n\n chart_data = chart_data.query('Date >= @start_date & Date <= @end_date')\n map_data = map_data.query('Date >= @start_date & Date <= @end_date')\n\n # fix error when groupby geometry or put it in the aggregate column\n temp = map_data.drop(['geometry', 'country_code', 'Date'], axis=1).groupby(['Country/Region']).agg(metrics).reset_index()\n map_data = join_country_code_data(temp, country_code_data)\n\n if is_perCapita(options):\n for metric in ['Confirmed', 'Deaths', 'Recovered']:\n chart_data[metric + '_per_capita'] = chart_data[metric] / chart_data['Population']\n map_data[metric + '_per_capita'] = map_data[metric] / map_data['Population']\n \n if is_perCapita(options):\n return plot(chart_data, 'Confirmed_per_capita', 'Confirmed Cases Per Capita'), \\\n plot(chart_data, 'Deaths_per_capita', 'Confirmed Deaths Per Capita'), \\\n plot(chart_data, 'Recovered_per_capita', 'Confirmed Recoveries Per Capita'), \\\n generate_map(map_data)\n\n return plot(chart_data, 'Confirmed', 'Confirmed Cases'), \\\n plot(chart_data, 'Deaths', 'Confirmed Deaths'), \\\n plot(chart_data, 'Recovered', 'Confirmed Recoveries'), \\\n generate_map(map_data)","def create_data_table(df: pd.DataFrame) -> pd.DataFrame:\n\n df = df.copy()\n\n # Normalize times by labeling all of today's data with its future label, 00:00\n # tomorrow (as that's the timestamp marking the end of the 24-hour data collection\n # period). No need to adjust data not from today; it's already been adjusted and is\n # labeled with the date whose 00:00 marked the end of data collection (i.e., data\n # generated on Mar 20 is labeled Mar 21).\n normalized_dates = df[Columns.DATE].dt.normalize()\n is_at_midnight = df[Columns.DATE] == normalized_dates\n df.loc[~is_at_midnight, Columns.DATE] = normalized_dates[\n ~is_at_midnight\n ] + pd.Timedelta(days=1)\n df[Columns.DATE] = df[Columns.DATE].dt.strftime(r\"%Y-%m-%d\")\n\n df = df.drop(\n columns=[\n Columns.IS_STATE,\n Columns.LOCATION_NAME,\n Columns.OUTBREAK_START_DATE_COL,\n Columns.DAYS_SINCE_OUTBREAK,\n Columns.POPULATION,\n Columns.STAGE,\n Columns.COUNT_TYPE,\n ]\n )\n\n df = (\n df.pivot_table(\n index=[\n c\n for c in df.columns\n if c not in [Columns.CASE_TYPE, Columns.CASE_COUNT]\n ],\n columns=Columns.CASE_TYPE,\n values=Columns.CASE_COUNT,\n aggfunc=\"first\",\n )\n .reset_index()\n .sort_values([Columns.COUNTRY, Columns.STATE, Columns.DATE])\n )\n\n for col in CaseInfo.get_info_items_for(\n InfoField.CASE_TYPE, count=Counting.TOTAL_CASES\n ):\n df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n\n # save_path = Paths.DATA / \"data_table.csv\"\n # df.to_csv(save_path, index=False)\n # print(f\"Saved data to {save_path.relative_to(Paths.ROOT)}\")\n\n return df","def prepare(gpi, start_date, end_date, models, satellites, kind=\"clim\", window=35):\n # read data\n ts_input = data_object.read_gpi(gpi, start_date, end_date, models, satellites)\n #ts_input = interp.add_nan(ts_input)\n ts_input = interp.iter_fill(ts_input, max_gap=5)\n\n # either calc climatology, apply moving average filter, or do nothing\n if kind == 'clim':\n ts_smooth = smooth.iter_climats(ts_input)\n plot_title = 'Climatology'\n elif kind == 'movav':\n ts_smooth = smooth.iter_movav(ts_input, window)\n #ts_smooth = ts_gapfill\n plot_title = 'Moving average'\n elif kind == None:\n # return original data\n ts_smooth = ts_input\n else:\n raise NotImplementedError\n pass\n\n # drop rows with missing values\n #ts_smooth = ts_smooth.dropna()\n\n # scale satellite data to model data\n ts_scaled = scaling.scale(ts_smooth, 'mean_std_nan', 0)\n # drop nan rows for slope funcs\n return ts_scaled #.dropna()","def time_stats(df):","def get_fact_time_filtered(self, category, selected_option, \\\r\n start_tmstmp, \\\r\n end_tmstmp):\r\n try:\r\n conn = self.create_connection()\r\n query = \"\"\"WITH sub_category_lookup AS (\r\n\t\t\t\t\t\t\t\tSELECT id \r\n\t\t\t\t\t\t\t\tFROM categories \r\n\t\t\t\t\t\t\t\tWHERE category = '%s' \r\n\t\t\t\t\t\t\t\tAND sub_category = '%s')\t\r\n\t\t\t\t\t SELECT date_time,\r\n\t\t\t\t\t\t\t data \r\n\t\t\t\t FROM fact\r\n\t\t\t\t\t WHERE category_id = (select id FROM sub_category_lookup) \r\n\t\t\t\t\t AND (date_time>= '%s' AND date_time<'%s'\t)\t\t\t\t \r\n\t\t\t\t\t ORDER BY date_time ;\"\"\"%(category, selected_option, \\\r\n start_tmstmp, end_tmstmp)\r\n \r\n data_frame = pd.read_sql(query, conn)\r\n print(query)\r\n conn.close()\r\n except (psycopg2.Error, ValueError):\r\n print(\"Error occured at get_fact_time_filtered, check connection or query\")\r\n return data_frame","def filter_time_slices(time_slices, apt_no, exp_no):\n # Removing the extraneous time slices\n if apt_no == '102A' and exp_no == '3':\n discard_ts = time_slices[\n (time_slices.phase == 'Not Found') & (time_slices.magnitude < 100)]\n time_slices = time_slices.ix[time_slices.index - discard_ts.index]\n\n elif apt_no == '603':\n print \"here\"\n # Likely power consumption of fridge is 110-150\n time_slices = time_slices[(time_slices.magnitude < 110) | (time_slices.magnitude > 150) &\n (time_slices.type == 'power')]\n # 25-26Nov\n if exp_no == '25-26Nov':\n time_slices = time_slices[time_slices.end_time < 1385404505]\n elif exp_no == '26-27Nov':\n time_slices = time_slices[time_slices.end_time < 1385492334]\n\n elif apt_no == '703':\n # Likely power consumption of fridge is 130-152\n fridge_ts = time_slices[(time_slices.magnitude >= 130) & (time_slices.magnitude <= 170) &\n (time_slices.type == 'power')]\n time_slices = time_slices.ix[time_slices.index - fridge_ts.index]\n\n # Likely power consumption of geyser > 2000 but on light phase > 1000\n geyser_ts = time_slices[(time_slices.magnitude > 1000) & (time_slices.type == 'light')]\n time_slices = time_slices.ix[time_slices.index - geyser_ts.index]\n\n # 26-27Nov\n if exp_no == '26-27Nov':\n washing_ts = time_slices[\n (time_slices.start_time >= 1385470967) & (time_slices.end_time <= 1385471880)]\n time_slices = time_slices.ix[time_slices.index - washing_ts.index]\n\n # 28-29Nov\n if exp_no == '28-29Nov':\n time_slices = time_slices[\n (time_slices.start_time < 1385646060) | (time_slices.end_time > 1385648143)]\n\n # Removing time slices with duration less than 30 seconds\n idx_list = []\n for idx in time_slices.index:\n start_time = time_slices.ix[idx]['start_time']\n end_time = time_slices.ix[idx]['end_time']\n magnitude = time_slices.ix[idx]['magnitude']\n\n time_diff = end_time - start_time\n\n if time_diff < 30 and magnitude < 80:\n print \"idx\", idx, \"time_diff\", time_diff, \"magnitude\", magnitude\n # Qualified for filtering it\n idx_list.append(idx)\n time_slices = time_slices.ix[time_slices.index - idx_list]\n\n return time_slices","def get_grid_data(df):\n \n bools = (df['Longitude'] % 60 == 0) & (df['Year'] % 10 == 0)\n return df[bools]","def load_data(city, month, day):\n# This code is refrenced from the practice problem on the project.\n # load data file into a dataframe\n df = pd.read_csv(CITY_DATA[city])\n\n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n # extract month and day of week and hour from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.weekday_name\n df['hour'] = df['Start Time'].dt.hour\n\n # filter by month if applicable\n if month != 'all':\n month = MONTHS.index(month) + 1\n df = df[ df['month'] == month ]\n\n # filter by day of week if applicable\n if day != 'all':\n # filter by day of week to create the new dataframe\n df = df[ df['day_of_week'] == day.title()]\n\n return df","def get_data(day, unixstart=None, unixend=None):\n global subnet\n df_data = pd.DataFrame([])\n while len(df_data.index)<=0:\n try:\n df_data = pd.read_feather(f'/home/pi/studies/ardmore/homeserver/h{subnet}_{day}.feather')\n except Exception as e:\n # print(f\"Error grid_server.get_data:{e}\")\n pass\n if unixstart!=None:\n df_data = df_data[(df_data['unixtime']>=unixstart)&(df_data['unixtime']<=unixend)]\n float_cols = [x for x in df_data.columns if not x.startswith('timezone')]\n df_data = df_data[float_cols].astype(float)\n return df_data","def _dataframe_preprocess(self):\n # 1. add baisc feature like date, time in day, ....\n if self.data_type != 'porto':\n self.df['TIMESTAMP'] = self.df.apply(lambda df: df['TIMESTAMPS'][0], axis=1)\n self.df['TIME'] = pd.to_datetime(self.df['TIMESTAMP'], unit='s', utc=True)\n \n self.df.TIME = self.df.TIME.dt.tz_convert(self.timezone)\n # 2. group df for specific driver analysis\n self.grouped_df = self.df.groupby('LABEL')\n if self.count_od_info:\n if 'SD' not in self.df.columns:\n self._add_OD_info()\n self.grouped_od = self.df.groupby('SD')","def summarise_data(trip_in, station_data, trip_out):\n # generate dictionary of station - city mapping\n station_map = create_station_mapping(station_data)\n \n with open(trip_out, 'w') as f_out:\n # set up csv writer object \n out_colnames = ['duration', 'start_date', 'start_year',\n 'start_month', 'start_hour', 'weekday',\n 'start_city', 'end_city', 'subscription_type'] \n trip_writer = csv.DictWriter(f_out, fieldnames = out_colnames)\n trip_writer.writeheader()\n \n for data_file in trip_in:\n with open(data_file, 'r') as f_in:\n # set up csv reader object\n trip_reader = csv.DictReader(f_in)\n\n # collect data from and process each row\n for row in trip_reader:\n new_point = {}\n \n # convert duration units from seconds to minutes\n ### Question 3a: Add a mathematical operation below ###\n ### to convert durations from seconds to minutes. ###\n new_point['duration'] = float(row['Duration'])/60\n \n # reformat datestrings into multiple columns\n ### Question 3b: Fill in the blanks below to generate ###\n ### the expected time values. ###\n trip_date = datetime.strptime(row['Start Date'], '%m/%d/%Y %H:%M')\n new_point['start_date'] = trip_date.strftime('%Y-%m-%d')\n new_point['start_year'] = trip_date.strftime('%Y') # or : trip_date.year\n new_point['start_month'] = trip_date.strftime('%m') # or : trip_date.month\n new_point['start_hour'] = trip_date.strftime('%H') # or : trip_date.hour\n new_point['weekday'] = trip_date.strftime('%a') # or : trip_date.weekday() OR trip_date.isoweekday()\n\n \n # remap start and end terminal with start and end city\n new_point['start_city'] = station_map[row['Start Terminal']]\n new_point['end_city'] = station_map[row['End Terminal']]\n # two different column names for subscribers depending on file\n if 'Subscription Type' in row:\n new_point['subscription_type'] = row['Subscription Type']\n else:\n new_point['subscription_type'] = row['Subscriber Type']\n\n # write the processed information to the output file.\n trip_writer.writerow(new_point)","def temporal_only(\n db_engine, station_id, start, end, sample_size=int(1.0e5),\n balance=None):\n\n data = get_and_adjust_data(\n db_engine, station_id, start, end, sample_size)\n\n # Balance or set to sample_size\n if balance is None:\n data = data.sample(n=sample_size)\n else:\n data = bal(data, balance)\n\n # Ensure shuffling.\n data = data.iloc[np.random.permutation(len(data))]\n\n X = []\n yempty = []\n yfull = []\n\n for row in data.iteritems():\n features = [\n (1 if row[0].dayofweek == 0 else 0),\n (1 if row[0].dayofweek == 1 else 0),\n (1 if row[0].dayofweek == 2 else 0),\n (1 if row[0].dayofweek == 3 else 0),\n (1 if row[0].dayofweek == 4 else 0),\n (1 if row[0].dayofweek == 5 else 0),\n (1 if row[0].dayofweek == 6 else 0),\n float(((row[0].hour * 60) + row[0].minute)) / 1440.0,\n float(row[0].month) / 12.0\n ]\n\n X.append(features)\n yempty.append(1 if row[1] == \"empty\" else 0)\n yfull.append(1 if row[1] == \"full\" else 0)\n\n return {'X': X, 'yempty': yempty, 'yfull': yfull}","def dataTimeSeries(timesteps,df,predictors,target,dropnan,out=2,dropVars=True): \r\n \r\n series = series_to_supervised(df[predictors+[target]].copy(),timesteps,out,dropnan=dropnan)\r\n \r\n if dropnan==False:\r\n series.replace(pd.np.nan,0,inplace=True)\r\n \r\n # Dropping other variables:\r\n if dropVars:\r\n index = list(np.arange(series.shape[1]-2,\r\n series.shape[1]-len(predictors)-2,\r\n -1))\r\n \r\n labels = [item for idx,item in enumerate(series.columns) \r\n if idx in index]\r\n \r\n #print(\"Eliminando variáveis: {}\".format(labels))\r\n series.drop(labels,axis=1,inplace=True) \r\n \r\n return series","def datetime_features(\n s: pd.Series, result: Optional[pd.DataFrame] = None\n) -> pd.DataFrame:\n result = date_features(s, result)\n return time_features(s, result)","def time_stats(dataframe, filters):\n\n print('**********************************************')\n print(' Calculating Most Frequent Times Of Travel')\n print(' Filter: ', filters)\n print('**********************************************')\n\n start_time = time.time()\n popular_month, count_popular_month = common_month(dataframe)\n popular_day, count_popular_day = common_day(dataframe)\n popular_hour = dataframe['Hour'].mode()[0]\n count_popular_hour = (dataframe['Hour'] == popular_hour).sum()\n\n if filters == 'None':\n print('\\nMost popular month for travelling: ', popular_month)\n print('Counts: ', count_popular_month)\n print('\\nMost popular day for travelling: ', get_day_name(popular_day))\n print('Counts: ', count_popular_day)\n print('\\nMost popular hour of day for travelling: ', popular_hour)\n print('Counts: ', count_popular_hour)\n elif filters == 'Both':\n print('\\nMost popular hour of day for travelling: ', popular_hour)\n print('Counts: ', count_popular_hour)\n elif filters == 'Month':\n print('\\nMost popular day for travelling: ', get_day_name(popular_day))\n print('Counts: ', count_popular_day)\n print('\\nMost popular hour of day for travelling: ', popular_hour)\n print('Counts: ', count_popular_hour)\n elif filters == 'Day':\n print('\\nMost popular month for travelling: ', popular_month)\n print('Counts: ', count_popular_month)\n print('\\nMost popular hour of day for travelling: ', popular_hour)\n print('Counts: ', count_popular_hour)\n\n print('\\nThis took about {} seconds'. format(time.time() - start_time))\n print('----------------------------------------------')","def build_regress_datafrane_for_one_newly_built_station(treatment_dataframe, control_dataframe,\n station_open_start_date, station_open_end_date,\n open_year_plus_month):\n # check the date\n assert open_year_plus_month in ['2016_10', '2016_12']\n result_dataframe = pd.DataFrame(columns=['Time', 'T_i_t', 'Post', 'Sentiment', 'Activity'])\n # build the T_i_t variable\n ones_list = [1] * treatment_dataframe.shape[0]\n treatment_dataframe['T_i_t'] = ones_list\n zeros_list = [0] * control_dataframe.shape[0]\n control_dataframe['T_i_t'] = zeros_list\n # build the post variable\n treatment_dataframe['Post'] = treatment_dataframe.apply(\n lambda row: add_post_variable(row['hk_time'], opening_start_date=station_open_start_date,\n opening_end_date=station_open_end_date), axis=1)\n print('Check the post variable distribution of treatment group: {}'.format(\n Counter(treatment_dataframe['Post'])))\n print('Check the T_i_t variable distribution of treatment group: {}'.format(\n Counter(treatment_dataframe['T_i_t'])))\n control_dataframe['Post'] = control_dataframe.apply(\n lambda row: add_post_variable(row['hk_time'], opening_start_date=station_open_start_date,\n opening_end_date=station_open_end_date), axis=1)\n print('Check the post variable distribution of control group: {}'.format(\n Counter(control_dataframe['Post'])))\n print('Check the T_i_t variable distribution of control group: {}'.format(\n Counter(control_dataframe['T_i_t'])))\n combined_dataframe = pd.concat([treatment_dataframe, control_dataframe], axis=0, sort=True)\n combined_dataframe = combined_dataframe.reset_index(drop=True)\n # We don't consider the tweets posted on the open date\n combined_dataframe_without_not_considered = \\\n combined_dataframe.loc[combined_dataframe['Post'] != 'not considered']\n combined_data_copy = combined_dataframe_without_not_considered.copy()\n combined_data_copy['month_plus_year'] = combined_data_copy.apply(\n lambda row: str(int(float(row['year']))) + '_' + str(int(float(row['month']))), axis=1)\n sentiment_dict = {}\n activity_dict = {}\n for _, dataframe in combined_data_copy.groupby(['month_plus_year', 'T_i_t', 'Post']):\n time = str(list(dataframe['month_plus_year'])[0])\n t_i_t = str(list(dataframe['T_i_t'])[0])\n post = str(list(dataframe['Post'])[0])\n sentiment_dict[time + '_' + t_i_t + '_' + post] = before_and_after_final_tpu.pos_percent_minus_neg_percent(dataframe)\n activity_dict[time + '_' + t_i_t + '_' + post] = np.log(dataframe.shape[0])\n result_dataframe_copy = result_dataframe.copy()\n time_list = []\n t_i_t_list = []\n post_list = []\n sentiment_list = []\n activity_list = []\n for key in list(sentiment_dict.keys()):\n # don't consider the tweets posted in 2016_10(for Whampoa and Ho Man Tin) or 2016_12(for other stations)\n if key[:7] != open_year_plus_month:\n time_list.append(key[:-4])\n t_i_t_list.append(int(key[-3]))\n post_list.append(int(key[-1]))\n sentiment_list.append(sentiment_dict[key])\n activity_list.append(activity_dict[key])\n else:\n pass\n result_dataframe_copy['Time'] = time_list\n result_dataframe_copy['T_i_t'] = t_i_t_list\n result_dataframe_copy['Post'] = post_list\n result_dataframe_copy['Sentiment'] = sentiment_list\n result_dataframe_copy['Activity'] = activity_list\n return result_dataframe_copy","def build_timeseries(self):\n timeseries = {\n \"metricKind\": \"DELTA\", \n \"metric\": {\n \"labels\": {\n \"response_code\": \"0\"}, \n \"type\": \"agent.googleapis.com/agent/request_count\"\n }, \n \"points\": [\n {\n \"interval\": {\n \"endTime\": \"2019-02-18T22:09:53.939194Z\", \n \"startTime\": \"2019-02-18T21:09:53.939194Z\"\n }, \n \"value\": {\n \"int64Value\": \"62\"\n }\n }, \n {\n \"interval\": {\n \"endTime\": \"2019-02-18T21:09:53.939194Z\", \n \"startTime\": \"2019-02-18T20:09:53.939194Z\"\n }, \n \"value\": {\n \"int64Value\": \"61\"\n }\n }\n ], \n \"resource\": {\n \"labels\": {\n \"instance_id\": \"9113659852587170607\", \n \"project_id\": \"YOUR_PROJECT_ID\", \n \"zone\": \"us-east4-a\"\n }, \n \"type\": \"gce_instance\"\n }, \n \"valueType\": \"INT64\"\n }\n\n return timeseries","def create_features(df,rsi_window = 14,macd_feat = [12,26,9]):\n df.dropna(inplace=True)\n ## day and month\n df['Date'] = pd.to_datetime(df['Date'])\n df['Month'] = df['Date'].dt.month\n df['dayowk'] = df['Date'].dt.dayofweek\n df = pd.get_dummies(data = df,columns = ['Month','dayowk'])\n \n ##Previos n-day pct_changes\n df['1day_pct'] = df['Adj Close'].pct_change()\n df['2day_pct'] = df['Adj Close'].pct_change(periods = 2)\n df['3day_pct'] = df['Adj Close'].pct_change(periods = 3)\n df['4day_pct'] = df['Adj Close'].pct_change(periods = 4)\n df['5day_pct'] = df['Adj Close'].pct_change(periods = 5)\n df['7day_pct'] = df['Adj Close'].pct_change(periods = 7)\n \n ##Cumulative sum of 1day_pct\n df['1day_pct_cs'] = df['Adj Close'].pct_change().cumsum()\n \n ##EWMA of 7, 50 and 200 days\n df['ewma_7'] = df['Adj Close'].ewm(span=7).mean()/df['Adj Close']\n df['ewma_50'] = df['Adj Close'].ewm(span=50).mean()/df['Adj Close']\n df['ewma_200'] = df['Adj Close'].ewm(span=200).mean()/df['Adj Close']\n ## Golden Cross vs Death Cross etc.\n #df['7g(50&200)'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\n #df['7l(50&200)'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\n #df['7g50'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\n #df['7g200'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\n \n ##RSI and MACD\n df = RSI(df,14)\n df = MACD_mod(df,nl=macd_feat[0],nh=macd_feat[1],nsig=macd_feat[2])\n \n df['day_var'] = (df['High'] - df['Low'])/df['Close']## Days variance\n df['open_close'] = (df['Open'] - df['Close'])/df['Close'] ## Days Open-Close\n df['high_close'] = (df['High'] - df['Close'])/df['Close'] ##Days High-Close\n df['open_prev_close'] = (df['Open'] - df['Close'].shift(1))/df['Close'] ## Days open - Previos Dyas Close\n \n ##Classification target\n df['target'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2) ## Target for classification\n #df['1_day_target'] = df['Adj Close'].shift(-1) - df['Adj Close'] ## Target for Regression\n #df['target2'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2)## Will the price go up intra-day\n \n ## IS the stock Overbought or Oversold based on RSI?\n df['RSI_overbought'] = df['RSI']>70\n df['RSI_oversold'] = df['RSI']<30\n \n \n #df.drop(['Open','High','Low','Close'],axis=1,inplace=True)\n# df = df.dropna()\n \n #df = df.reset_index(drop=True)\n \n ## Calculating how large the previos hot and cold streaks were\n f = 0\n df['prev_hot_streak'] = np.zeros(df.shape[0])\n for i in range(df.shape[0]-1):\n if df['target'][i] ==1:\n f += 1\n if df['target'][i+1] ==0:\n df['prev_hot_streak'][i+1] = f\n f = 0\n for i in range(1,df.shape[0]):\n #print(i)\n if df['prev_hot_streak'][i]==0:\n df['prev_hot_streak'][i]=df['prev_hot_streak'][i-1]\n \n \n df['prev_cold_streak'] = np.zeros(df.shape[0])\n for i in range(df.shape[0]-1):\n if df['target'][i] ==0:\n f += 1\n if df['target'][i+1] ==1:\n df['prev_cold_streak'][i+1] = f\n f = 0\n\n for i in range(1,df.shape[0]):\n #print(i)\n if df['prev_cold_streak'][i]==0:\n df['prev_cold_streak'][i] = df['prev_cold_streak'][i-1]\n \n ## Calculating current hot and cold streaks\n df['current_hot_streak'] = np.zeros(df.shape[0])\n df['current_cold_streak'] = np.zeros(df.shape[0])\n fhot=0\n fcold=0\n for i in range(df.shape[0]):\n if df['target'][i]==1:\n fhot += 1\n fcold = 0\n df['current_hot_streak'][i] = fhot\n elif df['target'][i]==0:\n fcold += 1\n fhot = 0\n df['current_cold_streak'][i] = fcold\n \n df['prev_hot_streak'] = df['prev_hot_streak'].shift(1)\n df['prev_cold_streak'] = df['prev_cold_streak'].shift(1)\n df['current_hot_streak'] = df['current_hot_streak'].shift(1)\n df['current_cold_streak'] = df['current_cold_streak'].shift(1)\n \n ## Combinations of previos streaks\n df['prev_current_hot'] = df['prev_hot_streak'] - df['current_hot_streak']\n df['prev_current_cold'] = df['prev_cold_streak'] - df['current_cold_streak']\n df['current_hot_prev_cold'] = df['current_hot_streak'] - df['prev_cold_streak']\n df['current_cold_prev_hot'] = df['current_cold_streak'] - df['prev_hot_streak']\n \n ##Calculating days since max\n current_max = df['Adj Close'][0]\n df['days_from_max'] = np.zeros(df.shape[0])\n df['pct_from_max'] = np.zeros(df.shape[0])\n #print('blah')\n for i in range(1,df.shape[0]):\n if df['Adj Close'][i] > current_max:\n current_max = df['Adj Close'][i]\n # print(current_max)\n else:\n df['days_from_max'][i] = df['days_from_max'][i-1]+1\n df['pct_from_max'][i] = (df['Adj Close'][i]-current_max)/current_max\n #print(df['days_from_max'][i])\n \n \n \n df.dropna(inplace=True)\n df = df.reset_index(drop=True)\n return df","def __init__(self, year=None, month=None, day=None, map_type='Cases'):\n self.covid_df = pd.DataFrame([])\n self.geo_data = pd.DataFrame([])\n self.name_iso2_mapping = {}\n self.countries_centroids = pd.DataFrame([])\n \n try:\n self.date = datetime(year=year, month=month, day=day)\n except:\n print('Invalid/empty date entry (year, month, day take valid int inputs)! Date defaulted to today.')\n self.date = datetime.today()\n \n if self.date > datetime.today():\n print('Can\\'t input future date! Date defaulted to today.')\n self.date = datetime.today()\n \n if map_type not in ['Cases', 'Deaths']:\n sys.exit('Please specify either \"Cases\" or \"Deaths\" as map type!')\n else:\n self.map_type = map_type","def create_demo_location_history() -> geopandas.GeoDataFrame:\n np.random.seed(123)\n\n time = pd.date_range(start=datetime.fromtimestamp(1624241116), end=datetime.now(), freq=\"1min\").values\n\n center_point = (-36.875990410695394, 174.76398830024274)\n lat = np.random.normal(loc=center_point[0], scale=0.01, size=len(time))\n lon = np.random.normal(loc=center_point[1], scale=0.01, size=len(time))\n\n geometry = [Point(lon, lat) for lon, lat in zip(lon, lat)]\n return geopandas.GeoDataFrame(pd.DataFrame(dict(time=time, lat=lat, lon=lon)), geometry=geometry)","def createnc(ncfout,xlat,xlon,times=None,zvals=None,wsvals=None,\\\n wdvals=None,olvals=None,attbts=None,ftype=\"timeseries\",dims=[7,180,180]):\n nc_out=nc.Dataset(ncfout,'w',clobber=True)\n\n # Set Attributes to the File\n if attbts is not None:\n final_attbts={}\n # Define projection\n proj_lcc = pj_lcc = Proj(\"+proj=lcc +lat_1={TRUELAT1} +lat_2={TRUELAT2} +lat_0={MOAD_CEN_LAT} +lon_0={STAND_LON} +x_0=0 +y_0=0 +a=6370000 +b=6370000\".format(**attbts))\n\n # Get x&y of domain center\n xcen, ycen = pj_lcc(attbts['CEN_LON'], attbts['CEN_LAT'])\n\n for key in attbts:\n if str(key).find(\"STAG\") <= 0 : # Remove Staggered Grid Information\n final_attbts.update({key:attbts[key]})\n nc_out.setncatts(final_attbts)\n # Create a CRS Variable for the Projection (GIS Readability)\n crsv=nc_out.createVariable('crs','c')\n crsv.semi_major_axis = 6370000.0\n crsv.inverse_flattening = 0.0\n crsv.grid_mapping_name = \"lambert_conformal_conic\"\n crsv.longitude_of_central_meridian = attbts[\"STAND_LON\"]\n crsv.false_easting = 0.0\n crsv.false_northing = 0.0\n crsv.latitude_of_projection_origin = attbts[\"MOAD_CEN_LAT\"]\n crsv.standard_parallel = [attbts[\"TRUELAT1\"],attbts[\"TRUELAT2\"]]\n crsv.longitude_of_prime_meridian = 0.0\n crsv.proj = proj_lcc.srs\n\n\n\n # Override Institution and Experiment\n nc_out.INSTITUTION=INSTITUTION\n nc_out.EXPERIMENT=EXPERIMENT\n nc_out.Conventions=\"CF-1.6\"\n\n # Create Dimensions First\n if ftype==\"timeseries\":\n nc_out.TITLE='Timeseries of the New European Wind Atlas from WRF V3.8.1'\n nc_out.createDimension('time',None)\n nc_out.createDimension('DateStrLen',19)\n nc_out.createDimension('height',dims[0])\n nc_out.createDimension('south_north',dims[1])\n nc_out.createDimension('west_east',dims[2])\n # Create Time Vector as Integer\n timesn = nc_out.createVariable('time','i8',('time',))\n timesn.units = \"minutes since 1900-01-01 00:00:00.0\"\n timesn.calendar = \"gregorian\"\n timesn.long_name = \"Time\"\n timesn.standard_name = \"time\"\n timesn[:] = nc.date2num(createdatv(times),units=timesn.units,calendar=timesn.calendar)\n # Create additional Time Vector as Character\n timesc = nc_out.createVariable('Times', 'c', ('time','DateStrLen'))\n timesc.format = \"YYYY-MM-DD_HH:MM:SS\"\n timesc.long_name = \"Time\"\n timesc[:] = times[:]\n # Height\n hgts = nc_out.createVariable('height','f4',('height',))\n hgts.units=\"m\"\n hgts.long_name=\"Height above Ground\"\n hgts.standard_name=\"height\"\n hgts[:] = zvals\n # y\n south_north = nc_out.createVariable('south_north','f4',('south_north',))\n south_north.long_name = \"y-coordinate in Cartesian system\"\n south_north.units = \"m\"\n\n dy = attbts[\"DY\"]\n ny = attbts[\"SOUTH-NORTH_PATCH_END_UNSTAG\"]\n ymin = ycen - dy * (ny - 1) / 2\n s_n = np.linspace(0, ny-1, ny) * dy + ymin\n south_north[:] = s_n\n\n # x\n west_east = nc_out.createVariable('west_east','f4',('west_east',))\n west_east.long_name = \"x-coordinate in Cartesian system\"\n west_east.units = \"m\"\n\n dx = attbts[\"DX\"]\n nx = attbts[\"WEST-EAST_PATCH_END_UNSTAG\"]\n xmin = xcen - dx * (nx - 1) / 2\n e_w = np.linspace(0, nx-1, nx) * dx + xmin\n west_east[:] = e_w\n\n elif ftype==\"roughness\":\n nc_out.title='NEWA Roughness'\n nc_out.createDimension('south_north',dims[0])\n nc_out.createDimension('west_east',dims[1])\n\n elif ftype==\"tabfile\":\n nc_out.title='NEWA WasP Tab File'\n nc_out.createDimension('south_north',dims[0])\n nc_out.createDimension('west_east',dims[1])\n nc_out.createDimension('sector',dims[2])\n nc_out.createDimension('wind',dims[3])\n nc_out.createDimension('stab',dims[4])\n\n # Wind Speed Class\n wscl = nc_out.createVariable('wspdCl','f4',('wind',))\n wscl.units=\"ms-1\"\n wscl.long_name=\"Velocity of bin centre\"\n wscl[:] = wsvals\n\n # Wind Speed Class\n wdcl = nc_out.createVariable('wdirCl','f4',('sector',))\n wdcl.units=\"ms-1\"\n wdcl.long_name=\"Velocity of bin centre\"\n wdcl[:] = wdvals\n\n # Stability\n lcl = nc_out.createVariable('Ltypical','f4',('stab',))\n lcl.units=\"m\"\n lcl.long_name=\"L typical\"\n lcl[:] = olvals\n\n # Lat and Lon\n lats = nc_out.createVariable(\"XLAT\", 'f4', ('south_north','west_east'), zlib=True,complevel=9)\n lats[:] = xlat[:]\n lats.units=\"degree_north\"\n lats.long_name=\"Center Latitude of Grid Cell\"\n lats.standard_name=\"latitude\"\n lons = nc_out.createVariable(\"XLON\", 'f4', ('south_north','west_east'), zlib=True,complevel=9)\n lons[:] = xlon[:]\n lons.units=\"degree_east\"\n lons.long_name=\"Center Longitude of Grid Cell\"\n lons.standard_name=\"longitude\"\n nc_out.close()\n return(None)","def get_profile_template_data(end_date, time_scale):\n\n intervals = -1\n HOURS = 0\n DAYS = 0\n WEEKS = 0\n time_format = \"%d %B %Y\"\n\n print(f\"Time scale: {time_scale}\")\n if time_scale == \"DAILY\":\n intervals = 12 # 24 hours per day\n HOURS = 2\n time_format = \"%I%p\"\n elif time_scale == \"WEEKLY\":\n intervals = 7 # 7 days per week\n DAYS = 1\n elif time_scale == \"MONTHLY\":\n intervals = 4 # 4 weeks per month\n WEEKS = 1\n\n # end_date = date.fromtimestamp(float(end_date))\n # end_date = datetime(end_date.year, end_date.month, end_date.day)\n end_date = datetime.now() + timedelta(hours=2)\n print(f\"TEMP current end date: {end_date.strftime('%I%p')}\")\n x_axis = []\n data = []\n inter = []\n TEMP = []\n time_gap = timedelta(hours=HOURS, days=DAYS, weeks=WEEKS)\n for i in range(intervals - 1, -1, -1):\n x_step = end_date - (i * time_gap)\n TEMP.append(x_step.strftime(\"%I%p\"))\n inter.append(str(x_step.timestamp()))\n data.append({\"images\": [], \"x\": x_step.strftime(time_format), \"y\": 0})\n print(f\"TEMP x-axis:{TEMP}\")\n return data, inter","def get_time_df(plan_type, stop=False):\n df = pd.DataFrame({\n 'patient': patients,\n 'plan_type': len(patients)*[plan_type],\n 'plan_time': [get_plan_time(patient, plan_type, stop)\n for patient in patients],\n 'plan_iter': [get_plan_iter(patient, plan_type, stop)\n for patient in patients]})\n return df","def ttest_region():\n\n cks = cksmet.io.load_table('cks-cuts',cache=1)\n cks = cks[~cks.isany]\n lamo = cksmet.io.load_table('lamost-cal-cuts',cache=2)\n lamo = lamo[~lamo.isany]\n #lamo = lamo.query('-1 < lamo_smet < 0.5')\n\n boxes = _boxes()\n boxes = boxes.copy()\n #boxes = boxes.sort_values(by=['per1','prad1'])\n boxes2 = []\n lines = []\n for i,row in boxes.iterrows():\n cut = cks[\n cks.koi_period.between(row.per1,row.per2) &\n cks.iso_prad.between(row.prad1,row.prad2)\n ]\n d = calculate_statistics(cut.cks_smet,lamo.lamo_smet)\n d = dict(d, **row)\n lines.append( to_string(d))\n \n return lines","def get_time_series(this_lat, this_lon, case, varnames):\n\n cesmdir = '/gpfs/fs1/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly'\n\n if 'LE' in case:\n\n from observational_large_ensemble.params import karen_params_cesm\n\n mode_lag = karen_params_cesm.mode_lag\n cvdp_loc = karen_params_cesm.cvdp_loc\n AMO_cutoff_freq = karen_params_cesm.AMO_cutoff_freq\n\n name_conversion = {'tas': 'TREFHT', 'pr': 'PRECC', 'slp': 'PSL'}\n cesm_names = [name_conversion[v] for v in varnames]\n this_member = int((case).split('-')[-1])\n cvdp_file = '%s/CESM1-CAM5-BGC-LE_#%i.cvdp_data.1920-2018.nc' % (cvdp_loc, this_member)\n\n # Historical filenames for CESM. Will need to append part of RCP8.5 to get full period\n filenames = []\n for var in cesm_names:\n file_str = '%s/%s/b.e11.B20TRC5CNBDRD.f09_g16.%03d.cam.h0.%s.??????-200512.nc' % (cesmdir, var,\n this_member, var)\n this_file = glob(file_str)[0]\n filenames.append(this_file)\n\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames,\n karen_params_cesm.valid_years, mode_lag,\n cvdp_file, AMO_cutoff_freq, name_conversion)\n\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\n\n else:\n\n from observational_large_ensemble.params import karen_params_obs\n\n mode_lag = karen_params_obs.mode_lag\n cvdp_loc = karen_params_obs.cvdp_loc\n AMO_cutoff_freq = karen_params_obs.AMO_cutoff_freq\n\n tas_dir = karen_params_obs.tas_dir\n pr_dir = karen_params_obs.pr_dir\n slp_dir = karen_params_obs.slp_dir\n cvdp_file = '%s/HadISST.cvdp_data.1920-2018.nc' % cvdp_loc\n file_dict = {'tas': '%s/Complete_TAVG_LatLong1.nc' % tas_dir,\n 'pr': '%s/full_data_monthly_v2020.nc' % pr_dir,\n 'slp': '%s/prmsl.mon.mean.nc' % slp_dir}\n\n filenames = []\n for var in varnames:\n filenames.append(file_dict[var])\n\n name_conversion = {'tas': 'temperature', 'pr': 'precip', 'slp': 'prmsl'}\n\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames[0],\n karen_params_obs.valid_years, mode_lag,\n cvdp_file, AMO_cutoff_freq, name_conversion)\n\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\n\n return this_ts, df_shifted","def load_data(city, month, day):\n # load data file into a dataframe\n df = pd.read_csv(CITY_DATA[city])\n\n # convert the Start Time column to datetime\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n\n # extract month and day of week from Start Time to create new columns\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.weekday_name\n df['Start and End Stations'] = df['Start Station'] + ' and ' + df['End Station']\n\n # filter by month if applicable\n if month != 'all':\n # use the index of the months list to get the corresponding int\n months = ['january', 'february', 'march', 'april', 'may', 'june']\n month = months.index(month) + 1\n\n # filter by month to create the new dataframe\n df = df[df['month'] == month]\n\n # filter by day of week if applicable\n if day != 'all':\n # filter by day of week to create the new dataframe\n df = df[df['day_of_week'] == day.title()]\n\n return df","def get_time_series_data():\r\n # Grab the requested years and columns from the query arguments\r\n ls_year = [int(year) for year in request.args.getlist(\"n\")]\r\n ls_col = request.args.getlist(\"m\")\r\n\r\n # Generate a list of all the months we need to get\r\n all_years = [str(year) for year in range(min(ls_year), max(ls_year) + 1)]\r\n\r\n # Grab all of the wanted months by filtering for the ones we want\r\n wanted_months = reduce(\r\n lambda a, b: a | b, (app.df[\"month\"].str.contains(year) for year in all_years)\r\n )\r\n\r\n # Create a new dataframe from the one that\r\n df_new = app.df[wanted_months][[\"month\"] + ls_col]\r\n\r\n # Convert all string dates into datetime objects and then sort them\r\n df_new[\"month\"] = pd.to_datetime(df_new[\"month\"])\r\n df_new = df_new.sort_values(by=[\"month\"])\r\n\r\n # Return the dataframe as json\r\n return df_new.to_json(), 200","def main():\n \n Y1, Y2 = 2005, 2017 ### range with coordinates supplied in pre-2018 generated archive\n\n if len(sys.argv) > 1 and int(sys.argv[1]) > 0:\n Y1 = int(sys.argv[1])\n \n if len(sys.argv) > 2 and int(sys.argv[2]) > Y1:\n Y2 = int(sys.argv[2])\n \n with open('data/audit.log','w') as output:\n for Y in range(Y1, Y2):\n df = pd.read_csv('data/{}.csv'.format(Y), low_memory = False)\n output.write('\\n--- {} --------------------\\n'.format(Y))\n\n # remove `deleted` records\n df['deleted'] = df['deleted'].apply(yes_no)\n df = df[df['deleted'] == 0]\n\n # remove misc misdemeanors\n df = df[~df['category'].isin(drop)]\n\n # validate date and expand into Y,N,D,W,H\n df['dt'] = df['incident_date'].apply(extract)\n df = df[~df['dt'].isnull()]\n\n # convert from plane state to longitude-latitude\n df['ll'] = df.apply(to_lnglat, axis = 1)\n\n # init features\n features = df.loc[:,['category','stat','address','city','zip']]\n features['id'] = df['incident_id']\n dt = ['year','month','day','weekday','hour']\n for i in range(len(dt)):\n features[dt[i]] = df['dt'].apply(lambda x: x[i] )\n\n features['lng'] = df['ll'].apply(lambda x: x[0])\n features['lat'] = df['ll'].apply(lambda x: x[1])\n\n features['gang'] = df['gang_related'].apply(yes_no)\n features['category'] = df['category'].apply(collapse)\n cat = set(features.groupby(['category']).size().reset_index(name='count')['category'].tolist())\n output.write('Categories: {}\\n'.format(len(cat)))\n\n output.write('Date miss: {:.4f}%\\n'\\\n .format(100 * (1 - len(features[(features['year'] > 2000) & (~features['weekday'].isnull())])/len(features))))\n output.write('Location miss: {:.4f}%\\n'\\\n .format(100 * (1 - len(features[(features['zip'] > 0) | (features['lat'] > 0)])/len(features))))\n\n # keep records with valid date\n features['date'] = df['dt'].apply(lambda x: datetime.date(x[0], x[1], x[2]))\n features = features[(features['year'] > 2000) & (~features['weekday'].isnull())]\n output.write('Time miss: {:.4f}%\\n'.format(100 * len(features[features['hour'] == -1])/len(features)))\n\n # potential `time-unknown` issue\n output.write('Hour ZERO: {:.4f}%\\n'.format(100 * len(features[features['hour'] == 0])/len(features)))\n output.write('Hour NOON: {:.4f}%\\n'.format(100 * len(features[features['hour'] == 12])/len(features)))\n\n features = features[(features['zip'] > 0) | (features['lat'] > 0)]\n\n # get the best possible coordinates + zipcode assessment\n features[['zip','lng','lat']] = features[['zip','lng','lat']].apply(fix_location, axis = 1)\n output.write('Failed location: {:.4f}%\\n'.format(100 * len(features[features['zip'].isnull()])/len(features)))\n features = features[~features['zip'].isnull()]\n features['zip'] = df['zip'].apply(lambda x: str(x)[:5])\n \n # normalize city attr\n features = features.join(zipcodes[['zip','city']].set_index('zip'), on = 'zip', lsuffix = '_orig', rsuffix = '')\n features.loc[features['city'].isnull(), 'city'] = features.loc[features['city'].isnull(), 'city_orig']\\\n .apply(lambda x: x if type(x) == float else ' '.join([l[0].upper() + l[1:] for l in x.split()]))\n\n # reduce to LA bounding-box\n features = features[(features['lng'] > -119) & (features['lng'] < -116)]\n features = features[(features['lat'] > 32) & (features['lat'] < 35)]\n\n # save csv\n features[fields].to_csv('data/F{}.csv'.format(Y), index = False)\n features[fields].to_json('data/F{}.json'.format(Y), orient = 'records')\n output.close()","def coarse_dataframe(geodf, side_square):\n\n # initialise the categories\n\n geodf['category'] = -1\n\n # do calculations on the first date, then extrapolate to the rest\n data_df = geodf[geodf['date'] == np.unique(geodf['date'])[0]]\n\n data_df = data_df.sort_values(by=['longitude', 'latitude'])\n\n n_grids = int(math.sqrt(data_df.shape[0]))\n\n category = 0\n\n for n in range(data_df.shape[0]):\n\n # only process lat,long point that do not have a category\n if data_df['category'].iloc[n] == -1:\n\n # get the side_square^2 nearest indexes to the point.\n indexes = []\n for i in range(side_square):\n for j in range(side_square):\n\n if n + n_grids * i + j < n_grids * n_grids and data_df['category'].iloc[n + n_grids * i + j] == -1:\n indexes.append(n + n_grids * i + j)\n\n # assing them all to the same categorty\n data_df['category'].iloc[indexes] = str(category)\n\n # get the geometry points of that catery\n cat_geometry = data_df[data_df['category'] == str(category)]['geometry']\n\n # get indexes of each point belonging to the category\n indexes_all = []\n for point in cat_geometry:\n indexes_all.append(geodf[geodf['geometry'] == point].index.tolist())\n\n indexes_all_flat = [item for sublist in indexes_all for item in sublist]\n\n geodf['category'].iloc[indexes_all_flat] = str(category)\n\n category = category + 1\n\n geodf['category'] = (geodf['category'].astype(str)).str.cat(geodf['date'], sep=\"_\")\n\n geodf = geodf.dissolve(by=['category', 'date'], aggfunc='mean')\n\n # re-assing the date because we are losing it\n geodf['date'] = [i[1] for i in geodf.index]\n\n geodf['category'] = [i[0] for i in geodf.index]\n\n return geodf","def load_data(city, month, day):\r\n df = pd.read_csv(CITY_DATA[city])\r\n\r\n # convert the Start Time column to datetime\r\n df['Start Time'] = pd.to_datetime(df['Start Time'])\r\n\r\n # extract month and day of week from Start Time to create new columns\r\n df['month'] = df['Start Time'].dt.month\r\n df['day_of_week'] =df['Start Time'].dt.weekday_name\r\n\r\n\r\n # filter by month if applicable\r\n if month != 'all':\r\n # use the index of the months list to get the corresponding int\r\n months = ['january', 'february', 'march', 'april', 'may', 'june']\r\n month = months.index(month) + 1\r\n\r\n # filter by month to create the new dataframe\r\n df = df[df['month'] == month]\r\n\r\n # filter by day of week if applicable\r\n if day != 'all':\r\n # filter by day of week to create the new dataframe\r\n df = df[df['day_of_week'] == day.title()]\r\n\r\n return df","def preprocess_time(data, metadata):\n timestamp_name = metadata[\"timestamp_name\"]\n if timestamp_name == \"\":\n timestamp_name = \"fake_ts\"\n data[timestamp_name] = data.index\n\n data[timestamp_name] = pd.to_datetime(data[timestamp_name])\n data.sort_values(by=[timestamp_name], inplace=True)\n data.set_index([timestamp_name], inplace=True)\n\n return data","def load_data(city, month, day):\n \n start_time = time.time()\n\n # load data file into a dataframe\n df = pd.read_csv(CITY_DATA[city])\n\n df['Start Time'] = pd.to_datetime(df['Start Time'], errors='coerce')\n\n # extract month, day of week and hour from Start Time to create new columns\n \n # Months will take values from 1 through 12\n df['month'] = df['Start Time'].dt.month \n \n # day of the week will take values in the range of 1 through 7\n df['day_of_week'] = df['Start Time'].dt.dayofweek \n \n # hour will take values from 0 through 23\n df['hour'] = df['Start Time'].dt.hour # range (0-23)\n\n # Here, we are filtering by month\n df['End Time'] = pd.to_datetime(df['End Time'])\n if month != 'all':\n months = ['january', 'february', 'march', 'april', 'may', 'june']\n month = months.index(month) + 1 \n\n df = df[df['Start Time'].dt.month == month]\n\n # Here, we are filtering by day of week\n if day != 'all': \n df = df[df['Start Time'].dt.weekday_name == day.title()]\n \n return df","def load_data(city, month, day):\n data_to_use = CITY_DATA[city]\n df = pd.read_csv(data_to_use)\n # drop rows containing NAN fields\n df2 = df.dropna()\n\n # Ensure the Start and End Time are Date\n pd.to_datetime(df2['Start Time'])\n pd.to_datetime(df2['End Time'])\n df = df2.sort_values(by='Start Time')\n\n # For each Start Time create additional columns to store year, month, day_of_week and hour\n # df['Start Year'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'year'))\n df['Start Month'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'month'))\n df['Start Day'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'day_of_week'))\n df['Start Hour'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'hour'))\n\n # filter month if month is not all\n if month.title() != 'All':\n df = df.loc[df['Start Month'] == month.title()]\n\n # filter day if day is not all\n if day.title() != 'All':\n df = df.loc[df['Start Day'] == day.title()]\n\n return df","def make_result_table(self, for_time):\n time = [t.AsPython()\n for t in cmf.timerange(cmf.AsCMFtime(self.starttime),\n cmf.AsCMFtime(self.starttime + for_time),\n self.dt)]\n depth = self.depth\n layer_template = np.zeros((len(time), len(depth))) * np.NaN\n ds = xr.Dataset(\n {'N': (('time', 'depth'), layer_template.copy()),\n 'Corg': (('time', 'depth'), layer_template.copy()),\n 'DOC': (('time', 'depth'), layer_template.copy()),\n 'Temp': (('time', 'depth'), layer_template.copy()),\n 'wetness': (('time', 'depth'), layer_template.copy()),\n },\n {'time': time, 'depth': list(depth)}\n )\n return ds"],"string":"[\n \"def filter(df: pd.DataFrame = pd.DataFrame()):\\n if df.empty:\\n df = read()\\n print('Filtering data...')\\n df = df.dropna()\\n df2 = pd.DataFrame()\\n df2['Longitude'] = df['Longitude']\\n df2['Latitude'] = df['Latitude']\\n df2['Month'] = df['Date'].dt.strftime('%m').astype(int)\\n df2['Day'] = df['Date'].dt.strftime('%d').astype(int)\\n df2['Day_of_Week'] = df['Day_of_Week']\\n df2['Time'] = np.array([t.timestamp() for t in df['Time']]) - df['Time'].min().timestamp()\\n df2['Weather_Conditions'] = df['Weather_Conditions']\\n return pd.get_dummies(df2)\",\n \"def extract_tt_by_periods(ttri, periods, start_time, end_time, filters):\\n logger = getLogger(__name__)\\n # sess = conn.get_session()\\n das = {}\\n all_wz_features = {}\\n all_wz_laneconfigs = {}\\n\\n # collecting daily data\\n for prd in periods:\\n logger.debug('>>>> retrieving data for %s' % prd.get_date_string())\\n year = prd.start_date.year\\n sdate = prd.start_date\\n edate = prd.end_date\\n if year not in das:\\n da_tt = tt.TravelTimeDataAccess(year)\\n da_tt_wz = tt_workzone.TTWorkZoneDataAccess(year)\\n da_tt_wz_feature = wz_feature.WZFeatureDataAccess()\\n da_tt_wz_lncfg = wz_laneconfig.WZLaneConfigDataAccess()\\n da_tt_weather = tt_weather.TTWeatherDataAccess(year)\\n da_tt_snowmgmt = tt_snowmgmt.TTSnowManagementDataAccess(year)\\n da_tt_incident = tt_incident.TTIncidentDataAccess(year)\\n da_tt_specialevent = tt_specialevent.TTSpecialeventDataAccess(year)\\n das[year] = (\\n da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\\n da_tt_specialevent)\\n\\n (da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\\n da_tt_specialevent) = das[year]\\n\\n # traveltimes = da_tt.list_by_period(ttri.id, self.prd)\\n weathers = da_tt_weather.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.WeatherInfo] \\\"\\\"\\\"\\n workzones = da_tt_wz.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.WorkZoneInfo] \\\"\\\"\\\"\\n incidents = da_tt_incident.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.IncidentInfo] \\\"\\\"\\\"\\n snowmgmts = da_tt_snowmgmt.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.SnowManagementInfo] \\\"\\\"\\\"\\n specialevents = da_tt_specialevent.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.SpecialEventInfo] \\\"\\\"\\\"\\n traveltimes = da_tt.list_by_period(ttri.id, prd)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.TravelTimeInfo] \\\"\\\"\\\"\\n\\n if not any(weathers):\\n logger.debug('>>>> end of retrieving data for %s (no weather data)' % prd.get_date_string())\\n continue\\n\\n extras = {\\n 'weathers': {_tt.id: [] for _tt in traveltimes},\\n 'workzones': {_tt.id: [] for _tt in traveltimes},\\n 'incidents': {_tt.id: [] for _tt in traveltimes},\\n 'specialevents': {_tt.id: [] for _tt in traveltimes},\\n 'snowmgmts': {_tt.id: [] for _tt in traveltimes},\\n }\\n \\\"\\\"\\\":type: dict[str, dict[int, list]]\\\"\\\"\\\"\\n\\n _put_to_bucket(ttri, weathers, extras['weathers'], da_tt_weather, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, workzones, extras['workzones'], da_tt_wz, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, incidents, extras['incidents'], da_tt_incident, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, snowmgmts, extras['snowmgmts'], da_tt_snowmgmt, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, specialevents, extras['specialevents'], da_tt_specialevent, year, all_wz_features, all_wz_laneconfigs, das)\\n\\n for tti in traveltimes:\\n _tt_weathers = extras['weathers'][tti.id]\\n extdata = ExtData(tti,\\n _tt_weathers[0] if _tt_weathers else None,\\n extras['incidents'][tti.id],\\n extras['workzones'][tti.id],\\n extras['specialevents'][tti.id],\\n extras['snowmgmts'][tti.id])\\n\\n if start_time <= tti.str2datetime(tti.time).time() <= end_time:\\n for ef in filters:\\n try:\\n ef.check(extdata)\\n except Exception as ex:\\n tb.traceback(ex)\\n logger.debug('>>>> end of retrieving data for %s (error occured 1)' % prd.get_date_string())\\n continue\\n else:\\n for ef in filters:\\n try:\\n ef.check_outofrange(extdata)\\n except Exception as ex:\\n tb.traceback(ex)\\n logger.debug('>>>> end of retrieving data for %s (error occured 2)' % prd.get_date_string())\\n continue\\n\\n del extras\\n logger.debug('>>>> end of retrieving data for %s' % prd.get_date_string())\\n\\n # sess.close()\",\n \"def clean_and_save_timeseries(df):\\n drop_columns = ['Lat', \\n 'Long', \\n 'Province/State']\\n\\n df.drop(columns=drop_columns, inplace = True)\\n \\n df_grouped = df.groupby(['Country/Region'], as_index=False).sum()\\n df_grouped = df_grouped.set_index('Country/Region').transpose()\\n df_grouped.reset_index(level=0, inplace=True)\\n df_grouped.rename(columns={'index': 'Date'}, inplace=True)\\n df_grouped['Date'] = pd.to_datetime(df_grouped['Date'])\\n\\n df_grouped.to_csv('../data/worldwide_timeseries.csv', index=False)\",\n \"def get_time_series_at_location(data, lat, lon, feature):\\n\\n ts = data.sel(lat=lat, lon=lon, method='nearest', drop=True).to_series()\\n index = ts.index.get_level_values('time')\\n values = ts.values\\n\\n return pd.DataFrame({'Date': index.values, feature: values})\",\n \"def zonal_statistics(wrksppath, timestamp, region, model):\\n logging.info('\\\\nDoing Zonal Statistics on ' + region)\\n # Define app workspace and sub-paths\\n resampleds = os.path.join(wrksppath, region, model + '_GeoTIFFs_resampled')\\n shp_path = os.path.join(wrksppath, region, 'shapefiles', 'ffgs_' + region + '.shp')\\n\\n stat_file = os.path.join(wrksppath, region, model + 'results.csv')\\n\\n # check that there are resampled tiffs to do zonal statistics on\\n if not os.path.exists(resampleds):\\n logging.info('There are no resampled tiffs to do zonal statistics on. Skipping Zonal Statistics')\\n return\\n\\n # List all Resampled GeoTIFFs\\n files = os.listdir(resampleds)\\n files = [tif for tif in files if tif.endswith('.tif')]\\n files.sort()\\n\\n # do zonal statistics for each resampled tiff file and put it in the stats dataframe\\n stats_df = pd.DataFrame()\\n for i in range(len(files)):\\n logging.info('starting zonal statistics for ' + files[i])\\n ras_path = os.path.join(resampleds, files[i])\\n stats = rasterstats.zonal_stats(\\n shp_path,\\n ras_path,\\n stats=['count', 'max', 'mean'],\\n geojson_out=True\\n )\\n\\n timestep = files[i][:10]\\n\\n # for each stat that you get out, write it to the dataframe\\n logging.info('writing the statistics for this file to the dataframe')\\n for j in range(len(stats)):\\n\\n temp_data = stats[j]['properties']\\n temp_data.update({'Forecast Timestamp': timestamp})\\n temp_data.update({'Timestep': timestep})\\n\\n temp_df = pd.DataFrame([temp_data])\\n stats_df = stats_df.append(temp_df, ignore_index=True)\\n\\n # write the resulting dataframe to a csv\\n logging.info('\\\\ndone with zonal statistics, rounding values, writing to a csv file')\\n stats_df = stats_df.round({'max': 1, 'mean': 1})\\n stats_df.to_csv(stat_file, index=False)\\n\\n # delete the resampled tiffs now that we dont need them\\n logging.info('deleting the resampled tiffs directory')\\n shutil.rmtree(resampleds)\\n\\n return\",\n \"def time_of_visit_bins(df):\\n mask_8_5 = (df[\\\"admit_hour_code\\\"] >= 8) & (df[\\\"admit_hour_code\\\"] < 17)\\n mask_5_11 = (df[\\\"admit_hour_code\\\"] >= 17) & (df[\\\"admit_hour_code\\\"] < 23)\\n mask_11_8 = (df[\\\"admit_hour_code\\\"] >= 23) | (df[\\\"admit_hour_code\\\"] < 8)\\n\\n df[\\\"hour_category\\\"] = np.where(mask_8_5, \\\"Open Hours\\\", \\\"Unknown\\\")\\n df[\\\"hour_category\\\"] = np.where(mask_5_11, \\\"Evening\\\", df[\\\"hour_category\\\"])\\n df[\\\"hour_category\\\"] = np.where(mask_11_8, \\\"Overnight\\\", df[\\\"hour_category\\\"])\\n\\n return df\",\n \"def prep_data():\\n # read data\\n df = pd.read_csv(\\\"data/SF-crime-data_2016.csv\\\")\\n\\n # convert to datetime and extract hour\\n df['datetime'] = pd.to_datetime(df[[\\\"Date\\\",\\\"Time\\\"]].apply(lambda x: x[0].split()[0] +\\\" \\\"+x[1], axis=1), format=\\\"%m/%d/%Y %H:%M\\\")\\n df['hour'] = df['datetime'].dt.hour \\n df.dropna(inplace=True)\\n \\n # filter out top 4 crimes\\n top_4_crimes = df['Category'].value_counts()[:6].index.to_list()\\n top_4_crimes\\n top_4_crimes.remove(\\\"NON-CRIMINAL\\\")\\n top_4_crimes.remove(\\\"OTHER OFFENSES\\\")\\n\\n return df[df[\\\"Category\\\"].isin(top_4_crimes)]\",\n \"def get_post_df(temp_post_df):\\n \\n cat_dfs = []\\n for num in np.arange(1080/2,0,-30):\\n # making > null i.e keeping <=\\n temp_post_df.loc[temp_post_df['event_time'] > int(num), 'event_time'] = np.nan\\n for col in ['event_name', 'specialty', 'plan_type']:\\n cat_df = temp_post_df.groupby([\\\"id\\\", col]).agg({\\\"event_time\\\": 'count'}).unstack(level=col)\\n cat_df = cat_df/num\\n cat_df.columns = ['__'.join(['normChange', col, name, str(int(num))]) for name in cat_df.columns.droplevel()]\\n cat_dfs.append(cat_df) \\n post_df = pd.concat(cat_dfs, axis = 1)\\n return post_df.fillna(0)\",\n \"def hotmap(datetime, room, query):\\n\\n #IMPORTANT: this query returns M-F values\\n\\n # get a df with date and count values, for a given room and date\\n week_counts = pd.read_sql_query(query.format(date=datetime, room=room),\\n connectDB())\\n\\n headcount = list(week_counts['count'])\\n headcount = [linear_predictor(x) for x in headcount] # predict on all supplied values\\n\\n bins = list(week_counts['count'])\\n # bins = [tertiary_classifier(x) for x in bins] # predict on all supplied values\\n tert_dictionary = {'Empty': 0, 'Medium': 0.5, 'High': 1} # map to int as only ints can go in heatmap\\n # bins = [tert_dictionary[x] for x in bins] # map with listcomp\\n\\n # 5 * 9 = 45, so all elements need to have 45 values. Days and hours are just multipled out.\\n data = {'days': ['fri']*9 + ['thu']*9 + ['wed']*9 + ['tue']*9 + ['mon']*9,\\n 'occupancy': headcount,\\n 'hours': ['9', '10', '11', '12', '13', '14', '15', '16', '17']*5}\\n\\n week_counts['headcount'] = week_counts['count'].apply(linear_predictor)\\n week_counts['bins'] = week_counts['count'].apply(tertiary_classifier)\\n\\n source = ColumnDataSource(ColumnDataSource.from_df(week_counts))\\n\\n\\n # hover = HoverTool()\\n #\\n # tooltips = [(\\\"headcount\\\", \\\"@headcount\\\")]\\n #\\n # tools = [hover]\\n\\n # colors = ['#1abc9c', '#ec583a', '#474e5d'] # set colors; currently not shading on continuous features\\n\\n hm = HeatMap(data, x='hours', y='days', values='occupancy',\\n title='Occupancy in room over the week'.format(room), stat=None,\\n palette=palette, tools=None,\\n hover_tool=True)\\n\\n legend = Legend(location=(0, -30))\\n\\n hm.add_layout(legend, 'right')\\n\\n all_plots = gridplot([[hm]])\\n\\n return all_plots\",\n \"def load_data(city, month, day):\\n\\n if city == 'all':\\n df = pd.read_csv(CITY_DATA['chicago'])\\n df['city'] = 'chicago'\\n ny = pd.read_csv(CITY_DATA['new york city'])\\n ny['city'] = 'new york city'\\n df = df.append(ny,sort = True)\\n wa = pd.read_csv(CITY_DATA['washington'])\\n wa['city'] = 'washington'\\n df = df.append(wa,sort = True)\\n else:\\n df = pd.read_csv(CITY_DATA[city])\\n df['city'] = CITY_DATA[city]\\n\\n df['Start Time'] = pd.to_datetime(df['Start Time']) #converts Start Time to datetime\\n df['End Time'] = pd.to_datetime(df['End Time']) #converts End Time to datetime\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n #print(df) #DataFrame\\n\\n #filter by month\\n if month != 'all':\\n month_name = ['january', 'february', 'march', 'april', 'may', 'june','july','august','september','october','november','december']\\n month_num = month_name.index(month) + 1\\n df = df[df['month'] == month_num]\\n #filter by day\\n if day != 'all':\\n df = df[df['day_of_week'] == day.title()]\\n\\n recs = df['Start Time'].count()\\n\\n return df, recs\",\n \"def manipulate_data(ds, var, predef_clim, predef_trnd, trn_yrs, all_yrs, \\n apply_latw=True, apply_detrending=True, dropna=True):\\n\\n \\n if((var=='SD')|(var=='sd')|(var=='snowc')): \\n ds[var] = ds[var].where(ds[var]>=0, other=0.0)\\n ds[var] = ds[var].where(ds[var]==0, other=1.0)\\n #ds[var].values = Gauss_filter(ds[var].values, (0,3,3))\\n \\n \\\"\\\"\\\"\\n if((var=='hgt')|(var=='z')|(var=='GPT')):\\n months = ds.time.to_index().month; ssn_ends = (months==2)|(months==5)|(months==8)|(months==11)\\n ds = ds.sel(time=ssn_ends)\\n else: \\n ds = ds.resample(time='3M').mean()\\n \\\"\\\"\\\"\\n \\n ds = ds.resample(time='3M').mean()\\n\\n ds = ds.sel(time=slice(str(all_yrs[0])+'-01-01', str(all_yrs[-1])+'-12-31')) \\n \\n try: \\n clim = predef_clim\\n ds = ds.groupby('time.season') - clim\\n print('Predefined climatology used')\\n except:\\n clim = ds.sel(time=slice(str(trn_yrs[0])+'-01-01', str(trn_yrs[-1])+'-12-31')).groupby('time.season').mean('time')\\n ds = ds.groupby('time.season') - clim\\n print('Climatology calculated from data')\\n \\n if(apply_latw): ds[var].values = lat_weighting(ds[var].values, \\n ds.lat, ds.lon)\\n if(dropna):\\n ds = ds.stack(gridcell=('lat', 'lon')).dropna(dim='gridcell',how='any')\\n else: \\n ds = ds.stack(gridcell=('lat', 'lon')).fillna(0)\\n \\n \\n trend_models = { }\\n if(apply_detrending): \\n ds = ds.load()\\n for ssn in ('DJF', 'MAM', 'JJA', 'SON'):\\n #ssn_idx = ds['time.season'] == ssn\\n \\n trn_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], trn_yrs))\\n all_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], all_yrs))\\n \\n trn_x = np.array(ds.time[trn_idx].values.tolist()).reshape(-1,1)\\n all_x = np.array(ds.time[all_idx].values.tolist()).reshape(-1,1)\\n try:\\n trend = predef_trnd[ssn].predict(all_x)\\n trend_models[ssn] = predef_trnd[ssn]\\n print('Predefined trend model used')\\n except:\\n #_, trend_model = define_trends(ds[var][trn_idx], trn_x)\\n _, trend_model = define_trends(ds[var][all_idx], all_x)\\n trend = trend_model.predict(all_x)\\n trend_models[ssn] = trend_model\\n print('Trends calculated from data')\\n \\n ds[var][all_idx] = ds[var][all_idx] - trend\\n \\n\\n \\n return ds, clim, trend_models\",\n \"def warren_time_series(lat, lon, dates, variable='snow_depth'):\\n\\n my_func = {'snow_depth': snow_depth,\\n 'swe': swe}\\n\\n #if not all([isinstance(d, dt.datetime) for d in dates]):\\n # print ('Expects datetime objects')\\n\\n # If lat, lon are vectors, generate 2d grids\\n # Need to add code to make sure x and y are DataArrays\\n if (count_dims(lat) == 1):\\n x, y = np.meshgrid(lon, lat)\\n else:\\n x, y = lon, lat\\n \\n if dates.size == 1:\\n cube = my_func[variable](x,y,dates.dt.month)\\n da = xr.DataArray(cube,\\n coords={'lat': x, 'lon': y},\\n dims=['lat', 'lon'])\\n else:\\n cube = [my_func[variable](x, y, m) for m in dates.dt.month.values]\\n da = xr.concat(cube, dim='time')\\n da['time'] = dates\\n\\n return da\",\n \"def compliance_time_series(county, core_path , patterns_path, backfill = False, GEOID_type = 'CBG'):\\n #Load data about places and patterns\\n county_places = pd.read_csv(core_path+'places-'+ county +'.csv', index_col='safegraph_place_id') \\n \\n pattern_dates = [x[5:9] for x in sorted(os.listdir(patterns_path+'main-file-'+ county +'/'))]\\n w = 0\\n next_date = pattern_dates[w]\\n #Create visitor table\\n place_cts = pd.DataFrame() #First two months don't have weekly patterns, use empty data.frame\\n\\n #List files for social distancing metrics in that county\\n months_path = '../social_distancing/social_dist_'+ county +'/'\\n month_list = sorted(os.listdir(months_path))\\n os.makedirs( '../stats/time_series/', exist_ok = True)\\n #metrics dictionary to be filled looping through every day\\n if not os.path.isfile('../stats/time_series/metrics_{}_CT.csv'.format(county)) or backfill:\\n metrics = pd.DataFrame()\\n existing_dates = []\\n else:\\n metrics = pd.read_csv('../stats/time_series/metrics_{}_CT.csv'.format(county), dtype = {'origin_census_tract':str})\\n #dates already processed\\n existing_dates = metrics['date'].unique() #series of unique dates\\n\\n #Initialize columns of new data frame\\n if GEOID_type == 'CBG':\\n columns= {'date':[], 'origin_census_block_group':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'total_expected_contacts':[], 'places_visited':[], 'cbgs_visited':[]}\\n if GEOID_type == 'CT':\\n columns= {'date':[], 'origin_census_tract':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'places_visited':[]} \\n changed = False\\n for month in month_list:\\n #Loop through every day\\n day_list = sorted(os.listdir(months_path + month))\\n for day in day_list: \\n date_name = month + '-' + day\\n print(date_name)\\n if month+'-'+day == next_date:\\n w = w+1\\n if w < len(pattern_dates):\\n next_date = pattern_dates[w]\\n if date_name in existing_dates:\\n continue\\n print(\\\"--changing to next patterns file\\\")\\n county_patterns = pd.read_csv(patterns_path + 'main-file-{}/2020-{}-weekly-patterns.csv.gz'.format(county, date_name),\\n index_col='safegraph_place_id')\\n norm_factors = pd.read_csv(\\n '../social_distancing/normalization/'+'normalization_{}.csv'.format(county),\\n dtype={'origin_census_block_group':str})\\n norm_factors.set_index('origin_census_block_group', drop =True, inplace=True)\\n\\n\\n #Establish prior for hourly distribution of visits at the top_category level\\n county_patterns= county_patterns.join(county_places[['top_category','sub_category']], how='inner')\\n restaurants = county_patterns['top_category'] == 'Restaurants and Other Eating Places'\\n county_patterns.loc[restaurants, 'top_category'] = county_patterns.loc[restaurants, 'sub_category']\\n norm_factor = norm_factors.loc[norm_factors.date == date_name].norm_factor\\n\\n prior_dict = {}\\n for category in county_patterns.top_category.value_counts().index:\\n places_in_cat = county_patterns['top_category'] == category\\n dirich_samples = [np.array(json.loads(x)) for x in county_patterns.loc[places_in_cat, 'visits_by_each_hour'] ] \\n prior_dict[category] = dirichlet.getInitAlphas(dirich_samples)\\n if GEOID_type == 'CBG':\\n place_cbgs = ccc.place_cbg_contacts_table(\\n county_patterns,\\n prior_dict,\\n norm_factor,\\n GEOID_type)\\n place_cbgs = place_cbgs.loc[place_cbgs['expected_contacts']>1]\\n place_cbgs = place_cbgs.join(county_places[['location_name','latitude','longitude']], how='inner')\\n place_cbgs.reset_index(inplace=True, drop=False)\\n place_cbgs.set_index('origin_census_block_group', inplace=True, drop=True)\\n if GEOID_type == 'CT':\\n place_cts = ccc.place_cbg_contacts_table(\\n county_patterns,\\n prior_dict,\\n norm_factor,\\n GEOID_type)\\n place_cts = place_cts.join(county_places[['location_name','latitude','longitude']], how='inner')\\n place_cts.reset_index(inplace=True, drop=False)\\n place_cts.set_index('origin_census_tract', inplace=True, drop=True)\\n print('--computed bipartite contact network')\\n\\n if date_name in existing_dates:\\n continue\\n\\n\\n file_name = os.listdir(months_path+ '/' + month+'/'+day)[0]\\n data_soc_dist = pd.read_csv(months_path+ '/' + month+'/'+day+'/'+file_name, dtype={'origin_census_block_group':str})\\n data_soc_dist['median_distance_traveled_from_home'] = [0 if math.isnan(x) else x for x in data_soc_dist['distance_traveled_from_home']] #NOT NEEDED?\\n \\n if GEOID_type == 'CBG':\\n [update_metrics_columns(row, date_name, place_cbgs, county_places, columns) for i, row in data_soc_dist.iterrows()]\\n else:\\n ####CREATE FUNCTION THAT AGGREGATES TO CENSUS TRACT\\n data_soc_dist['origin_census_tract'] = [x[:-1] for x in data_soc_dist['origin_census_block_group']]\\n ct_data_soc_dist = data_soc_dist.groupby('origin_census_tract').apply(ddd.aggregate_to_ct).reset_index()\\n [update_metrics_columns_CT(row, date_name, place_cts, county_places, columns) for i, row in ct_data_soc_dist.iterrows()]\\n changed = True\\n\\n print(\\\"--merging rows from new dates\\\")\\n new_metrics = pd.DataFrame.from_dict(columns, orient='index').transpose()\\n\\n metrics = pd.concat([metrics, new_metrics], ignore_index=True)\\n if changed:\\n if GEOID_type == 'CT':\\n metrics.to_csv('../stats/time_series/metrics_{}_CT.csv'.format(county),index=False)\\n else:\\n metrics.to_csv('../stats/time_series/metrics_{}_CBG.csv'.format(county),index=False)\\n print(\\\"--finished updating time series for {}\\\".format(county))\\n return(0)\",\n \"def load_data(city, time_filter, month, day):\\n print(\\\"\\\\nLoading data and preprocessing...\\\")\\n start_time = time.time()\\n df = pd.read_csv(CITY_DATA[city])\\n\\n #Create columns for the dataframe for convinience in calculations\\n df['start_year'] = [int(value[0:4]) for value in df[\\\"Start Time\\\"]]\\n df['start_month'] = [int(value[5:7]) for value in df[\\\"Start Time\\\"]]\\n df['start_day'] = [int(value[8:10]) for value in df[\\\"Start Time\\\"]]\\n df['start_hour'] = [int(value[11:13]) for value in df[\\\"Start Time\\\"]]\\n df['start_min'] = [int(value[14:16]) for value in df[\\\"Start Time\\\"]]\\n df['start_sec'] = [int(value[17:20]) for value in df[\\\"Start Time\\\"]]\\n\\n df['end_year'] = [int(value[0:4]) for value in df[\\\"End Time\\\"]]\\n df['end_month'] = [int(value[5:7]) for value in df[\\\"End Time\\\"]]\\n df['end_day'] = [int(value[8:10]) for value in df[\\\"End Time\\\"]]\\n df['end_hour'] = [int(value[11:13]) for value in df[\\\"End Time\\\"]]\\n df['end_min'] = [int(value[14:16]) for value in df[\\\"End Time\\\"]]\\n df['end_sec'] = [int(value[17:20]) for value in df[\\\"End Time\\\"]]\\n\\n #Create a new column for the dataframe containing the day of the week\\n day_of_week = []\\n for index, row in df.iterrows():\\n day_of_week.append( dt.date(row[\\\"start_year\\\"], row[\\\"start_month\\\"], row[\\\"start_day\\\"]).weekday())\\n df['day_of_week'] = day_of_week\\n\\n #Apply the required filters and return the dataframe\\n if time_filter == 'none':\\n load_data_print(start_time)\\n return df\\n elif time_filter == 'month':\\n df = df[(df['start_month']==MONTH[month])]\\n load_data_print(start_time)\\n return df\\n elif time_filter == 'day':\\n df = df[(df['day_of_week']==WEEKDAY[day])]\\n load_data_print(start_time)\\n return df\\n else:\\n print(\\\"\\\\nERROR on viable_unput_time value\\\")\\n return 1\",\n \"def filter_data(city, data_filter, Month, Day):\\r\\n directory = 'C:/Users/The Presence/Documents/Personal Document/Personal Development/Data Science and Analytics/Udacity (Python Course)/Labs Tasks-20200924T221854Z-001/Lab Results/Capstone Project/bikeshare-2/' + city \\r\\n df = pd.read_csv(directory)\\r\\n df_1 = df.copy() \\r\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\r\\n df['Months'] = df['Start Time'].dt.month_name(locale='English')\\r\\n df['Days'] = df['Start Time'].dt.day_name(locale='English')\\r\\n df['Hours'] = df['Start Time'].dt.hour\\r\\n if data_filter == 'None':\\r\\n df = df\\r\\n elif data_filter == 'Month':\\r\\n spef_mon = Month\\r\\n df_month = df[df.Months == spef_mon]\\r\\n df = df_month\\r\\n elif data_filter == 'Day':\\r\\n spef_day = Day\\r\\n df_day = df[df.Days == spef_day]\\r\\n df = df_day\\r\\n elif data_filter == 'Both':\\r\\n spef_mon, spef_day = Month, Day\\r\\n df_month = df[df.Months == spef_mon]\\r\\n df_month_day = df_month[df_month.Days == spef_day]\\r\\n df = df_month_day\\r\\n return(df, df_1)\",\n \"def __init__(self, location, type, log_df = TWEET_HISTORY_DF):\\n self.location = location\\n self.type = type\\n self.lat = c.LOCATIONS[self.location]['lat']\\n self.lon = c.LOCATIONS[self.location]['lon']\\n self.sunsetwx_response = py_sunsetwx.get_quality(self.lat, self.lon, self.type)\\n self.log_df = log_df \\n \\n ## Find the time of the sunrise/sunset\\n #### Lookup civil time of \\\"dawn\\\" if sunrise, \\\"dusk\\\" if sunset\\n if type == 'sunrise':\\n self.dawn_dusk = 'dawn'\\n elif type == 'sunset':\\n self.dawn_dusk = 'dusk'\\n self.utc_time = pytz.utc.localize(datetime.strptime(self.sunsetwx_response['features'][0]['properties'][self.dawn_dusk]['civil'], '%Y-%m-%dT%H:%M:%SZ'))\\n self.time_converted = self.utc_time.astimezone(pytz.timezone(c.LOCATIONS[location]['timezone']))\",\n \"def prepare_for_influxdb(df):\\n df = df.drop(columns=\\\"landkreis\\\", errors=\\\"ignore\\\") # prevent name collision in get_ags()\\n df = get_ags(df)\\n df[\\\"time\\\"] = df.apply(lambda x: 1000000000*int(datetime.timestamp((pd.to_datetime(x[\\\"timestamp\\\"])))), 1)\\n df[\\\"measurement\\\"] = \\\"hystreet\\\"\\n df[\\\"origin\\\"] = \\\"https://hystreet.com\\\"\\n df = df.rename(columns={\\n 'station_id': '_id',\\n 'pedestrians_count': 'pedestrian_count',\\n 'state': 'bundesland'\\n })\\n df['ags'] = pd.to_numeric(df['ags'])\\n # import pdb; pdb.set_trace()\\n return df\",\n \"def streaming_weather_data(**kwargs):\\n df = weather_data(['San Francisco'])\\n df['time'] = [pd.Timestamp.now()]\\n return df.set_index('time')\",\n \"def create_table(f, geoinfo):\\n bounds_cols = xb_points + yb_points\\n df = pd.read_csv(f, delimiter=\\\";\\\", index_col=\\\"INDEX_RC\\\")\\n df[duration_name] = parse_duration_level(f)\\n df = df.join(geoinfo[[\\\"X_CENT_GEO\\\", \\\"Y_CENT_GEO\\\", \\\"Col\\\", \\\"Row\\\"]])\\n df = df.rename(columns={\\\"Col\\\": x, \\\"Row\\\": y, \\\"X_CENT_GEO\\\": lon, \\\"Y_CENT_GEO\\\": lat})\\n return df\",\n \"def flat_ftr_hotspot(cities, perm_time_ranges=None, hourly=True, hotspot_types='loubar'):\\n if isinstance(hotspot_types, str):\\n hotspot_types = [hotspot_types]\\n elif not isinstance(hotspot_types, (list, tuple)):\\n raise ValueError('hotspot_types should be one type of str, list or tuple')\\n\\n if perm_time_ranges is None:\\n perm_time_ranges = ['all_day', 'home_hour', 'work_hour']\\n elif isinstance(perm_time_ranges, str):\\n if perm_time_ranges not in ['all_day', 'home_hour', 'work_hour']:\\n raise ValueError('perm_time_ranges should be one of [all_day, home_hour, work_hour]')\\n perm_time_ranges = [perm_time_ranges]\\n elif not isinstance(perm_time_ranges, (list, tuple)):\\n raise ValueError('perm_time_ranges should be one type of None, str, list or tuple')\\n\\n flat_ftrs = []\\n for i, city in enumerate(cities):\\n # print(i, city['admin_id'])\\n flat_ftr = {city['admin_lvl']: city['admin_id']}\\n for hs_type in hotspot_types:\\n features = city['features'][f'hs-{hs_type}']\\n # add permanent hotspots indices\\n for fname, values in features.items():\\n for time_range in perm_time_ranges:\\n flat_ftr[f'{hs_type}-{fname}-{time_range}'] = values[time_range]\\n # add hourly hotspots indices\\n if hourly:\\n for item in values['hourly']:\\n hour = item['hour']\\n value = item['value']\\n flat_ftr[f'{hs_type}-{fname}-hour{hour}'] = value\\n flat_ftrs.append(flat_ftr)\\n return flat_ftrs\",\n \"def filter_dataframe(df, start_date_dt, end_date_dt):\\n\\n dff = df \\n # df[\\n # (df[\\\"timestamp\\\"].dt.date >= dt.date(start_date_dt.year, start_date_dt.month, start_date_dt.day))\\n # & (df[\\\"timestamp\\\"].dt.date <= dt.date(end_date_dt.year, end_date_dt.month, end_date_dt.day))\\n # ]\\n # if (lat_min != -90) or (lat_max != 90):\\n # dff = dff[\\n # (dff[\\\"lat\\\"] >= lat_min)\\n # & (dff[\\\"lat\\\"] <= lat_max)\\n # ]\\n # if (lon_min != -90) or (lon_max != 90):\\n # dff = dff[\\n # (dff[\\\"lon\\\"] >= lon_min)\\n # & (dff[\\\"lon\\\"] <= lon_max)\\n # ]\\n\\n return dff\",\n \"def filter_meteo_data(self, startdate, enddate):\\n self.all_meteo_data.columns.values[0]='Datum-tijd'\\n self.all_meteo_data['datetime']=pd.to_datetime(self.all_meteo_data['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\\n self.all_meteo_data.drop(['Datum-tijd'],axis=1, inplace=True)\\n mask = (self.all_meteo_data['datetime'] > startdate) & (self.all_meteo_data['datetime'] <= enddate)\\n meteodata = self.all_meteo_data.loc[mask].copy()\\n meteodata.set_index('datetime',inplace=True)\\n return meteodata\",\n \"def test_plt_mag_time():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n # create the arrays per filter and readout pattern\\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\\n for i, val in enumerate(max_val_box):\\n if '140' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(val)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(val)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif '110' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(val)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(val)\\n nrsrapidd6_clear.append(np.NaN)\\n else:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(val)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(val)\\n # add to the bokeh data structure\\n ta.source.data[\\\"nrsrapid_f140x\\\"] = nrsrapid_f140x\\n ta.source.data[\\\"nrsrapid_f110w\\\"] = nrsrapid_f110w\\n ta.source.data[\\\"nrsrapid_clear\\\"] = nrsrapid_clear\\n ta.source.data[\\\"nrsrapidd6_f140x\\\"] = nrsrapidd6_f140x\\n ta.source.data[\\\"nrsrapidd6_f110w\\\"] = nrsrapidd6_f110w\\n ta.source.data[\\\"nrsrapidd6_clear\\\"] = nrsrapidd6_clear\\n result = ta.plt_mag_time()\\n\\n assert bokeh_plot_type == type(result)\",\n \"def select_features(dfp):\\n df = dfp[['5', '7', '12']].copy() \\n df.columns=['type', 'duration','location']\\n for col in df.columns:\\n strings = df[col].unique()\\n if col != \\\"location\\\":\\n mapper = dict( zip(strings, range(len(strings))) )\\n df[col].replace(mapper, inplace=True)\\n else:\\n df[col] = df[col].str.contains(\\\"LONDON\\\").astype(int)\\n return df\",\n \"def pull_traffic():\\n ga_conn = initialize_analyticsreporting()\\n\\n device_traffic = get_report(\\n ga_conn=ga_conn,\\n dimensions=[{'name': 'ga:deviceCategory'},{'name': 'ga:yearMonth'},])\\n device_traffic = melt_ga(device_traffic,\\n a='Traffic',\\n c='Device',\\n d='deviceCategory')\\n\\n geo_traffic = get_report(\\n ga_conn=ga_conn,\\n dimensions=[{'name': 'ga:country'},{'name': 'ga:yearMonth'},])\\n bucketed_countries = geo_traffic.apply(lambda row: bucket_countries(row), axis=1)\\n geo_traffic['country'] = bucketed_countries\\n geo_traffic = geo_traffic.groupby(['yearMonth','country'])\\\\\\n .sum()\\\\\\n .reset_index()\\n geo_traffic = melt_ga(geo_traffic,\\n a='Traffic',\\n c='Geography',\\n d='country')\\n\\n channel_traffic = get_report(\\n ga_conn=ga_conn,\\n dimensions=[{'name': 'ga:channelGrouping'},{'name': 'ga:yearMonth'},])\\n bucketed_channels = channel_traffic.apply(lambda row: bucket_channels(row), axis=1)\\n channel_traffic['channelGrouping'] = bucketed_channels\\n channel_traffic = channel_traffic.groupby(['yearMonth','channelGrouping'])\\\\\\n .sum()\\\\\\n .reset_index()\\n channel_traffic = melt_ga(channel_traffic,\\n a='Traffic',\\n c='Channel',\\n d='channelGrouping')\\n df_out = pd.concat([device_traffic, geo_traffic, channel_traffic])\\n return df_out\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n #convert Start Time to datetime for filtering\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n #add columns for filters to match on\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n\\n #filter by month and day if not all\\n #limited index options to available date range in the data set\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month_index = 1 + months.index(month)\\n\\n # filter by month to create the new dataframe\\n df = df.loc[df['month'] == month_index]\\n\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df.loc[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def filtering(self, Z):\\n coord_x_est = [0]\\n coord_y_est = [0]\\n time_est = [Z[['time']].iloc[0][0]]\\n for i in range(1, len(Z.x)):\\n new_dt = Z[['time']].iloc[i][0] - time_est[-1]\\n while (new_dt > 150):\\n new_state = [coord_x_est[-1], coord_y_est[-1], time_est[-1]]\\n new_state = self._restore(new_state)\\n coord_x_est.append(new_state[0])\\n coord_y_est.append(new_state[1])\\n time_est.append(new_state[2])\\n new_dt -= 100\\n\\n# self.configure(new_dt)\\n self._predict()\\n mes = Z[['x', 'y']].iloc[i].to_numpy()\\n self._update(np.resize(mes, (2, 1)))\\n # save for latter plotting\\n coord_x_est.append(self.x[0])\\n coord_y_est.append(self.x[4])\\n time_est.append(int(Z[['time']].iloc[i]))\\n\\n return pd.DataFrame({'X_f': coord_x_est,\\n 'Y_f': coord_y_est,\\n 'time': time_est\\n })\",\n \"def _tseries_from_nifti_helper(coords, data, TR, filter, normalize, average):\\r\\n if coords is not None:\\r\\n out_data = np.asarray(data[coords[0], coords[1], coords[2]])\\r\\n else:\\r\\n out_data = data\\r\\n\\r\\n tseries = ts.TimeSeries(out_data, sampling_interval=TR)\\r\\n\\r\\n if filter is not None:\\r\\n if filter['method'] not in ('boxcar', 'fourier', 'fir', 'iir'):\\r\\n e_s = \\\"Filter method %s is not recognized\\\" % filter['method']\\r\\n raise ValueError(e_s)\\r\\n else:\\r\\n #Construct the key-word arguments to FilterAnalyzer:\\r\\n kwargs = dict(lb=filter.get('lb', 0),\\r\\n ub=filter.get('ub', None),\\r\\n boxcar_iterations=filter.get('boxcar_iterations', 2),\\r\\n filt_order=filter.get('filt_order', 64),\\r\\n gpass=filter.get('gpass', 1),\\r\\n gstop=filter.get('gstop', 60),\\r\\n iir_ftype=filter.get('iir_ftype', 'ellip'),\\r\\n fir_win=filter.get('fir_win', 'hamming'))\\r\\n\\r\\n F = tsa.FilterAnalyzer(tseries, **kwargs)\\r\\n\\r\\n if filter['method'] == 'boxcar':\\r\\n tseries = F.filtered_boxcar\\r\\n elif filter['method'] == 'fourier':\\r\\n tseries = F.filtered_fourier\\r\\n elif filter['method'] == 'fir':\\r\\n tseries = F.fir\\r\\n elif filter['method'] == 'iir':\\r\\n tseries = F.iir\\r\\n\\r\\n if normalize == 'percent':\\r\\n tseries = tsa.NormalizationAnalyzer(tseries).percent_change\\r\\n elif normalize == 'zscore':\\r\\n tseries = tsa.NormalizationAnalyzer(tseries).z_score\\r\\n\\r\\n if average:\\r\\n if coords is None:\\r\\n tseries.data = np.mean(np.reshape(tseries.data,\\r\\n (np.array(tseries.shape[:-1]).prod(),\\r\\n tseries.shape[-1])),0)\\r\\n else:\\r\\n tseries.data = np.mean(tseries.data, 0)\\r\\n\\r\\n return tseries\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n#changed 'weekday_name' to just 'weekday' which outputs the weekday as integer\\n # extract month, day of week and hour from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['dow'] = df['Start Time'].dt.weekday\\n df['hour'] = df['Start Time'].dt.hour\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n month = months.index(month) + 1\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n# problem with the 'day'-filter, if a day (not 'all') is applied, the output is not right\\n # filter by day of week if applicable\\n if day != 'all':\\n\\n # filter by day of week to create the new dataframe\\n day = days.index(day) + 1\\n df = df[df['dow'] == day]\\n\\n return df\",\n \"def create_dataframe():\\n df = pd.read_csv(\\\"data/311-calls.csv\\\", parse_dates=[\\\"created\\\"])\\n df[\\\"created\\\"] = df[\\\"created\\\"].dt.date\\n df.drop(columns=[\\\"incident_zip\\\"], inplace=True)\\n num_complaints = df[\\\"complaint_type\\\"].value_counts()\\n to_remove = num_complaints[num_complaints <= 30].index\\n df.replace(to_remove, np.nan, inplace=True)\\n return df\",\n \"def prep_data(df, datatype):\\n\\n start = time.time()\\n if datatype == \\\"training\\\":\\n df = drop_cols(df, \\\"gross_bookings_usd\\\")\\n df = add_category(df)\\n df = get_train_data(df)\\n\\n df = stats(df)\\n print(\\\"(1/6 - train only) add categories and downsample train data: \\\", np.round((time.time() - start)*1000 / 60, 2), \\\"min\\\")\\n\\n start = time.time()\\n df = competitors(df)\\n print(\\\"(2/6) competitors: \\\", np.round((time.time() - start) / 60, 2), \\\"min\\\")\\n\\n start = time.time()\\n df = seasonality(df)\\n print(\\\"(3/6) seasons: \\\", np.round((time.time() - start)*1000 / 60, 2), \\\"min\\\")\\n\\n start = time.time()\\n df = missing_values(df)\\n print(\\\"(4/6) missing values: \\\", np.round((time.time() - start)*1000 / 60, 2), \\\"min\\\")\\n\\n start = time.time()\\n df = price_star_diff(df)\\n print(\\\"(5/6) price and star difference: \\\", np.round((time.time() - start)*1000 / 60, 2), \\\"min\\\")\\n\\n # print(\\\"er zijn nog zoveel nans(2): \\\", df.isnull().sum().sum())\\n # pd.set_option('display.max_rows', None)\\n # print(df.isnull().sum())\\n\\n\\n start = time.time()\\n df = scale(df)\\n print(\\\"(6/6) scaling: \\\", np.round((time.time() - start) / 60, 2), \\\"min\\\")\\n\\n start = time.time()\\n df = categorical(df)\\n print(\\\"(6/6) categorical transformation: \\\", np.round((time.time() - start) / 60, 2), \\\"min\\\")\\n\\n return df\",\n \"def outliers_solid_tsds():\\n timestamp1 = np.arange(np.datetime64(\\\"2021-01-01\\\"), np.datetime64(\\\"2021-02-10\\\"))\\n target1 = [np.sin(i) for i in range(len(timestamp1))]\\n target1[10] += 10\\n\\n timestamp2 = np.arange(np.datetime64(\\\"2021-01-01\\\"), np.datetime64(\\\"2021-02-10\\\"))\\n target2 = [np.sin(i) for i in range(len(timestamp2))]\\n target2[8] += 8\\n target2[15] = 2\\n target2[26] -= 12\\n\\n df1 = pd.DataFrame({\\\"timestamp\\\": timestamp1, \\\"target\\\": target1, \\\"segment\\\": \\\"1\\\"})\\n df2 = pd.DataFrame({\\\"timestamp\\\": timestamp2, \\\"target\\\": target2, \\\"segment\\\": \\\"2\\\"})\\n\\n df = pd.concat([df1, df2], ignore_index=True)\\n\\n df = df.pivot(index=\\\"timestamp\\\", columns=\\\"segment\\\")\\n df = df.reorder_levels([1, 0], axis=1)\\n df = df.sort_index(axis=1)\\n df.columns.names = [\\\"segment\\\", \\\"feature\\\"]\\n tsds = TSDataset(df, \\\"1d\\\")\\n return tsds\",\n \"def create_dt(is_train=True):\\n train = pd.read_csv(\\\"/Users/milesklingenberg/Documents/UWMSBA/590/Data/train_house-1.csv\\\", dtype=CAT_DTYPES)\\n train = pd.DataFrame(train)\\n for col, col_dtype in CAT_DTYPES.items():\\n if col_dtype == \\\"category\\\":\\n train[col] = train[col].astype('category').cat.codes\\n\\n #drop_columns = ['']\\n #was going to drop columns\\n\\n dt = train\\n\\n return dt\",\n \"def shapely_tileset(processed_query,min_ovp = 0,max_ovp = 1,\\n n_neg = None,buffer = 0):\\n types, xx, yy, qual, tags = [],[],[],[],[]\\n z = processed_query['zoom']\\n for elem in processed_query['elements']:\\n for tile in elem['tiles']:\\n qq = tile[1]\\n if qq >= min_ovp and qq <= max_ovp:\\n x,y,_ = find_tile_coords(tile[0],z)\\n xx.append(x)\\n yy.append(y)\\n qual.append(tile[1])\\n tags.append(json.dumps(elem['tags']))\\n types.append(elem['type'])\\n \\n pos_df = pd.DataFrame({\\n 'z': z, 'x' : xx, 'y': yy, \\n 'entity': types,\\n 'overlap': qual,'tags': tags,\\n 'placename': processed_query['query_info']['placename']\\n }) \\\\\\n .drop_duplicates(subset = ['x','y']) \\\\\\n .sort_values(by = ['x','y'])\\n if n_neg is None: n_neg = pos_df.shape[0]\\n negt = sample_complement(pos_df['x'],pos_df['y'],n_neg,buffer)\\n neg_df = pd.DataFrame({'z': z,'x': negt[0],'y': negt[1]}) \\\\\\n .sort_values(by = ['x','y'])\\n return { \\n 'positive': add_latlon(pos_df),\\n 'negative': add_latlon(neg_df)\\n }\",\n \"def process_underway():\\n uway_path = CMORE_BULA_path + \\\"underway/bu1uw2.dat\\\"\\n df = pd.read_csv(\\n uway_path,\\n delim_whitespace=True,\\n names=[\\n \\\"year\\\",\\n \\\"doy\\\",\\n \\\"hour\\\",\\n \\\"minute\\\",\\n \\\"lat\\\",\\n \\\"lon\\\",\\n \\\"salinity\\\",\\n \\\"temperature\\\",\\n \\\"chloropigment\\\",\\n \\\"quality_flags\\\",\\n ],\\n )\\n # builds time out of year, doy,hour,min\\n df[\\\"time\\\"] = pd.to_datetime(\\n (df[\\\"year\\\"]).astype(str)\\n + (df[\\\"doy\\\"]).astype(str)\\n + (df[\\\"hour\\\"].astype(str))\\n + (df[\\\"minute\\\"].astype(str)),\\n format=\\\"%Y%j%H%M\\\",\\n )\\n # split flags into variable flag columns\\n df[\\\"salinity_flag\\\"] = (\\n df[\\\"quality_flags\\\"].astype(str).str.split(\\\"\\\", expand=True).iloc[:, [1]]\\n )\\n df[\\\"temperature_flag\\\"] = (\\n df[\\\"quality_flags\\\"].astype(str).str.split(\\\"\\\", expand=True).iloc[:, [2]]\\n )\\n df[\\\"chloropigment_flag\\\"] = (\\n df[\\\"quality_flags\\\"].astype(str).str.split(\\\"\\\", expand=True).iloc[:, [3]]\\n )\\n # removes old cols and reorders\\n df.drop([\\\"year\\\", \\\"doy\\\", \\\"hour\\\", \\\"minute\\\", \\\"quality_flags\\\"], axis=1, inplace=True)\\n df = df[\\n [\\n \\\"time\\\",\\n \\\"lat\\\",\\n \\\"lon\\\",\\n \\\"salinity\\\",\\n \\\"temperature\\\",\\n \\\"chloropigment\\\",\\n \\\"salinity_flag\\\",\\n \\\"temperature_flag\\\",\\n \\\"chloropigment_flag\\\",\\n ]\\n ]\\n return df\",\n \"def make_dataset_for_time_series(date_key):\\n by_date_key = time_series_data[time_series_data['date_key'] == date_key]\\n by_date_key.sort_values('datetime', inplace=True)\\n return ColumnDataSource(by_date_key), ColumnDataSource(by_date_key.interpolate('slinear'))\",\n \"def make_time_series(rics: list, fields: list, start_date: str, end_date: str, name: str) -> None:\\n df = get_time_series(rics, fields, start_date, end_date)\\n df.dropna(inplace=True)\\n to_csv(df, name, time_series=True)\",\n \"def read_and_prepare_dataframe(start_date='1980-01-01'):\\n \\n # Read the dataset and rename 'dt' to 'Date'\\n df = pd.read_csv('Data/GlobalLandTemperaturesByCountry.csv', parse_dates=['dt'])\\n df.rename(columns={'dt':'Date'}, inplace=True)\\n \\n # Filter for Canada\\n df = df[df['Country']=='Canada']\\n \\n # Filter out data prior to start date\\n df = df[df['Date'] >= start_date]\\n \\n # To ensure data is sorted\\n df = df.sort_values('Date')\\n \\n # Set index to Date and return the final dataframe\\n return df.set_index('Date')\",\n \"def get_time_series(self, tile, year, loc, px, py, cols=1, rows=1, step=1, save_dir='txt/'):\\n\\n # keys = ['sza', 'saa', 'vza', 'vaa', 'qa', 'b01', 'b02', 'b03', 'b04', 'b05', 'b06', 'b07']\\n\\n timeBefore = time.clock()\\n\\n f_list = np.array(glob.glob(loc + '%s_%d_*_1km.nc' % (tile, year)))\\n\\n # if there are no files for this year\\n if f_list.shape[0] == 0:\\n print 'Data for year %d and tile %s not found' % (year, tile)\\n return -1\\n\\n # get DOYs from file name\\n doys0 = np.sort([int(s.split('_')[4]) for s in f_list])\\n # print 'doys0', doys0\\n\\n # arrange DOYs according to step. I.e. we assume that...\\n # step = 7\\n doys = np.zeros(doys0.shape).astype(int)\\n doys_real = np.zeros(doys0.shape).astype(int)\\n\\n for jj, ii in enumerate(xrange(0, doys0.shape[0], step)):\\n # doys = np.append(doys, doys0[ii:ii+7])\\n doys[ii:ii+step] = jj + 1#doys0[ii]\\n doys_real[ii:ii+step] = doys0[ii]\\n\\n ind = np.argsort([int(s.split('_')[4]) for s in f_list])\\n f_list = f_list[ind]\\n\\n # print 'loc:', loc\\n # print 'tile:', tile\\n # print 'year:', year\\n # print 'f_list:', f_list[0]\\n\\n output = {}\\n for f in f_list:\\n print 'f:', f\\n try:\\n ds = nc.Dataset(f)\\n except:\\n print 'something wrong with %s' % f\\n continue\\n\\n # if a dataset is empty\\n # i.e. if it has less than 2 bands\\n # i.e. vza, vaa, sza, saa, proj, geo, qa, b1, b2\\n if len(ds.variables.keys()) < 9:\\n print 'Dataset exists but empty:'\\n print 'loc:', loc\\n print 'tile:', tile\\n print 'year:', year\\n print 'f_list:', f_list[0]\\n print ''\\n # return -1\\n else:\\n break\\n if f == f_list[-1]:\\n print 'all datasets are empty'\\n return -1\\n\\n for key in ds.variables.keys():\\n if len(ds.variables[key].shape) == 2:\\n # output[key] = np.zeros((f_list.shape[0], ds.variables[key].shape[0], ds.variables[key].shape[1]))\\n output[key] = np.zeros((f_list.shape[0], cols, rows))\\n if len(ds.variables[key].shape) == 1:\\n output[key] = np.zeros(ds.variables[key].shape[0]).astype(str)\\n\\n for i, fname in enumerate(f_list):\\n #print fname\\n ds = nc.Dataset(fname)\\n for key in ds.variables.keys():\\n if len(ds.variables[key].shape) == 2:\\n\\n try:\\n # print ds.variables[key][px:px+cols, py:py+rows]\\n output[key][i, :, :] = ds.variables[key][px:px+cols, py:py+rows]\\n except:\\n print 'something wrong in output[%s][%d, :, :]' % (key, i)\\n print 'output:', output[key][i, :, :].shape\\n print 'ds.variables:', ds.variables[key][px:px+cols, py:py+rows].shape\\n\\n if len(ds.variables[key].shape) == 1:\\n output[key][:] = ds.variables[key][:]\\n\\n # print 'output.keys:', output.keys()\\n\\n QA_OK = np.array([8, 72, 136, 200, 1288, 2056, 2120, 2184, 2248])\\n qa_passer = np.logical_or.reduce([output['qa'] == x for x in QA_OK])\\n\\n for b in [1,2,3,4,5,7]:\\n\\n qa_passer[output[\\\"b0%d\\\" % b] <= 0] = 0.\\n qa_passer[output[\\\"b0%d\\\" % b] >= 10000] = 0.\\n\\n # if, for this pixel, we have just a few observations we don't need them\\n if np.sum(qa_passer) < 2:\\n qa_passer[:] = 0\\n\\n output['qa_passer'] = qa_passer\\n [bin(b) for b in QA_OK]\\n\\n #doys = np.array([int(g.GetRasterBand(b+1).GetMetadata()['DoY']) for b in xrange(g.RasterCount)])\\n #years = np.array([int(g.GetRasterBand(b + 1).GetMetadata()['Year']) for b in xrange(g.RasterCount)])\\n output['doys'] = doys\\n output['doys_real'] = np.unique(doys_real)\\n output['years'] = np.ones(doys.shape) * year\\n\\n output[\\\"sza\\\"] = output[\\\"sza\\\"] / 100.\\n output[\\\"saa\\\"] = output[\\\"saa\\\"] / 100.\\n output[\\\"vza\\\"] = output[\\\"vza\\\"] / 100.\\n output[\\\"vaa\\\"] = output[\\\"vaa\\\"] / 100.\\n output['b01'] = output['b01'] / 10000.\\n output['b02'] = output['b02'] / 10000.\\n output['b03'] = output['b03'] / 10000.\\n output['b04'] = output['b04'] / 10000.\\n output['b05'] = output['b05'] / 10000.\\n output['b06'] = output['b06'] / 10000.\\n output['b07'] = output['b07'] / 10000.\\n\\n # print 'qa_passer:', output['qa_passer']\\n #print output['b01']\\n\\n #pkl.dump(output, open(f_out, 'wb'))\\n\\n timeAfter = time.clock()\\n elapsed_time = timeAfter - timeBefore\\n print 'read time series time (s): ', elapsed_time\\n\\n print 'Read MODIS for year %d data is done' % year\\n return output\",\n \"def temperature_range_analysis(temp_df, dt_from, dt_to):\\n df = copy.deepcopy(temp_df)\\n\\n df_unique_sensors = df[[\\\"sensor_id\\\", \\\"name\\\"]].drop_duplicates([\\\"sensor_id\\\", \\\"name\\\"])\\n\\n sensor_ids = df_unique_sensors[\\\"sensor_id\\\"].tolist()\\n sensor_names = df_unique_sensors[\\\"name\\\"].tolist()\\n\\n # extracting date from datetime\\n df[\\\"date\\\"] = pd.to_datetime(df[\\\"timestamp\\\"].dt.date)\\n\\n # Reseting index\\n df.sort_values(by=[\\\"timestamp\\\"], ascending=True).reset_index(inplace=True)\\n\\n data_by_sensor_id = {}\\n for sensor_name, sensor_id in zip(sensor_names, sensor_ids):\\n df_sensor = df[df[\\\"sensor_id\\\"] == sensor_id]\\n # grouping data by date-hour and sensor id\\n sensor_grp = df_sensor.groupby(\\n by=[\\n df_sensor.timestamp.map(\\n lambda x: \\\"%04d-%02d-%02d-%02d\\\" % (x.year, x.month, x.day, x.hour)\\n ),\\n \\\"date\\\",\\n ]\\n )\\n\\n # estimating hourly temperature mean values\\n sensor_grp_temp = sensor_grp[\\\"temperature\\\"].mean().reset_index()\\n\\n try:\\n bins = TEMP_BINS[SENSOR_CATEORIES[sensor_id]]\\n except KeyError:\\n logging.error(\\n f\\\"Don't know how to categorise or bin sensor {sensor_id} \\\"\\n \\\"in the dashboard.\\\"\\n )\\n continue\\n # binning temperature values\\n sensor_grp_temp[\\\"temp_bin\\\"] = pd.cut(sensor_grp_temp[\\\"temperature\\\"], bins)\\n\\n # converting bins to str\\n sensor_grp_temp[\\\"temp_bin\\\"] = sensor_grp_temp[\\\"temp_bin\\\"].astype(str)\\n\\n # get bin counts for each sensor-day combination\\n sensor_grp_date = sensor_grp_temp.groupby(by=[\\\"date\\\", \\\"temp_bin\\\"])\\n\\n sensor_cnt = sensor_grp_date[\\\"temperature\\\"].count().reset_index()\\n sensor_cnt.rename(columns={\\\"temperature\\\": \\\"temp_cnt\\\"}, inplace=True)\\n\\n # Adding missing date/temp_bin combos\\n bins_list, df_list = resample(sensor_cnt, bins, dt_from, dt_to)\\n\\n data_by_sensor_id[sensor_id] = {\\n \\\"name\\\": sensor_name,\\n \\\"bins\\\": bins_list,\\n \\\"data\\\": [\\n {\\n \\\"date\\\": df[\\\"date\\\"].dt.strftime(\\\"%Y-%m-%d\\\").to_list(),\\n \\\"count\\\": df[\\\"temp_cnt\\\"].to_list(),\\n }\\n for df in df_list\\n ],\\n }\\n return len(data_by_sensor_id.keys()), json.dumps(data_by_sensor_id)\",\n \"def get_cut_level_dataframe(start, end, level):\\n entries = get_cut_dataframe(start, end)\\n data = entries.copy()\\n break_point = break_level(start, end, level)\\n if len(break_point) != 0:\\n data['datetime'] = data['to'].map(ts2date)\\n\\n ind = 0\\n new_row = 0\\n for index, row in entries.iterrows():\\n if row['to'] == break_point[ind]:\\n ind += 1\\n elif row['from'] < break_point[ind] < row['to']:\\n row = entries.iloc[[index]].copy()\\n row.loc[index, 'delta'] = abs(break_point[ind]-row.loc[index, 'from'])\\n row.loc[index, 'to'] = break_point[ind]\\n row.loc[index, 'datetime'] = ts2date(row.loc[index, 'from'])\\n new_ind = index + new_row\\n data.loc[new_ind, 'delta'] = abs(break_point[ind]-data.loc[new_ind, 'to'])\\n data.loc[new_ind, 'from'] = break_point[ind]\\n data = pd.concat([data[:new_ind], row, data[new_ind:]])\\n data = data.reset_index(drop=True)\\n new_row += 1\\n ind += 1\\n if ind >= len(break_point):\\n break\\n\\n data['datetime'] = data['from'].map(ts2datetime)\\n data['date_agg'] = data['from'].map(lambda x: ts2str_level(x, level))\\n return data\",\n \"def load_data(city, month, day):\\n #create the DataFrame\\n #I'll be honest, I was struggling with this bit of code so I searched the internet and found what I needed to get started.\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week and hour from Start Time to create new columns. New columns are needed for filtering.\\n df['month'] = df['Start Time'].dt.month\\n df['day'] = df['Start Time'].dt.day\\n df['hour'] = df['Start Time'].dt.hour\\n\\n # filter by month if applicable\\n if month != 'all':\\n month = MONTHS.index(month) + 1\\n df = df[ df['month'] == month ]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[ df['day'] == day.title()]\\n\\n return df\",\n \"def load_data(city, month, day, filters):\\n print('\\\\n\\\\n*****************LOADING DATA*****************')\\n start_time = time.time()\\n\\n dataframe = pd.read_csv(CITY_DATA[city])\\n print('City: ', city)\\n print('Total data points found: ', len(dataframe))\\n\\n # Changing start time to datetime format\\n dataframe['Start Time'] = pd.to_datetime(dataframe['Start Time'])\\n dataframe['Day'] = dataframe['Start Time'].dt.weekday\\n dataframe['Month'] = dataframe['Start Time'].dt.month\\n dataframe['Hour'] = dataframe['Start Time'].dt.hour\\n\\n # Displaying statistics for whole data\\n if filters == 'Month':\\n popular_month, count_popular_month = common_month(dataframe)\\n print('Most popular month for travelling: ', popular_month)\\n print('Counts: ', count_popular_month)\\n\\n elif filters == 'Day':\\n popular_day, count_popular_day = common_day(dataframe)\\n print('Most popular day for travelling: ', get_day_name(popular_day))\\n print('Counts: ', count_popular_day)\\n\\n elif filters == 'Both':\\n popular_month, count_popular_month = common_month(dataframe)\\n popular_day, count_popular_day = common_day(dataframe)\\n print('\\\\nMost popular month for travelling: ', popular_month)\\n print('Counts: ', count_popular_month)\\n print('\\\\nMost popular day for travelling: ', get_day_name(popular_day))\\n print('Counts: ', count_popular_day)\\n\\n print(\\\"\\\\nThis took {} seconds.\\\".format(time.time() - start_time))\\n print('----------------------------------------------')\\n\\n print('\\\\n\\\\n***************APPLYING FILTERS***************')\\n start_time = time.time()\\n months = ['January', 'February', 'March', 'April', 'May', 'June']\\n\\n if filters == 'Month':\\n print('Filter:\\\\n Month = ', month.title())\\n dataframe = dataframe[dataframe['Month'] == months.index(month) + 1]\\n elif filters == 'Day':\\n print('Filter: Day = ', day)\\n dataframe = dataframe[dataframe['Day'] == get_day_number(day)]\\n elif filters == 'Both':\\n print('Filter:\\\\n Month = {}\\\\n Day = {}'.format(month.title(), day))\\n dataframe = dataframe[dataframe['Month'] == months.index(month) + 1]\\n dataframe = dataframe[dataframe['Day'] == get_day_number(day)]\\n else:\\n print('Filter: ', filters)\\n\\n print('Total data points after applying filter: ', len(dataframe))\\n print(\\\"\\\\nThis took {} seconds.\\\".format(time.time() - start_time))\\n print('----------------------------------------------')\\n\\n return dataframe\",\n \"def load_data(city, month, day):\\n # Read csv file for city\\n df = pd.read_csv(city)\\n ## Make sure numbers/dates/etc. are treated appropriately for our needs\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n #df[['Start Time', 'End Time']] = df[['Start Time', 'End Time']].apply(pd.to_datetime)\\n # Filter dates/times where applicable\\n ## Define tuples for months/days so we can index as numerals\\n month_tuple = ('january', 'february', 'march', 'april', 'may', 'june')\\n day_tuple = ('monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday')\\n if month != '':\\n #Index months as integers; in this case we need to add 1 to each to convert them\\n month_int = month_tuple.index(str(month.lower()))+1\\n #Create new columns indicating the start and end months respectively\\n #This preserves the original data while still allowing us to filter\\n df['Start Month'] = df['Start Time'].dt.month\\n df['End Month'] = df['End Time'].dt.month\\n df = df[df['Start Month']== month_int]\\n return df\\n elif day != '':\\n #Index days as integers\\n day_int = day_tuple.index(str(day.lower()))\\n #Create new columns indicating the start and end weekdays respectively\\n #This preserves the original data while still allowing us to filter\\n df['Start Weekday'] = df['Start Time'].dt.dayofweek\\n df['End Weekday'] = df['End Time'].dt.dayofweek\\n df = df[df['Start Weekday']== day_int]\\n return df\\n else:\\n return df\",\n \"def load_data(city, month, day):\\n df = pd.read_csv(CITY_DATA [city])\\n \\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n \\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['hour'] = df['Start Time'].dt.hour\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n \\n # Do the filter below\\n # no filter is applied\\n if month == 0 and day == 0:\\n return df\\n # only filter by day\\n elif month == 0:\\n df = df[df['day_of_week']==day]\\n # only filter by month\\n elif day == 0:\\n df = df[df['month']== month]\\n else:\\n df = df[df['day_of_week']==day]\\n df = df[df['month']== month]\\n \\n return df\",\n \"def get_category_timetable(categ_ids, start_dt, end_dt, detail_level='event', tz=utc, from_categ=None, grouped=True,\\n includible=lambda item: True):\\n day_start = start_dt.astimezone(utc)\\n day_end = end_dt.astimezone(utc)\\n dates_overlap = lambda t: (t.start_dt >= day_start) & (t.start_dt <= day_end)\\n\\n items = defaultdict(lambda: defaultdict(list))\\n\\n # first of all, query TimetableEntries/events that fall within\\n # specified range of dates (and category set)\\n events = _query_events(categ_ids, day_start, day_end)\\n if from_categ:\\n events = events.filter(Event.is_visible_in(from_categ.id))\\n for eid, tt_start_dt in events:\\n if tt_start_dt:\\n items[eid][tt_start_dt.astimezone(tz).date()].append(tt_start_dt)\\n else:\\n items[eid] = None\\n\\n # then, retrieve detailed information about the events\\n event_ids = set(items)\\n query = (Event.query\\n .filter(Event.id.in_(event_ids))\\n .options(subqueryload(Event.person_links).joinedload(EventPersonLink.person),\\n joinedload(Event.own_room).noload('owner'),\\n joinedload(Event.own_venue),\\n joinedload(Event.category).undefer('effective_icon_data'),\\n undefer('effective_protection_mode')))\\n scheduled_events = defaultdict(list)\\n ongoing_events = []\\n events = []\\n for e in query:\\n if not includible(e):\\n continue\\n if grouped:\\n local_start_dt = e.start_dt.astimezone(tz).date()\\n local_end_dt = e.end_dt.astimezone(tz).date()\\n if items[e.id] is None:\\n # if there is no TimetableEntry, this means the event has not timetable on that interval\\n for day in iterdays(max(start_dt.date(), local_start_dt), min(end_dt.date(), local_end_dt)):\\n # if the event starts on this date, we've got a time slot\\n if day.date() == local_start_dt:\\n scheduled_events[day.date()].append((e.start_dt, e))\\n else:\\n ongoing_events.append(e)\\n else:\\n for start_d, start_dts in items[e.id].items():\\n scheduled_events[start_d].append((start_dts[0], e))\\n else:\\n events.append(e)\\n\\n # result['events'][date(...)] -> [(datetime(....), Event(...))]\\n # result[event_id]['contribs'][date(...)] -> [(TimetableEntry(...), Contribution(...))]\\n # result['ongoing_events'] = [Event(...)]\\n if grouped:\\n result = defaultdict(lambda: defaultdict(lambda: defaultdict(list)))\\n else:\\n result = defaultdict(lambda: defaultdict(list))\\n\\n result.update({\\n 'events': scheduled_events if grouped else events,\\n 'ongoing_events': ongoing_events\\n })\\n\\n # according to detail level, ask for extra information from the DB\\n if detail_level != 'event':\\n query = _query_blocks(event_ids, dates_overlap, detail_level)\\n if grouped:\\n for b in query:\\n start_date = b.timetable_entry.start_dt.astimezone(tz).date()\\n result[b.session.event_id]['blocks'][start_date].append((b.timetable_entry, b))\\n else:\\n for b in query:\\n result[b.session.event_id]['blocks'].append(b)\\n\\n if detail_level == 'contribution':\\n query = (Contribution.query\\n .filter(Contribution.event_id.in_(event_ids),\\n dates_overlap(TimetableEntry),\\n ~Contribution.is_deleted)\\n .options(contains_eager(Contribution.timetable_entry),\\n joinedload(Contribution.person_links))\\n .join(TimetableEntry))\\n if grouped:\\n for c in query:\\n start_date = c.timetable_entry.start_dt.astimezone(tz).date()\\n result[c.event_id]['contribs'][start_date].append((c.timetable_entry, c))\\n else:\\n for c in query:\\n result[c.event_id]['contributions'].append(c)\\n\\n query = (Break.query\\n .filter(TimetableEntry.event_id.in_(event_ids), dates_overlap(TimetableEntry))\\n .options(contains_eager(Break.timetable_entry))\\n .join(TimetableEntry))\\n if grouped:\\n for b in query:\\n start_date = b.timetable_entry.start_dt.astimezone(tz).date()\\n result[b.timetable_entry.event_id]['breaks'][start_date].append((b.timetable_entry, b))\\n else:\\n for b in query:\\n result[b.timetable_entry.event_id]['breaks'].append(b)\\n return result\",\n \"def get_meter_data_for_time_slice(apt_no, start_time, end_time):\\n if apt_no in ['102A', 102]:\\n apt_no = '102A'\\n\\n logger.debug(\\\"sMap: Getting meter data for %s between %s and %s\\\", apt_no, start_time, end_time)\\n\\n query = (\\\"select data in ('\\\" + str(start_time) + \\\"','\\\" + str(end_time) + \\\"') \\\"\\n \\\"limit 200000 \\\"\\n \\\"where Metadata/LoadLocation/FlatNumber ='\\\" + str(apt_no) + \\\"' and \\\"\\n \\\"Metadata/Extra/PhysicalParameter='Power'\\\")\\n\\n r = requests.post(url, data=query)\\n # logger.debug (\\\"%s\\\",r)\\n payload = r.json()\\n # logger.debug(\\\"Payload:%s\\\", payload)\\n\\n if apt_no in ['102A', 102]:\\n apt_no = 102\\n meters = retrieve_meter_info(apt_no)\\n logger.debug(\\\"Meters: %s\\\", meters)\\n\\n streams = []\\n meter_type = []\\n l_meters = range(0, len(meters))\\n for i in l_meters:\\n uuid = payload[i]['uuid']\\n\\n # Get meter type based on uuid\\n for meter in meters:\\n if meter['uuid'] == uuid:\\n m_type = meter['type']\\n # logger.debug (uuid, m_type)\\n\\n meter_type.append(m_type)\\n streams.append(np.array(payload[i]['Readings']))\\n # logger.debug(\\\"Streams: %s\\\", streams)\\n\\n if len(streams[0]) > 0:\\n\\n df = [pd.DataFrame({'time': readings[:, 0] / 1000, 'power': readings[:, 1],\\n 'type': [meter_type[i]] * len(readings)},\\n columns=['time', 'power', 'type']) for i, readings in enumerate(streams)]\\n else:\\n df = []\\n\\n return df\",\n \"def get_traffic_sensor_df(sensorURI: str, fromTime: str, toTime: str, resampleFreq: str = None, remove_outliers=False):\\n values = [\\\"count\\\", \\\"sumSpeed\\\"]\\n result = None\\n for v in values:\\n # data = query_ensor(sensorURI, fromTime, toTime, v)\\n data = multiday_query(sensorURI, fromTime, toTime, v)\\n df = pd.DataFrame(data, columns=[\\\"measuredTime\\\", v])\\n df[\\\"measuredTime\\\"] = pd.to_datetime(df[\\\"measuredTime\\\"])\\n df.index = df[\\\"measuredTime\\\"]\\n del df[\\\"measuredTime\\\"]\\n if remove_outliers:\\n z_scores = np.abs(stats.zscore(df))\\n print(f\\\"Removed outliers: {df.size - df[(z_scores < 3).all(axis=1)].size}\\\")\\n df = df[(z_scores < 3).all(axis=1)]\\n if resampleFreq is not None:\\n df = df.resample(resampleFreq).sum()\\n if result is not None:\\n result = pd.merge_ordered(result, df, left_on=\\\"measuredTime\\\", right_on=\\\"measuredTime\\\")\\n result.index = result[\\\"measuredTime\\\"]\\n del result[\\\"measuredTime\\\"]\\n else:\\n result = df\\n # avg speed\\n result[\\\"avgSpeed\\\"] = result[\\\"sumSpeed\\\"] / result[\\\"count\\\"]\\n result.loc[~np.isfinite(result[\\\"avgSpeed\\\"]), \\\"avgSpeed\\\"] = np.nan\\n result[\\\"avgSpeed\\\"] = result[\\\"avgSpeed\\\"].interpolate()\\n return result\",\n \"def load_data(city, month, day):\\n #load the data of the specified city in a dataframe:\\n df = pd.read_csv(CITY_DATA[city])\\n\\n #convert the type of data in 'Start Time' column to datetime:\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n #create new columns required to calculate time_stats:\\n df['month'] = df['Start Time'].dt.month\\n df['weekday'] = df['Start Time'].dt.day_name()\\n df['hour'] = df['Start Time'].dt.hour\\n\\n #unless user input is all, filter by month:\\n if month != 'all':\\n month = months.index(month) + 1 #get the index of the month\\n df = df[df['month'] == month]\\n\\n #uless user input is all, filter by weekday:\\n if day != 'all':\\n df = df[df['weekday'] == day.title()]\\n\\n\\n return df.set_index(pd.Series([i for i in range(df.shape[0])])) #reset the indices of the filterd df\",\n \"def get_moorings_urls(varname=None, site=None, featuretype=None, fileversion=None, datacategory=None, realtime=None, timestart=None, timeend=None, filterout=None):\\n\\n WEBROOT = 'http://thredds.aodn.org.au/thredds/dodsC/'\\n\\n if realtime:\\n if realtime.lower() == \\\"yes\\\":\\n url = \\\"http://geoserver-123.aodn.org.au/geoserver/ows?typeName=moorings_all_map&SERVICE=WFS&REQUEST=GetFeature&VERSION=1.0.0&outputFormat=csv&CQL_FILTER=(realtime=TRUE)\\\"\\n elif realtime.lower() == \\\"no\\\":\\n url = \\\"http://geoserver-123.aodn.org.au/geoserver/ows?typeName=moorings_all_map&SERVICE=WFS&REQUEST=GetFeature&VERSION=1.0.0&outputFormat=csv&CQL_FILTER=(realtime=FALSE)\\\"\\n else:\\n raise ValueError('ERROR: realtime %s is not valid' % realtime)\\n else:\\n url = \\\"http://geoserver-123.aodn.org.au/geoserver/ows?typeName=moorings_all_map&SERVICE=WFS&REQUEST=GetFeature&VERSION=1.0.0&outputFormat=csv\\\"\\n\\n df = pd.read_csv(url)\\n df = df.sort_values(by='time_coverage_start')\\n criteria_all = df.url != None\\n\\n if varname:\\n separator = ', '\\n varnames_all = set(separator.join(list(df.variables)).split(', '))\\n if varname in varnames_all:\\n criteria_all = criteria_all & df.variables.str.contains('.*\\\\\\\\b'+varname+'\\\\\\\\b.*', regex=True)\\n else:\\n raise ValueError('ERROR: %s not a valid variable name' % varname)\\n\\n if site:\\n site_all = list(df.site_code.unique())\\n if site in site_all:\\n criteria_all = criteria_all & df.site_code.str.contains(site, regex=False)\\n else:\\n raise ValueError('ERROR: %s is not a valid site code' % site)\\n\\n if featuretype:\\n if featuretype in [\\\"timeseries\\\", \\\"profile\\\", \\\"timeseriesprofile\\\"]:\\n criteria_all = criteria_all & (df.feature_type.str.lower() == featuretype.lower())\\n else:\\n raise ValueError('ERROR: %s is not a valid feature type' % featuretype)\\n\\n if datacategory:\\n datacategory_all = list(df.data_category.str.lower().unique())\\n if datacategory.lower() in datacategory_all:\\n criteria_all = criteria_all & (df.data_category.str.lower() == datacategory.lower())\\n else:\\n raise ValueError('ERROR: %s is not a valid data category' % datacategory)\\n\\n if fileversion is not None:\\n if fileversion in [0, 1, 2]:\\n criteria_all = criteria_all & (df.file_version == fileversion)\\n else:\\n raise ValueError('ERROR: %s is not a valid file version' % fileversion)\\n\\n if timestart:\\n try:\\n criteria_all = criteria_all & (pd.to_datetime(df.time_coverage_end) >= datetime.strptime(timestart, '%Y-%m-%d'))\\n except ValueError:\\n raise ValueError('ERROR: invalid start date.')\\n\\n if timeend:\\n try:\\n criteria_all = criteria_all & (pd.to_datetime(df.time_coverage_start) <= datetime.strptime(timeend, '%Y-%m-%d'))\\n except ValueError:\\n raise ValueError('ERROR: invalid end date.')\\n\\n if filterout is not None:\\n for keyword in filterout:\\n criteria_all = criteria_all & (~df.url.str.contains(keyword, regex=True))\\n\\n\\n return list(WEBROOT + df.url[criteria_all])\",\n \"def get_weather_data(lat='40.761440',lng='-73.981806'):\\r\\n key ='********************************'\\r\\n x = pd.DataFrame()\\r\\n unix_now = int((dt.datetime.now()- dt.datetime(1970,1,1)).total_seconds())\\r\\n for time in range(unix_now-86400, unix_now+604800, 86400):\\r\\n rsp = rq.get('https://api.darksky.net/forecast/{}/{},{},{}'.format(key, lat, lng, time))\\r\\n rsp_json = json.loads(rsp.text)\\r\\n row = json_normalize(rsp_json[\\\"daily\\\"]['data'])\\r\\n x = x.append(row)\\r\\n \\r\\n x = x[['icon','apparentTemperatureHigh','apparentTemperatureLow','cloudCover','humidity','precipProbability',\\r\\n 'pressure','visibility','windBearing','windGust','windSpeed']].reset_index(drop=True)\\r\\n return x\",\n \"def get_outdoor_data(temp_dir,site):\\n if site == 'berk':\\n files_od = glob(join(temp_dir,'outdoor','20*.xlsx'))\\n elif site == 'bus':\\n files_od = glob(join(temp_dir,'outdoor','Busara*.csv'))\\n else:\\n raise NameError(site)\\n\\n dfs = []\\n for f in files_od:\\n if site == 'berk':\\n this_df = pd.read_excel(f,sheet_name=0,usecols='B:D',index_col=0,parse_dates=True, header=1)\\n elif site == 'bus':\\n this_df = pd.read_csv(f,usecols=[0,1,2],index_col=0,parse_dates=True,header=2)\\n \\n # drop missing values that prevented conversion to float type\\n if this_df.iloc[:,0].dtype != np.float64:\\n this_df = this_df[this_df.iloc[:,0] != ' ']\\n this_df = this_df.astype(np.float64)\\n\\n # correct for weird timezones in berkeley datalogger\\n this_df = correct_tz(this_df,site)\\n \\n this_df.columns = ['T','RH']\\n this_df.index.name = 'time'\\n\\n # convert to celsius\\n this_df['T'] = (this_df['T'] - 32) * 5/9\\n dfs.append(this_df)\\n \\n df_od = pd.concat(dfs)\\n\\n # drop duplicated measurements\\n df_od = df_od[~df_od.index.duplicated(keep='last')].sort_index()\\n \\n # separate out into daily min,mean,max\\n groups = df_od.groupby(df_od.index.date)\\n dfs_od = {'all':df_od,\\n 'min': groups.min(),\\n 'mean': groups.mean(),\\n 'max': groups.max()}\\n \\n for i in ['min','mean','max']:\\n # remove first and last day to ignore days where we did not get full recording\\n dfs_od[i] = dfs_od[i].iloc[1:-1,:]\\n \\n # name index so that we can merge onto multiIndex'd dataframe\\n dfs_od[i].index.name = 'date'\\n \\n return dfs_od\",\n \"def convert_data (data_taxi,density):\\n \\n n_trips = len(data_taxi)\\n \\n min_longitude = min(min(list(data_taxi.loc[:,'pickup_longitude'])),\\n min(list(data_taxi.loc[:,'dropoff_longitude'])))\\n max_longitude = max(max(list(data_taxi.loc[:,'pickup_longitude'])),\\n max(list(data_taxi.loc[:,'dropoff_longitude'])))\\n min_latitude = min(min(list(data_taxi.loc[:,'pickup_latitude'])),\\n min(list(data_taxi.loc[:,'dropoff_latitude'])))\\n max_latitude = max(max(list(data_taxi.loc[:,'pickup_latitude'])),\\n max(list(data_taxi.loc[:,'dropoff_latitude'])))\\n \\n e_longitude = max_longitude - min_longitude\\n \\n e_latitude = max_latitude - min_latitude\\n \\n scale =np.sqrt( n_trips/( e_longitude* e_latitude * density) )\\n\\n taxis = []\\n \\n for i in range(n_trips):\\n selected_taxi = data_taxi.iloc[i]\\n departure = [int((selected_taxi.pickup_longitude - min_longitude) * scale),\\n int((selected_taxi.pickup_latitude - min_latitude) * scale),\\n ]\\n \\n arrival = [\\n int((selected_taxi.dropoff_longitude - min_longitude) * scale),\\n int((selected_taxi.dropoff_latitude - min_latitude) * scale)]\\n \\n taxis.append(taxi(departure,arrival,departure))\\n return taxis,int(scale*(e_latitude))+1,int(scale*(e_longitude))+1\",\n \"def create_dataframe(euctr_cond):\\n def f(x):\\n d = {}\\n d['number_of_countries'] = x.eudract_number_with_country.nunique()\\n d['min_end_date'] = x.date_of_the_global_end_of_the_trial.min()\\n d['max_end_date'] = x.date_of_the_global_end_of_the_trial.max()\\n d['comp_date'] = np.where(pd.notnull(x.date_of_the_global_end_of_the_trial),1,0).sum()\\n d['has_results'] = x.trial_results.sum()\\n d['includes_pip'] = x.trial_is_part_of_a_paediatric_investigation_plan.sum()\\n d['single_blind'] = x.trial_single_blind.sum()\\n d['not_single_blind'] = x.not_single_blind.sum()\\n d['rare_disease'] = x.trial_condition_being_studied_is_a_rare_disease.sum()\\n d['not_rare_disease'] = x.not_rare_disease.sum()\\n d['rare_disease_blank'] = x.rare_disease_blank.sum()\\n d['completed'] = np.where(x.end_of_trial_status == 'Completed', 1, 0).sum()\\n d['ongoing'] = np.where((x.end_of_trial_status == 'Ongoing') | (x.end_of_trial_status == 'Restarted'), 1, 0).sum()\\n d['terminated'] = np.where(x.end_of_trial_status == 'Prematurely Ended', 1, 0).sum()\\n d['suspended'] = np.where((x.end_of_trial_status == 'Temporarily Halted') | (x.end_of_trial_status == 'Suspended by CA'), 1, 0).sum()\\n d['other_status'] = np.where((x.end_of_trial_status == 'Not Authorised') | (x.end_of_trial_status == 'Prohibited by CA'), 1, 0).sum()\\n d['no_status'] = np.where(pd.isnull(x.end_of_trial_status),1,0).sum()\\n d['phase_1'] = x.trial_human_pharmacology_phase_i.sum()\\n d['phase_2'] = x.trial_therapeutic_exploratory_phase_ii.sum()\\n d['phase_3'] = x.trial_therapeutic_confirmatory_phase_iii.sum()\\n d['phase_4'] = x.trial_therapeutic_use_phase_iv.sum()\\n d['bioequivalence'] = x.trial_bioequivalence_study.sum()\\n d['not_bioequivalence'] = x.not_bioequivalence_study.sum()\\n d['healthy_volunteers'] = x.subject_healthy_volunteers.sum()\\n d['not_healthy_volunteers'] = x.not_healthy_volunteers.sum()\\n d['full_title'] = x.full_title.astype('str').min()\\n d['abbreviated_title'] = x.abbreviated_title.astype('str').max()\\n d['non_eu'] = x.non_eu.sum()\\n return pd.Series(d)\\n\\n return euctr_cond.groupby('eudract_number').apply(f).reset_index()\",\n \"def use_ground_truth_time_slice(gt, df_p, df_l, app):\\n\\n gt = gt.reset_index(drop=True)\\n gt['start_time'] = gt.start_time / 1000\\n gt['end_time'] = gt.end_time / 1000\\n gt['pred_location'] = np.zeros(len(gt))\\n gt['true_location'] = gt.wlabel\\n gt['true_appliance'] = gt.slabel\\n\\n gt['type'] = np.zeros(len(gt))\\n gt['magnitude'] = np.zeros(len(gt))\\n gt['phase'] = np.zeros(len(gt))\\n\\n print \\\"\\\\n\\\"\\n # Label lights and fans as \\\"light\\\" type and magnitude as the fall magnitude of the time slice\\n for i in gt.index:\\n appl = gt.ix[i]['slabel']\\n etype = ''\\n\\n if appl in ['Fan', 'Light']:\\n gt.ix[i, 'type'] = etype = \\\"light\\\"\\n else:\\n gt.ix[i, 'type'] = etype = \\\"power\\\"\\n\\n # Get index from the main light/power data stream\\n fall_time = gt.ix[i]['end_time']\\n logger.debug(\\\"Fall_time:: %s (%s)\\\", dt.datetime.fromtimestamp(fall_time), fall_time)\\n if etype == \\\"light\\\":\\n idx = 0\\n try:\\n idx = np.where(df_l.time == fall_time)[0][0]\\n except IndexError, e:\\n print \\\"-----Index not found--------\\\", e\\n continue\\n\\n edge = get_magnitude_metadata(df_l, idx, \\\"lightpower\\\", lwinmin)\\n if edge:\\n gt.ix[i, 'magnitude'] = math.fabs(edge['magnitude'])\\n print i, \\\"Light:: Index\\\", idx, \\\"Magnitude::\\\", math.fabs(edge['magnitude'])\\n else:\\n logger.debug(\\\"--------Edge not found---------\\\")\\n continue\\n\\n # Get phase of the edge\\n gt.ix[i, 'phase'] = get_phase(idx, df_l, \\\"light\\\")\\n # print \\\"LIGHT:: Index\\\", idx, \\\"magnitude\\\", magnitude\\n else:\\n idx = 0\\n try:\\n idx = np.where(df_p.time == fall_time)[0][0]\\n except IndexError, e:\\n print \\\"-----Index not found--------\\\", e\\n continue\\n\\n edge = get_magnitude_metadata(df_p, idx, \\\"power\\\", pwinmin)\\n if edge:\\n gt.ix[i, 'magnitude'] = math.fabs(edge['magnitude'])\\n print i, \\\"Power:: Index\\\", idx, \\\"Magnitude::\\\", math.fabs(edge['magnitude'])\\n else:\\n logger.debug(\\\"--------Edge not found---------\\\")\\n continue\\n\\n # Get phase of the edge\\n gt.ix[i, 'phase'] = get_phase(idx, df_p, \\\"power\\\")\\n\\n # print \\\"POWER:: Index\\\", idx, \\\"magnitude\\\", magnitude\\n\\n gt.pop('wlabel')\\n gt.pop('slabel')\\n\\n if app in [4, 7]:\\n gt = gt[(gt.phase != 0) & (gt.magnitude > lthresmin)]\\n else:\\n gt = gt[gt.magnitude > lthresmin]\\n\\n gt['act_start_time'] = [dt.datetime.fromtimestamp(i)\\n for i in gt['start_time']]\\n gt['act_end_time'] = [dt.datetime.fromtimestamp(i)\\n for i in gt['end_time']]\\n gt = gt.sort(['act_start_time'])\\n\\n print \\\"\\\\nGround Truth Time slices:\\\\n\\\", gt\\n\\n return gt\",\n \"def time_features(s: pd.Series, result: Optional[pd.DataFrame] = None) -> pd.DataFrame:\\n if result is None:\\n result = pd.DataFrame(s, copy=False)\\n index = cast(pd.DatetimeIndex, s.index)\\n\\n result[\\\"hour\\\"] = index.hour\\n result[\\\"minute\\\"] = index.minute\\n result[\\\"second\\\"] = index.second\\n result[\\\"milliseconds\\\"] = index.microsecond / 1000\\n result[\\\"quarterhour\\\"] = index.minute // 15 + 1\\n result[\\\"hourofweek\\\"] = index.dayofweek * 24 + index.hour\\n result[\\\"daytime\\\"] = _map(index.hour, _DAYTIME_MAP)\\n\\n # No vectorized methods for these:\\n dst = np.zeros(len(result))\\n utcoffset = np.zeros(len(result))\\n for i, dt in enumerate(index):\\n try:\\n dst[i] = dt.dst().total_seconds()\\n utcoffset[i] = dt.utcoffset().total_seconds()\\n except AttributeError:\\n # No timezone\\n dst[i] = np.nan\\n utcoffset[i] = np.nan\\n result[\\\"dst\\\"] = dst\\n result[\\\"utcoffset\\\"] = utcoffset\\n return result\",\n \"def dropoff_features_fn(df, ts_column, start_date, end_date):\\n df = filter_df_by_ts(\\n df, ts_column, start_date, end_date\\n )\\n dropoffzip_features = (\\n df.groupBy(\\\"dropoff_zip\\\", window(\\\"tpep_dropoff_datetime\\\", \\\"30 minute\\\"))\\n .agg(count(\\\"*\\\").alias(\\\"count_trips_window_30m_dropoff_zip\\\"))\\n .select(\\n col(\\\"dropoff_zip\\\").alias(\\\"zip\\\"),\\n unix_timestamp(col(\\\"window.end\\\")).alias(\\\"ts\\\").cast(IntegerType()),\\n partition_id(to_timestamp(col(\\\"window.end\\\"))).alias(\\\"yyyy_mm\\\"),\\n col(\\\"count_trips_window_30m_dropoff_zip\\\").cast(IntegerType()),\\n is_weekend(col(\\\"window.end\\\")).alias(\\\"dropoff_is_weekend\\\"),\\n )\\n )\\n return dropoffzip_features\",\n \"def get_date_features(gt_ids=[], gt_masks=None, gt_shifts=None, first_year=None):\\n # If particular arguments aren't lists, replace with repeating iterators\\n if not isinstance(gt_masks, list):\\n gt_masks = itertools.repeat(gt_masks)\\n if not isinstance(gt_shifts, list):\\n gt_shifts = itertools.repeat(gt_shifts)\\n\\n # Add each ground truth feature to dataframe\\n df = None\\n for gt_id, gt_mask, gt_shift in zip(gt_ids, gt_masks, gt_shifts):\\n print \\\"Getting {}_shift{}\\\".format(gt_id, gt_shift)\\n t = time.time()\\n # Load ground truth data\\n gt = get_ground_truth(gt_id, gt_mask, gt_shift)\\n # Discard years prior to first_year\\n gt = year_slice(gt, first_year = first_year)\\n # If lat, lon columns exist, pivot to wide format\\n if 'lat' in gt.columns and 'lon' in gt.columns:\\n if gt_shift == None:\\n measurement_variable = get_measurement_variable(gt_id)\\n else:\\n measurement_variable = get_measurement_variable(gt_id)+'_shift'+str(gt_shift)\\n gt = pd.pivot_table(gt, values=measurement_variable, index='start_date',\\n columns=['lat', 'lon']).reset_index()\\n gt = pd.DataFrame(gt.to_records())\\n gt.drop(\\\"index\\\", axis=1, inplace=True)\\n # Rename columns to start_date and precip_(27.0,261.0), etc.\\n gt.rename(columns={gt.columns[0]: 'start_date'}, inplace=True)\\n gt.rename(columns=lambda x: x.replace('(',\\n measurement_variable +\\n '_('), inplace=True)\\n # Use outer merge to include union of start_date values across all features\\n # combinations across all features\\n df = df_merge(df, gt, on=\\\"start_date\\\")\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n return df\",\n \"def create_sample_dataframe():\\n ax_readings = []\\n ay_readings = []\\n az_readings = []\\n mx_readings = []\\n my_readings = []\\n mz_readings = []\\n gx_readings = []\\n gy_readings = []\\n gz_readings = []\\n activity_list = [LABELS_NAMES[0] for _ in range(SEGMENT_TIME_SIZE)]\\n\\n\\n for _ in range(SEGMENT_TIME_SIZE):\\n ax_readings.append(random.uniform(-10,10))\\n ay_readings.append(random.uniform(-10,10))\\n az_readings.append(random.uniform(-10,10))\\n mx_readings.append(random.uniform(-10,10))\\n my_readings.append(random.uniform(-10,10))\\n mz_readings.append(random.uniform(-10,10))\\n gx_readings.append(random.uniform(-10,10))\\n gy_readings.append(random.uniform(-10,10))\\n gz_readings.append(random.uniform(-10,10))\\n\\n data_dict = {\\n COLUMN_NAMES[0]: activity_list, COLUMN_NAMES[1]: ax_readings,\\n COLUMN_NAMES[2]: ay_readings, COLUMN_NAMES[3]: az_readings,\\n COLUMN_NAMES[4]: gx_readings, COLUMN_NAMES[5]: gy_readings,\\n COLUMN_NAMES[6]: gz_readings, COLUMN_NAMES[7]: mx_readings,\\n COLUMN_NAMES[8]: my_readings, COLUMN_NAMES[9]: mz_readings\\n }\\n\\n df = pd.DataFrame(data=data_dict)\\n return df\",\n \"def get_weather_timeseries_2017():\\n weather_df = pd.read_csv(\\\"src/kumpula-weather-2017.csv\\\")\\n\\n # -1 value in columns \\\"Precipitation amount (mm)\\\" and \\\"Snow depth (cm)\\\" mean\\n # that there was no absolutely no rain or snow that day, whereas 0 can mean\\n # a little of either. Let's convert the -1 values to 0 to make the dataset\\n # more logical to read.\\n weather_df.loc[weather_df[\\\"Precipitation amount (mm)\\\"] == -1,\\n \\\"Precipitation amount (mm)\\\"] = 0\\n weather_df.loc[weather_df[\\\"Snow depth (cm)\\\"] == -1, \\\"Snow depth (cm)\\\"] = 0\\n\\n # Create datetime index\\n weather_df[\\\"Month\\\"] = weather_df[\\\"m\\\"]\\n weather_df[\\\"Day\\\"] = weather_df[\\\"d\\\"]\\n weather_df[\\\"Date\\\"] = pd.to_datetime(weather_df[[\\\"Year\\\", \\\"Month\\\", \\\"Day\\\"]])\\n\\n # Reindex dataset\\n weather_df = weather_df.set_index(\\\"Date\\\")\\n\\n # Drop redundant columns\\n weather_df.drop([\\\"Time\\\", \\\"Time zone\\\", \\\"m\\\", \\\"d\\\", \\\"Year\\\",\\n \\\"Month\\\", \\\"Day\\\"], axis=\\\"columns\\\", inplace=True)\\n\\n return weather_df\",\n \"def _create_ts_features(df, tscol):\\r\\n df = copy.deepcopy(df)\\r\\n dt_adds = []\\r\\n try:\\r\\n df[tscol+'_hour'] = df[tscol].dt.hour.fillna(0).astype(int)\\r\\n df[tscol+'_minute'] = df[tscol].dt.minute.fillna(0).astype(int)\\r\\n dt_adds.append(tscol+'_hour')\\r\\n dt_adds.append(tscol+'_minute')\\r\\n except:\\r\\n print(' Error in creating hour-second derived features. Continuing...')\\r\\n try:\\r\\n df[tscol+'_dayofweek'] = df[tscol].dt.dayofweek.fillna(0).astype(int)\\r\\n dt_adds.append(tscol+'_dayofweek')\\r\\n if tscol+'_hour' in dt_adds:\\r\\n DAYS = dict(zip(range(7),['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']))\\r\\n df[tscol+'_dayofweek'] = df[tscol+'_dayofweek'].map(DAYS)\\r\\n df.loc[:,tscol+'_dayofweek_hour_cross'] = df[tscol+'_dayofweek'] +\\\" \\\"+ df[tscol+'_hour'].astype(str)\\r\\n dt_adds.append(tscol+'_dayofweek_hour_cross')\\r\\n df[tscol+'_quarter'] = df[tscol].dt.quarter.fillna(0).astype(int)\\r\\n dt_adds.append(tscol+'_quarter')\\r\\n df[tscol+'_month'] = df[tscol].dt.month.fillna(0).astype(int)\\r\\n MONTHS = dict(zip(range(1,13),['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul',\\r\\n 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']))\\r\\n df[tscol+'_month'] = df[tscol+'_month'].map(MONTHS)\\r\\n dt_adds.append(tscol+'_month')\\r\\n #### Add some features for months ########################################\\r\\n festives = ['Oct','Nov','Dec']\\r\\n name_col = tscol+\\\"_is_festive\\\"\\r\\n df[name_col] = 0\\r\\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in festives else 0).values\\r\\n df[name_col].fillna(0,inplace=True)\\r\\n dt_adds.append(name_col)\\r\\n summer = ['Jun','Jul','Aug']\\r\\n name_col = tscol+\\\"_is_summer\\\"\\r\\n df[name_col] = 0\\r\\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in summer else 0).values\\r\\n df[name_col].fillna(0,inplace=True)\\r\\n dt_adds.append(name_col)\\r\\n winter = ['Dec','Jan','Feb']\\r\\n name_col = tscol+\\\"_is_winter\\\"\\r\\n df[name_col] = 0\\r\\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in winter else 0).values\\r\\n df[name_col].fillna(0,inplace=True)\\r\\n dt_adds.append(name_col)\\r\\n cold = ['Oct','Nov','Dec','Jan','Feb','Mar']\\r\\n name_col = tscol+\\\"_is_cold\\\"\\r\\n df[name_col] = 0\\r\\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in cold else 0).values\\r\\n df[name_col].fillna(0,inplace=True)\\r\\n dt_adds.append(name_col)\\r\\n warm = ['Apr','May','Jun','Jul','Aug','Sep']\\r\\n name_col = tscol+\\\"_is_warm\\\"\\r\\n df[name_col] = 0\\r\\n df[name_col] = df[tscol+'_month'].map(lambda x: 1 if x in warm else 0).values\\r\\n df[name_col].fillna(0,inplace=True)\\r\\n dt_adds.append(name_col)\\r\\n #########################################################################\\r\\n if tscol+'_dayofweek' in dt_adds:\\r\\n df.loc[:,tscol+'_month_dayofweek_cross'] = df[tscol+'_month'] +\\\" \\\"+ df[tscol+'_dayofweek']\\r\\n dt_adds.append(tscol+'_month_dayofweek_cross')\\r\\n df[tscol+'_year'] = df[tscol].dt.year.fillna(0).astype(int)\\r\\n dt_adds.append(tscol+'_year')\\r\\n today = date.today()\\r\\n df[tscol+'_age_in_years'] = today.year - df[tscol].dt.year.fillna(0).astype(int)\\r\\n dt_adds.append(tscol+'_age_in_years')\\r\\n df[tscol+'_dayofyear'] = df[tscol].dt.dayofyear.fillna(0).astype(int)\\r\\n dt_adds.append(tscol+'_dayofyear')\\r\\n df[tscol+'_dayofmonth'] = df[tscol].dt.day.fillna(0).astype(int)\\r\\n dt_adds.append(tscol+'_dayofmonth')\\r\\n df[tscol+'_weekofyear'] = df[tscol].dt.weekofyear.fillna(0).astype(int)\\r\\n dt_adds.append(tscol+'_weekofyear')\\r\\n weekends = (df[tscol+'_dayofweek'] == 'Sat') | (df[tscol+'_dayofweek'] == 'Sun')\\r\\n df[tscol+'_typeofday'] = 'weekday'\\r\\n df.loc[weekends, tscol+'_typeofday'] = 'weekend'\\r\\n dt_adds.append(tscol+'_typeofday')\\r\\n if tscol+'_typeofday' in dt_adds:\\r\\n df.loc[:,tscol+'_month_typeofday_cross'] = df[tscol+'_month'] +\\\" \\\"+ df[tscol+'_typeofday']\\r\\n dt_adds.append(tscol+'_month_typeofday_cross')\\r\\n except:\\r\\n print(' Error in creating date time derived features. Continuing...')\\r\\n print(' created %d columns from time series %s column' %(len(dt_adds),tscol))\\r\\n return df, dt_adds\",\n \"def load_data(city, month, day):\\n ##task0.4 now the user choices are all ready and saved, start preparing your data \\n ## first, load the data of the selected city using the pd.read_csv command for the user specified city \\n df = pd.read_csv(CITY_DATA[city])\\n ## read the column start time as a date, to start extracting information from it\\n df['Start Time']=pd.to_datetime(df['Start Time'])\\n ##define a new column\\\"month\\\" as a function from start time, which reads the month from the date\\n df['month'] = df['Start Time'].dt.month\\n ## extract the day name from the start time, append a new column\\\"day_of_week\\\" to the dataframe \\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n ## extract the hour of the start time and save to a new variable\\\"hour\\\" \\n df['hour'] = df['Start Time'].dt.hour\\n \\n ## now, the panel data is complete with variables of interest created\\n ## time to consider user filters on month and day!!\\n ## for the month, if user choice is all, no changes to dataset\\n ## if user specifies a month,filter the dataset column\\\"month\\\" with the selected value\\n if month !=\\\"all\\\":\\n df=(df[df['month']==int(month)])\\n \\n ## for the day column, if user doesnt choose a specific day and select\\\"all\\\", no change to df\\n ## if the user chooses a day, filter the \\\"day_of_week\\\" column for the specified day\\n if day !=\\\"All\\\":\\n df=(df[df['day_of_week']==day])\\n \\n ## print the first five rows of data to confirm filters are applied\\n ## return the data frame to start calculating statistics\\n #print(df.head())\\n return df\",\n \"def load_data(city, month, day):\\n\\n data = pd.read_csv(\\\"{}.csv\\\".format(city))\\n data.drop(data.columns[0], axis = 1, inplace = True) #dropping this strange column\\n\\n data['Start Time'] = pd.to_datetime(data['Start Time'], format='%Y-%m-%d %H:%M:%S')\\n data['End Time'] = pd.to_datetime(data['End Time'], format='%Y-%m-%d %H:%M:%S')\\n\\n data['weekday'] = data['Start Time'].dt.dayofweek #0 - monday\\n data['month'] = data['Start Time'].dt.month #1 - january\\n data['hour'] = data['Start Time'].dt.hour # 1 - hour 1\\n\\n day_dict = {\\\"Mon\\\":0, \\\"Tue\\\":1, \\\"Wed\\\":2, \\\"Thu\\\":3, \\\"Fry\\\":4, \\\"Sat\\\":5, \\\"Sun\\\":6}\\n\\n month_dict = {\\\"Jan\\\":1, \\\"Feb\\\":2, \\\"Mar\\\":3, \\\"Apr\\\":4, \\\"May\\\":5, \\\"Jun\\\":6}\\n\\n if month == 'all_months' and day != 'all_days': # filter just by day\\n day = day_dict.get(day)\\n df = data[data['weekday'] == day]\\n elif day == 'all_days' and month != 'all_months': # filter just by month\\n month = month_dict.get(month)\\n df = data[data['month'] == month]\\n elif day == 'all_days' and month == 'all_months': # no filters\\n df = data\\n else: # filter both by day and month\\n day = day_dict.get(day)\\n month = month_dict.get(month)\\n df = data[(data['weekday']== day) & (data['month']==month)]\\n return df\",\n \"def create_features(energy_data, label=None):\\n energy_data['date'] = energy_data.index\\n energy_data['hour'] = energy_data['Datetime'].dt.hour\\n energy_data['dayofweek'] = energy_data['Datetime'].dt.dayofweek\\n energy_data['month'] = energy_data['Datetime'].dt.month\\n energy_data['quarter'] = energy_data['Datetime'].dt.quarter\\n energy_data['year'] = energy_data['Datetime'].dt.year\\n energy_data['dayofyear'] = energy_data['Datetime'].dt.dayofyear\\n energy_data['dayofmonth'] = energy_data['Datetime'].dt.day\\n energy_data['weekofyear'] = energy_data['Datetime'].dt.weekofyear\\n energy_data['pjme_2_hrs_lag'] = energy_data['PJME_MW'].shift(2)\\n energy_data['pjme_4_hrs_lag'] = energy_data['PJME_MW'].shift(4)\\n energy_data['pjme_8_hrs_lag'] = energy_data['PJME_MW'].shift(8)\\n energy_data['pjme_12_hrs_lag'] = energy_data['PJME_MW'].shift(12)\\n energy_data['pjme_24_hrs_lag'] = energy_data['PJME_MW'].shift(24)\\n energy_data['pjme_4_hrs_mean'] = energy_data['PJME_MW'].rolling(window=4).mean()\\n energy_data['pjme_8_hrs_mean'] = energy_data['PJME_MW'].rolling(window=8).mean()\\n energy_data['pjme_12_hrs_mean'] = energy_data['PJME_MW'].rolling(window=12).mean()\\n energy_data['pjme_24_hrs_mean'] = energy_data['PJME_MW'].rolling(window=24).mean()\\n energy_data['pjme_4_hrs_std'] = energy_data['PJME_MW'].rolling(window=4).std()\\n energy_data['pjme_8_hrs_std'] = energy_data['PJME_MW'].rolling(window=8).std()\\n energy_data['pjme_12_hrs_std'] = energy_data['PJME_MW'].rolling(window=12).std()\\n energy_data['pjme_24_hrs_std'] = energy_data['PJME_MW'].rolling(window=24).std()\\n energy_data['pjme_4_hrs_max'] = energy_data['PJME_MW'].rolling(window=4).max()\\n energy_data['pjme_8_hrs_max'] = energy_data['PJME_MW'].rolling(window=8).max()\\n energy_data['pjme_12_hrs_max'] = energy_data['PJME_MW'].rolling(window=12).max()\\n energy_data['pjme_24_hrs_max'] = energy_data['PJME_MW'].rolling(window=24).max()\\n energy_data['pjme_4_hrs_min'] = energy_data['PJME_MW'].rolling(window=4).min()\\n energy_data['pjme_8_hrs_min'] = energy_data['PJME_MW'].rolling(window=8).min()\\n energy_data['pjme_12_hrs_min'] = energy_data['PJME_MW'].rolling(window=12).min()\\n energy_data['pjme_24_hrs_min'] = energy_data['PJME_MW'].rolling(window=24).min()\\n\\n features = energy_data[['hour', 'dayofweek', 'quarter', 'month', 'year',\\n 'dayofyear', 'dayofmonth', 'weekofyear', 'pjme_2_hrs_lag', 'pjme_4_hrs_lag',\\n 'pjme_8_hrs_lag', 'pjme_12_hrs_lag', 'pjme_24_hrs_lag', 'pjme_4_hrs_mean',\\n \\\"pjme_8_hrs_mean\\\", \\\"pjme_12_hrs_mean\\\", \\\"pjme_24_hrs_mean\\\", \\\"pjme_4_hrs_std\\\",\\n \\\"pjme_8_hrs_std\\\", \\\"pjme_12_hrs_std\\\", \\\"pjme_24_hrs_std\\\",\\n \\\"pjme_4_hrs_max\\\", \\\"pjme_8_hrs_max\\\", \\\"pjme_12_hrs_max\\\", \\\"pjme_24_hrs_max\\\",\\n \\\"pjme_4_hrs_min\\\", \\\"pjme_8_hrs_min\\\", \\\"pjme_12_hrs_min\\\", \\\"pjme_24_hrs_min\\\"]]\\n if label:\\n label = energy_data[label]\\n return features, label\\n return features\",\n \"def make_time_features(ts, index=None, epoch=None, epoch_span=None):\\n # input validation\\n try:\\n if len(ts) == 1:\\n _singleton = True\\n elif len(ts) > 1:\\n _singleton = False\\n elif len(ts) < 1:\\n raise ValueError(\\\"must pass non-empty iterable of timestamps\\\")\\n except TypeError:\\n return make_time_features([ts], index=index, epoch=epoch, epoch_span=epoch_span)\\n\\n if not isinstance(ts, pd.DatetimeIndex):\\n ts = pd.Series(0, index=ts).index\\n if not isinstance(ts, pd.DatetimeIndex):\\n raise ValueError(\\\"must pass non-empty iterable of timestamps\\\")\\n\\n if index is None:\\n index = pd.RangeIndex(len(ts))\\n if epoch is None:\\n epoch = min(ts)\\n if epoch_span is None:\\n epoch_span = float((end - epoch).total_seconds())\\n\\n time_features = {}\\n start = min(ts)\\n end = max(ts)\\n\\n # Major US holidays\\n NewYearsDay = pd.tseries.holiday.Holiday('New Years Day', month=1, day=1)\\n MemorialDay = pd.tseries.holiday.Holiday('Memorial Day', month=6, day=1, offset=pd.DateOffset(weekday=MO(-1)))\\n IndependenceDay = pd.tseries.holiday.Holiday('Independence Day', month=7, day=4)\\n LaborDay = pd.tseries.holiday.Holiday('Labor Day', month=9, day=1, offset=pd.DateOffset(weekday=MO(1)))\\n ThanksgivingDay = pd.tseries.holiday.Holiday('Thanksgiving Day', month=11, day=1, offset=pd.DateOffset(weekday=TH(4)))\\n ChristmasDay = pd.tseries.holiday.Holiday('Christmas Day', month=12, day=25)\\n holidays = \\\\\\n NewYearsDay.dates(start.date(), end.date()).tolist() +\\\\\\n MemorialDay.dates(start.date(), end.date()).tolist() +\\\\\\n IndependenceDay.dates(start.date(), end.date()).tolist() +\\\\\\n LaborDay.dates(start.date(), end.date()).tolist() +\\\\\\n ThanksgivingDay.dates(start.date(), end.date()).tolist() +\\\\\\n ChristmasDay.dates(start.date(), end.date()).tolist()\\n holidays = set([h.date() for h in holidays])\\n\\n # projections onto unit circle\\n time_features['day_cos'] = np.cos((ts.hour * 3600 + ts.minute * 60 + ts.second) * 2 * np.pi / 86400.)\\n time_features['day_sin'] = np.sin((ts.hour * 3600 + ts.minute * 60 + ts.second) * 2 * np.pi / 86400.)\\n time_features['week_cos'] = np.cos(ts.dayofweek * 2 * np.pi / 7.)\\n time_features['week_sin'] = np.sin(ts.dayofweek * 2 * np.pi / 7.)\\n time_features['year_cos'] = np.cos(ts.dayofyear * 2 * np.pi / 365.)\\n time_features['year_sin'] = np.sin(ts.dayofyear * 2 * np.pi / 365.)\\n # linear march through time\\n time_features['epoch'] = (ts - epoch).total_seconds() / epoch_span\\n # workday indicator\\n time_features['workday'] = [int(weekday < 5 and date not in holidays) for weekday, date in zip(ts.weekday, ts.date)]\\n\\n if _singleton:\\n return {k: v[0] for k, v in time_features.iteritems()}\\n else:\\n return pd.DataFrame(time_features, index=index)\",\n \"def get_canada(data_path: str, filter_: Union[dict, bool] = True,\\n fixes: bool = False) -> None:\\n dfs = [] # we will append dfs for cases, deaths, recovered here\\n # URL for API call to get Province-level timeseries data starting on Jan 22 2020\\n url_template = 'https://api.opencovid.ca/timeseries?stat=%s&loc=prov&date=01-22-2020'\\n for kind in ['cases', 'mortality', 'recovered']:\\n url_path = url_template % kind # Create the full data URL\\n with urllib.request.urlopen(url_path) as url:\\n data = json.loads(url.read().decode())\\n source = pd.json_normalize(data[kind])\\n if kind == 'cases':\\n source.drop('cases', axis=1, inplace=True) # removing this column so\\n # we can index into date on all 3 dfs at same position\\n source.rename(columns={source.columns[1]: \\\"date\\\" }, inplace=True)\\n dfs.append(source)\\n cases = dfs[0]\\n deaths = dfs[1]\\n recovered = dfs[2]\\n # combine dfs\\n df_rawtemp = cases.merge(recovered, on=['date', 'province'], how='outer')\\n df_raw = df_rawtemp.merge(deaths, on=['date', 'province'], how='outer')\\n df_raw.fillna(0, inplace=True)\\n\\n provinces = ['Alberta', 'BC', 'Manitoba', 'New Brunswick', 'NL',\\n 'Nova Scotia', 'Nunavut', 'NWT', 'Ontario', 'PEI', 'Quebec',\\n 'Saskatchewan', 'Yukon']\\n\\n # Export timeseries data for each province\\n for province in tqdm(provinces, desc='Canadian Provinces'):\\n source = df_raw[df_raw['province'] == province] # Only the given province\\n df = pd.DataFrame(columns=['dates2','cum_cases', 'cum_deaths',\\n 'cum_recover', 'new_cases',\\n 'new_deaths', 'new_recover',\\n 'new_uninfected'])\\n df['dates2'] = source['date'].apply(fix_canada_dates) # Convert date format\\n df['cum_cases'] = source['cumulative_cases'].values\\n df['cum_deaths'] = source['cumulative_deaths'].values\\n df['cum_recover'] = source['cumulative_recovered'].values\\n\\n df[['new_cases', 'new_deaths', 'new_recover']] = \\\\\\n df[['cum_cases', 'cum_deaths', 'cum_recover']].diff()\\n df['new_uninfected'] = df['new_recover'] + df['new_deaths']\\n\\n try:\\n population = get_population_count(data_path, 'CA_' + province)\\n df['population'] = population\\n except:\\n pass\\n\\n df.sort_values(by=['dates2'], inplace=True) # sort by datetime obj before converting to string\\n df['dates2'] = pd.to_datetime(df['dates2']).dt.strftime('%m/%d/%y') # convert dates to string\\n df = df.set_index('dates2').fillna(0).astype(int) # Fill NaN with 0 and convert to int\\n df.to_csv(data_path / ('covidtimeseries_CA_%s.csv' % province))\",\n \"def filter_plot(mode, country, continent, start_date, end_date, options):\\n # Default is World mode\\n chart_data = world_daywise_df\\n map_data = countries_daywise_df\\n print(country, continent)\\n if mode == SelectionMode.Continents.value:\\n #Continents mode\\n if not isinstance(continent, list):\\n continent = [continent]\\n\\n chart_data = continents_daywise_df[continents_daywise_df['WHO Region'].isin(continent)]\\n map_data = map_data[map_data['WHO Region'].isin(continent)]\\n elif mode == SelectionMode.Countries.value:\\n # Countries mode\\n if not isinstance(country, list):\\n country = [country]\\n\\n chart_data = countries_daywise_df[countries_daywise_df['Country/Region'].isin(country)]\\n map_data = chart_data\\n\\n chart_data = chart_data.query('Date >= @start_date & Date <= @end_date')\\n map_data = map_data.query('Date >= @start_date & Date <= @end_date')\\n\\n # fix error when groupby geometry or put it in the aggregate column\\n temp = map_data.drop(['geometry', 'country_code', 'Date'], axis=1).groupby(['Country/Region']).agg(metrics).reset_index()\\n map_data = join_country_code_data(temp, country_code_data)\\n\\n if is_perCapita(options):\\n for metric in ['Confirmed', 'Deaths', 'Recovered']:\\n chart_data[metric + '_per_capita'] = chart_data[metric] / chart_data['Population']\\n map_data[metric + '_per_capita'] = map_data[metric] / map_data['Population']\\n \\n if is_perCapita(options):\\n return plot(chart_data, 'Confirmed_per_capita', 'Confirmed Cases Per Capita'), \\\\\\n plot(chart_data, 'Deaths_per_capita', 'Confirmed Deaths Per Capita'), \\\\\\n plot(chart_data, 'Recovered_per_capita', 'Confirmed Recoveries Per Capita'), \\\\\\n generate_map(map_data)\\n\\n return plot(chart_data, 'Confirmed', 'Confirmed Cases'), \\\\\\n plot(chart_data, 'Deaths', 'Confirmed Deaths'), \\\\\\n plot(chart_data, 'Recovered', 'Confirmed Recoveries'), \\\\\\n generate_map(map_data)\",\n \"def create_data_table(df: pd.DataFrame) -> pd.DataFrame:\\n\\n df = df.copy()\\n\\n # Normalize times by labeling all of today's data with its future label, 00:00\\n # tomorrow (as that's the timestamp marking the end of the 24-hour data collection\\n # period). No need to adjust data not from today; it's already been adjusted and is\\n # labeled with the date whose 00:00 marked the end of data collection (i.e., data\\n # generated on Mar 20 is labeled Mar 21).\\n normalized_dates = df[Columns.DATE].dt.normalize()\\n is_at_midnight = df[Columns.DATE] == normalized_dates\\n df.loc[~is_at_midnight, Columns.DATE] = normalized_dates[\\n ~is_at_midnight\\n ] + pd.Timedelta(days=1)\\n df[Columns.DATE] = df[Columns.DATE].dt.strftime(r\\\"%Y-%m-%d\\\")\\n\\n df = df.drop(\\n columns=[\\n Columns.IS_STATE,\\n Columns.LOCATION_NAME,\\n Columns.OUTBREAK_START_DATE_COL,\\n Columns.DAYS_SINCE_OUTBREAK,\\n Columns.POPULATION,\\n Columns.STAGE,\\n Columns.COUNT_TYPE,\\n ]\\n )\\n\\n df = (\\n df.pivot_table(\\n index=[\\n c\\n for c in df.columns\\n if c not in [Columns.CASE_TYPE, Columns.CASE_COUNT]\\n ],\\n columns=Columns.CASE_TYPE,\\n values=Columns.CASE_COUNT,\\n aggfunc=\\\"first\\\",\\n )\\n .reset_index()\\n .sort_values([Columns.COUNTRY, Columns.STATE, Columns.DATE])\\n )\\n\\n for col in CaseInfo.get_info_items_for(\\n InfoField.CASE_TYPE, count=Counting.TOTAL_CASES\\n ):\\n df[col] = pd.to_numeric(df[col], downcast=\\\"integer\\\")\\n\\n # save_path = Paths.DATA / \\\"data_table.csv\\\"\\n # df.to_csv(save_path, index=False)\\n # print(f\\\"Saved data to {save_path.relative_to(Paths.ROOT)}\\\")\\n\\n return df\",\n \"def prepare(gpi, start_date, end_date, models, satellites, kind=\\\"clim\\\", window=35):\\n # read data\\n ts_input = data_object.read_gpi(gpi, start_date, end_date, models, satellites)\\n #ts_input = interp.add_nan(ts_input)\\n ts_input = interp.iter_fill(ts_input, max_gap=5)\\n\\n # either calc climatology, apply moving average filter, or do nothing\\n if kind == 'clim':\\n ts_smooth = smooth.iter_climats(ts_input)\\n plot_title = 'Climatology'\\n elif kind == 'movav':\\n ts_smooth = smooth.iter_movav(ts_input, window)\\n #ts_smooth = ts_gapfill\\n plot_title = 'Moving average'\\n elif kind == None:\\n # return original data\\n ts_smooth = ts_input\\n else:\\n raise NotImplementedError\\n pass\\n\\n # drop rows with missing values\\n #ts_smooth = ts_smooth.dropna()\\n\\n # scale satellite data to model data\\n ts_scaled = scaling.scale(ts_smooth, 'mean_std_nan', 0)\\n # drop nan rows for slope funcs\\n return ts_scaled #.dropna()\",\n \"def time_stats(df):\",\n \"def get_fact_time_filtered(self, category, selected_option, \\\\\\r\\n start_tmstmp, \\\\\\r\\n end_tmstmp):\\r\\n try:\\r\\n conn = self.create_connection()\\r\\n query = \\\"\\\"\\\"WITH sub_category_lookup AS (\\r\\n\\t\\t\\t\\t\\t\\t\\t\\tSELECT id \\r\\n\\t\\t\\t\\t\\t\\t\\t\\tFROM categories \\r\\n\\t\\t\\t\\t\\t\\t\\t\\tWHERE category = '%s' \\r\\n\\t\\t\\t\\t\\t\\t\\t\\tAND sub_category = '%s')\\t\\r\\n\\t\\t\\t\\t\\t SELECT date_time,\\r\\n\\t\\t\\t\\t\\t\\t\\t data \\r\\n\\t\\t\\t\\t FROM fact\\r\\n\\t\\t\\t\\t\\t WHERE category_id = (select id FROM sub_category_lookup) \\r\\n\\t\\t\\t\\t\\t AND (date_time>= '%s' AND date_time<'%s'\\t)\\t\\t\\t\\t \\r\\n\\t\\t\\t\\t\\t ORDER BY date_time ;\\\"\\\"\\\"%(category, selected_option, \\\\\\r\\n start_tmstmp, end_tmstmp)\\r\\n \\r\\n data_frame = pd.read_sql(query, conn)\\r\\n print(query)\\r\\n conn.close()\\r\\n except (psycopg2.Error, ValueError):\\r\\n print(\\\"Error occured at get_fact_time_filtered, check connection or query\\\")\\r\\n return data_frame\",\n \"def filter_time_slices(time_slices, apt_no, exp_no):\\n # Removing the extraneous time slices\\n if apt_no == '102A' and exp_no == '3':\\n discard_ts = time_slices[\\n (time_slices.phase == 'Not Found') & (time_slices.magnitude < 100)]\\n time_slices = time_slices.ix[time_slices.index - discard_ts.index]\\n\\n elif apt_no == '603':\\n print \\\"here\\\"\\n # Likely power consumption of fridge is 110-150\\n time_slices = time_slices[(time_slices.magnitude < 110) | (time_slices.magnitude > 150) &\\n (time_slices.type == 'power')]\\n # 25-26Nov\\n if exp_no == '25-26Nov':\\n time_slices = time_slices[time_slices.end_time < 1385404505]\\n elif exp_no == '26-27Nov':\\n time_slices = time_slices[time_slices.end_time < 1385492334]\\n\\n elif apt_no == '703':\\n # Likely power consumption of fridge is 130-152\\n fridge_ts = time_slices[(time_slices.magnitude >= 130) & (time_slices.magnitude <= 170) &\\n (time_slices.type == 'power')]\\n time_slices = time_slices.ix[time_slices.index - fridge_ts.index]\\n\\n # Likely power consumption of geyser > 2000 but on light phase > 1000\\n geyser_ts = time_slices[(time_slices.magnitude > 1000) & (time_slices.type == 'light')]\\n time_slices = time_slices.ix[time_slices.index - geyser_ts.index]\\n\\n # 26-27Nov\\n if exp_no == '26-27Nov':\\n washing_ts = time_slices[\\n (time_slices.start_time >= 1385470967) & (time_slices.end_time <= 1385471880)]\\n time_slices = time_slices.ix[time_slices.index - washing_ts.index]\\n\\n # 28-29Nov\\n if exp_no == '28-29Nov':\\n time_slices = time_slices[\\n (time_slices.start_time < 1385646060) | (time_slices.end_time > 1385648143)]\\n\\n # Removing time slices with duration less than 30 seconds\\n idx_list = []\\n for idx in time_slices.index:\\n start_time = time_slices.ix[idx]['start_time']\\n end_time = time_slices.ix[idx]['end_time']\\n magnitude = time_slices.ix[idx]['magnitude']\\n\\n time_diff = end_time - start_time\\n\\n if time_diff < 30 and magnitude < 80:\\n print \\\"idx\\\", idx, \\\"time_diff\\\", time_diff, \\\"magnitude\\\", magnitude\\n # Qualified for filtering it\\n idx_list.append(idx)\\n time_slices = time_slices.ix[time_slices.index - idx_list]\\n\\n return time_slices\",\n \"def get_grid_data(df):\\n \\n bools = (df['Longitude'] % 60 == 0) & (df['Year'] % 10 == 0)\\n return df[bools]\",\n \"def load_data(city, month, day):\\n# This code is refrenced from the practice problem on the project.\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week and hour from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['hour'] = df['Start Time'].dt.hour\\n\\n # filter by month if applicable\\n if month != 'all':\\n month = MONTHS.index(month) + 1\\n df = df[ df['month'] == month ]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[ df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def get_data(day, unixstart=None, unixend=None):\\n global subnet\\n df_data = pd.DataFrame([])\\n while len(df_data.index)<=0:\\n try:\\n df_data = pd.read_feather(f'/home/pi/studies/ardmore/homeserver/h{subnet}_{day}.feather')\\n except Exception as e:\\n # print(f\\\"Error grid_server.get_data:{e}\\\")\\n pass\\n if unixstart!=None:\\n df_data = df_data[(df_data['unixtime']>=unixstart)&(df_data['unixtime']<=unixend)]\\n float_cols = [x for x in df_data.columns if not x.startswith('timezone')]\\n df_data = df_data[float_cols].astype(float)\\n return df_data\",\n \"def _dataframe_preprocess(self):\\n # 1. add baisc feature like date, time in day, ....\\n if self.data_type != 'porto':\\n self.df['TIMESTAMP'] = self.df.apply(lambda df: df['TIMESTAMPS'][0], axis=1)\\n self.df['TIME'] = pd.to_datetime(self.df['TIMESTAMP'], unit='s', utc=True)\\n \\n self.df.TIME = self.df.TIME.dt.tz_convert(self.timezone)\\n # 2. group df for specific driver analysis\\n self.grouped_df = self.df.groupby('LABEL')\\n if self.count_od_info:\\n if 'SD' not in self.df.columns:\\n self._add_OD_info()\\n self.grouped_od = self.df.groupby('SD')\",\n \"def summarise_data(trip_in, station_data, trip_out):\\n # generate dictionary of station - city mapping\\n station_map = create_station_mapping(station_data)\\n \\n with open(trip_out, 'w') as f_out:\\n # set up csv writer object \\n out_colnames = ['duration', 'start_date', 'start_year',\\n 'start_month', 'start_hour', 'weekday',\\n 'start_city', 'end_city', 'subscription_type'] \\n trip_writer = csv.DictWriter(f_out, fieldnames = out_colnames)\\n trip_writer.writeheader()\\n \\n for data_file in trip_in:\\n with open(data_file, 'r') as f_in:\\n # set up csv reader object\\n trip_reader = csv.DictReader(f_in)\\n\\n # collect data from and process each row\\n for row in trip_reader:\\n new_point = {}\\n \\n # convert duration units from seconds to minutes\\n ### Question 3a: Add a mathematical operation below ###\\n ### to convert durations from seconds to minutes. ###\\n new_point['duration'] = float(row['Duration'])/60\\n \\n # reformat datestrings into multiple columns\\n ### Question 3b: Fill in the blanks below to generate ###\\n ### the expected time values. ###\\n trip_date = datetime.strptime(row['Start Date'], '%m/%d/%Y %H:%M')\\n new_point['start_date'] = trip_date.strftime('%Y-%m-%d')\\n new_point['start_year'] = trip_date.strftime('%Y') # or : trip_date.year\\n new_point['start_month'] = trip_date.strftime('%m') # or : trip_date.month\\n new_point['start_hour'] = trip_date.strftime('%H') # or : trip_date.hour\\n new_point['weekday'] = trip_date.strftime('%a') # or : trip_date.weekday() OR trip_date.isoweekday()\\n\\n \\n # remap start and end terminal with start and end city\\n new_point['start_city'] = station_map[row['Start Terminal']]\\n new_point['end_city'] = station_map[row['End Terminal']]\\n # two different column names for subscribers depending on file\\n if 'Subscription Type' in row:\\n new_point['subscription_type'] = row['Subscription Type']\\n else:\\n new_point['subscription_type'] = row['Subscriber Type']\\n\\n # write the processed information to the output file.\\n trip_writer.writerow(new_point)\",\n \"def temporal_only(\\n db_engine, station_id, start, end, sample_size=int(1.0e5),\\n balance=None):\\n\\n data = get_and_adjust_data(\\n db_engine, station_id, start, end, sample_size)\\n\\n # Balance or set to sample_size\\n if balance is None:\\n data = data.sample(n=sample_size)\\n else:\\n data = bal(data, balance)\\n\\n # Ensure shuffling.\\n data = data.iloc[np.random.permutation(len(data))]\\n\\n X = []\\n yempty = []\\n yfull = []\\n\\n for row in data.iteritems():\\n features = [\\n (1 if row[0].dayofweek == 0 else 0),\\n (1 if row[0].dayofweek == 1 else 0),\\n (1 if row[0].dayofweek == 2 else 0),\\n (1 if row[0].dayofweek == 3 else 0),\\n (1 if row[0].dayofweek == 4 else 0),\\n (1 if row[0].dayofweek == 5 else 0),\\n (1 if row[0].dayofweek == 6 else 0),\\n float(((row[0].hour * 60) + row[0].minute)) / 1440.0,\\n float(row[0].month) / 12.0\\n ]\\n\\n X.append(features)\\n yempty.append(1 if row[1] == \\\"empty\\\" else 0)\\n yfull.append(1 if row[1] == \\\"full\\\" else 0)\\n\\n return {'X': X, 'yempty': yempty, 'yfull': yfull}\",\n \"def dataTimeSeries(timesteps,df,predictors,target,dropnan,out=2,dropVars=True): \\r\\n \\r\\n series = series_to_supervised(df[predictors+[target]].copy(),timesteps,out,dropnan=dropnan)\\r\\n \\r\\n if dropnan==False:\\r\\n series.replace(pd.np.nan,0,inplace=True)\\r\\n \\r\\n # Dropping other variables:\\r\\n if dropVars:\\r\\n index = list(np.arange(series.shape[1]-2,\\r\\n series.shape[1]-len(predictors)-2,\\r\\n -1))\\r\\n \\r\\n labels = [item for idx,item in enumerate(series.columns) \\r\\n if idx in index]\\r\\n \\r\\n #print(\\\"Eliminando variáveis: {}\\\".format(labels))\\r\\n series.drop(labels,axis=1,inplace=True) \\r\\n \\r\\n return series\",\n \"def datetime_features(\\n s: pd.Series, result: Optional[pd.DataFrame] = None\\n) -> pd.DataFrame:\\n result = date_features(s, result)\\n return time_features(s, result)\",\n \"def time_stats(dataframe, filters):\\n\\n print('**********************************************')\\n print(' Calculating Most Frequent Times Of Travel')\\n print(' Filter: ', filters)\\n print('**********************************************')\\n\\n start_time = time.time()\\n popular_month, count_popular_month = common_month(dataframe)\\n popular_day, count_popular_day = common_day(dataframe)\\n popular_hour = dataframe['Hour'].mode()[0]\\n count_popular_hour = (dataframe['Hour'] == popular_hour).sum()\\n\\n if filters == 'None':\\n print('\\\\nMost popular month for travelling: ', popular_month)\\n print('Counts: ', count_popular_month)\\n print('\\\\nMost popular day for travelling: ', get_day_name(popular_day))\\n print('Counts: ', count_popular_day)\\n print('\\\\nMost popular hour of day for travelling: ', popular_hour)\\n print('Counts: ', count_popular_hour)\\n elif filters == 'Both':\\n print('\\\\nMost popular hour of day for travelling: ', popular_hour)\\n print('Counts: ', count_popular_hour)\\n elif filters == 'Month':\\n print('\\\\nMost popular day for travelling: ', get_day_name(popular_day))\\n print('Counts: ', count_popular_day)\\n print('\\\\nMost popular hour of day for travelling: ', popular_hour)\\n print('Counts: ', count_popular_hour)\\n elif filters == 'Day':\\n print('\\\\nMost popular month for travelling: ', popular_month)\\n print('Counts: ', count_popular_month)\\n print('\\\\nMost popular hour of day for travelling: ', popular_hour)\\n print('Counts: ', count_popular_hour)\\n\\n print('\\\\nThis took about {} seconds'. format(time.time() - start_time))\\n print('----------------------------------------------')\",\n \"def build_regress_datafrane_for_one_newly_built_station(treatment_dataframe, control_dataframe,\\n station_open_start_date, station_open_end_date,\\n open_year_plus_month):\\n # check the date\\n assert open_year_plus_month in ['2016_10', '2016_12']\\n result_dataframe = pd.DataFrame(columns=['Time', 'T_i_t', 'Post', 'Sentiment', 'Activity'])\\n # build the T_i_t variable\\n ones_list = [1] * treatment_dataframe.shape[0]\\n treatment_dataframe['T_i_t'] = ones_list\\n zeros_list = [0] * control_dataframe.shape[0]\\n control_dataframe['T_i_t'] = zeros_list\\n # build the post variable\\n treatment_dataframe['Post'] = treatment_dataframe.apply(\\n lambda row: add_post_variable(row['hk_time'], opening_start_date=station_open_start_date,\\n opening_end_date=station_open_end_date), axis=1)\\n print('Check the post variable distribution of treatment group: {}'.format(\\n Counter(treatment_dataframe['Post'])))\\n print('Check the T_i_t variable distribution of treatment group: {}'.format(\\n Counter(treatment_dataframe['T_i_t'])))\\n control_dataframe['Post'] = control_dataframe.apply(\\n lambda row: add_post_variable(row['hk_time'], opening_start_date=station_open_start_date,\\n opening_end_date=station_open_end_date), axis=1)\\n print('Check the post variable distribution of control group: {}'.format(\\n Counter(control_dataframe['Post'])))\\n print('Check the T_i_t variable distribution of control group: {}'.format(\\n Counter(control_dataframe['T_i_t'])))\\n combined_dataframe = pd.concat([treatment_dataframe, control_dataframe], axis=0, sort=True)\\n combined_dataframe = combined_dataframe.reset_index(drop=True)\\n # We don't consider the tweets posted on the open date\\n combined_dataframe_without_not_considered = \\\\\\n combined_dataframe.loc[combined_dataframe['Post'] != 'not considered']\\n combined_data_copy = combined_dataframe_without_not_considered.copy()\\n combined_data_copy['month_plus_year'] = combined_data_copy.apply(\\n lambda row: str(int(float(row['year']))) + '_' + str(int(float(row['month']))), axis=1)\\n sentiment_dict = {}\\n activity_dict = {}\\n for _, dataframe in combined_data_copy.groupby(['month_plus_year', 'T_i_t', 'Post']):\\n time = str(list(dataframe['month_plus_year'])[0])\\n t_i_t = str(list(dataframe['T_i_t'])[0])\\n post = str(list(dataframe['Post'])[0])\\n sentiment_dict[time + '_' + t_i_t + '_' + post] = before_and_after_final_tpu.pos_percent_minus_neg_percent(dataframe)\\n activity_dict[time + '_' + t_i_t + '_' + post] = np.log(dataframe.shape[0])\\n result_dataframe_copy = result_dataframe.copy()\\n time_list = []\\n t_i_t_list = []\\n post_list = []\\n sentiment_list = []\\n activity_list = []\\n for key in list(sentiment_dict.keys()):\\n # don't consider the tweets posted in 2016_10(for Whampoa and Ho Man Tin) or 2016_12(for other stations)\\n if key[:7] != open_year_plus_month:\\n time_list.append(key[:-4])\\n t_i_t_list.append(int(key[-3]))\\n post_list.append(int(key[-1]))\\n sentiment_list.append(sentiment_dict[key])\\n activity_list.append(activity_dict[key])\\n else:\\n pass\\n result_dataframe_copy['Time'] = time_list\\n result_dataframe_copy['T_i_t'] = t_i_t_list\\n result_dataframe_copy['Post'] = post_list\\n result_dataframe_copy['Sentiment'] = sentiment_list\\n result_dataframe_copy['Activity'] = activity_list\\n return result_dataframe_copy\",\n \"def build_timeseries(self):\\n timeseries = {\\n \\\"metricKind\\\": \\\"DELTA\\\", \\n \\\"metric\\\": {\\n \\\"labels\\\": {\\n \\\"response_code\\\": \\\"0\\\"}, \\n \\\"type\\\": \\\"agent.googleapis.com/agent/request_count\\\"\\n }, \\n \\\"points\\\": [\\n {\\n \\\"interval\\\": {\\n \\\"endTime\\\": \\\"2019-02-18T22:09:53.939194Z\\\", \\n \\\"startTime\\\": \\\"2019-02-18T21:09:53.939194Z\\\"\\n }, \\n \\\"value\\\": {\\n \\\"int64Value\\\": \\\"62\\\"\\n }\\n }, \\n {\\n \\\"interval\\\": {\\n \\\"endTime\\\": \\\"2019-02-18T21:09:53.939194Z\\\", \\n \\\"startTime\\\": \\\"2019-02-18T20:09:53.939194Z\\\"\\n }, \\n \\\"value\\\": {\\n \\\"int64Value\\\": \\\"61\\\"\\n }\\n }\\n ], \\n \\\"resource\\\": {\\n \\\"labels\\\": {\\n \\\"instance_id\\\": \\\"9113659852587170607\\\", \\n \\\"project_id\\\": \\\"YOUR_PROJECT_ID\\\", \\n \\\"zone\\\": \\\"us-east4-a\\\"\\n }, \\n \\\"type\\\": \\\"gce_instance\\\"\\n }, \\n \\\"valueType\\\": \\\"INT64\\\"\\n }\\n\\n return timeseries\",\n \"def create_features(df,rsi_window = 14,macd_feat = [12,26,9]):\\n df.dropna(inplace=True)\\n ## day and month\\n df['Date'] = pd.to_datetime(df['Date'])\\n df['Month'] = df['Date'].dt.month\\n df['dayowk'] = df['Date'].dt.dayofweek\\n df = pd.get_dummies(data = df,columns = ['Month','dayowk'])\\n \\n ##Previos n-day pct_changes\\n df['1day_pct'] = df['Adj Close'].pct_change()\\n df['2day_pct'] = df['Adj Close'].pct_change(periods = 2)\\n df['3day_pct'] = df['Adj Close'].pct_change(periods = 3)\\n df['4day_pct'] = df['Adj Close'].pct_change(periods = 4)\\n df['5day_pct'] = df['Adj Close'].pct_change(periods = 5)\\n df['7day_pct'] = df['Adj Close'].pct_change(periods = 7)\\n \\n ##Cumulative sum of 1day_pct\\n df['1day_pct_cs'] = df['Adj Close'].pct_change().cumsum()\\n \\n ##EWMA of 7, 50 and 200 days\\n df['ewma_7'] = df['Adj Close'].ewm(span=7).mean()/df['Adj Close']\\n df['ewma_50'] = df['Adj Close'].ewm(span=50).mean()/df['Adj Close']\\n df['ewma_200'] = df['Adj Close'].ewm(span=200).mean()/df['Adj Close']\\n ## Golden Cross vs Death Cross etc.\\n #df['7g(50&200)'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\\n #df['7l(50&200)'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\\n #df['7g50'] = (df['ewma_7'] > df['ewma_50']) & (df['ewma_7'] < df['ewma_200'])\\n #df['7g200'] = (df['ewma_7'] < df['ewma_50']) & (df['ewma_7'] > df['ewma_200'])\\n \\n ##RSI and MACD\\n df = RSI(df,14)\\n df = MACD_mod(df,nl=macd_feat[0],nh=macd_feat[1],nsig=macd_feat[2])\\n \\n df['day_var'] = (df['High'] - df['Low'])/df['Close']## Days variance\\n df['open_close'] = (df['Open'] - df['Close'])/df['Close'] ## Days Open-Close\\n df['high_close'] = (df['High'] - df['Close'])/df['Close'] ##Days High-Close\\n df['open_prev_close'] = (df['Open'] - df['Close'].shift(1))/df['Close'] ## Days open - Previos Dyas Close\\n \\n ##Classification target\\n df['target'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2) ## Target for classification\\n #df['1_day_target'] = df['Adj Close'].shift(-1) - df['Adj Close'] ## Target for Regression\\n #df['target2'] = round((np.sign(df['1day_pct']).shift(-1)+1)/2)## Will the price go up intra-day\\n \\n ## IS the stock Overbought or Oversold based on RSI?\\n df['RSI_overbought'] = df['RSI']>70\\n df['RSI_oversold'] = df['RSI']<30\\n \\n \\n #df.drop(['Open','High','Low','Close'],axis=1,inplace=True)\\n# df = df.dropna()\\n \\n #df = df.reset_index(drop=True)\\n \\n ## Calculating how large the previos hot and cold streaks were\\n f = 0\\n df['prev_hot_streak'] = np.zeros(df.shape[0])\\n for i in range(df.shape[0]-1):\\n if df['target'][i] ==1:\\n f += 1\\n if df['target'][i+1] ==0:\\n df['prev_hot_streak'][i+1] = f\\n f = 0\\n for i in range(1,df.shape[0]):\\n #print(i)\\n if df['prev_hot_streak'][i]==0:\\n df['prev_hot_streak'][i]=df['prev_hot_streak'][i-1]\\n \\n \\n df['prev_cold_streak'] = np.zeros(df.shape[0])\\n for i in range(df.shape[0]-1):\\n if df['target'][i] ==0:\\n f += 1\\n if df['target'][i+1] ==1:\\n df['prev_cold_streak'][i+1] = f\\n f = 0\\n\\n for i in range(1,df.shape[0]):\\n #print(i)\\n if df['prev_cold_streak'][i]==0:\\n df['prev_cold_streak'][i] = df['prev_cold_streak'][i-1]\\n \\n ## Calculating current hot and cold streaks\\n df['current_hot_streak'] = np.zeros(df.shape[0])\\n df['current_cold_streak'] = np.zeros(df.shape[0])\\n fhot=0\\n fcold=0\\n for i in range(df.shape[0]):\\n if df['target'][i]==1:\\n fhot += 1\\n fcold = 0\\n df['current_hot_streak'][i] = fhot\\n elif df['target'][i]==0:\\n fcold += 1\\n fhot = 0\\n df['current_cold_streak'][i] = fcold\\n \\n df['prev_hot_streak'] = df['prev_hot_streak'].shift(1)\\n df['prev_cold_streak'] = df['prev_cold_streak'].shift(1)\\n df['current_hot_streak'] = df['current_hot_streak'].shift(1)\\n df['current_cold_streak'] = df['current_cold_streak'].shift(1)\\n \\n ## Combinations of previos streaks\\n df['prev_current_hot'] = df['prev_hot_streak'] - df['current_hot_streak']\\n df['prev_current_cold'] = df['prev_cold_streak'] - df['current_cold_streak']\\n df['current_hot_prev_cold'] = df['current_hot_streak'] - df['prev_cold_streak']\\n df['current_cold_prev_hot'] = df['current_cold_streak'] - df['prev_hot_streak']\\n \\n ##Calculating days since max\\n current_max = df['Adj Close'][0]\\n df['days_from_max'] = np.zeros(df.shape[0])\\n df['pct_from_max'] = np.zeros(df.shape[0])\\n #print('blah')\\n for i in range(1,df.shape[0]):\\n if df['Adj Close'][i] > current_max:\\n current_max = df['Adj Close'][i]\\n # print(current_max)\\n else:\\n df['days_from_max'][i] = df['days_from_max'][i-1]+1\\n df['pct_from_max'][i] = (df['Adj Close'][i]-current_max)/current_max\\n #print(df['days_from_max'][i])\\n \\n \\n \\n df.dropna(inplace=True)\\n df = df.reset_index(drop=True)\\n return df\",\n \"def __init__(self, year=None, month=None, day=None, map_type='Cases'):\\n self.covid_df = pd.DataFrame([])\\n self.geo_data = pd.DataFrame([])\\n self.name_iso2_mapping = {}\\n self.countries_centroids = pd.DataFrame([])\\n \\n try:\\n self.date = datetime(year=year, month=month, day=day)\\n except:\\n print('Invalid/empty date entry (year, month, day take valid int inputs)! Date defaulted to today.')\\n self.date = datetime.today()\\n \\n if self.date > datetime.today():\\n print('Can\\\\'t input future date! Date defaulted to today.')\\n self.date = datetime.today()\\n \\n if map_type not in ['Cases', 'Deaths']:\\n sys.exit('Please specify either \\\"Cases\\\" or \\\"Deaths\\\" as map type!')\\n else:\\n self.map_type = map_type\",\n \"def create_demo_location_history() -> geopandas.GeoDataFrame:\\n np.random.seed(123)\\n\\n time = pd.date_range(start=datetime.fromtimestamp(1624241116), end=datetime.now(), freq=\\\"1min\\\").values\\n\\n center_point = (-36.875990410695394, 174.76398830024274)\\n lat = np.random.normal(loc=center_point[0], scale=0.01, size=len(time))\\n lon = np.random.normal(loc=center_point[1], scale=0.01, size=len(time))\\n\\n geometry = [Point(lon, lat) for lon, lat in zip(lon, lat)]\\n return geopandas.GeoDataFrame(pd.DataFrame(dict(time=time, lat=lat, lon=lon)), geometry=geometry)\",\n \"def createnc(ncfout,xlat,xlon,times=None,zvals=None,wsvals=None,\\\\\\n wdvals=None,olvals=None,attbts=None,ftype=\\\"timeseries\\\",dims=[7,180,180]):\\n nc_out=nc.Dataset(ncfout,'w',clobber=True)\\n\\n # Set Attributes to the File\\n if attbts is not None:\\n final_attbts={}\\n # Define projection\\n proj_lcc = pj_lcc = Proj(\\\"+proj=lcc +lat_1={TRUELAT1} +lat_2={TRUELAT2} +lat_0={MOAD_CEN_LAT} +lon_0={STAND_LON} +x_0=0 +y_0=0 +a=6370000 +b=6370000\\\".format(**attbts))\\n\\n # Get x&y of domain center\\n xcen, ycen = pj_lcc(attbts['CEN_LON'], attbts['CEN_LAT'])\\n\\n for key in attbts:\\n if str(key).find(\\\"STAG\\\") <= 0 : # Remove Staggered Grid Information\\n final_attbts.update({key:attbts[key]})\\n nc_out.setncatts(final_attbts)\\n # Create a CRS Variable for the Projection (GIS Readability)\\n crsv=nc_out.createVariable('crs','c')\\n crsv.semi_major_axis = 6370000.0\\n crsv.inverse_flattening = 0.0\\n crsv.grid_mapping_name = \\\"lambert_conformal_conic\\\"\\n crsv.longitude_of_central_meridian = attbts[\\\"STAND_LON\\\"]\\n crsv.false_easting = 0.0\\n crsv.false_northing = 0.0\\n crsv.latitude_of_projection_origin = attbts[\\\"MOAD_CEN_LAT\\\"]\\n crsv.standard_parallel = [attbts[\\\"TRUELAT1\\\"],attbts[\\\"TRUELAT2\\\"]]\\n crsv.longitude_of_prime_meridian = 0.0\\n crsv.proj = proj_lcc.srs\\n\\n\\n\\n # Override Institution and Experiment\\n nc_out.INSTITUTION=INSTITUTION\\n nc_out.EXPERIMENT=EXPERIMENT\\n nc_out.Conventions=\\\"CF-1.6\\\"\\n\\n # Create Dimensions First\\n if ftype==\\\"timeseries\\\":\\n nc_out.TITLE='Timeseries of the New European Wind Atlas from WRF V3.8.1'\\n nc_out.createDimension('time',None)\\n nc_out.createDimension('DateStrLen',19)\\n nc_out.createDimension('height',dims[0])\\n nc_out.createDimension('south_north',dims[1])\\n nc_out.createDimension('west_east',dims[2])\\n # Create Time Vector as Integer\\n timesn = nc_out.createVariable('time','i8',('time',))\\n timesn.units = \\\"minutes since 1900-01-01 00:00:00.0\\\"\\n timesn.calendar = \\\"gregorian\\\"\\n timesn.long_name = \\\"Time\\\"\\n timesn.standard_name = \\\"time\\\"\\n timesn[:] = nc.date2num(createdatv(times),units=timesn.units,calendar=timesn.calendar)\\n # Create additional Time Vector as Character\\n timesc = nc_out.createVariable('Times', 'c', ('time','DateStrLen'))\\n timesc.format = \\\"YYYY-MM-DD_HH:MM:SS\\\"\\n timesc.long_name = \\\"Time\\\"\\n timesc[:] = times[:]\\n # Height\\n hgts = nc_out.createVariable('height','f4',('height',))\\n hgts.units=\\\"m\\\"\\n hgts.long_name=\\\"Height above Ground\\\"\\n hgts.standard_name=\\\"height\\\"\\n hgts[:] = zvals\\n # y\\n south_north = nc_out.createVariable('south_north','f4',('south_north',))\\n south_north.long_name = \\\"y-coordinate in Cartesian system\\\"\\n south_north.units = \\\"m\\\"\\n\\n dy = attbts[\\\"DY\\\"]\\n ny = attbts[\\\"SOUTH-NORTH_PATCH_END_UNSTAG\\\"]\\n ymin = ycen - dy * (ny - 1) / 2\\n s_n = np.linspace(0, ny-1, ny) * dy + ymin\\n south_north[:] = s_n\\n\\n # x\\n west_east = nc_out.createVariable('west_east','f4',('west_east',))\\n west_east.long_name = \\\"x-coordinate in Cartesian system\\\"\\n west_east.units = \\\"m\\\"\\n\\n dx = attbts[\\\"DX\\\"]\\n nx = attbts[\\\"WEST-EAST_PATCH_END_UNSTAG\\\"]\\n xmin = xcen - dx * (nx - 1) / 2\\n e_w = np.linspace(0, nx-1, nx) * dx + xmin\\n west_east[:] = e_w\\n\\n elif ftype==\\\"roughness\\\":\\n nc_out.title='NEWA Roughness'\\n nc_out.createDimension('south_north',dims[0])\\n nc_out.createDimension('west_east',dims[1])\\n\\n elif ftype==\\\"tabfile\\\":\\n nc_out.title='NEWA WasP Tab File'\\n nc_out.createDimension('south_north',dims[0])\\n nc_out.createDimension('west_east',dims[1])\\n nc_out.createDimension('sector',dims[2])\\n nc_out.createDimension('wind',dims[3])\\n nc_out.createDimension('stab',dims[4])\\n\\n # Wind Speed Class\\n wscl = nc_out.createVariable('wspdCl','f4',('wind',))\\n wscl.units=\\\"ms-1\\\"\\n wscl.long_name=\\\"Velocity of bin centre\\\"\\n wscl[:] = wsvals\\n\\n # Wind Speed Class\\n wdcl = nc_out.createVariable('wdirCl','f4',('sector',))\\n wdcl.units=\\\"ms-1\\\"\\n wdcl.long_name=\\\"Velocity of bin centre\\\"\\n wdcl[:] = wdvals\\n\\n # Stability\\n lcl = nc_out.createVariable('Ltypical','f4',('stab',))\\n lcl.units=\\\"m\\\"\\n lcl.long_name=\\\"L typical\\\"\\n lcl[:] = olvals\\n\\n # Lat and Lon\\n lats = nc_out.createVariable(\\\"XLAT\\\", 'f4', ('south_north','west_east'), zlib=True,complevel=9)\\n lats[:] = xlat[:]\\n lats.units=\\\"degree_north\\\"\\n lats.long_name=\\\"Center Latitude of Grid Cell\\\"\\n lats.standard_name=\\\"latitude\\\"\\n lons = nc_out.createVariable(\\\"XLON\\\", 'f4', ('south_north','west_east'), zlib=True,complevel=9)\\n lons[:] = xlon[:]\\n lons.units=\\\"degree_east\\\"\\n lons.long_name=\\\"Center Longitude of Grid Cell\\\"\\n lons.standard_name=\\\"longitude\\\"\\n nc_out.close()\\n return(None)\",\n \"def get_profile_template_data(end_date, time_scale):\\n\\n intervals = -1\\n HOURS = 0\\n DAYS = 0\\n WEEKS = 0\\n time_format = \\\"%d %B %Y\\\"\\n\\n print(f\\\"Time scale: {time_scale}\\\")\\n if time_scale == \\\"DAILY\\\":\\n intervals = 12 # 24 hours per day\\n HOURS = 2\\n time_format = \\\"%I%p\\\"\\n elif time_scale == \\\"WEEKLY\\\":\\n intervals = 7 # 7 days per week\\n DAYS = 1\\n elif time_scale == \\\"MONTHLY\\\":\\n intervals = 4 # 4 weeks per month\\n WEEKS = 1\\n\\n # end_date = date.fromtimestamp(float(end_date))\\n # end_date = datetime(end_date.year, end_date.month, end_date.day)\\n end_date = datetime.now() + timedelta(hours=2)\\n print(f\\\"TEMP current end date: {end_date.strftime('%I%p')}\\\")\\n x_axis = []\\n data = []\\n inter = []\\n TEMP = []\\n time_gap = timedelta(hours=HOURS, days=DAYS, weeks=WEEKS)\\n for i in range(intervals - 1, -1, -1):\\n x_step = end_date - (i * time_gap)\\n TEMP.append(x_step.strftime(\\\"%I%p\\\"))\\n inter.append(str(x_step.timestamp()))\\n data.append({\\\"images\\\": [], \\\"x\\\": x_step.strftime(time_format), \\\"y\\\": 0})\\n print(f\\\"TEMP x-axis:{TEMP}\\\")\\n return data, inter\",\n \"def get_time_df(plan_type, stop=False):\\n df = pd.DataFrame({\\n 'patient': patients,\\n 'plan_type': len(patients)*[plan_type],\\n 'plan_time': [get_plan_time(patient, plan_type, stop)\\n for patient in patients],\\n 'plan_iter': [get_plan_iter(patient, plan_type, stop)\\n for patient in patients]})\\n return df\",\n \"def ttest_region():\\n\\n cks = cksmet.io.load_table('cks-cuts',cache=1)\\n cks = cks[~cks.isany]\\n lamo = cksmet.io.load_table('lamost-cal-cuts',cache=2)\\n lamo = lamo[~lamo.isany]\\n #lamo = lamo.query('-1 < lamo_smet < 0.5')\\n\\n boxes = _boxes()\\n boxes = boxes.copy()\\n #boxes = boxes.sort_values(by=['per1','prad1'])\\n boxes2 = []\\n lines = []\\n for i,row in boxes.iterrows():\\n cut = cks[\\n cks.koi_period.between(row.per1,row.per2) &\\n cks.iso_prad.between(row.prad1,row.prad2)\\n ]\\n d = calculate_statistics(cut.cks_smet,lamo.lamo_smet)\\n d = dict(d, **row)\\n lines.append( to_string(d))\\n \\n return lines\",\n \"def get_time_series(this_lat, this_lon, case, varnames):\\n\\n cesmdir = '/gpfs/fs1/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly'\\n\\n if 'LE' in case:\\n\\n from observational_large_ensemble.params import karen_params_cesm\\n\\n mode_lag = karen_params_cesm.mode_lag\\n cvdp_loc = karen_params_cesm.cvdp_loc\\n AMO_cutoff_freq = karen_params_cesm.AMO_cutoff_freq\\n\\n name_conversion = {'tas': 'TREFHT', 'pr': 'PRECC', 'slp': 'PSL'}\\n cesm_names = [name_conversion[v] for v in varnames]\\n this_member = int((case).split('-')[-1])\\n cvdp_file = '%s/CESM1-CAM5-BGC-LE_#%i.cvdp_data.1920-2018.nc' % (cvdp_loc, this_member)\\n\\n # Historical filenames for CESM. Will need to append part of RCP8.5 to get full period\\n filenames = []\\n for var in cesm_names:\\n file_str = '%s/%s/b.e11.B20TRC5CNBDRD.f09_g16.%03d.cam.h0.%s.??????-200512.nc' % (cesmdir, var,\\n this_member, var)\\n this_file = glob(file_str)[0]\\n filenames.append(this_file)\\n\\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames,\\n karen_params_cesm.valid_years, mode_lag,\\n cvdp_file, AMO_cutoff_freq, name_conversion)\\n\\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\\n\\n else:\\n\\n from observational_large_ensemble.params import karen_params_obs\\n\\n mode_lag = karen_params_obs.mode_lag\\n cvdp_loc = karen_params_obs.cvdp_loc\\n AMO_cutoff_freq = karen_params_obs.AMO_cutoff_freq\\n\\n tas_dir = karen_params_obs.tas_dir\\n pr_dir = karen_params_obs.pr_dir\\n slp_dir = karen_params_obs.slp_dir\\n cvdp_file = '%s/HadISST.cvdp_data.1920-2018.nc' % cvdp_loc\\n file_dict = {'tas': '%s/Complete_TAVG_LatLong1.nc' % tas_dir,\\n 'pr': '%s/full_data_monthly_v2020.nc' % pr_dir,\\n 'slp': '%s/prmsl.mon.mean.nc' % slp_dir}\\n\\n filenames = []\\n for var in varnames:\\n filenames.append(file_dict[var])\\n\\n name_conversion = {'tas': 'temperature', 'pr': 'precip', 'slp': 'prmsl'}\\n\\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames[0],\\n karen_params_obs.valid_years, mode_lag,\\n cvdp_file, AMO_cutoff_freq, name_conversion)\\n\\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\\n\\n return this_ts, df_shifted\",\n \"def load_data(city, month, day):\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n # convert the Start Time column to datetime\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n\\n # extract month and day of week from Start Time to create new columns\\n df['month'] = df['Start Time'].dt.month\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['Start and End Stations'] = df['Start Station'] + ' and ' + df['End Station']\\n\\n # filter by month if applicable\\n if month != 'all':\\n # use the index of the months list to get the corresponding int\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1\\n\\n # filter by month to create the new dataframe\\n df = df[df['month'] == month]\\n\\n # filter by day of week if applicable\\n if day != 'all':\\n # filter by day of week to create the new dataframe\\n df = df[df['day_of_week'] == day.title()]\\n\\n return df\",\n \"def get_time_series_data():\\r\\n # Grab the requested years and columns from the query arguments\\r\\n ls_year = [int(year) for year in request.args.getlist(\\\"n\\\")]\\r\\n ls_col = request.args.getlist(\\\"m\\\")\\r\\n\\r\\n # Generate a list of all the months we need to get\\r\\n all_years = [str(year) for year in range(min(ls_year), max(ls_year) + 1)]\\r\\n\\r\\n # Grab all of the wanted months by filtering for the ones we want\\r\\n wanted_months = reduce(\\r\\n lambda a, b: a | b, (app.df[\\\"month\\\"].str.contains(year) for year in all_years)\\r\\n )\\r\\n\\r\\n # Create a new dataframe from the one that\\r\\n df_new = app.df[wanted_months][[\\\"month\\\"] + ls_col]\\r\\n\\r\\n # Convert all string dates into datetime objects and then sort them\\r\\n df_new[\\\"month\\\"] = pd.to_datetime(df_new[\\\"month\\\"])\\r\\n df_new = df_new.sort_values(by=[\\\"month\\\"])\\r\\n\\r\\n # Return the dataframe as json\\r\\n return df_new.to_json(), 200\",\n \"def main():\\n \\n Y1, Y2 = 2005, 2017 ### range with coordinates supplied in pre-2018 generated archive\\n\\n if len(sys.argv) > 1 and int(sys.argv[1]) > 0:\\n Y1 = int(sys.argv[1])\\n \\n if len(sys.argv) > 2 and int(sys.argv[2]) > Y1:\\n Y2 = int(sys.argv[2])\\n \\n with open('data/audit.log','w') as output:\\n for Y in range(Y1, Y2):\\n df = pd.read_csv('data/{}.csv'.format(Y), low_memory = False)\\n output.write('\\\\n--- {} --------------------\\\\n'.format(Y))\\n\\n # remove `deleted` records\\n df['deleted'] = df['deleted'].apply(yes_no)\\n df = df[df['deleted'] == 0]\\n\\n # remove misc misdemeanors\\n df = df[~df['category'].isin(drop)]\\n\\n # validate date and expand into Y,N,D,W,H\\n df['dt'] = df['incident_date'].apply(extract)\\n df = df[~df['dt'].isnull()]\\n\\n # convert from plane state to longitude-latitude\\n df['ll'] = df.apply(to_lnglat, axis = 1)\\n\\n # init features\\n features = df.loc[:,['category','stat','address','city','zip']]\\n features['id'] = df['incident_id']\\n dt = ['year','month','day','weekday','hour']\\n for i in range(len(dt)):\\n features[dt[i]] = df['dt'].apply(lambda x: x[i] )\\n\\n features['lng'] = df['ll'].apply(lambda x: x[0])\\n features['lat'] = df['ll'].apply(lambda x: x[1])\\n\\n features['gang'] = df['gang_related'].apply(yes_no)\\n features['category'] = df['category'].apply(collapse)\\n cat = set(features.groupby(['category']).size().reset_index(name='count')['category'].tolist())\\n output.write('Categories: {}\\\\n'.format(len(cat)))\\n\\n output.write('Date miss: {:.4f}%\\\\n'\\\\\\n .format(100 * (1 - len(features[(features['year'] > 2000) & (~features['weekday'].isnull())])/len(features))))\\n output.write('Location miss: {:.4f}%\\\\n'\\\\\\n .format(100 * (1 - len(features[(features['zip'] > 0) | (features['lat'] > 0)])/len(features))))\\n\\n # keep records with valid date\\n features['date'] = df['dt'].apply(lambda x: datetime.date(x[0], x[1], x[2]))\\n features = features[(features['year'] > 2000) & (~features['weekday'].isnull())]\\n output.write('Time miss: {:.4f}%\\\\n'.format(100 * len(features[features['hour'] == -1])/len(features)))\\n\\n # potential `time-unknown` issue\\n output.write('Hour ZERO: {:.4f}%\\\\n'.format(100 * len(features[features['hour'] == 0])/len(features)))\\n output.write('Hour NOON: {:.4f}%\\\\n'.format(100 * len(features[features['hour'] == 12])/len(features)))\\n\\n features = features[(features['zip'] > 0) | (features['lat'] > 0)]\\n\\n # get the best possible coordinates + zipcode assessment\\n features[['zip','lng','lat']] = features[['zip','lng','lat']].apply(fix_location, axis = 1)\\n output.write('Failed location: {:.4f}%\\\\n'.format(100 * len(features[features['zip'].isnull()])/len(features)))\\n features = features[~features['zip'].isnull()]\\n features['zip'] = df['zip'].apply(lambda x: str(x)[:5])\\n \\n # normalize city attr\\n features = features.join(zipcodes[['zip','city']].set_index('zip'), on = 'zip', lsuffix = '_orig', rsuffix = '')\\n features.loc[features['city'].isnull(), 'city'] = features.loc[features['city'].isnull(), 'city_orig']\\\\\\n .apply(lambda x: x if type(x) == float else ' '.join([l[0].upper() + l[1:] for l in x.split()]))\\n\\n # reduce to LA bounding-box\\n features = features[(features['lng'] > -119) & (features['lng'] < -116)]\\n features = features[(features['lat'] > 32) & (features['lat'] < 35)]\\n\\n # save csv\\n features[fields].to_csv('data/F{}.csv'.format(Y), index = False)\\n features[fields].to_json('data/F{}.json'.format(Y), orient = 'records')\\n output.close()\",\n \"def coarse_dataframe(geodf, side_square):\\n\\n # initialise the categories\\n\\n geodf['category'] = -1\\n\\n # do calculations on the first date, then extrapolate to the rest\\n data_df = geodf[geodf['date'] == np.unique(geodf['date'])[0]]\\n\\n data_df = data_df.sort_values(by=['longitude', 'latitude'])\\n\\n n_grids = int(math.sqrt(data_df.shape[0]))\\n\\n category = 0\\n\\n for n in range(data_df.shape[0]):\\n\\n # only process lat,long point that do not have a category\\n if data_df['category'].iloc[n] == -1:\\n\\n # get the side_square^2 nearest indexes to the point.\\n indexes = []\\n for i in range(side_square):\\n for j in range(side_square):\\n\\n if n + n_grids * i + j < n_grids * n_grids and data_df['category'].iloc[n + n_grids * i + j] == -1:\\n indexes.append(n + n_grids * i + j)\\n\\n # assing them all to the same categorty\\n data_df['category'].iloc[indexes] = str(category)\\n\\n # get the geometry points of that catery\\n cat_geometry = data_df[data_df['category'] == str(category)]['geometry']\\n\\n # get indexes of each point belonging to the category\\n indexes_all = []\\n for point in cat_geometry:\\n indexes_all.append(geodf[geodf['geometry'] == point].index.tolist())\\n\\n indexes_all_flat = [item for sublist in indexes_all for item in sublist]\\n\\n geodf['category'].iloc[indexes_all_flat] = str(category)\\n\\n category = category + 1\\n\\n geodf['category'] = (geodf['category'].astype(str)).str.cat(geodf['date'], sep=\\\"_\\\")\\n\\n geodf = geodf.dissolve(by=['category', 'date'], aggfunc='mean')\\n\\n # re-assing the date because we are losing it\\n geodf['date'] = [i[1] for i in geodf.index]\\n\\n geodf['category'] = [i[0] for i in geodf.index]\\n\\n return geodf\",\n \"def load_data(city, month, day):\\r\\n df = pd.read_csv(CITY_DATA[city])\\r\\n\\r\\n # convert the Start Time column to datetime\\r\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\r\\n\\r\\n # extract month and day of week from Start Time to create new columns\\r\\n df['month'] = df['Start Time'].dt.month\\r\\n df['day_of_week'] =df['Start Time'].dt.weekday_name\\r\\n\\r\\n\\r\\n # filter by month if applicable\\r\\n if month != 'all':\\r\\n # use the index of the months list to get the corresponding int\\r\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\r\\n month = months.index(month) + 1\\r\\n\\r\\n # filter by month to create the new dataframe\\r\\n df = df[df['month'] == month]\\r\\n\\r\\n # filter by day of week if applicable\\r\\n if day != 'all':\\r\\n # filter by day of week to create the new dataframe\\r\\n df = df[df['day_of_week'] == day.title()]\\r\\n\\r\\n return df\",\n \"def preprocess_time(data, metadata):\\n timestamp_name = metadata[\\\"timestamp_name\\\"]\\n if timestamp_name == \\\"\\\":\\n timestamp_name = \\\"fake_ts\\\"\\n data[timestamp_name] = data.index\\n\\n data[timestamp_name] = pd.to_datetime(data[timestamp_name])\\n data.sort_values(by=[timestamp_name], inplace=True)\\n data.set_index([timestamp_name], inplace=True)\\n\\n return data\",\n \"def load_data(city, month, day):\\n \\n start_time = time.time()\\n\\n # load data file into a dataframe\\n df = pd.read_csv(CITY_DATA[city])\\n\\n df['Start Time'] = pd.to_datetime(df['Start Time'], errors='coerce')\\n\\n # extract month, day of week and hour from Start Time to create new columns\\n \\n # Months will take values from 1 through 12\\n df['month'] = df['Start Time'].dt.month \\n \\n # day of the week will take values in the range of 1 through 7\\n df['day_of_week'] = df['Start Time'].dt.dayofweek \\n \\n # hour will take values from 0 through 23\\n df['hour'] = df['Start Time'].dt.hour # range (0-23)\\n\\n # Here, we are filtering by month\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n if month != 'all':\\n months = ['january', 'february', 'march', 'april', 'may', 'june']\\n month = months.index(month) + 1 \\n\\n df = df[df['Start Time'].dt.month == month]\\n\\n # Here, we are filtering by day of week\\n if day != 'all': \\n df = df[df['Start Time'].dt.weekday_name == day.title()]\\n \\n return df\",\n \"def load_data(city, month, day):\\n data_to_use = CITY_DATA[city]\\n df = pd.read_csv(data_to_use)\\n # drop rows containing NAN fields\\n df2 = df.dropna()\\n\\n # Ensure the Start and End Time are Date\\n pd.to_datetime(df2['Start Time'])\\n pd.to_datetime(df2['End Time'])\\n df = df2.sort_values(by='Start Time')\\n\\n # For each Start Time create additional columns to store year, month, day_of_week and hour\\n # df['Start Year'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'year'))\\n df['Start Month'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'month'))\\n df['Start Day'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'day_of_week'))\\n df['Start Hour'] = df['Start Time'].apply(lambda x: get_part_of_datetime(x, 'hour'))\\n\\n # filter month if month is not all\\n if month.title() != 'All':\\n df = df.loc[df['Start Month'] == month.title()]\\n\\n # filter day if day is not all\\n if day.title() != 'All':\\n df = df.loc[df['Start Day'] == day.title()]\\n\\n return df\",\n \"def make_result_table(self, for_time):\\n time = [t.AsPython()\\n for t in cmf.timerange(cmf.AsCMFtime(self.starttime),\\n cmf.AsCMFtime(self.starttime + for_time),\\n self.dt)]\\n depth = self.depth\\n layer_template = np.zeros((len(time), len(depth))) * np.NaN\\n ds = xr.Dataset(\\n {'N': (('time', 'depth'), layer_template.copy()),\\n 'Corg': (('time', 'depth'), layer_template.copy()),\\n 'DOC': (('time', 'depth'), layer_template.copy()),\\n 'Temp': (('time', 'depth'), layer_template.copy()),\\n 'wetness': (('time', 'depth'), layer_template.copy()),\\n },\\n {'time': time, 'depth': list(depth)}\\n )\\n return ds\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6118439","0.579679","0.5540201","0.55036247","0.5491236","0.5480218","0.5434643","0.54258335","0.54027617","0.5400543","0.53993887","0.539527","0.5395043","0.53789306","0.53724855","0.5362963","0.5352141","0.53505415","0.5327444","0.5325912","0.53214043","0.5317872","0.53176177","0.5309722","0.5293582","0.5276143","0.5262892","0.525614","0.5243122","0.52268857","0.5214312","0.5206717","0.5206076","0.52046233","0.520376","0.51820964","0.51767874","0.5167578","0.5156011","0.5155063","0.51532185","0.5151431","0.51471907","0.51464254","0.5144911","0.51372606","0.51338214","0.51274496","0.51264954","0.5124133","0.5123075","0.5116841","0.5107943","0.5104118","0.50941896","0.5085329","0.5082668","0.50808823","0.50692177","0.50565785","0.50457966","0.50405186","0.5039353","0.5028577","0.5023151","0.50185245","0.5010007","0.5007894","0.5007045","0.5006016","0.5003963","0.49951744","0.49870408","0.49828604","0.49825528","0.49769625","0.49732313","0.49692294","0.4967019","0.49653468","0.49650168","0.4961472","0.49512208","0.49504045","0.49496356","0.49393535","0.49381268","0.49345475","0.493356","0.49308908","0.4930712","0.49305108","0.4928879","0.49248728","0.49187613","0.49175915","0.49098593","0.4906722","0.48952273","0.4887452"],"string":"[\n \"0.6118439\",\n \"0.579679\",\n \"0.5540201\",\n \"0.55036247\",\n \"0.5491236\",\n \"0.5480218\",\n \"0.5434643\",\n \"0.54258335\",\n \"0.54027617\",\n \"0.5400543\",\n \"0.53993887\",\n \"0.539527\",\n \"0.5395043\",\n \"0.53789306\",\n \"0.53724855\",\n \"0.5362963\",\n \"0.5352141\",\n \"0.53505415\",\n \"0.5327444\",\n \"0.5325912\",\n \"0.53214043\",\n \"0.5317872\",\n \"0.53176177\",\n \"0.5309722\",\n \"0.5293582\",\n \"0.5276143\",\n \"0.5262892\",\n \"0.525614\",\n \"0.5243122\",\n \"0.52268857\",\n \"0.5214312\",\n \"0.5206717\",\n \"0.5206076\",\n \"0.52046233\",\n \"0.520376\",\n \"0.51820964\",\n \"0.51767874\",\n \"0.5167578\",\n \"0.5156011\",\n \"0.5155063\",\n \"0.51532185\",\n \"0.5151431\",\n \"0.51471907\",\n \"0.51464254\",\n \"0.5144911\",\n \"0.51372606\",\n \"0.51338214\",\n \"0.51274496\",\n \"0.51264954\",\n \"0.5124133\",\n \"0.5123075\",\n \"0.5116841\",\n \"0.5107943\",\n \"0.5104118\",\n \"0.50941896\",\n \"0.5085329\",\n \"0.5082668\",\n \"0.50808823\",\n \"0.50692177\",\n \"0.50565785\",\n \"0.50457966\",\n \"0.50405186\",\n \"0.5039353\",\n \"0.5028577\",\n \"0.5023151\",\n \"0.50185245\",\n \"0.5010007\",\n \"0.5007894\",\n \"0.5007045\",\n \"0.5006016\",\n \"0.5003963\",\n \"0.49951744\",\n \"0.49870408\",\n \"0.49828604\",\n \"0.49825528\",\n \"0.49769625\",\n \"0.49732313\",\n \"0.49692294\",\n \"0.4967019\",\n \"0.49653468\",\n \"0.49650168\",\n \"0.4961472\",\n \"0.49512208\",\n \"0.49504045\",\n \"0.49496356\",\n \"0.49393535\",\n \"0.49381268\",\n \"0.49345475\",\n \"0.493356\",\n \"0.49308908\",\n \"0.4930712\",\n \"0.49305108\",\n \"0.4928879\",\n \"0.49248728\",\n \"0.49187613\",\n \"0.49175915\",\n \"0.49098593\",\n \"0.4906722\",\n \"0.48952273\",\n \"0.4887452\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5729195"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":11097,"cells":{"query":{"kind":"string","value":"Utilities for spatio temporal analysis zed.uchicago.edu Creates DataFrame of location tiles and their respective timeseries from input datasource with significance threshold THRESHOLD latitude, longitude coordinate boundaries given by LAT, LON calls on getTS for individual tile then concats them together"},"document":{"kind":"string","value":"def timeseries(self,LAT,LON,EPS,_types,CSVfile='TS.csv',THRESHOLD=None):\n if THRESHOLD is None:\n if self._THRESHOLD is None:\n THRESHOLD=0.1\n else:\n THRESHOLD=self._THRESHOLD\n\n if self._trng is None:\n self._trng = pd.date_range(start=self._INIT,\n end=self._END,freq=self._FREQ)\n\n _TS = pd.concat([self.getTS(tile=[i, i + EPS, j, j + EPS],\n _types=_types) for i in tqdm(LAT)\n for j in tqdm(LON)])\n\n LEN=pd.date_range(start=self._INIT,\n end=self._END,freq=self._FREQ).size+0.0\n\n statbool = _TS.astype(bool).sum(axis=1) / LEN\n _TS = _TS.loc[statbool > THRESHOLD]\n self._ts_dict[repr(_types)] = _TS\n\n if CSVfile is not None:\n _TS.to_csv(CSVfile, sep=' ')\n\n return"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def outliers_solid_tsds():\n timestamp1 = np.arange(np.datetime64(\"2021-01-01\"), np.datetime64(\"2021-02-10\"))\n target1 = [np.sin(i) for i in range(len(timestamp1))]\n target1[10] += 10\n\n timestamp2 = np.arange(np.datetime64(\"2021-01-01\"), np.datetime64(\"2021-02-10\"))\n target2 = [np.sin(i) for i in range(len(timestamp2))]\n target2[8] += 8\n target2[15] = 2\n target2[26] -= 12\n\n df1 = pd.DataFrame({\"timestamp\": timestamp1, \"target\": target1, \"segment\": \"1\"})\n df2 = pd.DataFrame({\"timestamp\": timestamp2, \"target\": target2, \"segment\": \"2\"})\n\n df = pd.concat([df1, df2], ignore_index=True)\n\n df = df.pivot(index=\"timestamp\", columns=\"segment\")\n df = df.reorder_levels([1, 0], axis=1)\n df = df.sort_index(axis=1)\n df.columns.names = [\"segment\", \"feature\"]\n tsds = TSDataset(df, \"1d\")\n return tsds","def getTS(self,_types=None,tile=None):\n\n assert(self._END is not None)\n TS_NAME = ('#'.join(str(x) for x in tile))+\"#\"+stringify(_types)\n\n lat_ = tile[0:2]\n lon_ = tile[2:4]\n\n if self._value_limits is None:\n df = self._logdf[self._columns]\\\n .loc[self._logdf[self._EVENT].isin(_types)]\\\n .sort_values(by=self._DATE).dropna()\n else:\n df = self._logdf[self._columns]\\\n .loc[self._logdf[self._EVENT]\\\n .between(self._value_limits[0],\n self._value_limits[1])]\\\n .sort_values(by=self._DATE).dropna()\n\n df = df.loc[(df[self._coord1] > lat_[0])\n & (df[self._coord1] <= lat_[1])\n & (df[self._coord2] > lon_[0])\n & (df[self._coord2] <= lon_[1])]\n df.index = df[self._DATE]\n df=df[[self._EVENT]]\n\n ts = [df.loc[self._trng[i]:self._trng[i + 1]].size for i in\n np.arange(self._trng.size - 1)]\n\n return pd.DataFrame(ts, columns=[TS_NAME],\n index=self._trng[:-1]).transpose()","def zonal_statistics(wrksppath, timestamp, region, model):\n logging.info('\\nDoing Zonal Statistics on ' + region)\n # Define app workspace and sub-paths\n resampleds = os.path.join(wrksppath, region, model + '_GeoTIFFs_resampled')\n shp_path = os.path.join(wrksppath, region, 'shapefiles', 'ffgs_' + region + '.shp')\n\n stat_file = os.path.join(wrksppath, region, model + 'results.csv')\n\n # check that there are resampled tiffs to do zonal statistics on\n if not os.path.exists(resampleds):\n logging.info('There are no resampled tiffs to do zonal statistics on. Skipping Zonal Statistics')\n return\n\n # List all Resampled GeoTIFFs\n files = os.listdir(resampleds)\n files = [tif for tif in files if tif.endswith('.tif')]\n files.sort()\n\n # do zonal statistics for each resampled tiff file and put it in the stats dataframe\n stats_df = pd.DataFrame()\n for i in range(len(files)):\n logging.info('starting zonal statistics for ' + files[i])\n ras_path = os.path.join(resampleds, files[i])\n stats = rasterstats.zonal_stats(\n shp_path,\n ras_path,\n stats=['count', 'max', 'mean'],\n geojson_out=True\n )\n\n timestep = files[i][:10]\n\n # for each stat that you get out, write it to the dataframe\n logging.info('writing the statistics for this file to the dataframe')\n for j in range(len(stats)):\n\n temp_data = stats[j]['properties']\n temp_data.update({'Forecast Timestamp': timestamp})\n temp_data.update({'Timestep': timestep})\n\n temp_df = pd.DataFrame([temp_data])\n stats_df = stats_df.append(temp_df, ignore_index=True)\n\n # write the resulting dataframe to a csv\n logging.info('\\ndone with zonal statistics, rounding values, writing to a csv file')\n stats_df = stats_df.round({'max': 1, 'mean': 1})\n stats_df.to_csv(stat_file, index=False)\n\n # delete the resampled tiffs now that we dont need them\n logging.info('deleting the resampled tiffs directory')\n shutil.rmtree(resampleds)\n\n return","def test_plt_mag_time():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n # create the arrays per filter and readout pattern\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\n for i, val in enumerate(max_val_box):\n if '140' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(val)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(val)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif '110' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(val)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(val)\n nrsrapidd6_clear.append(np.NaN)\n else:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(val)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(val)\n # add to the bokeh data structure\n ta.source.data[\"nrsrapid_f140x\"] = nrsrapid_f140x\n ta.source.data[\"nrsrapid_f110w\"] = nrsrapid_f110w\n ta.source.data[\"nrsrapid_clear\"] = nrsrapid_clear\n ta.source.data[\"nrsrapidd6_f140x\"] = nrsrapidd6_f140x\n ta.source.data[\"nrsrapidd6_f110w\"] = nrsrapidd6_f110w\n ta.source.data[\"nrsrapidd6_clear\"] = nrsrapidd6_clear\n result = ta.plt_mag_time()\n\n assert bokeh_plot_type == type(result)","def get_weather_data(lat='40.761440',lng='-73.981806'):\r\n key ='********************************'\r\n x = pd.DataFrame()\r\n unix_now = int((dt.datetime.now()- dt.datetime(1970,1,1)).total_seconds())\r\n for time in range(unix_now-86400, unix_now+604800, 86400):\r\n rsp = rq.get('https://api.darksky.net/forecast/{}/{},{},{}'.format(key, lat, lng, time))\r\n rsp_json = json.loads(rsp.text)\r\n row = json_normalize(rsp_json[\"daily\"]['data'])\r\n x = x.append(row)\r\n \r\n x = x[['icon','apparentTemperatureHigh','apparentTemperatureLow','cloudCover','humidity','precipProbability',\r\n 'pressure','visibility','windBearing','windGust','windSpeed']].reset_index(drop=True)\r\n return x","def create_dat_from_geodataframe(gdf, DAT_header, output_file, wind_field='gust_mph'):\n t0 = time()\n t1 = time()\n db = HazusDB()\n sql = \"\"\"\n select Tract as FIPS, CenLongit as lon, CenLat as lat from\n (select * from\n syHazus.dbo.syTract) a\n inner join \n (select StateFips\n FROM [syHazus].[dbo].[syState]\n where HUState = 1\n and StateFips < 55) b\n on Substring(a.CountyFips, 1, 2) like b.StateFips+ '%'\n \"\"\"\n queryset = db.query(sql)\n queryset['geometry'] = [Point(x, y) for x, y in zip(\n queryset['lon'], queryset['lat'])]\n centroids_all = gpd.GeoDataFrame(queryset, geometry='geometry')\n buff = gdf.geometry.buffer(0.2)\n buff_gdf = gpd.GeoDataFrame(geometry=buff.geometry)\n buff_gdf['dis'] = 1\n dissolve = buff_gdf.dissolve(by='dis')\n centroids_intersect = centroids_all.intersects(dissolve.unary_union)\n centroids = centroids_all[centroids_intersect == True]\n print(time() - t1)\n\n print('formatting data for idw')\n t1 = time()\n xy = np.asarray([[x.x, x.y] for x in gdf.geometry])\n z = np.asarray([x for x in gdf[wind_field]])\n xis = np.asarray([x.x for x in centroids.geometry])\n yis = np.asarray([x.y for x in centroids.geometry])\n print(time() - t1)\n\n print('interpolating values')\n t1 = time()\n kdtree = cKDTree(xy)\n zis = []\n for xi, yi in zip(xis, yis):\n zi = idw(kdtree, z, xi, yi)\n zis.append(zi)\n print(time() - t1)\n zis = np.asarray(zis)\n zis = zis * 0.44704\n\n print('formatting dataframe for output')\n t1 = time()\n tracts = list(map(lambda x: x + ' ', centroids.FIPS))\n longs = list(map(lambda x: '{0:.4f}'.format(x) + ' ', xis))\n lats = list(map(lambda x: '{0:.4f}'.format(x) + ' ', yis))\n windSpeeds = list(map(lambda x: '{0:.5f}'.format(x) + ' ', zis))\n zeros = list(map(lambda x: '0' + '{0:.5f}'.format(x * 0) + ' ', zis))\n windSpeedsLast = list(map(lambda x: '{0:.5f}'.format(x), zis))\n df = pd.DataFrame({'tracts': tracts, 'longs': longs, 'lats': lats,\n 'windSpeeds': windSpeeds, 'zeros': zeros, 'windSpeedsLast': windSpeedsLast})\n print(time() - t1)\n\n print('writing output DAT file')\n t1 = time()\n # creates and opens the export DAT file\n pd.DataFrame().to_csv(output_file, header=False, index=False)\n export = open(output_file, \"w\")\n\n # adds columns to the DAT file header\n DAT_header.append('')\n DAT_header.append(\n ' ident elon nlat ux vy w (m/s)')\n\n # writes header to DAT file\n for row in DAT_header:\n export.write(row + '\\n')\n\n # writes data to DAT file\n for row in range(len(df[df.columns[0]])):\n writeRow = ''\n for item in df.iloc[row]:\n writeRow = writeRow + item\n export.write(writeRow + '\\n')\n export.close()\n print(time() - t1)\n print('Total elasped time: ' + str(time() - t0))","def prepare_for_influxdb(df):\n df = df.drop(columns=\"landkreis\", errors=\"ignore\") # prevent name collision in get_ags()\n df = get_ags(df)\n df[\"time\"] = df.apply(lambda x: 1000000000*int(datetime.timestamp((pd.to_datetime(x[\"timestamp\"])))), 1)\n df[\"measurement\"] = \"hystreet\"\n df[\"origin\"] = \"https://hystreet.com\"\n df = df.rename(columns={\n 'station_id': '_id',\n 'pedestrians_count': 'pedestrian_count',\n 'state': 'bundesland'\n })\n df['ags'] = pd.to_numeric(df['ags'])\n # import pdb; pdb.set_trace()\n return df","def get_traffic_sensor_df(sensorURI: str, fromTime: str, toTime: str, resampleFreq: str = None, remove_outliers=False):\n values = [\"count\", \"sumSpeed\"]\n result = None\n for v in values:\n # data = query_ensor(sensorURI, fromTime, toTime, v)\n data = multiday_query(sensorURI, fromTime, toTime, v)\n df = pd.DataFrame(data, columns=[\"measuredTime\", v])\n df[\"measuredTime\"] = pd.to_datetime(df[\"measuredTime\"])\n df.index = df[\"measuredTime\"]\n del df[\"measuredTime\"]\n if remove_outliers:\n z_scores = np.abs(stats.zscore(df))\n print(f\"Removed outliers: {df.size - df[(z_scores < 3).all(axis=1)].size}\")\n df = df[(z_scores < 3).all(axis=1)]\n if resampleFreq is not None:\n df = df.resample(resampleFreq).sum()\n if result is not None:\n result = pd.merge_ordered(result, df, left_on=\"measuredTime\", right_on=\"measuredTime\")\n result.index = result[\"measuredTime\"]\n del result[\"measuredTime\"]\n else:\n result = df\n # avg speed\n result[\"avgSpeed\"] = result[\"sumSpeed\"] / result[\"count\"]\n result.loc[~np.isfinite(result[\"avgSpeed\"]), \"avgSpeed\"] = np.nan\n result[\"avgSpeed\"] = result[\"avgSpeed\"].interpolate()\n return result","def get_time_series_at_location(data, lat, lon, feature):\n\n ts = data.sel(lat=lat, lon=lon, method='nearest', drop=True).to_series()\n index = ts.index.get_level_values('time')\n values = ts.values\n\n return pd.DataFrame({'Date': index.values, feature: values})","def make_dataset_temperatures(filename, points):\r\n\r\n ds = Dataset(filename)\r\n\r\n lats, lons, cities, countries = get_lat_lon()\r\n\r\n # total lat,lon pairs: 1366\r\n print('The number of rows is ' + str(len(lats)*points))\r\n lon = ds.variables['longitude']\r\n lat = ds.variables['latitude']\r\n time = ds.variables['date_number']\r\n\r\n lon_array = lon[:]\r\n lat_array = lat[:]\r\n time_array = time[:]\r\n\r\n temperature = ds.variables['temperature']\r\n\r\n dates = []\r\n for time in time_array[:]:\r\n year = int(time)\r\n rem = time - year\r\n base = datetime(year, 1, 1)\r\n dates.append((base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)).date())\r\n\r\n # second approach\r\n # for t in time_array[:]:\r\n # dates.append(num2date(t, units=time.units))\r\n\r\n dateResult = []\r\n temperatureResult = []\r\n latitudeResult = []\r\n longitudeResult = []\r\n cityResult = []\r\n countryResult = []\r\n\r\n for latitude, longitude, city, country in zip(lats, lons, cities, countries):\r\n\r\n # We want to find data for latitude, longitude\r\n # We first need to find the indexes\r\n i = np.abs(lon_array - longitude).argmin()\r\n j = np.abs(lat_array - latitude).argmin()\r\n\r\n for d in dates:\r\n dateResult.append(d)\r\n\r\n resultTemperature = temperature[:, j, i]\r\n for t in resultTemperature:\r\n temperatureResult.append(t)\r\n\r\n resultLatitues = np.full(\r\n shape=points,\r\n fill_value=latitude,\r\n dtype=np.float\r\n )\r\n for l in resultLatitues:\r\n latitudeResult.append(l)\r\n\r\n resultLongitudes = np.full(\r\n shape=points,\r\n fill_value=longitude,\r\n dtype=np.float\r\n )\r\n for l in resultLongitudes:\r\n longitudeResult.append(l)\r\n\r\n resultCities = np.full(\r\n shape=points,\r\n fill_value=city\r\n )\r\n for c in resultCities:\r\n cityResult.append(c)\r\n\r\n resultCountries = np.full(\r\n shape=points,\r\n fill_value=country\r\n )\r\n for c in resultCountries:\r\n countryResult.append(c)\r\n\r\n print('iteration no:' + str(i))\r\n\r\n df = pd.DataFrame()\r\n df['date'] = dateResult\r\n df['temperature'] = temperatureResult\r\n df['latitude'] = latitudeResult\r\n df['longitude'] = longitudeResult\r\n df['city'] = cityResult\r\n df['country'] = countryResult\r\n\r\n df.to_csv(r'C:\\Users\\stoja\\Desktop\\Temperatures.csv', index=False)\r\n return df","def get_date_features(gt_ids=[], gt_masks=None, gt_shifts=None, first_year=None):\n # If particular arguments aren't lists, replace with repeating iterators\n if not isinstance(gt_masks, list):\n gt_masks = itertools.repeat(gt_masks)\n if not isinstance(gt_shifts, list):\n gt_shifts = itertools.repeat(gt_shifts)\n\n # Add each ground truth feature to dataframe\n df = None\n for gt_id, gt_mask, gt_shift in zip(gt_ids, gt_masks, gt_shifts):\n print \"Getting {}_shift{}\".format(gt_id, gt_shift)\n t = time.time()\n # Load ground truth data\n gt = get_ground_truth(gt_id, gt_mask, gt_shift)\n # Discard years prior to first_year\n gt = year_slice(gt, first_year = first_year)\n # If lat, lon columns exist, pivot to wide format\n if 'lat' in gt.columns and 'lon' in gt.columns:\n if gt_shift == None:\n measurement_variable = get_measurement_variable(gt_id)\n else:\n measurement_variable = get_measurement_variable(gt_id)+'_shift'+str(gt_shift)\n gt = pd.pivot_table(gt, values=measurement_variable, index='start_date',\n columns=['lat', 'lon']).reset_index()\n gt = pd.DataFrame(gt.to_records())\n gt.drop(\"index\", axis=1, inplace=True)\n # Rename columns to start_date and precip_(27.0,261.0), etc.\n gt.rename(columns={gt.columns[0]: 'start_date'}, inplace=True)\n gt.rename(columns=lambda x: x.replace('(',\n measurement_variable +\n '_('), inplace=True)\n # Use outer merge to include union of start_date values across all features\n # combinations across all features\n df = df_merge(df, gt, on=\"start_date\")\n print \"Elapsed: {}s\".format(time.time() - t)\n\n return df","def create_demo_location_history() -> geopandas.GeoDataFrame:\n np.random.seed(123)\n\n time = pd.date_range(start=datetime.fromtimestamp(1624241116), end=datetime.now(), freq=\"1min\").values\n\n center_point = (-36.875990410695394, 174.76398830024274)\n lat = np.random.normal(loc=center_point[0], scale=0.01, size=len(time))\n lon = np.random.normal(loc=center_point[1], scale=0.01, size=len(time))\n\n geometry = [Point(lon, lat) for lon, lat in zip(lon, lat)]\n return geopandas.GeoDataFrame(pd.DataFrame(dict(time=time, lat=lat, lon=lon)), geometry=geometry)","def getExample1Data(conditionData, conditions, shockTubeEffectiveHeatingTimes, smilesInExperiments):\n #First get the time points close to\n allResults=[]\n for T, timepoint in shockTubeEffectiveHeatingTimes.iteritems():\n for smiles in smilesInExperiments:\n temperatureResults=GenericData(label=label='Temperature',\n data = []\n units = 'K')\n for conIndex, data in conditionData:\n if conditions[conIndex].T0==T:\n #index1 is the index of time from the simulation closest to timepoint\n #data is organized as (timeArray, rest of data)\n index1=findNearest(data[0], timepoint)\n for speciesData in data[1]:\n if smiles==speciesData.species:\n\n if smiles not in exptCompDict1: exptCompDict1[smiles]=[]\n #need to multiply by 20, in the experiment, they don't seem to count the Ar in the mole fraction\n exptCompDict1[smiles].append(resultsDictionary[T][1][index1][index2+1]*20)\n (index1, timepoint2)=rt.getNearestTime(timepoint1, resultsDictionary[T][2])\n for index2, smiles in enumerate(majorSpecies[0:4]):\n if smiles not in exptCompDict2: exptCompDict2[smiles]=[]\n exptCompDict2[smiles].append(resultsDictionary[T][3][index1][index2+1]*20)","def use_ground_truth_time_slice(gt, df_p, df_l, app):\n\n gt = gt.reset_index(drop=True)\n gt['start_time'] = gt.start_time / 1000\n gt['end_time'] = gt.end_time / 1000\n gt['pred_location'] = np.zeros(len(gt))\n gt['true_location'] = gt.wlabel\n gt['true_appliance'] = gt.slabel\n\n gt['type'] = np.zeros(len(gt))\n gt['magnitude'] = np.zeros(len(gt))\n gt['phase'] = np.zeros(len(gt))\n\n print \"\\n\"\n # Label lights and fans as \"light\" type and magnitude as the fall magnitude of the time slice\n for i in gt.index:\n appl = gt.ix[i]['slabel']\n etype = ''\n\n if appl in ['Fan', 'Light']:\n gt.ix[i, 'type'] = etype = \"light\"\n else:\n gt.ix[i, 'type'] = etype = \"power\"\n\n # Get index from the main light/power data stream\n fall_time = gt.ix[i]['end_time']\n logger.debug(\"Fall_time:: %s (%s)\", dt.datetime.fromtimestamp(fall_time), fall_time)\n if etype == \"light\":\n idx = 0\n try:\n idx = np.where(df_l.time == fall_time)[0][0]\n except IndexError, e:\n print \"-----Index not found--------\", e\n continue\n\n edge = get_magnitude_metadata(df_l, idx, \"lightpower\", lwinmin)\n if edge:\n gt.ix[i, 'magnitude'] = math.fabs(edge['magnitude'])\n print i, \"Light:: Index\", idx, \"Magnitude::\", math.fabs(edge['magnitude'])\n else:\n logger.debug(\"--------Edge not found---------\")\n continue\n\n # Get phase of the edge\n gt.ix[i, 'phase'] = get_phase(idx, df_l, \"light\")\n # print \"LIGHT:: Index\", idx, \"magnitude\", magnitude\n else:\n idx = 0\n try:\n idx = np.where(df_p.time == fall_time)[0][0]\n except IndexError, e:\n print \"-----Index not found--------\", e\n continue\n\n edge = get_magnitude_metadata(df_p, idx, \"power\", pwinmin)\n if edge:\n gt.ix[i, 'magnitude'] = math.fabs(edge['magnitude'])\n print i, \"Power:: Index\", idx, \"Magnitude::\", math.fabs(edge['magnitude'])\n else:\n logger.debug(\"--------Edge not found---------\")\n continue\n\n # Get phase of the edge\n gt.ix[i, 'phase'] = get_phase(idx, df_p, \"power\")\n\n # print \"POWER:: Index\", idx, \"magnitude\", magnitude\n\n gt.pop('wlabel')\n gt.pop('slabel')\n\n if app in [4, 7]:\n gt = gt[(gt.phase != 0) & (gt.magnitude > lthresmin)]\n else:\n gt = gt[gt.magnitude > lthresmin]\n\n gt['act_start_time'] = [dt.datetime.fromtimestamp(i)\n for i in gt['start_time']]\n gt['act_end_time'] = [dt.datetime.fromtimestamp(i)\n for i in gt['end_time']]\n gt = gt.sort(['act_start_time'])\n\n print \"\\nGround Truth Time slices:\\n\", gt\n\n return gt","def add_columns_for_taps(full_data: DataFrame, tap_data: DataFrame):\n for tap_file in tap_file_names:\n tap_type = tap_file_to_feature_name[tap_file]\n data = tap_data[tap_data['Type'] == tap_type].reset_index(drop = True)\n\n lead_file = 'Accelerometer.csv'\n time_column_name = x_columns[lead_file]\n data_times = full_data[time_column_name]\n data_index = 0\n\n new_column = []\n\n for tap_index in range(data.shape[0]):\n try:\n while data_times[data_index] < (data['Start'][tap_index] * 1000000):\n new_column.append(0) # Not in the midst of a tap\n data_index += 1\n if data_index >= full_data.shape[0]: break\n if data_index >= full_data.shape[0]: break\n new_column.append(1) # At least one value in the midst of the tap\n data_index += 1\n if data_index >= full_data.shape[0]: break\n while data_times[data_index] < (data['End'][tap_index] * 1000000):\n new_column.append(1)\n data_index += 1\n if data_index >= full_data.shape[0]: break\n if data_index >= full_data.shape[0]: break\n except KeyError:\n print(\"Okay, here's that thing again\")\n return\n\n \n while data_index < full_data.shape[0]:\n new_column.append(0)\n data_index += 1\n\n full_data[tap_type] = new_column","def filter(df: pd.DataFrame = pd.DataFrame()):\n if df.empty:\n df = read()\n print('Filtering data...')\n df = df.dropna()\n df2 = pd.DataFrame()\n df2['Longitude'] = df['Longitude']\n df2['Latitude'] = df['Latitude']\n df2['Month'] = df['Date'].dt.strftime('%m').astype(int)\n df2['Day'] = df['Date'].dt.strftime('%d').astype(int)\n df2['Day_of_Week'] = df['Day_of_Week']\n df2['Time'] = np.array([t.timestamp() for t in df['Time']]) - df['Time'].min().timestamp()\n df2['Weather_Conditions'] = df['Weather_Conditions']\n return pd.get_dummies(df2)","def create_lat_lon_date_data(gt_id,\n target_horizon,\n experiment,\n past_gt_ids=[\"contest_precip\", \"contest_tmp2m\"],\n forecast_models=[\"nmme\",\"nmme0\"],\n other_lat_lon_date_features=[\"contest_rhum.sig995\",\n \"contest_pres.sfc.gauss\"]):\n\n time_start = time.time()\n\n # Add forecasts to list of forecast IDs\n forecast_variable = get_forecast_variable(gt_id) # 'prate' or 'tmp2m'\n forecast_ids = ['{}-{}-{}'.format(forecast, forecast_variable, target_horizon)\n for forecast in forecast_models]\n\n # -----------\n # Generate relevant variable and column names\n # -----------\n\n # Identify measurement variable name\n measurement_variable = get_measurement_variable(gt_id) # 'tmp2m' or 'prate'\n\n # Keep track of relevant column names\n gt_col = measurement_variable\n clim_col = measurement_variable+\"_clim\"\n anom_col = measurement_variable+\"_anom\"\n\n # Inverse of standard deviation of anomalies for each start_date\n anom_inv_std_col = anom_col+\"_inv_std\"\n\n # --------\n # Prepare experiment cache directory and saved file names\n # --------\n\n # Name of cache directory for storing non-submission-date specific\n # intermediate files\n cache_dir = os.path.join('results', experiment, 'shared',\n '{}_{}'.format(gt_id, target_horizon))\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\n\n # if cache_dir doesn't exist, create it\n if not os.path.isdir(cache_dir):\n os.makedirs(cache_dir)\n\n # Filenames for data file to be stored in cache_dir\n lat_lon_date_data_file = os.path.join(\n cache_dir, \"lat_lon_date_data-{}_{}.h5\".format(gt_id, target_horizon))\n\n # --------\n # Load mask indicating which grid points count in the contest (1=in, 0=out)\n # --------\n print \"Loading contest mask\"\n t = time.time()\n mask_df = get_contest_mask()\n print \"Elapsed: {}s\".format(time.time() - t)\n\n # --------\n # Creates and saves lat_lon_date_data dataframe\n # --------\n # Load masked lat lon date features restricted to years >= get_first_year(gt_id)\n # Note: contest lat lon date features and forecasts are pre-masked, so there\n # is no need to mask explcitily\n print \"Loading lat lon date features\"\n num_gt_ids = len(past_gt_ids)\n # For each measurement,\n # get number of days between start date of observation period used for prediction\n # (2 weeks + 1 submission day behind for most predictors) and start date of\n # target period (2 or 4 weeks ahead)\n past_start_deltas = [get_start_delta(target_horizon, past_gt_id)\n for past_gt_id in past_gt_ids]\n other_start_deltas = [get_start_delta(target_horizon, other_gt_id)\n for other_gt_id in other_lat_lon_date_features]\n # Additionally keep track of days between forecast date and start date of\n # target period\n forecast_delta = get_forecast_delta(target_horizon)\n\n lat_lon_date_data = get_lat_lon_date_features(\n gt_ids=other_lat_lon_date_features + other_lat_lon_date_features\n + other_lat_lon_date_features,\n gt_masks=None,\n gt_shifts=other_start_deltas +\n [2*delta for delta in other_start_deltas] +\n [365]*len(other_lat_lon_date_features),\n forecast_ids=forecast_ids + forecast_ids,\n forecast_masks=None,\n forecast_shifts=[None]*len(forecast_ids) + [forecast_delta]*len(forecast_ids),\n anom_ids=[gt_id] + past_gt_ids + past_gt_ids + past_gt_ids,\n anom_masks=None,\n anom_shifts=[None] + past_start_deltas +\n [2*delta for delta in past_start_deltas] +\n [365]*len(past_gt_ids),\n first_year=get_first_year(gt_id)\n )\n\n print \"Loading additional lat lon date features\"\n t = time.time()\n # Add CFSv2 mean as feature\n if 'cfsv2' in forecast_models:\n cfsv2_models = ['cfsv2_op_delta_2w_1d_6h', 'cfsv2_op_delta_2w_1d_12h',\n 'cfsv2_op_delta_2w_1d_18h', 'cfsv2_op_delta_2w_2d_0h',\n 'cfsv2_op_delta_2w_2d_6h', 'cfsv2_op_delta_2w_2d_12h',\n 'cfsv2_op_delta_2w_2d_18h', 'cfsv2_op_delta_2w_3d_0h']\n lat_lon_date_data['cfsv2_mean'] = lat_lon_date_data[cfsv2_models].mean(axis=1)\n lat_lon_date_data[\"cfsv2_mean_shift\"+str(start_delta)] = lat_lon_date_data[\n [model+\"_shift\"+str(start_delta) for model in cfsv2_models]].mean(axis=1)\n # Add inverse of standard deviation of anomalies for each start_date\n lat_lon_date_data[anom_inv_std_col] = \\\n 1.0/lat_lon_date_data.groupby([\"start_date\"])[anom_col].transform('std')\n\n print \"Elapsed: {}s\".format(time.time() - t)\n\n # Save lat lon date features to disk\n print \"Saving lat lon date features to \"+lat_lon_date_data_file\n t = time.time()\n lat_lon_date_data.to_hdf(lat_lon_date_data_file, key=\"data\", mode=\"w\")\n subprocess.call(\"chmod a+w \"+lat_lon_date_data_file, shell=True)\n print \"Elapsed: {}s\".format(time.time() - t)\n print \"Finished generating lat_lon_date_data matrix.\"\n print \"Total time elapsed: {}s\".format(time.time()-time_start)\n return list(lat_lon_date_data)","def create_interpolated_turnstile_data(\n start_date: datetime,\n end_date: datetime = None,\n group_by: List[str] = ['UNIT', 'SCP'],\n frequency: str = '1H') -> pd.DataFrame:\n\n if not set(group_by).issubset(['STATION', 'LINENAME', 'UNIT', 'SCP']):\n raise Exception(\"Unsupported group by keys: \" + str(group_by))\n\n\n raw = download_turnstile_data(start_date, end_date)\n raw['date'] = pd.to_datetime(raw.DATE)\n raw = raw[(raw.date <= (end_date + timedelta(1))) & (raw.date >= (start_date - timedelta(1)))]\n raw.drop('date',axis=1,inplace=True)\n\n interpolated = _interpolate(_process_raw_data(raw, group_by), group_by, frequency)\n end_date = end_date or interpolated.index.max()\n return interpolated[interpolated.index.to_series().between(\n start_date, end_date)] .drop(columns=[\"entry_diffs\", \"exit_diffs\"])","def shapely_tileset(processed_query,min_ovp = 0,max_ovp = 1,\n n_neg = None,buffer = 0):\n types, xx, yy, qual, tags = [],[],[],[],[]\n z = processed_query['zoom']\n for elem in processed_query['elements']:\n for tile in elem['tiles']:\n qq = tile[1]\n if qq >= min_ovp and qq <= max_ovp:\n x,y,_ = find_tile_coords(tile[0],z)\n xx.append(x)\n yy.append(y)\n qual.append(tile[1])\n tags.append(json.dumps(elem['tags']))\n types.append(elem['type'])\n \n pos_df = pd.DataFrame({\n 'z': z, 'x' : xx, 'y': yy, \n 'entity': types,\n 'overlap': qual,'tags': tags,\n 'placename': processed_query['query_info']['placename']\n }) \\\n .drop_duplicates(subset = ['x','y']) \\\n .sort_values(by = ['x','y'])\n if n_neg is None: n_neg = pos_df.shape[0]\n negt = sample_complement(pos_df['x'],pos_df['y'],n_neg,buffer)\n neg_df = pd.DataFrame({'z': z,'x': negt[0],'y': negt[1]}) \\\n .sort_values(by = ['x','y'])\n return { \n 'positive': add_latlon(pos_df),\n 'negative': add_latlon(neg_df)\n }","def get_meter_data_for_time_slice(apt_no, start_time, end_time):\n if apt_no in ['102A', 102]:\n apt_no = '102A'\n\n logger.debug(\"sMap: Getting meter data for %s between %s and %s\", apt_no, start_time, end_time)\n\n query = (\"select data in ('\" + str(start_time) + \"','\" + str(end_time) + \"') \"\n \"limit 200000 \"\n \"where Metadata/LoadLocation/FlatNumber ='\" + str(apt_no) + \"' and \"\n \"Metadata/Extra/PhysicalParameter='Power'\")\n\n r = requests.post(url, data=query)\n # logger.debug (\"%s\",r)\n payload = r.json()\n # logger.debug(\"Payload:%s\", payload)\n\n if apt_no in ['102A', 102]:\n apt_no = 102\n meters = retrieve_meter_info(apt_no)\n logger.debug(\"Meters: %s\", meters)\n\n streams = []\n meter_type = []\n l_meters = range(0, len(meters))\n for i in l_meters:\n uuid = payload[i]['uuid']\n\n # Get meter type based on uuid\n for meter in meters:\n if meter['uuid'] == uuid:\n m_type = meter['type']\n # logger.debug (uuid, m_type)\n\n meter_type.append(m_type)\n streams.append(np.array(payload[i]['Readings']))\n # logger.debug(\"Streams: %s\", streams)\n\n if len(streams[0]) > 0:\n\n df = [pd.DataFrame({'time': readings[:, 0] / 1000, 'power': readings[:, 1],\n 'type': [meter_type[i]] * len(readings)},\n columns=['time', 'power', 'type']) for i, readings in enumerate(streams)]\n else:\n df = []\n\n return df","def get_temp():\n epts = [\"cage_coldPlate_temp\", \"cage_pressure\"]\n # t_earlier_aug = '2019-10-02T00:00'\n # t_later_aug = datetime.utcnow().isoformat()\n t_earlier_aug = '2019-09-27T13:00'\n t_later_aug = '2019-09-28T19:49'\n dfs = pandas_db_query(epts, t_earlier_aug, t_later_aug)\n print(dfs[epts[0]].tail())\n\n exit()\n\n xv = dfs[epts[0]][\"timestamp\"]\n yv = dfs[epts[0]][epts[0]]\n plt.plot(xv, yv, '-b')\n plt.ylabel(epts[0], ha='right', y=1)\n\n p1a = plt.gca().twinx()\n xv = dfs[epts[1]][\"timestamp\"]\n yv = dfs[epts[1]][epts[1]]\n p1a.set_ylabel(epts[1], color='r', ha='right', y=1)\n p1a.tick_params('y', colors='r')\n p1a.semilogy(xv, yv, '-r')\n\n plt.gcf().autofmt_xdate()\n plt.tight_layout()\n plt.show()","def main():\n df_turnstiles = get_mta_data2()\n df_turnstiles = clean_data(df_turnstiles)\n df_turnstiles = add_dt_cols(df_turnstiles)\n df_turnstiles = merge_zipcode_agi(df_turnstiles)\n\n df_ampm = fixup_entries_exits(df_turnstiles)\n return df_turnstiles, df_ampm","def get_time_series(this_lat, this_lon, case, varnames):\n\n cesmdir = '/gpfs/fs1/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly'\n\n if 'LE' in case:\n\n from observational_large_ensemble.params import karen_params_cesm\n\n mode_lag = karen_params_cesm.mode_lag\n cvdp_loc = karen_params_cesm.cvdp_loc\n AMO_cutoff_freq = karen_params_cesm.AMO_cutoff_freq\n\n name_conversion = {'tas': 'TREFHT', 'pr': 'PRECC', 'slp': 'PSL'}\n cesm_names = [name_conversion[v] for v in varnames]\n this_member = int((case).split('-')[-1])\n cvdp_file = '%s/CESM1-CAM5-BGC-LE_#%i.cvdp_data.1920-2018.nc' % (cvdp_loc, this_member)\n\n # Historical filenames for CESM. Will need to append part of RCP8.5 to get full period\n filenames = []\n for var in cesm_names:\n file_str = '%s/%s/b.e11.B20TRC5CNBDRD.f09_g16.