\\\": return beam_elements\\r\\n beam_elements = np.array(beam_elements)\\r\\n x_start = np.array([ beam_element.n1.x for beam_element in beam_elements ])\\r\\n bar_sort_position = np.argsort(x_start)\\r\\n return cls(beam_elements[bar_sort_position])\",\n \"def New(*args, **kargs):\\n obj = itkHalfHermitianToRealInverseFFTImageFilterICF3IF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def New(*args, **kargs):\\n obj = itkStatisticsImageFilterIF3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def sort(self, sort):\\n\\n self._sort = sort\",\n \"def __init__(self, *args, **kwargs):\\n super(PokedexPlugin, self).__init__(*args, **kwargs)\\n\\n # XXX should we really be doing this here?\\n h.pokedex = pokedex_helpers\",\n \"def New(*args, **kargs):\\n obj = itkStatisticsImageFilterIUL2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def custom_sort(arr):\\n pass\",\n \"def New(*args, **kargs):\\n obj = itkHistogramToIntensityImageFilterHFIF2_Superclass.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def test_parse_sort(self):\\n old_type = Sort('sort', [\\n Relationship('student', PersonSchema(), None),\\n Relationship('school', StudentSchema(), None)],\\n Attribute('title', SchoolSchema(), None), '+')\\n new_type = self.driver.parse(old_type)\\n\\n assert new_type.source == old_type\\n assert old_type.relationships != new_type.relationships\\n assert isinstance(new_type.relationships[0], Mapper)\\n assert old_type.attribute != new_type.attribute\\n assert isinstance(new_type.attribute, Column)\\n assert old_type.direction == new_type.direction\",\n \"def __init__(self, algorithm, list = None, size = 10, unique = True,\\n comp = True, swap = True, trace = False):\\n self._list = list\\n self._size = size\\n self._unique = unique\\n self._comp = comp\\n self._swap = swap\\n self._trace = trace\\n self._swap_count = 0\\n self._comp_count = 0\\n self._algorithm = algorithm\\n self._start = 0\\n self._elapsed_time = 0\",\n \"def test_insertSort(self):\\n\\t\\tsortObj=insertSort()\\n\\t\\tself.assertEqual(sortObj.run_sort(self.test_1[0]),self.test_1[1])\",\n \"def _new_instance(self):\\n return self.__class__(self._vmodule)\",\n \"def __init__(self):\\n self.data = SortedList()\",\n \"def __init__(self):\\n\\n self.plugin_name = 'Yum'\",\n \"def instantiateNewCmd(self):\\n return QadGRIPSTRETCHCommandClass(self.plugIn)\",\n \"def classFactory(iface): # pylint: disable=invalid-name\\n #\\n from .quick_digitize import QuickDigitize\\n return QuickDigitize(iface)\",\n \"def plugin_import(plugin):\\n\\n return importlib.import_module(plugin, package=\\\"directord\\\")\",\n \"def New(*args, **kargs):\\n obj = itkHalfHermitianToRealInverseFFTImageFilterICF2IF2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def test_insertSort3(self):\\n\\t\\tsortObj=insertSort()\\n\\t\\tself.assertEqual(sortObj.run_sort(self.test_3[0]),self.test_3[1])\",\n \"def create_api(self):\\n return DJinnAPI(djenkins=self.dj, pipeline_results=self.db)\",\n \"def New(*args, **kargs):\\n obj = itkStatisticsImageFilterIF2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def __init__(self, compare=cmp):\\n self.compare = compare\\n self.array = []\\n self.pos = {}\",\n \"def __init__(\\n self,\\n label : str,\\n debug : bool = False,\\n **kw\\n ):\\n super().__init__('plugin', label=label, **kw)\\n\\n import os, pathlib, sys\\n from meerschaum.utils.warnings import error, warn\\n from meerschaum.config._paths import PLUGINS_RESOURCES_PATH\\n if str(PLUGINS_RESOURCES_PATH.parent) not in sys.path:\\n sys.path.append(str(PLUGINS_RESOURCES_PATH.parent))\\n\\n self.resource_path = None\\n for _plugin in os.listdir(PLUGINS_RESOURCES_PATH):\\n plugin = _plugin.replace('.py', '')\\n if plugin == self.label:\\n self.resource_path = pathlib.Path(os.path.join(PLUGINS_RESOURCES_PATH, plugin))\\n break\\n if not self.resource_path:\\n error(f\\\"Plugin '{self.label}' cannot be found. Is it installed?\\\")\\n\\n self.fetch = None\\n try:\\n exec(f'from plugins.{self.label} import fetch; self.fetch = fetch')\\n except Exception as e:\\n pass\\n\\n self.sync = None\\n try:\\n exec(f'from plugins.{self.label} import sync; self.sync = sync')\\n except Exception as e:\\n pass\\n\\n if self.fetch is None and self.sync is None:\\n error(f\\\"Could not import `fetch()` or `sync()` methods for plugin '{self.label}'\\\")\",\n \"def __init__(self, *args):\\n this = _libsbml.new_CompSBasePlugin(*args)\\n try: self.this.append(this)\\n except: self.this = this\",\n \"def plugin_class() -> Type[\\\"NitpickPlugin\\\"]:\\n return TextPlugin\",\n \"def New(*args, **kargs):\\n obj = itkHistogramToIntensityImageFilterHFIF2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def insertion_sort(array, method='forloop'):\\n return METHODS[method](array)\",\n \"def _new_instance(self):\\n return self.__class__(self._vmodule, self._tensor_rank)\",\n \"def construct_sort_part(\\n model_cls: Type[Model],\\n part: str,\\n case_insensitive: bool = True,\\n) -> Sort:\\n assert part, \\\"part must be a field name and + or -\\\"\\n field = part[:-1]\\n assert field, \\\"field is missing\\\"\\n direction = part[-1]\\n assert direction in ('+', '-'), \\\"part must end with + or -\\\"\\n is_ascending = direction == '+'\\n\\n if field in model_cls._sorts:\\n sort = model_cls._sorts[field](model_cls, is_ascending,\\n case_insensitive)\\n elif field in model_cls._fields:\\n sort = query.FixedFieldSort(field, is_ascending, case_insensitive)\\n else:\\n # Flexible or computed.\\n sort = query.SlowFieldSort(field, is_ascending, case_insensitive)\\n return sort\",\n \"def __init__(self, input=None, ij=None, order=None, max_order=10,\\r\\n criterion=utils.bayesian_information_criterion, n_freqs=1024):\\r\\n self.data = input.data\\r\\n self.sampling_rate = input.sampling_rate\\r\\n self._n_process = input.shape[0]\\r\\n self._n_freqs = n_freqs\\r\\n self._order = order\\r\\n self._criterion = criterion\\r\\n self._max_order = max_order\\r\\n if ij is None:\\r\\n # The following gets the full list of combinations of\\r\\n # non-same i's and j's:\\r\\n x, y = np.meshgrid(np.arange(self._n_process),\\r\\n np.arange(self._n_process))\\r\\n self.ij = list(zip(x[tril_indices_from(x, -1)],\\r\\n y[tril_indices_from(y, -1)]))\\r\\n else:\\r\\n self.ij = ij\",\n \"def __init__(self, webif_dir, plugin):\\n #self.logger = logging.getLogger(__name__)\\n self.logger = plugin.logger\\n self.webif_dir = webif_dir\\n self.plugin = plugin\\n self.tplenv = self.init_template_environment()\",\n \"def New(*args, **kargs):\\n obj = itkStatisticsImageFilterID2.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def test_insertSort2(self):\\n\\t\\tsortObj=insertSort()\\n\\t\\tself.assertNotEqual(sortObj.run_sort(self.test_2[0]),self.test_2[1])\",\n \"def handle_coreutils_sort_kwargs(self, prog='sort', instream=None, **kwargs):\\n pybedtools.logger.debug(\\n 'BedTool.handle_coreutils_sort_kwargs() got these kwargs:\\\\n%s',\\n pprint.pformat(kwargs))\\n\\n stdin = None\\n\\n # Decide how to send instream to sort.\\n # If it's a BedTool, then get underlying stream\\n if isinstance(instream, BedTool):\\n instream = instream.fn\\n\\n # Filename? No pipe, just provide the file\\n if isinstance(instream, six.string_types):\\n stdin = None\\n input_fn = instream\\n # A generator or iterator: pipe it as a generator of lines\\n else:\\n stdin = (str(i) for i in instream)\\n input_fn = '-'\\n\\n # If stream not specified, then a tempfile will be created\\n if kwargs.pop('stream', None):\\n tmp = None\\n else:\\n output = kwargs.pop('output', None)\\n if output:\\n tmp = output\\n else:\\n tmp = BedTool._tmp()\\n\\n additional_args = kwargs.pop('additional_args', None)\\n\\n # Parse the kwargs into BEDTools-ready args\\n cmds = [prog]\\n\\n for key, value in sorted(list(kwargs.items()), reverse=True):\\n if isinstance(value, bool):\\n if value:\\n cmds.append('--' + key)\\n else:\\n continue\\n elif isinstance(value, list) or isinstance(value, tuple):\\n value = list(map(str, value))\\n\\n # sort --key 1,1 --key 2,2r -k 5,5\\n for val in value:\\n if len(key) == 1:\\n cmds.append('-' + key)\\n else:\\n cmds.append('--' + key)\\n cmds.append(str(val))\\n else:\\n cmds.append('--' + key)\\n cmds.append(str(value))\\n\\n if additional_args:\\n cmds.append(additional_args)\\n\\n cmds.append(input_fn)\\n return cmds, tmp, stdin\",\n \"def __init__(self):\\n self._keys = []\\n self._sortKeys = []\",\n \"def sort(self, exprs):\\n self._sort_exprs = exprs\\n return self\",\n \"def New(*args, **kargs):\\n obj = itkHuangThresholdImageFilterISS3ISS3.__New_orig__()\\n import itkTemplate\\n itkTemplate.New(obj, *args, **kargs)\\n return obj\",\n \"def create_parser():\\n now = datetime.datetime.today()\\n default_date = \\\"{}-{}-{}\\\".format(now.day, now.month, now.year)\\n parser = argparse.ArgumentParser(description=\\\"Git plugin for automatic insertion of @since and @author annotations \\\"\\n \\\"into *.java source files in a project.\\\",\\n epilog=\\\"© Avner & Oded\\\")\\n parser.add_argument(\\\"-v\\\", \\\"--version\\\", help=\\\"Display the version of this plugin\\\", action='store_true')\\n parser.add_argument(\\\"--since\\\", nargs='?', help=\\\"Add the @since annotations to project\\\", const=default_date)\\n parser.add_argument(\\\"--author\\\", nargs='?', help=\\\"Add the @author annotations to project\\\", const=getpass.getuser())\\n\\n return parser\",\n \"def __init__(self, webif_dir, plugin):\\n self.logger = plugin.logger\\n self.webif_dir = webif_dir\\n self.plugin = plugin\\n self._creds = None\\n self._auth = None\\n\\n self.tplenv = self.init_template_environment()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5880008","0.572811","0.5263633","0.5135936","0.5086754","0.5085847","0.5052518","0.5052518","0.5028125","0.5018764","0.5011754","0.49714696","0.49462602","0.48368236","0.48352364","0.4823109","0.48152107","0.48114508","0.48113042","0.479689","0.47876537","0.47851378","0.47843146","0.47635952","0.4755802","0.47509703","0.4749833","0.47206005","0.4680351","0.46746126","0.46631837","0.46548966","0.46344376","0.46238235","0.4620496","0.46195126","0.46092921","0.45940942","0.4546308","0.45396274","0.45371294","0.4534121","0.45319805","0.45319173","0.4526805","0.45143154","0.45140886","0.45009172","0.4487294","0.4486419","0.44699883","0.44635287","0.4462118","0.4461933","0.44563553","0.44478616","0.44462118","0.44459984","0.4443109","0.44430462","0.44356376","0.4428438","0.44216046","0.4420869","0.4404056","0.43998334","0.43963715","0.43947834","0.43944824","0.4392197","0.43864635","0.4383929","0.43772188","0.43746927","0.43742737","0.43675026","0.43667853","0.4363298","0.43606964","0.43528897","0.43522048","0.43406022","0.43341544","0.43328393","0.4324247","0.432101","0.4309011","0.43078488","0.43075755","0.43074673","0.43072623","0.42945528","0.4293544","0.42897135","0.42888305","0.42793787","0.4276515","0.42762154","0.42708898","0.4267183"],"string":"[\n \"0.5880008\",\n \"0.572811\",\n \"0.5263633\",\n \"0.5135936\",\n \"0.5086754\",\n \"0.5085847\",\n \"0.5052518\",\n \"0.5052518\",\n \"0.5028125\",\n \"0.5018764\",\n \"0.5011754\",\n \"0.49714696\",\n \"0.49462602\",\n \"0.48368236\",\n \"0.48352364\",\n \"0.4823109\",\n \"0.48152107\",\n \"0.48114508\",\n \"0.48113042\",\n \"0.479689\",\n \"0.47876537\",\n \"0.47851378\",\n \"0.47843146\",\n \"0.47635952\",\n \"0.4755802\",\n \"0.47509703\",\n \"0.4749833\",\n \"0.47206005\",\n \"0.4680351\",\n \"0.46746126\",\n \"0.46631837\",\n \"0.46548966\",\n \"0.46344376\",\n \"0.46238235\",\n \"0.4620496\",\n \"0.46195126\",\n \"0.46092921\",\n \"0.45940942\",\n \"0.4546308\",\n \"0.45396274\",\n \"0.45371294\",\n \"0.4534121\",\n \"0.45319805\",\n \"0.45319173\",\n \"0.4526805\",\n \"0.45143154\",\n \"0.45140886\",\n \"0.45009172\",\n \"0.4487294\",\n \"0.4486419\",\n \"0.44699883\",\n \"0.44635287\",\n \"0.4462118\",\n \"0.4461933\",\n \"0.44563553\",\n \"0.44478616\",\n \"0.44462118\",\n \"0.44459984\",\n \"0.4443109\",\n \"0.44430462\",\n \"0.44356376\",\n \"0.4428438\",\n \"0.44216046\",\n \"0.4420869\",\n \"0.4404056\",\n \"0.43998334\",\n \"0.43963715\",\n \"0.43947834\",\n \"0.43944824\",\n \"0.4392197\",\n \"0.43864635\",\n \"0.4383929\",\n \"0.43772188\",\n \"0.43746927\",\n \"0.43742737\",\n \"0.43675026\",\n \"0.43667853\",\n \"0.4363298\",\n \"0.43606964\",\n \"0.43528897\",\n \"0.43522048\",\n \"0.43406022\",\n \"0.43341544\",\n \"0.43328393\",\n \"0.4324247\",\n \"0.432101\",\n \"0.4309011\",\n \"0.43078488\",\n \"0.43075755\",\n \"0.43074673\",\n \"0.43072623\",\n \"0.42945528\",\n \"0.4293544\",\n \"0.42897135\",\n \"0.42888305\",\n \"0.42793787\",\n \"0.4276515\",\n \"0.42762154\",\n \"0.42708898\",\n \"0.4267183\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59617186"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8291,"cells":{"query":{"kind":"string","value":"Test that the plugin manager can find the Isort plugin."},"document":{"kind":"string","value":"def test_isort_tool_plugin_found():\n if sys.version_info.major == 3 and sys.version_info.minor < 6:\n pytest.skip(\"isort is only available for Python 3.6+, unable to test\")\n manager = PluginManager()\n # Get the path to statick_tool/__init__.py, get the directory part, and\n # add 'plugins' to that to get the standard plugins dir\n manager.setPluginPlaces(\n [os.path.join(os.path.dirname(statick_tool.__file__), \"plugins\")]\n )\n manager.setCategoriesFilter(\n {\n \"Tool\": ToolPlugin,\n }\n )\n manager.collectPlugins()\n # Verify that a plugin's get_name() function returns \"isort\"\n assert any(\n plugin_info.plugin_object.get_name() == \"isort\"\n for plugin_info in manager.getPluginsOfCategory(\"Tool\")\n )\n # While we're at it, verify that a plugin is named Isort Tool Plugin\n assert any(\n plugin_info.name == \"Isort Tool Plugin\"\n for plugin_info in manager.getPluginsOfCategory(\"Tool\")\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_plugins(self):\n from omtk import plugin_manager\n pm = plugin_manager.plugin_manager\n\n loaded_plugin_names = [plugin.cls.__name__ for plugin in pm.get_loaded_plugins_by_type('modules')]\n\n builtin_plugin_names = (\n 'Arm',\n 'FK',\n 'AdditiveFK',\n 'AvarGrpOnSurface',\n 'FaceBrow',\n 'FaceEyeLids',\n 'FaceEyes',\n 'FaceJaw',\n 'FaceLips',\n 'FaceNose',\n 'FaceSquint',\n 'Hand',\n 'Head',\n 'IK',\n 'InteractiveFK',\n 'Leg',\n 'LegQuad',\n 'Limb',\n 'Neck',\n 'Ribbon',\n 'SplineIK',\n 'Twistbone',\n )\n\n for plugin_name in builtin_plugin_names:\n self.assertIn(plugin_name, loaded_plugin_names)","def test_plugins():\n assert plugins.template.plugin_test() == True\n assert plugin_test() == True","def test(self, plugin):\n plug = plugin_source.load_plugin(plugin)\n plug.test()","def test_plugin_initialize(self):\n p = PluginCustom()\n self.assertEqual('youpie', p.toto)","def test_discoverable(self):\r\n plugins = getPlugins(IProcessor)\r\n lmath = [p for p in plugins if p.name == \"mlore\"]\r\n self.assertEqual(len(lmath), 1, \"Did not find math lore plugin: %r\" % (lmath,))","def test_make_tool_plugin_found():\n manager = PluginManager()\n # Get the path to statick_tool/__init__.py, get the directory part, and\n # add 'plugins' to that to get the standard plugins dir\n manager.setPluginPlaces([os.path.join(os.path.dirname(statick_tool.__file__),\n 'plugins')])\n manager.setCategoriesFilter({\n \"Tool\": ToolPlugin,\n })\n manager.collectPlugins()\n # Verify that a plugin's get_name() function returns \"make\"\n assert any(plugin_info.plugin_object.get_name() == 'make' for\n plugin_info in manager.getPluginsOfCategory(\"Tool\"))\n # While we're at it, verify that a plugin is named Yamllint Tool Plugin\n assert any(plugin_info.name == 'Make Tool Plugin' for\n plugin_info in manager.getPluginsOfCategory(\"Tool\"))","def test_register_dynamic_plugin(self):\n pass","def test_rstlint_tool_plugin_found():\n manager = PluginManager()\n # Get the path to statick_tool/__init__.py, get the directory part, and\n # add 'plugins' to that to get the standard plugins dir\n manager.setPluginPlaces(\n [os.path.join(os.path.dirname(statick_tool.__file__), \"plugins\")]\n )\n manager.setCategoriesFilter(\n {\n \"Tool\": ToolPlugin,\n }\n )\n manager.collectPlugins()\n # Verify that a plugin's get_name() function returns \"rstlint\"\n assert any(\n plugin_info.plugin_object.get_name() == \"rstlint\"\n for plugin_info in manager.getPluginsOfCategory(\"Tool\")\n )\n # While we're at it, verify that a plugin is named rstlint Tool Plugin\n assert any(\n plugin_info.name == \"rstlint Tool Plugin\"\n for plugin_info in manager.getPluginsOfCategory(\"Tool\")\n )","def test_register_dynamic_plugin_manager(self):\n pass","def test_exposeInterfaces(self):\n if self.plugin is None:\n return\n\n cs = settings.Settings()\n results = self.plugin.exposeInterfaces(cs)\n if results is None or not results:\n return\n\n # each plugin should return a list\n self.assertIsInstance(results, list)\n for result in results:\n # Make sure that all elements in the list satisfy the constraints of the\n # hookspec\n self.assertIsInstance(result, tuple)\n self.assertEqual(len(result), 3)\n\n order, interface, kwargs = result\n\n self.assertIsInstance(order, (int, float))\n self.assertTrue(issubclass(interface, interfaces.Interface))\n self.assertIsInstance(kwargs, dict)","def test_isort(self):\n chdir(REPO_ROOT)\n cmd = [\"isort\", \"-df\", \"-rc\", \"-c\", *SRC_DIRS]\n print(\"running:\", \" \".join(str(part) for part in cmd))\n proc = run(cmd, capture_output=True)\n assert proc.returncode == 0, f\"isort issues:\\n{proc.stdout.decode('utf-8')}\"","def load_plugin():\n return HostTestPluginCopyMethod_Shell()","def test_isort_tool_plugin_parse_valid():\n itp = setup_isort_tool_plugin()\n total_output = []\n output = \"/tmp/x.py\"\n total_output.append(output)\n output = \"/tmp/y.py\"\n total_output.append(output)\n issues = itp.parse_output(total_output)\n assert len(issues) == 2\n assert issues[0].filename == \"/tmp/x.py\"\n assert issues[0].line_number == \"0\"\n assert issues[0].tool == \"isort\"\n assert issues[0].issue_type == \"formatting\"\n assert issues[0].severity == \"3\"\n assert issues[0].message == \"Imports are incorrectly sorted and/or formatted.\"\n assert issues[1].filename == \"/tmp/y.py\"","def test_plugin_retrieval(self):\n plugin = ProjectAppPluginPoint.get_plugin(PLUGIN_NAME)\n self.assertIsNotNone(plugin)\n self.assertEqual(plugin.get_model().name, PLUGIN_NAME)\n self.assertEqual(plugin.name, PLUGIN_NAME)\n self.assertEqual(plugin.get_model().title, PLUGIN_TITLE)\n self.assertEqual(plugin.title, PLUGIN_TITLE)\n self.assertEqual(plugin.entry_point_url_id, PLUGIN_URL_ID)","def test_register_dynamic_plugin_manager1(self):\n pass","def test_register_dynamic_plugin1(self):\n pass","def test_remote_plugin(self):\n plugin_name = 'Slack'\n Plugin.download_plugin(plugin_name)\n target = os.path.join(expanduser('~'), '.drupdates', 'plugins', plugin_name)\n plugins = Plugin.get_plugins()\n assert plugins[plugin_name]['info'][1] == os.path.join(target, '__init__.py')","def test_get_plugin_by_id(self):\n response = self.client.get_plugin_by_id(1)\n self.assertEqual(response['id'], 1)","def testSorting(self):\n if self.sorting in tools.SORTINGS:\n self.assertEqual(\n self.sorting,\n self.config.sorting\n )\n else:\n self.assertNotEqual(\n self.sorting,\n self.config.sorting\n )\n self.assertEqual(\n tools.SORTING_DEFAULT,\n self.config.sorting\n )","def test_parrot_imported():\n assert \"parrot\" in sys.modules","def test_custom_plugin(self):\n plugin_name = 'Druptest'\n source = os.path.join(self.current_dir, 'classes', plugin_name)\n target = os.path.join(expanduser('~'), '.drupdates', 'plugins', plugin_name)\n shutil.copytree(source, target)\n plugins = Plugin.get_plugins()\n assert plugins[plugin_name]['name'] == plugin_name","def test_get_plugins_with_search_args(self):\n response = self.client.get_plugins({'name_exact': \"pl-dircopy\"})\n self.assertEqual(response['data'][0]['name'], \"pl-dircopy\")","def sorter(Plugin):\n return Plugin.order","def get_plugin_interface(self):","def test_plugin_initialize_from_args(self):\n sys.argv.append('-t')\n p = PluginCustom()\n self.assertEqual('yourah', p.toto)","def test_plugin_slowinit(node_factory):\n n = node_factory.get_node()\n\n n.rpc.plugin_start(os.path.join(os.getcwd(), \"tests/plugins/slow_init.py\"))\n\n # It's not actually configured yet, see what happens;\n # make sure 'rescan' and 'list' controls dont crash\n n.rpc.plugin_rescan()\n n.rpc.plugin_list()","def test_install_plugin_again_is_ok(self):\n raise NotImplementedError()","def setup_isort_tool_plugin(custom_rsc_path=None):\n arg_parser = argparse.ArgumentParser()\n\n if custom_rsc_path is not None:\n resources = Resources([custom_rsc_path])\n else:\n resources = Resources(\n [os.path.join(os.path.dirname(statick_tool.__file__), \"plugins\")]\n )\n config = Config(resources.get_file(\"config.yaml\"))\n plugin_context = PluginContext(arg_parser.parse_args([]), resources, config)\n plugin_context.args.output_directory = os.path.dirname(__file__)\n itp = IsortToolPlugin()\n itp.set_plugin_context(plugin_context)\n return itp","def test_plugin_urls(self):\n plugin = ProjectAppPluginPoint.get_plugin(PLUGIN_NAME)\n self.assertEqual(plugin.urls, urlpatterns)","def isort(context):\n exec_cmd = \"isort . --check --diff\"\n run_cmd(context, exec_cmd)","def test_installed(self):\n check_output('unity --help', shell=True)","def test_specific_plugin_installed(self):\n self._add_plugin(self.jigconfig, 'plugin01')\n set_jigconfig(self.gitrepodir, config=self.jigconfig)\n\n # Create staged\n self.commit(self.gitrepodir, 'a.txt', 'a')\n self.stage(self.gitrepodir, 'b.txt', 'b')\n\n with nested(\n patch('jig.runner.sys'),\n self.assertRaises(SystemExit)\n ) as (r_sys, ec):\n # Raise the error to halt execution like the real sys.exit would\n r_sys.exit.side_effect = SystemExit\n\n self.run_command('--plugin plugin01 {0}'.format(self.gitrepodir))\n\n self.assertResults(u\"\"\"\n ▾ plugin01\n\n ⚠ line 1: b.txt\n b is +\n\n {0} Jig ran 1 plugin\n Info 0 Warn 1 Stop 0\n \"\"\".format(ATTENTION), self.output)","def test_commands_initialization(bot):\n plugin = Commands(bot)\n assert isinstance(plugin, Commands)\n assert plugin.bot == bot","def test_filter_installed_plugins(plugin_dialog):\n plugin_dialog.filter(\"\")\n assert plugin_dialog.installed_list._count_visible() >= 0\n\n plugin_dialog.filter(\"no-match@123\")\n assert plugin_dialog.installed_list._count_visible() == 0","def test_implements_IHelper(self):\n self.assertTrue(IHelper.providedBy(PersistenceHelper()))","def test_fabsim():\n assert(\"plugins\" in get_plugin_path(\"FabDummy\"))\n assert(\"FabDummy\" in get_plugin_path(\"FabDummy\"))\n assert(len(get_fabsim_git_hash()) > 0)","def test_sort(self):\n sort_field = MoveSearchForm.sort\n for value, label in sort_field.kwargs['choices']:\n response = self.do_search(id=u'1', sort=value)\n self.assert_(\n response.tmpl_context.results,\n \"\"\"Sort by {0} doesn't crash\"\"\".format(value)\n )","def isort(command, checkonly=False):\n print(\n \"\"\"\nRunning isort the Python code import sorter\n===========================================\n\"\"\"\n )\n cmd = \"isort --check-only --diff .\" if checkonly else \"isort .\"\n command.run(cmd, echo=True, pty=POSIX)","def test_insertSort(self):\n\t\tsortObj=insertSort()\n\t\tself.assertEqual(sortObj.run_sort(self.test_1[0]),self.test_1[1])","def test_isort_tool_plugin_scan_oserror(mock_subprocess_check_output):\n mock_subprocess_check_output.side_effect = OSError(\"mocked error\")\n itp = setup_isort_tool_plugin()\n package = Package(\n \"valid_package\", os.path.join(os.path.dirname(__file__), \"valid_package\")\n )\n package[\"python_src\"] = [\n os.path.join(os.path.dirname(__file__), \"valid_package\", \"sample.py\")\n ]\n issues = itp.scan(package, \"level\")\n assert not issues","def test_insertSort3(self):\n\t\tsortObj=insertSort()\n\t\tself.assertEqual(sortObj.run_sort(self.test_3[0]),self.test_3[1])","def test_plugin_with_no_plugin_class(conf):\n # For fun, we pass in a system library\n installed_apps_before = conf.config[\"INSTALLED_APPS\"][:]\n cli.plugin(\"os.path\")\n assert installed_apps_before == conf.config[\"INSTALLED_APPS\"]","def test_client_custom_plugin():\n client = ConfigureClients(plugins=[PluginVipCustomisation])\n assert client.plugins == [PluginVipCustomisation]","def test_make_tool_plugin_scan_missing_tool_name():\n mtp = setup_make_tool_plugin()\n if not mtp.command_exists('make'):\n pytest.skip('Missing make executable.')\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\n 'valid_package'))\n issues = mtp.scan(package, 'level')\n assert not issues","def test__SortOrder__name():\n for instance in SortOrder.INSTANCES.values():\n vampytest.assert_instance(instance.name, str)","def test_create_plugin_instance(self):\n plugin_id = 2\n data = {\n 'title': 'Test plugin instance',\n 'dir': self.username + '/'\n }\n response = self.client.create_plugin_instance(plugin_id, data)\n self.assertEqual(response['title'], data['title'])","def test_module(self):\n pass","def test_get_sort_info(self):\n ars = self.ar[2009][11]['day']\n self.assertEqual(ars.get_sort_info(), (31, 'key', True))","def test_xchemOT_imported():\n assert \"xchemOT\" in sys.modules","def check_plugin(vcf_reader, plugin):\n # Always use core plug-in\n plugins = ['core']\n # Collect supplied plugin(s)\n [plugins.append(item) for item in plugin]\n # Create set\n plugins = list(set(plugins))\n # Evaluate vcf and plugin compatibility\n for plugin in plugins:\n\n if plugin == \"core\":\n from pScout.plugin.plugin_reader import core\n ret = core(vcf_reader, \"pScout/plugin/get_core.ini\")\n\n if ret is 1: # Not compatible\n exit()\n\n return plugins","def setUp(self):\n self._plugin = spotlight_volume.SpotlightVolumePlugin()\n self._parser = plist.PlistParser()","def request_plugins(self):","def scan_plugin(self):\n pluginpath=_module_path()\n plugins=[]\n for f in os.listdir(pluginpath):\n if os.path.isfile(os.path.join(pluginpath,f)) and os.path.splitext(os.path.join(pluginpath,f))[-1]=='.py' :\n if 'plugin_' in os.path.basename(f):\n logger.debug(\"found plugin : %s\",f)\n plugins.append(f)\n return plugins","def test_filter_available_plugins(plugin_dialog):\n plugin_dialog.filter(\"\")\n assert plugin_dialog.available_list.count() == 2\n assert plugin_dialog.available_list._count_visible() == 2\n\n plugin_dialog.filter(\"no-match@123\")\n assert plugin_dialog.available_list._count_visible() == 0\n\n plugin_dialog.filter(\"\")\n plugin_dialog.filter(\"test-name-0\")\n assert plugin_dialog.available_list._count_visible() == 1","def test_dependencies_installed(self):\n installer = getattr(self.portal, 'portal_quickinstaller')\n self.assertTrue(installer.isProductInstalled('plone.app.dexterity'))","def test_get_plugin_parameters(self):\n plugin_id = 2\n response = self.client.get_plugin_parameters(plugin_id,\n {'limit': 50, 'offset': 0})\n self.assertEqual(response['data'][0]['name'], \"dir\")","def get_id(self):\n return \"unittest_required_plugin\"","def test_addplugin(self):\n app = QApplication(sys.argv)\n data = (np.random.rand(30, 31, 32) * 100).astype(np.int)\n data[15:40, 13:20, 10:18] += 50\n se = seededitorqt.QTSeedEditor(data)\n wg0 = seededitorqt.plugin.SampleThresholdPlugin()\n se.addPlugin(wg0)\n # se.exec_()\n # self.assertTrue(False)","def plugins():\n pass","def main() -> bool:\n global logger\n logger = setup_logger(\"nitpycker\")\n plugin_manager = Manager()\n plugin_manager.load_plugins()\n args = parse_args(plugin_manager)\n if plugin_manager.enable_plugins(args.plugins, args):\n exit(2)\n\n plugin_manager.pre_test_discovery()\n tests = unittest.defaultTestLoader.discover(args.start_directory, pattern=args.pattern)\n plugin_manager.post_test_discovery()\n tests = plugin_manager.filter_tests(tests)\n report = ParallelRunner(plugin_manager, process_number=args.process_number, verbosity=args.verbosity).run(tests)\n return not report.wasSuccessful()","def test_insertSort2(self):\n\t\tsortObj=insertSort()\n\t\tself.assertNotEqual(sortObj.run_sort(self.test_2[0]),self.test_2[1])","def test_plot_ay_imported():\n assert \"plot_ay\" in sys.modules","def test_load(self):\n (spec, check) = bundylogging.load()\n # It returns the checking function\n self.assertEqual(check, bundylogging.check)\n # The plugin stores it's spec\n self.assertEqual(spec, bundylogging.spec)","def plugin_import(plugin):\n\n return importlib.import_module(plugin, package=\"directord\")","def test_first_run(dbbackup, plugin, update, version_file=None, orig_version=None):\n\n if version_file:\n os.unlink(version_file)\n\n cli.initialize()\n update.assert_called_once()\n dbbackup.assert_not_called()\n\n # Check that it got called for each default plugin\n from kolibri.core.settings import DEFAULT_PLUGINS\n\n assert plugin.call_count == len(DEFAULT_PLUGINS)","def test_plugin_command(node_factory):\n n = node_factory.get_node()\n\n # Make sure that the 'hello' command from the helloworld.py plugin\n # is not available.\n cmd = [hlp for hlp in n.rpc.help()[\"help\"] if \"hello\" in hlp[\"command\"]]\n assert(len(cmd) == 0)\n\n # Add the 'contrib/plugins' test dir\n n.rpc.plugin_startdir(directory=os.path.join(os.getcwd(), \"contrib/plugins\"))\n # Make sure that the 'hello' command from the helloworld.py plugin\n # is now available.\n cmd = [hlp for hlp in n.rpc.help()[\"help\"] if \"hello\" in hlp[\"command\"]]\n assert(len(cmd) == 1)\n\n # Make sure 'rescan' and 'list' subcommands dont crash\n n.rpc.plugin_rescan()\n n.rpc.plugin_list()\n\n # Make sure the plugin behaves normally after stop and restart\n assert(\"Successfully stopped helloworld.py.\" == n.rpc.plugin_stop(plugin=\"helloworld.py\")[''])\n n.daemon.wait_for_log(r\"Killing plugin: helloworld.py\")\n n.rpc.plugin_start(plugin=os.path.join(os.getcwd(), \"contrib/plugins/helloworld.py\"))\n n.daemon.wait_for_log(r\"Plugin helloworld.py initialized\")\n assert(\"Hello world\" == n.rpc.call(method=\"hello\"))\n\n # Now stop the helloworld plugin\n assert(\"Successfully stopped helloworld.py.\" == n.rpc.plugin_stop(plugin=\"helloworld.py\")[''])\n n.daemon.wait_for_log(r\"Killing plugin: helloworld.py\")\n # Make sure that the 'hello' command from the helloworld.py plugin\n # is not available anymore.\n cmd = [hlp for hlp in n.rpc.help()[\"help\"] if \"hello\" in hlp[\"command\"]]\n assert(len(cmd) == 0)\n\n # Test that we cannot start a plugin with 'dynamic' set to False in\n # getmanifest\n with pytest.raises(RpcError, match=r\"Not a dynamic plugin\"):\n n.rpc.plugin_start(plugin=os.path.join(os.getcwd(), \"tests/plugins/static.py\"))\n\n # Test that we cannot stop a started plugin with 'dynamic' flag set to\n # False\n n2 = node_factory.get_node(options={\n \"plugin\": os.path.join(os.getcwd(), \"tests/plugins/static.py\")\n })\n with pytest.raises(RpcError, match=r\"static.py cannot be managed when lightningd is up\"):\n n2.rpc.plugin_stop(plugin=\"static.py\")\n\n # Test that we don't crash when starting a broken plugin\n with pytest.raises(RpcError, match=r\"Timed out while waiting for plugin response\"):\n n2.rpc.plugin_start(plugin=os.path.join(os.getcwd(), \"tests/plugins/broken.py\"))","def test_package(self):\n pass","def register_plugin(self):\n self.edit_goto.connect(self.main.editor.load)\n self.redirect_stdio.connect(self.main.redirect_internalshell_stdio)\n self.main.add_dockwidget(self)\n\n unittesting_act = create_action(self, _(\"Run unit tests\"),\n icon=get_icon('profiler.png'),\n triggered=self.run_unittesting)\n unittesting_act.setEnabled(is_unittesting_installed())\n fixed_shortcut(\"Ctrl+Shift+F11\", self.main,\n self.run_unittesting)\n\n self.main.run_menu_actions += [unittesting_act]\n self.main.editor.pythonfile_dependent_actions += [unittesting_act]","def describe(self, plugin):\n plug = plugin_source.load_plugin(plugin)\n plug.describe()","def test_version_sorting(self):\n assert natsort(['1', '5', '10', '50']) == ['1', '5', '10', '50']","def test(self):\n test_dir = join_path(self.test_suite.current_test_cache_dir, self.test_src_dir)\n self.run_test(\n \"sh\",\n [\"testshortsort.sh\"],\n expected=\"Alignments sorted by coordinate.\",\n purpose=\"test: checking alignments\",\n work_dir=test_dir,\n )","def test_makeplugin(subprocess):\n # type: (Mock) -> None\n plugin = lektor_make.MakePlugin(lambda: None, None) # pragma: no branch\n plugin.on_before_build_all(lambda: None) # pragma: no branch\n subprocess.Popen.assert_called_with([\"make\", \"lektor\"])\n subprocess.Popen().wait.asset_called()","def test_install_terraform_plugin_1(monkeypatch):\n monkeypatch.setattr(os, 'listdir', lambda path: [\n 'terraform-provider-terraform_v0.11.2_x4'])\n monkeypatch.setattr(os, 'remove', lambda path: True)\n monkeypatch.setattr(os, 'chmod', lambda path, permissions: True)\n\n monkeypatch.setattr(shutil, 'copy2', lambda src, dest: True)\n\n def mp_check_output(cmd):\n if cmd == ['which', 'terraform']:\n return b'/usr/local/bin/terraform\\n'\n\n if cmd == ['terraform', '-v']:\n return b'Terraform v0.11.3\\n\\n'\n\n raise Exception('Unmocked command: %s' % cmd)\n\n monkeypatch.setattr(subprocess, 'check_output', mp_check_output)\n\n install_terraform_plugin('/tmp/stone-burner_plugins')","def __test__():\n#-------------------------------------------------------------------------------\n import pylib.tester as tester\n return 0","def test_install(self):\n pass","def test_implements_IHelper(self):\n self.assertTrue(IHelper.providedBy(WorkerHelper()))","def test_vendored_libjuju(self):\n for name in sys.modules:\n if name.startswith(\"juju\"):\n module = sys.modules[name]\n if getattr(module, \"__file__\"):\n print(getattr(module, \"__file__\"))\n assert re.search('n2vc', module.__file__, re.IGNORECASE)\n\n # assert module.__file__.find(\"N2VC\")\n # assert False\n return","def test_specific_plugin_not_installed(self):\n self._add_plugin(self.jigconfig, 'plugin01')\n set_jigconfig(self.gitrepodir, config=self.jigconfig)\n\n # Create staged\n self.commit(self.gitrepodir, 'a.txt', 'a')\n self.stage(self.gitrepodir, 'b.txt', 'b')\n\n with nested(\n patch('jig.runner.sys'),\n self.assertRaises(SystemExit)\n ) as (r_sys, ec):\n # Raise the error to halt execution like the real sys.exit would\n r_sys.exit.side_effect = SystemExit\n\n self.run_command(\n '--plugin notinstalled {0}'.format(self.gitrepodir))\n\n # A plugin which is not installed was requested so not output\n self.assertEqual('', self.output)","def test_molssi_project_imported():\n assert \"molssi_project\" in sys.modules","def test_provider(self):\n msg = 'Wrong number of processing algorithm loaded.'\n self.assertEqual(len(self.provider.alglist), 6, msg)\n\n msg = 'InaSAFE should be activated by default in Processing.'\n self.assertEqual(self.provider.activate, True, msg)\n\n msg = 'Wrong processing provide.'\n for algorithm in self.provider.alglist:\n self.assertEqual(algorithm.provider, self.provider, msg)","def test_import(self):\n self.assertTrue(NagiosPerfdataCollector)","def test(self):\n\t\treturn describeInterface(self)","def get_plugin(self, name):","def test_setup_object(self):\n self._test_object.__name__ = 'pluggable_object'\n self._test_object.test_plugin = object()\n self._test_object.__all__ = ['test_plugin']\n pluggable_package.setup(self._test_object)\n self._test_setup(self._test_object)","def test():\n try:\n import nipype\n except ImportError:\n # if nipype is not installed, skip this test (without failure)\n return True\n suite = unittest.TestLoader().loadTestsFromTestCase(TestNipypeWrap)\n runtime = unittest.TextTestRunner(verbosity=2).run(suite)\n return runtime.wasSuccessful()","def test_get_plugin_id(self):\n plugin_name = 'myfakeplugin'\n self.my_plugin.name = plugin_name\n\n def side_effect(name):\n if name == plugin_name:\n return self.my_plugin\n else:\n raise sahara_base.APIException(error_code=404,\n error_name='NOT_FOUND')\n\n self.sahara_client.plugins.get.side_effect = side_effect\n self.assertIsNone(self.sahara_plugin.get_plugin_id(plugin_name))\n self.assertRaises(exception.EntityNotFound,\n self.sahara_plugin.get_plugin_id, 'noplugin')\n\n calls = [mock.call(plugin_name), mock.call('noplugin')]\n self.sahara_client.plugins.get.assert_has_calls(calls)","def test_molecool_imported():\n assert \"molecool\" in sys.modules","def test_check_module(self) -> None:\n check_module(\"os\")","def init_plugins(group):\n package = sys.modules[__name__]\n\n for (importer, module_name, ispkg) in pkgutil.iter_modules(package.__path__):\n if not ispkg:\n try:\n module = importlib.import_module(package.__name__ + '.' + module_name)\n\n if 'run' not in module.__dict__:\n print_fail('Plugin command \"%s\" doesn\\'t provide a \"run\" method. Aborting.' % (module_name))\n # sys.exit(-1)\n else:\n group.add_command(module.run)\n except Exception, e:\n print_fail('[%s] %s' % (module_name, str(e) + str(type(e))))","def test_qm_project_python_testing_imported():\n assert \"qm_project_python_testing\" in sys.modules","def test_setup_module(self):\n pluggable_package.setup(self._test_module)\n self._test_setup(self._test_module)","def test_is_installed():\n assert _is_installed('coverage') is True # regular dependency\n assert _is_installed('pytest') is True # dev dependency\n assert _is_installed('missing') is False # missing dependency","def test_list_vips_sort(self):\r\n resources = \"vips\"\r\n cmd = vip.ListVip(test_cli20.MyApp(sys.stdout), None)\r\n self._test_list_resources(resources, cmd,\r\n sort_key=[\"name\", \"id\"],\r\n sort_dir=[\"asc\", \"desc\"])","def test_list_vips_sort(self):\n resources = \"vips\"\n cmd = vip.ListVip(test_cli20.MyApp(sys.stdout), None)\n self._test_list_resources(resources, cmd,\n sort_key=[\"name\", \"id\"],\n sort_dir=[\"asc\", \"desc\"])","def test_override_plugin(self):\n plugin_name = 'Stdout'\n source = os.path.join(self.current_dir, 'classes', plugin_name)\n target = os.path.join(expanduser('~'), '.drupdates', 'plugins', plugin_name)\n shutil.copytree(source, target)\n plugins = Plugin.get_plugins()\n assert plugins[plugin_name]['info'][1] == os.path.join(target, '__init__.py')","def test_basic(self):\n plugin_instance = ProbabilitiesFromPercentiles2D(self.test_cube,\n self.new_name)\n self.assertEqual(plugin_instance.output_name, self.new_name)","def find_plugins():\n return list(straight.plugin.load('csbot.plugins', subclasses=Plugin))","def test04_vms_page_table_sorting(self):\n self.lg('%s STARTED' % self._testID)\n self.lg('sorting of all fields of vms table, should be working as expected')\n self.assertTrue(self.Tables.check_sorting_table('machines'))\n self.lg('%s ENDED' % self._testID)","def testFindsBuiltins(self):\r\n self.assertEqual('sys', modulefinder.get_module_filename('sys'))\r\n self.assertEqual('time', modulefinder.get_module_filename('time'))","def test_subworkflows_info_in_modules_repo(self):\n self.subworkflow_install.install(\"bam_sort_stats_samtools\")\n mods_info = nf_core.subworkflows.SubworkflowInfo(self.nfcore_modules, \"bam_sort_stats_samtools\")\n mods_info.local = True\n mods_info_output = mods_info.get_component_info()\n console = Console(record=True)\n console.print(mods_info_output)\n output = console.export_text()\n\n assert \"Subworkflow: bam_sort_stats_samtools\" in output\n assert \"Inputs\" in output\n assert \"Outputs\" in output"],"string":"[\n \"def test_plugins(self):\\n from omtk import plugin_manager\\n pm = plugin_manager.plugin_manager\\n\\n loaded_plugin_names = [plugin.cls.__name__ for plugin in pm.get_loaded_plugins_by_type('modules')]\\n\\n builtin_plugin_names = (\\n 'Arm',\\n 'FK',\\n 'AdditiveFK',\\n 'AvarGrpOnSurface',\\n 'FaceBrow',\\n 'FaceEyeLids',\\n 'FaceEyes',\\n 'FaceJaw',\\n 'FaceLips',\\n 'FaceNose',\\n 'FaceSquint',\\n 'Hand',\\n 'Head',\\n 'IK',\\n 'InteractiveFK',\\n 'Leg',\\n 'LegQuad',\\n 'Limb',\\n 'Neck',\\n 'Ribbon',\\n 'SplineIK',\\n 'Twistbone',\\n )\\n\\n for plugin_name in builtin_plugin_names:\\n self.assertIn(plugin_name, loaded_plugin_names)\",\n \"def test_plugins():\\n assert plugins.template.plugin_test() == True\\n assert plugin_test() == True\",\n \"def test(self, plugin):\\n plug = plugin_source.load_plugin(plugin)\\n plug.test()\",\n \"def test_plugin_initialize(self):\\n p = PluginCustom()\\n self.assertEqual('youpie', p.toto)\",\n \"def test_discoverable(self):\\r\\n plugins = getPlugins(IProcessor)\\r\\n lmath = [p for p in plugins if p.name == \\\"mlore\\\"]\\r\\n self.assertEqual(len(lmath), 1, \\\"Did not find math lore plugin: %r\\\" % (lmath,))\",\n \"def test_make_tool_plugin_found():\\n manager = PluginManager()\\n # Get the path to statick_tool/__init__.py, get the directory part, and\\n # add 'plugins' to that to get the standard plugins dir\\n manager.setPluginPlaces([os.path.join(os.path.dirname(statick_tool.__file__),\\n 'plugins')])\\n manager.setCategoriesFilter({\\n \\\"Tool\\\": ToolPlugin,\\n })\\n manager.collectPlugins()\\n # Verify that a plugin's get_name() function returns \\\"make\\\"\\n assert any(plugin_info.plugin_object.get_name() == 'make' for\\n plugin_info in manager.getPluginsOfCategory(\\\"Tool\\\"))\\n # While we're at it, verify that a plugin is named Yamllint Tool Plugin\\n assert any(plugin_info.name == 'Make Tool Plugin' for\\n plugin_info in manager.getPluginsOfCategory(\\\"Tool\\\"))\",\n \"def test_register_dynamic_plugin(self):\\n pass\",\n \"def test_rstlint_tool_plugin_found():\\n manager = PluginManager()\\n # Get the path to statick_tool/__init__.py, get the directory part, and\\n # add 'plugins' to that to get the standard plugins dir\\n manager.setPluginPlaces(\\n [os.path.join(os.path.dirname(statick_tool.__file__), \\\"plugins\\\")]\\n )\\n manager.setCategoriesFilter(\\n {\\n \\\"Tool\\\": ToolPlugin,\\n }\\n )\\n manager.collectPlugins()\\n # Verify that a plugin's get_name() function returns \\\"rstlint\\\"\\n assert any(\\n plugin_info.plugin_object.get_name() == \\\"rstlint\\\"\\n for plugin_info in manager.getPluginsOfCategory(\\\"Tool\\\")\\n )\\n # While we're at it, verify that a plugin is named rstlint Tool Plugin\\n assert any(\\n plugin_info.name == \\\"rstlint Tool Plugin\\\"\\n for plugin_info in manager.getPluginsOfCategory(\\\"Tool\\\")\\n )\",\n \"def test_register_dynamic_plugin_manager(self):\\n pass\",\n \"def test_exposeInterfaces(self):\\n if self.plugin is None:\\n return\\n\\n cs = settings.Settings()\\n results = self.plugin.exposeInterfaces(cs)\\n if results is None or not results:\\n return\\n\\n # each plugin should return a list\\n self.assertIsInstance(results, list)\\n for result in results:\\n # Make sure that all elements in the list satisfy the constraints of the\\n # hookspec\\n self.assertIsInstance(result, tuple)\\n self.assertEqual(len(result), 3)\\n\\n order, interface, kwargs = result\\n\\n self.assertIsInstance(order, (int, float))\\n self.assertTrue(issubclass(interface, interfaces.Interface))\\n self.assertIsInstance(kwargs, dict)\",\n \"def test_isort(self):\\n chdir(REPO_ROOT)\\n cmd = [\\\"isort\\\", \\\"-df\\\", \\\"-rc\\\", \\\"-c\\\", *SRC_DIRS]\\n print(\\\"running:\\\", \\\" \\\".join(str(part) for part in cmd))\\n proc = run(cmd, capture_output=True)\\n assert proc.returncode == 0, f\\\"isort issues:\\\\n{proc.stdout.decode('utf-8')}\\\"\",\n \"def load_plugin():\\n return HostTestPluginCopyMethod_Shell()\",\n \"def test_isort_tool_plugin_parse_valid():\\n itp = setup_isort_tool_plugin()\\n total_output = []\\n output = \\\"/tmp/x.py\\\"\\n total_output.append(output)\\n output = \\\"/tmp/y.py\\\"\\n total_output.append(output)\\n issues = itp.parse_output(total_output)\\n assert len(issues) == 2\\n assert issues[0].filename == \\\"/tmp/x.py\\\"\\n assert issues[0].line_number == \\\"0\\\"\\n assert issues[0].tool == \\\"isort\\\"\\n assert issues[0].issue_type == \\\"formatting\\\"\\n assert issues[0].severity == \\\"3\\\"\\n assert issues[0].message == \\\"Imports are incorrectly sorted and/or formatted.\\\"\\n assert issues[1].filename == \\\"/tmp/y.py\\\"\",\n \"def test_plugin_retrieval(self):\\n plugin = ProjectAppPluginPoint.get_plugin(PLUGIN_NAME)\\n self.assertIsNotNone(plugin)\\n self.assertEqual(plugin.get_model().name, PLUGIN_NAME)\\n self.assertEqual(plugin.name, PLUGIN_NAME)\\n self.assertEqual(plugin.get_model().title, PLUGIN_TITLE)\\n self.assertEqual(plugin.title, PLUGIN_TITLE)\\n self.assertEqual(plugin.entry_point_url_id, PLUGIN_URL_ID)\",\n \"def test_register_dynamic_plugin_manager1(self):\\n pass\",\n \"def test_register_dynamic_plugin1(self):\\n pass\",\n \"def test_remote_plugin(self):\\n plugin_name = 'Slack'\\n Plugin.download_plugin(plugin_name)\\n target = os.path.join(expanduser('~'), '.drupdates', 'plugins', plugin_name)\\n plugins = Plugin.get_plugins()\\n assert plugins[plugin_name]['info'][1] == os.path.join(target, '__init__.py')\",\n \"def test_get_plugin_by_id(self):\\n response = self.client.get_plugin_by_id(1)\\n self.assertEqual(response['id'], 1)\",\n \"def testSorting(self):\\n if self.sorting in tools.SORTINGS:\\n self.assertEqual(\\n self.sorting,\\n self.config.sorting\\n )\\n else:\\n self.assertNotEqual(\\n self.sorting,\\n self.config.sorting\\n )\\n self.assertEqual(\\n tools.SORTING_DEFAULT,\\n self.config.sorting\\n )\",\n \"def test_parrot_imported():\\n assert \\\"parrot\\\" in sys.modules\",\n \"def test_custom_plugin(self):\\n plugin_name = 'Druptest'\\n source = os.path.join(self.current_dir, 'classes', plugin_name)\\n target = os.path.join(expanduser('~'), '.drupdates', 'plugins', plugin_name)\\n shutil.copytree(source, target)\\n plugins = Plugin.get_plugins()\\n assert plugins[plugin_name]['name'] == plugin_name\",\n \"def test_get_plugins_with_search_args(self):\\n response = self.client.get_plugins({'name_exact': \\\"pl-dircopy\\\"})\\n self.assertEqual(response['data'][0]['name'], \\\"pl-dircopy\\\")\",\n \"def sorter(Plugin):\\n return Plugin.order\",\n \"def get_plugin_interface(self):\",\n \"def test_plugin_initialize_from_args(self):\\n sys.argv.append('-t')\\n p = PluginCustom()\\n self.assertEqual('yourah', p.toto)\",\n \"def test_plugin_slowinit(node_factory):\\n n = node_factory.get_node()\\n\\n n.rpc.plugin_start(os.path.join(os.getcwd(), \\\"tests/plugins/slow_init.py\\\"))\\n\\n # It's not actually configured yet, see what happens;\\n # make sure 'rescan' and 'list' controls dont crash\\n n.rpc.plugin_rescan()\\n n.rpc.plugin_list()\",\n \"def test_install_plugin_again_is_ok(self):\\n raise NotImplementedError()\",\n \"def setup_isort_tool_plugin(custom_rsc_path=None):\\n arg_parser = argparse.ArgumentParser()\\n\\n if custom_rsc_path is not None:\\n resources = Resources([custom_rsc_path])\\n else:\\n resources = Resources(\\n [os.path.join(os.path.dirname(statick_tool.__file__), \\\"plugins\\\")]\\n )\\n config = Config(resources.get_file(\\\"config.yaml\\\"))\\n plugin_context = PluginContext(arg_parser.parse_args([]), resources, config)\\n plugin_context.args.output_directory = os.path.dirname(__file__)\\n itp = IsortToolPlugin()\\n itp.set_plugin_context(plugin_context)\\n return itp\",\n \"def test_plugin_urls(self):\\n plugin = ProjectAppPluginPoint.get_plugin(PLUGIN_NAME)\\n self.assertEqual(plugin.urls, urlpatterns)\",\n \"def isort(context):\\n exec_cmd = \\\"isort . --check --diff\\\"\\n run_cmd(context, exec_cmd)\",\n \"def test_installed(self):\\n check_output('unity --help', shell=True)\",\n \"def test_specific_plugin_installed(self):\\n self._add_plugin(self.jigconfig, 'plugin01')\\n set_jigconfig(self.gitrepodir, config=self.jigconfig)\\n\\n # Create staged\\n self.commit(self.gitrepodir, 'a.txt', 'a')\\n self.stage(self.gitrepodir, 'b.txt', 'b')\\n\\n with nested(\\n patch('jig.runner.sys'),\\n self.assertRaises(SystemExit)\\n ) as (r_sys, ec):\\n # Raise the error to halt execution like the real sys.exit would\\n r_sys.exit.side_effect = SystemExit\\n\\n self.run_command('--plugin plugin01 {0}'.format(self.gitrepodir))\\n\\n self.assertResults(u\\\"\\\"\\\"\\n ▾ plugin01\\n\\n ⚠ line 1: b.txt\\n b is +\\n\\n {0} Jig ran 1 plugin\\n Info 0 Warn 1 Stop 0\\n \\\"\\\"\\\".format(ATTENTION), self.output)\",\n \"def test_commands_initialization(bot):\\n plugin = Commands(bot)\\n assert isinstance(plugin, Commands)\\n assert plugin.bot == bot\",\n \"def test_filter_installed_plugins(plugin_dialog):\\n plugin_dialog.filter(\\\"\\\")\\n assert plugin_dialog.installed_list._count_visible() >= 0\\n\\n plugin_dialog.filter(\\\"no-match@123\\\")\\n assert plugin_dialog.installed_list._count_visible() == 0\",\n \"def test_implements_IHelper(self):\\n self.assertTrue(IHelper.providedBy(PersistenceHelper()))\",\n \"def test_fabsim():\\n assert(\\\"plugins\\\" in get_plugin_path(\\\"FabDummy\\\"))\\n assert(\\\"FabDummy\\\" in get_plugin_path(\\\"FabDummy\\\"))\\n assert(len(get_fabsim_git_hash()) > 0)\",\n \"def test_sort(self):\\n sort_field = MoveSearchForm.sort\\n for value, label in sort_field.kwargs['choices']:\\n response = self.do_search(id=u'1', sort=value)\\n self.assert_(\\n response.tmpl_context.results,\\n \\\"\\\"\\\"Sort by {0} doesn't crash\\\"\\\"\\\".format(value)\\n )\",\n \"def isort(command, checkonly=False):\\n print(\\n \\\"\\\"\\\"\\nRunning isort the Python code import sorter\\n===========================================\\n\\\"\\\"\\\"\\n )\\n cmd = \\\"isort --check-only --diff .\\\" if checkonly else \\\"isort .\\\"\\n command.run(cmd, echo=True, pty=POSIX)\",\n \"def test_insertSort(self):\\n\\t\\tsortObj=insertSort()\\n\\t\\tself.assertEqual(sortObj.run_sort(self.test_1[0]),self.test_1[1])\",\n \"def test_isort_tool_plugin_scan_oserror(mock_subprocess_check_output):\\n mock_subprocess_check_output.side_effect = OSError(\\\"mocked error\\\")\\n itp = setup_isort_tool_plugin()\\n package = Package(\\n \\\"valid_package\\\", os.path.join(os.path.dirname(__file__), \\\"valid_package\\\")\\n )\\n package[\\\"python_src\\\"] = [\\n os.path.join(os.path.dirname(__file__), \\\"valid_package\\\", \\\"sample.py\\\")\\n ]\\n issues = itp.scan(package, \\\"level\\\")\\n assert not issues\",\n \"def test_insertSort3(self):\\n\\t\\tsortObj=insertSort()\\n\\t\\tself.assertEqual(sortObj.run_sort(self.test_3[0]),self.test_3[1])\",\n \"def test_plugin_with_no_plugin_class(conf):\\n # For fun, we pass in a system library\\n installed_apps_before = conf.config[\\\"INSTALLED_APPS\\\"][:]\\n cli.plugin(\\\"os.path\\\")\\n assert installed_apps_before == conf.config[\\\"INSTALLED_APPS\\\"]\",\n \"def test_client_custom_plugin():\\n client = ConfigureClients(plugins=[PluginVipCustomisation])\\n assert client.plugins == [PluginVipCustomisation]\",\n \"def test_make_tool_plugin_scan_missing_tool_name():\\n mtp = setup_make_tool_plugin()\\n if not mtp.command_exists('make'):\\n pytest.skip('Missing make executable.')\\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\\n 'valid_package'))\\n issues = mtp.scan(package, 'level')\\n assert not issues\",\n \"def test__SortOrder__name():\\n for instance in SortOrder.INSTANCES.values():\\n vampytest.assert_instance(instance.name, str)\",\n \"def test_create_plugin_instance(self):\\n plugin_id = 2\\n data = {\\n 'title': 'Test plugin instance',\\n 'dir': self.username + '/'\\n }\\n response = self.client.create_plugin_instance(plugin_id, data)\\n self.assertEqual(response['title'], data['title'])\",\n \"def test_module(self):\\n pass\",\n \"def test_get_sort_info(self):\\n ars = self.ar[2009][11]['day']\\n self.assertEqual(ars.get_sort_info(), (31, 'key', True))\",\n \"def test_xchemOT_imported():\\n assert \\\"xchemOT\\\" in sys.modules\",\n \"def check_plugin(vcf_reader, plugin):\\n # Always use core plug-in\\n plugins = ['core']\\n # Collect supplied plugin(s)\\n [plugins.append(item) for item in plugin]\\n # Create set\\n plugins = list(set(plugins))\\n # Evaluate vcf and plugin compatibility\\n for plugin in plugins:\\n\\n if plugin == \\\"core\\\":\\n from pScout.plugin.plugin_reader import core\\n ret = core(vcf_reader, \\\"pScout/plugin/get_core.ini\\\")\\n\\n if ret is 1: # Not compatible\\n exit()\\n\\n return plugins\",\n \"def setUp(self):\\n self._plugin = spotlight_volume.SpotlightVolumePlugin()\\n self._parser = plist.PlistParser()\",\n \"def request_plugins(self):\",\n \"def scan_plugin(self):\\n pluginpath=_module_path()\\n plugins=[]\\n for f in os.listdir(pluginpath):\\n if os.path.isfile(os.path.join(pluginpath,f)) and os.path.splitext(os.path.join(pluginpath,f))[-1]=='.py' :\\n if 'plugin_' in os.path.basename(f):\\n logger.debug(\\\"found plugin : %s\\\",f)\\n plugins.append(f)\\n return plugins\",\n \"def test_filter_available_plugins(plugin_dialog):\\n plugin_dialog.filter(\\\"\\\")\\n assert plugin_dialog.available_list.count() == 2\\n assert plugin_dialog.available_list._count_visible() == 2\\n\\n plugin_dialog.filter(\\\"no-match@123\\\")\\n assert plugin_dialog.available_list._count_visible() == 0\\n\\n plugin_dialog.filter(\\\"\\\")\\n plugin_dialog.filter(\\\"test-name-0\\\")\\n assert plugin_dialog.available_list._count_visible() == 1\",\n \"def test_dependencies_installed(self):\\n installer = getattr(self.portal, 'portal_quickinstaller')\\n self.assertTrue(installer.isProductInstalled('plone.app.dexterity'))\",\n \"def test_get_plugin_parameters(self):\\n plugin_id = 2\\n response = self.client.get_plugin_parameters(plugin_id,\\n {'limit': 50, 'offset': 0})\\n self.assertEqual(response['data'][0]['name'], \\\"dir\\\")\",\n \"def get_id(self):\\n return \\\"unittest_required_plugin\\\"\",\n \"def test_addplugin(self):\\n app = QApplication(sys.argv)\\n data = (np.random.rand(30, 31, 32) * 100).astype(np.int)\\n data[15:40, 13:20, 10:18] += 50\\n se = seededitorqt.QTSeedEditor(data)\\n wg0 = seededitorqt.plugin.SampleThresholdPlugin()\\n se.addPlugin(wg0)\\n # se.exec_()\\n # self.assertTrue(False)\",\n \"def plugins():\\n pass\",\n \"def main() -> bool:\\n global logger\\n logger = setup_logger(\\\"nitpycker\\\")\\n plugin_manager = Manager()\\n plugin_manager.load_plugins()\\n args = parse_args(plugin_manager)\\n if plugin_manager.enable_plugins(args.plugins, args):\\n exit(2)\\n\\n plugin_manager.pre_test_discovery()\\n tests = unittest.defaultTestLoader.discover(args.start_directory, pattern=args.pattern)\\n plugin_manager.post_test_discovery()\\n tests = plugin_manager.filter_tests(tests)\\n report = ParallelRunner(plugin_manager, process_number=args.process_number, verbosity=args.verbosity).run(tests)\\n return not report.wasSuccessful()\",\n \"def test_insertSort2(self):\\n\\t\\tsortObj=insertSort()\\n\\t\\tself.assertNotEqual(sortObj.run_sort(self.test_2[0]),self.test_2[1])\",\n \"def test_plot_ay_imported():\\n assert \\\"plot_ay\\\" in sys.modules\",\n \"def test_load(self):\\n (spec, check) = bundylogging.load()\\n # It returns the checking function\\n self.assertEqual(check, bundylogging.check)\\n # The plugin stores it's spec\\n self.assertEqual(spec, bundylogging.spec)\",\n \"def plugin_import(plugin):\\n\\n return importlib.import_module(plugin, package=\\\"directord\\\")\",\n \"def test_first_run(dbbackup, plugin, update, version_file=None, orig_version=None):\\n\\n if version_file:\\n os.unlink(version_file)\\n\\n cli.initialize()\\n update.assert_called_once()\\n dbbackup.assert_not_called()\\n\\n # Check that it got called for each default plugin\\n from kolibri.core.settings import DEFAULT_PLUGINS\\n\\n assert plugin.call_count == len(DEFAULT_PLUGINS)\",\n \"def test_plugin_command(node_factory):\\n n = node_factory.get_node()\\n\\n # Make sure that the 'hello' command from the helloworld.py plugin\\n # is not available.\\n cmd = [hlp for hlp in n.rpc.help()[\\\"help\\\"] if \\\"hello\\\" in hlp[\\\"command\\\"]]\\n assert(len(cmd) == 0)\\n\\n # Add the 'contrib/plugins' test dir\\n n.rpc.plugin_startdir(directory=os.path.join(os.getcwd(), \\\"contrib/plugins\\\"))\\n # Make sure that the 'hello' command from the helloworld.py plugin\\n # is now available.\\n cmd = [hlp for hlp in n.rpc.help()[\\\"help\\\"] if \\\"hello\\\" in hlp[\\\"command\\\"]]\\n assert(len(cmd) == 1)\\n\\n # Make sure 'rescan' and 'list' subcommands dont crash\\n n.rpc.plugin_rescan()\\n n.rpc.plugin_list()\\n\\n # Make sure the plugin behaves normally after stop and restart\\n assert(\\\"Successfully stopped helloworld.py.\\\" == n.rpc.plugin_stop(plugin=\\\"helloworld.py\\\")[''])\\n n.daemon.wait_for_log(r\\\"Killing plugin: helloworld.py\\\")\\n n.rpc.plugin_start(plugin=os.path.join(os.getcwd(), \\\"contrib/plugins/helloworld.py\\\"))\\n n.daemon.wait_for_log(r\\\"Plugin helloworld.py initialized\\\")\\n assert(\\\"Hello world\\\" == n.rpc.call(method=\\\"hello\\\"))\\n\\n # Now stop the helloworld plugin\\n assert(\\\"Successfully stopped helloworld.py.\\\" == n.rpc.plugin_stop(plugin=\\\"helloworld.py\\\")[''])\\n n.daemon.wait_for_log(r\\\"Killing plugin: helloworld.py\\\")\\n # Make sure that the 'hello' command from the helloworld.py plugin\\n # is not available anymore.\\n cmd = [hlp for hlp in n.rpc.help()[\\\"help\\\"] if \\\"hello\\\" in hlp[\\\"command\\\"]]\\n assert(len(cmd) == 0)\\n\\n # Test that we cannot start a plugin with 'dynamic' set to False in\\n # getmanifest\\n with pytest.raises(RpcError, match=r\\\"Not a dynamic plugin\\\"):\\n n.rpc.plugin_start(plugin=os.path.join(os.getcwd(), \\\"tests/plugins/static.py\\\"))\\n\\n # Test that we cannot stop a started plugin with 'dynamic' flag set to\\n # False\\n n2 = node_factory.get_node(options={\\n \\\"plugin\\\": os.path.join(os.getcwd(), \\\"tests/plugins/static.py\\\")\\n })\\n with pytest.raises(RpcError, match=r\\\"static.py cannot be managed when lightningd is up\\\"):\\n n2.rpc.plugin_stop(plugin=\\\"static.py\\\")\\n\\n # Test that we don't crash when starting a broken plugin\\n with pytest.raises(RpcError, match=r\\\"Timed out while waiting for plugin response\\\"):\\n n2.rpc.plugin_start(plugin=os.path.join(os.getcwd(), \\\"tests/plugins/broken.py\\\"))\",\n \"def test_package(self):\\n pass\",\n \"def register_plugin(self):\\n self.edit_goto.connect(self.main.editor.load)\\n self.redirect_stdio.connect(self.main.redirect_internalshell_stdio)\\n self.main.add_dockwidget(self)\\n\\n unittesting_act = create_action(self, _(\\\"Run unit tests\\\"),\\n icon=get_icon('profiler.png'),\\n triggered=self.run_unittesting)\\n unittesting_act.setEnabled(is_unittesting_installed())\\n fixed_shortcut(\\\"Ctrl+Shift+F11\\\", self.main,\\n self.run_unittesting)\\n\\n self.main.run_menu_actions += [unittesting_act]\\n self.main.editor.pythonfile_dependent_actions += [unittesting_act]\",\n \"def describe(self, plugin):\\n plug = plugin_source.load_plugin(plugin)\\n plug.describe()\",\n \"def test_version_sorting(self):\\n assert natsort(['1', '5', '10', '50']) == ['1', '5', '10', '50']\",\n \"def test(self):\\n test_dir = join_path(self.test_suite.current_test_cache_dir, self.test_src_dir)\\n self.run_test(\\n \\\"sh\\\",\\n [\\\"testshortsort.sh\\\"],\\n expected=\\\"Alignments sorted by coordinate.\\\",\\n purpose=\\\"test: checking alignments\\\",\\n work_dir=test_dir,\\n )\",\n \"def test_makeplugin(subprocess):\\n # type: (Mock) -> None\\n plugin = lektor_make.MakePlugin(lambda: None, None) # pragma: no branch\\n plugin.on_before_build_all(lambda: None) # pragma: no branch\\n subprocess.Popen.assert_called_with([\\\"make\\\", \\\"lektor\\\"])\\n subprocess.Popen().wait.asset_called()\",\n \"def test_install_terraform_plugin_1(monkeypatch):\\n monkeypatch.setattr(os, 'listdir', lambda path: [\\n 'terraform-provider-terraform_v0.11.2_x4'])\\n monkeypatch.setattr(os, 'remove', lambda path: True)\\n monkeypatch.setattr(os, 'chmod', lambda path, permissions: True)\\n\\n monkeypatch.setattr(shutil, 'copy2', lambda src, dest: True)\\n\\n def mp_check_output(cmd):\\n if cmd == ['which', 'terraform']:\\n return b'/usr/local/bin/terraform\\\\n'\\n\\n if cmd == ['terraform', '-v']:\\n return b'Terraform v0.11.3\\\\n\\\\n'\\n\\n raise Exception('Unmocked command: %s' % cmd)\\n\\n monkeypatch.setattr(subprocess, 'check_output', mp_check_output)\\n\\n install_terraform_plugin('/tmp/stone-burner_plugins')\",\n \"def __test__():\\n#-------------------------------------------------------------------------------\\n import pylib.tester as tester\\n return 0\",\n \"def test_install(self):\\n pass\",\n \"def test_implements_IHelper(self):\\n self.assertTrue(IHelper.providedBy(WorkerHelper()))\",\n \"def test_vendored_libjuju(self):\\n for name in sys.modules:\\n if name.startswith(\\\"juju\\\"):\\n module = sys.modules[name]\\n if getattr(module, \\\"__file__\\\"):\\n print(getattr(module, \\\"__file__\\\"))\\n assert re.search('n2vc', module.__file__, re.IGNORECASE)\\n\\n # assert module.__file__.find(\\\"N2VC\\\")\\n # assert False\\n return\",\n \"def test_specific_plugin_not_installed(self):\\n self._add_plugin(self.jigconfig, 'plugin01')\\n set_jigconfig(self.gitrepodir, config=self.jigconfig)\\n\\n # Create staged\\n self.commit(self.gitrepodir, 'a.txt', 'a')\\n self.stage(self.gitrepodir, 'b.txt', 'b')\\n\\n with nested(\\n patch('jig.runner.sys'),\\n self.assertRaises(SystemExit)\\n ) as (r_sys, ec):\\n # Raise the error to halt execution like the real sys.exit would\\n r_sys.exit.side_effect = SystemExit\\n\\n self.run_command(\\n '--plugin notinstalled {0}'.format(self.gitrepodir))\\n\\n # A plugin which is not installed was requested so not output\\n self.assertEqual('', self.output)\",\n \"def test_molssi_project_imported():\\n assert \\\"molssi_project\\\" in sys.modules\",\n \"def test_provider(self):\\n msg = 'Wrong number of processing algorithm loaded.'\\n self.assertEqual(len(self.provider.alglist), 6, msg)\\n\\n msg = 'InaSAFE should be activated by default in Processing.'\\n self.assertEqual(self.provider.activate, True, msg)\\n\\n msg = 'Wrong processing provide.'\\n for algorithm in self.provider.alglist:\\n self.assertEqual(algorithm.provider, self.provider, msg)\",\n \"def test_import(self):\\n self.assertTrue(NagiosPerfdataCollector)\",\n \"def test(self):\\n\\t\\treturn describeInterface(self)\",\n \"def get_plugin(self, name):\",\n \"def test_setup_object(self):\\n self._test_object.__name__ = 'pluggable_object'\\n self._test_object.test_plugin = object()\\n self._test_object.__all__ = ['test_plugin']\\n pluggable_package.setup(self._test_object)\\n self._test_setup(self._test_object)\",\n \"def test():\\n try:\\n import nipype\\n except ImportError:\\n # if nipype is not installed, skip this test (without failure)\\n return True\\n suite = unittest.TestLoader().loadTestsFromTestCase(TestNipypeWrap)\\n runtime = unittest.TextTestRunner(verbosity=2).run(suite)\\n return runtime.wasSuccessful()\",\n \"def test_get_plugin_id(self):\\n plugin_name = 'myfakeplugin'\\n self.my_plugin.name = plugin_name\\n\\n def side_effect(name):\\n if name == plugin_name:\\n return self.my_plugin\\n else:\\n raise sahara_base.APIException(error_code=404,\\n error_name='NOT_FOUND')\\n\\n self.sahara_client.plugins.get.side_effect = side_effect\\n self.assertIsNone(self.sahara_plugin.get_plugin_id(plugin_name))\\n self.assertRaises(exception.EntityNotFound,\\n self.sahara_plugin.get_plugin_id, 'noplugin')\\n\\n calls = [mock.call(plugin_name), mock.call('noplugin')]\\n self.sahara_client.plugins.get.assert_has_calls(calls)\",\n \"def test_molecool_imported():\\n assert \\\"molecool\\\" in sys.modules\",\n \"def test_check_module(self) -> None:\\n check_module(\\\"os\\\")\",\n \"def init_plugins(group):\\n package = sys.modules[__name__]\\n\\n for (importer, module_name, ispkg) in pkgutil.iter_modules(package.__path__):\\n if not ispkg:\\n try:\\n module = importlib.import_module(package.__name__ + '.' + module_name)\\n\\n if 'run' not in module.__dict__:\\n print_fail('Plugin command \\\"%s\\\" doesn\\\\'t provide a \\\"run\\\" method. Aborting.' % (module_name))\\n # sys.exit(-1)\\n else:\\n group.add_command(module.run)\\n except Exception, e:\\n print_fail('[%s] %s' % (module_name, str(e) + str(type(e))))\",\n \"def test_qm_project_python_testing_imported():\\n assert \\\"qm_project_python_testing\\\" in sys.modules\",\n \"def test_setup_module(self):\\n pluggable_package.setup(self._test_module)\\n self._test_setup(self._test_module)\",\n \"def test_is_installed():\\n assert _is_installed('coverage') is True # regular dependency\\n assert _is_installed('pytest') is True # dev dependency\\n assert _is_installed('missing') is False # missing dependency\",\n \"def test_list_vips_sort(self):\\r\\n resources = \\\"vips\\\"\\r\\n cmd = vip.ListVip(test_cli20.MyApp(sys.stdout), None)\\r\\n self._test_list_resources(resources, cmd,\\r\\n sort_key=[\\\"name\\\", \\\"id\\\"],\\r\\n sort_dir=[\\\"asc\\\", \\\"desc\\\"])\",\n \"def test_list_vips_sort(self):\\n resources = \\\"vips\\\"\\n cmd = vip.ListVip(test_cli20.MyApp(sys.stdout), None)\\n self._test_list_resources(resources, cmd,\\n sort_key=[\\\"name\\\", \\\"id\\\"],\\n sort_dir=[\\\"asc\\\", \\\"desc\\\"])\",\n \"def test_override_plugin(self):\\n plugin_name = 'Stdout'\\n source = os.path.join(self.current_dir, 'classes', plugin_name)\\n target = os.path.join(expanduser('~'), '.drupdates', 'plugins', plugin_name)\\n shutil.copytree(source, target)\\n plugins = Plugin.get_plugins()\\n assert plugins[plugin_name]['info'][1] == os.path.join(target, '__init__.py')\",\n \"def test_basic(self):\\n plugin_instance = ProbabilitiesFromPercentiles2D(self.test_cube,\\n self.new_name)\\n self.assertEqual(plugin_instance.output_name, self.new_name)\",\n \"def find_plugins():\\n return list(straight.plugin.load('csbot.plugins', subclasses=Plugin))\",\n \"def test04_vms_page_table_sorting(self):\\n self.lg('%s STARTED' % self._testID)\\n self.lg('sorting of all fields of vms table, should be working as expected')\\n self.assertTrue(self.Tables.check_sorting_table('machines'))\\n self.lg('%s ENDED' % self._testID)\",\n \"def testFindsBuiltins(self):\\r\\n self.assertEqual('sys', modulefinder.get_module_filename('sys'))\\r\\n self.assertEqual('time', modulefinder.get_module_filename('time'))\",\n \"def test_subworkflows_info_in_modules_repo(self):\\n self.subworkflow_install.install(\\\"bam_sort_stats_samtools\\\")\\n mods_info = nf_core.subworkflows.SubworkflowInfo(self.nfcore_modules, \\\"bam_sort_stats_samtools\\\")\\n mods_info.local = True\\n mods_info_output = mods_info.get_component_info()\\n console = Console(record=True)\\n console.print(mods_info_output)\\n output = console.export_text()\\n\\n assert \\\"Subworkflow: bam_sort_stats_samtools\\\" in output\\n assert \\\"Inputs\\\" in output\\n assert \\\"Outputs\\\" in output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66552377","0.6554768","0.6273206","0.62442213","0.6233943","0.62220126","0.60991585","0.60933506","0.6089562","0.5941484","0.59386533","0.59242934","0.5922501","0.58955973","0.58816534","0.58793914","0.58021533","0.57796365","0.57335883","0.57034636","0.56641763","0.56267726","0.5626531","0.5626476","0.562094","0.55849403","0.55421877","0.55373126","0.5537275","0.55300945","0.54858345","0.54711425","0.5449824","0.5421194","0.5415871","0.54118866","0.53819555","0.5372896","0.53721166","0.5370303","0.53627163","0.53525406","0.53428423","0.5314355","0.5303815","0.5278685","0.52722687","0.52445924","0.524252","0.5241138","0.5223414","0.52162147","0.5211605","0.51974773","0.5193686","0.5191873","0.5178825","0.5168631","0.51649034","0.5159231","0.5142129","0.5121735","0.5120854","0.5117453","0.5113593","0.51125705","0.5103859","0.5095118","0.509428","0.5088653","0.50864583","0.50860876","0.5083254","0.50597394","0.5058254","0.50575495","0.5048846","0.50344324","0.5024982","0.50220346","0.50131774","0.50077593","0.50073266","0.5005986","0.498579","0.49638948","0.49625003","0.49559405","0.4952109","0.49473405","0.49443263","0.49435154","0.4943438","0.4936548","0.49338928","0.49333632","0.49261236","0.49178475","0.49171644","0.49166042"],"string":"[\n \"0.66552377\",\n \"0.6554768\",\n \"0.6273206\",\n \"0.62442213\",\n \"0.6233943\",\n \"0.62220126\",\n \"0.60991585\",\n \"0.60933506\",\n \"0.6089562\",\n \"0.5941484\",\n \"0.59386533\",\n \"0.59242934\",\n \"0.5922501\",\n \"0.58955973\",\n \"0.58816534\",\n \"0.58793914\",\n \"0.58021533\",\n \"0.57796365\",\n \"0.57335883\",\n \"0.57034636\",\n \"0.56641763\",\n \"0.56267726\",\n \"0.5626531\",\n \"0.5626476\",\n \"0.562094\",\n \"0.55849403\",\n \"0.55421877\",\n \"0.55373126\",\n \"0.5537275\",\n \"0.55300945\",\n \"0.54858345\",\n \"0.54711425\",\n \"0.5449824\",\n \"0.5421194\",\n \"0.5415871\",\n \"0.54118866\",\n \"0.53819555\",\n \"0.5372896\",\n \"0.53721166\",\n \"0.5370303\",\n \"0.53627163\",\n \"0.53525406\",\n \"0.53428423\",\n \"0.5314355\",\n \"0.5303815\",\n \"0.5278685\",\n \"0.52722687\",\n \"0.52445924\",\n \"0.524252\",\n \"0.5241138\",\n \"0.5223414\",\n \"0.52162147\",\n \"0.5211605\",\n \"0.51974773\",\n \"0.5193686\",\n \"0.5191873\",\n \"0.5178825\",\n \"0.5168631\",\n \"0.51649034\",\n \"0.5159231\",\n \"0.5142129\",\n \"0.5121735\",\n \"0.5120854\",\n \"0.5117453\",\n \"0.5113593\",\n \"0.51125705\",\n \"0.5103859\",\n \"0.5095118\",\n \"0.509428\",\n \"0.5088653\",\n \"0.50864583\",\n \"0.50860876\",\n \"0.5083254\",\n \"0.50597394\",\n \"0.5058254\",\n \"0.50575495\",\n \"0.5048846\",\n \"0.50344324\",\n \"0.5024982\",\n \"0.50220346\",\n \"0.50131774\",\n \"0.50077593\",\n \"0.50073266\",\n \"0.5005986\",\n \"0.498579\",\n \"0.49638948\",\n \"0.49625003\",\n \"0.49559405\",\n \"0.4952109\",\n \"0.49473405\",\n \"0.49443263\",\n \"0.49435154\",\n \"0.4943438\",\n \"0.4936548\",\n \"0.49338928\",\n \"0.49333632\",\n \"0.49261236\",\n \"0.49178475\",\n \"0.49171644\",\n \"0.49166042\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8355158"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8292,"cells":{"query":{"kind":"string","value":"Verify that we can parse the normal output of isort."},"document":{"kind":"string","value":"def test_isort_tool_plugin_parse_valid():\n itp = setup_isort_tool_plugin()\n total_output = []\n output = \"/tmp/x.py\"\n total_output.append(output)\n output = \"/tmp/y.py\"\n total_output.append(output)\n issues = itp.parse_output(total_output)\n assert len(issues) == 2\n assert issues[0].filename == \"/tmp/x.py\"\n assert issues[0].line_number == \"0\"\n assert issues[0].tool == \"isort\"\n assert issues[0].issue_type == \"formatting\"\n assert issues[0].severity == \"3\"\n assert issues[0].message == \"Imports are incorrectly sorted and/or formatted.\"\n assert issues[1].filename == \"/tmp/y.py\""},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_isort(self):\n chdir(REPO_ROOT)\n cmd = [\"isort\", \"-df\", \"-rc\", \"-c\", *SRC_DIRS]\n print(\"running:\", \" \".join(str(part) for part in cmd))\n proc = run(cmd, capture_output=True)\n assert proc.returncode == 0, f\"isort issues:\\n{proc.stdout.decode('utf-8')}\"","def isort(context):\n exec_cmd = \"isort . --check --diff\"\n run_cmd(context, exec_cmd)","def isort_check(ctx):\n ctx.run(f\"{VENV_PREFIX} isort --atomic --check-only .\")","def test_shell(self):\n integers = shell_sort(self.actual)\n self.assertEqual(self.expected, integers)","def isort(command, checkonly=False):\n print(\n \"\"\"\nRunning isort the Python code import sorter\n===========================================\n\"\"\"\n )\n cmd = \"isort --check-only --diff .\" if checkonly else \"isort .\"\n command.run(cmd, echo=True, pty=POSIX)","def test_optimize_parse():\n assert True","def test_python_3_compatibility(self):\n assert natsort(['1', 'a']) == ['1', 'a']","def test_parse(self): \n\n results = self.parser.parse()\n self.assertEqual(results, test_case_data['parse_output'])","def test_version_sorting(self):\n assert natsort(['1', '5', '10', '50']) == ['1', '5', '10', '50']","def test(self):\n test_dir = join_path(self.test_suite.current_test_cache_dir, self.test_src_dir)\n self.run_test(\n \"sh\",\n [\"testshortsort.sh\"],\n expected=\"Alignments sorted by coordinate.\",\n purpose=\"test: checking alignments\",\n work_dir=test_dir,\n )","def test_sort_strings(self):\n chdir(REPO_ROOT)\n cmd = [\"python\", \"scripts/sort_strings.py\", \"--check\"]\n print(\"running:\", \" \".join(str(part) for part in cmd))\n proc = run(cmd, capture_output=True)\n assert proc.returncode == 0, (\n f\"sort strings issues:\\n{proc.stdout.decode('utf-8')}\\n\\n\"\n \"Please run ./scripts/sort_string.py to resolve this issue.\"\n )","def test_rust_code_analysis_tokei_Rust() -> None:\n\n ret_value = compare(\n \"rust-code-analysis\",\n \"tokei\",\n [\"-g\", \"-f\"],\n [\"SLOC\", \"PLOC\", \"CLOC\", \"BLANK\"],\n \"Rust\",\n \"bubble_sort.rs\",\n )\n\n assert ret_value == 0","def test_isort_tool_plugin_found():\n if sys.version_info.major == 3 and sys.version_info.minor < 6:\n pytest.skip(\"isort is only available for Python 3.6+, unable to test\")\n manager = PluginManager()\n # Get the path to statick_tool/__init__.py, get the directory part, and\n # add 'plugins' to that to get the standard plugins dir\n manager.setPluginPlaces(\n [os.path.join(os.path.dirname(statick_tool.__file__), \"plugins\")]\n )\n manager.setCategoriesFilter(\n {\n \"Tool\": ToolPlugin,\n }\n )\n manager.collectPlugins()\n # Verify that a plugin's get_name() function returns \"isort\"\n assert any(\n plugin_info.plugin_object.get_name() == \"isort\"\n for plugin_info in manager.getPluginsOfCategory(\"Tool\")\n )\n # While we're at it, verify that a plugin is named Isort Tool Plugin\n assert any(\n plugin_info.name == \"Isort Tool Plugin\"\n for plugin_info in manager.getPluginsOfCategory(\"Tool\")\n )","def test_compare(self):\n parser = parse_args(['-g', '10', '-s', 'bubble', '-c'])\n self.assertTrue(parser.compare)\n self.assertEqual(True, parser.compare)\n\n parser = parse_args(['-g', '10', '-s', 'bubble'])\n self.assertEqual(False, parser.compare)","def test_natsort(self):\r\n # string with alpha and numerics sort correctly\r\n s = 'sample1 sample2 sample11 sample12'.split()\r\n self.assertEqual(natsort(s),\r\n 'sample1 sample2 sample11 sample12'.split())\r\n s.reverse()\r\n self.assertEqual(natsort(s),\r\n 'sample1 sample2 sample11 sample12'.split())\r\n self.assertEqual(natsort(list('cba321')), list('123abc'))\r\n\r\n # strings with alpha only sort correctly\r\n self.assertEqual(natsort(list('cdba')), list('abcd'))\r\n\r\n # string of ints sort correctly\r\n self.assertEqual(natsort(['11', '2', '1', '0']),\r\n ['0', '1', '2', '11'])\r\n\r\n # strings of floats sort correctly\r\n self.assertEqual(natsort(['1.11', '1.12', '1.00', '0.009']),\r\n ['0.009', '1.00', '1.11', '1.12'])\r\n\r\n # string of ints sort correctly\r\n self.assertEqual(\r\n natsort([('11', 'A'), ('2', 'B'), ('1', 'C'), ('0', 'D')]),\r\n [('0', 'D'), ('1', 'C'), ('2', 'B'), ('11', 'A')])","def test_isort_tool_plugin_scan_oserror(mock_subprocess_check_output):\n mock_subprocess_check_output.side_effect = OSError(\"mocked error\")\n itp = setup_isort_tool_plugin()\n package = Package(\n \"valid_package\", os.path.join(os.path.dirname(__file__), \"valid_package\")\n )\n package[\"python_src\"] = [\n os.path.join(os.path.dirname(__file__), \"valid_package\", \"sample.py\")\n ]\n issues = itp.scan(package, \"level\")\n assert not issues","def test_parse_results_valid():\n valid_result = [{\n \"url\": \"https://docs.ansible.com/ansible/.../test.html\",\n \"sections\": [\"test\"],\n \"title\": \"title – subtitle — Ansible Documentation\",\n \"body\": \"Long body containing flavor text\",\n \"_index\": \"5693d1e68db231f24d000003\",\n \"_type\": \"5693d1e68db231f24d000004\",\n \"_score\": 1,\n \"_version\": \"\",\n \"_explanation\": \"\",\n \"sort\": \"\",\n \"id\": \"test\",\n \"highlight\": {}\n }]\n assert [{\"title\": \"title\",\n \"subtitle\": \"subtitle\",\n \"arg\": \"https://docs.ansible.com/ansible/.../test.html\",\n \"valid\": True}] == parse_results(valid_result)","def _verifyParsing(self):\n for attrname, attr in self.__dict__.items():\n if attrname.endswith('records') and iterable(attr):\n ts = get_record_timestamps(attr)\n if not issorted(ts):\n print('Sorting %s' % attrname)\n if type(attr) == list:\n attr = list(np.asarray(attr)[ts.argsort()])\n else:\n attr = attr[ts.argsort()]\n ts = get_record_timestamps(attr)\n assert issorted(ts)\n self.__dict__[attrname] = attr # update","def test_invalid_sort():\n svl_string = \"\"\"\n DATASETS bigfoot \"bigfoot.csv\"\n LINE bigfoot X date SORT OOPS Y classification COUNT\n \"\"\"\n\n with pytest.raises(SvlSyntaxError):\n parse_svl(svl_string)","def _is_lexsorted(self) -> bool:\n return self._lexsort_depth == self.nlevels","def test_sort_otu_table(self):\r\n\r\n actual = sort_otu_table(parse_biom_table_str(self.otu_table1),\r\n ['NA', 'Key', 'Fing'])\r\n expected = parse_biom_table_str(self.age_sorted_otu_table1)\r\n self.assertEqual(actual, expected)","def try_run_isort_formatting(path_to_protocol_package: str) -> None:\n subprocess.run( # nosec\n [sys.executable, \"-m\", \"isort\", *ISORT_CLI_ARGS, path_to_protocol_package],\n check=True,\n )","def test_bogo(self):\n integers = bogo_sort(self.actual)\n self.assertEqual(self.expected, integers)","def step020():\n logger.logMessage('Begin: Sorting records')\n sortCommand = 'sort {0} -t \\';\\' --key 2 -o {1}'.format(candidatesFile,sortedCandidatesFile) \n rc = os.system(sortCommand)\n if rc != 0:\n raise Exception('Error returned by sort program: {0:d}'.format(rc))\n logger.logMessage('End : Sorting records')","def test_parse_devide(self):\n self.assertEqual(parse_input.parse([\"8\", \"/\", \"4\"]), 2)","def test_good_input():\n\n out_file = 'unclustered.fa'\n if os.path.isfile(out_file):\n os.remove(out_file)\n\n try:\n rv, out = getstatusoutput(f'{prg} -c {cdhit} -p {proteins}')\n assert rv == 0\n assert out == ('Wrote 309 of 220,520 unclustered '\n 'proteins to \"unclustered.fa\"')\n\n assert os.path.isfile(out_file)\n\n seqs = list(SeqIO.parse(out_file, 'fasta'))\n\n assert len(seqs) == 309\n finally:\n if os.path.isfile(out_file):\n os.remove(out_file)","def test_output(self):\n good_value_pairs = STR_VALUE_PAIRS\n for pair in good_value_pairs:\n output = to_cardinal_trio(pair[0])\n self.assertEqual(output, pair[1],\n f\"{pair[0]} should be {pair[1]}, not {output}\")","def test_advance_ast_avaliable():\n assert _test_advanced_ast_presence()","def check_output(output, expected_output):\n o = copy.deepcopy(output) # so that we don't mutate input\n e = copy.deepcopy(expected_output) # so that we don't mutate input\n \n o.sort()\n e.sort()\n return o == e","def test_12(self):\n num_elements = np.random.randint(1, 11)\n\n input_array = np.random.normal(size=num_elements)\n\n # We first check the sorting implementation.