%03d.cam.h0.%s.??????-200512.nc' % (cesmdir, var,\n this_member, var)\n this_file = glob(file_str)[0]\n filenames.append(this_file)\n\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames,\n karen_params_cesm.valid_years, mode_lag,\n cvdp_file, AMO_cutoff_freq, name_conversion)\n\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\n\n else:\n\n from observational_large_ensemble.params import karen_params_obs\n\n mode_lag = karen_params_obs.mode_lag\n cvdp_loc = karen_params_obs.cvdp_loc\n AMO_cutoff_freq = karen_params_obs.AMO_cutoff_freq\n\n tas_dir = karen_params_obs.tas_dir\n pr_dir = karen_params_obs.pr_dir\n slp_dir = karen_params_obs.slp_dir\n cvdp_file = '%s/HadISST.cvdp_data.1920-2018.nc' % cvdp_loc\n file_dict = {'tas': '%s/Complete_TAVG_LatLong1.nc' % tas_dir,\n 'pr': '%s/full_data_monthly_v2020.nc' % pr_dir,\n 'slp': '%s/prmsl.mon.mean.nc' % slp_dir}\n\n filenames = []\n for var in varnames:\n filenames.append(file_dict[var])\n\n name_conversion = {'tas': 'temperature', 'pr': 'precip', 'slp': 'prmsl'}\n\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames[0],\n karen_params_obs.valid_years, mode_lag,\n cvdp_file, AMO_cutoff_freq, name_conversion)\n\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\n\n return this_ts, df_shifted","def trajcheck(file, lat_diff=1., lon_diff=1., search=None, path = '/home/ollie/muali/Data/winter_all/'):\n \n \n \n \n file_name = path + file\n \n df_IGR = pd.read_excel('IGR_25N_v2.xlsx')\n #f = open(file_name,'rb+')\n \n # getting launch date\n #for i, file_ in enumerate(f):\n # if i ==4: # reads 6th line\n # date_str = f.read(25)\n # f.close()\n #l_date = parse(date_str)\n \n df = pd.read_csv(file_name, skiprows=7, header=None, delim_whitespace=True) \n \n # dict to rename columns\n cols_renames = {2: 'year', 3: 'month', 4: 'day', 5: 'hour', 6: 'minute'}\n\n # converting year to 4 digits\n df.iloc[:,2] = df.iloc[:,2] + 1900\n\n # df_subset is used to merge yy, mm, dd, hh values into BT_time column\n df_subset = df.loc[:, list(cols_renames.keys())].rename(columns=cols_renames)\n # df_subset is extracting the time from the original dataframe to be used to make a time index for the final dataframe\n dt_series = pd.to_datetime(df_subset) # converts the df into a time series which we will add as extra column\n dt_series.head()\n # adding a new backtrajectory column\n df['BT_time'] = dt_series # adding the back trajectory time column to df\n \n # dropping the not required columns\n df.drop([0,1,2,3,4,5,6,7], axis =1, inplace=True)\n df.head()\n \n col_rename2 = {8: 'back_hr', 9:'Lat', 10: 'Lon', 11:'AGL', 12:'Pressure'}\n df.rename(columns=col_rename2, inplace=True)\n\n del df_subset\n del dt_series\n \n count = 0\n # count will be used to tell how many times our condition is met\n \n string_list =[] # to store the output of print to be used by regex\n for row_IRG in df_IGR.itertuples():\n for row in df.itertuples():\n # lat_diff lon_diff provided by the user\n if ((-lat_diff < (row_IRG.Lat - row.Lat) < lat_diff) & (-lon_diff < (row_IRG.Lon - row.Lon) < lon_diff)):\n # print(row.back_hr, row.BT_time, row_IRG.ID, row_IRG.Lat, row_IRG.Lon ,row_IRG.Lat - row.Lat, row_IRG.Lon - row.Lon)\n# print(row.back_hr, row.BT_time.strftime('%d-%m-%Y %H:%M'), row_IRG.ID, row_IRG.Lat, row_IRG.Lon , row.Lat - row_IRG.Lat, row.Lon - row_IRG.Lon, geodesic((row.Lat, row.Lon), (row_IRG.Lat, row_IRG.Lon)).km )\n string_list.append((row.back_hr, row.BT_time.strftime('%d-%m-%Y %H:%M'), row_IRG.ID, row_IRG.Lat, row_IRG.Lon , row.Lat - row_IRG.Lat, row.Lon - row_IRG.Lon, geodesic((row.Lat, row.Lon), (row_IRG.Lat, row_IRG.Lon)).km ))\n count = count + 1\n \n print(\"The following are the counts:\")\n print(count)\n \n if search is not None:\n for item in string_list:\n if search in item:\n print(item,'\\n')\n \n else:\n for item in string_list:\n print(item,'\\n')","def process_tir_image(ds, data_res, t_thresh=-50, min_mcs_size=5000):\n ctt = (ds['tb']).squeeze()-273.15\n min_pix_nb = min_mcs_size / data_res**2\n\n max_pix_nb = 300000 / data_res**2 # this is to capture satellite artefacts that come in large contiguous stripes.\n labels, goodinds = mcs_define(ctt.values, t_thresh, minmax_area=[min_pix_nb, max_pix_nb]) # 7.7x7.7km = 64km2 per pix in gridsat? 83 pix is 5000km2\n dic = dictionary()\n #plt.figure()\n #plt.pcolormesh(labels)\n #plt.colorbar()\n #plt.show()\n for g in goodinds:\n\n if g==0:\n continue\n\n pos = np.where(labels==g)\n npos = np.where(labels!=g)\n datestr = str(int(ctt['time.year'].values))+'-'+str(int(ctt['time.month'].values)).zfill(2)+'-'+str(int(ctt['time.day'].values)).zfill(2)+'_'+\\\n str(int(ctt['time.hour'].values)).zfill(2)+':'+str(int(ctt['time.minute'].values)).zfill(2)\n \n dic['date'].append(datestr)\n dic['month'].append(int(ctt['time.month']))\n dic['hour'].append(int(ctt['time.hour']))\n dic['year'].append(int(ctt['time.year']))\n dic['day'].append(int(ctt['time.day']))\n dic['minute'].append(int(ctt['time.minute']))\n\n storm = ctt.copy()\n storm.values[npos] = np.nan\n tmin_pos = np.nanargmin(storm.values)\n tpos_2d = np.unravel_index(tmin_pos, storm.shape)\n \n latmin = np.nanmin(ctt.lat.values[pos[0]])\n latmax = np.nanmax(ctt.lat.values[pos[0]])\n lonmin = np.nanmin(ctt.lon.values[pos[1]])\n lonmax = np.nanmax(ctt.lon.values[pos[1]])\n dic['area'].append(np.sum(np.isfinite(storm.values))*data_res**2)\n dic['70area'].append(np.sum(storm.values<=-70)*data_res**2)\n dic['minlon'].append(lonmin)\n dic['minlat'].append(latmin)\n dic['maxlon'].append(lonmax)\n dic['maxlat'].append(latmax)\n dic['clon'].append(lonmin + (lonmax - lonmin)/2)\n dic['clat'].append(latmin + (latmax - latmin)/2)\n dic['tmin'].append(np.nanmin(storm))\n dic['tminlat'].append(float(ctt.lat[tpos_2d[0]].values))\n dic['tminlon'].append(float(ctt.lon[tpos_2d[1]].values))\n dic['tmean'].append(float(np.nanmean(storm)))\n dic['tp1'].append(float(np.nanpercentile(storm, 1)))\n dic['tp99'].append(float(np.nanpercentile(storm, 99)))\n dic['stormID'].append(datestr + '_' + str(g))\n dic['cloudMask'].append(labels==g)\n dic['tir'].append(storm.values)\n\n # for k in dic.keys():\n # print(k, len(dic[k]))\n return dic","def build_sea_data(\n start_year=1999,\n end_year=2016,\n netcdf_path=\"data/sea_level/netcdf/\",\n target_lon=175.8606890,\n target_lat=-36.993684,\n buffer_degrees=0.5,\n path_out=\".\",\n):\n # tairua_coords = (-36.993684, 175.8606890)\n df_sea_data = pd.DataFrame()\n\n for year in range(start_year, end_year + 1):\n ds_first = xr.open_mfdataset(\n os.path.join(netcdf_path, f\"dt_global_twosat_phy_l4_{year}*.nc\")\n )\n\n target_lon = xr.DataArray(\n list(target_lon + np.linspace(-buffer_degrees, buffer_degrees))\n )\n target_lat = xr.DataArray(\n list(target_lat + np.linspace(-buffer_degrees, buffer_degrees))\n )\n\n ds_tairua = ds_first[[\"adt\", \"ugos\", \"vgos\"]].sel(\n longitude=target_lon, latitude=target_lat, method=\"nearest\"\n )\n df_sealevel_pandas = (\n ds_tairua.resample(time=\"MS\")\n .mean()\n .mean(dim=\"dim_0\")\n .to_dataframe()\n )\n\n df_sea_data = pd.concat([df_sea_data, df_sealevel_pandas])\n\n print(\n f\"************************Done {year} ************************************\"\n )\n print(df_sea_data.tail(10))\n\n df_sea_data.to_csv(os.path.join(path_out, \"df_sea_data.csv\"))","def testdata_gps():\n lon = np.array(\n [\n 4.54,\n 104.0,\n -14.9,\n 56.26,\n 103.46,\n 103.37,\n 54.22,\n 23.3,\n 25.53,\n 23.31,\n 118.0,\n 103.53,\n 54.40,\n 103.48,\n 6.14,\n 7.25,\n 2.38,\n 18.18,\n 103.54,\n 103.40,\n 28.59,\n 25.21,\n 29.35,\n 25.20,\n ]\n )\n\n lat = np.array(\n [\n 52.22,\n 1.14,\n 27.34,\n 25.16,\n 1.16,\n 1.11,\n 24.30,\n 37.54,\n 37.26,\n 38.1,\n 24.25,\n 1.13,\n 24.49,\n 1.13,\n 42.33,\n 43.44,\n 39.34,\n 70.30,\n 1.16,\n 1.10,\n 40.58,\n 37.34,\n 41.18,\n 38.35,\n ]\n )\n\n time = np.zeros(len(lon))\n\n X_data = np.vstack((time, lat, lon)).T\n\n X_name = np.array([0, 1, 2, 2, 2, 2, 3, 3, 3]) # NOQA\n X_data = np.array(\n [\n (0, 42.727985, -73.683994), # MRC\n (0, 42.657872, -73.764148), # Home\n (0, 42.657414, -73.774448), # Park1\n (0, 42.658333, -73.770993), # Park2\n (0, 42.654384, -73.768919), # Park3\n (0, 42.655039, -73.769048), # Park4\n (0, 42.876974, -73.819311), # CP1\n (0, 42.862946, -73.804977), # CP2\n (0, 42.849809, -73.758486), # CP3\n ]\n )\n return X_name, X_data","def preprocess():\n #get a list of all sentinel-image filenames\n s2files = [f for f in listdir(s2path) if endswith(join(s2path, f),\".tif\")==True]\n #read in a csv-file with information about the cluster\n csvpath = os.path.abspath(os.path.join(os.path.abspath(__file__),\"../../dataResearch/Data_with_Pooled.csv\"))\n df = pd.read_csv(csvpath)\n #get the min and max values per band \n minmaxlist = minmax()\n timelist = []\n print(\"STEP 2/2\")\n print(\"CREATING TFRECORDS\")\n for i in s2files:\n start = time.time()\n s2file = s2path + \"/\" + i\n #Get Features out of the Dataframe\n #get the name of the label (equals the SurveyID in the data)\n labelname = i.replace(\".tif\",\"\")\n #get the index of the entry to get the information out of the dataframe\n index = df.ID[df.ID == labelname].index\n wealthpooled = float(df['wealthpooled'].loc[index].max().replace(\",\",\".\"))\n wealthpooled5country = float(df['wealthpooled5country'].loc[index].max().replace(\",\",\".\"))\n country = bytes(df['country'].loc[index].max(), 'utf-8')\n urbanrural = bytes(df['URBAN_RURA'].loc[index].max(), 'utf-8')\n csvlat = float(df['LATNUM'].loc[index].max().replace(\",\",\".\"))\n csvlon = float(df['LONGNUM'].loc[index].max().replace(\",\",\".\"))\n year = int(df['year'].loc[index].max())\n wealth = float(df['wealth'].loc[index].max().replace(\",\",\".\"))\n #Get all Bands out of the GEOTIFF File\n s2raster = gdal.Open(s2file)\n bandlist = []\n for n in range(s2raster.RasterCount):\n f = n+1\n if n not in [13,14,15]:\n s2band = s2raster.GetRasterBand(f)\n s2band = s2band.ReadAsArray()\n s2band = np.resize(s2band,(1050,1050)).flatten()\n min = minmaxlist[n][0]\n max = minmaxlist[n][1]\n s2band = (s2band-min)/(max-min)\n bandlist.append(s2band.flatten())\n #get the Nightlight Band out of the GEOTIFF File\n nlfile = nlpath + \"/\" + i\n nlraster = gdal.Open(nlfile)\n nlband = nlraster.GetRasterBand(1)\n nlband = nlband.ReadAsArray()\n nlband = np.resize(nlband,(1050,1050)).flatten()\n min = minmaxlist[13][0]\n max = minmaxlist[13][1]\n nlband = (nlband-min)/(max-min)\n bandlist.append(nlband)\n #create a TFRecords-File with the TFRecordWriter\n with tf.io.TFRecordWriter(exportpath + '/' + labelname + '.tfrec') as writer:\n example = serialize_example(B1=bandlist[0],\n B2=bandlist[1],\n B3=bandlist[2],\n B4=bandlist[3],\n B5=bandlist[4],\n B6=bandlist[5],\n B7=bandlist[6],\n B8=bandlist[7],\n B8A=bandlist[8],\n B9=bandlist[9],\n B10=bandlist[10],\n B11=bandlist[11],\n B12=bandlist[12],\n NL=bandlist[13],\n wealth=wealth,\n wealthpooled=wealthpooled,\n wealthpooled5country=wealthpooled5country,\n country=country,\n urbanrural=urbanrural,\n lon_coord=csvlon,\n lat_coord=csvlat,\n year=year)\n writer.write(example)\n end = time.time()\n timelist.append(end-start)\n print(\"Done!\",str(s2files.index(i)+1) + \"/\" + str(len(s2files)),\"Est. time left:\",time.strftime('%d:%H:%M:%S',time.gmtime(int(sum(timelist)/len(timelist)*(len(s2files)-s2files.index(i))))))","def read_wcfat_results(filename, locations=[\"LPH Weld\", \"HPH Weld\", \"BOP Connector\"]):\n\n df = pd.read_csv(filename, skiprows=8, header=None, sep=\"\\t\")\n\n # Drop the event length column and last column, which is just redundant \",\"\n df = df.drop([1, 5], axis=1)\n df.columns = [\"Timestamp\"] + locations\n df[\"Timestamp\"] = df[\"Timestamp\"].str.strip()\n df = df.set_index(\"Timestamp\")\n df.index = pd.to_datetime(df.index, format=\"%Y_%m_%d_%H_%M\")\n\n return df","def filtering(self, Z):\n coord_x_est = [0]\n coord_y_est = [0]\n time_est = [Z[['time']].iloc[0][0]]\n for i in range(1, len(Z.x)):\n new_dt = Z[['time']].iloc[i][0] - time_est[-1]\n while (new_dt > 150):\n new_state = [coord_x_est[-1], coord_y_est[-1], time_est[-1]]\n new_state = self._restore(new_state)\n coord_x_est.append(new_state[0])\n coord_y_est.append(new_state[1])\n time_est.append(new_state[2])\n new_dt -= 100\n\n# self.configure(new_dt)\n self._predict()\n mes = Z[['x', 'y']].iloc[i].to_numpy()\n self._update(np.resize(mes, (2, 1)))\n # save for latter plotting\n coord_x_est.append(self.x[0])\n coord_y_est.append(self.x[4])\n time_est.append(int(Z[['time']].iloc[i]))\n\n return pd.DataFrame({'X_f': coord_x_est,\n 'Y_f': coord_y_est,\n 'time': time_est\n })","def create_table(f, geoinfo):\n bounds_cols = xb_points + yb_points\n df = pd.read_csv(f, delimiter=\";\", index_col=\"INDEX_RC\")\n df[duration_name] = parse_duration_level(f)\n df = df.join(geoinfo[[\"X_CENT_GEO\", \"Y_CENT_GEO\", \"Col\", \"Row\"]])\n df = df.rename(columns={\"Col\": x, \"Row\": y, \"X_CENT_GEO\": lon, \"Y_CENT_GEO\": lat})\n return df","def get_lat_lon_date_features(gt_ids=[], gt_masks=None, gt_shifts=None,\n forecast_ids=[], forecast_masks=None, forecast_shifts=None,\n anom_ids=[], anom_masks=None, anom_shifts=None,\n first_year = None):\n # If particular arguments aren't lists, replace with repeating iterators\n if not isinstance(gt_masks, list):\n gt_masks = itertools.repeat(gt_masks)\n if not isinstance(gt_shifts, list):\n gt_shifts = itertools.repeat(gt_shifts)\n if not isinstance(forecast_masks, list):\n forecast_masks = itertools.repeat(forecast_masks)\n if not isinstance(forecast_shifts, list):\n forecast_shifts = itertools.repeat(forecast_shifts)\n if not isinstance(anom_masks, list):\n anom_masks = itertools.repeat(anom_masks)\n if not isinstance(anom_shifts, list):\n anom_shifts = itertools.repeat(anom_shifts)\n\n # Define canonical name for target start date column\n date_col = \"start_date\"\n # Add each ground truth feature to dataframe\n df = None\n for gt_id, gt_mask, gt_shift in zip(gt_ids, gt_masks, gt_shifts):\n print \"Getting {}_shift{}\".format(gt_id, gt_shift)\n t = time.time()\n # Load ground truth data\n gt = get_ground_truth(gt_id, gt_mask, shift=gt_shift)\n # Discard years prior to first_year\n gt = year_slice(gt, first_year = first_year)\n # Use outer merge to include union of (lat,lon,date_col)\n # combinations across all features\n df = df_merge(df, gt)\n print \"Elapsed: {}s\".format(time.time() - t)\n\n # Add each forecast feature to dataframe\n for forecast_id, forecast_mask, forecast_shift in zip(forecast_ids,\n forecast_masks,\n forecast_shifts):\n print \"Getting {}_shift{}\".format(forecast_id, forecast_shift)\n t = time.time()\n # Load forecast with years >= first_year\n forecast = get_forecast(forecast_id, forecast_mask, shift=forecast_shift)\n # Rename target start date column to \"start_date\"\n fcst_date_col = get_target_start_date_col(forecast_id)\n forecast.rename(columns={fcst_date_col: date_col}, inplace=True)\n # Discard years prior to first_year\n forecast = year_slice(forecast, first_year = first_year)\n # Use outer merge to include union of (lat,lon,date_col)\n # combinations across all features\n df = df_merge(df, forecast)\n print \"Elapsed: {}s\".format(time.time() - t)\n\n # Add anomaly features and climatology last so that climatology\n # is produced for all previously added start dates\n for anom_id, anom_mask, anom_shift in zip(anom_ids, anom_masks, anom_shifts):\n print \"Getting {}_shift{} with anomalies\".format(anom_id, anom_shift)\n t = time.time()\n # Check if ground truth column already exists\n gt_col = get_measurement_variable(anom_id, shift=anom_shift)\n if df is None or gt_col not in df.columns:\n # Add masked ground truth data if absent\n gt = get_ground_truth(anom_id, anom_mask, shift=anom_shift)\n # Discard years prior to first_year\n gt = year_slice(gt, first_year = first_year)\n # Use outer merge to include union of (lat,lon,date_col)\n # combinations across all features\n df = df_merge(df, gt)\n\n # Load masked ground truth data climatology\n climatology = get_climatology(anom_id, anom_mask, anom_shift)\n # Merge climatology into dataset\n df = pd.merge(df, climatology[[gt_col]],\n left_on=['lat', 'lon', df[date_col].dt.month,\n df[date_col].dt.day],\n right_on=[climatology.lat, climatology.lon,\n climatology[date_col].dt.month,\n climatology[date_col].dt.day],\n how='left', suffixes=('', '_clim'))\n clim_col = gt_col+\"_clim\"\n # Compute ground-truth anomalies\n anom_col = gt_col+\"_anom\"\n df[anom_col] = df[gt_col] - df[clim_col]\n print \"Elapsed: {}s\".format(time.time() - t)\n\n return df","def prepare(gpi, start_date, end_date, models, satellites, kind=\"clim\", window=35):\n # read data\n ts_input = data_object.read_gpi(gpi, start_date, end_date, models, satellites)\n #ts_input = interp.add_nan(ts_input)\n ts_input = interp.iter_fill(ts_input, max_gap=5)\n\n # either calc climatology, apply moving average filter, or do nothing\n if kind == 'clim':\n ts_smooth = smooth.iter_climats(ts_input)\n plot_title = 'Climatology'\n elif kind == 'movav':\n ts_smooth = smooth.iter_movav(ts_input, window)\n #ts_smooth = ts_gapfill\n plot_title = 'Moving average'\n elif kind == None:\n # return original data\n ts_smooth = ts_input\n else:\n raise NotImplementedError\n pass\n\n # drop rows with missing values\n #ts_smooth = ts_smooth.dropna()\n\n # scale satellite data to model data\n ts_scaled = scaling.scale(ts_smooth, 'mean_std_nan', 0)\n # drop nan rows for slope funcs\n return ts_scaled #.dropna()","def QUERY_LATERALISATION(inspect_result, df, one_map, gif_lat_file,\n side_of_symptoms_signs=None,\n pts_dominant_hemisphere_R_or_L=None,\n normalise_lat_to_loc=False,\n disable_tqdm=True):\n pd.options.mode.chained_assignment = 'raise'\n df = df.copy()\n\n # ensure there is patient's lateralised signs and check dominant known or not\n if not side_of_symptoms_signs and not pts_dominant_hemisphere_R_or_L:\n # print('Please note you must determine at least one of side_of_symptoms_signs or')\n # print('pts_dominant_hemisphere_R_or_L keyword arguments for lateralised data extraction.')\n return None, None, None, None, None, None, None\n\n # check there is lateralising value\n try:\n num_QL_lat = inspect_result['Lateralising'].sum()\n if num_QL_lat > 0:\n logging.debug(\n f'\\n\\nLateralisation based on: {num_QL_lat.sum()} datapoints')\n else:\n num_QL_lat = None\n return None, None, None, None, None, None, None\n except KeyError:\n # logging.debug(\n # f'No Lateralising values found for this query of the database.')\n num_QL_lat = None\n return None, None, None, None, None, None, None\n\n lat_vars = [i for i in lateralisation_vars() if i not in ['Lateralising']]\n\n # check that the lateralising columns isn't null where it shouldn't be i.e. CL/IL/DomH/NonDomH not null:\n # but not 'BL (Non-lateralising)'\n # first ensure other columns all feature in this inspect_result:\n inspect_result2 = inspect_result.copy()\n for col in lat_vars:\n if col not in inspect_result2.columns:\n inspect_result2[col] = np.nan\n # now can check lateralising columns isn't null where it shouldn't be:\n missing_lat = inspect_result2.loc[(inspect_result2['CL'].notnull()) |\n (inspect_result2['IL'].notnull()) |\n (inspect_result2['DomH'].notnull()) |\n (inspect_result2['NonDomH'].notnull()), :].copy()\n missing_lat_null_mask = missing_lat['Lateralising'].isnull()\n if not missing_lat_null_mask.all():\n # logging.debug('\\nNo missing Lateralising data points.')\n pass\n else:\n logging.debug(\n 'The inspect_result lat col has NaNs/zero where it should not: autofilled')\n df_of_missing_lats = missing_lat.loc[missing_lat_null_mask].copy()\n df.loc[df_of_missing_lats.index, 'Lateralising'] = df_of_missing_lats[[\n 'CL', 'IL', 'DomH', 'NonDomH']].sum(axis=1)\n\n # check columns exist (not removed in preceding notnull steps from other functions):\n for col in lat_vars:\n if col not in inspect_result.columns:\n inspect_result[col] = 0\n\n # summarise overall lat values\n IL = inspect_result['IL']\n CL = inspect_result['CL']\n DomH = inspect_result['DomH']\n NonDomH = inspect_result['NonDomH']\n BL = inspect_result['BL (Non-lateralising)']\n\n num_QL_CL = CL.sum()\n num_QL_IL = IL.sum()\n num_QL_BL = BL.sum()\n num_QL_DomH = DomH.sum()\n num_QL_NonDomH = NonDomH.sum()\n\n logging.debug(f'\\n\\nOverall Contralateral: {num_QL_CL} datapoints')\n logging.debug(f'Ipsilateral: {num_QL_IL} datapoints')\n logging.debug(\n f'Bilateral/Non-lateralising: {num_QL_BL} datapoints. This is not utilised in our analysis/visualisation.')\n logging.debug(f'Dominant Hemisphere: {num_QL_DomH} datapoints')\n logging.debug(f'Non-Dominant Hemisphere: {num_QL_NonDomH} datapoints')\n\n # Global initialisation:\n lat_only_Right = 0\n lat_only_Left = 0\n inspect_result_lat = inspect_result.loc[inspect_result['Lateralising'].notnull(\n ), :].copy() # only those with lat (with or without localising)\n no_rows = inspect_result_lat.shape[0]\n all_combined_gifs = None\n gifs_right, gifs_left = gifs_lat(gif_lat_file)\n\n # cycle through rows of inspect_result_lat:\n id_cols = [i for i in full_id_vars() if i not in ['Localising']\n ] # note 'Localising' is in id_cols\n\n for i in (range(no_rows) if disable_tqdm else tqdm(range(no_rows), desc='QUERY LATERALISTION: main',\n bar_format=\"{l_bar}%s{bar}%s{r_bar}\" % (Fore.BLUE, Fore.RESET))):\n # logging.debug(str(i))\n Right = 0\n Left = 0\n\n full_row = inspect_result_lat.iloc[[i], :]\n row = full_row.drop(labels=id_cols, axis='columns',\n inplace=False, errors='ignore')\n row = row.dropna(how='all', axis='columns')\n # row = row.dropna(how='all', axis='rows')\n\n #\n #\n #\n # some pts/rows will have lateralising but no localising values:\n if (('Localising' not in row.columns) | (full_row['Localising'].sum() == 0)):\n logging.debug(\n '\\n\\nSome extracted lateralisations have no specific localisation - these are mapped to entire hemispheric GIFs')\n # logging.debug(f'row# = {i}')\n # probably, in future, instead of break we want to compare this row's:\n # full_row['Lateralising'] to the overall inspect_result['Lateralising'] and use that proportion\n # or actually, to count this full_row['Lateralising'] as data for localising, using the lateralised gif parcellations from the sheet called\n # 'Full GIF Map for Review '\n\n lat_only_Right, lat_only_Left = lateralising_but_not_localising(full_row,\n side_of_symptoms_signs,\n pts_dominant_hemisphere_R_or_L,\n lat_only_Right,\n lat_only_Left)\n continue\n #\n #\n #\n # otherwise if there is localising value (and lateralising value):\n row_to_one_map = pivot_result_to_one_map(row, one_map, raw_pt_numbers_string='pt #s',\n )\n # ^ row_to_one_map now contains all the lateralising gif parcellations\n\n # set the scale of influence of lateralisation on the gif parcellations:\n proportion_lateralising = full_row['Lateralising'].sum(\n ) / full_row['Localising'].sum()\n\n # some rows will have lateralisng exceed localising values:\n #\n #\n #\n if proportion_lateralising > 1:\n proportion_lateralising = 1\n logging.debug(\n '\\n\\nSome lateralising data exceed localising data, excess are mapped to entire hemispheric GIFs')\n\n # now deal with lat_exceed_loc excess as we did with lat_but_not_loc\n lat_exceed_loc = True\n else:\n lat_exceed_loc = False\n\n # check columns exist in this particular row:\n for col in lat_vars:\n if col not in row.columns:\n row[col] = 0\n else:\n continue\n\n # summarise overall lat values\n Right, Left = summarise_overall_lat_values(row,\n side_of_symptoms_signs,\n pts_dominant_hemisphere_R_or_L,\n Right,\n Left)\n\n Total = Right+Left\n if Right == Left:\n # no point as this is 50:50 as it already is, so skip\n # before continuing, ensure there is a all_combined_gifs...\n # e.g. for blink there isn't as the first row is 50:50...\n # ... and all future codes fail\n if all_combined_gifs is None:\n all_combined_gifs = row_to_one_map\n elif all_combined_gifs is not None:\n all_combined_gifs = pd.concat(\n [all_combined_gifs, row_to_one_map], join='outer', sort=False, ignore_index=True)\n continue\n\n # now should be able to use above to lateralise the localising gif parcellations:\n # if there are 100 localisations in one row, and only 1 IL And 3 CL, it would be too much\n # to say the IL side gets one third of the CL side as number of lat is too low\n # hence normalise by dividing by proportion_lateralising (which is between (0,1])\n\n # find lowest value of R or L\n lower_postn = np.argmin([Right, Left])\n if lower_postn == 0:\n isin = gifs_right # reduce right sided intensities/pt #s\n isin_right = True\n isin_left = False\n elif lower_postn == 1:\n isin = gifs_left\n isin_left = True\n isin_right = False\n\n lower_value = [Right, Left][lower_postn]\n higher_value = [Right, Left]\n higher_value.remove(lower_value)\n\n ratio = lower_value / Total\n\n if normalise_lat_to_loc == True:\n # see comments on section above about why we should normalise\n norm_ratio = ratio / proportion_lateralising\n if norm_ratio > 1:\n norm_ratio = 1\n logging.debug(\n 'norm_ratio capped at 1: small proportion of data lateralised')\n elif normalise_lat_to_loc == False:\n norm_ratio = lower_value / higher_value\n\n # if proportion_lateralising is 1, straightforward: return dataframe of right/left gifs whichever lower\n df_lower_lat_to_be_reduced = row_to_one_map.loc[row_to_one_map['Gif Parcellations'].isin(\n list(isin))].copy()\n # now make these values lower by a proportion = norm_ratio (in this case norm_ratio = ratio as denom is 1)\n reduce_these = df_lower_lat_to_be_reduced.loc[:, 'pt #s'].copy()\n df_lower_lat_to_be_reduced.loc[:, 'pt #s'] = norm_ratio * reduce_these\n # re attribute these corrected reduced lateralised values to the entire row's data:\n row_to_one_map.loc[df_lower_lat_to_be_reduced.index,\n :] = df_lower_lat_to_be_reduced\n\n #\n #\n #\n # now deal with lat_exceed_loc excess as we did with lat_but_not_loc\n if lat_exceed_loc:\n lat_only_Right, lat_only_Left = lat_exceeding_loc_mapped_to_hemisphericGIFs_adjusted_for_locs(\n full_row, lat_vars,\n side_of_symptoms_signs,\n pts_dominant_hemisphere_R_or_L,\n lat_only_Right,\n lat_only_Left,\n isin_left=isin_left, isin_right=isin_right,\n )\n\n #\n #\n #\n\n # now need to merge/concat these rows-(pivot-result)-to-one-map as the cycle goes through each row:\n if i == 0:\n # can't merge first row\n all_combined_gifs = row_to_one_map\n # logging.debug('end of zeroo')\n continue\n elif i != 0:\n all_combined_gifs = pd.concat(\n [all_combined_gifs, row_to_one_map], join='outer', sort=False, ignore_index=True)\n # logging.debug(f'end of i {i}')\n\n # Need to recombine the inspect_result_lat (also had loc) used in for loop to give all_combined_gifs\n # with inspect_result that had null lateralising:\n inspect_result_nulllateralising = inspect_result.loc[inspect_result['Lateralising'].isnull(\n ), :].copy()\n # now clean ready to map:\n inspect_result_nulllateralising.drop(\n labels=id_cols, axis='columns', inplace=True, errors='ignore')\n inspect_result_nulllateralising.dropna(\n how='all', axis='columns', inplace=True)\n # now map row by row otherwise you get a \"TypeError: only size-1 arrays can be converted to Python scalars\":\n nonlat_no_rows = inspect_result_nulllateralising.shape[0]\n if (nonlat_no_rows == 0) | inspect_result_nulllateralising.empty:\n gifs_not_lat = None\n elif nonlat_no_rows != 0:\n for j in (range(nonlat_no_rows) if disable_tqdm else tqdm(range(nonlat_no_rows), desc='QUERY LATERALISATION: non-lateralising data',\n bar_format=\"{l_bar}%s{bar}%s{r_bar}\" % (Fore.CYAN, Fore.RESET))):\n full_row = inspect_result_nulllateralising.iloc[[j], :]\n row = full_row.drop(labels=id_cols, axis='columns',\n inplace=False, errors='ignore')\n row = row.dropna(how='all', axis='columns')\n row_nonlat_to_one_map = pivot_result_to_one_map(row,\n one_map, raw_pt_numbers_string='pt #s',\n )\n if j == 0:\n # can't merge first row\n gifs_not_lat = row_nonlat_to_one_map\n # logging.debug(\n # 'j zero gifs_not_lat set to row_nonlat_to_one_map')\n continue\n elif j != 0:\n gifs_not_lat = pd.concat(\n [gifs_not_lat, row_nonlat_to_one_map], join='outer', sort=False, ignore_index=True)\n # now combine the lateralised+loc and non-lateralised(=only localised):\n # all_combined_gifs may still be None if after running above with some lateralised...\n # ...semiology or dominance, there is no lateralising data.\n if all_combined_gifs is None:\n all_combined_gifs = gifs_not_lat\n elif all_combined_gifs is not None:\n all_combined_gifs = pd.concat(\n [all_combined_gifs, gifs_not_lat], join='outer', sort=False, ignore_index=True)\n\n # here add the lateralising_but_not_localising data to the all_combined_gifs (or fixed, or fixed2):\n # if all_combined_gifs is None then use the unilteral_gifs\n # if all_combined_Gifs is not None then only add values to the unilateral gifs if they are already in all_combined_gifs\n if (lat_only_Right != 0) | (lat_only_Left != 0):\n # means both lateralising+loc and gifs_not_lat were none. occurs usually in dummy_data only.\n if all_combined_gifs is None:\n lat_only_df = lateralising_but_not_localising_GIF(all_combined_gifs,\n lat_only_Right, lat_only_Left,\n gifs_right, gifs_left)\n all_combined_gifs = lat_only_df.copy()\n else:\n lat_only_df = lateralising_but_not_localising_GIF(all_combined_gifs,\n lat_only_Right, lat_only_Left,\n gifs_right, gifs_left)\n all_combined_gifs = pd.concat([all_combined_gifs, lat_only_df],\n join='outer', sort=False, ignore_index=True)\n # all_combined_gifs.append(lat_only_df) # equivalent to above concat.\n\n # if EpiNav doesn't sum the pixel intensities: (infact even if it does)\n fixed = all_combined_gifs.pivot_table(\n columns='Gif Parcellations', values='pt #s', aggfunc='sum')\n fixed2 = fixed.melt(value_name='pt #s')\n fixed2.insert(0, 'Semiology Term', np.nan)\n # fixed2.loc[0, 'Semiology Term'] = str( list(inspect_result.index.values) )\n all_combined_gifs = fixed2\n all_combined_gifs\n\n return (all_combined_gifs,\n num_QL_lat, num_QL_CL, num_QL_IL, num_QL_BL, num_QL_DomH, num_QL_NonDomH)","def preproc_data(base_dir):\n n_users = 182\n user_datas = []\n\n for user in tqdm(range(n_users)): \n #iterate through each trace of user \n user_traces = []\n if (len(str(user)) == 1): \n user_id = '00' + str(user)\n elif (len(str(user)) == 2): \n user_id = '0' + str(user)\n else: \n user_id = str(user)\n dir_name = base_dir + user_id + '/Trajectory/'\n for filename in os.listdir(dir_name): \n #load trajectory\n trajectory_raw = pd.read_csv(\n dir_name + filename,\n delimiter = ',', header = None, skiprows = 6,\n names = ['lat','lon', '0', 'altitude', 't_1899', 'date', 'time' ])\n\n #modify time\n traj = trajectory_raw.copy()\n traj.drop(columns = '0', inplace = True)\n\n traj['t_1899'] = (trajectory_raw['t_1899'] - trajectory_raw['t_1899'].values[0]) * 24 * 60 * 60\n traj_newcols = np.array(traj.columns)\n traj_newcols[traj_newcols == 't_1899'] = 'seconds'\n traj.columns = traj_newcols\n\n #get X_vals: \n R_earth = 6.371e6\n deg_to_rad = np.pi / 180\n traj['X'] = R_earth * np.cos(deg_to_rad * traj['lat'].values) * np.cos(deg_to_rad * traj['lon'].values)\n traj['Y'] = R_earth * np.cos(deg_to_rad * traj['lat'].values) * np.sin(deg_to_rad * traj['lon'].values)\n\n #Normalize to first point: \n origin_x = traj['X'].values[0]\n origin_y = traj['Y'].values[0]\n\n traj['X'] = traj['X'] - origin_x\n traj['Y'] = traj['Y'] - origin_y\n\n txy = traj.copy()\n txy = traj[['seconds', 'X', 'Y']]\n txy.columns = ['T', 'X', 'Y']\n if not np.isnan(txy.values).any(): \n user_traces.append(txy.values)\n\n user_datas.append(user_traces)\n return user_datas","def identify_peaks_nowind(xCar, xDate, xDir, xFilename, outDir, processedFileLoc, Engineering, threshold='.1',\n rthresh = '.7',\n xTimeThreshold='5.0', minElevated='2', xB='102', basePerc='50'):\n import csv, numpy\n import shutil\n from shapely.geometry import Point\n import pandas as pd\n import geopandas as gpd\n\n\n try:\n baseCalc = float(basePerc)\n xABThreshold = float(threshold)\n minElevated = float(minElevated)\n rMin = float(rthresh)\n xDistThreshold = 160.0 # find the maximum CH4 reading of observations within street segments of this grouping distance in meters\n xSDF = 4 # multiplier times standard deviation for floating baseline added to mean\n\n xB = int(xB)\n xTimeThreshold = float(xTimeThreshold)\n fn = xDir + xFilename # set raw text file to read in\n fnOut = outDir + \"Peaks\" + \"_\" + xCar + \"_\" + xDate.replace(\"-\", \"\") + \".csv\"\n fnShape = outDir + \"Peaks\" + \"_\" + xCar + \"_\" + xDate.replace(\"-\", \"\") + \".shp\"\n fnLog = outDir + \"Peaks\" + \"_\" + xCar + \"_\" + xDate.replace(\"-\", \"\") + \".log\"\n pkLog = outDir + \"Peaks\" + \"_\" + xCar + \"_\" + xDate.replace(\"-\",\"\") + \"_info.csv\"\n jsonOut = outDir + \"Peaks\" + \"_\" + xCar + \"_\" + xDate.replace(\"-\",\"\") + \".geojson\"\n infOut = processedFileLoc + xCar + \"_\" + xDate.replace(\"-\", \"\") + \"_info.csv\"\n\n ### TEST THING\n fn = xDir + xFilename # set raw text file to read in\n filenames = nameFiles(outDir,processedFileLoc,xCar,xDate,True)\n fnOut = filenames['fnOut']\n fnShape = filenames['fnShape']\n fnLog = filenames['fnLog']\n pkLog = filenames['pkLog']\n jsonOut = filenames['jsonOut']\n infOut = filenames['infOut']\n\n print(f\"{outDir}Peaks_{xCar}_{xDate}_info.csv\")\n fLog = open(fnLog, 'w')\n shutil.copy(infOut, pkLog)\n\n # field column indices for various variables\n if Engineering == True:\n fDate = 0; fTime = 1; fEpochTime = 2\n fNanoSeconds = 3; fVelocity = 4; fU = 5\n fV = 6; fW = 7; fBCH4 = 10\n fBCH4 = 8; fBRSSI = 9; fTCH4 = 10\n TRSSI = 11;PRESS = 12; INLET = 13\n TEMP = 14; CH4 = 15;H20 = 16\n C2H6 = 17; R = 18; C2C1 = 19\n BATT = 20; POWER = 21; CURR = 22\n SOCPER = 23;fLat = 24; fLon = 25\n elif not Engineering:\n fDate = 0; fTime = 1; fEpochTime = 2\n fNanoSeconds = 3;fVelocity = 4; fU = 5\n fV = 6; fW = 7\n fBCH4 = 8; fBRSSI = 9\n fTCH4 = 10; TRSSI = 11; PRESS = 12\n INLET = 13; TEMP = 14; CH4 = 15\n H20 = 16;C2H6 = 17; R = 18; C2C1 = 19\n BATT = 20; POWER = 21; CURR = 22\n SOCPER = 23; fLat = 24;fLon = 25; fDist = 33; fOdometer = 34\n fUavg = 35; fVavg = 36; fWavg = 37\n\n # read data in from text file and extract desired fields into a list, padding with 5 minute and hourly average\n x1, x2, x3, x4, x5, x6, x7, x8, x9, x10,x11,x12,x13,x14,x15,x16 = [[] for _ in range(16)]\n\n count = -1\n with open(fn, 'r') as f:\n t = csv.reader(f)\n for row in t:\n woo = row\n # print(count)\n if count < 0:\n count += 1\n continue\n elif count >= 0:\n datet = row[fDate].replace(\"-\", \"\") + row[fTime].replace(\":\", \"\")\n ## if not engineering\n epoch = float(row[fEpochTime] + \".\" + row[fNanoSeconds][0])\n datetime = row[fDate].replace(\"-\", \"\") + row[fTime].replace(\":\", \"\")\n x1.append(epoch); x2.append(datetime)\n if row[fLat] == '':\n x3.append('')\n elif row[fLat] != '':\n x3.append(float(row[fLat]))\n if row[fLon] == '':\n x4.append('')\n elif row[fLon] != '':\n x4.append(float(row[fLon]))\n\n x5.append(float(row[fBCH4]))\n x6.append(float(row[fTCH4]))\n x7.append(0.0)\n x8.append(0.0)\n x9.append(row[fOdometer])\n x11.append(float(row[C2H6]))\n x12.append(float(row[C2C1]))\n x13.append(float(row[R]))\n x14.append(float(row[fUavg]))\n x15.append(float(row[fVavg]))\n x16.append(float(row[fWavg]))\n\n count += 1\n print(f\"Number of observations processed:{count}\")\n\n # convert lists to numpy arrays\n aEpochTime = numpy.array(x1)\n aDateTime = numpy.array(x2)\n aLat = numpy.array(x3)\n aLon = numpy.array(x4)\n aCH4 = numpy.array(x5)\n aTCH4 = numpy.array(x6)\n aMean = numpy.array(x7)\n arealMean = numpy.array(x7)\n astd = numpy.array(x7)\n\n aMeanC2H6 = numpy.array(x7)\n aThreshold = numpy.array(x8)\n aOdom = numpy.array(x9)\n\n # adding ethane stuff\n aC2H6 = numpy.array(x11)\n aC2C1 = numpy.array(x12)\n aR = numpy.array(x13)\n aUavg = numpy.array(x14)\n aVavg = numpy.array(x15)\n aWavg = numpy.array(x16)\n\n\n xLatMean = numpy.mean(aLat)\n xLonMean = numpy.mean(aLon)\n #xCH4Mean = numpy.mean(aCH4)\n #xC2H6Mean = numpy.mean(aC2H6)\n #xC2C1Mean = numpy.mean(aC2C1)\n\n fLog.write(\"Day CH4_mean = \" + str(numpy.mean(aCH4)) +\n \", Day CH4 SD = \" + str(numpy.std(aCH4)) + \"\\n\")\n fLog.write(\"Day C2H6 Mean = \" + str(numpy.mean(aC2H6)) +\n \", Day C2H6 SD = \" + str(numpy.std(aC2H6)) + \"\\n\")\n fLog.write(\"Center lon/lat = \" + str(xLonMean) + \", \" + str(xLatMean) + \"\\n\")\n\n lstCH4_AB = []\n\n # generate list of the index for observations that were above the threshold\n for i in range(0, count - 2):\n if ((count - 2) > xB):\n topBound = min((i + xB), (count - 2))\n botBound = max((i - xB), 0)\n\n for t in range(min((i + xB), (count - 2)), i, -1):\n if aEpochTime[t] < (aEpochTime[i] + (xB / 2)):\n topBound = t\n break\n for b in range(max((i - xB), 0), i):\n if aEpochTime[b] > (aEpochTime[i] - (xB / 2)):\n botBound = b\n break\n\n xCH4Mean = numpy.percentile(aCH4[botBound:topBound], baseCalc)\n xCH4_actualMean = numpy.mean(aCH4[botBound:topBound])\n xCH4_stdev = numpy.mean(aCH4[botBound:topBound])\n xC2H6Mean = numpy.percentile(aC2H6[botBound:topBound], baseCalc)\n\n # xCH4SD = numpy.std(aCH4[botBound:topBound])\n else:\n xCH4Mean = numpy.percentile(aCH4[0:(count - 2)], baseCalc)\n xCH4_actualMean = numpy.mean(aCH4[0:(count - 2)], baseCalc)\n xCH4_stdev = numpy.std(aCH4[0:(count - 2)], baseCalc)\n xC2H6Mean = numpy.percentile(aC2H6[0:(count - 2)], baseCalc)\n\n # xCH4SD = numpy.std(aCH4[0:(count-2)])\n xThreshold = xCH4Mean + (xCH4Mean * xABThreshold)\n xThreshold_c2h6 = xC2H6Mean + (xC2H6Mean * xABThreshold)\n\n if (aCH4[i] > xThreshold and aR[i]>rMin):\n #if (aCH4[i] > xThreshold):\n lstCH4_AB.append(i)\n aMean[i] = xCH4Mean\n aMeanC2H6[i] = xC2H6Mean\n aThreshold[i] = xThreshold\n arealMean[i] = xCH4_actualMean\n astd[i] = xCH4_stdev\n\n # now group the above baseline threshold observations into groups based on distance threshold\n lstCH4_ABP = []\n xDistPeak = 0.0\n xCH4Peak = 0.0\n xTime = 0.0\n cntPeak = 0\n cnt = 0\n sID = \"\"\n sPeriod5Min = \"\"\n prevIndex = 0\n for i in lstCH4_AB:\n if (cnt == 0):\n xLon1 = aLon[i]\n xLat1 = aLat[i]\n xOdom = aOdom[i]\n else:\n # calculate distance between points\n xDist = haversine(xLat1, xLon1, aLat[i], aLon[i])\n xDistPeak += xDist\n xCH4Peak += (xDist * (aCH4[i] - aMean[i]))\n xLon1 = aLon[i]\n xLat1 = aLat[i]\n xOdom = aOdom[i]\n if (sID == \"\"):\n xTime = aEpochTime[i]\n sID = str(xCar) + \"_\" + str(xTime)\n sPeriod5Min = str(int((aEpochTime[i] - 1350000000) / (30 * 1))) # 30 sec\n if ((aEpochTime[i] - aEpochTime[prevIndex]) > xTimeThreshold): # initial start of a observed peak\n cntPeak += 1\n xTime = aEpochTime[i]\n xDistPeak = 0.0\n xCH4Peak = 0.0\n sID = str(xCar) + \"_\" + str(xTime)\n sPeriod5Min = str(int((aEpochTime[i] - 1350000000) / (30 * 1))) # 30 sec\n # print str(i) +\", \" + str(xDist) + \",\" + str(cntPeak) +\",\" + str(xDistPeak)\n lstCH4_ABP.append(\n [sID, xTime, aEpochTime[i], aDateTime[i], aCH4[i], aLon[i], aLat[i], aMean[i], aThreshold[i],\n xDistPeak, xCH4Peak, aTCH4[i],aC2H6[i],aC2C1[i],aR[i],aMeanC2H6[i], sPeriod5Min, xOdom,\n aUavg[i],aVavg[i],aWavg[i]])\n cnt += 1\n prevIndex = i\n\n # Finding peak_id larger than 160.0 m\n tmpsidlist = []\n for r in lstCH4_ABP:\n if (float(r[9]) > 160.0) and (r[0] not in tmpsidlist):\n tmpsidlist.append(r[0])\n cntPeak -= len(tmpsidlist)\n\n fLog.write(\"Number of peaks found: \" + str(cntPeak) + \"\\n\")\n print(f\"{xCar} \\t {xDate} \\t {xFilename} \\t {count} \\t {len(lstCH4_ABP)}\")\n\n # write out the observed peaks to a csv to be read into a GIS\n fOut = open(fnOut, 'w')\n # s = \"PEAK_NUM,EPOCHSTART,EPOCH,DATETIME,CH4,LON,LAT,CH4_BASELINE,CH4_THRESHOLD,PEAK_DIST_M,PEAK_CH4,TCH4,PERIOD5MIN\\n\"\n s = \"OP_NUM,OP_EPOCHSTART,OB_EPOCH,OB_DATETIME,OB_CH4,OB_LON,OB_LAT,OB_CH4_BASELINE,\" \\\n \"OB_CH4_THRESHOLD,OP_PEAK_DIST_M,OP_PEAK_CH4,OB_TCH4,OB_C2H6,\" \\\n \"OB_C2C1,OB_R,OB_C2H6_BASELINE,OB_PERIOD5MIN,ODOMETER,OB_U_AVG,OB_V_AVG,OB_W_AVG\\n\"\n fOut.write(s)\n\n truecount = 0\n for r in lstCH4_ABP:\n if r[0] not in tmpsidlist:\n s = ''\n for rr in r:\n s += str(rr) + ','\n s = s[:-1]\n s += '\\n'\n fOut.write(s)\n truecount += 1\n fOut.close()\n fLog.close()\n\n openFile = pd.read_csv(fnOut)\n if openFile.shape[0] != 0:\n pkDistDf = openFile.copy().groupby('OP_NUM', as_index=False).apply(\n lambda x: max(x.ODOMETER) - min(x.ODOMETER))\n pkDistDf.columns = ['OP_NUM', 'OP_DISTANCE']\n openFile = pd.merge(openFile.copy(), pkDistDf)\n tempCount = openFile.groupby('OP_NUM', as_index=False).OP_EPOCHSTART.count().rename(\n columns={'OP_EPOCHSTART': 'Frequency'})\n tempCount = tempCount.loc[tempCount.Frequency >= minElevated, :]\n if tempCount.shape[0] == 0:\n print(f\"No Observed Peaks with enough Elevated Readings Found in the file: {xFilename}\")\n\n elif tempCount.shape[0] != 0:\n oFile = pd.merge(openFile, tempCount, on=['OP_NUM'])\n openFile = oFile.copy()\n del (oFile)\n openFile[\"minElevated\"] = openFile.apply(lambda x: int(minElevated), axis=1)\n openFile['OB_CH4_AB'] = openFile.loc[:, 'OB_CH4'].sub(openFile.loc[:, 'OB_CH4_BASELINE'], axis=0)\n openFile['OB_C2H6_AB'] = openFile.loc[:, 'OB_C2H6'].sub(openFile.loc[:, 'OB_C2H6_BASELINE'],axis=0)\n openFile.to_csv(fnOut, index=False)\n\n\n fileWt = weighted_loc(openFile, 'OB_LAT', 'OB_LON', 'OP_NUM', 'OB_CH4_AB').loc[:, :].rename(\n columns={'OB_LAT': 'pk_LAT', 'OB_LON': 'pk_LON'}).reset_index(drop=True)\n geometry_temp = [Point(lon, lat) for lon, lat in zip(fileWt['pk_LON'], fileWt['pk_LAT'])]\n crs = 'EPSG:4326'\n # geometry is the point of the lat/lon\n # gdf_buff = gpd.GeoDataFrame(datFram, crs=crs, geometry=geometry_temp)\n\n ## BUFFER AROUND EACH 'OP_NUM' WITH BUFFER DISTANCE\n gdf_buff = gpd.GeoDataFrame(fileWt, crs=crs, geometry=geometry_temp)\n # gdf_buff = makeGPD(datFram,'LON','LAT')\n\n ##maybe this is the issue?\n #gdf_buff = gdf_buff.to_crs(epsg=32610)\n #gdf_buff['geometry'] = gdf_buff.loc[:, 'geometry'].buffer(30)\n try:\n gdf_buff.to_file(jsonOut, driver=\"GeoJSON\")\n #gdf_buff.to_file('testthing.geojson', driver=\"GeoJSON\")\n except:\n print(\"Error Saving JSON File\")\n elif openFile.shape[0] == 0:\n print(f\"No Observed Peaks Found in the file:{xFilename}\")\n except ValueError:\n print(\"Error in Identify Peaks\")\n return False","def get_time_series(self, tile, year, loc, px, py, cols=1, rows=1, step=1, save_dir='txt/'):\n\n # keys = ['sza', 'saa', 'vza', 'vaa', 'qa', 'b01', 'b02', 'b03', 'b04', 'b05', 'b06', 'b07']\n\n timeBefore = time.clock()\n\n f_list = np.array(glob.glob(loc + '%s_%d_*_1km.nc' % (tile, year)))\n\n # if there are no files for this year\n if f_list.shape[0] == 0:\n print 'Data for year %d and tile %s not found' % (year, tile)\n return -1\n\n # get DOYs from file name\n doys0 = np.sort([int(s.split('_')[4]) for s in f_list])\n # print 'doys0', doys0\n\n # arrange DOYs according to step. I.e. we assume that...\n # step = 7\n doys = np.zeros(doys0.shape).astype(int)\n doys_real = np.zeros(doys0.shape).astype(int)\n\n for jj, ii in enumerate(xrange(0, doys0.shape[0], step)):\n # doys = np.append(doys, doys0[ii:ii+7])\n doys[ii:ii+step] = jj + 1#doys0[ii]\n doys_real[ii:ii+step] = doys0[ii]\n\n ind = np.argsort([int(s.split('_')[4]) for s in f_list])\n f_list = f_list[ind]\n\n # print 'loc:', loc\n # print 'tile:', tile\n # print 'year:', year\n # print 'f_list:', f_list[0]\n\n output = {}\n for f in f_list:\n print 'f:', f\n try:\n ds = nc.Dataset(f)\n except:\n print 'something wrong with %s' % f\n continue\n\n # if a dataset is empty\n # i.e. if it has less than 2 bands\n # i.e. vza, vaa, sza, saa, proj, geo, qa, b1, b2\n if len(ds.variables.keys()) < 9:\n print 'Dataset exists but empty:'\n print 'loc:', loc\n print 'tile:', tile\n print 'year:', year\n print 'f_list:', f_list[0]\n print ''\n # return -1\n else:\n break\n if f == f_list[-1]:\n print 'all datasets are empty'\n return -1\n\n for key in ds.variables.keys():\n if len(ds.variables[key].shape) == 2:\n # output[key] = np.zeros((f_list.shape[0], ds.variables[key].shape[0], ds.variables[key].shape[1]))\n output[key] = np.zeros((f_list.shape[0], cols, rows))\n if len(ds.variables[key].shape) == 1:\n output[key] = np.zeros(ds.variables[key].shape[0]).astype(str)\n\n for i, fname in enumerate(f_list):\n #print fname\n ds = nc.Dataset(fname)\n for key in ds.variables.keys():\n if len(ds.variables[key].shape) == 2:\n\n try:\n # print ds.variables[key][px:px+cols, py:py+rows]\n output[key][i, :, :] = ds.variables[key][px:px+cols, py:py+rows]\n except:\n print 'something wrong in output[%s][%d, :, :]' % (key, i)\n print 'output:', output[key][i, :, :].shape\n print 'ds.variables:', ds.variables[key][px:px+cols, py:py+rows].shape\n\n if len(ds.variables[key].shape) == 1:\n output[key][:] = ds.variables[key][:]\n\n # print 'output.keys:', output.keys()\n\n QA_OK = np.array([8, 72, 136, 200, 1288, 2056, 2120, 2184, 2248])\n qa_passer = np.logical_or.reduce([output['qa'] == x for x in QA_OK])\n\n for b in [1,2,3,4,5,7]:\n\n qa_passer[output[\"b0%d\" % b] <= 0] = 0.\n qa_passer[output[\"b0%d\" % b] >= 10000] = 0.\n\n # if, for this pixel, we have just a few observations we don't need them\n if np.sum(qa_passer) < 2:\n qa_passer[:] = 0\n\n output['qa_passer'] = qa_passer\n [bin(b) for b in QA_OK]\n\n #doys = np.array([int(g.GetRasterBand(b+1).GetMetadata()['DoY']) for b in xrange(g.RasterCount)])\n #years = np.array([int(g.GetRasterBand(b + 1).GetMetadata()['Year']) for b in xrange(g.RasterCount)])\n output['doys'] = doys\n output['doys_real'] = np.unique(doys_real)\n output['years'] = np.ones(doys.shape) * year\n\n output[\"sza\"] = output[\"sza\"] / 100.\n output[\"saa\"] = output[\"saa\"] / 100.\n output[\"vza\"] = output[\"vza\"] / 100.\n output[\"vaa\"] = output[\"vaa\"] / 100.\n output['b01'] = output['b01'] / 10000.\n output['b02'] = output['b02'] / 10000.\n output['b03'] = output['b03'] / 10000.\n output['b04'] = output['b04'] / 10000.\n output['b05'] = output['b05'] / 10000.\n output['b06'] = output['b06'] / 10000.\n output['b07'] = output['b07'] / 10000.\n\n # print 'qa_passer:', output['qa_passer']\n #print output['b01']\n\n #pkl.dump(output, open(f_out, 'wb'))\n\n timeAfter = time.clock()\n elapsed_time = timeAfter - timeBefore\n print 'read time series time (s): ', elapsed_time\n\n print 'Read MODIS for year %d data is done' % year\n return output","def kpi_function(kwargs):\n start_time = kwargs['start_time']\n end_time = kwargs['end_time']\n project = kwargs['project']\n client_id = kwargs['client_id']\n device_id = kwargs['device_id']\n dataset_src = kwargs['dataset_src']\n dataset_dest = kwargs['dataset_dest']\n table_src = kwargs['table_src']\n table_dest = kwargs['table_dest']\n location = kwargs['location']\n config = kwargs['config']\n def station_data(station_id, start, end):\n data = Daily(station_id, start, end)\n data = data.fetch()\n data.reset_index(inplace=True)\n data = data[['time', 'tavg', 'tmin', 'tmax']]\n # print(data)\n return data\n def costic(df):\n alias_a = df['tmin']\n alias_b = df['tmax']\n\n if 15 > alias_b:\n return 15 - (alias_a + alias_b) / 2\n elif 15 <= alias_a:\n return 0\n else:\n return (15 - alias_a) * (0.08 + 0.42 * (15 - alias_a) / (alias_b - alias_a))\n\n current_time = arrow.utcnow().floor('day')\n # if end_time <= current_time:\n output = config\n rules = pd.DataFrame(output['rules'])\n rules.index = rules.index.astype(int)\n rules.sort_index(inplace=True)\n rules = rules[['kpi_name',\n 'alias_a',\n 'alias_b',\n 'alias_c',\n 'alias_d',\n 'alias_e',\n 'alias_f',\n 'equation_1',\n 'equation_2',\n 'aggregation',\n 'start_time',\n 'end_time',\n 'mute_logs']].copy()\n rules = rules.where(pd.notnull(rules), None)\n settings = output['settings']['mute_rules']\n station_id = output['settings']['station_id']\n country = output['settings']['country']\n state = output['settings']['state']\n df = rules\n # create an empty dataframe\n logs = pd.DataFrame()\n # get access to bigqyery\n # credentials, project_id = google.auth.default()\n # client = bigquery.Client(credentials=credentials)\n if settings is False:\n data_rd = pd.read_csv('main_data.csv')\n data_rd['time'] = pd.to_datetime(data_rd['time_local'])\n if data_rd.shape[0] > 0:\n left_table = data_rd.drop(['alias','value','time_local'],axis=1)\n left_table = left_table.groupby(\"time\").min().reset_index()\n sub_df = data_rd[['time','alias','value']].copy()\n sub_df['value'] = pd.to_numeric(sub_df['value'])\n right_table = pd.pivot_table(sub_df,index='time',columns='alias',values='value').reset_index()\n data_rd = pd.merge(left_table,right_table,on='time', how='outer')\n if data_rd.shape[0] > 0:\n min_time = data_rd.time.min()\n max_time = data_rd.time.max()\n if station_id is not None:\n meteo_data = station_data(station_id, min_time, max_time)\n # print(meteo_data)\n data_rd['costic_HDD_15C'] = meteo_data.apply(costic, axis=1)\n data_rd = data_rd.merge(meteo_data, on='time', how='left')\n data_rd[['tavg', 'tmax', 'tmin']] = data_rd[['tavg', 'tmax', 'tmin']].astype('float')\n print(data_rd.head())\n data_rd.to_csv(\"file_name.csv\", index=False)\n for i in range(df.shape[0]):\n if df.iloc[i]['kpi_name'] == 'costic_HDD_15C':\n obj = kpi(df.iloc[i], data_rd, country, state, logs)\n dff = obj.return_log()\n logs = pd.concat([logs, dff], axis=0)\n if df.iloc[i]['mute_logs']:\n aliases_list = df.iloc[i]['mute_logs'].split(',')\n for alias in aliases_list:\n logs = logs[~logs.aliases.str.contains(alias.strip())]\n else:\n print(f\"Data is not available from {start_time} to {end_time}\")\n if logs.shape[0]>0:\n logs['time'] = logs['time'].dt.date\n logs.rename(columns={\"time\":\"date\"},inplace=True)\n logs['client_id'] = client_id\n logs['device_id'] = device_id\n logs['date'] = logs['date'].astype(str)\n print(tabulate(logs[['date','value']]))\n # load the logs dataframe to destination table in the bigquery\n # table = client.get_table(f\"{project}.{dataset_dest}.{table_dest}\")\n else:\n print('no logs saved')\n else:\n print(\"All rules are muted\")","def build_polling_location_txt(self):\n self.base_df['address_line'] = self.base_df.apply(\n lambda row: self.get_address_line(row['index'], row['address1'], row['address2'], row['city'],\n row['state'], row['zip_code']), axis=1)\n\n self.base_df['directions'] = self.base_df.apply(\n lambda row: self.get_directions(), axis=1)\n #\n self.base_df['hours'] = self.base_df.apply(\n lambda row: self.get_hours(row['index'],row['start_time'], row['end_time']), axis=1)\n\n self.base_df['photo_uri'] = self.base_df.apply(\n lambda row: self.get_photo_uri(), axis=1)\n\n self.base_df['hours_open_id'] = self.base_df.apply(\n lambda row: self.create_hours_open_id(row['index'], row['address1'], row['address2'], row['city'],\n row['state'], row['zip_code']), axis=1)\n\n self.base_df['is_drop_box'] = self.base_df.apply(\n lambda row: self.is_drop_box(), axis=1)\n\n self.base_df['is_early_voting'] = self.base_df.apply(\n lambda row: self.is_early_voting(), axis=1)\n\n self.base_df['latitude'] = self.base_df.apply(\n lambda row: self.get_latitude(), axis=1)\n\n self.base_df['longitude'] = self.base_df.apply(\n lambda row: self.get_longitude(), axis=1)\n\n self.base_df['latlng_source'] = self.base_df.apply(\n lambda row: self.get_latlng_source(), axis=1)\n\n self.base_df['id'] = self.base_df.apply(\n lambda row: self.create_id(row['index'], row['ocd_division'],row['address1'], row['address2'],\n row['city'], row['state'], row['zip_code']), axis=1)\n\n return self.base_df","def Find_nearest_dwd_stations(inpt_data,\r\n date_start='20051201',\r\n date_end='20201231',\r\n dwd_time_format='%Y%m%d%H',\r\n data_category='air_temperature',\r\n temp_resolution='hourly',\r\n no_of_nearest_stations=4,\r\n memory_save=True,\r\n Output='True'):\r\n if isinstance(data_category,list):\r\n if len(list(data_category)) > 1:\r\n print(\r\n 'Currently only one dwd category allowed, please run function multiple times for each category'\r\n )\r\n return None\r\n \r\n #convert time to datetime\r\n dt_start=datetime.strptime(date_start,'%Y%m%d')\r\n dt_end=datetime.strptime(date_end,'%Y%m%d')\r\n print('Start quering data from DWD')\r\n #define the database folder\r\n pypath = os.path.dirname(os.path.abspath(__file__))\r\n table_dir = pypath + '\\\\' + 'tables'\r\n dbase_dir = pypath + '\\\\' + 'dbase' \r\n #%% we check all available stations and create a valid list\r\n filename_stations=update_stationlist(time_res='hourly',dbase_dir=table_dir)\r\n stations_all=pd.read_csv(filename_stations, dtype={'STATIONS_ID': object})\r\n # delete all stations which do not cover the category\r\n dwd_stations=stations_all[stations_all[data_category]==True].copy()\r\n #correct to datetime\r\n dwd_stations['date_end']=pd.to_datetime(stations_all.date_end,format='%Y%m%d')\r\n dwd_stations['date_start']=pd.to_datetime(stations_all.date_start,format='%Y%m%d')\r\n # clean to stations which cover the campaign time #dt_low <= dt <= dt_high:\r\n dwd_stations=dwd_stations[(dwd_stations.date_start<=dt_start) & (dwd_stations.date_end>=dt_end)]\r\n #make a geodataframe out of it\r\n dwd_stations=gpd.GeoDataFrame(dwd_stations,geometry=gpd.points_from_xy(dwd_stations.geo_lon, dwd_stations.geo_lat))\r\n \r\n #loop through all rows to get the n closest points\r\n distances=pd.DataFrame()\r\n for _, station in dwd_stations.iterrows():\r\n distances[station.STATIONS_ID]=inpt_data.distance(station.geometry)\r\n \r\n #%% get the n stations with smallest distance and update database\r\n id_nearest_stations=distances.apply(lambda s: s.nsmallest(no_of_nearest_stations).index.tolist(), axis=1).values.tolist() #station ids\r\n #get them as unique values by sum a list of lists https://bit.ly/353iZQB\r\n id_dwd_stations=list(set(sum(id_nearest_stations,[])))\r\n \r\n #update the database\r\n db_dwd_stations=import_stations(time_res=temp_resolution,time_format=dwd_time_format,campaign_time=[dt_start,dt_end],data_category=data_category,station_ids=id_dwd_stations,dbase_dir=dbase_dir,Output=Output,table_dir=table_dir,memory_save=memory_save)\r\n \r\n #distance of nearest stattions\r\n dist_nearest_stations=pd.DataFrame(np.sort(distances.values)[:,:no_of_nearest_stations]).values.tolist() #distances themself\r\n #create new columns in the input data\r\n station_col_nm=list()\r\n for i in range(0,no_of_nearest_stations):\r\n station_col_nm.append(data_category+'_station_'+str(i))\r\n for i in range(0,no_of_nearest_stations):\r\n station_col_nm.append(data_category+'_distance_'+str(i))\r\n #create new dataframe\r\n distance_data=pd.concat([pd.DataFrame(id_nearest_stations).astype(int),pd.DataFrame(dist_nearest_stations)],axis=1)\r\n distance_data.columns=station_col_nm\r\n #add to main dataset\r\n inpt_data=pd.concat([inpt_data, distance_data],axis=1) \r\n \r\n return inpt_data,db_dwd_stations","def getEPADailyData(dateint, dt_ind, month, epa_df, yr):\n\n try:\n start = dateint + dt_ind * 10000\n end = start + 10001\n dly_epa_df = epa_df[(epa_df.created >= start) & (epa_df.created < end)]\n dly_epa_df.reset_index(inplace=True, drop=True)\n\n new_df = pd.DataFrame(columns=['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'epa_pm25_value', 'raw_concentration', 'aqi', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code', 'created'])\n for sitenm in dly_epa_df.site_name.unique():\n indx_ct = 0\n site_df = dly_epa_df[dly_epa_df.site_name == sitenm]\n for i in site_df.created.unique():\n indx_ct += 1\n new_df = pd.concat([new_df,site_df.iloc[indx_ct - 1:indx_ct]],ignore_index=True)\n\n if i != site_df.created.max(): # Don't interpolate the last record\n tmp_df = site_df.iloc[indx_ct - 1:indx_ct][['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code']]\n for j in range(1,6):\n new_dt = i + j * 10\n tmp_df['created'] = int(new_dt)\n tmp_df['epa_pm25_value'] = np.nan\n tmp_df['raw_concentration'] = np.nan\n tmp_df['aqi'] = np.nan\n new_df = pd.concat([new_df,tmp_df],ignore_index=True)\n\n # Convert aqi to numerica for so that it gets interpolated\n new_df[['aqi']] = new_df[['aqi']].replace(\"nan\", np.nan, regex=True)\n new_df[['aqi']] = new_df[['aqi']].apply(pd.to_numeric)\n\n new_df = new_df.interpolate(method='linear', limit_direction='forward', axis=0)\n\n int_epa_df = new_df[(new_df.created >= start) & (new_df.created < (end - 1))]\n int_epa_df.reset_index(inplace=True, drop=True)\n \n # Write to S3\n s3 = s3fs.S3FileSystem()\n myopen = s3.open\n write('midscapstone-whos-polluting-my-air/EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr), int_epa_df, compression='GZIP', open_with=myopen)\n s3_resource = boto3.resource('s3')\n s3_resource.Object('midscapstone-whos-polluting-my-air', 'EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr)).Acl().put(ACL='public-read')\n\n except Exception as e:\n print(\"*** EXCEPTION IN GET EPA DAILY DATA *** {}\".format(e))\n return int_epa_df","def get_sunspot_data(yy, time1):\n master = []\n num_of_ss = np.max(yy.flatten()) # get number of different SS's\n centroids = []\n sizes = []\n numbers = []\n\n for i in np.arange(1, num_of_ss + 1): # for each SS:\n temp_sunspot = SunSpot(1, 1, 1)\n copy_yy = np.array(yy, copy = True)\n copy_yy[copy_yy != i] = 0 # get only points == i\n copy_yy[copy_yy == i] = 1\n\n indices_x, indices_y = np.where(yy == i)\n\n max_lat = np.max(indices_x)\n min_lat = np.min(indices_x)\n mean_lat = max_lat - (max_lat - min_lat)/2\n \n max_lon = np.max(indices_y)\n min_lon = np.min(indices_y)\n mean_lon = max_lon - (max_lon - min_lon)/2\n \n temp_sunspot.mask = copy_yy\n temp_sunspot.centroid = [mean_lon, mean_lat]\n temp_sunspot.size = len(indices_x)\n temp_sunspot.number = i\n temp_sunspot.x_points = indices_x\n temp_sunspot.y_points = indices_y\n temp_sunspot.timestamp = time1\n temp_sunspot.min_x = min_lon\n temp_sunspot.max_x = max_lon\n temp_sunspot.min_y = min_lat\n temp_sunspot.max_y = max_lat\n\n master.append(temp_sunspot)\n\n return num_of_ss, master","def streaming_weather_data(**kwargs):\n df = weather_data(['San Francisco'])\n df['time'] = [pd.Timestamp.now()]\n return df.set_index('time')","def hotmap(datetime, room, query):\n\n #IMPORTANT: this query returns M-F values\n\n # get a df with date and count values, for a given room and date\n week_counts = pd.read_sql_query(query.format(date=datetime, room=room),\n connectDB())\n\n headcount = list(week_counts['count'])\n headcount = [linear_predictor(x) for x in headcount] # predict on all supplied values\n\n bins = list(week_counts['count'])\n # bins = [tertiary_classifier(x) for x in bins] # predict on all supplied values\n tert_dictionary = {'Empty': 0, 'Medium': 0.5, 'High': 1} # map to int as only ints can go in heatmap\n # bins = [tert_dictionary[x] for x in bins] # map with listcomp\n\n # 5 * 9 = 45, so all elements need to have 45 values. Days and hours are just multipled out.\n data = {'days': ['fri']*9 + ['thu']*9 + ['wed']*9 + ['tue']*9 + ['mon']*9,\n 'occupancy': headcount,\n 'hours': ['9', '10', '11', '12', '13', '14', '15', '16', '17']*5}\n\n week_counts['headcount'] = week_counts['count'].apply(linear_predictor)\n week_counts['bins'] = week_counts['count'].apply(tertiary_classifier)\n\n source = ColumnDataSource(ColumnDataSource.from_df(week_counts))\n\n\n # hover = HoverTool()\n #\n # tooltips = [(\"headcount\", \"@headcount\")]\n #\n # tools = [hover]\n\n # colors = ['#1abc9c', '#ec583a', '#474e5d'] # set colors; currently not shading on continuous features\n\n hm = HeatMap(data, x='hours', y='days', values='occupancy',\n title='Occupancy in room over the week'.format(room), stat=None,\n palette=palette, tools=None,\n hover_tool=True)\n\n legend = Legend(location=(0, -30))\n\n hm.add_layout(legend, 'right')\n\n all_plots = gridplot([[hm]])\n\n return all_plots","def F_interp_geos_mat(sounding_lon,sounding_lat,sounding_datenum,\\\n geos_dir='/mnt/Data2/GEOS/s5p_interp/',\\\n interp_fields=['TROPPT']):\n from scipy.io import loadmat\n from scipy.interpolate import RegularGridInterpolator\n \n start_datenum = np.amin(sounding_datenum)\n end_datenum = np.amax(sounding_datenum)\n start_datetime = datedev_py(start_datenum)\n start_year = start_datetime.year\n start_month = start_datetime.month\n start_day = start_datetime.day\n start_hour = start_datetime.hour\n \n end_datetime = datedev_py(end_datenum)\n end_year = end_datetime.year\n end_month = end_datetime.month\n end_day = end_datetime.day\n end_hour = end_datetime.hour\n end_minute = end_datetime.minute\n end_second = end_datetime.second\n \n step_hour = 3 # geos fp data are 3-hourly\n \n geos_start_hour = start_hour-start_hour%step_hour\n geos_start_datetime = datetime.datetime(year=start_year,month=start_month,day=start_day,hour=geos_start_hour)\n if end_hour > 24-step_hour or (end_hour == 24-step_hour and (end_minute > 0 or end_second > 0)):\n geos_end_hour = 0\n geos_end_datetime = datetime.datetime(year=end_year,month=end_month,day=end_day,hour=geos_end_hour) +datetime.timedelta(days=1)\n elif end_hour%step_hour == 0 and end_minute == 0 and end_second == 0:\n geos_end_hour = end_hour\n geos_end_datetime = datetime.datetime(year=end_year,month=end_month,day=end_day,hour=geos_end_hour)\n else:\n geos_end_hour = (step_hour-(end_hour+1)%step_hour)%step_hour+end_hour+1\n geos_end_datetime = datetime.datetime(year=end_year,month=end_month,day=end_day,hour=geos_end_hour)\n \n nstep = (geos_end_datetime-geos_start_datetime).total_seconds()/3600/step_hour+1\n nstep = int(nstep)\n \n geos_data = {}\n # load narr data\n for istep in range(nstep):\n file_datetime = geos_start_datetime+datetime.timedelta(hours=step_hour*istep)\n file_dir = os.path.join(geos_dir,file_datetime.strftime('Y%Y'),\\\n file_datetime.strftime('M%m'),\\\n file_datetime.strftime('D%d'))\n file_path = os.path.join(file_dir,'subset_'+file_datetime.strftime('%Y%m%d_%H')+'.mat')\n if not geos_data:\n mat_data = loadmat(file_path,variable_names=np.concatenate((['lat','lon'],interp_fields)))\n geos_data['lon'] = mat_data['lon'].flatten()\n geos_data['lat'] = mat_data['lat'].flatten()\n geos_data['datenum'] = np.zeros((nstep),dtype=np.float64)\n for fn in interp_fields:\n geos_data[fn] = np.zeros((len(geos_data['lon']),len(geos_data['lat']),nstep))\n geos_data[fn][...,istep] = mat_data[fn]\n else:\n mat_data = loadmat(file_path,variable_names=interp_fields)\n for fn in interp_fields:\n geos_data[fn][...,istep] = mat_data[fn]\n \n geos_data['datenum'][istep] = (file_datetime.toordinal()\\\n +file_datetime.hour/24.\\\n +file_datetime.minute/1440.\\\n +file_datetime.second/86400.+366.)\n # interpolate\n sounding_interp = {}\n for fn in interp_fields:\n my_interpolating_function = \\\n RegularGridInterpolator((geos_data['lon'],geos_data['lat'],geos_data['datenum']),\\\n geos_data[fn],bounds_error=False,fill_value=np.nan)\n sounding_interp[fn] = my_interpolating_function((sounding_lon,sounding_lat,sounding_datenum))\n return sounding_interp","def get_hr_radar_dap_data(dap_urls, st_list, jd_start, jd_stop):\n df_list = []\n for url in dap_urls:\n # Only look at 6 km hf radar.\n if ('http://hfrnet.ucsd.edu/thredds/dodsC/HFRNet/USWC/' in url and\n \"6km\" in url and \"GNOME\" in url):\n print(url)\n # Get URL.\n nc = Dataset(url, 'r')\n lat_dim = nc.variables['lat']\n lon_dim = nc.variables['lon']\n time_dim = nc.variables['time']\n u_var, v_var = None, None\n standard_name_u = \"surface_eastward_sea_water_velocity\"\n standard_name_v = \"surface_northward_sea_water_velocity\"\n for key in nc.variables.keys():\n key_dim = nc.variables[key]\n try:\n if key_dim.standard_name == standard_name_u:\n u_var = key_dim\n elif key_dim.standard_name == standard_name_v:\n v_var = key_dim\n elif key_dim.standard_name == \"time\":\n time = key_dim\n except:\n # Only if the standard name is not available.\n pass\n # Manage dates.\n dates = num2date(time_dim[:], units=time_dim.units,\n calendar='gregorian')\n date_idx = []\n date_list = []\n for i, date in enumerate(dates):\n if jd_start < date < jd_stop:\n date_idx.append(i)\n date_list.append(date)\n # Manage location.\n for st in st_list:\n station = st_list[st]\n f_lat = station['lat']\n f_lon = station['lon']\n\n ret = find_nearest(f_lat, f_lon, lat_dim[:], lon_dim[:])\n lat_idx = ret[0]\n lon_idx = ret[1]\n dist_deg = ret[2]\n\n if len(u_var.dimensions) == 3:\n # 3D.\n ret = cycleAndGetData(u_var, v_var, date_idx,\n lat_idx, lon_idx)\n u_vals = ret[0]\n v_vals = ret[1]\n\n lat_idx = ret[2]\n lon_idx = ret[3]\n\n print(\"lat, lon, dist = {} {}\".format(ret[2], ret[3]))\n try:\n # Turn vectors in the speed and direction.\n ws = uv2ws(u_vals, v_vals)\n wd = uv2wd(u_vals, v_vals)\n\n data = dict()\n data['sea_water_speed (cm/s)'] = ws\n data['direction_of_sea_water_velocity (degree)'] = wd\n time = np.array(date_list)\n columns = ['sea_water_speed (cm/s)',\n 'direction_of_sea_water_velocity (degree)']\n df = DataFrame(data=data, index=time, columns=columns)\n df_list.append({\"name\": st,\n \"data\": df,\n \"lat\": lat_dim[lat_idx],\n \"lon\": lon_dim[lon_idx],\n \"ws_pts\": np.count_nonzero(~np.isnan(ws)),\n \"wd_pts\": np.count_nonzero(~np.isnan(wd)),\n \"dist\": dist_deg,\n \"from\": url})\n except Exception as e:\n print(\"\\t\\terror: {}\".format(e))\n else:\n pass\n return df_list","def extract_tt_by_periods(ttri, periods, start_time, end_time, filters):\n logger = getLogger(__name__)\n # sess = conn.get_session()\n das = {}\n all_wz_features = {}\n all_wz_laneconfigs = {}\n\n # collecting daily data\n for prd in periods:\n logger.debug('>>>> retrieving data for %s' % prd.get_date_string())\n year = prd.start_date.year\n sdate = prd.start_date\n edate = prd.end_date\n if year not in das:\n da_tt = tt.TravelTimeDataAccess(year)\n da_tt_wz = tt_workzone.TTWorkZoneDataAccess(year)\n da_tt_wz_feature = wz_feature.WZFeatureDataAccess()\n da_tt_wz_lncfg = wz_laneconfig.WZLaneConfigDataAccess()\n da_tt_weather = tt_weather.TTWeatherDataAccess(year)\n da_tt_snowmgmt = tt_snowmgmt.TTSnowManagementDataAccess(year)\n da_tt_incident = tt_incident.TTIncidentDataAccess(year)\n da_tt_specialevent = tt_specialevent.TTSpecialeventDataAccess(year)\n das[year] = (\n da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\n da_tt_specialevent)\n\n (da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\n da_tt_specialevent) = das[year]\n\n # traveltimes = da_tt.list_by_period(ttri.id, self.prd)\n weathers = da_tt_weather.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.WeatherInfo] \"\"\"\n workzones = da_tt_wz.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.WorkZoneInfo] \"\"\"\n incidents = da_tt_incident.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.IncidentInfo] \"\"\"\n snowmgmts = da_tt_snowmgmt.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.SnowManagementInfo] \"\"\"\n specialevents = da_tt_specialevent.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.SpecialEventInfo] \"\"\"\n traveltimes = da_tt.list_by_period(ttri.id, prd)\n \"\"\":type: list[pyticas_tetres.ttrms_types.TravelTimeInfo] \"\"\"\n\n if not any(weathers):\n logger.debug('>>>> end of retrieving data for %s (no weather data)' % prd.get_date_string())\n continue\n\n extras = {\n 'weathers': {_tt.id: [] for _tt in traveltimes},\n 'workzones': {_tt.id: [] for _tt in traveltimes},\n 'incidents': {_tt.id: [] for _tt in traveltimes},\n 'specialevents': {_tt.id: [] for _tt in traveltimes},\n 'snowmgmts': {_tt.id: [] for _tt in traveltimes},\n }\n \"\"\":type: dict[str, dict[int, list]]\"\"\"\n\n _put_to_bucket(ttri, weathers, extras['weathers'], da_tt_weather, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, workzones, extras['workzones'], da_tt_wz, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, incidents, extras['incidents'], da_tt_incident, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, snowmgmts, extras['snowmgmts'], da_tt_snowmgmt, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, specialevents, extras['specialevents'], da_tt_specialevent, year, all_wz_features, all_wz_laneconfigs, das)\n\n for tti in traveltimes:\n _tt_weathers = extras['weathers'][tti.id]\n extdata = ExtData(tti,\n _tt_weathers[0] if _tt_weathers else None,\n extras['incidents'][tti.id],\n extras['workzones'][tti.id],\n extras['specialevents'][tti.id],\n extras['snowmgmts'][tti.id])\n\n if start_time <= tti.str2datetime(tti.time).time() <= end_time:\n for ef in filters:\n try:\n ef.check(extdata)\n except Exception as ex:\n tb.traceback(ex)\n logger.debug('>>>> end of retrieving data for %s (error occured 1)' % prd.get_date_string())\n continue\n else:\n for ef in filters:\n try:\n ef.check_outofrange(extdata)\n except Exception as ex:\n tb.traceback(ex)\n logger.debug('>>>> end of retrieving data for %s (error occured 2)' % prd.get_date_string())\n continue\n\n del extras\n logger.debug('>>>> end of retrieving data for %s' % prd.get_date_string())\n\n # sess.close()","def compute_templates(filename, TDUR, filt, ratios, dt, ncor, window, \\\n winlength, nattempts, waittime, method='RMS'):\n # To transform latitude and longitude into kilometers\n a = 6378.136\n e = 0.006694470\n lat0 = 41.0\n lon0 = -123.0\n dx = (pi / 180.0) * a * cos(lat0 * pi / 180.0) / sqrt(1.0 - e * e * \\\n sin(lat0 * pi / 180.0) * sin(lat0 * pi / 180.0))\n dy = (3.6 * pi / 648.0) * a * (1.0 - e * e) / ((1.0 - e * e * sin(lat0 * \\\n pi / 180.0) * sin(lat0 * pi / 180.0)) ** 1.5)\n\n # Get the names of the stations which have a waveform for this LFE family\n file = open('../data/Plourde_2015/detections/' + filename + \\\n '_detect5_cull.txt')\n first_line = file.readline().strip()\n staNames = first_line.split()\n file.close()\n\n # Get the time of LFE detections\n LFEtime = np.loadtxt('../data/Plourde_2015/detections/' + filename + \\\n '_detect5_cull.txt', \\\n dtype={'names': ('unknown', 'day', 'hour', 'second', 'threshold'), \\\n 'formats': (np.float, '|S6', np.int, np.float, np.float)}, \\\n skiprows=2)\n\n # Get the network, channels, and location of the stations\n staloc = pd.read_csv('../data/Plourde_2015/station_locations.txt', \\\n sep=r'\\s{1,}', header=None)\n staloc.columns = ['station', 'network', 'channels', 'location', \\\n 'server', 'latitude', 'longitude']\n\n # Get the location of the source of the LFE\n LFEloc = np.loadtxt('../data/Plourde_2015/templates_list.txt', \\\n dtype={'names': ('name', 'family', 'lat', 'lon', 'depth', 'eH', \\\n 'eZ', 'nb'), \\\n 'formats': ('S13', 'S3', np.float, np.float, np.float, \\\n np.float, np.float, np.int)}, \\\n skiprows=1)\n for ie in range(0, len(LFEloc)):\n if (filename == LFEloc[ie][0].decode('utf-8')):\n lats = LFEloc[ie][2]\n lons = LFEloc[ie][3]\n xs = dx * (lons - lon0)\n ys = dy * (lats - lat0)\n\n # Create directory to store the waveforms\n namedir = 'templates/' + filename\n if not os.path.exists(namedir):\n os.makedirs(namedir)\n\n # Read origin time and station slowness files\n origintime = pickle.load(open('timearrival/origintime.pkl', 'rb'))\n slowness = pickle.load(open('timearrival/slowness.pkl', 'rb'))\n\n # File to write error messages\n errorfile = 'error/' + filename + '.txt'\n\n # Loop over stations\n for station in staNames:\n # Create streams\n EW = Stream()\n NS = Stream()\n UD = Stream()\n # Get station metadata for downloading\n for ir in range(0, len(staloc)):\n if (station == staloc['station'][ir]):\n network = staloc['network'][ir]\n channels = staloc['channels'][ir]\n location = staloc['location'][ir]\n server = staloc['server'][ir]\n # Compute source-receiver distance\n latitude = staloc['latitude'][ir]\n longitude = staloc['longitude'][ir]\n xr = dx * (longitude - lon0)\n yr = dy * (latitude - lat0)\n distance = sqrt((xr - xs) ** 2.0 + (yr - ys) ** 2.0)\n # Loop on LFEs\n for i in range(0, np.shape(LFEtime)[0]):\n YMD = LFEtime[i][1]\n myYear = 2000 + int(YMD[0 : 2])\n myMonth = int(YMD[2 : 4])\n myDay = int(YMD[4 : 6])\n myHour = LFEtime[i][2] - 1\n myMinute = int(LFEtime[i][3] / 60.0)\n mySecond = int(LFEtime[i][3] - 60.0 * myMinute)\n myMicrosecond = int(1000000.0 * \\\n (LFEtime[i][3] - 60.0 * myMinute - mySecond))\n Tori = UTCDateTime(year=myYear, month=myMonth, day=myDay, \\\n hour=myHour, minute=myMinute, second=mySecond, \\\n microsecond=myMicrosecond)\n Tstart = Tori - TDUR\n Tend = Tori + 60.0 + TDUR\n # First case: we can get the data from IRIS\n if (server == 'IRIS'):\n (D, orientation) = get_from_IRIS(station, network, channels, \\\n location, Tstart, Tend, filt, dt, nattempts, waittime, \\\n errorfile)\n # Second case: we get the data from NCEDC\n elif (server == 'NCEDC'):\n (D, orientation) = get_from_NCEDC(station, network, channels, \\\n location, Tstart, Tend, filt, dt, nattempts, waittime, \\\n errorfile)\n else:\n raise ValueError( \\\n 'You can only download data from IRIS and NCEDC')\n if (type(D) == obspy.core.stream.Stream):\n # Add to stream\n if (channels == 'EH1,EH2,EHZ'):\n EW.append(D.select(channel='EH1').slice(Tori, \\\n Tori + 60.0)[0])\n NS.append(D.select(channel='EH2').slice(Tori, \\\n Tori + 60.0)[0])\n UD.append(D.select(channel='EHZ').slice(Tori, \\\n Tori + 60.0)[0])\n else:\n EW.append(D.select(component='E').slice(Tori, \\\n Tori + 60.0)[0])\n NS.append(D.select(component='N').slice(Tori, \\\n Tori + 60.0)[0])\n UD.append(D.select(component='Z').slice(Tori, \\\n Tori + 60.0)[0])\n else:\n print('Failed at downloading data')\n # Stack\n if (len(EW) > 0 and len(NS) > 0 and len(UD) > 0):\n # Stack waveforms\n EWstack = linstack([EW], normalize=True, method=method) \n NSstack = linstack([NS], normalize=True, method=method)\n UDstack = linstack([UD], normalize=True, method=method)\n # Initializations\n maxCC = np.zeros(len(EW))\n cc0EW = np.zeros(len(EW))\n cc0NS = np.zeros(len(EW))\n cc0UD = np.zeros(len(EW))\n if (window == True):\n # Get time arrival\n arrivaltime = origintime[filename] + \\\n slowness[station] * distance\n Tmin = arrivaltime - winlength / 2.0\n Tmax = arrivaltime + winlength / 2.0\n if Tmin < 0.0:\n Tmin = 0.0\n if Tmax > EWstack[0].stats.delta * (EWstack[0].stats.npts - 1):\n Tmax = EWstack[0].stats.delta * (EWstack[0].stats.npts - 1)\n ibegin = int(Tmin / EWstack[0].stats.delta)\n iend = int(Tmax / EWstack[0].stats.delta) + 1\n # Cross correlation\n for i in range(0, len(EW)):\n ccEW = correlate(EWstack[0].data[ibegin : iend], \\\n EW[i].data[ibegin : iend], ncor)\n ccNS = correlate(NSstack[0].data[ibegin : iend], \\\n NS[i].data[ibegin : iend], ncor)\n ccUD = correlate(UDstack[0].data[ibegin : iend], \\\n UD[i].data[ibegin : iend], ncor)\n maxCC[i] = np.max(ccEW) + np.max(ccNS) + np.max(ccUD)\n cc0EW[i] = ccEW[ncor]\n cc0NS[i] = ccNS[ncor]\n cc0UD[i] = ccUD[ncor]\n else:\n # Cross correlation\n for i in range(0, len(EW)):\n ccEW = correlate(EWstack[0].data, EW[i].data, ncor)\n ccNS = correlate(NSstack[0].data, NS[i].data, ncor)\n ccUD = correlate(UDstack[0].data, UD[i].data, ncor)\n maxCC[i] = np.max(ccEW) + np.max(ccNS) + np.max(ccUD)\n cc0EW[i] = ccEW[ncor]\n cc0NS[i] = ccNS[ncor]\n cc0UD[i] = ccUD[ncor]\n # Sort cross correlations\n index = np.flip(np.argsort(maxCC), axis=0)\n EWbest = Stream()\n NSbest = Stream()\n UDbest = Stream()\n # Compute stack of best LFEs\n for j in range(0, len(ratios)):\n nLFE = int(ratios[j] * len(EW) / 100.0)\n EWselect = Stream()\n NSselect = Stream()\n UDselect = Stream()\n for i in range(0, nLFE):\n EWselect.append(EW[index[i]])\n NSselect.append(NS[index[i]])\n UDselect.append(UD[index[i]])\n # Stack best LFEs\n EWbest.append(linstack([EWselect], normalize=True, \\\n method=method)[0])\n NSbest.append(linstack([NSselect], normalize=True, \\\n method=method)[0])\n UDbest.append(linstack([UDselect], normalize=True, \\\n method=method)[0])\n # Plot figure\n plt.figure(1, figsize=(20, 15))\n params = {'xtick.labelsize':16,\n 'ytick.labelsize':16}\n pylab.rcParams.update(params) \n colors = cm.rainbow(np.linspace(0, 1, len(ratios)))\n # East - West component\n ax1 = plt.subplot(311)\n dt = EWstack[0].stats.delta\n nt = EWstack[0].stats.npts\n t = dt * np.arange(0, nt)\n for j in range(0, len(ratios)):\n if (method == 'RMS'):\n norm = EWbest[j].data / np.sqrt(np.mean(np.square( \\\n EWbest[j].data)))\n elif (method == 'MAD'):\n norm = EWbest[j].data / np.median(np.abs(EWbest[j].data - \\\n np.median(EWbest[j].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, color = colors[j], \\\n label = str(int(ratios[j])) + '%')\n if (method == 'RMS'):\n norm = EWstack[0].data / np.sqrt(np.mean(np.square( \\\n EWstack[0].data)))\n elif (method == 'MAD'):\n norm = EWstack[0].data / np.median(np.abs(EWstack[0].data - \\\n np.median(EWstack[0].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, 'k', label='All')\n if (window == True):\n plt.axvline(Tmin, linewidth=2, color='grey')\n plt.axvline(Tmax, linewidth=2, color='grey')\n plt.xlim([np.min(t), np.max(t)])\n plt.title('East - West component', fontsize=24)\n plt.xlabel('Time (s)', fontsize=24)\n plt.legend(loc=1)\n # North - South component\n ax2 = plt.subplot(312)\n dt = NSstack[0].stats.delta\n nt = NSstack[0].stats.npts\n t = dt * np.arange(0, nt)\n for j in range(0, len(ratios)):\n if (method == 'RMS'):\n norm = NSbest[j].data / np.sqrt(np.mean(np.square( \\\n NSbest[j].data)))\n elif (method == 'MAD'):\n norm = NSbest[j].data / np.median(np.abs(NSbest[j].data - \\\n np.median(NSbest[j].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, color = colors[j], \\\n label = str(int(ratios[j])) + '%')\n if (method == 'RMS'):\n norm = NSstack[0].data / np.sqrt(np.mean(np.square( \\\n NSstack[0].data)))\n elif (method == 'MAD'):\n norm = NSstack[0].data / np.median(np.abs(NSstack[0].data - \\\n np.median(NSstack[0].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, 'k', label='All')\n if (window == True):\n plt.axvline(Tmin, linewidth=2, color='grey')\n plt.axvline(Tmax, linewidth=2, color='grey')\n plt.xlim([np.min(t), np.max(t)])\n plt.title('North - South component', fontsize=24)\n plt.xlabel('Time (s)', fontsize=24)\n plt.legend(loc=1)\n # Vertical component\n ax3 = plt.subplot(313)\n dt = UDstack[0].stats.delta\n nt = UDstack[0].stats.npts\n t = dt * np.arange(0, nt)\n for j in range(0, len(ratios)):\n if (method == 'RMS'):\n norm = UDbest[j].data / np.sqrt(np.mean(np.square( \\\n UDbest[j].data)))\n elif (method == 'MAD'):\n norm = UDbest[j].data / np.median(np.abs(UDbest[j].data - \\\n np.median(UDbest[j].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, color = colors[j], \\\n label = str(int(ratios[j])) + '%')\n if (method == 'RMS'):\n norm = UDstack[0].data / np.sqrt(np.mean(np.square( \\\n UDstack[0].data)))\n elif (method == 'MAD'):\n norm = UDstack[0].data / np.median(np.abs(UDstack[0].data - \\\n np.median(UDstack[0].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, 'k', label='All')\n if (window == True):\n plt.axvline(Tmin, linewidth=2, color='grey')\n plt.axvline(Tmax, linewidth=2, color='grey')\n plt.xlim([np.min(t), np.max(t)])\n plt.title('Vertical component', fontsize=24)\n plt.xlabel('Time (s)', fontsize=24)\n plt.legend(loc=1)\n # End figure\n plt.suptitle(station, fontsize=24)\n plt.savefig(namedir + '/' + station + '.eps', format='eps')\n ax1.clear()\n ax2.clear()\n ax3.clear()\n plt.close(1)\n # Save stacks into files\n savename = namedir + '/' + station +'.pkl'\n pickle.dump([EWstack[0], NSstack[0], UDstack[0]], \\\n open(savename, 'wb'))\n for j in range(0, len(ratios)):\n savename = namedir + '/' + station + '_' + \\\n str(int(ratios[j])) + '.pkl'\n pickle.dump([EWbest[j], NSbest[j], UDbest[j]], \\\n open(savename, 'wb'))\n # Save cross correlations into files\n savename = namedir + '/' + station + '_cc.pkl'\n pickle.dump([cc0EW, cc0NS, cc0UD], \\\n open(savename, 'wb'))","def windTimeSeries(ftp, rootFolder, selectedDate, outputFolder, ymax):\n \n dataType = 'wind_reconstruction_data'\n file_list = []\n \n\n # Retrieve data from folders in FTP\n \n possible_hours = [format(x,'02d') for x in range(24)]\n from_zone = tz.gettz('America/Mexico_City')\n to_zone = tz.gettz('UTC')\n \n for item in possible_hours:\n \n local_time = datetime.strptime(selectedDate+item, '%Y-%m-%d%H')\n local_time = local_time.replace(tzinfo=from_zone)\n \n utc_date = local_time.astimezone(to_zone)\n \n utc_day = utc_date.strftime(\"%Y-%m-%d\")\n \n utc_hour = utc_date.strftime(\"%H\")\n \n time_folder = rootFolder+utc_day+'/'+dataType+'/'+utc_hour+'-00/'\n \n creat_min = []\n try:\n\n ftp.dir(time_folder, creat_min.append)\n dbs = []\n for i in range(len(creat_min)):\n if creat_min[i].find('DBS') != -1:\n dbs.append(creat_min[i])\n\n if len(dbs) == 0:\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\n\n elif len(dbs) == 1:\n if dbs[0].find('-25-'):\n file_list.append(time_folder+dbs[0][55:])\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\n elif dbs[0].find('-55-'):\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\n file_list.append(time_folder+dbs[0][55:])\n else:\n for i in range(len(dbs)):\n file_list.append(time_folder+dbs[i][55:])\n\n except:\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\n\n\n \n # Create output folder\n outputFolder = outputFolder+'/'+selectedDate\n try:\n mkdirp(outputFolder)\n except:\n print('warning: folder '+outputFolder+' already exists')\n \n # Make 'generic' outputfile\n outFile = outputFolder+'/'\n\n try:\n \n meas_time = []\n Usets = []\n Vsets = []\n Wsets = []\n \n for working_file in file_list:\n\n if working_file.find('missing_data') != -1:\n Xset = pd.DataFrame(np.nan,columns=['Xwind'],index=range(100,2550,50))\n Yset = pd.DataFrame(np.nan,columns=['Ywind'],index=range(100,2550,50))\n Zset = pd.DataFrame(np.nan,columns=['Zwind'],index=range(100,2550,50))\n match = (re.search('\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}',working_file)).group(0)\n meas_time.append(match)\n\n else: \n match = (re.search('\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}',working_file)).group(0)\n #hour_min = re.search('_\\d{2}-\\d{2}-\\d{2}',working_file).group(0)\n meas_time.append(match)\n \n obj = DataContainer()\n ftp.retrbinary('RETR %s' % working_file, obj.readFromFTP)\n columns = ['Timestamp', 'Azimuth', 'Elevation','Range','Xwind','Ywind','Zwind',\\\n 'CNR','ConfIdx']\n\n # Get the date from the file as a DataFrame\n data = obj.dataToArray(columns)\n data.drop(data[data.Range > ymax].index, inplace=True)\n data = data[['Range','Xwind','Ywind','Zwind']]\n # Replace abs values Xwind and Ywind greater than 50 with NaN\n \n data.Xwind.where((data.Xwind < 50) | (data.Xwind > -50),np.nan,inplace=True)\n data.Ywind.where((data.Ywind > 50) | (data.Ywind > -50),np.nan,inplace=True)\n\n dataByRange = data.groupby('Range').mean() \n \n Xset = dataByRange[['Xwind']]\n Yset = dataByRange[['Ywind']]\n Zset = dataByRange[['Zwind']]\n \n Usets.append(Xset)\n Vsets.append(Yset)\n Wsets.append(Zset)\n \n df_U = pd.concat(Usets,axis=1)\n df_V = pd.concat(Vsets,axis=1)\n df_W = pd.concat(Wsets,axis=1)\n \n U = df_U.values;\n V = df_V.values;\n W = df_W.values;\n \n # Vertical wind grid\n x = np.linspace(0,48,49)\n y = np.linspace(100,2500,49)\n X, Y = np.meshgrid(x, y)\n \n # If the plot is going to have a variable pcolormesh uncomment next section\n# cmapLim = 0\n# if abs(np.nanmax(W)) > abs(np.nanmin(W)):\n# cmapLim = abs(np.nanmax(W))\n# else:\n# cmapLim = abs(np.nanmin(W))\n \n # Change back the UTC times to local time\n \n timelabels = []\n to_zone = tz.gettz('America/Mexico_City')\n from_zone = tz.gettz('UTC')\n \n for entry in meas_time:\n utc_time = datetime.strptime(entry, '%Y-%m-%d_%H-%M')\n utc_time = utc_time.replace(tzinfo=from_zone)\n \n local = utc_time.astimezone(to_zone)\n \n timelabels.append(local.strftime(\"%H:%M\"))\n \n timelabels = [i.replace('25','00') for i in timelabels[2:48:4]]\n \n # Plot \n title = ('Perfil vertical del viento, %s (serie de tiempo)' % selectedDate)\n fig, ax = plt.subplots(figsize=(20,8))\n \n # For variable pcolormesh uncomment next line\n #cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= (-cmapLim), vmax=cmapLim)\n \n cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= -3, vmax=3)\n ax.set_title(title,fontsize = 24)\n ax.set_xlabel('Hora Local [hh:mm]', fontsize = 18, labelpad = 14)\n ax.set_xticks(np.arange(2.5,48, step=4))\n ax.set_xticklabels(timelabels, rotation=45, size=12)\n ax.set_ylabel('Altura [m]', fontsize = 18, labelpad = 18)\n ax.yaxis.set_tick_params(labelsize=12)\n ax.text(0.18,-0.08,'Datos de perfilador LIDAR Leosphere Windcube 100s. Lat 19.3262, Lon -99.1761, Alt 2280 msnm',transform=plt.gcf().transFigure, fontdict={'size': 18})\n \n # Colorbar\n cbaxes = fig.add_axes([0.91, 0.125, 0.02, 0.4]) \n cbar = fig.colorbar(cax, cax = cbaxes)\n cbar.set_label(r'Viento vertical [$m\\,s^{-1}$]', fontsize = 18)\n cbaxes.text(-0.3,1.07,'(Hacia arriba)')\n cbaxes.text(-0.3,-0.07,'(Hacia abajo)') \n \n \n #Other a artists\n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/time_adv.png')\n newax = fig.add_axes([0.6, -.03, 0.06, 0.06], anchor='NW', zorder=0) #[xo,yo,width,height]\n newax.imshow(im)\n newax.axis('off')\n \n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/directions.png')\n newax1 = fig.add_axes([0.90, 0.75, 0.15, 0.15], anchor='NW', zorder=0) #[xo,yo,width,height]\n newax1.imshow(im)\n newax1.axis('off')\n \n \n # Horizontal wind grid\n \n x_h = np.arange(0, len(meas_time), 1)\n Xh, Yh = np.meshgrid(x_h, y)\n \n # Plot arrows\n Q = ax.quiver(Xh[::2], Yh[::2], U[::2], V[::2], pivot = 'mid',alpha=0.75)\n ax.quiverkey(Q, 0.93, 0.68, 10, r'$10\\,m\\,s^{-1}$',coordinates = 'figure', labelpos='N' )\n \n \n finalOutputFile = outFile+'timeseries_daily_'+selectedDate\n fig.savefig(finalOutputFile,bbox_inches='tight')\n plt.close()\n \n except Exception as e:\n print(\"Problema calculando Wind Time Series\", e)","def export_lateness_data( gpssched, sched_error ):\n \n sql = \"\"\"\ninsert into datamining_table dt\n( gps_segment_id, gtfs_trip_id, rms_schedule_error, vehicle_id,\n route_name, vehicle_type, service_id, direction_id,\n stop_lat, stop_lon, stop_id, stop_sequence,\n scheduled_arrival_time, scheduled_departure_time,\n actual_arrival_time, lateness, prev_stop_id )\nvalues\n( null, %(trip_id)s, %(sched_err)s, %(vehid)s,\n %(routename)s, %(vehtype)s, %(service_id)s, %(dir_id)s,\n %(stoplat)s,%(stoplon)s,%(stopid)s,%(stopseq)s,\n %(sched_arr)s,%(sched_dep)s,%(actual_arr)s,%(lateness)s,\n %(prev_stop_id)s )\n\"\"\"\n \n gtfs = gpssched.getGTFSSchedule()\n\n basedict = { 'trip_id' : gtfs.trip_id,\n 'sched_err' : sched_error,\n 'vehid' : gpssched.segment.vehicle_id,\n 'routename' : gtfs.route_short_name,\n 'vehtype' : gtfs.route_type,\n 'service_id' : gtfs.service_id,\n 'dir_id' : gtfs.direction_id\n }\n\n cur = get_cursor()\n \n for arrival in gpssched.getGPSSchedule():\n print dict(arrival)\n stopdict = dict(basedict)\n stopdict.update ( { 'stoplat' : arrival['stop_lat'],\n 'stoplon' : arrival['stop_lon'],\n 'stopid' : arrival['stop_id'],\n 'stopseq' : arrival['stop_sequence'],\n 'sched_arr' : arrival['arrival_time_seconds'],\n 'sched_dep' : arrival['departure_time_seconds'],\n 'actual_arr' : arrival['actual_arrival_time_seconds'],\n 'lateness' : arrival['actual_arrival_time_seconds'] \\\n - arrival['departure_time_seconds'],\n 'prev_stop_id' : arrival['prev_stop_id']\n } )\n SQLExec(cur, sql, stopdict)\n\n\n cur.close()","def Collect_data(clo, cla, FD, LD, ZC, idx_time, cntry, TZ):\r\n #initiate the console response\r\n rep = \"\"\r\n global ddf\r\n \r\n #define the noaa server access\r\n server_noaa = \"https://www.ncei.noaa.gov/data/global-hourly/access/\"\r\n \r\n #Initate dateframe\r\n data = pd.DataFrame()\r\n \r\n #Convert date from specified timezone to UTC\r\n FDc = Date_calibration(FD, 1, TZ)\r\n LDc = Date_calibration(LD, 1, TZ)\r\n \r\n #define timestep\r\n timestep = \"1 H\"\r\n \r\n #Loop on the range time by each year\r\n for y in range(FDc.year, (LDc.year + 1)):\r\n rep += '--- Collect Year ['+str(y) + '] --- \\n' \r\n \r\n #Loop on each weather station while the ouput data is good\r\n #weather station in the year instancied dataframe \r\n for i in range(len(ddf[y])) :\r\n \r\n #Define the memory key : year_zipcode\r\n key_d_z = str(y)+'_'+str(ZC)\r\n \r\n #Verify if the data is already in the memory\r\n if(key_d_z in memory_df.keys()) :\r\n #The data is already in the memory :\r\n #Collect the data and go next (save compute time and server solicitation)\r\n ext_data = memory_df[key_d_z]\r\n NS, DO = memory_NS_DO[key_d_z].split('_')[0], memory_NS_DO[key_d_z].split('_')[1]\r\n break\r\n else:\r\n \r\n #The data isn't in the memory :\r\n \r\n #Collect information about the nearest weather station from the zip code\r\n rs, NS, DO = Fetch_station(clo, cla, y)\r\n \r\n #Generate the ftp key weather station\r\n code_station = str(ddf[y]['USAF'][rs])+str(ddf[y]['WBAN'][rs])\r\n \r\n #Define the server access\r\n url = server_noaa+str(y)+'/'+code_station+'.csv'\r\n \r\n #Get the data\r\n req = requests.get(url)\r\n \r\n #The server answer\r\n if(req.status_code == 200):\r\n \r\n #Extract the data (only date and temperature)\r\n ext_data = pd.read_csv(url, usecols=['DATE','TMP'])\r\n \r\n #Check if the data isn't empty (1000 is arbitrary)\r\n if(len(ext_data) > 1000):\r\n \r\n #Format data\r\n ext_data, qual = Data_formatting(ext_data, y, timestep, TZ)\r\n \r\n #Check if the data quality respect the quality rule\r\n if(qual > quality_rule):\r\n \r\n #Save the date in the memory\r\n memory_df[key_d_z] = ext_data\r\n memory_NS_DO[key_d_z] = str(NS)+'_'+str(DO)\r\n \r\n #Response for the GUI\r\n rep += \"# Station [\"+str(NS)+\"] valid : \\n\"\r\n rep += \"- Quality density data : \"+str(round(qual, 2))+\"% \\n\"\r\n rep += \"- Great circle distance : \"+str(round(DO,2))+\"km \\n\"\r\n break\r\n else:\r\n #The data quality is too bad\r\n #Response for the GUI\r\n rep += \"# Station [\"+str(NS)+\"] invalid : \\n\"\r\n rep += \"- Quality density data : \"+str(round(qual,2))+\"% \\n\"\r\n rep += \"> Quality criterion unsatisfied \\n\"\r\n \r\n #Delete the weather station in the dataframe (instancied by year)\r\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\r\n continue\r\n \r\n else:\r\n #The data density is too low\r\n \r\n #Response for the GUI\r\n rep += \"# Station [\"+str(NS)+\"] invalid : \\n\"\r\n rep += \"> Low data volume \\n\"\r\n \r\n #Delete the weather station in the dataframe (instancied by year)\r\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\r\n\r\n continue\r\n else:\r\n #The NOAA doesn't answer for the code station\r\n rep += \"# Station [\"+str(NS)+\"] invalid : \\n\"\r\n rep += \"> Server doesn't answer\\n\"\r\n \r\n #Delete the weather station in the dataframe (instancied by year)\r\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\r\n\r\n continue\r\n #Add data in the Dataframe\r\n data = data.append(ext_data)\r\n \r\n #Define a new dataframe mark out by the specified time range\r\n range_time = pd.DataFrame(index=pd.DatetimeIndex(start=FD, end=LD, freq=timestep))\r\n \r\n #Paste data in the time marked out Dataframe\r\n range_time['Temp'] = data['TMP']\r\n \r\n #Calculate the amount of NaN (global)\r\n nb_nan = range_time['Temp'].isnull().sum()\r\n \r\n #Calculate the global quality\r\n quality = (1 - (nb_nan) / len(range_time)) * 100\r\n \r\n #Fill the gap data by temporal interpolation\r\n data = range_time.interpolate(method='time')\r\n data = data.ffill().bfill()\r\n data['Temp'] = round(data['Temp'], 2)\r\n \r\n \r\n #If specified index time, cut the data for it\r\n if(len(idx_time)> 2):\r\n final_data = pd.DataFrame(index = pd.to_datetime(idx_time))\r\n final_data['Temp'] = round(data['Temp'], 2)\r\n #final_data = final_data.resample(rule = str(timestep)).mean()\r\n #final_data = final_data.dropna()\r\n else:\r\n final_data = data\r\n \r\n return final_data, quality, NS, DO, rep","def filterEvents(intervals_dates,list_infected,distance):\n d=distance\n list_gpsevents=[]\n for z in range(len(intervals_dates)-1):\n print(\"Interval: \",intervals_dates[z], \"y\", intervals_dates[z+1])\n infected,uninfected=getTrazaTimestamp(intervals_dates[z],intervals_dates[z+1],GPSrecords,list_infected)\n events_gps = nearest_neighbor(infected, uninfected, d)\n events_gps = events_gps.drop(['geometry','closest_stop_geom'], axis=1)\n print(len(events_gps))\n if(len(events_gps)!=0):\n list_gpsevents.append(events_gps.reset_index(drop=True))\n else:\n events_gps=pd.DataFrame()\n list_gpsevents.append(events_gps)\n #GPSevents=pd.concat(list_gpsevents).reset_index(drop=True)\n #return GPSevents\n return list_gpsevents","def ttest_region():\n\n cks = cksmet.io.load_table('cks-cuts',cache=1)\n cks = cks[~cks.isany]\n lamo = cksmet.io.load_table('lamost-cal-cuts',cache=2)\n lamo = lamo[~lamo.isany]\n #lamo = lamo.query('-1 < lamo_smet < 0.5')\n\n boxes = _boxes()\n boxes = boxes.copy()\n #boxes = boxes.sort_values(by=['per1','prad1'])\n boxes2 = []\n lines = []\n for i,row in boxes.iterrows():\n cut = cks[\n cks.koi_period.between(row.per1,row.per2) &\n cks.iso_prad.between(row.prad1,row.prad2)\n ]\n d = calculate_statistics(cut.cks_smet,lamo.lamo_smet)\n d = dict(d, **row)\n lines.append( to_string(d))\n \n return lines","def create_lat_lon_data(gt_id,\n target_horizon,\n experiment,\n lat_lon_features=[\"elevation\", \"climate_regions\"]):\n\n time_start = time.time()\n\n # --------\n # Prepare experiment cache directory and saved file names\n # --------\n\n # Name of cache directory for storing non-submission-date specific\n # intermediate files\n cache_dir = os.path.join('results', experiment, 'shared',\n '{}_{}'.format(gt_id, target_horizon))\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\n\n # if cache_dir doesn't exist, create it\n if not os.path.isdir(cache_dir):\n os.makedirs(cache_dir)\n\n # Filenames for data file to be stored in cache_dir\n lat_lon_data_file = os.path.join(\n cache_dir, \"lat_lon_data-{}_{}.h5\".format(gt_id, target_horizon))\n\n # --------\n # Load mask indicating which grid points count in the contest (1=in, 0=out)\n # --------\n print \"Loading contest mask\"\n t = time.time()\n mask_df = get_contest_mask()\n print \"Elapsed: {}s\".format(time.time() - t)\n\n # --------\n # Creates lat_lon_data dataframe.\n # --------\n # Load masked lat lon features\n print \"Loading lat lon features\"\n lat_lon_data = get_lat_lon_features(gt_ids=lat_lon_features, gt_masks=mask_df)\n # Convert qualitative variable (climate_region) to dummies; all dummies included\n if lat_lon_features:\n lat_lon_data = pd.get_dummies(lat_lon_data,\n columns=[\"climate_region\"],\n drop_first=False)\n print \"Saving lat lon features to \"+lat_lon_data_file\n t = time.time()\n # Save lat lon features to disk\n lat_lon_data.to_hdf(lat_lon_data_file, key=\"data\", mode=\"w\")\n subprocess.call(\"chmod a+w \"+lat_lon_data_file, shell=True)\n print \"Elapsed: {}s\".format(time.time() - t)\n else:\n print \"No lat lon features requested\"\n # Delete any old version of the data\n if os.path.isfile(lat_lon_data_file):\n os.remove(lat_lon_data_file)\n\n print \"Finished generating lat_lon_data matrix.\"\n print \"Total time elapsed: {}s\".format(time.time()-time_start)\n return list(lat_lon_data) if lat_lon_features else 0","def create_sample_dataframe():\n ax_readings = []\n ay_readings = []\n az_readings = []\n mx_readings = []\n my_readings = []\n mz_readings = []\n gx_readings = []\n gy_readings = []\n gz_readings = []\n activity_list = [LABELS_NAMES[0] for _ in range(SEGMENT_TIME_SIZE)]\n\n\n for _ in range(SEGMENT_TIME_SIZE):\n ax_readings.append(random.uniform(-10,10))\n ay_readings.append(random.uniform(-10,10))\n az_readings.append(random.uniform(-10,10))\n mx_readings.append(random.uniform(-10,10))\n my_readings.append(random.uniform(-10,10))\n mz_readings.append(random.uniform(-10,10))\n gx_readings.append(random.uniform(-10,10))\n gy_readings.append(random.uniform(-10,10))\n gz_readings.append(random.uniform(-10,10))\n\n data_dict = {\n COLUMN_NAMES[0]: activity_list, COLUMN_NAMES[1]: ax_readings,\n COLUMN_NAMES[2]: ay_readings, COLUMN_NAMES[3]: az_readings,\n COLUMN_NAMES[4]: gx_readings, COLUMN_NAMES[5]: gy_readings,\n COLUMN_NAMES[6]: gz_readings, COLUMN_NAMES[7]: mx_readings,\n COLUMN_NAMES[8]: my_readings, COLUMN_NAMES[9]: mz_readings\n }\n\n df = pd.DataFrame(data=data_dict)\n return df","def manipulate_data(ds, var, predef_clim, predef_trnd, trn_yrs, all_yrs, \n apply_latw=True, apply_detrending=True, dropna=True):\n\n \n if((var=='SD')|(var=='sd')|(var=='snowc')): \n ds[var] = ds[var].where(ds[var]>=0, other=0.0)\n ds[var] = ds[var].where(ds[var]==0, other=1.0)\n #ds[var].values = Gauss_filter(ds[var].values, (0,3,3))\n \n \"\"\"\n if((var=='hgt')|(var=='z')|(var=='GPT')):\n months = ds.time.to_index().month; ssn_ends = (months==2)|(months==5)|(months==8)|(months==11)\n ds = ds.sel(time=ssn_ends)\n else: \n ds = ds.resample(time='3M').mean()\n \"\"\"\n \n ds = ds.resample(time='3M').mean()\n\n ds = ds.sel(time=slice(str(all_yrs[0])+'-01-01', str(all_yrs[-1])+'-12-31')) \n \n try: \n clim = predef_clim\n ds = ds.groupby('time.season') - clim\n print('Predefined climatology used')\n except:\n clim = ds.sel(time=slice(str(trn_yrs[0])+'-01-01', str(trn_yrs[-1])+'-12-31')).groupby('time.season').mean('time')\n ds = ds.groupby('time.season') - clim\n print('Climatology calculated from data')\n \n if(apply_latw): ds[var].values = lat_weighting(ds[var].values, \n ds.lat, ds.lon)\n if(dropna):\n ds = ds.stack(gridcell=('lat', 'lon')).dropna(dim='gridcell',how='any')\n else: \n ds = ds.stack(gridcell=('lat', 'lon')).fillna(0)\n \n \n trend_models = { }\n if(apply_detrending): \n ds = ds.load()\n for ssn in ('DJF', 'MAM', 'JJA', 'SON'):\n #ssn_idx = ds['time.season'] == ssn\n \n trn_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], trn_yrs))\n all_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], all_yrs))\n \n trn_x = np.array(ds.time[trn_idx].values.tolist()).reshape(-1,1)\n all_x = np.array(ds.time[all_idx].values.tolist()).reshape(-1,1)\n try:\n trend = predef_trnd[ssn].predict(all_x)\n trend_models[ssn] = predef_trnd[ssn]\n print('Predefined trend model used')\n except:\n #_, trend_model = define_trends(ds[var][trn_idx], trn_x)\n _, trend_model = define_trends(ds[var][all_idx], all_x)\n trend = trend_model.predict(all_x)\n trend_models[ssn] = trend_model\n print('Trends calculated from data')\n \n ds[var][all_idx] = ds[var][all_idx] - trend\n \n\n \n return ds, clim, trend_models","def artificial_data(dt1, dt2, minutes=1):\n\n def fxweek(x):\n return 2 - x * (1 - x)\n\n def sat(x):\n return 2 * x + 2\n\n data = []\n dt = datetime.timedelta(minutes=minutes)\n while dt1 < dt2:\n if dt1.weekday() == 6:\n dt1 += dt\n continue\n if minutes <= 120 and not (dt1.hour >= 8 and dt1.hour <= 18):\n dt1 += dt\n continue\n x = (dt1.hour - 8) / 10\n if dt1.weekday() == 5:\n y = sat(x)\n else:\n y = fxweek(x)\n data.append({'time': dt1, 'y': y})\n dt1 += dt\n df = pandas.DataFrame(data)\n df['y'] += numpy.random.randn(df.shape[0]) * 0.1\n df['time'] = pandas.DatetimeIndex(df['time'])\n return df","def rowify(url: str, additional_info: list = []) -> list:\n try:\n rawdata = req.get(url).json() # does exactly what you think\n except json.decoder.JSONDecodeError as err:\n splits = url.split('/') # get the parts of the URL\n intvl = f\"[{splits[-3]}, {splits[-2]}]\" # create a string of interval\n logger.warning(\"[rowify] No data found for interval %s. \"\n \"Msg: %s.\", intvl, err) # use exc_info=1 for traceback\n return []\n\n fulldata = []\n for point in rawdata: # iterating over all measured datapoints\n uid = additional_info[0]\n idstring = stringifyID(point, uid) # create unique IdString\n\n # check for duplicates\n if idstring not in genIdStrings and idstring not in queriedIds:\n genIdStrings.append(idstring)\n\n # first part of data\n row = [point[i] for i in [\"TBTimestamp\", \"TETimestamp\", \"Latitude\",\n \"Longitude\", \"Altitude\"]]\n\n # Channel part of data\n channels = point[\"Channels\"]\n tableChannels = [\"AIRPRES\", \"CO\", \"HUM\", \"NO\", \"NO2\", \"O3\",\n \"SO2\", \"PM1\", \"PM10\", \"PM2.5\", \"PARTICLE_COUNT\",\n \"TEMP\", \"TSP\", \"VOLTAGE\"]\n\n # dict comprehension to get sensorlabel: sensorchannel pairs\n channelDict = {ch[\"SensorLabel\"]: ch for ch in channels}\n channelDictKeys = list(channelDict.keys())\n\n # log a warning if unrecognized channel labels appear\n for cdk in channelDictKeys:\n if cdk not in tableChannels:\n logger.warning(\"Unrecognized channel label %s for IdString\"\n \" %s.\", cdk, idstring)\n\n # kind of diagnostics so see, whether all the data fits in nicely\n if len(tableChannels) != len(channels):\n logger.debug(\"Number of expected channels [%s] and received \"\n \"channels [%s] do not match. Received: %s.\",\n len(tableChannels), len(channels), channelDictKeys)\n\n # create the actual row\n for tch in tableChannels:\n if tch in channelDictKeys:\n ch = channelDict[tch]\n row = [*row, *[ch[\"PreScaled\"], ch[\"Slope\"], ch[\"Offset\"],\n ch[\"Scaled\"], ch[\"UnitName\"], ch[\"Status\"]]]\n else:\n filler = [None] * 6 # no data -> fill with None\n row = [*row, *filler]\n\n row = tuple([*row, *additional_info, idstring])\n fulldata.append(row)\n\n else:\n logger.warning(\"Encountered duplicate IdString %s.\", idstring)\n\n del rawdata[:] # freeing memory\n del genIdStrings[:] # if API not broken this shouldn't do harm+free memory\n\n return fulldata","def _interpolate_meteorological_data(dset, data, rundate):\n rundate = datetime(rundate.year, rundate.month, rundate.day)\n for field, station in [(f, f[4:]) for f in data.keys() if f.startswith(\"met_\")]:\n log.debug(f\"Meteorological data available for station {station}\")\n\n met_time = data[field].pop(\"met_time\")\n flat_list = [item for sublist in met_time for item in sublist]\n met_time_float = np.array([(flat_list[i] - rundate).total_seconds() for i in range(0, len(flat_list))])\n met_time_unique, met_index = np.unique(met_time_float, return_index=True)\n\n diff = len(met_time_float) - len(met_time_unique)\n if diff > 0:\n log.dev(f\"Removed duplicate met data for station {station}\")\n log.dev(\"Do this for the actual obs data also!\")\n if len(met_time_unique) == 1:\n for met_type in data[field].keys():\n data[field][met_type] = np.repeat(data[field][met_type][0], dset.num_obs)\n continue\n\n # Extrapolation one month before/after\n # (this is overkill, most of these values will be removed later when taking the diagonal)\n min_time = min(met_time_unique) - 31 * 86400\n max_time = max(met_time_unique) + 31 * 86400\n met_time_unique = np.hstack((np.array(min_time), met_time_unique, np.array(max_time)))\n\n for met_type in data[field].keys():\n met_data_array = data[field][met_type]\n flat_list = [item for sublist in met_data_array for item in sublist]\n met_data_array = np.array([flat_list[i] for i in met_index])\n met_data_array = np.hstack((met_data_array[0], met_data_array, met_data_array[-1]))\n data[field][met_type] = interpolation.interpolate(\n met_time_unique, met_data_array, dset.obs_time, kind=\"cubic\"\n )\n\n return data","def generate_base_tiles(self):\n\n gdal.SetConfigOption(\"GDAL_PAM_ENABLED\", \"NO\")\n\n print \"Generating Base Tiles:\"\n if self.options.verbose:\n #mx, my = self.out_gt[0], self.out_gt[3] # OriginX, OriginY\n #px, py = self.mercator.MetersToPixels( mx, my, self.tmaxz)\n #print \"Pixel coordinates:\", px, py, (mx, my)\n print\n print \"Tiles generated from the max zoom level:\"\n print \"----------------------------------------\"\n print\n\n\n # Set the bounds\n tminx, tminy, tmaxx, tmaxy = self.tminmax[self.tmaxz]\n querysize = self.querysize\n\n # Just the center tile\n #tminx = tminx+ (tmaxx - tminx)/2\n #tminy = tminy+ (tmaxy - tminy)/2\n #tmaxx = tminx\n #tmaxy = tminy\n\n #print tminx, tminy, tmaxx, tmaxy\n tcount = (1+abs(tmaxx-tminx)) * (1+abs(tmaxy-tminy))\n #print tcount\n ti = 0\n i_y_column_count=((tmaxy-tminy)+1)\n ds = self.out_ds\n tz = self.tmaxz\n if self.options.verbose:\n # tx in range(tminx, tmaxx+1) tminx[ 281596 ] tmaxx[ 281744 ] ; ((tmaxx-tmaxy)+1) x_tiles[ 23393 ]\n print \"\\ttz=[\",tz,\"] : tx in range(tminx, tmaxx+1) tminx[\",tminx,\"] tmaxx[\",tmaxx,\"] ; ((tmaxx-tmaxy)+1) x_tiles[\",tcount,\"]\"\n # ty_tms in range(tmaxy, tminy-1, -1) tmaxy[ 352409 ] tminy[ 352253 ] ; ((tmaxy-tminy)) y_tiles[ 157 ] 352409-(352253-1)\n print \"\\ttz=[\",tz,\"] : ty_tms in range(tmaxy, tminy-1, -1) tmaxy[\",tmaxy,\"] tminy[\",tminy,\"] ; ((tmaxy-tminy+1)) y_tiles[\",i_y_column_count,\"]\"\n if self.options.resume:\n i_count = self.tile_exists(0, 0, tz,2)\n if i_count == tcount:\n if self.options.verbose:\n print \"\\tTile generation skipped because of --resume ; x/y-tiles of z[\",tz,\"] y_tiles[\",tcount,\"]\"\n return\n for tx in range(tminx, tmaxx+1):\n tmaxy_work=tmaxy\n if self.options.resume:\n i_count = self.tile_exists(tx, 0, tz,3)\n if i_count == i_y_column_count:\n if self.options.verbose:\n print \"\\tTile generation skipped because of --resume ; z =\",tz,\" ; y-tiles of x[\",tx,\"] y_tiles[\",i_y_column_count,\"]\"\n break\n else:\n if i_count > 0:\n # this assums the rows are compleate, which may NOT be true\n tmaxy_work-=i_count\n if self.options.verbose:\n print \"\\tTile generation skipped to tmaxy[\",tmaxy_work,\"] because of --resume ; z =\",tz,\" ; y-tiles of x[\",tx,\"] y_tiles[\",i_y_column_count,\"]\"\n for ty_tms in range(tmaxy_work, tminy-1, -1): #range(tminy, tmaxy+1):\n ty_osm=self.flip_y(tz,ty_tms)\n ty=ty_tms\n if self.options.tms_osm:\n ty=ty_osm\n if self.stopped:\n if self.options.mbtiles:\n if self.mbtiles_db:\n self.mbtiles_db.close_db()\n break\n ti += 1\n\n if self.options.resume:\n exists = self.tile_exists(tx, ty, tz,0)\n if exists and self.options.verbose:\n print \"\\tTile generation skipped because of --resume ; z =\",tz,\" ; x =\",tx,\" ; y_tms =\",ty_tms, \"; y_osm =\",ty_osm\n else:\n exists = False\n\n if not exists:\n if self.options.verbose:\n print ti, '/', tcount, self.get_verbose_tile_name(tx, ty, tz)\n # Don't scale up by nearest neighbour, better change the querysize\n # to the native resolution (and return smaller query tile) for scaling\n if self.options.profile in ('mercator','geodetic'):\n if self.options.profile == 'mercator':\n # Tile bounds in EPSG:900913\n b = self.mercator.TileBounds(tx, ty_tms, tz)\n elif self.options.profile == 'geodetic':\n b = self.geodetic.TileBounds(tx, ty_tms, tz)\n\n rb, wb = self.geo_query( ds, b[0], b[3], b[2], b[1])\n nativesize = wb[0]+wb[2] # Pixel size in the raster covering query geo extent\n if self.options.verbose:\n print \"\\tNative Extent (querysize\",nativesize,\"): \", rb, wb\n\n querysize = self.querysize\n # Tile bounds in raster coordinates for ReadRaster query\n rb, wb = self.geo_query( ds, b[0], b[3], b[2], b[1], querysize=querysize)\n\n rx, ry, rxsize, rysize = rb\n wx, wy, wxsize, wysize = wb\n else: # 'raster' or 'gearth' or 'garmin' profile:\n tsize = int(self.tsize[tz]) # tilesize in raster coordinates for actual zoom\n xsize = self.out_ds.RasterXSize # size of the raster in pixels\n ysize = self.out_ds.RasterYSize\n if tz >= self.nativezoom:\n querysize = self.tilesize # int(2**(self.nativezoom-tz) * self.tilesize)\n\n rx = (tx) * tsize\n rxsize = 0\n if tx == tmaxx:\n rxsize = xsize % tsize\n if rxsize == 0:\n rxsize = tsize\n\n rysize = 0\n if ty_tms == tmaxy:\n rysize = ysize % tsize\n if rysize == 0:\n rysize = tsize\n ry = ysize - (ty_tms * tsize) - rysize\n\n wx, wy = 0, 0\n\n wxsize, wysize = int(rxsize/float(tsize) * querysize), int(rysize/float(tsize) * querysize)\n if wysize != querysize:\n wy = querysize - wysize\n xyzzy = Xyzzy(querysize, rx, ry, rxsize, rysize, wx, wy, wxsize, wysize)\n try:\n if self.options.verbose:\n print ti,'/',tcount,' total ; z =',tz,' ; x =',tx,' ; y_tms =',ty_tms,' ; y_osm =',ty_osm\n print \"\\tReadRaster Extent: \", (rx, ry, rxsize, rysize), (wx, wy, wxsize, wysize)\n self.write_base_tile(tx, ty, tz, xyzzy)\n except ImageOutputException, e:\n self.error(\"'%d/%d/%d': %s\" % (tz, tx, ty, e.message))\n\n if not self.options.verbose or self.is_subprocess:\n self.progressbar( ti / float(tcount) )\n if self.options.mbtiles:\n if self.mbtiles_db:\n self.mbtiles_db.close_db()\n self.mbtiles_db=None","def get_gridcell_history(\n lat,\n lon,\n dataset,\n also_return_snapped_coordinates=False,\n also_return_metadata=False,\n use_imperial_units=True,\n convert_to_local_time=True,\n as_of=None,\n ipfs_timeout=None):\n try:\n metadata = get_metadata(get_heads()[dataset])\n except KeyError:\n raise DatasetError(\"No such dataset in dClimate\")\n\n # set up units\n converter, dweather_unit = get_unit_converter(metadata[\"unit of measurement\"], use_imperial_units)\n\n # get dataset-specific \"no observation\" value\n missing_value = metadata[\"missing value\"]\n try:\n dataset_obj = GRIDDED_DATASETS[dataset](as_of=as_of, ipfs_timeout=ipfs_timeout)\n except KeyError:\n raise DatasetError(\"No such dataset in dClimate\")\n\n try:\n (lat, lon), resp_series = dataset_obj.get_data(lat, lon)\n\n except (ipfshttpclient.exceptions.ErrorResponse, ipfshttpclient.exceptions.TimeoutError, KeyError, FileNotFoundError) as e:\n raise CoordinateNotFoundError(\"Invalid coordinate for dataset\")\n\n # try a timezone-based transformation on the times in case we're using an hourly set.\n if convert_to_local_time:\n try:\n tf = TimezoneFinder()\n local_tz = pytz.timezone(tf.timezone_at(lng=lon, lat=lat))\n resp_series = resp_series.tz_localize(\"UTC\").tz_convert(local_tz)\n except (AttributeError, TypeError): # datetime.date (daily sets) doesn't work with this, only datetime.datetime (hourly sets)\n pass\n\n if type(missing_value) == str:\n resp_series = resp_series.replace(missing_value, np.NaN).astype(float)\n else:\n resp_series.loc[resp_series.astype(float) == missing_value] = np.NaN\n resp_series = resp_series.astype(float)\n \n resp_series = resp_series * dweather_unit\n if converter is not None:\n resp_series = pd.Series(converter(resp_series.values), resp_series.index)\n result = {k: convert_nans_to_none(v) for k, v in resp_series.to_dict().items()}\n \n if also_return_metadata:\n result = tupleify(result) + ({\"metadata\": metadata},)\n if also_return_snapped_coordinates:\n result = tupleify(result) + ({\"snapped to\": (lat, lon)},)\n return result","def process_location(pid, data_source, out_loc, start_date, end_date, debug=False):\n\n #query = client.query_data_points(page_size=PAGE_SIZE, source=pid)\n query = try_query(pid)\n\n location_query = try_filter(query, pid, 'pdk-location', start_date, end_date)\n \"\"\" location_query = query.filter(source=pid, \n generator_identifier='pdk-location',\n created__gte=start_date,\n created__lte=end_date).order_by('created')\n \"\"\"\n tot_count = location_query.count()\n count = 0\n frac = int(tot_count / 100)\n\n loc_df = pd.DataFrame()\n for point in location_query:\n point_df = pd.DataFrame.from_dict(point).iloc[0].to_frame().transpose()\n metadata_df = pd.Series(point['passive-data-metadata']).to_frame().transpose()\n # TODO check if ignoring errors is safe\n metadata_df = metadata_df.drop(['latitude', 'longitude'], axis='columns', errors=\"ignore\")\n point_df.reset_index(inplace=True, drop=True)\n point_df = pd.concat([metadata_df, point_df], axis=1, sort=True)\n \n point_df.drop('passive-data-metadata', axis='columns', inplace=True)\n missing_cols = [col for col in loc_df.columns.values if col not in point_df.columns.values]\n \n if len(missing_cols) > 0 and loc_df.shape[0] > 0:\n for col in missing_cols:\n point_df[col] = np.nan\n point_df = point_df[loc_df.columns]\n loc_df = loc_df.append(point_df)\n count += 1\n if debug and (count % frac == 0):\n print(\"{0:.2f}% complete\".format(float(count)/float(tot_count)*100))\n\n loc_df['pid'] = pid \n loc_df['data_source'] = data_source\n print(loc_df.shape)\n \n pickle.dump(loc_df, open(\"{}/pdk-location/{}.df\".format(out_loc, pid), 'wb'), -1)","def prepare_input(self, only_center = True):\n \n if only_center:\n nx = [0]\n ny = [0]\n else:\n nx = [0,1,-1]\n ny = [0,1,-1]\n gauge = dd.read_csv(str(Path(self.db_location, 'gauge', '*.csv.gz')), \n compression='gzip', \n assume_missing=True,\n dtype = {'TIMESTAMP':int, 'STATION': str})\n \n gauge = gauge.compute().drop_duplicates()\n gauge = gauge.replace(-9999,np.nan)\n for x in nx:\n for y in ny:\n logging.info('Processing neighbour {:d}{:d}'.format(x, y))\n radar = dd.read_parquet(str(Path(self.db_location, 'radar',\n '*.parquet')))\n refer = dd.read_parquet(str(Path(self.db_location, 'reference', \n '*.parquet')))\n \n # Select only required pixel\n radar = radar.loc[np.logical_and(radar['NX'] == x, \n radar['NY'] == y)]\n refer = refer.loc[np.logical_and(refer['NX'] == x, \n refer['NY'] == y)]\n \n # Convert to pandas and remove duplicates \n radar = radar.compute().drop_duplicates(subset = ['TIMESTAMP',\n 'STATION',\n 'RADAR',\n 'NX','NY',\n 'SWEEP'])\n \n refer = refer.compute().drop_duplicates(subset = ['TIMESTAMP',\n 'STATION'])\n \n radar = radar.sort_values(by = ['TIMESTAMP','STATION','SWEEP'])\n refer = refer.sort_values(by = ['TIMESTAMP','STATION'])\n gauge = gauge.sort_values(by = ['TIMESTAMP','STATION'])\n # Get only valid precip data\n gauge = gauge[np.isfinite(gauge['RRE150Z0'])]\n \n # Create individual 10 min - station stamps\n gauge['s-tstamp'] = np.array(gauge['STATION'] + \n gauge['TIMESTAMP'].astype(str)).astype(str)\n radar['s-tstamp'] = np.array(radar['STATION'] + \n radar['TIMESTAMP'].astype(str)).astype(str)\n refer['s-tstamp'] = np.array(refer['STATION'] + \n refer['TIMESTAMP'].astype(str)).astype(str)\n \n # Get gauge and reference only when radar data available\n \n # Find timestamps that are in the three datasets\n ststamp_common = np.array(pd.Series(list(set(gauge['s-tstamp'])\n .intersection(set(refer['s-tstamp'])))))\n ststamp_common = np.array(pd.Series(list(set(radar['s-tstamp'])\n .intersection(set(ststamp_common)))))\n radar = radar.loc[radar['s-tstamp'].isin(ststamp_common)]\n gauge = gauge.loc[gauge['s-tstamp'].isin(ststamp_common)]\n refer = refer.loc[refer['s-tstamp'].isin(ststamp_common)]\n \n \n # Filter incomplete hours\n stahour = np.array(gauge['STATION'] + \n ((gauge['TIMESTAMP'] - 600 ) - \n (gauge['TIMESTAMP'] - 600 ) % 3600).astype(str)).astype(str)\n \n full_hours = np.array(gauge.groupby(stahour)['STATION']\n .transform('count') == 6)\n \n refer = refer.reindex[full_hours]\n gauge = gauge.reindex[full_hours] \n radar = radar.reindex[radar['s-tstamp'].\n isin(np.array(gauge['s-tstamp']))]\n \n stahour = stahour[full_hours]\n \n # Creating vertical grouping index\n \n _, idx, grp_vertical = np.unique(radar['s-tstamp'],\n return_inverse = True,\n return_index = True)\n # Get original order\n sta_tstamp_unique = radar['s-tstamp'][np.sort(idx)]\n # Preserves order and avoids sorting radar_statstamp\n grp_vertical = idx[grp_vertical]\n # However one issue is that the indexes are not starting from zero with increment\n # of one, though they are sorted, they are like 0,7,7,7,15,15,23,23\n # We want them starting from zero with step of one\n grp_vertical = rankdata(grp_vertical,method='dense') - 1\n \n # Repeat operation with gauge hours\n sta_hourly_unique, idx, grp_hourly = np.unique(stahour, \n return_inverse = True,\n return_index = True)\n grp_hourly = idx[grp_hourly]\n \n # Add derived variables height iso0 (HISO) and height above ground (HAG)\n # Radar\n stations = constants.METSTATIONS\n cols = list(stations.columns)\n cols[1] = 'STATION'\n stations.columns = cols\n radar = pd.merge(radar,stations, how = 'left', on = 'STATION',\n sort = False)\n \n radar['HISO'] = -radar['T'] / constants.LAPSE_RATE * 100\n radar['HAG'] = radar['HEIGHT'] - radar['Z']\n radar['HAG'][radar['HAG'] < 0] = 0\n \n # Gauge\n gauge['minutes'] = (gauge['TIMESTAMP'] % 3600)/60\n \n # Save all to file\n refer.to_parquet(str(Path(self.input_location, \n 'reference_x{:d}y{:d}.parquet'.format(x,y))),\n compression = 'gzip', index = False)\n \n radar.to_parquet(str(Path(self.input_location, \n 'radar_x{:d}y{:d}.parquet'.format(x,y))),\n compression = 'gzip', index = False)\n \n grp_idx = {}\n grp_idx['grp_vertical'] = grp_vertical\n grp_idx['grp_hourly'] = grp_hourly\n grp_idx['tstamp_unique'] = sta_tstamp_unique\n \n pickle.dump(grp_idx, \n open(str(Path(self.input_location, \n 'grouping_idx_x{:d}y{:d}.p'.format(x,y))),'wb'))\n \n if x == 0 and y == 0:\n # Save only gauge for center pixel since it's available only there\n gauge.to_parquet(str(Path(self.input_location, 'gauge.parquet')),\n compression = 'gzip', index = False)","def calculate_psf_tilts():\n for order in [1, 2]:\n\n # Get the file\n path = 'files/SOSS_PSF_tilt_order{}.npy'.format(order)\n psf_file = resource_filename('awesimsoss', path)\n\n # Dimensions\n subarray = 'SUBSTRIP256'\n X = range(2048)\n Y = range(256)\n\n # Get the wave map\n wave_map = utils.wave_solutions(subarray, order).astype(float)\n\n # Get the y-coordinate of the trace polynomial in this column\n # (center of the trace)\n coeffs = trace_polynomials(subarray=subarray, order=order)\n trace = np.polyval(coeffs, X)\n\n # Interpolate to get the wavelength value at the center\n wave = interp2d(X, Y, wave_map)\n\n # Get the wavelength of the trace center in each column\n trace_wave = []\n for x, y in zip(X, trace):\n trace_wave.append(wave(x, y)[0])\n\n # For each column wavelength (defined by the wavelength at\n # the trace center) define an isowavelength contour\n angles = []\n for n, x in enumerate(X):\n\n w = trace_wave[x]\n\n # Edge cases\n try:\n w0 = trace_wave[x-1]\n except IndexError:\n w0 = 0\n\n try:\n w1 = trace_wave[x+1]\n except IndexError:\n w1 = 10\n\n # Define the width of the wavelength bin as half-way\n # between neighboring points\n dw0 = np.mean([w0, w])\n dw1 = np.mean([w1, w])\n\n # Get the coordinates of all the pixels in that range\n yy, xx = np.where(np.logical_and(wave_map >= dw0, wave_map < dw1))\n\n # Find the angle between the vertical and the tilted wavelength bin\n if len(xx) >= 1:\n angle = get_angle([xx[-1], yy[-1]], [x, trace[x]])\n else:\n angle = 0\n\n # Don't flip them upside down\n angle = angle % 180\n\n # Add to the array\n angles.append(angle)\n\n # Save the file\n np.save(psf_file, np.array(angles))\n print('Angles saved to', psf_file)","def create_features(energy_data, label=None):\n energy_data['date'] = energy_data.index\n energy_data['hour'] = energy_data['Datetime'].dt.hour\n energy_data['dayofweek'] = energy_data['Datetime'].dt.dayofweek\n energy_data['month'] = energy_data['Datetime'].dt.month\n energy_data['quarter'] = energy_data['Datetime'].dt.quarter\n energy_data['year'] = energy_data['Datetime'].dt.year\n energy_data['dayofyear'] = energy_data['Datetime'].dt.dayofyear\n energy_data['dayofmonth'] = energy_data['Datetime'].dt.day\n energy_data['weekofyear'] = energy_data['Datetime'].dt.weekofyear\n energy_data['pjme_2_hrs_lag'] = energy_data['PJME_MW'].shift(2)\n energy_data['pjme_4_hrs_lag'] = energy_data['PJME_MW'].shift(4)\n energy_data['pjme_8_hrs_lag'] = energy_data['PJME_MW'].shift(8)\n energy_data['pjme_12_hrs_lag'] = energy_data['PJME_MW'].shift(12)\n energy_data['pjme_24_hrs_lag'] = energy_data['PJME_MW'].shift(24)\n energy_data['pjme_4_hrs_mean'] = energy_data['PJME_MW'].rolling(window=4).mean()\n energy_data['pjme_8_hrs_mean'] = energy_data['PJME_MW'].rolling(window=8).mean()\n energy_data['pjme_12_hrs_mean'] = energy_data['PJME_MW'].rolling(window=12).mean()\n energy_data['pjme_24_hrs_mean'] = energy_data['PJME_MW'].rolling(window=24).mean()\n energy_data['pjme_4_hrs_std'] = energy_data['PJME_MW'].rolling(window=4).std()\n energy_data['pjme_8_hrs_std'] = energy_data['PJME_MW'].rolling(window=8).std()\n energy_data['pjme_12_hrs_std'] = energy_data['PJME_MW'].rolling(window=12).std()\n energy_data['pjme_24_hrs_std'] = energy_data['PJME_MW'].rolling(window=24).std()\n energy_data['pjme_4_hrs_max'] = energy_data['PJME_MW'].rolling(window=4).max()\n energy_data['pjme_8_hrs_max'] = energy_data['PJME_MW'].rolling(window=8).max()\n energy_data['pjme_12_hrs_max'] = energy_data['PJME_MW'].rolling(window=12).max()\n energy_data['pjme_24_hrs_max'] = energy_data['PJME_MW'].rolling(window=24).max()\n energy_data['pjme_4_hrs_min'] = energy_data['PJME_MW'].rolling(window=4).min()\n energy_data['pjme_8_hrs_min'] = energy_data['PJME_MW'].rolling(window=8).min()\n energy_data['pjme_12_hrs_min'] = energy_data['PJME_MW'].rolling(window=12).min()\n energy_data['pjme_24_hrs_min'] = energy_data['PJME_MW'].rolling(window=24).min()\n\n features = energy_data[['hour', 'dayofweek', 'quarter', 'month', 'year',\n 'dayofyear', 'dayofmonth', 'weekofyear', 'pjme_2_hrs_lag', 'pjme_4_hrs_lag',\n 'pjme_8_hrs_lag', 'pjme_12_hrs_lag', 'pjme_24_hrs_lag', 'pjme_4_hrs_mean',\n \"pjme_8_hrs_mean\", \"pjme_12_hrs_mean\", \"pjme_24_hrs_mean\", \"pjme_4_hrs_std\",\n \"pjme_8_hrs_std\", \"pjme_12_hrs_std\", \"pjme_24_hrs_std\",\n \"pjme_4_hrs_max\", \"pjme_8_hrs_max\", \"pjme_12_hrs_max\", \"pjme_24_hrs_max\",\n \"pjme_4_hrs_min\", \"pjme_8_hrs_min\", \"pjme_12_hrs_min\", \"pjme_24_hrs_min\"]]\n if label:\n label = energy_data[label]\n return features, label\n return features","def get_tap_events(user_id: str, user_session_id: str) -> DataFrame:\n full_df = pd.DataFrame()\n for tap_file in tap_file_names:\n columns = tap_file_important_columns[tap_file]\n data = read_file(user_id, user_session_id, tap_file)\n time_data = pd.DataFrame()\n time_data['Start'] = data[columns[0]]\n time_data['End'] = data[columns[-2]]\n time_data['Type'] = tap_file_to_feature_name[tap_file]\n full_df = pd.concat([full_df, time_data], ignore_index = True)\n return full_df.dropna().sort_values(by = 'Start').reset_index(drop = True)","def build_full_temp(df_temperature):\n df_interp_full = build_missing(df_temperature)\n df_full = pd.concat([df_interp_full, df_temperature], ignore_index=True)\n df_full = df_full.sort_values(by=['datetime'])\n return df_full","def tidy_val_df(df_val, m_sample_scm):\n \n df_val = df_val.copy()\n # Set values to 0 prior to isothermal\n # look from min_search_start minutes to min_search_end minutes\n min_search_start = 60 \n min_search_end = 600\n idx_min = np.argmin(\n df_val['Power1,W'].values[min_search_start:min_search_end]) + min_search_start\n if idx_min >= 599:\n idx_min = 0\n# idx_min = 0\n df_val = df_val[idx_min:]\n df_val['Heat1,J'] = df_val['Heat1,J'].apply(lambda x: x - df_val['Heat1,J'].values[0])\n \n # create time in decimal days for RG charts 20180111\n # header names require numbers for cc1 data exported with cc2\n df_val['Power/SCM,W/g'] = df_val['Power1,W'].values / m_sample_scm\n df_val['Heat/SCM,J/g'] = df_val['Heat1,J'].values / m_sample_scm\n# df_val['Tmix,s'] = df_val['Tlog,s'].values + time_difference\n df_val['Tmix,days'] = df_val['Tmix1,s'].values / 86400 # 60 * 60 * 24\n# df_val = df_val.drop('Tlog,s', axis=1) # remove for cc1 data exported with cc2\n\n# rearrange columns to place Tmixs first \n cols = df_val.columns.tolist()\n cols = cols[0:1] + cols[-1:] + cols[1:-1]\n# cols = cols[0:1] + cols[-1:] + cols[1:-1] # For cc1 data exported with cc2\n df_val = df_val[cols]\n \n return df_val","def get_data_subsets(t0, t1):\n\n # Iridium data:\n irid = iridium[(iridium.time >= t0) & (iridium.time <= t1)]\n irid_B = np.vstack((irid.B_e.values, irid.B_n.values, irid.B_r.values))\n irid_coords = np.vstack((irid.lon.values, irid.lat.values, irid.r.values))\n\n # SuperMAG data:\n smag = supermag.loc[t0:t1, :]\n smag_B = np.vstack((smag.Be.values, smag.Bn.values, smag.Bu.values))\n smag_coords = np.vstack((smag.lon.values, smag.lat.values))\n\n # SuperDARN data:\n sd = superdarn.loc[t0:t1, :]\n vlos = sd['vlos'].values\n sd_coords = np.vstack((sd['glon'].values, sd['glat'].values))\n los = np.vstack((sd['le'].values, sd['ln'].values))\n\n\n # Make the data objects. The scale keyword determines a weight for the dataset. Increase it to reduce weight\n iridium_data = lompe.Data(irid_B * 1e-9, irid_coords, datatype = 'space_mag_fac', scale = 200e-9)\n supermag_data = lompe.Data(smag_B * 1e-9, smag_coords, datatype = 'ground_mag' , scale = 100e-9)\n superdarn_data = lompe.Data(vlos , sd_coords , LOS = los, datatype = 'convection' , scale = 500 )\n\n return(iridium_data, supermag_data, superdarn_data)","def tile_read_utm(source, bounds, tilesize, indexes=[1], nodata=None, alpha=None, dst_crs='EPSG:3857', \n verbose=False,\n boundless=False):\n w, s, e, n = bounds\n\n if alpha is not None and nodata is not None:\n raise RioTilerError('cannot pass alpha and nodata option')\n\n if isinstance(indexes, int):\n indexes = [indexes]\n (e - w) / tilesize\n out_shape = (len(indexes), tilesize, tilesize)\n if verbose:\n print(dst_crs)\n vrt_params = dict(\n crs=dst_crs,\n resampling=Resampling.bilinear,\n src_nodata=nodata,\n dst_nodata=nodata)\n\n if isinstance(source, DatasetReader):\n with WarpedVRT(source, **vrt_params) as vrt:\n window = vrt.window(w, s, e, n, precision=21)\n if verbose:\n print(window)\n #window_transform = windows.transform(window, vrt.transform)\n window_transform = transform.from_bounds(w,s,e,n, tilesize, tilesize)\n \n data = vrt.read(window=window,\n resampling=Resampling.bilinear,\n out_shape=out_shape,\n indexes=indexes,\n boundless=boundless)\n if False: #except:\n print(bounds)\n print(window)\n print(out_shape)\n print(indexes)\n print(boundless)\n print(window_transform)\n\n if nodata is not None:\n mask = np.all(data != nodata, axis=0).astype(np.uint8) * 255\n elif alpha is not None:\n mask = vrt.read(alpha, window=window,\n out_shape=(tilesize, tilesize),\n boundless=boundless,\n resampling=Resampling.bilinear)\n else:\n mask = vrt.read_masks(1, window=window,\n out_shape=(tilesize, tilesize),\n boundless=boundless,\n resampling=Resampling.bilinear)\n else:\n with rasterio.open(source) as src:\n with WarpedVRT(src, **vrt_params) as vrt:\n window = vrt.window(w, s, e, n, precision=21)\n window_transform = windows.transform(window, vrt.transform)\n window_transform = transform.from_bounds(w, s, e, n, tilesize, tilesize)\n\n data = vrt.read(window=window,\n boundless=boundless,\n resampling=Resampling.bilinear,\n out_shape=out_shape,\n indexes=indexes)\n\n if nodata is not None:\n mask = np.all(data != nodata, axis=0).astype(np.uint8) * 255\n elif alpha is not None:\n mask = vrt.read(alpha, window=window,\n out_shape=(tilesize, tilesize),\n boundless=boundless,\n resampling=Resampling.bilinear)\n else:\n mask = vrt.read_masks(1, window=window,\n out_shape=(tilesize, tilesize),\n boundless=boundless,\n resampling=Resampling.bilinear)\n\n return data, mask, window, window_transform","def download_data(study, engine, has_trips=True, filter_coverage=False):\n\n def to_datetime(df):\n df[\"started_at\"] = pd.to_datetime(df[\"started_at\"], utc=True)\n df[\"finished_at\"] = pd.to_datetime(df[\"finished_at\"], utc=True)\n return df\n\n exclude_purpose_tist = [\n \"Light Rail\",\n \"Subway\",\n \"Platform\",\n \"Trail\",\n \"Road\",\n \"Train\",\n \"Bus Line\",\n ]\n\n print(\"\\t download staypoints\")\n sp = gpd.read_postgis(\n sql=\"select * from {}.{}\".format(study, sp_name_dict[study]),\n con=engine,\n geom_col=\"geom\",\n index_col=\"id\",\n )\n sp = to_datetime(sp)\n\n print(\"\\t download locations\")\n sql = f\"SELECT * FROM {study}.{locs_name_dict[study]}\"\n locs = ti.io.read_locations_postgis(sql, con=engine, index_col=\"id\")\n\n # studies with trips\n gap_treshold = 12\n if has_trips:\n print(\"\\t download trips\")\n trips = ti.io.read_trips_postgis(\n f\"select * from {study}.trips\", con=engine\n )\n if filter_coverage:\n # get_triplegs(study=study, engine=engine)\n print(\"\\t download triplegs\")\n tpls = pd.read_sql(\n sql=f\"select id, user_id, started_at, finished_at from {study}.triplegs\",\n con=engine,\n index_col=\"id\",\n )\n tpls = to_datetime(tpls)\n sp, locs, trips = filter_tracking_coverage(sp, locs, tpls, trips)\n\n # studies without trips (Foursquare)\n else:\n sp = sp[~sp[\"purpose\"].isin(exclude_purpose_tist)]\n trips = None\n return (sp, locs, trips, gap_treshold)","def get_weather_data():\n keys = ['1364038.csv',\n '1364041.csv',\n '1364042.csv',\n '1364043.csv',\n '1364044.csv',\n '1364046.csv',\n '1364047.csv',\n '1364048.csv',\n '1364051.csv',\n '1364052.csv',\n '1364053.csv',\n '1364054.csv',\n '1364055.csv',\n '1364058.csv',\n '1364059.csv',\n '1364060.csv',\n '1364061.csv',\n '1364062.csv',\n '1364063.csv',\n '1364064.csv',\n '1364066.csv']\n df_weather = import_weather(keys)\n df_weather_dist = df_weather[[\n 'LATITUDE', 'LONGITUDE', 'name']].drop_duplicates().reset_index()\n return df_weather, df_weather_dist","def point_interp_ts(df, time_col, x_col, y_col, data_col, point_shp, point_site_col, from_crs, to_crs=None, interp_fun='cubic', agg_ts_fun=None, period=None, digits=2):\n\n #### Read in points\n if isinstance(point_shp, str) & isinstance(point_site_col, str):\n points = read_file(point_shp)[[point_site_col, 'geometry']]\n to_crs1 = points.crs\n elif isinstance(point_shp, GeoDataFrame) & isinstance(point_site_col, str):\n points = point_shp[[point_site_col, 'geometry']]\n to_crs1 = points.crs\n else:\n raise ValueError('point_shp must be a str path to a shapefile or a GeoDataFrame and point_site_col must be a str.')\n\n #### Create the grids\n df1 = df.copy()\n\n #### Resample the time series data\n if agg_ts_fun is not None:\n df1a = df1.set_index(time_col)\n if agg_ts_fun == 'sum':\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].sum().reset_index()\n elif agg_ts_fun == 'mean':\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].mean().reset_index()\n else:\n raise ValueError(\"agg_ts_fun should be either 'sum' or 'mean'.\")\n time = df2[time_col].unique()\n else:\n df2 = df1\n\n time = df2[time_col].sort_values().unique()\n\n #### Convert input data to crs of points shp and create input xy\n data1 = df2.loc[df2[time_col] == time[0]]\n from_crs1 = convert_crs(from_crs, pass_str=True)\n\n if to_crs is not None:\n to_crs1 = convert_crs(to_crs, pass_str=True)\n points = points.to_crs(to_crs1)\n geometry = [Point(xy) for xy in zip(data1[x_col], data1[y_col])]\n gpd = GeoDataFrame(data1.index, geometry=geometry, crs=from_crs1)\n gpd1 = gpd.to_crs(crs=to_crs1)\n x = gpd1.geometry.apply(lambda p: p.x).round(digits).values\n y = gpd1.geometry.apply(lambda p: p.y).round(digits).values\n\n xy = column_stack((x, y))\n\n #### Prepare the x and y of the points geodataframe output\n x_int = points.geometry.apply(lambda p: p.x).round(digits).values\n y_int = points.geometry.apply(lambda p: p.y).round(digits).values\n sites = points[point_site_col]\n\n xy_int = column_stack((x_int, y_int))\n\n #### Create new df\n sites_ar = tile(sites, len(time))\n time_ar = repeat(time, len(xy_int))\n x_ar = tile(x_int, len(time))\n y_ar = tile(y_int, len(time))\n new_df = DataFrame({'site': sites_ar, 'time': time_ar, 'x': x_ar, 'y': y_ar, data_col: repeat(0, len(time) * len(xy_int))})\n\n new_lst = []\n for t in to_datetime(time):\n set1 = df2.loc[df2[time_col] == t, data_col]\n new_z = griddata(xy, set1.values, xy_int, method=interp_fun).round(digits)\n new_z[new_z < 0] = 0\n new_lst.extend(new_z.tolist())\n# print(t)\n new_df.loc[:, data_col] = new_lst\n\n #### Export results\n return(new_df[new_df[data_col].notnull()])","def generate_timeseries(data_list, setname=\"MagneticFields\"):\n full_data = TimeSeriesList()\n for seg in sorted(data_list):\n hfile = h5py.File(data_list[seg], \"r\")\n full_data.append(retrieve_data_timeseries(hfile, \"MagneticFields\"))\n hfile.close()\n return full_data","def write_overview_tile(self, tx, ty, tz,tms_osm):\n\n image_format = self.get_overview_tile_format(tx, ty, tz)\n\n if image_format is None:\n return\n else:\n num_bands = self.get_num_bands(image_format)\n\n dsquery = self.mem_drv.Create('', 2*self.tile_size, 2*self.tile_size, num_bands)\n self.fill_init_dest(dsquery)\n # tms: z=19: 281626\n # -z=18-140813 176168*2=352336; 176168*2+1=352337\n # -- 352336,352337\n y_from=2*ty\n y_to=2*ty + 1\n ty_tms=ty;\n s_y_type=\"tms\"\n if tms_osm:\n # osm: z=19: 281626\n # -z=18-140813 85975*2+1=171951; 85975*2=171950\n # -- 171951,171950 [in range: last/end not used]\n y_from=2*ty + 1\n y_to=2*ty\n ty_tms=(2**tz-1) - ty\n s_y_type=\"osm\"\n s_tile_id=\"{0}-{1}-{2}.{3}\".format(str(tz), str(tx),str(ty),s_y_type)\n if self.verbose:\n # Build from zoom 19 tiles: (281626, 171951) (281627, 171951) (281626, 171950) (281627, 171950)\n print \"\\tBuild [\",s_tile_id,\"] from [\",self.output_dir,\"] zoom\", tz+1,\" tiles [\",s_y_type,\"]: \", (2*tx, y_from), (2*tx+1, y_from),(2*tx, y_to), (2*tx+1, y_to)\n\n for cx, cy, child_image_format in self.iter_children(tx, ty, tz):\n if (ty_tms==0 and cy==1) or (ty_tms!=0 and (cy % (y_from)) != 0):\n tileposy = 0\n else:\n tileposy = self.tile_size\n if tx:\n tileposx = cx % (2*tx) * self.tile_size\n elif tx==0 and cx==1:\n tileposx = self.tile_size\n else:\n tileposx = 0\n\n path = self.get_full_path(cx, cy, tz+1, format_extension[child_image_format])\n\n dsquerytile = gdal.Open(path, gdal.GA_ReadOnly)\n\n dsquery.WriteRaster(tileposx, tileposy, self.tile_size, self.tile_size,\n dsquerytile.ReadRaster(0, 0, self.tile_size, self.tile_size),\n band_list=range(1, dsquerytile.RasterCount+1))\n\n if image_format == \"PNG\" and dsquerytile.RasterCount != num_bands:\n dsquery.WriteRaster(tileposx, tileposy, self.tile_size, self.tile_size,\n self.get_alpha_filler(), band_list=[num_bands])\n\n dstile = self.mem_drv.Create('', self.tile_size, self.tile_size, num_bands)\n path = self.get_full_path(tx, ty, tz, format_extension[image_format])\n self.resampler(path, dsquery, dstile, image_format)","def pull_traffic():\n ga_conn = initialize_analyticsreporting()\n\n device_traffic = get_report(\n ga_conn=ga_conn,\n dimensions=[{'name': 'ga:deviceCategory'},{'name': 'ga:yearMonth'},])\n device_traffic = melt_ga(device_traffic,\n a='Traffic',\n c='Device',\n d='deviceCategory')\n\n geo_traffic = get_report(\n ga_conn=ga_conn,\n dimensions=[{'name': 'ga:country'},{'name': 'ga:yearMonth'},])\n bucketed_countries = geo_traffic.apply(lambda row: bucket_countries(row), axis=1)\n geo_traffic['country'] = bucketed_countries\n geo_traffic = geo_traffic.groupby(['yearMonth','country'])\\\n .sum()\\\n .reset_index()\n geo_traffic = melt_ga(geo_traffic,\n a='Traffic',\n c='Geography',\n d='country')\n\n channel_traffic = get_report(\n ga_conn=ga_conn,\n dimensions=[{'name': 'ga:channelGrouping'},{'name': 'ga:yearMonth'},])\n bucketed_channels = channel_traffic.apply(lambda row: bucket_channels(row), axis=1)\n channel_traffic['channelGrouping'] = bucketed_channels\n channel_traffic = channel_traffic.groupby(['yearMonth','channelGrouping'])\\\n .sum()\\\n .reset_index()\n channel_traffic = melt_ga(channel_traffic,\n a='Traffic',\n c='Channel',\n d='channelGrouping')\n df_out = pd.concat([device_traffic, geo_traffic, channel_traffic])\n return df_out","def temperature_range_analysis(temp_df, dt_from, dt_to):\n df = copy.deepcopy(temp_df)\n\n df_unique_sensors = df[[\"sensor_id\", \"name\"]].drop_duplicates([\"sensor_id\", \"name\"])\n\n sensor_ids = df_unique_sensors[\"sensor_id\"].tolist()\n sensor_names = df_unique_sensors[\"name\"].tolist()\n\n # extracting date from datetime\n df[\"date\"] = pd.to_datetime(df[\"timestamp\"].dt.date)\n\n # Reseting index\n df.sort_values(by=[\"timestamp\"], ascending=True).reset_index(inplace=True)\n\n data_by_sensor_id = {}\n for sensor_name, sensor_id in zip(sensor_names, sensor_ids):\n df_sensor = df[df[\"sensor_id\"] == sensor_id]\n # grouping data by date-hour and sensor id\n sensor_grp = df_sensor.groupby(\n by=[\n df_sensor.timestamp.map(\n lambda x: \"%04d-%02d-%02d-%02d\" % (x.year, x.month, x.day, x.hour)\n ),\n \"date\",\n ]\n )\n\n # estimating hourly temperature mean values\n sensor_grp_temp = sensor_grp[\"temperature\"].mean().reset_index()\n\n try:\n bins = TEMP_BINS[SENSOR_CATEORIES[sensor_id]]\n except KeyError:\n logging.error(\n f\"Don't know how to categorise or bin sensor {sensor_id} \"\n \"in the dashboard.\"\n )\n continue\n # binning temperature values\n sensor_grp_temp[\"temp_bin\"] = pd.cut(sensor_grp_temp[\"temperature\"], bins)\n\n # converting bins to str\n sensor_grp_temp[\"temp_bin\"] = sensor_grp_temp[\"temp_bin\"].astype(str)\n\n # get bin counts for each sensor-day combination\n sensor_grp_date = sensor_grp_temp.groupby(by=[\"date\", \"temp_bin\"])\n\n sensor_cnt = sensor_grp_date[\"temperature\"].count().reset_index()\n sensor_cnt.rename(columns={\"temperature\": \"temp_cnt\"}, inplace=True)\n\n # Adding missing date/temp_bin combos\n bins_list, df_list = resample(sensor_cnt, bins, dt_from, dt_to)\n\n data_by_sensor_id[sensor_id] = {\n \"name\": sensor_name,\n \"bins\": bins_list,\n \"data\": [\n {\n \"date\": df[\"date\"].dt.strftime(\"%Y-%m-%d\").to_list(),\n \"count\": df[\"temp_cnt\"].to_list(),\n }\n for df in df_list\n ],\n }\n return len(data_by_sensor_id.keys()), json.dumps(data_by_sensor_id)","def create_df(file_path):\n columns = ['Lat', 'Lat_dir', 'Long', 'Long_dir', 'Quality', 'Dilution']\n speeds = []\n validity = []\n rows = []\n with open(file_path, encoding='utf-8', errors='ignore') as f:\n for line in f.readlines()[5:]:\n words = line.strip().split(\",\")\n if len(words) == 0 or len(words) > 15:\n continue\n elif words[0] == \"$GPGGA\": # if line starts with GPGGA store lat, long, quality and dilution of precision\n if len(rows) == 0:\n row = [words[2], words[3], words[4], words[5], words[6], words[8]]\n speeds.append(np.nan)\n validity.append(np.nan)\n rows.append(row)\n else:\n if rows[len(rows) - 1][0] is np.nan:\n row = [words[2], words[3], words[4], words[5], words[6], words[8]]\n rows[len(rows) - 1] = row\n else:\n row = [words[2], words[3], words[4], words[5], words[6], words[8]]\n speeds.append(np.nan)\n validity.append(np.nan)\n rows.append(row)\n elif words[0] == \"$GPRMC\": # if lines start with GPRMC store speed and validity\n if len(rows) == 0:\n row = [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]\n speeds.append(float(words[7]) * 1.15078)\n validity.append(words[2])\n rows.append(row)\n else:\n if speeds[len(speeds) - 1] is np.nan:\n speeds[len(speeds) - 1] = float(words[7]) * 1.15078\n validity[len(speeds) - 1] = words[2]\n else:\n row = [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]\n speeds.append(float(words[7]) * 1.15078)\n validity.append(words[2])\n rows.append(row)\n else:\n continue\n gps_df = pd.DataFrame(rows, columns=columns)\n gps_df['Speed'] = speeds # combine both speed and validity back to dataframe\n gps_df['Validity'] = validity\n return gps_df","def create_geodata(x):\n list_len = len(x)\n pilot_log = pd.concat(x[i][['time','Cn0DbHz','svid','geometry']] for i in range(list_len))\n \n return pilot_log","def grid_interp_ts(df, time_col, x_col, y_col, data_col, grid_res, from_crs=None, to_crs=2193, interp_fun='cubic', agg_ts_fun=None, period=None, digits=2):\n\n #### Create the grids\n df1 = df.copy()\n\n #### Resample the time series data\n if agg_ts_fun is not None:\n df1a = df1.set_index(time_col)\n if agg_ts_fun == 'sum':\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].sum().reset_index()\n elif agg_ts_fun == 'mean':\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].mean().reset_index()\n else:\n raise ValueError(\"agg_ts_fun should be either 'sum' or 'mean'.\")\n time = df2[time_col].unique()\n else:\n df2 = df1\n\n time = df2[time_col].sort_values().unique()\n\n if from_crs is None:\n x = df2.loc[df2[time_col] == time[0], x_col].values\n y = df2.loc[df2[time_col] == time[0], y_col].values\n else:\n data1 = df2.loc[df2[time_col] == time[0]]\n from_crs1 = convert_crs(from_crs, pass_str=True)\n to_crs1 = convert_crs(to_crs, pass_str=True)\n geometry = [Point(xy) for xy in zip(data1[x_col], data1[y_col])]\n gpd = GeoDataFrame(data1.index, geometry=geometry, crs=from_crs1)\n gpd1 = gpd.to_crs(crs=to_crs1)\n x = gpd1.geometry.apply(lambda p: p.x).round(digits).values\n y = gpd1.geometry.apply(lambda p: p.y).round(digits).values\n\n xy = column_stack((x, y))\n\n max_x = x.max()\n min_x = x.min()\n\n max_y = y.max()\n min_y = y.min()\n\n new_x = arange(min_x, max_x, grid_res)\n new_y = arange(min_y, max_y, grid_res)\n x_int, y_int = meshgrid(new_x, new_y)\n\n #### Create new df\n x_int2 = x_int.flatten()\n y_int2 = y_int.flatten()\n xy_int = column_stack((x_int2, y_int2))\n time_df = repeat(time, len(x_int2))\n x_df = tile(x_int2, len(time))\n y_df = tile(y_int2, len(time))\n new_df = DataFrame({'time': time_df, 'x': x_df, 'y': y_df, data_col: repeat(0, len(time) * len(x_int2))})\n\n new_lst = []\n for t in to_datetime(time):\n set1 = df2.loc[df2[time_col] == t, data_col]\n# index = new_df[new_df['time'] == t].index\n new_z = griddata(xy, set1.values, xy_int, method=interp_fun).round(digits)\n new_z[new_z < 0] = 0\n new_lst.extend(new_z.tolist())\n# print(t)\n new_df.loc[:, data_col] = new_lst\n\n #### Export results\n return(new_df[new_df[data_col].notnull()])","def preproc_data(data):\n # Load data manually from Yahoo! finance\n\n # Initialize TP Matrix\n # 3-dimension: # of stock * 18 * 18\n # narray\n _TP_matrixs = np.zeros(\n (len(data.ix[stockname]) - 230, 18, 18), dtype=np.bool)\n old = data.ix[stockname]['close'][229]\n TP_matrixs = pd.Panel(_TP_matrixs, items=data.ix[stockname].index[230:])\n label = np.zeros((len(data.ix[stockname]) - 230), dtype=np.float)\n dataindex = 0\n dataset = []\n # Construct TP Matrix\n for TP_matrix in TP_matrixs.iteritems():\n # Extract raw close price of last 230 days\n # pdb.set_trace()\n tp_features = np.zeros((18, 18), dtype=np.bool)\n _list_CP = data.ix[stockname][data.ix[stockname].index <\n TP_matrix[0]]['close'].tolist()\n list_CP = _list_CP[len(_list_CP) - 230: len(_list_CP)]\n close = data.ix[stockname]['close'][dataindex + 230]\n label = (close - old) / old\n old = close\n # col[0, 8] for Upward TP Matrix\n # col[9, 17] for Downward TP Matrix\n for col in range(0, 18):\n D = columns[col][0] - 1\n for row in range(0, 18):\n # For each element of TP Matrix\n for TP in range(D, columns[col][1]):\n # Change ratio of stock on day D with repect to the price\n # at TP\n C_TPD = (list_CP[TP] - list_CP[D]) / list_CP[D]\n if C_TPD * 100 >= rows[row][0] and C_TPD * 100 < rows[row][1]:\n TP_matrix[1][row][col] = True\n tp_features[row][col] = True\n break\n\n sample = DataSet()\n sample.tp_features = tp_features\n sample.labels = label\n dataindex += 1\n dataset.append(sample)\n\n filename = 'data/TP_matrix_' + stockname + '.pkl'\n output = open(filename, 'wb')\n # # Pickle dictionary using protocol 0.\n pickle.dump(TP_matrixs, output)\n output.close()\n return dataset","def warren_time_series(lat, lon, dates, variable='snow_depth'):\n\n my_func = {'snow_depth': snow_depth,\n 'swe': swe}\n\n #if not all([isinstance(d, dt.datetime) for d in dates]):\n # print ('Expects datetime objects')\n\n # If lat, lon are vectors, generate 2d grids\n # Need to add code to make sure x and y are DataArrays\n if (count_dims(lat) == 1):\n x, y = np.meshgrid(lon, lat)\n else:\n x, y = lon, lat\n \n if dates.size == 1:\n cube = my_func[variable](x,y,dates.dt.month)\n da = xr.DataArray(cube,\n coords={'lat': x, 'lon': y},\n dims=['lat', 'lon'])\n else:\n cube = [my_func[variable](x, y, m) for m in dates.dt.month.values]\n da = xr.concat(cube, dim='time')\n da['time'] = dates\n\n return da","def link_tariffs(meas: pd.DataFrame, data_location: str, filename: str,\n local_tz: str = 'Australia/Sydney') -> pd.Series:\n datetime_series = unit_conversion.local_t_from_dataframe_index(meas, local_tz=local_tz)\n tariff_series = load_tariff(data_location, filename, meas.index, datetime_series)\n\n return tariff_series","def main(parameters_file, token):\n\n print(f\"--\\nReading parameters file: {parameters_file}\\n--\")\n\n set_token(token=token)\n\n\n try:\n with open(parameters_file, 'r') as f:\n\n parameters = load(f,\n Loader=SafeLoader)\n maggeo_params = parameters[\"maggeo\"] \n gpsfilename = maggeo_params[\"gpsfilename\"]\n Lat = maggeo_params[\"Lat\"]\n Long = maggeo_params[\"Long\"] \n DateTime = maggeo_params[\"DateTime\"]\n altitude = maggeo_params[\"altitude\"]\n\n except Exception as error:\n print('Error in parameters file format')\n raise error\n \n base_dir=os.getcwd()\n temp_results_dir = os.path.join(base_dir, \"temp_data\")\n results_dir = os.path.join(base_dir, \"results\")\n data_dir = os.path.join(base_dir, \"data\")\n utilities_dir = os.path.join(base_dir, \"utilities\")\n\n GPSData = getGPSData(data_dir,gpsfilename,Lat,Long,DateTime,altitude)\n \n datestimeslist = []\n for index, row in GPSData.iterrows():\n datetimerow = row['gpsDateTime']\n daterow = row['dates']\n hourrow = row['times']\n hourrow = hourrow.strftime('%H:%M:%S')\n if hourrow < '04:00:00':\n date_bfr = daterow - (timedelta(days=1))\n datestimeslist.append(daterow)\n datestimeslist.append(date_bfr)\n if hourrow > '20:00:00':\n Date_aft = daterow + (timedelta(days=1))\n datestimeslist.append(daterow)\n datestimeslist.append(Date_aft) \n else:\n datestimeslist.append(daterow)\n\n def uniquelistdates(list): \n x = np.array(list) \n uniquelist = np.unique(x)\n return uniquelist\n\n uniquelist_dates = uniquelistdates(datestimeslist)\n\n hours_t_day = 24 #MagGeo needs the entire Swarm data for each day of the identified day.\n hours_added = dt.timedelta(hours = hours_t_day)\n\n listdfa = []\n listdfb = []\n listdfc = []\n\n for d in tqdm(uniquelist_dates, desc=\"Getting Swarm Data\"):\n #print(\"Getting Swarm data for date:\",d )\n startdate = dt.datetime.combine(d, dt.datetime.min.time())\n enddate = startdate + hours_added\n SwarmResidualsA,SwarmResidualsB,SwarmResidualsC = Get_Swarm_residuals(startdate, enddate)\n listdfa.append(SwarmResidualsA)\n listdfb.append(SwarmResidualsB)\n listdfc.append(SwarmResidualsC)\n\n base_dir = os.getcwd() # Get main MagGeo directory\n temp_results_dir = os.path.join(base_dir, \"temp_data\")\n results_dir = os.path.join(base_dir, \"results\")\n data_dir = os.path.join(base_dir, \"data\")\n\n #TODO:\n #Find a more elegant way to concante the the list from Swarm and then read it to get the type of columns is requiered, Timestamp as datetime and epoch as index.\n \n PdSwarmRes_A = pd.concat(listdfa, join='outer', axis=0)\n PdSwarmRes_A.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_A.csv'), header=True)\n PdSwarmRes_B = pd.concat(listdfb, join='outer', axis=0)\n PdSwarmRes_B.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_B.csv'), header=True)\n PdSwarmRes_C = pd.concat(listdfc, join='outer', axis=0)\n PdSwarmRes_C.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_C.csv'), header=True)\n\n TotalSwarmRes_A = pd.read_csv(os.path.join(temp_results_dir,\"TotalSwarmRes_A.csv\"),low_memory=False, index_col='epoch')\n TotalSwarmRes_A['timestamp'] = pd.to_datetime(TotalSwarmRes_A['timestamp'])\n TotalSwarmRes_B = pd.read_csv(os.path.join(temp_results_dir,\"TotalSwarmRes_B.csv\"),low_memory=False, index_col='epoch')\n TotalSwarmRes_B['timestamp'] = pd.to_datetime(TotalSwarmRes_B['timestamp'])\n TotalSwarmRes_C = pd.read_csv(os.path.join(temp_results_dir,\"TotalSwarmRes_C.csv\"),low_memory=False, index_col='epoch')\n TotalSwarmRes_C['timestamp'] = pd.to_datetime(TotalSwarmRes_C['timestamp'])\n\n dn = [] ## List used to add all the GPS points with the annotated MAG Data. See the last bullet point of this process \n for index, row in tqdm(GPSData.iterrows(), total=GPSData.shape[0], desc=\"Annotating the GPS Trayectory\"):\n GPSLat = row['gpsLat'] \n GPSLong = row['gpsLong']\n GPSDateTime = row['gpsDateTime']\n GPSTime = row['epoch']\n GPSAltitude = row['gpsAltitude']\n #print(\"Process for:\", index,\"DateTime:\",GPSDateTime)\n try:\n result=ST_IDW_Process(GPSLat,GPSLong,GPSAltitude, GPSDateTime,GPSTime, TotalSwarmRes_A, TotalSwarmRes_B, TotalSwarmRes_C)\n dn.append(result)\n except:\n #print(\"Ups!.That was a bad Swarm Point, let's keep working with the next point\")\n result_badPoint= {'Latitude': GPSLat, 'Longitude': GPSLong, 'Altitude':GPSAltitude, 'DateTime': GPSDateTime, 'N_res': np.nan, 'E_res': np.nan, 'C_res':np.nan, 'TotalPoints':0, 'Minimum_Distance':np.nan, 'Average_Distance':np.nan} \n dn.append(result_badPoint)\n continue\n \n \n GPS_ResInt = pd.DataFrame(dn)\n GPS_ResInt.to_csv (os.path.join(temp_results_dir,\"GPS_ResInt.csv\"), header=True)\n \n X_obs, Y_obs, Z_obs, X_obs_internal, Y_obs_internal, Z_obs_internal = CHAOS_ground_values(utilities_dir,GPS_ResInt)\n\n GPS_ResInt['N'] =pd.Series(X_obs)\n GPS_ResInt['E'] =pd.Series(Y_obs)\n GPS_ResInt['C'] =pd.Series(Z_obs)\n GPS_ResInt['N_Obs'] =pd.Series(X_obs_internal)\n GPS_ResInt['E_Obs'] =pd.Series(Y_obs_internal)\n GPS_ResInt['C_Obs'] =pd.Series(Z_obs_internal)\n\n GPS_ResInt.drop(columns=['N_res', 'E_res','C_res'], inplace=True)\n\n # Having Intepolated and weighted the magnetic values, we can compute the other magnectic components. \n GPS_ResInt['H'] = np.sqrt((GPS_ResInt['N']**2)+(GPS_ResInt['E']**2))\n #check the arcgtan in python., From arctan2 is saver.\n DgpsRad = np.arctan2(GPS_ResInt['E'],GPS_ResInt['N'])\n GPS_ResInt['D'] = np.degrees(DgpsRad)\n IgpsRad = np.arctan2(GPS_ResInt['C'],GPS_ResInt['H'])\n GPS_ResInt['I'] = np.degrees(IgpsRad)\n GPS_ResInt['F'] = np.sqrt((GPS_ResInt['N']**2)+(GPS_ResInt['E']**2)+(GPS_ResInt['C']**2))\n\n \n originalGPSTrack=pd.read_csv(os.path.join(data_dir,gpsfilename))\n MagGeoResult = pd.concat([originalGPSTrack, GPS_ResInt], axis=1)\n #Drop duplicated columns. Latitude, Longitued, and DateTime will not be part of the final result.\n MagGeoResult.drop(columns=['Latitude', 'Longitude', 'DateTime'], inplace=True)\n \n #Exporting the CSV file\n \n outputfile =\"GeoMagResult_\"+gpsfilename\n export_csv = MagGeoResult.to_csv (os.path.join(results_dir,outputfile), index = None, header=True) \n print(\"Congrats! MagGeo has processed your GPS trayectory. Find the annotated table: \" + outputfile + \" in the folder results.\")","def time_info(input_file):\n original_path = os.getcwd() #set original directory\n save_path = input_file['save_path']\n planet = input_file['exoplanet'] #set exoplanet name\n print '\\nObtain the images .... \\n'\n print 'Change to ', save_path\n os.chdir(save_path) #change to save directory where is our scvience images\n images = sorted(glob.glob('AB'+input_file['exoplanet']+'*.fits'))\n print '\\nImages = \\n',images\n tempo_loc = [] #time object\n SUN = [] #Sun coordinate object\n ra_sun, dec_sun, dsun = np.zeros(len(images)),np.zeros(len(images)),np.zeros(len(images)) #sun coordinates\n JD = np.zeros(len(images)) #julian date from time object\n ST = np.zeros(len(images))\n HJD = np.zeros(len(images))\n #create the exoplanet object coordianate\n exoplanet = SkyCoord(dec=input_file['DEC'],ra=input_file['RA'],unit=('deg','deg'),frame=input_file['frame'])\n print '\\nObtain data info from header ....\\n'\n for i in range(len(images)):\n hdr = fits.getheader(images[i])\n UTC = hdr['date-obs']+'T'+hdr['UT'] #string that contain the time in UTC in isot format\n tempo_loc.append(Time(UTC,scale=input_file['scale-time'],format='isot',location=(input_file['lon-obs'],input_file['lat-obs'])))#,input_data['altitude'])))\n JD[i] = tempo_loc[i].jd\n ST[i] = tempo_loc[i].sidereal_time('apparent').hour\n SUN.append(get_sun(tempo_loc[i]))\n ra_sun[i],dec_sun[i] = SUN[i].ra.deg, SUN[i].dec.deg\n dsun[i] = SUN[i].distance.value\n HJD[i] = use.hjd_date(JD[i],dsun[i],dec_sun[i],ra_sun[i],exoplanet.dec.deg,exoplanet.ra.deg,circular_orbit=input_file['circular_orbit'])\n use.update_progress((i+1.)/len(images))\n print '\\n.... done.\\n'\n print '\\n Time from header = \\n'\n #print '\\nImages ** UTC (YYYY-MM-DDTHH:MM:SS) ** JD (7d.5d) ** ST (hours) ** ST (HH:MM:SS) ** Sun Coordinate (epoch,RA,DEC,Distance) (deg,deg,AU) \\n'\n ST_string = []\n for i in range(len(images)):\n ST1 = int(ST[i])\n ST2 = int((ST[i]-ST1)*60.)\n ST3 = (((ST[i]-ST1)*60.)-ST2)*60\n ST_string.append(str(ST1)+':'+str(ST2)+':'+str(ST3))\n tempo_loc[i] = tempo_loc[i].value\n use.update_progress((i+1.)/len(images))\n #print images[i], ' ** ',tempo_loc[i], ' ** ', JD[i], ' ** ', ST[i],' ** ',ST_string[i],' ** ',sun_loc[i],' ** ',HJD[i]\n print '\\nSave data file ... \\n'\n data = DataFrame([images,tempo_loc,list(JD),list(ST),list(ST_string),list(ra_sun),list(dec_sun),list(dsun),list(HJD)]).T\n data.columns=['images','UTC','JD','ST','ST_isot','RA_SUN','DEC_SUN','D_SUN','HJD']\n print data\n data.to_csv('results.csv')\n os.chdir(original_path)\n return","def grid_events(self, scale, threshold=100, distrib=False, dataname=''):\n\n def plot_notimeres(thres):\n \"\"\"\n All time colapsed\n @return:\n \"\"\"\n cont = np.zeros((scale, scale))\n for i in range(self.dataset.shape[0]):\n posx = int(((self.dataset[i][1] - minLon) * normLon))\n posy = int(((self.dataset[i][0] - minLat) * normLat))\n\n cont[posx - 1, posy - 1] += 1\n\n lgeo = []\n\n for i in range(cont.shape[0]):\n for j in range(cont.shape[1]):\n if cont[i, j] >= thres:\n path = [(minLon + (i + 0.5) / normLon, maxLat - (scale - j - 1.5) / normLat),\n (minLon + (i + 0.5) / normLon, maxLat - (scale - j - 0.5) / normLat),\n (minLon + (i + 1.5) / normLon, maxLat - (scale - j - 0.5) / normLat),\n (minLon + (i + 1.5) / normLon, maxLat - (scale - j - 1.5) / normLat)\n ]\n lgeo.append(Feature(geometry=Polygon([path])))\n\n return lgeo\n\n minLat, maxLat, minLon, maxLon = self.city[1]\n normLat = scale / (maxLat - minLat)\n normLon = scale / (maxLon - minLon)\n mymap = folium.Map(location=[(minLat + maxLat) / 2.0, (minLon + maxLon) / 2.0], zoom_start=12, width=1400,\n height=1000)\n lgeog = plot_notimeres(threshold)\n\n nfile = self.application + '-' + dataname\n if self.mnhh is not None and self.mnhh is not None:\n nfile += '-nusr' + str(self.mxhh) + '-' + str(self.mnhh)\n nfile += '-s' + str(scale)\n nfile += '-tr' + str(threshold)\n\n geoc = FeatureCollection(lgeog)\n dump = geojson.dumps(geoc)\n jsfile = open(homepath + 'Results/' + nfile + '.json', 'w')\n jsfile.write(dump)\n jsfile.close()\n mymap.geo_json(geo_path=homepath + 'Results/' + nfile + '.json', fill_opacity=0.2)\n\n mymap.create_map(path=homepath + 'Results/' + self.city[2] + nfile + '.html')","def get_data(station,starttime,endtime,activity=False,\n rep='/GNOMEDrive/gnome/serverdata/',resample=None):\n setname = \"MagneticFields\"\n dstr = ['%Y','%m','%d','%H','%M']\n dsplit = '-'.join(dstr[:starttime.count('-')+1])\n start = datetime.strptime(starttime,dsplit)\n starttime = construct_utc_from_metadata(start.strftime(\"%Y/%m/%d\"),\n start.strftime(\"%H:%M:%S.%d\"))\n dsplit = '-'.join(dstr[:endtime.count('-')+1])\n end = datetime.strptime(endtime,dsplit)\n endtime = construct_utc_from_metadata(end.strftime(\"%Y/%m/%d\"),\n end.strftime(\"%H:%M:%S.%d\"))\n dataset = []\n for date in numpy.arange(start,end,timedelta(minutes=1)):\n date = date.astype(datetime)\n path1 = rep+station+'/'+date.strftime(\"%Y/%m/%d/\")\n path2 = station+'_'+date.strftime(\"%Y%m%d_%H%M*.hdf5\")\n fullpath = os.path.join(path1,path2)\n dataset += glob.glob(fullpath)\n if len(dataset)==0:\n print \"ERROR: No data files were found...\"\n quit()\n file_order,data_order = {},{}\n for fname in dataset:\n hfile = h5py.File(fname, \"r\")\n segfile = file_to_segment(hfile,setname)\n file_order[segfile] = fname\n data_order[segfile] = hfile\n # Extract sample rate from metadata of last read data file\n sample_rate = hfile[setname].attrs[\"SamplingRate(Hz)\"]\n # Estimate full segment activity list\n activity = create_activity_list(station,data_order)\n # Generate an ASCII representation of the GPS timestamped\n # segments of time covered by the input data\n seglist = segmentlist(data_order.keys())\n # Sort the segment list\n seglist.sort()\n # Create list of time series from every segment\n ts_list = generate_timeseries(file_order,setname)\n # Retrieve channel data for all the segments\n full_data = numpy.hstack([retrieve_channel_data(data_order[seg],setname)\n for seg in seglist])\n new_sample_rate = sample_rate if resample==None else resample\n new_data_length = len(full_data)*new_sample_rate/float(sample_rate)\n full_data = scipy.signal.resample(full_data,int(new_data_length))\n # Models a time series consisting of uniformly sampled scalar values\n ts_data = types.TimeSeries(full_data,delta_t=1./new_sample_rate,\n epoch=seglist[0][0])\n for v in data_order.values():\n v.close()\n return ts_data,ts_list,activity,int(starttime),int(endtime)","def get_data(day, unixstart=None, unixend=None):\n global subnet\n df_data = pd.DataFrame([])\n while len(df_data.index)<=0:\n try:\n df_data = pd.read_feather(f'/home/pi/studies/ardmore/homeserver/h{subnet}_{day}.feather')\n except Exception as e:\n # print(f\"Error grid_server.get_data:{e}\")\n pass\n if unixstart!=None:\n df_data = df_data[(df_data['unixtime']>=unixstart)&(df_data['unixtime']<=unixend)]\n float_cols = [x for x in df_data.columns if not x.startswith('timezone')]\n df_data = df_data[float_cols].astype(float)\n return df_data","def main():\n\n pathfolder = \"/home/vanessa/DATA_SEEG/PKL_FILE/\"\n filename = \"/data.pkl\"\n # pathfolder = argv[1]\n # filename = argv[2]\n\n ti = 10. # initial time\n tf = 590. # final time\n t_split = 300. # split\n fs = 1000. # sampling frequency\n powerline = 50.\n\n thresholds = np.load(\"threshold.npy\") # load the threshold file\n meanthresh = thresholds.mean(axis=0)[1::2]\n stdthresh = thresholds.std(axis=0)[1::2]\n\n # features = 159 # classification features + (x,y,z)-coordinates\n\n for ii, id in enumerate(os.listdir(pathfolder)):\n\n print(id)\n\n df = pd.read_pickle(pathfolder + id + filename)\n\n validchannels = np.where(~df.loc[:, \"PTD\"].isnull())[0] # remove NaN values\n\n df = df.iloc[validchannels, :]\n _, p = df.shape\n\n timeseries = df.values[:, :-5] # we are not considering Y, ptd, coordinates\n\n data = remove_powerline(timeseries, fs) # remove power line effects\n\n #################### split into 2 fragments ############################\n\n split1half = data[:, int(fs*ti):int(fs*t_split)]\n split2half = data[:, int(fs*t_split):int(fs*tf)]\n\n timefeat1half = merge_temporal_features(split1half, fs, powerline,\n meanthresh)\n timefeat2half = merge_temporal_features(split2half, fs, powerline,\n meanthresh)\n\n ########################################################################\n\n cc = [df.index[t] for t in range(len(df.index))]\n arrays = [[id]*(2*len(df.index)), cc + cc]\n\n tuples = list(zip(*arrays))\n index = pd.MultiIndex.from_tuples(tuples, names=['patient', 'channel'])\n\n # temporal features from SEEG\n timefeatdf = pd.DataFrame(data=np.vstack((timefeat1half,\n timefeat2half)), index=index)\n\n # spatial features for MRI\n spacefeat = df.values[:, -4:]\n spacefeatdf = pd.DataFrame(data=np.vstack((spacefeat, spacefeat)),\n index=index, columns=\n ['PTD', 'xcoor', 'ycoor', 'zcoor'])\n\n # y labels\n ylab = df.values[:, -5]\n Ylabel = pd.DataFrame(data=np.append(ylab, ylab), index=index,\n columns=[\"Y\"])\n\n # pickle file in output\n outputpkl = pd.concat([timefeatdf, spacefeatdf, Ylabel], axis=1)\n\n outputpkl.to_pickle(pathfolder + id + \"/features.pkl\")\n\n if ii == 0:\n ddd = outputpkl\n else:\n ddd = pd.concat([ddd, outputpkl], axis=0)\n\n ddd.to_pickle(pathfolder + \"classificationset.pkl\")","def make_dataframe(self):\n logging.info('*** Creating the dataframes from the source files ' )\n \n for k in self.datasets_keys:\n #for k in ['igra2' , 'ncar']:\n \n logging.info('*** Creating the dataframe for the dataset: %s ' , k ) \n \n p_levels = self.data[k]['df']['observations_table']['z_coordinate'][:]\n logging.debug(' Loaded the z_coordinate')\n \n z_type = self.data[k]['df']['observations_table']['z_coordinate_type'][:]\n logging.debug(' Loaded the z_coordinate_type')\n \n obs_variable = self.data[k]['df']['observations_table']['observed_variable'][:]\n logging.debug(' Loaded the observed_variable')\n \n obs_values = self.data[k]['df']['observations_table']['observation_value'][:]\n logging.debug(' Loaded the observation_value')\n \n observation_id = self.data[k]['df']['observations_table']['observation_id'][:]\n logging.debug(' Loaded the observation_id')\n \n units = self.data[k]['df']['observations_table']['units'][:].astype(int)\n logging.debug(' Loaded the units') \n \n report_id = self.data[k]['df']['observations_table']['report_id'][:] \n logging.debug(' Loaded the report_id')\n \n date_time = self.data[k]['df']['observations_table']['date_time'][:]\n logging.debug(' Loaded the date_time (deltas)')\n \n lat , lon = self.data[k]['df']['observations_table']['latitude'][:] , self.data[k]['df']['observations_table']['longitude'][:]\n logging.debug(' Loaded the lat,lon ')\n \n \n self.obs_table_columns = list(self.data[k]['df']['observations_table'].keys() )\n \n self.data[k]['df'].close()\n \n \"\"\" Creating a dataframe \"\"\"\n columns = ['date_time', 'z_coordinate' , 'z_coordinate_type', 'observed_variable' , 'observation_value' , 'report_id' , 'observation_id' , 'latitude' , 'longitude', 'units']\n logging.info(' Loaded the data, creating dataframe ')\n \n df = pd.DataFrame( list(zip( date_time, p_levels, z_type, obs_variable , obs_values, report_id, observation_id , lat , lon, units ) ) , columns = columns ) \n \n \n \"\"\" Storing the dataframe \"\"\" ### try using xarrays ??? \n logging.debug('Storing the DF ' ) \n self.data[k]['dataframe'] = df\n \n logging.debug(' PD dataframe created !!! ')","def add_shortest_route(df):\n\n df['gmaps_dist'] = df.apply(lambda row: gmaps.getTotDist((row['pick_lon'], row['pick_lat']), (row['drop_lon'], row['drop_lat'])), axis=1)\n df['gmaps_dur'] = df.apply(lambda row: gmaps.getTotDur((row['pick_lon'], row['pick_lat']), (row['drop_lon'], row['drop_lat'])), axis=1)","def get_mta_data2():\n\n url = \"http://web.mta.info/developers/data/nyct/turnstile/turnstile_{}.txt\"\n dfs = []\n\n for week_num in week_nums:\n file_url = url.format(week_num)\n dfs.append(\n pd.read_csv(\n file_url, parse_dates=[[\"DATE\", \"TIME\"]], keep_date_col=True\n )\n )\n return pd.concat(dfs)","def result(self):\n\n # determine the X and Y sensors to plot from those sensors selected by the user.\n sensorX = bmsapp.models.Sensor.objects.get(pk=self.request_params['select_sensor_x'])\n sensorY = bmsapp.models.Sensor.objects.get(pk=self.request_params['select_sensor_y'])\n\n # determine the averaging time\n averaging_hours = float(self.request_params['averaging_time_xy'])\n\n # get the building's timezone\n tz = pytz.timezone(self.timezone)\n\n # determine the start and end time for selecting records\n st_ts, end_ts = self.get_ts_range()\n\n # get the dividing date, if there is one\n div_datestring = self.request_params['div_date']\n div_dt = tz.localize(parser.parse(div_datestring)) if len(div_datestring) else None\n\n\n # The list that will hold each series\n series = []\n\n # get the X and Y sensor records and perform the requested averaging\n dfX = self.reading_db.dataframeForOneID(sensorX.sensor_id, st_ts, end_ts, tz)\n dfY = self.reading_db.dataframeForOneID(sensorY.sensor_id, st_ts, end_ts, tz)\n\n if not dfX.empty and not dfY.empty: # both sensors have some data, so proceed to average the data points\n \n dfX = bmsapp.data_util.resample_timeseries(dfX,averaging_hours)\n dfX.rename(columns = {'val':'X'}, inplace = True)\n\n dfY = bmsapp.data_util.resample_timeseries(dfY,averaging_hours)\n dfY.rename(columns = {'val':'Y','ts':'tsY'}, inplace = True)\n\n # Join the X and Y values for the overlapping time intervals and make\n # a list of points.\n df_all = dfX.join(dfY, how='inner') # inner join does intersection of timestamps\n\n # make sure there are matched records before continuing\n if len(df_all):\n\n # add a point name column to be used in the tooltip.\n df_all['name'] = df_all.index.strftime('%a %m/%d/%y %H:%M')\n\n # add a column identifying whether point is in occupied or unoccupied period.\n resolution = self.occupied_resolution()\n if (self.schedule is None) or (resolution is None):\n # no schedule or data doesn't lend itself to classifying\n # consider all points to be occupied\n df_all['occupied'] = 1\n else:\n df_all['occupied'] = [self.schedule.is_occupied(ts, resolution=resolution) for ts in df_all.ts]\n\n # Set up the parameters for the different series of data\n # Required Info is (starting datetime, ending datetime, occupied status (0 or 1), series name, \n # series color, series symbol, series radius, series zindex).\n now_dt = datetime.now()\n if div_dt:\n # A dividing date was provided by the user.\n div_dt = div_dt.replace(tzinfo=None) # needs to be naive\n ser_params = ( (datetime(1970,1,1), div_dt, 1, 'Prior to %s' % div_datestring, '#2f7ed8', 'circle', 4.5),\n (datetime(1970,1,1), div_dt, 0, 'Prior to %s, Unoccupied' % div_datestring, '#2f7ed8', 'triangle-up', 3),\n (div_dt, now_dt, 1, '%s and beyond' % div_datestring, '#FF0000', 'circle', 4.5),\n (div_dt, now_dt, 0, '%s and beyond, Unoccupied' % div_datestring, '#FF0000', 'triangle-up', 3) )\n else:\n # Divide data by how recent it is.\n ser_params = ( (now_dt - timedelta(days=1), now_dt, 1, 'Last 24 Hours', '#FF0000', 'circle', 4.5),\n (now_dt - timedelta(days=1), now_dt, 0, 'Last 24 Hours, Unoccupied', '#FF0000', 'triangle-up', 3),\n (now_dt - timedelta(days=7), now_dt - timedelta(days=1), 1, 'Last 7 Days', '#00CC00', 'circle', 4.5),\n (now_dt - timedelta(days=7), now_dt - timedelta(days=1), 0, 'Last 7 Days, Unoccupied', '#00CC00', 'triangle-up', 3),\n (datetime(1970,1,1), now_dt - timedelta(days=7), 1, '7+ Days Old', '#2f7ed8', 'circle', 4.5),\n (datetime(1970,1,1), now_dt - timedelta(days=7), 0, '7+ Days Old, Unoccupied', '#2f7ed8', 'triangle-up', 3),\n )\n\n for t_start, t_end, occup, ser_name, ser_color, ser_symbol, radius in reversed(ser_params):\n mask = (df_all.index >= t_start) & (df_all.index < t_end) & (df_all.occupied==occup)\n if mask.max():\n series.append( {'x': np.char.mod('%.4g',df_all[mask].X.values).astype(float).tolist(),\n 'y': np.char.mod('%.4g',df_all[mask].Y.values).astype(float).tolist(),\n 'text': df_all[mask].name.values.tolist(),\n 'type': 'scatter',\n 'mode': 'markers', \n 'name': ser_name,\n 'marker': { 'color': ser_color,\n 'symbol': ser_symbol,\n 'size': radius * 2\n }\n } )\n\n # create the X and Y axis labels and the series\n x_label = '%s, %s' % (sensorX.title, sensorX.unit.label)\n y_label = '%s, %s' % (sensorY.title, sensorY.unit.label)\n\n opt = self.get_chart_options('plotly')\n opt['data'] = series\n opt['layout']['title'] = sensorY.title + \" vs. \" + sensorX.title\n opt['layout']['xaxis']['title'] = x_label\n opt['layout']['yaxis']['title'] = y_label\n opt['layout']['legend']['traceorder'] = 'reversed'\n\n html = basechart.chart_config.chart_container_html(opt['layout']['title'])\n\n return {'html': html, 'objects': [('plotly', opt)]}","def coarse_dataframe(geodf, side_square):\n\n # initialise the categories\n\n geodf['category'] = -1\n\n # do calculations on the first date, then extrapolate to the rest\n data_df = geodf[geodf['date'] == np.unique(geodf['date'])[0]]\n\n data_df = data_df.sort_values(by=['longitude', 'latitude'])\n\n n_grids = int(math.sqrt(data_df.shape[0]))\n\n category = 0\n\n for n in range(data_df.shape[0]):\n\n # only process lat,long point that do not have a category\n if data_df['category'].iloc[n] == -1:\n\n # get the side_square^2 nearest indexes to the point.\n indexes = []\n for i in range(side_square):\n for j in range(side_square):\n\n if n + n_grids * i + j < n_grids * n_grids and data_df['category'].iloc[n + n_grids * i + j] == -1:\n indexes.append(n + n_grids * i + j)\n\n # assing them all to the same categorty\n data_df['category'].iloc[indexes] = str(category)\n\n # get the geometry points of that catery\n cat_geometry = data_df[data_df['category'] == str(category)]['geometry']\n\n # get indexes of each point belonging to the category\n indexes_all = []\n for point in cat_geometry:\n indexes_all.append(geodf[geodf['geometry'] == point].index.tolist())\n\n indexes_all_flat = [item for sublist in indexes_all for item in sublist]\n\n geodf['category'].iloc[indexes_all_flat] = str(category)\n\n category = category + 1\n\n geodf['category'] = (geodf['category'].astype(str)).str.cat(geodf['date'], sep=\"_\")\n\n geodf = geodf.dissolve(by=['category', 'date'], aggfunc='mean')\n\n # re-assing the date because we are losing it\n geodf['date'] = [i[1] for i in geodf.index]\n\n geodf['category'] = [i[0] for i in geodf.index]\n\n return geodf","def import_stations(time_res='hourly',time_format='%Y%m%d%H',\r\n campaign_time=[datetime(2018,12,9), datetime(2018,12,12)],\r\n data_category='air_temperature', station_ids=['00044','00091'],\r\n dbase_dir='dbase', table_dir='tables',Output=True,\r\n memory_save=True):\r\n timeranges=['recent','historical']\r\n #%%load the datasets available at each timestep\r\n dwd_datasets_meta=dwd_datasets_meta=json.load(open(table_dir+\"\\\\dwd_station_meta.txt\"))\r\n #try to get a variable from the category, otherwise use interpolation of higher frequency data\r\n resample_frequency=None\r\n time_res_dbase=time_res\r\n try:\r\n dwd_datasets_meta[time_res][data_category]\r\n except Exception:\r\n if time_res=='daily':\r\n try:\r\n dwd_datasets_meta['hourly'][data_category]\r\n print(data_category,' is not provided at the required resolution, daily_mean of hourly data used instead')\r\n resample_frequency='D'\r\n time_res_dbase='hourly'\r\n except Exception:\r\n try: \r\n dwd_datasets_meta['10_minutes'][data_category]\r\n print(data_category,' is not provided at the required resolution, daily_mean of 10_minutes data used instead')\r\n resample_frequency='D'\r\n time_res_dbase='10_minutes'\r\n except Exception:\r\n print(data_category, 'not available')\r\n sys.exit(1)\r\n if time_res=='hourly':\r\n try: \r\n dwd_datasets_meta['10_minutes'][data_category]\r\n print(data_category,' is not provided at the required resolution, hourly_mean of 10_minutes data used instead')\r\n resample_frequency='H'\r\n time_res_dbase='10_minutes'\r\n except Exception:\r\n print(data_category, 'not available')\r\n sys.exit(1)\r\n \r\n \r\n #%% download from dwd if necessary\r\n #connect to server\r\n server='opendata.dwd.de'\r\n ftp=connect_ftp(server = server,connected = False)\r\n #get the mean time of the campaign\r\n date_mean=campaign_time[0]+(campaign_time[1]-campaign_time[0])/2 \r\n # load the inititial ds\r\n dbase_path=dbase_dir+'\\\\db_stations_'+time_res+'_'+data_category+'.nc'\r\n if os.path.exists(dbase_path):\r\n with xr.open_dataset(dbase_path) as dwd_dbase:\r\n dwd_dbase.load()\r\n print('Existing database imported')\r\n #get the non_nans stations\r\n current_stations=np.array(dwd_dbase[list(dwd_dbase.keys())[0]].sel(time=date_mean,method='nearest').dropna('STATIONS_ID').coords['STATIONS_ID'])\r\n else:\r\n print(dbase_path, 'does not exist, we create a new netcdf_file')\r\n dwd_dbase=xr.Dataset()\r\n current_stations=np.array((-9999)).reshape(1)\r\n #change directory on server\r\n for timerange in timeranges:\r\n archive_url='/climate_environment/CDC/observations_germany/climate/'+time_res_dbase+'/'+data_category+'/'+timerange \r\n ftp.cwd(archive_url)\r\n #get the archive\r\n for station_id in station_ids:\r\n #we check whether the station is in the database with this parameter already\r\n if int(station_id) in current_stations:\r\n print('Station', station_id, 'with category', data_category,'in ',timerange,'dbase already')\r\n continue\r\n try:\r\n archive_name=[s for s in ftp.nlst() if station_id in s][0]\r\n except:\r\n print('No ',timerange,'data for station',station_id)\r\n continue\r\n print('Retrieving {}...'.format(archive_name))\r\n retrieved = False\r\n archive = io.BytesIO()\r\n # try to retrieve file\r\n while not retrieved:\r\n try:\r\n ftp.retrbinary(\"RETR \" + archive_name, archive.write)\r\n retrieved = True\r\n except:\r\n ftp=connect_ftp(server = server,connected = False)\r\n ftp.cwd(archive_url)\r\n archive.seek(0)\r\n with ZipFile(archive) as myzip:\r\n for f in myzip.infolist():\r\n # This is the data file\r\n #print('zip content:', f.filename)\r\n if f.filename.startswith('produkt_'):\r\n product = io.StringIO(str(myzip.read(f.filename),'utf-8'))\r\n #get dataframe from product \r\n dwd_product=pd.read_csv(product,sep=';',skipinitialspace=True)\r\n #get datetime\r\n dwd_product['time']=pd.to_datetime(dwd_product['MESS_DATUM'],format=time_format) \r\n dwd_product=dwd_product.rename(columns=dwd_datasets_meta[time_res_dbase][data_category])\r\n dwd_product=dwd_product.reset_index()\r\n dwd_product=dwd_product.set_index(['time','STATIONS_ID'])\r\n dwd_product=dwd_product.drop(columns=['MESS_DATUM','quality_level_of_next_columns','end_of_record','index'])\r\n #append to database\r\n dwd_xr=dwd_product.to_xarray()\r\n #replace all values equal to -999 to nan\r\n for data_var in dwd_xr.data_vars:\r\n dwd_xr[data_var]=dwd_xr[data_var].where(dwd_xr[data_var]>-999)\r\n if station_id=='05009':\r\n print('ok') \r\n #only add relevant dates if available memoryis rather small\r\n \r\n if memory_save and timerange=='historical':\r\n dwd_xr=dwd_xr.sel(time=slice(campaign_time[0]-timedelta(days=1),campaign_time[1]+timedelta(days=1)))\r\n #dwd_xr=dwd_xr.squeeze()\r\n \r\n try:\r\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr])\r\n except Exception as e:\r\n print(e)\r\n print('try merging with compat=override')\r\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr],compat='override')\r\n print(archive_name,' added to database')\r\n #upscale to required temporal resolution\r\n if resample_frequency is not None:\r\n dwd_dbase=dwd_dbase.resample(time=resample_frequency).mean(skipna=True)\r\n print('DWD data upscaled to',time_res,'averages')\r\n if Output==True:\r\n dwd_dbase.to_netcdf(dbase_path)\r\n print('Updated database' ,dbase_path)\r\n return dwd_dbase","def get_data(f, zoom_level, start_pos_1, end_pos_1, start_pos_2, end_pos_2):\n \n c = cooler.Cooler(f[str(zoom_level)])\n \n (chroms, chrom_sizes, chrom_cum_lengths) = get_chromosome_names_cumul_lengths(c)\n \n i0 = abs_coord_2_bin(c, start_pos_1, chroms, chrom_cum_lengths, chrom_sizes)\n i1 = abs_coord_2_bin(c, end_pos_1, chroms, chrom_cum_lengths, chrom_sizes)\n j0 = abs_coord_2_bin(c, start_pos_2, chroms, chrom_cum_lengths, chrom_sizes)\n j1 = abs_coord_2_bin(c, end_pos_2, chroms, chrom_cum_lengths, chrom_sizes)\n \n pixels = c.matrix(as_pixels=True, balance=False, max_chunk=np.inf)[i0:i1+1, j0:j1+1]\n \n if not len(pixels):\n return pd.DataFrame(columns=['genome_start1', 'genome_start2', 'balanced'])\n \n bins = c.bins()[['chrom', 'start', 'end', 'weight']]\n pixels = annotate(pixels, bins)\n\n pixels['genome_start1'] = chrom_cum_lengths[pixels['chrom1']] + pixels['start1']\n pixels['genome_start2'] = chrom_cum_lengths[pixels['chrom2']] + pixels['start2']\n pixels['balanced'] = (\n pixels['count'] * pixels['weight1'] * pixels['weight2']\n )\n \n return pixels[['genome_start1', 'genome_start2', 'balanced']]","def filter_meteo_data(self, startdate, enddate):\n self.all_meteo_data.columns.values[0]='Datum-tijd'\n self.all_meteo_data['datetime']=pd.to_datetime(self.all_meteo_data['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\n self.all_meteo_data.drop(['Datum-tijd'],axis=1, inplace=True)\n mask = (self.all_meteo_data['datetime'] > startdate) & (self.all_meteo_data['datetime'] <= enddate)\n meteodata = self.all_meteo_data.loc[mask].copy()\n meteodata.set_index('datetime',inplace=True)\n return meteodata","def get_outdoor_data(temp_dir,site):\n if site == 'berk':\n files_od = glob(join(temp_dir,'outdoor','20*.xlsx'))\n elif site == 'bus':\n files_od = glob(join(temp_dir,'outdoor','Busara*.csv'))\n else:\n raise NameError(site)\n\n dfs = []\n for f in files_od:\n if site == 'berk':\n this_df = pd.read_excel(f,sheet_name=0,usecols='B:D',index_col=0,parse_dates=True, header=1)\n elif site == 'bus':\n this_df = pd.read_csv(f,usecols=[0,1,2],index_col=0,parse_dates=True,header=2)\n \n # drop missing values that prevented conversion to float type\n if this_df.iloc[:,0].dtype != np.float64:\n this_df = this_df[this_df.iloc[:,0] != ' ']\n this_df = this_df.astype(np.float64)\n\n # correct for weird timezones in berkeley datalogger\n this_df = correct_tz(this_df,site)\n \n this_df.columns = ['T','RH']\n this_df.index.name = 'time'\n\n # convert to celsius\n this_df['T'] = (this_df['T'] - 32) * 5/9\n dfs.append(this_df)\n \n df_od = pd.concat(dfs)\n\n # drop duplicated measurements\n df_od = df_od[~df_od.index.duplicated(keep='last')].sort_index()\n \n # separate out into daily min,mean,max\n groups = df_od.groupby(df_od.index.date)\n dfs_od = {'all':df_od,\n 'min': groups.min(),\n 'mean': groups.mean(),\n 'max': groups.max()}\n \n for i in ['min','mean','max']:\n # remove first and last day to ignore days where we did not get full recording\n dfs_od[i] = dfs_od[i].iloc[1:-1,:]\n \n # name index so that we can merge onto multiIndex'd dataframe\n dfs_od[i].index.name = 'date'\n \n return dfs_od","def _timeseries_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):\n if params[cf.TIME_SERIES_CAL] == 0:\n log.info('Time Series Calculation not required')\n return\n\n if params[cf.TIME_SERIES_METHOD] == 1:\n log.info('Calculating time series using Laplacian Smoothing method')\n elif params[cf.TIME_SERIES_METHOD] == 2:\n log.info('Calculating time series using SVD method')\n\n output_dir = params[cf.TMPDIR]\n total_tiles = len(tiles)\n process_tiles = mpiops.array_split(tiles)\n for t in process_tiles:\n log.debug(\"Calculating time series for tile \"+str(t.index)+\" out of \"+str(total_tiles))\n ifg_parts = [shared.IfgPart(p, t, preread_ifgs, params) for p in ifg_paths]\n mst_tile = np.load(os.path.join(output_dir, 'mst_mat_{}.npy'.format(t.index)))\n res = timeseries.time_series(ifg_parts, params, vcmt, mst_tile)\n tsincr, tscum, _ = res\n np.save(file=os.path.join(output_dir, 'tsincr_{}.npy'.format(t.index)), arr=tsincr)\n np.save(file=os.path.join(output_dir, 'tscuml_{}.npy'.format(t.index)), arr=tscum)\n mpiops.comm.barrier()\n log.debug(\"Finished timeseries calc!\")","def get_tomo_data(self,threshold=20.):\n\t\tdset = raytomo.RayTomoDataSet(self.attrs['tomo_f'])\n\t\tfor prd in self.attrs['prd_arr']:\n\t\t\tgroup = self['%g_sec'%( prd )]\n\t\t\tdset.get_data4plot(dataid=self.attrs['dataid'].decode('utf-8'), period=prd)\n\t\t\tpdens = dset.pdens\n\t\t\tmask_pdens = dset.pdens < threshold\n\t\t\ttomo_data = np.ma.masked_array(dset.vel_iso, mask=mask_pdens)\n\t\t\tgroup.create_dataset(name='tomo_data', data=dset.vel_iso) # phase velocity map\n\t\t\tgroup.create_dataset(name='tomo_data_msk', data=mask_pdens) # save the mask array seperately. h5 file doesn't support masked array\n\t\t\tgroup.create_dataset(name='latArr', data=dset.latArr)\n\t\t\tgroup.create_dataset(name='lonArr', data=dset.lonArr)\n\t\treturn"],"string":"[\n \"def outliers_solid_tsds():\\n timestamp1 = np.arange(np.datetime64(\\\"2021-01-01\\\"), np.datetime64(\\\"2021-02-10\\\"))\\n target1 = [np.sin(i) for i in range(len(timestamp1))]\\n target1[10] += 10\\n\\n timestamp2 = np.arange(np.datetime64(\\\"2021-01-01\\\"), np.datetime64(\\\"2021-02-10\\\"))\\n target2 = [np.sin(i) for i in range(len(timestamp2))]\\n target2[8] += 8\\n target2[15] = 2\\n target2[26] -= 12\\n\\n df1 = pd.DataFrame({\\\"timestamp\\\": timestamp1, \\\"target\\\": target1, \\\"segment\\\": \\\"1\\\"})\\n df2 = pd.DataFrame({\\\"timestamp\\\": timestamp2, \\\"target\\\": target2, \\\"segment\\\": \\\"2\\\"})\\n\\n df = pd.concat([df1, df2], ignore_index=True)\\n\\n df = df.pivot(index=\\\"timestamp\\\", columns=\\\"segment\\\")\\n df = df.reorder_levels([1, 0], axis=1)\\n df = df.sort_index(axis=1)\\n df.columns.names = [\\\"segment\\\", \\\"feature\\\"]\\n tsds = TSDataset(df, \\\"1d\\\")\\n return tsds\",\n \"def getTS(self,_types=None,tile=None):\\n\\n assert(self._END is not None)\\n TS_NAME = ('#'.join(str(x) for x in tile))+\\\"#\\\"+stringify(_types)\\n\\n lat_ = tile[0:2]\\n lon_ = tile[2:4]\\n\\n if self._value_limits is None:\\n df = self._logdf[self._columns]\\\\\\n .loc[self._logdf[self._EVENT].isin(_types)]\\\\\\n .sort_values(by=self._DATE).dropna()\\n else:\\n df = self._logdf[self._columns]\\\\\\n .loc[self._logdf[self._EVENT]\\\\\\n .between(self._value_limits[0],\\n self._value_limits[1])]\\\\\\n .sort_values(by=self._DATE).dropna()\\n\\n df = df.loc[(df[self._coord1] > lat_[0])\\n & (df[self._coord1] <= lat_[1])\\n & (df[self._coord2] > lon_[0])\\n & (df[self._coord2] <= lon_[1])]\\n df.index = df[self._DATE]\\n df=df[[self._EVENT]]\\n\\n ts = [df.loc[self._trng[i]:self._trng[i + 1]].size for i in\\n np.arange(self._trng.size - 1)]\\n\\n return pd.DataFrame(ts, columns=[TS_NAME],\\n index=self._trng[:-1]).transpose()\",\n \"def zonal_statistics(wrksppath, timestamp, region, model):\\n logging.info('\\\\nDoing Zonal Statistics on ' + region)\\n # Define app workspace and sub-paths\\n resampleds = os.path.join(wrksppath, region, model + '_GeoTIFFs_resampled')\\n shp_path = os.path.join(wrksppath, region, 'shapefiles', 'ffgs_' + region + '.shp')\\n\\n stat_file = os.path.join(wrksppath, region, model + 'results.csv')\\n\\n # check that there are resampled tiffs to do zonal statistics on\\n if not os.path.exists(resampleds):\\n logging.info('There are no resampled tiffs to do zonal statistics on. Skipping Zonal Statistics')\\n return\\n\\n # List all Resampled GeoTIFFs\\n files = os.listdir(resampleds)\\n files = [tif for tif in files if tif.endswith('.tif')]\\n files.sort()\\n\\n # do zonal statistics for each resampled tiff file and put it in the stats dataframe\\n stats_df = pd.DataFrame()\\n for i in range(len(files)):\\n logging.info('starting zonal statistics for ' + files[i])\\n ras_path = os.path.join(resampleds, files[i])\\n stats = rasterstats.zonal_stats(\\n shp_path,\\n ras_path,\\n stats=['count', 'max', 'mean'],\\n geojson_out=True\\n )\\n\\n timestep = files[i][:10]\\n\\n # for each stat that you get out, write it to the dataframe\\n logging.info('writing the statistics for this file to the dataframe')\\n for j in range(len(stats)):\\n\\n temp_data = stats[j]['properties']\\n temp_data.update({'Forecast Timestamp': timestamp})\\n temp_data.update({'Timestep': timestep})\\n\\n temp_df = pd.DataFrame([temp_data])\\n stats_df = stats_df.append(temp_df, ignore_index=True)\\n\\n # write the resulting dataframe to a csv\\n logging.info('\\\\ndone with zonal statistics, rounding values, writing to a csv file')\\n stats_df = stats_df.round({'max': 1, 'mean': 1})\\n stats_df.to_csv(stat_file, index=False)\\n\\n # delete the resampled tiffs now that we dont need them\\n logging.info('deleting the resampled tiffs directory')\\n shutil.rmtree(resampleds)\\n\\n return\",\n \"def test_plt_mag_time():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n # create the arrays per filter and readout pattern\\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\\n for i, val in enumerate(max_val_box):\\n if '140' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(val)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(val)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif '110' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(val)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(val)\\n nrsrapidd6_clear.append(np.NaN)\\n else:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(val)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(val)\\n # add to the bokeh data structure\\n ta.source.data[\\\"nrsrapid_f140x\\\"] = nrsrapid_f140x\\n ta.source.data[\\\"nrsrapid_f110w\\\"] = nrsrapid_f110w\\n ta.source.data[\\\"nrsrapid_clear\\\"] = nrsrapid_clear\\n ta.source.data[\\\"nrsrapidd6_f140x\\\"] = nrsrapidd6_f140x\\n ta.source.data[\\\"nrsrapidd6_f110w\\\"] = nrsrapidd6_f110w\\n ta.source.data[\\\"nrsrapidd6_clear\\\"] = nrsrapidd6_clear\\n result = ta.plt_mag_time()\\n\\n assert bokeh_plot_type == type(result)\",\n \"def get_weather_data(lat='40.761440',lng='-73.981806'):\\r\\n key ='********************************'\\r\\n x = pd.DataFrame()\\r\\n unix_now = int((dt.datetime.now()- dt.datetime(1970,1,1)).total_seconds())\\r\\n for time in range(unix_now-86400, unix_now+604800, 86400):\\r\\n rsp = rq.get('https://api.darksky.net/forecast/{}/{},{},{}'.format(key, lat, lng, time))\\r\\n rsp_json = json.loads(rsp.text)\\r\\n row = json_normalize(rsp_json[\\\"daily\\\"]['data'])\\r\\n x = x.append(row)\\r\\n \\r\\n x = x[['icon','apparentTemperatureHigh','apparentTemperatureLow','cloudCover','humidity','precipProbability',\\r\\n 'pressure','visibility','windBearing','windGust','windSpeed']].reset_index(drop=True)\\r\\n return x\",\n \"def create_dat_from_geodataframe(gdf, DAT_header, output_file, wind_field='gust_mph'):\\n t0 = time()\\n t1 = time()\\n db = HazusDB()\\n sql = \\\"\\\"\\\"\\n select Tract as FIPS, CenLongit as lon, CenLat as lat from\\n (select * from\\n syHazus.dbo.syTract) a\\n inner join \\n (select StateFips\\n FROM [syHazus].[dbo].[syState]\\n where HUState = 1\\n and StateFips < 55) b\\n on Substring(a.CountyFips, 1, 2) like b.StateFips+ '%'\\n \\\"\\\"\\\"\\n queryset = db.query(sql)\\n queryset['geometry'] = [Point(x, y) for x, y in zip(\\n queryset['lon'], queryset['lat'])]\\n centroids_all = gpd.GeoDataFrame(queryset, geometry='geometry')\\n buff = gdf.geometry.buffer(0.2)\\n buff_gdf = gpd.GeoDataFrame(geometry=buff.geometry)\\n buff_gdf['dis'] = 1\\n dissolve = buff_gdf.dissolve(by='dis')\\n centroids_intersect = centroids_all.intersects(dissolve.unary_union)\\n centroids = centroids_all[centroids_intersect == True]\\n print(time() - t1)\\n\\n print('formatting data for idw')\\n t1 = time()\\n xy = np.asarray([[x.x, x.y] for x in gdf.geometry])\\n z = np.asarray([x for x in gdf[wind_field]])\\n xis = np.asarray([x.x for x in centroids.geometry])\\n yis = np.asarray([x.y for x in centroids.geometry])\\n print(time() - t1)\\n\\n print('interpolating values')\\n t1 = time()\\n kdtree = cKDTree(xy)\\n zis = []\\n for xi, yi in zip(xis, yis):\\n zi = idw(kdtree, z, xi, yi)\\n zis.append(zi)\\n print(time() - t1)\\n zis = np.asarray(zis)\\n zis = zis * 0.44704\\n\\n print('formatting dataframe for output')\\n t1 = time()\\n tracts = list(map(lambda x: x + ' ', centroids.FIPS))\\n longs = list(map(lambda x: '{0:.4f}'.format(x) + ' ', xis))\\n lats = list(map(lambda x: '{0:.4f}'.format(x) + ' ', yis))\\n windSpeeds = list(map(lambda x: '{0:.5f}'.format(x) + ' ', zis))\\n zeros = list(map(lambda x: '0' + '{0:.5f}'.format(x * 0) + ' ', zis))\\n windSpeedsLast = list(map(lambda x: '{0:.5f}'.format(x), zis))\\n df = pd.DataFrame({'tracts': tracts, 'longs': longs, 'lats': lats,\\n 'windSpeeds': windSpeeds, 'zeros': zeros, 'windSpeedsLast': windSpeedsLast})\\n print(time() - t1)\\n\\n print('writing output DAT file')\\n t1 = time()\\n # creates and opens the export DAT file\\n pd.DataFrame().to_csv(output_file, header=False, index=False)\\n export = open(output_file, \\\"w\\\")\\n\\n # adds columns to the DAT file header\\n DAT_header.append('')\\n DAT_header.append(\\n ' ident elon nlat ux vy w (m/s)')\\n\\n # writes header to DAT file\\n for row in DAT_header:\\n export.write(row + '\\\\n')\\n\\n # writes data to DAT file\\n for row in range(len(df[df.columns[0]])):\\n writeRow = ''\\n for item in df.iloc[row]:\\n writeRow = writeRow + item\\n export.write(writeRow + '\\\\n')\\n export.close()\\n print(time() - t1)\\n print('Total elasped time: ' + str(time() - t0))\",\n \"def prepare_for_influxdb(df):\\n df = df.drop(columns=\\\"landkreis\\\", errors=\\\"ignore\\\") # prevent name collision in get_ags()\\n df = get_ags(df)\\n df[\\\"time\\\"] = df.apply(lambda x: 1000000000*int(datetime.timestamp((pd.to_datetime(x[\\\"timestamp\\\"])))), 1)\\n df[\\\"measurement\\\"] = \\\"hystreet\\\"\\n df[\\\"origin\\\"] = \\\"https://hystreet.com\\\"\\n df = df.rename(columns={\\n 'station_id': '_id',\\n 'pedestrians_count': 'pedestrian_count',\\n 'state': 'bundesland'\\n })\\n df['ags'] = pd.to_numeric(df['ags'])\\n # import pdb; pdb.set_trace()\\n return df\",\n \"def get_traffic_sensor_df(sensorURI: str, fromTime: str, toTime: str, resampleFreq: str = None, remove_outliers=False):\\n values = [\\\"count\\\", \\\"sumSpeed\\\"]\\n result = None\\n for v in values:\\n # data = query_ensor(sensorURI, fromTime, toTime, v)\\n data = multiday_query(sensorURI, fromTime, toTime, v)\\n df = pd.DataFrame(data, columns=[\\\"measuredTime\\\", v])\\n df[\\\"measuredTime\\\"] = pd.to_datetime(df[\\\"measuredTime\\\"])\\n df.index = df[\\\"measuredTime\\\"]\\n del df[\\\"measuredTime\\\"]\\n if remove_outliers:\\n z_scores = np.abs(stats.zscore(df))\\n print(f\\\"Removed outliers: {df.size - df[(z_scores < 3).all(axis=1)].size}\\\")\\n df = df[(z_scores < 3).all(axis=1)]\\n if resampleFreq is not None:\\n df = df.resample(resampleFreq).sum()\\n if result is not None:\\n result = pd.merge_ordered(result, df, left_on=\\\"measuredTime\\\", right_on=\\\"measuredTime\\\")\\n result.index = result[\\\"measuredTime\\\"]\\n del result[\\\"measuredTime\\\"]\\n else:\\n result = df\\n # avg speed\\n result[\\\"avgSpeed\\\"] = result[\\\"sumSpeed\\\"] / result[\\\"count\\\"]\\n result.loc[~np.isfinite(result[\\\"avgSpeed\\\"]), \\\"avgSpeed\\\"] = np.nan\\n result[\\\"avgSpeed\\\"] = result[\\\"avgSpeed\\\"].interpolate()\\n return result\",\n \"def get_time_series_at_location(data, lat, lon, feature):\\n\\n ts = data.sel(lat=lat, lon=lon, method='nearest', drop=True).to_series()\\n index = ts.index.get_level_values('time')\\n values = ts.values\\n\\n return pd.DataFrame({'Date': index.values, feature: values})\",\n \"def make_dataset_temperatures(filename, points):\\r\\n\\r\\n ds = Dataset(filename)\\r\\n\\r\\n lats, lons, cities, countries = get_lat_lon()\\r\\n\\r\\n # total lat,lon pairs: 1366\\r\\n print('The number of rows is ' + str(len(lats)*points))\\r\\n lon = ds.variables['longitude']\\r\\n lat = ds.variables['latitude']\\r\\n time = ds.variables['date_number']\\r\\n\\r\\n lon_array = lon[:]\\r\\n lat_array = lat[:]\\r\\n time_array = time[:]\\r\\n\\r\\n temperature = ds.variables['temperature']\\r\\n\\r\\n dates = []\\r\\n for time in time_array[:]:\\r\\n year = int(time)\\r\\n rem = time - year\\r\\n base = datetime(year, 1, 1)\\r\\n dates.append((base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)).date())\\r\\n\\r\\n # second approach\\r\\n # for t in time_array[:]:\\r\\n # dates.append(num2date(t, units=time.units))\\r\\n\\r\\n dateResult = []\\r\\n temperatureResult = []\\r\\n latitudeResult = []\\r\\n longitudeResult = []\\r\\n cityResult = []\\r\\n countryResult = []\\r\\n\\r\\n for latitude, longitude, city, country in zip(lats, lons, cities, countries):\\r\\n\\r\\n # We want to find data for latitude, longitude\\r\\n # We first need to find the indexes\\r\\n i = np.abs(lon_array - longitude).argmin()\\r\\n j = np.abs(lat_array - latitude).argmin()\\r\\n\\r\\n for d in dates:\\r\\n dateResult.append(d)\\r\\n\\r\\n resultTemperature = temperature[:, j, i]\\r\\n for t in resultTemperature:\\r\\n temperatureResult.append(t)\\r\\n\\r\\n resultLatitues = np.full(\\r\\n shape=points,\\r\\n fill_value=latitude,\\r\\n dtype=np.float\\r\\n )\\r\\n for l in resultLatitues:\\r\\n latitudeResult.append(l)\\r\\n\\r\\n resultLongitudes = np.full(\\r\\n shape=points,\\r\\n fill_value=longitude,\\r\\n dtype=np.float\\r\\n )\\r\\n for l in resultLongitudes:\\r\\n longitudeResult.append(l)\\r\\n\\r\\n resultCities = np.full(\\r\\n shape=points,\\r\\n fill_value=city\\r\\n )\\r\\n for c in resultCities:\\r\\n cityResult.append(c)\\r\\n\\r\\n resultCountries = np.full(\\r\\n shape=points,\\r\\n fill_value=country\\r\\n )\\r\\n for c in resultCountries:\\r\\n countryResult.append(c)\\r\\n\\r\\n print('iteration no:' + str(i))\\r\\n\\r\\n df = pd.DataFrame()\\r\\n df['date'] = dateResult\\r\\n df['temperature'] = temperatureResult\\r\\n df['latitude'] = latitudeResult\\r\\n df['longitude'] = longitudeResult\\r\\n df['city'] = cityResult\\r\\n df['country'] = countryResult\\r\\n\\r\\n df.to_csv(r'C:\\\\Users\\\\stoja\\\\Desktop\\\\Temperatures.csv', index=False)\\r\\n return df\",\n \"def get_date_features(gt_ids=[], gt_masks=None, gt_shifts=None, first_year=None):\\n # If particular arguments aren't lists, replace with repeating iterators\\n if not isinstance(gt_masks, list):\\n gt_masks = itertools.repeat(gt_masks)\\n if not isinstance(gt_shifts, list):\\n gt_shifts = itertools.repeat(gt_shifts)\\n\\n # Add each ground truth feature to dataframe\\n df = None\\n for gt_id, gt_mask, gt_shift in zip(gt_ids, gt_masks, gt_shifts):\\n print \\\"Getting {}_shift{}\\\".format(gt_id, gt_shift)\\n t = time.time()\\n # Load ground truth data\\n gt = get_ground_truth(gt_id, gt_mask, gt_shift)\\n # Discard years prior to first_year\\n gt = year_slice(gt, first_year = first_year)\\n # If lat, lon columns exist, pivot to wide format\\n if 'lat' in gt.columns and 'lon' in gt.columns:\\n if gt_shift == None:\\n measurement_variable = get_measurement_variable(gt_id)\\n else:\\n measurement_variable = get_measurement_variable(gt_id)+'_shift'+str(gt_shift)\\n gt = pd.pivot_table(gt, values=measurement_variable, index='start_date',\\n columns=['lat', 'lon']).reset_index()\\n gt = pd.DataFrame(gt.to_records())\\n gt.drop(\\\"index\\\", axis=1, inplace=True)\\n # Rename columns to start_date and precip_(27.0,261.0), etc.\\n gt.rename(columns={gt.columns[0]: 'start_date'}, inplace=True)\\n gt.rename(columns=lambda x: x.replace('(',\\n measurement_variable +\\n '_('), inplace=True)\\n # Use outer merge to include union of start_date values across all features\\n # combinations across all features\\n df = df_merge(df, gt, on=\\\"start_date\\\")\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n return df\",\n \"def create_demo_location_history() -> geopandas.GeoDataFrame:\\n np.random.seed(123)\\n\\n time = pd.date_range(start=datetime.fromtimestamp(1624241116), end=datetime.now(), freq=\\\"1min\\\").values\\n\\n center_point = (-36.875990410695394, 174.76398830024274)\\n lat = np.random.normal(loc=center_point[0], scale=0.01, size=len(time))\\n lon = np.random.normal(loc=center_point[1], scale=0.01, size=len(time))\\n\\n geometry = [Point(lon, lat) for lon, lat in zip(lon, lat)]\\n return geopandas.GeoDataFrame(pd.DataFrame(dict(time=time, lat=lat, lon=lon)), geometry=geometry)\",\n \"def getExample1Data(conditionData, conditions, shockTubeEffectiveHeatingTimes, smilesInExperiments):\\n #First get the time points close to\\n allResults=[]\\n for T, timepoint in shockTubeEffectiveHeatingTimes.iteritems():\\n for smiles in smilesInExperiments:\\n temperatureResults=GenericData(label=label='Temperature',\\n data = []\\n units = 'K')\\n for conIndex, data in conditionData:\\n if conditions[conIndex].T0==T:\\n #index1 is the index of time from the simulation closest to timepoint\\n #data is organized as (timeArray, rest of data)\\n index1=findNearest(data[0], timepoint)\\n for speciesData in data[1]:\\n if smiles==speciesData.species:\\n\\n if smiles not in exptCompDict1: exptCompDict1[smiles]=[]\\n #need to multiply by 20, in the experiment, they don't seem to count the Ar in the mole fraction\\n exptCompDict1[smiles].append(resultsDictionary[T][1][index1][index2+1]*20)\\n (index1, timepoint2)=rt.getNearestTime(timepoint1, resultsDictionary[T][2])\\n for index2, smiles in enumerate(majorSpecies[0:4]):\\n if smiles not in exptCompDict2: exptCompDict2[smiles]=[]\\n exptCompDict2[smiles].append(resultsDictionary[T][3][index1][index2+1]*20)\",\n \"def use_ground_truth_time_slice(gt, df_p, df_l, app):\\n\\n gt = gt.reset_index(drop=True)\\n gt['start_time'] = gt.start_time / 1000\\n gt['end_time'] = gt.end_time / 1000\\n gt['pred_location'] = np.zeros(len(gt))\\n gt['true_location'] = gt.wlabel\\n gt['true_appliance'] = gt.slabel\\n\\n gt['type'] = np.zeros(len(gt))\\n gt['magnitude'] = np.zeros(len(gt))\\n gt['phase'] = np.zeros(len(gt))\\n\\n print \\\"\\\\n\\\"\\n # Label lights and fans as \\\"light\\\" type and magnitude as the fall magnitude of the time slice\\n for i in gt.index:\\n appl = gt.ix[i]['slabel']\\n etype = ''\\n\\n if appl in ['Fan', 'Light']:\\n gt.ix[i, 'type'] = etype = \\\"light\\\"\\n else:\\n gt.ix[i, 'type'] = etype = \\\"power\\\"\\n\\n # Get index from the main light/power data stream\\n fall_time = gt.ix[i]['end_time']\\n logger.debug(\\\"Fall_time:: %s (%s)\\\", dt.datetime.fromtimestamp(fall_time), fall_time)\\n if etype == \\\"light\\\":\\n idx = 0\\n try:\\n idx = np.where(df_l.time == fall_time)[0][0]\\n except IndexError, e:\\n print \\\"-----Index not found--------\\\", e\\n continue\\n\\n edge = get_magnitude_metadata(df_l, idx, \\\"lightpower\\\", lwinmin)\\n if edge:\\n gt.ix[i, 'magnitude'] = math.fabs(edge['magnitude'])\\n print i, \\\"Light:: Index\\\", idx, \\\"Magnitude::\\\", math.fabs(edge['magnitude'])\\n else:\\n logger.debug(\\\"--------Edge not found---------\\\")\\n continue\\n\\n # Get phase of the edge\\n gt.ix[i, 'phase'] = get_phase(idx, df_l, \\\"light\\\")\\n # print \\\"LIGHT:: Index\\\", idx, \\\"magnitude\\\", magnitude\\n else:\\n idx = 0\\n try:\\n idx = np.where(df_p.time == fall_time)[0][0]\\n except IndexError, e:\\n print \\\"-----Index not found--------\\\", e\\n continue\\n\\n edge = get_magnitude_metadata(df_p, idx, \\\"power\\\", pwinmin)\\n if edge:\\n gt.ix[i, 'magnitude'] = math.fabs(edge['magnitude'])\\n print i, \\\"Power:: Index\\\", idx, \\\"Magnitude::\\\", math.fabs(edge['magnitude'])\\n else:\\n logger.debug(\\\"--------Edge not found---------\\\")\\n continue\\n\\n # Get phase of the edge\\n gt.ix[i, 'phase'] = get_phase(idx, df_p, \\\"power\\\")\\n\\n # print \\\"POWER:: Index\\\", idx, \\\"magnitude\\\", magnitude\\n\\n gt.pop('wlabel')\\n gt.pop('slabel')\\n\\n if app in [4, 7]:\\n gt = gt[(gt.phase != 0) & (gt.magnitude > lthresmin)]\\n else:\\n gt = gt[gt.magnitude > lthresmin]\\n\\n gt['act_start_time'] = [dt.datetime.fromtimestamp(i)\\n for i in gt['start_time']]\\n gt['act_end_time'] = [dt.datetime.fromtimestamp(i)\\n for i in gt['end_time']]\\n gt = gt.sort(['act_start_time'])\\n\\n print \\\"\\\\nGround Truth Time slices:\\\\n\\\", gt\\n\\n return gt\",\n \"def add_columns_for_taps(full_data: DataFrame, tap_data: DataFrame):\\n for tap_file in tap_file_names:\\n tap_type = tap_file_to_feature_name[tap_file]\\n data = tap_data[tap_data['Type'] == tap_type].reset_index(drop = True)\\n\\n lead_file = 'Accelerometer.csv'\\n time_column_name = x_columns[lead_file]\\n data_times = full_data[time_column_name]\\n data_index = 0\\n\\n new_column = []\\n\\n for tap_index in range(data.shape[0]):\\n try:\\n while data_times[data_index] < (data['Start'][tap_index] * 1000000):\\n new_column.append(0) # Not in the midst of a tap\\n data_index += 1\\n if data_index >= full_data.shape[0]: break\\n if data_index >= full_data.shape[0]: break\\n new_column.append(1) # At least one value in the midst of the tap\\n data_index += 1\\n if data_index >= full_data.shape[0]: break\\n while data_times[data_index] < (data['End'][tap_index] * 1000000):\\n new_column.append(1)\\n data_index += 1\\n if data_index >= full_data.shape[0]: break\\n if data_index >= full_data.shape[0]: break\\n except KeyError:\\n print(\\\"Okay, here's that thing again\\\")\\n return\\n\\n \\n while data_index < full_data.shape[0]:\\n new_column.append(0)\\n data_index += 1\\n\\n full_data[tap_type] = new_column\",\n \"def filter(df: pd.DataFrame = pd.DataFrame()):\\n if df.empty:\\n df = read()\\n print('Filtering data...')\\n df = df.dropna()\\n df2 = pd.DataFrame()\\n df2['Longitude'] = df['Longitude']\\n df2['Latitude'] = df['Latitude']\\n df2['Month'] = df['Date'].dt.strftime('%m').astype(int)\\n df2['Day'] = df['Date'].dt.strftime('%d').astype(int)\\n df2['Day_of_Week'] = df['Day_of_Week']\\n df2['Time'] = np.array([t.timestamp() for t in df['Time']]) - df['Time'].min().timestamp()\\n df2['Weather_Conditions'] = df['Weather_Conditions']\\n return pd.get_dummies(df2)\",\n \"def create_lat_lon_date_data(gt_id,\\n target_horizon,\\n experiment,\\n past_gt_ids=[\\\"contest_precip\\\", \\\"contest_tmp2m\\\"],\\n forecast_models=[\\\"nmme\\\",\\\"nmme0\\\"],\\n other_lat_lon_date_features=[\\\"contest_rhum.sig995\\\",\\n \\\"contest_pres.sfc.gauss\\\"]):\\n\\n time_start = time.time()\\n\\n # Add forecasts to list of forecast IDs\\n forecast_variable = get_forecast_variable(gt_id) # 'prate' or 'tmp2m'\\n forecast_ids = ['{}-{}-{}'.format(forecast, forecast_variable, target_horizon)\\n for forecast in forecast_models]\\n\\n # -----------\\n # Generate relevant variable and column names\\n # -----------\\n\\n # Identify measurement variable name\\n measurement_variable = get_measurement_variable(gt_id) # 'tmp2m' or 'prate'\\n\\n # Keep track of relevant column names\\n gt_col = measurement_variable\\n clim_col = measurement_variable+\\\"_clim\\\"\\n anom_col = measurement_variable+\\\"_anom\\\"\\n\\n # Inverse of standard deviation of anomalies for each start_date\\n anom_inv_std_col = anom_col+\\\"_inv_std\\\"\\n\\n # --------\\n # Prepare experiment cache directory and saved file names\\n # --------\\n\\n # Name of cache directory for storing non-submission-date specific\\n # intermediate files\\n cache_dir = os.path.join('results', experiment, 'shared',\\n '{}_{}'.format(gt_id, target_horizon))\\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\\n\\n # if cache_dir doesn't exist, create it\\n if not os.path.isdir(cache_dir):\\n os.makedirs(cache_dir)\\n\\n # Filenames for data file to be stored in cache_dir\\n lat_lon_date_data_file = os.path.join(\\n cache_dir, \\\"lat_lon_date_data-{}_{}.h5\\\".format(gt_id, target_horizon))\\n\\n # --------\\n # Load mask indicating which grid points count in the contest (1=in, 0=out)\\n # --------\\n print \\\"Loading contest mask\\\"\\n t = time.time()\\n mask_df = get_contest_mask()\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n # --------\\n # Creates and saves lat_lon_date_data dataframe\\n # --------\\n # Load masked lat lon date features restricted to years >= get_first_year(gt_id)\\n # Note: contest lat lon date features and forecasts are pre-masked, so there\\n # is no need to mask explcitily\\n print \\\"Loading lat lon date features\\\"\\n num_gt_ids = len(past_gt_ids)\\n # For each measurement,\\n # get number of days between start date of observation period used for prediction\\n # (2 weeks + 1 submission day behind for most predictors) and start date of\\n # target period (2 or 4 weeks ahead)\\n past_start_deltas = [get_start_delta(target_horizon, past_gt_id)\\n for past_gt_id in past_gt_ids]\\n other_start_deltas = [get_start_delta(target_horizon, other_gt_id)\\n for other_gt_id in other_lat_lon_date_features]\\n # Additionally keep track of days between forecast date and start date of\\n # target period\\n forecast_delta = get_forecast_delta(target_horizon)\\n\\n lat_lon_date_data = get_lat_lon_date_features(\\n gt_ids=other_lat_lon_date_features + other_lat_lon_date_features\\n + other_lat_lon_date_features,\\n gt_masks=None,\\n gt_shifts=other_start_deltas +\\n [2*delta for delta in other_start_deltas] +\\n [365]*len(other_lat_lon_date_features),\\n forecast_ids=forecast_ids + forecast_ids,\\n forecast_masks=None,\\n forecast_shifts=[None]*len(forecast_ids) + [forecast_delta]*len(forecast_ids),\\n anom_ids=[gt_id] + past_gt_ids + past_gt_ids + past_gt_ids,\\n anom_masks=None,\\n anom_shifts=[None] + past_start_deltas +\\n [2*delta for delta in past_start_deltas] +\\n [365]*len(past_gt_ids),\\n first_year=get_first_year(gt_id)\\n )\\n\\n print \\\"Loading additional lat lon date features\\\"\\n t = time.time()\\n # Add CFSv2 mean as feature\\n if 'cfsv2' in forecast_models:\\n cfsv2_models = ['cfsv2_op_delta_2w_1d_6h', 'cfsv2_op_delta_2w_1d_12h',\\n 'cfsv2_op_delta_2w_1d_18h', 'cfsv2_op_delta_2w_2d_0h',\\n 'cfsv2_op_delta_2w_2d_6h', 'cfsv2_op_delta_2w_2d_12h',\\n 'cfsv2_op_delta_2w_2d_18h', 'cfsv2_op_delta_2w_3d_0h']\\n lat_lon_date_data['cfsv2_mean'] = lat_lon_date_data[cfsv2_models].mean(axis=1)\\n lat_lon_date_data[\\\"cfsv2_mean_shift\\\"+str(start_delta)] = lat_lon_date_data[\\n [model+\\\"_shift\\\"+str(start_delta) for model in cfsv2_models]].mean(axis=1)\\n # Add inverse of standard deviation of anomalies for each start_date\\n lat_lon_date_data[anom_inv_std_col] = \\\\\\n 1.0/lat_lon_date_data.groupby([\\\"start_date\\\"])[anom_col].transform('std')\\n\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n # Save lat lon date features to disk\\n print \\\"Saving lat lon date features to \\\"+lat_lon_date_data_file\\n t = time.time()\\n lat_lon_date_data.to_hdf(lat_lon_date_data_file, key=\\\"data\\\", mode=\\\"w\\\")\\n subprocess.call(\\\"chmod a+w \\\"+lat_lon_date_data_file, shell=True)\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n print \\\"Finished generating lat_lon_date_data matrix.\\\"\\n print \\\"Total time elapsed: {}s\\\".format(time.time()-time_start)\\n return list(lat_lon_date_data)\",\n \"def create_interpolated_turnstile_data(\\n start_date: datetime,\\n end_date: datetime = None,\\n group_by: List[str] = ['UNIT', 'SCP'],\\n frequency: str = '1H') -> pd.DataFrame:\\n\\n if not set(group_by).issubset(['STATION', 'LINENAME', 'UNIT', 'SCP']):\\n raise Exception(\\\"Unsupported group by keys: \\\" + str(group_by))\\n\\n\\n raw = download_turnstile_data(start_date, end_date)\\n raw['date'] = pd.to_datetime(raw.DATE)\\n raw = raw[(raw.date <= (end_date + timedelta(1))) & (raw.date >= (start_date - timedelta(1)))]\\n raw.drop('date',axis=1,inplace=True)\\n\\n interpolated = _interpolate(_process_raw_data(raw, group_by), group_by, frequency)\\n end_date = end_date or interpolated.index.max()\\n return interpolated[interpolated.index.to_series().between(\\n start_date, end_date)] .drop(columns=[\\\"entry_diffs\\\", \\\"exit_diffs\\\"])\",\n \"def shapely_tileset(processed_query,min_ovp = 0,max_ovp = 1,\\n n_neg = None,buffer = 0):\\n types, xx, yy, qual, tags = [],[],[],[],[]\\n z = processed_query['zoom']\\n for elem in processed_query['elements']:\\n for tile in elem['tiles']:\\n qq = tile[1]\\n if qq >= min_ovp and qq <= max_ovp:\\n x,y,_ = find_tile_coords(tile[0],z)\\n xx.append(x)\\n yy.append(y)\\n qual.append(tile[1])\\n tags.append(json.dumps(elem['tags']))\\n types.append(elem['type'])\\n \\n pos_df = pd.DataFrame({\\n 'z': z, 'x' : xx, 'y': yy, \\n 'entity': types,\\n 'overlap': qual,'tags': tags,\\n 'placename': processed_query['query_info']['placename']\\n }) \\\\\\n .drop_duplicates(subset = ['x','y']) \\\\\\n .sort_values(by = ['x','y'])\\n if n_neg is None: n_neg = pos_df.shape[0]\\n negt = sample_complement(pos_df['x'],pos_df['y'],n_neg,buffer)\\n neg_df = pd.DataFrame({'z': z,'x': negt[0],'y': negt[1]}) \\\\\\n .sort_values(by = ['x','y'])\\n return { \\n 'positive': add_latlon(pos_df),\\n 'negative': add_latlon(neg_df)\\n }\",\n \"def get_meter_data_for_time_slice(apt_no, start_time, end_time):\\n if apt_no in ['102A', 102]:\\n apt_no = '102A'\\n\\n logger.debug(\\\"sMap: Getting meter data for %s between %s and %s\\\", apt_no, start_time, end_time)\\n\\n query = (\\\"select data in ('\\\" + str(start_time) + \\\"','\\\" + str(end_time) + \\\"') \\\"\\n \\\"limit 200000 \\\"\\n \\\"where Metadata/LoadLocation/FlatNumber ='\\\" + str(apt_no) + \\\"' and \\\"\\n \\\"Metadata/Extra/PhysicalParameter='Power'\\\")\\n\\n r = requests.post(url, data=query)\\n # logger.debug (\\\"%s\\\",r)\\n payload = r.json()\\n # logger.debug(\\\"Payload:%s\\\", payload)\\n\\n if apt_no in ['102A', 102]:\\n apt_no = 102\\n meters = retrieve_meter_info(apt_no)\\n logger.debug(\\\"Meters: %s\\\", meters)\\n\\n streams = []\\n meter_type = []\\n l_meters = range(0, len(meters))\\n for i in l_meters:\\n uuid = payload[i]['uuid']\\n\\n # Get meter type based on uuid\\n for meter in meters:\\n if meter['uuid'] == uuid:\\n m_type = meter['type']\\n # logger.debug (uuid, m_type)\\n\\n meter_type.append(m_type)\\n streams.append(np.array(payload[i]['Readings']))\\n # logger.debug(\\\"Streams: %s\\\", streams)\\n\\n if len(streams[0]) > 0:\\n\\n df = [pd.DataFrame({'time': readings[:, 0] / 1000, 'power': readings[:, 1],\\n 'type': [meter_type[i]] * len(readings)},\\n columns=['time', 'power', 'type']) for i, readings in enumerate(streams)]\\n else:\\n df = []\\n\\n return df\",\n \"def get_temp():\\n epts = [\\\"cage_coldPlate_temp\\\", \\\"cage_pressure\\\"]\\n # t_earlier_aug = '2019-10-02T00:00'\\n # t_later_aug = datetime.utcnow().isoformat()\\n t_earlier_aug = '2019-09-27T13:00'\\n t_later_aug = '2019-09-28T19:49'\\n dfs = pandas_db_query(epts, t_earlier_aug, t_later_aug)\\n print(dfs[epts[0]].tail())\\n\\n exit()\\n\\n xv = dfs[epts[0]][\\\"timestamp\\\"]\\n yv = dfs[epts[0]][epts[0]]\\n plt.plot(xv, yv, '-b')\\n plt.ylabel(epts[0], ha='right', y=1)\\n\\n p1a = plt.gca().twinx()\\n xv = dfs[epts[1]][\\\"timestamp\\\"]\\n yv = dfs[epts[1]][epts[1]]\\n p1a.set_ylabel(epts[1], color='r', ha='right', y=1)\\n p1a.tick_params('y', colors='r')\\n p1a.semilogy(xv, yv, '-r')\\n\\n plt.gcf().autofmt_xdate()\\n plt.tight_layout()\\n plt.show()\",\n \"def main():\\n df_turnstiles = get_mta_data2()\\n df_turnstiles = clean_data(df_turnstiles)\\n df_turnstiles = add_dt_cols(df_turnstiles)\\n df_turnstiles = merge_zipcode_agi(df_turnstiles)\\n\\n df_ampm = fixup_entries_exits(df_turnstiles)\\n return df_turnstiles, df_ampm\",\n \"def get_time_series(this_lat, this_lon, case, varnames):\\n\\n cesmdir = '/gpfs/fs1/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly'\\n\\n if 'LE' in case:\\n\\n from observational_large_ensemble.params import karen_params_cesm\\n\\n mode_lag = karen_params_cesm.mode_lag\\n cvdp_loc = karen_params_cesm.cvdp_loc\\n AMO_cutoff_freq = karen_params_cesm.AMO_cutoff_freq\\n\\n name_conversion = {'tas': 'TREFHT', 'pr': 'PRECC', 'slp': 'PSL'}\\n cesm_names = [name_conversion[v] for v in varnames]\\n this_member = int((case).split('-')[-1])\\n cvdp_file = '%s/CESM1-CAM5-BGC-LE_#%i.cvdp_data.1920-2018.nc' % (cvdp_loc, this_member)\\n\\n # Historical filenames for CESM. Will need to append part of RCP8.5 to get full period\\n filenames = []\\n for var in cesm_names:\\n file_str = '%s/%s/b.e11.B20TRC5CNBDRD.f09_g16.%03d.cam.h0.%s.??????-200512.nc' % (cesmdir, var,\\n this_member, var)\\n this_file = glob(file_str)[0]\\n filenames.append(this_file)\\n\\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames,\\n karen_params_cesm.valid_years, mode_lag,\\n cvdp_file, AMO_cutoff_freq, name_conversion)\\n\\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\\n\\n else:\\n\\n from observational_large_ensemble.params import karen_params_obs\\n\\n mode_lag = karen_params_obs.mode_lag\\n cvdp_loc = karen_params_obs.cvdp_loc\\n AMO_cutoff_freq = karen_params_obs.AMO_cutoff_freq\\n\\n tas_dir = karen_params_obs.tas_dir\\n pr_dir = karen_params_obs.pr_dir\\n slp_dir = karen_params_obs.slp_dir\\n cvdp_file = '%s/HadISST.cvdp_data.1920-2018.nc' % cvdp_loc\\n file_dict = {'tas': '%s/Complete_TAVG_LatLong1.nc' % tas_dir,\\n 'pr': '%s/full_data_monthly_v2020.nc' % pr_dir,\\n 'slp': '%s/prmsl.mon.mean.nc' % slp_dir}\\n\\n filenames = []\\n for var in varnames:\\n filenames.append(file_dict[var])\\n\\n name_conversion = {'tas': 'temperature', 'pr': 'precip', 'slp': 'prmsl'}\\n\\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames[0],\\n karen_params_obs.valid_years, mode_lag,\\n cvdp_file, AMO_cutoff_freq, name_conversion)\\n\\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\\n\\n return this_ts, df_shifted\",\n \"def trajcheck(file, lat_diff=1., lon_diff=1., search=None, path = '/home/ollie/muali/Data/winter_all/'):\\n \\n \\n \\n \\n file_name = path + file\\n \\n df_IGR = pd.read_excel('IGR_25N_v2.xlsx')\\n #f = open(file_name,'rb+')\\n \\n # getting launch date\\n #for i, file_ in enumerate(f):\\n # if i ==4: # reads 6th line\\n # date_str = f.read(25)\\n # f.close()\\n #l_date = parse(date_str)\\n \\n df = pd.read_csv(file_name, skiprows=7, header=None, delim_whitespace=True) \\n \\n # dict to rename columns\\n cols_renames = {2: 'year', 3: 'month', 4: 'day', 5: 'hour', 6: 'minute'}\\n\\n # converting year to 4 digits\\n df.iloc[:,2] = df.iloc[:,2] + 1900\\n\\n # df_subset is used to merge yy, mm, dd, hh values into BT_time column\\n df_subset = df.loc[:, list(cols_renames.keys())].rename(columns=cols_renames)\\n # df_subset is extracting the time from the original dataframe to be used to make a time index for the final dataframe\\n dt_series = pd.to_datetime(df_subset) # converts the df into a time series which we will add as extra column\\n dt_series.head()\\n # adding a new backtrajectory column\\n df['BT_time'] = dt_series # adding the back trajectory time column to df\\n \\n # dropping the not required columns\\n df.drop([0,1,2,3,4,5,6,7], axis =1, inplace=True)\\n df.head()\\n \\n col_rename2 = {8: 'back_hr', 9:'Lat', 10: 'Lon', 11:'AGL', 12:'Pressure'}\\n df.rename(columns=col_rename2, inplace=True)\\n\\n del df_subset\\n del dt_series\\n \\n count = 0\\n # count will be used to tell how many times our condition is met\\n \\n string_list =[] # to store the output of print to be used by regex\\n for row_IRG in df_IGR.itertuples():\\n for row in df.itertuples():\\n # lat_diff lon_diff provided by the user\\n if ((-lat_diff < (row_IRG.Lat - row.Lat) < lat_diff) & (-lon_diff < (row_IRG.Lon - row.Lon) < lon_diff)):\\n # print(row.back_hr, row.BT_time, row_IRG.ID, row_IRG.Lat, row_IRG.Lon ,row_IRG.Lat - row.Lat, row_IRG.Lon - row.Lon)\\n# print(row.back_hr, row.BT_time.strftime('%d-%m-%Y %H:%M'), row_IRG.ID, row_IRG.Lat, row_IRG.Lon , row.Lat - row_IRG.Lat, row.Lon - row_IRG.Lon, geodesic((row.Lat, row.Lon), (row_IRG.Lat, row_IRG.Lon)).km )\\n string_list.append((row.back_hr, row.BT_time.strftime('%d-%m-%Y %H:%M'), row_IRG.ID, row_IRG.Lat, row_IRG.Lon , row.Lat - row_IRG.Lat, row.Lon - row_IRG.Lon, geodesic((row.Lat, row.Lon), (row_IRG.Lat, row_IRG.Lon)).km ))\\n count = count + 1\\n \\n print(\\\"The following are the counts:\\\")\\n print(count)\\n \\n if search is not None:\\n for item in string_list:\\n if search in item:\\n print(item,'\\\\n')\\n \\n else:\\n for item in string_list:\\n print(item,'\\\\n')\",\n \"def process_tir_image(ds, data_res, t_thresh=-50, min_mcs_size=5000):\\n ctt = (ds['tb']).squeeze()-273.15\\n min_pix_nb = min_mcs_size / data_res**2\\n\\n max_pix_nb = 300000 / data_res**2 # this is to capture satellite artefacts that come in large contiguous stripes.