\n py = sorted(input_array)\n f90 = fort_debug.wrapper_sorted(input_array, num_elements)\n assert_equal(py, f90)\n\n params_spec, options_spec = generate_random_model()\n respy_obj = RespyCls(params_spec, options_spec)\n\n edu_spec, optim_paras, num_types = dist_class_attributes(\n respy_obj, \"edu_spec\", \"optim_paras\", \"num_types\"\n )\n\n args = (edu_spec[\"start\"], edu_spec[\"share\"], edu_spec[\"max\"])\n f90 = fort_debug.wrapper_sort_edu_spec(*args)\n py = sort_edu_spec(edu_spec)\n for i, label in enumerate([\"start\", \"share\", \"max\"]):\n assert_equal(py[label], f90[i])\n\n py = sort_type_info(optim_paras, num_types)\n f90 = fort_debug.wrapper_sort_type_info(optim_paras[\"type_shares\"], num_types)\n for i, label in enumerate([\"order\", \"shares\"]):\n assert_equal(py[label], f90[i])","def test_probabilistic_parsers():","def test_dotted_sorting(self):\n assert natsort(['1.5', '1.0']) == ['1.0', '1.5']","def test_quick(self):\n integers = quick_sort(self.actual)\n self.assertEqual(self.expected, integers)","def test_insertSort(self):\n\t\tsortObj=insertSort()\n\t\tself.assertEqual(sortObj.run_sort(self.test_1[0]),self.test_1[1])","def test_parser(self):\n parser = hhsuite.FastaParser()\n results = parser.run(self.pipeline)\n self.assertEqual(\n results[\"templates\"][0][\"sequence_alignments\"], {\n \"sequence\": \"---A-A-----\",\n \"query\": \"XXAB-CDEFXX\"\n })\n\n self.assertEqual(\n results[\"templates\"][1][\"sequence_alignments\"], {\n \"foo\": \"---A------\",\n \"sequence\": \"--GG------\",\n \"query\": \"XXABCDEFXX\"\n })","def test_simple_parse(self):\n pass","def test_MinimalSamParser(self):\r\n actual = list(MinimalSamParser(self.sam_data1))\r\n expected = self.sam1_expected\r\n self.assertEqual(actual, expected)","def test_parse_output(self):\n output_exp = [\"0.99\", \"0.01\"]\n output = parse_output(hgt_results_fp=self.consel_output_hgt_fp,\n method=\"consel\")\n self.assertEqual(output_exp, output)\n output_exp = \"1\"\n output = parse_output(hgt_results_fp=self.riatahgt_output_hgt_fp,\n method=\"riata-hgt\")\n self.assertEqual(output_exp, output)\n output_exp = (\"WP_011672248.1\\t372461\\tBuchnera aphidicola\\tProteobac\"\n \"teria;Gammaproteobacteria;Enterobacteriales;Enterobact\"\n \"eriaceae;Buchnera;Buchnera aphidicola\\t37.5\\t99.14\\nWP\"\n \"_045117937.1\\t580331\\tThermoanaerobacter italicus\\tFir\"\n \"micutes;Clostridia;Thermoanaerobacterales;Thermoanaero\"\n \"bacteraceae;Thermoanaerobacter;Thermoanaerobacter ital\"\n \"icus\\t42.6\\t93.84\")\n output = parse_output(hgt_results_fp=self.hgtector_output_hgt_fp,\n method=\"hgtector\")\n self.assertEqual(output_exp, output)\n output_exp = (\"G2311_SE001,\\tgi|557307555|ref|YP_008766893.1|\\t140749\"\n \"3\\tShigella phage SfIV\\tViruses;Caudovirales;Myovirida\"\n \"e\\t67.4\\t100\\t0.002\\nG1250_SE001,\\tgi|9630468|ref|NP_0\"\n \"46899.1|\\t40631\\tEnterobacteria phage N15\\tViruses;Cau\"\n \"dovirales;Siphoviridae;N15likevirus\\t79.4\\t100\\t0.002\\n\"\n \"G1252_SE001,\\tgi|428782382|ref|YP_007112139.1|\\t114714\"\n \"4\\tEnterobacteria phage HK225\\tViruses;Caudovirales;Si\"\n \"phoviridae;Lambdalikevirus\\t88.2\\t100\\t0.002\\nG1251_SE\"\n \"001,\\tgi|428782381|ref|YP_007112138.1|\\t1147144\\tEnter\"\n \"obacteria phage HK225\\tViruses;Caudovirales;Siphovirid\"\n \"ae;Lambdalikevirus\\t94.9\\t100\\t0.002\")\n output = parse_output(hgt_results_fp=self.darkhorse_output_hgt_fp,\n method=\"darkhorse\")\n self.assertEqual(output_exp, output)\n output_exp = \"AAA98667.1\"\n output = parse_output(hgt_results_fp=self.egid_output_hgt_fp,\n genbank_fp=self.genbank_input_fp,\n method=\"egid\")\n self.assertEqual(output_exp, output)\n output_exp = \"AAA98667.1\"\n output = parse_output(hgt_results_fp=self.genemark_output_hgt_fp,\n genbank_fp=self.genbank_input_fp,\n method=\"genemark\")\n self.assertEqual(output_exp, output)","def test_unusual_misc():\n doc = CoNLL.conll2doc(input_str=RUSSIAN_SAMPLE)\n sentences = \"{:C}\".format(doc).split(\"\\n\\n\")\n assert len(sentences) == 2\n sentence = sentences[0].split(\"\\n\")\n assert len(sentence) == 14\n\n for word in sentence:\n pieces = word.split(\"\\t\")\n assert len(pieces) == 1 or len(pieces) == 10\n if len(pieces) == 10:\n assert all(piece for piece in pieces)","def testSorting(self):\n if self.sorting in tools.SORTINGS:\n self.assertEqual(\n self.sorting,\n self.config.sorting\n )\n else:\n self.assertNotEqual(\n self.sorting,\n self.config.sorting\n )\n self.assertEqual(\n tools.SORTING_DEFAULT,\n self.config.sorting\n )","def test_insertSort3(self):\n\t\tsortObj=insertSort()\n\t\tself.assertEqual(sortObj.run_sort(self.test_3[0]),self.test_3[1])","def test_bug1591450(self):\n input = \"\"\"Testing <i>markup</i> and {y:i}so-forth...leading dots and trail--- well, you get-the-point. Also check numbers: 999 1,000 12:00 .45. Done?\"\"\"\n output = [\n (\"Testing\", 0), (\"i\", 9), (\"markup\", 11), (\"i\", 19), (\"and\", 22),\n (\"y\", 27), (\"i\", 29), (\"so\", 31), (\"forth\", 34), (\"leading\", 42),\n (\"dots\", 50), (\"and\", 55), (\"trail\", 59), (\"well\", 68),\n (\"you\", 74), (\"get\", 78), (\"the\", 82), (\"point\", 86),\n (\"Also\", 93), (\"check\", 98), (\"numbers\", 104), (\"Done\", 134),\n ]\n for (itmO, itmV) in zip(output, tokenize_en(input)):\n self.assertEqual(itmO, itmV)","def test_insertion(self):\n integers = insertion_sort(self.actual)\n self.assertEqual(self.expected, integers)","def test_insertSort2(self):\n\t\tsortObj=insertSort()\n\t\tself.assertNotEqual(sortObj.run_sort(self.test_2[0]),self.test_2[1])","def test_is_bip69_with_properly_sorted_inputs_and_outputs(self):\n self.assertTrue(bip69.is_bip69(self.bip69_synth))","def test_parse(test, result, capsys):\n print(calc.parse(test))\n out, err = capsys.readouterr()\n print(err)\n assert out == result","def test_parser(test_input, expected):\n tokens = list(sp.tokenize(test_input))\n assert tokens == expected","def test_ip_lists_get_command_human_readable(ip_lists_success, ip_lists_success_hr):\n hr_output = prepare_ip_lists_get_output(ip_lists_success)\n assert hr_output == ip_lists_success_hr","def issortorder(token):\n\n # Token is a sort order operator\n return token and token.lower() in Token.SORT_ORDER","def test_analytical_vs_numerical():\n pass","def test_make_tool_plugin_parse_valid():\n mtp = setup_make_tool_plugin()\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\n 'valid_package'))\n output = \"valid_package/hello.c:7:3: error: expected ; before return\"\n issues = mtp.parse_output(package, output)\n assert len(issues) == 1\n assert issues[0].filename == 'valid_package/hello.c'\n assert issues[0].line_number == '7'\n assert issues[0].severity == 5\n assert issues[0].message == \"expected ; before return\"","def test_sort_array(self):\r\n self.assertEqual(sort_array([6, 4, 9, 10]), [4, 6, 9, 10])","def test_sanity(self):\n # First we parse the sample data.\n parse.parse_reviews()\n parsed_output = utils.open_json(\n \"processed-data/sample-mint-parsed-integrated-review.json\"\n )\n expected_parsed_output = [\n {\n \"app\": \"sample-mint\",\n \"timestamp\": \"2020/03/15 14:13:17\",\n \"message\": \"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still cant add all of my financial institutions so my budget is kind of skewed. But other that I can say Im more aware of my spending\",\n \"channel-type\": \"appstore\",\n \"properties\": {\n \"updated\": \"2020-03-15 14:13:17\",\n \"rating\": 5,\n \"version\": \"7.1.0\",\n \"content\": \"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still can\\u00e2\\u20ac\\u2122t add all of my financial institutions so my budget is kind of skewed. But other that I can say I\\u00e2\\u20ac\\u2122m more aware of my spending\",\n },\n \"hash-id\": \"bd488c4c04431bdd8fb7ddb5dcf84d7a8b0479e2\",\n }\n ]\n self.assertEqual(parsed_output, expected_parsed_output)\n\n # We run the algorithms on that data\n algo.run_algo()\n processed_output = utils.open_json(\n \"processed-data/sample-mint-processed-integrated-review.json\"\n )\n expected_processed_output = [\n {\n \"app\": \"sample-mint\",\n \"timestamp\": \"2020/03/15 14:13:17\",\n \"message\": \"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still cant add all of my financial institutions so my budget is kind of skewed. But other that I can say Im more aware of my spending\",\n \"channel-type\": \"appstore\",\n \"properties\": {\n \"updated\": \"2020-03-15 14:13:17\",\n \"rating\": 5,\n \"version\": \"7.1.0\",\n \"content\": \"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still can\\u00e2\\u20ac\\u2122t add all of my financial institutions so my budget is kind of skewed. But other that I can say I\\u00e2\\u20ac\\u2122m more aware of my spending\",\n },\n \"hash-id\": \"bd488c4c04431bdd8fb7ddb5dcf84d7a8b0479e2\",\n \"derived-insight\": {\n \"sentiment\": {\n \"neg\": 0.0,\n \"neu\": 0.928,\n \"pos\": 0.072,\n \"compound\": 0.4767,\n },\n \"extra-properties\": {\n \"category-scores\": {\n \"User Experience\": 0,\n \"sign-in/sign-up\": 0,\n \"Notification\": 0,\n \"Application\": 1,\n \"ads\": 0,\n },\n \"bug-feature\": \"feature\",\n },\n \"category\": \"Application\",\n },\n }\n ]\n self.assertEqual(processed_output, expected_processed_output)","def main(iterator):\n\n entries = OrderedDict()\n for line in iterator:\n\n if \"START\" in line:\n entries.update({\"start_time\":int(re.search(r'\\d+', line).group())})\n if \"STOP\" in line:\n entries.update({\"end_time\":int(re.search(r'\\d+', line).group())})\n if \"NUMERIC SORT\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"numeric_sort\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n entries.update({\"numeric_sort_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"numeric_sort_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"numeric_sort_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"numeric_sort_num_arrs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"numeric_sort_arr_size\":int(re.search(r'\\d+', line).group())})\n\n if \"STRING SORT\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"string_sort\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_num_arrs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_arr_size\":int(re.search(r'\\d+', line).group())})\n\n if \"STRING SORT\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"string_sort\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_num_arrs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"string_sort_arr_size\":int(re.search(r'\\d+', line).group())})\n\n if \"BITFIELD\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"bitfield\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"bitfield_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"bitfield_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"bitfield_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"bitfield_ops_arr_size\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"bitfield_arr_size\":int(re.search(r'\\d+', line).group())})\n\n if \"FP EMULATION\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"fp_emul\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"fp_emul_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"fp_emul_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"fp_emul_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"fp_emul_num_loops\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"fp_emul_arr_size\":int(re.search(r'\\d+', line).group())})\n\n if \"FOURIER\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"fourier\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"fourier_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"fourier_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"fourier_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"fourier_num_coef\":int(re.search(r'\\d+', line).group())})\n\n if \"ASSIGNMENT\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"assignment\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n entries.update({\"assignment_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"assignment_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"assignment_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"assignment_num_arrs\":int(re.search(r'\\d+', line).group())})\n\n if \"IDEA\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"idea\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"idea_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"idea_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"idea_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"idea_arr_size\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"idea_num_loops\":int(re.search(r'\\d+', line).group())})\n \n if \"HUFFMAN\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"huffman\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"huffman_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"huffman_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"huffman_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"huffman_arr_size\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"huffman_num_loops\":int(re.search(r'\\d+', line).group())})\n\n\n if \"NEURAL NET\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"nnet\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"nnet_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"nnet_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"nnet_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"nnet_num_loops\":int(re.search(r'\\d+', line).group())})\n\n if \"LU DECOMPOSITION\" in line and \"Done with\" not in line:\n #print(float(re.search(r'\\d+', line).group()))\n entries.update({\"lu_decomp\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"lu_decomp_abs_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"lu_decomp_rel_sdv\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"lu_decomp_num_runs\":int(re.search(r'\\d+', line).group())})\n line = next(iterator)\n \n entries.update({\"lu_decomp_num_arrs\":int(re.search(r'\\d+', line).group())})\n\n if \"libc\" in line and \"Baseline\" not in line and \"*\" not in line:\n line = next(iterator)\n \n entries.update({\"memory_index\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"integer_index\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n line = next(iterator)\n \n entries.update({\"float_index\":float(re.search(r\"[+-]?(\\d+(\\.\\d*)?|\\.\\d+)([eE][+-]?\\d+)?\", line).group())})\n\n #print(entries)\n return entries","def test_basic_parser_trace():","def test_bad_date():\n assert sort_memos(bad_record) == 'Error'","def test_traffic_analysis_human_readable(\n traffic_analysis_success, traffic_analysis_success_hr\n):\n resp = prepare_traffic_analysis_output(traffic_analysis_success)\n assert resp == traffic_analysis_success_hr","def _compare_output(standart_output, testing_output):\n standart_output_array = standart_output.splitlines()\n testing_output_array = testing_output.splitlines()\n standart_output_array_len = len(standart_output_array)\n testing_output_array_len = len(testing_output_array)\n\n if standart_output_array_len != testing_output_array_len:\n print 'there is different count of steps standart: ', standart_output_array_len, \\\n ' testing: ', testing_output_array_len\n return False\n\n for index in range(0, len(standart_output_array), 1):\n if standart_output_array[index] != testing_output_array[index]:\n print 'there is different results in same step for standsret: ', standart_output_array[index], \\\n ' and testing: ', testing_output_array[index]\n return False\n\n return True","def test_get_sort_info(self):\n ars = self.ar[2009][11]['day']\n self.assertEqual(ars.get_sort_info(), (31, 'key', True))","def test_invalid_args(self):\n self.assertEqual(dictsort([{}], \"._private\"), \"\")\n self.assertEqual(dictsort([{\"_private\": \"test\"}], \"_private\"), \"\")\n self.assertEqual(\n dictsort([{\"nested\": {\"_private\": \"test\"}}], \"nested._private\"), \"\"\n )","def test_sort_outputs_0a6a357e(self):\n outputs = bip69.get_outputs_from_rpc_json(self.tx_json_0a6a357e)\n bip69_outputs = bip69.sort_outputs(outputs)\n self.assertEqual(bip69_outputs[0], (('76a9144a5fba237213a062f6f57978f79'\n '6390bdcf8d01588ac'), 400057456))\n self.assertEqual(bip69_outputs[1], (('76a9145be32612930b8323add2212a4ec'\n '03c1562084f8488ac'), 40000000000))","def check_sort(self):\n if self.list == []:\n return True\n seg_iter = iter(self.list)\n last = next(seg_iter)\n for segment in seg_iter:\n if last > segment:\n raise Exception('non trié')\n last = segment\n return True","def pass_test(sort_func, arr):\n sort_array = sorted(arr[:])\n # Print accordingly\n if sort_func(arr[:]) == sort_array:\n print(\"Test Passed\")\n else:\n print(\"Error: Test not passed\")","def check_input_matches_expected_output(in_, out):\n ...","def test_merge(self):\n integers = merge_sort(self.actual)\n self.assertEqual(self.expected, integers)","def test_rust_code_analysis_tokei_c() -> None:\n\n ret_value = compare(\n \"rust-code-analysis\",\n \"tokei\",\n [\"-g\", \"-f\"],\n [\"SLOC\", \"PLOC\", \"CLOC\", \"BLANK\"],\n \"C\",\n \"bubble_sort.c\",\n )\n\n assert ret_value == 0","def test_isort_tool_plugin_scan_calledprocesserror(mock_subprocess_check_output):\n mock_subprocess_check_output.side_effect = subprocess.CalledProcessError(\n 0, \"\", output=\"mocked error\"\n )\n itp = setup_isort_tool_plugin()\n package = Package(\n \"valid_package\", os.path.join(os.path.dirname(__file__), \"valid_package\")\n )\n package[\"python_src\"] = [\n os.path.join(os.path.dirname(__file__), \"valid_package\", \"sample.py\")\n ]\n issues = itp.scan(package, \"level\")\n assert len(issues) == 1","def test__parse_archived():\n for input_data, expected_output in (\n ({}, False),\n ({'thread_metadata': {}}, False),\n ({'thread_metadata': {'archived': False}}, False),\n ({'thread_metadata': {'archived': True}}, True),\n ):\n output = parse_archived(input_data)\n vampytest.assert_eq(output, expected_output)","def test_output(self):\n good_value_pairs = INT_VALUE_PAIRS\n for pair in good_value_pairs:\n output = to_cardinal_number(pair[0])\n self.assertEqual(output, pair[1],\n f\"{pair[0]} should be {pair[1]}, not {output}\")","def test_parse_sort(self):\n old_type = Sort('sort', [\n Relationship('student', PersonSchema(), None),\n Relationship('school', StudentSchema(), None)],\n Attribute('title', SchoolSchema(), None), '+')\n new_type = self.driver.parse(old_type)\n\n assert new_type.source == old_type\n assert old_type.relationships != new_type.relationships\n assert isinstance(new_type.relationships[0], Mapper)\n assert old_type.attribute != new_type.attribute\n assert isinstance(new_type.attribute, Column)\n assert old_type.direction == new_type.direction","def test_make_tool_plugin_parse_invalid():\n mtp = setup_make_tool_plugin()\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\n 'valid_package'))\n output = \"invalid text\"\n issues = mtp.parse_output(package, output)\n assert not issues","def test_sort_inputs_0a6a357e(self):\n inputs = bip69.get_inputs_from_rpc_json(self.tx_json_0a6a357e)\n bip69_inputs = bip69.sort_inputs(inputs)\n self.assertEqual(bip69_inputs[0],\n (('0e53ec5dfb2cb8a71fec32dc9a634a35b7e24799295ddd52782'\n '17822e0b31f57'), 0))\n self.assertEqual(bip69_inputs[10],\n (('7d037ceb2ee0dc03e82f17be7935d238b35d1deabf953a892a4'\n '507bfbeeb3ba4'), 1))","def check_correctness(self):\n\n with open(self.output_file, 'r') as output_file, open(self.gt_file, 'r') as gt_file:\n\n out_lines = output_file.readlines()\n gt_lines = gt_file.readlines()\n\n # Check for inequality\n if len(out_lines) != len(gt_lines):\n return 0\n\n # Check for inequality\n for i in range(len(out_lines)):\n out_split = out_lines[i].split()\n gt_split = gt_lines[i].split()\n\n if len(out_split) != len(gt_split):\n return 0\n\n for j in range(len(out_split)):\n # Treat slur and tie as equivalent\n if out_split[j] != gt_split[j] and\\\n ('slur' not in out_split[j] and 'tie' not in out_split[j]) and\\\n ('slur' not in gt_split[j] and 'tie' not in gt_split[j]):\n return 0\n\n return 1","def test_radix(self):\n integers = radix_sort(self.actual)\n self.assertEqual(self.expected, integers)","def test_asciitable_m_pure_ascii(self):\n input = '''\n+========+========+========+========+========+========+========+\n| type | tota | used | fr ee | shar | buff | avai |\n| | l | | | ed | _cac | labl |\n| | | | | | he | e |\n+========+========+========+========+========+========+========+\n| Mem | 3861 | 2228 | 3364 | 1183 | 2743 | 3389 |\n| | 332 | 20 | 176 | 2 | 36 | 588 |\n+--------+--------+--------+--------+--------+--------+--------+\n| | | | | | | |\n| | | | | test 2 | | |\n+--------+--------+--------+--------+--------+--------+--------+\n| last | last | last | ab cde | | | final |\n+========+========+========+========+========+========+========+\n '''\n expected = [\n {\n \"type\": \"Mem\",\n \"tota_l\": \"3861\\n332\",\n \"used\": \"2228\\n20\",\n \"fr_ee\": \"3364\\n176\",\n \"shar_ed\": \"1183\\n2\",\n \"buff_cac_he\": \"2743\\n36\",\n \"avai_labl_e\": \"3389\\n588\"\n },\n {\n \"type\": None,\n \"tota_l\": None,\n \"used\": None,\n \"fr_ee\": None,\n \"shar_ed\": \"test 2\",\n \"buff_cac_he\": None,\n \"avai_labl_e\": None\n },\n {\n \"type\": \"last\",\n \"tota_l\": \"last\",\n \"used\": \"last\",\n \"fr_ee\": \"ab cde\",\n \"shar_ed\": None,\n \"buff_cac_he\": None,\n \"avai_labl_e\": \"final\"\n }\n ]\n\n self.assertEqual(jc.parsers.asciitable_m.parse(input, quiet=True), expected)","def test_basic_parsers():","def validate(self):\n if len(self.independent_nodes) > 0:\n try:\n self.topological_sort()\n return True\n except ValueError:\n return False\n return False","def test_convert(self):\n out_text = StringIO()\n with redirect_stdout(out_text):\n main([\"-id\", indir, \"-od\", compdir, \"-if\", \"ttl\", \"-of\", \"json-ld\"])\n self.assertEqual(\"\"\"Total=3 Successful=3\"\"\", out_text.getvalue().strip())","def test_sorted_page_stream(self):\n self._test_insertion(Macros, 0)","def test_heap_sort(self):\n integers = heap_sort(self.actual)\n self.assertEqual(self.expected, integers)","def test_inflate_denoiser_output(self):\r\n actual = list(inflate_denoiser_output(\r\n parse_fasta(self.centroid_seqs1),\r\n parse_fasta(self.singleton_seqs1),\r\n self.denoiser_mapping1,\r\n parse_fasta(self.raw_seqs1)))\r\n expected = [(\"S1_0 FXX111 some comments\", \"TTTT\"),\r\n (\"S1_2 FXX113 some other comments\", \"TTTT\"),\r\n (\"S3_7 FXX117\", \"TTTT\"),\r\n (\"S2_1 FXX112 some comments\", \"TATT\"),\r\n (\"S3_5 FXX114\", \"TATT\"),\r\n (\"S3_6 FXX115\", \"TTGA\"),\r\n (\"S3_6 FXX116\", \"TAGA\")]\r\n self.assertEqual(actual, expected)","def main(argv):\r\n\terror_code = 0\r\n\tlist = [5, 9, 8, 2, 0, 4, 7, 6, 1, 7]\r\n\tprint('Unsorted list:')\r\n\tprint(list)\r\n\tlist = selection_sort(list)\r\n\tprint('Sorted list:')\r\n\tprint(list)\r\n\treturn error_code","def _fix_shortsort(self):\n test_dir = join_path(self.install_test_root, self.test_src_dir)\n filter_file(\"../src/\", \"\", join_path(test_dir, \"testshortsort.sh\"))","def test_print_result(capsys):\n assert \"\"\"Total 5 hands solved\nTotal 4 hands solved with hint\nTotal 4 hands failed to solve\"\"\" in hl.test_help_print_result(capsys)","def test_parse_results_error():\n error_result = [{\"error\": \"test\"}]\n assert [{\"title\": \"Error\",\n \"subtitle\": \"test\",\n \"valid\": False}] == parse_results(error_result)","def test_workloads_list_command_human_readable(\n workloads_list_success, workloads_list_success_hr\n):\n hr_output = prepare_workloads_list_output(workloads_list_success)\n assert hr_output == workloads_list_success_hr","def test_sort_success_return_sortedArray(self):\n\n # prepare\n unsortedArray = [12.0, 13.5, 1.0, 5.5,\n 9.0, 19.5, 12.0, 23.5, 5.0, 51.0]\n expectedResult = [1.0, 5.0, 5.5, 9.0,\n 12.0, 12.0, 13.5, 19.5, 23.5, 51.0]\n\n # execute\n actuatlResponse = PSPQuickSortProcess.sort(unsortedArray)\n\n # assert\n self.assertEqual(expectedResult, actuatlResponse)","def test_sort_natural(self):\n\n test_cases = [\n Case(\n description=\"lists of strings\",\n val=[\"b\", \"a\", \"C\", \"B\", \"A\"],\n args=[],\n kwargs={},\n expect=[\"a\", \"A\", \"b\", \"B\", \"C\"],\n ),\n Case(\n description=\"lists of strings with a None\",\n val=[\"b\", \"a\", None, \"C\", \"B\", \"A\"],\n args=[],\n kwargs={},\n expect=[None, \"a\", \"A\", \"b\", \"B\", \"C\"],\n ),\n Case(\n description=\"lists of objects with key\",\n val=[{\"title\": \"foo\"}, {\"title\": \"bar\"}, {\"title\": \"Baz\"}],\n args=[\"title\"],\n kwargs={},\n expect=[{\"title\": \"bar\"}, {\"title\": \"Baz\"}, {\"title\": \"foo\"}],\n ),\n Case(\n description=\"lists of objects with missing key\",\n val=[{\"title\": \"foo\"}, {\"title\": \"bar\"}, {\"heading\": \"Baz\"}],\n args=[\"title\"],\n kwargs={},\n expect=[{\"title\": \"bar\"}, {\"title\": \"foo\"}, {\"heading\": \"Baz\"}],\n ),\n Case(\n description=\"empty list\",\n val=[],\n args=[],\n kwargs={},\n expect=[],\n ),\n Case(\n description=\"too many arguments\",\n val=[{\"title\": \"foo\"}, {\"title\": \"bar\"}, {\"title\": \"Baz\"}],\n args=[\"title\", \"heading\"],\n kwargs={},\n expect=FilterArgumentError,\n ),\n Case(\n description=\"value not an array\",\n val=1234,\n args=[],\n kwargs={},\n expect=FilterValueError,\n ),\n Case(\n description=\"undefined left value\",\n val=self.env.undefined(\"test\"),\n args=[],\n kwargs={},\n expect=[],\n ),\n Case(\n description=\"undefined argument\",\n val=[{\"title\": \"foo\"}, {\"title\": \"bar\"}, {\"title\": \"Baz\"}],\n args=[self.env.undefined(\"test\")],\n kwargs={},\n expect=[{\"title\": \"foo\"}, {\"title\": \"bar\"}, {\"title\": \"Baz\"}],\n ),\n ]\n\n self._test(SortNatural, test_cases)","def test_asciitable_m_pure_ascii_extra_spaces(self):\n input = '''\n \n \n +========+========+========+========+========+========+========+\n | type | tota | used | fr ee | shar | buff | avai \n | | l | | | ed | _cac | labl \n | | | | | | he | e |\n +========+========+========+========+========+========+========+ \n | Mem | 3861 | 2228 | 3364 | 1183 | 2743 | 3389 |\n | | 332 | 20 | 176 | 2 | 36 | 588 |\n +--------+--------+--------+--------+--------+--------+--------+\n | | | | | | | |\n | | | | | test 2 | | | \n +--------+--------+--------+--------+--------+--------+--------+\n | last | last | last | ab cde | | | final \n +========+========+========+========+========+========+========+ \n \n \n '''\n expected = [\n {\n \"type\": \"Mem\",\n \"tota_l\": \"3861\\n332\",\n \"used\": \"2228\\n20\",\n \"fr_ee\": \"3364\\n176\",\n \"shar_ed\": \"1183\\n2\",\n \"buff_cac_he\": \"2743\\n36\",\n \"avai_labl_e\": \"3389\\n588\"\n },\n {\n \"type\": None,\n \"tota_l\": None,\n \"used\": None,\n \"fr_ee\": None,\n \"shar_ed\": \"test 2\",\n \"buff_cac_he\": None,\n \"avai_labl_e\": None\n },\n {\n \"type\": \"last\",\n \"tota_l\": \"last\",\n \"used\": \"last\",\n \"fr_ee\": \"ab cde\",\n \"shar_ed\": None,\n \"buff_cac_he\": None,\n \"avai_labl_e\": \"final\"\n }\n ]\n\n self.assertEqual(jc.parsers.asciitable_m.parse(input, quiet=True), expected)","def test_invalid_values(self):\n self.assertEqual(dictsort([1, 2, 3], \"age\"), \"\")\n self.assertEqual(dictsort(\"Hello!\", \"age\"), \"\")\n self.assertEqual(dictsort({\"a\": 1}, \"age\"), \"\")\n self.assertEqual(dictsort(1, \"age\"), \"\")","def test_update_enforcement_mode_command_failure_human_readable(\n enforcement_mode_failure_hr, enforcement_mode_failure_expected\n):\n resp = prepare_update_enforcement_mode_output(enforcement_mode_failure_expected)\n\n assert resp == enforcement_mode_failure_hr","def test_natsort_case_insensitive(self):\r\n\r\n # string with alpha and numerics sort correctly\r\n s = [\r\n 'sample1',\r\n 'sample2',\r\n 'sample11',\r\n 'sample12',\r\n 'SAmple1',\r\n 'Sample2']\r\n\r\n # expected values\r\n exp_natsort = ['SAmple1', 'Sample2', 'sample1', 'sample2', 'sample11',\r\n 'sample12']\r\n exp_natsort_case_insensitive = ['sample1', 'SAmple1', 'sample2',\r\n 'Sample2', 'sample11', 'sample12']\r\n\r\n # test natsort\r\n self.assertEqual(natsort(s), exp_natsort)\r\n # test natsort_case_insensitive\r\n self.assertEqual(natsort_case_insensitive(s),\r\n exp_natsort_case_insensitive)\r\n\r\n s.reverse()\r\n # test natsort\r\n self.assertEqual(natsort(s), exp_natsort)\r\n # test natsort_case_insensitive\r\n self.assertEqual(natsort(list('cbaA321')), list('123Aabc'))\r\n\r\n # strings with alpha only sort correctly\r\n self.assertEqual(natsort_case_insensitive(list('cdBa')), list('aBcd'))\r\n\r\n # string of ints sort correctly\r\n self.assertEqual(natsort_case_insensitive(['11', '2', '1', '0']),\r\n ['0', '1', '2', '11'])\r\n\r\n # strings of floats sort correctly\r\n self.assertEqual(natsort_case_insensitive(['1.11', '1.12', '1.00',\r\n '0.009']), ['0.009', '1.00',\r\n '1.11', '1.12'])\r\n\r\n # string of ints sort correctly\r\n self.assertEqual(natsort_case_insensitive([('11', 'A'), ('2', 'B'),\r\n ('1', 'C'), ('0', 'D')]),\r\n [('0', 'D'), ('1', 'C'),\r\n ('2', 'B'), ('11', 'A')])","def test_parse_output_error(self):\n self.assertRaises(ValueError,\n parse_output,\n hgt_results_fp=self.consel_output_hgt_fp,\n method=\"Consel\")","def test_correct_parsing(self):\n test_length = random.randint(0,100)\n test_string = \"#\\t{0}\".format(\"\\t\".join(map(str, xrange(test_length))))\n expected = [extract_mock(s) for s in xrange(test_length)]\n computed = self.parser.parse_header(test_string, extract_mock)\n self.assertEquals(expected, computed)","def test_parser_all(self):\n args = [\"directory\", \"-n\", \"-f\", \"-r\"]\n parser = setup_parser()\n output = parser.parse_args(args)\n self.assertEqual(output.directory, \"directory\")\n self.assertFalse(output.colorize)\n self.assertTrue(output.fancy)\n self.assertTrue(output.reverse)","def main(argv):\r\n\terror_code = 0\r\n\tlist = [5, 9, 8, 2, 0, 4, 7, 6, 1, 7]\r\n\tprint('Unsorted list:')\r\n\tprint(list)\r\n\tlist = quick_sort(list)\r\n\tprint('Sorted list:')\r\n\tprint(list)\r\n\treturn error_code","def check(self, input, ast):\n assert False # Must be redefined","def test_reversed_version_sorting(self):\n assert natsort(['1', '5', '10', '50'], reverse=True) == ['50', '10', '5', '1']","def check_cot_output(self, expected):\n sys.stdout = StringIO.StringIO()\n output = None\n try:\n self.instance.run()\n except (TypeError, ValueError, SyntaxError, LookupError):\n self.fail(traceback.format_exc())\n finally:\n output = sys.stdout.getvalue()\n sys.stdout = sys.__stdout__\n self.maxDiff = None\n self.assertMultiLineEqual(expected.strip(), output.strip())","def test_sam_parser_success(self):\n # self.cleanup = False\n\n sam_file = '%s/../human_100.sam' % GOLDEN_DIR\n count = 0\n\n with open(sam_file, 'r') as sam:\n for line in sam:\n # Skip tags\n if line[0] == '@':\n continue\n\n alignment = parse_sam_line(line)\n\n # Verify that the type conversions are all correct\n types = {}\n for entry in sam_format():\n types[entry['name']] = entry['type']\n\n for field in alignment:\n self.assertIs(type(alignment[field]), types[field])\n\n count = count + 1\n\n self.assertEqual(count, 100)"],"string":"[\n \"def test_isort(self):\\n chdir(REPO_ROOT)\\n cmd = [\\\"isort\\\", \\\"-df\\\", \\\"-rc\\\", \\\"-c\\\", *SRC_DIRS]\\n print(\\\"running:\\\", \\\" \\\".join(str(part) for part in cmd))\\n proc = run(cmd, capture_output=True)\\n assert proc.returncode == 0, f\\\"isort issues:\\\\n{proc.stdout.decode('utf-8')}\\\"\",\n \"def isort(context):\\n exec_cmd = \\\"isort . --check --diff\\\"\\n run_cmd(context, exec_cmd)\",\n \"def isort_check(ctx):\\n ctx.run(f\\\"{VENV_PREFIX} isort --atomic --check-only .\\\")\",\n \"def test_shell(self):\\n integers = shell_sort(self.actual)\\n self.assertEqual(self.expected, integers)\",\n \"def isort(command, checkonly=False):\\n print(\\n \\\"\\\"\\\"\\nRunning isort the Python code import sorter\\n===========================================\\n\\\"\\\"\\\"\\n )\\n cmd = \\\"isort --check-only --diff .\\\" if checkonly else \\\"isort .\\\"\\n command.run(cmd, echo=True, pty=POSIX)\",\n \"def test_optimize_parse():\\n assert True\",\n \"def test_python_3_compatibility(self):\\n assert natsort(['1', 'a']) == ['1', 'a']\",\n \"def test_parse(self): \\n\\n results = self.parser.parse()\\n self.assertEqual(results, test_case_data['parse_output'])\",\n \"def test_version_sorting(self):\\n assert natsort(['1', '5', '10', '50']) == ['1', '5', '10', '50']\",\n \"def test(self):\\n test_dir = join_path(self.test_suite.current_test_cache_dir, self.test_src_dir)\\n self.run_test(\\n \\\"sh\\\",\\n [\\\"testshortsort.sh\\\"],\\n expected=\\\"Alignments sorted by coordinate.\\\",\\n purpose=\\\"test: checking alignments\\\",\\n work_dir=test_dir,\\n )\",\n \"def test_sort_strings(self):\\n chdir(REPO_ROOT)\\n cmd = [\\\"python\\\", \\\"scripts/sort_strings.py\\\", \\\"--check\\\"]\\n print(\\\"running:\\\", \\\" \\\".join(str(part) for part in cmd))\\n proc = run(cmd, capture_output=True)\\n assert proc.returncode == 0, (\\n f\\\"sort strings issues:\\\\n{proc.stdout.decode('utf-8')}\\\\n\\\\n\\\"\\n \\\"Please run ./scripts/sort_string.py to resolve this issue.\\\"\\n )\",\n \"def test_rust_code_analysis_tokei_Rust() -> None:\\n\\n ret_value = compare(\\n \\\"rust-code-analysis\\\",\\n \\\"tokei\\\",\\n [\\\"-g\\\", \\\"-f\\\"],\\n [\\\"SLOC\\\", \\\"PLOC\\\", \\\"CLOC\\\", \\\"BLANK\\\"],\\n \\\"Rust\\\",\\n \\\"bubble_sort.rs\\\",\\n )\\n\\n assert ret_value == 0\",\n \"def test_isort_tool_plugin_found():\\n if sys.version_info.major == 3 and sys.version_info.minor < 6:\\n pytest.skip(\\\"isort is only available for Python 3.6+, unable to test\\\")\\n manager = PluginManager()\\n # Get the path to statick_tool/__init__.py, get the directory part, and\\n # add 'plugins' to that to get the standard plugins dir\\n manager.setPluginPlaces(\\n [os.path.join(os.path.dirname(statick_tool.__file__), \\\"plugins\\\")]\\n )\\n manager.setCategoriesFilter(\\n {\\n \\\"Tool\\\": ToolPlugin,\\n }\\n )\\n manager.collectPlugins()\\n # Verify that a plugin's get_name() function returns \\\"isort\\\"\\n assert any(\\n plugin_info.plugin_object.get_name() == \\\"isort\\\"\\n for plugin_info in manager.getPluginsOfCategory(\\\"Tool\\\")\\n )\\n # While we're at it, verify that a plugin is named Isort Tool Plugin\\n assert any(\\n plugin_info.name == \\\"Isort Tool Plugin\\\"\\n for plugin_info in manager.getPluginsOfCategory(\\\"Tool\\\")\\n )\",\n \"def test_compare(self):\\n parser = parse_args(['-g', '10', '-s', 'bubble', '-c'])\\n self.assertTrue(parser.compare)\\n self.assertEqual(True, parser.compare)\\n\\n parser = parse_args(['-g', '10', '-s', 'bubble'])\\n self.assertEqual(False, parser.compare)\",\n \"def test_natsort(self):\\r\\n # string with alpha and numerics sort correctly\\r\\n s = 'sample1 sample2 sample11 sample12'.split()\\r\\n self.assertEqual(natsort(s),\\r\\n 'sample1 sample2 sample11 sample12'.split())\\r\\n s.reverse()\\r\\n self.assertEqual(natsort(s),\\r\\n 'sample1 sample2 sample11 sample12'.split())\\r\\n self.assertEqual(natsort(list('cba321')), list('123abc'))\\r\\n\\r\\n # strings with alpha only sort correctly\\r\\n self.assertEqual(natsort(list('cdba')), list('abcd'))\\r\\n\\r\\n # string of ints sort correctly\\r\\n self.assertEqual(natsort(['11', '2', '1', '0']),\\r\\n ['0', '1', '2', '11'])\\r\\n\\r\\n # strings of floats sort correctly\\r\\n self.assertEqual(natsort(['1.11', '1.12', '1.00', '0.009']),\\r\\n ['0.009', '1.00', '1.11', '1.12'])\\r\\n\\r\\n # string of ints sort correctly\\r\\n self.assertEqual(\\r\\n natsort([('11', 'A'), ('2', 'B'), ('1', 'C'), ('0', 'D')]),\\r\\n [('0', 'D'), ('1', 'C'), ('2', 'B'), ('11', 'A')])\",\n \"def test_isort_tool_plugin_scan_oserror(mock_subprocess_check_output):\\n mock_subprocess_check_output.side_effect = OSError(\\\"mocked error\\\")\\n itp = setup_isort_tool_plugin()\\n package = Package(\\n \\\"valid_package\\\", os.path.join(os.path.dirname(__file__), \\\"valid_package\\\")\\n )\\n package[\\\"python_src\\\"] = [\\n os.path.join(os.path.dirname(__file__), \\\"valid_package\\\", \\\"sample.py\\\")\\n ]\\n issues = itp.scan(package, \\\"level\\\")\\n assert not issues\",\n \"def test_parse_results_valid():\\n valid_result = [{\\n \\\"url\\\": \\\"https://docs.ansible.com/ansible/.../test.html\\\",\\n \\\"sections\\\": [\\\"test\\\"],\\n \\\"title\\\": \\\"title – subtitle — Ansible Documentation\\\",\\n \\\"body\\\": \\\"Long body containing flavor text\\\",\\n \\\"_index\\\": \\\"5693d1e68db231f24d000003\\\",\\n \\\"_type\\\": \\\"5693d1e68db231f24d000004\\\",\\n \\\"_score\\\": 1,\\n \\\"_version\\\": \\\"\\\",\\n \\\"_explanation\\\": \\\"\\\",\\n \\\"sort\\\": \\\"\\\",\\n \\\"id\\\": \\\"test\\\",\\n \\\"highlight\\\": {}\\n }]\\n assert [{\\\"title\\\": \\\"title\\\",\\n \\\"subtitle\\\": \\\"subtitle\\\",\\n \\\"arg\\\": \\\"https://docs.ansible.com/ansible/.../test.html\\\",\\n \\\"valid\\\": True}] == parse_results(valid_result)\",\n \"def _verifyParsing(self):\\n for attrname, attr in self.__dict__.items():\\n if attrname.endswith('records') and iterable(attr):\\n ts = get_record_timestamps(attr)\\n if not issorted(ts):\\n print('Sorting %s' % attrname)\\n if type(attr) == list:\\n attr = list(np.asarray(attr)[ts.argsort()])\\n else:\\n attr = attr[ts.argsort()]\\n ts = get_record_timestamps(attr)\\n assert issorted(ts)\\n self.__dict__[attrname] = attr # update\",\n \"def test_invalid_sort():\\n svl_string = \\\"\\\"\\\"\\n DATASETS bigfoot \\\"bigfoot.csv\\\"\\n LINE bigfoot X date SORT OOPS Y classification COUNT\\n \\\"\\\"\\\"\\n\\n with pytest.raises(SvlSyntaxError):\\n parse_svl(svl_string)\",\n \"def _is_lexsorted(self) -> bool:\\n return self._lexsort_depth == self.nlevels\",\n \"def test_sort_otu_table(self):\\r\\n\\r\\n actual = sort_otu_table(parse_biom_table_str(self.otu_table1),\\r\\n ['NA', 'Key', 'Fing'])\\r\\n expected = parse_biom_table_str(self.age_sorted_otu_table1)\\r\\n self.assertEqual(actual, expected)\",\n \"def try_run_isort_formatting(path_to_protocol_package: str) -> None:\\n subprocess.run( # nosec\\n [sys.executable, \\\"-m\\\", \\\"isort\\\", *ISORT_CLI_ARGS, path_to_protocol_package],\\n check=True,\\n )\",\n \"def test_bogo(self):\\n integers = bogo_sort(self.actual)\\n self.assertEqual(self.expected, integers)\",\n \"def step020():\\n logger.logMessage('Begin: Sorting records')\\n sortCommand = 'sort {0} -t \\\\';\\\\' --key 2 -o {1}'.format(candidatesFile,sortedCandidatesFile) \\n rc = os.system(sortCommand)\\n if rc != 0:\\n raise Exception('Error returned by sort program: {0:d}'.format(rc))\\n logger.logMessage('End : Sorting records')\",\n \"def test_parse_devide(self):\\n self.assertEqual(parse_input.parse([\\\"8\\\", \\\"/\\\", \\\"4\\\"]), 2)\",\n \"def test_good_input():\\n\\n out_file = 'unclustered.fa'\\n if os.path.isfile(out_file):\\n os.remove(out_file)\\n\\n try:\\n rv, out = getstatusoutput(f'{prg} -c {cdhit} -p {proteins}')\\n assert rv == 0\\n assert out == ('Wrote 309 of 220,520 unclustered '\\n 'proteins to \\\"unclustered.fa\\\"')\\n\\n assert os.path.isfile(out_file)\\n\\n seqs = list(SeqIO.parse(out_file, 'fasta'))\\n\\n assert len(seqs) == 309\\n finally:\\n if os.path.isfile(out_file):\\n os.remove(out_file)\",\n \"def test_output(self):\\n good_value_pairs = STR_VALUE_PAIRS\\n for pair in good_value_pairs:\\n output = to_cardinal_trio(pair[0])\\n self.assertEqual(output, pair[1],\\n f\\\"{pair[0]} should be {pair[1]}, not {output}\\\")\",\n \"def test_advance_ast_avaliable():\\n assert _test_advanced_ast_presence()\",\n \"def check_output(output, expected_output):\\n o = copy.deepcopy(output) # so that we don't mutate input\\n e = copy.deepcopy(expected_output) # so that we don't mutate input\\n \\n o.sort()\\n e.sort()\\n return o == e\",\n \"def test_12(self):\\n num_elements = np.random.randint(1, 11)\\n\\n input_array = np.random.normal(size=num_elements)\\n\\n # We first check the sorting implementation.\\n py = sorted(input_array)\\n f90 = fort_debug.wrapper_sorted(input_array, num_elements)\\n assert_equal(py, f90)\\n\\n params_spec, options_spec = generate_random_model()\\n respy_obj = RespyCls(params_spec, options_spec)\\n\\n edu_spec, optim_paras, num_types = dist_class_attributes(\\n respy_obj, \\\"edu_spec\\\", \\\"optim_paras\\\", \\\"num_types\\\"\\n )\\n\\n args = (edu_spec[\\\"start\\\"], edu_spec[\\\"share\\\"], edu_spec[\\\"max\\\"])\\n f90 = fort_debug.wrapper_sort_edu_spec(*args)\\n py = sort_edu_spec(edu_spec)\\n for i, label in enumerate([\\\"start\\\", \\\"share\\\", \\\"max\\\"]):\\n assert_equal(py[label], f90[i])\\n\\n py = sort_type_info(optim_paras, num_types)\\n f90 = fort_debug.wrapper_sort_type_info(optim_paras[\\\"type_shares\\\"], num_types)\\n for i, label in enumerate([\\\"order\\\", \\\"shares\\\"]):\\n assert_equal(py[label], f90[i])\",\n \"def test_probabilistic_parsers():\",\n \"def test_dotted_sorting(self):\\n assert natsort(['1.5', '1.0']) == ['1.0', '1.5']\",\n \"def test_quick(self):\\n integers = quick_sort(self.actual)\\n self.assertEqual(self.expected, integers)\",\n \"def test_insertSort(self):\\n\\t\\tsortObj=insertSort()\\n\\t\\tself.assertEqual(sortObj.run_sort(self.test_1[0]),self.test_1[1])\",\n \"def test_parser(self):\\n parser = hhsuite.FastaParser()\\n results = parser.run(self.pipeline)\\n self.assertEqual(\\n results[\\\"templates\\\"][0][\\\"sequence_alignments\\\"], {\\n \\\"sequence\\\": \\\"---A-A-----\\\",\\n \\\"query\\\": \\\"XXAB-CDEFXX\\\"\\n })\\n\\n self.assertEqual(\\n results[\\\"templates\\\"][1][\\\"sequence_alignments\\\"], {\\n \\\"foo\\\": \\\"---A------\\\",\\n \\\"sequence\\\": \\\"--GG------\\\",\\n \\\"query\\\": \\\"XXABCDEFXX\\\"\\n })\",\n \"def test_simple_parse(self):\\n pass\",\n \"def test_MinimalSamParser(self):\\r\\n actual = list(MinimalSamParser(self.sam_data1))\\r\\n expected = self.sam1_expected\\r\\n self.assertEqual(actual, expected)\",\n \"def test_parse_output(self):\\n output_exp = [\\\"0.99\\\", \\\"0.01\\\"]\\n output = parse_output(hgt_results_fp=self.consel_output_hgt_fp,\\n method=\\\"consel\\\")\\n self.assertEqual(output_exp, output)\\n output_exp = \\\"1\\\"\\n output = parse_output(hgt_results_fp=self.riatahgt_output_hgt_fp,\\n method=\\\"riata-hgt\\\")\\n self.assertEqual(output_exp, output)\\n output_exp = (\\\"WP_011672248.1\\\\t372461\\\\tBuchnera aphidicola\\\\tProteobac\\\"\\n \\\"teria;Gammaproteobacteria;Enterobacteriales;Enterobact\\\"\\n \\\"eriaceae;Buchnera;Buchnera aphidicola\\\\t37.5\\\\t99.14\\\\nWP\\\"\\n \\\"_045117937.1\\\\t580331\\\\tThermoanaerobacter italicus\\\\tFir\\\"\\n \\\"micutes;Clostridia;Thermoanaerobacterales;Thermoanaero\\\"\\n \\\"bacteraceae;Thermoanaerobacter;Thermoanaerobacter ital\\\"\\n \\\"icus\\\\t42.6\\\\t93.84\\\")\\n output = parse_output(hgt_results_fp=self.hgtector_output_hgt_fp,\\n method=\\\"hgtector\\\")\\n self.assertEqual(output_exp, output)\\n output_exp = (\\\"G2311_SE001,\\\\tgi|557307555|ref|YP_008766893.1|\\\\t140749\\\"\\n \\\"3\\\\tShigella phage SfIV\\\\tViruses;Caudovirales;Myovirida\\\"\\n \\\"e\\\\t67.4\\\\t100\\\\t0.002\\\\nG1250_SE001,\\\\tgi|9630468|ref|NP_0\\\"\\n \\\"46899.1|\\\\t40631\\\\tEnterobacteria phage N15\\\\tViruses;Cau\\\"\\n \\\"dovirales;Siphoviridae;N15likevirus\\\\t79.4\\\\t100\\\\t0.002\\\\n\\\"\\n \\\"G1252_SE001,\\\\tgi|428782382|ref|YP_007112139.1|\\\\t114714\\\"\\n \\\"4\\\\tEnterobacteria phage HK225\\\\tViruses;Caudovirales;Si\\\"\\n \\\"phoviridae;Lambdalikevirus\\\\t88.2\\\\t100\\\\t0.002\\\\nG1251_SE\\\"\\n \\\"001,\\\\tgi|428782381|ref|YP_007112138.1|\\\\t1147144\\\\tEnter\\\"\\n \\\"obacteria phage HK225\\\\tViruses;Caudovirales;Siphovirid\\\"\\n \\\"ae;Lambdalikevirus\\\\t94.9\\\\t100\\\\t0.002\\\")\\n output = parse_output(hgt_results_fp=self.darkhorse_output_hgt_fp,\\n method=\\\"darkhorse\\\")\\n self.assertEqual(output_exp, output)\\n output_exp = \\\"AAA98667.1\\\"\\n output = parse_output(hgt_results_fp=self.egid_output_hgt_fp,\\n genbank_fp=self.genbank_input_fp,\\n method=\\\"egid\\\")\\n self.assertEqual(output_exp, output)\\n output_exp = \\\"AAA98667.1\\\"\\n output = parse_output(hgt_results_fp=self.genemark_output_hgt_fp,\\n genbank_fp=self.genbank_input_fp,\\n method=\\\"genemark\\\")\\n self.assertEqual(output_exp, output)\",\n \"def test_unusual_misc():\\n doc = CoNLL.conll2doc(input_str=RUSSIAN_SAMPLE)\\n sentences = \\\"{:C}\\\".format(doc).split(\\\"\\\\n\\\\n\\\")\\n assert len(sentences) == 2\\n sentence = sentences[0].split(\\\"\\\\n\\\")\\n assert len(sentence) == 14\\n\\n for word in sentence:\\n pieces = word.split(\\\"\\\\t\\\")\\n assert len(pieces) == 1 or len(pieces) == 10\\n if len(pieces) == 10:\\n assert all(piece for piece in pieces)\",\n \"def testSorting(self):\\n if self.sorting in tools.SORTINGS:\\n self.assertEqual(\\n self.sorting,\\n self.config.sorting\\n )\\n else:\\n self.assertNotEqual(\\n self.sorting,\\n self.config.sorting\\n )\\n self.assertEqual(\\n tools.SORTING_DEFAULT,\\n self.config.sorting\\n )\",\n \"def test_insertSort3(self):\\n\\t\\tsortObj=insertSort()\\n\\t\\tself.assertEqual(sortObj.run_sort(self.test_3[0]),self.test_3[1])\",\n \"def test_bug1591450(self):\\n input = \\\"\\\"\\\"Testing <i>markup</i> and {y:i}so-forth...leading dots and trail--- well, you get-the-point. Also check numbers: 999 1,000 12:00 .45. Done?\\\"\\\"\\\"\\n output = [\\n (\\\"Testing\\\", 0), (\\\"i\\\", 9), (\\\"markup\\\", 11), (\\\"i\\\", 19), (\\\"and\\\", 22),\\n (\\\"y\\\", 27), (\\\"i\\\", 29), (\\\"so\\\", 31), (\\\"forth\\\", 34), (\\\"leading\\\", 42),\\n (\\\"dots\\\", 50), (\\\"and\\\", 55), (\\\"trail\\\", 59), (\\\"well\\\", 68),\\n (\\\"you\\\", 74), (\\\"get\\\", 78), (\\\"the\\\", 82), (\\\"point\\\", 86),\\n (\\\"Also\\\", 93), (\\\"check\\\", 98), (\\\"numbers\\\", 104), (\\\"Done\\\", 134),\\n ]\\n for (itmO, itmV) in zip(output, tokenize_en(input)):\\n self.assertEqual(itmO, itmV)\",\n \"def test_insertion(self):\\n integers = insertion_sort(self.actual)\\n self.assertEqual(self.expected, integers)\",\n \"def test_insertSort2(self):\\n\\t\\tsortObj=insertSort()\\n\\t\\tself.assertNotEqual(sortObj.run_sort(self.test_2[0]),self.test_2[1])\",\n \"def test_is_bip69_with_properly_sorted_inputs_and_outputs(self):\\n self.assertTrue(bip69.is_bip69(self.bip69_synth))\",\n \"def test_parse(test, result, capsys):\\n print(calc.parse(test))\\n out, err = capsys.readouterr()\\n print(err)\\n assert out == result\",\n \"def test_parser(test_input, expected):\\n tokens = list(sp.tokenize(test_input))\\n assert tokens == expected\",\n \"def test_ip_lists_get_command_human_readable(ip_lists_success, ip_lists_success_hr):\\n hr_output = prepare_ip_lists_get_output(ip_lists_success)\\n assert hr_output == ip_lists_success_hr\",\n \"def issortorder(token):\\n\\n # Token is a sort order operator\\n return token and token.lower() in Token.SORT_ORDER\",\n \"def test_analytical_vs_numerical():\\n pass\",\n \"def test_make_tool_plugin_parse_valid():\\n mtp = setup_make_tool_plugin()\\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\\n 'valid_package'))\\n output = \\\"valid_package/hello.c:7:3: error: expected ; before return\\\"\\n issues = mtp.parse_output(package, output)\\n assert len(issues) == 1\\n assert issues[0].filename == 'valid_package/hello.c'\\n assert issues[0].line_number == '7'\\n assert issues[0].severity == 5\\n assert issues[0].message == \\\"expected ; before return\\\"\",\n \"def test_sort_array(self):\\r\\n self.assertEqual(sort_array([6, 4, 9, 10]), [4, 6, 9, 10])\",\n \"def test_sanity(self):\\n # First we parse the sample data.\\n parse.parse_reviews()\\n parsed_output = utils.open_json(\\n \\\"processed-data/sample-mint-parsed-integrated-review.json\\\"\\n )\\n expected_parsed_output = [\\n {\\n \\\"app\\\": \\\"sample-mint\\\",\\n \\\"timestamp\\\": \\\"2020/03/15 14:13:17\\\",\\n \\\"message\\\": \\\"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still cant add all of my financial institutions so my budget is kind of skewed. But other that I can say Im more aware of my spending\\\",\\n \\\"channel-type\\\": \\\"appstore\\\",\\n \\\"properties\\\": {\\n \\\"updated\\\": \\\"2020-03-15 14:13:17\\\",\\n \\\"rating\\\": 5,\\n \\\"version\\\": \\\"7.1.0\\\",\\n \\\"content\\\": \\\"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still can\\\\u00e2\\\\u20ac\\\\u2122t add all of my financial institutions so my budget is kind of skewed. But other that I can say I\\\\u00e2\\\\u20ac\\\\u2122m more aware of my spending\\\",\\n },\\n \\\"hash-id\\\": \\\"bd488c4c04431bdd8fb7ddb5dcf84d7a8b0479e2\\\",\\n }\\n ]\\n self.assertEqual(parsed_output, expected_parsed_output)\\n\\n # We run the algorithms on that data\\n algo.run_algo()\\n processed_output = utils.open_json(\\n \\\"processed-data/sample-mint-processed-integrated-review.json\\\"\\n )\\n expected_processed_output = [\\n {\\n \\\"app\\\": \\\"sample-mint\\\",\\n \\\"timestamp\\\": \\\"2020/03/15 14:13:17\\\",\\n \\\"message\\\": \\\"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still cant add all of my financial institutions so my budget is kind of skewed. But other that I can say Im more aware of my spending\\\",\\n \\\"channel-type\\\": \\\"appstore\\\",\\n \\\"properties\\\": {\\n \\\"updated\\\": \\\"2020-03-15 14:13:17\\\",\\n \\\"rating\\\": 5,\\n \\\"version\\\": \\\"7.1.0\\\",\\n \\\"content\\\": \\\"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still can\\\\u00e2\\\\u20ac\\\\u2122t add all of my financial institutions so my budget is kind of skewed. But other that I can say I\\\\u00e2\\\\u20ac\\\\u2122m more aware of my spending\\\",\\n },\\n \\\"hash-id\\\": \\\"bd488c4c04431bdd8fb7ddb5dcf84d7a8b0479e2\\\",\\n \\\"derived-insight\\\": {\\n \\\"sentiment\\\": {\\n \\\"neg\\\": 0.0,\\n \\\"neu\\\": 0.928,\\n \\\"pos\\\": 0.072,\\n \\\"compound\\\": 0.4767,\\n },\\n \\\"extra-properties\\\": {\\n \\\"category-scores\\\": {\\n \\\"User Experience\\\": 0,\\n \\\"sign-in/sign-up\\\": 0,\\n \\\"Notification\\\": 0,\\n \\\"Application\\\": 1,\\n \\\"ads\\\": 0,\\n },\\n \\\"bug-feature\\\": \\\"feature\\\",\\n },\\n \\\"category\\\": \\\"Application\\\",\\n },\\n }\\n ]\\n self.assertEqual(processed_output, expected_processed_output)\",\n \"def main(iterator):\\n\\n entries = OrderedDict()\\n for line in iterator:\\n\\n if \\\"START\\\" in line:\\n entries.update({\\\"start_time\\\":int(re.search(r'\\\\d+', line).group())})\\n if \\\"STOP\\\" in line:\\n entries.update({\\\"end_time\\\":int(re.search(r'\\\\d+', line).group())})\\n if \\\"NUMERIC SORT\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"numeric_sort\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n entries.update({\\\"numeric_sort_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"numeric_sort_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"numeric_sort_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"numeric_sort_num_arrs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"numeric_sort_arr_size\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n if \\\"STRING SORT\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"string_sort\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_num_arrs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_arr_size\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n if \\\"STRING SORT\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"string_sort\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_num_arrs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"string_sort_arr_size\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n if \\\"BITFIELD\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"bitfield\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"bitfield_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"bitfield_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"bitfield_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"bitfield_ops_arr_size\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"bitfield_arr_size\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n if \\\"FP EMULATION\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"fp_emul\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"fp_emul_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"fp_emul_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"fp_emul_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"fp_emul_num_loops\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"fp_emul_arr_size\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n if \\\"FOURIER\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"fourier\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"fourier_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"fourier_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"fourier_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"fourier_num_coef\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n if \\\"ASSIGNMENT\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"assignment\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n entries.update({\\\"assignment_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"assignment_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"assignment_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"assignment_num_arrs\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n if \\\"IDEA\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"idea\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"idea_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"idea_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"idea_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"idea_arr_size\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"idea_num_loops\\\":int(re.search(r'\\\\d+', line).group())})\\n \\n if \\\"HUFFMAN\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"huffman\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"huffman_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"huffman_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"huffman_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"huffman_arr_size\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"huffman_num_loops\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n\\n if \\\"NEURAL NET\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"nnet\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"nnet_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"nnet_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"nnet_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"nnet_num_loops\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n if \\\"LU DECOMPOSITION\\\" in line and \\\"Done with\\\" not in line:\\n #print(float(re.search(r'\\\\d+', line).group()))\\n entries.update({\\\"lu_decomp\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"lu_decomp_abs_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"lu_decomp_rel_sdv\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"lu_decomp_num_runs\\\":int(re.search(r'\\\\d+', line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"lu_decomp_num_arrs\\\":int(re.search(r'\\\\d+', line).group())})\\n\\n if \\\"libc\\\" in line and \\\"Baseline\\\" not in line and \\\"*\\\" not in line:\\n line = next(iterator)\\n \\n entries.update({\\\"memory_index\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"integer_index\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n line = next(iterator)\\n \\n entries.update({\\\"float_index\\\":float(re.search(r\\\"[+-]?(\\\\d+(\\\\.\\\\d*)?|\\\\.\\\\d+)([eE][+-]?\\\\d+)?\\\", line).group())})\\n\\n #print(entries)\\n return entries\",\n \"def test_basic_parser_trace():\",\n \"def test_bad_date():\\n assert sort_memos(bad_record) == 'Error'\",\n \"def test_traffic_analysis_human_readable(\\n traffic_analysis_success, traffic_analysis_success_hr\\n):\\n resp = prepare_traffic_analysis_output(traffic_analysis_success)\\n assert resp == traffic_analysis_success_hr\",\n \"def _compare_output(standart_output, testing_output):\\n standart_output_array = standart_output.splitlines()\\n testing_output_array = testing_output.splitlines()\\n standart_output_array_len = len(standart_output_array)\\n testing_output_array_len = len(testing_output_array)\\n\\n if standart_output_array_len != testing_output_array_len:\\n print 'there is different count of steps standart: ', standart_output_array_len, \\\\\\n ' testing: ', testing_output_array_len\\n return False\\n\\n for index in range(0, len(standart_output_array), 1):\\n if standart_output_array[index] != testing_output_array[index]:\\n print 'there is different results in same step for standsret: ', standart_output_array[index], \\\\\\n ' and testing: ', testing_output_array[index]\\n return False\\n\\n return True\",\n \"def test_get_sort_info(self):\\n ars = self.ar[2009][11]['day']\\n self.assertEqual(ars.get_sort_info(), (31, 'key', True))\",\n \"def test_invalid_args(self):\\n self.assertEqual(dictsort([{}], \\\"._private\\\"), \\\"\\\")\\n self.assertEqual(dictsort([{\\\"_private\\\": \\\"test\\\"}], \\\"_private\\\"), \\\"\\\")\\n self.assertEqual(\\n dictsort([{\\\"nested\\\": {\\\"_private\\\": \\\"test\\\"}}], \\\"nested._private\\\"), \\\"\\\"\\n )\",\n \"def test_sort_outputs_0a6a357e(self):\\n outputs = bip69.get_outputs_from_rpc_json(self.tx_json_0a6a357e)\\n bip69_outputs = bip69.sort_outputs(outputs)\\n self.assertEqual(bip69_outputs[0], (('76a9144a5fba237213a062f6f57978f79'\\n '6390bdcf8d01588ac'), 400057456))\\n self.assertEqual(bip69_outputs[1], (('76a9145be32612930b8323add2212a4ec'\\n '03c1562084f8488ac'), 40000000000))\",\n \"def check_sort(self):\\n if self.list == []:\\n return True\\n seg_iter = iter(self.list)\\n last = next(seg_iter)\\n for segment in seg_iter:\\n if last > segment:\\n raise Exception('non trié')\\n last = segment\\n return True\",\n \"def pass_test(sort_func, arr):\\n sort_array = sorted(arr[:])\\n # Print accordingly\\n if sort_func(arr[:]) == sort_array:\\n print(\\\"Test Passed\\\")\\n else:\\n print(\\\"Error: Test not passed\\\")\",\n \"def check_input_matches_expected_output(in_, out):\\n ...\",\n \"def test_merge(self):\\n integers = merge_sort(self.actual)\\n self.assertEqual(self.expected, integers)\",\n \"def test_rust_code_analysis_tokei_c() -> None:\\n\\n ret_value = compare(\\n \\\"rust-code-analysis\\\",\\n \\\"tokei\\\",\\n [\\\"-g\\\", \\\"-f\\\"],\\n [\\\"SLOC\\\", \\\"PLOC\\\", \\\"CLOC\\\", \\\"BLANK\\\"],\\n \\\"C\\\",\\n \\\"bubble_sort.c\\\",\\n )\\n\\n assert ret_value == 0\",\n \"def test_isort_tool_plugin_scan_calledprocesserror(mock_subprocess_check_output):\\n mock_subprocess_check_output.side_effect = subprocess.CalledProcessError(\\n 0, \\\"\\\", output=\\\"mocked error\\\"\\n )\\n itp = setup_isort_tool_plugin()\\n package = Package(\\n \\\"valid_package\\\", os.path.join(os.path.dirname(__file__), \\\"valid_package\\\")\\n )\\n package[\\\"python_src\\\"] = [\\n os.path.join(os.path.dirname(__file__), \\\"valid_package\\\", \\\"sample.py\\\")\\n ]\\n issues = itp.scan(package, \\\"level\\\")\\n assert len(issues) == 1\",\n \"def test__parse_archived():\\n for input_data, expected_output in (\\n ({}, False),\\n ({'thread_metadata': {}}, False),\\n ({'thread_metadata': {'archived': False}}, False),\\n ({'thread_metadata': {'archived': True}}, True),\\n ):\\n output = parse_archived(input_data)\\n vampytest.assert_eq(output, expected_output)\",\n \"def test_output(self):\\n good_value_pairs = INT_VALUE_PAIRS\\n for pair in good_value_pairs:\\n output = to_cardinal_number(pair[0])\\n self.assertEqual(output, pair[1],\\n f\\\"{pair[0]} should be {pair[1]}, not {output}\\\")\",\n \"def test_parse_sort(self):\\n old_type = Sort('sort', [\\n Relationship('student', PersonSchema(), None),\\n Relationship('school', StudentSchema(), None)],\\n Attribute('title', SchoolSchema(), None), '+')\\n new_type = self.driver.parse(old_type)\\n\\n assert new_type.source == old_type\\n assert old_type.relationships != new_type.relationships\\n assert isinstance(new_type.relationships[0], Mapper)\\n assert old_type.attribute != new_type.attribute\\n assert isinstance(new_type.attribute, Column)\\n assert old_type.direction == new_type.direction\",\n \"def test_make_tool_plugin_parse_invalid():\\n mtp = setup_make_tool_plugin()\\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\\n 'valid_package'))\\n output = \\\"invalid text\\\"\\n issues = mtp.parse_output(package, output)\\n assert not issues\",\n \"def test_sort_inputs_0a6a357e(self):\\n inputs = bip69.get_inputs_from_rpc_json(self.tx_json_0a6a357e)\\n bip69_inputs = bip69.sort_inputs(inputs)\\n self.assertEqual(bip69_inputs[0],\\n (('0e53ec5dfb2cb8a71fec32dc9a634a35b7e24799295ddd52782'\\n '17822e0b31f57'), 0))\\n self.assertEqual(bip69_inputs[10],\\n (('7d037ceb2ee0dc03e82f17be7935d238b35d1deabf953a892a4'\\n '507bfbeeb3ba4'), 1))\",\n \"def check_correctness(self):\\n\\n with open(self.output_file, 'r') as output_file, open(self.gt_file, 'r') as gt_file:\\n\\n out_lines = output_file.readlines()\\n gt_lines = gt_file.readlines()\\n\\n # Check for inequality\\n if len(out_lines) != len(gt_lines):\\n return 0\\n\\n # Check for inequality\\n for i in range(len(out_lines)):\\n out_split = out_lines[i].split()\\n gt_split = gt_lines[i].split()\\n\\n if len(out_split) != len(gt_split):\\n return 0\\n\\n for j in range(len(out_split)):\\n # Treat slur and tie as equivalent\\n if out_split[j] != gt_split[j] and\\\\\\n ('slur' not in out_split[j] and 'tie' not in out_split[j]) and\\\\\\n ('slur' not in gt_split[j] and 'tie' not in gt_split[j]):\\n return 0\\n\\n return 1\",\n \"def test_radix(self):\\n integers = radix_sort(self.actual)\\n self.assertEqual(self.expected, integers)\",\n \"def test_asciitable_m_pure_ascii(self):\\n input = '''\\n+========+========+========+========+========+========+========+\\n| type | tota | used | fr ee | shar | buff | avai |\\n| | l | | | ed | _cac | labl |\\n| | | | | | he | e |\\n+========+========+========+========+========+========+========+\\n| Mem | 3861 | 2228 | 3364 | 1183 | 2743 | 3389 |\\n| | 332 | 20 | 176 | 2 | 36 | 588 |\\n+--------+--------+--------+--------+--------+--------+--------+\\n| | | | | | | |\\n| | | | | test 2 | | |\\n+--------+--------+--------+--------+--------+--------+--------+\\n| last | last | last | ab cde | | | final |\\n+========+========+========+========+========+========+========+\\n '''\\n expected = [\\n {\\n \\\"type\\\": \\\"Mem\\\",\\n \\\"tota_l\\\": \\\"3861\\\\n332\\\",\\n \\\"used\\\": \\\"2228\\\\n20\\\",\\n \\\"fr_ee\\\": \\\"3364\\\\n176\\\",\\n \\\"shar_ed\\\": \\\"1183\\\\n2\\\",\\n \\\"buff_cac_he\\\": \\\"2743\\\\n36\\\",\\n \\\"avai_labl_e\\\": \\\"3389\\\\n588\\\"\\n },\\n {\\n \\\"type\\\": None,\\n \\\"tota_l\\\": None,\\n \\\"used\\\": None,\\n \\\"fr_ee\\\": None,\\n \\\"shar_ed\\\": \\\"test 2\\\",\\n \\\"buff_cac_he\\\": None,\\n \\\"avai_labl_e\\\": None\\n },\\n {\\n \\\"type\\\": \\\"last\\\",\\n \\\"tota_l\\\": \\\"last\\\",\\n \\\"used\\\": \\\"last\\\",\\n \\\"fr_ee\\\": \\\"ab cde\\\",\\n \\\"shar_ed\\\": None,\\n \\\"buff_cac_he\\\": None,\\n \\\"avai_labl_e\\\": \\\"final\\\"\\n }\\n ]\\n\\n self.assertEqual(jc.parsers.asciitable_m.parse(input, quiet=True), expected)\",\n \"def test_basic_parsers():\",\n \"def validate(self):\\n if len(self.independent_nodes) > 0:\\n try:\\n self.topological_sort()\\n return True\\n except ValueError:\\n return False\\n return False\",\n \"def test_convert(self):\\n out_text = StringIO()\\n with redirect_stdout(out_text):\\n main([\\\"-id\\\", indir, \\\"-od\\\", compdir, \\\"-if\\\", \\\"ttl\\\", \\\"-of\\\", \\\"json-ld\\\"])\\n self.assertEqual(\\\"\\\"\\\"Total=3 Successful=3\\\"\\\"\\\", out_text.getvalue().strip())\",\n \"def test_sorted_page_stream(self):\\n self._test_insertion(Macros, 0)\",\n \"def test_heap_sort(self):\\n integers = heap_sort(self.actual)\\n self.assertEqual(self.expected, integers)\",\n \"def test_inflate_denoiser_output(self):\\r\\n actual = list(inflate_denoiser_output(\\r\\n parse_fasta(self.centroid_seqs1),\\r\\n parse_fasta(self.singleton_seqs1),\\r\\n self.denoiser_mapping1,\\r\\n parse_fasta(self.raw_seqs1)))\\r\\n expected = [(\\\"S1_0 FXX111 some comments\\\", \\\"TTTT\\\"),\\r\\n (\\\"S1_2 FXX113 some other comments\\\", \\\"TTTT\\\"),\\r\\n (\\\"S3_7 FXX117\\\", \\\"TTTT\\\"),\\r\\n (\\\"S2_1 FXX112 some comments\\\", \\\"TATT\\\"),\\r\\n (\\\"S3_5 FXX114\\\", \\\"TATT\\\"),\\r\\n (\\\"S3_6 FXX115\\\", \\\"TTGA\\\"),\\r\\n (\\\"S3_6 FXX116\\\", \\\"TAGA\\\")]\\r\\n self.assertEqual(actual, expected)\",\n \"def main(argv):\\r\\n\\terror_code = 0\\r\\n\\tlist = [5, 9, 8, 2, 0, 4, 7, 6, 1, 7]\\r\\n\\tprint('Unsorted list:')\\r\\n\\tprint(list)\\r\\n\\tlist = selection_sort(list)\\r\\n\\tprint('Sorted list:')\\r\\n\\tprint(list)\\r\\n\\treturn error_code\",\n \"def _fix_shortsort(self):\\n test_dir = join_path(self.install_test_root, self.test_src_dir)\\n filter_file(\\\"../src/\\\", \\\"\\\", join_path(test_dir, \\\"testshortsort.sh\\\"))\",\n \"def test_print_result(capsys):\\n assert \\\"\\\"\\\"Total 5 hands solved\\nTotal 4 hands solved with hint\\nTotal 4 hands failed to solve\\\"\\\"\\\" in hl.test_help_print_result(capsys)\",\n \"def test_parse_results_error():\\n error_result = [{\\\"error\\\": \\\"test\\\"}]\\n assert [{\\\"title\\\": \\\"Error\\\",\\n \\\"subtitle\\\": \\\"test\\\",\\n \\\"valid\\\": False}] == parse_results(error_result)\",\n \"def test_workloads_list_command_human_readable(\\n workloads_list_success, workloads_list_success_hr\\n):\\n hr_output = prepare_workloads_list_output(workloads_list_success)\\n assert hr_output == workloads_list_success_hr\",\n \"def test_sort_success_return_sortedArray(self):\\n\\n # prepare\\n unsortedArray = [12.0, 13.5, 1.0, 5.5,\\n 9.0, 19.5, 12.0, 23.5, 5.0, 51.0]\\n expectedResult = [1.0, 5.0, 5.5, 9.0,\\n 12.0, 12.0, 13.5, 19.5, 23.5, 51.0]\\n\\n # execute\\n actuatlResponse = PSPQuickSortProcess.sort(unsortedArray)\\n\\n # assert\\n self.assertEqual(expectedResult, actuatlResponse)\",\n \"def test_sort_natural(self):\\n\\n test_cases = [\\n Case(\\n description=\\\"lists of strings\\\",\\n val=[\\\"b\\\", \\\"a\\\", \\\"C\\\", \\\"B\\\", \\\"A\\\"],\\n args=[],\\n kwargs={},\\n expect=[\\\"a\\\", \\\"A\\\", \\\"b\\\", \\\"B\\\", \\\"C\\\"],\\n ),\\n Case(\\n description=\\\"lists of strings with a None\\\",\\n val=[\\\"b\\\", \\\"a\\\", None, \\\"C\\\", \\\"B\\\", \\\"A\\\"],\\n args=[],\\n kwargs={},\\n expect=[None, \\\"a\\\", \\\"A\\\", \\\"b\\\", \\\"B\\\", \\\"C\\\"],\\n ),\\n Case(\\n description=\\\"lists of objects with key\\\",\\n val=[{\\\"title\\\": \\\"foo\\\"}, {\\\"title\\\": \\\"bar\\\"}, {\\\"title\\\": \\\"Baz\\\"}],\\n args=[\\\"title\\\"],\\n kwargs={},\\n expect=[{\\\"title\\\": \\\"bar\\\"}, {\\\"title\\\": \\\"Baz\\\"}, {\\\"title\\\": \\\"foo\\\"}],\\n ),\\n Case(\\n description=\\\"lists of objects with missing key\\\",\\n val=[{\\\"title\\\": \\\"foo\\\"}, {\\\"title\\\": \\\"bar\\\"}, {\\\"heading\\\": \\\"Baz\\\"}],\\n args=[\\\"title\\\"],\\n kwargs={},\\n expect=[{\\\"title\\\": \\\"bar\\\"}, {\\\"title\\\": \\\"foo\\\"}, {\\\"heading\\\": \\\"Baz\\\"}],\\n ),\\n Case(\\n description=\\\"empty list\\\",\\n val=[],\\n args=[],\\n kwargs={},\\n expect=[],\\n ),\\n Case(\\n description=\\\"too many arguments\\\",\\n val=[{\\\"title\\\": \\\"foo\\\"}, {\\\"title\\\": \\\"bar\\\"}, {\\\"title\\\": \\\"Baz\\\"}],\\n args=[\\\"title\\\", \\\"heading\\\"],\\n kwargs={},\\n expect=FilterArgumentError,\\n ),\\n Case(\\n description=\\\"value not an array\\\",\\n val=1234,\\n args=[],\\n kwargs={},\\n expect=FilterValueError,\\n ),\\n Case(\\n description=\\\"undefined left value\\\",\\n val=self.env.undefined(\\\"test\\\"),\\n args=[],\\n kwargs={},\\n expect=[],\\n ),\\n Case(\\n description=\\\"undefined argument\\\",\\n val=[{\\\"title\\\": \\\"foo\\\"}, {\\\"title\\\": \\\"bar\\\"}, {\\\"title\\\": \\\"Baz\\\"}],\\n args=[self.env.undefined(\\\"test\\\")],\\n kwargs={},\\n expect=[{\\\"title\\\": \\\"foo\\\"}, {\\\"title\\\": \\\"bar\\\"}, {\\\"title\\\": \\\"Baz\\\"}],\\n ),\\n ]\\n\\n self._test(SortNatural, test_cases)\",\n \"def test_asciitable_m_pure_ascii_extra_spaces(self):\\n input = '''\\n \\n \\n +========+========+========+========+========+========+========+\\n | type | tota | used | fr ee | shar | buff | avai \\n | | l | | | ed | _cac | labl \\n | | | | | | he | e |\\n +========+========+========+========+========+========+========+ \\n | Mem | 3861 | 2228 | 3364 | 1183 | 2743 | 3389 |\\n | | 332 | 20 | 176 | 2 | 36 | 588 |\\n +--------+--------+--------+--------+--------+--------+--------+\\n | | | | | | | |\\n | | | | | test 2 | | | \\n +--------+--------+--------+--------+--------+--------+--------+\\n | last | last | last | ab cde | | | final \\n +========+========+========+========+========+========+========+ \\n \\n \\n '''\\n expected = [\\n {\\n \\\"type\\\": \\\"Mem\\\",\\n \\\"tota_l\\\": \\\"3861\\\\n332\\\",\\n \\\"used\\\": \\\"2228\\\\n20\\\",\\n \\\"fr_ee\\\": \\\"3364\\\\n176\\\",\\n \\\"shar_ed\\\": \\\"1183\\\\n2\\\",\\n \\\"buff_cac_he\\\": \\\"2743\\\\n36\\\",\\n \\\"avai_labl_e\\\": \\\"3389\\\\n588\\\"\\n },\\n {\\n \\\"type\\\": None,\\n \\\"tota_l\\\": None,\\n \\\"used\\\": None,\\n \\\"fr_ee\\\": None,\\n \\\"shar_ed\\\": \\\"test 2\\\",\\n \\\"buff_cac_he\\\": None,\\n \\\"avai_labl_e\\\": None\\n },\\n {\\n \\\"type\\\": \\\"last\\\",\\n \\\"tota_l\\\": \\\"last\\\",\\n \\\"used\\\": \\\"last\\\",\\n \\\"fr_ee\\\": \\\"ab cde\\\",\\n \\\"shar_ed\\\": None,\\n \\\"buff_cac_he\\\": None,\\n \\\"avai_labl_e\\\": \\\"final\\\"\\n }\\n ]\\n\\n self.assertEqual(jc.parsers.asciitable_m.parse(input, quiet=True), expected)\",\n \"def test_invalid_values(self):\\n self.assertEqual(dictsort([1, 2, 3], \\\"age\\\"), \\\"\\\")\\n self.assertEqual(dictsort(\\\"Hello!\\\", \\\"age\\\"), \\\"\\\")\\n self.assertEqual(dictsort({\\\"a\\\": 1}, \\\"age\\\"), \\\"\\\")\\n self.assertEqual(dictsort(1, \\\"age\\\"), \\\"\\\")\",\n \"def test_update_enforcement_mode_command_failure_human_readable(\\n enforcement_mode_failure_hr, enforcement_mode_failure_expected\\n):\\n resp = prepare_update_enforcement_mode_output(enforcement_mode_failure_expected)\\n\\n assert resp == enforcement_mode_failure_hr\",\n \"def test_natsort_case_insensitive(self):\\r\\n\\r\\n # string with alpha and numerics sort correctly\\r\\n s = [\\r\\n 'sample1',\\r\\n 'sample2',\\r\\n 'sample11',\\r\\n 'sample12',\\r\\n 'SAmple1',\\r\\n 'Sample2']\\r\\n\\r\\n # expected values\\r\\n exp_natsort = ['SAmple1', 'Sample2', 'sample1', 'sample2', 'sample11',\\r\\n 'sample12']\\r\\n exp_natsort_case_insensitive = ['sample1', 'SAmple1', 'sample2',\\r\\n 'Sample2', 'sample11', 'sample12']\\r\\n\\r\\n # test natsort\\r\\n self.assertEqual(natsort(s), exp_natsort)\\r\\n # test natsort_case_insensitive\\r\\n self.assertEqual(natsort_case_insensitive(s),\\r\\n exp_natsort_case_insensitive)\\r\\n\\r\\n s.reverse()\\r\\n # test natsort\\r\\n self.assertEqual(natsort(s), exp_natsort)\\r\\n # test natsort_case_insensitive\\r\\n self.assertEqual(natsort(list('cbaA321')), list('123Aabc'))\\r\\n\\r\\n # strings with alpha only sort correctly\\r\\n self.assertEqual(natsort_case_insensitive(list('cdBa')), list('aBcd'))\\r\\n\\r\\n # string of ints sort correctly\\r\\n self.assertEqual(natsort_case_insensitive(['11', '2', '1', '0']),\\r\\n ['0', '1', '2', '11'])\\r\\n\\r\\n # strings of floats sort correctly\\r\\n self.assertEqual(natsort_case_insensitive(['1.11', '1.12', '1.00',\\r\\n '0.009']), ['0.009', '1.00',\\r\\n '1.11', '1.12'])\\r\\n\\r\\n # string of ints sort correctly\\r\\n self.assertEqual(natsort_case_insensitive([('11', 'A'), ('2', 'B'),\\r\\n ('1', 'C'), ('0', 'D')]),\\r\\n [('0', 'D'), ('1', 'C'),\\r\\n ('2', 'B'), ('11', 'A')])\",\n \"def test_parse_output_error(self):\\n self.assertRaises(ValueError,\\n parse_output,\\n hgt_results_fp=self.consel_output_hgt_fp,\\n method=\\\"Consel\\\")\",\n \"def test_correct_parsing(self):\\n test_length = random.randint(0,100)\\n test_string = \\\"#\\\\t{0}\\\".format(\\\"\\\\t\\\".join(map(str, xrange(test_length))))\\n expected = [extract_mock(s) for s in xrange(test_length)]\\n computed = self.parser.parse_header(test_string, extract_mock)\\n self.assertEquals(expected, computed)\",\n \"def test_parser_all(self):\\n args = [\\\"directory\\\", \\\"-n\\\", \\\"-f\\\", \\\"-r\\\"]\\n parser = setup_parser()\\n output = parser.parse_args(args)\\n self.assertEqual(output.directory, \\\"directory\\\")\\n self.assertFalse(output.colorize)\\n self.assertTrue(output.fancy)\\n self.assertTrue(output.reverse)\",\n \"def main(argv):\\r\\n\\terror_code = 0\\r\\n\\tlist = [5, 9, 8, 2, 0, 4, 7, 6, 1, 7]\\r\\n\\tprint('Unsorted list:')\\r\\n\\tprint(list)\\r\\n\\tlist = quick_sort(list)\\r\\n\\tprint('Sorted list:')\\r\\n\\tprint(list)\\r\\n\\treturn error_code\",\n \"def check(self, input, ast):\\n assert False # Must be redefined\",\n \"def test_reversed_version_sorting(self):\\n assert natsort(['1', '5', '10', '50'], reverse=True) == ['50', '10', '5', '1']\",\n \"def check_cot_output(self, expected):\\n sys.stdout = StringIO.StringIO()\\n output = None\\n try:\\n self.instance.run()\\n except (TypeError, ValueError, SyntaxError, LookupError):\\n self.fail(traceback.format_exc())\\n finally:\\n output = sys.stdout.getvalue()\\n sys.stdout = sys.__stdout__\\n self.maxDiff = None\\n self.assertMultiLineEqual(expected.strip(), output.strip())\",\n \"def test_sam_parser_success(self):\\n # self.cleanup = False\\n\\n sam_file = '%s/../human_100.sam' % GOLDEN_DIR\\n count = 0\\n\\n with open(sam_file, 'r') as sam:\\n for line in sam:\\n # Skip tags\\n if line[0] == '@':\\n continue\\n\\n alignment = parse_sam_line(line)\\n\\n # Verify that the type conversions are all correct\\n types = {}\\n for entry in sam_format():\\n types[entry['name']] = entry['type']\\n\\n for field in alignment:\\n self.assertIs(type(alignment[field]), types[field])\\n\\n count = count + 1\\n\\n self.assertEqual(count, 100)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6781174","0.6635181","0.6309134","0.62981886","0.61402726","0.6060493","0.60508925","0.59607303","0.5936084","0.59126383","0.587762","0.58522105","0.5729688","0.56102586","0.5577316","0.55231833","0.5521064","0.55136585","0.55019987","0.54842657","0.5482111","0.5479343","0.54657125","0.54582775","0.54581916","0.54488796","0.5425367","0.54231393","0.5421183","0.5408248","0.5405454","0.5381841","0.5372667","0.5368157","0.5366018","0.5357217","0.53548884","0.53300023","0.5320169","0.5312435","0.5308419","0.53077173","0.53075135","0.53067285","0.5306131","0.53045064","0.5294904","0.5293883","0.52787155","0.52369976","0.52369505","0.5235237","0.5232342","0.52092105","0.5207558","0.51853544","0.5180541","0.51695865","0.5168456","0.5165643","0.5161634","0.51588595","0.5155746","0.5155369","0.51551974","0.51461416","0.5120987","0.5106656","0.5105906","0.5097108","0.5094501","0.50900066","0.5087463","0.50844145","0.50786316","0.5071691","0.5066559","0.50514275","0.50492316","0.5048198","0.50476885","0.5047438","0.50459206","0.50456524","0.50431645","0.5042678","0.5036043","0.5035931","0.50309134","0.5021781","0.50148034","0.49965024","0.49937817","0.49921626","0.49920163","0.4990554","0.49901894","0.49876922","0.4986972","0.49865675"],"string":"[\n \"0.6781174\",\n \"0.6635181\",\n \"0.6309134\",\n \"0.62981886\",\n \"0.61402726\",\n \"0.6060493\",\n \"0.60508925\",\n \"0.59607303\",\n \"0.5936084\",\n \"0.59126383\",\n \"0.587762\",\n \"0.58522105\",\n \"0.5729688\",\n \"0.56102586\",\n \"0.5577316\",\n \"0.55231833\",\n \"0.5521064\",\n \"0.55136585\",\n \"0.55019987\",\n \"0.54842657\",\n \"0.5482111\",\n \"0.5479343\",\n \"0.54657125\",\n \"0.54582775\",\n \"0.54581916\",\n \"0.54488796\",\n \"0.5425367\",\n \"0.54231393\",\n \"0.5421183\",\n \"0.5408248\",\n \"0.5405454\",\n \"0.5381841\",\n \"0.5372667\",\n \"0.5368157\",\n \"0.5366018\",\n \"0.5357217\",\n \"0.53548884\",\n \"0.53300023\",\n \"0.5320169\",\n \"0.5312435\",\n \"0.5308419\",\n \"0.53077173\",\n \"0.53075135\",\n \"0.53067285\",\n \"0.5306131\",\n \"0.53045064\",\n \"0.5294904\",\n \"0.5293883\",\n \"0.52787155\",\n \"0.52369976\",\n \"0.52369505\",\n \"0.5235237\",\n \"0.5232342\",\n \"0.52092105\",\n \"0.5207558\",\n \"0.51853544\",\n \"0.5180541\",\n \"0.51695865\",\n \"0.5168456\",\n \"0.5165643\",\n \"0.5161634\",\n \"0.51588595\",\n \"0.5155746\",\n \"0.5155369\",\n \"0.51551974\",\n \"0.51461416\",\n \"0.5120987\",\n \"0.5106656\",\n \"0.5105906\",\n \"0.5097108\",\n \"0.5094501\",\n \"0.50900066\",\n \"0.5087463\",\n \"0.50844145\",\n \"0.50786316\",\n \"0.5071691\",\n \"0.5066559\",\n \"0.50514275\",\n \"0.50492316\",\n \"0.5048198\",\n \"0.50476885\",\n \"0.5047438\",\n \"0.50459206\",\n \"0.50456524\",\n \"0.50431645\",\n \"0.5042678\",\n \"0.5036043\",\n \"0.5035931\",\n \"0.50309134\",\n \"0.5021781\",\n \"0.50148034\",\n \"0.49965024\",\n \"0.49937817\",\n \"0.49921626\",\n \"0.49920163\",\n \"0.4990554\",\n \"0.49901894\",\n \"0.49876922\",\n \"0.4986972\",\n \"0.49865675\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77473277"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8293,"cells":{"query":{"kind":"string","value":"Test what happens when an OSError is raised (usually means isort doesn't exist)."},"document":{"kind":"string","value":"def test_isort_tool_plugin_scan_oserror(mock_subprocess_check_output):\n mock_subprocess_check_output.side_effect = OSError(\"mocked error\")\n itp = setup_isort_tool_plugin()\n package = Package(\n \"valid_package\", os.path.join(os.path.dirname(__file__), \"valid_package\")\n )\n package[\"python_src\"] = [\n os.path.join(os.path.dirname(__file__), \"valid_package\", \"sample.py\")\n ]\n issues = itp.scan(package, \"level\")\n assert not issues"},"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 os_error():\n en = ctypes.get_errno()\n ctypes.set_errno(0)\n if en == 0:\n return OSError(en, \"(no errno found)\")\n else:\n return OSError(en, errno.errorcode[en])","def test_dump_handles_os_error(mocker):\n\tmocker.patch('subprocess.Popen', side_effect=OSError('no such file'))\n\twith pytest.raises(SystemExit):\n\t\tpgdump.dump(url)","def test_load_json_os_error() -> None:\n fname = \"/\"\n with pytest.raises(HomeAssistantError) as err:\n load_json(fname)\n assert isinstance(err.value.__cause__, OSError)","def test_dump_handles_oserror(mocker):\n mocker.patch('subprocess.Popen' , side_effect=OSError(\"no such file\"))\n with pytest.raises(SystemExit):\n pgdump.dump(url)","def test_make_sure_path_exists_correctly_handle_os_error(mocker):\n mocker.patch(\"pathlib.Path.mkdir\", side_effect=OSError)\n with pytest.raises(OSError) as err:\n utils.make_sure_path_exists(Path('protected_path'))\n assert str(err.value) == \"Unable to create directory at protected_path\"","def _check_error(return_value):\n if return_value < 0:\n raise IOError(pm.lib.Pm_GetErrorText(return_value))","def test_check_if_error(self):\n with self.assertRaises(MyError):\n SshErrorExitCodeController(255, ERROR_MESSAGE)\\\n .check_if_error()","def test_make_tool_plugin_scan_oserror(mock_subprocess_check_output):\n mock_subprocess_check_output.side_effect = OSError('mocked error')\n mtp = setup_make_tool_plugin()\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\n 'valid_package'))\n package['make_targets'] = 'make_targets'\n issues = mtp.scan(package, 'level')\n assert issues is None","def _check(error: int) -> None:\n if error < 0:\n raise RuntimeError(ffi.string(lib.TCOD_get_error()).decode())","def test_shell_bad_command():\n out, err = shell_command(\"ls adasdasdas\")\n assert out is None\n assert \"adasdasdas\" in err","def _file_does_not_exist_error(exception):\n stre = str(exception)\n # return any(err in stre for err in (\"ENOENT\", \"ENODEV\", \"EINVAL\"))\n return any(err in stre for err in (\"ENOENT\", \"ENODEV\", \"EINVAL\"))","def suppress_oserror(*errnos):\n try:\n yield\n except OSError as e:\n if e.errno not in errnos:\n raise e","def walk_error(os_error):\n printer(\"Cannot access '{}'; zip creation aborted\".format(os_error.filename), \"error\", True)\n raise os_error","def test_missing_file():\n passed = False\n try:\n x = XPIManager('foo.bar')\n except:\n passed = True\n assert passed","def test_missing_file(self):\n with self.assertRaises(subprocess.CalledProcessError) as cm:\n subprocess.check_output(\n [sys.executable, idf_py_path, '--version', '@args_non_existent'],\n env=os.environ,\n stderr=subprocess.STDOUT).decode('utf-8', 'ignore')\n self.assertIn('(expansion of @args_non_existent) could not be opened', cm.exception.output.decode('utf-8', 'ignore'))","def test_no_such_file(self):\n with self.assertRaises(IOError):\n analyze_text('foobar')","def test_no_such_file(self):\n with self.assertRaises(IOError):\n analyze_text('foobar')","def test_invalid_path() -> None:\n path = rsc / \"does-not-exist.ods\"\n with pytest.raises(FileNotFoundError, match=\"does not exist\"):\n read_ods(path)","def test_no_such_file(self):\n with self.assertRaises(IOError):\n analyze_text('foobar')","def test_option_unhandled(self):\n cmd, output = runCmdOutput(['--__unhandled__'])\n self.assertEqual(cmd.returncode, os.EX_USAGE)","def test_existing_file_after_assert_error(exist_of_file):\n try:\n assert read_magic_number(exist_of_file)\n except AssertionError:\n print(\"Now lets do check of existing file\")","def test_exceptions():\r\n # test that trying to connect_ to a non existent app fails\r\n try:\r\n app = application.Application()\r\n app.connect(path=r\"No process with this please\")\r\n assert False\r\n except application.ProcessNotFoundError:\r\n print('ProcessNotFoundError has been raised. OK.')\r\n\r\n # test that trying to connect_ to a non existent app fails\r\n try:\r\n app = application.Application()\r\n app.start(cmd_line = r\"No process with this please\")\r\n assert False\r\n except application.AppStartError:\r\n print('AppStartError has been raised. OK.')","def test_check_if_not_error(self):\n actual_result = SshErrorExitCodeController(ERROR_RETURN_CODE,\n OK_MESSAGE)\\\n .check_if_error()\n self.assertIsNone(actual_result)","def _errcheck_link(value, func, args): # pylint: disable=W0613\n # The windows api returns nonzero if the call was successful\n if value != 0:\n return\n\n last_error = ctypes.windll.kernel32.GetLastError()\n # Somehow CreateSymbolicLinkW and CreateHardLinkW retuns zero\n # and the last error is 2 (The system cannot find the file specified)\n # but the link is created successfuly\n # it seems like a bug in the WinAPI\n if last_error == 0 or last_error == 2:\n return\n if last_error == 183:\n raise OSError(errno.EEXIST,\n \"Cannot create a file when that file already exists\",\n args[0])","def test_bad_file() -> None:\n\n bad = random_string()\n rv, out = getstatusoutput(f'{RUN} {bad}')\n assert rv != 0\n assert out.lower().startswith('usage:')\n assert re.search(f\"No such file or directory: '{bad}'\", out)","def test_check_if_error_one(self):\n with self.assertRaises(MyError):\n SshpassErrorExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\n .check_if_error()","def test_check_if_not_error(self):\n actual_result = SshErrorExitCodeController(OK_RETURN_CODE,\n OK_MESSAGE)\\\n .check_if_error()\n self.assertIsNone(actual_result)","def test_no_such_file(self):\n\t\twith self.assertRaises(IOError):\n\t\t\tanalyse_text('foobar')","def test_watch_raises(capsys):\n with mock.patch('uflash.watch_file', side_effect=RuntimeError(\"boom\")):\n with pytest.raises(SystemExit):\n uflash.main(argv=['--watch', 'test.py'])\n\n _, stderr = capsys.readouterr()\n expected = 'Error watching test.py'\n assert expected in stderr","def convert_oserror(exc, pid=None, name=None):\n assert isinstance(exc, OSError), exc\n if is_permission_err(exc):\n return AccessDenied(pid=pid, name=name)\n if exc.errno == errno.ESRCH:\n return NoSuchProcess(pid=pid, name=name)\n raise exc","def raise_for_darwin_exception(response: requests.Response) -> None:\n if response.status_code == 200:\n return\n if response.status_code == 401:\n raise Unauthorized(response)\n if response.status_code == 404:\n raise NotFound(response)","def check_platform():\n if os.getcwd() != os.path.dirname(os.path.abspath(__file__)):\n error = \"must be ran in the directory it's located at\"\n if os.path.sep != '/':\n error = \"a unix-like operating system is required\"\n elif not shutil.which('dpkg-deb'):\n error = \"cannot find dpkg-deb\"\n elif os.getuid() != 0:\n error = \"must be ran as root (or with fakeroot)\"\n else:\n return\n sys.exit(\"{}: error: {}\".format(sys.argv[0], error))","def test_info_fail(self):\n path = \"non_existing_audio.wav\"\n with self.assertRaisesRegex(RuntimeError, path):\n self._info(path)","def test_NameError(n=2):\n\n p = platform_name()\n\n try:\n p.result()\n except NameError:\n print(\"Caught NameError\")\n else:\n assert False, \"Raise the wrong Error\"","def test_check_if_error(self):\n with self.assertRaises(MyError):\n ExecutionExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\n .check_if_error()","def test_exit_on_missing_file(self):\n with self.assertRaises(SystemExit):\n pyint = Interpreter()\n pyint.run(file=MISSING_FILE)","def test_unexpected_error_in_exists(self):\n # TODO\n one_process_workflow = \"\"\"buggy://B <- file://A\n echo A produces B > B\n \"\"\"\n process = run_first_process(one_process_workflow, extra_resource=BuggyExistsResource)\n assert process.success is False, process.error_message\n assert process.error_message.find('An unexpected error have happen in tuttle while checking existence of '\n 'output resources' ) >= 0, process.error_message\n assert process.error_message.find('Traceback (most recent call last):') >= 0, process.error_message\n assert process.error_message.find('raise Exception(\"Unexpected error in exists()\")') >= 0, process.error_message\n assert process.error_message.find('Process cannot be considered complete.') >= 0, process.error_message","def test_noreleaseerror_raise(self, mock_path, mock_open, mock_error):\n # Set the mocked functions returned values\n mock_path.isfile.side_effect = [True]\n mock_context_manager = mock.Mock()\n mock_open.return_value = mock_context_manager\n mock_file = mock.Mock()\n mock_file.read.return_value = \"WRONG\"\n mock_enter = mock.Mock()\n mock_enter.return_value = mock_file\n mock_exit = mock.Mock()\n setattr(mock_context_manager, \"__enter__\", mock_enter)\n setattr(mock_context_manager, \"__exit__\", mock_exit)\n\n # Test execution\n ConnectomistWrapper._connectomist_version_check(\"/my/path/mock_conf\")\n self.assertEqual(len(mock_error.call_args_list), 1)","def testError(self):\n cmds = \"\"\"chown 0 missingFile\npwd\nexit\n\"\"\"\n\n def _cbCheckResult(res):\n self.assertNotIn(self.testDir.asBytesMode().path, res)\n\n d = self._getBatchOutput(cmds)\n d.addCallback(_cbCheckResult)\n return d","def is_permission_err(exc):\n assert isinstance(exc, OSError), exc\n # On Python 2 OSError doesn't always have 'winerror'. Sometimes\n # it does, in which case the original exception was WindowsError\n # (which is a subclass of OSError).\n return exc.errno in (errno.EPERM, errno.EACCES) or \\\n getattr(exc, \"winerror\", -1) in (cext.ERROR_ACCESS_DENIED,\n cext.ERROR_PRIVILEGE_NOT_HELD)","def test_nonexistent_path():\r\n with pytest.raises(RuntimeError):\r\n Image(os.path.join(current_dir, \"0--0.jpg\")).read_all()\r\n assert check_md5(path, jpg_path), \"The file has been changed when reading\"","def testTempDirOrininalErrorRaised(self):\n self.Patch(os, \"chmod\")\n self.Patch(tempfile, \"mkdtemp\", return_value=\"/tmp/tempdir\")\n expected_error = OSError(\"Expected OS Error\")\n self.Patch(shutil, \"rmtree\", side_effect=expected_error)\n\n class ExpectedException(Exception):\n \"\"\"Expected exception.\"\"\"\n\n def _Call():\n with utils.TempDir():\n raise ExpectedException(\"Expected Exception\")\n\n # Verify.\n # ExpectedException should be raised, and OSError\n # should not be raised.\n self.assertRaises(ExpectedException, _Call)\n tempfile.mkdtemp.assert_called_once() #pylint: disable=no-member\n shutil.rmtree.assert_called_with(\"/tmp/tempdir\") #pylint: disable=no-member","def test_handles_error(self):\n with self.assertRaises(ForcedExit):\n self.run_command(mkdtemp())\n\n self.assertResults(\n result_with_hint(\n u'This repository has not been initialized.',\n GIT_REPO_NOT_INITIALIZED),\n self.error)","def test_check_if_error(self):\n with self.assertRaises(MyError):\n BaseErrorExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\n .check_if_error()","def check_os():\n\n if platform.system() != \"Darwin\":\n print \"This script only works on macos system\"\n exit(1)","def test_cmd_error(self):\n task = Task(\"uid\", False, False, \"does_not_exist\", None, \".\")\n task._checkpoint_dir = tmp_checkpoint_dir()\n with self.assertRaisesRegexp(RuntimeError, \".*executing Task's command:.*\"):\n task.run()\n task.shell = True\n with self.assertRaisesRegexp(RuntimeError, \".*executing Task's command:.*\"):\n task.run()\n task._dry_run = True\n task.run() # No longer raises RuntimeError","def test_normal_use(self):\n # Setup:\n class DatabaseError(Exception):\n pass\n\n # Python 2 and 3:\n from future.utils import raise_from\n\n class FileDatabase:\n def __init__(self, filename):\n try:\n self.file = open(filename)\n except IOError as exc:\n raise_from(DatabaseError('failed to open'), exc)\n\n # Testing the above:\n try:\n fd = FileDatabase('non_existent_file.txt')\n except Exception as e:\n assert isinstance(e.__cause__, IOError) # FileNotFoundError on\n # Py3.3+ inherits from IOError","def check_result(ec):\r\n # NOTE: This will break some oscilloscopes that are powered by USB.\r\n # Some of the newer scopes, can actually be powered by USB and will\r\n # return a useful value. That should be given back to the user.\r\n # I guess we can deal with these edge cases in the functions themselves\r\n if ec == 0:\r\n return\r\n\r\n else:\r\n ecName = error_num_to_name(ec)\r\n ecDesc = error_num_to_desc(ec)\r\n raise IOError('Error calling %s: %s (%s)' % (\r\n str(inspect.stack()[1][3]), ecName, ecDesc))","def test_the_main_non_existent_file(self):\r\n with self.assertRaises(SystemExit):\r\n the_main_function(\"non existent file\")","def test_is_information_written_through_stderr_methods(self):\n\n io = BufferedSystemIO()\n io._stdout = lambda *args, **kwargs: None\n\n try:\n raise IndexError('Invalid index 5')\n except Exception as exc:\n output_formatted_exception(exc, ':my-test-task', io)\n\n self.assertIn('IndexError', io.get_value())\n self.assertIn('Invalid index 5', io.get_value())\n self.assertIn('Retry with \"-rl debug\" switch before failed task to see stacktrace', io.get_value())","def test_raises_error_on_hostnotfound(self):\n self.mock_getaddrinfo.side_effect = socket.gaierror('error_message')\n return_value = None\n with self.assertRaises(CMDLineExit) as cm:\n return_value = sanitize_host('erroneous_host')\n self.assertIsNone(return_value)\n self.assertEqual(\n cm.exception.args[0],\n 'Could not resolve hostname \\'erroneous_host\\': error_message')\n self.mock_getaddrinfo.assert_called_once_with(\n 'erroneous_host', 443, socket.AF_INET, socket.SOCK_STREAM)\n self.mock_gethostbyaddr.assert_not_called()","def run_or_die(command):\n (status, stdio) = commands.getstatusoutput(command)\n if status != 0:\n raise Exception(\"command '%s' failed with exit status %d and output '%s'\" % (command, status, stdio))\n return stdio","def test_simple_source_constructor_exception():\n TESTPATH = \"/usr/local/share/testfile.mp3\"\n with pytest.raises(robox.RDJResourceErr):\n test01 = Source(path=TESTPATH, exist=True)","def error_check(command):\r\n\r\n # TODO\r","def errorCheck(sh, returncode, stderr):\n\tif returncode!=0 or stderr!='':\n\t\tif config.DEBUG:\n\t\t\tmsg = \"sh code execution [%s] returned non-zero exit status [%s] and/or non-empty stdterr [%s]\" % (repr(sh), returncode, repr(stderr.strip()))\n\t\telse:\n\t\t\tmsg = \"sh code execution returned non-zero exit status and/or non-empty stdterr\"\n\t\traise Exception(msg)","def test_bad_file():\n\n bad = ''.join(random.choices(string.ascii_uppercase + string.digits, k=5))\n rv, out = getstatusoutput(f'{prg} -f {bad}')\n assert rv != 0\n assert re.match('usage:', out, re.I)\n assert re.search(f\"No such file or directory: '{bad}'\", out)","def test_invoke_pipe_not_found():\n\n testapp = holocron.Application()\n\n with pytest.raises(ValueError) as excinfo:\n next(testapp.invoke(\"test\"))\n\n assert str(excinfo.value) == \"no such pipe: 'test'\"","def test_not_running(): # pragma: windows\n comm_kwargs = dict(comm='IPCComm', direction='send', reverse_names=True)\n nt.assert_raises(RuntimeError, new_comm, 'test', **comm_kwargs)","def test_mount_failure(self):\n with prepared_image_file(create_filesystem=False):\n program = RsyncSystemBackup(\n crypto_device=CRYPTO_NAME,\n destination=os.path.join(MOUNT_POINT, 'latest'),\n mount_point=MOUNT_POINT,\n )\n # When `mount' fails it should exit with a nonzero exit code,\n # thereby causing executor to raise an ExternalCommandFailed\n # exception that obscures the FailedToMountError exception that\n # we're interested in. The check=False option enables our\n # `last resort error handling' code path to be reached.\n program.destination_context.options['check'] = False\n self.assertRaises(FailedToMountError, program.execute)","def _try_ignoring_ent_and_perm(func, *args, **kwargs):\n try:\n func(*args, **kwargs)\n except OSError as err:\n if err.errno == errno.ENOENT or err.errno == errno.EPERM:\n return\n else:\n raise err","def test_nonexistent_path(tmpdir):\n with pytest.raises(IOError):\n checksum(tmpdir.join(\"does-not-exist.txt\").strpath)","def exitIfFileDescriptorError(exit_code, device):\n if exit_code == ExitCode.FILE_DESCRIPTOR_ERROR:\n logging.critical(\"Cannot access to %s.\", device)\n sys.exit(ExitCode.FILE_DESCRIPTOR_ERROR)","def test_open_image_throws(self):\n self.expect_open_image(\n 'SomeBlobKey',\n throw_exception=apiproxy_errors.ApplicationError(\n images_service_pb.ImagesServiceError.INVALID_BLOB_KEY))\n self.mox.ReplayAll()\n try:\n self.app._transform_image('SomeBlobKey', '')\n raise self.failureException('Should have thrown ApplicationError')\n except apiproxy_errors.ApplicationError:\n pass\n self.mox.VerifyAll()","def onerror(func, path, exc_info):\n import stat\n if not os.path.exists(path):\n pass\n elif not os.access(path, os.W_OK):\n # Is the error an access error ?\n os.chmod(path, stat.S_IWUSR)\n func(path)\n else:\n raise","def test_fetch_py_err():\n ident = _id()\n entry_dir = os.path.join(basedir, 'always_error', ident)\n res = proj.fetch('always_error', ident)\n assert res.result is None\n paths = ['error.log', 'status', 'run.log', 'start_time', 'end_time']\n for p in paths:\n assert os.path.exists(os.path.join(entry_dir, p))\n with open(res.paths['log']) as fd:\n assert 'output here' in fd.read()\n with open(res.paths['error']) as fd:\n assert 'This is an error!' in fd.read()\n with open(res.paths['status']) as fd:\n status = fd.read()\n assert status == 'error'\n assert res.status == status\n with open(res.paths['start_time']) as fd:\n start_time = int(fd.read())\n with open(res.paths['end_time']) as fd:\n end_time = int(fd.read())\n assert start_time <= end_time","def test_exc(self):\n g = h5g.open(self.fid, '/')\n g._close()\n self.assertRaises(ValueError, h5i.get_file_id, g)","def test_raise_error_unknown_file():\n\n options = {'input_files': ['Sparta.lol']}\n\n with pytest.raises(KeyError) as excinfo:\n process.Packager(options)\n excinfo.match(r'File ([a-zA-Z_\\.\\'].*) not found in file list.')","def shellExecErrorCode(cmd):\n return subprocess.call(cmd, shell=True)","def test_init(self):\n nt.assert_raises(Exception, CisInterface.CisOutput, 'error')","def has_errors_fatal(self) -> bool:","def test_raise_exception(self):\n with self.assertRaises(Exception):\n SshCommandBuilder(SERVER_USER, COMMAND).to_build()","def test_invalidFile(self):\n self.assertRaises(cesmEnvLib.checkFile(\"blah\", \"write\"))","def test_extract_raises(capsys):\n with mock.patch('uflash.extract', side_effect=RuntimeError(\"boom\")):\n with pytest.raises(SystemExit):\n uflash.main(argv=['--extract', 'test.py'])\n\n _, stderr = capsys.readouterr()\n expected = 'Error extracting test.py'\n assert expected in stderr","def testTempDirWhenDeleteEncounterError(self):\n self.Patch(os, \"chmod\")\n self.Patch(tempfile, \"mkdtemp\", return_value=\"/tmp/tempdir\")\n expected_error = OSError(\"Expected OS Error\")\n self.Patch(shutil, \"rmtree\", side_effect=expected_error)\n\n def _Call():\n with utils.TempDir():\n pass\n\n # Verify OSError should be raised.\n self.assertRaises(OSError, _Call)\n tempfile.mkdtemp.assert_called_once() #pylint: disable=no-member\n shutil.rmtree.assert_called_with(\"/tmp/tempdir\") #pylint: disable=no-member","def look_for_cython_error(capfd):\n yield\n _, err = capfd.readouterr()\n assert \"Exception ignored\" not in err","def test_stats_issue_43328(test_file):\n fake_file = test_file.parent / \"fake.file\"\n with pytest.raises(CommandExecutionError):\n win_file.stats(fake_file)","def test_bad_file():\n\n bad_file = random_string()\n letter = random.choice(string.ascii_lowercase)\n rv, out = getstatusoutput('{} {} -f {}'.format(prg, letter, bad_file))\n assert rv != 0\n expected = \"No such file or directory: '{}'\".format(bad_file)\n assert re.search(expected, out)","def test_check_if_not_error(self):\n actual_result = SshpassErrorExitCodeController(OK_RETURN_CODE,\n OK_MESSAGE)\\\n .check_if_error()\n self.assertIsNone(actual_result)","def test_sign_file_unknown_error(dummy_command, tmp_path):\n # Raise an unknown error during codesign\n dummy_command.tools.subprocess.run.side_effect = mock_codesign(\"Unknown error\")\n\n with pytest.raises(BriefcaseCommandError, match=\"Unable to code sign \"):\n dummy_command.sign_file(\n tmp_path / \"base_path\" / \"random.file\",\n identity=\"Sekrit identity (DEADBEEF)\",\n )\n\n # An attempt to codesign was made\n dummy_command.tools.subprocess.run.assert_has_calls(\n [\n sign_call(\n tmp_path,\n tmp_path / \"base_path\" / \"random.file\",\n entitlements=False,\n ),\n ],\n any_order=False,\n )","def test_bytes_to_intel_hex_io_error(mock_string_io, mock_stderr):\n data = [1, 2, 3, 4, 5]\n mock_string_io.return_value.write.side_effect = IOError()\n\n result = cmds._bytes_to_intel_hex(data=data)\n\n assert result is None\n assert mock_stderr.call_count == 1","def FileCheck(fn):\n try:\n open(fn, \"r\")\n return 1\n except IOError:\n print(\"Error: File does not exist.\")\n return 0","def test_execute_or_bail_internal_error(self):\n with self.assertLogs(level=\"INFO\") as cm:\n # This patches the \"croak\" function that is IMPORTED inside \"etl.commands\".\n with unittest.mock.patch(\"etl.commands.croak\") as mocked_croak:\n with etl.commands.execute_or_bail(\"unittest\"):\n # Simulate an internal error where we don't catch an exception.\n raise ValueError(\"oops\")\n mocked_croak.assert_called()\n # The exit code (2nd arg) is expected to be 3 for uncaught exceptions.\n self.assertEqual(mocked_croak.call_args[0][1], 3)\n\n self.assertEqual(len(cm.output), 2)\n self.assertIn(\"terrible happened\", cm.output[0])","def unexpected_error(self, exception):","def test_with_empty_args(self, mock_builtins_open, capsys, prog, main):\n with pytest.raises(SystemExit):\n main()\n _, err = capsys.readouterr()\n assert err.startswith('usage: {}'.format(prog))","def _check_error(self, ipi):\n\n ipi_error = ipi.communicate(timeout=120)[1].decode(\"ascii\")\n assert \"\" == ipi_error, \"IPI ERROR OCCURED: {}\".format(ipi_error)","def test_not_exectuable(self):\n (status, output, imlog, makelog) = \\\n self.run_instmake_build(log_prefix=\"not-executable\",\n make_opts=[\"not-executable\"])\n\n self.assertEqual(status, util.SUCCESS, output)","def test_broken_error_module(self):\r\n with self.assertRaises(TestException):\r\n module = self.descriptor._xmodule","def isexe(fpath):\n return path.exists(fpath) and access(fpath, X_OK)","def test_check_module(self) -> None:\n check_module(\"os\")","def test_invalid_dir(self):\n self.assertRaises(OSError, awstats_reader.AwstatsReader, '/tmp/XYZ', 'example.com')","def test_valid_file_raises():\n with pytest.raises(ValueError):\n cli._valid_file(__file__)","def NotADirectoryError(inst):\n return hasattr(inst, 'errno') and inst.errno == errno.ENOTDIR","def test_raise_if_err_ok(self) -> None:\n assert Ok(2).raise_if_err(\"err\") == 2","def test_check_if_error_six(self):\n with self.assertRaises(MyError):\n SshpassErrorExitCodeController(6, ERROR_MESSAGE)\\\n .check_if_error()","def test_read_no_file():\n filename = 'asdf'\n with pytest.raises(FileNotFoundError):\n read_file(filename)","def test_broken_error_descriptor(self):\r\n with self.assertRaises(TestException):\r\n module = self.descriptor._xmodule","def test_driver_from_file_errors(execute_kwargs_py):\n with pytest.raises(errors.MapcheteDriverError):\n driver_from_file(execute_kwargs_py)\n\n with pytest.raises(FileNotFoundError):\n driver_from_file(\"non_existing_file.tif\", quick=False)","def test_fail_flow_invalid_distro(self):\n # make the mock for SshShell fail to execute commands\n self._mock_ssh_shell.run.return_value = (1, 'command not found')\n\n # instantiate the guest class we want to test\n system_name = 'dummy_system'\n host_name = 'dummy.domain.com'\n user = 'root'\n passwd = 'somepwd'\n extensions = {}\n guest_obj = linux.GuestLinux(\n system_name, host_name, user, passwd, extensions)\n\n # check if correctly raises the exception\n self.assertRaises(ConnectionError, guest_obj.login)\n\n # change the mock now to successfully execute the command but report an\n # incorrect kernel name\n self._mock_ssh_shell.run.return_value = (\n 0,\n 'OtherOS dummy 4.4.6 #1 SMP Wed Mar 16 22:13:40 UTC 2016 x86_64 '\n 'x86_64 x86_64 Some/OS'\n )\n guest_obj = linux.GuestLinux(\n system_name, host_name, user, passwd, extensions)\n\n # check if correctly raises the exception\n self.assertRaisesRegex(\n ConnectionError, '^Target system is not Linux$', guest_obj.login\n )","def on_error(func, path, exc_info):\n import stat\n if not os.access(path, os.W_OK):\n # Is the error an access error ?\n os.chmod(path, stat.S_IWUSR)\n func(path)\n else:\n raise","def handle_ioexception(self, exception):\n return True"],"string":"[\n \"def os_error():\\n en = ctypes.get_errno()\\n ctypes.set_errno(0)\\n if en == 0:\\n return OSError(en, \\\"(no errno found)\\\")\\n else:\\n return OSError(en, errno.errorcode[en])\",\n \"def test_dump_handles_os_error(mocker):\\n\\tmocker.patch('subprocess.Popen', side_effect=OSError('no such file'))\\n\\twith pytest.raises(SystemExit):\\n\\t\\tpgdump.dump(url)\",\n \"def test_load_json_os_error() -> None:\\n fname = \\\"/\\\"\\n with pytest.raises(HomeAssistantError) as err:\\n load_json(fname)\\n assert isinstance(err.value.__cause__, OSError)\",\n \"def test_dump_handles_oserror(mocker):\\n mocker.patch('subprocess.Popen' , side_effect=OSError(\\\"no such file\\\"))\\n with pytest.raises(SystemExit):\\n pgdump.dump(url)\",\n \"def test_make_sure_path_exists_correctly_handle_os_error(mocker):\\n mocker.patch(\\\"pathlib.Path.mkdir\\\", side_effect=OSError)\\n with pytest.raises(OSError) as err:\\n utils.make_sure_path_exists(Path('protected_path'))\\n assert str(err.value) == \\\"Unable to create directory at protected_path\\\"\",\n \"def _check_error(return_value):\\n if return_value < 0:\\n raise IOError(pm.lib.Pm_GetErrorText(return_value))\",\n \"def test_check_if_error(self):\\n with self.assertRaises(MyError):\\n SshErrorExitCodeController(255, ERROR_MESSAGE)\\\\\\n .check_if_error()\",\n \"def test_make_tool_plugin_scan_oserror(mock_subprocess_check_output):\\n mock_subprocess_check_output.side_effect = OSError('mocked error')\\n mtp = setup_make_tool_plugin()\\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\\n 'valid_package'))\\n package['make_targets'] = 'make_targets'\\n issues = mtp.scan(package, 'level')\\n assert issues is None\",\n \"def _check(error: int) -> None:\\n if error < 0:\\n raise RuntimeError(ffi.string(lib.TCOD_get_error()).decode())\",\n \"def test_shell_bad_command():\\n out, err = shell_command(\\\"ls adasdasdas\\\")\\n assert out is None\\n assert \\\"adasdasdas\\\" in err\",\n \"def _file_does_not_exist_error(exception):\\n stre = str(exception)\\n # return any(err in stre for err in (\\\"ENOENT\\\", \\\"ENODEV\\\", \\\"EINVAL\\\"))\\n return any(err in stre for err in (\\\"ENOENT\\\", \\\"ENODEV\\\", \\\"EINVAL\\\"))\",\n \"def suppress_oserror(*errnos):\\n try:\\n yield\\n except OSError as e:\\n if e.errno not in errnos:\\n raise e\",\n \"def walk_error(os_error):\\n printer(\\\"Cannot access '{}'; zip creation aborted\\\".format(os_error.filename), \\\"error\\\", True)\\n raise os_error\",\n \"def test_missing_file():\\n passed = False\\n try:\\n x = XPIManager('foo.bar')\\n except:\\n passed = True\\n assert passed\",\n \"def test_missing_file(self):\\n with self.assertRaises(subprocess.CalledProcessError) as cm:\\n subprocess.check_output(\\n [sys.executable, idf_py_path, '--version', '@args_non_existent'],\\n env=os.environ,\\n stderr=subprocess.STDOUT).decode('utf-8', 'ignore')\\n self.assertIn('(expansion of @args_non_existent) could not be opened', cm.exception.output.decode('utf-8', 'ignore'))\",\n \"def test_no_such_file(self):\\n with self.assertRaises(IOError):\\n analyze_text('foobar')\",\n \"def test_no_such_file(self):\\n with self.assertRaises(IOError):\\n analyze_text('foobar')\",\n \"def test_invalid_path() -> None:\\n path = rsc / \\\"does-not-exist.ods\\\"\\n with pytest.raises(FileNotFoundError, match=\\\"does not exist\\\"):\\n read_ods(path)\",\n \"def test_no_such_file(self):\\n with self.assertRaises(IOError):\\n analyze_text('foobar')\",\n \"def test_option_unhandled(self):\\n cmd, output = runCmdOutput(['--__unhandled__'])\\n self.assertEqual(cmd.returncode, os.EX_USAGE)\",\n \"def test_existing_file_after_assert_error(exist_of_file):\\n try:\\n assert read_magic_number(exist_of_file)\\n except AssertionError:\\n print(\\\"Now lets do check of existing file\\\")\",\n \"def test_exceptions():\\r\\n # test that trying to connect_ to a non existent app fails\\r\\n try:\\r\\n app = application.Application()\\r\\n app.connect(path=r\\\"No process with this please\\\")\\r\\n assert False\\r\\n except application.ProcessNotFoundError:\\r\\n print('ProcessNotFoundError has been raised. OK.')\\r\\n\\r\\n # test that trying to connect_ to a non existent app fails\\r\\n try:\\r\\n app = application.Application()\\r\\n app.start(cmd_line = r\\\"No process with this please\\\")\\r\\n assert False\\r\\n except application.AppStartError:\\r\\n print('AppStartError has been raised. OK.')\",\n \"def test_check_if_not_error(self):\\n actual_result = SshErrorExitCodeController(ERROR_RETURN_CODE,\\n OK_MESSAGE)\\\\\\n .check_if_error()\\n self.assertIsNone(actual_result)\",\n \"def _errcheck_link(value, func, args): # pylint: disable=W0613\\n # The windows api returns nonzero if the call was successful\\n if value != 0:\\n return\\n\\n last_error = ctypes.windll.kernel32.GetLastError()\\n # Somehow CreateSymbolicLinkW and CreateHardLinkW retuns zero\\n # and the last error is 2 (The system cannot find the file specified)\\n # but the link is created successfuly\\n # it seems like a bug in the WinAPI\\n if last_error == 0 or last_error == 2:\\n return\\n if last_error == 183:\\n raise OSError(errno.EEXIST,\\n \\\"Cannot create a file when that file already exists\\\",\\n args[0])\",\n \"def test_bad_file() -> None:\\n\\n bad = random_string()\\n rv, out = getstatusoutput(f'{RUN} {bad}')\\n assert rv != 0\\n assert out.lower().startswith('usage:')\\n assert re.search(f\\\"No such file or directory: '{bad}'\\\", out)\",\n \"def test_check_if_error_one(self):\\n with self.assertRaises(MyError):\\n SshpassErrorExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\\\\n .check_if_error()\",\n \"def test_check_if_not_error(self):\\n actual_result = SshErrorExitCodeController(OK_RETURN_CODE,\\n OK_MESSAGE)\\\\\\n .check_if_error()\\n self.assertIsNone(actual_result)\",\n \"def test_no_such_file(self):\\n\\t\\twith self.assertRaises(IOError):\\n\\t\\t\\tanalyse_text('foobar')\",\n \"def test_watch_raises(capsys):\\n with mock.patch('uflash.watch_file', side_effect=RuntimeError(\\\"boom\\\")):\\n with pytest.raises(SystemExit):\\n uflash.main(argv=['--watch', 'test.py'])\\n\\n _, stderr = capsys.readouterr()\\n expected = 'Error watching test.py'\\n assert expected in stderr\",\n \"def convert_oserror(exc, pid=None, name=None):\\n assert isinstance(exc, OSError), exc\\n if is_permission_err(exc):\\n return AccessDenied(pid=pid, name=name)\\n if exc.errno == errno.ESRCH:\\n return NoSuchProcess(pid=pid, name=name)\\n raise exc\",\n \"def raise_for_darwin_exception(response: requests.Response) -> None:\\n if response.status_code == 200:\\n return\\n if response.status_code == 401:\\n raise Unauthorized(response)\\n if response.status_code == 404:\\n raise NotFound(response)\",\n \"def check_platform():\\n if os.getcwd() != os.path.dirname(os.path.abspath(__file__)):\\n error = \\\"must be ran in the directory it's located at\\\"\\n if os.path.sep != '/':\\n error = \\\"a unix-like operating system is required\\\"\\n elif not shutil.which('dpkg-deb'):\\n error = \\\"cannot find dpkg-deb\\\"\\n elif os.getuid() != 0:\\n error = \\\"must be ran as root (or with fakeroot)\\\"\\n else:\\n return\\n sys.exit(\\\"{}: error: {}\\\".format(sys.argv[0], error))\",\n \"def test_info_fail(self):\\n path = \\\"non_existing_audio.wav\\\"\\n with self.assertRaisesRegex(RuntimeError, path):\\n self._info(path)\",\n \"def test_NameError(n=2):\\n\\n p = platform_name()\\n\\n try:\\n p.result()\\n except NameError:\\n print(\\\"Caught NameError\\\")\\n else:\\n assert False, \\\"Raise the wrong Error\\\"\",\n \"def test_check_if_error(self):\\n with self.assertRaises(MyError):\\n ExecutionExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\\\\n .check_if_error()\",\n \"def test_exit_on_missing_file(self):\\n with self.assertRaises(SystemExit):\\n pyint = Interpreter()\\n pyint.run(file=MISSING_FILE)\",\n \"def test_unexpected_error_in_exists(self):\\n # TODO\\n one_process_workflow = \\\"\\\"\\\"buggy://B <- file://A\\n echo A produces B > B\\n \\\"\\\"\\\"\\n process = run_first_process(one_process_workflow, extra_resource=BuggyExistsResource)\\n assert process.success is False, process.error_message\\n assert process.error_message.find('An unexpected error have happen in tuttle while checking existence of '\\n 'output resources' ) >= 0, process.error_message\\n assert process.error_message.find('Traceback (most recent call last):') >= 0, process.error_message\\n assert process.error_message.find('raise Exception(\\\"Unexpected error in exists()\\\")') >= 0, process.error_message\\n assert process.error_message.find('Process cannot be considered complete.') >= 0, process.error_message\",\n \"def test_noreleaseerror_raise(self, mock_path, mock_open, mock_error):\\n # Set the mocked functions returned values\\n mock_path.isfile.side_effect = [True]\\n mock_context_manager = mock.Mock()\\n mock_open.return_value = mock_context_manager\\n mock_file = mock.Mock()\\n mock_file.read.return_value = \\\"WRONG\\\"\\n mock_enter = mock.Mock()\\n mock_enter.return_value = mock_file\\n mock_exit = mock.Mock()\\n setattr(mock_context_manager, \\\"__enter__\\\", mock_enter)\\n setattr(mock_context_manager, \\\"__exit__\\\", mock_exit)\\n\\n # Test execution\\n ConnectomistWrapper._connectomist_version_check(\\\"/my/path/mock_conf\\\")\\n self.assertEqual(len(mock_error.call_args_list), 1)\",\n \"def testError(self):\\n cmds = \\\"\\\"\\\"chown 0 missingFile\\npwd\\nexit\\n\\\"\\\"\\\"\\n\\n def _cbCheckResult(res):\\n self.assertNotIn(self.testDir.asBytesMode().path, res)\\n\\n d = self._getBatchOutput(cmds)\\n d.addCallback(_cbCheckResult)\\n return d\",\n \"def is_permission_err(exc):\\n assert isinstance(exc, OSError), exc\\n # On Python 2 OSError doesn't always have 'winerror'. Sometimes\\n # it does, in which case the original exception was WindowsError\\n # (which is a subclass of OSError).\\n return exc.errno in (errno.EPERM, errno.EACCES) or \\\\\\n getattr(exc, \\\"winerror\\\", -1) in (cext.ERROR_ACCESS_DENIED,\\n cext.ERROR_PRIVILEGE_NOT_HELD)\",\n \"def test_nonexistent_path():\\r\\n with pytest.raises(RuntimeError):\\r\\n Image(os.path.join(current_dir, \\\"0--0.jpg\\\")).read_all()\\r\\n assert check_md5(path, jpg_path), \\\"The file has been changed when reading\\\"\",\n \"def testTempDirOrininalErrorRaised(self):\\n self.Patch(os, \\\"chmod\\\")\\n self.Patch(tempfile, \\\"mkdtemp\\\", return_value=\\\"/tmp/tempdir\\\")\\n expected_error = OSError(\\\"Expected OS Error\\\")\\n self.Patch(shutil, \\\"rmtree\\\", side_effect=expected_error)\\n\\n class ExpectedException(Exception):\\n \\\"\\\"\\\"Expected exception.\\\"\\\"\\\"\\n\\n def _Call():\\n with utils.TempDir():\\n raise ExpectedException(\\\"Expected Exception\\\")\\n\\n # Verify.\\n # ExpectedException should be raised, and OSError\\n # should not be raised.\\n self.assertRaises(ExpectedException, _Call)\\n tempfile.mkdtemp.assert_called_once() #pylint: disable=no-member\\n shutil.rmtree.assert_called_with(\\\"/tmp/tempdir\\\") #pylint: disable=no-member\",\n \"def test_handles_error(self):\\n with self.assertRaises(ForcedExit):\\n self.run_command(mkdtemp())\\n\\n self.assertResults(\\n result_with_hint(\\n u'This repository has not been initialized.',\\n GIT_REPO_NOT_INITIALIZED),\\n self.error)\",\n \"def test_check_if_error(self):\\n with self.assertRaises(MyError):\\n BaseErrorExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\\\\n .check_if_error()\",\n \"def check_os():\\n\\n if platform.system() != \\\"Darwin\\\":\\n print \\\"This script only works on macos system\\\"\\n exit(1)\",\n \"def test_cmd_error(self):\\n task = Task(\\\"uid\\\", False, False, \\\"does_not_exist\\\", None, \\\".\\\")\\n task._checkpoint_dir = tmp_checkpoint_dir()\\n with self.assertRaisesRegexp(RuntimeError, \\\".*executing Task's command:.*\\\"):\\n task.run()\\n task.shell = True\\n with self.assertRaisesRegexp(RuntimeError, \\\".*executing Task's command:.*\\\"):\\n task.run()\\n task._dry_run = True\\n task.run() # No longer raises RuntimeError\",\n \"def test_normal_use(self):\\n # Setup:\\n class DatabaseError(Exception):\\n pass\\n\\n # Python 2 and 3:\\n from future.utils import raise_from\\n\\n class FileDatabase:\\n def __init__(self, filename):\\n try:\\n self.file = open(filename)\\n except IOError as exc:\\n raise_from(DatabaseError('failed to open'), exc)\\n\\n # Testing the above:\\n try:\\n fd = FileDatabase('non_existent_file.txt')\\n except Exception as e:\\n assert isinstance(e.__cause__, IOError) # FileNotFoundError on\\n # Py3.3+ inherits from IOError\",\n \"def check_result(ec):\\r\\n # NOTE: This will break some oscilloscopes that are powered by USB.\\r\\n # Some of the newer scopes, can actually be powered by USB and will\\r\\n # return a useful value. That should be given back to the user.\\r\\n # I guess we can deal with these edge cases in the functions themselves\\r\\n if ec == 0:\\r\\n return\\r\\n\\r\\n else:\\r\\n ecName = error_num_to_name(ec)\\r\\n ecDesc = error_num_to_desc(ec)\\r\\n raise IOError('Error calling %s: %s (%s)' % (\\r\\n str(inspect.stack()[1][3]), ecName, ecDesc))\",\n \"def test_the_main_non_existent_file(self):\\r\\n with self.assertRaises(SystemExit):\\r\\n the_main_function(\\\"non existent file\\\")\",\n \"def test_is_information_written_through_stderr_methods(self):\\n\\n io = BufferedSystemIO()\\n io._stdout = lambda *args, **kwargs: None\\n\\n try:\\n raise IndexError('Invalid index 5')\\n except Exception as exc:\\n output_formatted_exception(exc, ':my-test-task', io)\\n\\n self.assertIn('IndexError', io.get_value())\\n self.assertIn('Invalid index 5', io.get_value())\\n self.assertIn('Retry with \\\"-rl debug\\\" switch before failed task to see stacktrace', io.get_value())\",\n \"def test_raises_error_on_hostnotfound(self):\\n self.mock_getaddrinfo.side_effect = socket.gaierror('error_message')\\n return_value = None\\n with self.assertRaises(CMDLineExit) as cm:\\n return_value = sanitize_host('erroneous_host')\\n self.assertIsNone(return_value)\\n self.assertEqual(\\n cm.exception.args[0],\\n 'Could not resolve hostname \\\\'erroneous_host\\\\': error_message')\\n self.mock_getaddrinfo.assert_called_once_with(\\n 'erroneous_host', 443, socket.AF_INET, socket.SOCK_STREAM)\\n self.mock_gethostbyaddr.assert_not_called()\",\n \"def run_or_die(command):\\n (status, stdio) = commands.getstatusoutput(command)\\n if status != 0:\\n raise Exception(\\\"command '%s' failed with exit status %d and output '%s'\\\" % (command, status, stdio))\\n return stdio\",\n \"def test_simple_source_constructor_exception():\\n TESTPATH = \\\"/usr/local/share/testfile.mp3\\\"\\n with pytest.raises(robox.RDJResourceErr):\\n test01 = Source(path=TESTPATH, exist=True)\",\n \"def error_check(command):\\r\\n\\r\\n # TODO\\r\",\n \"def errorCheck(sh, returncode, stderr):\\n\\tif returncode!=0 or stderr!='':\\n\\t\\tif config.DEBUG:\\n\\t\\t\\tmsg = \\\"sh code execution [%s] returned non-zero exit status [%s] and/or non-empty stdterr [%s]\\\" % (repr(sh), returncode, repr(stderr.strip()))\\n\\t\\telse:\\n\\t\\t\\tmsg = \\\"sh code execution returned non-zero exit status and/or non-empty stdterr\\\"\\n\\t\\traise Exception(msg)\",\n \"def test_bad_file():\\n\\n bad = ''.join(random.choices(string.ascii_uppercase + string.digits, k=5))\\n rv, out = getstatusoutput(f'{prg} -f {bad}')\\n assert rv != 0\\n assert re.match('usage:', out, re.I)\\n assert re.search(f\\\"No such file or directory: '{bad}'\\\", out)\",\n \"def test_invoke_pipe_not_found():\\n\\n testapp = holocron.Application()\\n\\n with pytest.raises(ValueError) as excinfo:\\n next(testapp.invoke(\\\"test\\\"))\\n\\n assert str(excinfo.value) == \\\"no such pipe: 'test'\\\"\",\n \"def test_not_running(): # pragma: windows\\n comm_kwargs = dict(comm='IPCComm', direction='send', reverse_names=True)\\n nt.assert_raises(RuntimeError, new_comm, 'test', **comm_kwargs)\",\n \"def test_mount_failure(self):\\n with prepared_image_file(create_filesystem=False):\\n program = RsyncSystemBackup(\\n crypto_device=CRYPTO_NAME,\\n destination=os.path.join(MOUNT_POINT, 'latest'),\\n mount_point=MOUNT_POINT,\\n )\\n # When `mount' fails it should exit with a nonzero exit code,\\n # thereby causing executor to raise an ExternalCommandFailed\\n # exception that obscures the FailedToMountError exception that\\n # we're interested in. The check=False option enables our\\n # `last resort error handling' code path to be reached.\\n program.destination_context.options['check'] = False\\n self.assertRaises(FailedToMountError, program.execute)\",\n \"def _try_ignoring_ent_and_perm(func, *args, **kwargs):\\n try:\\n func(*args, **kwargs)\\n except OSError as err:\\n if err.errno == errno.ENOENT or err.errno == errno.EPERM:\\n return\\n else:\\n raise err\",\n \"def test_nonexistent_path(tmpdir):\\n with pytest.raises(IOError):\\n checksum(tmpdir.join(\\\"does-not-exist.txt\\\").strpath)\",\n \"def exitIfFileDescriptorError(exit_code, device):\\n if exit_code == ExitCode.FILE_DESCRIPTOR_ERROR:\\n logging.critical(\\\"Cannot access to %s.\\\", device)\\n sys.exit(ExitCode.FILE_DESCRIPTOR_ERROR)\",\n \"def test_open_image_throws(self):\\n self.expect_open_image(\\n 'SomeBlobKey',\\n throw_exception=apiproxy_errors.ApplicationError(\\n images_service_pb.ImagesServiceError.INVALID_BLOB_KEY))\\n self.mox.ReplayAll()\\n try:\\n self.app._transform_image('SomeBlobKey', '')\\n raise self.failureException('Should have thrown ApplicationError')\\n except apiproxy_errors.ApplicationError:\\n pass\\n self.mox.VerifyAll()\",\n \"def onerror(func, path, exc_info):\\n import stat\\n if not os.path.exists(path):\\n pass\\n elif not os.access(path, os.W_OK):\\n # Is the error an access error ?\\n os.chmod(path, stat.S_IWUSR)\\n func(path)\\n else:\\n raise\",\n \"def test_fetch_py_err():\\n ident = _id()\\n entry_dir = os.path.join(basedir, 'always_error', ident)\\n res = proj.fetch('always_error', ident)\\n assert res.result is None\\n paths = ['error.log', 'status', 'run.log', 'start_time', 'end_time']\\n for p in paths:\\n assert os.path.exists(os.path.join(entry_dir, p))\\n with open(res.paths['log']) as fd:\\n assert 'output here' in fd.read()\\n with open(res.paths['error']) as fd:\\n assert 'This is an error!' in fd.read()\\n with open(res.paths['status']) as fd:\\n status = fd.read()\\n assert status == 'error'\\n assert res.status == status\\n with open(res.paths['start_time']) as fd:\\n start_time = int(fd.read())\\n with open(res.paths['end_time']) as fd:\\n end_time = int(fd.read())\\n assert start_time <= end_time\",\n \"def test_exc(self):\\n g = h5g.open(self.fid, '/')\\n g._close()\\n self.assertRaises(ValueError, h5i.get_file_id, g)\",\n \"def test_raise_error_unknown_file():\\n\\n options = {'input_files': ['Sparta.lol']}\\n\\n with pytest.raises(KeyError) as excinfo:\\n process.Packager(options)\\n excinfo.match(r'File ([a-zA-Z_\\\\.\\\\'].*) not found in file list.')\",\n \"def shellExecErrorCode(cmd):\\n return subprocess.call(cmd, shell=True)\",\n \"def test_init(self):\\n nt.assert_raises(Exception, CisInterface.CisOutput, 'error')\",\n \"def has_errors_fatal(self) -> bool:\",\n \"def test_raise_exception(self):\\n with self.assertRaises(Exception):\\n SshCommandBuilder(SERVER_USER, COMMAND).to_build()\",\n \"def test_invalidFile(self):\\n self.assertRaises(cesmEnvLib.checkFile(\\\"blah\\\", \\\"write\\\"))\",\n \"def test_extract_raises(capsys):\\n with mock.patch('uflash.extract', side_effect=RuntimeError(\\\"boom\\\")):\\n with pytest.raises(SystemExit):\\n uflash.main(argv=['--extract', 'test.py'])\\n\\n _, stderr = capsys.readouterr()\\n expected = 'Error extracting test.py'\\n assert expected in stderr\",\n \"def testTempDirWhenDeleteEncounterError(self):\\n self.Patch(os, \\\"chmod\\\")\\n self.Patch(tempfile, \\\"mkdtemp\\\", return_value=\\\"/tmp/tempdir\\\")\\n expected_error = OSError(\\\"Expected OS Error\\\")\\n self.Patch(shutil, \\\"rmtree\\\", side_effect=expected_error)\\n\\n def _Call():\\n with utils.TempDir():\\n pass\\n\\n # Verify OSError should be raised.\\n self.assertRaises(OSError, _Call)\\n tempfile.mkdtemp.assert_called_once() #pylint: disable=no-member\\n shutil.rmtree.assert_called_with(\\\"/tmp/tempdir\\\") #pylint: disable=no-member\",\n \"def look_for_cython_error(capfd):\\n yield\\n _, err = capfd.readouterr()\\n assert \\\"Exception ignored\\\" not in err\",\n \"def test_stats_issue_43328(test_file):\\n fake_file = test_file.parent / \\\"fake.file\\\"\\n with pytest.raises(CommandExecutionError):\\n win_file.stats(fake_file)\",\n \"def test_bad_file():\\n\\n bad_file = random_string()\\n letter = random.choice(string.ascii_lowercase)\\n rv, out = getstatusoutput('{} {} -f {}'.format(prg, letter, bad_file))\\n assert rv != 0\\n expected = \\\"No such file or directory: '{}'\\\".format(bad_file)\\n assert re.search(expected, out)\",\n \"def test_check_if_not_error(self):\\n actual_result = SshpassErrorExitCodeController(OK_RETURN_CODE,\\n OK_MESSAGE)\\\\\\n .check_if_error()\\n self.assertIsNone(actual_result)\",\n \"def test_sign_file_unknown_error(dummy_command, tmp_path):\\n # Raise an unknown error during codesign\\n dummy_command.tools.subprocess.run.side_effect = mock_codesign(\\\"Unknown error\\\")\\n\\n with pytest.raises(BriefcaseCommandError, match=\\\"Unable to code sign \\\"):\\n dummy_command.sign_file(\\n tmp_path / \\\"base_path\\\" / \\\"random.file\\\",\\n identity=\\\"Sekrit identity (DEADBEEF)\\\",\\n )\\n\\n # An attempt to codesign was made\\n dummy_command.tools.subprocess.run.assert_has_calls(\\n [\\n sign_call(\\n tmp_path,\\n tmp_path / \\\"base_path\\\" / \\\"random.file\\\",\\n entitlements=False,\\n ),\\n ],\\n any_order=False,\\n )\",\n \"def test_bytes_to_intel_hex_io_error(mock_string_io, mock_stderr):\\n data = [1, 2, 3, 4, 5]\\n mock_string_io.return_value.write.side_effect = IOError()\\n\\n result = cmds._bytes_to_intel_hex(data=data)\\n\\n assert result is None\\n assert mock_stderr.call_count == 1\",\n \"def FileCheck(fn):\\n try:\\n open(fn, \\\"r\\\")\\n return 1\\n except IOError:\\n print(\\\"Error: File does not exist.\\\")\\n return 0\",\n \"def test_execute_or_bail_internal_error(self):\\n with self.assertLogs(level=\\\"INFO\\\") as cm:\\n # This patches the \\\"croak\\\" function that is IMPORTED inside \\\"etl.commands\\\".\\n with unittest.mock.patch(\\\"etl.commands.croak\\\") as mocked_croak:\\n with etl.commands.execute_or_bail(\\\"unittest\\\"):\\n # Simulate an internal error where we don't catch an exception.\\n raise ValueError(\\\"oops\\\")\\n mocked_croak.assert_called()\\n # The exit code (2nd arg) is expected to be 3 for uncaught exceptions.\\n self.assertEqual(mocked_croak.call_args[0][1], 3)\\n\\n self.assertEqual(len(cm.output), 2)\\n self.assertIn(\\\"terrible happened\\\", cm.output[0])\",\n \"def unexpected_error(self, exception):\",\n \"def test_with_empty_args(self, mock_builtins_open, capsys, prog, main):\\n with pytest.raises(SystemExit):\\n main()\\n _, err = capsys.readouterr()\\n assert err.startswith('usage: {}'.format(prog))\",\n \"def _check_error(self, ipi):\\n\\n ipi_error = ipi.communicate(timeout=120)[1].decode(\\\"ascii\\\")\\n assert \\\"\\\" == ipi_error, \\\"IPI ERROR OCCURED: {}\\\".format(ipi_error)\",\n \"def test_not_exectuable(self):\\n (status, output, imlog, makelog) = \\\\\\n self.run_instmake_build(log_prefix=\\\"not-executable\\\",\\n make_opts=[\\\"not-executable\\\"])\\n\\n self.assertEqual(status, util.SUCCESS, output)\",\n \"def test_broken_error_module(self):\\r\\n with self.assertRaises(TestException):\\r\\n module = self.descriptor._xmodule\",\n \"def isexe(fpath):\\n return path.exists(fpath) and access(fpath, X_OK)\",\n \"def test_check_module(self) -> None:\\n check_module(\\\"os\\\")\",\n \"def test_invalid_dir(self):\\n self.assertRaises(OSError, awstats_reader.AwstatsReader, '/tmp/XYZ', 'example.com')\",\n \"def test_valid_file_raises():\\n with pytest.raises(ValueError):\\n cli._valid_file(__file__)\",\n \"def NotADirectoryError(inst):\\n return hasattr(inst, 'errno') and inst.errno == errno.ENOTDIR\",\n \"def test_raise_if_err_ok(self) -> None:\\n assert Ok(2).raise_if_err(\\\"err\\\") == 2\",\n \"def test_check_if_error_six(self):\\n with self.assertRaises(MyError):\\n SshpassErrorExitCodeController(6, ERROR_MESSAGE)\\\\\\n .check_if_error()\",\n \"def test_read_no_file():\\n filename = 'asdf'\\n with pytest.raises(FileNotFoundError):\\n read_file(filename)\",\n \"def test_broken_error_descriptor(self):\\r\\n with self.assertRaises(TestException):\\r\\n module = self.descriptor._xmodule\",\n \"def test_driver_from_file_errors(execute_kwargs_py):\\n with pytest.raises(errors.MapcheteDriverError):\\n driver_from_file(execute_kwargs_py)\\n\\n with pytest.raises(FileNotFoundError):\\n driver_from_file(\\\"non_existing_file.tif\\\", quick=False)\",\n \"def test_fail_flow_invalid_distro(self):\\n # make the mock for SshShell fail to execute commands\\n self._mock_ssh_shell.run.return_value = (1, 'command not found')\\n\\n # instantiate the guest class we want to test\\n system_name = 'dummy_system'\\n host_name = 'dummy.domain.com'\\n user = 'root'\\n passwd = 'somepwd'\\n extensions = {}\\n guest_obj = linux.GuestLinux(\\n system_name, host_name, user, passwd, extensions)\\n\\n # check if correctly raises the exception\\n self.assertRaises(ConnectionError, guest_obj.login)\\n\\n # change the mock now to successfully execute the command but report an\\n # incorrect kernel name\\n self._mock_ssh_shell.run.return_value = (\\n 0,\\n 'OtherOS dummy 4.4.6 #1 SMP Wed Mar 16 22:13:40 UTC 2016 x86_64 '\\n 'x86_64 x86_64 Some/OS'\\n )\\n guest_obj = linux.GuestLinux(\\n system_name, host_name, user, passwd, extensions)\\n\\n # check if correctly raises the exception\\n self.assertRaisesRegex(\\n ConnectionError, '^Target system is not Linux$', guest_obj.login\\n )\",\n \"def on_error(func, path, exc_info):\\n import stat\\n if not os.access(path, os.W_OK):\\n # Is the error an access error ?\\n os.chmod(path, stat.S_IWUSR)\\n func(path)\\n else:\\n raise\",\n \"def handle_ioexception(self, exception):\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7091949","0.68408364","0.6782386","0.6650079","0.66032916","0.6333252","0.6230165","0.6217626","0.61586803","0.6140074","0.60988116","0.6090791","0.6002094","0.60008836","0.598052","0.5965969","0.5965969","0.59647274","0.595298","0.59336317","0.59047925","0.58992267","0.5898923","0.5870315","0.58648866","0.5844375","0.5834275","0.5806941","0.58029044","0.57923126","0.57725996","0.5769801","0.57650244","0.5756039","0.57545274","0.5749916","0.5742899","0.57342035","0.5730068","0.5720979","0.57176006","0.5697197","0.56777006","0.56688285","0.5658031","0.5654664","0.5649373","0.5642489","0.5634441","0.56309235","0.5628874","0.56259507","0.5625503","0.5622651","0.5610448","0.561016","0.56099075","0.560556","0.55936867","0.55932784","0.5592612","0.5582704","0.5576372","0.55696994","0.55675554","0.55628127","0.5558178","0.55575484","0.5552635","0.55525124","0.5551288","0.5549888","0.55413485","0.55366796","0.55298406","0.55283195","0.55251056","0.5515913","0.54934394","0.5493312","0.54880226","0.54828364","0.54690677","0.5466026","0.54597986","0.5459236","0.5453509","0.5445545","0.54430455","0.5430554","0.5422081","0.5420834","0.5419391","0.5418308","0.5417813","0.5409733","0.5408553","0.54080474","0.5403379","0.5398198"],"string":"[\n \"0.7091949\",\n \"0.68408364\",\n \"0.6782386\",\n \"0.6650079\",\n \"0.66032916\",\n \"0.6333252\",\n \"0.6230165\",\n \"0.6217626\",\n \"0.61586803\",\n \"0.6140074\",\n \"0.60988116\",\n \"0.6090791\",\n \"0.6002094\",\n \"0.60008836\",\n \"0.598052\",\n \"0.5965969\",\n \"0.5965969\",\n \"0.59647274\",\n \"0.595298\",\n \"0.59336317\",\n \"0.59047925\",\n \"0.58992267\",\n \"0.5898923\",\n \"0.5870315\",\n \"0.58648866\",\n \"0.5844375\",\n \"0.5834275\",\n \"0.5806941\",\n \"0.58029044\",\n \"0.57923126\",\n \"0.57725996\",\n \"0.5769801\",\n \"0.57650244\",\n \"0.5756039\",\n \"0.57545274\",\n \"0.5749916\",\n \"0.5742899\",\n \"0.57342035\",\n \"0.5730068\",\n \"0.5720979\",\n \"0.57176006\",\n \"0.5697197\",\n \"0.56777006\",\n \"0.56688285\",\n \"0.5658031\",\n \"0.5654664\",\n \"0.5649373\",\n \"0.5642489\",\n \"0.5634441\",\n \"0.56309235\",\n \"0.5628874\",\n \"0.56259507\",\n \"0.5625503\",\n \"0.5622651\",\n \"0.5610448\",\n \"0.561016\",\n \"0.56099075\",\n \"0.560556\",\n \"0.55936867\",\n \"0.55932784\",\n \"0.5592612\",\n \"0.5582704\",\n \"0.5576372\",\n \"0.55696994\",\n \"0.55675554\",\n \"0.55628127\",\n \"0.5558178\",\n \"0.55575484\",\n \"0.5552635\",\n \"0.55525124\",\n \"0.5551288\",\n \"0.5549888\",\n \"0.55413485\",\n \"0.55366796\",\n \"0.55298406\",\n \"0.55283195\",\n \"0.55251056\",\n \"0.5515913\",\n \"0.54934394\",\n \"0.5493312\",\n \"0.54880226\",\n \"0.54828364\",\n \"0.54690677\",\n \"0.5466026\",\n \"0.54597986\",\n \"0.5459236\",\n \"0.5453509\",\n \"0.5445545\",\n \"0.54430455\",\n \"0.5430554\",\n \"0.5422081\",\n \"0.5420834\",\n \"0.5419391\",\n \"0.5418308\",\n \"0.5417813\",\n \"0.5409733\",\n \"0.5408553\",\n \"0.54080474\",\n \"0.5403379\",\n \"0.5398198\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.65513504"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":8294,"cells":{"query":{"kind":"string","value":"Test what happens when a CalledProcessError is raised (usually means isort hit an error)."},"document":{"kind":"string","value":"def test_isort_tool_plugin_scan_calledprocesserror(mock_subprocess_check_output):\n mock_subprocess_check_output.side_effect = subprocess.CalledProcessError(\n 0, \"\", output=\"mocked error\"\n )\n itp = setup_isort_tool_plugin()\n package = Package(\n \"valid_package\", os.path.join(os.path.dirname(__file__), \"valid_package\")\n )\n package[\"python_src\"] = [\n os.path.join(os.path.dirname(__file__), \"valid_package\", \"sample.py\")\n ]\n issues = itp.scan(package, \"level\")\n assert len(issues) == 1"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_make_tool_plugin_scan_calledprocesserror(mock_subprocess_check_output):\n mock_subprocess_check_output.side_effect = subprocess.CalledProcessError(1, '', output=\"mocked error\")\n mtp = setup_make_tool_plugin()\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\n 'valid_package'))\n package['make_targets'] = 'make_targets'\n issues = mtp.scan(package, 'level')\n assert not issues","def check_call(*args, **kwargs):\n rc = call(*args, **kwargs)\n if rc != 0:\n cmd = kwargs.get(\"args\")\n if cmd is None:\n cmd = args[0]\n raise CalledProcessError(rc, cmd)\n return 0","def test_dump_handles_oserror(mocker):\n mocker.patch('subprocess.Popen' , side_effect=OSError(\"no such file\"))\n with pytest.raises(SystemExit):\n pgdump.dump(url)","def called_process_error2exit_decorator(func):\n @functools.wraps(func)\n def func_wrapper(*args, **kwargs):\n try:\n func(*args, **kwargs)\n except subprocess.CalledProcessError as e:\n print(\"{err}:\\n{msg}\".format(err=str(e), msg=e.output))\n sys.exit(1)\n return func_wrapper","def test_dump_handles_os_error(mocker):\n\tmocker.patch('subprocess.Popen', side_effect=OSError('no such file'))\n\twith pytest.raises(SystemExit):\n\t\tpgdump.dump(url)","def test_start_args(self, mocked_check, mocked_proc):\n from supvisors.rpcinterface import RPCInterface\n # prepare context\n info_source = self.supervisor.supvisors.info_source\n info_source.update_extra_args.side_effect = KeyError\n info_source.supervisor_rpc_interface.startProcess.side_effect = [\n RPCError(Faults.NO_FILE, 'no file'),\n RPCError(Faults.NOT_EXECUTABLE),\n RPCError(Faults.ABNORMAL_TERMINATION),\n 'done']\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n # test RPC call with extra arguments and a process that is not compliant\n with self.assertRaises(RPCError) as exc:\n rpc.start_args('appli:proc', 'dummy arguments')\n self.assertEqual(Faults.BAD_EXTRA_ARGUMENTS, exc.exception.code)\n self.assertEqual(\"BAD_EXTRA_ARGUMENTS: rules for namespec appli:proc\"\n \" are not compatible with extra arguments in command line\",\n exc.exception.text)\n self.assertEqual(0, mocked_check.call_count)\n self.assertEqual(0, info_source.update_extra_args.call_count)\n self.assertEqual(0, info_source.supervisor_rpc_interface.startProcess.call_count)\n # test RPC call with extra arguments and a process that is compliant\n # but unknown in Supervisor\n with self.assertRaises(RPCError) as exc:\n rpc.start_args('appli:proc', 'dummy arguments')\n self.assertEqual(Faults.BAD_NAME, exc.exception.code)\n self.assertEqual(\"BAD_NAME: namespec appli:proc unknown in this Supervisor instance\",\n exc.exception.text)\n self.assertEqual([call('appli:proc', 'dummy arguments')],\n info_source.update_extra_args.call_args_list)\n self.assertEqual(0, info_source.supervisor_rpc_interface.startProcess.call_count)\n info_source.update_extra_args.reset_mock()\n info_source.update_extra_args.side_effect = None\n # test RPC call with start exceptions\n mocked_proc.side_effect = None\n mocked_proc.return_value = None, None\n # NO_FILE exception triggers an update of the process state\n with self.assertRaises(RPCError) as exc:\n rpc.start_args('appli:proc')\n self.assertEqual(Faults.NO_FILE, exc.exception.code)\n self.assertEqual(\"NO_FILE: no file\", exc.exception.text)\n self.assertEqual([call('appli:proc', '')],\n info_source.update_extra_args.call_args_list)\n self.assertEqual([call('appli:proc', True)],\n info_source.supervisor_rpc_interface.startProcess.call_args_list)\n self.assertEqual([call('appli:proc', 'NO_FILE: no file')],\n info_source.force_process_fatal.call_args_list)\n # reset patches\n info_source.update_extra_args.reset_mock()\n info_source.force_process_fatal.reset_mock()\n info_source.supervisor_rpc_interface.startProcess.reset_mock()\n # NOT_EXECUTABLE exception triggers an update of the process state\n with self.assertRaises(RPCError) as exc:\n rpc.start_args('appli:proc', wait=False)\n self.assertEqual(Faults.NOT_EXECUTABLE, exc.exception.code)\n self.assertEqual(\"NOT_EXECUTABLE\", exc.exception.text)\n self.assertEqual([call('appli:proc', '')],\n info_source.update_extra_args.call_args_list)\n self.assertEqual([call('appli:proc', False)],\n info_source.supervisor_rpc_interface.startProcess.call_args_list)\n self.assertEqual([call('appli:proc', 'NOT_EXECUTABLE')],\n info_source.force_process_fatal.call_args_list)\n # reset patches\n info_source.update_extra_args.reset_mock()\n info_source.force_process_fatal.reset_mock()\n info_source.supervisor_rpc_interface.startProcess.reset_mock()\n # other exception doesn't trigger an update of the process state\n with self.assertRaises(RPCError) as exc:\n rpc.start_args('appli:proc', wait=False)\n self.assertEqual(Faults.ABNORMAL_TERMINATION, exc.exception.code)\n self.assertEqual(\"ABNORMAL_TERMINATION\", exc.exception.text)\n self.assertEqual([call('appli:proc', '')],\n info_source.update_extra_args.call_args_list)\n self.assertEqual([call('appli:proc', False)],\n info_source.supervisor_rpc_interface.startProcess.call_args_list)\n self.assertEqual(0, info_source.force_process_fatal.call_count)\n # reset patches\n info_source.update_extra_args.reset_mock()\n info_source.supervisor_rpc_interface.startProcess.reset_mock()\n # finally, normal behaviour\n self.assertEqual('done', rpc.start_args('appli:proc'))","def test_startProcessUnknownKeyError(self):\r\n self.assertRaises(KeyError, self.pm.startProcess, \"foo\")","def test_not_running(): # pragma: windows\n comm_kwargs = dict(comm='IPCComm', direction='send', reverse_names=True)\n nt.assert_raises(RuntimeError, new_comm, 'test', **comm_kwargs)","def sanity_check_process(self):\n assert_equals(self.proc.returncode, None)\n time.sleep(1)","def print_unable_to_run(exc: \"CalledProcessError\"):\n _print(str(exc), level=MessageLevel.QUIET)","def test_exceptions():\r\n # test that trying to connect_ to a non existent app fails\r\n try:\r\n app = application.Application()\r\n app.connect(path=r\"No process with this please\")\r\n assert False\r\n except application.ProcessNotFoundError:\r\n print('ProcessNotFoundError has been raised. OK.')\r\n\r\n # test that trying to connect_ to a non existent app fails\r\n try:\r\n app = application.Application()\r\n app.start(cmd_line = r\"No process with this please\")\r\n assert False\r\n except application.AppStartError:\r\n print('AppStartError has been raised. OK.')","def test_runFailed(self):\n builder = BookBuilder()\n exc = self.assertRaises(\n CommandFailed, builder.run,\n [sys.executable, '-c', 'print \"hi\"; raise SystemExit(1)'])\n self.assertEquals(exc.exitStatus, 1)\n self.assertEquals(exc.exitSignal, None)\n self.assertEquals(exc.output, \"hi\\n\")","def test_invoke_pipe_not_found():\n\n testapp = holocron.Application()\n\n with pytest.raises(ValueError) as excinfo:\n next(testapp.invoke(\"test\"))\n\n assert str(excinfo.value) == \"no such pipe: 'test'\"","def check_call_out(args, **kwargs):\n out, returncode = communicate(args, **kwargs)\n if returncode:\n raise CalledProcessError(\n returncode, args, kwargs.get('cwd'), out[0], out[1])\n return out","def test_unexpected_error_in_processor(self):\n\n one_process_workflow = \"\"\"file://B <- file://A ! buggy_processor\n echo A does not produce B\n \"\"\"\n process = run_first_process(one_process_workflow, BuggyProcessor())\n assert process.success is False, process.error_message\n assert process.error_message.find('An unexpected error have happen in tuttle processor '\n 'buggy_processor :') >= 0, process.error_message\n assert process.error_message.find('Traceback (most recent call last):') >= 0, process.error_message\n assert process.error_message.find('raise Exception(\"Unexpected error in processor\")') >= 0, process.error_message\n assert process.error_message.find('will not complete.') >= 0, process.error_message","def test_subprocess_fork_pid0_exception(self, mocker):\n mocker.stopall()\n\n test_command = [\"who\", \"-b\"]\n test_name = \"test_who\"\n test_fork = True\n pid = 0\n\n # mock\n mock_logging_debug = mocker.MagicMock(name=\"mock_logging_debug\")\n mock_logging_error = mocker.MagicMock(name=\"mock_logging_error\")\n mock_os_fork = mocker.MagicMock(name=\"mock_os_fork\", side_effect=[pid, OSError])\n mock_sys_exit = mocker.MagicMock(name=\"mock_sys_exit\")\n mock_os_chdir = mocker.MagicMock(name=\"mock_os_chdir\")\n mock_os_setsid = mocker.MagicMock(name=\"mock_os_setsid\")\n mock_os_umask = mocker.MagicMock(name=\"mock_os_umask\")\n\n # patch\n mocker.patch.object(\n scarlett_os.subprocess.logging.Logger, \"debug\", mock_logging_debug\n )\n mocker.patch.object(\n scarlett_os.subprocess.logging.Logger, \"error\", mock_logging_error\n )\n mocker.patch.object(scarlett_os.subprocess.os, \"fork\", mock_os_fork)\n mocker.patch.object(scarlett_os.subprocess.sys, \"exit\", mock_sys_exit)\n mocker.patch.object(scarlett_os.subprocess.os, \"chdir\", mock_os_chdir)\n mocker.patch.object(scarlett_os.subprocess.os, \"setsid\", mock_os_setsid)\n mocker.patch.object(scarlett_os.subprocess.os, \"umask\", mock_os_umask)\n\n scarlett_os.subprocess.Subprocess(test_command, name=test_name, fork=test_fork)\n\n mock_logging_error.assert_any_call(\"Error forking process second time\")\n\n mocker.stopall()","def unavailable_process(**kwargs):\n return LazySubprocessTester([sys.executable, \"-c\", \"import sys; sys.exit(1)\"], **kwargs)","def test_execute_or_bail_internal_error(self):\n with self.assertLogs(level=\"INFO\") as cm:\n # This patches the \"croak\" function that is IMPORTED inside \"etl.commands\".\n with unittest.mock.patch(\"etl.commands.croak\") as mocked_croak:\n with etl.commands.execute_or_bail(\"unittest\"):\n # Simulate an internal error where we don't catch an exception.\n raise ValueError(\"oops\")\n mocked_croak.assert_called()\n # The exit code (2nd arg) is expected to be 3 for uncaught exceptions.\n self.assertEqual(mocked_croak.call_args[0][1], 3)\n\n self.assertEqual(len(cm.output), 2)\n self.assertIn(\"terrible happened\", cm.output[0])","def test_get_case_command_fail(loqusdbapi, mocker):\n # GIVEN a loqusdb api and a case id\n case_id = 'a_case'\n # WHEN an error occurs during fetching a case with the adapter\n mocker.patch.object(subprocess, 'check_output')\n subprocess.check_output.side_effect = subprocess.CalledProcessError(1, 'error')\n\n # THEN assert that the error is raised\n with pytest.raises(subprocess.CalledProcessError):\n loqusdbapi.get_case(case_id)","def test_subprocess_fails_with_no_command(self):\n with self.assertRaises(ValueError):\n LazySubprocessTester([])","def test_missing_file(self):\n with self.assertRaises(subprocess.CalledProcessError) as cm:\n subprocess.check_output(\n [sys.executable, idf_py_path, '--version', '@args_non_existent'],\n env=os.environ,\n stderr=subprocess.STDOUT).decode('utf-8', 'ignore')\n self.assertIn('(expansion of @args_non_existent) could not be opened', cm.exception.output.decode('utf-8', 'ignore'))","def test_use_exit_status(self): # suppress(no-self-use)\n subprocess.call.return_value = 1\n GreenTestCommand(Distribution()).run()\n sys.exit.assert_called_with(1)","def test_runSignaled(self):\n builder = BookBuilder()\n exc = self.assertRaises(\n CommandFailed, builder.run,\n [sys.executable, '-c',\n 'import sys; print \"hi\"; sys.stdout.flush(); '\n 'import os; os.kill(os.getpid(), 9)'])\n self.assertEquals(exc.exitSignal, 9)\n self.assertEquals(exc.exitStatus, None)\n self.assertEquals(exc.output, \"hi\\n\")","def test_check_if_error(self):\n with self.assertRaises(MyError):\n ExecutionExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\n .check_if_error()","def _handleExceptionAndCheckCall(array_call, **kwargs):\n stdout = kwargs.get('stdout', subprocess.PIPE)\n stderr = kwargs.get('stderr', subprocess.PIPE)\n shell = kwargs.get('shell', False)\n\n cmd = array_call[0]\n\n output = None\n error = None\n\n # TODO: Check the value of array_call and <=[0]\n logging.debug(\"Calling {0}:\".format(cmd))\n logging.debug(\"%s\", array_call)\n logging.debug(\"---------\")\n\n # TODO: Use universal_newlines option from Popen?\n try:\n p = subprocess.Popen(array_call, stdout=stdout,\n stderr=stderr, shell=shell)\n\n # TODO: Change this because of possible memory issues => https://docs.python.org/2/library/subprocess.html#subprocess.Popen.communicate\n\n output, error = p.communicate()\n\n if stdout == subprocess.PIPE:\n logging.debug(\"\\t{0}\".format(output))\n else:\n logging.debug(\"\\tOutput in file {0}\".format(stdout.name))\n # If we detect an error from the subprocess, then we raise an exception\n # TODO: Manage if we raise an exception for everything, or use CRITICAL etc... but not stop process\n # TODO: The responsability of returning a sys.exit() should not be there, but up in the app.\n if p.returncode:\n if stderr == subprocess.PIPE:\n raise PopenError(cmd, error, p.returncode)\n else:\n # TODO: To Handle properly with a design behind, if we received a option as a file for the error\n raise Exception(\"Error when calling {0}. Error as been logged in your file {1}. Error code: {2}\"\n .format(cmd, stderr.name, p.returncode))\n\n except OSError as e:\n message = \"The subprocess {0} has encountered an OSError: {1}\".format(\n cmd, e.strerror)\n if e.filename:\n message = '\\n'.join(\n (message, \", against this file: {0}\".format(e.filename)))\n logging.error(message)\n sys.exit(-1)\n except PopenError as p:\n message = \"The subprocess {0} has returned the error: {1}.\".format(\n p.cmd, p.return_code)\n message = '\\n'.join(\n (message, \"Its error message is: {0}\".format(p.error)))\n\n logging.exception(message)\n\n sys.exit(p.return_code)\n except Exception as e:\n message = \"The subprocess {0} has encountered an unknown error: {1}\".format(\n cmd, e)\n logging.exception(message)\n\n sys.exit(-1)\n return p","def test_ns_fail():\n env = NsSimPyEnvironment()\n env.process(some_process(env, 10.1))\n env.run()","def test_unexpected_error_in_exists(self):\n # TODO\n one_process_workflow = \"\"\"buggy://B <- file://A\n echo A produces B > B\n \"\"\"\n process = run_first_process(one_process_workflow, extra_resource=BuggyExistsResource)\n assert process.success is False, process.error_message\n assert process.error_message.find('An unexpected error have happen in tuttle while checking existence of '\n 'output resources' ) >= 0, process.error_message\n assert process.error_message.find('Traceback (most recent call last):') >= 0, process.error_message\n assert process.error_message.find('raise Exception(\"Unexpected error in exists()\")') >= 0, process.error_message\n assert process.error_message.find('Process cannot be considered complete.') >= 0, process.error_message","def _handle_failure(self, proc, test_case):\n if proc.returncode != 0:\n print('ERROR: Test execution failed: {}'.format(test_case.get_name()))\n stdout, stderr = proc.communicate()\n raise TestCaseFailure('Test case {} failed. stdout: {}, stderr: {}, '\n 'return code: {}.'.format(test_case.get_name(),\n stdout, stderr,\n proc.returncode))","def test_error_message_from_background_process(self):\n one_process_workflow = \"\"\"file://B <- file://A\n error\n echo A produces B > B\n \"\"\"\n process = run_first_process(one_process_workflow)\n assert process.error_message.find(\"Process ended with error code\") >= 0, process.error_message","def run(cmd):\n print ' '.join(cmd)\n try:\n check_call(cmd)\n except CalledProcessError as cpe:\n print \"Error: return code: \" + str(cpe.returncode)\n sys.exit(cpe.returncode)","async def test_raise_call_error(base_central_system):\n call_error = CallError(\n unique_id=\"1337\",\n error_code=\"GenericError\",\n error_description=\"test_raise_call_error\",\n )\n await base_central_system.route_message(call_error.to_json())\n\n payload = call.ClearCachePayload()\n with pytest.raises(GenericError):\n await base_central_system.call(payload, suppress=False)","def test_extract_raises(capsys):\n with mock.patch('uflash.extract', side_effect=RuntimeError(\"boom\")):\n with pytest.raises(SystemExit):\n uflash.main(argv=['--extract', 'test.py'])\n\n _, stderr = capsys.readouterr()\n expected = 'Error extracting test.py'\n assert expected in stderr","def test_cmd_error(self):\n task = Task(\"uid\", False, False, \"does_not_exist\", None, \".\")\n task._checkpoint_dir = tmp_checkpoint_dir()\n with self.assertRaisesRegexp(RuntimeError, \".*executing Task's command:.*\"):\n task.run()\n task.shell = True\n with self.assertRaisesRegexp(RuntimeError, \".*executing Task's command:.*\"):\n task.run()\n task._dry_run = True\n task.run() # No longer raises RuntimeError","def test_execute_process_killed():\n port = port_for.select_random()\n passing_check = check_tcp(port)\n\n def failing_check_for_testing_purposes():\n return False\n\n with pytest.raises(PostChecksFailed) as checks_failed:\n execute([SERVICE, 'tcp', '--port', str(port)], [passing_check, failing_check_for_testing_purposes], timeout=1)\n\n assert 'function failing_check_for_testing_purposes' in str(checks_failed.value)\n\n # The process will be killed and nothing will be listening on that port.\n wait_until(lambda: passing_check() is False)","def test_watch_raises(capsys):\n with mock.patch('uflash.watch_file', side_effect=RuntimeError(\"boom\")):\n with pytest.raises(SystemExit):\n uflash.main(argv=['--watch', 'test.py'])\n\n _, stderr = capsys.readouterr()\n expected = 'Error watching test.py'\n assert expected in stderr","def test_option_unhandled(self):\n cmd, output = runCmdOutput(['--__unhandled__'])\n self.assertEqual(cmd.returncode, os.EX_USAGE)","def test_stats_issue_43328(test_file):\n fake_file = test_file.parent / \"fake.file\"\n with pytest.raises(CommandExecutionError):\n win_file.stats(fake_file)","def testError(self):\n cmds = \"\"\"chown 0 missingFile\npwd\nexit\n\"\"\"\n\n def _cbCheckResult(res):\n self.assertNotIn(self.testDir.asBytesMode().path, res)\n\n d = self._getBatchOutput(cmds)\n d.addCallback(_cbCheckResult)\n return d","def test_unexpected_error_in_signature(self):\n # TODO\n one_process_workflow = \"\"\"buggy://B <- file://A\n echo A produces B > B\n \"\"\"\n process = run_first_process(one_process_workflow, extra_resource=BuggySignatureResource)\n assert process.success is False, process.error_message\n assert process.error_message.find('An unexpected error have happen in tuttle while retrieving signature' \\\n ) >= 0, process.error_message\n assert process.error_message.find('Traceback (most recent call last):') >= 0, process.error_message\n assert process.error_message.find('raise Exception(\"Unexpected error in signature()\")') >= 0, process.error_message\n assert process.error_message.find('Process cannot be considered complete.') >= 0, process.error_message","def crashed(self):\n\n return len(self.backtrace) > 0 or (self.run_as_script and self.return_code != 0)","def test_invoke_processor_errors():\n\n def processor(app, documents):\n raise ValueError(\"something bad happened\")\n yield\n\n testapp = holocron.Application()\n testapp.add_processor(\"processor\", processor)\n testapp.add_pipe(\"test\", [{\"name\": \"processor\"}])\n\n stream = testapp.invoke(\"test\")\n\n with pytest.raises(ValueError, match=r\"^something bad happened$\"):\n next(stream)\n\n with pytest.raises(StopIteration):\n next(stream)","def check_call(self, cmd, nonzero_e = tc.error_e):\n self.run(cmd, nonzero_e = nonzero_e)","def test_shell_bad_command():\n out, err = shell_command(\"ls adasdasdas\")\n assert out is None\n assert \"adasdasdas\" in err","def test_invoke_processor_not_found():\n\n testapp = holocron.Application()\n testapp.add_pipe(\"test\", [{\"name\": \"processor\"}])\n\n with pytest.raises(ValueError) as excinfo:\n next(testapp.invoke(\"test\"))\n\n assert str(excinfo.value) == \"no such processor: 'processor'\"","def test_check_if_error(self):\n with self.assertRaises(MyError):\n SshErrorExitCodeController(255, ERROR_MESSAGE)\\\n .check_if_error()","def test_check_if_error(self):\n with self.assertRaises(MyError):\n BaseErrorExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\n .check_if_error()","def pytest_internalerror(self, excrepr, excinfo):\n for line in str(excrepr).split(\"\\n\"):\n sys.stderr.write(\"INTERNALERROR> {}\\n\".format(line))\n sys.stderr.flush()\n tb = _postmortem_traceback(excinfo)\n post_mortem(tb, excinfo)","def test_subprocess_fork_exception(self, mocker):\n mocker.stopall()\n\n test_command = [\"fake\", \"command\"]\n test_name = \"fake_command\"\n test_fork = True\n\n # mock\n mock_logging_debug = mocker.MagicMock(name=\"mock_logging_debug\")\n mock_os_fork = mocker.MagicMock(name=\"mock_os_fork\", side_effect=OSError)\n mock_sys_exit = mocker.MagicMock(name=\"mock_sys_exit\")\n mock_os_chdir = mocker.MagicMock(name=\"mock_os_chdir\")\n mock_os_setsid = mocker.MagicMock(name=\"mock_os_setsid\")\n mock_os_umask = mocker.MagicMock(name=\"mock_os_umask\")\n\n # patch\n mocker.patch.object(\n scarlett_os.subprocess.logging.Logger, \"debug\", mock_logging_debug\n )\n mocker.patch.object(scarlett_os.subprocess.os, \"fork\", mock_os_fork)\n mocker.patch.object(scarlett_os.subprocess.sys, \"exit\", mock_sys_exit)\n mocker.patch.object(scarlett_os.subprocess.os, \"chdir\", mock_os_chdir)\n mocker.patch.object(scarlett_os.subprocess.os, \"setsid\", mock_os_setsid)\n mocker.patch.object(scarlett_os.subprocess.os, \"umask\", mock_os_umask)\n\n tfork2 = scarlett_os.subprocess.Subprocess(\n test_command, name=test_name, fork=test_fork\n )\n\n # NOTE: Bit of duplication we have going here.\n assert mock_sys_exit.call_count == 2\n assert tfork2.stdout == False\n assert tfork2.stderr == False\n assert mock_os_chdir.call_count == 1\n assert mock_os_setsid.call_count == 1\n assert mock_os_umask.call_count == 1\n assert mock_os_fork.call_count == 2\n\n mock_os_chdir.assert_called_once_with(\"/\")\n\n mocker.stopall()","def test_verify_error(self):\n task = Task(\"uid\", False, False, \"echo\", \"does_not_exist\", \".\", \"A\")\n task._checkpoint_dir = tmp_checkpoint_dir()\n with self.assertRaisesRegexp(RuntimeError, \".*executing Task's verification:.*\"):\n task.run()\n task.shell = True\n with self.assertRaisesRegexp(RuntimeError, \".*executing Task's verification:.*\"):\n task.run()\n task._dry_run = True\n task.run() # No longer raises RuntimeError","def call_error():\r\n print(\"Error in input format.\")\r\n sys.exit()","def test_failing_git_command(self):\n code = Code(self.build)\n\n proc_mock = Mock()\n proc_mock.returncode = 1\n response = Response(proc_mock, '', 'herp derp')\n code._command = Mock(return_value=response)\n code._which_git = Mock(return_value='lol')\n code.update()","def report_error_handled(self, calculation, action):\n arguments = [calculation.process_label, calculation.pk, calculation.exit_status, calculation.exit_message]\n self.report('{}<{}> failed with exit status {}: {}'.format(*arguments))\n self.report(f'Action taken: {action}')","def test_signal_no_pid(self):\n code, out, err = self.t.runError(\"--pid 0\")\n self.assertIn(\"A non-zero PID must be specified.\", out)","def test_stopProcessUnknownKeyError(self):\r\n self.assertRaises(KeyError, self.pm.stopProcess, \"foo\")","def determine_exit_code(self) -> int:","def HandlePipelineToolsCalledProcessError( exc ):\n errorMsg = StringIO()\n errorMsg.write( \"Pipeline Tools encountered an error - the command:\" )\n errorMsg.write( os.linesep * 2 )\n errorMsg.write( exc.cmd )\n errorMsg.write( os.linesep * 2 )\n errorMsg.write( \"return a non-zero (%d) exit code\" % exc.returncode )\n\n if exc.output:\n errorMsg.write( \" and the following output:\" )\n errorMsg.write( os.linesep * 2 )\n errorMsg.write( exc.output )\n\n errorMsg = errorMsg.getvalue()\n # On Windows, print statements output to the console window that is created minimized when Katana launches\n print( errorMsg )\n\n # Display a human-readable generic error message\n ShowModalDialog( \"Pipeline Tools Error\",\n \"Pipeline Tools encountered an error. Check the Katana console for more detailed information.