\\n labels, goodinds = mcs_define(ctt.values, t_thresh, minmax_area=[min_pix_nb, max_pix_nb]) # 7.7x7.7km = 64km2 per pix in gridsat? 83 pix is 5000km2\\n dic = dictionary()\\n #plt.figure()\\n #plt.pcolormesh(labels)\\n #plt.colorbar()\\n #plt.show()\\n for g in goodinds:\\n\\n if g==0:\\n continue\\n\\n pos = np.where(labels==g)\\n npos = np.where(labels!=g)\\n datestr = str(int(ctt['time.year'].values))+'-'+str(int(ctt['time.month'].values)).zfill(2)+'-'+str(int(ctt['time.day'].values)).zfill(2)+'_'+\\\\\\n str(int(ctt['time.hour'].values)).zfill(2)+':'+str(int(ctt['time.minute'].values)).zfill(2)\\n \\n dic['date'].append(datestr)\\n dic['month'].append(int(ctt['time.month']))\\n dic['hour'].append(int(ctt['time.hour']))\\n dic['year'].append(int(ctt['time.year']))\\n dic['day'].append(int(ctt['time.day']))\\n dic['minute'].append(int(ctt['time.minute']))\\n\\n storm = ctt.copy()\\n storm.values[npos] = np.nan\\n tmin_pos = np.nanargmin(storm.values)\\n tpos_2d = np.unravel_index(tmin_pos, storm.shape)\\n \\n latmin = np.nanmin(ctt.lat.values[pos[0]])\\n latmax = np.nanmax(ctt.lat.values[pos[0]])\\n lonmin = np.nanmin(ctt.lon.values[pos[1]])\\n lonmax = np.nanmax(ctt.lon.values[pos[1]])\\n dic['area'].append(np.sum(np.isfinite(storm.values))*data_res**2)\\n dic['70area'].append(np.sum(storm.values<=-70)*data_res**2)\\n dic['minlon'].append(lonmin)\\n dic['minlat'].append(latmin)\\n dic['maxlon'].append(lonmax)\\n dic['maxlat'].append(latmax)\\n dic['clon'].append(lonmin + (lonmax - lonmin)/2)\\n dic['clat'].append(latmin + (latmax - latmin)/2)\\n dic['tmin'].append(np.nanmin(storm))\\n dic['tminlat'].append(float(ctt.lat[tpos_2d[0]].values))\\n dic['tminlon'].append(float(ctt.lon[tpos_2d[1]].values))\\n dic['tmean'].append(float(np.nanmean(storm)))\\n dic['tp1'].append(float(np.nanpercentile(storm, 1)))\\n dic['tp99'].append(float(np.nanpercentile(storm, 99)))\\n dic['stormID'].append(datestr + '_' + str(g))\\n dic['cloudMask'].append(labels==g)\\n dic['tir'].append(storm.values)\\n\\n # for k in dic.keys():\\n # print(k, len(dic[k]))\\n return dic\",\n \"def build_sea_data(\\n start_year=1999,\\n end_year=2016,\\n netcdf_path=\\\"data/sea_level/netcdf/\\\",\\n target_lon=175.8606890,\\n target_lat=-36.993684,\\n buffer_degrees=0.5,\\n path_out=\\\".\\\",\\n):\\n # tairua_coords = (-36.993684, 175.8606890)\\n df_sea_data = pd.DataFrame()\\n\\n for year in range(start_year, end_year + 1):\\n ds_first = xr.open_mfdataset(\\n os.path.join(netcdf_path, f\\\"dt_global_twosat_phy_l4_{year}*.nc\\\")\\n )\\n\\n target_lon = xr.DataArray(\\n list(target_lon + np.linspace(-buffer_degrees, buffer_degrees))\\n )\\n target_lat = xr.DataArray(\\n list(target_lat + np.linspace(-buffer_degrees, buffer_degrees))\\n )\\n\\n ds_tairua = ds_first[[\\\"adt\\\", \\\"ugos\\\", \\\"vgos\\\"]].sel(\\n longitude=target_lon, latitude=target_lat, method=\\\"nearest\\\"\\n )\\n df_sealevel_pandas = (\\n ds_tairua.resample(time=\\\"MS\\\")\\n .mean()\\n .mean(dim=\\\"dim_0\\\")\\n .to_dataframe()\\n )\\n\\n df_sea_data = pd.concat([df_sea_data, df_sealevel_pandas])\\n\\n print(\\n f\\\"************************Done {year} ************************************\\\"\\n )\\n print(df_sea_data.tail(10))\\n\\n df_sea_data.to_csv(os.path.join(path_out, \\\"df_sea_data.csv\\\"))\",\n \"def testdata_gps():\\n lon = np.array(\\n [\\n 4.54,\\n 104.0,\\n -14.9,\\n 56.26,\\n 103.46,\\n 103.37,\\n 54.22,\\n 23.3,\\n 25.53,\\n 23.31,\\n 118.0,\\n 103.53,\\n 54.40,\\n 103.48,\\n 6.14,\\n 7.25,\\n 2.38,\\n 18.18,\\n 103.54,\\n 103.40,\\n 28.59,\\n 25.21,\\n 29.35,\\n 25.20,\\n ]\\n )\\n\\n lat = np.array(\\n [\\n 52.22,\\n 1.14,\\n 27.34,\\n 25.16,\\n 1.16,\\n 1.11,\\n 24.30,\\n 37.54,\\n 37.26,\\n 38.1,\\n 24.25,\\n 1.13,\\n 24.49,\\n 1.13,\\n 42.33,\\n 43.44,\\n 39.34,\\n 70.30,\\n 1.16,\\n 1.10,\\n 40.58,\\n 37.34,\\n 41.18,\\n 38.35,\\n ]\\n )\\n\\n time = np.zeros(len(lon))\\n\\n X_data = np.vstack((time, lat, lon)).T\\n\\n X_name = np.array([0, 1, 2, 2, 2, 2, 3, 3, 3]) # NOQA\\n X_data = np.array(\\n [\\n (0, 42.727985, -73.683994), # MRC\\n (0, 42.657872, -73.764148), # Home\\n (0, 42.657414, -73.774448), # Park1\\n (0, 42.658333, -73.770993), # Park2\\n (0, 42.654384, -73.768919), # Park3\\n (0, 42.655039, -73.769048), # Park4\\n (0, 42.876974, -73.819311), # CP1\\n (0, 42.862946, -73.804977), # CP2\\n (0, 42.849809, -73.758486), # CP3\\n ]\\n )\\n return X_name, X_data\",\n \"def preprocess():\\n #get a list of all sentinel-image filenames\\n s2files = [f for f in listdir(s2path) if endswith(join(s2path, f),\\\".tif\\\")==True]\\n #read in a csv-file with information about the cluster\\n csvpath = os.path.abspath(os.path.join(os.path.abspath(__file__),\\\"../../dataResearch/Data_with_Pooled.csv\\\"))\\n df = pd.read_csv(csvpath)\\n #get the min and max values per band \\n minmaxlist = minmax()\\n timelist = []\\n print(\\\"STEP 2/2\\\")\\n print(\\\"CREATING TFRECORDS\\\")\\n for i in s2files:\\n start = time.time()\\n s2file = s2path + \\\"/\\\" + i\\n #Get Features out of the Dataframe\\n #get the name of the label (equals the SurveyID in the data)\\n labelname = i.replace(\\\".tif\\\",\\\"\\\")\\n #get the index of the entry to get the information out of the dataframe\\n index = df.ID[df.ID == labelname].index\\n wealthpooled = float(df['wealthpooled'].loc[index].max().replace(\\\",\\\",\\\".\\\"))\\n wealthpooled5country = float(df['wealthpooled5country'].loc[index].max().replace(\\\",\\\",\\\".\\\"))\\n country = bytes(df['country'].loc[index].max(), 'utf-8')\\n urbanrural = bytes(df['URBAN_RURA'].loc[index].max(), 'utf-8')\\n csvlat = float(df['LATNUM'].loc[index].max().replace(\\\",\\\",\\\".\\\"))\\n csvlon = float(df['LONGNUM'].loc[index].max().replace(\\\",\\\",\\\".\\\"))\\n year = int(df['year'].loc[index].max())\\n wealth = float(df['wealth'].loc[index].max().replace(\\\",\\\",\\\".\\\"))\\n #Get all Bands out of the GEOTIFF File\\n s2raster = gdal.Open(s2file)\\n bandlist = []\\n for n in range(s2raster.RasterCount):\\n f = n+1\\n if n not in [13,14,15]:\\n s2band = s2raster.GetRasterBand(f)\\n s2band = s2band.ReadAsArray()\\n s2band = np.resize(s2band,(1050,1050)).flatten()\\n min = minmaxlist[n][0]\\n max = minmaxlist[n][1]\\n s2band = (s2band-min)/(max-min)\\n bandlist.append(s2band.flatten())\\n #get the Nightlight Band out of the GEOTIFF File\\n nlfile = nlpath + \\\"/\\\" + i\\n nlraster = gdal.Open(nlfile)\\n nlband = nlraster.GetRasterBand(1)\\n nlband = nlband.ReadAsArray()\\n nlband = np.resize(nlband,(1050,1050)).flatten()\\n min = minmaxlist[13][0]\\n max = minmaxlist[13][1]\\n nlband = (nlband-min)/(max-min)\\n bandlist.append(nlband)\\n #create a TFRecords-File with the TFRecordWriter\\n with tf.io.TFRecordWriter(exportpath + '/' + labelname + '.tfrec') as writer:\\n example = serialize_example(B1=bandlist[0],\\n B2=bandlist[1],\\n B3=bandlist[2],\\n B4=bandlist[3],\\n B5=bandlist[4],\\n B6=bandlist[5],\\n B7=bandlist[6],\\n B8=bandlist[7],\\n B8A=bandlist[8],\\n B9=bandlist[9],\\n B10=bandlist[10],\\n B11=bandlist[11],\\n B12=bandlist[12],\\n NL=bandlist[13],\\n wealth=wealth,\\n wealthpooled=wealthpooled,\\n wealthpooled5country=wealthpooled5country,\\n country=country,\\n urbanrural=urbanrural,\\n lon_coord=csvlon,\\n lat_coord=csvlat,\\n year=year)\\n writer.write(example)\\n end = time.time()\\n timelist.append(end-start)\\n print(\\\"Done!\\\",str(s2files.index(i)+1) + \\\"/\\\" + str(len(s2files)),\\\"Est. time left:\\\",time.strftime('%d:%H:%M:%S',time.gmtime(int(sum(timelist)/len(timelist)*(len(s2files)-s2files.index(i))))))\",\n \"def read_wcfat_results(filename, locations=[\\\"LPH Weld\\\", \\\"HPH Weld\\\", \\\"BOP Connector\\\"]):\\n\\n df = pd.read_csv(filename, skiprows=8, header=None, sep=\\\"\\\\t\\\")\\n\\n # Drop the event length column and last column, which is just redundant \\\",\\\"\\n df = df.drop([1, 5], axis=1)\\n df.columns = [\\\"Timestamp\\\"] + locations\\n df[\\\"Timestamp\\\"] = df[\\\"Timestamp\\\"].str.strip()\\n df = df.set_index(\\\"Timestamp\\\")\\n df.index = pd.to_datetime(df.index, format=\\\"%Y_%m_%d_%H_%M\\\")\\n\\n return df\",\n \"def filtering(self, Z):\\n coord_x_est = [0]\\n coord_y_est = [0]\\n time_est = [Z[['time']].iloc[0][0]]\\n for i in range(1, len(Z.x)):\\n new_dt = Z[['time']].iloc[i][0] - time_est[-1]\\n while (new_dt > 150):\\n new_state = [coord_x_est[-1], coord_y_est[-1], time_est[-1]]\\n new_state = self._restore(new_state)\\n coord_x_est.append(new_state[0])\\n coord_y_est.append(new_state[1])\\n time_est.append(new_state[2])\\n new_dt -= 100\\n\\n# self.configure(new_dt)\\n self._predict()\\n mes = Z[['x', 'y']].iloc[i].to_numpy()\\n self._update(np.resize(mes, (2, 1)))\\n # save for latter plotting\\n coord_x_est.append(self.x[0])\\n coord_y_est.append(self.x[4])\\n time_est.append(int(Z[['time']].iloc[i]))\\n\\n return pd.DataFrame({'X_f': coord_x_est,\\n 'Y_f': coord_y_est,\\n 'time': time_est\\n })\",\n \"def create_table(f, geoinfo):\\n bounds_cols = xb_points + yb_points\\n df = pd.read_csv(f, delimiter=\\\";\\\", index_col=\\\"INDEX_RC\\\")\\n df[duration_name] = parse_duration_level(f)\\n df = df.join(geoinfo[[\\\"X_CENT_GEO\\\", \\\"Y_CENT_GEO\\\", \\\"Col\\\", \\\"Row\\\"]])\\n df = df.rename(columns={\\\"Col\\\": x, \\\"Row\\\": y, \\\"X_CENT_GEO\\\": lon, \\\"Y_CENT_GEO\\\": lat})\\n return df\",\n \"def get_lat_lon_date_features(gt_ids=[], gt_masks=None, gt_shifts=None,\\n forecast_ids=[], forecast_masks=None, forecast_shifts=None,\\n anom_ids=[], anom_masks=None, anom_shifts=None,\\n first_year = None):\\n # If particular arguments aren't lists, replace with repeating iterators\\n if not isinstance(gt_masks, list):\\n gt_masks = itertools.repeat(gt_masks)\\n if not isinstance(gt_shifts, list):\\n gt_shifts = itertools.repeat(gt_shifts)\\n if not isinstance(forecast_masks, list):\\n forecast_masks = itertools.repeat(forecast_masks)\\n if not isinstance(forecast_shifts, list):\\n forecast_shifts = itertools.repeat(forecast_shifts)\\n if not isinstance(anom_masks, list):\\n anom_masks = itertools.repeat(anom_masks)\\n if not isinstance(anom_shifts, list):\\n anom_shifts = itertools.repeat(anom_shifts)\\n\\n # Define canonical name for target start date column\\n date_col = \\\"start_date\\\"\\n # Add each ground truth feature to dataframe\\n df = None\\n for gt_id, gt_mask, gt_shift in zip(gt_ids, gt_masks, gt_shifts):\\n print \\\"Getting {}_shift{}\\\".format(gt_id, gt_shift)\\n t = time.time()\\n # Load ground truth data\\n gt = get_ground_truth(gt_id, gt_mask, shift=gt_shift)\\n # Discard years prior to first_year\\n gt = year_slice(gt, first_year = first_year)\\n # Use outer merge to include union of (lat,lon,date_col)\\n # combinations across all features\\n df = df_merge(df, gt)\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n # Add each forecast feature to dataframe\\n for forecast_id, forecast_mask, forecast_shift in zip(forecast_ids,\\n forecast_masks,\\n forecast_shifts):\\n print \\\"Getting {}_shift{}\\\".format(forecast_id, forecast_shift)\\n t = time.time()\\n # Load forecast with years >= first_year\\n forecast = get_forecast(forecast_id, forecast_mask, shift=forecast_shift)\\n # Rename target start date column to \\\"start_date\\\"\\n fcst_date_col = get_target_start_date_col(forecast_id)\\n forecast.rename(columns={fcst_date_col: date_col}, inplace=True)\\n # Discard years prior to first_year\\n forecast = year_slice(forecast, first_year = first_year)\\n # Use outer merge to include union of (lat,lon,date_col)\\n # combinations across all features\\n df = df_merge(df, forecast)\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n # Add anomaly features and climatology last so that climatology\\n # is produced for all previously added start dates\\n for anom_id, anom_mask, anom_shift in zip(anom_ids, anom_masks, anom_shifts):\\n print \\\"Getting {}_shift{} with anomalies\\\".format(anom_id, anom_shift)\\n t = time.time()\\n # Check if ground truth column already exists\\n gt_col = get_measurement_variable(anom_id, shift=anom_shift)\\n if df is None or gt_col not in df.columns:\\n # Add masked ground truth data if absent\\n gt = get_ground_truth(anom_id, anom_mask, shift=anom_shift)\\n # Discard years prior to first_year\\n gt = year_slice(gt, first_year = first_year)\\n # Use outer merge to include union of (lat,lon,date_col)\\n # combinations across all features\\n df = df_merge(df, gt)\\n\\n # Load masked ground truth data climatology\\n climatology = get_climatology(anom_id, anom_mask, anom_shift)\\n # Merge climatology into dataset\\n df = pd.merge(df, climatology[[gt_col]],\\n left_on=['lat', 'lon', df[date_col].dt.month,\\n df[date_col].dt.day],\\n right_on=[climatology.lat, climatology.lon,\\n climatology[date_col].dt.month,\\n climatology[date_col].dt.day],\\n how='left', suffixes=('', '_clim'))\\n clim_col = gt_col+\\\"_clim\\\"\\n # Compute ground-truth anomalies\\n anom_col = gt_col+\\\"_anom\\\"\\n df[anom_col] = df[gt_col] - df[clim_col]\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n return df\",\n \"def prepare(gpi, start_date, end_date, models, satellites, kind=\\\"clim\\\", window=35):\\n # read data\\n ts_input = data_object.read_gpi(gpi, start_date, end_date, models, satellites)\\n #ts_input = interp.add_nan(ts_input)\\n ts_input = interp.iter_fill(ts_input, max_gap=5)\\n\\n # either calc climatology, apply moving average filter, or do nothing\\n if kind == 'clim':\\n ts_smooth = smooth.iter_climats(ts_input)\\n plot_title = 'Climatology'\\n elif kind == 'movav':\\n ts_smooth = smooth.iter_movav(ts_input, window)\\n #ts_smooth = ts_gapfill\\n plot_title = 'Moving average'\\n elif kind == None:\\n # return original data\\n ts_smooth = ts_input\\n else:\\n raise NotImplementedError\\n pass\\n\\n # drop rows with missing values\\n #ts_smooth = ts_smooth.dropna()\\n\\n # scale satellite data to model data\\n ts_scaled = scaling.scale(ts_smooth, 'mean_std_nan', 0)\\n # drop nan rows for slope funcs\\n return ts_scaled #.dropna()\",\n \"def QUERY_LATERALISATION(inspect_result, df, one_map, gif_lat_file,\\n side_of_symptoms_signs=None,\\n pts_dominant_hemisphere_R_or_L=None,\\n normalise_lat_to_loc=False,\\n disable_tqdm=True):\\n pd.options.mode.chained_assignment = 'raise'\\n df = df.copy()\\n\\n # ensure there is patient's lateralised signs and check dominant known or not\\n if not side_of_symptoms_signs and not pts_dominant_hemisphere_R_or_L:\\n # print('Please note you must determine at least one of side_of_symptoms_signs or')\\n # print('pts_dominant_hemisphere_R_or_L keyword arguments for lateralised data extraction.')\\n return None, None, None, None, None, None, None\\n\\n # check there is lateralising value\\n try:\\n num_QL_lat = inspect_result['Lateralising'].sum()\\n if num_QL_lat > 0:\\n logging.debug(\\n f'\\\\n\\\\nLateralisation based on: {num_QL_lat.sum()} datapoints')\\n else:\\n num_QL_lat = None\\n return None, None, None, None, None, None, None\\n except KeyError:\\n # logging.debug(\\n # f'No Lateralising values found for this query of the database.')\\n num_QL_lat = None\\n return None, None, None, None, None, None, None\\n\\n lat_vars = [i for i in lateralisation_vars() if i not in ['Lateralising']]\\n\\n # check that the lateralising columns isn't null where it shouldn't be i.e. CL/IL/DomH/NonDomH not null:\\n # but not 'BL (Non-lateralising)'\\n # first ensure other columns all feature in this inspect_result:\\n inspect_result2 = inspect_result.copy()\\n for col in lat_vars:\\n if col not in inspect_result2.columns:\\n inspect_result2[col] = np.nan\\n # now can check lateralising columns isn't null where it shouldn't be:\\n missing_lat = inspect_result2.loc[(inspect_result2['CL'].notnull()) |\\n (inspect_result2['IL'].notnull()) |\\n (inspect_result2['DomH'].notnull()) |\\n (inspect_result2['NonDomH'].notnull()), :].copy()\\n missing_lat_null_mask = missing_lat['Lateralising'].isnull()\\n if not missing_lat_null_mask.all():\\n # logging.debug('\\\\nNo missing Lateralising data points.')\\n pass\\n else:\\n logging.debug(\\n 'The inspect_result lat col has NaNs/zero where it should not: autofilled')\\n df_of_missing_lats = missing_lat.loc[missing_lat_null_mask].copy()\\n df.loc[df_of_missing_lats.index, 'Lateralising'] = df_of_missing_lats[[\\n 'CL', 'IL', 'DomH', 'NonDomH']].sum(axis=1)\\n\\n # check columns exist (not removed in preceding notnull steps from other functions):\\n for col in lat_vars:\\n if col not in inspect_result.columns:\\n inspect_result[col] = 0\\n\\n # summarise overall lat values\\n IL = inspect_result['IL']\\n CL = inspect_result['CL']\\n DomH = inspect_result['DomH']\\n NonDomH = inspect_result['NonDomH']\\n BL = inspect_result['BL (Non-lateralising)']\\n\\n num_QL_CL = CL.sum()\\n num_QL_IL = IL.sum()\\n num_QL_BL = BL.sum()\\n num_QL_DomH = DomH.sum()\\n num_QL_NonDomH = NonDomH.sum()\\n\\n logging.debug(f'\\\\n\\\\nOverall Contralateral: {num_QL_CL} datapoints')\\n logging.debug(f'Ipsilateral: {num_QL_IL} datapoints')\\n logging.debug(\\n f'Bilateral/Non-lateralising: {num_QL_BL} datapoints. This is not utilised in our analysis/visualisation.')\\n logging.debug(f'Dominant Hemisphere: {num_QL_DomH} datapoints')\\n logging.debug(f'Non-Dominant Hemisphere: {num_QL_NonDomH} datapoints')\\n\\n # Global initialisation:\\n lat_only_Right = 0\\n lat_only_Left = 0\\n inspect_result_lat = inspect_result.loc[inspect_result['Lateralising'].notnull(\\n ), :].copy() # only those with lat (with or without localising)\\n no_rows = inspect_result_lat.shape[0]\\n all_combined_gifs = None\\n gifs_right, gifs_left = gifs_lat(gif_lat_file)\\n\\n # cycle through rows of inspect_result_lat:\\n id_cols = [i for i in full_id_vars() if i not in ['Localising']\\n ] # note 'Localising' is in id_cols\\n\\n for i in (range(no_rows) if disable_tqdm else tqdm(range(no_rows), desc='QUERY LATERALISTION: main',\\n bar_format=\\\"{l_bar}%s{bar}%s{r_bar}\\\" % (Fore.BLUE, Fore.RESET))):\\n # logging.debug(str(i))\\n Right = 0\\n Left = 0\\n\\n full_row = inspect_result_lat.iloc[[i], :]\\n row = full_row.drop(labels=id_cols, axis='columns',\\n inplace=False, errors='ignore')\\n row = row.dropna(how='all', axis='columns')\\n # row = row.dropna(how='all', axis='rows')\\n\\n #\\n #\\n #\\n # some pts/rows will have lateralising but no localising values:\\n if (('Localising' not in row.columns) | (full_row['Localising'].sum() == 0)):\\n logging.debug(\\n '\\\\n\\\\nSome extracted lateralisations have no specific localisation - these are mapped to entire hemispheric GIFs')\\n # logging.debug(f'row# = {i}')\\n # probably, in future, instead of break we want to compare this row's:\\n # full_row['Lateralising'] to the overall inspect_result['Lateralising'] and use that proportion\\n # or actually, to count this full_row['Lateralising'] as data for localising, using the lateralised gif parcellations from the sheet called\\n # 'Full GIF Map for Review '\\n\\n lat_only_Right, lat_only_Left = lateralising_but_not_localising(full_row,\\n side_of_symptoms_signs,\\n pts_dominant_hemisphere_R_or_L,\\n lat_only_Right,\\n lat_only_Left)\\n continue\\n #\\n #\\n #\\n # otherwise if there is localising value (and lateralising value):\\n row_to_one_map = pivot_result_to_one_map(row, one_map, raw_pt_numbers_string='pt #s',\\n )\\n # ^ row_to_one_map now contains all the lateralising gif parcellations\\n\\n # set the scale of influence of lateralisation on the gif parcellations:\\n proportion_lateralising = full_row['Lateralising'].sum(\\n ) / full_row['Localising'].sum()\\n\\n # some rows will have lateralisng exceed localising values:\\n #\\n #\\n #\\n if proportion_lateralising > 1:\\n proportion_lateralising = 1\\n logging.debug(\\n '\\\\n\\\\nSome lateralising data exceed localising data, excess are mapped to entire hemispheric GIFs')\\n\\n # now deal with lat_exceed_loc excess as we did with lat_but_not_loc\\n lat_exceed_loc = True\\n else:\\n lat_exceed_loc = False\\n\\n # check columns exist in this particular row:\\n for col in lat_vars:\\n if col not in row.columns:\\n row[col] = 0\\n else:\\n continue\\n\\n # summarise overall lat values\\n Right, Left = summarise_overall_lat_values(row,\\n side_of_symptoms_signs,\\n pts_dominant_hemisphere_R_or_L,\\n Right,\\n Left)\\n\\n Total = Right+Left\\n if Right == Left:\\n # no point as this is 50:50 as it already is, so skip\\n # before continuing, ensure there is a all_combined_gifs...\\n # e.g. for blink there isn't as the first row is 50:50...\\n # ... and all future codes fail\\n if all_combined_gifs is None:\\n all_combined_gifs = row_to_one_map\\n elif all_combined_gifs is not None:\\n all_combined_gifs = pd.concat(\\n [all_combined_gifs, row_to_one_map], join='outer', sort=False, ignore_index=True)\\n continue\\n\\n # now should be able to use above to lateralise the localising gif parcellations:\\n # if there are 100 localisations in one row, and only 1 IL And 3 CL, it would be too much\\n # to say the IL side gets one third of the CL side as number of lat is too low\\n # hence normalise by dividing by proportion_lateralising (which is between (0,1])\\n\\n # find lowest value of R or L\\n lower_postn = np.argmin([Right, Left])\\n if lower_postn == 0:\\n isin = gifs_right # reduce right sided intensities/pt #s\\n isin_right = True\\n isin_left = False\\n elif lower_postn == 1:\\n isin = gifs_left\\n isin_left = True\\n isin_right = False\\n\\n lower_value = [Right, Left][lower_postn]\\n higher_value = [Right, Left]\\n higher_value.remove(lower_value)\\n\\n ratio = lower_value / Total\\n\\n if normalise_lat_to_loc == True:\\n # see comments on section above about why we should normalise\\n norm_ratio = ratio / proportion_lateralising\\n if norm_ratio > 1:\\n norm_ratio = 1\\n logging.debug(\\n 'norm_ratio capped at 1: small proportion of data lateralised')\\n elif normalise_lat_to_loc == False:\\n norm_ratio = lower_value / higher_value\\n\\n # if proportion_lateralising is 1, straightforward: return dataframe of right/left gifs whichever lower\\n df_lower_lat_to_be_reduced = row_to_one_map.loc[row_to_one_map['Gif Parcellations'].isin(\\n list(isin))].copy()\\n # now make these values lower by a proportion = norm_ratio (in this case norm_ratio = ratio as denom is 1)\\n reduce_these = df_lower_lat_to_be_reduced.loc[:, 'pt #s'].copy()\\n df_lower_lat_to_be_reduced.loc[:, 'pt #s'] = norm_ratio * reduce_these\\n # re attribute these corrected reduced lateralised values to the entire row's data:\\n row_to_one_map.loc[df_lower_lat_to_be_reduced.index,\\n :] = df_lower_lat_to_be_reduced\\n\\n #\\n #\\n #\\n # now deal with lat_exceed_loc excess as we did with lat_but_not_loc\\n if lat_exceed_loc:\\n lat_only_Right, lat_only_Left = lat_exceeding_loc_mapped_to_hemisphericGIFs_adjusted_for_locs(\\n full_row, lat_vars,\\n side_of_symptoms_signs,\\n pts_dominant_hemisphere_R_or_L,\\n lat_only_Right,\\n lat_only_Left,\\n isin_left=isin_left, isin_right=isin_right,\\n )\\n\\n #\\n #\\n #\\n\\n # now need to merge/concat these rows-(pivot-result)-to-one-map as the cycle goes through each row:\\n if i == 0:\\n # can't merge first row\\n all_combined_gifs = row_to_one_map\\n # logging.debug('end of zeroo')\\n continue\\n elif i != 0:\\n all_combined_gifs = pd.concat(\\n [all_combined_gifs, row_to_one_map], join='outer', sort=False, ignore_index=True)\\n # logging.debug(f'end of i {i}')\\n\\n # Need to recombine the inspect_result_lat (also had loc) used in for loop to give all_combined_gifs\\n # with inspect_result that had null lateralising:\\n inspect_result_nulllateralising = inspect_result.loc[inspect_result['Lateralising'].isnull(\\n ), :].copy()\\n # now clean ready to map:\\n inspect_result_nulllateralising.drop(\\n labels=id_cols, axis='columns', inplace=True, errors='ignore')\\n inspect_result_nulllateralising.dropna(\\n how='all', axis='columns', inplace=True)\\n # now map row by row otherwise you get a \\\"TypeError: only size-1 arrays can be converted to Python scalars\\\":\\n nonlat_no_rows = inspect_result_nulllateralising.shape[0]\\n if (nonlat_no_rows == 0) | inspect_result_nulllateralising.empty:\\n gifs_not_lat = None\\n elif nonlat_no_rows != 0:\\n for j in (range(nonlat_no_rows) if disable_tqdm else tqdm(range(nonlat_no_rows), desc='QUERY LATERALISATION: non-lateralising data',\\n bar_format=\\\"{l_bar}%s{bar}%s{r_bar}\\\" % (Fore.CYAN, Fore.RESET))):\\n full_row = inspect_result_nulllateralising.iloc[[j], :]\\n row = full_row.drop(labels=id_cols, axis='columns',\\n inplace=False, errors='ignore')\\n row = row.dropna(how='all', axis='columns')\\n row_nonlat_to_one_map = pivot_result_to_one_map(row,\\n one_map, raw_pt_numbers_string='pt #s',\\n )\\n if j == 0:\\n # can't merge first row\\n gifs_not_lat = row_nonlat_to_one_map\\n # logging.debug(\\n # 'j zero gifs_not_lat set to row_nonlat_to_one_map')\\n continue\\n elif j != 0:\\n gifs_not_lat = pd.concat(\\n [gifs_not_lat, row_nonlat_to_one_map], join='outer', sort=False, ignore_index=True)\\n # now combine the lateralised+loc and non-lateralised(=only localised):\\n # all_combined_gifs may still be None if after running above with some lateralised...\\n # ...semiology or dominance, there is no lateralising data.\\n if all_combined_gifs is None:\\n all_combined_gifs = gifs_not_lat\\n elif all_combined_gifs is not None:\\n all_combined_gifs = pd.concat(\\n [all_combined_gifs, gifs_not_lat], join='outer', sort=False, ignore_index=True)\\n\\n # here add the lateralising_but_not_localising data to the all_combined_gifs (or fixed, or fixed2):\\n # if all_combined_gifs is None then use the unilteral_gifs\\n # if all_combined_Gifs is not None then only add values to the unilateral gifs if they are already in all_combined_gifs\\n if (lat_only_Right != 0) | (lat_only_Left != 0):\\n # means both lateralising+loc and gifs_not_lat were none. occurs usually in dummy_data only.\\n if all_combined_gifs is None:\\n lat_only_df = lateralising_but_not_localising_GIF(all_combined_gifs,\\n lat_only_Right, lat_only_Left,\\n gifs_right, gifs_left)\\n all_combined_gifs = lat_only_df.copy()\\n else:\\n lat_only_df = lateralising_but_not_localising_GIF(all_combined_gifs,\\n lat_only_Right, lat_only_Left,\\n gifs_right, gifs_left)\\n all_combined_gifs = pd.concat([all_combined_gifs, lat_only_df],\\n join='outer', sort=False, ignore_index=True)\\n # all_combined_gifs.append(lat_only_df) # equivalent to above concat.\\n\\n # if EpiNav doesn't sum the pixel intensities: (infact even if it does)\\n fixed = all_combined_gifs.pivot_table(\\n columns='Gif Parcellations', values='pt #s', aggfunc='sum')\\n fixed2 = fixed.melt(value_name='pt #s')\\n fixed2.insert(0, 'Semiology Term', np.nan)\\n # fixed2.loc[0, 'Semiology Term'] = str( list(inspect_result.index.values) )\\n all_combined_gifs = fixed2\\n all_combined_gifs\\n\\n return (all_combined_gifs,\\n num_QL_lat, num_QL_CL, num_QL_IL, num_QL_BL, num_QL_DomH, num_QL_NonDomH)\",\n \"def preproc_data(base_dir):\\n n_users = 182\\n user_datas = []\\n\\n for user in tqdm(range(n_users)): \\n #iterate through each trace of user \\n user_traces = []\\n if (len(str(user)) == 1): \\n user_id = '00' + str(user)\\n elif (len(str(user)) == 2): \\n user_id = '0' + str(user)\\n else: \\n user_id = str(user)\\n dir_name = base_dir + user_id + '/Trajectory/'\\n for filename in os.listdir(dir_name): \\n #load trajectory\\n trajectory_raw = pd.read_csv(\\n dir_name + filename,\\n delimiter = ',', header = None, skiprows = 6,\\n names = ['lat','lon', '0', 'altitude', 't_1899', 'date', 'time' ])\\n\\n #modify time\\n traj = trajectory_raw.copy()\\n traj.drop(columns = '0', inplace = True)\\n\\n traj['t_1899'] = (trajectory_raw['t_1899'] - trajectory_raw['t_1899'].values[0]) * 24 * 60 * 60\\n traj_newcols = np.array(traj.columns)\\n traj_newcols[traj_newcols == 't_1899'] = 'seconds'\\n traj.columns = traj_newcols\\n\\n #get X_vals: \\n R_earth = 6.371e6\\n deg_to_rad = np.pi / 180\\n traj['X'] = R_earth * np.cos(deg_to_rad * traj['lat'].values) * np.cos(deg_to_rad * traj['lon'].values)\\n traj['Y'] = R_earth * np.cos(deg_to_rad * traj['lat'].values) * np.sin(deg_to_rad * traj['lon'].values)\\n\\n #Normalize to first point: \\n origin_x = traj['X'].values[0]\\n origin_y = traj['Y'].values[0]\\n\\n traj['X'] = traj['X'] - origin_x\\n traj['Y'] = traj['Y'] - origin_y\\n\\n txy = traj.copy()\\n txy = traj[['seconds', 'X', 'Y']]\\n txy.columns = ['T', 'X', 'Y']\\n if not np.isnan(txy.values).any(): \\n user_traces.append(txy.values)\\n\\n user_datas.append(user_traces)\\n return user_datas\",\n \"def identify_peaks_nowind(xCar, xDate, xDir, xFilename, outDir, processedFileLoc, Engineering, threshold='.1',\\n rthresh = '.7',\\n xTimeThreshold='5.0', minElevated='2', xB='102', basePerc='50'):\\n import csv, numpy\\n import shutil\\n from shapely.geometry import Point\\n import pandas as pd\\n import geopandas as gpd\\n\\n\\n try:\\n baseCalc = float(basePerc)\\n xABThreshold = float(threshold)\\n minElevated = float(minElevated)\\n rMin = float(rthresh)\\n xDistThreshold = 160.0 # find the maximum CH4 reading of observations within street segments of this grouping distance in meters\\n xSDF = 4 # multiplier times standard deviation for floating baseline added to mean\\n\\n xB = int(xB)\\n xTimeThreshold = float(xTimeThreshold)\\n fn = xDir + xFilename # set raw text file to read in\\n fnOut = outDir + \\\"Peaks\\\" + \\\"_\\\" + xCar + \\\"_\\\" + xDate.replace(\\\"-\\\", \\\"\\\") + \\\".csv\\\"\\n fnShape = outDir + \\\"Peaks\\\" + \\\"_\\\" + xCar + \\\"_\\\" + xDate.replace(\\\"-\\\", \\\"\\\") + \\\".shp\\\"\\n fnLog = outDir + \\\"Peaks\\\" + \\\"_\\\" + xCar + \\\"_\\\" + xDate.replace(\\\"-\\\", \\\"\\\") + \\\".log\\\"\\n pkLog = outDir + \\\"Peaks\\\" + \\\"_\\\" + xCar + \\\"_\\\" + xDate.replace(\\\"-\\\",\\\"\\\") + \\\"_info.csv\\\"\\n jsonOut = outDir + \\\"Peaks\\\" + \\\"_\\\" + xCar + \\\"_\\\" + xDate.replace(\\\"-\\\",\\\"\\\") + \\\".geojson\\\"\\n infOut = processedFileLoc + xCar + \\\"_\\\" + xDate.replace(\\\"-\\\", \\\"\\\") + \\\"_info.csv\\\"\\n\\n ### TEST THING\\n fn = xDir + xFilename # set raw text file to read in\\n filenames = nameFiles(outDir,processedFileLoc,xCar,xDate,True)\\n fnOut = filenames['fnOut']\\n fnShape = filenames['fnShape']\\n fnLog = filenames['fnLog']\\n pkLog = filenames['pkLog']\\n jsonOut = filenames['jsonOut']\\n infOut = filenames['infOut']\\n\\n print(f\\\"{outDir}Peaks_{xCar}_{xDate}_info.csv\\\")\\n fLog = open(fnLog, 'w')\\n shutil.copy(infOut, pkLog)\\n\\n # field column indices for various variables\\n if Engineering == True:\\n fDate = 0; fTime = 1; fEpochTime = 2\\n fNanoSeconds = 3; fVelocity = 4; fU = 5\\n fV = 6; fW = 7; fBCH4 = 10\\n fBCH4 = 8; fBRSSI = 9; fTCH4 = 10\\n TRSSI = 11;PRESS = 12; INLET = 13\\n TEMP = 14; CH4 = 15;H20 = 16\\n C2H6 = 17; R = 18; C2C1 = 19\\n BATT = 20; POWER = 21; CURR = 22\\n SOCPER = 23;fLat = 24; fLon = 25\\n elif not Engineering:\\n fDate = 0; fTime = 1; fEpochTime = 2\\n fNanoSeconds = 3;fVelocity = 4; fU = 5\\n fV = 6; fW = 7\\n fBCH4 = 8; fBRSSI = 9\\n fTCH4 = 10; TRSSI = 11; PRESS = 12\\n INLET = 13; TEMP = 14; CH4 = 15\\n H20 = 16;C2H6 = 17; R = 18; C2C1 = 19\\n BATT = 20; POWER = 21; CURR = 22\\n SOCPER = 23; fLat = 24;fLon = 25; fDist = 33; fOdometer = 34\\n fUavg = 35; fVavg = 36; fWavg = 37\\n\\n # read data in from text file and extract desired fields into a list, padding with 5 minute and hourly average\\n x1, x2, x3, x4, x5, x6, x7, x8, x9, x10,x11,x12,x13,x14,x15,x16 = [[] for _ in range(16)]\\n\\n count = -1\\n with open(fn, 'r') as f:\\n t = csv.reader(f)\\n for row in t:\\n woo = row\\n # print(count)\\n if count < 0:\\n count += 1\\n continue\\n elif count >= 0:\\n datet = row[fDate].replace(\\\"-\\\", \\\"\\\") + row[fTime].replace(\\\":\\\", \\\"\\\")\\n ## if not engineering\\n epoch = float(row[fEpochTime] + \\\".\\\" + row[fNanoSeconds][0])\\n datetime = row[fDate].replace(\\\"-\\\", \\\"\\\") + row[fTime].replace(\\\":\\\", \\\"\\\")\\n x1.append(epoch); x2.append(datetime)\\n if row[fLat] == '':\\n x3.append('')\\n elif row[fLat] != '':\\n x3.append(float(row[fLat]))\\n if row[fLon] == '':\\n x4.append('')\\n elif row[fLon] != '':\\n x4.append(float(row[fLon]))\\n\\n x5.append(float(row[fBCH4]))\\n x6.append(float(row[fTCH4]))\\n x7.append(0.0)\\n x8.append(0.0)\\n x9.append(row[fOdometer])\\n x11.append(float(row[C2H6]))\\n x12.append(float(row[C2C1]))\\n x13.append(float(row[R]))\\n x14.append(float(row[fUavg]))\\n x15.append(float(row[fVavg]))\\n x16.append(float(row[fWavg]))\\n\\n count += 1\\n print(f\\\"Number of observations processed:{count}\\\")\\n\\n # convert lists to numpy arrays\\n aEpochTime = numpy.array(x1)\\n aDateTime = numpy.array(x2)\\n aLat = numpy.array(x3)\\n aLon = numpy.array(x4)\\n aCH4 = numpy.array(x5)\\n aTCH4 = numpy.array(x6)\\n aMean = numpy.array(x7)\\n arealMean = numpy.array(x7)\\n astd = numpy.array(x7)\\n\\n aMeanC2H6 = numpy.array(x7)\\n aThreshold = numpy.array(x8)\\n aOdom = numpy.array(x9)\\n\\n # adding ethane stuff\\n aC2H6 = numpy.array(x11)\\n aC2C1 = numpy.array(x12)\\n aR = numpy.array(x13)\\n aUavg = numpy.array(x14)\\n aVavg = numpy.array(x15)\\n aWavg = numpy.array(x16)\\n\\n\\n xLatMean = numpy.mean(aLat)\\n xLonMean = numpy.mean(aLon)\\n #xCH4Mean = numpy.mean(aCH4)\\n #xC2H6Mean = numpy.mean(aC2H6)\\n #xC2C1Mean = numpy.mean(aC2C1)\\n\\n fLog.write(\\\"Day CH4_mean = \\\" + str(numpy.mean(aCH4)) +\\n \\\", Day CH4 SD = \\\" + str(numpy.std(aCH4)) + \\\"\\\\n\\\")\\n fLog.write(\\\"Day C2H6 Mean = \\\" + str(numpy.mean(aC2H6)) +\\n \\\", Day C2H6 SD = \\\" + str(numpy.std(aC2H6)) + \\\"\\\\n\\\")\\n fLog.write(\\\"Center lon/lat = \\\" + str(xLonMean) + \\\", \\\" + str(xLatMean) + \\\"\\\\n\\\")\\n\\n lstCH4_AB = []\\n\\n # generate list of the index for observations that were above the threshold\\n for i in range(0, count - 2):\\n if ((count - 2) > xB):\\n topBound = min((i + xB), (count - 2))\\n botBound = max((i - xB), 0)\\n\\n for t in range(min((i + xB), (count - 2)), i, -1):\\n if aEpochTime[t] < (aEpochTime[i] + (xB / 2)):\\n topBound = t\\n break\\n for b in range(max((i - xB), 0), i):\\n if aEpochTime[b] > (aEpochTime[i] - (xB / 2)):\\n botBound = b\\n break\\n\\n xCH4Mean = numpy.percentile(aCH4[botBound:topBound], baseCalc)\\n xCH4_actualMean = numpy.mean(aCH4[botBound:topBound])\\n xCH4_stdev = numpy.mean(aCH4[botBound:topBound])\\n xC2H6Mean = numpy.percentile(aC2H6[botBound:topBound], baseCalc)\\n\\n # xCH4SD = numpy.std(aCH4[botBound:topBound])\\n else:\\n xCH4Mean = numpy.percentile(aCH4[0:(count - 2)], baseCalc)\\n xCH4_actualMean = numpy.mean(aCH4[0:(count - 2)], baseCalc)\\n xCH4_stdev = numpy.std(aCH4[0:(count - 2)], baseCalc)\\n xC2H6Mean = numpy.percentile(aC2H6[0:(count - 2)], baseCalc)\\n\\n # xCH4SD = numpy.std(aCH4[0:(count-2)])\\n xThreshold = xCH4Mean + (xCH4Mean * xABThreshold)\\n xThreshold_c2h6 = xC2H6Mean + (xC2H6Mean * xABThreshold)\\n\\n if (aCH4[i] > xThreshold and aR[i]>rMin):\\n #if (aCH4[i] > xThreshold):\\n lstCH4_AB.append(i)\\n aMean[i] = xCH4Mean\\n aMeanC2H6[i] = xC2H6Mean\\n aThreshold[i] = xThreshold\\n arealMean[i] = xCH4_actualMean\\n astd[i] = xCH4_stdev\\n\\n # now group the above baseline threshold observations into groups based on distance threshold\\n lstCH4_ABP = []\\n xDistPeak = 0.0\\n xCH4Peak = 0.0\\n xTime = 0.0\\n cntPeak = 0\\n cnt = 0\\n sID = \\\"\\\"\\n sPeriod5Min = \\\"\\\"\\n prevIndex = 0\\n for i in lstCH4_AB:\\n if (cnt == 0):\\n xLon1 = aLon[i]\\n xLat1 = aLat[i]\\n xOdom = aOdom[i]\\n else:\\n # calculate distance between points\\n xDist = haversine(xLat1, xLon1, aLat[i], aLon[i])\\n xDistPeak += xDist\\n xCH4Peak += (xDist * (aCH4[i] - aMean[i]))\\n xLon1 = aLon[i]\\n xLat1 = aLat[i]\\n xOdom = aOdom[i]\\n if (sID == \\\"\\\"):\\n xTime = aEpochTime[i]\\n sID = str(xCar) + \\\"_\\\" + str(xTime)\\n sPeriod5Min = str(int((aEpochTime[i] - 1350000000) / (30 * 1))) # 30 sec\\n if ((aEpochTime[i] - aEpochTime[prevIndex]) > xTimeThreshold): # initial start of a observed peak\\n cntPeak += 1\\n xTime = aEpochTime[i]\\n xDistPeak = 0.0\\n xCH4Peak = 0.0\\n sID = str(xCar) + \\\"_\\\" + str(xTime)\\n sPeriod5Min = str(int((aEpochTime[i] - 1350000000) / (30 * 1))) # 30 sec\\n # print str(i) +\\\", \\\" + str(xDist) + \\\",\\\" + str(cntPeak) +\\\",\\\" + str(xDistPeak)\\n lstCH4_ABP.append(\\n [sID, xTime, aEpochTime[i], aDateTime[i], aCH4[i], aLon[i], aLat[i], aMean[i], aThreshold[i],\\n xDistPeak, xCH4Peak, aTCH4[i],aC2H6[i],aC2C1[i],aR[i],aMeanC2H6[i], sPeriod5Min, xOdom,\\n aUavg[i],aVavg[i],aWavg[i]])\\n cnt += 1\\n prevIndex = i\\n\\n # Finding peak_id larger than 160.0 m\\n tmpsidlist = []\\n for r in lstCH4_ABP:\\n if (float(r[9]) > 160.0) and (r[0] not in tmpsidlist):\\n tmpsidlist.append(r[0])\\n cntPeak -= len(tmpsidlist)\\n\\n fLog.write(\\\"Number of peaks found: \\\" + str(cntPeak) + \\\"\\\\n\\\")\\n print(f\\\"{xCar} \\\\t {xDate} \\\\t {xFilename} \\\\t {count} \\\\t {len(lstCH4_ABP)}\\\")\\n\\n # write out the observed peaks to a csv to be read into a GIS\\n fOut = open(fnOut, 'w')\\n # s = \\\"PEAK_NUM,EPOCHSTART,EPOCH,DATETIME,CH4,LON,LAT,CH4_BASELINE,CH4_THRESHOLD,PEAK_DIST_M,PEAK_CH4,TCH4,PERIOD5MIN\\\\n\\\"\\n s = \\\"OP_NUM,OP_EPOCHSTART,OB_EPOCH,OB_DATETIME,OB_CH4,OB_LON,OB_LAT,OB_CH4_BASELINE,\\\" \\\\\\n \\\"OB_CH4_THRESHOLD,OP_PEAK_DIST_M,OP_PEAK_CH4,OB_TCH4,OB_C2H6,\\\" \\\\\\n \\\"OB_C2C1,OB_R,OB_C2H6_BASELINE,OB_PERIOD5MIN,ODOMETER,OB_U_AVG,OB_V_AVG,OB_W_AVG\\\\n\\\"\\n fOut.write(s)\\n\\n truecount = 0\\n for r in lstCH4_ABP:\\n if r[0] not in tmpsidlist:\\n s = ''\\n for rr in r:\\n s += str(rr) + ','\\n s = s[:-1]\\n s += '\\\\n'\\n fOut.write(s)\\n truecount += 1\\n fOut.close()\\n fLog.close()\\n\\n openFile = pd.read_csv(fnOut)\\n if openFile.shape[0] != 0:\\n pkDistDf = openFile.copy().groupby('OP_NUM', as_index=False).apply(\\n lambda x: max(x.ODOMETER) - min(x.ODOMETER))\\n pkDistDf.columns = ['OP_NUM', 'OP_DISTANCE']\\n openFile = pd.merge(openFile.copy(), pkDistDf)\\n tempCount = openFile.groupby('OP_NUM', as_index=False).OP_EPOCHSTART.count().rename(\\n columns={'OP_EPOCHSTART': 'Frequency'})\\n tempCount = tempCount.loc[tempCount.Frequency >= minElevated, :]\\n if tempCount.shape[0] == 0:\\n print(f\\\"No Observed Peaks with enough Elevated Readings Found in the file: {xFilename}\\\")\\n\\n elif tempCount.shape[0] != 0:\\n oFile = pd.merge(openFile, tempCount, on=['OP_NUM'])\\n openFile = oFile.copy()\\n del (oFile)\\n openFile[\\\"minElevated\\\"] = openFile.apply(lambda x: int(minElevated), axis=1)\\n openFile['OB_CH4_AB'] = openFile.loc[:, 'OB_CH4'].sub(openFile.loc[:, 'OB_CH4_BASELINE'], axis=0)\\n openFile['OB_C2H6_AB'] = openFile.loc[:, 'OB_C2H6'].sub(openFile.loc[:, 'OB_C2H6_BASELINE'],axis=0)\\n openFile.to_csv(fnOut, index=False)\\n\\n\\n fileWt = weighted_loc(openFile, 'OB_LAT', 'OB_LON', 'OP_NUM', 'OB_CH4_AB').loc[:, :].rename(\\n columns={'OB_LAT': 'pk_LAT', 'OB_LON': 'pk_LON'}).reset_index(drop=True)\\n geometry_temp = [Point(lon, lat) for lon, lat in zip(fileWt['pk_LON'], fileWt['pk_LAT'])]\\n crs = 'EPSG:4326'\\n # geometry is the point of the lat/lon\\n # gdf_buff = gpd.GeoDataFrame(datFram, crs=crs, geometry=geometry_temp)\\n\\n ## BUFFER AROUND EACH 'OP_NUM' WITH BUFFER DISTANCE\\n gdf_buff = gpd.GeoDataFrame(fileWt, crs=crs, geometry=geometry_temp)\\n # gdf_buff = makeGPD(datFram,'LON','LAT')\\n\\n ##maybe this is the issue?\\n #gdf_buff = gdf_buff.to_crs(epsg=32610)\\n #gdf_buff['geometry'] = gdf_buff.loc[:, 'geometry'].buffer(30)\\n try:\\n gdf_buff.to_file(jsonOut, driver=\\\"GeoJSON\\\")\\n #gdf_buff.to_file('testthing.geojson', driver=\\\"GeoJSON\\\")\\n except:\\n print(\\\"Error Saving JSON File\\\")\\n elif openFile.shape[0] == 0:\\n print(f\\\"No Observed Peaks Found in the file:{xFilename}\\\")\\n except ValueError:\\n print(\\\"Error in Identify Peaks\\\")\\n return False\",\n \"def get_time_series(self, tile, year, loc, px, py, cols=1, rows=1, step=1, save_dir='txt/'):\\n\\n # keys = ['sza', 'saa', 'vza', 'vaa', 'qa', 'b01', 'b02', 'b03', 'b04', 'b05', 'b06', 'b07']\\n\\n timeBefore = time.clock()\\n\\n f_list = np.array(glob.glob(loc + '%s_%d_*_1km.nc' % (tile, year)))\\n\\n # if there are no files for this year\\n if f_list.shape[0] == 0:\\n print 'Data for year %d and tile %s not found' % (year, tile)\\n return -1\\n\\n # get DOYs from file name\\n doys0 = np.sort([int(s.split('_')[4]) for s in f_list])\\n # print 'doys0', doys0\\n\\n # arrange DOYs according to step. I.e. we assume that...\\n # step = 7\\n doys = np.zeros(doys0.shape).astype(int)\\n doys_real = np.zeros(doys0.shape).astype(int)\\n\\n for jj, ii in enumerate(xrange(0, doys0.shape[0], step)):\\n # doys = np.append(doys, doys0[ii:ii+7])\\n doys[ii:ii+step] = jj + 1#doys0[ii]\\n doys_real[ii:ii+step] = doys0[ii]\\n\\n ind = np.argsort([int(s.split('_')[4]) for s in f_list])\\n f_list = f_list[ind]\\n\\n # print 'loc:', loc\\n # print 'tile:', tile\\n # print 'year:', year\\n # print 'f_list:', f_list[0]\\n\\n output = {}\\n for f in f_list:\\n print 'f:', f\\n try:\\n ds = nc.Dataset(f)\\n except:\\n print 'something wrong with %s' % f\\n continue\\n\\n # if a dataset is empty\\n # i.e. if it has less than 2 bands\\n # i.e. vza, vaa, sza, saa, proj, geo, qa, b1, b2\\n if len(ds.variables.keys()) < 9:\\n print 'Dataset exists but empty:'\\n print 'loc:', loc\\n print 'tile:', tile\\n print 'year:', year\\n print 'f_list:', f_list[0]\\n print ''\\n # return -1\\n else:\\n break\\n if f == f_list[-1]:\\n print 'all datasets are empty'\\n return -1\\n\\n for key in ds.variables.keys():\\n if len(ds.variables[key].shape) == 2:\\n # output[key] = np.zeros((f_list.shape[0], ds.variables[key].shape[0], ds.variables[key].shape[1]))\\n output[key] = np.zeros((f_list.shape[0], cols, rows))\\n if len(ds.variables[key].shape) == 1:\\n output[key] = np.zeros(ds.variables[key].shape[0]).astype(str)\\n\\n for i, fname in enumerate(f_list):\\n #print fname\\n ds = nc.Dataset(fname)\\n for key in ds.variables.keys():\\n if len(ds.variables[key].shape) == 2:\\n\\n try:\\n # print ds.variables[key][px:px+cols, py:py+rows]\\n output[key][i, :, :] = ds.variables[key][px:px+cols, py:py+rows]\\n except:\\n print 'something wrong in output[%s][%d, :, :]' % (key, i)\\n print 'output:', output[key][i, :, :].shape\\n print 'ds.variables:', ds.variables[key][px:px+cols, py:py+rows].shape\\n\\n if len(ds.variables[key].shape) == 1:\\n output[key][:] = ds.variables[key][:]\\n\\n # print 'output.keys:', output.keys()\\n\\n QA_OK = np.array([8, 72, 136, 200, 1288, 2056, 2120, 2184, 2248])\\n qa_passer = np.logical_or.reduce([output['qa'] == x for x in QA_OK])\\n\\n for b in [1,2,3,4,5,7]:\\n\\n qa_passer[output[\\\"b0%d\\\" % b] <= 0] = 0.\\n qa_passer[output[\\\"b0%d\\\" % b] >= 10000] = 0.\\n\\n # if, for this pixel, we have just a few observations we don't need them\\n if np.sum(qa_passer) < 2:\\n qa_passer[:] = 0\\n\\n output['qa_passer'] = qa_passer\\n [bin(b) for b in QA_OK]\\n\\n #doys = np.array([int(g.GetRasterBand(b+1).GetMetadata()['DoY']) for b in xrange(g.RasterCount)])\\n #years = np.array([int(g.GetRasterBand(b + 1).GetMetadata()['Year']) for b in xrange(g.RasterCount)])\\n output['doys'] = doys\\n output['doys_real'] = np.unique(doys_real)\\n output['years'] = np.ones(doys.shape) * year\\n\\n output[\\\"sza\\\"] = output[\\\"sza\\\"] / 100.\\n output[\\\"saa\\\"] = output[\\\"saa\\\"] / 100.\\n output[\\\"vza\\\"] = output[\\\"vza\\\"] / 100.\\n output[\\\"vaa\\\"] = output[\\\"vaa\\\"] / 100.\\n output['b01'] = output['b01'] / 10000.\\n output['b02'] = output['b02'] / 10000.\\n output['b03'] = output['b03'] / 10000.\\n output['b04'] = output['b04'] / 10000.\\n output['b05'] = output['b05'] / 10000.\\n output['b06'] = output['b06'] / 10000.\\n output['b07'] = output['b07'] / 10000.\\n\\n # print 'qa_passer:', output['qa_passer']\\n #print output['b01']\\n\\n #pkl.dump(output, open(f_out, 'wb'))\\n\\n timeAfter = time.clock()\\n elapsed_time = timeAfter - timeBefore\\n print 'read time series time (s): ', elapsed_time\\n\\n print 'Read MODIS for year %d data is done' % year\\n return output\",\n \"def kpi_function(kwargs):\\n start_time = kwargs['start_time']\\n end_time = kwargs['end_time']\\n project = kwargs['project']\\n client_id = kwargs['client_id']\\n device_id = kwargs['device_id']\\n dataset_src = kwargs['dataset_src']\\n dataset_dest = kwargs['dataset_dest']\\n table_src = kwargs['table_src']\\n table_dest = kwargs['table_dest']\\n location = kwargs['location']\\n config = kwargs['config']\\n def station_data(station_id, start, end):\\n data = Daily(station_id, start, end)\\n data = data.fetch()\\n data.reset_index(inplace=True)\\n data = data[['time', 'tavg', 'tmin', 'tmax']]\\n # print(data)\\n return data\\n def costic(df):\\n alias_a = df['tmin']\\n alias_b = df['tmax']\\n\\n if 15 > alias_b:\\n return 15 - (alias_a + alias_b) / 2\\n elif 15 <= alias_a:\\n return 0\\n else:\\n return (15 - alias_a) * (0.08 + 0.42 * (15 - alias_a) / (alias_b - alias_a))\\n\\n current_time = arrow.utcnow().floor('day')\\n # if end_time <= current_time:\\n output = config\\n rules = pd.DataFrame(output['rules'])\\n rules.index = rules.index.astype(int)\\n rules.sort_index(inplace=True)\\n rules = rules[['kpi_name',\\n 'alias_a',\\n 'alias_b',\\n 'alias_c',\\n 'alias_d',\\n 'alias_e',\\n 'alias_f',\\n 'equation_1',\\n 'equation_2',\\n 'aggregation',\\n 'start_time',\\n 'end_time',\\n 'mute_logs']].copy()\\n rules = rules.where(pd.notnull(rules), None)\\n settings = output['settings']['mute_rules']\\n station_id = output['settings']['station_id']\\n country = output['settings']['country']\\n state = output['settings']['state']\\n df = rules\\n # create an empty dataframe\\n logs = pd.DataFrame()\\n # get access to bigqyery\\n # credentials, project_id = google.auth.default()\\n # client = bigquery.Client(credentials=credentials)\\n if settings is False:\\n data_rd = pd.read_csv('main_data.csv')\\n data_rd['time'] = pd.to_datetime(data_rd['time_local'])\\n if data_rd.shape[0] > 0:\\n left_table = data_rd.drop(['alias','value','time_local'],axis=1)\\n left_table = left_table.groupby(\\\"time\\\").min().reset_index()\\n sub_df = data_rd[['time','alias','value']].copy()\\n sub_df['value'] = pd.to_numeric(sub_df['value'])\\n right_table = pd.pivot_table(sub_df,index='time',columns='alias',values='value').reset_index()\\n data_rd = pd.merge(left_table,right_table,on='time', how='outer')\\n if data_rd.shape[0] > 0:\\n min_time = data_rd.time.min()\\n max_time = data_rd.time.max()\\n if station_id is not None:\\n meteo_data = station_data(station_id, min_time, max_time)\\n # print(meteo_data)\\n data_rd['costic_HDD_15C'] = meteo_data.apply(costic, axis=1)\\n data_rd = data_rd.merge(meteo_data, on='time', how='left')\\n data_rd[['tavg', 'tmax', 'tmin']] = data_rd[['tavg', 'tmax', 'tmin']].astype('float')\\n print(data_rd.head())\\n data_rd.to_csv(\\\"file_name.csv\\\", index=False)\\n for i in range(df.shape[0]):\\n if df.iloc[i]['kpi_name'] == 'costic_HDD_15C':\\n obj = kpi(df.iloc[i], data_rd, country, state, logs)\\n dff = obj.return_log()\\n logs = pd.concat([logs, dff], axis=0)\\n if df.iloc[i]['mute_logs']:\\n aliases_list = df.iloc[i]['mute_logs'].split(',')\\n for alias in aliases_list:\\n logs = logs[~logs.aliases.str.contains(alias.strip())]\\n else:\\n print(f\\\"Data is not available from {start_time} to {end_time}\\\")\\n if logs.shape[0]>0:\\n logs['time'] = logs['time'].dt.date\\n logs.rename(columns={\\\"time\\\":\\\"date\\\"},inplace=True)\\n logs['client_id'] = client_id\\n logs['device_id'] = device_id\\n logs['date'] = logs['date'].astype(str)\\n print(tabulate(logs[['date','value']]))\\n # load the logs dataframe to destination table in the bigquery\\n # table = client.get_table(f\\\"{project}.{dataset_dest}.{table_dest}\\\")\\n else:\\n print('no logs saved')\\n else:\\n print(\\\"All rules are muted\\\")\",\n \"def build_polling_location_txt(self):\\n self.base_df['address_line'] = self.base_df.apply(\\n lambda row: self.get_address_line(row['index'], row['address1'], row['address2'], row['city'],\\n row['state'], row['zip_code']), axis=1)\\n\\n self.base_df['directions'] = self.base_df.apply(\\n lambda row: self.get_directions(), axis=1)\\n #\\n self.base_df['hours'] = self.base_df.apply(\\n lambda row: self.get_hours(row['index'],row['start_time'], row['end_time']), axis=1)\\n\\n self.base_df['photo_uri'] = self.base_df.apply(\\n lambda row: self.get_photo_uri(), axis=1)\\n\\n self.base_df['hours_open_id'] = self.base_df.apply(\\n lambda row: self.create_hours_open_id(row['index'], row['address1'], row['address2'], row['city'],\\n row['state'], row['zip_code']), axis=1)\\n\\n self.base_df['is_drop_box'] = self.base_df.apply(\\n lambda row: self.is_drop_box(), axis=1)\\n\\n self.base_df['is_early_voting'] = self.base_df.apply(\\n lambda row: self.is_early_voting(), axis=1)\\n\\n self.base_df['latitude'] = self.base_df.apply(\\n lambda row: self.get_latitude(), axis=1)\\n\\n self.base_df['longitude'] = self.base_df.apply(\\n lambda row: self.get_longitude(), axis=1)\\n\\n self.base_df['latlng_source'] = self.base_df.apply(\\n lambda row: self.get_latlng_source(), axis=1)\\n\\n self.base_df['id'] = self.base_df.apply(\\n lambda row: self.create_id(row['index'], row['ocd_division'],row['address1'], row['address2'],\\n row['city'], row['state'], row['zip_code']), axis=1)\\n\\n return self.base_df\",\n \"def Find_nearest_dwd_stations(inpt_data,\\r\\n date_start='20051201',\\r\\n date_end='20201231',\\r\\n dwd_time_format='%Y%m%d%H',\\r\\n data_category='air_temperature',\\r\\n temp_resolution='hourly',\\r\\n no_of_nearest_stations=4,\\r\\n memory_save=True,\\r\\n Output='True'):\\r\\n if isinstance(data_category,list):\\r\\n if len(list(data_category)) > 1:\\r\\n print(\\r\\n 'Currently only one dwd category allowed, please run function multiple times for each category'\\r\\n )\\r\\n return None\\r\\n \\r\\n #convert time to datetime\\r\\n dt_start=datetime.strptime(date_start,'%Y%m%d')\\r\\n dt_end=datetime.strptime(date_end,'%Y%m%d')\\r\\n print('Start quering data from DWD')\\r\\n #define the database folder\\r\\n pypath = os.path.dirname(os.path.abspath(__file__))\\r\\n table_dir = pypath + '\\\\\\\\' + 'tables'\\r\\n dbase_dir = pypath + '\\\\\\\\' + 'dbase' \\r\\n #%% we check all available stations and create a valid list\\r\\n filename_stations=update_stationlist(time_res='hourly',dbase_dir=table_dir)\\r\\n stations_all=pd.read_csv(filename_stations, dtype={'STATIONS_ID': object})\\r\\n # delete all stations which do not cover the category\\r\\n dwd_stations=stations_all[stations_all[data_category]==True].copy()\\r\\n #correct to datetime\\r\\n dwd_stations['date_end']=pd.to_datetime(stations_all.date_end,format='%Y%m%d')\\r\\n dwd_stations['date_start']=pd.to_datetime(stations_all.date_start,format='%Y%m%d')\\r\\n # clean to stations which cover the campaign time #dt_low <= dt <= dt_high:\\r\\n dwd_stations=dwd_stations[(dwd_stations.date_start<=dt_start) & (dwd_stations.date_end>=dt_end)]\\r\\n #make a geodataframe out of it\\r\\n dwd_stations=gpd.GeoDataFrame(dwd_stations,geometry=gpd.points_from_xy(dwd_stations.geo_lon, dwd_stations.geo_lat))\\r\\n \\r\\n #loop through all rows to get the n closest points\\r\\n distances=pd.DataFrame()\\r\\n for _, station in dwd_stations.iterrows():\\r\\n distances[station.STATIONS_ID]=inpt_data.distance(station.geometry)\\r\\n \\r\\n #%% get the n stations with smallest distance and update database\\r\\n id_nearest_stations=distances.apply(lambda s: s.nsmallest(no_of_nearest_stations).index.tolist(), axis=1).values.tolist() #station ids\\r\\n #get them as unique values by sum a list of lists https://bit.ly/353iZQB\\r\\n id_dwd_stations=list(set(sum(id_nearest_stations,[])))\\r\\n \\r\\n #update the database\\r\\n db_dwd_stations=import_stations(time_res=temp_resolution,time_format=dwd_time_format,campaign_time=[dt_start,dt_end],data_category=data_category,station_ids=id_dwd_stations,dbase_dir=dbase_dir,Output=Output,table_dir=table_dir,memory_save=memory_save)\\r\\n \\r\\n #distance of nearest stattions\\r\\n dist_nearest_stations=pd.DataFrame(np.sort(distances.values)[:,:no_of_nearest_stations]).values.tolist() #distances themself\\r\\n #create new columns in the input data\\r\\n station_col_nm=list()\\r\\n for i in range(0,no_of_nearest_stations):\\r\\n station_col_nm.append(data_category+'_station_'+str(i))\\r\\n for i in range(0,no_of_nearest_stations):\\r\\n station_col_nm.append(data_category+'_distance_'+str(i))\\r\\n #create new dataframe\\r\\n distance_data=pd.concat([pd.DataFrame(id_nearest_stations).astype(int),pd.DataFrame(dist_nearest_stations)],axis=1)\\r\\n distance_data.columns=station_col_nm\\r\\n #add to main dataset\\r\\n inpt_data=pd.concat([inpt_data, distance_data],axis=1) \\r\\n \\r\\n return inpt_data,db_dwd_stations\",\n \"def getEPADailyData(dateint, dt_ind, month, epa_df, yr):\\n\\n try:\\n start = dateint + dt_ind * 10000\\n end = start + 10001\\n dly_epa_df = epa_df[(epa_df.created >= start) & (epa_df.created < end)]\\n dly_epa_df.reset_index(inplace=True, drop=True)\\n\\n new_df = pd.DataFrame(columns=['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'epa_pm25_value', 'raw_concentration', 'aqi', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code', 'created'])\\n for sitenm in dly_epa_df.site_name.unique():\\n indx_ct = 0\\n site_df = dly_epa_df[dly_epa_df.site_name == sitenm]\\n for i in site_df.created.unique():\\n indx_ct += 1\\n new_df = pd.concat([new_df,site_df.iloc[indx_ct - 1:indx_ct]],ignore_index=True)\\n\\n if i != site_df.created.max(): # Don't interpolate the last record\\n tmp_df = site_df.iloc[indx_ct - 1:indx_ct][['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code']]\\n for j in range(1,6):\\n new_dt = i + j * 10\\n tmp_df['created'] = int(new_dt)\\n tmp_df['epa_pm25_value'] = np.nan\\n tmp_df['raw_concentration'] = np.nan\\n tmp_df['aqi'] = np.nan\\n new_df = pd.concat([new_df,tmp_df],ignore_index=True)\\n\\n # Convert aqi to numerica for so that it gets interpolated\\n new_df[['aqi']] = new_df[['aqi']].replace(\\\"nan\\\", np.nan, regex=True)\\n new_df[['aqi']] = new_df[['aqi']].apply(pd.to_numeric)\\n\\n new_df = new_df.interpolate(method='linear', limit_direction='forward', axis=0)\\n\\n int_epa_df = new_df[(new_df.created >= start) & (new_df.created < (end - 1))]\\n int_epa_df.reset_index(inplace=True, drop=True)\\n \\n # Write to S3\\n s3 = s3fs.S3FileSystem()\\n myopen = s3.open\\n write('midscapstone-whos-polluting-my-air/EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr), int_epa_df, compression='GZIP', open_with=myopen)\\n s3_resource = boto3.resource('s3')\\n s3_resource.Object('midscapstone-whos-polluting-my-air', 'EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr)).Acl().put(ACL='public-read')\\n\\n except Exception as e:\\n print(\\\"*** EXCEPTION IN GET EPA DAILY DATA *** {}\\\".format(e))\\n return int_epa_df\",\n \"def get_sunspot_data(yy, time1):\\n master = []\\n num_of_ss = np.max(yy.flatten()) # get number of different SS's\\n centroids = []\\n sizes = []\\n numbers = []\\n\\n for i in np.arange(1, num_of_ss + 1): # for each SS:\\n temp_sunspot = SunSpot(1, 1, 1)\\n copy_yy = np.array(yy, copy = True)\\n copy_yy[copy_yy != i] = 0 # get only points == i\\n copy_yy[copy_yy == i] = 1\\n\\n indices_x, indices_y = np.where(yy == i)\\n\\n max_lat = np.max(indices_x)\\n min_lat = np.min(indices_x)\\n mean_lat = max_lat - (max_lat - min_lat)/2\\n \\n max_lon = np.max(indices_y)\\n min_lon = np.min(indices_y)\\n mean_lon = max_lon - (max_lon - min_lon)/2\\n \\n temp_sunspot.mask = copy_yy\\n temp_sunspot.centroid = [mean_lon, mean_lat]\\n temp_sunspot.size = len(indices_x)\\n temp_sunspot.number = i\\n temp_sunspot.x_points = indices_x\\n temp_sunspot.y_points = indices_y\\n temp_sunspot.timestamp = time1\\n temp_sunspot.min_x = min_lon\\n temp_sunspot.max_x = max_lon\\n temp_sunspot.min_y = min_lat\\n temp_sunspot.max_y = max_lat\\n\\n master.append(temp_sunspot)\\n\\n return num_of_ss, master\",\n \"def streaming_weather_data(**kwargs):\\n df = weather_data(['San Francisco'])\\n df['time'] = [pd.Timestamp.now()]\\n return df.set_index('time')\",\n \"def hotmap(datetime, room, query):\\n\\n #IMPORTANT: this query returns M-F values\\n\\n # get a df with date and count values, for a given room and date\\n week_counts = pd.read_sql_query(query.format(date=datetime, room=room),\\n connectDB())\\n\\n headcount = list(week_counts['count'])\\n headcount = [linear_predictor(x) for x in headcount] # predict on all supplied values\\n\\n bins = list(week_counts['count'])\\n # bins = [tertiary_classifier(x) for x in bins] # predict on all supplied values\\n tert_dictionary = {'Empty': 0, 'Medium': 0.5, 'High': 1} # map to int as only ints can go in heatmap\\n # bins = [tert_dictionary[x] for x in bins] # map with listcomp\\n\\n # 5 * 9 = 45, so all elements need to have 45 values. Days and hours are just multipled out.\\n data = {'days': ['fri']*9 + ['thu']*9 + ['wed']*9 + ['tue']*9 + ['mon']*9,\\n 'occupancy': headcount,\\n 'hours': ['9', '10', '11', '12', '13', '14', '15', '16', '17']*5}\\n\\n week_counts['headcount'] = week_counts['count'].apply(linear_predictor)\\n week_counts['bins'] = week_counts['count'].apply(tertiary_classifier)\\n\\n source = ColumnDataSource(ColumnDataSource.from_df(week_counts))\\n\\n\\n # hover = HoverTool()\\n #\\n # tooltips = [(\\\"headcount\\\", \\\"@headcount\\\")]\\n #\\n # tools = [hover]\\n\\n # colors = ['#1abc9c', '#ec583a', '#474e5d'] # set colors; currently not shading on continuous features\\n\\n hm = HeatMap(data, x='hours', y='days', values='occupancy',\\n title='Occupancy in room over the week'.format(room), stat=None,\\n palette=palette, tools=None,\\n hover_tool=True)\\n\\n legend = Legend(location=(0, -30))\\n\\n hm.add_layout(legend, 'right')\\n\\n all_plots = gridplot([[hm]])\\n\\n return all_plots\",\n \"def F_interp_geos_mat(sounding_lon,sounding_lat,sounding_datenum,\\\\\\n geos_dir='/mnt/Data2/GEOS/s5p_interp/',\\\\\\n interp_fields=['TROPPT']):\\n from scipy.io import loadmat\\n from scipy.interpolate import RegularGridInterpolator\\n \\n start_datenum = np.amin(sounding_datenum)\\n end_datenum = np.amax(sounding_datenum)\\n start_datetime = datedev_py(start_datenum)\\n start_year = start_datetime.year\\n start_month = start_datetime.month\\n start_day = start_datetime.day\\n start_hour = start_datetime.hour\\n \\n end_datetime = datedev_py(end_datenum)\\n end_year = end_datetime.year\\n end_month = end_datetime.month\\n end_day = end_datetime.day\\n end_hour = end_datetime.hour\\n end_minute = end_datetime.minute\\n end_second = end_datetime.second\\n \\n step_hour = 3 # geos fp data are 3-hourly\\n \\n geos_start_hour = start_hour-start_hour%step_hour\\n geos_start_datetime = datetime.datetime(year=start_year,month=start_month,day=start_day,hour=geos_start_hour)\\n if end_hour > 24-step_hour or (end_hour == 24-step_hour and (end_minute > 0 or end_second > 0)):\\n geos_end_hour = 0\\n geos_end_datetime = datetime.datetime(year=end_year,month=end_month,day=end_day,hour=geos_end_hour) +datetime.timedelta(days=1)\\n elif end_hour%step_hour == 0 and end_minute == 0 and end_second == 0:\\n geos_end_hour = end_hour\\n geos_end_datetime = datetime.datetime(year=end_year,month=end_month,day=end_day,hour=geos_end_hour)\\n else:\\n geos_end_hour = (step_hour-(end_hour+1)%step_hour)%step_hour+end_hour+1\\n geos_end_datetime = datetime.datetime(year=end_year,month=end_month,day=end_day,hour=geos_end_hour)\\n \\n nstep = (geos_end_datetime-geos_start_datetime).total_seconds()/3600/step_hour+1\\n nstep = int(nstep)\\n \\n geos_data = {}\\n # load narr data\\n for istep in range(nstep):\\n file_datetime = geos_start_datetime+datetime.timedelta(hours=step_hour*istep)\\n file_dir = os.path.join(geos_dir,file_datetime.strftime('Y%Y'),\\\\\\n file_datetime.strftime('M%m'),\\\\\\n file_datetime.strftime('D%d'))\\n file_path = os.path.join(file_dir,'subset_'+file_datetime.strftime('%Y%m%d_%H')+'.mat')\\n if not geos_data:\\n mat_data = loadmat(file_path,variable_names=np.concatenate((['lat','lon'],interp_fields)))\\n geos_data['lon'] = mat_data['lon'].flatten()\\n geos_data['lat'] = mat_data['lat'].flatten()\\n geos_data['datenum'] = np.zeros((nstep),dtype=np.float64)\\n for fn in interp_fields:\\n geos_data[fn] = np.zeros((len(geos_data['lon']),len(geos_data['lat']),nstep))\\n geos_data[fn][...,istep] = mat_data[fn]\\n else:\\n mat_data = loadmat(file_path,variable_names=interp_fields)\\n for fn in interp_fields:\\n geos_data[fn][...,istep] = mat_data[fn]\\n \\n geos_data['datenum'][istep] = (file_datetime.toordinal()\\\\\\n +file_datetime.hour/24.\\\\\\n +file_datetime.minute/1440.\\\\\\n +file_datetime.second/86400.+366.)\\n # interpolate\\n sounding_interp = {}\\n for fn in interp_fields:\\n my_interpolating_function = \\\\\\n RegularGridInterpolator((geos_data['lon'],geos_data['lat'],geos_data['datenum']),\\\\\\n geos_data[fn],bounds_error=False,fill_value=np.nan)\\n sounding_interp[fn] = my_interpolating_function((sounding_lon,sounding_lat,sounding_datenum))\\n return sounding_interp\",\n \"def get_hr_radar_dap_data(dap_urls, st_list, jd_start, jd_stop):\\n df_list = []\\n for url in dap_urls:\\n # Only look at 6 km hf radar.\\n if ('http://hfrnet.ucsd.edu/thredds/dodsC/HFRNet/USWC/' in url and\\n \\\"6km\\\" in url and \\\"GNOME\\\" in url):\\n print(url)\\n # Get URL.\\n nc = Dataset(url, 'r')\\n lat_dim = nc.variables['lat']\\n lon_dim = nc.variables['lon']\\n time_dim = nc.variables['time']\\n u_var, v_var = None, None\\n standard_name_u = \\\"surface_eastward_sea_water_velocity\\\"\\n standard_name_v = \\\"surface_northward_sea_water_velocity\\\"\\n for key in nc.variables.keys():\\n key_dim = nc.variables[key]\\n try:\\n if key_dim.standard_name == standard_name_u:\\n u_var = key_dim\\n elif key_dim.standard_name == standard_name_v:\\n v_var = key_dim\\n elif key_dim.standard_name == \\\"time\\\":\\n time = key_dim\\n except:\\n # Only if the standard name is not available.\\n pass\\n # Manage dates.\\n dates = num2date(time_dim[:], units=time_dim.units,\\n calendar='gregorian')\\n date_idx = []\\n date_list = []\\n for i, date in enumerate(dates):\\n if jd_start < date < jd_stop:\\n date_idx.append(i)\\n date_list.append(date)\\n # Manage location.\\n for st in st_list:\\n station = st_list[st]\\n f_lat = station['lat']\\n f_lon = station['lon']\\n\\n ret = find_nearest(f_lat, f_lon, lat_dim[:], lon_dim[:])\\n lat_idx = ret[0]\\n lon_idx = ret[1]\\n dist_deg = ret[2]\\n\\n if len(u_var.dimensions) == 3:\\n # 3D.\\n ret = cycleAndGetData(u_var, v_var, date_idx,\\n lat_idx, lon_idx)\\n u_vals = ret[0]\\n v_vals = ret[1]\\n\\n lat_idx = ret[2]\\n lon_idx = ret[3]\\n\\n print(\\\"lat, lon, dist = {} {}\\\".format(ret[2], ret[3]))\\n try:\\n # Turn vectors in the speed and direction.\\n ws = uv2ws(u_vals, v_vals)\\n wd = uv2wd(u_vals, v_vals)\\n\\n data = dict()\\n data['sea_water_speed (cm/s)'] = ws\\n data['direction_of_sea_water_velocity (degree)'] = wd\\n time = np.array(date_list)\\n columns = ['sea_water_speed (cm/s)',\\n 'direction_of_sea_water_velocity (degree)']\\n df = DataFrame(data=data, index=time, columns=columns)\\n df_list.append({\\\"name\\\": st,\\n \\\"data\\\": df,\\n \\\"lat\\\": lat_dim[lat_idx],\\n \\\"lon\\\": lon_dim[lon_idx],\\n \\\"ws_pts\\\": np.count_nonzero(~np.isnan(ws)),\\n \\\"wd_pts\\\": np.count_nonzero(~np.isnan(wd)),\\n \\\"dist\\\": dist_deg,\\n \\\"from\\\": url})\\n except Exception as e:\\n print(\\\"\\\\t\\\\terror: {}\\\".format(e))\\n else:\\n pass\\n return df_list\",\n \"def extract_tt_by_periods(ttri, periods, start_time, end_time, filters):\\n logger = getLogger(__name__)\\n # sess = conn.get_session()\\n das = {}\\n all_wz_features = {}\\n all_wz_laneconfigs = {}\\n\\n # collecting daily data\\n for prd in periods:\\n logger.debug('>>>> retrieving data for %s' % prd.get_date_string())\\n year = prd.start_date.year\\n sdate = prd.start_date\\n edate = prd.end_date\\n if year not in das:\\n da_tt = tt.TravelTimeDataAccess(year)\\n da_tt_wz = tt_workzone.TTWorkZoneDataAccess(year)\\n da_tt_wz_feature = wz_feature.WZFeatureDataAccess()\\n da_tt_wz_lncfg = wz_laneconfig.WZLaneConfigDataAccess()\\n da_tt_weather = tt_weather.TTWeatherDataAccess(year)\\n da_tt_snowmgmt = tt_snowmgmt.TTSnowManagementDataAccess(year)\\n da_tt_incident = tt_incident.TTIncidentDataAccess(year)\\n da_tt_specialevent = tt_specialevent.TTSpecialeventDataAccess(year)\\n das[year] = (\\n da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\\n da_tt_specialevent)\\n\\n (da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\\n da_tt_specialevent) = das[year]\\n\\n # traveltimes = da_tt.list_by_period(ttri.id, self.prd)\\n weathers = da_tt_weather.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.WeatherInfo] \\\"\\\"\\\"\\n workzones = da_tt_wz.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.WorkZoneInfo] \\\"\\\"\\\"\\n incidents = da_tt_incident.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.IncidentInfo] \\\"\\\"\\\"\\n snowmgmts = da_tt_snowmgmt.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.SnowManagementInfo] \\\"\\\"\\\"\\n specialevents = da_tt_specialevent.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.SpecialEventInfo] \\\"\\\"\\\"\\n traveltimes = da_tt.list_by_period(ttri.id, prd)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.TravelTimeInfo] \\\"\\\"\\\"\\n\\n if not any(weathers):\\n logger.debug('>>>> end of retrieving data for %s (no weather data)' % prd.get_date_string())\\n continue\\n\\n extras = {\\n 'weathers': {_tt.id: [] for _tt in traveltimes},\\n 'workzones': {_tt.id: [] for _tt in traveltimes},\\n 'incidents': {_tt.id: [] for _tt in traveltimes},\\n 'specialevents': {_tt.id: [] for _tt in traveltimes},\\n 'snowmgmts': {_tt.id: [] for _tt in traveltimes},\\n }\\n \\\"\\\"\\\":type: dict[str, dict[int, list]]\\\"\\\"\\\"\\n\\n _put_to_bucket(ttri, weathers, extras['weathers'], da_tt_weather, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, workzones, extras['workzones'], da_tt_wz, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, incidents, extras['incidents'], da_tt_incident, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, snowmgmts, extras['snowmgmts'], da_tt_snowmgmt, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, specialevents, extras['specialevents'], da_tt_specialevent, year, all_wz_features, all_wz_laneconfigs, das)\\n\\n for tti in traveltimes:\\n _tt_weathers = extras['weathers'][tti.id]\\n extdata = ExtData(tti,\\n _tt_weathers[0] if _tt_weathers else None,\\n extras['incidents'][tti.id],\\n extras['workzones'][tti.id],\\n extras['specialevents'][tti.id],\\n extras['snowmgmts'][tti.id])\\n\\n if start_time <= tti.str2datetime(tti.time).time() <= end_time:\\n for ef in filters:\\n try:\\n ef.check(extdata)\\n except Exception as ex:\\n tb.traceback(ex)\\n logger.debug('>>>> end of retrieving data for %s (error occured 1)' % prd.get_date_string())\\n continue\\n else:\\n for ef in filters:\\n try:\\n ef.check_outofrange(extdata)\\n except Exception as ex:\\n tb.traceback(ex)\\n logger.debug('>>>> end of retrieving data for %s (error occured 2)' % prd.get_date_string())\\n continue\\n\\n del extras\\n logger.debug('>>>> end of retrieving data for %s' % prd.get_date_string())\\n\\n # sess.close()\",\n \"def compute_templates(filename, TDUR, filt, ratios, dt, ncor, window, \\\\\\n winlength, nattempts, waittime, method='RMS'):\\n # To transform latitude and longitude into kilometers\\n a = 6378.136\\n e = 0.006694470\\n lat0 = 41.0\\n lon0 = -123.0\\n dx = (pi / 180.0) * a * cos(lat0 * pi / 180.0) / sqrt(1.0 - e * e * \\\\\\n sin(lat0 * pi / 180.0) * sin(lat0 * pi / 180.0))\\n dy = (3.6 * pi / 648.0) * a * (1.0 - e * e) / ((1.0 - e * e * sin(lat0 * \\\\\\n pi / 180.0) * sin(lat0 * pi / 180.0)) ** 1.5)\\n\\n # Get the names of the stations which have a waveform for this LFE family\\n file = open('../data/Plourde_2015/detections/' + filename + \\\\\\n '_detect5_cull.txt')\\n first_line = file.readline().strip()\\n staNames = first_line.split()\\n file.close()\\n\\n # Get the time of LFE detections\\n LFEtime = np.loadtxt('../data/Plourde_2015/detections/' + filename + \\\\\\n '_detect5_cull.txt', \\\\\\n dtype={'names': ('unknown', 'day', 'hour', 'second', 'threshold'), \\\\\\n 'formats': (np.float, '|S6', np.int, np.float, np.float)}, \\\\\\n skiprows=2)\\n\\n # Get the network, channels, and location of the stations\\n staloc = pd.read_csv('../data/Plourde_2015/station_locations.txt', \\\\\\n sep=r'\\\\s{1,}', header=None)\\n staloc.columns = ['station', 'network', 'channels', 'location', \\\\\\n 'server', 'latitude', 'longitude']\\n\\n # Get the location of the source of the LFE\\n LFEloc = np.loadtxt('../data/Plourde_2015/templates_list.txt', \\\\\\n dtype={'names': ('name', 'family', 'lat', 'lon', 'depth', 'eH', \\\\\\n 'eZ', 'nb'), \\\\\\n 'formats': ('S13', 'S3', np.float, np.float, np.float, \\\\\\n np.float, np.float, np.int)}, \\\\\\n skiprows=1)\\n for ie in range(0, len(LFEloc)):\\n if (filename == LFEloc[ie][0].decode('utf-8')):\\n lats = LFEloc[ie][2]\\n lons = LFEloc[ie][3]\\n xs = dx * (lons - lon0)\\n ys = dy * (lats - lat0)\\n\\n # Create directory to store the waveforms\\n namedir = 'templates/' + filename\\n if not os.path.exists(namedir):\\n os.makedirs(namedir)\\n\\n # Read origin time and station slowness files\\n origintime = pickle.load(open('timearrival/origintime.pkl', 'rb'))\\n slowness = pickle.load(open('timearrival/slowness.pkl', 'rb'))\\n\\n # File to write error messages\\n errorfile = 'error/' + filename + '.txt'\\n\\n # Loop over stations\\n for station in staNames:\\n # Create streams\\n EW = Stream()\\n NS = Stream()\\n UD = Stream()\\n # Get station metadata for downloading\\n for ir in range(0, len(staloc)):\\n if (station == staloc['station'][ir]):\\n network = staloc['network'][ir]\\n channels = staloc['channels'][ir]\\n location = staloc['location'][ir]\\n server = staloc['server'][ir]\\n # Compute source-receiver distance\\n latitude = staloc['latitude'][ir]\\n longitude = staloc['longitude'][ir]\\n xr = dx * (longitude - lon0)\\n yr = dy * (latitude - lat0)\\n distance = sqrt((xr - xs) ** 2.0 + (yr - ys) ** 2.0)\\n # Loop on LFEs\\n for i in range(0, np.shape(LFEtime)[0]):\\n YMD = LFEtime[i][1]\\n myYear = 2000 + int(YMD[0 : 2])\\n myMonth = int(YMD[2 : 4])\\n myDay = int(YMD[4 : 6])\\n myHour = LFEtime[i][2] - 1\\n myMinute = int(LFEtime[i][3] / 60.0)\\n mySecond = int(LFEtime[i][3] - 60.0 * myMinute)\\n myMicrosecond = int(1000000.0 * \\\\\\n (LFEtime[i][3] - 60.0 * myMinute - mySecond))\\n Tori = UTCDateTime(year=myYear, month=myMonth, day=myDay, \\\\\\n hour=myHour, minute=myMinute, second=mySecond, \\\\\\n microsecond=myMicrosecond)\\n Tstart = Tori - TDUR\\n Tend = Tori + 60.0 + TDUR\\n # First case: we can get the data from IRIS\\n if (server == 'IRIS'):\\n (D, orientation) = get_from_IRIS(station, network, channels, \\\\\\n location, Tstart, Tend, filt, dt, nattempts, waittime, \\\\\\n errorfile)\\n # Second case: we get the data from NCEDC\\n elif (server == 'NCEDC'):\\n (D, orientation) = get_from_NCEDC(station, network, channels, \\\\\\n location, Tstart, Tend, filt, dt, nattempts, waittime, \\\\\\n errorfile)\\n else:\\n raise ValueError( \\\\\\n 'You can only download data from IRIS and NCEDC')\\n if (type(D) == obspy.core.stream.Stream):\\n # Add to stream\\n if (channels == 'EH1,EH2,EHZ'):\\n EW.append(D.select(channel='EH1').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n NS.append(D.select(channel='EH2').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n UD.append(D.select(channel='EHZ').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n else:\\n EW.append(D.select(component='E').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n NS.append(D.select(component='N').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n UD.append(D.select(component='Z').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n else:\\n print('Failed at downloading data')\\n # Stack\\n if (len(EW) > 0 and len(NS) > 0 and len(UD) > 0):\\n # Stack waveforms\\n EWstack = linstack([EW], normalize=True, method=method) \\n NSstack = linstack([NS], normalize=True, method=method)\\n UDstack = linstack([UD], normalize=True, method=method)\\n # Initializations\\n maxCC = np.zeros(len(EW))\\n cc0EW = np.zeros(len(EW))\\n cc0NS = np.zeros(len(EW))\\n cc0UD = np.zeros(len(EW))\\n if (window == True):\\n # Get time arrival\\n arrivaltime = origintime[filename] + \\\\\\n slowness[station] * distance\\n Tmin = arrivaltime - winlength / 2.0\\n Tmax = arrivaltime + winlength / 2.0\\n if Tmin < 0.0:\\n Tmin = 0.0\\n if Tmax > EWstack[0].stats.delta * (EWstack[0].stats.npts - 1):\\n Tmax = EWstack[0].stats.delta * (EWstack[0].stats.npts - 1)\\n ibegin = int(Tmin / EWstack[0].stats.delta)\\n iend = int(Tmax / EWstack[0].stats.delta) + 1\\n # Cross correlation\\n for i in range(0, len(EW)):\\n ccEW = correlate(EWstack[0].data[ibegin : iend], \\\\\\n EW[i].data[ibegin : iend], ncor)\\n ccNS = correlate(NSstack[0].data[ibegin : iend], \\\\\\n NS[i].data[ibegin : iend], ncor)\\n ccUD = correlate(UDstack[0].data[ibegin : iend], \\\\\\n UD[i].data[ibegin : iend], ncor)\\n maxCC[i] = np.max(ccEW) + np.max(ccNS) + np.max(ccUD)\\n cc0EW[i] = ccEW[ncor]\\n cc0NS[i] = ccNS[ncor]\\n cc0UD[i] = ccUD[ncor]\\n else:\\n # Cross correlation\\n for i in range(0, len(EW)):\\n ccEW = correlate(EWstack[0].data, EW[i].data, ncor)\\n ccNS = correlate(NSstack[0].data, NS[i].data, ncor)\\n ccUD = correlate(UDstack[0].data, UD[i].data, ncor)\\n maxCC[i] = np.max(ccEW) + np.max(ccNS) + np.max(ccUD)\\n cc0EW[i] = ccEW[ncor]\\n cc0NS[i] = ccNS[ncor]\\n cc0UD[i] = ccUD[ncor]\\n # Sort cross correlations\\n index = np.flip(np.argsort(maxCC), axis=0)\\n EWbest = Stream()\\n NSbest = Stream()\\n UDbest = Stream()\\n # Compute stack of best LFEs\\n for j in range(0, len(ratios)):\\n nLFE = int(ratios[j] * len(EW) / 100.0)\\n EWselect = Stream()\\n NSselect = Stream()\\n UDselect = Stream()\\n for i in range(0, nLFE):\\n EWselect.append(EW[index[i]])\\n NSselect.append(NS[index[i]])\\n UDselect.append(UD[index[i]])\\n # Stack best LFEs\\n EWbest.append(linstack([EWselect], normalize=True, \\\\\\n method=method)[0])\\n NSbest.append(linstack([NSselect], normalize=True, \\\\\\n method=method)[0])\\n UDbest.append(linstack([UDselect], normalize=True, \\\\\\n method=method)[0])\\n # Plot figure\\n plt.figure(1, figsize=(20, 15))\\n params = {'xtick.labelsize':16,\\n 'ytick.labelsize':16}\\n pylab.rcParams.update(params) \\n colors = cm.rainbow(np.linspace(0, 1, len(ratios)))\\n # East - West component\\n ax1 = plt.subplot(311)\\n dt = EWstack[0].stats.delta\\n nt = EWstack[0].stats.npts\\n t = dt * np.arange(0, nt)\\n for j in range(0, len(ratios)):\\n if (method == 'RMS'):\\n norm = EWbest[j].data / np.sqrt(np.mean(np.square( \\\\\\n EWbest[j].data)))\\n elif (method == 'MAD'):\\n norm = EWbest[j].data / np.median(np.abs(EWbest[j].data - \\\\\\n np.median(EWbest[j].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, color = colors[j], \\\\\\n label = str(int(ratios[j])) + '%')\\n if (method == 'RMS'):\\n norm = EWstack[0].data / np.sqrt(np.mean(np.square( \\\\\\n EWstack[0].data)))\\n elif (method == 'MAD'):\\n norm = EWstack[0].data / np.median(np.abs(EWstack[0].data - \\\\\\n np.median(EWstack[0].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, 'k', label='All')\\n if (window == True):\\n plt.axvline(Tmin, linewidth=2, color='grey')\\n plt.axvline(Tmax, linewidth=2, color='grey')\\n plt.xlim([np.min(t), np.max(t)])\\n plt.title('East - West component', fontsize=24)\\n plt.xlabel('Time (s)', fontsize=24)\\n plt.legend(loc=1)\\n # North - South component\\n ax2 = plt.subplot(312)\\n dt = NSstack[0].stats.delta\\n nt = NSstack[0].stats.npts\\n t = dt * np.arange(0, nt)\\n for j in range(0, len(ratios)):\\n if (method == 'RMS'):\\n norm = NSbest[j].data / np.sqrt(np.mean(np.square( \\\\\\n NSbest[j].data)))\\n elif (method == 'MAD'):\\n norm = NSbest[j].data / np.median(np.abs(NSbest[j].data - \\\\\\n np.median(NSbest[j].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, color = colors[j], \\\\\\n label = str(int(ratios[j])) + '%')\\n if (method == 'RMS'):\\n norm = NSstack[0].data / np.sqrt(np.mean(np.square( \\\\\\n NSstack[0].data)))\\n elif (method == 'MAD'):\\n norm = NSstack[0].data / np.median(np.abs(NSstack[0].data - \\\\\\n np.median(NSstack[0].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, 'k', label='All')\\n if (window == True):\\n plt.axvline(Tmin, linewidth=2, color='grey')\\n plt.axvline(Tmax, linewidth=2, color='grey')\\n plt.xlim([np.min(t), np.max(t)])\\n plt.title('North - South component', fontsize=24)\\n plt.xlabel('Time (s)', fontsize=24)\\n plt.legend(loc=1)\\n # Vertical component\\n ax3 = plt.subplot(313)\\n dt = UDstack[0].stats.delta\\n nt = UDstack[0].stats.npts\\n t = dt * np.arange(0, nt)\\n for j in range(0, len(ratios)):\\n if (method == 'RMS'):\\n norm = UDbest[j].data / np.sqrt(np.mean(np.square( \\\\\\n UDbest[j].data)))\\n elif (method == 'MAD'):\\n norm = UDbest[j].data / np.median(np.abs(UDbest[j].data - \\\\\\n np.median(UDbest[j].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, color = colors[j], \\\\\\n label = str(int(ratios[j])) + '%')\\n if (method == 'RMS'):\\n norm = UDstack[0].data / np.sqrt(np.mean(np.square( \\\\\\n UDstack[0].data)))\\n elif (method == 'MAD'):\\n norm = UDstack[0].data / np.median(np.abs(UDstack[0].data - \\\\\\n np.median(UDstack[0].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, 'k', label='All')\\n if (window == True):\\n plt.axvline(Tmin, linewidth=2, color='grey')\\n plt.axvline(Tmax, linewidth=2, color='grey')\\n plt.xlim([np.min(t), np.max(t)])\\n plt.title('Vertical component', fontsize=24)\\n plt.xlabel('Time (s)', fontsize=24)\\n plt.legend(loc=1)\\n # End figure\\n plt.suptitle(station, fontsize=24)\\n plt.savefig(namedir + '/' + station + '.eps', format='eps')\\n ax1.clear()\\n ax2.clear()\\n ax3.clear()\\n plt.close(1)\\n # Save stacks into files\\n savename = namedir + '/' + station +'.pkl'\\n pickle.dump([EWstack[0], NSstack[0], UDstack[0]], \\\\\\n open(savename, 'wb'))\\n for j in range(0, len(ratios)):\\n savename = namedir + '/' + station + '_' + \\\\\\n str(int(ratios[j])) + '.pkl'\\n pickle.dump([EWbest[j], NSbest[j], UDbest[j]], \\\\\\n open(savename, 'wb'))\\n # Save cross correlations into files\\n savename = namedir + '/' + station + '_cc.pkl'\\n pickle.dump([cc0EW, cc0NS, cc0UD], \\\\\\n open(savename, 'wb'))\",\n \"def windTimeSeries(ftp, rootFolder, selectedDate, outputFolder, ymax):\\n \\n dataType = 'wind_reconstruction_data'\\n file_list = []\\n \\n\\n # Retrieve data from folders in FTP\\n \\n possible_hours = [format(x,'02d') for x in range(24)]\\n from_zone = tz.gettz('America/Mexico_City')\\n to_zone = tz.gettz('UTC')\\n \\n for item in possible_hours:\\n \\n local_time = datetime.strptime(selectedDate+item, '%Y-%m-%d%H')\\n local_time = local_time.replace(tzinfo=from_zone)\\n \\n utc_date = local_time.astimezone(to_zone)\\n \\n utc_day = utc_date.strftime(\\\"%Y-%m-%d\\\")\\n \\n utc_hour = utc_date.strftime(\\\"%H\\\")\\n \\n time_folder = rootFolder+utc_day+'/'+dataType+'/'+utc_hour+'-00/'\\n \\n creat_min = []\\n try:\\n\\n ftp.dir(time_folder, creat_min.append)\\n dbs = []\\n for i in range(len(creat_min)):\\n if creat_min[i].find('DBS') != -1:\\n dbs.append(creat_min[i])\\n\\n if len(dbs) == 0:\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\\n\\n elif len(dbs) == 1:\\n if dbs[0].find('-25-'):\\n file_list.append(time_folder+dbs[0][55:])\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\\n elif dbs[0].find('-55-'):\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\\n file_list.append(time_folder+dbs[0][55:])\\n else:\\n for i in range(len(dbs)):\\n file_list.append(time_folder+dbs[i][55:])\\n\\n except:\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\\n\\n\\n \\n # Create output folder\\n outputFolder = outputFolder+'/'+selectedDate\\n try:\\n mkdirp(outputFolder)\\n except:\\n print('warning: folder '+outputFolder+' already exists')\\n \\n # Make 'generic' outputfile\\n outFile = outputFolder+'/'\\n\\n try:\\n \\n meas_time = []\\n Usets = []\\n Vsets = []\\n Wsets = []\\n \\n for working_file in file_list:\\n\\n if working_file.find('missing_data') != -1:\\n Xset = pd.DataFrame(np.nan,columns=['Xwind'],index=range(100,2550,50))\\n Yset = pd.DataFrame(np.nan,columns=['Ywind'],index=range(100,2550,50))\\n Zset = pd.DataFrame(np.nan,columns=['Zwind'],index=range(100,2550,50))\\n match = (re.search('\\\\d{4}-\\\\d{2}-\\\\d{2}_\\\\d{2}-\\\\d{2}',working_file)).group(0)\\n meas_time.append(match)\\n\\n else: \\n match = (re.search('\\\\d{4}-\\\\d{2}-\\\\d{2}_\\\\d{2}-\\\\d{2}',working_file)).group(0)\\n #hour_min = re.search('_\\\\d{2}-\\\\d{2}-\\\\d{2}',working_file).group(0)\\n meas_time.append(match)\\n \\n obj = DataContainer()\\n ftp.retrbinary('RETR %s' % working_file, obj.readFromFTP)\\n columns = ['Timestamp', 'Azimuth', 'Elevation','Range','Xwind','Ywind','Zwind',\\\\\\n 'CNR','ConfIdx']\\n\\n # Get the date from the file as a DataFrame\\n data = obj.dataToArray(columns)\\n data.drop(data[data.Range > ymax].index, inplace=True)\\n data = data[['Range','Xwind','Ywind','Zwind']]\\n # Replace abs values Xwind and Ywind greater than 50 with NaN\\n \\n data.Xwind.where((data.Xwind < 50) | (data.Xwind > -50),np.nan,inplace=True)\\n data.Ywind.where((data.Ywind > 50) | (data.Ywind > -50),np.nan,inplace=True)\\n\\n dataByRange = data.groupby('Range').mean() \\n \\n Xset = dataByRange[['Xwind']]\\n Yset = dataByRange[['Ywind']]\\n Zset = dataByRange[['Zwind']]\\n \\n Usets.append(Xset)\\n Vsets.append(Yset)\\n Wsets.append(Zset)\\n \\n df_U = pd.concat(Usets,axis=1)\\n df_V = pd.concat(Vsets,axis=1)\\n df_W = pd.concat(Wsets,axis=1)\\n \\n U = df_U.values;\\n V = df_V.values;\\n W = df_W.values;\\n \\n # Vertical wind grid\\n x = np.linspace(0,48,49)\\n y = np.linspace(100,2500,49)\\n X, Y = np.meshgrid(x, y)\\n \\n # If the plot is going to have a variable pcolormesh uncomment next section\\n# cmapLim = 0\\n# if abs(np.nanmax(W)) > abs(np.nanmin(W)):\\n# cmapLim = abs(np.nanmax(W))\\n# else:\\n# cmapLim = abs(np.nanmin(W))\\n \\n # Change back the UTC times to local time\\n \\n timelabels = []\\n to_zone = tz.gettz('America/Mexico_City')\\n from_zone = tz.gettz('UTC')\\n \\n for entry in meas_time:\\n utc_time = datetime.strptime(entry, '%Y-%m-%d_%H-%M')\\n utc_time = utc_time.replace(tzinfo=from_zone)\\n \\n local = utc_time.astimezone(to_zone)\\n \\n timelabels.append(local.strftime(\\\"%H:%M\\\"))\\n \\n timelabels = [i.replace('25','00') for i in timelabels[2:48:4]]\\n \\n # Plot \\n title = ('Perfil vertical del viento, %s (serie de tiempo)' % selectedDate)\\n fig, ax = plt.subplots(figsize=(20,8))\\n \\n # For variable pcolormesh uncomment next line\\n #cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= (-cmapLim), vmax=cmapLim)\\n \\n cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= -3, vmax=3)\\n ax.set_title(title,fontsize = 24)\\n ax.set_xlabel('Hora Local [hh:mm]', fontsize = 18, labelpad = 14)\\n ax.set_xticks(np.arange(2.5,48, step=4))\\n ax.set_xticklabels(timelabels, rotation=45, size=12)\\n ax.set_ylabel('Altura [m]', fontsize = 18, labelpad = 18)\\n ax.yaxis.set_tick_params(labelsize=12)\\n ax.text(0.18,-0.08,'Datos de perfilador LIDAR Leosphere Windcube 100s. Lat 19.3262, Lon -99.1761, Alt 2280 msnm',transform=plt.gcf().transFigure, fontdict={'size': 18})\\n \\n # Colorbar\\n cbaxes = fig.add_axes([0.91, 0.125, 0.02, 0.4]) \\n cbar = fig.colorbar(cax, cax = cbaxes)\\n cbar.set_label(r'Viento vertical [$m\\\\,s^{-1}$]', fontsize = 18)\\n cbaxes.text(-0.3,1.07,'(Hacia arriba)')\\n cbaxes.text(-0.3,-0.07,'(Hacia abajo)') \\n \\n \\n #Other a artists\\n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/time_adv.png')\\n newax = fig.add_axes([0.6, -.03, 0.06, 0.06], anchor='NW', zorder=0) #[xo,yo,width,height]\\n newax.imshow(im)\\n newax.axis('off')\\n \\n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/directions.png')\\n newax1 = fig.add_axes([0.90, 0.75, 0.15, 0.15], anchor='NW', zorder=0) #[xo,yo,width,height]\\n newax1.imshow(im)\\n newax1.axis('off')\\n \\n \\n # Horizontal wind grid\\n \\n x_h = np.arange(0, len(meas_time), 1)\\n Xh, Yh = np.meshgrid(x_h, y)\\n \\n # Plot arrows\\n Q = ax.quiver(Xh[::2], Yh[::2], U[::2], V[::2], pivot = 'mid',alpha=0.75)\\n ax.quiverkey(Q, 0.93, 0.68, 10, r'$10\\\\,m\\\\,s^{-1}$',coordinates = 'figure', labelpos='N' )\\n \\n \\n finalOutputFile = outFile+'timeseries_daily_'+selectedDate\\n fig.savefig(finalOutputFile,bbox_inches='tight')\\n plt.close()\\n \\n except Exception as e:\\n print(\\\"Problema calculando Wind Time Series\\\", e)\",\n \"def export_lateness_data( gpssched, sched_error ):\\n \\n sql = \\\"\\\"\\\"\\ninsert into datamining_table dt\\n( gps_segment_id, gtfs_trip_id, rms_schedule_error, vehicle_id,\\n route_name, vehicle_type, service_id, direction_id,\\n stop_lat, stop_lon, stop_id, stop_sequence,\\n scheduled_arrival_time, scheduled_departure_time,\\n actual_arrival_time, lateness, prev_stop_id )\\nvalues\\n( null, %(trip_id)s, %(sched_err)s, %(vehid)s,\\n %(routename)s, %(vehtype)s, %(service_id)s, %(dir_id)s,\\n %(stoplat)s,%(stoplon)s,%(stopid)s,%(stopseq)s,\\n %(sched_arr)s,%(sched_dep)s,%(actual_arr)s,%(lateness)s,\\n %(prev_stop_id)s )\\n\\\"\\\"\\\"\\n \\n gtfs = gpssched.getGTFSSchedule()\\n\\n basedict = { 'trip_id' : gtfs.trip_id,\\n 'sched_err' : sched_error,\\n 'vehid' : gpssched.segment.vehicle_id,\\n 'routename' : gtfs.route_short_name,\\n 'vehtype' : gtfs.route_type,\\n 'service_id' : gtfs.service_id,\\n 'dir_id' : gtfs.direction_id\\n }\\n\\n cur = get_cursor()\\n \\n for arrival in gpssched.getGPSSchedule():\\n print dict(arrival)\\n stopdict = dict(basedict)\\n stopdict.update ( { 'stoplat' : arrival['stop_lat'],\\n 'stoplon' : arrival['stop_lon'],\\n 'stopid' : arrival['stop_id'],\\n 'stopseq' : arrival['stop_sequence'],\\n 'sched_arr' : arrival['arrival_time_seconds'],\\n 'sched_dep' : arrival['departure_time_seconds'],\\n 'actual_arr' : arrival['actual_arrival_time_seconds'],\\n 'lateness' : arrival['actual_arrival_time_seconds'] \\\\\\n - arrival['departure_time_seconds'],\\n 'prev_stop_id' : arrival['prev_stop_id']\\n } )\\n SQLExec(cur, sql, stopdict)\\n\\n\\n cur.close()\",\n \"def Collect_data(clo, cla, FD, LD, ZC, idx_time, cntry, TZ):\\r\\n #initiate the console response\\r\\n rep = \\\"\\\"\\r\\n global ddf\\r\\n \\r\\n #define the noaa server access\\r\\n server_noaa = \\\"https://www.ncei.noaa.gov/data/global-hourly/access/\\\"\\r\\n \\r\\n #Initate dateframe\\r\\n data = pd.DataFrame()\\r\\n \\r\\n #Convert date from specified timezone to UTC\\r\\n FDc = Date_calibration(FD, 1, TZ)\\r\\n LDc = Date_calibration(LD, 1, TZ)\\r\\n \\r\\n #define timestep\\r\\n timestep = \\\"1 H\\\"\\r\\n \\r\\n #Loop on the range time by each year\\r\\n for y in range(FDc.year, (LDc.year + 1)):\\r\\n rep += '--- Collect Year ['+str(y) + '] --- \\\\n' \\r\\n \\r\\n #Loop on each weather station while the ouput data is good\\r\\n #weather station in the year instancied dataframe \\r\\n for i in range(len(ddf[y])) :\\r\\n \\r\\n #Define the memory key : year_zipcode\\r\\n key_d_z = str(y)+'_'+str(ZC)\\r\\n \\r\\n #Verify if the data is already in the memory\\r\\n if(key_d_z in memory_df.keys()) :\\r\\n #The data is already in the memory :\\r\\n #Collect the data and go next (save compute time and server solicitation)\\r\\n ext_data = memory_df[key_d_z]\\r\\n NS, DO = memory_NS_DO[key_d_z].split('_')[0], memory_NS_DO[key_d_z].split('_')[1]\\r\\n break\\r\\n else:\\r\\n \\r\\n #The data isn't in the memory :\\r\\n \\r\\n #Collect information about the nearest weather station from the zip code\\r\\n rs, NS, DO = Fetch_station(clo, cla, y)\\r\\n \\r\\n #Generate the ftp key weather station\\r\\n code_station = str(ddf[y]['USAF'][rs])+str(ddf[y]['WBAN'][rs])\\r\\n \\r\\n #Define the server access\\r\\n url = server_noaa+str(y)+'/'+code_station+'.csv'\\r\\n \\r\\n #Get the data\\r\\n req = requests.get(url)\\r\\n \\r\\n #The server answer\\r\\n if(req.status_code == 200):\\r\\n \\r\\n #Extract the data (only date and temperature)\\r\\n ext_data = pd.read_csv(url, usecols=['DATE','TMP'])\\r\\n \\r\\n #Check if the data isn't empty (1000 is arbitrary)\\r\\n if(len(ext_data) > 1000):\\r\\n \\r\\n #Format data\\r\\n ext_data, qual = Data_formatting(ext_data, y, timestep, TZ)\\r\\n \\r\\n #Check if the data quality respect the quality rule\\r\\n if(qual > quality_rule):\\r\\n \\r\\n #Save the date in the memory\\r\\n memory_df[key_d_z] = ext_data\\r\\n memory_NS_DO[key_d_z] = str(NS)+'_'+str(DO)\\r\\n \\r\\n #Response for the GUI\\r\\n rep += \\\"# Station [\\\"+str(NS)+\\\"] valid : \\\\n\\\"\\r\\n rep += \\\"- Quality density data : \\\"+str(round(qual, 2))+\\\"% \\\\n\\\"\\r\\n rep += \\\"- Great circle distance : \\\"+str(round(DO,2))+\\\"km \\\\n\\\"\\r\\n break\\r\\n else:\\r\\n #The data quality is too bad\\r\\n #Response for the GUI\\r\\n rep += \\\"# Station [\\\"+str(NS)+\\\"] invalid : \\\\n\\\"\\r\\n rep += \\\"- Quality density data : \\\"+str(round(qual,2))+\\\"% \\\\n\\\"\\r\\n rep += \\\"> Quality criterion unsatisfied \\\\n\\\"\\r\\n \\r\\n #Delete the weather station in the dataframe (instancied by year)\\r\\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\\r\\n continue\\r\\n \\r\\n else:\\r\\n #The data density is too low\\r\\n \\r\\n #Response for the GUI\\r\\n rep += \\\"# Station [\\\"+str(NS)+\\\"] invalid : \\\\n\\\"\\r\\n rep += \\\"> Low data volume \\\\n\\\"\\r\\n \\r\\n #Delete the weather station in the dataframe (instancied by year)\\r\\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\\r\\n\\r\\n continue\\r\\n else:\\r\\n #The NOAA doesn't answer for the code station\\r\\n rep += \\\"# Station [\\\"+str(NS)+\\\"] invalid : \\\\n\\\"\\r\\n rep += \\\"> Server doesn't answer\\\\n\\\"\\r\\n \\r\\n #Delete the weather station in the dataframe (instancied by year)\\r\\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\\r\\n\\r\\n continue\\r\\n #Add data in the Dataframe\\r\\n data = data.append(ext_data)\\r\\n \\r\\n #Define a new dataframe mark out by the specified time range\\r\\n range_time = pd.DataFrame(index=pd.DatetimeIndex(start=FD, end=LD, freq=timestep))\\r\\n \\r\\n #Paste data in the time marked out Dataframe\\r\\n range_time['Temp'] = data['TMP']\\r\\n \\r\\n #Calculate the amount of NaN (global)\\r\\n nb_nan = range_time['Temp'].isnull().sum()\\r\\n \\r\\n #Calculate the global quality\\r\\n quality = (1 - (nb_nan) / len(range_time)) * 100\\r\\n \\r\\n #Fill the gap data by temporal interpolation\\r\\n data = range_time.interpolate(method='time')\\r\\n data = data.ffill().bfill()\\r\\n data['Temp'] = round(data['Temp'], 2)\\r\\n \\r\\n \\r\\n #If specified index time, cut the data for it\\r\\n if(len(idx_time)> 2):\\r\\n final_data = pd.DataFrame(index = pd.to_datetime(idx_time))\\r\\n final_data['Temp'] = round(data['Temp'], 2)\\r\\n #final_data = final_data.resample(rule = str(timestep)).mean()\\r\\n #final_data = final_data.dropna()\\r\\n else:\\r\\n final_data = data\\r\\n \\r\\n return final_data, quality, NS, DO, rep\",\n \"def filterEvents(intervals_dates,list_infected,distance):\\n d=distance\\n list_gpsevents=[]\\n for z in range(len(intervals_dates)-1):\\n print(\\\"Interval: \\\",intervals_dates[z], \\\"y\\\", intervals_dates[z+1])\\n infected,uninfected=getTrazaTimestamp(intervals_dates[z],intervals_dates[z+1],GPSrecords,list_infected)\\n events_gps = nearest_neighbor(infected, uninfected, d)\\n events_gps = events_gps.drop(['geometry','closest_stop_geom'], axis=1)\\n print(len(events_gps))\\n if(len(events_gps)!=0):\\n list_gpsevents.append(events_gps.reset_index(drop=True))\\n else:\\n events_gps=pd.DataFrame()\\n list_gpsevents.append(events_gps)\\n #GPSevents=pd.concat(list_gpsevents).reset_index(drop=True)\\n #return GPSevents\\n return list_gpsevents\",\n \"def ttest_region():\\n\\n cks = cksmet.io.load_table('cks-cuts',cache=1)\\n cks = cks[~cks.isany]\\n lamo = cksmet.io.load_table('lamost-cal-cuts',cache=2)\\n lamo = lamo[~lamo.isany]\\n #lamo = lamo.query('-1 < lamo_smet < 0.5')\\n\\n boxes = _boxes()\\n boxes = boxes.copy()\\n #boxes = boxes.sort_values(by=['per1','prad1'])\\n boxes2 = []\\n lines = []\\n for i,row in boxes.iterrows():\\n cut = cks[\\n cks.koi_period.between(row.per1,row.per2) &\\n cks.iso_prad.between(row.prad1,row.prad2)\\n ]\\n d = calculate_statistics(cut.cks_smet,lamo.lamo_smet)\\n d = dict(d, **row)\\n lines.append( to_string(d))\\n \\n return lines\",\n \"def create_lat_lon_data(gt_id,\\n target_horizon,\\n experiment,\\n lat_lon_features=[\\\"elevation\\\", \\\"climate_regions\\\"]):\\n\\n time_start = time.time()\\n\\n # --------\\n # Prepare experiment cache directory and saved file names\\n # --------\\n\\n # Name of cache directory for storing non-submission-date specific\\n # intermediate files\\n cache_dir = os.path.join('results', experiment, 'shared',\\n '{}_{}'.format(gt_id, target_horizon))\\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\\n\\n # if cache_dir doesn't exist, create it\\n if not os.path.isdir(cache_dir):\\n os.makedirs(cache_dir)\\n\\n # Filenames for data file to be stored in cache_dir\\n lat_lon_data_file = os.path.join(\\n cache_dir, \\\"lat_lon_data-{}_{}.h5\\\".format(gt_id, target_horizon))\\n\\n # --------\\n # Load mask indicating which grid points count in the contest (1=in, 0=out)\\n # --------\\n print \\\"Loading contest mask\\\"\\n t = time.time()\\n mask_df = get_contest_mask()\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n # --------\\n # Creates lat_lon_data dataframe.\\n # --------\\n # Load masked lat lon features\\n print \\\"Loading lat lon features\\\"\\n lat_lon_data = get_lat_lon_features(gt_ids=lat_lon_features, gt_masks=mask_df)\\n # Convert qualitative variable (climate_region) to dummies; all dummies included\\n if lat_lon_features:\\n lat_lon_data = pd.get_dummies(lat_lon_data,\\n columns=[\\\"climate_region\\\"],\\n drop_first=False)\\n print \\\"Saving lat lon features to \\\"+lat_lon_data_file\\n t = time.time()\\n # Save lat lon features to disk\\n lat_lon_data.to_hdf(lat_lon_data_file, key=\\\"data\\\", mode=\\\"w\\\")\\n subprocess.call(\\\"chmod a+w \\\"+lat_lon_data_file, shell=True)\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n else:\\n print \\\"No lat lon features requested\\\"\\n # Delete any old version of the data\\n if os.path.isfile(lat_lon_data_file):\\n os.remove(lat_lon_data_file)\\n\\n print \\\"Finished generating lat_lon_data matrix.\\\"\\n print \\\"Total time elapsed: {}s\\\".format(time.time()-time_start)\\n return list(lat_lon_data) if lat_lon_features else 0\",\n \"def create_sample_dataframe():\\n ax_readings = []\\n ay_readings = []\\n az_readings = []\\n mx_readings = []\\n my_readings = []\\n mz_readings = []\\n gx_readings = []\\n gy_readings = []\\n gz_readings = []\\n activity_list = [LABELS_NAMES[0] for _ in range(SEGMENT_TIME_SIZE)]\\n\\n\\n for _ in range(SEGMENT_TIME_SIZE):\\n ax_readings.append(random.uniform(-10,10))\\n ay_readings.append(random.uniform(-10,10))\\n az_readings.append(random.uniform(-10,10))\\n mx_readings.append(random.uniform(-10,10))\\n my_readings.append(random.uniform(-10,10))\\n mz_readings.append(random.uniform(-10,10))\\n gx_readings.append(random.uniform(-10,10))\\n gy_readings.append(random.uniform(-10,10))\\n gz_readings.append(random.uniform(-10,10))\\n\\n data_dict = {\\n COLUMN_NAMES[0]: activity_list, COLUMN_NAMES[1]: ax_readings,\\n COLUMN_NAMES[2]: ay_readings, COLUMN_NAMES[3]: az_readings,\\n COLUMN_NAMES[4]: gx_readings, COLUMN_NAMES[5]: gy_readings,\\n COLUMN_NAMES[6]: gz_readings, COLUMN_NAMES[7]: mx_readings,\\n COLUMN_NAMES[8]: my_readings, COLUMN_NAMES[9]: mz_readings\\n }\\n\\n df = pd.DataFrame(data=data_dict)\\n return df\",\n \"def manipulate_data(ds, var, predef_clim, predef_trnd, trn_yrs, all_yrs, \\n apply_latw=True, apply_detrending=True, dropna=True):\\n\\n \\n if((var=='SD')|(var=='sd')|(var=='snowc')): \\n ds[var] = ds[var].where(ds[var]>=0, other=0.0)\\n ds[var] = ds[var].where(ds[var]==0, other=1.0)\\n #ds[var].values = Gauss_filter(ds[var].values, (0,3,3))\\n \\n \\\"\\\"\\\"\\n if((var=='hgt')|(var=='z')|(var=='GPT')):\\n months = ds.time.to_index().month; ssn_ends = (months==2)|(months==5)|(months==8)|(months==11)\\n ds = ds.sel(time=ssn_ends)\\n else: \\n ds = ds.resample(time='3M').mean()\\n \\\"\\\"\\\"\\n \\n ds = ds.resample(time='3M').mean()\\n\\n ds = ds.sel(time=slice(str(all_yrs[0])+'-01-01', str(all_yrs[-1])+'-12-31')) \\n \\n try: \\n clim = predef_clim\\n ds = ds.groupby('time.season') - clim\\n print('Predefined climatology used')\\n except:\\n clim = ds.sel(time=slice(str(trn_yrs[0])+'-01-01', str(trn_yrs[-1])+'-12-31')).groupby('time.season').mean('time')\\n ds = ds.groupby('time.season') - clim\\n print('Climatology calculated from data')\\n \\n if(apply_latw): ds[var].values = lat_weighting(ds[var].values, \\n ds.lat, ds.lon)\\n if(dropna):\\n ds = ds.stack(gridcell=('lat', 'lon')).dropna(dim='gridcell',how='any')\\n else: \\n ds = ds.stack(gridcell=('lat', 'lon')).fillna(0)\\n \\n \\n trend_models = { }\\n if(apply_detrending): \\n ds = ds.load()\\n for ssn in ('DJF', 'MAM', 'JJA', 'SON'):\\n #ssn_idx = ds['time.season'] == ssn\\n \\n trn_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], trn_yrs))\\n all_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], all_yrs))\\n \\n trn_x = np.array(ds.time[trn_idx].values.tolist()).reshape(-1,1)\\n all_x = np.array(ds.time[all_idx].values.tolist()).reshape(-1,1)\\n try:\\n trend = predef_trnd[ssn].predict(all_x)\\n trend_models[ssn] = predef_trnd[ssn]\\n print('Predefined trend model used')\\n except:\\n #_, trend_model = define_trends(ds[var][trn_idx], trn_x)\\n _, trend_model = define_trends(ds[var][all_idx], all_x)\\n trend = trend_model.predict(all_x)\\n trend_models[ssn] = trend_model\\n print('Trends calculated from data')\\n \\n ds[var][all_idx] = ds[var][all_idx] - trend\\n \\n\\n \\n return ds, clim, trend_models\",\n \"def artificial_data(dt1, dt2, minutes=1):\\n\\n def fxweek(x):\\n return 2 - x * (1 - x)\\n\\n def sat(x):\\n return 2 * x + 2\\n\\n data = []\\n dt = datetime.timedelta(minutes=minutes)\\n while dt1 < dt2:\\n if dt1.weekday() == 6:\\n dt1 += dt\\n continue\\n if minutes <= 120 and not (dt1.hour >= 8 and dt1.hour <= 18):\\n dt1 += dt\\n continue\\n x = (dt1.hour - 8) / 10\\n if dt1.weekday() == 5:\\n y = sat(x)\\n else:\\n y = fxweek(x)\\n data.append({'time': dt1, 'y': y})\\n dt1 += dt\\n df = pandas.DataFrame(data)\\n df['y'] += numpy.random.randn(df.shape[0]) * 0.1\\n df['time'] = pandas.DatetimeIndex(df['time'])\\n return df\",\n \"def rowify(url: str, additional_info: list = []) -> list:\\n try:\\n rawdata = req.get(url).json() # does exactly what you think\\n except json.decoder.JSONDecodeError as err:\\n splits = url.split('/') # get the parts of the URL\\n intvl = f\\\"[{splits[-3]}, {splits[-2]}]\\\" # create a string of interval\\n logger.warning(\\\"[rowify] No data found for interval %s. \\\"\\n \\\"Msg: %s.\\\", intvl, err) # use exc_info=1 for traceback\\n return []\\n\\n fulldata = []\\n for point in rawdata: # iterating over all measured datapoints\\n uid = additional_info[0]\\n idstring = stringifyID(point, uid) # create unique IdString\\n\\n # check for duplicates\\n if idstring not in genIdStrings and idstring not in queriedIds:\\n genIdStrings.append(idstring)\\n\\n # first part of data\\n row = [point[i] for i in [\\\"TBTimestamp\\\", \\\"TETimestamp\\\", \\\"Latitude\\\",\\n \\\"Longitude\\\", \\\"Altitude\\\"]]\\n\\n # Channel part of data\\n channels = point[\\\"Channels\\\"]\\n tableChannels = [\\\"AIRPRES\\\", \\\"CO\\\", \\\"HUM\\\", \\\"NO\\\", \\\"NO2\\\", \\\"O3\\\",\\n \\\"SO2\\\", \\\"PM1\\\", \\\"PM10\\\", \\\"PM2.5\\\", \\\"PARTICLE_COUNT\\\",\\n \\\"TEMP\\\", \\\"TSP\\\", \\\"VOLTAGE\\\"]\\n\\n # dict comprehension to get sensorlabel: sensorchannel pairs\\n channelDict = {ch[\\\"SensorLabel\\\"]: ch for ch in channels}\\n channelDictKeys = list(channelDict.keys())\\n\\n # log a warning if unrecognized channel labels appear\\n for cdk in channelDictKeys:\\n if cdk not in tableChannels:\\n logger.warning(\\\"Unrecognized channel label %s for IdString\\\"\\n \\\" %s.\\\", cdk, idstring)\\n\\n # kind of diagnostics so see, whether all the data fits in nicely\\n if len(tableChannels) != len(channels):\\n logger.debug(\\\"Number of expected channels [%s] and received \\\"\\n \\\"channels [%s] do not match. Received: %s.\\\",\\n len(tableChannels), len(channels), channelDictKeys)\\n\\n # create the actual row\\n for tch in tableChannels:\\n if tch in channelDictKeys:\\n ch = channelDict[tch]\\n row = [*row, *[ch[\\\"PreScaled\\\"], ch[\\\"Slope\\\"], ch[\\\"Offset\\\"],\\n ch[\\\"Scaled\\\"], ch[\\\"UnitName\\\"], ch[\\\"Status\\\"]]]\\n else:\\n filler = [None] * 6 # no data -> fill with None\\n row = [*row, *filler]\\n\\n row = tuple([*row, *additional_info, idstring])\\n fulldata.append(row)\\n\\n else:\\n logger.warning(\\\"Encountered duplicate IdString %s.\\\", idstring)\\n\\n del rawdata[:] # freeing memory\\n del genIdStrings[:] # if API not broken this shouldn't do harm+free memory\\n\\n return fulldata\",\n \"def _interpolate_meteorological_data(dset, data, rundate):\\n rundate = datetime(rundate.year, rundate.month, rundate.day)\\n for field, station in [(f, f[4:]) for f in data.keys() if f.startswith(\\\"met_\\\")]:\\n log.debug(f\\\"Meteorological data available for station {station}\\\")\\n\\n met_time = data[field].pop(\\\"met_time\\\")\\n flat_list = [item for sublist in met_time for item in sublist]\\n met_time_float = np.array([(flat_list[i] - rundate).total_seconds() for i in range(0, len(flat_list))])\\n met_time_unique, met_index = np.unique(met_time_float, return_index=True)\\n\\n diff = len(met_time_float) - len(met_time_unique)\\n if diff > 0:\\n log.dev(f\\\"Removed duplicate met data for station {station}\\\")\\n log.dev(\\\"Do this for the actual obs data also!\\\")\\n if len(met_time_unique) == 1:\\n for met_type in data[field].keys():\\n data[field][met_type] = np.repeat(data[field][met_type][0], dset.num_obs)\\n continue\\n\\n # Extrapolation one month before/after\\n # (this is overkill, most of these values will be removed later when taking the diagonal)\\n min_time = min(met_time_unique) - 31 * 86400\\n max_time = max(met_time_unique) + 31 * 86400\\n met_time_unique = np.hstack((np.array(min_time), met_time_unique, np.array(max_time)))\\n\\n for met_type in data[field].keys():\\n met_data_array = data[field][met_type]\\n flat_list = [item for sublist in met_data_array for item in sublist]\\n met_data_array = np.array([flat_list[i] for i in met_index])\\n met_data_array = np.hstack((met_data_array[0], met_data_array, met_data_array[-1]))\\n data[field][met_type] = interpolation.interpolate(\\n met_time_unique, met_data_array, dset.obs_time, kind=\\\"cubic\\\"\\n )\\n\\n return data\",\n \"def generate_base_tiles(self):\\n\\n gdal.SetConfigOption(\\\"GDAL_PAM_ENABLED\\\", \\\"NO\\\")\\n\\n print \\\"Generating Base Tiles:\\\"\\n if self.options.verbose:\\n #mx, my = self.out_gt[0], self.out_gt[3] # OriginX, OriginY\\n #px, py = self.mercator.MetersToPixels( mx, my, self.tmaxz)\\n #print \\\"Pixel coordinates:\\\", px, py, (mx, my)\\n print\\n print \\\"Tiles generated from the max zoom level:\\\"\\n print \\\"----------------------------------------\\\"\\n print\\n\\n\\n # Set the bounds\\n tminx, tminy, tmaxx, tmaxy = self.tminmax[self.tmaxz]\\n querysize = self.querysize\\n\\n # Just the center tile\\n #tminx = tminx+ (tmaxx - tminx)/2\\n #tminy = tminy+ (tmaxy - tminy)/2\\n #tmaxx = tminx\\n #tmaxy = tminy\\n\\n #print tminx, tminy, tmaxx, tmaxy\\n tcount = (1+abs(tmaxx-tminx)) * (1+abs(tmaxy-tminy))\\n #print tcount\\n ti = 0\\n i_y_column_count=((tmaxy-tminy)+1)\\n ds = self.out_ds\\n tz = self.tmaxz\\n if self.options.verbose:\\n # tx in range(tminx, tmaxx+1) tminx[ 281596 ] tmaxx[ 281744 ] ; ((tmaxx-tmaxy)+1) x_tiles[ 23393 ]\\n print \\\"\\\\ttz=[\\\",tz,\\\"] : tx in range(tminx, tmaxx+1) tminx[\\\",tminx,\\\"] tmaxx[\\\",tmaxx,\\\"] ; ((tmaxx-tmaxy)+1) x_tiles[\\\",tcount,\\\"]\\\"\\n # ty_tms in range(tmaxy, tminy-1, -1) tmaxy[ 352409 ] tminy[ 352253 ] ; ((tmaxy-tminy)) y_tiles[ 157 ] 352409-(352253-1)\\n print \\\"\\\\ttz=[\\\",tz,\\\"] : ty_tms in range(tmaxy, tminy-1, -1) tmaxy[\\\",tmaxy,\\\"] tminy[\\\",tminy,\\\"] ; ((tmaxy-tminy+1)) y_tiles[\\\",i_y_column_count,\\\"]\\\"\\n if self.options.resume:\\n i_count = self.tile_exists(0, 0, tz,2)\\n if i_count == tcount:\\n if self.options.verbose:\\n print \\\"\\\\tTile generation skipped because of --resume ; x/y-tiles of z[\\\",tz,\\\"] y_tiles[\\\",tcount,\\\"]\\\"\\n return\\n for tx in range(tminx, tmaxx+1):\\n tmaxy_work=tmaxy\\n if self.options.resume:\\n i_count = self.tile_exists(tx, 0, tz,3)\\n if i_count == i_y_column_count:\\n if self.options.verbose:\\n print \\\"\\\\tTile generation skipped because of --resume ; z =\\\",tz,\\\" ; y-tiles of x[\\\",tx,\\\"] y_tiles[\\\",i_y_column_count,\\\"]\\\"\\n break\\n else:\\n if i_count > 0:\\n # this assums the rows are compleate, which may NOT be true\\n tmaxy_work-=i_count\\n if self.options.verbose:\\n print \\\"\\\\tTile generation skipped to tmaxy[\\\",tmaxy_work,\\\"] because of --resume ; z =\\\",tz,\\\" ; y-tiles of x[\\\",tx,\\\"] y_tiles[\\\",i_y_column_count,\\\"]\\\"\\n for ty_tms in range(tmaxy_work, tminy-1, -1): #range(tminy, tmaxy+1):\\n ty_osm=self.flip_y(tz,ty_tms)\\n ty=ty_tms\\n if self.options.tms_osm:\\n ty=ty_osm\\n if self.stopped:\\n if self.options.mbtiles:\\n if self.mbtiles_db:\\n self.mbtiles_db.close_db()\\n break\\n ti += 1\\n\\n if self.options.resume:\\n exists = self.tile_exists(tx, ty, tz,0)\\n if exists and self.options.verbose:\\n print \\\"\\\\tTile generation skipped because of --resume ; z =\\\",tz,\\\" ; x =\\\",tx,\\\" ; y_tms =\\\",ty_tms, \\\"; y_osm =\\\",ty_osm\\n else:\\n exists = False\\n\\n if not exists:\\n if self.options.verbose:\\n print ti, '/', tcount, self.get_verbose_tile_name(tx, ty, tz)\\n # Don't scale up by nearest neighbour, better change the querysize\\n # to the native resolution (and return smaller query tile) for scaling\\n if self.options.profile in ('mercator','geodetic'):\\n if self.options.profile == 'mercator':\\n # Tile bounds in EPSG:900913\\n b = self.mercator.TileBounds(tx, ty_tms, tz)\\n elif self.options.profile == 'geodetic':\\n b = self.geodetic.TileBounds(tx, ty_tms, tz)\\n\\n rb, wb = self.geo_query( ds, b[0], b[3], b[2], b[1])\\n nativesize = wb[0]+wb[2] # Pixel size in the raster covering query geo extent\\n if self.options.verbose:\\n print \\\"\\\\tNative Extent (querysize\\\",nativesize,\\\"): \\\", rb, wb\\n\\n querysize = self.querysize\\n # Tile bounds in raster coordinates for ReadRaster query\\n rb, wb = self.geo_query( ds, b[0], b[3], b[2], b[1], querysize=querysize)\\n\\n rx, ry, rxsize, rysize = rb\\n wx, wy, wxsize, wysize = wb\\n else: # 'raster' or 'gearth' or 'garmin' profile:\\n tsize = int(self.tsize[tz]) # tilesize in raster coordinates for actual zoom\\n xsize = self.out_ds.RasterXSize # size of the raster in pixels\\n ysize = self.out_ds.RasterYSize\\n if tz >= self.nativezoom:\\n querysize = self.tilesize # int(2**(self.nativezoom-tz) * self.tilesize)\\n\\n rx = (tx) * tsize\\n rxsize = 0\\n if tx == tmaxx:\\n rxsize = xsize % tsize\\n if rxsize == 0:\\n rxsize = tsize\\n\\n rysize = 0\\n if ty_tms == tmaxy:\\n rysize = ysize % tsize\\n if rysize == 0:\\n rysize = tsize\\n ry = ysize - (ty_tms * tsize) - rysize\\n\\n wx, wy = 0, 0\\n\\n wxsize, wysize = int(rxsize/float(tsize) * querysize), int(rysize/float(tsize) * querysize)\\n if wysize != querysize:\\n wy = querysize - wysize\\n xyzzy = Xyzzy(querysize, rx, ry, rxsize, rysize, wx, wy, wxsize, wysize)\\n try:\\n if self.options.verbose:\\n print ti,'/',tcount,' total ; z =',tz,' ; x =',tx,' ; y_tms =',ty_tms,' ; y_osm =',ty_osm\\n print \\\"\\\\tReadRaster Extent: \\\", (rx, ry, rxsize, rysize), (wx, wy, wxsize, wysize)\\n self.write_base_tile(tx, ty, tz, xyzzy)\\n except ImageOutputException, e:\\n self.error(\\\"'%d/%d/%d': %s\\\" % (tz, tx, ty, e.message))\\n\\n if not self.options.verbose or self.is_subprocess:\\n self.progressbar( ti / float(tcount) )\\n if self.options.mbtiles:\\n if self.mbtiles_db:\\n self.mbtiles_db.close_db()\\n self.mbtiles_db=None\",\n \"def get_gridcell_history(\\n lat,\\n lon,\\n dataset,\\n also_return_snapped_coordinates=False,\\n also_return_metadata=False,\\n use_imperial_units=True,\\n convert_to_local_time=True,\\n as_of=None,\\n ipfs_timeout=None):\\n try:\\n metadata = get_metadata(get_heads()[dataset])\\n except KeyError:\\n raise DatasetError(\\\"No such dataset in dClimate\\\")\\n\\n # set up units\\n converter, dweather_unit = get_unit_converter(metadata[\\\"unit of measurement\\\"], use_imperial_units)\\n\\n # get dataset-specific \\\"no observation\\\" value\\n missing_value = metadata[\\\"missing value\\\"]\\n try:\\n dataset_obj = GRIDDED_DATASETS[dataset](as_of=as_of, ipfs_timeout=ipfs_timeout)\\n except KeyError:\\n raise DatasetError(\\\"No such dataset in dClimate\\\")\\n\\n try:\\n (lat, lon), resp_series = dataset_obj.get_data(lat, lon)\\n\\n except (ipfshttpclient.exceptions.ErrorResponse, ipfshttpclient.exceptions.TimeoutError, KeyError, FileNotFoundError) as e:\\n raise CoordinateNotFoundError(\\\"Invalid coordinate for dataset\\\")\\n\\n # try a timezone-based transformation on the times in case we're using an hourly set.\\n if convert_to_local_time:\\n try:\\n tf = TimezoneFinder()\\n local_tz = pytz.timezone(tf.timezone_at(lng=lon, lat=lat))\\n resp_series = resp_series.tz_localize(\\\"UTC\\\").tz_convert(local_tz)\\n except (AttributeError, TypeError): # datetime.date (daily sets) doesn't work with this, only datetime.datetime (hourly sets)\\n pass\\n\\n if type(missing_value) == str:\\n resp_series = resp_series.replace(missing_value, np.NaN).astype(float)\\n else:\\n resp_series.loc[resp_series.astype(float) == missing_value] = np.NaN\\n resp_series = resp_series.astype(float)\\n \\n resp_series = resp_series * dweather_unit\\n if converter is not None:\\n resp_series = pd.Series(converter(resp_series.values), resp_series.index)\\n result = {k: convert_nans_to_none(v) for k, v in resp_series.to_dict().items()}\\n \\n if also_return_metadata:\\n result = tupleify(result) + ({\\\"metadata\\\": metadata},)\\n if also_return_snapped_coordinates:\\n result = tupleify(result) + ({\\\"snapped to\\\": (lat, lon)},)\\n return result\",\n \"def process_location(pid, data_source, out_loc, start_date, end_date, debug=False):\\n\\n #query = client.query_data_points(page_size=PAGE_SIZE, source=pid)\\n query = try_query(pid)\\n\\n location_query = try_filter(query, pid, 'pdk-location', start_date, end_date)\\n \\\"\\\"\\\" location_query = query.filter(source=pid, \\n generator_identifier='pdk-location',\\n created__gte=start_date,\\n created__lte=end_date).order_by('created')\\n \\\"\\\"\\\"\\n tot_count = location_query.count()\\n count = 0\\n frac = int(tot_count / 100)\\n\\n loc_df = pd.DataFrame()\\n for point in location_query:\\n point_df = pd.DataFrame.from_dict(point).iloc[0].to_frame().transpose()\\n metadata_df = pd.Series(point['passive-data-metadata']).to_frame().transpose()\\n # TODO check if ignoring errors is safe\\n metadata_df = metadata_df.drop(['latitude', 'longitude'], axis='columns', errors=\\\"ignore\\\")\\n point_df.reset_index(inplace=True, drop=True)\\n point_df = pd.concat([metadata_df, point_df], axis=1, sort=True)\\n \\n point_df.drop('passive-data-metadata', axis='columns', inplace=True)\\n missing_cols = [col for col in loc_df.columns.values if col not in point_df.columns.values]\\n \\n if len(missing_cols) > 0 and loc_df.shape[0] > 0:\\n for col in missing_cols:\\n point_df[col] = np.nan\\n point_df = point_df[loc_df.columns]\\n loc_df = loc_df.append(point_df)\\n count += 1\\n if debug and (count % frac == 0):\\n print(\\\"{0:.2f}% complete\\\".format(float(count)/float(tot_count)*100))\\n\\n loc_df['pid'] = pid \\n loc_df['data_source'] = data_source\\n print(loc_df.shape)\\n \\n pickle.dump(loc_df, open(\\\"{}/pdk-location/{}.df\\\".format(out_loc, pid), 'wb'), -1)\",\n \"def prepare_input(self, only_center = True):\\n \\n if only_center:\\n nx = [0]\\n ny = [0]\\n else:\\n nx = [0,1,-1]\\n ny = [0,1,-1]\\n gauge = dd.read_csv(str(Path(self.db_location, 'gauge', '*.csv.gz')), \\n compression='gzip', \\n assume_missing=True,\\n dtype = {'TIMESTAMP':int, 'STATION': str})\\n \\n gauge = gauge.compute().drop_duplicates()\\n gauge = gauge.replace(-9999,np.nan)\\n for x in nx:\\n for y in ny:\\n logging.info('Processing neighbour {:d}{:d}'.format(x, y))\\n radar = dd.read_parquet(str(Path(self.db_location, 'radar',\\n '*.parquet')))\\n refer = dd.read_parquet(str(Path(self.db_location, 'reference', \\n '*.parquet')))\\n \\n # Select only required pixel\\n radar = radar.loc[np.logical_and(radar['NX'] == x, \\n radar['NY'] == y)]\\n refer = refer.loc[np.logical_and(refer['NX'] == x, \\n refer['NY'] == y)]\\n \\n # Convert to pandas and remove duplicates \\n radar = radar.compute().drop_duplicates(subset = ['TIMESTAMP',\\n 'STATION',\\n 'RADAR',\\n 'NX','NY',\\n 'SWEEP'])\\n \\n refer = refer.compute().drop_duplicates(subset = ['TIMESTAMP',\\n 'STATION'])\\n \\n radar = radar.sort_values(by = ['TIMESTAMP','STATION','SWEEP'])\\n refer = refer.sort_values(by = ['TIMESTAMP','STATION'])\\n gauge = gauge.sort_values(by = ['TIMESTAMP','STATION'])\\n # Get only valid precip data\\n gauge = gauge[np.isfinite(gauge['RRE150Z0'])]\\n \\n # Create individual 10 min - station stamps\\n gauge['s-tstamp'] = np.array(gauge['STATION'] + \\n gauge['TIMESTAMP'].astype(str)).astype(str)\\n radar['s-tstamp'] = np.array(radar['STATION'] + \\n radar['TIMESTAMP'].astype(str)).astype(str)\\n refer['s-tstamp'] = np.array(refer['STATION'] + \\n refer['TIMESTAMP'].astype(str)).astype(str)\\n \\n # Get gauge and reference only when radar data available\\n \\n # Find timestamps that are in the three datasets\\n ststamp_common = np.array(pd.Series(list(set(gauge['s-tstamp'])\\n .intersection(set(refer['s-tstamp'])))))\\n ststamp_common = np.array(pd.Series(list(set(radar['s-tstamp'])\\n .intersection(set(ststamp_common)))))\\n radar = radar.loc[radar['s-tstamp'].isin(ststamp_common)]\\n gauge = gauge.loc[gauge['s-tstamp'].isin(ststamp_common)]\\n refer = refer.loc[refer['s-tstamp'].isin(ststamp_common)]\\n \\n \\n # Filter incomplete hours\\n stahour = np.array(gauge['STATION'] + \\n ((gauge['TIMESTAMP'] - 600 ) - \\n (gauge['TIMESTAMP'] - 600 ) % 3600).astype(str)).astype(str)\\n \\n full_hours = np.array(gauge.groupby(stahour)['STATION']\\n .transform('count') == 6)\\n \\n refer = refer.reindex[full_hours]\\n gauge = gauge.reindex[full_hours] \\n radar = radar.reindex[radar['s-tstamp'].\\n isin(np.array(gauge['s-tstamp']))]\\n \\n stahour = stahour[full_hours]\\n \\n # Creating vertical grouping index\\n \\n _, idx, grp_vertical = np.unique(radar['s-tstamp'],\\n return_inverse = True,\\n return_index = True)\\n # Get original order\\n sta_tstamp_unique = radar['s-tstamp'][np.sort(idx)]\\n # Preserves order and avoids sorting radar_statstamp\\n grp_vertical = idx[grp_vertical]\\n # However one issue is that the indexes are not starting from zero with increment\\n # of one, though they are sorted, they are like 0,7,7,7,15,15,23,23\\n # We want them starting from zero with step of one\\n grp_vertical = rankdata(grp_vertical,method='dense') - 1\\n \\n # Repeat operation with gauge hours\\n sta_hourly_unique, idx, grp_hourly = np.unique(stahour, \\n return_inverse = True,\\n return_index = True)\\n grp_hourly = idx[grp_hourly]\\n \\n # Add derived variables height iso0 (HISO) and height above ground (HAG)\\n # Radar\\n stations = constants.METSTATIONS\\n cols = list(stations.columns)\\n cols[1] = 'STATION'\\n stations.columns = cols\\n radar = pd.merge(radar,stations, how = 'left', on = 'STATION',\\n sort = False)\\n \\n radar['HISO'] = -radar['T'] / constants.LAPSE_RATE * 100\\n radar['HAG'] = radar['HEIGHT'] - radar['Z']\\n radar['HAG'][radar['HAG'] < 0] = 0\\n \\n # Gauge\\n gauge['minutes'] = (gauge['TIMESTAMP'] % 3600)/60\\n \\n # Save all to file\\n refer.to_parquet(str(Path(self.input_location, \\n 'reference_x{:d}y{:d}.parquet'.format(x,y))),\\n compression = 'gzip', index = False)\\n \\n radar.to_parquet(str(Path(self.input_location, \\n 'radar_x{:d}y{:d}.parquet'.format(x,y))),\\n compression = 'gzip', index = False)\\n \\n grp_idx = {}\\n grp_idx['grp_vertical'] = grp_vertical\\n grp_idx['grp_hourly'] = grp_hourly\\n grp_idx['tstamp_unique'] = sta_tstamp_unique\\n \\n pickle.dump(grp_idx, \\n open(str(Path(self.input_location, \\n 'grouping_idx_x{:d}y{:d}.p'.format(x,y))),'wb'))\\n \\n if x == 0 and y == 0:\\n # Save only gauge for center pixel since it's available only there\\n gauge.to_parquet(str(Path(self.input_location, 'gauge.parquet')),\\n compression = 'gzip', index = False)\",\n \"def calculate_psf_tilts():\\n for order in [1, 2]:\\n\\n # Get the file\\n path = 'files/SOSS_PSF_tilt_order{}.npy'.format(order)\\n psf_file = resource_filename('awesimsoss', path)\\n\\n # Dimensions\\n subarray = 'SUBSTRIP256'\\n X = range(2048)\\n Y = range(256)\\n\\n # Get the wave map\\n wave_map = utils.wave_solutions(subarray, order).astype(float)\\n\\n # Get the y-coordinate of the trace polynomial in this column\\n # (center of the trace)\\n coeffs = trace_polynomials(subarray=subarray, order=order)\\n trace = np.polyval(coeffs, X)\\n\\n # Interpolate to get the wavelength value at the center\\n wave = interp2d(X, Y, wave_map)\\n\\n # Get the wavelength of the trace center in each column\\n trace_wave = []\\n for x, y in zip(X, trace):\\n trace_wave.append(wave(x, y)[0])\\n\\n # For each column wavelength (defined by the wavelength at\\n # the trace center) define an isowavelength contour\\n angles = []\\n for n, x in enumerate(X):\\n\\n w = trace_wave[x]\\n\\n # Edge cases\\n try:\\n w0 = trace_wave[x-1]\\n except IndexError:\\n w0 = 0\\n\\n try:\\n w1 = trace_wave[x+1]\\n except IndexError:\\n w1 = 10\\n\\n # Define the width of the wavelength bin as half-way\\n # between neighboring points\\n dw0 = np.mean([w0, w])\\n dw1 = np.mean([w1, w])\\n\\n # Get the coordinates of all the pixels in that range\\n yy, xx = np.where(np.logical_and(wave_map >= dw0, wave_map < dw1))\\n\\n # Find the angle between the vertical and the tilted wavelength bin\\n if len(xx) >= 1:\\n angle = get_angle([xx[-1], yy[-1]], [x, trace[x]])\\n else:\\n angle = 0\\n\\n # Don't flip them upside down\\n angle = angle % 180\\n\\n # Add to the array\\n angles.append(angle)\\n\\n # Save the file\\n np.save(psf_file, np.array(angles))\\n print('Angles saved to', psf_file)\",\n \"def create_features(energy_data, label=None):\\n energy_data['date'] = energy_data.index\\n energy_data['hour'] = energy_data['Datetime'].dt.hour\\n energy_data['dayofweek'] = energy_data['Datetime'].dt.dayofweek\\n energy_data['month'] = energy_data['Datetime'].dt.month\\n energy_data['quarter'] = energy_data['Datetime'].dt.quarter\\n energy_data['year'] = energy_data['Datetime'].dt.year\\n energy_data['dayofyear'] = energy_data['Datetime'].dt.dayofyear\\n energy_data['dayofmonth'] = energy_data['Datetime'].dt.day\\n energy_data['weekofyear'] = energy_data['Datetime'].dt.weekofyear\\n energy_data['pjme_2_hrs_lag'] = energy_data['PJME_MW'].shift(2)\\n energy_data['pjme_4_hrs_lag'] = energy_data['PJME_MW'].shift(4)\\n energy_data['pjme_8_hrs_lag'] = energy_data['PJME_MW'].shift(8)\\n energy_data['pjme_12_hrs_lag'] = energy_data['PJME_MW'].shift(12)\\n energy_data['pjme_24_hrs_lag'] = energy_data['PJME_MW'].shift(24)\\n energy_data['pjme_4_hrs_mean'] = energy_data['PJME_MW'].rolling(window=4).mean()\\n energy_data['pjme_8_hrs_mean'] = energy_data['PJME_MW'].rolling(window=8).mean()\\n energy_data['pjme_12_hrs_mean'] = energy_data['PJME_MW'].rolling(window=12).mean()\\n energy_data['pjme_24_hrs_mean'] = energy_data['PJME_MW'].rolling(window=24).mean()\\n energy_data['pjme_4_hrs_std'] = energy_data['PJME_MW'].rolling(window=4).std()\\n energy_data['pjme_8_hrs_std'] = energy_data['PJME_MW'].rolling(window=8).std()\\n energy_data['pjme_12_hrs_std'] = energy_data['PJME_MW'].rolling(window=12).std()\\n energy_data['pjme_24_hrs_std'] = energy_data['PJME_MW'].rolling(window=24).std()\\n energy_data['pjme_4_hrs_max'] = energy_data['PJME_MW'].rolling(window=4).max()\\n energy_data['pjme_8_hrs_max'] = energy_data['PJME_MW'].rolling(window=8).max()\\n energy_data['pjme_12_hrs_max'] = energy_data['PJME_MW'].rolling(window=12).max()\\n energy_data['pjme_24_hrs_max'] = energy_data['PJME_MW'].rolling(window=24).max()\\n energy_data['pjme_4_hrs_min'] = energy_data['PJME_MW'].rolling(window=4).min()\\n energy_data['pjme_8_hrs_min'] = energy_data['PJME_MW'].rolling(window=8).min()\\n energy_data['pjme_12_hrs_min'] = energy_data['PJME_MW'].rolling(window=12).min()\\n energy_data['pjme_24_hrs_min'] = energy_data['PJME_MW'].rolling(window=24).min()\\n\\n features = energy_data[['hour', 'dayofweek', 'quarter', 'month', 'year',\\n 'dayofyear', 'dayofmonth', 'weekofyear', 'pjme_2_hrs_lag', 'pjme_4_hrs_lag',\\n 'pjme_8_hrs_lag', 'pjme_12_hrs_lag', 'pjme_24_hrs_lag', 'pjme_4_hrs_mean',\\n \\\"pjme_8_hrs_mean\\\", \\\"pjme_12_hrs_mean\\\", \\\"pjme_24_hrs_mean\\\", \\\"pjme_4_hrs_std\\\",\\n \\\"pjme_8_hrs_std\\\", \\\"pjme_12_hrs_std\\\", \\\"pjme_24_hrs_std\\\",\\n \\\"pjme_4_hrs_max\\\", \\\"pjme_8_hrs_max\\\", \\\"pjme_12_hrs_max\\\", \\\"pjme_24_hrs_max\\\",\\n \\\"pjme_4_hrs_min\\\", \\\"pjme_8_hrs_min\\\", \\\"pjme_12_hrs_min\\\", \\\"pjme_24_hrs_min\\\"]]\\n if label:\\n label = energy_data[label]\\n return features, label\\n return features\",\n \"def get_tap_events(user_id: str, user_session_id: str) -> DataFrame:\\n full_df = pd.DataFrame()\\n for tap_file in tap_file_names:\\n columns = tap_file_important_columns[tap_file]\\n data = read_file(user_id, user_session_id, tap_file)\\n time_data = pd.DataFrame()\\n time_data['Start'] = data[columns[0]]\\n time_data['End'] = data[columns[-2]]\\n time_data['Type'] = tap_file_to_feature_name[tap_file]\\n full_df = pd.concat([full_df, time_data], ignore_index = True)\\n return full_df.dropna().sort_values(by = 'Start').reset_index(drop = True)\",\n \"def build_full_temp(df_temperature):\\n df_interp_full = build_missing(df_temperature)\\n df_full = pd.concat([df_interp_full, df_temperature], ignore_index=True)\\n df_full = df_full.sort_values(by=['datetime'])\\n return df_full\",\n \"def tidy_val_df(df_val, m_sample_scm):\\n \\n df_val = df_val.copy()\\n # Set values to 0 prior to isothermal\\n # look from min_search_start minutes to min_search_end minutes\\n min_search_start = 60 \\n min_search_end = 600\\n idx_min = np.argmin(\\n df_val['Power1,W'].values[min_search_start:min_search_end]) + min_search_start\\n if idx_min >= 599:\\n idx_min = 0\\n# idx_min = 0\\n df_val = df_val[idx_min:]\\n df_val['Heat1,J'] = df_val['Heat1,J'].apply(lambda x: x - df_val['Heat1,J'].values[0])\\n \\n # create time in decimal days for RG charts 20180111\\n # header names require numbers for cc1 data exported with cc2\\n df_val['Power/SCM,W/g'] = df_val['Power1,W'].values / m_sample_scm\\n df_val['Heat/SCM,J/g'] = df_val['Heat1,J'].values / m_sample_scm\\n# df_val['Tmix,s'] = df_val['Tlog,s'].values + time_difference\\n df_val['Tmix,days'] = df_val['Tmix1,s'].values / 86400 # 60 * 60 * 24\\n# df_val = df_val.drop('Tlog,s', axis=1) # remove for cc1 data exported with cc2\\n\\n# rearrange columns to place Tmixs first \\n cols = df_val.columns.tolist()\\n cols = cols[0:1] + cols[-1:] + cols[1:-1]\\n# cols = cols[0:1] + cols[-1:] + cols[1:-1] # For cc1 data exported with cc2\\n df_val = df_val[cols]\\n \\n return df_val\",\n \"def get_data_subsets(t0, t1):\\n\\n # Iridium data:\\n irid = iridium[(iridium.time >= t0) & (iridium.time <= t1)]\\n irid_B = np.vstack((irid.B_e.values, irid.B_n.values, irid.B_r.values))\\n irid_coords = np.vstack((irid.lon.values, irid.lat.values, irid.r.values))\\n\\n # SuperMAG data:\\n smag = supermag.loc[t0:t1, :]\\n smag_B = np.vstack((smag.Be.values, smag.Bn.values, smag.Bu.values))\\n smag_coords = np.vstack((smag.lon.values, smag.lat.values))\\n\\n # SuperDARN data:\\n sd = superdarn.loc[t0:t1, :]\\n vlos = sd['vlos'].values\\n sd_coords = np.vstack((sd['glon'].values, sd['glat'].values))\\n los = np.vstack((sd['le'].values, sd['ln'].values))\\n\\n\\n # Make the data objects. The scale keyword determines a weight for the dataset. Increase it to reduce weight\\n iridium_data = lompe.Data(irid_B * 1e-9, irid_coords, datatype = 'space_mag_fac', scale = 200e-9)\\n supermag_data = lompe.Data(smag_B * 1e-9, smag_coords, datatype = 'ground_mag' , scale = 100e-9)\\n superdarn_data = lompe.Data(vlos , sd_coords , LOS = los, datatype = 'convection' , scale = 500 )\\n\\n return(iridium_data, supermag_data, superdarn_data)\",\n \"def tile_read_utm(source, bounds, tilesize, indexes=[1], nodata=None, alpha=None, dst_crs='EPSG:3857', \\n verbose=False,\\n boundless=False):\\n w, s, e, n = bounds\\n\\n if alpha is not None and nodata is not None:\\n raise RioTilerError('cannot pass alpha and nodata option')\\n\\n if isinstance(indexes, int):\\n indexes = [indexes]\\n (e - w) / tilesize\\n out_shape = (len(indexes), tilesize, tilesize)\\n if verbose:\\n print(dst_crs)\\n vrt_params = dict(\\n crs=dst_crs,\\n resampling=Resampling.bilinear,\\n src_nodata=nodata,\\n dst_nodata=nodata)\\n\\n if isinstance(source, DatasetReader):\\n with WarpedVRT(source, **vrt_params) as vrt:\\n window = vrt.window(w, s, e, n, precision=21)\\n if verbose:\\n print(window)\\n #window_transform = windows.transform(window, vrt.transform)\\n window_transform = transform.from_bounds(w,s,e,n, tilesize, tilesize)\\n \\n data = vrt.read(window=window,\\n resampling=Resampling.bilinear,\\n out_shape=out_shape,\\n indexes=indexes,\\n boundless=boundless)\\n if False: #except:\\n print(bounds)\\n print(window)\\n print(out_shape)\\n print(indexes)\\n print(boundless)\\n print(window_transform)\\n\\n if nodata is not None:\\n mask = np.all(data != nodata, axis=0).astype(np.uint8) * 255\\n elif alpha is not None:\\n mask = vrt.read(alpha, window=window,\\n out_shape=(tilesize, tilesize),\\n boundless=boundless,\\n resampling=Resampling.bilinear)\\n else:\\n mask = vrt.read_masks(1, window=window,\\n out_shape=(tilesize, tilesize),\\n boundless=boundless,\\n resampling=Resampling.bilinear)\\n else:\\n with rasterio.open(source) as src:\\n with WarpedVRT(src, **vrt_params) as vrt:\\n window = vrt.window(w, s, e, n, precision=21)\\n window_transform = windows.transform(window, vrt.transform)\\n window_transform = transform.from_bounds(w, s, e, n, tilesize, tilesize)\\n\\n data = vrt.read(window=window,\\n boundless=boundless,\\n resampling=Resampling.bilinear,\\n out_shape=out_shape,\\n indexes=indexes)\\n\\n if nodata is not None:\\n mask = np.all(data != nodata, axis=0).astype(np.uint8) * 255\\n elif alpha is not None:\\n mask = vrt.read(alpha, window=window,\\n out_shape=(tilesize, tilesize),\\n boundless=boundless,\\n resampling=Resampling.bilinear)\\n else:\\n mask = vrt.read_masks(1, window=window,\\n out_shape=(tilesize, tilesize),\\n boundless=boundless,\\n resampling=Resampling.bilinear)\\n\\n return data, mask, window, window_transform\",\n \"def download_data(study, engine, has_trips=True, filter_coverage=False):\\n\\n def to_datetime(df):\\n df[\\\"started_at\\\"] = pd.to_datetime(df[\\\"started_at\\\"], utc=True)\\n df[\\\"finished_at\\\"] = pd.to_datetime(df[\\\"finished_at\\\"], utc=True)\\n return df\\n\\n exclude_purpose_tist = [\\n \\\"Light Rail\\\",\\n \\\"Subway\\\",\\n \\\"Platform\\\",\\n \\\"Trail\\\",\\n \\\"Road\\\",\\n \\\"Train\\\",\\n \\\"Bus Line\\\",\\n ]\\n\\n print(\\\"\\\\t download staypoints\\\")\\n sp = gpd.read_postgis(\\n sql=\\\"select * from {}.{}\\\".format(study, sp_name_dict[study]),\\n con=engine,\\n geom_col=\\\"geom\\\",\\n index_col=\\\"id\\\",\\n )\\n sp = to_datetime(sp)\\n\\n print(\\\"\\\\t download locations\\\")\\n sql = f\\\"SELECT * FROM {study}.{locs_name_dict[study]}\\\"\\n locs = ti.io.read_locations_postgis(sql, con=engine, index_col=\\\"id\\\")\\n\\n # studies with trips\\n gap_treshold = 12\\n if has_trips:\\n print(\\\"\\\\t download trips\\\")\\n trips = ti.io.read_trips_postgis(\\n f\\\"select * from {study}.trips\\\", con=engine\\n )\\n if filter_coverage:\\n # get_triplegs(study=study, engine=engine)\\n print(\\\"\\\\t download triplegs\\\")\\n tpls = pd.read_sql(\\n sql=f\\\"select id, user_id, started_at, finished_at from {study}.triplegs\\\",\\n con=engine,\\n index_col=\\\"id\\\",\\n )\\n tpls = to_datetime(tpls)\\n sp, locs, trips = filter_tracking_coverage(sp, locs, tpls, trips)\\n\\n # studies without trips (Foursquare)\\n else:\\n sp = sp[~sp[\\\"purpose\\\"].isin(exclude_purpose_tist)]\\n trips = None\\n return (sp, locs, trips, gap_treshold)\",\n \"def get_weather_data():\\n keys = ['1364038.csv',\\n '1364041.csv',\\n '1364042.csv',\\n '1364043.csv',\\n '1364044.csv',\\n '1364046.csv',\\n '1364047.csv',\\n '1364048.csv',\\n '1364051.csv',\\n '1364052.csv',\\n '1364053.csv',\\n '1364054.csv',\\n '1364055.csv',\\n '1364058.csv',\\n '1364059.csv',\\n '1364060.csv',\\n '1364061.csv',\\n '1364062.csv',\\n '1364063.csv',\\n '1364064.csv',\\n '1364066.csv']\\n df_weather = import_weather(keys)\\n df_weather_dist = df_weather[[\\n 'LATITUDE', 'LONGITUDE', 'name']].drop_duplicates().reset_index()\\n return df_weather, df_weather_dist\",\n \"def point_interp_ts(df, time_col, x_col, y_col, data_col, point_shp, point_site_col, from_crs, to_crs=None, interp_fun='cubic', agg_ts_fun=None, period=None, digits=2):\\n\\n #### Read in points\\n if isinstance(point_shp, str) & isinstance(point_site_col, str):\\n points = read_file(point_shp)[[point_site_col, 'geometry']]\\n to_crs1 = points.crs\\n elif isinstance(point_shp, GeoDataFrame) & isinstance(point_site_col, str):\\n points = point_shp[[point_site_col, 'geometry']]\\n to_crs1 = points.crs\\n else:\\n raise ValueError('point_shp must be a str path to a shapefile or a GeoDataFrame and point_site_col must be a str.')\\n\\n #### Create the grids\\n df1 = df.copy()\\n\\n #### Resample the time series data\\n if agg_ts_fun is not None:\\n df1a = df1.set_index(time_col)\\n if agg_ts_fun == 'sum':\\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].sum().reset_index()\\n elif agg_ts_fun == 'mean':\\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].mean().reset_index()\\n else:\\n raise ValueError(\\\"agg_ts_fun should be either 'sum' or 'mean'.\\\")\\n time = df2[time_col].unique()\\n else:\\n df2 = df1\\n\\n time = df2[time_col].sort_values().unique()\\n\\n #### Convert input data to crs of points shp and create input xy\\n data1 = df2.loc[df2[time_col] == time[0]]\\n from_crs1 = convert_crs(from_crs, pass_str=True)\\n\\n if to_crs is not None:\\n to_crs1 = convert_crs(to_crs, pass_str=True)\\n points = points.to_crs(to_crs1)\\n geometry = [Point(xy) for xy in zip(data1[x_col], data1[y_col])]\\n gpd = GeoDataFrame(data1.index, geometry=geometry, crs=from_crs1)\\n gpd1 = gpd.to_crs(crs=to_crs1)\\n x = gpd1.geometry.apply(lambda p: p.x).round(digits).values\\n y = gpd1.geometry.apply(lambda p: p.y).round(digits).values\\n\\n xy = column_stack((x, y))\\n\\n #### Prepare the x and y of the points geodataframe output\\n x_int = points.geometry.apply(lambda p: p.x).round(digits).values\\n y_int = points.geometry.apply(lambda p: p.y).round(digits).values\\n sites = points[point_site_col]\\n\\n xy_int = column_stack((x_int, y_int))\\n\\n #### Create new df\\n sites_ar = tile(sites, len(time))\\n time_ar = repeat(time, len(xy_int))\\n x_ar = tile(x_int, len(time))\\n y_ar = tile(y_int, len(time))\\n new_df = DataFrame({'site': sites_ar, 'time': time_ar, 'x': x_ar, 'y': y_ar, data_col: repeat(0, len(time) * len(xy_int))})\\n\\n new_lst = []\\n for t in to_datetime(time):\\n set1 = df2.loc[df2[time_col] == t, data_col]\\n new_z = griddata(xy, set1.values, xy_int, method=interp_fun).round(digits)\\n new_z[new_z < 0] = 0\\n new_lst.extend(new_z.tolist())\\n# print(t)\\n new_df.loc[:, data_col] = new_lst\\n\\n #### Export results\\n return(new_df[new_df[data_col].notnull()])\",\n \"def generate_timeseries(data_list, setname=\\\"MagneticFields\\\"):\\n full_data = TimeSeriesList()\\n for seg in sorted(data_list):\\n hfile = h5py.File(data_list[seg], \\\"r\\\")\\n full_data.append(retrieve_data_timeseries(hfile, \\\"MagneticFields\\\"))\\n hfile.close()\\n return full_data\",\n \"def write_overview_tile(self, tx, ty, tz,tms_osm):\\n\\n image_format = self.get_overview_tile_format(tx, ty, tz)\\n\\n if image_format is None:\\n return\\n else:\\n num_bands = self.get_num_bands(image_format)\\n\\n dsquery = self.mem_drv.Create('', 2*self.tile_size, 2*self.tile_size, num_bands)\\n self.fill_init_dest(dsquery)\\n # tms: z=19: 281626\\n # -z=18-140813 176168*2=352336; 176168*2+1=352337\\n # -- 352336,352337\\n y_from=2*ty\\n y_to=2*ty + 1\\n ty_tms=ty;\\n s_y_type=\\\"tms\\\"\\n if tms_osm:\\n # osm: z=19: 281626\\n # -z=18-140813 85975*2+1=171951; 85975*2=171950\\n # -- 171951,171950 [in range: last/end not used]\\n y_from=2*ty + 1\\n y_to=2*ty\\n ty_tms=(2**tz-1) - ty\\n s_y_type=\\\"osm\\\"\\n s_tile_id=\\\"{0}-{1}-{2}.{3}\\\".format(str(tz), str(tx),str(ty),s_y_type)\\n if self.verbose:\\n # Build from zoom 19 tiles: (281626, 171951) (281627, 171951) (281626, 171950) (281627, 171950)\\n print \\\"\\\\tBuild [\\\",s_tile_id,\\\"] from [\\\",self.output_dir,\\\"] zoom\\\", tz+1,\\\" tiles [\\\",s_y_type,\\\"]: \\\", (2*tx, y_from), (2*tx+1, y_from),(2*tx, y_to), (2*tx+1, y_to)\\n\\n for cx, cy, child_image_format in self.iter_children(tx, ty, tz):\\n if (ty_tms==0 and cy==1) or (ty_tms!=0 and (cy % (y_from)) != 0):\\n tileposy = 0\\n else:\\n tileposy = self.tile_size\\n if tx:\\n tileposx = cx % (2*tx) * self.tile_size\\n elif tx==0 and cx==1:\\n tileposx = self.tile_size\\n else:\\n tileposx = 0\\n\\n path = self.get_full_path(cx, cy, tz+1, format_extension[child_image_format])\\n\\n dsquerytile = gdal.Open(path, gdal.GA_ReadOnly)\\n\\n dsquery.WriteRaster(tileposx, tileposy, self.tile_size, self.tile_size,\\n dsquerytile.ReadRaster(0, 0, self.tile_size, self.tile_size),\\n band_list=range(1, dsquerytile.RasterCount+1))\\n\\n if image_format == \\\"PNG\\\" and dsquerytile.RasterCount != num_bands:\\n dsquery.WriteRaster(tileposx, tileposy, self.tile_size, self.tile_size,\\n self.get_alpha_filler(), band_list=[num_bands])\\n\\n dstile = self.mem_drv.Create('', self.tile_size, self.tile_size, num_bands)\\n path = self.get_full_path(tx, ty, tz, format_extension[image_format])\\n self.resampler(path, dsquery, dstile, image_format)\",\n \"def pull_traffic():\\n ga_conn = initialize_analyticsreporting()\\n\\n device_traffic = get_report(\\n ga_conn=ga_conn,\\n dimensions=[{'name': 'ga:deviceCategory'},{'name': 'ga:yearMonth'},])\\n device_traffic = melt_ga(device_traffic,\\n a='Traffic',\\n c='Device',\\n d='deviceCategory')\\n\\n geo_traffic = get_report(\\n ga_conn=ga_conn,\\n dimensions=[{'name': 'ga:country'},{'name': 'ga:yearMonth'},])\\n bucketed_countries = geo_traffic.apply(lambda row: bucket_countries(row), axis=1)\\n geo_traffic['country'] = bucketed_countries\\n geo_traffic = geo_traffic.groupby(['yearMonth','country'])\\\\\\n .sum()\\\\\\n .reset_index()\\n geo_traffic = melt_ga(geo_traffic,\\n a='Traffic',\\n c='Geography',\\n d='country')\\n\\n channel_traffic = get_report(\\n ga_conn=ga_conn,\\n dimensions=[{'name': 'ga:channelGrouping'},{'name': 'ga:yearMonth'},])\\n bucketed_channels = channel_traffic.apply(lambda row: bucket_channels(row), axis=1)\\n channel_traffic['channelGrouping'] = bucketed_channels\\n channel_traffic = channel_traffic.groupby(['yearMonth','channelGrouping'])\\\\\\n .sum()\\\\\\n .reset_index()\\n channel_traffic = melt_ga(channel_traffic,\\n a='Traffic',\\n c='Channel',\\n d='channelGrouping')\\n df_out = pd.concat([device_traffic, geo_traffic, channel_traffic])\\n return df_out\",\n \"def temperature_range_analysis(temp_df, dt_from, dt_to):\\n df = copy.deepcopy(temp_df)\\n\\n df_unique_sensors = df[[\\\"sensor_id\\\", \\\"name\\\"]].drop_duplicates([\\\"sensor_id\\\", \\\"name\\\"])\\n\\n sensor_ids = df_unique_sensors[\\\"sensor_id\\\"].tolist()\\n sensor_names = df_unique_sensors[\\\"name\\\"].tolist()\\n\\n # extracting date from datetime\\n df[\\\"date\\\"] = pd.to_datetime(df[\\\"timestamp\\\"].dt.date)\\n\\n # Reseting index\\n df.sort_values(by=[\\\"timestamp\\\"], ascending=True).reset_index(inplace=True)\\n\\n data_by_sensor_id = {}\\n for sensor_name, sensor_id in zip(sensor_names, sensor_ids):\\n df_sensor = df[df[\\\"sensor_id\\\"] == sensor_id]\\n # grouping data by date-hour and sensor id\\n sensor_grp = df_sensor.groupby(\\n by=[\\n df_sensor.timestamp.map(\\n lambda x: \\\"%04d-%02d-%02d-%02d\\\" % (x.year, x.month, x.day, x.hour)\\n ),\\n \\\"date\\\",\\n ]\\n )\\n\\n # estimating hourly temperature mean values\\n sensor_grp_temp = sensor_grp[\\\"temperature\\\"].mean().reset_index()\\n\\n try:\\n bins = TEMP_BINS[SENSOR_CATEORIES[sensor_id]]\\n except KeyError:\\n logging.error(\\n f\\\"Don't know how to categorise or bin sensor {sensor_id} \\\"\\n \\\"in the dashboard.\\\"\\n )\\n continue\\n # binning temperature values\\n sensor_grp_temp[\\\"temp_bin\\\"] = pd.cut(sensor_grp_temp[\\\"temperature\\\"], bins)\\n\\n # converting bins to str\\n sensor_grp_temp[\\\"temp_bin\\\"] = sensor_grp_temp[\\\"temp_bin\\\"].astype(str)\\n\\n # get bin counts for each sensor-day combination\\n sensor_grp_date = sensor_grp_temp.groupby(by=[\\\"date\\\", \\\"temp_bin\\\"])\\n\\n sensor_cnt = sensor_grp_date[\\\"temperature\\\"].count().reset_index()\\n sensor_cnt.rename(columns={\\\"temperature\\\": \\\"temp_cnt\\\"}, inplace=True)\\n\\n # Adding missing date/temp_bin combos\\n bins_list, df_list = resample(sensor_cnt, bins, dt_from, dt_to)\\n\\n data_by_sensor_id[sensor_id] = {\\n \\\"name\\\": sensor_name,\\n \\\"bins\\\": bins_list,\\n \\\"data\\\": [\\n {\\n \\\"date\\\": df[\\\"date\\\"].dt.strftime(\\\"%Y-%m-%d\\\").to_list(),\\n \\\"count\\\": df[\\\"temp_cnt\\\"].to_list(),\\n }\\n for df in df_list\\n ],\\n }\\n return len(data_by_sensor_id.keys()), json.dumps(data_by_sensor_id)\",\n \"def create_df(file_path):\\n columns = ['Lat', 'Lat_dir', 'Long', 'Long_dir', 'Quality', 'Dilution']\\n speeds = []\\n validity = []\\n rows = []\\n with open(file_path, encoding='utf-8', errors='ignore') as f:\\n for line in f.readlines()[5:]:\\n words = line.strip().split(\\\",\\\")\\n if len(words) == 0 or len(words) > 15:\\n continue\\n elif words[0] == \\\"$GPGGA\\\": # if line starts with GPGGA store lat, long, quality and dilution of precision\\n if len(rows) == 0:\\n row = [words[2], words[3], words[4], words[5], words[6], words[8]]\\n speeds.append(np.nan)\\n validity.append(np.nan)\\n rows.append(row)\\n else:\\n if rows[len(rows) - 1][0] is np.nan:\\n row = [words[2], words[3], words[4], words[5], words[6], words[8]]\\n rows[len(rows) - 1] = row\\n else:\\n row = [words[2], words[3], words[4], words[5], words[6], words[8]]\\n speeds.append(np.nan)\\n validity.append(np.nan)\\n rows.append(row)\\n elif words[0] == \\\"$GPRMC\\\": # if lines start with GPRMC store speed and validity\\n if len(rows) == 0:\\n row = [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]\\n speeds.append(float(words[7]) * 1.15078)\\n validity.append(words[2])\\n rows.append(row)\\n else:\\n if speeds[len(speeds) - 1] is np.nan:\\n speeds[len(speeds) - 1] = float(words[7]) * 1.15078\\n validity[len(speeds) - 1] = words[2]\\n else:\\n row = [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]\\n speeds.append(float(words[7]) * 1.15078)\\n validity.append(words[2])\\n rows.append(row)\\n else:\\n continue\\n gps_df = pd.DataFrame(rows, columns=columns)\\n gps_df['Speed'] = speeds # combine both speed and validity back to dataframe\\n gps_df['Validity'] = validity\\n return gps_df\",\n \"def create_geodata(x):\\n list_len = len(x)\\n pilot_log = pd.concat(x[i][['time','Cn0DbHz','svid','geometry']] for i in range(list_len))\\n \\n return pilot_log\",\n \"def grid_interp_ts(df, time_col, x_col, y_col, data_col, grid_res, from_crs=None, to_crs=2193, interp_fun='cubic', agg_ts_fun=None, period=None, digits=2):\\n\\n #### Create the grids\\n df1 = df.copy()\\n\\n #### Resample the time series data\\n if agg_ts_fun is not None:\\n df1a = df1.set_index(time_col)\\n if agg_ts_fun == 'sum':\\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].sum().reset_index()\\n elif agg_ts_fun == 'mean':\\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].mean().reset_index()\\n else:\\n raise ValueError(\\\"agg_ts_fun should be either 'sum' or 'mean'.\\\")\\n time = df2[time_col].unique()\\n else:\\n df2 = df1\\n\\n time = df2[time_col].sort_values().unique()\\n\\n if from_crs is None:\\n x = df2.loc[df2[time_col] == time[0], x_col].values\\n y = df2.loc[df2[time_col] == time[0], y_col].values\\n else:\\n data1 = df2.loc[df2[time_col] == time[0]]\\n from_crs1 = convert_crs(from_crs, pass_str=True)\\n to_crs1 = convert_crs(to_crs, pass_str=True)\\n geometry = [Point(xy) for xy in zip(data1[x_col], data1[y_col])]\\n gpd = GeoDataFrame(data1.index, geometry=geometry, crs=from_crs1)\\n gpd1 = gpd.to_crs(crs=to_crs1)\\n x = gpd1.geometry.apply(lambda p: p.x).round(digits).values\\n y = gpd1.geometry.apply(lambda p: p.y).round(digits).values\\n\\n xy = column_stack((x, y))\\n\\n max_x = x.max()\\n min_x = x.min()\\n\\n max_y = y.max()\\n min_y = y.min()\\n\\n new_x = arange(min_x, max_x, grid_res)\\n new_y = arange(min_y, max_y, grid_res)\\n x_int, y_int = meshgrid(new_x, new_y)\\n\\n #### Create new df\\n x_int2 = x_int.flatten()\\n y_int2 = y_int.flatten()\\n xy_int = column_stack((x_int2, y_int2))\\n time_df = repeat(time, len(x_int2))\\n x_df = tile(x_int2, len(time))\\n y_df = tile(y_int2, len(time))\\n new_df = DataFrame({'time': time_df, 'x': x_df, 'y': y_df, data_col: repeat(0, len(time) * len(x_int2))})\\n\\n new_lst = []\\n for t in to_datetime(time):\\n set1 = df2.loc[df2[time_col] == t, data_col]\\n# index = new_df[new_df['time'] == t].index\\n new_z = griddata(xy, set1.values, xy_int, method=interp_fun).round(digits)\\n new_z[new_z < 0] = 0\\n new_lst.extend(new_z.tolist())\\n# print(t)\\n new_df.loc[:, data_col] = new_lst\\n\\n #### Export results\\n return(new_df[new_df[data_col].notnull()])\",\n \"def preproc_data(data):\\n # Load data manually from Yahoo! finance\\n\\n # Initialize TP Matrix\\n # 3-dimension: # of stock * 18 * 18\\n # narray\\n _TP_matrixs = np.zeros(\\n (len(data.ix[stockname]) - 230, 18, 18), dtype=np.bool)\\n old = data.ix[stockname]['close'][229]\\n TP_matrixs = pd.Panel(_TP_matrixs, items=data.ix[stockname].index[230:])\\n label = np.zeros((len(data.ix[stockname]) - 230), dtype=np.float)\\n dataindex = 0\\n dataset = []\\n # Construct TP Matrix\\n for TP_matrix in TP_matrixs.iteritems():\\n # Extract raw close price of last 230 days\\n # pdb.set_trace()\\n tp_features = np.zeros((18, 18), dtype=np.bool)\\n _list_CP = data.ix[stockname][data.ix[stockname].index <\\n TP_matrix[0]]['close'].tolist()\\n list_CP = _list_CP[len(_list_CP) - 230: len(_list_CP)]\\n close = data.ix[stockname]['close'][dataindex + 230]\\n label = (close - old) / old\\n old = close\\n # col[0, 8] for Upward TP Matrix\\n # col[9, 17] for Downward TP Matrix\\n for col in range(0, 18):\\n D = columns[col][0] - 1\\n for row in range(0, 18):\\n # For each element of TP Matrix\\n for TP in range(D, columns[col][1]):\\n # Change ratio of stock on day D with repect to the price\\n # at TP\\n C_TPD = (list_CP[TP] - list_CP[D]) / list_CP[D]\\n if C_TPD * 100 >= rows[row][0] and C_TPD * 100 < rows[row][1]:\\n TP_matrix[1][row][col] = True\\n tp_features[row][col] = True\\n break\\n\\n sample = DataSet()\\n sample.tp_features = tp_features\\n sample.labels = label\\n dataindex += 1\\n dataset.append(sample)\\n\\n filename = 'data/TP_matrix_' + stockname + '.pkl'\\n output = open(filename, 'wb')\\n # # Pickle dictionary using protocol 0.\\n pickle.dump(TP_matrixs, output)\\n output.close()\\n return dataset\",\n \"def warren_time_series(lat, lon, dates, variable='snow_depth'):\\n\\n my_func = {'snow_depth': snow_depth,\\n 'swe': swe}\\n\\n #if not all([isinstance(d, dt.datetime) for d in dates]):\\n # print ('Expects datetime objects')\\n\\n # If lat, lon are vectors, generate 2d grids\\n # Need to add code to make sure x and y are DataArrays\\n if (count_dims(lat) == 1):\\n x, y = np.meshgrid(lon, lat)\\n else:\\n x, y = lon, lat\\n \\n if dates.size == 1:\\n cube = my_func[variable](x,y,dates.dt.month)\\n da = xr.DataArray(cube,\\n coords={'lat': x, 'lon': y},\\n dims=['lat', 'lon'])\\n else:\\n cube = [my_func[variable](x, y, m) for m in dates.dt.month.values]\\n da = xr.concat(cube, dim='time')\\n da['time'] = dates\\n\\n return da\",\n \"def link_tariffs(meas: pd.DataFrame, data_location: str, filename: str,\\n local_tz: str = 'Australia/Sydney') -> pd.Series:\\n datetime_series = unit_conversion.local_t_from_dataframe_index(meas, local_tz=local_tz)\\n tariff_series = load_tariff(data_location, filename, meas.index, datetime_series)\\n\\n return tariff_series\",\n \"def main(parameters_file, token):\\n\\n print(f\\\"--\\\\nReading parameters file: {parameters_file}\\\\n--\\\")\\n\\n set_token(token=token)\\n\\n\\n try:\\n with open(parameters_file, 'r') as f:\\n\\n parameters = load(f,\\n Loader=SafeLoader)\\n maggeo_params = parameters[\\\"maggeo\\\"] \\n gpsfilename = maggeo_params[\\\"gpsfilename\\\"]\\n Lat = maggeo_params[\\\"Lat\\\"]\\n Long = maggeo_params[\\\"Long\\\"] \\n DateTime = maggeo_params[\\\"DateTime\\\"]\\n altitude = maggeo_params[\\\"altitude\\\"]\\n\\n except Exception as error:\\n print('Error in parameters file format')\\n raise error\\n \\n base_dir=os.getcwd()\\n temp_results_dir = os.path.join(base_dir, \\\"temp_data\\\")\\n results_dir = os.path.join(base_dir, \\\"results\\\")\\n data_dir = os.path.join(base_dir, \\\"data\\\")\\n utilities_dir = os.path.join(base_dir, \\\"utilities\\\")\\n\\n GPSData = getGPSData(data_dir,gpsfilename,Lat,Long,DateTime,altitude)\\n \\n datestimeslist = []\\n for index, row in GPSData.iterrows():\\n datetimerow = row['gpsDateTime']\\n daterow = row['dates']\\n hourrow = row['times']\\n hourrow = hourrow.strftime('%H:%M:%S')\\n if hourrow < '04:00:00':\\n date_bfr = daterow - (timedelta(days=1))\\n datestimeslist.append(daterow)\\n datestimeslist.append(date_bfr)\\n if hourrow > '20:00:00':\\n Date_aft = daterow + (timedelta(days=1))\\n datestimeslist.append(daterow)\\n datestimeslist.append(Date_aft) \\n else:\\n datestimeslist.append(daterow)\\n\\n def uniquelistdates(list): \\n x = np.array(list) \\n uniquelist = np.unique(x)\\n return uniquelist\\n\\n uniquelist_dates = uniquelistdates(datestimeslist)\\n\\n hours_t_day = 24 #MagGeo needs the entire Swarm data for each day of the identified day.\\n hours_added = dt.timedelta(hours = hours_t_day)\\n\\n listdfa = []\\n listdfb = []\\n listdfc = []\\n\\n for d in tqdm(uniquelist_dates, desc=\\\"Getting Swarm Data\\\"):\\n #print(\\\"Getting Swarm data for date:\\\",d )\\n startdate = dt.datetime.combine(d, dt.datetime.min.time())\\n enddate = startdate + hours_added\\n SwarmResidualsA,SwarmResidualsB,SwarmResidualsC = Get_Swarm_residuals(startdate, enddate)\\n listdfa.append(SwarmResidualsA)\\n listdfb.append(SwarmResidualsB)\\n listdfc.append(SwarmResidualsC)\\n\\n base_dir = os.getcwd() # Get main MagGeo directory\\n temp_results_dir = os.path.join(base_dir, \\\"temp_data\\\")\\n results_dir = os.path.join(base_dir, \\\"results\\\")\\n data_dir = os.path.join(base_dir, \\\"data\\\")\\n\\n #TODO:\\n #Find a more elegant way to concante the the list from Swarm and then read it to get the type of columns is requiered, Timestamp as datetime and epoch as index.\\n \\n PdSwarmRes_A = pd.concat(listdfa, join='outer', axis=0)\\n PdSwarmRes_A.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_A.csv'), header=True)\\n PdSwarmRes_B = pd.concat(listdfb, join='outer', axis=0)\\n PdSwarmRes_B.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_B.csv'), header=True)\\n PdSwarmRes_C = pd.concat(listdfc, join='outer', axis=0)\\n PdSwarmRes_C.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_C.csv'), header=True)\\n\\n TotalSwarmRes_A = pd.read_csv(os.path.join(temp_results_dir,\\\"TotalSwarmRes_A.csv\\\"),low_memory=False, index_col='epoch')\\n TotalSwarmRes_A['timestamp'] = pd.to_datetime(TotalSwarmRes_A['timestamp'])\\n TotalSwarmRes_B = pd.read_csv(os.path.join(temp_results_dir,\\\"TotalSwarmRes_B.csv\\\"),low_memory=False, index_col='epoch')\\n TotalSwarmRes_B['timestamp'] = pd.to_datetime(TotalSwarmRes_B['timestamp'])\\n TotalSwarmRes_C = pd.read_csv(os.path.join(temp_results_dir,\\\"TotalSwarmRes_C.csv\\\"),low_memory=False, index_col='epoch')\\n TotalSwarmRes_C['timestamp'] = pd.to_datetime(TotalSwarmRes_C['timestamp'])\\n\\n dn = [] ## List used to add all the GPS points with the annotated MAG Data. See the last bullet point of this process \\n for index, row in tqdm(GPSData.iterrows(), total=GPSData.shape[0], desc=\\\"Annotating the GPS Trayectory\\\"):\\n GPSLat = row['gpsLat'] \\n GPSLong = row['gpsLong']\\n GPSDateTime = row['gpsDateTime']\\n GPSTime = row['epoch']\\n GPSAltitude = row['gpsAltitude']\\n #print(\\\"Process for:\\\", index,\\\"DateTime:\\\",GPSDateTime)\\n try:\\n result=ST_IDW_Process(GPSLat,GPSLong,GPSAltitude, GPSDateTime,GPSTime, TotalSwarmRes_A, TotalSwarmRes_B, TotalSwarmRes_C)\\n dn.append(result)\\n except:\\n #print(\\\"Ups!.That was a bad Swarm Point, let's keep working with the next point\\\")\\n result_badPoint= {'Latitude': GPSLat, 'Longitude': GPSLong, 'Altitude':GPSAltitude, 'DateTime': GPSDateTime, 'N_res': np.nan, 'E_res': np.nan, 'C_res':np.nan, 'TotalPoints':0, 'Minimum_Distance':np.nan, 'Average_Distance':np.nan} \\n dn.append(result_badPoint)\\n continue\\n \\n \\n GPS_ResInt = pd.DataFrame(dn)\\n GPS_ResInt.to_csv (os.path.join(temp_results_dir,\\\"GPS_ResInt.csv\\\"), header=True)\\n \\n X_obs, Y_obs, Z_obs, X_obs_internal, Y_obs_internal, Z_obs_internal = CHAOS_ground_values(utilities_dir,GPS_ResInt)\\n\\n GPS_ResInt['N'] =pd.Series(X_obs)\\n GPS_ResInt['E'] =pd.Series(Y_obs)\\n GPS_ResInt['C'] =pd.Series(Z_obs)\\n GPS_ResInt['N_Obs'] =pd.Series(X_obs_internal)\\n GPS_ResInt['E_Obs'] =pd.Series(Y_obs_internal)\\n GPS_ResInt['C_Obs'] =pd.Series(Z_obs_internal)\\n\\n GPS_ResInt.drop(columns=['N_res', 'E_res','C_res'], inplace=True)\\n\\n # Having Intepolated and weighted the magnetic values, we can compute the other magnectic components. \\n GPS_ResInt['H'] = np.sqrt((GPS_ResInt['N']**2)+(GPS_ResInt['E']**2))\\n #check the arcgtan in python., From arctan2 is saver.\\n DgpsRad = np.arctan2(GPS_ResInt['E'],GPS_ResInt['N'])\\n GPS_ResInt['D'] = np.degrees(DgpsRad)\\n IgpsRad = np.arctan2(GPS_ResInt['C'],GPS_ResInt['H'])\\n GPS_ResInt['I'] = np.degrees(IgpsRad)\\n GPS_ResInt['F'] = np.sqrt((GPS_ResInt['N']**2)+(GPS_ResInt['E']**2)+(GPS_ResInt['C']**2))\\n\\n \\n originalGPSTrack=pd.read_csv(os.path.join(data_dir,gpsfilename))\\n MagGeoResult = pd.concat([originalGPSTrack, GPS_ResInt], axis=1)\\n #Drop duplicated columns. Latitude, Longitued, and DateTime will not be part of the final result.\\n MagGeoResult.drop(columns=['Latitude', 'Longitude', 'DateTime'], inplace=True)\\n \\n #Exporting the CSV file\\n \\n outputfile =\\\"GeoMagResult_\\\"+gpsfilename\\n export_csv = MagGeoResult.to_csv (os.path.join(results_dir,outputfile), index = None, header=True) \\n print(\\\"Congrats! MagGeo has processed your GPS trayectory. Find the annotated table: \\\" + outputfile + \\\" in the folder results.\\\")\",\n \"def time_info(input_file):\\n original_path = os.getcwd() #set original directory\\n save_path = input_file['save_path']\\n planet = input_file['exoplanet'] #set exoplanet name\\n print '\\\\nObtain the images .... \\\\n'\\n print 'Change to ', save_path\\n os.chdir(save_path) #change to save directory where is our scvience images\\n images = sorted(glob.glob('AB'+input_file['exoplanet']+'*.fits'))\\n print '\\\\nImages = \\\\n',images\\n tempo_loc = [] #time object\\n SUN = [] #Sun coordinate object\\n ra_sun, dec_sun, dsun = np.zeros(len(images)),np.zeros(len(images)),np.zeros(len(images)) #sun coordinates\\n JD = np.zeros(len(images)) #julian date from time object\\n ST = np.zeros(len(images))\\n HJD = np.zeros(len(images))\\n #create the exoplanet object coordianate\\n exoplanet = SkyCoord(dec=input_file['DEC'],ra=input_file['RA'],unit=('deg','deg'),frame=input_file['frame'])\\n print '\\\\nObtain data info from header ....\\\\n'\\n for i in range(len(images)):\\n hdr = fits.getheader(images[i])\\n UTC = hdr['date-obs']+'T'+hdr['UT'] #string that contain the time in UTC in isot format\\n tempo_loc.append(Time(UTC,scale=input_file['scale-time'],format='isot',location=(input_file['lon-obs'],input_file['lat-obs'])))#,input_data['altitude'])))\\n JD[i] = tempo_loc[i].jd\\n ST[i] = tempo_loc[i].sidereal_time('apparent').hour\\n SUN.append(get_sun(tempo_loc[i]))\\n ra_sun[i],dec_sun[i] = SUN[i].ra.deg, SUN[i].dec.deg\\n dsun[i] = SUN[i].distance.value\\n HJD[i] = use.hjd_date(JD[i],dsun[i],dec_sun[i],ra_sun[i],exoplanet.dec.deg,exoplanet.ra.deg,circular_orbit=input_file['circular_orbit'])\\n use.update_progress((i+1.)/len(images))\\n print '\\\\n.... done.\\\\n'\\n print '\\\\n Time from header = \\\\n'\\n #print '\\\\nImages ** UTC (YYYY-MM-DDTHH:MM:SS) ** JD (7d.5d) ** ST (hours) ** ST (HH:MM:SS) ** Sun Coordinate (epoch,RA,DEC,Distance) (deg,deg,AU) \\\\n'\\n ST_string = []\\n for i in range(len(images)):\\n ST1 = int(ST[i])\\n ST2 = int((ST[i]-ST1)*60.)\\n ST3 = (((ST[i]-ST1)*60.)-ST2)*60\\n ST_string.append(str(ST1)+':'+str(ST2)+':'+str(ST3))\\n tempo_loc[i] = tempo_loc[i].value\\n use.update_progress((i+1.)/len(images))\\n #print images[i], ' ** ',tempo_loc[i], ' ** ', JD[i], ' ** ', ST[i],' ** ',ST_string[i],' ** ',sun_loc[i],' ** ',HJD[i]\\n print '\\\\nSave data file ... \\\\n'\\n data = DataFrame([images,tempo_loc,list(JD),list(ST),list(ST_string),list(ra_sun),list(dec_sun),list(dsun),list(HJD)]).T\\n data.columns=['images','UTC','JD','ST','ST_isot','RA_SUN','DEC_SUN','D_SUN','HJD']\\n print data\\n data.to_csv('results.csv')\\n os.chdir(original_path)\\n return\",\n \"def grid_events(self, scale, threshold=100, distrib=False, dataname=''):\\n\\n def plot_notimeres(thres):\\n \\\"\\\"\\\"\\n All time colapsed\\n @return:\\n \\\"\\\"\\\"\\n cont = np.zeros((scale, scale))\\n for i in range(self.dataset.shape[0]):\\n posx = int(((self.dataset[i][1] - minLon) * normLon))\\n posy = int(((self.dataset[i][0] - minLat) * normLat))\\n\\n cont[posx - 1, posy - 1] += 1\\n\\n lgeo = []\\n\\n for i in range(cont.shape[0]):\\n for j in range(cont.shape[1]):\\n if cont[i, j] >= thres:\\n path = [(minLon + (i + 0.5) / normLon, maxLat - (scale - j - 1.5) / normLat),\\n (minLon + (i + 0.5) / normLon, maxLat - (scale - j - 0.5) / normLat),\\n (minLon + (i + 1.5) / normLon, maxLat - (scale - j - 0.5) / normLat),\\n (minLon + (i + 1.5) / normLon, maxLat - (scale - j - 1.5) / normLat)\\n ]\\n lgeo.append(Feature(geometry=Polygon([path])))\\n\\n return lgeo\\n\\n minLat, maxLat, minLon, maxLon = self.city[1]\\n normLat = scale / (maxLat - minLat)\\n normLon = scale / (maxLon - minLon)\\n mymap = folium.Map(location=[(minLat + maxLat) / 2.0, (minLon + maxLon) / 2.0], zoom_start=12, width=1400,\\n height=1000)\\n lgeog = plot_notimeres(threshold)\\n\\n nfile = self.application + '-' + dataname\\n if self.mnhh is not None and self.mnhh is not None:\\n nfile += '-nusr' + str(self.mxhh) + '-' + str(self.mnhh)\\n nfile += '-s' + str(scale)\\n nfile += '-tr' + str(threshold)\\n\\n geoc = FeatureCollection(lgeog)\\n dump = geojson.dumps(geoc)\\n jsfile = open(homepath + 'Results/' + nfile + '.json', 'w')\\n jsfile.write(dump)\\n jsfile.close()\\n mymap.geo_json(geo_path=homepath + 'Results/' + nfile + '.json', fill_opacity=0.2)\\n\\n mymap.create_map(path=homepath + 'Results/' + self.city[2] + nfile + '.html')\",\n \"def get_data(station,starttime,endtime,activity=False,\\n rep='/GNOMEDrive/gnome/serverdata/',resample=None):\\n setname = \\\"MagneticFields\\\"\\n dstr = ['%Y','%m','%d','%H','%M']\\n dsplit = '-'.join(dstr[:starttime.count('-')+1])\\n start = datetime.strptime(starttime,dsplit)\\n starttime = construct_utc_from_metadata(start.strftime(\\\"%Y/%m/%d\\\"),\\n start.strftime(\\\"%H:%M:%S.%d\\\"))\\n dsplit = '-'.join(dstr[:endtime.count('-')+1])\\n end = datetime.strptime(endtime,dsplit)\\n endtime = construct_utc_from_metadata(end.strftime(\\\"%Y/%m/%d\\\"),\\n end.strftime(\\\"%H:%M:%S.%d\\\"))\\n dataset = []\\n for date in numpy.arange(start,end,timedelta(minutes=1)):\\n date = date.astype(datetime)\\n path1 = rep+station+'/'+date.strftime(\\\"%Y/%m/%d/\\\")\\n path2 = station+'_'+date.strftime(\\\"%Y%m%d_%H%M*.hdf5\\\")\\n fullpath = os.path.join(path1,path2)\\n dataset += glob.glob(fullpath)\\n if len(dataset)==0:\\n print \\\"ERROR: No data files were found...\\\"\\n quit()\\n file_order,data_order = {},{}\\n for fname in dataset:\\n hfile = h5py.File(fname, \\\"r\\\")\\n segfile = file_to_segment(hfile,setname)\\n file_order[segfile] = fname\\n data_order[segfile] = hfile\\n # Extract sample rate from metadata of last read data file\\n sample_rate = hfile[setname].attrs[\\\"SamplingRate(Hz)\\\"]\\n # Estimate full segment activity list\\n activity = create_activity_list(station,data_order)\\n # Generate an ASCII representation of the GPS timestamped\\n # segments of time covered by the input data\\n seglist = segmentlist(data_order.keys())\\n # Sort the segment list\\n seglist.sort()\\n # Create list of time series from every segment\\n ts_list = generate_timeseries(file_order,setname)\\n # Retrieve channel data for all the segments\\n full_data = numpy.hstack([retrieve_channel_data(data_order[seg],setname)\\n for seg in seglist])\\n new_sample_rate = sample_rate if resample==None else resample\\n new_data_length = len(full_data)*new_sample_rate/float(sample_rate)\\n full_data = scipy.signal.resample(full_data,int(new_data_length))\\n # Models a time series consisting of uniformly sampled scalar values\\n ts_data = types.TimeSeries(full_data,delta_t=1./new_sample_rate,\\n epoch=seglist[0][0])\\n for v in data_order.values():\\n v.close()\\n return ts_data,ts_list,activity,int(starttime),int(endtime)\",\n \"def get_data(day, unixstart=None, unixend=None):\\n global subnet\\n df_data = pd.DataFrame([])\\n while len(df_data.index)<=0:\\n try:\\n df_data = pd.read_feather(f'/home/pi/studies/ardmore/homeserver/h{subnet}_{day}.feather')\\n except Exception as e:\\n # print(f\\\"Error grid_server.get_data:{e}\\\")\\n pass\\n if unixstart!=None:\\n df_data = df_data[(df_data['unixtime']>=unixstart)&(df_data['unixtime']<=unixend)]\\n float_cols = [x for x in df_data.columns if not x.startswith('timezone')]\\n df_data = df_data[float_cols].astype(float)\\n return df_data\",\n \"def main():\\n\\n pathfolder = \\\"/home/vanessa/DATA_SEEG/PKL_FILE/\\\"\\n filename = \\\"/data.pkl\\\"\\n # pathfolder = argv[1]\\n # filename = argv[2]\\n\\n ti = 10. # initial time\\n tf = 590. # final time\\n t_split = 300. # split\\n fs = 1000. # sampling frequency\\n powerline = 50.\\n\\n thresholds = np.load(\\\"threshold.npy\\\") # load the threshold file\\n meanthresh = thresholds.mean(axis=0)[1::2]\\n stdthresh = thresholds.std(axis=0)[1::2]\\n\\n # features = 159 # classification features + (x,y,z)-coordinates\\n\\n for ii, id in enumerate(os.listdir(pathfolder)):\\n\\n print(id)\\n\\n df = pd.read_pickle(pathfolder + id + filename)\\n\\n validchannels = np.where(~df.loc[:, \\\"PTD\\\"].isnull())[0] # remove NaN values\\n\\n df = df.iloc[validchannels, :]\\n _, p = df.shape\\n\\n timeseries = df.values[:, :-5] # we are not considering Y, ptd, coordinates\\n\\n data = remove_powerline(timeseries, fs) # remove power line effects\\n\\n #################### split into 2 fragments ############################\\n\\n split1half = data[:, int(fs*ti):int(fs*t_split)]\\n split2half = data[:, int(fs*t_split):int(fs*tf)]\\n\\n timefeat1half = merge_temporal_features(split1half, fs, powerline,\\n meanthresh)\\n timefeat2half = merge_temporal_features(split2half, fs, powerline,\\n meanthresh)\\n\\n ########################################################################\\n\\n cc = [df.index[t] for t in range(len(df.index))]\\n arrays = [[id]*(2*len(df.index)), cc + cc]\\n\\n tuples = list(zip(*arrays))\\n index = pd.MultiIndex.from_tuples(tuples, names=['patient', 'channel'])\\n\\n # temporal features from SEEG\\n timefeatdf = pd.DataFrame(data=np.vstack((timefeat1half,\\n timefeat2half)), index=index)\\n\\n # spatial features for MRI\\n spacefeat = df.values[:, -4:]\\n spacefeatdf = pd.DataFrame(data=np.vstack((spacefeat, spacefeat)),\\n index=index, columns=\\n ['PTD', 'xcoor', 'ycoor', 'zcoor'])\\n\\n # y labels\\n ylab = df.values[:, -5]\\n Ylabel = pd.DataFrame(data=np.append(ylab, ylab), index=index,\\n columns=[\\\"Y\\\"])\\n\\n # pickle file in output\\n outputpkl = pd.concat([timefeatdf, spacefeatdf, Ylabel], axis=1)\\n\\n outputpkl.to_pickle(pathfolder + id + \\\"/features.pkl\\\")\\n\\n if ii == 0:\\n ddd = outputpkl\\n else:\\n ddd = pd.concat([ddd, outputpkl], axis=0)\\n\\n ddd.to_pickle(pathfolder + \\\"classificationset.pkl\\\")\",\n \"def make_dataframe(self):\\n logging.info('*** Creating the dataframes from the source files ' )\\n \\n for k in self.datasets_keys:\\n #for k in ['igra2' , 'ncar']:\\n \\n logging.info('*** Creating the dataframe for the dataset: %s ' , k ) \\n \\n p_levels = self.data[k]['df']['observations_table']['z_coordinate'][:]\\n logging.debug(' Loaded the z_coordinate')\\n \\n z_type = self.data[k]['df']['observations_table']['z_coordinate_type'][:]\\n logging.debug(' Loaded the z_coordinate_type')\\n \\n obs_variable = self.data[k]['df']['observations_table']['observed_variable'][:]\\n logging.debug(' Loaded the observed_variable')\\n \\n obs_values = self.data[k]['df']['observations_table']['observation_value'][:]\\n logging.debug(' Loaded the observation_value')\\n \\n observation_id = self.data[k]['df']['observations_table']['observation_id'][:]\\n logging.debug(' Loaded the observation_id')\\n \\n units = self.data[k]['df']['observations_table']['units'][:].astype(int)\\n logging.debug(' Loaded the units') \\n \\n report_id = self.data[k]['df']['observations_table']['report_id'][:] \\n logging.debug(' Loaded the report_id')\\n \\n date_time = self.data[k]['df']['observations_table']['date_time'][:]\\n logging.debug(' Loaded the date_time (deltas)')\\n \\n lat , lon = self.data[k]['df']['observations_table']['latitude'][:] , self.data[k]['df']['observations_table']['longitude'][:]\\n logging.debug(' Loaded the lat,lon ')\\n \\n \\n self.obs_table_columns = list(self.data[k]['df']['observations_table'].keys() )\\n \\n self.data[k]['df'].close()\\n \\n \\\"\\\"\\\" Creating a dataframe \\\"\\\"\\\"\\n columns = ['date_time', 'z_coordinate' , 'z_coordinate_type', 'observed_variable' , 'observation_value' , 'report_id' , 'observation_id' , 'latitude' , 'longitude', 'units']\\n logging.info(' Loaded the data, creating dataframe ')\\n \\n df = pd.DataFrame( list(zip( date_time, p_levels, z_type, obs_variable , obs_values, report_id, observation_id , lat , lon, units ) ) , columns = columns ) \\n \\n \\n \\\"\\\"\\\" Storing the dataframe \\\"\\\"\\\" ### try using xarrays ??? \\n logging.debug('Storing the DF ' ) \\n self.data[k]['dataframe'] = df\\n \\n logging.debug(' PD dataframe created !!! ')\",\n \"def add_shortest_route(df):\\n\\n df['gmaps_dist'] = df.apply(lambda row: gmaps.getTotDist((row['pick_lon'], row['pick_lat']), (row['drop_lon'], row['drop_lat'])), axis=1)\\n df['gmaps_dur'] = df.apply(lambda row: gmaps.getTotDur((row['pick_lon'], row['pick_lat']), (row['drop_lon'], row['drop_lat'])), axis=1)\",\n \"def get_mta_data2():\\n\\n url = \\\"http://web.mta.info/developers/data/nyct/turnstile/turnstile_{}.txt\\\"\\n dfs = []\\n\\n for week_num in week_nums:\\n file_url = url.format(week_num)\\n dfs.append(\\n pd.read_csv(\\n file_url, parse_dates=[[\\\"DATE\\\", \\\"TIME\\\"]], keep_date_col=True\\n )\\n )\\n return pd.concat(dfs)\",\n \"def result(self):\\n\\n # determine the X and Y sensors to plot from those sensors selected by the user.\\n sensorX = bmsapp.models.Sensor.objects.get(pk=self.request_params['select_sensor_x'])\\n sensorY = bmsapp.models.Sensor.objects.get(pk=self.request_params['select_sensor_y'])\\n\\n # determine the averaging time\\n averaging_hours = float(self.request_params['averaging_time_xy'])\\n\\n # get the building's timezone\\n tz = pytz.timezone(self.timezone)\\n\\n # determine the start and end time for selecting records\\n st_ts, end_ts = self.get_ts_range()\\n\\n # get the dividing date, if there is one\\n div_datestring = self.request_params['div_date']\\n div_dt = tz.localize(parser.parse(div_datestring)) if len(div_datestring) else None\\n\\n\\n # The list that will hold each series\\n series = []\\n\\n # get the X and Y sensor records and perform the requested averaging\\n dfX = self.reading_db.dataframeForOneID(sensorX.sensor_id, st_ts, end_ts, tz)\\n dfY = self.reading_db.dataframeForOneID(sensorY.sensor_id, st_ts, end_ts, tz)\\n\\n if not dfX.empty and not dfY.empty: # both sensors have some data, so proceed to average the data points\\n \\n dfX = bmsapp.data_util.resample_timeseries(dfX,averaging_hours)\\n dfX.rename(columns = {'val':'X'}, inplace = True)\\n\\n dfY = bmsapp.data_util.resample_timeseries(dfY,averaging_hours)\\n dfY.rename(columns = {'val':'Y','ts':'tsY'}, inplace = True)\\n\\n # Join the X and Y values for the overlapping time intervals and make\\n # a list of points.\\n df_all = dfX.join(dfY, how='inner') # inner join does intersection of timestamps\\n\\n # make sure there are matched records before continuing\\n if len(df_all):\\n\\n # add a point name column to be used in the tooltip.\\n df_all['name'] = df_all.index.strftime('%a %m/%d/%y %H:%M')\\n\\n # add a column identifying whether point is in occupied or unoccupied period.\\n resolution = self.occupied_resolution()\\n if (self.schedule is None) or (resolution is None):\\n # no schedule or data doesn't lend itself to classifying\\n # consider all points to be occupied\\n df_all['occupied'] = 1\\n else:\\n df_all['occupied'] = [self.schedule.is_occupied(ts, resolution=resolution) for ts in df_all.ts]\\n\\n # Set up the parameters for the different series of data\\n # Required Info is (starting datetime, ending datetime, occupied status (0 or 1), series name, \\n # series color, series symbol, series radius, series zindex).\\n now_dt = datetime.now()\\n if div_dt:\\n # A dividing date was provided by the user.\\n div_dt = div_dt.replace(tzinfo=None) # needs to be naive\\n ser_params = ( (datetime(1970,1,1), div_dt, 1, 'Prior to %s' % div_datestring, '#2f7ed8', 'circle', 4.5),\\n (datetime(1970,1,1), div_dt, 0, 'Prior to %s, Unoccupied' % div_datestring, '#2f7ed8', 'triangle-up', 3),\\n (div_dt, now_dt, 1, '%s and beyond' % div_datestring, '#FF0000', 'circle', 4.5),\\n (div_dt, now_dt, 0, '%s and beyond, Unoccupied' % div_datestring, '#FF0000', 'triangle-up', 3) )\\n else:\\n # Divide data by how recent it is.\\n ser_params = ( (now_dt - timedelta(days=1), now_dt, 1, 'Last 24 Hours', '#FF0000', 'circle', 4.5),\\n (now_dt - timedelta(days=1), now_dt, 0, 'Last 24 Hours, Unoccupied', '#FF0000', 'triangle-up', 3),\\n (now_dt - timedelta(days=7), now_dt - timedelta(days=1), 1, 'Last 7 Days', '#00CC00', 'circle', 4.5),\\n (now_dt - timedelta(days=7), now_dt - timedelta(days=1), 0, 'Last 7 Days, Unoccupied', '#00CC00', 'triangle-up', 3),\\n (datetime(1970,1,1), now_dt - timedelta(days=7), 1, '7+ Days Old', '#2f7ed8', 'circle', 4.5),\\n (datetime(1970,1,1), now_dt - timedelta(days=7), 0, '7+ Days Old, Unoccupied', '#2f7ed8', 'triangle-up', 3),\\n )\\n\\n for t_start, t_end, occup, ser_name, ser_color, ser_symbol, radius in reversed(ser_params):\\n mask = (df_all.index >= t_start) & (df_all.index < t_end) & (df_all.occupied==occup)\\n if mask.max():\\n series.append( {'x': np.char.mod('%.4g',df_all[mask].X.values).astype(float).tolist(),\\n 'y': np.char.mod('%.4g',df_all[mask].Y.values).astype(float).tolist(),\\n 'text': df_all[mask].name.values.tolist(),\\n 'type': 'scatter',\\n 'mode': 'markers', \\n 'name': ser_name,\\n 'marker': { 'color': ser_color,\\n 'symbol': ser_symbol,\\n 'size': radius * 2\\n }\\n } )\\n\\n # create the X and Y axis labels and the series\\n x_label = '%s, %s' % (sensorX.title, sensorX.unit.label)\\n y_label = '%s, %s' % (sensorY.title, sensorY.unit.label)\\n\\n opt = self.get_chart_options('plotly')\\n opt['data'] = series\\n opt['layout']['title'] = sensorY.title + \\\" vs. \\\" + sensorX.title\\n opt['layout']['xaxis']['title'] = x_label\\n opt['layout']['yaxis']['title'] = y_label\\n opt['layout']['legend']['traceorder'] = 'reversed'\\n\\n html = basechart.chart_config.chart_container_html(opt['layout']['title'])\\n\\n return {'html': html, 'objects': [('plotly', opt)]}\",\n \"def coarse_dataframe(geodf, side_square):\\n\\n # initialise the categories\\n\\n geodf['category'] = -1\\n\\n # do calculations on the first date, then extrapolate to the rest\\n data_df = geodf[geodf['date'] == np.unique(geodf['date'])[0]]\\n\\n data_df = data_df.sort_values(by=['longitude', 'latitude'])\\n\\n n_grids = int(math.sqrt(data_df.shape[0]))\\n\\n category = 0\\n\\n for n in range(data_df.shape[0]):\\n\\n # only process lat,long point that do not have a category\\n if data_df['category'].iloc[n] == -1:\\n\\n # get the side_square^2 nearest indexes to the point.\\n indexes = []\\n for i in range(side_square):\\n for j in range(side_square):\\n\\n if n + n_grids * i + j < n_grids * n_grids and data_df['category'].iloc[n + n_grids * i + j] == -1:\\n indexes.append(n + n_grids * i + j)\\n\\n # assing them all to the same categorty\\n data_df['category'].iloc[indexes] = str(category)\\n\\n # get the geometry points of that catery\\n cat_geometry = data_df[data_df['category'] == str(category)]['geometry']\\n\\n # get indexes of each point belonging to the category\\n indexes_all = []\\n for point in cat_geometry:\\n indexes_all.append(geodf[geodf['geometry'] == point].index.tolist())\\n\\n indexes_all_flat = [item for sublist in indexes_all for item in sublist]\\n\\n geodf['category'].iloc[indexes_all_flat] = str(category)\\n\\n category = category + 1\\n\\n geodf['category'] = (geodf['category'].astype(str)).str.cat(geodf['date'], sep=\\\"_\\\")\\n\\n geodf = geodf.dissolve(by=['category', 'date'], aggfunc='mean')\\n\\n # re-assing the date because we are losing it\\n geodf['date'] = [i[1] for i in geodf.index]\\n\\n geodf['category'] = [i[0] for i in geodf.index]\\n\\n return geodf\",\n \"def import_stations(time_res='hourly',time_format='%Y%m%d%H',\\r\\n campaign_time=[datetime(2018,12,9), datetime(2018,12,12)],\\r\\n data_category='air_temperature', station_ids=['00044','00091'],\\r\\n dbase_dir='dbase', table_dir='tables',Output=True,\\r\\n memory_save=True):\\r\\n timeranges=['recent','historical']\\r\\n #%%load the datasets available at each timestep\\r\\n dwd_datasets_meta=dwd_datasets_meta=json.load(open(table_dir+\\\"\\\\\\\\dwd_station_meta.txt\\\"))\\r\\n #try to get a variable from the category, otherwise use interpolation of higher frequency data\\r\\n resample_frequency=None\\r\\n time_res_dbase=time_res\\r\\n try:\\r\\n dwd_datasets_meta[time_res][data_category]\\r\\n except Exception:\\r\\n if time_res=='daily':\\r\\n try:\\r\\n dwd_datasets_meta['hourly'][data_category]\\r\\n print(data_category,' is not provided at the required resolution, daily_mean of hourly data used instead')\\r\\n resample_frequency='D'\\r\\n time_res_dbase='hourly'\\r\\n except Exception:\\r\\n try: \\r\\n dwd_datasets_meta['10_minutes'][data_category]\\r\\n print(data_category,' is not provided at the required resolution, daily_mean of 10_minutes data used instead')\\r\\n resample_frequency='D'\\r\\n time_res_dbase='10_minutes'\\r\\n except Exception:\\r\\n print(data_category, 'not available')\\r\\n sys.exit(1)\\r\\n if time_res=='hourly':\\r\\n try: \\r\\n dwd_datasets_meta['10_minutes'][data_category]\\r\\n print(data_category,' is not provided at the required resolution, hourly_mean of 10_minutes data used instead')\\r\\n resample_frequency='H'\\r\\n time_res_dbase='10_minutes'\\r\\n except Exception:\\r\\n print(data_category, 'not available')\\r\\n sys.exit(1)\\r\\n \\r\\n \\r\\n #%% download from dwd if necessary\\r\\n #connect to server\\r\\n server='opendata.dwd.de'\\r\\n ftp=connect_ftp(server = server,connected = False)\\r\\n #get the mean time of the campaign\\r\\n date_mean=campaign_time[0]+(campaign_time[1]-campaign_time[0])/2 \\r\\n # load the inititial ds\\r\\n dbase_path=dbase_dir+'\\\\\\\\db_stations_'+time_res+'_'+data_category+'.nc'\\r\\n if os.path.exists(dbase_path):\\r\\n with xr.open_dataset(dbase_path) as dwd_dbase:\\r\\n dwd_dbase.load()\\r\\n print('Existing database imported')\\r\\n #get the non_nans stations\\r\\n current_stations=np.array(dwd_dbase[list(dwd_dbase.keys())[0]].sel(time=date_mean,method='nearest').dropna('STATIONS_ID').coords['STATIONS_ID'])\\r\\n else:\\r\\n print(dbase_path, 'does not exist, we create a new netcdf_file')\\r\\n dwd_dbase=xr.Dataset()\\r\\n current_stations=np.array((-9999)).reshape(1)\\r\\n #change directory on server\\r\\n for timerange in timeranges:\\r\\n archive_url='/climate_environment/CDC/observations_germany/climate/'+time_res_dbase+'/'+data_category+'/'+timerange \\r\\n ftp.cwd(archive_url)\\r\\n #get the archive\\r\\n for station_id in station_ids:\\r\\n #we check whether the station is in the database with this parameter already\\r\\n if int(station_id) in current_stations:\\r\\n print('Station', station_id, 'with category', data_category,'in ',timerange,'dbase already')\\r\\n continue\\r\\n try:\\r\\n archive_name=[s for s in ftp.nlst() if station_id in s][0]\\r\\n except:\\r\\n print('No ',timerange,'data for station',station_id)\\r\\n continue\\r\\n print('Retrieving {}...'.format(archive_name))\\r\\n retrieved = False\\r\\n archive = io.BytesIO()\\r\\n # try to retrieve file\\r\\n while not retrieved:\\r\\n try:\\r\\n ftp.retrbinary(\\\"RETR \\\" + archive_name, archive.write)\\r\\n retrieved = True\\r\\n except:\\r\\n ftp=connect_ftp(server = server,connected = False)\\r\\n ftp.cwd(archive_url)\\r\\n archive.seek(0)\\r\\n with ZipFile(archive) as myzip:\\r\\n for f in myzip.infolist():\\r\\n # This is the data file\\r\\n #print('zip content:', f.filename)\\r\\n if f.filename.startswith('produkt_'):\\r\\n product = io.StringIO(str(myzip.read(f.filename),'utf-8'))\\r\\n #get dataframe from product \\r\\n dwd_product=pd.read_csv(product,sep=';',skipinitialspace=True)\\r\\n #get datetime\\r\\n dwd_product['time']=pd.to_datetime(dwd_product['MESS_DATUM'],format=time_format) \\r\\n dwd_product=dwd_product.rename(columns=dwd_datasets_meta[time_res_dbase][data_category])\\r\\n dwd_product=dwd_product.reset_index()\\r\\n dwd_product=dwd_product.set_index(['time','STATIONS_ID'])\\r\\n dwd_product=dwd_product.drop(columns=['MESS_DATUM','quality_level_of_next_columns','end_of_record','index'])\\r\\n #append to database\\r\\n dwd_xr=dwd_product.to_xarray()\\r\\n #replace all values equal to -999 to nan\\r\\n for data_var in dwd_xr.data_vars:\\r\\n dwd_xr[data_var]=dwd_xr[data_var].where(dwd_xr[data_var]>-999)\\r\\n if station_id=='05009':\\r\\n print('ok') \\r\\n #only add relevant dates if available memoryis rather small\\r\\n \\r\\n if memory_save and timerange=='historical':\\r\\n dwd_xr=dwd_xr.sel(time=slice(campaign_time[0]-timedelta(days=1),campaign_time[1]+timedelta(days=1)))\\r\\n #dwd_xr=dwd_xr.squeeze()\\r\\n \\r\\n try:\\r\\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr])\\r\\n except Exception as e:\\r\\n print(e)\\r\\n print('try merging with compat=override')\\r\\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr],compat='override')\\r\\n print(archive_name,' added to database')\\r\\n #upscale to required temporal resolution\\r\\n if resample_frequency is not None:\\r\\n dwd_dbase=dwd_dbase.resample(time=resample_frequency).mean(skipna=True)\\r\\n print('DWD data upscaled to',time_res,'averages')\\r\\n if Output==True:\\r\\n dwd_dbase.to_netcdf(dbase_path)\\r\\n print('Updated database' ,dbase_path)\\r\\n return dwd_dbase\",\n \"def get_data(f, zoom_level, start_pos_1, end_pos_1, start_pos_2, end_pos_2):\\n \\n c = cooler.Cooler(f[str(zoom_level)])\\n \\n (chroms, chrom_sizes, chrom_cum_lengths) = get_chromosome_names_cumul_lengths(c)\\n \\n i0 = abs_coord_2_bin(c, start_pos_1, chroms, chrom_cum_lengths, chrom_sizes)\\n i1 = abs_coord_2_bin(c, end_pos_1, chroms, chrom_cum_lengths, chrom_sizes)\\n j0 = abs_coord_2_bin(c, start_pos_2, chroms, chrom_cum_lengths, chrom_sizes)\\n j1 = abs_coord_2_bin(c, end_pos_2, chroms, chrom_cum_lengths, chrom_sizes)\\n \\n pixels = c.matrix(as_pixels=True, balance=False, max_chunk=np.inf)[i0:i1+1, j0:j1+1]\\n \\n if not len(pixels):\\n return pd.DataFrame(columns=['genome_start1', 'genome_start2', 'balanced'])\\n \\n bins = c.bins()[['chrom', 'start', 'end', 'weight']]\\n pixels = annotate(pixels, bins)\\n\\n pixels['genome_start1'] = chrom_cum_lengths[pixels['chrom1']] + pixels['start1']\\n pixels['genome_start2'] = chrom_cum_lengths[pixels['chrom2']] + pixels['start2']\\n pixels['balanced'] = (\\n pixels['count'] * pixels['weight1'] * pixels['weight2']\\n )\\n \\n return pixels[['genome_start1', 'genome_start2', 'balanced']]\",\n \"def filter_meteo_data(self, startdate, enddate):\\n self.all_meteo_data.columns.values[0]='Datum-tijd'\\n self.all_meteo_data['datetime']=pd.to_datetime(self.all_meteo_data['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\\n self.all_meteo_data.drop(['Datum-tijd'],axis=1, inplace=True)\\n mask = (self.all_meteo_data['datetime'] > startdate) & (self.all_meteo_data['datetime'] <= enddate)\\n meteodata = self.all_meteo_data.loc[mask].copy()\\n meteodata.set_index('datetime',inplace=True)\\n return meteodata\",\n \"def get_outdoor_data(temp_dir,site):\\n if site == 'berk':\\n files_od = glob(join(temp_dir,'outdoor','20*.xlsx'))\\n elif site == 'bus':\\n files_od = glob(join(temp_dir,'outdoor','Busara*.csv'))\\n else:\\n raise NameError(site)\\n\\n dfs = []\\n for f in files_od:\\n if site == 'berk':\\n this_df = pd.read_excel(f,sheet_name=0,usecols='B:D',index_col=0,parse_dates=True, header=1)\\n elif site == 'bus':\\n this_df = pd.read_csv(f,usecols=[0,1,2],index_col=0,parse_dates=True,header=2)\\n \\n # drop missing values that prevented conversion to float type\\n if this_df.iloc[:,0].dtype != np.float64:\\n this_df = this_df[this_df.iloc[:,0] != ' ']\\n this_df = this_df.astype(np.float64)\\n\\n # correct for weird timezones in berkeley datalogger\\n this_df = correct_tz(this_df,site)\\n \\n this_df.columns = ['T','RH']\\n this_df.index.name = 'time'\\n\\n # convert to celsius\\n this_df['T'] = (this_df['T'] - 32) * 5/9\\n dfs.append(this_df)\\n \\n df_od = pd.concat(dfs)\\n\\n # drop duplicated measurements\\n df_od = df_od[~df_od.index.duplicated(keep='last')].sort_index()\\n \\n # separate out into daily min,mean,max\\n groups = df_od.groupby(df_od.index.date)\\n dfs_od = {'all':df_od,\\n 'min': groups.min(),\\n 'mean': groups.mean(),\\n 'max': groups.max()}\\n \\n for i in ['min','mean','max']:\\n # remove first and last day to ignore days where we did not get full recording\\n dfs_od[i] = dfs_od[i].iloc[1:-1,:]\\n \\n # name index so that we can merge onto multiIndex'd dataframe\\n dfs_od[i].index.name = 'date'\\n \\n return dfs_od\",\n \"def _timeseries_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):\\n if params[cf.TIME_SERIES_CAL] == 0:\\n log.info('Time Series Calculation not required')\\n return\\n\\n if params[cf.TIME_SERIES_METHOD] == 1:\\n log.info('Calculating time series using Laplacian Smoothing method')\\n elif params[cf.TIME_SERIES_METHOD] == 2:\\n log.info('Calculating time series using SVD method')\\n\\n output_dir = params[cf.TMPDIR]\\n total_tiles = len(tiles)\\n process_tiles = mpiops.array_split(tiles)\\n for t in process_tiles:\\n log.debug(\\\"Calculating time series for tile \\\"+str(t.index)+\\\" out of \\\"+str(total_tiles))\\n ifg_parts = [shared.IfgPart(p, t, preread_ifgs, params) for p in ifg_paths]\\n mst_tile = np.load(os.path.join(output_dir, 'mst_mat_{}.npy'.format(t.index)))\\n res = timeseries.time_series(ifg_parts, params, vcmt, mst_tile)\\n tsincr, tscum, _ = res\\n np.save(file=os.path.join(output_dir, 'tsincr_{}.npy'.format(t.index)), arr=tsincr)\\n np.save(file=os.path.join(output_dir, 'tscuml_{}.npy'.format(t.index)), arr=tscum)\\n mpiops.comm.barrier()\\n log.debug(\\\"Finished timeseries calc!\\\")\",\n \"def get_tomo_data(self,threshold=20.):\\n\\t\\tdset = raytomo.RayTomoDataSet(self.attrs['tomo_f'])\\n\\t\\tfor prd in self.attrs['prd_arr']:\\n\\t\\t\\tgroup = self['%g_sec'%( prd )]\\n\\t\\t\\tdset.get_data4plot(dataid=self.attrs['dataid'].decode('utf-8'), period=prd)\\n\\t\\t\\tpdens = dset.pdens\\n\\t\\t\\tmask_pdens = dset.pdens < threshold\\n\\t\\t\\ttomo_data = np.ma.masked_array(dset.vel_iso, mask=mask_pdens)\\n\\t\\t\\tgroup.create_dataset(name='tomo_data', data=dset.vel_iso) # phase velocity map\\n\\t\\t\\tgroup.create_dataset(name='tomo_data_msk', data=mask_pdens) # save the mask array seperately. h5 file doesn't support masked array\\n\\t\\t\\tgroup.create_dataset(name='latArr', data=dset.latArr)\\n\\t\\t\\tgroup.create_dataset(name='lonArr', data=dset.lonArr)\\n\\t\\treturn\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63389176","0.61668277","0.60331327","0.59823436","0.58627486","0.5774627","0.5769538","0.575066","0.574341","0.5693412","0.5659913","0.5631676","0.5619256","0.5589875","0.5581725","0.5581672","0.55765","0.55755305","0.5565926","0.5553959","0.5542568","0.55383873","0.5537667","0.5520269","0.5514772","0.55108446","0.547999","0.5479977","0.5472937","0.5469251","0.54603016","0.5433442","0.54096687","0.5401351","0.53927106","0.5384891","0.5381836","0.53795147","0.5373994","0.53714126","0.53642","0.53625923","0.534706","0.53468555","0.5345016","0.53294873","0.5314151","0.53115505","0.5310678","0.52996963","0.5298086","0.5296908","0.5292022","0.52901334","0.52832216","0.5275935","0.52704364","0.5266496","0.5262013","0.5260705","0.5258948","0.5258297","0.52536654","0.5248869","0.52464825","0.52464366","0.52432853","0.5242233","0.5241885","0.52336895","0.52317566","0.52267313","0.5226678","0.5220624","0.52156705","0.52101827","0.52071905","0.52048206","0.520476","0.51996595","0.5199406","0.5198756","0.5188327","0.517064","0.5168717","0.5168673","0.5168314","0.51649565","0.5163451","0.51625466","0.5151697","0.51495755","0.51489055","0.51488537","0.51417273","0.51408225","0.51334363","0.5128758","0.51278996","0.51233524"],"string":"[\n \"0.63389176\",\n \"0.61668277\",\n \"0.60331327\",\n \"0.59823436\",\n \"0.58627486\",\n \"0.5774627\",\n \"0.5769538\",\n \"0.575066\",\n \"0.574341\",\n \"0.5693412\",\n \"0.5659913\",\n \"0.5631676\",\n \"0.5619256\",\n \"0.5589875\",\n \"0.5581725\",\n \"0.5581672\",\n \"0.55765\",\n \"0.55755305\",\n \"0.5565926\",\n \"0.5553959\",\n \"0.5542568\",\n \"0.55383873\",\n \"0.5537667\",\n \"0.5520269\",\n \"0.5514772\",\n \"0.55108446\",\n \"0.547999\",\n \"0.5479977\",\n \"0.5472937\",\n \"0.5469251\",\n \"0.54603016\",\n \"0.5433442\",\n \"0.54096687\",\n \"0.5401351\",\n \"0.53927106\",\n \"0.5384891\",\n \"0.5381836\",\n \"0.53795147\",\n \"0.5373994\",\n \"0.53714126\",\n \"0.53642\",\n \"0.53625923\",\n \"0.534706\",\n \"0.53468555\",\n \"0.5345016\",\n \"0.53294873\",\n \"0.5314151\",\n \"0.53115505\",\n \"0.5310678\",\n \"0.52996963\",\n \"0.5298086\",\n \"0.5296908\",\n \"0.5292022\",\n \"0.52901334\",\n \"0.52832216\",\n \"0.5275935\",\n \"0.52704364\",\n \"0.5266496\",\n \"0.5262013\",\n \"0.5260705\",\n \"0.5258948\",\n \"0.5258297\",\n \"0.52536654\",\n \"0.5248869\",\n \"0.52464825\",\n \"0.52464366\",\n \"0.52432853\",\n \"0.5242233\",\n \"0.5241885\",\n \"0.52336895\",\n \"0.52317566\",\n \"0.52267313\",\n \"0.5226678\",\n \"0.5220624\",\n \"0.52156705\",\n \"0.52101827\",\n \"0.52071905\",\n \"0.52048206\",\n \"0.520476\",\n \"0.51996595\",\n \"0.5199406\",\n \"0.5198756\",\n \"0.5188327\",\n \"0.517064\",\n \"0.5168717\",\n \"0.5168673\",\n \"0.5168314\",\n \"0.51649565\",\n \"0.5163451\",\n \"0.51625466\",\n \"0.5151697\",\n \"0.51495755\",\n \"0.51489055\",\n \"0.51488537\",\n \"0.51417273\",\n \"0.51408225\",\n \"0.51334363\",\n \"0.5128758\",\n \"0.51278996\",\n \"0.51233524\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5494842"},"document_rank":{"kind":"string","value":"26"}}},{"rowIdx":11098,"cells":{"query":{"kind":"string","value":"Utilities for spatio temporal analysis zed.uchicago.edu Fit dataproc with specified grid parameters and create timeseries for date boundaries specified by INIT, THRESHOLD, and END which do not have to match the arguments first input to the dataproc"},"document":{"kind":"string","value":"def fit(self,grid=None,INIT=None,END=None,THRESHOLD=None,csvPREF='TS'):\n\n if INIT is not None:\n self._INIT=INIT\n if END is not None:\n self._END=END\n if grid is not None:\n self._grid=grid\n\n assert(self._END is not None)\n assert(self._coord1 in self._grid)\n assert(self._coord2 in self._grid)\n assert('Eps' in self._grid)\n\n if self._types is not None:\n for key in self._types:\n self.timeseries(self._grid[self._coord1],\n self._grid[self._coord2],\n self._grid['Eps'],\n key,\n CSVfile=csvPREF+stringify(key)+'.csv',\n THRESHOLD=THRESHOLD)\n return\n else:\n assert(self._value_limits is not None)\n self.timeseries(self._grid[self._coord1],\n self._grid[self._coord2],\n self._grid['Eps'],\n None,\n CSVfile=csvPREF+'.csv',\n THRESHOLD=THRESHOLD)\n return"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def fit_timeseries(xdates, ydata):\n\n pass","def fit_surface(tdata,\n scale_params=[40.0, 1.25]):\n agg_tdata = aggregate_data(tdata)\n\n # unpack data\n mt = agg_tdata.mean_temp\n dt = agg_tdata.daily_temp\n scaled_dt = utils.scale_daily_temp(mt, dt, scale_params)\n\n obs_mean = agg_tdata.obs_mean\n obs_std = agg_tdata.obs_std*15.0\n study_sizes = agg_tdata.study_sizes\n\n # mt = tdata.mean_temp\n # dt = tdata.daily_temp\n # scaled_dt = utils.scale_daily_temp(mt, dt, scale_params)\n\n # obs_mean = tdata.obs_mean\n # obs_std = tdata.obs_std\n # study_sizes = np.array([1]*obs_mean.size)\n\n # create spline\n mt_knots = np.linspace(mt.min(), mt.max(), 2)\n dt_knots = np.linspace(scaled_dt.min(), scaled_dt.max(), 2)\n mt_degree = 3\n dt_degree = 3\n spline_list = [xspline.ndxspline(2,\n [mt_knots, dt_knots],\n [mt_degree, dt_degree])]\n\n # create mrbrt object\n x_cov_list = [{\n 'cov_type': 'ndspline',\n 'spline_id': 0,\n 'mat': np.vstack((mt, scaled_dt))\n }]\n z_cov_list = [{\n 'cov_type': 'linear',\n 'mat': np.ones(mt.size)\n }]\n\n mr = mrbrt.MR_BRT(obs_mean,\n obs_std,\n study_sizes,\n x_cov_list,\n z_cov_list,\n spline_list)\n\n # add priors\n prior_list = [\n {\n 'prior_type': 'ndspline_shape_function_uprior',\n 'x_cov_id': 0,\n 'interval': [[mt.min(), mt.max()],\n [scaled_dt.min(), scaled_dt.max()]],\n 'indicator': [-1.0, 1.0],\n 'num_points': [20, 20]\n },\n # {\n # 'prior_type': 'x_cov_gprior',\n # 'x_cov_id': 0,\n # 'prior': np.array([[0.0]*mr.k_beta, [10.0]*mr.k_beta])\n # },\n {\n 'prior_type': 'ndspline_shape_monotonicity',\n 'x_cov_id': 0,\n 'dim_id': 1,\n 'interval': [[mt.min(), mt.max()],\n [0.5, scaled_dt.max()]],\n 'indicator': 'increasing',\n 'num_points': [20, 10]\n },\n {\n 'prior_type': 'ndspline_shape_monotonicity',\n 'x_cov_id': 0,\n 'dim_id': 1,\n 'interval': [[mt.min(), mt.max()],\n [scaled_dt.min(), -0.75]],\n 'indicator': 'decreasing',\n 'num_points': [20, 10]\n },\n {\n 'prior_type': 'z_cov_uprior',\n 'z_cov_id': 0,\n 'prior': np.array([[1e-6]*mr.k_gamma, [1e-6]*mr.k_gamma])\n }\n ]\n mr.addPriors(prior_list)\n\n # fit the model and store the result\n mr.fitModel()\n\n # compute posterior variance for the beta\n # extract the matrix\n k_beta = mr.lt.k_beta\n k_gamma = mr.lt.k_gamma\n X = mr.lt.JF(mr.lt.beta)\n S = mr.lt.S\n V = S**2\n\n beta_var = (X.T/V).dot(X)\n\n if mr.lt.use_regularizer:\n H = np.vstack([mr.lt.H(np.hstack(np.eye(1, k_beta, i).reshape(k_beta,),\n np.zeros(k_gamma)))\n for i in range(k_beta)]).T\n SH = mr.lt.h[1]\n VH = SH**2\n beta_var += (H.T/VH).dot(VH)\n\n beta_var = np.linalg.inv(beta_var)\n\n\n surface_result = utils.SurfaceResult(\n mr.beta_soln,\n beta_var,\n spline_list[0],\n scale_params=scale_params)\n\n return surface_result, agg_tdata","def manipulate_data(ds, var, predef_clim, predef_trnd, trn_yrs, all_yrs, \n apply_latw=True, apply_detrending=True, dropna=True):\n\n \n if((var=='SD')|(var=='sd')|(var=='snowc')): \n ds[var] = ds[var].where(ds[var]>=0, other=0.0)\n ds[var] = ds[var].where(ds[var]==0, other=1.0)\n #ds[var].values = Gauss_filter(ds[var].values, (0,3,3))\n \n \"\"\"\n if((var=='hgt')|(var=='z')|(var=='GPT')):\n months = ds.time.to_index().month; ssn_ends = (months==2)|(months==5)|(months==8)|(months==11)\n ds = ds.sel(time=ssn_ends)\n else: \n ds = ds.resample(time='3M').mean()\n \"\"\"\n \n ds = ds.resample(time='3M').mean()\n\n ds = ds.sel(time=slice(str(all_yrs[0])+'-01-01', str(all_yrs[-1])+'-12-31')) \n \n try: \n clim = predef_clim\n ds = ds.groupby('time.season') - clim\n print('Predefined climatology used')\n except:\n clim = ds.sel(time=slice(str(trn_yrs[0])+'-01-01', str(trn_yrs[-1])+'-12-31')).groupby('time.season').mean('time')\n ds = ds.groupby('time.season') - clim\n print('Climatology calculated from data')\n \n if(apply_latw): ds[var].values = lat_weighting(ds[var].values, \n ds.lat, ds.lon)\n if(dropna):\n ds = ds.stack(gridcell=('lat', 'lon')).dropna(dim='gridcell',how='any')\n else: \n ds = ds.stack(gridcell=('lat', 'lon')).fillna(0)\n \n \n trend_models = { }\n if(apply_detrending): \n ds = ds.load()\n for ssn in ('DJF', 'MAM', 'JJA', 'SON'):\n #ssn_idx = ds['time.season'] == ssn\n \n trn_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], trn_yrs))\n all_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], all_yrs))\n \n trn_x = np.array(ds.time[trn_idx].values.tolist()).reshape(-1,1)\n all_x = np.array(ds.time[all_idx].values.tolist()).reshape(-1,1)\n try:\n trend = predef_trnd[ssn].predict(all_x)\n trend_models[ssn] = predef_trnd[ssn]\n print('Predefined trend model used')\n except:\n #_, trend_model = define_trends(ds[var][trn_idx], trn_x)\n _, trend_model = define_trends(ds[var][all_idx], all_x)\n trend = trend_model.predict(all_x)\n trend_models[ssn] = trend_model\n print('Trends calculated from data')\n \n ds[var][all_idx] = ds[var][all_idx] - trend\n \n\n \n return ds, clim, trend_models","def __init__(self, ts_df, time_format=\"%Y-%m-%d %H:%M:%S\", freq='D',\n fill_method='ffill',\n n_test=0, n_val=0,\n hyper_params=None,\n test='adf',\n trend=None,\n seasonal=False,\n seasonal_periods=1,\n **kwds):\n self._ts_df_cols = ['ds', 'y']\n\n self.ts_df = ts_df\n self.time_format = time_format\n self.freq = freq\n self.fill_method = fill_method.lower()\n self.n_test = int(n_test)\n self.n_val = int(n_val)\n self.transform = None\n self._boxcox_lmbda = None\n\n self._mode = ''\n\n self._train_dt = None\n self._test_dt = None\n self._val_dt = None\n\n self.model_fit = None\n self.fittedvalues = None\n self.residuals = None\n self.rmse = 0\n self._gs = tsa.GridSearchClass()\n self.hyper_params = hyper_params\n self.best_model = dict()\n\n \"\"\"\n self.rmse_test = 0\n self.rmse_val = 0\n \"\"\"\n\n self.upper_whisker_res = None\n self.lower_conf_int = None\n self.upper_conf_int = None\n\n self.forecast = None\n self.residuals_forecast = None\n\n self._res_decomp = None\n self._arr_seasonal = None\n self._arr_trend = None\n self._arr_baseline = None\n\n self._test = test\n self._trend = trend\n if self._trend is not None:\n self._trend = self._trend.lower()\n self._seasonal = seasonal\n if isinstance(self._seasonal, str):\n self._seasonal = self._seasonal.lower()\n self._seasonal_periods = seasonal_periods\n\n self._uvts_cls_logger = Logger('uvts_cls')\n\n UVariateTimeSeriesClass.assertions(self)\n # work with ts_df\n self.ts_df = self.ts_df.reset_index()\n self.ts_df.columns = self._ts_df_cols\n self.ts_df['y'] = self.ts_df['y'].apply(np.float64, errors='coerce')\n self.ts_df.set_index('ds', inplace=True)\n self._uvts_cls_logger.info(\n \"Received time series data of range: \" + str(min(self.ts_df.index)) + ' - ' + str(\n max(self.ts_df.index)) + \" and shape: \" + str(self.ts_df.shape))\n\n if not isinstance(self.ts_df.index, pd.DatetimeIndex):\n self._uvts_cls_logger.warning(\"Time conversion required...\")\n self.ts_df = self.ts_df.reset_index()\n try:\n self.ts_df['ds'] = self.ts_df['ds'].apply(\n lambda x: datetime.datetime.strptime(\n str(x).translate({ord('T'): ' ', ord('Z'): None})[:-1],\n self.time_format))\n except ValueError as e:\n self._uvts_cls_logger.warning(\"Zulu time conversion not successful: {}\".format(e))\n self._uvts_cls_logger.warning(\"Will try without assuming zulu time...\")\n try:\n self.ts_df['ds'] = self.ts_df['ds'].apply(\n lambda x: datetime.datetime.strptime(str(x), self.time_format))\n except ValueError as e:\n self._uvts_cls_logger.info(\"Time conversion not successful. Check your time_format: {}\".format(e))\n sys.exit(\"STOP\")\n else:\n self._uvts_cls_logger.info(\"Time conversion successful!\")\n else:\n self._uvts_cls_logger.info(\"Time conversion successful!\")\n # set index\n self.ts_df.set_index('ds', inplace=True)\n #\n self.ts_df.index = pd.to_datetime(self.ts_df.index)\n self.ts_df.sort_index(inplace=True)\n # resample\n self.ts_resample()\n UVariateTimeSeriesClass.assertions(self, post=True)\n #\n if self.n_val > len(self.ts_df) - self.n_test:\n self.n_val = len(self.ts_df) - self.n_test\n\n if self.n_test == 0 and self.n_val == 0:\n self._mode = 'forecast'\n elif self.n_test > 0:\n self._mode = 'test'\n elif self.n_test == 0 and self.n_val > 0:\n self._mode = 'validate'\n \n # delegate just for good programming style here\n super(UVariateTimeSeriesClass, self).__init__(**kwds)","def prepare(gpi, start_date, end_date, models, satellites, kind=\"clim\", window=35):\n # read data\n ts_input = data_object.read_gpi(gpi, start_date, end_date, models, satellites)\n #ts_input = interp.add_nan(ts_input)\n ts_input = interp.iter_fill(ts_input, max_gap=5)\n\n # either calc climatology, apply moving average filter, or do nothing\n if kind == 'clim':\n ts_smooth = smooth.iter_climats(ts_input)\n plot_title = 'Climatology'\n elif kind == 'movav':\n ts_smooth = smooth.iter_movav(ts_input, window)\n #ts_smooth = ts_gapfill\n plot_title = 'Moving average'\n elif kind == None:\n # return original data\n ts_smooth = ts_input\n else:\n raise NotImplementedError\n pass\n\n # drop rows with missing values\n #ts_smooth = ts_smooth.dropna()\n\n # scale satellite data to model data\n ts_scaled = scaling.scale(ts_smooth, 'mean_std_nan', 0)\n # drop nan rows for slope funcs\n return ts_scaled #.dropna()","def prep_data_fn(self, st_train_dt, end_train_dt, st_val_dt, end_val_dt, st_test_dt, end_test_dt):\n df = self.get_prep_data()\n train = df[(df['ft_data_dt'] >= st_train_dt) & (df['ft_data_dt'] <= end_train_dt)]\n val = df[(df['ft_data_dt'] >= st_val_dt) & (df['ft_data_dt'] <= end_val_dt)].sample(frac=0.4, random_state=2021)\n test = df[(df['ft_data_dt'] >= st_test_dt) & (df['ft_data_dt'] <= end_test_dt)]\n print(f'----train----')\n print(train[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n print(f'----validation----')\n print(val[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n print(f'----test----')\n print(test[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n self.set_train(train)\n self.set_validation(val)\n self.set_test(test)\n train_X = train[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n train_y = train['target']\n val_X = val[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n val_y = val['target']\n test_X = test[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n test_y = test['target']\n self.set_train_X(train_X)\n self.set_train_y(train_y)\n self.set_val_X(val_X)\n self.set_val_y(val_y)\n self.set_test_X(test_X)\n self.set_test_y(test_y)","def prepare_data_for_dfe(data, size_timeseries, num_timeseries):\n num_timesteps = size_timeseries\n window_size = size_timeseries\n\n if num_timeseries > CORRELATION_MAX_NUM_TIMESERIES:\n sys.stderr.write(\n 'Number of Time series should be less or equal to ' +\n str(CORRELATION_MAX_NUM_TIMESERIES) +\n '. Terminating!')\n sys.stderr.flush()\n sys.exit(-1)\n elif window_size < 2:\n sys.stderr.write(\n 'Window size must be equal or greater than 2. Terminating!')\n sys.stderr.flush()\n sys.exit(-1)\n elif num_timesteps > size_timeseries:\n sys.stderr.write(\n 'Number of Time steps should be less or equal to' +\n ' size of Time series. Terminating!')\n sys.stderr.flush()\n sys.exit(-1)\n\n precalculations = []\n data_pairs = []\n\n sums = [[0.0] * num_timeseries] * num_timesteps\n sums_sq = [[0.0] * num_timeseries] * num_timesteps\n inv = [[0.0] * num_timeseries] * num_timesteps\n\n # 2 DFE input streams: precalculations and data pairs\n for i in range(num_timesteps):\n for j in range(num_timeseries):\n old = 0.0 if i < window_size else data[j][i - window_size]\n new = data[j][i]\n\n if i == 0:\n sums[i][j] = new\n sums_sq[i][j] = new * new\n else:\n sums[i][j] = sums[i-1][j] + new - old\n sums_sq[i][j] = sums_sq[i-1][j] + new * new - old * old\n\n inv[i][j] = 1 / math.sqrt(window_size * sums_sq[i][j] -\n sums[i][j] * sums[i][j])\n\n # Precalculations REORDERED in DFE ORDER\n precalculations.append(sums[i][j])\n precalculations.append(inv[i][j])\n\n # Data pairs REORDERED in DFE ORDER\n data_pairs.append(new)\n data_pairs.append(old)\n\n return (precalculations, data_pairs)","def build_shape_data(self, start=None, end=None):\n # If start and end are None, then set them to be min/max of self.df_demand\n if start is None:\n start = self.df_demand['date'].min()\n if end is None:\n end = self.df_demand['date'].max()\n print(f\"date range for shape data is from {start} to {end}\")\n # Extract part of df_demand that is within start and end\n df_sub = self.df_demand[(self.df_demand['date'] >= start) & (self.df_demand['date'] <= end)]\n assert df_sub['date'].min() >= start\n assert df_sub['date'].max() <= end\n num_days = len(pd.date_range(iso8601.parse_date(start), iso8601.parse_date(end), freq='d'))\n print(f\"number of days is {num_days}\")\n # When finding variance and mean, add in missing days as 0s\n # Obtain the counts for each lat/lng region\n counts = df_sub.groupby(['left_lng', 'right_lng', 'lower_lat', 'upper_lat']).size().reset_index(name='counts')\n # Group demand data by lat/lng region and average across other cols\n df = df_sub.groupby(['left_lng', 'right_lng', 'lower_lat', 'upper_lat'])[['avail_count', 'avail_mins', 'trips', 'prob_scooter_avail', 'adj_trips']].mean().reset_index()\n df = df.merge(counts, on=['left_lng', 'right_lng', 'lower_lat', 'upper_lat'])\n # print(df.head())\n # Modify averages by multiplying each by count and divide by num_days\n vars = ['avail_count', 'avail_mins', 'trips', 'prob_scooter_avail', 'adj_trips']\n for var in vars:\n df[var] = df[var]*df['counts']/num_days\n # print(df.head())\n # Calculate the variance for prob_scooter_avail\n probVariance = df_sub.groupby(['left_lng', 'right_lng', 'lower_lat', 'upper_lat']).apply(lambda x: ((x['prob_scooter_avail'] - (x['prob_scooter_avail'].sum()/num_days))**2).sum()/(num_days-1)).reset_index(name='prob_scooter_avail')\n # print(probVariance.head())\n df['prob_scooter_avail_var'] = probVariance['prob_scooter_avail']\n # Check to see if there are any Nan values\n print(f\"Nan values in df? {df.isnull().values.any()}\")\n # print(df.head())\n # For each var col, create corresponding color columns (log and unlog)\n # Also create the factors list that get passed into self.create_rectangle_lst\n factors = [('avail_count', 'decimal'), ('avail_mins', 'decimal'),\n ('trips', 'decimal'), ('prob_scooter_avail', 'percent'), ('adj_trips', 'decimal')]\n i = 0\n original_len = len(factors)\n while i < original_len:\n name, type = factors[i]\n # print(f\"name={name}, type={type}\")\n # Create color column\n df = self.map_values_to_color(df, name)\n # If type is not percent than create log version\n if type != 'percent':\n df = self.create_log_column(df, name)\n factors.append(('log_'+name, type))\n i += 1\n # Deal with estimated demand and unmet demand\n # Filter out rows where prob_scooter_avail sig diff from 0\n sigDiffIdx = df.apply(lambda x: utils.sig_diff_from_zero(x['prob_scooter_avail'], x['prob_scooter_avail_var']), axis=1)\n # print(sigDiffIdx.head())\n df_sig_diff = df[sigDiffIdx]\n # Calculate estimated demand and unmet demand\n df_sig_diff = self.calculate_demand(df_sig_diff)\n # print(df_sig_diff.head())\n # Create color column and log column for unmet demand\n df_sig_diff = self.map_values_to_color(df_sig_diff, 'unmet_demand')\n df_sig_diff = self.map_values_to_color(df_sig_diff, 'estimated_demand')\n df_sig_diff = self.create_log_column(df_sig_diff, 'unmet_demand')\n factors.extend([('estimated_demand', 'decimal'), ('unmet_demand', 'decimal'), ('log_unmet_demand', 'decimal')])\n # Fill in the colors for the grid cells that aren't significantly different\n df_not_sig_diff = df[~sigDiffIdx]\n # print(df_not_sig_diff.head())\n df = pd.concat([df_sig_diff, df_not_sig_diff])\n # df.to_csv('../../../data_files/20210427_estimatedDemand.csv', index=False)\n # Create Rectangle information\n rectangles = self.create_rectangle_lst(df, factors)\n return rectangles, start, end","def create_grid(spl,\n var_to_plot,\n xmin=None,\n xmax=None,\n ymin=None,\n ymax=None):\n\n # Find the lowest and the highest heights to calibrate the ylims\n\n max_heights, min_heights, dates = [], [], []\n for df in spl:\n max_heights.append(df['height [> 0: top, < 0: bottom of elem.] (cm)'].iloc[-1])\n min_heights.append(df['height [> 0: top, < 0: bottom of elem.] (cm)'].iloc[0])\n dates.append(df['dates'].iloc[0])\n\n if ymin and ymax:\n max_height = ymax\n min_height = ymin\n else:\n max_height = np.max(max_heights)\n min_height = np.min(min_heights)\n max_height = round(max_height ,5 ,'up')\n min_height = round(min_height ,5 ,'down')\n\n grid_resolution = 100\n vertical_grid = np.linspace(min_height ,max_height ,grid_resolution)\n\n # Trim spl to fit specified xmin, xmax\n\n if xmin and xmax:\n\n spl = [sp for (date,sp) in zip(dates,spl) if (xmin < date < xmax)]\n dates = [date for date in dates if (xmin < date < xmax)]\n\n grid = np.full((grid_resolution, len(spl)), np.nan)\n\n for count, df in enumerate(spl):\n\n heights = np.array(df['height [> 0: top, < 0: bottom of elem.] (cm)'])\n variables = np.array(df[var_to_plot])\n\n # regular_variables = np.interp(vertical_grid, heights, variables, left = np.nan, right = np.nan)\n\n kind = 'nearest'\n\n my_interp = interpolate.interp1d(heights,\n variables,\n kind=kind,\n bounds_error = False,\n fill_value = (np.nan ,np.nan))\n\n regular_variables = my_interp(vertical_grid)\n\n grid[: ,count] = np.flip(regular_variables, axis=0)\n\n return_dict = {'grid':grid,\n 'max_height':max_height,\n 'min_height':min_height,\n 'dates':dates}\n\n return(return_dict)","def _training(self, data, ts_start, ts_end, min_ts_length=None, min_ts_mean=None, min_ts_mean_window=None,\n max_ft_freq=None, include_holidays=None, optimize=None, **kwargs):\n from numpy.linalg import LinAlgError\n\n freq = self._params['freq']\n\n try:\n\n if data is None:\n raise ValueError('Not enough data to train due to recent change point')\n\n endog = data[self._imputed_metric]\n\n index = pd.date_range(start=ts_start, end=ts_end, freq=freq) # Holidays are always daily.\n\n de_obj = DataExploration()\n exog_data = de_obj._get_exog_data(ts_start, ts_end, index) if self._params[\n 'include_holidays_exog'] else None\n\n # always run the model first without holiday exogenous variables\n result, order = self._fit(endog=endog, endog_end=ts_end, min_ts_mean=min_ts_mean,\n min_ts_mean_window=min_ts_mean_window, include_holidays=include_holidays,\n min_ts_length=min_ts_length, max_ft_freq=max_ft_freq, exog_data=exog_data,\n optimize=optimize)\n\n result['training_tail'] = data.loc[:ts_end].values.tolist()[-3:]\n\n except(LinAlgError, ValueError, LADStructuralError) as e:\n result = {'ErrorMessage': str(e)}\n return result, None\n\n return result, order","def grid_data(self, method='mean'): \n \n # Add a few extra variables in that I print out each month as I can then\n # compare these with the IDL output to see if there are discrepancies in\n # the number of profiles being used:\n rej_profiles = 0.\n nWOD9 = 0.\n nGTSPP0 = 0.\n nGTSPP999 = 0.\n index = np.where(self.qc == False)\n print('No. of rejected temperature values', np.shape(index))\n \n # Make the salinity values missing where the salinity QC flag is bad:\n fv = self.z.fill_value\n \n # Having set fv to be the general fill_value, check that this is the \n # recognised fill value for the other variables that are assessed by it\n # and raise a ValueError if not:\n if not self.data.fill_value == fv:\n raise ValueError('Incompatible fill value between depth and data')\n \n if not self.ps.fill_value == fv:\n raise ValueError('Incompatible fill value between depth and psal.')\n \n # Now make the salinity values missing where the QC flag is bad:\n self.ps[np.where(self.psalqc == False)] = fv\n index = np.where(self.psalqc == False)\n print('No. of rejected salinity values', np.shape(index))\n\n # Restrict to profiles with good posqc:\n self.data = self.data[self.posqc] \n self.x = self.x[self.posqc]\n self.y = self.y[self.posqc]\n self.p = self.p[self.posqc]\n self.pn = self.pn[self.posqc]\n self.ir = self.ir[self.posqc]\n self.ps = self.ps[self.posqc]\n self.z = self.z[self.posqc]\n self.qc = self.qc[self.posqc]\n self.posqc = self.posqc[self.posqc]\n \n # Store the maximum depth (with a good quality flag) of each profile:\n zvar1 = np.ma.masked_where(self.qc == False, self.z)\n maxdepth = np.amax(zvar1, axis = 1)\n # Get only the profiles that aren't entirely bad:\n self.data = self.data[maxdepth.mask == False]\n self.x = self.x[maxdepth.mask == False]\n self.y = self.y[maxdepth.mask == False]\n self.p = self.p[maxdepth.mask == False]\n self.pn = self.pn[maxdepth.mask == False]\n self.ir = self.ir[maxdepth.mask == False]\n self.ps = self.ps[maxdepth.mask == False]\n self.z = self.z[maxdepth.mask == False]\n self.qc = self.qc[maxdepth.mask == False]\n self.posqc = self.posqc[maxdepth.mask == False] \n maxdepth = maxdepth[maxdepth.mask == False]\n \n # I could thin to the second element of zbounds as if profiles don't go\n # that deep then I won't be able to use them (this would be true even if\n # the first element of zbounds wasn't zero) - it also removes the need \n # for an OHC dep value in my configuration file:\n self.data = self.data[maxdepth >= self.zbounds[1]]\n self.x = self.x[maxdepth >= self.zbounds[1]]\n self.y = self.y[maxdepth >= self.zbounds[1]]\n self.p = self.p[maxdepth >= self.zbounds[1]]\n self.pn = self.pn[maxdepth >= self.zbounds[1]]\n self.ir = self.ir[maxdepth >= self.zbounds[1]]\n self.ps = self.ps[maxdepth >= self.zbounds[1]]\n self.z = self.z[maxdepth >= self.zbounds[1]]\n self.qc = self.qc[maxdepth >= self.zbounds[1]]\n self.posqc = self.posqc[maxdepth >= self.zbounds[1]]\n year = int(self.fname[-9:-5])\n \n # Filter out low quality XBTs:\n rem = []\n for p in range(len(self.p)):\n xbt = inst_type.is_xbt(self.pn[p], self.ir[p], self.ps[p], fv, \n self.z[p], fv)\n if xbt[0][0] >= 0:\n # Remove any XBTs sourced from WOD where the fall rate equation\n # is unknown:\n if xbt[3] == 9:\n rem.append(p)\n nWOD9 += 1\n # Remove any GTSPP XBTs where the type is unknown and year is \n # >= 1995. \n # Or if type is unknown and it may not be a T4/T6/T7/DB because \n # the depth it reaches is too deep. Some of these will have been\n # given the Hanawa correction and so be inaccurate - this \n # happens in EN processing if probe code is zero:\n projectName = ''.join(self.pn[p])\n if projectName[0:5] == 'GTSPP':\n if (xbt[4] == 0 or xbt[4] == 99 or xbt[4] == 999) and year >= 1995:\n rem.append(p)\n if xbt[4] == 0:\n nGTSPP0 += 1\n else:\n nGTSPP999 += 1\n if (xbt[4] == 0 and xbt[1] > 900):\n rem.append(p)\n \n # Get rid of the low quality XBTs:\n nolowxbt = np.array(np.setdiff1d(range(len(self.p)),rem))\n self.data = self.data[nolowxbt]\n self.x = self.x[nolowxbt]\n self.y = self.y[nolowxbt]\n self.p = self.p[nolowxbt]\n self.pn = self.pn[nolowxbt]\n self.ps = self.ps[nolowxbt]\n self.ir = self.ir[nolowxbt]\n self.z = self.z[nolowxbt]\n self.qc = self.qc[nolowxbt]\n self.posqc = self.posqc[nolowxbt]\n \n # Do the vertical averaging:\n self.p = np.array(range(len(self.p)))\n \n # Check if the maxgap really is fixed:\n print('Fixed maxgap? ', self.fixedgap == True)\n \n # Loop over these profiles and do vertical averages:\n # Storage vectors need to start off filled with fill values otherwise\n # you get zero values being included in the averages instead of being\n # discarded. Make these storage matrices, not vectors so you can store \n # avergae values for multiple depth levels in them.\n all_mT = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\n all_mT.fill(fv)\n all_lT = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\n all_lT.fill(fv)\n all_dep = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\n all_dep.fill(fv)\n all_x = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\n all_x.fill(fv)\n all_y = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\n all_y.fill(fv)\n for p in range(len(self.p)):\n # 1. Select the profile of interest and make sure no tar_t1 values\n # are carried over from a previous profile:\n x_p = self.x[p]\n y_p = self.y[p]\n qc_p = np.where(np.logical_and(self.qc[p] == True, self.z[p].mask == False))\n data_p = self.data[p][qc_p]\n z_p = self.z[p][qc_p].data\n tar_t1 = fv\n # 1a. Sanity check to make sure there are no missing data going into\n # the averaging process:\n tempanddeppres = np.where(np.logical_and(data_p != fv, z_p != fv))[0]\n data_p = data_p[tempanddeppres]\n z_p = z_p[tempanddeppres]\n # 2. Make sure that depths are in correct order, but sorting takes time\n # therefore only sort if I've identified non-ascending depths:\n if np.any((z_p[1:] - z_p[:-1]) < 0):\n sortz = np.argsort(z_p)\n data_p = data_p[sortz]\n z_p = z_p[sortz]\n # To be in line with the IDL code also need to check for very wrong \n # depth values that might have slipped through:\n udep = np.where(np.logical_and(z_p > -99.9, z_p < 10000))\n z_p = z_p[udep]\n data_p = data_p[udep]\n # 3. Find the temperature at the exact depth level - this is now \n # more in depth as there are multiple depth levels to look at - AT \n # THE MOMENT THIS STILL ASSUMES THAT THE FIRST LEVEL IS 0 TO SOME\n # DEPTH - THIS IS STILL A SLIGHT SIMPLIFICATION:\n dval = 0\n for dep in self.zbounds[1:]:\n # Have added in this switch to allow you to specify whether\n # you're using a fixed gap or not:\n if self.fixedgap == True:\n maxgap = 200\n else:\n maxgap = max(0.3*(self.zbounds[dval+1]),100)\n # Get only the levels of the profile in the depth range of \n # interest:\n LTi1 = np.where(np.logical_and(z_p < dep, \n z_p >= self.zbounds[dval]))\n GEi1 = np.where(z_p >= dep)\n if (np.shape(LTi1)[1] != 0 and np.shape(GEi1)[1] != 0):\n # Get the depth differences between layers and the mean temps across\n # layers:\n nk = np.shape(LTi1)[1] + 1\n dz = np.zeros(nk)\n mt = np.zeros(nk)\n if dval == 0:\n dz[0] = z_p[0]\n mt[0] = data_p[0]\n for kk in range(1, nk):\n dz[kk] = z_p[kk] - z_p[kk-1]\n mt[kk] = 0.5 * (data_p[kk] + data_p[kk-1])\n else:\n # Effectively missing the first layer as dz will be zero\n # there if you've calculated a temperature at that depth\n # for tar_t1 on the previous loop, but it won't exist if\n # you haven't been able to calculate a tar_t1 value, so\n # then you'll have to do what you do when you're at the \n # first depth level and aren't garunteed a value at 0m.\n if tar_t1 != fv:\n dz[0] = z_p[LTi1[0][0]] - self.zbounds[dval]\n mt[0] = 0.5 * (data_p[LTi1[0][0]] + tar_t1)\n else:\n dz[0] = z_p[LTi1[0][0]] - self.zbounds[dval]\n mt[0] = data_p[LTi1[0][0]]\n for kk in range(0, nk-1):\n dz[kk+1] = z_p[LTi1[0][kk]+1] - z_p[LTi1[0][kk]]\n mt[kk+1] = 0.5 * (data_p[LTi1[0][kk]+1] + data_p[LTi1[0][kk]])\n \n # Work out the temp at the target depth:\n if z_p[GEi1[0][0]] == dep:\n #print('A sampled depth is equal to the desired level')\n tar_t1 = data_p[GEi1[0][0]]\n else:\n deltaT = data_p[GEi1[0][0]] - data_p[GEi1[0][0] -1]\n deltaZ = z_p[GEi1[0][0]] - z_p[GEi1[0][0] -1]\n tar_t1 = (dep - z_p[GEi1[0][0] -1])*(deltaT/deltaZ) + data_p[GEi1[0][0] -1]\n dz[nk -1] = dep - z_p[GEi1[0][0] -1]\n mt[nk -1] = 0.5*(data_p[GEi1[0][0] -1] + tar_t1)\n \n # Check if there are unacceptable gaps between layers:\n test_gap = np.where(dz > maxgap)\n if np.shape(test_gap)[1] != 0:\n mean_t1 = fv\n mean_t2 = fv\n tar_t1 = fv\n else:\n mean_t1 = sum(np.multiply(mt,dz))/(self.zbounds[dval+1] - self.zbounds[dval])\n \n # Make sure there are no crazy mean values:\n if (abs(mean_t1) > 100 and mean_t1 != fv):\n raise ValueError('Extreme values found')\n # Save the mean temperature at that depth and the temp at the target \n # depth, also save the depth and profile number:\n all_mT[p,dval] = mean_t1\n all_lT[p,dval] = tar_t1\n all_dep[p,dval] = dep # Lower bound of depth\n all_x[p,dval] = x_p\n all_y[p,dval] = y_p\n else:\n tar_t1 = fv # Make sure that if you have no data in a \n # specific depth range you don't carry an old tar_t1 value\n # over.\n \n dval +=1\n\n # Reshape\n self.data_1d = self.reshape_1d(self.data)\n self.x_1d = self.reshape_1d(self.x)\n self.y_1d = self.reshape_1d(self.y)\n self.p_1d = self.reshape_1d(self.p)\n self.z_1d = self.reshape_1d(self.z)\n self.qc_1d = self.reshape_1d(self.qc)\n self.posqc_1d = self.reshape_1d(self.posqc)\n \n # Apply QC - Technically the self.posqc_1d step shouldn't be needed as \n # these profiles will have got filtered out with the earlier QC, but \n # it's good to leave it in there as then if I remove the depth \n # restriction step, this step will still catch badly positioned profiles:\n #qcind = (self.qc_1d == True) & (self.posqc_1d == True)\n qcind = (self.qc_1d == True)\n self.qc_1d = self.qc_1d[qcind]\n self.posqc_1d = self.posqc_1d[qcind]\n self.data_1d = self.data_1d[qcind] # Still seems to have 99999 values in\n # it, and I can't see where they get filtered out, but they must get \n # filtered out somewhere or the mean values wouldn't be sensible.\n self.x_1d = self.x_1d[qcind]\n self.y_1d = self.y_1d[qcind]\n self.p_1d = self.p_1d[qcind]\n self.z_1d = self.z_1d[qcind]\n # Trying to work out why I use self.posqc here, not qcind, I think it's \n # because qcind will be one value for each level for each profile,\n # whereas posqc is one value for a whole profile - though I don't think\n # there should be any values left with a bad self.posqc - so this step \n # is probably redundant:\n #all_mTqc = all_mT[self.posqc]\n # Make sure I'm not including values where a mean_t1 value couldn't be \n # calculated:\n #all_mTqc1 = all_mTqc[np.where(all_mTqc != 99999.0)]\n # Need to remember that all_mT may well now be an array not a vector...