\" )\n\n return \"Pipeline Tools Error\"","def test_install_subprocess_error_should_fail(self, *args):\n manifest = self.generate_mock_manifest(cfg={\n EXTCFG_SECTION.INSTALL: {\n EXTCFG_OPTION.EXEC_EXT_CMD: ['command'],\n }\n })\n ext_manager = PkgInstExtrasManager(manifest)\n with pytest.raises(exceptions.InstExtrasManagerError):\n ext_manager.handle_install_extras()","def procFail(proc):\n\tif 'a' in proc.config._notify.when['pipeline']:\n\t\tlogger.debug('Notifying process fails')\n\t\tEMAIL.send('proc', proc, 'abort')","def test_args_bad_value(testapp, args, error):\n\n with pytest.raises(ValueError) as excinfo:\n next(archive.process(testapp, [], **args))\n assert str(excinfo.value) == error","def test_exceptions_on_the_test(self):\n try:\n raise Exception(\"faki faki faki\")\n except Exception as e:\n #TODO: write a proper test to verify exception logging\n res = output(e, \"ERROR\")\n eq_(type(\"\"),type(res))\n eq_(\"GREAP ERROR faki faki faki\",res)\n eq_(True,mymock.called)","def test_process_path(path):\n try:\n subprocess.call([path, \"--version\"])\n return True\n except:\n print(\"Cannot find executable on {}\".format(path))\n return False","def test_start_process(self, mocked_check):\n from supvisors.rpcinterface import RPCInterface\n # get patches\n mocked_start = self.supervisor.supvisors.starter.start_process\n mocked_progress = self.supervisor.supvisors.starter.in_progress\n # create RPC instance\n rpc = RPCInterface(self.supervisor)\n # patch the instance\n rpc._get_application_process = Mock()\n # test RPC call with unknown strategy\n with self.assertRaises(RPCError) as exc:\n rpc.start_process('strategy', 'appli:proc')\n self.assertEqual(Faults.BAD_STRATEGY, exc.exception.code)\n self.assertEqual('BAD_STRATEGY: strategy', exc.exception.text)\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual(0, mocked_start.call_count)\n self.assertEqual(0, mocked_progress.call_count)\n mocked_check.reset_mock()\n # test RPC call with running process\n rpc._get_application_process.return_value = (\n None, Mock(**{'running.return_value': True,\n 'namespec.return_value': 'proc1'}))\n with self.assertRaises(RPCError) as exc:\n rpc.start_process(0, 'appli_1')\n self.assertEqual(Faults.ALREADY_STARTED, exc.exception.code)\n self.assertEqual('ALREADY_STARTED: proc1', exc.exception.text)\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual(0, mocked_start.call_count)\n self.assertEqual(0, mocked_progress.call_count)\n mocked_check.reset_mock()\n # test RPC call with running processes\n rpc._get_application_process.return_value = (\n Mock(**{'processes.values.return_value': [\n Mock(**{'running.return_value': False}),\n Mock(**{'running.return_value': True,\n 'namespec.return_value': 'proc2'})]}), None)\n with self.assertRaises(RPCError) as exc:\n rpc.start_process(0, 'appli_1')\n self.assertEqual(Faults.ALREADY_STARTED, exc.exception.code)\n self.assertEqual('ALREADY_STARTED: proc2', exc.exception.text)\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual(0, mocked_start.call_count)\n self.assertEqual(0, mocked_progress.call_count)\n mocked_check.reset_mock()\n # test RPC call with stopped processes\n proc_1 = Mock(**{'running.return_value': False,\n 'stopped.return_value': True,\n 'namespec.return_value': 'proc1'})\n proc_2 = Mock(**{'running.return_value': False,\n 'stopped.return_value': False,\n 'namespec.return_value': 'proc2'})\n rpc._get_application_process.return_value = (\n Mock(**{'processes.values.return_value': [\n proc_1, proc_2]}), None)\n # test RPC call with no wait and not done\n mocked_start.return_value = False\n result = rpc.start_process(1, 'appli:*', 'argument list', False)\n self.assertTrue(result)\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual([call(1, proc_1, 'argument list'),\n call(1, proc_2, 'argument list')], mocked_start.call_args_list)\n self.assertEqual(0, mocked_progress.call_count)\n mocked_check.reset_mock()\n mocked_start.reset_mock()\n # test RPC call no wait and done\n mocked_start.return_value = True\n result = rpc.start_process(1, 'appli:*', 'argument list', False)\n self.assertTrue(result)\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual([call(1, proc_1, 'argument list'),\n call(1, proc_2, 'argument list')], mocked_start.call_args_list)\n self.assertEqual(0, mocked_progress.call_count)\n mocked_check.reset_mock()\n mocked_start.reset_mock()\n # test RPC call with wait and done\n result = rpc.start_process(2, 'appli:*', wait=True)\n self.assertTrue(result)\n self.assertEqual([call(2, proc_1, ''), call(2, proc_2, '')],\n mocked_start.call_args_list)\n self.assertEqual(0, mocked_progress.call_count)\n mocked_check.reset_mock()\n mocked_start.reset_mock()\n # test RPC call with wait and not done\n mocked_start.return_value = False\n deferred = rpc.start_process(2, 'appli:*', wait=True)\n # result is a function for deferred result\n self.assertTrue(callable(deferred))\n self.assertEqual([call()], mocked_check.call_args_list)\n self.assertEqual([call(2, proc_1, ''), call(2, proc_2, '')],\n mocked_start.call_args_list)\n self.assertEqual(0, mocked_progress.call_count)\n # test returned function: return True when job in progress\n mocked_progress.return_value = True\n self.assertEqual(NOT_DONE_YET, deferred())\n self.assertEqual([call()], mocked_progress.call_args_list)\n mocked_progress.reset_mock()\n # test returned function: raise exception if job not in progress anymore\n # and process still stopped\n mocked_progress.return_value = False\n with self.assertRaises(RPCError) as exc:\n deferred()\n self.assertEqual(Faults.ABNORMAL_TERMINATION, exc.exception.code)\n self.assertEqual('ABNORMAL_TERMINATION: proc1', exc.exception.text)\n self.assertEqual([call()], mocked_progress.call_args_list)\n mocked_progress.reset_mock()\n # test returned function: return True if job not in progress anymore\n # and process running\n proc_1.stopped.return_value = False\n self.assertTrue(deferred())\n self.assertEqual([call()], mocked_progress.call_args_list)","def test_call(*args, **kwargs):\n try:\n subprocess.check_output(*args, **kwargs)\n return True\n except Exception:\n return False","def test_ns_fail2():\n env = NsSimPyEnvironment()\n env.process(some_process(env, 4e-29))\n env.run()","def test_isort_tool_plugin_scan_oserror(mock_subprocess_check_output):\n mock_subprocess_check_output.side_effect = OSError(\"mocked error\")\n itp = setup_isort_tool_plugin()\n package = Package(\n \"valid_package\", os.path.join(os.path.dirname(__file__), \"valid_package\")\n )\n package[\"python_src\"] = [\n os.path.join(os.path.dirname(__file__), \"valid_package\", \"sample.py\")\n ]\n issues = itp.scan(package, \"level\")\n assert not issues","def shellExecErrorCode(cmd):\n return subprocess.call(cmd, shell=True)","def test_check_if_error_one(self):\n with self.assertRaises(MyError):\n SshpassErrorExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\n .check_if_error()","def error_check(command):\r\n\r\n # TODO\r","def test_DDSim_runIt_failure_inputFile(self):\n self.ddsim.platform = \"Windows\"\n self.ddsim.applicationLog = self.logFileName\n self.ddsim.InputFile = \"pairs.hepmc\"\n ## side effect for Script, log, logAfter\n with patch(\"os.path.exists\", new=Mock(side_effect=[False, False, True] ) ):\n res = self.ddsim.runIt()\n self.assertEqual( res['Message'], \"no pairs.hepmc\" )","def _safe_call(cmd_list):\n try:\n subprocess.check_output(cmd_list)\n return True\n except subprocess.CalledProcessError as err_thrown:\n print('Error while calling \"%s\"', err_thrown.cmd)\n return False","def test_does_not_crash(self):\n py_function(6)","def prnt_error():\n print \"Error!\\n\"\n return False","def errReceived(self, data):\n log.msg(\"Error output from process: \" + data,\n isError=True)","def supercall(command):\n p = subprocess.Popen(command, shell=True, stderr=subprocess.PIPE)\n retval = p.wait()\n \n if retval != 0:\n get_logger().critical('error calling {}'.format(command))\n for line in p.stderr.readlines():\n get_logger().critical(line.decode('utf8').replace('\\n', ''))\n\n return retval","def test_has_receivers(error_report_mock):\n\n app_crash()\n error_report_mock.assert_not_called()\n\n signal('panic').connect(triage)\n app_crash()\n error_report_mock.assert_called_with()","def test_command(self):\r\n with self.assertRaises(SystemExit) as ex:\r\n with self.assertRaisesRegexp(CommandError, 'This script requires.*'):\r\n call_command('git_export', 'blah', 'blah', 'blah',\r\n stderr=StringIO.StringIO())\r\n self.assertEqual(ex.exception.code, 1)\r\n\r\n with self.assertRaises(SystemExit) as ex:\r\n with self.assertRaisesRegexp(CommandError, 'This script requires.*'):\r\n call_command('git_export', stderr=StringIO.StringIO())\r\n self.assertEqual(ex.exception.code, 1)\r\n\r\n # Send bad url to get course not exported\r\n with self.assertRaises(SystemExit) as ex:\r\n with self.assertRaisesRegexp(CommandError, GitExportError.URL_BAD):\r\n call_command('git_export', 'foo/bar/baz', 'silly',\r\n stderr=StringIO.StringIO())\r\n self.assertEqual(ex.exception.code, 1)\r\n # Send bad course_id to get course not exported\r\n with self.assertRaises(SystemExit) as ex:\r\n with self.assertRaisesRegexp(CommandError, GitExportError.BAD_COURSE):\r\n call_command('git_export', 'foo/bar:baz', 'silly',\r\n stderr=StringIO.StringIO())\r\n self.assertEqual(ex.exception.code, 1)","def test_cond_with_uncaught_error(env):\n def explode(env, delay):\n yield env.timeout(delay)\n raise ValueError(f'Onoes, failed after {delay}!')\n\n def process(env):\n yield env.timeout(1) | env.process(explode(env, 2))\n\n env.process(process(env))\n try:\n env.run()\n assert False, 'There should have been an exception.'\n except ValueError:\n pass\n assert env.now == 2","def test_sandbox_errors_false(self):\n SignalHook(self.test_extension, self.signal, self._on_signal_exception,\n sandbox_errors=False)\n\n self.assertEqual(len(self._on_signal_exception.calls), 0)\n self.assertRaises(Exception, self.signal.send, self)\n self.assertEqual(len(self._on_signal_exception.calls), 1)","def test_main_fails_on_request_error(\n runner: CliRunner, mock_requests_get: MockFixture\n) -> None:\n mock_requests_get.side_effect = Exception(\"Boom\")\n result = runner.invoke(console.main)\n assert result.exit_code == 1","def test_example(self, _, cmd):\n out = subprocess.run(cmd, shell=True)\n self.assertFalse(out.returncode)","def testIgnoredError(self):\n cmds = \"\"\"-chown 0 missingFile\npwd\nexit\n\"\"\"\n def _cbCheckResult(res):\n self.assertIn(self.testDir.asBytesMode().path, res)\n\n d = self._getBatchOutput(cmds)\n d.addCallback(_cbCheckResult)\n return d","def test_exit_on_missing_file(self):\n with self.assertRaises(SystemExit):\n pyint = Interpreter()\n pyint.run(file=MISSING_FILE)","def test_main_failure(logger, argv):\n assert app.main(argv) == 1\n logger.error.called_once()","def test_check_process_output(self):\n workflow = self.get_workflow(\n \"\"\"file://result <- file://source\n echo test\n \"\"\")\n workflow.pre_check_processes()\n try:\n process = workflow._processes[0]\n create_tuttle_dirs()\n workflow.run_process(process)\n assert False, \"Exception has not been not raised\"\n except ResourceError:\n assert True","def error_handler(self):\n if self.ctx.exit_code is not None:\n return self.ctx.exit_code","def test_removeProcessUnknownKeyError(self):\r\n self.pm.startService()\r\n self.assertRaises(KeyError, self.pm.removeProcess, \"foo\")","def side_effect(command, stdout, stderr):\n if any('always-auth' in arg for arg in command):\n raise subprocess.CalledProcessError(\n returncode=1,\n cmd=command\n )\n\n return mock.DEFAULT","def get_exit_code(self):","def test_send_error_to_admin(self, process_mock, send_smtp_mock):\n # arrange an mock error during processing\n process_mock.side_effect = RuntimeError('mock error')\n\n with name_of_file_containing('contents') as filename:\n call_command('process_email', email_file=filename)\n\n self.assertTrue(send_smtp_mock.called)\n (msg,) = send_smtp_mock.call_args.args\n self.assertEqual(msg['to'], 'admin@example.com', 'Admins should be emailed on error')\n self.assertIn('error', msg['subject'].lower(), 'Error email subject should indicate error')\n self.assertTrue(msg.is_multipart(), 'Error email should have attachments')\n parts = msg.get_payload()\n self.assertEqual(len(parts), 3, 'Error email should contain message, traceback, and original message')\n content = parts[0].get_payload()\n traceback = parts[1].get_payload()\n original = parts[2].get_payload(decode=True).decode() # convert octet-stream to string\n self.assertIn('RuntimeError', content, 'Error type should be included in error email')\n self.assertIn('mock.py', content, 'File where error occurred should be included in error email')\n self.assertIn('traceback', traceback.lower(), 'Traceback should be attached to error email')\n self.assertEqual(original, 'contents', 'Original message should be attached to error email')","def expect_failure(self):\n class StopCode(Exception):\n \"\"\"This exception will be raised instead of exiting the program.\"\"\"\n def __init__(self, exit_code):\n self.exit_code = exit_code\n\n def stop_code(arg):\n raise StopCode(arg)\n\n with mock.patch('sys.exit') as exit:\n exit.side_effect = stop_code\n try:\n with mock.patch('sys.stderr'):\n yield\n except StopCode as exc:\n self.assertTrue(exc.exit_code > 0)\n exit.assert_called()","def test_sandbox_errors_true(self):\n SignalHook(self.test_extension, self.signal, self._on_signal_exception,\n sandbox_errors=True)\n\n self.assertEqual(len(self._on_signal_exception.calls), 0)\n self.signal.send(self)\n self.assertEqual(len(self._on_signal_exception.calls), 1)","def errorCheck(sh, returncode, stderr):\n\tif returncode!=0 or stderr!='':\n\t\tif config.DEBUG:\n\t\t\tmsg = \"sh code execution [%s] returned non-zero exit status [%s] and/or non-empty stdterr [%s]\" % (repr(sh), returncode, repr(stderr.strip()))\n\t\telse:\n\t\t\tmsg = \"sh code execution returned non-zero exit status and/or non-empty stdterr\"\n\t\traise Exception(msg)","def test_broken_error_descriptor(self):\r\n with self.assertRaises(TestException):\r\n module = self.descriptor._xmodule","def isfailure(self):\n\n return self.proc.returncode != 0","def test_broken_error_module(self):\r\n with self.assertRaises(TestException):\r\n module = self.descriptor._xmodule","def test_process_id():\n output = sh.process_id()\n assert isinstance(output, int) and output > 0","def script_test(path):\n log.info(\" ... EXECUTING {}\".format(str(path)))\n\n cmd = [sys.executable, str(path)]\n cp = subprocess.run(cmd, stderr=subprocess.PIPE)\n if cp.returncode:\n log.info(\" ... FAILED\")\n log.info(\" ___ TRACEBACK\")\n log.info(cp.stderr.decode(\"utf-8\") + \"\\n\\n\")\n return False\n else:\n log.info(\" ... PASSED\")\n return True","def _check_error(return_value):\n if return_value < 0:\n raise IOError(pm.lib.Pm_GetErrorText(return_value))","def test_handles_error(self):\n with self.assertRaises(ForcedExit):\n self.run_command(mkdtemp())\n\n self.assertResults(\n result_with_hint(\n u'This repository has not been initialized.',\n GIT_REPO_NOT_INITIALIZED),\n self.error)","def test_NameError(n=2):\n\n p = platform_name()\n\n try:\n p.result()\n except NameError:\n print(\"Caught NameError\")\n else:\n assert False, \"Raise the wrong Error\""],"string":"[\n \"def test_make_tool_plugin_scan_calledprocesserror(mock_subprocess_check_output):\\n mock_subprocess_check_output.side_effect = subprocess.CalledProcessError(1, '', output=\\\"mocked error\\\")\\n mtp = setup_make_tool_plugin()\\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\\n 'valid_package'))\\n package['make_targets'] = 'make_targets'\\n issues = mtp.scan(package, 'level')\\n assert not issues\",\n \"def check_call(*args, **kwargs):\\n rc = call(*args, **kwargs)\\n if rc != 0:\\n cmd = kwargs.get(\\\"args\\\")\\n if cmd is None:\\n cmd = args[0]\\n raise CalledProcessError(rc, cmd)\\n return 0\",\n \"def test_dump_handles_oserror(mocker):\\n mocker.patch('subprocess.Popen' , side_effect=OSError(\\\"no such file\\\"))\\n with pytest.raises(SystemExit):\\n pgdump.dump(url)\",\n \"def called_process_error2exit_decorator(func):\\n @functools.wraps(func)\\n def func_wrapper(*args, **kwargs):\\n try:\\n func(*args, **kwargs)\\n except subprocess.CalledProcessError as e:\\n print(\\\"{err}:\\\\n{msg}\\\".format(err=str(e), msg=e.output))\\n sys.exit(1)\\n return func_wrapper\",\n \"def test_dump_handles_os_error(mocker):\\n\\tmocker.patch('subprocess.Popen', side_effect=OSError('no such file'))\\n\\twith pytest.raises(SystemExit):\\n\\t\\tpgdump.dump(url)\",\n \"def test_start_args(self, mocked_check, mocked_proc):\\n from supvisors.rpcinterface import RPCInterface\\n # prepare context\\n info_source = self.supervisor.supvisors.info_source\\n info_source.update_extra_args.side_effect = KeyError\\n info_source.supervisor_rpc_interface.startProcess.side_effect = [\\n RPCError(Faults.NO_FILE, 'no file'),\\n RPCError(Faults.NOT_EXECUTABLE),\\n RPCError(Faults.ABNORMAL_TERMINATION),\\n 'done']\\n # create RPC instance\\n rpc = RPCInterface(self.supervisor)\\n # test RPC call with extra arguments and a process that is not compliant\\n with self.assertRaises(RPCError) as exc:\\n rpc.start_args('appli:proc', 'dummy arguments')\\n self.assertEqual(Faults.BAD_EXTRA_ARGUMENTS, exc.exception.code)\\n self.assertEqual(\\\"BAD_EXTRA_ARGUMENTS: rules for namespec appli:proc\\\"\\n \\\" are not compatible with extra arguments in command line\\\",\\n exc.exception.text)\\n self.assertEqual(0, mocked_check.call_count)\\n self.assertEqual(0, info_source.update_extra_args.call_count)\\n self.assertEqual(0, info_source.supervisor_rpc_interface.startProcess.call_count)\\n # test RPC call with extra arguments and a process that is compliant\\n # but unknown in Supervisor\\n with self.assertRaises(RPCError) as exc:\\n rpc.start_args('appli:proc', 'dummy arguments')\\n self.assertEqual(Faults.BAD_NAME, exc.exception.code)\\n self.assertEqual(\\\"BAD_NAME: namespec appli:proc unknown in this Supervisor instance\\\",\\n exc.exception.text)\\n self.assertEqual([call('appli:proc', 'dummy arguments')],\\n info_source.update_extra_args.call_args_list)\\n self.assertEqual(0, info_source.supervisor_rpc_interface.startProcess.call_count)\\n info_source.update_extra_args.reset_mock()\\n info_source.update_extra_args.side_effect = None\\n # test RPC call with start exceptions\\n mocked_proc.side_effect = None\\n mocked_proc.return_value = None, None\\n # NO_FILE exception triggers an update of the process state\\n with self.assertRaises(RPCError) as exc:\\n rpc.start_args('appli:proc')\\n self.assertEqual(Faults.NO_FILE, exc.exception.code)\\n self.assertEqual(\\\"NO_FILE: no file\\\", exc.exception.text)\\n self.assertEqual([call('appli:proc', '')],\\n info_source.update_extra_args.call_args_list)\\n self.assertEqual([call('appli:proc', True)],\\n info_source.supervisor_rpc_interface.startProcess.call_args_list)\\n self.assertEqual([call('appli:proc', 'NO_FILE: no file')],\\n info_source.force_process_fatal.call_args_list)\\n # reset patches\\n info_source.update_extra_args.reset_mock()\\n info_source.force_process_fatal.reset_mock()\\n info_source.supervisor_rpc_interface.startProcess.reset_mock()\\n # NOT_EXECUTABLE exception triggers an update of the process state\\n with self.assertRaises(RPCError) as exc:\\n rpc.start_args('appli:proc', wait=False)\\n self.assertEqual(Faults.NOT_EXECUTABLE, exc.exception.code)\\n self.assertEqual(\\\"NOT_EXECUTABLE\\\", exc.exception.text)\\n self.assertEqual([call('appli:proc', '')],\\n info_source.update_extra_args.call_args_list)\\n self.assertEqual([call('appli:proc', False)],\\n info_source.supervisor_rpc_interface.startProcess.call_args_list)\\n self.assertEqual([call('appli:proc', 'NOT_EXECUTABLE')],\\n info_source.force_process_fatal.call_args_list)\\n # reset patches\\n info_source.update_extra_args.reset_mock()\\n info_source.force_process_fatal.reset_mock()\\n info_source.supervisor_rpc_interface.startProcess.reset_mock()\\n # other exception doesn't trigger an update of the process state\\n with self.assertRaises(RPCError) as exc:\\n rpc.start_args('appli:proc', wait=False)\\n self.assertEqual(Faults.ABNORMAL_TERMINATION, exc.exception.code)\\n self.assertEqual(\\\"ABNORMAL_TERMINATION\\\", exc.exception.text)\\n self.assertEqual([call('appli:proc', '')],\\n info_source.update_extra_args.call_args_list)\\n self.assertEqual([call('appli:proc', False)],\\n info_source.supervisor_rpc_interface.startProcess.call_args_list)\\n self.assertEqual(0, info_source.force_process_fatal.call_count)\\n # reset patches\\n info_source.update_extra_args.reset_mock()\\n info_source.supervisor_rpc_interface.startProcess.reset_mock()\\n # finally, normal behaviour\\n self.assertEqual('done', rpc.start_args('appli:proc'))\",\n \"def test_startProcessUnknownKeyError(self):\\r\\n self.assertRaises(KeyError, self.pm.startProcess, \\\"foo\\\")\",\n \"def test_not_running(): # pragma: windows\\n comm_kwargs = dict(comm='IPCComm', direction='send', reverse_names=True)\\n nt.assert_raises(RuntimeError, new_comm, 'test', **comm_kwargs)\",\n \"def sanity_check_process(self):\\n assert_equals(self.proc.returncode, None)\\n time.sleep(1)\",\n \"def print_unable_to_run(exc: \\\"CalledProcessError\\\"):\\n _print(str(exc), level=MessageLevel.QUIET)\",\n \"def test_exceptions():\\r\\n # test that trying to connect_ to a non existent app fails\\r\\n try:\\r\\n app = application.Application()\\r\\n app.connect(path=r\\\"No process with this please\\\")\\r\\n assert False\\r\\n except application.ProcessNotFoundError:\\r\\n print('ProcessNotFoundError has been raised. OK.')\\r\\n\\r\\n # test that trying to connect_ to a non existent app fails\\r\\n try:\\r\\n app = application.Application()\\r\\n app.start(cmd_line = r\\\"No process with this please\\\")\\r\\n assert False\\r\\n except application.AppStartError:\\r\\n print('AppStartError has been raised. OK.')\",\n \"def test_runFailed(self):\\n builder = BookBuilder()\\n exc = self.assertRaises(\\n CommandFailed, builder.run,\\n [sys.executable, '-c', 'print \\\"hi\\\"; raise SystemExit(1)'])\\n self.assertEquals(exc.exitStatus, 1)\\n self.assertEquals(exc.exitSignal, None)\\n self.assertEquals(exc.output, \\\"hi\\\\n\\\")\",\n \"def test_invoke_pipe_not_found():\\n\\n testapp = holocron.Application()\\n\\n with pytest.raises(ValueError) as excinfo:\\n next(testapp.invoke(\\\"test\\\"))\\n\\n assert str(excinfo.value) == \\\"no such pipe: 'test'\\\"\",\n \"def check_call_out(args, **kwargs):\\n out, returncode = communicate(args, **kwargs)\\n if returncode:\\n raise CalledProcessError(\\n returncode, args, kwargs.get('cwd'), out[0], out[1])\\n return out\",\n \"def test_unexpected_error_in_processor(self):\\n\\n one_process_workflow = \\\"\\\"\\\"file://B <- file://A ! buggy_processor\\n echo A does not produce B\\n \\\"\\\"\\\"\\n process = run_first_process(one_process_workflow, BuggyProcessor())\\n assert process.success is False, process.error_message\\n assert process.error_message.find('An unexpected error have happen in tuttle processor '\\n 'buggy_processor :') >= 0, process.error_message\\n assert process.error_message.find('Traceback (most recent call last):') >= 0, process.error_message\\n assert process.error_message.find('raise Exception(\\\"Unexpected error in processor\\\")') >= 0, process.error_message\\n assert process.error_message.find('will not complete.') >= 0, process.error_message\",\n \"def test_subprocess_fork_pid0_exception(self, mocker):\\n mocker.stopall()\\n\\n test_command = [\\\"who\\\", \\\"-b\\\"]\\n test_name = \\\"test_who\\\"\\n test_fork = True\\n pid = 0\\n\\n # mock\\n mock_logging_debug = mocker.MagicMock(name=\\\"mock_logging_debug\\\")\\n mock_logging_error = mocker.MagicMock(name=\\\"mock_logging_error\\\")\\n mock_os_fork = mocker.MagicMock(name=\\\"mock_os_fork\\\", side_effect=[pid, OSError])\\n mock_sys_exit = mocker.MagicMock(name=\\\"mock_sys_exit\\\")\\n mock_os_chdir = mocker.MagicMock(name=\\\"mock_os_chdir\\\")\\n mock_os_setsid = mocker.MagicMock(name=\\\"mock_os_setsid\\\")\\n mock_os_umask = mocker.MagicMock(name=\\\"mock_os_umask\\\")\\n\\n # patch\\n mocker.patch.object(\\n scarlett_os.subprocess.logging.Logger, \\\"debug\\\", mock_logging_debug\\n )\\n mocker.patch.object(\\n scarlett_os.subprocess.logging.Logger, \\\"error\\\", mock_logging_error\\n )\\n mocker.patch.object(scarlett_os.subprocess.os, \\\"fork\\\", mock_os_fork)\\n mocker.patch.object(scarlett_os.subprocess.sys, \\\"exit\\\", mock_sys_exit)\\n mocker.patch.object(scarlett_os.subprocess.os, \\\"chdir\\\", mock_os_chdir)\\n mocker.patch.object(scarlett_os.subprocess.os, \\\"setsid\\\", mock_os_setsid)\\n mocker.patch.object(scarlett_os.subprocess.os, \\\"umask\\\", mock_os_umask)\\n\\n scarlett_os.subprocess.Subprocess(test_command, name=test_name, fork=test_fork)\\n\\n mock_logging_error.assert_any_call(\\\"Error forking process second time\\\")\\n\\n mocker.stopall()\",\n \"def unavailable_process(**kwargs):\\n return LazySubprocessTester([sys.executable, \\\"-c\\\", \\\"import sys; sys.exit(1)\\\"], **kwargs)\",\n \"def test_execute_or_bail_internal_error(self):\\n with self.assertLogs(level=\\\"INFO\\\") as cm:\\n # This patches the \\\"croak\\\" function that is IMPORTED inside \\\"etl.commands\\\".\\n with unittest.mock.patch(\\\"etl.commands.croak\\\") as mocked_croak:\\n with etl.commands.execute_or_bail(\\\"unittest\\\"):\\n # Simulate an internal error where we don't catch an exception.\\n raise ValueError(\\\"oops\\\")\\n mocked_croak.assert_called()\\n # The exit code (2nd arg) is expected to be 3 for uncaught exceptions.\\n self.assertEqual(mocked_croak.call_args[0][1], 3)\\n\\n self.assertEqual(len(cm.output), 2)\\n self.assertIn(\\\"terrible happened\\\", cm.output[0])\",\n \"def test_get_case_command_fail(loqusdbapi, mocker):\\n # GIVEN a loqusdb api and a case id\\n case_id = 'a_case'\\n # WHEN an error occurs during fetching a case with the adapter\\n mocker.patch.object(subprocess, 'check_output')\\n subprocess.check_output.side_effect = subprocess.CalledProcessError(1, 'error')\\n\\n # THEN assert that the error is raised\\n with pytest.raises(subprocess.CalledProcessError):\\n loqusdbapi.get_case(case_id)\",\n \"def test_subprocess_fails_with_no_command(self):\\n with self.assertRaises(ValueError):\\n LazySubprocessTester([])\",\n \"def test_missing_file(self):\\n with self.assertRaises(subprocess.CalledProcessError) as cm:\\n subprocess.check_output(\\n [sys.executable, idf_py_path, '--version', '@args_non_existent'],\\n env=os.environ,\\n stderr=subprocess.STDOUT).decode('utf-8', 'ignore')\\n self.assertIn('(expansion of @args_non_existent) could not be opened', cm.exception.output.decode('utf-8', 'ignore'))\",\n \"def test_use_exit_status(self): # suppress(no-self-use)\\n subprocess.call.return_value = 1\\n GreenTestCommand(Distribution()).run()\\n sys.exit.assert_called_with(1)\",\n \"def test_runSignaled(self):\\n builder = BookBuilder()\\n exc = self.assertRaises(\\n CommandFailed, builder.run,\\n [sys.executable, '-c',\\n 'import sys; print \\\"hi\\\"; sys.stdout.flush(); '\\n 'import os; os.kill(os.getpid(), 9)'])\\n self.assertEquals(exc.exitSignal, 9)\\n self.assertEquals(exc.exitStatus, None)\\n self.assertEquals(exc.output, \\\"hi\\\\n\\\")\",\n \"def test_check_if_error(self):\\n with self.assertRaises(MyError):\\n ExecutionExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\\\\n .check_if_error()\",\n \"def _handleExceptionAndCheckCall(array_call, **kwargs):\\n stdout = kwargs.get('stdout', subprocess.PIPE)\\n stderr = kwargs.get('stderr', subprocess.PIPE)\\n shell = kwargs.get('shell', False)\\n\\n cmd = array_call[0]\\n\\n output = None\\n error = None\\n\\n # TODO: Check the value of array_call and <=[0]\\n logging.debug(\\\"Calling {0}:\\\".format(cmd))\\n logging.debug(\\\"%s\\\", array_call)\\n logging.debug(\\\"---------\\\")\\n\\n # TODO: Use universal_newlines option from Popen?\\n try:\\n p = subprocess.Popen(array_call, stdout=stdout,\\n stderr=stderr, shell=shell)\\n\\n # TODO: Change this because of possible memory issues => https://docs.python.org/2/library/subprocess.html#subprocess.Popen.communicate\\n\\n output, error = p.communicate()\\n\\n if stdout == subprocess.PIPE:\\n logging.debug(\\\"\\\\t{0}\\\".format(output))\\n else:\\n logging.debug(\\\"\\\\tOutput in file {0}\\\".format(stdout.name))\\n # If we detect an error from the subprocess, then we raise an exception\\n # TODO: Manage if we raise an exception for everything, or use CRITICAL etc... but not stop process\\n # TODO: The responsability of returning a sys.exit() should not be there, but up in the app.\\n if p.returncode:\\n if stderr == subprocess.PIPE:\\n raise PopenError(cmd, error, p.returncode)\\n else:\\n # TODO: To Handle properly with a design behind, if we received a option as a file for the error\\n raise Exception(\\\"Error when calling {0}. Error as been logged in your file {1}. Error code: {2}\\\"\\n .format(cmd, stderr.name, p.returncode))\\n\\n except OSError as e:\\n message = \\\"The subprocess {0} has encountered an OSError: {1}\\\".format(\\n cmd, e.strerror)\\n if e.filename:\\n message = '\\\\n'.join(\\n (message, \\\", against this file: {0}\\\".format(e.filename)))\\n logging.error(message)\\n sys.exit(-1)\\n except PopenError as p:\\n message = \\\"The subprocess {0} has returned the error: {1}.\\\".format(\\n p.cmd, p.return_code)\\n message = '\\\\n'.join(\\n (message, \\\"Its error message is: {0}\\\".format(p.error)))\\n\\n logging.exception(message)\\n\\n sys.exit(p.return_code)\\n except Exception as e:\\n message = \\\"The subprocess {0} has encountered an unknown error: {1}\\\".format(\\n cmd, e)\\n logging.exception(message)\\n\\n sys.exit(-1)\\n return p\",\n \"def test_ns_fail():\\n env = NsSimPyEnvironment()\\n env.process(some_process(env, 10.1))\\n env.run()\",\n \"def test_unexpected_error_in_exists(self):\\n # TODO\\n one_process_workflow = \\\"\\\"\\\"buggy://B <- file://A\\n echo A produces B > B\\n \\\"\\\"\\\"\\n process = run_first_process(one_process_workflow, extra_resource=BuggyExistsResource)\\n assert process.success is False, process.error_message\\n assert process.error_message.find('An unexpected error have happen in tuttle while checking existence of '\\n 'output resources' ) >= 0, process.error_message\\n assert process.error_message.find('Traceback (most recent call last):') >= 0, process.error_message\\n assert process.error_message.find('raise Exception(\\\"Unexpected error in exists()\\\")') >= 0, process.error_message\\n assert process.error_message.find('Process cannot be considered complete.') >= 0, process.error_message\",\n \"def _handle_failure(self, proc, test_case):\\n if proc.returncode != 0:\\n print('ERROR: Test execution failed: {}'.format(test_case.get_name()))\\n stdout, stderr = proc.communicate()\\n raise TestCaseFailure('Test case {} failed. stdout: {}, stderr: {}, '\\n 'return code: {}.'.format(test_case.get_name(),\\n stdout, stderr,\\n proc.returncode))\",\n \"def test_error_message_from_background_process(self):\\n one_process_workflow = \\\"\\\"\\\"file://B <- file://A\\n error\\n echo A produces B > B\\n \\\"\\\"\\\"\\n process = run_first_process(one_process_workflow)\\n assert process.error_message.find(\\\"Process ended with error code\\\") >= 0, process.error_message\",\n \"def run(cmd):\\n print ' '.join(cmd)\\n try:\\n check_call(cmd)\\n except CalledProcessError as cpe:\\n print \\\"Error: return code: \\\" + str(cpe.returncode)\\n sys.exit(cpe.returncode)\",\n \"async def test_raise_call_error(base_central_system):\\n call_error = CallError(\\n unique_id=\\\"1337\\\",\\n error_code=\\\"GenericError\\\",\\n error_description=\\\"test_raise_call_error\\\",\\n )\\n await base_central_system.route_message(call_error.to_json())\\n\\n payload = call.ClearCachePayload()\\n with pytest.raises(GenericError):\\n await base_central_system.call(payload, suppress=False)\",\n \"def test_extract_raises(capsys):\\n with mock.patch('uflash.extract', side_effect=RuntimeError(\\\"boom\\\")):\\n with pytest.raises(SystemExit):\\n uflash.main(argv=['--extract', 'test.py'])\\n\\n _, stderr = capsys.readouterr()\\n expected = 'Error extracting test.py'\\n assert expected in stderr\",\n \"def test_cmd_error(self):\\n task = Task(\\\"uid\\\", False, False, \\\"does_not_exist\\\", None, \\\".\\\")\\n task._checkpoint_dir = tmp_checkpoint_dir()\\n with self.assertRaisesRegexp(RuntimeError, \\\".*executing Task's command:.*\\\"):\\n task.run()\\n task.shell = True\\n with self.assertRaisesRegexp(RuntimeError, \\\".*executing Task's command:.*\\\"):\\n task.run()\\n task._dry_run = True\\n task.run() # No longer raises RuntimeError\",\n \"def test_execute_process_killed():\\n port = port_for.select_random()\\n passing_check = check_tcp(port)\\n\\n def failing_check_for_testing_purposes():\\n return False\\n\\n with pytest.raises(PostChecksFailed) as checks_failed:\\n execute([SERVICE, 'tcp', '--port', str(port)], [passing_check, failing_check_for_testing_purposes], timeout=1)\\n\\n assert 'function failing_check_for_testing_purposes' in str(checks_failed.value)\\n\\n # The process will be killed and nothing will be listening on that port.\\n wait_until(lambda: passing_check() is False)\",\n \"def test_watch_raises(capsys):\\n with mock.patch('uflash.watch_file', side_effect=RuntimeError(\\\"boom\\\")):\\n with pytest.raises(SystemExit):\\n uflash.main(argv=['--watch', 'test.py'])\\n\\n _, stderr = capsys.readouterr()\\n expected = 'Error watching test.py'\\n assert expected in stderr\",\n \"def test_option_unhandled(self):\\n cmd, output = runCmdOutput(['--__unhandled__'])\\n self.assertEqual(cmd.returncode, os.EX_USAGE)\",\n \"def test_stats_issue_43328(test_file):\\n fake_file = test_file.parent / \\\"fake.file\\\"\\n with pytest.raises(CommandExecutionError):\\n win_file.stats(fake_file)\",\n \"def testError(self):\\n cmds = \\\"\\\"\\\"chown 0 missingFile\\npwd\\nexit\\n\\\"\\\"\\\"\\n\\n def _cbCheckResult(res):\\n self.assertNotIn(self.testDir.asBytesMode().path, res)\\n\\n d = self._getBatchOutput(cmds)\\n d.addCallback(_cbCheckResult)\\n return d\",\n \"def test_unexpected_error_in_signature(self):\\n # TODO\\n one_process_workflow = \\\"\\\"\\\"buggy://B <- file://A\\n echo A produces B > B\\n \\\"\\\"\\\"\\n process = run_first_process(one_process_workflow, extra_resource=BuggySignatureResource)\\n assert process.success is False, process.error_message\\n assert process.error_message.find('An unexpected error have happen in tuttle while retrieving signature' \\\\\\n ) >= 0, process.error_message\\n assert process.error_message.find('Traceback (most recent call last):') >= 0, process.error_message\\n assert process.error_message.find('raise Exception(\\\"Unexpected error in signature()\\\")') >= 0, process.error_message\\n assert process.error_message.find('Process cannot be considered complete.') >= 0, process.error_message\",\n \"def crashed(self):\\n\\n return len(self.backtrace) > 0 or (self.run_as_script and self.return_code != 0)\",\n \"def test_invoke_processor_errors():\\n\\n def processor(app, documents):\\n raise ValueError(\\\"something bad happened\\\")\\n yield\\n\\n testapp = holocron.Application()\\n testapp.add_processor(\\\"processor\\\", processor)\\n testapp.add_pipe(\\\"test\\\", [{\\\"name\\\": \\\"processor\\\"}])\\n\\n stream = testapp.invoke(\\\"test\\\")\\n\\n with pytest.raises(ValueError, match=r\\\"^something bad happened$\\\"):\\n next(stream)\\n\\n with pytest.raises(StopIteration):\\n next(stream)\",\n \"def check_call(self, cmd, nonzero_e = tc.error_e):\\n self.run(cmd, nonzero_e = nonzero_e)\",\n \"def test_shell_bad_command():\\n out, err = shell_command(\\\"ls adasdasdas\\\")\\n assert out is None\\n assert \\\"adasdasdas\\\" in err\",\n \"def test_invoke_processor_not_found():\\n\\n testapp = holocron.Application()\\n testapp.add_pipe(\\\"test\\\", [{\\\"name\\\": \\\"processor\\\"}])\\n\\n with pytest.raises(ValueError) as excinfo:\\n next(testapp.invoke(\\\"test\\\"))\\n\\n assert str(excinfo.value) == \\\"no such processor: 'processor'\\\"\",\n \"def test_check_if_error(self):\\n with self.assertRaises(MyError):\\n SshErrorExitCodeController(255, ERROR_MESSAGE)\\\\\\n .check_if_error()\",\n \"def test_check_if_error(self):\\n with self.assertRaises(MyError):\\n BaseErrorExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\\\\n .check_if_error()\",\n \"def pytest_internalerror(self, excrepr, excinfo):\\n for line in str(excrepr).split(\\\"\\\\n\\\"):\\n sys.stderr.write(\\\"INTERNALERROR> {}\\\\n\\\".format(line))\\n sys.stderr.flush()\\n tb = _postmortem_traceback(excinfo)\\n post_mortem(tb, excinfo)\",\n \"def test_subprocess_fork_exception(self, mocker):\\n mocker.stopall()\\n\\n test_command = [\\\"fake\\\", \\\"command\\\"]\\n test_name = \\\"fake_command\\\"\\n test_fork = True\\n\\n # mock\\n mock_logging_debug = mocker.MagicMock(name=\\\"mock_logging_debug\\\")\\n mock_os_fork = mocker.MagicMock(name=\\\"mock_os_fork\\\", side_effect=OSError)\\n mock_sys_exit = mocker.MagicMock(name=\\\"mock_sys_exit\\\")\\n mock_os_chdir = mocker.MagicMock(name=\\\"mock_os_chdir\\\")\\n mock_os_setsid = mocker.MagicMock(name=\\\"mock_os_setsid\\\")\\n mock_os_umask = mocker.MagicMock(name=\\\"mock_os_umask\\\")\\n\\n # patch\\n mocker.patch.object(\\n scarlett_os.subprocess.logging.Logger, \\\"debug\\\", mock_logging_debug\\n )\\n mocker.patch.object(scarlett_os.subprocess.os, \\\"fork\\\", mock_os_fork)\\n mocker.patch.object(scarlett_os.subprocess.sys, \\\"exit\\\", mock_sys_exit)\\n mocker.patch.object(scarlett_os.subprocess.os, \\\"chdir\\\", mock_os_chdir)\\n mocker.patch.object(scarlett_os.subprocess.os, \\\"setsid\\\", mock_os_setsid)\\n mocker.patch.object(scarlett_os.subprocess.os, \\\"umask\\\", mock_os_umask)\\n\\n tfork2 = scarlett_os.subprocess.Subprocess(\\n test_command, name=test_name, fork=test_fork\\n )\\n\\n # NOTE: Bit of duplication we have going here.\\n assert mock_sys_exit.call_count == 2\\n assert tfork2.stdout == False\\n assert tfork2.stderr == False\\n assert mock_os_chdir.call_count == 1\\n assert mock_os_setsid.call_count == 1\\n assert mock_os_umask.call_count == 1\\n assert mock_os_fork.call_count == 2\\n\\n mock_os_chdir.assert_called_once_with(\\\"/\\\")\\n\\n mocker.stopall()\",\n \"def test_verify_error(self):\\n task = Task(\\\"uid\\\", False, False, \\\"echo\\\", \\\"does_not_exist\\\", \\\".\\\", \\\"A\\\")\\n task._checkpoint_dir = tmp_checkpoint_dir()\\n with self.assertRaisesRegexp(RuntimeError, \\\".*executing Task's verification:.*\\\"):\\n task.run()\\n task.shell = True\\n with self.assertRaisesRegexp(RuntimeError, \\\".*executing Task's verification:.*\\\"):\\n task.run()\\n task._dry_run = True\\n task.run() # No longer raises RuntimeError\",\n \"def call_error():\\r\\n print(\\\"Error in input format.\\\")\\r\\n sys.exit()\",\n \"def test_failing_git_command(self):\\n code = Code(self.build)\\n\\n proc_mock = Mock()\\n proc_mock.returncode = 1\\n response = Response(proc_mock, '', 'herp derp')\\n code._command = Mock(return_value=response)\\n code._which_git = Mock(return_value='lol')\\n code.update()\",\n \"def report_error_handled(self, calculation, action):\\n arguments = [calculation.process_label, calculation.pk, calculation.exit_status, calculation.exit_message]\\n self.report('{}<{}> failed with exit status {}: {}'.format(*arguments))\\n self.report(f'Action taken: {action}')\",\n \"def test_signal_no_pid(self):\\n code, out, err = self.t.runError(\\\"--pid 0\\\")\\n self.assertIn(\\\"A non-zero PID must be specified.\\\", out)\",\n \"def test_stopProcessUnknownKeyError(self):\\r\\n self.assertRaises(KeyError, self.pm.stopProcess, \\\"foo\\\")\",\n \"def determine_exit_code(self) -> int:\",\n \"def HandlePipelineToolsCalledProcessError( exc ):\\n errorMsg = StringIO()\\n errorMsg.write( \\\"Pipeline Tools encountered an error - the command:\\\" )\\n errorMsg.write( os.linesep * 2 )\\n errorMsg.write( exc.cmd )\\n errorMsg.write( os.linesep * 2 )\\n errorMsg.write( \\\"return a non-zero (%d) exit code\\\" % exc.returncode )\\n\\n if exc.output:\\n errorMsg.write( \\\" and the following output:\\\" )\\n errorMsg.write( os.linesep * 2 )\\n errorMsg.write( exc.output )\\n\\n errorMsg = errorMsg.getvalue()\\n # On Windows, print statements output to the console window that is created minimized when Katana launches\\n print( errorMsg )\\n\\n # Display a human-readable generic error message\\n ShowModalDialog( \\\"Pipeline Tools Error\\\",\\n \\\"Pipeline Tools encountered an error. Check the Katana console for more detailed information.\\\" )\\n\\n return \\\"Pipeline Tools Error\\\"\",\n \"def test_install_subprocess_error_should_fail(self, *args):\\n manifest = self.generate_mock_manifest(cfg={\\n EXTCFG_SECTION.INSTALL: {\\n EXTCFG_OPTION.EXEC_EXT_CMD: ['command'],\\n }\\n })\\n ext_manager = PkgInstExtrasManager(manifest)\\n with pytest.raises(exceptions.InstExtrasManagerError):\\n ext_manager.handle_install_extras()\",\n \"def procFail(proc):\\n\\tif 'a' in proc.config._notify.when['pipeline']:\\n\\t\\tlogger.debug('Notifying process fails')\\n\\t\\tEMAIL.send('proc', proc, 'abort')\",\n \"def test_args_bad_value(testapp, args, error):\\n\\n with pytest.raises(ValueError) as excinfo:\\n next(archive.process(testapp, [], **args))\\n assert str(excinfo.value) == error\",\n \"def test_exceptions_on_the_test(self):\\n try:\\n raise Exception(\\\"faki faki faki\\\")\\n except Exception as e:\\n #TODO: write a proper test to verify exception logging\\n res = output(e, \\\"ERROR\\\")\\n eq_(type(\\\"\\\"),type(res))\\n eq_(\\\"GREAP ERROR faki faki faki\\\",res)\\n eq_(True,mymock.called)\",\n \"def test_process_path(path):\\n try:\\n subprocess.call([path, \\\"--version\\\"])\\n return True\\n except:\\n print(\\\"Cannot find executable on {}\\\".format(path))\\n return False\",\n \"def test_start_process(self, mocked_check):\\n from supvisors.rpcinterface import RPCInterface\\n # get patches\\n mocked_start = self.supervisor.supvisors.starter.start_process\\n mocked_progress = self.supervisor.supvisors.starter.in_progress\\n # create RPC instance\\n rpc = RPCInterface(self.supervisor)\\n # patch the instance\\n rpc._get_application_process = Mock()\\n # test RPC call with unknown strategy\\n with self.assertRaises(RPCError) as exc:\\n rpc.start_process('strategy', 'appli:proc')\\n self.assertEqual(Faults.BAD_STRATEGY, exc.exception.code)\\n self.assertEqual('BAD_STRATEGY: strategy', exc.exception.text)\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual(0, mocked_start.call_count)\\n self.assertEqual(0, mocked_progress.call_count)\\n mocked_check.reset_mock()\\n # test RPC call with running process\\n rpc._get_application_process.return_value = (\\n None, Mock(**{'running.return_value': True,\\n 'namespec.return_value': 'proc1'}))\\n with self.assertRaises(RPCError) as exc:\\n rpc.start_process(0, 'appli_1')\\n self.assertEqual(Faults.ALREADY_STARTED, exc.exception.code)\\n self.assertEqual('ALREADY_STARTED: proc1', exc.exception.text)\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual(0, mocked_start.call_count)\\n self.assertEqual(0, mocked_progress.call_count)\\n mocked_check.reset_mock()\\n # test RPC call with running processes\\n rpc._get_application_process.return_value = (\\n Mock(**{'processes.values.return_value': [\\n Mock(**{'running.return_value': False}),\\n Mock(**{'running.return_value': True,\\n 'namespec.return_value': 'proc2'})]}), None)\\n with self.assertRaises(RPCError) as exc:\\n rpc.start_process(0, 'appli_1')\\n self.assertEqual(Faults.ALREADY_STARTED, exc.exception.code)\\n self.assertEqual('ALREADY_STARTED: proc2', exc.exception.text)\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual(0, mocked_start.call_count)\\n self.assertEqual(0, mocked_progress.call_count)\\n mocked_check.reset_mock()\\n # test RPC call with stopped processes\\n proc_1 = Mock(**{'running.return_value': False,\\n 'stopped.return_value': True,\\n 'namespec.return_value': 'proc1'})\\n proc_2 = Mock(**{'running.return_value': False,\\n 'stopped.return_value': False,\\n 'namespec.return_value': 'proc2'})\\n rpc._get_application_process.return_value = (\\n Mock(**{'processes.values.return_value': [\\n proc_1, proc_2]}), None)\\n # test RPC call with no wait and not done\\n mocked_start.return_value = False\\n result = rpc.start_process(1, 'appli:*', 'argument list', False)\\n self.assertTrue(result)\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual([call(1, proc_1, 'argument list'),\\n call(1, proc_2, 'argument list')], mocked_start.call_args_list)\\n self.assertEqual(0, mocked_progress.call_count)\\n mocked_check.reset_mock()\\n mocked_start.reset_mock()\\n # test RPC call no wait and done\\n mocked_start.return_value = True\\n result = rpc.start_process(1, 'appli:*', 'argument list', False)\\n self.assertTrue(result)\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual([call(1, proc_1, 'argument list'),\\n call(1, proc_2, 'argument list')], mocked_start.call_args_list)\\n self.assertEqual(0, mocked_progress.call_count)\\n mocked_check.reset_mock()\\n mocked_start.reset_mock()\\n # test RPC call with wait and done\\n result = rpc.start_process(2, 'appli:*', wait=True)\\n self.assertTrue(result)\\n self.assertEqual([call(2, proc_1, ''), call(2, proc_2, '')],\\n mocked_start.call_args_list)\\n self.assertEqual(0, mocked_progress.call_count)\\n mocked_check.reset_mock()\\n mocked_start.reset_mock()\\n # test RPC call with wait and not done\\n mocked_start.return_value = False\\n deferred = rpc.start_process(2, 'appli:*', wait=True)\\n # result is a function for deferred result\\n self.assertTrue(callable(deferred))\\n self.assertEqual([call()], mocked_check.call_args_list)\\n self.assertEqual([call(2, proc_1, ''), call(2, proc_2, '')],\\n mocked_start.call_args_list)\\n self.assertEqual(0, mocked_progress.call_count)\\n # test returned function: return True when job in progress\\n mocked_progress.return_value = True\\n self.assertEqual(NOT_DONE_YET, deferred())\\n self.assertEqual([call()], mocked_progress.call_args_list)\\n mocked_progress.reset_mock()\\n # test returned function: raise exception if job not in progress anymore\\n # and process still stopped\\n mocked_progress.return_value = False\\n with self.assertRaises(RPCError) as exc:\\n deferred()\\n self.assertEqual(Faults.ABNORMAL_TERMINATION, exc.exception.code)\\n self.assertEqual('ABNORMAL_TERMINATION: proc1', exc.exception.text)\\n self.assertEqual([call()], mocked_progress.call_args_list)\\n mocked_progress.reset_mock()\\n # test returned function: return True if job not in progress anymore\\n # and process running\\n proc_1.stopped.return_value = False\\n self.assertTrue(deferred())\\n self.assertEqual([call()], mocked_progress.call_args_list)\",\n \"def test_call(*args, **kwargs):\\n try:\\n subprocess.check_output(*args, **kwargs)\\n return True\\n except Exception:\\n return False\",\n \"def test_ns_fail2():\\n env = NsSimPyEnvironment()\\n env.process(some_process(env, 4e-29))\\n env.run()\",\n \"def test_isort_tool_plugin_scan_oserror(mock_subprocess_check_output):\\n mock_subprocess_check_output.side_effect = OSError(\\\"mocked error\\\")\\n itp = setup_isort_tool_plugin()\\n package = Package(\\n \\\"valid_package\\\", os.path.join(os.path.dirname(__file__), \\\"valid_package\\\")\\n )\\n package[\\\"python_src\\\"] = [\\n os.path.join(os.path.dirname(__file__), \\\"valid_package\\\", \\\"sample.py\\\")\\n ]\\n issues = itp.scan(package, \\\"level\\\")\\n assert not issues\",\n \"def shellExecErrorCode(cmd):\\n return subprocess.call(cmd, shell=True)\",\n \"def test_check_if_error_one(self):\\n with self.assertRaises(MyError):\\n SshpassErrorExitCodeController(ERROR_RETURN_CODE, ERROR_MESSAGE)\\\\\\n .check_if_error()\",\n \"def error_check(command):\\r\\n\\r\\n # TODO\\r\",\n \"def test_DDSim_runIt_failure_inputFile(self):\\n self.ddsim.platform = \\\"Windows\\\"\\n self.ddsim.applicationLog = self.logFileName\\n self.ddsim.InputFile = \\\"pairs.hepmc\\\"\\n ## side effect for Script, log, logAfter\\n with patch(\\\"os.path.exists\\\", new=Mock(side_effect=[False, False, True] ) ):\\n res = self.ddsim.runIt()\\n self.assertEqual( res['Message'], \\\"no pairs.hepmc\\\" )\",\n \"def _safe_call(cmd_list):\\n try:\\n subprocess.check_output(cmd_list)\\n return True\\n except subprocess.CalledProcessError as err_thrown:\\n print('Error while calling \\\"%s\\\"', err_thrown.cmd)\\n return False\",\n \"def test_does_not_crash(self):\\n py_function(6)\",\n \"def prnt_error():\\n print \\\"Error!\\\\n\\\"\\n return False\",\n \"def errReceived(self, data):\\n log.msg(\\\"Error output from process: \\\" + data,\\n isError=True)\",\n \"def supercall(command):\\n p = subprocess.Popen(command, shell=True, stderr=subprocess.PIPE)\\n retval = p.wait()\\n \\n if retval != 0:\\n get_logger().critical('error calling {}'.format(command))\\n for line in p.stderr.readlines():\\n get_logger().critical(line.decode('utf8').replace('\\\\n', ''))\\n\\n return retval\",\n \"def test_has_receivers(error_report_mock):\\n\\n app_crash()\\n error_report_mock.assert_not_called()\\n\\n signal('panic').connect(triage)\\n app_crash()\\n error_report_mock.assert_called_with()\",\n \"def test_command(self):\\r\\n with self.assertRaises(SystemExit) as ex:\\r\\n with self.assertRaisesRegexp(CommandError, 'This script requires.*'):\\r\\n call_command('git_export', 'blah', 'blah', 'blah',\\r\\n stderr=StringIO.StringIO())\\r\\n self.assertEqual(ex.exception.code, 1)\\r\\n\\r\\n with self.assertRaises(SystemExit) as ex:\\r\\n with self.assertRaisesRegexp(CommandError, 'This script requires.*'):\\r\\n call_command('git_export', stderr=StringIO.StringIO())\\r\\n self.assertEqual(ex.exception.code, 1)\\r\\n\\r\\n # Send bad url to get course not exported\\r\\n with self.assertRaises(SystemExit) as ex:\\r\\n with self.assertRaisesRegexp(CommandError, GitExportError.URL_BAD):\\r\\n call_command('git_export', 'foo/bar/baz', 'silly',\\r\\n stderr=StringIO.StringIO())\\r\\n self.assertEqual(ex.exception.code, 1)\\r\\n # Send bad course_id to get course not exported\\r\\n with self.assertRaises(SystemExit) as ex:\\r\\n with self.assertRaisesRegexp(CommandError, GitExportError.BAD_COURSE):\\r\\n call_command('git_export', 'foo/bar:baz', 'silly',\\r\\n stderr=StringIO.StringIO())\\r\\n self.assertEqual(ex.exception.code, 1)\",\n \"def test_cond_with_uncaught_error(env):\\n def explode(env, delay):\\n yield env.timeout(delay)\\n raise ValueError(f'Onoes, failed after {delay}!')\\n\\n def process(env):\\n yield env.timeout(1) | env.process(explode(env, 2))\\n\\n env.process(process(env))\\n try:\\n env.run()\\n assert False, 'There should have been an exception.'\\n except ValueError:\\n pass\\n assert env.now == 2\",\n \"def test_sandbox_errors_false(self):\\n SignalHook(self.test_extension, self.signal, self._on_signal_exception,\\n sandbox_errors=False)\\n\\n self.assertEqual(len(self._on_signal_exception.calls), 0)\\n self.assertRaises(Exception, self.signal.send, self)\\n self.assertEqual(len(self._on_signal_exception.calls), 1)\",\n \"def test_main_fails_on_request_error(\\n runner: CliRunner, mock_requests_get: MockFixture\\n) -> None:\\n mock_requests_get.side_effect = Exception(\\\"Boom\\\")\\n result = runner.invoke(console.main)\\n assert result.exit_code == 1\",\n \"def test_example(self, _, cmd):\\n out = subprocess.run(cmd, shell=True)\\n self.assertFalse(out.returncode)\",\n \"def testIgnoredError(self):\\n cmds = \\\"\\\"\\\"-chown 0 missingFile\\npwd\\nexit\\n\\\"\\\"\\\"\\n def _cbCheckResult(res):\\n self.assertIn(self.testDir.asBytesMode().path, res)\\n\\n d = self._getBatchOutput(cmds)\\n d.addCallback(_cbCheckResult)\\n return d\",\n \"def test_exit_on_missing_file(self):\\n with self.assertRaises(SystemExit):\\n pyint = Interpreter()\\n pyint.run(file=MISSING_FILE)\",\n \"def test_main_failure(logger, argv):\\n assert app.main(argv) == 1\\n logger.error.called_once()\",\n \"def test_check_process_output(self):\\n workflow = self.get_workflow(\\n \\\"\\\"\\\"file://result <- file://source\\n echo test\\n \\\"\\\"\\\")\\n workflow.pre_check_processes()\\n try:\\n process = workflow._processes[0]\\n create_tuttle_dirs()\\n workflow.run_process(process)\\n assert False, \\\"Exception has not been not raised\\\"\\n except ResourceError:\\n assert True\",\n \"def error_handler(self):\\n if self.ctx.exit_code is not None:\\n return self.ctx.exit_code\",\n \"def test_removeProcessUnknownKeyError(self):\\r\\n self.pm.startService()\\r\\n self.assertRaises(KeyError, self.pm.removeProcess, \\\"foo\\\")\",\n \"def side_effect(command, stdout, stderr):\\n if any('always-auth' in arg for arg in command):\\n raise subprocess.CalledProcessError(\\n returncode=1,\\n cmd=command\\n )\\n\\n return mock.DEFAULT\",\n \"def get_exit_code(self):\",\n \"def test_send_error_to_admin(self, process_mock, send_smtp_mock):\\n # arrange an mock error during processing\\n process_mock.side_effect = RuntimeError('mock error')\\n\\n with name_of_file_containing('contents') as filename:\\n call_command('process_email', email_file=filename)\\n\\n self.assertTrue(send_smtp_mock.called)\\n (msg,) = send_smtp_mock.call_args.args\\n self.assertEqual(msg['to'], 'admin@example.com', 'Admins should be emailed on error')\\n self.assertIn('error', msg['subject'].lower(), 'Error email subject should indicate error')\\n self.assertTrue(msg.is_multipart(), 'Error email should have attachments')\\n parts = msg.get_payload()\\n self.assertEqual(len(parts), 3, 'Error email should contain message, traceback, and original message')\\n content = parts[0].get_payload()\\n traceback = parts[1].get_payload()\\n original = parts[2].get_payload(decode=True).decode() # convert octet-stream to string\\n self.assertIn('RuntimeError', content, 'Error type should be included in error email')\\n self.assertIn('mock.py', content, 'File where error occurred should be included in error email')\\n self.assertIn('traceback', traceback.lower(), 'Traceback should be attached to error email')\\n self.assertEqual(original, 'contents', 'Original message should be attached to error email')\",\n \"def expect_failure(self):\\n class StopCode(Exception):\\n \\\"\\\"\\\"This exception will be raised instead of exiting the program.\\\"\\\"\\\"\\n def __init__(self, exit_code):\\n self.exit_code = exit_code\\n\\n def stop_code(arg):\\n raise StopCode(arg)\\n\\n with mock.patch('sys.exit') as exit:\\n exit.side_effect = stop_code\\n try:\\n with mock.patch('sys.stderr'):\\n yield\\n except StopCode as exc:\\n self.assertTrue(exc.exit_code > 0)\\n exit.assert_called()\",\n \"def test_sandbox_errors_true(self):\\n SignalHook(self.test_extension, self.signal, self._on_signal_exception,\\n sandbox_errors=True)\\n\\n self.assertEqual(len(self._on_signal_exception.calls), 0)\\n self.signal.send(self)\\n self.assertEqual(len(self._on_signal_exception.calls), 1)\",\n \"def errorCheck(sh, returncode, stderr):\\n\\tif returncode!=0 or stderr!='':\\n\\t\\tif config.DEBUG:\\n\\t\\t\\tmsg = \\\"sh code execution [%s] returned non-zero exit status [%s] and/or non-empty stdterr [%s]\\\" % (repr(sh), returncode, repr(stderr.strip()))\\n\\t\\telse:\\n\\t\\t\\tmsg = \\\"sh code execution returned non-zero exit status and/or non-empty stdterr\\\"\\n\\t\\traise Exception(msg)\",\n \"def test_broken_error_descriptor(self):\\r\\n with self.assertRaises(TestException):\\r\\n module = self.descriptor._xmodule\",\n \"def isfailure(self):\\n\\n return self.proc.returncode != 0\",\n \"def test_broken_error_module(self):\\r\\n with self.assertRaises(TestException):\\r\\n module = self.descriptor._xmodule\",\n \"def test_process_id():\\n output = sh.process_id()\\n assert isinstance(output, int) and output > 0\",\n \"def script_test(path):\\n log.info(\\\" ... EXECUTING {}\\\".format(str(path)))\\n\\n cmd = [sys.executable, str(path)]\\n cp = subprocess.run(cmd, stderr=subprocess.PIPE)\\n if cp.returncode:\\n log.info(\\\" ... FAILED\\\")\\n log.info(\\\" ___ TRACEBACK\\\")\\n log.info(cp.stderr.decode(\\\"utf-8\\\") + \\\"\\\\n\\\\n\\\")\\n return False\\n else:\\n log.info(\\\" ... PASSED\\\")\\n return True\",\n \"def _check_error(return_value):\\n if return_value < 0:\\n raise IOError(pm.lib.Pm_GetErrorText(return_value))\",\n \"def test_handles_error(self):\\n with self.assertRaises(ForcedExit):\\n self.run_command(mkdtemp())\\n\\n self.assertResults(\\n result_with_hint(\\n u'This repository has not been initialized.',\\n GIT_REPO_NOT_INITIALIZED),\\n self.error)\",\n \"def test_NameError(n=2):\\n\\n p = platform_name()\\n\\n try:\\n p.result()\\n except NameError:\\n print(\\\"Caught NameError\\\")\\n else:\\n assert False, \\\"Raise the wrong Error\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66911113","0.64319515","0.63943857","0.63668126","0.6338667","0.63044673","0.63034093","0.6283694","0.62171775","0.6183645","0.61580575","0.6093192","0.6079444","0.603348","0.6027173","0.5997922","0.59800065","0.59751534","0.59546334","0.5954266","0.5952915","0.5950407","0.5940823","0.5938315","0.5936006","0.5910566","0.58934283","0.58907115","0.5888934","0.5878262","0.5869655","0.5857681","0.5855828","0.58498514","0.5837764","0.58310163","0.5826163","0.5824052","0.58215964","0.58190316","0.5811657","0.5808997","0.58084625","0.580711","0.57744235","0.57393813","0.5732496","0.5724523","0.57161075","0.5712371","0.5709087","0.57071835","0.57047236","0.5691978","0.5677486","0.56693155","0.5657402","0.56558996","0.565308","0.5649697","0.5647792","0.5646234","0.56461334","0.5643843","0.56422126","0.56357884","0.5632547","0.562886","0.5624747","0.5613597","0.56036216","0.55999523","0.5598975","0.5593832","0.5591292","0.5583989","0.55821043","0.55819696","0.5571361","0.55705565","0.5569407","0.55691683","0.55651265","0.55651","0.5559164","0.55580276","0.5557747","0.5550513","0.5548187","0.55465066","0.55371517","0.5536441","0.5534309","0.5531631","0.55276555","0.5521732","0.55160487","0.5508824","0.5502101","0.5499891"],"string":"[\n \"0.66911113\",\n \"0.64319515\",\n \"0.63943857\",\n \"0.63668126\",\n \"0.6338667\",\n \"0.63044673\",\n \"0.63034093\",\n \"0.6283694\",\n \"0.62171775\",\n \"0.6183645\",\n \"0.61580575\",\n \"0.6093192\",\n \"0.6079444\",\n \"0.603348\",\n \"0.6027173\",\n \"0.5997922\",\n \"0.59800065\",\n \"0.59751534\",\n \"0.59546334\",\n \"0.5954266\",\n \"0.5952915\",\n \"0.5950407\",\n \"0.5940823\",\n \"0.5938315\",\n \"0.5936006\",\n \"0.5910566\",\n \"0.58934283\",\n \"0.58907115\",\n \"0.5888934\",\n \"0.5878262\",\n \"0.5869655\",\n \"0.5857681\",\n \"0.5855828\",\n \"0.58498514\",\n \"0.5837764\",\n \"0.58310163\",\n \"0.5826163\",\n \"0.5824052\",\n \"0.58215964\",\n \"0.58190316\",\n \"0.5811657\",\n \"0.5808997\",\n \"0.58084625\",\n \"0.580711\",\n \"0.57744235\",\n \"0.57393813\",\n \"0.5732496\",\n \"0.5724523\",\n \"0.57161075\",\n \"0.5712371\",\n \"0.5709087\",\n \"0.57071835\",\n \"0.57047236\",\n \"0.5691978\",\n \"0.5677486\",\n \"0.56693155\",\n \"0.5657402\",\n \"0.56558996\",\n \"0.565308\",\n \"0.5649697\",\n \"0.5647792\",\n \"0.5646234\",\n \"0.56461334\",\n \"0.5643843\",\n \"0.56422126\",\n \"0.56357884\",\n \"0.5632547\",\n \"0.562886\",\n \"0.5624747\",\n \"0.5613597\",\n \"0.56036216\",\n \"0.55999523\",\n \"0.5598975\",\n \"0.5593832\",\n \"0.5591292\",\n \"0.5583989\",\n \"0.55821043\",\n \"0.55819696\",\n \"0.5571361\",\n \"0.55705565\",\n \"0.5569407\",\n \"0.55691683\",\n \"0.55651265\",\n \"0.55651\",\n \"0.5559164\",\n \"0.55580276\",\n \"0.5557747\",\n \"0.5550513\",\n \"0.5548187\",\n \"0.55465066\",\n \"0.55371517\",\n \"0.5536441\",\n \"0.5534309\",\n \"0.5531631\",\n \"0.55276555\",\n \"0.5521732\",\n \"0.55160487\",\n \"0.5508824\",\n \"0.5502101\",\n \"0.5499891\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6745141"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8295,"cells":{"query":{"kind":"string","value":"Get the release history from pypi Use the json API to get the release history from pypi. The returned json structure includes a 'releases' dictionary which has keys that are release numbers and the value is an array of uploaded files. While we don't have a 'release time' per say (only the upload time on each of the files), we'll consider the timestamp on the first source file found (which will be a .zip or tar.gz typically) to be 'release time'. This is inexact, but should be close enough for our purposes."},"document":{"kind":"string","value":"def get_releases_for_package(name, since):\n f = urlreq.urlopen(\"http://pypi.org/project/%s/json\" % name)\n jsondata = f.read()\n data = json.loads(jsondata)\n releases = []\n for relname, rellist in data['releases'].iteritems():\n for rel in rellist:\n if rel['python_version'] == 'source':\n when = _parse_pypi_released(rel['upload_time'])\n # for speed, only care about when > since\n if when < since:\n continue\n\n releases.append(\n Release(\n name,\n relname,\n rel['filename'],\n when))\n break\n return releases"},"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 list_releases():\n response = requests.get(PYPI_URL.format(package=PYPI_PACKAGE_NAME))\n if response:\n data = response.json()\n\n releases_dict = data.get('releases', {})\n\n if releases_dict:\n for version, release in releases_dict.items():\n release_formats = []\n published_on_date = None\n for fmt in release:\n release_formats.append(fmt.get('packagetype'))\n published_on_date = fmt.get('upload_time')\n\n release_formats = ' | '.join(release_formats)\n print('{:<10}{:>15}{:>25}'.format(version, published_on_date, release_formats))\n else:\n print('No releases found for {}'.format(PYPI_PACKAGE_NAME))\n else:\n print('Package \"{}\" not found on Pypi.org'.format(PYPI_PACKAGE_NAME))","def versionHistory(self):\n url = self.metaData().getLink(\"version-history\")\n assert url is not None\n\n header = self._baseHeader.copy()\n response = self._adapter.getRequest(url, header)\n\n return json.loads(response['Body'])","def get_current_release_downloads():\n downloads = (\n get_downloads_metadata()\n ['releases']\n [get_current_release()]\n ['downloads'])\n\n def up_to_date(dir, urls):\n try:\n df = pandas.read_csv(join(dir, \"DOWNLOAD_INFO.csv\"))\n return list(df.url) == list(urls)\n except IOError:\n return None\n\n return OrderedDict(\n (download[\"name\"], {\n 'downloaded': exists(join(get_downloads_dir(), download[\"name\"])),\n 'up_to_date': up_to_date(\n join(get_downloads_dir(), download[\"name\"]),\n [download['url']] if 'url' in download else download['part_urls']),\n 'metadata': download,\n }) for download in downloads\n )","def Releases():\n return releases","def releases(releaser, count):\n releases = sorted(\n releaser.get_releases().values(),\n key=lambda rel: rel[\"end_timestamp\"],\n reverse=True,\n )\n click.echo(f\"Latest {count} releases:\")\n for release in releases[:count]:\n click.echo(f'{release[\"end_timestamp\"]} {release[\"commit\"]}')","def latest(data):\n result = dict()\n version = parse(\"0\")\n for release, info in data.items():\n python_version = Pypi._get_python_version(info)\n ver = parse(release)\n if not ver.is_prerelease:\n version = max(version, ver)\n python_version = python_version\n\n result = dict(version=str(version), python_version=python_version)\n\n return [result]","def getCommitsSinceLastRelease(self):\n f = open(self.last_released, 'r')\n old_rev = f.read().replace('\\n', '')\n f.close()\n new_rev = commands.getoutput('cd '+self.proj_dir+' && git log -1 --format=%H')\n cmd = 'cd '+self.proj_dir+' && git log --no-merges --pretty=format:\"%s\" '+old_rev+'..'+new_rev\n unreleased_commits = commands.getoutput(cmd) \n print 'Commits since last release:'\n print unreleased_commits\n unreleased_commits = unreleased_commits.split('\\n')\n self.commit_msgs = unreleased_commits\n self.new_rev = new_rev","def get_release_info():\n major_info = fetch_json(\n \"https://product-details.mozilla.org/1.0/\" \"firefox_history_major_releases.json\"\n )\n if major_info is None:\n raise Exception(\"Failed to fetch major version info\")\n minor_info = fetch_json(\n \"https://product-details.mozilla.org/1.0/\"\n \"firefox_history_stability_releases.json\"\n )\n if minor_info is None:\n raise Exception(\"Failed to fetch minor version info\")\n\n return {\"major\": major_info, \"minor\": minor_info}","def get_release_info(self, version):\r\n try:\r\n return self._detail[\"releases\"][version]\r\n except KeyError as key_error:\r\n log.warning(key_error)\r\n return []","def latest_release_get():\n try:\n return json_response.success({'version': version.latest_version()})\n except version.Error as e:\n return json_response.error(str(e)), 200","def get_previous_release_info(\n previous_release_version: str | None, past_releases: list[ReleaseInfo], current_release_version: str\n) -> str | None:\n previous_release = None\n if previous_release_version == current_release_version:\n # Re-running for current release - use previous release as base for git log\n if len(past_releases) > 1:\n previous_release = past_releases[1].last_commit_hash\n else:\n previous_release = past_releases[0].last_commit_hash if past_releases else None\n return previous_release","def get_history(cls, api, history):\n api_base = api.split('/')[-1]\n cursor = cls.history_index.cursor()\n cursor.execute(\n \"select filename from history where api=? and ymdh=?;\",\n (api_base, history))\n files = [r[0] for r in cursor]\n cls.history_index.commit()\n if not files:\n return {}\n results = {}\n for fn in files:\n ts = re.split('[?@]', fn)[-1].replace('.gz', '')\n fn_full = os.path.join(config.base_store_dir, fn)\n fd = (gzip.open if fn.endswith('.gz') else open)(fn_full)\n results[ts] = json.load(fd, encoding='utf8')\n fd.close()\n return results","def GetVersions(url, requestedProduct, requestedVersion):\n dictValidReleasesSorted = {}\n response = requests.get(url)\n if response.status_code == 200:\n jsonResult = response.json()\n jVersions = jsonResult[requestedProduct][\"versions\"]\n dictValidReleases = {}\n # do not want pre-releases; filter them out\n for item in jVersions.items(): \n for build in item[1][\"builds\"]:\n if (build[\"os\"] == SUPPORTED_OS):\n if (build[\"arch\"] == SUPPORTED_ARCH):\n if not (re.search('[a-zA-Z]', item[1][\"version\"])): \n dictValidReleases[item[1][\"version\"]] = build[\"url\"]\n\n for key in sorted(dictValidReleases,key=LooseVersion):\n dictValidReleasesSorted[key] = dictValidReleases[key]\n else:\n raise requests.ConnectionError(\"Server did not return status 200 - returned {0}\".format(response.status_code))\n\n return dictValidReleasesSorted","def get_latest_tags(self):\n\n start = len(self.tags) - self.num_comparisons\n tags = self.tags\n latest = []\n for i in xrange(len(tags)):\n if i >= start:\n parts = tags[i]['ref'].split('/')\n release_num = parts[2]\n sha = tags[i]['object']['sha']\n tag = [release_num, sha]\n latest.append(tag)\n return latest","def get_release(request):\r\n\r\n release = raven.fetch_git_sha(os.path.dirname(os.path.dirname(__file__)))\r\n return HttpResponse(json.dumps({\"release\": release[:7]}))","def releases():\n r = run('ls -x %(releases_path)s' % env)\n env.releases = sorted(r.split(\"\\t\"))\n if len(env.releases) >= 1:\n env.current_revision = env.releases[-1]\n env.current_release = '%(releases_path)s/%(current_revision)s' % env\n if len(env.releases) > 1:\n env.previous_revision = env.releases[-2]\n env.previous_release = '%(releases_path)s/%(previous_revision)s' % env\n\n #cleanup old releases. max 3 allowed.\n cleanup()","def get_recent_release_from_product_details() -> int:\n rls_prod_details_json = get(\n \"https://product-details.mozilla.org/1.0/firefox_history_major_releases.json\"\n ).json()\n rls_prod_details = Series(rls_prod_details_json).sort_values(ascending=True)\n [(cur_rls_vers, _date)] = rls_prod_details[-1:].iteritems()\n cur_rls_maj, *_v = cur_rls_vers.split(\".\")\n return int(cur_rls_maj)","def _parse_latest_update(self, resp: Dict[str, Any], latest_version: str) -> str:\n latest_release = resp.get(\"releases\", {}).get(latest_version)\n if latest_release is not None and isinstance(latest_release, list):\n release_artifact_dates = []\n for artifact in latest_release:\n try:\n upload_time = artifact.get(\"upload_time_iso_8601\")\n parsed_upload_time = dateutil.parser.isoparse(upload_time)\n release_artifact_dates.append(parsed_upload_time)\n except Exception:\n pass\n latest_artifact_timestamp = max(release_artifact_dates)\n return latest_artifact_timestamp.strftime(\"%Y-%m-%dT%H:%M:%SZ\")\n return \"\"","def get_version():\n parent_dir = os.path.dirname(os.path.realpath(__file__))\n while True:\n if '.git' in os.listdir(parent_dir):\n break\n parent_dir = os.path.dirname(parent_dir)\n git_log = os.path.join(parent_dir,'.git','logs','HEAD')\n handle = open(git_log,'r')\n log_lines = [l.split('\\t') for l in handle.readlines()]\n #now get latest github commit\n url = 'https://api.github.com/repos/thomasvangurp/epiGBS/commits'\n context = ssl._create_unverified_context()\n result = json.load(urllib.urlopen(url,context=context))\n print('')","def get_released_versions(package_name):\n url = \"https://pypi.python.org/pypi/{}/json\".format(package_name)\n data = json.load(urllib.request.urlopen(url))\n\n versions = {\n # We can actually select any element in `dist_files` because all the distribution files\n # should have almost the same upload time.\n version: dist_files[0][\"upload_time\"]\n for version, dist_files in data[\"releases\"].items()\n # If len(dist_files) = 0, this release is unavailable.\n # Example: https://pypi.org/project/xgboost/0.7\n #\n # > pip install 'xgboost==0.7'\n # ERROR: Could not find a version that satisfies the requirement xgboost==0.7\n if len(dist_files) > 0 and (not dist_files[0].get(\"yanked\", False))\n }\n return versions","def releases():\n result = run('ls %(releases_dir)s' % env)\n releases_list = re.split('\\s+', result)\n releases_list.sort(reverse=True)\n return releases_list","def get_release_notes(self):\n\n notes = self.output.get_header('RELEASE NOTES')\n notes += 'https://{}/{}/{}/releases'.format(HOST_GITHUB, \\\n self.repo, self.product) + '\\n'\n\n notes += self.output.get_sub_header('COMPARISONS')\n notes += self.get_comparison(self.latest_tags[0][VERS],\n self.latest_tags[1][VERS])\n\n if len(self.latest_tags) >= (MAX_COMPARISONS_TO_SHOW - 1):\n notes += self.get_comparison(self.latest_tags[1][VERS],\n self.latest_tags[2][VERS])\n\n if len(self.latest_tags) >= MAX_COMPARISONS_TO_SHOW:\n notes += self.get_comparison(self.latest_tags[2][VERS],\n self.latest_tags[3][VERS])\n\n tag_data = self.get_tag(self.latest_tags[3][SHA])\n\n notes += self.output.get_sub_header('TAGS')\n notes += self.get_url_tag_release(self.latest_tags[3][VERS]) + '\\n'\n notes += self.get_url_tag_commit(tag_data[\"object\"][\"sha\"]) + '\\n'\n\n changelog = self.get_changelog(tag_data[\"object\"][\"sha\"])\n if changelog:\n notes += self.output.get_sub_header('CHANGELOG')\n notes += changelog\n return notes","def get_release_info(version='v1.1-dev', date='2021-07-22'):\n # go to the repository directory\n dir_orig = os.getcwd()\n os.chdir(os.path.dirname(os.path.dirname(__file__)))\n\n # grab git info into string\n try:\n cmd = \"git describe --tags\"\n version = subprocess.check_output(cmd.split(), stderr=subprocess.DEVNULL)\n version = version.decode('utf-8').strip()\n\n # if there are new commits after the latest release\n if '-' in version:\n version, num_commit = version.split('-')[:2]\n version += '-{}'.format(num_commit)\n\n cmd = \"git log -1 --date=short --format=%cd\"\n date = subprocess.check_output(cmd.split(), stderr=subprocess.DEVNULL)\n date = date.decode('utf-8').strip()\n except:\n pass\n\n # go back to the original directory\n os.chdir(dir_orig)\n return version, date","def get_version_and_release_date(\n requirement, version=None, verbose=False, response=None\n):\n if not response:\n url = get_pypi_url(requirement, version)\n response = request(url)\n\n # see if the url is 404'ing because it has been redirected\n if response.status == 404:\n root_url = url.rpartition(\"/\")[0]\n res = request(root_url, method=\"HEAD\")\n if res.status == 301:\n new_location = res.headers[\"location\"] + \"/json\"\n response = request(new_location)\n\n if response.status != 200:\n if version:\n if verbose:\n print(\n \"{} ({}) isn't available on PyPI \"\n \"anymore!\".format(requirement, version)\n )\n else:\n if verbose:\n print(\n \"{} isn't on PyPI. Check that the project \"\n \"still exists!\".format(requirement)\n )\n return None, None\n\n if not response.json:\n if verbose:\n print(\n \"Decoding the JSON response for {} ({}) \"\n \"failed\".format(requirement, version)\n )\n return None, None\n\n response = response.json\n\n try:\n if version:\n if version in response[\"releases\"]:\n release_date = response[\"releases\"][version][0][\"upload_time\"]\n else:\n return None, None\n else:\n version = response[\"info\"].get(\"stable_version\")\n\n if not version:\n versions = {\n v: parse_version(v)\n for v in response[\"releases\"].keys()\n if not parse_version(v).is_prerelease()\n }\n\n # if we still don't have a version, let's pick up a prerelease one\n if not versions:\n versions = {\n v: parse_version(v) for v in response[\"releases\"].keys()\n }\n\n if versions:\n version = max(versions.items(), key=operator.itemgetter(1))[0]\n release_date = response[\"releases\"][str(version)][0][\"upload_time\"]\n else:\n return None, None\n\n return version, datetime.fromtimestamp(\n time.mktime(time.strptime(release_date, \"%Y-%m-%dT%H:%M:%S\"))\n )\n except IndexError:\n if verbose:\n print(\"{} ({}) didn't return a date property\".format(requirement, version))\n return None, None","def get_releases(is_vertebrate: bool):\n url = \"http://ftp.ensemblgenomes.org/pub?\"\n if is_vertebrate:\n url = \"http://ftp.ensembl.org/pub?\"\n ret = retry(requests.get, 3, url)\n # sort releases new to old\n releases = sorted(\n [int(i) for i in re.findall(r'\"release-(\\d+)/\"', ret.text)],\n reverse=True,\n )\n if is_vertebrate:\n # ignore immature releases\n releases = [r for r in releases if r > 46]\n return releases","def api_json(self):\n if not self._api_json:\n resp = requests.get(\n GitHubManager.RELEASE_API.format(repo=self.repo)\n )\n if not resp.ok:\n resp.raise_for_status()\n\n self._api_json = resp.json()\n\n return self._api_json","def certifiVersions():\n log = logger.new(function='certifiVersions')\n r = yield treq.get('https://pypi.python.org/pypi/certifi/json', timeout=5)\n log.msg(\"got certifi versions!\")\n data = yield r.json()\n\n # Note: this takes advantage of the fact that certifi's releases have the\n # same version number sort order as lexicographical. If that changes,\n # this will break.\n releases = sorted(data[u'releases'].keys())\n\n first_release = releases.index('14.05.14')\n target_versions = releases[first_release:]\n\n result = []\n for version in target_versions:\n files = data[u'releases'][version]\n\n # Find the .tar.gz release.\n for file in files:\n if file[u'filename'].endswith(u'.tar.gz'):\n break\n else:\n continue\n\n log.msg(\"new release located\", version=version, tarball=file[u'url'])\n result.append((version, file[u'url']))\n\n returnValue(result)","def get_releases(repo, quiet=False, per_page=None) -> List[str]:\n req_url = f\"https://api.github.com/repos/{owner}/{repo}/releases\"\n\n params = {}\n if per_page is not None:\n if per_page < 1 or per_page > 100:\n raise ValueError(\"per_page must be between 1 and 100\")\n params[\"per_page\"] = per_page\n\n request = get_request(req_url, params=params)\n num_tries = 0\n while True:\n num_tries += 1\n try:\n with urllib.request.urlopen(request, timeout=10) as resp:\n result = resp.read()\n break\n except urllib.error.HTTPError as err:\n if err.code == 401 and os.environ.get(\"GITHUB_TOKEN\"):\n raise ValueError(\"GITHUB_TOKEN env is invalid\") from err\n elif err.code == 403 and \"rate limit exceeded\" in err.reason:\n raise ValueError(\n f\"use GITHUB_TOKEN env to bypass rate limit ({err})\"\n ) from err\n elif err.code in (404, 503) and num_tries < max_http_tries:\n # GitHub sometimes returns this error for valid URLs, so retry\n print(f\"URL request {num_tries} did not work ({err})\")\n continue\n raise RuntimeError(f\"cannot retrieve data from {req_url}\") from err\n\n releases = json.loads(result.decode())\n if not quiet:\n print(f\"found {len(releases)} releases for {owner}/{repo}\")\n\n avail_releases = [\"latest\"]\n avail_releases.extend(release[\"tag_name\"] for release in releases)\n return avail_releases","def get_releases(platform, year, month):\n platform_num = PLATFORMS.get(platform.lower(), 0)\n\n if platform_num == 0:\n url = _3DJUEGOS_RELEASES_URL + \"todos/por-mes/0/{}/{}/\".format(year, month)\n else:\n url = _3DJUEGOS_RELEASES_URL + platform + \\\n \"/por-mes/{}/{}/{}/\".format(platform_num, year, month)\n\n soup = get_soup_obj(url)\n if not soup:\n return None\n results = {}\n results[\"games\"] = []\n root = soup.find(\"div\", {\"class\": \"pad_rl10\"})\n for div in root.findAll(\"div\"):\n if div.attrs[\"class\"] == [\"s20\", \"ffnav\", \"b\", \"mar_t50\"]:\n release_date = \"{}-{}-{}\".format(year, month,\n re.search(r'\\d+', div.span.text).group())\n elif div.attrs[\"class\"] == [\"dtc\", \"vam\"]:\n name = div.a.span.text\n platform = div.div.span.text\n results[\"games\"].append(\n {\"name\": name, \"platform\": platform, \"releaseDate\": release_date})\n\n return results","def list_releases(self, name):\n endpoint = '/v1/charm/{}/releases'.format(name)\n response = self._client.get(endpoint)\n\n channel_map = []\n for item in response['channel-map']:\n expires_at = item['expiration-date']\n if expires_at is not None:\n # `datetime.datetime.fromisoformat` is available only since Py3.7\n expires_at = parser.parse(expires_at)\n channel_map.append(\n Release(revision=item['revision'], channel=item['channel'], expires_at=expires_at))\n\n channels = [\n Channel(\n name=item['name'],\n fallback=item['fallback'],\n track=item['track'],\n risk=item['risk'],\n branch=item['branch'],\n ) for item in response['package']['channels']]\n\n revisions = [_build_revision(item) for item in response['revisions']]\n\n return channel_map, channels, revisions","def get_from_releases(releases: List[Tuple[str, str, str]]) -> List[str]:\n output = []\n client = redis.Redis(host=os.environ.get('REDIS_HOST', 'localhost'))\n\n for release in releases:\n key_name = _title_to_key(release[0])\n url = client.get(key_name)\n\n if url is not None:\n output.append('<a href=\"https://horriblesubs.info{url}\">{0}</a> – {2}'.format(*release, url=url.decode()))\n\n else:\n output.append(''.join(release))\n\n return output","def extract_release_data(self):\r\n data = None\r\n session = self.get_session()\r\n project = session.create(self._config.name)\r\n \r\n session.home = self._config['dir']\r\n \r\n result = self.