\n all_mTqc1 = all_mT[all_mT != 99999.0]\n all_depqc = all_dep[all_mT != 99999.0]\n all_xqc = all_x[all_mT != 99999.0]\n all_yqc = all_y[all_mT != 99999.0]\n \n # Prepare data for gridding\n self.init_xgrid()\n self.init_ygrid()\n self.init_zgrid()\n \n # Getting the unique profile references:\n punique = np.unique(self.p_1d, return_index = True)[1]\n \n # From the unique profile references selecting only those profiles that \n # have a mean temperature over the depth of interest:\n #puniqueqc = punique[all_mT != 99999.0]\n \n # Getting references for all the points - so every temp value will have \n # a depth, lat and long i.e. you will still have multiple points per \n # profile:\n points = np.vstack([self.z_1d, self.y_1d, self.x_1d]).transpose()\n \n # Getting a single reference for each profile, this will just take the \n # first value in points, for each profile => points2 will have a \n # latitude, a longitude and the shallowest accepted depth of the profile\n # => This point will always (pretty much) be put into the top set of \n # grid boxes, would therefore need to consider this further if I wanted\n # to populate multiple depth level grid boxes at once:\n points2 = np.vstack([self.z_1d[punique], self.y_1d[punique], \n self.x_1d[punique]]).transpose()\n \n # points3 is like points 2, but gets coordinates for only profiles that\n # have a mean temperature down to the depth of interest:\n #points3 = np.vstack([np.ones(len(puniqueqc)), self.y_1d[puniqueqc], \n # self.x_1d[puniqueqc]]).transpose()\n points3 = np.vstack([all_depqc - 1, all_yqc, all_xqc]).transpose()\n \n # Pretty self explanatory - the boundaries of the grid boxes:\n bins = [self.zbounds, self.ybounds, self.xbounds]\n \n # Grid data:\n grid_count, binedges, binno = scipy.stats.binned_statistic_dd(\n points, self.data_1d, statistic='count', bins=bins)\n grid_sum, binedges, binno = scipy.stats.binned_statistic_dd(\n points, self.data_1d, statistic='sum', bins=bins)\n grid_meansum, binedges, binno = scipy.stats.binned_statistic_dd(\n points3, all_mTqc1, statistic = 'sum', bins = bins)\n grid_pcount, binedges, binno = scipy.stats.binned_statistic_dd(\n points3, all_mTqc1, statistic='count', bins=bins)\n# grid_pcount, binedges, binno = scipy.stats.binned_statistic_dd(\n# points3, self.data_1d[puniqueqc], statistic='count', bins=bins)\n# grid_max, binedges, binno = scipy.stats.binned_statistic_dd(\n# points, self.data_1d, statistic = 'max', bins=bins)\n# grid_min, binedges, binno = scipy.stats.binned_statistic_dd(\n# points, self.data_1d, statistic = 'min', bins=bins)\n# grid_med, binedges, binno = scipy.stats.binned_statistic_dd(\n# points, all_mTqc1, statistic = 'median', bins = bins)\n \n # Sum of valid temps/ number of valid obs:\n grid_tmean = grid_sum / grid_count\n grid_tmean = np.ma.MaskedArray(grid_tmean, mask = (grid_count == 0))\n # Sum of valid mean temps/ number of valid profiles:\n grid_meantmean = grid_meansum / grid_pcount\n grid_meantmean = np.ma.MaskedArray(grid_meantmean, mask = (grid_pcount == 0))\n self.grid_tmean = grid_tmean\n self.grid_count = grid_count\n self.grid_sum = grid_sum\n self.grid_meansum = grid_meansum\n self.grid_pcount = grid_pcount\n self.grid_meantmean = grid_meantmean","def main(\n ts_dir: str,\n save_dir: str,\n figure_dir: str,\n arima_order: Tuple[int] = (1, 0, 1),\n start = datetime(2000, 1, 1),\n end = datetime(2019, 9, 30),\n verbose: bool = True\n) -> None:\n # Load dataset\n df = pd.read_csv(\n ts_dir,\n index_col=0,\n header=0,\n parse_dates=[\"DATE\"],\n date_parser=lambda d: datetime.strptime(d, \"%Y-%m-%d\")\n )\n df.replace(\".\", np.NaN, inplace=True)\n df = df.astype(np.float32)\n if verbose:\n print(\"** Raw Dataset **\")\n df.info()\n print(f\"Nan ratio: {np.mean(np.isnan(df.values.squeeze())) * 100: .2}%\")\n\n # select subset.\n def _select_range(df):\n return df[np.logical_and(\n df.index >= start, df.index <= end\n )]\n df = _select_range(df)\n if verbose:\n print(\"** Raw Dataset **\")\n df.info()\n\n df_filled = utils.arima_interpolate(\n raw=df,\n arima_order=arima_order,\n verbose=verbose\n )\n df_filled.to_csv(save_dir)\n # Visualize Interpolation results.\n utils.visualize_interpolation(df, df_filled, figure_dir)\n # return df, df_filled\n return None","def gendata(params,xmin,xmax,npts=4000):\n F = lorentzian.ForwardFactory\n def gensample(F, xmin, xmax):\n from numpy import arange\n import random\n a = arange(xmin, xmax, (xmax-xmin)/200.)\n ymin = 0\n ymax = F(a).max()\n while 1:\n t1 = random.random() * (xmax-xmin) + xmin\n t2 = random.random() * (ymax-ymin) + ymin\n t3 = F(t1)\n if t2 < t3:\n return t1\n fwd = F(params)\n return array([gensample(fwd, xmin,xmax) for i in xrange(npts)])","def _create_historic_forecasts(\n data, time_dt, frt_dt, standard_grid_metadata=\"uk_ens\", number_of_days=5, **kwargs\n):\n historic_forecasts = iris.cube.CubeList([])\n for day in range(number_of_days):\n new_frt_dt = frt_dt + datetime.timedelta(days=day)\n new_time_dt = time_dt + datetime.timedelta(days=day)\n historic_forecasts.append(\n set_up_variable_cube(\n data - 2 + 0.2 * day,\n time=new_time_dt,\n frt=new_frt_dt,\n standard_grid_metadata=standard_grid_metadata,\n **kwargs,\n )\n )\n return historic_forecasts","def generate_obs_grid(start_date, end_date, storm_report_path, model_grid_path, proj_str):\n\n grid = xr.open_dataset(model_grid_path)\n for coord in ['lon', 'lat']:\n grid[coord].values = grid[coord].astype('float32')\n valid_dates = pd.date_range(start_date, end_date, freq='1h')\n\n obs_list = []\n\n for report_type in ['filtered_torn', 'filtered_wind', 'filtered_hail']:\n\n ds_list = []\n\n obs = combine_storm_reports(valid_dates.min(), valid_dates.max(), storm_report_path, report_type)\n\n for valid_date in valid_dates:\n\n ds = grid.expand_dims('time').assign_coords(valid_time=('time', [valid_date]))\n ds[report_type.split('_')[-1]] = ds['lat'] * 0\n\n obs_sub = obs[obs['Actual_Date'] == valid_date]\n obs_indx = find_coord_indices(ds['lon'].values, ds['lat'].values, obs_sub['Lon'], obs_sub['Lat'], proj_str)\n for i in obs_indx:\n if i is not None:\n ds[report_type.split('_')[-1]][i[0], i[1]] += 1\n else:\n continue\n ds_list.append(ds)\n\n obs_list.append(xr.concat(ds_list, dim='time'))\n\n return xr.merge(obs_list)","def main(args, **kwargs):\n data_file = os.path.join(kwargs['data_dir'],\n '{}_{}m_{}_{}.npz'.format(args.area, args.resolution, args.year, args.tar_date))\n data = np.load(data_file)\n tar_label_mat = data['label_mat']\n tar_dynamic_mat = data['dynamic_mat']\n static_mat, tar_static_mat = data['static_mat'], data['static_mat']\n mapping_mat = data['mapping_mat']\n dynamic_features, static_features = list(data['dynamic_features']), list(data['static_features'])\n\n \"\"\" extract information from all time period \"\"\"\n dynamic_mat, label_mat = [], []\n for date in args.dates:\n data_file = os.path.join(kwargs['data_dir'],\n '{}_{}m_{}_{}.npz'.format(args.area, args.resolution, args.year, date))\n data = np.load(data_file)\n dynamic_mat.append(data['dynamic_mat'])\n label_mat.append(data['label_mat'])\n dynamic_mat = np.concatenate(dynamic_mat)\n label_mat = np.concatenate(label_mat)\n\n data_obj = DataObj(label_mat, dynamic_mat, static_mat,\n tar_label_mat, tar_dynamic_mat, tar_static_mat,\n dynamic_features, static_features, mapping_mat)\n\n \"\"\" load train, val, test locations \"\"\"\n data_obj.train_loc, data_obj.val_loc, data_obj.test_loc = load_train_val_test(kwargs['train_val_test_file'], args)\n\n data_obj.train_y = data_obj.gen_train_val_test_label(data_obj.label_mat, data_obj.train_loc)\n data_obj.val_y = data_obj.gen_train_val_test_label(data_obj.label_mat, data_obj.val_loc)\n data_obj.test_y = data_obj.gen_train_val_test_label(data_obj.tar_label_mat, data_obj.test_loc)\n\n logging.info('Number of features = {}.'.format(data_obj.n_features))\n logging.info('Number of dynamic features = {}.'.format(data_obj.n_dynamic_features))\n logging.info('Number of static features = {}.'.format(data_obj.n_static_features))\n logging.info('Number of time points = {}.'.format(data_obj.n_times))\n logging.info('Shape of the matrix = ({}, {}).'.format(data_obj.n_rows, data_obj.n_cols))\n\n \"\"\" normalize data \"\"\"\n data_obj.dynamic_x = normalize_mat(data_obj.dynamic_mat, if_retain_last_dim=True)\n data_obj.static_x = normalize_mat(data_obj.static_mat, if_retain_last_dim=True)\n data_obj.tar_dynamic_x = normalize_mat(data_obj.tar_dynamic_mat, if_retain_last_dim=True)\n data_obj.tar_static_x = normalize_mat(data_obj.tar_static_mat, if_retain_last_dim=True)\n\n \"\"\" load auto-encoder model \"\"\"\n ae = torch.load(os.path.join(kwargs['model_dir'], kwargs['ae_model_name'] + '.pkl'))\n\n \"\"\" define DeepAP model \"\"\"\n dap = DeepAP(in_dim=data_obj.n_features,\n ae_en_h_dims=[64, 32, 16],\n ae_de_h_dims=[16, 32, 64],\n\n conv_lstm_in_size=(data_obj.n_rows, data_obj.n_cols),\n conv_lstm_in_dim=args.ae_h_dim, # ae_h_dim\n conv_lstm_h_dim=[args.dap_h_dim], # dap_h_dim\n conv_lstm_kernel_sizes=args.kernel_sizes, # kernel_sizes\n conv_lstm_n_layers=1,\n\n fc_in_dim=args.dap_h_dim * len(args.kernel_sizes),\n fc_h_dims=args.fc_h_dims, # fc_h_dims\n fc_out_dim=1,\n\n ae_pretrain_weight=ae.state_dict(),\n if_trainable=True,\n fc_p_dropout=0.1,\n\n mask_thre=args.mask_thr,\n device=kwargs['device'])\n\n dap = dap.to(kwargs['device'])\n train(dap, data_obj, args, **kwargs)","def get_time_series(this_lat, this_lon, case, varnames):\n\n cesmdir = '/gpfs/fs1/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly'\n\n if 'LE' in case:\n\n from observational_large_ensemble.params import karen_params_cesm\n\n mode_lag = karen_params_cesm.mode_lag\n cvdp_loc = karen_params_cesm.cvdp_loc\n AMO_cutoff_freq = karen_params_cesm.AMO_cutoff_freq\n\n name_conversion = {'tas': 'TREFHT', 'pr': 'PRECC', 'slp': 'PSL'}\n cesm_names = [name_conversion[v] for v in varnames]\n this_member = int((case).split('-')[-1])\n cvdp_file = '%s/CESM1-CAM5-BGC-LE_#%i.cvdp_data.1920-2018.nc' % (cvdp_loc, this_member)\n\n # Historical filenames for CESM. Will need to append part of RCP8.5 to get full period\n filenames = []\n for var in cesm_names:\n file_str = '%s/%s/b.e11.B20TRC5CNBDRD.f09_g16.%03d.cam.h0.%s.??????-200512.nc' % (cesmdir, var,\n this_member, var)\n this_file = glob(file_str)[0]\n filenames.append(this_file)\n\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames,\n karen_params_cesm.valid_years, mode_lag,\n cvdp_file, AMO_cutoff_freq, name_conversion)\n\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\n\n else:\n\n from observational_large_ensemble.params import karen_params_obs\n\n mode_lag = karen_params_obs.mode_lag\n cvdp_loc = karen_params_obs.cvdp_loc\n AMO_cutoff_freq = karen_params_obs.AMO_cutoff_freq\n\n tas_dir = karen_params_obs.tas_dir\n pr_dir = karen_params_obs.pr_dir\n slp_dir = karen_params_obs.slp_dir\n cvdp_file = '%s/HadISST.cvdp_data.1920-2018.nc' % cvdp_loc\n file_dict = {'tas': '%s/Complete_TAVG_LatLong1.nc' % tas_dir,\n 'pr': '%s/full_data_monthly_v2020.nc' % pr_dir,\n 'slp': '%s/prmsl.mon.mean.nc' % slp_dir}\n\n filenames = []\n for var in varnames:\n filenames.append(file_dict[var])\n\n name_conversion = {'tas': 'temperature', 'pr': 'precip', 'slp': 'prmsl'}\n\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames[0],\n karen_params_obs.valid_years, mode_lag,\n cvdp_file, AMO_cutoff_freq, name_conversion)\n\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\n\n return this_ts, df_shifted","def run_regression(auto_input,\n variables: list = ['magasin', 'tilsig'],\n regions: list = ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'],\n jupyter: bool = False,\n backup: bool = False,\n loop: bool = False,\n write: bool = True,\n week_nb: int = False,\n year: int = False) -> None:\n\n start_tuning = utctime_now()\n\n for region in regions:\n\n if not region in ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4']:\n sys.exit(\"Region must be one out of: 'NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'\")\n\n for variable in variables:\n\n if not variable in ['magasin', 'tilsig']:\n sys.exit(\"Variable must be either 'tilsig' or 'magasin'\")\n\n print('---------------------------------------------------------------')\n print(' {}, {} '.format(region, variable))\n print('---------------------------------------------------------------')\n\n \n \n df_week, MagKap = auto_input[variable]\n \n period, forecast_time, read_start = rs.get_timeperiods(variable, week_nb, year)\n \n reg_end = (pd.to_datetime(time.strftime(forecast_time), format=\"%Y.%m.%d\") - Timedelta(days=7)).strftime(\n '%Y.%m.%d')\n\n if (0 <= today.weekday() <= 1) or (today.weekday() == 2 and today.hour < 14): # True for tipping\n last_forecast = forecast_time\n else:\n last_forecast = forecast_time\n\n df_cleaned = deletingNaNs(df_week.loc[:last_forecast])\n\n if loop:\n if variable == 'tilsig':\n max_kandidater = 196\n min_kandidater = 2\n\n else:\n max_kandidater = 171\n min_kandidater = 2\n\n max_weeks = 238 #288\n min_weeks = 16\n print('max ant. kandidater: {}, min ant. kandidater: {}'.format(max_kandidater, min_kandidater))\n print('max ant. uker: {}, min ant. uker: {}'.format(max_weeks, min_weeks))\n\n start_time_loop = utctime_now()\n fasit, fasit_key = rs.make_fasit(variable, region, reg_end, period)\n print('Fasit er lest inn.\\n')\n\n if fasit[fasit_key][:reg_end].isnull().any():\n print('OBS: Det mangler verdier på fasiten! Går videre til neste region i loopen..')\n print(fasit[fasit_key][:reg_end].isnull())\n continue\n\n sorted_r2 = get_R2_sorted(variable, df_cleaned, fasit, fasit_key)\n\n if loop:\n max_p = 0.001 # Beste testresultat SvF\n\n # First loop: Tuning number of candidates for best possible R2 combined\n df_ant_kandidater = pd.DataFrame(columns=columns)\n for antall in range(min_kandidater, max_kandidater + 1, 1):\n if antall > len(sorted_r2):\n chosen_r2 = sorted_r2\n else:\n chosen_r2 = sorted_r2[:antall]\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, first_period, max_p, chosen_r2,\n loop=True)\n df_ant_kandidater = df_ant_kandidater.append(\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\n if antall > len(sorted_r2):\n print('Feilmelding: Ønsket antall kandidater overskrider maks (%i).\\n' % len(sorted_r2))\n break\n idx_max = df_ant_kandidater.r2_samlet.idxmax(skipna=True)\n ant_kandidater_beste = int(df_ant_kandidater.ant_kandidater.values[idx_max])\n print('Beste ant_kandidater loop 1: ', ant_kandidater_beste)\n\n # Second loop: tuning length of the short regression for best possible R2 combined, using the best number of\n # candidates found in the First loop.\n final_chosen_r2 = sorted_r2[:ant_kandidater_beste]\n df_short_period = pd.DataFrame(columns=columns)\n for short_period in range(min_weeks, max_weeks + 1, 1):\n short_period = int(short_period)\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period, max_p,\n final_chosen_r2, loop=True)\n df_short_period = df_short_period.append(\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\n idx_max = df_short_period.r2_samlet.idxmax(skipna=True)\n short_period_beste = int(df_short_period.short_period.values[idx_max])\n print('Beste short_period loop 2: ', short_period_beste)\n\n # Getting the best input variables from loop and write to input_variables_from_tuning.txt\n df_all_methods = pd.concat([df_ant_kandidater, df_short_period], ignore_index=True, sort=False)\n idx_max = df_all_methods.r2_samlet.idxmax(skipna=True)\n ant_kandidater_beste = int(df_all_methods.ant_kandidater.values[idx_max])\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\n short_period_beste = df_all_methods.short_period.values[idx_max]\n ws.write_input_variables_to_file(region, variable, max_p, ant_kandidater_beste, short_period_beste)\n\n print('\\nTuning for regionen tok %.0f minutter. \\n' % ((utctime_now() - start_time_loop) / 60))\n\n else:\n # getting the best variables from input_variables_from_tuning.txt or input_variables_backup.txr\n short_period_beste, max_p, ant_kandidater_beste, input_file = rs.get_input_variables_from_file(variable,region,backup)\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\n print(\"Input variables was read from: \", input_file)\n\n # MAKE ESTIMATE AND SHOW RESULTS\n input1 = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period_beste, max_p,\n chosen_r2_beste, loop=False)\n input2 = fasit_key, ant_kandidater_beste, max_p, reg_end, read_start\n\n\n #WRITE RESULTS\n if write:\n # Write results from the regression to SMG.\n fasit, long_results, short_results, df_tot, chosen_p, chosen_r2, r2_modelled, r2_modelled_long, prediction, tipping_ps, short_period, nb_weeks_tipping = input1\n\n # write to SMG:\n ws.write_SMG_regresjon(variable, region, tipping_ps[-1:])\n\n # write to SMG, virtual:\n ws.write_V_SMG_Regresjon(short_results, chosen_p, fasit_key, r2_modelled, r2_modelled_long, short_period_beste, MagKap)\n\n if jupyter:\n ws.show_result_jupyter(input1, input2)\n else:\n ws.show_result(input1, input2, input_file)\n\n print('---------------------------------------------------------------')\n print(' SLUTT ')\n print('---------------------------------------------------------------')\n print('\\nRegresjon for alle regioner og variabler brukte totalt %.0f minutter. \\n' % (\n (utctime_now() - start_tuning) / 60))","def par_fit(init_file):\n check_presence_init(init_file)\n\n dict_ = read(init_file)\n np.random.seed(dict_[\"SIMULATION\"][\"seed\"])\n\n # We perform some basic consistency checks regarding the user's request.\n check_presence_estimation_dataset(dict_)\n # check_initialization_dict2(dict_)\n # check_init_file(dict_)\n\n # Distribute initialization information.\n data = read_data(dict_[\"ESTIMATION\"][\"file\"])\n num_treated = dict_[\"AUX\"][\"num_covars_treated\"]\n num_untreated = num_treated + dict_[\"AUX\"][\"num_covars_untreated\"]\n\n _, X1, X0, Z1, Z0, Y1, Y0 = process_data(data, dict_)\n\n if dict_[\"ESTIMATION\"][\"maxiter\"] == 0:\n option = \"init\"\n else:\n option = dict_[\"ESTIMATION\"][\"start\"]\n\n # Read data frame\n\n # define starting values\n x0 = start_values(dict_, data, option)\n opts, method = optimizer_options(dict_)\n dict_[\"AUX\"][\"criteria\"] = calculate_criteria(dict_, X1, X0, Z1, Z0, Y1, Y0, x0)\n dict_[\"AUX\"][\"starting_values\"] = backward_transformation(x0)\n rslt_dict = bfgs_dict()\n if opts[\"maxiter\"] == 0:\n rslt = adjust_output(None, dict_, x0, X1, X0, Z1, Z0, Y1, Y0, rslt_dict)\n else:\n opt_rslt = minimize(\n minimizing_interface,\n x0,\n args=(dict_, X1, X0, Z1, Z0, Y1, Y0, num_treated, num_untreated, rslt_dict),\n method=method,\n options=opts,\n )\n rslt = adjust_output(\n opt_rslt, dict_, opt_rslt[\"x\"], X1, X0, Z1, Z0, Y1, Y0, rslt_dict\n )\n # Print Output files\n print_logfile(dict_, rslt)\n\n if \"comparison\" in dict_[\"ESTIMATION\"].keys():\n if dict_[\"ESTIMATION\"][\"comparison\"] == 0:\n pass\n else:\n write_comparison(data, rslt)\n else:\n write_comparison(data, rslt)\n\n return rslt","def form_data(stocks, init_param):\r\n \r\n rs = stocks[1].rsi\r\n ts = stocks[1].tsi\r\n a = 1\r\n \r\n \r\n for date in init_param.train_dates:\r\n try:\r\n training_data\r\n except NameError:\r\n training_data = LearningData()\r\n training_data.construct(stocks, date, init_param.future_day, init_param.features)\r\n else:\r\n training_data.append(stocks, date, init_param.future_day, init_param.features)\r\n \r\n for date in init_param.test_dates:\r\n try:\r\n test_data\r\n except NameError:\r\n test_data = LearningData()\r\n test_data.construct(stocks, date, init_param.future_day, init_param.features)\r\n else:\r\n test_data.append(stocks, date, init_param.future_day, init_param.features)\r\n \r\n #reference_date = dateutl.days_since_1900('1991-01-01')\r\n #test_data.construct(stocks,[reference_date, day_history, init_param.future_day])\r\n \r\n return training_data, test_data","def grid_interp_ts(df, time_col, x_col, y_col, data_col, grid_res, from_crs=None, to_crs=2193, interp_fun='cubic', agg_ts_fun=None, period=None, digits=2):\n\n #### Create the grids\n df1 = df.copy()\n\n #### Resample the time series data\n if agg_ts_fun is not None:\n df1a = df1.set_index(time_col)\n if agg_ts_fun == 'sum':\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].sum().reset_index()\n elif agg_ts_fun == 'mean':\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].mean().reset_index()\n else:\n raise ValueError(\"agg_ts_fun should be either 'sum' or 'mean'.\")\n time = df2[time_col].unique()\n else:\n df2 = df1\n\n time = df2[time_col].sort_values().unique()\n\n if from_crs is None:\n x = df2.loc[df2[time_col] == time[0], x_col].values\n y = df2.loc[df2[time_col] == time[0], y_col].values\n else:\n data1 = df2.loc[df2[time_col] == time[0]]\n from_crs1 = convert_crs(from_crs, pass_str=True)\n to_crs1 = convert_crs(to_crs, pass_str=True)\n geometry = [Point(xy) for xy in zip(data1[x_col], data1[y_col])]\n gpd = GeoDataFrame(data1.index, geometry=geometry, crs=from_crs1)\n gpd1 = gpd.to_crs(crs=to_crs1)\n x = gpd1.geometry.apply(lambda p: p.x).round(digits).values\n y = gpd1.geometry.apply(lambda p: p.y).round(digits).values\n\n xy = column_stack((x, y))\n\n max_x = x.max()\n min_x = x.min()\n\n max_y = y.max()\n min_y = y.min()\n\n new_x = arange(min_x, max_x, grid_res)\n new_y = arange(min_y, max_y, grid_res)\n x_int, y_int = meshgrid(new_x, new_y)\n\n #### Create new df\n x_int2 = x_int.flatten()\n y_int2 = y_int.flatten()\n xy_int = column_stack((x_int2, y_int2))\n time_df = repeat(time, len(x_int2))\n x_df = tile(x_int2, len(time))\n y_df = tile(y_int2, len(time))\n new_df = DataFrame({'time': time_df, 'x': x_df, 'y': y_df, data_col: repeat(0, len(time) * len(x_int2))})\n\n new_lst = []\n for t in to_datetime(time):\n set1 = df2.loc[df2[time_col] == t, data_col]\n# index = new_df[new_df['time'] == t].index\n new_z = griddata(xy, set1.values, xy_int, method=interp_fun).round(digits)\n new_z[new_z < 0] = 0\n new_lst.extend(new_z.tolist())\n# print(t)\n new_df.loc[:, data_col] = new_lst\n\n #### Export results\n return(new_df[new_df[data_col].notnull()])","def build_params(clsite, dt):\n hp = GaugeParams()\n lp = GaugeParams()\n if clsite is not None:\n with get_sqlalchemy_conn(\"coop\") as conn:\n df = pd.read_sql(\n \"SELECT year, high, low from alldata WHERE \"\n \"station = %s and sday = %s\",\n conn,\n params=(clsite, f\"{dt:%m%d}\"),\n index_col=\"year\",\n )\n hp.minval = df[\"high\"].min()\n hp.maxval = df[\"high\"].max()\n hp.avgval = df[\"high\"].mean()\n hp.stddev = df[\"high\"].std()\n hp.ptiles = df[\"high\"].quantile(np.arange(0.1, 0.91, 0.1)).to_list()\n lp.maxval = df[\"low\"].max()\n lp.minval = df[\"low\"].min()\n lp.avgval = df[\"low\"].mean()\n lp.stddev = df[\"low\"].std()\n lp.ptiles = df[\"low\"].quantile(np.arange(0.1, 0.91, 0.1)).to_list()\n\n return hp, lp","def __init__(self, prefix, params, begin, end, simthreshold_NS = 0.5, \r\n simthreshold_pbias = 25., simthreshold_rsr = 0.7, \r\n with_valid_data = False, shift_one_day = False):\r\n self.simthreshold_NS = simthreshold_NS\r\n self.simthreshold_pbias = simthreshold_pbias\r\n self.simthreshold_rsr = simthreshold_rsr\r\n self.with_valid_data = with_valid_data\r\n self.shift_one_day = shift_one_day\r\n # Open the simulation file\r\n try:\r\n task_id = int(os.environ.get('SGE_TASK_ID',0))\r\n except:\r\n task_id = 0\r\n postfix = '.%04i' % task_id if task_id else ''\r\n self.outfile_sim = open(prefix + '-simulation.csv' + postfix,'w')\r\n # Open the parameter file\r\n self.outfile_param = open(prefix + '-parameters.csv' + postfix,'w')\r\n # Make the header\r\n if task_id<2:\r\n header = 'logNS,' + \"pbias,\" + \"rsr,\" + ','.join(p.name for p in params)\r\n self.outfile_param.write(header + '\\n')\r\n # Prolong the header with the data\r\n t = begin\r\n # add the validation period if wanted\r\n if self.with_valid_data:\r\n end = datetime.datetime(1988,12,31)\r\n # if the whole timeseries is shifted add one day to the header\r\n if self.shift_one_day:\r\n end = datetime.datetime(1989,1,1)\r\n while t <= end:\r\n header += ', ' + datetime.datetime.strftime(t,'%Y-%m-%d')\r\n t += datetime.timedelta(days=1)\r\n self.outfile_sim.write(header + '\\n')","def extract_dates(self,dates, tol= 0.05 , in_place=False, verbose=True):\n###################################################################\n\n # import \n import inspect\n import numpy as np\n import pyacs.gts\n\n # check data is not None\n from pyacs.gts.lib.errors import GtsInputDataNone\n \n try:\n if self.data is None:\n # raise exception\n raise GtsInputDataNone(inspect.stack()[0][3],__name__,self)\n except GtsInputDataNone as error:\n # print PYACS WARNING\n print( error )\n return( self )\n\n # working gts\n new_gts = self.copy()\n \n # case .data_xyz is None\n \n if new_gts.data_xyz is None:\n new_gts.neu2xyz(corr=True)\n\n else:\n # check data/data_xyz consistency\n try:\n if not new_gts.cdata(data=True):\n # raise exception\n from pyacs.gts.lib.errors import GtsCDataError\n raise GtsCDataError( inspect.stack()[0][3],__name__,self )\n except GtsCDataError as error:\n print( error )\n return( self )\n \n \n new_data=None\n \n # extract dates\n \n index = np.array( pyacs.gts.Gts.get_index_from_dates(dates, self.data, tol=tol) )\n \n if verbose:\n print('-- Extracting ',index.shape[0],' entries from Gts or code: ',self.code)\n \n if index.shape[0] > 0:\n new_data_xyz= self.data_xyz[index,:]\n new_sigma = self.data[index,4:]\n else:\n new_data=None\n if verbose:\n print(\"-- time series \",self.code,\" does not have dates at the requested dates \")\n\n \n # handles outliers\n\n if new_data is not None: \n ldate_outliers=self.data[:,0][self.outliers]\n lupdated_outliers=pyacs.gts.Gts.get_index_from_dates(ldate_outliers, new_data, tol=tol)\n else:\n lupdated_outliers = []\n\n if verbose:\n print('-- Transmitting ',len(lupdated_outliers),' outliers to the extracted Gts ')\n \n # case observations\n \n new_gts.data_xyz = new_data_xyz\n \n # handle outliers\n\n ldate_outliers=self.data[:,0][self.outliers]\n lupdated_outliers=pyacs.gts.Gts.get_index_from_dates(ldate_outliers, new_data_xyz, tol=0.05)\n \n # handles offsets_date\n \n upd_offsets=[]\n for offset_date in self.offsets_dates:\n if offset_date>=new_data_xyz[0,0] and offset_date<=new_data_xyz[-1,0]:\n upd_offsets.append(offset_date)\n \n # handles X0,Y0,Z0\n \n new_gts.X0 = new_data_xyz[0,1]\n new_gts.Y0 = new_data_xyz[0,2]\n new_gts.Z0 = new_data_xyz[0,3]\n \n # re-generate NEU time series\n new_gts.xyz2neu(corr=False)\n\n # re-populate the uncertainties columns\n new_gts.data[:,4:] = new_sigma\n \n # offsets & outliers\n \n new_gts.offsets_dates=upd_offsets\n new_gts.outliers=lupdated_outliers\n \n if in_place:\n self = new_gts\n return(self)\n else:\n return(new_gts)","def make_data(args):\n mass_MJ = 1.142\n radius_RJ = 1.138\n gravity_SI = 23.970 \n Rs_Rsun = 0.805\n inc = 85.71\n t0 = 2454037.612\n sma = 8.839304998 # semi major axis in stellar radiu\n orb_per = 2.21857545 #in days\n ecc = 0.0041\n w_peri = -24.1 # longiutude of periastron\n limbdark = \"linear\"\n \n u_limbdark = [0.35]\n \n num_transit = 1\n \n dates = [2458383.77055943, 2458383.77384704, 2458383.77707875,\n 2458383.78030307, 2458383.78358918, 2458383.78681399,\n 2458383.79004101, 2458383.79326712, 2458383.79655574,\n 2458383.79984545, 2458383.80307906, 2458383.80629228,\n 2458383.80958299, 2458383.8128124 , 2458383.81603942,\n 2458383.81925973, 2458383.82248474, 2458383.82577195,\n 2458383.82900097, 2458383.83223048, 2458383.8354501 ,\n 2458383.83874811, 2458383.84196822, 2458383.84520053,\n 2458383.84847654, 2458383.85170346, 2458383.85493727,\n 2458383.85821578, 2458383.86144419, 2458383.86466921,\n 2458383.86790322, 2458383.87118233, 2458383.87441074,\n 2458383.87763435, 2458383.88092406, 2458383.88414957],\n #don't forget the coma at the end if there is only one transit !!!!!\n \n\n\n # Wmean = [2400.695909757236,2328.5343131275904,1972.9809993156186,\n # 1927.2107049022654,]\n # Wmean = [1634.5200937047302,1600.8109822367207],[1670.071564637037,1634.5459486709924,1600.8124596368639],\n Wmean = [2328.5343131275904], \n orderstot = [33]\n orders = [33],\n # orderstot = [46,47,48]\n # orders = [47,48],[46,47,48],\n \n # Vfiles = [\"Vcorr47_DRS2.txt\",\n # \"Vcorr48_DRS2.txt\",\n # ],[\"Vcorr46_Jun19-1_DRS2.txt\",\n # \"Vcorr47_Jun19-1_DRS2.txt\",\n # \"Vcorr48_Jun19-1_DRS2.txt\"\n # ],\n Vfiles = [\"V33_CO.txt\"], \n \n Ifiles = [\"I33_CO.txt\"],\n \n # if Stdfiles are not needed, for example with the Brogi likelihood, \n # uncomment the next line\n #Stdfiles = []\n Stdfiles = [\"Std33_CO.txt\"],\n \n lambdas = np.array([[ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [2291.84518119, 2362.55271775],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [1939.42197854, 1998.81548771],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [1758.50261646, 1812.39702422],\n [1718.50054581, 1771.64067835],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [1512.43747007, 1558.89713666],\n [1484.77586677, 1528.30354258],\n [1457.06015806, 1498.88570675],\n [1429.75333156, 1470.19096444],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [1306.967007 , 1343.21643463],\n [1285.02046052, 1320.56072659],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [1167.78440327, 1198.13940642],\n [1150.59417256, 1178.48372217],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ],\n [ 0. , 0. ]])\n\n return dict(\n mass_MJ=mass_MJ,\n radius_RJ=radius_RJ,\n\t\tgravity_SI = gravity_SI,\n\t\tRs_Rsun = Rs_Rsun,\n\t\tinc = inc,\n\t\tt0 = t0,\n\t\tsma = sma,\n\t\torb_per = orb_per,\n\t\tecc = ecc,\n\t\tw_peri = w_peri,\n Wmean = Wmean,\n\t\tlimbdark = limbdark,\n\t\tu_limbdark = u_limbdark,\n\t\tdates = dates,\n\t\tlambdas = lambdas,\n orders = orders,\n orderstot=orderstot,\n num_transit=num_transit,\n\t\tVfiles = Vfiles,\n\t\tIfiles = Ifiles,\n\t\tStdfiles = Stdfiles\n\t\t )","def params(timeseries_input):\n # Settings for Nelder Mead Algorithm\n global timeseries\n timeseries=timeseries_input\n\n NumIters = 1 # First Iteration\n MaxIters = 1e3 # Maximum number of iterations\n Tolerance = 1e-5 # Tolerance on best and worst function values\n N = 5 # Number of Heston and Nandi parameters\n r = 0.01 / 252.0 # Risk Free Rate\n\n # Heston and Nandi parameter starting values (vertices) in vector form\n\n x = [[0 for i in range(N + 1)] for j in range(N)]\n x[0][0] = 5.02e-6;\n x[0][1] = 5.12e-6;\n x[0][2] = 5.00e-6;\n x[0][3] = 4.90e-6;\n x[0][4] = 4.95e-6;\n x[0][5] = 4.99e-6 # omega\n x[1][0] = 1.32e-6;\n x[1][1] = 1.25e-6;\n x[1][2] = 1.35e-6;\n x[1][3] = 1.36e-6;\n x[1][4] = 1.30e-6;\n x[1][5] = 1.44e-6 # alpha\n x[2][0] = 0.79;\n x[2][1] = 0.80;\n x[2][2] = 0.78;\n x[2][3] = 0.77;\n x[2][4] = 0.81;\n x[2][5] = 0.82 # beta\n x[3][0] = 427.0;\n x[3][1] = 421.0;\n x[3][2] = 425.0;\n x[3][3] = 419.1;\n x[3][4] = 422.1;\n x[3][5] = 430.0 # gamma\n x[4][0] = 0.21;\n x[4][1] = 0.20;\n x[4][2] = 0.22;\n x[4][3] = 0.19;\n x[4][4] = 0.18;\n x[4][5] = 0.205 # lambda\n\n # Run Nelder Mead and output Nelder Mead results\n B = NelderMead(LogLike, N, NumIters, MaxIters, Tolerance, x, r)\n\n #\tprint(\"Nelder Mead Minimization of Log-Likelihood for Heston and Nandi parameters\")\n #\tprint(\"---------------------------------\")\n #\tprint(\"omega = \", B[0])\n #\tprint(\"alpha = \", B[1])\n #\tprint(\"beta = \", B[2])\n #\tprint(\"gamma = \", B[3])\n #\tprint(\"lambda = \", B[4])\n #\tprint(\"Value of Objective Function = \", B[N])\n #\tprint(\"Number of Iterations = \", B[N+1])\n #\tprint(\"Persistence \", B[2]+B[1]*(B[3]**2) )\n #\tprint(\"---------------------------------\")\n\n # alpha,beta,gamma,omega,lambda\n return [B[1], B[2], B[3], B[0], B[4]]","def find_calibration_parameters(df, temperature, cal_mode, calibration_statistics, num_iterations, optimal_t=25):\n\n if \"temp\" in cal_mode:\n # create a column of T - optimal_T (mean temperature for each still bout minus the optimal temperature)\n # i.e. the deviation in T from the optimal\n df[\"T_dev\"] = temperature.data - optimal_t\n\n for i in range(num_iterations):\n # do linear regression:\n x_results, y_results, z_results = dataframe_regression(df, cal_mode, do_or_undo=\"do\")\n\n # results.params() gives the calibration parameters thus:\n # x_results.params() = [x_scale, x_offset, x_temp_offset] (last item only applies if temperature is used)\n df = dataframe_transformation(df, x_results.params, y_results.params, z_results.params,\n cal_mode)\n # update the \"matched\" arrays to reflect the new \"closest points\" after the dataframe transformation\n update_matched(df)\n\n # Regress the backup copy of the original input against the transformed version,\n # to calculate offset, scale and temperature offset scalar (if temperature used)\n x_results_final, y_results_final, z_results_final = dataframe_regression(df, cal_mode, do_or_undo=\"undo\")\n\n calibration_parameters = {\"x_offset\": x_results_final.params[1],\n \"x_scale\": x_results_final.params[0],\n \"y_offset\": y_results_final.params[1],\n \"y_scale\": y_results_final.params[0],\n \"z_offset\": z_results_final.params[1],\n \"z_scale\": z_results_final.params[0]\n }\n\n if \"temp\" in cal_mode:\n calibration_parameters[\"x_temp_offset\"] = x_results_final.params[2]\n calibration_parameters[\"y_temp_offset\"] = y_results_final.params[2]\n calibration_parameters[\"z_temp_offset\"] = z_results_final.params[2]\n else:\n calibration_parameters[\"x_temp_offset\"] = 0\n calibration_parameters[\"y_temp_offset\"] = 0\n calibration_parameters[\"z_temp_offset\"] = 0\n\n # if enhanced calibration statistics are required...\n if calibration_statistics:\n\n ######################\n\n # extract the error in the final regression fit for each axis\n calibration_parameters[\"x_rsquared\"] = x_results_final.rsquared\n calibration_parameters[\"y_rsquared\"] = y_results_final.rsquared\n calibration_parameters[\"z_rsquared\"] = z_results_final.rsquared\n\n x_bse = x_results_final.bse\n y_bse = y_results_final.bse\n z_bse = z_results_final.bse\n\n calibration_parameters[\"x_scale_se\"] = x_bse[0]\n calibration_parameters[\"y_scale_se\"] = y_bse[0]\n calibration_parameters[\"z_scale_se\"] = z_bse[0]\n\n calibration_parameters[\"x_offset_se\"] = x_bse[1]\n calibration_parameters[\"y_offset_se\"] = y_bse[1]\n calibration_parameters[\"z_offset_se\"] = z_bse[1]\n\n if \"temp\" in cal_mode:\n calibration_parameters[\"x_temp_offset_se\"] = x_bse[2]\n calibration_parameters[\"y_temp_offset_se\"] = y_bse[2]\n calibration_parameters[\"z_temp_offset_se\"] = z_bse[2]\n\n #########################\n\n return calibration_parameters","def start_params(unique_id):\n\n temp = unique_id.GradientTemp\n temp_k = unique_id.Temp_K\n logtraitval = unique_id.logTraitValue\n midpoint = unique_id.logTraitValue.idxmax()\n midtemp = unique_id.GradientTemp[unique_id.logTraitValue.idxmax()]\n y = NaN\n\n # at midpoint, split the data into two arrays\n EhVect = where(unique_id.GradientTemp >= midtemp, logtraitval, y)\n EVect = where(unique_id.GradientTemp < midtemp, logtraitval, y)\n\n Len_EVect = len(EVect)\n Len_EhVect = len(EhVect)\n\n if Len_EVect < 3: # all the points are one side of the 'midpoint'):\n EGrad = stats.linregress(temp, logtraitval)\n else:\n try:\n EGrad = stats.linregress(\n temp[:midpoint], logtraitval[:midpoint])\n except ValueError as error:\n logging.error(\"ValueError encountered %r\", error)\n EGrad = stats.linregress(temp, logtraitval)\n\n if Len_EhVect < 3:\n EhGrad = stats.linregress(temp, logtraitval)\n else:\n try:\n EhGrad = stats.linregress(\n temp[midpoint:], logtraitval[midpoint:])\n except ValueError as error:\n #logging.error(\"ValueError encountered %r\", error)\n EhGrad = stats.linregress(temp, logtraitval)\n\n B0 = exp(EGrad[0] * (1 / (k * 283.15)) + EGrad[1])\n\n if Len_EhVect < 2:\n Th = midtemp\n else:\n try:\n Th = (\n ((mean(logtraitval) - logtraitval[B0]) / EhGrad[0]))**(-1) / k\n except BaseException:\n Th = temp_k[logtraitval.idxmax()] # close to where 50% deactivation would occur\n\n return DataFrame({\n \"E\": EGrad[0],\n \"Eh\": EhGrad[0],\n \"B0\": B0,\n \"Th\": Th\n }, index=[0]\n ) # index assignment as passing scalar values","def create_lat_lon_date_data(gt_id,\n target_horizon,\n experiment,\n past_gt_ids=[\"contest_precip\", \"contest_tmp2m\"],\n forecast_models=[\"nmme\",\"nmme0\"],\n other_lat_lon_date_features=[\"contest_rhum.sig995\",\n \"contest_pres.sfc.gauss\"]):\n\n time_start = time.time()\n\n # Add forecasts to list of forecast IDs\n forecast_variable = get_forecast_variable(gt_id) # 'prate' or 'tmp2m'\n forecast_ids = ['{}-{}-{}'.format(forecast, forecast_variable, target_horizon)\n for forecast in forecast_models]\n\n # -----------\n # Generate relevant variable and column names\n # -----------\n\n # Identify measurement variable name\n measurement_variable = get_measurement_variable(gt_id) # 'tmp2m' or 'prate'\n\n # Keep track of relevant column names\n gt_col = measurement_variable\n clim_col = measurement_variable+\"_clim\"\n anom_col = measurement_variable+\"_anom\"\n\n # Inverse of standard deviation of anomalies for each start_date\n anom_inv_std_col = anom_col+\"_inv_std\"\n\n # --------\n # Prepare experiment cache directory and saved file names\n # --------\n\n # Name of cache directory for storing non-submission-date specific\n # intermediate files\n cache_dir = os.path.join('results', experiment, 'shared',\n '{}_{}'.format(gt_id, target_horizon))\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\n\n # if cache_dir doesn't exist, create it\n if not os.path.isdir(cache_dir):\n os.makedirs(cache_dir)\n\n # Filenames for data file to be stored in cache_dir\n lat_lon_date_data_file = os.path.join(\n cache_dir, \"lat_lon_date_data-{}_{}.h5\".format(gt_id, target_horizon))\n\n # --------\n # Load mask indicating which grid points count in the contest (1=in, 0=out)\n # --------\n print \"Loading contest mask\"\n t = time.time()\n mask_df = get_contest_mask()\n print \"Elapsed: {}s\".format(time.time() - t)\n\n # --------\n # Creates and saves lat_lon_date_data dataframe\n # --------\n # Load masked lat lon date features restricted to years >= get_first_year(gt_id)\n # Note: contest lat lon date features and forecasts are pre-masked, so there\n # is no need to mask explcitily\n print \"Loading lat lon date features\"\n num_gt_ids = len(past_gt_ids)\n # For each measurement,\n # get number of days between start date of observation period used for prediction\n # (2 weeks + 1 submission day behind for most predictors) and start date of\n # target period (2 or 4 weeks ahead)\n past_start_deltas = [get_start_delta(target_horizon, past_gt_id)\n for past_gt_id in past_gt_ids]\n other_start_deltas = [get_start_delta(target_horizon, other_gt_id)\n for other_gt_id in other_lat_lon_date_features]\n # Additionally keep track of days between forecast date and start date of\n # target period\n forecast_delta = get_forecast_delta(target_horizon)\n\n lat_lon_date_data = get_lat_lon_date_features(\n gt_ids=other_lat_lon_date_features + other_lat_lon_date_features\n + other_lat_lon_date_features,\n gt_masks=None,\n gt_shifts=other_start_deltas +\n [2*delta for delta in other_start_deltas] +\n [365]*len(other_lat_lon_date_features),\n forecast_ids=forecast_ids + forecast_ids,\n forecast_masks=None,\n forecast_shifts=[None]*len(forecast_ids) + [forecast_delta]*len(forecast_ids),\n anom_ids=[gt_id] + past_gt_ids + past_gt_ids + past_gt_ids,\n anom_masks=None,\n anom_shifts=[None] + past_start_deltas +\n [2*delta for delta in past_start_deltas] +\n [365]*len(past_gt_ids),\n first_year=get_first_year(gt_id)\n )\n\n print \"Loading additional lat lon date features\"\n t = time.time()\n # Add CFSv2 mean as feature\n if 'cfsv2' in forecast_models:\n cfsv2_models = ['cfsv2_op_delta_2w_1d_6h', 'cfsv2_op_delta_2w_1d_12h',\n 'cfsv2_op_delta_2w_1d_18h', 'cfsv2_op_delta_2w_2d_0h',\n 'cfsv2_op_delta_2w_2d_6h', 'cfsv2_op_delta_2w_2d_12h',\n 'cfsv2_op_delta_2w_2d_18h', 'cfsv2_op_delta_2w_3d_0h']\n lat_lon_date_data['cfsv2_mean'] = lat_lon_date_data[cfsv2_models].mean(axis=1)\n lat_lon_date_data[\"cfsv2_mean_shift\"+str(start_delta)] = lat_lon_date_data[\n [model+\"_shift\"+str(start_delta) for model in cfsv2_models]].mean(axis=1)\n # Add inverse of standard deviation of anomalies for each start_date\n lat_lon_date_data[anom_inv_std_col] = \\\n 1.0/lat_lon_date_data.groupby([\"start_date\"])[anom_col].transform('std')\n\n print \"Elapsed: {}s\".format(time.time() - t)\n\n # Save lat lon date features to disk\n print \"Saving lat lon date features to \"+lat_lon_date_data_file\n t = time.time()\n lat_lon_date_data.to_hdf(lat_lon_date_data_file, key=\"data\", mode=\"w\")\n subprocess.call(\"chmod a+w \"+lat_lon_date_data_file, shell=True)\n print \"Elapsed: {}s\".format(time.time() - t)\n print \"Finished generating lat_lon_date_data matrix.\"\n print \"Total time elapsed: {}s\".format(time.time()-time_start)\n return list(lat_lon_date_data)","def generate_testdata(dt, maxt=10., meanrate=10., amp=20.e-12, ampvar=5.e-12, \n noise=2.5e-12, taus=[0.001, 0.010], baseclass=None, func=None, sign=1, \n expseed=None, noiseseed=None,\n bigevent=None):\n if baseclass is None and func is not None:\n raise ValueError('Need base class definition')\n tdur = 0.020\n timebase = np.arange(0., maxt, dt) # in ms\n t_psc = np.arange(0., tdur, dt) # time base for single event template in ms\n if func is None:\n tau_1 = taus[0] # ms\n tau_2 = taus[1] # ms\n Apeak = amp # pA\n Aprime = (tau_2/tau_1)**(tau_1/(tau_1-tau_2))\n g = Aprime * (-np.exp(-t_psc/tau_1) + np.exp((-t_psc/tau_2)))\n gmax = np.max(g)\n print('gmax: ', gmax)\n g = sign*g*amp/gmax\n print(f'max g: {np.min(g):.6e}')\n else:\n baseclass._make_template()\n gmax = np.min(baseclass.template)\n g = sign*amp*baseclass.template/gmax\n print('gmaxb: ', np.max(gmax))\n \n testpsc = np.zeros(timebase.shape)\n if expseed is None:\n eventintervals = np.random.exponential(1./meanrate, int(maxt*meanrate))\n else:\n np.random.seed(expseed)\n eventintervals = np.random.exponential(1./meanrate, int(maxt*meanrate))\n eventintervals = eventintervals[eventintervals < 10.]\n events = np.cumsum(eventintervals)\n if bigevent is not None:\n events = np.append(events, bigevent['t'])\n events = np.sort(events)\n t_events = events[events < maxt] # time of events with exp distribution\n i_events = np.array([int(x/dt) for x in t_events])\n testpsc[i_events] = np.random.normal(1., ampvar/amp, len(i_events))\n if bigevent is not None:\n ipos = int(bigevent['t']/dt) # position in array\n testpsc[ipos] = bigevent['I']\n testpsc = scipy.signal.convolve(testpsc, g, mode='full')[:timebase.shape[0]]\n # f, ax = mpl.subplots(1,1)\n # ax.plot(dt*np.arange(len(testpsc)), testpsc)\n # mpl.show()\n if noise > 0:\n if noiseseed is None:\n testpscn = testpsc + np.random.normal(0., noise, testpsc.shape)\n else:\n np.random.seed(noiseseed)\n testpscn = testpsc + np.random.normal(0., noise, testpsc.shape)\n else:\n testpscn = testpsc\n return timebase, testpsc, testpscn, i_events","def process_data(self):\n timer_start = time.time()\n # ensure self.df_events and self.df_locations are not None\n if self.df_events is None or self.df_locations is None:\n print(\"Missing data: either df_events or df_locations is None\")\n return\n # set start and end based on self.df_events if not already set\n if not self.start:\n self.start = self.df_events['event_time'].min()\n if not self.end:\n self.end = self.df_events['event_time'].max()\n print(f\"date range for events data is from {self.start} to {self.end}\")\n # create Grid object before processing any data\n grid = self.compute_grid_cells(self.df_locations)\n # clean and combine events and locations data\n df_data = self.combine_events_and_locations(grid)\n print(df_data.shape)\n # df_data.to_csv('../../../data_files/20210506_cleanedInputDataCumSum.csv', index=False)\n # df_data = pd.read_csv('../../../data_files/20210415_cleanedInputDataAprilCumSum.csv')\n # process data within grid class\n df_processed = grid.process_data(df_data, 'weekly')\n # df_processed = self.calculate_demand(df_processed)\n # df_processed.to_csv('../../../data_files/20210506_processedGridCellData.csv')\n # set df_demand to be df_processed\n df_processed.reset_index(inplace=True)\n df_processed = df_processed.astype({'date': 'str', 'avail_count': 'float', 'avail_mins': 'float', 'prob_scooter_avail': 'float', 'trips': 'float', 'adj_trips': 'float'})\n # make sure dates are within start and end dates\n start_date = str(iso8601.parse_date(self.start).date())\n end_date = str(iso8601.parse_date(self.end).date())\n df_processed = df_processed[(df_processed['date'] >= start_date) & (df_processed['date'] <= end_date)]\n self.set_demand(df_processed)\n timer_end = time.time()\n print('Elapsed time to process data:', (timer_end - timer_start)/60.0, 'minutes')","def run_global(start_year, end_year, depth_from, depth_to, animate=True):\n# years, times, rootgrps = retrieve(1950,2018)\n# rootgrps_1950 = [nt.Dataset(\"EN.4.2.1.analyses.g10.1950\\EN.4.2.1.f.analysis.g10.195001.nc\", \"r+\", format=\"NETCDF4\")]\n# rootgrps_1951 = [nt.Dataset(\"EN.4.2.1.analyses.g10.1951\\EN.4.2.1.f.analysis.g10.195101.nc\", \"r+\", format=\"NETCDF4\")]\n# rootgrps_1952 = [nt.Dataset(\"EN.4.2.1.analyses.g10.1952\\EN.4.2.1.f.analysis.g10.195201.nc\", \"r+\", format=\"NETCDF4\")]\n# rootgrps_1953 = [nt.Dataset(\"EN.4.2.1.analyses.g10.1953\\EN.4.2.1.f.analysis.g10.195301.nc\", \"r+\", format=\"NETCDF4\")]\n#\n#\n# rootgrps_2015 = [nt.Dataset(\"EN.4.2.1.analyses.g10.2015\\EN.4.2.1.f.analysis.g10.201501.nc\", \"r+\", format=\"NETCDF4\")]\n# rootgrps_2016 = [nt.Dataset(\"EN.4.2.1.analyses.g10.2016\\EN.4.2.1.f.analysis.g10.201601.nc\", \"r+\", format=\"NETCDF4\")]\n# rootgrps_2017 = [nt.Dataset(\"EN.4.2.1.analyses.g10.2017\\EN.4.2.1.f.analysis.g10.201701.nc\", \"r+\", format=\"NETCDF4\")]\n rootgrps_2018 = [nt.Dataset(\"EN.4.2.1.analyses.g10.2018\\EN.4.2.1.f.analysis.g10.201801.nc\", \"r+\", format=\"NETCDF4\")]\n\n# HC_1950 = calculate_HC_global(rootgrps_1950, 0, 2000)\n# print('1950', time.time()-start)\n# HC_1951 = calculate_HC_global(rootgrps_1951, 0, 2000)\n# print('1951', time.time()-start)\n# HC_1952 = calculate_HC_global(rootgrps_1952, 0, 2000)\n# print('1952', time.time()-start)\n# HC_1953 = calculate_HC_global(rootgrps_1953, 0, 2000)\n# print('1953', time.time()-start) \n#\n# HC_2015 = calculate_HC_global(rootgrps_2015, 0, 2000)\n# print('2015', time.time()-start)\n# HC_2016 = calculate_HC_global(rootgrps_2016, 0, 2000)\n# print('2016', time.time()-start)\n# HC_2017 = calculate_HC_global(rootgrps_2017, 0, 2000)\n# print('2017', time.time()-start)\n HC_2018 = calculate_HC_global(rootgrps_2018, 0, 2000)\n# print('2018', time.time()-start)\n# HC_1950_mean = (HC_1950+HC_1951+HC_1952+HC_1953)/4\n# HC_2018_mean = (HC_2015+HC_2016+HC_2017+HC_2018)/4\n\n# dHC = (HC_2018_mean-HC_1950_mean)/(65*365*24*3600)\n if animate == True:\n plot(rootgrps_2018, HC_2018)\n return HC_2018","def main(parameters_file, token):\n\n print(f\"--\\nReading parameters file: {parameters_file}\\n--\")\n\n set_token(token=token)\n\n\n try:\n with open(parameters_file, 'r') as f:\n\n parameters = load(f,\n Loader=SafeLoader)\n maggeo_params = parameters[\"maggeo\"] \n gpsfilename = maggeo_params[\"gpsfilename\"]\n Lat = maggeo_params[\"Lat\"]\n Long = maggeo_params[\"Long\"] \n DateTime = maggeo_params[\"DateTime\"]\n altitude = maggeo_params[\"altitude\"]\n\n except Exception as error:\n print('Error in parameters file format')\n raise error\n \n base_dir=os.getcwd()\n temp_results_dir = os.path.join(base_dir, \"temp_data\")\n results_dir = os.path.join(base_dir, \"results\")\n data_dir = os.path.join(base_dir, \"data\")\n utilities_dir = os.path.join(base_dir, \"utilities\")\n\n GPSData = getGPSData(data_dir,gpsfilename,Lat,Long,DateTime,altitude)\n \n datestimeslist = []\n for index, row in GPSData.iterrows():\n datetimerow = row['gpsDateTime']\n daterow = row['dates']\n hourrow = row['times']\n hourrow = hourrow.strftime('%H:%M:%S')\n if hourrow < '04:00:00':\n date_bfr = daterow - (timedelta(days=1))\n datestimeslist.append(daterow)\n datestimeslist.append(date_bfr)\n if hourrow > '20:00:00':\n Date_aft = daterow + (timedelta(days=1))\n datestimeslist.append(daterow)\n datestimeslist.append(Date_aft) \n else:\n datestimeslist.append(daterow)\n\n def uniquelistdates(list): \n x = np.array(list) \n uniquelist = np.unique(x)\n return uniquelist\n\n uniquelist_dates = uniquelistdates(datestimeslist)\n\n hours_t_day = 24 #MagGeo needs the entire Swarm data for each day of the identified day.\n hours_added = dt.timedelta(hours = hours_t_day)\n\n listdfa = []\n listdfb = []\n listdfc = []\n\n for d in tqdm(uniquelist_dates, desc=\"Getting Swarm Data\"):\n #print(\"Getting Swarm data for date:\",d )\n startdate = dt.datetime.combine(d, dt.datetime.min.time())\n enddate = startdate + hours_added\n SwarmResidualsA,SwarmResidualsB,SwarmResidualsC = Get_Swarm_residuals(startdate, enddate)\n listdfa.append(SwarmResidualsA)\n listdfb.append(SwarmResidualsB)\n listdfc.append(SwarmResidualsC)\n\n base_dir = os.getcwd() # Get main MagGeo directory\n temp_results_dir = os.path.join(base_dir, \"temp_data\")\n results_dir = os.path.join(base_dir, \"results\")\n data_dir = os.path.join(base_dir, \"data\")\n\n #TODO:\n #Find a more elegant way to concante the the list from Swarm and then read it to get the type of columns is requiered, Timestamp as datetime and epoch as index.\n \n PdSwarmRes_A = pd.concat(listdfa, join='outer', axis=0)\n PdSwarmRes_A.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_A.csv'), header=True)\n PdSwarmRes_B = pd.concat(listdfb, join='outer', axis=0)\n PdSwarmRes_B.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_B.csv'), header=True)\n PdSwarmRes_C = pd.concat(listdfc, join='outer', axis=0)\n PdSwarmRes_C.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_C.csv'), header=True)\n\n TotalSwarmRes_A = pd.read_csv(os.path.join(temp_results_dir,\"TotalSwarmRes_A.csv\"),low_memory=False, index_col='epoch')\n TotalSwarmRes_A['timestamp'] = pd.to_datetime(TotalSwarmRes_A['timestamp'])\n TotalSwarmRes_B = pd.read_csv(os.path.join(temp_results_dir,\"TotalSwarmRes_B.csv\"),low_memory=False, index_col='epoch')\n TotalSwarmRes_B['timestamp'] = pd.to_datetime(TotalSwarmRes_B['timestamp'])\n TotalSwarmRes_C = pd.read_csv(os.path.join(temp_results_dir,\"TotalSwarmRes_C.csv\"),low_memory=False, index_col='epoch')\n TotalSwarmRes_C['timestamp'] = pd.to_datetime(TotalSwarmRes_C['timestamp'])\n\n dn = [] ## List used to add all the GPS points with the annotated MAG Data. See the last bullet point of this process \n for index, row in tqdm(GPSData.iterrows(), total=GPSData.shape[0], desc=\"Annotating the GPS Trayectory\"):\n GPSLat = row['gpsLat'] \n GPSLong = row['gpsLong']\n GPSDateTime = row['gpsDateTime']\n GPSTime = row['epoch']\n GPSAltitude = row['gpsAltitude']\n #print(\"Process for:\", index,\"DateTime:\",GPSDateTime)\n try:\n result=ST_IDW_Process(GPSLat,GPSLong,GPSAltitude, GPSDateTime,GPSTime, TotalSwarmRes_A, TotalSwarmRes_B, TotalSwarmRes_C)\n dn.append(result)\n except:\n #print(\"Ups!.That was a bad Swarm Point, let's keep working with the next point\")\n result_badPoint= {'Latitude': GPSLat, 'Longitude': GPSLong, 'Altitude':GPSAltitude, 'DateTime': GPSDateTime, 'N_res': np.nan, 'E_res': np.nan, 'C_res':np.nan, 'TotalPoints':0, 'Minimum_Distance':np.nan, 'Average_Distance':np.nan} \n dn.append(result_badPoint)\n continue\n \n \n GPS_ResInt = pd.DataFrame(dn)\n GPS_ResInt.to_csv (os.path.join(temp_results_dir,\"GPS_ResInt.csv\"), header=True)\n \n X_obs, Y_obs, Z_obs, X_obs_internal, Y_obs_internal, Z_obs_internal = CHAOS_ground_values(utilities_dir,GPS_ResInt)\n\n GPS_ResInt['N'] =pd.Series(X_obs)\n GPS_ResInt['E'] =pd.Series(Y_obs)\n GPS_ResInt['C'] =pd.Series(Z_obs)\n GPS_ResInt['N_Obs'] =pd.Series(X_obs_internal)\n GPS_ResInt['E_Obs'] =pd.Series(Y_obs_internal)\n GPS_ResInt['C_Obs'] =pd.Series(Z_obs_internal)\n\n GPS_ResInt.drop(columns=['N_res', 'E_res','C_res'], inplace=True)\n\n # Having Intepolated and weighted the magnetic values, we can compute the other magnectic components. \n GPS_ResInt['H'] = np.sqrt((GPS_ResInt['N']**2)+(GPS_ResInt['E']**2))\n #check the arcgtan in python., From arctan2 is saver.\n DgpsRad = np.arctan2(GPS_ResInt['E'],GPS_ResInt['N'])\n GPS_ResInt['D'] = np.degrees(DgpsRad)\n IgpsRad = np.arctan2(GPS_ResInt['C'],GPS_ResInt['H'])\n GPS_ResInt['I'] = np.degrees(IgpsRad)\n GPS_ResInt['F'] = np.sqrt((GPS_ResInt['N']**2)+(GPS_ResInt['E']**2)+(GPS_ResInt['C']**2))\n\n \n originalGPSTrack=pd.read_csv(os.path.join(data_dir,gpsfilename))\n MagGeoResult = pd.concat([originalGPSTrack, GPS_ResInt], axis=1)\n #Drop duplicated columns. Latitude, Longitued, and DateTime will not be part of the final result.\n MagGeoResult.drop(columns=['Latitude', 'Longitude', 'DateTime'], inplace=True)\n \n #Exporting the CSV file\n \n outputfile =\"GeoMagResult_\"+gpsfilename\n export_csv = MagGeoResult.to_csv (os.path.join(results_dir,outputfile), index = None, header=True) \n print(\"Congrats! MagGeo has processed your GPS trayectory. Find the annotated table: \" + outputfile + \" in the folder results.\")","def <start>/<end>(<start>/<end>)\ndef calc_temps(start_date, end_date):","def gridsearch(model_class,\n parameters: dict,\n training_series: TimeSeries,\n target_series: Optional[TimeSeries] = None,\n forecast_horizon: Optional[int] = None,\n start: Union[pd.Timestamp, float, int] = 0.5,\n use_full_output_length: Optional[bool] = None,\n val_target_series: Optional[TimeSeries] = None,\n use_fitted_values: bool = False,\n metric: Callable[[TimeSeries, TimeSeries], float] = metrics.mape,\n verbose=False) -> TimeSeries:\n raise_if_not((forecast_horizon is not None) + (val_target_series is not None) + use_fitted_values == 1,\n \"Please pass exactly one of the arguments 'forecast_horizon', \"\n \"'val_target_series' or 'use_fitted_values'.\", logger)\n\n # check target and training series\n if target_series is None:\n target_series = training_series\n raise_if_not(all(training_series.time_index() == target_series.time_index()), \"the target and training series\"\n \" must have the same time indices.\")\n\n # construct predict kwargs dictionary\n predict_kwargs = {}\n if use_full_output_length is not None:\n predict_kwargs['use_full_output_length'] = use_full_output_length\n\n if use_fitted_values:\n raise_if_not(hasattr(model_class(), \"fitted_values\"), \"The model must have a fitted_values attribute\"\n \" to compare with the train TimeSeries\", logger)\n\n elif val_target_series is not None:\n raise_if_not(training_series.width == val_target_series.width, \"Training and validation series require the\"\n \" same number of components.\", logger)\n\n min_error = float('inf')\n best_param_combination = {}\n\n # compute all hyperparameter combinations from selection\n params_cross_product = list(product(*parameters.values()))\n\n # iterate through all combinations of the provided parameters and choose the best one\n iterator = _build_tqdm_iterator(params_cross_product, verbose)\n for param_combination in iterator:\n param_combination_dict = dict(list(zip(parameters.keys(), param_combination)))\n model = model_class(**param_combination_dict)\n if use_fitted_values: # fitted value mode\n model.fit(training_series)\n fitted_values = TimeSeries.from_times_and_values(training_series.time_index(), model.fitted_values)\n error = metric(fitted_values, target_series)\n elif val_target_series is None: # expanding window mode\n backtest_forecast = model.backtest(training_series, target_series, start,\n forecast_horizon, use_full_output_length=use_full_output_length)\n error = metric(backtest_forecast, target_series)\n else: # split mode\n if isinstance(model, MultivariateForecastingModel):\n model.fit(training_series, target_series)\n else:\n model.fit(training_series)\n error = metric(model.predict(len(val_target_series), **predict_kwargs), val_target_series)\n if error < min_error:\n min_error = error\n best_param_combination = param_combination_dict\n logger.info('Chosen parameters: ' + str(best_param_combination))\n\n return model_class(**best_param_combination)","def grid(config, keep_flag_values=True, treat_coast_as_land=False):\n if config.get('daily', False) and config.get('median', False):\n start_year, end_year = config['range']\n\n gridset = sid.extent_daily_median(config['hemi'],\n start_year,\n end_year,\n dayofyear=config['dayofyear'],\n search_paths=config['search_paths'],\n extent_threshold=config['extent_threshold'],\n allow_empty_gridset=True,\n drop_invalid_ice=True,\n allow_bad_dates=False)\n\n elif config.get('monthly', False) and config.get('median', False):\n start_year, end_year = config['range']\n\n gridset = sid.extent_monthly_median(config['hemi'],\n start_year,\n end_year,\n config['month'],\n search_paths=config['search_paths'],\n extent_threshold=config['extent_threshold'],\n drop_invalid_ice=True,\n allow_empty_gridset=True)\n\n elif config.get('daily', False):\n gridset = sid.extent_daily(config['hemi'],\n config['year'],\n config['month'],\n config['day'],\n search_paths=config['search_paths'],\n extent_threshold=config['extent_threshold'],\n allow_empty_gridset=False,\n drop_invalid_ice=True,\n allow_bad_dates=False)\n\n elif config.get('monthly', False):\n gridset = sid.extent_monthly(config['hemi'],\n config['year'],\n config['month'],\n search_paths=config['search_paths'],\n extent_threshold=config['extent_threshold'],\n drop_invalid_ice=True,\n allow_empty_gridset=False)\n\n return _massage_grid(gridset['data'], keep_flag_values, treat_coast_as_land)","def find_besthist(\n grid_lat, grid_long, grid_dates, grid_z_value,\n lat, long, date, z_value,\n latitude_large, latitude_small, longitude_large, longitude_small,\n phi_large, phi_small, age_large, age_small, map_pv_use, max_casts\n):\n\n # make sure arrays are 1 dimensional\n grid_lat = grid_lat.flatten()\n grid_long = grid_long.flatten()\n grid_z_value = grid_z_value.flatten()\n grid_dates = grid_dates.flatten()\n\n # set up potential vorticity\n potential_vorticity_vec = np.vectorize(potential_vorticity)\n pv_float = 0\n pv_hist = 0\n\n # if we are using potential vorticity, calculate it\n if map_pv_use == 1:\n pv_float = potential_vorticity(lat, z_value)\n pv_hist = potential_vorticity_vec(grid_lat, grid_z_value)\n\n # calculate ellipse\n find_ellipse_vec = np.vectorize(find_ellipse)\n ellipse = find_ellipse_vec(grid_long, long, longitude_large, grid_lat, lat, latitude_large,\n phi_large, pv_hist, pv_float)\n\n # find points that lie within the ellipse\n hist_long = []\n hist_lat = []\n hist_dates = []\n hist_z_value = []\n index_ellipse = np.argwhere(ellipse < 1)\n for i in index_ellipse:\n hist_long.append(grid_long[i])\n hist_lat.append(grid_lat[i])\n hist_dates.append(grid_dates[i])\n hist_z_value.append(grid_z_value[i])\n\n index = index_ellipse\n # check to see if too many data points were found\n if index.__len__() > max_casts:\n\n # uses pseudo-random numbers so the same random numbers are selected for each run\n np.random.seed(index_ellipse.__len__())\n\n # pick max_casts/3 random points\n rand_matrix = np.random.rand(math.ceil(max_casts / 3))\n index_rand = np.round(rand_matrix * (index_ellipse.__len__() - 1))\n\n # make sure the points are all unique\n index_rand = np.unique(index_rand)\n # sort them into ascending order\n index_rand.sort()\n\n # create an array containing the indices of the remaining reference data\n # (index_ellipse array without index_rand array)\n # Then create arrays containing the remaining reference lat, long, age, and z_value\n index_remain = []\n remain_hist_lat = []\n remain_hist_long = []\n remain_hist_z_value = []\n remain_hist_dates = []\n\n for i in index_ellipse.flatten():\n if i not in index_rand:\n index_remain.append(i)\n\n for i in index_remain:\n remain_hist_lat = np.append(remain_hist_lat, grid_lat[int(i)])\n remain_hist_long = np.append(remain_hist_long, grid_long[int(i)])\n remain_hist_z_value = np.append(remain_hist_z_value, grid_z_value[int(i)])\n remain_hist_dates = np.append(remain_hist_dates, grid_dates[int(i)])\n\n # sort remaining points by large spatial correlations\n # if using potential vorticity, calculate it for remaining data\n if map_pv_use == 1:\n pv_hist = potential_vorticity_vec(remain_hist_lat, remain_hist_z_value)\n\n # calculate the large spatial correlation for each point\n spatial_correlation_vec = np.vectorize(spatial_correlation)\n correlation_large = spatial_correlation_vec(remain_hist_long, long, longitude_large,\n remain_hist_lat, lat, latitude_large,\n remain_hist_dates, date, age_large,\n pv_hist, pv_float, phi_large)\n\n # combine the large spatial correlation with their indices in a 2d matrix\n correlation_large_combined = np.stack((index_remain, correlation_large)).transpose()\n # sort the matrix in ascending order of correlation\n correlation_large_combined_sorted = correlation_large_combined[\n correlation_large_combined[:, 1].argsort()\n ]\n\n # work out how many large spatial data points needed\n lsegment2 = 2 * math.ceil(max_casts / 3) - index_rand.__len__()\n\n # select the best large spatial data points\n index_large_spatial = []\n for i in range(0, lsegment2):\n index_large_spatial = np.append(\n index_large_spatial, correlation_large_combined_sorted[i][0]\n )\n\n # create array to hold selected indices from random and spatial selection\n rand_large_index = np.concatenate((index_rand, index_large_spatial))\n # sort into ascending order\n rand_large_index.sort()\n\n # remove the currently selected indices again\n index_remain = []\n remain_hist_lat = []\n remain_hist_long = []\n remain_hist_z_value = []\n remain_hist_dates = []\n for i in index_ellipse.flatten():\n if i not in rand_large_index:\n index_remain.append(i)\n\n for i in index_remain:\n remain_hist_lat = np.append(remain_hist_lat, grid_lat[int(i)])\n remain_hist_long = np.append(remain_hist_long, grid_long[int(i)])\n remain_hist_z_value = np.append(remain_hist_z_value, grid_z_value[int(i)])\n remain_hist_dates = np.append(remain_hist_dates, grid_dates[int(i)])\n\n # sort the remaining points by short spatial and temporal correlations\n # if using potential vorticity, calculate it\n if map_pv_use == 1:\n pv_hist = potential_vorticity_vec(\n remain_hist_lat, remain_hist_z_value)\n\n # calculate the small spatial correlation for each point\n correlation_small = spatial_correlation_vec(remain_hist_long,\n long, longitude_small,\n remain_hist_lat,\n lat, latitude_small,\n remain_hist_dates,\n date, age_small,\n pv_hist, pv_float, phi_small)\n\n # combine the small spatial correlation with their indices in a 2d matrix\n correlation_small_combined = np.stack((index_remain, correlation_small)).transpose()\n # sort the matrix in ascending order of correlation\n correlation_small_combined_sorted = correlation_small_combined[\n correlation_small_combined[:, 1].argsort()\n ]\n\n # select the amount we need for short spatial/temporal correlation\n leftover = max_casts - index_rand.__len__() - lsegment2\n\n index_small_spatial = []\n for i in range(0, leftover):\n index_small_spatial = np.append(\n index_small_spatial, correlation_small_combined_sorted[i][0]\n )\n\n # put together the best random data, large spatial data, and small spatial data\n index = np.concatenate((index_rand, index_large_spatial, index_small_spatial))\n if index.__len__() != np.unique(index).__len__():\n print(\"WARNING: not all points are unique\")\n rand_large = np.concatenate((index_rand, index_large_spatial))\n rand_small = np.concatenate((index_rand, index_small_spatial))\n large_small = np.concatenate((index_large_spatial, index_small_spatial))\n print(\"unique points: \", np.unique(index).__len__())\n print(\"unique rand large: \", rand_large.__len__(),\n \" : \", np.unique(rand_large).__len__())\n print(\"unique rand small: \", rand_small.__len__(),\n \" : \", np.unique(rand_small).__len__())\n print(\"unique large small: \", large_small.__len__(),\n \" : \", np.unique(large_small).__len__())\n\n # ensure that the index is integers\n index = index.flatten()\n index = index.astype(int)\n\n return index","def output_grib2(forecast,proj_dict,grid_dict,start_hour,end_hour,\n stride,size,run_date,target_dataset,smoothing,config):\n for d,date in enumerate(run_date):\n date_outpath = config.forecast_out_path+'20{0}/grib/'.format(\n date.strftime(config.run_date_format))\n \n if not os.path.exists(date_outpath):\n os.makedirs(date_outpath)\n\n lscale = 1e6\n grib_id_start = [7, 0, 14, 14, 2]\n \n filtered_forecast = gaussian_filter(forecast[d],smoothing,mode='constant')\n \n gdsinfo = np.array([0, np.product(filtered_forecast.shape[-2:]), 0, 0, 30], dtype=np.int32)\n \n lon_0 = proj_dict[\"lon_0\"]\n sw_lon = grid_dict[\"sw_lon\"]\n \n if lon_0 < 0:\n lon_0 += 360\n if sw_lon < 0:\n sw_lon += 360\n\n gdtmp1 = [1, 0, proj_dict['a'], 0, float(proj_dict['a']), 0, float(proj_dict['b']),\n filtered_forecast.shape[-1], filtered_forecast.shape[-2], grid_dict[\"sw_lat\"] * lscale,\n sw_lon * lscale, 0, proj_dict[\"lat_0\"] * lscale,\n lon_0 * lscale,\n grid_dict[\"dx\"] * 1e3 * stride, grid_dict[\"dy\"] * 1e3 * stride, 0b00000000, 0b01000000,\n proj_dict[\"lat_1\"] * lscale,\n proj_dict[\"lat_2\"] * lscale, -90 * lscale, 0]\n pdtmp1 = np.array([1, 31, 4, 0, 31, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1], dtype=np.int32)\n drtmp1 = np.array([0, 0, 4, 8, 0], dtype=np.int32)\n time_list = list(date.utctimetuple()[0:6])\n grib_objects = Grib2Encode(0, np.array(grib_id_start + time_list + [2, 1], dtype=np.int32))\n grib_objects.addgrid(gdsinfo, gdtmp1)\n pdtmp1[8] = end_hour\n pdtmp1[-2] = 0\n grib_objects.addfield(1, pdtmp1, 0, drtmp1, filtered_forecast)\n grib_objects.end()\n filename = date_outpath + \"{0}_{6}_Hail_{1}_Cali_NMEP_{2}mm_{3}_Hours_{4}-{5}.grib2\".format(\n config.ensemble_name,\n target_dataset,\n size,\n date.strftime(config.run_date_format),\n start_hour,end_hour,config.forecast_model_names)\n print(\"Writing to \" + filename )\n \n grib_file = open(filename, \"wb\")\n grib_file.write(grib_objects.msg)\n grib_file.close()\n\n return","def create_date_data(gt_id,\n target_horizon,\n experiment,\n date_features=[\"mei\", \"mjo\",\n \"pca_sst_2010\", \"pca_icec_2010\",\n \"pca_wind_hgt_10_2010\",\n \"pca_wind_hgt_100_2010\",\n \"pca_wind_hgt_500_2010\",\n \"pca_wind_hgt_850_2010\"]):\n\n time_start = time.time()\n\n # --------\n # Prepare experiment cache directory and saved file names\n # --------\n\n # Name of cache directory for storing non-submission-date specific\n # intermediate files\n cache_dir = os.path.join('results', experiment, 'shared',\n '{}_{}'.format(gt_id, target_horizon))\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\n\n # if cache_dir doesn't exist, create it\n if not os.path.isdir(cache_dir):\n os.makedirs(cache_dir)\n\n # Filenames for data file to be stored in cache_dir\n date_data_file = os.path.join(\n cache_dir, \"date_data-{}_{}.h5\".format(gt_id, target_horizon))\n\n # --------\n # Creates date_data dataframe.\n # --------\n # Get number of days between start date of observation period used for prediction\n # (2 weeks behind) and start date of target period (2 or 4 weeks ahead)\n start_deltas = [get_start_delta(target_horizon, gt_id)\n for gt_id in date_features]\n\n # Load masked date features\n print \"Loading date features\"\n date_data = get_date_features(gt_ids=date_features, gt_shifts=start_deltas,\n first_year=get_first_year(gt_id))\n\n print \"Loading additional date features\"\n t = time.time()\n if 'mjo' in date_features:\n # Add cosine and sine transforms of MJO phase\n mjo_phase_name = 'phase_shift'+str(get_start_delta(target_horizon, 'mjo'))\n date_data['cos_'+mjo_phase_name] = np.cos((2*np.pi*date_data[mjo_phase_name])/8)\n date_data['sin_'+mjo_phase_name] = np.sin((2*np.pi*date_data[mjo_phase_name])/8)\n print \"Elapsed: {}s\".format(time.time() - t)\n # Save date features to disk\n print \"Saving date features to \"+date_data_file\n t = time.time()\n date_data.to_hdf(date_data_file, key=\"data\", mode=\"w\")\n subprocess.call(\"chmod a+w \"+date_data_file, shell=True)\n print \"Elapsed: {}s\".format(time.time() - t)\n\n print \"Finished generating date_data matrix.\"\n print \"Total time elapsed: {}s\".format(time.time()-time_start)\n return list(date_data)","def fit_grid():\r\n\t\r\n\tp = 'results\\\\mnist_filter'\r\n\t# try:\r\n\t\t# os.makedirs(p)\r\n\t# except OSError:\r\n\t\t# pass\r\n\tnp.random.seed(123456789)\r\n\t# kargs = {\r\n\t\t# 'ninputs': 9,\r\n\t\t# 'ncolumns': 100,\r\n\t\t# 'nsynapses': 5,\r\n\t\t# 'random_permanence': True,\r\n\t\t# 'pinc':0.03, 'pdec':0.05,\r\n\t\t# 'seg_th': 3,\r\n\t\t# 'nactive': 10,\r\n\t\t# 'duty_cycle': 100,\r\n\t\t# 'max_boost': 10,\r\n\t\t# 'global_inhibition': True,\r\n\t\t# 'trim': 1e-4\r\n\t# }\r\n\tkargs2 = {\r\n\t\t'ninputs': 100 * (26 ** 2),\r\n\t\t'ncolumns': 2048,\r\n\t\t'nsynapses': 1000,\r\n\t\t'random_permanence': True,\r\n\t\t'pinc':0.03, 'pdec':0.05,\r\n\t\t'seg_th': 5,\r\n\t\t'nactive': 20,\r\n\t\t'duty_cycle': 100,\r\n\t\t'max_boost': 10,\r\n\t\t'global_inhibition': True,\r\n\t\t'trim': 1e-4\r\n\t}\r\n\t\r\n\t# Get the data\r\n\t(tr_x, tr_y), (te_x, te_y) = get_data()\r\n\tnwindows = 26 ** 2\r\n\t\r\n\t# # Make the SPs\r\n\t# sps = [SPRegion(**kargs) for _ in xrange(nwindows)]\r\n\t\r\n\t# # Train the SPs\r\n\t# nepochs = 10\r\n\t# t = time.time()\r\n\t# for i in xrange(nepochs):\r\n\t\t# print i\r\n\t\t# for j, x in enumerate(tr_x):\r\n\t\t\t# print '\\t{0}'.format(j)\r\n\t\t\t# nx = extract_patches_2d(x.reshape(28, 28), (3, 3)).reshape(\r\n\t\t\t\t# nwindows, 9)\r\n\t\t\t# for xi, sp in izip(nx, sps):\r\n\t\t\t\t# sp.step(xi)\r\n\t# t1 = time.time() - t\r\n\t# print t1\r\n\t\r\n\t# # Save this batch of SPs\r\n\t# for i, sp in enumerate(sps):\r\n\t\t# sp.learn = False\r\n\t\t# sp.save(os.path.join(p, 'sp0-{0}.pkl'.format(i)))\r\n\t\r\n\t# Make the top level SP\r\n\tsp2 = SPRegion(**kargs2)\r\n\t\r\n\t# Get the SPs\r\n\tsps = [load(os.path.join(p, sp)) for sp in os.listdir(p) if sp[2] == '0']\r\n\t\r\n\t# Train the top SP\r\n\tnepochs = 10\r\n\tt = time.time()\r\n\tfor i in xrange(nepochs):\r\n\t\tprint i\r\n\t\tfor j, x in enumerate(tr_x):\r\n\t\t\tprint '\\t{0}'.format(j)\r\n\t\t\tnx = extract_patches_2d(x.reshape(28, 28), (3, 3)).reshape(\r\n\t\t\t\tnwindows, 9)\r\n\t\t\toutput = np.array(np.zeros(100 * nwindows), dtype='bool')\r\n\t\t\tfor k, (xi, sp) in enumerate(izip(nx, sps)):\r\n\t\t\t\tsp.step(xi)\r\n\t\t\t\toutput[k*100:(k*100)+100] = sp.y[:, 0]\r\n\t\t\tsp2.step(output)\r\n\tt2 = time.time() - t\r\n\tprint t2\r\n\t\r\n\t# Save the top SP\r\n\tsp2.learn = False\r\n\tsp2.save(os.path.join(p, 'sp1-0.pkl'))","def cell_params(x,y,**kwargs):\n\n GR = glo.global_results()\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n cloudy_library = clo.library()\n lookup_table = cloudy_library._restore_lookup_table()\n lookup_table['logG0s'] = lookup_table['logFUVs']\n if x == 'NH': \n x_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\n else:\n x_cloudy = np.unique(lookup_table['log'+x+'s'])\n if y == 'NH': \n y_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\n else:\n y_cloudy = np.unique(lookup_table['log'+y+'s'])\n\n if not p.ylim:\n p.ylim = [1e-3,30]\n if not p.xlim:\n p.xlim = [1e-7,1e3]\n \n # SELECT GALAXIES\n rand_gal_index = np.random.randint(0, GR.N_gal, size=(p.bins))\n if p.bins == GR.N_gal: rand_gal_index = np.arange(GR.N_gal)\n if p.gal_index: \n rand_gal_index = [p.gal_index]\n print(rand_gal_index)\n xs = np.array([])\n ys = np.array([])\n m_tot,m_encomp,m_y0 = 0,0,0\n for gal_index in rand_gal_index:\n print(gal_index)\n gal_ob = gal.galaxy(gal_index)\n df = gal_ob.cell_data.get_dataframe()\n df['nSFR'] = df.nSFR.values#/(0.2**3)\n #df['nSFR'] = df['SFR_density']\n #df['NH'] = 10.**df['NH']\n x1 = df[x].values\n y1 = df[y].values\n x1[x1 <= p.xlim[0]] = p.xlim[0]\n y1[y1 <= p.ylim[0]] = p.ylim[0]\n m_tot += np.sum(df.m.values)\n m_encomp += np.sum(df.m[(x1>=p.xlim[0]) & (y1>=p.ylim[0])].values)\n m_y0 += np.sum(df.m[(y1 == 0)].values)\n #print(x,x1.min(),x1.max())\n #print(y,y1.min(),y1.max())\n ys = np.append(ys,y1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\n xs = np.append(xs,x1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\n print('Min max of %s:' % x)\n print(xs.min(),xs.max())\n print('Min max of %s:' % y)\n print(ys.min(),ys.max())\n fig,ax = plt.subplots(figsize=(10,8))\n hb = ax.hexbin(xs,ys,xscale='log',yscale='log',bins='log',mincnt=1,lw=None,gridsize=50,cmap='inferno')\n cb = fig.colorbar(hb, ax=ax)\n cb.set_label('Number of cells in %i galaxies' % len(rand_gal_index))\n ax.set_xlabel(getlabel(x))\n ax.set_ylabel(getlabel(y))\n print('Total gas mass fraction encompassed: %.4f%%' % (m_encomp/m_tot*100))\n print('Total gas mass fraction with y = 0: %.4f%%' % (m_y0/m_tot*100))\n ax.set_xlim(p.xlim)\n ax.set_ylim(p.ylim)\n # Overplot Cloudy grid params\n print(x,x_cloudy)\n print(y,y_cloudy)\n for x1 in x_cloudy:\n ax.plot([10**x1,10**x1],ax.get_ylim(),'-',color='white',alpha=0.7)\n ax.plot([10**x1,10**x1],ax.get_ylim(),'--k',alpha=0.7)\n for y1 in y_cloudy:\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'-',color='white',alpha=0.7)\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'--k',alpha=0.7)\n\n if not os.path.isdir(p.d_plot + 'cell_data/'): os.mkdir(p.d_plot + 'cell_data/') \n plt.savefig('plots/cell_data/%s%s_cell_params_%s_%s_%s.png' % (p.sim_name,p.sim_run,p.z1,x,y),dpi=250, facecolor='w')","def samples_timesteps_features(dataframe, columns, start_date, timesteps=72, \n steps_ahead=24, window_days=100, train_percent=80.):\n \n def overlap_windows(dataset, timesteps, steps_ahead):\n \"\"\" Create overlaping window of time-series data\n \n Parameters\n ----------\n dataset: pd.DataFrame\n time-series pandas dataset\n timesteps: int\n number of time steps from the past for creating output arrays\n steps_ahead: int\n number of time steps into the future for making predictions\n \n Returns\n -------\n X, y: np.array\n input and output 3-d arrays of overlaping time windows\n \"\"\"\n X = []; y = []\n \n start = 0\n for i in range(len(dataset)):\n # Define the end of the input sequence\n in_end = start + timesteps\n out_end = in_end + steps_ahead\n # Ensure that there is enough data\n if out_end <= len(dataset):\n X.append(dataset[start:in_end, :])\n # First column holds load values\n y.append(dataset[in_end:out_end, 0])\n # Move along one time step\n start += 1\n \n # Convert list to np.array\n X = np.asarray(X)\n y = np.asarray(y)\n \n return X, y\n\n\n data = dataframe.copy()\n \n if window_days*24 > data.values.shape[0]:\n raise ValueError('Variable window_days has too large value: {}*24h = {} > {}, which is more than there is data!'.format(window_days, window_days*24, \n data.values.shape[0]))\n \n # Training period\n # ---------------\n train_percent = train_percent/100.\n st = pd.to_datetime(start_date) # start date\n et = st + dt.timedelta(days=int(train_percent*window_days)) # end date\n train = data.loc[st:et].values\n \n # Standardize and transform training data set\n mean_std_values = {}\n for i, column in enumerate(columns):\n # Calculate mean and standard deviation only\n # from the training data set values\n mu = train[:,i].mean() # axis=0\n sd = train[:,i].std()\n mean_std_values[column] = (mu, sd)\n # Standardize training data\n train[:,i] = (train[:,i] - mu)/sd\n \n # Create overlapping windows with training data\n X_train, y_train = overlap_windows(train, timesteps, steps_ahead)\n \n # Testing / Validation period\n # ---------------------------\n sv = et \n ev = sv + dt.timedelta(days=int((1-train_percent)*window_days)+1)\n test = data.loc[sv:ev].values\n \n # Transform testing/validation data set\n for i, column in enumerate(columns):\n # Use mean and standard deviation from the\n # training data set\n mu = mean_std_values[column][0]\n sd = mean_std_values[column][1]\n # Standardize test data\n test[:,i] = (test[:,i] - mu)/sd\n \n # Create overlaping windows with test data\n X_test, y_test = overlap_windows(test, timesteps, steps_ahead)\n \n return mean_std_values, X_train, y_train, X_test, y_test","def grid_search(train_fun, decode_fun, eval_fun, train_set, dev_set, FLAGS):\n FLAGS.create_fresh_params = True\n\n hyperparameters = FLAGS.tuning.split(',')\n num_hps = len(hyperparameters)\n hp_range = hyperparam_range\n\n print(\"======== Grid Search ========\")\n print(\"%d hyperparameter(s): \" % num_hps)\n for i in xrange(num_hps):\n print(\"{}: {}\".format(\n hyperparameters[i], hp_range[hyperparameters[i]]))\n print()\n\n param_grid = [v for v in hp_range[hyperparameters[0]]]\n for i in xrange(1, num_hps):\n param_grid = itertools.product(param_grid, hp_range[hyperparameters[i]])\n\n best_hp_set = [-1] * num_hps\n best_seed = -1\n best_metrics_value = 0\n\n for row in param_grid:\n row = nest.flatten(row)\n for i in xrange(num_hps):\n setattr(FLAGS, hyperparameters[i], row[i])\n if hyperparameters[i] == 'universal_keep':\n setattr(FLAGS, 'sc_input_keep', row[i])\n setattr(FLAGS, 'sc_output_keep', row[i])\n setattr(FLAGS, 'tg_input_keep', row[i])\n setattr(FLAGS, 'tg_output_keep', row[i])\n setattr(FLAGS, 'attention_input_keep', row[i])\n setattr(FLAGS, 'attention_output_keep', row[i])\n\n print(\"Trying parameter set: \")\n for i in xrange(num_hps):\n print(\"* {}: {}\".format(hyperparameters[i], row[i]))\n\n # Try different random seed if tuning initialization\n num_trials = 5 if FLAGS.initialization else 1\n\n if FLAGS.dataset.startswith('bash'):\n metrics = [\"top1_temp_ms\", \"top1_cms\", \"top3_temp_ms\", \"top3_cms\"]\n metrics_weights = [0.4, 0.4, 0.1, 0.1]\n else:\n metrics = [\"top1_temp_ms\"]\n metrics_weights = [1]\n metrics_signature = '+'.join(\n ['{}x{}'.format(m, mw) for m, mw in zip(metrics, metrics_weights)])\n\n for t in xrange(num_trials):\n seed = random.getrandbits(32)\n tf.set_random_seed(seed)\n metrics_value = single_round_model_eval(train_fun, decode_fun,\n eval_fun, train_set, dev_set, metrics, metrics_weights)\n print(\"Parameter set: \")\n for i in xrange(num_hps):\n print(\"* {}: {}\".format(hyperparameters[i], row[i]))\n print(\"random seed: {}\".format(seed))\n print(\"{} = {}\".format(metrics_signature, metrics_value))\n print(\"Best parameter set so far: \")\n for i in xrange(num_hps):\n print(\"* {}: {}\".format(hyperparameters[i], best_hp_set[i]))\n print(\"Best random seed so far: {}\".format(best_seed))\n print(\"Best evaluation metrics so far = {}\".format(best_metrics_value))\n if metrics_value > best_metrics_value:\n best_hp_set = row\n best_seed = seed\n best_metrics_value = metrics_value\n print(\"☺ New best parameter setting found\")\n\n print()\n print(\"*****************************\")\n print(\"Best parameter set: \")\n for i in xrange(num_hps):\n print(\"* {}: {}\".format(hyperparameters[i], best_hp_set[i]))\n print(\"Best seed = {}\".format(best_seed))\n print(\"Best {} = {}\".format(metrics, best_metrics_value))\n print(\"*****************************\")","def __gxg__(series, year_agg, tmin, tmax, fill_method, limit, output,\n min_n_meas, min_n_years, year_offset):\n # handle tmin and tmax\n if tmin is not None:\n series = series.loc[tmin:]\n if tmax is not None:\n series = series.loc[:tmax]\n if series.empty:\n if output.startswith('year'):\n return pd.Series()\n elif output == 'mean':\n return np.nan\n else:\n ValueError('{output:} is not a valid output option'.format(\n output=output))\n\n # resample the series to values at the 14th and 28th of every month\n # first generate a daily series by averaging multiple measurements during the day\n series = series.resample('d').mean()\n select14or28 = True\n if fill_method is None:\n series = series.dropna()\n elif fill_method == 'ffill':\n series = series.ffill(limit=limit)\n elif fill_method == 'bfill':\n series = series.bfill(limit=limit)\n elif fill_method == 'nearest':\n if limit == 0:\n # limit=0 is a trick to only use each measurements once\n # only keep days with measurements\n series = series.dropna()\n # generate an index at the 14th and 28th of every month\n buf = pd.to_timedelta(8, 'd')\n ref_index = pd.date_range(series.index.min() - buf,\n series.index.max() + buf)\n mask = [(x.day == 14) or (x.day == 28) for x in ref_index]\n ref_index = ref_index[mask]\n # only keep the days that are closest to series.index\n ref_index = get_sample(ref_index, series.index)\n # and set the index of series to this index\n # (and remove rows in series that are not in ref_index)\n series = series.reindex(ref_index, method=fill_method)\n select14or28 = False\n else:\n # with a large limit (larger than 6) it is possible that one measurement is used more than once\n series = series.dropna().reindex(series.index, method=fill_method,\n limit=limit)\n else:\n series = series.interpolate(method=fill_method, limit=limit,\n limit_direction='both')\n\n # and select the 14th and 28th of each month (if needed still)\n if select14or28:\n mask = [(x.day == 14) or (x.day == 28) for x in series.index]\n series = series.loc[mask]\n\n # remove NaNs that may have formed in the process above\n series.dropna(inplace=True)\n\n # resample the series to yearly values\n yearly = series.resample(year_offset).apply(year_agg,\n min_n_meas=min_n_meas)\n\n # return statements\n if output.startswith('year'):\n return yearly\n elif output == 'mean':\n if yearly.notna().sum() < min_n_years:\n return np.nan\n else:\n return yearly.mean()\n else:\n ValueError('{output:} is not a valid output option'.format(\n output=output))","def source_adj_gsdf(gmdata_sim,gmdata_obs,IsolationFilter,num_pts,dt): \n t = np.arange(num_pts)*dt\n ts=np.flip(-t[1:], axis=0)\n lTime = np.concatenate((ts,t), axis=0)#Lag time \n \n #convolve the waveforms for the cross- and auto-correlagrams \n cross = np.correlate(IsolationFilter,gmdata_obs,'full')\n auto = np.correlate(IsolationFilter,gmdata_sim,'full') \n \n #GSDF Parameters \n w0=2*np.pi/(lTime[-1]) \n# wN=2*np.pi/(2*dt)\n# w(:,1)=-wN:w0:wN\n wf=w0*np.linspace(-int(num_pts/2),int(num_pts/2),num_pts) \n fi = [0.05, 0.075, 0.1]\n# fi = [0.02, 0.03, 0.04, 0.05]\n# fi = [0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2]\n Tw = 2/np.mean(fi) # Effective window\n# sw = 2*np.pi*0.72/Tw; # Sigma w ~ 0.2827433388230814\n sw=0.1 \n \n# #% A local maximum will be selected closest to 0-lag\n# I_O=np.argmax(cross)\n# I_S=np.argmax(auto) \n\n I_O, peaks_O = find_peaks(np.abs(hilbert(cross))/np.max(np.abs(hilbert(cross))), height=0.25)\n I_S, peaks_S = find_peaks(np.abs(hilbert(auto))/np.max(np.abs(hilbert(auto))), height=0.25)\n\n PkO = peaks_O.get(\"peak_heights\", \"\")\n PkS = peaks_S.get(\"peak_heights\", \"\")\n\n if (I_O==[] or I_S==[]):\n I_O=np.argmax(cross)\n I_S=np.argmax(auto)\n else:\n I_O_min = np.argmin(np.multiply((1+np.abs(lTime[I_O]))**2,np.abs(1-PkO)))\n I_O = I_O[I_O_min]\n\n I_S_min = np.argmin(np.multiply((1+np.abs(lTime[I_S]))**2,np.abs(1-PkS)))\n I_S = I_S[I_S_min]\n \n ##Windowing\n win1=np.exp(-(0.5*sw**2)*(lTime-lTime[I_O])**2)\n win2=np.exp(-(0.5*sw**2)*(lTime-lTime[I_S])**2) \n \n #\n WO = np.multiply(win1,cross)\n WS = np.multiply(win2,auto)\n WS = WS*np.max(WO)/np.max(WS) #Normalized window by amplitude\n #% Parameters for \"bootstraping\"\n InOR=np.argmax(WO)\n InSR=np.argmax(WS) \n \n #% Isolation filter FFT for perturbation kernel\n tff=np.conj(fftshift(fft(IsolationFilter)))*1/num_pts \n \n adj_sim_decompose = np.zeros((len(fi),num_pts))\n adj_sim_sum = np.zeros(num_pts)\n TauP_arr = np.zeros(len(fi)) \n \n ne = int(np.min([2/np.min(fi)/dt,num_pts/2])) #% Effective bandwidth for inversion\n \n for i in range(0,len(fi)): \n si = 0.1*fi[i]\n #Crosscorrelagram and Autocorrelagram filtering\n dO=computebandfftfilter_gauss(WO,dt,fi[i],si,lTime);\n dS=computebandfftfilter_gauss(WS,dt,fi[i],si,lTime); \n \n # % Check bootstraping\n InO=np.argmax(np.real(dO))\n InS=np.argmax(np.real(dS)) \n \n BS = 1; Cn = 0;\n while BS == 1 or Cn < 10:\n InO=int(InO)\n if (lTime[InO] < lTime[InOR]+0.51/fi[i]) and (lTime[InO] >= lTime[InOR]-0.51/fi[i]):\n BS = 0\n elif (lTime[InO] >= (lTime[InOR]+0.45/fi[i])):\n InO=InO-np.round(1/fi[i]/dt)\n elif (lTime[InO] < lTime[InOR]-0.45/fi[i]):\n InO=InO+np.round(1/fi[i]/dt)\n Cn = Cn+1\n \n BS = 1; Cn = 0;\n while BS == 1 or Cn < 10:\n InS=int(InS) \n if (lTime[InS] < lTime[InSR]+0.51/fi[i]) and (lTime[InS] >= lTime[InSR]-0.51/fi[i]):\n BS = 0\n elif (lTime[InS] >= (lTime[InSR]+0.45/fi[i])):\n InS=InS-np.round(1/fi[i]/dt)\n elif (lTime[InS] < lTime[InSR]-0.45/fi[i]):\n InS=InS+np.round(1/fi[i]/dt)\n Cn = Cn+1 \n\n # Five parameter Gaussian wavelet fitting \n Ao = np.max(envelope(np.real(dO))); Io = np.argmax(envelope(np.real(dO)));\n As = np.max(envelope(np.real(dS))); Is = np.argmax(envelope(np.real(dS))); \n ##Constrain the initial values \n # Parameters for curve_fit\n wi=2*np.pi*fi[i] \n \n try:\n GaO, params_covariance = curve_fit(Eqn, lTime[Io-ne-1:Io+ne], np.real(dO[Io-ne-1:Io+ne]))\n GaS, params_covariance = curve_fit(Eqn, lTime[Is-ne-1:Is+ne], np.real(dS[Is-ne-1:Is+ne])) \n except:\n GaO = [Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]\n GaS = [As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]] \n\n# GaO, params_covariance = curve_fit(Eqn, lTime[Io-ne-1:Io+ne], np.real(dO[Io-ne-1:Io+ne]),bounds=(0,[Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]))\n# GaS, params_covariance = curve_fit(Eqn, lTime[Is-ne-1:Is+ne], np.real(dS[Is-ne-1:Is+ne]),bounds=(0,[As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]])) \n \n# % Check fitting\n if ((GaO[0]/GaS[0]) > 10**5) or np.abs(GaO[4]-GaS[4]) > lTime[-1]/2:\n GaO = [Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]\n GaS = [As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]] \n \n wP=((si**2)*wf+(sw**2)*wi)/(sw**2+si**2)\n wPP=((si**2)*wf-(sw**2)*wi)/(sw**2+si**2)\n siP=((si**2)*(sw**2)/(sw**2+si**2))**0.5 \n #Estimate waveform perturbation kernel (WPK)\n IW=(siP/(sw*GaS[0]))*np.multiply(np.exp(-0.5*(wf-2*np.pi*fi[i])**2/(sw**2+si**2)),np.divide(tff,wP))+\\\n (siP/(sw*GaS[0]))*np.exp(-0.5*(wf+2*np.pi*fi[i])**2/(sw**2+si**2))*tff/wPP\n \n IW[0:int(len(IW)/2)]=0*IW[0:int(len(IW)/2)]\n \n itff = ifft(fftshift(num_pts*IW)) \n \n #Save the GSDF measurements\n TauP_arr[i] = GaO[4]-GaS[4]; #% delta_P\n \n# Jp = np.real(itff)\n# Jp = np.imag(itff)\n Jp = -np.imag(itff) \n adj_sim_decompose[i,:] = np.flip(Jp,axis=0)*TauP_arr[i] \n \n #if i>0:\n adj_sim_sum = adj_sim_sum + adj_sim_decompose[i,:] \n \n return adj_sim_sum, TauP_arr","def test_plt_mag_time():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n # create the arrays per filter and readout pattern\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\n for i, val in enumerate(max_val_box):\n if '140' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(val)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(val)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif '110' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(val)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(val)\n nrsrapidd6_clear.append(np.NaN)\n else:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(val)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(val)\n # add to the bokeh data structure\n ta.source.data[\"nrsrapid_f140x\"] = nrsrapid_f140x\n ta.source.data[\"nrsrapid_f110w\"] = nrsrapid_f110w\n ta.source.data[\"nrsrapid_clear\"] = nrsrapid_clear\n ta.source.data[\"nrsrapidd6_f140x\"] = nrsrapidd6_f140x\n ta.source.data[\"nrsrapidd6_f110w\"] = nrsrapidd6_f110w\n ta.source.data[\"nrsrapidd6_clear\"] = nrsrapidd6_clear\n result = ta.plt_mag_time()\n\n assert bokeh_plot_type == type(result)","def _timeseries_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):\n if params[cf.TIME_SERIES_CAL] == 0:\n log.info('Time Series Calculation not required')\n return\n\n if params[cf.TIME_SERIES_METHOD] == 1:\n log.info('Calculating time series using Laplacian Smoothing method')\n elif params[cf.TIME_SERIES_METHOD] == 2:\n log.info('Calculating time series using SVD method')\n\n output_dir = params[cf.TMPDIR]\n total_tiles = len(tiles)\n process_tiles = mpiops.array_split(tiles)\n for t in process_tiles:\n log.debug(\"Calculating time series for tile \"+str(t.index)+\" out of \"+str(total_tiles))\n ifg_parts = [shared.IfgPart(p, t, preread_ifgs, params) for p in ifg_paths]\n mst_tile = np.load(os.path.join(output_dir, 'mst_mat_{}.npy'.format(t.index)))\n res = timeseries.time_series(ifg_parts, params, vcmt, mst_tile)\n tsincr, tscum, _ = res\n np.save(file=os.path.join(output_dir, 'tsincr_{}.npy'.format(t.index)), arr=tsincr)\n np.save(file=os.path.join(output_dir, 'tscuml_{}.npy'.format(t.index)), arr=tscum)\n mpiops.comm.barrier()\n log.debug(\"Finished timeseries calc!\")","def createParkingCostDataSet(baseyear_tazdata_df, tazdata_df):\n # compute the employment density\n tazdata_df[\"employmentDensity\"] = 0.0\n tazdata_df.loc[ tazdata_df[\"CIACRE\"] > 0, \"employmentDensity\" ] = tazdata_df[\"TOTEMP\"]/tazdata_df[\"CIACRE\"]\n\n baseyear_tazdata_df[\"employmentDensity_baseyear\"] = 0.0\n baseyear_tazdata_df.loc[ baseyear_tazdata_df[\"CIACRE\"] > 0, \"employmentDensity_baseyear\" ] = baseyear_tazdata_df[\"TOTEMP\"]/baseyear_tazdata_df[\"CIACRE\"]\n\n # if these are set, clear them since we'll set them\n if \"PRKCST\" in list(tazdata_df.columns.values):\n tazdata_df.drop(columns=[\"PRKCST\",\"OPRKCST\"], inplace=True)\n\n # compute the ratio of densities between the base year and the forecast year\n tazdata_df = pandas.merge(left=tazdata_df, right=baseyear_tazdata_df[[\"ZONE\",\"employmentDensity_baseyear\",\"PRKCST\",\"OPRKCST\"]], how=\"left\", on=\"ZONE\")\n tazdata_df[\"employmentDensityRatio\"] = 0.0\n tazdata_df.loc[ tazdata_df[\"employmentDensity_baseyear\"] > 0, \"employmentDensityRatio\" ] = tazdata_df[\"employmentDensity\"]/tazdata_df[\"employmentDensity_baseyear\"]\n # do not allow decreasing parking costs\n tazdata_df.loc[ tazdata_df[\"employmentDensityRatio\"] <1.0, \"employmentDensityRatio\" ] = 1.0\n\n tazdata_df[\"PRKCST\"] = tazdata_df[\"PRKCST\"]*tazdata_df[\"employmentDensityRatio\"]\n tazdata_df[\"OPRKCST\"] = tazdata_df[\"OPRKCST\"]*tazdata_df[\"employmentDensityRatio\"]\n\n # remove intermediate variables\n baseyear_tazdata_df.drop(columns=[\"employmentDensity_baseyear\"], inplace=True)\n tazdata_df.drop(columns=[\"employmentDensity\",\"employmentDensity_baseyear\",\"employmentDensityRatio\"], inplace=True)\n return tazdata_df","def initializeDistribution(self):\n\n f = open(self.dataFilename, 'r')\n reader = csv.reader(f)\n headers = next(reader)\n indexFunctionID = headers.index(self.functionID)\n indexVariableID = headers.index(self.variableID)\n f.close()\n rawData = np.genfromtxt(self.dataFilename, delimiter=\",\" , skip_header=1, usecols=(indexVariableID,indexFunctionID))\n\n self.data = rawData[rawData[:,0].argsort()]\n self.lowerBound = self.data[0,0]\n self.upperBound = self.data[-1,0]\n\n if self.functionType == 'cdf':\n self.cdfFunc = UnivariateSpline(self.data[:,0], self.data[:,1], k=self.k, s=self.s)\n self.pdfFunc = self.cdfFunc.derivative()\n self.invCDF = UnivariateSpline(self.data[:,1], self.data[:,0], k=self.k, s=self.s)\n else:\n self.pdfFunc = UnivariateSpline(self.data[:,0], self.data[:,1], k=self.k, s=self.s)\n cdfValues = np.zeros(self.data[:,0].size)\n for i in range(self.data[:,0].size):\n cdfValues[i] = self.pdfFunc.integral(self.data[0][0],self.data[i,0])\n self.invCDF = UnivariateSpline(cdfValues, self.data[:,0] , k=self.k, s=self.s)\n\n # Note that self.invCDF is creating a new spline where I switch its term.\n # Instead of doing spline(x,f(x)) I am creating its inverse spline(f(x),x)\n # This can be done if f(x) is monothonic increasing with x (which is true for cdf)","def SC_generation(hourly_radiation, prop_observers, number_groups, weather_data, g, Sz, Az, ha, Tin_C, height,\n panel_properties, latitude):\n\n\n n0 = panel_properties['n0']\n c1 = panel_properties['c1']\n c2 = panel_properties['c2']\n mB0_r = panel_properties['mB0_r']\n mB_max_r = panel_properties['mB_max_r']\n mB_min_r = panel_properties['mB_min_r']\n C_eff = panel_properties['C_eff']\n t_max = panel_properties['t_max']\n IAM_d = panel_properties['IAM_d']\n Aratio = panel_properties['aperture_area_ratio']\n Apanel = panel_properties['module_area']\n dP1 = panel_properties['dP1']\n dP2 = panel_properties['dP2']\n dP3 = panel_properties['dP3']\n dP4 = panel_properties['dP4']\n Cp_fluid_JperkgK = panel_properties['Cp_fluid'] # J/kgK\n\n # create lists to store results\n list_results = [None] * number_groups\n list_areas_groups = [None] * number_groups\n Sum_mcp_kWperC = np.zeros(8760)\n Sum_qout_kWh = np.zeros(8760)\n Sum_Eaux_kWh = np.zeros(8760)\n Sum_qloss = np.zeros(8760)\n Sum_radiation_kWh = np.zeros(8760)\n\n Tin_array_C = np.zeros(8760) + Tin_C\n aperature_area_per_module = Aratio * Apanel\n total_area_module = prop_observers['total_area_module'].sum() # total area for panel installation\n\n # calculate equivalent length of pipes\n lv = panel_properties['module_length'] # module length\n number_modules = round(total_area_module/Apanel) # this is an estimation\n l_ext_mperm2 = (2 * lv * number_modules/ (total_area_module * Aratio)) # pipe length within the collectors\n l_int_mperm2 = 2 * height / (total_area_module * Aratio) # pipe length from building substation to roof top collectors\n Leq_mperm2 = l_int_mperm2 + l_ext_mperm2 # in m/m2 aperture\n\n if panel_properties['type'] == 'ET': # for evacuated tubes\n Nseg = 100 # default number of subsdivisions for the calculation\n else:\n Nseg = 10 # default number of subsdivisions for the calculation\n\n for group in range(number_groups):\n # load panel angles from group\n teta_z = prop_observers.loc[group, 'surface_azimuth'] # azimuth of panels of group\n area_per_group = prop_observers.loc[group, 'total_area_module']\n tilt_angle_deg = prop_observers.loc[group, 'tilt'] # tilt angle of panels\n\n # create dataframe with irradiation from group\n\n radiation_Wh = pd.DataFrame({'I_sol': hourly_radiation[group]})\n radiation_Wh['I_diffuse'] = weather_data.ratio_diffhout * radiation_Wh.I_sol # calculate diffuse radiation\n radiation_Wh['I_direct'] = radiation_Wh['I_sol'] - radiation_Wh['I_diffuse'] # calculate direct radiation\n radiation_Wh.fillna(0, inplace=True) # set nan to zero\n\n # calculate incidence angle modifier for beam radiation\n IAM_b = calc_IAM_beam_SC(Az, g, ha, teta_z, tilt_angle_deg, panel_properties['type'], Sz, latitude)\n\n # calculate heat production from a solar collector of each group\n list_results[group] = calc_SC_module(tilt_angle_deg, IAM_b, IAM_d, radiation_Wh.I_direct,\n radiation_Wh.I_diffuse, weather_data.drybulb_C, n0,\n c1, c2, mB0_r, mB_max_r, mB_min_r, C_eff, t_max,\n aperature_area_per_module, dP1, dP2, dP3, dP4,\n Cp_fluid_JperkgK, Tin_C, Leq_mperm2, l_ext_mperm2,\n l_int_mperm2, Nseg)\n\n\n # multiplying the results with the number of panels in each group and write to list\n number_modules_per_group = area_per_group / Apanel\n list_areas_groups[group] = area_per_group\n radiation_array = hourly_radiation[group] * list_areas_groups[group] / 1000 # kWh\n Sum_qout_kWh = Sum_qout_kWh + list_results[group][1] * number_modules_per_group\n Sum_Eaux_kWh = Sum_Eaux_kWh + list_results[group][2] * number_modules_per_group\n Sum_qloss = Sum_qloss + list_results[group][0] * number_modules_per_group\n Sum_mcp_kWperC = Sum_mcp_kWperC + list_results[group][5] * number_modules_per_group\n Sum_radiation_kWh = Sum_radiation_kWh + radiation_Wh['I_sol']*area_per_group/1000\n\n Tout_group_C = (Sum_qout_kWh / Sum_mcp_kWperC) + Tin_C # in C assuming all collectors are connected in parallel\n\n Final = pd.DataFrame(\n {'Q_SC_gen_kWh': Sum_qout_kWh, 'T_SC_sup_C': Tin_array_C, 'T_SC_re_C': Tout_group_C, 'mcp_SC_kWperC': Sum_mcp_kWperC, 'Eaux_SC_kWh': Sum_Eaux_kWh,\n 'Q_SC_l_kWh': Sum_qloss, 'Area_SC_m2': sum(list_areas_groups), 'radiation_kWh': Sum_radiation_kWh}, index=range(8760))\n\n return list_results, Final","def __gxg__(series, year_agg, tmin, tmax, fill_method, limit, output,\n min_n_meas, min_n_years, year_offset):\n # handle tmin and tmax\n if tmin is not None:\n series = series.loc[tmin:]\n if tmax is not None:\n series = series.loc[:tmax]\n if series.empty:\n if output.startswith('year'):\n return Series()\n elif output == 'mean':\n return nan\n else:\n ValueError('{output:} is not a valid output option'.format(\n output=output))\n\n # resample the series to values at the 14th and 28th of every month\n # first generate a daily series by averaging multiple measurements during the day\n series = series.resample('d').mean()\n select14or28 = True\n if fill_method is None:\n series = series.dropna()\n elif fill_method == 'ffill':\n series = series.ffill(limit=limit)\n elif fill_method == 'bfill':\n series = series.bfill(limit=limit)\n elif fill_method == 'nearest':\n if limit == 0:\n # limit=0 is a trick to only use each measurements once\n # only keep days with measurements\n series = series.dropna()\n # generate an index at the 14th and 28th of every month\n buf = to_timedelta(8, 'd')\n ref_index = date_range(series.index.min() - buf,\n series.index.max() + buf)\n mask = [(x.day == 14) or (x.day == 28) for x in ref_index]\n ref_index = ref_index[mask]\n # only keep the days that are closest to series.index\n ref_index = get_sample(ref_index, series.index)\n # and set the index of series to this index\n # (and remove rows in series that are not in ref_index)\n series = series.reindex(ref_index, method=fill_method)\n select14or28 = False\n else:\n # with a large limit (larger than 6) it is possible that one measurement is used more than once\n series = series.dropna().reindex(series.index, method=fill_method,\n limit=limit)\n else:\n series = series.interpolate(method=fill_method, limit=limit,\n limit_direction='both')\n\n # and select the 14th and 28th of each month (if needed still)\n if select14or28:\n mask = [(x.day == 14) or (x.day == 28) for x in series.index]\n series = series.loc[mask]\n\n # remove NaNs that may have formed in the process above\n series.dropna(inplace=True)\n\n # resample the series to yearly values\n yearly = series.resample(year_offset).apply(year_agg,\n min_n_meas=min_n_meas)\n\n # return statements\n if output.startswith('year'):\n return yearly\n elif output == 'mean':\n if yearly.notna().sum() < min_n_years:\n return nan\n else:\n return yearly.mean()\n else:\n msg = '{} is not a valid output option'.format(output)\n raise (ValueError(msg))","def repeat(start, end, roi_times=None, timeres=2, coords=None, ar=None,\n split_temps=None, em_wlen=None, plotminmax=False, plotstd=False,\n hist_type='plain', loaddata=False):#, output=None):\n #if isinstance(output, str):\n # from matplotlib import use\n # use(output)\n import matplotlib.pyplot as plt\n import matplotlib.dates as mdates\n\n #loaddata = False\n\n print start, end\n start, end = parse(start), parse(end)\n \n s = []\n t = []\n p = []\n \n #if flares == []:\n # return s, t, p\n\n timerange = tr(start, end)\n delta = dt.timedelta(hours=timeres)\n ntimes = int(timerange.seconds()/delta.total_seconds())\n times = [time.start() for time in timerange.split(ntimes)]\n \n ntemps = 141\n tempsovertime = np.zeros((ntemps, ntimes))\n \n means = np.zeros(len(times))\n p95s = np.zeros(len(times))\n loopmeans = np.zeros(len(times))\n if plotminmax:\n maxes = np.zeros(len(times))\n mins = np.zeros(len(times))\n if plotstd:\n stds = np.zeros(len(times))\n loopstds = np.zeros(len(times))\n if em_wlen:\n meanem = np.zeros(len(times))\n if plotminmax:\n maxem = np.zeros(len(times))\n minem = np.zeros(len(times))\n if plotstd:\n stdem = np.zeros(len(times))\n\n for i, date in enumerate(times):\n data_only = True\n try:\n if ar == 'all':\n plotar = None\n else:\n plotar = ar\n results = output_maps(date, plotar, coords, 'data', split_temps,\n subimsize=50, calc_em=em_wlen, data_only=data_only)#True)#, linear=True)\n if isinstance(results, tuple):\n tempmap, emmap = results\n else:\n tempmap = results\n data = tempmap.data\n except DownloadError as de:\n data = np.zeros((512, 512))\n print de.msg\n except:\n print 'KHAAAAAAAN! Some part of the temperature-plotting process failed.'\n raise\n data = np.zeros((512, 512))\n if em_wlen:\n emmap = np.zeros((512, 512))\n \n t.append(np.nanmean(data))\n p.append(np.nanmax(data))\n \n data = data.flatten()\n data2 = data.copy()\n data2[data == 0.0] = np.NaN\n data2 = data2[np.isfinite(data)]\n data2.sort()\n temps, bins = np.histogram(data, bins=ntemps, density=False, range=(5.6, 7.0))\n temps = (temps/float(data.size))*100.0\n tempsovertime[:, i] = temps\n\n #loops = data[data >= split_temps]\n #data = data[data < split_temps]\n\n means[i] = np.nanmean(data2)\n try:\n p95s[i] = data2[round(0.95 * len(data2))-1]\n except IndexError:\n p95s[i] = np.NaN\n #loopmeans[i] = np.nanmean(loops)\n if plotminmax:\n maxes[i] = np.nanmax(data)\n mins[i] = np.nanmin(data)\n if em_wlen:\n maxem[i] = np.nanmax(emmap)\n minem[i] = np.nanmin(emmap)\n if plotstd:\n stds[i] = np.nanstd(data)\n if em_wlen:\n stdem[i] = np.nanstd(emmap)\n #loopstds[i] = np.nanstd(loops)\n \n tempsovertime[tempsovertime <= 0.1] = np.nan\n\n xmin, xmax = mdates.datestr2num([str(start), str(end)])\n fig = plt.figure(figsize=(36, 18))\n ax = fig.add_subplot(111, axisbg='k')\n plot_title = 'Temperature distribution of corona\\n{:%Y/%m/%d %H:%M} - {:%Y/%m/%d %H:%M}'.format(start, end)\n if roi_times:\n plot_title += '\\nRegion observed: {:%Y/%m/%d %H:%M} - {:%Y/%m/%d %H:%M}'.format(*roi_times)\n plt.title(plot_title)\n if hist_type == 'plain':\n plt.imshow(tempsovertime[30:106, :], extent=[xmin, xmax, 5.9, 6.65],\n aspect='auto', interpolation='none', origin='lower',\n cmap='coolwarm', vmin=np.nanmin(tempsovertime[65:106, :]),\n vmax=np.nanmax(tempsovertime[65:106, :]))\n elif hist_type == 'loops':\n plt.imshow(tempsovertime[65:106, :], extent=[xmin, xmax, 6.25, 6.65],\n aspect='auto', interpolation='none', origin='lower',\n cmap='coolwarm', vmin=np.nanmin(tempsovertime[65:106, :]),\n vmax=np.nanmax(tempsovertime[65:106, :]))\n elif hist_type == 'full':\n plt.imshow(tempsovertime, extent=[xmin, xmax, 5.6, 7.0],\n aspect='auto', interpolation='none', origin='lower',\n cmap='coolwarm', vmin=np.nanmin(tempsovertime),\n vmax=np.nanmax(tempsovertime))\n plt.tight_layout()\n ax.xaxis_date()\n fig.autofmt_xdate()\n plt.colorbar(orientation='horizontal')\n plt.savefig('/media/huw/temp-time_hists/distribution_over_time_{}'.format(ar))\n plt.close()\n\n\n means[np.where(means == 0.0)] = np.nan\n if plotstd:\n stds[np.where(stds == 0.0)] = np.nan\n loopstds[loopstds == 0.0] = np.nan\n\n try:\n tnums = mdates.date2num([ti for ti in times])\n print maxes\n print len(maxes)\n fig = plt.figure(figsize=(18, 14))\n ax = fig.add_subplot(111)\n plt.title('Variation of temperature over time; AR{}'.format(ar), \n fontsize=32)\n plt.plot(tnums, maxes, label='Maximum temperature', color='red')\n plt.axhline(np.nanmean(maxes))\n print tnums\n print len(tnums)\n ax.xaxis_date()\n fig.autofmt_xdate()\n plt.legend(loc=4, fontsize=16)\n plt.xlabel('Date', fontsize=24)\n plt.ylabel('log(T)', fontsize=24)\n #plt.savefig('/media/huw/temp_plots/temp_plot_{}_b'.format(ar))\n plt.savefig('/home/drew/Dropbox/ARs/temps_{}_b'.format(ar))\n plt.close()\n\n \"\"\"diff = ((maxes-p95s)/p95s)*100.0\n fig = plt.figure(figsize=(18, 14))\n ax = fig.add_subplot(1, 1, 1)\n plt.title('Percentage difference between max and 95th %-ile; AR{}'.format(ar), \n fontsize=32)\n plt.plot(tnums, diff, color='black')\n plt.scatter(fldates, [np.nanmax(diff)]*len(fldates))\n for flare in flares:\n ax.text(sunpy.time.parse_time(flare['event_peaktime']), np.nanmax(diff)+0.01, flare['fl_goescls'][0])\n ax.xaxis_date()\n fig.autofmt_xdate()\n plt.xlabel('Date', fontsize=24)\n plt.ylabel('log(T)', fontsize=24)\n #plt.savefig('/media/huw/temp_plots/temp_plot_{}'.format(ar))\n plt.savefig('Dropbox/ARs/diffs_{}'.format(ar))\n plt.close()\"\"\"\n \n except:# ValueError:\n print \"Can't plot the temperature graph because matplotlib is being a whiney douche\"\n print tnums\n raise\n\n return s, t, p, times","def _curve_fitting(self, phase, info):\n start_date = info[self.START]\n end_date = info[self.END]\n population = info[self.N]\n trend = Trend(\n self.jhu_data, population, self.country, province=self.province,\n start_date=start_date, end_date=end_date\n )\n trend.analyse()\n df = trend.result()\n if trend.rmsle() > self.max_rmsle:\n df[f\"{self.S}{self.P}\"] = None\n # Get min value for vline\n r_value = int(df[self.R].min())\n # Rename the columns\n phase = self.INITIAL if phase == \"0th\" else phase\n df = df.rename({f\"{self.S}{self.P}\": f\"{phase}{self.P}\"}, axis=1)\n df = df.rename({f\"{self.S}{self.A}\": f\"{phase}{self.A}\"}, axis=1)\n df = df.rename({f\"{self.R}\": f\"{phase}_{self.R}\"}, axis=1)\n return (df, r_value)","def generate_data(params, N, rng=(-7, 7)):\n hp = np.array(params)\n print(\"parameters for data generated from gp are : {0}\".format(hp))\n print(\"using a ExpSquared kernel\")\n gp = george.GP(hp[0] * kernels.ExpSquaredKernel(hp[1]))\n t = rng[0] + np.diff(rng) * np.sort(np.random.rand(N))\n\n #y = model(params, t)\n y = gp.sample(t)\n yerr = 1.e-5 #1 + 0.1 * np.random.randn(N)\n y += yerr\n\n return t, y, yerr","def prepare_data(self):\r\n annual_df = self.annual_df\r\n coef_df = self.coef_df\r\n quarter_df = self.quarter_df\r\n # historical_df = self.historical_df\r\n Event_Buffer = self.Event_Buffer\r\n\r\n Tot_Prod = coef_df[\"Product\"].nunique()\r\n # Tot_Week = coef_df[\"wk\"].nunique()\r\n Tot_Week = 52\r\n\r\n EDLP_Events = list(annual_df[\"RP_Events\"])\r\n Min_EDLP_Events = [\r\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in EDLP_Events\r\n ]\r\n Max_EDLP_Events = [\r\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\r\n for i in EDLP_Events\r\n ]\r\n\r\n TPR_Events = list(annual_df[\"TPR_Events\"])\r\n Min_TPR_Events = [\r\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in TPR_Events\r\n ]\r\n Max_TPR_Events = [\r\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\r\n for i in TPR_Events\r\n ]\r\n\r\n Target_EDLP_Spend = [i for i in annual_df[\"PPG_RP_Spend\"]]\r\n Target_TPR_Spend = [i for i in annual_df[\"PPG_TPR_Spend\"]]\r\n Target_Trade_Spend = [i for i in annual_df[\"PPG_Total_Spend\"]]\r\n\r\n Mapping = {}\r\n Prod_Ind = coef_df[\"Product\"][0:Tot_Prod]\r\n for i, j in zip(Prod_Ind.index, Prod_Ind.values):\r\n Mapping[j] = i\r\n Mapping_reverse = {i: j for j, i in Mapping.items()}\r\n\r\n constants = [i for i in coef_df[\"constant\"]]\r\n\r\n Cat_Coef = coef_df[\"Catalogue\"][0:Tot_Prod]\r\n\r\n Disp_Coef = coef_df[\"Display\"][0:Tot_Prod]\r\n\r\n Base_Price_stg1 = [i for i in quarter_df[\"Final_baseprice\"]]\r\n Intercepts_stg1 = []\r\n for pr in range(Tot_Prod):\r\n Intercepts_stg1.append(\r\n np.mean([constants[j * Tot_Prod + pr] for j in range(0, Tot_Week)])\r\n )\r\n\r\n Base_Price_stg2 = [[i] * Tot_Week for i in quarter_df[\"Final_baseprice\"]]\r\n Intercepts_stg2 = [\r\n constants[j : j + Tot_Prod] for j in range(0, len(constants), Tot_Prod)\r\n ] # noqa\r\n\r\n EDLP_Coef = np.array(\r\n coef_df[[i for i in coef_df.columns if i.count(\"Retailer_Regular\") == 1]]\r\n )\r\n TPR_Coef = np.array(\r\n coef_df[[i for i in coef_df.columns if i.count(\"Retailer_Promoted\") == 1]]\r\n )\r\n\r\n # ################################ Available EDLP Interactions pairs ##############################\r\n\r\n EDLP = [\r\n re.findall(r\"[0-9]+\", i)\r\n for i in coef_df.columns\r\n if i.count(\"Retailer_Regular\") > 1\r\n ]\r\n EDLP_Interactions = []\r\n for i in EDLP:\r\n temp = []\r\n for j in i:\r\n temp.append(int(j))\r\n EDLP_Interactions.append(temp)\r\n\r\n # ###################################### Available TPR Interactions pairs #########################\r\n\r\n TPR = [\r\n re.findall(r\"[0-9]+\", i)\r\n for i in coef_df.columns\r\n if i.count(\"Retailer_Promoted\") > 1\r\n ]\r\n TPR_Interactions = []\r\n for i in TPR:\r\n temp = []\r\n for j in i:\r\n temp.append(int(j))\r\n TPR_Interactions.append(temp)\r\n\r\n # ###################################### EDLP_Interaction_Coef_Values ############################\r\n\r\n EDLP_Int_Coef_Values = {}\r\n for col in coef_df.columns:\r\n if col.count(\"Retailer_Regular\") > 1:\r\n Pair_name = \"_\".join([str(int(i)) for i in re.findall(r\"[0-9]+\", col)])\r\n EDLP_Int_Coef_Values[Pair_name] = list(coef_df[col])\r\n\r\n # ###################################### TPR_Interaction_Coef_Values #############################\r\n\r\n TPR_Int_Coef_Values = {}\r\n for col in coef_df.columns:\r\n if col.count(\"Retailer_Promoted\") > 1:\r\n Pair_name = \"_\".join([str(int(i)) for i in re.findall(r\"[0-9]+\", col)])\r\n TPR_Int_Coef_Values[Pair_name] = list(coef_df[col])\r\n\r\n # ##################################### Loading Pantry Loading Coefficients #######################\r\n\r\n Pantry_1 = list(coef_df[\"Pantry_Loading_1\"])\r\n Pantry_1 = [\r\n Pantry_1[j : j + Tot_Prod] for j in range(0, len(Pantry_1), Tot_Prod)\r\n ]\r\n Pantry_2 = list(coef_df[\"Pantry_Loading_2\"])\r\n Pantry_2 = [\r\n Pantry_2[j : j + Tot_Prod] for j in range(0, len(Pantry_2), Tot_Prod)\r\n ]\r\n\r\n # TE_Coeff = np.array(Promo_df[[\"TE_Promo\",\"TE_NoPromo\"]])\r\n self.Tot_Prod = Tot_Prod\r\n self.Tot_Week = Tot_Week\r\n self.EDLP_Events = EDLP_Events\r\n self.Min_EDLP_Events = Min_EDLP_Events\r\n self.Max_EDLP_Events = Max_EDLP_Events\r\n self.TPR_Events = TPR_Events\r\n self.Min_TPR_Events = Min_TPR_Events\r\n self.Max_TPR_Events = Max_TPR_Events\r\n\r\n self.Target_EDLP_Spend = Target_EDLP_Spend\r\n self.Target_TPR_Spend = Target_TPR_Spend\r\n self.Target_Trade_Spend = Target_Trade_Spend\r\n self.Mapping = Mapping\r\n self.Mapping_reverse = Mapping_reverse\r\n self.constants = constants\r\n self.EDLP_Coef = EDLP_Coef\r\n self.TPR_Coef = TPR_Coef\r\n\r\n self.EDLP_Interactions = EDLP_Interactions\r\n self.TPR_Interactions = TPR_Interactions\r\n self.EDLP_Int_Coef_Values = EDLP_Int_Coef_Values\r\n self.TPR_Int_Coef_Values = TPR_Int_Coef_Values\r\n self.Pantry_1 = Pantry_1\r\n self.Pantry_2 = Pantry_2\r\n\r\n self.Base_Price_stg1 = Base_Price_stg1\r\n self.Intercepts_stg1 = Intercepts_stg1\r\n self.Base_Price_stg2 = Base_Price_stg2\r\n self.Intercepts_stg2 = Intercepts_stg2\r\n\r\n self.Cat_Coef = Cat_Coef\r\n self.Disp_Coef = Disp_Coef","def setup_fitting_init_pars(inparam, night, band, masterbeam, order):\n\n # Determine whether IGRINS mounting was loose or\n # the night of interest is in question\n if (int(night) < 20180401) or (int(night) > 20190531):\n IPpars = inparam.ips_tightmount_pars[band][masterbeam][order]\n else:\n IPpars = inparam.ips_loosemount_pars[band][masterbeam][order]\n\n # start at bucket loc = 1250 +- 100, width = 250 +- 100,\n # depth = 100 +- 5000 but floor at 0\n centerloc = 1250 if band == 'H' else 1180\n\n # Initialize parameter array for optimization as well as half-range values\n # for each parameter during the various steps of the optimization.\n # Many of the parameters initialized here will be changed throughout the\n # code before optimization and in between optimization steps.\n\n parA0 = np.array([\n 0.0, # 0: The shift of the stellar template (km/s)\n 0.0, # 1: The scale factor for the stellar template\n 0.0, # 2: The shift of the telluric template (km/s)\n 1.0, # 3: The scale factor for the telluric template\n 0.0, # 4: vsini (km/s)\n IPpars[2], # 5: The instrumental resolution (FWHM) in pixels\n 0.0, # 6: Wavelength 0-pt\n 0.0, # 7: Wavelength linear component\n 0.0, # 8: Wavelength quadratic component\n 0.0, # 9: Wavelength cubic component\n 1.0, #10: Continuum zero point\n 0.0, #11: Continuum linear component\n 0.0, #12: Continuum quadratic component\n IPpars[1], #13: Instrumental resolution linear component\n IPpars[0], #14: Instrumental resolution quadratic component\n centerloc, #15: Blaze dip center location\n 330, #16: Blaze dip full width\n 0.05, #17: Blaze dip depth\n 90, #18: Secondary blaze dip full width\n 0.05, #19: Blaze dip depth\n 0.0, #20: Continuum cubic component\n 0.0, #21: Continuum quartic component\n 0.0, #22: Continuum quintic component\n 0.0, #23: Continuum hexic component\n 0.0, #24: secondary par\n 0.0, #25: secondary par\n 0.0, #26: secondary par\n 0.0 #27: secondary par\n ])\n\n return parA0","def generateTSPlot(startTime, endTime, dcs, imgDir=None, ylim={},\n obsTag='obs', **kwargs):\n\n doMurphyScore = True\n\n # a collection for all the data\n dataDir = 'data'\n sc = stationCollection.StationCollection(startTime, endTime, obsTag)\n\n # add data from the files\n for dc in dcs:\n if dc.data.shape[1] > 1: # more than one component\n for i in range(dc.data.shape[1]):\n # add each component as separate time series\n dci = dc.extractField(i)\n # change variable to component name: 'hvel' -> 'u','v'\n dci.setMetaData('variable', dci.fieldNames[0])\n sc.addSample(dci)\n else:\n sc.addSample(dc)\n\n # plot\n # master container to hold all the axes, x axis will be shared\n canvas = plotBase.stackPlotBase(rightPad=1.25)\n\n modelColors = plot.makeColorsForModels(sc)\n comKeys = sc.getComparableKeys(requireObs=False, requireMod=False)\n for c in comKeys:\n if c[0][2] is None:\n print c\n c[0] = (c[0][0], c[0][1], '0')\n # fancy sort: sort by 'entry' tuple with msldepth converted to float\n comKeys.sort(key=lambda tup: (tup[0][0], tup[0][1], float(tup[0][2])))\n for entry, obsKey, modKeys in comKeys:\n station, var, msldepth = entry\n if obsKey:\n o = sc.getSample(**obsKey)\n if len(o.time) < 3:\n print 'observation time series too short:', obsKey\n continue\n else:\n o = None\n\n title = ' '.join(\n (plot.VARS.get(var, var),\n '[' + plot.UNITS.get(var, '-') + ']'))\n xlabel = startTime.year if startTime.year == endTime.year else 'Date'\n dia = timeSeriesPlot.timeSeriesPlotDC2(\n title=title,\n ylabel=station.upper() +\n ' ' +\n msldepth,\n ylim=ylim.get(\n var,\n None),\n xlabel=xlabel)\n canvas.addPlot(dia, tag=plot.VARS.get(var, var) + station + msldepth)\n if obsKey:\n dia.addSample(o, color='r', label=obsKey['tag'])\n for modKey in modKeys:\n m = sc.getSample(**modKey)\n l = modKey['tag'].split('-')[0]\n if doMurphyScore and obsKey:\n o2, m2 = o.alignTimes(m)\n murphy = statistics.murphySkillScore(\n o2.data.ravel(), m2.data.ravel())\n l += ' MS={ms:.2f}'.format(ms=murphy)\n dia.addSample(m, color=modelColors[modKey['tag']], label=l)\n dia.showLegend()\n\n if 'title' in kwargs:\n canvas.addTitle(kwargs.pop('title'))\n if 'xaxis_tight' in kwargs and kwargs['xaxis_tight'] == True:\n\n xLim = [canvas.axGrid[0].dataLim.xmin, canvas.axGrid[0].dataLim.xmax]\n for ax in canvas.axGrid[1:]:\n a, b = ax.dataLim.xmin, ax.dataLim.xmax\n xLim = [min(xLim[0], a), max(xLim[1], b)]\n canvas.axGrid[0].set_xlim(xLim)\n if imgDir:\n # ----- Save file -----\n sT = str(sc.startTime.date())\n eT = str(sc.endTime.date())\n imgDir = createDirectory(imgDir)\n varList = uniqueList([tup[0][1] for tup in comKeys])\n #depSet = list(depSet)\n tagStr = '-'.join(uniqueList([dc.getMetaData('tag') for dc in dcs]))\n stationList = []\n for dc in dcs:\n meta = dc.getMetaData()\n entry = meta.get('location')\n if 'msldepth' in meta:\n entry += '-' + meta['msldepth']\n stationList.append(entry)\n stationStr = '-'.join(uniqueList(stationList))\n varStr = '-'.join(varList)\n fname = '_'.join(['ts', varStr, stationStr, sT, eT])\n fn = kwargs.get('filename', fname)\n saveFigure(imgDir, fn, 'png', verbose=True, bbox_tight=True)\n plt.close()\n else:\n # ----- show plot -----\n plt.show()","def schedule_maker(dates, locator, list_uses):\n\n def get_yearly_vectors(dates, occ_schedules, el_schedules, dhw_schedules, pro_schedules, month_schedule):\n \"\"\"\n For a given use type, this script generates yearly schedules for occupancy, electricity demand,\n hot water demand, process electricity demand based on the daily and monthly schedules obtained from the\n archetype database.\n\n :param dates: dates and times throughout the year\n :type dates: DatetimeIndex\n :param occ_schedules: occupancy schedules for a weekdays, Saturdays and Sundays from the archetype database\n :type occ_schedules: list[array]\n :param el_schedules: electricity schedules for a weekdays, Saturdays and Sundays from the archetype database\n :type el_schedules: list[array]\n :param dhw_schedules: domestic hot water schedules for a weekdays, Saturdays and Sundays from the archetype\n database\n :type dhw_schedules: list[array]\n :param pro_schedules: process electricity schedules for a weekdays, Saturdays and Sundays from the archetype\n database\n :type pro_schedules: list[array]\n :param month_schedule: monthly schedules from the archetype database\n :type month_schedule: ndarray\n\n :return occ: occupancy schedule for each hour of the year\n :type occ: list[float]\n :return el: electricity schedule for each hour of the year\n :type el: list[float]\n :return dhw: domestic hot water schedule for each hour of the year\n :type dhw: list[float]\n :return pro: process electricity schedule for each hour of the year\n :type pro: list[float]\n\n \"\"\"\n\n occ = []\n el = []\n dhw = []\n pro = []\n\n if dhw_schedules[0].sum() != 0:\n dhw_weekday_max = dhw_schedules[0].sum() ** -1\n else: dhw_weekday_max = 0\n\n if dhw_schedules[1].sum() != 0:\n dhw_sat_max = dhw_schedules[1].sum() ** -1\n else: dhw_sat_max = 0\n\n if dhw_schedules[2].sum() != 0:\n dhw_sun_max = dhw_schedules[2].sum() ** -1\n else: dhw_sun_max = 0\n\n for date in dates:\n month_year = month_schedule[date.month - 1]\n hour_day = date.hour\n dayofweek = date.dayofweek\n if 0 <= dayofweek < 5: # weekday\n occ.append(occ_schedules[0][hour_day] * month_year)\n el.append(el_schedules[0][hour_day] * month_year)\n dhw.append(dhw_schedules[0][hour_day] * month_year * dhw_weekday_max) # normalized dhw demand flow rates\n pro.append(pro_schedules[0][hour_day] * month_year)\n elif dayofweek is 5: # saturday\n occ.append(occ_schedules[1][hour_day] * month_year)\n el.append(el_schedules[1][hour_day] * month_year)\n dhw.append(dhw_schedules[1][hour_day] * month_year * dhw_sat_max) # normalized dhw demand flow rates\n pro.append(pro_schedules[1][hour_day] * month_year)\n else: # sunday\n occ.append(occ_schedules[2][hour_day] * month_year)\n el.append(el_schedules[2][hour_day] * month_year)\n dhw.append(dhw_schedules[2][hour_day] * month_year * dhw_sun_max) # normalized dhw demand flow rates\n pro.append(pro_schedules[2][hour_day] * month_year)\n\n return occ, el, dhw, pro\n\n # get internal loads and indoor comfort from archetypes\n archetypes_internal_loads = pd.read_excel(locator.get_archetypes_properties(), 'INTERNAL_LOADS').set_index('Code')\n archetypes_indoor_comfort = pd.read_excel(locator.get_archetypes_properties(), 'INDOOR_COMFORT').set_index('Code')\n\n # create empty list of archetypal schedules and occupant densities\n schedules = []\n occ_densities = []\n\n # create empty lists for the values of each archetype's ventilation and internal loads\n Qs_Wm2 = []\n X_ghm2 = []\n Ea_Wm2 = []\n El_Wm2 = []\n Epro_Wm2 = []\n Ere_Wm2 = []\n Ed_Wm2 = []\n Vww_ldm2 = []\n Vw_ldm2 = []\n Ve_lsm2 = []\n Qhpro_Wm2 = []\n\n for use in list_uses:\n # read from archetypes_schedules and properties\n archetypes_schedules = pd.read_excel(locator.get_archetypes_schedules(), use).T\n\n # read lists of every daily profile\n occ_schedules, el_schedules, dhw_schedules, pro_schedules, month_schedule, area_per_occupant = read_schedules(\n use, archetypes_schedules)\n\n # get occupancy density per schedule in a list\n if area_per_occupant != 0:\n occ_densities.append(1 / area_per_occupant)\n else:\n occ_densities.append(area_per_occupant)\n\n # get internal loads per schedule in a list\n Ea_Wm2.append(archetypes_internal_loads['Ea_Wm2'][use])\n El_Wm2.append(archetypes_internal_loads['El_Wm2'][use])\n Epro_Wm2.append(archetypes_internal_loads['Epro_Wm2'][use])\n Ere_Wm2.append(archetypes_internal_loads['Ere_Wm2'][use])\n Ed_Wm2.append(archetypes_internal_loads['Ed_Wm2'][use])\n Qs_Wm2.append(archetypes_internal_loads['Qs_Wp'][use])\n X_ghm2.append(archetypes_internal_loads['X_ghp'][use])\n Vww_ldm2.append(archetypes_internal_loads['Vww_lpd'][use])\n Vw_ldm2.append(archetypes_internal_loads['Vw_lpd'][use])\n Ve_lsm2.append(archetypes_indoor_comfort['Ve_lps'][use])\n Qhpro_Wm2.append(archetypes_internal_loads['Qhpro_Wm2'][use])\n\n # get yearly schedules in a list\n schedule = get_yearly_vectors(dates, occ_schedules, el_schedules, dhw_schedules, pro_schedules, month_schedule)\n schedules.append(schedule)\n\n archetype_values = {'people': occ_densities, 'Qs': Qs_Wm2, 'X': X_ghm2, 'Ea': Ea_Wm2, 'El': El_Wm2,\n 'Epro': Epro_Wm2, 'Ere': Ere_Wm2, 'Ed': Ed_Wm2, 'Vww': Vww_ldm2,\n 'Vw': Vw_ldm2, 've': Ve_lsm2, 'Qhpro': Qhpro_Wm2}\n\n return schedules, archetype_values","def optimize_parameters(model, grid, X_train, y_train):\n \n tss = TimeSeriesSplit(n_splits=10)\n \n \n print(\"[INFO] performing random search...\")\n searcher = RandomizedSearchCV(estimator=model, n_jobs=-1, n_iter=10, cv=tss,\n param_distributions=grid, scoring=('neg_mean_squared_error','neg_mean_absolute_error'), refit='neg_mean_squared_error')\n search_results = searcher.fit(X_train, y_train)\n best_params = search_results.best_params_\n print(\"Best parameters are: {}\".format(best_params))\n \n return best_params","def getEPADailyData(dateint, dt_ind, month, epa_df, yr):\n\n try:\n start = dateint + dt_ind * 10000\n end = start + 10001\n dly_epa_df = epa_df[(epa_df.created >= start) & (epa_df.created < end)]\n dly_epa_df.reset_index(inplace=True, drop=True)\n\n new_df = pd.DataFrame(columns=['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'epa_pm25_value', 'raw_concentration', 'aqi', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code', 'created'])\n for sitenm in dly_epa_df.site_name.unique():\n indx_ct = 0\n site_df = dly_epa_df[dly_epa_df.site_name == sitenm]\n for i in site_df.created.unique():\n indx_ct += 1\n new_df = pd.concat([new_df,site_df.iloc[indx_ct - 1:indx_ct]],ignore_index=True)\n\n if i != site_df.created.max(): # Don't interpolate the last record\n tmp_df = site_df.iloc[indx_ct - 1:indx_ct][['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code']]\n for j in range(1,6):\n new_dt = i + j * 10\n tmp_df['created'] = int(new_dt)\n tmp_df['epa_pm25_value'] = np.nan\n tmp_df['raw_concentration'] = np.nan\n tmp_df['aqi'] = np.nan\n new_df = pd.concat([new_df,tmp_df],ignore_index=True)\n\n # Convert aqi to numerica for so that it gets interpolated\n new_df[['aqi']] = new_df[['aqi']].replace(\"nan\", np.nan, regex=True)\n new_df[['aqi']] = new_df[['aqi']].apply(pd.to_numeric)\n\n new_df = new_df.interpolate(method='linear', limit_direction='forward', axis=0)\n\n int_epa_df = new_df[(new_df.created >= start) & (new_df.created < (end - 1))]\n int_epa_df.reset_index(inplace=True, drop=True)\n \n # Write to S3\n s3 = s3fs.S3FileSystem()\n myopen = s3.open\n write('midscapstone-whos-polluting-my-air/EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr), int_epa_df, compression='GZIP', open_with=myopen)\n s3_resource = boto3.resource('s3')\n s3_resource.Object('midscapstone-whos-polluting-my-air', 'EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr)).Acl().put(ACL='public-read')\n\n except Exception as e:\n print(\"*** EXCEPTION IN GET EPA DAILY DATA *** {}\".format(e))\n return int_epa_df","def prior_bounds_from_data(npl, ts, rvs):\n\n nobs=len(ts)\n\n dts=[np.diff(t) for t in ts]\n min_dt=reduce(min, [np.min(dt) for dt in dts])\n\n tobss=[t[-1]-t[0] for t in ts]\n max_obst=reduce(max, tobss)\n\n min_dv=reduce(min, [np.min(np.abs(np.diff(rv))) for rv in rvs])\n\n maxspread=reduce(max, [np.max(rv)-np.min(rv) for rv in rvs])\n\n pmin=params.Parameters(nobs=nobs,npl=npl)\n pmax=params.Parameters(nobs=nobs,npl=npl)\n\n Vmin=[]\n Vmax=[]\n taumin=[]\n taumax=[]\n sigma0min=[]\n sigma0max=[]\n sigmamin=[]\n sigmamax=[]\n for t,rv in zip(ts, rvs):\n spread=np.max(rv) - np.min(rv)\n Vmin.append(np.min(rv) - spread)\n Vmax.append(np.max(rv) + spread)\n\n mindt=np.min(np.diff(t))\n mindv=np.min(np.abs(np.diff(rv)))\n T=t[-1] - t[0]\n\n taumin.append(mindt/2.0)\n taumax.append(T*2.0)\n\n sigma0min.append(mindv/2.0)\n sigma0max.append(2.0*np.std(rv))\n\n sigmamin.append(mindv/2.0)\n sigmamax.append(2.0*np.std(rv))\n\n pmin.V = np.array(Vmin)\n pmax.V = np.array(Vmax)\n pmin.tau = np.array(taumin)\n pmax.tau = np.array(taumax)\n pmin.sigma0 = np.array(sigma0min)\n pmax.sigma0 = np.array(sigma0max)\n pmin.sigma = np.array(sigmamin)\n pmax.sigma = np.array(sigmamax)\n\n if npl >= 1:\n pmin.n = 2.0*np.pi/(max_obst)\n pmax.n = 2.0*np.pi/(min_dt)\n\n pmin.chi = 0.0\n pmax.chi = 1.0\n \n pmin.e = 0.0\n pmax.e = 1.0\n \n pmin.omega = 0.0\n pmax.omega = 2.0*np.pi\n\n pmin.K = min_dv/2.0\n pmax.K = 2.0*maxspread\n\n return pmin, pmax","def data_read(variable):\t\t\r\n\tdef day2datetime(scenario,days):\r\n\t\t\"\"\"\r\n\t\t# convert days from a reference into int datetime \r\n\t\t# do not take leap years into account\r\n\t\t\"\"\"\r\n\t\tdate_int = np.empty((len(days)));date_int[:]=np.nan\r\n\t\tif scenario =='T1970C': start_year =1970\r\n\t\telse: start_year =2010\r\n\t\tstart =(start_year*365)\r\n\t\tith=0\t\r\n\t\tfor iday in days:\r\n\t\t\tmonth_days =np.array([31,28,31,30,31,30,31,31,30,31,30,31])\r\n\t\t\tcalendar_days = np.array([0,31,59,90,120,151,181,212,243,273,304,334,365])\r\n\t\t\ttotal_days = int(iday) + start; \r\n\t\t\tyear = total_days//365; \r\n\t\t\tremainder = total_days%365\r\n\t\t\tif remainder ==0: year=year-1;month=12;day=31\r\n\t\t\telse: \r\n\t\t\t\tmonth = 1+[layer for layer in range(len(calendar_days)) if calendar_days[layer]< remainder and calendar_days[layer+1]>=remainder][0]\r\n\t\t\t\tday = int(remainder - calendar_days[month-1])\r\n\t\t\t\tif day == 0: day = month_days[month-1]\r\n\t\t\tdate_int[ith] = year*10000+month*100+day\r\n\t\t\tith=ith+1\r\n\t\treturn date_int.astype(int)\r\n\t\t\r\n\tdef mon_mean2annual_mean(scenario,time,data):\r\n\t\tannual_mean=np.empty((30,192,288));annual_mean[:]=np.nan\r\n\t\tcalendar_day = np.array([31,28,31,30,31,30,31,31,30,31,30,31])\r\n\t\tif scenario=='T1970RCP':\r\n\t\t\tyear_series = range(2020,2050)\r\n\t\telif scenario=='EdgEne':\r\n\t\t\tyear_series = range(2200,2230)\r\n\t\telif scenario=='Edg70GO':\r\n\t\t\tyear_series = range(2070,2100)\r\n\t\telse:\r\n\t\t\tyear_series = range(2130,2160)\r\n\t\tfor iyear in year_series:\r\n\t\t\t\r\n\t\t\tif (iyear == year_series[0] and time[0]//100 >= year_series[0] *100+1):\r\n\t\t\t\tlayer_b=0\r\n\t\t\telse:\r\n\t\t\t\tlayer_b = [layer for layer in range(len(time)) if time[layer]//100 == iyear*100+1][0] #June01\r\n\t\t\tif (iyear == year_series[-1] and time[-1]//100 <= year_series[-1] *100+12):\r\n\t\t\t\tlayer_e=-2\r\n\t\t\telse:\r\n\t\t\t\tlayer_e = [layer for layer in range(len(time)) if time[layer]//100 == iyear*100+12][0] #August 31\r\n\t\t\tdata_cache = data[layer_b:layer_e+1,:,:]\r\n\t\t\tannual_mean[iyear-year_series[0],:,:] = stats.nanmean(data_cache,axis=0)\r\n\t\treturn annual_mean\r\n\r\n\tdef data_netcdf(scenario,variable):\r\n\t\tinput_path ='/exports/csce/datastore/geos/users/s1667168/CESM_EDGAR/ModelOutput/FullCp/'\r\n\t\tvar_path = input_path+scenario+'/mon/atm/'+scenario+'.atm.mon.'+variable+'.nc'\r\n\t\t# print var_path\r\n\t\tnc_fid = nc4.Dataset(var_path,mode='r')\r\n\t\tlat = nc_fid.variables['lat'][:]\r\n\t\tlon = nc_fid.variables['lon'][:]\r\n\t\tdays = nc_fid.variables['time'][:]; time = day2datetime(scenario,days);#print time\r\n\t\tif variable ==\"VQ\" or variable == \"VT\":\r\n\t\t\tdata = np.nanmean(nc_fid.variables[variable][:,23:30,:,:],axis=1) # 850hpa\r\n\t\telse:\r\n\t\t\tdata = nc_fid.variables[variable][:]#-273.15\r\n\t\tnc_fid.close()\r\n\t\tvar40map = mon_mean2annual_mean(scenario,time,data)\r\n\t\treturn lon,lat,var40map\r\n\t\r\n\tlon,lat,Edg70GO = data_netcdf('Edg70GO',variable)\r\n\t_,_,T1970 = data_netcdf('T1970RCP',variable)\r\n\t_,_,EdgRef = data_netcdf('EdgRef',variable)\r\n\t_,_,Edg70Oz = data_netcdf('Edg70Oz',variable)\r\n\t_,_,EdgEne = data_netcdf('EdgEne',variable)\r\n\t_,_,EdgTech = data_netcdf('EdgTech',variable)\r\n\treturn lon,lat,T1970,Edg70GO,Edg70Oz,EdgRef,EdgEne,EdgTech","def kpi_function(kwargs):\n start_time = kwargs['start_time']\n end_time = kwargs['end_time']\n project = kwargs['project']\n client_id = kwargs['client_id']\n device_id = kwargs['device_id']\n dataset_src = kwargs['dataset_src']\n dataset_dest = kwargs['dataset_dest']\n table_src = kwargs['table_src']\n table_dest = kwargs['table_dest']\n location = kwargs['location']\n config = kwargs['config']\n def station_data(station_id, start, end):\n data = Daily(station_id, start, end)\n data = data.fetch()\n data.reset_index(inplace=True)\n data = data[['time', 'tavg', 'tmin', 'tmax']]\n # print(data)\n return data\n def costic(df):\n alias_a = df['tmin']\n alias_b = df['tmax']\n\n if 15 > alias_b:\n return 15 - (alias_a + alias_b) / 2\n elif 15 <= alias_a:\n return 0\n else:\n return (15 - alias_a) * (0.08 + 0.42 * (15 - alias_a) / (alias_b - alias_a))\n\n current_time = arrow.utcnow().floor('day')\n # if end_time <= current_time:\n output = config\n rules = pd.DataFrame(output['rules'])\n rules.index = rules.index.astype(int)\n rules.sort_index(inplace=True)\n rules = rules[['kpi_name',\n 'alias_a',\n 'alias_b',\n 'alias_c',\n 'alias_d',\n 'alias_e',\n 'alias_f',\n 'equation_1',\n 'equation_2',\n 'aggregation',\n 'start_time',\n 'end_time',\n 'mute_logs']].copy()\n rules = rules.where(pd.notnull(rules), None)\n settings = output['settings']['mute_rules']\n station_id = output['settings']['station_id']\n country = output['settings']['country']\n state = output['settings']['state']\n df = rules\n # create an empty dataframe\n logs = pd.DataFrame()\n # get access to bigqyery\n # credentials, project_id = google.auth.default()\n # client = bigquery.Client(credentials=credentials)\n if settings is False:\n data_rd = pd.read_csv('main_data.csv')\n data_rd['time'] = pd.to_datetime(data_rd['time_local'])\n if data_rd.shape[0] > 0:\n left_table = data_rd.drop(['alias','value','time_local'],axis=1)\n left_table = left_table.groupby(\"time\").min().reset_index()\n sub_df = data_rd[['time','alias','value']].copy()\n sub_df['value'] = pd.to_numeric(sub_df['value'])\n right_table = pd.pivot_table(sub_df,index='time',columns='alias',values='value').reset_index()\n data_rd = pd.merge(left_table,right_table,on='time', how='outer')\n if data_rd.shape[0] > 0:\n min_time = data_rd.time.min()\n max_time = data_rd.time.max()\n if station_id is not None:\n meteo_data = station_data(station_id, min_time, max_time)\n # print(meteo_data)\n data_rd['costic_HDD_15C'] = meteo_data.apply(costic, axis=1)\n data_rd = data_rd.merge(meteo_data, on='time', how='left')\n data_rd[['tavg', 'tmax', 'tmin']] = data_rd[['tavg', 'tmax', 'tmin']].astype('float')\n print(data_rd.head())\n data_rd.to_csv(\"file_name.csv\", index=False)\n for i in range(df.shape[0]):\n if df.iloc[i]['kpi_name'] == 'costic_HDD_15C':\n obj = kpi(df.iloc[i], data_rd, country, state, logs)\n dff = obj.return_log()\n logs = pd.concat([logs, dff], axis=0)\n if df.iloc[i]['mute_logs']:\n aliases_list = df.iloc[i]['mute_logs'].split(',')\n for alias in aliases_list:\n logs = logs[~logs.aliases.str.contains(alias.strip())]\n else:\n print(f\"Data is not available from {start_time} to {end_time}\")\n if logs.shape[0]>0:\n logs['time'] = logs['time'].dt.date\n logs.rename(columns={\"time\":\"date\"},inplace=True)\n logs['client_id'] = client_id\n logs['device_id'] = device_id\n logs['date'] = logs['date'].astype(str)\n print(tabulate(logs[['date','value']]))\n # load the logs dataframe to destination table in the bigquery\n # table = client.get_table(f\"{project}.{dataset_dest}.{table_dest}\")\n else:\n print('no logs saved')\n else:\n print(\"All rules are muted\")","def main():\n yesterday_datestring = (datetime.date.today() - datetime.timedelta(1)).isoformat()\n parser = argparse.ArgumentParser(description='generate TimeseriesArrays on local backend')\n parser.add_argument('--url', default=\"https://datalogger-api.tirol-kliniken.cc/DataLogger\", help=\"url of DataLogger Webapplication\")\n parser.add_argument('--logdir', default=\"/data1/haproxy_daily/\", help=\"directory where to find day sorted haproxylogs\")\n parser.add_argument(\"-b\", '--back', help=\"how many days back from now\")\n parser.add_argument(\"-s\", '--startdate', help=\"start date in isoformat YYY-MM-DD\")\n parser.add_argument(\"-e\", '--enddate', default=yesterday_datestring, help=\"stop date in isoformat YYY-MM-DD\")\n parser.add_argument(\"-q\", '--quiet', action='store_true', help=\"set to loglevel ERROR\")\n parser.add_argument(\"-v\", '--verbose', action='store_true', help=\"set to loglevel DEBUG\")\n args = parser.parse_args()\n if args.quiet is True:\n logging.getLogger(\"\").setLevel(logging.ERROR)\n if args.verbose is True:\n logging.getLogger(\"\").setLevel(logging.DEBUG)\n if (args.back is not None) == (args.startdate is not None):\n logging.error(\"option -b and -e are mutual exclusive, use only one\")\n sys.exit(1)\n startdate = None\n if args.back is not None:\n startdate = (datetime.date.today() - datetime.timedelta(int(args.back))).isoformat()\n elif args.startdate is not None:\n startdate = args.startdate\n else:\n logging.error(\"you have to provide either -b or -s\")\n sys.exit(1)\n # lets get started\n datalogger = DataLoggerWeb(args.url)\n project = \"haproxy\"\n tablename = \"http_host\"\n baseurl = \"%s/upload_raw_file/\" % args.url\n logdir = args.logdir # where to find haproxy logs\n keys = (\"http_host\", )\n values = (\"bytes_read\", \"rsp_1xx\", \"rsp_2xx\", \"rsp_3xx\", \"rsp_4xx\", \"rsp_5xx\", \"rsp_other\", \"srv_queue\", \"backend_queue\", \"actconn\", \"feconn\", \"beconn\", \"srv_conn\", \"retries\", \"tq\", \"tw\", \"tc\", \"tr\", \"tt\", \"hits\")\n ts_keyname = \"ts\"\n for datestring in datewalk(startdate, args.enddate):\n caches = datalogger.get_caches(project, tablename, datestring)\n if caches[\"tsa\"][\"raw\"] is not None:\n logging.info(\"Skipping this datestring, raw data is already available\")\n continue\n try:\n stringio = generate_datalogger_csv(logdir, datestring, keys, values, ts_keyname)\n #upload data\n files = {'myfile': stringio}\n url = \"/\".join((baseurl, project, tablename, datestring))\n logging.info(\"calling %s\", url)\n response = requests.post(url, files=files)\n print response.content\n except StandardError as exc:\n logging.error(\"Exception on file datestring %si, skipping this date\", datestring)\n except zlib.error as exc:\n logging.error(exc)","def _generate_hourly_time_series(\n self,\n size: int,\n hourly_seasonality: float,\n hourly_seasonality_params: Optional[List[Tuple[float, float]]] = None,\n trend_params: Optional[List[TrendParams]] = None,\n weekday_dummy_params: Optional[List[float]] = None,\n annual_seasonality_params: Optional[List[Tuple[float, float]]] = None,\n holiday_alpha: float = 3.5,\n outlier_alpha: float = 2.5,\n noise_scale: float = 1.0,\n ):\n if hourly_seasonality_params is None:\n # Create 10 random hourly seasonality parameters\n hourly_seasonality_params = [\n (\n np.random.normal() / 100,\n np.random.normal() / 100,\n )\n for i in range(4)\n ]\n\n time_range = np.arange(size)\n\n hourly_seasonality_series = (\n self._generate_hourly_seasonality(\n time_range=time_range,\n hourly_seasonality_params=hourly_seasonality_params,\n )\n * hourly_seasonality\n )\n\n hourly_outliers = self._generate_posneg_pareto(outlier_alpha, size)\n\n daily_time_series = self._generate_daily_time_series(\n size=ceil(size / 24),\n trend_params=trend_params,\n weekday_dummy_params=weekday_dummy_params,\n annual_seasonality_params=annual_seasonality_params,\n holiday_alpha=holiday_alpha,\n outlier_alpha=1000000, # outlier_alpha,\n noise_scale=noise_scale,\n )\n return (\n np.exp(hourly_seasonality_series) * np.repeat(daily_time_series, 24)[:size]\n + hourly_outliers\n )","def runtest(params_fc):\n\n npz = {}\n\n fold, cost = params_fc\n dset = np.load(DSET)\n\n idx_tr = dset['split'] != fold\n idx_ts = dset['split'] == fold\n\n lgt_tr, gt_tr = dset['wlogit'][idx_tr], dset['gt'][idx_tr]\n lgt_ts = dset['wlogit'][idx_ts]\n tinv = ut.calib(lgt_tr, gt_tr)\n npz['tinv'] = tinv\n\n wrank_tr, srank_tr = dset['wrank'][idx_tr], dset['srank'][idx_tr]\n wrank_ts, srank_ts = dset['wrank'][idx_ts], dset['srank'][idx_ts]\n\n entr_tr, entr_ts = ut.entropy(lgt_tr, tinv), ut.entropy(lgt_ts, tinv)\n wcost_tr, scost_tr = ut.cost(wrank_tr, srank_tr, cost)\n wcost_ts, scost_ts = ut.cost(wrank_ts, srank_ts, cost)\n npz['wcost_tr'], npz['scost_tr'] = wcost_tr, scost_tr\n npz['wcost_ts'], npz['scost_ts'] = wcost_ts, scost_ts\n npz['entr_tr'] = entr_tr\n\n rew_tr = wcost_tr - scost_tr\n mtog = po.fitmetric(entr_tr, rew_tr)\n npz['mtog'] = np.stack(mtog)\n\n npz['metric_tr'] = np.interp(entr_tr, *mtog)\n npz['metric_ts'] = np.interp(entr_ts, *mtog)\n\n np.savez_compressed(SPATH % (fold, cost), **npz)\n print(\"Completed fold %d, cost %d\" % (fold, cost))","def fit(self, Y, STATUS, ntop=100, nrecent=100, nmax=400, ntopmu=100, ntopvar=100, nkmeans=300, nkeamnsdata=5000,\n lam=1e-6):\n X = self.X\n untested = [i for i in range(self.n) if STATUS[i] == 0]\n tested = [i for i in range(self.n) if STATUS[i] == 2]\n ytested = Y[tested].reshape(-1)\n self.y_max = np.max(ytested)\n # each 10 fits we update the hyperparameters, otherwise we just update the data which is a lot faster\n if np.mod(self.update_counter, self.updates_per_big_fit) == 0:\n print('fitting hyperparameters')\n # how many training points are there\n ntested = len(tested)\n # if more than nmax we will subsample and use the subsample to fit hyperparametesr\n if ntested > nmax:\n # subsample is uniion of 100 best points, 100 most recent points and then random points \n top = list(np.argsort(ytested)[-ntop:])\n recent = list(range(ntested - nrecent, ntested))\n topandrecent = list(set(top + recent))\n rand = list(\n np.random.choice([i for i in range(ntested) if i not in topandrecent], nmax - len(topandrecent),\n False))\n testedtrain = topandrecent + rand\n ytrain = ytested[testedtrain]\n train = [tested[i] for i in testedtrain]\n else:\n train = tested\n ytrain = ytested\n \n # use GPy code to fit hyperparameters to minimize NLL on train data\n mfy = GPy.mappings.Constant(input_dim=self.d, output_dim=1) # fit dense GPy model to this data\n ky = GPy.kern.RBF(self.d, ARD=True, lengthscale=np.ones(self.d))\n self.GP = GPy.models.GPRegression(X[train], ytrain.reshape(-1, 1), kernel=ky, mean_function=mfy)\n self.GP.optimize('bfgs')\n # strip out fitted hyperparameters from GPy model, because cant do high(ish) dim sparse inference\n self.mu = self.GP.flattened_parameters[0]\n self.a = self.GP.flattened_parameters[1]\n self.l = self.GP.flattened_parameters[2]\n self.b = self.GP.flattened_parameters[3]\n # selecting inducing points for sparse inference \n print('selecting inducing points')\n # get prediction from GPy model \n self.py = self.GP.predict(X)\n # points with 100 highest means\n topmu = [untested[i] for i in np.argsort(self.py[0][untested].reshape(-1))[-ntopmu:]]\n # points with 100 highest uncertatinty\n topvar = [untested[i] for i in np.argsort(self.py[1][untested].reshape(-1))[-ntopvar:]]\n # combine with train set above to give nystrom inducing points (inducing points that are also actual trainingdata points) \n nystrom = topmu + topvar + train\n # also get some inducing points spread throughout domain by using kmeans\n # kmeans is very slow on full dataset so choose random subset \n # also scale using length scales l so that kmeans uses approproate distance measure\n kms = KMeans(n_clusters=nkmeans, max_iter=5).fit(\n np.divide(X[list(np.random.choice(untested, nkeamnsdata))], self.l))\n # matrix of inducing points \n self.M = np.vstack((X[nystrom], np.multiply(kms.cluster_centers_, self.l)))\n # dragons...\n # email james.l.hook@gmail.com if this bit goes wrong!\n print('fitting sparse model')\n DXM = euclidean_distances(np.divide(X, self.l), np.divide(self.M, self.l), squared=True)\n self.SIG_XM = self.a * np.exp(-DXM / 2)\n DMM = euclidean_distances(np.divide(self.M, self.l), np.divide(self.M, self.l), squared=True)\n self.SIG_MM = self.a * np.exp(-DMM / 2) + np.identity(self.M.shape[0]) * lam * self.a\n self.B = self.a + self.b - np.sum(np.multiply(np.linalg.solve(self.SIG_MM, self.SIG_XM.T), self.SIG_XM.T),0)\n K = np.matmul(self.SIG_XM[tested].T, np.divide(self.SIG_XM[tested], self.B[tested].reshape(-1, 1)))\n self.SIG_MM_pos = self.SIG_MM - K + np.matmul(K, np.linalg.solve(K + self.SIG_MM, K))\n J = np.matmul(self.SIG_XM[tested].T, np.divide(ytested - self.mu, self.B[tested]))\n self.mu_M_pos = self.mu + J - np.matmul(K, np.linalg.solve(K + self.SIG_MM, J))\n else:\n K = np.matmul(self.SIG_XM[tested].T, np.divide(self.SIG_XM[tested], self.B[tested].reshape(-1, 1)))\n self.SIG_MM_pos = self.SIG_MM - K + np.matmul(K, np.linalg.solve(K + self.SIG_MM, K))\n J = np.matmul(self.SIG_XM[tested].T, np.divide(ytested - self.mu, self.B[tested]))\n self.mu_M_pos = self.mu + J - np.matmul(K, np.linalg.solve(K + self.SIG_MM, J))\n self.update_counter += 1\n \"\"\" \n key attributes updated by fit \n \n self.SIG_XM : prior covarience matrix between data and inducing points\n self.SIG_MM : prior covarience matrix at inducing points\n \n self.SIG_MM_pos : posterior covarience matrix at inducing points\n self.mu_M_pos : posterior mean at inducing points \n \n \"\"\"","def _range_for_dates(X_dt, percentiles, grid_resolution):\n timestamps = np.array(\n [X_dt - pd.Timestamp(\"1970-01-01\")] // np.timedelta64(1, \"s\")\n ).reshape(-1, 1)\n timestamps = pd.DataFrame(timestamps)\n grid, values = _grid_from_X(\n timestamps,\n percentiles=percentiles,\n grid_resolution=grid_resolution,\n custom_range={},\n )\n grid_dates = pd.to_datetime(pd.Series(grid.squeeze()), unit=\"s\")\n return grid_dates","def KSm_gof(data, relative_time_grid, sf, args=(), alternative='two-sided'):\n \n def deltaNA (d, Y):\n return np.sum([ np.power(Y-l,-1) for l in range(int(d))])\n \n mortalities = pd.Series(data.loc[data['status']==1,'mortality'].value_counts(), index=relative_time_grid).fillna(0)\n censored = pd.Series(data.loc[data['status']==2,'mortality'].value_counts(), index=relative_time_grid).fillna(0)\n counts = mortalities + censored\n n = len(counts)\n atRisk = pd.Series(data=np.dot(np.triu(np.ones((n,n)),k=0), counts), index=relative_time_grid)\n survived = pd.Series(data=np.dot(np.triu(np.ones((n,n)),k=1), counts) + np.array(censored), index=relative_time_grid)\n \n sfvals = sf(relative_time_grid, *args)\n \n def deltaNA (d, Y):\n return np.sum([ np.power(Y-l,-1) for l in range(int(d))])\n beta_NelsonAalen = np.dot(np.tril(np.ones((n,n)),k=0), [deltaNA(mortalities.loc[ind],atRisk.loc[ind]) for ind in atRisk.index] )\n alpha_CumCensor = np.dot(np.tril(np.ones((n,n)),k=0), [deltaNA(censored.loc[ind],survived.loc[ind]) for ind in survived.index] )\n A_CumHazardCenAdjusted = np.dot(np.tril(np.ones((n,n)),k=0),\n [0]+[np.exp(-0.5*alpha_CumCensor[i-1])*np.log(sfvals[i-1]/sfvals[i]) for i in range(1,len(atRisk))] )\n B_NelsonAalenCenAdjusted = np.dot(np.tril(np.ones((n,n)),k=0),\n [0]+[np.exp(-0.5*alpha_CumCensor[i-1])*deltaNA(mortalities.iloc[i],atRisk.iloc[i]) for i in range(1,len(atRisk))] )\n Yminus = np.sqrt(n) * 0.5 * (sfvals[1:]+np.exp(-beta_NelsonAalen[:-1])) * (A_CumHazardCenAdjusted[1:] - B_NelsonAalenCenAdjusted[:-1])\n Yplus = np.sqrt(n) * 0.5 * (sfvals+np.exp(-beta_NelsonAalen)) * (A_CumHazardCenAdjusted - B_NelsonAalenCenAdjusted)\n Yconcat = pd.concat([pd.Series(data=Yplus,index=atRisk.index), pd.Series(data=Yminus,index=atRisk.index[1:])]).sort_index()\n R_MaxCDF = 1 - 0.5*(np.exp(-beta_NelsonAalen[-1])+sfvals[-1])\n \n return KSmResidues(Yconcat, R_MaxCDF)","def _postprocess_timeseries(rows, cols, params):\n # pylint: disable=too-many-locals\n xlks, _, crop = cf.transform_params(params)\n base_unw_paths = cf.original_ifg_paths(params[cf.IFG_FILE_LIST])\n dest_tifs = cf.get_dest_paths(base_unw_paths, crop, params, xlks)\n output_dir = params[cf.TMPDIR]\n\n # load previously saved prepread_ifgs dict\n preread_ifgs_file = join(output_dir, 'preread_ifgs.pk')\n ifgs = cp.load(open(preread_ifgs_file, 'rb'))\n\n # metadata and projections\n gt, md, wkt = ifgs['gt'], ifgs['md'], ifgs['wkt']\n epochlist = ifgs['epochlist']\n ifgs = [v for v in ifgs.values() if isinstance(v, PrereadIfg)]\n\n tiles = shared.get_tiles(dest_tifs[0], rows, cols)\n\n # load the first tsincr file to determine the number of time series tifs\n tsincr_file = os.path.join(output_dir, 'tsincr_0.npy')\n tsincr = np.load(file=tsincr_file)\n\n # pylint: disable=no-member\n no_ts_tifs = tsincr.shape[2]\n # we create 2 x no_ts_tifs as we are splitting tsincr and tscuml\n # to all processes.\n process_tifs = mpiops.array_split(range(2 * no_ts_tifs))\n\n # depending on nvelpar, this will not fit in memory\n # e.g. nvelpar=100, nrows=10000, ncols=10000, 32bit floats need 40GB memory\n # 32 * 100 * 10000 * 10000 / 8 bytes = 4e10 bytes = 40 GB\n # the double for loop helps us overcome the memory limit\n log.info('process {} will write {} ts (incr/cuml) tifs of '\n 'total {}'.format(mpiops.rank, len(process_tifs), no_ts_tifs * 2))\n for i in process_tifs:\n tscum_g = np.empty(shape=ifgs[0].shape, dtype=np.float32)\n if i < no_ts_tifs:\n for n, t in enumerate(tiles):\n _assemble_tiles(i, n, t, tscum_g, output_dir, 'tscuml')\n md[ifc.EPOCH_DATE] = epochlist.dates[i + 1]\n # sequence position; first time slice is #0\n md['SEQUENCE_POSITION'] = i+1\n dest = os.path.join(params[cf.OUT_DIR],\n 'tscuml' + \"_\" +\n str(epochlist.dates[i + 1]) + \".tif\")\n md[ifc.DATA_TYPE] = ifc.CUML\n shared.write_output_geotiff(md, gt, wkt, tscum_g, dest, np.nan)\n else:\n tsincr_g = np.empty(shape=ifgs[0].shape, dtype=np.float32)\n i %= no_ts_tifs\n for n, t in enumerate(tiles):\n _assemble_tiles(i, n, t, tsincr_g, output_dir, 'tsincr')\n md[ifc.EPOCH_DATE] = epochlist.dates[i + 1]\n # sequence position; first time slice is #0\n md['SEQUENCE_POSITION'] = i+1\n dest = os.path.join(params[cf.OUT_DIR],\n 'tsincr' + \"_\" + str(\n epochlist.dates[i + 1]) + \".tif\")\n md[ifc.DATA_TYPE] = ifc.INCR\n shared.write_output_geotiff(md, gt, wkt, tsincr_g, dest, np.nan)\n log.info('process {} finished writing {} ts (incr/cuml) tifs of '\n 'total {}'.format(mpiops.rank, len(process_tifs), no_ts_tifs * 2))","def probabilistic_forecasting(tau,nr_steps_ahead,values,levy_seed,levy_seed_params,envelope,\n envelope_params, nr_samples, envelope_function = None, max_gaussian_lag = None):\n \n probabilistic_forecasting_sanity_checks(values,tau,nr_steps_ahead,levy_seed,levy_seed_params,nr_samples,envelope_params)\n \n acf_function_helper = trawl_acf(envelope, envelope_function)\n acf_function = lambda x: acf_function_helper(x,envelope_params)\n overlap_area = acf_function(tau*nr_steps_ahead)\n \n\n if levy_seed == 'gaussian':\n \n assert isinstance(max_gaussian_lag,int) and max_gaussian_lag > 0 \n \n conditional_mean,conditional_scale = conditional_gaussian_distribution(values,tau,nr_steps_ahead,max_gaussian_lag,\n levy_seed_params,envelope,envelope_params,envelope_function)\n \n return np.array([norm.rvs(loc = i, scale = conditional_scale, size=nr_samples) for i in conditional_mean])\n \n \n elif levy_seed == 'gamma':\n \n alpha,theta = levy_seed_params\n \n \n \n alpha0 = alpha * overlap_area\n alpha1 = alpha * (1-overlap_area)\n print('before overlap')\n overlap_samples = values[:,np.newaxis] * beta.rvs(a = alpha0, b = alpha1, size = [len(values),nr_samples])\n print('before independent')\n independent_samples = gamma.rvs(a = alpha1, loc = 0, scale = theta, size = nr_samples)[np.newaxis,:]\n print('after independent')\n \n elif levy_seed in ['invgauss','gig','cauchy','student']:\n raise ValueError('not yet implemented')\n \n return overlap_samples + independent_samples","def get_profile_template_data(end_date, time_scale):\n\n intervals = -1\n HOURS = 0\n DAYS = 0\n WEEKS = 0\n time_format = \"%d %B %Y\"\n\n print(f\"Time scale: {time_scale}\")\n if time_scale == \"DAILY\":\n intervals = 12 # 24 hours per day\n HOURS = 2\n time_format = \"%I%p\"\n elif time_scale == \"WEEKLY\":\n intervals = 7 # 7 days per week\n DAYS = 1\n elif time_scale == \"MONTHLY\":\n intervals = 4 # 4 weeks per month\n WEEKS = 1\n\n # end_date = date.fromtimestamp(float(end_date))\n # end_date = datetime(end_date.year, end_date.month, end_date.day)\n end_date = datetime.now() + timedelta(hours=2)\n print(f\"TEMP current end date: {end_date.strftime('%I%p')}\")\n x_axis = []\n data = []\n inter = []\n TEMP = []\n time_gap = timedelta(hours=HOURS, days=DAYS, weeks=WEEKS)\n for i in range(intervals - 1, -1, -1):\n x_step = end_date - (i * time_gap)\n TEMP.append(x_step.strftime(\"%I%p\"))\n inter.append(str(x_step.timestamp()))\n data.append({\"images\": [], \"x\": x_step.strftime(time_format), \"y\": 0})\n print(f\"TEMP x-axis:{TEMP}\")\n return data, inter","def semipar_fit(dict_, data):\n # Process information specified in the initialization file and\n # assign default values if missing\n bins, logit, bandwidth, gridsize, startgrid, endgrid = process_primary_inputs(dict_)\n trim, rbandwidth, reestimate_p, show_output = process_secondary_inputs(dict_)\n\n # Prepare the sample for the estimation process\n # Compute propensity score, define common support and trim the sample\n # data, prop_score = process_mte_data(\n # dict_, data, logit, bins, trim, reestimate_p, show_output\n # )\n data = estimate_treatment_propensity(dict_, data, logit, show_output)\n\n X, Y, prop_score = trim_support(\n dict_, data, logit, bins, trim, reestimate_p, show_output\n )\n\n b0, b1_b0 = double_residual_reg(X, Y, prop_score)\n\n # # Construct the MTE\n # Generate the quantiles of the final MTE\n quantiles = np.linspace(startgrid, endgrid, gridsize)\n\n mte_x = mte_observed(X, b1_b0)\n mte_u = mte_unobserved_semipar(\n X, Y, b0, b1_b0, prop_score, bandwidth, gridsize, startgrid, endgrid\n )\n\n # Put the MTE together\n mte = mte_x.mean(axis=0) + mte_u\n\n # Account for variation in X\n mte_min = np.min(mte_x) + mte_u\n mte_max = np.max(mte_x) + mte_u\n\n rslt = {\n \"quantiles\": quantiles,\n \"mte\": mte,\n \"mte_x\": mte_x,\n \"mte_u\": mte_u,\n \"mte_min\": mte_min,\n \"mte_max\": mte_max,\n \"X\": X,\n \"b0\": b0,\n \"b1\": b1_b0 + b0,\n }\n\n return rslt","def run(config, tim=None):\n import common_lib\n import dr_lib\n import DST\n \n if tim is not None:\n tim.getTime(False)\n old_time = tim.getOldTime()\n\n if config.data is None:\n raise RuntimeError(\"Need to pass a data filename to the driver \"\\\n +\"script.\")\n\n # Read in geometry if one is provided\n if config.inst_geom is not None:\n if config.verbose:\n print \"Reading in instrument geometry file\"\n \n inst_geom_dst = DST.getInstance(\"application/x-NxsGeom\",\n config.inst_geom)\n else:\n inst_geom_dst = None\n\n config.so_axis = \"time_of_flight\"\n\n # Steps 1-3: Produce a scaled summed dark current dataset\n dc_som = dr_lib.scaled_summed_data(config.dkcur, config,\n dataset_type=\"dark_current\",\n timer=tim)\n\n # Perform Steps 3-6 on black can data\n if config.bcan is not None:\n b_som1 = dr_lib.calibrate_dgs_data(config.bcan, config, dc_som,\n dataset_type=\"black_can\",\n inst_geom_dst=inst_geom_dst,\n tib_const=config.tib_const,\n cwp=config.cwp_bcan,\n timer=tim)\n else:\n b_som1 = None\n\n # Perform Steps 3-6 on empty can data \n if config.ecan is not None:\n e_som1 = dr_lib.calibrate_dgs_data(config.ecan, config, dc_som,\n dataset_type=\"empty_can\",\n inst_geom_dst=inst_geom_dst,\n tib_const=config.tib_const,\n cwp=config.cwp_ecan,\n timer=tim)\n else:\n e_som1 = None\n\n # Perform Steps 3-6 on normalization data\n n_som1 = dr_lib.calibrate_dgs_data(config.data, config, dc_som,\n dataset_type=\"normalization\",\n inst_geom_dst=inst_geom_dst,\n tib_const=config.tib_const,\n cwp=config.cwp_data,\n timer=tim)\n\n # Perform Steps 7-16 on normalization data\n if config.norm_trans_coeff is None:\n norm_trans_coeff = None\n else:\n norm_trans_coeff = config.norm_trans_coeff.toValErrTuple()\n\n # Determine if we need to rebin the empty or black can data\n if config.ecan is not None and e_som1 is not None:\n ecan_cwp = True\n else:\n ecan_cwp = False\n\n if config.bcan is not None and b_som1 is not None:\n bcan_cwp = True\n else:\n bcan_cwp = False \n\n cwp_used = ecan_cwp or bcan_cwp\n\n n_som2 = dr_lib.process_dgs_data(n_som1, config, b_som1, e_som1,\n norm_trans_coeff,\n dataset_type=\"normalization\",\n cwp_used=cwp_used,\n timer=tim)\n \n del n_som1, b_som1, e_som1\n\n # Step 17: Integrate normalization spectra\n if config.verbose:\n print \"Integrating normalization spectra\"\n\n if tim is not None:\n tim.getTime(False)\n\n if config.norm_int_range is None:\n start_val = float(\"inf\")\n end_val = float(\"inf\")\n else:\n if not config.wb_norm:\n # Translate energy transfer to final energy\n ef_start = config.initial_energy.getValue() - \\\n config.norm_int_range[0]\n ef_end = config.initial_energy.getValue() - \\\n config.norm_int_range[1]\n # Convert final energy to final wavelength\n start_val = common_lib.energy_to_wavelength((ef_start, 0.0))[0]\n end_val = common_lib.energy_to_wavelength((ef_end, 0.0))[0]\n else:\n start_val = config.norm_int_range[0]\n end_val = config.norm_int_range[1]\n \n n_som3 = dr_lib.integrate_spectra(n_som2, start=start_val,\n end=end_val, width=True)\n\n del n_som2\n \n if tim is not None:\n tim.getTime(msg=\"After integrating normalization spectra \")\n\n file_comment = \"Normalization Integration range: %0.3fA, %0.3fA\" \\\n % (start_val, end_val)\n \n hlr_utils.write_file(config.output, \"text/num-info\", n_som3,\n output_ext=\"norm\",\n data_ext=config.ext_replacement,\n path_replacement=config.path_replacement,\n verbose=config.verbose,\n message=\"normalization values\",\n comments=[file_comment],\n tag=\"Integral\", units=\"counts\") \n \n if tim is not None:\n tim.getTime(False)\n\n if config.verbose:\n print \"Making mask file\"\n\n # Make mask file from threshold\n dr_lib.filter_normalization(n_som3, config.lo_threshold,\n config.hi_threshold, config)\n\n if tim is not None:\n tim.getTime(msg=\"After making mask file \")\n\n # Write out RMD file\n n_som3.attr_list[\"config\"] = config\n\n hlr_utils.write_file(config.output, \"text/rmd\", n_som3,\n output_ext=\"rmd\",\n data_ext=config.ext_replacement, \n path_replacement=config.path_replacement,\n verbose=config.verbose,\n message=\"metadata\")\n \n if tim is not None:\n tim.setOldTime(old_time)\n tim.getTime(msg=\"Total Running Time\")","def execute(self, in_nc, in_weight_table, out_nc, grid_name, conversion_flag, in_time_interval=\"6hr\"): # modified this line CJB 20190218\r\n # MJS I might consider netCDF4.Dataset.variables['RO'].units\r\n # and upstream correction of the cdo grid conversion units attribute.\r\n\r\n # Validate the netcdf dataset\r\n vars_oi_index = self.dataValidation(in_nc)\r\n \r\n \"\"\"get conversion factor the flag is used to differentiate forecasts converted \r\n to netCDF from GRIB and the original netCDF. They both use the same weight tables\r\n but the original netCDF is in mm whereas the stock GRIB forecasts are in meters.\r\n Set the conversion_flag in the run.py configuration file.\r\n \"\"\"\r\n if conversion_flag: # Line Added CJB 20190218\r\n conversion_factor = 1.0 #Line Modified CJB 20190218\r\n elif grid_name == 'ecmwf_t1279' or grid_name == 'ecmwf_tco639': # Line Modified CJB 20190218\r\n #if grid_name == 'ecmwf_HRES_F' or grid_name == 'ecmwf_ENS_F': # Line Added/Modified CJB 20190108\r\n #new grids in mm instead of m\r\n conversion_factor = 0.001\r\n else: #set the conversion factor to 1 for everything else (data is in m but legacy installations do not have a flag) Line Added CJB 20190218\r\n conversion_factor = 1.0 # Line Added CJB 20190218\r\n # MJS I might consider netCDF4.Dataset.variables['RO'].units\r\n # and upstream correction of the cdo grid conversion units attribute.\r\n # identify if the input netcdf data is the High Resolution data with three different time intervals\r\n id_data = self.dataIdentify(in_nc)\r\n if id_data is None:\r\n raise Exception(self.errorMessages[3])\r\n\r\n ''' Read the netcdf dataset'''\r\n data_in_nc = NET.Dataset(in_nc)\r\n time = data_in_nc.variables['time'][:]\r\n\r\n # Check the size of time variable in the netcdf data\r\n if len(time) == 0: # *** MJS This change seems like it is too loose an error trap; should it account for instances when nc file time var is != in length with id_data lenght?\r\n raise Exception(self.errorMessages[3])\r\n #if len(time) != self.length_time[id_data]:\r\n # raise Exception(self.errorMessages[3])\r\n\r\n ''' Read the weight table '''\r\n print(\"Reading the weight table...\", in_weight_table)\r\n dict_list = {self.header_wt[0]:[], self.header_wt[1]:[], self.header_wt[2]:[],\r\n self.header_wt[3]:[], self.header_wt[4]:[]}\r\n\r\n with open(in_weight_table, \"r\") as csvfile:\r\n reader = csv.reader(csvfile)\r\n count = 0\r\n for row in reader:\r\n if count == 0:\r\n #check number of columns in the weight table\r\n if len(row) < len(self.header_wt):\r\n raise Exception(self.errorMessages[4])\r\n #check header\r\n if row[1:len(self.header_wt)] != self.header_wt[1:]:\r\n raise Exception(self.errorMessages[5])\r\n count += 1\r\n else:\r\n for i in range(len(self.header_wt)):\r\n dict_list[self.header_wt[i]].append(row[i])\r\n count += 1\r\n\r\n ''' Calculate water inflows\r\n as a reminder, the first 91 time steps are T=0 to T=90 and are 1-hourly for HRES\r\n\t\t the next 18 time steps for HRES are T=93 to T=144 at 3-hourly\r\n then the final 16 time steps are T=150 to T=240 at 6-hourly for a total of 125 records\r\n\t\t\tFor ENS, the first 49 time steps are T=0 to T=144 at 3-hourly\r\n\t\t\tthe final 35 time steps are T=150 to T=360 at 6-hourly for a total of 84 records\r\n '''\r\n\t\t\t\r\n print(\"Calculating water inflows...\")\r\n\t\t\r\n ''' \r\n added the next section CJB 20180122 \r\n '''\r\n\r\n\t\t# Get the overall number of time steps\r\n size_time = self.getTimeSize(in_nc) #CJB 20180122\r\n # Determine the size of time steps in each group (1-hourly, 3-hourly, and/or 6-hourly)\r\n if id_data == \"HRES1\": # T <= 90 \r\n time_size = (size_time - 1)\r\n elif id_data == \"HRES13\": # 93 <= T <= 144\r\n if in_time_interval == \"1hr\":\r\n time_size = self.length_time_opt[\"HighRes-1hr\"]\r\n else:\r\n time_size = (size_time - self.length_time_opt[\"HighRes-1hr\"] - 1)\r\n elif id_data == \"HRES136\": # 150 <= T <= 240\r\n if in_time_interval == \"1hr\":\r\n time_size = self.length_time_opt[\"HighRes-1hr\"]\r\n elif in_time_interval == \"3hr\": # MJS Doesn't seem to be a case used currently, but added just in case later need.\r\n time_size = self.length_time_opt[\"HighRes-3hr-sub\"] # MJS This is HRES136, i.e., if for some reason in ecmwf_rapid_multi a 3 hr is asked for for this case, it should still have the 3hr_sub number of times\r\n elif in_time_interval == \"3hr_subset\":\r\n time_size = self.length_time_opt[\"HighRes-3hr-Sub\"]\r\n else:\r\n time_size = (size_time - self.length_time_opt[\"HighRes-1hr\"] - self.length_time_opt[\"HighRes-3hr-Sub\"] - 1)\r\n elif id_data == \"ENS3\": # T <= 144\r\n time_size = (size_time - 1)\r\n elif id_data == \"ENS36\": # 150 <= T <= 360\r\n if in_time_interval == \"3hr_subset\":\r\n time_size = self.length_time_opt[\"LowResFull-3hr-Sub\"]\r\n else:\r\n time_size = (size_time - self.length_time_opt[\"LowResFull-3hr-Sub\"] - 1)\r\n else: # id_data == \"ENS6\": # T <= 360 but all 6-hourly\r\n time_size = (size_time - 1)\r\n #else: # something is wrong and need to throw an error message - likely a corrupt forecast file\r\n # raise Exception(self.errorMessages[3])\r\n #''' end of added section CJB 20180122 \r\n #'''\r\n\r\n #if id_data == \"LowRes\":\r\n # size_time = self.length_time_opt[\"LowRes-6hr\"]\r\n #elif id_data == \"Low3HrRes\":\r\n # size_time = self.length_time_opt[\"LowRes-3hr\"]\r\n #elif id_data == \"LowResFull\":\r\n # if in_time_interval == \"3hr_subset\":\r\n # size_time = self.length_time_opt[\"LowResFull-3hr-Sub\"]\r\n # elif in_time_interval == \"6hr_subset\":\r\n # size_time = self.length_time_opt[\"LowResFull-6hr-Sub\"]\r\n # else:\r\n # size_time = self.length_time_opt[\"LowRes-6hr\"]\r\n #else: #HighRes\r\n # if in_time_interval == \"1hr\":\r\n # size_time = self.length_time_opt[\"HighRes-1hr\"]\r\n # elif in_time_interval == \"3hr\":\r\n # size_time = self.length_time_opt[\"HighRes-3hr\"]\r\n # elif in_time_interval == \"3hr_subset\":\r\n # size_time = self.length_time_opt[\"HighRes-3hr-Sub\"]\r\n # elif in_time_interval == \"6hr_subset\":\r\n # size_time = self.length_time_opt[\"HighRes-6hr-Sub\"]\r\n # else:\r\n # size_time = self.length_time_opt[\"HighRes-6hr\"]\r\n\r\n size_streamID = len(set(dict_list[self.header_wt[0]]))\r\n\r\n # Create output inflow netcdf data\r\n # data_out_nc = NET.Dataset(out_nc, \"w\") # by default format = \"NETCDF4\"\r\n data_out_nc = NET.Dataset(out_nc, \"w\", format = \"NETCDF3_CLASSIC\")\r\n #dim_Time = data_out_nc.createDimension('Time', size_time)\r\n dim_Time = data_out_nc.createDimension('Time', time_size)\r\n dim_RiverID = data_out_nc.createDimension('rivid', size_streamID)\r\n var_m3_riv = data_out_nc.createVariable('m3_riv', 'f4', \r\n ('Time', 'rivid'),\r\n fill_value=0)\r\n \r\n #data_temp = NUM.empty(shape = [size_time, size_streamID])\r\n data_temp = NUM.empty(shape = [time_size, size_streamID])\r\n\r\n lon_ind_all = [int(i) for i in dict_list[self.header_wt[2]]]\r\n lat_ind_all = [int(j) for j in dict_list[self.header_wt[3]]]\r\n\r\n # Obtain a subset of runoff data based on the indices in the weight table\r\n min_lon_ind_all = min(lon_ind_all)\r\n max_lon_ind_all = max(lon_ind_all)\r\n min_lat_ind_all = min(lat_ind_all)\r\n max_lat_ind_all = max(lat_ind_all)\r\n\r\n # self.vars_oi[vars_oi_index][3] = RO; get that variable's 3D structure (time, lat_index, lon_index) ready to reshape into 2D (time, lat_index x lon_index)\r\n data_subset_all = data_in_nc.variables[self.vars_oi[vars_oi_index][3]][:, min_lat_ind_all:max_lat_ind_all+1, min_lon_ind_all:max_lon_ind_all+1]\r\n len_time_subset_all = data_subset_all.shape[0]\r\n len_lat_subset_all = data_subset_all.shape[1]\r\n len_lon_subset_all = data_subset_all.shape[2]\r\n data_subset_all = data_subset_all.reshape(len_time_subset_all, (len_lat_subset_all * len_lon_subset_all))\r\n\r\n # compute new indices based on the data_subset_all\r\n index_new = []\r\n for r in range(0,count-1):\r\n ind_lat_orig = lat_ind_all[r]\r\n ind_lon_orig = lon_ind_all[r]\r\n index_new.append((ind_lat_orig - min_lat_ind_all)*len_lon_subset_all + (ind_lon_orig - min_lon_ind_all))\r\n\r\n # obtain a new subset of data\r\n data_subset_new = data_subset_all[:,index_new]*conversion_factor\r\n\r\n # start compute inflow\r\n pointer = 0\r\n for s in range(0, size_streamID):\r\n npoints = int(dict_list[self.header_wt[4]][pointer])\r\n # Check if all npoints points correspond to the same streamID\r\n if len(set(dict_list[self.header_wt[0]][pointer : (pointer + npoints)])) != 1:\r\n print(\"ROW INDEX {0}\".format(pointer))\r\n print(\"RIVID {0}\".format(dict_list[self.header_wt[0]][pointer]))\r\n raise Exception(self.errorMessages[2])\r\n\r\n area_sqm_npoints = [float(k) for k in dict_list[self.header_wt[1]][pointer : (pointer + npoints)]]\r\n area_sqm_npoints = NUM.array(area_sqm_npoints)\r\n area_sqm_npoints = area_sqm_npoints.reshape(1, npoints)\r\n data_goal = data_subset_new[:, pointer:(pointer + npoints)]\r\n \r\n \r\n #remove noise from data\r\n data_goal[data_goal<=0.00001] = 0\r\n\r\n ''' IMPORTANT NOTE: runoff variable in ECMWF dataset is cumulative instead of incremental through time\r\n '''\r\n # For data with Low Resolution, there's only one time interval 6 hrs\r\n if id_data == \"ENS6\": # Line Added/Modified CJB 20190108\r\n #ro_stream = data_goal * area_sqm_npoints\r\n ro_stream = NUM.subtract(data_goal[1:,],data_goal[:-1,]) * area_sqm_npoints\r\n elif id_data == \"ENS3\": # there's only one time interval 3 hrs # Line Added/Modified CJB 20190108\r\n #ro_stream = data_goal * area_sqm_npoints\r\n ro_stream = NUM.subtract(data_goal[1:,],data_goal[:-1,]) * area_sqm_npoints # Line Added/Modified CJB 20190108\r\n elif id_data == \"HRES1\": # there's only one time interval 1 hrs # Line Added/Modified CJB 20190108\r\n #ro_stream = data_goal * area_sqm_npoints\r\n ro_stream = NUM.subtract(data_goal[1:,],data_goal[:-1,]) * area_sqm_npoints # Line Added/Modified CJB 20190108\t\r\n #For data with the full version of Low Resolution, from Hour 0 to 144 (the first 49 time points) are of 3 hr time interval,\r\n # then from Hour 144 to 360 (36 time points) are of 6 hour time interval\r\n elif id_data == \"ENS36\": # Line Added/Modified CJB 20190108\r\n if in_time_interval == \"3hr_subset\":\r\n #use only the 3hr time interval\r\n ro_stream = NUM.subtract(data_goal[1:49,], data_goal[:48,]) * area_sqm_npoints\r\n elif in_time_interval == \"6hr_subset\":\r\n #use only the 6hr time interval\r\n ro_stream = NUM.subtract(data_goal[49:,], data_goal[48:-1,]) * area_sqm_npoints\r\n else: #\"LowRes-6hr\"\r\n ######################################################\r\n # MJS Always assume this case will have a full ECMWF 240\r\n # hour forecast to work with. It's actually never re-\r\n # quested by ecmwf_rapid_multiprocess anyhow.\r\n ######################################################\r\n #convert all to 6hr\r\n # calculate time series of 6 hr data from 3 hr data\r\n ro_6hr_a = NUM.subtract(data_goal[2:49:2,], data_goal[:48:2,])\r\n # get the time series of 6 hr data\r\n ro_6hr_b = NUM.subtract(data_goal[49:,], data_goal[48:-1,])\r\n # concatenate all time series\r\n ro_stream = NUM.concatenate([ro_6hr_a, ro_6hr_b]) * area_sqm_npoints\r\n #For data with High Resolution, from Hour 0 to 90 (the first 91 time points) are of 1 hr time interval,\r\n # then from Hour 90 to 144 (18 time points) are of 3 hour time interval, and from Hour 144 to 240 (16 time points)\r\n # are of 6 hour time interval\r\n ##########################################################\r\n # MJS The following should handle id_data = HRES13 and HRES136\r\n ##########################################################\r\n else:\r\n if in_time_interval == \"1hr\":\r\n #ro_stream = NUM.subtract(data_goal[1:91,],data_goal[:90,]) * area_sqm_npoints\r\n ro_stream = NUM.subtract(data_goal[1:1+time_size,],data_goal[:time_size,]) * area_sqm_npoints # Line Added/Modified CJB, MJS 20190108\r\n elif in_time_interval == \"3hr\": # MJS HRES 3hr not currently used\r\n # calculate time series of 3 hr data from 1 hr data\r\n ro_3hr_a = NUM.subtract(data_goal[3:91:3,],data_goal[:88:3,])\r\n # get the time series of 3 hr data\r\n #ro_3hr_b = NUM.subtract(data_goal[91:109,], data_goal[90:108,])\r\n ro_3hr_b = NUM.subtract(data_goal[91:91+time_size,], data_goal[90:90+time_size,]) # MJS modified again; seems no case for this, but just in case later... Line Added/Modified CJB 20190108\r\n # concatenate all time series\r\n ro_stream = NUM.concatenate([ro_3hr_a, ro_3hr_b]) * area_sqm_npoints\r\n elif in_time_interval == \"3hr_subset\":\r\n #use only the 3hr time interval\r\n #ro_stream = NUM.subtract(data_goal[91:109,], data_goal[90:108,]) * area_sqm_npoints\r\n ro_stream = NUM.subtract(data_goal[91:91+time_size,], data_goal[90:90+time_size,]) * area_sqm_npoints # MJS modified again; needs to handle HRES13 that might not have complete 3hr set... Line Added/Modified CJB 20190108\r\n elif in_time_interval == \"6hr_subset\":\r\n #use only the 6hr time interval\r\n ro_stream = NUM.subtract(data_goal[109:,], data_goal[108:-1,]) * area_sqm_npoints\r\n ######################################################\r\n # MJS Always assume this case will have a full ECMWF 240 \r\n # hour forecast to work with. It's actually never re-\r\n # quested by ecmwf_rapid_multiprocess anyhow.\r\n ######################################################\r\n else: # in_time_interval == \"6hr\"\r\n #arcpy.AddMessage(\"6hr\")\r\n # calculate time series of 6 hr data from 1 hr data\r\n ro_6hr_a = NUM.subtract(data_goal[6:91:6,], data_goal[:85:6,])\r\n # calculate time series of 6 hr data from 3 hr data\r\n ro_6hr_b = NUM.subtract(data_goal[92:109:2,], data_goal[90:107:2,])\r\n # get the time series of 6 hr data\r\n ro_6hr_c = NUM.subtract(data_goal[109:,], data_goal[108:-1,])\r\n # concatenate all time series\r\n ro_stream = NUM.concatenate([ro_6hr_a, ro_6hr_b, ro_6hr_c]) * area_sqm_npoints\r\n \r\n #remove negative values\r\n ro_stream[ro_stream<0] = 0\r\n data_temp[:,s] = ro_stream.sum(axis = 1)\r\n\r\n pointer += npoints\r\n\r\n\r\n '''Write inflow data'''\r\n print(\"Writing inflow data...\")\r\n var_m3_riv[:] = data_temp\r\n # close the input and output netcdf datasets\r\n data_in_nc.close()\r\n data_out_nc.close()","def statistical_analysis(var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, param):\n \n valueCount_prov1 = round(valueCounter([var1, var4, var7, var10]),3) #how many valid OpenWeatherMap forecasts are available\n valueCount_prov2 = round(valueCounter([var2, var5, var8, var11]),3) #how many valid Weatherbit forecasts are available\n valueCount_prov4 = round(valueCounter([var3, var6, var9, var12]),3) #how many valid WorldWeatherOnline forecasts are available\n \n average_discrepancy_prov1 = round(averageOfList([var1, var4, var7, var10]),3) #average forecast discrepancy for each provider\n average_discrepancy_prov2 = round(averageOfList([var2, var5, var8, var11]),3)\n average_discrepancy_prov4 = round(averageOfList([var3, var6, var9, var12]),3)\n \n absolute_average_discrepancy_prov1 = round(absoluteAverageOfList([var1, var4, var7, var10]),3) #average absolute forecast discrepancy for each provider\n absolute_average_discrepancy_prov2 = round(absoluteAverageOfList([var2, var5, var8, var11]),3)\n absolute_average_discrepancy_prov4 = round(absoluteAverageOfList([var3, var6, var9, var12]),3)\n \n median_discrepancy_prov1 = round(medianErrorOfList([var1, var4, var7, var10]),3) #median forecast discrepancy for each provider\n median_discrepancy_prov2 = round(medianErrorOfList([var2, var5, var8, var11]),3)\n median_discrepancy_prov4 = round(medianErrorOfList([var3, var6, var9, var12]),3)\n\n sd_discrepancy_prov1 = sampleStandardDeviation([var1, var4, var7, var10]) #standard deviation forecast discrepancy for each provider\n sd_discrepancy_prov2 = sampleStandardDeviation([var2, var5, var8, var11]) #if there are at least two values available\n sd_discrepancy_prov4 = sampleStandardDeviation([var3, var6, var9, var12])\n\n #which is the best forecast: defined by minimal absolute average discrepancy\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov1:\n most_accurate_forecast = \"OpenWeatherMap\"\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov2:\n most_accurate_forecast = \"Weatherbit\"\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov4:\n most_accurate_forecast = \"WorldWeatherOnline\"\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov1 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov2:\n most_accurate_forecast = \"OpenWeatherMap and Weatherbit\"\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov1 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov4:\n most_accurate_forecast = \"OpenWeatherMap and WorldWeatherOnline\" \n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov2 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov4:\n most_accurate_forecast = \"Weatherbit and WorldWeatherOnline\"\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov2 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov4 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov1:\n most_accurate_forecast = \"all three forecasts\"\n \n dictionary = { #save the values to dictionary to make them accessible for the HTML template\n 'valueCount_prov1_'+str(param) : valueCount_prov1,\n 'valueCount_prov2_'+str(param) : valueCount_prov2,\n 'valueCount_prov4_'+str(param) : valueCount_prov4,\n 'average_discrepancy_prov1_'+str(param) : average_discrepancy_prov1,\n 'average_discrepancy_prov2_'+str(param) : average_discrepancy_prov2,\n 'average_discrepancy_prov4_'+str(param) : average_discrepancy_prov4,\n 'absolute_average_discrepancy_prov1_'+str(param) : absolute_average_discrepancy_prov1,\n 'absolute_average_discrepancy_prov2_'+str(param) : absolute_average_discrepancy_prov2,\n 'absolute_average_discrepancy_prov4_'+str(param) : absolute_average_discrepancy_prov4,\n 'median_discrepancy_prov1_'+str(param) : median_discrepancy_prov1,\n 'median_discrepancy_prov2_'+str(param) : median_discrepancy_prov2,\n 'median_discrepancy_prov4_'+str(param) : median_discrepancy_prov4,\n 'sd_discrepancy_prov1_'+str(param) : sd_discrepancy_prov1,\n 'sd_discrepancy_prov2_'+str(param) : sd_discrepancy_prov2,\n 'sd_discrepancy_prov4_'+str(param) : sd_discrepancy_prov4,\n 'weatherDay_'+str(param) : most_accurate_forecast,\n }\n \n return dictionary","def regrid_custom_hourly_netcdf(input_forcings, config_options, wrf_hydro_geo_meta, mpi_config):\n # If the expected file is missing, this means we are allowing missing files, simply\n # exit out of this routine as the regridded fields have already been set to NDV.\n if not os.path.isfile(input_forcings.file_in2):\n return\n\n # Check to see if the regrid complete flag for this\n # output time step is true. This entails the necessary\n # inputs have already been regridded and we can move on.\n if input_forcings.regridComplete:\n if mpi_config.rank == 0:\n config_options.statusMsg = \"No Custom Hourly NetCDF regridding required for this timestep.\"\n err_handler.log_msg(config_options, mpi_config)\n return\n\n # Open the input NetCDF file containing necessary data.\n id_tmp = ioMod.open_netcdf_forcing(input_forcings.file_in2, config_options, mpi_config, open_on_all_procs=True)\n\n fill_values = {'TMP': 288.0, 'SPFH': 0.005, 'PRES': 101300.0, 'APCP': 0,\n 'UGRD': 1.0, 'VGRD': 1.0, 'DSWRF': 80.0, 'DLWRF': 310.0}\n\n for force_count, nc_var in enumerate(input_forcings.netcdf_var_names):\n if mpi_config.rank == 0:\n config_options.statusMsg = \"Processing Custom NetCDF Forcing Variable: \" + nc_var\n err_handler.log_msg(config_options, mpi_config)\n calc_regrid_flag = check_regrid_status(id_tmp, force_count, input_forcings,\n config_options, wrf_hydro_geo_meta, mpi_config)\n\n if calc_regrid_flag:\n calculate_weights(id_tmp, force_count, input_forcings, config_options, mpi_config)\n\n # Read in the RAP height field, which is used for downscaling purposes.\n if 'HGT_surface' in id_tmp.variables.keys():\n # Regrid the height variable.\n if mpi_config.rank == 0:\n var_tmp = id_tmp.variables['HGT_surface'][0, :, :]\n else:\n var_tmp = None\n err_handler.check_program_status(config_options, mpi_config)\n\n var_sub_tmp = mpi_config.scatter_array(input_forcings, var_tmp, config_options)\n err_handler.check_program_status(config_options, mpi_config)\n\n try:\n input_forcings.esmf_field_in.data[:, :] = var_sub_tmp\n except (ValueError, KeyError, AttributeError) as err:\n config_options.errMsg = \"Unable to place NetCDF elevation data into the ESMF field object: \" \\\n + str(err)\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n\n if mpi_config.rank == 0:\n config_options.statusMsg = \"Regridding elevation data to the WRF-Hydro domain.\"\n err_handler.log_msg(config_options, mpi_config)\n try:\n input_forcings.esmf_field_out = input_forcings.regridObj(input_forcings.esmf_field_in,\n input_forcings.esmf_field_out)\n except ValueError as ve:\n config_options.errMsg = \"Unable to regrid elevation data to the WRF-Hydro domain \" \\\n \"using ESMF: \" + str(ve)\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n\n # Set any pixel cells outside the input domain to the global missing value.\n try:\n input_forcings.esmf_field_out.data[np.where(input_forcings.regridded_mask == 0)] = \\\n config_options.globalNdv\n except (ValueError, ArithmeticError) as npe:\n config_options.errMsg = \"Unable to compute mask on elevation data: \" + str(npe)\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n\n try:\n input_forcings.height[:, :] = input_forcings.esmf_field_out.data\n except (ValueError, KeyError, AttributeError) as err:\n config_options.errMsg = \"Unable to extract ESMF regridded elevation data to a local \" \\\n \"array: \" + str(err)\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n else:\n input_forcings.height = None\n if mpi_config.rank == 0:\n config_options.statusMsg = f\"Unable to locate HGT_surface in: {input_forcings.file_in2}. \" \\\n f\"Downscaling will not be available.\"\n err_handler.log_msg(config_options, mpi_config)\n\n # close netCDF file on non-root ranks\n if mpi_config.rank != 0:\n id_tmp.close()\n\n # Regrid the input variables.\n var_tmp = None\n fill = fill_values.get(input_forcings.grib_vars[force_count], config_options.globalNdv)\n if mpi_config.rank == 0:\n config_options.statusMsg = \"Regridding Custom netCDF input variable: \" + nc_var\n err_handler.log_msg(config_options, mpi_config)\n try:\n config_options.statusMsg = f\"Using {fill} to replace missing values in input\"\n err_handler.log_msg(config_options, mpi_config)\n var_tmp = id_tmp.variables[nc_var][:].filled(fill)[0, :, :]\n except Exception as err:\n config_options.errMsg = \"Unable to extract \" + nc_var + \\\n \" from: \" + input_forcings.file_in2 + \" (\" + str(err) + \")\"\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n\n var_sub_tmp = mpi_config.scatter_array(input_forcings, var_tmp, config_options)\n err_handler.check_program_status(config_options, mpi_config)\n\n try:\n input_forcings.esmf_field_in.data[:, :] = var_sub_tmp\n except (ValueError, KeyError, AttributeError) as err:\n config_options.errMsg = \"Unable to place local array into local ESMF field: \" + str(err)\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n\n try:\n input_forcings.esmf_field_out = input_forcings.regridObj(input_forcings.esmf_field_in,\n input_forcings.esmf_field_out)\n except ValueError as ve:\n config_options.errMsg = \"Unable to regrid input Custom netCDF forcing variables using ESMF: \" + str(ve)\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n\n # Set any pixel cells outside the input domain to the global missing value.\n try:\n input_forcings.esmf_field_out.data[np.where(input_forcings.regridded_mask == 0)] = fill\n except (ValueError, ArithmeticError) as npe:\n config_options.errMsg = \"Unable to calculate mask from input Custom netCDF regridded forcings: \" + str(\n npe)\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n\n # Convert the hourly precipitation total to a rate of mm/s\n if nc_var == 'APCP_surface':\n try:\n ind_valid = np.where(input_forcings.esmf_field_out.data != fill)\n input_forcings.esmf_field_out.data[ind_valid] = input_forcings.esmf_field_out.data[\n ind_valid] / 3600.0\n del ind_valid\n except (ValueError, ArithmeticError, AttributeError, KeyError) as npe:\n config_options.errMsg = \"Unable to run NDV search on Custom netCDF precipitation: \" + str(npe)\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n\n try:\n input_forcings.regridded_forcings2[input_forcings.input_map_output[force_count], :, :] = \\\n input_forcings.esmf_field_out.data\n except (ValueError, KeyError, AttributeError) as err:\n config_options.errMsg = \"Unable to place local ESMF regridded data into local array: \" + str(err)\n err_handler.log_critical(config_options, mpi_config)\n err_handler.check_program_status(config_options, mpi_config)\n\n # If we are on the first timestep, set the previous regridded field to be\n # the latest as there are no states for time 0.\n if config_options.current_output_step == 1:\n input_forcings.regridded_forcings1[input_forcings.input_map_output[force_count], :, :] = \\\n input_forcings.regridded_forcings2[input_forcings.input_map_output[force_count], :, :]\n err_handler.check_program_status(config_options, mpi_config)\n\n # Close the NetCDF file\n if mpi_config.rank == 0:\n try:\n id_tmp.close()\n except OSError:\n config_options.errMsg = \"Unable to close NetCDF file: \" + input_forcings.tmpFile\n err_handler.err_out(config_options)","def preprocess_data(study_period_data: pd.DataFrame, unique_indices: pd.MultiIndex, cols: list, split_index: int,\n configs: dict, full_date_range: pd.Index, parent_model_type: str, from_mixed=False,\n return_validation_data=False, validation_range: list = None) -> tuple:\n\n cols = cols.copy()\n print(f'{Style.BRIGHT}{Fore.YELLOW}Pre-processing data ...{Style.RESET_ALL}')\n timer = Timer().start()\n\n if from_mixed and parent_model_type == 'tree_based':\n cols.extend(['return_index'])\n cols.remove('stand_d_return')\n\n if parent_model_type == 'mixed':\n # JOB: Pre-process data separately for each mixed ensemble component\n return np.array([preprocess_data(\n study_period_data=study_period_data, unique_indices=unique_indices, cols=cols, split_index=split_index,\n configs=configs, full_date_range=full_date_range, parent_model_type=parent_model_type,\n from_mixed=True, return_validation_data=True, validation_range=validation_range) for\n parent_model_type in ['deep_learning', 'tree_based']]).T.tolist()\n\n # JOB: Instantiate training and test data\n data = None\n x_train = None\n y_train = None\n x_val = None\n y_val = None\n x_test = None\n y_test = None\n validation_mask = []\n train_data_index = pd.Index([])\n validation_data_index = pd.Index([])\n test_data_index = pd.Index([])\n\n train_range = full_date_range[configs['data']['sequence_length'] + 1:split_index + 1]\n print(f'Last training date: {full_date_range[split_index]}')\n\n print(f'Training range length: {len(train_range)}')\n\n if return_validation_data:\n print(f'Validation range length: {len(validation_range)}')\n\n # Instantiate progress bar\n progress_bar = ProgressBar(len(unique_indices))\n # JOB: Iterate through individual stocks and generate training and test data\n for stock_id in unique_indices:\n progress_bar.print_progress()\n id_data = study_period_data.loc[stock_id].set_index('datadate')\n\n try:\n # JOB: Initialize DataLoader\n data = DataLoader(\n id_data, cols=cols,\n seq_len=configs['data']['sequence_length'], full_date_range=full_date_range, stock_id=stock_id,\n split_index=split_index, model_type=parent_model_type\n )\n except AssertionError as ae:\n print(ae)\n continue\n\n # print('Length of data for ID %s: %d' % (stock_id, len(id_data)))\n\n # JOB: Generate training data\n x_train_i, y_train_i = data.get_train_data()\n\n # JOB: Generate test data\n x_test_i, y_test_i = data.get_test_data(\n seq_len=configs['data']['sequence_length'],\n )\n\n # JOB: In case training/test set is empty, set to first batch, otherwise append data\n if (len(x_train_i) > 0) and (len(y_train_i) > 0):\n train_data_index = train_data_index.append(data.data_train_index)\n mask = [date_i in validation_range for date_i in data.data_train_index.get_level_values(level=0)]\n validation_mask.extend(mask)\n if x_train is None:\n x_train = x_train_i\n y_train = y_train_i\n else:\n x_train = np.append(x_train, x_train_i, axis=0)\n y_train = np.append(y_train, y_train_i, axis=0)\n\n if (len(x_test_i) > 0) and (len(y_test_i) > 0):\n test_data_index = test_data_index.append(data.data_test_index)\n if x_test is None:\n x_test = x_test_i\n y_test = y_test_i\n else:\n x_test = np.append(x_test, x_test_i, axis=0)\n y_test = np.append(y_test, y_test_i, axis=0)\n\n if len(y_test_i) != len(data.data_test_index):\n print(f'\\nMismatch for index {stock_id}')\n print(f'{Fore.YELLOW}{Back.RED}{Style.BRIGHT}Lengths do not conform!{Style.RESET_ALL}')\n print(f'Target length: {len(y_test_i)}, index length: {len(data.data_test_index)}')\n raise AssertionError('Data length is not conforming.')\n\n # print('Length of test labels: %d' % len(y_test))\n # print('Length of test index: %d\\n' % len(test_data_index))\n\n if len(y_test) != len(test_data_index):\n raise AssertionError('Data length is not conforming.')\n\n print(f'Length training data (before): {len(x_train)}')\n\n if return_validation_data:\n # JOB: Split up training data in training and validation set if needed\n inverse_mask = [not m for m in validation_mask]\n x_val = x_train[validation_mask]\n y_val = y_train[validation_mask]\n validation_data_index = train_data_index[validation_mask]\n x_train = x_train[inverse_mask]\n y_train = y_train[inverse_mask]\n train_data_index = train_data_index[inverse_mask]\n\n print(f'Length x_train: {len(x_train)}')\n print(f'Length x_val: {len(x_val)}')\n\n print(f'Length x_train: {len(x_train)}')\n\n print(f'{Style.BRIGHT}{Fore.YELLOW}Done pre-processing data.{Style.RESET_ALL}')\n timer.stop()\n\n return x_train, y_train, x_test, y_test, train_data_index, test_data_index, data.cols, (\n x_val, y_val, validation_data_index)","def compliance_time_series(county, core_path , patterns_path, backfill = False, GEOID_type = 'CBG'):\n #Load data about places and patterns\n county_places = pd.read_csv(core_path+'places-'+ county +'.csv', index_col='safegraph_place_id') \n \n pattern_dates = [x[5:9] for x in sorted(os.listdir(patterns_path+'main-file-'+ county +'/'))]\n w = 0\n next_date = pattern_dates[w]\n #Create visitor table\n place_cts = pd.DataFrame() #First two months don't have weekly patterns, use empty data.frame\n\n #List files for social distancing metrics in that county\n months_path = '../social_distancing/social_dist_'+ county +'/'\n month_list = sorted(os.listdir(months_path))\n os.makedirs( '../stats/time_series/', exist_ok = True)\n #metrics dictionary to be filled looping through every day\n if not os.path.isfile('../stats/time_series/metrics_{}_CT.csv'.format(county)) or backfill:\n metrics = pd.DataFrame()\n existing_dates = []\n else:\n metrics = pd.read_csv('../stats/time_series/metrics_{}_CT.csv'.format(county), dtype = {'origin_census_tract':str})\n #dates already processed\n existing_dates = metrics['date'].unique() #series of unique dates\n\n #Initialize columns of new data frame\n if GEOID_type == 'CBG':\n columns= {'date':[], 'origin_census_block_group':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'total_expected_contacts':[], 'places_visited':[], 'cbgs_visited':[]}\n if GEOID_type == 'CT':\n columns= {'date':[], 'origin_census_tract':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'places_visited':[]} \n changed = False\n for month in month_list:\n #Loop through every day\n day_list = sorted(os.listdir(months_path + month))\n for day in day_list: \n date_name = month + '-' + day\n print(date_name)\n if month+'-'+day == next_date:\n w = w+1\n if w < len(pattern_dates):\n next_date = pattern_dates[w]\n if date_name in existing_dates:\n continue\n print(\"--changing to next patterns file\")\n county_patterns = pd.read_csv(patterns_path + 'main-file-{}/2020-{}-weekly-patterns.csv.gz'.format(county, date_name),\n index_col='safegraph_place_id')\n norm_factors = pd.read_csv(\n '../social_distancing/normalization/'+'normalization_{}.csv'.format(county),\n dtype={'origin_census_block_group':str})\n norm_factors.set_index('origin_census_block_group', drop =True, inplace=True)\n\n\n #Establish prior for hourly distribution of visits at the top_category level\n county_patterns= county_patterns.join(county_places[['top_category','sub_category']], how='inner')\n restaurants = county_patterns['top_category'] == 'Restaurants and Other Eating Places'\n county_patterns.loc[restaurants, 'top_category'] = county_patterns.loc[restaurants, 'sub_category']\n norm_factor = norm_factors.loc[norm_factors.date == date_name].norm_factor\n\n prior_dict = {}\n for category in county_patterns.top_category.value_counts().index:\n places_in_cat = county_patterns['top_category'] == category\n dirich_samples = [np.array(json.loads(x)) for x in county_patterns.loc[places_in_cat, 'visits_by_each_hour'] ] \n prior_dict[category] = dirichlet.getInitAlphas(dirich_samples)\n if GEOID_type == 'CBG':\n place_cbgs = ccc.place_cbg_contacts_table(\n county_patterns,\n prior_dict,\n norm_factor,\n GEOID_type)\n place_cbgs = place_cbgs.loc[place_cbgs['expected_contacts']>1]\n place_cbgs = place_cbgs.join(county_places[['location_name','latitude','longitude']], how='inner')\n place_cbgs.reset_index(inplace=True, drop=False)\n place_cbgs.set_index('origin_census_block_group', inplace=True, drop=True)\n if GEOID_type == 'CT':\n place_cts = ccc.place_cbg_contacts_table(\n county_patterns,\n prior_dict,\n norm_factor,\n GEOID_type)\n place_cts = place_cts.join(county_places[['location_name','latitude','longitude']], how='inner')\n place_cts.reset_index(inplace=True, drop=False)\n place_cts.set_index('origin_census_tract', inplace=True, drop=True)\n print('--computed bipartite contact network')\n\n if date_name in existing_dates:\n continue\n\n\n file_name = os.listdir(months_path+ '/' + month+'/'+day)[0]\n data_soc_dist = pd.read_csv(months_path+ '/' + month+'/'+day+'/'+file_name, dtype={'origin_census_block_group':str})\n data_soc_dist['median_distance_traveled_from_home'] = [0 if math.isnan(x) else x for x in data_soc_dist['distance_traveled_from_home']] #NOT NEEDED?\n \n if GEOID_type == 'CBG':\n [update_metrics_columns(row, date_name, place_cbgs, county_places, columns) for i, row in data_soc_dist.iterrows()]\n else:\n ####CREATE FUNCTION THAT AGGREGATES TO CENSUS TRACT\n data_soc_dist['origin_census_tract'] = [x[:-1] for x in data_soc_dist['origin_census_block_group']]\n ct_data_soc_dist = data_soc_dist.groupby('origin_census_tract').apply(ddd.aggregate_to_ct).reset_index()\n [update_metrics_columns_CT(row, date_name, place_cts, county_places, columns) for i, row in ct_data_soc_dist.iterrows()]\n changed = True\n\n print(\"--merging rows from new dates\")\n new_metrics = pd.DataFrame.from_dict(columns, orient='index').transpose()\n\n metrics = pd.concat([metrics, new_metrics], ignore_index=True)\n if changed:\n if GEOID_type == 'CT':\n metrics.to_csv('../stats/time_series/metrics_{}_CT.csv'.format(county),index=False)\n else:\n metrics.to_csv('../stats/time_series/metrics_{}_CBG.csv'.format(county),index=False)\n print(\"--finished updating time series for {}\".format(county))\n return(0)","def _util(\n self,\n hist: bool = False,\n prop: str = None,\n start_date_range: str = None,\n end_date_range: str = None,\n **_,\n ):\n outputs: dict = {}\n\n # List of properties needed by the plot, properties are a set of tuples and\n # contain 3 parts: required True/False, property name and scenarios required,\n # scenarios must be a list.\n properties = [\n (True, \"line_Flow\", self.Scenarios),\n (True, \"line_Import_Limit\", self.Scenarios),\n ]\n\n # Runs get_formatted_data within PlotDataStoreAndProcessor to populate PlotDataStoreAndProcessor dictionary\n # with all required properties, returns a 1 if required data is missing\n check_input_data = self.get_formatted_data(properties)\n\n if 1 in check_input_data:\n return MissingInputData()\n\n # sets up x, y dimensions of plot\n ncols, nrows = set_facet_col_row_dimensions(\n self.xlabels, self.ylabels, facet=True, multi_scenario=self.Scenarios\n )\n grid_size = ncols * nrows\n # Used to calculate any excess axis to delete\n plot_number = len(self.Scenarios)\n excess_axs = grid_size - plot_number\n\n for zone_input in self.Zones:\n logger.info(f\"For all lines touching Zone = {zone_input}\")\n\n mplt = PlotLibrary(nrows, ncols, sharey=True, squeeze=False, ravel_axs=True)\n fig, axs = mplt.get_figure()\n plt.subplots_adjust(wspace=0.1, hspace=0.25)\n\n # Get and process all line limits\n line_limits = self.get_line_interface_limits(\n [\"line_Import_Limit\"],\n )\n data_table = []\n for n, scenario in enumerate(self.Scenarios):\n logger.info(f\"Scenario = {str(scenario)}\")\n # gets correct metadata based on area aggregation\n if self.AGG_BY == \"zone\":\n zone_lines = self.meta.zone_lines(scenario)\n else:\n zone_lines = self.meta.region_lines(scenario)\n try:\n zone_lines = zone_lines.set_index([self.AGG_BY])\n except:\n logger.warning(\"Column to Aggregate by is missing\")\n continue\n try:\n zone_lines = zone_lines.xs(zone_input)\n zone_lines = zone_lines[\"line_name\"].unique()\n except KeyError:\n logger.warning(\"No data to plot for scenario\")\n outputs[zone_input] = MissingZoneData()\n continue\n\n flow = self[\"line_Flow\"].get(scenario)\n # Limit to only lines touching to this zone\n flow = flow[flow.index.get_level_values(\"line_name\").isin(zone_lines)]\n\n if pd.notna(start_date_range):\n flow = set_timestamp_date_range(\n flow, start_date_range, end_date_range\n )\n if flow.empty is True:\n logger.warning(\"No data in selected Date Range\")\n continue\n\n if shift_leapday:\n flow = adjust_for_leapday(flow)\n\n if \"Scenario\" in line_limits.index.names:\n limits = line_limits.xs(scenario, level=\"Scenario\")\n else:\n limits = line_limits\n limits = limits.groupby(\"line_name\").max().abs()\n\n # Filters on specifed line category if prop is not None.\n if prop:\n logger.info(f\"Line category = {str(prop)}\")\n try:\n flow = flow.xs(prop, level=\"category\")\n except KeyError:\n logger.warning(\n f\"Line category {prop}, Not found in data! \"\n \"All categories will be plotted.\"\n )\n\n flow = pd.merge(flow, limits, on=\"line_name\", how=\"left\")\n flow[\"Util\"] = (\n flow[\"values\"].abs() / flow[\"line_Import_Limit\"]\n ).fillna(0)\n # If greater than 1 because exceeds flow limit, report as 1\n flow[\"Util\"][flow[\"Util\"] > 1] = 1\n annual_util = (\n flow[\"Util\"].groupby([\"line_name\"]).mean().rename(scenario)\n )\n # top annual utilized lines\n top_utilization = annual_util.nlargest(10, keep=\"first\")\n\n color_dict = dict(zip(self.Scenarios, self.color_list))\n\n if hist is True:\n mplt.histogram(annual_util, color_dict, label=scenario, sub_pos=n)\n else:\n for line in top_utilization.index.get_level_values(\n level=\"line_name\"\n ).unique():\n duration_curve = (\n flow.loc[line]\n .sort_values(by=\"Util\", ascending=False)\n .reset_index(drop=True)\n )\n mplt.lineplot(duration_curve, \"Util\", label=line, sub_pos=n)\n axs[n].set_ylim((0, 1.1))\n data_table.append(annual_util)\n\n mplt.add_legend()\n # Remove extra axes\n mplt.remove_excess_axs(excess_axs, grid_size)\n # add facet labels\n mplt.add_facet_labels(xlabels=self.xlabels, ylabels=self.ylabels)\n if hist is True:\n if prop:\n prop_name = prop\n else:\n prop_name = \"All Lines\"\n plt.ylabel(\n \"Number of lines\", color=\"black\", rotation=\"vertical\", labelpad=30\n )\n plt.xlabel(\n f\"Line Utilization: {prop_name}\",\n color=\"black\",\n rotation=\"horizontal\",\n labelpad=30,\n )\n else:\n if prop:\n prop_name = prop\n else:\n prop_name = \"Top 10 Lines\"\n plt.ylabel(\n f\"Line Utilization: {prop_name}\",\n color=\"black\",\n rotation=\"vertical\",\n labelpad=60,\n )\n plt.xlabel(\n \"Intervals\", color=\"black\", rotation=\"horizontal\", labelpad=20\n )\n\n if plot_data_settings[\"plot_title_as_region\"]:\n mplt.add_main_title(zone_input)\n\n Data_Out = pd.concat(data_table)\n\n outputs[zone_input] = {\"fig\": fig, \"data_table\": Data_Out}\n return outputs","def _fit(self, endog, endog_end, min_ts_mean, min_ts_mean_window, include_holidays=False,\n min_ts_length=None, max_ft_freq=None, exog_data=None, optimize=None):\n import numpy as np\n from pykalman import KalmanFilter\n import warnings\n warnings.filterwarnings('ignore')\n\n p, q = self._params['p'], self._params['q']\n freq = self._params['freq']\n pred_len = self.max_scoring_length\n x_matrix_train = None\n x_matrix_score = None\n\n # set exogenous (holiday) variables for input data\n if include_holidays and len(endog) + pred_len > 385:\n exog = exog_data.loc[endog.index.min():endog_end]\n else:\n include_holidays = False\n exog = None\n\n if min_ts_length is not None and len(endog) < min_ts_length:\n raise ValueError('TimeSeries length less than minimum length specified')\n\n if min_ts_mean is not None:\n if (min_ts_mean_window is not None and endog[-min_ts_mean_window:].fillna(0).mean() < min_ts_mean) or \\\n (min_ts_mean_window is None and endog.fillna(0).mean() < min_ts_mean):\n raise ValueError('Metric values too small to model.')\n\n # Smoothing the given time series as a pre-processing for modeling seasonalities through Fourier\n # transformation\n kf = KalmanFilter()\n endog_smoothed, filtered_state_covariances = kf.em(endog).smooth(endog)\n endog_smoothed = endog_smoothed[:, 0]\n\n endog, diff_order, actual_previous_per_diff = DataExploration._stationarizer(endog=pd.Series(endog),\n diff_min=0,\n diff_max=1,\n obs_incl=False)\n if diff_order:\n endog_smoothed = np.diff(endog_smoothed)\n\n if freq == 'D':\n complete_cycle = int(len(endog) / 7)\n endog = endog[- (complete_cycle * 7):]\n endog_smoothed = endog_smoothed[- (complete_cycle * 7):]\n elif freq == 'H':\n complete_cycle = int(len(endog) / 24)\n endog = endog[- (complete_cycle * 24):]\n endog_smoothed = endog_smoothed[- (complete_cycle * 24):]\n\n exog = exog.iloc[-len(endog):] if exog is not None else None\n\n if include_holidays:\n exog = exog.loc[:, (exog != 0).any(axis=0)]\n ext_training_features = list(exog.columns)\n else:\n ext_training_features = None\n\n stepwise_fit = []\n\n # Updating the user specified maximum number of frequencies to consider for the Fourier transformation\n # based on the length of the smoothed endogenous variable\n max_ft_freq = int(min(max_ft_freq, len(endog_smoothed) / 4))\n\n # Running the Fourier transformation extrapolating one point ahead in future that is going to be used\n # for predicting\n\n if max_ft_freq > 0:\n x_matrix = self._fourier_extp(series=endog_smoothed, max_trun=(2 * max_ft_freq),\n forecast_period=pred_len)\n if not optimize and np.all(x_matrix[0] == x_matrix[0][0]):\n x_matrix_train = None\n x_matrix_score = None\n max_ft_freq = 0\n else:\n x_matrix_train = x_matrix[:, :(x_matrix.shape[1] - pred_len)]\n x_matrix_score = x_matrix[:, (x_matrix.shape[1] - pred_len):]\n\n\n self._seasonal_arima(endog=endog, exog=exog, p=p, d=0, q=q, imodels=max_ft_freq,\n include_holidays=include_holidays, ift_matrix=x_matrix_train,\n stepwise_fit=stepwise_fit, optimize=optimize)\n model = stepwise_fit[0]\n\n seasonal_feature_scoring = x_matrix_score[0, :].tolist() if not x_matrix_score is None else None\n\n result = {\n 'model': model,\n 'diff_order': diff_order,\n 'seasonal_feature_scoring': seasonal_feature_scoring,\n 'ext_training_features': ext_training_features,\n }\n\n p_selected = model.k_ar if hasattr(model, 'k_ar') else 0\n d_selected = diff_order\n q_selected = model.k_ma if hasattr(model, 'k_ma') else 0\n order = (p_selected, d_selected, q_selected)\n\n return result, order","def grid(df, N = 100, l = 1200, side = \"sx\"):\n #variables for the intervals of the grid\n min_x = 0\n max_x = l\n delta = l/float(N)\n grid = df\n grid.columns = [0,1]\n grid = grid.sort_values(by = 1)\n #makes a new aray where will be the smoothed fronts\n grid_smooth=np.empty((2,N))\n\n #makes the new grid taking the maximum value of the y coordinate in the interval\n for i in np.arange(N):\n #intervals\n bin_m = min_x +i*delta\n bin_M = min_x +(i+1)*delta\n #difference from sx front and dx front: in one case i take the max, in the\n #other i take the min\n if side == \"sx\":\n grid_smooth[1,i]=min_x +(i+1/2.)*delta\n #takes the max value only if in the interval there are values\n if len(grid[(grid[1]>bin_m) & (grid[1]<bin_M)][0]) != 0:\n grid_smooth[0,i]=max(grid[(grid[1]>bin_m)&(grid[1]<bin_M)][0])\n else:\n #if there are not points in that interval, takes the latest value computed\n grid_smooth[0,i] = grid_smooth[0,i-1]\n if i == 0:\n grid_smooth[0,i] = np.mean(grid[0])\n else:\n grid_smooth[1,i]=min_x +(i+1/2.)*delta\n if len(grid[(grid[1]>bin_m)&(grid[1]<bin_M)][0]) != 0:\n grid_smooth[0,i]=min(grid[(grid[1]>bin_m)&(grid[1]<bin_M)][0])\n else:\n grid_smooth[0,i] = grid_smooth[0,i-1]\n if i == 0:\n grid_smooth[0,i] = np.mean(grid[0])\n\n df = pd.DataFrame()\n df[0] = pd.Series(grid_smooth[0])\n df[1] = pd.Series(grid_smooth[1])\n #return the dataframe with the smoothed grid\n return df","def get_data(temp_estimate_source='historic'):\n\n print 'get data from files'\n load = process_load_data(datafoldername+loadfilename_train)\n load_test = process_load_data(datafoldername+loadfilename_test)\n temp = process_temp_data(datafoldername+tempfilename_train)\n holidays = process_holiday_data(datafoldername+holidayfilename)\n\n print 'merge load with temp data'\n X_train_df = load.merge(temp, on='datetime', how='left')\n X_test_df = load_test.merge(temp, on='datetime', how='left')\n\n print 'estimate missing temps'\n # find rows with missing temperatures\n missingtemp = X_test_df[X_test_df.isnull().any(axis=1)][['datetime', 'zone_id']].copy()\n\n # source estimates for missing periods\n if temp_estimate_source == 'arima':\n # use preprocessed arima estimates\n estimatedtemps = process_arima_temp_data(datafoldername+arimafilename)\n elif temp_estimate_source == 'actuals':\n # use actual temperatures - as provided after conclusion of kaggle competition\n estimatedtemps = process_temp_data(datafoldername+tempfilename_solution)\n else:\n # use means of historical temps at same day/time.\n estimatedtemps = get_estimated_temps(missingtemp[['datetime']].drop_duplicates(), temp)\n\n # merge estimates against missing rows, and use to update original dataframe in place\n replacementtemps = missingtemp.merge(estimatedtemps, left_on='datetime', right_on='datetime', how='left')\n replace_unknown_temps(X_test_df, replacementtemps)\n\n print 'merge in holiday dates'\n X_train_df = X_train_df.merge(holidays, on='datetime', how='left')\n X_train_df['holiday'].fillna(0, inplace=True)\n X_test_df = X_test_df.merge(holidays, on='datetime', how='left')\n X_test_df['holiday'].fillna(0, inplace=True)\n\n print 'add datetime categorical variables'\n X_train_df = add_datetime_categories(X_train_df)\n X_test_df = add_datetime_categories(X_test_df)\n\n return X_train_df, X_test_df","def cloudy_grid_map(**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n cloudy_library = clo.library()\n model_number_matrix,grid_table = cloudy_library._restore_grid_table(grid_ext=p.grid_ext)\n # print(len(grid_table))\n # print(len(grid_table)/len(np.unique(grid_table.nH)))\n\n grid_table = grid_table.fillna(-10)\n grid_table['DTM'] = np.round(grid_table['DTM'] * 10.) / 10.\n grid_table['NH'] = np.round(grid_table['NH'] * 10.) / 10.\n\n # print(grid_table.DTM[np.isnan(grid_table['DTM'])])\n # print(grid_table.NH[np.isnan(grid_table['NH'])])\n # print(grid_table.FUV[np.isnan(grid_table['FUV'])])\n # print(grid_table.nH[np.isnan(grid_table.nH)])\n # print(grid_table.Z[np.isnan(grid_table.Z)])\n\n print('nHs: ',np.unique(grid_table.nH))\n print('DTMs: ',np.unique(grid_table.DTM))\n print('FUVs: ',np.unique(grid_table.FUV))\n print('NHs: ',np.unique(grid_table.NH))\n print('Zs: ',np.unique(grid_table.Z))\n\n fig,ax = plt.subplots(figsize=(8,5))\n\n key1, key2, key3 = list(p.cloudy_param.keys())[0],list(p.cloudy_param.keys())[1],list(p.cloudy_param.keys())[2]\n value1, value2, value3 = list(p.cloudy_param.values())[0],list(p.cloudy_param.values())[1],list(p.cloudy_param.values())[2]\n\n # Decide on what goes on x and y axis\n cloudy_parameters = np.array(['NH','FUV','nH','Z','DTM'])\n x_index = cloudy_parameters[(cloudy_parameters != key1) &\\\n (cloudy_parameters != key2) &\\\n (cloudy_parameters != key3)][0]\n y_index = cloudy_parameters[(cloudy_parameters != key1) &\\\n (cloudy_parameters != key2) &\\\n (cloudy_parameters != key3)][1]\n print(x_index,y_index)\n # Cut in grid table\n grid_table_cut = grid_table.iloc[np.where((grid_table[key1].values == value1) & \\\n (grid_table[key2].values == value2) & \\\n (grid_table[key3].values == value3))[0]]\n\n x, y = grid_table_cut[x_index].values, grid_table_cut[y_index].values\n X, Y = np.meshgrid(np.unique(grid_table_cut[x_index].values), np.unique(grid_table_cut[y_index].values))\n\n # Plot line ratio?\n if '_' in p.line:\n L1 = grid_table_cut[p.line.split('_')[0]].values\n L2 = grid_table_cut[p.line.split('_')[1]].values\n L2[L2 == 0] = 1e9\n line_lum = (L1/L2).astype(float)\n vmin = np.min(np.log10(line_lum[L2 < 1e9]))\n\n else:\n line_lum = grid_table_cut[p.line].values.astype(float)\n vmin = np.min(np.log10(line_lum[line_lum > 0]))\n\n\n # ########## Patching the grid !!\n # line_lum[np.isnan(line_lum)] = -1 # what are these?\n # # 0 values: not sure if we have any?\n # # Negative numbers: missing grid point\n # i_missing = np.where(line_lum <= 0)[0]\n # line_lum[line_lum == 0] = np.min(line_lum[line_lum > 0])\n # while len(i_missing) > 0:\n # print(i_missing)\n # lum = np.log10(line_lum)\n # for i in i_missing:\n # # print(lum[i-1],lum[i+1])\n # try: \n # lum[i] = (lum[i-1] + lum[i+1])/ 2\n # except:\n # pass\n # # print('he',np.isnan(lum[i]))\n # if np.isnan(lum[i]):\n # try:\n # lum[i] = lum[i-1] \n # except:\n # pass\n # if np.isnan(lum[i]):\n # try:\n # lum[i] = lum[i+1] \n # except:\n # pass \n # line_lum[i] = 10.**lum[i]\n # # print(i,lum[i])\n # i_missing = np.where(line_lum < 0)[0]\n # ########## End of patching\n \n lum = np.log10(line_lum)\n lum = lum.reshape([len(np.unique(x)), len(np.unique(y))]).T\n\n\n # pdb.set_trace()\n print(p.zlim)\n if p.zlim:\n print(p.zlim)\n lum[lum < p.zlim[0]] = p.zlim[0]\n lum[lum > p.zlim[1]] = p.zlim[1]\n cf = ax.contourf(X,Y, lum, cmap=\"jet\", vmin=p.zlim[0], vmax=p.zlim[1], lw=0, rstride=1, cstride=1,alpha=0.8, levels=20)\n else:\n cf = ax.contourf(X,Y, lum, cmap=\"jet\", vmin=vmin, lw=0, rstride=1, cstride=1,alpha=0.8)\n # print(lum)\n ax.set_xlabel('\\n\\n' + getlabel('l'+x_index))\n ax.set_ylabel('\\n\\n' + getlabel('l'+y_index))\n\n ax.set_xlim([np.min(X),np.max(X)])\n ax.set_ylim([np.min(Y),np.max(Y)])\n\n plt.colorbar(cf)\n\n plt.tight_layout()\n if p.savefig:\n if not os.path.isdir(p.d_plot + 'look-up/'): os.mkdir(p.d_plot + 'look-up/') \n plt.savefig(p.d_plot + 'look-up/cloudy_grid_map_%s%s%s.%s' % (p.line, p.grid_ext, p.ext, p.format), format=p.format, dpi=300)","def READ_DEFINE_AND_PROCESS_EVERYTHING(basedir, in__dir):\n\n import sys, ast, multiprocessing\n import pandas as pd\n import numpy as np\n\n # Output dictionanry\n dc = {}\n \n # Read command line arguments\n try:\n y_var = dc['y_var'] = str(sys.argv[1]) \n y_area = dc['y_area'] = str(sys.argv[2])\n experiment = dc['experiment'] = str(sys.argv[3])\n X_source = dc['X_source'] = str(sys.argv[4]) \n except:\n y_var = dc['y_var'] = 'T2M'\n y_area = dc['y_area'] = 'scandi'\n experiment = dc['experiment'] = 'CONTROL'\n X_source = dc['X_source'] = 'ERA-20C'\n \n # Define details \n if(experiment=='CUTFIRSTYRS'): \n yr1 = dc['yr1'] = '1945' \n else:\n yr1 = dc['yr1'] = '1915' #'1900' \n \n yr2 = dc['yr2'] = '2010'\n \n \n rstate = dc['rstate'] = 70 \n n_folds = dc['n_folds'] = 5\n p_smpl = dc['p_smpl'] = 0.50\n p_feat = dc['p_feat'] = 0.33\n n_smpls = dc['n_smpls'] = 1000\n tst_len = dc['tst_len'] = 25\n\n ncomps_sst = dc['ncomps_sst'] = 5\n ncomps_snc = dc['ncomps_snc'] = 3\n ncomps_gpt = dc['ncomps_gpt'] = 3\n\n if(experiment=='NO_LAGS'):\n lags_sst = dc['lags_sst'] = (1,)\n lags_snc = dc['lags_snc'] = (1,)\n lags_gpt = dc['lags_gpt'] = (1,)\n else:\n lags_sst = dc['lags_sst'] = (1,2,3,4,5)\n lags_snc = dc['lags_snc'] = (1,2)\n lags_gpt = dc['lags_gpt'] = (1,2)\n \n \n \n n_jobs = dc['n_jobs'] = np.min([28, int(0.9*(multiprocessing.cpu_count()))])\n seasons = dc['seasons'] = ('DJF', 'MAM' ,'JJA', 'SON')\n\n # Define training and test periods\n all_yrs = dc['all_yrs'] = list(np.arange(int(yr1),int(yr2)+1))\n tst_yrs = dc['tst_yrs'] = all_yrs[-tst_len:] \n trn_yrs = dc['trn_yrs'] = list(np.array(all_yrs)[~np.isin(all_yrs,tst_yrs)])\n\n # Define a skeleton for naming output files \n basename = dc['basename'] = 'fittings_'+experiment+'_HadCRUT4-'+y_var+ \\\n '_nsmpls'+str(n_smpls)+'_ntestyrs'+str(tst_len)+ \\\n '_'+X_source+'-SST'+str(ncomps_sst)+'-'+str(lags_sst[-1])+ \\\n '_'+X_source+'-GPT'+str(ncomps_gpt)+'-'+str(lags_gpt[-1])+ \\\n '_'+yr1+'-'+yr2+'_'+y_area\n\n # Variables, form: 'name_of_variable': ['domain', n_comps, lags, year_range]\n X_var_definitions = dc['X_var_definitions'] = {\n 'SST': ['global', ncomps_sst, lags_sst, trn_yrs, all_yrs, X_source],\n 'GPT': ['norhem', ncomps_gpt, lags_gpt, trn_yrs, all_yrs, X_source],\n #'SNC': ['norhem', ncomps_snc, lags_snc, trn_yrs, all_yrs, X_source],\n }\n \n # Optional variables from https://climexp.knmi.nl/, form: 'name_of_index': [lags]\n X_clxp_definitions = dc['X_clxp_definitions'] = {\n #'M1i':(1,), 'M2i':(1,), 'M3i':(1,), \n #'M4i':(1,), 'M5i':(1,), 'M6i':(1,),\n #'NAO':(1,), 'NINO12':(1,), 'NINO3':(1,), 'NINO34':(1,), 'NINO4',:(1,),\n #'AMO1':(1,), 'AMO2':(1,), 'PDO1':(1,), 'PDO2':(1,), 'SOI',:(1,),\n }\n\n # Read and preprocess the predictand data using xarray etc.\n y_eur, Y, cl, tr = dc['y_eur'], dc['Y'], dc['Y_clim'], dc['Y_trend'] = \\\n read_manipulate_Y_data(y_var, in__dir, {}, {}, all_yrs, all_yrs, y_area)\n\n # Read and preprocess the raw predictor data using xarray etc.\n X_vars, cl, tr = dc['X_vars'], dc['X_clim'], dc['X_trnd'] = \\\n read_manipulate_X_data(in__dir, X_var_definitions, {}, {})\n\n\n if(experiment=='INCLPERSIS'): \n include_persistence=True\n else:\n include_persistence=False\n\n # Compress raw data with PCA, apply lagging, and create a Pandas dataframe \n X,p,ei,er = dc['X'], dc['X_PCAs'], dc['X_EIGs'], dc['X_ERRs'] = prepare_X_array(Y, \n y_var, X_vars, {}, X_var_definitions, X_clxp_definitions, include_persistence=include_persistence)\n\n if(experiment=='FOLLAND'):\n # Folland et al. 2012, Hall et al. 2017\n for i,vrb in enumerate(X.columns):\n if((vrb[0:4] == 'SST1')|(vrb[0:4] == 'sst1')):\n X[vrb] = StandardScaler().fit_transform(X[vrb][:,np.newaxis])\n X[vrb][ np.abs(X[vrb]) < 1 ] = 0\n X[vrb][ X[vrb] < -1 ] = -1\n X[vrb][ X[vrb] > 1.75 ] = 0\n X[vrb][ X[vrb] > 1 ] = 1\n print(X[vrb])\n\n # Extract variable names\n vrbl_names = dc['vrbl_names'] = X.columns\n \n return dc","def autoset_numerical_parameters():\n testenv = env(\n Ndim=N_DIMS,\n lambda_over_dx=LAMBDA_OVER_DX,\n R_dt=R_DT,\n norm_Poisson=NORM_POISSON,\n Ngrid=N_GRID,\n Nhits=N_HITS,\n dummy=True,\n )\n if STOP_t is None:\n if N_DIMS == 1:\n stop_t = int(round(4 * testenv.N))\n else:\n if testenv.mu0_Poisson < 1e-3:\n stop_t = 10 * testenv.N ** N_DIMS\n elif testenv.mu0_Poisson < 1:\n stop_t = int(round(5 * 10 ** N_DIMS * LAMBDA_OVER_DX / np.sqrt(testenv.mu0_Poisson)))\n else:\n stop_t = int(round(5 * 10 ** N_DIMS * LAMBDA_OVER_DX))\n else:\n stop_t = STOP_t\n\n if N_RUNS is None:\n # predefined for REL_TOL = 0.01\n if N_DIMS == 1:\n Nruns = 16000\n elif N_DIMS == 2:\n Nruns = 6400\n elif N_DIMS == 3:\n Nruns = 25600\n elif N_DIMS == 4:\n Nruns = 102400\n else:\n raise Exception(\"Nruns not pre-defined for N_DIMS > 4\")\n Nruns = int(Nruns * (0.01 / REL_TOL) ** 2)\n else:\n Nruns = N_RUNS\n\n if MAX_N_RUNS is None:\n max_Nruns = MAX_N_RUNS\n else:\n max_Nruns = 10 * Nruns\n\n if ADAPTIVE_N_RUNS or WITH_MPI:\n Nruns = int(N_PARALLEL * (np.ceil(Nruns / N_PARALLEL))) # make it multiple of N_PARALLEL\n max_Nruns = int(N_PARALLEL * (np.ceil(max_Nruns / N_PARALLEL))) # make it multiple of N_PARALLEL\n\n return testenv.N, testenv.Nhits, stop_t, Nruns, max_Nruns, testenv.mu0_Poisson","def ts_fit(series: TimeSeries) -> TimeSeries:\n pass","def run_main(sst, ft_qv, use_NT):\n\n dtout=10. #minutes\n end_time=8*24. #hours\n del_time=dtout*60. #seconds\n end_time=end_time*3600. #seconds\n #sst=297\n D=5.e-6 #s-1\n U=7 #m/s\n psfc=100. #kPa\n qsfc=tf.qs_tp(sst,psfc)\n ft_intercept = 292 #K\n ft_gamma = 6.e-3 #K/m\n #ft_qv = 2.e-3\n k=0.2 #entrainment efficiency\n Cd = 1.e-3 #drag coefficient\n tspan = np.arange(0.,end_time,del_time)\n vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start\n the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,\n qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT\n the_tup=make_tuple(the_tup,'coeffs')\n output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))\n result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])\n\n # save time/computation by only doing calculations for the last timestep (equilibrium)\n result['time']=tspan[-1]/3600./24. #days\n result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]\n result['delqv'] = ft_qv - result['qv'].iloc[-1]\n result['LCL'] = calc_lcl(result.iloc[-1], psfc)\n result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)\n result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)\n result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)\n \n # decide how to calculate entrainment\n the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]\n if use_NT:\n result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1], \n result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])\n else:\n result['went']=calc_went(result.iloc[-1],the_tup)\n\n result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)\n\n with open('dumpmodel.csv','w') as f:\n result.to_csv(f,index=False)\n \n return None","def parameter_forecast_plot(model_obj,time_index,start,end,num_samples = 100,cached_samples=None,col_labels = ['P','PET','Lag-1 Q','Lag-1 P','Seasonal','P$^2$','Constant']):\n \n f = plt.figure(figsize = (8,10))\n num_components = len(col_labels)\n gs = gridspec.GridSpec(8+2*num_components,6)\n ax0 = plt.subplot(gs[-8:-6,:])\n ax1 = plt.subplot(gs[-6::,:])\n col_labels = ['P','PET','Lag-1 Q','Lag-1 P','Seasonal','P$^2$','Constant']\n ffbs = model_obj # 120 is French Broad River at Blantyre, NC\n if cached_samples is None:\n samples = ffbs.backward_sample(num_samples=num_samples)\n else: \n samples = cached_samples\n for i in range(7):\n ax_new = plt.subplot(gs[2*i:2*i+2,:])\n\n upper = np.percentile(samples[start:end,i,:],75,axis = 1)\n mid = np.percentile(samples[start:end,i,:],50,axis = 1)\n lower = np.percentile(samples[start:end,i,:],25,axis = 1)\n\n ax_new.plot(time_index[start:end],mid,color='k')\n ax_new.fill_between(time_index[start:end],upper,lower,color='0.8')\n ax_new.tick_params(labelbottom=False,direction='in')\n ax_new.text(0.02, 0.82,col_labels[i],\n horizontalalignment='left',\n verticalalignment='center',transform=ax_new.transAxes)\n\n ax1.plot(time_index[start:end],ffbs.f[start:end],color='k',label='1-step forecast')\n ax1.plot(time_index[start:end],ffbs.Y[start:end],color='k',linestyle='',marker='+',\n markersize = 10,label='Observed streamflow')\n\n ax1.fill_between(time_index[start:end],\n np.squeeze(ffbs.f[start:end] + 2*ffbs.Q[start:end,0]),\n np.squeeze(ffbs.f[start:end] - 2*ffbs.Q[start:end,0]),color='0.8',\n label = 'Forecast $\\pm 2V_t$')\n ax1.tick_params(direction='in')\n ax1.legend(loc='upper right',ncol=1,frameon=True)\n #ax1.set_ylabel('Standardized streamflow')\n ax1.set_xlabel('Date',fontsize=16)\n ax1.get_yaxis().set_label_coords(-0.1,0.5)\n ax1.text(0.02, 0.92,'Standardized streamflow',\n horizontalalignment='left',\n verticalalignment='center',transform=ax1.transAxes,)\n ax0.plot(time_index[start:end],ffbs.s[start:end],color='k')\n ax0.text(0.02, 0.82,'$E[V_t]$',\n horizontalalignment='left',\n verticalalignment='center',transform=ax0.transAxes,)\n ax0.get_yaxis().set_label_coords(-0.1,0.5)\n return f,samples","def create_experiment_dataset(df_degrees, params):\n \n print ('[*] Creating Experiment Dataset')\n if params.strategy == 'random':\n degree_thr = np.array([float(x)/100.0 for x in np.arange(10, 101, params.step_size)])\n else: \n degree_thr = np.array([float(x)/100.0 for x in np.logspace(-2, 2, params.step_size, dtype=float)])\n\n n_users_thr, n_items_thr = [], []\n user_degree_thr, item_degree_thr = [], []\n n_users_max = len(df_degrees['from'].unique())\n n_items_max = len(df_degrees['to'].unique())\n \n for threshold in degree_thr: \n if params.strategy == 'upper_degree_centrality':\n df_temp = df_degrees[df_degrees['degree_centrality_user'] > threshold]\n elif params.strategy == 'lower_degree_centrality':\n df_temp = df_degrees[df_degrees['degree_centrality_user'] < threshold] \n elif params.strategy == 'random':\n df_temp = utils.filter_dataset(df_degrees, degree_user=threshold, degree_item=1, strategy='random')\n\n n_users = len(df_temp['from'].unique()) \n n_users_thr.append(n_users)\n user_degree_thr.append(threshold)\n if n_users == 0 and params.strategy == 'upper_degree_centrality':\n break\n if n_users == n_users_max and params.strategy == 'lower_degree_centrality':\n break \n \n for threshold in degree_thr: \n if params.strategy == 'upper_degree_centrality':\n df_temp = df_degrees[df_degrees['degree_centrality_item'] > threshold]\n elif params.strategy == 'lower_degree_centrality':\n df_temp = df_degrees[df_degrees['degree_centrality_item'] < threshold] \n elif params.strategy == 'random':\n df_temp = utils.filter_dataset(df_degrees, degree_user=1, degree_item=threshold, strategy='random')\n\n n_items = len(df_temp['to'].unique()) \n n_items_thr.append(n_items)\n item_degree_thr.append(threshold)\n if n_items == 0 and params.strategy == 'upper_degree_centrality':\n break\n if n_items == n_items_max and params.strategy == 'lower_degree_centrality':\n break \n\n arr = []\n for u, u_thr in enumerate(user_degree_thr):\n for i, i_thr in enumerate(item_degree_thr):\n arr.append([u_thr, n_users_thr[u], i_thr, n_items_thr[i]])\n df_exp = pd.DataFrame(arr, columns=['degree_centrality_users', 'n_users', 'degree_centrality_items', 'n_items'])\n cond1 = df_exp['n_users'] > 5\n cond2 = df_exp['n_items'] > 5\n df_exp = df_exp[cond1 & cond2]\n df_exp.to_csv(os.path.join('.', 'Variables', 'df_exp.csv'), sep=';', index=None)\n print ('[+] Experiment Dataset Created')\n\n return df_exp","def heston(base_price: int = 1,\n base_volume: int = 1,\n start_date: str = '2010-01-01',\n start_date_format: str = '%Y-%m-%d',\n times_to_generate: int = 1000,\n time_frame: str = '1h',\n params: 'ModelParameters' = None) -> 'pd.DataFrame':\n\n data_frame = generate(\n price_fn=lambda p: heston_model_levels(p)[0],\n base_price=base_price,\n base_volume=base_volume,\n start_date=start_date,\n start_date_format=start_date_format,\n times_to_generate=times_to_generate,\n time_frame=time_frame,\n params=params\n )\n return data_frame","def _get_input_data(self, var, start_date, end_date):\n logging.info(self._print_verbose(\"Getting input data:\", var))\n # Pass numerical constants as is.