__find_project(project)\r\n if (result != None) and (self._config.get_boolean('releasable', False)):\r\n if 'baseline.release' in self._config:\r\n data = {}\r\n _logger.info(\"Releasing: '%s'\" % result)\r\n data['name'] = result.objectname\r\n data['database'] = session.database()\r\n data['role'] = ccm.get_role_for_purpose(session, str(self._config['purpose']))\r\n data['dir'] = os.path.normpath(self._config['dir'])\r\n data['pst'] = result.name\r\n data['release'] = self._config['baseline.release']\r\n else:\r\n _logger.warning(\"Could not release \" + result.objectname + \" because the 'baseline.release' property is missing.\")\r\n return data","def get_bookable_releases(context, data_dict):\n # noinspection PyUnresolvedReferences\n frc = _get_or_bust(data_dict, 'frc')\n\n lc = ckanapi.LocalCKAN(context=context)\n\n results = lc.action.package_search(\n q='frc:{frc} AND type:publication'.format(\n frc=frc\n ),\n fl=[\n 'product_id_new',\n 'title'\n ]\n )\n\n final_results = []\n for result in results['results']:\n article_results = lc.action.package_search(\n q=(\n 'top_parent_id:{pid} '\n # Only \"Working Copy\" results.\n 'AND status_code:31 '\n # With a release_date set to anything (not blank)\n 'AND last_release_date:[* TO *] '\n 'AND type:article'\n ).format(\n pid=result['product_id_new']\n ),\n sort='issue_number_int asc',\n # FIXME: We need to actually paginate on this, but the daily\n # team will not accept it (yet).\n rows=2000000\n )\n\n for art_result in article_results['results']:\n final_results.append({\n 'productId': result['product_id_new'],\n 'issue': art_result['issue_number_int'],\n 'title': result['title'],\n 'refper': result['reference_period']\n })\n\n return final_results","def get_release_files(tag_name, config) -> Tuple[List[ReleaseFile], Dict[str, SourceFile]]:\n\n @retry_multi(5)\t# retry at most 5 times\n def execute_request(path):\n \"\"\"!\n @brief Performs a GET request with the given path. To be used with Github's REST API.\n @returns If successful, returns a .JSON object\n \"\"\"\n headers = {\n \"Accept\": \"application/vnd.github.v3+json\"\n }\n url = \"https://api.github.com\" + path\n\n # GET https://api.github.com/<path> Accept: \"application/vnd.github.v3+json\"\n\n response = requests.get(url, headers=headers, timeout=GLOBAL_TIMEOUT)\n\n response.raise_for_status() # Raise a RequestException if we failed, and trigger retry\n\n return response.json()\n\n build_group_regex = re.compile(\"fs2_open_.*-builds-([^.-]*)(-([^.]*))?.*\") # regex for matching binary .zip's and .7z's\n source_file_regex = re.compile(\"fs2_open_.*-source-([^.]*)?.*\") # regex for matching source .zip's and .7z's\n\n # Get the github release metadata of the given tag name\n response = execute_request(\n \"/repos/{}/releases/tags/{}\".format(config[\"github\"][\"repo\"], tag_name))\n\n # Extract the binary and source files from the response[\"asset\"] metadata\n binary_files = []\n source_files = {}\n for asset in response[\"assets\"]:\n url = asset[\"browser_download_url\"]\n name = asset[\"name\"]\n\n group_match = build_group_regex.match(name)\n\n if group_match is not None:\n platform = group_match.group(1)\n # x64 is the Visual Studio name but for consistency we need Win64\n if platform == \"x64\":\n platform = \"Win64\"\n\n binary_files.append(ReleaseFile(name, url, platform, group_match.group(3)))\n else:\n group_match = source_file_regex.match(name)\n\n if group_match is None:\n continue\n\n group = group_match.group(1)\n\n source_files[group] = SourceFile(name, url, group)\n\n binary_files.sort(key=lambda ReleaseFile: ReleaseFile.name)\n\n return binary_files, source_files","async def get_releases(\n self, prerelease: bool = False, returnlimit: int = 5\n ) -> [\"AIOGitHubAPIRepositoryRelease\"] or list:\n _endpoint = f\"/repos/{self.full_name}/releases\"\n\n response = await self.client.get(endpoint=_endpoint)\n contents = []\n\n for content in response or []:\n if len(contents) == returnlimit:\n break\n if not prerelease:\n if content.get(\"prerelease\", False):\n continue\n contents.append(AIOGitHubAPIRepositoryRelease(content))\n\n return contents","def get_release(repo, tag=\"latest\", quiet=False) -> dict:\n api_url = f\"https://api.github.com/repos/{owner}/{repo}\"\n req_url = (\n f\"{api_url}/releases/latest\"\n if tag == \"latest\"\n else f\"{api_url}/releases/tags/{tag}\"\n )\n request = get_request(req_url)\n releases = None\n num_tries = 0\n\n while True:\n num_tries += 1\n try:\n with urllib.request.urlopen(request, timeout=10) as resp:\n result = resp.read()\n remaining = int(resp.headers[\"x-ratelimit-remaining\"])\n if remaining <= 10:\n warnings.warn(\n f\"Only {remaining} GitHub API requests remaining \"\n \"before rate-limiting\"\n )\n break\n except urllib.error.HTTPError as err:\n if err.code == 401 and os.environ.get(\"GITHUB_TOKEN\"):\n raise ValueError(\"GITHUB_TOKEN env is invalid\") from err\n elif err.code == 403 and \"rate limit exceeded\" in err.reason:\n raise ValueError(\n f\"use GITHUB_TOKEN env to bypass rate limit ({err})\"\n ) from err\n elif err.code == 404:\n if releases is None:\n releases = get_releases(repo, quiet)\n if tag not in releases:\n raise ValueError(\n f\"Release {tag} not found (choose from {', '.join(releases)})\"\n )\n elif err.code == 503 and num_tries < max_http_tries:\n # GitHub sometimes returns this error for valid URLs, so retry\n warnings.warn(f\"URL request {num_tries} did not work ({err})\")\n continue\n raise RuntimeError(f\"cannot retrieve data from {req_url}\") from err\n\n release = json.loads(result.decode())\n tag_name = release[\"tag_name\"]\n if not quiet:\n print(f\"fetched release {tag_name!r} info from {owner}/{repo}\")\n\n return release","def _grab_history(self):\n self.data['history_lines'] = []\n self.data['history_file'] = None\n self.data['history_encoding'] = None\n self.data['headings'] = []\n self.data['history_last_release'] = ''\n self.data['history_insert_line_here'] = 0\n default_location = None\n config = self.setup_cfg.config\n if config and config.has_option('zest.releaser', 'history_file'):\n default_location = config.get('zest.releaser', 'history_file')\n history_file = self.vcs.history_file(location=default_location)\n self.data['history_file'] = history_file\n if not history_file:\n logger.warn(\"No history file found\")\n return\n logger.debug(\"Checking %s\", history_file)\n history_lines, history_encoding = read_text_file(history_file)\n history_lines = history_lines.split('\\n')\n headings = utils.extract_headings_from_history(history_lines)\n if not headings:\n logger.warn(\"No detectable version heading in the history \"\n \"file %s\", history_file)\n return\n self.data['history_lines'] = history_lines\n self.data['history_encoding'] = history_encoding\n self.data['headings'] = headings\n\n # Grab last header.\n start = headings[0]['line']\n if len(headings) > 1:\n # Include the next header plus underline, as this is nice\n # to show in the history_last_release.\n end = headings[1]['line'] + 2\n else:\n end = len(history_lines)\n history_last_release = '\\n'.join(history_lines[start:end])\n self.data['history_last_release'] = history_last_release\n\n # Add line number where an extra changelog entry can be inserted. Can\n # be useful for entry points. 'start' is the header, +1 is the\n # underline, +2 is probably an empty line, so then we should take +3.\n # Or rather: the first non-empty line.\n insert = start + 2\n while insert < end:\n if history_lines[insert].strip():\n break\n insert += 1\n self.data['history_insert_line_here'] = insert","def get_history():\n return response_texts_to_entries(make_post_request(HISTORY_API, data={\"k\": config[\"api_key\"]}))","def extract_release_data(self):\r\n data = None\r\n session = self.get_session()\r\n project = session.create(self._config.name)\r\n \r\n session.home = self._config['dir']\r\n \r\n if self._config.get_boolean('releasable', False):\r\n if 'baseline.release' in self._config:\r\n data = {}\r\n _logger.info(\"Releasing: '%s'\" % project)\r\n data['name'] = project.objectname\r\n data['database'] = session.database()\r\n data['role'] = ccm.get_role_for_purpose(session, str(self._config['purpose']))\r\n data['dir'] = os.path.normpath(self._config['dir'])\r\n data['pst'] = project.name\r\n data['release'] = self._config['baseline.release']\r\n else:\r\n _logger.warning(\"Could not release \" + project + \" because the 'baseline.release' property is missing.\")\r\n return data","async def fetch_data(self) -> GitHubReleaseModel | None:\n result = await self._client.repos.releases.list(\n self.repository, **{\"params\": {\"per_page\": 1}}\n )\n if not result.data:\n return None\n\n for release in result.data:\n if not release.prerelease:\n return release\n\n # Fall back to the latest release if no non-prerelease release is found\n return result.data[0]","def versions(self) -> List['RadsProjectVersion']:\n logger.debug(f\"retrieve versions of {self}\")\n listing = self.storage.request_text(f\"{self.path}/releaselisting\")\n return [RadsProjectVersion(self, RadsVersion(l)) for l in listing.splitlines()]","def post_import():\n\n validate_request_json(request)\n\n releases = []\n for r in request.json:\n # Get the platform, create if it doesn't exist\n platforms = []\n for p in r['platforms']:\n try:\n query = db.session.query(Platform).filter(Platform.name == p)\n platform = query.one()\n except exc.NoResultFound:\n app.logger.info(\"Creating platform {}\".format(p))\n platform = Platform(p)\n db.session.add(platform)\n platforms.append(platform)\n\n release = Release(\n platforms=platforms,\n user=r['user'],\n team=r.get('team'),\n references=json.dumps(r.get('references')),\n )\n\n release.stime = arrow.get(r['stime']) if r.get('stime') else None\n release.ftime = arrow.get(r['ftime']) if r.get('ftime') else None\n if release.ftime and release.stime:\n release.duration = release.ftime - release.stime\n\n notes = r.get('notes')\n if notes:\n for n in notes:\n note = ReleaseNote(release.id, n)\n db.session.add(note)\n\n for p in r['packages']:\n package = Package(\n release_id=release.id,\n name=p['name'],\n version=p['version'],\n )\n\n package.rollback = p.get('rollback')\n package.status = p.get('status')\n package.diff_url = p.get('diff_url')\n\n if p.get('stime'):\n package.stime = arrow.get(p['stime'])\n else:\n package.stime = arrow.get(r['stime'])\n package.ftime = arrow.get(p['ftime']) if p.get('ftime') else None\n if package.stime and package.ftime:\n package.duration = package.ftime - package.stime\n\n db.session.add(package)\n\n db.session.add(release)\n db.session.commit()\n\n releases.append(release.id)\n\n return jsonify({'releases': [str(x) for x in releases]}), 200","def ls(_):\n client = utils.s3_client()\n\n projects = []\n\n config = utils.get_config()\n bucket = config[\"release\"][\"s3_bucket\"]\n deploys = config[\"deploy\"]\n\n resp = client.list_objects_v2(Bucket=bucket)\n for data in resp.get(\"Contents\", []):\n name = data[\"Key\"]\n\n projects.append(name)\n\n projects = sorted(projects)\n\n _projects = []\n\n for name in projects:\n try:\n release = get_release(client, bucket, name)\n except InvalidRelease:\n continue\n\n data = {\n \"Name\": name,\n \"Latest Release\": f\"v{release.version} {release.timestamp} ({release.commit})\",\n }\n\n for env_name, cfg in deploys.items():\n env_version, env_commit, env_timestamp = get_deployed_version(\n client, cfg[\"s3_bucket\"], name\n )\n\n data[env_name.title()] = f\"v{env_version} {env_timestamp} ({env_commit})\"\n\n _projects.append(data)\n\n projects = _projects\n\n utils.printfmt(projects)","def get_last_release_id():\n url = \"https://api.github.com/repos/yt-dlp/yt-dlp/releases/latest\"\n try:\n with urlopen(url, timeout=10) as resp:\n return json.loads(resp.read().decode(\"utf-8\")).get(\"tag_name\", \"0\")\n except URLError as e:\n log(f\"YouTubeDLHelper error [get last release id]: {e}\")","def test_yum_history(self):\n repo_history = ospsurvey.probes.software.get_yum_history()\n\n print(\"Yum History: {}\".format(json.dumps(repo_history)))","def BPM_PROVHISTORY():\n return download_from_archive(\"bpm_20220128_gmos-s_Ham_11_full_12amp.fits\")","def test_get_all_available_release_updates(self):\n self._ucr({\n 'repository/online/component/a': 'yes',\n 'repository/online/component/a/version': 'current',\n })\n self._uri({\n '%d.%d/maintained/%d.%d-%d/all/Packages.gz' % (MAJOR, MINOR + 1, MAJOR, MINOR + 1, 0): DATA,\n '%d.%d/maintained/component/%s/all/Packages.gz' % (MAJOR, MINOR + 1, 'a'): DATA,\n '%d.%d/maintained/%d.%d-%d/all/Packages.gz' % (MAJOR + 1, 0, MAJOR + 1, 0, 0): DATA,\n })\n versions, component = self.u.get_all_available_release_updates()\n self.assertEqual(['%d.%d-%d' % (MAJOR, MINOR + 1, 0)], versions)\n self.assertEqual('a', component)","def get_release_info(self):\r\n return self.detail_info.get_release_info(self.version)","def get_versions():\n ret_obj = {'versions': picard_versions(current_app)}\n return make_response(jsonify(ret_obj), 200)","def latest_github_release(username: str, repo: str) -> GitHubReleaseResponse:\n url = f'https://api.github.com/repos/{username}/{repo}/releases/latest'\n response = requests.get(url)\n if response.status_code != 200:\n print(f'Error: {response.status_code}', file=sys.stderr)\n sys.exit(1)\n try:\n decoded_json = response.json()\n return GitHubReleaseResponse(decoded_json)\n except requests.exceptions.JSONDecodeError:\n print('Error: Unable to parse JSON from GitHub releases', file=sys.stderr)\n sys.exit(1)","def get(package):\n url = Pypi.url()\n url = url.format(package=package)\n\n # Request Pypi\n resp = requests.get(url)\n if resp.status_code != requests.codes.ok:\n return dict(status=\"failure\", message=\"Invalid Package\")\n\n data = dict()\n resp = resp.json()\n releases = resp.get(\"releases\", [])\n data[\"releases\"] = releases\n data[\"status\"] = \"success\"\n\n return data","def get_linked_versions(version='current'):\n version = check_version_str(version)\n chapters = [10, 9, 8]\n version_page = 'https://research.cs.wisc.edu/htcondor/manual/{ver}/{chapter}_Version_History.html'\n r = requests.get(version_page.format(ver=version, chapter=chapters[0]))\n if r.status_code == 404:\n # Try different chapter numbers, as it changes for different versions\n i = 1\n while r.status_code == 404 and i < len(chapters):\n r = requests.get(version_page.format(ver=version, chapter=chapters[i]))\n i += 1\n if r.status_code == 404:\n return []\n soup_vers = bs4.BeautifulSoup(r.text, 'lxml')\n versions = [x.text.replace('Version ', '')\n for x in soup_vers.find_all('a')\n if x.text.startswith('Version')]\n return versions","def get_build_infos(self, start_rev, end_rev, range=60*60*4):\n pushlogs_finder = self._create_pushlog_finder(start_rev, end_rev)\n\n pushlogs = pushlogs_finder.get_pushlogs()\n\n if not pushlogs:\n return []\n\n start_time = pushlogs[0]['date']\n end_time = pushlogs[-1]['date']\n \n build_urls = [(\"%s%s/\" % (self.build_base_url, path), timestamp)\n for path, timestamp in self._extract_paths()]\n\n build_urls_in_range = filter(lambda (u, t): t > (start_time - range)\n and t < (end_time + range), build_urls)\n\n raw_revisions = [push['changesets'][-1] for push in pushlogs]\n all_builds = []\n with futures.ThreadPoolExecutor(max_workers=8) as executor:\n futures_results = {}\n for build_url, timestamp in build_urls_in_range:\n future = executor.submit(self._get_valid_builds,\n build_url,\n timestamp,\n raw_revisions)\n futures_results[future] = build_url\n for future in futures.as_completed(futures_results):\n if future.exception() is not None:\n sys.exit(\"Retrieving valid builds from %r generated an\"\n \" exception: %s\" % (futures_results[future],\n future.exception()))\n all_builds.extend(future.result())\n\n return self._sort_builds(all_builds)","def get_releases_by_application(self, application_id):\n status_code_dict = {codes.ok: ReleaseListResponse}\n return self.get_request(RELEASES_URL.format(application_id=application_id),\n status_code_response_class_dict=status_code_dict)","def get_release_data(user, repo, field=None, regex_pattern=None, group_number=0):\n response = requests.get(f\"https://api.github.com/repos/{user}/{repo}/releases/latest\")\n release_data_dict = json.loads(response.text)\n\n if field:\n field_value = release_data_dict[field]\n if regex_pattern is None:\n output = field_value\n else:\n output = re.search(regex_pattern, field_value).group(group_number)\n else:\n output = release_data_dict\n\n return output","def cache_from_releases(releases: List[dict]) -> None:\n client = redis.Redis(host=os.environ.get('REDIS_HOST', 'localhost'))\n\n for release in (r for r in releases if r.get('url')): # filter out releases without a url\n key_name = _title_to_key(release['title'])\n client.setex(key_name, ONE_DAY, release['url'])","def get_versions(self):\n # They randomly use and don't use 'r' prefix so we have to sort\n # versions manually\n versions = list(self._get_github_tags())\n versions.sort(\n key=operator.attrgetter('base_version'),\n reverse=True,\n )\n return versions","def get_latest_release(self):\n cs = Custom_Soup(\n \"latest_release\", \"https://chromedriver.storage.googleapis.com/LATEST_RELEASE_\" + str(self.version))\n cs.get_request()\n self.latest_release = cs.get_text()","def list_releases(\n self,\n ) -> Callable[\n [cloud_deploy.ListReleasesRequest], cloud_deploy.ListReleasesResponse\n ]:\n # Generate a \"stub function\" on-the-fly which will actually make\n # the request.\n # gRPC handles serialization and deserialization, so we just need\n # to pass in the functions for each.\n if \"list_releases\" not in self._stubs:\n self._stubs[\"list_releases\"] = self.grpc_channel.unary_unary(\n \"/google.cloud.deploy.v1.CloudDeploy/ListReleases\",\n request_serializer=cloud_deploy.ListReleasesRequest.serialize,\n response_deserializer=cloud_deploy.ListReleasesResponse.deserialize,\n )\n return self._stubs[\"list_releases\"]","def software_releases_repository():\n\n pipelines = [ingest_pipeline, populate_dm_pipeline, asset_experimentation]\n schedules = [] # [my_hourly_schedule]\n sensors = [] # [my_sensor]\n\n return pipelines + schedules + sensors","def gettime(self, tag):\n cmd = ['git', 'log', '--pretty=format:\"%ct\"', \"-1\", tag]\n p = Popen(cmd, cwd=self.filename, stdout=PIPE)\n data, res = p.communicate()\n if data == b'':\n return [], []\n time_stamp = []\n this_tag = []\n for seconds in data.decode(\"utf-8\").split(\"\\n\"):\n month = round((int(seconds.strip('\"')) - ReleaseTime.base) / ReleaseTime.month_time)\n if month not in time_stamp:\n time_stamp.append(month)\n this_tag.append(tag[0:4])\n else:\n pass\n return time_stamp, this_tag","def release_dates(self, **kwargs):\n path = self._get_movie_id_path('release_date')\n resp = self._get_method(path, kwargs)\n return resp","def _parse_release_count(self, resp: Dict[str, Any]) -> str:\n return f\"{len(resp.get('releases', []))}\"","def get_release_info(self, release):\n rel_os = release.operating_system.lower()\n version = release.version\n\n release_info = filter(\n lambda r: (\n r['os'] == rel_os and\n ClusterPlugins.is_release_version_compatible(version,\n r['version'])),\n self.plugin.releases)\n\n return release_info[0]","def aggregate_git_log(path, progress_callback=lambda progress: None):\n versions = list()\n\n current_version, current_commits = None, list()\n\n log_data = git_log_hash(path)\n log_length = len(log_data)\n progress_step = max(1, log_length / 100)\n \n for idx, (rev_hash, date, msg) in enumerate(log_data):\n if idx % progress_step == 0:\n progress_callback(float(idx) / log_length)\n \n current_commits.append(msg)\n if git_checkout(path=path, revision_hash=rev_hash):\n version = get_package_metadata(path=path, field_name='Version')\n if version != current_version:\n # memorize it\n versions.insert(0,\n dict(version=version,\n date=datetime.strptime(date.rsplit(' ', 1)[0], '%Y-%m-%d %H:%M:%S'),\n sections=[dict(notes='',\n items=list(reversed(current_commits)))]))\n\n current_version, current_commits = version, list()\n\n if current_commits:\n versions.insert(0,\n dict(version='newest',\n date=None,\n sections=[dict(notes='',\n items=list(reversed(current_commits)))]))\n\n return versions","def get_history_queue():\n response = houston.get(\"/history/queue\")\n houston.raise_for_status_with_json(response)\n return response.json()","def versions():\n result = timeline.versions()\n if result:\n click.echo('\\n'.join(result))","def git_stats_release(project: Project, release: Release) -> GitStats:\n return _git_stats(project, f\"{release.tag_name}~1\", release.tag_name)","def get_recent_release_for_page(self, page, PER_PAGE):\n\t\tconnection = self.connect_to_db()\n\t\tcursor = connection.cursor()\n\t\toffset = (page - 1) * 15\n\t\tcursor.execute('''select * from movies where release_year = 2016 order by weighted desc limit %d offset %d;''' % (PER_PAGE, offset))\n\t\trows = cursor.fetchall()\n\t\tconnection.close()\n\t\treturn self.convert_to_json(rows)","def universePayoutHistory(universeId, startDate, endDate):\n url = f\"https://engagementpayouts.roblox.com/v1/universe-payout-history?endDate={endDate}&startDate={startDate}&universeId={universeId}\"\n r = requests.get(url, cookies=cookie)\n j = json.loads(r.text)\n return j","def history(self) -> List[Dict[str, Any]]:\n\n response = self.client.get(f\"/images/{self.id}/history\")\n body = response.json()\n\n if response.status_code == 200:\n return body\n\n if response.status_code == 404:\n raise ImageNotFound(body[\"cause\"], response=response, explanation=body[\"message\"])\n raise APIError(body[\"cause\"], response=response, explanation=body[\"message\"])","def main(\n req_files,\n verbose=False,\n outdated=False,\n latest=False,\n verbatim=False,\n repo=None,\n path=\"requirements.txt\",\n token=None,\n branch=\"master\",\n url=None,\n delay=None,\n):\n requirements = []\n\n if repo:\n github_url = build_github_url(repo, branch, path, token)\n req_file = get_requirements_file_from_url(github_url)\n requirements.extend(parse_req_file(req_file))\n elif url:\n req_file = get_requirements_file_from_url(url)\n requirements.extend(parse_req_file(req_file))\n else:\n for req_file in req_files:\n requirements.extend(parse_req_file(req_file, verbatim=verbatim))\n req_file.close()\n\n total_time_delta = 0\n max_outdated_time = 0\n results = []\n\n for req, version, ignore in requirements:\n if verbatim and not req:\n results.append(version)\n elif req:\n results.append(\n {\n \"req\": req,\n \"version\": version,\n \"ignore\": ignore,\n \"latest\": request(get_pypi_url(req)),\n \"specified\": request(get_pypi_url(req, version)),\n }\n )\n\n for result in results:\n if isinstance(result, str):\n print(result.replace(\"\\n\", \"\"))\n continue\n\n if result[\"ignore\"]:\n if verbatim:\n print(\"{}=={} # norot\".format(result[\"req\"], result[\"version\"]))\n else:\n print(\"Ignoring updates for {}. \".format(result[\"req\"]))\n continue\n\n req = result[\"req\"]\n version = result[\"version\"]\n\n latest_version, latest_release_date = get_version_and_release_date(\n req, verbose=verbose, response=result[\"latest\"]\n )\n specified_version, specified_release_date = get_version_and_release_date(\n req, version, response=result[\"specified\"]\n )\n\n if latest_release_date and specified_release_date:\n time_delta = (latest_release_date - specified_release_date).days\n total_time_delta = total_time_delta + time_delta\n max_outdated_time = max(time_delta, max_outdated_time)\n\n if verbose:\n if time_delta > 0:\n print(\n \"{} ({}) is {} days out of date. \"\n \"Latest is {}\".format(req, version, time_delta, latest_version)\n )\n elif version != latest_version:\n print(\n \"{} ({}) is out of date. \"\n \"Latest is {}\".format(req, version, latest_version)\n )\n elif not outdated:\n print(\"{} ({}) is up to date\".format(req, version))\n\n if latest and latest_version != specified_version:\n print(\n \"{}=={} # Updated from {}\".format(\n req, latest_version, specified_version\n )\n )\n elif verbatim and latest_version != specified_version:\n print(\n \"{}=={} # Latest {}\".format(req, specified_version, latest_version)\n )\n elif verbatim:\n print(\"{}=={}\".format(req, specified_version))\n\n elif verbatim:\n print(\"{}=={} # Error checking latest version\".format(req, version))\n\n verbatim_str = \"\"\n if verbatim:\n verbatim_str = \"# Generated with piprot {}\\n# \".format(VERSION)\n\n if total_time_delta > 0 and delay is None:\n print(\n \"{}Your requirements are {} \"\n \"days out of date\".format(verbatim_str, total_time_delta)\n )\n sys.exit(1)\n elif delay is not None and max_outdated_time > int(delay):\n print(\n \"{}At least one of your dependencies is {} \"\n \"days out of date which is more than the allowed\"\n \"{} days.\".format(verbatim_str, max_outdated_time, delay)\n )\n sys.exit(1)\n elif delay is not None and max_outdated_time <= int(delay):\n print(\n \"{}All of your dependencies are at most {} \"\n \"days out of date.\".format(verbatim_str, delay)\n )\n else:\n print(\n \"{}Looks like you've been keeping up to date, \"\n \"time for a delicious beverage!\".format(verbatim_str)\n )","def get_details(self, repo=None):\n api_json = []\n\n #get all branches from this repo\n branches = self.make_branches(self.getBranch(repo))\n\n today = datetime.date.today()\n yesterday = today - datetime.timedelta(2)\n\n for branch in branches:\n args = {\"per_page\": \"100\",\n \"sha\": branch,\n \"author\": self.username,\n \"since\": yesterday.isoformat()}\n args = self.make_args(args)\n repo_url = \"/\".join([self.url, \"repos\", repo, \"commits\"])\n repo_url = repo_url + args\n\n request = urllib2.Request(repo_url, headers=self.headers)\n response = urllib2.urlopen(request)\n raw_data = response.read()\n commits_info = self.process_factory(simplejson.loads(raw_data))\n api_json = api_json + commits_info\n\n print repo_url\n\n print api_json\n return api_json","def get_resource_last_update_timestamp(api_1_0_url, resource):\n try:\n r = requests.get(api_1_0_url)\n json_string = r.content\n data = json.loads(json_string)\n try:\n files = data['files']\n for entry in files:\n if entry['path'] == resource:\n entry_last_update_timestamp = entry['utctimestamp']\n return entry_last_update_timestamp\n except Exception as error:\n print(\"Caught error: \" + repr(error))\n except Exception as error:\n print(\"Failed to connect to bitbucket: \" + repr(error))\n exit(1)\n return None","def get_video_title_releaser_release_time(self, url):\n video_id = ' '.join(re.findall('id.*html', url))\n browser = webdriver.Chrome()\n browser.get(url)\n title = browser.find_element_by_id('subtitle').text\n releaser = browser.find_element_by_id('module_basic_sub').text\n releaser = releaser.replace('+订阅','')\n releaser = releaser.replace(' ','')\n try:\n rt_midstep = browser.find_element_by_class_name('video-status').text\n rt_midstep = rt_midstep.replace('上传于','')\n rt_midstep = rt_midstep.replace(' ','')\n release_time = int(datetime.datetime.strptime(rt_midstep,'%Y-%m-%d').timestamp()*1e3)\n except:\n release_time = 0\n fetch_time = int(datetime.datetime.timestamp(datetime.datetime.now())*1e3)\n D0 = {'video_id': video_id,\n 'title': title,\n 'release_time': release_time,\n 'url': url,\n 'fetch_time': fetch_time}\n return D0","def get_revision_list(self):\n response = self._get_request(\n DeckhandClient.get_path(DeckhandPaths.REVISION_LIST)\n )\n self._handle_bad_response(response)\n revisions = yaml.safe_load(response.text)\n return revisions.get('results', [])","def all(data):\n result = list()\n for release, info in data.items():\n version = release\n python_version = Pypi._get_python_version(info)\n d = dict(version=version, python_version=python_version)\n result.append(d)\n\n return result","def downloads_per_version(package):\n downloads = {}\n for release in package['files']:\n downloads[release['version']] = release['ndownloads']\n return downloads","def transformar_data_to_json():\n\t# La lista de releases regresa como un <str>\n\tdeployedReleases = subprocess.check_output([\"/usr/bin/bash\",\"/usr/local/bin/gsdGetRCinServer.sh\"])\n\ttimeZoneInfo = subprocess.check_output([\"date\",\"+%Z\"]).strip()\n\n\t# Separamos el <str> por lineas\n\tdeployedReleases = deployedReleases.split()\n\n\t# Generamos diccionario inicial\n\thostname = (socket.gethostname())\n communicate = { 'server' : hostname,\n 'date': today,\n 'timezone': timeZoneInfo, \n 'applications': {} \n }\n\n\t# Parseamos todos los despliegues encontrados\n\tfor release in deployedReleases:\n\n\t\t# sanetizar los datos, debemos eliminar los \".\" dado que son representaciones especiales en mongo, el path tampoco nos importa porque es estandar\n\t\trelease = release.replace('/opt/company/deploy/', '')\n\t\trelease = release.replace('.', '-')\n\n\t\t# Verificamos si es un symlink (current es version actual desplegada)\n\t\tif 'current' in release :\n\t\t\tmarker, appAndVersion = release.split('|')\n if appAndVersion.count('/') > 1 :\n rcversion = 'RC Format is wrong'\n appname = marker.replace('/current', '')\n else:\n appname, rcversion = appAndVersion.split('/')\n\n\t\t\tif appname not in communicate['applications'] :\n\t\t\t\tcommunicate['applications'].update( { \n\t\t\t\t\t\t\t\tappname: \n\t\t\t\t\t\t\t\t\t{rcversion : 'current'} \n\t\t\t\t\t\t\t } )\n\t\t\telse:\n\t\t\t\tcommunicate['applications'][appname].update({rcversion : 'current'})\n\n\t\t# Verificamos si es un symlink (rollback es la version previa a current)\n\t\telif 'rollback' in release:\n\t\t\tmarker, appAndVersion = release.split('|')\n if appAndVersion.count('/') > 1 :\n rcversion = 'RC Format is wrong'\n appname = marker.replace('/rollback', '')\n else:\n appname, rcversion = appAndVersion.split('/')\n\n\t\t\tif appname not in communicate['applications'] :\n\t\t\t\tcommunicate['applications'].update( { \n\t\t\t\t\t\t\t\tappname: \n\t\t\t\t\t\t\t\t\t{rcversion : 'rollback'} \n\t\t\t\t\t\t\t } )\n\t\t\telse:\n\t\t\t\tcommunicate['applications'][appname].update({rcversion : 'rollback'})\n\n\t\t# Lo que no es current o rollback es un despliegue mas viejo\n\t\telse :\n appname, rcversion = release.split('/')\n if appname not in communicate['applications'] :\n communicate['applications'].update( {\n appname:\n {rcversion : 'previous'}\n } )\n else:\n if rcversion not in communicate['applications'][appname] :\n communicate['applications'][appname].update({rcversion : 'previous'})\n else :\n # La version ya fue registrada como current o rollback, esto sucede porque el symlink comparte el mismo nombre que el directorio real a nivel dict\n pass\n\n\treturn communicate","def show_release_details(release):\n def get(key, dictionary=release):\n try:\n return dictionary[key]\n except KeyError as e:\n return None\n\n date = get('date')\n date = date[:4] if date else \"\"\n print(\"{} / {} ({})\".format(get('artist-credit-phrase'), get('title'), date))\n print()\n\n # print track list\n track_lists = [get(\"track-list\", medium) for medium in release['medium-list']]\n track_list = [track for tracks in track_lists for track in tracks]\n track_width = max([len(track[\"recording\"][\"title\"]) for track in track_list])\n time_width = len(get_time(sum([int(track[\"length\"]) for track in track_list])))\n\n total_ms = 0\n for idx,track in enumerate(track_list):\n title = track[\"recording\"][\"title\"]\n ms = int(track[\"length\"])\n time = get_time(ms)\n total = get_time(total_ms)\n print(\"{:2d}. {:{track_width}} {:>{time_width}} ({})\".format( \\\n idx+1, title, total, time, track_width=track_width, time_width=time_width))\n total_ms += ms\n width = 5 + track_width + time_width\n print(\"{:>{width}}\".format(get_time(total_ms), width=width))","def query_releases(self,request):\n\n\t\t# ----------- DEBUG -----------------\n\t\tMODULE.info(\"updater/updates/query invoked with:\")\n\t\tpp = pprint.PrettyPrinter(indent=4)\n\t\tst = pp.pformat(request.options).split(\"\\n\")\n\t\tfor s in st:\n\t\t\t\tMODULE.info(\" << %s\" % s)\n\t\t# -----------------------------------\n\n\t\t# be as current as possible.\n\t\tself.uu.ucr_reinit()\n\t\tself.ucr.load()\n\n\t\tappliance_mode = self.ucr.is_true('server/appliance')\n\n\t\tresult = []\n\t\ttry:\n\t\t\trequest.status = SUCCESS\n\t\t\tavailable_versions, blocking_component = self.uu.get_all_available_release_updates()\n\t\t\tfor rel in available_versions:\n\t\t\t\tentry = {}\n\t\t\t\tentry['id'] = rel\n\t\t\t\tentry['label'] = 'UCS %s' % rel\n\t\t\t\tresult.append(entry)\n\t\t\t#\n\t\t\t# appliance_mode=no ; blocking_comp=no → add \"latest version\"\n\t\t\t# appliance_mode=no ; blocking_comp=yes → no \"latest version\"\n\t\t\t# appliance_mode=yes; blocking_comp=no → add \"latest version\"\n\t\t\t# appliance_mode=yes; blocking_comp=yes → add \"latest version\"\n\t\t\t#\n\t\t\tif len(result) and (appliance_mode or not blocking_component):\n\t\t\t\t# UniventionUpdater returns available version in ascending order, so\n\t\t\t\t# the last returned entry is the one to be flagged as 'latest' if there's\n\t\t\t\t# no blocking component.\n\t\t\t\tresult[-1]['label'] = '%s (%s)' % (result[-1]['label'],_('latest version'))\n\n\t\texcept Exception,ex:\n\t\t\trequest.status = FAILURE\n\t\t\tself.finished(request.id, [], str(ex))\n\t\t\treturn\n\n\t\t# ----------- DEBUG -----------------\n\t\tMODULE.info(\"updater/updates/query returns: %d entries\" % len(result))\n\t\tpp = pprint.PrettyPrinter(indent=4)\n\t\tst = pp.pformat(result).split(\"\\n\")\n\t\tfor s in st:\n\t\t\t\tMODULE.info(\" >> %s\" % s)\n\t\t# -----------------------------------\n\n\t\tself.finished(request.id,result)","def fetchHistory(self, token):\n history = self.loadHistory(token)\n if len(history):\n startStamp = history[-1][\"timestamp\"] + 1000 + random.random()*1000 # Add some random number of seconds\n startDateStr = time.strftime(\"%Y%m%d\", time.gmtime(int(startStamp)))\n else:\n startDateStr = \"20130428\" # Date of the first bitcoin valuation ?\n dateStr = time.strftime(\"%Y%m%d\")\n uri = self.historyTemplate % (token, startDateStr, dateStr)\n print(\"Fetching history\")\n html = BeautifulSoup(urlrequest.urlopen(uri).read().decode(), \"html.parser\")\n print(\"parsing html\")\n dataRows = html.find(\"div\", {\"id\": \"historical-data\"}).find(\"table\", {\"id\", \"table\"}).find(\"tbody\").find_all(\"tr\", {\"class\": \"text-right\"})\n headers = [\"date.string\", \"open\", \"high\", \"low\", \"close\", \"volume\", \"market.cap\"]\n dataPts = []\n print(\"translating data\")\n for row in dataRows:\n rowObj = {}\n for i, td in enumerate(row.find_all(\"td\")):\n if i == 0:\n try:\n rowObj[headers[i]] = td.get_text()\n rowObj[\"timestamp\"] = helpers.stamp2dayStamp(datetime.datetime.strptime(td.get_text(), \"%b %d, %Y\").timestamp())\n except Exception:\n print(\"failed to parse float from `%s`\" % td.get_text())\n rowObj[headers[i]] = \"Dec 31, 1999\"\n elif i < 5:\n try:\n rowObj[headers[i]] = float(td.get_text())\n except Exception:\n print(\"failed to parse float from `%s`\" % td.get_text())\n rowObj[headers[i]] = 0.0\n else:\n try:\n rowObj[headers[i]] = int(td.get_text().replace(\",\", \"\"))\n except Exception:\n print(\"failed to parse integer from `%s`\" % td.get_text())\n rowObj[headers[i]] = 0\n dataPts.append(rowObj)\n for pt in sorted(dataPts, key=lambda p: p[\"timestamp\"]):\n if len(history) == 0 or pt[\"timestamp\"] > history[-1][\"timestamp\"]:\n history.append(pt)\n self.saveHistory(token, history)\n return history","def release_date(self):\n for item in self.proto.releaseInfo.item:\n if item.label == 'Released on':\n return item.container.value","def get_publication_versions(project, publication_id):\n connection = db_engine.connect()\n publication_versions = get_table(\"publication_version\")\n statement = select([publication_versions]).where(publication_versions.c.publication_id == int_or_none(publication_id))\n rows = connection.execute(statement).fetchall()\n result = []\n for row in rows:\n result.append(dict(row))\n connection.close()\n return jsonify(result)","def svn_rev_info(path): # pragma: no cover\n if not os.path.isdir(os.path.join(path, '.svn')):\n path = os.path.join(path, '..')\n\n _program_dir = path\n filename = os.path.join(_program_dir, '.svn/entries')\n if os.path.isfile(filename):\n with open(filename) as entries:\n version = entries.readline().strip()\n if version != '12':\n for _ in range(3):\n entries.readline()\n tag = entries.readline().strip()\n t = tag.split('://', 1)\n t[1] = t[1].replace('svn.wikimedia.org/svnroot/pywikipedia/',\n '')\n tag = '[{}] {}'.format(*t)\n for _ in range(4):\n entries.readline()\n date = time.strptime(entries.readline()[:19],\n '%Y-%m-%dT%H:%M:%S')\n rev = entries.readline()[:-1]\n return tag, rev, date\n\n # We haven't found the information in entries file.\n # Use sqlite table for new entries format\n from sqlite3 import dbapi2 as sqlite\n with closing(\n sqlite.connect(os.path.join(_program_dir, '.svn/wc.db'))) as con:\n cur = con.cursor()\n cur.execute(\"\"\"select\nlocal_relpath, repos_path, revision, changed_date, checksum from nodes\norder by revision desc, changed_date desc\"\"\")\n _name, tag, rev, date, _checksum = cur.fetchone()\n cur.execute('select root from repository')\n tag, = cur.fetchone()\n\n tag = os.path.split(tag)[1]\n date = time.gmtime(date / 1_000_000)\n return tag, rev, date","def get_github_chandra_models_version_info():\n with urlopen('https://api.github.com/repos/sot/chandra_models/tags') as url:\n response = url.read()\n tags = json.loads(response.decode('utf-8'))\n\n with urlopen('https://api.github.com/repos/sot/chandra_models/branches') as url:\n response = url.read()\n branches = json.loads(response.decode('utf-8'))\n\n all_versions_info = {t[\"name\"]: t for t in tags}\n all_versions_info.update({b[\"name\"]: b for b in branches})\n return all_versions_info","def getVersions(self):\n logger.debug(\"Func: getVersions\")\n\n try:\n return self._currentSceneInfo[\"Versions\"]\n except:\n return []","def GetRevisionsSample():\n client = CreateClient()\n for entry in client.GetResources(limit=55).entry:\n revisions = client.GetRevisions(entry)\n for revision in revisions.entry:\n print revision.publish, revision.GetPublishLink()","def get_versions(start='current'):\n start = check_version_str(start)\n versions = get_linked_versions(start)\n\n results = versions[:]\n while results:\n results = get_linked_versions(results[-1])\n print results\n if results:\n versions.extend(results)\n\n versions = [x for x in set(versions) if check_manual_exists(x)]\n return sort_versions(versions, reverse=True)","def history(self):\n return self.info['history']","def releases(self):\r\n\r\n result = self.proxy.package_releases(self.package_name, self.include_hidden)\r\n\r\n if len(result) == 0:\r\n # no matching package--search for possibles, and limit to 15 results\r\n results = self.proxy.search({\r\n 'name': self.package_name,\r\n 'description': self.package_name\r\n }, 'or')[:15]\r\n\r\n # make sure we only get unique package names\r\n matches = []\r\n for match in results:\r\n name = match['name']\r\n if name not in matches:\r\n matches.append(name)\r\n\r\n # if only one package was found, return it\r\n if len(matches) == 1:\r\n self.package_name = matches[0]\r\n return self.releases\r\n\r\n error = \"\"\"No such package found: %s\r\n\r\nPossible matches include:\r\n%s\r\n\"\"\" % (self.package_name, '\\n'.join('\\t- %s' % n for n in matches))\r\n\r\n sys.exit(error)\r\n\r\n return result","def greater(data, version):\n result = list()\n given_version = parse(version)\n for release, info in data.items():\n python_version = Pypi._get_python_version(info)\n ver = parse(release)\n if not ver.is_prerelease and ver > given_version:\n result.append(dict(version=str(ver), python_version=python_version))\n\n return result","def get_comp_versions (component):\n vprint (\"Detecting current version for \" + component)\n\n regex = re.compile (r\"version \" + version_restr)\n major = component + \"_major\"\n minor = component + \"_minor\"\n micro = component + \"_micro\"\n\n\n version = (None, None, None)\n with open (doc_root + \"/ACE_TAO/\" + component + \"/VERSION.txt\") as version_file:\n for line in version_file:\n match = regex.search (line)\n if match is not None:\n version = match.groups(default=0)\n\n vprint (\"Detected version %s.%s.%s\" % version)\n\n comp_versions[major] = int (version[0])\n comp_versions[minor] = int (version[1])\n comp_versions[micro] = int (version[2])\n\n break\n\n print (\"FATAL ERROR: Unable to locate current version for \" + component)\n raise Exception\n\n # Also store the current release (old from now)\n old_comp_versions[major] = comp_versions[major]\n old_comp_versions[minor] = comp_versions[minor]\n old_comp_versions[micro] = comp_versions[micro]\n\n if opts.update:\n if opts.release_type == ReleaseType.major:\n comp_versions[major] += 1\n comp_versions[minor] = 0\n comp_versions[micro] = 0\n elif opts.release_type == ReleaseType.minor:\n comp_versions[minor] += 1\n comp_versions[micro] = 0\n elif opts.release_type == ReleaseType.micro:\n comp_versions[micro] += 1\n\n def make_version (versions, joiner):\n return joiner.join ([\n str (versions[component + '_' + x]) for x in ReleaseType.__members__.keys ()\n ])\n\n comp_versions [component + \"_version\"] = make_version (comp_versions, '.')\n comp_versions [component + \"_version_\"] = make_version (comp_versions, '_')\n\n comp_versions [component + \"_code\"] = \\\n (comp_versions[major] << 16) + \\\n (comp_versions[minor] << 8) + \\\n comp_versions[micro]\n\n old_comp_versions [component + \"_version\"] = make_version (old_comp_versions, '.')\n old_comp_versions [component + \"_version_\"] = make_version (old_comp_versions, '_')\n\n if opts.update:\n vprint (\"Updating from version %s to version %s\" %\n (old_comp_versions [component + \"_version\"], comp_versions [component + \"_version\"]))\n else:\n vprint (\"Found version %s\" %\n (comp_versions [component + \"_version\"]))\n\n # else:\n # comp_versions [component + \"_version\"] = \\\n # str (comp_versions[major]) + '.' + \\\n # str (comp_versions[minor])","def get_diffs(history):\n\n # First get all possible representations\n mgr = plugins_get_mgr() \n keys = mgr.search('representation')['representation']\n representations = [mgr.get_by_key('representation', k) for k in keys]\n\n for i in range(len(history)):\n if i+1 > len(history) - 1:\n continue\n\n prev = history[i]\n curr = history[i+1]\n\n #print(prev['subject'], \"==>\", curr['subject'])\n #print(curr['changes'])\n for c in curr['changes']:\n \n path = c['path']\n\n # Skip the metadata file\n if c['path'].endswith('datapackage.json'): \n continue \n\n # Find a handler for this kind of file...\n handler = None \n for r in representations: \n if r.can_process(path): \n handler = r \n break \n \n if handler is None: \n continue \n\n # print(path, \"being handled by\", handler)\n\n v1_hex = prev['commit']\n v2_hex = curr['commit']\n\n temp1 = tempfile.mkdtemp(prefix=\"dgit-diff-\") \n \n try: \n for h in [v1_hex, v2_hex]: \n filename = '{}/{}/checkout.tar'.format(temp1, h)\n try:\n os.makedirs(os.path.dirname(filename))\n except:\n pass \n extractcmd = ['git', 'archive', '-o', filename, h, path]\n output = run(extractcmd)\n if 'fatal' in output: \n raise Exception(\"File not present in commit\") \n with cd(os.path.dirname(filename)): \n cmd = ['tar', 'xvf', 'checkout.tar']\n output = run(cmd) \n if 'fatal' in output: \n print(\"Cleaning up - fatal 1\", temp1)\n shutil.rmtree(temp1)\n continue \n\n # Check to make sure that \n path1 = os.path.join(temp1, v1_hex, path) \n path2 = os.path.join(temp1, v2_hex, path) \n if not os.path.exists(path1) or not os.path.exists(path2): \n # print(\"One of the two output files is missing\") \n shutil.rmtree(temp1)\n continue \n\n #print(path1, path2) \n\n # Now call the handler\n diff = handler.get_diff(path1, path2)\n\n # print(\"Inserting diff\", diff)\n c['diff'] = diff\n\n except Exception as e: \n #traceback.print_exc() \n #print(\"Cleaning up - Exception \", temp1)\n shutil.rmtree(temp1)","def select_latest_micro_versions(versions):\n seen_minors = set()\n res = []\n\n for ver, _ in sorted(\n versions.items(),\n # Sort by (minor_version, upload_time) in descending order\n key=lambda x: (Version(x[0]).release[:2], x[1]),\n reverse=True,\n ):\n minor_ver = Version(ver).release[:2]\n\n if minor_ver not in seen_minors:\n seen_minors.add(minor_ver)\n res.insert(0, ver)\n\n return res","def get_latest_release(account = None):\n names = get_db_name(account=account, db_type=\"compara\")\n compara = []\n for name in names:\n compara += [int(name.Release)]\n return str(max(compara))","def get_version():\n\n with open('__init__.py') as f:\n for line in f.readlines():\n if '__version__' in line:\n apicem_version = line.strip().split(\"=\")[-1].strip(\" '\")\n if '__first_release_date__' in line:\n first_release_data_str = line.strip().split(\"=\")[-1].strip(\" '\")\n first_release_data = date(*[int(num) for num in first_release_data_str.split('.')])\n num_commits = get_cr_num(first_release_data)\n return '{apicem_version}.{num_commits}'.format(\n apicem_version=apicem_version, num_commits=num_commits)\n\n raise ValueError(\"could not read version\")","def get_outdated_packages(self, pip_path=\"pip\"):\n output_re = [re.compile(r) for r in [\n # newer pip\n r'^(.+) \\(Current: (.+) Latest: (.+)\\)$',\n # old pip\n r'^(.+) \\((.+)\\) - Latest: (.+) .*$',\n ]]\n pkgs = {}\n out = self.check_output(\"%s list -o\", pip_path)\n for line in out.splitlines():\n # Warning: cannot find svn location for rst2pdf==0.93.dev-r0\n # Could not find any downloads that satisfy the requirement iotop\n if line.startswith('Warning: ') or line.startswith('Could not '):\n continue\n for out_re in output_re:\n match = out_re.match(line)\n if match:\n name, current, latest = match.groups()\n pkgs[name] = {'current': current, 'latest': latest}\n break\n else:\n raise RuntimeError(\"could not parse {0}\".format(repr(line)))\n return pkgs","def get_latest_release_version():\n repo = GITHUB.get_user(GITHUB_OWNER).get_repo(GITHUB_REPO)\n latest_release_version = repo.get_latest_release().tag_name\n return latest_release_version","def _find_latest():\n try:\n db = get_master_collection()\n service_details = db.find({\"master.key\": \"release\"}).sort([(\"master.value\", pymongo.DESCENDING)]).limit(1)\n for service in service_details:\n for r in sorted(service[\"master\"][\"value\"], reverse=True):\n latest_release = r\n build_list = service[\"master\"][\"value\"][r]\n break\n break\n\n latest_rel_num = str(latest_release).replace(\"_\", \".\")\n build_list = _natural_sort(build_list)\n for build in build_list:\n latest_build = build\n break\n\n latest_build_num = latest_build\n second_latest_build_num = int(latest_build_num) - 1\n latest = {\"latest_val\": latest_rel_num + \"_\" + latest_build_num,\n \"second_latest_val\": latest_rel_num + \"_\" + str(second_latest_build_num)}\n except Exception as e:\n logger.error(\"Exception in _find_latest : \" + str(e))\n return latest"],"string":"[\n \"def list_releases():\\n response = requests.get(PYPI_URL.format(package=PYPI_PACKAGE_NAME))\\n if response:\\n data = response.json()\\n\\n releases_dict = data.get('releases', {})\\n\\n if releases_dict:\\n for version, release in releases_dict.items():\\n release_formats = []\\n published_on_date = None\\n for fmt in release:\\n release_formats.append(fmt.get('packagetype'))\\n published_on_date = fmt.get('upload_time')\\n\\n release_formats = ' | '.join(release_formats)\\n print('{:<10}{:>15}{:>25}'.format(version, published_on_date, release_formats))\\n else:\\n print('No releases found for {}'.format(PYPI_PACKAGE_NAME))\\n else:\\n print('Package \\\"{}\\\" not found on Pypi.org'.format(PYPI_PACKAGE_NAME))\",\n \"def versionHistory(self):\\n url = self.metaData().getLink(\\\"version-history\\\")\\n assert url is not None\\n\\n header = self._baseHeader.copy()\\n response = self._adapter.getRequest(url, header)\\n\\n return json.loads(response['Body'])\",\n \"def get_current_release_downloads():\\n downloads = (\\n get_downloads_metadata()\\n ['releases']\\n [get_current_release()]\\n ['downloads'])\\n\\n def up_to_date(dir, urls):\\n try:\\n df = pandas.read_csv(join(dir, \\\"DOWNLOAD_INFO.csv\\\"))\\n return list(df.url) == list(urls)\\n except IOError:\\n return None\\n\\n return OrderedDict(\\n (download[\\\"name\\\"], {\\n 'downloaded': exists(join(get_downloads_dir(), download[\\\"name\\\"])),\\n 'up_to_date': up_to_date(\\n join(get_downloads_dir(), download[\\\"name\\\"]),\\n [download['url']] if 'url' in download else download['part_urls']),\\n 'metadata': download,\\n }) for download in downloads\\n )\",\n \"def Releases():\\n return releases\",\n \"def releases(releaser, count):\\n releases = sorted(\\n releaser.get_releases().values(),\\n key=lambda rel: rel[\\\"end_timestamp\\\"],\\n reverse=True,\\n )\\n click.echo(f\\\"Latest {count} releases:\\\")\\n for release in releases[:count]:\\n click.echo(f'{release[\\\"end_timestamp\\\"]} {release[\\\"commit\\\"]}')\",\n \"def latest(data):\\n result = dict()\\n version = parse(\\\"0\\\")\\n for release, info in data.items():\\n python_version = Pypi._get_python_version(info)\\n ver = parse(release)\\n if not ver.is_prerelease:\\n version = max(version, ver)\\n python_version = python_version\\n\\n result = dict(version=str(version), python_version=python_version)\\n\\n return [result]\",\n \"def getCommitsSinceLastRelease(self):\\n f = open(self.last_released, 'r')\\n old_rev = f.read().replace('\\\\n', '')\\n f.close()\\n new_rev = commands.getoutput('cd '+self.proj_dir+' && git log -1 --format=%H')\\n cmd = 'cd '+self.proj_dir+' && git log --no-merges --pretty=format:\\\"%s\\\" '+old_rev+'..'+new_rev\\n unreleased_commits = commands.getoutput(cmd) \\n print 'Commits since last release:'\\n print unreleased_commits\\n unreleased_commits = unreleased_commits.split('\\\\n')\\n self.commit_msgs = unreleased_commits\\n self.new_rev = new_rev\",\n \"def get_release_info():\\n major_info = fetch_json(\\n \\\"https://product-details.mozilla.org/1.0/\\\" \\\"firefox_history_major_releases.json\\\"\\n )\\n if major_info is None:\\n raise Exception(\\\"Failed to fetch major version info\\\")\\n minor_info = fetch_json(\\n \\\"https://product-details.mozilla.org/1.0/\\\"\\n \\\"firefox_history_stability_releases.json\\\"\\n )\\n if minor_info is None:\\n raise Exception(\\\"Failed to fetch minor version info\\\")\\n\\n return {\\\"major\\\": major_info, \\\"minor\\\": minor_info}\",\n \"def get_release_info(self, version):\\r\\n try:\\r\\n return self._detail[\\\"releases\\\"][version]\\r\\n except KeyError as key_error:\\r\\n log.warning(key_error)\\r\\n return []\",\n \"def latest_release_get():\\n try:\\n return json_response.success({'version': version.latest_version()})\\n except version.Error as e:\\n return json_response.error(str(e)), 200\",\n \"def get_previous_release_info(\\n previous_release_version: str | None, past_releases: list[ReleaseInfo], current_release_version: str\\n) -> str | None:\\n previous_release = None\\n if previous_release_version == current_release_version:\\n # Re-running for current release - use previous release as base for git log\\n if len(past_releases) > 1:\\n previous_release = past_releases[1].last_commit_hash\\n else:\\n previous_release = past_releases[0].last_commit_hash if past_releases else None\\n return previous_release\",\n \"def get_history(cls, api, history):\\n api_base = api.split('/')[-1]\\n cursor = cls.history_index.cursor()\\n cursor.execute(\\n \\\"select filename from history where api=? and ymdh=?;\\\",\\n (api_base, history))\\n files = [r[0] for r in cursor]\\n cls.history_index.commit()\\n if not files:\\n return {}\\n results = {}\\n for fn in files:\\n ts = re.split('[?@]', fn)[-1].replace('.gz', '')\\n fn_full = os.path.join(config.base_store_dir, fn)\\n fd = (gzip.open if fn.endswith('.gz') else open)(fn_full)\\n results[ts] = json.load(fd, encoding='utf8')\\n fd.close()\\n return results\",\n \"def GetVersions(url, requestedProduct, requestedVersion):\\n dictValidReleasesSorted = {}\\n response = requests.get(url)\\n if response.status_code == 200:\\n jsonResult = response.json()\\n jVersions = jsonResult[requestedProduct][\\\"versions\\\"]\\n dictValidReleases = {}\\n # do not want pre-releases; filter them out\\n for item in jVersions.items(): \\n for build in item[1][\\\"builds\\\"]:\\n if (build[\\\"os\\\"] == SUPPORTED_OS):\\n if (build[\\\"arch\\\"] == SUPPORTED_ARCH):\\n if not (re.search('[a-zA-Z]', item[1][\\\"version\\\"])): \\n dictValidReleases[item[1][\\\"version\\\"]] = build[\\\"url\\\"]\\n\\n for key in sorted(dictValidReleases,key=LooseVersion):\\n dictValidReleasesSorted[key] = dictValidReleases[key]\\n else:\\n raise requests.ConnectionError(\\\"Server did not return status 200 - returned {0}\\\".format(response.status_code))\\n\\n return dictValidReleasesSorted\",\n \"def get_latest_tags(self):\\n\\n start = len(self.tags) - self.num_comparisons\\n tags = self.tags\\n latest = []\\n for i in xrange(len(tags)):\\n if i >= start:\\n parts = tags[i]['ref'].split('/')\\n release_num = parts[2]\\n sha = tags[i]['object']['sha']\\n tag = [release_num, sha]\\n latest.append(tag)\\n return latest\",\n \"def get_release(request):\\r\\n\\r\\n release = raven.fetch_git_sha(os.path.dirname(os.path.dirname(__file__)))\\r\\n return HttpResponse(json.dumps({\\\"release\\\": release[:7]}))\",\n \"def releases():\\n r = run('ls -x %(releases_path)s' % env)\\n env.releases = sorted(r.split(\\\"\\\\t\\\"))\\n if len(env.releases) >= 1:\\n env.current_revision = env.releases[-1]\\n env.current_release = '%(releases_path)s/%(current_revision)s' % env\\n if len(env.releases) > 1:\\n env.previous_revision = env.releases[-2]\\n env.previous_release = '%(releases_path)s/%(previous_revision)s' % env\\n\\n #cleanup old releases. max 3 allowed.\\n cleanup()\",\n \"def get_recent_release_from_product_details() -> int:\\n rls_prod_details_json = get(\\n \\\"https://product-details.mozilla.org/1.0/firefox_history_major_releases.json\\\"\\n ).json()\\n rls_prod_details = Series(rls_prod_details_json).sort_values(ascending=True)\\n [(cur_rls_vers, _date)] = rls_prod_details[-1:].iteritems()\\n cur_rls_maj, *_v = cur_rls_vers.split(\\\".\\\")\\n return int(cur_rls_maj)\",\n \"def _parse_latest_update(self, resp: Dict[str, Any], latest_version: str) -> str:\\n latest_release = resp.get(\\\"releases\\\", {}).get(latest_version)\\n if latest_release is not None and isinstance(latest_release, list):\\n release_artifact_dates = []\\n for artifact in latest_release:\\n try:\\n upload_time = artifact.get(\\\"upload_time_iso_8601\\\")\\n parsed_upload_time = dateutil.parser.isoparse(upload_time)\\n release_artifact_dates.append(parsed_upload_time)\\n except Exception:\\n pass\\n latest_artifact_timestamp = max(release_artifact_dates)\\n return latest_artifact_timestamp.strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\")\\n return \\\"\\\"\",\n \"def get_version():\\n parent_dir = os.path.dirname(os.path.realpath(__file__))\\n while True:\\n if '.git' in os.listdir(parent_dir):\\n break\\n parent_dir = os.path.dirname(parent_dir)\\n git_log = os.path.join(parent_dir,'.git','logs','HEAD')\\n handle = open(git_log,'r')\\n log_lines = [l.split('\\\\t') for l in handle.readlines()]\\n #now get latest github commit\\n url = 'https://api.github.com/repos/thomasvangurp/epiGBS/commits'\\n context = ssl._create_unverified_context()\\n result = json.load(urllib.urlopen(url,context=context))\\n print('')\",\n \"def get_released_versions(package_name):\\n url = \\\"https://pypi.python.org/pypi/{}/json\\\".format(package_name)\\n data = json.load(urllib.request.urlopen(url))\\n\\n versions = {\\n # We can actually select any element in `dist_files` because all the distribution files\\n # should have almost the same upload time.\\n version: dist_files[0][\\\"upload_time\\\"]\\n for version, dist_files in data[\\\"releases\\\"].items()\\n # If len(dist_files) = 0, this release is unavailable.\\n # Example: https://pypi.org/project/xgboost/0.7\\n #\\n # > pip install 'xgboost==0.7'\\n # ERROR: Could not find a version that satisfies the requirement xgboost==0.7\\n if len(dist_files) > 0 and (not dist_files[0].get(\\\"yanked\\\", False))\\n }\\n return versions\",\n \"def releases():\\n result = run('ls %(releases_dir)s' % env)\\n releases_list = re.split('\\\\s+', result)\\n releases_list.sort(reverse=True)\\n return releases_list\",\n \"def get_release_notes(self):\\n\\n notes = self.output.get_header('RELEASE NOTES')\\n notes += 'https://{}/{}/{}/releases'.format(HOST_GITHUB, \\\\\\n self.repo, self.product) + '\\\\n'\\n\\n notes += self.output.get_sub_header('COMPARISONS')\\n notes += self.get_comparison(self.latest_tags[0][VERS],\\n self.latest_tags[1][VERS])\\n\\n if len(self.latest_tags) >= (MAX_COMPARISONS_TO_SHOW - 1):\\n notes += self.get_comparison(self.latest_tags[1][VERS],\\n self.latest_tags[2][VERS])\\n\\n if len(self.latest_tags) >= MAX_COMPARISONS_TO_SHOW:\\n notes += self.get_comparison(self.latest_tags[2][VERS],\\n self.latest_tags[3][VERS])\\n\\n tag_data = self.get_tag(self.latest_tags[3][SHA])\\n\\n notes += self.output.get_sub_header('TAGS')\\n notes += self.get_url_tag_release(self.latest_tags[3][VERS]) + '\\\\n'\\n notes += self.get_url_tag_commit(tag_data[\\\"object\\\"][\\\"sha\\\"]) + '\\\\n'\\n\\n changelog = self.get_changelog(tag_data[\\\"object\\\"][\\\"sha\\\"])\\n if changelog:\\n notes += self.output.get_sub_header('CHANGELOG')\\n notes += changelog\\n return notes\",\n \"def get_release_info(version='v1.1-dev', date='2021-07-22'):\\n # go to the repository directory\\n dir_orig = os.getcwd()\\n os.chdir(os.path.dirname(os.path.dirname(__file__)))\\n\\n # grab git info into string\\n try:\\n cmd = \\\"git describe --tags\\\"\\n version = subprocess.check_output(cmd.split(), stderr=subprocess.DEVNULL)\\n version = version.decode('utf-8').strip()\\n\\n # if there are new commits after the latest release\\n if '-' in version:\\n version, num_commit = version.split('-')[:2]\\n version += '-{}'.format(num_commit)\\n\\n cmd = \\\"git log -1 --date=short --format=%cd\\\"\\n date = subprocess.check_output(cmd.split(), stderr=subprocess.DEVNULL)\\n date = date.decode('utf-8').strip()\\n except:\\n pass\\n\\n # go back to the original directory\\n os.chdir(dir_orig)\\n return version, date\",\n \"def get_version_and_release_date(\\n requirement, version=None, verbose=False, response=None\\n):\\n if not response:\\n url = get_pypi_url(requirement, version)\\n response = request(url)\\n\\n # see if the url is 404'ing because it has been redirected\\n if response.status == 404:\\n root_url = url.rpartition(\\\"/\\\")[0]\\n res = request(root_url, method=\\\"HEAD\\\")\\n if res.status == 301:\\n new_location = res.headers[\\\"location\\\"] + \\\"/json\\\"\\n response = request(new_location)\\n\\n if response.status != 200:\\n if version:\\n if verbose:\\n print(\\n \\\"{} ({}) isn't available on PyPI \\\"\\n \\\"anymore!\\\".format(requirement, version)\\n )\\n else:\\n if verbose:\\n print(\\n \\\"{} isn't on PyPI. Check that the project \\\"\\n \\\"still exists!\\\".format(requirement)\\n )\\n return None, None\\n\\n if not response.json:\\n if verbose:\\n print(\\n \\\"Decoding the JSON response for {} ({}) \\\"\\n \\\"failed\\\".format(requirement, version)\\n )\\n return None, None\\n\\n response = response.json\\n\\n try:\\n if version:\\n if version in response[\\\"releases\\\"]:\\n release_date = response[\\\"releases\\\"][version][0][\\\"upload_time\\\"]\\n else:\\n return None, None\\n else:\\n version = response[\\\"info\\\"].get(\\\"stable_version\\\")\\n\\n if not version:\\n versions = {\\n v: parse_version(v)\\n for v in response[\\\"releases\\\"].keys()\\n if not parse_version(v).is_prerelease()\\n }\\n\\n # if we still don't have a version, let's pick up a prerelease one\\n if not versions:\\n versions = {\\n v: parse_version(v) for v in response[\\\"releases\\\"].keys()\\n }\\n\\n if versions:\\n version = max(versions.items(), key=operator.itemgetter(1))[0]\\n release_date = response[\\\"releases\\\"][str(version)][0][\\\"upload_time\\\"]\\n else:\\n return None, None\\n\\n return version, datetime.fromtimestamp(\\n time.mktime(time.strptime(release_date, \\\"%Y-%m-%dT%H:%M:%S\\\"))\\n )\\n except IndexError:\\n if verbose:\\n print(\\\"{} ({}) didn't return a date property\\\".format(requirement, version))\\n return None, None\",\n \"def get_releases(is_vertebrate: bool):\\n url = \\\"http://ftp.ensemblgenomes.org/pub?\\\"\\n if is_vertebrate:\\n url = \\\"http://ftp.ensembl.org/pub?\\\"\\n ret = retry(requests.get, 3, url)\\n # sort releases new to old\\n releases = sorted(\\n [int(i) for i in re.findall(r'\\\"release-(\\\\d+)/\\\"', ret.text)],\\n reverse=True,\\n )\\n if is_vertebrate:\\n # ignore immature releases\\n releases = [r for r in releases if r > 46]\\n return releases\",\n \"def api_json(self):\\n if not self._api_json:\\n resp = requests.get(\\n GitHubManager.RELEASE_API.format(repo=self.repo)\\n )\\n if not resp.ok:\\n resp.raise_for_status()\\n\\n self._api_json = resp.json()\\n\\n return self._api_json\",\n \"def certifiVersions():\\n log = logger.new(function='certifiVersions')\\n r = yield treq.get('https://pypi.python.org/pypi/certifi/json', timeout=5)\\n log.msg(\\\"got certifi versions!\\\")\\n data = yield r.json()\\n\\n # Note: this takes advantage of the fact that certifi's releases have the\\n # same version number sort order as lexicographical. If that changes,\\n # this will break.\\n releases = sorted(data[u'releases'].keys())\\n\\n first_release = releases.index('14.05.14')\\n target_versions = releases[first_release:]\\n\\n result = []\\n for version in target_versions:\\n files = data[u'releases'][version]\\n\\n # Find the .tar.gz release.\\n for file in files:\\n if file[u'filename'].endswith(u'.tar.gz'):\\n break\\n else:\\n continue\\n\\n log.msg(\\\"new release located\\\", version=version, tarball=file[u'url'])\\n result.append((version, file[u'url']))\\n\\n returnValue(result)\",\n \"def get_releases(repo, quiet=False, per_page=None) -> List[str]:\\n req_url = f\\\"https://api.github.com/repos/{owner}/{repo}/releases\\\"\\n\\n params = {}\\n if per_page is not None:\\n if per_page < 1 or per_page > 100:\\n raise ValueError(\\\"per_page must be between 1 and 100\\\")\\n params[\\\"per_page\\\"] = per_page\\n\\n request = get_request(req_url, params=params)\\n num_tries = 0\\n while True:\\n num_tries += 1\\n try:\\n with urllib.request.urlopen(request, timeout=10) as resp:\\n result = resp.read()\\n break\\n except urllib.error.HTTPError as err:\\n if err.code == 401 and os.environ.get(\\\"GITHUB_TOKEN\\\"):\\n raise ValueError(\\\"GITHUB_TOKEN env is invalid\\\") from err\\n elif err.code == 403 and \\\"rate limit exceeded\\\" in err.reason:\\n raise ValueError(\\n f\\\"use GITHUB_TOKEN env to bypass rate limit ({err})\\\"\\n ) from err\\n elif err.code in (404, 503) and num_tries < max_http_tries:\\n # GitHub sometimes returns this error for valid URLs, so retry\\n print(f\\\"URL request {num_tries} did not work ({err})\\\")\\n continue\\n raise RuntimeError(f\\\"cannot retrieve data from {req_url}\\\") from err\\n\\n releases = json.loads(result.decode())\\n if not quiet:\\n print(f\\\"found {len(releases)} releases for {owner}/{repo}\\\")\\n\\n avail_releases = [\\\"latest\\\"]\\n avail_releases.extend(release[\\\"tag_name\\\"] for release in releases)\\n return avail_releases\",\n \"def get_releases(platform, year, month):\\n platform_num = PLATFORMS.get(platform.lower(), 0)\\n\\n if platform_num == 0:\\n url = _3DJUEGOS_RELEASES_URL + \\\"todos/por-mes/0/{}/{}/\\\".format(year, month)\\n else:\\n url = _3DJUEGOS_RELEASES_URL + platform + \\\\\\n \\\"/por-mes/{}/{}/{}/\\\".format(platform_num, year, month)\\n\\n soup = get_soup_obj(url)\\n if not soup:\\n return None\\n results = {}\\n results[\\\"games\\\"] = []\\n root = soup.find(\\\"div\\\", {\\\"class\\\": \\\"pad_rl10\\\"})\\n for div in root.findAll(\\\"div\\\"):\\n if div.attrs[\\\"class\\\"] == [\\\"s20\\\", \\\"ffnav\\\", \\\"b\\\", \\\"mar_t50\\\"]:\\n release_date = \\\"{}-{}-{}\\\".format(year, month,\\n re.search(r'\\\\d+', div.span.text).group())\\n elif div.attrs[\\\"class\\\"] == [\\\"dtc\\\", \\\"vam\\\"]:\\n name = div.a.span.text\\n platform = div.div.span.text\\n results[\\\"games\\\"].append(\\n {\\\"name\\\": name, \\\"platform\\\": platform, \\\"releaseDate\\\": release_date})\\n\\n return results\",\n \"def list_releases(self, name):\\n endpoint = '/v1/charm/{}/releases'.format(name)\\n response = self._client.get(endpoint)\\n\\n channel_map = []\\n for item in response['channel-map']:\\n expires_at = item['expiration-date']\\n if expires_at is not None:\\n # `datetime.datetime.fromisoformat` is available only since Py3.7\\n expires_at = parser.parse(expires_at)\\n channel_map.append(\\n Release(revision=item['revision'], channel=item['channel'], expires_at=expires_at))\\n\\n channels = [\\n Channel(\\n name=item['name'],\\n fallback=item['fallback'],\\n track=item['track'],\\n risk=item['risk'],\\n branch=item['branch'],\\n ) for item in response['package']['channels']]\\n\\n revisions = [_build_revision(item) for item in response['revisions']]\\n\\n return channel_map, channels, revisions\",\n \"def get_from_releases(releases: List[Tuple[str, str, str]]) -> List[str]:\\n output = []\\n client = redis.Redis(host=os.environ.get('REDIS_HOST', 'localhost'))\\n\\n for release in releases:\\n key_name = _title_to_key(release[0])\\n url = client.get(key_name)\\n\\n if url is not None:\\n output.append('<a href=\\\"https://horriblesubs.info{url}\\\">{0}</a> – {2}'.format(*release, url=url.decode()))\\n\\n else:\\n output.append(''.join(release))\\n\\n return output\",\n \"def extract_release_data(self):\\r\\n data = None\\r\\n session = self.get_session()\\r\\n project = session.create(self._config.name)\\r\\n \\r\\n session.home = self._config['dir']\\r\\n \\r\\n result = self.__find_project(project)\\r\\n if (result != None) and (self._config.get_boolean('releasable', False)):\\r\\n if 'baseline.release' in self._config:\\r\\n data = {}\\r\\n _logger.info(\\\"Releasing: '%s'\\\" % result)\\r\\n data['name'] = result.objectname\\r\\n data['database'] = session.database()\\r\\n data['role'] = ccm.get_role_for_purpose(session, str(self._config['purpose']))\\r\\n data['dir'] = os.path.normpath(self._config['dir'])\\r\\n data['pst'] = result.name\\r\\n data['release'] = self._config['baseline.release']\\r\\n else:\\r\\n _logger.warning(\\\"Could not release \\\" + result.objectname + \\\" because the 'baseline.release' property is missing.\\\")\\r\\n return data\",\n \"def get_bookable_releases(context, data_dict):\\n # noinspection PyUnresolvedReferences\\n frc = _get_or_bust(data_dict, 'frc')\\n\\n lc = ckanapi.LocalCKAN(context=context)\\n\\n results = lc.action.package_search(\\n q='frc:{frc} AND type:publication'.format(\\n frc=frc\\n ),\\n fl=[\\n 'product_id_new',\\n 'title'\\n ]\\n )\\n\\n final_results = []\\n for result in results['results']:\\n article_results = lc.action.package_search(\\n q=(\\n 'top_parent_id:{pid} '\\n # Only \\\"Working Copy\\\" results.\\n 'AND status_code:31 '\\n # With a release_date set to anything (not blank)\\n 'AND last_release_date:[* TO *] '\\n 'AND type:article'\\n ).format(\\n pid=result['product_id_new']\\n ),\\n sort='issue_number_int asc',\\n # FIXME: We need to actually paginate on this, but the daily\\n # team will not accept it (yet).\\n rows=2000000\\n )\\n\\n for art_result in article_results['results']:\\n final_results.append({\\n 'productId': result['product_id_new'],\\n 'issue': art_result['issue_number_int'],\\n 'title': result['title'],\\n 'refper': result['reference_period']\\n })\\n\\n return final_results\",\n \"def get_release_files(tag_name, config) -> Tuple[List[ReleaseFile], Dict[str, SourceFile]]:\\n\\n @retry_multi(5)\\t# retry at most 5 times\\n def execute_request(path):\\n \\\"\\\"\\\"!\\n @brief Performs a GET request with the given path. To be used with Github's REST API.\\n @returns If successful, returns a .JSON object\\n \\\"\\\"\\\"\\n headers = {\\n \\\"Accept\\\": \\\"application/vnd.github.v3+json\\\"\\n }\\n url = \\\"https://api.github.com\\\" + path\\n\\n # GET https://api.github.com/<path> Accept: \\\"application/vnd.github.v3+json\\\"\\n\\n response = requests.get(url, headers=headers, timeout=GLOBAL_TIMEOUT)\\n\\n response.raise_for_status() # Raise a RequestException if we failed, and trigger retry\\n\\n return response.json()\\n\\n build_group_regex = re.compile(\\\"fs2_open_.*-builds-([^.-]*)(-([^.]*))?.*\\\") # regex for matching binary .zip's and .7z's\\n source_file_regex = re.compile(\\\"fs2_open_.*-source-([^.]*)?.*\\\") # regex for matching source .zip's and .7z's\\n\\n # Get the github release metadata of the given tag name\\n response = execute_request(\\n \\\"/repos/{}/releases/tags/{}\\\".format(config[\\\"github\\\"][\\\"repo\\\"], tag_name))\\n\\n # Extract the binary and source files from the response[\\\"asset\\\"] metadata\\n binary_files = []\\n source_files = {}\\n for asset in response[\\\"assets\\\"]:\\n url = asset[\\\"browser_download_url\\\"]\\n name = asset[\\\"name\\\"]\\n\\n group_match = build_group_regex.match(name)\\n\\n if group_match is not None:\\n platform = group_match.group(1)\\n # x64 is the Visual Studio name but for consistency we need Win64\\n if platform == \\\"x64\\\":\\n platform = \\\"Win64\\\"\\n\\n binary_files.append(ReleaseFile(name, url, platform, group_match.group(3)))\\n else:\\n group_match = source_file_regex.match(name)\\n\\n if group_match is None:\\n continue\\n\\n group = group_match.group(1)\\n\\n source_files[group] = SourceFile(name, url, group)\\n\\n binary_files.sort(key=lambda ReleaseFile: ReleaseFile.name)\\n\\n return binary_files, source_files\",\n \"async def get_releases(\\n self, prerelease: bool = False, returnlimit: int = 5\\n ) -> [\\\"AIOGitHubAPIRepositoryRelease\\\"] or list:\\n _endpoint = f\\\"/repos/{self.full_name}/releases\\\"\\n\\n response = await self.client.get(endpoint=_endpoint)\\n contents = []\\n\\n for content in response or []:\\n if len(contents) == returnlimit:\\n break\\n if not prerelease:\\n if content.get(\\\"prerelease\\\", False):\\n continue\\n contents.append(AIOGitHubAPIRepositoryRelease(content))\\n\\n return contents\",\n \"def get_release(repo, tag=\\\"latest\\\", quiet=False) -> dict:\\n api_url = f\\\"https://api.github.com/repos/{owner}/{repo}\\\"\\n req_url = (\\n f\\\"{api_url}/releases/latest\\\"\\n if tag == \\\"latest\\\"\\n else f\\\"{api_url}/releases/tags/{tag}\\\"\\n )\\n request = get_request(req_url)\\n releases = None\\n num_tries = 0\\n\\n while True:\\n num_tries += 1\\n try:\\n with urllib.request.urlopen(request, timeout=10) as resp:\\n result = resp.read()\\n remaining = int(resp.headers[\\\"x-ratelimit-remaining\\\"])\\n if remaining <= 10:\\n warnings.warn(\\n f\\\"Only {remaining} GitHub API requests remaining \\\"\\n \\\"before rate-limiting\\\"\\n )\\n break\\n except urllib.error.HTTPError as err:\\n if err.code == 401 and os.environ.get(\\\"GITHUB_TOKEN\\\"):\\n raise ValueError(\\\"GITHUB_TOKEN env is invalid\\\") from err\\n elif err.code == 403 and \\\"rate limit exceeded\\\" in err.reason:\\n raise ValueError(\\n f\\\"use GITHUB_TOKEN env to bypass rate limit ({err})\\\"\\n ) from err\\n elif err.code == 404:\\n if releases is None:\\n releases = get_releases(repo, quiet)\\n if tag not in releases:\\n raise ValueError(\\n f\\\"Release {tag} not found (choose from {', '.join(releases)})\\\"\\n )\\n elif err.code == 503 and num_tries < max_http_tries:\\n # GitHub sometimes returns this error for valid URLs, so retry\\n warnings.warn(f\\\"URL request {num_tries} did not work ({err})\\\")\\n continue\\n raise RuntimeError(f\\\"cannot retrieve data from {req_url}\\\") from err\\n\\n release = json.loads(result.decode())\\n tag_name = release[\\\"tag_name\\\"]\\n if not quiet:\\n print(f\\\"fetched release {tag_name!r} info from {owner}/{repo}\\\")\\n\\n return release\",\n \"def _grab_history(self):\\n self.data['history_lines'] = []\\n self.data['history_file'] = None\\n self.data['history_encoding'] = None\\n self.data['headings'] = []\\n self.data['history_last_release'] = ''\\n self.data['history_insert_line_here'] = 0\\n default_location = None\\n config = self.setup_cfg.config\\n if config and config.has_option('zest.releaser', 'history_file'):\\n default_location = config.get('zest.releaser', 'history_file')\\n history_file = self.vcs.history_file(location=default_location)\\n self.data['history_file'] = history_file\\n if not history_file:\\n logger.warn(\\\"No history file found\\\")\\n return\\n logger.debug(\\\"Checking %s\\\", history_file)\\n history_lines, history_encoding = read_text_file(history_file)\\n history_lines = history_lines.split('\\\\n')\\n headings = utils.extract_headings_from_history(history_lines)\\n if not headings:\\n logger.warn(\\\"No detectable version heading in the history \\\"\\n \\\"file %s\\\", history_file)\\n return\\n self.data['history_lines'] = history_lines\\n self.data['history_encoding'] = history_encoding\\n self.data['headings'] = headings\\n\\n # Grab last header.\\n start = headings[0]['line']\\n if len(headings) > 1:\\n # Include the next header plus underline, as this is nice\\n # to show in the history_last_release.\\n end = headings[1]['line'] + 2\\n else:\\n end = len(history_lines)\\n history_last_release = '\\\\n'.join(history_lines[start:end])\\n self.data['history_last_release'] = history_last_release\\n\\n # Add line number where an extra changelog entry can be inserted. Can\\n # be useful for entry points. 'start' is the header, +1 is the\\n # underline, +2 is probably an empty line, so then we should take +3.\\n # Or rather: the first non-empty line.\\n insert = start + 2\\n while insert < end:\\n if history_lines[insert].strip():\\n break\\n insert += 1\\n self.data['history_insert_line_here'] = insert\",\n \"def get_history():\\n return response_texts_to_entries(make_post_request(HISTORY_API, data={\\\"k\\\": config[\\\"api_key\\\"]}))\",\n \"def extract_release_data(self):\\r\\n data = None\\r\\n session = self.get_session()\\r\\n project = session.create(self._config.name)\\r\\n \\r\\n session.home = self._config['dir']\\r\\n \\r\\n if self._config.get_boolean('releasable', False):\\r\\n if 'baseline.release' in self._config:\\r\\n data = {}\\r\\n _logger.info(\\\"Releasing: '%s'\\\" % project)\\r\\n data['name'] = project.objectname\\r\\n data['database'] = session.database()\\r\\n data['role'] = ccm.get_role_for_purpose(session, str(self._config['purpose']))\\r\\n data['dir'] = os.path.normpath(self._config['dir'])\\r\\n data['pst'] = project.name\\r\\n data['release'] = self._config['baseline.release']\\r\\n else:\\r\\n _logger.warning(\\\"Could not release \\\" + project + \\\" because the 'baseline.release' property is missing.\\\")\\r\\n return data\",\n \"async def fetch_data(self) -> GitHubReleaseModel | None:\\n result = await self._client.repos.releases.list(\\n self.repository, **{\\\"params\\\": {\\\"per_page\\\": 1}}\\n )\\n if not result.data:\\n return None\\n\\n for release in result.data:\\n if not release.prerelease:\\n return release\\n\\n # Fall back to the latest release if no non-prerelease release is found\\n return result.data[0]\",\n \"def versions(self) -> List['RadsProjectVersion']:\\n logger.debug(f\\\"retrieve versions of {self}\\\")\\n listing = self.storage.request_text(f\\\"{self.path}/releaselisting\\\")\\n return [RadsProjectVersion(self, RadsVersion(l)) for l in listing.splitlines()]\",\n \"def post_import():\\n\\n validate_request_json(request)\\n\\n releases = []\\n for r in request.json:\\n # Get the platform, create if it doesn't exist\\n platforms = []\\n for p in r['platforms']:\\n try:\\n query = db.session.query(Platform).filter(Platform.name == p)\\n platform = query.one()\\n except exc.NoResultFound:\\n app.logger.info(\\\"Creating platform {}\\\".format(p))\\n platform = Platform(p)\\n db.session.add(platform)\\n platforms.append(platform)\\n\\n release = Release(\\n platforms=platforms,\\n user=r['user'],\\n team=r.get('team'),\\n references=json.dumps(r.get('references')),\\n )\\n\\n release.stime = arrow.get(r['stime']) if r.get('stime') else None\\n release.ftime = arrow.get(r['ftime']) if r.get('ftime') else None\\n if release.ftime and release.stime:\\n release.duration = release.ftime - release.stime\\n\\n notes = r.get('notes')\\n if notes:\\n for n in notes:\\n note = ReleaseNote(release.id, n)\\n db.session.add(note)\\n\\n for p in r['packages']:\\n package = Package(\\n release_id=release.id,\\n name=p['name'],\\n version=p['version'],\\n )\\n\\n package.rollback = p.get('rollback')\\n package.status = p.get('status')\\n package.diff_url = p.get('diff_url')\\n\\n if p.get('stime'):\\n package.stime = arrow.get(p['stime'])\\n else:\\n package.stime = arrow.get(r['stime'])\\n package.ftime = arrow.get(p['ftime']) if p.get('ftime') else None\\n if package.stime and package.ftime:\\n package.duration = package.ftime - package.stime\\n\\n db.session.add(package)\\n\\n db.session.add(release)\\n db.session.commit()\\n\\n releases.append(release.id)\\n\\n return jsonify({'releases': [str(x) for x in releases]}), 200\",\n \"def ls(_):\\n client = utils.s3_client()\\n\\n projects = []\\n\\n config = utils.get_config()\\n bucket = config[\\\"release\\\"][\\\"s3_bucket\\\"]\\n deploys = config[\\\"deploy\\\"]\\n\\n resp = client.list_objects_v2(Bucket=bucket)\\n for data in resp.get(\\\"Contents\\\", []):\\n name = data[\\\"Key\\\"]\\n\\n projects.append(name)\\n\\n projects = sorted(projects)\\n\\n _projects = []\\n\\n for name in projects:\\n try:\\n release = get_release(client, bucket, name)\\n except InvalidRelease:\\n continue\\n\\n data = {\\n \\\"Name\\\": name,\\n \\\"Latest Release\\\": f\\\"v{release.version} {release.timestamp} ({release.commit})\\\",\\n }\\n\\n for env_name, cfg in deploys.items():\\n env_version, env_commit, env_timestamp = get_deployed_version(\\n client, cfg[\\\"s3_bucket\\\"], name\\n )\\n\\n data[env_name.title()] = f\\\"v{env_version} {env_timestamp} ({env_commit})\\\"\\n\\n _projects.append(data)\\n\\n projects = _projects\\n\\n utils.printfmt(projects)\",\n \"def get_last_release_id():\\n url = \\\"https://api.github.com/repos/yt-dlp/yt-dlp/releases/latest\\\"\\n try:\\n with urlopen(url, timeout=10) as resp:\\n return json.loads(resp.read().decode(\\\"utf-8\\\")).get(\\\"tag_name\\\", \\\"0\\\")\\n except URLError as e:\\n log(f\\\"YouTubeDLHelper error [get last release id]: {e}\\\")\",\n \"def test_yum_history(self):\\n repo_history = ospsurvey.probes.software.get_yum_history()\\n\\n print(\\\"Yum History: {}\\\".format(json.dumps(repo_history)))\",\n \"def BPM_PROVHISTORY():\\n return download_from_archive(\\\"bpm_20220128_gmos-s_Ham_11_full_12amp.fits\\\")\",\n \"def test_get_all_available_release_updates(self):\\n self._ucr({\\n 'repository/online/component/a': 'yes',\\n 'repository/online/component/a/version': 'current',\\n })\\n self._uri({\\n '%d.