\n if isinstance(var, (float, int)):\n return var\n # aospy.Var objects remain.\n # Pressure handled specially due to complications from sigma vs. p.\n elif var.name in ('p', 'dp'):\n data = self._get_pressure_vals(var, start_date, end_date)\n if self.dtype_in_vert == internal_names.ETA_STR:\n return self._to_desired_dates(data)\n return data\n # Get grid, time, etc. arrays directly from model object\n elif var.name in (internal_names.LAT_STR, internal_names.LON_STR,\n internal_names.TIME_STR, internal_names.PLEVEL_STR,\n internal_names.PK_STR, internal_names.BK_STR,\n internal_names.SFC_AREA_STR):\n data = getattr(self.model, var.name)\n else:\n cond_pfull = ((not hasattr(self, internal_names.PFULL_STR))\n and var.def_vert and\n self.dtype_in_vert == internal_names.ETA_STR)\n data = self.data_loader.load_variable(var, start_date, end_date,\n self.time_offset,\n **self.data_loader_attrs)\n name = data.name\n data = self._add_grid_attributes(data.to_dataset(name=data.name))\n data = data[name]\n if cond_pfull:\n try:\n self.pfull_coord = data[internal_names.PFULL_STR]\n except KeyError:\n pass\n # Force all data to be at full pressure levels, not half levels.\n bool_to_pfull = (self.dtype_in_vert == internal_names.ETA_STR and\n var.def_vert == internal_names.PHALF_STR)\n if bool_to_pfull:\n data = utils.vertcoord.to_pfull_from_phalf(data,\n self.pfull_coord)\n if var.def_time:\n # Restrict to the desired dates within each year.\n if self.dtype_in_time != 'av':\n return self._to_desired_dates(data)\n else:\n return data","def sample_multirange_empirical_variogram(dh: np.ndarray, gsd: float = None, coords: np.ndarray = None,\n nsamp: int = 10000, range_list: list = None, nrun: int = 1, nproc: int = 1,\n **kwargs) -> pd.DataFrame:\n # checks\n dh = dh.squeeze()\n if coords is None and gsd is None:\n raise TypeError('Must provide either coordinates or ground sampling distance.')\n elif gsd is not None and dh.ndim == 1:\n raise TypeError('Array must be 2-dimensional when providing only ground sampling distance')\n elif coords is not None and dh.ndim != 1:\n raise TypeError('Coordinate array must be provided with 1-dimensional input array')\n elif coords is not None and (coords.shape[0] != 2 and coords.shape[1] != 2):\n raise TypeError('One dimension of the coordinates array must be of length equal to 2')\n\n # defaulting to xx and yy if those are provided\n if coords is not None:\n if coords.shape[0] == 2 and coords.shape[1] != 2:\n coords = np.transpose(coords)\n else:\n x, y = np.meshgrid(np.arange(0, dh.shape[0] * gsd, gsd), np.arange(0, dh.shape[1] * gsd, gsd))\n coords = np.dstack((x.flatten(), y.flatten())).squeeze()\n dh = dh.flatten()\n\n # COMMENTING: custom binning is not supported by skgstat yet...\n # if no range list is specified, define a default one based on the spatial extent of the data and its resolution\n # if 'bin_func' not in kwargs.keys():\n # if range_list is None:\n #\n # # define max range as half the maximum distance between coordinates\n # max_range = np.sqrt((np.max(coords[:,0])-np.min(coords[:,0]))**2+(np.max(coords[:,1])-np.min(coords[:,1]))**2)/2\n #\n # # get the ground sampling distance\n # if gsd is None:\n # est_gsd = np.abs(coords[0,0] - coords[0,1])\n # else:\n # est_gsd = gsd\n #\n # # define ranges as multiple of the resolution until they get close to the maximum range\n # range_list = []\n # new_range = gsd\n # while new_range < max_range/10:\n # range_list.append(new_range)\n # new_range *= 10\n # range_list.append(max_range)\n #\n # else:\n # if range_list is not None:\n # print('Both range_list and bin_func are defined for binning: defaulting to bin_func')\n\n # default value we want to use (kmeans is failing)\n if 'bin_func' not in kwargs.keys():\n kwargs.update({'bin_func': 'even'})\n if 'n_lags' not in kwargs.keys():\n kwargs.update({'n_lags': 100})\n\n # estimate variogram\n if nrun == 1:\n # subsetting\n dh_sub, coords_sub = random_subset(dh, coords, nsamp)\n # getting empirical variogram\n print(dh_sub.shape)\n print(coords_sub.shape)\n df = get_empirical_variogram(dh=dh_sub, coords=coords_sub, **kwargs)\n df['exp_sigma'] = np.nan\n\n else:\n\n # multiple run only work for an even binning function for now (would need a customized binning not supported by skgstat)\n if kwargs.get('bin_func') is None:\n raise ValueError('Binning function must be \"even\" when doing multiple runs.')\n\n # define max range as half the maximum distance between coordinates\n max_range = np.sqrt((np.max(coords[:, 0])-np.min(coords[:, 0]))**2 +\n (np.max(coords[:, 1])-np.min(coords[:, 1]))**2)/2\n # also need a cutoff value to get the exact same bins\n if 'maxlag' not in kwargs.keys():\n kwargs.update({'maxlag': max_range})\n\n # TODO: somewhere here we could think of adding random sampling without replacement\n if nproc == 1:\n print('Using 1 core...')\n list_df_nb = []\n for i in range(nrun):\n dh_sub, coords_sub = random_subset(dh, coords, nsamp)\n df = get_empirical_variogram(dh=dh_sub, coords=coords_sub, **kwargs)\n df['run'] = i\n list_df_nb.append(df)\n else:\n print('Using '+str(nproc) + ' cores...')\n list_dh_sub = []\n list_coords_sub = []\n for i in range(nrun):\n dh_sub, coords_sub = random_subset(dh, coords, nsamp)\n list_dh_sub.append(dh_sub)\n list_coords_sub.append(coords_sub)\n\n pool = mp.Pool(nproc, maxtasksperchild=1)\n argsin = [{'dh': list_dh_sub[i], 'coords': list_coords_sub[i], 'i':i, 'max_i':nrun} for i in range(nrun)]\n list_df = pool.map(partial(wrapper_get_empirical_variogram, **kwargs), argsin, chunksize=1)\n pool.close()\n pool.join()\n\n list_df_nb = []\n for i in range(10):\n df_nb = list_df[i]\n df_nb['run'] = i\n list_df_nb.append(df_nb)\n df = pd.concat(list_df_nb)\n\n # group results, use mean as empirical variogram, estimate sigma, and sum the counts\n df_grouped = df.groupby('bins', dropna=False)\n df_mean = df_grouped[['exp']].mean()\n df_sig = df_grouped[['exp']].std()\n df_count = df_grouped[['count']].sum()\n df_mean['bins'] = df_mean.index.values\n df_mean['exp_sigma'] = df_sig['exp']\n df_mean['count'] = df_count['count']\n df = df_mean\n\n return df","def FE_start_end_date_time_features(smalldf, startTime, endTime, splitter_date_string=\"/\",splitter_hour_string=\":\"):\r\n smalldf = smalldf.copy()\r\n add_cols = []\r\n date_time_variable_flag = False\r\n if smalldf[startTime].dtype in ['datetime64[ns]','datetime16[ns]','datetime32[ns]']:\r\n print('%s variable is a date-time variable' %startTime)\r\n date_time_variable_flag = True\r\n if date_time_variable_flag:\r\n view_days = 'processing'+startTime+'_elapsed_days'\r\n smalldf[view_days] = (smalldf[endTime] - smalldf[startTime]).astype('timedelta64[s]')/(60*60*24)\r\n smalldf[view_days] = smalldf[view_days].astype(int)\r\n add_cols.append(view_days)\r\n view_time = 'processing'+startTime+'_elapsed_time'\r\n smalldf[view_time] = (smalldf[endTime] - smalldf[startTime]).astype('timedelta64[s]').values\r\n add_cols.append(view_time)\r\n else:\r\n start_date = 'processing'+startTime+'_start_date'\r\n smalldf[start_date] = smalldf[startTime].map(lambda x: x.split(\" \")[0])\r\n add_cols.append(start_date) \r\n try:\r\n start_time = 'processing'+startTime+'_start_time'\r\n smalldf[start_time] = smalldf[startTime].map(lambda x: x.split(\" \")[1])\r\n add_cols.append(start_time)\r\n except:\r\n ### there is no hour-minutes part of this date time stamp field. You can just skip it if it is not there\r\n pass\r\n end_date = 'processing'+endTime+'_end_date'\r\n smalldf[end_date] = smalldf[endTime].map(lambda x: x.split(\" \")[0])\r\n add_cols.append(end_date)\r\n try:\r\n end_time = 'processing'+endTime+'_end_time'\r\n smalldf[end_time] = smalldf[endTime].map(lambda x: x.split(\" \")[1])\r\n add_cols.append(end_time)\r\n except:\r\n ### there is no hour-minutes part of this date time stamp field. You can just skip it if it is not there\r\n pass\r\n view_days = 'processing'+startTime+'_elapsed_days'\r\n smalldf[view_days] = (pd.to_datetime(smalldf[end_date]) - pd.to_datetime(smalldf[start_date])).values.astype(int)\r\n add_cols.append(view_days)\r\n try:\r\n view_time = 'processing'+startTime+'_elapsed_time'\r\n smalldf[view_time] = (pd.to_datetime(smalldf[end_time]) - pd.to_datetime(smalldf[start_time])).astype('timedelta64[s]').values\r\n add_cols.append(view_time)\r\n except:\r\n ### In some date time fields this gives an error so skip it in that case\r\n pass\r\n #### The reason we chose endTime here is that startTime is usually taken care of by another library. So better to do this alone.\r\n year = 'processing'+endTime+'_end_year'\r\n smalldf[year] = smalldf[end_date].map(lambda x: str(x).split(splitter_date_string)[0]).values\r\n add_cols.append(year)\r\n #### The reason we chose endTime here is that startTime is usually taken care of by another library. So better to do this alone.\r\n month = 'processing'+endTime+'_end_month'\r\n smalldf[month] = smalldf[end_date].map(lambda x: str(x).split(splitter_date_string)[1]).values\r\n add_cols.append(month)\r\n try:\r\n #### The reason we chose endTime here is that startTime is usually taken care of by another library. So better to do this alone.\r\n daynum = 'processing'+endTime+'_end_day_number'\r\n smalldf[daynum] = smalldf[end_date].map(lambda x: str(x).split(splitter_date_string)[2]).values\r\n add_cols.append(daynum)\r\n except:\r\n ### In some date time fields the day number is not there. If not, just skip it ####\r\n pass\r\n #### In some date time fields, the hour and minute is not there, so skip it in that case if it errors!\r\n try:\r\n start_hour = 'processing'+startTime+'_start_hour'\r\n smalldf[start_hour] = smalldf[start_time].map(lambda x: str(x).split(splitter_hour_string)[0]).values\r\n add_cols.append(start_hour)\r\n start_min = 'processing'+startTime+'_start_hour'\r\n smalldf[start_min] = smalldf[start_time].map(lambda x: str(x).split(splitter_hour_string)[1]).values\r\n add_cols.append(start_min)\r\n except:\r\n ### If it errors, skip it\r\n pass\r\n #### Check if there is a weekday and weekends in date time columns using endTime only\r\n weekday_num = 'processing'+endTime+'_end_weekday_number'\r\n smalldf[weekday_num] = pd.to_datetime(smalldf[end_date]).dt.weekday.values\r\n add_cols.append(weekday_num)\r\n weekend = 'processing'+endTime+'_end_weekend_flag'\r\n smalldf[weekend] = smalldf[weekday_num].map(lambda x: 1 if x in[5,6] else 0)\r\n add_cols.append(weekend)\r\n #### If everything works well, there should be 13 new columns added by module. All the best!\r\n print('%d columns added using start date=%s and end date=%s processing...' %(len(add_cols),startTime,endTime))\r\n return smalldf","def run(self):\n self.timestamp['start'] = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\n\n for point in self._prepare_grid():\n graph = self._prepare_graph(**point)\n env = self._prepare_env(graph, **point)\n log = self._prepare_logger(graph, env, **point)\n\n try:\n env.run(until=self.runtime)\n except Exception as e:\n print(e)\n log.close()\n\n # self.timestamp[grid.hash_grid_point(point)].append(datetime.datetime.now().strftime('%Y%m%dT%H%M%S'))\n\n self.timestamp['end'] = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')","def create_batch(batch, source, params, dv,\n t_end=1.3e5, exclude=None, basename='xrb', walltime=96,\n auto_t_end=True, notes='No notes given', nbursts=20, kgrid=None,\n nuc_heat=True, setup_test=False,\n auto_qnuc=False, grid_version=None, qnuc_source='heat',\n substrate='fe54', substrate_off=True, adapnet_filename=None,\n bdat_filename=None, params_full=None,\n numerical_params=None, scratch_file_sys=False):\n # TODO:\n # - WRITE ALL PARAM DESCRIPTIONS\n # - Overhaul/tidy up\n # - use pd table instead of dicts of arrays\n\n print_batch(batch=batch)\n source = grid_strings.source_shorthand(source=source)\n mass_ref = 1.4 # reference NS mass (Msun)\n radius_ref = 10 # default NS radius (km)\n\n specified = {'params': params,\n 'dv': dv,\n 'numerical_params': numerical_params}\n\n if specified['numerical_params'] is None:\n specified['numerical_params'] = {}\n\n config = grid_tools.setup_config(specified=specified, source=source)\n # TODO: print numerical_params being used\n\n if params_full is None:\n params_expanded, var = expand_params(params=config['params'], dv=config['dv'])\n params_full = exclude_params(params_expanded=params_expanded, exclude=exclude)\n\n n_models = len(params_full['x'])\n\n if kgrid is None:\n print('No kgrid provided. Loading default:')\n kgrid = grid_analyser.Kgrid(load_lc=False, linregress_burst_rate=True,\n source=source)\n\n params_full['y'] = 1 - params_full['x'] - params_full['z'] # helium-4 values\n params_full['geemult'] = params_full['mass'] / mass_ref # Gravity multiplier\n\n gravities = gravity.get_acceleration_newtonian(r=radius_ref,\n m=params_full['mass']).value\n params_full['radius'] = np.full(n_models, radius_ref)\n params_full['gravity'] = gravities\n\n if auto_qnuc:\n predict_qnuc(params_full=params_full, qnuc_source=qnuc_source,\n grid_version=grid_version)\n\n # ===== Create top grid folder =====\n batch_model_path = grid_strings.get_batch_models_path(batch, source)\n grid_tools.try_mkdir(batch_model_path)\n\n # Directory to keep MonARCH logs and sbatch files\n logpath = grid_strings.get_source_subdir(batch_model_path, 'logs')\n grid_tools.try_mkdir(logpath)\n\n # ===== Write parameter table MODELS.txt and NOTES.txt=====\n write_model_table(n=n_models, params=params_full, path=batch_model_path)\n filepath = os.path.join(batch_model_path, 'NOTES.txt')\n with open(filepath, 'w') as f:\n f.write(notes)\n\n # ===== Write jobscripts for submission on clusters =====\n print_dashes()\n kepler_jobs.write_both_jobscripts(run0=1, run1=n_models, batch=batch,\n source=source, basename=basename,\n path=logpath, walltime=walltime,\n adapnet_filename=adapnet_filename,\n bdat_filename=bdat_filename,\n scratch_file_sys=scratch_file_sys)\n\n # ===== Directories and templates for each model =====\n for i in range(n_models):\n # ==== Create directory tree ====\n print_dashes()\n model = i + 1\n run_path = grid_strings.get_model_path(model, batch, source, basename=basename)\n\n # ==== Create task directory ====\n grid_tools.try_mkdir(run_path)\n\n # ==== Write burn file, set initial composition ====\n x0 = params_full['x'][i]\n z0 = params_full['z'][i]\n kepler_files.write_rpabg(x0, z0, run_path, substrate=substrate)\n\n # ==== Create model generator file ====\n if auto_t_end:\n mdot = params_full['accrate'][i] * params_full['acc_mult'][i]\n rate_params = {}\n for param in ('x', 'z', 'qb', 'mass'):\n rate_params[param] = params_full[param][i]\n fudge = 0.5 # extra time to ensure complete final burst\n tdel = kgrid.predict_recurrence(accrate=mdot, params=rate_params)\n t_end = (nbursts + fudge) * tdel\n print(f'Using predicted dt={tdel/3600:.1f} hr')\n if t_end < 0:\n print('WARN! negative dt predicted. Defaulting n * 1.5hr')\n t_end = nbursts * 1.5 * 3600\n\n run = i + 1\n print(f'Writing genfile for xrb{run}')\n header = f'This generator belongs to model: {source}_{batch}/{basename}{run}'\n\n accdepth = params_full['accdepth'][i]\n if (params_full['x'][i] > 0.0) and (accdepth > 1e20):\n print(f\"!!!WARNING!!!: accdepth of {accdepth:.0e} may be too deep for\" +\n \" models accreting hydrogen\")\n print(f'Using accdepth = {accdepth:.1e}')\n\n kepler_files.write_genfile(h1=params_full['x'][i], he4=params_full['y'][i],\n n14=params_full['z'][i], qb=params_full['qb'][i],\n acc_mult=params_full['acc_mult'][i],\n qnuc=params_full['qnuc'][i],\n geemult=params_full['geemult'][i],\n accrate0=params_full['accrate'][i],\n accmass=params_full['accmass'][i],\n accdepth=params_full['accdepth'][i],\n path=run_path, t_end=t_end, header=header,\n nuc_heat=nuc_heat, setup_test=setup_test,\n substrate_off=substrate_off,\n numerical_params=config['numerical_params'])","def stfbss(X,nsources=5,ng=2**5-1,nh=2**9-1,tstep=2**6-1,df=1.0,nfbins=2**10,\r\n tftol=1.E-8,normalize=True):\r\n \r\n \r\n #get shape of timeseries matrix, \r\n #m=number of channels\r\n #tlen=length of timeseries\r\n m,maxn=X.shape\r\n \r\n n=nsources\r\n #get number of time bins\r\n ntbins=int(float(maxn)/tstep)\r\n \r\n tfkwargs={'ng':ng,'nh':nh,'df':df,'nfbins':nfbins,'tstep':tstep}\r\n \r\n \r\n #remove dc component from time series and normalize\r\n if normalize==True:\r\n for ii in range(m):\r\n X[ii,:]=X[ii,:]-np.mean(X[ii,:])\r\n X[ii,:]=X[ii,:]/X[ii,:].std() \r\n \r\n #===============================================================================\r\n # Whiten data and Compute Whitening matrix\r\n #===============================================================================\r\n #whiten data to get a unitary matrix with unit variance and zero mean\r\n #compute covariance matrix\r\n Rxx=(np.dot(X,X.T))/float(maxn)\r\n \r\n #calculate eigen decomposition\r\n [l,u]=np.linalg.eig(Rxx)\r\n \r\n #sort eigenvalues from smallest to largest assuming largest are sources and \r\n #smallest are noise\r\n lspot=l.argsort()\r\n eigval=l[lspot]\r\n eigvec=u[:,lspot]\r\n \r\n #calculate the noise variance as mean of non-principal components\r\n sigman=np.mean(eigval[0:m-n])\r\n \r\n #compute scaling factor for whitening matrix\r\n wscale=1/np.sqrt(eigval[m-n:m]-sigman)\r\n \r\n #compute whitening matrix\r\n W=np.zeros((m,n))\r\n for kk in range(n):\r\n W[:,kk]=wscale[kk]*eigvec[:,m-n+kk].T\r\n W=W.T\r\n #compute whitened signal vector. Note the dimensionality is reduced from [mxn] \r\n #to [nxn] making the computation simpler.\r\n Z=np.dot(W,X)\r\n \r\n #===============================================================================\r\n # Compute Spatial Time Frequency Distribution\r\n #===============================================================================\r\n \r\n stfd=np.zeros((n,n,nfbins,ntbins+1),dtype='complex128')\r\n Za=np.array(Z.copy())\r\n #compute auto terms\r\n for ii in range(n):\r\n pswvd,tswvd,fswvd=spwvd(Za[ii].reshape(maxn),**tfkwargs)\r\n stfd[ii,ii,:,:]=pswvd\r\n \r\n #compute cross terms\r\n for jj in range(n):\r\n for kk in range(jj,n):\r\n pswvd,tswvd,fswvd=spwvd([Za[jj].reshape(maxn),Za[kk].reshape(maxn)],\r\n **tfkwargs)\r\n stfd[jj,kk,:,:]=pswvd\r\n stfd[kk,jj,:,:]=pswvd.conj()\r\n \r\n #===============================================================================\r\n # Compute criteria for cross terms \r\n #===============================================================================\r\n \r\n stfdTr=np.zeros((nfbins,ntbins))\r\n C=np.zeros((nfbins,ntbins))\r\n \r\n for ff in range(nfbins):\r\n for tt in range(ntbins):\r\n #compensate for noise\r\n stfd[:,:,ff,tt]=stfd[:,:,ff,tt]-sigman*np.matrix(W)*np.matrix(W.T)\r\n #compute the trace\r\n stfdTr[ff,tt]=abs(np.trace(stfd[:,:,ff,tt]))\r\n #compute mean over entire t-f plane\r\n trmean=stfdTr.mean()\r\n \r\n #find t-f points that meet the criteria\r\n fspot,tspot=np.nonzero(stfdTr>trmean)\r\n \r\n for ll in range(len(fspot)):\r\n treig=abs(np.linalg.eig(stfd[:,:,fspot[ll],tspot[ll]])[0])\r\n if sum(treig)!=0 and sum(treig)>tftol:\r\n C[fspot[ll],tspot[ll]]=max(treig)/sum(treig)\r\n else:\r\n C[fspot[ll],tspot[ll]]=0\r\n \r\n #compute gradients and jacobi matrices\r\n negjacobi=np.zeros((nfbins,ntbins))\r\n smallgrad=np.zeros((nfbins,ntbins))\r\n maxpoints=np.zeros((nfbins,ntbins))\r\n \r\n gradt,gradf=np.gradient(C)\r\n Jtt,Jtf=np.gradient(gradt)\r\n Jft,Jff=np.gradient(gradf)\r\n \r\n #get points when L2 of gradient is smaller than tolerance level\r\n smallgrad=np.where(np.sqrt(gradt**2+gradf**2)<tftol,1,0)\r\n \r\n #get points where the Jacobi is negative definite\r\n detjacobi=Jtt*Jff-Jtf*Jft\r\n negjacobi=np.where(detjacobi>0,1,0)*np.where(Jtt<0,1,0)\\\r\n *np.where((Jtt+Jff)<0,1,0)\r\n \r\n maxpoints=smallgrad*negjacobi\r\n \r\n gfspot,gtspot=np.nonzero(maxpoints)\r\n ntfpoints=len(gfspot)\r\n \r\n if ntfpoints==0:\r\n raise ValueError('Found no tf points, relax tolerance')\r\n else:\r\n print 'Found '+str(ntfpoints)+' t-f points'\r\n \r\n for rr in range(ntfpoints):\r\n if rr==0:\r\n Rjd=stfd[:,:,gfspot[rr],gtspot[rr]]\r\n else:\r\n Rjd=np.concatenate((Rjd,stfd[:,:,gfspot[rr],gtspot[rr]]),axis=1)\r\n Rjd=np.array(Rjd)\r\n \r\n #===============================================================================\r\n # Calculate Joint Diagonalization\r\n #===============================================================================\r\n #get size of array of matrices to be diagonalized\r\n mtf,nm=Rjd.shape #mtf is number of t-f points, nm is number of matrices\r\n #set up some initial parameters\r\n V=np.eye(mtf)\r\n \r\n #update boolean\r\n encore=True \r\n #Total number of rotations\r\n updates=0\r\n sweep=0\r\n #print 'Computing Joint Diagonalization'\r\n # Joint diagonalization proper\r\n # ============================\r\n while encore:\r\n #reset some parameters\r\n encore=False\r\n sweep+=1 \r\n upds=0\r\n Vkeep=V\r\n for p in range(mtf):\r\n \r\n for q in range(p+1,mtf):\r\n #set up indices\r\n qi=np.arange(start=q,stop=nm,step=mtf)\r\n pi=np.arange(start=p,stop=nm,step=mtf)\r\n # computation of Givens angle\r\n g=np.array([Rjd[p,pi]-Rjd[q,qi],Rjd[p,qi],Rjd[q,pi]])\r\n gg=np.real(np.dot(g,g.T))\r\n ton=gg[0,0]-gg[1,1] \r\n toff=gg[0,1]+gg[1,0]\r\n theta=0.5*np.arctan2(toff,ton+np.sqrt(ton**2+toff**2))\r\n # Givens update\r\n if abs(theta) > tftol:\r\n encore=True\r\n upds+=1\r\n c=np.cos(theta) \r\n s=np.sin(theta)\r\n G=np.matrix([[c,-s],[s,c]])\r\n pair =np.array([p,q])\r\n V[:,pair]=V[:,pair]*G\r\n Rjd[pair,:]=G.T*Rjd[pair,:]\r\n Rjd[:,np.concatenate([pi,qi])]=np.append(\r\n c*Rjd[:,pi]+s*Rjd[:,qi],\r\n -s*Rjd[:,pi]+c*Rjd[:,qi],axis=1)\r\n updates+=upds\r\n print 'Updated '+str(updates)+' times.'\r\n \r\n #compute estimated signal matrix\r\n Se=np.dot(V.T,Z)\r\n #compute estimated mixing matrix\r\n Ae=np.dot(np.linalg.pinv(W),V)\r\n \r\n return Se,Ae","def groups_of_train_test_set(df, config, fbprophet=None):\n \n tvar = config.variables['tvar']\n xvar = config.variables['xvar'] + config.variables['xvar_derived']\n \n begin_date = config.timestamps['begin_date']\n end_date = config.timestamps['end_date']\n deltat = config.timestamps['deltat']\n\n begin_date = datetime.datetime.strptime(begin_date, \"%Y-%m-%d %H:%M:%S\")\n end_date = datetime.datetime.strptime(end_date, \"%Y-%m-%d %H:%M:%S\")\n\n \n # Check if ustar threshold is provided for year of interest\n if config.data['ustar']==True:\n if not begin_date.year in config.data['ustar_map'].keys():\n raise ValueError('{} is missing from config/data/ustar_map'.format(begin_date.year))\n if not end_date.year in config.data['ustar_map'].keys():\n raise ValueError('{} is missing from config/data/ustar_map'.format(end_date.year))\n\n\n if (end_date - begin_date).days < deltat:\n raise ValueError(\"Time difference in days between begin and end date\" + \n \"must be greater than deltat.\")\n\n\n df = df.loc[df[tvar] <= end_date]\n\n number_of_train_test_sets = int((end_date - begin_date).total_seconds()/\\\n datetime.timedelta(deltat).total_seconds())\n\n begin_test_timestamp = begin_date\n \n\n test_df = []\n train_df = [] \n for i in range(number_of_train_test_sets):\n if i == number_of_train_test_sets-1:\n end_test_timestamp = end_date\n else:\n end_test_timestamp = None\n\n\n i_test_set, i_train_set, end_test_timestamp =\\\n _train_test_split(df.copy(), config,\n begin_test_timestamp,\n deltat, end_test_timestamp,\n fbprophet)\n begin_test_timestamp = end_test_timestamp\n \n # Interpolating where x-var is nan.\n i_test_set[xvar] = i_test_set[xvar].interpolate()\n i_train_set[xvar] = i_train_set[xvar].interpolate()\n \n\n i_test_set['Set_rank'] = i\n i_train_set['Set_rank'] = i\n \n if i == 0:\n test_df = i_test_set\n train_df = i_train_set\n else:\n test_df = pd.concat((test_df, i_test_set))\n train_df = pd.concat((train_df, i_train_set))\n\n\n return test_df, train_df","def test_fit_prep_proba():\n args = get_layer('fit', 'manual', 'temporal', True, True, window=2, step_size=3)\n run_layer(*args)","def add_environment_toTable(tab, in_ds, envvar_take=[],tabvar_skip=[], rainvar_name=None, env_tformat=\"%Y-%m-%d %H:%M:%S\", env_hour=12):\n\n tab_tformat = \"%Y-%m-%d_%H:%M\"\n dic = {}\n for k in tab.keys():\n if k in tabvar_skip: # option to add variables to be excluded from environment table\n continue\n dic[k] = tab[k]\n\n ds = in_ds\n envdates = pd.to_datetime(ds.time.dt.floor('T'), format=env_tformat)\n ###### sample variables\n for tlat, tlon, date, mask, tir in zip(dic['tminlat'], dic['tminlon'], dic['date'], dic['cloudMask'], dic['tir']):\n\n\n #save cloud-wide rainfall stats, and rainfall maximum at ~0.15deg\n if rainvar_name is not None:\n tabdate = pd.to_datetime(date, format=tab_tformat) # rainfall sampling same time as TIR\n\n pos = envdates == tabdate\n rain = ds[rainvar_name].isel(time=pos).where(mask).squeeze() # to mm/h\n pmax_pos = np.nanargmax(rain.values)\n ppos_2d = np.unravel_index(pmax_pos, rain.shape)\n pmax_lon = rain.lon[ppos_2d[1]]\n pmax_lat = rain.lat[ppos_2d[0]]\n pmax = rain.sel(lon=slice(pmax_lon - 0.075, pmax_lon + 0.075), lat=slice(pmax_lat - 0.075, pmax_lat + 0.075))\n # ipdb.set_trace()\n pmax = pmax.mean().values\n if (rainvar_name + '_mean') not in dic.keys():\n for tag in ['_mean', '_max', '_p95', '_p99']:\n dic[rainvar_name + tag] = []\n dic[rainvar_name + '_mean'].append(float(rain.mean().values)) # full cloud mean\n dic[rainvar_name + '_max'].append(float(pmax)) # ~0.15deg rain max\n dic[rainvar_name + '_p95'].append(float(rain.quantile(0.95).values))\n dic[rainvar_name + '_p99'].append(float(rain.quantile(0.99).values))\n\n ## save mean environments at ~0.7deg centred on location of minimum storm temperature\n if len(envvar_take) > 0:\n tabdate = pd.to_datetime(date, format=tab_tformat).replace(hour=env_hour, minute=0) # hour of environment sampling\n pos = envdates == tabdate\n single = ds.isel(time=pos).sel(longitude=slice(tlon - 0.375, tlon + 0.375), latitude=slice(tlat - 0.375, tlat + 0.375))\n # ipdb.set_trace()\n single = single.mean()\n for vt in envvar_take:\n if vt in dic.keys():\n dic[vt].append(single[vt].values)\n else:\n dic[vt] = [single[vt].values]\n return dic"],"string":"[\n \"def fit_timeseries(xdates, ydata):\\n\\n pass\",\n \"def fit_surface(tdata,\\n scale_params=[40.0, 1.25]):\\n agg_tdata = aggregate_data(tdata)\\n\\n # unpack data\\n mt = agg_tdata.mean_temp\\n dt = agg_tdata.daily_temp\\n scaled_dt = utils.scale_daily_temp(mt, dt, scale_params)\\n\\n obs_mean = agg_tdata.obs_mean\\n obs_std = agg_tdata.obs_std*15.0\\n study_sizes = agg_tdata.study_sizes\\n\\n # mt = tdata.mean_temp\\n # dt = tdata.daily_temp\\n # scaled_dt = utils.scale_daily_temp(mt, dt, scale_params)\\n\\n # obs_mean = tdata.obs_mean\\n # obs_std = tdata.obs_std\\n # study_sizes = np.array([1]*obs_mean.size)\\n\\n # create spline\\n mt_knots = np.linspace(mt.min(), mt.max(), 2)\\n dt_knots = np.linspace(scaled_dt.min(), scaled_dt.max(), 2)\\n mt_degree = 3\\n dt_degree = 3\\n spline_list = [xspline.ndxspline(2,\\n [mt_knots, dt_knots],\\n [mt_degree, dt_degree])]\\n\\n # create mrbrt object\\n x_cov_list = [{\\n 'cov_type': 'ndspline',\\n 'spline_id': 0,\\n 'mat': np.vstack((mt, scaled_dt))\\n }]\\n z_cov_list = [{\\n 'cov_type': 'linear',\\n 'mat': np.ones(mt.size)\\n }]\\n\\n mr = mrbrt.MR_BRT(obs_mean,\\n obs_std,\\n study_sizes,\\n x_cov_list,\\n z_cov_list,\\n spline_list)\\n\\n # add priors\\n prior_list = [\\n {\\n 'prior_type': 'ndspline_shape_function_uprior',\\n 'x_cov_id': 0,\\n 'interval': [[mt.min(), mt.max()],\\n [scaled_dt.min(), scaled_dt.max()]],\\n 'indicator': [-1.0, 1.0],\\n 'num_points': [20, 20]\\n },\\n # {\\n # 'prior_type': 'x_cov_gprior',\\n # 'x_cov_id': 0,\\n # 'prior': np.array([[0.0]*mr.k_beta, [10.0]*mr.k_beta])\\n # },\\n {\\n 'prior_type': 'ndspline_shape_monotonicity',\\n 'x_cov_id': 0,\\n 'dim_id': 1,\\n 'interval': [[mt.min(), mt.max()],\\n [0.5, scaled_dt.max()]],\\n 'indicator': 'increasing',\\n 'num_points': [20, 10]\\n },\\n {\\n 'prior_type': 'ndspline_shape_monotonicity',\\n 'x_cov_id': 0,\\n 'dim_id': 1,\\n 'interval': [[mt.min(), mt.max()],\\n [scaled_dt.min(), -0.75]],\\n 'indicator': 'decreasing',\\n 'num_points': [20, 10]\\n },\\n {\\n 'prior_type': 'z_cov_uprior',\\n 'z_cov_id': 0,\\n 'prior': np.array([[1e-6]*mr.k_gamma, [1e-6]*mr.k_gamma])\\n }\\n ]\\n mr.addPriors(prior_list)\\n\\n # fit the model and store the result\\n mr.fitModel()\\n\\n # compute posterior variance for the beta\\n # extract the matrix\\n k_beta = mr.lt.k_beta\\n k_gamma = mr.lt.k_gamma\\n X = mr.lt.JF(mr.lt.beta)\\n S = mr.lt.S\\n V = S**2\\n\\n beta_var = (X.T/V).dot(X)\\n\\n if mr.lt.use_regularizer:\\n H = np.vstack([mr.lt.H(np.hstack(np.eye(1, k_beta, i).reshape(k_beta,),\\n np.zeros(k_gamma)))\\n for i in range(k_beta)]).T\\n SH = mr.lt.h[1]\\n VH = SH**2\\n beta_var += (H.T/VH).dot(VH)\\n\\n beta_var = np.linalg.inv(beta_var)\\n\\n\\n surface_result = utils.SurfaceResult(\\n mr.beta_soln,\\n beta_var,\\n spline_list[0],\\n scale_params=scale_params)\\n\\n return surface_result, agg_tdata\",\n \"def manipulate_data(ds, var, predef_clim, predef_trnd, trn_yrs, all_yrs, \\n apply_latw=True, apply_detrending=True, dropna=True):\\n\\n \\n if((var=='SD')|(var=='sd')|(var=='snowc')): \\n ds[var] = ds[var].where(ds[var]>=0, other=0.0)\\n ds[var] = ds[var].where(ds[var]==0, other=1.0)\\n #ds[var].values = Gauss_filter(ds[var].values, (0,3,3))\\n \\n \\\"\\\"\\\"\\n if((var=='hgt')|(var=='z')|(var=='GPT')):\\n months = ds.time.to_index().month; ssn_ends = (months==2)|(months==5)|(months==8)|(months==11)\\n ds = ds.sel(time=ssn_ends)\\n else: \\n ds = ds.resample(time='3M').mean()\\n \\\"\\\"\\\"\\n \\n ds = ds.resample(time='3M').mean()\\n\\n ds = ds.sel(time=slice(str(all_yrs[0])+'-01-01', str(all_yrs[-1])+'-12-31')) \\n \\n try: \\n clim = predef_clim\\n ds = ds.groupby('time.season') - clim\\n print('Predefined climatology used')\\n except:\\n clim = ds.sel(time=slice(str(trn_yrs[0])+'-01-01', str(trn_yrs[-1])+'-12-31')).groupby('time.season').mean('time')\\n ds = ds.groupby('time.season') - clim\\n print('Climatology calculated from data')\\n \\n if(apply_latw): ds[var].values = lat_weighting(ds[var].values, \\n ds.lat, ds.lon)\\n if(dropna):\\n ds = ds.stack(gridcell=('lat', 'lon')).dropna(dim='gridcell',how='any')\\n else: \\n ds = ds.stack(gridcell=('lat', 'lon')).fillna(0)\\n \\n \\n trend_models = { }\\n if(apply_detrending): \\n ds = ds.load()\\n for ssn in ('DJF', 'MAM', 'JJA', 'SON'):\\n #ssn_idx = ds['time.season'] == ssn\\n \\n trn_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], trn_yrs))\\n all_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], all_yrs))\\n \\n trn_x = np.array(ds.time[trn_idx].values.tolist()).reshape(-1,1)\\n all_x = np.array(ds.time[all_idx].values.tolist()).reshape(-1,1)\\n try:\\n trend = predef_trnd[ssn].predict(all_x)\\n trend_models[ssn] = predef_trnd[ssn]\\n print('Predefined trend model used')\\n except:\\n #_, trend_model = define_trends(ds[var][trn_idx], trn_x)\\n _, trend_model = define_trends(ds[var][all_idx], all_x)\\n trend = trend_model.predict(all_x)\\n trend_models[ssn] = trend_model\\n print('Trends calculated from data')\\n \\n ds[var][all_idx] = ds[var][all_idx] - trend\\n \\n\\n \\n return ds, clim, trend_models\",\n \"def __init__(self, ts_df, time_format=\\\"%Y-%m-%d %H:%M:%S\\\", freq='D',\\n fill_method='ffill',\\n n_test=0, n_val=0,\\n hyper_params=None,\\n test='adf',\\n trend=None,\\n seasonal=False,\\n seasonal_periods=1,\\n **kwds):\\n self._ts_df_cols = ['ds', 'y']\\n\\n self.ts_df = ts_df\\n self.time_format = time_format\\n self.freq = freq\\n self.fill_method = fill_method.lower()\\n self.n_test = int(n_test)\\n self.n_val = int(n_val)\\n self.transform = None\\n self._boxcox_lmbda = None\\n\\n self._mode = ''\\n\\n self._train_dt = None\\n self._test_dt = None\\n self._val_dt = None\\n\\n self.model_fit = None\\n self.fittedvalues = None\\n self.residuals = None\\n self.rmse = 0\\n self._gs = tsa.GridSearchClass()\\n self.hyper_params = hyper_params\\n self.best_model = dict()\\n\\n \\\"\\\"\\\"\\n self.rmse_test = 0\\n self.rmse_val = 0\\n \\\"\\\"\\\"\\n\\n self.upper_whisker_res = None\\n self.lower_conf_int = None\\n self.upper_conf_int = None\\n\\n self.forecast = None\\n self.residuals_forecast = None\\n\\n self._res_decomp = None\\n self._arr_seasonal = None\\n self._arr_trend = None\\n self._arr_baseline = None\\n\\n self._test = test\\n self._trend = trend\\n if self._trend is not None:\\n self._trend = self._trend.lower()\\n self._seasonal = seasonal\\n if isinstance(self._seasonal, str):\\n self._seasonal = self._seasonal.lower()\\n self._seasonal_periods = seasonal_periods\\n\\n self._uvts_cls_logger = Logger('uvts_cls')\\n\\n UVariateTimeSeriesClass.assertions(self)\\n # work with ts_df\\n self.ts_df = self.ts_df.reset_index()\\n self.ts_df.columns = self._ts_df_cols\\n self.ts_df['y'] = self.ts_df['y'].apply(np.float64, errors='coerce')\\n self.ts_df.set_index('ds', inplace=True)\\n self._uvts_cls_logger.info(\\n \\\"Received time series data of range: \\\" + str(min(self.ts_df.index)) + ' - ' + str(\\n max(self.ts_df.index)) + \\\" and shape: \\\" + str(self.ts_df.shape))\\n\\n if not isinstance(self.ts_df.index, pd.DatetimeIndex):\\n self._uvts_cls_logger.warning(\\\"Time conversion required...\\\")\\n self.ts_df = self.ts_df.reset_index()\\n try:\\n self.ts_df['ds'] = self.ts_df['ds'].apply(\\n lambda x: datetime.datetime.strptime(\\n str(x).translate({ord('T'): ' ', ord('Z'): None})[:-1],\\n self.time_format))\\n except ValueError as e:\\n self._uvts_cls_logger.warning(\\\"Zulu time conversion not successful: {}\\\".format(e))\\n self._uvts_cls_logger.warning(\\\"Will try without assuming zulu time...\\\")\\n try:\\n self.ts_df['ds'] = self.ts_df['ds'].apply(\\n lambda x: datetime.datetime.strptime(str(x), self.time_format))\\n except ValueError as e:\\n self._uvts_cls_logger.info(\\\"Time conversion not successful. Check your time_format: {}\\\".format(e))\\n sys.exit(\\\"STOP\\\")\\n else:\\n self._uvts_cls_logger.info(\\\"Time conversion successful!\\\")\\n else:\\n self._uvts_cls_logger.info(\\\"Time conversion successful!\\\")\\n # set index\\n self.ts_df.set_index('ds', inplace=True)\\n #\\n self.ts_df.index = pd.to_datetime(self.ts_df.index)\\n self.ts_df.sort_index(inplace=True)\\n # resample\\n self.ts_resample()\\n UVariateTimeSeriesClass.assertions(self, post=True)\\n #\\n if self.n_val > len(self.ts_df) - self.n_test:\\n self.n_val = len(self.ts_df) - self.n_test\\n\\n if self.n_test == 0 and self.n_val == 0:\\n self._mode = 'forecast'\\n elif self.n_test > 0:\\n self._mode = 'test'\\n elif self.n_test == 0 and self.n_val > 0:\\n self._mode = 'validate'\\n \\n # delegate just for good programming style here\\n super(UVariateTimeSeriesClass, self).__init__(**kwds)\",\n \"def prepare(gpi, start_date, end_date, models, satellites, kind=\\\"clim\\\", window=35):\\n # read data\\n ts_input = data_object.read_gpi(gpi, start_date, end_date, models, satellites)\\n #ts_input = interp.add_nan(ts_input)\\n ts_input = interp.iter_fill(ts_input, max_gap=5)\\n\\n # either calc climatology, apply moving average filter, or do nothing\\n if kind == 'clim':\\n ts_smooth = smooth.iter_climats(ts_input)\\n plot_title = 'Climatology'\\n elif kind == 'movav':\\n ts_smooth = smooth.iter_movav(ts_input, window)\\n #ts_smooth = ts_gapfill\\n plot_title = 'Moving average'\\n elif kind == None:\\n # return original data\\n ts_smooth = ts_input\\n else:\\n raise NotImplementedError\\n pass\\n\\n # drop rows with missing values\\n #ts_smooth = ts_smooth.dropna()\\n\\n # scale satellite data to model data\\n ts_scaled = scaling.scale(ts_smooth, 'mean_std_nan', 0)\\n # drop nan rows for slope funcs\\n return ts_scaled #.dropna()\",\n \"def prep_data_fn(self, st_train_dt, end_train_dt, st_val_dt, end_val_dt, st_test_dt, end_test_dt):\\n df = self.get_prep_data()\\n train = df[(df['ft_data_dt'] >= st_train_dt) & (df['ft_data_dt'] <= end_train_dt)]\\n val = df[(df['ft_data_dt'] >= st_val_dt) & (df['ft_data_dt'] <= end_val_dt)].sample(frac=0.4, random_state=2021)\\n test = df[(df['ft_data_dt'] >= st_test_dt) & (df['ft_data_dt'] <= end_test_dt)]\\n print(f'----train----')\\n print(train[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n print(f'----validation----')\\n print(val[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n print(f'----test----')\\n print(test[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n self.set_train(train)\\n self.set_validation(val)\\n self.set_test(test)\\n train_X = train[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n train_y = train['target']\\n val_X = val[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n val_y = val['target']\\n test_X = test[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n test_y = test['target']\\n self.set_train_X(train_X)\\n self.set_train_y(train_y)\\n self.set_val_X(val_X)\\n self.set_val_y(val_y)\\n self.set_test_X(test_X)\\n self.set_test_y(test_y)\",\n \"def prepare_data_for_dfe(data, size_timeseries, num_timeseries):\\n num_timesteps = size_timeseries\\n window_size = size_timeseries\\n\\n if num_timeseries > CORRELATION_MAX_NUM_TIMESERIES:\\n sys.stderr.write(\\n 'Number of Time series should be less or equal to ' +\\n str(CORRELATION_MAX_NUM_TIMESERIES) +\\n '. Terminating!')\\n sys.stderr.flush()\\n sys.exit(-1)\\n elif window_size < 2:\\n sys.stderr.write(\\n 'Window size must be equal or greater than 2. Terminating!')\\n sys.stderr.flush()\\n sys.exit(-1)\\n elif num_timesteps > size_timeseries:\\n sys.stderr.write(\\n 'Number of Time steps should be less or equal to' +\\n ' size of Time series. Terminating!')\\n sys.stderr.flush()\\n sys.exit(-1)\\n\\n precalculations = []\\n data_pairs = []\\n\\n sums = [[0.0] * num_timeseries] * num_timesteps\\n sums_sq = [[0.0] * num_timeseries] * num_timesteps\\n inv = [[0.0] * num_timeseries] * num_timesteps\\n\\n # 2 DFE input streams: precalculations and data pairs\\n for i in range(num_timesteps):\\n for j in range(num_timeseries):\\n old = 0.0 if i < window_size else data[j][i - window_size]\\n new = data[j][i]\\n\\n if i == 0:\\n sums[i][j] = new\\n sums_sq[i][j] = new * new\\n else:\\n sums[i][j] = sums[i-1][j] + new - old\\n sums_sq[i][j] = sums_sq[i-1][j] + new * new - old * old\\n\\n inv[i][j] = 1 / math.sqrt(window_size * sums_sq[i][j] -\\n sums[i][j] * sums[i][j])\\n\\n # Precalculations REORDERED in DFE ORDER\\n precalculations.append(sums[i][j])\\n precalculations.append(inv[i][j])\\n\\n # Data pairs REORDERED in DFE ORDER\\n data_pairs.append(new)\\n data_pairs.append(old)\\n\\n return (precalculations, data_pairs)\",\n \"def build_shape_data(self, start=None, end=None):\\n # If start and end are None, then set them to be min/max of self.df_demand\\n if start is None:\\n start = self.df_demand['date'].min()\\n if end is None:\\n end = self.df_demand['date'].max()\\n print(f\\\"date range for shape data is from {start} to {end}\\\")\\n # Extract part of df_demand that is within start and end\\n df_sub = self.df_demand[(self.df_demand['date'] >= start) & (self.df_demand['date'] <= end)]\\n assert df_sub['date'].min() >= start\\n assert df_sub['date'].max() <= end\\n num_days = len(pd.date_range(iso8601.parse_date(start), iso8601.parse_date(end), freq='d'))\\n print(f\\\"number of days is {num_days}\\\")\\n # When finding variance and mean, add in missing days as 0s\\n # Obtain the counts for each lat/lng region\\n counts = df_sub.groupby(['left_lng', 'right_lng', 'lower_lat', 'upper_lat']).size().reset_index(name='counts')\\n # Group demand data by lat/lng region and average across other cols\\n df = df_sub.groupby(['left_lng', 'right_lng', 'lower_lat', 'upper_lat'])[['avail_count', 'avail_mins', 'trips', 'prob_scooter_avail', 'adj_trips']].mean().reset_index()\\n df = df.merge(counts, on=['left_lng', 'right_lng', 'lower_lat', 'upper_lat'])\\n # print(df.head())\\n # Modify averages by multiplying each by count and divide by num_days\\n vars = ['avail_count', 'avail_mins', 'trips', 'prob_scooter_avail', 'adj_trips']\\n for var in vars:\\n df[var] = df[var]*df['counts']/num_days\\n # print(df.head())\\n # Calculate the variance for prob_scooter_avail\\n probVariance = df_sub.groupby(['left_lng', 'right_lng', 'lower_lat', 'upper_lat']).apply(lambda x: ((x['prob_scooter_avail'] - (x['prob_scooter_avail'].sum()/num_days))**2).sum()/(num_days-1)).reset_index(name='prob_scooter_avail')\\n # print(probVariance.head())\\n df['prob_scooter_avail_var'] = probVariance['prob_scooter_avail']\\n # Check to see if there are any Nan values\\n print(f\\\"Nan values in df? {df.isnull().values.any()}\\\")\\n # print(df.head())\\n # For each var col, create corresponding color columns (log and unlog)\\n # Also create the factors list that get passed into self.create_rectangle_lst\\n factors = [('avail_count', 'decimal'), ('avail_mins', 'decimal'),\\n ('trips', 'decimal'), ('prob_scooter_avail', 'percent'), ('adj_trips', 'decimal')]\\n i = 0\\n original_len = len(factors)\\n while i < original_len:\\n name, type = factors[i]\\n # print(f\\\"name={name}, type={type}\\\")\\n # Create color column\\n df = self.map_values_to_color(df, name)\\n # If type is not percent than create log version\\n if type != 'percent':\\n df = self.create_log_column(df, name)\\n factors.append(('log_'+name, type))\\n i += 1\\n # Deal with estimated demand and unmet demand\\n # Filter out rows where prob_scooter_avail sig diff from 0\\n sigDiffIdx = df.apply(lambda x: utils.sig_diff_from_zero(x['prob_scooter_avail'], x['prob_scooter_avail_var']), axis=1)\\n # print(sigDiffIdx.head())\\n df_sig_diff = df[sigDiffIdx]\\n # Calculate estimated demand and unmet demand\\n df_sig_diff = self.calculate_demand(df_sig_diff)\\n # print(df_sig_diff.head())\\n # Create color column and log column for unmet demand\\n df_sig_diff = self.map_values_to_color(df_sig_diff, 'unmet_demand')\\n df_sig_diff = self.map_values_to_color(df_sig_diff, 'estimated_demand')\\n df_sig_diff = self.create_log_column(df_sig_diff, 'unmet_demand')\\n factors.extend([('estimated_demand', 'decimal'), ('unmet_demand', 'decimal'), ('log_unmet_demand', 'decimal')])\\n # Fill in the colors for the grid cells that aren't significantly different\\n df_not_sig_diff = df[~sigDiffIdx]\\n # print(df_not_sig_diff.head())\\n df = pd.concat([df_sig_diff, df_not_sig_diff])\\n # df.to_csv('../../../data_files/20210427_estimatedDemand.csv', index=False)\\n # Create Rectangle information\\n rectangles = self.create_rectangle_lst(df, factors)\\n return rectangles, start, end\",\n \"def create_grid(spl,\\n var_to_plot,\\n xmin=None,\\n xmax=None,\\n ymin=None,\\n ymax=None):\\n\\n # Find the lowest and the highest heights to calibrate the ylims\\n\\n max_heights, min_heights, dates = [], [], []\\n for df in spl:\\n max_heights.append(df['height [> 0: top, < 0: bottom of elem.] (cm)'].iloc[-1])\\n min_heights.append(df['height [> 0: top, < 0: bottom of elem.] (cm)'].iloc[0])\\n dates.append(df['dates'].iloc[0])\\n\\n if ymin and ymax:\\n max_height = ymax\\n min_height = ymin\\n else:\\n max_height = np.max(max_heights)\\n min_height = np.min(min_heights)\\n max_height = round(max_height ,5 ,'up')\\n min_height = round(min_height ,5 ,'down')\\n\\n grid_resolution = 100\\n vertical_grid = np.linspace(min_height ,max_height ,grid_resolution)\\n\\n # Trim spl to fit specified xmin, xmax\\n\\n if xmin and xmax:\\n\\n spl = [sp for (date,sp) in zip(dates,spl) if (xmin < date < xmax)]\\n dates = [date for date in dates if (xmin < date < xmax)]\\n\\n grid = np.full((grid_resolution, len(spl)), np.nan)\\n\\n for count, df in enumerate(spl):\\n\\n heights = np.array(df['height [> 0: top, < 0: bottom of elem.] (cm)'])\\n variables = np.array(df[var_to_plot])\\n\\n # regular_variables = np.interp(vertical_grid, heights, variables, left = np.nan, right = np.nan)\\n\\n kind = 'nearest'\\n\\n my_interp = interpolate.interp1d(heights,\\n variables,\\n kind=kind,\\n bounds_error = False,\\n fill_value = (np.nan ,np.nan))\\n\\n regular_variables = my_interp(vertical_grid)\\n\\n grid[: ,count] = np.flip(regular_variables, axis=0)\\n\\n return_dict = {'grid':grid,\\n 'max_height':max_height,\\n 'min_height':min_height,\\n 'dates':dates}\\n\\n return(return_dict)\",\n \"def _training(self, data, ts_start, ts_end, min_ts_length=None, min_ts_mean=None, min_ts_mean_window=None,\\n max_ft_freq=None, include_holidays=None, optimize=None, **kwargs):\\n from numpy.linalg import LinAlgError\\n\\n freq = self._params['freq']\\n\\n try:\\n\\n if data is None:\\n raise ValueError('Not enough data to train due to recent change point')\\n\\n endog = data[self._imputed_metric]\\n\\n index = pd.date_range(start=ts_start, end=ts_end, freq=freq) # Holidays are always daily.\\n\\n de_obj = DataExploration()\\n exog_data = de_obj._get_exog_data(ts_start, ts_end, index) if self._params[\\n 'include_holidays_exog'] else None\\n\\n # always run the model first without holiday exogenous variables\\n result, order = self._fit(endog=endog, endog_end=ts_end, min_ts_mean=min_ts_mean,\\n min_ts_mean_window=min_ts_mean_window, include_holidays=include_holidays,\\n min_ts_length=min_ts_length, max_ft_freq=max_ft_freq, exog_data=exog_data,\\n optimize=optimize)\\n\\n result['training_tail'] = data.loc[:ts_end].values.tolist()[-3:]\\n\\n except(LinAlgError, ValueError, LADStructuralError) as e:\\n result = {'ErrorMessage': str(e)}\\n return result, None\\n\\n return result, order\",\n \"def grid_data(self, method='mean'): \\n \\n # Add a few extra variables in that I print out each month as I can then\\n # compare these with the IDL output to see if there are discrepancies in\\n # the number of profiles being used:\\n rej_profiles = 0.\\n nWOD9 = 0.\\n nGTSPP0 = 0.\\n nGTSPP999 = 0.\\n index = np.where(self.qc == False)\\n print('No. of rejected temperature values', np.shape(index))\\n \\n # Make the salinity values missing where the salinity QC flag is bad:\\n fv = self.z.fill_value\\n \\n # Having set fv to be the general fill_value, check that this is the \\n # recognised fill value for the other variables that are assessed by it\\n # and raise a ValueError if not:\\n if not self.data.fill_value == fv:\\n raise ValueError('Incompatible fill value between depth and data')\\n \\n if not self.ps.fill_value == fv:\\n raise ValueError('Incompatible fill value between depth and psal.')\\n \\n # Now make the salinity values missing where the QC flag is bad:\\n self.ps[np.where(self.psalqc == False)] = fv\\n index = np.where(self.psalqc == False)\\n print('No. of rejected salinity values', np.shape(index))\\n\\n # Restrict to profiles with good posqc:\\n self.data = self.data[self.posqc] \\n self.x = self.x[self.posqc]\\n self.y = self.y[self.posqc]\\n self.p = self.p[self.posqc]\\n self.pn = self.pn[self.posqc]\\n self.ir = self.ir[self.posqc]\\n self.ps = self.ps[self.posqc]\\n self.z = self.z[self.posqc]\\n self.qc = self.qc[self.posqc]\\n self.posqc = self.posqc[self.posqc]\\n \\n # Store the maximum depth (with a good quality flag) of each profile:\\n zvar1 = np.ma.masked_where(self.qc == False, self.z)\\n maxdepth = np.amax(zvar1, axis = 1)\\n # Get only the profiles that aren't entirely bad:\\n self.data = self.data[maxdepth.mask == False]\\n self.x = self.x[maxdepth.mask == False]\\n self.y = self.y[maxdepth.mask == False]\\n self.p = self.p[maxdepth.mask == False]\\n self.pn = self.pn[maxdepth.mask == False]\\n self.ir = self.ir[maxdepth.mask == False]\\n self.ps = self.ps[maxdepth.mask == False]\\n self.z = self.z[maxdepth.mask == False]\\n self.qc = self.qc[maxdepth.mask == False]\\n self.posqc = self.posqc[maxdepth.mask == False] \\n maxdepth = maxdepth[maxdepth.mask == False]\\n \\n # I could thin to the second element of zbounds as if profiles don't go\\n # that deep then I won't be able to use them (this would be true even if\\n # the first element of zbounds wasn't zero) - it also removes the need \\n # for an OHC dep value in my configuration file:\\n self.data = self.data[maxdepth >= self.zbounds[1]]\\n self.x = self.x[maxdepth >= self.zbounds[1]]\\n self.y = self.y[maxdepth >= self.zbounds[1]]\\n self.p = self.p[maxdepth >= self.zbounds[1]]\\n self.pn = self.pn[maxdepth >= self.zbounds[1]]\\n self.ir = self.ir[maxdepth >= self.zbounds[1]]\\n self.ps = self.ps[maxdepth >= self.zbounds[1]]\\n self.z = self.z[maxdepth >= self.zbounds[1]]\\n self.qc = self.qc[maxdepth >= self.zbounds[1]]\\n self.posqc = self.posqc[maxdepth >= self.zbounds[1]]\\n year = int(self.fname[-9:-5])\\n \\n # Filter out low quality XBTs:\\n rem = []\\n for p in range(len(self.p)):\\n xbt = inst_type.is_xbt(self.pn[p], self.ir[p], self.ps[p], fv, \\n self.z[p], fv)\\n if xbt[0][0] >= 0:\\n # Remove any XBTs sourced from WOD where the fall rate equation\\n # is unknown:\\n if xbt[3] == 9:\\n rem.append(p)\\n nWOD9 += 1\\n # Remove any GTSPP XBTs where the type is unknown and year is \\n # >= 1995. \\n # Or if type is unknown and it may not be a T4/T6/T7/DB because \\n # the depth it reaches is too deep. Some of these will have been\\n # given the Hanawa correction and so be inaccurate - this \\n # happens in EN processing if probe code is zero:\\n projectName = ''.join(self.pn[p])\\n if projectName[0:5] == 'GTSPP':\\n if (xbt[4] == 0 or xbt[4] == 99 or xbt[4] == 999) and year >= 1995:\\n rem.append(p)\\n if xbt[4] == 0:\\n nGTSPP0 += 1\\n else:\\n nGTSPP999 += 1\\n if (xbt[4] == 0 and xbt[1] > 900):\\n rem.append(p)\\n \\n # Get rid of the low quality XBTs:\\n nolowxbt = np.array(np.setdiff1d(range(len(self.p)),rem))\\n self.data = self.data[nolowxbt]\\n self.x = self.x[nolowxbt]\\n self.y = self.y[nolowxbt]\\n self.p = self.p[nolowxbt]\\n self.pn = self.pn[nolowxbt]\\n self.ps = self.ps[nolowxbt]\\n self.ir = self.ir[nolowxbt]\\n self.z = self.z[nolowxbt]\\n self.qc = self.qc[nolowxbt]\\n self.posqc = self.posqc[nolowxbt]\\n \\n # Do the vertical averaging:\\n self.p = np.array(range(len(self.p)))\\n \\n # Check if the maxgap really is fixed:\\n print('Fixed maxgap? ', self.fixedgap == True)\\n \\n # Loop over these profiles and do vertical averages:\\n # Storage vectors need to start off filled with fill values otherwise\\n # you get zero values being included in the averages instead of being\\n # discarded. Make these storage matrices, not vectors so you can store \\n # avergae values for multiple depth levels in them.\\n all_mT = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\\n all_mT.fill(fv)\\n all_lT = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\\n all_lT.fill(fv)\\n all_dep = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\\n all_dep.fill(fv)\\n all_x = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\\n all_x.fill(fv)\\n all_y = np.zeros((len(self.p),np.shape(self.zbounds)[0]-1))\\n all_y.fill(fv)\\n for p in range(len(self.p)):\\n # 1. Select the profile of interest and make sure no tar_t1 values\\n # are carried over from a previous profile:\\n x_p = self.x[p]\\n y_p = self.y[p]\\n qc_p = np.where(np.logical_and(self.qc[p] == True, self.z[p].mask == False))\\n data_p = self.data[p][qc_p]\\n z_p = self.z[p][qc_p].data\\n tar_t1 = fv\\n # 1a. Sanity check to make sure there are no missing data going into\\n # the averaging process:\\n tempanddeppres = np.where(np.logical_and(data_p != fv, z_p != fv))[0]\\n data_p = data_p[tempanddeppres]\\n z_p = z_p[tempanddeppres]\\n # 2. Make sure that depths are in correct order, but sorting takes time\\n # therefore only sort if I've identified non-ascending depths:\\n if np.any((z_p[1:] - z_p[:-1]) < 0):\\n sortz = np.argsort(z_p)\\n data_p = data_p[sortz]\\n z_p = z_p[sortz]\\n # To be in line with the IDL code also need to check for very wrong \\n # depth values that might have slipped through:\\n udep = np.where(np.logical_and(z_p > -99.9, z_p < 10000))\\n z_p = z_p[udep]\\n data_p = data_p[udep]\\n # 3. Find the temperature at the exact depth level - this is now \\n # more in depth as there are multiple depth levels to look at - AT \\n # THE MOMENT THIS STILL ASSUMES THAT THE FIRST LEVEL IS 0 TO SOME\\n # DEPTH - THIS IS STILL A SLIGHT SIMPLIFICATION:\\n dval = 0\\n for dep in self.zbounds[1:]:\\n # Have added in this switch to allow you to specify whether\\n # you're using a fixed gap or not:\\n if self.fixedgap == True:\\n maxgap = 200\\n else:\\n maxgap = max(0.3*(self.zbounds[dval+1]),100)\\n # Get only the levels of the profile in the depth range of \\n # interest:\\n LTi1 = np.where(np.logical_and(z_p < dep, \\n z_p >= self.zbounds[dval]))\\n GEi1 = np.where(z_p >= dep)\\n if (np.shape(LTi1)[1] != 0 and np.shape(GEi1)[1] != 0):\\n # Get the depth differences between layers and the mean temps across\\n # layers:\\n nk = np.shape(LTi1)[1] + 1\\n dz = np.zeros(nk)\\n mt = np.zeros(nk)\\n if dval == 0:\\n dz[0] = z_p[0]\\n mt[0] = data_p[0]\\n for kk in range(1, nk):\\n dz[kk] = z_p[kk] - z_p[kk-1]\\n mt[kk] = 0.5 * (data_p[kk] + data_p[kk-1])\\n else:\\n # Effectively missing the first layer as dz will be zero\\n # there if you've calculated a temperature at that depth\\n # for tar_t1 on the previous loop, but it won't exist if\\n # you haven't been able to calculate a tar_t1 value, so\\n # then you'll have to do what you do when you're at the \\n # first depth level and aren't garunteed a value at 0m.\\n if tar_t1 != fv:\\n dz[0] = z_p[LTi1[0][0]] - self.zbounds[dval]\\n mt[0] = 0.5 * (data_p[LTi1[0][0]] + tar_t1)\\n else:\\n dz[0] = z_p[LTi1[0][0]] - self.zbounds[dval]\\n mt[0] = data_p[LTi1[0][0]]\\n for kk in range(0, nk-1):\\n dz[kk+1] = z_p[LTi1[0][kk]+1] - z_p[LTi1[0][kk]]\\n mt[kk+1] = 0.5 * (data_p[LTi1[0][kk]+1] + data_p[LTi1[0][kk]])\\n \\n # Work out the temp at the target depth:\\n if z_p[GEi1[0][0]] == dep:\\n #print('A sampled depth is equal to the desired level')\\n tar_t1 = data_p[GEi1[0][0]]\\n else:\\n deltaT = data_p[GEi1[0][0]] - data_p[GEi1[0][0] -1]\\n deltaZ = z_p[GEi1[0][0]] - z_p[GEi1[0][0] -1]\\n tar_t1 = (dep - z_p[GEi1[0][0] -1])*(deltaT/deltaZ) + data_p[GEi1[0][0] -1]\\n dz[nk -1] = dep - z_p[GEi1[0][0] -1]\\n mt[nk -1] = 0.5*(data_p[GEi1[0][0] -1] + tar_t1)\\n \\n # Check if there are unacceptable gaps between layers:\\n test_gap = np.where(dz > maxgap)\\n if np.shape(test_gap)[1] != 0:\\n mean_t1 = fv\\n mean_t2 = fv\\n tar_t1 = fv\\n else:\\n mean_t1 = sum(np.multiply(mt,dz))/(self.zbounds[dval+1] - self.zbounds[dval])\\n \\n # Make sure there are no crazy mean values:\\n if (abs(mean_t1) > 100 and mean_t1 != fv):\\n raise ValueError('Extreme values found')\\n # Save the mean temperature at that depth and the temp at the target \\n # depth, also save the depth and profile number:\\n all_mT[p,dval] = mean_t1\\n all_lT[p,dval] = tar_t1\\n all_dep[p,dval] = dep # Lower bound of depth\\n all_x[p,dval] = x_p\\n all_y[p,dval] = y_p\\n else:\\n tar_t1 = fv # Make sure that if you have no data in a \\n # specific depth range you don't carry an old tar_t1 value\\n # over.\\n \\n dval +=1\\n\\n # Reshape\\n self.data_1d = self.reshape_1d(self.data)\\n self.x_1d = self.reshape_1d(self.x)\\n self.y_1d = self.reshape_1d(self.y)\\n self.p_1d = self.reshape_1d(self.p)\\n self.z_1d = self.reshape_1d(self.z)\\n self.qc_1d = self.reshape_1d(self.qc)\\n self.posqc_1d = self.reshape_1d(self.posqc)\\n \\n # Apply QC - Technically the self.posqc_1d step shouldn't be needed as \\n # these profiles will have got filtered out with the earlier QC, but \\n # it's good to leave it in there as then if I remove the depth \\n # restriction step, this step will still catch badly positioned profiles:\\n #qcind = (self.qc_1d == True) & (self.posqc_1d == True)\\n qcind = (self.qc_1d == True)\\n self.qc_1d = self.qc_1d[qcind]\\n self.posqc_1d = self.posqc_1d[qcind]\\n self.data_1d = self.data_1d[qcind] # Still seems to have 99999 values in\\n # it, and I can't see where they get filtered out, but they must get \\n # filtered out somewhere or the mean values wouldn't be sensible.\\n self.x_1d = self.x_1d[qcind]\\n self.y_1d = self.y_1d[qcind]\\n self.p_1d = self.p_1d[qcind]\\n self.z_1d = self.z_1d[qcind]\\n # Trying to work out why I use self.posqc here, not qcind, I think it's \\n # because qcind will be one value for each level for each profile,\\n # whereas posqc is one value for a whole profile - though I don't think\\n # there should be any values left with a bad self.posqc - so this step \\n # is probably redundant:\\n #all_mTqc = all_mT[self.posqc]\\n # Make sure I'm not including values where a mean_t1 value couldn't be \\n # calculated:\\n #all_mTqc1 = all_mTqc[np.where(all_mTqc != 99999.0)]\\n # Need to remember that all_mT may well now be an array not a vector...\\n all_mTqc1 = all_mT[all_mT != 99999.0]\\n all_depqc = all_dep[all_mT != 99999.0]\\n all_xqc = all_x[all_mT != 99999.0]\\n all_yqc = all_y[all_mT != 99999.0]\\n \\n # Prepare data for gridding\\n self.init_xgrid()\\n self.init_ygrid()\\n self.init_zgrid()\\n \\n # Getting the unique profile references:\\n punique = np.unique(self.p_1d, return_index = True)[1]\\n \\n # From the unique profile references selecting only those profiles that \\n # have a mean temperature over the depth of interest:\\n #puniqueqc = punique[all_mT != 99999.0]\\n \\n # Getting references for all the points - so every temp value will have \\n # a depth, lat and long i.e. you will still have multiple points per \\n # profile:\\n points = np.vstack([self.z_1d, self.y_1d, self.x_1d]).transpose()\\n \\n # Getting a single reference for each profile, this will just take the \\n # first value in points, for each profile => points2 will have a \\n # latitude, a longitude and the shallowest accepted depth of the profile\\n # => This point will always (pretty much) be put into the top set of \\n # grid boxes, would therefore need to consider this further if I wanted\\n # to populate multiple depth level grid boxes at once:\\n points2 = np.vstack([self.z_1d[punique], self.y_1d[punique], \\n self.x_1d[punique]]).transpose()\\n \\n # points3 is like points 2, but gets coordinates for only profiles that\\n # have a mean temperature down to the depth of interest:\\n #points3 = np.vstack([np.ones(len(puniqueqc)), self.y_1d[puniqueqc], \\n # self.x_1d[puniqueqc]]).transpose()\\n points3 = np.vstack([all_depqc - 1, all_yqc, all_xqc]).transpose()\\n \\n # Pretty self explanatory - the boundaries of the grid boxes:\\n bins = [self.zbounds, self.ybounds, self.xbounds]\\n \\n # Grid data:\\n grid_count, binedges, binno = scipy.stats.binned_statistic_dd(\\n points, self.data_1d, statistic='count', bins=bins)\\n grid_sum, binedges, binno = scipy.stats.binned_statistic_dd(\\n points, self.data_1d, statistic='sum', bins=bins)\\n grid_meansum, binedges, binno = scipy.stats.binned_statistic_dd(\\n points3, all_mTqc1, statistic = 'sum', bins = bins)\\n grid_pcount, binedges, binno = scipy.stats.binned_statistic_dd(\\n points3, all_mTqc1, statistic='count', bins=bins)\\n# grid_pcount, binedges, binno = scipy.stats.binned_statistic_dd(\\n# points3, self.data_1d[puniqueqc], statistic='count', bins=bins)\\n# grid_max, binedges, binno = scipy.stats.binned_statistic_dd(\\n# points, self.data_1d, statistic = 'max', bins=bins)\\n# grid_min, binedges, binno = scipy.stats.binned_statistic_dd(\\n# points, self.data_1d, statistic = 'min', bins=bins)\\n# grid_med, binedges, binno = scipy.stats.binned_statistic_dd(\\n# points, all_mTqc1, statistic = 'median', bins = bins)\\n \\n # Sum of valid temps/ number of valid obs:\\n grid_tmean = grid_sum / grid_count\\n grid_tmean = np.ma.MaskedArray(grid_tmean, mask = (grid_count == 0))\\n # Sum of valid mean temps/ number of valid profiles:\\n grid_meantmean = grid_meansum / grid_pcount\\n grid_meantmean = np.ma.MaskedArray(grid_meantmean, mask = (grid_pcount == 0))\\n self.grid_tmean = grid_tmean\\n self.grid_count = grid_count\\n self.grid_sum = grid_sum\\n self.grid_meansum = grid_meansum\\n self.grid_pcount = grid_pcount\\n self.grid_meantmean = grid_meantmean\",\n \"def main(\\n ts_dir: str,\\n save_dir: str,\\n figure_dir: str,\\n arima_order: Tuple[int] = (1, 0, 1),\\n start = datetime(2000, 1, 1),\\n end = datetime(2019, 9, 30),\\n verbose: bool = True\\n) -> None:\\n # Load dataset\\n df = pd.read_csv(\\n ts_dir,\\n index_col=0,\\n header=0,\\n parse_dates=[\\\"DATE\\\"],\\n date_parser=lambda d: datetime.strptime(d, \\\"%Y-%m-%d\\\")\\n )\\n df.replace(\\\".\\\", np.NaN, inplace=True)\\n df = df.astype(np.float32)\\n if verbose:\\n print(\\\"** Raw Dataset **\\\")\\n df.info()\\n print(f\\\"Nan ratio: {np.mean(np.isnan(df.values.squeeze())) * 100: .2}%\\\")\\n\\n # select subset.\\n def _select_range(df):\\n return df[np.logical_and(\\n df.index >= start, df.index <= end\\n )]\\n df = _select_range(df)\\n if verbose:\\n print(\\\"** Raw Dataset **\\\")\\n df.info()\\n\\n df_filled = utils.arima_interpolate(\\n raw=df,\\n arima_order=arima_order,\\n verbose=verbose\\n )\\n df_filled.to_csv(save_dir)\\n # Visualize Interpolation results.\\n utils.visualize_interpolation(df, df_filled, figure_dir)\\n # return df, df_filled\\n return None\",\n \"def gendata(params,xmin,xmax,npts=4000):\\n F = lorentzian.ForwardFactory\\n def gensample(F, xmin, xmax):\\n from numpy import arange\\n import random\\n a = arange(xmin, xmax, (xmax-xmin)/200.)\\n ymin = 0\\n ymax = F(a).max()\\n while 1:\\n t1 = random.random() * (xmax-xmin) + xmin\\n t2 = random.random() * (ymax-ymin) + ymin\\n t3 = F(t1)\\n if t2 < t3:\\n return t1\\n fwd = F(params)\\n return array([gensample(fwd, xmin,xmax) for i in xrange(npts)])\",\n \"def _create_historic_forecasts(\\n data, time_dt, frt_dt, standard_grid_metadata=\\\"uk_ens\\\", number_of_days=5, **kwargs\\n):\\n historic_forecasts = iris.cube.CubeList([])\\n for day in range(number_of_days):\\n new_frt_dt = frt_dt + datetime.timedelta(days=day)\\n new_time_dt = time_dt + datetime.timedelta(days=day)\\n historic_forecasts.append(\\n set_up_variable_cube(\\n data - 2 + 0.2 * day,\\n time=new_time_dt,\\n frt=new_frt_dt,\\n standard_grid_metadata=standard_grid_metadata,\\n **kwargs,\\n )\\n )\\n return historic_forecasts\",\n \"def generate_obs_grid(start_date, end_date, storm_report_path, model_grid_path, proj_str):\\n\\n grid = xr.open_dataset(model_grid_path)\\n for coord in ['lon', 'lat']:\\n grid[coord].values = grid[coord].astype('float32')\\n valid_dates = pd.date_range(start_date, end_date, freq='1h')\\n\\n obs_list = []\\n\\n for report_type in ['filtered_torn', 'filtered_wind', 'filtered_hail']:\\n\\n ds_list = []\\n\\n obs = combine_storm_reports(valid_dates.min(), valid_dates.max(), storm_report_path, report_type)\\n\\n for valid_date in valid_dates:\\n\\n ds = grid.expand_dims('time').assign_coords(valid_time=('time', [valid_date]))\\n ds[report_type.split('_')[-1]] = ds['lat'] * 0\\n\\n obs_sub = obs[obs['Actual_Date'] == valid_date]\\n obs_indx = find_coord_indices(ds['lon'].values, ds['lat'].values, obs_sub['Lon'], obs_sub['Lat'], proj_str)\\n for i in obs_indx:\\n if i is not None:\\n ds[report_type.split('_')[-1]][i[0], i[1]] += 1\\n else:\\n continue\\n ds_list.append(ds)\\n\\n obs_list.append(xr.concat(ds_list, dim='time'))\\n\\n return xr.merge(obs_list)\",\n \"def main(args, **kwargs):\\n data_file = os.path.join(kwargs['data_dir'],\\n '{}_{}m_{}_{}.npz'.format(args.area, args.resolution, args.year, args.tar_date))\\n data = np.load(data_file)\\n tar_label_mat = data['label_mat']\\n tar_dynamic_mat = data['dynamic_mat']\\n static_mat, tar_static_mat = data['static_mat'], data['static_mat']\\n mapping_mat = data['mapping_mat']\\n dynamic_features, static_features = list(data['dynamic_features']), list(data['static_features'])\\n\\n \\\"\\\"\\\" extract information from all time period \\\"\\\"\\\"\\n dynamic_mat, label_mat = [], []\\n for date in args.dates:\\n data_file = os.path.join(kwargs['data_dir'],\\n '{}_{}m_{}_{}.npz'.format(args.area, args.resolution, args.year, date))\\n data = np.load(data_file)\\n dynamic_mat.append(data['dynamic_mat'])\\n label_mat.append(data['label_mat'])\\n dynamic_mat = np.concatenate(dynamic_mat)\\n label_mat = np.concatenate(label_mat)\\n\\n data_obj = DataObj(label_mat, dynamic_mat, static_mat,\\n tar_label_mat, tar_dynamic_mat, tar_static_mat,\\n dynamic_features, static_features, mapping_mat)\\n\\n \\\"\\\"\\\" load train, val, test locations \\\"\\\"\\\"\\n data_obj.train_loc, data_obj.val_loc, data_obj.test_loc = load_train_val_test(kwargs['train_val_test_file'], args)\\n\\n data_obj.train_y = data_obj.gen_train_val_test_label(data_obj.label_mat, data_obj.train_loc)\\n data_obj.val_y = data_obj.gen_train_val_test_label(data_obj.label_mat, data_obj.val_loc)\\n data_obj.test_y = data_obj.gen_train_val_test_label(data_obj.tar_label_mat, data_obj.test_loc)\\n\\n logging.info('Number of features = {}.'.format(data_obj.n_features))\\n logging.info('Number of dynamic features = {}.'.format(data_obj.n_dynamic_features))\\n logging.info('Number of static features = {}.'.format(data_obj.n_static_features))\\n logging.info('Number of time points = {}.'.format(data_obj.n_times))\\n logging.info('Shape of the matrix = ({}, {}).'.format(data_obj.n_rows, data_obj.n_cols))\\n\\n \\\"\\\"\\\" normalize data \\\"\\\"\\\"\\n data_obj.dynamic_x = normalize_mat(data_obj.dynamic_mat, if_retain_last_dim=True)\\n data_obj.static_x = normalize_mat(data_obj.static_mat, if_retain_last_dim=True)\\n data_obj.tar_dynamic_x = normalize_mat(data_obj.tar_dynamic_mat, if_retain_last_dim=True)\\n data_obj.tar_static_x = normalize_mat(data_obj.tar_static_mat, if_retain_last_dim=True)\\n\\n \\\"\\\"\\\" load auto-encoder model \\\"\\\"\\\"\\n ae = torch.load(os.path.join(kwargs['model_dir'], kwargs['ae_model_name'] + '.pkl'))\\n\\n \\\"\\\"\\\" define DeepAP model \\\"\\\"\\\"\\n dap = DeepAP(in_dim=data_obj.n_features,\\n ae_en_h_dims=[64, 32, 16],\\n ae_de_h_dims=[16, 32, 64],\\n\\n conv_lstm_in_size=(data_obj.n_rows, data_obj.n_cols),\\n conv_lstm_in_dim=args.ae_h_dim, # ae_h_dim\\n conv_lstm_h_dim=[args.dap_h_dim], # dap_h_dim\\n conv_lstm_kernel_sizes=args.kernel_sizes, # kernel_sizes\\n conv_lstm_n_layers=1,\\n\\n fc_in_dim=args.dap_h_dim * len(args.kernel_sizes),\\n fc_h_dims=args.fc_h_dims, # fc_h_dims\\n fc_out_dim=1,\\n\\n ae_pretrain_weight=ae.state_dict(),\\n if_trainable=True,\\n fc_p_dropout=0.1,\\n\\n mask_thre=args.mask_thr,\\n device=kwargs['device'])\\n\\n dap = dap.to(kwargs['device'])\\n train(dap, data_obj, args, **kwargs)\",\n \"def get_time_series(this_lat, this_lon, case, varnames):\\n\\n cesmdir = '/gpfs/fs1/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly'\\n\\n if 'LE' in case:\\n\\n from observational_large_ensemble.params import karen_params_cesm\\n\\n mode_lag = karen_params_cesm.mode_lag\\n cvdp_loc = karen_params_cesm.cvdp_loc\\n AMO_cutoff_freq = karen_params_cesm.AMO_cutoff_freq\\n\\n name_conversion = {'tas': 'TREFHT', 'pr': 'PRECC', 'slp': 'PSL'}\\n cesm_names = [name_conversion[v] for v in varnames]\\n this_member = int((case).split('-')[-1])\\n cvdp_file = '%s/CESM1-CAM5-BGC-LE_#%i.cvdp_data.1920-2018.nc' % (cvdp_loc, this_member)\\n\\n # Historical filenames for CESM. Will need to append part of RCP8.5 to get full period\\n filenames = []\\n for var in cesm_names:\\n file_str = '%s/%s/b.e11.B20TRC5CNBDRD.f09_g16.%03d.cam.h0.%s.??????-200512.nc' % (cesmdir, var,\\n this_member, var)\\n this_file = glob(file_str)[0]\\n filenames.append(this_file)\\n\\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames,\\n karen_params_cesm.valid_years, mode_lag,\\n cvdp_file, AMO_cutoff_freq, name_conversion)\\n\\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\\n\\n else:\\n\\n from observational_large_ensemble.params import karen_params_obs\\n\\n mode_lag = karen_params_obs.mode_lag\\n cvdp_loc = karen_params_obs.cvdp_loc\\n AMO_cutoff_freq = karen_params_obs.AMO_cutoff_freq\\n\\n tas_dir = karen_params_obs.tas_dir\\n pr_dir = karen_params_obs.pr_dir\\n slp_dir = karen_params_obs.slp_dir\\n cvdp_file = '%s/HadISST.cvdp_data.1920-2018.nc' % cvdp_loc\\n file_dict = {'tas': '%s/Complete_TAVG_LatLong1.nc' % tas_dir,\\n 'pr': '%s/full_data_monthly_v2020.nc' % pr_dir,\\n 'slp': '%s/prmsl.mon.mean.nc' % slp_dir}\\n\\n filenames = []\\n for var in varnames:\\n filenames.append(file_dict[var])\\n\\n name_conversion = {'tas': 'temperature', 'pr': 'precip', 'slp': 'prmsl'}\\n\\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames[0],\\n karen_params_obs.valid_years, mode_lag,\\n cvdp_file, AMO_cutoff_freq, name_conversion)\\n\\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\\n\\n return this_ts, df_shifted\",\n \"def run_regression(auto_input,\\n variables: list = ['magasin', 'tilsig'],\\n regions: list = ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'],\\n jupyter: bool = False,\\n backup: bool = False,\\n loop: bool = False,\\n write: bool = True,\\n week_nb: int = False,\\n year: int = False) -> None:\\n\\n start_tuning = utctime_now()\\n\\n for region in regions:\\n\\n if not region in ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4']:\\n sys.exit(\\\"Region must be one out of: 'NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'\\\")\\n\\n for variable in variables:\\n\\n if not variable in ['magasin', 'tilsig']:\\n sys.exit(\\\"Variable must be either 'tilsig' or 'magasin'\\\")\\n\\n print('---------------------------------------------------------------')\\n print(' {}, {} '.format(region, variable))\\n print('---------------------------------------------------------------')\\n\\n \\n \\n df_week, MagKap = auto_input[variable]\\n \\n period, forecast_time, read_start = rs.get_timeperiods(variable, week_nb, year)\\n \\n reg_end = (pd.to_datetime(time.strftime(forecast_time), format=\\\"%Y.%m.%d\\\") - Timedelta(days=7)).strftime(\\n '%Y.%m.%d')\\n\\n if (0 <= today.weekday() <= 1) or (today.weekday() == 2 and today.hour < 14): # True for tipping\\n last_forecast = forecast_time\\n else:\\n last_forecast = forecast_time\\n\\n df_cleaned = deletingNaNs(df_week.loc[:last_forecast])\\n\\n if loop:\\n if variable == 'tilsig':\\n max_kandidater = 196\\n min_kandidater = 2\\n\\n else:\\n max_kandidater = 171\\n min_kandidater = 2\\n\\n max_weeks = 238 #288\\n min_weeks = 16\\n print('max ant. kandidater: {}, min ant. kandidater: {}'.format(max_kandidater, min_kandidater))\\n print('max ant. uker: {}, min ant. uker: {}'.format(max_weeks, min_weeks))\\n\\n start_time_loop = utctime_now()\\n fasit, fasit_key = rs.make_fasit(variable, region, reg_end, period)\\n print('Fasit er lest inn.\\\\n')\\n\\n if fasit[fasit_key][:reg_end].isnull().any():\\n print('OBS: Det mangler verdier på fasiten! Går videre til neste region i loopen..')\\n print(fasit[fasit_key][:reg_end].isnull())\\n continue\\n\\n sorted_r2 = get_R2_sorted(variable, df_cleaned, fasit, fasit_key)\\n\\n if loop:\\n max_p = 0.001 # Beste testresultat SvF\\n\\n # First loop: Tuning number of candidates for best possible R2 combined\\n df_ant_kandidater = pd.DataFrame(columns=columns)\\n for antall in range(min_kandidater, max_kandidater + 1, 1):\\n if antall > len(sorted_r2):\\n chosen_r2 = sorted_r2\\n else:\\n chosen_r2 = sorted_r2[:antall]\\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, first_period, max_p, chosen_r2,\\n loop=True)\\n df_ant_kandidater = df_ant_kandidater.append(\\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\\n if antall > len(sorted_r2):\\n print('Feilmelding: Ønsket antall kandidater overskrider maks (%i).\\\\n' % len(sorted_r2))\\n break\\n idx_max = df_ant_kandidater.r2_samlet.idxmax(skipna=True)\\n ant_kandidater_beste = int(df_ant_kandidater.ant_kandidater.values[idx_max])\\n print('Beste ant_kandidater loop 1: ', ant_kandidater_beste)\\n\\n # Second loop: tuning length of the short regression for best possible R2 combined, using the best number of\\n # candidates found in the First loop.\\n final_chosen_r2 = sorted_r2[:ant_kandidater_beste]\\n df_short_period = pd.DataFrame(columns=columns)\\n for short_period in range(min_weeks, max_weeks + 1, 1):\\n short_period = int(short_period)\\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period, max_p,\\n final_chosen_r2, loop=True)\\n df_short_period = df_short_period.append(\\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\\n idx_max = df_short_period.r2_samlet.idxmax(skipna=True)\\n short_period_beste = int(df_short_period.short_period.values[idx_max])\\n print('Beste short_period loop 2: ', short_period_beste)\\n\\n # Getting the best input variables from loop and write to input_variables_from_tuning.txt\\n df_all_methods = pd.concat([df_ant_kandidater, df_short_period], ignore_index=True, sort=False)\\n idx_max = df_all_methods.r2_samlet.idxmax(skipna=True)\\n ant_kandidater_beste = int(df_all_methods.ant_kandidater.values[idx_max])\\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\\n short_period_beste = df_all_methods.short_period.values[idx_max]\\n ws.write_input_variables_to_file(region, variable, max_p, ant_kandidater_beste, short_period_beste)\\n\\n print('\\\\nTuning for regionen tok %.0f minutter. \\\\n' % ((utctime_now() - start_time_loop) / 60))\\n\\n else:\\n # getting the best variables from input_variables_from_tuning.txt or input_variables_backup.txr\\n short_period_beste, max_p, ant_kandidater_beste, input_file = rs.get_input_variables_from_file(variable,region,backup)\\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\\n print(\\\"Input variables was read from: \\\", input_file)\\n\\n # MAKE ESTIMATE AND SHOW RESULTS\\n input1 = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period_beste, max_p,\\n chosen_r2_beste, loop=False)\\n input2 = fasit_key, ant_kandidater_beste, max_p, reg_end, read_start\\n\\n\\n #WRITE RESULTS\\n if write:\\n # Write results from the regression to SMG.\\n fasit, long_results, short_results, df_tot, chosen_p, chosen_r2, r2_modelled, r2_modelled_long, prediction, tipping_ps, short_period, nb_weeks_tipping = input1\\n\\n # write to SMG:\\n ws.write_SMG_regresjon(variable, region, tipping_ps[-1:])\\n\\n # write to SMG, virtual:\\n ws.write_V_SMG_Regresjon(short_results, chosen_p, fasit_key, r2_modelled, r2_modelled_long, short_period_beste, MagKap)\\n\\n if jupyter:\\n ws.show_result_jupyter(input1, input2)\\n else:\\n ws.show_result(input1, input2, input_file)\\n\\n print('---------------------------------------------------------------')\\n print(' SLUTT ')\\n print('---------------------------------------------------------------')\\n print('\\\\nRegresjon for alle regioner og variabler brukte totalt %.0f minutter. \\\\n' % (\\n (utctime_now() - start_tuning) / 60))\",\n \"def par_fit(init_file):\\n check_presence_init(init_file)\\n\\n dict_ = read(init_file)\\n np.random.seed(dict_[\\\"SIMULATION\\\"][\\\"seed\\\"])\\n\\n # We perform some basic consistency checks regarding the user's request.\\n check_presence_estimation_dataset(dict_)\\n # check_initialization_dict2(dict_)\\n # check_init_file(dict_)\\n\\n # Distribute initialization information.\\n data = read_data(dict_[\\\"ESTIMATION\\\"][\\\"file\\\"])\\n num_treated = dict_[\\\"AUX\\\"][\\\"num_covars_treated\\\"]\\n num_untreated = num_treated + dict_[\\\"AUX\\\"][\\\"num_covars_untreated\\\"]\\n\\n _, X1, X0, Z1, Z0, Y1, Y0 = process_data(data, dict_)\\n\\n if dict_[\\\"ESTIMATION\\\"][\\\"maxiter\\\"] == 0:\\n option = \\\"init\\\"\\n else:\\n option = dict_[\\\"ESTIMATION\\\"][\\\"start\\\"]\\n\\n # Read data frame\\n\\n # define starting values\\n x0 = start_values(dict_, data, option)\\n opts, method = optimizer_options(dict_)\\n dict_[\\\"AUX\\\"][\\\"criteria\\\"] = calculate_criteria(dict_, X1, X0, Z1, Z0, Y1, Y0, x0)\\n dict_[\\\"AUX\\\"][\\\"starting_values\\\"] = backward_transformation(x0)\\n rslt_dict = bfgs_dict()\\n if opts[\\\"maxiter\\\"] == 0:\\n rslt = adjust_output(None, dict_, x0, X1, X0, Z1, Z0, Y1, Y0, rslt_dict)\\n else:\\n opt_rslt = minimize(\\n minimizing_interface,\\n x0,\\n args=(dict_, X1, X0, Z1, Z0, Y1, Y0, num_treated, num_untreated, rslt_dict),\\n method=method,\\n options=opts,\\n )\\n rslt = adjust_output(\\n opt_rslt, dict_, opt_rslt[\\\"x\\\"], X1, X0, Z1, Z0, Y1, Y0, rslt_dict\\n )\\n # Print Output files\\n print_logfile(dict_, rslt)\\n\\n if \\\"comparison\\\" in dict_[\\\"ESTIMATION\\\"].keys():\\n if dict_[\\\"ESTIMATION\\\"][\\\"comparison\\\"] == 0:\\n pass\\n else:\\n write_comparison(data, rslt)\\n else:\\n write_comparison(data, rslt)\\n\\n return rslt\",\n \"def form_data(stocks, init_param):\\r\\n \\r\\n rs = stocks[1].rsi\\r\\n ts = stocks[1].tsi\\r\\n a = 1\\r\\n \\r\\n \\r\\n for date in init_param.train_dates:\\r\\n try:\\r\\n training_data\\r\\n except NameError:\\r\\n training_data = LearningData()\\r\\n training_data.construct(stocks, date, init_param.future_day, init_param.features)\\r\\n else:\\r\\n training_data.append(stocks, date, init_param.future_day, init_param.features)\\r\\n \\r\\n for date in init_param.test_dates:\\r\\n try:\\r\\n test_data\\r\\n except NameError:\\r\\n test_data = LearningData()\\r\\n test_data.construct(stocks, date, init_param.future_day, init_param.features)\\r\\n else:\\r\\n test_data.append(stocks, date, init_param.future_day, init_param.features)\\r\\n \\r\\n #reference_date = dateutl.days_since_1900('1991-01-01')\\r\\n #test_data.construct(stocks,[reference_date, day_history, init_param.future_day])\\r\\n \\r\\n return training_data, test_data\",\n \"def grid_interp_ts(df, time_col, x_col, y_col, data_col, grid_res, from_crs=None, to_crs=2193, interp_fun='cubic', agg_ts_fun=None, period=None, digits=2):\\n\\n #### Create the grids\\n df1 = df.copy()\\n\\n #### Resample the time series data\\n if agg_ts_fun is not None:\\n df1a = df1.set_index(time_col)\\n if agg_ts_fun == 'sum':\\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].sum().reset_index()\\n elif agg_ts_fun == 'mean':\\n df2 = df1a.groupby([TimeGrouper(period), Grouper(y_col), Grouper(x_col)])[data_col].mean().reset_index()\\n else:\\n raise ValueError(\\\"agg_ts_fun should be either 'sum' or 'mean'.\\\")\\n time = df2[time_col].unique()\\n else:\\n df2 = df1\\n\\n time = df2[time_col].sort_values().unique()\\n\\n if from_crs is None:\\n x = df2.loc[df2[time_col] == time[0], x_col].values\\n y = df2.loc[df2[time_col] == time[0], y_col].values\\n else:\\n data1 = df2.loc[df2[time_col] == time[0]]\\n from_crs1 = convert_crs(from_crs, pass_str=True)\\n to_crs1 = convert_crs(to_crs, pass_str=True)\\n geometry = [Point(xy) for xy in zip(data1[x_col], data1[y_col])]\\n gpd = GeoDataFrame(data1.index, geometry=geometry, crs=from_crs1)\\n gpd1 = gpd.to_crs(crs=to_crs1)\\n x = gpd1.geometry.apply(lambda p: p.x).round(digits).values\\n y = gpd1.geometry.apply(lambda p: p.y).round(digits).values\\n\\n xy = column_stack((x, y))\\n\\n max_x = x.max()\\n min_x = x.min()\\n\\n max_y = y.max()\\n min_y = y.min()\\n\\n new_x = arange(min_x, max_x, grid_res)\\n new_y = arange(min_y, max_y, grid_res)\\n x_int, y_int = meshgrid(new_x, new_y)\\n\\n #### Create new df\\n x_int2 = x_int.flatten()\\n y_int2 = y_int.flatten()\\n xy_int = column_stack((x_int2, y_int2))\\n time_df = repeat(time, len(x_int2))\\n x_df = tile(x_int2, len(time))\\n y_df = tile(y_int2, len(time))\\n new_df = DataFrame({'time': time_df, 'x': x_df, 'y': y_df, data_col: repeat(0, len(time) * len(x_int2))})\\n\\n new_lst = []\\n for t in to_datetime(time):\\n set1 = df2.loc[df2[time_col] == t, data_col]\\n# index = new_df[new_df['time'] == t].index\\n new_z = griddata(xy, set1.values, xy_int, method=interp_fun).round(digits)\\n new_z[new_z < 0] = 0\\n new_lst.extend(new_z.tolist())\\n# print(t)\\n new_df.loc[:, data_col] = new_lst\\n\\n #### Export results\\n return(new_df[new_df[data_col].notnull()])\",\n \"def build_params(clsite, dt):\\n hp = GaugeParams()\\n lp = GaugeParams()\\n if clsite is not None:\\n with get_sqlalchemy_conn(\\\"coop\\\") as conn:\\n df = pd.read_sql(\\n \\\"SELECT year, high, low from alldata WHERE \\\"\\n \\\"station = %s and sday = %s\\\",\\n conn,\\n params=(clsite, f\\\"{dt:%m%d}\\\"),\\n index_col=\\\"year\\\",\\n )\\n hp.minval = df[\\\"high\\\"].min()\\n hp.maxval = df[\\\"high\\\"].max()\\n hp.avgval = df[\\\"high\\\"].mean()\\n hp.stddev = df[\\\"high\\\"].std()\\n hp.ptiles = df[\\\"high\\\"].quantile(np.arange(0.1, 0.91, 0.1)).to_list()\\n lp.maxval = df[\\\"low\\\"].max()\\n lp.minval = df[\\\"low\\\"].min()\\n lp.avgval = df[\\\"low\\\"].mean()\\n lp.stddev = df[\\\"low\\\"].std()\\n lp.ptiles = df[\\\"low\\\"].quantile(np.arange(0.1, 0.91, 0.1)).to_list()\\n\\n return hp, lp\",\n \"def __init__(self, prefix, params, begin, end, simthreshold_NS = 0.5, \\r\\n simthreshold_pbias = 25., simthreshold_rsr = 0.7, \\r\\n with_valid_data = False, shift_one_day = False):\\r\\n self.simthreshold_NS = simthreshold_NS\\r\\n self.simthreshold_pbias = simthreshold_pbias\\r\\n self.simthreshold_rsr = simthreshold_rsr\\r\\n self.with_valid_data = with_valid_data\\r\\n self.shift_one_day = shift_one_day\\r\\n # Open the simulation file\\r\\n try:\\r\\n task_id = int(os.environ.get('SGE_TASK_ID',0))\\r\\n except:\\r\\n task_id = 0\\r\\n postfix = '.%04i' % task_id if task_id else ''\\r\\n self.outfile_sim = open(prefix + '-simulation.csv' + postfix,'w')\\r\\n # Open the parameter file\\r\\n self.outfile_param = open(prefix + '-parameters.csv' + postfix,'w')\\r\\n # Make the header\\r\\n if task_id<2:\\r\\n header = 'logNS,' + \\\"pbias,\\\" + \\\"rsr,\\\" + ','.join(p.name for p in params)\\r\\n self.outfile_param.write(header + '\\\\n')\\r\\n # Prolong the header with the data\\r\\n t = begin\\r\\n # add the validation period if wanted\\r\\n if self.with_valid_data:\\r\\n end = datetime.datetime(1988,12,31)\\r\\n # if the whole timeseries is shifted add one day to the header\\r\\n if self.shift_one_day:\\r\\n end = datetime.datetime(1989,1,1)\\r\\n while t <= end:\\r\\n header += ', ' + datetime.datetime.strftime(t,'%Y-%m-%d')\\r\\n t += datetime.timedelta(days=1)\\r\\n self.outfile_sim.write(header + '\\\\n')\",\n \"def extract_dates(self,dates, tol= 0.05 , in_place=False, verbose=True):\\n###################################################################\\n\\n # import \\n import inspect\\n import numpy as np\\n import pyacs.gts\\n\\n # check data is not None\\n from pyacs.gts.lib.errors import GtsInputDataNone\\n \\n try:\\n if self.data is None:\\n # raise exception\\n raise GtsInputDataNone(inspect.stack()[0][3],__name__,self)\\n except GtsInputDataNone as error:\\n # print PYACS WARNING\\n print( error )\\n return( self )\\n\\n # working gts\\n new_gts = self.copy()\\n \\n # case .data_xyz is None\\n \\n if new_gts.data_xyz is None:\\n new_gts.neu2xyz(corr=True)\\n\\n else:\\n # check data/data_xyz consistency\\n try:\\n if not new_gts.cdata(data=True):\\n # raise exception\\n from pyacs.gts.lib.errors import GtsCDataError\\n raise GtsCDataError( inspect.stack()[0][3],__name__,self )\\n except GtsCDataError as error:\\n print( error )\\n return( self )\\n \\n \\n new_data=None\\n \\n # extract dates\\n \\n index = np.array( pyacs.gts.Gts.get_index_from_dates(dates, self.data, tol=tol) )\\n \\n if verbose:\\n print('-- Extracting ',index.shape[0],' entries from Gts or code: ',self.code)\\n \\n if index.shape[0] > 0:\\n new_data_xyz= self.data_xyz[index,:]\\n new_sigma = self.data[index,4:]\\n else:\\n new_data=None\\n if verbose:\\n print(\\\"-- time series \\\",self.code,\\\" does not have dates at the requested dates \\\")\\n\\n \\n # handles outliers\\n\\n if new_data is not None: \\n ldate_outliers=self.data[:,0][self.outliers]\\n lupdated_outliers=pyacs.gts.Gts.get_index_from_dates(ldate_outliers, new_data, tol=tol)\\n else:\\n lupdated_outliers = []\\n\\n if verbose:\\n print('-- Transmitting ',len(lupdated_outliers),' outliers to the extracted Gts ')\\n \\n # case observations\\n \\n new_gts.data_xyz = new_data_xyz\\n \\n # handle outliers\\n\\n ldate_outliers=self.data[:,0][self.outliers]\\n lupdated_outliers=pyacs.gts.Gts.get_index_from_dates(ldate_outliers, new_data_xyz, tol=0.05)\\n \\n # handles offsets_date\\n \\n upd_offsets=[]\\n for offset_date in self.offsets_dates:\\n if offset_date>=new_data_xyz[0,0] and offset_date<=new_data_xyz[-1,0]:\\n upd_offsets.append(offset_date)\\n \\n # handles X0,Y0,Z0\\n \\n new_gts.X0 = new_data_xyz[0,1]\\n new_gts.Y0 = new_data_xyz[0,2]\\n new_gts.Z0 = new_data_xyz[0,3]\\n \\n # re-generate NEU time series\\n new_gts.xyz2neu(corr=False)\\n\\n # re-populate the uncertainties columns\\n new_gts.data[:,4:] = new_sigma\\n \\n # offsets & outliers\\n \\n new_gts.offsets_dates=upd_offsets\\n new_gts.outliers=lupdated_outliers\\n \\n if in_place:\\n self = new_gts\\n return(self)\\n else:\\n return(new_gts)\",\n \"def make_data(args):\\n mass_MJ = 1.142\\n radius_RJ = 1.138\\n gravity_SI = 23.970 \\n Rs_Rsun = 0.805\\n inc = 85.71\\n t0 = 2454037.612\\n sma = 8.839304998 # semi major axis in stellar radiu\\n orb_per = 2.21857545 #in days\\n ecc = 0.0041\\n w_peri = -24.1 # longiutude of periastron\\n limbdark = \\\"linear\\\"\\n \\n u_limbdark = [0.35]\\n \\n num_transit = 1\\n \\n dates = [2458383.77055943, 2458383.77384704, 2458383.77707875,\\n 2458383.78030307, 2458383.78358918, 2458383.78681399,\\n 2458383.79004101, 2458383.79326712, 2458383.79655574,\\n 2458383.79984545, 2458383.80307906, 2458383.80629228,\\n 2458383.80958299, 2458383.8128124 , 2458383.81603942,\\n 2458383.81925973, 2458383.82248474, 2458383.82577195,\\n 2458383.82900097, 2458383.83223048, 2458383.8354501 ,\\n 2458383.83874811, 2458383.84196822, 2458383.84520053,\\n 2458383.84847654, 2458383.85170346, 2458383.85493727,\\n 2458383.85821578, 2458383.86144419, 2458383.86466921,\\n 2458383.86790322, 2458383.87118233, 2458383.87441074,\\n 2458383.87763435, 2458383.88092406, 2458383.88414957],\\n #don't forget the coma at the end if there is only one transit !!!!!\\n \\n\\n\\n # Wmean = [2400.695909757236,2328.5343131275904,1972.9809993156186,\\n # 1927.2107049022654,]\\n # Wmean = [1634.5200937047302,1600.8109822367207],[1670.071564637037,1634.5459486709924,1600.8124596368639],\\n Wmean = [2328.5343131275904], \\n orderstot = [33]\\n orders = [33],\\n # orderstot = [46,47,48]\\n # orders = [47,48],[46,47,48],\\n \\n # Vfiles = [\\\"Vcorr47_DRS2.txt\\\",\\n # \\\"Vcorr48_DRS2.txt\\\",\\n # ],[\\\"Vcorr46_Jun19-1_DRS2.txt\\\",\\n # \\\"Vcorr47_Jun19-1_DRS2.txt\\\",\\n # \\\"Vcorr48_Jun19-1_DRS2.txt\\\"\\n # ],\\n Vfiles = [\\\"V33_CO.txt\\\"], \\n \\n Ifiles = [\\\"I33_CO.txt\\\"],\\n \\n # if Stdfiles are not needed, for example with the Brogi likelihood, \\n # uncomment the next line\\n #Stdfiles = []\\n Stdfiles = [\\\"Std33_CO.txt\\\"],\\n \\n lambdas = np.array([[ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [2291.84518119, 2362.55271775],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [1939.42197854, 1998.81548771],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [1758.50261646, 1812.39702422],\\n [1718.50054581, 1771.64067835],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [1512.43747007, 1558.89713666],\\n [1484.77586677, 1528.30354258],\\n [1457.06015806, 1498.88570675],\\n [1429.75333156, 1470.19096444],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [1306.967007 , 1343.21643463],\\n [1285.02046052, 1320.56072659],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [1167.78440327, 1198.13940642],\\n [1150.59417256, 1178.48372217],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ],\\n [ 0. , 0. ]])\\n\\n return dict(\\n mass_MJ=mass_MJ,\\n radius_RJ=radius_RJ,\\n\\t\\tgravity_SI = gravity_SI,\\n\\t\\tRs_Rsun = Rs_Rsun,\\n\\t\\tinc = inc,\\n\\t\\tt0 = t0,\\n\\t\\tsma = sma,\\n\\t\\torb_per = orb_per,\\n\\t\\tecc = ecc,\\n\\t\\tw_peri = w_peri,\\n Wmean = Wmean,\\n\\t\\tlimbdark = limbdark,\\n\\t\\tu_limbdark = u_limbdark,\\n\\t\\tdates = dates,\\n\\t\\tlambdas = lambdas,\\n orders = orders,\\n orderstot=orderstot,\\n num_transit=num_transit,\\n\\t\\tVfiles = Vfiles,\\n\\t\\tIfiles = Ifiles,\\n\\t\\tStdfiles = Stdfiles\\n\\t\\t )\",\n \"def params(timeseries_input):\\n # Settings for Nelder Mead Algorithm\\n global timeseries\\n timeseries=timeseries_input\\n\\n NumIters = 1 # First Iteration\\n MaxIters = 1e3 # Maximum number of iterations\\n Tolerance = 1e-5 # Tolerance on best and worst function values\\n N = 5 # Number of Heston and Nandi parameters\\n r = 0.01 / 252.0 # Risk Free Rate\\n\\n # Heston and Nandi parameter starting values (vertices) in vector form\\n\\n x = [[0 for i in range(N + 1)] for j in range(N)]\\n x[0][0] = 5.02e-6;\\n x[0][1] = 5.12e-6;\\n x[0][2] = 5.00e-6;\\n x[0][3] = 4.90e-6;\\n x[0][4] = 4.95e-6;\\n x[0][5] = 4.99e-6 # omega\\n x[1][0] = 1.32e-6;\\n x[1][1] = 1.25e-6;\\n x[1][2] = 1.35e-6;\\n x[1][3] = 1.36e-6;\\n x[1][4] = 1.30e-6;\\n x[1][5] = 1.44e-6 # alpha\\n x[2][0] = 0.79;\\n x[2][1] = 0.80;\\n x[2][2] = 0.78;\\n x[2][3] = 0.77;\\n x[2][4] = 0.81;\\n x[2][5] = 0.82 # beta\\n x[3][0] = 427.0;\\n x[3][1] = 421.0;\\n x[3][2] = 425.0;\\n x[3][3] = 419.1;\\n x[3][4] = 422.1;\\n x[3][5] = 430.0 # gamma\\n x[4][0] = 0.21;\\n x[4][1] = 0.20;\\n x[4][2] = 0.22;\\n x[4][3] = 0.19;\\n x[4][4] = 0.18;\\n x[4][5] = 0.205 # lambda\\n\\n # Run Nelder Mead and output Nelder Mead results\\n B = NelderMead(LogLike, N, NumIters, MaxIters, Tolerance, x, r)\\n\\n #\\tprint(\\\"Nelder Mead Minimization of Log-Likelihood for Heston and Nandi parameters\\\")\\n #\\tprint(\\\"---------------------------------\\\")\\n #\\tprint(\\\"omega = \\\", B[0])\\n #\\tprint(\\\"alpha = \\\", B[1])\\n #\\tprint(\\\"beta = \\\", B[2])\\n #\\tprint(\\\"gamma = \\\", B[3])\\n #\\tprint(\\\"lambda = \\\", B[4])\\n #\\tprint(\\\"Value of Objective Function = \\\", B[N])\\n #\\tprint(\\\"Number of Iterations = \\\", B[N+1])\\n #\\tprint(\\\"Persistence \\\", B[2]+B[1]*(B[3]**2) )\\n #\\tprint(\\\"---------------------------------\\\")\\n\\n # alpha,beta,gamma,omega,lambda\\n return [B[1], B[2], B[3], B[0], B[4]]\",\n \"def find_calibration_parameters(df, temperature, cal_mode, calibration_statistics, num_iterations, optimal_t=25):\\n\\n if \\\"temp\\\" in cal_mode:\\n # create a column of T - optimal_T (mean temperature for each still bout minus the optimal temperature)\\n # i.e. the deviation in T from the optimal\\n df[\\\"T_dev\\\"] = temperature.data - optimal_t\\n\\n for i in range(num_iterations):\\n # do linear regression:\\n x_results, y_results, z_results = dataframe_regression(df, cal_mode, do_or_undo=\\\"do\\\")\\n\\n # results.params() gives the calibration parameters thus:\\n # x_results.params() = [x_scale, x_offset, x_temp_offset] (last item only applies if temperature is used)\\n df = dataframe_transformation(df, x_results.params, y_results.params, z_results.params,\\n cal_mode)\\n # update the \\\"matched\\\" arrays to reflect the new \\\"closest points\\\" after the dataframe transformation\\n update_matched(df)\\n\\n # Regress the backup copy of the original input against the transformed version,\\n # to calculate offset, scale and temperature offset scalar (if temperature used)\\n x_results_final, y_results_final, z_results_final = dataframe_regression(df, cal_mode, do_or_undo=\\\"undo\\\")\\n\\n calibration_parameters = {\\\"x_offset\\\": x_results_final.params[1],\\n \\\"x_scale\\\": x_results_final.params[0],\\n \\\"y_offset\\\": y_results_final.params[1],\\n \\\"y_scale\\\": y_results_final.params[0],\\n \\\"z_offset\\\": z_results_final.params[1],\\n \\\"z_scale\\\": z_results_final.params[0]\\n }\\n\\n if \\\"temp\\\" in cal_mode:\\n calibration_parameters[\\\"x_temp_offset\\\"] = x_results_final.params[2]\\n calibration_parameters[\\\"y_temp_offset\\\"] = y_results_final.params[2]\\n calibration_parameters[\\\"z_temp_offset\\\"] = z_results_final.params[2]\\n else:\\n calibration_parameters[\\\"x_temp_offset\\\"] = 0\\n calibration_parameters[\\\"y_temp_offset\\\"] = 0\\n calibration_parameters[\\\"z_temp_offset\\\"] = 0\\n\\n # if enhanced calibration statistics are required...\\n if calibration_statistics:\\n\\n ######################\\n\\n # extract the error in the final regression fit for each axis\\n calibration_parameters[\\\"x_rsquared\\\"] = x_results_final.rsquared\\n calibration_parameters[\\\"y_rsquared\\\"] = y_results_final.rsquared\\n calibration_parameters[\\\"z_rsquared\\\"] = z_results_final.rsquared\\n\\n x_bse = x_results_final.bse\\n y_bse = y_results_final.bse\\n z_bse = z_results_final.bse\\n\\n calibration_parameters[\\\"x_scale_se\\\"] = x_bse[0]\\n calibration_parameters[\\\"y_scale_se\\\"] = y_bse[0]\\n calibration_parameters[\\\"z_scale_se\\\"] = z_bse[0]\\n\\n calibration_parameters[\\\"x_offset_se\\\"] = x_bse[1]\\n calibration_parameters[\\\"y_offset_se\\\"] = y_bse[1]\\n calibration_parameters[\\\"z_offset_se\\\"] = z_bse[1]\\n\\n if \\\"temp\\\" in cal_mode:\\n calibration_parameters[\\\"x_temp_offset_se\\\"] = x_bse[2]\\n calibration_parameters[\\\"y_temp_offset_se\\\"] = y_bse[2]\\n calibration_parameters[\\\"z_temp_offset_se\\\"] = z_bse[2]\\n\\n #########################\\n\\n return calibration_parameters\",\n \"def start_params(unique_id):\\n\\n temp = unique_id.GradientTemp\\n temp_k = unique_id.Temp_K\\n logtraitval = unique_id.logTraitValue\\n midpoint = unique_id.logTraitValue.idxmax()\\n midtemp = unique_id.GradientTemp[unique_id.logTraitValue.idxmax()]\\n y = NaN\\n\\n # at midpoint, split the data into two arrays\\n EhVect = where(unique_id.GradientTemp >= midtemp, logtraitval, y)\\n EVect = where(unique_id.GradientTemp < midtemp, logtraitval, y)\\n\\n Len_EVect = len(EVect)\\n Len_EhVect = len(EhVect)\\n\\n if Len_EVect < 3: # all the points are one side of the 'midpoint'):\\n EGrad = stats.linregress(temp, logtraitval)\\n else:\\n try:\\n EGrad = stats.linregress(\\n temp[:midpoint], logtraitval[:midpoint])\\n except ValueError as error:\\n logging.error(\\\"ValueError encountered %r\\\", error)\\n EGrad = stats.linregress(temp, logtraitval)\\n\\n if Len_EhVect < 3:\\n EhGrad = stats.linregress(temp, logtraitval)\\n else:\\n try:\\n EhGrad = stats.linregress(\\n temp[midpoint:], logtraitval[midpoint:])\\n except ValueError as error:\\n #logging.error(\\\"ValueError encountered %r\\\", error)\\n EhGrad = stats.linregress(temp, logtraitval)\\n\\n B0 = exp(EGrad[0] * (1 / (k * 283.15)) + EGrad[1])\\n\\n if Len_EhVect < 2:\\n Th = midtemp\\n else:\\n try:\\n Th = (\\n ((mean(logtraitval) - logtraitval[B0]) / EhGrad[0]))**(-1) / k\\n except BaseException:\\n Th = temp_k[logtraitval.idxmax()] # close to where 50% deactivation would occur\\n\\n return DataFrame({\\n \\\"E\\\": EGrad[0],\\n \\\"Eh\\\": EhGrad[0],\\n \\\"B0\\\": B0,\\n \\\"Th\\\": Th\\n }, index=[0]\\n ) # index assignment as passing scalar values\",\n \"def create_lat_lon_date_data(gt_id,\\n target_horizon,\\n experiment,\\n past_gt_ids=[\\\"contest_precip\\\", \\\"contest_tmp2m\\\"],\\n forecast_models=[\\\"nmme\\\",\\\"nmme0\\\"],\\n other_lat_lon_date_features=[\\\"contest_rhum.sig995\\\",\\n \\\"contest_pres.sfc.gauss\\\"]):\\n\\n time_start = time.time()\\n\\n # Add forecasts to list of forecast IDs\\n forecast_variable = get_forecast_variable(gt_id) # 'prate' or 'tmp2m'\\n forecast_ids = ['{}-{}-{}'.format(forecast, forecast_variable, target_horizon)\\n for forecast in forecast_models]\\n\\n # -----------\\n # Generate relevant variable and column names\\n # -----------\\n\\n # Identify measurement variable name\\n measurement_variable = get_measurement_variable(gt_id) # 'tmp2m' or 'prate'\\n\\n # Keep track of relevant column names\\n gt_col = measurement_variable\\n clim_col = measurement_variable+\\\"_clim\\\"\\n anom_col = measurement_variable+\\\"_anom\\\"\\n\\n # Inverse of standard deviation of anomalies for each start_date\\n anom_inv_std_col = anom_col+\\\"_inv_std\\\"\\n\\n # --------\\n # Prepare experiment cache directory and saved file names\\n # --------\\n\\n # Name of cache directory for storing non-submission-date specific\\n # intermediate files\\n cache_dir = os.path.join('results', experiment, 'shared',\\n '{}_{}'.format(gt_id, target_horizon))\\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\\n\\n # if cache_dir doesn't exist, create it\\n if not os.path.isdir(cache_dir):\\n os.makedirs(cache_dir)\\n\\n # Filenames for data file to be stored in cache_dir\\n lat_lon_date_data_file = os.path.join(\\n cache_dir, \\\"lat_lon_date_data-{}_{}.h5\\\".format(gt_id, target_horizon))\\n\\n # --------\\n # Load mask indicating which grid points count in the contest (1=in, 0=out)\\n # --------\\n print \\\"Loading contest mask\\\"\\n t = time.time()\\n mask_df = get_contest_mask()\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n # --------\\n # Creates and saves lat_lon_date_data dataframe\\n # --------\\n # Load masked lat lon date features restricted to years >= get_first_year(gt_id)\\n # Note: contest lat lon date features and forecasts are pre-masked, so there\\n # is no need to mask explcitily\\n print \\\"Loading lat lon date features\\\"\\n num_gt_ids = len(past_gt_ids)\\n # For each measurement,\\n # get number of days between start date of observation period used for prediction\\n # (2 weeks + 1 submission day behind for most predictors) and start date of\\n # target period (2 or 4 weeks ahead)\\n past_start_deltas = [get_start_delta(target_horizon, past_gt_id)\\n for past_gt_id in past_gt_ids]\\n other_start_deltas = [get_start_delta(target_horizon, other_gt_id)\\n for other_gt_id in other_lat_lon_date_features]\\n # Additionally keep track of days between forecast date and start date of\\n # target period\\n forecast_delta = get_forecast_delta(target_horizon)\\n\\n lat_lon_date_data = get_lat_lon_date_features(\\n gt_ids=other_lat_lon_date_features + other_lat_lon_date_features\\n + other_lat_lon_date_features,\\n gt_masks=None,\\n gt_shifts=other_start_deltas +\\n [2*delta for delta in other_start_deltas] +\\n [365]*len(other_lat_lon_date_features),\\n forecast_ids=forecast_ids + forecast_ids,\\n forecast_masks=None,\\n forecast_shifts=[None]*len(forecast_ids) + [forecast_delta]*len(forecast_ids),\\n anom_ids=[gt_id] + past_gt_ids + past_gt_ids + past_gt_ids,\\n anom_masks=None,\\n anom_shifts=[None] + past_start_deltas +\\n [2*delta for delta in past_start_deltas] +\\n [365]*len(past_gt_ids),\\n first_year=get_first_year(gt_id)\\n )\\n\\n print \\\"Loading additional lat lon date features\\\"\\n t = time.time()\\n # Add CFSv2 mean as feature\\n if 'cfsv2' in forecast_models:\\n cfsv2_models = ['cfsv2_op_delta_2w_1d_6h', 'cfsv2_op_delta_2w_1d_12h',\\n 'cfsv2_op_delta_2w_1d_18h', 'cfsv2_op_delta_2w_2d_0h',\\n 'cfsv2_op_delta_2w_2d_6h', 'cfsv2_op_delta_2w_2d_12h',\\n 'cfsv2_op_delta_2w_2d_18h', 'cfsv2_op_delta_2w_3d_0h']\\n lat_lon_date_data['cfsv2_mean'] = lat_lon_date_data[cfsv2_models].mean(axis=1)\\n lat_lon_date_data[\\\"cfsv2_mean_shift\\\"+str(start_delta)] = lat_lon_date_data[\\n [model+\\\"_shift\\\"+str(start_delta) for model in cfsv2_models]].mean(axis=1)\\n # Add inverse of standard deviation of anomalies for each start_date\\n lat_lon_date_data[anom_inv_std_col] = \\\\\\n 1.0/lat_lon_date_data.groupby([\\\"start_date\\\"])[anom_col].transform('std')\\n\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n # Save lat lon date features to disk\\n print \\\"Saving lat lon date features to \\\"+lat_lon_date_data_file\\n t = time.time()\\n lat_lon_date_data.to_hdf(lat_lon_date_data_file, key=\\\"data\\\", mode=\\\"w\\\")\\n subprocess.call(\\\"chmod a+w \\\"+lat_lon_date_data_file, shell=True)\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n print \\\"Finished generating lat_lon_date_data matrix.\\\"\\n print \\\"Total time elapsed: {}s\\\".format(time.time()-time_start)\\n return list(lat_lon_date_data)\",\n \"def generate_testdata(dt, maxt=10., meanrate=10., amp=20.e-12, ampvar=5.e-12, \\n noise=2.5e-12, taus=[0.001, 0.010], baseclass=None, func=None, sign=1, \\n expseed=None, noiseseed=None,\\n bigevent=None):\\n if baseclass is None and func is not None:\\n raise ValueError('Need base class definition')\\n tdur = 0.020\\n timebase = np.arange(0., maxt, dt) # in ms\\n t_psc = np.arange(0., tdur, dt) # time base for single event template in ms\\n if func is None:\\n tau_1 = taus[0] # ms\\n tau_2 = taus[1] # ms\\n Apeak = amp # pA\\n Aprime = (tau_2/tau_1)**(tau_1/(tau_1-tau_2))\\n g = Aprime * (-np.exp(-t_psc/tau_1) + np.exp((-t_psc/tau_2)))\\n gmax = np.max(g)\\n print('gmax: ', gmax)\\n g = sign*g*amp/gmax\\n print(f'max g: {np.min(g):.6e}')\\n else:\\n baseclass._make_template()\\n gmax = np.min(baseclass.template)\\n g = sign*amp*baseclass.template/gmax\\n print('gmaxb: ', np.max(gmax))\\n \\n testpsc = np.zeros(timebase.shape)\\n if expseed is None:\\n eventintervals = np.random.exponential(1./meanrate, int(maxt*meanrate))\\n else:\\n np.random.seed(expseed)\\n eventintervals = np.random.exponential(1./meanrate, int(maxt*meanrate))\\n eventintervals = eventintervals[eventintervals < 10.]\\n events = np.cumsum(eventintervals)\\n if bigevent is not None:\\n events = np.append(events, bigevent['t'])\\n events = np.sort(events)\\n t_events = events[events < maxt] # time of events with exp distribution\\n i_events = np.array([int(x/dt) for x in t_events])\\n testpsc[i_events] = np.random.normal(1., ampvar/amp, len(i_events))\\n if bigevent is not None:\\n ipos = int(bigevent['t']/dt) # position in array\\n testpsc[ipos] = bigevent['I']\\n testpsc = scipy.signal.convolve(testpsc, g, mode='full')[:timebase.shape[0]]\\n # f, ax = mpl.subplots(1,1)\\n # ax.plot(dt*np.arange(len(testpsc)), testpsc)\\n # mpl.show()\\n if noise > 0:\\n if noiseseed is None:\\n testpscn = testpsc + np.random.normal(0., noise, testpsc.shape)\\n else:\\n np.random.seed(noiseseed)\\n testpscn = testpsc + np.random.normal(0., noise, testpsc.shape)\\n else:\\n testpscn = testpsc\\n return timebase, testpsc, testpscn, i_events\",\n \"def process_data(self):\\n timer_start = time.time()\\n # ensure self.df_events and self.df_locations are not None\\n if self.df_events is None or self.df_locations is None:\\n print(\\\"Missing data: either df_events or df_locations is None\\\")\\n return\\n # set start and end based on self.df_events if not already set\\n if not self.start:\\n self.start = self.df_events['event_time'].min()\\n if not self.end:\\n self.end = self.df_events['event_time'].max()\\n print(f\\\"date range for events data is from {self.start} to {self.end}\\\")\\n # create Grid object before processing any data\\n grid = self.compute_grid_cells(self.df_locations)\\n # clean and combine events and locations data\\n df_data = self.combine_events_and_locations(grid)\\n print(df_data.shape)\\n # df_data.to_csv('../../../data_files/20210506_cleanedInputDataCumSum.csv', index=False)\\n # df_data = pd.read_csv('../../../data_files/20210415_cleanedInputDataAprilCumSum.csv')\\n # process data within grid class\\n df_processed = grid.process_data(df_data, 'weekly')\\n # df_processed = self.calculate_demand(df_processed)\\n # df_processed.to_csv('../../../data_files/20210506_processedGridCellData.csv')\\n # set df_demand to be df_processed\\n df_processed.reset_index(inplace=True)\\n df_processed = df_processed.astype({'date': 'str', 'avail_count': 'float', 'avail_mins': 'float', 'prob_scooter_avail': 'float', 'trips': 'float', 'adj_trips': 'float'})\\n # make sure dates are within start and end dates\\n start_date = str(iso8601.parse_date(self.start).date())\\n end_date = str(iso8601.parse_date(self.end).date())\\n df_processed = df_processed[(df_processed['date'] >= start_date) & (df_processed['date'] <= end_date)]\\n self.set_demand(df_processed)\\n timer_end = time.time()\\n print('Elapsed time to process data:', (timer_end - timer_start)/60.0, 'minutes')\",\n \"def run_global(start_year, end_year, depth_from, depth_to, animate=True):\\n# years, times, rootgrps = retrieve(1950,2018)\\n# rootgrps_1950 = [nt.Dataset(\\\"EN.4.2.1.analyses.g10.1950\\\\EN.4.2.1.f.analysis.g10.195001.nc\\\", \\\"r+\\\", format=\\\"NETCDF4\\\")]\\n# rootgrps_1951 = [nt.Dataset(\\\"EN.4.2.1.analyses.g10.1951\\\\EN.4.2.1.f.analysis.g10.195101.nc\\\", \\\"r+\\\", format=\\\"NETCDF4\\\")]\\n# rootgrps_1952 = [nt.Dataset(\\\"EN.4.2.1.analyses.g10.1952\\\\EN.4.2.1.f.analysis.g10.195201.nc\\\", \\\"r+\\\", format=\\\"NETCDF4\\\")]\\n# rootgrps_1953 = [nt.Dataset(\\\"EN.4.2.1.analyses.g10.1953\\\\EN.4.2.1.f.analysis.g10.195301.nc\\\", \\\"r+\\\", format=\\\"NETCDF4\\\")]\\n#\\n#\\n# rootgrps_2015 = [nt.Dataset(\\\"EN.4.2.1.analyses.g10.2015\\\\EN.4.2.1.f.analysis.g10.201501.nc\\\", \\\"r+\\\", format=\\\"NETCDF4\\\")]\\n# rootgrps_2016 = [nt.Dataset(\\\"EN.4.2.1.analyses.g10.2016\\\\EN.4.2.1.f.analysis.g10.201601.nc\\\", \\\"r+\\\", format=\\\"NETCDF4\\\")]\\n# rootgrps_2017 = [nt.Dataset(\\\"EN.4.2.1.analyses.g10.2017\\\\EN.4.2.1.f.analysis.g10.201701.nc\\\", \\\"r+\\\", format=\\\"NETCDF4\\\")]\\n rootgrps_2018 = [nt.Dataset(\\\"EN.4.2.1.analyses.g10.2018\\\\EN.4.2.1.f.analysis.g10.201801.nc\\\", \\\"r+\\\", format=\\\"NETCDF4\\\")]\\n\\n# HC_1950 = calculate_HC_global(rootgrps_1950, 0, 2000)\\n# print('1950', time.time()-start)\\n# HC_1951 = calculate_HC_global(rootgrps_1951, 0, 2000)\\n# print('1951', time.time()-start)\\n# HC_1952 = calculate_HC_global(rootgrps_1952, 0, 2000)\\n# print('1952', time.time()-start)\\n# HC_1953 = calculate_HC_global(rootgrps_1953, 0, 2000)\\n# print('1953', time.time()-start) \\n#\\n# HC_2015 = calculate_HC_global(rootgrps_2015, 0, 2000)\\n# print('2015', time.time()-start)\\n# HC_2016 = calculate_HC_global(rootgrps_2016, 0, 2000)\\n# print('2016', time.time()-start)\\n# HC_2017 = calculate_HC_global(rootgrps_2017, 0, 2000)\\n# print('2017', time.time()-start)\\n HC_2018 = calculate_HC_global(rootgrps_2018, 0, 2000)\\n# print('2018', time.time()-start)\\n# HC_1950_mean = (HC_1950+HC_1951+HC_1952+HC_1953)/4\\n# HC_2018_mean = (HC_2015+HC_2016+HC_2017+HC_2018)/4\\n\\n# dHC = (HC_2018_mean-HC_1950_mean)/(65*365*24*3600)\\n if animate == True:\\n plot(rootgrps_2018, HC_2018)\\n return HC_2018\",\n \"def main(parameters_file, token):\\n\\n print(f\\\"--\\\\nReading parameters file: {parameters_file}\\\\n--\\\")\\n\\n set_token(token=token)\\n\\n\\n try:\\n with open(parameters_file, 'r') as f:\\n\\n parameters = load(f,\\n Loader=SafeLoader)\\n maggeo_params = parameters[\\\"maggeo\\\"] \\n gpsfilename = maggeo_params[\\\"gpsfilename\\\"]\\n Lat = maggeo_params[\\\"Lat\\\"]\\n Long = maggeo_params[\\\"Long\\\"] \\n DateTime = maggeo_params[\\\"DateTime\\\"]\\n altitude = maggeo_params[\\\"altitude\\\"]\\n\\n except Exception as error:\\n print('Error in parameters file format')\\n raise error\\n \\n base_dir=os.getcwd()\\n temp_results_dir = os.path.join(base_dir, \\\"temp_data\\\")\\n results_dir = os.path.join(base_dir, \\\"results\\\")\\n data_dir = os.path.join(base_dir, \\\"data\\\")\\n utilities_dir = os.path.join(base_dir, \\\"utilities\\\")\\n\\n GPSData = getGPSData(data_dir,gpsfilename,Lat,Long,DateTime,altitude)\\n \\n datestimeslist = []\\n for index, row in GPSData.iterrows():\\n datetimerow = row['gpsDateTime']\\n daterow = row['dates']\\n hourrow = row['times']\\n hourrow = hourrow.strftime('%H:%M:%S')\\n if hourrow < '04:00:00':\\n date_bfr = daterow - (timedelta(days=1))\\n datestimeslist.append(daterow)\\n datestimeslist.append(date_bfr)\\n if hourrow > '20:00:00':\\n Date_aft = daterow + (timedelta(days=1))\\n datestimeslist.append(daterow)\\n datestimeslist.append(Date_aft) \\n else:\\n datestimeslist.append(daterow)\\n\\n def uniquelistdates(list): \\n x = np.array(list) \\n uniquelist = np.unique(x)\\n return uniquelist\\n\\n uniquelist_dates = uniquelistdates(datestimeslist)\\n\\n hours_t_day = 24 #MagGeo needs the entire Swarm data for each day of the identified day.\\n hours_added = dt.timedelta(hours = hours_t_day)\\n\\n listdfa = []\\n listdfb = []\\n listdfc = []\\n\\n for d in tqdm(uniquelist_dates, desc=\\\"Getting Swarm Data\\\"):\\n #print(\\\"Getting Swarm data for date:\\\",d )\\n startdate = dt.datetime.combine(d, dt.datetime.min.time())\\n enddate = startdate + hours_added\\n SwarmResidualsA,SwarmResidualsB,SwarmResidualsC = Get_Swarm_residuals(startdate, enddate)\\n listdfa.append(SwarmResidualsA)\\n listdfb.append(SwarmResidualsB)\\n listdfc.append(SwarmResidualsC)\\n\\n base_dir = os.getcwd() # Get main MagGeo directory\\n temp_results_dir = os.path.join(base_dir, \\\"temp_data\\\")\\n results_dir = os.path.join(base_dir, \\\"results\\\")\\n data_dir = os.path.join(base_dir, \\\"data\\\")\\n\\n #TODO:\\n #Find a more elegant way to concante the the list from Swarm and then read it to get the type of columns is requiered, Timestamp as datetime and epoch as index.\\n \\n PdSwarmRes_A = pd.concat(listdfa, join='outer', axis=0)\\n PdSwarmRes_A.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_A.csv'), header=True)\\n PdSwarmRes_B = pd.concat(listdfb, join='outer', axis=0)\\n PdSwarmRes_B.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_B.csv'), header=True)\\n PdSwarmRes_C = pd.concat(listdfc, join='outer', axis=0)\\n PdSwarmRes_C.to_csv (os.path.join(temp_results_dir,'TotalSwarmRes_C.csv'), header=True)\\n\\n TotalSwarmRes_A = pd.read_csv(os.path.join(temp_results_dir,\\\"TotalSwarmRes_A.csv\\\"),low_memory=False, index_col='epoch')\\n TotalSwarmRes_A['timestamp'] = pd.to_datetime(TotalSwarmRes_A['timestamp'])\\n TotalSwarmRes_B = pd.read_csv(os.path.join(temp_results_dir,\\\"TotalSwarmRes_B.csv\\\"),low_memory=False, index_col='epoch')\\n TotalSwarmRes_B['timestamp'] = pd.to_datetime(TotalSwarmRes_B['timestamp'])\\n TotalSwarmRes_C = pd.read_csv(os.path.join(temp_results_dir,\\\"TotalSwarmRes_C.csv\\\"),low_memory=False, index_col='epoch')\\n TotalSwarmRes_C['timestamp'] = pd.to_datetime(TotalSwarmRes_C['timestamp'])\\n\\n dn = [] ## List used to add all the GPS points with the annotated MAG Data. See the last bullet point of this process \\n for index, row in tqdm(GPSData.iterrows(), total=GPSData.shape[0], desc=\\\"Annotating the GPS Trayectory\\\"):\\n GPSLat = row['gpsLat'] \\n GPSLong = row['gpsLong']\\n GPSDateTime = row['gpsDateTime']\\n GPSTime = row['epoch']\\n GPSAltitude = row['gpsAltitude']\\n #print(\\\"Process for:\\\", index,\\\"DateTime:\\\",GPSDateTime)\\n try:\\n result=ST_IDW_Process(GPSLat,GPSLong,GPSAltitude, GPSDateTime,GPSTime, TotalSwarmRes_A, TotalSwarmRes_B, TotalSwarmRes_C)\\n dn.append(result)\\n except:\\n #print(\\\"Ups!.That was a bad Swarm Point, let's keep working with the next point\\\")\\n result_badPoint= {'Latitude': GPSLat, 'Longitude': GPSLong, 'Altitude':GPSAltitude, 'DateTime': GPSDateTime, 'N_res': np.nan, 'E_res': np.nan, 'C_res':np.nan, 'TotalPoints':0, 'Minimum_Distance':np.nan, 'Average_Distance':np.nan} \\n dn.append(result_badPoint)\\n continue\\n \\n \\n GPS_ResInt = pd.DataFrame(dn)\\n GPS_ResInt.to_csv (os.path.join(temp_results_dir,\\\"GPS_ResInt.csv\\\"), header=True)\\n \\n X_obs, Y_obs, Z_obs, X_obs_internal, Y_obs_internal, Z_obs_internal = CHAOS_ground_values(utilities_dir,GPS_ResInt)\\n\\n GPS_ResInt['N'] =pd.Series(X_obs)\\n GPS_ResInt['E'] =pd.Series(Y_obs)\\n GPS_ResInt['C'] =pd.Series(Z_obs)\\n GPS_ResInt['N_Obs'] =pd.Series(X_obs_internal)\\n GPS_ResInt['E_Obs'] =pd.Series(Y_obs_internal)\\n GPS_ResInt['C_Obs'] =pd.Series(Z_obs_internal)\\n\\n GPS_ResInt.drop(columns=['N_res', 'E_res','C_res'], inplace=True)\\n\\n # Having Intepolated and weighted the magnetic values, we can compute the other magnectic components. \\n GPS_ResInt['H'] = np.sqrt((GPS_ResInt['N']**2)+(GPS_ResInt['E']**2))\\n #check the arcgtan in python., From arctan2 is saver.\\n DgpsRad = np.arctan2(GPS_ResInt['E'],GPS_ResInt['N'])\\n GPS_ResInt['D'] = np.degrees(DgpsRad)\\n IgpsRad = np.arctan2(GPS_ResInt['C'],GPS_ResInt['H'])\\n GPS_ResInt['I'] = np.degrees(IgpsRad)\\n GPS_ResInt['F'] = np.sqrt((GPS_ResInt['N']**2)+(GPS_ResInt['E']**2)+(GPS_ResInt['C']**2))\\n\\n \\n originalGPSTrack=pd.read_csv(os.path.join(data_dir,gpsfilename))\\n MagGeoResult = pd.concat([originalGPSTrack, GPS_ResInt], axis=1)\\n #Drop duplicated columns. Latitude, Longitued, and DateTime will not be part of the final result.\\n MagGeoResult.drop(columns=['Latitude', 'Longitude', 'DateTime'], inplace=True)\\n \\n #Exporting the CSV file\\n \\n outputfile =\\\"GeoMagResult_\\\"+gpsfilename\\n export_csv = MagGeoResult.to_csv (os.path.join(results_dir,outputfile), index = None, header=True) \\n print(\\\"Congrats! MagGeo has processed your GPS trayectory. Find the annotated table: \\\" + outputfile + \\\" in the folder results.\\\")\",\n \"def <start>/<end>(<start>/<end>)\\ndef calc_temps(start_date, end_date):\",\n \"def gridsearch(model_class,\\n parameters: dict,\\n training_series: TimeSeries,\\n target_series: Optional[TimeSeries] = None,\\n forecast_horizon: Optional[int] = None,\\n start: Union[pd.Timestamp, float, int] = 0.5,\\n use_full_output_length: Optional[bool] = None,\\n val_target_series: Optional[TimeSeries] = None,\\n use_fitted_values: bool = False,\\n metric: Callable[[TimeSeries, TimeSeries], float] = metrics.mape,\\n verbose=False) -> TimeSeries:\\n raise_if_not((forecast_horizon is not None) + (val_target_series is not None) + use_fitted_values == 1,\\n \\\"Please pass exactly one of the arguments 'forecast_horizon', \\\"\\n \\\"'val_target_series' or 'use_fitted_values'.\\\", logger)\\n\\n # check target and training series\\n if target_series is None:\\n target_series = training_series\\n raise_if_not(all(training_series.time_index() == target_series.time_index()), \\\"the target and training series\\\"\\n \\\" must have the same time indices.\\\")\\n\\n # construct predict kwargs dictionary\\n predict_kwargs = {}\\n if use_full_output_length is not None:\\n predict_kwargs['use_full_output_length'] = use_full_output_length\\n\\n if use_fitted_values:\\n raise_if_not(hasattr(model_class(), \\\"fitted_values\\\"), \\\"The model must have a fitted_values attribute\\\"\\n \\\" to compare with the train TimeSeries\\\", logger)\\n\\n elif val_target_series is not None:\\n raise_if_not(training_series.width == val_target_series.width, \\\"Training and validation series require the\\\"\\n \\\" same number of components.\\\", logger)\\n\\n min_error = float('inf')\\n best_param_combination = {}\\n\\n # compute all hyperparameter combinations from selection\\n params_cross_product = list(product(*parameters.values()))\\n\\n # iterate through all combinations of the provided parameters and choose the best one\\n iterator = _build_tqdm_iterator(params_cross_product, verbose)\\n for param_combination in iterator:\\n param_combination_dict = dict(list(zip(parameters.keys(), param_combination)))\\n model = model_class(**param_combination_dict)\\n if use_fitted_values: # fitted value mode\\n model.fit(training_series)\\n fitted_values = TimeSeries.from_times_and_values(training_series.time_index(), model.fitted_values)\\n error = metric(fitted_values, target_series)\\n elif val_target_series is None: # expanding window mode\\n backtest_forecast = model.backtest(training_series, target_series, start,\\n forecast_horizon, use_full_output_length=use_full_output_length)\\n error = metric(backtest_forecast, target_series)\\n else: # split mode\\n if isinstance(model, MultivariateForecastingModel):\\n model.fit(training_series, target_series)\\n else:\\n model.fit(training_series)\\n error = metric(model.predict(len(val_target_series), **predict_kwargs), val_target_series)\\n if error < min_error:\\n min_error = error\\n best_param_combination = param_combination_dict\\n logger.info('Chosen parameters: ' + str(best_param_combination))\\n\\n return model_class(**best_param_combination)\",\n \"def grid(config, keep_flag_values=True, treat_coast_as_land=False):\\n if config.get('daily', False) and config.get('median', False):\\n start_year, end_year = config['range']\\n\\n gridset = sid.extent_daily_median(config['hemi'],\\n start_year,\\n end_year,\\n dayofyear=config['dayofyear'],\\n search_paths=config['search_paths'],\\n extent_threshold=config['extent_threshold'],\\n allow_empty_gridset=True,\\n drop_invalid_ice=True,\\n allow_bad_dates=False)\\n\\n elif config.get('monthly', False) and config.get('median', False):\\n start_year, end_year = config['range']\\n\\n gridset = sid.extent_monthly_median(config['hemi'],\\n start_year,\\n end_year,\\n config['month'],\\n search_paths=config['search_paths'],\\n extent_threshold=config['extent_threshold'],\\n drop_invalid_ice=True,\\n allow_empty_gridset=True)\\n\\n elif config.get('daily', False):\\n gridset = sid.extent_daily(config['hemi'],\\n config['year'],\\n config['month'],\\n config['day'],\\n search_paths=config['search_paths'],\\n extent_threshold=config['extent_threshold'],\\n allow_empty_gridset=False,\\n drop_invalid_ice=True,\\n allow_bad_dates=False)\\n\\n elif config.get('monthly', False):\\n gridset = sid.extent_monthly(config['hemi'],\\n config['year'],\\n config['month'],\\n search_paths=config['search_paths'],\\n extent_threshold=config['extent_threshold'],\\n drop_invalid_ice=True,\\n allow_empty_gridset=False)\\n\\n return _massage_grid(gridset['data'], keep_flag_values, treat_coast_as_land)\",\n \"def find_besthist(\\n grid_lat, grid_long, grid_dates, grid_z_value,\\n lat, long, date, z_value,\\n latitude_large, latitude_small, longitude_large, longitude_small,\\n phi_large, phi_small, age_large, age_small, map_pv_use, max_casts\\n):\\n\\n # make sure arrays are 1 dimensional\\n grid_lat = grid_lat.flatten()\\n grid_long = grid_long.flatten()\\n grid_z_value = grid_z_value.flatten()\\n grid_dates = grid_dates.flatten()\\n\\n # set up potential vorticity\\n potential_vorticity_vec = np.vectorize(potential_vorticity)\\n pv_float = 0\\n pv_hist = 0\\n\\n # if we are using potential vorticity, calculate it\\n if map_pv_use == 1:\\n pv_float = potential_vorticity(lat, z_value)\\n pv_hist = potential_vorticity_vec(grid_lat, grid_z_value)\\n\\n # calculate ellipse\\n find_ellipse_vec = np.vectorize(find_ellipse)\\n ellipse = find_ellipse_vec(grid_long, long, longitude_large, grid_lat, lat, latitude_large,\\n phi_large, pv_hist, pv_float)\\n\\n # find points that lie within the ellipse\\n hist_long = []\\n hist_lat = []\\n hist_dates = []\\n hist_z_value = []\\n index_ellipse = np.argwhere(ellipse < 1)\\n for i in index_ellipse:\\n hist_long.append(grid_long[i])\\n hist_lat.append(grid_lat[i])\\n hist_dates.append(grid_dates[i])\\n hist_z_value.append(grid_z_value[i])\\n\\n index = index_ellipse\\n # check to see if too many data points were found\\n if index.__len__() > max_casts:\\n\\n # uses pseudo-random numbers so the same random numbers are selected for each run\\n np.random.seed(index_ellipse.__len__())\\n\\n # pick max_casts/3 random points\\n rand_matrix = np.random.rand(math.ceil(max_casts / 3))\\n index_rand = np.round(rand_matrix * (index_ellipse.__len__() - 1))\\n\\n # make sure the points are all unique\\n index_rand = np.unique(index_rand)\\n # sort them into ascending order\\n index_rand.sort()\\n\\n # create an array containing the indices of the remaining reference data\\n # (index_ellipse array without index_rand array)\\n # Then create arrays containing the remaining reference lat, long, age, and z_value\\n index_remain = []\\n remain_hist_lat = []\\n remain_hist_long = []\\n remain_hist_z_value = []\\n remain_hist_dates = []\\n\\n for i in index_ellipse.flatten():\\n if i not in index_rand:\\n index_remain.append(i)\\n\\n for i in index_remain:\\n remain_hist_lat = np.append(remain_hist_lat, grid_lat[int(i)])\\n remain_hist_long = np.append(remain_hist_long, grid_long[int(i)])\\n remain_hist_z_value = np.append(remain_hist_z_value, grid_z_value[int(i)])\\n remain_hist_dates = np.append(remain_hist_dates, grid_dates[int(i)])\\n\\n # sort remaining points by large spatial correlations\\n # if using potential vorticity, calculate it for remaining data\\n if map_pv_use == 1:\\n pv_hist = potential_vorticity_vec(remain_hist_lat, remain_hist_z_value)\\n\\n # calculate the large spatial correlation for each point\\n spatial_correlation_vec = np.vectorize(spatial_correlation)\\n correlation_large = spatial_correlation_vec(remain_hist_long, long, longitude_large,\\n remain_hist_lat, lat, latitude_large,\\n remain_hist_dates, date, age_large,\\n pv_hist, pv_float, phi_large)\\n\\n # combine the large spatial correlation with their indices in a 2d matrix\\n correlation_large_combined = np.stack((index_remain, correlation_large)).transpose()\\n # sort the matrix in ascending order of correlation\\n correlation_large_combined_sorted = correlation_large_combined[\\n correlation_large_combined[:, 1].argsort()\\n ]\\n\\n # work out how many large spatial data points needed\\n lsegment2 = 2 * math.ceil(max_casts / 3) - index_rand.__len__()\\n\\n # select the best large spatial data points\\n index_large_spatial = []\\n for i in range(0, lsegment2):\\n index_large_spatial = np.append(\\n index_large_spatial, correlation_large_combined_sorted[i][0]\\n )\\n\\n # create array to hold selected indices from random and spatial selection\\n rand_large_index = np.concatenate((index_rand, index_large_spatial))\\n # sort into ascending order\\n rand_large_index.sort()\\n\\n # remove the currently selected indices again\\n index_remain = []\\n remain_hist_lat = []\\n remain_hist_long = []\\n remain_hist_z_value = []\\n remain_hist_dates = []\\n for i in index_ellipse.flatten():\\n if i not in rand_large_index:\\n index_remain.append(i)\\n\\n for i in index_remain:\\n remain_hist_lat = np.append(remain_hist_lat, grid_lat[int(i)])\\n remain_hist_long = np.append(remain_hist_long, grid_long[int(i)])\\n remain_hist_z_value = np.append(remain_hist_z_value, grid_z_value[int(i)])\\n remain_hist_dates = np.append(remain_hist_dates, grid_dates[int(i)])\\n\\n # sort the remaining points by short spatial and temporal correlations\\n # if using potential vorticity, calculate it\\n if map_pv_use == 1:\\n pv_hist = potential_vorticity_vec(\\n remain_hist_lat, remain_hist_z_value)\\n\\n # calculate the small spatial correlation for each point\\n correlation_small = spatial_correlation_vec(remain_hist_long,\\n long, longitude_small,\\n remain_hist_lat,\\n lat, latitude_small,\\n remain_hist_dates,\\n date, age_small,\\n pv_hist, pv_float, phi_small)\\n\\n # combine the small spatial correlation with their indices in a 2d matrix\\n correlation_small_combined = np.stack((index_remain, correlation_small)).transpose()\\n # sort the matrix in ascending order of correlation\\n correlation_small_combined_sorted = correlation_small_combined[\\n correlation_small_combined[:, 1].argsort()\\n ]\\n\\n # select the amount we need for short spatial/temporal correlation\\n leftover = max_casts - index_rand.__len__() - lsegment2\\n\\n index_small_spatial = []\\n for i in range(0, leftover):\\n index_small_spatial = np.append(\\n index_small_spatial, correlation_small_combined_sorted[i][0]\\n )\\n\\n # put together the best random data, large spatial data, and small spatial data\\n index = np.concatenate((index_rand, index_large_spatial, index_small_spatial))\\n if index.__len__() != np.unique(index).__len__():\\n print(\\\"WARNING: not all points are unique\\\")\\n rand_large = np.concatenate((index_rand, index_large_spatial))\\n rand_small = np.concatenate((index_rand, index_small_spatial))\\n large_small = np.concatenate((index_large_spatial, index_small_spatial))\\n print(\\\"unique points: \\\", np.unique(index).__len__())\\n print(\\\"unique rand large: \\\", rand_large.__len__(),\\n \\\" : \\\", np.unique(rand_large).__len__())\\n print(\\\"unique rand small: \\\", rand_small.__len__(),\\n \\\" : \\\", np.unique(rand_small).__len__())\\n print(\\\"unique large small: \\\", large_small.__len__(),\\n \\\" : \\\", np.unique(large_small).__len__())\\n\\n # ensure that the index is integers\\n index = index.flatten()\\n index = index.astype(int)\\n\\n return index\",\n \"def output_grib2(forecast,proj_dict,grid_dict,start_hour,end_hour,\\n stride,size,run_date,target_dataset,smoothing,config):\\n for d,date in enumerate(run_date):\\n date_outpath = config.forecast_out_path+'20{0}/grib/'.format(\\n date.strftime(config.run_date_format))\\n \\n if not os.path.exists(date_outpath):\\n os.makedirs(date_outpath)\\n\\n lscale = 1e6\\n grib_id_start = [7, 0, 14, 14, 2]\\n \\n filtered_forecast = gaussian_filter(forecast[d],smoothing,mode='constant')\\n \\n gdsinfo = np.array([0, np.product(filtered_forecast.shape[-2:]), 0, 0, 30], dtype=np.int32)\\n \\n lon_0 = proj_dict[\\\"lon_0\\\"]\\n sw_lon = grid_dict[\\\"sw_lon\\\"]\\n \\n if lon_0 < 0:\\n lon_0 += 360\\n if sw_lon < 0:\\n sw_lon += 360\\n\\n gdtmp1 = [1, 0, proj_dict['a'], 0, float(proj_dict['a']), 0, float(proj_dict['b']),\\n filtered_forecast.shape[-1], filtered_forecast.shape[-2], grid_dict[\\\"sw_lat\\\"] * lscale,\\n sw_lon * lscale, 0, proj_dict[\\\"lat_0\\\"] * lscale,\\n lon_0 * lscale,\\n grid_dict[\\\"dx\\\"] * 1e3 * stride, grid_dict[\\\"dy\\\"] * 1e3 * stride, 0b00000000, 0b01000000,\\n proj_dict[\\\"lat_1\\\"] * lscale,\\n proj_dict[\\\"lat_2\\\"] * lscale, -90 * lscale, 0]\\n pdtmp1 = np.array([1, 31, 4, 0, 31, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1], dtype=np.int32)\\n drtmp1 = np.array([0, 0, 4, 8, 0], dtype=np.int32)\\n time_list = list(date.utctimetuple()[0:6])\\n grib_objects = Grib2Encode(0, np.array(grib_id_start + time_list + [2, 1], dtype=np.int32))\\n grib_objects.addgrid(gdsinfo, gdtmp1)\\n pdtmp1[8] = end_hour\\n pdtmp1[-2] = 0\\n grib_objects.addfield(1, pdtmp1, 0, drtmp1, filtered_forecast)\\n grib_objects.end()\\n filename = date_outpath + \\\"{0}_{6}_Hail_{1}_Cali_NMEP_{2}mm_{3}_Hours_{4}-{5}.grib2\\\".format(\\n config.ensemble_name,\\n target_dataset,\\n size,\\n date.strftime(config.run_date_format),\\n start_hour,end_hour,config.forecast_model_names)\\n print(\\\"Writing to \\\" + filename )\\n \\n grib_file = open(filename, \\\"wb\\\")\\n grib_file.write(grib_objects.msg)\\n grib_file.close()\\n\\n return\",\n \"def create_date_data(gt_id,\\n target_horizon,\\n experiment,\\n date_features=[\\\"mei\\\", \\\"mjo\\\",\\n \\\"pca_sst_2010\\\", \\\"pca_icec_2010\\\",\\n \\\"pca_wind_hgt_10_2010\\\",\\n \\\"pca_wind_hgt_100_2010\\\",\\n \\\"pca_wind_hgt_500_2010\\\",\\n \\\"pca_wind_hgt_850_2010\\\"]):\\n\\n time_start = time.time()\\n\\n # --------\\n # Prepare experiment cache directory and saved file names\\n # --------\\n\\n # Name of cache directory for storing non-submission-date specific\\n # intermediate files\\n cache_dir = os.path.join('results', experiment, 'shared',\\n '{}_{}'.format(gt_id, target_horizon))\\n # e.g., cache_dir = 'results/regression/shared/contest_precip_34w'\\n\\n # if cache_dir doesn't exist, create it\\n if not os.path.isdir(cache_dir):\\n os.makedirs(cache_dir)\\n\\n # Filenames for data file to be stored in cache_dir\\n date_data_file = os.path.join(\\n cache_dir, \\\"date_data-{}_{}.h5\\\".format(gt_id, target_horizon))\\n\\n # --------\\n # Creates date_data dataframe.\\n # --------\\n # Get number of days between start date of observation period used for prediction\\n # (2 weeks behind) and start date of target period (2 or 4 weeks ahead)\\n start_deltas = [get_start_delta(target_horizon, gt_id)\\n for gt_id in date_features]\\n\\n # Load masked date features\\n print \\\"Loading date features\\\"\\n date_data = get_date_features(gt_ids=date_features, gt_shifts=start_deltas,\\n first_year=get_first_year(gt_id))\\n\\n print \\\"Loading additional date features\\\"\\n t = time.time()\\n if 'mjo' in date_features:\\n # Add cosine and sine transforms of MJO phase\\n mjo_phase_name = 'phase_shift'+str(get_start_delta(target_horizon, 'mjo'))\\n date_data['cos_'+mjo_phase_name] = np.cos((2*np.pi*date_data[mjo_phase_name])/8)\\n date_data['sin_'+mjo_phase_name] = np.sin((2*np.pi*date_data[mjo_phase_name])/8)\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n # Save date features to disk\\n print \\\"Saving date features to \\\"+date_data_file\\n t = time.time()\\n date_data.to_hdf(date_data_file, key=\\\"data\\\", mode=\\\"w\\\")\\n subprocess.call(\\\"chmod a+w \\\"+date_data_file, shell=True)\\n print \\\"Elapsed: {}s\\\".format(time.time() - t)\\n\\n print \\\"Finished generating date_data matrix.\\\"\\n print \\\"Total time elapsed: {}s\\\".format(time.time()-time_start)\\n return list(date_data)\",\n \"def fit_grid():\\r\\n\\t\\r\\n\\tp = 'results\\\\\\\\mnist_filter'\\r\\n\\t# try:\\r\\n\\t\\t# os.makedirs(p)\\r\\n\\t# except OSError:\\r\\n\\t\\t# pass\\r\\n\\tnp.random.seed(123456789)\\r\\n\\t# kargs = {\\r\\n\\t\\t# 'ninputs': 9,\\r\\n\\t\\t# 'ncolumns': 100,\\r\\n\\t\\t# 'nsynapses': 5,\\r\\n\\t\\t# 'random_permanence': True,\\r\\n\\t\\t# 'pinc':0.03, 'pdec':0.05,\\r\\n\\t\\t# 'seg_th': 3,\\r\\n\\t\\t# 'nactive': 10,\\r\\n\\t\\t# 'duty_cycle': 100,\\r\\n\\t\\t# 'max_boost': 10,\\r\\n\\t\\t# 'global_inhibition': True,\\r\\n\\t\\t# 'trim': 1e-4\\r\\n\\t# }\\r\\n\\tkargs2 = {\\r\\n\\t\\t'ninputs': 100 * (26 ** 2),\\r\\n\\t\\t'ncolumns': 2048,\\r\\n\\t\\t'nsynapses': 1000,\\r\\n\\t\\t'random_permanence': True,\\r\\n\\t\\t'pinc':0.03, 'pdec':0.05,\\r\\n\\t\\t'seg_th': 5,\\r\\n\\t\\t'nactive': 20,\\r\\n\\t\\t'duty_cycle': 100,\\r\\n\\t\\t'max_boost': 10,\\r\\n\\t\\t'global_inhibition': True,\\r\\n\\t\\t'trim': 1e-4\\r\\n\\t}\\r\\n\\t\\r\\n\\t# Get the data\\r\\n\\t(tr_x, tr_y), (te_x, te_y) = get_data()\\r\\n\\tnwindows = 26 ** 2\\r\\n\\t\\r\\n\\t# # Make the SPs\\r\\n\\t# sps = [SPRegion(**kargs) for _ in xrange(nwindows)]\\r\\n\\t\\r\\n\\t# # Train the SPs\\r\\n\\t# nepochs = 10\\r\\n\\t# t = time.time()\\r\\n\\t# for i in xrange(nepochs):\\r\\n\\t\\t# print i\\r\\n\\t\\t# for j, x in enumerate(tr_x):\\r\\n\\t\\t\\t# print '\\\\t{0}'.format(j)\\r\\n\\t\\t\\t# nx = extract_patches_2d(x.reshape(28, 28), (3, 3)).reshape(\\r\\n\\t\\t\\t\\t# nwindows, 9)\\r\\n\\t\\t\\t# for xi, sp in izip(nx, sps):\\r\\n\\t\\t\\t\\t# sp.step(xi)\\r\\n\\t# t1 = time.time() - t\\r\\n\\t# print t1\\r\\n\\t\\r\\n\\t# # Save this batch of SPs\\r\\n\\t# for i, sp in enumerate(sps):\\r\\n\\t\\t# sp.learn = False\\r\\n\\t\\t# sp.save(os.path.join(p, 'sp0-{0}.pkl'.format(i)))\\r\\n\\t\\r\\n\\t# Make the top level SP\\r\\n\\tsp2 = SPRegion(**kargs2)\\r\\n\\t\\r\\n\\t# Get the SPs\\r\\n\\tsps = [load(os.path.join(p, sp)) for sp in os.listdir(p) if sp[2] == '0']\\r\\n\\t\\r\\n\\t# Train the top SP\\r\\n\\tnepochs = 10\\r\\n\\tt = time.time()\\r\\n\\tfor i in xrange(nepochs):\\r\\n\\t\\tprint i\\r\\n\\t\\tfor j, x in enumerate(tr_x):\\r\\n\\t\\t\\tprint '\\\\t{0}'.format(j)\\r\\n\\t\\t\\tnx = extract_patches_2d(x.reshape(28, 28), (3, 3)).reshape(\\r\\n\\t\\t\\t\\tnwindows, 9)\\r\\n\\t\\t\\toutput = np.array(np.zeros(100 * nwindows), dtype='bool')\\r\\n\\t\\t\\tfor k, (xi, sp) in enumerate(izip(nx, sps)):\\r\\n\\t\\t\\t\\tsp.step(xi)\\r\\n\\t\\t\\t\\toutput[k*100:(k*100)+100] = sp.y[:, 0]\\r\\n\\t\\t\\tsp2.step(output)\\r\\n\\tt2 = time.time() - t\\r\\n\\tprint t2\\r\\n\\t\\r\\n\\t# Save the top SP\\r\\n\\tsp2.learn = False\\r\\n\\tsp2.save(os.path.join(p, 'sp1-0.pkl'))\",\n \"def cell_params(x,y,**kwargs):\\n\\n GR = glo.global_results()\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n cloudy_library = clo.library()\\n lookup_table = cloudy_library._restore_lookup_table()\\n lookup_table['logG0s'] = lookup_table['logFUVs']\\n if x == 'NH': \\n x_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\\n else:\\n x_cloudy = np.unique(lookup_table['log'+x+'s'])\\n if y == 'NH': \\n y_cloudy,R_NIR_FUV_cl = aux.get_NH_from_cloudy()\\n else:\\n y_cloudy = np.unique(lookup_table['log'+y+'s'])\\n\\n if not p.ylim:\\n p.ylim = [1e-3,30]\\n if not p.xlim:\\n p.xlim = [1e-7,1e3]\\n \\n # SELECT GALAXIES\\n rand_gal_index = np.random.randint(0, GR.N_gal, size=(p.bins))\\n if p.bins == GR.N_gal: rand_gal_index = np.arange(GR.N_gal)\\n if p.gal_index: \\n rand_gal_index = [p.gal_index]\\n print(rand_gal_index)\\n xs = np.array([])\\n ys = np.array([])\\n m_tot,m_encomp,m_y0 = 0,0,0\\n for gal_index in rand_gal_index:\\n print(gal_index)\\n gal_ob = gal.galaxy(gal_index)\\n df = gal_ob.cell_data.get_dataframe()\\n df['nSFR'] = df.nSFR.values#/(0.2**3)\\n #df['nSFR'] = df['SFR_density']\\n #df['NH'] = 10.**df['NH']\\n x1 = df[x].values\\n y1 = df[y].values\\n x1[x1 <= p.xlim[0]] = p.xlim[0]\\n y1[y1 <= p.ylim[0]] = p.ylim[0]\\n m_tot += np.sum(df.m.values)\\n m_encomp += np.sum(df.m[(x1>=p.xlim[0]) & (y1>=p.ylim[0])].values)\\n m_y0 += np.sum(df.m[(y1 == 0)].values)\\n #print(x,x1.min(),x1.max())\\n #print(y,y1.min(),y1.max())\\n ys = np.append(ys,y1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\\n xs = np.append(xs,x1[(x1>=p.xlim[0]) & (y1>=p.ylim[0])])\\n print('Min max of %s:' % x)\\n print(xs.min(),xs.max())\\n print('Min max of %s:' % y)\\n print(ys.min(),ys.max())\\n fig,ax = plt.subplots(figsize=(10,8))\\n hb = ax.hexbin(xs,ys,xscale='log',yscale='log',bins='log',mincnt=1,lw=None,gridsize=50,cmap='inferno')\\n cb = fig.colorbar(hb, ax=ax)\\n cb.set_label('Number of cells in %i galaxies' % len(rand_gal_index))\\n ax.set_xlabel(getlabel(x))\\n ax.set_ylabel(getlabel(y))\\n print('Total gas mass fraction encompassed: %.4f%%' % (m_encomp/m_tot*100))\\n print('Total gas mass fraction with y = 0: %.4f%%' % (m_y0/m_tot*100))\\n ax.set_xlim(p.xlim)\\n ax.set_ylim(p.ylim)\\n # Overplot Cloudy grid params\\n print(x,x_cloudy)\\n print(y,y_cloudy)\\n for x1 in x_cloudy:\\n ax.plot([10**x1,10**x1],ax.get_ylim(),'-',color='white',alpha=0.7)\\n ax.plot([10**x1,10**x1],ax.get_ylim(),'--k',alpha=0.7)\\n for y1 in y_cloudy:\\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'-',color='white',alpha=0.7)\\n ax.plot(ax.get_xlim(),[10.**y1,10.**y1],'--k',alpha=0.7)\\n\\n if not os.path.isdir(p.d_plot + 'cell_data/'): os.mkdir(p.d_plot + 'cell_data/') \\n plt.savefig('plots/cell_data/%s%s_cell_params_%s_%s_%s.png' % (p.sim_name,p.sim_run,p.z1,x,y),dpi=250, facecolor='w')\",\n \"def samples_timesteps_features(dataframe, columns, start_date, timesteps=72, \\n steps_ahead=24, window_days=100, train_percent=80.):\\n \\n def overlap_windows(dataset, timesteps, steps_ahead):\\n \\\"\\\"\\\" Create overlaping window of time-series data\\n \\n Parameters\\n ----------\\n dataset: pd.DataFrame\\n time-series pandas dataset\\n timesteps: int\\n number of time steps from the past for creating output arrays\\n steps_ahead: int\\n number of time steps into the future for making predictions\\n \\n Returns\\n -------\\n X, y: np.array\\n input and output 3-d arrays of overlaping time windows\\n \\\"\\\"\\\"\\n X = []; y = []\\n \\n start = 0\\n for i in range(len(dataset)):\\n # Define the end of the input sequence\\n in_end = start + timesteps\\n out_end = in_end + steps_ahead\\n # Ensure that there is enough data\\n if out_end <= len(dataset):\\n X.append(dataset[start:in_end, :])\\n # First column holds load values\\n y.append(dataset[in_end:out_end, 0])\\n # Move along one time step\\n start += 1\\n \\n # Convert list to np.array\\n X = np.asarray(X)\\n y = np.asarray(y)\\n \\n return X, y\\n\\n\\n data = dataframe.copy()\\n \\n if window_days*24 > data.values.shape[0]:\\n raise ValueError('Variable window_days has too large value: {}*24h = {} > {}, which is more than there is data!'.format(window_days, window_days*24, \\n data.values.shape[0]))\\n \\n # Training period\\n # ---------------\\n train_percent = train_percent/100.\\n st = pd.to_datetime(start_date) # start date\\n et = st + dt.timedelta(days=int(train_percent*window_days)) # end date\\n train = data.loc[st:et].values\\n \\n # Standardize and transform training data set\\n mean_std_values = {}\\n for i, column in enumerate(columns):\\n # Calculate mean and standard deviation only\\n # from the training data set values\\n mu = train[:,i].mean() # axis=0\\n sd = train[:,i].std()\\n mean_std_values[column] = (mu, sd)\\n # Standardize training data\\n train[:,i] = (train[:,i] - mu)/sd\\n \\n # Create overlapping windows with training data\\n X_train, y_train = overlap_windows(train, timesteps, steps_ahead)\\n \\n # Testing / Validation period\\n # ---------------------------\\n sv = et \\n ev = sv + dt.timedelta(days=int((1-train_percent)*window_days)+1)\\n test = data.loc[sv:ev].values\\n \\n # Transform testing/validation data set\\n for i, column in enumerate(columns):\\n # Use mean and standard deviation from the\\n # training data set\\n mu = mean_std_values[column][0]\\n sd = mean_std_values[column][1]\\n # Standardize test data\\n test[:,i] = (test[:,i] - mu)/sd\\n \\n # Create overlaping windows with test data\\n X_test, y_test = overlap_windows(test, timesteps, steps_ahead)\\n \\n return mean_std_values, X_train, y_train, X_test, y_test\",\n \"def grid_search(train_fun, decode_fun, eval_fun, train_set, dev_set, FLAGS):\\n FLAGS.create_fresh_params = True\\n\\n hyperparameters = FLAGS.tuning.split(',')\\n num_hps = len(hyperparameters)\\n hp_range = hyperparam_range\\n\\n print(\\\"======== Grid Search ========\\\")\\n print(\\\"%d hyperparameter(s): \\\" % num_hps)\\n for i in xrange(num_hps):\\n print(\\\"{}: {}\\\".format(\\n hyperparameters[i], hp_range[hyperparameters[i]]))\\n print()\\n\\n param_grid = [v for v in hp_range[hyperparameters[0]]]\\n for i in xrange(1, num_hps):\\n param_grid = itertools.product(param_grid, hp_range[hyperparameters[i]])\\n\\n best_hp_set = [-1] * num_hps\\n best_seed = -1\\n best_metrics_value = 0\\n\\n for row in param_grid:\\n row = nest.flatten(row)\\n for i in xrange(num_hps):\\n setattr(FLAGS, hyperparameters[i], row[i])\\n if hyperparameters[i] == 'universal_keep':\\n setattr(FLAGS, 'sc_input_keep', row[i])\\n setattr(FLAGS, 'sc_output_keep', row[i])\\n setattr(FLAGS, 'tg_input_keep', row[i])\\n setattr(FLAGS, 'tg_output_keep', row[i])\\n setattr(FLAGS, 'attention_input_keep', row[i])\\n setattr(FLAGS, 'attention_output_keep', row[i])\\n\\n print(\\\"Trying parameter set: \\\")\\n for i in xrange(num_hps):\\n print(\\\"* {}: {}\\\".format(hyperparameters[i], row[i]))\\n\\n # Try different random seed if tuning initialization\\n num_trials = 5 if FLAGS.initialization else 1\\n\\n if FLAGS.dataset.startswith('bash'):\\n metrics = [\\\"top1_temp_ms\\\", \\\"top1_cms\\\", \\\"top3_temp_ms\\\", \\\"top3_cms\\\"]\\n metrics_weights = [0.4, 0.4, 0.1, 0.1]\\n else:\\n metrics = [\\\"top1_temp_ms\\\"]\\n metrics_weights = [1]\\n metrics_signature = '+'.join(\\n ['{}x{}'.format(m, mw) for m, mw in zip(metrics, metrics_weights)])\\n\\n for t in xrange(num_trials):\\n seed = random.getrandbits(32)\\n tf.set_random_seed(seed)\\n metrics_value = single_round_model_eval(train_fun, decode_fun,\\n eval_fun, train_set, dev_set, metrics, metrics_weights)\\n print(\\\"Parameter set: \\\")\\n for i in xrange(num_hps):\\n print(\\\"* {}: {}\\\".format(hyperparameters[i], row[i]))\\n print(\\\"random seed: {}\\\".format(seed))\\n print(\\\"{} = {}\\\".format(metrics_signature, metrics_value))\\n print(\\\"Best parameter set so far: \\\")\\n for i in xrange(num_hps):\\n print(\\\"* {}: {}\\\".format(hyperparameters[i], best_hp_set[i]))\\n print(\\\"Best random seed so far: {}\\\".format(best_seed))\\n print(\\\"Best evaluation metrics so far = {}\\\".format(best_metrics_value))\\n if metrics_value > best_metrics_value:\\n best_hp_set = row\\n best_seed = seed\\n best_metrics_value = metrics_value\\n print(\\\"☺ New best parameter setting found\\\")\\n\\n print()\\n print(\\\"*****************************\\\")\\n print(\\\"Best parameter set: \\\")\\n for i in xrange(num_hps):\\n print(\\\"* {}: {}\\\".format(hyperparameters[i], best_hp_set[i]))\\n print(\\\"Best seed = {}\\\".format(best_seed))\\n print(\\\"Best {} = {}\\\".format(metrics, best_metrics_value))\\n print(\\\"*****************************\\\")\",\n \"def __gxg__(series, year_agg, tmin, tmax, fill_method, limit, output,\\n min_n_meas, min_n_years, year_offset):\\n # handle tmin and tmax\\n if tmin is not None:\\n series = series.loc[tmin:]\\n if tmax is not None:\\n series = series.loc[:tmax]\\n if series.empty:\\n if output.startswith('year'):\\n return pd.Series()\\n elif output == 'mean':\\n return np.nan\\n else:\\n ValueError('{output:} is not a valid output option'.format(\\n output=output))\\n\\n # resample the series to values at the 14th and 28th of every month\\n # first generate a daily series by averaging multiple measurements during the day\\n series = series.resample('d').mean()\\n select14or28 = True\\n if fill_method is None:\\n series = series.dropna()\\n elif fill_method == 'ffill':\\n series = series.ffill(limit=limit)\\n elif fill_method == 'bfill':\\n series = series.bfill(limit=limit)\\n elif fill_method == 'nearest':\\n if limit == 0:\\n # limit=0 is a trick to only use each measurements once\\n # only keep days with measurements\\n series = series.dropna()\\n # generate an index at the 14th and 28th of every month\\n buf = pd.to_timedelta(8, 'd')\\n ref_index = pd.date_range(series.index.min() - buf,\\n series.index.max() + buf)\\n mask = [(x.day == 14) or (x.day == 28) for x in ref_index]\\n ref_index = ref_index[mask]\\n # only keep the days that are closest to series.index\\n ref_index = get_sample(ref_index, series.index)\\n # and set the index of series to this index\\n # (and remove rows in series that are not in ref_index)\\n series = series.reindex(ref_index, method=fill_method)\\n select14or28 = False\\n else:\\n # with a large limit (larger than 6) it is possible that one measurement is used more than once\\n series = series.dropna().reindex(series.index, method=fill_method,\\n limit=limit)\\n else:\\n series = series.interpolate(method=fill_method, limit=limit,\\n limit_direction='both')\\n\\n # and select the 14th and 28th of each month (if needed still)\\n if select14or28:\\n mask = [(x.day == 14) or (x.day == 28) for x in series.index]\\n series = series.loc[mask]\\n\\n # remove NaNs that may have formed in the process above\\n series.dropna(inplace=True)\\n\\n # resample the series to yearly values\\n yearly = series.resample(year_offset).apply(year_agg,\\n min_n_meas=min_n_meas)\\n\\n # return statements\\n if output.startswith('year'):\\n return yearly\\n elif output == 'mean':\\n if yearly.notna().sum() < min_n_years:\\n return np.nan\\n else:\\n return yearly.mean()\\n else:\\n ValueError('{output:} is not a valid output option'.format(\\n output=output))\",\n \"def source_adj_gsdf(gmdata_sim,gmdata_obs,IsolationFilter,num_pts,dt): \\n t = np.arange(num_pts)*dt\\n ts=np.flip(-t[1:], axis=0)\\n lTime = np.concatenate((ts,t), axis=0)#Lag time \\n \\n #convolve the waveforms for the cross- and auto-correlagrams \\n cross = np.correlate(IsolationFilter,gmdata_obs,'full')\\n auto = np.correlate(IsolationFilter,gmdata_sim,'full') \\n \\n #GSDF Parameters \\n w0=2*np.pi/(lTime[-1]) \\n# wN=2*np.pi/(2*dt)\\n# w(:,1)=-wN:w0:wN\\n wf=w0*np.linspace(-int(num_pts/2),int(num_pts/2),num_pts) \\n fi = [0.05, 0.075, 0.1]\\n# fi = [0.02, 0.03, 0.04, 0.05]\\n# fi = [0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2]\\n Tw = 2/np.mean(fi) # Effective window\\n# sw = 2*np.pi*0.72/Tw; # Sigma w ~ 0.2827433388230814\\n sw=0.1 \\n \\n# #% A local maximum will be selected closest to 0-lag\\n# I_O=np.argmax(cross)\\n# I_S=np.argmax(auto) \\n\\n I_O, peaks_O = find_peaks(np.abs(hilbert(cross))/np.max(np.abs(hilbert(cross))), height=0.25)\\n I_S, peaks_S = find_peaks(np.abs(hilbert(auto))/np.max(np.abs(hilbert(auto))), height=0.25)\\n\\n PkO = peaks_O.get(\\\"peak_heights\\\", \\\"\\\")\\n PkS = peaks_S.get(\\\"peak_heights\\\", \\\"\\\")\\n\\n if (I_O==[] or I_S==[]):\\n I_O=np.argmax(cross)\\n I_S=np.argmax(auto)\\n else:\\n I_O_min = np.argmin(np.multiply((1+np.abs(lTime[I_O]))**2,np.abs(1-PkO)))\\n I_O = I_O[I_O_min]\\n\\n I_S_min = np.argmin(np.multiply((1+np.abs(lTime[I_S]))**2,np.abs(1-PkS)))\\n I_S = I_S[I_S_min]\\n \\n ##Windowing\\n win1=np.exp(-(0.5*sw**2)*(lTime-lTime[I_O])**2)\\n win2=np.exp(-(0.5*sw**2)*(lTime-lTime[I_S])**2) \\n \\n #\\n WO = np.multiply(win1,cross)\\n WS = np.multiply(win2,auto)\\n WS = WS*np.max(WO)/np.max(WS) #Normalized window by amplitude\\n #% Parameters for \\\"bootstraping\\\"\\n InOR=np.argmax(WO)\\n InSR=np.argmax(WS) \\n \\n #% Isolation filter FFT for perturbation kernel\\n tff=np.conj(fftshift(fft(IsolationFilter)))*1/num_pts \\n \\n adj_sim_decompose = np.zeros((len(fi),num_pts))\\n adj_sim_sum = np.zeros(num_pts)\\n TauP_arr = np.zeros(len(fi)) \\n \\n ne = int(np.min([2/np.min(fi)/dt,num_pts/2])) #% Effective bandwidth for inversion\\n \\n for i in range(0,len(fi)): \\n si = 0.1*fi[i]\\n #Crosscorrelagram and Autocorrelagram filtering\\n dO=computebandfftfilter_gauss(WO,dt,fi[i],si,lTime);\\n dS=computebandfftfilter_gauss(WS,dt,fi[i],si,lTime); \\n \\n # % Check bootstraping\\n InO=np.argmax(np.real(dO))\\n InS=np.argmax(np.real(dS)) \\n \\n BS = 1; Cn = 0;\\n while BS == 1 or Cn < 10:\\n InO=int(InO)\\n if (lTime[InO] < lTime[InOR]+0.51/fi[i]) and (lTime[InO] >= lTime[InOR]-0.51/fi[i]):\\n BS = 0\\n elif (lTime[InO] >= (lTime[InOR]+0.45/fi[i])):\\n InO=InO-np.round(1/fi[i]/dt)\\n elif (lTime[InO] < lTime[InOR]-0.45/fi[i]):\\n InO=InO+np.round(1/fi[i]/dt)\\n Cn = Cn+1\\n \\n BS = 1; Cn = 0;\\n while BS == 1 or Cn < 10:\\n InS=int(InS) \\n if (lTime[InS] < lTime[InSR]+0.51/fi[i]) and (lTime[InS] >= lTime[InSR]-0.51/fi[i]):\\n BS = 0\\n elif (lTime[InS] >= (lTime[InSR]+0.45/fi[i])):\\n InS=InS-np.round(1/fi[i]/dt)\\n elif (lTime[InS] < lTime[InSR]-0.45/fi[i]):\\n InS=InS+np.round(1/fi[i]/dt)\\n Cn = Cn+1 \\n\\n # Five parameter Gaussian wavelet fitting \\n Ao = np.max(envelope(np.real(dO))); Io = np.argmax(envelope(np.real(dO)));\\n As = np.max(envelope(np.real(dS))); Is = np.argmax(envelope(np.real(dS))); \\n ##Constrain the initial values \\n # Parameters for curve_fit\\n wi=2*np.pi*fi[i] \\n \\n try:\\n GaO, params_covariance = curve_fit(Eqn, lTime[Io-ne-1:Io+ne], np.real(dO[Io-ne-1:Io+ne]))\\n GaS, params_covariance = curve_fit(Eqn, lTime[Is-ne-1:Is+ne], np.real(dS[Is-ne-1:Is+ne])) \\n except:\\n GaO = [Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]\\n GaS = [As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]] \\n\\n# GaO, params_covariance = curve_fit(Eqn, lTime[Io-ne-1:Io+ne], np.real(dO[Io-ne-1:Io+ne]),bounds=(0,[Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]))\\n# GaS, params_covariance = curve_fit(Eqn, lTime[Is-ne-1:Is+ne], np.real(dS[Is-ne-1:Is+ne]),bounds=(0,[As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]])) \\n \\n# % Check fitting\\n if ((GaO[0]/GaS[0]) > 10**5) or np.abs(GaO[4]-GaS[4]) > lTime[-1]/2:\\n GaO = [Ao, 2*np.pi*si, lTime[Io], 2*np.pi*fi[i], lTime[InO]]\\n GaS = [As, 2*np.pi*si, lTime[Is], 2*np.pi*fi[i], lTime[InS]] \\n \\n wP=((si**2)*wf+(sw**2)*wi)/(sw**2+si**2)\\n wPP=((si**2)*wf-(sw**2)*wi)/(sw**2+si**2)\\n siP=((si**2)*(sw**2)/(sw**2+si**2))**0.5 \\n #Estimate waveform perturbation kernel (WPK)\\n IW=(siP/(sw*GaS[0]))*np.multiply(np.exp(-0.5*(wf-2*np.pi*fi[i])**2/(sw**2+si**2)),np.divide(tff,wP))+\\\\\\n (siP/(sw*GaS[0]))*np.exp(-0.5*(wf+2*np.pi*fi[i])**2/(sw**2+si**2))*tff/wPP\\n \\n IW[0:int(len(IW)/2)]=0*IW[0:int(len(IW)/2)]\\n \\n itff = ifft(fftshift(num_pts*IW)) \\n \\n #Save the GSDF measurements\\n TauP_arr[i] = GaO[4]-GaS[4]; #% delta_P\\n \\n# Jp = np.real(itff)\\n# Jp = np.imag(itff)\\n Jp = -np.imag(itff) \\n adj_sim_decompose[i,:] = np.flip(Jp,axis=0)*TauP_arr[i] \\n \\n #if i>0:\\n adj_sim_sum = adj_sim_sum + adj_sim_decompose[i,:] \\n \\n return adj_sim_sum, TauP_arr\",\n \"def test_plt_mag_time():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n # create the arrays per filter and readout pattern\\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\\n for i, val in enumerate(max_val_box):\\n if '140' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(val)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(val)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif '110' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(val)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(val)\\n nrsrapidd6_clear.append(np.NaN)\\n else:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(val)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(val)\\n # add to the bokeh data structure\\n ta.source.data[\\\"nrsrapid_f140x\\\"] = nrsrapid_f140x\\n ta.source.data[\\\"nrsrapid_f110w\\\"] = nrsrapid_f110w\\n ta.source.data[\\\"nrsrapid_clear\\\"] = nrsrapid_clear\\n ta.source.data[\\\"nrsrapidd6_f140x\\\"] = nrsrapidd6_f140x\\n ta.source.data[\\\"nrsrapidd6_f110w\\\"] = nrsrapidd6_f110w\\n ta.source.data[\\\"nrsrapidd6_clear\\\"] = nrsrapidd6_clear\\n result = ta.plt_mag_time()\\n\\n assert bokeh_plot_type == type(result)\",\n \"def _timeseries_calc(ifg_paths, params, vcmt, tiles, preread_ifgs):\\n if params[cf.TIME_SERIES_CAL] == 0:\\n log.info('Time Series Calculation not required')\\n return\\n\\n if params[cf.TIME_SERIES_METHOD] == 1:\\n log.info('Calculating time series using Laplacian Smoothing method')\\n elif params[cf.TIME_SERIES_METHOD] == 2:\\n log.info('Calculating time series using SVD method')\\n\\n output_dir = params[cf.TMPDIR]\\n total_tiles = len(tiles)\\n process_tiles = mpiops.array_split(tiles)\\n for t in process_tiles:\\n log.debug(\\\"Calculating time series for tile \\\"+str(t.index)+\\\" out of \\\"+str(total_tiles))\\n ifg_parts = [shared.IfgPart(p, t, preread_ifgs, params) for p in ifg_paths]\\n mst_tile = np.load(os.path.join(output_dir, 'mst_mat_{}.npy'.format(t.index)))\\n res = timeseries.time_series(ifg_parts, params, vcmt, mst_tile)\\n tsincr, tscum, _ = res\\n np.save(file=os.path.join(output_dir, 'tsincr_{}.npy'.format(t.index)), arr=tsincr)\\n np.save(file=os.path.join(output_dir, 'tscuml_{}.npy'.format(t.index)), arr=tscum)\\n mpiops.comm.barrier()\\n log.debug(\\\"Finished timeseries calc!\\\")\",\n \"def createParkingCostDataSet(baseyear_tazdata_df, tazdata_df):\\n # compute the employment density\\n tazdata_df[\\\"employmentDensity\\\"] = 0.0\\n tazdata_df.loc[ tazdata_df[\\\"CIACRE\\\"] > 0, \\\"employmentDensity\\\" ] = tazdata_df[\\\"TOTEMP\\\"]/tazdata_df[\\\"CIACRE\\\"]\\n\\n baseyear_tazdata_df[\\\"employmentDensity_baseyear\\\"] = 0.0\\n baseyear_tazdata_df.loc[ baseyear_tazdata_df[\\\"CIACRE\\\"] > 0, \\\"employmentDensity_baseyear\\\" ] = baseyear_tazdata_df[\\\"TOTEMP\\\"]/baseyear_tazdata_df[\\\"CIACRE\\\"]\\n\\n # if these are set, clear them since we'll set them\\n if \\\"PRKCST\\\" in list(tazdata_df.columns.values):\\n tazdata_df.drop(columns=[\\\"PRKCST\\\",\\\"OPRKCST\\\"], inplace=True)\\n\\n # compute the ratio of densities between the base year and the forecast year\\n tazdata_df = pandas.merge(left=tazdata_df, right=baseyear_tazdata_df[[\\\"ZONE\\\",\\\"employmentDensity_baseyear\\\",\\\"PRKCST\\\",\\\"OPRKCST\\\"]], how=\\\"left\\\", on=\\\"ZONE\\\")\\n tazdata_df[\\\"employmentDensityRatio\\\"] = 0.0\\n tazdata_df.loc[ tazdata_df[\\\"employmentDensity_baseyear\\\"] > 0, \\\"employmentDensityRatio\\\" ] = tazdata_df[\\\"employmentDensity\\\"]/tazdata_df[\\\"employmentDensity_baseyear\\\"]\\n # do not allow decreasing parking costs\\n tazdata_df.loc[ tazdata_df[\\\"employmentDensityRatio\\\"] <1.0, \\\"employmentDensityRatio\\\" ] = 1.0\\n\\n tazdata_df[\\\"PRKCST\\\"] = tazdata_df[\\\"PRKCST\\\"]*tazdata_df[\\\"employmentDensityRatio\\\"]\\n tazdata_df[\\\"OPRKCST\\\"] = tazdata_df[\\\"OPRKCST\\\"]*tazdata_df[\\\"employmentDensityRatio\\\"]\\n\\n # remove intermediate variables\\n baseyear_tazdata_df.drop(columns=[\\\"employmentDensity_baseyear\\\"], inplace=True)\\n tazdata_df.drop(columns=[\\\"employmentDensity\\\",\\\"employmentDensity_baseyear\\\",\\\"employmentDensityRatio\\\"], inplace=True)\\n return tazdata_df\",\n \"def initializeDistribution(self):\\n\\n f = open(self.dataFilename, 'r')\\n reader = csv.reader(f)\\n headers = next(reader)\\n indexFunctionID = headers.index(self.functionID)\\n indexVariableID = headers.index(self.variableID)\\n f.close()\\n rawData = np.genfromtxt(self.dataFilename, delimiter=\\\",\\\" , skip_header=1, usecols=(indexVariableID,indexFunctionID))\\n\\n self.data = rawData[rawData[:,0].argsort()]\\n self.lowerBound = self.data[0,0]\\n self.upperBound = self.data[-1,0]\\n\\n if self.functionType == 'cdf':\\n self.cdfFunc = UnivariateSpline(self.data[:,0], self.data[:,1], k=self.k, s=self.s)\\n self.pdfFunc = self.cdfFunc.derivative()\\n self.invCDF = UnivariateSpline(self.data[:,1], self.data[:,0], k=self.k, s=self.s)\\n else:\\n self.pdfFunc = UnivariateSpline(self.data[:,0], self.data[:,1], k=self.k, s=self.s)\\n cdfValues = np.zeros(self.data[:,0].size)\\n for i in range(self.data[:,0].size):\\n cdfValues[i] = self.pdfFunc.integral(self.data[0][0],self.data[i,0])\\n self.invCDF = UnivariateSpline(cdfValues, self.data[:,0] , k=self.k, s=self.s)\\n\\n # Note that self.invCDF is creating a new spline where I switch its term.\\n # Instead of doing spline(x,f(x)) I am creating its inverse spline(f(x),x)\\n # This can be done if f(x) is monothonic increasing with x (which is true for cdf)\",\n \"def SC_generation(hourly_radiation, prop_observers, number_groups, weather_data, g, Sz, Az, ha, Tin_C, height,\\n panel_properties, latitude):\\n\\n\\n n0 = panel_properties['n0']\\n c1 = panel_properties['c1']\\n c2 = panel_properties['c2']\\n mB0_r = panel_properties['mB0_r']\\n mB_max_r = panel_properties['mB_max_r']\\n mB_min_r = panel_properties['mB_min_r']\\n C_eff = panel_properties['C_eff']\\n t_max = panel_properties['t_max']\\n IAM_d = panel_properties['IAM_d']\\n Aratio = panel_properties['aperture_area_ratio']\\n Apanel = panel_properties['module_area']\\n dP1 = panel_properties['dP1']\\n dP2 = panel_properties['dP2']\\n dP3 = panel_properties['dP3']\\n dP4 = panel_properties['dP4']\\n Cp_fluid_JperkgK = panel_properties['Cp_fluid'] # J/kgK\\n\\n # create lists to store results\\n list_results = [None] * number_groups\\n list_areas_groups = [None] * number_groups\\n Sum_mcp_kWperC = np.zeros(8760)\\n Sum_qout_kWh = np.zeros(8760)\\n Sum_Eaux_kWh = np.zeros(8760)\\n Sum_qloss = np.zeros(8760)\\n Sum_radiation_kWh = np.zeros(8760)\\n\\n Tin_array_C = np.zeros(8760) + Tin_C\\n aperature_area_per_module = Aratio * Apanel\\n total_area_module = prop_observers['total_area_module'].sum() # total area for panel installation\\n\\n # calculate equivalent length of pipes\\n lv = panel_properties['module_length'] # module length\\n number_modules = round(total_area_module/Apanel) # this is an estimation\\n l_ext_mperm2 = (2 * lv * number_modules/ (total_area_module * Aratio)) # pipe length within the collectors\\n l_int_mperm2 = 2 * height / (total_area_module * Aratio) # pipe length from building substation to roof top collectors\\n Leq_mperm2 = l_int_mperm2 + l_ext_mperm2 # in m/m2 aperture\\n\\n if panel_properties['type'] == 'ET': # for evacuated tubes\\n Nseg = 100 # default number of subsdivisions for the calculation\\n else:\\n Nseg = 10 # default number of subsdivisions for the calculation\\n\\n for group in range(number_groups):\\n # load panel angles from group\\n teta_z = prop_observers.loc[group, 'surface_azimuth'] # azimuth of panels of group\\n area_per_group = prop_observers.loc[group, 'total_area_module']\\n tilt_angle_deg = prop_observers.loc[group, 'tilt'] # tilt angle of panels\\n\\n # create dataframe with irradiation from group\\n\\n radiation_Wh = pd.DataFrame({'I_sol': hourly_radiation[group]})\\n radiation_Wh['I_diffuse'] = weather_data.ratio_diffhout * radiation_Wh.I_sol # calculate diffuse radiation\\n radiation_Wh['I_direct'] = radiation_Wh['I_sol'] - radiation_Wh['I_diffuse'] # calculate direct radiation\\n radiation_Wh.fillna(0, inplace=True) # set nan to zero\\n\\n # calculate incidence angle modifier for beam radiation\\n IAM_b = calc_IAM_beam_SC(Az, g, ha, teta_z, tilt_angle_deg, panel_properties['type'], Sz, latitude)\\n\\n # calculate heat production from a solar collector of each group\\n list_results[group] = calc_SC_module(tilt_angle_deg, IAM_b, IAM_d, radiation_Wh.I_direct,\\n radiation_Wh.I_diffuse, weather_data.drybulb_C, n0,\\n c1, c2, mB0_r, mB_max_r, mB_min_r, C_eff, t_max,\\n aperature_area_per_module, dP1, dP2, dP3, dP4,\\n Cp_fluid_JperkgK, Tin_C, Leq_mperm2, l_ext_mperm2,\\n l_int_mperm2, Nseg)\\n\\n\\n # multiplying the results with the number of panels in each group and write to list\\n number_modules_per_group = area_per_group / Apanel\\n list_areas_groups[group] = area_per_group\\n radiation_array = hourly_radiation[group] * list_areas_groups[group] / 1000 # kWh\\n Sum_qout_kWh = Sum_qout_kWh + list_results[group][1] * number_modules_per_group\\n Sum_Eaux_kWh = Sum_Eaux_kWh + list_results[group][2] * number_modules_per_group\\n Sum_qloss = Sum_qloss + list_results[group][0] * number_modules_per_group\\n Sum_mcp_kWperC = Sum_mcp_kWperC + list_results[group][5] * number_modules_per_group\\n Sum_radiation_kWh = Sum_radiation_kWh + radiation_Wh['I_sol']*area_per_group/1000\\n\\n Tout_group_C = (Sum_qout_kWh / Sum_mcp_kWperC) + Tin_C # in C assuming all collectors are connected in parallel\\n\\n Final = pd.DataFrame(\\n {'Q_SC_gen_kWh': Sum_qout_kWh, 'T_SC_sup_C': Tin_array_C, 'T_SC_re_C': Tout_group_C, 'mcp_SC_kWperC': Sum_mcp_kWperC, 'Eaux_SC_kWh': Sum_Eaux_kWh,\\n 'Q_SC_l_kWh': Sum_qloss, 'Area_SC_m2': sum(list_areas_groups), 'radiation_kWh': Sum_radiation_kWh}, index=range(8760))\\n\\n return list_results, Final\",\n \"def __gxg__(series, year_agg, tmin, tmax, fill_method, limit, output,\\n min_n_meas, min_n_years, year_offset):\\n # handle tmin and tmax\\n if tmin is not None:\\n series = series.loc[tmin:]\\n if tmax is not None:\\n series = series.loc[:tmax]\\n if series.empty:\\n if output.startswith('year'):\\n return Series()\\n elif output == 'mean':\\n return nan\\n else:\\n ValueError('{output:} is not a valid output option'.format(\\n output=output))\\n\\n # resample the series to values at the 14th and 28th of every month\\n # first generate a daily series by averaging multiple measurements during the day\\n series = series.resample('d').mean()\\n select14or28 = True\\n if fill_method is None:\\n series = series.dropna()\\n elif fill_method == 'ffill':\\n series = series.ffill(limit=limit)\\n elif fill_method == 'bfill':\\n series = series.bfill(limit=limit)\\n elif fill_method == 'nearest':\\n if limit == 0:\\n # limit=0 is a trick to only use each measurements once\\n # only keep days with measurements\\n series = series.dropna()\\n # generate an index at the 14th and 28th of every month\\n buf = to_timedelta(8, 'd')\\n ref_index = date_range(series.index.min() - buf,\\n series.index.max() + buf)\\n mask = [(x.day == 14) or (x.day == 28) for x in ref_index]\\n ref_index = ref_index[mask]\\n # only keep the days that are closest to series.index\\n ref_index = get_sample(ref_index, series.index)\\n # and set the index of series to this index\\n # (and remove rows in series that are not in ref_index)\\n series = series.reindex(ref_index, method=fill_method)\\n select14or28 = False\\n else:\\n # with a large limit (larger than 6) it is possible that one measurement is used more than once\\n series = series.dropna().reindex(series.index, method=fill_method,\\n limit=limit)\\n else:\\n series = series.interpolate(method=fill_method, limit=limit,\\n limit_direction='both')\\n\\n # and select the 14th and 28th of each month (if needed still)\\n if select14or28:\\n mask = [(x.day == 14) or (x.day == 28) for x in series.index]\\n series = series.loc[mask]\\n\\n # remove NaNs that may have formed in the process above\\n series.dropna(inplace=True)\\n\\n # resample the series to yearly values\\n yearly = series.resample(year_offset).apply(year_agg,\\n min_n_meas=min_n_meas)\\n\\n # return statements\\n if output.startswith('year'):\\n return yearly\\n elif output == 'mean':\\n if yearly.notna().sum() < min_n_years:\\n return nan\\n else:\\n return yearly.mean()\\n else:\\n msg = '{} is not a valid output option'.format(output)\\n raise (ValueError(msg))\",\n \"def repeat(start, end, roi_times=None, timeres=2, coords=None, ar=None,\\n split_temps=None, em_wlen=None, plotminmax=False, plotstd=False,\\n hist_type='plain', loaddata=False):#, output=None):\\n #if isinstance(output, str):\\n # from matplotlib import use\\n # use(output)\\n import matplotlib.pyplot as plt\\n import matplotlib.dates as mdates\\n\\n #loaddata = False\\n\\n print start, end\\n start, end = parse(start), parse(end)\\n \\n s = []\\n t = []\\n p = []\\n \\n #if flares == []:\\n # return s, t, p\\n\\n timerange = tr(start, end)\\n delta = dt.timedelta(hours=timeres)\\n ntimes = int(timerange.seconds()/delta.total_seconds())\\n times = [time.start() for time in timerange.split(ntimes)]\\n \\n ntemps = 141\\n tempsovertime = np.zeros((ntemps, ntimes))\\n \\n means = np.zeros(len(times))\\n p95s = np.zeros(len(times))\\n loopmeans = np.zeros(len(times))\\n if plotminmax:\\n maxes = np.zeros(len(times))\\n mins = np.zeros(len(times))\\n if plotstd:\\n stds = np.zeros(len(times))\\n loopstds = np.zeros(len(times))\\n if em_wlen:\\n meanem = np.zeros(len(times))\\n if plotminmax:\\n maxem = np.zeros(len(times))\\n minem = np.zeros(len(times))\\n if plotstd:\\n stdem = np.zeros(len(times))\\n\\n for i, date in enumerate(times):\\n data_only = True\\n try:\\n if ar == 'all':\\n plotar = None\\n else:\\n plotar = ar\\n results = output_maps(date, plotar, coords, 'data', split_temps,\\n subimsize=50, calc_em=em_wlen, data_only=data_only)#True)#, linear=True)\\n if isinstance(results, tuple):\\n tempmap, emmap = results\\n else:\\n tempmap = results\\n data = tempmap.data\\n except DownloadError as de:\\n data = np.zeros((512, 512))\\n print de.msg\\n except:\\n print 'KHAAAAAAAN! Some part of the temperature-plotting process failed.'\\n raise\\n data = np.zeros((512, 512))\\n if em_wlen:\\n emmap = np.zeros((512, 512))\\n \\n t.append(np.nanmean(data))\\n p.append(np.nanmax(data))\\n \\n data = data.flatten()\\n data2 = data.copy()\\n data2[data == 0.0] = np.NaN\\n data2 = data2[np.isfinite(data)]\\n data2.sort()\\n temps, bins = np.histogram(data, bins=ntemps, density=False, range=(5.6, 7.0))\\n temps = (temps/float(data.size))*100.0\\n tempsovertime[:, i] = temps\\n\\n #loops = data[data >= split_temps]\\n #data = data[data < split_temps]\\n\\n means[i] = np.nanmean(data2)\\n try:\\n p95s[i] = data2[round(0.95 * len(data2))-1]\\n except IndexError:\\n p95s[i] = np.NaN\\n #loopmeans[i] = np.nanmean(loops)\\n if plotminmax:\\n maxes[i] = np.nanmax(data)\\n mins[i] = np.nanmin(data)\\n if em_wlen:\\n maxem[i] = np.nanmax(emmap)\\n minem[i] = np.nanmin(emmap)\\n if plotstd:\\n stds[i] = np.nanstd(data)\\n if em_wlen:\\n stdem[i] = np.nanstd(emmap)\\n #loopstds[i] = np.nanstd(loops)\\n \\n tempsovertime[tempsovertime <= 0.1] = np.nan\\n\\n xmin, xmax = mdates.datestr2num([str(start), str(end)])\\n fig = plt.figure(figsize=(36, 18))\\n ax = fig.add_subplot(111, axisbg='k')\\n plot_title = 'Temperature distribution of corona\\\\n{:%Y/%m/%d %H:%M} - {:%Y/%m/%d %H:%M}'.format(start, end)\\n if roi_times:\\n plot_title += '\\\\nRegion observed: {:%Y/%m/%d %H:%M} - {:%Y/%m/%d %H:%M}'.format(*roi_times)\\n plt.title(plot_title)\\n if hist_type == 'plain':\\n plt.imshow(tempsovertime[30:106, :], extent=[xmin, xmax, 5.9, 6.65],\\n aspect='auto', interpolation='none', origin='lower',\\n cmap='coolwarm', vmin=np.nanmin(tempsovertime[65:106, :]),\\n vmax=np.nanmax(tempsovertime[65:106, :]))\\n elif hist_type == 'loops':\\n plt.imshow(tempsovertime[65:106, :], extent=[xmin, xmax, 6.25, 6.65],\\n aspect='auto', interpolation='none', origin='lower',\\n cmap='coolwarm', vmin=np.nanmin(tempsovertime[65:106, :]),\\n vmax=np.nanmax(tempsovertime[65:106, :]))\\n elif hist_type == 'full':\\n plt.imshow(tempsovertime, extent=[xmin, xmax, 5.6, 7.0],\\n aspect='auto', interpolation='none', origin='lower',\\n cmap='coolwarm', vmin=np.nanmin(tempsovertime),\\n vmax=np.nanmax(tempsovertime))\\n plt.tight_layout()\\n ax.xaxis_date()\\n fig.autofmt_xdate()\\n plt.colorbar(orientation='horizontal')\\n plt.savefig('/media/huw/temp-time_hists/distribution_over_time_{}'.format(ar))\\n plt.close()\\n\\n\\n means[np.where(means == 0.0)] = np.nan\\n if plotstd:\\n stds[np.where(stds == 0.0)] = np.nan\\n loopstds[loopstds == 0.0] = np.nan\\n\\n try:\\n tnums = mdates.date2num([ti for ti in times])\\n print maxes\\n print len(maxes)\\n fig = plt.figure(figsize=(18, 14))\\n ax = fig.add_subplot(111)\\n plt.title('Variation of temperature over time; AR{}'.format(ar), \\n fontsize=32)\\n plt.plot(tnums, maxes, label='Maximum temperature', color='red')\\n plt.axhline(np.nanmean(maxes))\\n print tnums\\n print len(tnums)\\n ax.xaxis_date()\\n fig.autofmt_xdate()\\n plt.legend(loc=4, fontsize=16)\\n plt.xlabel('Date', fontsize=24)\\n plt.ylabel('log(T)', fontsize=24)\\n #plt.savefig('/media/huw/temp_plots/temp_plot_{}_b'.format(ar))\\n plt.savefig('/home/drew/Dropbox/ARs/temps_{}_b'.format(ar))\\n plt.close()\\n\\n \\\"\\\"\\\"diff = ((maxes-p95s)/p95s)*100.0\\n fig = plt.figure(figsize=(18, 14))\\n ax = fig.add_subplot(1, 1, 1)\\n plt.title('Percentage difference between max and 95th %-ile; AR{}'.format(ar), \\n fontsize=32)\\n plt.plot(tnums, diff, color='black')\\n plt.scatter(fldates, [np.nanmax(diff)]*len(fldates))\\n for flare in flares:\\n ax.text(sunpy.time.parse_time(flare['event_peaktime']), np.nanmax(diff)+0.01, flare['fl_goescls'][0])\\n ax.xaxis_date()\\n fig.autofmt_xdate()\\n plt.xlabel('Date', fontsize=24)\\n plt.ylabel('log(T)', fontsize=24)\\n #plt.savefig('/media/huw/temp_plots/temp_plot_{}'.format(ar))\\n plt.savefig('Dropbox/ARs/diffs_{}'.format(ar))\\n plt.close()\\\"\\\"\\\"\\n \\n except:# ValueError:\\n print \\\"Can't plot the temperature graph because matplotlib is being a whiney douche\\\"\\n print tnums\\n raise\\n\\n return s, t, p, times\",\n \"def _curve_fitting(self, phase, info):\\n start_date = info[self.START]\\n end_date = info[self.END]\\n population = info[self.N]\\n trend = Trend(\\n self.jhu_data, population, self.country, province=self.province,\\n start_date=start_date, end_date=end_date\\n )\\n trend.analyse()\\n df = trend.result()\\n if trend.rmsle() > self.max_rmsle:\\n df[f\\\"{self.S}{self.P}\\\"] = None\\n # Get min value for vline\\n r_value = int(df[self.R].min())\\n # Rename the columns\\n phase = self.INITIAL if phase == \\\"0th\\\" else phase\\n df = df.rename({f\\\"{self.S}{self.P}\\\": f\\\"{phase}{self.P}\\\"}, axis=1)\\n df = df.rename({f\\\"{self.S}{self.A}\\\": f\\\"{phase}{self.A}\\\"}, axis=1)\\n df = df.rename({f\\\"{self.R}\\\": f\\\"{phase}_{self.R}\\\"}, axis=1)\\n return (df, r_value)\",\n \"def generate_data(params, N, rng=(-7, 7)):\\n hp = np.array(params)\\n print(\\\"parameters for data generated from gp are : {0}\\\".format(hp))\\n print(\\\"using a ExpSquared kernel\\\")\\n gp = george.GP(hp[0] * kernels.ExpSquaredKernel(hp[1]))\\n t = rng[0] + np.diff(rng) * np.sort(np.random.rand(N))\\n\\n #y = model(params, t)\\n y = gp.sample(t)\\n yerr = 1.e-5 #1 + 0.1 * np.random.randn(N)\\n y += yerr\\n\\n return t, y, yerr\",\n \"def prepare_data(self):\\r\\n annual_df = self.annual_df\\r\\n coef_df = self.coef_df\\r\\n quarter_df = self.quarter_df\\r\\n # historical_df = self.historical_df\\r\\n Event_Buffer = self.Event_Buffer\\r\\n\\r\\n Tot_Prod = coef_df[\\\"Product\\\"].nunique()\\r\\n # Tot_Week = coef_df[\\\"wk\\\"].nunique()\\r\\n Tot_Week = 52\\r\\n\\r\\n EDLP_Events = list(annual_df[\\\"RP_Events\\\"])\\r\\n Min_EDLP_Events = [\\r\\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in EDLP_Events\\r\\n ]\\r\\n Max_EDLP_Events = [\\r\\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\\r\\n for i in EDLP_Events\\r\\n ]\\r\\n\\r\\n TPR_Events = list(annual_df[\\\"TPR_Events\\\"])\\r\\n Min_TPR_Events = [\\r\\n i - Event_Buffer if i - Event_Buffer >= 0 else 0 for i in TPR_Events\\r\\n ]\\r\\n Max_TPR_Events = [\\r\\n i + Event_Buffer if i + Event_Buffer < Tot_Week + 1 else Tot_Week\\r\\n for i in TPR_Events\\r\\n ]\\r\\n\\r\\n Target_EDLP_Spend = [i for i in annual_df[\\\"PPG_RP_Spend\\\"]]\\r\\n Target_TPR_Spend = [i for i in annual_df[\\\"PPG_TPR_Spend\\\"]]\\r\\n Target_Trade_Spend = [i for i in annual_df[\\\"PPG_Total_Spend\\\"]]\\r\\n\\r\\n Mapping = {}\\r\\n Prod_Ind = coef_df[\\\"Product\\\"][0:Tot_Prod]\\r\\n for i, j in zip(Prod_Ind.index, Prod_Ind.values):\\r\\n Mapping[j] = i\\r\\n Mapping_reverse = {i: j for j, i in Mapping.items()}\\r\\n\\r\\n constants = [i for i in coef_df[\\\"constant\\\"]]\\r\\n\\r\\n Cat_Coef = coef_df[\\\"Catalogue\\\"][0:Tot_Prod]\\r\\n\\r\\n Disp_Coef = coef_df[\\\"Display\\\"][0:Tot_Prod]\\r\\n\\r\\n Base_Price_stg1 = [i for i in quarter_df[\\\"Final_baseprice\\\"]]\\r\\n Intercepts_stg1 = []\\r\\n for pr in range(Tot_Prod):\\r\\n Intercepts_stg1.append(\\r\\n np.mean([constants[j * Tot_Prod + pr] for j in range(0, Tot_Week)])\\r\\n )\\r\\n\\r\\n Base_Price_stg2 = [[i] * Tot_Week for i in quarter_df[\\\"Final_baseprice\\\"]]\\r\\n Intercepts_stg2 = [\\r\\n constants[j : j + Tot_Prod] for j in range(0, len(constants), Tot_Prod)\\r\\n ] # noqa\\r\\n\\r\\n EDLP_Coef = np.array(\\r\\n coef_df[[i for i in coef_df.columns if i.count(\\\"Retailer_Regular\\\") == 1]]\\r\\n )\\r\\n TPR_Coef = np.array(\\r\\n coef_df[[i for i in coef_df.columns if i.count(\\\"Retailer_Promoted\\\") == 1]]\\r\\n )\\r\\n\\r\\n # ################################ Available EDLP Interactions pairs ##############################\\r\\n\\r\\n EDLP = [\\r\\n re.findall(r\\\"[0-9]+\\\", i)\\r\\n for i in coef_df.columns\\r\\n if i.count(\\\"Retailer_Regular\\\") > 1\\r\\n ]\\r\\n EDLP_Interactions = []\\r\\n for i in EDLP:\\r\\n temp = []\\r\\n for j in i:\\r\\n temp.append(int(j))\\r\\n EDLP_Interactions.append(temp)\\r\\n\\r\\n # ###################################### Available TPR Interactions pairs #########################\\r\\n\\r\\n TPR = [\\r\\n re.findall(r\\\"[0-9]+\\\", i)\\r\\n for i in coef_df.columns\\r\\n if i.count(\\\"Retailer_Promoted\\\") > 1\\r\\n ]\\r\\n TPR_Interactions = []\\r\\n for i in TPR:\\r\\n temp = []\\r\\n for j in i:\\r\\n temp.append(int(j))\\r\\n TPR_Interactions.append(temp)\\r\\n\\r\\n # ###################################### EDLP_Interaction_Coef_Values ############################\\r\\n\\r\\n EDLP_Int_Coef_Values = {}\\r\\n for col in coef_df.columns:\\r\\n if col.count(\\\"Retailer_Regular\\\") > 1:\\r\\n Pair_name = \\\"_\\\".join([str(int(i)) for i in re.findall(r\\\"[0-9]+\\\", col)])\\r\\n EDLP_Int_Coef_Values[Pair_name] = list(coef_df[col])\\r\\n\\r\\n # ###################################### TPR_Interaction_Coef_Values #############################\\r\\n\\r\\n TPR_Int_Coef_Values = {}\\r\\n for col in coef_df.columns:\\r\\n if col.count(\\\"Retailer_Promoted\\\") > 1:\\r\\n Pair_name = \\\"_\\\".join([str(int(i)) for i in re.findall(r\\\"[0-9]+\\\", col)])\\r\\n TPR_Int_Coef_Values[Pair_name] = list(coef_df[col])\\r\\n\\r\\n # ##################################### Loading Pantry Loading Coefficients #######################\\r\\n\\r\\n Pantry_1 = list(coef_df[\\\"Pantry_Loading_1\\\"])\\r\\n Pantry_1 = [\\r\\n Pantry_1[j : j + Tot_Prod] for j in range(0, len(Pantry_1), Tot_Prod)\\r\\n ]\\r\\n Pantry_2 = list(coef_df[\\\"Pantry_Loading_2\\\"])\\r\\n Pantry_2 = [\\r\\n Pantry_2[j : j + Tot_Prod] for j in range(0, len(Pantry_2), Tot_Prod)\\r\\n ]\\r\\n\\r\\n # TE_Coeff = np.array(Promo_df[[\\\"TE_Promo\\\",\\\"TE_NoPromo\\\"]])\\r\\n self.Tot_Prod = Tot_Prod\\r\\n self.Tot_Week = Tot_Week\\r\\n self.EDLP_Events = EDLP_Events\\r\\n self.Min_EDLP_Events = Min_EDLP_Events\\r\\n self.Max_EDLP_Events = Max_EDLP_Events\\r\\n self.TPR_Events = TPR_Events\\r\\n self.Min_TPR_Events = Min_TPR_Events\\r\\n self.Max_TPR_Events = Max_TPR_Events\\r\\n\\r\\n self.Target_EDLP_Spend = Target_EDLP_Spend\\r\\n self.Target_TPR_Spend = Target_TPR_Spend\\r\\n self.Target_Trade_Spend = Target_Trade_Spend\\r\\n self.Mapping = Mapping\\r\\n self.Mapping_reverse = Mapping_reverse\\r\\n self.constants = constants\\r\\n self.EDLP_Coef = EDLP_Coef\\r\\n self.TPR_Coef = TPR_Coef\\r\\n\\r\\n self.EDLP_Interactions = EDLP_Interactions\\r\\n self.TPR_Interactions = TPR_Interactions\\r\\n self.EDLP_Int_Coef_Values = EDLP_Int_Coef_Values\\r\\n self.TPR_Int_Coef_Values = TPR_Int_Coef_Values\\r\\n self.Pantry_1 = Pantry_1\\r\\n self.Pantry_2 = Pantry_2\\r\\n\\r\\n self.Base_Price_stg1 = Base_Price_stg1\\r\\n self.Intercepts_stg1 = Intercepts_stg1\\r\\n self.Base_Price_stg2 = Base_Price_stg2\\r\\n self.Intercepts_stg2 = Intercepts_stg2\\r\\n\\r\\n self.Cat_Coef = Cat_Coef\\r\\n self.Disp_Coef = Disp_Coef\",\n \"def setup_fitting_init_pars(inparam, night, band, masterbeam, order):\\n\\n # Determine whether IGRINS mounting was loose or\\n # the night of interest is in question\\n if (int(night) < 20180401) or (int(night) > 20190531):\\n IPpars = inparam.ips_tightmount_pars[band][masterbeam][order]\\n else:\\n IPpars = inparam.ips_loosemount_pars[band][masterbeam][order]\\n\\n # start at bucket loc = 1250 +- 100, width = 250 +- 100,\\n # depth = 100 +- 5000 but floor at 0\\n centerloc = 1250 if band == 'H' else 1180\\n\\n # Initialize parameter array for optimization as well as half-range values\\n # for each parameter during the various steps of the optimization.\\n # Many of the parameters initialized here will be changed throughout the\\n # code before optimization and in between optimization steps.\\n\\n parA0 = np.array([\\n 0.0, # 0: The shift of the stellar template (km/s)\\n 0.0, # 1: The scale factor for the stellar template\\n 0.0, # 2: The shift of the telluric template (km/s)\\n 1.0, # 3: The scale factor for the telluric template\\n 0.0, # 4: vsini (km/s)\\n IPpars[2], # 5: The instrumental resolution (FWHM) in pixels\\n 0.0, # 6: Wavelength 0-pt\\n 0.0, # 7: Wavelength linear component\\n 0.0, # 8: Wavelength quadratic component\\n 0.0, # 9: Wavelength cubic component\\n 1.0, #10: Continuum zero point\\n 0.0, #11: Continuum linear component\\n 0.0, #12: Continuum quadratic component\\n IPpars[1], #13: Instrumental resolution linear component\\n IPpars[0], #14: Instrumental resolution quadratic component\\n centerloc, #15: Blaze dip center location\\n 330, #16: Blaze dip full width\\n 0.05, #17: Blaze dip depth\\n 90, #18: Secondary blaze dip full width\\n 0.05, #19: Blaze dip depth\\n 0.0, #20: Continuum cubic component\\n 0.0, #21: Continuum quartic component\\n 0.0, #22: Continuum quintic component\\n 0.0, #23: Continuum hexic component\\n 0.0, #24: secondary par\\n 0.0, #25: secondary par\\n 0.0, #26: secondary par\\n 0.0 #27: secondary par\\n ])\\n\\n return parA0\",\n \"def generateTSPlot(startTime, endTime, dcs, imgDir=None, ylim={},\\n obsTag='obs', **kwargs):\\n\\n doMurphyScore = True\\n\\n # a collection for all the data\\n dataDir = 'data'\\n sc = stationCollection.StationCollection(startTime, endTime, obsTag)\\n\\n # add data from the files\\n for dc in dcs:\\n if dc.data.shape[1] > 1: # more than one component\\n for i in range(dc.data.shape[1]):\\n # add each component as separate time series\\n dci = dc.extractField(i)\\n # change variable to component name: 'hvel' -> 'u','v'\\n dci.setMetaData('variable', dci.fieldNames[0])\\n sc.addSample(dci)\\n else:\\n sc.addSample(dc)\\n\\n # plot\\n # master container to hold all the axes, x axis will be shared\\n canvas = plotBase.stackPlotBase(rightPad=1.25)\\n\\n modelColors = plot.makeColorsForModels(sc)\\n comKeys = sc.getComparableKeys(requireObs=False, requireMod=False)\\n for c in comKeys:\\n if c[0][2] is None:\\n print c\\n c[0] = (c[0][0], c[0][1], '0')\\n # fancy sort: sort by 'entry' tuple with msldepth converted to float\\n comKeys.sort(key=lambda tup: (tup[0][0], tup[0][1], float(tup[0][2])))\\n for entry, obsKey, modKeys in comKeys:\\n station, var, msldepth = entry\\n if obsKey:\\n o = sc.getSample(**obsKey)\\n if len(o.time) < 3:\\n print 'observation time series too short:', obsKey\\n continue\\n else:\\n o = None\\n\\n title = ' '.join(\\n (plot.VARS.get(var, var),\\n '[' + plot.UNITS.get(var, '-') + ']'))\\n xlabel = startTime.year if startTime.year == endTime.year else 'Date'\\n dia = timeSeriesPlot.timeSeriesPlotDC2(\\n title=title,\\n ylabel=station.upper() +\\n ' ' +\\n msldepth,\\n ylim=ylim.get(\\n var,\\n None),\\n xlabel=xlabel)\\n canvas.addPlot(dia, tag=plot.VARS.get(var, var) + station + msldepth)\\n if obsKey:\\n dia.addSample(o, color='r', label=obsKey['tag'])\\n for modKey in modKeys:\\n m = sc.getSample(**modKey)\\n l = modKey['tag'].split('-')[0]\\n if doMurphyScore and obsKey:\\n o2, m2 = o.alignTimes(m)\\n murphy = statistics.murphySkillScore(\\n o2.data.ravel(), m2.data.ravel())\\n l += ' MS={ms:.2f}'.format(ms=murphy)\\n dia.addSample(m, color=modelColors[modKey['tag']], label=l)\\n dia.showLegend()\\n\\n if 'title' in kwargs:\\n canvas.addTitle(kwargs.pop('title'))\\n if 'xaxis_tight' in kwargs and kwargs['xaxis_tight'] == True:\\n\\n xLim = [canvas.axGrid[0].dataLim.xmin, canvas.axGrid[0].dataLim.xmax]\\n for ax in canvas.axGrid[1:]:\\n a, b = ax.dataLim.xmin, ax.dataLim.xmax\\n xLim = [min(xLim[0], a), max(xLim[1], b)]\\n canvas.axGrid[0].set_xlim(xLim)\\n if imgDir:\\n # ----- Save file -----\\n sT = str(sc.startTime.date())\\n eT = str(sc.endTime.date())\\n imgDir = createDirectory(imgDir)\\n varList = uniqueList([tup[0][1] for tup in comKeys])\\n #depSet = list(depSet)\\n tagStr = '-'.join(uniqueList([dc.getMetaData('tag') for dc in dcs]))\\n stationList = []\\n for dc in dcs:\\n meta = dc.getMetaData()\\n entry = meta.get('location')\\n if 'msldepth' in meta:\\n entry += '-' + meta['msldepth']\\n stationList.append(entry)\\n stationStr = '-'.join(uniqueList(stationList))\\n varStr = '-'.join(varList)\\n fname = '_'.join(['ts', varStr, stationStr, sT, eT])\\n fn = kwargs.get('filename', fname)\\n saveFigure(imgDir, fn, 'png', verbose=True, bbox_tight=True)\\n plt.close()\\n else:\\n # ----- show plot -----\\n plt.show()\",\n \"def schedule_maker(dates, locator, list_uses):\\n\\n def get_yearly_vectors(dates, occ_schedules, el_schedules, dhw_schedules, pro_schedules, month_schedule):\\n \\\"\\\"\\\"\\n For a given use type, this script generates yearly schedules for occupancy, electricity demand,\\n hot water demand, process electricity demand based on the daily and monthly schedules obtained from the\\n archetype database.\\n\\n :param dates: dates and times throughout the year\\n :type dates: DatetimeIndex\\n :param occ_schedules: occupancy schedules for a weekdays, Saturdays and Sundays from the archetype database\\n :type occ_schedules: list[array]\\n :param el_schedules: electricity schedules for a weekdays, Saturdays and Sundays from the archetype database\\n :type el_schedules: list[array]\\n :param dhw_schedules: domestic hot water schedules for a weekdays, Saturdays and Sundays from the archetype\\n database\\n :type dhw_schedules: list[array]\\n :param pro_schedules: process electricity schedules for a weekdays, Saturdays and Sundays from the archetype\\n database\\n :type pro_schedules: list[array]\\n :param month_schedule: monthly schedules from the archetype database\\n :type month_schedule: ndarray\\n\\n :return occ: occupancy schedule for each hour of the year\\n :type occ: list[float]\\n :return el: electricity schedule for each hour of the year\\n :type el: list[float]\\n :return dhw: domestic hot water schedule for each hour of the year\\n :type dhw: list[float]\\n :return pro: process electricity schedule for each hour of the year\\n :type pro: list[float]\\n\\n \\\"\\\"\\\"\\n\\n occ = []\\n el = []\\n dhw = []\\n pro = []\\n\\n if dhw_schedules[0].sum() != 0:\\n dhw_weekday_max = dhw_schedules[0].sum() ** -1\\n else: dhw_weekday_max = 0\\n\\n if dhw_schedules[1].sum() != 0:\\n dhw_sat_max = dhw_schedules[1].sum() ** -1\\n else: dhw_sat_max = 0\\n\\n if dhw_schedules[2].sum() != 0:\\n dhw_sun_max = dhw_schedules[2].sum() ** -1\\n else: dhw_sun_max = 0\\n\\n for date in dates:\\n month_year = month_schedule[date.month - 1]\\n hour_day = date.hour\\n dayofweek = date.dayofweek\\n if 0 <= dayofweek < 5: # weekday\\n occ.append(occ_schedules[0][hour_day] * month_year)\\n el.append(el_schedules[0][hour_day] * month_year)\\n dhw.append(dhw_schedules[0][hour_day] * month_year * dhw_weekday_max) # normalized dhw demand flow rates\\n pro.append(pro_schedules[0][hour_day] * month_year)\\n elif dayofweek is 5: # saturday\\n occ.append(occ_schedules[1][hour_day] * month_year)\\n el.append(el_schedules[1][hour_day] * month_year)\\n dhw.append(dhw_schedules[1][hour_day] * month_year * dhw_sat_max) # normalized dhw demand flow rates\\n pro.append(pro_schedules[1][hour_day] * month_year)\\n else: # sunday\\n occ.append(occ_schedules[2][hour_day] * month_year)\\n el.append(el_schedules[2][hour_day] * month_year)\\n dhw.append(dhw_schedules[2][hour_day] * month_year * dhw_sun_max) # normalized dhw demand flow rates\\n pro.append(pro_schedules[2][hour_day] * month_year)\\n\\n return occ, el, dhw, pro\\n\\n # get internal loads and indoor comfort from archetypes\\n archetypes_internal_loads = pd.read_excel(locator.get_archetypes_properties(), 'INTERNAL_LOADS').set_index('Code')\\n archetypes_indoor_comfort = pd.read_excel(locator.get_archetypes_properties(), 'INDOOR_COMFORT').set_index('Code')\\n\\n # create empty list of archetypal schedules and occupant densities\\n schedules = []\\n occ_densities = []\\n\\n # create empty lists for the values of each archetype's ventilation and internal loads\\n Qs_Wm2 = []\\n X_ghm2 = []\\n Ea_Wm2 = []\\n El_Wm2 = []\\n Epro_Wm2 = []\\n Ere_Wm2 = []\\n Ed_Wm2 = []\\n Vww_ldm2 = []\\n Vw_ldm2 = []\\n Ve_lsm2 = []\\n Qhpro_Wm2 = []\\n\\n for use in list_uses:\\n # read from archetypes_schedules and properties\\n archetypes_schedules = pd.read_excel(locator.get_archetypes_schedules(), use).T\\n\\n # read lists of every daily profile\\n occ_schedules, el_schedules, dhw_schedules, pro_schedules, month_schedule, area_per_occupant = read_schedules(\\n use, archetypes_schedules)\\n\\n # get occupancy density per schedule in a list\\n if area_per_occupant != 0:\\n occ_densities.append(1 / area_per_occupant)\\n else:\\n occ_densities.append(area_per_occupant)\\n\\n # get internal loads per schedule in a list\\n Ea_Wm2.append(archetypes_internal_loads['Ea_Wm2'][use])\\n El_Wm2.append(archetypes_internal_loads['El_Wm2'][use])\\n Epro_Wm2.append(archetypes_internal_loads['Epro_Wm2'][use])\\n Ere_Wm2.append(archetypes_internal_loads['Ere_Wm2'][use])\\n Ed_Wm2.append(archetypes_internal_loads['Ed_Wm2'][use])\\n Qs_Wm2.append(archetypes_internal_loads['Qs_Wp'][use])\\n X_ghm2.append(archetypes_internal_loads['X_ghp'][use])\\n Vww_ldm2.append(archetypes_internal_loads['Vww_lpd'][use])\\n Vw_ldm2.append(archetypes_internal_loads['Vw_lpd'][use])\\n Ve_lsm2.append(archetypes_indoor_comfort['Ve_lps'][use])\\n Qhpro_Wm2.append(archetypes_internal_loads['Qhpro_Wm2'][use])\\n\\n # get yearly schedules in a list\\n schedule = get_yearly_vectors(dates, occ_schedules, el_schedules, dhw_schedules, pro_schedules, month_schedule)\\n schedules.append(schedule)\\n\\n archetype_values = {'people': occ_densities, 'Qs': Qs_Wm2, 'X': X_ghm2, 'Ea': Ea_Wm2, 'El': El_Wm2,\\n 'Epro': Epro_Wm2, 'Ere': Ere_Wm2, 'Ed': Ed_Wm2, 'Vww': Vww_ldm2,\\n 'Vw': Vw_ldm2, 've': Ve_lsm2, 'Qhpro': Qhpro_Wm2}\\n\\n return schedules, archetype_values\",\n \"def optimize_parameters(model, grid, X_train, y_train):\\n \\n tss = TimeSeriesSplit(n_splits=10)\\n \\n \\n print(\\\"[INFO] performing random search...\\\")\\n searcher = RandomizedSearchCV(estimator=model, n_jobs=-1, n_iter=10, cv=tss,\\n param_distributions=grid, scoring=('neg_mean_squared_error','neg_mean_absolute_error'), refit='neg_mean_squared_error')\\n search_results = searcher.fit(X_train, y_train)\\n best_params = search_results.best_params_\\n print(\\\"Best parameters are: {}\\\".format(best_params))\\n \\n return best_params\",\n \"def getEPADailyData(dateint, dt_ind, month, epa_df, yr):\\n\\n try:\\n start = dateint + dt_ind * 10000\\n end = start + 10001\\n dly_epa_df = epa_df[(epa_df.created >= start) & (epa_df.created < end)]\\n dly_epa_df.reset_index(inplace=True, drop=True)\\n\\n new_df = pd.DataFrame(columns=['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'epa_pm25_value', 'raw_concentration', 'aqi', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code', 'created'])\\n for sitenm in dly_epa_df.site_name.unique():\\n indx_ct = 0\\n site_df = dly_epa_df[dly_epa_df.site_name == sitenm]\\n for i in site_df.created.unique():\\n indx_ct += 1\\n new_df = pd.concat([new_df,site_df.iloc[indx_ct - 1:indx_ct]],ignore_index=True)\\n\\n if i != site_df.created.max(): # Don't interpolate the last record\\n tmp_df = site_df.iloc[indx_ct - 1:indx_ct][['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code']]\\n for j in range(1,6):\\n new_dt = i + j * 10\\n tmp_df['created'] = int(new_dt)\\n tmp_df['epa_pm25_value'] = np.nan\\n tmp_df['raw_concentration'] = np.nan\\n tmp_df['aqi'] = np.nan\\n new_df = pd.concat([new_df,tmp_df],ignore_index=True)\\n\\n # Convert aqi to numerica for so that it gets interpolated\\n new_df[['aqi']] = new_df[['aqi']].replace(\\\"nan\\\", np.nan, regex=True)\\n new_df[['aqi']] = new_df[['aqi']].apply(pd.to_numeric)\\n\\n new_df = new_df.interpolate(method='linear', limit_direction='forward', axis=0)\\n\\n int_epa_df = new_df[(new_df.created >= start) & (new_df.created < (end - 1))]\\n int_epa_df.reset_index(inplace=True, drop=True)\\n \\n # Write to S3\\n s3 = s3fs.S3FileSystem()\\n myopen = s3.open\\n write('midscapstone-whos-polluting-my-air/EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr), int_epa_df, compression='GZIP', open_with=myopen)\\n s3_resource = boto3.resource('s3')\\n s3_resource.Object('midscapstone-whos-polluting-my-air', 'EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr)).Acl().put(ACL='public-read')\\n\\n except Exception as e:\\n print(\\\"*** EXCEPTION IN GET EPA DAILY DATA *** {}\\\".format(e))\\n return int_epa_df\",\n \"def prior_bounds_from_data(npl, ts, rvs):\\n\\n nobs=len(ts)\\n\\n dts=[np.diff(t) for t in ts]\\n min_dt=reduce(min, [np.min(dt) for dt in dts])\\n\\n tobss=[t[-1]-t[0] for t in ts]\\n max_obst=reduce(max, tobss)\\n\\n min_dv=reduce(min, [np.min(np.abs(np.diff(rv))) for rv in rvs])\\n\\n maxspread=reduce(max, [np.max(rv)-np.min(rv) for rv in rvs])\\n\\n pmin=params.Parameters(nobs=nobs,npl=npl)\\n pmax=params.Parameters(nobs=nobs,npl=npl)\\n\\n Vmin=[]\\n Vmax=[]\\n taumin=[]\\n taumax=[]\\n sigma0min=[]\\n sigma0max=[]\\n sigmamin=[]\\n sigmamax=[]\\n for t,rv in zip(ts, rvs):\\n spread=np.max(rv) - np.min(rv)\\n Vmin.append(np.min(rv) - spread)\\n Vmax.append(np.max(rv) + spread)\\n\\n mindt=np.min(np.diff(t))\\n mindv=np.min(np.abs(np.diff(rv)))\\n T=t[-1] - t[0]\\n\\n taumin.append(mindt/2.0)\\n taumax.append(T*2.0)\\n\\n sigma0min.append(mindv/2.0)\\n sigma0max.append(2.0*np.std(rv))\\n\\n sigmamin.append(mindv/2.0)\\n sigmamax.append(2.0*np.std(rv))\\n\\n pmin.V = np.array(Vmin)\\n pmax.V = np.array(Vmax)\\n pmin.tau = np.array(taumin)\\n pmax.tau = np.array(taumax)\\n pmin.sigma0 = np.array(sigma0min)\\n pmax.sigma0 = np.array(sigma0max)\\n pmin.sigma = np.array(sigmamin)\\n pmax.sigma = np.array(sigmamax)\\n\\n if npl >= 1:\\n pmin.n = 2.0*np.pi/(max_obst)\\n pmax.n = 2.0*np.pi/(min_dt)\\n\\n pmin.chi = 0.0\\n pmax.chi = 1.0\\n \\n pmin.e = 0.0\\n pmax.e = 1.0\\n \\n pmin.omega = 0.0\\n pmax.omega = 2.0*np.pi\\n\\n pmin.K = min_dv/2.0\\n pmax.K = 2.0*maxspread\\n\\n return pmin, pmax\",\n \"def data_read(variable):\\t\\t\\r\\n\\tdef day2datetime(scenario,days):\\r\\n\\t\\t\\\"\\\"\\\"\\r\\n\\t\\t# convert days from a reference into int datetime \\r\\n\\t\\t# do not take leap years into account\\r\\n\\t\\t\\\"\\\"\\\"\\r\\n\\t\\tdate_int = np.empty((len(days)));date_int[:]=np.nan\\r\\n\\t\\tif scenario =='T1970C': start_year =1970\\r\\n\\t\\telse: start_year =2010\\r\\n\\t\\tstart =(start_year*365)\\r\\n\\t\\tith=0\\t\\r\\n\\t\\tfor iday in days:\\r\\n\\t\\t\\tmonth_days =np.array([31,28,31,30,31,30,31,31,30,31,30,31])\\r\\n\\t\\t\\tcalendar_days = np.array([0,31,59,90,120,151,181,212,243,273,304,334,365])\\r\\n\\t\\t\\ttotal_days = int(iday) + start; \\r\\n\\t\\t\\tyear = total_days//365; \\r\\n\\t\\t\\tremainder = total_days%365\\r\\n\\t\\t\\tif remainder ==0: year=year-1;month=12;day=31\\r\\n\\t\\t\\telse: \\r\\n\\t\\t\\t\\tmonth = 1+[layer for layer in range(len(calendar_days)) if calendar_days[layer]< remainder and calendar_days[layer+1]>=remainder][0]\\r\\n\\t\\t\\t\\tday = int(remainder - calendar_days[month-1])\\r\\n\\t\\t\\t\\tif day == 0: day = month_days[month-1]\\r\\n\\t\\t\\tdate_int[ith] = year*10000+month*100+day\\r\\n\\t\\t\\tith=ith+1\\r\\n\\t\\treturn date_int.astype(int)\\r\\n\\t\\t\\r\\n\\tdef mon_mean2annual_mean(scenario,time,data):\\r\\n\\t\\tannual_mean=np.empty((30,192,288));annual_mean[:]=np.nan\\r\\n\\t\\tcalendar_day = np.array([31,28,31,30,31,30,31,31,30,31,30,31])\\r\\n\\t\\tif scenario=='T1970RCP':\\r\\n\\t\\t\\tyear_series = range(2020,2050)\\r\\n\\t\\telif scenario=='EdgEne':\\r\\n\\t\\t\\tyear_series = range(2200,2230)\\r\\n\\t\\telif scenario=='Edg70GO':\\r\\n\\t\\t\\tyear_series = range(2070,2100)\\r\\n\\t\\telse:\\r\\n\\t\\t\\tyear_series = range(2130,2160)\\r\\n\\t\\tfor iyear in year_series:\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tif (iyear == year_series[0] and time[0]//100 >= year_series[0] *100+1):\\r\\n\\t\\t\\t\\tlayer_b=0\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tlayer_b = [layer for layer in range(len(time)) if time[layer]//100 == iyear*100+1][0] #June01\\r\\n\\t\\t\\tif (iyear == year_series[-1] and time[-1]//100 <= year_series[-1] *100+12):\\r\\n\\t\\t\\t\\tlayer_e=-2\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tlayer_e = [layer for layer in range(len(time)) if time[layer]//100 == iyear*100+12][0] #August 31\\r\\n\\t\\t\\tdata_cache = data[layer_b:layer_e+1,:,:]\\r\\n\\t\\t\\tannual_mean[iyear-year_series[0],:,:] = stats.nanmean(data_cache,axis=0)\\r\\n\\t\\treturn annual_mean\\r\\n\\r\\n\\tdef data_netcdf(scenario,variable):\\r\\n\\t\\tinput_path ='/exports/csce/datastore/geos/users/s1667168/CESM_EDGAR/ModelOutput/FullCp/'\\r\\n\\t\\tvar_path = input_path+scenario+'/mon/atm/'+scenario+'.atm.mon.'+variable+'.nc'\\r\\n\\t\\t# print var_path\\r\\n\\t\\tnc_fid = nc4.Dataset(var_path,mode='r')\\r\\n\\t\\tlat = nc_fid.variables['lat'][:]\\r\\n\\t\\tlon = nc_fid.variables['lon'][:]\\r\\n\\t\\tdays = nc_fid.variables['time'][:]; time = day2datetime(scenario,days);#print time\\r\\n\\t\\tif variable ==\\\"VQ\\\" or variable == \\\"VT\\\":\\r\\n\\t\\t\\tdata = np.nanmean(nc_fid.variables[variable][:,23:30,:,:],axis=1) # 850hpa\\r\\n\\t\\telse:\\r\\n\\t\\t\\tdata = nc_fid.variables[variable][:]#-273.15\\r\\n\\t\\tnc_fid.close()\\r\\n\\t\\tvar40map = mon_mean2annual_mean(scenario,time,data)\\r\\n\\t\\treturn lon,lat,var40map\\r\\n\\t\\r\\n\\tlon,lat,Edg70GO = data_netcdf('Edg70GO',variable)\\r\\n\\t_,_,T1970 = data_netcdf('T1970RCP',variable)\\r\\n\\t_,_,EdgRef = data_netcdf('EdgRef',variable)\\r\\n\\t_,_,Edg70Oz = data_netcdf('Edg70Oz',variable)\\r\\n\\t_,_,EdgEne = data_netcdf('EdgEne',variable)\\r\\n\\t_,_,EdgTech = data_netcdf('EdgTech',variable)\\r\\n\\treturn lon,lat,T1970,Edg70GO,Edg70Oz,EdgRef,EdgEne,EdgTech\",\n \"def kpi_function(kwargs):\\n start_time = kwargs['start_time']\\n end_time = kwargs['end_time']\\n project = kwargs['project']\\n client_id = kwargs['client_id']\\n device_id = kwargs['device_id']\\n dataset_src = kwargs['dataset_src']\\n dataset_dest = kwargs['dataset_dest']\\n table_src = kwargs['table_src']\\n table_dest = kwargs['table_dest']\\n location = kwargs['location']\\n config = kwargs['config']\\n def station_data(station_id, start, end):\\n data = Daily(station_id, start, end)\\n data = data.fetch()\\n data.reset_index(inplace=True)\\n data = data[['time', 'tavg', 'tmin', 'tmax']]\\n # print(data)\\n return data\\n def costic(df):\\n alias_a = df['tmin']\\n alias_b = df['tmax']\\n\\n if 15 > alias_b:\\n return 15 - (alias_a + alias_b) / 2\\n elif 15 <= alias_a:\\n return 0\\n else:\\n return (15 - alias_a) * (0.08 + 0.42 * (15 - alias_a) / (alias_b - alias_a))\\n\\n current_time = arrow.utcnow().floor('day')\\n # if end_time <= current_time:\\n output = config\\n rules = pd.DataFrame(output['rules'])\\n rules.index = rules.index.astype(int)\\n rules.sort_index(inplace=True)\\n rules = rules[['kpi_name',\\n 'alias_a',\\n 'alias_b',\\n 'alias_c',\\n 'alias_d',\\n 'alias_e',\\n 'alias_f',\\n 'equation_1',\\n 'equation_2',\\n 'aggregation',\\n 'start_time',\\n 'end_time',\\n 'mute_logs']].copy()\\n rules = rules.where(pd.notnull(rules), None)\\n settings = output['settings']['mute_rules']\\n station_id = output['settings']['station_id']\\n country = output['settings']['country']\\n state = output['settings']['state']\\n df = rules\\n # create an empty dataframe\\n logs = pd.DataFrame()\\n # get access to bigqyery\\n # credentials, project_id = google.auth.default()\\n # client = bigquery.Client(credentials=credentials)\\n if settings is False:\\n data_rd = pd.read_csv('main_data.csv')\\n data_rd['time'] = pd.to_datetime(data_rd['time_local'])\\n if data_rd.shape[0] > 0:\\n left_table = data_rd.drop(['alias','value','time_local'],axis=1)\\n left_table = left_table.groupby(\\\"time\\\").min().reset_index()\\n sub_df = data_rd[['time','alias','value']].copy()\\n sub_df['value'] = pd.to_numeric(sub_df['value'])\\n right_table = pd.pivot_table(sub_df,index='time',columns='alias',values='value').reset_index()\\n data_rd = pd.merge(left_table,right_table,on='time', how='outer')\\n if data_rd.shape[0] > 0:\\n min_time = data_rd.time.min()\\n max_time = data_rd.time.max()\\n if station_id is not None:\\n meteo_data = station_data(station_id, min_time, max_time)\\n # print(meteo_data)\\n data_rd['costic_HDD_15C'] = meteo_data.apply(costic, axis=1)\\n data_rd = data_rd.merge(meteo_data, on='time', how='left')\\n data_rd[['tavg', 'tmax', 'tmin']] = data_rd[['tavg', 'tmax', 'tmin']].astype('float')\\n print(data_rd.head())\\n data_rd.to_csv(\\\"file_name.csv\\\", index=False)\\n for i in range(df.shape[0]):\\n if df.iloc[i]['kpi_name'] == 'costic_HDD_15C':\\n obj = kpi(df.iloc[i], data_rd, country, state, logs)\\n dff = obj.return_log()\\n logs = pd.concat([logs, dff], axis=0)\\n if df.iloc[i]['mute_logs']:\\n aliases_list = df.iloc[i]['mute_logs'].split(',')\\n for alias in aliases_list:\\n logs = logs[~logs.aliases.str.contains(alias.strip())]\\n else:\\n print(f\\\"Data is not available from {start_time} to {end_time}\\\")\\n if logs.shape[0]>0:\\n logs['time'] = logs['time'].dt.date\\n logs.rename(columns={\\\"time\\\":\\\"date\\\"},inplace=True)\\n logs['client_id'] = client_id\\n logs['device_id'] = device_id\\n logs['date'] = logs['date'].astype(str)\\n print(tabulate(logs[['date','value']]))\\n # load the logs dataframe to destination table in the bigquery\\n # table = client.get_table(f\\\"{project}.{dataset_dest}.{table_dest}\\\")\\n else:\\n print('no logs saved')\\n else:\\n print(\\\"All rules are muted\\\")\",\n \"def main():\\n yesterday_datestring = (datetime.date.today() - datetime.timedelta(1)).isoformat()\\n parser = argparse.ArgumentParser(description='generate TimeseriesArrays on local backend')\\n parser.add_argument('--url', default=\\\"https://datalogger-api.tirol-kliniken.cc/DataLogger\\\", help=\\\"url of DataLogger Webapplication\\\")\\n parser.add_argument('--logdir', default=\\\"/data1/haproxy_daily/\\\", help=\\\"directory where to find day sorted haproxylogs\\\")\\n parser.add_argument(\\\"-b\\\", '--back', help=\\\"how many days back from now\\\")\\n parser.add_argument(\\\"-s\\\", '--startdate', help=\\\"start date in isoformat YYY-MM-DD\\\")\\n parser.add_argument(\\\"-e\\\", '--enddate', default=yesterday_datestring, help=\\\"stop date in isoformat YYY-MM-DD\\\")\\n parser.add_argument(\\\"-q\\\", '--quiet', action='store_true', help=\\\"set to loglevel ERROR\\\")\\n parser.add_argument(\\\"-v\\\", '--verbose', action='store_true', help=\\\"set to loglevel DEBUG\\\")\\n args = parser.parse_args()\\n if args.quiet is True:\\n logging.getLogger(\\\"\\\").setLevel(logging.ERROR)\\n if args.verbose is True:\\n logging.getLogger(\\\"\\\").setLevel(logging.DEBUG)\\n if (args.back is not None) == (args.startdate is not None):\\n logging.error(\\\"option -b and -e are mutual exclusive, use only one\\\")\\n sys.exit(1)\\n startdate = None\\n if args.back is not None:\\n startdate = (datetime.date.today() - datetime.timedelta(int(args.back))).isoformat()\\n elif args.startdate is not None:\\n startdate = args.startdate\\n else:\\n logging.error(\\\"you have to provide either -b or -s\\\")\\n sys.exit(1)\\n # lets get started\\n datalogger = DataLoggerWeb(args.url)\\n project = \\\"haproxy\\\"\\n tablename = \\\"http_host\\\"\\n baseurl = \\\"%s/upload_raw_file/\\\" % args.url\\n logdir = args.logdir # where to find haproxy logs\\n keys = (\\\"http_host\\\", )\\n values = (\\\"bytes_read\\\", \\\"rsp_1xx\\\", \\\"rsp_2xx\\\", \\\"rsp_3xx\\\", \\\"rsp_4xx\\\", \\\"rsp_5xx\\\", \\\"rsp_other\\\", \\\"srv_queue\\\", \\\"backend_queue\\\", \\\"actconn\\\", \\\"feconn\\\", \\\"beconn\\\", \\\"srv_conn\\\", \\\"retries\\\", \\\"tq\\\", \\\"tw\\\", \\\"tc\\\", \\\"tr\\\", \\\"tt\\\", \\\"hits\\\")\\n ts_keyname = \\\"ts\\\"\\n for datestring in datewalk(startdate, args.enddate):\\n caches = datalogger.get_caches(project, tablename, datestring)\\n if caches[\\\"tsa\\\"][\\\"raw\\\"] is not None:\\n logging.info(\\\"Skipping this datestring, raw data is already available\\\")\\n continue\\n try:\\n stringio = generate_datalogger_csv(logdir, datestring, keys, values, ts_keyname)\\n #upload data\\n files = {'myfile': stringio}\\n url = \\\"/\\\".join((baseurl, project, tablename, datestring))\\n logging.info(\\\"calling %s\\\", url)\\n response = requests.post(url, files=files)\\n print response.content\\n except StandardError as exc:\\n logging.error(\\\"Exception on file datestring %si, skipping this date\\\", datestring)\\n except zlib.error as exc:\\n logging.error(exc)\",\n \"def _generate_hourly_time_series(\\n self,\\n size: int,\\n hourly_seasonality: float,\\n hourly_seasonality_params: Optional[List[Tuple[float, float]]] = None,\\n trend_params: Optional[List[TrendParams]] = None,\\n weekday_dummy_params: Optional[List[float]] = None,\\n annual_seasonality_params: Optional[List[Tuple[float, float]]] = None,\\n holiday_alpha: float = 3.5,\\n outlier_alpha: float = 2.5,\\n noise_scale: float = 1.0,\\n ):\\n if hourly_seasonality_params is None:\\n # Create 10 random hourly seasonality parameters\\n hourly_seasonality_params = [\\n (\\n np.random.normal() / 100,\\n np.random.normal() / 100,\\n )\\n for i in range(4)\\n ]\\n\\n time_range = np.arange(size)\\n\\n hourly_seasonality_series = (\\n self._generate_hourly_seasonality(\\n time_range=time_range,\\n hourly_seasonality_params=hourly_seasonality_params,\\n )\\n * hourly_seasonality\\n )\\n\\n hourly_outliers = self._generate_posneg_pareto(outlier_alpha, size)\\n\\n daily_time_series = self._generate_daily_time_series(\\n size=ceil(size / 24),\\n trend_params=trend_params,\\n weekday_dummy_params=weekday_dummy_params,\\n annual_seasonality_params=annual_seasonality_params,\\n holiday_alpha=holiday_alpha,\\n outlier_alpha=1000000, # outlier_alpha,\\n noise_scale=noise_scale,\\n )\\n return (\\n np.exp(hourly_seasonality_series) * np.repeat(daily_time_series, 24)[:size]\\n + hourly_outliers\\n )\",\n \"def runtest(params_fc):\\n\\n npz = {}\\n\\n fold, cost = params_fc\\n dset = np.load(DSET)\\n\\n idx_tr = dset['split'] != fold\\n idx_ts = dset['split'] == fold\\n\\n lgt_tr, gt_tr = dset['wlogit'][idx_tr], dset['gt'][idx_tr]\\n lgt_ts = dset['wlogit'][idx_ts]\\n tinv = ut.calib(lgt_tr, gt_tr)\\n npz['tinv'] = tinv\\n\\n wrank_tr, srank_tr = dset['wrank'][idx_tr], dset['srank'][idx_tr]\\n wrank_ts, srank_ts = dset['wrank'][idx_ts], dset['srank'][idx_ts]\\n\\n entr_tr, entr_ts = ut.entropy(lgt_tr, tinv), ut.entropy(lgt_ts, tinv)\\n wcost_tr, scost_tr = ut.cost(wrank_tr, srank_tr, cost)\\n wcost_ts, scost_ts = ut.cost(wrank_ts, srank_ts, cost)\\n npz['wcost_tr'], npz['scost_tr'] = wcost_tr, scost_tr\\n npz['wcost_ts'], npz['scost_ts'] = wcost_ts, scost_ts\\n npz['entr_tr'] = entr_tr\\n\\n rew_tr = wcost_tr - scost_tr\\n mtog = po.fitmetric(entr_tr, rew_tr)\\n npz['mtog'] = np.stack(mtog)\\n\\n npz['metric_tr'] = np.interp(entr_tr, *mtog)\\n npz['metric_ts'] = np.interp(entr_ts, *mtog)\\n\\n np.savez_compressed(SPATH % (fold, cost), **npz)\\n print(\\\"Completed fold %d, cost %d\\\" % (fold, cost))\",\n \"def fit(self, Y, STATUS, ntop=100, nrecent=100, nmax=400, ntopmu=100, ntopvar=100, nkmeans=300, nkeamnsdata=5000,\\n lam=1e-6):\\n X = self.X\\n untested = [i for i in range(self.n) if STATUS[i] == 0]\\n tested = [i for i in range(self.n) if STATUS[i] == 2]\\n ytested = Y[tested].reshape(-1)\\n self.y_max = np.max(ytested)\\n # each 10 fits we update the hyperparameters, otherwise we just update the data which is a lot faster\\n if np.mod(self.update_counter, self.updates_per_big_fit) == 0:\\n print('fitting hyperparameters')\\n # how many training points are there\\n ntested = len(tested)\\n # if more than nmax we will subsample and use the subsample to fit hyperparametesr\\n if ntested > nmax:\\n # subsample is uniion of 100 best points, 100 most recent points and then random points \\n top = list(np.argsort(ytested)[-ntop:])\\n recent = list(range(ntested - nrecent, ntested))\\n topandrecent = list(set(top + recent))\\n rand = list(\\n np.random.choice([i for i in range(ntested) if i not in topandrecent], nmax - len(topandrecent),\\n False))\\n testedtrain = topandrecent + rand\\n ytrain = ytested[testedtrain]\\n train = [tested[i] for i in testedtrain]\\n else:\\n train = tested\\n ytrain = ytested\\n \\n # use GPy code to fit hyperparameters to minimize NLL on train data\\n mfy = GPy.mappings.Constant(input_dim=self.d, output_dim=1) # fit dense GPy model to this data\\n ky = GPy.kern.RBF(self.d, ARD=True, lengthscale=np.ones(self.d))\\n self.GP = GPy.models.GPRegression(X[train], ytrain.reshape(-1, 1), kernel=ky, mean_function=mfy)\\n self.GP.optimize('bfgs')\\n # strip out fitted hyperparameters from GPy model, because cant do high(ish) dim sparse inference\\n self.mu = self.GP.flattened_parameters[0]\\n self.a = self.GP.flattened_parameters[1]\\n self.l = self.GP.flattened_parameters[2]\\n self.b = self.GP.flattened_parameters[3]\\n # selecting inducing points for sparse inference \\n print('selecting inducing points')\\n # get prediction from GPy model \\n self.py = self.GP.predict(X)\\n # points with 100 highest means\\n topmu = [untested[i] for i in np.argsort(self.py[0][untested].reshape(-1))[-ntopmu:]]\\n # points with 100 highest uncertatinty\\n topvar = [untested[i] for i in np.argsort(self.py[1][untested].reshape(-1))[-ntopvar:]]\\n # combine with train set above to give nystrom inducing points (inducing points that are also actual trainingdata points) \\n nystrom = topmu + topvar + train\\n # also get some inducing points spread throughout domain by using kmeans\\n # kmeans is very slow on full dataset so choose random subset \\n # also scale using length scales l so that kmeans uses approproate distance measure\\n kms = KMeans(n_clusters=nkmeans, max_iter=5).fit(\\n np.divide(X[list(np.random.choice(untested, nkeamnsdata))], self.l))\\n # matrix of inducing points \\n self.M = np.vstack((X[nystrom], np.multiply(kms.cluster_centers_, self.l)))\\n # dragons...\\n # email james.l.hook@gmail.com if this bit goes wrong!\\n print('fitting sparse model')\\n DXM = euclidean_distances(np.divide(X, self.l), np.divide(self.M, self.l), squared=True)\\n self.SIG_XM = self.a * np.exp(-DXM / 2)\\n DMM = euclidean_distances(np.divide(self.M, self.l), np.divide(self.M, self.l), squared=True)\\n self.SIG_MM = self.a * np.exp(-DMM / 2) + np.identity(self.M.shape[0]) * lam * self.a\\n self.B = self.a + self.b - np.sum(np.multiply(np.linalg.solve(self.SIG_MM, self.SIG_XM.T), self.SIG_XM.T),0)\\n K = np.matmul(self.SIG_XM[tested].T, np.divide(self.SIG_XM[tested], self.B[tested].reshape(-1, 1)))\\n self.SIG_MM_pos = self.SIG_MM - K + np.matmul(K, np.linalg.solve(K + self.SIG_MM, K))\\n J = np.matmul(self.SIG_XM[tested].T, np.divide(ytested - self.mu, self.B[tested]))\\n self.mu_M_pos = self.mu + J - np.matmul(K, np.linalg.solve(K + self.SIG_MM, J))\\n else:\\n K = np.matmul(self.SIG_XM[tested].T, np.divide(self.SIG_XM[tested], self.B[tested].reshape(-1, 1)))\\n self.SIG_MM_pos = self.SIG_MM - K + np.matmul(K, np.linalg.solve(K + self.SIG_MM, K))\\n J = np.matmul(self.SIG_XM[tested].T, np.divide(ytested - self.mu, self.B[tested]))\\n self.mu_M_pos = self.mu + J - np.matmul(K, np.linalg.solve(K + self.SIG_MM, J))\\n self.update_counter += 1\\n \\\"\\\"\\\" \\n key attributes updated by fit \\n \\n self.SIG_XM : prior covarience matrix between data and inducing points\\n self.SIG_MM : prior covarience matrix at inducing points\\n \\n self.SIG_MM_pos : posterior covarience matrix at inducing points\\n self.mu_M_pos : posterior mean at inducing points \\n \\n \\\"\\\"\\\"\",\n \"def _range_for_dates(X_dt, percentiles, grid_resolution):\\n timestamps = np.array(\\n [X_dt - pd.Timestamp(\\\"1970-01-01\\\")] // np.timedelta64(1, \\\"s\\\")\\n ).reshape(-1, 1)\\n timestamps = pd.DataFrame(timestamps)\\n grid, values = _grid_from_X(\\n timestamps,\\n percentiles=percentiles,\\n grid_resolution=grid_resolution,\\n custom_range={},\\n )\\n grid_dates = pd.to_datetime(pd.Series(grid.squeeze()), unit=\\\"s\\\")\\n return grid_dates\",\n \"def KSm_gof(data, relative_time_grid, sf, args=(), alternative='two-sided'):\\n \\n def deltaNA (d, Y):\\n return np.sum([ np.power(Y-l,-1) for l in range(int(d))])\\n \\n mortalities = pd.Series(data.loc[data['status']==1,'mortality'].value_counts(), index=relative_time_grid).fillna(0)\\n censored = pd.Series(data.loc[data['status']==2,'mortality'].value_counts(), index=relative_time_grid).fillna(0)\\n counts = mortalities + censored\\n n = len(counts)\\n atRisk = pd.Series(data=np.dot(np.triu(np.ones((n,n)),k=0), counts), index=relative_time_grid)\\n survived = pd.Series(data=np.dot(np.triu(np.ones((n,n)),k=1), counts) + np.array(censored), index=relative_time_grid)\\n \\n sfvals = sf(relative_time_grid, *args)\\n \\n def deltaNA (d, Y):\\n return np.sum([ np.power(Y-l,-1) for l in range(int(d))])\\n beta_NelsonAalen = np.dot(np.tril(np.ones((n,n)),k=0), [deltaNA(mortalities.loc[ind],atRisk.loc[ind]) for ind in atRisk.index] )\\n alpha_CumCensor = np.dot(np.tril(np.ones((n,n)),k=0), [deltaNA(censored.loc[ind],survived.loc[ind]) for ind in survived.index] )\\n A_CumHazardCenAdjusted = np.dot(np.tril(np.ones((n,n)),k=0),\\n [0]+[np.exp(-0.5*alpha_CumCensor[i-1])*np.log(sfvals[i-1]/sfvals[i]) for i in range(1,len(atRisk))] )\\n B_NelsonAalenCenAdjusted = np.dot(np.tril(np.ones((n,n)),k=0),\\n [0]+[np.exp(-0.5*alpha_CumCensor[i-1])*deltaNA(mortalities.iloc[i],atRisk.iloc[i]) for i in range(1,len(atRisk))] )\\n Yminus = np.sqrt(n) * 0.5 * (sfvals[1:]+np.exp(-beta_NelsonAalen[:-1])) * (A_CumHazardCenAdjusted[1:] - B_NelsonAalenCenAdjusted[:-1])\\n Yplus = np.sqrt(n) * 0.5 * (sfvals+np.exp(-beta_NelsonAalen)) * (A_CumHazardCenAdjusted - B_NelsonAalenCenAdjusted)\\n Yconcat = pd.concat([pd.Series(data=Yplus,index=atRisk.index), pd.Series(data=Yminus,index=atRisk.index[1:])]).sort_index()\\n R_MaxCDF = 1 - 0.5*(np.exp(-beta_NelsonAalen[-1])+sfvals[-1])\\n \\n return KSmResidues(Yconcat, R_MaxCDF)\",\n \"def _postprocess_timeseries(rows, cols, params):\\n # pylint: disable=too-many-locals\\n xlks, _, crop = cf.transform_params(params)\\n base_unw_paths = cf.original_ifg_paths(params[cf.IFG_FILE_LIST])\\n dest_tifs = cf.get_dest_paths(base_unw_paths, crop, params, xlks)\\n output_dir = params[cf.TMPDIR]\\n\\n # load previously saved prepread_ifgs dict\\n preread_ifgs_file = join(output_dir, 'preread_ifgs.pk')\\n ifgs = cp.load(open(preread_ifgs_file, 'rb'))\\n\\n # metadata and projections\\n gt, md, wkt = ifgs['gt'], ifgs['md'], ifgs['wkt']\\n epochlist = ifgs['epochlist']\\n ifgs = [v for v in ifgs.values() if isinstance(v, PrereadIfg)]\\n\\n tiles = shared.get_tiles(dest_tifs[0], rows, cols)\\n\\n # load the first tsincr file to determine the number of time series tifs\\n tsincr_file = os.path.join(output_dir, 'tsincr_0.npy')\\n tsincr = np.load(file=tsincr_file)\\n\\n # pylint: disable=no-member\\n no_ts_tifs = tsincr.shape[2]\\n # we create 2 x no_ts_tifs as we are splitting tsincr and tscuml\\n # to all processes.\\n process_tifs = mpiops.array_split(range(2 * no_ts_tifs))\\n\\n # depending on nvelpar, this will not fit in memory\\n # e.g. nvelpar=100, nrows=10000, ncols=10000, 32bit floats need 40GB memory\\n # 32 * 100 * 10000 * 10000 / 8 bytes = 4e10 bytes = 40 GB\\n # the double for loop helps us overcome the memory limit\\n log.info('process {} will write {} ts (incr/cuml) tifs of '\\n 'total {}'.format(mpiops.rank, len(process_tifs), no_ts_tifs * 2))\\n for i in process_tifs:\\n tscum_g = np.empty(shape=ifgs[0].shape, dtype=np.float32)\\n if i < no_ts_tifs:\\n for n, t in enumerate(tiles):\\n _assemble_tiles(i, n, t, tscum_g, output_dir, 'tscuml')\\n md[ifc.EPOCH_DATE] = epochlist.dates[i + 1]\\n # sequence position; first time slice is #0\\n md['SEQUENCE_POSITION'] = i+1\\n dest = os.path.join(params[cf.OUT_DIR],\\n 'tscuml' + \\\"_\\\" +\\n str(epochlist.dates[i + 1]) + \\\".tif\\\")\\n md[ifc.DATA_TYPE] = ifc.CUML\\n shared.write_output_geotiff(md, gt, wkt, tscum_g, dest, np.nan)\\n else:\\n tsincr_g = np.empty(shape=ifgs[0].shape, dtype=np.float32)\\n i %= no_ts_tifs\\n for n, t in enumerate(tiles):\\n _assemble_tiles(i, n, t, tsincr_g, output_dir, 'tsincr')\\n md[ifc.EPOCH_DATE] = epochlist.dates[i + 1]\\n # sequence position; first time slice is #0\\n md['SEQUENCE_POSITION'] = i+1\\n dest = os.path.join(params[cf.OUT_DIR],\\n 'tsincr' + \\\"_\\\" + str(\\n epochlist.dates[i + 1]) + \\\".tif\\\")\\n md[ifc.DATA_TYPE] = ifc.INCR\\n shared.write_output_geotiff(md, gt, wkt, tsincr_g, dest, np.nan)\\n log.info('process {} finished writing {} ts (incr/cuml) tifs of '\\n 'total {}'.format(mpiops.rank, len(process_tifs), no_ts_tifs * 2))\",\n \"def probabilistic_forecasting(tau,nr_steps_ahead,values,levy_seed,levy_seed_params,envelope,\\n envelope_params, nr_samples, envelope_function = None, max_gaussian_lag = None):\\n \\n probabilistic_forecasting_sanity_checks(values,tau,nr_steps_ahead,levy_seed,levy_seed_params,nr_samples,envelope_params)\\n \\n acf_function_helper = trawl_acf(envelope, envelope_function)\\n acf_function = lambda x: acf_function_helper(x,envelope_params)\\n overlap_area = acf_function(tau*nr_steps_ahead)\\n \\n\\n if levy_seed == 'gaussian':\\n \\n assert isinstance(max_gaussian_lag,int) and max_gaussian_lag > 0 \\n \\n conditional_mean,conditional_scale = conditional_gaussian_distribution(values,tau,nr_steps_ahead,max_gaussian_lag,\\n levy_seed_params,envelope,envelope_params,envelope_function)\\n \\n return np.array([norm.rvs(loc = i, scale = conditional_scale, size=nr_samples) for i in conditional_mean])\\n \\n \\n elif levy_seed == 'gamma':\\n \\n alpha,theta = levy_seed_params\\n \\n \\n \\n alpha0 = alpha * overlap_area\\n alpha1 = alpha * (1-overlap_area)\\n print('before overlap')\\n overlap_samples = values[:,np.newaxis] * beta.rvs(a = alpha0, b = alpha1, size = [len(values),nr_samples])\\n print('before independent')\\n independent_samples = gamma.rvs(a = alpha1, loc = 0, scale = theta, size = nr_samples)[np.newaxis,:]\\n print('after independent')\\n \\n elif levy_seed in ['invgauss','gig','cauchy','student']:\\n raise ValueError('not yet implemented')\\n \\n return overlap_samples + independent_samples\",\n \"def get_profile_template_data(end_date, time_scale):\\n\\n intervals = -1\\n HOURS = 0\\n DAYS = 0\\n WEEKS = 0\\n time_format = \\\"%d %B %Y\\\"\\n\\n print(f\\\"Time scale: {time_scale}\\\")\\n if time_scale == \\\"DAILY\\\":\\n intervals = 12 # 24 hours per day\\n HOURS = 2\\n time_format = \\\"%I%p\\\"\\n elif time_scale == \\\"WEEKLY\\\":\\n intervals = 7 # 7 days per week\\n DAYS = 1\\n elif time_scale == \\\"MONTHLY\\\":\\n intervals = 4 # 4 weeks per month\\n WEEKS = 1\\n\\n # end_date = date.fromtimestamp(float(end_date))\\n # end_date = datetime(end_date.year, end_date.month, end_date.day)\\n end_date = datetime.now() + timedelta(hours=2)\\n print(f\\\"TEMP current end date: {end_date.strftime('%I%p')}\\\")\\n x_axis = []\\n data = []\\n inter = []\\n TEMP = []\\n time_gap = timedelta(hours=HOURS, days=DAYS, weeks=WEEKS)\\n for i in range(intervals - 1, -1, -1):\\n x_step = end_date - (i * time_gap)\\n TEMP.append(x_step.strftime(\\\"%I%p\\\"))\\n inter.append(str(x_step.timestamp()))\\n data.append({\\\"images\\\": [], \\\"x\\\": x_step.strftime(time_format), \\\"y\\\": 0})\\n print(f\\\"TEMP x-axis:{TEMP}\\\")\\n return data, inter\",\n \"def semipar_fit(dict_, data):\\n # Process information specified in the initialization file and\\n # assign default values if missing\\n bins, logit, bandwidth, gridsize, startgrid, endgrid = process_primary_inputs(dict_)\\n trim, rbandwidth, reestimate_p, show_output = process_secondary_inputs(dict_)\\n\\n # Prepare the sample for the estimation process\\n # Compute propensity score, define common support and trim the sample\\n # data, prop_score = process_mte_data(\\n # dict_, data, logit, bins, trim, reestimate_p, show_output\\n # )\\n data = estimate_treatment_propensity(dict_, data, logit, show_output)\\n\\n X, Y, prop_score = trim_support(\\n dict_, data, logit, bins, trim, reestimate_p, show_output\\n )\\n\\n b0, b1_b0 = double_residual_reg(X, Y, prop_score)\\n\\n # # Construct the MTE\\n # Generate the quantiles of the final MTE\\n quantiles = np.linspace(startgrid, endgrid, gridsize)\\n\\n mte_x = mte_observed(X, b1_b0)\\n mte_u = mte_unobserved_semipar(\\n X, Y, b0, b1_b0, prop_score, bandwidth, gridsize, startgrid, endgrid\\n )\\n\\n # Put the MTE together\\n mte = mte_x.mean(axis=0) + mte_u\\n\\n # Account for variation in X\\n mte_min = np.min(mte_x) + mte_u\\n mte_max = np.max(mte_x) + mte_u\\n\\n rslt = {\\n \\\"quantiles\\\": quantiles,\\n \\\"mte\\\": mte,\\n \\\"mte_x\\\": mte_x,\\n \\\"mte_u\\\": mte_u,\\n \\\"mte_min\\\": mte_min,\\n \\\"mte_max\\\": mte_max,\\n \\\"X\\\": X,\\n \\\"b0\\\": b0,\\n \\\"b1\\\": b1_b0 + b0,\\n }\\n\\n return rslt\",\n \"def run(config, tim=None):\\n import common_lib\\n import dr_lib\\n import DST\\n \\n if tim is not None:\\n tim.getTime(False)\\n old_time = tim.getOldTime()\\n\\n if config.data is None:\\n raise RuntimeError(\\\"Need to pass a data filename to the driver \\\"\\\\\\n +\\\"script.\\\")\\n\\n # Read in geometry if one is provided\\n if config.inst_geom is not None:\\n if config.verbose:\\n print \\\"Reading in instrument geometry file\\\"\\n \\n inst_geom_dst = DST.getInstance(\\\"application/x-NxsGeom\\\",\\n config.inst_geom)\\n else:\\n inst_geom_dst = None\\n\\n config.so_axis = \\\"time_of_flight\\\"\\n\\n # Steps 1-3: Produce a scaled summed dark current dataset\\n dc_som = dr_lib.scaled_summed_data(config.dkcur, config,\\n dataset_type=\\\"dark_current\\\",\\n timer=tim)\\n\\n # Perform Steps 3-6 on black can data\\n if config.bcan is not None:\\n b_som1 = dr_lib.calibrate_dgs_data(config.bcan, config, dc_som,\\n dataset_type=\\\"black_can\\\",\\n inst_geom_dst=inst_geom_dst,\\n tib_const=config.tib_const,\\n cwp=config.cwp_bcan,\\n timer=tim)\\n else:\\n b_som1 = None\\n\\n # Perform Steps 3-6 on empty can data \\n if config.ecan is not None:\\n e_som1 = dr_lib.calibrate_dgs_data(config.ecan, config, dc_som,\\n dataset_type=\\\"empty_can\\\",\\n inst_geom_dst=inst_geom_dst,\\n tib_const=config.tib_const,\\n cwp=config.cwp_ecan,\\n timer=tim)\\n else:\\n e_som1 = None\\n\\n # Perform Steps 3-6 on normalization data\\n n_som1 = dr_lib.calibrate_dgs_data(config.data, config, dc_som,\\n dataset_type=\\\"normalization\\\",\\n inst_geom_dst=inst_geom_dst,\\n tib_const=config.tib_const,\\n cwp=config.cwp_data,\\n timer=tim)\\n\\n # Perform Steps 7-16 on normalization data\\n if config.norm_trans_coeff is None:\\n norm_trans_coeff = None\\n else:\\n norm_trans_coeff = config.norm_trans_coeff.toValErrTuple()\\n\\n # Determine if we need to rebin the empty or black can data\\n if config.ecan is not None and e_som1 is not None:\\n ecan_cwp = True\\n else:\\n ecan_cwp = False\\n\\n if config.bcan is not None and b_som1 is not None:\\n bcan_cwp = True\\n else:\\n bcan_cwp = False \\n\\n cwp_used = ecan_cwp or bcan_cwp\\n\\n n_som2 = dr_lib.process_dgs_data(n_som1, config, b_som1, e_som1,\\n norm_trans_coeff,\\n dataset_type=\\\"normalization\\\",\\n cwp_used=cwp_used,\\n timer=tim)\\n \\n del n_som1, b_som1, e_som1\\n\\n # Step 17: Integrate normalization spectra\\n if config.verbose:\\n print \\\"Integrating normalization spectra\\\"\\n\\n if tim is not None:\\n tim.getTime(False)\\n\\n if config.norm_int_range is None:\\n start_val = float(\\\"inf\\\")\\n end_val = float(\\\"inf\\\")\\n else:\\n if not config.wb_norm:\\n # Translate energy transfer to final energy\\n ef_start = config.initial_energy.getValue() - \\\\\\n config.norm_int_range[0]\\n ef_end = config.initial_energy.getValue() - \\\\\\n config.norm_int_range[1]\\n # Convert final energy to final wavelength\\n start_val = common_lib.energy_to_wavelength((ef_start, 0.0))[0]\\n end_val = common_lib.energy_to_wavelength((ef_end, 0.0))[0]\\n else:\\n start_val = config.norm_int_range[0]\\n end_val = config.norm_int_range[1]\\n \\n n_som3 = dr_lib.integrate_spectra(n_som2, start=start_val,\\n end=end_val, width=True)\\n\\n del n_som2\\n \\n if tim is not None:\\n tim.getTime(msg=\\\"After integrating normalization spectra \\\")\\n\\n file_comment = \\\"Normalization Integration range: %0.3fA, %0.3fA\\\" \\\\\\n % (start_val, end_val)\\n \\n hlr_utils.write_file(config.output, \\\"text/num-info\\\", n_som3,\\n output_ext=\\\"norm\\\",\\n data_ext=config.ext_replacement,\\n path_replacement=config.path_replacement,\\n verbose=config.verbose,\\n message=\\\"normalization values\\\",\\n comments=[file_comment],\\n tag=\\\"Integral\\\", units=\\\"counts\\\") \\n \\n if tim is not None:\\n tim.getTime(False)\\n\\n if config.verbose:\\n print \\\"Making mask file\\\"\\n\\n # Make mask file from threshold\\n dr_lib.filter_normalization(n_som3, config.lo_threshold,\\n config.hi_threshold, config)\\n\\n if tim is not None:\\n tim.getTime(msg=\\\"After making mask file \\\")\\n\\n # Write out RMD file\\n n_som3.attr_list[\\\"config\\\"] = config\\n\\n hlr_utils.write_file(config.output, \\\"text/rmd\\\", n_som3,\\n output_ext=\\\"rmd\\\",\\n data_ext=config.ext_replacement, \\n path_replacement=config.path_replacement,\\n verbose=config.verbose,\\n message=\\\"metadata\\\")\\n \\n if tim is not None:\\n tim.setOldTime(old_time)\\n tim.getTime(msg=\\\"Total Running Time\\\")\",\n \"def execute(self, in_nc, in_weight_table, out_nc, grid_name, conversion_flag, in_time_interval=\\\"6hr\\\"): # modified this line CJB 20190218\\r\\n # MJS I might consider netCDF4.Dataset.variables['RO'].units\\r\\n # and upstream correction of the cdo grid conversion units attribute.\\r\\n\\r\\n # Validate the netcdf dataset\\r\\n vars_oi_index = self.dataValidation(in_nc)\\r\\n \\r\\n \\\"\\\"\\\"get conversion factor the flag is used to differentiate forecasts converted \\r\\n to netCDF from GRIB and the original netCDF. They both use the same weight tables\\r\\n but the original netCDF is in mm whereas the stock GRIB forecasts are in meters.\\r\\n Set the conversion_flag in the run.py configuration file.\\r\\n \\\"\\\"\\\"\\r\\n if conversion_flag: # Line Added CJB 20190218\\r\\n conversion_factor = 1.0 #Line Modified CJB 20190218\\r\\n elif grid_name == 'ecmwf_t1279' or grid_name == 'ecmwf_tco639': # Line Modified CJB 20190218\\r\\n #if grid_name == 'ecmwf_HRES_F' or grid_name == 'ecmwf_ENS_F': # Line Added/Modified CJB 20190108\\r\\n #new grids in mm instead of m\\r\\n conversion_factor = 0.001\\r\\n else: #set the conversion factor to 1 for everything else (data is in m but legacy installations do not have a flag) Line Added CJB 20190218\\r\\n conversion_factor = 1.0 # Line Added CJB 20190218\\r\\n # MJS I might consider netCDF4.Dataset.variables['RO'].units\\r\\n # and upstream correction of the cdo grid conversion units attribute.\\r\\n # identify if the input netcdf data is the High Resolution data with three different time intervals\\r\\n id_data = self.dataIdentify(in_nc)\\r\\n if id_data is None:\\r\\n raise Exception(self.errorMessages[3])\\r\\n\\r\\n ''' Read the netcdf dataset'''\\r\\n data_in_nc = NET.Dataset(in_nc)\\r\\n time = data_in_nc.variables['time'][:]\\r\\n\\r\\n # Check the size of time variable in the netcdf data\\r\\n if len(time) == 0: # *** MJS This change seems like it is too loose an error trap; should it account for instances when nc file time var is != in length with id_data lenght?\\r\\n raise Exception(self.errorMessages[3])\\r\\n #if len(time) != self.length_time[id_data]:\\r\\n # raise Exception(self.errorMessages[3])\\r\\n\\r\\n ''' Read the weight table '''\\r\\n print(\\\"Reading the weight table...\\\", in_weight_table)\\r\\n dict_list = {self.header_wt[0]:[], self.header_wt[1]:[], self.header_wt[2]:[],\\r\\n self.header_wt[3]:[], self.header_wt[4]:[]}\\r\\n\\r\\n with open(in_weight_table, \\\"r\\\") as csvfile:\\r\\n reader = csv.reader(csvfile)\\r\\n count = 0\\r\\n for row in reader:\\r\\n if count == 0:\\r\\n #check number of columns in the weight table\\r\\n if len(row) < len(self.header_wt):\\r\\n raise Exception(self.errorMessages[4])\\r\\n #check header\\r\\n if row[1:len(self.header_wt)] != self.header_wt[1:]:\\r\\n raise Exception(self.errorMessages[5])\\r\\n count += 1\\r\\n else:\\r\\n for i in range(len(self.header_wt)):\\r\\n dict_list[self.header_wt[i]].append(row[i])\\r\\n count += 1\\r\\n\\r\\n ''' Calculate water inflows\\r\\n as a reminder, the first 91 time steps are T=0 to T=90 and are 1-hourly for HRES\\r\\n\\t\\t the next 18 time steps for HRES are T=93 to T=144 at 3-hourly\\r\\n then the final 16 time steps are T=150 to T=240 at 6-hourly for a total of 125 records\\r\\n\\t\\t\\tFor ENS, the first 49 time steps are T=0 to T=144 at 3-hourly\\r\\n\\t\\t\\tthe final 35 time steps are T=150 to T=360 at 6-hourly for a total of 84 records\\r\\n '''\\r\\n\\t\\t\\t\\r\\n print(\\\"Calculating water inflows...\\\")\\r\\n\\t\\t\\r\\n ''' \\r\\n added the next section CJB 20180122 \\r\\n '''\\r\\n\\r\\n\\t\\t# Get the overall number of time steps\\r\\n size_time = self.getTimeSize(in_nc) #CJB 20180122\\r\\n # Determine the size of time steps in each group (1-hourly, 3-hourly, and/or 6-hourly)\\r\\n if id_data == \\\"HRES1\\\": # T <= 90 \\r\\n time_size = (size_time - 1)\\r\\n elif id_data == \\\"HRES13\\\": # 93 <= T <= 144\\r\\n if in_time_interval == \\\"1hr\\\":\\r\\n time_size = self.length_time_opt[\\\"HighRes-1hr\\\"]\\r\\n else:\\r\\n time_size = (size_time - self.length_time_opt[\\\"HighRes-1hr\\\"] - 1)\\r\\n elif id_data == \\\"HRES136\\\": # 150 <= T <= 240\\r\\n if in_time_interval == \\\"1hr\\\":\\r\\n time_size = self.length_time_opt[\\\"HighRes-1hr\\\"]\\r\\n elif in_time_interval == \\\"3hr\\\": # MJS Doesn't seem to be a case used currently, but added just in case later need.\\r\\n time_size = self.length_time_opt[\\\"HighRes-3hr-sub\\\"] # MJS This is HRES136, i.e., if for some reason in ecmwf_rapid_multi a 3 hr is asked for for this case, it should still have the 3hr_sub number of times\\r\\n elif in_time_interval == \\\"3hr_subset\\\":\\r\\n time_size = self.length_time_opt[\\\"HighRes-3hr-Sub\\\"]\\r\\n else:\\r\\n time_size = (size_time - self.length_time_opt[\\\"HighRes-1hr\\\"] - self.length_time_opt[\\\"HighRes-3hr-Sub\\\"] - 1)\\r\\n elif id_data == \\\"ENS3\\\": # T <= 144\\r\\n time_size = (size_time - 1)\\r\\n elif id_data == \\\"ENS36\\\": # 150 <= T <= 360\\r\\n if in_time_interval == \\\"3hr_subset\\\":\\r\\n time_size = self.length_time_opt[\\\"LowResFull-3hr-Sub\\\"]\\r\\n else:\\r\\n time_size = (size_time - self.length_time_opt[\\\"LowResFull-3hr-Sub\\\"] - 1)\\r\\n else: # id_data == \\\"ENS6\\\": # T <= 360 but all 6-hourly\\r\\n time_size = (size_time - 1)\\r\\n #else: # something is wrong and need to throw an error message - likely a corrupt forecast file\\r\\n # raise Exception(self.errorMessages[3])\\r\\n #''' end of added section CJB 20180122 \\r\\n #'''\\r\\n\\r\\n #if id_data == \\\"LowRes\\\":\\r\\n # size_time = self.length_time_opt[\\\"LowRes-6hr\\\"]\\r\\n #elif id_data == \\\"Low3HrRes\\\":\\r\\n # size_time = self.length_time_opt[\\\"LowRes-3hr\\\"]\\r\\n #elif id_data == \\\"LowResFull\\\":\\r\\n # if in_time_interval == \\\"3hr_subset\\\":\\r\\n # size_time = self.length_time_opt[\\\"LowResFull-3hr-Sub\\\"]\\r\\n # elif in_time_interval == \\\"6hr_subset\\\":\\r\\n # size_time = self.length_time_opt[\\\"LowResFull-6hr-Sub\\\"]\\r\\n # else:\\r\\n # size_time = self.length_time_opt[\\\"LowRes-6hr\\\"]\\r\\n #else: #HighRes\\r\\n # if in_time_interval == \\\"1hr\\\":\\r\\n # size_time = self.length_time_opt[\\\"HighRes-1hr\\\"]\\r\\n # elif in_time_interval == \\\"3hr\\\":\\r\\n # size_time = self.length_time_opt[\\\"HighRes-3hr\\\"]\\r\\n # elif in_time_interval == \\\"3hr_subset\\\":\\r\\n # size_time = self.length_time_opt[\\\"HighRes-3hr-Sub\\\"]\\r\\n # elif in_time_interval == \\\"6hr_subset\\\":\\r\\n # size_time = self.length_time_opt[\\\"HighRes-6hr-Sub\\\"]\\r\\n # else:\\r\\n # size_time = self.length_time_opt[\\\"HighRes-6hr\\\"]\\r\\n\\r\\n size_streamID = len(set(dict_list[self.header_wt[0]]))\\r\\n\\r\\n # Create output inflow netcdf data\\r\\n # data_out_nc = NET.Dataset(out_nc, \\\"w\\\") # by default format = \\\"NETCDF4\\\"\\r\\n data_out_nc = NET.Dataset(out_nc, \\\"w\\\", format = \\\"NETCDF3_CLASSIC\\\")\\r\\n #dim_Time = data_out_nc.createDimension('Time', size_time)\\r\\n dim_Time = data_out_nc.createDimension('Time', time_size)\\r\\n dim_RiverID = data_out_nc.createDimension('rivid', size_streamID)\\r\\n var_m3_riv = data_out_nc.createVariable('m3_riv', 'f4', \\r\\n ('Time', 'rivid'),\\r\\n fill_value=0)\\r\\n \\r\\n #data_temp = NUM.empty(shape = [size_time, size_streamID])\\r\\n data_temp = NUM.empty(shape = [time_size, size_streamID])\\r\\n\\r\\n lon_ind_all = [int(i) for i in dict_list[self.header_wt[2]]]\\r\\n lat_ind_all = [int(j) for j in dict_list[self.header_wt[3]]]\\r\\n\\r\\n # Obtain a subset of runoff data based on the indices in the weight table\\r\\n min_lon_ind_all = min(lon_ind_all)\\r\\n max_lon_ind_all = max(lon_ind_all)\\r\\n min_lat_ind_all = min(lat_ind_all)\\r\\n max_lat_ind_all = max(lat_ind_all)\\r\\n\\r\\n # self.vars_oi[vars_oi_index][3] = RO; get that variable's 3D structure (time, lat_index, lon_index) ready to reshape into 2D (time, lat_index x lon_index)\\r\\n data_subset_all = data_in_nc.variables[self.vars_oi[vars_oi_index][3]][:, min_lat_ind_all:max_lat_ind_all+1, min_lon_ind_all:max_lon_ind_all+1]\\r\\n len_time_subset_all = data_subset_all.shape[0]\\r\\n len_lat_subset_all = data_subset_all.shape[1]\\r\\n len_lon_subset_all = data_subset_all.shape[2]\\r\\n data_subset_all = data_subset_all.reshape(len_time_subset_all, (len_lat_subset_all * len_lon_subset_all))\\r\\n\\r\\n # compute new indices based on the data_subset_all\\r\\n index_new = []\\r\\n for r in range(0,count-1):\\r\\n ind_lat_orig = lat_ind_all[r]\\r\\n ind_lon_orig = lon_ind_all[r]\\r\\n index_new.append((ind_lat_orig - min_lat_ind_all)*len_lon_subset_all + (ind_lon_orig - min_lon_ind_all))\\r\\n\\r\\n # obtain a new subset of data\\r\\n data_subset_new = data_subset_all[:,index_new]*conversion_factor\\r\\n\\r\\n # start compute inflow\\r\\n pointer = 0\\r\\n for s in range(0, size_streamID):\\r\\n npoints = int(dict_list[self.header_wt[4]][pointer])\\r\\n # Check if all npoints points correspond to the same streamID\\r\\n if len(set(dict_list[self.header_wt[0]][pointer : (pointer + npoints)])) != 1:\\r\\n print(\\\"ROW INDEX {0}\\\".format(pointer))\\r\\n print(\\\"RIVID {0}\\\".format(dict_list[self.header_wt[0]][pointer]))\\r\\n raise Exception(self.errorMessages[2])\\r\\n\\r\\n area_sqm_npoints = [float(k) for k in dict_list[self.header_wt[1]][pointer : (pointer + npoints)]]\\r\\n area_sqm_npoints = NUM.array(area_sqm_npoints)\\r\\n area_sqm_npoints = area_sqm_npoints.reshape(1, npoints)\\r\\n data_goal = data_subset_new[:, pointer:(pointer + npoints)]\\r\\n \\r\\n \\r\\n #remove noise from data\\r\\n data_goal[data_goal<=0.00001] = 0\\r\\n\\r\\n ''' IMPORTANT NOTE: runoff variable in ECMWF dataset is cumulative instead of incremental through time\\r\\n '''\\r\\n # For data with Low Resolution, there's only one time interval 6 hrs\\r\\n if id_data == \\\"ENS6\\\": # Line Added/Modified CJB 20190108\\r\\n #ro_stream = data_goal * area_sqm_npoints\\r\\n ro_stream = NUM.subtract(data_goal[1:,],data_goal[:-1,]) * area_sqm_npoints\\r\\n elif id_data == \\\"ENS3\\\": # there's only one time interval 3 hrs # Line Added/Modified CJB 20190108\\r\\n #ro_stream = data_goal * area_sqm_npoints\\r\\n ro_stream = NUM.subtract(data_goal[1:,],data_goal[:-1,]) * area_sqm_npoints # Line Added/Modified CJB 20190108\\r\\n elif id_data == \\\"HRES1\\\": # there's only one time interval 1 hrs # Line Added/Modified CJB 20190108\\r\\n #ro_stream = data_goal * area_sqm_npoints\\r\\n ro_stream = NUM.subtract(data_goal[1:,],data_goal[:-1,]) * area_sqm_npoints # Line Added/Modified CJB 20190108\\t\\r\\n #For data with the full version of Low Resolution, from Hour 0 to 144 (the first 49 time points) are of 3 hr time interval,\\r\\n # then from Hour 144 to 360 (36 time points) are of 6 hour time interval\\r\\n elif id_data == \\\"ENS36\\\": # Line Added/Modified CJB 20190108\\r\\n if in_time_interval == \\\"3hr_subset\\\":\\r\\n #use only the 3hr time interval\\r\\n ro_stream = NUM.subtract(data_goal[1:49,], data_goal[:48,]) * area_sqm_npoints\\r\\n elif in_time_interval == \\\"6hr_subset\\\":\\r\\n #use only the 6hr time interval\\r\\n ro_stream = NUM.subtract(data_goal[49:,], data_goal[48:-1,]) * area_sqm_npoints\\r\\n else: #\\\"LowRes-6hr\\\"\\r\\n ######################################################\\r\\n # MJS Always assume this case will have a full ECMWF 240\\r\\n # hour forecast to work with. It's actually never re-\\r\\n # quested by ecmwf_rapid_multiprocess anyhow.\\r\\n ######################################################\\r\\n #convert all to 6hr\\r\\n # calculate time series of 6 hr data from 3 hr data\\r\\n ro_6hr_a = NUM.subtract(data_goal[2:49:2,], data_goal[:48:2,])\\r\\n # get the time series of 6 hr data\\r\\n ro_6hr_b = NUM.subtract(data_goal[49:,], data_goal[48:-1,])\\r\\n # concatenate all time series\\r\\n ro_stream = NUM.concatenate([ro_6hr_a, ro_6hr_b]) * area_sqm_npoints\\r\\n #For data with High Resolution, from Hour 0 to 90 (the first 91 time points) are of 1 hr time interval,\\r\\n # then from Hour 90 to 144 (18 time points) are of 3 hour time interval, and from Hour 144 to 240 (16 time points)\\r\\n # are of 6 hour time interval\\r\\n ##########################################################\\r\\n # MJS The following should handle id_data = HRES13 and HRES136\\r\\n ##########################################################\\r\\n else:\\r\\n if in_time_interval == \\\"1hr\\\":\\r\\n #ro_stream = NUM.subtract(data_goal[1:91,],data_goal[:90,]) * area_sqm_npoints\\r\\n ro_stream = NUM.subtract(data_goal[1:1+time_size,],data_goal[:time_size,]) * area_sqm_npoints # Line Added/Modified CJB, MJS 20190108\\r\\n elif in_time_interval == \\\"3hr\\\": # MJS HRES 3hr not currently used\\r\\n # calculate time series of 3 hr data from 1 hr data\\r\\n ro_3hr_a = NUM.subtract(data_goal[3:91:3,],data_goal[:88:3,])\\r\\n # get the time series of 3 hr data\\r\\n #ro_3hr_b = NUM.subtract(data_goal[91:109,], data_goal[90:108,])\\r\\n ro_3hr_b = NUM.subtract(data_goal[91:91+time_size,], data_goal[90:90+time_size,]) # MJS modified again; seems no case for this, but just in case later... Line Added/Modified CJB 20190108\\r\\n # concatenate all time series\\r\\n ro_stream = NUM.concatenate([ro_3hr_a, ro_3hr_b]) * area_sqm_npoints\\r\\n elif in_time_interval == \\\"3hr_subset\\\":\\r\\n #use only the 3hr time interval\\r\\n #ro_stream = NUM.subtract(data_goal[91:109,], data_goal[90:108,]) * area_sqm_npoints\\r\\n ro_stream = NUM.subtract(data_goal[91:91+time_size,], data_goal[90:90+time_size,]) * area_sqm_npoints # MJS modified again; needs to handle HRES13 that might not have complete 3hr set... Line Added/Modified CJB 20190108\\r\\n elif in_time_interval == \\\"6hr_subset\\\":\\r\\n #use only the 6hr time interval\\r\\n ro_stream = NUM.subtract(data_goal[109:,], data_goal[108:-1,]) * area_sqm_npoints\\r\\n ######################################################\\r\\n # MJS Always assume this case will have a full ECMWF 240 \\r\\n # hour forecast to work with. It's actually never re-\\r\\n # quested by ecmwf_rapid_multiprocess anyhow.\\r\\n ######################################################\\r\\n else: # in_time_interval == \\\"6hr\\\"\\r\\n #arcpy.AddMessage(\\\"6hr\\\")\\r\\n # calculate time series of 6 hr data from 1 hr data\\r\\n ro_6hr_a = NUM.subtract(data_goal[6:91:6,], data_goal[:85:6,])\\r\\n # calculate time series of 6 hr data from 3 hr data\\r\\n ro_6hr_b = NUM.subtract(data_goal[92:109:2,], data_goal[90:107:2,])\\r\\n # get the time series of 6 hr data\\r\\n ro_6hr_c = NUM.subtract(data_goal[109:,], data_goal[108:-1,])\\r\\n # concatenate all time series\\r\\n ro_stream = NUM.concatenate([ro_6hr_a, ro_6hr_b, ro_6hr_c]) * area_sqm_npoints\\r\\n \\r\\n #remove negative values\\r\\n ro_stream[ro_stream<0] = 0\\r\\n data_temp[:,s] = ro_stream.sum(axis = 1)\\r\\n\\r\\n pointer += npoints\\r\\n\\r\\n\\r\\n '''Write inflow data'''\\r\\n print(\\\"Writing inflow data...\\\")\\r\\n var_m3_riv[:] = data_temp\\r\\n # close the input and output netcdf datasets\\r\\n data_in_nc.close()\\r\\n data_out_nc.close()\",\n \"def statistical_analysis(var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, param):\\n \\n valueCount_prov1 = round(valueCounter([var1, var4, var7, var10]),3) #how many valid OpenWeatherMap forecasts are available\\n valueCount_prov2 = round(valueCounter([var2, var5, var8, var11]),3) #how many valid Weatherbit forecasts are available\\n valueCount_prov4 = round(valueCounter([var3, var6, var9, var12]),3) #how many valid WorldWeatherOnline forecasts are available\\n \\n average_discrepancy_prov1 = round(averageOfList([var1, var4, var7, var10]),3) #average forecast discrepancy for each provider\\n average_discrepancy_prov2 = round(averageOfList([var2, var5, var8, var11]),3)\\n average_discrepancy_prov4 = round(averageOfList([var3, var6, var9, var12]),3)\\n \\n absolute_average_discrepancy_prov1 = round(absoluteAverageOfList([var1, var4, var7, var10]),3) #average absolute forecast discrepancy for each provider\\n absolute_average_discrepancy_prov2 = round(absoluteAverageOfList([var2, var5, var8, var11]),3)\\n absolute_average_discrepancy_prov4 = round(absoluteAverageOfList([var3, var6, var9, var12]),3)\\n \\n median_discrepancy_prov1 = round(medianErrorOfList([var1, var4, var7, var10]),3) #median forecast discrepancy for each provider\\n median_discrepancy_prov2 = round(medianErrorOfList([var2, var5, var8, var11]),3)\\n median_discrepancy_prov4 = round(medianErrorOfList([var3, var6, var9, var12]),3)\\n\\n sd_discrepancy_prov1 = sampleStandardDeviation([var1, var4, var7, var10]) #standard deviation forecast discrepancy for each provider\\n sd_discrepancy_prov2 = sampleStandardDeviation([var2, var5, var8, var11]) #if there are at least two values available\\n sd_discrepancy_prov4 = sampleStandardDeviation([var3, var6, var9, var12])\\n\\n #which is the best forecast: defined by minimal absolute average discrepancy\\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov1:\\n most_accurate_forecast = \\\"OpenWeatherMap\\\"\\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov2:\\n most_accurate_forecast = \\\"Weatherbit\\\"\\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov4:\\n most_accurate_forecast = \\\"WorldWeatherOnline\\\"\\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov1 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov2:\\n most_accurate_forecast = \\\"OpenWeatherMap and Weatherbit\\\"\\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov1 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov4:\\n most_accurate_forecast = \\\"OpenWeatherMap and WorldWeatherOnline\\\" \\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov2 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov4:\\n most_accurate_forecast = \\\"Weatherbit and WorldWeatherOnline\\\"\\n if min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov2 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov4 and min([absolute_average_discrepancy_prov1,absolute_average_discrepancy_prov2,absolute_average_discrepancy_prov4]) == absolute_average_discrepancy_prov1:\\n most_accurate_forecast = \\\"all three forecasts\\\"\\n \\n dictionary = { #save the values to dictionary to make them accessible for the HTML template\\n 'valueCount_prov1_'+str(param) : valueCount_prov1,\\n 'valueCount_prov2_'+str(param) : valueCount_prov2,\\n 'valueCount_prov4_'+str(param) : valueCount_prov4,\\n 'average_discrepancy_prov1_'+str(param) : average_discrepancy_prov1,\\n 'average_discrepancy_prov2_'+str(param) : average_discrepancy_prov2,\\n 'average_discrepancy_prov4_'+str(param) : average_discrepancy_prov4,\\n 'absolute_average_discrepancy_prov1_'+str(param) : absolute_average_discrepancy_prov1,\\n 'absolute_average_discrepancy_prov2_'+str(param) : absolute_average_discrepancy_prov2,\\n 'absolute_average_discrepancy_prov4_'+str(param) : absolute_average_discrepancy_prov4,\\n 'median_discrepancy_prov1_'+str(param) : median_discrepancy_prov1,\\n 'median_discrepancy_prov2_'+str(param) : median_discrepancy_prov2,\\n 'median_discrepancy_prov4_'+str(param) : median_discrepancy_prov4,\\n 'sd_discrepancy_prov1_'+str(param) : sd_discrepancy_prov1,\\n 'sd_discrepancy_prov2_'+str(param) : sd_discrepancy_prov2,\\n 'sd_discrepancy_prov4_'+str(param) : sd_discrepancy_prov4,\\n 'weatherDay_'+str(param) : most_accurate_forecast,\\n }\\n \\n return dictionary\",\n \"def regrid_custom_hourly_netcdf(input_forcings, config_options, wrf_hydro_geo_meta, mpi_config):\\n # If the expected file is missing, this means we are allowing missing files, simply\\n # exit out of this routine as the regridded fields have already been set to NDV.\\n if not os.path.isfile(input_forcings.file_in2):\\n return\\n\\n # Check to see if the regrid complete flag for this\\n # output time step is true. This entails the necessary\\n # inputs have already been regridded and we can move on.\\n if input_forcings.regridComplete:\\n if mpi_config.rank == 0:\\n config_options.statusMsg = \\\"No Custom Hourly NetCDF regridding required for this timestep.\\\"\\n err_handler.log_msg(config_options, mpi_config)\\n return\\n\\n # Open the input NetCDF file containing necessary data.\\n id_tmp = ioMod.open_netcdf_forcing(input_forcings.file_in2, config_options, mpi_config, open_on_all_procs=True)\\n\\n fill_values = {'TMP': 288.0, 'SPFH': 0.005, 'PRES': 101300.0, 'APCP': 0,\\n 'UGRD': 1.0, 'VGRD': 1.0, 'DSWRF': 80.0, 'DLWRF': 310.0}\\n\\n for force_count, nc_var in enumerate(input_forcings.netcdf_var_names):\\n if mpi_config.rank == 0:\\n config_options.statusMsg = \\\"Processing Custom NetCDF Forcing Variable: \\\" + nc_var\\n err_handler.log_msg(config_options, mpi_config)\\n calc_regrid_flag = check_regrid_status(id_tmp, force_count, input_forcings,\\n config_options, wrf_hydro_geo_meta, mpi_config)\\n\\n if calc_regrid_flag:\\n calculate_weights(id_tmp, force_count, input_forcings, config_options, mpi_config)\\n\\n # Read in the RAP height field, which is used for downscaling purposes.\\n if 'HGT_surface' in id_tmp.variables.keys():\\n # Regrid the height variable.\\n if mpi_config.rank == 0:\\n var_tmp = id_tmp.variables['HGT_surface'][0, :, :]\\n else:\\n var_tmp = None\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n var_sub_tmp = mpi_config.scatter_array(input_forcings, var_tmp, config_options)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n try:\\n input_forcings.esmf_field_in.data[:, :] = var_sub_tmp\\n except (ValueError, KeyError, AttributeError) as err:\\n config_options.errMsg = \\\"Unable to place NetCDF elevation data into the ESMF field object: \\\" \\\\\\n + str(err)\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n if mpi_config.rank == 0:\\n config_options.statusMsg = \\\"Regridding elevation data to the WRF-Hydro domain.\\\"\\n err_handler.log_msg(config_options, mpi_config)\\n try:\\n input_forcings.esmf_field_out = input_forcings.regridObj(input_forcings.esmf_field_in,\\n input_forcings.esmf_field_out)\\n except ValueError as ve:\\n config_options.errMsg = \\\"Unable to regrid elevation data to the WRF-Hydro domain \\\" \\\\\\n \\\"using ESMF: \\\" + str(ve)\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n # Set any pixel cells outside the input domain to the global missing value.\\n try:\\n input_forcings.esmf_field_out.data[np.where(input_forcings.regridded_mask == 0)] = \\\\\\n config_options.globalNdv\\n except (ValueError, ArithmeticError) as npe:\\n config_options.errMsg = \\\"Unable to compute mask on elevation data: \\\" + str(npe)\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n try:\\n input_forcings.height[:, :] = input_forcings.esmf_field_out.data\\n except (ValueError, KeyError, AttributeError) as err:\\n config_options.errMsg = \\\"Unable to extract ESMF regridded elevation data to a local \\\" \\\\\\n \\\"array: \\\" + str(err)\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n else:\\n input_forcings.height = None\\n if mpi_config.rank == 0:\\n config_options.statusMsg = f\\\"Unable to locate HGT_surface in: {input_forcings.file_in2}. \\\" \\\\\\n f\\\"Downscaling will not be available.\\\"\\n err_handler.log_msg(config_options, mpi_config)\\n\\n # close netCDF file on non-root ranks\\n if mpi_config.rank != 0:\\n id_tmp.close()\\n\\n # Regrid the input variables.\\n var_tmp = None\\n fill = fill_values.get(input_forcings.grib_vars[force_count], config_options.globalNdv)\\n if mpi_config.rank == 0:\\n config_options.statusMsg = \\\"Regridding Custom netCDF input variable: \\\" + nc_var\\n err_handler.log_msg(config_options, mpi_config)\\n try:\\n config_options.statusMsg = f\\\"Using {fill} to replace missing values in input\\\"\\n err_handler.log_msg(config_options, mpi_config)\\n var_tmp = id_tmp.variables[nc_var][:].filled(fill)[0, :, :]\\n except Exception as err:\\n config_options.errMsg = \\\"Unable to extract \\\" + nc_var + \\\\\\n \\\" from: \\\" + input_forcings.file_in2 + \\\" (\\\" + str(err) + \\\")\\\"\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n var_sub_tmp = mpi_config.scatter_array(input_forcings, var_tmp, config_options)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n try:\\n input_forcings.esmf_field_in.data[:, :] = var_sub_tmp\\n except (ValueError, KeyError, AttributeError) as err:\\n config_options.errMsg = \\\"Unable to place local array into local ESMF field: \\\" + str(err)\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n try:\\n input_forcings.esmf_field_out = input_forcings.regridObj(input_forcings.esmf_field_in,\\n input_forcings.esmf_field_out)\\n except ValueError as ve:\\n config_options.errMsg = \\\"Unable to regrid input Custom netCDF forcing variables using ESMF: \\\" + str(ve)\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n # Set any pixel cells outside the input domain to the global missing value.\\n try:\\n input_forcings.esmf_field_out.data[np.where(input_forcings.regridded_mask == 0)] = fill\\n except (ValueError, ArithmeticError) as npe:\\n config_options.errMsg = \\\"Unable to calculate mask from input Custom netCDF regridded forcings: \\\" + str(\\n npe)\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n # Convert the hourly precipitation total to a rate of mm/s\\n if nc_var == 'APCP_surface':\\n try:\\n ind_valid = np.where(input_forcings.esmf_field_out.data != fill)\\n input_forcings.esmf_field_out.data[ind_valid] = input_forcings.esmf_field_out.data[\\n ind_valid] / 3600.0\\n del ind_valid\\n except (ValueError, ArithmeticError, AttributeError, KeyError) as npe:\\n config_options.errMsg = \\\"Unable to run NDV search on Custom netCDF precipitation: \\\" + str(npe)\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n try:\\n input_forcings.regridded_forcings2[input_forcings.input_map_output[force_count], :, :] = \\\\\\n input_forcings.esmf_field_out.data\\n except (ValueError, KeyError, AttributeError) as err:\\n config_options.errMsg = \\\"Unable to place local ESMF regridded data into local array: \\\" + str(err)\\n err_handler.log_critical(config_options, mpi_config)\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n # If we are on the first timestep, set the previous regridded field to be\\n # the latest as there are no states for time 0.\\n if config_options.current_output_step == 1:\\n input_forcings.regridded_forcings1[input_forcings.input_map_output[force_count], :, :] = \\\\\\n input_forcings.regridded_forcings2[input_forcings.input_map_output[force_count], :, :]\\n err_handler.check_program_status(config_options, mpi_config)\\n\\n # Close the NetCDF file\\n if mpi_config.rank == 0:\\n try:\\n id_tmp.close()\\n except OSError:\\n config_options.errMsg = \\\"Unable to close NetCDF file: \\\" + input_forcings.tmpFile\\n err_handler.err_out(config_options)\",\n \"def preprocess_data(study_period_data: pd.DataFrame, unique_indices: pd.MultiIndex, cols: list, split_index: int,\\n configs: dict, full_date_range: pd.Index, parent_model_type: str, from_mixed=False,\\n return_validation_data=False, validation_range: list = None) -> tuple:\\n\\n cols = cols.copy()\\n print(f'{Style.BRIGHT}{Fore.YELLOW}Pre-processing data ...{Style.RESET_ALL}')\\n timer = Timer().start()\\n\\n if from_mixed and parent_model_type == 'tree_based':\\n cols.extend(['return_index'])\\n cols.remove('stand_d_return')\\n\\n if parent_model_type == 'mixed':\\n # JOB: Pre-process data separately for each mixed ensemble component\\n return np.array([preprocess_data(\\n study_period_data=study_period_data, unique_indices=unique_indices, cols=cols, split_index=split_index,\\n configs=configs, full_date_range=full_date_range, parent_model_type=parent_model_type,\\n from_mixed=True, return_validation_data=True, validation_range=validation_range) for\\n parent_model_type in ['deep_learning', 'tree_based']]).T.tolist()\\n\\n # JOB: Instantiate training and test data\\n data = None\\n x_train = None\\n y_train = None\\n x_val = None\\n y_val = None\\n x_test = None\\n y_test = None\\n validation_mask = []\\n train_data_index = pd.Index([])\\n validation_data_index = pd.Index([])\\n test_data_index = pd.Index([])\\n\\n train_range = full_date_range[configs['data']['sequence_length'] + 1:split_index + 1]\\n print(f'Last training date: {full_date_range[split_index]}')\\n\\n print(f'Training range length: {len(train_range)}')\\n\\n if return_validation_data:\\n print(f'Validation range length: {len(validation_range)}')\\n\\n # Instantiate progress bar\\n progress_bar = ProgressBar(len(unique_indices))\\n # JOB: Iterate through individual stocks and generate training and test data\\n for stock_id in unique_indices:\\n progress_bar.print_progress()\\n id_data = study_period_data.loc[stock_id].set_index('datadate')\\n\\n try:\\n # JOB: Initialize DataLoader\\n data = DataLoader(\\n id_data, cols=cols,\\n seq_len=configs['data']['sequence_length'], full_date_range=full_date_range, stock_id=stock_id,\\n split_index=split_index, model_type=parent_model_type\\n )\\n except AssertionError as ae:\\n print(ae)\\n continue\\n\\n # print('Length of data for ID %s: %d' % (stock_id, len(id_data)))\\n\\n # JOB: Generate training data\\n x_train_i, y_train_i = data.get_train_data()\\n\\n # JOB: Generate test data\\n x_test_i, y_test_i = data.get_test_data(\\n seq_len=configs['data']['sequence_length'],\\n )\\n\\n # JOB: In case training/test set is empty, set to first batch, otherwise append data\\n if (len(x_train_i) > 0) and (len(y_train_i) > 0):\\n train_data_index = train_data_index.append(data.data_train_index)\\n mask = [date_i in validation_range for date_i in data.data_train_index.get_level_values(level=0)]\\n validation_mask.extend(mask)\\n if x_train is None:\\n x_train = x_train_i\\n y_train = y_train_i\\n else:\\n x_train = np.append(x_train, x_train_i, axis=0)\\n y_train = np.append(y_train, y_train_i, axis=0)\\n\\n if (len(x_test_i) > 0) and (len(y_test_i) > 0):\\n test_data_index = test_data_index.append(data.data_test_index)\\n if x_test is None:\\n x_test = x_test_i\\n y_test = y_test_i\\n else:\\n x_test = np.append(x_test, x_test_i, axis=0)\\n y_test = np.append(y_test, y_test_i, axis=0)\\n\\n if len(y_test_i) != len(data.data_test_index):\\n print(f'\\\\nMismatch for index {stock_id}')\\n print(f'{Fore.YELLOW}{Back.RED}{Style.BRIGHT}Lengths do not conform!{Style.RESET_ALL}')\\n print(f'Target length: {len(y_test_i)}, index length: {len(data.data_test_index)}')\\n raise AssertionError('Data length is not conforming.')\\n\\n # print('Length of test labels: %d' % len(y_test))\\n # print('Length of test index: %d\\\\n' % len(test_data_index))\\n\\n if len(y_test) != len(test_data_index):\\n raise AssertionError('Data length is not conforming.')\\n\\n print(f'Length training data (before): {len(x_train)}')\\n\\n if return_validation_data:\\n # JOB: Split up training data in training and validation set if needed\\n inverse_mask = [not m for m in validation_mask]\\n x_val = x_train[validation_mask]\\n y_val = y_train[validation_mask]\\n validation_data_index = train_data_index[validation_mask]\\n x_train = x_train[inverse_mask]\\n y_train = y_train[inverse_mask]\\n train_data_index = train_data_index[inverse_mask]\\n\\n print(f'Length x_train: {len(x_train)}')\\n print(f'Length x_val: {len(x_val)}')\\n\\n print(f'Length x_train: {len(x_train)}')\\n\\n print(f'{Style.BRIGHT}{Fore.YELLOW}Done pre-processing data.{Style.RESET_ALL}')\\n timer.stop()\\n\\n return x_train, y_train, x_test, y_test, train_data_index, test_data_index, data.cols, (\\n x_val, y_val, validation_data_index)\",\n \"def compliance_time_series(county, core_path , patterns_path, backfill = False, GEOID_type = 'CBG'):\\n #Load data about places and patterns\\n county_places = pd.read_csv(core_path+'places-'+ county +'.csv', index_col='safegraph_place_id') \\n \\n pattern_dates = [x[5:9] for x in sorted(os.listdir(patterns_path+'main-file-'+ county +'/'))]\\n w = 0\\n next_date = pattern_dates[w]\\n #Create visitor table\\n place_cts = pd.DataFrame() #First two months don't have weekly patterns, use empty data.frame\\n\\n #List files for social distancing metrics in that county\\n months_path = '../social_distancing/social_dist_'+ county +'/'\\n month_list = sorted(os.listdir(months_path))\\n os.makedirs( '../stats/time_series/', exist_ok = True)\\n #metrics dictionary to be filled looping through every day\\n if not os.path.isfile('../stats/time_series/metrics_{}_CT.csv'.format(county)) or backfill:\\n metrics = pd.DataFrame()\\n existing_dates = []\\n else:\\n metrics = pd.read_csv('../stats/time_series/metrics_{}_CT.csv'.format(county), dtype = {'origin_census_tract':str})\\n #dates already processed\\n existing_dates = metrics['date'].unique() #series of unique dates\\n\\n #Initialize columns of new data frame\\n if GEOID_type == 'CBG':\\n columns= {'date':[], 'origin_census_block_group':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'total_expected_contacts':[], 'places_visited':[], 'cbgs_visited':[]}\\n if GEOID_type == 'CT':\\n columns= {'date':[], 'origin_census_tract':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'places_visited':[]} \\n changed = False\\n for month in month_list:\\n #Loop through every day\\n day_list = sorted(os.listdir(months_path + month))\\n for day in day_list: \\n date_name = month + '-' + day\\n print(date_name)\\n if month+'-'+day == next_date:\\n w = w+1\\n if w < len(pattern_dates):\\n next_date = pattern_dates[w]\\n if date_name in existing_dates:\\n continue\\n print(\\\"--changing to next patterns file\\\")\\n county_patterns = pd.read_csv(patterns_path + 'main-file-{}/2020-{}-weekly-patterns.csv.gz'.format(county, date_name),\\n index_col='safegraph_place_id')\\n norm_factors = pd.read_csv(\\n '../social_distancing/normalization/'+'normalization_{}.csv'.format(county),\\n dtype={'origin_census_block_group':str})\\n norm_factors.set_index('origin_census_block_group', drop =True, inplace=True)\\n\\n\\n #Establish prior for hourly distribution of visits at the top_category level\\n county_patterns= county_patterns.join(county_places[['top_category','sub_category']], how='inner')\\n restaurants = county_patterns['top_category'] == 'Restaurants and Other Eating Places'\\n county_patterns.loc[restaurants, 'top_category'] = county_patterns.loc[restaurants, 'sub_category']\\n norm_factor = norm_factors.loc[norm_factors.date == date_name].norm_factor\\n\\n prior_dict = {}\\n for category in county_patterns.top_category.value_counts().index:\\n places_in_cat = county_patterns['top_category'] == category\\n dirich_samples = [np.array(json.loads(x)) for x in county_patterns.loc[places_in_cat, 'visits_by_each_hour'] ] \\n prior_dict[category] = dirichlet.getInitAlphas(dirich_samples)\\n if GEOID_type == 'CBG':\\n place_cbgs = ccc.place_cbg_contacts_table(\\n county_patterns,\\n prior_dict,\\n norm_factor,\\n GEOID_type)\\n place_cbgs = place_cbgs.loc[place_cbgs['expected_contacts']>1]\\n place_cbgs = place_cbgs.join(county_places[['location_name','latitude','longitude']], how='inner')\\n place_cbgs.reset_index(inplace=True, drop=False)\\n place_cbgs.set_index('origin_census_block_group', inplace=True, drop=True)\\n if GEOID_type == 'CT':\\n place_cts = ccc.place_cbg_contacts_table(\\n county_patterns,\\n prior_dict,\\n norm_factor,\\n GEOID_type)\\n place_cts = place_cts.join(county_places[['location_name','latitude','longitude']], how='inner')\\n place_cts.reset_index(inplace=True, drop=False)\\n place_cts.set_index('origin_census_tract', inplace=True, drop=True)\\n print('--computed bipartite contact network')\\n\\n if date_name in existing_dates:\\n continue\\n\\n\\n file_name = os.listdir(months_path+ '/' + month+'/'+day)[0]\\n data_soc_dist = pd.read_csv(months_path+ '/' + month+'/'+day+'/'+file_name, dtype={'origin_census_block_group':str})\\n data_soc_dist['median_distance_traveled_from_home'] = [0 if math.isnan(x) else x for x in data_soc_dist['distance_traveled_from_home']] #NOT NEEDED?\\n \\n if GEOID_type == 'CBG':\\n [update_metrics_columns(row, date_name, place_cbgs, county_places, columns) for i, row in data_soc_dist.iterrows()]\\n else:\\n ####CREATE FUNCTION THAT AGGREGATES TO CENSUS TRACT\\n data_soc_dist['origin_census_tract'] = [x[:-1] for x in data_soc_dist['origin_census_block_group']]\\n ct_data_soc_dist = data_soc_dist.groupby('origin_census_tract').apply(ddd.aggregate_to_ct).reset_index()\\n [update_metrics_columns_CT(row, date_name, place_cts, county_places, columns) for i, row in ct_data_soc_dist.iterrows()]\\n changed = True\\n\\n print(\\\"--merging rows from new dates\\\")\\n new_metrics = pd.DataFrame.from_dict(columns, orient='index').transpose()\\n\\n metrics = pd.concat([metrics, new_metrics], ignore_index=True)\\n if changed:\\n if GEOID_type == 'CT':\\n metrics.to_csv('../stats/time_series/metrics_{}_CT.csv'.format(county),index=False)\\n else:\\n metrics.to_csv('../stats/time_series/metrics_{}_CBG.csv'.format(county),index=False)\\n print(\\\"--finished updating time series for {}\\\".format(county))\\n return(0)\",\n \"def _util(\\n self,\\n hist: bool = False,\\n prop: str = None,\\n start_date_range: str = None,\\n end_date_range: str = None,\\n **_,\\n ):\\n outputs: dict = {}\\n\\n # List of properties needed by the plot, properties are a set of tuples and\\n # contain 3 parts: required True/False, property name and scenarios required,\\n # scenarios must be a list.\\n properties = [\\n (True, \\\"line_Flow\\\", self.Scenarios),\\n (True, \\\"line_Import_Limit\\\", self.Scenarios),\\n ]\\n\\n # Runs get_formatted_data within PlotDataStoreAndProcessor to populate PlotDataStoreAndProcessor dictionary\\n # with all required properties, returns a 1 if required data is missing\\n check_input_data = self.get_formatted_data(properties)\\n\\n if 1 in check_input_data:\\n return MissingInputData()\\n\\n # sets up x, y dimensions of plot\\n ncols, nrows = set_facet_col_row_dimensions(\\n self.xlabels, self.ylabels, facet=True, multi_scenario=self.Scenarios\\n )\\n grid_size = ncols * nrows\\n # Used to calculate any excess axis to delete\\n plot_number = len(self.Scenarios)\\n excess_axs = grid_size - plot_number\\n\\n for zone_input in self.Zones:\\n logger.info(f\\\"For all lines touching Zone = {zone_input}\\\")\\n\\n mplt = PlotLibrary(nrows, ncols, sharey=True, squeeze=False, ravel_axs=True)\\n fig, axs = mplt.get_figure()\\n plt.subplots_adjust(wspace=0.1, hspace=0.25)\\n\\n # Get and process all line limits\\n line_limits = self.get_line_interface_limits(\\n [\\\"line_Import_Limit\\\"],\\n )\\n data_table = []\\n for n, scenario in enumerate(self.Scenarios):\\n logger.info(f\\\"Scenario = {str(scenario)}\\\")\\n # gets correct metadata based on area aggregation\\n if self.AGG_BY == \\\"zone\\\":\\n zone_lines = self.meta.zone_lines(scenario)\\n else:\\n zone_lines = self.meta.region_lines(scenario)\\n try:\\n zone_lines = zone_lines.set_index([self.AGG_BY])\\n except:\\n logger.warning(\\\"Column to Aggregate by is missing\\\")\\n continue\\n try:\\n zone_lines = zone_lines.xs(zone_input)\\n zone_lines = zone_lines[\\\"line_name\\\"].unique()\\n except KeyError:\\n logger.warning(\\\"No data to plot for scenario\\\")\\n outputs[zone_input] = MissingZoneData()\\n continue\\n\\n flow = self[\\\"line_Flow\\\"].get(scenario)\\n # Limit to only lines touching to this zone\\n flow = flow[flow.index.get_level_values(\\\"line_name\\\").isin(zone_lines)]\\n\\n if pd.notna(start_date_range):\\n flow = set_timestamp_date_range(\\n flow, start_date_range, end_date_range\\n )\\n if flow.empty is True:\\n logger.warning(\\\"No data in selected Date Range\\\")\\n continue\\n\\n if shift_leapday:\\n flow = adjust_for_leapday(flow)\\n\\n if \\\"Scenario\\\" in line_limits.index.names:\\n limits = line_limits.xs(scenario, level=\\\"Scenario\\\")\\n else:\\n limits = line_limits\\n limits = limits.groupby(\\\"line_name\\\").max().abs()\\n\\n # Filters on specifed line category if prop is not None.\\n if prop:\\n logger.info(f\\\"Line category = {str(prop)}\\\")\\n try:\\n flow = flow.xs(prop, level=\\\"category\\\")\\n except KeyError:\\n logger.warning(\\n f\\\"Line category {prop}, Not found in data! \\\"\\n \\\"All categories will be plotted.\\\"\\n )\\n\\n flow = pd.merge(flow, limits, on=\\\"line_name\\\", how=\\\"left\\\")\\n flow[\\\"Util\\\"] = (\\n flow[\\\"values\\\"].abs() / flow[\\\"line_Import_Limit\\\"]\\n ).fillna(0)\\n # If greater than 1 because exceeds flow limit, report as 1\\n flow[\\\"Util\\\"][flow[\\\"Util\\\"] > 1] = 1\\n annual_util = (\\n flow[\\\"Util\\\"].groupby([\\\"line_name\\\"]).mean().rename(scenario)\\n )\\n # top annual utilized lines\\n top_utilization = annual_util.nlargest(10, keep=\\\"first\\\")\\n\\n color_dict = dict(zip(self.Scenarios, self.color_list))\\n\\n if hist is True:\\n mplt.histogram(annual_util, color_dict, label=scenario, sub_pos=n)\\n else:\\n for line in top_utilization.index.get_level_values(\\n level=\\\"line_name\\\"\\n ).unique():\\n duration_curve = (\\n flow.loc[line]\\n .sort_values(by=\\\"Util\\\", ascending=False)\\n .reset_index(drop=True)\\n )\\n mplt.lineplot(duration_curve, \\\"Util\\\", label=line, sub_pos=n)\\n axs[n].set_ylim((0, 1.1))\\n data_table.append(annual_util)\\n\\n mplt.add_legend()\\n # Remove extra axes\\n mplt.remove_excess_axs(excess_axs, grid_size)\\n # add facet labels\\n mplt.add_facet_labels(xlabels=self.xlabels, ylabels=self.ylabels)\\n if hist is True:\\n if prop:\\n prop_name = prop\\n else:\\n prop_name = \\\"All Lines\\\"\\n plt.ylabel(\\n \\\"Number of lines\\\", color=\\\"black\\\", rotation=\\\"vertical\\\", labelpad=30\\n )\\n plt.xlabel(\\n f\\\"Line Utilization: {prop_name}\\\",\\n color=\\\"black\\\",\\n rotation=\\\"horizontal\\\",\\n labelpad=30,\\n )\\n else:\\n if prop:\\n prop_name = prop\\n else:\\n prop_name = \\\"Top 10 Lines\\\"\\n plt.ylabel(\\n f\\\"Line Utilization: {prop_name}\\\",\\n color=\\\"black\\\",\\n rotation=\\\"vertical\\\",\\n labelpad=60,\\n )\\n plt.xlabel(\\n \\\"Intervals\\\", color=\\\"black\\\", rotation=\\\"horizontal\\\", labelpad=20\\n )\\n\\n if plot_data_settings[\\\"plot_title_as_region\\\"]:\\n mplt.add_main_title(zone_input)\\n\\n Data_Out = pd.concat(data_table)\\n\\n outputs[zone_input] = {\\\"fig\\\": fig, \\\"data_table\\\": Data_Out}\\n return outputs\",\n \"def _fit(self, endog, endog_end, min_ts_mean, min_ts_mean_window, include_holidays=False,\\n min_ts_length=None, max_ft_freq=None, exog_data=None, optimize=None):\\n import numpy as np\\n from pykalman import KalmanFilter\\n import warnings\\n warnings.filterwarnings('ignore')\\n\\n p, q = self._params['p'], self._params['q']\\n freq = self._params['freq']\\n pred_len = self.max_scoring_length\\n x_matrix_train = None\\n x_matrix_score = None\\n\\n # set exogenous (holiday) variables for input data\\n if include_holidays and len(endog) + pred_len > 385:\\n exog = exog_data.loc[endog.index.min():endog_end]\\n else:\\n include_holidays = False\\n exog = None\\n\\n if min_ts_length is not None and len(endog) < min_ts_length:\\n raise ValueError('TimeSeries length less than minimum length specified')\\n\\n if min_ts_mean is not None:\\n if (min_ts_mean_window is not None and endog[-min_ts_mean_window:].fillna(0).mean() < min_ts_mean) or \\\\\\n (min_ts_mean_window is None and endog.fillna(0).mean() < min_ts_mean):\\n raise ValueError('Metric values too small to model.')\\n\\n # Smoothing the given time series as a pre-processing for modeling seasonalities through Fourier\\n # transformation\\n kf = KalmanFilter()\\n endog_smoothed, filtered_state_covariances = kf.em(endog).smooth(endog)\\n endog_smoothed = endog_smoothed[:, 0]\\n\\n endog, diff_order, actual_previous_per_diff = DataExploration._stationarizer(endog=pd.Series(endog),\\n diff_min=0,\\n diff_max=1,\\n obs_incl=False)\\n if diff_order:\\n endog_smoothed = np.diff(endog_smoothed)\\n\\n if freq == 'D':\\n complete_cycle = int(len(endog) / 7)\\n endog = endog[- (complete_cycle * 7):]\\n endog_smoothed = endog_smoothed[- (complete_cycle * 7):]\\n elif freq == 'H':\\n complete_cycle = int(len(endog) / 24)\\n endog = endog[- (complete_cycle * 24):]\\n endog_smoothed = endog_smoothed[- (complete_cycle * 24):]\\n\\n exog = exog.iloc[-len(endog):] if exog is not None else None\\n\\n if include_holidays:\\n exog = exog.loc[:, (exog != 0).any(axis=0)]\\n ext_training_features = list(exog.columns)\\n else:\\n ext_training_features = None\\n\\n stepwise_fit = []\\n\\n # Updating the user specified maximum number of frequencies to consider for the Fourier transformation\\n # based on the length of the smoothed endogenous variable\\n max_ft_freq = int(min(max_ft_freq, len(endog_smoothed) / 4))\\n\\n # Running the Fourier transformation extrapolating one point ahead in future that is going to be used\\n # for predicting\\n\\n if max_ft_freq > 0:\\n x_matrix = self._fourier_extp(series=endog_smoothed, max_trun=(2 * max_ft_freq),\\n forecast_period=pred_len)\\n if not optimize and np.all(x_matrix[0] == x_matrix[0][0]):\\n x_matrix_train = None\\n x_matrix_score = None\\n max_ft_freq = 0\\n else:\\n x_matrix_train = x_matrix[:, :(x_matrix.shape[1] - pred_len)]\\n x_matrix_score = x_matrix[:, (x_matrix.shape[1] - pred_len):]\\n\\n\\n self._seasonal_arima(endog=endog, exog=exog, p=p, d=0, q=q, imodels=max_ft_freq,\\n include_holidays=include_holidays, ift_matrix=x_matrix_train,\\n stepwise_fit=stepwise_fit, optimize=optimize)\\n model = stepwise_fit[0]\\n\\n seasonal_feature_scoring = x_matrix_score[0, :].tolist() if not x_matrix_score is None else None\\n\\n result = {\\n 'model': model,\\n 'diff_order': diff_order,\\n 'seasonal_feature_scoring': seasonal_feature_scoring,\\n 'ext_training_features': ext_training_features,\\n }\\n\\n p_selected = model.k_ar if hasattr(model, 'k_ar') else 0\\n d_selected = diff_order\\n q_selected = model.k_ma if hasattr(model, 'k_ma') else 0\\n order = (p_selected, d_selected, q_selected)\\n\\n return result, order\",\n \"def grid(df, N = 100, l = 1200, side = \\\"sx\\\"):\\n #variables for the intervals of the grid\\n min_x = 0\\n max_x = l\\n delta = l/float(N)\\n grid = df\\n grid.columns = [0,1]\\n grid = grid.sort_values(by = 1)\\n #makes a new aray where will be the smoothed fronts\\n grid_smooth=np.empty((2,N))\\n\\n #makes the new grid taking the maximum value of the y coordinate in the interval\\n for i in np.arange(N):\\n #intervals\\n bin_m = min_x +i*delta\\n bin_M = min_x +(i+1)*delta\\n #difference from sx front and dx front: in one case i take the max, in the\\n #other i take the min\\n if side == \\\"sx\\\":\\n grid_smooth[1,i]=min_x +(i+1/2.)*delta\\n #takes the max value only if in the interval there are values\\n if len(grid[(grid[1]>bin_m) & (grid[1]<bin_M)][0]) != 0:\\n grid_smooth[0,i]=max(grid[(grid[1]>bin_m)&(grid[1]<bin_M)][0])\\n else:\\n #if there are not points in that interval, takes the latest value computed\\n grid_smooth[0,i] = grid_smooth[0,i-1]\\n if i == 0:\\n grid_smooth[0,i] = np.mean(grid[0])\\n else:\\n grid_smooth[1,i]=min_x +(i+1/2.)*delta\\n if len(grid[(grid[1]>bin_m)&(grid[1]<bin_M)][0]) != 0:\\n grid_smooth[0,i]=min(grid[(grid[1]>bin_m)&(grid[1]<bin_M)][0])\\n else:\\n grid_smooth[0,i] = grid_smooth[0,i-1]\\n if i == 0:\\n grid_smooth[0,i] = np.mean(grid[0])\\n\\n df = pd.DataFrame()\\n df[0] = pd.Series(grid_smooth[0])\\n df[1] = pd.Series(grid_smooth[1])\\n #return the dataframe with the smoothed grid\\n return df\",\n \"def get_data(temp_estimate_source='historic'):\\n\\n print 'get data from files'\\n load = process_load_data(datafoldername+loadfilename_train)\\n load_test = process_load_data(datafoldername+loadfilename_test)\\n temp = process_temp_data(datafoldername+tempfilename_train)\\n holidays = process_holiday_data(datafoldername+holidayfilename)\\n\\n print 'merge load with temp data'\\n X_train_df = load.merge(temp, on='datetime', how='left')\\n X_test_df = load_test.merge(temp, on='datetime', how='left')\\n\\n print 'estimate missing temps'\\n # find rows with missing temperatures\\n missingtemp = X_test_df[X_test_df.isnull().any(axis=1)][['datetime', 'zone_id']].copy()\\n\\n # source estimates for missing periods\\n if temp_estimate_source == 'arima':\\n # use preprocessed arima estimates\\n estimatedtemps = process_arima_temp_data(datafoldername+arimafilename)\\n elif temp_estimate_source == 'actuals':\\n # use actual temperatures - as provided after conclusion of kaggle competition\\n estimatedtemps = process_temp_data(datafoldername+tempfilename_solution)\\n else:\\n # use means of historical temps at same day/time.\\n estimatedtemps = get_estimated_temps(missingtemp[['datetime']].drop_duplicates(), temp)\\n\\n # merge estimates against missing rows, and use to update original dataframe in place\\n replacementtemps = missingtemp.merge(estimatedtemps, left_on='datetime', right_on='datetime', how='left')\\n replace_unknown_temps(X_test_df, replacementtemps)\\n\\n print 'merge in holiday dates'\\n X_train_df = X_train_df.merge(holidays, on='datetime', how='left')\\n X_train_df['holiday'].fillna(0, inplace=True)\\n X_test_df = X_test_df.merge(holidays, on='datetime', how='left')\\n X_test_df['holiday'].fillna(0, inplace=True)\\n\\n print 'add datetime categorical variables'\\n X_train_df = add_datetime_categories(X_train_df)\\n X_test_df = add_datetime_categories(X_test_df)\\n\\n return X_train_df, X_test_df\",\n \"def cloudy_grid_map(**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n cloudy_library = clo.library()\\n model_number_matrix,grid_table = cloudy_library._restore_grid_table(grid_ext=p.grid_ext)\\n # print(len(grid_table))\\n # print(len(grid_table)/len(np.unique(grid_table.nH)))\\n\\n grid_table = grid_table.fillna(-10)\\n grid_table['DTM'] = np.round(grid_table['DTM'] * 10.) / 10.\\n grid_table['NH'] = np.round(grid_table['NH'] * 10.) / 10.\\n\\n # print(grid_table.DTM[np.isnan(grid_table['DTM'])])\\n # print(grid_table.NH[np.isnan(grid_table['NH'])])\\n # print(grid_table.FUV[np.isnan(grid_table['FUV'])])\\n # print(grid_table.nH[np.isnan(grid_table.nH)])\\n # print(grid_table.Z[np.isnan(grid_table.Z)])\\n\\n print('nHs: ',np.unique(grid_table.nH))\\n print('DTMs: ',np.unique(grid_table.DTM))\\n print('FUVs: ',np.unique(grid_table.FUV))\\n print('NHs: ',np.unique(grid_table.NH))\\n print('Zs: ',np.unique(grid_table.Z))\\n\\n fig,ax = plt.subplots(figsize=(8,5))\\n\\n key1, key2, key3 = list(p.cloudy_param.keys())[0],list(p.cloudy_param.keys())[1],list(p.cloudy_param.keys())[2]\\n value1, value2, value3 = list(p.cloudy_param.values())[0],list(p.cloudy_param.values())[1],list(p.cloudy_param.values())[2]\\n\\n # Decide on what goes on x and y axis\\n cloudy_parameters = np.array(['NH','FUV','nH','Z','DTM'])\\n x_index = cloudy_parameters[(cloudy_parameters != key1) &\\\\\\n (cloudy_parameters != key2) &\\\\\\n (cloudy_parameters != key3)][0]\\n y_index = cloudy_parameters[(cloudy_parameters != key1) &\\\\\\n (cloudy_parameters != key2) &\\\\\\n (cloudy_parameters != key3)][1]\\n print(x_index,y_index)\\n # Cut in grid table\\n grid_table_cut = grid_table.iloc[np.where((grid_table[key1].values == value1) & \\\\\\n (grid_table[key2].values == value2) & \\\\\\n (grid_table[key3].values == value3))[0]]\\n\\n x, y = grid_table_cut[x_index].values, grid_table_cut[y_index].values\\n X, Y = np.meshgrid(np.unique(grid_table_cut[x_index].values), np.unique(grid_table_cut[y_index].values))\\n\\n # Plot line ratio?\\n if '_' in p.line:\\n L1 = grid_table_cut[p.line.split('_')[0]].values\\n L2 = grid_table_cut[p.line.split('_')[1]].values\\n L2[L2 == 0] = 1e9\\n line_lum = (L1/L2).astype(float)\\n vmin = np.min(np.log10(line_lum[L2 < 1e9]))\\n\\n else:\\n line_lum = grid_table_cut[p.line].values.astype(float)\\n vmin = np.min(np.log10(line_lum[line_lum > 0]))\\n\\n\\n # ########## Patching the grid !!\\n # line_lum[np.isnan(line_lum)] = -1 # what are these?\\n # # 0 values: not sure if we have any?\\n # # Negative numbers: missing grid point\\n # i_missing = np.where(line_lum <= 0)[0]\\n # line_lum[line_lum == 0] = np.min(line_lum[line_lum > 0])\\n # while len(i_missing) > 0:\\n # print(i_missing)\\n # lum = np.log10(line_lum)\\n # for i in i_missing:\\n # # print(lum[i-1],lum[i+1])\\n # try: \\n # lum[i] = (lum[i-1] + lum[i+1])/ 2\\n # except:\\n # pass\\n # # print('he',np.isnan(lum[i]))\\n # if np.isnan(lum[i]):\\n # try:\\n # lum[i] = lum[i-1] \\n # except:\\n # pass\\n # if np.isnan(lum[i]):\\n # try:\\n # lum[i] = lum[i+1] \\n # except:\\n # pass \\n # line_lum[i] = 10.**lum[i]\\n # # print(i,lum[i])\\n # i_missing = np.where(line_lum < 0)[0]\\n # ########## End of patching\\n \\n lum = np.log10(line_lum)\\n lum = lum.reshape([len(np.unique(x)), len(np.unique(y))]).T\\n\\n\\n # pdb.set_trace()\\n print(p.zlim)\\n if p.zlim:\\n print(p.zlim)\\n lum[lum < p.zlim[0]] = p.zlim[0]\\n lum[lum > p.zlim[1]] = p.zlim[1]\\n cf = ax.contourf(X,Y, lum, cmap=\\\"jet\\\", vmin=p.zlim[0], vmax=p.zlim[1], lw=0, rstride=1, cstride=1,alpha=0.8, levels=20)\\n else:\\n cf = ax.contourf(X,Y, lum, cmap=\\\"jet\\\", vmin=vmin, lw=0, rstride=1, cstride=1,alpha=0.8)\\n # print(lum)\\n ax.set_xlabel('\\\\n\\\\n' + getlabel('l'+x_index))\\n ax.set_ylabel('\\\\n\\\\n' + getlabel('l'+y_index))\\n\\n ax.set_xlim([np.min(X),np.max(X)])\\n ax.set_ylim([np.min(Y),np.max(Y)])\\n\\n plt.colorbar(cf)\\n\\n plt.tight_layout()\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'look-up/'): os.mkdir(p.d_plot + 'look-up/') \\n plt.savefig(p.d_plot + 'look-up/cloudy_grid_map_%s%s%s.%s' % (p.line, p.grid_ext, p.ext, p.format), format=p.format, dpi=300)\",\n \"def READ_DEFINE_AND_PROCESS_EVERYTHING(basedir, in__dir):\\n\\n import sys, ast, multiprocessing\\n import pandas as pd\\n import numpy as np\\n\\n # Output dictionanry\\n dc = {}\\n \\n # Read command line arguments\\n try:\\n y_var = dc['y_var'] = str(sys.argv[1]) \\n y_area = dc['y_area'] = str(sys.argv[2])\\n experiment = dc['experiment'] = str(sys.argv[3])\\n X_source = dc['X_source'] = str(sys.argv[4]) \\n except:\\n y_var = dc['y_var'] = 'T2M'\\n y_area = dc['y_area'] = 'scandi'\\n experiment = dc['experiment'] = 'CONTROL'\\n X_source = dc['X_source'] = 'ERA-20C'\\n \\n # Define details \\n if(experiment=='CUTFIRSTYRS'): \\n yr1 = dc['yr1'] = '1945' \\n else:\\n yr1 = dc['yr1'] = '1915' #'1900' \\n \\n yr2 = dc['yr2'] = '2010'\\n \\n \\n rstate = dc['rstate'] = 70 \\n n_folds = dc['n_folds'] = 5\\n p_smpl = dc['p_smpl'] = 0.50\\n p_feat = dc['p_feat'] = 0.33\\n n_smpls = dc['n_smpls'] = 1000\\n tst_len = dc['tst_len'] = 25\\n\\n ncomps_sst = dc['ncomps_sst'] = 5\\n ncomps_snc = dc['ncomps_snc'] = 3\\n ncomps_gpt = dc['ncomps_gpt'] = 3\\n\\n if(experiment=='NO_LAGS'):\\n lags_sst = dc['lags_sst'] = (1,)\\n lags_snc = dc['lags_snc'] = (1,)\\n lags_gpt = dc['lags_gpt'] = (1,)\\n else:\\n lags_sst = dc['lags_sst'] = (1,2,3,4,5)\\n lags_snc = dc['lags_snc'] = (1,2)\\n lags_gpt = dc['lags_gpt'] = (1,2)\\n \\n \\n \\n n_jobs = dc['n_jobs'] = np.min([28, int(0.9*(multiprocessing.cpu_count()))])\\n seasons = dc['seasons'] = ('DJF', 'MAM' ,'JJA', 'SON')\\n\\n # Define training and test periods\\n all_yrs = dc['all_yrs'] = list(np.arange(int(yr1),int(yr2)+1))\\n tst_yrs = dc['tst_yrs'] = all_yrs[-tst_len:] \\n trn_yrs = dc['trn_yrs'] = list(np.array(all_yrs)[~np.isin(all_yrs,tst_yrs)])\\n\\n # Define a skeleton for naming output files \\n basename = dc['basename'] = 'fittings_'+experiment+'_HadCRUT4-'+y_var+ \\\\\\n '_nsmpls'+str(n_smpls)+'_ntestyrs'+str(tst_len)+ \\\\\\n '_'+X_source+'-SST'+str(ncomps_sst)+'-'+str(lags_sst[-1])+ \\\\\\n '_'+X_source+'-GPT'+str(ncomps_gpt)+'-'+str(lags_gpt[-1])+ \\\\\\n '_'+yr1+'-'+yr2+'_'+y_area\\n\\n # Variables, form: 'name_of_variable': ['domain', n_comps, lags, year_range]\\n X_var_definitions = dc['X_var_definitions'] = {\\n 'SST': ['global', ncomps_sst, lags_sst, trn_yrs, all_yrs, X_source],\\n 'GPT': ['norhem', ncomps_gpt, lags_gpt, trn_yrs, all_yrs, X_source],\\n #'SNC': ['norhem', ncomps_snc, lags_snc, trn_yrs, all_yrs, X_source],\\n }\\n \\n # Optional variables from https://climexp.knmi.nl/, form: 'name_of_index': [lags]\\n X_clxp_definitions = dc['X_clxp_definitions'] = {\\n #'M1i':(1,), 'M2i':(1,), 'M3i':(1,), \\n #'M4i':(1,), 'M5i':(1,), 'M6i':(1,),\\n #'NAO':(1,), 'NINO12':(1,), 'NINO3':(1,), 'NINO34':(1,), 'NINO4',:(1,),\\n #'AMO1':(1,), 'AMO2':(1,), 'PDO1':(1,), 'PDO2':(1,), 'SOI',:(1,),\\n }\\n\\n # Read and preprocess the predictand data using xarray etc.\\n y_eur, Y, cl, tr = dc['y_eur'], dc['Y'], dc['Y_clim'], dc['Y_trend'] = \\\\\\n read_manipulate_Y_data(y_var, in__dir, {}, {}, all_yrs, all_yrs, y_area)\\n\\n # Read and preprocess the raw predictor data using xarray etc.\\n X_vars, cl, tr = dc['X_vars'], dc['X_clim'], dc['X_trnd'] = \\\\\\n read_manipulate_X_data(in__dir, X_var_definitions, {}, {})\\n\\n\\n if(experiment=='INCLPERSIS'): \\n include_persistence=True\\n else:\\n include_persistence=False\\n\\n # Compress raw data with PCA, apply lagging, and create a Pandas dataframe \\n X,p,ei,er = dc['X'], dc['X_PCAs'], dc['X_EIGs'], dc['X_ERRs'] = prepare_X_array(Y, \\n y_var, X_vars, {}, X_var_definitions, X_clxp_definitions, include_persistence=include_persistence)\\n\\n if(experiment=='FOLLAND'):\\n # Folland et al. 2012, Hall et al. 2017\\n for i,vrb in enumerate(X.columns):\\n if((vrb[0:4] == 'SST1')|(vrb[0:4] == 'sst1')):\\n X[vrb] = StandardScaler().fit_transform(X[vrb][:,np.newaxis])\\n X[vrb][ np.abs(X[vrb]) < 1 ] = 0\\n X[vrb][ X[vrb] < -1 ] = -1\\n X[vrb][ X[vrb] > 1.75 ] = 0\\n X[vrb][ X[vrb] > 1 ] = 1\\n print(X[vrb])\\n\\n # Extract variable names\\n vrbl_names = dc['vrbl_names'] = X.columns\\n \\n return dc\",\n \"def autoset_numerical_parameters():\\n testenv = env(\\n Ndim=N_DIMS,\\n lambda_over_dx=LAMBDA_OVER_DX,\\n R_dt=R_DT,\\n norm_Poisson=NORM_POISSON,\\n Ngrid=N_GRID,\\n Nhits=N_HITS,\\n dummy=True,\\n )\\n if STOP_t is None:\\n if N_DIMS == 1:\\n stop_t = int(round(4 * testenv.N))\\n else:\\n if testenv.mu0_Poisson < 1e-3:\\n stop_t = 10 * testenv.N ** N_DIMS\\n elif testenv.mu0_Poisson < 1:\\n stop_t = int(round(5 * 10 ** N_DIMS * LAMBDA_OVER_DX / np.sqrt(testenv.mu0_Poisson)))\\n else:\\n stop_t = int(round(5 * 10 ** N_DIMS * LAMBDA_OVER_DX))\\n else:\\n stop_t = STOP_t\\n\\n if N_RUNS is None:\\n # predefined for REL_TOL = 0.01\\n if N_DIMS == 1:\\n Nruns = 16000\\n elif N_DIMS == 2:\\n Nruns = 6400\\n elif N_DIMS == 3:\\n Nruns = 25600\\n elif N_DIMS == 4:\\n Nruns = 102400\\n else:\\n raise Exception(\\\"Nruns not pre-defined for N_DIMS > 4\\\")\\n Nruns = int(Nruns * (0.01 / REL_TOL) ** 2)\\n else:\\n Nruns = N_RUNS\\n\\n if MAX_N_RUNS is None:\\n max_Nruns = MAX_N_RUNS\\n else:\\n max_Nruns = 10 * Nruns\\n\\n if ADAPTIVE_N_RUNS or WITH_MPI:\\n Nruns = int(N_PARALLEL * (np.ceil(Nruns / N_PARALLEL))) # make it multiple of N_PARALLEL\\n max_Nruns = int(N_PARALLEL * (np.ceil(max_Nruns / N_PARALLEL))) # make it multiple of N_PARALLEL\\n\\n return testenv.N, testenv.Nhits, stop_t, Nruns, max_Nruns, testenv.mu0_Poisson\",\n \"def ts_fit(series: TimeSeries) -> TimeSeries:\\n pass\",\n \"def run_main(sst, ft_qv, use_NT):\\n\\n dtout=10. #minutes\\n end_time=8*24. #hours\\n del_time=dtout*60. #seconds\\n end_time=end_time*3600. #seconds\\n #sst=297\\n D=5.e-6 #s-1\\n U=7 #m/s\\n psfc=100. #kPa\\n qsfc=tf.qs_tp(sst,psfc)\\n ft_intercept = 292 #K\\n ft_gamma = 6.e-3 #K/m\\n #ft_qv = 2.e-3\\n k=0.2 #entrainment efficiency\\n Cd = 1.e-3 #drag coefficient\\n tspan = np.arange(0.,end_time,del_time)\\n vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start\\n the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,\\n qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT\\n the_tup=make_tuple(the_tup,'coeffs')\\n output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))\\n result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])\\n\\n # save time/computation by only doing calculations for the last timestep (equilibrium)\\n result['time']=tspan[-1]/3600./24. #days\\n result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]\\n result['delqv'] = ft_qv - result['qv'].iloc[-1]\\n result['LCL'] = calc_lcl(result.iloc[-1], psfc)\\n result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)\\n result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)\\n result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)\\n \\n # decide how to calculate entrainment\\n the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]\\n if use_NT:\\n result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1], \\n result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])\\n else:\\n result['went']=calc_went(result.iloc[-1],the_tup)\\n\\n result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)\\n\\n with open('dumpmodel.csv','w') as f:\\n result.to_csv(f,index=False)\\n \\n return None\",\n \"def parameter_forecast_plot(model_obj,time_index,start,end,num_samples = 100,cached_samples=None,col_labels = ['P','PET','Lag-1 Q','Lag-1 P','Seasonal','P$^2$','Constant']):\\n \\n f = plt.figure(figsize = (8,10))\\n num_components = len(col_labels)\\n gs = gridspec.GridSpec(8+2*num_components,6)\\n ax0 = plt.subplot(gs[-8:-6,:])\\n ax1 = plt.subplot(gs[-6::,:])\\n col_labels = ['P','PET','Lag-1 Q','Lag-1 P','Seasonal','P$^2$','Constant']\\n ffbs = model_obj # 120 is French Broad River at Blantyre, NC\\n if cached_samples is None:\\n samples = ffbs.backward_sample(num_samples=num_samples)\\n else: \\n samples = cached_samples\\n for i in range(7):\\n ax_new = plt.subplot(gs[2*i:2*i+2,:])\\n\\n upper = np.percentile(samples[start:end,i,:],75,axis = 1)\\n mid = np.percentile(samples[start:end,i,:],50,axis = 1)\\n lower = np.percentile(samples[start:end,i,:],25,axis = 1)\\n\\n ax_new.plot(time_index[start:end],mid,color='k')\\n ax_new.fill_between(time_index[start:end],upper,lower,color='0.8')\\n ax_new.tick_params(labelbottom=False,direction='in')\\n ax_new.text(0.02, 0.82,col_labels[i],\\n horizontalalignment='left',\\n verticalalignment='center',transform=ax_new.transAxes)\\n\\n ax1.plot(time_index[start:end],ffbs.f[start:end],color='k',label='1-step forecast')\\n ax1.plot(time_index[start:end],ffbs.Y[start:end],color='k',linestyle='',marker='+',\\n markersize = 10,label='Observed streamflow')\\n\\n ax1.fill_between(time_index[start:end],\\n np.squeeze(ffbs.f[start:end] + 2*ffbs.Q[start:end,0]),\\n np.squeeze(ffbs.f[start:end] - 2*ffbs.Q[start:end,0]),color='0.8',\\n label = 'Forecast $\\\\pm 2V_t$')\\n ax1.tick_params(direction='in')\\n ax1.legend(loc='upper right',ncol=1,frameon=True)\\n #ax1.set_ylabel('Standardized streamflow')\\n ax1.set_xlabel('Date',fontsize=16)\\n ax1.get_yaxis().set_label_coords(-0.1,0.5)\\n ax1.text(0.02, 0.92,'Standardized streamflow',\\n horizontalalignment='left',\\n verticalalignment='center',transform=ax1.transAxes,)\\n ax0.plot(time_index[start:end],ffbs.s[start:end],color='k')\\n ax0.text(0.02, 0.82,'$E[V_t]$',\\n horizontalalignment='left',\\n verticalalignment='center',transform=ax0.transAxes,)\\n ax0.get_yaxis().set_label_coords(-0.1,0.5)\\n return f,samples\",\n \"def create_experiment_dataset(df_degrees, params):\\n \\n print ('[*] Creating Experiment Dataset')\\n if params.strategy == 'random':\\n degree_thr = np.array([float(x)/100.0 for x in np.arange(10, 101, params.step_size)])\\n else: \\n degree_thr = np.array([float(x)/100.0 for x in np.logspace(-2, 2, params.step_size, dtype=float)])\\n\\n n_users_thr, n_items_thr = [], []\\n user_degree_thr, item_degree_thr = [], []\\n n_users_max = len(df_degrees['from'].unique())\\n n_items_max = len(df_degrees['to'].unique())\\n \\n for threshold in degree_thr: \\n if params.strategy == 'upper_degree_centrality':\\n df_temp = df_degrees[df_degrees['degree_centrality_user'] > threshold]\\n elif params.strategy == 'lower_degree_centrality':\\n df_temp = df_degrees[df_degrees['degree_centrality_user'] < threshold] \\n elif params.strategy == 'random':\\n df_temp = utils.filter_dataset(df_degrees, degree_user=threshold, degree_item=1, strategy='random')\\n\\n n_users = len(df_temp['from'].unique()) \\n n_users_thr.append(n_users)\\n user_degree_thr.append(threshold)\\n if n_users == 0 and params.strategy == 'upper_degree_centrality':\\n break\\n if n_users == n_users_max and params.strategy == 'lower_degree_centrality':\\n break \\n \\n for threshold in degree_thr: \\n if params.strategy == 'upper_degree_centrality':\\n df_temp = df_degrees[df_degrees['degree_centrality_item'] > threshold]\\n elif params.strategy == 'lower_degree_centrality':\\n df_temp = df_degrees[df_degrees['degree_centrality_item'] < threshold] \\n elif params.strategy == 'random':\\n df_temp = utils.filter_dataset(df_degrees, degree_user=1, degree_item=threshold, strategy='random')\\n\\n n_items = len(df_temp['to'].unique()) \\n n_items_thr.append(n_items)\\n item_degree_thr.append(threshold)\\n if n_items == 0 and params.strategy == 'upper_degree_centrality':\\n break\\n if n_items == n_items_max and params.strategy == 'lower_degree_centrality':\\n break \\n\\n arr = []\\n for u, u_thr in enumerate(user_degree_thr):\\n for i, i_thr in enumerate(item_degree_thr):\\n arr.append([u_thr, n_users_thr[u], i_thr, n_items_thr[i]])\\n df_exp = pd.DataFrame(arr, columns=['degree_centrality_users', 'n_users', 'degree_centrality_items', 'n_items'])\\n cond1 = df_exp['n_users'] > 5\\n cond2 = df_exp['n_items'] > 5\\n df_exp = df_exp[cond1 & cond2]\\n df_exp.to_csv(os.path.join('.', 'Variables', 'df_exp.csv'), sep=';', index=None)\\n print ('[+] Experiment Dataset Created')\\n\\n return df_exp\",\n \"def heston(base_price: int = 1,\\n base_volume: int = 1,\\n start_date: str = '2010-01-01',\\n start_date_format: str = '%Y-%m-%d',\\n times_to_generate: int = 1000,\\n time_frame: str = '1h',\\n params: 'ModelParameters' = None) -> 'pd.DataFrame':\\n\\n data_frame = generate(\\n price_fn=lambda p: heston_model_levels(p)[0],\\n base_price=base_price,\\n base_volume=base_volume,\\n start_date=start_date,\\n start_date_format=start_date_format,\\n times_to_generate=times_to_generate,\\n time_frame=time_frame,\\n params=params\\n )\\n return data_frame\",\n \"def _get_input_data(self, var, start_date, end_date):\\n logging.info(self._print_verbose(\\\"Getting input data:\\\", var))\\n # Pass numerical constants as is.\\n if isinstance(var, (float, int)):\\n return var\\n # aospy.Var objects remain.\\n # Pressure handled specially due to complications from sigma vs. p.\\n elif var.name in ('p', 'dp'):\\n data = self._get_pressure_vals(var, start_date, end_date)\\n if self.dtype_in_vert == internal_names.ETA_STR:\\n return self._to_desired_dates(data)\\n return data\\n # Get grid, time, etc. arrays directly from model object\\n elif var.name in (internal_names.LAT_STR, internal_names.LON_STR,\\n internal_names.TIME_STR, internal_names.PLEVEL_STR,\\n internal_names.PK_STR, internal_names.BK_STR,\\n internal_names.SFC_AREA_STR):\\n data = getattr(self.model, var.name)\\n else:\\n cond_pfull = ((not hasattr(self, internal_names.PFULL_STR))\\n and var.def_vert and\\n self.dtype_in_vert == internal_names.ETA_STR)\\n data = self.data_loader.load_variable(var, start_date, end_date,\\n self.time_offset,\\n **self.data_loader_attrs)\\n name = data.name\\n data = self._add_grid_attributes(data.to_dataset(name=data.name))\\n data = data[name]\\n if cond_pfull:\\n try:\\n self.pfull_coord = data[internal_names.PFULL_STR]\\n except KeyError:\\n pass\\n # Force all data to be at full pressure levels, not half levels.\\n bool_to_pfull = (self.dtype_in_vert == internal_names.ETA_STR and\\n var.def_vert == internal_names.PHALF_STR)\\n if bool_to_pfull:\\n data = utils.vertcoord.to_pfull_from_phalf(data,\\n self.pfull_coord)\\n if var.def_time:\\n # Restrict to the desired dates within each year.\\n if self.dtype_in_time != 'av':\\n return self._to_desired_dates(data)\\n else:\\n return data\",\n \"def sample_multirange_empirical_variogram(dh: np.ndarray, gsd: float = None, coords: np.ndarray = None,\\n nsamp: int = 10000, range_list: list = None, nrun: int = 1, nproc: int = 1,\\n **kwargs) -> pd.DataFrame:\\n # checks\\n dh = dh.squeeze()\\n if coords is None and gsd is None:\\n raise TypeError('Must provide either coordinates or ground sampling distance.')\\n elif gsd is not None and dh.ndim == 1:\\n raise TypeError('Array must be 2-dimensional when providing only ground sampling distance')\\n elif coords is not None and dh.ndim != 1:\\n raise TypeError('Coordinate array must be provided with 1-dimensional input array')\\n elif coords is not None and (coords.shape[0] != 2 and coords.shape[1] != 2):\\n raise TypeError('One dimension of the coordinates array must be of length equal to 2')\\n\\n # defaulting to xx and yy if those are provided\\n if coords is not None:\\n if coords.shape[0] == 2 and coords.shape[1] != 2:\\n coords = np.transpose(coords)\\n else:\\n x, y = np.meshgrid(np.arange(0, dh.shape[0] * gsd, gsd), np.arange(0, dh.shape[1] * gsd, gsd))\\n coords = np.dstack((x.flatten(), y.flatten())).squeeze()\\n dh = dh.flatten()\\n\\n # COMMENTING: custom binning is not supported by skgstat yet...\\n # if no range list is specified, define a default one based on the spatial extent of the data and its resolution\\n # if 'bin_func' not in kwargs.keys():\\n # if range_list is None:\\n #\\n # # define max range as half the maximum distance between coordinates\\n # max_range = np.sqrt((np.max(coords[:,0])-np.min(coords[:,0]))**2+(np.max(coords[:,1])-np.min(coords[:,1]))**2)/2\\n #\\n # # get the ground sampling distance\\n # if gsd is None:\\n # est_gsd = np.abs(coords[0,0] - coords[0,1])\\n # else:\\n # est_gsd = gsd\\n #\\n # # define ranges as multiple of the resolution until they get close to the maximum range\\n # range_list = []\\n # new_range = gsd\\n # while new_range < max_range/10:\\n # range_list.append(new_range)\\n # new_range *= 10\\n # range_list.append(max_range)\\n #\\n # else:\\n # if range_list is not None:\\n # print('Both range_list and bin_func are defined for binning: defaulting to bin_func')\\n\\n # default value we want to use (kmeans is failing)\\n if 'bin_func' not in kwargs.keys():\\n kwargs.update({'bin_func': 'even'})\\n if 'n_lags' not in kwargs.keys():\\n kwargs.update({'n_lags': 100})\\n\\n # estimate variogram\\n if nrun == 1:\\n # subsetting\\n dh_sub, coords_sub = random_subset(dh, coords, nsamp)\\n # getting empirical variogram\\n print(dh_sub.shape)\\n print(coords_sub.shape)\\n df = get_empirical_variogram(dh=dh_sub, coords=coords_sub, **kwargs)\\n df['exp_sigma'] = np.nan\\n\\n else:\\n\\n # multiple run only work for an even binning function for now (would need a customized binning not supported by skgstat)\\n if kwargs.get('bin_func') is None:\\n raise ValueError('Binning function must be \\\"even\\\" when doing multiple runs.')\\n\\n # define max range as half the maximum distance between coordinates\\n max_range = np.sqrt((np.max(coords[:, 0])-np.min(coords[:, 0]))**2 +\\n (np.max(coords[:, 1])-np.min(coords[:, 1]))**2)/2\\n # also need a cutoff value to get the exact same bins\\n if 'maxlag' not in kwargs.keys():\\n kwargs.update({'maxlag': max_range})\\n\\n # TODO: somewhere here we could think of adding random sampling without replacement\\n if nproc == 1:\\n print('Using 1 core...')\\n list_df_nb = []\\n for i in range(nrun):\\n dh_sub, coords_sub = random_subset(dh, coords, nsamp)\\n df = get_empirical_variogram(dh=dh_sub, coords=coords_sub, **kwargs)\\n df['run'] = i\\n list_df_nb.append(df)\\n else:\\n print('Using '+str(nproc) + ' cores...')\\n list_dh_sub = []\\n list_coords_sub = []\\n for i in range(nrun):\\n dh_sub, coords_sub = random_subset(dh, coords, nsamp)\\n list_dh_sub.append(dh_sub)\\n list_coords_sub.append(coords_sub)\\n\\n pool = mp.Pool(nproc, maxtasksperchild=1)\\n argsin = [{'dh': list_dh_sub[i], 'coords': list_coords_sub[i], 'i':i, 'max_i':nrun} for i in range(nrun)]\\n list_df = pool.map(partial(wrapper_get_empirical_variogram, **kwargs), argsin, chunksize=1)\\n pool.close()\\n pool.join()\\n\\n list_df_nb = []\\n for i in range(10):\\n df_nb = list_df[i]\\n df_nb['run'] = i\\n list_df_nb.append(df_nb)\\n df = pd.concat(list_df_nb)\\n\\n # group results, use mean as empirical variogram, estimate sigma, and sum the counts\\n df_grouped = df.groupby('bins', dropna=False)\\n df_mean = df_grouped[['exp']].mean()\\n df_sig = df_grouped[['exp']].std()\\n df_count = df_grouped[['count']].sum()\\n df_mean['bins'] = df_mean.index.values\\n df_mean['exp_sigma'] = df_sig['exp']\\n df_mean['count'] = df_count['count']\\n df = df_mean\\n\\n return df\",\n \"def FE_start_end_date_time_features(smalldf, startTime, endTime, splitter_date_string=\\\"/\\\",splitter_hour_string=\\\":\\\"):\\r\\n smalldf = smalldf.copy()\\r\\n add_cols = []\\r\\n date_time_variable_flag = False\\r\\n if smalldf[startTime].dtype in ['datetime64[ns]','datetime16[ns]','datetime32[ns]']:\\r\\n print('%s variable is a date-time variable' %startTime)\\r\\n date_time_variable_flag = True\\r\\n if date_time_variable_flag:\\r\\n view_days = 'processing'+startTime+'_elapsed_days'\\r\\n smalldf[view_days] = (smalldf[endTime] - smalldf[startTime]).astype('timedelta64[s]')/(60*60*24)\\r\\n smalldf[view_days] = smalldf[view_days].astype(int)\\r\\n add_cols.append(view_days)\\r\\n view_time = 'processing'+startTime+'_elapsed_time'\\r\\n smalldf[view_time] = (smalldf[endTime] - smalldf[startTime]).astype('timedelta64[s]').values\\r\\n add_cols.append(view_time)\\r\\n else:\\r\\n start_date = 'processing'+startTime+'_start_date'\\r\\n smalldf[start_date] = smalldf[startTime].map(lambda x: x.split(\\\" \\\")[0])\\r\\n add_cols.append(start_date) \\r\\n try:\\r\\n start_time = 'processing'+startTime+'_start_time'\\r\\n smalldf[start_time] = smalldf[startTime].map(lambda x: x.split(\\\" \\\")[1])\\r\\n add_cols.append(start_time)\\r\\n except:\\r\\n ### there is no hour-minutes part of this date time stamp field. You can just skip it if it is not there\\r\\n pass\\r\\n end_date = 'processing'+endTime+'_end_date'\\r\\n smalldf[end_date] = smalldf[endTime].map(lambda x: x.split(\\\" \\\")[0])\\r\\n add_cols.append(end_date)\\r\\n try:\\r\\n end_time = 'processing'+endTime+'_end_time'\\r\\n smalldf[end_time] = smalldf[endTime].map(lambda x: x.split(\\\" \\\")[1])\\r\\n add_cols.append(end_time)\\r\\n except:\\r\\n ### there is no hour-minutes part of this date time stamp field. You can just skip it if it is not there\\r\\n pass\\r\\n view_days = 'processing'+startTime+'_elapsed_days'\\r\\n smalldf[view_days] = (pd.to_datetime(smalldf[end_date]) - pd.to_datetime(smalldf[start_date])).values.astype(int)\\r\\n add_cols.append(view_days)\\r\\n try:\\r\\n view_time = 'processing'+startTime+'_elapsed_time'\\r\\n smalldf[view_time] = (pd.to_datetime(smalldf[end_time]) - pd.to_datetime(smalldf[start_time])).astype('timedelta64[s]').values\\r\\n add_cols.append(view_time)\\r\\n except:\\r\\n ### In some date time fields this gives an error so skip it in that case\\r\\n pass\\r\\n #### The reason we chose endTime here is that startTime is usually taken care of by another library. So better to do this alone.\\r\\n year = 'processing'+endTime+'_end_year'\\r\\n smalldf[year] = smalldf[end_date].map(lambda x: str(x).split(splitter_date_string)[0]).values\\r\\n add_cols.append(year)\\r\\n #### The reason we chose endTime here is that startTime is usually taken care of by another library. So better to do this alone.\\r\\n month = 'processing'+endTime+'_end_month'\\r\\n smalldf[month] = smalldf[end_date].map(lambda x: str(x).split(splitter_date_string)[1]).values\\r\\n add_cols.append(month)\\r\\n try:\\r\\n #### The reason we chose endTime here is that startTime is usually taken care of by another library. So better to do this alone.\\r\\n daynum = 'processing'+endTime+'_end_day_number'\\r\\n smalldf[daynum] = smalldf[end_date].map(lambda x: str(x).split(splitter_date_string)[2]).values\\r\\n add_cols.append(daynum)\\r\\n except:\\r\\n ### In some date time fields the day number is not there. If not, just skip it ####\\r\\n pass\\r\\n #### In some date time fields, the hour and minute is not there, so skip it in that case if it errors!\\r\\n try:\\r\\n start_hour = 'processing'+startTime+'_start_hour'\\r\\n smalldf[start_hour] = smalldf[start_time].map(lambda x: str(x).split(splitter_hour_string)[0]).values\\r\\n add_cols.append(start_hour)\\r\\n start_min = 'processing'+startTime+'_start_hour'\\r\\n smalldf[start_min] = smalldf[start_time].map(lambda x: str(x).split(splitter_hour_string)[1]).values\\r\\n add_cols.append(start_min)\\r\\n except:\\r\\n ### If it errors, skip it\\r\\n pass\\r\\n #### Check if there is a weekday and weekends in date time columns using endTime only\\r\\n weekday_num = 'processing'+endTime+'_end_weekday_number'\\r\\n smalldf[weekday_num] = pd.to_datetime(smalldf[end_date]).dt.weekday.values\\r\\n add_cols.append(weekday_num)\\r\\n weekend = 'processing'+endTime+'_end_weekend_flag'\\r\\n smalldf[weekend] = smalldf[weekday_num].map(lambda x: 1 if x in[5,6] else 0)\\r\\n add_cols.append(weekend)\\r\\n #### If everything works well, there should be 13 new columns added by module. All the best!\\r\\n print('%d columns added using start date=%s and end date=%s processing...' %(len(add_cols),startTime,endTime))\\r\\n return smalldf\",\n \"def run(self):\\n self.timestamp['start'] = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\\n\\n for point in self._prepare_grid():\\n graph = self._prepare_graph(**point)\\n env = self._prepare_env(graph, **point)\\n log = self._prepare_logger(graph, env, **point)\\n\\n try:\\n env.run(until=self.runtime)\\n except Exception as e:\\n print(e)\\n log.close()\\n\\n # self.timestamp[grid.hash_grid_point(point)].append(datetime.datetime.now().strftime('%Y%m%dT%H%M%S'))\\n\\n self.timestamp['end'] = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')\",\n \"def create_batch(batch, source, params, dv,\\n t_end=1.3e5, exclude=None, basename='xrb', walltime=96,\\n auto_t_end=True, notes='No notes given', nbursts=20, kgrid=None,\\n nuc_heat=True, setup_test=False,\\n auto_qnuc=False, grid_version=None, qnuc_source='heat',\\n substrate='fe54', substrate_off=True, adapnet_filename=None,\\n bdat_filename=None, params_full=None,\\n numerical_params=None, scratch_file_sys=False):\\n # TODO:\\n # - WRITE ALL PARAM DESCRIPTIONS\\n # - Overhaul/tidy up\\n # - use pd table instead of dicts of arrays\\n\\n print_batch(batch=batch)\\n source = grid_strings.source_shorthand(source=source)\\n mass_ref = 1.4 # reference NS mass (Msun)\\n radius_ref = 10 # default NS radius (km)\\n\\n specified = {'params': params,\\n 'dv': dv,\\n 'numerical_params': numerical_params}\\n\\n if specified['numerical_params'] is None:\\n specified['numerical_params'] = {}\\n\\n config = grid_tools.setup_config(specified=specified, source=source)\\n # TODO: print numerical_params being used\\n\\n if params_full is None:\\n params_expanded, var = expand_params(params=config['params'], dv=config['dv'])\\n params_full = exclude_params(params_expanded=params_expanded, exclude=exclude)\\n\\n n_models = len(params_full['x'])\\n\\n if kgrid is None:\\n print('No kgrid provided. Loading default:')\\n kgrid = grid_analyser.Kgrid(load_lc=False, linregress_burst_rate=True,\\n source=source)\\n\\n params_full['y'] = 1 - params_full['x'] - params_full['z'] # helium-4 values\\n params_full['geemult'] = params_full['mass'] / mass_ref # Gravity multiplier\\n\\n gravities = gravity.get_acceleration_newtonian(r=radius_ref,\\n m=params_full['mass']).value\\n params_full['radius'] = np.full(n_models, radius_ref)\\n params_full['gravity'] = gravities\\n\\n if auto_qnuc:\\n predict_qnuc(params_full=params_full, qnuc_source=qnuc_source,\\n grid_version=grid_version)\\n\\n # ===== Create top grid folder =====\\n batch_model_path = grid_strings.get_batch_models_path(batch, source)\\n grid_tools.try_mkdir(batch_model_path)\\n\\n # Directory to keep MonARCH logs and sbatch files\\n logpath = grid_strings.get_source_subdir(batch_model_path, 'logs')\\n grid_tools.try_mkdir(logpath)\\n\\n # ===== Write parameter table MODELS.txt and NOTES.txt=====\\n write_model_table(n=n_models, params=params_full, path=batch_model_path)\\n filepath = os.path.join(batch_model_path, 'NOTES.txt')\\n with open(filepath, 'w') as f:\\n f.write(notes)\\n\\n # ===== Write jobscripts for submission on clusters =====\\n print_dashes()\\n kepler_jobs.write_both_jobscripts(run0=1, run1=n_models, batch=batch,\\n source=source, basename=basename,\\n path=logpath, walltime=walltime,\\n adapnet_filename=adapnet_filename,\\n bdat_filename=bdat_filename,\\n scratch_file_sys=scratch_file_sys)\\n\\n # ===== Directories and templates for each model =====\\n for i in range(n_models):\\n # ==== Create directory tree ====\\n print_dashes()\\n model = i + 1\\n run_path = grid_strings.get_model_path(model, batch, source, basename=basename)\\n\\n # ==== Create task directory ====\\n grid_tools.try_mkdir(run_path)\\n\\n # ==== Write burn file, set initial composition ====\\n x0 = params_full['x'][i]\\n z0 = params_full['z'][i]\\n kepler_files.write_rpabg(x0, z0, run_path, substrate=substrate)\\n\\n # ==== Create model generator file ====\\n if auto_t_end:\\n mdot = params_full['accrate'][i] * params_full['acc_mult'][i]\\n rate_params = {}\\n for param in ('x', 'z', 'qb', 'mass'):\\n rate_params[param] = params_full[param][i]\\n fudge = 0.5 # extra time to ensure complete final burst\\n tdel = kgrid.predict_recurrence(accrate=mdot, params=rate_params)\\n t_end = (nbursts + fudge) * tdel\\n print(f'Using predicted dt={tdel/3600:.1f} hr')\\n if t_end < 0:\\n print('WARN! negative dt predicted. Defaulting n * 1.5hr')\\n t_end = nbursts * 1.5 * 3600\\n\\n run = i + 1\\n print(f'Writing genfile for xrb{run}')\\n header = f'This generator belongs to model: {source}_{batch}/{basename}{run}'\\n\\n accdepth = params_full['accdepth'][i]\\n if (params_full['x'][i] > 0.0) and (accdepth > 1e20):\\n print(f\\\"!!!WARNING!!!: accdepth of {accdepth:.0e} may be too deep for\\\" +\\n \\\" models accreting hydrogen\\\")\\n print(f'Using accdepth = {accdepth:.1e}')\\n\\n kepler_files.write_genfile(h1=params_full['x'][i], he4=params_full['y'][i],\\n n14=params_full['z'][i], qb=params_full['qb'][i],\\n acc_mult=params_full['acc_mult'][i],\\n qnuc=params_full['qnuc'][i],\\n geemult=params_full['geemult'][i],\\n accrate0=params_full['accrate'][i],\\n accmass=params_full['accmass'][i],\\n accdepth=params_full['accdepth'][i],\\n path=run_path, t_end=t_end, header=header,\\n nuc_heat=nuc_heat, setup_test=setup_test,\\n substrate_off=substrate_off,\\n numerical_params=config['numerical_params'])\",\n \"def stfbss(X,nsources=5,ng=2**5-1,nh=2**9-1,tstep=2**6-1,df=1.0,nfbins=2**10,\\r\\n tftol=1.E-8,normalize=True):\\r\\n \\r\\n \\r\\n #get shape of timeseries matrix, \\r\\n #m=number of channels\\r\\n #tlen=length of timeseries\\r\\n m,maxn=X.shape\\r\\n \\r\\n n=nsources\\r\\n #get number of time bins\\r\\n ntbins=int(float(maxn)/tstep)\\r\\n \\r\\n tfkwargs={'ng':ng,'nh':nh,'df':df,'nfbins':nfbins,'tstep':tstep}\\r\\n \\r\\n \\r\\n #remove dc component from time series and normalize\\r\\n if normalize==True:\\r\\n for ii in range(m):\\r\\n X[ii,:]=X[ii,:]-np.mean(X[ii,:])\\r\\n X[ii,:]=X[ii,:]/X[ii,:].std() \\r\\n \\r\\n #===============================================================================\\r\\n # Whiten data and Compute Whitening matrix\\r\\n #===============================================================================\\r\\n #whiten data to get a unitary matrix with unit variance and zero mean\\r\\n #compute covariance matrix\\r\\n Rxx=(np.dot(X,X.T))/float(maxn)\\r\\n \\r\\n #calculate eigen decomposition\\r\\n [l,u]=np.linalg.eig(Rxx)\\r\\n \\r\\n #sort eigenvalues from smallest to largest assuming largest are sources and \\r\\n #smallest are noise\\r\\n lspot=l.argsort()\\r\\n eigval=l[lspot]\\r\\n eigvec=u[:,lspot]\\r\\n \\r\\n #calculate the noise variance as mean of non-principal components\\r\\n sigman=np.mean(eigval[0:m-n])\\r\\n \\r\\n #compute scaling factor for whitening matrix\\r\\n wscale=1/np.sqrt(eigval[m-n:m]-sigman)\\r\\n \\r\\n #compute whitening matrix\\r\\n W=np.zeros((m,n))\\r\\n for kk in range(n):\\r\\n W[:,kk]=wscale[kk]*eigvec[:,m-n+kk].T\\r\\n W=W.T\\r\\n #compute whitened signal vector. Note the dimensionality is reduced from [mxn] \\r\\n #to [nxn] making the computation simpler.\\r\\n Z=np.dot(W,X)\\r\\n \\r\\n #===============================================================================\\r\\n # Compute Spatial Time Frequency Distribution\\r\\n #===============================================================================\\r\\n \\r\\n stfd=np.zeros((n,n,nfbins,ntbins+1),dtype='complex128')\\r\\n Za=np.array(Z.copy())\\r\\n #compute auto terms\\r\\n for ii in range(n):\\r\\n pswvd,tswvd,fswvd=spwvd(Za[ii].reshape(maxn),**tfkwargs)\\r\\n stfd[ii,ii,:,:]=pswvd\\r\\n \\r\\n #compute cross terms\\r\\n for jj in range(n):\\r\\n for kk in range(jj,n):\\r\\n pswvd,tswvd,fswvd=spwvd([Za[jj].reshape(maxn),Za[kk].reshape(maxn)],\\r\\n **tfkwargs)\\r\\n stfd[jj,kk,:,:]=pswvd\\r\\n stfd[kk,jj,:,:]=pswvd.conj()\\r\\n \\r\\n #===============================================================================\\r\\n # Compute criteria for cross terms \\r\\n #===============================================================================\\r\\n \\r\\n stfdTr=np.zeros((nfbins,ntbins))\\r\\n C=np.zeros((nfbins,ntbins))\\r\\n \\r\\n for ff in range(nfbins):\\r\\n for tt in range(ntbins):\\r\\n #compensate for noise\\r\\n stfd[:,:,ff,tt]=stfd[:,:,ff,tt]-sigman*np.matrix(W)*np.matrix(W.T)\\r\\n #compute the trace\\r\\n stfdTr[ff,tt]=abs(np.trace(stfd[:,:,ff,tt]))\\r\\n #compute mean over entire t-f plane\\r\\n trmean=stfdTr.mean()\\r\\n \\r\\n #find t-f points that meet the criteria\\r\\n fspot,tspot=np.nonzero(stfdTr>trmean)\\r\\n \\r\\n for ll in range(len(fspot)):\\r\\n treig=abs(np.linalg.eig(stfd[:,:,fspot[ll],tspot[ll]])[0])\\r\\n if sum(treig)!=0 and sum(treig)>tftol:\\r\\n C[fspot[ll],tspot[ll]]=max(treig)/sum(treig)\\r\\n else:\\r\\n C[fspot[ll],tspot[ll]]=0\\r\\n \\r\\n #compute gradients and jacobi matrices\\r\\n negjacobi=np.zeros((nfbins,ntbins))\\r\\n smallgrad=np.zeros((nfbins,ntbins))\\r\\n maxpoints=np.zeros((nfbins,ntbins))\\r\\n \\r\\n gradt,gradf=np.gradient(C)\\r\\n Jtt,Jtf=np.gradient(gradt)\\r\\n Jft,Jff=np.gradient(gradf)\\r\\n \\r\\n #get points when L2 of gradient is smaller than tolerance level\\r\\n smallgrad=np.where(np.sqrt(gradt**2+gradf**2)<tftol,1,0)\\r\\n \\r\\n #get points where the Jacobi is negative definite\\r\\n detjacobi=Jtt*Jff-Jtf*Jft\\r\\n negjacobi=np.where(detjacobi>0,1,0)*np.where(Jtt<0,1,0)\\\\\\r\\n *np.where((Jtt+Jff)<0,1,0)\\r\\n \\r\\n maxpoints=smallgrad*negjacobi\\r\\n \\r\\n gfspot,gtspot=np.nonzero(maxpoints)\\r\\n ntfpoints=len(gfspot)\\r\\n \\r\\n if ntfpoints==0:\\r\\n raise ValueError('Found no tf points, relax tolerance')\\r\\n else:\\r\\n print 'Found '+str(ntfpoints)+' t-f points'\\r\\n \\r\\n for rr in range(ntfpoints):\\r\\n if rr==0:\\r\\n Rjd=stfd[:,:,gfspot[rr],gtspot[rr]]\\r\\n else:\\r\\n Rjd=np.concatenate((Rjd,stfd[:,:,gfspot[rr],gtspot[rr]]),axis=1)\\r\\n Rjd=np.array(Rjd)\\r\\n \\r\\n #===============================================================================\\r\\n # Calculate Joint Diagonalization\\r\\n #===============================================================================\\r\\n #get size of array of matrices to be diagonalized\\r\\n mtf,nm=Rjd.shape #mtf is number of t-f points, nm is number of matrices\\r\\n #set up some initial parameters\\r\\n V=np.eye(mtf)\\r\\n \\r\\n #update boolean\\r\\n encore=True \\r\\n #Total number of rotations\\r\\n updates=0\\r\\n sweep=0\\r\\n #print 'Computing Joint Diagonalization'\\r\\n # Joint diagonalization proper\\r\\n # ============================\\r\\n while encore:\\r\\n #reset some parameters\\r\\n encore=False\\r\\n sweep+=1 \\r\\n upds=0\\r\\n Vkeep=V\\r\\n for p in range(mtf):\\r\\n \\r\\n for q in range(p+1,mtf):\\r\\n #set up indices\\r\\n qi=np.arange(start=q,stop=nm,step=mtf)\\r\\n pi=np.arange(start=p,stop=nm,step=mtf)\\r\\n # computation of Givens angle\\r\\n g=np.array([Rjd[p,pi]-Rjd[q,qi],Rjd[p,qi],Rjd[q,pi]])\\r\\n gg=np.real(np.dot(g,g.T))\\r\\n ton=gg[0,0]-gg[1,1] \\r\\n toff=gg[0,1]+gg[1,0]\\r\\n theta=0.5*np.arctan2(toff,ton+np.sqrt(ton**2+toff**2))\\r\\n # Givens update\\r\\n if abs(theta) > tftol:\\r\\n encore=True\\r\\n upds+=1\\r\\n c=np.cos(theta) \\r\\n s=np.sin(theta)\\r\\n G=np.matrix([[c,-s],[s,c]])\\r\\n pair =np.array([p,q])\\r\\n V[:,pair]=V[:,pair]*G\\r\\n Rjd[pair,:]=G.T*Rjd[pair,:]\\r\\n Rjd[:,np.concatenate([pi,qi])]=np.append(\\r\\n c*Rjd[:,pi]+s*Rjd[:,qi],\\r\\n -s*Rjd[:,pi]+c*Rjd[:,qi],axis=1)\\r\\n updates+=upds\\r\\n print 'Updated '+str(updates)+' times.'\\r\\n \\r\\n #compute estimated signal matrix\\r\\n Se=np.dot(V.T,Z)\\r\\n #compute estimated mixing matrix\\r\\n Ae=np.dot(np.linalg.pinv(W),V)\\r\\n \\r\\n return Se,Ae\",\n \"def groups_of_train_test_set(df, config, fbprophet=None):\\n \\n tvar = config.variables['tvar']\\n xvar = config.variables['xvar'] + config.variables['xvar_derived']\\n \\n begin_date = config.timestamps['begin_date']\\n end_date = config.timestamps['end_date']\\n deltat = config.timestamps['deltat']\\n\\n begin_date = datetime.datetime.strptime(begin_date, \\\"%Y-%m-%d %H:%M:%S\\\")\\n end_date = datetime.datetime.strptime(end_date, \\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n \\n # Check if ustar threshold is provided for year of interest\\n if config.data['ustar']==True:\\n if not begin_date.year in config.data['ustar_map'].keys():\\n raise ValueError('{} is missing from config/data/ustar_map'.format(begin_date.year))\\n if not end_date.year in config.data['ustar_map'].keys():\\n raise ValueError('{} is missing from config/data/ustar_map'.format(end_date.year))\\n\\n\\n if (end_date - begin_date).days < deltat:\\n raise ValueError(\\\"Time difference in days between begin and end date\\\" + \\n \\\"must be greater than deltat.\\\")\\n\\n\\n df = df.loc[df[tvar] <= end_date]\\n\\n number_of_train_test_sets = int((end_date - begin_date).total_seconds()/\\\\\\n datetime.timedelta(deltat).total_seconds())\\n\\n begin_test_timestamp = begin_date\\n \\n\\n test_df = []\\n train_df = [] \\n for i in range(number_of_train_test_sets):\\n if i == number_of_train_test_sets-1:\\n end_test_timestamp = end_date\\n else:\\n end_test_timestamp = None\\n\\n\\n i_test_set, i_train_set, end_test_timestamp =\\\\\\n _train_test_split(df.copy(), config,\\n begin_test_timestamp,\\n deltat, end_test_timestamp,\\n fbprophet)\\n begin_test_timestamp = end_test_timestamp\\n \\n # Interpolating where x-var is nan.\\n i_test_set[xvar] = i_test_set[xvar].interpolate()\\n i_train_set[xvar] = i_train_set[xvar].interpolate()\\n \\n\\n i_test_set['Set_rank'] = i\\n i_train_set['Set_rank'] = i\\n \\n if i == 0:\\n test_df = i_test_set\\n train_df = i_train_set\\n else:\\n test_df = pd.concat((test_df, i_test_set))\\n train_df = pd.concat((train_df, i_train_set))\\n\\n\\n return test_df, train_df\",\n \"def test_fit_prep_proba():\\n args = get_layer('fit', 'manual', 'temporal', True, True, window=2, step_size=3)\\n run_layer(*args)\",\n \"def add_environment_toTable(tab, in_ds, envvar_take=[],tabvar_skip=[], rainvar_name=None, env_tformat=\\\"%Y-%m-%d %H:%M:%S\\\", env_hour=12):\\n\\n tab_tformat = \\\"%Y-%m-%d_%H:%M\\\"\\n dic = {}\\n for k in tab.keys():\\n if k in tabvar_skip: # option to add variables to be excluded from environment table\\n continue\\n dic[k] = tab[k]\\n\\n ds = in_ds\\n envdates = pd.to_datetime(ds.time.dt.floor('T'), format=env_tformat)\\n ###### sample variables\\n for tlat, tlon, date, mask, tir in zip(dic['tminlat'], dic['tminlon'], dic['date'], dic['cloudMask'], dic['tir']):\\n\\n\\n #save cloud-wide rainfall stats, and rainfall maximum at ~0.15deg\\n if rainvar_name is not None:\\n tabdate = pd.to_datetime(date, format=tab_tformat) # rainfall sampling same time as TIR\\n\\n pos = envdates == tabdate\\n rain = ds[rainvar_name].isel(time=pos).where(mask).squeeze() # to mm/h\\n pmax_pos = np.nanargmax(rain.values)\\n ppos_2d = np.unravel_index(pmax_pos, rain.shape)\\n pmax_lon = rain.lon[ppos_2d[1]]\\n pmax_lat = rain.lat[ppos_2d[0]]\\n pmax = rain.sel(lon=slice(pmax_lon - 0.075, pmax_lon + 0.075), lat=slice(pmax_lat - 0.075, pmax_lat + 0.075))\\n # ipdb.set_trace()\\n pmax = pmax.mean().values\\n if (rainvar_name + '_mean') not in dic.keys():\\n for tag in ['_mean', '_max', '_p95', '_p99']:\\n dic[rainvar_name + tag] = []\\n dic[rainvar_name + '_mean'].append(float(rain.mean().values)) # full cloud mean\\n dic[rainvar_name + '_max'].append(float(pmax)) # ~0.15deg rain max\\n dic[rainvar_name + '_p95'].append(float(rain.quantile(0.95).values))\\n dic[rainvar_name + '_p99'].append(float(rain.quantile(0.99).values))\\n\\n ## save mean environments at ~0.7deg centred on location of minimum storm temperature\\n if len(envvar_take) > 0:\\n tabdate = pd.to_datetime(date, format=tab_tformat).replace(hour=env_hour, minute=0) # hour of environment sampling\\n pos = envdates == tabdate\\n single = ds.isel(time=pos).sel(longitude=slice(tlon - 0.375, tlon + 0.375), latitude=slice(tlat - 0.375, tlat + 0.375))\\n # ipdb.set_trace()\\n single = single.mean()\\n for vt in envvar_take:\\n if vt in dic.keys():\\n dic[vt].append(single[vt].values)\\n else:\\n dic[vt] = [single[vt].values]\\n return dic\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6216409","0.61393374","0.60502726","0.59144783","0.5902388","0.56675595","0.56113106","0.55587673","0.55573833","0.5554575","0.55389374","0.55208224","0.55186224","0.54923666","0.5484279","0.5448603","0.5447749","0.5442355","0.5438024","0.5410568","0.5405809","0.54031134","0.5401165","0.5400915","0.53947645","0.53747934","0.5360897","0.53567594","0.53286064","0.53182304","0.53019243","0.52919006","0.52858835","0.5271701","0.52660483","0.5265205","0.5257574","0.525046","0.5247383","0.52178097","0.52038366","0.51867193","0.51802707","0.5179336","0.5174405","0.5163546","0.5163435","0.5163369","0.51621103","0.5161112","0.5155189","0.5149232","0.5144993","0.5141698","0.51373386","0.5133565","0.51244944","0.51189727","0.5114872","0.51052356","0.5098208","0.5088633","0.5088423","0.50883335","0.5087922","0.50854224","0.5077994","0.5077075","0.50710875","0.5066024","0.5065414","0.5061198","0.50611717","0.505524","0.5054448","0.5049424","0.50458723","0.50447655","0.50416005","0.5038187","0.50366825","0.50330323","0.50053525","0.49983776","0.49980786","0.49948275","0.49935165","0.49900594","0.49898994","0.4979265","0.49728328","0.4969122","0.49689138","0.4965254","0.49614236","0.49586827","0.49573272","0.4955782","0.49522206","0.49514347"],"string":"[\n \"0.6216409\",\n \"0.61393374\",\n \"0.60502726\",\n \"0.59144783\",\n \"0.5902388\",\n \"0.56675595\",\n \"0.56113106\",\n \"0.55587673\",\n \"0.55573833\",\n \"0.5554575\",\n \"0.55389374\",\n \"0.55208224\",\n \"0.55186224\",\n \"0.54923666\",\n \"0.5484279\",\n \"0.5448603\",\n \"0.5447749\",\n \"0.5442355\",\n \"0.5438024\",\n \"0.5410568\",\n \"0.5405809\",\n \"0.54031134\",\n \"0.5401165\",\n \"0.5400915\",\n \"0.53947645\",\n \"0.53747934\",\n \"0.5360897\",\n \"0.53567594\",\n \"0.53286064\",\n \"0.53182304\",\n \"0.53019243\",\n \"0.52919006\",\n \"0.52858835\",\n \"0.5271701\",\n \"0.52660483\",\n \"0.5265205\",\n \"0.5257574\",\n \"0.525046\",\n \"0.5247383\",\n \"0.52178097\",\n \"0.52038366\",\n \"0.51867193\",\n \"0.51802707\",\n \"0.5179336\",\n \"0.5174405\",\n \"0.5163546\",\n \"0.5163435\",\n \"0.5163369\",\n \"0.51621103\",\n \"0.5161112\",\n \"0.5155189\",\n \"0.5149232\",\n \"0.5144993\",\n \"0.5141698\",\n \"0.51373386\",\n \"0.5133565\",\n \"0.51244944\",\n \"0.51189727\",\n \"0.5114872\",\n \"0.51052356\",\n \"0.5098208\",\n \"0.5088633\",\n \"0.5088423\",\n \"0.50883335\",\n \"0.5087922\",\n \"0.50854224\",\n \"0.5077994\",\n \"0.5077075\",\n \"0.50710875\",\n \"0.5066024\",\n \"0.5065414\",\n \"0.5061198\",\n \"0.50611717\",\n \"0.505524\",\n \"0.5054448\",\n \"0.5049424\",\n \"0.50458723\",\n \"0.50447655\",\n \"0.50416005\",\n \"0.5038187\",\n \"0.50366825\",\n \"0.50330323\",\n \"0.50053525\",\n \"0.49983776\",\n \"0.49980786\",\n \"0.49948275\",\n \"0.49935165\",\n \"0.49900594\",\n \"0.49898994\",\n \"0.4979265\",\n \"0.49728328\",\n \"0.4969122\",\n \"0.49689138\",\n \"0.4965254\",\n \"0.49614236\",\n \"0.49586827\",\n \"0.49573272\",\n \"0.4955782\",\n \"0.49522206\",\n \"0.49514347\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6764931"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11099,"cells":{"query":{"kind":"string","value":"Utilities for spatio temporal analysis zed.uchicago.edu Pulls new entries from datasource"},"document":{"kind":"string","value":"def pull(self, domain=\"data.cityofchicago.org\",dataset_id=\"crimes\",\\\n token=\"ZIgqoPrBu0rsvhRr7WfjyPOzW\",store=True, out_fname=\"pull_df.p\",\n pull_all=False):\n\n pdb.set_trace()\n client = Socrata(domain, token)\n if domain == \"data.cityofchicago.org\" and dataset_id==\"crimes\":\n self._coord1 = \"latitude\"\n self._coord2 = \"longitude\"\n self._EVENT = \"primary_type\"\n\n if pull_all:\n new_data = client.get(dataset_id)\n # pull_df = pd.DataFrame(new_data).dropna(\\\n # subset=[self._coord1, self._coord2, self._DATE, self._EVENT],\\\n # axis=1).sort_values(self._DATE)\n # NOTE: running into columns encoded in unicode not accepting subset\n # specific filtering of NaN's by column error\n # columns defined in subset aren't columns in the pulled DataFrame\n pull_df = pd.DataFrame(new_data).dropna().sort_values(self._DATE)\n self._logdf = pull_df\n else:\n self._logdf.sort_values(self._DATE)\n pull_after_date = \"'\"+str(self._logdf[self._DATE].iloc[-1]).replace(\\\n \" \", \"T\")+\"'\"\n new_data = client.get(dataset_id, where=\\\n (\"date > \"+pull_after_date))\n if domain == \"data.cityofchicago.org\" and dataset_id==\"crimes\":\n self._DATE = \"date\"\n # pull_df = pd.DataFrame(new_data).dropna(\\\n # subset=[self._coord1, self._coord2, self._DATE, self._EVENT],\\\n # axis=1).sort_values(self._DATE)\n pull_df = pd.DataFrame(new_data).dropna().sort_values(self._DATE)\n self._logdf = self._logdf.append(pull_df)\n\n if store:\n assert out_fname is not None, \"Out filename not specified\"\n self._logdf.to_pickle(out_fname)\n\n return"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _load_data(self, datasource):\n import pandas as pd\n if not isinstance(datasource, pd.DataFrame):\n raise TypeError('DfFeature must loaded from pd.DataFrame')\n self.data = datasource\n self.data['thetime']=self.data['thetime'].apply(lambda x:try_to_parse_date(x))","def process_data(self):\n logging.debug('process_data called')\n\n pd_time_series = pd.read_csv(f'{self.out_dir}docs/downloaded/'\n f'{self.filename}')\n\n pd_time_series = pd_time_series.drop('Lat', axis=1)\n pd_time_series = pd_time_series.drop('Long', axis=1)\n no_of_dates = len(pd_time_series.columns) - 2\n dateindex = pd.date_range(start='1-22-2020',\n periods=no_of_dates,\n freq='D').strftime('%d-%m')\n\n new_cols = ['Province/State', 'Country/Region']\n for index in dateindex:\n new_cols.append(index)\n pd_time_series.columns = new_cols\n\n pd_time_series = pd_time_series.drop('Province/State', axis=1)\n pd_edit_series = pd_time_series.set_index('Country/Region')\n\n pd_edit_series = pd_edit_series.T\n\n return pd_edit_series","def update_datasource():\n ds = Datasource(TDSX_FILE)\n ds.replace_extract(HYPER_FILE)","def import_stations(time_res='hourly',time_format='%Y%m%d%H',\r\n campaign_time=[datetime(2018,12,9), datetime(2018,12,12)],\r\n data_category='air_temperature', station_ids=['00044','00091'],\r\n dbase_dir='dbase', table_dir='tables',Output=True,\r\n memory_save=True):\r\n timeranges=['recent','historical']\r\n #%%load the datasets available at each timestep\r\n dwd_datasets_meta=dwd_datasets_meta=json.load(open(table_dir+\"\\\\dwd_station_meta.txt\"))\r\n #try to get a variable from the category, otherwise use interpolation of higher frequency data\r\n resample_frequency=None\r\n time_res_dbase=time_res\r\n try:\r\n dwd_datasets_meta[time_res][data_category]\r\n except Exception:\r\n if time_res=='daily':\r\n try:\r\n dwd_datasets_meta['hourly'][data_category]\r\n print(data_category,' is not provided at the required resolution, daily_mean of hourly data used instead')\r\n resample_frequency='D'\r\n time_res_dbase='hourly'\r\n except Exception:\r\n try: \r\n dwd_datasets_meta['10_minutes'][data_category]\r\n print(data_category,' is not provided at the required resolution, daily_mean of 10_minutes data used instead')\r\n resample_frequency='D'\r\n time_res_dbase='10_minutes'\r\n except Exception:\r\n print(data_category, 'not available')\r\n sys.exit(1)\r\n if time_res=='hourly':\r\n try: \r\n dwd_datasets_meta['10_minutes'][data_category]\r\n print(data_category,' is not provided at the required resolution, hourly_mean of 10_minutes data used instead')\r\n resample_frequency='H'\r\n time_res_dbase='10_minutes'\r\n except Exception:\r\n print(data_category, 'not available')\r\n sys.exit(1)\r\n \r\n \r\n #%% download from dwd if necessary\r\n #connect to server\r\n server='opendata.dwd.de'\r\n ftp=connect_ftp(server = server,connected = False)\r\n #get the mean time of the campaign\r\n date_mean=campaign_time[0]+(campaign_time[1]-campaign_time[0])/2 \r\n # load the inititial ds\r\n dbase_path=dbase_dir+'\\\\db_stations_'+time_res+'_'+data_category+'.nc'\r\n if os.path.exists(dbase_path):\r\n with xr.open_dataset(dbase_path) as dwd_dbase:\r\n dwd_dbase.load()\r\n print('Existing database imported')\r\n #get the non_nans stations\r\n current_stations=np.array(dwd_dbase[list(dwd_dbase.keys())[0]].sel(time=date_mean,method='nearest').dropna('STATIONS_ID').coords['STATIONS_ID'])\r\n else:\r\n print(dbase_path, 'does not exist, we create a new netcdf_file')\r\n dwd_dbase=xr.Dataset()\r\n current_stations=np.array((-9999)).reshape(1)\r\n #change directory on server\r\n for timerange in timeranges:\r\n archive_url='/climate_environment/CDC/observations_germany/climate/'+time_res_dbase+'/'+data_category+'/'+timerange \r\n ftp.cwd(archive_url)\r\n #get the archive\r\n for station_id in station_ids:\r\n #we check whether the station is in the database with this parameter already\r\n if int(station_id) in current_stations:\r\n print('Station', station_id, 'with category', data_category,'in ',timerange,'dbase already')\r\n continue\r\n try:\r\n archive_name=[s for s in ftp.nlst() if station_id in s][0]\r\n except:\r\n print('No ',timerange,'data for station',station_id)\r\n continue\r\n print('Retrieving {}...'.format(archive_name))\r\n retrieved = False\r\n archive = io.BytesIO()\r\n # try to retrieve file\r\n while not retrieved:\r\n try:\r\n ftp.retrbinary(\"RETR \" + archive_name, archive.write)\r\n retrieved = True\r\n except:\r\n ftp=connect_ftp(server = server,connected = False)\r\n ftp.cwd(archive_url)\r\n archive.seek(0)\r\n with ZipFile(archive) as myzip:\r\n for f in myzip.infolist():\r\n # This is the data file\r\n #print('zip content:', f.filename)\r\n if f.filename.startswith('produkt_'):\r\n product = io.StringIO(str(myzip.read(f.filename),'utf-8'))\r\n #get dataframe from product \r\n dwd_product=pd.read_csv(product,sep=';',skipinitialspace=True)\r\n #get datetime\r\n dwd_product['time']=pd.to_datetime(dwd_product['MESS_DATUM'],format=time_format) \r\n dwd_product=dwd_product.rename(columns=dwd_datasets_meta[time_res_dbase][data_category])\r\n dwd_product=dwd_product.reset_index()\r\n dwd_product=dwd_product.set_index(['time','STATIONS_ID'])\r\n dwd_product=dwd_product.drop(columns=['MESS_DATUM','quality_level_of_next_columns','end_of_record','index'])\r\n #append to database\r\n dwd_xr=dwd_product.to_xarray()\r\n #replace all values equal to -999 to nan\r\n for data_var in dwd_xr.data_vars:\r\n dwd_xr[data_var]=dwd_xr[data_var].where(dwd_xr[data_var]>-999)\r\n if station_id=='05009':\r\n print('ok') \r\n #only add relevant dates if available memoryis rather small\r\n \r\n if memory_save and timerange=='historical':\r\n dwd_xr=dwd_xr.sel(time=slice(campaign_time[0]-timedelta(days=1),campaign_time[1]+timedelta(days=1)))\r\n #dwd_xr=dwd_xr.squeeze()\r\n \r\n try:\r\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr])\r\n except Exception as e:\r\n print(e)\r\n print('try merging with compat=override')\r\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr],compat='override')\r\n print(archive_name,' added to database')\r\n #upscale to required temporal resolution\r\n if resample_frequency is not None:\r\n dwd_dbase=dwd_dbase.resample(time=resample_frequency).mean(skipna=True)\r\n print('DWD data upscaled to',time_res,'averages')\r\n if Output==True:\r\n dwd_dbase.to_netcdf(dbase_path)\r\n print('Updated database' ,dbase_path)\r\n return dwd_dbase","def refresh():\n DB.drop_all()\n DB.create_all()\n samples = pull_pm('Los Angeles', 'pm25')\n for sample in samples:\n measure = Record(datetime = str(sample[0]), value = sample[1])\n DB.session.add(measure)\n DB.session.commit()\n return 'Data refreshed!'","def entries_from_goes_ts_files(*files, default_waveunit=None, source=None):\n\n\n \"\"\"\n ts_goes = ts.TimeSeries(file)\n statinfo = os.stat(file)\n entry = DatabaseEntry(path=file)\n entry.size = statinfo.st_size\n\n #header['INSTRUME'] = header['TELESCOP']# So E.G. 'GOES 6' instead 'X-ray Detector'\n entry.instrument = ts_goes.meta.get('TELESCOP').values()\n entry.instrument = ts_goes.meta.get('TELESCOP').values()\n\n entry.wavemax = 0.8 # XRSB '1.0--8.0 $\\AA$'\n entry.wavemin = 0.05 # XRSA '0.5--4.0 $\\AA$'\n\n #\n entry.observation_time_start = ts_goes.meta.get('date-beg').values()[0]\n entry.observation_time_end = ts_goes.meta.get('date-end').values()[0]\n\n entry.metadata = ts_goes.meta.metadata[0][2]\n\n #entry.tags = [ sunpy.database.attrs.Tag('raw') ]\n \"\"\"\n\n\n for file in files:\n headers = fits.get_header(file)\n if isinstance(file, (str, six.text_type)):\n filename = file\n else:\n filename = getattr(file, 'name', None)\n statinfo = os.stat(file)\n #print('a header')\n entry = DatabaseEntry(path=filename)\n entry.size = statinfo.st_size\n\n # Add/tweak start/end entries for GOES\n if headers[0].get('TELESCOP','') != '':\n #header['INSTRUME'] = header['TELESCOP']# So E.G. 'GOES 6' instead 'X-ray Detector'\n entry.instrument = headers[0]['TELESCOP']\n elif headers[1].get('TELESCOP','') != '':\n entry.instrument = headers[1]['TELESCOP']\n if (headers[0].get('DATE-OBS','') != ''):\n if is_time_in_given_format(headers[0]['DATE-OBS'], '%d/%m/%Y'):\n start_time = datetime.strptime(headers[0]['DATE-OBS'], '%d/%m/%Y')\n elif is_time_in_given_format(headers[0]['DATE-OBS'], '%d/%m/%y'):\n start_time = datetime.strptime(headers[0]['DATE-OBS'], '%d/%m/%y')\n else:\n start_time = parse_time(headers[0]['DATE-OBS'])\n elif (headers[1].get('DATE-OBS','') != ''):\n if is_time_in_given_format(headers[1]['DATE-OBS'], '%d/%m/%Y'):\n start_time = datetime.strptime(headers[1]['DATE-OBS'], '%d/%m/%Y')\n elif is_time_in_given_format(headers[1]['DATE-OBS'], '%d/%m/%y'):\n start_time = datetime.strptime(headers[1]['DATE-OBS'], '%d/%m/%y')\n else:\n start_time = parse_time(headers[1]['DATE-OBS'])\n\n if (headers[0].get('DATE-END','') != ''):\n if is_time_in_given_format(headers[0]['DATE-END'], '%d/%m/%Y'):\n end_time = datetime.strptime(headers[0]['DATE-END'], '%d/%m/%Y')\n elif is_time_in_given_format(headers[0]['DATE-END'], '%d/%m/%y'):\n end_time = datetime.strptime(headers[0]['DATE-END'], '%d/%m/%y')\n else:\n end_time = parse_time(headers[0]['DATE-END'])\n elif (headers[1].get('DATE-END','') != ''):\n if is_time_in_given_format(headers[1]['DATE-END'], '%d/%m/%Y'):\n end_time = datetime.strptime(headers[1]['DATE-END'], '%d/%m/%Y')\n elif is_time_in_given_format(headers[1]['DATE-END'], '%d/%m/%y'):\n end_time = datetime.strptime(headers[1]['DATE-END'], '%d/%m/%y')\n else:\n end_time = parse_time(headers[1]['DATE-END'])\n else:\n end_time = start_time + timedelta(days=1,seconds=-1)\n\n # Add these to the entry\n entry.observation_time_start = start_time\n entry.observation_time_end = end_time\n\n entry.wavemax = 0.8 # XRSB '1.0--8.0 $\\AA$'\n entry.wavemin = 0.05 # XRSA '0.5--4.0 $\\AA$'\n\n if source:\n entry.source = source\n\n entry.metadata = MetaDict(headers[1])\n #entry.tags = sunpy.database.attrs.Tag('raw')\n\n #entry = DatabaseEntry(instrument='EIT', wavemin=25.0)\n\n #return entry\n yield entry","def temporal_database():\n return TimeHistory()","def update_historical_data():\n print('updating historical data')\n for sp in SupplyPoint.objects.filter(supplypointwarehouserecord__isnull=True).exclude(type__code=SupplyPointCodes.ZONE):\n update_historical_data_for_supply_point(sp)","def pull_data_from_geodb(sids, verbose=True):\n api_key = \"f125f4c130e61d9f4ad5874aadfe07ff\"\n geodb = dpf.data(api_key).geo\n\n df_a = pd.DataFrame() # for annual features\n df_m = pd.DataFrame() # for monthly features\n\n # gets meta data for each feature from its series id, \n # then uses meta data to pull all of the feature's regional data\n for sid in sids:\n print(f\"Series ID: {sid}\")\n\n print(\" > Collecting meta data...\")\n meta = geodb['meta'](series_id=sid).iloc[0, :]\n\n for col in ['min_date', 'max_date']:\n meta[col] = pd.to_datetime(meta[col]).date()\n\n print(\" > Pulling dataframe...\")\n data = geodb['data'](\n series_group=meta['series_group'],\n date=meta['max_date'],\n start_date=meta['min_date'],\n region_type=meta['region_type'],\n units=meta['units'],\n frequency=meta['frequency'],\n season=meta['season']\n )\n # swap series id for its feature name (change colname later)\n data['series_id'] = meta['title']\n\n if verbose:\n print(f\" > Title: {meta['title']}\")\n print(f\" > Min date: {meta['min_date']}\")\n print(f\" > Max date: {meta['max_date']}\")\n print(f\" > Units: {meta['units']}\")\n print(f\" > Season: {meta['season']}\")\n print(f\" > Frequency: {meta['frequency']}\")\n print(f\" > Region type: {meta['region_type']}\")\n\n print(f\" > Appending to df_{meta['frequency']}...\")\n if meta['frequency'] == 'a':\n df_a = df_a.append(data)\n else:\n df_m = df_m.append(data)\n continue\n\n df_a = Wrangler.transform_fresh_columns(df_a)\n df_m = Wrangler.transform_fresh_columns(df_m)\n\n return df_a, df_m","def test_data_source_soaps_get(self):\n pass","def update_data():\n etf_prices = get_prices(start=START_DATE, end=END_DATE)\n etf_returns = compute_returns(etf_prices)\n merged_etf_data = etf_prices.merge(etf_returns, right_index=True, left_index=True)\n indicators = compute_indicators(merged_etf_data) # this uses the \"ta\" lib, but it does not need\n # to be imported\n merged_etf_data = merged_etf_data.merge(indicators, right_index=True, left_index=True)\n vix_data = get_vix()\n data = merged_etf_data.merge(vix_data, right_index=True, left_index=True)\n data.to_csv('Data/database.csv')\n return","def get_data(day, unixstart=None, unixend=None):\n global subnet\n df_data = pd.DataFrame([])\n while len(df_data.index)<=0:\n try:\n df_data = pd.read_feather(f'/home/pi/studies/ardmore/homeserver/h{subnet}_{day}.feather')\n except Exception as e:\n # print(f\"Error grid_server.get_data:{e}\")\n pass\n if unixstart!=None:\n df_data = df_data[(df_data['unixtime']>=unixstart)&(df_data['unixtime']<=unixend)]\n float_cols = [x for x in df_data.columns if not x.startswith('timezone')]\n df_data = df_data[float_cols].astype(float)\n return df_data","def get_data(temp_estimate_source='historic'):\n\n print 'get data from files'\n load = process_load_data(datafoldername+loadfilename_train)\n load_test = process_load_data(datafoldername+loadfilename_test)\n temp = process_temp_data(datafoldername+tempfilename_train)\n holidays = process_holiday_data(datafoldername+holidayfilename)\n\n print 'merge load with temp data'\n X_train_df = load.merge(temp, on='datetime', how='left')\n X_test_df = load_test.merge(temp, on='datetime', how='left')\n\n print 'estimate missing temps'\n # find rows with missing temperatures\n missingtemp = X_test_df[X_test_df.isnull().any(axis=1)][['datetime', 'zone_id']].copy()\n\n # source estimates for missing periods\n if temp_estimate_source == 'arima':\n # use preprocessed arima estimates\n estimatedtemps = process_arima_temp_data(datafoldername+arimafilename)\n elif temp_estimate_source == 'actuals':\n # use actual temperatures - as provided after conclusion of kaggle competition\n estimatedtemps = process_temp_data(datafoldername+tempfilename_solution)\n else:\n # use means of historical temps at same day/time.\n estimatedtemps = get_estimated_temps(missingtemp[['datetime']].drop_duplicates(), temp)\n\n # merge estimates against missing rows, and use to update original dataframe in place\n replacementtemps = missingtemp.merge(estimatedtemps, left_on='datetime', right_on='datetime', how='left')\n replace_unknown_temps(X_test_df, replacementtemps)\n\n print 'merge in holiday dates'\n X_train_df = X_train_df.merge(holidays, on='datetime', how='left')\n X_train_df['holiday'].fillna(0, inplace=True)\n X_test_df = X_test_df.merge(holidays, on='datetime', how='left')\n X_test_df['holiday'].fillna(0, inplace=True)\n\n print 'add datetime categorical variables'\n X_train_df = add_datetime_categories(X_train_df)\n X_test_df = add_datetime_categories(X_test_df)\n\n return X_train_df, X_test_df","def test_4_data_fetching_unix_time_and_insertion(self):\n d1 = date.today()\n dt1 = datetime(d1.year, d1.month, d1.day) + timedelta(hours=8)\n result, success = self.fitness.get_columns_given_range(dt1, dt1+timedelta(days=1))\n self.assertTrue(success)\n self.assertEqual(result[0]['Datetime'],self.unix_time)","def prepare_for_influxdb(df):\n df = df.drop(columns=\"landkreis\", errors=\"ignore\") # prevent name collision in get_ags()\n df = get_ags(df)\n df[\"time\"] = df.apply(lambda x: 1000000000*int(datetime.timestamp((pd.to_datetime(x[\"timestamp\"])))), 1)\n df[\"measurement\"] = \"hystreet\"\n df[\"origin\"] = \"https://hystreet.com\"\n df = df.rename(columns={\n 'station_id': '_id',\n 'pedestrians_count': 'pedestrian_count',\n 'state': 'bundesland'\n })\n df['ags'] = pd.to_numeric(df['ags'])\n # import pdb; pdb.set_trace()\n return df","def Find_nearest_dwd_stations(inpt_data,\r\n date_start='20051201',\r\n date_end='20201231',\r\n dwd_time_format='%Y%m%d%H',\r\n data_category='air_temperature',\r\n temp_resolution='hourly',\r\n no_of_nearest_stations=4,\r\n memory_save=True,\r\n Output='True'):\r\n if isinstance(data_category,list):\r\n if len(list(data_category)) > 1:\r\n print(\r\n 'Currently only one dwd category allowed, please run function multiple times for each category'\r\n )\r\n return None\r\n \r\n #convert time to datetime\r\n dt_start=datetime.strptime(date_start,'%Y%m%d')\r\n dt_end=datetime.strptime(date_end,'%Y%m%d')\r\n print('Start quering data from DWD')\r\n #define the database folder\r\n pypath = os.path.dirname(os.path.abspath(__file__))\r\n table_dir = pypath + '\\\\' + 'tables'\r\n dbase_dir = pypath + '\\\\' + 'dbase' \r\n #%% we check all available stations and create a valid list\r\n filename_stations=update_stationlist(time_res='hourly',dbase_dir=table_dir)\r\n stations_all=pd.read_csv(filename_stations, dtype={'STATIONS_ID': object})\r\n # delete all stations which do not cover the category\r\n dwd_stations=stations_all[stations_all[data_category]==True].copy()\r\n #correct to datetime\r\n dwd_stations['date_end']=pd.to_datetime(stations_all.date_end,format='%Y%m%d')\r\n dwd_stations['date_start']=pd.to_datetime(stations_all.date_start,format='%Y%m%d')\r\n # clean to stations which cover the campaign time #dt_low <= dt <= dt_high:\r\n dwd_stations=dwd_stations[(dwd_stations.date_start<=dt_start) & (dwd_stations.date_end>=dt_end)]\r\n #make a geodataframe out of it\r\n dwd_stations=gpd.GeoDataFrame(dwd_stations,geometry=gpd.points_from_xy(dwd_stations.geo_lon, dwd_stations.geo_lat))\r\n \r\n #loop through all rows to get the n closest points\r\n distances=pd.DataFrame()\r\n for _, station in dwd_stations.iterrows():\r\n distances[station.STATIONS_ID]=inpt_data.distance(station.geometry)\r\n \r\n #%% get the n stations with smallest distance and update database\r\n id_nearest_stations=distances.apply(lambda s: s.nsmallest(no_of_nearest_stations).index.tolist(), axis=1).values.tolist() #station ids\r\n #get them as unique values by sum a list of lists https://bit.ly/353iZQB\r\n id_dwd_stations=list(set(sum(id_nearest_stations,[])))\r\n \r\n #update the database\r\n db_dwd_stations=import_stations(time_res=temp_resolution,time_format=dwd_time_format,campaign_time=[dt_start,dt_end],data_category=data_category,station_ids=id_dwd_stations,dbase_dir=dbase_dir,Output=Output,table_dir=table_dir,memory_save=memory_save)\r\n \r\n #distance of nearest stattions\r\n dist_nearest_stations=pd.DataFrame(np.sort(distances.values)[:,:no_of_nearest_stations]).values.tolist() #distances themself\r\n #create new columns in the input data\r\n station_col_nm=list()\r\n for i in range(0,no_of_nearest_stations):\r\n station_col_nm.append(data_category+'_station_'+str(i))\r\n for i in range(0,no_of_nearest_stations):\r\n station_col_nm.append(data_category+'_distance_'+str(i))\r\n #create new dataframe\r\n distance_data=pd.concat([pd.DataFrame(id_nearest_stations).astype(int),pd.DataFrame(dist_nearest_stations)],axis=1)\r\n distance_data.columns=station_col_nm\r\n #add to main dataset\r\n inpt_data=pd.concat([inpt_data, distance_data],axis=1) \r\n \r\n return inpt_data,db_dwd_stations","def update(start_date, end_date, binning):\n global mean_source1,mean_source,median_source1,median_source, difference_source1,difference_source, difference_source2 , difference_source3, mean_source2, median_source2\n\n # if type of start/end date is date, turn it into a datetime,\n # set time of start/end date time to 12:00\n\n def convert_time(t):\n if type(t) == datetime.date:\n return datetime.datetime(t.year, t.month, t.day, 12, 0, 0, 0)\n else:\n return t.replace(hour=12, minute=0, second=0, microsecond=0)\n\n start_date = convert_time(start_date)\n end_date = convert_time(end_date)\n if binning is None:\n binning = ''\n\n first_timestamp = start_date.timestamp() + time_offset\n second_timestamp = end_date.timestamp() + time_offset\n\n # query data in mysql database\n sql1 = 'SELECT str_to_date(datetime,\"%%Y-%%m-%%d %%H:%%i:%%s\") AS datetime, seeing from seeing ' \\\n ' where datetime >= str_to_date(\"{start_date_}\",\"%%Y-%%m-%%d %%H:%%i:%%s\")' \\\n ' and datetime <= str_to_date(\"{end_date_}\",\"%%Y-%%m-%%d %%H:%%i:%%s\") ' \\\n .format(start_date_=str(start_date), end_date_=str(end_date))\n\n sql2 = 'select _timestamp_,ee50,fwhm,timestamp from tpc_guidance_status__timestamp where timestamp >= {start_date_}' \\\n ' and timestamp<= {end_date_} and guidance_available=\"T\" ' \\\n ' order by _timestamp_' \\\n .format(start_date_=str(first_timestamp), end_date_=str(second_timestamp))\n\n df2 = pd.read_sql(sql2, db.get_engine(app=current_app, bind='els'))\n df1 = pd.read_sql(sql1, db.get_engine(app=current_app, bind='suthweather'))\n\n # setting index time for calculating mean and average\n df2.index = df2[\"_timestamp_\"]\n df1.index = df1['datetime']\n\n # It seems that Pandas doesn't change the index type if the data frame is empty, which means that resampling\n # would fail for an empty data frame. As there will be no row for median or mean , it is safe to just use the\n # original data frame to avoid this problem.\n\n # for external seeing calculating median and mean\n if not df1.empty:\n mean1_all = df1.resample(str(binning) + 'T').mean()\n else:\n mean1_all = df1.copy(deep=True)\n source1 = ColumnDataSource(mean1_all)\n mean_source1.data = source1.data\n\n if not df1.empty:\n median1_all = df1.resample(str(binning) + 'T').median()\n else:\n median1_all = df1.copy(deep=True)\n source = ColumnDataSource(median1_all)\n median_source1.data = source.data\n\n # calculate mean and median for ee50\n if not df2.empty:\n mean_all = df2.resample(str(binning) + 'T').mean()\n else:\n mean_all = df2.copy(deep=True)\n source3 = ColumnDataSource(mean_all)\n mean_source.data = source3.data\n\n if not df2.empty:\n median_all = df2.resample(str(binning) + 'T').median()\n else:\n median_all = df2.copy(deep=True)\n source4 = ColumnDataSource(median_all)\n median_source.data = source4.data\n\n #calculate mean and median for fwhm\n if not df2.empty:\n mean_all1 = df2.resample(str(binning) + 'T').mean()\n else:\n mean_all1 = df2.copy(deep=True)\n source4 = ColumnDataSource(mean_all)\n mean_source2.data = source4.data\n\n if not df2.empty:\n median_all = df2.resample(str(binning) + 'T').median()\n else:\n median_all = df2.copy(deep=True)\n source5 = ColumnDataSource(median_all)\n median_source2.data = source5.data\n\n # calculate difference for external seeing against fwhm and ee50\n dataframes = [mean1_all, mean_all]\n add_dataframes = pd.concat(dataframes, axis=1)\n add_dataframes.index.name = '_timestamp_'\n add_dataframes['difference'] = add_dataframes['seeing'] - add_dataframes['ee50']\n datasource2 = ColumnDataSource(add_dataframes)\n difference_source.data = datasource2.data\n\n dataframes = [mean1_all, mean_all1]\n add_dataframes = pd.concat(dataframes, axis=1)\n add_dataframes.index.name = '_timestamp_'\n add_dataframes['difference1'] = add_dataframes['seeing'] - add_dataframes['fwhm']\n datasource1 = ColumnDataSource(add_dataframes)\n difference_source1.data = datasource1.data\n\n # #difference using the median\n # dataframes2 = [median_all, median1_all]\n # add_dataframes2 = pd.concat(dataframes2, axis=1)\n # add_dataframes2.index.name = '_timestamp_'\n # add_dataframes2['difference2'] = add_dataframes2['seeing'] - add_dataframes2['ee50']\n # datasource2 = ColumnDataSource(add_dataframes2)\n # difference_source2.data = datasource2.data\n #\n # dataframes3 = [median_all, median1_all]\n # add_dataframes3 = pd.concat(dataframes3, axis=1)\n # add_dataframes3.index.name = '_timestamp_'\n # add_dataframes3['difference3'] = add_dataframes3['seeing'] - add_dataframes3['fwhm']\n # datasource3 = ColumnDataSource(add_dataframes3)\n # difference_source3.data = datasource3.data\n\n # plot labels\n p = figure(title=\"external vs internal seeing ({binning} minute bins)\".format(binning=binning), x_axis_type='datetime'\n , x_axis_label='datetime', y_axis_label='seeing',plot_width=1000, plot_height=500,tools=TOOLS)\n dif=figure(title='difference between average internal and external seeing ({binning} minute bins)'.format(binning=binning), x_axis_type='datetime',\n x_axis_label='datetime', y_axis_label='seeing',plot_width=1000, plot_height=500,tools=TOOLS)\n\n #plots\n # plots for external seeing\n p.circle(source=mean_source1, x='datetime',y='seeing', legend=\"external average\" ,fill_color=\"white\",color='green')\n p.line(source=median_source1, x='datetime',y='seeing', legend=\"external median\" ,color='blue')\n\n #plots showing median and mean for ee50 and fwhm\n p.circle(source=mean_source, x='_timestamp_', y='ee50', legend='ee50 average')\n p.circle(source=mean_source, x='_timestamp_', y='fwhm', legend='fwhm average', color='red', fill_color='white')\n\n p.line(source=median_source, x='_timestamp_', y='ee50', legend='ee50 median', color='green')\n p.line(source=median_source, x='_timestamp_', y='fwhm', legend='fwhm median', color='orange')\n\n #for difference\n dif.circle(source=difference_source, x='_timestamp_', y='difference', legend='ee50_mean difference', color='red')\n dif.circle(source=difference_source1, x='_timestamp_', y='difference1', legend='fwhm_mean difference', fill_color='green')\n\n #\n # dif.circle(source=difference_source2, x='_timestamp_', y='difference2', legend='ee50_median difference', fill_color='blue')\n # dif.circle(source=difference_source3, x='_timestamp_', y='difference3', legend='fwhm_median difference', color='orange')\n\n p.xaxis.formatter = date_formatter\n p.legend.location = \"top_left\"\n p.legend.click_policy=\"hide\"\n\n dif.xaxis.formatter = date_formatter\n dif.legend.click_policy=\"hide\"\n\n script, div = components(p)\n content1 = '<div>{script}{div}</div>'.format(script=script, div=div)\n\n script, div = components(dif)\n content2 = '<div>{script}{div}</div>'.format(script=script, div=div)\n\n return '{cont} {cont2}'.format(cont=content1,cont2=content2)","def load_data(city, month, day):\n ##task0.4 now the user choices are all ready and saved, start preparing your data \n ## first, load the data of the selected city using the pd.read_csv command for the user specified city \n df = pd.read_csv(CITY_DATA[city])\n ## read the column start time as a date, to start extracting information from it\n df['Start Time']=pd.to_datetime(df['Start Time'])\n ##define a new column\"month\" as a function from start time, which reads the month from the date\n df['month'] = df['Start Time'].dt.month\n ## extract the day name from the start time, append a new column\"day_of_week\" to the dataframe \n df['day_of_week'] = df['Start Time'].dt.day_name()\n ## extract the hour of the start time and save to a new variable\"hour\" \n df['hour'] = df['Start Time'].dt.hour\n \n ## now, the panel data is complete with variables of interest created\n ## time to consider user filters on month and day!!\n ## for the month, if user choice is all, no changes to dataset\n ## if user specifies a month,filter the dataset column\"month\" with the selected value\n if month !=\"all\":\n df=(df[df['month']==int(month)])\n \n ## for the day column, if user doesnt choose a specific day and select\"all\", no change to df\n ## if the user chooses a day, filter the \"day_of_week\" column for the specified day\n if day !=\"All\":\n df=(df[df['day_of_week']==day])\n \n ## print the first five rows of data to confirm filters are applied\n ## return the data frame to start calculating statistics\n #print(df.head())\n return df","def update_data(self, data):\n start_time = data.index[-1].strftime(\"%Y-%m-%dT%H:%M:%S.000000Z\")\n temp_data = self.gather_data(start=start_time)\n temp_data = self._list_to_df(temp_data)\n if (len(temp_data) > 1):\n # temp_data[0] is the same as data[-1]\n out_data = data.append(temp_data[1:])\n return out_data","def add_stlouis_data(self):\n stlfsi = endpoints['stlfsi']\n url = stlfsi['base_url']\n params = stlfsi['params']\n params[''] = get_str_date()\n stlfsi_data = get_api_data(url, params, 'data/stlfsi.csv')\n self.data = self.data.join(stlfsi_data, how='left')\n self.data['STLFSI'] = self.data['STLFSI'].fillna(method='ffill')\n if isnull(self.data.ix[0]['STLFSI']):\n for i, v in self.data.iterrows():\n if isnull(v['STLFSI']):\n continue\n try:\n self.data.ix[0]['STLFSI'] = stlfsi_data.ix[i - dt.timedelta(weeks=1)]\n break\n except Exception:\n self.data['STLFSI'] = self.data['STLFSI'].fillna(method='bfill')\n break\n self.data['STLFSI'] = self.data['STLFSI'].fillna(method='ffill')","def get_historical_data(userID):\n\n # import satz ID\n infile = open(\"satzIDs.pkl\", \"rb\")\n saetze = pickle.load(infile)\n infile.close()\n satz_ID_list = list(saetze.satzID)\n satz_ID_list = [str(item) for item in satz_ID_list]\n indexlist = [userID]\n\n # create emtpy DF\n df = pd.DataFrame(0, index=indexlist, columns=satz_ID_list)\n\n # get xmlsaetze by userID\n retrieve = xmlsaetze.objects.filter(UserID=userID)\n data = serializers.serialize(\"json\", retrieve, fields=(\"SatzID\", \"Erfolg\", \"Datum\"))\n struct = json.loads(data)\n df_obj = pd.DataFrame(columns=[\"SatzID\", \"Erfolg\", \"Datum\"])\n for x in struct:\n satz_ID = x[\"fields\"][\"SatzID\"]\n erfolg = x[\"fields\"][\"Erfolg\"]\n datum = x[\"fields\"][\"Datum\"]\n df2 = pd.DataFrame({\"SatzID\": [satz_ID], \"Erfolg\": erfolg, \"Datum\": datum})\n df_obj = pd.concat([df_obj, df2], ignore_index=True, axis=0)\n\n # iterate trough dataframe and updates erfolg where user did something\n for i in range(df_obj.shape[0]):\n satz_ID_cell = df_obj.iloc[i, 0]\n erfolg_cell = df_obj.iloc[i, 1]\n datum_cell = df_obj.iloc[i, 2]\n current_time = datetime.datetime.now()\n accepted_date = current_time + pd.DateOffset(\n months=-3\n ) # accepted date calculates the date of the last login minus 3 months\n\n if satz_ID_cell in df.columns:\n if str(datum_cell) > str(accepted_date):\n if erfolg_cell == 1 | erfolg_cell == True:\n df.loc[userID, satz_ID_cell] = 1\n if erfolg_cell == 0 | erfolg_cell == False:\n df.loc[userID, satz_ID_cell] = -1\n\n df = df.reset_index()\n df = df.rename(columns={\"index\": \"UserID\"})\n\n global historical_data\n historical_data = df\n\n return df","def Collect_data(clo, cla, FD, LD, ZC, idx_time, cntry, TZ):\r\n #initiate the console response\r\n rep = \"\"\r\n global ddf\r\n \r\n #define the noaa server access\r\n server_noaa = \"https://www.ncei.noaa.gov/data/global-hourly/access/\"\r\n \r\n #Initate dateframe\r\n data = pd.DataFrame()\r\n \r\n #Convert date from specified timezone to UTC\r\n FDc = Date_calibration(FD, 1, TZ)\r\n LDc = Date_calibration(LD, 1, TZ)\r\n \r\n #define timestep\r\n timestep = \"1 H\"\r\n \r\n #Loop on the range time by each year\r\n for y in range(FDc.year, (LDc.year + 1)):\r\n rep += '--- Collect Year ['+str(y) + '] --- \\n' \r\n \r\n #Loop on each weather station while the ouput data is good\r\n #weather station in the year instancied dataframe \r\n for i in range(len(ddf[y])) :\r\n \r\n #Define the memory key : year_zipcode\r\n key_d_z = str(y)+'_'+str(ZC)\r\n \r\n #Verify if the data is already in the memory\r\n if(key_d_z in memory_df.keys()) :\r\n #The data is already in the memory :\r\n #Collect the data and go next (save compute time and server solicitation)\r\n ext_data = memory_df[key_d_z]\r\n NS, DO = memory_NS_DO[key_d_z].split('_')[0], memory_NS_DO[key_d_z].split('_')[1]\r\n break\r\n else:\r\n \r\n #The data isn't in the memory :\r\n \r\n #Collect information about the nearest weather station from the zip code\r\n rs, NS, DO = Fetch_station(clo, cla, y)\r\n \r\n #Generate the ftp key weather station\r\n code_station = str(ddf[y]['USAF'][rs])+str(ddf[y]['WBAN'][rs])\r\n \r\n #Define the server access\r\n url = server_noaa+str(y)+'/'+code_station+'.csv'\r\n \r\n #Get the data\r\n req = requests.get(url)\r\n \r\n #The server answer\r\n if(req.status_code == 200):\r\n \r\n #Extract the data (only date and temperature)\r\n ext_data = pd.read_csv(url, usecols=['DATE','TMP'])\r\n \r\n #Check if the data isn't empty (1000 is arbitrary)\r\n if(len(ext_data) > 1000):\r\n \r\n #Format data\r\n ext_data, qual = Data_formatting(ext_data, y, timestep, TZ)\r\n \r\n #Check if the data quality respect the quality rule\r\n if(qual > quality_rule):\r\n \r\n #Save the date in the memory\r\n memory_df[key_d_z] = ext_data\r\n memory_NS_DO[key_d_z] = str(NS)+'_'+str(DO)\r\n \r\n #Response for the GUI\r\n rep += \"# Station [\"+str(NS)+\"] valid : \\n\"\r\n rep += \"- Quality density data : \"+str(round(qual, 2))+\"% \\n\"\r\n rep += \"- Great circle distance : \"+str(round(DO,2))+\"km \\n\"\r\n break\r\n else:\r\n #The data quality is too bad\r\n #Response for the GUI\r\n rep += \"# Station [\"+str(NS)+\"] invalid : \\n\"\r\n rep += \"- Quality density data : \"+str(round(qual,2))+\"% \\n\"\r\n rep += \"> Quality criterion unsatisfied \\n\"\r\n \r\n #Delete the weather station in the dataframe (instancied by year)\r\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\r\n continue\r\n \r\n else:\r\n #The data density is too low\r\n \r\n #Response for the GUI\r\n rep += \"# Station [\"+str(NS)+\"] invalid : \\n\"\r\n rep += \"> Low data volume \\n\"\r\n \r\n #Delete the weather station in the dataframe (instancied by year)\r\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\r\n\r\n continue\r\n else:\r\n #The NOAA doesn't answer for the code station\r\n rep += \"# Station [\"+str(NS)+\"] invalid : \\n\"\r\n rep += \"> Server doesn't answer\\n\"\r\n \r\n #Delete the weather station in the dataframe (instancied by year)\r\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\r\n\r\n continue\r\n #Add data in the Dataframe\r\n data = data.append(ext_data)\r\n \r\n #Define a new dataframe mark out by the specified time range\r\n range_time = pd.DataFrame(index=pd.DatetimeIndex(start=FD, end=LD, freq=timestep))\r\n \r\n #Paste data in the time marked out Dataframe\r\n range_time['Temp'] = data['TMP']\r\n \r\n #Calculate the amount of NaN (global)\r\n nb_nan = range_time['Temp'].isnull().sum()\r\n \r\n #Calculate the global quality\r\n quality = (1 - (nb_nan) / len(range_time)) * 100\r\n \r\n #Fill the gap data by temporal interpolation\r\n data = range_time.interpolate(method='time')\r\n data = data.ffill().bfill()\r\n data['Temp'] = round(data['Temp'], 2)\r\n \r\n \r\n #If specified index time, cut the data for it\r\n if(len(idx_time)> 2):\r\n final_data = pd.DataFrame(index = pd.to_datetime(idx_time))\r\n final_data['Temp'] = round(data['Temp'], 2)\r\n #final_data = final_data.resample(rule = str(timestep)).mean()\r\n #final_data = final_data.dropna()\r\n else:\r\n final_data = data\r\n \r\n return final_data, quality, NS, DO, rep","def _assess(self):\n # get eruptions\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\n # check if data file exists\n self.exists = os.path.isfile(self.file)\n if not self.exists:\n t0 = datetime(2011,1,1)\n t1 = datetime(2011,1,2)\n self.update(t0,t1)\n # check date of latest data in file\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\n self.ti = self.df.index[0]\n self.tf = self.df.index[-1]","def _update_modified_since(self, timestamp):\n new_data_sources = [\n source\n for provider in self.data_source_providers\n for source in provider.get_data_sources_modified_since(timestamp)\n ]\n filtered_data_sources = self.get_filtered_configs(new_data_sources)\n invalid_data_sources = {ds._id for ds in new_data_sources} - {ds._id for ds in filtered_data_sources}\n self._add_data_sources_to_table_adapters(filtered_data_sources, invalid_data_sources)","def get_data(self, date_time):\n id_columns = ','.join([col for col in self.table_primary_keys if col not in ['EFFECTIVEDATE', 'VERSIONNO']])\n return_columns = ','.join(self.table_columns)\n with self.con:\n cur = self.con.cursor()\n cur.execute(\"DROP TABLE IF EXISTS temp;\")\n cur.execute(\"DROP TABLE IF EXISTS temp2;\")\n cur.execute(\"DROP TABLE IF EXISTS temp3;\")\n cur.execute(\"DROP TABLE IF EXISTS temp4;\")\n # Store just the unique sets of ids that came into effect before the the datetime in a temporary table.\n query = \"\"\"CREATE TEMPORARY TABLE temp AS \n SELECT * \n FROM {table} \n WHERE EFFECTIVEDATE <= '{datetime}';\"\"\"\n cur.execute(query.format(table=self.table_name, datetime=date_time))\n # For each unique set of ids and effective dates get the latest versionno and sore in temporary table.\n query = \"\"\"CREATE TEMPORARY TABLE temp2 AS\n SELECT {id}, EFFECTIVEDATE, MAX(VERSIONNO) AS VERSIONNO\n FROM temp\n GROUP BY {id}, EFFECTIVEDATE;\"\"\"\n cur.execute(query.format(id=id_columns))\n # For each unique set of ids get the record with the most recent effective date.\n query = \"\"\"CREATE TEMPORARY TABLE temp3 as\n SELECT {id}, VERSIONNO, max(EFFECTIVEDATE) as EFFECTIVEDATE\n FROM temp2\n GROUP BY {id};\"\"\"\n cur.execute(query.format(id=id_columns))\n # Inner join the original table to the set of most recent effective dates and version no.\n query = \"\"\"CREATE TEMPORARY TABLE temp4 AS\n SELECT * \n FROM {table} \n INNER JOIN temp3 \n USING ({id}, VERSIONNO, EFFECTIVEDATE);\"\"\"\n cur.execute(query.format(table=self.table_name, id=id_columns))\n # Inner join the most recent data with the interconnectors used in the actual interval of interest.\n query = \"\"\"SELECT {cols} FROM temp4 ;\"\"\"\n query = query.format(cols=return_columns)\n data = pd.read_sql_query(query, con=self.con)\n return data","def get_new_modelling_data():\n # get latest epidemic data from OWID \n\n df = pd.read_json(requests.get(\"https://covid.ourworldindata.org/data/owid-covid-data.json\").content)\n data = pd.DataFrame(df[\"POL\"][\"data\"])\n\n # get latest government restriction data from Oxford tracker\n response = requests.get(\"https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest.csv\").content\n rest = pd.read_csv(io.StringIO(response.decode('utf-8')))\n rest = rest[rest.CountryName == \"Poland\"]\n\n modelling = pd.DataFrame(Mobility.objects.values())\n prepare_model_data(data,rest,modelling)","def getOLAPSource():","def get_SequencedFischerData():\n filteredOn = { #For reference, to know how dataset has been filtered\n 'minimalSurvivalLastFollowup': 365*5\n }\n print('Filtering settings:', filteredOn)\n\n #Metadata\n metadata = pd.read_table(\n gzip.open(\n os.path.join(\n privatedir,\n \"R2_grabbed_data/Fischer498/metadata_src/GSE49710_series_matrix.txt.gz\"\n ), 'rt', encoding=\"UTF-8\"\n ), skiprows=47,skipfooter=44799-66,engine='python',header=None\n )\n metadata.index = metadata[0].apply(lambda x: x.replace('!',''))\n del metadata[0]\n metadata = metadata.T\n i = count()\n metadata.columns = [c.replace('ch1',str(next(i))) if c.startswith('Sample_char') else c for c in metadata.columns]\n del i, metadata['Sample_source_name_ch1'], metadata['Sample_status'], metadata['Sample_organism_ch1']\n metadata.columns = [metadata[c][metadata.first_valid_index()].split(':')[0].replace(' ','_')\n if c.startswith('Sample_char') else c for c in metadata.columns]\n metadata = metadata.applymap(lambda x: x.split(': ')[1] if ': ' in x else x)\n \n metadatasurv = pd.read_table(\n gzip.open(\n os.path.join(\n privatedir,\n \"R2_grabbed_data/Fischer498/metadata_src/GSE62564_series_matrix.txt.gz\"\n ),'rt',encoding=\"UTF-8\"\n ), skiprows=51,skipfooter=102-74,engine='python'\n )\n metadatasurv.index = metadatasurv['!Sample_geo_accession'].apply(lambda x: x.replace('!',''))\n del metadatasurv['!Sample_geo_accession']\n metadatasurv = metadatasurv.T\n metadatasurv.columns = range(len(metadatasurv.columns))\n metadatasurv = metadatasurv[list(range(7,21))]\n metadatasurv.columns = [v.split(':')[0].replace(' ','_') for v in metadatasurv.ix[metadatasurv.first_valid_index()]]\n metadatasurv = metadatasurv.applymap(lambda x: x.split(': ')[1] if not x is np.nan else x)\n metadatasurv.index = metadata.index #Both sample sets are sorted the same way, but different gse names\n assert sum(metadatasurv.age == metadata.age_at_diagnosis) == len(metadata)\n metadata['overall_survival'] = metadatasurv.os_day.apply(int)\n metadata['eventfree_survival'] = metadatasurv.efs_day.apply(int)\n del metadatasurv\n metadata.Sample_geo_accession = metadata.Sample_geo_accession.apply(lambda x: x.lower())\n metadata.set_index(\"Sample_geo_accession\",inplace=True)\n metadata.Sample_title = metadata.Sample_title.apply(lambda x: x[5:].replace(' patient ',''))\n metadata.death_from_disease = metadata.death_from_disease == '1'\n metadata.progression = metadata.progression == '1'\n\n #Expression data\n ## Array expression\n exprdata_A = pd.read_table(os.path.join(privatedir,'R2_grabbed_data/Fischer498/GSE49710_R2.txt'))\n exprdata_A.index = exprdata_A.pop('#H:hugo')\n del exprdata_A['probeset']\n \n ## RNAseq gene expression\n exprdata_G = pd.read_table(\n gzip.open(\n os.path.join(\n privatedir,\n 'R2_grabbed_data/Fischer498/sequencedData/GSE49711_SEQC_NB_TUC_G_log2.txt.gz'\n ), 'rt',encoding=\"UTF-8\"\n ),\n index_col = '00gene_id'\n )\n exprdata_G.columns = [\n metadata.reset_index().set_index('Sample_title').ix[c.split('_')[1]].Sample_geo_accession\n for c in exprdata_G.columns\n ]\n\n ## RNAseq transcript level expression\n exprdata_T = pd.read_table(\n gzip.open(\n os.path.join(\n privatedir,\n 'R2_grabbed_data/Fischer498/sequencedData/GSE49711_SEQC_NB_TUC_T_log2.txt.gz'\n ), 'rt',encoding=\"UTF-8\"\n ),\n index_col = '00transcript_id'\n )\n exprdata_T.columns = exprdata_G.columns\n\n ## RNAseq junction level expression\n exprdata_J = pd.read_table(\n gzip.open(\n os.path.join(\n privatedir,\n 'R2_grabbed_data/Fischer498/sequencedData/GSE49711_SEQC_NB_TUC_J_log2.txt.gz'\n ), 'rt',encoding=\"UTF-8\"\n ),\n index_col = 'sample_ID'\n )\n exprdata_J.columns = exprdata_G.columns\n\n # Default expression dataset -> exprdata_G\n exprdata = exprdata_G\n \n #aCGH\n aCGH = pd.read_table(\n os.path.join(privatedir,'R2_grabbed_data/Fischer498/SEQC_aCGH/SEQC_aCGH_all_146.txt')\n )\n geosearch = metadata[['Sample_title']].copy()\n geosearch.reset_index(inplace=True)\n geosearch.set_index('Sample_title',inplace=True)\n aCGH.Sample = aCGH.Sample.apply(lambda x: geosearch.ix[x].Sample_geo_accession)\n del geosearch\n aCGH['log2ratio'] = (aCGH.CN/2).apply(np.log2)\n #Convert coordinates to hg38\n lo = LSD.get_lift19to38()\n aCGH['Start38'] = aCGH.T.apply(lambda x: lo.convert_coordinate(x.Chromosome,x.Start)).apply(lambda x: x[0][1] if x else np.nan)\n aCGH['End38'] = aCGH.T.apply(lambda x: lo.convert_coordinate(x.Chromosome,x.End)).apply(lambda x: x[0][1] if x else np.nan)\n del lo, aCGH['Start'], aCGH['End']\n aCGH = aCGH.dropna().copy()\n #Assign genes to regions\n genannot = LSD.get_ensemblGeneannot()\n aCGH['genes'] = aCGH.T.apply(lambda x: {f.attributes['gene_name'][0] for f in genannot.region('{}:{}-{}'\n .format(x.Chromosome[3:],int(x.Start38),int(x.End38)),featuretype='gene')})\n aCGH['nrGenes'] = aCGH.genes.apply(len)\n del genannot\n #To set cut offs look at hist => aCGH.log2ratio.hist(ax=ax,bins='auto')\n aCGH['annotation'] = aCGH.log2ratio.apply(lambda x: 'gain' if x > 0.3 else ('loss' if x < -0.3 else 'normal'))\n\n # Filter patients whom according to metadata survived, but had last follow up before 5 years\n metadata = metadata[metadata.death_from_disease |\n (~metadata.death_from_disease &\n (metadata.overall_survival > filteredOn['minimalSurvivalLastFollowup']))]\n exprdata_A = exprdata_A[metadata.index]\n exprdata_G = exprdata_G[metadata.index]\n exprdata_T = exprdata_T[metadata.index]\n exprdata_J = exprdata_J[metadata.index]\n aCGH = aCGH[aCGH.Sample.isin(metadata.index)]\n \n return Dataset(**locals())","def testWeatherFetch(self):\n\n timeCol = 'timestamp'\n rows = []\n for row in self.aggregator.rawData(dataType = 'weather',\n orderBy = [timeCol],\n timestampCol = timeCol,\n startDate = self.testStart,\n endDate = self.testEnd):\n rows.append(row)\n self.assertIsNotNone(rows, 'Rows are present.')","def test_plt_mag_time():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n # create the arrays per filter and readout pattern\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\n for i, val in enumerate(max_val_box):\n if '140' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(val)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(val)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif '110' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(val)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(val)\n nrsrapidd6_clear.append(np.NaN)\n else:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(val)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(val)\n # add to the bokeh data structure\n ta.source.data[\"nrsrapid_f140x\"] = nrsrapid_f140x\n ta.source.data[\"nrsrapid_f110w\"] = nrsrapid_f110w\n ta.source.data[\"nrsrapid_clear\"] = nrsrapid_clear\n ta.source.data[\"nrsrapidd6_f140x\"] = nrsrapidd6_f140x\n ta.source.data[\"nrsrapidd6_f110w\"] = nrsrapidd6_f110w\n ta.source.data[\"nrsrapidd6_clear\"] = nrsrapidd6_clear\n result = ta.plt_mag_time()\n\n assert bokeh_plot_type == type(result)","def get_data(last):\n Table = \"ServerRoom\"\n filter = \"\"\n if last == \"lastone\":\n data = request_meteodata(\"SELECT * from `ServerRoom` ORDER BY id DESC LIMIT 1 \")\n if len(data) == 0:\n return [SensorData(datetime.datetime.now(), 0, 0)]\n res = []\n for d in data:\n res.append(SensorData(d[1], d[2], d[3]))\n return res\n if last != \"All\":\n limit = datetime.datetime.now().astimezone(utz)\n if last == \"24hours\":\n limit -= datetime.timedelta(hours=24)\n else:\n limit = limit.replace(hour=0, minute=0, second=0, microsecond=0)\n if last == \"3days\":\n limit -= datetime.timedelta(days=3)\n elif last == \"7days\":\n limit -= datetime.timedelta(days=7)\n elif last == \"month\":\n limit = limit.replace(day=1)\n elif last == \"30days\":\n limit -= datetime.timedelta(days=30)\n elif last == \"year\":\n limit = limit.replace(day=1, month=1)\n filter = \" WHERE `date` > '\" + str(limit) + \"'\"\n order = \" ORDER BY `date` ASC\"\n req = \"SELECT * FROM `\" + Table + \"`\" + filter + order\n data = request_meteodata(req)\n if len(data) == 0:\n print(\"no data: get all\")\n req = \"SELECT * FROM `\" + Table + \"`\" + order\n data = request_meteodata(req)\n res = []\n for d in data:\n res.append(SensorData(d[1], d[2], d[3]))\n return res","def getEPADailyData(dateint, dt_ind, month, epa_df, yr):\n\n try:\n start = dateint + dt_ind * 10000\n end = start + 10001\n dly_epa_df = epa_df[(epa_df.created >= start) & (epa_df.created < end)]\n dly_epa_df.reset_index(inplace=True, drop=True)\n\n new_df = pd.DataFrame(columns=['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'epa_pm25_value', 'raw_concentration', 'aqi', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code', 'created'])\n for sitenm in dly_epa_df.site_name.unique():\n indx_ct = 0\n site_df = dly_epa_df[dly_epa_df.site_name == sitenm]\n for i in site_df.created.unique():\n indx_ct += 1\n new_df = pd.concat([new_df,site_df.iloc[indx_ct - 1:indx_ct]],ignore_index=True)\n\n if i != site_df.created.max(): # Don't interpolate the last record\n tmp_df = site_df.iloc[indx_ct - 1:indx_ct][['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code']]\n for j in range(1,6):\n new_dt = i + j * 10\n tmp_df['created'] = int(new_dt)\n tmp_df['epa_pm25_value'] = np.nan\n tmp_df['raw_concentration'] = np.nan\n tmp_df['aqi'] = np.nan\n new_df = pd.concat([new_df,tmp_df],ignore_index=True)\n\n # Convert aqi to numerica for so that it gets interpolated\n new_df[['aqi']] = new_df[['aqi']].replace(\"nan\", np.nan, regex=True)\n new_df[['aqi']] = new_df[['aqi']].apply(pd.to_numeric)\n\n new_df = new_df.interpolate(method='linear', limit_direction='forward', axis=0)\n\n int_epa_df = new_df[(new_df.created >= start) & (new_df.created < (end - 1))]\n int_epa_df.reset_index(inplace=True, drop=True)\n \n # Write to S3\n s3 = s3fs.S3FileSystem()\n myopen = s3.open\n write('midscapstone-whos-polluting-my-air/EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr), int_epa_df, compression='GZIP', open_with=myopen)\n s3_resource = boto3.resource('s3')\n s3_resource.Object('midscapstone-whos-polluting-my-air', 'EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr)).Acl().put(ACL='public-read')\n\n except Exception as e:\n print(\"*** EXCEPTION IN GET EPA DAILY DATA *** {}\".format(e))\n return int_epa_df","def get_physio_data_old(subject_num, session_num):\n \n subject_path = pathlib.Path(PHYSIO_PATH + '/subject_' + subject_num)\n meta_file_path = subject_path.joinpath('physio_meta_' + subject_num + '.yml')\n\n if not subject_path.exists() :\n raise DataAccessError('Subject folder not found: '+str(subject_path))\n \n # open metadata file\n with meta_file_path.open() as yaml_file :\n physio_meta = yaml.load(yaml_file.read())\n\n # construct list of record starting times\n starting_times = [(rec['number'], float(rec['start_time'])) for rec in physio_meta['records'] if rec['session']==session_num]\n starting_times = dict(starting_times)\n #print(starting_times)\n\n # load recodrs and set to absolute time\n column_names = ['time','ecg','gsr']\n physio_data = pd.DataFrame(columns=column_names)\n pattern = 'physio_record_' + subject_num + '_' + session_num + '_*.csv'\n\n # raise error is no data files found or starting times are missing\n record_file_num = len(list(subject_path.glob(pattern)))\n if record_file_num == 0:\n raise DataAccessError('No record files: '+str(subject_path))\n if record_file_num != len(starting_times):\n raise DataIntegrityError('Number of record files and number of starting times in meta file not the same!')\n \n for record_path in subject_path.glob(pattern):\n \n # load physio_data\n physio_record = pd.read_csv(\n str(record_path),\n comment='#',\n header=None,\n names = column_names\n )\n \n # convert physio time to seconds\n physio_record['time'] /= 1000\n \n # convert to absolute time\n #record_number = int(record_path.stem.split('_')[-1])\n record_number = int(min(starting_times.keys()))\n s_time = starting_times[record_number]\n physio_record['time'] += s_time\n \n # concatinate to other records\n physio_data = physio_data.append(physio_record, ignore_index=True)\n \n # sort records by time\n physio_data = physio_data.sort_values(by='time')\n \n return physio_data","def entries_from_goes_ts_file2(file, default_waveunit=None):\n\n headers = fits.get_header(file)\n if isinstance(file, (str, six.text_type)):\n filename = file\n else:\n filename = getattr(file, 'name', None)\n\n statinfo = os.stat(file)\n #print('a header')\n entry = DatabaseEntry(path=filename)\n size = statinfo.st_size\n\n # Add/tweak start/end entries for GOES\n if headers[0].get('TELESCOP','') != '':\n #header['INSTRUME'] = header['TELESCOP']# So E.G. 'GOES 6' instead 'X-ray Detector'\n entry.instrument = headers[0]['TELESCOP']\n if (headers[0].get('DATE-OBS','') != ''):\n if is_time_in_given_format(headers[0]['DATE-OBS'], '%d/%m/%Y'):\n start_time = datetime.strptime(headers[0]['DATE-OBS'], '%d/%m/%Y')\n elif is_time_in_given_format(headers[0]['DATE-OBS'], '%d/%m/%y'):\n start_time = datetime.strptime(headers[0]['DATE-OBS'], '%d/%m/%y')\n else:\n start_time = parse_time(headers[0]['DATE-OBS'])\n elif (headers[1].get('DATE-OBS','') != ''):\n if is_time_in_given_format(headers[1]['DATE-OBS'], '%d/%m/%Y'):\n start_time = datetime.strptime(headers[1]['DATE-OBS'], '%d/%m/%Y')\n elif is_time_in_given_format(headers[1]['DATE-OBS'], '%d/%m/%y'):\n start_time = datetime.strptime(headers[1]['DATE-OBS'], '%d/%m/%y')\n else:\n start_time = parse_time(headers[1]['DATE-OBS'])\n\n if (headers[0].get('DATE-END','') != ''):\n if is_time_in_given_format(headers[0]['DATE-END'], '%d/%m/%Y'):\n end_time = datetime.strptime(headers[0]['DATE-END'], '%d/%m/%Y')\n elif is_time_in_given_format(headers[0]['DATE-END'], '%d/%m/%y'):\n end_time = datetime.strptime(headers[0]['DATE-END'], '%d/%m/%y')\n else:\n end_time = parse_time(headers[0]['DATE-END'])\n elif (headers[1].get('DATE-END','') != ''):\n if is_time_in_given_format(headers[1]['DATE-END'], '%d/%m/%Y'):\n end_time = datetime.strptime(headers[1]['DATE-END'], '%d/%m/%Y')\n elif is_time_in_given_format(headers[1]['DATE-END'], '%d/%m/%y'):\n end_time = datetime.strptime(headers[1]['DATE-END'], '%d/%m/%y')\n else:\n end_time = parse_time(headers[1]['DATE-END'])\n else:\n end_time = start_time + timedelta(days=1,seconds=-1)\n\n # Add these to the entry\n observation_time_start = start_time\n observation_time_end = end_time\n\n wavemax = 0.8 # XRSB '1.0--8.0 $\\AA$'\n wavemin = 0.05 # XRSA '0.5--4.0 $\\AA$'\n\n metadata = MetaDict(headers[1])\n #entry.tags = sunpy.database.attrs.Tag('raw')\n\n entry = DatabaseEntry(observation_time_start=start_time,\n observation_time_end = end_time,\n instrument='EIT',\n wavemin=wavemin,\n wavemax=wavemax,\n metadata=metadata,\n size=size)\n\n return entry","def loadDBstation(dbfile,stationID,varname,timeinfo=None,filttype=None,cutoff=3600.0,output_meta=False):\r\n from netcdfio import queryNC\r\n \r\n outvar = ['NetCDF_Filename','NetCDF_GroupID','StationName']\r\n tablename = 'observations'\r\n #condition = 'Variable_Name = \"%s\" and StationID = \"%s\"' % (varname,stationID)\r\n condition = 'Variable_Name = \"%s\" and StationID LIKE \"%%%s\"' % (varname,stationID)\r\n \r\n print 'Querying database...'\r\n print condition\r\n data, query = queryNC(dbfile,outvar,tablename,condition) \r\n \r\n if len(data)==0:\r\n print '!!! Warning - Did not find any stations matching query. Returning -1 !!!'\r\n return -1\r\n else:\r\n ts = timeseries(data[0]['time'],data[0][varname].squeeze())\r\n \r\n \r\n if not timeinfo==None:\r\n print 'Interpolating station data between %s and %s\\n'%(timeinfo[0],timeinfo[1])\r\n tnew,ynew = ts.interp((timeinfo[0],timeinfo[1],timeinfo[2]))\r\n ts = timeseries(tnew,ynew)\r\n ts.dt = timeinfo[2] # This needs updating\r\n \r\n if not filttype==None:\r\n print '%s-pass filtering output data. Cutoff period = %f [s].'%(filttype,cutoff)\r\n yfilt = ts.filt(cutoff,btype=filttype,axis=-1)\r\n ts.y = yfilt.copy()\r\n \r\n if output_meta:\r\n if data[0].has_key('elevation'):\r\n ele = data[0]['elevation']\r\n else:\r\n ele = 0.0\r\n meta = {'longitude':data[0]['longitude'],'latitude':data[0]['latitude'],'elevation':ele,'StationName':query['StationName'][0]}\r\n return ts, meta \r\n else:\r\n return ts","def export_lateness_data( gpssched, sched_error ):\n \n sql = \"\"\"\ninsert into datamining_table dt\n( gps_segment_id, gtfs_trip_id, rms_schedule_error, vehicle_id,\n route_name, vehicle_type, service_id, direction_id,\n stop_lat, stop_lon, stop_id, stop_sequence,\n scheduled_arrival_time, scheduled_departure_time,\n actual_arrival_time, lateness, prev_stop_id )\nvalues\n( null, %(trip_id)s, %(sched_err)s, %(vehid)s,\n %(routename)s, %(vehtype)s, %(service_id)s, %(dir_id)s,\n %(stoplat)s,%(stoplon)s,%(stopid)s,%(stopseq)s,\n %(sched_arr)s,%(sched_dep)s,%(actual_arr)s,%(lateness)s,\n %(prev_stop_id)s )\n\"\"\"\n \n gtfs = gpssched.getGTFSSchedule()\n\n basedict = { 'trip_id' : gtfs.trip_id,\n 'sched_err' : sched_error,\n 'vehid' : gpssched.segment.vehicle_id,\n 'routename' : gtfs.route_short_name,\n 'vehtype' : gtfs.route_type,\n 'service_id' : gtfs.service_id,\n 'dir_id' : gtfs.direction_id\n }\n\n cur = get_cursor()\n \n for arrival in gpssched.getGPSSchedule():\n print dict(arrival)\n stopdict = dict(basedict)\n stopdict.update ( { 'stoplat' : arrival['stop_lat'],\n 'stoplon' : arrival['stop_lon'],\n 'stopid' : arrival['stop_id'],\n 'stopseq' : arrival['stop_sequence'],\n 'sched_arr' : arrival['arrival_time_seconds'],\n 'sched_dep' : arrival['departure_time_seconds'],\n 'actual_arr' : arrival['actual_arrival_time_seconds'],\n 'lateness' : arrival['actual_arrival_time_seconds'] \\\n - arrival['departure_time_seconds'],\n 'prev_stop_id' : arrival['prev_stop_id']\n } )\n SQLExec(cur, sql, stopdict)\n\n\n cur.close()","def test_data_source_soaps_post(self):\n pass","def load_data(city, month, day):\n \n# Using pandas accessor to find month, day, hour from the Start Time column in the source data\n print(\"A moment please while I find the data....\\n\")\n df = pd.read_csv(CITY_DATA[city])\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n df['day_of_week'] = df['Start Time'].dt.weekday_name\n df['month'] = df['Start Time'].dt.month\n df['hour'] = df['Start Time'].dt.hour\n\n return df","def manipulate_data(ds, var, predef_clim, predef_trnd, trn_yrs, all_yrs, \n apply_latw=True, apply_detrending=True, dropna=True):\n\n \n if((var=='SD')|(var=='sd')|(var=='snowc')): \n ds[var] = ds[var].where(ds[var]>=0, other=0.0)\n ds[var] = ds[var].where(ds[var]==0, other=1.0)\n #ds[var].values = Gauss_filter(ds[var].values, (0,3,3))\n \n \"\"\"\n if((var=='hgt')|(var=='z')|(var=='GPT')):\n months = ds.time.to_index().month; ssn_ends = (months==2)|(months==5)|(months==8)|(months==11)\n ds = ds.sel(time=ssn_ends)\n else: \n ds = ds.resample(time='3M').mean()\n \"\"\"\n \n ds = ds.resample(time='3M').mean()\n\n ds = ds.sel(time=slice(str(all_yrs[0])+'-01-01', str(all_yrs[-1])+'-12-31')) \n \n try: \n clim = predef_clim\n ds = ds.groupby('time.season') - clim\n print('Predefined climatology used')\n except:\n clim = ds.sel(time=slice(str(trn_yrs[0])+'-01-01', str(trn_yrs[-1])+'-12-31')).groupby('time.season').mean('time')\n ds = ds.groupby('time.season') - clim\n print('Climatology calculated from data')\n \n if(apply_latw): ds[var].values = lat_weighting(ds[var].values, \n ds.lat, ds.lon)\n if(dropna):\n ds = ds.stack(gridcell=('lat', 'lon')).dropna(dim='gridcell',how='any')\n else: \n ds = ds.stack(gridcell=('lat', 'lon')).fillna(0)\n \n \n trend_models = { }\n if(apply_detrending): \n ds = ds.load()\n for ssn in ('DJF', 'MAM', 'JJA', 'SON'):\n #ssn_idx = ds['time.season'] == ssn\n \n trn_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], trn_yrs))\n all_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], all_yrs))\n \n trn_x = np.array(ds.time[trn_idx].values.tolist()).reshape(-1,1)\n all_x = np.array(ds.time[all_idx].values.tolist()).reshape(-1,1)\n try:\n trend = predef_trnd[ssn].predict(all_x)\n trend_models[ssn] = predef_trnd[ssn]\n print('Predefined trend model used')\n except:\n #_, trend_model = define_trends(ds[var][trn_idx], trn_x)\n _, trend_model = define_trends(ds[var][all_idx], all_x)\n trend = trend_model.predict(all_x)\n trend_models[ssn] = trend_model\n print('Trends calculated from data')\n \n ds[var][all_idx] = ds[var][all_idx] - trend\n \n\n \n return ds, clim, trend_models","def gatherStationData():\n flist = list_files()\n station_dics = {}\n print(\"Reading in csv data...\")\n for f_in in flist:\n start,end = find_timespan(f_in)\n station = station_name(f=f_in)\n print(\"File: {0} Station: {1} {2}--{3}\".format(f_in, \n station, start, end))\n station_dics[station] = read_precip(fname=f_in, \n label=station, start_year=start, end_year=end)\n data_list = []\n for s in station_dics:\n data_list.append(station_dics[s]) \n return pd.concat(data_list,axis=1)","def extract_dates(self,dates, tol= 0.05 , in_place=False, verbose=True):\n###################################################################\n\n # import \n import inspect\n import numpy as np\n import pyacs.gts\n\n # check data is not None\n from pyacs.gts.lib.errors import GtsInputDataNone\n \n try:\n if self.data is None:\n # raise exception\n raise GtsInputDataNone(inspect.stack()[0][3],__name__,self)\n except GtsInputDataNone as error:\n # print PYACS WARNING\n print( error )\n return( self )\n\n # working gts\n new_gts = self.copy()\n \n # case .data_xyz is None\n \n if new_gts.data_xyz is None:\n new_gts.neu2xyz(corr=True)\n\n else:\n # check data/data_xyz consistency\n try:\n if not new_gts.cdata(data=True):\n # raise exception\n from pyacs.gts.lib.errors import GtsCDataError\n raise GtsCDataError( inspect.stack()[0][3],__name__,self )\n except GtsCDataError as error:\n print( error )\n return( self )\n \n \n new_data=None\n \n # extract dates\n \n index = np.array( pyacs.gts.Gts.get_index_from_dates(dates, self.data, tol=tol) )\n \n if verbose:\n print('-- Extracting ',index.shape[0],' entries from Gts or code: ',self.code)\n \n if index.shape[0] > 0:\n new_data_xyz= self.data_xyz[index,:]\n new_sigma = self.data[index,4:]\n else:\n new_data=None\n if verbose:\n print(\"-- time series \",self.code,\" does not have dates at the requested dates \")\n\n \n # handles outliers\n\n if new_data is not None: \n ldate_outliers=self.data[:,0][self.outliers]\n lupdated_outliers=pyacs.gts.Gts.get_index_from_dates(ldate_outliers, new_data, tol=tol)\n else:\n lupdated_outliers = []\n\n if verbose:\n print('-- Transmitting ',len(lupdated_outliers),' outliers to the extracted Gts ')\n \n # case observations\n \n new_gts.data_xyz = new_data_xyz\n \n # handle outliers\n\n ldate_outliers=self.data[:,0][self.outliers]\n lupdated_outliers=pyacs.gts.Gts.get_index_from_dates(ldate_outliers, new_data_xyz, tol=0.05)\n \n # handles offsets_date\n \n upd_offsets=[]\n for offset_date in self.offsets_dates:\n if offset_date>=new_data_xyz[0,0] and offset_date<=new_data_xyz[-1,0]:\n upd_offsets.append(offset_date)\n \n # handles X0,Y0,Z0\n \n new_gts.X0 = new_data_xyz[0,1]\n new_gts.Y0 = new_data_xyz[0,2]\n new_gts.Z0 = new_data_xyz[0,3]\n \n # re-generate NEU time series\n new_gts.xyz2neu(corr=False)\n\n # re-populate the uncertainties columns\n new_gts.data[:,4:] = new_sigma\n \n # offsets & outliers\n \n new_gts.offsets_dates=upd_offsets\n new_gts.outliers=lupdated_outliers\n \n if in_place:\n self = new_gts\n return(self)\n else:\n return(new_gts)","def windTimeSeries(ftp, rootFolder, selectedDate, outputFolder, ymax):\n \n dataType = 'wind_reconstruction_data'\n file_list = []\n \n\n # Retrieve data from folders in FTP\n \n possible_hours = [format(x,'02d') for x in range(24)]\n from_zone = tz.gettz('America/Mexico_City')\n to_zone = tz.gettz('UTC')\n \n for item in possible_hours:\n \n local_time = datetime.strptime(selectedDate+item, '%Y-%m-%d%H')\n local_time = local_time.replace(tzinfo=from_zone)\n \n utc_date = local_time.astimezone(to_zone)\n \n utc_day = utc_date.strftime(\"%Y-%m-%d\")\n \n utc_hour = utc_date.strftime(\"%H\")\n \n time_folder = rootFolder+utc_day+'/'+dataType+'/'+utc_hour+'-00/'\n \n creat_min = []\n try:\n\n ftp.dir(time_folder, creat_min.append)\n dbs = []\n for i in range(len(creat_min)):\n if creat_min[i].find('DBS') != -1:\n dbs.append(creat_min[i])\n\n if len(dbs) == 0:\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\n\n elif len(dbs) == 1:\n if dbs[0].find('-25-'):\n file_list.append(time_folder+dbs[0][55:])\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\n elif dbs[0].find('-55-'):\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\n file_list.append(time_folder+dbs[0][55:])\n else:\n for i in range(len(dbs)):\n file_list.append(time_folder+dbs[i][55:])\n\n except:\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\n\n\n \n # Create output folder\n outputFolder = outputFolder+'/'+selectedDate\n try:\n mkdirp(outputFolder)\n except:\n print('warning: folder '+outputFolder+' already exists')\n \n # Make 'generic' outputfile\n outFile = outputFolder+'/'\n\n try:\n \n meas_time = []\n Usets = []\n Vsets = []\n Wsets = []\n \n for working_file in file_list:\n\n if working_file.find('missing_data') != -1:\n Xset = pd.DataFrame(np.nan,columns=['Xwind'],index=range(100,2550,50))\n Yset = pd.DataFrame(np.nan,columns=['Ywind'],index=range(100,2550,50))\n Zset = pd.DataFrame(np.nan,columns=['Zwind'],index=range(100,2550,50))\n match = (re.search('\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}',working_file)).group(0)\n meas_time.append(match)\n\n else: \n match = (re.search('\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}',working_file)).group(0)\n #hour_min = re.search('_\\d{2}-\\d{2}-\\d{2}',working_file).group(0)\n meas_time.append(match)\n \n obj = DataContainer()\n ftp.retrbinary('RETR %s' % working_file, obj.readFromFTP)\n columns = ['Timestamp', 'Azimuth', 'Elevation','Range','Xwind','Ywind','Zwind',\\\n 'CNR','ConfIdx']\n\n # Get the date from the file as a DataFrame\n data = obj.dataToArray(columns)\n data.drop(data[data.Range > ymax].index, inplace=True)\n data = data[['Range','Xwind','Ywind','Zwind']]\n # Replace abs values Xwind and Ywind greater than 50 with NaN\n \n data.Xwind.where((data.Xwind < 50) | (data.Xwind > -50),np.nan,inplace=True)\n data.Ywind.where((data.Ywind > 50) | (data.Ywind > -50),np.nan,inplace=True)\n\n dataByRange = data.groupby('Range').mean() \n \n Xset = dataByRange[['Xwind']]\n Yset = dataByRange[['Ywind']]\n Zset = dataByRange[['Zwind']]\n \n Usets.append(Xset)\n Vsets.append(Yset)\n Wsets.append(Zset)\n \n df_U = pd.concat(Usets,axis=1)\n df_V = pd.concat(Vsets,axis=1)\n df_W = pd.concat(Wsets,axis=1)\n \n U = df_U.values;\n V = df_V.values;\n W = df_W.values;\n \n # Vertical wind grid\n x = np.linspace(0,48,49)\n y = np.linspace(100,2500,49)\n X, Y = np.meshgrid(x, y)\n \n # If the plot is going to have a variable pcolormesh uncomment next section\n# cmapLim = 0\n# if abs(np.nanmax(W)) > abs(np.nanmin(W)):\n# cmapLim = abs(np.nanmax(W))\n# else:\n# cmapLim = abs(np.nanmin(W))\n \n # Change back the UTC times to local time\n \n timelabels = []\n to_zone = tz.gettz('America/Mexico_City')\n from_zone = tz.gettz('UTC')\n \n for entry in meas_time:\n utc_time = datetime.strptime(entry, '%Y-%m-%d_%H-%M')\n utc_time = utc_time.replace(tzinfo=from_zone)\n \n local = utc_time.astimezone(to_zone)\n \n timelabels.append(local.strftime(\"%H:%M\"))\n \n timelabels = [i.replace('25','00') for i in timelabels[2:48:4]]\n \n # Plot \n title = ('Perfil vertical del viento, %s (serie de tiempo)' % selectedDate)\n fig, ax = plt.subplots(figsize=(20,8))\n \n # For variable pcolormesh uncomment next line\n #cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= (-cmapLim), vmax=cmapLim)\n \n cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= -3, vmax=3)\n ax.set_title(title,fontsize = 24)\n ax.set_xlabel('Hora Local [hh:mm]', fontsize = 18, labelpad = 14)\n ax.set_xticks(np.arange(2.5,48, step=4))\n ax.set_xticklabels(timelabels, rotation=45, size=12)\n ax.set_ylabel('Altura [m]', fontsize = 18, labelpad = 18)\n ax.yaxis.set_tick_params(labelsize=12)\n ax.text(0.18,-0.08,'Datos de perfilador LIDAR Leosphere Windcube 100s. Lat 19.3262, Lon -99.1761, Alt 2280 msnm',transform=plt.gcf().transFigure, fontdict={'size': 18})\n \n # Colorbar\n cbaxes = fig.add_axes([0.91, 0.125, 0.02, 0.4]) \n cbar = fig.colorbar(cax, cax = cbaxes)\n cbar.set_label(r'Viento vertical [$m\\,s^{-1}$]', fontsize = 18)\n cbaxes.text(-0.3,1.07,'(Hacia arriba)')\n cbaxes.text(-0.3,-0.07,'(Hacia abajo)') \n \n \n #Other a artists\n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/time_adv.png')\n newax = fig.add_axes([0.6, -.03, 0.06, 0.06], anchor='NW', zorder=0) #[xo,yo,width,height]\n newax.imshow(im)\n newax.axis('off')\n \n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/directions.png')\n newax1 = fig.add_axes([0.90, 0.75, 0.15, 0.15], anchor='NW', zorder=0) #[xo,yo,width,height]\n newax1.imshow(im)\n newax1.axis('off')\n \n \n # Horizontal wind grid\n \n x_h = np.arange(0, len(meas_time), 1)\n Xh, Yh = np.meshgrid(x_h, y)\n \n # Plot arrows\n Q = ax.quiver(Xh[::2], Yh[::2], U[::2], V[::2], pivot = 'mid',alpha=0.75)\n ax.quiverkey(Q, 0.93, 0.68, 10, r'$10\\,m\\,s^{-1}$',coordinates = 'figure', labelpos='N' )\n \n \n finalOutputFile = outFile+'timeseries_daily_'+selectedDate\n fig.savefig(finalOutputFile,bbox_inches='tight')\n plt.close()\n \n except Exception as e:\n print(\"Problema calculando Wind Time Series\", e)","def load_data(city, month, week_day):\n# name for day variable changed from \"day_name\" into week_day to take into account new pandas method \".day_name()\"\n# read in file form selected city\n df = pd.read_csv(CITY_DATA[city])\n# create additional columns for months, days, start/ end times, hours and station combinations\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n df['End Time'] = pd.to_datetime(df['End Time'])\n df['month_start'] = df['Start Time'].dt.month\n df['month_end'] = df['End Time'].dt.month\n df['day_start'] = df['Start Time'].dt.day_name()\n df['day_end'] = df['End Time'].dt.day_name()\n df['hour'] = df['Start Time'].dt.hour\n df['station_comb'] = df['Start Station'] + ' &AND& ' + df['End Station']\n# filter data file by month: capture start and end months\n if month != 7:\n df1 = df[df['month_start'] == month]\n df2 = df1.append(df[df['month_end'] == month])\n df = df2.drop_duplicates()\n# filter data file by day: capture start and end days\n if week_day != 'All':\n df3 = df[df['day_start'] == week_day]\n df4 = df3.append(df[df['day_end'] == week_day])\n df = df4.drop_duplicates()\n# reset index to facilitate looping in station_stats function\n df = df.reset_index()\n# check if user wants to check first data lines\n req_check_df = input('\\nIf you want to check the selected data please enter y.')\n if req_check_df[0:1].lower() == 'y':\n print('check df = \\n', df.head())\n wait = input('Press Enter to continue. ')\n\n return df","def database_extraction(mod_start_time,mod_end_time,instrument_chosen,subevent_bool,\r\n detect_previous_event = False,thresholds='100,1',\r\n one_thresh = False):\r\n obs_file_created = False\r\n\r\n #extending time window\r\n window_end_time = (mod_end_time + timedelta(days=2))\r\n window_start_time = (mod_start_time - timedelta(days=2))\r\n \r\n #making a list of all dates within window\r\n day_list=[]\r\n for d in range(10):\r\n day_list.append((window_start_time + timedelta(days=d)).date())\r\n print('day list = %s' %day_list)\r\n \r\n print('determining if an instrument has been chosen')\r\n\r\n if instrument_chosen:\r\n #if an instrument has been chosen, checking to make sure it still works for this date\r\n if inst_end < window_end_time:\r\n instrument_chosen = False\r\n else:\r\n #if insturment hasn't been chosen, figuring out what it should be for given date\r\n try:\r\n #if instrument is specified in cfg using that\r\n instrument = cfg.instrument\r\n inst_end = datetime.today()\r\n print('using %s as our instrument for observations' %instrument)\r\n instrument_chosen = True\r\n\r\n except:\r\n #choosing instrument using function if not given in cfg\r\n instrument_stuff = choose_prime_inst(window_start_time.date(),\r\n window_end_time.date())\r\n instrument = instrument_stuff[0]\r\n #figuring out how long we can use this instrument\r\n inst_end = instrument_stuff[1]\r\n instrument_chosen = True\r\n \r\n #running katie's code to extract data using chosen instrument and dates\r\n print('extracting data from GOES website')\r\n \r\n #running for only one threshold if one_thresh is true, otherwise running for default\r\n #thresholds as well as any additional threshold given\r\n if one_thresh:\r\n one_sep.run_all(str(window_start_time), str(window_end_time), str(instrument),\r\n 'integral', '', '', True, detect_previous_event, thresholds) \r\n print('ran for threshold %s' %thresholds)\r\n else:\r\n if subevent_bool:\r\n thresholds = '10,1'\r\n #if event is a subevent, changing the threshold in katie's code to\r\n #10 MeV > 1pfu so that it will be recorded\r\n print('********************SUBEVENT**************************')\r\n sep.run_all(str(window_start_time), str(window_end_time), str(instrument),\r\n 'integral', '', '', True, detect_previous_event, thresholds)\r\n print('ran for subevent')\r\n else:\r\n #if an event, running with usual thresholds\r\n print('********************EVENT*****************************')\r\n sep.run_all(str(window_start_time), str(window_end_time),str(instrument), \r\n 'integral', '', '', True, detect_previous_event, thresholds)\r\n \r\n #reloading function so it doesn't keep old data \r\n reload(sep)\r\n \r\n #reformatting csv created from katie's code to json\r\n print('extracted - reformatting') \r\n for day in day_list: \r\n if not obs_file_created:\r\n #checking each day within the window to find the csv file if it hasn't\r\n #already been found\r\n print('thresholds: %s' %thresholds)\r\n \r\n if one_thresh:\r\n #name includes threshold if only ran for one threshold\r\n new_obs_name = ('sep_values_' + str(instrument) + '_integral_gt' +\r\n str(thresholds).split(',')[0] + '_' + str(thresholds).split(',')[1] + 'pfu_' +\r\n day.strftime('%Y_%m_%d').replace('_0','_') + '.csv')\r\n else:\r\n #otherwise only includes date ran for\r\n new_obs_name = ('sep_values_' + str(instrument) + '_integral_' +\r\n day.strftime('%Y_%m_%d').replace('_0','_') + '.csv')\r\n \r\n print('new_os_name %s' %new_obs_name) \r\n \r\n #checking if that file exists\r\n if os.path.exists(katies_path / new_obs_name):\r\n #if a file with this date exists, creating the corresponding json file\r\n \r\n #json name\r\n if one_thresh:\r\n obs_name = (str(instrument) + '_' + str(day) + 'only_' + str(thresholds).split(',')[0] + 'MeV_event.json')\r\n else:\r\n obs_name = (str(instrument) + '_' +\r\n str(day) + '.json')\r\n #creating json file\r\n obs_csv2json((katies_path / new_obs_name), obs_name,\r\n (ref_path/'example_sepscoreboard_json_file_v20190228.json'),\r\n instrument)\r\n \r\n print('obs file created')\r\n #file is created - will not run for anymore dates within window\r\n obs_file_created = True\r\n \r\n return(obs_name)\r\n else:\r\n print('no csv file found with this date, checking next one')","def _write_to_dataset(parser1, parser2, dset, rundate):\n\n data_all1 = parser1.as_dict()\n data_all2 = parser2.as_dict()\n if parser1.file_path == parser2.file_path:\n collection = [data_all1]\n else:\n collection = [data_all1, data_all2]\n\n # Meta information\n dset.meta[\"tech\"] = \"slr\"\n dset.meta.add(\"file\", parser1.file_path.stem, section=\"input\")\n dset.meta.add(\"file\", parser2.file_path.stem, section=\"input\")\n dset.meta.add(\"type\", config.tech.obs_format.str.upper(), section=\"input\")\n\n # Make new dict \"obs_data\" containing only data in relevant time interval:\n arc_length = config.tech.arc_length.float\n rundate_datetime = datetime(rundate.year, rundate.month, rundate.day)\n obs_data = dict()\n for data_all in collection:\n for i, x in enumerate(data_all[\"meta\"][\"obs_time\"]):\n if rundate_datetime <= x < rundate_datetime + timedelta(days=arc_length):\n for key in (\"meta\", \"obs\", \"obs_str\"):\n for field, val in data_all[key].items():\n obs_data.setdefault(key, dict()).setdefault(field, list()).append(val[i])\n\n data_all.pop(\"meta\")\n data_all.pop(\"obs\")\n data_all.pop(\"obs_str\")\n\n for key in data_all.keys():\n if key.startswith(\"met_\"):\n for key2, val in data_all[key].items():\n obs_data.setdefault(key, dict()).setdefault(key2, list()).append(val)\n elif key.startswith(\"satellite_\"):\n # TODO: Use this information in the future?\n continue\n elif key.startswith(\"station_\"):\n # TODO: Use this information in the future?\n continue\n else:\n log.fatal(f\"Unknown data type{key}\")\n\n obs_date = obs_data[\"meta\"][\"obs_date\"]\n time = [obs_date[i] + timedelta(seconds=obs_data[\"meta\"][\"obs_sec\"][i]) for i in range(0, len(obs_date))]\n dset.num_obs = len(obs_data[\"meta\"][\"obs_time\"])\n dset.add_time(\"time\", val=time, scale=\"utc\", fmt=\"datetime\")\n dset.add_text(val=obs_data[\"meta\"][\"station\"], name=\"station\")\n dset.add_text(val=obs_data[\"meta\"][\"satellite\"], name=\"satellite\")\n dset.add_float(val=obs_data[\"meta\"][\"bin_rms\"], unit=\"picoseconds\", name=\"bin_rms\")\n # Positions\n trf = apriori.get(\"trf\", time=dset.time)\n for station in dset.unique(\"station\"):\n trf_site = trf[station]\n station_pos = trf_site.pos.trs.val\n log.debug(f\"Station position for {station} ({trf_site.name}) is (x,y,z) = {station_pos.mean(axis=0)}\")\n domes = trf_site.meta[\"domes\"]\n obs_data[\"pos_\" + station] = station_pos\n obs_data[\"station-other_\" + station] = dict(domes=domes, cdp=station, site_id=station)\n dset.add_position(\n \"site_pos\",\n time=dset.time,\n system=\"trs\",\n val=np.array([obs_data[\"pos_\" + s][idx] for idx, s in enumerate(dset.station)]),\n )\n # Station data\n sta_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\"station_\")])\n for field in sta_fields:\n dset.add_float(field, val=np.array([float(obs_data[\"station_\" + s][field]) for s in dset.station]))\n sta_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\"station-other_\")])\n for field in sta_fields:\n dset.add_text(field, val=[obs_data[\"station-other_\" + s][field] for s in dset.station])\n\n # Station meta\n station_keys = sorted([k for k, v in obs_data.items() if k.startswith(\"station-other_\")])\n pos_keys = sorted([k for k, v in obs_data.items() if k.startswith(\"pos_\")])\n\n for sta_key, pos_key in zip(station_keys, pos_keys):\n sta_name = sta_key.replace(\"station-other_\", \"\")\n cdp = obs_data[sta_key][\"cdp\"]\n dset.meta.add(sta_name, \"site_id\", cdp)\n longitude, latitude, height, _ = sofa.iau_gc2gd(2, obs_data[pos_key][0, :]) # TODO: Reference ellipsoid\n dset.meta[\"station\"].setdefault(sta_name, {})[\"cdp\"] = cdp\n dset.meta[\"station\"].setdefault(sta_name, {})[\"site_id\"] = cdp\n dset.meta[\"station\"].setdefault(sta_name, {})[\"domes\"] = obs_data[sta_key][\"domes\"]\n dset.meta[\"station\"].setdefault(sta_name, {})[\"marker\"] = \" \"\n dset.meta[\"station\"].setdefault(sta_name, {})[\"description\"] = \" \"\n dset.meta[\"station\"].setdefault(sta_name, {})[\"longitude\"] = longitude\n dset.meta[\"station\"].setdefault(sta_name, {})[\"latitude\"] = latitude\n dset.meta[\"station\"].setdefault(sta_name, {})[\"height\"] = height\n\n # Satellite data\n sat_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\"satellite_\")])\n for field in sat_fields:\n dset.add_float(field, val=np.array([float(obs_data[\"satellite_\" + s][field]) for s in dset.satellite]))\n\n # Observations\n # In the dataset, obs_time is seconds since rundate:\n v = [\n (obs_data[\"meta\"][\"obs_date\"][i] - rundate_datetime).total_seconds() + obs_data[\"meta\"][\"obs_sec\"][i]\n for i in range(0, dset.num_obs)\n ]\n\n obs_data[\"obs\"].pop(\"obs_time\")\n dset.add_float(\"obs_time\", val=v)\n for field, values in obs_data[\"obs\"].items():\n dset.add_float(field, val=np.array(values))\n\n for field, values in obs_data[\"obs_str\"].items():\n dset.add_text(field, val=values)\n\n return obs_data","def main(args):\r\n print(\"Anonymizing ...\")\r\n print(args)\r\n\r\n file_loc = args.file\r\n origdata = pd.read_csv(file_loc, sep=',', low_memory=False)\r\n\r\n print(origdata)\r\n\r\n print(args.col_customer)\r\n\r\n ID_field = args.col_customer #TBU CustomerID field name\r\n TS_field = args.col_time #TBU TS field name \r\n\r\n sub = origdata.loc[:,:] #Uncomment for applying on complete dataset\r\n\r\n def anonymizeID(data,ID_field):\r\n df=pd.DataFrame(data.loc[:,ID_field])\r\n df2=df.applymap(lambda x: ((HMAC.new(b\"key\", bytes(x), MD5)).hexdigest()))\r\n return df2.loc[:,ID_field]\r\n\r\n def shiftTimeForTrace(data,TS_field):\r\n # print(df.head())\r\n df=pd.DataFrame(data.loc[:,TS_field])\r\n df2 = df.loc[:,TS_field].apply(lambda x: pd.to_datetime(x)) \r\n # print(df2.head())\r\n rand_days = random.randint(-5,5) #range can be updated\r\n df2 = df2 + pd.DateOffset(days=rand_days)\r\n # print(df2.head())\r\n return df2\r\n\r\n #OG subset for reference\r\n print(sub.head())\r\n\r\n # sub1=sub.sort_values(by=ID_field)\r\n sub1 = sub.loc[:,:]\r\n uniqueIDs = list(sub[ID_field].unique())\r\n # sub2 = sub.loc[:,TS_field].apply(lambda x: pd.to_datetime(x))\r\n for ID in uniqueIDs:\r\n sub3=sub1.loc[sub1[ID_field] == ID]\r\n sub3.loc[:,TS_field]=shiftTimeForTrace(sub3,TS_field)\r\n # print(sub1.loc[sub1[ID_field] == ID][TS_field])\r\n # print(pd.DataFrame(sub3[TS_field]))\r\n sub1.loc[sub1[ID_field] == ID,TS_field] = pd.DataFrame(sub3[TS_field])\r\n\r\n # Results post TS shift\r\n print(sub1.head())\r\n\r\n sub4 = sub1.loc[:,:]\r\n sub4.loc[:,ID_field] = anonymizeID(sub4,ID_field)\r\n\r\n #Results post ID anonymization\r\n print(sub4.head())\r\n sub4.to_csv('out.csv',index=False)","def get_new_local_alerts():\n ip_details = my_ip_details()\n m = int(minutes_a_record_remains_new())\n new_date = make_date_time(seconds_to_subtract=m * 60)\n f = []\n if not ip_details:\n return None\n data = get_feeds().find({\"time.observation\": {\"$gt\": new_date}, \"source.network\": ip_details['network'],\n \"$or\": [{\"source.ip\": {\"$exists\": True}, \"source.network\": {\"$exists\": True}}]})\\\n .sort([(\"time_observation\", DESCENDING)])\n if data and data.count() > 0:\n data = normalize_dict(data)\n for x, value in data.items():\n value['id'] = x\n if '_id' in value:\n del value['_id']\n if 'raw' in value:\n del value['raw']\n\n f.append(value)\n\n create_log(None, None, \"{0} Local attacks detected : \".format(len(f)), LOG_SYSTEM_TYPE)\n return f","def soho_ephin_loader(startdate, enddate, resample=None, path=None, all_columns=False, pos_timestamp=None, use_uncorrected_data_on_own_risk=False):\n\n if not path:\n path = sunpy.config.get('downloads', 'download_dir') + os.sep\n\n # create list of files to load:\n dates = pd.date_range(start=startdate, end=enddate, freq='D')\n filelist = []\n for i, doy in enumerate(dates.day_of_year):\n if dates[i].year<2000:\n pre = \"eph\"\n yy = dates[i].year-1900\n else:\n pre = \"epi\"\n yy = dates[i].year-2000\n name = \"%s%02d%03d\" %(pre, yy, doy)\n try:\n file = glob.glob(f\"{path}{os.sep}{name}.rl2\")[0]\n except IndexError:\n print(f\"File {name}.rl2 not found locally at {path}.\")\n file = soho_ephin_download(dates[i], path)\n if len(file) > 0:\n filelist.append(file)\n if len(filelist) > 0:\n filelist = np.sort(filelist)\n\n col_names = ['Year', 'DOY', 'MS', 'S/C Epoch', 'Status Word part 1', 'Status Word part 2',\n 'E150', 'E300', 'E1300', 'E3000', 'P4', 'P8', 'P25', 'P41',\n 'H4', 'H8', 'H25', 'H41', 'INT',\n 'P4 GM', 'P4 GR', 'P4 S', 'P8 GM', 'P8 GR', 'P8 S',\n 'P25 GM', 'P25 GR', 'P25 S', 'P41 GM', 'P41 GR', 'P41 S',\n 'H4 GM', 'H4 GR', 'H4 S1', 'H4 S23', 'H8 GM', 'H8 GR', 'H8 S1', 'H8 S23',\n 'H25 GM', 'H25 GR', 'H25 S1', 'H25 S23', 'H41 GM', 'H41 GR', 'H41 S1', 'H41 S23',\n 'Status Flag', 'Spare 1', 'Spare 2', 'Spare 3']\n\n # read files into Pandas dataframes:\n df = pd.read_csv(filelist[0], header=None, sep=r'\\s+', names=col_names)\n if len(filelist) > 1:\n for file in filelist[1:]:\n t_df = pd.read_csv(file, header=None, sep=r'\\s+', names=col_names)\n df = pd.concat([df, t_df])\n\n # # generate datetime index from year, day of year, and milisec of day:\n df.index = doy2dt(df.Year.values, df.DOY.values + df.MS.values/1000./86400.)\n df.index.name = 'time'\n\n # drop some unused columns:\n if not all_columns:\n df = df.drop(columns=['Year', 'DOY', 'MS', 'S/C Epoch',\n 'Status Word part 1', 'Status Word part 2',\n 'P4 GM', 'P4 GR', 'P4 S',\n 'P8 GM', 'P8 GR', 'P8 S',\n 'P25 GM', 'P25 GR', 'P25 S',\n 'P41 GM', 'P41 GR', 'P41 S',\n 'H4 GM', 'H4 GR', 'H4 S1', 'H4 S23',\n 'H8 GM', 'H8 GR', 'H8 S1', 'H8 S23',\n 'H25 GM', 'H25 GR', 'H25 S1', 'H25 S23',\n 'H41 GM', 'H41 GR', 'H41 S1', 'H41 S23',\n 'Spare 1', 'Spare 2', 'Spare 3'])\n\n # Proton and helium measurements need to be corrected for effects determined post-launch,\n # cf. chapter 2.3 of https://www.ieap.uni-kiel.de/et/ag-heber/costep/materials/L2_spec_ephin.pdf\n # Until this correction has been implemented here, these data products are set to -9e9.\n # Setting use_uncorrected_data_on_own_risk=True skips this replacement, so that the uncorrected\n # data can be obtained at own risk!\n if use_uncorrected_data_on_own_risk:\n warnings.warn(\"Proton and helium data is still uncorrected! Know what you're doing and use at own risk!\")\n else:\n df.P4 = -9e9\n df.P8 = -9e9\n df.P25 = -9e9\n df.P41 = -9e9\n df.H4 = -9e9\n df.H8 = -9e9\n df.H25 = -9e9\n df.H41 = -9e9\n\n # replace bad data with np.nan:\n # there shouldn't be bad data in rl2 files!\n # df = df.replace(-9999.900, np.nan)\n\n # derive instrument status and dependencies\n status = df['Status Flag'].values\n\n fmodes = np.zeros(len(status))\n for q in range(len(status)):\n binaries = '{0:08b}'.format(int(status[q]))\n if int(binaries[-1]) == 1:\n if int(binaries[-3]) == 1:\n fmodes[q] = 1\n else:\n fmodes[q] = 2\n\n ringoff = np.zeros(len(status))\n for q in range(len(status)):\n binaries = '{0:08b}'.format(int(status[q]))\n if int(binaries[-2]):\n ringoff[q] = 1\n\n cs_e300 = '0.67 - 3.0 MeV'\n cs_e1300 = '2.64 - 6.18 MeV'\n cs_p25 = '25 - 41 MeV'\n cs_he25 = '25 - 41 MeV/N'\n if max(fmodes)==1:\n cs_e1300 = \"2.64 - 10.4 MeV\"\n cs_p25 = '25 - 53 MeV'\n cs_he25 = '25 - 53 MeV/n'\n if max(fmodes)==2:\n warnings.warn('Careful: EPHIN ring off!')\n\n # failure mode D since 4 Oct 2017:\n # dates[-1].date() is enddate, used to catch cases when enddate is a string\n if dates[-1].date() >= dt.date(2017, 10, 4):\n cs_e300 = 'deactivated bc. of failure mode D'\n cs_e1300 = \"0.67 - 10.4 MeV\"\n # dates[0].date() is startdate, used to catch cases when startdate is a string\n if dates[0].date() <= dt.date(2017, 10, 4):\n warnings.warn('EPHIN instrument status (i.e., electron energy channels) changed during selected period (on Oct 4, 2017)!')\n\n # careful!\n # adjusting the position of the timestamp manually.\n # requires knowledge of the original time resolution and timestamp position!\n if pos_timestamp == 'center':\n df.index = df.index+pd.Timedelta('30s')\n\n # optional resampling:\n if isinstance(resample, str):\n df = resample_df(df, resample, pos_timestamp=pos_timestamp)\n else:\n df = []\n\n meta = {'E150': '0.25 - 0.7 MeV',\n 'E300': cs_e300,\n 'E1300': cs_e1300,\n 'E3000': '4.80 - 10.4 MeV',\n 'P4': '4.3 - 7.8 MeV',\n 'P8': '7.8 - 25 MeV',\n 'P25': cs_p25,\n 'P41': '41 - 53 MeV',\n 'H4': '4.3 - 7.8 MeV/n',\n 'H8': '7.8 - 25.0 MeV/n',\n 'H25': cs_he25,\n 'H41': '40.9 - 53.0 MeV/n',\n 'INT': '>25 MeV integral'}\n\n return df, meta","def get(url: str, dp: frictionless.package.Package, force: bool = False):\n ld = utilities.get_ld_json(url)\n csv_file = ld[\"distribution\"][0][\"contentUrl\"]\n datePublished = ld[\"datePublished\"]\n name = ld[\"name\"].replace(\" \", \"_\")\n\n # Inferring and completing metadata\n logging.info(\"Creating datapackage for input data\")\n new_dp = frictionless.describe_package(csv_file, stats=True,) # Add stats\n # Add date\n new_dp[\"datePublished\"] = datePublished\n\n # Add missing valies\n new_dp.resources[0][\"schema\"][\"missingValues\"] = [\"NULL\"]\n for field in VALUE_VARS:\n new_dp.resources[0].schema.get_field(field).type = \"number\"\n\n # Logic for update\n if dp is not None: # Existing dataset\n # check stats\n isChangedStats = dp[\"resources\"][0][\"stats\"] != new_dp[\"resources\"][0][\"stats\"]\n isChangedDate = dp[\"datePublished\"] != new_dp[\"datePublished\"]\n\n if (\n isChangedStats or isChangedDate\n ): # Data integration will continue, regardless of force argument\n logging.info(\"Data has changed\")\n if isValid(dp, new_dp):\n enermaps_data, spatial = prepare(new_dp, name)\n elif force: # Data integration will continue, even if data has not changed\n logging.info(\"Forced update\")\n if isValid(dp, new_dp):\n enermaps_data, spatial = prepare(new_dp, name)\n else: # Data integration will stop here, returning Nones\n logging.info(\"Data has not changed. Use --force if you want to reupload.\")\n return None, None, None\n else: # New dataset\n dp = new_dp # this is just for the sake of the schema control\n if isValid(dp, new_dp):\n enermaps_data, spatial = prepare(new_dp, name)\n\n return enermaps_data, spatial, new_dp","def GetLambData(removerepeats=True, days_from_year = 30,interval_min = 0,interval_max = None ,earliest_date = None,\\\nlatest_date = None, userwhere = \"\",verbose = False,orderby=None,join='inner',longest_interval=False,get_geom=False,get_geog=False,\\\nby_column=True,as_object=False,omit=True,generalize=None,results=False,density=0.850, density_err= 0.06,acrossgl_err=0.0,get_hypsometry=False):\n \n select1 = \"\"\"select \\\nlamb.gid,\\\nlamb.glid,\\\nlamb.date1,\\\nlamb.date2,\\\nlamb.interval,\\\nlamb.volmodel,\\\nlamb.vol25diff,\\\nlamb.vol75diff,\\\nlamb.balmodel,\\\nlamb.bal25diff,\\\nlamb.bal75diff,\\\nergi.glimsid,\\\ngltype.surge,\\\ngltype.tidewater,\\\ngltype.lake,\\\ngltype.river,\\\ngltype.glaciertype,\\\nglnames.name as lambname,\\\nglnames.region,\\\nlamb.e,\\\nlamb.dz,\\\nlamb.dz25,\\\nlamb.dz75,\\\nlamb.aad,\\\nlamb.masschange,\\\nlamb.massbal,\\\nlamb.numdata,\\\nergi.gltype,\\\nergi.glactype,\\\nergi.region,\\\nergi.max::real,\\\nergi.min::real,\\\nflx.eb_bm_flx,\\\nflx.eb_best_flx,\\\nflx.eb_low_flx,\\\nflx.eb_high_flx,\\\nflx.eb_bm_err,\\\nflx.bm_length,\\\nflx.smb,\\\nergi.continentality,\\\nergi.area::double precision,\\\nergi.name,\\\n\"\"\"\n\n#eb_bm_flx\n\n#ergi.max,\\\n#ergi.mn_dist_coast,\\\n#ergi.mn_elev,\\\n#ergi.meanVel,\\\n\n\n if get_geom:\n if generalize != None: select2 = \"ST_Simplify(ergi.albersgeom, %s) as albersgeom,\" % generalize\n else: select2 = \"ergi.albersgeom as albersgeom,\"\n elif get_geog: \n if generalize != None: select2 = \"ST_Simplify(ergi.geog::geometry, %s) as geog,\" % generalize\n else: select2 = \"ergi.geog as geog,\"\n else: select2 = ''\n\n select3 = \"\"\"lamb.numdata \\\nfrom lamb inner join glnames on glnames.gid=lamb.glid inner join gltype on glnames.gid=gltype.glid\ninner join ergi on glnames.glimsid=ergi.glimsid left join tidewater_flux as flx on ergi.glimsid=flx.glimsid \"\"\" \n\n if results:\n select1 = select1 + \"rlt.rlt_totalGt,rlt.rlt_totalkgm2,rlt.rlt_errGt,rlt.rlt_errkgm2,rlt.rlt_singlerrGt,rlt.rlt_singlerrkgm2,\"\n \n #select3 = select3 + \"\"\" LEFT JOIN (SELECT rlt.glimsid,\n #SUM(rlt.area)/1e6::real as area, \n #SUM(rlt.mean*rlt.area)/1e9*%5.3f::real as rlt_totalGt,\n #(((((SUM(rlt.error*rlt.area)/SUM(rlt.mean*rlt.area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(rlt.mean*rlt.area)/1e9*%5.3f)^2 + (%5.3f)^2)^0.5::real as rlt_errGt,\n #SUM(rlt.mean*rlt.area)/SUM(rlt.area)*%5.3f::real as rlt_totalkgm2,\n #(((((SUM(rlt.error*rlt.area)/SUM(rlt.mean*rlt.area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(rlt.mean*rlt.area)/SUM(rlt.area)*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_errkgm2,\n #(((((SUM(rlt.singl_std*rlt.area)/SUM(rlt.mean*rlt.area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(rlt.mean*rlt.area)/1e9*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_singlerrkgm2,\n #(((((SUM(rlt.singl_std*rlt.area)/SUM(rlt.mean*rlt.area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(rlt.mean*rlt.area)/SUM(rlt.area)*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_singlerrGt\n #FROM resultsauto as rlt GROUP BY rlt.glimsid) as rlt on ergi.glimsid=rlt.glimsid \"\"\" % (density,density_err,density, density, acrossgl_err,density,\n #density_err,density, density, acrossgl_err,density_err,density, density, acrossgl_err,density_err,density, density, acrossgl_err)\n\n select3 = select3 + \"\"\" LEFT JOIN (SELECT glimsid,\n SUM(area)/1000000. as area,\n SUM(mean*area)/1e9*%5.3f::real as rlt_totalGt,\n SUM(mean*area)/SUM(area)*%5.3f::real as rlt_totalkgm2,\n (((((SUM(error*area)/SUM(mean*area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(mean*area)/1e9*%5.3f)^2 + (%5.3f)^2)^0.5::real as rlt_errGt,\n (((((SUM(error*area)/SUM(mean*area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(mean*area)/SUM(area)*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_errkgm2,\n (((((SUM(singl_std*area)/SUM(mean*area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(mean*area)/SUM(area)*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_singlerrkgm2,\n (((((SUM(singl_std*area)/SUM(mean*area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(mean*area)/1e9*%5.3f)^2 + (%5.3f)^2)^0.5::real as rlt_singlerrGt \n FROM resultsauto GROUP BY glimsid) as rlt on ergi.glimsid=rlt.glimsid \"\"\" % (density,density,density_err,density,density, acrossgl_err,density_err,density,density,acrossgl_err,density_err,density,density,acrossgl_err,density_err,density,density,acrossgl_err)\n\n select = select1+select2+select3\n\n\n#ST_AsText(ergi.albersgeom) as geom \\\n\n if longest_interval:removerepeats = False\n\n if join != 'inner': re.sub('inner join','left join',select) \n \n if longest_interval:\n order = \"\"\" order by glnames.name,lamb.interval DESC;\"\"\"\n removerepeats = False\n else:order = \"\"\" order by glnames.name,lamb.date1,lamb.interval;\"\"\"\n \n where = 'WHERE' \n \n if days_from_year != None: where = where+' AND ((interval % 365) > '+str(365-days_from_year)+' OR (interval % 365) < '+str(days_from_year)+')'\n if interval_min != None: where = where+' AND (ROUND(interval/365.) >= '+str(interval_min)+')'\n if interval_max != None: where = where+' AND (ROUND(interval/365.) <= '+str(interval_max)+')'\n if earliest_date != None: where = where+\" AND date1 >= '\"+str(earliest_date)+\"'\"\n if latest_date != None: where = where+\" AND date2 <= '\"+str(latest_date)+\"'\"\n if omit: where = where+\" AND omit='f'\"\n \n \n userwhere2 = re.sub('where','',userwhere, re.IGNORECASE)\n userwhere2 = re.sub('^\\s*and\\s*','',userwhere2, re.IGNORECASE)\n userwhere2 = re.sub('^\\s*','',userwhere2)\n userwhere2 = re.sub('\\s*$','',userwhere2)\n if userwhere != '':userwhere2 = ' AND '+userwhere2+' '\n \n where = where+userwhere2\n \n where = re.sub('WHERE AND','WHERE',where)\n \n if verbose:print select+where+order\n \n s = GetSqlData2(select+where+order,bycolumn=False)\n if type(s)==NoneType: return None\n #print s[0].keys()\n deletelist = []\n keeplist = []\n lastgl = ''\n lastdate = dtm.date(1900,1,1)\n \n if removerepeats:\n if verbose: print'Filtering lamb entries:'\n for i,row in enumerate(s):\n\n if row['name'] == lastgl:\n #print ' ',row['lamb.date1'],lastdate\n if row['date1'] < lastdate:\n deletelist.append(i)\n if verbose:print ' ',row['name'],row['date1'],row['date2'],'-- Omitted'\n else:\n lastdate = row['date2']\n if verbose:print row['name'],row['date1'],row['date2']\n keeplist.append(row['gid'])\n else: \n lastgl = row['name']\n lastdate = row['date2']\n if verbose:print row['name'],row['date1'],row['date2']\n keeplist.append(row['gid'])\n \n if longest_interval:\n if verbose: print'Filering for longest interval:'\n for i,row in enumerate(s):\n\n if row['name'] == lastgl:\n #print ' ',row['lamb.date1'],lastdate\n #if row['lamb.date1'] < lastdate:\n deletelist.append(i)\n if verbose:print ' ',row['name'],row['date1'],row['date2'],row['interval'],'-- Omitted'\n #else:\n # lastdate = row['lamb.date2']\n # if verbose:print row['glnames.name'],row['lamb.date1'],row['lamb.date2']\n # keeplist.append(row['lamb.gid'])\n else: \n lastgl = row['name']\n lastdate = row['date2']\n if verbose:print row['name'],row['date1'],row['date2'],row['interval']\n keeplist.append(row['gid'])\n \n if orderby == None:\n #print len(s)\n s = N.delete(N.array(s),deletelist)\n if get_hypsometry:\n for i in s:\n hyps = GetSqlData2(\"SELECT area::real as binned_area,bins::real,normbins::real FROM ergibins3 WHERE glimsid='%s' ORDER BY normbins\" % i['glimsid'])\n for key in ('binned_area','bins','normbins'):i[key]=hyps[key]\n \n if by_column:s = LambToColumn(s)\n else:\n s = GetSqlData2(select+'WHERE lamb.gid IN ('+re.sub('[\\[\\]]','',str(keeplist))+\") ORDER BY \"+orderby+\";\", bycolumn=by_column)\n \n if get_hypsometry:\n s['binned_area'] = []\n s['bins'] = []\n s['normbins'] = []\n for glimsidt in s['glimsid']:\n print glimsidt\n hyps = GetSqlData2(\"SELECT area as binned_area,bins,normbins FROM ergibins3 WHERE glimsid='%s' ORDER BY normbins\" % glimsidt)\n s['binned_area'].append(hyps['binned_area'])\n s['bins'].append(hyps['bins'])\n s['normbins'].append(hyps['normbins'])\n \n # \n #if orderby == None:\n # #print len(s)\n # s = N.delete(N.array(s),deletelist)\n # #print len(s)\n # if by_column:s = LambToColumn(s)\n #else:\n # s = GetSqlData2(select+'WHERE lamb.gid IN ('+re.sub('[\\[\\]]','',str(keeplist))+\") ORDER BY \"+orderby+\";\", bycolumn=by_column)\n # \n #if verbose:print len(s),' of rows in lamb selected.'\n\n\n if len(s) == 0: return None\n\n if as_object:\n if type(s) == dict:\n s=LambObject(s)\n print 'object'\n elif type(s) == list or type(s) == N.ndarray:\n s = [LambObject(row) for row in s]\n print 'list'\n \n return s \n \n #def norm_e(self):return (self.e - self.min) / (self.max - self.min)","def read_weather_data():\n # Check if UTC to gmt+1 conversion is being handled correctly\n weather = pd.read_csv('//datc//opschaler//weather_data//knmi_10_min_raw_data//output//df_combined_uncleaned.csv',\n delimiter='\\t', comment='#',\n parse_dates=['datetime'])\n weather = weather.set_index(['datetime'])\n return weather","def tobsdata():\n # query for the dates and temperature observations from a year from the last data point.\n # * Return a JSON list of Temperature Observations (tobs) for the previous year.\n # as this should be a list, I'm just grabbing the station name\n session = Session(engine)\n results = session.query(Measurement.tobs).filter(Measurement.date>='2016-08-23').all()\n session.close()\n tobs_list = list(np.ravel(results))\n\n # * Return the JSON representation of your dictionary.\n return jsonify(tobs_list)","def get_time_series(this_lat, this_lon, case, varnames):\n\n cesmdir = '/gpfs/fs1/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly'\n\n if 'LE' in case:\n\n from observational_large_ensemble.params import karen_params_cesm\n\n mode_lag = karen_params_cesm.mode_lag\n cvdp_loc = karen_params_cesm.cvdp_loc\n AMO_cutoff_freq = karen_params_cesm.AMO_cutoff_freq\n\n name_conversion = {'tas': 'TREFHT', 'pr': 'PRECC', 'slp': 'PSL'}\n cesm_names = [name_conversion[v] for v in varnames]\n this_member = int((case).split('-')[-1])\n cvdp_file = '%s/CESM1-CAM5-BGC-LE_#%i.cvdp_data.1920-2018.nc' % (cvdp_loc, this_member)\n\n # Historical filenames for CESM. Will need to append part of RCP8.5 to get full period\n filenames = []\n for var in cesm_names:\n file_str = '%s/%s/b.e11.B20TRC5CNBDRD.f09_g16.%03d.cam.h0.%s.??????-200512.nc' % (cesmdir, var,\n this_member, var)\n this_file = glob(file_str)[0]\n filenames.append(this_file)\n\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames,\n karen_params_cesm.valid_years, mode_lag,\n cvdp_file, AMO_cutoff_freq, name_conversion)\n\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\n\n else:\n\n from observational_large_ensemble.params import karen_params_obs\n\n mode_lag = karen_params_obs.mode_lag\n cvdp_loc = karen_params_obs.cvdp_loc\n AMO_cutoff_freq = karen_params_obs.AMO_cutoff_freq\n\n tas_dir = karen_params_obs.tas_dir\n pr_dir = karen_params_obs.pr_dir\n slp_dir = karen_params_obs.slp_dir\n cvdp_file = '%s/HadISST.cvdp_data.1920-2018.nc' % cvdp_loc\n file_dict = {'tas': '%s/Complete_TAVG_LatLong1.nc' % tas_dir,\n 'pr': '%s/full_data_monthly_v2020.nc' % pr_dir,\n 'slp': '%s/prmsl.mon.mean.nc' % slp_dir}\n\n filenames = []\n for var in varnames:\n filenames.append(file_dict[var])\n\n name_conversion = {'tas': 'temperature', 'pr': 'precip', 'slp': 'prmsl'}\n\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames[0],\n karen_params_obs.valid_years, mode_lag,\n cvdp_file, AMO_cutoff_freq, name_conversion)\n\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\n\n return this_ts, df_shifted","def extract_ssi_data():\r\n test_pos_over_time_file = \"data/Data-Epidemiologiske-Rapport-04012021-ja21/Test_pos_over_time.csv\"\r\n\r\n # Read data\r\n test_pos_over_time = pd.read_csv(test_pos_over_time_file, sep=\";\", decimal=\",\")\r\n\r\n # Remove last two rows\r\n df = test_pos_over_time[\"NewPositive\"].iloc[:-2]\r\n\r\n # Set index to datetime\r\n df.index = pd.to_datetime(test_pos_over_time[\"Date\"].iloc[:-2])\r\n\r\n # Remove . from data\r\n df = df.str.replace(\".\",\"\").astype(int)\r\n\r\n return df","def filter_meteo_data(self, startdate, enddate):\n self.all_meteo_data.columns.values[0]='Datum-tijd'\n self.all_meteo_data['datetime']=pd.to_datetime(self.all_meteo_data['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\n self.all_meteo_data.drop(['Datum-tijd'],axis=1, inplace=True)\n mask = (self.all_meteo_data['datetime'] > startdate) & (self.all_meteo_data['datetime'] <= enddate)\n meteodata = self.all_meteo_data.loc[mask].copy()\n meteodata.set_index('datetime',inplace=True)\n return meteodata","def collect_new_data(datafile):\n user_input = float(input('How far did you travel today? '))\n existingData['distanceTravelled'].append(user_input)\n write_data(datafile, existingData) # write changes to file using write_date function\n return existingData","def data(pd_edit_series):\n country_dict = {\n 'europe': ['United Kingdom', 'France', 'Spain', 'Belgium',\n 'Finland', 'Sweden', 'Germany', 'Croatia',\n 'Switzerland', 'Austria', 'Greece', 'Hungary',\n 'Slovenia', 'Poland', 'Bosnia and Herzegovina',\n 'Denmark', 'Liechtenstein', 'Ukraine',\n 'North Macedonia', 'Latvia', 'Andorra',\n 'Norway', 'Portugal', 'Romania', 'Estonia',\n 'Netherlands', 'San Marino', 'Belarus',\n 'Iceland', 'Lithuania', 'Ireland', 'Luxembourg',\n 'Monaco', 'Czechia', 'Slovakia', 'Holy See',\n 'Serbia', 'Malta', 'Bulgaria', 'Albania',\n 'Cyprus', 'Moldova', 'Andorra', 'Armenia',\n 'Austria', 'Cyprus', 'Estonia', 'Georgia',\n 'Gibraltar', 'Greenland', 'Croatia',\n 'Israel', 'Iceland', 'Luxembourg',\n 'Latvia', 'Monaco', 'Portugal', 'Romania',\n 'Svalbard and Jan Mayen', 'Slovakia',\n 'Turkey', 'Serbia', 'Montenegro',\n 'Aland Islands', 'Guernsey',\n 'Island of Man', 'Jersey', 'Kosovo'],\n\n 'asia': ['Thailand', 'Japan', 'Singapore', 'Mongolia',\n 'Nepal', 'Malaysia', 'Sri Lanka', 'Philippines',\n 'India', 'Cambodia', 'Pakistan',\n 'Indonesia', 'United Arab Emirates', 'Lebanon',\n 'Iraq', 'Oman', 'Afghanistan', 'Bahrain',\n 'Kuwait', 'Qatar', 'Saudi Arabia',\n 'Jordan', 'Azerbaijan', 'Bhutan', 'Maldives',\n 'Bangladesh', 'Brunei', 'Korea, South', 'Vietnam',\n 'Russia', 'Iran', 'Reunion', 'Taiwan*', 'Yemen',\n 'American Samoa', 'Brunei Darussalam',\n 'Guam', 'Hong Kong',\n 'Heard Island and McDonald Islands',\n 'British Indian Ocean Territory',\n 'Kyrgystan', 'Kiribati', 'Korea, North',\n 'Kazakhstan', 'Sri Lanka', 'Marshall Islands',\n 'Lao People\\'s Democratic Republic',\n 'Myanmar', 'Mongolia', 'Macau', 'Macao SAR',\n 'North Mariana Islands', 'Maldives',\n 'Malaysia', 'Papua New Guinea', 'Palau',\n 'Singapore', 'Syrian Arab Republic',\n 'Tajikistan', 'Turkmenistan', 'Timor-Leste',\n 'United States Minor Outlying Islands',\n 'Uzbekistan', 'Kyrgyzstan',\n 'occupied Palestinian territory', 'Mauritania',\n 'Comoros', 'Djibouti', 'Bahrain', 'Nepal',\n 'Malaysia', 'Singapore', 'East Timor', 'Syria'],\n\n 'africa': ['Egypt', 'Algeria', 'Nigeria',\n 'Morocco', 'Senegal', 'Tunisia',\n 'South Africa', 'Togo', 'Cameroon',\n 'Burkina Faso', 'Cote d\\'Ivoire',\n 'Congo (Kinshasa)', 'Congo (Brazzaville)',\n 'Republic of the Congo',\n 'Central African Republic', 'Eswatini',\n 'Eswatini', 'Ethiopia', 'Gabon', 'Ghana',\n 'Guinea', 'Equatorial Guinea', 'Kenya',\n 'Namibia', 'Rwanda', 'Sudan', 'Seychelles',\n 'Republic of Congo', 'Tanzania', 'Mayotte',\n 'Benin', 'Liberia', 'Somalia', 'The Gambia',\n 'Gambia, The', 'Zambia', 'Mauritius', 'Chad',\n 'Madagascar', 'Cabo Verde', 'Niger', 'Zimbabwe',\n 'Cape Verde', 'Angola', 'Eritrea', 'Uganda',\n 'Mozambique'],\n\n 'americas': ['Brazil', 'Mexico', 'Ecuador',\n 'Dominican Republic', 'Argentina',\n 'Chile', 'Peru', 'Netherlands Antilles',\n 'Costa Rica', 'Colombia', 'French Guiana',\n 'Martinique', 'Paraguay', 'Panama',\n 'Canada', 'US', 'Jamaica', 'Honduras',\n 'Bolivia', 'Antigua and Barbuda', 'Anguilla',\n 'Argentina', 'Aruba', 'Barbados',\n 'Bouvet Island', 'Belize', 'Cuba', 'Dominica',\n 'Equador', 'Falkland Islands', 'Malvinas',\n 'Grenada', 'Guadeloupe', 'Guyana',\n 'South Georgia and the South Sandwich '\n 'Islands', 'US',\n 'Guatemala', 'Haiti', 'Saint Kitts and Nevis',\n 'Cayman Islands', 'Saint Lucia', 'Montserrat',\n 'Mexico', 'Nicaragua', 'Puerto Rico',\n 'Paraguay', 'Suriname', 'El Salvador',\n 'Turks and Caicos Islands',\n 'Trinidad and Tobago', 'Uruguay',\n 'Saint Vincent and the Grenadines',\n 'Venezuela', 'Virgin Islands (British)',\n 'Virgin Islands (US)', 'Saint Martin',\n 'Saint Berthelemy', 'Bermuda',\n 'Saint Pierre and Miquelon', 'Cuba', 'Guyana',\n 'Curacao', 'The Bahamas', 'Bahamas, The'],\n\n 'oceania': ['Australia', 'New Zealand', 'New Caledonia',\n 'Norfolk Island', 'Nauru', 'Niue',\n 'Micronesia (federated States of)', 'Fiji',\n 'Cook Islands', 'Christmas Island',\n 'Cocos (Keeling) Islands', 'French Polynesia',\n 'Pitcairn Islands', 'Solomon Islands',\n 'French Southern Territories',\n 'American Samoa', 'Tokelau', 'Tonga', 'Tuvalu',\n 'Vanuatu', 'Wallis and Futuna', 'Samoa']}\n\n europe = []\n asia = []\n oceania = []\n americas = []\n africa = []\n uk_list = []\n italy = []\n china = []\n others = []\n ship = []\n all_lists = [europe, asia, oceania, americas, africa, uk_list,\n italy, china, ship, others]\n for_total = [europe, asia, oceania, americas, africa, china,\n others, ship, italy]\n\n for region, countries in country_dict.items():\n for column in pd_edit_series:\n if column in countries:\n if region == 'europe':\n if column == 'United Kingdom':\n if column not in uk_list:\n uk_list.append(column)\n if column not in europe:\n europe.append(column)\n\n elif region == 'asia':\n if column not in asia:\n asia.append(column)\n elif region == 'africa':\n if column not in africa:\n africa.append(column)\n elif region == 'americas':\n if column not in americas:\n americas.append(column)\n elif region == 'oceania':\n if column not in oceania:\n oceania.append(column)\n\n else:\n if column == 'Italy':\n if column not in italy:\n italy.append(column)\n\n elif column == 'China':\n if column not in china:\n china.append(column)\n\n elif column == 'Cruise Ship':\n if column not in ship:\n ship.append(column)\n\n else:\n others.append(column)\n\n # -----------------------------------------------------------\n # Segment of code it to catch any straggler countries not\n # accounted for in the country_dict\n remove_list = []\n for region in all_lists:\n for countries in region:\n if countries in others:\n if countries not in remove_list:\n remove_list.append(countries)\n\n others_final = [item for item in others\n if item not in remove_list]\n\n if len(others_final) > 0:\n logging.debug(others_final)\n print('Exiting due to unaccounted countries')\n sys.exit()\n\n total_count_list = []\n for region in for_total:\n for country in region:\n if country not in total_count_list:\n total_count_list.append(country)\n # -----------------------------------------------------------\n\n diamond_csv = pd_edit_series[ship].copy()\n main_china_csv = pd_edit_series[china].copy()\n europe_csv = pd_edit_series[europe].copy()\n americas_csv = pd_edit_series[americas].copy()\n asia_csv = pd_edit_series[asia].copy()\n africa_csv = pd_edit_series[africa].copy()\n uk_csv = pd_edit_series[uk_list].copy()\n italy_csv = pd_edit_series[italy].copy()\n oceania_csv = pd_edit_series[oceania].copy()\n\n csv_list = {'europe': europe_csv, 'america': americas_csv,\n 'asia': asia_csv, 'main_china': main_china_csv,\n 'UK': uk_csv, 'diamond': diamond_csv,\n 'italy': italy_csv, 'oceania': oceania_csv,\n 'africa': africa_csv}\n\n backup_frame = pd_edit_series.copy()\n backup_frame['Global_Cases'] = \\\n backup_frame.sum(axis=1)\n\n pd_edit_series['Mainland_China_Total'] = \\\n pd_edit_series[china].sum(axis=1)\n\n pd_edit_series['Oceania_Total'] = \\\n pd_edit_series[oceania].sum(axis=1)\n\n pd_edit_series['Europe_Total'] = \\\n pd_edit_series[europe + ['Italy']].sum(axis=1)\n\n pd_edit_series['Diamond_Princess'] = \\\n pd_edit_series[ship]\n\n pd_edit_series['UK_Total'] = \\\n pd_edit_series[uk_list].sum(axis=1)\n\n pd_edit_series['Asian_Total'] = \\\n pd_edit_series[asia].sum(axis=1)\n\n pd_edit_series['Americas_Total'] = \\\n pd_edit_series[americas].sum(axis=1)\n\n pd_edit_series['African_Total'] = \\\n pd_edit_series[africa].sum(axis=1)\n\n # As China is being kept separate\n pd_edit_series = pd_edit_series.drop('China', axis=1)\n pd_edit_series = pd_edit_series.drop('Cruise Ship', axis=1)\n\n for place in asia:\n pd_edit_series = pd_edit_series.drop(place, axis=1)\n for place in europe:\n pd_edit_series = pd_edit_series.drop(place, axis=1)\n for place in americas:\n pd_edit_series = pd_edit_series.drop(place, axis=1)\n for place in africa:\n pd_edit_series = pd_edit_series.drop(place, axis=1)\n for place in oceania:\n pd_edit_series = pd_edit_series.drop(place, axis=1)\n\n return csv_list, pd_edit_series, backup_frame","def temporal_only(\n db_engine, station_id, start, end, sample_size=int(1.0e5),\n balance=None):\n\n data = get_and_adjust_data(\n db_engine, station_id, start, end, sample_size)\n\n # Balance or set to sample_size\n if balance is None:\n data = data.sample(n=sample_size)\n else:\n data = bal(data, balance)\n\n # Ensure shuffling.\n data = data.iloc[np.random.permutation(len(data))]\n\n X = []\n yempty = []\n yfull = []\n\n for row in data.iteritems():\n features = [\n (1 if row[0].dayofweek == 0 else 0),\n (1 if row[0].dayofweek == 1 else 0),\n (1 if row[0].dayofweek == 2 else 0),\n (1 if row[0].dayofweek == 3 else 0),\n (1 if row[0].dayofweek == 4 else 0),\n (1 if row[0].dayofweek == 5 else 0),\n (1 if row[0].dayofweek == 6 else 0),\n float(((row[0].hour * 60) + row[0].minute)) / 1440.0,\n float(row[0].month) / 12.0\n ]\n\n X.append(features)\n yempty.append(1 if row[1] == \"empty\" else 0)\n yfull.append(1 if row[1] == \"full\" else 0)\n\n return {'X': X, 'yempty': yempty, 'yfull': yfull}","def addMissingData():\n\n conn = sqlite3.connect(\"./transactions.db\")\n\n person = pd.read_sql(\n \"\"\"\n select * from person;\n \"\"\",\n conn,\n )\n\n record = pd.read_sql(\n \"\"\"\n select * from record;\n \"\"\",\n conn,\n )\n\n tracked = set([_id for _id in record[\"doc_id\"]])\n\n untracked = []\n\n for url, _id in zip(person[\"url\"], person[\"doc_id\"]):\n if not _id in tracked:\n untracked.append((url[-17:-13], _id))\n\n untracked = pd.DataFrame(untracked, columns=[\"date\", \"doc_id\"])\n\n res = transaction.extractData(untracked)\n\n res.to_sql(\"record\", conn, index=False, if_exists=\"append\")\n\n conn.close()","def get_data():\n wga_df = pd.read_csv(os.path.join(MODEL_DIR, 'wga/sculptures/wga_sculpture_periods.csv'), index_col=0)\n wikiart_df = pd.read_csv(os.path.join(MODEL_DIR, 'wikiart/sculptures/wikiart_sculpture_periods.csv'), index_col=0)\n nga_df = pd.read_csv(os.path.join(MODEL_DIR, 'nga/sculptures/nga_sculpture_periods.csv'), index_col=0)\n\n ######## Fix name for WGA and WikiaRt ###########\n wga_df['Author'] = wga_df.apply(lambda x: fix_name_wga(x['Author']), axis=1)\n wikiart_df['Author'] = wikiart_df.apply(lambda x: fix_name_wiki(x['Author']), axis=1)\n nga_df['Author'] = nga_df.apply(lambda x: fix_name_nga(x['Author']), axis=1)\n\n df = pd.concat([wga_df, wikiart_df, nga_df], ignore_index=True, sort=True)\n\n df['Author_Fixed'] = df.apply(lambda x: fix_text(x['Author']), axis=1)\n df['title_fixed'] = df.apply(lambda x: fix_text(x['title']), axis=1)\n\n periods = [\"BAROQUE\", \"EARLY RENAISSANCE\", \"MEDIEVAL\", \"NEOCLASSICISM\", \"HIGH RENAISSANCE\", \"MINIMALISM\", \"REALISM\",\n \"IMPRESSIONISM\", \"ROCOCO\", \"SURREALISM\", \"MANNERISM\", \"ROMANTICISM\",\n ]\n df['Period'] = df.apply(lambda row: row['Period'].upper(), axis=1)\n\n # Get Desired Periods\n df['Period'] = df.apply(lambda x: \"SURREALISM\" if \"SURREALISM\" in x['Period'] else x['Period'], axis=1)\n df = df[(df['Period'].isin(periods))]\n df = df.sort_values(['Author_Fixed', 'title_fixed'])\n\n #print(\"Combined Drop Rows:\", df.shape[0] - df.drop_duplicates(subset=['Author_Fixed', 'title_fixed']).shape[0])\n\n df = df.drop_duplicates(subset=['Author_Fixed', 'title_fixed'], keep='last')\n\n # Drop Duplicate Sculptures\n df = df[~df['file'].isin(DUP_SCULPTURES)].reset_index(drop=True)\n\n #print(df['Period'].value_counts())\n\n return df","def get_weather_data(filename, dates, highs, lows, date_index, high_index,\n low_index):\n with open(filename) as f:\n reader = csv.reader(f)\n header_row = next(reader)\n\n # Get data temp.