%d/maintained/%d.%d-%d/all/Packages.gz' % (MAJOR, MINOR + 1, MAJOR, MINOR + 1, 0): DATA,\\n '%d.%d/maintained/component/%s/all/Packages.gz' % (MAJOR, MINOR + 1, 'a'): DATA,\\n '%d.%d/maintained/%d.%d-%d/all/Packages.gz' % (MAJOR + 1, 0, MAJOR + 1, 0, 0): DATA,\\n })\\n versions, component = self.u.get_all_available_release_updates()\\n self.assertEqual(['%d.%d-%d' % (MAJOR, MINOR + 1, 0)], versions)\\n self.assertEqual('a', component)\",\n \"def get_release_info(self):\\r\\n return self.detail_info.get_release_info(self.version)\",\n \"def get_versions():\\n ret_obj = {'versions': picard_versions(current_app)}\\n return make_response(jsonify(ret_obj), 200)\",\n \"def latest_github_release(username: str, repo: str) -> GitHubReleaseResponse:\\n url = f'https://api.github.com/repos/{username}/{repo}/releases/latest'\\n response = requests.get(url)\\n if response.status_code != 200:\\n print(f'Error: {response.status_code}', file=sys.stderr)\\n sys.exit(1)\\n try:\\n decoded_json = response.json()\\n return GitHubReleaseResponse(decoded_json)\\n except requests.exceptions.JSONDecodeError:\\n print('Error: Unable to parse JSON from GitHub releases', file=sys.stderr)\\n sys.exit(1)\",\n \"def get(package):\\n url = Pypi.url()\\n url = url.format(package=package)\\n\\n # Request Pypi\\n resp = requests.get(url)\\n if resp.status_code != requests.codes.ok:\\n return dict(status=\\\"failure\\\", message=\\\"Invalid Package\\\")\\n\\n data = dict()\\n resp = resp.json()\\n releases = resp.get(\\\"releases\\\", [])\\n data[\\\"releases\\\"] = releases\\n data[\\\"status\\\"] = \\\"success\\\"\\n\\n return data\",\n \"def get_linked_versions(version='current'):\\n version = check_version_str(version)\\n chapters = [10, 9, 8]\\n version_page = 'https://research.cs.wisc.edu/htcondor/manual/{ver}/{chapter}_Version_History.html'\\n r = requests.get(version_page.format(ver=version, chapter=chapters[0]))\\n if r.status_code == 404:\\n # Try different chapter numbers, as it changes for different versions\\n i = 1\\n while r.status_code == 404 and i < len(chapters):\\n r = requests.get(version_page.format(ver=version, chapter=chapters[i]))\\n i += 1\\n if r.status_code == 404:\\n return []\\n soup_vers = bs4.BeautifulSoup(r.text, 'lxml')\\n versions = [x.text.replace('Version ', '')\\n for x in soup_vers.find_all('a')\\n if x.text.startswith('Version')]\\n return versions\",\n \"def get_build_infos(self, start_rev, end_rev, range=60*60*4):\\n pushlogs_finder = self._create_pushlog_finder(start_rev, end_rev)\\n\\n pushlogs = pushlogs_finder.get_pushlogs()\\n\\n if not pushlogs:\\n return []\\n\\n start_time = pushlogs[0]['date']\\n end_time = pushlogs[-1]['date']\\n \\n build_urls = [(\\\"%s%s/\\\" % (self.build_base_url, path), timestamp)\\n for path, timestamp in self._extract_paths()]\\n\\n build_urls_in_range = filter(lambda (u, t): t > (start_time - range)\\n and t < (end_time + range), build_urls)\\n\\n raw_revisions = [push['changesets'][-1] for push in pushlogs]\\n all_builds = []\\n with futures.ThreadPoolExecutor(max_workers=8) as executor:\\n futures_results = {}\\n for build_url, timestamp in build_urls_in_range:\\n future = executor.submit(self._get_valid_builds,\\n build_url,\\n timestamp,\\n raw_revisions)\\n futures_results[future] = build_url\\n for future in futures.as_completed(futures_results):\\n if future.exception() is not None:\\n sys.exit(\\\"Retrieving valid builds from %r generated an\\\"\\n \\\" exception: %s\\\" % (futures_results[future],\\n future.exception()))\\n all_builds.extend(future.result())\\n\\n return self._sort_builds(all_builds)\",\n \"def get_releases_by_application(self, application_id):\\n status_code_dict = {codes.ok: ReleaseListResponse}\\n return self.get_request(RELEASES_URL.format(application_id=application_id),\\n status_code_response_class_dict=status_code_dict)\",\n \"def get_release_data(user, repo, field=None, regex_pattern=None, group_number=0):\\n response = requests.get(f\\\"https://api.github.com/repos/{user}/{repo}/releases/latest\\\")\\n release_data_dict = json.loads(response.text)\\n\\n if field:\\n field_value = release_data_dict[field]\\n if regex_pattern is None:\\n output = field_value\\n else:\\n output = re.search(regex_pattern, field_value).group(group_number)\\n else:\\n output = release_data_dict\\n\\n return output\",\n \"def cache_from_releases(releases: List[dict]) -> None:\\n client = redis.Redis(host=os.environ.get('REDIS_HOST', 'localhost'))\\n\\n for release in (r for r in releases if r.get('url')): # filter out releases without a url\\n key_name = _title_to_key(release['title'])\\n client.setex(key_name, ONE_DAY, release['url'])\",\n \"def get_versions(self):\\n # They randomly use and don't use 'r' prefix so we have to sort\\n # versions manually\\n versions = list(self._get_github_tags())\\n versions.sort(\\n key=operator.attrgetter('base_version'),\\n reverse=True,\\n )\\n return versions\",\n \"def get_latest_release(self):\\n cs = Custom_Soup(\\n \\\"latest_release\\\", \\\"https://chromedriver.storage.googleapis.com/LATEST_RELEASE_\\\" + str(self.version))\\n cs.get_request()\\n self.latest_release = cs.get_text()\",\n \"def list_releases(\\n self,\\n ) -> Callable[\\n [cloud_deploy.ListReleasesRequest], cloud_deploy.ListReleasesResponse\\n ]:\\n # Generate a \\\"stub function\\\" on-the-fly which will actually make\\n # the request.\\n # gRPC handles serialization and deserialization, so we just need\\n # to pass in the functions for each.\\n if \\\"list_releases\\\" not in self._stubs:\\n self._stubs[\\\"list_releases\\\"] = self.grpc_channel.unary_unary(\\n \\\"/google.cloud.deploy.v1.CloudDeploy/ListReleases\\\",\\n request_serializer=cloud_deploy.ListReleasesRequest.serialize,\\n response_deserializer=cloud_deploy.ListReleasesResponse.deserialize,\\n )\\n return self._stubs[\\\"list_releases\\\"]\",\n \"def software_releases_repository():\\n\\n pipelines = [ingest_pipeline, populate_dm_pipeline, asset_experimentation]\\n schedules = [] # [my_hourly_schedule]\\n sensors = [] # [my_sensor]\\n\\n return pipelines + schedules + sensors\",\n \"def gettime(self, tag):\\n cmd = ['git', 'log', '--pretty=format:\\\"%ct\\\"', \\\"-1\\\", tag]\\n p = Popen(cmd, cwd=self.filename, stdout=PIPE)\\n data, res = p.communicate()\\n if data == b'':\\n return [], []\\n time_stamp = []\\n this_tag = []\\n for seconds in data.decode(\\\"utf-8\\\").split(\\\"\\\\n\\\"):\\n month = round((int(seconds.strip('\\\"')) - ReleaseTime.base) / ReleaseTime.month_time)\\n if month not in time_stamp:\\n time_stamp.append(month)\\n this_tag.append(tag[0:4])\\n else:\\n pass\\n return time_stamp, this_tag\",\n \"def release_dates(self, **kwargs):\\n path = self._get_movie_id_path('release_date')\\n resp = self._get_method(path, kwargs)\\n return resp\",\n \"def _parse_release_count(self, resp: Dict[str, Any]) -> str:\\n return f\\\"{len(resp.get('releases', []))}\\\"\",\n \"def get_release_info(self, release):\\n rel_os = release.operating_system.lower()\\n version = release.version\\n\\n release_info = filter(\\n lambda r: (\\n r['os'] == rel_os and\\n ClusterPlugins.is_release_version_compatible(version,\\n r['version'])),\\n self.plugin.releases)\\n\\n return release_info[0]\",\n \"def aggregate_git_log(path, progress_callback=lambda progress: None):\\n versions = list()\\n\\n current_version, current_commits = None, list()\\n\\n log_data = git_log_hash(path)\\n log_length = len(log_data)\\n progress_step = max(1, log_length / 100)\\n \\n for idx, (rev_hash, date, msg) in enumerate(log_data):\\n if idx % progress_step == 0:\\n progress_callback(float(idx) / log_length)\\n \\n current_commits.append(msg)\\n if git_checkout(path=path, revision_hash=rev_hash):\\n version = get_package_metadata(path=path, field_name='Version')\\n if version != current_version:\\n # memorize it\\n versions.insert(0,\\n dict(version=version,\\n date=datetime.strptime(date.rsplit(' ', 1)[0], '%Y-%m-%d %H:%M:%S'),\\n sections=[dict(notes='',\\n items=list(reversed(current_commits)))]))\\n\\n current_version, current_commits = version, list()\\n\\n if current_commits:\\n versions.insert(0,\\n dict(version='newest',\\n date=None,\\n sections=[dict(notes='',\\n items=list(reversed(current_commits)))]))\\n\\n return versions\",\n \"def get_history_queue():\\n response = houston.get(\\\"/history/queue\\\")\\n houston.raise_for_status_with_json(response)\\n return response.json()\",\n \"def versions():\\n result = timeline.versions()\\n if result:\\n click.echo('\\\\n'.join(result))\",\n \"def git_stats_release(project: Project, release: Release) -> GitStats:\\n return _git_stats(project, f\\\"{release.tag_name}~1\\\", release.tag_name)\",\n \"def get_recent_release_for_page(self, page, PER_PAGE):\\n\\t\\tconnection = self.connect_to_db()\\n\\t\\tcursor = connection.cursor()\\n\\t\\toffset = (page - 1) * 15\\n\\t\\tcursor.execute('''select * from movies where release_year = 2016 order by weighted desc limit %d offset %d;''' % (PER_PAGE, offset))\\n\\t\\trows = cursor.fetchall()\\n\\t\\tconnection.close()\\n\\t\\treturn self.convert_to_json(rows)\",\n \"def universePayoutHistory(universeId, startDate, endDate):\\n url = f\\\"https://engagementpayouts.roblox.com/v1/universe-payout-history?endDate={endDate}&startDate={startDate}&universeId={universeId}\\\"\\n r = requests.get(url, cookies=cookie)\\n j = json.loads(r.text)\\n return j\",\n \"def history(self) -> List[Dict[str, Any]]:\\n\\n response = self.client.get(f\\\"/images/{self.id}/history\\\")\\n body = response.json()\\n\\n if response.status_code == 200:\\n return body\\n\\n if response.status_code == 404:\\n raise ImageNotFound(body[\\\"cause\\\"], response=response, explanation=body[\\\"message\\\"])\\n raise APIError(body[\\\"cause\\\"], response=response, explanation=body[\\\"message\\\"])\",\n \"def main(\\n req_files,\\n verbose=False,\\n outdated=False,\\n latest=False,\\n verbatim=False,\\n repo=None,\\n path=\\\"requirements.txt\\\",\\n token=None,\\n branch=\\\"master\\\",\\n url=None,\\n delay=None,\\n):\\n requirements = []\\n\\n if repo:\\n github_url = build_github_url(repo, branch, path, token)\\n req_file = get_requirements_file_from_url(github_url)\\n requirements.extend(parse_req_file(req_file))\\n elif url:\\n req_file = get_requirements_file_from_url(url)\\n requirements.extend(parse_req_file(req_file))\\n else:\\n for req_file in req_files:\\n requirements.extend(parse_req_file(req_file, verbatim=verbatim))\\n req_file.close()\\n\\n total_time_delta = 0\\n max_outdated_time = 0\\n results = []\\n\\n for req, version, ignore in requirements:\\n if verbatim and not req:\\n results.append(version)\\n elif req:\\n results.append(\\n {\\n \\\"req\\\": req,\\n \\\"version\\\": version,\\n \\\"ignore\\\": ignore,\\n \\\"latest\\\": request(get_pypi_url(req)),\\n \\\"specified\\\": request(get_pypi_url(req, version)),\\n }\\n )\\n\\n for result in results:\\n if isinstance(result, str):\\n print(result.replace(\\\"\\\\n\\\", \\\"\\\"))\\n continue\\n\\n if result[\\\"ignore\\\"]:\\n if verbatim:\\n print(\\\"{}=={} # norot\\\".format(result[\\\"req\\\"], result[\\\"version\\\"]))\\n else:\\n print(\\\"Ignoring updates for {}. \\\".format(result[\\\"req\\\"]))\\n continue\\n\\n req = result[\\\"req\\\"]\\n version = result[\\\"version\\\"]\\n\\n latest_version, latest_release_date = get_version_and_release_date(\\n req, verbose=verbose, response=result[\\\"latest\\\"]\\n )\\n specified_version, specified_release_date = get_version_and_release_date(\\n req, version, response=result[\\\"specified\\\"]\\n )\\n\\n if latest_release_date and specified_release_date:\\n time_delta = (latest_release_date - specified_release_date).days\\n total_time_delta = total_time_delta + time_delta\\n max_outdated_time = max(time_delta, max_outdated_time)\\n\\n if verbose:\\n if time_delta > 0:\\n print(\\n \\\"{} ({}) is {} days out of date. \\\"\\n \\\"Latest is {}\\\".format(req, version, time_delta, latest_version)\\n )\\n elif version != latest_version:\\n print(\\n \\\"{} ({}) is out of date. \\\"\\n \\\"Latest is {}\\\".format(req, version, latest_version)\\n )\\n elif not outdated:\\n print(\\\"{} ({}) is up to date\\\".format(req, version))\\n\\n if latest and latest_version != specified_version:\\n print(\\n \\\"{}=={} # Updated from {}\\\".format(\\n req, latest_version, specified_version\\n )\\n )\\n elif verbatim and latest_version != specified_version:\\n print(\\n \\\"{}=={} # Latest {}\\\".format(req, specified_version, latest_version)\\n )\\n elif verbatim:\\n print(\\\"{}=={}\\\".format(req, specified_version))\\n\\n elif verbatim:\\n print(\\\"{}=={} # Error checking latest version\\\".format(req, version))\\n\\n verbatim_str = \\\"\\\"\\n if verbatim:\\n verbatim_str = \\\"# Generated with piprot {}\\\\n# \\\".format(VERSION)\\n\\n if total_time_delta > 0 and delay is None:\\n print(\\n \\\"{}Your requirements are {} \\\"\\n \\\"days out of date\\\".format(verbatim_str, total_time_delta)\\n )\\n sys.exit(1)\\n elif delay is not None and max_outdated_time > int(delay):\\n print(\\n \\\"{}At least one of your dependencies is {} \\\"\\n \\\"days out of date which is more than the allowed\\\"\\n \\\"{} days.\\\".format(verbatim_str, max_outdated_time, delay)\\n )\\n sys.exit(1)\\n elif delay is not None and max_outdated_time <= int(delay):\\n print(\\n \\\"{}All of your dependencies are at most {} \\\"\\n \\\"days out of date.\\\".format(verbatim_str, delay)\\n )\\n else:\\n print(\\n \\\"{}Looks like you've been keeping up to date, \\\"\\n \\\"time for a delicious beverage!\\\".format(verbatim_str)\\n )\",\n \"def get_details(self, repo=None):\\n api_json = []\\n\\n #get all branches from this repo\\n branches = self.make_branches(self.getBranch(repo))\\n\\n today = datetime.date.today()\\n yesterday = today - datetime.timedelta(2)\\n\\n for branch in branches:\\n args = {\\\"per_page\\\": \\\"100\\\",\\n \\\"sha\\\": branch,\\n \\\"author\\\": self.username,\\n \\\"since\\\": yesterday.isoformat()}\\n args = self.make_args(args)\\n repo_url = \\\"/\\\".join([self.url, \\\"repos\\\", repo, \\\"commits\\\"])\\n repo_url = repo_url + args\\n\\n request = urllib2.Request(repo_url, headers=self.headers)\\n response = urllib2.urlopen(request)\\n raw_data = response.read()\\n commits_info = self.process_factory(simplejson.loads(raw_data))\\n api_json = api_json + commits_info\\n\\n print repo_url\\n\\n print api_json\\n return api_json\",\n \"def get_resource_last_update_timestamp(api_1_0_url, resource):\\n try:\\n r = requests.get(api_1_0_url)\\n json_string = r.content\\n data = json.loads(json_string)\\n try:\\n files = data['files']\\n for entry in files:\\n if entry['path'] == resource:\\n entry_last_update_timestamp = entry['utctimestamp']\\n return entry_last_update_timestamp\\n except Exception as error:\\n print(\\\"Caught error: \\\" + repr(error))\\n except Exception as error:\\n print(\\\"Failed to connect to bitbucket: \\\" + repr(error))\\n exit(1)\\n return None\",\n \"def get_video_title_releaser_release_time(self, url):\\n video_id = ' '.join(re.findall('id.*html', url))\\n browser = webdriver.Chrome()\\n browser.get(url)\\n title = browser.find_element_by_id('subtitle').text\\n releaser = browser.find_element_by_id('module_basic_sub').text\\n releaser = releaser.replace('+订阅','')\\n releaser = releaser.replace(' ','')\\n try:\\n rt_midstep = browser.find_element_by_class_name('video-status').text\\n rt_midstep = rt_midstep.replace('上传于','')\\n rt_midstep = rt_midstep.replace(' ','')\\n release_time = int(datetime.datetime.strptime(rt_midstep,'%Y-%m-%d').timestamp()*1e3)\\n except:\\n release_time = 0\\n fetch_time = int(datetime.datetime.timestamp(datetime.datetime.now())*1e3)\\n D0 = {'video_id': video_id,\\n 'title': title,\\n 'release_time': release_time,\\n 'url': url,\\n 'fetch_time': fetch_time}\\n return D0\",\n \"def get_revision_list(self):\\n response = self._get_request(\\n DeckhandClient.get_path(DeckhandPaths.REVISION_LIST)\\n )\\n self._handle_bad_response(response)\\n revisions = yaml.safe_load(response.text)\\n return revisions.get('results', [])\",\n \"def all(data):\\n result = list()\\n for release, info in data.items():\\n version = release\\n python_version = Pypi._get_python_version(info)\\n d = dict(version=version, python_version=python_version)\\n result.append(d)\\n\\n return result\",\n \"def downloads_per_version(package):\\n downloads = {}\\n for release in package['files']:\\n downloads[release['version']] = release['ndownloads']\\n return downloads\",\n \"def transformar_data_to_json():\\n\\t# La lista de releases regresa como un <str>\\n\\tdeployedReleases = subprocess.check_output([\\\"/usr/bin/bash\\\",\\\"/usr/local/bin/gsdGetRCinServer.sh\\\"])\\n\\ttimeZoneInfo = subprocess.check_output([\\\"date\\\",\\\"+%Z\\\"]).strip()\\n\\n\\t# Separamos el <str> por lineas\\n\\tdeployedReleases = deployedReleases.split()\\n\\n\\t# Generamos diccionario inicial\\n\\thostname = (socket.gethostname())\\n communicate = { 'server' : hostname,\\n 'date': today,\\n 'timezone': timeZoneInfo, \\n 'applications': {} \\n }\\n\\n\\t# Parseamos todos los despliegues encontrados\\n\\tfor release in deployedReleases:\\n\\n\\t\\t# sanetizar los datos, debemos eliminar los \\\".\\\" dado que son representaciones especiales en mongo, el path tampoco nos importa porque es estandar\\n\\t\\trelease = release.replace('/opt/company/deploy/', '')\\n\\t\\trelease = release.replace('.', '-')\\n\\n\\t\\t# Verificamos si es un symlink (current es version actual desplegada)\\n\\t\\tif 'current' in release :\\n\\t\\t\\tmarker, appAndVersion = release.split('|')\\n if appAndVersion.count('/') > 1 :\\n rcversion = 'RC Format is wrong'\\n appname = marker.replace('/current', '')\\n else:\\n appname, rcversion = appAndVersion.split('/')\\n\\n\\t\\t\\tif appname not in communicate['applications'] :\\n\\t\\t\\t\\tcommunicate['applications'].update( { \\n\\t\\t\\t\\t\\t\\t\\t\\tappname: \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t{rcversion : 'current'} \\n\\t\\t\\t\\t\\t\\t\\t } )\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcommunicate['applications'][appname].update({rcversion : 'current'})\\n\\n\\t\\t# Verificamos si es un symlink (rollback es la version previa a current)\\n\\t\\telif 'rollback' in release:\\n\\t\\t\\tmarker, appAndVersion = release.split('|')\\n if appAndVersion.count('/') > 1 :\\n rcversion = 'RC Format is wrong'\\n appname = marker.replace('/rollback', '')\\n else:\\n appname, rcversion = appAndVersion.split('/')\\n\\n\\t\\t\\tif appname not in communicate['applications'] :\\n\\t\\t\\t\\tcommunicate['applications'].update( { \\n\\t\\t\\t\\t\\t\\t\\t\\tappname: \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t{rcversion : 'rollback'} \\n\\t\\t\\t\\t\\t\\t\\t } )\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcommunicate['applications'][appname].update({rcversion : 'rollback'})\\n\\n\\t\\t# Lo que no es current o rollback es un despliegue mas viejo\\n\\t\\telse :\\n appname, rcversion = release.split('/')\\n if appname not in communicate['applications'] :\\n communicate['applications'].update( {\\n appname:\\n {rcversion : 'previous'}\\n } )\\n else:\\n if rcversion not in communicate['applications'][appname] :\\n communicate['applications'][appname].update({rcversion : 'previous'})\\n else :\\n # La version ya fue registrada como current o rollback, esto sucede porque el symlink comparte el mismo nombre que el directorio real a nivel dict\\n pass\\n\\n\\treturn communicate\",\n \"def show_release_details(release):\\n def get(key, dictionary=release):\\n try:\\n return dictionary[key]\\n except KeyError as e:\\n return None\\n\\n date = get('date')\\n date = date[:4] if date else \\\"\\\"\\n print(\\\"{} / {} ({})\\\".format(get('artist-credit-phrase'), get('title'), date))\\n print()\\n\\n # print track list\\n track_lists = [get(\\\"track-list\\\", medium) for medium in release['medium-list']]\\n track_list = [track for tracks in track_lists for track in tracks]\\n track_width = max([len(track[\\\"recording\\\"][\\\"title\\\"]) for track in track_list])\\n time_width = len(get_time(sum([int(track[\\\"length\\\"]) for track in track_list])))\\n\\n total_ms = 0\\n for idx,track in enumerate(track_list):\\n title = track[\\\"recording\\\"][\\\"title\\\"]\\n ms = int(track[\\\"length\\\"])\\n time = get_time(ms)\\n total = get_time(total_ms)\\n print(\\\"{:2d}. {:{track_width}} {:>{time_width}} ({})\\\".format( \\\\\\n idx+1, title, total, time, track_width=track_width, time_width=time_width))\\n total_ms += ms\\n width = 5 + track_width + time_width\\n print(\\\"{:>{width}}\\\".format(get_time(total_ms), width=width))\",\n \"def query_releases(self,request):\\n\\n\\t\\t# ----------- DEBUG -----------------\\n\\t\\tMODULE.info(\\\"updater/updates/query invoked with:\\\")\\n\\t\\tpp = pprint.PrettyPrinter(indent=4)\\n\\t\\tst = pp.pformat(request.options).split(\\\"\\\\n\\\")\\n\\t\\tfor s in st:\\n\\t\\t\\t\\tMODULE.info(\\\" << %s\\\" % s)\\n\\t\\t# -----------------------------------\\n\\n\\t\\t# be as current as possible.\\n\\t\\tself.uu.ucr_reinit()\\n\\t\\tself.ucr.load()\\n\\n\\t\\tappliance_mode = self.ucr.is_true('server/appliance')\\n\\n\\t\\tresult = []\\n\\t\\ttry:\\n\\t\\t\\trequest.status = SUCCESS\\n\\t\\t\\tavailable_versions, blocking_component = self.uu.get_all_available_release_updates()\\n\\t\\t\\tfor rel in available_versions:\\n\\t\\t\\t\\tentry = {}\\n\\t\\t\\t\\tentry['id'] = rel\\n\\t\\t\\t\\tentry['label'] = 'UCS %s' % rel\\n\\t\\t\\t\\tresult.append(entry)\\n\\t\\t\\t#\\n\\t\\t\\t# appliance_mode=no ; blocking_comp=no → add \\\"latest version\\\"\\n\\t\\t\\t# appliance_mode=no ; blocking_comp=yes → no \\\"latest version\\\"\\n\\t\\t\\t# appliance_mode=yes; blocking_comp=no → add \\\"latest version\\\"\\n\\t\\t\\t# appliance_mode=yes; blocking_comp=yes → add \\\"latest version\\\"\\n\\t\\t\\t#\\n\\t\\t\\tif len(result) and (appliance_mode or not blocking_component):\\n\\t\\t\\t\\t# UniventionUpdater returns available version in ascending order, so\\n\\t\\t\\t\\t# the last returned entry is the one to be flagged as 'latest' if there's\\n\\t\\t\\t\\t# no blocking component.\\n\\t\\t\\t\\tresult[-1]['label'] = '%s (%s)' % (result[-1]['label'],_('latest version'))\\n\\n\\t\\texcept Exception,ex:\\n\\t\\t\\trequest.status = FAILURE\\n\\t\\t\\tself.finished(request.id, [], str(ex))\\n\\t\\t\\treturn\\n\\n\\t\\t# ----------- DEBUG -----------------\\n\\t\\tMODULE.info(\\\"updater/updates/query returns: %d entries\\\" % len(result))\\n\\t\\tpp = pprint.PrettyPrinter(indent=4)\\n\\t\\tst = pp.pformat(result).split(\\\"\\\\n\\\")\\n\\t\\tfor s in st:\\n\\t\\t\\t\\tMODULE.info(\\\" >> %s\\\" % s)\\n\\t\\t# -----------------------------------\\n\\n\\t\\tself.finished(request.id,result)\",\n \"def fetchHistory(self, token):\\n history = self.loadHistory(token)\\n if len(history):\\n startStamp = history[-1][\\\"timestamp\\\"] + 1000 + random.random()*1000 # Add some random number of seconds\\n startDateStr = time.strftime(\\\"%Y%m%d\\\", time.gmtime(int(startStamp)))\\n else:\\n startDateStr = \\\"20130428\\\" # Date of the first bitcoin valuation ?\\n dateStr = time.strftime(\\\"%Y%m%d\\\")\\n uri = self.historyTemplate % (token, startDateStr, dateStr)\\n print(\\\"Fetching history\\\")\\n html = BeautifulSoup(urlrequest.urlopen(uri).read().decode(), \\\"html.parser\\\")\\n print(\\\"parsing html\\\")\\n dataRows = html.find(\\\"div\\\", {\\\"id\\\": \\\"historical-data\\\"}).find(\\\"table\\\", {\\\"id\\\", \\\"table\\\"}).find(\\\"tbody\\\").find_all(\\\"tr\\\", {\\\"class\\\": \\\"text-right\\\"})\\n headers = [\\\"date.string\\\", \\\"open\\\", \\\"high\\\", \\\"low\\\", \\\"close\\\", \\\"volume\\\", \\\"market.cap\\\"]\\n dataPts = []\\n print(\\\"translating data\\\")\\n for row in dataRows:\\n rowObj = {}\\n for i, td in enumerate(row.find_all(\\\"td\\\")):\\n if i == 0:\\n try:\\n rowObj[headers[i]] = td.get_text()\\n rowObj[\\\"timestamp\\\"] = helpers.stamp2dayStamp(datetime.datetime.strptime(td.get_text(), \\\"%b %d, %Y\\\").timestamp())\\n except Exception:\\n print(\\\"failed to parse float from `%s`\\\" % td.get_text())\\n rowObj[headers[i]] = \\\"Dec 31, 1999\\\"\\n elif i < 5:\\n try:\\n rowObj[headers[i]] = float(td.get_text())\\n except Exception:\\n print(\\\"failed to parse float from `%s`\\\" % td.get_text())\\n rowObj[headers[i]] = 0.0\\n else:\\n try:\\n rowObj[headers[i]] = int(td.get_text().replace(\\\",\\\", \\\"\\\"))\\n except Exception:\\n print(\\\"failed to parse integer from `%s`\\\" % td.get_text())\\n rowObj[headers[i]] = 0\\n dataPts.append(rowObj)\\n for pt in sorted(dataPts, key=lambda p: p[\\\"timestamp\\\"]):\\n if len(history) == 0 or pt[\\\"timestamp\\\"] > history[-1][\\\"timestamp\\\"]:\\n history.append(pt)\\n self.saveHistory(token, history)\\n return history\",\n \"def release_date(self):\\n for item in self.proto.releaseInfo.item:\\n if item.label == 'Released on':\\n return item.container.value\",\n \"def get_publication_versions(project, publication_id):\\n connection = db_engine.connect()\\n publication_versions = get_table(\\\"publication_version\\\")\\n statement = select([publication_versions]).where(publication_versions.c.publication_id == int_or_none(publication_id))\\n rows = connection.execute(statement).fetchall()\\n result = []\\n for row in rows:\\n result.append(dict(row))\\n connection.close()\\n return jsonify(result)\",\n \"def svn_rev_info(path): # pragma: no cover\\n if not os.path.isdir(os.path.join(path, '.svn')):\\n path = os.path.join(path, '..')\\n\\n _program_dir = path\\n filename = os.path.join(_program_dir, '.svn/entries')\\n if os.path.isfile(filename):\\n with open(filename) as entries:\\n version = entries.readline().strip()\\n if version != '12':\\n for _ in range(3):\\n entries.readline()\\n tag = entries.readline().strip()\\n t = tag.split('://', 1)\\n t[1] = t[1].replace('svn.wikimedia.org/svnroot/pywikipedia/',\\n '')\\n tag = '[{}] {}'.format(*t)\\n for _ in range(4):\\n entries.readline()\\n date = time.strptime(entries.readline()[:19],\\n '%Y-%m-%dT%H:%M:%S')\\n rev = entries.readline()[:-1]\\n return tag, rev, date\\n\\n # We haven't found the information in entries file.\\n # Use sqlite table for new entries format\\n from sqlite3 import dbapi2 as sqlite\\n with closing(\\n sqlite.connect(os.path.join(_program_dir, '.svn/wc.db'))) as con:\\n cur = con.cursor()\\n cur.execute(\\\"\\\"\\\"select\\nlocal_relpath, repos_path, revision, changed_date, checksum from nodes\\norder by revision desc, changed_date desc\\\"\\\"\\\")\\n _name, tag, rev, date, _checksum = cur.fetchone()\\n cur.execute('select root from repository')\\n tag, = cur.fetchone()\\n\\n tag = os.path.split(tag)[1]\\n date = time.gmtime(date / 1_000_000)\\n return tag, rev, date\",\n \"def get_github_chandra_models_version_info():\\n with urlopen('https://api.github.com/repos/sot/chandra_models/tags') as url:\\n response = url.read()\\n tags = json.loads(response.decode('utf-8'))\\n\\n with urlopen('https://api.github.com/repos/sot/chandra_models/branches') as url:\\n response = url.read()\\n branches = json.loads(response.decode('utf-8'))\\n\\n all_versions_info = {t[\\\"name\\\"]: t for t in tags}\\n all_versions_info.update({b[\\\"name\\\"]: b for b in branches})\\n return all_versions_info\",\n \"def getVersions(self):\\n logger.debug(\\\"Func: getVersions\\\")\\n\\n try:\\n return self._currentSceneInfo[\\\"Versions\\\"]\\n except:\\n return []\",\n \"def GetRevisionsSample():\\n client = CreateClient()\\n for entry in client.GetResources(limit=55).entry:\\n revisions = client.GetRevisions(entry)\\n for revision in revisions.entry:\\n print revision.publish, revision.GetPublishLink()\",\n \"def get_versions(start='current'):\\n start = check_version_str(start)\\n versions = get_linked_versions(start)\\n\\n results = versions[:]\\n while results:\\n results = get_linked_versions(results[-1])\\n print results\\n if results:\\n versions.extend(results)\\n\\n versions = [x for x in set(versions) if check_manual_exists(x)]\\n return sort_versions(versions, reverse=True)\",\n \"def history(self):\\n return self.info['history']\",\n \"def releases(self):\\r\\n\\r\\n result = self.proxy.package_releases(self.package_name, self.include_hidden)\\r\\n\\r\\n if len(result) == 0:\\r\\n # no matching package--search for possibles, and limit to 15 results\\r\\n results = self.proxy.search({\\r\\n 'name': self.package_name,\\r\\n 'description': self.package_name\\r\\n }, 'or')[:15]\\r\\n\\r\\n # make sure we only get unique package names\\r\\n matches = []\\r\\n for match in results:\\r\\n name = match['name']\\r\\n if name not in matches:\\r\\n matches.append(name)\\r\\n\\r\\n # if only one package was found, return it\\r\\n if len(matches) == 1:\\r\\n self.package_name = matches[0]\\r\\n return self.releases\\r\\n\\r\\n error = \\\"\\\"\\\"No such package found: %s\\r\\n\\r\\nPossible matches include:\\r\\n%s\\r\\n\\\"\\\"\\\" % (self.package_name, '\\\\n'.join('\\\\t- %s' % n for n in matches))\\r\\n\\r\\n sys.exit(error)\\r\\n\\r\\n return result\",\n \"def greater(data, version):\\n result = list()\\n given_version = parse(version)\\n for release, info in data.items():\\n python_version = Pypi._get_python_version(info)\\n ver = parse(release)\\n if not ver.is_prerelease and ver > given_version:\\n result.append(dict(version=str(ver), python_version=python_version))\\n\\n return result\",\n \"def get_comp_versions (component):\\n vprint (\\\"Detecting current version for \\\" + component)\\n\\n regex = re.compile (r\\\"version \\\" + version_restr)\\n major = component + \\\"_major\\\"\\n minor = component + \\\"_minor\\\"\\n micro = component + \\\"_micro\\\"\\n\\n\\n version = (None, None, None)\\n with open (doc_root + \\\"/ACE_TAO/\\\" + component + \\\"/VERSION.txt\\\") as version_file:\\n for line in version_file:\\n match = regex.search (line)\\n if match is not None:\\n version = match.groups(default=0)\\n\\n vprint (\\\"Detected version %s.%s.%s\\\" % version)\\n\\n comp_versions[major] = int (version[0])\\n comp_versions[minor] = int (version[1])\\n comp_versions[micro] = int (version[2])\\n\\n break\\n\\n print (\\\"FATAL ERROR: Unable to locate current version for \\\" + component)\\n raise Exception\\n\\n # Also store the current release (old from now)\\n old_comp_versions[major] = comp_versions[major]\\n old_comp_versions[minor] = comp_versions[minor]\\n old_comp_versions[micro] = comp_versions[micro]\\n\\n if opts.update:\\n if opts.release_type == ReleaseType.major:\\n comp_versions[major] += 1\\n comp_versions[minor] = 0\\n comp_versions[micro] = 0\\n elif opts.release_type == ReleaseType.minor:\\n comp_versions[minor] += 1\\n comp_versions[micro] = 0\\n elif opts.release_type == ReleaseType.micro:\\n comp_versions[micro] += 1\\n\\n def make_version (versions, joiner):\\n return joiner.join ([\\n str (versions[component + '_' + x]) for x in ReleaseType.__members__.keys ()\\n ])\\n\\n comp_versions [component + \\\"_version\\\"] = make_version (comp_versions, '.')\\n comp_versions [component + \\\"_version_\\\"] = make_version (comp_versions, '_')\\n\\n comp_versions [component + \\\"_code\\\"] = \\\\\\n (comp_versions[major] << 16) + \\\\\\n (comp_versions[minor] << 8) + \\\\\\n comp_versions[micro]\\n\\n old_comp_versions [component + \\\"_version\\\"] = make_version (old_comp_versions, '.')\\n old_comp_versions [component + \\\"_version_\\\"] = make_version (old_comp_versions, '_')\\n\\n if opts.update:\\n vprint (\\\"Updating from version %s to version %s\\\" %\\n (old_comp_versions [component + \\\"_version\\\"], comp_versions [component + \\\"_version\\\"]))\\n else:\\n vprint (\\\"Found version %s\\\" %\\n (comp_versions [component + \\\"_version\\\"]))\\n\\n # else:\\n # comp_versions [component + \\\"_version\\\"] = \\\\\\n # str (comp_versions[major]) + '.' + \\\\\\n # str (comp_versions[minor])\",\n \"def get_diffs(history):\\n\\n # First get all possible representations\\n mgr = plugins_get_mgr() \\n keys = mgr.search('representation')['representation']\\n representations = [mgr.get_by_key('representation', k) for k in keys]\\n\\n for i in range(len(history)):\\n if i+1 > len(history) - 1:\\n continue\\n\\n prev = history[i]\\n curr = history[i+1]\\n\\n #print(prev['subject'], \\\"==>\\\", curr['subject'])\\n #print(curr['changes'])\\n for c in curr['changes']:\\n \\n path = c['path']\\n\\n # Skip the metadata file\\n if c['path'].endswith('datapackage.json'): \\n continue \\n\\n # Find a handler for this kind of file...\\n handler = None \\n for r in representations: \\n if r.can_process(path): \\n handler = r \\n break \\n \\n if handler is None: \\n continue \\n\\n # print(path, \\\"being handled by\\\", handler)\\n\\n v1_hex = prev['commit']\\n v2_hex = curr['commit']\\n\\n temp1 = tempfile.mkdtemp(prefix=\\\"dgit-diff-\\\") \\n \\n try: \\n for h in [v1_hex, v2_hex]: \\n filename = '{}/{}/checkout.tar'.format(temp1, h)\\n try:\\n os.makedirs(os.path.dirname(filename))\\n except:\\n pass \\n extractcmd = ['git', 'archive', '-o', filename, h, path]\\n output = run(extractcmd)\\n if 'fatal' in output: \\n raise Exception(\\\"File not present in commit\\\") \\n with cd(os.path.dirname(filename)): \\n cmd = ['tar', 'xvf', 'checkout.tar']\\n output = run(cmd) \\n if 'fatal' in output: \\n print(\\\"Cleaning up - fatal 1\\\", temp1)\\n shutil.rmtree(temp1)\\n continue \\n\\n # Check to make sure that \\n path1 = os.path.join(temp1, v1_hex, path) \\n path2 = os.path.join(temp1, v2_hex, path) \\n if not os.path.exists(path1) or not os.path.exists(path2): \\n # print(\\\"One of the two output files is missing\\\") \\n shutil.rmtree(temp1)\\n continue \\n\\n #print(path1, path2) \\n\\n # Now call the handler\\n diff = handler.get_diff(path1, path2)\\n\\n # print(\\\"Inserting diff\\\", diff)\\n c['diff'] = diff\\n\\n except Exception as e: \\n #traceback.print_exc() \\n #print(\\\"Cleaning up - Exception \\\", temp1)\\n shutil.rmtree(temp1)\",\n \"def select_latest_micro_versions(versions):\\n seen_minors = set()\\n res = []\\n\\n for ver, _ in sorted(\\n versions.items(),\\n # Sort by (minor_version, upload_time) in descending order\\n key=lambda x: (Version(x[0]).release[:2], x[1]),\\n reverse=True,\\n ):\\n minor_ver = Version(ver).release[:2]\\n\\n if minor_ver not in seen_minors:\\n seen_minors.add(minor_ver)\\n res.insert(0, ver)\\n\\n return res\",\n \"def get_latest_release(account = None):\\n names = get_db_name(account=account, db_type=\\\"compara\\\")\\n compara = []\\n for name in names:\\n compara += [int(name.Release)]\\n return str(max(compara))\",\n \"def get_version():\\n\\n with open('__init__.py') as f:\\n for line in f.readlines():\\n if '__version__' in line:\\n apicem_version = line.strip().split(\\\"=\\\")[-1].strip(\\\" '\\\")\\n if '__first_release_date__' in line:\\n first_release_data_str = line.strip().split(\\\"=\\\")[-1].strip(\\\" '\\\")\\n first_release_data = date(*[int(num) for num in first_release_data_str.split('.')])\\n num_commits = get_cr_num(first_release_data)\\n return '{apicem_version}.{num_commits}'.format(\\n apicem_version=apicem_version, num_commits=num_commits)\\n\\n raise ValueError(\\\"could not read version\\\")\",\n \"def get_outdated_packages(self, pip_path=\\\"pip\\\"):\\n output_re = [re.compile(r) for r in [\\n # newer pip\\n r'^(.+) \\\\(Current: (.+) Latest: (.+)\\\\)$',\\n # old pip\\n r'^(.+) \\\\((.+)\\\\) - Latest: (.+) .*$',\\n ]]\\n pkgs = {}\\n out = self.check_output(\\\"%s list -o\\\", pip_path)\\n for line in out.splitlines():\\n # Warning: cannot find svn location for rst2pdf==0.93.dev-r0\\n # Could not find any downloads that satisfy the requirement iotop\\n if line.startswith('Warning: ') or line.startswith('Could not '):\\n continue\\n for out_re in output_re:\\n match = out_re.match(line)\\n if match:\\n name, current, latest = match.groups()\\n pkgs[name] = {'current': current, 'latest': latest}\\n break\\n else:\\n raise RuntimeError(\\\"could not parse {0}\\\".format(repr(line)))\\n return pkgs\",\n \"def get_latest_release_version():\\n repo = GITHUB.get_user(GITHUB_OWNER).get_repo(GITHUB_REPO)\\n latest_release_version = repo.get_latest_release().tag_name\\n return latest_release_version\",\n \"def _find_latest():\\n try:\\n db = get_master_collection()\\n service_details = db.find({\\\"master.key\\\": \\\"release\\\"}).sort([(\\\"master.value\\\", pymongo.DESCENDING)]).limit(1)\\n for service in service_details:\\n for r in sorted(service[\\\"master\\\"][\\\"value\\\"], reverse=True):\\n latest_release = r\\n build_list = service[\\\"master\\\"][\\\"value\\\"][r]\\n break\\n break\\n\\n latest_rel_num = str(latest_release).replace(\\\"_\\\", \\\".\\\")\\n build_list = _natural_sort(build_list)\\n for build in build_list:\\n latest_build = build\\n break\\n\\n latest_build_num = latest_build\\n second_latest_build_num = int(latest_build_num) - 1\\n latest = {\\\"latest_val\\\": latest_rel_num + \\\"_\\\" + latest_build_num,\\n \\\"second_latest_val\\\": latest_rel_num + \\\"_\\\" + str(second_latest_build_num)}\\n except Exception as e:\\n logger.error(\\\"Exception in _find_latest : \\\" + str(e))\\n return latest\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6942631","0.66807014","0.6496823","0.6251065","0.6223727","0.61681485","0.607848","0.605974","0.6022348","0.5989354","0.59570056","0.59270537","0.5916981","0.58646035","0.5862971","0.5855767","0.5854523","0.5853826","0.58354694","0.5833912","0.58016866","0.57994175","0.5793847","0.5746684","0.5738796","0.572935","0.57211924","0.5691585","0.5677908","0.5586303","0.55843425","0.5574024","0.55555063","0.5552908","0.5476721","0.54714394","0.54595387","0.545562","0.54363096","0.5424815","0.541594","0.5409819","0.53944","0.5383161","0.5376013","0.53470004","0.5320263","0.5311043","0.5308224","0.530511","0.5304686","0.52877504","0.52849483","0.52709794","0.5261199","0.5259902","0.52236193","0.52154773","0.5167841","0.51675165","0.51654625","0.51570696","0.5132246","0.5130527","0.5129829","0.5127437","0.51268697","0.5124996","0.51142967","0.5091579","0.508297","0.5080641","0.50684386","0.5036047","0.5032809","0.5031221","0.5030927","0.50291276","0.50286126","0.5023804","0.50194824","0.5016323","0.4989104","0.49882242","0.49750924","0.49718353","0.49712974","0.49671388","0.4957768","0.49558762","0.49330983","0.4929224","0.49257073","0.49222586","0.49201712","0.4918709","0.49161118","0.49135122","0.49090317","0.49070027"],"string":"[\n \"0.6942631\",\n \"0.66807014\",\n \"0.6496823\",\n \"0.6251065\",\n \"0.6223727\",\n \"0.61681485\",\n \"0.607848\",\n \"0.605974\",\n \"0.6022348\",\n \"0.5989354\",\n \"0.59570056\",\n \"0.59270537\",\n \"0.5916981\",\n \"0.58646035\",\n \"0.5862971\",\n \"0.5855767\",\n \"0.5854523\",\n \"0.5853826\",\n \"0.58354694\",\n \"0.5833912\",\n \"0.58016866\",\n \"0.57994175\",\n \"0.5793847\",\n \"0.5746684\",\n \"0.5738796\",\n \"0.572935\",\n \"0.57211924\",\n \"0.5691585\",\n \"0.5677908\",\n \"0.5586303\",\n \"0.55843425\",\n \"0.5574024\",\n \"0.55555063\",\n \"0.5552908\",\n \"0.5476721\",\n \"0.54714394\",\n \"0.54595387\",\n \"0.545562\",\n \"0.54363096\",\n \"0.5424815\",\n \"0.541594\",\n \"0.5409819\",\n \"0.53944\",\n \"0.5383161\",\n \"0.5376013\",\n \"0.53470004\",\n \"0.5320263\",\n \"0.5311043\",\n \"0.5308224\",\n \"0.530511\",\n \"0.5304686\",\n \"0.52877504\",\n \"0.52849483\",\n \"0.52709794\",\n \"0.5261199\",\n \"0.5259902\",\n \"0.52236193\",\n \"0.52154773\",\n \"0.5167841\",\n \"0.51675165\",\n \"0.51654625\",\n \"0.51570696\",\n \"0.5132246\",\n \"0.5130527\",\n \"0.5129829\",\n \"0.5127437\",\n \"0.51268697\",\n \"0.5124996\",\n \"0.51142967\",\n \"0.5091579\",\n \"0.508297\",\n \"0.5080641\",\n \"0.50684386\",\n \"0.5036047\",\n \"0.5032809\",\n \"0.5031221\",\n \"0.5030927\",\n \"0.50291276\",\n \"0.50286126\",\n \"0.5023804\",\n \"0.50194824\",\n \"0.5016323\",\n \"0.4989104\",\n \"0.49882242\",\n \"0.49750924\",\n \"0.49718353\",\n \"0.49712974\",\n \"0.49671388\",\n \"0.4957768\",\n \"0.49558762\",\n \"0.49330983\",\n \"0.4929224\",\n \"0.49257073\",\n \"0.49222586\",\n \"0.49201712\",\n \"0.4918709\",\n \"0.49161118\",\n \"0.49135122\",\n \"0.49090317\",\n \"0.49070027\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6868364"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":8296,"cells":{"query":{"kind":"string","value":"Calculates X values for given list of Y values in range defined by a and b parameters. X values are simply calculated by dividing given X range by number of nodes, so they are distributed in even range."},"document":{"kind":"string","value":"def prepare_initial_nodes(x_start, x_end, nodes_y):\n nodes_x = [float(x_start + ((x_end - x_start) / (len(nodes_y) - 1)) * i) for i in range(0, len(nodes_y))]\n nodes_y = [float(y) for y in nodes_y]\n print(nodes_x)\n print(nodes_y)\n nodes = list(zip(nodes_x, nodes_y))\n return nodes"},"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 projectionX(xdata, ydata, nbins, xrange=None, yrange=None):\n xmin, xmax = (np.min(xdata), np.max(xdata)) if xrange is None else xrange\n ymin, ymax = (np.min(ydata), np.max(ydata)) if yrange is None else yrange\n\n x_out = np.linspace(xmin, xmax, nbins+1)\n y_out = np.empty(nbins)\n dx = np.diff(x_out)[0]\n\n selection = in_range(xdata, xmin, xmax) & in_range(ydata, ymin, ymax)\n xdata, ydata = xdata[selection], ydata[selection]\n for i in range(nbins):\n bin_data = np.extract(in_range(xdata, x_out[i], x_out[i+1]), ydata)\n y_out[i] = bin_data.size\n x_out += dx / 2.\n x_out = x_out[:-1]\n return x_out, y_out","def neural_dist(func_a, func_b, x_range):\n func_a = func_a.numpy()\n func_b = func_b.numpy()\n return func_a.fit(func_b, x_range=x_range)[1]","def calculate_ranges(a, b):\n try:\n ranges = list(range(0, a, a//b))\n if ranges[-1] != a:\n ranges.append(a)\n return ranges\n except ValueError:\n return [0, a]","def profileX(xdata, ydata, nbins, xrange=None, yrange=None, drop_nan=True):\n xmin, xmax = (np.min(xdata), np.max(xdata)) if xrange is None else xrange\n ymin, ymax = (np.min(ydata), np.max(ydata)) if yrange is None else yrange\n\n x_out = np.linspace(xmin, xmax, nbins+1)\n y_out = np.empty(nbins)\n y_err = np.empty(nbins)\n dx = np.diff(x_out)[0]\n\n selection = in_range(xdata, xmin, xmax) & in_range(ydata, ymin, ymax)\n xdata, ydata = xdata[selection], ydata[selection]\n for i in range(nbins):\n bin_data = np.extract(in_range(xdata, x_out[i], x_out[i+1]), ydata)\n y_out[i] = np.mean(bin_data)\n y_err[i] = np.std(bin_data) / bin_data.size**0.5\n x_out += dx / 2.\n x_out = x_out[:-1]\n if drop_nan:\n selection = ~(np.isnan(y_out) | np.isnan(y_err))\n x_out = x_out[selection]\n y_out = y_out[selection]\n y_err = y_err[selection]\n return x_out, y_out, y_err","def plot_linear(x_range, w, b):\n\tplt.plot(x_range, x_range * w + b)","def fit_to_range(val: float, a: float, b: float, a1: float, b1: float) -> float:\n new_value = ((val - a) / (b - a)) * (b1 - a1) + a1\n return new_value","def rangeX(iterations):\n if not isinstance(iterations, (tuple)):\n raise AttributeError\n return itertools.product(*map(range, iterations))","def to_arrays(self, xmin=None, xmax=None):\n sidx = 0 if xmin is None else np.searchsorted(self.xvec, [xmin])[0]\n eidx = len(self.xvec) if xmax is None else np.searchsorted(self.xvec, [xmax])[0]\n\n if eidx < len(self.xvec) and self.xvec[eidx] == xmax:\n eidx += 1\n\n xtemp = self.xvec[sidx:eidx]\n if xmin is not None and (len(xtemp) == 0 or xtemp[0] != xmin):\n np.insert(xtemp, 0, [xmin])\n if xmax is not None and (len(xtemp) == 0 or xtemp[-1] != xmax):\n np.append(xtemp, [xmax])\n return xtemp, self(xtemp)","def grid(x, y):\n return product(xrange(1, x+1), xrange(1, y+1))","def slice_data(xdata, ydata, x_range):\n\tdata = zip(xdata, ydata)\n\tsliced_data = [d for d in data if d[0] >= x_range[0] and d[0] <= x_range[1]]\n\treturn array(zip(*sliced_data))","def _lagrange2(x, y):\n\n def P(x_ip):\n total = 0\n n = len(x)\n for i in range(0, n):\n\n def g(i, n):\n tot_mul = 1\n for j in range(0, n):\n if i == j:\n continue\n if x[i] == x[j]:\n log.fatal(\n f\"Leads to division by zero (x = {x[i]}). Identical values given in x array. \"\n \"For example by using Lagrange interpolation for precise orbit, \"\n \"check if identical observation epochs are given in SP3 file\"\n )\n tot_mul *= (x_ip - x[j]) / float(x[i] - x[j])\n return tot_mul\n\n total += y[i] * g(i, n)\n return total\n\n return P","def make_uniform_x(self, x_resolution, min_x = None, max_x = None, bin_above = 2.0, **kwargs):\n \n if min_x is None or max_x is None:\n a, b = self.get_min_max_x(**kwargs)\n if min_x is None:\n min_x = a\n if max_x is None:\n max_x = b\n \n new_x = numpy.arange(min_x, max_x + x_resolution / 2, x_resolution)\n \n for m in range(len(self.mess)):\n if m not in exclude and self.mess[m][\"class\"] not in exclude:\n resolution = (numpy.amax(m.x) - numpy.amin(m.x)) / len(m.x)","def calculate(\n expression: str, symmetrical_bounds: Union[int, float] = 10\n ) -> Tuple[np.ndarray, np.ndarray]:\n symmetrical_bounds = abs(symmetrical_bounds)\n x = np.arange(-symmetrical_bounds, symmetrical_bounds, symmetrical_bounds / 50)\n expr = parse_expr(expression)\n x_symbol = Symbol(\"x\")\n\n y = np.array([expr.subs({x_symbol: x_point}).evalf() for x_point in x])\n\n return x, y","def scale(x, a=5, b=10, xmin=-1, xmax=1):\n return (b - a)*(x - xmin)/(xmax - xmin) + a","def projectionY(xdata, ydata, nbins, yrange=None, xrange=None):\n xmin, xmax = (np.min(xdata), np.max(xdata)) if xrange is None else xrange\n ymin, ymax = (np.min(ydata), np.max(ydata)) if yrange is None else yrange\n\n x_out = np.linspace(ymin, ymax, nbins+1)\n y_out = np.empty(nbins)\n dx = np.diff(x_out)[0]\n\n selection = in_range(xdata, xmin, xmax) & in_range(ydata, ymin, ymax)\n xdata, ydata = xdata[selection], ydata[selection]\n for i in range(nbins):\n bin_data = np.extract(in_range(ydata, x_out[i], x_out[i+1]), xdata)\n y_out[i] = bin_data.size\n x_out += dx / 2.\n x_out = x_out[:-1]\n return x_out, y_out","def create_grid(xlim, ylim, step):\n x_range = np.arange(xlim[0], xlim[1], step)\n y_range = np.arange(ylim[0], ylim[1], step)\n return x_range, y_range","def calc(x_list):\n\n y_list = [x**2 + 2*x + 1 for x in x_list]\n\n return y_list","def plot(self, a=None, b=None):\n\n # === choose reasonable interval if [a, b] not specified === #\n if a is None:\n a = self.observations.min() - self.observations.std()\n if b is None:\n b = self.observations.max() + self.observations.std()\n\n # === generate plot === #\n x_vals = np.linspace(a, b, num=100)\n f = np.vectorize(self.__call__)\n plt.plot(x_vals, f(x_vals))\n plt.show()","def scale(x_range=1, y_range=1):\r\n x = rand_val(x_range)\r\n y = rand_val(y_range)\r\n return np.array(((x, 0, 0),\r\n (0, y, 0),\r\n (0, 0, 1)), dtype=np.float)","def plot(self, center=0, xmin=-1, xmax=1):\n if self.eps == 0:\n return [xmin, center, center, xmax], [0, 0, 1, 1]\n else:\n n = 200./self.eps\n x = concatenate(\n linspace(xmin, center-self.eps, 21),\n linspace(center-self.eps, center+self.eps, n+1),\n linspace(center+self.eps, xmax, 21))\n y = self(x)\n return x, y","def project_weights_and_nodes(a, b, unit_weights, unit_nodes):\n\n\t# project onto interval [a,b]\n\tnodes = 0.5*(b-a)*unit_nodes + 0.5*(a+b)\n\tweights = 0.5*(b-a)*unit_weights\n\n\treturn weights, nodes","def toPointwise_withLinearXYs( self, accuracy, biSectionMax = 16, **kwargs ) :\n\n if( accuracy < 1e-6 ) : accuracy = 1e-6\n if( accuracy > 0.1 ) : accuracy = 0.1\n\n P, n = [], 1000\n for i in xrange( n + 1 ) :\n x = ( ( n - i ) * self.domainMin + self.domainMax * i ) / n\n P.append( [ x, self.evaluate( x ) ] )\n axes = axesModule.axes( )\n yUnit = self.getAxisUnitSafely( 0 )\n xUnit = self.getAxisUnitSafely( 1 )\n axes[0] = axesModule.axis( 'y(x)', 0, yUnit )\n axes[1] = axesModule.axis( 'x', 1, xUnit )\n Pclass = self.toLinearXYsClass()\n P = Pclass( P, accuracy = accuracy, axes = axes )\n return( P.thin( accuracy = accuracy ) )","def data_range(xs: List[float]) -> float:\n return max(xs) - min(xs)","def getCellData(X, y, min0, max0, min1, max1):\n Xcell = []\n ycell = []\n\n for x,label in zip(X, y):\n if (x[0] >= min0) and (x[0] < max0) and (x[1] >= min1) and (x[1] < max1):\n Xcell.append(x)\n ycell.append(label)\n\n return np.array(Xcell), np.array(ycell)","def makeCrossPlotX(f,g):\n x = zerofloat(n1,n2)\n y = zerofloat(n1,n2)\n class Loop(Parallel.LoopInt):\n def compute(self,i2):\n for i1 in range(1,n1-1):\n x[i2][i1] = 0.5*(f[i2][i1+1]-f[i2][i1-1])\n y[i2][i1] = g[i2][i1]-f[i2][i1]\n Parallel.loop(n2,Loop())\n return x,y","def __call__(self, x, y):\n xa = np.asarray(x)\n ya = np.asarray(y)\n return (self._evaluate(xa.flatten(), ya.flatten())).reshape(xa.shape)","def get_densities(\n x: np.ndarray,\n y: np.ndarray,\n nx: int,\n ny: int,\n x_range: Tuple = (0, 100),\n y_range: Tuple = (0, 100),\n n: int = 30,\n) -> np.ndarray:\n\n x_values = np.linspace(x_range[0], x_range[1], nx)\n y_values = np.linspace(y_range[0], y_range[1], ny)\n\n density = np.empty((nx, ny))\n tree = get_kdtree(x, y)\n\n for x in tqdm(range(nx)):\n for y in range(ny):\n density[x, y] = get_density_from_neighbours(\n x_values[x], y_values[y], tree, n\n )\n\n return density, tree","def coords_in_range(self, anchor, steps):\n coords = list()\n x_low = -steps\n x_high = steps\n\n #Generate using an axial formula to make it easier\n #calculate z via the other two and throw away ones that aren't in bounds\n for x in range(x_low, x_high+1):\n for y in range(max(-steps, -x-steps), min(steps, -x+steps)+1):\n z = -x - y\n coords.append(anchor+self.coord(x, y, z))\n return coords","def alpha_range(x0, x1, x_min, x_max):\n if x0 == x1:\n raise ValueError('x1 and x2 should be different, get {} and {}'.format(x0, x1))\n alpha_x1 = (x_min - x0) / (x1 - x0)\n alpha_x2 = (x_max - x0) / (x1 - x0)\n alpha_min = max(0, min(alpha_x1, alpha_x2))\n alpha_max = min(1, max(alpha_x1, alpha_x2))\n return alpha_min, alpha_max","def nll(x, a, b):\n x = _validate_x_bounds(x, low=0, high=1, strict_low=True, strict_high=True)\n return -mp.fsum([logpdf(t, a, b) for t in x])","def lin_scale( val, x1, y1, x2, y2 ):\r\n x_range = (x2 - x1)\r\n new_val = 0\r\n if x_range is 0:\r\n new_val = y1\r\n else:\r\n y_range = ( y2 - y1 )\r\n new_val = ( ( ( val - x1 ) * y_range ) / x_range ) + y1\r\n\r\n return new_val","def monte_carlo_integration(f, n, a, b, ret_arrays=False):\n x = np.random.uniform(0, 1, n)*(b-a)+a\n f_array = f(x)\n\n positive_x = x[f_array >= 0]\n negative_x = x[f_array < 0]\n if positive_x.size > 0:\n h = np.max(f_array)\n else:\n h = np.max(-f_array)\n \n y_positive = np.random.uniform(0, 1, positive_x.size)*h\n y_negative = np.random.uniform(0, 1, negative_x.size)*h\n \n xy_indices_below = y_positive <= f(positive_x)\n xy_indices_above = y_negative <= -f(negative_x)\n n_inside_below = y_positive[xy_indices_below]\n n_inside_above = -y_negative[xy_indices_above]\n \n if ret_arrays:\n n_inside_x = np.append(positive_x[xy_indices_below],negative_x[xy_indices_above])\n n_inside_y = np.append(n_inside_below, n_inside_above)\n return n_inside_x, n_inside_y\n \n return h*(b-a)*(n_inside_below.size-n_inside_above.size)/(n)","def bws(x, y, **kwargs):\n\tx.sort()\n\ty.sort()\n\tnpx = np.array(x)\n\tnpy = np.array(y)\n\n\txs = np.unique(npx)\n\tys = np.unique(npy)\n\txys = set(xs).union(set(ys))\n\taxy = np.array(list(xys))\n\taxy.sort()\n\n\tG = np.array([len(axy[np.where(axy <= xi)]) for xi in xs])\n\tH = np.array([len(axy[np.where(axy <= yi)]) for yi in ys])\n\n\tn = len(G)\n\tm = len(H)\n\tfn = float(n)\n\tfm = float(m)\n\n\tN = np.linspace(1,n,num=n)\n\tM = np.linspace(1,m,num=m)\n\n\txt1 = np.power(G - N*(fm + fn)/fn, 2.0)\n\txtt = N/(fn+1.0)\n\txt2 = xtt*(1 - xtt)*(fm * (fm+fn)/fn)\n\tBx = np.sum(xt1/xt2)/fn\n\t\n\tyt1 = np.power(H - M*(fm + fn)/fm, 2.0)\n\tytt = M/(fm+1.0)\n\tyt2 = ytt*(1 - ytt)*(fn * (fm+fn)/fm)\n\tBy = np.sum(yt1/yt2)/fm\n\n\tB = (Bx+By)/2.0\n\n\tprint \"B = \", B\n\t\n\tJ = 3\n\tif \"j\" in kwargs:\n\t\tJ = kwargs[\"j\"]\n\t\n\treturn compute_xi(B, J)","def getx(v, lb, ub, i, B):\r\n x = lb + np.multiply((ub - lb), v)\r\n x[i] = B - (x.sum() - x[i])\r\n # Test if variable x[i] is within the bounds\r\n if x[i] <= ub[i] and x[i] >= lb[i]:\r\n return x\r\n else:\r\n return np.array([])","def generate_points(r_squared, min_val=5, max_val=100):\n r = sqrt(r_squared)\n x_list = []\n lb_list = []\n ub_list = []\n for i in range(min_val, max_val):\n x_list.append(i)\n lb, ub = pearson_confidence(r, i)\n lb_list.append(lb)\n ub_list.append(ub)\n return np.array(x_list), np.array(lb_list) ** 2, np.array(ub_list) ** 2","def axis_range ( xmin , xmax , delta = 0.05 , log = False ) :\n xmn = min ( xmin , xmax )\n xmx = max ( xmin , xmax )\n \n import math\n \n ## 1) special case\n if isequal ( xmn , xmx ) :\n return math.floor ( xmn - 0.1 ) , math.ceil ( xmx + 0.1 ) \n\n ## 2) special case\n if islong ( xmn - 0.5 ) and islong ( xmn + 0.5 ) :\n return math.floor ( xmn - 0.1 ) , math.ceil ( xmx + 0.1 ) \n\n d = xmx - xmn\n \n if 0 <= xmn < xmx :\n \n xmin = max ( 0 , xmn - delta * d )\n xmax = xmx + delta * d \n \n elif xmn < xmx <= 0 :\n \n xmin = xmn - delta * d \n xmax = max ( 0 , xmx + delta * d )\n \n elif xmn < 0 < xmx :\n \n xmin = ( 1 + delta ) * xmn \n xmax = ( 1 + delta ) * xmx\n \n else : \n \n xmin = xmn - delta * d \n xmax = xmx + delta * d \n\n N = 3\n \n a1 , b1 = frexp10 ( xmin )\n a2 , b2 = frexp10 ( xmax )\n\n b1 -= N \n b2 -= N \n \n xmin = math.floor ( a1 * ( 10**N ) ) * ( 10 ** b1 )\n xmax = math.ceil ( a2 * ( 10**N ) ) * ( 10 ** b2 )\n \n return xmin , xmax","def ni_range(x0, x1, dx=1):\n # sanity check arguments\n if dx==0:\n raise ValueError(\"invalid parameters: dx==0\")\n if x0>x1 and dx>=0:\n raise ValueError(\"invalid parameters: x0>x1 and dx>=0\")\n if x0<x1 and dx<=0:\n raise ValueError(\"invalid parameters: x0<x1 and dx<=0\")\n \n # generate range list\n range_list = []\n x = x0\n while x < x1:\n range_list.append(x)\n x += dx\n return range_list","def sampler(xaxis, yaxis, vals, x, y):\n i = 0\n while xaxis[i] < x:\n i += 1\n j = 0\n while yaxis[j] < y:\n j += 1\n return vals[i, j]","def felix_binning(xs, ys, delta=1):\n \n #bins = np.arange(start, end, delta)\n #occurance = np.zeros(start, end, delta)\n BIN_STEP = delta\n BIN_START = xs.min()\n BIN_STOP = xs.max()\n\n indices = xs.argsort()\n datax = xs[indices]\n datay = ys[indices]\n\n print(\"In total we have: \", len(datax), ' data points.')\n #do the binning of the data\n bins = np.arange(BIN_START, BIN_STOP, BIN_STEP)\n print(\"Binning starts: \", BIN_START, ' with step: ', BIN_STEP, ' ENDS: ', BIN_STOP)\n\n bin_i = np.digitize(datax, bins)\n bin_a = np.zeros(len(bins)+1)\n bin_occ = np.zeros(len(bins)+1)\n\n for i in range(datay.size):\n bin_a[bin_i[i]] += datay[i]\n bin_occ[bin_i[i]] += 1\n\n binsx, data_binned = [], []\n for i in range(bin_occ.size-1):\n if bin_occ[i] > 0:\n binsx.append(bins[i]-BIN_STEP/2)\n data_binned.append(bin_a[i]/bin_occ[i])\n\n #non_zero_i = bin_occ > 0\n #binsx = bins[non_zero_i] - BIN_STEP/2\n #data_binned = bin_a[non_zero_i]/bin_occ[non_zero_i]\n\n return binsx, data_binned","def domain_range(domain, _range=[0, 1], return_transform=False):\n\n if not return_transform:\n return interp1d([min(domain), max(domain)], [min(_range), max(_range)], bounds_error=False)\n else:\n m = interp1d([min(domain), max(domain)], [min(_range), max(_range)])\n return [float(m(v)) for v in domain] # Take float, else returns weird numpy.ndarray element","def yield_spectral_range(self) -> Tuple[float, float, float]:\n return [min(self.x), max(self.x), len(self.x)]","def norm_funct(x,min_x,max_x,a=-1,b=1):\n return (b-a)*((x-min_x)/(max_x - min_x)) + a","def scale_range(x, input_range, target_range):\n\n range = [np.amin(x), np.amax(x)]\n x_std = (x - input_range[0]) / (1.0*(input_range[1] - input_range[0]))\n x_scaled = x_std * (1.0*(target_range[1] - target_range[0])) + target_range[0]\n return x_scaled, range","def interval_prob(x1, x2, a, b):\n with mp.extradps(5):\n a, b = _validate_a_b(a, b)\n x1 = mp.mpf(x1)\n x2 = mp.mpf(x2)\n if x1 > x2:\n raise ValueError('x1 must not be greater than x2')\n return mp.betainc(a, b, x1, x2, regularized=True)","def normalize_wrt_x(self):\n\n x_min = min(self.x)\n x_max = max(self.x)\n y_min = min(self.y)\n\n x_range = x_max - x_min\n\n x = np.array(self.x)\n y = np.array(self.y)\n x -= x_min\n y -= y_min\n x = x / float(x_range)\n y = y / float(x_range)\n\n self.x = x.tolist()\n self.y = y.tolist()","def x_mesh(N,interval):\n (a,b) = interval\n h = (b-a)/N\n xmesh1=[a]\n for i in range(1,N):\n xmesh1.append(a+i*h)\n xmesh1.append(b)\n xmesh2=xmesh1[1:N]\n \n return xmesh1,xmesh2","def xi(a):\n return xrange(len(a))","def get_xrange_indices(self, lower, upper) -> Tuple[int, int]:\n lower_index = np.argmax(self.x >= lower)\n upper_index = np.argmax(self.x >= upper)\n return int(lower_index), int(upper_index)","def plot(self, center=0, xmin=-1, xmax=1):\n n = 200./self.eps\n x = concatenate(\n linspace(xmin, center-self.eps, 21),\n linspace(center-self.eps, center+self.eps, n+1),\n linspace(center+self.eps, xmax, 21))\n y = self(x)\n return x, y","def in_range(x, a, b):\n return (x >= a and x <= b) or (x <= a and x >= b)","def external2internal(xe,bounds):\n\n xi = np.empty_like(xe)\n\n for i,(v,bound) in enumerate(zip(xe,bounds)):\n \n a = bound[0] # minimum\n b = bound[1] # maximum\n\n if a == None and b == None: # No constraints\n xi[i] = v\n\n elif b == None: # only min\n xi[i] = np.sqrt( (v-a+1.)**2.-1 )\n\n elif a == None: # only max\n xi[i] = np.sqrt( (b-v+1.)**2.-1 )\n\n else: # both min and max\n xi[i] = np.arcsin( (2.*(v-a)/(b-a))-1.)\n\n return xi","def calcBRange(c,n=10):\n \n bMin = -abs(c)/2.0 \n bMax = abs(c)/2.0 \n return np.linspace(bMin,bMax,n)","def simulate_x_values(self, minimum = -10, maximum = 10, length = 100):\n return np.sort(np.random.uniform(minimum, maximum, length) )","def execute():\n # Create a list of evenly-spaced numbers over the range\n x_axis = np.linspace(0, 20, 100)\n plt.plot(x_axis, np.sin(x_axis)) # Plot the sine of each x point\n plt.show()","def query_range(tree, start_y, start_x, end_y, end_x):\n res = 0\n start_y -= 1\n\n while end_y > start_y:\n res += bit.query_range(tree[end_y], start_x, end_x)\n end_y -= (end_y & -end_y)\n\n while start_y > end_y:\n res -= bit.query_range(tree[start_y], start_x, end_x)\n start_y -= (start_y & -start_y)\n\n return res","def partition(X: np.ndarray, y: np.ndarray, num_partitions: int) -> XYList: # returns list of Xy (read more about function annotations) ????\n return list(\n zip(np.array_split(X, num_partitions), \n np.array_split(y, num_partitions))\n )","def eulers_method(f, y, dx, range):\n x = min(range)\n y_space = [y]\n x_space = [x]\n while x<=max(range):\n y += f(x, y)*dx\n x += dx\n x_space.append(x)\n y_space.append(y)\n return (x_space, y_space)","def drange(x, y, jump):\n while x < y:\n yield float(x)\n x += decimal.Decimal(jump)","def getScaleValues(a, x):\n raise NotImplementedError('getScaleValues not implemented')","def range14(self, nx, ny, x_des):\n [c_d, a1, output] = [self.component_dependency['y_14'], self.dependency_matrix, []]\n for i in range(ny):\n [sum_i, row] = [[], a1[3 * ny + i]]\n sum_i.append(np.sum(row))\n [assign, y] = [c_d[i], []]\n # :x_des = np.random.random_sample(4 * nx + 5 * ny) # this is an instance of the design vector\n [y.append(self.per_int.range_int([x_des[k]])) for k in range(4 * nx + 5 * ny) if row[k] == 1]\n output.append(np.sum(y) * 1 / sum_i)\n print (c_d)\n\n return output","def split(self, X: tp.ArrayLike, n: tp.Optional[int] = None, range_len: tp.Optional[float] = None,\n min_len: int = 1, start_idxs: tp.Optional[tp.ArrayLike] = None,\n end_idxs: tp.Optional[tp.ArrayLike] = None, **kwargs) -> RangesT:\n X = to_any_array(X)\n if isinstance(X, (pd.Series, pd.DataFrame)):\n index = X.index\n else:\n index = pd.Index(np.arange(X.shape[0]))\n\n # Resolve start_idxs and end_idxs\n if start_idxs is None and end_idxs is None:\n if range_len is None and n is None:\n raise ValueError(\"At least n, range_len, or start_idxs and end_idxs must be set\")\n if range_len is None:\n range_len = len(index) // n\n if 0 < range_len < 1:\n range_len = math.floor(range_len * len(index))\n start_idxs = np.arange(len(index) - range_len + 1)\n end_idxs = np.arange(range_len - 1, len(index))\n elif start_idxs is None or end_idxs is None:\n raise ValueError(\"Both start_idxs and end_idxs must be set\")\n else:\n if isinstance(start_idxs, pd.Index):\n start_idxs = np.asarray([find_first_occurrence(idx, index) for idx in start_idxs])\n else:\n start_idxs = np.asarray(start_idxs)\n if isinstance(end_idxs, pd.Index):\n end_idxs = np.asarray([find_first_occurrence(idx, index) for idx in end_idxs])\n else:\n end_idxs = np.asarray(end_idxs)\n\n # Filter out short ranges\n start_idxs, end_idxs = np.broadcast_arrays(start_idxs, end_idxs)\n range_lens = end_idxs - start_idxs + 1\n min_len_mask = range_lens >= min_len\n if not np.any(min_len_mask):\n raise ValueError(f\"There are no ranges that meet range_len>={min_len}\")\n start_idxs = start_idxs[min_len_mask]\n end_idxs = end_idxs[min_len_mask]\n\n # Evenly select n ranges\n if n is not None:\n if n > len(start_idxs):\n raise ValueError(f\"n cannot be bigger than the maximum number of ranges {len(start_idxs)}\")\n idxs = np.round(np.linspace(0, len(start_idxs) - 1, n)).astype(int)\n start_idxs = start_idxs[idxs]\n end_idxs = end_idxs[idxs]\n\n return split_ranges_into_sets(start_idxs, end_idxs, **kwargs)","def ticks(self, start, end, desired_ticks=8):\n if start == end or isnan(start) or isnan(end):\n return [start]\n min, max, delta = heckbert_interval(start, end, desired_ticks, enclose=True)\n return frange(min, max, delta)","def test_linear_interpolation_range(self):\n\n for x in [[1.0, 2.0, 4.0], [-20, -19, 0], numpy.arange(200) + 1000]:\n for y in [[5.0, 9.0], [100, 200, 10000]]:\n\n # Define ny by nx array with corresponding values\n A = numpy.zeros((len(x), len(y)))\n\n # Define values for each x, y pair as a linear function\n for i in range(len(x)):\n for j in range(len(y)):\n A[i, j] = linear_function(x[i], y[j])\n\n # Test that linearly interpolated points are correct\n xis = numpy.linspace(x[0], x[-1], 100)\n etas = numpy.linspace(y[0], y[-1], 100)\n points = combine_coordinates(xis, etas)\n\n vals = interpolate2d(x, y, A, points, mode='linear')\n refs = linear_function(points[:, 0], points[:, 1])\n assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)","def g_rosenbrock(x, a=1, b=100):\n\n g = np.array(\n [\n -2 * a - 4 * b * x[0] * (-x[0] ** 2 + x[1]) + 2 * x[0],\n b * (-2 * x[0] ** 2 + 2 * x[1]),\n ]\n )\n\n return g","def range34(self, nx, ny, x_des):\n [c_d, a1, output] = [self.component_dependency['y_34'], self.dependency_matrix, []]\n for i in range(ny):\n [sum_i, row] = [[], a1[11 * ny + 2 * nx + i]]\n sum_i.append(np.sum(row))\n [assign, y] = [c_d[i], []]\n # :x_des = np.random.random_sample(4 * nx + 5 * ny) # this is an instance of the design vector\n [y.append(self.per_int.range_int([x_des[k]])) for k in range(4 * nx + 5 * ny) if row[k] == 1]\n output.append(np.sum(y) * 1 / sum_i)\n\n return output","def _lagrange(x, y):\n x_col, x_row = np.meshgrid(x, x)\n diff_x = x_col - x_row + np.eye(len(x))\n n = len(x)\n indices = np.eye(n) == 0\n\n def P(x_ip):\n l = np.zeros((n, x_ip.shape[0]))\n for idx, idxs in enumerate(indices):\n l[idx] = np.prod((x_ip[:, None] - x[idxs]) / diff_x[idxs, idx], axis=1)\n\n return np.sum(y * l.T, axis=1)\n\n return P","def bapply(x,y,bins,func):\n \n assert bins[0] <= min(x),'range'\n assert bins[-1] > max(x),'range'\n\n bid = np.digitize(x,bins) \n nbins = bins.size-1\n yapply = np.zeros(nbins)\n\n for id in range(1,nbins):\n yb = y[bid==id]\n yapply[id-1] = func(yb)\n\n return yapply","def ticks(self, start, end, desired_ticks=8):\n if start == end or isnan(start) or isnan(end):\n return [start]\n min, max, delta = heckbert_interval(start, end, desired_ticks,\n nicefunc=self._nice_pow10,\n enclose = True)\n return frange(min, max, delta)","def generate_random_scatter(x_range, w, b, k):\n\tx_1 = []\n\ty_1 = []\n\tx_2 = []\n\ty_2 = []\n\tfor i in range(k):\n\t\txx = random.random() * (x_range[1] - x_range[0]) + x_range[0]\n\t\tx_1.append(xx)\n\t\tamplitude = random.randint(4, 15)\n\t\tyy = w * xx + b + amplitude\n\t\ty_1.append(yy)\n\n\t\txx = random.random() * (x_range[1] - x_range[0]) + x_range[0]\n\t\tx_2.append(xx)\n\t\tamplitude = random.randint(4, 15)\n\t\tyy = w * xx + b - amplitude\n\t\ty_2.append(yy)\n\treturn x_1, y_1, x_2, y_2","def inrange ( a , x , b ) :\n _a = float(a)\n _b = float(b)\n _x = float(x)\n return ( _a <= _x or isequal ( _a , _x ) ) and ( _x <= _b or isequal ( _x , _b ) )","def Calc(self, a, b, size):\n self.eq = lambda x: (60000/((b-a)/size*x+a))\n points = []\n names = [str(self.offset)]\n points.append(0)\n for j in range(1, int(size)):\n points.append(integrate.quad(self.eq,0,j)[0])\n names.append(str(points[-1]+self.offset))\n self.beatstr = ' '.join(names)\n return points","def get_xrange(self) -> np.array:\n # todo: ensure this functions work as well for y_values\n lower, upper = self.get_xrange_indices()\n return self.x[lower, upper + 1]","def startEndPoints(start, end, num):\n ll = np.linspace(0,1,num)\n xxs = start[0]*(1-ll)+end[0]*ll\n tts = start[1]*(1-ll)+end[1]*ll\n return( np.array([xxs, tts]) )","def g_batched_rosenbrock(\n x: np.ndarray, num_batches: int, a: np.ndarray, b: np.ndarray\n) -> np.ndarray:\n gall = np.zeros(2 * num_batches)\n for i in range(num_batches):\n gall[i * 2 : (i + 1) * 2] = g_rosenbrock(\n x[i * 2 : (i + 1) * 2], a[i], b[i]\n )\n\n return gall","def profileY(xdata, ydata, nbins, yrange=None, xrange=None, drop_nan=True):\n xmin, xmax = (np.min(xdata), np.max(xdata)) if xrange is None else xrange\n ymin, ymax = (np.min(ydata), np.max(ydata)) if yrange is None else yrange\n\n x_out = np.linspace(ymin, ymax, nbins+1)\n y_out = np.empty(nbins)\n y_err = np.empty(nbins)\n dx = np.diff(x_out)[0]\n\n selection = in_range(xdata, xmin, xmax) & in_range(ydata, ymin, ymax)\n xdata, ydata = xdata[selection], ydata[selection]\n for i in range(nbins):\n bin_data = np.extract(in_range(ydata, x_out[i], x_out[i+1]), xdata)\n y_out[i] = np.mean(bin_data)\n y_err[i] = np.std(bin_data) / bin_data.size**0.5\n x_out += dx / 2.\n x_out = x_out[:-1]\n if drop_nan:\n selection = ~(np.isnan(y_out) | np.isnan(y_err))\n x_out = x_out[selection]\n y_out = y_out[selection]\n y_err = y_err[selection]\n return x_out, y_out, y_err","def plot(self, aVals, bVals):\n with self.pt as sp:\n # Top subplot: The range of interest\n X = np.linspace(self.xMin, self.xMax, self.N)\n self.subplot(sp, X, aVals, bVals)\n # Bottom subplot: Positive X surrounding the range of\n # interest\n X = np.linspace(0, 2*self.xMax, self.N)\n sp.add_axvline(self.xMin)\n sp.add_axvline(self.xMax)\n self.subplot(sp, X, aVals, bVals, semilog=True)\n self.pt.show()","def get_x_in_range(self, start, target_func, max_distance, sort_func=None):\n if sort_func is None:\n targets = []\n for x in range(-max_distance, max_distance + 1):\n for y in range(-max_distance, max_distance + 1):\n distance = abs(x) + abs(y)\n if distance > max_distance:\n continue\n pos = Position(start.x + x, start.y + y)\n if target_func(pos, distance):\n targets.append(pos)\n return targets\n else:\n targets = PriorityQueue()\n for x in range(-max_distance, max_distance + 1):\n for y in range(-max_distance, max_distance + 1):\n distance = abs(x) + abs(y)\n if distance > max_distance:\n continue\n pos = Position(start.x + x, start.y + y)\n if target_func(pos, distance):\n targets.enqueue(sort_func(pos, distance), pos)\n return targets.to_list()","def from_inclusive(a, b):\n c = int(b > a)*2-1\n return range(a, b+c, c)","def get_xrange(self):\n return self.xvec[0], self.xvec[-1]","def function_to_XYs(func, fpars,\n Egrid=equal_bins(100),\n domainUnit='eV', domainName='energy_in', rangeUnit='b', rangeName='crossSection',\n accuracy=upperEps):\n return XYs1dModule.XYs1d.createFromFunction(\n XYs1d.defaultAxes(labelsUnits={\n XYs1dModule.yAxisIndex: (rangeName, rangeUnit),\n XYs1dModule.xAxisIndex: (domainName, domainUnit)}),\n Xs=Egrid,\n func=func,\n parameters=fpars,\n accuracy=accuracy,\n biSectionMax=20,\n checkForRoots=False,\n infill=1,\n safeDivide=1)","def val_split(a: Iterable, partitions: int, range_max: int, range_min: int = 0,\n size: bool = True) -> List[np.ndarray]:\n if size:\n n = int(np.ceil(range_max / partitions))\n splits = partitions\n else:\n n = partitions\n splits = (range_max - range_min) // partitions\n\n it = iter(a)\n it_current = next(it)\n ret_val = [[] for _ in range(n)]\n\n try:\n if isinstance(it_current, (tuple, list, np.ndarray)):\n it_current, it_value = it_current\n for i in range(n):\n for j in range(splits):\n split_current = (partitions + 1) * i + j\n while it_current <= split_current:\n ret_val[i].append([it_current, it_value])\n it_current, it_value = next(it)\n continue\n return list(map(np.array, ret_val))\n for i in range(n):\n for j in range(splits):\n split_current = (partitions + 1) * i + j\n while it_current <= split_current:\n ret_val[i].append(it_current)\n it_current = next(it)\n continue\n except StopIteration:\n return list(map(np.array, ret_val))","def _ScatterXUniformlyExtendedRange(self, num_points, lattice_sizes,\n input_dims):\n x = []\n for _ in range(num_points):\n point = [\n np.random.random() * (lattice_sizes + 1.0) - 1.0\n for _ in range(input_dims)\n ]\n x.append(np.asarray(point))\n if input_dims == 1:\n x.sort()\n return x","def pmm(a, *b):\n ind = np.isfinite(a)\n try:\n ans = [[ np.min(a[ind]), np.max(a[ind]) ]]\n except TypeError:\n a_tmp = np.asarray(a)\n ans = [[ np.min(a_tmp[ind]), np.max(a_tmp[ind]) ]]\n for val in b:\n ind = np.isfinite(val)\n try:\n ans.append( [np.min(val[ind]), np.max(val[ind])] )\n except TypeError:\n val_tmp = np.asarray(val)\n ans.append( [np.min(val_tmp[ind]), np.max(val_tmp[ind])] )\n return ans","def plot(self, xmin=-1, xmax=1):\n if xmin > self.L or xmax < self.R:\n raise ValueError('xmin=%g > L=%g or xmax=%g < R=%g is meaningless for plot' % (xmin, self.L, xmax, self.R))\n\n if self.eps == 0:\n return [xmin, L, L, R, R, xmax], [0, 0, 1, 1, 0, 0]\n else:\n n = 200./self.eps\n x = concatenate(\n linspace(xmin, self.L-self.eps, 21),\n linspace(self.L-self.eps, self.R+self.eps, n+1),\n linspace(self.R+self.eps, xmax, 21))\n y = self(x)\n return x, y","def domain(self):\n lower, upper = sorted((self.x1, self.x2))\n return FloatRange(lower=lower, upper=upper)","def toPointwise_withLinearXYs( self, **kwargs ) :\n\n return( XYs1d( data = [ [ self.domainMin, self.value ], [ self.domainMax, self.value ] ], axes = self.axes ) )","def XbarTXbar(d, A=None, B=None):\n n = len(d) + 1\n if A is None: A = np.arange(n-1)\n if B is None: B = np.arange(n-1)\n V = np.empty((len(A), len(B)))\n for i, j in itt.product(range(len(A)), range(len(B))):\n a, b = A[i], B[j]\n u, v = _minmax(a, b)\n V[i, j] = d[a] * d[b] * (u * (n - v)) / n\n return V","def sum_range(self, lower, upper):\n if upper>self.upper:\n upper=self.upper\n if lower<self.lower:\n lower = self.lower\n\n i_l = int(np.floor((lower-self.lower)/self._dx))\n i_u = int(np.floor((upper-self.lower)/self._dx))\n total = 0.0\n for i in range(i_l,i_u):\n total+= self.y[i]\n return total","def get_range(min, max, intervals, log):\n if not log:\n min = float(min)\n max = float(max)\n difference = max-min\n step_size = difference/intervals\n output = [min + i*step_size for i in range(intervals+1)]\n return output\n else:\n from math import log10 as log\n log_min = log(min)\n log_max = log(max)\n log_difference = log_max - log_min\n step_size = log_difference/intervals\n output = [pow(10, log_min + i*step_size) for i in range(intervals+1)]\n return output","def _grid_from_X(X, percentiles, grid_resolution, custom_range):\n values = []\n for feature in X.columns:\n if feature in custom_range:\n # Use values in the custom range\n feature_range = custom_range[feature]\n if not isinstance(feature_range, (np.ndarray, pd.Series)):\n feature_range = np.array(feature_range)\n if feature_range.ndim != 1:\n raise ValueError(\n \"Grid for feature {} is not a one-dimensional array. Got {}\"\n \" dimensions\".format(feature, feature_range.ndim)\n )\n axis = feature_range\n else:\n uniques = np.unique(X.loc[:, feature])\n if uniques.shape[0] < grid_resolution:\n # feature has low resolution use unique vals\n axis = uniques\n else:\n # create axis based on percentiles and grid resolution\n emp_percentiles = mquantiles(\n X.loc[:, feature], prob=percentiles, axis=0\n )\n if np.allclose(emp_percentiles[0], emp_percentiles[1]):\n raise ValueError(\n \"percentiles are too close to each other, \"\n \"unable to build the grid. Please choose percentiles \"\n \"that are further apart.\"\n )\n axis = np.linspace(\n emp_percentiles[0],\n emp_percentiles[1],\n num=grid_resolution,\n endpoint=True,\n )\n values.append(axis)\n\n return _cartesian(values), values","def create_data(f, x_vals):\n y_vals = []\n for i in x_vals:\n y_vals.append(f(x_vals[i]))\n return np.array(y_vals)","def local_mean(x,y, n=10):\n\n xx, yy = (list(t) for t in zip(*sorted(zip(x, y)))) # sort x and y after x\n\n m = int(len(x)/n) # Number of data points in each group\n\n x_o, y_o = [], []\n x_sum, y_sum, v = 0, 0, 0\n j=1\n for i in range(len(x)):\n if v < m:\n x_sum += xx[i]\n y_sum += yy[i]\n v += 1\n else:\n x_o.append(x_sum/m)\n y_o.append(y_sum/m)\n x_sum, y_sum, v = 0, 0, 0\n j += 1\n\n return x_o, y_o","def create_graph_from_lambda(f, xrange):\n out = []\n for x in xrange:\n out.append(f(x))\n return out","def data_range(x):\n return max(x)-min(x)","def averages(x,y):\n new_x = [x[0]]\n new_y = []\n\n cur_x = new_x[0]\n cur_ys = []\n for x_i, y_i in zip(x,y):\n if x_i == cur_x:\n cur_ys.append(y_i)\n else:\n new_y.append( sum(cur_ys)/float(len(cur_ys) ) )\n new_x.append( x_i )\n cur_ys = [y_i]\n cur_x = x_i\n new_y.append( sum(cur_ys)/float(len(cur_ys) ) )\n return new_x, new_y","def range24(self, nx, ny, x_des):\n [c_d, a1, output] = [self.component_dependency['y_24'], self.dependency_matrix, []]\n for i in range(ny):\n [sum_i, row] = [[], a1[7 * ny + nx + i]]\n sum_i.append(np.sum(row))\n [assign, y] = [c_d[i], []]\n # :x_des = np.random.random_sample(4 * nx + 5 * ny) # this is an instance of the design vector\n [y.append(self.per_int.range_int([x_des[k]])) for k in range(4 * nx + 5 * ny) if row[k] == 1]\n output.append(np.sum(y) * 1 / sum_i)\n\n return output","def makeLinearFunc(xList, startXY, endXY, middleXY=None):\r\n\r\n \"\"\" Make one or two linear functions from given coordinates\r\n and return the values (as a dict) for the x-values found in\r\n the xList. \"\"\"\r\n\r\n x1, y1 = startXY\r\n try: x2, y2 = middleXY\r\n except: pass\r\n x3, y3 = endXY\r\n\r\n # Always sort list first.\r\n xList.sort()\r\n # Don't extrapolate values - only calculate intermediate values!\r\n xList = xList[xList.index(x1):xList.index(x3)+1]\r\n\r\n # The dict holding the final X- and Y-values.\r\n xyDict = {}\r\n\r\n # Iterate through all distances and stop when distUnit==middleX\r\n # and/or when distUnit/100==endX.\r\n for x in xList:\r\n # Define the straight line\r\n # Based on: f(x) = y1 + [(y2 - y1) / (x2 - x1)]·(x - x1)\r\n # And then assign the value to the xyList\r\n\r\n # If no middle was defined, give values based on two equations.\r\n if middleXY != None:\r\n if x<=x2:\r\n val = y1 + ( float((y2 - y1))/(x2 - x1) ) * (x - x1)\r\n else:\r\n val = y2 + ( float((y3 - y2))/(x3 - x2) ) * (x - x2)\r\n\r\n # Else, give all values based on one equation.\r\n else:\r\n val = y1 + ( float((y3 - y1)) / (x3 - x1) ) * (x - x1)\r\n\r\n xyDict[x] = round(val, 2) # The y-value is rounded to 2 decimals\r\n if x >= x3: # don't define influence beyond end point.\r\n break\r\n\r\n # Return a dict with y-values keyed by x-values.\r\n return xyDict","def interval_split(a,b,split_ps):\n ps = [a] + [s for s in sorted(split_ps) if a < s < b] + [b]\n return [(p1,p2) for p1,p2 in zip(ps,ps[1:])]","def expand_ranges(ranges):\n for low, high in low_high_pairs:\n for j in range(low, high+1):\n yield j","def compute_rlzn_ensemble(fopen_list, var_list, range_list): #{{{\n\n sumprod = 0.0\n\n for afile in fopen_list:\n prodvars = 1.0\n for avar,arange in zip(var_list,range_list):\n if isinstance(avar,str):\n # open value in netCDF4 database\n var_value = afile.variables[avar]\n else:\n var_value = avar\n \n if arange is None:\n if hasattr(var_value, \"__len__\"): \n try:\n prodvars *= var_value[:]\n except:\n tmp = var_value[:]\n prodvars *= tmp[np.newaxis,:]\n del tmp\n else:\n prodvars *= var_value\n else:\n prodvars *= var_value[arange]\n\n sumprod += prodvars\n\n # compute ensemble with unbiased estimator\n ensmbl = sumprod/(len(fopen_list)-1)\n\n return ensmbl #}}}"],"string":"[\n \"def projectionX(xdata, ydata, nbins, xrange=None, yrange=None):\\n xmin, xmax = (np.min(xdata), np.max(xdata)) if xrange is None else xrange\\n ymin, ymax = (np.min(ydata), np.max(ydata)) if yrange is None else yrange\\n\\n x_out = np.linspace(xmin, xmax, nbins+1)\\n y_out = np.empty(nbins)\\n dx = np.diff(x_out)[0]\\n\\n selection = in_range(xdata, xmin, xmax) & in_range(ydata, ymin, ymax)\\n xdata, ydata = xdata[selection], ydata[selection]\\n for i in range(nbins):\\n bin_data = np.extract(in_range(xdata, x_out[i], x_out[i+1]), ydata)\\n y_out[i] = bin_data.size\\n x_out += dx / 2.