\n for row in reader:\n current_date = datetime.strptime(row[date_index], '%Y-%m-%d')\n try:\n high = int(row[high_index])\n low = int(row[low_index])\n except ValueError:\n print(f\"No data for {current_date}\")\n else:\n dates.append(current_date)\n highs.append(high)\n lows.append(low)","def getSFData(self, since, stream=\"all\"):\r\n brId=None;\r\n queryStr1 =\"Select c.Id, c.CaseNumber, c.ClosedDate, c.OwnerId, c.Status from Case c \"\r\n if stream != \"all\":\r\n queryStr1 += \" WHERE ClosedDate = LAST_N_DAYS:%s AND X1st_Stream__c = %s order by ClosedDate\"%(since, stream)\r\n else:\r\n queryStr1 += \" WHERE ClosedDate = LAST_N_DAYS:%s order by ClosedDate\"%(since)\r\n \r\n #queryStr1 =\"Select c.Id, c.CaseNumber, c.ClosedDate, c.OwnerId, c.Status from Case c WHERE ClosedDate = LAST_N_DAYS:%s order by ClosedDate\"%since\r\n queryList1=self.cxn.query(queryStr1)\r\n \r\n if queryList1 not in BAD_INFO_LIST:\r\n found = len(queryList1)\r\n print 'Found %s Cases closed in the last %s days'%(found,since)\r\n return queryList1\r\n #for qr1 in queryList1:\r\n # brId=qr1.get('Id')\r\n else:\r\n print \"No Cases found in the last '%s' days\" %since\r\n return []","def test_importtleUpdatesDataSourceUpdateDate(self):\n\n # Edit the DataSource to make sure the date must be updated\n datasource = DataSource.objects.get(system_name='tle_test')\n datasource.last_time_checked = timezone.make_aware(datetime(2000, 1, 1))\n datasource.save()\n\n timestamp = format(datasource.last_time_checked, 'U')\n\n try:\n call_command('importtle', 'tle_test', stdout=StringIO())\n except ConnectionError:\n pass\n\n datasource = DataSource.objects.get(system_name='tle_test')\n\n self.assertNotEquals(\n timestamp,\n format(datasource.last_time_checked, 'U')\n )","def retrieve_data_timeseries(hfile, setname):\n dset = hfile[setname]\n sample_rate = dset.attrs[\"SamplingRate(Hz)\"]\n gps_epoch = construct_utc_from_metadata(dset.attrs[\"Date\"], dset.attrs[\"t0\"])\n data = retrieve_channel_data(hfile, setname)\n ts_data = TimeSeries(data, sample_rate=sample_rate, epoch=gps_epoch)\n return ts_data","def extract_tt_by_periods(ttri, periods, start_time, end_time, filters):\n logger = getLogger(__name__)\n # sess = conn.get_session()\n das = {}\n all_wz_features = {}\n all_wz_laneconfigs = {}\n\n # collecting daily data\n for prd in periods:\n logger.debug('>>>> retrieving data for %s' % prd.get_date_string())\n year = prd.start_date.year\n sdate = prd.start_date\n edate = prd.end_date\n if year not in das:\n da_tt = tt.TravelTimeDataAccess(year)\n da_tt_wz = tt_workzone.TTWorkZoneDataAccess(year)\n da_tt_wz_feature = wz_feature.WZFeatureDataAccess()\n da_tt_wz_lncfg = wz_laneconfig.WZLaneConfigDataAccess()\n da_tt_weather = tt_weather.TTWeatherDataAccess(year)\n da_tt_snowmgmt = tt_snowmgmt.TTSnowManagementDataAccess(year)\n da_tt_incident = tt_incident.TTIncidentDataAccess(year)\n da_tt_specialevent = tt_specialevent.TTSpecialeventDataAccess(year)\n das[year] = (\n da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\n da_tt_specialevent)\n\n (da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\n da_tt_specialevent) = das[year]\n\n # traveltimes = da_tt.list_by_period(ttri.id, self.prd)\n weathers = da_tt_weather.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.WeatherInfo] \"\"\"\n workzones = da_tt_wz.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.WorkZoneInfo] \"\"\"\n incidents = da_tt_incident.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.IncidentInfo] \"\"\"\n snowmgmts = da_tt_snowmgmt.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.SnowManagementInfo] \"\"\"\n specialevents = da_tt_specialevent.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.SpecialEventInfo] \"\"\"\n traveltimes = da_tt.list_by_period(ttri.id, prd)\n \"\"\":type: list[pyticas_tetres.ttrms_types.TravelTimeInfo] \"\"\"\n\n if not any(weathers):\n logger.debug('>>>> end of retrieving data for %s (no weather data)' % prd.get_date_string())\n continue\n\n extras = {\n 'weathers': {_tt.id: [] for _tt in traveltimes},\n 'workzones': {_tt.id: [] for _tt in traveltimes},\n 'incidents': {_tt.id: [] for _tt in traveltimes},\n 'specialevents': {_tt.id: [] for _tt in traveltimes},\n 'snowmgmts': {_tt.id: [] for _tt in traveltimes},\n }\n \"\"\":type: dict[str, dict[int, list]]\"\"\"\n\n _put_to_bucket(ttri, weathers, extras['weathers'], da_tt_weather, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, workzones, extras['workzones'], da_tt_wz, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, incidents, extras['incidents'], da_tt_incident, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, snowmgmts, extras['snowmgmts'], da_tt_snowmgmt, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, specialevents, extras['specialevents'], da_tt_specialevent, year, all_wz_features, all_wz_laneconfigs, das)\n\n for tti in traveltimes:\n _tt_weathers = extras['weathers'][tti.id]\n extdata = ExtData(tti,\n _tt_weathers[0] if _tt_weathers else None,\n extras['incidents'][tti.id],\n extras['workzones'][tti.id],\n extras['specialevents'][tti.id],\n extras['snowmgmts'][tti.id])\n\n if start_time <= tti.str2datetime(tti.time).time() <= end_time:\n for ef in filters:\n try:\n ef.check(extdata)\n except Exception as ex:\n tb.traceback(ex)\n logger.debug('>>>> end of retrieving data for %s (error occured 1)' % prd.get_date_string())\n continue\n else:\n for ef in filters:\n try:\n ef.check_outofrange(extdata)\n except Exception as ex:\n tb.traceback(ex)\n logger.debug('>>>> end of retrieving data for %s (error occured 2)' % prd.get_date_string())\n continue\n\n del extras\n logger.debug('>>>> end of retrieving data for %s' % prd.get_date_string())\n\n # sess.close()","def rd_pst_parameters(pst_file):\n skiplines_top = 0\n nrows=-1 #to account for the extra that gets generated\n start = False\n stop=False\n with open(pst_file) as f:\n while not stop:\n if start:\n nrows +=1\n if '* observation groups' in f.readline():\n stop =True\n else:\n skiplines_top +=1\n if '* parameter data' in f.readline():\n start=True\n data = pd.read_table(pst_file,skiprows=skiplines_top,nrows=nrows,delim_whitespace=True,\n names=['temp1','temp2','initial','lower','upper','temp3','scale','offset','flag'],\n index_col=0)\n\n data.loc[:,'initial'] +=data.loc[:,'offset']\n data.loc[:,'lower'] +=data.loc[:,'offset']\n data.loc[:,'upper'] +=data.loc[:,'offset']\n return data","def _update_modified_data_sources(self):\n new_last_imported = datetime.utcnow()\n self._update_modified_since(self.last_imported)\n self.last_imported = new_last_imported","def __get_updated_data(self, table_name='derivatives_economicindicatorstandard'):\n # get the latest data and add 1 day to date column\n df = self.__get_latest_data(table_name)\n df['date'] = pd.to_datetime(df['date']) + timedelta(days=1)\n df['date'] = df['date'].apply(lambda x: x.strftime('%Y-%m-%d'))\n\n # get updated data\n result = []\n df.apply(lambda row: result.append(self.__call_api_data(row, table_name)), axis=1)\n df = pd.concat(result, sort=True)\n df['Date'] = df['Date'].apply(lambda x: x.strftime('%Y-%m-%d'))\n return df","def get_data(self, date_time):\n id_columns = ','.join([col for col in self.table_primary_keys if col not in ['EFFECTIVEDATE', 'VERSIONNO']])\n return_columns = ','.join(self.table_columns)\n with self.con:\n cur = self.con.cursor()\n cur.execute(\"DROP TABLE IF EXISTS temp;\")\n cur.execute(\"DROP TABLE IF EXISTS temp2;\")\n cur.execute(\"DROP TABLE IF EXISTS temp3;\")\n cur.execute(\"DROP TABLE IF EXISTS temp4;\")\n # Store just the unique sets of ids that came into effect before the the datetime in a temporary table.\n query = \"\"\"CREATE TEMPORARY TABLE temp AS \n SELECT * \n FROM {table} \n WHERE EFFECTIVEDATE <= '{datetime}';\"\"\"\n cur.execute(query.format(table=self.table_name, datetime=date_time))\n # For each unique set of ids and effective dates get the latest versionno and sore in temporary table.\n query = \"\"\"CREATE TEMPORARY TABLE temp2 AS\n SELECT {id}, EFFECTIVEDATE, MAX(VERSIONNO) AS VERSIONNO\n FROM temp\n GROUP BY {id}, EFFECTIVEDATE;\"\"\"\n cur.execute(query.format(id=id_columns))\n # For each unique set of ids get the record with the most recent effective date.\n query = \"\"\"CREATE TEMPORARY TABLE temp3 as\n SELECT {id}, VERSIONNO, max(EFFECTIVEDATE) as EFFECTIVEDATE\n FROM temp2\n GROUP BY {id};\"\"\"\n cur.execute(query.format(id=id_columns))\n # Inner join the original table to the set of most recent effective dates and version no.\n query = \"\"\"CREATE TEMPORARY TABLE temp4 AS\n SELECT * \n FROM {table} \n INNER JOIN temp3 \n USING ({id}, VERSIONNO, EFFECTIVEDATE);\"\"\"\n cur.execute(query.format(table=self.table_name, id=id_columns))\n # Inner join the most recent data with the interconnectors used in the actual interval of interest.\n query = \"\"\"SELECT {cols} \n FROM temp4 \n INNER JOIN (SELECT * \n FROM DISPATCHINTERCONNECTORRES \n WHERE SETTLEMENTDATE == '{datetime}') \n USING (INTERCONNECTORID);\"\"\"\n query = query.format(datetime=date_time, id=id_columns, cols=return_columns)\n data = pd.read_sql_query(query, con=self.con)\n return data","def __add__ ( self, other, resample_opts=None ):\n result = ObservationStorage (datadir=self.datadir, \\\n resample_opts=resample_opts )\n if self.date[0] > other.date[0]:\n start_date = other.date[0]\n else:\n start_date = self.date[0]\n if self.date[-1] > other.date[-1]:\n end_date = other.date[-1]\n else:\n end_date = self.date[-1]\n \n delta = datetime.timedelta ( days=1 )\n this_date = start_date.date()\n end_date = end_date.date() + delta\n \n this_obs_dates = [ x.date() for x in self.date ]\n other_obs_dates = [ x.date() for x in other.date ]\n \n date = [] ; vza = [] ; vaa = [] ; sza = [] ; saa = []\n emulator = [] ; mask = [] ; data_pntr = [] ; spectral = []\n sensor = []\n \n while this_date < end_date:\n if this_date in this_obs_dates:\n iloc = this_obs_dates.index ( this_date )\n date.append ( self.date[iloc] )\n emulator.append ( self.emulator[iloc] )\n vza.append ( self.vza[iloc] )\n sza.append ( self.sza[iloc] )\n vaa.append ( self.vaa[iloc] )\n saa.append ( self.saa[iloc] )\n spectral.append ( self.spectral )\n mask.append ( ( self.get_mask, [iloc] ) )\n sensor.append ( self.sensor )\n \n data_pntr.append ( self._data_pntr[iloc] )\n if this_date in other_obs_dates:\n iloc = other_obs_dates.index ( this_date )\n date.append ( other.date[iloc] )\n emulator.append ( other.emulator[iloc] )\n vza.append ( other.vza[iloc] )\n sza.append ( other.sza[iloc] )\n vaa.append ( other.vaa[iloc] )\n saa.append ( other.saa[iloc] )\n spectral.append ( other.spectral )\n mask.append ( ( other.get_mask, [iloc] ) )\n sensor.append ( other.sensor )\n data_pntr.append ( other._data_pntr[iloc] )\n this_date += delta\n result.vza = vza\n result.vaa = vaa\n result.sza = sza \n result.saa = saa \n result.date = date\n result.spectral = spectral\n result.masks = mask\n result.sensor = sensor\n result.emulator = emulator\n result._data_pntr = data_pntr\n return result","def update_rainfall_obs(target_model, method, timestep, start_time, end_time):\n obs_start = datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S')\n try:\n\n # Connect to the database\n curw_obs_pool = get_Pool(host=con_params.CURW_OBS_HOST, user=con_params.CURW_OBS_USERNAME,\n password=con_params.CURW_OBS_PASSWORD, port=con_params.CURW_OBS_PORT,\n db=con_params.CURW_OBS_DATABASE)\n\n curw_obs_connection = curw_obs_pool.connection()\n\n curw_sim_pool = get_Pool(host=con_params.CURW_SIM_HOST, user=con_params.CURW_SIM_USERNAME,\n password=con_params.CURW_SIM_PASSWORD, port=con_params.CURW_SIM_PORT,\n db=con_params.CURW_SIM_DATABASE)\n\n TS = Timeseries(pool=curw_sim_pool)\n\n # [hash_id, station_id, station_name, latitude, longitude]\n active_obs_stations = extract_active_curw_obs_rainfall_stations(start_time=start_time, end_time=end_time)[1:]\n obs_stations_dict = { } # keys: obs station id , value: [hash id, name, latitude, longitude]\n\n for obs_index in range(len(active_obs_stations)):\n obs_stations_dict[active_obs_stations[obs_index][1]] = [active_obs_stations[obs_index][0],\n active_obs_stations[obs_index][2],\n active_obs_stations[obs_index][3],\n active_obs_stations[obs_index][4]]\n\n for obs_id in obs_stations_dict.keys():\n meta_data = {\n 'latitude': float('%.6f' % float(obs_stations_dict.get(obs_id)[2])),\n 'longitude': float('%.6f' % float(obs_stations_dict.get(obs_id)[3])),\n 'model': target_model, 'method': method,\n 'grid_id': 'rainfall_{}_{}'.format(obs_id, obs_stations_dict.get(obs_id)[1])\n }\n\n tms_id = TS.get_timeseries_id_if_exists(meta_data=meta_data)\n\n if tms_id is None:\n tms_id = TS.generate_timeseries_id(meta_data=meta_data)\n meta_data['id'] = tms_id\n TS.insert_run(meta_data=meta_data)\n\n TS.update_grid_id(id_=tms_id, grid_id=meta_data['grid_id'])\n\n obs_hash_id = obs_stations_dict.get(obs_id)[0]\n\n obs_timeseries = []\n\n if timestep == 5:\n ts = extract_obs_rain_5_min_ts(connection=curw_obs_connection, start_time=obs_start, end_time=end_time,\n id=obs_hash_id)\n if ts is not None and len(ts) > 1:\n obs_timeseries.extend(process_5_min_ts(newly_extracted_timeseries=ts, expected_start=obs_start)[1:])\n # obs_start = ts[-1][0]\n elif timestep == 15:\n ts = extract_obs_rain_15_min_ts(connection=curw_obs_connection, start_time=obs_start, end_time=end_time,\n id=obs_hash_id)\n if ts is not None and len(ts) > 1:\n obs_timeseries.extend(process_15_min_ts(newly_extracted_timeseries=ts, expected_start=obs_start)[1:])\n # obs_start = ts[-1][0]\n\n # for i in range(len(obs_timeseries)):\n # if obs_timeseries[i][1] == -99999:\n # obs_timeseries[i][1] = 0\n\n if obs_timeseries is not None and len(obs_timeseries) > 0:\n TS.replace_data(timeseries=obs_timeseries, tms_id=tms_id)\n\n except Exception as e:\n traceback.print_exc()\n logger.error(\"Exception occurred while updating obs rainfalls in curw_sim.\")\n finally:\n curw_obs_connection.close()\n destroy_Pool(pool=curw_sim_pool)\n destroy_Pool(pool=curw_obs_pool)","def get_new_sourcedatasets(self):\n previous_study_version = self.get_previous_version()\n SourceDataset = apps.get_model('trait_browser', 'SourceDataset')\n if previous_study_version is not None:\n qs = SourceDataset.objects.filter(source_study_version=self)\n # We can probably write this with a join to be more efficient.\n previous_dataset_accessions = SourceDataset.objects.filter(\n source_study_version=previous_study_version\n ).values_list('i_accession', flat=True)\n qs = qs.exclude(i_accession__in=previous_dataset_accessions)\n return qs\n else:\n return SourceDataset.objects.none()","def read_scada_data(dt_from, dt_to, class_name, read_from_hdf=True, conf_fn='config.json'):\r\n\r\n if read_from_hdf:\r\n all_data = pd.read_hdf(class_name + '.hdf')\r\n else:\r\n with open(conf_fn, 'r') as fh_conf:\r\n conf = json.load(fh_conf)\r\n\r\n usr = conf['scada_params']['username']\r\n psw = conf['scada_params']['password']\r\n url = conf['scada_params']['url']\r\n project_id = conf['scada_params']['project_id']\r\n\r\n tout_guid = conf['variables'][class_name]['t_out']\r\n tin_guid = conf['variables'][class_name]['t_in']\r\n ahu_fan_guid = conf['variables'][class_name]['ahu_fan']\r\n t_supply = conf['variables'][class_name]['t_supply']\r\n\r\n list_guids = [tin_guid, tout_guid, t_supply, ahu_fan_guid]\r\n list_cols = ['T_in', 'T_out', 'T_ahu', 'P_ahu']\r\n\r\n from ScadaClient3 import ScadaClient\r\n sc = ScadaClient(url, usr, psw)\r\n\r\n raw_data = sc.get_history(project_id, list_guids, dt_from, dt_to)\r\n all_data = pd.DataFrame()\r\n\r\n for idx, guid in enumerate(list_guids):\r\n guid_s = pd.Series(\r\n name=list_cols[idx], index=raw_data[guid]['raw']['tx_dt'], data=raw_data[guid]['raw']['values']\r\n )\r\n\r\n all_data = all_data.combine_first(pd.DataFrame(guid_s))\r\n\r\n all_data.index = all_data.index.map(lambda x: x.replace(microsecond=0))\r\n all_data.to_hdf(class_name + '.hdf', 'libeznice', mode='w')\r\n print('All data successfully downloaded from Mervis DB.')\r\n all_data = all_data.loc[(all_data.index >= dt_from) & (all_data.index <= dt_to), :]\r\n return all_data","def _interpolate_meteorological_data(dset, data, rundate):\n rundate = datetime(rundate.year, rundate.month, rundate.day)\n for field, station in [(f, f[4:]) for f in data.keys() if f.startswith(\"met_\")]:\n log.debug(f\"Meteorological data available for station {station}\")\n\n met_time = data[field].pop(\"met_time\")\n flat_list = [item for sublist in met_time for item in sublist]\n met_time_float = np.array([(flat_list[i] - rundate).total_seconds() for i in range(0, len(flat_list))])\n met_time_unique, met_index = np.unique(met_time_float, return_index=True)\n\n diff = len(met_time_float) - len(met_time_unique)\n if diff > 0:\n log.dev(f\"Removed duplicate met data for station {station}\")\n log.dev(\"Do this for the actual obs data also!\")\n if len(met_time_unique) == 1:\n for met_type in data[field].keys():\n data[field][met_type] = np.repeat(data[field][met_type][0], dset.num_obs)\n continue\n\n # Extrapolation one month before/after\n # (this is overkill, most of these values will be removed later when taking the diagonal)\n min_time = min(met_time_unique) - 31 * 86400\n max_time = max(met_time_unique) + 31 * 86400\n met_time_unique = np.hstack((np.array(min_time), met_time_unique, np.array(max_time)))\n\n for met_type in data[field].keys():\n met_data_array = data[field][met_type]\n flat_list = [item for sublist in met_data_array for item in sublist]\n met_data_array = np.array([flat_list[i] for i in met_index])\n met_data_array = np.hstack((met_data_array[0], met_data_array, met_data_array[-1]))\n data[field][met_type] = interpolation.interpolate(\n met_time_unique, met_data_array, dset.obs_time, kind=\"cubic\"\n )\n\n return data","def get_drought_data():\n\n # Base URL for the DM homepage & AJAX URL for simulated request\n url = 'http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx'\n ajax_url = 'http://droughtmonitor.unl.edu/Ajax.aspx/ReturnDMWeeks'\n\n # Based on AJAX HTTP POST request\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/38.0.2125.104 Safari/537.36',\n 'Accept': 'application/json, text/javascript, */*; q=0.01',\n 'Content-Type': 'application/json; charset=UTF-8',\n 'X-Requested-With': 'XMLHttpRequest'\n }\n\n # Sets requests Session & sends simulated AJAX request\n s = requests.Session()\n s.head(url)\n r = s.post(ajax_url, data='{}', headers=headers)\n\n # Grabs data, a list of valid dates for file reference\n json_data = json.loads(r.text)\n file_dates = json_data['d']\n\n print 'Writing file dates...'\n\n # Writes the USDM dates to a file for D3 reference\n datefile = open(basedir + '/app/static/data/saved/dates.json', 'w')\n datefile.write(json.dumps(sorted(file_dates), indent=1))\n datefile.close()\n\n print 'File dates from the USDM:'\n print file_dates\n print ''\n print 'Now comparing...'\n print ''\n\n dates = check_date(file_dates)\n if dates:\n file_dates = dates\n print 'Now collecting...'\n\n # Base URL for data files\n base_url = 'http://usdmdataservices.unl.edu/?mode=table'\n\n # Loops thru dates, saves files based on geographical focus & date\n for date in file_dates:\n fileout = urllib.URLopener()\n fileout.retrieve(base_url + '&aoi=county&date=' + date, basedir + '/app/static/data/raw/' + date + '.csv')\n\n print 'Finished.'\n else:\n print 'All data collected to date.'","def process_location(pid, data_source, out_loc, start_date, end_date, debug=False):\n\n #query = client.query_data_points(page_size=PAGE_SIZE, source=pid)\n query = try_query(pid)\n\n location_query = try_filter(query, pid, 'pdk-location', start_date, end_date)\n \"\"\" location_query = query.filter(source=pid, \n generator_identifier='pdk-location',\n created__gte=start_date,\n created__lte=end_date).order_by('created')\n \"\"\"\n tot_count = location_query.count()\n count = 0\n frac = int(tot_count / 100)\n\n loc_df = pd.DataFrame()\n for point in location_query:\n point_df = pd.DataFrame.from_dict(point).iloc[0].to_frame().transpose()\n metadata_df = pd.Series(point['passive-data-metadata']).to_frame().transpose()\n # TODO check if ignoring errors is safe\n metadata_df = metadata_df.drop(['latitude', 'longitude'], axis='columns', errors=\"ignore\")\n point_df.reset_index(inplace=True, drop=True)\n point_df = pd.concat([metadata_df, point_df], axis=1, sort=True)\n \n point_df.drop('passive-data-metadata', axis='columns', inplace=True)\n missing_cols = [col for col in loc_df.columns.values if col not in point_df.columns.values]\n \n if len(missing_cols) > 0 and loc_df.shape[0] > 0:\n for col in missing_cols:\n point_df[col] = np.nan\n point_df = point_df[loc_df.columns]\n loc_df = loc_df.append(point_df)\n count += 1\n if debug and (count % frac == 0):\n print(\"{0:.2f}% complete\".format(float(count)/float(tot_count)*100))\n\n loc_df['pid'] = pid \n loc_df['data_source'] = data_source\n print(loc_df.shape)\n \n pickle.dump(loc_df, open(\"{}/pdk-location/{}.df\".format(out_loc, pid), 'wb'), -1)","def ddf_parser():\n num_available, total = 0, 0\n indicator_twn_tuples = list() # format of a single tuple: (indicator_name, #twn rows, earliest available year)\n concept_metadata = dict() # {top_tag: second_layer_tag:\n\n # parse all ddf files provided by GapMinder and find how many of them with Taiwan statistics\n for f_path in glob.glob(os.path.join('statistics', '*datapoints*.csv')):\n total += 1\n df = pd.read_csv(f_path)\n if 'twn' in df.geo.unique():\n num_available += 1\n indicator = f_path.replace('statistics/ddf--datapoints--', '').replace('--by--geo--time.csv', '')\n # print('[Indicator]', indicator)\n print(f\"\\t{len(df[df.geo == 'twn'])} indicators including Taiwan statistics.\")\n\n # stat_name = df.columns[-1]\n # df_p = df.pivot(index='geo', columns='time')[stat_name]\n # df_p.insert(loc=0, column='indicator', value=stat_name)\n # df_p.to_csv(f'statistics_transformed/{stat_name}.csv', sep=';')\n\n indicators.append(indicator)\n\n\n # print(\"{:.1f}% datapoints have Taiwan statistics\".format(num_available / float(total) * 100))\n\n\n\n df_c = pd.read_csv(CONCEPT_CSV_PATH)\n df_t = pd.read_csv(TAG_CSV_PATH)\n df = pd.merge(df_c, df_t, how='left', left_on='tags', right_on='tag')\n for idr, num_rows, earliest_year in indicator_twn_tuples:\n ancestors = list()\n\n row_values = df[df['concept'] == idr].values[0]\n name_catalog, parent, ancestor = (row_values[i] for i in [9, 17, 18])\n if type(parent) is str:\n ancestors.append(parent)\n\n # get ancestors recursively\n while type(ancestor) is str:\n tag_row_values = df_t[df_t['tag'] == ancestor].values[0]\n ancestors.append(tag_row_values[1])\n ancestor = tag_row_values[2]\n\n # build concept structure\n ancestors.insert(0, name_catalog)\n print('/'.join(ancestors[::-1]))","def ingest(self,check=True):\n\t\tdata=self.data_all\n\t\tpubdate=time_utils.parseISO(self.api.last_update).date()\n\t\t\n\t\tcounter=0\n\t\tfor item in data:\n\t\t\tareacode=item['areaCode']\n\t\t\tdatestring=item['specimenDate']\n\t\t\t_date=fetchdate(datestring)\n\t\t\trow,created=DailyCases.objects.get_or_create(specimenDate=_date,areacode=areacode)\n\t\t\trow.areaname=item['areaName']\n\t\t\tdaily=item['newCasesBySpecimenDate']\n\t\t\ttotal=item['cumCasesBySpecimenDate']\n\t\t\t\n\t\t\t#log.debug(f'{row.areaname}: {datestring}')\t\t\t\n\t\t\tif created:\n\t\t\t\trow.dailyLabConfirmedCases=daily\n\t\t\t\trow.totalLabConfirmedCases=total\n\t\t\t\trow.save()\n\t\t\t\t\n\t\t\t\tif daily:\n\t\t\t\t\tlag=(pubdate-_date.date()).days\n\t\t\t\t\tlog.debug(f'date:{_date} lag: {lag} daily:{daily}')\n\t\t\t\t\tdrow,dcreated=DailyReport.objects.get_or_create(specimenDate=_date,areacode=areacode,publag=lag)\n\t\t\t\t\tdrow.dailycases=daily\n\t\t\t\t\tdrow.add_cases=daily #if a new daily case, assume no prior report\n\t\t\t\t\tdrow.save()\n\t\t\t\n\t\t\tif not created:\n\t\t\t\texisting_daily=row.dailyLabConfirmedCases\n\t\t\t\texisting_total=row.totalLabConfirmedCases\n\t\t\t\tif daily is not None:\n\t\t\t\t\tif existing_daily !=daily or existing_total!=total:\n\t\t\t\t\t\trow.dailyLabConfirmedCases=daily\n\t\t\t\t\t\trow.totalLabConfirmedCases=total\n\t\t\t\t\t\trow.save()\n\t\t\t\t\t\tif existing_daily !=daily:\n\t\t\t\t\t\t\tlog.debug(f'Updating {row.areaname} on {datestring}: Daily: {existing_daily} to {daily} Total: {existing_total} to {total}')\n\t\t\t\t\t\t\tif existing_daily:\n\t\t\t\t\t\t\t\t_increase=daily-existing_daily\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t_increase=daily\n\t\t\t\t\t\t\tlag=(pubdate-_date.date()).days\n\t\t\t\t\t\t\tdrow,dcreated=DailyReport.objects.get_or_create(specimenDate=_date,areacode=areacode,publag=lag)\n\t\t\t\t\t\t\tdrow.dailycases=daily\n\t\t\t\t\t\t\tdrow.add_cases=_increase\n\t\t\t\t\t\t\tdrow.save()\n\t\t\t\t\t\n\t\t\tcounter+=1\n\t\t\tif counter%1000==0:\n\t\t\t\tlog.info(f'Processing row {counter}')\n\t\tlog.info(f'Processed: {counter} rows')\n\n\t\tif self.edition:\n\t\t\tconfigs.userconfig.update('PHE','latest_update',self.edition)\n\t\telse:\n\t\t\tlog.info('Latest update not updated')","def make_dataset_temperatures(filename, points):\r\n\r\n ds = Dataset(filename)\r\n\r\n lats, lons, cities, countries = get_lat_lon()\r\n\r\n # total lat,lon pairs: 1366\r\n print('The number of rows is ' + str(len(lats)*points))\r\n lon = ds.variables['longitude']\r\n lat = ds.variables['latitude']\r\n time = ds.variables['date_number']\r\n\r\n lon_array = lon[:]\r\n lat_array = lat[:]\r\n time_array = time[:]\r\n\r\n temperature = ds.variables['temperature']\r\n\r\n dates = []\r\n for time in time_array[:]:\r\n year = int(time)\r\n rem = time - year\r\n base = datetime(year, 1, 1)\r\n dates.append((base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)).date())\r\n\r\n # second approach\r\n # for t in time_array[:]:\r\n # dates.append(num2date(t, units=time.units))\r\n\r\n dateResult = []\r\n temperatureResult = []\r\n latitudeResult = []\r\n longitudeResult = []\r\n cityResult = []\r\n countryResult = []\r\n\r\n for latitude, longitude, city, country in zip(lats, lons, cities, countries):\r\n\r\n # We want to find data for latitude, longitude\r\n # We first need to find the indexes\r\n i = np.abs(lon_array - longitude).argmin()\r\n j = np.abs(lat_array - latitude).argmin()\r\n\r\n for d in dates:\r\n dateResult.append(d)\r\n\r\n resultTemperature = temperature[:, j, i]\r\n for t in resultTemperature:\r\n temperatureResult.append(t)\r\n\r\n resultLatitues = np.full(\r\n shape=points,\r\n fill_value=latitude,\r\n dtype=np.float\r\n )\r\n for l in resultLatitues:\r\n latitudeResult.append(l)\r\n\r\n resultLongitudes = np.full(\r\n shape=points,\r\n fill_value=longitude,\r\n dtype=np.float\r\n )\r\n for l in resultLongitudes:\r\n longitudeResult.append(l)\r\n\r\n resultCities = np.full(\r\n shape=points,\r\n fill_value=city\r\n )\r\n for c in resultCities:\r\n cityResult.append(c)\r\n\r\n resultCountries = np.full(\r\n shape=points,\r\n fill_value=country\r\n )\r\n for c in resultCountries:\r\n countryResult.append(c)\r\n\r\n print('iteration no:' + str(i))\r\n\r\n df = pd.DataFrame()\r\n df['date'] = dateResult\r\n df['temperature'] = temperatureResult\r\n df['latitude'] = latitudeResult\r\n df['longitude'] = longitudeResult\r\n df['city'] = cityResult\r\n df['country'] = countryResult\r\n\r\n df.to_csv(r'C:\\Users\\stoja\\Desktop\\Temperatures.csv', index=False)\r\n return df","def load_daily_data():\n return pd.read_csv(os.path.join('data', 'raw', 'full_grouped.csv'))","def temp():\n \n #Query temp from a year from last data point\n query = session.query(func.max(Measurement.date)).first()\n maxDate = dt.datetime.strptime(query[0],'%Y-%m-%d')\n year_ago = maxDate - dt.timedelta(days=365)\n\n results = session.query(Measurement.date,Measurement.tobs).filter(Measurement.date>=year_ago).all()\n\n # convert list of tuples into normal list\n all_dates = (result[0] for result in results)\n all_tobs = (result[1] for result in results)\n\n # Convert result to dictionary using date as key and tobs as value\n tobs_dict = dict(zip(all_dates,all_tobs))\n\n # Return on webpage\n return jsonify(tobs_dict)","def retrieve_time_series(api, series_ID):\r\n #Retrieve Data By Series ID \r\n series_search = api.data_by_series(series=series_ID)\r\n ##Create a pandas dataframe from the retrieved time series\r\n df = pd.DataFrame(series_search)\r\n return df","def compliance_time_series(county, core_path , patterns_path, backfill = False, GEOID_type = 'CBG'):\n #Load data about places and patterns\n county_places = pd.read_csv(core_path+'places-'+ county +'.csv', index_col='safegraph_place_id') \n \n pattern_dates = [x[5:9] for x in sorted(os.listdir(patterns_path+'main-file-'+ county +'/'))]\n w = 0\n next_date = pattern_dates[w]\n #Create visitor table\n place_cts = pd.DataFrame() #First two months don't have weekly patterns, use empty data.frame\n\n #List files for social distancing metrics in that county\n months_path = '../social_distancing/social_dist_'+ county +'/'\n month_list = sorted(os.listdir(months_path))\n os.makedirs( '../stats/time_series/', exist_ok = True)\n #metrics dictionary to be filled looping through every day\n if not os.path.isfile('../stats/time_series/metrics_{}_CT.csv'.format(county)) or backfill:\n metrics = pd.DataFrame()\n existing_dates = []\n else:\n metrics = pd.read_csv('../stats/time_series/metrics_{}_CT.csv'.format(county), dtype = {'origin_census_tract':str})\n #dates already processed\n existing_dates = metrics['date'].unique() #series of unique dates\n\n #Initialize columns of new data frame\n if GEOID_type == 'CBG':\n columns= {'date':[], 'origin_census_block_group':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'total_expected_contacts':[], 'places_visited':[], 'cbgs_visited':[]}\n if GEOID_type == 'CT':\n columns= {'date':[], 'origin_census_tract':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'places_visited':[]} \n changed = False\n for month in month_list:\n #Loop through every day\n day_list = sorted(os.listdir(months_path + month))\n for day in day_list: \n date_name = month + '-' + day\n print(date_name)\n if month+'-'+day == next_date:\n w = w+1\n if w < len(pattern_dates):\n next_date = pattern_dates[w]\n if date_name in existing_dates:\n continue\n print(\"--changing to next patterns file\")\n county_patterns = pd.read_csv(patterns_path + 'main-file-{}/2020-{}-weekly-patterns.csv.gz'.format(county, date_name),\n index_col='safegraph_place_id')\n norm_factors = pd.read_csv(\n '../social_distancing/normalization/'+'normalization_{}.csv'.format(county),\n dtype={'origin_census_block_group':str})\n norm_factors.set_index('origin_census_block_group', drop =True, inplace=True)\n\n\n #Establish prior for hourly distribution of visits at the top_category level\n county_patterns= county_patterns.join(county_places[['top_category','sub_category']], how='inner')\n restaurants = county_patterns['top_category'] == 'Restaurants and Other Eating Places'\n county_patterns.loc[restaurants, 'top_category'] = county_patterns.loc[restaurants, 'sub_category']\n norm_factor = norm_factors.loc[norm_factors.date == date_name].norm_factor\n\n prior_dict = {}\n for category in county_patterns.top_category.value_counts().index:\n places_in_cat = county_patterns['top_category'] == category\n dirich_samples = [np.array(json.loads(x)) for x in county_patterns.loc[places_in_cat, 'visits_by_each_hour'] ] \n prior_dict[category] = dirichlet.getInitAlphas(dirich_samples)\n if GEOID_type == 'CBG':\n place_cbgs = ccc.place_cbg_contacts_table(\n county_patterns,\n prior_dict,\n norm_factor,\n GEOID_type)\n place_cbgs = place_cbgs.loc[place_cbgs['expected_contacts']>1]\n place_cbgs = place_cbgs.join(county_places[['location_name','latitude','longitude']], how='inner')\n place_cbgs.reset_index(inplace=True, drop=False)\n place_cbgs.set_index('origin_census_block_group', inplace=True, drop=True)\n if GEOID_type == 'CT':\n place_cts = ccc.place_cbg_contacts_table(\n county_patterns,\n prior_dict,\n norm_factor,\n GEOID_type)\n place_cts = place_cts.join(county_places[['location_name','latitude','longitude']], how='inner')\n place_cts.reset_index(inplace=True, drop=False)\n place_cts.set_index('origin_census_tract', inplace=True, drop=True)\n print('--computed bipartite contact network')\n\n if date_name in existing_dates:\n continue\n\n\n file_name = os.listdir(months_path+ '/' + month+'/'+day)[0]\n data_soc_dist = pd.read_csv(months_path+ '/' + month+'/'+day+'/'+file_name, dtype={'origin_census_block_group':str})\n data_soc_dist['median_distance_traveled_from_home'] = [0 if math.isnan(x) else x for x in data_soc_dist['distance_traveled_from_home']] #NOT NEEDED?\n \n if GEOID_type == 'CBG':\n [update_metrics_columns(row, date_name, place_cbgs, county_places, columns) for i, row in data_soc_dist.iterrows()]\n else:\n ####CREATE FUNCTION THAT AGGREGATES TO CENSUS TRACT\n data_soc_dist['origin_census_tract'] = [x[:-1] for x in data_soc_dist['origin_census_block_group']]\n ct_data_soc_dist = data_soc_dist.groupby('origin_census_tract').apply(ddd.aggregate_to_ct).reset_index()\n [update_metrics_columns_CT(row, date_name, place_cts, county_places, columns) for i, row in ct_data_soc_dist.iterrows()]\n changed = True\n\n print(\"--merging rows from new dates\")\n new_metrics = pd.DataFrame.from_dict(columns, orient='index').transpose()\n\n metrics = pd.concat([metrics, new_metrics], ignore_index=True)\n if changed:\n if GEOID_type == 'CT':\n metrics.to_csv('../stats/time_series/metrics_{}_CT.csv'.format(county),index=False)\n else:\n metrics.to_csv('../stats/time_series/metrics_{}_CBG.csv'.format(county),index=False)\n print(\"--finished updating time series for {}\".format(county))\n return(0)","def get_extension_list(self, timespan, db_lookup):\n if (self.search_list_extension != None):\n return [self.search_list_extension]\n \n t1 = time.time()\n\n # Get UTC offset\n stop_struct = time.localtime(timespan.stop)\n utc_offset = (calendar.timegm(stop_struct) - calendar.timegm(time.gmtime(time.mktime(stop_struct))))/60\n\n # Our start time is one year ago from midnight at the start of today\n # first get the start of today\n _ts = startOfDay(timespan.stop)\n # then go back 1 year\n _ts_dt = datetime.datetime.fromtimestamp(_ts)\n try:\n _start_dt = _ts_dt.replace(year=_ts_dt.year-1)\n except ValueError:\n _start_dt = _ts_dt.replace(year=_ts_dt.year-1, day=_ts_dt.day-1)\n _start_ts = time.mktime(_start_dt.timetuple())\n _timespan = TimeSpan(_start_ts, timespan.stop)\n\n # Get our outTemp vectors\n (outTemp_time_vt, outTemp_dict) = getDaySummaryVectors(db_lookup(), 'outTemp', _timespan, ['min', 'max', 'avg'])\n (inTemp_time_vt, inTemp_dict) = getDaySummaryVectors(db_lookup(), 'inTemp', _timespan, ['min', 'max', 'avg'])\n # Get our vector ValueTuple out of the dictionary and convert it\n outTempMin_vt = self.generator.converter.convert(outTemp_dict['min'])\n outTempMax_vt = self.generator.converter.convert(outTemp_dict['max'])\n outTempAvg_vt = self.generator.converter.convert(outTemp_dict['avg'])\n\n inTempMin_vt = self.generator.converter.convert(inTemp_dict['min'])\n inTempMax_vt = self.generator.converter.convert(inTemp_dict['max'])\n inTempAvg_vt = self.generator.converter.convert(inTemp_dict['avg'])\n \n # Get our dewpoint vectors\n (dewpoint_time_vt, dewpoint_dict) = getDaySummaryVectors(db_lookup(), 'dewpoint', _timespan, ['min', 'max', 'avg'])\n # Get our vector ValueTuple out of the dictionary and convert it\n dewpointMin_vt = self.generator.converter.convert(dewpoint_dict['min'])\n dewpointMax_vt = self.generator.converter.convert(dewpoint_dict['max'])\n dewpointAvg_vt = self.generator.converter.convert(dewpoint_dict['avg'])\n\n # Get our appTemp vectors. appTemp data is not normally archived so\n # only try to get it if we have a binding for it. Wrap in a try..except\n # to catch any errors. If we don't have a binding then set the vectors\n # to None\n if self.apptemp_binding is not None:\n try:\n (appTemp_time_vt, appTemp_dict) = getDaySummaryVectors(db_lookup('wd_binding'),\n 'appTemp',\n _timespan,\n ['min', 'max', 'avg'])\n # Get our vector ValueTuple out of the dictionary and convert it\n appTempMin_vt = self.generator.converter.convert(appTemp_dict['min'])\n appTempMax_vt = self.generator.converter.convert(appTemp_dict['max'])\n appTempAvg_vt = self.generator.converter.convert(appTemp_dict['avg'])\n except weewx.UnknownBinding:\n raise\n else:\n appTempMin_vt = None\n appTempMax_vt = None\n appTempAvg_vt = None\n\n # Get our windchill vector\n (windchill_time_vt, windchill_dict) = getDaySummaryVectors(db_lookup(),\n 'windchill',\n _timespan,\n ['min', 'max', 'avg'])\n # Get our vector ValueTuple out of the dictionary and convert it\n windchillAvg_vt = self.generator.converter.convert(windchill_dict['avg'])\n windchillMin_vt = self.generator.converter.convert(windchill_dict['min'])\n windchillMax_vt = self.generator.converter.convert(windchill_dict['max'])\n\n # Get our heatindex vector\n (heatindex_time_vt, heatindex_dict) = getDaySummaryVectors(db_lookup(),\n 'heatindex',\n _timespan,\n ['min', 'max', 'avg'])\n # Get our vector ValueTuple out of the dictionary and convert it\n heatindexAvg_vt = self.generator.converter.convert(heatindex_dict['avg'])\n heatindexMin_vt = self.generator.converter.convert(heatindex_dict['min'])\n heatindexMax_vt = self.generator.converter.convert(heatindex_dict['max'])\n # Get our humidity vectors\n (outHumidity_time_vt, outHumidity_dict) = getDaySummaryVectors(db_lookup(),'outHumidity',_timespan,['min', 'max', 'avg'])\n (inHumidity_time_vt, inHumidity_dict) = getDaySummaryVectors(db_lookup(),'inHumidity',_timespan,['min', 'max', 'avg'])\n\n # Get our barometer vectors\n (barometer_time_vt, barometer_dict) = getDaySummaryVectors(db_lookup(),\n 'barometer',\n _timespan,\n ['min', 'max', 'avg'])\n # Get our vector ValueTuple out of the dictionary and convert it\n barometerMin_vt = self.generator.converter.convert(barometer_dict['min'])\n barometerMax_vt = self.generator.converter.convert(barometer_dict['max'])\n barometerAvg_vt = self.generator.converter.convert(barometer_dict['avg'])\n\n # Get our wind vectors\n (wind_time_vt, wind_dict) = getDaySummaryVectors(db_lookup(),\n 'wind',\n _timespan,\n ['max', 'avg'])\n # Get our vector ValueTuple out of the dictionary and convert it\n windMax_vt = self.generator.converter.convert(wind_dict['max'])\n windAvg_vt = self.generator.converter.convert(wind_dict['avg'])\n\n # Get our windSpeed vectors\n (windSpeed_time_vt, windSpeed_dict) = getDaySummaryVectors(db_lookup(),\n 'windSpeed',\n _timespan,\n ['min', 'max', 'avg'])\n # Get our vector ValueTuple out of the dictionary and convert it\n windSpeedMax_vt = self.generator.converter.convert(windSpeed_dict['max'])\n windSpeedAvg_vt = self.generator.converter.convert(windSpeed_dict['avg'])\n\n # Get our windDir vectors\n (windDir_time_vt, windDir_dict) = getDaySummaryVectors(db_lookup(),'wind',_timespan,['vecdir'])\n # Get our rain vectors\n (rain_time_vt, rain_dict) = getDaySummaryVectors(db_lookup(),\n 'rain',\n _timespan,\n ['sum'])\n # Get our vector ValueTuple out of the dictionary and convert it\n rainSum_vt = self.generator.converter.convert(rain_dict['sum'])\n\n # Get our radiation vectors\n (radiation_time_vt, radiation_dict) = getDaySummaryVectors(db_lookup(),\n 'radiation',\n _timespan,\n ['min', 'max', 'avg'])\n\n # Get our UV vectors\n (uv_time_vt, uv_dict) = getDaySummaryVectors(db_lookup(),\n 'UV',\n _timespan,\n ['min', 'max', 'avg'])\n \n # Get no of decimal places to use when formatting results\n tempPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(outTempMin_vt[1], \"1f\")[-2])\n outHumidityPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(outHumidity_dict['min'][1], \"1f\")[-2])\n inHumidityPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(inHumidity_dict['min'][1], \"1f\")[-2])\n barometerPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(barometerMin_vt[1], \"1f\")[-2])\n windPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(windMax_vt[1], \"1f\")[-2])\n windSpeedPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(windSpeedMax_vt[1], \"1f\")[-2])\n windDirPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(windDir_dict['vecdir'][1], \"1f\")[-2])\n rainPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(rainSum_vt[1], \"1f\")[-2])\n radiationPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(radiation_dict['max'][1], \"1f\")[-2])\n uvPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uv_dict['max'][1], \"1f\")[-2])\n\n # Get our time vector in ms\n time_ms = [outTemp_time_vt[0][0] if (x == 0) else outTemp_time_vt[0][x] - outTemp_time_vt[0][0] for x in range(len(outTemp_time_vt[0]))]\n #time_ms = [float(x) * 1000 for x in outTemp_time_vt[0]]\n\n # Round our values from our ValueTuples\n outTempMinRound = [roundNone(x,tempPlaces) for x in outTempMin_vt[0]]\n outTempMaxRound = [roundNone(x,tempPlaces) for x in outTempMax_vt[0]]\n outTempAvgRound = [roundNone(x,tempPlaces) for x in outTempAvg_vt[0]]\n inTempMinRound = [roundNone(x,tempPlaces) for x in inTempMin_vt[0]]\n inTempMaxRound = [roundNone(x,tempPlaces) for x in inTempMax_vt[0]]\n inTempAvgRound = [roundNone(x,tempPlaces) for x in inTempAvg_vt[0]]\n # round our appTemp values, if we don't have any then set it to None\n try:\n appTempMinRound = [roundNone(x,tempPlaces) for x in appTempMin_vt[0]]\n appTempMaxRound = [roundNone(x,tempPlaces) for x in appTempMax_vt[0]]\n appTempAvgRound = [roundNone(x,tempPlaces) for x in appTempAvg_vt[0]]\n except TypeError:\n appTempMinRound = None\n appTempMaxRound = None\n appTempAvgRound = None\n dewpointMinRound = [roundNone(x,tempPlaces) for x in dewpointMin_vt[0]]\n dewpointMaxRound = [roundNone(x,tempPlaces) for x in dewpointMax_vt[0]]\n dewpointAvgRound = [roundNone(x,tempPlaces) for x in dewpointAvg_vt[0]]\n windchillMinRound = [roundNone(x,tempPlaces) for x in windchillMin_vt[0]]\n windchillMaxRound = [roundNone(x,tempPlaces) for x in windchillMax_vt[0]]\n windchillAvgRound = [roundNone(x,tempPlaces) for x in windchillAvg_vt[0]]\n heatindexMinRound = [roundNone(x,tempPlaces) for x in heatindexMin_vt[0]]\n heatindexMaxRound = [roundNone(x,tempPlaces) for x in heatindexMax_vt[0]]\n heatindexAvgRound = [roundNone(x,tempPlaces) for x in heatindexAvg_vt[0]]\n outHumidityMinRound = [roundNone(x,outHumidityPlaces) for x in outHumidity_dict['min'][0]]\n outHumidityMaxRound = [roundNone(x,outHumidityPlaces) for x in outHumidity_dict['max'][0]]\n outHumidityAvgRound = [roundNone(x,outHumidityPlaces) for x in outHumidity_dict['avg'][0]]\n inHumidityMinRound = [roundNone(x,inHumidityPlaces) for x in inHumidity_dict['min'][0]]\n inHumidityMaxRound = [roundNone(x,inHumidityPlaces) for x in inHumidity_dict['max'][0]]\n inHumidityAvgRound = [roundNone(x,inHumidityPlaces) for x in inHumidity_dict['avg'][0]]\n barometerMinRound = [roundNone(x,tempPlaces) for x in barometerMin_vt[0]]\n barometerMaxRound = [roundNone(x,tempPlaces) for x in barometerMax_vt[0]]\n barometerAvgRound = [roundNone(x,tempPlaces) for x in barometerAvg_vt[0]]\n windMaxRound = [roundNone(x,windPlaces) for x in windMax_vt[0]]\n windAvgRound = [roundNone(x,windPlaces) for x in windAvg_vt[0]]\n windSpeedMaxRound = [roundNone(x,windSpeedPlaces) for x in windSpeedMax_vt[0]]\n windSpeedAvgRound = [roundNone(x,windSpeedPlaces) for x in windSpeedAvg_vt[0]]\n windDirRound = [roundNone(x,windDirPlaces) for x in windDir_dict['vecdir'][0]]\n rainSumRound = [roundNone(x,rainPlaces) for x in rainSum_vt[0]]\n radiationMinRound = [roundNone(x,radiationPlaces) for x in radiation_dict['min'][0]]\n radiationMaxRound = [roundNone(x,radiationPlaces) for x in radiation_dict['max'][0]]\n radiationAvgRound = [roundNone(x,radiationPlaces) for x in radiation_dict['avg'][0]]\n uvMinRound = [roundNone(x,uvPlaces) for x in uv_dict['min'][0]]\n uvMaxRound = [roundNone(x,uvPlaces) for x in uv_dict['max'][0]]\n uvAvgRound = [roundNone(x,uvPlaces) for x in uv_dict['avg'][0]]\n\n # Produce our JSON strings\n outTempMinMax_json = json.dumps(zip(time_ms, outTempMinRound, outTempMaxRound))\n outTempMin_json = json.dumps(zip(time_ms, outTempMinRound))\n outTempMax_json = json.dumps(zip(time_ms, outTempMaxRound))\n outTempAvg_json = json.dumps(zip(time_ms, outTempAvgRound))\n inTempMinMax_json = json.dumps(zip(time_ms, inTempMinRound, inTempMaxRound))\n inTempMin_json = json.dumps(zip(time_ms, inTempMinRound))\n inTempMax_json = json.dumps(zip(time_ms, inTempMaxRound))\n inTempAvg_json = json.dumps(zip(time_ms, inTempAvgRound))\n # appTemp. If we don't have any source data then set our JSON string to\n # None\n if appTempMinRound is not None and appTempMaxRound is not None:\n appTempMinMax_json = json.dumps(zip(time_ms, appTempMinRound, appTempMaxRound))\n else:\n appTempMinMax_json = None\n if appTempMinRound is not None:\n appTempMin_json = json.dumps(zip(time_ms, appTempMinRound))\n else:\n appTempMin_json = None\n if appTempMaxRound is not None:\n appTempMax_json = json.dumps(zip(time_ms, appTempMaxRound))\n else:\n appTempMax_json = None\n if appTempAvgRound is not None:\n appTempAvg_json = json.dumps(zip(time_ms, appTempAvgRound))\n else:\n appTempAvg_json = None\n dewpointMinMax_json = json.dumps(zip(time_ms, dewpointMinRound, dewpointMaxRound))\n dewpointMin_json = json.dumps(zip(time_ms, dewpointMinRound))\n dewpointMax_json = json.dumps(zip(time_ms, dewpointMaxRound))\n dewpointAvg_json = json.dumps(zip(time_ms, dewpointAvgRound))\n windchillMinMax_json = json.dumps(zip(time_ms, windchillMinRound, windchillMaxRound))\n windchillMin_json = json.dumps(zip(time_ms, windchillMinRound))\n windchillMax_json = json.dumps(zip(time_ms, windchillMaxRound))\n windchillAvg_json = json.dumps(zip(time_ms, windchillAvgRound))\n heatindexMinMax_json = json.dumps(zip(time_ms, heatindexMinRound, heatindexMaxRound))\n heatindexMin_json = json.dumps(zip(time_ms, heatindexMinRound))\n heatindexMax_json = json.dumps(zip(time_ms, heatindexMaxRound))\n heatindexAvg_json = json.dumps(zip(time_ms, heatindexAvgRound))\n outHumidityMinMax_json = json.dumps(zip(time_ms, outHumidityMinRound, outHumidityMaxRound))\n outHumidityMin_json = json.dumps(zip(time_ms, outHumidityMinRound))\n outHumidityMax_json = json.dumps(zip(time_ms, outHumidityMaxRound))\n outHumidityAvg_json = json.dumps(zip(time_ms, outHumidityAvgRound))\n inHumidityMinMax_json = json.dumps(zip(time_ms, inHumidityMinRound, inHumidityMaxRound))\n inHumidityMin_json = json.dumps(zip(time_ms, inHumidityMinRound))\n inHumidityMax_json = json.dumps(zip(time_ms, inHumidityMaxRound))\n inHumidityAvg_json = json.dumps(zip(time_ms, inHumidityAvgRound))\n barometerMinMax_json = json.dumps(zip(time_ms, barometerMinRound, barometerMaxRound))\n barometerMin_json = json.dumps(zip(time_ms, barometerMinRound))\n barometerMax_json = json.dumps(zip(time_ms, barometerMaxRound))\n barometerAvg_json = json.dumps(zip(time_ms, barometerAvgRound))\n windMax_json = json.dumps(zip(time_ms, windMaxRound))\n windAvg_json = json.dumps(zip(time_ms, windAvgRound))\n windSpeedMax_json = json.dumps(zip(time_ms, windSpeedMaxRound))\n windSpeedAvg_json = json.dumps(zip(time_ms, windSpeedAvgRound))\n windDir_json = json.dumps(zip(time_ms, windDirRound))\n rainSum_json = json.dumps(zip(time_ms, rainSumRound))\n radiationMax_json = json.dumps(zip(time_ms, radiationMaxRound))\n radiationAvg_json = json.dumps(zip(time_ms, radiationAvgRound))\n uvMax_json = json.dumps(zip(time_ms, uvMaxRound))\n uvAvg_json = json.dumps(zip(time_ms, uvAvgRound))\n\n # Create uva json\n try:\n (uva_time_vt, uva_dict) = getDaySummaryVectors(db_lookup(), 'uva', timespan,['max', 'avg'])\n uvaPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uva_dict['max'][1], \"1f\")[-2])\n uvaMaxRound = [roundNone(x,uvaPlaces) for x in uva_dict['max'][0]]\n uvaAvgRound = [roundNone(x,uvaPlaces) for x in uva_dict['avg'][0]]\n uvaMax_json = json.dumps(zip(time_ms, uvaMaxRound))\n uvaAvg_json = json.dumps(zip(time_ms, uvaAvgRound))\n except:\n uvaMax_json = None\n uvaAvg_json = None\n \n # Create uvb json\n try:\n (uvb_time_vt, uvb_dict) = getDaySummaryVectors(db_lookup(), 'uvb', timespan,['max', 'avg'])\n uvbPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uvb_dict['max'][1], \"1f\")[-2])\n uvbMaxRound = [roundNone(x,uvbPlaces) for x in uvb_dict['max'][0]]\n uvbAvgRound = [roundNone(x,uvbPlaces) for x in uvb_dict['avg'][0]]\n uvbMax_json = json.dumps(zip(time_ms, uvbMaxRound))\n uvbAvg_json = json.dumps(zip(time_ms, uvbAvgRound))\n except:\n uvbMax_json = None\n uvbAvg_json = None\n \n # Create uvaWm json\n try:\n (uvaWm_time_vt, uvaWm_dict) = getDaySummaryVectors(db_lookup(), 'uvaWm', timespan,['max', 'avg'])\n uvaWmPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uvaWm_dict['max'][1], \"1f\")[-2])\n uvaWmMaxRound = [roundNone(x,uvaWmPlaces) for x in uvaWm_dict['max'][0]]\n uvaWmAvgRound = [roundNone(x,uvaWmPlaces) for x in uvaWm_dict['avg'][0]]\n uvaWmMax_json = json.dumps(zip(time_ms, uvaWmMaxRound))\n uvaWmAvg_json = json.dumps(zip(time_ms, uvaWmAvgRound))\n except:\n uvaWmMax_json = None\n uvaWmAvg_json = None\n \n # Create uvbWm json\n try:\n (uvbWm_time_vt, uvbWm_dict) = getDaySummaryVectors(db_lookup(), 'uvbWm', timespan,['max', 'avg'])\n uvbWmPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uvbWm_dict['max'][1], \"1f\")[-2])\n uvbWmMaxRound = [roundNone(x,uvbWmPlaces) for x in uvbWm_dict['max'][0]]\n uvbWmAvgRound = [roundNone(x,uvbWmPlaces) for x in uvbWm_dict['avg'][0]]\n uvbWmMax_json = json.dumps(zip(time_ms, uvbWmMaxRound))\n uvbWmAvg_json = json.dumps(zip(time_ms, uvbWmAvgRound))\n except:\n uvbWmMax_json = None\n uvbWmAvg_json = None\n \n # Create energy json\n try:\n (energy_time_vt, energy_dict) = getDaySummaryVectors(db_lookup(), 'energy', timespan,['max', 'avg'])\n energyPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(energy_dict['max'][1], \"1f\")[-2])\n energyMaxRound = [roundNone(x,energyPlaces) for x in energy_dict['max'][0]]\n energyAvgRound = [roundNone(x,energyPlaces) for x in energy_dict['avg'][0]]\n energyMax_json = json.dumps(zip(time_ms, energyMaxRound))\n energyAvg_json = json.dumps(zip(time_ms, energyAvgRound))\n except:\n energyMax_json = None\n energyAvg_json = None\n \n # Create distance json\n try:\n (distance_time_vt, distance_dict) = getDaySummaryVectors(db_lookup(), 'avg_distance', timespan,['max', 'avg'])\n distancePlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(distance_dict['max'][1], \"1f\")[-2])\n distanceMaxRound = [roundNone(x,distancePlaces) for x in distance_dict['max'][0]]\n distanceAvgRound = [roundNone(x,distancePlaces) for x in distance_dict['avg'][0]]\n distanceMax_json = json.dumps(zip(time_ms, distanceMaxRound))\n distanceAvg_json = json.dumps(zip(time_ms, distanceAvgRound))\n except:\n distanceMax_json = None\n distanceAvg_json = None\n \n # Create strikes json\n try:\n (strikes_time_vt, strikes_dict) = getDaySummaryVectors(db_lookup(), 'lightning_strikes', timespan,['sum'])\n strikesPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(strikes_dict['sum'][1], \"1f\")[-2])\n strikesSumRound = [roundNone(x,strikesPlaces) for x in strikes_dict['sum'][0]]\n strikesSum_json = json.dumps(zip(time_ms, strikesSumRound))\n except:\n strikesSum_json = None\n\n # Create full_spectrum json\n try:\n (full_spectrum_time_vt, full_spectrum_dict) = getDaySummaryVectors(db_lookup(), 'full_spectrum', timespan,['max', 'avg'])\n full_spectrumPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(full_spectrum_dict['max'][1], \"1f\")[-2])\n full_spectrumMaxRound = [roundNone(x,full_spectrumPlaces) for x in full_spectrum_dict['max'][0]]\n full_spectrumAvgRound = [roundNone(x,full_spectrumPlaces) for x in full_spectrum_dict['avg'][0]]\n full_spectrumMax_json = json.dumps(zip(time_ms, full_spectrumMaxRound))\n full_spectrumAvg_json = json.dumps(zip(time_ms, full_spectrumAvgRound))\n except:\n full_spectrumMax_json = None\n full_spectrumAvg_json = None\n \n # Create lux json\n try:\n (lux_time_vt, lux_dict) = getDaySummaryVectors(db_lookup(), 'lux', timespan,['max', 'avg'])\n luxPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(lux_dict['max'][1], \"1f\")[-2])\n luxMaxRound = [roundNone(x,luxPlaces) for x in lux_dict['max'][0]]\n luxAvgRound = [roundNone(x,luxPlaces) for x in lux_dict['avg'][0]]\n luxMax_json = json.dumps(zip(time_ms, luxMaxRound))\n luxAvg_json = json.dumps(zip(time_ms, luxAvgRound))\n except:\n luxMax_json = None\n luxAvg_json = None\n\n # Create infrared json\n try:\n (infrared_time_vt, infrared_dict) = getDaySummaryVectors(db_lookup(), 'infrared', timespan,['max', 'avg'])\n infraredPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(infrared_dict['max'][1], \"1f\")[-2])\n infraredMaxRound = [roundNone(x,infraredPlaces) for x in infrared_dict['max'][0]]\n infraredAvgRound = [roundNone(x,infraredPlaces) for x in infrared_dict['avg'][0]]\n infraredMax_json = json.dumps(zip(time_ms, infraredMaxRound))\n infraredAvg_json = json.dumps(zip(time_ms, infraredAvgRound))\n except:\n infraredMax_json = None\n infraredAvg_json = None\n \n # Put into a dictionary to return\n self.search_list_extension = {'outTempMinMax_json' : outTempMinMax_json,\n 'outTempAvg_json' : outTempAvg_json,\n 'inTempMinMax_json' : inTempMinMax_json,\n 'inTempAvg_json' : inTempAvg_json,\n 'appTempMinMax_json' : appTempMinMax_json,\n 'appTempMin_json' : appTempMin_json,\n 'appTempMax_json' : appTempMax_json,\n 'appTempAvg_json' : appTempAvg_json,\n 'dewpointMinMax_json' : dewpointMinMax_json,\n 'dewpointAvg_json' : dewpointAvg_json,\n 'windchillMinMax_json' : windchillMinMax_json,\n 'windchillAvg_json' : windchillAvg_json,\n 'heatindexMinMax_json' : heatindexMinMax_json,\n 'heatindexAvg_json' : heatindexAvg_json,\n 'outHumidityMinMax_json' : outHumidityMinMax_json,\n 'outHumidityMin_json' : outHumidityMin_json,\n 'outHumidityMax_json' : outHumidityMax_json,\n 'outHumidityAvg_json' : outHumidityAvg_json,\n 'inHumidityMinMax_json' : inHumidityMinMax_json,\n 'inHumidityMin_json' : inHumidityMin_json,\n 'inHumidityMax_json' : inHumidityMax_json,\n 'inHumidityAvg_json' : inHumidityAvg_json,\n 'barometerMinMax_json' : barometerMinMax_json,\n 'barometerMin_json' : barometerMin_json,\n 'barometerMax_json' : barometerMax_json,\n 'barometerAvg_json' : barometerAvg_json,\n 'windMax_json' : windMax_json,\n 'windAvg_json' : windAvg_json,\n 'windSpeedMax_json' : windSpeedMax_json,\n 'windSpeedAvg_json' : windSpeedAvg_json,\n 'windDir_json' : windDir_json,\n 'rainSum_json' : rainSum_json,\n 'radiationMax_json' : radiationMax_json,\n 'radiationAvg_json' : radiationAvg_json,\n 'uvMax_json' : uvMax_json,\n 'uvAvg_json' : uvAvg_json,\n 'uvaMax_json' : uvaMax_json,\n 'uvaAvg_json' : uvaAvg_json,\n 'uvbMax_json' : uvbMax_json,\n 'uvbAvg_json' : uvbAvg_json,\n 'uvaWmMax_json' : uvaWmMax_json,\n 'uvaWmAvg_json' : uvaWmAvg_json,\n 'uvbWmMax_json' : uvbWmMax_json,\n 'uvbWmAvg_json' : uvbWmAvg_json,\n 'strikesSum_json' : strikesSum_json,\n 'distanceMax_json' : distanceMax_json ,\n 'distanceAvg_json' : distanceAvg_json,\n 'energyMax_json' : energyMax_json,\n 'energyAvg_json' : energyAvg_json,\n 'full_spectrumMax_json' : full_spectrumMax_json,\n 'full_spectrumAvg_json' : full_spectrumAvg_json,\n 'luxMax_json' : luxMax_json,\n 'luxAvg_json' : luxAvg_json,\n 'infraredMax_json' : infraredMax_json,\n 'infraredAvg_json' : infraredAvg_json,\n 'utcOffset': utc_offset,\n 'yearPlotStart' : _timespan.start * 1000,\n 'yearPlotEnd' : _timespan.stop * 1000}\n t2 = time.time()\n logdbg2(\"w34highchartsYear SLE executed in %0.3f seconds\" % (t2 - t1))\n\n # Return our json data\n return [self.search_list_extension]","def read_SVE_runoff(ID, start_date,end_date, sourcefile):\n # Runoffs compiled from Hertta-database (Syke) and Metla/Luke old observations.\n # Span: 1935-2015, missing data=-999.99\n # File columns:\n # pvm;14_Paunulanpuro;15_Katajaluoma;16_Huhtisuonoja;17_Kesselinpuro;18_Korpijoki;20_Vaarajoki;\n # 22_Vaha-Askanjoki;26_Iittovuoma;24_Kotioja;27_Laanioja;1_Lompolojanganoja;10_Kelopuro;\n # 13_Rudbacken;3_Porkkavaara;11_Hauklammenoja;19_Pahkaoja;21_Myllypuro;23_Ylijoki;2_Liuhapuro;\n # 501_Kauheanpuro;502_Korsukorvenpuro;503_Kangasvaaranpuro;504_Kangaslammenpuro;56_Suopuro;\n # 57_Valipuro;28_Kroopinsuo;30_Pakopirtti;31_Ojakorpi;32_Rantainrahka\n\n # import data\n sourcefile = os.path.join(sourcefile)\n dat = pd.read_csv(sourcefile, sep=';', header='infer', parse_dates=['pvm'], index_col='pvm', na_values=-999)\n\n # split column names so that they equal ID's\n cols = [x.split(\"_\")[0] for x in dat.columns]\n dat.columns = cols\n\n # get desired period & rename column ID to Qm\n dat = dat[(dat.index >= start_date) & (dat.index <= end_date)]\n dat = dat[ID]\n if type(ID) is str:\n dat.columns = ['Qm']\n return dat","def update_data(self):\n data, meta_data = ts.get_daily(symbol=self.stock_ticker, outputsize='full')\n self.data = data\n self.meta_data = meta_data","def update(self, ti=None, tf=None):\n if failedobspyimport:\n raise ImportError('ObsPy import failed, cannot update data.')\n\n makedir('_tmp')\n\n # default data range if not given \n ti = ti or datetime(self.tf.year,self.tf.month,self.tf.day,0,0,0)\n tf = tf or datetime.today() + _DAY\n \n ti = datetimeify(ti)\n tf = datetimeify(tf)\n\n ndays = (tf-ti).days\n\n # parallel data collection - creates temporary files in ./_tmp\n pars = [[i,ti] for i in range(ndays)]\n p = Pool(6)\n p.starmap(get_data_for_day, pars)\n p.close()\n p.join()\n\n # special case of no file to update - create new file\n if not self.exists:\n shutil.copyfile('_tmp/_tmp_fl_00000.dat',self.file)\n self.exists = True\n shutil.rmtree('_tmp')\n return\n\n # read temporary files in as dataframes for concatenation with existing data\n dfs = [self.df[datas]]\n for i in range(ndays):\n fl = '_tmp/_tmp_fl_{:05d}.dat'.format(i)\n if not os.path.isfile(fl): \n continue\n dfs.append(pd.read_csv(fl, index_col=0, parse_dates=[0,], infer_datetime_format=True))\n shutil.rmtree('_tmp')\n self.df = pd.concat(dfs)\n\n # impute missing data using linear interpolation and save file\n self.df = self.df.loc[~self.df.index.duplicated(keep='last')]\n self.df = self.df.resample('10T').interpolate('linear')\n\n # remove artefact in computing dsar\n for i in range(1,int(np.floor(self.df.shape[0]/(24*6)))): \n ind = i*24*6\n self.df['dsar'][ind] = 0.5*(self.df['dsar'][ind-1]+self.df['dsar'][ind+1])\n\n self.df.to_csv(self.file, index=True)\n self.ti = self.df.index[0]\n self.tf = self.df.index[-1]","def get_data(station,starttime,endtime,activity=False,\n rep='/GNOMEDrive/gnome/serverdata/',resample=None):\n setname = \"MagneticFields\"\n dstr = ['%Y','%m','%d','%H','%M']\n dsplit = '-'.join(dstr[:starttime.count('-')+1])\n start = datetime.strptime(starttime,dsplit)\n starttime = construct_utc_from_metadata(start.strftime(\"%Y/%m/%d\"),\n start.strftime(\"%H:%M:%S.%d\"))\n dsplit = '-'.join(dstr[:endtime.count('-')+1])\n end = datetime.strptime(endtime,dsplit)\n endtime = construct_utc_from_metadata(end.strftime(\"%Y/%m/%d\"),\n end.strftime(\"%H:%M:%S.%d\"))\n dataset = []\n for date in numpy.arange(start,end,timedelta(minutes=1)):\n date = date.astype(datetime)\n path1 = rep+station+'/'+date.strftime(\"%Y/%m/%d/\")\n path2 = station+'_'+date.strftime(\"%Y%m%d_%H%M*.hdf5\")\n fullpath = os.path.join(path1,path2)\n dataset += glob.glob(fullpath)\n if len(dataset)==0:\n print \"ERROR: No data files were found...\"\n quit()\n file_order,data_order = {},{}\n for fname in dataset:\n hfile = h5py.File(fname, \"r\")\n segfile = file_to_segment(hfile,setname)\n file_order[segfile] = fname\n data_order[segfile] = hfile\n # Extract sample rate from metadata of last read data file\n sample_rate = hfile[setname].attrs[\"SamplingRate(Hz)\"]\n # Estimate full segment activity list\n activity = create_activity_list(station,data_order)\n # Generate an ASCII representation of the GPS timestamped\n # segments of time covered by the input data\n seglist = segmentlist(data_order.keys())\n # Sort the segment list\n seglist.sort()\n # Create list of time series from every segment\n ts_list = generate_timeseries(file_order,setname)\n # Retrieve channel data for all the segments\n full_data = numpy.hstack([retrieve_channel_data(data_order[seg],setname)\n for seg in seglist])\n new_sample_rate = sample_rate if resample==None else resample\n new_data_length = len(full_data)*new_sample_rate/float(sample_rate)\n full_data = scipy.signal.resample(full_data,int(new_data_length))\n # Models a time series consisting of uniformly sampled scalar values\n ts_data = types.TimeSeries(full_data,delta_t=1./new_sample_rate,\n epoch=seglist[0][0])\n for v in data_order.values():\n v.close()\n return ts_data,ts_list,activity,int(starttime),int(endtime)","def refresh():\r\n DB.drop_all()\r\n DB.create_all()\r\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\r\n for i in time_x_values():\r\n DB.session.add(Record(datetime=i[0], value=i[1]))\r\n DB.session.commit()\r\n return 'Data refreshed!'","def preprocess_time(data, metadata):\n timestamp_name = metadata[\"timestamp_name\"]\n if timestamp_name == \"\":\n timestamp_name = \"fake_ts\"\n data[timestamp_name] = data.index\n\n data[timestamp_name] = pd.to_datetime(data[timestamp_name])\n data.sort_values(by=[timestamp_name], inplace=True)\n data.set_index([timestamp_name], inplace=True)\n\n return data","def Data_formatting(dz, y, timestep, TZ):\r\n \r\n #reindex data by datetime\r\n dz.index = pd.to_datetime(dz['DATE'])\r\n \r\n #Isolate temperature data\r\n dz = dz[['TMP']]\r\n \r\n #Delete data mistake\r\n dz = dz[dz['TMP'] != \"+9999,9\"]\r\n \r\n #Format data\r\n dz['TMP'] = dz['TMP'].str.replace(',', '.')\r\n dz['TMP'] = pd.to_numeric(dz['TMP'], errors='coerce')\r\n \r\n #Delete NaN data\r\n dz = dz.dropna()\r\n \r\n #Convert temperature\r\n dz['TMP'] = dz['TMP'] / 10\r\n dz['TMP'] = dz['TMP'] * (9/5) + 32\r\n \r\n #Convert datetime index utc to specified timezone\r\n dz.index = dz.index.tz_localize(pytz.utc).tz_convert(pytz.timezone(str(TZ))).strftime(\"%Y-%m-%d %H:%M:%S\")\r\n dz.index = pd.to_datetime(dz.index)\r\n \r\n #Resample data by average on timestep\r\n dz = dz.resample(rule = str(timestep)).mean()\r\n \r\n #Define the first date of the instance year\r\n fdy = dt.datetime.strptime(\"01/01/\"+str(y)+\" 00:00\", '%m/%d/%Y %H:%M')\r\n #Convert first date of the year to timezone\r\n fdy = Date_calibration(fdy, 0, TZ)\r\n \r\n #If we collect the date from the current year we limit the collect from 2days before now\r\n \r\n #Define the datetime 2 days before now\r\n dbeyest = dt.datetime.now(tz=pytz.timezone(str(TZ))) - dt.timedelta(days=2)\r\n \r\n #If the instance year is the current year\r\n if(y == dbeyest.year):\r\n #We limit the collect 2 days before now\r\n ldy = dt.datetime.strptime(str(dbeyest.month)+\"/\"+str(dbeyest.day)+\"/\"+str(y)+\" 23:59\", '%m/%d/%Y %H:%M')\r\n else:\r\n #Else, we collect the full year\r\n ldy = dt.datetime.strptime(\"12/31/\"+str(y)+\" 23:59\", '%m/%d/%Y %H:%M')\r\n \r\n #Convert the last date of the year to specified timezone\r\n ldy = Date_calibration(ldy, 0, TZ)\r\n \r\n #Set up dataframe for the specified datetime index and timestep\r\n ph = pd.DataFrame(index=pd.DatetimeIndex(start=fdy, end=ldy, freq=str(timestep)))\r\n \r\n #Past original data temperature in the time fitted dataframe (with the datetimeindex position)\r\n ph['TMP'] = dz['TMP']\r\n \r\n #Calculate the quality of the instance data\r\n nb_nan = ph['TMP'].isnull().sum()\r\n qual = (1 - (nb_nan) / len(ph)) * 100\r\n \r\n return dz, qual","def ec_data_processor_precip(path, x='TIMESTAMP_END', y='LE', daily=True):\n\n\n # Get the data from the path and turn the path into a data frame\n # ec_dataset = pd.read_csv(path, header=2)\n\n ec_dataset = pd.read_csv(path, header=2, engine='python')\n\n # print ec_dataset.head()\n print ec_dataset['LE'].head()\n print ec_dataset[ec_dataset[y] != -9999].head()\n # === get rid of no data values in any category of the energy balance ===\n precip_dataset = ec_dataset[ec_dataset['P'] != -9999]\n ec_dataset = ec_dataset[ec_dataset[y] != -9999]\n ec_dataset = ec_dataset[ec_dataset['NETRAD'] != -9999]\n ec_dataset = ec_dataset[ec_dataset['H'] != -9999]\n ec_dataset = ec_dataset[ec_dataset['LE'] != -9999]\n # # You probably won't need these because Marcy Doesn't think they are valid for her towers\n # ec_dataset = ec_dataset[ec_dataset['SH'] != -9999]\n # ec_dataset = ec_dataset[ec_dataset['SLE'] != -9999]\n\n if x.startswith(\"TIMESTAMP\"):\n a = ec_dataset[x].apply(lambda b: dt.strptime(str(b), '%Y%m%d%H%M'))\n aa = precip_dataset[x].apply(lambda d: dt.strptime(str(d), '%Y%m%d%H%M'))\n\n # # TODO - if converting PRISM to MTN time.\n # # Convert to PRISM time (Mtn Standard + 5 hours) PRISM midnight is 12:00 UTC - 7 hours for mountain. Net +5 hrs\n # a = [i + timedelta(hours=19) for i in a]\n # aa = [i + timedelta(hours=19) for i in aa]\n\n\n else:\n a = ec_dataset[x]\n\n # ===== Time Series Processing =====\n\n timeseries = a\n p_timeseries = aa\n # print 'timeseries\\n', timeseries\n Rn = ec_dataset['NETRAD'].values\n H = ec_dataset['H'].values\n LE = ec_dataset['LE'].values\n P = precip_dataset['P']\n print 'P \\n', P\n # indexed_datetimes = pd.DataFrame(pd.DatetimeIndex(timeseries))\n\n # # testing\n # plt.plot(timeseries, P, color='black')\n # plt.show()\n\n # recreate a dataframe of the variables you want to time average on a monthly timestep\n halfhour_data = pd.DataFrame({'timeseries': timeseries, 'Rn': Rn, 'LE': LE, 'H': H}) # took out precip. no good vals? 'P': P\n\n halfhour_precip = pd.DataFrame({'timeseries': p_timeseries, 'P': P})\n # set the timeseries column to the index so groupby function can group by year and month of the index.\n halfhour_data = halfhour_data.set_index(pd.DatetimeIndex(halfhour_data['timeseries']))\n halfhour_precip = halfhour_precip.set_index(pd.DatetimeIndex(halfhour_precip['timeseries']))\n # convert latent heat to mmH2O by dividing by latent heat of vaporization.\n halfhour_data['mmh20'] = halfhour_data['LE'] * 7.962e-4\n\n if daily:\n\n daily_cum_data = halfhour_data.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()\n daily_cum_precip = halfhour_precip.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()\n\n # get each day in the timeseries. there are duplicates from the groupby function, so use set() to get rid of\n # duplicates\n daily_cum_time = daily_time_parse(timeseries)\n daily_cum_precip_time = daily_time_parse(p_timeseries)\n\n # # testing\n # daily_cum_data.to_csv('/Users/dcadol/Desktop/daily_cumulative_df.csv')\n\n # format daily_cum_data to have datetimes\n daily_cum_data['date'] = daily_cum_time\n daily_cum_precip['date'] = daily_cum_precip_time\n\n return daily_cum_data, daily_cum_precip","def feature_engineer_ts(self, month=12):\n st_data_dt = self.get_st_data_dt()\n end_data_dt = self.get_end_data_dt()\n date_list = pd.date_range(*(pd.to_datetime([st_data_dt, end_data_dt]) + pd.offsets.MonthEnd()), freq='M').to_list()\n population = self.get_population()\n is_raw_partition = self.get_is_raw_partition()\n# Lag 2 months\n all_data = []\n# join past is_raw columns\n for d in date_list:\n \n population_partition = population[population['ft_data_dt'] == d] \n old_date = d - relativedelta(months=month)\n y = old_date.year\n m = old_date.month\n day = calendar.monthrange(y, m)[1]\n old_date = date(y, m, day)\n old_date = max(old_date, st_data_dt)\n date_list_join = pd.date_range(*(pd.to_datetime([old_date, d]) + pd.offsets.MonthEnd()), freq='M').to_list()\n date_list_join.reverse()\n for index, date_join in enumerate(date_list_join):\n if date_join.strftime(\"%Y-%m-%d\") not in is_raw_partition.keys():\n continue\n \n tmp_is_raw_partition = is_raw_partition[date_join.strftime(\"%Y-%m-%d\")]\n \n rename_col = [c for c in list(tmp_is_raw_partition.columns) if c not in ['idd', 'ft_data_dt']]\n new_col = [c+'_'+str(index+1) for c in rename_col]\n name_dict = dict(list(zip(rename_col, new_col)))\n tmp_is_raw_partition = tmp_is_raw_partition.rename(columns = name_dict)\n population_partition = population_partition.merge(tmp_is_raw_partition.drop(columns=['ft_data_dt']), on=['idd'], how='left')\n all_data.append(population_partition)\n ts_df = pd.concat(all_data)\n threshold_null = len(ts_df.columns) - 4\n ts_df = ts_df[ts_df.isnull().sum(axis=1) < threshold_null]\n \n def sum_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_sum_'+str(duration)+'mth'\n tmp_df = df[col_list].sum(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def mean_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_avg_'+str(duration)+'mth'\n tmp_df = df[col_list].mean(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def std_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_std_'+str(duration)+'mth'\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def med_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_med_'+str(duration)+'mth'\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def min_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_min_'+str(duration)+'mth'\n tmp_df = df[col_list].min(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def max_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_max_'+str(duration)+'mth'\n tmp_df = df[col_list].max(axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def q1_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_q1_'+str(duration)+'mth'\n tmp_df = df[col_list].quantile(q=0.25, axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def q3_ts(self, df, col_list, feature, duration):\n ft_name = feature+ '_q3_'+str(duration)+'mth'\n tmp_df = df[col_list].quantile(q=0.75, axis = 1).to_frame(name=ft_name)\n return tmp_df\n \n def last_ts(self, df, feature):\n ft_name = feature+ '_last'\n tmp_df = df[feature+'_'+str(1)].to_frame(name=ft_name)\n return tmp_df\n \n ts_duration = [1, 3, 6, 9, 12]\n feature_list = self.get_is_raw_col()\n df = ts_df[['idd', 'ft_data_dt']]\n# Time Series Features\n for duration in ts_duration:\n for col in feature_list:\n col_list = [col+'_'+str(i) for i in range(1, duration+1)]\n df = pd.concat([df\\\n , sum_ts(self, ts_df, col_list, col, duration)\\\n , mean_ts(self, ts_df, col_list, col, duration)\\\n , med_ts(self, ts_df, col_list, col, duration)\\\n , q1_ts(self, ts_df, col_list, col, duration)\\\n , q3_ts(self, ts_df, col_list, col, duration)\\\n , min_ts(self, ts_df, col_list, col, duration)\\\n , max_ts(self, ts_df, col_list, col, duration)]\n , axis=1)\n self.set_all_data(df)","def main():\n \n Y1, Y2 = 2005, 2017 ### range with coordinates supplied in pre-2018 generated archive\n\n if len(sys.argv) > 1 and int(sys.argv[1]) > 0:\n Y1 = int(sys.argv[1])\n \n if len(sys.argv) > 2 and int(sys.argv[2]) > Y1:\n Y2 = int(sys.argv[2])\n \n with open('data/audit.log','w') as output:\n for Y in range(Y1, Y2):\n df = pd.read_csv('data/{}.csv'.format(Y), low_memory = False)\n output.write('\\n--- {} --------------------\\n'.format(Y))\n\n # remove `deleted` records\n df['deleted'] = df['deleted'].apply(yes_no)\n df = df[df['deleted'] == 0]\n\n # remove misc misdemeanors\n df = df[~df['category'].isin(drop)]\n\n # validate date and expand into Y,N,D,W,H\n df['dt'] = df['incident_date'].apply(extract)\n df = df[~df['dt'].isnull()]\n\n # convert from plane state to longitude-latitude\n df['ll'] = df.apply(to_lnglat, axis = 1)\n\n # init features\n features = df.loc[:,['category','stat','address','city','zip']]\n features['id'] = df['incident_id']\n dt = ['year','month','day','weekday','hour']\n for i in range(len(dt)):\n features[dt[i]] = df['dt'].apply(lambda x: x[i] )\n\n features['lng'] = df['ll'].apply(lambda x: x[0])\n features['lat'] = df['ll'].apply(lambda x: x[1])\n\n features['gang'] = df['gang_related'].apply(yes_no)\n features['category'] = df['category'].apply(collapse)\n cat = set(features.groupby(['category']).size().reset_index(name='count')['category'].tolist())\n output.write('Categories: {}\\n'.format(len(cat)))\n\n output.write('Date miss: {:.4f}%\\n'\\\n .format(100 * (1 - len(features[(features['year'] > 2000) & (~features['weekday'].isnull())])/len(features))))\n output.write('Location miss: {:.4f}%\\n'\\\n .format(100 * (1 - len(features[(features['zip'] > 0) | (features['lat'] > 0)])/len(features))))\n\n # keep records with valid date\n features['date'] = df['dt'].apply(lambda x: datetime.date(x[0], x[1], x[2]))\n features = features[(features['year'] > 2000) & (~features['weekday'].isnull())]\n output.write('Time miss: {:.4f}%\\n'.format(100 * len(features[features['hour'] == -1])/len(features)))\n\n # potential `time-unknown` issue\n output.write('Hour ZERO: {:.4f}%\\n'.format(100 * len(features[features['hour'] == 0])/len(features)))\n output.write('Hour NOON: {:.4f}%\\n'.format(100 * len(features[features['hour'] == 12])/len(features)))\n\n features = features[(features['zip'] > 0) | (features['lat'] > 0)]\n\n # get the best possible coordinates + zipcode assessment\n features[['zip','lng','lat']] = features[['zip','lng','lat']].apply(fix_location, axis = 1)\n output.write('Failed location: {:.4f}%\\n'.format(100 * len(features[features['zip'].isnull()])/len(features)))\n features = features[~features['zip'].isnull()]\n features['zip'] = df['zip'].apply(lambda x: str(x)[:5])\n \n # normalize city attr\n features = features.join(zipcodes[['zip','city']].set_index('zip'), on = 'zip', lsuffix = '_orig', rsuffix = '')\n features.loc[features['city'].isnull(), 'city'] = features.loc[features['city'].isnull(), 'city_orig']\\\n .apply(lambda x: x if type(x) == float else ' '.join([l[0].upper() + l[1:] for l in x.split()]))\n\n # reduce to LA bounding-box\n features = features[(features['lng'] > -119) & (features['lng'] < -116)]\n features = features[(features['lat'] > 32) & (features['lat'] < 35)]\n\n # save csv\n features[fields].to_csv('data/F{}.csv'.format(Y), index = False)\n features[fields].to_json('data/F{}.json'.format(Y), orient = 'records')\n output.close()","def get_gdax_historical_data():\n \n start = None\n while not start:\n start,end,tid = getStartAndEndHistoric()\n if not start:\n time.sleep(60)\n #Todo: change this to 1min\n firsttimestamp = start\n engine = sa.create_engine(sql_address)\n products = [\"LTC-USD\",\"LTC-BTC\",\"ETH-USD\",\"ETH-BTC\",\"BTC-USD\"]\n public_client = gdax.PublicClient()\n deltat = datetime.timedelta(seconds = 200)\n timewindows = []\n while end - start > datetime.timedelta(seconds=0):\n if start + deltat > end:\n endx = end\n else:\n endx = start + deltat\n timewindows.append([start,endx])\n start += deltat\n results = []\n total = len(timewindows)\n current_idx = 0\n timeold = time.time()\n numofqueries = 0\n engine = sa.create_engine(sql_address)\n Base.metadata.bind = engine\n DBSession = sa.orm.sessionmaker()\n DBSession.bind = engine\n session = DBSession()\n for startx,endx in timewindows:\n\n current_idx += 1\n for i in products:\n repeat = True\n while repeat:\n\n #delay if ratelimts are close\n if numofqueries < 3:\n while time.time() - timeold < 1:\n time.sleep(0.05)\n \n timeold = time.time()\n numofqueries = 0\n try:\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n except:\n time.sleep(30)\n public_client = gdax.PublicClient()\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\n\n numofqueries += 1\n\n #rate limit exceeded has 'message' as dict.\n if not 'message' in alist:\n repeat = False\n for a in alist:\n a[0] = datetime.datetime.fromtimestamp(float(a[0]))\n tmp = i.split('-')\n d = dict(coin = tmp[0], currency = tmp[1], timestamp = a[0], low=a[1], high=a[2], open=a[3], close=a[4], volume=a[5])\n results.append(d)\n lasttimestamp = a[0]\n\n #upload with batch size of 10000\n if len(results) > 10000:\n engine.execute(\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\n results\n )\n results = []\n \n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n if update:\n update.end = lasttimestamp\n session.commit()\n else:\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\n session.add(new_update)\n session.commit()\n if len(results) > 0:\n engine.execute(\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\n results\n )\n results = []\n \n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n if update:\n update.end = lasttimestamp\n session.commit()\n else:\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\n session.add(new_update)\n session.commit()\n\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\n update.status='complete'\n order = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'order')).first()\n order.status='complete'\n session.commit()","def get_SWAS_data4flight(flight_ID=None):\n # Where is the data?\n folder = '{}/{}/'.format(get_local_folder('ARNA_data'), 'SWAS')\n# filename = 'ARNA-FIRSTLOOK-SWAS_JDL_typo_fix.csv'\n# var2use = 'comments4'\n# format = '%d/%m/%Y %H:%M:%S'\n # Or use latest file (NOTE: issue with column formating)\n# filename = 'ARNA-SECONDLOOK-SWAS.csv'\n # Use the updated second look file\n filename = 'ARNA-SECONDLOOK-SWAS_v2.csv'\n df = pd.read_csv(folder+filename)\n print(filename)\n # Update the index to use the SWAS fire fime\n var2use = 'SAMPLE START TIME'\n format = '%d/%m/%Y %H:%M:%S'\n df.index = pd.to_datetime(df[var2use].values, format=format)\n # If a flight ID stated, then only return points for that flight\n if isinstance(flight_ID, type(None)):\n pass\n else:\n # Get the beginning and end of the flight\n dfS = get_summary4flight(flight_ID=flight_ID)\n sdate = dfS.index.min()\n edate = dfS.index.max()\n # Only consider samples within this time\n df = df.loc[df.index >= sdate, :]\n df = df.loc[df.index <= edate, :]\n return df","def step030():\n logger.logMessage('Begin: get data from table')\n \n query = 'select tsa,time at time zone \\'utc\\' from weather_dupes ' + \\\n 'order by time;'\n \n pgConn = pg.connect(host=host,user=user,password=password,database=database) \n with pgConn:\n with pgConn.cursor() as c:\n c.execute(query)\n numrecs = 0\n with open(dbDumpFile,'w') as f:\n for row in c.fetchall():\n tsa = row[0]\n time= row[1].isoformat()\n f.write('{0:14d};{1:25s}\\n'.format(tsa,time))\n numrecs += 1\n if numrecs % 1000 == 0:\n logger.logMessage(level='DEBUG',message=\"{0:9d} rows dumped\".format(numrecs))\n logger.logMessage(\"Total rows: {0:d}\".format(numrecs))\n \n logger.logMessage('End : get data from table')","def find_inflections(study, record, sensor, segment, range):\n\n # check if the inflections have already been found\n path = [study, 'analyzed', 'inflections', 'all_times', str(range), record, segment]\n pathJoined = os.path.join(*path)\n file = os.path.join(pathJoined, sensor + \".csv\")\n\n if os.path.isfile(file):\n print('file found, not recalculated.')\n return\n\n print('finding inflections to build : ' + file)\n\n # retrieve the timestamped measurements for the study - record - sensor - segment\n format_type = 'truncate'\n source = os.path.join(study, 'formatted', format_type, record, segment, sensor + '.csv')\n print('source = ' + source)\n df = pd.read_csv(source)\n\n for colName in df.columns:\n\n # remove extra columns because the dataframe will be saved\n if 'Unnamed' in str(colName):\n del df[colName]\n\n # save the timestamps as a list\n elif 'Minutes' in str(colName):\n timeMinutes = list(df[colName])\n\n # find the measurement\n elif 'meas' in colName:\n\n # add new columns to the dataframe to save the new variables\n newColNames = ['inflectionDecision', 'inflectionLocation', 'polyfitCoefficients', 'polyfitEquation', 'polyfitSolution', 'derivativeEquation', 'derivativeSolution']\n colNameSplit = colName.split('_')\n print('colNameSplit[0] = ' + colNameSplit[0])\n\n for suffix in newColNames:\n label = str(colNameSplit[0] + '_' + suffix)\n print('label = ' + label)\n if label not in df.columns:\n df[label] = [None]*len((list(df['timeMinutes'])))\n\n df['timeBegin'] = [None]*len((list(df['timeMinutes'])))\n df['timeEnd'] = [None]*len((list(df['timeMinutes'])))\n\n for timeMinute in timeMinutes:\n\n i = df[ df['timeMinutes']== timeMinute].index.values[0]\n\n timeDif = (float(df.loc[2,'timeMinutes']) - float(df.loc[1,'timeMinutes']))\n timeTolerance = timeDif/2\n iRange = int(range/60*1/(timeDif))\n # print('iRange = ' + str(iRange))\n\n if len(list(df['timeMinutes'])) - i <= iRange+2:\n continue\n\n timeMedian = df.loc[int(i+iRange/2), 'timeMinutes']\n timeBegin = df.loc[int(i), 'timeMinutes']\n timeEnd = df.loc[int(i+iRange), 'timeMinutes']\n\n # print('timeMedian = ' + str(timeMedian) + ' timeBegin = ' + str(timeBegin) + ' timeEnd = ' + str(timeEnd))\n # print('range = ' + str(range/60) + ' timeEnd-timeBegin = ' + str(timeEnd-timeBegin) + ' % = ' + str(range/60/(timeEnd-timeBegin)))\n\n df_truncate = df[df['timeMinutes'] >= timeMinute]\n df_truncate = df_truncate[df_truncate['timeMinutes'] <= timeMinute + range/60]\n # df_truncate = df[df['timeMinutes'] >= timeMinute & df_truncate['timeMinutes'] <= timeMinute + range/60]\n\n timeTruncate = list(df_truncate['timeMinutes'])\n df.loc[int(i+iRange/2), 'timeBegin'] = min(timeTruncate)\n df.loc[int(i+iRange/2), 'timeEnd'] = max(timeTruncate)\n\n measTruncate = list(df_truncate[colName])\n\n coef = np.polyfit(timeTruncate, measTruncate, 2)\n # coef = [float(x) for x in coef]\n\n x = sym.Symbol('x')\n\n f = coef[0]*x*x+coef[1]*x+coef[2]\n # print('f = ')\n # print(f)\n\n dff = sym.diff(f,x)\n # print('dff = ')\n # print(dff)\n\n solf = sym.solve(f)\n soldf = sym.solve(dff)\n soldf = soldf[0]\n\n\n label = str(colNameSplit[0] + '_' + 'inflectionDecision')\n df.loc[int(i+iRange/2), label] = 'No'\n\n label = str(colNameSplit[0] + '_' + 'inflectionLocation')\n df.loc[int(i+iRange/2), label] = timeMinute\n\n label = str(colNameSplit[0] + '_' + 'polyfitCoefficients')\n df.loc[int(i+iRange/2), label] = str(''.join([str(x) for x in coef]))\n\n label = str(colNameSplit[0] + '_' + 'polyfitEquation')\n df.loc[int(i+iRange/2), label] = str(f)\n\n label = str(colNameSplit[0] + '_' + 'polyfitSolution')\n df.loc[int(i+iRange/2), label] = str(''.join([str(x) for x in solf]))\n\n label = str(colNameSplit[0] + '_' + 'derivativeEquation')\n df.loc[int(i+iRange/2), label] = str(dff)\n\n label = str(colNameSplit[0] + '_' + 'derivativeSolution')\n df.loc[int(i+iRange/2), label] = str(soldf)\n\n if soldf < timeMedian + timeTolerance:\n\n if soldf > timeMedian - timeTolerance:\n\n print('inflection found at time = ' + str(soldf))\n label = str(colNameSplit[0] + '_' + 'inflectionDecision')\n df.loc[int(i+iRange/2), label] = 'Yes'\n\n path = build_path(path)\n file = os.path.join(path, sensor + \".csv\")\n df.to_csv(file)\n print('inflection list saved : ' + file)\n return(file)","def get_weather_data():\n keys = ['1364038.csv',\n '1364041.csv',\n '1364042.csv',\n '1364043.csv',\n '1364044.csv',\n '1364046.csv',\n '1364047.csv',\n '1364048.csv',\n '1364051.csv',\n '1364052.csv',\n '1364053.csv',\n '1364054.csv',\n '1364055.csv',\n '1364058.csv',\n '1364059.csv',\n '1364060.csv',\n '1364061.csv',\n '1364062.csv',\n '1364063.csv',\n '1364064.csv',\n '1364066.csv']\n df_weather = import_weather(keys)\n df_weather_dist = df_weather[[\n 'LATITUDE', 'LONGITUDE', 'name']].drop_duplicates().reset_index()\n return df_weather, df_weather_dist","def overheadstats(sdb, obsdate, update=True):\n bvs_updated = 0\n scams=0\n #for a given obsdate get the night info\n nid=getnightinfo(sdb, obsdate)\n\n #get the times for the night\n record=sdb.select('EveningTwilightEnd, MorningTwilightStart', 'NightInfo', 'NightInfo_Id=%i' % nid)\n stime=record[0][0]\n etime=record[0][1]\n totaltime=(etime-stime).seconds\n\n #From the sdb, get the SoLogEvent table\n record=sdb.select('EventType_Id, EventTime', 'SoLogEvent', 'NightInfo_Id=%i' % nid)\n event_list=[]\n for i in range(len(record)):\n r=record[i]\n if r[1].seconds>43200:\n t=datetime.datetime(int(obsdate[0:4]), int(obsdate[5:7]), int(obsdate[8:10]), 0, 0, 0)+r[1]\n else:\n t=datetime.datetime(int(obsdate[0:4]), int(obsdate[5:7]), int(obsdate[8:10]), 0, 0, 0)+datetime.timedelta(days=1)+r[1]\n event_list.append([r[0], t])\n #sort the list by the datetimes\n event_list.sort(key=lambda e:e[1])\n\n #get the night's list of blocks\n selcmd='BlockVisit_Id'\n tabcmd='BlockVisit'\n logcmd='NightInfo_Id=%i and BlockVisitStatus_Id=%i' % (nid, 1)\n bvid_list=sdb.select(selcmd, tabcmd, logcmd)\n bvid_list[:]=[bvid[0] for bvid in bvid_list]\n\n #get a list of all images from the night\n select_state='FileName, Proposal_Code, Target_Name, ExposureTime, UTSTART, h.INSTRUME, h.OBSMODE, h.DETMODE, h.CCDTYPE, NExposures, BlockVisit_Id'\n table_state='FileData Join ProposalCode on (FileData.ProposalCode_Id = ProposalCode.ProposalCode_Id) join FitsHeaderImage as h using (FileData_Id)'\n formatteddate = obsdate.replace('-','')\n logic_state=\"FileName like '%\"+formatteddate+\"%' order by UTSTART\"\n img_list=sdb.select(select_state, table_state, logic_state)\n img_list[:] = [img for img in img_list if not \"CAL_\" in img[1] and not \"ENG_\" in img[1] and not \"JUNK\" in img[1]]\n\n #get a list of all RSS images from the night\n select_state='FileName, Proposal_Code, Target_Name, ExposureTime, UTSTART, h.INSTRUME, '\n select_state+='h.OBSMODE, h.DETMODE, h.CCDTYPE, NExposures, BlockVisit_Id, r.GRATING, r.GR_STA, r.AR_STA'\n table_state='FileData Join ProposalCode on (FileData.ProposalCode_Id = ProposalCode.ProposalCode_Id) '\n table_state+='join FitsHeaderImage as h using (FileData_Id) join FitsHeaderRss as r using (FileData_Id)'\n formatteddate = obsdate.replace('-','')\n logic_state=\"FileName like '%\"+formatteddate+\"%' order by UTSTART\"\n rss_imglist=sdb.select(select_state, table_state, logic_state)\n rss_imglist[:] = [img for img in rss_imglist if not \"CAL_\" in img[1] and not \"ENG_\" in img[1] and not \"JUNK\" in img[1]]\n\n #get a list of all point commands from the night\n select_state= 'BlockVisit_Id, EventTime, Block_Id, Target_Name, NightInfo_Id, EventData'\n table_state='PointEvent join SoLogEvent using (SoLogEvent_Id)'\n plist=sdb.select(select_state, table_state, 'NightInfo_Id=%i' % nid)\n point_list=[]\n for i in range(len(plist)):\n p=plist[i]\n if p[1].seconds>43200:\n t=datetime.datetime(int(obsdate[0:4]), int(obsdate[5:7]), int(obsdate[8:10]), 0, 0, 0)+p[1]\n else:\n t=datetime.datetime(int(obsdate[0:4]), int(obsdate[5:7]), int(obsdate[8:10]), 0, 0, 0)+datetime.timedelta(days=1)+p[1]\n point_list.append([p[0], t, p[2], p[3], p[4], p[5]])\n\n #deal with accepted blocks\n for bvid in bvid_list:\n\n #determine start time (point) and end time (track end)\n pointtime = findpointcommand(bvid, point_list)\n if pointtime is None:\n #print('no point')\n continue\n starttime=pointtime\n endtime=findguidingstop(starttime, event_list)\n if endtime is None:\n #print('no end')\n continue\n\n #determine total time\n tottime=endtime-starttime\n #some limit to avoid crazy stats\n if tottime.seconds > 10000:\n #print('total too long, took %i s'%tottime.seconds)\n continue\n\n #determine the slew time\n guidestart=findguidingstart(starttime, event_list)\n if guidestart is None:\n #print('no trackstart')\n continue\n slewtime=guidestart-starttime\n if slewtime.seconds > 1000:\n #print('slew too long, took %i s'%slewtime.seconds)\n continue\n\n #determine the time between TrackStart and OnTarget\n ontarget=findontarget(starttime, event_list)\n if ontarget is None:\n #print('no on target?')\n continue\n trackerslewtime=ontarget-guidestart\n if trackerslewtime.seconds > 500:\n #print('trackslew too long, took %i s'%trackerslewtime.seconds)\n continue\n\n #get primary instrument, check if MOS\n scams=0\n instr, primary_mode=getprimarymode(img_list, bvid)\n if instr == 'SALTICAM':\n #print('its a scam!')\n scams+=1\n continue\n\n select_state= 'Block_Id'\n table_state='BlockVisit'\n logic_state='BlockVisit_Id=%i' % (bvid)\n bids=sdb.select(select_state, table_state, logic_state)\n if not bids:\n #print(\"no bid!\")\n continue\n else:\n bid=bids[0]\n selcmd='Block_Id, Barcode'\n tabcmd='Block join Pointing using (Block_Id) join Observation using (Pointing_Id) '\n tabcmd+='join TelescopeConfigObsConfig using (Pointing_Id) join ObsConfig on (PlannedObsConfig_Id=ObsConfig_Id) '\n tabcmd+='join RssPatternDetail using (RssPattern_Id) join Rss using (Rss_Id) join RssProcedure using (RssProcedure_Id) '\n tabcmd+='join RssConfig using (RssConfig_Id) join RssMask using (RssMask_Id)'\n logcmd='RssProcedureType_Id = \\'7\\' and Block_Id = %i group by Block_Id order by Block_Id' % bid\n mos=sdb.select(selcmd, tabcmd, logcmd)\n if mos:\n instr='MOS'\n\n if instr == 'MOS':\n #special case for MOS science acquisition\n mosacq=getfirstimage(rss_imglist, ontarget, instr, primary_mode, bvid)\n mosacqtime=mosacq-ontarget\n if mosacqtime.seconds > 1000:\n #print(\"MOS Acquisition too long, took %i s\"%mosacqtime.seconds)\n continue\n else:\n #determine the Salticam acquisition time after being on target\n scamstart=getfirstscam(img_list, starttime, 'SALTICAM', 'IMAGING', bvid)\n if scamstart is None:\n #print(\"Did not find SCAM image\")\n continue\n acqtime=scamstart-ontarget\n if acqtime.seconds > 1000:\n #print(\"Target Acquisition too long, took %i s\"%acqtime.seconds)\n continue\n #determine the time between acquisition and first science image\n sciencestart=getfirstimage(img_list, scamstart, instr, primary_mode, bvid)\n if sciencestart is None:\n #print(\"Did not find science image for BV %i using %s\" % (bvid, instr))\n continue\n sciacqtime=sciencestart-scamstart\n if sciacqtime.seconds > 1000:\n #print(\"Science Acquisition too long, took %i s\"%sciacqtime.seconds)\n continue\n\n #update results in sdb\n if update:\n bvs_updated+=1\n inscmd='SlewTime=%i, TrackerSlewTime=%i' % (slewtime.seconds, trackerslewtime.seconds)\n sdb.update(inscmd, 'BlockVisit', 'BlockVisit_Id=%i' % bvid)\n if instr == 'MOS':\n inscmd='MOSAcquisitionTime=%i' % (mosacqtime.seconds)\n sdb.update(inscmd, 'BlockVisit', 'BlockVisit_Id=%i' % bvid)\n else:\n inscmd='TargetAcquisitionTime=%i, InstrumentAcquisitionTime=%i' % (acqtime.seconds, sciacqtime.seconds)\n sdb.update(inscmd, 'BlockVisit', 'BlockVisit_Id=%i' % bvid)\n\n print(bvs_updated)\n print(len(bvid_list)-scams)\n return bvid_list"],"string":"[\n \"def _load_data(self, datasource):\\n import pandas as pd\\n if not isinstance(datasource, pd.DataFrame):\\n raise TypeError('DfFeature must loaded from pd.DataFrame')\\n self.data = datasource\\n self.data['thetime']=self.data['thetime'].apply(lambda x:try_to_parse_date(x))\",\n \"def process_data(self):\\n logging.debug('process_data called')\\n\\n pd_time_series = pd.read_csv(f'{self.out_dir}docs/downloaded/'\\n f'{self.filename}')\\n\\n pd_time_series = pd_time_series.drop('Lat', axis=1)\\n pd_time_series = pd_time_series.drop('Long', axis=1)\\n no_of_dates = len(pd_time_series.columns) - 2\\n dateindex = pd.date_range(start='1-22-2020',\\n periods=no_of_dates,\\n freq='D').strftime('%d-%m')\\n\\n new_cols = ['Province/State', 'Country/Region']\\n for index in dateindex:\\n new_cols.append(index)\\n pd_time_series.columns = new_cols\\n\\n pd_time_series = pd_time_series.drop('Province/State', axis=1)\\n pd_edit_series = pd_time_series.set_index('Country/Region')\\n\\n pd_edit_series = pd_edit_series.T\\n\\n return pd_edit_series\",\n \"def update_datasource():\\n ds = Datasource(TDSX_FILE)\\n ds.replace_extract(HYPER_FILE)\",\n \"def import_stations(time_res='hourly',time_format='%Y%m%d%H',\\r\\n campaign_time=[datetime(2018,12,9), datetime(2018,12,12)],\\r\\n data_category='air_temperature', station_ids=['00044','00091'],\\r\\n dbase_dir='dbase', table_dir='tables',Output=True,\\r\\n memory_save=True):\\r\\n timeranges=['recent','historical']\\r\\n #%%load the datasets available at each timestep\\r\\n dwd_datasets_meta=dwd_datasets_meta=json.load(open(table_dir+\\\"\\\\\\\\dwd_station_meta.txt\\\"))\\r\\n #try to get a variable from the category, otherwise use interpolation of higher frequency data\\r\\n resample_frequency=None\\r\\n time_res_dbase=time_res\\r\\n try:\\r\\n dwd_datasets_meta[time_res][data_category]\\r\\n except Exception:\\r\\n if time_res=='daily':\\r\\n try:\\r\\n dwd_datasets_meta['hourly'][data_category]\\r\\n print(data_category,' is not provided at the required resolution, daily_mean of hourly data used instead')\\r\\n resample_frequency='D'\\r\\n time_res_dbase='hourly'\\r\\n except Exception:\\r\\n try: \\r\\n dwd_datasets_meta['10_minutes'][data_category]\\r\\n print(data_category,' is not provided at the required resolution, daily_mean of 10_minutes data used instead')\\r\\n resample_frequency='D'\\r\\n time_res_dbase='10_minutes'\\r\\n except Exception:\\r\\n print(data_category, 'not available')\\r\\n sys.exit(1)\\r\\n if time_res=='hourly':\\r\\n try: \\r\\n dwd_datasets_meta['10_minutes'][data_category]\\r\\n print(data_category,' is not provided at the required resolution, hourly_mean of 10_minutes data used instead')\\r\\n resample_frequency='H'\\r\\n time_res_dbase='10_minutes'\\r\\n except Exception:\\r\\n print(data_category, 'not available')\\r\\n sys.exit(1)\\r\\n \\r\\n \\r\\n #%% download from dwd if necessary\\r\\n #connect to server\\r\\n server='opendata.dwd.de'\\r\\n ftp=connect_ftp(server = server,connected = False)\\r\\n #get the mean time of the campaign\\r\\n date_mean=campaign_time[0]+(campaign_time[1]-campaign_time[0])/2 \\r\\n # load the inititial ds\\r\\n dbase_path=dbase_dir+'\\\\\\\\db_stations_'+time_res+'_'+data_category+'.nc'\\r\\n if os.path.exists(dbase_path):\\r\\n with xr.open_dataset(dbase_path) as dwd_dbase:\\r\\n dwd_dbase.load()\\r\\n print('Existing database imported')\\r\\n #get the non_nans stations\\r\\n current_stations=np.array(dwd_dbase[list(dwd_dbase.keys())[0]].sel(time=date_mean,method='nearest').dropna('STATIONS_ID').coords['STATIONS_ID'])\\r\\n else:\\r\\n print(dbase_path, 'does not exist, we create a new netcdf_file')\\r\\n dwd_dbase=xr.Dataset()\\r\\n current_stations=np.array((-9999)).reshape(1)\\r\\n #change directory on server\\r\\n for timerange in timeranges:\\r\\n archive_url='/climate_environment/CDC/observations_germany/climate/'+time_res_dbase+'/'+data_category+'/'+timerange \\r\\n ftp.cwd(archive_url)\\r\\n #get the archive\\r\\n for station_id in station_ids:\\r\\n #we check whether the station is in the database with this parameter already\\r\\n if int(station_id) in current_stations:\\r\\n print('Station', station_id, 'with category', data_category,'in ',timerange,'dbase already')\\r\\n continue\\r\\n try:\\r\\n archive_name=[s for s in ftp.nlst() if station_id in s][0]\\r\\n except:\\r\\n print('No ',timerange,'data for station',station_id)\\r\\n continue\\r\\n print('Retrieving {}...'.format(archive_name))\\r\\n retrieved = False\\r\\n archive = io.BytesIO()\\r\\n # try to retrieve file\\r\\n while not retrieved:\\r\\n try:\\r\\n ftp.retrbinary(\\\"RETR \\\" + archive_name, archive.write)\\r\\n retrieved = True\\r\\n except:\\r\\n ftp=connect_ftp(server = server,connected = False)\\r\\n ftp.cwd(archive_url)\\r\\n archive.seek(0)\\r\\n with ZipFile(archive) as myzip:\\r\\n for f in myzip.infolist():\\r\\n # This is the data file\\r\\n #print('zip content:', f.filename)\\r\\n if f.filename.startswith('produkt_'):\\r\\n product = io.StringIO(str(myzip.read(f.filename),'utf-8'))\\r\\n #get dataframe from product \\r\\n dwd_product=pd.read_csv(product,sep=';',skipinitialspace=True)\\r\\n #get datetime\\r\\n dwd_product['time']=pd.to_datetime(dwd_product['MESS_DATUM'],format=time_format) \\r\\n dwd_product=dwd_product.rename(columns=dwd_datasets_meta[time_res_dbase][data_category])\\r\\n dwd_product=dwd_product.reset_index()\\r\\n dwd_product=dwd_product.set_index(['time','STATIONS_ID'])\\r\\n dwd_product=dwd_product.drop(columns=['MESS_DATUM','quality_level_of_next_columns','end_of_record','index'])\\r\\n #append to database\\r\\n dwd_xr=dwd_product.to_xarray()\\r\\n #replace all values equal to -999 to nan\\r\\n for data_var in dwd_xr.data_vars:\\r\\n dwd_xr[data_var]=dwd_xr[data_var].where(dwd_xr[data_var]>-999)\\r\\n if station_id=='05009':\\r\\n print('ok') \\r\\n #only add relevant dates if available memoryis rather small\\r\\n \\r\\n if memory_save and timerange=='historical':\\r\\n dwd_xr=dwd_xr.sel(time=slice(campaign_time[0]-timedelta(days=1),campaign_time[1]+timedelta(days=1)))\\r\\n #dwd_xr=dwd_xr.squeeze()\\r\\n \\r\\n try:\\r\\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr])\\r\\n except Exception as e:\\r\\n print(e)\\r\\n print('try merging with compat=override')\\r\\n dwd_dbase=xr.merge([dwd_dbase,dwd_xr],compat='override')\\r\\n print(archive_name,' added to database')\\r\\n #upscale to required temporal resolution\\r\\n if resample_frequency is not None:\\r\\n dwd_dbase=dwd_dbase.resample(time=resample_frequency).mean(skipna=True)\\r\\n print('DWD data upscaled to',time_res,'averages')\\r\\n if Output==True:\\r\\n dwd_dbase.to_netcdf(dbase_path)\\r\\n print('Updated database' ,dbase_path)\\r\\n return dwd_dbase\",\n \"def refresh():\\n DB.drop_all()\\n DB.create_all()\\n samples = pull_pm('Los Angeles', 'pm25')\\n for sample in samples:\\n measure = Record(datetime = str(sample[0]), value = sample[1])\\n DB.session.add(measure)\\n DB.session.commit()\\n return 'Data refreshed!'\",\n \"def entries_from_goes_ts_files(*files, default_waveunit=None, source=None):\\n\\n\\n \\\"\\\"\\\"\\n ts_goes = ts.TimeSeries(file)\\n statinfo = os.stat(file)\\n entry = DatabaseEntry(path=file)\\n entry.size = statinfo.st_size\\n\\n #header['INSTRUME'] = header['TELESCOP']# So E.G. 'GOES 6' instead 'X-ray Detector'\\n entry.instrument = ts_goes.meta.get('TELESCOP').values()\\n entry.instrument = ts_goes.meta.get('TELESCOP').values()\\n\\n entry.wavemax = 0.8 # XRSB '1.0--8.0 $\\\\AA$'\\n entry.wavemin = 0.05 # XRSA '0.5--4.0 $\\\\AA$'\\n\\n #\\n entry.observation_time_start = ts_goes.meta.get('date-beg').values()[0]\\n entry.observation_time_end = ts_goes.meta.get('date-end').values()[0]\\n\\n entry.metadata = ts_goes.meta.metadata[0][2]\\n\\n #entry.tags = [ sunpy.database.attrs.Tag('raw') ]\\n \\\"\\\"\\\"\\n\\n\\n for file in files:\\n headers = fits.get_header(file)\\n if isinstance(file, (str, six.text_type)):\\n filename = file\\n else:\\n filename = getattr(file, 'name', None)\\n statinfo = os.stat(file)\\n #print('a header')\\n entry = DatabaseEntry(path=filename)\\n entry.size = statinfo.st_size\\n\\n # Add/tweak start/end entries for GOES\\n if headers[0].get('TELESCOP','') != '':\\n #header['INSTRUME'] = header['TELESCOP']# So E.G. 'GOES 6' instead 'X-ray Detector'\\n entry.instrument = headers[0]['TELESCOP']\\n elif headers[1].get('TELESCOP','') != '':\\n entry.instrument = headers[1]['TELESCOP']\\n if (headers[0].get('DATE-OBS','') != ''):\\n if is_time_in_given_format(headers[0]['DATE-OBS'], '%d/%m/%Y'):\\n start_time = datetime.strptime(headers[0]['DATE-OBS'], '%d/%m/%Y')\\n elif is_time_in_given_format(headers[0]['DATE-OBS'], '%d/%m/%y'):\\n start_time = datetime.strptime(headers[0]['DATE-OBS'], '%d/%m/%y')\\n else:\\n start_time = parse_time(headers[0]['DATE-OBS'])\\n elif (headers[1].get('DATE-OBS','') != ''):\\n if is_time_in_given_format(headers[1]['DATE-OBS'], '%d/%m/%Y'):\\n start_time = datetime.strptime(headers[1]['DATE-OBS'], '%d/%m/%Y')\\n elif is_time_in_given_format(headers[1]['DATE-OBS'], '%d/%m/%y'):\\n start_time = datetime.strptime(headers[1]['DATE-OBS'], '%d/%m/%y')\\n else:\\n start_time = parse_time(headers[1]['DATE-OBS'])\\n\\n if (headers[0].get('DATE-END','') != ''):\\n if is_time_in_given_format(headers[0]['DATE-END'], '%d/%m/%Y'):\\n end_time = datetime.strptime(headers[0]['DATE-END'], '%d/%m/%Y')\\n elif is_time_in_given_format(headers[0]['DATE-END'], '%d/%m/%y'):\\n end_time = datetime.strptime(headers[0]['DATE-END'], '%d/%m/%y')\\n else:\\n end_time = parse_time(headers[0]['DATE-END'])\\n elif (headers[1].get('DATE-END','') != ''):\\n if is_time_in_given_format(headers[1]['DATE-END'], '%d/%m/%Y'):\\n end_time = datetime.strptime(headers[1]['DATE-END'], '%d/%m/%Y')\\n elif is_time_in_given_format(headers[1]['DATE-END'], '%d/%m/%y'):\\n end_time = datetime.strptime(headers[1]['DATE-END'], '%d/%m/%y')\\n else:\\n end_time = parse_time(headers[1]['DATE-END'])\\n else:\\n end_time = start_time + timedelta(days=1,seconds=-1)\\n\\n # Add these to the entry\\n entry.observation_time_start = start_time\\n entry.observation_time_end = end_time\\n\\n entry.wavemax = 0.8 # XRSB '1.0--8.0 $\\\\AA$'\\n entry.wavemin = 0.05 # XRSA '0.5--4.0 $\\\\AA$'\\n\\n if source:\\n entry.source = source\\n\\n entry.metadata = MetaDict(headers[1])\\n #entry.tags = sunpy.database.attrs.Tag('raw')\\n\\n #entry = DatabaseEntry(instrument='EIT', wavemin=25.0)\\n\\n #return entry\\n yield entry\",\n \"def temporal_database():\\n return TimeHistory()\",\n \"def update_historical_data():\\n print('updating historical data')\\n for sp in SupplyPoint.objects.filter(supplypointwarehouserecord__isnull=True).exclude(type__code=SupplyPointCodes.ZONE):\\n update_historical_data_for_supply_point(sp)\",\n \"def pull_data_from_geodb(sids, verbose=True):\\n api_key = \\\"f125f4c130e61d9f4ad5874aadfe07ff\\\"\\n geodb = dpf.data(api_key).geo\\n\\n df_a = pd.DataFrame() # for annual features\\n df_m = pd.DataFrame() # for monthly features\\n\\n # gets meta data for each feature from its series id, \\n # then uses meta data to pull all of the feature's regional data\\n for sid in sids:\\n print(f\\\"Series ID: {sid}\\\")\\n\\n print(\\\" > Collecting meta data...\\\")\\n meta = geodb['meta'](series_id=sid).iloc[0, :]\\n\\n for col in ['min_date', 'max_date']:\\n meta[col] = pd.to_datetime(meta[col]).date()\\n\\n print(\\\" > Pulling dataframe...\\\")\\n data = geodb['data'](\\n series_group=meta['series_group'],\\n date=meta['max_date'],\\n start_date=meta['min_date'],\\n region_type=meta['region_type'],\\n units=meta['units'],\\n frequency=meta['frequency'],\\n season=meta['season']\\n )\\n # swap series id for its feature name (change colname later)\\n data['series_id'] = meta['title']\\n\\n if verbose:\\n print(f\\\" > Title: {meta['title']}\\\")\\n print(f\\\" > Min date: {meta['min_date']}\\\")\\n print(f\\\" > Max date: {meta['max_date']}\\\")\\n print(f\\\" > Units: {meta['units']}\\\")\\n print(f\\\" > Season: {meta['season']}\\\")\\n print(f\\\" > Frequency: {meta['frequency']}\\\")\\n print(f\\\" > Region type: {meta['region_type']}\\\")\\n\\n print(f\\\" > Appending to df_{meta['frequency']}...\\\")\\n if meta['frequency'] == 'a':\\n df_a = df_a.append(data)\\n else:\\n df_m = df_m.append(data)\\n continue\\n\\n df_a = Wrangler.transform_fresh_columns(df_a)\\n df_m = Wrangler.transform_fresh_columns(df_m)\\n\\n return df_a, df_m\",\n \"def test_data_source_soaps_get(self):\\n pass\",\n \"def update_data():\\n etf_prices = get_prices(start=START_DATE, end=END_DATE)\\n etf_returns = compute_returns(etf_prices)\\n merged_etf_data = etf_prices.merge(etf_returns, right_index=True, left_index=True)\\n indicators = compute_indicators(merged_etf_data) # this uses the \\\"ta\\\" lib, but it does not need\\n # to be imported\\n merged_etf_data = merged_etf_data.merge(indicators, right_index=True, left_index=True)\\n vix_data = get_vix()\\n data = merged_etf_data.merge(vix_data, right_index=True, left_index=True)\\n data.to_csv('Data/database.csv')\\n return\",\n \"def get_data(day, unixstart=None, unixend=None):\\n global subnet\\n df_data = pd.DataFrame([])\\n while len(df_data.index)<=0:\\n try:\\n df_data = pd.read_feather(f'/home/pi/studies/ardmore/homeserver/h{subnet}_{day}.feather')\\n except Exception as e:\\n # print(f\\\"Error grid_server.get_data:{e}\\\")\\n pass\\n if unixstart!=None:\\n df_data = df_data[(df_data['unixtime']>=unixstart)&(df_data['unixtime']<=unixend)]\\n float_cols = [x for x in df_data.columns if not x.startswith('timezone')]\\n df_data = df_data[float_cols].astype(float)\\n return df_data\",\n \"def get_data(temp_estimate_source='historic'):\\n\\n print 'get data from files'\\n load = process_load_data(datafoldername+loadfilename_train)\\n load_test = process_load_data(datafoldername+loadfilename_test)\\n temp = process_temp_data(datafoldername+tempfilename_train)\\n holidays = process_holiday_data(datafoldername+holidayfilename)\\n\\n print 'merge load with temp data'\\n X_train_df = load.merge(temp, on='datetime', how='left')\\n X_test_df = load_test.merge(temp, on='datetime', how='left')\\n\\n print 'estimate missing temps'\\n # find rows with missing temperatures\\n missingtemp = X_test_df[X_test_df.isnull().any(axis=1)][['datetime', 'zone_id']].copy()\\n\\n # source estimates for missing periods\\n if temp_estimate_source == 'arima':\\n # use preprocessed arima estimates\\n estimatedtemps = process_arima_temp_data(datafoldername+arimafilename)\\n elif temp_estimate_source == 'actuals':\\n # use actual temperatures - as provided after conclusion of kaggle competition\\n estimatedtemps = process_temp_data(datafoldername+tempfilename_solution)\\n else:\\n # use means of historical temps at same day/time.\\n estimatedtemps = get_estimated_temps(missingtemp[['datetime']].drop_duplicates(), temp)\\n\\n # merge estimates against missing rows, and use to update original dataframe in place\\n replacementtemps = missingtemp.merge(estimatedtemps, left_on='datetime', right_on='datetime', how='left')\\n replace_unknown_temps(X_test_df, replacementtemps)\\n\\n print 'merge in holiday dates'\\n X_train_df = X_train_df.merge(holidays, on='datetime', how='left')\\n X_train_df['holiday'].fillna(0, inplace=True)\\n X_test_df = X_test_df.merge(holidays, on='datetime', how='left')\\n X_test_df['holiday'].fillna(0, inplace=True)\\n\\n print 'add datetime categorical variables'\\n X_train_df = add_datetime_categories(X_train_df)\\n X_test_df = add_datetime_categories(X_test_df)\\n\\n return X_train_df, X_test_df\",\n \"def test_4_data_fetching_unix_time_and_insertion(self):\\n d1 = date.today()\\n dt1 = datetime(d1.year, d1.month, d1.day) + timedelta(hours=8)\\n result, success = self.fitness.get_columns_given_range(dt1, dt1+timedelta(days=1))\\n self.assertTrue(success)\\n self.assertEqual(result[0]['Datetime'],self.unix_time)\",\n \"def prepare_for_influxdb(df):\\n df = df.drop(columns=\\\"landkreis\\\", errors=\\\"ignore\\\") # prevent name collision in get_ags()\\n df = get_ags(df)\\n df[\\\"time\\\"] = df.apply(lambda x: 1000000000*int(datetime.timestamp((pd.to_datetime(x[\\\"timestamp\\\"])))), 1)\\n df[\\\"measurement\\\"] = \\\"hystreet\\\"\\n df[\\\"origin\\\"] = \\\"https://hystreet.com\\\"\\n df = df.rename(columns={\\n 'station_id': '_id',\\n 'pedestrians_count': 'pedestrian_count',\\n 'state': 'bundesland'\\n })\\n df['ags'] = pd.to_numeric(df['ags'])\\n # import pdb; pdb.set_trace()\\n return df\",\n \"def Find_nearest_dwd_stations(inpt_data,\\r\\n date_start='20051201',\\r\\n date_end='20201231',\\r\\n dwd_time_format='%Y%m%d%H',\\r\\n data_category='air_temperature',\\r\\n temp_resolution='hourly',\\r\\n no_of_nearest_stations=4,\\r\\n memory_save=True,\\r\\n Output='True'):\\r\\n if isinstance(data_category,list):\\r\\n if len(list(data_category)) > 1:\\r\\n print(\\r\\n 'Currently only one dwd category allowed, please run function multiple times for each category'\\r\\n )\\r\\n return None\\r\\n \\r\\n #convert time to datetime\\r\\n dt_start=datetime.strptime(date_start,'%Y%m%d')\\r\\n dt_end=datetime.strptime(date_end,'%Y%m%d')\\r\\n print('Start quering data from DWD')\\r\\n #define the database folder\\r\\n pypath = os.path.dirname(os.path.abspath(__file__))\\r\\n table_dir = pypath + '\\\\\\\\' + 'tables'\\r\\n dbase_dir = pypath + '\\\\\\\\' + 'dbase' \\r\\n #%% we check all available stations and create a valid list\\r\\n filename_stations=update_stationlist(time_res='hourly',dbase_dir=table_dir)\\r\\n stations_all=pd.read_csv(filename_stations, dtype={'STATIONS_ID': object})\\r\\n # delete all stations which do not cover the category\\r\\n dwd_stations=stations_all[stations_all[data_category]==True].copy()\\r\\n #correct to datetime\\r\\n dwd_stations['date_end']=pd.to_datetime(stations_all.date_end,format='%Y%m%d')\\r\\n dwd_stations['date_start']=pd.to_datetime(stations_all.date_start,format='%Y%m%d')\\r\\n # clean to stations which cover the campaign time #dt_low <= dt <= dt_high:\\r\\n dwd_stations=dwd_stations[(dwd_stations.date_start<=dt_start) & (dwd_stations.date_end>=dt_end)]\\r\\n #make a geodataframe out of it\\r\\n dwd_stations=gpd.GeoDataFrame(dwd_stations,geometry=gpd.points_from_xy(dwd_stations.geo_lon, dwd_stations.geo_lat))\\r\\n \\r\\n #loop through all rows to get the n closest points\\r\\n distances=pd.DataFrame()\\r\\n for _, station in dwd_stations.iterrows():\\r\\n distances[station.STATIONS_ID]=inpt_data.distance(station.geometry)\\r\\n \\r\\n #%% get the n stations with smallest distance and update database\\r\\n id_nearest_stations=distances.apply(lambda s: s.nsmallest(no_of_nearest_stations).index.tolist(), axis=1).values.tolist() #station ids\\r\\n #get them as unique values by sum a list of lists https://bit.ly/353iZQB\\r\\n id_dwd_stations=list(set(sum(id_nearest_stations,[])))\\r\\n \\r\\n #update the database\\r\\n db_dwd_stations=import_stations(time_res=temp_resolution,time_format=dwd_time_format,campaign_time=[dt_start,dt_end],data_category=data_category,station_ids=id_dwd_stations,dbase_dir=dbase_dir,Output=Output,table_dir=table_dir,memory_save=memory_save)\\r\\n \\r\\n #distance of nearest stattions\\r\\n dist_nearest_stations=pd.DataFrame(np.sort(distances.values)[:,:no_of_nearest_stations]).values.tolist() #distances themself\\r\\n #create new columns in the input data\\r\\n station_col_nm=list()\\r\\n for i in range(0,no_of_nearest_stations):\\r\\n station_col_nm.append(data_category+'_station_'+str(i))\\r\\n for i in range(0,no_of_nearest_stations):\\r\\n station_col_nm.append(data_category+'_distance_'+str(i))\\r\\n #create new dataframe\\r\\n distance_data=pd.concat([pd.DataFrame(id_nearest_stations).astype(int),pd.DataFrame(dist_nearest_stations)],axis=1)\\r\\n distance_data.columns=station_col_nm\\r\\n #add to main dataset\\r\\n inpt_data=pd.concat([inpt_data, distance_data],axis=1) \\r\\n \\r\\n return inpt_data,db_dwd_stations\",\n \"def update(start_date, end_date, binning):\\n global mean_source1,mean_source,median_source1,median_source, difference_source1,difference_source, difference_source2 , difference_source3, mean_source2, median_source2\\n\\n # if type of start/end date is date, turn it into a datetime,\\n # set time of start/end date time to 12:00\\n\\n def convert_time(t):\\n if type(t) == datetime.date:\\n return datetime.datetime(t.year, t.month, t.day, 12, 0, 0, 0)\\n else:\\n return t.replace(hour=12, minute=0, second=0, microsecond=0)\\n\\n start_date = convert_time(start_date)\\n end_date = convert_time(end_date)\\n if binning is None:\\n binning = ''\\n\\n first_timestamp = start_date.timestamp() + time_offset\\n second_timestamp = end_date.timestamp() + time_offset\\n\\n # query data in mysql database\\n sql1 = 'SELECT str_to_date(datetime,\\\"%%Y-%%m-%%d %%H:%%i:%%s\\\") AS datetime, seeing from seeing ' \\\\\\n ' where datetime >= str_to_date(\\\"{start_date_}\\\",\\\"%%Y-%%m-%%d %%H:%%i:%%s\\\")' \\\\\\n ' and datetime <= str_to_date(\\\"{end_date_}\\\",\\\"%%Y-%%m-%%d %%H:%%i:%%s\\\") ' \\\\\\n .format(start_date_=str(start_date), end_date_=str(end_date))\\n\\n sql2 = 'select _timestamp_,ee50,fwhm,timestamp from tpc_guidance_status__timestamp where timestamp >= {start_date_}' \\\\\\n ' and timestamp<= {end_date_} and guidance_available=\\\"T\\\" ' \\\\\\n ' order by _timestamp_' \\\\\\n .format(start_date_=str(first_timestamp), end_date_=str(second_timestamp))\\n\\n df2 = pd.read_sql(sql2, db.get_engine(app=current_app, bind='els'))\\n df1 = pd.read_sql(sql1, db.get_engine(app=current_app, bind='suthweather'))\\n\\n # setting index time for calculating mean and average\\n df2.index = df2[\\\"_timestamp_\\\"]\\n df1.index = df1['datetime']\\n\\n # It seems that Pandas doesn't change the index type if the data frame is empty, which means that resampling\\n # would fail for an empty data frame. As there will be no row for median or mean , it is safe to just use the\\n # original data frame to avoid this problem.\\n\\n # for external seeing calculating median and mean\\n if not df1.empty:\\n mean1_all = df1.resample(str(binning) + 'T').mean()\\n else:\\n mean1_all = df1.copy(deep=True)\\n source1 = ColumnDataSource(mean1_all)\\n mean_source1.data = source1.data\\n\\n if not df1.empty:\\n median1_all = df1.resample(str(binning) + 'T').median()\\n else:\\n median1_all = df1.copy(deep=True)\\n source = ColumnDataSource(median1_all)\\n median_source1.data = source.data\\n\\n # calculate mean and median for ee50\\n if not df2.empty:\\n mean_all = df2.resample(str(binning) + 'T').mean()\\n else:\\n mean_all = df2.copy(deep=True)\\n source3 = ColumnDataSource(mean_all)\\n mean_source.data = source3.data\\n\\n if not df2.empty:\\n median_all = df2.resample(str(binning) + 'T').median()\\n else:\\n median_all = df2.copy(deep=True)\\n source4 = ColumnDataSource(median_all)\\n median_source.data = source4.data\\n\\n #calculate mean and median for fwhm\\n if not df2.empty:\\n mean_all1 = df2.resample(str(binning) + 'T').mean()\\n else:\\n mean_all1 = df2.copy(deep=True)\\n source4 = ColumnDataSource(mean_all)\\n mean_source2.data = source4.data\\n\\n if not df2.empty:\\n median_all = df2.resample(str(binning) + 'T').median()\\n else:\\n median_all = df2.copy(deep=True)\\n source5 = ColumnDataSource(median_all)\\n median_source2.data = source5.data\\n\\n # calculate difference for external seeing against fwhm and ee50\\n dataframes = [mean1_all, mean_all]\\n add_dataframes = pd.concat(dataframes, axis=1)\\n add_dataframes.index.name = '_timestamp_'\\n add_dataframes['difference'] = add_dataframes['seeing'] - add_dataframes['ee50']\\n datasource2 = ColumnDataSource(add_dataframes)\\n difference_source.data = datasource2.data\\n\\n dataframes = [mean1_all, mean_all1]\\n add_dataframes = pd.concat(dataframes, axis=1)\\n add_dataframes.index.name = '_timestamp_'\\n add_dataframes['difference1'] = add_dataframes['seeing'] - add_dataframes['fwhm']\\n datasource1 = ColumnDataSource(add_dataframes)\\n difference_source1.data = datasource1.data\\n\\n # #difference using the median\\n # dataframes2 = [median_all, median1_all]\\n # add_dataframes2 = pd.concat(dataframes2, axis=1)\\n # add_dataframes2.index.name = '_timestamp_'\\n # add_dataframes2['difference2'] = add_dataframes2['seeing'] - add_dataframes2['ee50']\\n # datasource2 = ColumnDataSource(add_dataframes2)\\n # difference_source2.data = datasource2.data\\n #\\n # dataframes3 = [median_all, median1_all]\\n # add_dataframes3 = pd.concat(dataframes3, axis=1)\\n # add_dataframes3.index.name = '_timestamp_'\\n # add_dataframes3['difference3'] = add_dataframes3['seeing'] - add_dataframes3['fwhm']\\n # datasource3 = ColumnDataSource(add_dataframes3)\\n # difference_source3.data = datasource3.data\\n\\n # plot labels\\n p = figure(title=\\\"external vs internal seeing ({binning} minute bins)\\\".format(binning=binning), x_axis_type='datetime'\\n , x_axis_label='datetime', y_axis_label='seeing',plot_width=1000, plot_height=500,tools=TOOLS)\\n dif=figure(title='difference between average internal and external seeing ({binning} minute bins)'.format(binning=binning), x_axis_type='datetime',\\n x_axis_label='datetime', y_axis_label='seeing',plot_width=1000, plot_height=500,tools=TOOLS)\\n\\n #plots\\n # plots for external seeing\\n p.circle(source=mean_source1, x='datetime',y='seeing', legend=\\\"external average\\\" ,fill_color=\\\"white\\\",color='green')\\n p.line(source=median_source1, x='datetime',y='seeing', legend=\\\"external median\\\" ,color='blue')\\n\\n #plots showing median and mean for ee50 and fwhm\\n p.circle(source=mean_source, x='_timestamp_', y='ee50', legend='ee50 average')\\n p.circle(source=mean_source, x='_timestamp_', y='fwhm', legend='fwhm average', color='red', fill_color='white')\\n\\n p.line(source=median_source, x='_timestamp_', y='ee50', legend='ee50 median', color='green')\\n p.line(source=median_source, x='_timestamp_', y='fwhm', legend='fwhm median', color='orange')\\n\\n #for difference\\n dif.circle(source=difference_source, x='_timestamp_', y='difference', legend='ee50_mean difference', color='red')\\n dif.circle(source=difference_source1, x='_timestamp_', y='difference1', legend='fwhm_mean difference', fill_color='green')\\n\\n #\\n # dif.circle(source=difference_source2, x='_timestamp_', y='difference2', legend='ee50_median difference', fill_color='blue')\\n # dif.circle(source=difference_source3, x='_timestamp_', y='difference3', legend='fwhm_median difference', color='orange')\\n\\n p.xaxis.formatter = date_formatter\\n p.legend.location = \\\"top_left\\\"\\n p.legend.click_policy=\\\"hide\\\"\\n\\n dif.xaxis.formatter = date_formatter\\n dif.legend.click_policy=\\\"hide\\\"\\n\\n script, div = components(p)\\n content1 = '<div>{script}{div}</div>'.format(script=script, div=div)\\n\\n script, div = components(dif)\\n content2 = '<div>{script}{div}</div>'.format(script=script, div=div)\\n\\n return '{cont} {cont2}'.format(cont=content1,cont2=content2)\",\n \"def load_data(city, month, day):\\n ##task0.4 now the user choices are all ready and saved, start preparing your data \\n ## first, load the data of the selected city using the pd.read_csv command for the user specified city \\n df = pd.read_csv(CITY_DATA[city])\\n ## read the column start time as a date, to start extracting information from it\\n df['Start Time']=pd.to_datetime(df['Start Time'])\\n ##define a new column\\\"month\\\" as a function from start time, which reads the month from the date\\n df['month'] = df['Start Time'].dt.month\\n ## extract the day name from the start time, append a new column\\\"day_of_week\\\" to the dataframe \\n df['day_of_week'] = df['Start Time'].dt.day_name()\\n ## extract the hour of the start time and save to a new variable\\\"hour\\\" \\n df['hour'] = df['Start Time'].dt.hour\\n \\n ## now, the panel data is complete with variables of interest created\\n ## time to consider user filters on month and day!!\\n ## for the month, if user choice is all, no changes to dataset\\n ## if user specifies a month,filter the dataset column\\\"month\\\" with the selected value\\n if month !=\\\"all\\\":\\n df=(df[df['month']==int(month)])\\n \\n ## for the day column, if user doesnt choose a specific day and select\\\"all\\\", no change to df\\n ## if the user chooses a day, filter the \\\"day_of_week\\\" column for the specified day\\n if day !=\\\"All\\\":\\n df=(df[df['day_of_week']==day])\\n \\n ## print the first five rows of data to confirm filters are applied\\n ## return the data frame to start calculating statistics\\n #print(df.head())\\n return df\",\n \"def update_data(self, data):\\n start_time = data.index[-1].strftime(\\\"%Y-%m-%dT%H:%M:%S.000000Z\\\")\\n temp_data = self.gather_data(start=start_time)\\n temp_data = self._list_to_df(temp_data)\\n if (len(temp_data) > 1):\\n # temp_data[0] is the same as data[-1]\\n out_data = data.append(temp_data[1:])\\n return out_data\",\n \"def add_stlouis_data(self):\\n stlfsi = endpoints['stlfsi']\\n url = stlfsi['base_url']\\n params = stlfsi['params']\\n params[''] = get_str_date()\\n stlfsi_data = get_api_data(url, params, 'data/stlfsi.csv')\\n self.data = self.data.join(stlfsi_data, how='left')\\n self.data['STLFSI'] = self.data['STLFSI'].fillna(method='ffill')\\n if isnull(self.data.ix[0]['STLFSI']):\\n for i, v in self.data.iterrows():\\n if isnull(v['STLFSI']):\\n continue\\n try:\\n self.data.ix[0]['STLFSI'] = stlfsi_data.ix[i - dt.timedelta(weeks=1)]\\n break\\n except Exception:\\n self.data['STLFSI'] = self.data['STLFSI'].fillna(method='bfill')\\n break\\n self.data['STLFSI'] = self.data['STLFSI'].fillna(method='ffill')\",\n \"def get_historical_data(userID):\\n\\n # import satz ID\\n infile = open(\\\"satzIDs.pkl\\\", \\\"rb\\\")\\n saetze = pickle.load(infile)\\n infile.close()\\n satz_ID_list = list(saetze.satzID)\\n satz_ID_list = [str(item) for item in satz_ID_list]\\n indexlist = [userID]\\n\\n # create emtpy DF\\n df = pd.DataFrame(0, index=indexlist, columns=satz_ID_list)\\n\\n # get xmlsaetze by userID\\n retrieve = xmlsaetze.objects.filter(UserID=userID)\\n data = serializers.serialize(\\\"json\\\", retrieve, fields=(\\\"SatzID\\\", \\\"Erfolg\\\", \\\"Datum\\\"))\\n struct = json.loads(data)\\n df_obj = pd.DataFrame(columns=[\\\"SatzID\\\", \\\"Erfolg\\\", \\\"Datum\\\"])\\n for x in struct:\\n satz_ID = x[\\\"fields\\\"][\\\"SatzID\\\"]\\n erfolg = x[\\\"fields\\\"][\\\"Erfolg\\\"]\\n datum = x[\\\"fields\\\"][\\\"Datum\\\"]\\n df2 = pd.DataFrame({\\\"SatzID\\\": [satz_ID], \\\"Erfolg\\\": erfolg, \\\"Datum\\\": datum})\\n df_obj = pd.concat([df_obj, df2], ignore_index=True, axis=0)\\n\\n # iterate trough dataframe and updates erfolg where user did something\\n for i in range(df_obj.shape[0]):\\n satz_ID_cell = df_obj.iloc[i, 0]\\n erfolg_cell = df_obj.iloc[i, 1]\\n datum_cell = df_obj.iloc[i, 2]\\n current_time = datetime.datetime.now()\\n accepted_date = current_time + pd.DateOffset(\\n months=-3\\n ) # accepted date calculates the date of the last login minus 3 months\\n\\n if satz_ID_cell in df.columns:\\n if str(datum_cell) > str(accepted_date):\\n if erfolg_cell == 1 | erfolg_cell == True:\\n df.loc[userID, satz_ID_cell] = 1\\n if erfolg_cell == 0 | erfolg_cell == False:\\n df.loc[userID, satz_ID_cell] = -1\\n\\n df = df.reset_index()\\n df = df.rename(columns={\\\"index\\\": \\\"UserID\\\"})\\n\\n global historical_data\\n historical_data = df\\n\\n return df\",\n \"def Collect_data(clo, cla, FD, LD, ZC, idx_time, cntry, TZ):\\r\\n #initiate the console response\\r\\n rep = \\\"\\\"\\r\\n global ddf\\r\\n \\r\\n #define the noaa server access\\r\\n server_noaa = \\\"https://www.ncei.noaa.gov/data/global-hourly/access/\\\"\\r\\n \\r\\n #Initate dateframe\\r\\n data = pd.DataFrame()\\r\\n \\r\\n #Convert date from specified timezone to UTC\\r\\n FDc = Date_calibration(FD, 1, TZ)\\r\\n LDc = Date_calibration(LD, 1, TZ)\\r\\n \\r\\n #define timestep\\r\\n timestep = \\\"1 H\\\"\\r\\n \\r\\n #Loop on the range time by each year\\r\\n for y in range(FDc.year, (LDc.year + 1)):\\r\\n rep += '--- Collect Year ['+str(y) + '] --- \\\\n' \\r\\n \\r\\n #Loop on each weather station while the ouput data is good\\r\\n #weather station in the year instancied dataframe \\r\\n for i in range(len(ddf[y])) :\\r\\n \\r\\n #Define the memory key : year_zipcode\\r\\n key_d_z = str(y)+'_'+str(ZC)\\r\\n \\r\\n #Verify if the data is already in the memory\\r\\n if(key_d_z in memory_df.keys()) :\\r\\n #The data is already in the memory :\\r\\n #Collect the data and go next (save compute time and server solicitation)\\r\\n ext_data = memory_df[key_d_z]\\r\\n NS, DO = memory_NS_DO[key_d_z].split('_')[0], memory_NS_DO[key_d_z].split('_')[1]\\r\\n break\\r\\n else:\\r\\n \\r\\n #The data isn't in the memory :\\r\\n \\r\\n #Collect information about the nearest weather station from the zip code\\r\\n rs, NS, DO = Fetch_station(clo, cla, y)\\r\\n \\r\\n #Generate the ftp key weather station\\r\\n code_station = str(ddf[y]['USAF'][rs])+str(ddf[y]['WBAN'][rs])\\r\\n \\r\\n #Define the server access\\r\\n url = server_noaa+str(y)+'/'+code_station+'.csv'\\r\\n \\r\\n #Get the data\\r\\n req = requests.get(url)\\r\\n \\r\\n #The server answer\\r\\n if(req.status_code == 200):\\r\\n \\r\\n #Extract the data (only date and temperature)\\r\\n ext_data = pd.read_csv(url, usecols=['DATE','TMP'])\\r\\n \\r\\n #Check if the data isn't empty (1000 is arbitrary)\\r\\n if(len(ext_data) > 1000):\\r\\n \\r\\n #Format data\\r\\n ext_data, qual = Data_formatting(ext_data, y, timestep, TZ)\\r\\n \\r\\n #Check if the data quality respect the quality rule\\r\\n if(qual > quality_rule):\\r\\n \\r\\n #Save the date in the memory\\r\\n memory_df[key_d_z] = ext_data\\r\\n memory_NS_DO[key_d_z] = str(NS)+'_'+str(DO)\\r\\n \\r\\n #Response for the GUI\\r\\n rep += \\\"# Station [\\\"+str(NS)+\\\"] valid : \\\\n\\\"\\r\\n rep += \\\"- Quality density data : \\\"+str(round(qual, 2))+\\\"% \\\\n\\\"\\r\\n rep += \\\"- Great circle distance : \\\"+str(round(DO,2))+\\\"km \\\\n\\\"\\r\\n break\\r\\n else:\\r\\n #The data quality is too bad\\r\\n #Response for the GUI\\r\\n rep += \\\"# Station [\\\"+str(NS)+\\\"] invalid : \\\\n\\\"\\r\\n rep += \\\"- Quality density data : \\\"+str(round(qual,2))+\\\"% \\\\n\\\"\\r\\n rep += \\\"> Quality criterion unsatisfied \\\\n\\\"\\r\\n \\r\\n #Delete the weather station in the dataframe (instancied by year)\\r\\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\\r\\n continue\\r\\n \\r\\n else:\\r\\n #The data density is too low\\r\\n \\r\\n #Response for the GUI\\r\\n rep += \\\"# Station [\\\"+str(NS)+\\\"] invalid : \\\\n\\\"\\r\\n rep += \\\"> Low data volume \\\\n\\\"\\r\\n \\r\\n #Delete the weather station in the dataframe (instancied by year)\\r\\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\\r\\n\\r\\n continue\\r\\n else:\\r\\n #The NOAA doesn't answer for the code station\\r\\n rep += \\\"# Station [\\\"+str(NS)+\\\"] invalid : \\\\n\\\"\\r\\n rep += \\\"> Server doesn't answer\\\\n\\\"\\r\\n \\r\\n #Delete the weather station in the dataframe (instancied by year)\\r\\n ddf[y] = ddf[y].drop(rs).reset_index(drop=True)\\r\\n\\r\\n continue\\r\\n #Add data in the Dataframe\\r\\n data = data.append(ext_data)\\r\\n \\r\\n #Define a new dataframe mark out by the specified time range\\r\\n range_time = pd.DataFrame(index=pd.DatetimeIndex(start=FD, end=LD, freq=timestep))\\r\\n \\r\\n #Paste data in the time marked out Dataframe\\r\\n range_time['Temp'] = data['TMP']\\r\\n \\r\\n #Calculate the amount of NaN (global)\\r\\n nb_nan = range_time['Temp'].isnull().sum()\\r\\n \\r\\n #Calculate the global quality\\r\\n quality = (1 - (nb_nan) / len(range_time)) * 100\\r\\n \\r\\n #Fill the gap data by temporal interpolation\\r\\n data = range_time.interpolate(method='time')\\r\\n data = data.ffill().bfill()\\r\\n data['Temp'] = round(data['Temp'], 2)\\r\\n \\r\\n \\r\\n #If specified index time, cut the data for it\\r\\n if(len(idx_time)> 2):\\r\\n final_data = pd.DataFrame(index = pd.to_datetime(idx_time))\\r\\n final_data['Temp'] = round(data['Temp'], 2)\\r\\n #final_data = final_data.resample(rule = str(timestep)).mean()\\r\\n #final_data = final_data.dropna()\\r\\n else:\\r\\n final_data = data\\r\\n \\r\\n return final_data, quality, NS, DO, rep\",\n \"def _assess(self):\\n # get eruptions\\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\\n # check if data file exists\\n self.exists = os.path.isfile(self.file)\\n if not self.exists:\\n t0 = datetime(2011,1,1)\\n t1 = datetime(2011,1,2)\\n self.update(t0,t1)\\n # check date of latest data in file\\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\\n self.ti = self.df.index[0]\\n self.tf = self.df.index[-1]\",\n \"def _update_modified_since(self, timestamp):\\n new_data_sources = [\\n source\\n for provider in self.data_source_providers\\n for source in provider.get_data_sources_modified_since(timestamp)\\n ]\\n filtered_data_sources = self.get_filtered_configs(new_data_sources)\\n invalid_data_sources = {ds._id for ds in new_data_sources} - {ds._id for ds in filtered_data_sources}\\n self._add_data_sources_to_table_adapters(filtered_data_sources, invalid_data_sources)\",\n \"def get_data(self, date_time):\\n id_columns = ','.join([col for col in self.table_primary_keys if col not in ['EFFECTIVEDATE', 'VERSIONNO']])\\n return_columns = ','.join(self.table_columns)\\n with self.con:\\n cur = self.con.cursor()\\n cur.execute(\\\"DROP TABLE IF EXISTS temp;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp2;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp3;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp4;\\\")\\n # Store just the unique sets of ids that came into effect before the the datetime in a temporary table.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp AS \\n SELECT * \\n FROM {table} \\n WHERE EFFECTIVEDATE <= '{datetime}';\\\"\\\"\\\"\\n cur.execute(query.format(table=self.table_name, datetime=date_time))\\n # For each unique set of ids and effective dates get the latest versionno and sore in temporary table.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp2 AS\\n SELECT {id}, EFFECTIVEDATE, MAX(VERSIONNO) AS VERSIONNO\\n FROM temp\\n GROUP BY {id}, EFFECTIVEDATE;\\\"\\\"\\\"\\n cur.execute(query.format(id=id_columns))\\n # For each unique set of ids get the record with the most recent effective date.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp3 as\\n SELECT {id}, VERSIONNO, max(EFFECTIVEDATE) as EFFECTIVEDATE\\n FROM temp2\\n GROUP BY {id};\\\"\\\"\\\"\\n cur.execute(query.format(id=id_columns))\\n # Inner join the original table to the set of most recent effective dates and version no.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp4 AS\\n SELECT * \\n FROM {table} \\n INNER JOIN temp3 \\n USING ({id}, VERSIONNO, EFFECTIVEDATE);\\\"\\\"\\\"\\n cur.execute(query.format(table=self.table_name, id=id_columns))\\n # Inner join the most recent data with the interconnectors used in the actual interval of interest.\\n query = \\\"\\\"\\\"SELECT {cols} FROM temp4 ;\\\"\\\"\\\"\\n query = query.format(cols=return_columns)\\n data = pd.read_sql_query(query, con=self.con)\\n return data\",\n \"def get_new_modelling_data():\\n # get latest epidemic data from OWID \\n\\n df = pd.read_json(requests.get(\\\"https://covid.ourworldindata.org/data/owid-covid-data.json\\\").content)\\n data = pd.DataFrame(df[\\\"POL\\\"][\\\"data\\\"])\\n\\n # get latest government restriction data from Oxford tracker\\n response = requests.get(\\\"https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest.csv\\\").content\\n rest = pd.read_csv(io.StringIO(response.decode('utf-8')))\\n rest = rest[rest.CountryName == \\\"Poland\\\"]\\n\\n modelling = pd.DataFrame(Mobility.objects.values())\\n prepare_model_data(data,rest,modelling)\",\n \"def getOLAPSource():\",\n \"def get_SequencedFischerData():\\n filteredOn = { #For reference, to know how dataset has been filtered\\n 'minimalSurvivalLastFollowup': 365*5\\n }\\n print('Filtering settings:', filteredOn)\\n\\n #Metadata\\n metadata = pd.read_table(\\n gzip.open(\\n os.path.join(\\n privatedir,\\n \\\"R2_grabbed_data/Fischer498/metadata_src/GSE49710_series_matrix.txt.gz\\\"\\n ), 'rt', encoding=\\\"UTF-8\\\"\\n ), skiprows=47,skipfooter=44799-66,engine='python',header=None\\n )\\n metadata.index = metadata[0].apply(lambda x: x.replace('!',''))\\n del metadata[0]\\n metadata = metadata.T\\n i = count()\\n metadata.columns = [c.replace('ch1',str(next(i))) if c.startswith('Sample_char') else c for c in metadata.columns]\\n del i, metadata['Sample_source_name_ch1'], metadata['Sample_status'], metadata['Sample_organism_ch1']\\n metadata.columns = [metadata[c][metadata.first_valid_index()].split(':')[0].replace(' ','_')\\n if c.startswith('Sample_char') else c for c in metadata.columns]\\n metadata = metadata.applymap(lambda x: x.split(': ')[1] if ': ' in x else x)\\n \\n metadatasurv = pd.read_table(\\n gzip.open(\\n os.path.join(\\n privatedir,\\n \\\"R2_grabbed_data/Fischer498/metadata_src/GSE62564_series_matrix.txt.gz\\\"\\n ),'rt',encoding=\\\"UTF-8\\\"\\n ), skiprows=51,skipfooter=102-74,engine='python'\\n )\\n metadatasurv.index = metadatasurv['!Sample_geo_accession'].apply(lambda x: x.replace('!',''))\\n del metadatasurv['!Sample_geo_accession']\\n metadatasurv = metadatasurv.T\\n metadatasurv.columns = range(len(metadatasurv.columns))\\n metadatasurv = metadatasurv[list(range(7,21))]\\n metadatasurv.columns = [v.split(':')[0].replace(' ','_') for v in metadatasurv.ix[metadatasurv.first_valid_index()]]\\n metadatasurv = metadatasurv.applymap(lambda x: x.split(': ')[1] if not x is np.nan else x)\\n metadatasurv.index = metadata.index #Both sample sets are sorted the same way, but different gse names\\n assert sum(metadatasurv.age == metadata.age_at_diagnosis) == len(metadata)\\n metadata['overall_survival'] = metadatasurv.os_day.apply(int)\\n metadata['eventfree_survival'] = metadatasurv.efs_day.apply(int)\\n del metadatasurv\\n metadata.Sample_geo_accession = metadata.Sample_geo_accession.apply(lambda x: x.lower())\\n metadata.set_index(\\\"Sample_geo_accession\\\",inplace=True)\\n metadata.Sample_title = metadata.Sample_title.apply(lambda x: x[5:].replace(' patient ',''))\\n metadata.death_from_disease = metadata.death_from_disease == '1'\\n metadata.progression = metadata.progression == '1'\\n\\n #Expression data\\n ## Array expression\\n exprdata_A = pd.read_table(os.path.join(privatedir,'R2_grabbed_data/Fischer498/GSE49710_R2.txt'))\\n exprdata_A.index = exprdata_A.pop('#H:hugo')\\n del exprdata_A['probeset']\\n \\n ## RNAseq gene expression\\n exprdata_G = pd.read_table(\\n gzip.open(\\n os.path.join(\\n privatedir,\\n 'R2_grabbed_data/Fischer498/sequencedData/GSE49711_SEQC_NB_TUC_G_log2.txt.gz'\\n ), 'rt',encoding=\\\"UTF-8\\\"\\n ),\\n index_col = '00gene_id'\\n )\\n exprdata_G.columns = [\\n metadata.reset_index().set_index('Sample_title').ix[c.split('_')[1]].Sample_geo_accession\\n for c in exprdata_G.columns\\n ]\\n\\n ## RNAseq transcript level expression\\n exprdata_T = pd.read_table(\\n gzip.open(\\n os.path.join(\\n privatedir,\\n 'R2_grabbed_data/Fischer498/sequencedData/GSE49711_SEQC_NB_TUC_T_log2.txt.gz'\\n ), 'rt',encoding=\\\"UTF-8\\\"\\n ),\\n index_col = '00transcript_id'\\n )\\n exprdata_T.columns = exprdata_G.columns\\n\\n ## RNAseq junction level expression\\n exprdata_J = pd.read_table(\\n gzip.open(\\n os.path.join(\\n privatedir,\\n 'R2_grabbed_data/Fischer498/sequencedData/GSE49711_SEQC_NB_TUC_J_log2.txt.gz'\\n ), 'rt',encoding=\\\"UTF-8\\\"\\n ),\\n index_col = 'sample_ID'\\n )\\n exprdata_J.columns = exprdata_G.columns\\n\\n # Default expression dataset -> exprdata_G\\n exprdata = exprdata_G\\n \\n #aCGH\\n aCGH = pd.read_table(\\n os.path.join(privatedir,'R2_grabbed_data/Fischer498/SEQC_aCGH/SEQC_aCGH_all_146.txt')\\n )\\n geosearch = metadata[['Sample_title']].copy()\\n geosearch.reset_index(inplace=True)\\n geosearch.set_index('Sample_title',inplace=True)\\n aCGH.Sample = aCGH.Sample.apply(lambda x: geosearch.ix[x].Sample_geo_accession)\\n del geosearch\\n aCGH['log2ratio'] = (aCGH.CN/2).apply(np.log2)\\n #Convert coordinates to hg38\\n lo = LSD.get_lift19to38()\\n aCGH['Start38'] = aCGH.T.apply(lambda x: lo.convert_coordinate(x.Chromosome,x.Start)).apply(lambda x: x[0][1] if x else np.nan)\\n aCGH['End38'] = aCGH.T.apply(lambda x: lo.convert_coordinate(x.Chromosome,x.End)).apply(lambda x: x[0][1] if x else np.nan)\\n del lo, aCGH['Start'], aCGH['End']\\n aCGH = aCGH.dropna().copy()\\n #Assign genes to regions\\n genannot = LSD.get_ensemblGeneannot()\\n aCGH['genes'] = aCGH.T.apply(lambda x: {f.attributes['gene_name'][0] for f in genannot.region('{}:{}-{}'\\n .format(x.Chromosome[3:],int(x.Start38),int(x.End38)),featuretype='gene')})\\n aCGH['nrGenes'] = aCGH.genes.apply(len)\\n del genannot\\n #To set cut offs look at hist => aCGH.log2ratio.hist(ax=ax,bins='auto')\\n aCGH['annotation'] = aCGH.log2ratio.apply(lambda x: 'gain' if x > 0.3 else ('loss' if x < -0.3 else 'normal'))\\n\\n # Filter patients whom according to metadata survived, but had last follow up before 5 years\\n metadata = metadata[metadata.death_from_disease |\\n (~metadata.death_from_disease &\\n (metadata.overall_survival > filteredOn['minimalSurvivalLastFollowup']))]\\n exprdata_A = exprdata_A[metadata.index]\\n exprdata_G = exprdata_G[metadata.index]\\n exprdata_T = exprdata_T[metadata.index]\\n exprdata_J = exprdata_J[metadata.index]\\n aCGH = aCGH[aCGH.Sample.isin(metadata.index)]\\n \\n return Dataset(**locals())\",\n \"def testWeatherFetch(self):\\n\\n timeCol = 'timestamp'\\n rows = []\\n for row in self.aggregator.rawData(dataType = 'weather',\\n orderBy = [timeCol],\\n timestampCol = timeCol,\\n startDate = self.testStart,\\n endDate = self.testEnd):\\n rows.append(row)\\n self.assertIsNotNone(rows, 'Rows are present.')\",\n \"def test_plt_mag_time():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n # create the arrays per filter and readout pattern\\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\\n for i, val in enumerate(max_val_box):\\n if '140' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(val)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(val)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif '110' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(val)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(val)\\n nrsrapidd6_clear.append(np.NaN)\\n else:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(val)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(val)\\n # add to the bokeh data structure\\n ta.source.data[\\\"nrsrapid_f140x\\\"] = nrsrapid_f140x\\n ta.source.data[\\\"nrsrapid_f110w\\\"] = nrsrapid_f110w\\n ta.source.data[\\\"nrsrapid_clear\\\"] = nrsrapid_clear\\n ta.source.data[\\\"nrsrapidd6_f140x\\\"] = nrsrapidd6_f140x\\n ta.source.data[\\\"nrsrapidd6_f110w\\\"] = nrsrapidd6_f110w\\n ta.source.data[\\\"nrsrapidd6_clear\\\"] = nrsrapidd6_clear\\n result = ta.plt_mag_time()\\n\\n assert bokeh_plot_type == type(result)\",\n \"def get_data(last):\\n Table = \\\"ServerRoom\\\"\\n filter = \\\"\\\"\\n if last == \\\"lastone\\\":\\n data = request_meteodata(\\\"SELECT * from `ServerRoom` ORDER BY id DESC LIMIT 1 \\\")\\n if len(data) == 0:\\n return [SensorData(datetime.datetime.now(), 0, 0)]\\n res = []\\n for d in data:\\n res.append(SensorData(d[1], d[2], d[3]))\\n return res\\n if last != \\\"All\\\":\\n limit = datetime.datetime.now().astimezone(utz)\\n if last == \\\"24hours\\\":\\n limit -= datetime.timedelta(hours=24)\\n else:\\n limit = limit.replace(hour=0, minute=0, second=0, microsecond=0)\\n if last == \\\"3days\\\":\\n limit -= datetime.timedelta(days=3)\\n elif last == \\\"7days\\\":\\n limit -= datetime.timedelta(days=7)\\n elif last == \\\"month\\\":\\n limit = limit.replace(day=1)\\n elif last == \\\"30days\\\":\\n limit -= datetime.timedelta(days=30)\\n elif last == \\\"year\\\":\\n limit = limit.replace(day=1, month=1)\\n filter = \\\" WHERE `date` > '\\\" + str(limit) + \\\"'\\\"\\n order = \\\" ORDER BY `date` ASC\\\"\\n req = \\\"SELECT * FROM `\\\" + Table + \\\"`\\\" + filter + order\\n data = request_meteodata(req)\\n if len(data) == 0:\\n print(\\\"no data: get all\\\")\\n req = \\\"SELECT * FROM `\\\" + Table + \\\"`\\\" + order\\n data = request_meteodata(req)\\n res = []\\n for d in data:\\n res.append(SensorData(d[1], d[2], d[3]))\\n return res\",\n \"def getEPADailyData(dateint, dt_ind, month, epa_df, yr):\\n\\n try:\\n start = dateint + dt_ind * 10000\\n end = start + 10001\\n dly_epa_df = epa_df[(epa_df.created >= start) & (epa_df.created < end)]\\n dly_epa_df.reset_index(inplace=True, drop=True)\\n\\n new_df = pd.DataFrame(columns=['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'epa_pm25_value', 'raw_concentration', 'aqi', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code', 'created'])\\n for sitenm in dly_epa_df.site_name.unique():\\n indx_ct = 0\\n site_df = dly_epa_df[dly_epa_df.site_name == sitenm]\\n for i in site_df.created.unique():\\n indx_ct += 1\\n new_df = pd.concat([new_df,site_df.iloc[indx_ct - 1:indx_ct]],ignore_index=True)\\n\\n if i != site_df.created.max(): # Don't interpolate the last record\\n tmp_df = site_df.iloc[indx_ct - 1:indx_ct][['lat', 'lon', 'utc', 'parameter', 'epa_pm25_unit', 'category', 'site_name', 'agency_name', 'full_aqs_code', 'intl_aqs_code']]\\n for j in range(1,6):\\n new_dt = i + j * 10\\n tmp_df['created'] = int(new_dt)\\n tmp_df['epa_pm25_value'] = np.nan\\n tmp_df['raw_concentration'] = np.nan\\n tmp_df['aqi'] = np.nan\\n new_df = pd.concat([new_df,tmp_df],ignore_index=True)\\n\\n # Convert aqi to numerica for so that it gets interpolated\\n new_df[['aqi']] = new_df[['aqi']].replace(\\\"nan\\\", np.nan, regex=True)\\n new_df[['aqi']] = new_df[['aqi']].apply(pd.to_numeric)\\n\\n new_df = new_df.interpolate(method='linear', limit_direction='forward', axis=0)\\n\\n int_epa_df = new_df[(new_df.created >= start) & (new_df.created < (end - 1))]\\n int_epa_df.reset_index(inplace=True, drop=True)\\n \\n # Write to S3\\n s3 = s3fs.S3FileSystem()\\n myopen = s3.open\\n write('midscapstone-whos-polluting-my-air/EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr), int_epa_df, compression='GZIP', open_with=myopen)\\n s3_resource = boto3.resource('s3')\\n s3_resource.Object('midscapstone-whos-polluting-my-air', 'EpaDaily/epa_20{2}{0}{1:02}.parquet'.format(month, dt_ind, yr)).Acl().put(ACL='public-read')\\n\\n except Exception as e:\\n print(\\\"*** EXCEPTION IN GET EPA DAILY DATA *** {}\\\".format(e))\\n return int_epa_df\",\n \"def get_physio_data_old(subject_num, session_num):\\n \\n subject_path = pathlib.Path(PHYSIO_PATH + '/subject_' + subject_num)\\n meta_file_path = subject_path.joinpath('physio_meta_' + subject_num + '.yml')\\n\\n if not subject_path.exists() :\\n raise DataAccessError('Subject folder not found: '+str(subject_path))\\n \\n # open metadata file\\n with meta_file_path.open() as yaml_file :\\n physio_meta = yaml.load(yaml_file.read())\\n\\n # construct list of record starting times\\n starting_times = [(rec['number'], float(rec['start_time'])) for rec in physio_meta['records'] if rec['session']==session_num]\\n starting_times = dict(starting_times)\\n #print(starting_times)\\n\\n # load recodrs and set to absolute time\\n column_names = ['time','ecg','gsr']\\n physio_data = pd.DataFrame(columns=column_names)\\n pattern = 'physio_record_' + subject_num + '_' + session_num + '_*.csv'\\n\\n # raise error is no data files found or starting times are missing\\n record_file_num = len(list(subject_path.glob(pattern)))\\n if record_file_num == 0:\\n raise DataAccessError('No record files: '+str(subject_path))\\n if record_file_num != len(starting_times):\\n raise DataIntegrityError('Number of record files and number of starting times in meta file not the same!')\\n \\n for record_path in subject_path.glob(pattern):\\n \\n # load physio_data\\n physio_record = pd.read_csv(\\n str(record_path),\\n comment='#',\\n header=None,\\n names = column_names\\n )\\n \\n # convert physio time to seconds\\n physio_record['time'] /= 1000\\n \\n # convert to absolute time\\n #record_number = int(record_path.stem.split('_')[-1])\\n record_number = int(min(starting_times.keys()))\\n s_time = starting_times[record_number]\\n physio_record['time'] += s_time\\n \\n # concatinate to other records\\n physio_data = physio_data.append(physio_record, ignore_index=True)\\n \\n # sort records by time\\n physio_data = physio_data.sort_values(by='time')\\n \\n return physio_data\",\n \"def entries_from_goes_ts_file2(file, default_waveunit=None):\\n\\n headers = fits.get_header(file)\\n if isinstance(file, (str, six.text_type)):\\n filename = file\\n else:\\n filename = getattr(file, 'name', None)\\n\\n statinfo = os.stat(file)\\n #print('a header')\\n entry = DatabaseEntry(path=filename)\\n size = statinfo.st_size\\n\\n # Add/tweak start/end entries for GOES\\n if headers[0].get('TELESCOP','') != '':\\n #header['INSTRUME'] = header['TELESCOP']# So E.G. 'GOES 6' instead 'X-ray Detector'\\n entry.instrument = headers[0]['TELESCOP']\\n if (headers[0].get('DATE-OBS','') != ''):\\n if is_time_in_given_format(headers[0]['DATE-OBS'], '%d/%m/%Y'):\\n start_time = datetime.strptime(headers[0]['DATE-OBS'], '%d/%m/%Y')\\n elif is_time_in_given_format(headers[0]['DATE-OBS'], '%d/%m/%y'):\\n start_time = datetime.strptime(headers[0]['DATE-OBS'], '%d/%m/%y')\\n else:\\n start_time = parse_time(headers[0]['DATE-OBS'])\\n elif (headers[1].get('DATE-OBS','') != ''):\\n if is_time_in_given_format(headers[1]['DATE-OBS'], '%d/%m/%Y'):\\n start_time = datetime.strptime(headers[1]['DATE-OBS'], '%d/%m/%Y')\\n elif is_time_in_given_format(headers[1]['DATE-OBS'], '%d/%m/%y'):\\n start_time = datetime.strptime(headers[1]['DATE-OBS'], '%d/%m/%y')\\n else:\\n start_time = parse_time(headers[1]['DATE-OBS'])\\n\\n if (headers[0].get('DATE-END','') != ''):\\n if is_time_in_given_format(headers[0]['DATE-END'], '%d/%m/%Y'):\\n end_time = datetime.strptime(headers[0]['DATE-END'], '%d/%m/%Y')\\n elif is_time_in_given_format(headers[0]['DATE-END'], '%d/%m/%y'):\\n end_time = datetime.strptime(headers[0]['DATE-END'], '%d/%m/%y')\\n else:\\n end_time = parse_time(headers[0]['DATE-END'])\\n elif (headers[1].get('DATE-END','') != ''):\\n if is_time_in_given_format(headers[1]['DATE-END'], '%d/%m/%Y'):\\n end_time = datetime.strptime(headers[1]['DATE-END'], '%d/%m/%Y')\\n elif is_time_in_given_format(headers[1]['DATE-END'], '%d/%m/%y'):\\n end_time = datetime.strptime(headers[1]['DATE-END'], '%d/%m/%y')\\n else:\\n end_time = parse_time(headers[1]['DATE-END'])\\n else:\\n end_time = start_time + timedelta(days=1,seconds=-1)\\n\\n # Add these to the entry\\n observation_time_start = start_time\\n observation_time_end = end_time\\n\\n wavemax = 0.8 # XRSB '1.0--8.0 $\\\\AA$'\\n wavemin = 0.05 # XRSA '0.5--4.0 $\\\\AA$'\\n\\n metadata = MetaDict(headers[1])\\n #entry.tags = sunpy.database.attrs.Tag('raw')\\n\\n entry = DatabaseEntry(observation_time_start=start_time,\\n observation_time_end = end_time,\\n instrument='EIT',\\n wavemin=wavemin,\\n wavemax=wavemax,\\n metadata=metadata,\\n size=size)\\n\\n return entry\",\n \"def loadDBstation(dbfile,stationID,varname,timeinfo=None,filttype=None,cutoff=3600.0,output_meta=False):\\r\\n from netcdfio import queryNC\\r\\n \\r\\n outvar = ['NetCDF_Filename','NetCDF_GroupID','StationName']\\r\\n tablename = 'observations'\\r\\n #condition = 'Variable_Name = \\\"%s\\\" and StationID = \\\"%s\\\"' % (varname,stationID)\\r\\n condition = 'Variable_Name = \\\"%s\\\" and StationID LIKE \\\"%%%s\\\"' % (varname,stationID)\\r\\n \\r\\n print 'Querying database...'\\r\\n print condition\\r\\n data, query = queryNC(dbfile,outvar,tablename,condition) \\r\\n \\r\\n if len(data)==0:\\r\\n print '!!! Warning - Did not find any stations matching query. Returning -1 !!!'\\r\\n return -1\\r\\n else:\\r\\n ts = timeseries(data[0]['time'],data[0][varname].squeeze())\\r\\n \\r\\n \\r\\n if not timeinfo==None:\\r\\n print 'Interpolating station data between %s and %s\\\\n'%(timeinfo[0],timeinfo[1])\\r\\n tnew,ynew = ts.interp((timeinfo[0],timeinfo[1],timeinfo[2]))\\r\\n ts = timeseries(tnew,ynew)\\r\\n ts.dt = timeinfo[2] # This needs updating\\r\\n \\r\\n if not filttype==None:\\r\\n print '%s-pass filtering output data. Cutoff period = %f [s].'%(filttype,cutoff)\\r\\n yfilt = ts.filt(cutoff,btype=filttype,axis=-1)\\r\\n ts.y = yfilt.copy()\\r\\n \\r\\n if output_meta:\\r\\n if data[0].has_key('elevation'):\\r\\n ele = data[0]['elevation']\\r\\n else:\\r\\n ele = 0.0\\r\\n meta = {'longitude':data[0]['longitude'],'latitude':data[0]['latitude'],'elevation':ele,'StationName':query['StationName'][0]}\\r\\n return ts, meta \\r\\n else:\\r\\n return ts\",\n \"def export_lateness_data( gpssched, sched_error ):\\n \\n sql = \\\"\\\"\\\"\\ninsert into datamining_table dt\\n( gps_segment_id, gtfs_trip_id, rms_schedule_error, vehicle_id,\\n route_name, vehicle_type, service_id, direction_id,\\n stop_lat, stop_lon, stop_id, stop_sequence,\\n scheduled_arrival_time, scheduled_departure_time,\\n actual_arrival_time, lateness, prev_stop_id )\\nvalues\\n( null, %(trip_id)s, %(sched_err)s, %(vehid)s,\\n %(routename)s, %(vehtype)s, %(service_id)s, %(dir_id)s,\\n %(stoplat)s,%(stoplon)s,%(stopid)s,%(stopseq)s,\\n %(sched_arr)s,%(sched_dep)s,%(actual_arr)s,%(lateness)s,\\n %(prev_stop_id)s )\\n\\\"\\\"\\\"\\n \\n gtfs = gpssched.getGTFSSchedule()\\n\\n basedict = { 'trip_id' : gtfs.trip_id,\\n 'sched_err' : sched_error,\\n 'vehid' : gpssched.segment.vehicle_id,\\n 'routename' : gtfs.route_short_name,\\n 'vehtype' : gtfs.route_type,\\n 'service_id' : gtfs.service_id,\\n 'dir_id' : gtfs.direction_id\\n }\\n\\n cur = get_cursor()\\n \\n for arrival in gpssched.getGPSSchedule():\\n print dict(arrival)\\n stopdict = dict(basedict)\\n stopdict.update ( { 'stoplat' : arrival['stop_lat'],\\n 'stoplon' : arrival['stop_lon'],\\n 'stopid' : arrival['stop_id'],\\n 'stopseq' : arrival['stop_sequence'],\\n 'sched_arr' : arrival['arrival_time_seconds'],\\n 'sched_dep' : arrival['departure_time_seconds'],\\n 'actual_arr' : arrival['actual_arrival_time_seconds'],\\n 'lateness' : arrival['actual_arrival_time_seconds'] \\\\\\n - arrival['departure_time_seconds'],\\n 'prev_stop_id' : arrival['prev_stop_id']\\n } )\\n SQLExec(cur, sql, stopdict)\\n\\n\\n cur.close()\",\n \"def test_data_source_soaps_post(self):\\n pass\",\n \"def load_data(city, month, day):\\n \\n# Using pandas accessor to find month, day, hour from the Start Time column in the source data\\n print(\\\"A moment please while I find the data....\\\\n\\\")\\n df = pd.read_csv(CITY_DATA[city])\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['day_of_week'] = df['Start Time'].dt.weekday_name\\n df['month'] = df['Start Time'].dt.month\\n df['hour'] = df['Start Time'].dt.hour\\n\\n return df\",\n \"def manipulate_data(ds, var, predef_clim, predef_trnd, trn_yrs, all_yrs, \\n apply_latw=True, apply_detrending=True, dropna=True):\\n\\n \\n if((var=='SD')|(var=='sd')|(var=='snowc')): \\n ds[var] = ds[var].where(ds[var]>=0, other=0.0)\\n ds[var] = ds[var].where(ds[var]==0, other=1.0)\\n #ds[var].values = Gauss_filter(ds[var].values, (0,3,3))\\n \\n \\\"\\\"\\\"\\n if((var=='hgt')|(var=='z')|(var=='GPT')):\\n months = ds.time.to_index().month; ssn_ends = (months==2)|(months==5)|(months==8)|(months==11)\\n ds = ds.sel(time=ssn_ends)\\n else: \\n ds = ds.resample(time='3M').mean()\\n \\\"\\\"\\\"\\n \\n ds = ds.resample(time='3M').mean()\\n\\n ds = ds.sel(time=slice(str(all_yrs[0])+'-01-01', str(all_yrs[-1])+'-12-31')) \\n \\n try: \\n clim = predef_clim\\n ds = ds.groupby('time.season') - clim\\n print('Predefined climatology used')\\n except:\\n clim = ds.sel(time=slice(str(trn_yrs[0])+'-01-01', str(trn_yrs[-1])+'-12-31')).groupby('time.season').mean('time')\\n ds = ds.groupby('time.season') - clim\\n print('Climatology calculated from data')\\n \\n if(apply_latw): ds[var].values = lat_weighting(ds[var].values, \\n ds.lat, ds.lon)\\n if(dropna):\\n ds = ds.stack(gridcell=('lat', 'lon')).dropna(dim='gridcell',how='any')\\n else: \\n ds = ds.stack(gridcell=('lat', 'lon')).fillna(0)\\n \\n \\n trend_models = { }\\n if(apply_detrending): \\n ds = ds.load()\\n for ssn in ('DJF', 'MAM', 'JJA', 'SON'):\\n #ssn_idx = ds['time.season'] == ssn\\n \\n trn_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], trn_yrs))\\n all_idx = bool_index_to_int_index(np.isin(ds['time.season'], ssn) & np.isin(ds['time.year'], all_yrs))\\n \\n trn_x = np.array(ds.time[trn_idx].values.tolist()).reshape(-1,1)\\n all_x = np.array(ds.time[all_idx].values.tolist()).reshape(-1,1)\\n try:\\n trend = predef_trnd[ssn].predict(all_x)\\n trend_models[ssn] = predef_trnd[ssn]\\n print('Predefined trend model used')\\n except:\\n #_, trend_model = define_trends(ds[var][trn_idx], trn_x)\\n _, trend_model = define_trends(ds[var][all_idx], all_x)\\n trend = trend_model.predict(all_x)\\n trend_models[ssn] = trend_model\\n print('Trends calculated from data')\\n \\n ds[var][all_idx] = ds[var][all_idx] - trend\\n \\n\\n \\n return ds, clim, trend_models\",\n \"def gatherStationData():\\n flist = list_files()\\n station_dics = {}\\n print(\\\"Reading in csv data...\\\")\\n for f_in in flist:\\n start,end = find_timespan(f_in)\\n station = station_name(f=f_in)\\n print(\\\"File: {0} Station: {1} {2}--{3}\\\".format(f_in, \\n station, start, end))\\n station_dics[station] = read_precip(fname=f_in, \\n label=station, start_year=start, end_year=end)\\n data_list = []\\n for s in station_dics:\\n data_list.append(station_dics[s]) \\n return pd.concat(data_list,axis=1)\",\n \"def extract_dates(self,dates, tol= 0.05 , in_place=False, verbose=True):\\n###################################################################\\n\\n # import \\n import inspect\\n import numpy as np\\n import pyacs.gts\\n\\n # check data is not None\\n from pyacs.gts.lib.errors import GtsInputDataNone\\n \\n try:\\n if self.data is None:\\n # raise exception\\n raise GtsInputDataNone(inspect.stack()[0][3],__name__,self)\\n except GtsInputDataNone as error:\\n # print PYACS WARNING\\n print( error )\\n return( self )\\n\\n # working gts\\n new_gts = self.copy()\\n \\n # case .data_xyz is None\\n \\n if new_gts.data_xyz is None:\\n new_gts.neu2xyz(corr=True)\\n\\n else:\\n # check data/data_xyz consistency\\n try:\\n if not new_gts.cdata(data=True):\\n # raise exception\\n from pyacs.gts.lib.errors import GtsCDataError\\n raise GtsCDataError( inspect.stack()[0][3],__name__,self )\\n except GtsCDataError as error:\\n print( error )\\n return( self )\\n \\n \\n new_data=None\\n \\n # extract dates\\n \\n index = np.array( pyacs.gts.Gts.get_index_from_dates(dates, self.data, tol=tol) )\\n \\n if verbose:\\n print('-- Extracting ',index.shape[0],' entries from Gts or code: ',self.code)\\n \\n if index.shape[0] > 0:\\n new_data_xyz= self.data_xyz[index,:]\\n new_sigma = self.data[index,4:]\\n else:\\n new_data=None\\n if verbose:\\n print(\\\"-- time series \\\",self.code,\\\" does not have dates at the requested dates \\\")\\n\\n \\n # handles outliers\\n\\n if new_data is not None: \\n ldate_outliers=self.data[:,0][self.outliers]\\n lupdated_outliers=pyacs.gts.Gts.get_index_from_dates(ldate_outliers, new_data, tol=tol)\\n else:\\n lupdated_outliers = []\\n\\n if verbose:\\n print('-- Transmitting ',len(lupdated_outliers),' outliers to the extracted Gts ')\\n \\n # case observations\\n \\n new_gts.data_xyz = new_data_xyz\\n \\n # handle outliers\\n\\n ldate_outliers=self.data[:,0][self.outliers]\\n lupdated_outliers=pyacs.gts.Gts.get_index_from_dates(ldate_outliers, new_data_xyz, tol=0.05)\\n \\n # handles offsets_date\\n \\n upd_offsets=[]\\n for offset_date in self.offsets_dates:\\n if offset_date>=new_data_xyz[0,0] and offset_date<=new_data_xyz[-1,0]:\\n upd_offsets.append(offset_date)\\n \\n # handles X0,Y0,Z0\\n \\n new_gts.X0 = new_data_xyz[0,1]\\n new_gts.Y0 = new_data_xyz[0,2]\\n new_gts.Z0 = new_data_xyz[0,3]\\n \\n # re-generate NEU time series\\n new_gts.xyz2neu(corr=False)\\n\\n # re-populate the uncertainties columns\\n new_gts.data[:,4:] = new_sigma\\n \\n # offsets & outliers\\n \\n new_gts.offsets_dates=upd_offsets\\n new_gts.outliers=lupdated_outliers\\n \\n if in_place:\\n self = new_gts\\n return(self)\\n else:\\n return(new_gts)\",\n \"def windTimeSeries(ftp, rootFolder, selectedDate, outputFolder, ymax):\\n \\n dataType = 'wind_reconstruction_data'\\n file_list = []\\n \\n\\n # Retrieve data from folders in FTP\\n \\n possible_hours = [format(x,'02d') for x in range(24)]\\n from_zone = tz.gettz('America/Mexico_City')\\n to_zone = tz.gettz('UTC')\\n \\n for item in possible_hours:\\n \\n local_time = datetime.strptime(selectedDate+item, '%Y-%m-%d%H')\\n local_time = local_time.replace(tzinfo=from_zone)\\n \\n utc_date = local_time.astimezone(to_zone)\\n \\n utc_day = utc_date.strftime(\\\"%Y-%m-%d\\\")\\n \\n utc_hour = utc_date.strftime(\\\"%H\\\")\\n \\n time_folder = rootFolder+utc_day+'/'+dataType+'/'+utc_hour+'-00/'\\n \\n creat_min = []\\n try:\\n\\n ftp.dir(time_folder, creat_min.append)\\n dbs = []\\n for i in range(len(creat_min)):\\n if creat_min[i].find('DBS') != -1:\\n dbs.append(creat_min[i])\\n\\n if len(dbs) == 0:\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\\n\\n elif len(dbs) == 1:\\n if dbs[0].find('-25-'):\\n file_list.append(time_folder+dbs[0][55:])\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\\n elif dbs[0].find('-55-'):\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\\n file_list.append(time_folder+dbs[0][55:])\\n else:\\n for i in range(len(dbs)):\\n file_list.append(time_folder+dbs[i][55:])\\n\\n except:\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\\n\\n\\n \\n # Create output folder\\n outputFolder = outputFolder+'/'+selectedDate\\n try:\\n mkdirp(outputFolder)\\n except:\\n print('warning: folder '+outputFolder+' already exists')\\n \\n # Make 'generic' outputfile\\n outFile = outputFolder+'/'\\n\\n try:\\n \\n meas_time = []\\n Usets = []\\n Vsets = []\\n Wsets = []\\n \\n for working_file in file_list:\\n\\n if working_file.find('missing_data') != -1:\\n Xset = pd.DataFrame(np.nan,columns=['Xwind'],index=range(100,2550,50))\\n Yset = pd.DataFrame(np.nan,columns=['Ywind'],index=range(100,2550,50))\\n Zset = pd.DataFrame(np.nan,columns=['Zwind'],index=range(100,2550,50))\\n match = (re.search('\\\\d{4}-\\\\d{2}-\\\\d{2}_\\\\d{2}-\\\\d{2}',working_file)).group(0)\\n meas_time.append(match)\\n\\n else: \\n match = (re.search('\\\\d{4}-\\\\d{2}-\\\\d{2}_\\\\d{2}-\\\\d{2}',working_file)).group(0)\\n #hour_min = re.search('_\\\\d{2}-\\\\d{2}-\\\\d{2}',working_file).group(0)\\n meas_time.append(match)\\n \\n obj = DataContainer()\\n ftp.retrbinary('RETR %s' % working_file, obj.readFromFTP)\\n columns = ['Timestamp', 'Azimuth', 'Elevation','Range','Xwind','Ywind','Zwind',\\\\\\n 'CNR','ConfIdx']\\n\\n # Get the date from the file as a DataFrame\\n data = obj.dataToArray(columns)\\n data.drop(data[data.Range > ymax].index, inplace=True)\\n data = data[['Range','Xwind','Ywind','Zwind']]\\n # Replace abs values Xwind and Ywind greater than 50 with NaN\\n \\n data.Xwind.where((data.Xwind < 50) | (data.Xwind > -50),np.nan,inplace=True)\\n data.Ywind.where((data.Ywind > 50) | (data.Ywind > -50),np.nan,inplace=True)\\n\\n dataByRange = data.groupby('Range').mean() \\n \\n Xset = dataByRange[['Xwind']]\\n Yset = dataByRange[['Ywind']]\\n Zset = dataByRange[['Zwind']]\\n \\n Usets.append(Xset)\\n Vsets.append(Yset)\\n Wsets.append(Zset)\\n \\n df_U = pd.concat(Usets,axis=1)\\n df_V = pd.concat(Vsets,axis=1)\\n df_W = pd.concat(Wsets,axis=1)\\n \\n U = df_U.values;\\n V = df_V.values;\\n W = df_W.values;\\n \\n # Vertical wind grid\\n x = np.linspace(0,48,49)\\n y = np.linspace(100,2500,49)\\n X, Y = np.meshgrid(x, y)\\n \\n # If the plot is going to have a variable pcolormesh uncomment next section\\n# cmapLim = 0\\n# if abs(np.nanmax(W)) > abs(np.nanmin(W)):\\n# cmapLim = abs(np.nanmax(W))\\n# else:\\n# cmapLim = abs(np.nanmin(W))\\n \\n # Change back the UTC times to local time\\n \\n timelabels = []\\n to_zone = tz.gettz('America/Mexico_City')\\n from_zone = tz.gettz('UTC')\\n \\n for entry in meas_time:\\n utc_time = datetime.strptime(entry, '%Y-%m-%d_%H-%M')\\n utc_time = utc_time.replace(tzinfo=from_zone)\\n \\n local = utc_time.astimezone(to_zone)\\n \\n timelabels.append(local.strftime(\\\"%H:%M\\\"))\\n \\n timelabels = [i.replace('25','00') for i in timelabels[2:48:4]]\\n \\n # Plot \\n title = ('Perfil vertical del viento, %s (serie de tiempo)' % selectedDate)\\n fig, ax = plt.subplots(figsize=(20,8))\\n \\n # For variable pcolormesh uncomment next line\\n #cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= (-cmapLim), vmax=cmapLim)\\n \\n cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= -3, vmax=3)\\n ax.set_title(title,fontsize = 24)\\n ax.set_xlabel('Hora Local [hh:mm]', fontsize = 18, labelpad = 14)\\n ax.set_xticks(np.arange(2.5,48, step=4))\\n ax.set_xticklabels(timelabels, rotation=45, size=12)\\n ax.set_ylabel('Altura [m]', fontsize = 18, labelpad = 18)\\n ax.yaxis.set_tick_params(labelsize=12)\\n ax.text(0.18,-0.08,'Datos de perfilador LIDAR Leosphere Windcube 100s. Lat 19.3262, Lon -99.1761, Alt 2280 msnm',transform=plt.gcf().transFigure, fontdict={'size': 18})\\n \\n # Colorbar\\n cbaxes = fig.add_axes([0.91, 0.125, 0.02, 0.4]) \\n cbar = fig.colorbar(cax, cax = cbaxes)\\n cbar.set_label(r'Viento vertical [$m\\\\,s^{-1}$]', fontsize = 18)\\n cbaxes.text(-0.3,1.07,'(Hacia arriba)')\\n cbaxes.text(-0.3,-0.07,'(Hacia abajo)') \\n \\n \\n #Other a artists\\n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/time_adv.png')\\n newax = fig.add_axes([0.6, -.03, 0.06, 0.06], anchor='NW', zorder=0) #[xo,yo,width,height]\\n newax.imshow(im)\\n newax.axis('off')\\n \\n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/directions.png')\\n newax1 = fig.add_axes([0.90, 0.75, 0.15, 0.15], anchor='NW', zorder=0) #[xo,yo,width,height]\\n newax1.imshow(im)\\n newax1.axis('off')\\n \\n \\n # Horizontal wind grid\\n \\n x_h = np.arange(0, len(meas_time), 1)\\n Xh, Yh = np.meshgrid(x_h, y)\\n \\n # Plot arrows\\n Q = ax.quiver(Xh[::2], Yh[::2], U[::2], V[::2], pivot = 'mid',alpha=0.75)\\n ax.quiverkey(Q, 0.93, 0.68, 10, r'$10\\\\,m\\\\,s^{-1}$',coordinates = 'figure', labelpos='N' )\\n \\n \\n finalOutputFile = outFile+'timeseries_daily_'+selectedDate\\n fig.savefig(finalOutputFile,bbox_inches='tight')\\n plt.close()\\n \\n except Exception as e:\\n print(\\\"Problema calculando Wind Time Series\\\", e)\",\n \"def load_data(city, month, week_day):\\n# name for day variable changed from \\\"day_name\\\" into week_day to take into account new pandas method \\\".day_name()\\\"\\n# read in file form selected city\\n df = pd.read_csv(CITY_DATA[city])\\n# create additional columns for months, days, start/ end times, hours and station combinations\\n df['Start Time'] = pd.to_datetime(df['Start Time'])\\n df['End Time'] = pd.to_datetime(df['End Time'])\\n df['month_start'] = df['Start Time'].dt.month\\n df['month_end'] = df['End Time'].dt.month\\n df['day_start'] = df['Start Time'].dt.day_name()\\n df['day_end'] = df['End Time'].dt.day_name()\\n df['hour'] = df['Start Time'].dt.hour\\n df['station_comb'] = df['Start Station'] + ' &AND& ' + df['End Station']\\n# filter data file by month: capture start and end months\\n if month != 7:\\n df1 = df[df['month_start'] == month]\\n df2 = df1.append(df[df['month_end'] == month])\\n df = df2.drop_duplicates()\\n# filter data file by day: capture start and end days\\n if week_day != 'All':\\n df3 = df[df['day_start'] == week_day]\\n df4 = df3.append(df[df['day_end'] == week_day])\\n df = df4.drop_duplicates()\\n# reset index to facilitate looping in station_stats function\\n df = df.reset_index()\\n# check if user wants to check first data lines\\n req_check_df = input('\\\\nIf you want to check the selected data please enter y.')\\n if req_check_df[0:1].lower() == 'y':\\n print('check df = \\\\n', df.head())\\n wait = input('Press Enter to continue. ')\\n\\n return df\",\n \"def database_extraction(mod_start_time,mod_end_time,instrument_chosen,subevent_bool,\\r\\n detect_previous_event = False,thresholds='100,1',\\r\\n one_thresh = False):\\r\\n obs_file_created = False\\r\\n\\r\\n #extending time window\\r\\n window_end_time = (mod_end_time + timedelta(days=2))\\r\\n window_start_time = (mod_start_time - timedelta(days=2))\\r\\n \\r\\n #making a list of all dates within window\\r\\n day_list=[]\\r\\n for d in range(10):\\r\\n day_list.append((window_start_time + timedelta(days=d)).date())\\r\\n print('day list = %s' %day_list)\\r\\n \\r\\n print('determining if an instrument has been chosen')\\r\\n\\r\\n if instrument_chosen:\\r\\n #if an instrument has been chosen, checking to make sure it still works for this date\\r\\n if inst_end < window_end_time:\\r\\n instrument_chosen = False\\r\\n else:\\r\\n #if insturment hasn't been chosen, figuring out what it should be for given date\\r\\n try:\\r\\n #if instrument is specified in cfg using that\\r\\n instrument = cfg.instrument\\r\\n inst_end = datetime.today()\\r\\n print('using %s as our instrument for observations' %instrument)\\r\\n instrument_chosen = True\\r\\n\\r\\n except:\\r\\n #choosing instrument using function if not given in cfg\\r\\n instrument_stuff = choose_prime_inst(window_start_time.date(),\\r\\n window_end_time.date())\\r\\n instrument = instrument_stuff[0]\\r\\n #figuring out how long we can use this instrument\\r\\n inst_end = instrument_stuff[1]\\r\\n instrument_chosen = True\\r\\n \\r\\n #running katie's code to extract data using chosen instrument and dates\\r\\n print('extracting data from GOES website')\\r\\n \\r\\n #running for only one threshold if one_thresh is true, otherwise running for default\\r\\n #thresholds as well as any additional threshold given\\r\\n if one_thresh:\\r\\n one_sep.run_all(str(window_start_time), str(window_end_time), str(instrument),\\r\\n 'integral', '', '', True, detect_previous_event, thresholds) \\r\\n print('ran for threshold %s' %thresholds)\\r\\n else:\\r\\n if subevent_bool:\\r\\n thresholds = '10,1'\\r\\n #if event is a subevent, changing the threshold in katie's code to\\r\\n #10 MeV > 1pfu so that it will be recorded\\r\\n print('********************SUBEVENT**************************')\\r\\n sep.run_all(str(window_start_time), str(window_end_time), str(instrument),\\r\\n 'integral', '', '', True, detect_previous_event, thresholds)\\r\\n print('ran for subevent')\\r\\n else:\\r\\n #if an event, running with usual thresholds\\r\\n print('********************EVENT*****************************')\\r\\n sep.run_all(str(window_start_time), str(window_end_time),str(instrument), \\r\\n 'integral', '', '', True, detect_previous_event, thresholds)\\r\\n \\r\\n #reloading function so it doesn't keep old data \\r\\n reload(sep)\\r\\n \\r\\n #reformatting csv created from katie's code to json\\r\\n print('extracted - reformatting') \\r\\n for day in day_list: \\r\\n if not obs_file_created:\\r\\n #checking each day within the window to find the csv file if it hasn't\\r\\n #already been found\\r\\n print('thresholds: %s' %thresholds)\\r\\n \\r\\n if one_thresh:\\r\\n #name includes threshold if only ran for one threshold\\r\\n new_obs_name = ('sep_values_' + str(instrument) + '_integral_gt' +\\r\\n str(thresholds).split(',')[0] + '_' + str(thresholds).split(',')[1] + 'pfu_' +\\r\\n day.strftime('%Y_%m_%d').replace('_0','_') + '.csv')\\r\\n else:\\r\\n #otherwise only includes date ran for\\r\\n new_obs_name = ('sep_values_' + str(instrument) + '_integral_' +\\r\\n day.strftime('%Y_%m_%d').replace('_0','_') + '.csv')\\r\\n \\r\\n print('new_os_name %s' %new_obs_name) \\r\\n \\r\\n #checking if that file exists\\r\\n if os.path.exists(katies_path / new_obs_name):\\r\\n #if a file with this date exists, creating the corresponding json file\\r\\n \\r\\n #json name\\r\\n if one_thresh:\\r\\n obs_name = (str(instrument) + '_' + str(day) + 'only_' + str(thresholds).split(',')[0] + 'MeV_event.json')\\r\\n else:\\r\\n obs_name = (str(instrument) + '_' +\\r\\n str(day) + '.json')\\r\\n #creating json file\\r\\n obs_csv2json((katies_path / new_obs_name), obs_name,\\r\\n (ref_path/'example_sepscoreboard_json_file_v20190228.json'),\\r\\n instrument)\\r\\n \\r\\n print('obs file created')\\r\\n #file is created - will not run for anymore dates within window\\r\\n obs_file_created = True\\r\\n \\r\\n return(obs_name)\\r\\n else:\\r\\n print('no csv file found with this date, checking next one')\",\n \"def _write_to_dataset(parser1, parser2, dset, rundate):\\n\\n data_all1 = parser1.as_dict()\\n data_all2 = parser2.as_dict()\\n if parser1.file_path == parser2.file_path:\\n collection = [data_all1]\\n else:\\n collection = [data_all1, data_all2]\\n\\n # Meta information\\n dset.meta[\\\"tech\\\"] = \\\"slr\\\"\\n dset.meta.add(\\\"file\\\", parser1.file_path.stem, section=\\\"input\\\")\\n dset.meta.add(\\\"file\\\", parser2.file_path.stem, section=\\\"input\\\")\\n dset.meta.add(\\\"type\\\", config.tech.obs_format.str.upper(), section=\\\"input\\\")\\n\\n # Make new dict \\\"obs_data\\\" containing only data in relevant time interval:\\n arc_length = config.tech.arc_length.float\\n rundate_datetime = datetime(rundate.year, rundate.month, rundate.day)\\n obs_data = dict()\\n for data_all in collection:\\n for i, x in enumerate(data_all[\\\"meta\\\"][\\\"obs_time\\\"]):\\n if rundate_datetime <= x < rundate_datetime + timedelta(days=arc_length):\\n for key in (\\\"meta\\\", \\\"obs\\\", \\\"obs_str\\\"):\\n for field, val in data_all[key].items():\\n obs_data.setdefault(key, dict()).setdefault(field, list()).append(val[i])\\n\\n data_all.pop(\\\"meta\\\")\\n data_all.pop(\\\"obs\\\")\\n data_all.pop(\\\"obs_str\\\")\\n\\n for key in data_all.keys():\\n if key.startswith(\\\"met_\\\"):\\n for key2, val in data_all[key].items():\\n obs_data.setdefault(key, dict()).setdefault(key2, list()).append(val)\\n elif key.startswith(\\\"satellite_\\\"):\\n # TODO: Use this information in the future?\\n continue\\n elif key.startswith(\\\"station_\\\"):\\n # TODO: Use this information in the future?\\n continue\\n else:\\n log.fatal(f\\\"Unknown data type{key}\\\")\\n\\n obs_date = obs_data[\\\"meta\\\"][\\\"obs_date\\\"]\\n time = [obs_date[i] + timedelta(seconds=obs_data[\\\"meta\\\"][\\\"obs_sec\\\"][i]) for i in range(0, len(obs_date))]\\n dset.num_obs = len(obs_data[\\\"meta\\\"][\\\"obs_time\\\"])\\n dset.add_time(\\\"time\\\", val=time, scale=\\\"utc\\\", fmt=\\\"datetime\\\")\\n dset.add_text(val=obs_data[\\\"meta\\\"][\\\"station\\\"], name=\\\"station\\\")\\n dset.add_text(val=obs_data[\\\"meta\\\"][\\\"satellite\\\"], name=\\\"satellite\\\")\\n dset.add_float(val=obs_data[\\\"meta\\\"][\\\"bin_rms\\\"], unit=\\\"picoseconds\\\", name=\\\"bin_rms\\\")\\n # Positions\\n trf = apriori.get(\\\"trf\\\", time=dset.time)\\n for station in dset.unique(\\\"station\\\"):\\n trf_site = trf[station]\\n station_pos = trf_site.pos.trs.val\\n log.debug(f\\\"Station position for {station} ({trf_site.name}) is (x,y,z) = {station_pos.mean(axis=0)}\\\")\\n domes = trf_site.meta[\\\"domes\\\"]\\n obs_data[\\\"pos_\\\" + station] = station_pos\\n obs_data[\\\"station-other_\\\" + station] = dict(domes=domes, cdp=station, site_id=station)\\n dset.add_position(\\n \\\"site_pos\\\",\\n time=dset.time,\\n system=\\\"trs\\\",\\n val=np.array([obs_data[\\\"pos_\\\" + s][idx] for idx, s in enumerate(dset.station)]),\\n )\\n # Station data\\n sta_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\\\"station_\\\")])\\n for field in sta_fields:\\n dset.add_float(field, val=np.array([float(obs_data[\\\"station_\\\" + s][field]) for s in dset.station]))\\n sta_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\\\"station-other_\\\")])\\n for field in sta_fields:\\n dset.add_text(field, val=[obs_data[\\\"station-other_\\\" + s][field] for s in dset.station])\\n\\n # Station meta\\n station_keys = sorted([k for k, v in obs_data.items() if k.startswith(\\\"station-other_\\\")])\\n pos_keys = sorted([k for k, v in obs_data.items() if k.startswith(\\\"pos_\\\")])\\n\\n for sta_key, pos_key in zip(station_keys, pos_keys):\\n sta_name = sta_key.replace(\\\"station-other_\\\", \\\"\\\")\\n cdp = obs_data[sta_key][\\\"cdp\\\"]\\n dset.meta.add(sta_name, \\\"site_id\\\", cdp)\\n longitude, latitude, height, _ = sofa.iau_gc2gd(2, obs_data[pos_key][0, :]) # TODO: Reference ellipsoid\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"cdp\\\"] = cdp\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"site_id\\\"] = cdp\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"domes\\\"] = obs_data[sta_key][\\\"domes\\\"]\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"marker\\\"] = \\\" \\\"\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"description\\\"] = \\\" \\\"\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"longitude\\\"] = longitude\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"latitude\\\"] = latitude\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"height\\\"] = height\\n\\n # Satellite data\\n sat_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\\\"satellite_\\\")])\\n for field in sat_fields:\\n dset.add_float(field, val=np.array([float(obs_data[\\\"satellite_\\\" + s][field]) for s in dset.satellite]))\\n\\n # Observations\\n # In the dataset, obs_time is seconds since rundate:\\n v = [\\n (obs_data[\\\"meta\\\"][\\\"obs_date\\\"][i] - rundate_datetime).total_seconds() + obs_data[\\\"meta\\\"][\\\"obs_sec\\\"][i]\\n for i in range(0, dset.num_obs)\\n ]\\n\\n obs_data[\\\"obs\\\"].pop(\\\"obs_time\\\")\\n dset.add_float(\\\"obs_time\\\", val=v)\\n for field, values in obs_data[\\\"obs\\\"].items():\\n dset.add_float(field, val=np.array(values))\\n\\n for field, values in obs_data[\\\"obs_str\\\"].items():\\n dset.add_text(field, val=values)\\n\\n return obs_data\",\n \"def main(args):\\r\\n print(\\\"Anonymizing ...\\\")\\r\\n print(args)\\r\\n\\r\\n file_loc = args.file\\r\\n origdata = pd.read_csv(file_loc, sep=',', low_memory=False)\\r\\n\\r\\n print(origdata)\\r\\n\\r\\n print(args.col_customer)\\r\\n\\r\\n ID_field = args.col_customer #TBU CustomerID field name\\r\\n TS_field = args.col_time #TBU TS field name \\r\\n\\r\\n sub = origdata.loc[:,:] #Uncomment for applying on complete dataset\\r\\n\\r\\n def anonymizeID(data,ID_field):\\r\\n df=pd.DataFrame(data.loc[:,ID_field])\\r\\n df2=df.applymap(lambda x: ((HMAC.new(b\\\"key\\\", bytes(x), MD5)).hexdigest()))\\r\\n return df2.loc[:,ID_field]\\r\\n\\r\\n def shiftTimeForTrace(data,TS_field):\\r\\n # print(df.head())\\r\\n df=pd.DataFrame(data.loc[:,TS_field])\\r\\n df2 = df.loc[:,TS_field].apply(lambda x: pd.to_datetime(x)) \\r\\n # print(df2.head())\\r\\n rand_days = random.randint(-5,5) #range can be updated\\r\\n df2 = df2 + pd.DateOffset(days=rand_days)\\r\\n # print(df2.head())\\r\\n return df2\\r\\n\\r\\n #OG subset for reference\\r\\n print(sub.head())\\r\\n\\r\\n # sub1=sub.sort_values(by=ID_field)\\r\\n sub1 = sub.loc[:,:]\\r\\n uniqueIDs = list(sub[ID_field].unique())\\r\\n # sub2 = sub.loc[:,TS_field].apply(lambda x: pd.to_datetime(x))\\r\\n for ID in uniqueIDs:\\r\\n sub3=sub1.loc[sub1[ID_field] == ID]\\r\\n sub3.loc[:,TS_field]=shiftTimeForTrace(sub3,TS_field)\\r\\n # print(sub1.loc[sub1[ID_field] == ID][TS_field])\\r\\n # print(pd.DataFrame(sub3[TS_field]))\\r\\n sub1.loc[sub1[ID_field] == ID,TS_field] = pd.DataFrame(sub3[TS_field])\\r\\n\\r\\n # Results post TS shift\\r\\n print(sub1.head())\\r\\n\\r\\n sub4 = sub1.loc[:,:]\\r\\n sub4.loc[:,ID_field] = anonymizeID(sub4,ID_field)\\r\\n\\r\\n #Results post ID anonymization\\r\\n print(sub4.head())\\r\\n sub4.to_csv('out.csv',index=False)\",\n \"def get_new_local_alerts():\\n ip_details = my_ip_details()\\n m = int(minutes_a_record_remains_new())\\n new_date = make_date_time(seconds_to_subtract=m * 60)\\n f = []\\n if not ip_details:\\n return None\\n data = get_feeds().find({\\\"time.observation\\\": {\\\"$gt\\\": new_date}, \\\"source.network\\\": ip_details['network'],\\n \\\"$or\\\": [{\\\"source.ip\\\": {\\\"$exists\\\": True}, \\\"source.network\\\": {\\\"$exists\\\": True}}]})\\\\\\n .sort([(\\\"time_observation\\\", DESCENDING)])\\n if data and data.count() > 0:\\n data = normalize_dict(data)\\n for x, value in data.items():\\n value['id'] = x\\n if '_id' in value:\\n del value['_id']\\n if 'raw' in value:\\n del value['raw']\\n\\n f.append(value)\\n\\n create_log(None, None, \\\"{0} Local attacks detected : \\\".format(len(f)), LOG_SYSTEM_TYPE)\\n return f\",\n \"def soho_ephin_loader(startdate, enddate, resample=None, path=None, all_columns=False, pos_timestamp=None, use_uncorrected_data_on_own_risk=False):\\n\\n if not path:\\n path = sunpy.config.get('downloads', 'download_dir') + os.sep\\n\\n # create list of files to load:\\n dates = pd.date_range(start=startdate, end=enddate, freq='D')\\n filelist = []\\n for i, doy in enumerate(dates.day_of_year):\\n if dates[i].year<2000:\\n pre = \\\"eph\\\"\\n yy = dates[i].year-1900\\n else:\\n pre = \\\"epi\\\"\\n yy = dates[i].year-2000\\n name = \\\"%s%02d%03d\\\" %(pre, yy, doy)\\n try:\\n file = glob.glob(f\\\"{path}{os.sep}{name}.rl2\\\")[0]\\n except IndexError:\\n print(f\\\"File {name}.rl2 not found locally at {path}.\\\")\\n file = soho_ephin_download(dates[i], path)\\n if len(file) > 0:\\n filelist.append(file)\\n if len(filelist) > 0:\\n filelist = np.sort(filelist)\\n\\n col_names = ['Year', 'DOY', 'MS', 'S/C Epoch', 'Status Word part 1', 'Status Word part 2',\\n 'E150', 'E300', 'E1300', 'E3000', 'P4', 'P8', 'P25', 'P41',\\n 'H4', 'H8', 'H25', 'H41', 'INT',\\n 'P4 GM', 'P4 GR', 'P4 S', 'P8 GM', 'P8 GR', 'P8 S',\\n 'P25 GM', 'P25 GR', 'P25 S', 'P41 GM', 'P41 GR', 'P41 S',\\n 'H4 GM', 'H4 GR', 'H4 S1', 'H4 S23', 'H8 GM', 'H8 GR', 'H8 S1', 'H8 S23',\\n 'H25 GM', 'H25 GR', 'H25 S1', 'H25 S23', 'H41 GM', 'H41 GR', 'H41 S1', 'H41 S23',\\n 'Status Flag', 'Spare 1', 'Spare 2', 'Spare 3']\\n\\n # read files into Pandas dataframes:\\n df = pd.read_csv(filelist[0], header=None, sep=r'\\\\s+', names=col_names)\\n if len(filelist) > 1:\\n for file in filelist[1:]:\\n t_df = pd.read_csv(file, header=None, sep=r'\\\\s+', names=col_names)\\n df = pd.concat([df, t_df])\\n\\n # # generate datetime index from year, day of year, and milisec of day:\\n df.index = doy2dt(df.Year.values, df.DOY.values + df.MS.values/1000./86400.)\\n df.index.name = 'time'\\n\\n # drop some unused columns:\\n if not all_columns:\\n df = df.drop(columns=['Year', 'DOY', 'MS', 'S/C Epoch',\\n 'Status Word part 1', 'Status Word part 2',\\n 'P4 GM', 'P4 GR', 'P4 S',\\n 'P8 GM', 'P8 GR', 'P8 S',\\n 'P25 GM', 'P25 GR', 'P25 S',\\n 'P41 GM', 'P41 GR', 'P41 S',\\n 'H4 GM', 'H4 GR', 'H4 S1', 'H4 S23',\\n 'H8 GM', 'H8 GR', 'H8 S1', 'H8 S23',\\n 'H25 GM', 'H25 GR', 'H25 S1', 'H25 S23',\\n 'H41 GM', 'H41 GR', 'H41 S1', 'H41 S23',\\n 'Spare 1', 'Spare 2', 'Spare 3'])\\n\\n # Proton and helium measurements need to be corrected for effects determined post-launch,\\n # cf. chapter 2.3 of https://www.ieap.uni-kiel.de/et/ag-heber/costep/materials/L2_spec_ephin.pdf\\n # Until this correction has been implemented here, these data products are set to -9e9.\\n # Setting use_uncorrected_data_on_own_risk=True skips this replacement, so that the uncorrected\\n # data can be obtained at own risk!\\n if use_uncorrected_data_on_own_risk:\\n warnings.warn(\\\"Proton and helium data is still uncorrected! Know what you're doing and use at own risk!\\\")\\n else:\\n df.P4 = -9e9\\n df.P8 = -9e9\\n df.P25 = -9e9\\n df.P41 = -9e9\\n df.H4 = -9e9\\n df.H8 = -9e9\\n df.H25 = -9e9\\n df.H41 = -9e9\\n\\n # replace bad data with np.nan:\\n # there shouldn't be bad data in rl2 files!\\n # df = df.replace(-9999.900, np.nan)\\n\\n # derive instrument status and dependencies\\n status = df['Status Flag'].values\\n\\n fmodes = np.zeros(len(status))\\n for q in range(len(status)):\\n binaries = '{0:08b}'.format(int(status[q]))\\n if int(binaries[-1]) == 1:\\n if int(binaries[-3]) == 1:\\n fmodes[q] = 1\\n else:\\n fmodes[q] = 2\\n\\n ringoff = np.zeros(len(status))\\n for q in range(len(status)):\\n binaries = '{0:08b}'.format(int(status[q]))\\n if int(binaries[-2]):\\n ringoff[q] = 1\\n\\n cs_e300 = '0.67 - 3.0 MeV'\\n cs_e1300 = '2.64 - 6.18 MeV'\\n cs_p25 = '25 - 41 MeV'\\n cs_he25 = '25 - 41 MeV/N'\\n if max(fmodes)==1:\\n cs_e1300 = \\\"2.64 - 10.4 MeV\\\"\\n cs_p25 = '25 - 53 MeV'\\n cs_he25 = '25 - 53 MeV/n'\\n if max(fmodes)==2:\\n warnings.warn('Careful: EPHIN ring off!')\\n\\n # failure mode D since 4 Oct 2017:\\n # dates[-1].date() is enddate, used to catch cases when enddate is a string\\n if dates[-1].date() >= dt.date(2017, 10, 4):\\n cs_e300 = 'deactivated bc. of failure mode D'\\n cs_e1300 = \\\"0.67 - 10.4 MeV\\\"\\n # dates[0].date() is startdate, used to catch cases when startdate is a string\\n if dates[0].date() <= dt.date(2017, 10, 4):\\n warnings.warn('EPHIN instrument status (i.e., electron energy channels) changed during selected period (on Oct 4, 2017)!')\\n\\n # careful!\\n # adjusting the position of the timestamp manually.\\n # requires knowledge of the original time resolution and timestamp position!\\n if pos_timestamp == 'center':\\n df.index = df.index+pd.Timedelta('30s')\\n\\n # optional resampling:\\n if isinstance(resample, str):\\n df = resample_df(df, resample, pos_timestamp=pos_timestamp)\\n else:\\n df = []\\n\\n meta = {'E150': '0.25 - 0.7 MeV',\\n 'E300': cs_e300,\\n 'E1300': cs_e1300,\\n 'E3000': '4.80 - 10.4 MeV',\\n 'P4': '4.3 - 7.8 MeV',\\n 'P8': '7.8 - 25 MeV',\\n 'P25': cs_p25,\\n 'P41': '41 - 53 MeV',\\n 'H4': '4.3 - 7.8 MeV/n',\\n 'H8': '7.8 - 25.0 MeV/n',\\n 'H25': cs_he25,\\n 'H41': '40.9 - 53.0 MeV/n',\\n 'INT': '>25 MeV integral'}\\n\\n return df, meta\",\n \"def get(url: str, dp: frictionless.package.Package, force: bool = False):\\n ld = utilities.get_ld_json(url)\\n csv_file = ld[\\\"distribution\\\"][0][\\\"contentUrl\\\"]\\n datePublished = ld[\\\"datePublished\\\"]\\n name = ld[\\\"name\\\"].replace(\\\" \\\", \\\"_\\\")\\n\\n # Inferring and completing metadata\\n logging.info(\\\"Creating datapackage for input data\\\")\\n new_dp = frictionless.describe_package(csv_file, stats=True,) # Add stats\\n # Add date\\n new_dp[\\\"datePublished\\\"] = datePublished\\n\\n # Add missing valies\\n new_dp.resources[0][\\\"schema\\\"][\\\"missingValues\\\"] = [\\\"NULL\\\"]\\n for field in VALUE_VARS:\\n new_dp.resources[0].schema.get_field(field).type = \\\"number\\\"\\n\\n # Logic for update\\n if dp is not None: # Existing dataset\\n # check stats\\n isChangedStats = dp[\\\"resources\\\"][0][\\\"stats\\\"] != new_dp[\\\"resources\\\"][0][\\\"stats\\\"]\\n isChangedDate = dp[\\\"datePublished\\\"] != new_dp[\\\"datePublished\\\"]\\n\\n if (\\n isChangedStats or isChangedDate\\n ): # Data integration will continue, regardless of force argument\\n logging.info(\\\"Data has changed\\\")\\n if isValid(dp, new_dp):\\n enermaps_data, spatial = prepare(new_dp, name)\\n elif force: # Data integration will continue, even if data has not changed\\n logging.info(\\\"Forced update\\\")\\n if isValid(dp, new_dp):\\n enermaps_data, spatial = prepare(new_dp, name)\\n else: # Data integration will stop here, returning Nones\\n logging.info(\\\"Data has not changed. Use --force if you want to reupload.\\\")\\n return None, None, None\\n else: # New dataset\\n dp = new_dp # this is just for the sake of the schema control\\n if isValid(dp, new_dp):\\n enermaps_data, spatial = prepare(new_dp, name)\\n\\n return enermaps_data, spatial, new_dp\",\n \"def GetLambData(removerepeats=True, days_from_year = 30,interval_min = 0,interval_max = None ,earliest_date = None,\\\\\\nlatest_date = None, userwhere = \\\"\\\",verbose = False,orderby=None,join='inner',longest_interval=False,get_geom=False,get_geog=False,\\\\\\nby_column=True,as_object=False,omit=True,generalize=None,results=False,density=0.850, density_err= 0.06,acrossgl_err=0.0,get_hypsometry=False):\\n \\n select1 = \\\"\\\"\\\"select \\\\\\nlamb.gid,\\\\\\nlamb.glid,\\\\\\nlamb.date1,\\\\\\nlamb.date2,\\\\\\nlamb.interval,\\\\\\nlamb.volmodel,\\\\\\nlamb.vol25diff,\\\\\\nlamb.vol75diff,\\\\\\nlamb.balmodel,\\\\\\nlamb.bal25diff,\\\\\\nlamb.bal75diff,\\\\\\nergi.glimsid,\\\\\\ngltype.surge,\\\\\\ngltype.tidewater,\\\\\\ngltype.lake,\\\\\\ngltype.river,\\\\\\ngltype.glaciertype,\\\\\\nglnames.name as lambname,\\\\\\nglnames.region,\\\\\\nlamb.e,\\\\\\nlamb.dz,\\\\\\nlamb.dz25,\\\\\\nlamb.dz75,\\\\\\nlamb.aad,\\\\\\nlamb.masschange,\\\\\\nlamb.massbal,\\\\\\nlamb.numdata,\\\\\\nergi.gltype,\\\\\\nergi.glactype,\\\\\\nergi.region,\\\\\\nergi.max::real,\\\\\\nergi.min::real,\\\\\\nflx.eb_bm_flx,\\\\\\nflx.eb_best_flx,\\\\\\nflx.eb_low_flx,\\\\\\nflx.eb_high_flx,\\\\\\nflx.eb_bm_err,\\\\\\nflx.bm_length,\\\\\\nflx.smb,\\\\\\nergi.continentality,\\\\\\nergi.area::double precision,\\\\\\nergi.name,\\\\\\n\\\"\\\"\\\"\\n\\n#eb_bm_flx\\n\\n#ergi.max,\\\\\\n#ergi.mn_dist_coast,\\\\\\n#ergi.mn_elev,\\\\\\n#ergi.meanVel,\\\\\\n\\n\\n if get_geom:\\n if generalize != None: select2 = \\\"ST_Simplify(ergi.albersgeom, %s) as albersgeom,\\\" % generalize\\n else: select2 = \\\"ergi.albersgeom as albersgeom,\\\"\\n elif get_geog: \\n if generalize != None: select2 = \\\"ST_Simplify(ergi.geog::geometry, %s) as geog,\\\" % generalize\\n else: select2 = \\\"ergi.geog as geog,\\\"\\n else: select2 = ''\\n\\n select3 = \\\"\\\"\\\"lamb.numdata \\\\\\nfrom lamb inner join glnames on glnames.gid=lamb.glid inner join gltype on glnames.gid=gltype.glid\\ninner join ergi on glnames.glimsid=ergi.glimsid left join tidewater_flux as flx on ergi.glimsid=flx.glimsid \\\"\\\"\\\" \\n\\n if results:\\n select1 = select1 + \\\"rlt.rlt_totalGt,rlt.rlt_totalkgm2,rlt.rlt_errGt,rlt.rlt_errkgm2,rlt.rlt_singlerrGt,rlt.rlt_singlerrkgm2,\\\"\\n \\n #select3 = select3 + \\\"\\\"\\\" LEFT JOIN (SELECT rlt.glimsid,\\n #SUM(rlt.area)/1e6::real as area, \\n #SUM(rlt.mean*rlt.area)/1e9*%5.3f::real as rlt_totalGt,\\n #(((((SUM(rlt.error*rlt.area)/SUM(rlt.mean*rlt.area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(rlt.mean*rlt.area)/1e9*%5.3f)^2 + (%5.3f)^2)^0.5::real as rlt_errGt,\\n #SUM(rlt.mean*rlt.area)/SUM(rlt.area)*%5.3f::real as rlt_totalkgm2,\\n #(((((SUM(rlt.error*rlt.area)/SUM(rlt.mean*rlt.area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(rlt.mean*rlt.area)/SUM(rlt.area)*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_errkgm2,\\n #(((((SUM(rlt.singl_std*rlt.area)/SUM(rlt.mean*rlt.area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(rlt.mean*rlt.area)/1e9*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_singlerrkgm2,\\n #(((((SUM(rlt.singl_std*rlt.area)/SUM(rlt.mean*rlt.area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(rlt.mean*rlt.area)/SUM(rlt.area)*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_singlerrGt\\n #FROM resultsauto as rlt GROUP BY rlt.glimsid) as rlt on ergi.glimsid=rlt.glimsid \\\"\\\"\\\" % (density,density_err,density, density, acrossgl_err,density,\\n #density_err,density, density, acrossgl_err,density_err,density, density, acrossgl_err,density_err,density, density, acrossgl_err)\\n\\n select3 = select3 + \\\"\\\"\\\" LEFT JOIN (SELECT glimsid,\\n SUM(area)/1000000. as area,\\n SUM(mean*area)/1e9*%5.3f::real as rlt_totalGt,\\n SUM(mean*area)/SUM(area)*%5.3f::real as rlt_totalkgm2,\\n (((((SUM(error*area)/SUM(mean*area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(mean*area)/1e9*%5.3f)^2 + (%5.3f)^2)^0.5::real as rlt_errGt,\\n (((((SUM(error*area)/SUM(mean*area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(mean*area)/SUM(area)*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_errkgm2,\\n (((((SUM(singl_std*area)/SUM(mean*area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(mean*area)/SUM(area)*%5.3f)^2+(%5.3f)^2)^0.5::real as rlt_singlerrkgm2,\\n (((((SUM(singl_std*area)/SUM(mean*area))^2+(%5.3f/%5.3f)^2)^0.5)*SUM(mean*area)/1e9*%5.3f)^2 + (%5.3f)^2)^0.5::real as rlt_singlerrGt \\n FROM resultsauto GROUP BY glimsid) as rlt on ergi.glimsid=rlt.glimsid \\\"\\\"\\\" % (density,density,density_err,density,density, acrossgl_err,density_err,density,density,acrossgl_err,density_err,density,density,acrossgl_err,density_err,density,density,acrossgl_err)\\n\\n select = select1+select2+select3\\n\\n\\n#ST_AsText(ergi.albersgeom) as geom \\\\\\n\\n if longest_interval:removerepeats = False\\n\\n if join != 'inner': re.sub('inner join','left join',select) \\n \\n if longest_interval:\\n order = \\\"\\\"\\\" order by glnames.name,lamb.interval DESC;\\\"\\\"\\\"\\n removerepeats = False\\n else:order = \\\"\\\"\\\" order by glnames.name,lamb.date1,lamb.interval;\\\"\\\"\\\"\\n \\n where = 'WHERE' \\n \\n if days_from_year != None: where = where+' AND ((interval % 365) > '+str(365-days_from_year)+' OR (interval % 365) < '+str(days_from_year)+')'\\n if interval_min != None: where = where+' AND (ROUND(interval/365.) >= '+str(interval_min)+')'\\n if interval_max != None: where = where+' AND (ROUND(interval/365.) <= '+str(interval_max)+')'\\n if earliest_date != None: where = where+\\\" AND date1 >= '\\\"+str(earliest_date)+\\\"'\\\"\\n if latest_date != None: where = where+\\\" AND date2 <= '\\\"+str(latest_date)+\\\"'\\\"\\n if omit: where = where+\\\" AND omit='f'\\\"\\n \\n \\n userwhere2 = re.sub('where','',userwhere, re.IGNORECASE)\\n userwhere2 = re.sub('^\\\\s*and\\\\s*','',userwhere2, re.IGNORECASE)\\n userwhere2 = re.sub('^\\\\s*','',userwhere2)\\n userwhere2 = re.sub('\\\\s*$','',userwhere2)\\n if userwhere != '':userwhere2 = ' AND '+userwhere2+' '\\n \\n where = where+userwhere2\\n \\n where = re.sub('WHERE AND','WHERE',where)\\n \\n if verbose:print select+where+order\\n \\n s = GetSqlData2(select+where+order,bycolumn=False)\\n if type(s)==NoneType: return None\\n #print s[0].keys()\\n deletelist = []\\n keeplist = []\\n lastgl = ''\\n lastdate = dtm.date(1900,1,1)\\n \\n if removerepeats:\\n if verbose: print'Filtering lamb entries:'\\n for i,row in enumerate(s):\\n\\n if row['name'] == lastgl:\\n #print ' ',row['lamb.date1'],lastdate\\n if row['date1'] < lastdate:\\n deletelist.append(i)\\n if verbose:print ' ',row['name'],row['date1'],row['date2'],'-- Omitted'\\n else:\\n lastdate = row['date2']\\n if verbose:print row['name'],row['date1'],row['date2']\\n keeplist.append(row['gid'])\\n else: \\n lastgl = row['name']\\n lastdate = row['date2']\\n if verbose:print row['name'],row['date1'],row['date2']\\n keeplist.append(row['gid'])\\n \\n if longest_interval:\\n if verbose: print'Filering for longest interval:'\\n for i,row in enumerate(s):\\n\\n if row['name'] == lastgl:\\n #print ' ',row['lamb.date1'],lastdate\\n #if row['lamb.date1'] < lastdate:\\n deletelist.append(i)\\n if verbose:print ' ',row['name'],row['date1'],row['date2'],row['interval'],'-- Omitted'\\n #else:\\n # lastdate = row['lamb.date2']\\n # if verbose:print row['glnames.name'],row['lamb.date1'],row['lamb.date2']\\n # keeplist.append(row['lamb.gid'])\\n else: \\n lastgl = row['name']\\n lastdate = row['date2']\\n if verbose:print row['name'],row['date1'],row['date2'],row['interval']\\n keeplist.append(row['gid'])\\n \\n if orderby == None:\\n #print len(s)\\n s = N.delete(N.array(s),deletelist)\\n if get_hypsometry:\\n for i in s:\\n hyps = GetSqlData2(\\\"SELECT area::real as binned_area,bins::real,normbins::real FROM ergibins3 WHERE glimsid='%s' ORDER BY normbins\\\" % i['glimsid'])\\n for key in ('binned_area','bins','normbins'):i[key]=hyps[key]\\n \\n if by_column:s = LambToColumn(s)\\n else:\\n s = GetSqlData2(select+'WHERE lamb.gid IN ('+re.sub('[\\\\[\\\\]]','',str(keeplist))+\\\") ORDER BY \\\"+orderby+\\\";\\\", bycolumn=by_column)\\n \\n if get_hypsometry:\\n s['binned_area'] = []\\n s['bins'] = []\\n s['normbins'] = []\\n for glimsidt in s['glimsid']:\\n print glimsidt\\n hyps = GetSqlData2(\\\"SELECT area as binned_area,bins,normbins FROM ergibins3 WHERE glimsid='%s' ORDER BY normbins\\\" % glimsidt)\\n s['binned_area'].append(hyps['binned_area'])\\n s['bins'].append(hyps['bins'])\\n s['normbins'].append(hyps['normbins'])\\n \\n # \\n #if orderby == None:\\n # #print len(s)\\n # s = N.delete(N.array(s),deletelist)\\n # #print len(s)\\n # if by_column:s = LambToColumn(s)\\n #else:\\n # s = GetSqlData2(select+'WHERE lamb.gid IN ('+re.sub('[\\\\[\\\\]]','',str(keeplist))+\\\") ORDER BY \\\"+orderby+\\\";\\\", bycolumn=by_column)\\n # \\n #if verbose:print len(s),' of rows in lamb selected.'\\n\\n\\n if len(s) == 0: return None\\n\\n if as_object:\\n if type(s) == dict:\\n s=LambObject(s)\\n print 'object'\\n elif type(s) == list or type(s) == N.ndarray:\\n s = [LambObject(row) for row in s]\\n print 'list'\\n \\n return s \\n \\n #def norm_e(self):return (self.e - self.min) / (self.max - self.min)\",\n \"def read_weather_data():\\n # Check if UTC to gmt+1 conversion is being handled correctly\\n weather = pd.read_csv('//datc//opschaler//weather_data//knmi_10_min_raw_data//output//df_combined_uncleaned.csv',\\n delimiter='\\\\t', comment='#',\\n parse_dates=['datetime'])\\n weather = weather.set_index(['datetime'])\\n return weather\",\n \"def tobsdata():\\n # query for the dates and temperature observations from a year from the last data point.\\n # * Return a JSON list of Temperature Observations (tobs) for the previous year.\\n # as this should be a list, I'm just grabbing the station name\\n session = Session(engine)\\n results = session.query(Measurement.tobs).filter(Measurement.date>='2016-08-23').all()\\n session.close()\\n tobs_list = list(np.ravel(results))\\n\\n # * Return the JSON representation of your dictionary.\\n return jsonify(tobs_list)\",\n \"def get_time_series(this_lat, this_lon, case, varnames):\\n\\n cesmdir = '/gpfs/fs1/collections/cdg/data/cesmLE/CESM-CAM5-BGC-LE/atm/proc/tseries/monthly'\\n\\n if 'LE' in case:\\n\\n from observational_large_ensemble.params import karen_params_cesm\\n\\n mode_lag = karen_params_cesm.mode_lag\\n cvdp_loc = karen_params_cesm.cvdp_loc\\n AMO_cutoff_freq = karen_params_cesm.AMO_cutoff_freq\\n\\n name_conversion = {'tas': 'TREFHT', 'pr': 'PRECC', 'slp': 'PSL'}\\n cesm_names = [name_conversion[v] for v in varnames]\\n this_member = int((case).split('-')[-1])\\n cvdp_file = '%s/CESM1-CAM5-BGC-LE_#%i.cvdp_data.1920-2018.nc' % (cvdp_loc, this_member)\\n\\n # Historical filenames for CESM. Will need to append part of RCP8.5 to get full period\\n filenames = []\\n for var in cesm_names:\\n file_str = '%s/%s/b.e11.B20TRC5CNBDRD.f09_g16.%03d.cam.h0.%s.??????-200512.nc' % (cesmdir, var,\\n this_member, var)\\n this_file = glob(file_str)[0]\\n filenames.append(this_file)\\n\\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames,\\n karen_params_cesm.valid_years, mode_lag,\\n cvdp_file, AMO_cutoff_freq, name_conversion)\\n\\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\\n\\n else:\\n\\n from observational_large_ensemble.params import karen_params_obs\\n\\n mode_lag = karen_params_obs.mode_lag\\n cvdp_loc = karen_params_obs.cvdp_loc\\n AMO_cutoff_freq = karen_params_obs.AMO_cutoff_freq\\n\\n tas_dir = karen_params_obs.tas_dir\\n pr_dir = karen_params_obs.pr_dir\\n slp_dir = karen_params_obs.slp_dir\\n cvdp_file = '%s/HadISST.cvdp_data.1920-2018.nc' % cvdp_loc\\n file_dict = {'tas': '%s/Complete_TAVG_LatLong1.nc' % tas_dir,\\n 'pr': '%s/full_data_monthly_v2020.nc' % pr_dir,\\n 'slp': '%s/prmsl.mon.mean.nc' % slp_dir}\\n\\n filenames = []\\n for var in varnames:\\n filenames.append(file_dict[var])\\n\\n name_conversion = {'tas': 'temperature', 'pr': 'precip', 'slp': 'prmsl'}\\n\\n daX, df_shifted, _ = get_obs(case, varnames[0], filenames[0],\\n karen_params_obs.valid_years, mode_lag,\\n cvdp_file, AMO_cutoff_freq, name_conversion)\\n\\n this_ts = daX.sel({'lat': this_lat, 'lon': this_lon}, method='nearest')\\n\\n return this_ts, df_shifted\",\n \"def extract_ssi_data():\\r\\n test_pos_over_time_file = \\\"data/Data-Epidemiologiske-Rapport-04012021-ja21/Test_pos_over_time.csv\\\"\\r\\n\\r\\n # Read data\\r\\n test_pos_over_time = pd.read_csv(test_pos_over_time_file, sep=\\\";\\\", decimal=\\\",\\\")\\r\\n\\r\\n # Remove last two rows\\r\\n df = test_pos_over_time[\\\"NewPositive\\\"].iloc[:-2]\\r\\n\\r\\n # Set index to datetime\\r\\n df.index = pd.to_datetime(test_pos_over_time[\\\"Date\\\"].iloc[:-2])\\r\\n\\r\\n # Remove . from data\\r\\n df = df.str.replace(\\\".\\\",\\\"\\\").astype(int)\\r\\n\\r\\n return df\",\n \"def filter_meteo_data(self, startdate, enddate):\\n self.all_meteo_data.columns.values[0]='Datum-tijd'\\n self.all_meteo_data['datetime']=pd.to_datetime(self.all_meteo_data['Datum-tijd'], format='%Y-%m-%dT%H:%M:%SZ')\\n self.all_meteo_data.drop(['Datum-tijd'],axis=1, inplace=True)\\n mask = (self.all_meteo_data['datetime'] > startdate) & (self.all_meteo_data['datetime'] <= enddate)\\n meteodata = self.all_meteo_data.loc[mask].copy()\\n meteodata.set_index('datetime',inplace=True)\\n return meteodata\",\n \"def collect_new_data(datafile):\\n user_input = float(input('How far did you travel today? '))\\n existingData['distanceTravelled'].append(user_input)\\n write_data(datafile, existingData) # write changes to file using write_date function\\n return existingData\",\n \"def data(pd_edit_series):\\n country_dict = {\\n 'europe': ['United Kingdom', 'France', 'Spain', 'Belgium',\\n 'Finland', 'Sweden', 'Germany', 'Croatia',\\n 'Switzerland', 'Austria', 'Greece', 'Hungary',\\n 'Slovenia', 'Poland', 'Bosnia and Herzegovina',\\n 'Denmark', 'Liechtenstein', 'Ukraine',\\n 'North Macedonia', 'Latvia', 'Andorra',\\n 'Norway', 'Portugal', 'Romania', 'Estonia',\\n 'Netherlands', 'San Marino', 'Belarus',\\n 'Iceland', 'Lithuania', 'Ireland', 'Luxembourg',\\n 'Monaco', 'Czechia', 'Slovakia', 'Holy See',\\n 'Serbia', 'Malta', 'Bulgaria', 'Albania',\\n 'Cyprus', 'Moldova', 'Andorra', 'Armenia',\\n 'Austria', 'Cyprus', 'Estonia', 'Georgia',\\n 'Gibraltar', 'Greenland', 'Croatia',\\n 'Israel', 'Iceland', 'Luxembourg',\\n 'Latvia', 'Monaco', 'Portugal', 'Romania',\\n 'Svalbard and Jan Mayen', 'Slovakia',\\n 'Turkey', 'Serbia', 'Montenegro',\\n 'Aland Islands', 'Guernsey',\\n 'Island of Man', 'Jersey', 'Kosovo'],\\n\\n 'asia': ['Thailand', 'Japan', 'Singapore', 'Mongolia',\\n 'Nepal', 'Malaysia', 'Sri Lanka', 'Philippines',\\n 'India', 'Cambodia', 'Pakistan',\\n 'Indonesia', 'United Arab Emirates', 'Lebanon',\\n 'Iraq', 'Oman', 'Afghanistan', 'Bahrain',\\n 'Kuwait', 'Qatar', 'Saudi Arabia',\\n 'Jordan', 'Azerbaijan', 'Bhutan', 'Maldives',\\n 'Bangladesh', 'Brunei', 'Korea, South', 'Vietnam',\\n 'Russia', 'Iran', 'Reunion', 'Taiwan*', 'Yemen',\\n 'American Samoa', 'Brunei Darussalam',\\n 'Guam', 'Hong Kong',\\n 'Heard Island and McDonald Islands',\\n 'British Indian Ocean Territory',\\n 'Kyrgystan', 'Kiribati', 'Korea, North',\\n 'Kazakhstan', 'Sri Lanka', 'Marshall Islands',\\n 'Lao People\\\\'s Democratic Republic',\\n 'Myanmar', 'Mongolia', 'Macau', 'Macao SAR',\\n 'North Mariana Islands', 'Maldives',\\n 'Malaysia', 'Papua New Guinea', 'Palau',\\n 'Singapore', 'Syrian Arab Republic',\\n 'Tajikistan', 'Turkmenistan', 'Timor-Leste',\\n 'United States Minor Outlying Islands',\\n 'Uzbekistan', 'Kyrgyzstan',\\n 'occupied Palestinian territory', 'Mauritania',\\n 'Comoros', 'Djibouti', 'Bahrain', 'Nepal',\\n 'Malaysia', 'Singapore', 'East Timor', 'Syria'],\\n\\n 'africa': ['Egypt', 'Algeria', 'Nigeria',\\n 'Morocco', 'Senegal', 'Tunisia',\\n 'South Africa', 'Togo', 'Cameroon',\\n 'Burkina Faso', 'Cote d\\\\'Ivoire',\\n 'Congo (Kinshasa)', 'Congo (Brazzaville)',\\n 'Republic of the Congo',\\n 'Central African Republic', 'Eswatini',\\n 'Eswatini', 'Ethiopia', 'Gabon', 'Ghana',\\n 'Guinea', 'Equatorial Guinea', 'Kenya',\\n 'Namibia', 'Rwanda', 'Sudan', 'Seychelles',\\n 'Republic of Congo', 'Tanzania', 'Mayotte',\\n 'Benin', 'Liberia', 'Somalia', 'The Gambia',\\n 'Gambia, The', 'Zambia', 'Mauritius', 'Chad',\\n 'Madagascar', 'Cabo Verde', 'Niger', 'Zimbabwe',\\n 'Cape Verde', 'Angola', 'Eritrea', 'Uganda',\\n 'Mozambique'],\\n\\n 'americas': ['Brazil', 'Mexico', 'Ecuador',\\n 'Dominican Republic', 'Argentina',\\n 'Chile', 'Peru', 'Netherlands Antilles',\\n 'Costa Rica', 'Colombia', 'French Guiana',\\n 'Martinique', 'Paraguay', 'Panama',\\n 'Canada', 'US', 'Jamaica', 'Honduras',\\n 'Bolivia', 'Antigua and Barbuda', 'Anguilla',\\n 'Argentina', 'Aruba', 'Barbados',\\n 'Bouvet Island', 'Belize', 'Cuba', 'Dominica',\\n 'Equador', 'Falkland Islands', 'Malvinas',\\n 'Grenada', 'Guadeloupe', 'Guyana',\\n 'South Georgia and the South Sandwich '\\n 'Islands', 'US',\\n 'Guatemala', 'Haiti', 'Saint Kitts and Nevis',\\n 'Cayman Islands', 'Saint Lucia', 'Montserrat',\\n 'Mexico', 'Nicaragua', 'Puerto Rico',\\n 'Paraguay', 'Suriname', 'El Salvador',\\n 'Turks and Caicos Islands',\\n 'Trinidad and Tobago', 'Uruguay',\\n 'Saint Vincent and the Grenadines',\\n 'Venezuela', 'Virgin Islands (British)',\\n 'Virgin Islands (US)', 'Saint Martin',\\n 'Saint Berthelemy', 'Bermuda',\\n 'Saint Pierre and Miquelon', 'Cuba', 'Guyana',\\n 'Curacao', 'The Bahamas', 'Bahamas, The'],\\n\\n 'oceania': ['Australia', 'New Zealand', 'New Caledonia',\\n 'Norfolk Island', 'Nauru', 'Niue',\\n 'Micronesia (federated States of)', 'Fiji',\\n 'Cook Islands', 'Christmas Island',\\n 'Cocos (Keeling) Islands', 'French Polynesia',\\n 'Pitcairn Islands', 'Solomon Islands',\\n 'French Southern Territories',\\n 'American Samoa', 'Tokelau', 'Tonga', 'Tuvalu',\\n 'Vanuatu', 'Wallis and Futuna', 'Samoa']}\\n\\n europe = []\\n asia = []\\n oceania = []\\n americas = []\\n africa = []\\n uk_list = []\\n italy = []\\n china = []\\n others = []\\n ship = []\\n all_lists = [europe, asia, oceania, americas, africa, uk_list,\\n italy, china, ship, others]\\n for_total = [europe, asia, oceania, americas, africa, china,\\n others, ship, italy]\\n\\n for region, countries in country_dict.items():\\n for column in pd_edit_series:\\n if column in countries:\\n if region == 'europe':\\n if column == 'United Kingdom':\\n if column not in uk_list:\\n uk_list.append(column)\\n if column not in europe:\\n europe.append(column)\\n\\n elif region == 'asia':\\n if column not in asia:\\n asia.append(column)\\n elif region == 'africa':\\n if column not in africa:\\n africa.append(column)\\n elif region == 'americas':\\n if column not in americas:\\n americas.append(column)\\n elif region == 'oceania':\\n if column not in oceania:\\n oceania.append(column)\\n\\n else:\\n if column == 'Italy':\\n if column not in italy:\\n italy.append(column)\\n\\n elif column == 'China':\\n if column not in china:\\n china.append(column)\\n\\n elif column == 'Cruise Ship':\\n if column not in ship:\\n ship.append(column)\\n\\n else:\\n others.append(column)\\n\\n # -----------------------------------------------------------\\n # Segment of code it to catch any straggler countries not\\n # accounted for in the country_dict\\n remove_list = []\\n for region in all_lists:\\n for countries in region:\\n if countries in others:\\n if countries not in remove_list:\\n remove_list.append(countries)\\n\\n others_final = [item for item in others\\n if item not in remove_list]\\n\\n if len(others_final) > 0:\\n logging.debug(others_final)\\n print('Exiting due to unaccounted countries')\\n sys.exit()\\n\\n total_count_list = []\\n for region in for_total:\\n for country in region:\\n if country not in total_count_list:\\n total_count_list.append(country)\\n # -----------------------------------------------------------\\n\\n diamond_csv = pd_edit_series[ship].copy()\\n main_china_csv = pd_edit_series[china].copy()\\n europe_csv = pd_edit_series[europe].copy()\\n americas_csv = pd_edit_series[americas].copy()\\n asia_csv = pd_edit_series[asia].copy()\\n africa_csv = pd_edit_series[africa].copy()\\n uk_csv = pd_edit_series[uk_list].copy()\\n italy_csv = pd_edit_series[italy].copy()\\n oceania_csv = pd_edit_series[oceania].copy()\\n\\n csv_list = {'europe': europe_csv, 'america': americas_csv,\\n 'asia': asia_csv, 'main_china': main_china_csv,\\n 'UK': uk_csv, 'diamond': diamond_csv,\\n 'italy': italy_csv, 'oceania': oceania_csv,\\n 'africa': africa_csv}\\n\\n backup_frame = pd_edit_series.copy()\\n backup_frame['Global_Cases'] = \\\\\\n backup_frame.sum(axis=1)\\n\\n pd_edit_series['Mainland_China_Total'] = \\\\\\n pd_edit_series[china].sum(axis=1)\\n\\n pd_edit_series['Oceania_Total'] = \\\\\\n pd_edit_series[oceania].sum(axis=1)\\n\\n pd_edit_series['Europe_Total'] = \\\\\\n pd_edit_series[europe + ['Italy']].sum(axis=1)\\n\\n pd_edit_series['Diamond_Princess'] = \\\\\\n pd_edit_series[ship]\\n\\n pd_edit_series['UK_Total'] = \\\\\\n pd_edit_series[uk_list].sum(axis=1)\\n\\n pd_edit_series['Asian_Total'] = \\\\\\n pd_edit_series[asia].sum(axis=1)\\n\\n pd_edit_series['Americas_Total'] = \\\\\\n pd_edit_series[americas].sum(axis=1)\\n\\n pd_edit_series['African_Total'] = \\\\\\n pd_edit_series[africa].sum(axis=1)\\n\\n # As China is being kept separate\\n pd_edit_series = pd_edit_series.drop('China', axis=1)\\n pd_edit_series = pd_edit_series.drop('Cruise Ship', axis=1)\\n\\n for place in asia:\\n pd_edit_series = pd_edit_series.drop(place, axis=1)\\n for place in europe:\\n pd_edit_series = pd_edit_series.drop(place, axis=1)\\n for place in americas:\\n pd_edit_series = pd_edit_series.drop(place, axis=1)\\n for place in africa:\\n pd_edit_series = pd_edit_series.drop(place, axis=1)\\n for place in oceania:\\n pd_edit_series = pd_edit_series.drop(place, axis=1)\\n\\n return csv_list, pd_edit_series, backup_frame\",\n \"def temporal_only(\\n db_engine, station_id, start, end, sample_size=int(1.0e5),\\n balance=None):\\n\\n data = get_and_adjust_data(\\n db_engine, station_id, start, end, sample_size)\\n\\n # Balance or set to sample_size\\n if balance is None:\\n data = data.sample(n=sample_size)\\n else:\\n data = bal(data, balance)\\n\\n # Ensure shuffling.\\n data = data.iloc[np.random.permutation(len(data))]\\n\\n X = []\\n yempty = []\\n yfull = []\\n\\n for row in data.iteritems():\\n features = [\\n (1 if row[0].dayofweek == 0 else 0),\\n (1 if row[0].dayofweek == 1 else 0),\\n (1 if row[0].dayofweek == 2 else 0),\\n (1 if row[0].dayofweek == 3 else 0),\\n (1 if row[0].dayofweek == 4 else 0),\\n (1 if row[0].dayofweek == 5 else 0),\\n (1 if row[0].dayofweek == 6 else 0),\\n float(((row[0].hour * 60) + row[0].minute)) / 1440.0,\\n float(row[0].month) / 12.0\\n ]\\n\\n X.append(features)\\n yempty.append(1 if row[1] == \\\"empty\\\" else 0)\\n yfull.append(1 if row[1] == \\\"full\\\" else 0)\\n\\n return {'X': X, 'yempty': yempty, 'yfull': yfull}\",\n \"def addMissingData():\\n\\n conn = sqlite3.connect(\\\"./transactions.db\\\")\\n\\n person = pd.read_sql(\\n \\\"\\\"\\\"\\n select * from person;\\n \\\"\\\"\\\",\\n conn,\\n )\\n\\n record = pd.read_sql(\\n \\\"\\\"\\\"\\n select * from record;\\n \\\"\\\"\\\",\\n conn,\\n )\\n\\n tracked = set([_id for _id in record[\\\"doc_id\\\"]])\\n\\n untracked = []\\n\\n for url, _id in zip(person[\\\"url\\\"], person[\\\"doc_id\\\"]):\\n if not _id in tracked:\\n untracked.append((url[-17:-13], _id))\\n\\n untracked = pd.DataFrame(untracked, columns=[\\\"date\\\", \\\"doc_id\\\"])\\n\\n res = transaction.extractData(untracked)\\n\\n res.to_sql(\\\"record\\\", conn, index=False, if_exists=\\\"append\\\")\\n\\n conn.close()\",\n \"def get_data():\\n wga_df = pd.read_csv(os.path.join(MODEL_DIR, 'wga/sculptures/wga_sculpture_periods.csv'), index_col=0)\\n wikiart_df = pd.read_csv(os.path.join(MODEL_DIR, 'wikiart/sculptures/wikiart_sculpture_periods.csv'), index_col=0)\\n nga_df = pd.read_csv(os.path.join(MODEL_DIR, 'nga/sculptures/nga_sculpture_periods.csv'), index_col=0)\\n\\n ######## Fix name for WGA and WikiaRt ###########\\n wga_df['Author'] = wga_df.apply(lambda x: fix_name_wga(x['Author']), axis=1)\\n wikiart_df['Author'] = wikiart_df.apply(lambda x: fix_name_wiki(x['Author']), axis=1)\\n nga_df['Author'] = nga_df.apply(lambda x: fix_name_nga(x['Author']), axis=1)\\n\\n df = pd.concat([wga_df, wikiart_df, nga_df], ignore_index=True, sort=True)\\n\\n df['Author_Fixed'] = df.apply(lambda x: fix_text(x['Author']), axis=1)\\n df['title_fixed'] = df.apply(lambda x: fix_text(x['title']), axis=1)\\n\\n periods = [\\\"BAROQUE\\\", \\\"EARLY RENAISSANCE\\\", \\\"MEDIEVAL\\\", \\\"NEOCLASSICISM\\\", \\\"HIGH RENAISSANCE\\\", \\\"MINIMALISM\\\", \\\"REALISM\\\",\\n \\\"IMPRESSIONISM\\\", \\\"ROCOCO\\\", \\\"SURREALISM\\\", \\\"MANNERISM\\\", \\\"ROMANTICISM\\\",\\n ]\\n df['Period'] = df.apply(lambda row: row['Period'].upper(), axis=1)\\n\\n # Get Desired Periods\\n df['Period'] = df.apply(lambda x: \\\"SURREALISM\\\" if \\\"SURREALISM\\\" in x['Period'] else x['Period'], axis=1)\\n df = df[(df['Period'].isin(periods))]\\n df = df.sort_values(['Author_Fixed', 'title_fixed'])\\n\\n #print(\\\"Combined Drop Rows:\\\", df.shape[0] - df.drop_duplicates(subset=['Author_Fixed', 'title_fixed']).shape[0])\\n\\n df = df.drop_duplicates(subset=['Author_Fixed', 'title_fixed'], keep='last')\\n\\n # Drop Duplicate Sculptures\\n df = df[~df['file'].isin(DUP_SCULPTURES)].reset_index(drop=True)\\n\\n #print(df['Period'].value_counts())\\n\\n return df\",\n \"def get_weather_data(filename, dates, highs, lows, date_index, high_index,\\n low_index):\\n with open(filename) as f:\\n reader = csv.reader(f)\\n header_row = next(reader)\\n\\n # Get data temp.\\n for row in reader:\\n current_date = datetime.strptime(row[date_index], '%Y-%m-%d')\\n try:\\n high = int(row[high_index])\\n low = int(row[low_index])\\n except ValueError:\\n print(f\\\"No data for {current_date}\\\")\\n else:\\n dates.append(current_date)\\n highs.append(high)\\n lows.append(low)\",\n \"def getSFData(self, since, stream=\\\"all\\\"):\\r\\n brId=None;\\r\\n queryStr1 =\\\"Select c.Id, c.CaseNumber, c.ClosedDate, c.OwnerId, c.Status from Case c \\\"\\r\\n if stream != \\\"all\\\":\\r\\n queryStr1 += \\\" WHERE ClosedDate = LAST_N_DAYS:%s AND X1st_Stream__c = %s order by ClosedDate\\\"%(since, stream)\\r\\n else:\\r\\n queryStr1 += \\\" WHERE ClosedDate = LAST_N_DAYS:%s order by ClosedDate\\\"%(since)\\r\\n \\r\\n #queryStr1 =\\\"Select c.Id, c.CaseNumber, c.ClosedDate, c.OwnerId, c.Status from Case c WHERE ClosedDate = LAST_N_DAYS:%s order by ClosedDate\\\"%since\\r\\n queryList1=self.cxn.query(queryStr1)\\r\\n \\r\\n if queryList1 not in BAD_INFO_LIST:\\r\\n found = len(queryList1)\\r\\n print 'Found %s Cases closed in the last %s days'%(found,since)\\r\\n return queryList1\\r\\n #for qr1 in queryList1:\\r\\n # brId=qr1.get('Id')\\r\\n else:\\r\\n print \\\"No Cases found in the last '%s' days\\\" %since\\r\\n return []\",\n \"def test_importtleUpdatesDataSourceUpdateDate(self):\\n\\n # Edit the DataSource to make sure the date must be updated\\n datasource = DataSource.objects.get(system_name='tle_test')\\n datasource.last_time_checked = timezone.make_aware(datetime(2000, 1, 1))\\n datasource.save()\\n\\n timestamp = format(datasource.last_time_checked, 'U')\\n\\n try:\\n call_command('importtle', 'tle_test', stdout=StringIO())\\n except ConnectionError:\\n pass\\n\\n datasource = DataSource.objects.get(system_name='tle_test')\\n\\n self.assertNotEquals(\\n timestamp,\\n format(datasource.last_time_checked, 'U')\\n )\",\n \"def retrieve_data_timeseries(hfile, setname):\\n dset = hfile[setname]\\n sample_rate = dset.attrs[\\\"SamplingRate(Hz)\\\"]\\n gps_epoch = construct_utc_from_metadata(dset.attrs[\\\"Date\\\"], dset.attrs[\\\"t0\\\"])\\n data = retrieve_channel_data(hfile, setname)\\n ts_data = TimeSeries(data, sample_rate=sample_rate, epoch=gps_epoch)\\n return ts_data\",\n \"def extract_tt_by_periods(ttri, periods, start_time, end_time, filters):\\n logger = getLogger(__name__)\\n # sess = conn.get_session()\\n das = {}\\n all_wz_features = {}\\n all_wz_laneconfigs = {}\\n\\n # collecting daily data\\n for prd in periods:\\n logger.debug('>>>> retrieving data for %s' % prd.get_date_string())\\n year = prd.start_date.year\\n sdate = prd.start_date\\n edate = prd.end_date\\n if year not in das:\\n da_tt = tt.TravelTimeDataAccess(year)\\n da_tt_wz = tt_workzone.TTWorkZoneDataAccess(year)\\n da_tt_wz_feature = wz_feature.WZFeatureDataAccess()\\n da_tt_wz_lncfg = wz_laneconfig.WZLaneConfigDataAccess()\\n da_tt_weather = tt_weather.TTWeatherDataAccess(year)\\n da_tt_snowmgmt = tt_snowmgmt.TTSnowManagementDataAccess(year)\\n da_tt_incident = tt_incident.TTIncidentDataAccess(year)\\n da_tt_specialevent = tt_specialevent.TTSpecialeventDataAccess(year)\\n das[year] = (\\n da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\\n da_tt_specialevent)\\n\\n (da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\\n da_tt_specialevent) = das[year]\\n\\n # traveltimes = da_tt.list_by_period(ttri.id, self.prd)\\n weathers = da_tt_weather.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.WeatherInfo] \\\"\\\"\\\"\\n workzones = da_tt_wz.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.WorkZoneInfo] \\\"\\\"\\\"\\n incidents = da_tt_incident.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.IncidentInfo] \\\"\\\"\\\"\\n snowmgmts = da_tt_snowmgmt.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.SnowManagementInfo] \\\"\\\"\\\"\\n specialevents = da_tt_specialevent.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.SpecialEventInfo] \\\"\\\"\\\"\\n traveltimes = da_tt.list_by_period(ttri.id, prd)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.TravelTimeInfo] \\\"\\\"\\\"\\n\\n if not any(weathers):\\n logger.debug('>>>> end of retrieving data for %s (no weather data)' % prd.get_date_string())\\n continue\\n\\n extras = {\\n 'weathers': {_tt.id: [] for _tt in traveltimes},\\n 'workzones': {_tt.id: [] for _tt in traveltimes},\\n 'incidents': {_tt.id: [] for _tt in traveltimes},\\n 'specialevents': {_tt.id: [] for _tt in traveltimes},\\n 'snowmgmts': {_tt.id: [] for _tt in traveltimes},\\n }\\n \\\"\\\"\\\":type: dict[str, dict[int, list]]\\\"\\\"\\\"\\n\\n _put_to_bucket(ttri, weathers, extras['weathers'], da_tt_weather, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, workzones, extras['workzones'], da_tt_wz, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, incidents, extras['incidents'], da_tt_incident, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, snowmgmts, extras['snowmgmts'], da_tt_snowmgmt, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, specialevents, extras['specialevents'], da_tt_specialevent, year, all_wz_features, all_wz_laneconfigs, das)\\n\\n for tti in traveltimes:\\n _tt_weathers = extras['weathers'][tti.id]\\n extdata = ExtData(tti,\\n _tt_weathers[0] if _tt_weathers else None,\\n extras['incidents'][tti.id],\\n extras['workzones'][tti.id],\\n extras['specialevents'][tti.id],\\n extras['snowmgmts'][tti.id])\\n\\n if start_time <= tti.str2datetime(tti.time).time() <= end_time:\\n for ef in filters:\\n try:\\n ef.check(extdata)\\n except Exception as ex:\\n tb.traceback(ex)\\n logger.debug('>>>> end of retrieving data for %s (error occured 1)' % prd.get_date_string())\\n continue\\n else:\\n for ef in filters:\\n try:\\n ef.check_outofrange(extdata)\\n except Exception as ex:\\n tb.traceback(ex)\\n logger.debug('>>>> end of retrieving data for %s (error occured 2)' % prd.get_date_string())\\n continue\\n\\n del extras\\n logger.debug('>>>> end of retrieving data for %s' % prd.get_date_string())\\n\\n # sess.close()\",\n \"def rd_pst_parameters(pst_file):\\n skiplines_top = 0\\n nrows=-1 #to account for the extra that gets generated\\n start = False\\n stop=False\\n with open(pst_file) as f:\\n while not stop:\\n if start:\\n nrows +=1\\n if '* observation groups' in f.readline():\\n stop =True\\n else:\\n skiplines_top +=1\\n if '* parameter data' in f.readline():\\n start=True\\n data = pd.read_table(pst_file,skiprows=skiplines_top,nrows=nrows,delim_whitespace=True,\\n names=['temp1','temp2','initial','lower','upper','temp3','scale','offset','flag'],\\n index_col=0)\\n\\n data.loc[:,'initial'] +=data.loc[:,'offset']\\n data.loc[:,'lower'] +=data.loc[:,'offset']\\n data.loc[:,'upper'] +=data.loc[:,'offset']\\n return data\",\n \"def _update_modified_data_sources(self):\\n new_last_imported = datetime.utcnow()\\n self._update_modified_since(self.last_imported)\\n self.last_imported = new_last_imported\",\n \"def __get_updated_data(self, table_name='derivatives_economicindicatorstandard'):\\n # get the latest data and add 1 day to date column\\n df = self.__get_latest_data(table_name)\\n df['date'] = pd.to_datetime(df['date']) + timedelta(days=1)\\n df['date'] = df['date'].apply(lambda x: x.strftime('%Y-%m-%d'))\\n\\n # get updated data\\n result = []\\n df.apply(lambda row: result.append(self.__call_api_data(row, table_name)), axis=1)\\n df = pd.concat(result, sort=True)\\n df['Date'] = df['Date'].apply(lambda x: x.strftime('%Y-%m-%d'))\\n return df\",\n \"def get_data(self, date_time):\\n id_columns = ','.join([col for col in self.table_primary_keys if col not in ['EFFECTIVEDATE', 'VERSIONNO']])\\n return_columns = ','.join(self.table_columns)\\n with self.con:\\n cur = self.con.cursor()\\n cur.execute(\\\"DROP TABLE IF EXISTS temp;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp2;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp3;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp4;\\\")\\n # Store just the unique sets of ids that came into effect before the the datetime in a temporary table.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp AS \\n SELECT * \\n FROM {table} \\n WHERE EFFECTIVEDATE <= '{datetime}';\\\"\\\"\\\"\\n cur.execute(query.format(table=self.table_name, datetime=date_time))\\n # For each unique set of ids and effective dates get the latest versionno and sore in temporary table.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp2 AS\\n SELECT {id}, EFFECTIVEDATE, MAX(VERSIONNO) AS VERSIONNO\\n FROM temp\\n GROUP BY {id}, EFFECTIVEDATE;\\\"\\\"\\\"\\n cur.execute(query.format(id=id_columns))\\n # For each unique set of ids get the record with the most recent effective date.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp3 as\\n SELECT {id}, VERSIONNO, max(EFFECTIVEDATE) as EFFECTIVEDATE\\n FROM temp2\\n GROUP BY {id};\\\"\\\"\\\"\\n cur.execute(query.format(id=id_columns))\\n # Inner join the original table to the set of most recent effective dates and version no.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp4 AS\\n SELECT * \\n FROM {table} \\n INNER JOIN temp3 \\n USING ({id}, VERSIONNO, EFFECTIVEDATE);\\\"\\\"\\\"\\n cur.execute(query.format(table=self.table_name, id=id_columns))\\n # Inner join the most recent data with the interconnectors used in the actual interval of interest.\\n query = \\\"\\\"\\\"SELECT {cols} \\n FROM temp4 \\n INNER JOIN (SELECT * \\n FROM DISPATCHINTERCONNECTORRES \\n WHERE SETTLEMENTDATE == '{datetime}') \\n USING (INTERCONNECTORID);\\\"\\\"\\\"\\n query = query.format(datetime=date_time, id=id_columns, cols=return_columns)\\n data = pd.read_sql_query(query, con=self.con)\\n return data\",\n \"def __add__ ( self, other, resample_opts=None ):\\n result = ObservationStorage (datadir=self.datadir, \\\\\\n resample_opts=resample_opts )\\n if self.date[0] > other.date[0]:\\n start_date = other.date[0]\\n else:\\n start_date = self.date[0]\\n if self.date[-1] > other.date[-1]:\\n end_date = other.date[-1]\\n else:\\n end_date = self.date[-1]\\n \\n delta = datetime.timedelta ( days=1 )\\n this_date = start_date.date()\\n end_date = end_date.date() + delta\\n \\n this_obs_dates = [ x.date() for x in self.date ]\\n other_obs_dates = [ x.date() for x in other.date ]\\n \\n date = [] ; vza = [] ; vaa = [] ; sza = [] ; saa = []\\n emulator = [] ; mask = [] ; data_pntr = [] ; spectral = []\\n sensor = []\\n \\n while this_date < end_date:\\n if this_date in this_obs_dates:\\n iloc = this_obs_dates.index ( this_date )\\n date.append ( self.date[iloc] )\\n emulator.append ( self.emulator[iloc] )\\n vza.append ( self.vza[iloc] )\\n sza.append ( self.sza[iloc] )\\n vaa.append ( self.vaa[iloc] )\\n saa.append ( self.saa[iloc] )\\n spectral.append ( self.spectral )\\n mask.append ( ( self.get_mask, [iloc] ) )\\n sensor.append ( self.sensor )\\n \\n data_pntr.append ( self._data_pntr[iloc] )\\n if this_date in other_obs_dates:\\n iloc = other_obs_dates.index ( this_date )\\n date.append ( other.date[iloc] )\\n emulator.append ( other.emulator[iloc] )\\n vza.append ( other.vza[iloc] )\\n sza.append ( other.sza[iloc] )\\n vaa.append ( other.vaa[iloc] )\\n saa.append ( other.saa[iloc] )\\n spectral.append ( other.spectral )\\n mask.append ( ( other.get_mask, [iloc] ) )\\n sensor.append ( other.sensor )\\n data_pntr.append ( other._data_pntr[iloc] )\\n this_date += delta\\n result.vza = vza\\n result.vaa = vaa\\n result.sza = sza \\n result.saa = saa \\n result.date = date\\n result.spectral = spectral\\n result.masks = mask\\n result.sensor = sensor\\n result.emulator = emulator\\n result._data_pntr = data_pntr\\n return result\",\n \"def update_rainfall_obs(target_model, method, timestep, start_time, end_time):\\n obs_start = datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S')\\n try:\\n\\n # Connect to the database\\n curw_obs_pool = get_Pool(host=con_params.CURW_OBS_HOST, user=con_params.CURW_OBS_USERNAME,\\n password=con_params.CURW_OBS_PASSWORD, port=con_params.CURW_OBS_PORT,\\n db=con_params.CURW_OBS_DATABASE)\\n\\n curw_obs_connection = curw_obs_pool.connection()\\n\\n curw_sim_pool = get_Pool(host=con_params.CURW_SIM_HOST, user=con_params.CURW_SIM_USERNAME,\\n password=con_params.CURW_SIM_PASSWORD, port=con_params.CURW_SIM_PORT,\\n db=con_params.CURW_SIM_DATABASE)\\n\\n TS = Timeseries(pool=curw_sim_pool)\\n\\n # [hash_id, station_id, station_name, latitude, longitude]\\n active_obs_stations = extract_active_curw_obs_rainfall_stations(start_time=start_time, end_time=end_time)[1:]\\n obs_stations_dict = { } # keys: obs station id , value: [hash id, name, latitude, longitude]\\n\\n for obs_index in range(len(active_obs_stations)):\\n obs_stations_dict[active_obs_stations[obs_index][1]] = [active_obs_stations[obs_index][0],\\n active_obs_stations[obs_index][2],\\n active_obs_stations[obs_index][3],\\n active_obs_stations[obs_index][4]]\\n\\n for obs_id in obs_stations_dict.keys():\\n meta_data = {\\n 'latitude': float('%.6f' % float(obs_stations_dict.get(obs_id)[2])),\\n 'longitude': float('%.6f' % float(obs_stations_dict.get(obs_id)[3])),\\n 'model': target_model, 'method': method,\\n 'grid_id': 'rainfall_{}_{}'.format(obs_id, obs_stations_dict.get(obs_id)[1])\\n }\\n\\n tms_id = TS.get_timeseries_id_if_exists(meta_data=meta_data)\\n\\n if tms_id is None:\\n tms_id = TS.generate_timeseries_id(meta_data=meta_data)\\n meta_data['id'] = tms_id\\n TS.insert_run(meta_data=meta_data)\\n\\n TS.update_grid_id(id_=tms_id, grid_id=meta_data['grid_id'])\\n\\n obs_hash_id = obs_stations_dict.get(obs_id)[0]\\n\\n obs_timeseries = []\\n\\n if timestep == 5:\\n ts = extract_obs_rain_5_min_ts(connection=curw_obs_connection, start_time=obs_start, end_time=end_time,\\n id=obs_hash_id)\\n if ts is not None and len(ts) > 1:\\n obs_timeseries.extend(process_5_min_ts(newly_extracted_timeseries=ts, expected_start=obs_start)[1:])\\n # obs_start = ts[-1][0]\\n elif timestep == 15:\\n ts = extract_obs_rain_15_min_ts(connection=curw_obs_connection, start_time=obs_start, end_time=end_time,\\n id=obs_hash_id)\\n if ts is not None and len(ts) > 1:\\n obs_timeseries.extend(process_15_min_ts(newly_extracted_timeseries=ts, expected_start=obs_start)[1:])\\n # obs_start = ts[-1][0]\\n\\n # for i in range(len(obs_timeseries)):\\n # if obs_timeseries[i][1] == -99999:\\n # obs_timeseries[i][1] = 0\\n\\n if obs_timeseries is not None and len(obs_timeseries) > 0:\\n TS.replace_data(timeseries=obs_timeseries, tms_id=tms_id)\\n\\n except Exception as e:\\n traceback.print_exc()\\n logger.error(\\\"Exception occurred while updating obs rainfalls in curw_sim.\\\")\\n finally:\\n curw_obs_connection.close()\\n destroy_Pool(pool=curw_sim_pool)\\n destroy_Pool(pool=curw_obs_pool)\",\n \"def get_new_sourcedatasets(self):\\n previous_study_version = self.get_previous_version()\\n SourceDataset = apps.get_model('trait_browser', 'SourceDataset')\\n if previous_study_version is not None:\\n qs = SourceDataset.objects.filter(source_study_version=self)\\n # We can probably write this with a join to be more efficient.\\n previous_dataset_accessions = SourceDataset.objects.filter(\\n source_study_version=previous_study_version\\n ).values_list('i_accession', flat=True)\\n qs = qs.exclude(i_accession__in=previous_dataset_accessions)\\n return qs\\n else:\\n return SourceDataset.objects.none()\",\n \"def read_scada_data(dt_from, dt_to, class_name, read_from_hdf=True, conf_fn='config.json'):\\r\\n\\r\\n if read_from_hdf:\\r\\n all_data = pd.read_hdf(class_name + '.hdf')\\r\\n else:\\r\\n with open(conf_fn, 'r') as fh_conf:\\r\\n conf = json.load(fh_conf)\\r\\n\\r\\n usr = conf['scada_params']['username']\\r\\n psw = conf['scada_params']['password']\\r\\n url = conf['scada_params']['url']\\r\\n project_id = conf['scada_params']['project_id']\\r\\n\\r\\n tout_guid = conf['variables'][class_name]['t_out']\\r\\n tin_guid = conf['variables'][class_name]['t_in']\\r\\n ahu_fan_guid = conf['variables'][class_name]['ahu_fan']\\r\\n t_supply = conf['variables'][class_name]['t_supply']\\r\\n\\r\\n list_guids = [tin_guid, tout_guid, t_supply, ahu_fan_guid]\\r\\n list_cols = ['T_in', 'T_out', 'T_ahu', 'P_ahu']\\r\\n\\r\\n from ScadaClient3 import ScadaClient\\r\\n sc = ScadaClient(url, usr, psw)\\r\\n\\r\\n raw_data = sc.get_history(project_id, list_guids, dt_from, dt_to)\\r\\n all_data = pd.DataFrame()\\r\\n\\r\\n for idx, guid in enumerate(list_guids):\\r\\n guid_s = pd.Series(\\r\\n name=list_cols[idx], index=raw_data[guid]['raw']['tx_dt'], data=raw_data[guid]['raw']['values']\\r\\n )\\r\\n\\r\\n all_data = all_data.combine_first(pd.DataFrame(guid_s))\\r\\n\\r\\n all_data.index = all_data.index.map(lambda x: x.replace(microsecond=0))\\r\\n all_data.to_hdf(class_name + '.hdf', 'libeznice', mode='w')\\r\\n print('All data successfully downloaded from Mervis DB.')\\r\\n all_data = all_data.loc[(all_data.index >= dt_from) & (all_data.index <= dt_to), :]\\r\\n return all_data\",\n \"def _interpolate_meteorological_data(dset, data, rundate):\\n rundate = datetime(rundate.year, rundate.month, rundate.day)\\n for field, station in [(f, f[4:]) for f in data.keys() if f.startswith(\\\"met_\\\")]:\\n log.debug(f\\\"Meteorological data available for station {station}\\\")\\n\\n met_time = data[field].pop(\\\"met_time\\\")\\n flat_list = [item for sublist in met_time for item in sublist]\\n met_time_float = np.array([(flat_list[i] - rundate).total_seconds() for i in range(0, len(flat_list))])\\n met_time_unique, met_index = np.unique(met_time_float, return_index=True)\\n\\n diff = len(met_time_float) - len(met_time_unique)\\n if diff > 0:\\n log.dev(f\\\"Removed duplicate met data for station {station}\\\")\\n log.dev(\\\"Do this for the actual obs data also!\\\")\\n if len(met_time_unique) == 1:\\n for met_type in data[field].keys():\\n data[field][met_type] = np.repeat(data[field][met_type][0], dset.num_obs)\\n continue\\n\\n # Extrapolation one month before/after\\n # (this is overkill, most of these values will be removed later when taking the diagonal)\\n min_time = min(met_time_unique) - 31 * 86400\\n max_time = max(met_time_unique) + 31 * 86400\\n met_time_unique = np.hstack((np.array(min_time), met_time_unique, np.array(max_time)))\\n\\n for met_type in data[field].keys():\\n met_data_array = data[field][met_type]\\n flat_list = [item for sublist in met_data_array for item in sublist]\\n met_data_array = np.array([flat_list[i] for i in met_index])\\n met_data_array = np.hstack((met_data_array[0], met_data_array, met_data_array[-1]))\\n data[field][met_type] = interpolation.interpolate(\\n met_time_unique, met_data_array, dset.obs_time, kind=\\\"cubic\\\"\\n )\\n\\n return data\",\n \"def get_drought_data():\\n\\n # Base URL for the DM homepage & AJAX URL for simulated request\\n url = 'http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx'\\n ajax_url = 'http://droughtmonitor.unl.edu/Ajax.aspx/ReturnDMWeeks'\\n\\n # Based on AJAX HTTP POST request\\n headers = {\\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/38.0.2125.104 Safari/537.36',\\n 'Accept': 'application/json, text/javascript, */*; q=0.01',\\n 'Content-Type': 'application/json; charset=UTF-8',\\n 'X-Requested-With': 'XMLHttpRequest'\\n }\\n\\n # Sets requests Session & sends simulated AJAX request\\n s = requests.Session()\\n s.head(url)\\n r = s.post(ajax_url, data='{}', headers=headers)\\n\\n # Grabs data, a list of valid dates for file reference\\n json_data = json.loads(r.text)\\n file_dates = json_data['d']\\n\\n print 'Writing file dates...'\\n\\n # Writes the USDM dates to a file for D3 reference\\n datefile = open(basedir + '/app/static/data/saved/dates.json', 'w')\\n datefile.write(json.dumps(sorted(file_dates), indent=1))\\n datefile.close()\\n\\n print 'File dates from the USDM:'\\n print file_dates\\n print ''\\n print 'Now comparing...'\\n print ''\\n\\n dates = check_date(file_dates)\\n if dates:\\n file_dates = dates\\n print 'Now collecting...'\\n\\n # Base URL for data files\\n base_url = 'http://usdmdataservices.unl.edu/?mode=table'\\n\\n # Loops thru dates, saves files based on geographical focus & date\\n for date in file_dates:\\n fileout = urllib.URLopener()\\n fileout.retrieve(base_url + '&aoi=county&date=' + date, basedir + '/app/static/data/raw/' + date + '.csv')\\n\\n print 'Finished.'\\n else:\\n print 'All data collected to date.'\",\n \"def process_location(pid, data_source, out_loc, start_date, end_date, debug=False):\\n\\n #query = client.query_data_points(page_size=PAGE_SIZE, source=pid)\\n query = try_query(pid)\\n\\n location_query = try_filter(query, pid, 'pdk-location', start_date, end_date)\\n \\\"\\\"\\\" location_query = query.filter(source=pid, \\n generator_identifier='pdk-location',\\n created__gte=start_date,\\n created__lte=end_date).order_by('created')\\n \\\"\\\"\\\"\\n tot_count = location_query.count()\\n count = 0\\n frac = int(tot_count / 100)\\n\\n loc_df = pd.DataFrame()\\n for point in location_query:\\n point_df = pd.DataFrame.from_dict(point).iloc[0].to_frame().transpose()\\n metadata_df = pd.Series(point['passive-data-metadata']).to_frame().transpose()\\n # TODO check if ignoring errors is safe\\n metadata_df = metadata_df.drop(['latitude', 'longitude'], axis='columns', errors=\\\"ignore\\\")\\n point_df.reset_index(inplace=True, drop=True)\\n point_df = pd.concat([metadata_df, point_df], axis=1, sort=True)\\n \\n point_df.drop('passive-data-metadata', axis='columns', inplace=True)\\n missing_cols = [col for col in loc_df.columns.values if col not in point_df.columns.values]\\n \\n if len(missing_cols) > 0 and loc_df.shape[0] > 0:\\n for col in missing_cols:\\n point_df[col] = np.nan\\n point_df = point_df[loc_df.columns]\\n loc_df = loc_df.append(point_df)\\n count += 1\\n if debug and (count % frac == 0):\\n print(\\\"{0:.2f}% complete\\\".format(float(count)/float(tot_count)*100))\\n\\n loc_df['pid'] = pid \\n loc_df['data_source'] = data_source\\n print(loc_df.shape)\\n \\n pickle.dump(loc_df, open(\\\"{}/pdk-location/{}.df\\\".format(out_loc, pid), 'wb'), -1)\",\n \"def ddf_parser():\\n num_available, total = 0, 0\\n indicator_twn_tuples = list() # format of a single tuple: (indicator_name, #twn rows, earliest available year)\\n concept_metadata = dict() # {top_tag: second_layer_tag:\\n\\n # parse all ddf files provided by GapMinder and find how many of them with Taiwan statistics\\n for f_path in glob.glob(os.path.join('statistics', '*datapoints*.csv')):\\n total += 1\\n df = pd.read_csv(f_path)\\n if 'twn' in df.geo.unique():\\n num_available += 1\\n indicator = f_path.replace('statistics/ddf--datapoints--', '').replace('--by--geo--time.csv', '')\\n # print('[Indicator]', indicator)\\n print(f\\\"\\\\t{len(df[df.geo == 'twn'])} indicators including Taiwan statistics.\\\")\\n\\n # stat_name = df.columns[-1]\\n # df_p = df.pivot(index='geo', columns='time')[stat_name]\\n # df_p.insert(loc=0, column='indicator', value=stat_name)\\n # df_p.to_csv(f'statistics_transformed/{stat_name}.csv', sep=';')\\n\\n indicators.append(indicator)\\n\\n\\n # print(\\\"{:.1f}% datapoints have Taiwan statistics\\\".format(num_available / float(total) * 100))\\n\\n\\n\\n df_c = pd.read_csv(CONCEPT_CSV_PATH)\\n df_t = pd.read_csv(TAG_CSV_PATH)\\n df = pd.merge(df_c, df_t, how='left', left_on='tags', right_on='tag')\\n for idr, num_rows, earliest_year in indicator_twn_tuples:\\n ancestors = list()\\n\\n row_values = df[df['concept'] == idr].values[0]\\n name_catalog, parent, ancestor = (row_values[i] for i in [9, 17, 18])\\n if type(parent) is str:\\n ancestors.append(parent)\\n\\n # get ancestors recursively\\n while type(ancestor) is str:\\n tag_row_values = df_t[df_t['tag'] == ancestor].values[0]\\n ancestors.append(tag_row_values[1])\\n ancestor = tag_row_values[2]\\n\\n # build concept structure\\n ancestors.insert(0, name_catalog)\\n print('/'.join(ancestors[::-1]))\",\n \"def ingest(self,check=True):\\n\\t\\tdata=self.data_all\\n\\t\\tpubdate=time_utils.parseISO(self.api.last_update).date()\\n\\t\\t\\n\\t\\tcounter=0\\n\\t\\tfor item in data:\\n\\t\\t\\tareacode=item['areaCode']\\n\\t\\t\\tdatestring=item['specimenDate']\\n\\t\\t\\t_date=fetchdate(datestring)\\n\\t\\t\\trow,created=DailyCases.objects.get_or_create(specimenDate=_date,areacode=areacode)\\n\\t\\t\\trow.areaname=item['areaName']\\n\\t\\t\\tdaily=item['newCasesBySpecimenDate']\\n\\t\\t\\ttotal=item['cumCasesBySpecimenDate']\\n\\t\\t\\t\\n\\t\\t\\t#log.debug(f'{row.areaname}: {datestring}')\\t\\t\\t\\n\\t\\t\\tif created:\\n\\t\\t\\t\\trow.dailyLabConfirmedCases=daily\\n\\t\\t\\t\\trow.totalLabConfirmedCases=total\\n\\t\\t\\t\\trow.save()\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif daily:\\n\\t\\t\\t\\t\\tlag=(pubdate-_date.date()).days\\n\\t\\t\\t\\t\\tlog.debug(f'date:{_date} lag: {lag} daily:{daily}')\\n\\t\\t\\t\\t\\tdrow,dcreated=DailyReport.objects.get_or_create(specimenDate=_date,areacode=areacode,publag=lag)\\n\\t\\t\\t\\t\\tdrow.dailycases=daily\\n\\t\\t\\t\\t\\tdrow.add_cases=daily #if a new daily case, assume no prior report\\n\\t\\t\\t\\t\\tdrow.save()\\n\\t\\t\\t\\n\\t\\t\\tif not created:\\n\\t\\t\\t\\texisting_daily=row.dailyLabConfirmedCases\\n\\t\\t\\t\\texisting_total=row.totalLabConfirmedCases\\n\\t\\t\\t\\tif daily is not None:\\n\\t\\t\\t\\t\\tif existing_daily !=daily or existing_total!=total:\\n\\t\\t\\t\\t\\t\\trow.dailyLabConfirmedCases=daily\\n\\t\\t\\t\\t\\t\\trow.totalLabConfirmedCases=total\\n\\t\\t\\t\\t\\t\\trow.save()\\n\\t\\t\\t\\t\\t\\tif existing_daily !=daily:\\n\\t\\t\\t\\t\\t\\t\\tlog.debug(f'Updating {row.areaname} on {datestring}: Daily: {existing_daily} to {daily} Total: {existing_total} to {total}')\\n\\t\\t\\t\\t\\t\\t\\tif existing_daily:\\n\\t\\t\\t\\t\\t\\t\\t\\t_increase=daily-existing_daily\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\t_increase=daily\\n\\t\\t\\t\\t\\t\\t\\tlag=(pubdate-_date.date()).days\\n\\t\\t\\t\\t\\t\\t\\tdrow,dcreated=DailyReport.objects.get_or_create(specimenDate=_date,areacode=areacode,publag=lag)\\n\\t\\t\\t\\t\\t\\t\\tdrow.dailycases=daily\\n\\t\\t\\t\\t\\t\\t\\tdrow.add_cases=_increase\\n\\t\\t\\t\\t\\t\\t\\tdrow.save()\\n\\t\\t\\t\\t\\t\\n\\t\\t\\tcounter+=1\\n\\t\\t\\tif counter%1000==0:\\n\\t\\t\\t\\tlog.info(f'Processing row {counter}')\\n\\t\\tlog.info(f'Processed: {counter} rows')\\n\\n\\t\\tif self.edition:\\n\\t\\t\\tconfigs.userconfig.update('PHE','latest_update',self.edition)\\n\\t\\telse:\\n\\t\\t\\tlog.info('Latest update not updated')\",\n \"def make_dataset_temperatures(filename, points):\\r\\n\\r\\n ds = Dataset(filename)\\r\\n\\r\\n lats, lons, cities, countries = get_lat_lon()\\r\\n\\r\\n # total lat,lon pairs: 1366\\r\\n print('The number of rows is ' + str(len(lats)*points))\\r\\n lon = ds.variables['longitude']\\r\\n lat = ds.variables['latitude']\\r\\n time = ds.variables['date_number']\\r\\n\\r\\n lon_array = lon[:]\\r\\n lat_array = lat[:]\\r\\n time_array = time[:]\\r\\n\\r\\n temperature = ds.variables['temperature']\\r\\n\\r\\n dates = []\\r\\n for time in time_array[:]:\\r\\n year = int(time)\\r\\n rem = time - year\\r\\n base = datetime(year, 1, 1)\\r\\n dates.append((base + timedelta(seconds=(base.replace(year=base.year + 1) - base).total_seconds() * rem)).date())\\r\\n\\r\\n # second approach\\r\\n # for t in time_array[:]:\\r\\n # dates.append(num2date(t, units=time.units))\\r\\n\\r\\n dateResult = []\\r\\n temperatureResult = []\\r\\n latitudeResult = []\\r\\n longitudeResult = []\\r\\n cityResult = []\\r\\n countryResult = []\\r\\n\\r\\n for latitude, longitude, city, country in zip(lats, lons, cities, countries):\\r\\n\\r\\n # We want to find data for latitude, longitude\\r\\n # We first need to find the indexes\\r\\n i = np.abs(lon_array - longitude).argmin()\\r\\n j = np.abs(lat_array - latitude).argmin()\\r\\n\\r\\n for d in dates:\\r\\n dateResult.append(d)\\r\\n\\r\\n resultTemperature = temperature[:, j, i]\\r\\n for t in resultTemperature:\\r\\n temperatureResult.append(t)\\r\\n\\r\\n resultLatitues = np.full(\\r\\n shape=points,\\r\\n fill_value=latitude,\\r\\n dtype=np.float\\r\\n )\\r\\n for l in resultLatitues:\\r\\n latitudeResult.append(l)\\r\\n\\r\\n resultLongitudes = np.full(\\r\\n shape=points,\\r\\n fill_value=longitude,\\r\\n dtype=np.float\\r\\n )\\r\\n for l in resultLongitudes:\\r\\n longitudeResult.append(l)\\r\\n\\r\\n resultCities = np.full(\\r\\n shape=points,\\r\\n fill_value=city\\r\\n )\\r\\n for c in resultCities:\\r\\n cityResult.append(c)\\r\\n\\r\\n resultCountries = np.full(\\r\\n shape=points,\\r\\n fill_value=country\\r\\n )\\r\\n for c in resultCountries:\\r\\n countryResult.append(c)\\r\\n\\r\\n print('iteration no:' + str(i))\\r\\n\\r\\n df = pd.DataFrame()\\r\\n df['date'] = dateResult\\r\\n df['temperature'] = temperatureResult\\r\\n df['latitude'] = latitudeResult\\r\\n df['longitude'] = longitudeResult\\r\\n df['city'] = cityResult\\r\\n df['country'] = countryResult\\r\\n\\r\\n df.to_csv(r'C:\\\\Users\\\\stoja\\\\Desktop\\\\Temperatures.csv', index=False)\\r\\n return df\",\n \"def load_daily_data():\\n return pd.read_csv(os.path.join('data', 'raw', 'full_grouped.csv'))\",\n \"def temp():\\n \\n #Query temp from a year from last data point\\n query = session.query(func.max(Measurement.date)).first()\\n maxDate = dt.datetime.strptime(query[0],'%Y-%m-%d')\\n year_ago = maxDate - dt.timedelta(days=365)\\n\\n results = session.query(Measurement.date,Measurement.tobs).filter(Measurement.date>=year_ago).all()\\n\\n # convert list of tuples into normal list\\n all_dates = (result[0] for result in results)\\n all_tobs = (result[1] for result in results)\\n\\n # Convert result to dictionary using date as key and tobs as value\\n tobs_dict = dict(zip(all_dates,all_tobs))\\n\\n # Return on webpage\\n return jsonify(tobs_dict)\",\n \"def retrieve_time_series(api, series_ID):\\r\\n #Retrieve Data By Series ID \\r\\n series_search = api.data_by_series(series=series_ID)\\r\\n ##Create a pandas dataframe from the retrieved time series\\r\\n df = pd.DataFrame(series_search)\\r\\n return df\",\n \"def compliance_time_series(county, core_path , patterns_path, backfill = False, GEOID_type = 'CBG'):\\n #Load data about places and patterns\\n county_places = pd.read_csv(core_path+'places-'+ county +'.csv', index_col='safegraph_place_id') \\n \\n pattern_dates = [x[5:9] for x in sorted(os.listdir(patterns_path+'main-file-'+ county +'/'))]\\n w = 0\\n next_date = pattern_dates[w]\\n #Create visitor table\\n place_cts = pd.DataFrame() #First two months don't have weekly patterns, use empty data.frame\\n\\n #List files for social distancing metrics in that county\\n months_path = '../social_distancing/social_dist_'+ county +'/'\\n month_list = sorted(os.listdir(months_path))\\n os.makedirs( '../stats/time_series/', exist_ok = True)\\n #metrics dictionary to be filled looping through every day\\n if not os.path.isfile('../stats/time_series/metrics_{}_CT.csv'.format(county)) or backfill:\\n metrics = pd.DataFrame()\\n existing_dates = []\\n else:\\n metrics = pd.read_csv('../stats/time_series/metrics_{}_CT.csv'.format(county), dtype = {'origin_census_tract':str})\\n #dates already processed\\n existing_dates = metrics['date'].unique() #series of unique dates\\n\\n #Initialize columns of new data frame\\n if GEOID_type == 'CBG':\\n columns= {'date':[], 'origin_census_block_group':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'total_expected_contacts':[], 'places_visited':[], 'cbgs_visited':[]}\\n if GEOID_type == 'CT':\\n columns= {'date':[], 'origin_census_tract':[], 'low_device_count':[], 'pct_at_home':[], 'pct_within_neighborhood':[], 'median_distance_traveled':[],\\n 'median_percentage_time_home':[], 'total_visits_to_places':[], 'normalized_visits_to_places':[], 'places_visited':[]} \\n changed = False\\n for month in month_list:\\n #Loop through every day\\n day_list = sorted(os.listdir(months_path + month))\\n for day in day_list: \\n date_name = month + '-' + day\\n print(date_name)\\n if month+'-'+day == next_date:\\n w = w+1\\n if w < len(pattern_dates):\\n next_date = pattern_dates[w]\\n if date_name in existing_dates:\\n continue\\n print(\\\"--changing to next patterns file\\\")\\n county_patterns = pd.read_csv(patterns_path + 'main-file-{}/2020-{}-weekly-patterns.csv.gz'.format(county, date_name),\\n index_col='safegraph_place_id')\\n norm_factors = pd.read_csv(\\n '../social_distancing/normalization/'+'normalization_{}.csv'.format(county),\\n dtype={'origin_census_block_group':str})\\n norm_factors.set_index('origin_census_block_group', drop =True, inplace=True)\\n\\n\\n #Establish prior for hourly distribution of visits at the top_category level\\n county_patterns= county_patterns.join(county_places[['top_category','sub_category']], how='inner')\\n restaurants = county_patterns['top_category'] == 'Restaurants and Other Eating Places'\\n county_patterns.loc[restaurants, 'top_category'] = county_patterns.loc[restaurants, 'sub_category']\\n norm_factor = norm_factors.loc[norm_factors.date == date_name].norm_factor\\n\\n prior_dict = {}\\n for category in county_patterns.top_category.value_counts().index:\\n places_in_cat = county_patterns['top_category'] == category\\n dirich_samples = [np.array(json.loads(x)) for x in county_patterns.loc[places_in_cat, 'visits_by_each_hour'] ] \\n prior_dict[category] = dirichlet.getInitAlphas(dirich_samples)\\n if GEOID_type == 'CBG':\\n place_cbgs = ccc.place_cbg_contacts_table(\\n county_patterns,\\n prior_dict,\\n norm_factor,\\n GEOID_type)\\n place_cbgs = place_cbgs.loc[place_cbgs['expected_contacts']>1]\\n place_cbgs = place_cbgs.join(county_places[['location_name','latitude','longitude']], how='inner')\\n place_cbgs.reset_index(inplace=True, drop=False)\\n place_cbgs.set_index('origin_census_block_group', inplace=True, drop=True)\\n if GEOID_type == 'CT':\\n place_cts = ccc.place_cbg_contacts_table(\\n county_patterns,\\n prior_dict,\\n norm_factor,\\n GEOID_type)\\n place_cts = place_cts.join(county_places[['location_name','latitude','longitude']], how='inner')\\n place_cts.reset_index(inplace=True, drop=False)\\n place_cts.set_index('origin_census_tract', inplace=True, drop=True)\\n print('--computed bipartite contact network')\\n\\n if date_name in existing_dates:\\n continue\\n\\n\\n file_name = os.listdir(months_path+ '/' + month+'/'+day)[0]\\n data_soc_dist = pd.read_csv(months_path+ '/' + month+'/'+day+'/'+file_name, dtype={'origin_census_block_group':str})\\n data_soc_dist['median_distance_traveled_from_home'] = [0 if math.isnan(x) else x for x in data_soc_dist['distance_traveled_from_home']] #NOT NEEDED?\\n \\n if GEOID_type == 'CBG':\\n [update_metrics_columns(row, date_name, place_cbgs, county_places, columns) for i, row in data_soc_dist.iterrows()]\\n else:\\n ####CREATE FUNCTION THAT AGGREGATES TO CENSUS TRACT\\n data_soc_dist['origin_census_tract'] = [x[:-1] for x in data_soc_dist['origin_census_block_group']]\\n ct_data_soc_dist = data_soc_dist.groupby('origin_census_tract').apply(ddd.aggregate_to_ct).reset_index()\\n [update_metrics_columns_CT(row, date_name, place_cts, county_places, columns) for i, row in ct_data_soc_dist.iterrows()]\\n changed = True\\n\\n print(\\\"--merging rows from new dates\\\")\\n new_metrics = pd.DataFrame.from_dict(columns, orient='index').transpose()\\n\\n metrics = pd.concat([metrics, new_metrics], ignore_index=True)\\n if changed:\\n if GEOID_type == 'CT':\\n metrics.to_csv('../stats/time_series/metrics_{}_CT.csv'.format(county),index=False)\\n else:\\n metrics.to_csv('../stats/time_series/metrics_{}_CBG.csv'.format(county),index=False)\\n print(\\\"--finished updating time series for {}\\\".format(county))\\n return(0)\",\n \"def get_extension_list(self, timespan, db_lookup):\\n if (self.search_list_extension != None):\\n return [self.search_list_extension]\\n \\n t1 = time.time()\\n\\n # Get UTC offset\\n stop_struct = time.localtime(timespan.stop)\\n utc_offset = (calendar.timegm(stop_struct) - calendar.timegm(time.gmtime(time.mktime(stop_struct))))/60\\n\\n # Our start time is one year ago from midnight at the start of today\\n # first get the start of today\\n _ts = startOfDay(timespan.stop)\\n # then go back 1 year\\n _ts_dt = datetime.datetime.fromtimestamp(_ts)\\n try:\\n _start_dt = _ts_dt.replace(year=_ts_dt.year-1)\\n except ValueError:\\n _start_dt = _ts_dt.replace(year=_ts_dt.year-1, day=_ts_dt.day-1)\\n _start_ts = time.mktime(_start_dt.timetuple())\\n _timespan = TimeSpan(_start_ts, timespan.stop)\\n\\n # Get our outTemp vectors\\n (outTemp_time_vt, outTemp_dict) = getDaySummaryVectors(db_lookup(), 'outTemp', _timespan, ['min', 'max', 'avg'])\\n (inTemp_time_vt, inTemp_dict) = getDaySummaryVectors(db_lookup(), 'inTemp', _timespan, ['min', 'max', 'avg'])\\n # Get our vector ValueTuple out of the dictionary and convert it\\n outTempMin_vt = self.generator.converter.convert(outTemp_dict['min'])\\n outTempMax_vt = self.generator.converter.convert(outTemp_dict['max'])\\n outTempAvg_vt = self.generator.converter.convert(outTemp_dict['avg'])\\n\\n inTempMin_vt = self.generator.converter.convert(inTemp_dict['min'])\\n inTempMax_vt = self.generator.converter.convert(inTemp_dict['max'])\\n inTempAvg_vt = self.generator.converter.convert(inTemp_dict['avg'])\\n \\n # Get our dewpoint vectors\\n (dewpoint_time_vt, dewpoint_dict) = getDaySummaryVectors(db_lookup(), 'dewpoint', _timespan, ['min', 'max', 'avg'])\\n # Get our vector ValueTuple out of the dictionary and convert it\\n dewpointMin_vt = self.generator.converter.convert(dewpoint_dict['min'])\\n dewpointMax_vt = self.generator.converter.convert(dewpoint_dict['max'])\\n dewpointAvg_vt = self.generator.converter.convert(dewpoint_dict['avg'])\\n\\n # Get our appTemp vectors. appTemp data is not normally archived so\\n # only try to get it if we have a binding for it. Wrap in a try..except\\n # to catch any errors. If we don't have a binding then set the vectors\\n # to None\\n if self.apptemp_binding is not None:\\n try:\\n (appTemp_time_vt, appTemp_dict) = getDaySummaryVectors(db_lookup('wd_binding'),\\n 'appTemp',\\n _timespan,\\n ['min', 'max', 'avg'])\\n # Get our vector ValueTuple out of the dictionary and convert it\\n appTempMin_vt = self.generator.converter.convert(appTemp_dict['min'])\\n appTempMax_vt = self.generator.converter.convert(appTemp_dict['max'])\\n appTempAvg_vt = self.generator.converter.convert(appTemp_dict['avg'])\\n except weewx.UnknownBinding:\\n raise\\n else:\\n appTempMin_vt = None\\n appTempMax_vt = None\\n appTempAvg_vt = None\\n\\n # Get our windchill vector\\n (windchill_time_vt, windchill_dict) = getDaySummaryVectors(db_lookup(),\\n 'windchill',\\n _timespan,\\n ['min', 'max', 'avg'])\\n # Get our vector ValueTuple out of the dictionary and convert it\\n windchillAvg_vt = self.generator.converter.convert(windchill_dict['avg'])\\n windchillMin_vt = self.generator.converter.convert(windchill_dict['min'])\\n windchillMax_vt = self.generator.converter.convert(windchill_dict['max'])\\n\\n # Get our heatindex vector\\n (heatindex_time_vt, heatindex_dict) = getDaySummaryVectors(db_lookup(),\\n 'heatindex',\\n _timespan,\\n ['min', 'max', 'avg'])\\n # Get our vector ValueTuple out of the dictionary and convert it\\n heatindexAvg_vt = self.generator.converter.convert(heatindex_dict['avg'])\\n heatindexMin_vt = self.generator.converter.convert(heatindex_dict['min'])\\n heatindexMax_vt = self.generator.converter.convert(heatindex_dict['max'])\\n # Get our humidity vectors\\n (outHumidity_time_vt, outHumidity_dict) = getDaySummaryVectors(db_lookup(),'outHumidity',_timespan,['min', 'max', 'avg'])\\n (inHumidity_time_vt, inHumidity_dict) = getDaySummaryVectors(db_lookup(),'inHumidity',_timespan,['min', 'max', 'avg'])\\n\\n # Get our barometer vectors\\n (barometer_time_vt, barometer_dict) = getDaySummaryVectors(db_lookup(),\\n 'barometer',\\n _timespan,\\n ['min', 'max', 'avg'])\\n # Get our vector ValueTuple out of the dictionary and convert it\\n barometerMin_vt = self.generator.converter.convert(barometer_dict['min'])\\n barometerMax_vt = self.generator.converter.convert(barometer_dict['max'])\\n barometerAvg_vt = self.generator.converter.convert(barometer_dict['avg'])\\n\\n # Get our wind vectors\\n (wind_time_vt, wind_dict) = getDaySummaryVectors(db_lookup(),\\n 'wind',\\n _timespan,\\n ['max', 'avg'])\\n # Get our vector ValueTuple out of the dictionary and convert it\\n windMax_vt = self.generator.converter.convert(wind_dict['max'])\\n windAvg_vt = self.generator.converter.convert(wind_dict['avg'])\\n\\n # Get our windSpeed vectors\\n (windSpeed_time_vt, windSpeed_dict) = getDaySummaryVectors(db_lookup(),\\n 'windSpeed',\\n _timespan,\\n ['min', 'max', 'avg'])\\n # Get our vector ValueTuple out of the dictionary and convert it\\n windSpeedMax_vt = self.generator.converter.convert(windSpeed_dict['max'])\\n windSpeedAvg_vt = self.generator.converter.convert(windSpeed_dict['avg'])\\n\\n # Get our windDir vectors\\n (windDir_time_vt, windDir_dict) = getDaySummaryVectors(db_lookup(),'wind',_timespan,['vecdir'])\\n # Get our rain vectors\\n (rain_time_vt, rain_dict) = getDaySummaryVectors(db_lookup(),\\n 'rain',\\n _timespan,\\n ['sum'])\\n # Get our vector ValueTuple out of the dictionary and convert it\\n rainSum_vt = self.generator.converter.convert(rain_dict['sum'])\\n\\n # Get our radiation vectors\\n (radiation_time_vt, radiation_dict) = getDaySummaryVectors(db_lookup(),\\n 'radiation',\\n _timespan,\\n ['min', 'max', 'avg'])\\n\\n # Get our UV vectors\\n (uv_time_vt, uv_dict) = getDaySummaryVectors(db_lookup(),\\n 'UV',\\n _timespan,\\n ['min', 'max', 'avg'])\\n \\n # Get no of decimal places to use when formatting results\\n tempPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(outTempMin_vt[1], \\\"1f\\\")[-2])\\n outHumidityPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(outHumidity_dict['min'][1], \\\"1f\\\")[-2])\\n inHumidityPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(inHumidity_dict['min'][1], \\\"1f\\\")[-2])\\n barometerPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(barometerMin_vt[1], \\\"1f\\\")[-2])\\n windPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(windMax_vt[1], \\\"1f\\\")[-2])\\n windSpeedPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(windSpeedMax_vt[1], \\\"1f\\\")[-2])\\n windDirPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(windDir_dict['vecdir'][1], \\\"1f\\\")[-2])\\n rainPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(rainSum_vt[1], \\\"1f\\\")[-2])\\n radiationPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(radiation_dict['max'][1], \\\"1f\\\")[-2])\\n uvPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uv_dict['max'][1], \\\"1f\\\")[-2])\\n\\n # Get our time vector in ms\\n time_ms = [outTemp_time_vt[0][0] if (x == 0) else outTemp_time_vt[0][x] - outTemp_time_vt[0][0] for x in range(len(outTemp_time_vt[0]))]\\n #time_ms = [float(x) * 1000 for x in outTemp_time_vt[0]]\\n\\n # Round our values from our ValueTuples\\n outTempMinRound = [roundNone(x,tempPlaces) for x in outTempMin_vt[0]]\\n outTempMaxRound = [roundNone(x,tempPlaces) for x in outTempMax_vt[0]]\\n outTempAvgRound = [roundNone(x,tempPlaces) for x in outTempAvg_vt[0]]\\n inTempMinRound = [roundNone(x,tempPlaces) for x in inTempMin_vt[0]]\\n inTempMaxRound = [roundNone(x,tempPlaces) for x in inTempMax_vt[0]]\\n inTempAvgRound = [roundNone(x,tempPlaces) for x in inTempAvg_vt[0]]\\n # round our appTemp values, if we don't have any then set it to None\\n try:\\n appTempMinRound = [roundNone(x,tempPlaces) for x in appTempMin_vt[0]]\\n appTempMaxRound = [roundNone(x,tempPlaces) for x in appTempMax_vt[0]]\\n appTempAvgRound = [roundNone(x,tempPlaces) for x in appTempAvg_vt[0]]\\n except TypeError:\\n appTempMinRound = None\\n appTempMaxRound = None\\n appTempAvgRound = None\\n dewpointMinRound = [roundNone(x,tempPlaces) for x in dewpointMin_vt[0]]\\n dewpointMaxRound = [roundNone(x,tempPlaces) for x in dewpointMax_vt[0]]\\n dewpointAvgRound = [roundNone(x,tempPlaces) for x in dewpointAvg_vt[0]]\\n windchillMinRound = [roundNone(x,tempPlaces) for x in windchillMin_vt[0]]\\n windchillMaxRound = [roundNone(x,tempPlaces) for x in windchillMax_vt[0]]\\n windchillAvgRound = [roundNone(x,tempPlaces) for x in windchillAvg_vt[0]]\\n heatindexMinRound = [roundNone(x,tempPlaces) for x in heatindexMin_vt[0]]\\n heatindexMaxRound = [roundNone(x,tempPlaces) for x in heatindexMax_vt[0]]\\n heatindexAvgRound = [roundNone(x,tempPlaces) for x in heatindexAvg_vt[0]]\\n outHumidityMinRound = [roundNone(x,outHumidityPlaces) for x in outHumidity_dict['min'][0]]\\n outHumidityMaxRound = [roundNone(x,outHumidityPlaces) for x in outHumidity_dict['max'][0]]\\n outHumidityAvgRound = [roundNone(x,outHumidityPlaces) for x in outHumidity_dict['avg'][0]]\\n inHumidityMinRound = [roundNone(x,inHumidityPlaces) for x in inHumidity_dict['min'][0]]\\n inHumidityMaxRound = [roundNone(x,inHumidityPlaces) for x in inHumidity_dict['max'][0]]\\n inHumidityAvgRound = [roundNone(x,inHumidityPlaces) for x in inHumidity_dict['avg'][0]]\\n barometerMinRound = [roundNone(x,tempPlaces) for x in barometerMin_vt[0]]\\n barometerMaxRound = [roundNone(x,tempPlaces) for x in barometerMax_vt[0]]\\n barometerAvgRound = [roundNone(x,tempPlaces) for x in barometerAvg_vt[0]]\\n windMaxRound = [roundNone(x,windPlaces) for x in windMax_vt[0]]\\n windAvgRound = [roundNone(x,windPlaces) for x in windAvg_vt[0]]\\n windSpeedMaxRound = [roundNone(x,windSpeedPlaces) for x in windSpeedMax_vt[0]]\\n windSpeedAvgRound = [roundNone(x,windSpeedPlaces) for x in windSpeedAvg_vt[0]]\\n windDirRound = [roundNone(x,windDirPlaces) for x in windDir_dict['vecdir'][0]]\\n rainSumRound = [roundNone(x,rainPlaces) for x in rainSum_vt[0]]\\n radiationMinRound = [roundNone(x,radiationPlaces) for x in radiation_dict['min'][0]]\\n radiationMaxRound = [roundNone(x,radiationPlaces) for x in radiation_dict['max'][0]]\\n radiationAvgRound = [roundNone(x,radiationPlaces) for x in radiation_dict['avg'][0]]\\n uvMinRound = [roundNone(x,uvPlaces) for x in uv_dict['min'][0]]\\n uvMaxRound = [roundNone(x,uvPlaces) for x in uv_dict['max'][0]]\\n uvAvgRound = [roundNone(x,uvPlaces) for x in uv_dict['avg'][0]]\\n\\n # Produce our JSON strings\\n outTempMinMax_json = json.dumps(zip(time_ms, outTempMinRound, outTempMaxRound))\\n outTempMin_json = json.dumps(zip(time_ms, outTempMinRound))\\n outTempMax_json = json.dumps(zip(time_ms, outTempMaxRound))\\n outTempAvg_json = json.dumps(zip(time_ms, outTempAvgRound))\\n inTempMinMax_json = json.dumps(zip(time_ms, inTempMinRound, inTempMaxRound))\\n inTempMin_json = json.dumps(zip(time_ms, inTempMinRound))\\n inTempMax_json = json.dumps(zip(time_ms, inTempMaxRound))\\n inTempAvg_json = json.dumps(zip(time_ms, inTempAvgRound))\\n # appTemp. If we don't have any source data then set our JSON string to\\n # None\\n if appTempMinRound is not None and appTempMaxRound is not None:\\n appTempMinMax_json = json.dumps(zip(time_ms, appTempMinRound, appTempMaxRound))\\n else:\\n appTempMinMax_json = None\\n if appTempMinRound is not None:\\n appTempMin_json = json.dumps(zip(time_ms, appTempMinRound))\\n else:\\n appTempMin_json = None\\n if appTempMaxRound is not None:\\n appTempMax_json = json.dumps(zip(time_ms, appTempMaxRound))\\n else:\\n appTempMax_json = None\\n if appTempAvgRound is not None:\\n appTempAvg_json = json.dumps(zip(time_ms, appTempAvgRound))\\n else:\\n appTempAvg_json = None\\n dewpointMinMax_json = json.dumps(zip(time_ms, dewpointMinRound, dewpointMaxRound))\\n dewpointMin_json = json.dumps(zip(time_ms, dewpointMinRound))\\n dewpointMax_json = json.dumps(zip(time_ms, dewpointMaxRound))\\n dewpointAvg_json = json.dumps(zip(time_ms, dewpointAvgRound))\\n windchillMinMax_json = json.dumps(zip(time_ms, windchillMinRound, windchillMaxRound))\\n windchillMin_json = json.dumps(zip(time_ms, windchillMinRound))\\n windchillMax_json = json.dumps(zip(time_ms, windchillMaxRound))\\n windchillAvg_json = json.dumps(zip(time_ms, windchillAvgRound))\\n heatindexMinMax_json = json.dumps(zip(time_ms, heatindexMinRound, heatindexMaxRound))\\n heatindexMin_json = json.dumps(zip(time_ms, heatindexMinRound))\\n heatindexMax_json = json.dumps(zip(time_ms, heatindexMaxRound))\\n heatindexAvg_json = json.dumps(zip(time_ms, heatindexAvgRound))\\n outHumidityMinMax_json = json.dumps(zip(time_ms, outHumidityMinRound, outHumidityMaxRound))\\n outHumidityMin_json = json.dumps(zip(time_ms, outHumidityMinRound))\\n outHumidityMax_json = json.dumps(zip(time_ms, outHumidityMaxRound))\\n outHumidityAvg_json = json.dumps(zip(time_ms, outHumidityAvgRound))\\n inHumidityMinMax_json = json.dumps(zip(time_ms, inHumidityMinRound, inHumidityMaxRound))\\n inHumidityMin_json = json.dumps(zip(time_ms, inHumidityMinRound))\\n inHumidityMax_json = json.dumps(zip(time_ms, inHumidityMaxRound))\\n inHumidityAvg_json = json.dumps(zip(time_ms, inHumidityAvgRound))\\n barometerMinMax_json = json.dumps(zip(time_ms, barometerMinRound, barometerMaxRound))\\n barometerMin_json = json.dumps(zip(time_ms, barometerMinRound))\\n barometerMax_json = json.dumps(zip(time_ms, barometerMaxRound))\\n barometerAvg_json = json.dumps(zip(time_ms, barometerAvgRound))\\n windMax_json = json.dumps(zip(time_ms, windMaxRound))\\n windAvg_json = json.dumps(zip(time_ms, windAvgRound))\\n windSpeedMax_json = json.dumps(zip(time_ms, windSpeedMaxRound))\\n windSpeedAvg_json = json.dumps(zip(time_ms, windSpeedAvgRound))\\n windDir_json = json.dumps(zip(time_ms, windDirRound))\\n rainSum_json = json.dumps(zip(time_ms, rainSumRound))\\n radiationMax_json = json.dumps(zip(time_ms, radiationMaxRound))\\n radiationAvg_json = json.dumps(zip(time_ms, radiationAvgRound))\\n uvMax_json = json.dumps(zip(time_ms, uvMaxRound))\\n uvAvg_json = json.dumps(zip(time_ms, uvAvgRound))\\n\\n # Create uva json\\n try:\\n (uva_time_vt, uva_dict) = getDaySummaryVectors(db_lookup(), 'uva', timespan,['max', 'avg'])\\n uvaPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uva_dict['max'][1], \\\"1f\\\")[-2])\\n uvaMaxRound = [roundNone(x,uvaPlaces) for x in uva_dict['max'][0]]\\n uvaAvgRound = [roundNone(x,uvaPlaces) for x in uva_dict['avg'][0]]\\n uvaMax_json = json.dumps(zip(time_ms, uvaMaxRound))\\n uvaAvg_json = json.dumps(zip(time_ms, uvaAvgRound))\\n except:\\n uvaMax_json = None\\n uvaAvg_json = None\\n \\n # Create uvb json\\n try:\\n (uvb_time_vt, uvb_dict) = getDaySummaryVectors(db_lookup(), 'uvb', timespan,['max', 'avg'])\\n uvbPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uvb_dict['max'][1], \\\"1f\\\")[-2])\\n uvbMaxRound = [roundNone(x,uvbPlaces) for x in uvb_dict['max'][0]]\\n uvbAvgRound = [roundNone(x,uvbPlaces) for x in uvb_dict['avg'][0]]\\n uvbMax_json = json.dumps(zip(time_ms, uvbMaxRound))\\n uvbAvg_json = json.dumps(zip(time_ms, uvbAvgRound))\\n except:\\n uvbMax_json = None\\n uvbAvg_json = None\\n \\n # Create uvaWm json\\n try:\\n (uvaWm_time_vt, uvaWm_dict) = getDaySummaryVectors(db_lookup(), 'uvaWm', timespan,['max', 'avg'])\\n uvaWmPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uvaWm_dict['max'][1], \\\"1f\\\")[-2])\\n uvaWmMaxRound = [roundNone(x,uvaWmPlaces) for x in uvaWm_dict['max'][0]]\\n uvaWmAvgRound = [roundNone(x,uvaWmPlaces) for x in uvaWm_dict['avg'][0]]\\n uvaWmMax_json = json.dumps(zip(time_ms, uvaWmMaxRound))\\n uvaWmAvg_json = json.dumps(zip(time_ms, uvaWmAvgRound))\\n except:\\n uvaWmMax_json = None\\n uvaWmAvg_json = None\\n \\n # Create uvbWm json\\n try:\\n (uvbWm_time_vt, uvbWm_dict) = getDaySummaryVectors(db_lookup(), 'uvbWm', timespan,['max', 'avg'])\\n uvbWmPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(uvbWm_dict['max'][1], \\\"1f\\\")[-2])\\n uvbWmMaxRound = [roundNone(x,uvbWmPlaces) for x in uvbWm_dict['max'][0]]\\n uvbWmAvgRound = [roundNone(x,uvbWmPlaces) for x in uvbWm_dict['avg'][0]]\\n uvbWmMax_json = json.dumps(zip(time_ms, uvbWmMaxRound))\\n uvbWmAvg_json = json.dumps(zip(time_ms, uvbWmAvgRound))\\n except:\\n uvbWmMax_json = None\\n uvbWmAvg_json = None\\n \\n # Create energy json\\n try:\\n (energy_time_vt, energy_dict) = getDaySummaryVectors(db_lookup(), 'energy', timespan,['max', 'avg'])\\n energyPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(energy_dict['max'][1], \\\"1f\\\")[-2])\\n energyMaxRound = [roundNone(x,energyPlaces) for x in energy_dict['max'][0]]\\n energyAvgRound = [roundNone(x,energyPlaces) for x in energy_dict['avg'][0]]\\n energyMax_json = json.dumps(zip(time_ms, energyMaxRound))\\n energyAvg_json = json.dumps(zip(time_ms, energyAvgRound))\\n except:\\n energyMax_json = None\\n energyAvg_json = None\\n \\n # Create distance json\\n try:\\n (distance_time_vt, distance_dict) = getDaySummaryVectors(db_lookup(), 'avg_distance', timespan,['max', 'avg'])\\n distancePlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(distance_dict['max'][1], \\\"1f\\\")[-2])\\n distanceMaxRound = [roundNone(x,distancePlaces) for x in distance_dict['max'][0]]\\n distanceAvgRound = [roundNone(x,distancePlaces) for x in distance_dict['avg'][0]]\\n distanceMax_json = json.dumps(zip(time_ms, distanceMaxRound))\\n distanceAvg_json = json.dumps(zip(time_ms, distanceAvgRound))\\n except:\\n distanceMax_json = None\\n distanceAvg_json = None\\n \\n # Create strikes json\\n try:\\n (strikes_time_vt, strikes_dict) = getDaySummaryVectors(db_lookup(), 'lightning_strikes', timespan,['sum'])\\n strikesPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(strikes_dict['sum'][1], \\\"1f\\\")[-2])\\n strikesSumRound = [roundNone(x,strikesPlaces) for x in strikes_dict['sum'][0]]\\n strikesSum_json = json.dumps(zip(time_ms, strikesSumRound))\\n except:\\n strikesSum_json = None\\n\\n # Create full_spectrum json\\n try:\\n (full_spectrum_time_vt, full_spectrum_dict) = getDaySummaryVectors(db_lookup(), 'full_spectrum', timespan,['max', 'avg'])\\n full_spectrumPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(full_spectrum_dict['max'][1], \\\"1f\\\")[-2])\\n full_spectrumMaxRound = [roundNone(x,full_spectrumPlaces) for x in full_spectrum_dict['max'][0]]\\n full_spectrumAvgRound = [roundNone(x,full_spectrumPlaces) for x in full_spectrum_dict['avg'][0]]\\n full_spectrumMax_json = json.dumps(zip(time_ms, full_spectrumMaxRound))\\n full_spectrumAvg_json = json.dumps(zip(time_ms, full_spectrumAvgRound))\\n except:\\n full_spectrumMax_json = None\\n full_spectrumAvg_json = None\\n \\n # Create lux json\\n try:\\n (lux_time_vt, lux_dict) = getDaySummaryVectors(db_lookup(), 'lux', timespan,['max', 'avg'])\\n luxPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(lux_dict['max'][1], \\\"1f\\\")[-2])\\n luxMaxRound = [roundNone(x,luxPlaces) for x in lux_dict['max'][0]]\\n luxAvgRound = [roundNone(x,luxPlaces) for x in lux_dict['avg'][0]]\\n luxMax_json = json.dumps(zip(time_ms, luxMaxRound))\\n luxAvg_json = json.dumps(zip(time_ms, luxAvgRound))\\n except:\\n luxMax_json = None\\n luxAvg_json = None\\n\\n # Create infrared json\\n try:\\n (infrared_time_vt, infrared_dict) = getDaySummaryVectors(db_lookup(), 'infrared', timespan,['max', 'avg'])\\n infraredPlaces = int(self.generator.skin_dict['Units']['StringFormats'].get(infrared_dict['max'][1], \\\"1f\\\")[-2])\\n infraredMaxRound = [roundNone(x,infraredPlaces) for x in infrared_dict['max'][0]]\\n infraredAvgRound = [roundNone(x,infraredPlaces) for x in infrared_dict['avg'][0]]\\n infraredMax_json = json.dumps(zip(time_ms, infraredMaxRound))\\n infraredAvg_json = json.dumps(zip(time_ms, infraredAvgRound))\\n except:\\n infraredMax_json = None\\n infraredAvg_json = None\\n \\n # Put into a dictionary to return\\n self.search_list_extension = {'outTempMinMax_json' : outTempMinMax_json,\\n 'outTempAvg_json' : outTempAvg_json,\\n 'inTempMinMax_json' : inTempMinMax_json,\\n 'inTempAvg_json' : inTempAvg_json,\\n 'appTempMinMax_json' : appTempMinMax_json,\\n 'appTempMin_json' : appTempMin_json,\\n 'appTempMax_json' : appTempMax_json,\\n 'appTempAvg_json' : appTempAvg_json,\\n 'dewpointMinMax_json' : dewpointMinMax_json,\\n 'dewpointAvg_json' : dewpointAvg_json,\\n 'windchillMinMax_json' : windchillMinMax_json,\\n 'windchillAvg_json' : windchillAvg_json,\\n 'heatindexMinMax_json' : heatindexMinMax_json,\\n 'heatindexAvg_json' : heatindexAvg_json,\\n 'outHumidityMinMax_json' : outHumidityMinMax_json,\\n 'outHumidityMin_json' : outHumidityMin_json,\\n 'outHumidityMax_json' : outHumidityMax_json,\\n 'outHumidityAvg_json' : outHumidityAvg_json,\\n 'inHumidityMinMax_json' : inHumidityMinMax_json,\\n 'inHumidityMin_json' : inHumidityMin_json,\\n 'inHumidityMax_json' : inHumidityMax_json,\\n 'inHumidityAvg_json' : inHumidityAvg_json,\\n 'barometerMinMax_json' : barometerMinMax_json,\\n 'barometerMin_json' : barometerMin_json,\\n 'barometerMax_json' : barometerMax_json,\\n 'barometerAvg_json' : barometerAvg_json,\\n 'windMax_json' : windMax_json,\\n 'windAvg_json' : windAvg_json,\\n 'windSpeedMax_json' : windSpeedMax_json,\\n 'windSpeedAvg_json' : windSpeedAvg_json,\\n 'windDir_json' : windDir_json,\\n 'rainSum_json' : rainSum_json,\\n 'radiationMax_json' : radiationMax_json,\\n 'radiationAvg_json' : radiationAvg_json,\\n 'uvMax_json' : uvMax_json,\\n 'uvAvg_json' : uvAvg_json,\\n 'uvaMax_json' : uvaMax_json,\\n 'uvaAvg_json' : uvaAvg_json,\\n 'uvbMax_json' : uvbMax_json,\\n 'uvbAvg_json' : uvbAvg_json,\\n 'uvaWmMax_json' : uvaWmMax_json,\\n 'uvaWmAvg_json' : uvaWmAvg_json,\\n 'uvbWmMax_json' : uvbWmMax_json,\\n 'uvbWmAvg_json' : uvbWmAvg_json,\\n 'strikesSum_json' : strikesSum_json,\\n 'distanceMax_json' : distanceMax_json ,\\n 'distanceAvg_json' : distanceAvg_json,\\n 'energyMax_json' : energyMax_json,\\n 'energyAvg_json' : energyAvg_json,\\n 'full_spectrumMax_json' : full_spectrumMax_json,\\n 'full_spectrumAvg_json' : full_spectrumAvg_json,\\n 'luxMax_json' : luxMax_json,\\n 'luxAvg_json' : luxAvg_json,\\n 'infraredMax_json' : infraredMax_json,\\n 'infraredAvg_json' : infraredAvg_json,\\n 'utcOffset': utc_offset,\\n 'yearPlotStart' : _timespan.start * 1000,\\n 'yearPlotEnd' : _timespan.stop * 1000}\\n t2 = time.time()\\n logdbg2(\\\"w34highchartsYear SLE executed in %0.3f seconds\\\" % (t2 - t1))\\n\\n # Return our json data\\n return [self.search_list_extension]\",\n \"def read_SVE_runoff(ID, start_date,end_date, sourcefile):\\n # Runoffs compiled from Hertta-database (Syke) and Metla/Luke old observations.\\n # Span: 1935-2015, missing data=-999.99\\n # File columns:\\n # pvm;14_Paunulanpuro;15_Katajaluoma;16_Huhtisuonoja;17_Kesselinpuro;18_Korpijoki;20_Vaarajoki;\\n # 22_Vaha-Askanjoki;26_Iittovuoma;24_Kotioja;27_Laanioja;1_Lompolojanganoja;10_Kelopuro;\\n # 13_Rudbacken;3_Porkkavaara;11_Hauklammenoja;19_Pahkaoja;21_Myllypuro;23_Ylijoki;2_Liuhapuro;\\n # 501_Kauheanpuro;502_Korsukorvenpuro;503_Kangasvaaranpuro;504_Kangaslammenpuro;56_Suopuro;\\n # 57_Valipuro;28_Kroopinsuo;30_Pakopirtti;31_Ojakorpi;32_Rantainrahka\\n\\n # import data\\n sourcefile = os.path.join(sourcefile)\\n dat = pd.read_csv(sourcefile, sep=';', header='infer', parse_dates=['pvm'], index_col='pvm', na_values=-999)\\n\\n # split column names so that they equal ID's\\n cols = [x.split(\\\"_\\\")[0] for x in dat.columns]\\n dat.columns = cols\\n\\n # get desired period & rename column ID to Qm\\n dat = dat[(dat.index >= start_date) & (dat.index <= end_date)]\\n dat = dat[ID]\\n if type(ID) is str:\\n dat.columns = ['Qm']\\n return dat\",\n \"def update_data(self):\\n data, meta_data = ts.get_daily(symbol=self.stock_ticker, outputsize='full')\\n self.data = data\\n self.meta_data = meta_data\",\n \"def update(self, ti=None, tf=None):\\n if failedobspyimport:\\n raise ImportError('ObsPy import failed, cannot update data.')\\n\\n makedir('_tmp')\\n\\n # default data range if not given \\n ti = ti or datetime(self.tf.year,self.tf.month,self.tf.day,0,0,0)\\n tf = tf or datetime.today() + _DAY\\n \\n ti = datetimeify(ti)\\n tf = datetimeify(tf)\\n\\n ndays = (tf-ti).days\\n\\n # parallel data collection - creates temporary files in ./_tmp\\n pars = [[i,ti] for i in range(ndays)]\\n p = Pool(6)\\n p.starmap(get_data_for_day, pars)\\n p.close()\\n p.join()\\n\\n # special case of no file to update - create new file\\n if not self.exists:\\n shutil.copyfile('_tmp/_tmp_fl_00000.dat',self.file)\\n self.exists = True\\n shutil.rmtree('_tmp')\\n return\\n\\n # read temporary files in as dataframes for concatenation with existing data\\n dfs = [self.df[datas]]\\n for i in range(ndays):\\n fl = '_tmp/_tmp_fl_{:05d}.dat'.format(i)\\n if not os.path.isfile(fl): \\n continue\\n dfs.append(pd.read_csv(fl, index_col=0, parse_dates=[0,], infer_datetime_format=True))\\n shutil.rmtree('_tmp')\\n self.df = pd.concat(dfs)\\n\\n # impute missing data using linear interpolation and save file\\n self.df = self.df.loc[~self.df.index.duplicated(keep='last')]\\n self.df = self.df.resample('10T').interpolate('linear')\\n\\n # remove artefact in computing dsar\\n for i in range(1,int(np.floor(self.df.shape[0]/(24*6)))): \\n ind = i*24*6\\n self.df['dsar'][ind] = 0.5*(self.df['dsar'][ind-1]+self.df['dsar'][ind+1])\\n\\n self.df.to_csv(self.file, index=True)\\n self.ti = self.df.index[0]\\n self.tf = self.df.index[-1]\",\n \"def get_data(station,starttime,endtime,activity=False,\\n rep='/GNOMEDrive/gnome/serverdata/',resample=None):\\n setname = \\\"MagneticFields\\\"\\n dstr = ['%Y','%m','%d','%H','%M']\\n dsplit = '-'.join(dstr[:starttime.count('-')+1])\\n start = datetime.strptime(starttime,dsplit)\\n starttime = construct_utc_from_metadata(start.strftime(\\\"%Y/%m/%d\\\"),\\n start.strftime(\\\"%H:%M:%S.%d\\\"))\\n dsplit = '-'.join(dstr[:endtime.count('-')+1])\\n end = datetime.strptime(endtime,dsplit)\\n endtime = construct_utc_from_metadata(end.strftime(\\\"%Y/%m/%d\\\"),\\n end.strftime(\\\"%H:%M:%S.%d\\\"))\\n dataset = []\\n for date in numpy.arange(start,end,timedelta(minutes=1)):\\n date = date.astype(datetime)\\n path1 = rep+station+'/'+date.strftime(\\\"%Y/%m/%d/\\\")\\n path2 = station+'_'+date.strftime(\\\"%Y%m%d_%H%M*.hdf5\\\")\\n fullpath = os.path.join(path1,path2)\\n dataset += glob.glob(fullpath)\\n if len(dataset)==0:\\n print \\\"ERROR: No data files were found...\\\"\\n quit()\\n file_order,data_order = {},{}\\n for fname in dataset:\\n hfile = h5py.File(fname, \\\"r\\\")\\n segfile = file_to_segment(hfile,setname)\\n file_order[segfile] = fname\\n data_order[segfile] = hfile\\n # Extract sample rate from metadata of last read data file\\n sample_rate = hfile[setname].attrs[\\\"SamplingRate(Hz)\\\"]\\n # Estimate full segment activity list\\n activity = create_activity_list(station,data_order)\\n # Generate an ASCII representation of the GPS timestamped\\n # segments of time covered by the input data\\n seglist = segmentlist(data_order.keys())\\n # Sort the segment list\\n seglist.sort()\\n # Create list of time series from every segment\\n ts_list = generate_timeseries(file_order,setname)\\n # Retrieve channel data for all the segments\\n full_data = numpy.hstack([retrieve_channel_data(data_order[seg],setname)\\n for seg in seglist])\\n new_sample_rate = sample_rate if resample==None else resample\\n new_data_length = len(full_data)*new_sample_rate/float(sample_rate)\\n full_data = scipy.signal.resample(full_data,int(new_data_length))\\n # Models a time series consisting of uniformly sampled scalar values\\n ts_data = types.TimeSeries(full_data,delta_t=1./new_sample_rate,\\n epoch=seglist[0][0])\\n for v in data_order.values():\\n v.close()\\n return ts_data,ts_list,activity,int(starttime),int(endtime)\",\n \"def refresh():\\r\\n DB.drop_all()\\r\\n DB.create_all()\\r\\n # TODO Get data from OpenAQ, make Record objects with it, and add to db\\r\\n for i in time_x_values():\\r\\n DB.session.add(Record(datetime=i[0], value=i[1]))\\r\\n DB.session.commit()\\r\\n return 'Data refreshed!'\",\n \"def preprocess_time(data, metadata):\\n timestamp_name = metadata[\\\"timestamp_name\\\"]\\n if timestamp_name == \\\"\\\":\\n timestamp_name = \\\"fake_ts\\\"\\n data[timestamp_name] = data.index\\n\\n data[timestamp_name] = pd.to_datetime(data[timestamp_name])\\n data.sort_values(by=[timestamp_name], inplace=True)\\n data.set_index([timestamp_name], inplace=True)\\n\\n return data\",\n \"def Data_formatting(dz, y, timestep, TZ):\\r\\n \\r\\n #reindex data by datetime\\r\\n dz.index = pd.to_datetime(dz['DATE'])\\r\\n \\r\\n #Isolate temperature data\\r\\n dz = dz[['TMP']]\\r\\n \\r\\n #Delete data mistake\\r\\n dz = dz[dz['TMP'] != \\\"+9999,9\\\"]\\r\\n \\r\\n #Format data\\r\\n dz['TMP'] = dz['TMP'].str.replace(',', '.')\\r\\n dz['TMP'] = pd.to_numeric(dz['TMP'], errors='coerce')\\r\\n \\r\\n #Delete NaN data\\r\\n dz = dz.dropna()\\r\\n \\r\\n #Convert temperature\\r\\n dz['TMP'] = dz['TMP'] / 10\\r\\n dz['TMP'] = dz['TMP'] * (9/5) + 32\\r\\n \\r\\n #Convert datetime index utc to specified timezone\\r\\n dz.index = dz.index.tz_localize(pytz.utc).tz_convert(pytz.timezone(str(TZ))).strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\r\\n dz.index = pd.to_datetime(dz.index)\\r\\n \\r\\n #Resample data by average on timestep\\r\\n dz = dz.resample(rule = str(timestep)).mean()\\r\\n \\r\\n #Define the first date of the instance year\\r\\n fdy = dt.datetime.strptime(\\\"01/01/\\\"+str(y)+\\\" 00:00\\\", '%m/%d/%Y %H:%M')\\r\\n #Convert first date of the year to timezone\\r\\n fdy = Date_calibration(fdy, 0, TZ)\\r\\n \\r\\n #If we collect the date from the current year we limit the collect from 2days before now\\r\\n \\r\\n #Define the datetime 2 days before now\\r\\n dbeyest = dt.datetime.now(tz=pytz.timezone(str(TZ))) - dt.timedelta(days=2)\\r\\n \\r\\n #If the instance year is the current year\\r\\n if(y == dbeyest.year):\\r\\n #We limit the collect 2 days before now\\r\\n ldy = dt.datetime.strptime(str(dbeyest.month)+\\\"/\\\"+str(dbeyest.day)+\\\"/\\\"+str(y)+\\\" 23:59\\\", '%m/%d/%Y %H:%M')\\r\\n else:\\r\\n #Else, we collect the full year\\r\\n ldy = dt.datetime.strptime(\\\"12/31/\\\"+str(y)+\\\" 23:59\\\", '%m/%d/%Y %H:%M')\\r\\n \\r\\n #Convert the last date of the year to specified timezone\\r\\n ldy = Date_calibration(ldy, 0, TZ)\\r\\n \\r\\n #Set up dataframe for the specified datetime index and timestep\\r\\n ph = pd.DataFrame(index=pd.DatetimeIndex(start=fdy, end=ldy, freq=str(timestep)))\\r\\n \\r\\n #Past original data temperature in the time fitted dataframe (with the datetimeindex position)\\r\\n ph['TMP'] = dz['TMP']\\r\\n \\r\\n #Calculate the quality of the instance data\\r\\n nb_nan = ph['TMP'].isnull().sum()\\r\\n qual = (1 - (nb_nan) / len(ph)) * 100\\r\\n \\r\\n return dz, qual\",\n \"def ec_data_processor_precip(path, x='TIMESTAMP_END', y='LE', daily=True):\\n\\n\\n # Get the data from the path and turn the path into a data frame\\n # ec_dataset = pd.read_csv(path, header=2)\\n\\n ec_dataset = pd.read_csv(path, header=2, engine='python')\\n\\n # print ec_dataset.head()\\n print ec_dataset['LE'].head()\\n print ec_dataset[ec_dataset[y] != -9999].head()\\n # === get rid of no data values in any category of the energy balance ===\\n precip_dataset = ec_dataset[ec_dataset['P'] != -9999]\\n ec_dataset = ec_dataset[ec_dataset[y] != -9999]\\n ec_dataset = ec_dataset[ec_dataset['NETRAD'] != -9999]\\n ec_dataset = ec_dataset[ec_dataset['H'] != -9999]\\n ec_dataset = ec_dataset[ec_dataset['LE'] != -9999]\\n # # You probably won't need these because Marcy Doesn't think they are valid for her towers\\n # ec_dataset = ec_dataset[ec_dataset['SH'] != -9999]\\n # ec_dataset = ec_dataset[ec_dataset['SLE'] != -9999]\\n\\n if x.startswith(\\\"TIMESTAMP\\\"):\\n a = ec_dataset[x].apply(lambda b: dt.strptime(str(b), '%Y%m%d%H%M'))\\n aa = precip_dataset[x].apply(lambda d: dt.strptime(str(d), '%Y%m%d%H%M'))\\n\\n # # TODO - if converting PRISM to MTN time.\\n # # Convert to PRISM time (Mtn Standard + 5 hours) PRISM midnight is 12:00 UTC - 7 hours for mountain. Net +5 hrs\\n # a = [i + timedelta(hours=19) for i in a]\\n # aa = [i + timedelta(hours=19) for i in aa]\\n\\n\\n else:\\n a = ec_dataset[x]\\n\\n # ===== Time Series Processing =====\\n\\n timeseries = a\\n p_timeseries = aa\\n # print 'timeseries\\\\n', timeseries\\n Rn = ec_dataset['NETRAD'].values\\n H = ec_dataset['H'].values\\n LE = ec_dataset['LE'].values\\n P = precip_dataset['P']\\n print 'P \\\\n', P\\n # indexed_datetimes = pd.DataFrame(pd.DatetimeIndex(timeseries))\\n\\n # # testing\\n # plt.plot(timeseries, P, color='black')\\n # plt.show()\\n\\n # recreate a dataframe of the variables you want to time average on a monthly timestep\\n halfhour_data = pd.DataFrame({'timeseries': timeseries, 'Rn': Rn, 'LE': LE, 'H': H}) # took out precip. no good vals? 'P': P\\n\\n halfhour_precip = pd.DataFrame({'timeseries': p_timeseries, 'P': P})\\n # set the timeseries column to the index so groupby function can group by year and month of the index.\\n halfhour_data = halfhour_data.set_index(pd.DatetimeIndex(halfhour_data['timeseries']))\\n halfhour_precip = halfhour_precip.set_index(pd.DatetimeIndex(halfhour_precip['timeseries']))\\n # convert latent heat to mmH2O by dividing by latent heat of vaporization.\\n halfhour_data['mmh20'] = halfhour_data['LE'] * 7.962e-4\\n\\n if daily:\\n\\n daily_cum_data = halfhour_data.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()\\n daily_cum_precip = halfhour_precip.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()\\n\\n # get each day in the timeseries. there are duplicates from the groupby function, so use set() to get rid of\\n # duplicates\\n daily_cum_time = daily_time_parse(timeseries)\\n daily_cum_precip_time = daily_time_parse(p_timeseries)\\n\\n # # testing\\n # daily_cum_data.to_csv('/Users/dcadol/Desktop/daily_cumulative_df.csv')\\n\\n # format daily_cum_data to have datetimes\\n daily_cum_data['date'] = daily_cum_time\\n daily_cum_precip['date'] = daily_cum_precip_time\\n\\n return daily_cum_data, daily_cum_precip\",\n \"def feature_engineer_ts(self, month=12):\\n st_data_dt = self.get_st_data_dt()\\n end_data_dt = self.get_end_data_dt()\\n date_list = pd.date_range(*(pd.to_datetime([st_data_dt, end_data_dt]) + pd.offsets.MonthEnd()), freq='M').to_list()\\n population = self.get_population()\\n is_raw_partition = self.get_is_raw_partition()\\n# Lag 2 months\\n all_data = []\\n# join past is_raw columns\\n for d in date_list:\\n \\n population_partition = population[population['ft_data_dt'] == d] \\n old_date = d - relativedelta(months=month)\\n y = old_date.year\\n m = old_date.month\\n day = calendar.monthrange(y, m)[1]\\n old_date = date(y, m, day)\\n old_date = max(old_date, st_data_dt)\\n date_list_join = pd.date_range(*(pd.to_datetime([old_date, d]) + pd.offsets.MonthEnd()), freq='M').to_list()\\n date_list_join.reverse()\\n for index, date_join in enumerate(date_list_join):\\n if date_join.strftime(\\\"%Y-%m-%d\\\") not in is_raw_partition.keys():\\n continue\\n \\n tmp_is_raw_partition = is_raw_partition[date_join.strftime(\\\"%Y-%m-%d\\\")]\\n \\n rename_col = [c for c in list(tmp_is_raw_partition.columns) if c not in ['idd', 'ft_data_dt']]\\n new_col = [c+'_'+str(index+1) for c in rename_col]\\n name_dict = dict(list(zip(rename_col, new_col)))\\n tmp_is_raw_partition = tmp_is_raw_partition.rename(columns = name_dict)\\n population_partition = population_partition.merge(tmp_is_raw_partition.drop(columns=['ft_data_dt']), on=['idd'], how='left')\\n all_data.append(population_partition)\\n ts_df = pd.concat(all_data)\\n threshold_null = len(ts_df.columns) - 4\\n ts_df = ts_df[ts_df.isnull().sum(axis=1) < threshold_null]\\n \\n def sum_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_sum_'+str(duration)+'mth'\\n tmp_df = df[col_list].sum(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def mean_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_avg_'+str(duration)+'mth'\\n tmp_df = df[col_list].mean(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def std_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_std_'+str(duration)+'mth'\\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def med_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_med_'+str(duration)+'mth'\\n tmp_df = df[col_list].std(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def min_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_min_'+str(duration)+'mth'\\n tmp_df = df[col_list].min(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def max_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_max_'+str(duration)+'mth'\\n tmp_df = df[col_list].max(axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def q1_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_q1_'+str(duration)+'mth'\\n tmp_df = df[col_list].quantile(q=0.25, axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def q3_ts(self, df, col_list, feature, duration):\\n ft_name = feature+ '_q3_'+str(duration)+'mth'\\n tmp_df = df[col_list].quantile(q=0.75, axis = 1).to_frame(name=ft_name)\\n return tmp_df\\n \\n def last_ts(self, df, feature):\\n ft_name = feature+ '_last'\\n tmp_df = df[feature+'_'+str(1)].to_frame(name=ft_name)\\n return tmp_df\\n \\n ts_duration = [1, 3, 6, 9, 12]\\n feature_list = self.get_is_raw_col()\\n df = ts_df[['idd', 'ft_data_dt']]\\n# Time Series Features\\n for duration in ts_duration:\\n for col in feature_list:\\n col_list = [col+'_'+str(i) for i in range(1, duration+1)]\\n df = pd.concat([df\\\\\\n , sum_ts(self, ts_df, col_list, col, duration)\\\\\\n , mean_ts(self, ts_df, col_list, col, duration)\\\\\\n , med_ts(self, ts_df, col_list, col, duration)\\\\\\n , q1_ts(self, ts_df, col_list, col, duration)\\\\\\n , q3_ts(self, ts_df, col_list, col, duration)\\\\\\n , min_ts(self, ts_df, col_list, col, duration)\\\\\\n , max_ts(self, ts_df, col_list, col, duration)]\\n , axis=1)\\n self.set_all_data(df)\",\n \"def main():\\n \\n Y1, Y2 = 2005, 2017 ### range with coordinates supplied in pre-2018 generated archive\\n\\n if len(sys.argv) > 1 and int(sys.argv[1]) > 0:\\n Y1 = int(sys.argv[1])\\n \\n if len(sys.argv) > 2 and int(sys.argv[2]) > Y1:\\n Y2 = int(sys.argv[2])\\n \\n with open('data/audit.log','w') as output:\\n for Y in range(Y1, Y2):\\n df = pd.read_csv('data/{}.csv'.format(Y), low_memory = False)\\n output.write('\\\\n--- {} --------------------\\\\n'.format(Y))\\n\\n # remove `deleted` records\\n df['deleted'] = df['deleted'].apply(yes_no)\\n df = df[df['deleted'] == 0]\\n\\n # remove misc misdemeanors\\n df = df[~df['category'].isin(drop)]\\n\\n # validate date and expand into Y,N,D,W,H\\n df['dt'] = df['incident_date'].apply(extract)\\n df = df[~df['dt'].isnull()]\\n\\n # convert from plane state to longitude-latitude\\n df['ll'] = df.apply(to_lnglat, axis = 1)\\n\\n # init features\\n features = df.loc[:,['category','stat','address','city','zip']]\\n features['id'] = df['incident_id']\\n dt = ['year','month','day','weekday','hour']\\n for i in range(len(dt)):\\n features[dt[i]] = df['dt'].apply(lambda x: x[i] )\\n\\n features['lng'] = df['ll'].apply(lambda x: x[0])\\n features['lat'] = df['ll'].apply(lambda x: x[1])\\n\\n features['gang'] = df['gang_related'].apply(yes_no)\\n features['category'] = df['category'].apply(collapse)\\n cat = set(features.groupby(['category']).size().reset_index(name='count')['category'].tolist())\\n output.write('Categories: {}\\\\n'.format(len(cat)))\\n\\n output.write('Date miss: {:.4f}%\\\\n'\\\\\\n .format(100 * (1 - len(features[(features['year'] > 2000) & (~features['weekday'].isnull())])/len(features))))\\n output.write('Location miss: {:.4f}%\\\\n'\\\\\\n .format(100 * (1 - len(features[(features['zip'] > 0) | (features['lat'] > 0)])/len(features))))\\n\\n # keep records with valid date\\n features['date'] = df['dt'].apply(lambda x: datetime.date(x[0], x[1], x[2]))\\n features = features[(features['year'] > 2000) & (~features['weekday'].isnull())]\\n output.write('Time miss: {:.4f}%\\\\n'.format(100 * len(features[features['hour'] == -1])/len(features)))\\n\\n # potential `time-unknown` issue\\n output.write('Hour ZERO: {:.4f}%\\\\n'.format(100 * len(features[features['hour'] == 0])/len(features)))\\n output.write('Hour NOON: {:.4f}%\\\\n'.format(100 * len(features[features['hour'] == 12])/len(features)))\\n\\n features = features[(features['zip'] > 0) | (features['lat'] > 0)]\\n\\n # get the best possible coordinates + zipcode assessment\\n features[['zip','lng','lat']] = features[['zip','lng','lat']].apply(fix_location, axis = 1)\\n output.write('Failed location: {:.4f}%\\\\n'.format(100 * len(features[features['zip'].isnull()])/len(features)))\\n features = features[~features['zip'].isnull()]\\n features['zip'] = df['zip'].apply(lambda x: str(x)[:5])\\n \\n # normalize city attr\\n features = features.join(zipcodes[['zip','city']].set_index('zip'), on = 'zip', lsuffix = '_orig', rsuffix = '')\\n features.loc[features['city'].isnull(), 'city'] = features.loc[features['city'].isnull(), 'city_orig']\\\\\\n .apply(lambda x: x if type(x) == float else ' '.join([l[0].upper() + l[1:] for l in x.split()]))\\n\\n # reduce to LA bounding-box\\n features = features[(features['lng'] > -119) & (features['lng'] < -116)]\\n features = features[(features['lat'] > 32) & (features['lat'] < 35)]\\n\\n # save csv\\n features[fields].to_csv('data/F{}.csv'.format(Y), index = False)\\n features[fields].to_json('data/F{}.json'.format(Y), orient = 'records')\\n output.close()\",\n \"def get_gdax_historical_data():\\n \\n start = None\\n while not start:\\n start,end,tid = getStartAndEndHistoric()\\n if not start:\\n time.sleep(60)\\n #Todo: change this to 1min\\n firsttimestamp = start\\n engine = sa.create_engine(sql_address)\\n products = [\\\"LTC-USD\\\",\\\"LTC-BTC\\\",\\\"ETH-USD\\\",\\\"ETH-BTC\\\",\\\"BTC-USD\\\"]\\n public_client = gdax.PublicClient()\\n deltat = datetime.timedelta(seconds = 200)\\n timewindows = []\\n while end - start > datetime.timedelta(seconds=0):\\n if start + deltat > end:\\n endx = end\\n else:\\n endx = start + deltat\\n timewindows.append([start,endx])\\n start += deltat\\n results = []\\n total = len(timewindows)\\n current_idx = 0\\n timeold = time.time()\\n numofqueries = 0\\n engine = sa.create_engine(sql_address)\\n Base.metadata.bind = engine\\n DBSession = sa.orm.sessionmaker()\\n DBSession.bind = engine\\n session = DBSession()\\n for startx,endx in timewindows:\\n\\n current_idx += 1\\n for i in products:\\n repeat = True\\n while repeat:\\n\\n #delay if ratelimts are close\\n if numofqueries < 3:\\n while time.time() - timeold < 1:\\n time.sleep(0.05)\\n \\n timeold = time.time()\\n numofqueries = 0\\n try:\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n except:\\n time.sleep(30)\\n public_client = gdax.PublicClient()\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n\\n alist = public_client.get_product_historic_rates(i, start = startx, end = endx, granularity = 1)\\n\\n numofqueries += 1\\n\\n #rate limit exceeded has 'message' as dict.\\n if not 'message' in alist:\\n repeat = False\\n for a in alist:\\n a[0] = datetime.datetime.fromtimestamp(float(a[0]))\\n tmp = i.split('-')\\n d = dict(coin = tmp[0], currency = tmp[1], timestamp = a[0], low=a[1], high=a[2], open=a[3], close=a[4], volume=a[5])\\n results.append(d)\\n lasttimestamp = a[0]\\n\\n #upload with batch size of 10000\\n if len(results) > 10000:\\n engine.execute(\\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\\n results\\n )\\n results = []\\n \\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n if update:\\n update.end = lasttimestamp\\n session.commit()\\n else:\\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\\n session.add(new_update)\\n session.commit()\\n if len(results) > 0:\\n engine.execute(\\n GADXHistoricalDataOneSecondOHLC.__table__.insert(),\\n results\\n )\\n results = []\\n \\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n if update:\\n update.end = lasttimestamp\\n session.commit()\\n else:\\n new_update = historicalDataProgramState(entry_type = 'update',transaction_id = tid,start=firsttimestamp,end=lasttimestamp,platform='GDAX',status='incomplete')\\n session.add(new_update)\\n session.commit()\\n\\n update = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'update')).first()\\n update.status='complete'\\n order = session.query(historicalDataProgramState).filter(sa.and_(historicalDataProgramState.transaction_id == tid,historicalDataProgramState.entry_type == 'order')).first()\\n order.status='complete'\\n session.commit()\",\n \"def get_SWAS_data4flight(flight_ID=None):\\n # Where is the data?\\n folder = '{}/{}/'.format(get_local_folder('ARNA_data'), 'SWAS')\\n# filename = 'ARNA-FIRSTLOOK-SWAS_JDL_typo_fix.csv'\\n# var2use = 'comments4'\\n# format = '%d/%m/%Y %H:%M:%S'\\n # Or use latest file (NOTE: issue with column formating)\\n# filename = 'ARNA-SECONDLOOK-SWAS.csv'\\n # Use the updated second look file\\n filename = 'ARNA-SECONDLOOK-SWAS_v2.csv'\\n df = pd.read_csv(folder+filename)\\n print(filename)\\n # Update the index to use the SWAS fire fime\\n var2use = 'SAMPLE START TIME'\\n format = '%d/%m/%Y %H:%M:%S'\\n df.index = pd.to_datetime(df[var2use].values, format=format)\\n # If a flight ID stated, then only return points for that flight\\n if isinstance(flight_ID, type(None)):\\n pass\\n else:\\n # Get the beginning and end of the flight\\n dfS = get_summary4flight(flight_ID=flight_ID)\\n sdate = dfS.index.min()\\n edate = dfS.index.max()\\n # Only consider samples within this time\\n df = df.loc[df.index >= sdate, :]\\n df = df.loc[df.index <= edate, :]\\n return df\",\n \"def step030():\\n logger.logMessage('Begin: get data from table')\\n \\n query = 'select tsa,time at time zone \\\\'utc\\\\' from weather_dupes ' + \\\\\\n 'order by time;'\\n \\n pgConn = pg.connect(host=host,user=user,password=password,database=database) \\n with pgConn:\\n with pgConn.cursor() as c:\\n c.execute(query)\\n numrecs = 0\\n with open(dbDumpFile,'w') as f:\\n for row in c.fetchall():\\n tsa = row[0]\\n time= row[1].isoformat()\\n f.write('{0:14d};{1:25s}\\\\n'.format(tsa,time))\\n numrecs += 1\\n if numrecs % 1000 == 0:\\n logger.logMessage(level='DEBUG',message=\\\"{0:9d} rows dumped\\\".format(numrecs))\\n logger.logMessage(\\\"Total rows: {0:d}\\\".format(numrecs))\\n \\n logger.logMessage('End : get data from table')\",\n \"def find_inflections(study, record, sensor, segment, range):\\n\\n # check if the inflections have already been found\\n path = [study, 'analyzed', 'inflections', 'all_times', str(range), record, segment]\\n pathJoined = os.path.join(*path)\\n file = os.path.join(pathJoined, sensor + \\\".csv\\\")\\n\\n if os.path.isfile(file):\\n print('file found, not recalculated.')\\n return\\n\\n print('finding inflections to build : ' + file)\\n\\n # retrieve the timestamped measurements for the study - record - sensor - segment\\n format_type = 'truncate'\\n source = os.path.join(study, 'formatted', format_type, record, segment, sensor + '.csv')\\n print('source = ' + source)\\n df = pd.read_csv(source)\\n\\n for colName in df.columns:\\n\\n # remove extra columns because the dataframe will be saved\\n if 'Unnamed' in str(colName):\\n del df[colName]\\n\\n # save the timestamps as a list\\n elif 'Minutes' in str(colName):\\n timeMinutes = list(df[colName])\\n\\n # find the measurement\\n elif 'meas' in colName:\\n\\n # add new columns to the dataframe to save the new variables\\n newColNames = ['inflectionDecision', 'inflectionLocation', 'polyfitCoefficients', 'polyfitEquation', 'polyfitSolution', 'derivativeEquation', 'derivativeSolution']\\n colNameSplit = colName.split('_')\\n print('colNameSplit[0] = ' + colNameSplit[0])\\n\\n for suffix in newColNames:\\n label = str(colNameSplit[0] + '_' + suffix)\\n print('label = ' + label)\\n if label not in df.columns:\\n df[label] = [None]*len((list(df['timeMinutes'])))\\n\\n df['timeBegin'] = [None]*len((list(df['timeMinutes'])))\\n df['timeEnd'] = [None]*len((list(df['timeMinutes'])))\\n\\n for timeMinute in timeMinutes:\\n\\n i = df[ df['timeMinutes']== timeMinute].index.values[0]\\n\\n timeDif = (float(df.loc[2,'timeMinutes']) - float(df.loc[1,'timeMinutes']))\\n timeTolerance = timeDif/2\\n iRange = int(range/60*1/(timeDif))\\n # print('iRange = ' + str(iRange))\\n\\n if len(list(df['timeMinutes'])) - i <= iRange+2:\\n continue\\n\\n timeMedian = df.loc[int(i+iRange/2), 'timeMinutes']\\n timeBegin = df.loc[int(i), 'timeMinutes']\\n timeEnd = df.loc[int(i+iRange), 'timeMinutes']\\n\\n # print('timeMedian = ' + str(timeMedian) + ' timeBegin = ' + str(timeBegin) + ' timeEnd = ' + str(timeEnd))\\n # print('range = ' + str(range/60) + ' timeEnd-timeBegin = ' + str(timeEnd-timeBegin) + ' % = ' + str(range/60/(timeEnd-timeBegin)))\\n\\n df_truncate = df[df['timeMinutes'] >= timeMinute]\\n df_truncate = df_truncate[df_truncate['timeMinutes'] <= timeMinute + range/60]\\n # df_truncate = df[df['timeMinutes'] >= timeMinute & df_truncate['timeMinutes'] <= timeMinute + range/60]\\n\\n timeTruncate = list(df_truncate['timeMinutes'])\\n df.loc[int(i+iRange/2), 'timeBegin'] = min(timeTruncate)\\n df.loc[int(i+iRange/2), 'timeEnd'] = max(timeTruncate)\\n\\n measTruncate = list(df_truncate[colName])\\n\\n coef = np.polyfit(timeTruncate, measTruncate, 2)\\n # coef = [float(x) for x in coef]\\n\\n x = sym.Symbol('x')\\n\\n f = coef[0]*x*x+coef[1]*x+coef[2]\\n # print('f = ')\\n # print(f)\\n\\n dff = sym.diff(f,x)\\n # print('dff = ')\\n # print(dff)\\n\\n solf = sym.solve(f)\\n soldf = sym.solve(dff)\\n soldf = soldf[0]\\n\\n\\n label = str(colNameSplit[0] + '_' + 'inflectionDecision')\\n df.loc[int(i+iRange/2), label] = 'No'\\n\\n label = str(colNameSplit[0] + '_' + 'inflectionLocation')\\n df.loc[int(i+iRange/2), label] = timeMinute\\n\\n label = str(colNameSplit[0] + '_' + 'polyfitCoefficients')\\n df.loc[int(i+iRange/2), label] = str(''.join([str(x) for x in coef]))\\n\\n label = str(colNameSplit[0] + '_' + 'polyfitEquation')\\n df.loc[int(i+iRange/2), label] = str(f)\\n\\n label = str(colNameSplit[0] + '_' + 'polyfitSolution')\\n df.loc[int(i+iRange/2), label] = str(''.join([str(x) for x in solf]))\\n\\n label = str(colNameSplit[0] + '_' + 'derivativeEquation')\\n df.loc[int(i+iRange/2), label] = str(dff)\\n\\n label = str(colNameSplit[0] + '_' + 'derivativeSolution')\\n df.loc[int(i+iRange/2), label] = str(soldf)\\n\\n if soldf < timeMedian + timeTolerance:\\n\\n if soldf > timeMedian - timeTolerance:\\n\\n print('inflection found at time = ' + str(soldf))\\n label = str(colNameSplit[0] + '_' + 'inflectionDecision')\\n df.loc[int(i+iRange/2), label] = 'Yes'\\n\\n path = build_path(path)\\n file = os.path.join(path, sensor + \\\".csv\\\")\\n df.to_csv(file)\\n print('inflection list saved : ' + file)\\n return(file)\",\n \"def get_weather_data():\\n keys = ['1364038.csv',\\n '1364041.csv',\\n '1364042.csv',\\n '1364043.csv',\\n '1364044.csv',\\n '1364046.csv',\\n '1364047.csv',\\n '1364048.csv',\\n '1364051.csv',\\n '1364052.csv',\\n '1364053.csv',\\n '1364054.csv',\\n '1364055.csv',\\n '1364058.csv',\\n '1364059.csv',\\n '1364060.csv',\\n '1364061.csv',\\n '1364062.csv',\\n '1364063.csv',\\n '1364064.csv',\\n '1364066.csv']\\n df_weather = import_weather(keys)\\n df_weather_dist = df_weather[[\\n 'LATITUDE', 'LONGITUDE', 'name']].drop_duplicates().reset_index()\\n return df_weather, df_weather_dist\",\n \"def overheadstats(sdb, obsdate, update=True):\\n bvs_updated = 0\\n scams=0\\n #for a given obsdate get the night info\\n nid=getnightinfo(sdb, obsdate)\\n\\n #get the times for the night\\n record=sdb.select('EveningTwilightEnd, MorningTwilightStart', 'NightInfo', 'NightInfo_Id=%i' % nid)\\n stime=record[0][0]\\n etime=record[0][1]\\n totaltime=(etime-stime).seconds\\n\\n #From the sdb, get the SoLogEvent table\\n record=sdb.select('EventType_Id, EventTime', 'SoLogEvent', 'NightInfo_Id=%i' % nid)\\n event_list=[]\\n for i in range(len(record)):\\n r=record[i]\\n if r[1].seconds>43200:\\n t=datetime.datetime(int(obsdate[0:4]), int(obsdate[5:7]), int(obsdate[8:10]), 0, 0, 0)+r[1]\\n else:\\n t=datetime.datetime(int(obsdate[0:4]), int(obsdate[5:7]), int(obsdate[8:10]), 0, 0, 0)+datetime.timedelta(days=1)+r[1]\\n event_list.append([r[0], t])\\n #sort the list by the datetimes\\n event_list.sort(key=lambda e:e[1])\\n\\n #get the night's list of blocks\\n selcmd='BlockVisit_Id'\\n tabcmd='BlockVisit'\\n logcmd='NightInfo_Id=%i and BlockVisitStatus_Id=%i' % (nid, 1)\\n bvid_list=sdb.select(selcmd, tabcmd, logcmd)\\n bvid_list[:]=[bvid[0] for bvid in bvid_list]\\n\\n #get a list of all images from the night\\n select_state='FileName, Proposal_Code, Target_Name, ExposureTime, UTSTART, h.INSTRUME, h.OBSMODE, h.DETMODE, h.CCDTYPE, NExposures, BlockVisit_Id'\\n table_state='FileData Join ProposalCode on (FileData.ProposalCode_Id = ProposalCode.ProposalCode_Id) join FitsHeaderImage as h using (FileData_Id)'\\n formatteddate = obsdate.replace('-','')\\n logic_state=\\\"FileName like '%\\\"+formatteddate+\\\"%' order by UTSTART\\\"\\n img_list=sdb.select(select_state, table_state, logic_state)\\n img_list[:] = [img for img in img_list if not \\\"CAL_\\\" in img[1] and not \\\"ENG_\\\" in img[1] and not \\\"JUNK\\\" in img[1]]\\n\\n #get a list of all RSS images from the night\\n select_state='FileName, Proposal_Code, Target_Name, ExposureTime, UTSTART, h.INSTRUME, '\\n select_state+='h.OBSMODE, h.DETMODE, h.CCDTYPE, NExposures, BlockVisit_Id, r.GRATING, r.GR_STA, r.AR_STA'\\n table_state='FileData Join ProposalCode on (FileData.ProposalCode_Id = ProposalCode.ProposalCode_Id) '\\n table_state+='join FitsHeaderImage as h using (FileData_Id) join FitsHeaderRss as r using (FileData_Id)'\\n formatteddate = obsdate.replace('-','')\\n logic_state=\\\"FileName like '%\\\"+formatteddate+\\\"%' order by UTSTART\\\"\\n rss_imglist=sdb.select(select_state, table_state, logic_state)\\n rss_imglist[:] = [img for img in rss_imglist if not \\\"CAL_\\\" in img[1] and not \\\"ENG_\\\" in img[1] and not \\\"JUNK\\\" in img[1]]\\n\\n #get a list of all point commands from the night\\n select_state= 'BlockVisit_Id, EventTime, Block_Id, Target_Name, NightInfo_Id, EventData'\\n table_state='PointEvent join SoLogEvent using (SoLogEvent_Id)'\\n plist=sdb.select(select_state, table_state, 'NightInfo_Id=%i' % nid)\\n point_list=[]\\n for i in range(len(plist)):\\n p=plist[i]\\n if p[1].seconds>43200:\\n t=datetime.datetime(int(obsdate[0:4]), int(obsdate[5:7]), int(obsdate[8:10]), 0, 0, 0)+p[1]\\n else:\\n t=datetime.datetime(int(obsdate[0:4]), int(obsdate[5:7]), int(obsdate[8:10]), 0, 0, 0)+datetime.timedelta(days=1)+p[1]\\n point_list.append([p[0], t, p[2], p[3], p[4], p[5]])\\n\\n #deal with accepted blocks\\n for bvid in bvid_list:\\n\\n #determine start time (point) and end time (track end)\\n pointtime = findpointcommand(bvid, point_list)\\n if pointtime is None:\\n #print('no point')\\n continue\\n starttime=pointtime\\n endtime=findguidingstop(starttime, event_list)\\n if endtime is None:\\n #print('no end')\\n continue\\n\\n #determine total time\\n tottime=endtime-starttime\\n #some limit to avoid crazy stats\\n if tottime.seconds > 10000:\\n #print('total too long, took %i s'%tottime.seconds)\\n continue\\n\\n #determine the slew time\\n guidestart=findguidingstart(starttime, event_list)\\n if guidestart is None:\\n #print('no trackstart')\\n continue\\n slewtime=guidestart-starttime\\n if slewtime.seconds > 1000:\\n #print('slew too long, took %i s'%slewtime.seconds)\\n continue\\n\\n #determine the time between TrackStart and OnTarget\\n ontarget=findontarget(starttime, event_list)\\n if ontarget is None:\\n #print('no on target?')\\n continue\\n trackerslewtime=ontarget-guidestart\\n if trackerslewtime.seconds > 500:\\n #print('trackslew too long, took %i s'%trackerslewtime.seconds)\\n continue\\n\\n #get primary instrument, check if MOS\\n scams=0\\n instr, primary_mode=getprimarymode(img_list, bvid)\\n if instr == 'SALTICAM':\\n #print('its a scam!')\\n scams+=1\\n continue\\n\\n select_state= 'Block_Id'\\n table_state='BlockVisit'\\n logic_state='BlockVisit_Id=%i' % (bvid)\\n bids=sdb.select(select_state, table_state, logic_state)\\n if not bids:\\n #print(\\\"no bid!\\\")\\n continue\\n else:\\n bid=bids[0]\\n selcmd='Block_Id, Barcode'\\n tabcmd='Block join Pointing using (Block_Id) join Observation using (Pointing_Id) '\\n tabcmd+='join TelescopeConfigObsConfig using (Pointing_Id) join ObsConfig on (PlannedObsConfig_Id=ObsConfig_Id) '\\n tabcmd+='join RssPatternDetail using (RssPattern_Id) join Rss using (Rss_Id) join RssProcedure using (RssProcedure_Id) '\\n tabcmd+='join RssConfig using (RssConfig_Id) join RssMask using (RssMask_Id)'\\n logcmd='RssProcedureType_Id = \\\\'7\\\\' and Block_Id = %i group by Block_Id order by Block_Id' % bid\\n mos=sdb.select(selcmd, tabcmd, logcmd)\\n if mos:\\n instr='MOS'\\n\\n if instr == 'MOS':\\n #special case for MOS science acquisition\\n mosacq=getfirstimage(rss_imglist, ontarget, instr, primary_mode, bvid)\\n mosacqtime=mosacq-ontarget\\n if mosacqtime.seconds > 1000:\\n #print(\\\"MOS Acquisition too long, took %i s\\\"%mosacqtime.seconds)\\n continue\\n else:\\n #determine the Salticam acquisition time after being on target\\n scamstart=getfirstscam(img_list, starttime, 'SALTICAM', 'IMAGING', bvid)\\n if scamstart is None:\\n #print(\\\"Did not find SCAM image\\\")\\n continue\\n acqtime=scamstart-ontarget\\n if acqtime.seconds > 1000:\\n #print(\\\"Target Acquisition too long, took %i s\\\"%acqtime.seconds)\\n continue\\n #determine the time between acquisition and first science image\\n sciencestart=getfirstimage(img_list, scamstart, instr, primary_mode, bvid)\\n if sciencestart is None:\\n #print(\\\"Did not find science image for BV %i using %s\\\" % (bvid, instr))\\n continue\\n sciacqtime=sciencestart-scamstart\\n if sciacqtime.seconds > 1000:\\n #print(\\\"Science Acquisition too long, took %i s\\\"%sciacqtime.seconds)\\n continue\\n\\n #update results in sdb\\n if update:\\n bvs_updated+=1\\n inscmd='SlewTime=%i, TrackerSlewTime=%i' % (slewtime.seconds, trackerslewtime.seconds)\\n sdb.update(inscmd, 'BlockVisit', 'BlockVisit_Id=%i' % bvid)\\n if instr == 'MOS':\\n inscmd='MOSAcquisitionTime=%i' % (mosacqtime.seconds)\\n sdb.update(inscmd, 'BlockVisit', 'BlockVisit_Id=%i' % bvid)\\n else:\\n inscmd='TargetAcquisitionTime=%i, InstrumentAcquisitionTime=%i' % (acqtime.seconds, sciacqtime.seconds)\\n sdb.update(inscmd, 'BlockVisit', 'BlockVisit_Id=%i' % bvid)\\n\\n print(bvs_updated)\\n print(len(bvid_list)-scams)\\n return bvid_list\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6014438","0.5713395","0.5670081","0.5654937","0.55900925","0.5535862","0.55139977","0.5509106","0.5435431","0.54309124","0.54095095","0.53560346","0.53487986","0.5339302","0.53324765","0.5331317","0.5316162","0.5309659","0.52839315","0.52791053","0.5277503","0.5259976","0.5258335","0.52554417","0.52516705","0.5236573","0.52333957","0.5225","0.5218668","0.52167106","0.5213278","0.52109003","0.5210538","0.52034897","0.52027273","0.51867366","0.5173706","0.51722795","0.5171379","0.51697135","0.51420087","0.5139998","0.513903","0.51364756","0.5135996","0.5133151","0.51329046","0.5132083","0.5131093","0.5124735","0.5121029","0.511999","0.5115114","0.5108846","0.5101635","0.50899345","0.5081496","0.5078998","0.50778157","0.50770915","0.5074503","0.5074397","0.5071","0.5069955","0.50637966","0.50571454","0.5054803","0.5052196","0.504425","0.5042908","0.5042766","0.5042302","0.50361526","0.5035877","0.5032745","0.5031219","0.5028067","0.50271857","0.5023236","0.50198233","0.5018709","0.5018451","0.5017483","0.50170314","0.501304","0.5013033","0.50114447","0.5009793","0.5009683","0.5002779","0.5002486","0.50016737","0.49994206","0.49981245","0.4996059","0.4996041","0.49959543","0.49928322","0.49923152","0.49910387"],"string":"[\n \"0.6014438\",\n \"0.5713395\",\n \"0.5670081\",\n \"0.5654937\",\n \"0.55900925\",\n \"0.5535862\",\n \"0.55139977\",\n \"0.5509106\",\n \"0.5435431\",\n \"0.54309124\",\n \"0.54095095\",\n \"0.53560346\",\n \"0.53487986\",\n \"0.5339302\",\n \"0.53324765\",\n \"0.5331317\",\n \"0.5316162\",\n \"0.5309659\",\n \"0.52839315\",\n \"0.52791053\",\n \"0.5277503\",\n \"0.5259976\",\n \"0.5258335\",\n \"0.52554417\",\n \"0.52516705\",\n \"0.5236573\",\n \"0.52333957\",\n \"0.5225\",\n \"0.5218668\",\n \"0.52167106\",\n \"0.5213278\",\n \"0.52109003\",\n \"0.5210538\",\n \"0.52034897\",\n \"0.52027273\",\n \"0.51867366\",\n \"0.5173706\",\n \"0.51722795\",\n \"0.5171379\",\n \"0.51697135\",\n \"0.51420087\",\n \"0.5139998\",\n \"0.513903\",\n \"0.51364756\",\n \"0.5135996\",\n \"0.5133151\",\n \"0.51329046\",\n \"0.5132083\",\n \"0.5131093\",\n \"0.5124735\",\n \"0.5121029\",\n \"0.511999\",\n \"0.5115114\",\n \"0.5108846\",\n \"0.5101635\",\n \"0.50899345\",\n \"0.5081496\",\n \"0.5078998\",\n \"0.50778157\",\n \"0.50770915\",\n \"0.5074503\",\n \"0.5074397\",\n \"0.5071\",\n \"0.5069955\",\n \"0.50637966\",\n \"0.50571454\",\n \"0.5054803\",\n \"0.5052196\",\n \"0.504425\",\n \"0.5042908\",\n \"0.5042766\",\n \"0.5042302\",\n \"0.50361526\",\n \"0.5035877\",\n \"0.5032745\",\n \"0.5031219\",\n \"0.5028067\",\n \"0.50271857\",\n \"0.5023236\",\n \"0.50198233\",\n \"0.5018709\",\n \"0.5018451\",\n \"0.5017483\",\n \"0.50170314\",\n \"0.501304\",\n \"0.5013033\",\n \"0.50114447\",\n \"0.5009793\",\n \"0.5009683\",\n \"0.5002779\",\n \"0.5002486\",\n \"0.50016737\",\n \"0.49994206\",\n \"0.49981245\",\n \"0.4996059\",\n \"0.4996041\",\n \"0.49959543\",\n \"0.49928322\",\n \"0.49923152\",\n \"0.49910387\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5656978"},"document_rank":{"kind":"string","value":"3"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":110,"numItemsPerPage":100,"numTotalItems":95772,"offset":11000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDQ4OTIxMiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzQ0OTI4MTIsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.wT1y2C2kMLBhoxKKEGbMxZ46hDG3sf118oKIAMAFF6GNbw50VwpZQrllZr9EiE8fM7iJQQrzPok_Z91bwIczBg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Map MXNet's log_softmax operator attributes to onnx's LogSoftMax operator and return the created node. | def convert_logsoftmax(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to int
axis = int(attrs.get("axis", -1))
temp = attrs.get("temperature", 'None')
if temp != 'None':
raise AttributeError("LogSoftMax: ONNX supports only temperature=None")
node = onnx.helper.make_node(
'LogSoftmax',
input_nodes,
[name],
axis=axis,
name=name
)
return [node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_softmax(cls, op, op_t):\n node = cls._common_singa_tensor_to_onnx_node(op, op_t)\n\n node.attribute.extend([\n helper.make_attribute('axis', op.axis),\n ])\n return node",
"def convert_logsoftmax(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n axis... | [
"0.72433263",
"0.6945794",
"0.6428142",
"0.63807946",
"0.6325919",
"0.62042975",
"0.6159842",
"0.60255045",
"0.5992672",
"0.58925354",
"0.5820617",
"0.5788801",
"0.5781208",
"0.55802464",
"0.55784833",
"0.5528118",
"0.55134857",
"0.55087835",
"0.5452463",
"0.5428512",
"0.5405... | 0.75760037 | 0 |
Map MXNet's norm operator attributes to onnx's ReduceL1 and ReduceL2 operators and return the created node. | def convert_norm(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
mx_axis = attrs.get("axis", None)
axes = convert_string_to_list(str(mx_axis)) if mx_axis else None
keepdims = get_boolean_attribute_value(attrs, "keepdims")
ord = int(attrs.get("ord", 2))
onnx_op_name = "ReduceL1" if ord == 1 else "ReduceL2"
if axes:
reduce_node = onnx.helper.make_node(
onnx_op_name,
input_nodes,
[name],
axes=axes,
keepdims=keepdims,
name=name
)
return [reduce_node]
else:
reduce_node = onnx.helper.make_node(
onnx_op_name,
input_nodes,
[name],
keepdims=keepdims,
name=name
)
return [reduce_node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_norm(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n axis = op.attr(\"axis\")\n axis_l = [axis]\n epsilon = op.attr(\"epsilon\")\n out = _op.nn.l2_normalize(x, epsilon, axis_l)\n g.add_node(op.output(\"Out\")[0], out)",
"def convert_l2normalization(node, **kwargs):\n name... | [
"0.5756237",
"0.56087494",
"0.5600301",
"0.55728614",
"0.55290496",
"0.54875606",
"0.5486537",
"0.5459905",
"0.53227746",
"0.52392274",
"0.5229864",
"0.5215761",
"0.52136004",
"0.5174905",
"0.5169228",
"0.5152919",
"0.5142199",
"0.51399326",
"0.51165926",
"0.5102471",
"0.5090... | 0.7403831 | 0 |
Map MXNet's multinomial operator attributes to onnx's Multinomial operator and return the created node. | def convert_multinomial(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get("dtype", 'int32'))]
sample_size = convert_string_to_list(attrs.get("shape", '1'))
if len(sample_size) < 2:
sample_size = sample_size[-1]
else:
raise AttributeError("ONNX currently supports integer sample_size only")
node = onnx.helper.make_node(
"Multinomial",
input_nodes,
[name],
dtype=dtype,
sample_size=sample_size,
name=name,
)
return [node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_broadcast_mul(node, **kwargs):\n return create_basic_op_node('Mul', node, kwargs)",
"def _create_gemm(cls, op, op_t):\n node = cls._common_singa_tensor_to_onnx_node(op, op_t)\n\n node.attribute.extend([\n helper.make_attribute('alpha', float(op.alpha)),\n helper... | [
"0.58884233",
"0.5768025",
"0.54848015",
"0.5398994",
"0.5315125",
"0.52907187",
"0.5282063",
"0.5165217",
"0.51454043",
"0.51417667",
"0.5104819",
"0.50990736",
"0.50983584",
"0.5056548",
"0.5052555",
"0.5041364",
"0.50398886",
"0.5031336",
"0.5016045",
"0.5006198",
"0.50032... | 0.71358234 | 0 |
Map MXNet's random_uniform operator attributes to onnx's RandomUniform operator and return the created node. | def convert_random_uniform(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
low = float(attrs.get("low", 0))
high = float(attrs.get("high", 1.0))
shape = convert_string_to_list(attrs.get('shape', '[]'))
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
node = onnx.helper.make_node(
'RandomUniform',
input_nodes,
[name],
low=low,
high=high,
dtype=dtype,
shape=shape,
name=name
)
return [node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen(self, op, *args, **kwargs):\r\n random_state_variable = raw_random.random_state_type()\r\n new_r, out = op(random_state_variable, *args, **kwargs)\r\n out.rng = random_state_variable\r\n self.random_state_variables.append((random_state_variable, new_r))\r\n return out",
... | [
"0.60839343",
"0.5966107",
"0.5965341",
"0.59418684",
"0.5821455",
"0.55140215",
"0.5499431",
"0.5494168",
"0.5481163",
"0.5478966",
"0.5458399",
"0.5442405",
"0.54335564",
"0.5417929",
"0.53986716",
"0.539796",
"0.539796",
"0.5386957",
"0.53401625",
"0.5309333",
"0.5306019",... | 0.7275685 | 0 |
Map MXNet's random_normal operator attributes to onnx's RandomNormal operator and return the created node. | def convert_random_normal(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
# Converting to float32
mean = float(attrs.get("loc", 0))
scale = float(attrs.get("scale", 1.0))
shape = convert_string_to_list(attrs.get('shape', '[]'))
dtype = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(attrs.get('dtype', 'float32'))]
node = onnx.helper.make_node(
'RandomNormal',
input_nodes,
[name],
mean=mean,
scale=scale,
dtype=dtype,
shape=shape,
name=name
)
return [node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_random_uniform(node, **kwargs):\n name, input_nodes, attrs = get_inputs(node, kwargs)\n\n # Converting to float32\n low = float(attrs.get(\"low\", 0))\n high = float(attrs.get(\"high\", 1.0))\n shape = convert_string_to_list(attrs.get('shape', '[]'))\n dtype = onnx.mapping.NP_TYPE_TO_... | [
"0.62901884",
"0.60207254",
"0.5817178",
"0.57946956",
"0.5718311",
"0.5678145",
"0.56305474",
"0.5630399",
"0.5541356",
"0.55390555",
"0.5524819",
"0.55016184",
"0.5497462",
"0.54788977",
"0.54603964",
"0.54529095",
"0.5433405",
"0.5368286",
"0.5364437",
"0.534533",
"0.53327... | 0.7576377 | 0 |
Map MXNet's ROIPooling operator attributes to onnx's MaxRoiPool operator and return the created node. | def convert_roipooling(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
pooled_shape = convert_string_to_list(attrs.get('pooled_size'))
scale = float(attrs.get("spatial_scale"))
node = onnx.helper.make_node(
'MaxRoiPool',
input_nodes,
[name],
pooled_shape=pooled_shape,
spatial_scale=scale,
name=name
)
return [node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_pooling(node, **kwargs):\n name, input_nodes, attrs = get_inputs(node, kwargs)\n\n kernel = eval(attrs[\"kernel\"])\n pool_type = attrs[\"pool_type\"] if attrs.get(\"pool_type\") else \"max\"\n stride = eval(attrs[\"stride\"]) if attrs.get(\"stride\") else (1, 1)\n global_pool = get_bool... | [
"0.641093",
"0.58088446",
"0.56600386",
"0.5579279",
"0.55671954",
"0.55426383",
"0.5540293",
"0.54378605",
"0.5431979",
"0.54058385",
"0.5381407",
"0.5371967",
"0.5365166",
"0.53410465",
"0.5257729",
"0.52336967",
"0.5228986",
"0.5204838",
"0.51902246",
"0.5179581",
"0.51613... | 0.7807513 | 0 |
Map MXNet's Tile operator attributes to onnx's Tile operator and return the created node. | def convert_tile(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
reps_list = convert_string_to_list(attrs["reps"])
initializer = kwargs["initializer"]
reps_shape_np = np.array(reps_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[reps_shape_np.dtype]
dims = np.shape(reps_shape_np)
output_shape_name = "reps_attr_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(output_shape_name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=output_shape_name,
data_type=data_type,
dims=dims,
vals=reps_list,
raw=False,
)
)
input_nodes.append(output_shape_name)
tile_node = onnx.helper.make_node(
"Tile",
input_nodes,
[name],
name=name
)
return [tensor_node, tile_node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_tile(cls, op, op_t):\n node = cls._common_singa_tensor_to_onnx_node(op, op_t)\n\n node.input.append(op.name + \":repeats\")\n return node",
"def _create_tile(cls, onnx_node, inputs, opset_version):\n # we move several inputs to singa's attribuates\n # and mark them ... | [
"0.6590867",
"0.5910745",
"0.58112407",
"0.5749959",
"0.5646954",
"0.5629699",
"0.5605283",
"0.5601611",
"0.55009604",
"0.54751515",
"0.5461425",
"0.5446524",
"0.5431833",
"0.542485",
"0.5410572",
"0.53914165",
"0.53725374",
"0.5355028",
"0.53471506",
"0.5346676",
"0.53449744... | 0.67022306 | 0 |
Map MXNet's broadcast_to operator attributes to onnx's Expand operator and return the created node. | def convert_broadcast_to(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
shape_list = convert_string_to_list(attrs["shape"])
initializer = kwargs["initializer"]
output_shape_np = np.array(shape_list, dtype='int64')
data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[output_shape_np.dtype]
dims = np.shape(output_shape_np)
output_shape_name = "expand_attr_tensor" + str(kwargs["idx"])
tensor_node = onnx.helper.make_tensor_value_info(output_shape_name, data_type, dims)
initializer.append(
onnx.helper.make_tensor(
name=output_shape_name,
data_type=data_type,
dims=dims,
vals=shape_list,
raw=False,
)
)
input_nodes.append(output_shape_name)
expand_node = onnx.helper.make_node(
"Expand",
input_nodes,
[name],
name=name
)
return [tensor_node, expand_node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_expand_as(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n target_shape = op.attr(\"target_shape\")\n out = _op.broadcast_to(x, target_shape)\n g.add_node(op.output(\"Out\")[0], out)",
"def convert_broadcast_power(node, **kwargs):\n return create_basic_op_node('Pow', node, kwarg... | [
"0.6505846",
"0.604694",
"0.60059506",
"0.59943974",
"0.5957492",
"0.5825268",
"0.5748968",
"0.57150435",
"0.5620855",
"0.5597498",
"0.55732626",
"0.5556347",
"0.5554119",
"0.5502286",
"0.54921335",
"0.5468958",
"0.5464709",
"0.5461847",
"0.5448856",
"0.54340446",
"0.53191674... | 0.74339557 | 0 |
Map MXNet's topk operator attributes to onnx's TopK operator and return the created node. | def convert_topk(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get('axis', '-1'))
k = int(attrs.get('k', '1'))
ret_type = attrs.get('ret_typ')
dtype = attrs.get('dtype')
outputs = [name + '_output0']
if ret_type and ret_type == 'both':
if dtype and dtype == 'int64':
outputs.append(name + '_output1')
else:
raise NotImplementedError("ONNX expects indices to be of type int64")
else:
raise NotImplementedError("ONNX expects both value and indices as output")
export_nodes = []
k = np.asarray([k], dtype=np.int)
k_node = create_helper_tensor_node(k, name + '__k', kwargs)
export_nodes.extend(k_node)
k_node = k_node[-1].name
input_node = input_nodes[0]
topk_node = onnx.helper.make_node(
"TopK",
[input_node, k_node],
outputs,
axis=axis,
name=name
)
export_nodes.extend([topk_node])
return [topk_node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_topk(g, op, block):\n\n data = g.get_node(op.input(\"X\")[0])\n if op.input(\"K\"):\n k = g.get_node(op.input(\"K\")[0])\n else:\n k = op.attr(\"k\")\n\n largest = True\n axis = -1\n if op.has_attr(\"axis\"):\n axis = op.attr(\"axis\")\n if op.has_attr(\"larges... | [
"0.6419405",
"0.6140541",
"0.5742067",
"0.54392713",
"0.5406331",
"0.5386023",
"0.5383082",
"0.5365803",
"0.5237811",
"0.5237811",
"0.51466364",
"0.51442844",
"0.5112016",
"0.509088",
"0.5069989",
"0.50558615",
"0.50066954",
"0.49857953",
"0.49676558",
"0.49675217",
"0.496543... | 0.68704766 | 0 |
Map MXNet's Take operator attributes to onnx's Gather operator. | def convert_take(node, **kwargs):
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get('axis', 0))
node = onnx.helper.make_node(
"Gather",
input_nodes,
[name],
axis=axis,
name=name,
)
return [node] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_gather(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n index = g.get_node(op.input(\"Index\")[0])\n axis = op.attr(\"axis\")\n out = _op.take(x, index, axis)\n g.add_node(op.output(\"Out\")[0], out)",
"def _create_gather(cls, onnx_node, inputs, opset_version):\n axis = o... | [
"0.5603602",
"0.5088366",
"0.4996854",
"0.49716508",
"0.49634594",
"0.4908585",
"0.48814094",
"0.4826999",
"0.46374422",
"0.46165276",
"0.46148214",
"0.46006873",
"0.4598241",
"0.45922422",
"0.45850176",
"0.4572346",
"0.45681974",
"0.45681974",
"0.45681974",
"0.45255744",
"0.... | 0.6138117 | 0 |
Returns list of detected objects. | def get_output(interpreter, score_threshold, top_k, image_scale=1.0):
boxes = common.output_tensor(interpreter, 0)
class_ids = common.output_tensor(interpreter, 1)
scores = common.output_tensor(interpreter, 2)
count = int(common.output_tensor(interpreter, 3))
def make(i):
ymin, xmin, ymax, xmax = boxes[i]
return Object(
id=int(class_ids[i]),
score=scores[i],
bbox=BBox(xmin=np.maximum(0.0, xmin),
ymin=np.maximum(0.0, ymin),
xmax=np.minimum(1.0, xmax),
ymax=np.minimum(1.0, ymax)))
return [make(i) for i in range(top_k) if scores[i] >= score_threshold] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_object_detections(self):\n detections = self.__get_cropped_detections(self.image)\n return detections",
"def detect_objects(snap):\n client = vision.ImageAnnotatorClient()\n print(snap)\n\n with open(snap, 'rb') as im_file:\n content = im_file.read()\n image = vision.Imag... | [
"0.7872937",
"0.7200342",
"0.7165433",
"0.7144725",
"0.7090058",
"0.695256",
"0.69507074",
"0.6947512",
"0.69345987",
"0.6881886",
"0.6862112",
"0.68335724",
"0.6699529",
"0.6509898",
"0.6494214",
"0.6395166",
"0.6335874",
"0.6323961",
"0.630811",
"0.6308098",
"0.6254732",
... | 0.0 | -1 |
raise from_none(ValueError('a')) == raise ValueError('a') from None | def from_none(exc):
exc.__cause__ = None
exc.__suppress_context__ = True
return exc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _fail(raise_):\n if raise_:\n raise _UnexpectedForm()\n return None",
"def test_invalid_argument_type(self):\n t = TruthTable('A or B')\n\n with self.assertRaises(InvalidArgumentTypeError):\n t.equivalent_to(float())\n\n with self.assertRaises(InvalidArgumentTypeE... | [
"0.65491164",
"0.6507355",
"0.63295954",
"0.61693627",
"0.6143416",
"0.60782343",
"0.59815896",
"0.5925763",
"0.5909486",
"0.5908904",
"0.5904242",
"0.58995235",
"0.5848055",
"0.5834902",
"0.5818783",
"0.57958984",
"0.5760411",
"0.5742847",
"0.57380986",
"0.5723444",
"0.57004... | 0.68841505 | 0 |
Try and get the option out of os.enviorn and cast it, otherwise return the default (casted) | def getenv(self, section, option, key=None, type=str, context=None):
if key is None:
key = option
value = os.environ.get(key, None)
if value is not None:
try:
return type(value)
except TypeError:
pass
value = self.get(section, option, context=context)
if value:
return type(value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getenv(option, default=undefined, cast=undefined):\n\n # We can't avoid __contains__ because value may be empty.\n if option in os.environ:\n value = os.environ[option]\n else:\n if isinstance(default, Undefined):\n raise UndefinedValueError('{} not found. Declare it as envvar... | [
"0.72295177",
"0.70083475",
"0.6605337",
"0.6365441",
"0.63379735",
"0.63131976",
"0.6185211",
"0.6164362",
"0.6162968",
"0.6154077",
"0.6150953",
"0.6140299",
"0.6121057",
"0.6113948",
"0.60905766",
"0.6068026",
"0.6061009",
"0.6054365",
"0.6050812",
"0.60491484",
"0.6044577... | 0.69680715 | 2 |
Calculates the average price we would pay / receive per unit of `symbol` if we wanted to trade `quantity` of that `symbol`, based on its order book | def getOrderBookPrice(exchange, symbol, side, quantity, order_book=None):
# TODO test it
# print("obap1")
order_book_side = order_book['asks'] \
if side == exchange.SIDE_SELL else order_book['bids']
quantity = Decimal(quantity)
i, orders, price = 0, [], Decimal(0)
accounted_for_quantity = Decimal(0)
qtdif = Decimal(1)
# print("obap2")
while accounted_for_quantity < quantity or qtdif > Decimal(0.0001):
try:
order = order_book_side[i]
except IndexError:
raise Exception("There are not enough orders in the Order Book.")
# return False
qty = min(Decimal(order[1]), quantity - accounted_for_quantity)
price += Decimal(order[0]) * qty
accounted_for_quantity += qty
qtdif = abs(Decimal(1) - accounted_for_quantity / quantity)
i += 1
# print("obap3")
return price / quantity | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cost(self, symbol) -> float:\n if len(symbol) <= 6:\n search = self.trader.stock_positions + self.trader.crypto_positions\n for p in search:\n if p['symbol'] == symbol:\n return p['avg_price']\n return None\n else:\n ... | [
"0.64651424",
"0.6410142",
"0.63740027",
"0.63389856",
"0.6299657",
"0.6286352",
"0.6263074",
"0.6189815",
"0.6114266",
"0.6032858",
"0.60058963",
"0.5941486",
"0.5917772",
"0.5853512",
"0.5828471",
"0.5826779",
"0.580022",
"0.57563347",
"0.57001877",
"0.5657362",
"0.5588201"... | 0.7126667 | 0 |
Inserts multiple new asks in the order book (assumes that the order book AND the new_asks list are sorted) | def insertAsks(previous_asks, received_asks):
new_asks = []
if len(received_asks) < 1:
return previous_asks
if len(previous_asks) < 1:
return received_asks
# print("Prev")
# pprint(previous_asks)
# print("Recv")
# pprint(received_asks)
# Uses the merge-sort idea of popping the first element in the lists
# (which should also be the lowest)
while len(previous_asks) > 0 and len(received_asks) > 0:
ask = None
if Decimal(previous_asks[0][0]) < Decimal(received_asks[0][0]):
ask = previous_asks.pop(0)
# print('popped from prev')
elif Decimal(previous_asks[0][0]) > Decimal(received_asks[0][0]):
# print('popped from recv')
ask = received_asks.pop(0)
else:
# print('equal, popped from both')
previous_asks.pop(0)
ask = received_asks.pop(0)
# print(ask)
if Decimal(ask[1]) > Decimal(0):
# print("appended")
new_asks.append(ask)
# print("After Merge")
# pprint(new_asks)
if len(previous_asks) > 0:
new_asks.extend(previous_asks)
elif len(received_asks) > 0:
new_asks.extend(received_asks)
# print("Complete")
# pprint(new_asks)
return new_asks | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_answers(conn, cur, answers):\n \n print 'Adding answers...',\n \n for i, answer in enumerate(answers):\n cur.execute('INSERT INTO answers VALUES (\"{_id}\", \"{task_id}\", \"{text}\")'.format(\n _id = i+1,\n task_id = answer['task_id'],\n ... | [
"0.5846932",
"0.5842225",
"0.5782144",
"0.5523276",
"0.5449554",
"0.53335917",
"0.5256855",
"0.5231915",
"0.5231915",
"0.51975715",
"0.5142276",
"0.51192087",
"0.50733495",
"0.5046175",
"0.5029141",
"0.49793863",
"0.4966205",
"0.49656373",
"0.49622992",
"0.49538177",
"0.48968... | 0.7092049 | 0 |
Inserts multiple new bids in the order book (assumes that the order book AND the new_bids list are sorted) | def insertBids(previous_bids, received_bids):
new_bids = []
while len(previous_bids) > 0 and len(received_bids) > 0:
bid = None
if Decimal(previous_bids[0][0]) > Decimal(received_bids[0][0]):
bid = previous_bids.pop(0)
elif Decimal(previous_bids[0][0]) < Decimal(received_bids[0][0]):
bid = received_bids.pop(0)
else:
previous_bids.pop(0)
bid = received_bids.pop(0)
if Decimal(bid[1]) > Decimal(0):
new_bids.append(bid)
if len(previous_bids) > 0:
new_bids.extend(previous_bids)
elif len(received_bids) > 0:
new_bids.extend(received_bids)
return new_bids | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_new_bids(self):\n for bidder in self._bidders:\n if bidder != self._highest_current_bidder:\n bid_price = bidder(self)\n if bid_price > self.current_bid:\n self.update_bid(bid_price, bidder)",
"def add_boid(self, new_boid):\r\n s... | [
"0.6216382",
"0.6018596",
"0.5861003",
"0.57200617",
"0.5687406",
"0.5627169",
"0.5626909",
"0.5589654",
"0.5553824",
"0.5513555",
"0.54484546",
"0.544297",
"0.5307633",
"0.52903914",
"0.5283958",
"0.5282783",
"0.5269406",
"0.52541256",
"0.5221003",
"0.51885104",
"0.51821554"... | 0.72524697 | 0 |
Calculate the straight line distance between two gps coordinates | def dist(coord_a, coord_b):
lat1 = math.radians(coord_a[0])
lat2 = math.radians(coord_a[0])
lng1 = math.radians(coord_a[1])
lng2 = math.radians(coord_b[1])
# Haversine formula
dlat = lat2 - lat1
dlng = lng2 - lng1
a = math.sin(dlat / 2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlng / 2)**2
distance_radians = 2 * math.asin(math.sqrt(a))
# Radius of earth in meters.
r_earth = 6371000
# error in reading
val_error = 1.7
# calculate the distane in meters
return (distance_radians * r_earth)* val_error | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def distance(gps1, gps2):\n return haversine(gps1.lng, gps1.lat, gps2.lng, gps2.lat)",
"def calculateDistanceBetweenPoints(lat1,lon1,lat2,lon2):\n\treturn Geodesic.WGS84.Inverse(lat1,lon1, lat2, lon2)['s12']",
"def calculate_line_length(x1, y1, x2, y2):\n distance = np.sqrt((x2 - x1)**2 + (y2 - y1)**2)\n... | [
"0.7674099",
"0.7567361",
"0.7443959",
"0.7391137",
"0.7256764",
"0.7159113",
"0.7153204",
"0.71042436",
"0.70898855",
"0.702371",
"0.7000356",
"0.69934964",
"0.69846",
"0.6979073",
"0.6970385",
"0.6955696",
"0.69483423",
"0.69429255",
"0.6940593",
"0.6924824",
"0.6917241",
... | 0.0 | -1 |
Populate paths with lengths in database | def postPathLengths(map_area):
paths = Path.query.filter(Path.map_area==map_area).all()
for path in paths:
start = Node.query.filter_by(id=path.start).first()
end = Node.query.filter_by(id=path.end).first()
# get the length of the two side of the paths
avg_length = calculatePathLength(start, end)
path.length = avg_length
print(avg_length)
# save the changes to the database
db.session.commit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculateWireLenght(path_list):\n\n total_length = 0\n for path in path_list:\n total_length += len(path)\n return total_length",
"def path_length(self,path,num_repeats=10):\n begin_time=datetime.datetime.now()\n #num_repeats=100\n for i in range(num_repeats):\n ... | [
"0.58111256",
"0.58005226",
"0.5792587",
"0.54623866",
"0.5386073",
"0.5357159",
"0.5356317",
"0.5353135",
"0.5353135",
"0.53270024",
"0.53158593",
"0.5290157",
"0.52883023",
"0.52871025",
"0.5284866",
"0.5262351",
"0.52564347",
"0.5213889",
"0.5204303",
"0.5164193",
"0.51576... | 0.6926739 | 0 |
Return true if x is in the range [start, end] | def time_in_range(start, end, time):
if start <= end:
return start <= time <= end
else:
return start <= time or time <= end | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def in_range(start, end, x):\n if start <= end:\n return start <= x <= end\n else:\n return start <= x or x <= end",
"def date_in_range(start, end, x):\n\n if start <= end:\n return start <= x <= end\n else:\n return start <= x or x <= end",
"def in_range(x, a, b):\n return (x >=... | [
"0.8966769",
"0.7992362",
"0.77772695",
"0.7776177",
"0.766966",
"0.74990565",
"0.74736047",
"0.72713387",
"0.72193664",
"0.71821845",
"0.7163457",
"0.7140041",
"0.7109383",
"0.70654154",
"0.7048866",
"0.70405066",
"0.7038097",
"0.7010097",
"0.6977196",
"0.6975511",
"0.696325... | 0.6845803 | 26 |
Loggin In With Facebook | def login():
authorization_url, state = facebook.authorization_url(authorization_base_url)
print 'Please authorize', authorization_url
return redirect(authorization_url, code=302) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def facebook_login():\n if not facebook.authorized:\n return redirect(url_for('facebook.login'))\n account_info = facebook.get('me?fields=id,name,email')\n# print(account_info)\n if account_info.ok:\n account_info_json = account_info.json()\n user = {}\n user['email'] = acco... | [
"0.7702685",
"0.7581268",
"0.7554234",
"0.75297165",
"0.7491286",
"0.7259731",
"0.7058176",
"0.6954533",
"0.6849566",
"0.6809354",
"0.68011945",
"0.6796922",
"0.679441",
"0.6732429",
"0.66742224",
"0.6617167",
"0.65538794",
"0.65296173",
"0.6522063",
"0.6515471",
"0.64445364"... | 0.7205264 | 6 |
Walk through all files located under a root path. | def iter_files(path):
for dirpath, _, filenames in os.walk(path):
for f in filenames:
if f != '.DS_Store':
yield os.path.join(dirpath, f) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index_all_files(self, root_dir):\n pass",
"def files_from_root(root, accept):\n for (dir_path, _dir_names, file_names) in os.walk(root, followlinks=True):\n for file_name in file_names:\n if accept(file_name):\n path = os.path.join(dir_path, file_name)\n ... | [
"0.7381147",
"0.72097903",
"0.7066387",
"0.7058464",
"0.7056831",
"0.6993204",
"0.6956753",
"0.69543415",
"0.6945345",
"0.68726885",
"0.6805766",
"0.67303014",
"0.6628771",
"0.66223794",
"0.6598162",
"0.65677375",
"0.65590584",
"0.6550217",
"0.65464854",
"0.6530529",
"0.65129... | 0.59631664 | 81 |
Create the matrices from the datasets. | def prepare_data(seqs, labels, maxlen=None, xdim=3):
# Trim all output seqs to have only maxlen steps
if maxlen is not None:
Iseqs = []
Oseqs = []
for i_seq, o_seq in zip(seqs, labels):
if len(o_seq) < maxlen:
Iseqs.append(i_seq)
Oseqs.append(o_seq)
seqs = Iseqs
labels = Oseqs
else:
maxlen = 40
# Pad and compute masks
ret_X = np.zeros((maxlen, len(seqs), xdim))
mask_X = np.zeros((maxlen, len(seqs)))
labels_X = np.zeros((maxlen, len(seqs)))
for k in range(len(seqs)):
mask_X[:len(labels[k]), k] = 1
ret_X[:len(labels[k]), k] = np.asarray(seqs[k])
labels_X[:len(labels[k]), k] = labels[k]
return ret_X, mask_X, labels_X | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_datasets(self):\n self._build_datasets_sis3302()\n self._build_datasets_sis3305()",
"def matrices(self):\n return [ self.__class__(labels=self.labels,\n labels_map=self.labels_map,\n sets=[x]) for x in self.sets]",
"d... | [
"0.6748821",
"0.6716942",
"0.6337572",
"0.63359183",
"0.62707675",
"0.6173715",
"0.6111475",
"0.60522753",
"0.6028459",
"0.6023709",
"0.6017404",
"0.5932755",
"0.58969504",
"0.5868365",
"0.58672017",
"0.5865364",
"0.58570284",
"0.585554",
"0.5840747",
"0.5836187",
"0.583551",... | 0.0 | -1 |
Optimize a neural network that classifies MNIST data. | def sgd_experiment():
batch_size = 128
layer_1_hidden_nodes = 80 ## Starting small so my computer can keep up with the ram requirements of LEEA :)
(train_dataset, train_labels), (valid_dataset, valid_labels), (test_dataset, test_labels) = get_mnist()
graph = tf.Graph()
with graph.as_default():
## Data variables.
tf_train_dataset = tf.placeholder(tf.float32,
shape=(batch_size, image_size * image_size))
tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
## Weights describing single layer.
weights1 = tf.Variable(
tf.truncated_normal([image_size * image_size, layer_1_hidden_nodes])
)
biases1 = tf.Variable(tf.zeros([layer_1_hidden_nodes]))
weights2 = tf.Variable(
tf.truncated_normal([layer_1_hidden_nodes, num_labels])
)
biases2 = tf.Variable(tf.zeros([num_labels]))
## Training variables.
lay1_train = tf.nn.relu(tf.matmul(tf_train_dataset, weights1) + biases1)
logits = tf.matmul(lay1_train, weights2) + biases2
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=tf_train_labels)
)
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
train_prediction = tf.nn.softmax(logits)
lay1_valid = tf.nn.relu(tf.matmul(tf_valid_dataset, weights1) + biases1)
valid_prediction = tf.nn.softmax(tf.matmul(lay1_valid, weights2) + biases2)
lay1_test = tf.nn.relu(tf.matmul(tf_test_dataset, weights1) + biases1)
test_prediction = tf.nn.softmax(tf.matmul(lay1_test, weights2) + biases2)
num_steps = 3001
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
for step in range(num_steps):
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
batch_data = train_dataset[offset:(offset + batch_size), :]
batch_labels = train_labels[offset:(offset + batch_size), :]
feed_dict = {tf_train_dataset: batch_data, tf_train_labels: batch_labels}
_, l, predictions = session.run(
[optimizer, loss, train_prediction], feed_dict=feed_dict)
if (step % 250) == 0:
print("Minibatch loss at step %d: %f" % (step, l))
print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
print("Validation accuracy: %.1f%%" % accuracy(
valid_prediction.eval(), valid_labels)
)
print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000,\r\n dataset='mnist.pkl.gz',\r\n batch_size=600):\r\n datasets = load_data(dataset)\r\n\r\n train_set_x, train_set_y = datasets[0]\r\n valid_set_x, valid_set_y = datasets[1]\r\n test_set_x, ... | [
"0.68616706",
"0.67666817",
"0.639884",
"0.62000936",
"0.6169988",
"0.6159703",
"0.6147306",
"0.608073",
"0.60804075",
"0.6076149",
"0.6037147",
"0.6005561",
"0.6005367",
"0.59805745",
"0.59763664",
"0.59754896",
"0.5955019",
"0.5887888",
"0.58547395",
"0.58541465",
"0.584930... | 0.0 | -1 |
Optimize a neural network with LEEA and the MNIST dataset. | def leea_experiment():
layer_1_hidden_nodes = 80 ## Starting small so my computer can keep up with the ram requirements of LEEA :)
(train_dataset, train_labels), (valid_dataset, valid_labels), (test_dataset, test_labels) = get_mnist()
## Copy pasted, I'm so sorry.
graph = tf.Graph()
with graph.as_default():
## Data variables.
tf_train_dataset = tf.placeholder(tf.float32,
shape=(Params.SAMPLE_COUNT, image_size * image_size))
tf_train_labels = tf.placeholder(tf.float32, shape=(Params.SAMPLE_COUNT, num_labels))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
## Weights describing single layer.
weights1 = tf.Variable(
tf.truncated_normal([image_size * image_size, layer_1_hidden_nodes])
)
biases1 = tf.Variable(tf.zeros([layer_1_hidden_nodes]))
weights2 = tf.Variable(
tf.truncated_normal([layer_1_hidden_nodes, num_labels])
)
biases2 = tf.Variable(tf.zeros([num_labels]))
## Training variables.
lay1_train = tf.nn.relu(tf.matmul(tf_train_dataset, weights1) + biases1)
logits = tf.matmul(lay1_train, weights2) + biases2
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=tf_train_labels)
)
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
train_prediction = tf.nn.softmax(logits)
lay1_valid = tf.nn.relu(tf.matmul(tf_valid_dataset, weights1) + biases1)
valid_prediction = tf.nn.softmax(tf.matmul(lay1_valid, weights2) + biases2)
lay1_test = tf.nn.relu(tf.matmul(tf_test_dataset, weights1) + biases1)
test_prediction = tf.nn.softmax(tf.matmul(lay1_test, weights2) + biases2)
with tf.Session(graph=graph) as session:
op = tf.variables_initializer(tf.trainable_variables())
session.run(op)
evaluator = Evaluator(session, loss, train_prediction) ## Take in the loss as a function (rather than TF operation).
evolver = Evolver(tf.trainable_variables(), evaluator)
for gen in range(Params.MAX_GENERATIONS):
print("Generation: ", gen)
offset = (gen * Params.SAMPLE_COUNT) % (train_labels.shape[0] - Params.SAMPLE_COUNT)
batch_data = train_dataset[offset:(offset + Params.SAMPLE_COUNT), :]
batch_labels = train_labels[offset:(offset + Params.SAMPLE_COUNT), :]
evolver.doGeneration({tf_train_dataset: batch_data, tf_train_labels: batch_labels})
best = evolver.getBest()
evolver.restore_variables(evolver.variables,
session,
*evolver.unflatten_tensors(best.weights, evolver.variables))
print("Minimum achieved loss: %f" % (-1 * best.fitness))
print("Validation accuracy: %.1f%%" % accuracy(valid_prediction.eval(), valid_labels))
print("Test accuracy: %f%%" % accuracy(test_prediction.eval(), test_labels)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(tetrode_number=TETRODE_NUMBER,num_hidden_units=500,num_hidden_units_2=300,num_hidden_units_3=200,num_code_units=50):\n \n print(\"Making the model...\")\n network = model((None,200),200,num_hidden_units,num_hidden_units_2,num_hidden_units_3,num_code_units)\n print(\"Done!\")\n\n\n for tetrod... | [
"0.6583897",
"0.65585095",
"0.65471804",
"0.6538894",
"0.6405767",
"0.6403417",
"0.6328416",
"0.63117754",
"0.6307124",
"0.6301749",
"0.62865406",
"0.62524986",
"0.62325215",
"0.61932445",
"0.61917716",
"0.6189975",
"0.61607337",
"0.6145277",
"0.6128466",
"0.61265934",
"0.611... | 0.6448221 | 4 |
Create a database according to schema in JSON format. | def create_db(db, schema_json):
with open(schema_json) as of:
schema = json.load(of, object_pairs_hook=OrderedDict)
# OrderedDict so that tables are created in the order specified,
# allowing foreign keys to reference previously defined tables
for table_name, columns in schema.items():
col_types = columns.items() # dict -> tuple
make_table(db, table_name, col_types) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(db_path, schema_json):\n create_db(db_path, schema_json)",
"def create_db():\n db.create_all()\n print('Database structure created successfully')",
"def create_db():\n db.create_all()\n print(\"DB Created\")",
"def create_schema(db_name, schema_name):\n # 1. Connect to database\n ... | [
"0.82075125",
"0.7394558",
"0.7111433",
"0.7067049",
"0.70018756",
"0.7000087",
"0.6994893",
"0.6987741",
"0.696631",
"0.6925411",
"0.69109344",
"0.69109344",
"0.69109344",
"0.69109344",
"0.69109344",
"0.69109344",
"0.69109344",
"0.69109344",
"0.69109344",
"0.69109344",
"0.69... | 0.81120163 | 1 |
Create a database from a schema and populate it with CSV/JSON data. | def main(db_path, schema_json):
create_db(db_path, schema_json) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_db(db, schema_json):\n with open(schema_json) as of:\n schema = json.load(of, object_pairs_hook=OrderedDict)\n # OrderedDict so that tables are created in the order specified,\n # allowing foreign keys to reference previously defined tables\n\n for table_name, columns in schem... | [
"0.7358652",
"0.6983746",
"0.6980258",
"0.6947382",
"0.68650365",
"0.6854215",
"0.68521404",
"0.68436974",
"0.6828379",
"0.678138",
"0.67692417",
"0.67509025",
"0.67440456",
"0.67321527",
"0.67321527",
"0.67321527",
"0.67321527",
"0.67321527",
"0.67321527",
"0.67321527",
"0.6... | 0.7520764 | 0 |
Fails if log handler does not output expected log message | def test_sqs_log_handler_basic_json_format(self):
try:
self.logger.info("test info message")
body = self.retrieve_message()
expected = ("""{"asctime": "2016-01-01 00:00:00,000","""
""" "levelname": "INFO", "message": "test info message"}""")
except BaseException as err:
self.fail("Should not raise exception, got {} instead".format(err))
self.assertEqual(body, expected) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _log(self, message):\n pass",
"def log(self, message):",
"def _stab_log_error(self, logconf, msg):\n\t\tprint \"Error when logging %s: %s\" % (logconf.name, msg)",
"def do_handle_log(self, workunit, level, *msg_elements):\r\n pass",
"def log(self, message: str):",
"def _stab_log_error(self,... | [
"0.6798006",
"0.6634195",
"0.65810263",
"0.65154",
"0.64977425",
"0.6463785",
"0.64605075",
"0.6457076",
"0.64166117",
"0.6404903",
"0.6383126",
"0.63817513",
"0.6376192",
"0.636835",
"0.63453645",
"0.63350356",
"0.63286597",
"0.6290296",
"0.6282357",
"0.62702614",
"0.6269678... | 0.0 | -1 |
Fails if log handler does not output expected log message with extras | def test_sqs_log_handler_extra_json_data(self):
try:
extra = {
"test": "test logging",
"num": 1,
5: "9",
"float": 1.75,
"nested": {"more": "data"}
}
self.logger.info("test info message with properties", extra=extra)
body = self.retrieve_message()
expected = ("""{"asctime": "2016-01-01 00:00:00,000", "levelname": "INFO","""
""" "message": "test info message with properties","""
""" "5": "9", "float": 1.75, "num": 1,"""
""" "test": "test logging", "nested": {"more": "data"}}""")
except BaseException as err:
self.fail("Should not raise exception, got {} instead".format(err))
self.assertEqual(body, expected) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_extra(self, msg, *args):\n if self.settings.EXTRA_LOGGING.value:\n self.log.info(\"(Extra logging) \" + msg, *args)",
"def log_message(self, format, *args):",
"def _log(self, message):\n pass",
"def log(self, message):",
"def do_handle_log(self, workunit, level, *msg_elemen... | [
"0.6871636",
"0.6594983",
"0.65777576",
"0.64990383",
"0.6497",
"0.6418214",
"0.63011897",
"0.6265279",
"0.62583315",
"0.6248939",
"0.6216916",
"0.61940885",
"0.61799824",
"0.6179446",
"0.6169949",
"0.6157681",
"0.61575145",
"0.61328405",
"0.6126881",
"0.6124526",
"0.60975176... | 0.5951401 | 37 |
Fails if log handler does not output expected log message at error level | def test_sqs_log_handler_error(self):
try:
extra = {
"test": "test logging",
"num": 1,
5: "9",
"float": 1.75,
"nested": {"more": "data"}
}
self.logger.error("test info message with properties", extra=extra)
body = self.retrieve_message()
expected = ("""{"asctime": "2016-01-01 00:00:00,000", "levelname": "ERROR","""
""" "message": "test info message with properties","""
""" "5": "9", "float": 1.75, "num": 1,"""
""" "test": "test logging", "nested": {"more": "data"}}""")
except BaseException as err:
self.fail("Should not raise exception, got {} instead".format(err))
self.assertEqual(body, expected) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def error(self, msg, *args):\n if self.lvl<=logging.ERROR: return self._log(msg, *args)",
"def _stab_log_error(self, logconf, msg):\n\t\tprint \"Error when logging %s: %s\" % (logconf.name, msg)",
"def _stab_log_error(self, logconf, msg):\n print('Error when logging %s: %s' % (logconf.name, msg))... | [
"0.75329125",
"0.74146676",
"0.73945475",
"0.718149",
"0.71730816",
"0.71730816",
"0.7140554",
"0.71289593",
"0.7127636",
"0.7127636",
"0.7125438",
"0.7094133",
"0.7039443",
"0.70359004",
"0.70328724",
"0.70328724",
"0.70328724",
"0.70328724",
"0.70328724",
"0.70328724",
"0.7... | 0.6979829 | 27 |
Fails if log handler does not output expected log message at error level | def test_sqs_log_global_extra(self):
try:
extra = {
"test": "test logging",
"num": 1,
5: "9",
"float": 1.75,
"nested": {"more": "data"}
}
global_extra = {
"cluster_name": "regression",
"node_name": "localhost",
}
log_handler = sqsloghandler.SQSHandler(self.log_queue_name, global_extra=global_extra)
formatter = jsonlogger.JsonFormatter('%(asctime) %(levelname) %(message)')
log_handler.setFormatter(formatter)
logger = logging.getLogger(TestSQSLogHandler.__name__ + "global-extra")
logger.addHandler(log_handler)
logger.error("test info message with properties", extra=extra)
body = self.retrieve_message()
expected = ("""{"asctime": "2016-01-01 00:00:00,000", "levelname": "ERROR","""
""" "message": "test info message with properties", "5": "9", """
""""float": 1.75, "num": 1, "cluster_name": "regression","""
""" "test": "test logging", "nested": {"more": "data"}, "node_name": "localhost"}""")
except BaseException as err:
self.fail("Should not raise exception, got {} instead".format(err))
self.assertEqual(body, expected) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def error(self, msg, *args):\n if self.lvl<=logging.ERROR: return self._log(msg, *args)",
"def _stab_log_error(self, logconf, msg):\n\t\tprint \"Error when logging %s: %s\" % (logconf.name, msg)",
"def _stab_log_error(self, logconf, msg):\n print('Error when logging %s: %s' % (logconf.name, msg))... | [
"0.75332403",
"0.7415585",
"0.7395565",
"0.7182279",
"0.71737486",
"0.71737486",
"0.7142053",
"0.7128932",
"0.7128264",
"0.7128264",
"0.71270907",
"0.7094948",
"0.70400184",
"0.7036283",
"0.70337623",
"0.70337623",
"0.70337623",
"0.70337623",
"0.70337623",
"0.70337623",
"0.70... | 0.0 | -1 |
Traverse a tree rooted at in postorder | def leaves(node, res):
leaf = True
if node.lesser:
leaf = False
leaves(node.lesser, res)
if node.greater:
leaf = False
leaves(node.greater, res)
if leaf:
res.append(node.indices) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def traverse_postorder(self, root):\n if root is not None:\n self.traverse_postorder(root.left)\n self.traverse_postorder(root.right)\n print(root.data)",
"def postorder(root):\n if not root:\n return\n inorder(root.left)\n inorder(root.right)\n print(ro... | [
"0.81746197",
"0.7900327",
"0.77919316",
"0.76695967",
"0.7652187",
"0.76341856",
"0.75693804",
"0.7548271",
"0.75037867",
"0.74601656",
"0.7442373",
"0.7406432",
"0.73614275",
"0.73541087",
"0.7322688",
"0.7294151",
"0.72874355",
"0.7265556",
"0.7239447",
"0.7193696",
"0.716... | 0.0 | -1 |
Create a recursive dictionary mapping of a directory structure starting at pathname A leaf is filename>full path of file | def treedir(pathname):
(t, d, f) = next(os.walk(pathname))
return {**{_dir : treedir(os.path.join(pathname, _dir)) for _dir in d}, **{file:os.path.join(t, file) for file in f}} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def path_to_dict(self, someDir, level=9001, relativeFolders=True, relativeFiles=False):\n someDir = someDir.rstrip(os.path.sep)\n assert os.path.isdir(someDir)\n numSep = someDir.count(os.path.sep)\n\n outputDict = {}\n for root, dirs, files in os.walk(someDir):\n for ... | [
"0.71291417",
"0.70161325",
"0.67469096",
"0.6707382",
"0.6544313",
"0.6477862",
"0.64685744",
"0.63074756",
"0.6306378",
"0.62825865",
"0.6236606",
"0.61944944",
"0.61629915",
"0.6132351",
"0.6112128",
"0.6096042",
"0.6063349",
"0.60022354",
"0.59963495",
"0.5958212",
"0.593... | 0.7011454 | 2 |
Map x,y coordinates to pixel size | def topix(x, y, pxsz):
assert (x >= 0)
assert (y >= 0)
return np.array([int(round(x / pxsz)), int(round(y / pxsz))], dtype=np.uint) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPixelSize(self):\n return (0.000013, 0.000013)",
"def GetPixelDimensions(self):\n _, width, _, _, _, height = self.GetGeoTransform()\n return XY(x=width, y=height)",
"def px_size(self):\n xp, yp = ct.c_float(), ct.c_float()\n\n self.lib.GetPixelSize(ct.pointer(xp), ct.... | [
"0.7402808",
"0.73856735",
"0.73046285",
"0.7287127",
"0.7168122",
"0.7099318",
"0.69940174",
"0.6974272",
"0.6839358",
"0.68056744",
"0.6799161",
"0.6775825",
"0.6729077",
"0.67045385",
"0.6631168",
"0.6580784",
"0.65413487",
"0.653211",
"0.64739186",
"0.64416355",
"0.643086... | 0.0 | -1 |
Compute Local Effective Resolution | def computerecondensity(d3d, label, leafs=16, PIX=10, IMGMAX=40000):
points = d3d[:, :2].copy()
mx, MX = np.min(points[:, 0]), np.max(points[:, 0])
my, MY = np.min(points[:, 1]), np.max(points[:, 1])
if mx < 0:
points[:, 0] += np.abs(mx)
print("Neg pos for {}".format(label))
if my < 0:
points[:, 1] += np.abs(my)
print("Neg pos for {}".format(label))
tr = cKDTree(points, leafsize=leafs)
lfs = []
root = tr.tree
leaves(root, lfs)
assert (MX < IMGMAX)
assert (MY < IMGMAX)
pixels = np.empty((len(lfs), 2), dtype=np.float64)
imgsize = int(np.round(IMGMAX / PIX))
imarray = np.zeros((imgsize, imgsize), dtype=np.float32)
for ip, p in enumerate(lfs):
ps = points[p]
# assert(ps.shape[0] <= leafs)
assert (ps.shape[1] == 2)
_mx = min(ps[:, 0])
_Mx = max(ps[:, 0])
_my = min(ps[:, 1])
_My = max(ps[:, 1])
_x, _y = topix(_mx, _my, PIX) # Lower left
_X, _Y = topix(_Mx, _My, PIX) # Upper right
# ELR = nr of points over area xy - XY
# imarray[_x:_X, _y:_Y] += ps.shape[0]
pixels[ip, 0] = ((_X - _x) + 1) * ((_Y - _y) + 1) # Superpixel area
pixels[ip, 1] = ps.shape[0] / pixels[ip, 0] # Locs / px^2
for pri in range(ps.shape[0]):
pti = ps[pri, :2]
xp, yp = topix(pti[0], pti[1], PIX)
assert (xp < IMGMAX)
assert (
yp < IMGMAX) # You'd assume this check is not nec. as an index error would follow, but you'd be wrong in interesting cases
try:
imarray[xp, yp] += pixels[ip, 1]
except OverflowError as e:
print("OE {} , {} <- {} {}".format(xp, yp, pixels[ip, 1], pixels[ip, 0]))
print("OE {} , {} <- {:.2f} {:.2f}".format(xp, yp, pti[0], pti[1]))
raise
return tr, imarray, pixels | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _residual_edp(self, params):\n data = self.F**2\n model = np.absolute(self._model())**2\n sigma = self.sigma\n return (data[self.mask]-model[self.mask]) / sigma[self.mask] \n \n # The following three lines do not reproduce Sun's results, which proves\n # that the fits were done through... | [
"0.64042413",
"0.5862268",
"0.5787392",
"0.5745422",
"0.5741281",
"0.5721242",
"0.56675434",
"0.566455",
"0.5620853",
"0.55921644",
"0.5583884",
"0.5534082",
"0.5525383",
"0.55130315",
"0.5511572",
"0.5489824",
"0.54889446",
"0.5474182",
"0.54559463",
"0.5435168",
"0.5424118"... | 0.0 | -1 |
Compute the local effective resolution for data | def computeSNRLE(leafs=2, pix=10, MAX=60000, data=None, outpath="."):
lgr.info("Computing LRE for leaf size {}, {} nm/pixel".format(leafs, pix))
if data is None:
return
pixelmap ={}
SNR = np.sqrt(leafs/2)
for cell in data:
lgr.info("Cell {}".format(cell))
for channel in data[cell]:
lgr.info("Channel {}".format(channel))
d3d = data[cell][channel].points
label = "{}_{}".format(cell, channel)
tr, imarray, pixels = computerecondensity(d3d, label, leafs, pix, MAX)
img = Image.fromarray((imarray/np.max(imarray)*255).astype(np.uint8), mode='L')
img.save(os.path.join(outpath, '{}_oct_{:.2f}.tiff'.format(label, SNR)))
# sns.distplot(pixels[:,1])
pixelmap[label] = pixels, imarray
return pixelmap | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _residual_edp(self, params):\n data = self.F**2\n model = np.absolute(self._model())**2\n sigma = self.sigma\n return (data[self.mask]-model[self.mask]) / sigma[self.mask] \n \n # The following three lines do not reproduce Sun's results, which proves\n # that the fits were done through... | [
"0.6412861",
"0.6363401",
"0.6193123",
"0.618786",
"0.6109423",
"0.59960175",
"0.5980494",
"0.5975609",
"0.58518046",
"0.5812452",
"0.5804847",
"0.57916963",
"0.574673",
"0.57214296",
"0.56793344",
"0.567626",
"0.5666228",
"0.56486475",
"0.56465364",
"0.564494",
"0.56279874",... | 0.0 | -1 |
verify that, once send() is called, a tenant has been setup | def test_tenant_setup_celery(self):
class interceptor(mock.Mock):
tenant = None
def send(self, *kw, **args):
self.tenant = properties.tenant
msg = interceptor()
tenant = mock.Mock()
tenant.client_name = 'mock-tenant'
_send_celery_mail(msg, tenant, send=True)
self.assertTrue(msg.tenant is tenant) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_tenant_setup_celery_reset(self):\n msg = mock.Mock()\n tenant = mock.Mock()\n tenant.client_name = 'mock-tenant'\n\n _send_celery_mail(msg, tenant, send=False)\n\n self.assertFalse(hasattr(properties, 'tenant'))\n self.assertEqual(properties.tenant_properties, {})... | [
"0.6928082",
"0.60256696",
"0.5895501",
"0.5891009",
"0.5843694",
"0.57787097",
"0.577437",
"0.56721795",
"0.56186384",
"0.55741477",
"0.5572975",
"0.557291",
"0.55664927",
"0.5555025",
"0.54957616",
"0.54935724",
"0.5484652",
"0.5469827",
"0.54646283",
"0.5463046",
"0.544083... | 0.7089597 | 0 |
after _send_celery_mail finishes, the tenant should be cleared again | def test_tenant_setup_celery_reset(self):
msg = mock.Mock()
tenant = mock.Mock()
tenant.client_name = 'mock-tenant'
_send_celery_mail(msg, tenant, send=False)
self.assertFalse(hasattr(properties, 'tenant'))
self.assertEqual(properties.tenant_properties, {}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_tenant_setup_celery(self):\n\n class interceptor(mock.Mock):\n tenant = None\n\n def send(self, *kw, **args):\n self.tenant = properties.tenant\n\n msg = interceptor()\n tenant = mock.Mock()\n tenant.client_name = 'mock-tenant'\n\n _s... | [
"0.60660076",
"0.5897273",
"0.5827525",
"0.58136255",
"0.57590765",
"0.57500565",
"0.57095784",
"0.56320375",
"0.5629329",
"0.56141365",
"0.560764",
"0.55990505",
"0.55975515",
"0.5582091",
"0.5547078",
"0.5544799",
"0.55407256",
"0.5533396",
"0.5488413",
"0.54429895",
"0.540... | 0.7174306 | 0 |
Process a prepared payment | def process(self):
result = self.processor.process(self.request)
self.processorResults = result.success
if result.payment:
reason_code = result.payment.reason_code
else:
reason_code = ""
self.processorReasonCode = reason_code
self.processorMessage = result.message
log.info("""Processing %s transaction with %s
Order %i
Results=%s
Response=%s
Reason=%s""", self.paymentModule.LABEL.value, self.paymentModule.KEY.value,
self.order.id, self.processorResults, self.processorReasonCode, self.processorMessage)
return self.processorResults | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_payment():\n\n url = 'https://api.worldpay.com/v1/orders'\n headers = {'Authorization': environ.get('WORLDPAY_API_KEY'),\n 'Content-type': 'application/json'}\n body = {\n \"paymentMethod\": {\n \"type\": \"Card\",\n \"name\": session['caller_name'],\... | [
"0.71470153",
"0.6825788",
"0.6774326",
"0.6768605",
"0.66278535",
"0.6614318",
"0.65593857",
"0.651435",
"0.6356622",
"0.63481003",
"0.6293071",
"0.6278738",
"0.6272433",
"0.6199742",
"0.61738306",
"0.61488736",
"0.6109061",
"0.6077295",
"0.6046448",
"0.6045506",
"0.6034854"... | 0.6704594 | 4 |
Handles a success in payment. If the order is paidoff, sends success, else return page to pay remaining. | def _onSuccess(self, controller):
if controller.order.paid_in_full:
controller.cart.empty()
for item in controller.order.orderitem_set.all():
if item.product.is_subscription:
item.completed = True
item.save()
try:
curr_status = controller.order.orderstatus_set.latest()
except OrderStatus.DoesNotExist:
curr_status = None
if (curr_status is None) or (curr_status.notes and curr_status.status == "New"):
controller.order.add_status(status='New', notes = "Order successfully submitted")
else:
# otherwise just update and save
if not curr_status.notes:
curr_status.notes = _("Order successfully submitted")
curr_status.save()
#Redirect to the success page
url = controller.lookup_url('satchmo_checkout-success')
return HttpResponseRedirect(url)
else:
log.debug('Order #%i not paid in full, sending to pay rest of balance', controller.order.id)
#url = controller.order.get_balance_remaining_url()
url = reverse('satchmo_balance_remaining')
return HttpResponseRedirect(url) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ui_redirect_success(self, order: Order = None) -> HttpResponse:\n ui_return_url = self.extract_ui_return_url()\n if ui_return_url:\n return self._redirect_to_ui(\n ui_return_url, \"success\", order, path=\"/payment-result\"\n )\n else:\n retu... | [
"0.7352169",
"0.72179747",
"0.7183236",
"0.7149302",
"0.70583147",
"0.700066",
"0.69343185",
"0.68865967",
"0.6824747",
"0.68121576",
"0.6804239",
"0.6716359",
"0.6692291",
"0.661997",
"0.6476802",
"0.6473549",
"0.6418588",
"0.6369197",
"0.63250667",
"0.6308499",
"0.6287491",... | 0.74831283 | 0 |
| Ground Truth | Forehand Backhand Serve Forehand num_FF num_BF num_SF Backhand num_FB num_BB num_SB Serve num_FS num_BS num_SS No_action num_FN num_BN num_SN | def show_eval_class_level(num_BB, num_BF, num_BS, num_BN, num_FB, num_FF, num_FS, num_FN, num_SB, num_SF, num_SS, num_SN):
print("************************************************")
print(" | Ground Truth | ")
print(" Forehand Backhand Serve ")
print("Forehand " + str(num_FF) + " " + str(num_BF) + " " + str(num_SF))
print("Backhand " + str(num_FB) + " " + str(num_BB) + " " + str(num_SB))
print("Serve " + str(num_FS) + " " + str(num_BS) + " " + str(num_SS))
print("Noaction " + str(num_FN) + " " + str(num_BN) + " " + str(num_SN))
print("************************************************") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handDecision(handIn):",
"def part1b_2():\n xs = exampleInput\n z = 5.881\n forward = [\n Counter({'-FEAT-': 0.622, '-SIZE-': 0.377}), \n Counter({'-SIZE-': 0.761, '-FEAT-': 0.238}), \n Counter({'-SIZE-': 0.741, '-FEAT-': 0.258})]\n \n z_, forward_ = submission.... | [
"0.58027256",
"0.5786682",
"0.5755825",
"0.5679587",
"0.56194",
"0.56090933",
"0.5567905",
"0.5535476",
"0.5521466",
"0.54943043",
"0.5431216",
"0.5410346",
"0.5410346",
"0.54039884",
"0.53975636",
"0.53964627",
"0.53595126",
"0.5350653",
"0.5346474",
"0.53431875",
"0.5331558... | 0.58535993 | 0 |
This bidirectional stream makes it possible to send and receive Notes between 2 persons | def ReceiveMsg(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def outgoing(self,message,isBinary=False,identifier=None):\n pass",
"def bidi_streaming(self) -> global___Snippet.BidiStreaming:",
"def play_protobuf(self, notes):\n with open_output(self.midi_port) as outport:\n\n outport.send(Message('program_change', program=12))\n \n ... | [
"0.5810446",
"0.5711371",
"0.55302083",
"0.54603386",
"0.5386901",
"0.5341167",
"0.5337179",
"0.52391213",
"0.52031493",
"0.51937705",
"0.5192062",
"0.518014",
"0.51069796",
"0.50645936",
"0.50393647",
"0.50284076",
"0.50284076",
"0.50284076",
"0.49854276",
"0.49460483",
"0.4... | 0.0 | -1 |
This bidirectional stream makes it possible to send and receive Notes between 2 persons | def AddUser(self, request, context):
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def outgoing(self,message,isBinary=False,identifier=None):\n pass",
"def bidi_streaming(self) -> global___Snippet.BidiStreaming:",
"def play_protobuf(self, notes):\n with open_output(self.midi_port) as outport:\n\n outport.send(Message('program_change', program=12))\n \n ... | [
"0.58094895",
"0.5711503",
"0.55307317",
"0.54599893",
"0.5386648",
"0.53406537",
"0.5336743",
"0.52399313",
"0.5202152",
"0.51940846",
"0.5191432",
"0.51793194",
"0.510636",
"0.50639737",
"0.5037723",
"0.5028408",
"0.5028408",
"0.5028408",
"0.49852347",
"0.49451035",
"0.4945... | 0.0 | -1 |
Decoding Key Value Json String | def loads(kv_data):
dict_kv = {}
if isinstance(kv_data, str):
kvs = json.loads(kv_data)
for kv in kvs:
dict_kv[kv['Key']] = kv['Value']
else:
print("To load Key Value Data it must be String Type")
return dict_kv | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def json_decode(value):\r\n return json.loads(to_basestring(value))",
"def JsonDecode(json_str):\n return JSON_DECODER.decode(json_str)",
"def json_decode(json_str):\n return JSON_DECODER.decode(json_str)",
"def _decode_key(self, key):\n return key if not key or isinstance(key, str) else key.de... | [
"0.7216333",
"0.671068",
"0.66782916",
"0.6641796",
"0.6561169",
"0.6467437",
"0.64546573",
"0.63418275",
"0.6303222",
"0.6267742",
"0.6266026",
"0.6266026",
"0.6258209",
"0.625522",
"0.62400687",
"0.62128145",
"0.620793",
"0.62034374",
"0.61571765",
"0.6144871",
"0.6067123",... | 0.55143964 | 70 |
Remove Key from a Key Value pair Can be performed on Dictionary or Json key value string | def remove(kv_data, key):
if isinstance(kv_data, str):
kv_data = loads(kv_data) # Turn into Dictionary
try:
del kv_data[key]
except NameError:
print(key, " does not exists in key value pair.")
kv_data = dumps(kv_data)
else:
print("Provide a Json Key Value String")
sys.exit(6)
return kv_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_value(self, thing_key, dkey):\n if thing_key in self.things_dict:\n dic = self.things_dict[thing_key]\n if type(dic) != type({}):\n return\n dic.pop(dkey, None)",
"def remove_value(self, key: str) -> None:\n raise NotImplementedError",
"d... | [
"0.7145345",
"0.7130893",
"0.7018091",
"0.6990667",
"0.6903429",
"0.6875397",
"0.6862628",
"0.6806682",
"0.6771876",
"0.6739055",
"0.67074156",
"0.6704598",
"0.6693101",
"0.6646352",
"0.6637673",
"0.66145486",
"0.6510989",
"0.6470834",
"0.64574033",
"0.64387095",
"0.6428868",... | 0.8426328 | 0 |
This will add a key, if key already exists it will overwrite | def add(kv_data, key, value):
if isinstance(kv_data, str):
kvs = loads(kv_data) # Turn json into dictionary
kvs[key] = value # Add key
kvs = dumps(kvs) # Turn back into json Key Value String
else:
print("Provide A JSON Key Value String")
return kvs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add(self, key, value):\n if not key in self:\n self.keys.append(key)\n self.dict[key] = value",
"def add(self, key, value):",
"def _newKey(self, key):\n self._testKeySubNsAdd()\n self._getKeyList().append(key)",
"def addKey(self, key, val):\n self.dict[ke... | [
"0.76844144",
"0.7508933",
"0.7368967",
"0.7365639",
"0.72025496",
"0.7181988",
"0.7153343",
"0.7138765",
"0.7047213",
"0.70134836",
"0.6962179",
"0.6927866",
"0.69101334",
"0.6902198",
"0.6863071",
"0.68489337",
"0.6820259",
"0.67976147",
"0.67973626",
"0.67802364",
"0.67700... | 0.0 | -1 |
If JSON Key Value, Value contains this value | def contains_value(kv_json, value):
if isinstance(kv_json, str):
kv_dict = loads(kv_json)
for key in kv_dict:
if kv_dict[key] == value: # Found value in dictionary
return True
return False
else:
print("Provide A JSON Key Value String") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __is_key_in_json(self, key=str, json_dict=json):\n if key in json_dict:\n # noinspection PyUnresolvedReferences\n return json_dict[key]\n else:\n return self.NO_KEY_VALUE_FOR_ENTRY",
"def is_item_in_the_response(key, value, jsonResponse):\n flag = False\n ... | [
"0.71973366",
"0.68866235",
"0.66816956",
"0.64885634",
"0.6401842",
"0.6400266",
"0.6142757",
"0.61393195",
"0.60865045",
"0.60471123",
"0.60368866",
"0.60345954",
"0.6024064",
"0.6017378",
"0.5997288",
"0.59498996",
"0.59224445",
"0.58714145",
"0.5839041",
"0.58378595",
"0.... | 0.71492994 | 1 |
If the JSON Key Value Pair conatins a specific key | def contains_key(kv_json, key):
if isinstance(kv_json, str):
kv_dict = loads(kv_json)
try:
res = kv_dict[key]
return True
except KeyError:
return False
else:
print("Provide A JSON Key Value String") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __is_key_in_json(self, key=str, json_dict=json):\n if key in json_dict:\n # noinspection PyUnresolvedReferences\n return json_dict[key]\n else:\n return self.NO_KEY_VALUE_FOR_ENTRY",
"def contains_value(kv_json, value):\n if isinstance(kv_json, str):\n ... | [
"0.74435276",
"0.7416881",
"0.71410507",
"0.70549554",
"0.65186876",
"0.6266555",
"0.6243399",
"0.6222261",
"0.62155443",
"0.6150396",
"0.61433333",
"0.6076848",
"0.60425204",
"0.603984",
"0.5970664",
"0.5963968",
"0.5870145",
"0.5867332",
"0.5800207",
"0.57809585",
"0.577749... | 0.73385584 | 2 |
Checks if the JSON Key Value String are equal | def equals(kv_data, kv_data2):
kv_dict1 = loads(kv_data)
kv_dict2 = loads(kv_data2)
if kv_dict1 == kv_dict2:
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dict_keys_duplicate(self):\n assert (\n orjson.dumps({\"1\": True, 1: False}, option=orjson.OPT_NON_STR_KEYS)\n == b'{\"1\":true,\"1\":false}'\n )",
"def contains_value(kv_json, value):\n if isinstance(kv_json, str):\n kv_dict = loads(kv_json)\n for k... | [
"0.6808316",
"0.6743237",
"0.6582611",
"0.64539456",
"0.6340551",
"0.6310905",
"0.6183923",
"0.6180304",
"0.61636674",
"0.6138103",
"0.61157376",
"0.6100204",
"0.6092481",
"0.6077344",
"0.6060001",
"0.6018096",
"0.5998856",
"0.59183186",
"0.59001577",
"0.58963",
"0.58931094",... | 0.6284483 | 6 |
from all the information provided by the ONCat template, we are only interested by the following infos [name, path and units]. We isolate those into the template_information dictionary | def isolate_relevant_information(self):
def get_formula(oncat_formula):
"""will need to go from something like
"${value/10e11}`"
to something more pythonic
"{value/10e11}"""
regular_expression = r'\$(?P<formula>.+)\`'
m = re.search(regular_expression, oncat_formula)
if m:
return m.group('formula')
else:
return ""
template_information = {}
for _index, _element in enumerate(self._oncat_default_template):
_title = _element["name"]
_path = _element["path"]
if "units" in _element:
_units = _element["units"]
else:
_units = ""
if "transform" in _element:
_formula = get_formula(_element["transform"])
else:
_formula = ""
template_information[_index] = {'title': _title,
'path': _path,
'units': _units,
'formula': _formula}
self.template_information = template_information | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_template(self):\n for line in self.raw_template.split(\"\\n\"):\n line = line.strip()\n if line.startswith('#m3'):\n key, val = line[3:].strip().split('=', 1)\n key = key.strip()\n val = val.strip()\n self.variables[... | [
"0.6129451",
"0.5694503",
"0.5597461",
"0.5589702",
"0.5520051",
"0.5466003",
"0.5434218",
"0.5390082",
"0.5387614",
"0.53731686",
"0.53682715",
"0.53276026",
"0.5297058",
"0.5292366",
"0.52901614",
"0.52713674",
"0.5267604",
"0.5235461",
"0.52188444",
"0.5214309",
"0.5210592... | 0.7443054 | 0 |
will need to go from something like "${value/10e11}`" to something more pythonic "{value/10e11} | def get_formula(oncat_formula):
regular_expression = r'\$(?P<formula>.+)\`'
m = re.search(regular_expression, oncat_formula)
if m:
return m.group('formula')
else:
return "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def usd(value):\n return f\"${value:,.2f}\"",
"def usd(value):\n return f\"${value:,.2f}\"",
"def _fmt(x, pos):\n a, b = '{:.2e}'.format(x).split('e')\n b = int(b)\n return r'${} \\times 10^{{{}}}$'.format(a, b)",
"def usd(value):\n return f\"${int(value):,}\"",
"def format_val(val):\n ... | [
"0.6952612",
"0.6952612",
"0.6528751",
"0.6438116",
"0.6148548",
"0.607353",
"0.6007281",
"0.6003679",
"0.6000689",
"0.5998228",
"0.5958189",
"0.5958189",
"0.5906514",
"0.58572143",
"0.5845689",
"0.5830315",
"0.5819316",
"0.58074373",
"0.5799571",
"0.57672465",
"0.5750976",
... | 0.0 | -1 |
Using the ONCat template to create projection used by oncat to return full information | def create_oncat_projection_from_template(with_location=False,
template={}):
projection = []
if with_location:
projection = ['location']
nbr_columns = len(template)
for _col in np.arange(nbr_columns):
projection.append(template[_col]['path'])
return projection | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def projection(self):\n pass",
"def describe(self, template='projection_default.txt', engine='default'):\n raise NotImplementedError",
"def output_projection(self):\n return self.projection(what='output')",
"def makeProjection(self, variable, token, typed_token, constituent_dict):\n m... | [
"0.6503911",
"0.61083275",
"0.58378226",
"0.53846645",
"0.52837193",
"0.52689284",
"0.5229088",
"0.52271664",
"0.518275",
"0.5146785",
"0.51318693",
"0.51146895",
"0.5093305",
"0.5028556",
"0.50255054",
"0.49924508",
"0.49750137",
"0.4933599",
"0.49226946",
"0.49170607",
"0.4... | 0.67057735 | 0 |
Applies selected activation function to intermediate output. | def apply_activation(intermediate_output, intermediate_activation):
if intermediate_activation is None:
return intermediate_output
if intermediate_activation == 'gelu':
intermediate_output = nn.gelu(intermediate_output)
elif intermediate_activation == 'relu':
intermediate_output = nn.relu(intermediate_output)
elif intermediate_activation == 'sigmoid':
intermediate_output = nn.sigmoid(intermediate_output)
elif intermediate_activation == 'softmax':
intermediate_output = nn.softmax(intermediate_output)
elif intermediate_activation == 'celu':
intermediate_output = nn.celu(intermediate_output)
elif intermediate_activation == 'elu':
intermediate_output = nn.elu(intermediate_output)
elif intermediate_activation == 'log_sigmoid':
intermediate_output = nn.log_sigmoid(intermediate_output)
elif intermediate_activation == 'log_softmax':
intermediate_output = nn.log_softmax(intermediate_output)
elif intermediate_activation == 'soft_sign':
intermediate_output = nn.soft_sign(intermediate_output)
elif intermediate_activation == 'softplus':
intermediate_output = nn.softplus(intermediate_output)
elif intermediate_activation == 'swish':
intermediate_output = nn.swish(intermediate_output)
elif intermediate_activation == 'tanh':
intermediate_output = jnp.tanh(intermediate_output)
else:
raise NotImplementedError('%s activation function is not yet supported.' %
intermediate_activation)
return intermediate_output | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output_layer_activation(x):\n return x",
"def uf_activate(self, output_reg):\n if len(self.inputs) is 2:\n self.two_activation(output_reg)\n elif len(self.inputs) is 3:\n self.three_activation(output_reg)\n else:\n self.large_activation(output_reg)",
... | [
"0.7604806",
"0.7194517",
"0.6661993",
"0.6523873",
"0.64537185",
"0.6431659",
"0.64278513",
"0.6370167",
"0.63165534",
"0.6285312",
"0.6285312",
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"0.6194766",
"0.6191587",
"0.6181813",
"0.614133",
"0.6128814",
"0.6084095",
"0.607957",
"0.6078586",
"0.60711646",
... | 0.7784417 | 0 |
Get loss and log probs for the masked LM. | def get_masked_lm_output(bert_config, input_tensor, output_weights, positions,
label_ids, label_weights):
input_tensor = gather_indexes(input_tensor, positions)
with tf.variable_scope('cls/predictions'):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.variable_scope('transform'):
input_tensor = tf.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
'output_bias',
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return (loss, per_example_loss, log_probs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_loss_inputs(cls):\n return LOGITS",
"def losses(self):\n # compute all kinds of losses \n\n # 1. Logits losses for classification \n\n # 2. regression loss for bbox \n\n return classification_loss, bbox_reg_loss",
"def masked_l1_loss(prediction, target, mask):\n ab... | [
"0.6298937",
"0.6172792",
"0.6055308",
"0.5973929",
"0.58460975",
"0.58436185",
"0.58152527",
"0.58060163",
"0.5798398",
"0.5763899",
"0.5735554",
"0.5732799",
"0.57132846",
"0.5705625",
"0.5683144",
"0.56716806",
"0.5659647",
"0.5657345",
"0.5654184",
"0.5651408",
"0.565015"... | 0.0 | -1 |
Get loss and log probs for the next sentence prediction. | def get_next_sentence_output(bert_config, input_tensor, labels):
# Simple binary classification. Note that 0 is "next sentence" and 1 is
# "random sentence". This weight matrix is not used after pre-training.
with tf.variable_scope('cls/seq_relationship'):
output_weights = tf.get_variable(
'output_weights',
shape=[2, bert_config.hidden_size],
initializer=modeling.create_initializer(bert_config.initializer_range))
output_bias = tf.get_variable(
'output_bias', shape=[2], initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
labels = tf.reshape(labels, [-1])
one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, log_probs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict(self, X):\n words = X.split()\n chance = []\n for cur_label in self.model[\"labels\"]:\n probability = self.model[\"labels\"][cur_label][\"probability\"]\n total_grade = math.log(probability, math.e)\n for word in words:\n word_dict =... | [
"0.6818383",
"0.67636436",
"0.6717865",
"0.6672301",
"0.6578229",
"0.6562069",
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"0.6378741",
"0.6371581",
"0.6352471",
"0.6344506",
"0.63184255",
"0.629813",
"0.62947714"... | 0.63347507 | 18 |
Gathers the vectors at the specific positions over a minibatch. | def gather_indexes(sequence_tensor, positions):
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
return output_tensor | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def map_vects(self, input_vects):\n \n if not self._trained:\n raise ValueError(\"SOM not trained yet\")\n \n to_return = []\n \n distances = []\n \n \n contador_adyacentes = 0\n \n matriz = np.array(list(self._neuron_locations(self._m, self... | [
"0.6250158",
"0.6235054",
"0.5918158",
"0.590376",
"0.5815052",
"0.567955",
"0.566976",
"0.56412804",
"0.55624056",
"0.5546037",
"0.55153716",
"0.55076957",
"0.54924893",
"0.54811513",
"0.5472844",
"0.54588175",
"0.54577035",
"0.5457554",
"0.54365367",
"0.5420255",
"0.5397297... | 0.0 | -1 |
Returns TF Bert config.. | def get_tf_config(config_path):
return modeling.BertConfig.from_json_file(config_path).__dict__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bert_config(config):\n if config.model_size == \"large\":\n args = {\"hidden_size\": 1024, \"num_hidden_layers\": 24}\n elif config.model_size == \"base\":\n args = {\"hidden_size\": 768, \"num_hidden_layers\": 12}\n elif config.model_size == \"small\":\n args = {\"hidden_size\": 256, \"num_hid... | [
"0.66096175",
"0.6400916",
"0.6374486",
"0.63508105",
"0.6265168",
"0.61243945",
"0.6115964",
"0.61093426",
"0.61093426",
"0.60153896",
"0.601387",
"0.5967145",
"0.59554666",
"0.59397674",
"0.5929531",
"0.59017366",
"0.5877108",
"0.5862486",
"0.5856384",
"0.585002",
"0.584904... | 0.7430698 | 0 |
Return tf mlperf model parameters in a dictionary format. Use get_tf_model_variables if using kerasBERT checkpoint. This function works | def get_mlperf_model_variables(config_path, init_checkpoint):
# Load saved model configuration
bert_config = modeling.BertConfig.from_json_file(config_path)
seq_length = bert_config.max_position_embeddings
tf_variables = {}
max_predictions_per_seq = 76
# Generate BERT TF model and initiate variable update from checkpoint
graph = tf.Graph()
sess = tf.Session(graph=graph)
with graph.as_default():
input_ids = tf.zeros((4, seq_length), dtype=tf.int32)
input_mask = tf.zeros((4, seq_length), dtype=tf.int32)
segment_ids = tf.zeros((4, seq_length), dtype=tf.int32)
masked_lm_positions = tf.zeros((4, max_predictions_per_seq), dtype=tf.int32)
masked_lm_ids = tf.zeros((4, max_predictions_per_seq), dtype=tf.int32)
masked_lm_weights = tf.zeros((4, max_predictions_per_seq), dtype=tf.float32)
next_sentence_labels = tf.zeros((4), dtype=tf.int32)
tf_model = modeling.BertModel(
config=bert_config,
is_training=True,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=True)
(masked_lm_loss, _,
_) = get_masked_lm_output(bert_config, tf_model.get_sequence_output(),
tf_model.get_embedding_table(),
masked_lm_positions, masked_lm_ids,
masked_lm_weights)
(next_sentence_loss, _,
_) = get_next_sentence_output(bert_config, tf_model.get_pooled_output(),
next_sentence_labels)
_ = masked_lm_loss + next_sentence_loss
tvars = tf.trainable_variables()
(assignment_map,
_) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
tf.compat.v1.train.init_from_checkpoint(init_checkpoint, assignment_map)
sess.run(tf.initializers.global_variables())
tvars_vals = sess.run(tvars)
for var, val in zip(tvars, tvars_vals):
tf_variables[var.name[:-2]] = val
tf_config = bert_config.__dict__
return tf_config, tf_variables, tf_model | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def describe_model():\n train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n msg = [\"\"]\n total = 0\n for v in train_vars:\n shape = v.get_shape()\n ele = shape.num_elements()\n total += ele\n msg.append(\"{}: shape={}, dim={}\".format(\n v.name, s... | [
"0.65184647",
"0.65112454",
"0.638817",
"0.63505626",
"0.6223993",
"0.6148675",
"0.6122482",
"0.6062248",
"0.6055513",
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"0.600799",
"0.59653383",
"0.59652144",
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"0.59522676",
"0.5892172",
"0.58268005",
"0.5810189",
"0.58081865",
"0.57768404",
"0.577381... | 0.7301645 | 0 |
Return tf model parameters in a dictionary format. | def get_tf_model_variables(config_path, init_checkpoint):
# Load saved model configuration
config = configs.BertConfig.from_json_file(config_path)
# Generate BERT TF model and initiate variable update from checkpoint
seq_len = 20
_, tf_model = bert_models.squad_model(config, seq_len)
checkpoint = tf.train.Checkpoint(model=tf_model)
checkpoint.restore(init_checkpoint).assert_existing_objects_matched()
tf_config = config.__dict__
tf_variables = {v.name: v.numpy() for v in tf_model.variables}
return tf_config, tf_variables, tf_model | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_model(self):\n\n parameters = {keys._topology:self.topology,\n keys._size:self.size,\n keys._name:self.name,\n #keys._output_activation:self._outActiv_fun_key,\n #keys._hidden_activation:self._hiddenActiv_fun_key,\n... | [
"0.73577815",
"0.7218848",
"0.7189309",
"0.7117365",
"0.71154326",
"0.7032748",
"0.70010006",
"0.6994405",
"0.69852734",
"0.69549537",
"0.6953729",
"0.6948936",
"0.69394284",
"0.6904722",
"0.6866829",
"0.6861575",
"0.6858673",
"0.6855758",
"0.6850728",
"0.68482053",
"0.681446... | 0.0 | -1 |
Convert TF BERT model config to be compatible with JAX BERT model. | def convert_tf_config_to_jax_bert(config):
unnecessary_keys = ['initializer_range', 'backward_compatible',
'embedding_size']
for key in unnecessary_keys:
if key in config:
config.pop(key)
# change TF parameter names to match JAX parameter names
mapping = {
'attention_dropout_rate': 'attention_probs_dropout_prob',
'hidden_activation': 'hidden_act',
'dropout_rate': 'hidden_dropout_prob',
'emb_dim': 'hidden_size',
'mlp_dim': 'intermediate_size',
'max_len': 'max_position_embeddings',
'num_heads': 'num_attention_heads',
'num_layers': 'num_hidden_layers'
}
for jax_key, tf_key in mapping.items():
config[jax_key] = config.pop(tf_key)
return config | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bert_config(config):\n if config.model_size == \"large\":\n args = {\"hidden_size\": 1024, \"num_hidden_layers\": 24}\n elif config.model_size == \"base\":\n args = {\"hidden_size\": 768, \"num_hidden_layers\": 12}\n elif config.model_size == \"small\":\n args = {\"hidden_size\": 256, \"num_hid... | [
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"0.5420582",
"0.5410694",
"0.54047483",
"0.53842753",
"0.535301",
"0.5317... | 0.7478179 | 0 |
Modify TF mlperf model parameter dict to be compatible with JAX parameter dict. Convert parameter names in tf_params to match JAX parameter names and create a nested dictionary of parameters for each layer in the model using `/` in each key as a delimeter. This function uses mlperf model naming convention. Use convert_tf_param_dict_to_jax when using kerasBERT model configuration. | def convert_mlperf_param_dict_to_jax(tf_params, emb_dim, num_heads):
jax_params = {}
# mapping between mlperf model and JAX model
# works for model in //third_party/tensorflow_models/mlperf/models/rough/bert
tf_key_to_jax_key = [
('cls/seq_relationship/', 'classification/predictions_transform_logits/'),
('output_weights', 'kernel'),
('transform_logits/output_bias', 'transform_logits/bias'),
('cls/predictions/', 'masked_lm/cls_predictions_'),
('transform/dense', 'transform_dense'),
('transform/LayerNorm', 'transform_layernorm'),
('predictions_output_bias', 'predictions_output_bias/bias'),
('bert/embeddings/word_embeddings', 'word_embeddings/embedding'),
('bert/', 'transformer_encoder/'),
('embeddings/token_type_embeddings', 'type_embeddings/embedding'),
('embeddings/position_embeddings', 'position_embeddings/embedding'),
('attention/self', 'self_attention'),
('attention/output', 'self_attention_output'),
('layer_norm/layer_norm_', 'layer_norm/'),
('output/LayerNorm', 'output_layer_norm'),
('intermediate/dense', 'intermediate'),
('output/dense', 'output'),
('pooler/dense/', 'pooler_transform/'),
('self_attention_output_layer_norm', 'self_attention_layer_norm'),
('embeddings/LayerNorm', 'embeddings_layer_norm'),
('encoder/layer', 'encoder_layer'),
(':0', ''),
('beta', 'bias'),
('gamma', 'scale')
]
for tf_key, val in tf_params.items():
jax_key = tf_key
for tf_name, jax_name in tf_key_to_jax_key:
jax_key = jax_key.replace(tf_name, jax_name)
# Reshape kernels if necessary
jax_params[jax_key] = tf_params[tf_key]
if 'self_attention_output/kernel' in jax_key:
param = tf_params[tf_key]
jax_params[jax_key] = param.reshape(
(num_heads, -1, emb_dim))
# jax position embedding kernel has additional dimension
pos_embedding = jax_params[
'transformer_encoder/position_embeddings/embedding']
jax_params[
'transformer_encoder/position_embeddings/embedding'] = pos_embedding[
np.newaxis, ...]
# convert flat param dict into nested dict using `/` as delimeter
outer_dict = {}
for key, val in jax_params.items():
tokens = key.split('/')
inner_dict = outer_dict
# each token except the very last should add a layer to the nested dict
for token in tokens[:-1]:
if token not in inner_dict:
inner_dict[token] = {}
inner_dict = inner_dict[token]
inner_dict[tokens[-1]] = val
return outer_dict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_tf_param_dict_to_jax(tf_params):\n jax_params = {}\n tf_key_to_jax_key = [\n ('embeddings/layer_norm', 'embeddings_layer_norm'),\n ('transformer/layer', 'encoder_layer'), ('embeddings:0', 'embedding'),\n (':0', ''), ('beta', 'bias'), ('gamma', 'scale'),\n ('position_embedding/', '... | [
"0.7303152",
"0.59408104",
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"0.5378973",
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"0.5166872",
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"0.5018701",
"0.49990663",
"0.4953636",
"0.49412417",
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"0.48552454",
"0.48360878",
"0.48341933",
"0.48306793",
"0.48175794",
"0.48... | 0.80498666 | 0 |
Modify TF parameter dict to be compatible with JAX parameter dict. Convert parameter names in tf_params to match JAX parameter names and create a nested dictionary of parameters for each layer in the model using `/` in each key as a delimeter. | def convert_tf_param_dict_to_jax(tf_params):
jax_params = {}
tf_key_to_jax_key = [
('embeddings/layer_norm', 'embeddings_layer_norm'),
('transformer/layer', 'encoder_layer'), ('embeddings:0', 'embedding'),
(':0', ''), ('beta', 'bias'), ('gamma', 'scale'),
('position_embedding/', 'position_embeddings/')
]
for tf_key in tf_params:
jax_key = tf_key
for tf_name, jax_name in tf_key_to_jax_key:
jax_key = jax_key.replace(tf_name, jax_name)
jax_params[jax_key] = tf_params[tf_key]
# jax position embedding kernel has additional dimension
pos_embedding = jax_params['position_embeddings/embedding']
jax_params['position_embeddings/embedding'] = pos_embedding[np.newaxis, ...]
# convert flat param dict into nested dict using `/` as delimeter
outer_dict = {}
for key, val in jax_params.items():
tokens = key.split('/')
inner_dict = outer_dict
# each token except the very last should add a layer to the nested dict
for token in tokens[:-1]:
if token not in inner_dict:
inner_dict[token] = {}
inner_dict = inner_dict[token]
inner_dict[tokens[-1]] = val
# this layer doesn't have parameters, but key is required to be present
outer_dict['self_attention_mask'] = 0.
return outer_dict | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_mlperf_param_dict_to_jax(tf_params, emb_dim, num_heads):\n jax_params = {}\n # mapping between mlperf model and JAX model\n # works for model in //third_party/tensorflow_models/mlperf/models/rough/bert\n tf_key_to_jax_key = [\n ('cls/seq_relationship/', 'classification/predictions_transform_lo... | [
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"0.52044886",
"0.5198123",
"0.5191683",
"0.51802343",
"0.51680446",
"0.51627... | 0.7722715 | 0 |
>>> get_comparison_value('AS KS QS JS TS'.split())[1] 'Royal Flush' >>> get_comparison_value('KS QS JS TS 9S'.split())[1] 'Straight Flush' >>> get_comparison_value('8S 8C 8D 8H 3S'.split())[1] 'Four of a Kind' >>> get_comparison_value('8S 8C 8D 3H 3S'.split())[1] 'Full House' >>> get_comparison_value('2S QS JS TS 9S'.split())[1] 'Flush' >>> get_comparison_value('KD QS JS TS 9S'.split())[1] 'Straight' >>> get_comparison_value('8S 8C 8D 4H 3S'.split())[1] 'Three of a Kind' >>> get_comparison_value('8S 8C 4D 4H 3S'.split())[1] 'Two Pairs' >>> get_comparison_value('8S 8C 5D 4H 3S'.split())[1] 'One Pair' >>> get_comparison_value('8S 7C 5D 4H 3S'.split())[1] 'High Card' | def get_comparison_value(hand):
suits = set(get_suit(card) for card in hand)
values = set(get_value(card) for card in hand)
is_flush = len(suits) == 1
is_straight = (len(values) == 5 and
min(values) + 4 == max(values))
kinds = get_kinds(hand)
kind_counts = [k.count for k in kinds]
if is_flush and values == {10, 11, 12, 13, 14}:
result = (100, 'Royal Flush')
elif is_flush and is_straight:
result = (90, 'Straight Flush')
elif kind_counts == [4, 1]:
result = (80, 'Four of a Kind')
elif kind_counts == [3, 2]:
result = (70, 'Full House')
elif is_flush:
result = (60, 'Flush')
elif is_straight:
result = (50, 'Straight')
elif kind_counts == [3, 1, 1]:
result = (40, 'Three of a Kind')
elif kind_counts == [2, 2, 1]:
result = (30, 'Two Pairs')
elif kind_counts == [2, 1, 1, 1]:
result = (20, 'One Pair', kinds[0].value)
else:
assert kind_counts == [1]*5
result = (10, 'High Card')
return result + (max(values),) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def comparison(self) -> str:\n return self._values.get('comparison')",
"def comparison(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"comparison\")",
"def testWinkler(self): # - - - - - - - - - - - - - - - - - - - - - - - - - -\n\n for pair in self.string_pairs:\n\n a... | [
"0.578843",
"0.5683622",
"0.5322386",
"0.5317316",
"0.5244176",
"0.5244176",
"0.52301955",
"0.51247126",
"0.5097643",
"0.50959706",
"0.5091257",
"0.5083387",
"0.50707597",
"0.5040682",
"0.5037145",
"0.5023796",
"0.5014674",
"0.50132555",
"0.50123906",
"0.50123906",
"0.4993311... | 0.6191718 | 0 |
Compute the transformation matrix from Galactic spherical to Magellanic Stream coordinates. | def galactic_to_MS():
return MS_MATRIX | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MS_to_galactic():\n return matrix_transpose(MS_MATRIX)",
"def get_transformation_matrix(theta=45):\n\n theta = theta/360 * 2 * np.pi # in radians\n hx = np.cos(theta)\n sy = np.sin(theta)\n\n S = np.array([[1, hx, 0],\n [0, sy, 0],\n [0, 0, 1]])\n #S_inv = ... | [
"0.6793291",
"0.6397168",
"0.6347205",
"0.6225888",
"0.6009966",
"0.5733559",
"0.5673262",
"0.56630766",
"0.56511235",
"0.5618374",
"0.5604634",
"0.5580951",
"0.5580951",
"0.5563582",
"0.55473304",
"0.5542603",
"0.55239236",
"0.547165",
"0.546832",
"0.5466667",
"0.54391974",
... | 0.6850105 | 0 |
Compute the transformation matrix from Magellanic Stream coordinates to spherical Galactic. | def MS_to_galactic():
return matrix_transpose(MS_MATRIX) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def galactic_to_MS():\n return MS_MATRIX",
"def get_transformation_matrix(theta=45):\n\n theta = theta/360 * 2 * np.pi # in radians\n hx = np.cos(theta)\n sy = np.sin(theta)\n\n S = np.array([[1, hx, 0],\n [0, sy, 0],\n [0, 0, 1]])\n #S_inv = np.linalg.inv(S)\n... | [
"0.6758652",
"0.6351473",
"0.6289319",
"0.6249192",
"0.5900926",
"0.58043545",
"0.5739055",
"0.55864763",
"0.558487",
"0.55819327",
"0.55792",
"0.55535346",
"0.5547185",
"0.5533851",
"0.55322266",
"0.55322266",
"0.55189437",
"0.551152",
"0.5510809",
"0.54767853",
"0.5444235",... | 0.67547804 | 1 |
Add the ``request.raven`` method and configure the `ravenjs` panel. | def includeme(config, get_raven=None, panel=None):
# Compose.
if get_raven is None: #pragma: no cover
get_raven = get_raven_client
if panel is None: #pragma: no cover
panel = raven_js_panel
# Unpack.
settings = config.registry.settings
# Provide the client as ``request.raven``.
config.add_request_method(get_raven, 'raven', reify=True)
# Configure the ``raven-js`` panel.
if hasattr(config, "add_panel"):
# Soft detect if we have pyramid_layout installed
default_tmpl = 'pyramid_raven:templates/panel.mako'
panel_tmpl = settings.get('pyramid_raven.panel_tmpl', default_tmpl)
config.add_panel(panel, 'raven-js', renderer=panel_tmpl) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def includeme(config):\n config.add_subscriber(add_renderer_globals, BeforeRender)\n config.add_subscriber(add_localizer, NewRequest)\n config.add_subscriber(add_csrf_validation, NewRequest)\n config.add_subscriber(add_resources, NewRequest)",
"def enable(self):\n LOGGER.info('Enabling WebAPI ... | [
"0.59271264",
"0.52645963",
"0.5064856",
"0.5035225",
"0.5023455",
"0.50157934",
"0.5003367",
"0.49658138",
"0.49607152",
"0.49281287",
"0.48860234",
"0.4869646",
"0.48251504",
"0.48205826",
"0.48137787",
"0.47873187",
"0.47489318",
"0.46791732",
"0.46751204",
"0.46577823",
"... | 0.7125961 | 0 |
This is the method to be inherited for adding policies | def _get_policies(self, cr, uid, context=None):
return [('optional', _('Optional')),
('always', _('Always')),
('never', _('Never'))] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_policies(self, policy_collection, options):",
"def add_policy(self, policy_name, policy_text): \n self.policies.add(policy_name, policy_text)\n self.policies = set()",
"def update_policy(self):\n pass",
"def policies(self, policies):\n\n self._policies = policies",
... | [
"0.72758013",
"0.72136664",
"0.7203031",
"0.71865726",
"0.704924",
"0.7013781",
"0.6969776",
"0.67410517",
"0.66044307",
"0.65886885",
"0.64111924",
"0.64080495",
"0.6374745",
"0.6372226",
"0.6354436",
"0.63291216",
"0.6327667",
"0.6274823",
"0.6273386",
"0.62506497",
"0.6214... | 0.6071868 | 23 |
Extension point to obtain analytic policy for an account | def _get_asset_category_policy(self, cr, uid, account, context=None):
return account.user_type.asset_policy | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_policy(self, *args, **kwargs):\r\n pass",
"def get_policy(usage_id):\r\n return policy.get(policy_key(usage_id), {})",
"def get_account_settings():\n pass",
"def policy(agent):",
"def get_policies():\r\n policy = policies.values()\r\n return policy",
"def PolicyStat... | [
"0.62874985",
"0.592912",
"0.592754",
"0.5838627",
"0.5768842",
"0.5702864",
"0.56812865",
"0.5668994",
"0.56547016",
"0.5649048",
"0.5649048",
"0.5625525",
"0.55135316",
"0.5442621",
"0.5390839",
"0.53589445",
"0.5355903",
"0.5345013",
"0.5333619",
"0.5252388",
"0.5235518",
... | 0.47461304 | 80 |
Preprocess the inputs to a list. | def _inputs_to_list(self, inputs: InputsType) -> list:
if isinstance(inputs, str):
backend = get_file_backend(inputs)
if hasattr(backend, 'isdir') and isdir(inputs):
# Backends like HttpsBackend do not implement `isdir`, so only
# those backends that implement `isdir` could accept the inputs
# as a directory
filename_list = list_dir_or_file(inputs, list_dir=False)
inputs = [
join_path(inputs, filename) for filename in filename_list
]
if not isinstance(inputs, (list, tuple)):
inputs = [inputs]
return list(inputs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess(self, inputs, is_list_of_str=False):\n return self.vocab.transform(inputs, is_list_of_str)",
"def controlled_list(input_list):\n output_list = input_list\n\n if not isinstance(input_list, list):\n\n dummy_list = []\n dummy_list.append(input_list)\n output_list = d... | [
"0.7020911",
"0.6580381",
"0.63203806",
"0.62907684",
"0.62907684",
"0.62907684",
"0.6103207",
"0.6083461",
"0.6045474",
"0.6014713",
"0.60038537",
"0.59911287",
"0.59911287",
"0.59911287",
"0.5957472",
"0.5921157",
"0.59046984",
"0.58629376",
"0.5854231",
"0.5832996",
"0.582... | 0.60973823 | 7 |
Process the inputs into a modelfeedable format. Customize your preprocess by overriding this method. Preprocess should return an iterable object, of which each item will be used as the input of ``model.test_step``. ``BaseInferencer.preprocess`` will return an iterable chunked data, | def preprocess(self, inputs: InputsType, batch_size: int = 1, **kwargs):
chunked_data = self._get_chunk_data(
map(self.pipeline, inputs), batch_size)
yield from map(self.collate_fn, chunked_data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess_data(self):\n\n self._preprocess_train_data()\n self._preprocess_test_data()",
"def handle(self, data, context):\n \n model_input = self.preprocess(data)\n model_out = self.inference(model_input)\n return self.postprocess(model_out)",
"def inference(self... | [
"0.6673395",
"0.6537345",
"0.6424362",
"0.63573146",
"0.6273335",
"0.6219987",
"0.6157909",
"0.6074433",
"0.60611945",
"0.60131466",
"0.5940389",
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"0.591146",
"0.5900656",
"0.5848106",
"0.5789562",
"0.5781073",
"0.5774665",
"0.57714385... | 0.64281446 | 2 |
Feed the inputs to the model. | def forward(self, inputs: Union[dict, tuple], **kwargs) -> Any:
return self.model.test_step(inputs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def feed_inputs(self):\n feed_dict = collections.defaultdict(list)\n for i in range(self._pipe.batch_size):\n data = self.example_to_data(self._buffer.get())\n for k, v in data.items():\n feed_dict[k].append(v)\n for k, v in self.features.items():\n ... | [
"0.72261435",
"0.71358067",
"0.7098541",
"0.6972215",
"0.694485",
"0.67344826",
"0.67055213",
"0.6651103",
"0.6630101",
"0.6612315",
"0.6608851",
"0.6608851",
"0.6608851",
"0.6608851",
"0.65922177",
"0.65685606",
"0.651799",
"0.6496179",
"0.6496179",
"0.6453505",
"0.6434099",... | 0.6084333 | 39 |
Visualize predictions. Customize your visualization by overriding this method. visualize should return visualization results, which could be np.ndarray or any other objects. | def visualize(self,
inputs: list,
preds: Any,
show: bool = False,
**kwargs) -> List[np.ndarray]: | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_predictions(self):\n self.vis.draw_predictions()",
"def visualizePredictions(testData,knn_predictions):\r\n testData.visualize.scatterPlot('Petal length','Petal width')\r\n testData.dataDict[testData.reference] = knn_predictions\r\n testData.visualize.scatterPlot('Petal length','Petal wi... | [
"0.8056009",
"0.70886505",
"0.70653015",
"0.6991291",
"0.69617945",
"0.692556",
"0.68410033",
"0.68099743",
"0.67139244",
"0.6713704",
"0.6668432",
"0.6661632",
"0.66547585",
"0.6653662",
"0.66434425",
"0.66227466",
"0.6610092",
"0.65519696",
"0.6549795",
"0.6493545",
"0.6469... | 0.6486966 | 20 |
Process the predictions and visualization results from ``forward`` and ``visualize``. | def postprocess(
self,
preds: Any,
visualization: List[np.ndarray],
return_datasample=False,
**kwargs,
) -> dict: | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def walk_forward_prediction(self):\r\n for output_name in self.output_names:\r\n print('\\t\\t\\t|--Prediction type: {}'.format(output_name))\r\n predictions_by_model = {}\r\n pred_metadata_by_model = {}\r\n \r\n print('\\t\\t\\t\\t|--SVM Model')\r\n ... | [
"0.67429686",
"0.6646426",
"0.6335832",
"0.6304872",
"0.6192201",
"0.61361736",
"0.6050557",
"0.60094845",
"0.59930474",
"0.59825176",
"0.59754235",
"0.5935713",
"0.59297657",
"0.59130555",
"0.5906629",
"0.5894044",
"0.5871319",
"0.5870498",
"0.58653355",
"0.5862726",
"0.5836... | 0.55523545 | 70 |
Load config and weights from metafile. | def _load_model_from_metafile(self, model: str) -> Tuple[Config, str]:
model = model.lower()
assert self.scope is not None, (
'scope should be initialized if you want '
'to load config from metafile.')
assert self.scope in MODULE2PACKAGE, (
f'{self.scope} not in {MODULE2PACKAGE}!,'
'please pass a valid scope.')
repo_or_mim_dir = BaseInferencer._get_repo_or_mim_dir(self.scope)
for model_cfg in BaseInferencer._get_models_from_metafile(
repo_or_mim_dir):
model_name = model_cfg['Name'].lower()
model_aliases = model_cfg.get('Alias', [])
if isinstance(model_aliases, str):
model_aliases = [model_aliases.lower()]
else:
model_aliases = [alias.lower() for alias in model_aliases]
if (model_name == model or model in model_aliases):
cfg = Config.fromfile(
osp.join(repo_or_mim_dir, model_cfg['Config']))
weights = model_cfg['Weights']
weights = weights[0] if isinstance(weights, list) else weights
return cfg, weights
raise ValueError(f'Cannot find model: {model} in {self.scope}') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_meta(self, meta_file):\n self._w2i = pickle.load(open(meta_file, \"rb\"))",
"def _load_from_file(self):\n try:\n self.logger.debug('Load metafile %s.', self.meta_file_path)\n with codecs.open(self.meta_file_path, 'r', 'utf-8') as meta_file:\n self._meta... | [
"0.66893715",
"0.611689",
"0.61168337",
"0.59983677",
"0.59396636",
"0.59336466",
"0.5892341",
"0.58317614",
"0.5799049",
"0.5787442",
"0.578168",
"0.5773452",
"0.5759801",
"0.57340556",
"0.5724864",
"0.5721602",
"0.5648196",
"0.5646341",
"0.56409925",
"0.56261986",
"0.562360... | 0.5922421 | 6 |
Get the directory where the ``Configs`` located when the package is installed or ``PYTHONPATH`` is set. | def _get_repo_or_mim_dir(scope):
try:
module = importlib.import_module(scope)
except ImportError:
if scope not in MODULE2PACKAGE:
raise KeyError(
f'{scope} is not a valid scope. The available scopes '
f'are {MODULE2PACKAGE.keys()}')
else:
project = MODULE2PACKAGE[scope]
raise ImportError(
f'Cannot import {scope} correctly, please try to install '
f'the {project} by "pip install {project}"')
# Since none of OpenMMLab series packages are namespace packages
# (https://docs.python.org/3/glossary.html#term-namespace-package),
# The first element of module.__path__ means package installation path.
package_path = module.__path__[0]
if osp.exists(osp.join(osp.dirname(package_path), 'configs')):
repo_dir = osp.dirname(package_path)
return repo_dir
else:
mim_dir = osp.join(package_path, '.mim')
if not osp.exists(osp.join(mim_dir, 'configs')):
raise FileNotFoundError(
f'Cannot find `configs` directory in {package_path}!, '
f'please check the completeness of the {scope}.')
return mim_dir | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_config_dir():\n return Path(environ.get(CONFIG_DIR_ENV_VAR, _default_dir))",
"def get_package_dir():\n return Path(__file__).parent",
"def GetPackageDirectory():\n return os.path.dirname(__file__)",
"def configPath(self):\n return os.path.dirname(__file__)",
"def getConfigPath():\n\... | [
"0.75922424",
"0.74677104",
"0.739525",
"0.73235977",
"0.7267069",
"0.7208713",
"0.7202483",
"0.71300155",
"0.7091457",
"0.70678365",
"0.70390385",
"0.70278746",
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"0.69215524",
"0.69165456",
"0.6855893",
"0.6833579",
"0.68331736",
"0.6795784",
"0.6786... | 0.60847396 | 84 |
Initialize the model with the given config and checkpoint on the specific device. | def _init_model(
self,
cfg: ConfigType,
weights: Optional[str],
device: str = 'cpu',
) -> nn.Module:
checkpoint: Optional[dict] = None
if weights is not None:
checkpoint = _load_checkpoint(weights, map_location='cpu')
if not cfg:
assert checkpoint is not None
try:
# Prefer to get config from `message_hub` since `message_hub`
# is a more stable module to store all runtime information.
# However, the early version of MMEngine will not save config
# in `message_hub`, so we will try to load config from `meta`.
cfg_string = checkpoint['message_hub']['runtime_info']['cfg']
except KeyError:
assert 'meta' in checkpoint, (
'If model(config) is not provided, the checkpoint must'
'contain the config string in `meta` or `message_hub`, '
'but both `meta` and `message_hub` are not found in the '
'checkpoint.')
meta = checkpoint['meta']
if 'cfg' in meta:
cfg_string = meta['cfg']
else:
raise ValueError(
'Cannot find the config in the checkpoint.')
cfg.update(
Config.fromstring(cfg_string, file_format='.py')._cfg_dict)
# Delete the `pretrained` field to prevent model from loading the
# the pretrained weights unnecessarily.
if cfg.model.get('pretrained') is not None:
del cfg.model.pretrained
model = MODELS.build(cfg.model)
model.cfg = cfg
self._load_weights_to_model(model, checkpoint, cfg)
model.to(device)
model.eval()
return model | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_model(config: Union[str, Path, Config],\n checkpoint: Optional[str] = None,\n device: str = 'cuda:0',\n cfg_options: Optional[dict] = None):\n if isinstance(config, (str, Path)):\n config = Config.fromfile(config)\n elif not isinstance(config, Config)... | [
"0.77449846",
"0.7433742",
"0.73220265",
"0.72701585",
"0.7264649",
"0.6990384",
"0.6840998",
"0.6713714",
"0.65702015",
"0.65702015",
"0.6462767",
"0.64526856",
"0.6444339",
"0.6407412",
"0.63909423",
"0.6390203",
"0.63637805",
"0.6350734",
"0.62798935",
"0.62494826",
"0.623... | 0.70993656 | 5 |
Loading model weights and meta information from cfg and checkpoint. Subclasses could override this method to load extra meta information from ``checkpoint`` and ``cfg`` to model. | def _load_weights_to_model(self, model: nn.Module,
checkpoint: Optional[dict],
cfg: Optional[ConfigType]) -> None:
if checkpoint is not None:
_load_checkpoint_to_model(model, checkpoint)
else:
warnings.warn('Checkpoint is not loaded, and the inference '
'result is calculated by the randomly initialized '
'model!') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_model(self, ckpt_name=\"best_model.pth\"):\n path = \"/\".join(ckpt_name.split(\"/\")[:-1])\n chkpt = torch.load(ckpt_name)\n self.start_epoch = chkpt['epoch']\n self.best_metric = chkpt['best_metric']\n\n # fix the DataParallel caused problem with keys names\n if... | [
"0.6934471",
"0.69131255",
"0.69076866",
"0.67372805",
"0.6686757",
"0.6633907",
"0.66300243",
"0.6628675",
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"0.64553356",
"0.64483273",
"0.64396846",
"0.6426327",
"0.6404203",
"0.6404203",
"0.63859... | 0.7453869 | 0 |
Initialize the ``collate_fn`` with the given config. The returned ``collate_fn`` will be used to collate the batch data. | def _init_collate(self, cfg: ConfigType) -> Callable:
try:
with FUNCTIONS.switch_scope_and_registry(self.scope) as registry:
collate_fn = registry.get(cfg.test_dataloader.collate_fn)
except AttributeError:
collate_fn = pseudo_collate
return collate_fn # type: ignore | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collate_fn(self, *args):\n return TupleMiniBatch(default_collate(*args))",
"def build_collate_fn(\n cls, args: argparse.Namespace, train: bool\n ) -> Callable[[Sequence[Dict[str, np.ndarray]]], Dict[str, torch.Tensor]]:\n raise NotImplementedError",
"def collate_fn(batch):\n\n fl... | [
"0.6244159",
"0.599894",
"0.5865807",
"0.5771731",
"0.56962913",
"0.5471204",
"0.5424939",
"0.5283143",
"0.52531415",
"0.52518207",
"0.5231545",
"0.51880026",
"0.5077874",
"0.5077874",
"0.49971217",
"0.49632436",
"0.49623293",
"0.4961835",
"0.4936827",
"0.49166656",
"0.489373... | 0.7657155 | 0 |
Initialize the test pipeline. Return a pipeline to handle various input data, such as ``str``, ``np.ndarray``. It is an abstract method in BaseInferencer, and should be implemented in subclasses. The returned pipeline will be used to process a single data. | def _init_pipeline(self, cfg: ConfigType) -> Callable: | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, pipeline, **kwargs):\n self.pipeline = pipeline\n\n self.batch_size = pipeline.batch_size\n self.X_train = None\n self.y_train = None\n self.X_valid = None\n self.y_valid = None\n\n # Child classes should have init signature:\n # (self, bat... | [
"0.6728112",
"0.66963494",
"0.64707565",
"0.63527936",
"0.63097256",
"0.62483305",
"0.62239105",
"0.62218165",
"0.62119925",
"0.61996466",
"0.61975336",
"0.61403424",
"0.61403066",
"0.60585195",
"0.60382646",
"0.60304",
"0.6011767",
"0.60076314",
"0.6005523",
"0.5986289",
"0.... | 0.59325844 | 21 |
Get batch data from dataset. | def _get_chunk_data(self, inputs: Iterable, chunk_size: int):
inputs_iter = iter(inputs)
while True:
try:
chunk_data = []
for _ in range(chunk_size):
processed_data = next(inputs_iter)
chunk_data.append(processed_data)
yield chunk_data
except StopIteration:
if chunk_data:
yield chunk_data
break | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_batch_data(batch, ctx):\n data, label = batch\n return (mx.gluon.utils.split_and_load(data, ctx),\n mx.gluon.utils.split_and_load(label, ctx),\n data.shape[0])",
"def get_batch(self, idxs):\r\n return self.data[(self.start + idxs) % self.maxlen]",
"def _get_batch_dat... | [
"0.7814823",
"0.73819196",
"0.7299499",
"0.71846867",
"0.71441984",
"0.7113388",
"0.7087958",
"0.704405",
"0.6958586",
"0.6905176",
"0.6773217",
"0.67312944",
"0.6721615",
"0.67039114",
"0.6651075",
"0.66494566",
"0.66444975",
"0.66299015",
"0.6618811",
"0.66071945",
"0.65729... | 0.0 | -1 |
Dispatch kwargs to preprocess(), forward(), visualize() and postprocess() according to the actual demands. | def _dispatch_kwargs(self, **kwargs) -> Tuple[Dict, Dict, Dict, Dict]:
# Ensure each argument only matches one function
method_kwargs = self.preprocess_kwargs | self.forward_kwargs | \
self.visualize_kwargs | self.postprocess_kwargs
union_kwargs = method_kwargs | set(kwargs.keys())
if union_kwargs != method_kwargs:
unknown_kwargs = union_kwargs - method_kwargs
raise ValueError(
f'unknown argument {unknown_kwargs} for `preprocess`, '
'`forward`, `visualize` and `postprocess`')
preprocess_kwargs = {}
forward_kwargs = {}
visualize_kwargs = {}
postprocess_kwargs = {}
for key, value in kwargs.items():
if key in self.preprocess_kwargs:
preprocess_kwargs[key] = value
elif key in self.forward_kwargs:
forward_kwargs[key] = value
elif key in self.visualize_kwargs:
visualize_kwargs[key] = value
else:
postprocess_kwargs[key] = value
return (
preprocess_kwargs,
forward_kwargs,
visualize_kwargs,
postprocess_kwargs,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pre_process(cls, *args, **kwargs):\n pass",
"def pre_process(cls, *args, **kwargs):\n pass",
"def pre_process(cls, *args, **kwargs):\n pass",
"def pre_process(cls, *args, **kwargs):\n pass",
"def preprocess(self, real, **kwargs):\n self._preprocessed = True",
"def p... | [
"0.6335919",
"0.6335919",
"0.6335919",
"0.6335919",
"0.6038999",
"0.5977718",
"0.5867384",
"0.5712548",
"0.56914",
"0.5670015",
"0.5632651",
"0.5632651",
"0.5632651",
"0.5632651",
"0.56184596",
"0.5548038",
"0.55419624",
"0.55088145",
"0.5503097",
"0.5503062",
"0.54987526",
... | 0.5567695 | 15 |
Load model config defined in metafile from package path. | def _get_models_from_metafile(dir: str):
meta_indexes = load(osp.join(dir, 'model-index.yml'))
for meta_path in meta_indexes['Import']:
# meta_path example: mmcls/.mim/configs/conformer/metafile.yml
meta_path = osp.join(dir, meta_path)
metainfo = load(meta_path)
yield from metainfo['Models'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_model_from_metafile(self, model: str) -> Tuple[Config, str]:\n model = model.lower()\n\n assert self.scope is not None, (\n 'scope should be initialized if you want '\n 'to load config from metafile.')\n assert self.scope in MODULE2PACKAGE, (\n f'{sel... | [
"0.6878292",
"0.6540713",
"0.64107656",
"0.6372208",
"0.63531864",
"0.625004",
"0.6243981",
"0.6207827",
"0.6201223",
"0.6128199",
"0.6121089",
"0.6106509",
"0.6062811",
"0.5985855",
"0.5977003",
"0.5932948",
"0.59321964",
"0.58767205",
"0.58388513",
"0.5798539",
"0.5768722",... | 0.5608092 | 35 |
List models defined in metafile of corresponding packages. | def list_models(scope: Optional[str] = None, patterns: str = r'.*'):
matched_models = []
if scope is None:
default_scope = DefaultScope.get_current_instance()
assert default_scope is not None, (
'scope should be initialized if you want '
'to load config from metafile.')
assert scope in MODULE2PACKAGE, (
f'{scope} not in {MODULE2PACKAGE}!, please make pass a valid '
'scope.')
root_or_mim_dir = BaseInferencer._get_repo_or_mim_dir(scope)
for model_cfg in BaseInferencer._get_models_from_metafile(
root_or_mim_dir):
model_name = [model_cfg['Name']]
model_name.extend(model_cfg.get('Alias', []))
for name in model_name:
if re.match(patterns, name) is not None:
matched_models.append(name)
output_str = ''
for name in matched_models:
output_str += f'model_name: {name}\n'
print_log(output_str, logger='current')
return matched_models | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_models_from_metafile(dir: str):\n meta_indexes = load(osp.join(dir, 'model-index.yml'))\n for meta_path in meta_indexes['Import']:\n # meta_path example: mmcls/.mim/configs/conformer/metafile.yml\n meta_path = osp.join(dir, meta_path)\n metainfo = load(meta_p... | [
"0.72860724",
"0.6853126",
"0.6804148",
"0.6785551",
"0.6757944",
"0.66406256",
"0.6595081",
"0.65213096",
"0.6515239",
"0.64433444",
"0.6440567",
"0.6412613",
"0.6402561",
"0.63477504",
"0.63247734",
"0.63229364",
"0.6300605",
"0.6296517",
"0.6291255",
"0.6263",
"0.62552387"... | 0.70225334 | 1 |
By this save method we create user and make a relationship with contact model and secoud depend on conatc type. | def save(self,commit=True):
instance = super(ClientSignupForm, self).save(commit=False)
if commit:
instance.username = self.cleaned_data['email']
instance.is_active = False
instance.save()
contact = Client.objects.create(first_name=self.cleaned_data['first_name'],last_name=self.cleaned_data['last_name'],
user_role=self.cleaned_data['user_role'],
department=self.cleaned_data['department'],
user=instance)
return instance | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_model(self, request, obj, form, change):\n if not change:\n if form.is_valid():\n user = form.save()\n user.identity = Users.SUPERVISOR\n user.set_password(form.data.get('password'))\n user.iCode = InviteCls.encode_invite_code(u... | [
"0.64130485",
"0.6218375",
"0.61476517",
"0.60348797",
"0.6014504",
"0.5965818",
"0.59625626",
"0.5833716",
"0.5819595",
"0.5818768",
"0.5805604",
"0.5791148",
"0.5760427",
"0.575658",
"0.5729577",
"0.56765777",
"0.5676002",
"0.5647934",
"0.56215864",
"0.56215864",
"0.5614676... | 0.64635587 | 0 |
Must preserve data used at construction. Specifically for default averaging/length adjustments. averaging/length adjustments recalculate the underlying data | def _original_data(self, data: np.ndarray):
if self._raw_data is None:
self._raw_data = data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_avg(self):\n if self._data_type == 'coords':\n # default averaging is supported only for 'matrix' dataTypes\n return\n elif self._data_type == 'image':\n\n x, y = self._averaging, self._averaging\n\n if (x,y) == (1, 1):\n self.vec... | [
"0.60771775",
"0.5992512",
"0.5975735",
"0.5974339",
"0.58970016",
"0.5867703",
"0.58252496",
"0.58230865",
"0.58149874",
"0.57894266",
"0.5773829",
"0.5726125",
"0.5725895",
"0.570828",
"0.56946415",
"0.56483364",
"0.56139916",
"0.56139916",
"0.5607044",
"0.5600587",
"0.5596... | 0.60240406 | 1 |
Check on input data for proper shape and dtype | def _convert_to_vector_type(self, vectors):
if vectors.shape[-1] == 4 and vectors.ndim == 2:
coord_list = self._convert_coords_to_coordinates(vectors)
self._data_type = self._data_types[1]
elif vectors.shape[-1] == 2 and vectors.ndim == 3:
coord_list = self._convert_image_to_coordinates(vectors)
self._data_type = self._data_types[0]
else:
raise TypeError(
"Vector data of shape %s is not supported" %
str(vectors.shape))
return coord_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_shape(placeholder_shape, data_shape):\n\n return True",
"def check_dims(self, data):\n if np.ndim(data) != 2:\n raise ValueError('Input data must be a two dimensional numpy array. '\n 'Data received has shape (%g, %g).' % data.shape)",
"def check_data_shape(self, dat... | [
"0.7713703",
"0.76080585",
"0.74882436",
"0.7184162",
"0.71101797",
"0.70855606",
"0.7055508",
"0.7006338",
"0.6966901",
"0.6945867",
"0.6939449",
"0.69268495",
"0.69028056",
"0.68751085",
"0.6828708",
"0.6807963",
"0.67849463",
"0.6784447",
"0.6716908",
"0.67096233",
"0.6709... | 0.0 | -1 |
To convert an imagelike array with elements (yproj, xproj) into a position list of coordinates Every pixel position (n, m) results in two output coordinates of (N,2) | def _convert_image_to_coordinates(self, vect) -> np.ndarray:
xdim = vect.shape[0]
ydim = vect.shape[1]
# stride is used during averaging and length adjustment
stride_x, stride_y = self._averaging, self._averaging
# create empty vector of necessary shape
# every "pixel" has 2 coordinates
pos = np.empty((2 * xdim * ydim, 2), dtype=np.float32)
# create coordinate spacing for x-y
# double the num of elements by doubling x sampling
xspace = np.linspace(0, stride_x*xdim, 2 * xdim, endpoint=False)
yspace = np.linspace(0, stride_y*ydim, ydim, endpoint=False)
xv, yv = np.meshgrid(xspace, yspace)
# assign coordinates (pos) to all pixels
pos[:, 0] = xv.flatten()
pos[:, 1] = yv.flatten()
# pixel midpoints are the first x-values of positions
midpt = np.zeros((xdim * ydim, 2), dtype=np.float32)
midpt[:, 0] = pos[0::2, 0]+(stride_x-1)/2
midpt[:, 1] = pos[0::2, 1]+(stride_y-1)/2
# rotate coordinates about midpoint to represent angle and length
pos[0::2, 0] = midpt[:, 0] - (stride_x / 2) * (self._length/2) * \
vect.reshape((xdim*ydim, 2))[:, 0]
pos[0::2, 1] = midpt[:, 1] - (stride_y / 2) * (self._length/2) * \
vect.reshape((xdim*ydim, 2))[:, 1]
pos[1::2, 0] = midpt[:, 0] + (stride_x / 2) * (self._length/2) * \
vect.reshape((xdim*ydim, 2))[:, 0]
pos[1::2, 1] = midpt[:, 1] + (stride_y / 2) * (self._length/2) * \
vect.reshape((xdim*ydim, 2))[:, 1]
return pos | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def proj_coords(coords, proj_in, proj_out): \n return [proj_coord(coord, proj_in, proj_out) for coord in coords]",
"def coordinates_to_imgpts(x, y):\n pts = np.array([np.flipud(np.transpose(np.vstack([x, y])))])\n return pts",
"def get_pix_coordinates(pts, proj_mat, w, h):\n points_t = np.ones(shap... | [
"0.71438277",
"0.68676543",
"0.68333745",
"0.6638456",
"0.66094863",
"0.65278304",
"0.6524649",
"0.6480489",
"0.64521027",
"0.63857985",
"0.63771254",
"0.636936",
"0.6328122",
"0.6313248",
"0.62968314",
"0.62734854",
"0.62452483",
"0.6242931",
"0.6173009",
"0.6140261",
"0.613... | 0.6165102 | 19 |
To convert a list of coordinates of shape (ycenter, xcenter, yproj, xproj) into a list of coordinates Input coordinate of (N,4) becomes two output coordinates of (N,2) | def _convert_coords_to_coordinates(self, vect) -> np.ndarray:
# create empty vector of necessary shape
# one coordinate for each endpoint of the vector
pos = np.empty((2 * len(vect), 2), dtype=np.float32)
# create pairs of points
pos[0::2, 0] = vect[:, 0]
pos[1::2, 0] = vect[:, 0]
pos[0::2, 1] = vect[:, 1]
pos[1::2, 1] = vect[:, 1]
# adjust second of each pair according to x-y projection
pos[1::2, 0] += vect[:, 2]
pos[1::2, 1] += vect[:, 3]
return pos | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def proj_coords(coords, proj_in, proj_out): \n return [proj_coord(coord, proj_in, proj_out) for coord in coords]",
"def get_coordinate_lists(self, crs=None):\n x, y = self.vertices.vectors()[:2]\n if crs is not None and (crs != self.crs):\n x, y = _reproject((x,y), self.crs, crs)\n ... | [
"0.7183775",
"0.6618891",
"0.6581095",
"0.652824",
"0.64622325",
"0.6417996",
"0.63773674",
"0.6365129",
"0.63329947",
"0.63210684",
"0.6282502",
"0.626608",
"0.6254573",
"0.6216185",
"0.61284137",
"0.61090726",
"0.6102427",
"0.6072899",
"0.60686827",
"0.6067336",
"0.6059041"... | 0.5870924 | 44 |
Calculates an average vector over a kernel | def averaging(self, value: int):
self._averaging = value
self.events.averaging()
self._update_avg()
self.refresh() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def avg(u: np.ndarray, v: np.ndarray) -> np.ndarray:\n \n return (u + v) / 2.0",
"def kernels_bayes_average(g_y, w):\n return np.dot(g_y, w)",
"def get_input_vector(img):\n parts = partition_matrix(blurmap(img), 10)\n return numpy.array([numpy.mean(part) for part in parts],\n ... | [
"0.6827808",
"0.65671843",
"0.6564396",
"0.654793",
"0.6526386",
"0.63922465",
"0.63613415",
"0.6356005",
"0.6356005",
"0.6356005",
"0.63499135",
"0.63343555",
"0.63088894",
"0.63088894",
"0.62725526",
"0.6247251",
"0.62032187",
"0.6195959",
"0.6177069",
"0.61551315",
"0.6155... | 0.0 | -1 |
Method for calculating average Implemented ONLY for imagelike vector data | def _update_avg(self):
if self._data_type == 'coords':
# default averaging is supported only for 'matrix' dataTypes
return
elif self._data_type == 'image':
x, y = self._averaging, self._averaging
if (x,y) == (1, 1):
self.vectors = self._original_data
# calling original data
return
tempdat = self._original_data
range_x = tempdat.shape[0]
range_y = tempdat.shape[1]
x_offset = int((x - 1) / 2)
y_offset = int((y - 1) / 2)
kernel = np.ones(shape=(x, y)) / (x*y)
output_mat = np.zeros_like(tempdat)
output_mat_x = signal.convolve2d(tempdat[:, :, 0], kernel,
mode='same', boundary='wrap')
output_mat_y = signal.convolve2d(tempdat[:, :, 1], kernel,
mode='same', boundary='wrap')
output_mat[:, :, 0] = output_mat_x
output_mat[:, :, 1] = output_mat_y
self.vectors = (output_mat[x_offset:range_x-x_offset:x,
y_offset:range_y-y_offset:y]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def average(data):\n return np.average(data)",
"def avg(vector):\n if len(vector) == 0:\n return 0\n return sum(vector) / len(vector)",
"def _calc_avg_img(self, data: Union[Sequence[np.ndarray],\n Sequence[Sequence[np.ndarray]]]\n ) ->... | [
"0.7302006",
"0.7168423",
"0.7156485",
"0.71019864",
"0.694467",
"0.6885104",
"0.6851713",
"0.6831221",
"0.6690518",
"0.6667299",
"0.65953726",
"0.65504265",
"0.64968616",
"0.6474742",
"0.64647216",
"0.6450318",
"0.6422171",
"0.6420864",
"0.64044243",
"0.63954943",
"0.6390917... | 0.7117909 | 3 |
width of the line in pixels widths greater than 1px only guaranteed to work with "agg" method | def width(self, width: Union[int, float]):
self._width = width
vertices, triangles = self._generate_meshes(self.vectors, self._width)
self._mesh_vertices = vertices
self._mesh_triangles = triangles
self.events.width()
self.refresh() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def line_width(et: pd.DataFrame, lw_by: Hashable):\n if lw_by is not None:\n return encodings.data_linewidth(et[lw_by], et[lw_by])\n return pd.Series([1] * len(et), name=\"lw\")",
"def get_line_width(self):\n return self.lwidth",
"def line_width(self, width=2.0):\n self._impl.line_wi... | [
"0.67635524",
"0.64742523",
"0.6465246",
"0.639043",
"0.639043",
"0.61956435",
"0.6169401",
"0.61666083",
"0.6136135",
"0.6110745",
"0.6110745",
"0.6110745",
"0.602749",
"0.602131",
"0.5990465",
"0.5978512",
"0.59784377",
"0.59361607",
"0.5847792",
"0.5825186",
"0.5816536",
... | 0.0 | -1 |
Change the length of all lines | def length(self, length: Union[int, float]):
self._length = length
self._update_length()
self.events.length()
self.refresh() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setLength(self, new_length):\n\n self.length = new_length",
"def set_max_lines(self, n):\n\t\tself._maxLines = n\n\t\tself._trunc_lines()",
"def set_line_width(self, val):\n self.lwidth = val",
"def change_tail_length(self, value):\n self.layer.tail_length = value",
"def longlines(... | [
"0.67900187",
"0.66235274",
"0.6572527",
"0.64778185",
"0.630202",
"0.62923074",
"0.62599736",
"0.62461704",
"0.62156516",
"0.6177423",
"0.6118437",
"0.6068556",
"0.6058712",
"0.60584587",
"0.60579914",
"0.5999391",
"0.5994094",
"0.5986776",
"0.59459186",
"0.5893659",
"0.5886... | 0.5355139 | 86 |
Method for calculating vector lengths Implemented ONLY for imagelike vector data | def _update_length(self):
if self._data_type == 'coords':
return "length adjustment not allowed for coordinate-style data"
elif self._data_type == 'image':
self._vectors = self._convert_to_vector_type(self._current_data)
vertices, triangles = self._generate_meshes(self.vectors,
self.width)
self._mesh_vertices = vertices
self._mesh_triangles = triangles | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def length_vector(v):\n return sqrt(dot_vectors(v, v))",
"def veclength(vec):\n vec = np.array(vec, copy=False).reshape(-1, 3)\n return np.sqrt(np.einsum('ij,ij->i', vec, vec))",
"def vec_len(x):\r\n \r\n length = math.sqrt(x[0]**2 + x[1]**2)\r\n return length",
"def get_vector_length(vecto... | [
"0.69771564",
"0.6968557",
"0.69287777",
"0.68751884",
"0.68681043",
"0.6843323",
"0.67979896",
"0.6776573",
"0.6688724",
"0.66632444",
"0.6648538",
"0.6634671",
"0.6530027",
"0.65078795",
"0.64724904",
"0.6456348",
"0.64492345",
"0.6435357",
"0.6399516",
"0.6285586",
"0.6256... | 0.5496276 | 84 |
Fully refresh the underlying visual. | def _refresh(self):
self._need_display_update = True
self._update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def redraw(self):\n self.vispy_viewer.canvas.update()",
"def refresh(self):\n\t\tself.win.refresh()\n\t\tfor c in self.components:\n\t\t\tc.refresh()",
"def refresh(self):\n self.getWindow().getDecorView().postInvalidate()",
"def update_visualization(self) -> None:\n pass",
"def redraw... | [
"0.7797752",
"0.76777506",
"0.7624245",
"0.7607786",
"0.75885034",
"0.74661344",
"0.74655384",
"0.74655384",
"0.7456086",
"0.7437477",
"0.7407238",
"0.7366163",
"0.7329085",
"0.7314451",
"0.722998",
"0.7220857",
"0.7200671",
"0.7197712",
"0.7181533",
"0.71769416",
"0.7154286"... | 0.80525124 | 0 |
Generates list of mesh vertices and triangles from a list of vectors | def _generate_meshes(self, vectors, width):
centers = np.repeat(vectors, 2, axis=0)
offsets = segment_normal(vectors[::2, :], vectors[1::2, :])
offsets = np.repeat(offsets, 4, axis=0)
signs = np.ones((len(offsets), 2))
signs[::2] = -1
offsets = offsets*signs
vertices = centers + width*offsets/2
triangles = np.array([[2*i, 2*i+1, 2*i+2] if i % 2 == 0 else
[2*i-1, 2*i, 2*i+1] for i in
range(len(vectors))]).astype(np.uint32)
return vertices, triangles | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vertices(tri, vertex_list):\n dim = len(vertex_list[0])\n p = numpy.zeros((3, dim))\n for j in range(3):\n p[j] = vertex_list[tri[j]]\n return p",
"def get_vertices(self):\n vertices = []\n V = [[-self.base_vectors[:,n], self.base_vectors[:,n]] for n in range(self.base_vector... | [
"0.70674706",
"0.67971975",
"0.6688014",
"0.6595674",
"0.65747494",
"0.65282524",
"0.6382806",
"0.63815814",
"0.63469017",
"0.623005",
"0.62253344",
"0.6019658",
"0.60096925",
"0.60038024",
"0.6003564",
"0.59902227",
"0.59812725",
"0.5978787",
"0.59492654",
"0.59366006",
"0.5... | 0.6878562 | 1 |
Update the underlying visual. | def _update(self):
if self._need_display_update:
self._need_display_update = False
self._set_view_slice(self.viewer.dims.indices)
if self._need_visual_update:
self._need_visual_update = False
self._node.update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_visualization(self) -> None:\n pass",
"def update(self):\n self.redraw()\n self._changed = False",
"def update(self):\n self.redraw()\n self._changed = False",
"def update_figure(self):\n\n self.draw()",
"def redraw(self):\n self.vispy_viewer.canv... | [
"0.80167747",
"0.767551",
"0.767551",
"0.7650333",
"0.7572356",
"0.7398728",
"0.735658",
"0.73552585",
"0.7344015",
"0.7229653",
"0.71321124",
"0.7061494",
"0.70430195",
"0.7035277",
"0.70231766",
"0.69140226",
"0.68243873",
"0.6794981",
"0.67679983",
"0.6736001",
"0.6723893"... | 0.72630036 | 9 |
Sets the view given the indices to slice with. | def _set_view_slice(self, indices):
vertices = self._mesh_vertices
faces = self._mesh_triangles
if len(faces) == 0:
self._node.set_data(vertices=None, faces=None)
else:
self._node.set_data(vertices=vertices[:, ::-1], faces=faces,
color=self.color)
self._need_visual_update = True
self._update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, indices: Tuple[int, ...], slices: Tuple[slice, ...] = (slice(0, 0, 0),)):\n self.indices = indices\n self.slices = slices",
"def _set_neighs_slice(self, key):\n ## Condition to use slice type\n self._constant_neighs = True\n self.ks = range(1) if self.ks is N... | [
"0.62833464",
"0.61686224",
"0.6001683",
"0.58963263",
"0.5595757",
"0.5592054",
"0.5561091",
"0.55347645",
"0.5515838",
"0.55009526",
"0.5443674",
"0.5426867",
"0.5424508",
"0.53574157",
"0.52848315",
"0.52843475",
"0.5270382",
"0.5265261",
"0.52636987",
"0.5232416",
"0.5228... | 0.77993363 | 0 |
Utilities for spatio temporal analysis zed.uchicago.edu Utilities for spatio temporal analysis zed.uchicago.edu Given location tile boundaries and type category filter, creates the corresponding timeseries as a pandas DataFrame | def getTS(self,_types=None,tile=None):
assert(self._END is not None)
TS_NAME = ('#'.join(str(x) for x in tile))+"#"+stringify(_types)
lat_ = tile[0:2]
lon_ = tile[2:4]
if self._value_limits is None:
df = self._logdf[self._columns]\
.loc[self._logdf[self._EVENT].isin(_types)]\
.sort_values(by=self._DATE).dropna()
else:
df = self._logdf[self._columns]\
.loc[self._logdf[self._EVENT]\
.between(self._value_limits[0],
self._value_limits[1])]\
.sort_values(by=self._DATE).dropna()
df = df.loc[(df[self._coord1] > lat_[0])
& (df[self._coord1] <= lat_[1])
& (df[self._coord2] > lon_[0])
& (df[self._coord2] <= lon_[1])]
df.index = df[self._DATE]
df=df[[self._EVENT]]
ts = [df.loc[self._trng[i]:self._trng[i + 1]].size for i in
np.arange(self._trng.size - 1)]
return pd.DataFrame(ts, columns=[TS_NAME],
index=self._trng[:-1]).transpose() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter(df: pd.DataFrame = pd.DataFrame()):\n if df.empty:\n df = read()\n print('Filtering data...')\n df = df.dropna()\n df2 = pd.DataFrame()\n df2['Longitude'] = df['Longitude']\n df2['Latitude'] = df['Latitude']\n df2['Month'] = df['Date'].dt.strftime('%m').astype(int)\n df2['... | [
"0.6118439",
"0.579679",
"0.5540201",
"0.55036247",
"0.5491236",
"0.5480218",
"0.5434643",
"0.54258335",
"0.54027617",
"0.5400543",
"0.53993887",
"0.539527",
"0.5395043",
"0.53789306",
"0.53724855",
"0.5362963",
"0.5352141",
"0.53505415",
"0.5327444",
"0.5325912",
"0.53214043... | 0.5729195 | 2 |
Utilities for spatio temporal analysis zed.uchicago.edu Creates DataFrame of location tiles and their respective timeseries from input datasource with significance threshold THRESHOLD latitude, longitude coordinate boundaries given by LAT, LON calls on getTS for individual tile then concats them together | def timeseries(self,LAT,LON,EPS,_types,CSVfile='TS.csv',THRESHOLD=None):
if THRESHOLD is None:
if self._THRESHOLD is None:
THRESHOLD=0.1
else:
THRESHOLD=self._THRESHOLD
if self._trng is None:
self._trng = pd.date_range(start=self._INIT,
end=self._END,freq=self._FREQ)
_TS = pd.concat([self.getTS(tile=[i, i + EPS, j, j + EPS],
_types=_types) for i in tqdm(LAT)
for j in tqdm(LON)])
LEN=pd.date_range(start=self._INIT,
end=self._END,freq=self._FREQ).size+0.0
statbool = _TS.astype(bool).sum(axis=1) / LEN
_TS = _TS.loc[statbool > THRESHOLD]
self._ts_dict[repr(_types)] = _TS
if CSVfile is not None:
_TS.to_csv(CSVfile, sep=' ')
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def outliers_solid_tsds():\n timestamp1 = np.arange(np.datetime64(\"2021-01-01\"), np.datetime64(\"2021-02-10\"))\n target1 = [np.sin(i) for i in range(len(timestamp1))]\n target1[10] += 10\n\n timestamp2 = np.arange(np.datetime64(\"2021-01-01\"), np.datetime64(\"2021-02-10\"))\n target2 = [np.sin(i... | [
"0.63389176",
"0.61668277",
"0.60331327",
"0.59823436",
"0.58627486",
"0.5774627",
"0.5769538",
"0.575066",
"0.574341",
"0.5693412",
"0.5659913",
"0.5631676",
"0.5619256",
"0.5589875",
"0.5581725",
"0.5581672",
"0.55765",
"0.55755305",
"0.5565926",
"0.5553959",
"0.5542568",
... | 0.5494842 | 26 |
Utilities for spatio temporal analysis zed.uchicago.edu Fit dataproc with specified grid parameters and create timeseries for date boundaries specified by INIT, THRESHOLD, and END which do not have to match the arguments first input to the dataproc | def fit(self,grid=None,INIT=None,END=None,THRESHOLD=None,csvPREF='TS'):
if INIT is not None:
self._INIT=INIT
if END is not None:
self._END=END
if grid is not None:
self._grid=grid
assert(self._END is not None)
assert(self._coord1 in self._grid)
assert(self._coord2 in self._grid)
assert('Eps' in self._grid)
if self._types is not None:
for key in self._types:
self.timeseries(self._grid[self._coord1],
self._grid[self._coord2],
self._grid['Eps'],
key,
CSVfile=csvPREF+stringify(key)+'.csv',
THRESHOLD=THRESHOLD)
return
else:
assert(self._value_limits is not None)
self.timeseries(self._grid[self._coord1],
self._grid[self._coord2],
self._grid['Eps'],
None,
CSVfile=csvPREF+'.csv',
THRESHOLD=THRESHOLD)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit_timeseries(xdates, ydata):\n\n pass",
"def fit_surface(tdata,\n scale_params=[40.0, 1.25]):\n agg_tdata = aggregate_data(tdata)\n\n # unpack data\n mt = agg_tdata.mean_temp\n dt = agg_tdata.daily_temp\n scaled_dt = utils.scale_daily_temp(mt, dt, scale_params)\n\n obs_m... | [
"0.6216409",
"0.61393374",
"0.60502726",
"0.59144783",
"0.5902388",
"0.56675595",
"0.56113106",
"0.55587673",
"0.55573833",
"0.5554575",
"0.55389374",
"0.55208224",
"0.55186224",
"0.54923666",
"0.5484279",
"0.5448603",
"0.5447749",
"0.5442355",
"0.5438024",
"0.5410568",
"0.54... | 0.6764931 | 0 |
Utilities for spatio temporal analysis zed.uchicago.edu Pulls new entries from datasource | def pull(self, domain="data.cityofchicago.org",dataset_id="crimes",\
token="ZIgqoPrBu0rsvhRr7WfjyPOzW",store=True, out_fname="pull_df.p",
pull_all=False):
pdb.set_trace()
client = Socrata(domain, token)
if domain == "data.cityofchicago.org" and dataset_id=="crimes":
self._coord1 = "latitude"
self._coord2 = "longitude"
self._EVENT = "primary_type"
if pull_all:
new_data = client.get(dataset_id)
# pull_df = pd.DataFrame(new_data).dropna(\
# subset=[self._coord1, self._coord2, self._DATE, self._EVENT],\
# axis=1).sort_values(self._DATE)
# NOTE: running into columns encoded in unicode not accepting subset
# specific filtering of NaN's by column error
# columns defined in subset aren't columns in the pulled DataFrame
pull_df = pd.DataFrame(new_data).dropna().sort_values(self._DATE)
self._logdf = pull_df
else:
self._logdf.sort_values(self._DATE)
pull_after_date = "'"+str(self._logdf[self._DATE].iloc[-1]).replace(\
" ", "T")+"'"
new_data = client.get(dataset_id, where=\
("date > "+pull_after_date))
if domain == "data.cityofchicago.org" and dataset_id=="crimes":
self._DATE = "date"
# pull_df = pd.DataFrame(new_data).dropna(\
# subset=[self._coord1, self._coord2, self._DATE, self._EVENT],\
# axis=1).sort_values(self._DATE)
pull_df = pd.DataFrame(new_data).dropna().sort_values(self._DATE)
self._logdf = self._logdf.append(pull_df)
if store:
assert out_fname is not None, "Out filename not specified"
self._logdf.to_pickle(out_fname)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_data(self, datasource):\n import pandas as pd\n if not isinstance(datasource, pd.DataFrame):\n raise TypeError('DfFeature must loaded from pd.DataFrame')\n self.data = datasource\n self.data['thetime']=self.data['thetime'].apply(lambda x:try_to_parse_date(x))",
"d... | [
"0.6014438",
"0.5713395",
"0.5670081",
"0.5654937",
"0.55900925",
"0.5535862",
"0.55139977",
"0.5509106",
"0.5435431",
"0.54309124",
"0.54095095",
"0.53560346",
"0.53487986",
"0.5339302",
"0.53324765",
"0.5331317",
"0.5316162",
"0.5309659",
"0.52839315",
"0.52791053",
"0.5277... | 0.5656978 | 3 |
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