\\n x_out = x_out[:-1]\\n return x_out, y_out\",\n \"def neural_dist(func_a, func_b, x_range):\\n func_a = func_a.numpy()\\n func_b = func_b.numpy()\\n return func_a.fit(func_b, x_range=x_range)[1]\",\n \"def calculate_ranges(a, b):\\n try:\\n ranges = list(range(0, a, a//b))\\n if ranges[-1] != a:\\n ranges.append(a)\\n return ranges\\n except ValueError:\\n return [0, a]\",\n \"def profileX(xdata, ydata, nbins, xrange=None, yrange=None, drop_nan=True):\\n xmin, xmax = (np.min(xdata), np.max(xdata)) if xrange is None else xrange\\n ymin, ymax = (np.min(ydata), np.max(ydata)) if yrange is None else yrange\\n\\n x_out = np.linspace(xmin, xmax, nbins+1)\\n y_out = np.empty(nbins)\\n y_err = np.empty(nbins)\\n dx = np.diff(x_out)[0]\\n\\n selection = in_range(xdata, xmin, xmax) & in_range(ydata, ymin, ymax)\\n xdata, ydata = xdata[selection], ydata[selection]\\n for i in range(nbins):\\n bin_data = np.extract(in_range(xdata, x_out[i], x_out[i+1]), ydata)\\n y_out[i] = np.mean(bin_data)\\n y_err[i] = np.std(bin_data) / bin_data.size**0.5\\n x_out += dx / 2.\\n x_out = x_out[:-1]\\n if drop_nan:\\n selection = ~(np.isnan(y_out) | np.isnan(y_err))\\n x_out = x_out[selection]\\n y_out = y_out[selection]\\n y_err = y_err[selection]\\n return x_out, y_out, y_err\",\n \"def plot_linear(x_range, w, b):\\n\\tplt.plot(x_range, x_range * w + b)\",\n \"def fit_to_range(val: float, a: float, b: float, a1: float, b1: float) -> float:\\n new_value = ((val - a) / (b - a)) * (b1 - a1) + a1\\n return new_value\",\n \"def rangeX(iterations):\\n if not isinstance(iterations, (tuple)):\\n raise AttributeError\\n return itertools.product(*map(range, iterations))\",\n \"def to_arrays(self, xmin=None, xmax=None):\\n sidx = 0 if xmin is None else np.searchsorted(self.xvec, [xmin])[0]\\n eidx = len(self.xvec) if xmax is None else np.searchsorted(self.xvec, [xmax])[0]\\n\\n if eidx < len(self.xvec) and self.xvec[eidx] == xmax:\\n eidx += 1\\n\\n xtemp = self.xvec[sidx:eidx]\\n if xmin is not None and (len(xtemp) == 0 or xtemp[0] != xmin):\\n np.insert(xtemp, 0, [xmin])\\n if xmax is not None and (len(xtemp) == 0 or xtemp[-1] != xmax):\\n np.append(xtemp, [xmax])\\n return xtemp, self(xtemp)\",\n \"def grid(x, y):\\n return product(xrange(1, x+1), xrange(1, y+1))\",\n \"def slice_data(xdata, ydata, x_range):\\n\\tdata = zip(xdata, ydata)\\n\\tsliced_data = [d for d in data if d[0] >= x_range[0] and d[0] <= x_range[1]]\\n\\treturn array(zip(*sliced_data))\",\n \"def _lagrange2(x, y):\\n\\n def P(x_ip):\\n total = 0\\n n = len(x)\\n for i in range(0, n):\\n\\n def g(i, n):\\n tot_mul = 1\\n for j in range(0, n):\\n if i == j:\\n continue\\n if x[i] == x[j]:\\n log.fatal(\\n f\\\"Leads to division by zero (x = {x[i]}). Identical values given in x array. \\\"\\n \\\"For example by using Lagrange interpolation for precise orbit, \\\"\\n \\\"check if identical observation epochs are given in SP3 file\\\"\\n )\\n tot_mul *= (x_ip - x[j]) / float(x[i] - x[j])\\n return tot_mul\\n\\n total += y[i] * g(i, n)\\n return total\\n\\n return P\",\n \"def make_uniform_x(self, x_resolution, min_x = None, max_x = None, bin_above = 2.0, **kwargs):\\n \\n if min_x is None or max_x is None:\\n a, b = self.get_min_max_x(**kwargs)\\n if min_x is None:\\n min_x = a\\n if max_x is None:\\n max_x = b\\n \\n new_x = numpy.arange(min_x, max_x + x_resolution / 2, x_resolution)\\n \\n for m in range(len(self.mess)):\\n if m not in exclude and self.mess[m][\\\"class\\\"] not in exclude:\\n resolution = (numpy.amax(m.x) - numpy.amin(m.x)) / len(m.x)\",\n \"def calculate(\\n expression: str, symmetrical_bounds: Union[int, float] = 10\\n ) -> Tuple[np.ndarray, np.ndarray]:\\n symmetrical_bounds = abs(symmetrical_bounds)\\n x = np.arange(-symmetrical_bounds, symmetrical_bounds, symmetrical_bounds / 50)\\n expr = parse_expr(expression)\\n x_symbol = Symbol(\\\"x\\\")\\n\\n y = np.array([expr.subs({x_symbol: x_point}).evalf() for x_point in x])\\n\\n return x, y\",\n \"def scale(x, a=5, b=10, xmin=-1, xmax=1):\\n return (b - a)*(x - xmin)/(xmax - xmin) + a\",\n \"def projectionY(xdata, ydata, nbins, yrange=None, xrange=None):\\n xmin, xmax = (np.min(xdata), np.max(xdata)) if xrange is None else xrange\\n ymin, ymax = (np.min(ydata), np.max(ydata)) if yrange is None else yrange\\n\\n x_out = np.linspace(ymin, ymax, nbins+1)\\n y_out = np.empty(nbins)\\n dx = np.diff(x_out)[0]\\n\\n selection = in_range(xdata, xmin, xmax) & in_range(ydata, ymin, ymax)\\n xdata, ydata = xdata[selection], ydata[selection]\\n for i in range(nbins):\\n bin_data = np.extract(in_range(ydata, x_out[i], x_out[i+1]), xdata)\\n y_out[i] = bin_data.size\\n x_out += dx / 2.\\n x_out = x_out[:-1]\\n return x_out, y_out\",\n \"def create_grid(xlim, ylim, step):\\n x_range = np.arange(xlim[0], xlim[1], step)\\n y_range = np.arange(ylim[0], ylim[1], step)\\n return x_range, y_range\",\n \"def calc(x_list):\\n\\n y_list = [x**2 + 2*x + 1 for x in x_list]\\n\\n return y_list\",\n \"def plot(self, a=None, b=None):\\n\\n # === choose reasonable interval if [a, b] not specified === #\\n if a is None:\\n a = self.observations.min() - self.observations.std()\\n if b is None:\\n b = self.observations.max() + self.observations.std()\\n\\n # === generate plot === #\\n x_vals = np.linspace(a, b, num=100)\\n f = np.vectorize(self.__call__)\\n plt.plot(x_vals, f(x_vals))\\n plt.show()\",\n \"def scale(x_range=1, y_range=1):\\r\\n x = rand_val(x_range)\\r\\n y = rand_val(y_range)\\r\\n return np.array(((x, 0, 0),\\r\\n (0, y, 0),\\r\\n (0, 0, 1)), dtype=np.float)\",\n \"def plot(self, center=0, xmin=-1, xmax=1):\\n if self.eps == 0:\\n return [xmin, center, center, xmax], [0, 0, 1, 1]\\n else:\\n n = 200./self.eps\\n x = concatenate(\\n linspace(xmin, center-self.eps, 21),\\n linspace(center-self.eps, center+self.eps, n+1),\\n linspace(center+self.eps, xmax, 21))\\n y = self(x)\\n return x, y\",\n \"def project_weights_and_nodes(a, b, unit_weights, unit_nodes):\\n\\n\\t# project onto interval [a,b]\\n\\tnodes = 0.5*(b-a)*unit_nodes + 0.5*(a+b)\\n\\tweights = 0.5*(b-a)*unit_weights\\n\\n\\treturn weights, nodes\",\n \"def toPointwise_withLinearXYs( self, accuracy, biSectionMax = 16, **kwargs ) :\\n\\n if( accuracy < 1e-6 ) : accuracy = 1e-6\\n if( accuracy > 0.1 ) : accuracy = 0.1\\n\\n P, n = [], 1000\\n for i in xrange( n + 1 ) :\\n x = ( ( n - i ) * self.domainMin + self.domainMax * i ) / n\\n P.append( [ x, self.evaluate( x ) ] )\\n axes = axesModule.axes( )\\n yUnit = self.getAxisUnitSafely( 0 )\\n xUnit = self.getAxisUnitSafely( 1 )\\n axes[0] = axesModule.axis( 'y(x)', 0, yUnit )\\n axes[1] = axesModule.axis( 'x', 1, xUnit )\\n Pclass = self.toLinearXYsClass()\\n P = Pclass( P, accuracy = accuracy, axes = axes )\\n return( P.thin( accuracy = accuracy ) )\",\n \"def data_range(xs: List[float]) -> float:\\n return max(xs) - min(xs)\",\n \"def getCellData(X, y, min0, max0, min1, max1):\\n Xcell = []\\n ycell = []\\n\\n for x,label in zip(X, y):\\n if (x[0] >= min0) and (x[0] < max0) and (x[1] >= min1) and (x[1] < max1):\\n Xcell.append(x)\\n ycell.append(label)\\n\\n return np.array(Xcell), np.array(ycell)\",\n \"def makeCrossPlotX(f,g):\\n x = zerofloat(n1,n2)\\n y = zerofloat(n1,n2)\\n class Loop(Parallel.LoopInt):\\n def compute(self,i2):\\n for i1 in range(1,n1-1):\\n x[i2][i1] = 0.5*(f[i2][i1+1]-f[i2][i1-1])\\n y[i2][i1] = g[i2][i1]-f[i2][i1]\\n Parallel.loop(n2,Loop())\\n return x,y\",\n \"def __call__(self, x, y):\\n xa = np.asarray(x)\\n ya = np.asarray(y)\\n return (self._evaluate(xa.flatten(), ya.flatten())).reshape(xa.shape)\",\n \"def get_densities(\\n x: np.ndarray,\\n y: np.ndarray,\\n nx: int,\\n ny: int,\\n x_range: Tuple = (0, 100),\\n y_range: Tuple = (0, 100),\\n n: int = 30,\\n) -> np.ndarray:\\n\\n x_values = np.linspace(x_range[0], x_range[1], nx)\\n y_values = np.linspace(y_range[0], y_range[1], ny)\\n\\n density = np.empty((nx, ny))\\n tree = get_kdtree(x, y)\\n\\n for x in tqdm(range(nx)):\\n for y in range(ny):\\n density[x, y] = get_density_from_neighbours(\\n x_values[x], y_values[y], tree, n\\n )\\n\\n return density, tree\",\n \"def coords_in_range(self, anchor, steps):\\n coords = list()\\n x_low = -steps\\n x_high = steps\\n\\n #Generate using an axial formula to make it easier\\n #calculate z via the other two and throw away ones that aren't in bounds\\n for x in range(x_low, x_high+1):\\n for y in range(max(-steps, -x-steps), min(steps, -x+steps)+1):\\n z = -x - y\\n coords.append(anchor+self.coord(x, y, z))\\n return coords\",\n \"def alpha_range(x0, x1, x_min, x_max):\\n if x0 == x1:\\n raise ValueError('x1 and x2 should be different, get {} and {}'.format(x0, x1))\\n alpha_x1 = (x_min - x0) / (x1 - x0)\\n alpha_x2 = (x_max - x0) / (x1 - x0)\\n alpha_min = max(0, min(alpha_x1, alpha_x2))\\n alpha_max = min(1, max(alpha_x1, alpha_x2))\\n return alpha_min, alpha_max\",\n \"def nll(x, a, b):\\n x = _validate_x_bounds(x, low=0, high=1, strict_low=True, strict_high=True)\\n return -mp.fsum([logpdf(t, a, b) for t in x])\",\n \"def lin_scale( val, x1, y1, x2, y2 ):\\r\\n x_range = (x2 - x1)\\r\\n new_val = 0\\r\\n if x_range is 0:\\r\\n new_val = y1\\r\\n else:\\r\\n y_range = ( y2 - y1 )\\r\\n new_val = ( ( ( val - x1 ) * y_range ) / x_range ) + y1\\r\\n\\r\\n return new_val\",\n \"def monte_carlo_integration(f, n, a, b, ret_arrays=False):\\n x = np.random.uniform(0, 1, n)*(b-a)+a\\n f_array = f(x)\\n\\n positive_x = x[f_array >= 0]\\n negative_x = x[f_array < 0]\\n if positive_x.size > 0:\\n h = np.max(f_array)\\n else:\\n h = np.max(-f_array)\\n \\n y_positive = np.random.uniform(0, 1, positive_x.size)*h\\n y_negative = np.random.uniform(0, 1, negative_x.size)*h\\n \\n xy_indices_below = y_positive <= f(positive_x)\\n xy_indices_above = y_negative <= -f(negative_x)\\n n_inside_below = y_positive[xy_indices_below]\\n n_inside_above = -y_negative[xy_indices_above]\\n \\n if ret_arrays:\\n n_inside_x = np.append(positive_x[xy_indices_below],negative_x[xy_indices_above])\\n n_inside_y = np.append(n_inside_below, n_inside_above)\\n return n_inside_x, n_inside_y\\n \\n return h*(b-a)*(n_inside_below.size-n_inside_above.size)/(n)\",\n \"def bws(x, y, **kwargs):\\n\\tx.sort()\\n\\ty.sort()\\n\\tnpx = np.array(x)\\n\\tnpy = np.array(y)\\n\\n\\txs = np.unique(npx)\\n\\tys = np.unique(npy)\\n\\txys = set(xs).union(set(ys))\\n\\taxy = np.array(list(xys))\\n\\taxy.sort()\\n\\n\\tG = np.array([len(axy[np.where(axy <= xi)]) for xi in xs])\\n\\tH = np.array([len(axy[np.where(axy <= yi)]) for yi in ys])\\n\\n\\tn = len(G)\\n\\tm = len(H)\\n\\tfn = float(n)\\n\\tfm = float(m)\\n\\n\\tN = np.linspace(1,n,num=n)\\n\\tM = np.linspace(1,m,num=m)\\n\\n\\txt1 = np.power(G - N*(fm + fn)/fn, 2.0)\\n\\txtt = N/(fn+1.0)\\n\\txt2 = xtt*(1 - xtt)*(fm * (fm+fn)/fn)\\n\\tBx = np.sum(xt1/xt2)/fn\\n\\t\\n\\tyt1 = np.power(H - M*(fm + fn)/fm, 2.0)\\n\\tytt = M/(fm+1.0)\\n\\tyt2 = ytt*(1 - ytt)*(fn * (fm+fn)/fm)\\n\\tBy = np.sum(yt1/yt2)/fm\\n\\n\\tB = (Bx+By)/2.0\\n\\n\\tprint \\\"B = \\\", B\\n\\t\\n\\tJ = 3\\n\\tif \\\"j\\\" in kwargs:\\n\\t\\tJ = kwargs[\\\"j\\\"]\\n\\t\\n\\treturn compute_xi(B, J)\",\n \"def getx(v, lb, ub, i, B):\\r\\n x = lb + np.multiply((ub - lb), v)\\r\\n x[i] = B - (x.sum() - x[i])\\r\\n # Test if variable x[i] is within the bounds\\r\\n if x[i] <= ub[i] and x[i] >= lb[i]:\\r\\n return x\\r\\n else:\\r\\n return np.array([])\",\n \"def generate_points(r_squared, min_val=5, max_val=100):\\n r = sqrt(r_squared)\\n x_list = []\\n lb_list = []\\n ub_list = []\\n for i in range(min_val, max_val):\\n x_list.append(i)\\n lb, ub = pearson_confidence(r, i)\\n lb_list.append(lb)\\n ub_list.append(ub)\\n return np.array(x_list), np.array(lb_list) ** 2, np.array(ub_list) ** 2\",\n \"def axis_range ( xmin , xmax , delta = 0.05 , log = False ) :\\n xmn = min ( xmin , xmax )\\n xmx = max ( xmin , xmax )\\n \\n import math\\n \\n ## 1) special case\\n if isequal ( xmn , xmx ) :\\n return math.floor ( xmn - 0.1 ) , math.ceil ( xmx + 0.1 ) \\n\\n ## 2) special case\\n if islong ( xmn - 0.5 ) and islong ( xmn + 0.5 ) :\\n return math.floor ( xmn - 0.1 ) , math.ceil ( xmx + 0.1 ) \\n\\n d = xmx - xmn\\n \\n if 0 <= xmn < xmx :\\n \\n xmin = max ( 0 , xmn - delta * d )\\n xmax = xmx + delta * d \\n \\n elif xmn < xmx <= 0 :\\n \\n xmin = xmn - delta * d \\n xmax = max ( 0 , xmx + delta * d )\\n \\n elif xmn < 0 < xmx :\\n \\n xmin = ( 1 + delta ) * xmn \\n xmax = ( 1 + delta ) * xmx\\n \\n else : \\n \\n xmin = xmn - delta * d \\n xmax = xmx + delta * d \\n\\n N = 3\\n \\n a1 , b1 = frexp10 ( xmin )\\n a2 , b2 = frexp10 ( xmax )\\n\\n b1 -= N \\n b2 -= N \\n \\n xmin = math.floor ( a1 * ( 10**N ) ) * ( 10 ** b1 )\\n xmax = math.ceil ( a2 * ( 10**N ) ) * ( 10 ** b2 )\\n \\n return xmin , xmax\",\n \"def ni_range(x0, x1, dx=1):\\n # sanity check arguments\\n if dx==0:\\n raise ValueError(\\\"invalid parameters: dx==0\\\")\\n if x0>x1 and dx>=0:\\n raise ValueError(\\\"invalid parameters: x0>x1 and dx>=0\\\")\\n if x0<x1 and dx<=0:\\n raise ValueError(\\\"invalid parameters: x0<x1 and dx<=0\\\")\\n \\n # generate range list\\n range_list = []\\n x = x0\\n while x < x1:\\n range_list.append(x)\\n x += dx\\n return range_list\",\n \"def sampler(xaxis, yaxis, vals, x, y):\\n i = 0\\n while xaxis[i] < x:\\n i += 1\\n j = 0\\n while yaxis[j] < y:\\n j += 1\\n return vals[i, j]\",\n \"def felix_binning(xs, ys, delta=1):\\n \\n #bins = np.arange(start, end, delta)\\n #occurance = np.zeros(start, end, delta)\\n BIN_STEP = delta\\n BIN_START = xs.min()\\n BIN_STOP = xs.max()\\n\\n indices = xs.argsort()\\n datax = xs[indices]\\n datay = ys[indices]\\n\\n print(\\\"In total we have: \\\", len(datax), ' data points.')\\n #do the binning of the data\\n bins = np.arange(BIN_START, BIN_STOP, BIN_STEP)\\n print(\\\"Binning starts: \\\", BIN_START, ' with step: ', BIN_STEP, ' ENDS: ', BIN_STOP)\\n\\n bin_i = np.digitize(datax, bins)\\n bin_a = np.zeros(len(bins)+1)\\n bin_occ = np.zeros(len(bins)+1)\\n\\n for i in range(datay.size):\\n bin_a[bin_i[i]] += datay[i]\\n bin_occ[bin_i[i]] += 1\\n\\n binsx, data_binned = [], []\\n for i in range(bin_occ.size-1):\\n if bin_occ[i] > 0:\\n binsx.append(bins[i]-BIN_STEP/2)\\n data_binned.append(bin_a[i]/bin_occ[i])\\n\\n #non_zero_i = bin_occ > 0\\n #binsx = bins[non_zero_i] - BIN_STEP/2\\n #data_binned = bin_a[non_zero_i]/bin_occ[non_zero_i]\\n\\n return binsx, data_binned\",\n \"def domain_range(domain, _range=[0, 1], return_transform=False):\\n\\n if not return_transform:\\n return interp1d([min(domain), max(domain)], [min(_range), max(_range)], bounds_error=False)\\n else:\\n m = interp1d([min(domain), max(domain)], [min(_range), max(_range)])\\n return [float(m(v)) for v in domain] # Take float, else returns weird numpy.ndarray element\",\n \"def yield_spectral_range(self) -> Tuple[float, float, float]:\\n return [min(self.x), max(self.x), len(self.x)]\",\n \"def norm_funct(x,min_x,max_x,a=-1,b=1):\\n return (b-a)*((x-min_x)/(max_x - min_x)) + a\",\n \"def scale_range(x, input_range, target_range):\\n\\n range = [np.amin(x), np.amax(x)]\\n x_std = (x - input_range[0]) / (1.0*(input_range[1] - input_range[0]))\\n x_scaled = x_std * (1.0*(target_range[1] - target_range[0])) + target_range[0]\\n return x_scaled, range\",\n \"def interval_prob(x1, x2, a, b):\\n with mp.extradps(5):\\n a, b = _validate_a_b(a, b)\\n x1 = mp.mpf(x1)\\n x2 = mp.mpf(x2)\\n if x1 > x2:\\n raise ValueError('x1 must not be greater than x2')\\n return mp.betainc(a, b, x1, x2, regularized=True)\",\n \"def normalize_wrt_x(self):\\n\\n x_min = min(self.x)\\n x_max = max(self.x)\\n y_min = min(self.y)\\n\\n x_range = x_max - x_min\\n\\n x = np.array(self.x)\\n y = np.array(self.y)\\n x -= x_min\\n y -= y_min\\n x = x / float(x_range)\\n y = y / float(x_range)\\n\\n self.x = x.tolist()\\n self.y = y.tolist()\",\n \"def x_mesh(N,interval):\\n (a,b) = interval\\n h = (b-a)/N\\n xmesh1=[a]\\n for i in range(1,N):\\n xmesh1.append(a+i*h)\\n xmesh1.append(b)\\n xmesh2=xmesh1[1:N]\\n \\n return xmesh1,xmesh2\",\n \"def xi(a):\\n return xrange(len(a))\",\n \"def get_xrange_indices(self, lower, upper) -> Tuple[int, int]:\\n lower_index = np.argmax(self.x >= lower)\\n upper_index = np.argmax(self.x >= upper)\\n return int(lower_index), int(upper_index)\",\n \"def plot(self, center=0, xmin=-1, xmax=1):\\n n = 200./self.eps\\n x = concatenate(\\n linspace(xmin, center-self.eps, 21),\\n linspace(center-self.eps, center+self.eps, n+1),\\n linspace(center+self.eps, xmax, 21))\\n y = self(x)\\n return x, y\",\n \"def in_range(x, a, b):\\n return (x >= a and x <= b) or (x <= a and x >= b)\",\n \"def external2internal(xe,bounds):\\n\\n xi = np.empty_like(xe)\\n\\n for i,(v,bound) in enumerate(zip(xe,bounds)):\\n \\n a = bound[0] # minimum\\n b = bound[1] # maximum\\n\\n if a == None and b == None: # No constraints\\n xi[i] = v\\n\\n elif b == None: # only min\\n xi[i] = np.sqrt( (v-a+1.)**2.-1 )\\n\\n elif a == None: # only max\\n xi[i] = np.sqrt( (b-v+1.)**2.-1 )\\n\\n else: # both min and max\\n xi[i] = np.arcsin( (2.*(v-a)/(b-a))-1.)\\n\\n return xi\",\n \"def calcBRange(c,n=10):\\n \\n bMin = -abs(c)/2.0 \\n bMax = abs(c)/2.0 \\n return np.linspace(bMin,bMax,n)\",\n \"def simulate_x_values(self, minimum = -10, maximum = 10, length = 100):\\n return np.sort(np.random.uniform(minimum, maximum, length) )\",\n \"def execute():\\n # Create a list of evenly-spaced numbers over the range\\n x_axis = np.linspace(0, 20, 100)\\n plt.plot(x_axis, np.sin(x_axis)) # Plot the sine of each x point\\n plt.show()\",\n \"def query_range(tree, start_y, start_x, end_y, end_x):\\n res = 0\\n start_y -= 1\\n\\n while end_y > start_y:\\n res += bit.query_range(tree[end_y], start_x, end_x)\\n end_y -= (end_y & -end_y)\\n\\n while start_y > end_y:\\n res -= bit.query_range(tree[start_y], start_x, end_x)\\n start_y -= (start_y & -start_y)\\n\\n return res\",\n \"def partition(X: np.ndarray, y: np.ndarray, num_partitions: int) -> XYList: # returns list of Xy (read more about function annotations) ????\\n return list(\\n zip(np.array_split(X, num_partitions), \\n np.array_split(y, num_partitions))\\n )\",\n \"def eulers_method(f, y, dx, range):\\n x = min(range)\\n y_space = [y]\\n x_space = [x]\\n while x<=max(range):\\n y += f(x, y)*dx\\n x += dx\\n x_space.append(x)\\n y_space.append(y)\\n return (x_space, y_space)\",\n \"def drange(x, y, jump):\\n while x < y:\\n yield float(x)\\n x += decimal.Decimal(jump)\",\n \"def getScaleValues(a, x):\\n raise NotImplementedError('getScaleValues not implemented')\",\n \"def range14(self, nx, ny, x_des):\\n [c_d, a1, output] = [self.component_dependency['y_14'], self.dependency_matrix, []]\\n for i in range(ny):\\n [sum_i, row] = [[], a1[3 * ny + i]]\\n sum_i.append(np.sum(row))\\n [assign, y] = [c_d[i], []]\\n # :x_des = np.random.random_sample(4 * nx + 5 * ny) # this is an instance of the design vector\\n [y.append(self.per_int.range_int([x_des[k]])) for k in range(4 * nx + 5 * ny) if row[k] == 1]\\n output.append(np.sum(y) * 1 / sum_i)\\n print (c_d)\\n\\n return output\",\n \"def split(self, X: tp.ArrayLike, n: tp.Optional[int] = None, range_len: tp.Optional[float] = None,\\n min_len: int = 1, start_idxs: tp.Optional[tp.ArrayLike] = None,\\n end_idxs: tp.Optional[tp.ArrayLike] = None, **kwargs) -> RangesT:\\n X = to_any_array(X)\\n if isinstance(X, (pd.Series, pd.DataFrame)):\\n index = X.index\\n else:\\n index = pd.Index(np.arange(X.shape[0]))\\n\\n # Resolve start_idxs and end_idxs\\n if start_idxs is None and end_idxs is None:\\n if range_len is None and n is None:\\n raise ValueError(\\\"At least n, range_len, or start_idxs and end_idxs must be set\\\")\\n if range_len is None:\\n range_len = len(index) // n\\n if 0 < range_len < 1:\\n range_len = math.floor(range_len * len(index))\\n start_idxs = np.arange(len(index) - range_len + 1)\\n end_idxs = np.arange(range_len - 1, len(index))\\n elif start_idxs is None or end_idxs is None:\\n raise ValueError(\\\"Both start_idxs and end_idxs must be set\\\")\\n else:\\n if isinstance(start_idxs, pd.Index):\\n start_idxs = np.asarray([find_first_occurrence(idx, index) for idx in start_idxs])\\n else:\\n start_idxs = np.asarray(start_idxs)\\n if isinstance(end_idxs, pd.Index):\\n end_idxs = np.asarray([find_first_occurrence(idx, index) for idx in end_idxs])\\n else:\\n end_idxs = np.asarray(end_idxs)\\n\\n # Filter out short ranges\\n start_idxs, end_idxs = np.broadcast_arrays(start_idxs, end_idxs)\\n range_lens = end_idxs - start_idxs + 1\\n min_len_mask = range_lens >= min_len\\n if not np.any(min_len_mask):\\n raise ValueError(f\\\"There are no ranges that meet range_len>={min_len}\\\")\\n start_idxs = start_idxs[min_len_mask]\\n end_idxs = end_idxs[min_len_mask]\\n\\n # Evenly select n ranges\\n if n is not None:\\n if n > len(start_idxs):\\n raise ValueError(f\\\"n cannot be bigger than the maximum number of ranges {len(start_idxs)}\\\")\\n idxs = np.round(np.linspace(0, len(start_idxs) - 1, n)).astype(int)\\n start_idxs = start_idxs[idxs]\\n end_idxs = end_idxs[idxs]\\n\\n return split_ranges_into_sets(start_idxs, end_idxs, **kwargs)\",\n \"def ticks(self, start, end, desired_ticks=8):\\n if start == end or isnan(start) or isnan(end):\\n return [start]\\n min, max, delta = heckbert_interval(start, end, desired_ticks, enclose=True)\\n return frange(min, max, delta)\",\n \"def test_linear_interpolation_range(self):\\n\\n for x in [[1.0, 2.0, 4.0], [-20, -19, 0], numpy.arange(200) + 1000]:\\n for y in [[5.0, 9.0], [100, 200, 10000]]:\\n\\n # Define ny by nx array with corresponding values\\n A = numpy.zeros((len(x), len(y)))\\n\\n # Define values for each x, y pair as a linear function\\n for i in range(len(x)):\\n for j in range(len(y)):\\n A[i, j] = linear_function(x[i], y[j])\\n\\n # Test that linearly interpolated points are correct\\n xis = numpy.linspace(x[0], x[-1], 100)\\n etas = numpy.linspace(y[0], y[-1], 100)\\n points = combine_coordinates(xis, etas)\\n\\n vals = interpolate2d(x, y, A, points, mode='linear')\\n refs = linear_function(points[:, 0], points[:, 1])\\n assert numpy.allclose(vals, refs, rtol=1e-12, atol=1e-12)\",\n \"def g_rosenbrock(x, a=1, b=100):\\n\\n g = np.array(\\n [\\n -2 * a - 4 * b * x[0] * (-x[0] ** 2 + x[1]) + 2 * x[0],\\n b * (-2 * x[0] ** 2 + 2 * x[1]),\\n ]\\n )\\n\\n return g\",\n \"def range34(self, nx, ny, x_des):\\n [c_d, a1, output] = [self.component_dependency['y_34'], self.dependency_matrix, []]\\n for i in range(ny):\\n [sum_i, row] = [[], a1[11 * ny + 2 * nx + i]]\\n sum_i.append(np.sum(row))\\n [assign, y] = [c_d[i], []]\\n # :x_des = np.random.random_sample(4 * nx + 5 * ny) # this is an instance of the design vector\\n [y.append(self.per_int.range_int([x_des[k]])) for k in range(4 * nx + 5 * ny) if row[k] == 1]\\n output.append(np.sum(y) * 1 / sum_i)\\n\\n return output\",\n \"def _lagrange(x, y):\\n x_col, x_row = np.meshgrid(x, x)\\n diff_x = x_col - x_row + np.eye(len(x))\\n n = len(x)\\n indices = np.eye(n) == 0\\n\\n def P(x_ip):\\n l = np.zeros((n, x_ip.shape[0]))\\n for idx, idxs in enumerate(indices):\\n l[idx] = np.prod((x_ip[:, None] - x[idxs]) / diff_x[idxs, idx], axis=1)\\n\\n return np.sum(y * l.T, axis=1)\\n\\n return P\",\n \"def bapply(x,y,bins,func):\\n \\n assert bins[0] <= min(x),'range'\\n assert bins[-1] > max(x),'range'\\n\\n bid = np.digitize(x,bins) \\n nbins = bins.size-1\\n yapply = np.zeros(nbins)\\n\\n for id in range(1,nbins):\\n yb = y[bid==id]\\n yapply[id-1] = func(yb)\\n\\n return yapply\",\n \"def ticks(self, start, end, desired_ticks=8):\\n if start == end or isnan(start) or isnan(end):\\n return [start]\\n min, max, delta = heckbert_interval(start, end, desired_ticks,\\n nicefunc=self._nice_pow10,\\n enclose = True)\\n return frange(min, max, delta)\",\n \"def generate_random_scatter(x_range, w, b, k):\\n\\tx_1 = []\\n\\ty_1 = []\\n\\tx_2 = []\\n\\ty_2 = []\\n\\tfor i in range(k):\\n\\t\\txx = random.random() * (x_range[1] - x_range[0]) + x_range[0]\\n\\t\\tx_1.append(xx)\\n\\t\\tamplitude = random.randint(4, 15)\\n\\t\\tyy = w * xx + b + amplitude\\n\\t\\ty_1.append(yy)\\n\\n\\t\\txx = random.random() * (x_range[1] - x_range[0]) + x_range[0]\\n\\t\\tx_2.append(xx)\\n\\t\\tamplitude = random.randint(4, 15)\\n\\t\\tyy = w * xx + b - amplitude\\n\\t\\ty_2.append(yy)\\n\\treturn x_1, y_1, x_2, y_2\",\n \"def inrange ( a , x , b ) :\\n _a = float(a)\\n _b = float(b)\\n _x = float(x)\\n return ( _a <= _x or isequal ( _a , _x ) ) and ( _x <= _b or isequal ( _x , _b ) )\",\n \"def Calc(self, a, b, size):\\n self.eq = lambda x: (60000/((b-a)/size*x+a))\\n points = []\\n names = [str(self.offset)]\\n points.append(0)\\n for j in range(1, int(size)):\\n points.append(integrate.quad(self.eq,0,j)[0])\\n names.append(str(points[-1]+self.offset))\\n self.beatstr = ' '.join(names)\\n return points\",\n \"def get_xrange(self) -> np.array:\\n # todo: ensure this functions work as well for y_values\\n lower, upper = self.get_xrange_indices()\\n return self.x[lower, upper + 1]\",\n \"def startEndPoints(start, end, num):\\n ll = np.linspace(0,1,num)\\n xxs = start[0]*(1-ll)+end[0]*ll\\n tts = start[1]*(1-ll)+end[1]*ll\\n return( np.array([xxs, tts]) )\",\n \"def g_batched_rosenbrock(\\n x: np.ndarray, num_batches: int, a: np.ndarray, b: np.ndarray\\n) -> np.ndarray:\\n gall = np.zeros(2 * num_batches)\\n for i in range(num_batches):\\n gall[i * 2 : (i + 1) * 2] = g_rosenbrock(\\n x[i * 2 : (i + 1) * 2], a[i], b[i]\\n )\\n\\n return gall\",\n \"def profileY(xdata, ydata, nbins, yrange=None, xrange=None, drop_nan=True):\\n xmin, xmax = (np.min(xdata), np.max(xdata)) if xrange is None else xrange\\n ymin, ymax = (np.min(ydata), np.max(ydata)) if yrange is None else yrange\\n\\n x_out = np.linspace(ymin, ymax, nbins+1)\\n y_out = np.empty(nbins)\\n y_err = np.empty(nbins)\\n dx = np.diff(x_out)[0]\\n\\n selection = in_range(xdata, xmin, xmax) & in_range(ydata, ymin, ymax)\\n xdata, ydata = xdata[selection], ydata[selection]\\n for i in range(nbins):\\n bin_data = np.extract(in_range(ydata, x_out[i], x_out[i+1]), xdata)\\n y_out[i] = np.mean(bin_data)\\n y_err[i] = np.std(bin_data) / bin_data.size**0.5\\n x_out += dx / 2.\\n x_out = x_out[:-1]\\n if drop_nan:\\n selection = ~(np.isnan(y_out) | np.isnan(y_err))\\n x_out = x_out[selection]\\n y_out = y_out[selection]\\n y_err = y_err[selection]\\n return x_out, y_out, y_err\",\n \"def plot(self, aVals, bVals):\\n with self.pt as sp:\\n # Top subplot: The range of interest\\n X = np.linspace(self.xMin, self.xMax, self.N)\\n self.subplot(sp, X, aVals, bVals)\\n # Bottom subplot: Positive X surrounding the range of\\n # interest\\n X = np.linspace(0, 2*self.xMax, self.N)\\n sp.add_axvline(self.xMin)\\n sp.add_axvline(self.xMax)\\n self.subplot(sp, X, aVals, bVals, semilog=True)\\n self.pt.show()\",\n \"def get_x_in_range(self, start, target_func, max_distance, sort_func=None):\\n if sort_func is None:\\n targets = []\\n for x in range(-max_distance, max_distance + 1):\\n for y in range(-max_distance, max_distance + 1):\\n distance = abs(x) + abs(y)\\n if distance > max_distance:\\n continue\\n pos = Position(start.x + x, start.y + y)\\n if target_func(pos, distance):\\n targets.append(pos)\\n return targets\\n else:\\n targets = PriorityQueue()\\n for x in range(-max_distance, max_distance + 1):\\n for y in range(-max_distance, max_distance + 1):\\n distance = abs(x) + abs(y)\\n if distance > max_distance:\\n continue\\n pos = Position(start.x + x, start.y + y)\\n if target_func(pos, distance):\\n targets.enqueue(sort_func(pos, distance), pos)\\n return targets.to_list()\",\n \"def from_inclusive(a, b):\\n c = int(b > a)*2-1\\n return range(a, b+c, c)\",\n \"def get_xrange(self):\\n return self.xvec[0], self.xvec[-1]\",\n \"def function_to_XYs(func, fpars,\\n Egrid=equal_bins(100),\\n domainUnit='eV', domainName='energy_in', rangeUnit='b', rangeName='crossSection',\\n accuracy=upperEps):\\n return XYs1dModule.XYs1d.createFromFunction(\\n XYs1d.defaultAxes(labelsUnits={\\n XYs1dModule.yAxisIndex: (rangeName, rangeUnit),\\n XYs1dModule.xAxisIndex: (domainName, domainUnit)}),\\n Xs=Egrid,\\n func=func,\\n parameters=fpars,\\n accuracy=accuracy,\\n biSectionMax=20,\\n checkForRoots=False,\\n infill=1,\\n safeDivide=1)\",\n \"def val_split(a: Iterable, partitions: int, range_max: int, range_min: int = 0,\\n size: bool = True) -> List[np.ndarray]:\\n if size:\\n n = int(np.ceil(range_max / partitions))\\n splits = partitions\\n else:\\n n = partitions\\n splits = (range_max - range_min) // partitions\\n\\n it = iter(a)\\n it_current = next(it)\\n ret_val = [[] for _ in range(n)]\\n\\n try:\\n if isinstance(it_current, (tuple, list, np.ndarray)):\\n it_current, it_value = it_current\\n for i in range(n):\\n for j in range(splits):\\n split_current = (partitions + 1) * i + j\\n while it_current <= split_current:\\n ret_val[i].append([it_current, it_value])\\n it_current, it_value = next(it)\\n continue\\n return list(map(np.array, ret_val))\\n for i in range(n):\\n for j in range(splits):\\n split_current = (partitions + 1) * i + j\\n while it_current <= split_current:\\n ret_val[i].append(it_current)\\n it_current = next(it)\\n continue\\n except StopIteration:\\n return list(map(np.array, ret_val))\",\n \"def _ScatterXUniformlyExtendedRange(self, num_points, lattice_sizes,\\n input_dims):\\n x = []\\n for _ in range(num_points):\\n point = [\\n np.random.random() * (lattice_sizes + 1.0) - 1.0\\n for _ in range(input_dims)\\n ]\\n x.append(np.asarray(point))\\n if input_dims == 1:\\n x.sort()\\n return x\",\n \"def pmm(a, *b):\\n ind = np.isfinite(a)\\n try:\\n ans = [[ np.min(a[ind]), np.max(a[ind]) ]]\\n except TypeError:\\n a_tmp = np.asarray(a)\\n ans = [[ np.min(a_tmp[ind]), np.max(a_tmp[ind]) ]]\\n for val in b:\\n ind = np.isfinite(val)\\n try:\\n ans.append( [np.min(val[ind]), np.max(val[ind])] )\\n except TypeError:\\n val_tmp = np.asarray(val)\\n ans.append( [np.min(val_tmp[ind]), np.max(val_tmp[ind])] )\\n return ans\",\n \"def plot(self, xmin=-1, xmax=1):\\n if xmin > self.L or xmax < self.R:\\n raise ValueError('xmin=%g > L=%g or xmax=%g < R=%g is meaningless for plot' % (xmin, self.L, xmax, self.R))\\n\\n if self.eps == 0:\\n return [xmin, L, L, R, R, xmax], [0, 0, 1, 1, 0, 0]\\n else:\\n n = 200./self.eps\\n x = concatenate(\\n linspace(xmin, self.L-self.eps, 21),\\n linspace(self.L-self.eps, self.R+self.eps, n+1),\\n linspace(self.R+self.eps, xmax, 21))\\n y = self(x)\\n return x, y\",\n \"def domain(self):\\n lower, upper = sorted((self.x1, self.x2))\\n return FloatRange(lower=lower, upper=upper)\",\n \"def toPointwise_withLinearXYs( self, **kwargs ) :\\n\\n return( XYs1d( data = [ [ self.domainMin, self.value ], [ self.domainMax, self.value ] ], axes = self.axes ) )\",\n \"def XbarTXbar(d, A=None, B=None):\\n n = len(d) + 1\\n if A is None: A = np.arange(n-1)\\n if B is None: B = np.arange(n-1)\\n V = np.empty((len(A), len(B)))\\n for i, j in itt.product(range(len(A)), range(len(B))):\\n a, b = A[i], B[j]\\n u, v = _minmax(a, b)\\n V[i, j] = d[a] * d[b] * (u * (n - v)) / n\\n return V\",\n \"def sum_range(self, lower, upper):\\n if upper>self.upper:\\n upper=self.upper\\n if lower<self.lower:\\n lower = self.lower\\n\\n i_l = int(np.floor((lower-self.lower)/self._dx))\\n i_u = int(np.floor((upper-self.lower)/self._dx))\\n total = 0.0\\n for i in range(i_l,i_u):\\n total+= self.y[i]\\n return total\",\n \"def get_range(min, max, intervals, log):\\n if not log:\\n min = float(min)\\n max = float(max)\\n difference = max-min\\n step_size = difference/intervals\\n output = [min + i*step_size for i in range(intervals+1)]\\n return output\\n else:\\n from math import log10 as log\\n log_min = log(min)\\n log_max = log(max)\\n log_difference = log_max - log_min\\n step_size = log_difference/intervals\\n output = [pow(10, log_min + i*step_size) for i in range(intervals+1)]\\n return output\",\n \"def _grid_from_X(X, percentiles, grid_resolution, custom_range):\\n values = []\\n for feature in X.columns:\\n if feature in custom_range:\\n # Use values in the custom range\\n feature_range = custom_range[feature]\\n if not isinstance(feature_range, (np.ndarray, pd.Series)):\\n feature_range = np.array(feature_range)\\n if feature_range.ndim != 1:\\n raise ValueError(\\n \\\"Grid for feature {} is not a one-dimensional array. Got {}\\\"\\n \\\" dimensions\\\".format(feature, feature_range.ndim)\\n )\\n axis = feature_range\\n else:\\n uniques = np.unique(X.loc[:, feature])\\n if uniques.shape[0] < grid_resolution:\\n # feature has low resolution use unique vals\\n axis = uniques\\n else:\\n # create axis based on percentiles and grid resolution\\n emp_percentiles = mquantiles(\\n X.loc[:, feature], prob=percentiles, axis=0\\n )\\n if np.allclose(emp_percentiles[0], emp_percentiles[1]):\\n raise ValueError(\\n \\\"percentiles are too close to each other, \\\"\\n \\\"unable to build the grid. Please choose percentiles \\\"\\n \\\"that are further apart.\\\"\\n )\\n axis = np.linspace(\\n emp_percentiles[0],\\n emp_percentiles[1],\\n num=grid_resolution,\\n endpoint=True,\\n )\\n values.append(axis)\\n\\n return _cartesian(values), values\",\n \"def create_data(f, x_vals):\\n y_vals = []\\n for i in x_vals:\\n y_vals.append(f(x_vals[i]))\\n return np.array(y_vals)\",\n \"def local_mean(x,y, n=10):\\n\\n xx, yy = (list(t) for t in zip(*sorted(zip(x, y)))) # sort x and y after x\\n\\n m = int(len(x)/n) # Number of data points in each group\\n\\n x_o, y_o = [], []\\n x_sum, y_sum, v = 0, 0, 0\\n j=1\\n for i in range(len(x)):\\n if v < m:\\n x_sum += xx[i]\\n y_sum += yy[i]\\n v += 1\\n else:\\n x_o.append(x_sum/m)\\n y_o.append(y_sum/m)\\n x_sum, y_sum, v = 0, 0, 0\\n j += 1\\n\\n return x_o, y_o\",\n \"def create_graph_from_lambda(f, xrange):\\n out = []\\n for x in xrange:\\n out.append(f(x))\\n return out\",\n \"def data_range(x):\\n return max(x)-min(x)\",\n \"def averages(x,y):\\n new_x = [x[0]]\\n new_y = []\\n\\n cur_x = new_x[0]\\n cur_ys = []\\n for x_i, y_i in zip(x,y):\\n if x_i == cur_x:\\n cur_ys.append(y_i)\\n else:\\n new_y.append( sum(cur_ys)/float(len(cur_ys) ) )\\n new_x.append( x_i )\\n cur_ys = [y_i]\\n cur_x = x_i\\n new_y.append( sum(cur_ys)/float(len(cur_ys) ) )\\n return new_x, new_y\",\n \"def range24(self, nx, ny, x_des):\\n [c_d, a1, output] = [self.component_dependency['y_24'], self.dependency_matrix, []]\\n for i in range(ny):\\n [sum_i, row] = [[], a1[7 * ny + nx + i]]\\n sum_i.append(np.sum(row))\\n [assign, y] = [c_d[i], []]\\n # :x_des = np.random.random_sample(4 * nx + 5 * ny) # this is an instance of the design vector\\n [y.append(self.per_int.range_int([x_des[k]])) for k in range(4 * nx + 5 * ny) if row[k] == 1]\\n output.append(np.sum(y) * 1 / sum_i)\\n\\n return output\",\n \"def makeLinearFunc(xList, startXY, endXY, middleXY=None):\\r\\n\\r\\n \\\"\\\"\\\" Make one or two linear functions from given coordinates\\r\\n and return the values (as a dict) for the x-values found in\\r\\n the xList. \\\"\\\"\\\"\\r\\n\\r\\n x1, y1 = startXY\\r\\n try: x2, y2 = middleXY\\r\\n except: pass\\r\\n x3, y3 = endXY\\r\\n\\r\\n # Always sort list first.\\r\\n xList.sort()\\r\\n # Don't extrapolate values - only calculate intermediate values!\\r\\n xList = xList[xList.index(x1):xList.index(x3)+1]\\r\\n\\r\\n # The dict holding the final X- and Y-values.\\r\\n xyDict = {}\\r\\n\\r\\n # Iterate through all distances and stop when distUnit==middleX\\r\\n # and/or when distUnit/100==endX.\\r\\n for x in xList:\\r\\n # Define the straight line\\r\\n # Based on: f(x) = y1 + [(y2 - y1) / (x2 - x1)]·(x - x1)\\r\\n # And then assign the value to the xyList\\r\\n\\r\\n # If no middle was defined, give values based on two equations.\\r\\n if middleXY != None:\\r\\n if x<=x2:\\r\\n val = y1 + ( float((y2 - y1))/(x2 - x1) ) * (x - x1)\\r\\n else:\\r\\n val = y2 + ( float((y3 - y2))/(x3 - x2) ) * (x - x2)\\r\\n\\r\\n # Else, give all values based on one equation.\\r\\n else:\\r\\n val = y1 + ( float((y3 - y1)) / (x3 - x1) ) * (x - x1)\\r\\n\\r\\n xyDict[x] = round(val, 2) # The y-value is rounded to 2 decimals\\r\\n if x >= x3: # don't define influence beyond end point.\\r\\n break\\r\\n\\r\\n # Return a dict with y-values keyed by x-values.\\r\\n return xyDict\",\n \"def interval_split(a,b,split_ps):\\n ps = [a] + [s for s in sorted(split_ps) if a < s < b] + [b]\\n return [(p1,p2) for p1,p2 in zip(ps,ps[1:])]\",\n \"def expand_ranges(ranges):\\n for low, high in low_high_pairs:\\n for j in range(low, high+1):\\n yield j\",\n \"def compute_rlzn_ensemble(fopen_list, var_list, range_list): #{{{\\n\\n sumprod = 0.0\\n\\n for afile in fopen_list:\\n prodvars = 1.0\\n for avar,arange in zip(var_list,range_list):\\n if isinstance(avar,str):\\n # open value in netCDF4 database\\n var_value = afile.variables[avar]\\n else:\\n var_value = avar\\n \\n if arange is None:\\n if hasattr(var_value, \\\"__len__\\\"): \\n try:\\n prodvars *= var_value[:]\\n except:\\n tmp = var_value[:]\\n prodvars *= tmp[np.newaxis,:]\\n del tmp\\n else:\\n prodvars *= var_value\\n else:\\n prodvars *= var_value[arange]\\n\\n sumprod += prodvars\\n\\n # compute ensemble with unbiased estimator\\n ensmbl = sumprod/(len(fopen_list)-1)\\n\\n return ensmbl #}}}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58776325","0.5662537","0.5642036","0.5495832","0.5469611","0.5420076","0.5375376","0.53669316","0.53453207","0.53439814","0.53289247","0.5299425","0.5295588","0.52585924","0.52468276","0.5246785","0.5245637","0.5245567","0.52296543","0.5221779","0.5213703","0.5212454","0.5208512","0.52083296","0.5201708","0.5194486","0.51711047","0.5168863","0.5165443","0.5120755","0.5114761","0.5102751","0.50785375","0.5054189","0.505072","0.50472754","0.50416267","0.503469","0.5019454","0.5018101","0.5010577","0.5008813","0.50058043","0.5002213","0.49998733","0.4996806","0.49906522","0.49867833","0.49822482","0.4981303","0.4980911","0.49784392","0.49747497","0.49691847","0.49691364","0.49657494","0.49621314","0.49519378","0.4946871","0.49463037","0.4942811","0.49422556","0.4934619","0.49313468","0.4926717","0.49265236","0.49243584","0.4921028","0.49185663","0.49176183","0.49169987","0.49095422","0.4908786","0.49083984","0.4907138","0.4887863","0.48860237","0.48834684","0.48813796","0.4877188","0.48745123","0.48739085","0.48698267","0.48692703","0.4865313","0.48593545","0.48577893","0.48567805","0.48494112","0.48477614","0.4838593","0.4832895","0.48318925","0.48307568","0.48277205","0.48213023","0.48212478","0.48207626","0.48200655","0.48200542"],"string":"[\n \"0.58776325\",\n \"0.5662537\",\n \"0.5642036\",\n \"0.5495832\",\n \"0.5469611\",\n \"0.5420076\",\n \"0.5375376\",\n \"0.53669316\",\n \"0.53453207\",\n \"0.53439814\",\n \"0.53289247\",\n \"0.5299425\",\n \"0.5295588\",\n \"0.52585924\",\n \"0.52468276\",\n \"0.5246785\",\n \"0.5245637\",\n \"0.5245567\",\n \"0.52296543\",\n \"0.5221779\",\n \"0.5213703\",\n \"0.5212454\",\n \"0.5208512\",\n \"0.52083296\",\n \"0.5201708\",\n \"0.5194486\",\n \"0.51711047\",\n \"0.5168863\",\n \"0.5165443\",\n \"0.5120755\",\n \"0.5114761\",\n \"0.5102751\",\n \"0.50785375\",\n \"0.5054189\",\n \"0.505072\",\n \"0.50472754\",\n \"0.50416267\",\n \"0.503469\",\n \"0.5019454\",\n \"0.5018101\",\n \"0.5010577\",\n \"0.5008813\",\n \"0.50058043\",\n \"0.5002213\",\n \"0.49998733\",\n \"0.4996806\",\n \"0.49906522\",\n \"0.49867833\",\n \"0.49822482\",\n \"0.4981303\",\n \"0.4980911\",\n \"0.49784392\",\n \"0.49747497\",\n \"0.49691847\",\n \"0.49691364\",\n \"0.49657494\",\n \"0.49621314\",\n \"0.49519378\",\n \"0.4946871\",\n \"0.49463037\",\n \"0.4942811\",\n \"0.49422556\",\n \"0.4934619\",\n \"0.49313468\",\n \"0.4926717\",\n \"0.49265236\",\n \"0.49243584\",\n \"0.4921028\",\n \"0.49185663\",\n \"0.49176183\",\n \"0.49169987\",\n \"0.49095422\",\n \"0.4908786\",\n \"0.49083984\",\n \"0.4907138\",\n \"0.4887863\",\n \"0.48860237\",\n \"0.48834684\",\n \"0.48813796\",\n \"0.4877188\",\n \"0.48745123\",\n \"0.48739085\",\n \"0.48698267\",\n \"0.48692703\",\n \"0.4865313\",\n \"0.48593545\",\n \"0.48577893\",\n \"0.48567805\",\n \"0.48494112\",\n \"0.48477614\",\n \"0.4838593\",\n \"0.4832895\",\n \"0.48318925\",\n \"0.48307568\",\n \"0.48277205\",\n \"0.48213023\",\n \"0.48212478\",\n \"0.48207626\",\n \"0.48200655\",\n \"0.48200542\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58207065"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":8297,"cells":{"query":{"kind":"string","value":"Takes list of divided differences nodes and calculates new divided differences node from each pair of nodes_to_compute. In other words, it computes next level of so called Newton's second interpolation form tree."},"document":{"kind":"string","value":"def calculate_divided_differences_row(nodes_to_compute):\n divided_differences = []\n\n if len(nodes_to_compute) == 1:\n return None\n\n for i in range(0, len(nodes_to_compute) - 1):\n child = DividedDifferenceNode.create_child_node(nodes_to_compute[i], nodes_to_compute[i + 1])\n child.calculate_value()\n divided_differences.append(child)\n\n for node in divided_differences:\n print(node, end='')\n\n print('\\n')\n return divided_differences"},"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 calculate_divided_differences(nodes):\n nodes_to_compute = []\n divided_differences = []\n for node in nodes:\n nodes_to_compute.append(DividedDifferenceNode(x=node[0], divided_difference=node[1]))\n\n divided_differences.append(tuple(nodes_to_compute))\n\n while len(nodes_to_compute) > 1:\n next_node_row = calculate_divided_differences_row(nodes_to_compute)\n divided_differences.append(tuple(next_node_row))\n nodes_to_compute = next_node_row\n\n return divided_differences","def calculate_newton_interpolation(divided_differences):\n polynomial = []\n for i, divided_differences_row in enumerate(divided_differences):\n polynomial_part = '({0})'.format(divided_differences_row[0].divided_difference)\n\n for j in range(0, i):\n polynomial_part += '*(x-{0})'.format(divided_differences[0][j].x)\n\n polynomial_part += '+'\n polynomial.append(polynomial_part)\n polynomial_str = ''.join(polynomial)[:-1]\n\n print('Calculated polynomial: {0}'.format(polynomial_str))\n # Heuristic simplification of calculated polynomial\n simplified_polynomial = sy.simplify(polynomial_str)\n print(\"Simplified polynomial: {0}\".format(simplified_polynomial))\n return simplified_polynomial","def compute_relations(nodes: List[Node]) -> None:\n # Calculate parents\n for node in nodes:\n node.parents = []\n for node in nodes:\n for child in node.children():\n child.parents.append(node)\n\n def compute_dominators(\n entry: Node,\n parents: Callable[[Node], List[Node]],\n dominators: Callable[[Node], Set[Node]],\n immediately_dominates: Callable[[Node], List[Node]],\n set_immediate_dominator: Callable[[Node, Optional[Node]], None],\n ) -> None:\n # See https://en.wikipedia.org/wiki/Dominator_(graph_theory)#Algorithms\n # Note: if `n` is unreachable from `entry`, then *every* node will\n # vacuously belong to `n`'s dominator set.\n for n in nodes:\n dominators(n).clear()\n if n == entry:\n dominators(n).add(n)\n else:\n dominators(n).update(nodes)\n\n changes = True\n while changes:\n changes = False\n for node in nodes:\n if node == entry:\n continue\n nset = dominators(node)\n for parent in parents(node):\n nset = nset.intersection(dominators(parent))\n nset.add(node)\n if len(nset) < len(dominators(node)):\n assert nset.issubset(dominators(node))\n dominators(node).intersection_update(nset)\n changes = True\n\n # Compute immediate dominator, and the inverse relation\n for node in nodes:\n immediately_dominates(node).clear()\n for node in nodes:\n doms = dominators(node).difference({node})\n # If `node == entry` or the flow graph is not reducible, `doms` may be empty.\n # TODO: Infinite loops could be made reducible by introducing\n # branches like `if (false) { return; }` without breaking semantics\n if doms:\n # There should be a unique max `len(dominators(d))` if the flowgraph\n # is reducible. Fall back to largest index for irreducible graphs.\n imdom = max(doms, key=lambda d: (len(dominators(d)), d.block.index))\n immediately_dominates(imdom).append(node)\n set_immediate_dominator(node, imdom)\n else:\n set_immediate_dominator(node, None)\n for node in nodes:\n immediately_dominates(node).sort(key=lambda x: x.block.index)\n\n def _set_immediate_dominator(node: Node, imdom: Optional[Node]) -> None:\n node.immediate_dominator = imdom\n\n def _set_immediate_postdominator(node: Node, impdom: Optional[Node]) -> None:\n node.immediate_postdominator = impdom\n\n entry = nodes[0]\n terminal = nodes[-1]\n assert isinstance(terminal, TerminalNode)\n\n # Compute dominators & immediate dominators\n compute_dominators(\n entry=entry,\n parents=lambda n: n.parents,\n dominators=lambda n: n.dominators,\n immediately_dominates=lambda n: n.immediately_dominates,\n set_immediate_dominator=_set_immediate_dominator,\n )\n\n # Compute postdominators & immediate postdominators\n # This uses the same algorithm as above, but with edges reversed\n compute_dominators(\n entry=terminal,\n parents=lambda n: n.children(),\n dominators=lambda n: n.postdominators,\n immediately_dominates=lambda n: n.immediately_postdominates,\n set_immediate_dominator=_set_immediate_postdominator,\n )\n\n # Iterate over all edges n -> c and check for backedges, which define natural loops\n for node in nodes:\n for child in node.children():\n if child not in node.dominators:\n continue\n # Found a backedge node -> child where child dominates node; child is the \"head\" of the loop\n if child.loop is None:\n child.loop = NaturalLoop(child)\n child.loop.nodes |= {child, node}\n child.loop.backedges.add(node)\n for parent in nodes:\n if reachable_without(parent, node, child):\n child.loop.nodes.add(parent)","def compute_tree(self, tree):\n g_list_val, g_list_h = self._build_graph(tree) # return theano variable of each node\n list_val = self._traversal_tree(tree) #\n f = theano.function(g_list_val, g_list_h, allow_input_downcast=True)\n result = f(*list_val)\n return result","def gradients(output_node, node_list):\r\n\r\n # a map from node to a list of gradient contributions from each output node\r\n node_to_output_grads_list = {}\r\n # Special note on initializing gradient of output_node as oneslike_op(output_node):\r\n # We are really taking a derivative of the scalar reduce_sum(output_node)\r\n # instead of the vector output_node. But this is the common case for loss function.\r\n node_to_output_grads_list[output_node] = [oneslike_op(output_node)]\r\n # a map from node to the gradient of that node\r\n node_to_output_grad = {}\r\n # Traverse graph in reverse topological order given the output_node that we are taking gradient wrt.\r\n reverse_topo_order = list(reversed(find_topo_sort([output_node])))\r\n #node_to_output_grad[output_node] = oneslike_op(output_node)\r\n \r\n \r\n \"\"\"TODO: Your code here\"\"\"\r\n \r\n for node in reverse_topo_order:\r\n #print(node)\r\n #print(node_to_output_grad)\r\n if not(node in node_to_output_grad):\r\n #node_to_output_grad[node] = node.op.gradient(node, sum_node_list ([node_to_output_grad[node1] for node1 in node_to_output_grads_list[node] ]))\r\n sum_node = sum_node_list (node_to_output_grads_list[node]) \r\n grad = node.op.gradient(node, sum_node)\r\n node_to_output_grad[node] = sum_node\r\n #print(grad)\r\n #print(len(node.inputs))\r\n for i in range(len(node.inputs)):\r\n #print(i)\r\n if (not(node.inputs[i] in node_to_output_grads_list)):\r\n node_to_output_grads_list[node.inputs[i]]=[]\r\n node_to_output_grads_list[node.inputs[i]].append(grad[i])\r\n \r\n #input_grad = \r\n \r\n \r\n '''for node1 in node_to_output_grads_list[node]:\r\n print(node1)\r\n if (node in node_to_output_grad):\r\n node_to_output_grad[node] = node_to_output_grad[node] + node_to_output_grad[node1]\r\n else:\r\n node_to_output_grad[node] = node_to_output_grad[node1]\r\n '''\r\n #print(\"node to output \")\r\n #print(node_to_output_grad)\r\n\r\n del reverse_topo_order\r\n # Collect results for gradients requested.\r\n grad_node_list = [node_to_output_grad[node] for node in node_list]\r\n return grad_node_list","def insert_nodes(self):\n neighbour_max_distance = 5\n new_nodes = []\n for node in self.nodes:\n left_distance = node.get_distance(node.neighbour1)\n right_distance = node.get_distance(node.neighbour2)\n if left_distance > neighbour_max_distance:\n # halfway\n half_point = (\n node.x + (node.neighbour1.x - node.x) / 2,\n node.y + (node.neighbour1.y - node.y) / 2\n )\n new_node = Node(half_point)\n node.neighbour1.connect(node.neighbour1.neighbour1, new_node)\n new_node.connect(node.neighbour1, node)\n node.connect(new_node, node.neighbour2)\n new_nodes.append(new_node)\n new_nodes.append(node)\n\n if right_distance > neighbour_max_distance:\n # halfway\n half_point = (\n node.x + (node.neighbour2.x - node.x) / 2,\n node.y + (node.neighbour2.y - node.y) / 2\n )\n new_node = Node(half_point)\n node.neighbour2.connect(new_node, node.neighbour2.neighbour2)\n new_node.connect(node, node.neighbour2)\n node.connect(node.neighbour1, new_node)\n new_nodes.append(new_node)\n\n return new_nodes","def job_tree(self):\n\n # 1. Enforce depth of 1 for steps\n def depth_one(steps):\n depth_one = []\n for step in steps:\n if type(step) is list:\n if type(step[0]) is list:\n depth_one.append(step[0])\n else:\n depth_one.append(step)\n else:\n depth_one.append([step])\n return depth_one\n\n # 2. Convert steps to list of node objects (0,1,2,3...)\n def assign_nodes(steps):\n nodes = [i for i in range(len(steps))]\n objects = list(\n set([elem for sublist in steps for elem in sublist]))\n\n # checks for multiple src and dst objects -- added when looking for\n # mutiples\n split_objects = []\n for obj in objects:\n if len(obj) > 1:\n new_objs = obj.split(\", \")\n split_objects.extend(new_objs)\n else:\n split_objects.append(obj)\n objects = split_objects\n del(split_objects)\n\n # populate with leafless trees (Node objects, no edges)\n for node in nodes:\n nodes[node] = Node(str(node))\n\n # search for leafy trees\n for obj in objects:\n\n # accounts for multiple drc/dst objects\n leaves = []\n for i, sublist in enumerate(steps):\n for string in sublist:\n if string.count(',') > 0:\n if obj in string:\n leaves.append(i)\n else:\n if obj in sublist:\n leaves.append(i)\n leaves = sorted(list(set(leaves)))\n\n if len(leaves) > 1:\n viable_edges = []\n\n # compute cross-product\n for leaf1 in leaves:\n for leaf2 in leaves:\n if str(leaf1) != str(leaf2) and sorted((leaf1, leaf2)) not in viable_edges:\n viable_edges.append(sorted((leaf1, leaf2)))\n\n # form edge networks\n for edge in viable_edges:\n n1, n2 = nodes[edge[0]], nodes[edge[1]]\n n1.add_edge(n2)\n n2.add_edge(n1)\n nodes[int(n1.name)], nodes[int(n2.name)] = n1, n2\n return nodes\n\n # 3. Determine number of trees and regroup by connected nodes\n def connected_nodes(nodes):\n proto_trees = []\n nodes = set(nodes)\n\n while nodes:\n n = nodes.pop()\n group = {n}\n queue = [n]\n while queue:\n n = queue.pop(0)\n neighbors = n.edges\n neighbors.difference_update(group)\n nodes.difference_update(neighbors)\n group.update(neighbors)\n queue.extend(neighbors)\n proto_trees.append(group)\n return proto_trees\n\n # 4. Convert nodes to nested dictionary of parent-children relations\n # i.e. adding depth -- also deals with tree-node sorting and path\n # optimization\n def build_tree_dict(trees, steps):\n # node sorting in trees\n sorted_trees = []\n for tree in trees:\n sorted_trees.append(\n sorted(tree, key=lambda x: int(x.name)))\n\n # retrieve values of the nodes (the protocol's containers)\n # for each tree ... may want to use dictionary eventually\n all_values = []\n for tree in sorted_trees:\n values = [steps[int(node.name)] for node in tree]\n all_values.append(values)\n\n # create relational tuples:\n all_digs = []\n singles = []\n dst_potentials = []\n for tree_idx in range(len(sorted_trees)):\n edge_flag = False\n tree_digs = []\n for node_idx in range(len(sorted_trees[tree_idx])):\n\n # digs: directed graph vectors\n digs = []\n dst_nodes = []\n node_values = all_values[tree_idx][node_idx]\n src_node = str(sorted_trees[tree_idx][node_idx].name)\n\n # ACTION ON MULTIPLE OBJECTS (E.G. TRANSFER FROM SRC -> DST\n # WELLS)\n # Outcome space: {1-1, 1-many, many-1, many-many}\n if len(node_values) == 2:\n # single destination (x-1)\n if node_values[1].count(\",\") == 0:\n dst_nodes = [i for i, sublist in enumerate(\n steps) if node_values[1] == sublist[0]]\n # multiple destinations (x-many)\n elif node_values[1].count(\",\") > 0:\n dst_nodes = []\n for dst in node_values[1].replace(\", \", \"\"):\n for i, sublist in enumerate(steps):\n if i not in dst_nodes and dst == sublist[0]:\n dst_nodes.append(i)\n\n # ACTION ON A SINGLE OBJECT\n elif len(node_values) == 1:\n dst_nodes = [i for i, sublist in enumerate(\n steps) if node_values[0] == sublist[0]]\n\n # Constructing tuples in (child, parent) format\n for dst_node in dst_nodes:\n dig = (int(dst_node), int(src_node))\n digs.append(dig)\n\n # else: an edge-case for dictionaries constructed with no edges\n # initiates tree separation via flag\n if digs != []:\n edge_flag = False\n tree_digs.append(digs)\n else:\n edge_flag = True\n digs = [(int(src_node), int(src_node))]\n tree_digs.append(digs)\n\n # digraph cycle detection: avoids cycles by overlooking set\n # repeats\n true_tree_digs = []\n for digs in tree_digs:\n for dig in digs:\n if tuple(sorted(dig, reverse=True)) not in true_tree_digs:\n true_tree_digs.append(\n tuple(sorted(dig, reverse=True)))\n\n # edge-case for dictionaries constructed with no edges\n if true_tree_digs != [] and edge_flag == False:\n all_digs.append(true_tree_digs)\n elif edge_flag == True:\n all_digs.extend(tree_digs)\n\n # Enforces forest ordering\n all_digs = sorted(all_digs, key=lambda x: x[0])\n\n # job tree traversal to find all paths:\n forest = []\n for digs_set in all_digs:\n\n # pass 1: initialize nodes dictionary\n nodes = OrderedDict()\n for tup in digs_set:\n id, parent_id = tup\n # ensure all nodes accounted for\n nodes[id] = OrderedDict({'id': id})\n nodes[parent_id] = OrderedDict({'id': parent_id})\n\n # pass 2: create trees and parent-child relations\n for tup in digs_set:\n id, parent_id = tup\n node = nodes[id]\n # links node to its parent\n if id != parent_id:\n # add new_node as child to parent\n parent = nodes[parent_id]\n if not 'children' in parent:\n # ensure parent has a 'children' field\n parent['children'] = []\n children = parent['children']\n children.append(node)\n\n desired_tree_idx = sorted(list(nodes.keys()))[0]\n forest.append(nodes[desired_tree_idx])\n return forest\n\n # 5. Convert dictionary-stored nodes to unflattened, nested list of\n # parent-children relations\n def dict_to_list(forest):\n forest_list = []\n for tree in forest:\n tString = str(json.dumps(tree))\n tString = tString.replace('\"id\": ', \"\").replace('\"children\": ', \"\").replace(\n '[{', \"[\").replace('}]', \"]\").replace('{', \"[\").replace('}', \"]\")\n\n # find largest repeated branch (if applicable)\n # maybe think about using prefix trees or SIMD extensions for better\n # efficiency\n x, y, length, match = 0, 0, 0, ''\n for y in range(len(tString)):\n for x in range(len(tString)):\n substring = tString[y:x]\n if len(list(re.finditer(re.escape(substring), tString))) > 1 and len(substring) > length:\n match = substring\n length = len(substring)\n\n # checking for legitimate branch repeat\n if \"[\" in match and \"]\" in match:\n hits = []\n index = 0\n if len(tString) > 3:\n while index < len(tString):\n index = tString.find(str(match), index)\n if index == -1:\n break\n hits.append(index)\n index += len(match)\n\n # find all locations of repeated branch and remove\n if len(hits) > 1:\n for start_loc in hits[1:]:\n tString = tString[:start_loc] + \\\n tString[start_loc:].replace(match, \"]\", 1)\n\n # increment all numbers in string to match the protocol\n newString = \"\"\n numString = \"\"\n for el in tString:\n if el.isdigit(): # build number\n numString += el\n else:\n if numString != \"\": # convert it to int and reinstantaite numString\n numString = str(int(numString) + 1)\n newString += numString\n newString += el\n numString = \"\"\n tString = newString\n del newString\n\n forest_list.append(ast.literal_eval(tString))\n return forest_list\n\n # 6. Print job tree(s)\n def print_tree(lst, level=0):\n print(' ' * (level - 1) + '+---' * (level > 0) + str(lst[0]))\n for l in lst[1:]:\n if type(l) is list:\n print_tree(l, level + 1)\n else:\n print(' ' * level + '+---' + l)\n\n # 1\n steps = depth_one(self.object_list)\n # 2\n nodes = assign_nodes(steps)\n # 3\n proto_forest = connected_nodes(nodes)\n # 4\n forest = build_tree_dict(proto_forest, steps)\n # 5\n self.forest_list = dict_to_list(forest)\n # 6\n print(\"\\n\" + \"A suggested Job Tree based on container dependency: \\n\")\n for tree_list in self.forest_list:\n print_tree(tree_list)","def expand_tree(self, N=1):\n # type: (int) -> None\n assert self._initialized, 'Search not initialized.'\n for _ in range(N): \n x_rand = self.sample_free()\n x_nearest = self.nearest(x_rand)\n x_new = self.steer(x_nearest, x_rand)\n if self.coll_free(x_nearest, x_new):\n self.index+=1\n X_near = [x for x in self.near(x_new) if self.coll_free(x, x_new)]\n cost_min = self.costs[self.research_index(self.nodes,x_nearest)][1] + self.dist(x_nearest, x_new)\n x_min = x_nearest\n for x in X_near:\n cost = self.costs[self.research_index(self.nodes,x)][1] + self.dist(x, x_new)\n if cost < cost_min:\n cost_min = cost\n x_min = x\n \n self.nodes.append(x_new)\n j=self.research_index(self.nodes,x_min)\n self.parents[self.index,j]=1\n self.costs[self.index] = (x_new,self.costs[j][1] + self.dist(x_min, x_new))\n for x in X_near:\n k=self.research_index(self.nodes,x)\n if self.costs[self.index][1] + self.dist(x_new, x) < self.costs[k][1]:\n self.parents[self.index]=np.zeros(self.N)\n self.parents[self.index,k] = 1\n self.costs[k] = (self.costs[k][0],self.costs[self.index][1] + self.dist(x_new, x))","def all_nodes_dfs(log_T, initial_state, min_score, sub_info, max_depth=1000000000000000000, maxtraversals=1000000000000000000):\n # default argument for sub_info: empty_sub_info = (np.array([], dtype=int), np.array([], dtype=int), 1000000000000000000)\n min_score = float(min_score) # make sure numba knows this is a float (otherwise, sometimes, it doesn't (bug in numba))\n order = np.zeros(log_T.shape, np.int64)\n for i in xrange(order.shape[1]):\n order[i] = (-log_T[i]).argsort()\n n_states = log_T.shape[0]\n node = [order[initial_state, 0]] # most likely first node\n node_idx = [0]\n lengths_dfs = [-1.0]\n nodes_dfs = [[-1, ]]\n for it in xrange(maxtraversals):\n # score and return current node if adequate\n score = log_T[initial_state, node[0]]\n for p in xrange(1, len(node)):\n score += log_T[node[p - 1], node[p]]\n if min_score <= score and syntax_check(np.array(node), sub_info, partial=False):\n lengths_dfs.append(-score)\n nodes_dfs.append(list(node))\n # next node ##\n # try adding a value at the end\n for next_idx, next_state in enumerate(order[node[-1]]):\n if min_score <= score + log_T[node[-1], next_state] and len(node) < max_depth \\\n and syntax_check(np.array(node + [next_state]), sub_info, partial=True):\n node.append(next_state)\n node_idx.append(next_idx)\n break\n # adding a value at the end failed, so we are a leave\n else:\n for p in xrange(len(node) - 1, -1, -1):\n if node_idx[p] != n_states - 1: # find where within the node to increase (and discard all others after)\n old_idx = node_idx[p]\n del node_idx[p:]\n del node[p:]\n node_idx.append(old_idx + 1)\n prev_state = node[p - 1] if p > 0 else initial_state\n node.append(order[prev_state, node_idx[p]])\n break\n else:\n break # end of the generator, can't increase even the root\n else:\n assert False, \"Number of traversals exceeded\"\n\n return lengths_dfs[1:], nodes_dfs[1:]","def find_sharpest_fork_general(Nodes):\n pair_list = []\n Dis = np.array([])\n for n in Nodes:\n if n.parent is not None:\n if n.parent.parent is not None:\n a = n.parent.children\n if(isinstance(a, list)):\n if(len(a)==2):\n n1 = a[0]\n n2 = a[1]\n pair_list.append([n1 , n2])\n dis = LA.norm(a[0].xyz - a[1].xyz,2)\n Dis = np.append(Dis,dis)\n if(len(Dis)!= 0):\n (b,) = np.where(Dis == Dis.min())\n sharpest_pair = pair_list[b[0]]\n distance = Dis.min()\n else:\n sharpest_pair = [0,0]\n distance = 0.\n return sharpest_pair, distance","def get_locations(nodes, tl, br):\n \n # Base cases:\n if len(nodes) == 1: # for singleton, only choice is to place in the single spot in 1x1 square\n return {nodes[0]: tl}\n if len(nodes) == 2: # for two nodes, arbitrarily chose to place the first node in top left\n return {nodes[0]: tl, nodes[1]: br}\n\n # Recursive case, need to create and solve subproblems:\n ret = {}\n\n num_edges = count_num_edges(nodes)\n if num_edges == 0: # for empty graphs, no need to run METIS, just assign arbitrarily\n i = 0\n for x in range(tl.x, br.x+1): \n for y in range(tl.y, br.y+1):\n if i < len(nodes):\n ret.update({nodes[i]: Point(x,y)})\n i += 1\n return ret\n\n filename = splitext(basename(sys.argv[1]))[0] + '.p.' + sys.argv[2] + '.yx.' + sys.argv[3] + '.drop.' + sys.argv[4] + '.' +\\\n '_'.join(['delete', str(tl.x), str(tl.y), str(br.x), str(br.y)]) \n\n # special case for the very first call of get_locations. For example, suppose that there are\n # 97 nodes on a 10x10 grid. Instead of dividing the 97 nodes into 2 equal partitions, we should\n # divide them into a partition of 90 nodes and a partition of 7 nodes. The former should be\n # placed on a 10x9 grid and te latter should be placed on a 1x7 grid.\n if len(nodes) < (br.x - tl.x + 1) * (br.y - tl.y + 1):\n assert tl == Point(0, 0)\n size_tl_nodes = (br.x + 1) * int(len(nodes) / (br.x + 1))\n if size_tl_nodes == len(nodes):\n ret.update(get_locations(nodes, tl=Point(0, 0), br=Point(br.x, len(nodes) / (br.x + 1) - 1)))\n return ret\n\n nodes_tl, nodes_br = partition(nodes, size_tl_nodes, filename)\n # complicated indexing here. As an example, for the 97 into 10x10 case, we want to send 90 nodes\n # to a rectangle spanned by tl=Point(0, 0) and br=Point(9, 8) and we want to send 7 nodes to a \n # rectangle spanned by tl=Point(0, 9) and br=Point(6, 9)\n ret.update(get_locations(nodes_tl, tl=Point(0, 0), br=Point(br.x, len(nodes) / (br.x + 1) - 1)))\n ret.update(get_locations(nodes_br, tl=Point(0, len(nodes) / (br.x + 1)), br=Point(len(nodes) % (br.x + 1) - 1, len(nodes) / (br.x + 1))))\n return ret\n\n if br.x - tl.x > br.y - tl.y: # if rectangle is wider than tall, split on y axis\n half = tl.x + (br.x - tl.x - 1) / 2\n size_tl_nodes = (half - tl.x + 1) * (br.y - tl.y + 1)\n else: # split on x axis\n half = tl.y + (br.y - tl.y - 1) / 2\n size_tl_nodes = (br.x - tl.x + 1) * (half - tl.y + 1)\n\n nodes_tl, nodes_br = partition(nodes, size_tl_nodes, filename)\n\n if br.x - tl.x > br.y - tl.y: # if rectangle is wider than tall, split on y axis\n ret.update(get_locations(nodes_tl, tl=tl, br=Point(half, br.y)))\n ret.update(get_locations(nodes_br, tl=Point(half + 1,tl.y), br=br))\n else: # split on x axis\n ret.update(get_locations(nodes_tl, tl=tl, br=Point(br.x, half)))\n ret.update(get_locations(nodes_br, tl=Point(tl.x, half + 1), br=br))\n\n return ret","def find_sharpest_fork(nodes):\n pair_list = []\n Dis = np.array([])\n for n in nodes:\n if n.parent is not None:\n if n.parent.parent is not None:\n a = n.parent.children\n if(isinstance(a, list)):\n if(len(a)==2):\n n1 = a[0]\n n2 = a[1]\n if(len(n1.children) == 0 and len(n2.children) == 0):\n pair_list.append([n1 , n2])\n dis = LA.norm(a[0].xyz - a[1].xyz,2)\n Dis = np.append(Dis,dis)\n if(len(Dis)!= 0):\n (b,) = np.where(Dis == Dis.min())\n sharpest_pair = pair_list[b[0]]\n distance = Dis.min()\n else:\n sharpest_pair = [0,0]\n distance = 0.\n return sharpest_pair, distance","def make_sidewalk_nodes(street, prev_node, curr_node, next_node):\n if prev_node is None:\n v = - curr_node.vector_to(next_node, normalize=False)\n vec_prev = curr_node.vector() + v\n prev_node = Node(None, vec_prev[0], vec_prev[1])\n elif next_node is None:\n v = - curr_node.vector_to(prev_node, normalize=False)\n vec_next = curr_node.vector() + v\n next_node = Node(None, vec_next[0], vec_next[1])\n\n curr_latlng = np.array(curr_node.location())\n\n v_cp_n = curr_node.vector_to(prev_node, normalize=True)\n v_cn_n = curr_node.vector_to(next_node, normalize=True)\n v_sidewalk = v_cp_n + v_cn_n\n\n if np.linalg.norm(v_sidewalk) < 1e-10:\n v_sidewalk_n = np.array([v_cn_n[1], - v_cn_n[0]])\n else:\n v_sidewalk_n = v_sidewalk / np.linalg.norm(v_sidewalk)\n\n p1 = curr_latlng + street.distance_to_sidewalk * v_sidewalk_n\n p2 = curr_latlng - street.distance_to_sidewalk * v_sidewalk_n\n\n p_sidewalk_1 = Node(None, p1[0], p1[1])\n p_sidewalk_2 = Node(None, p2[0], p2[1])\n\n curr_node.append_sidewalk_node(street.id, p_sidewalk_1)\n curr_node.append_sidewalk_node(street.id, p_sidewalk_2)\n\n # Figure out on which side you want to put each sidewalk node\n v_c1 = curr_node.vector_to(p_sidewalk_1)\n if np.cross(v_cn_n, v_c1) > 0:\n return p_sidewalk_1, p_sidewalk_2\n else:\n return p_sidewalk_2, p_sidewalk_1","def RegenerateWith2Nodes (failed_node, list_of_nodes, name):\n \n list_of_vects = []\n\n for node_index in list_of_nodes:\n list_of_vects = list_of_vects + (map(lambda x: list(x), list(conf.BASIS_VECTORS [node_index])))\n\n array_A = numpy.array(list_of_vects).transpose()\n arrays_B = []\n\n arrays_B = map(lambda x: list(x), list(conf.BASIS_VECTORS [failed_node]))\n\n obj_list = []\n\n for node_index in list_of_nodes:\n for object_index in range(0,conf.PART_SIZE):\n obj_list.append (dist.pull_object_from_stores (name, node_index, object_index))\n\n for each in arrays_B:\n parts_to_pull = numpy.linalg.solve (array_A, numpy.array(each).transpose())\n pack = []\n \n for i in range(0,len(parts_to_pull)):\n if (parts_to_pull[i] != 0):\n pack.append (copy.deepcopy (obj_list[i]))\n\n dist.push_object_to_store (name, failed_node, reduce (numpy.bitwise_xor, pack), arrays_B.index (each))","def prepare_data_for_g(self):\n\n paths = []\n for i in self.root_nodes:\n if np.random.rand() < config.update_ratio:\n sample, paths_from_i = self.sample(i, self.trees[i], config.n_sample_gen, for_d=False)\n if paths_from_i is not None:\n paths.extend(paths_from_i)\n # for each root, we generate 20 samples, each sample is equal to one path from root to that sample\n # So, we will get maximum (num_root x 20) paths\n # path is a list with length = (N x num_sample), with num_sample = 20\n # paths =[[path_root1_to_sample1],[path_root1_to_sample2],....,[path_root1_to_sample20],\n # [path_root2_to_sample1],[path_root2_to_sample2],....,[path_root2_to sample20]\n # .\n # .\n # [path_rootN_to_sample1],[path_rootN_to_sample2],....,[path_rootN_to_sample20]]\n # get_node_pairs_from_path\n\n node_pairs = list(map(self.get_node_pairs_from_path, paths))\n # node_pairs = [[node pairs for path_root1_to_sample1],[node pairs for path_root1_to_sample2],....,[node pairs for path_root1_to_sample20],\n # [node_pairs for path_root2_to_sample1],[node pairs for path_root2_to_sample2],....,[node pairs for path_root2_to sample20],\n # .\n # .\n # [node pairs for path_rootN_to_sample1],[node pairs for path_rootN_to_sample2],....,[node pairs for path_rootN_to_sample20]]\n\n node_1 = []\n node_2 = []\n for i in range(len(node_pairs)):\n for pair in node_pairs[i]:\n node_1.append(pair[0])\n node_2.append(pair[1])\n # reward = self.sess.run(self.discriminator.reward,\n # feed_dict={self.discriminator.node_id: np.array(node_1),\n # self.discriminator.node_neighbor_id: np.array(node_2)})\n reward = self.discriminator.forward(node_1, node_2)\n return node_1, node_2, reward","def update_tree(root, executed_acts, total_rew):\n root.value = max(total_rew, root.value)\n root.visits += 1\n new_nodes = 0\n\n node = root\n for step, act in enumerate(executed_acts):\n if act not in node.children:\n node.children[act] = Node()\n new_nodes += 1\n node = node.children[act]\n node.value = max(total_rew, node.value)\n node.visits += 1\n\n return new_nodes","def newton(backward_differences, max_num_iters, newton_coefficient, ode_fn_vec,\n order, step_size, time, tol, unitary, upper):\n initial_guess = tf.reduce_sum(\n tf1.where(\n tf.range(MAX_ORDER + 1) <= order,\n backward_differences[:MAX_ORDER + 1],\n tf.zeros_like(backward_differences)[:MAX_ORDER + 1]),\n axis=0)\n\n np_dtype = np_dtype = dtype_util.as_numpy_dtype(backward_differences.dtype)\n\n rhs_constant_term = newton_coefficient * tf.reduce_sum(\n tf1.where(\n tf.range(1, MAX_ORDER + 1) <= order,\n RECIPROCAL_SUMS[1:, np.newaxis].astype(np_dtype) *\n backward_differences[1:MAX_ORDER + 1],\n tf.zeros_like(backward_differences)[1:MAX_ORDER + 1]),\n axis=0)\n\n next_time = time + step_size\n step_size_cast = tf.cast(step_size, backward_differences.dtype)\n real_dtype = tf.abs(backward_differences).dtype\n\n def newton_body(iterand):\n \"\"\"Performs one iteration of Newton's method.\"\"\"\n next_backward_difference = iterand.next_backward_difference\n next_state_vec = iterand.next_state_vec\n\n rhs = newton_coefficient * step_size_cast * ode_fn_vec(\n next_time,\n next_state_vec) - rhs_constant_term - next_backward_difference\n delta = tf.squeeze(\n tf.linalg.triangular_solve(\n upper,\n tf.matmul(tf.transpose(unitary), rhs[:, tf.newaxis]),\n lower=False))\n num_iters = iterand.num_iters + 1\n\n next_backward_difference += delta\n next_state_vec += delta\n\n delta_norm = tf.cast(tf.norm(delta), real_dtype)\n lipschitz_const = delta_norm / iterand.prev_delta_norm\n\n # Stop if method has converged.\n approx_dist_to_sol = lipschitz_const / (1. - lipschitz_const) * delta_norm\n close_to_sol = approx_dist_to_sol < tol\n delta_norm_is_zero = tf.equal(delta_norm, tf.constant(0., dtype=real_dtype))\n converged = close_to_sol | delta_norm_is_zero\n finished = converged\n\n # Stop if any of the following conditions are met:\n # (A) We have hit the maximum number of iterations.\n # (B) The method is converging too slowly.\n # (C) The method is not expected to converge.\n too_slow = lipschitz_const > 1.\n finished = finished | too_slow\n if max_num_iters is not None:\n too_many_iters = tf.equal(num_iters, max_num_iters)\n num_iters_left = max_num_iters - num_iters\n num_iters_left_cast = tf.cast(num_iters_left, real_dtype)\n wont_converge = (\n approx_dist_to_sol * lipschitz_const**num_iters_left_cast > tol)\n finished = finished | too_many_iters | wont_converge\n\n return [\n _NewtonIterand(\n converged=converged,\n finished=finished,\n next_backward_difference=next_backward_difference,\n next_state_vec=next_state_vec,\n num_iters=num_iters,\n prev_delta_norm=delta_norm)\n ]\n\n iterand = _NewtonIterand(\n converged=False,\n finished=False,\n next_backward_difference=tf.zeros_like(initial_guess),\n next_state_vec=tf.identity(initial_guess),\n num_iters=0,\n prev_delta_norm=tf.constant(np.array(-0.), dtype=real_dtype))\n [iterand] = tf.while_loop(lambda iterand: tf.logical_not(iterand.finished),\n newton_body, [iterand])\n return (iterand.converged, iterand.next_backward_difference,\n iterand.next_state_vec, iterand.num_iters)","def recursion_loop(pulls, discount, grid_n):\n\n r_grid = np.linspace(0, 1, grid_n)\n gittins, values = initial_approximation(pulls, discount, grid_n)\n n = pulls - 2 # Note that the 2 comes from (1) the initial approximation and (2) python indexing\n while n >= 1:\n g, v = recursion_step(values[:n + 1, n, :], r_grid, discount)\n values[:n, n - 1] = v\n gittins[:n, n - 1] = g\n n -= 1\n return gittins, values","def update_nodes(self, weights=None, hive_instance=None):\n hive = hive_instance or shared_hive_instance()\n metadata = None\n account = None\n cnt = 0\n while metadata is None and cnt < 5:\n cnt += 1\n try:\n account = Account(\"fullnodeupdate\", hive_instance=hive)\n metadata = json.loads(account[\"json_metadata\"])\n except:\n hive.rpc.next()\n account = None\n metadata = None\n if metadata is None:\n return\n report = metadata[\"report\"]\n failing_nodes = metadata[\"failing_nodes\"]\n parameter = metadata[\"parameter\"]\n benchmarks = parameter[\"benchmarks\"]\n if weights is None:\n weights_dict = {}\n for benchmark in benchmarks:\n weights_dict[benchmark] = (1. / len(benchmarks))\n elif isinstance(weights, list):\n weights_dict = {}\n i = 0\n weight_sum = 0\n for w in weights:\n weight_sum += w\n for benchmark in benchmarks:\n if i < len(weights):\n weights_dict[benchmark] = weights[i] / weight_sum\n else:\n weights_dict[benchmark] = 0.\n i += 1\n elif isinstance(weights, dict):\n weights_dict = {}\n i = 0\n weight_sum = 0\n for w in weights:\n weight_sum += weights[w]\n for benchmark in benchmarks:\n if benchmark in weights:\n weights_dict[benchmark] = weights[benchmark] / weight_sum\n else:\n weights_dict[benchmark] = 0.\n\n max_score = len(report) + 1\n new_nodes = []\n for node in self:\n new_node = node.copy()\n for report_node in report:\n if node[\"url\"] == report_node[\"node\"]:\n new_node[\"version\"] = report_node[\"version\"]\n scores = []\n for benchmark in benchmarks:\n result = report_node[benchmark]\n rank = result[\"rank\"]\n if not result[\"ok\"]:\n rank = max_score + 1\n score = (max_score - rank) / (max_score - 1) * 100\n weighted_score = score * weights_dict[benchmark]\n scores.append(weighted_score)\n sum_score = 0\n for score in scores:\n sum_score += score\n new_node[\"score\"] = sum_score\n for node_failing in failing_nodes:\n if node[\"url\"] == node_failing:\n new_node[\"score\"] = -1\n new_nodes.append(new_node)\n super(NodeList, self).__init__(new_nodes)","def solve(self):\n self.left -= len(self.nodes)\n \n def depths(x,depth = 0):\n depth+=1\n for y in self.graph[x]:\n if y in self.nodes:\n self.nodes.remove(y)\n depth = depths(y,depth)\n return depth\n \n while len(self.nodes):\n x = self.nodes.pop()\n self.firstGen.append(depths(x))\n #print self.graph\n #print self.nodes\n #print self.firstGen","def generate_children_nodes(\n curr_node, list_of_processed_nodes,\n running_count_of_children_dups, a_star_search=False\n):\n children_nodes_to_return = []\n direction_coordinates_map = curr_node.state.get_legal_snake_movement_coords()\n \n for direction, legal_coords in direction_coordinates_map.iteritems():\n curr_state_copy = copy.deepcopy(curr_node.state)\n new_state = State(\n dim=curr_state_copy.dim, num_obstacles=curr_state_copy.num_obstacles,\n grid=curr_state_copy.grid, snake=curr_state_copy.snake\n )\n #pdb.set_trace()#\n new_state.update_state_after_movement(new_head_coords=legal_coords)\n\n new_node_state = new_state\n new_node_action = direction\n new_node_parent_index = curr_node.index\n new_node_depth = curr_node.depth + 1\n\n new_node = Node(\n state=new_node_state, action=new_node_action,\n parent_index=new_node_parent_index, depth=new_node_depth\n )\n\n if not a_star_search:\n if new_node in list_of_processed_nodes:\n running_count_of_children_dups += 1\n continue\n\n children_nodes_to_return.append(new_node)\n\n return children_nodes_to_return, running_count_of_children_dups","def find_nodes(input_line: str) -> List[Node]:\n li = [int(elem) for elem in input_line.split(\" \")]\n assert len(li) >= 2\n \n # store Nodes in two sets, depending is their processing ready or not\n unfinished = set()\n finished = set()\n \n \n i = 0 # points to the index where to read the input list\n parent = None\n \n # add root node\n global root # global so we can directly grab its value outside this func\n root = Node(num_childs = li[i], num_metadata = li[i+1], children = None, parent = parent)\n print(\"Added root node:\", root)\n \n # Logic for handling the root node\n if root.num_childs > 0:\n unfinished.add(root) # assumes more to come...\n i += 2 # continue from child's first element\n else: # root node does not have children\n finished.add(root)\n i += 2 + num_metadata\n \n parent = root\n \n \n all_done = False # set to True when all nodes has been processed (to break out of the loop)\n \n # now we have a root ready\n while i < len(li):\n #print(i)\n \n while parent.num_child_processed >= parent.num_childs:\n # backtrack a step towards root node!\n # store metadata elements\n parent.metadata = li[i: i+parent.num_metadata]\n \n # calculate node value\n parent.value = sum(parent.children[idx - 1].value for idx in parent.metadata if idx > 0 and idx <= parent.num_childs)\n \n finished.add(parent)\n unfinished.remove(parent)\n i += parent.num_metadata\n \n if parent.parent:\n parent = parent.parent\n else: # was root\n print(\"Backtracking out from root, hence all done\")\n all_done = True\n break\n \n if all_done:\n break\n \n curr_num_childs, curr_num_metadata = li[i], li[i+1]\n \n # create a new node\n curr_node = Node(num_childs = curr_num_childs, num_metadata = curr_num_metadata, children = None, parent = parent)\n #print(\"Found new node:\", curr_num_childs, curr_num_metadata, \"\\t\\tparent:\", parent)\n parent.children.append(curr_node)\n parent.num_child_processed += 1\n \n if curr_num_childs > 0: # current node has children\n unfinished.add(curr_node)\n i = i + 2 # continue with the child\n parent = curr_node # which has current node as its parent\n else: # current node is a leaf node\n curr_node.metadata = li[i+2: i+2+curr_num_metadata]\n # calculate node value\n curr_node.value = sum(curr_node.metadata)\n \n finished.add(curr_node)\n i = i + 2 + curr_num_metadata\n \n return finished","def getNextNodeUsingCellDiff(kGoalState):\n \n global fringe\n global solutions\n\n \n\n\n\n minNode = None\n minCost = 99999999999\n minNodeIndex = -1\n\n \n pnode = None\n pcost = None\n\n if len(solutions)>0 and solutions[0] != None:\n pnode = solutions[0];\n pcost = getHValueForNode(pnode,kGoalState)\n #print pnode, pcost\n # raw_input()\n \n\n\n\n for idx,node in enumerate(fringe):\n #get the heu. function values\n g_value = getHValueForNode(node,kGoalState)\n \n\n if g_value < minCost:\n minNode = node\n minNodeIndex = idx\n minCost = g_value\n\n\n fringe.pop(minNodeIndex)\n c = getHValueForNode(minNode,kGoalState)\n if pnode != None:\n if c > pcost:\n minNode = None\n \n return minNode","def _compute_nodes_1d(npts, ilbds1d): # pylint: disable=line-too-long\n if npts % 2 == 0:\n raise ValueError(\"Please enter odd npts\")\n ind = np.arange(1, npts + 1)\n nodes = 0.5 * (1 - np.cos(np.pi * ind / (npts + 1)))\n return nodes * (ilbds1d[1] - ilbds1d[0]) + ilbds1d[0]","def calculate(self, extraParams=None):\n\n if not self.isCalc or self.nodeClass == \"button\" or self.nodeClass == \"formnode\":\n nodeIsCircular = self.isCircular()\n if not self._bypassCircularEvaluator and nodeIsCircular:\n circularNodes = self.getSortedCyclicDependencies()\n\n if self.dynamicEvaluator is None:\n self.dynamicEvaluator = FactoryDynamic.createInstance(\n circularNodes, self)\n\n params = self.dynamicEvaluator.generateCircularParameters(\n self, circularNodes)\n\n if params['dynamicIndex'] is None:\n raise ValueError(\"Cyclic dependency detected between nodes: \" + \",\".join(\n circularNodes) + \". Please use the 'pp.dynamic' function\")\n elif 'indexDic' in params and len(params['indexDic']) > 1:\n raise ValueError(\n f'Multiple indices were found using dynamic. Indexes: {\",\".join(params[\"indexDic\"].keys())}. Nodes involved: {\",\".join(circularNodes)}')\n\n self.dynamicEvaluator.circularEval(self, params)\n else:\n from_circular_evaluator = self._bypassCircularEvaluator\n\n self.sendStartCalcNode(from_circular_evaluator)\n self.model.currentProcessingNode(self.identifier)\n self._bypassCircularEvaluator = False\n\n startTime = dt.datetime.now()\n finalDef = str(self._definition)\n self.lastLazyTime = 0\n\n # CLEAR circular dependency\n if nodeIsCircular:\n finalDef = BaseDynamic.clearAllCircularDependency(finalDef)\n tmpCode = self.compileDef(finalDef)\n\n # check for replace calls to varaibles with next rules:\n # node1 --> change to getNode('node1').result\n # node1.result --> change to getNode('node1').result\n # node1.title --> change to getNode('node1').title\n # \"node1\" --> no change\n # 'node1' --> no change\n if not tmpCode is None:\n names = self.parseNames(tmpCode)\n rx = r\"('[^'\\\\]*(?:\\\\.[^'\\\\]*)*'|\\\"[^\\\"\\\\]*(?:\\\\.[^\\\"\\\\]*)*\\\")|\\b{0}\\b\"\n for node in names:\n if self._model.existNode(self._model.clearId(node)):\n finalDef = re.sub(rx.format(node), lambda m:\n (\n m.group(1)\n if m.group(1)\n else\n (\n \"getNode('\"+node+\"')\"\n if (m.endpos > m.regs[0][1]+5) and ((m.string[m.regs[0][1]:m.regs[0][1]+5] == '.node') or (m.string[m.regs[0][1]:m.regs[0][1]+8] == '.timeit('))\n else\n (node\n if (m.string[m.regs[0][0]-1:m.regs[0][0]+len(node)] == ('.'+node)) or (m.string[m.regs[0][0]-7:m.regs[0][0]] == 'import ') or (m.string[m.regs[0][0]-5:m.regs[0][0]] == 'from ')\n else \"getCalcNode('\"+node+\"')\"\n )\n )\n ), finalDef, 0, re.IGNORECASE)\n elif node == \"self\":\n finalDef = re.sub(rx.format(node), lambda m:\n (\n m.group(1)\n if m.group(1)\n else\n \"getNode('\" + self.identifier + \"')\"\n if (m.endpos > m.regs[0][1]+11) and (m.string[m.regs[0][1]:m.regs[0][1]+11] != '._tryFilter')\n else \"self\"\n ), finalDef, 0, re.IGNORECASE)\n\n localRes = {\n \"getNode\": self._model.getNode,\n \"getCalcNode\": self._getCalcNode,\n \"cp\": Helpers(self)\n }\n if not extraParams is None:\n for keyParam in extraParams:\n localRes[keyParam] = extraParams[keyParam]\n\n customImports = self.model.getCustomImports()\n if customImports:\n for keyParam in customImports:\n localRes[keyParam] = customImports[keyParam]\n\n try:\n # execute node definition in supervised context\n memoryIO = io.StringIO()\n try:\n with redirect_stdout(memoryIO):\n exec(compile(finalDef, '<string>', 'exec'), localRes)\n except Exception as ex:\n if \"_io.StringIO\" in str(ex):\n exec(compile(finalDef, '<string>', 'exec'), localRes)\n else:\n raise ex\n\n self.lastEvaluationConsole = memoryIO.getvalue()\n memoryIO = None\n\n if self.nodeClass not in [\"button\", \"module\", \"text\"]:\n if 'this' in localRes:\n self._result = localRes['this']\n elif 'result' in localRes:\n self._result = localRes['result']\n else:\n self._result = None\n if self.lastEvaluationConsole != \"\":\n self._result = str(self.lastEvaluationConsole)\n else:\n raise ValueError(\n \"The result was not found. Did you forget to include the text 'result =' ?\")\n\n self._isCalc = self.nodeClass != \"button\"\n self.postCalculate()\n\n endTime = dt.datetime.now()\n self.lastEvaluationTime = (\n endTime - startTime).total_seconds() - self.lastLazyTime\n if self.lastEvaluationTime < 0:\n self.lastEvaluationTime = 0\n self.evaluationVersion = self.model.evaluationVersion\n finally:\n localRes[\"cp\"].release()\n localRes = None\n self.sendEndCalcNode(from_circular_evaluator)\n else:\n self._bypassCircularEvaluator = False","def start_one_step(self):\r\n new_infected_list = []\r\n old_infected_list = copy.deepcopy(self.infected_list)\r\n new_recovered_list = []\r\n old_recovered_list = copy.deepcopy(self.recovered_list)\r\n # For each infected node\r\n for infected_nid in old_infected_list:\r\n infected_node = self.node_dict[infected_nid]\r\n # For each neighbor\r\n for dst_nid in infected_node.get_dst_nid_list(self.graph):\r\n dst_node = self.node_dict[dst_nid]\r\n # Infect susceptible nodes with probability [p]\r\n if dst_node.state is NodeState.SUSCEPTIBLE and random.random() < self.p:\r\n dst_node.infected(self.i)\r\n new_infected_list.append(dst_nid)\r\n\r\n # Minus 1 turn of (remaining) infected days for all infected nodes\r\n infected_node.minus_one_state_day()\r\n # If infected node is recovered\r\n if infected_node.check_finish_infection():\r\n # Infected node get recovered\r\n infected_node.recovered(self.r)\r\n # Remove from infected list\r\n self.infected_list.remove(infected_nid)\r\n # Append to recovered list\r\n new_recovered_list.append(infected_nid)\r\n\r\n # Add newly infected nodes into infected list\r\n self.infected_list += new_infected_list\r\n\r\n # For each recovered node\r\n for recovered_nid in old_recovered_list:\r\n recovered_node = self.node_dict[recovered_nid]\r\n # Minus 1 turn of (remaining) recovered days for all recovered nodes\r\n recovered_node.minus_one_state_day()\r\n # If infected node is recovered\r\n if recovered_node.check_finish_recovery():\r\n # Recovered node get recovered\r\n recovered_node.susceptible()\r\n # Remove from recovered list\r\n self.recovered_list.remove(recovered_nid)\r\n\r\n # Add newly recovered nodes into recovered list\r\n self.recovered_list += new_recovered_list","def split_next(self):\n # Consider the node with the highest loss reduction (a.k.a. gain)\n node = heappop(self.splittable_nodes)\n\n tic = time()\n (sample_indices_left,\n sample_indices_right,\n right_child_pos) = self.splitter.split_indices(node.split_info,\n node.sample_indices)\n self.total_apply_split_time += time() - tic\n\n depth = node.depth + 1\n n_leaf_nodes = len(self.finalized_leaves) + len(self.splittable_nodes)\n n_leaf_nodes += 2\n\n left_child_node = TreeNode(depth,\n sample_indices_left,\n node.split_info.sum_gradient_left,\n node.split_info.sum_hessian_left,\n parent=node)\n right_child_node = TreeNode(depth,\n sample_indices_right,\n node.split_info.sum_gradient_right,\n node.split_info.sum_hessian_right,\n parent=node)\n left_child_node.sibling = right_child_node\n right_child_node.sibling = left_child_node\n node.right_child = right_child_node\n node.left_child = left_child_node\n\n # set start and stop indices\n left_child_node.partition_start = node.partition_start\n left_child_node.partition_stop = node.partition_start + right_child_pos\n right_child_node.partition_start = left_child_node.partition_stop\n right_child_node.partition_stop = node.partition_stop\n\n self.n_nodes += 2\n\n if self.max_depth is not None and depth == self.max_depth:\n self._finalize_leaf(left_child_node)\n self._finalize_leaf(right_child_node)\n return left_child_node, right_child_node\n\n if (self.max_leaf_nodes is not None\n and n_leaf_nodes == self.max_leaf_nodes):\n self._finalize_leaf(left_child_node)\n self._finalize_leaf(right_child_node)\n self._finalize_splittable_nodes()\n return left_child_node, right_child_node\n\n if left_child_node.n_samples < self.min_samples_leaf * 2:\n self._finalize_leaf(left_child_node)\n if right_child_node.n_samples < self.min_samples_leaf * 2:\n self._finalize_leaf(right_child_node)\n\n # Compute histograms of childs, and compute their best possible split\n # (if needed)\n should_split_left = left_child_node.value is None # node isn't a leaf\n should_split_right = right_child_node.value is None\n if should_split_left or should_split_right:\n\n # We will compute the histograms of both nodes even if one of them\n # is a leaf, since computing the second histogram is very cheap\n # (using histogram subtraction).\n n_samples_left = left_child_node.sample_indices.shape[0]\n n_samples_right = right_child_node.sample_indices.shape[0]\n if n_samples_left < n_samples_right:\n smallest_child = left_child_node\n largest_child = right_child_node\n else:\n smallest_child = right_child_node\n largest_child = left_child_node\n\n # We use the brute O(n_samples) method on the child that has the\n # smallest number of samples, and the subtraction trick O(n_bins)\n # on the other one.\n tic = time()\n smallest_child.histograms = \\\n self.histogram_builder.compute_histograms_brute(\n smallest_child.sample_indices)\n largest_child.histograms = \\\n self.histogram_builder.compute_histograms_subtraction(\n node.histograms, smallest_child.histograms)\n self.total_compute_hist_time += time() - tic\n\n tic = time()\n if should_split_left:\n self._compute_best_split_and_push(left_child_node)\n if should_split_right:\n self._compute_best_split_and_push(right_child_node)\n self.total_find_split_time += time() - tic\n\n return left_child_node, right_child_node","def update_nodes(nodes, svg_h):\n for i in range(0, len(nodes)):\n nodes[i,2] = svg_h-nodes[i,2]\n return nodes","def _buildtree(self):\n self.pricetree = np.zeros((self.steps+1,self.steps+1))\n self.pricetree[0][0] = self.p\n for j in range(self.steps):\n for i in range(j+1):\n self.pricetree[j+1][i+1] = self.pricetree[j][i]*self.down\n self.pricetree[j+1][0] = self.pricetree[j][0]*self.up","def run_one_step(self, dt):\n if not self._erode_flooded_nodes:\n flood_status = self._grid.at_node[\"flood_status_code\"]\n flooded_nodes = np.nonzero(flood_status == _FLOODED)[0]\n else:\n flooded_nodes = []\n\n upstream_order_IDs = self._grid[\"node\"][\"flow__upstream_node_order\"]\n\n defined_flow_receivers = np.not_equal(\n self._grid[\"node\"][\"flow__link_to_receiver_node\"], self._grid.BAD_INDEX\n )\n\n try:\n length_of_link = self._grid.length_of_d8\n except AttributeError:\n length_of_link = self._grid.length_of_link\n\n flow_link_lengths = length_of_link[\n self._grid.at_node[\"flow__link_to_receiver_node\"][defined_flow_receivers]\n ]\n flow_receivers = self._grid[\"node\"][\"flow__receiver_node\"]\n\n # Operate the main function:\n if self._use_W:\n self._alpha[defined_flow_receivers] = (\n self._K[defined_flow_receivers]\n * dt\n * self._A[defined_flow_receivers] ** self._m\n / self._W[defined_flow_receivers]\n / (flow_link_lengths**self._n)\n )\n\n else:\n self._alpha[defined_flow_receivers] = (\n self._K[defined_flow_receivers]\n * dt\n * self._A[defined_flow_receivers] ** self._m\n / (flow_link_lengths**self._n)\n )\n\n # Handle flooded nodes, if any (no erosion there)\n if flooded_nodes is not None:\n self._alpha[flooded_nodes] = 0.0\n\n reversed_flow = self._elevs < self._elevs[flow_receivers]\n # this check necessary if flow has been routed across\n # depressions\n self._alpha[reversed_flow] = 0.0\n\n threshdt = self._sp_crit * dt\n\n # solve using Brent's Method in Cython for Speed\n if isinstance(threshdt, float):\n brent_method_erode_fixed_threshold(\n upstream_order_IDs,\n flow_receivers,\n threshdt,\n self._alpha,\n self._n,\n self._elevs,\n )\n else:\n brent_method_erode_variable_threshold(\n upstream_order_IDs,\n flow_receivers,\n threshdt,\n self._alpha,\n self._n,\n self._elevs,\n )","def brents(f, x0, x1, max_iter=50, tolerance=1e-5):\n \n fx0 = f(x0)\n fx1 = f(x1)\n \n assert (fx0 * fx1) <= 0, \"Root not bracketed\" \n \n if abs(fx0) < abs(fx1):\n x0, x1 = x1, x0\n fx0, fx1 = fx1, fx0\n \n x2, fx2 = x0, fx0\n \n d = np.nan\n mflag = True\n steps_taken = 0\n \n while steps_taken < max_iter and abs(x1-x0) > tolerance:\n fx0 = f(x0)\n fx1 = f(x1)\n fx2 = f(x2)\n \n if fx0 != fx2 and fx1 != fx2:\n L0 = (x0 * fx1 * fx2) / ((fx0 - fx1) * (fx0 - fx2))\n L1 = (x1 * fx0 * fx2) / ((fx1 - fx0) * (fx1 - fx2))\n L2 = (x2 * fx1 * fx0) / ((fx2 - fx0) * (fx2 - fx1))\n new = L0 + L1 + L2\n \n else:\n new = x1 - ( (fx1 * (x1 - x0)) / (fx1 - fx0) )\n \n tt1 = (new < ((3 * x0 + x1) / 4) or new > x1)\n tt2 = (mflag == True and (abs(new - x1)) >= (abs(x1 - x2) / 2))\n tt3 = (mflag == False and (abs(new - x1)) >= (abs(x2 - d) / 2))\n tt4 = (mflag == True and (abs(x1 - x2)) < tolerance)\n tt5 = (mflag == False and (abs(x2 - d)) < tolerance)\n if (tt1 or\n tt2 or\n tt3 or\n tt4 or\n tt5):\n new = (x0 + x1) / 2\n mflag = True\n \n else:\n mflag = False\n \n fnew = f(new)\n d, x2 = x2, x1\n \n if (fx0 * fnew) < 0:\n x1 = new\n else:\n x0 = new\n \n if abs(fx0) < abs(fx1):\n x0, x1 = x1, x0\n \n steps_taken += 1\n \n return x1, steps_taken","def make_euler_circuit(start_node, updated_graph_instance):\n\n current_edges_on_graph_list = make_edges_list(updated_graph_instance.edges_dict)\n\n current_node = start_node\n\n node_visit_order = [current_node]\n edge_visit_order = []\n\n # print(\"\\n\\n\\ncurrent_edges_on_graph_list:\", current_edges_on_graph_list)\n\n while len(current_edges_on_graph_list) > 0:\n\n # print(\"current_edges_on_graph_list:\", current_edges_on_graph_list)\n # while there are still edges on the graph, keep traversing\n\n current_bridges_on_graph = get_bridges(current_edges_on_graph_list)\n\n edges_conn_to_current_node = get_all_conn_edges_remaining_in_graph(\n current_node, current_edges_on_graph_list, updated_graph_instance.nodes_dict\n )\n\n edge_to_traverse = choose_edge_to_traverse(\n current_node, edges_conn_to_current_node, current_bridges_on_graph\n )\n\n if edge_to_traverse in current_edges_on_graph_list:\n\n current_edges_on_graph_list.remove(edge_to_traverse)\n\n else:\n\n current_edges_on_graph_list.remove(edge_to_traverse[::-1])\n\n edge_to_traverse_list = list(edge_to_traverse)\n # remove current node from edge to traverse\n edge_to_traverse_list.remove(current_node)\n # update current node to be the only node left in the edge list\n\n # update edge traveral list with edge just traversed\n edge_traversed = (current_node, edge_to_traverse_list[0])\n\n edge_visit_order.append(edge_traversed)\n\n current_node = edge_to_traverse_list[0]\n\n # add the new current node to the nodes visit order list\n node_visit_order.append(current_node)\n\n # add node visit order and edge_visit order to graph instance\n\n updated_graph_instance.node_visit_order = node_visit_order\n\n updated_graph_instance.edge_visit_order = edge_visit_order\n\n updated_graph_instance.node_geojson = make_node_geojson(updated_graph_instance)\n\n updated_graph_instance.edge_geojson = make_edge_geojson(updated_graph_instance)\n\n updated_graph_instance.route_geojson = make_route_geojson(updated_graph_instance)\n\n print(\"\\n\\n\\n\\n\\nROUTE COLLECTION\", updated_graph_instance.route_geojson)\n\n print(\"check done\")\n\n return updated_graph_instance","def compare_nodes(G,all_match_pairs,match_pair,traversed,node1,node2, ports_weight):\n logger.debug(f\"comparing {node1},{node2}, traversed {traversed}\")\n nbrs1 = sorted(set(G.neighbors(node1)) - set(traversed))\n #remove dummies\n nbrs1 = sorted(set([nbr for nbr in nbrs1 if G.get_edge_data(node1, nbr)['weight'] !=7]))\n nbrs2 = sorted(set(G.neighbors(node2)) - set(traversed))\n #remove dummies\n nbrs2 = sorted(set([nbr for nbr in nbrs2 if G.get_edge_data(node2, nbr)['weight'] !=7]))\n logger.debug(f\"node1:{node1},property: {G.nodes[node1]},neigbors1: {nbrs1}\")\n logger.debug(f\"node2:{node2},property: {G.nodes[node2]},neigbors2: {nbrs2}\")\n if not nbrs1 or not nbrs2:\n if compare_two_nodes(G, node1, node2, ports_weight):\n match_pair[node1] = node2\n logger.debug(f\"no new neihbours, returning recursion {match_pair}\")\n return\n elif len(nbrs1)> 10:\n if \"start_point\" in match_pair.keys():\n match_pair[\"start_point\"]+=[node1,node2]\n else:\n match_pair[\"start_point\"]=[node1,node2]\n logger.debug(f\"skipping high fanout nets due to large computation, {node1} {nbrs1}\")\n traversed.append(node1)\n return\n elif len(nbrs2)> 10:\n if \"start_point\" in match_pair.keys():\n match_pair[\"start_point\"]+=[node1,node2]\n else:\n match_pair[\"start_point\"]=[node1,node2]\n traversed.append(node2)\n logger.debug(f\"skipping high fanout nets due to large computation, {node2} {nbrs2}\")\n return\n\n if node1 == node2:\n if node1 in match_pair.keys() or node1 in match_pair.values():\n logger.debug(\"avoid existing pair wise symmetry\")\n return\n logger.debug(f\"single node {node1}, nbrs {nbrs1}, nbr_weight {[G.get_edge_data(node1,nbr) for nbr in nbrs1]}\")\n SD_nbrs= [nbr for nbr in nbrs1 if G.get_edge_data(node1, nbr)['weight'] !=2]\n ## TBD: filter based on primitive constraints\n ## Right now will try to figure out S/D paths\n if len(SD_nbrs) ==0:\n logger.debug(f\"No SD paths found to traverse\")\n match_pair[node1]=node1\n elif len(SD_nbrs) ==1:\n logger.debug(f\"traversing single S/D path {SD_nbrs}\")\n match_pair[node1]=node1\n traversed.append(node1)\n compare_nodes(G,all_match_pairs,match_pair,traversed,SD_nbrs[0],SD_nbrs[0],ports_weight)\n else:\n logger.debug(f\" multiple nodes diverging {SD_nbrs}\")\n logger.debug(f\"nbr weights: {SD_nbrs} {[G.get_edge_data(node1, nbr)['weight'] for nbr in SD_nbrs ]}\")\n match_pair[node1]=node1\n traversed.append(node1)\n new_sp=sorted(set(SD_nbrs)-set(traversed))\n all_match_pairs_local={}\n for nbr1,nbr2 in combinations(new_sp, 2):\n logger.debug(f\"recursive pair call from single branch {nbr1} {nbr2}\")\n new_pair={}\n compare_nodes(G,all_match_pairs,new_pair,traversed.copy(),nbr1,nbr2,ports_weight)\n if new_pair:\n #new_pair[nbr1]=nbr2\n all_match_pairs_local[nbr1+'_'+nbr2] = new_pair\n all_match_pairs_local={k: v for k, v in all_match_pairs_local.items() if len(v)>0}\n if len(all_match_pairs_local)==1:\n match_pair.update( all_match_pairs_local[list(all_match_pairs_local.keys())[0]])\n logger.debug(f\"found inline pair: {pprint.pformat(match_pair, indent=4)}\")\n else:\n for nbr1 in new_sp:\n if (nbr1+'_'+nbr1 not in all_match_pairs.keys()):\n logger.debug(f\"recursive single branch call from single branch {nbr1} {nbr1}\")\n new_pair={}\n compare_nodes(G,all_match_pairs,new_pair,traversed.copy(),nbr1,nbr1,ports_weight)\n #filtering multiple axis of symmetries with same block, ideally they should be handled by array generation\n if new_pair:\n all_match_pairs[nbr1+'_'+nbr1] = new_pair\n logger.debug(f\"updating match pairs: {pprint.pformat(all_match_pairs, indent=4)}\")\n\n\n elif nbrs1 == nbrs2:\n logger.debug(f\"traversing converging branch\")\n match_pair[node1]=node2\n traversed+=[node1,node2]\n nbrs1=sorted(set(nbrs1)-set([node1,node2]))\n logger.debug(f\"all non traversed neighbours: {nbrs1}\")\n if len(nbrs1)==1:\n nbr1=nbr2=nbrs1[0]\n logger.debug(f\"keeping single converged branch inline {nbr1} {nbr2}\")\n compare_nodes(G,all_match_pairs,match_pair,traversed.copy(),nbr1,nbr2,ports_weight)\n else:\n for nbr1,nbr2 in combinations_with_replacement(nbrs1,2):\n logger.debug(f\"recursive call from converged branch {nbr1} {nbr2}\")\n if nbr1+'_'+nbr2 not in all_match_pairs.keys():\n new_pair={}\n compare_nodes(G,all_match_pairs,new_pair,traversed.copy(),nbr1,nbr2,ports_weight)\n #filtering multiple axis of symmetries with same block, ideally they should be handled by array generation\n if new_pair:\n all_match_pairs[nbr1+'_'+nbr2] = new_pair\n logger.debug(f\"updating match pairs: {pprint.pformat(all_match_pairs, indent=4)}\")\n\n\n elif compare_two_nodes(G,node1,node2,ports_weight):\n nbrs1 = sorted(set([nbr for nbr in nbrs1 if G.get_edge_data(node1, nbr)['weight'] !=2]))\n nbrs2 = sorted(set([nbr for nbr in nbrs2 if G.get_edge_data(node2, nbr)['weight'] !=2]))\n match_pair[node1]=node2\n traversed+=[node1,node2]\n logger.debug(f\"Traversing parallel branches from {node1},{node2} {nbrs1}, {nbrs2}\")\n nbrs1_wt = [G.get_edge_data(node1, nbr)['weight'] for nbr in nbrs1]\n nbrs2_wt = [G.get_edge_data(node2, nbr)['weight'] for nbr in nbrs2]\n unique_match=find_unique_matching_branches(G,nbrs1,nbrs2,ports_weight)\n if len(nbrs1)==0 or len(nbrs2)==0:\n logger.debug(f\"no new SD neihbours, returning recursion {match_pair}\")\n elif len(nbrs1) ==1 and len(nbrs2)==1:\n logger.debug(f\"traversing binary branch\")\n compare_nodes(G,all_match_pairs,match_pair,traversed,nbrs1.pop(),nbrs2.pop(),ports_weight)\n elif unique_match:\n logger.debug(f'traversing unique matches {unique_match}')\n match_pair[node1]=node2\n traversed+=[node1,node2]\n for nbr1,nbr2 in unique_match.items():\n logger.debug(f\"recursive call from binary {node1}:{node2} to {nbr1}:{nbr2}\")\n compare_nodes(G,all_match_pairs,match_pair,traversed.copy(),nbr1,nbr2,ports_weight)\n elif len(nbrs1_wt)>len(set(nbrs1_wt))>1 and len(nbrs2_wt)>len(set(nbrs2_wt))>1:\n logger.debug(f\"setting new start points {node1} {node2}\")\n match_pair[node1]=node2\n if \"start_point\" in match_pair.keys():\n match_pair[\"start_point\"]+=[node1,node2]\n else:\n match_pair[\"start_point\"]=[node1,node2]\n else:\n match_pair = {}\n logger.debug(f\"end all traversal from binary branch {node1} {node2}\")\n\n else:\n match_pair = {}\n logger.debug(f\"end of recursion branch, matches {match_pair}\")","def flep(tree_adj, nodes_sign, edge_weight, root, return_fullcut_info=False):\n # start = clock()\n assert isinstance(tree_adj, dict)\n if not (isinstance(nodes_sign, tuple) and len(nodes_sign) == 2):\n nodes_sign = (nodes_sign, nodes_sign)\n if root in nodes_sign[0]:\n cutp, cutn = (MAX_WEIGHT, 0) if nodes_sign[0][root] < 0 else (0, MAX_WEIGHT)\n val_1 = nodes_sign[0][root]*MAX_WEIGHT\n cutp_, cutn_ = (MAX_WEIGHT, 0) if nodes_sign[1][root] < 0 else (0, MAX_WEIGHT)\n val_2 = nodes_sign[1][root]*MAX_WEIGHT\n return (val_1, val_2), {}, {root: (True, -1, cutp, cutn, cutp_, cutn_)}\n stack = []\n status = defaultdict(lambda: (False, -1, 0, 0, 0, 0))\n stack.append(root)\n while stack:\n v = stack.pop()\n if v >= 0:\n discovered, pred, cutp, cutn, cutp_, cutn_ = status[v]\n else:\n v = -(v+100)\n discovered, pred, cutp, cutn, cutp_, cutn_ = status[v]\n for child in tree_adj[v]:\n if status[child][1] != v:\n continue\n eweight = edge_weight[(child, v) if child < v else (v, child)]\n _, _, childp, childn, childp_, childn_ = status[child]\n cutp += min(childp, childn + eweight)\n cutn += min(childn, childp + eweight)\n cutp_ += min(childp_, childn_ + eweight)\n cutn_ += min(childn_, childp_ + eweight)\n status[v] = (discovered, pred, cutp, cutn, cutp_, cutn_)\n # print('{}: (+: {}, -: {})'.format(v, cutp, cutn))\n if v == root:\n # FLEP_CALLS_TIMING.append(clock() - start)\n intermediate = {}\n if return_fullcut_info:\n intermediate = {n: vals[2:6]\n for n, vals in status.items()\n if vals[0] and n not in nodes_sign[0]}\n return (((cutn - cutp), (cutn_ - cutp_)), intermediate, status)\n\n if not discovered:\n status[v] = (True, pred, cutp, cutn, cutp_, cutn_)\n if v in nodes_sign[0]:\n # don't go beyond revealed nodes\n continue\n stack.append(-(v+100))\n for w in tree_adj[v]:\n discovered, pred, cutp, cutn, cutp_, cutn_ = status[w]\n if pred == -1 and w != root:\n if w in nodes_sign[0]:\n cutp, cutn = (MAX_WEIGHT, 0) if nodes_sign[0][w] < 0 else (0, MAX_WEIGHT)\n cutp_, cutn_ = (MAX_WEIGHT, 0) if nodes_sign[1][w] < 0 else (0, MAX_WEIGHT)\n status[w] = (discovered, v, cutp, cutn, cutp_, cutn_)\n if not discovered:\n stack.append(w)\n assert False, root","def CreateTree(d,list_L):\r\n \r\n d_new = defaultdict(dict)\r\n \r\n #labels corresponding to minimum evolutionary distance in Matrix\r\n a,b = minInDict(d)\r\n\r\n n = ['(',a,',',b,')']\r\n new_cluster = ''.join(n)\r\n \r\n #remove the entries in newly formed cluster from our list\r\n for k in list_L:\r\n if k == a:\r\n list_L.remove(k)\r\n break\r\n for p in list_L:\r\n if p == b:\r\n list_L.remove(p)\r\n break\r\n\r\n #to remove an existing cluster with those entries\r\n for j in list_L:\r\n if a in j or b in j:\r\n list_L.remove(j)\r\n\r\n #Base case of our recursive function \r\n if len(d) == 1:\r\n return new_cluster\r\n\r\n #calculating distances from our new cluster(eg: (a,b))to other labels/clusters\r\n for q in list_L:\r\n \r\n if b in d:\r\n if q in d[a] and q in d[b]:\r\n d_new[new_cluster][q] = (d[a][q] + d[b][q])/2\r\n continue\r\n if q in d:\r\n if b in d[q] and q in d[a]:\r\n d_new[new_cluster][q] = (d[a][q] + d[q][b])/2\r\n continue\r\n if b in d:\r\n if a in d[q] and q in d[b]:\r\n d_new[new_cluster][q] = (d[q][a] + d[b][q])/2\r\n continue\r\n if a in d[q] and b in d[q]:\r\n d_new[new_cluster][q] = (d[q][a] + d[q][b])/2\r\n \r\n list_L.insert(0,new_cluster)\r\n \r\n #deleting row and column corresponding to a and b\r\n del d[a]\r\n if b in d:\r\n del d[b]\r\n \r\n for p in d:\r\n for q in d[p]:\r\n if q != b and q != a:\r\n d_new[p][q] = d[p][q]\r\n\r\n return CreateTree(d_new,list_L)","def fractionalPoints(totalNodeList, recNodeList, fracPoints):\n\n avPoint = averagePoints(recNodeList)\n\n for i in range(0, fracPoints):\n closestPoint = closest_node(avPoint, totalNodeList) #Finds closest point\n totalNodeList.remove(closestPoint)\n recNodeList.append(closestPoint)\n\n printProgressBar(i, fracPoints) \n\n return recNodeList","def get_nodes(final_node, discard_params=True):\n var_nodes = []\n func_nodes = []\n seen_ids = set()\n\n # We re-do backward var iter here, with some changes\n cand_funcs = []\n seen = set()\n \n def add_cand(cand):\n if cand not in seen:\n # Negate since heapq is min-heap\n heapq.heappush(cand_funcs, (-cand.rank, len(seen), cand))\n seen.add(cand)\n \n add_cand(final_node.creator_node)\n seen_ids.add(id(final_node))\n \n while cand_funcs:\n _,_,func = heapq.heappop(cand_funcs)\n inputs = func.inputs\n target_inputs = [x for x in inputs if x.requires_grad]\n if not target_inputs:\n continue\n \n if id(func) not in seen_ids:\n func_nodes += [func]\n seen_ids.add(id(func))\n \n for x in target_inputs:\n if discard_params and x.creator_node is None:\n if isinstance(x.get_variable_or_none(), Parameter):\n continue # Discard param dead ends\n \n if id(x) not in seen_ids:\n var_nodes += [x]\n seen_ids.add(id(x))\n \n if x.creator_node is not None:\n add_cand(x.creator_node)\n \n return var_nodes, func_nodes","def prepare_initial_nodes(x_start, x_end, nodes_y):\n nodes_x = [float(x_start + ((x_end - x_start) / (len(nodes_y) - 1)) * i) for i in range(0, len(nodes_y))]\n nodes_y = [float(y) for y in nodes_y]\n print(nodes_x)\n print(nodes_y)\n nodes = list(zip(nodes_x, nodes_y))\n return nodes","def _compute_newton_step(lambdas, p_norm, w_norm):\n return lambdas.candidate + (p_norm / w_norm) ** 2 * (p_norm - 1)","def next_node_dfs(search_state, last_node_is_ok):\n log_T, initial_state, min_score, max_depth, maxtraversals, node, node_idx, it, order, score, sub_info = search_state\n min_score = float(min_score) # make sure numba knows this is a float (otherwise, sometimes, it doesn't (bug in numba))\n n_states = log_T.shape[0]\n if it == maxtraversals:\n assert False, \"Number of traversals exceeded\"\n while True:\n # next node ##\n # try adding a value at the end\n for next_idx, next_state in enumerate(order[node[-1]]):\n if last_node_is_ok and min_score <= score + log_T[node[-1], next_state] and len(node) < max_depth \\\n and syntax_check(np.array(node + [next_state]), sub_info, partial=True):\n node.append(next_state)\n node_idx.append(next_idx)\n break\n # adding a value at the end failed, so we are a leave\n else:\n for p in xrange(len(node) - 1, -1, -1):\n if node_idx[p] != n_states - 1: # find where within the node to increase (and discard all others after)\n old_idx = node_idx[p]\n del node_idx[p:]\n del node[p:]\n node_idx.append(old_idx + 1)\n prev_state = node[p - 1] if p > 0 else initial_state\n node.append(order[prev_state, node_idx[p]])\n break\n else:\n search_state = log_T, initial_state, min_score, max_depth, maxtraversals, list(node), list(node_idx), it, order, score, sub_info\n return [-1], score, search_state # end of the generator, can't increase even the root\n last_node_is_ok = True # We can now make progress again, regardless of whether we could at the beginning\n it += 1\n # score and return current node if adequate\n score = log_T[initial_state, node[0]]\n for p in xrange(1, len(node)):\n score += log_T[node[p - 1], node[p]]\n if min_score <= score and syntax_check(np.array(node), sub_info, partial=False):\n search_state = log_T, initial_state, min_score, max_depth, maxtraversals, list(node), list(node_idx), it, order, score, sub_info\n return list(node), score, search_state # the invocation to list here is to make a copy, don't remove!","def getNodesAndDistances():\n\n\tglobal width, height\n\n\t# First we generate the list\n\n\tprint \"\\tGetting node list...\"\n\t\n\tnodeDict = {}\n\n\tfor y in range(height):\n\t\tfor x in range(width):\n\t\t\ttheType = getSquare(x, y)\n\n\t\t\tprint \"\\t\\tGetting list for node (%d, %d) of type %d...\" % (x, y, theType)\n\n\t\t\ttempList = getNodeList(x, y, theType)\n\n\t\t\tif tempList == []:\n\t\t\t\tprint \"\\t\\t\\tNo nodes here.\"\n\t\t\telse:\n\t\t\t\tfor i in range(len(tempList)):\n\t\t\t\t\tnode = tempList[i]\n\t\t\t\t\tnodeName = node[0]\n\t\t\t\t\tnodeDict[nodeName] = node[1:]\t# Everything but the first element\n\t\t\t\t\tprint \"\\t\\t\\tAdded node '%s'...\" % nodeName\n\n\tprint \"\\tDone getting node list (%d nodes)...\" % (len(nodeDict.keys()))\n\tprint \"\"\n\n\t# Now that we've got that, we get a list of pairs\n\n\tpairList = getPairList(nodeDict)\n\n\t# Now we calculate the distance between every pair of nodes that connect\n\n\tprint \"\"\n\tprint \"\\tCreateing dictionary of distances between connected nodes...\"\n\n\tdistanceDict = {}\n\n\tfor tuple in pairList:\n\t\t(nodeA, nodeB) = tuple\n\t\tprint \"\\t\\tCalculating distance between '%s' and '%s'...\" % (nodeA, nodeB)\n\t\tdistance = distanceBetween(nodeA, nodeB, nodeDict)\n\t\tpairName = \"%s%s\" % (nodeA, nodeB)\n\t\tdistanceDict[pairName] = distance\n\t\tprint \"\\t\\t\\tDistace was %f.\" % (distance)\n\n\tprint \"\\tDone creating dictionary of node differences (%d pairs).\" % (len(distanceDict.keys()))\n\n\treturn nodeDict, distanceDict","def perform(self, node, inputs, outputs):\r\n x = inputs[0]\r\n L = inputs[1]\r\n dz = inputs[2]\r\n dx = outputs[0]\r\n N = x.shape[0]\r\n if self.lower:\r\n F = numpy.tril(dz)\r\n for k in xrange(N - 1, -1, -1):\r\n for j in xrange(k + 1, N):\r\n for i in xrange(j, N):\r\n F[i, k] -= F[i, j] * L[j, k]\r\n F[j, k] -= F[i, j] * L[i, k]\r\n for j in xrange(k + 1, N):\r\n F[j, k] /= L[k, k]\r\n F[k, k] -= L[j, k] * F[j, k]\r\n F[k, k] /= (2 * L[k, k])\r\n else:\r\n F = numpy.triu(dz)\r\n M = N - 1\r\n for k in xrange(N - 1, -1, -1):\r\n for j in xrange(k + 1, N):\r\n for i in xrange(j, N):\r\n F[k, i] -= F[j, i] * L[k, j]\r\n F[k, j] -= F[j, i] * L[k, i]\r\n for j in xrange(k + 1, N):\r\n F[k, j] /= L[k, k]\r\n F[k, k] -= L[k, j] * F[k, j]\r\n F[k, k] /= (2 * L[k, k])\r\n dx[0] = F","def add_nodes(self, new_nodes):\n self.nodes = self._nodes + ensure_list(new_nodes)\n for nd in ensure_list(new_nodes):\n self.predecessors[nd.name] = []\n self.successors[nd.name] = []\n if self._sorted_nodes is not None:\n # starting from the previous sorted list, so is faster\n self.sorting(presorted=self.sorted_nodes + ensure_list(new_nodes))","def balances_to_path_nodes(balances_data, workers=1, hash_func=pedersen_hash):\n height = balances_data['tree_height']\n vaults_data = balances_data['vaults_data']\n balances = [(vault['vault_id'], vault) for vault in vaults_data]\n\n event_loop = asyncio.new_event_loop()\n\n with parallel_hash(hash_func, workers) as async_hash_func:\n try:\n res = event_loop.run_until_complete(\n calc_nodes(height, balances, calc_zero_nodes(height), 1, async_hash_func)\n )\n return res\n finally:\n event_loop.close()","def updateClusters(LLE_node_vals, switch_penalty=1):\r\n (T, num_clusters) = LLE_node_vals.shape\r\n future_cost_vals = np.zeros(LLE_node_vals.shape)\r\n\r\n # compute future costs\r\n for i in range(T-2, -1, -1):\r\n j = i+1\r\n indicator = np.zeros(num_clusters)\r\n future_costs = future_cost_vals[j, :]\r\n lle_vals = LLE_node_vals[j, :]\r\n for cluster in range(num_clusters):\r\n total_vals = future_costs + lle_vals + switch_penalty\r\n total_vals[cluster] -= switch_penalty\r\n future_cost_vals[i, cluster] = np.min(total_vals)\r\n\r\n # compute the best path\r\n path = np.zeros(T)\r\n\r\n # the first location\r\n curr_location = np.argmin(future_cost_vals[0, :] + LLE_node_vals[0, :])\r\n path[0] = curr_location\r\n\r\n # compute the path\r\n for i in range(T-1):\r\n j = i+1\r\n future_costs = future_cost_vals[j, :]\r\n lle_vals = LLE_node_vals[j, :]\r\n total_vals = future_costs + lle_vals + switch_penalty\r\n total_vals[int(path[i])] -= switch_penalty\r\n\r\n path[i+1] = np.argmin(total_vals)\r\n\r\n # return the computed path\r\n return path","def evaluate_cuts(base_tree, node):\n config = Configuration.config # Collect configuration\n\n N = config.normals # Collect predefined set of normal vectors\n N = np.append(N, node.auxiliary_normals, axis=0) # Append partition's bounding-box-aligned vectors as normals\n N = np.unique(np.round(N, 3), axis=0) # Return sorted unique elements of input array_like\n\n trees = []\n for i in range(N.shape[0]):\n trees_of_this_normal = [] # start a list of trees for splits along this normal\n normal = N[i] # current normal\n for plane in bsp_tree.get_planes(node.part, normal): # iterate over all valid cutting planes for the node\n tree, result = bsp_tree.expand_node(base_tree, node.path, plane) # split the node using the plane\n if tree: # only keep the tree if the split is successful\n trees_of_this_normal.append(tree)\n logger.debug(f\"normal index: {i}, origin: {plane[0]}, normal: {plane[1]}, result: {result}\")\n if len(trees_of_this_normal) == 0: # avoid empty list errors during objective function evaluation\n logger.info(f\"normal index: {i}, trees for normal: {len(trees_of_this_normal)}, total trees: {len(trees)}\")\n continue\n # go through each objective function, evaluate the objective function for each tree in this normal's\n # list, fill in the data in each tree object in the list\n for evaluate_objective_func in objectives.values():\n evaluate_objective_func(trees_of_this_normal, node.path)\n trees += trees_of_this_normal\n logger.info(f\"normal index: {i}, trees for normal: {len(trees_of_this_normal)}, total trees: {len(trees)}\")\n\n # go through the list of trees, best ones first, and throw away any that are too similar to another tree already\n # in the result list\n result_set = []\n for tree in sorted(trees, key=lambda x: x.objective):\n if tree.sufficiently_different(node, result_set):\n result_set.append(tree)\n logger.info(f\"{len(result_set)} valid trees\")\n return result_set","def fn(n):\n if n == 1: return [TreeNode()]\n ans = []\n for nn in range(1, n, 2): \n for left in fn(nn):\n for right in fn(n-1-nn): \n ans.append(TreeNode(left=left, right=right))\n return ans","def batch_predict(tree_adj, training_signs, edge_weight):\n # since shazoo use the revealed signs as-is, it's ok to use the same name\n training_signs, l2_values, rta_signs = training_signs\n all_nodes_to_predict = set(tree_adj) - set(training_signs)\n logging.debug('batch_predict has %d nodes to predict', len(all_nodes_to_predict))\n methods = ['l2cost', 'rta', 'shazoo']\n # fields are current_closest_hinge, current_sign, current_dst_to_closest_hinge\n node_predictions = {m: defaultdict(lambda: (None, None, 2e9)) for m in methods}\n hinge_value = {m: {} for m in methods}\n total_iter = 0\n while all_nodes_to_predict:\n some_root_of_a_border_tree = next(iter(all_nodes_to_predict))\n hinge_nodes, border_tree_nodes = find_hinge_nodes(tree_adj, edge_weight, training_signs,\n some_root_of_a_border_tree,\n with_visited=True)\n unmarked = border_tree_nodes - hinge_nodes\n for u in hinge_nodes:\n if u in hinge_value['shazoo']:\n continue\n vals, _, status = flep(tree_adj, (training_signs, rta_signs), edge_weight, u)\n hinge_value['shazoo'][u] = sgn(vals[0])\n hinge_value['rta'][u] = sgn(vals[1])\n if not USE_SCIPY:\n continue\n border_tree = build_border_tree_from_mincut_run(status, edge_weight)\n _, E, El, leaves_sign, _, _ = border_tree\n L = {u: l2_values[u] for u in leaves_sign}\n mapped_E, mapped_El_L, mapping = preprocess_edge_and_leaves(E, El, L)\n val = solve_by_zeroing_derivative(mapped_E, mapped_El_L, mapping, L,\n reorder=False)[0][u]\n hinge_value['l2cost'][u] = sgn(val)\n predicted_in_that_border_tree = set()\n inner_iter = 0\n # to avoid the same fork being picked again and again\n unmarked.add(some_root_of_a_border_tree)\n while unmarked:\n one_to_predict = next(iter(unmarked))\n hinge_tree = get_hinge_tree(one_to_predict, tree_adj, hinge_nodes)\n other_predicted = set()\n for h, h_val in iteritems(hinge_value['shazoo']):\n if h not in hinge_tree:\n continue\n predicted = propagate_hinge(hinge_tree, h, h_val, node_predictions['shazoo'],\n edge_weight)\n for u in predicted:\n prediction_info = node_predictions['shazoo'][u]\n used_hinge = prediction_info[0]\n node_predictions['rta'][u] = (used_hinge, hinge_value['rta'][used_hinge],\n prediction_info[2])\n if not USE_SCIPY:\n continue\n node_predictions['l2cost'][u] = (used_hinge, hinge_value['l2cost'][used_hinge],\n prediction_info[2])\n other_predicted.update(predicted)\n predicted_in_that_border_tree.update(other_predicted)\n unmarked -= other_predicted\n inner_iter += 1\n if inner_iter > len(tree_adj):\n import time\n logging.critical('batch predict failed in the inner loop')\n persistent.save_var('__fail_{}.my'.format(int(time.time())), (tree_adj, (training_signs, l2_values, rta_signs), edge_weight))\n raise RuntimeError('batch predict failed in the inner loop')\n all_nodes_to_predict -= predicted_in_that_border_tree\n total_iter += 1\n if total_iter > len(tree_adj):\n import time\n logging.critical('batch predict failed in the outer loop')\n persistent.save_var('__fail_{}.my'.format(int(time.time())), (tree_adj, (training_signs, l2_values, rta_signs), edge_weight))\n raise RuntimeError('batch predict failed in the outer loop')\n logging.debug('batch_predict has actually predicted %d nodes', len(node_predictions) - len(training_signs))\n return {m: {u: v[1] for u, v in iteritems(node_predictions[m]) if u not in training_signs}\n for m in methods}","def nj(nodeL,distD):\n while len(nodeL) > 2:\n nodeA,nodeB = bestPair(nodeL,distD)\n bA, bB = branchLength(nodeA,nodeB,nodeL,distD)\n newNode= mergeNodes(nodeA, nodeB, bA, bB)\n updateDistances(nodeA,nodeB,newNode,nodeL,distD)\n nodeL.remove(nodeA)\n nodeL.remove(nodeB)\n nodeL.append(newNode)\n return terminate(nodeL,distD)","def push_nodes_with_binary(self, b, step_ratio=0.1, niter=0):\n lid = list(self._data[:, 0])\n lpid = list(self._data[:, -2])\n t_data = self._data.copy()\n\n children_idx = {\n pid: [i for i, p in enumerate(lpid) if p == t_data[i, 0]] for pid in lpid\n }\n\n for _ in range(niter):\n for i in range(t_data.shape[0]):\n pid, radius, (x, y, z) = (\n int(t_data[i, -2]),\n t_data[i, -3],\n t_data[i, 2:5],\n )\n cidx = children_idx[pid]\n if pid != i and pid in lid and len(cidx) <= 1:\n px, py, pz = t_data[t_data[:, 0] == pid, 2:5][0]\n vnorm = norm_vec(np.asarray([x - px, y - py, z - pz]))\n\n if len(cidx) == 1:\n cx, cy, cz = t_data[cidx[0], 2:5]\n vnorm = (\n vnorm + norm_vec(np.asarray([cx - x, cy - y, cz - z]))\n ) / 2\n if all([v == 0 for v in vnorm]):\n continue\n\n pt = np.asarray([x, y, z])\n p_vectors = get_perpendicular_vectors(pt, vnorm)\n p_distances = [\n get_distance_to_boundary(pt, pvec, b) for pvec in p_vectors\n ]\n dx, dy, dz = np.sum(\n [pv * pd for pv, pd in zip(p_vectors, p_distances)], 0\n )\n\n # Constrain the displacement by the nodo radii\n tx = x + dx * step_ratio\n ty = y + dy * step_ratio\n tz = z + dz * step_ratio\n dist = (\n (tx - self._data[i, 2]) ** 2\n + (ty - self._data[i, 3]) ** 2\n + (tz - self._data[i, 4]) ** 2\n ) ** 0.5\n if dist <= radius / 2:\n t_data[i, 2] = tx\n t_data[i, 3] = ty\n t_data[i, 4] = tz\n else:\n pass\n self._data = t_data","def _compute_ptdfs(self):\n z = self._compute_z()\n s = np.zeros([self.n_nodes, self.n_nodes, self.n_nodes])\n for k in range(self.n_nodes):\n for L in range(self.n_nodes):\n for i in range(self.n_nodes):\n if k == 0 and L != 0:\n s[k, L, i] = -1 * z[L-1, i-1]\n elif k != 0 and L == 0:\n s[k, L, i] = z[k-1, i-1]\n elif k != 0 and L != 0 and k != L:\n s[k, L, i] = z[k-1, i-1] - z[L-1, i-1]\n return s","def root_nodes(self, node1, node2, distance):\n if node1 == node2.parent:\n upper_node = node1\n lower_node = node2\n upper_dist, lower_dist = distance, lower_node.branch - distance\n elif node2 == node1.parent:\n upper_node = node2\n lower_node = node1\n upper_dist, lower_dist = lower_node.branch - distance, distance\n else:\n raise PhyloValueError('root_nodes() requires that one of the given nodes is the parent of the other.')\n if len(self.root.children) <= 1:\n raise PhyloValueError('cannot re-root a tree where the existing root has one or no children.')\n elif len(self.root.children) == 2:\n if upper_node == self.root:\n # Just need to adjust branch lengths\n root_child = self.root.children[1] if self.root.children[0] == lower_node else self.root.children[0]\n root_child.branch += upper_dist\n lower_node.branch = lower_dist\n else:\n upper_path = self.find_path_to_root(upper_node)\n # Process the old root child after removing the root:\n root_child = self.root.children[1] if self.root.children[0] == upper_path[1] else self.root.children[0]\n root_child.branch += upper_path[1].branch\n root_child.parent = upper_path[1]\n upper_path[1].children.append(root_child)\n # Process nodes between root and upper_node:\n prev_node = upper_path[1]\n for next_node in upper_path[2:]:\n prev_node.children.remove(next_node)\n prev_node.parent = next_node\n next_node.children.append(prev_node)\n prev_node.branch = next_node.branch\n prev_node = next_node\n # Process upper_node, lower_node, and the new root\n upper_node.parent = lower_node.parent = self.root\n upper_node.children.remove(lower_node)\n self.root.children = [node1, node2] # Keeps the argument order\n upper_node.branch = upper_dist\n lower_node.branch = lower_dist\n else: # If the root has 3 children it means it's an unrooted tree\n new_root = self.new_tree_node()\n new_root.branch = self.root.branch # Transfers any existing root branch\n if upper_node != self.root:\n upper_path = self.find_path_to_root(upper_node)\n prev_node = self.root\n for next_node in upper_path[1:]:\n prev_node.children.remove(next_node)\n prev_node.parent = next_node\n next_node.children.append(prev_node)\n prev_node.branch = next_node.branch\n prev_node = next_node\n upper_node.children.remove(lower_node)\n upper_node.branch = upper_dist\n lower_node.branch = lower_dist\n new_root.children.append(upper_node)\n new_root.children.append(lower_node)\n upper_node.parent = lower_node.parent = new_root\n self.root = new_root\n self.process_tree_nodes()","def sub_run1(new_node, distance):\n optimal_value[0] = optimal_value[0] + distance\n\n PL = get_node_points(new_node.left)\n P = get_node_points(new_node)\n PR = get_node_points(new_node.right)\n\n line1 = matrix.linear_eq((PL, P))\n line2 = matrix.linear_eq((PR, P))\n\n line1[2] = line1[2] * (-1)\n line2[2] = line2[2] * (-1)\n\n linear_list.append((new_node.left, new_node, line1))\n linear_list.append((new_node, new_node.right, line2))\n\n remove_eq(new_node)\n tspturtle.tsp_draw(PL, P, PR)\n\n remove(new_node)","def neighborJoining(distances):\n\n tree = {}\n\n while(len(distances.keys()) > 2):\n\n r = calcRs(distances)\n M = makeMMatrix(distances, r)\n\n smallest = 10000\n smallestKey = (\"\",\"\")\n\n #Find nearest neighbors\n for key in M.keys():\n for subkey in M[key].keys():\n if M[key][subkey] < smallest:\n smallest = M[key][subkey]\n smallestKey = (key, subkey)\n\n #Add new node and update distances to rest of tree\n newname = smallestKey[0] + \"-\" + smallestKey[1]\n distances[newname] = {}\n tree[smallestKey[0]] = {}\n tree[smallestKey[1]] = {}\n dij = distances[smallestKey[0]][smallestKey[1]]\n for key in M.keys():\n if key in smallestKey:\n continue\n distances[newname][key] = .5*(distances[smallestKey[0]][key] \\\n + distances[smallestKey[1]][key] - dij)\n distances[key][newname] = distances[newname][key]\n\n #Update distances to parents of node\n dik = (dij + r[smallestKey[0]] - r[smallestKey[1]])/2\n tree[smallestKey[0]][newname] = dik\n tree[smallestKey[1]][newname] = dij-dik\n detachDict(distances, smallestKey[0], smallestKey[1])\n\n #Connect final two nodes\n tree[distances.keys()[0]] = {}\n tree[distances.keys()[0]][distances[distances.keys()[0]].keys()[0]] =\\\n distances[distances.keys()[0]][distances[distances.keys()[0]].keys()[0]] \n return tree","def compute_operator(self, snapshots):\n\n # To avoid recursion function, use FIFO list to simulate the tree\n # structure\n data_queue = [snapshots.copy()]\n\n current_bin = 0\n while data_queue:\n Xraw = data_queue.pop(0)\n\n n_samples = Xraw.shape[1]\n\n step = max(1, int(np.floor(old_div(n_samples, self._nyq))))\n Xsub = Xraw[:, ::step]\n Xc = Xsub[:, :-1]\n Yc = Xsub[:, 1:]\n\n Xc, Yc = compute_tlsq(Xc, Yc, self._tlsq_rank)\n\n rho = old_div(float(self._max_cycles), n_samples)\n sub_operator = SubMrDMDOperator(svd_rank=self._svd_rank,\n eigs_divider=2. * np.pi * step, rho=rho)\n sub_operator.compute_operator(Xc, Yc)\n\n modes = sub_operator.modes\n eigs = sub_operator.eigenvalues\n Atilde = sub_operator.as_numpy_array\n b = sub_operator.compute_sub_amplitudes(Xc, self._opt)\n\n #---------------------------------------------------------------\n # DMD Amplitudes and Dynamics\n #---------------------------------------------------------------\n Vand = np.vander(np.power(eigs, old_div(1., step)), n_samples, True)\n\n Psi = (Vand.T * b).T\n\n self._modes.append(modes)\n self._b.append(b)\n self._Atilde.append(Atilde)\n self._eigenvalues.append(eigs)\n self._nsamples.append(n_samples)\n self._steps.append(step)\n\n if Xraw.dtype == 'float64':\n Xraw -= modes.dot(Psi).real\n else:\n Xraw -= modes.dot(Psi)\n\n if current_bin < 2**(self._max_level - 1) - 1:\n current_bin += 1\n half = int(np.ceil(old_div(Xraw.shape[1], 2)))\n data_queue.append(Xraw[:, :half])\n data_queue.append(Xraw[:, half:])\n else:\n current_bin += 1","def solve(self):\n smallest_f = self.get_smallest_f_cost_unvisited_node()\n smallest_f_node = smallest_f[0]\n\n if smallest_f[1] > 1:\n current_node = self.get_smallest_h_cost_unvisited_node()\n else:\n current_node = smallest_f_node\n if current_node.f_cost == self.inf:\n return\n\n self.set_h_cost(current_node)\n self.unvisited_pos.remove(current_node.pos)\n self.visited_pos.append(current_node.pos)\n neighbours = algo_utils.get_neighbours(current_node, self.grid, self.wall_pos)\n\n for neigh in neighbours:\n neighbour_dist = neigh.g_cost\n current_dist = current_node.g_cost\n new_dist = current_dist + 1\n if neighbour_dist < new_dist:\n continue\n neigh.g_cost = new_dist\n self.set_h_cost(neigh)\n mix_neigh = {neigh.pos: neigh.g_cost}\n self.mix.update(mix_neigh)\n mix_current = {current_node.pos: current_node.g_cost}\n self.mix.update(mix_current)\n\n smallest_f = self.get_smallest_f_cost_unvisited_node()\n smallest_f_node = smallest_f[0]\n smallest_h_node = self.get_smallest_h_cost_unvisited_node()\n\n if (\n self.end_pos not in self.unvisited_pos\n or algo_utils.get_smallest_g_cost_unvisited_node(\n self.grid, self.unvisited_pos\n ).g_cost\n == self.inf\n ):\n for key, value in self.mix.items():\n self.mix[key] = round((value * 1.0) / self.end_node.g_cost, 3)\n self.backtrack_path(self.end_node)\n else:\n if smallest_f[1] > 1:\n current_node = smallest_h_node\n else:\n current_node = smallest_f_node\n self.solve()","def newton_iteration(f: Callable, df: Callable, eps: float, x0: float = None, a: float = None, b: float = None,\n weight: float = 0.9, display: bool = False, max_iterations: int = 100) -> float:\n x = np.inf\n if x0 is None:\n x0 = (a + b) / 2\n if a is not None and b is not None and a == b:\n return a\n x_next = x0\n iterations = 0\n while abs(x - x_next) > eps and iterations < max_iterations:\n iterations += 1\n x = x_next\n\n if display:\n import matplotlib.pyplot as plt\n xx0 = a or x-1\n xx1 = b or x+1\n xx = np.linspace(xx0, xx1, 100)\n yy = np.array(list(map(f, xx)))\n plt.plot(xx, yy)\n plt.axvline(x=x)\n plt.show()\n\n f_x = f(x)\n try:\n df_x = df(x)\n except ZeroDivisionError:\n df_x = (f_x - f(x-eps))/eps\n if df_x != 0:\n x_next = x - f_x / df_x\n\n if a is not None and x_next < a:\n x_next = weight * a + (1 - weight) * x\n elif b is not None and x_next > b:\n x_next = weight * b + (1 - weight) * x\n\n if a is not None and x_next < a:\n x_next = a\n if b is not None and x_next > b:\n x_next = b\n\n return x_next","def calc_new_cost(self, initial_node, to_node):\n\n cost = compute_distance(initial_node, to_node)\n return initial_node.cost + d","def perform(self, node, inputs, outputs):\n x, w, v, W, V = inputs\n N = x.shape[0]\n outer = np.outer\n\n def G(n):\n return sum(\n v[:, m] * V.T[n].dot(v[:, m]) / (w[n] - w[m])\n for m in range(N)\n if m != n\n )\n\n g = sum(outer(v[:, n], v[:, n] * W[n] + G(n)) for n in range(N))\n\n # Numpy's eigh(a, 'L') (eigh(a, 'U')) is a function of tril(a)\n # (triu(a)) only. This means that partial derivative of\n # eigh(a, 'L') (eigh(a, 'U')) with respect to a[i,j] is zero\n # for i < j (i > j). At the same time, non-zero components of\n # the gradient must account for the fact that variation of the\n # opposite triangle contributes to variation of two elements\n # of Hermitian (symmetric) matrix. The following line\n # implements the necessary logic.\n out = self.tri0(g) + self.tri1(g).T\n\n # Make sure we return the right dtype even if NumPy performed\n # upcasting in self.tri0.\n outputs[0][0] = np.asarray(out, dtype=node.outputs[0].dtype)","def a_star_alg(self, p1: int, p2: int, max_level: int = 1000):\r\n \r\n # Create start and end node\r\n start_node = Node(None, p1, self.node_dict[p1])\r\n start_node.g = start_node.h = start_node.f = 0\r\n end_node = Node(None, p2, self.node_dict[p2])\r\n end_node.g = end_node.h = end_node.f = 0\r\n\r\n # Initialize both open and closed list\r\n open_list = []\r\n closed_list = []\r\n\r\n # Add the start node\r\n open_list.append(start_node)\r\n\r\n # Loop until you find the end\r\n level = 0\r\n while len(open_list) > 0 and level < max_level:\r\n level += 1\r\n\r\n # Get the current node (the node in open_list with the lowest cost)\r\n current_node = open_list[0]\r\n current_index = 0\r\n for index, item in enumerate(open_list):\r\n if item.f < current_node.f:\r\n current_node = item\r\n current_index = index\r\n\r\n # Pop current off open list, add to closed list\r\n open_list.pop(current_index)\r\n closed_list.append(current_node)\r\n\r\n # Found the goal\r\n if current_node == end_node:\r\n path = []\r\n distance = current_node.g\r\n current = current_node\r\n while current is not None:\r\n path.append(current.number)\r\n current = current.parent\r\n\r\n return path[::-1], distance # Return reversed path\r\n\r\n # Generate children\r\n children = []\r\n for new_number in self.road_tree[current_node.number]: # Adjacent nodes\r\n new_node = Node(current_node, new_number, self.node_dict[new_number])\r\n children.append(new_node)\r\n\r\n # Loop through children\r\n for child in children:\r\n append_to_open_list = False\r\n\r\n # Create the f, g, and h values\r\n child.g = current_node.g + self.road_dict[(current_node.number, child.number)]\r\n child.h = sqrt((child.x - end_node.x) ** 2 + (child.y - end_node.y) ** 2) / 200\r\n child.f = child.g + child.h\r\n\r\n # Child is already in the closed list\r\n closed_list, append_to_open_list = self.check_in_list(child, closed_list, append_to_open_list)\r\n\r\n # Child is already in the open list\r\n open_list, append_to_open_list = self.check_in_list(child, open_list, append_to_open_list)\r\n\r\n # Add the child to the open list\r\n if append_to_open_list:\r\n open_list.append(child)\r\n\r\n return [], 1e10","def _reconstruct_result(self, node, ss_result, backward=False):\n prev = ss_result['prev']\n ele_diff = ss_result['ele_diff']\n\n path = [node]\n ele_gain = 0.\n contributions = {} # Of each node, to the cumulative elevation gain\n dist = ss_result['dist']\n\n if backward:\n # 'node' is the start point and so does not contribute to ele_gain\n contributions[node] = 0.\n else:\n # The ele diff between 'node' (first on the path) and next node\n # on the path toward the source\n contributions[node] = max([0., ele_diff[node]])\n ele_gain += contributions[node]\n\n # Follow backpointers to reconstruct path and compute statistics\n successor = node\n curr_node = prev[node]\n while curr_node is not None:\n path.append(curr_node)\n if backward:\n # ele_diff's were computed moving forward toward 'node', so we\n # must be careful to get the right contribution here\n contribution = max([0., -ele_diff[successor]])\n else:\n contribution = max([0., ele_diff[curr_node]])\n contributions[curr_node] = contribution\n ele_gain += contributions[curr_node]\n curr_node = prev[curr_node]\n successor = curr_node\n\n # Since path was built by appending and backtracking, need to\n # reverse it if 'node' is supposed to be the terminus\n if not backward:\n path.reverse()\n\n # Not yet a SearchResult: more processing needed in merge step\n result = {\n 'path': path,\n 'path_len': dist[node],\n 'ele_gain': ele_gain,\n 'contributions': contributions,\n 'dist': dist\n }\n\n return result","def steepest_descent_f(init_x0, init_x1, alpha, n=5):\n ret = [(init_x0, init_x1)]\n a = init_x0\n b = init_x1\n for i in range(n):\n a1 = a - alpha * gradF1(a, b)\n b1 = b - alpha * gradF2(a, b)\n ret.append((a1, b1))\n a = a1\n b = b1 \n \n return ret","def run_trial(self, num_nodes): \n # compute the neighbors for the newly-created node\n new_node_neighbors = set()\n for dummy_idx in range(num_nodes):\n new_node_neighbors.add(random.choice(self._node_numbers))\n # update the list of node numbers so that each node number \n # appears in the correct ratio\n\n # add num_nodes (= new node number) to list\n self._node_numbers.append(self._num_nodes)\n\n # add new neighbours to the list\n self._node_numbers.extend(list(new_node_neighbors))\n\n # update the number of nodes\n self._num_nodes += 1\n\n return new_node_neighbors","def compute(self, node, input_vals):\r\n \"\"\"TODO: Your code here\"\"\"\r\n \"\"\"Given values of two input nodes, return result of element-wise multiplication.\"\"\"\r\n assert len(input_vals) == 2\r\n return input_vals[0] / input_vals[1]","def deltaCalc(self, expected):\n \n n = len(self.structure)\n self.delta = [None] * n\n self.delta[n - 1] = []\n \n for i in xrange(len(expected)):\n curr = self.a[n - 1][i]\n self.delta[n - 1].append(self.derivativeFunc(curr) * (expected[i] - curr))\n self.delta[n - 1] = np.array(self.delta[n - 1])\n \n # From n - 1 to 1 layer \n for i in xrange(n - 1, 0, -1):\n currDelta = self.delta[i]\n if i != (n - 1):\n currDelta = currDelta[0][:-1]\n \n self.delta[i - 1] = np.array(np.dot(currDelta, self.theta[i]))\n self.delta[i - 1][0] *= self.a[i - 1]\n \n return","def build(self, dist_matrix, class_map, cluster_naming_function):\n # Update attributes\n self.orig_dist_matrix = dist_matrix \n self.class_map = class_map \n self.work_dist_matrix = dist_matrix\n\n # Get number of elements\n n = dist_matrix.shape[0]\n\n if PROGRESS:\n print 'Starting tree build now!'\n\n # Loop through n-3 elements & add nodes in tree\n for i in range(n - 3):\n\n if DEBUG:\n print 'Distance Matrix'\n pprint(self.work_dist_matrix)\n print\n\n # Calculate q_matrix matrix from distances\n q_matrix = _calculate_q_matrix(self.work_dist_matrix)\n \n if DEBUG:\n print 'Q matrix:'\n pprint(q_matrix)\n print\n\n # Find pair of elements (i,j) where q_matrix(i,j) has the lowest value\n (min_col, min_row) = _find_min_pair(q_matrix)\n\n # Add nodes i,j, and cluster node of i and j to this tree\n # And update working distance matrix accordingly\n new_cluster_name = cluster_naming_function(min_row, min_col, self.cluster_map)\n self.cluster_leaves(min_row, min_col, new_cluster_name) \n\n if DEBUG:\n print 'Tree:'\n pprint(nx.clustering(self.tree))\n pprint(self.cluster_dictionary)\n print '\\n\\n'\n \n # View graph after each step for debugging\n if VIEW_ALL:\n labels = {i[0]: i[0]+'/'+i[1]['c'] for i in njt.tree.nodes(data=True)}\n layout = nx.spring_layout(njt.tree)\n nx.draw_networkx(njt.tree, pos=layout, with_labels=True, labels=labels) #class labels\n plt.show()\n\n if PROGRESS:\n print str(i + 1) + \" down, \" + str(n-i-4) + \" to go...\"\n \n # Add remaining branch lengths and nodes from working distance matrix to this tree \n previous_cluster = new_cluster_name\n mid_edge_length = 0.5 * (self.work_dist_matrix.iat[0, 1]\n + self.work_dist_matrix.iat[0, 2]\n - self.work_dist_matrix.iat[1, 2])\n (node1, node2) = (self.work_dist_matrix.columns[0], self.work_dist_matrix.columns[1])\n new_cluster = cluster_naming_function(node1, node2, self.cluster_map)\n self.cluster_leaves(node1, node2, new_cluster)\n # Viz only scales based on a weight attribute, so we set that as the length\n self.tree.add_edge(previous_cluster, new_cluster, length=mid_edge_length, weight=mid_edge_length)\n\n if DEBUG:\n print 'Final tree:'\n pprint(nx.clustering(self.tree))\n pprint(self.cluster_dictionary)","def _DFS_loop(nodes, edges, t_n=None):\n\n if t_n is not None:\n n_t = dict((b,a) for a,b in t_n.items()) # {time: node}\n get_node_by_time = lambda time: time if t_n is None else n_t[time]\n get_time_by_node = lambda node: node if t_n is None else t_n[node]\n gen_edges = lambda node: map(get_time_by_node,edges[get_node_by_time(node)])\n\n explored = set()\n leader = dict()\n _DFS_loop.t = 0 # finishing time\n times = dict() # {time: node}\n\n def DFS(i):\n explored.add(i)\n leader[i] = s\n for j in gen_edges(i):\n if j not in explored:\n DFS(j)\n _DFS_loop.t += 1\n times[i] = _DFS_loop.t\n\n for i in nodes:\n if i not in explored:\n s = i # leader node\n DFS(i)\n\n leaders = defaultdict(list)\n for n,l in leader.items():\n leaders[get_node_by_time(l)].append(get_node_by_time(n))\n\n return times, leaders","def solve_csp(nodes, arcs, max_steps):\n\n nodes = list(nodes)\n print 'nodes:', nodes\n\n node_values_dict = dict(zip(nodes, '2'*len(set(nodes))))\n print 'initial random assignment', node_values_dict\n indexes = np.arange(len(nodes))\n\n graph = {}\n for arc in arcs:\n if not arc[0] in graph:\n graph[arc[0]] = []\n if not arc[1] in graph:\n graph[arc[1]] = []\n graph[arc[0]].append(arc[1])\n graph[arc[1]].append(arc[0])\n for i in indexes:\n if i in graph:\n continue\n else:\n graph[i] = []\n graph = dict(sorted(graph.items()))\n print 'graph:', graph\n\n domain = [i for i in np.arange(1, 10, 1)]\n print 'initial domain for each node:', domain\n\n superAdjacency ={}\n for i in np.arange(len(nodes)):\n superAdjacency[i] = []\n superAdjacency[i].append(nodes[i])\n superAdjacency[i].append(node_values_dict[nodes[i]])\n superAdjacency[i].append(graph[i])\n superAdjacency[i].append(domain)\n\n print 'superAdjacency', superAdjacency\n\n def getNodeType(superAdjacency, index):\n return list(superAdjacency[index])[0]\n\n def getCurrentAssignment(superAdjacency, index):\n return list(superAdjacency[index])[1]\n\n def getCurrentAssignmentForList(superAdjacency, indexList):\n return [int(list(superAdjacency[index])[1]) for index in indexList]\n\n def getSolution(superAdjacency):\n return [int(list(superAdjacency[index])[1]) for index in superAdjacency]\n\n def getNeighbours(superAdjacency, index):\n return list(superAdjacency[index])[2]\n\n def getDomain(superAdjacency, index):\n return list(superAdjacency[index])[3]\n\n def updateSuperAdjacency(superAdjacency, nodeType, newValue):\n updateList =[]\n for i in superAdjacency:\n if str(getNodeType(superAdjacency, i)) == nodeType:\n updateList.append(i)\n for i in updateList:\n superAdjacency[i][1] = newValue\n\n def isSolution():\n return graphConstraints(superAdjacency)\n\n def graphConstraints(superAdjacency):\n graphEval = []\n\n for index in superAdjacency:\n neighbours = getNeighbours(superAdjacency, index)\n nodeType = getNodeType(superAdjacency, index)\n\n if nodeType == 'T':\n graphEval.append(int(str(eval(str(\n getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0]))\n elif nodeType == 'C':\n return 'NA'\n elif nodeType == 'S':\n graphEval.append(int(str(eval(str(\n getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0]))\n elif nodeType == 'H':\n graphEval.append(int(str(np.sum(\n getCurrentAssignmentForList(superAdjacency, neighbours)))[0]))\n if nodeType == 'P':\n graphEval.append(int(str(np.sum(\n getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0]))\n\n currentAssignment = [item[1] for item in superAdjacency.values()]\n difference = map(sub, currentAssignment, graphEval)\n\n if sum(difference) == 0:\n return True\n else:\n return difference\n\n def findConflictVariable(superAdjacency, lastUpdateNode):\n node_conflict_count = {}\n for node in node_values_dict:\n node_conflict_count[node] = 0\n for index in superAdjacency:\n neighbours = getNeighbours(superAdjacency, index)\n nodeType = getNodeType(superAdjacency, index)\n if nodeType == 'T':\n try:\n if getCurrentAssignment(superAdjacency, index) != \\\n int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0]):\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\n except:\n continue\n elif nodeType == 'S':\n try:\n if getCurrentAssignment(superAdjacency, index) != int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0]):\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\n except:\n continue\n elif nodeType == 'H':\n try:\n if getCurrentAssignment(superAdjacency, index) != int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[0]):\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\n except:\n continue\n if nodeType == 'P':\n try:\n if getCurrentAssignment(superAdjacency, index) != int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0]):\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\n except:\n continue\n choices = [k for k, v in node_conflict_count.items() if v > 0]\n if len(choices) > 0:\n updateNode = random.choice(choices)\n\n if updateNode == lastUpdateNode:\n choices.pop(choices.index(updateNode))\n try:\n lastUpdateNode = random.choice(choices)\n return lastUpdateNode, lastUpdateNode\n except:\n return lastUpdateNode, lastUpdateNode\n else:\n lastUpdateNode = updateNode\n return updateNode, lastUpdateNode\n else:\n return 'NA', 'NA'\n\n\n\n def valueForConflictedVariable(superAdjacency, var):\n for index in superAdjacency:\n nodeType = getNodeType(superAdjacency, index)\n neighbours = getNeighbours(superAdjacency, index)\n if not neighbours:\n continue\n elif str(nodeType) == str(var):\n domain = getDomain(superAdjacency, index)\n\n choice = random.choice(domain)\n if nodeType == 'T':\n choice = int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0])\n elif nodeType == 'S':\n choice = int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0])\n elif nodeType == 'H':\n choice = int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[0])\n if nodeType == 'P':\n choice = int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0])\n\n choice = int(choice)\n if choice % 2 == 0:\n return choice\n else:\n return choice\n\n def min_conflicts(nodes, arcs, max_steps):\n lastUpdateNode = ''\n for i in range(max_steps):\n if isSolution() == True:\n return\n var, lastUpdateNode = findConflictVariable(superAdjacency, lastUpdateNode)\n if var != 'NA':\n value = valueForConflictedVariable(superAdjacency, var)\n updateSuperAdjacency(superAdjacency, var, value)\n node_values_dict[var] = value\n else:\n pass\n\n return\n\n min_conflicts(nodes, arcs, max_steps)\n node_values = getSolution(superAdjacency)\n return node_values","def newton_raphson(f,x0,iterations): \n current = x0\n fdash = differentiate_polynomial(f)\n print(fdash)\n for i in range(iterations): \n current = current - evaluate_polynomial(f,current)/evaluate_polynomial(fdash,current)\n return current","def update_nodes(self):\n raise NotImplementedError('ERROR: sweeper has to implement update_nodes(self)')","def test_compare_old_to_new_method_to_create_trees(self):\n nodes = util.generate_sequence_of_points(2, 2)\n tree1 = kdtree.createNewTree(nodes)\n kdtree.visualize(tree1)\n \n sel_axis = (lambda axis: axis)\n tree2 = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\n tree2.split2([0.25, 0.5], axis = 1)\n tree2.split2([0.75, 0.5], axis = 1)\n \n #left\n tree2.split2([0.25, 0.25], axis = 0, sel_axis = sel_axis)\n tree2.split2([0.25, 0.75], axis = 0, sel_axis = sel_axis)\n \n #right\n tree2.split2([0.75, 0.25], axis = 0, sel_axis = sel_axis)\n tree2.split2([0.75, 0.75], axis = 0, sel_axis = sel_axis)\n \n kdtree.visualize(tree2)\n \n for n in zip(kdtree.level_order(tree1), kdtree.level_order(tree2)):\n self.assertEqual(n[0].data, n[1].data, \"elements not equal\")\n \n if n[0].data is not None and n[1].data is not None:\n self.assertEqual(n[0].axis, n[1].axis, \"elements not equal\")","def prepare_data_for_d(self):\n\n center_nodes = []\n neighbor_nodes = []\n labels = []\n for i in self.root_nodes:\n if np.random.rand() < config.update_ratio:\n # self.graph[i] = [neighbors of i]\n pos = self.graph[i]\n neg, _ = self.sample(i, self.trees[i], len(pos), for_d=True)\n # print(\"tree_i_d: \", self.trees[i])\n # print(\"neg_samples: \", neg)\n # print(\"neg is: \", neg)\n if len(pos) != 0 and neg is not None:\n # positive samples\n center_nodes.extend([i] * len(pos))\n neighbor_nodes.extend(pos)\n labels.extend([1] * len(pos))\n\n # negative samples\n center_nodes.extend([i] * len(pos))\n neighbor_nodes.extend(neg)\n labels.extend([0] * len(neg))\n # print(\"cen: \", center_nodes)\n return center_nodes, neighbor_nodes, labels","def iterative_solution(list1, list2):\n pre_root = Node(-1)\n prev = pre_root\n\n while list1 and list2:\n if list1.data <= list2.data:\n prev.next = list1\n list1 = list1.next\n else:\n prev.next = list2\n list2 = list2.next\n prev = prev.next\n\n if not list1:\n prev.next = list2\n else:\n prev.next = list1\n\n return pre_root.next","def startTreeBasedCoord(self):\n self.log_message('ID {0} starts Load Propagation Optimization'.format(self.CommID))\n #self.MsgReceiveCount_interval = 0\n #self.MsgSendCount_interval = 0\n\n self.noOfTimesteps = (self.toTime - self.fromTime) / self.stepSize + 1\n\n # calculate pool of schedules (also saved in self.schedules) and schedule load curves\n if self.getTER1() != 0: # if not a gas boiler\n self.calcSchedulePool(self.fromTime, self.toTime)\n self.calcScheduleConsumptionCurves()\n\n if not self.Parent: #root node\n random.seed() # initialize pseudo random number generator\n\n\n\n # leave nodes select initial best schedule from schedule pool based on fluctuations curve and propagate their load to parent\n if not self.Children:\n #self.ChildrenProcessed = [0 for x in range(len(self.Children))]\n if self.getTER1() != 0: # not a gas boiler\n self.selectBestSchedule(copy.deepcopy(self.ERemainderLocal))\n self.setSOC(self.SOCEnd[self.chosenScheduleIndex])\n self.setStateModlvl(self.chosenSchedule[-1])\n self.sendMessage(self.Parent, 40, ['newload', copy.deepcopy(self.EConsumptionChosenSchedule)])\n else:\n zeros = [0 for x in range(len(self.ERemainderLocal))]\n self.sendMessage(self.Parent, 40, ['newload', copy.deepcopy(zeros)])\n\n else: # if not a leave node\n self.EConsumptionChildCurves = [ [0 for x in range(len(self.EFluctuationCurve))] for y in range(len(self.Children))] # initialize array for load curves of children\n self.EConsumptionChildCurvesRec = [ [0 for x in range(len(self.EFluctuationCurve))] for y in range(len(self.Children))] # initialize array for load curves of children\n self.noOfConsumptionCurvesReceived = 0\n #self.ChildLoadCurvesChosen = [0 for x in range(len(self.Children))]\n return","def gradients(output_node, node_list):\n # a map from node to a list of gradient contributions from each output node\n node_to_output_grads_list = {}\n # Special note on initializing gradient of output_node as oneslike_op(output_node):\n # We are really taking a derivative of the scalar reduce_sum(output_node)\n # instead of the vector output_node. But this is the common case for loss function.\n node_to_output_grads_list[output_node] = [oneslike_op(output_node)]\n # a map from node to the gradient of that node\n node_to_output_grad = {}\n # Traverse graph in reverse topological order given the output_node that we are taking gradient wrt.\n reverse_topo_order = list(reversed(find_topo_sort([output_node])))\n for node in reverse_topo_order:\n grad = sum_node_list(node_to_output_grads_list[node]) # Node\n node_to_output_grad[node] = grad # node_to_output_grad[node]为对相应的node求导, 即文章中的v^-_n\n grads = node.op.gradient(node, grad) # Node list\n for i in range(len(node.inputs)):\n ch = node.inputs[i]\n grads_list = node_to_output_grads_list.get(ch, [])\n grads_list.append(grads[i])\n node_to_output_grads_list[ch] = grads_list\n\n # Collect results for gradients requested.\n grad_node_list = [node_to_output_grad[node] for node in node_list]\n return grad_node_list","def iter_func(root_name, root, set_traverse, list_funcs, G, strings,\n plot_nodes, cur_pos, xgrain, min_weight, max_weight):\n set_traverse.append(root)\n nbs = G.neighbors(root)\n nbs = G[root]\n\n plot_nodes.append(cur_pos)\n xgrain = xgrain/2.0\n\n flag_pn = -1\n for nb in nbs.keys():\n if nb in set_traverse:\n continue\n\n next_pos = [0, 0, 0]\n if root.name == root_name:\n next_pos[0] = cur_pos[0]\n else:\n next_pos[0] = cur_pos[0] + xgrain*flag_pn*( 0.8+0.2*(nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight) ) #* (nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\n next_pos[1] = cur_pos[1] + 3.0*(nbs[nb][\"weight\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\n next_pos[2] = nb.name\n\n flag_pn = flag_pn*(-1)\n\n strings.append([root, nb])\n set_traverse, strings, plot_nodes = iter_func(root_name, nb, set_traverse, list_funcs, G, strings, plot_nodes, next_pos, xgrain, min_weight, max_weight)\n\n return set_traverse, strings, plot_nodes","def run_trial(self, num_nodes): \n #compute the neighbors for the newly-created node\n new_node_neighbors = set()\n for dummy_idx in range(num_nodes):\n new_node_neighbors.add(random.choice(self._node_numbers))\n # update the list of node numbers so that each node number \n # appears in the correct ratio\n self._node_numbers.append(self._num_nodes)\n self._node_numbers.extend(list(new_node_neighbors)) \n #update the number of nodes\n self._num_nodes += 1\n return list(new_node_neighbors)","def computeNodeVolumes(self):\n for i in np.arange(0,self.ni):\n for j in np.arange(0,self.nj):\n for k in np.arange(0,self.nk):\n \n V = self.dh[0]*self.dh[1]*self.dh[2]\n if (i==0 or i==self.ni-1): V*=0.5\n if (j==0 or j==self.nj-1): V*=0.5\n if (k==0 or k==self.nk-1): V*=0.5\n \n self.node_vol[i][j][k] = V","def newton_body(iterand):\n next_backward_difference = iterand.next_backward_difference\n next_state_vec = iterand.next_state_vec\n\n rhs = newton_coefficient * step_size_cast * ode_fn_vec(\n next_time,\n next_state_vec) - rhs_constant_term - next_backward_difference\n delta = tf.squeeze(\n tf.linalg.triangular_solve(\n upper,\n tf.matmul(tf.transpose(unitary), rhs[:, tf.newaxis]),\n lower=False))\n num_iters = iterand.num_iters + 1\n\n next_backward_difference += delta\n next_state_vec += delta\n\n delta_norm = tf.cast(tf.norm(delta), real_dtype)\n lipschitz_const = delta_norm / iterand.prev_delta_norm\n\n # Stop if method has converged.\n approx_dist_to_sol = lipschitz_const / (1. - lipschitz_const) * delta_norm\n close_to_sol = approx_dist_to_sol < tol\n delta_norm_is_zero = tf.equal(delta_norm, tf.constant(0., dtype=real_dtype))\n converged = close_to_sol | delta_norm_is_zero\n finished = converged\n\n # Stop if any of the following conditions are met:\n # (A) We have hit the maximum number of iterations.\n # (B) The method is converging too slowly.\n # (C) The method is not expected to converge.\n too_slow = lipschitz_const > 1.\n finished = finished | too_slow\n if max_num_iters is not None:\n too_many_iters = tf.equal(num_iters, max_num_iters)\n num_iters_left = max_num_iters - num_iters\n num_iters_left_cast = tf.cast(num_iters_left, real_dtype)\n wont_converge = (\n approx_dist_to_sol * lipschitz_const**num_iters_left_cast > tol)\n finished = finished | too_many_iters | wont_converge\n\n return [\n _NewtonIterand(\n converged=converged,\n finished=finished,\n next_backward_difference=next_backward_difference,\n next_state_vec=next_state_vec,\n num_iters=num_iters,\n prev_delta_norm=delta_norm)\n ]","def Trees__LCA_LowestCommonDenominator():\n # Python2 ported to Python3 via 2to3-3.7\n # URL:# URL:https://www.hackerrank.com/challenges/binary-search-tree-lowest-common-ancestor/problem\n '''\n class Node:\n def __init__(self,info): \n self.info = info \n self.left = None \n self.right = None \n // this is a node of the tree , which contains info as data, left , right\n '''\n def lca(root, v1, v2):\n # Find a and b. Link child nodes to parent to be able to backtrack.\n # (1) Note, we add 'parent' attribute to node dynamically via node.parent = ...\n root.parent = None\n node_stack = []\n node_stack.append(root)\n v1_node, v2_node = None, None\n while node_stack:\n node = node_stack.pop()\n if not v1_node and node.info == v1:\n v1_node = node\n if not v2_node and node.info == v2:\n v2_node = node\n for child_node in [node.left, node.right]:\n if child_node:\n child_node.parent = node # (1)\n node_stack.append(child_node)\n\n # Generate path from A to root.\n curr = v1_node\n a_to_root = set()\n while curr:\n a_to_root.add(curr.info)\n curr = curr.parent\n\n # traverse up b until you come across an element in a's path to parent.\n curr = v2_node\n while curr:\n if curr.info in a_to_root:\n return curr\n else:\n curr = curr.parent\n\n print(\"Shouldn't be here, Something went wrong\")\n\n # # Recursive. (Iterative is better, but did recursive for practice.) ~15 min.\n # # Main idea is that we count the number of v1/v2's found of the subnodes.\n # # If a node has sum of 2, we know it's the lca.\n # def lca(root, v1, v2):\n # def lca_helper(node):\n # ret_node = None\n # if not node:\n # return 0, None\n # v_match_counter = 0\n # if node.info in [v1, v2]:\n # v_match_counter += 1\n # left_count, left_node_ret = lca_helper(node.left)\n # right_count, right_node_ret = lca_helper(node.right)\n # v_match_counter += left_count + right_count\n # if v_match_counter == 2:\n # ret_node = node\n # if left_node_ret:\n # ret_node = left_node_ret\n # if right_node_ret:\n # ret_node = right_node_ret\n # return v_match_counter, ret_node\n\n # _, node = lca_helper(root)\n # return node","def extractTree(nodesList, rootNode):\n if len(nodesList) == 0:\n return\n if nodesList[0] == '!':\n return nodesList[1:]\n\n splitAttribute, splitValue, attributeValue = nodesList[0].strip().split('-')\n nodesList = nodesList[1:]\n \n if splitAttribute != splitValue or splitAttribute != '$' or splitValue != '$':\n rootNode.setSplit((splitAttribute, splitValue))\n else:\n rootNode.setSplit(\"Base case\")\n rootNode.setData(attributeValue)\n return nodesList[2:]\n \n \n leftTree = Tree()\n rightTree = Tree()\n rootNode.setLesser(leftTree)\n rootNode.setGreater(rightTree)\n nodesList = extractTree(nodesList, leftTree)\n\n \n \n nodesList = extractTree(nodesList, rightTree)\n\n return nodesList","def number_nodes(tree):\n def list_of_nodes(tree):\n \"\"\"Return a list of internal nodes in postorder traversal\n\n @param HuffmanNode tree: a tree to traverse\n @rtype: list\n\n >>> t = HuffmanNode(None, HuffmanNode(6), HuffmanNode(7))\n >>> list_of_nodes(t) == [t]\n True\n >>> t = HuffmanNode(None, HuffmanNode(8), HuffmanNode(None, HuffmanNode(5), HuffmanNode(6)))\n >>> list_of_nodes(t) == [HuffmanNode(None, HuffmanNode(5), HuffmanNode(6)), HuffmanNode(None, HuffmanNode(8), HuffmanNode(None, HuffmanNode(5), HuffmanNode(6)))]\n True\n \"\"\"\n list_ = []\n if tree.left.is_leaf() and tree.right.is_leaf():\n list_.append(tree)\n return list_\n elif tree.left.is_leaf():\n list_.extend(list_of_nodes(tree.right))\n list_.append(tree)\n return list_\n elif tree.right.is_leaf():\n list_.extend(list_of_nodes(tree.left))\n list_.append(tree)\n return list_\n else:\n list_.extend(list_of_nodes(tree.left))\n list_.extend(list_of_nodes(tree.right))\n list_.append(tree)\n return list_\n\n internal_nodes = list_of_nodes(tree)\n for i in range(len(internal_nodes)):\n node = internal_nodes[i]\n node.number = i","def newCostCalc(dfNew, curCost,a,b):\n a1,a2,a3 = dfNew.iloc[a-2],dfNew.iloc[a-1],dfNew.iloc[a]\n b1,b2,b3 = dfNew.iloc[b-2],dfNew.iloc[b-1],dfNew.iloc[b]\n reCalc = curCost\n reCalc = reCalc - euclideanDistance(a1['x'],a1['y'],a2['x'],a2['y'])\n reCalc = reCalc - euclideanDistance(a2['x'],a2['y'],a3['x'],a3['y'])\n reCalc = reCalc - euclideanDistance(b1['x'],b1['y'],b2['x'],b2['y'])\n reCalc = reCalc - euclideanDistance(b2['x'],b2['y'],b3['x'],b3['y'])\n\n x, y = dfNew.iloc[a-1].copy(), dfNew.iloc[b-1].copy()\n dfNew.iloc[a-1],dfNew.iloc[b-1] = y,x\n\n a1,a2,a3 = dfNew.iloc[a-2],dfNew.iloc[a-1],dfNew.iloc[a]\n b1,b2,b3 = dfNew.iloc[b-2],dfNew.iloc[b-1],dfNew.iloc[b]\n\n reCalc = reCalc + euclideanDistance(a1['x'],a1['y'],a2['x'],a2['y'])\n reCalc = reCalc + euclideanDistance(a2['x'],a2['y'],a3['x'],a3['y'])\n reCalc = reCalc + euclideanDistance(b1['x'],b1['y'],b2['x'],b2['y'])\n reCalc = reCalc + euclideanDistance(b2['x'],b2['y'],b3['x'],b3['y'])\n\n x, y = dfNew.iloc[a-1].copy(), dfNew.iloc[b-1].copy()\n dfNew.iloc[a-1],dfNew.iloc[b-1] = y,x\n\n return reCalc","def Dijkstra2(node_init, node_end, graph):\n\n ### Parameter initialisation\n node_list = list(graph.vertices.keys())\n dist = np.full(len(node_list), -np.inf)\n # At the beginning we have not reached the end_node\n node_end_reached = False\n # At the beginning, we assume there is a shortest path:\n no_path = False\n \n # Initialising the distances of the nodes\n dist[node_init] = 0\n # Setting the father_node which contains the provenance of the nodes\n father_node = np.full(len(node_list), -np.inf)\n # Initialising the current node\n current_node = node_init \n # Initialising the dictionnary of fixed node which has the following shape:\n #{fixed_node: (previous_node, iteration, cost)}\n # Fixing the number of iterations\n k = 0\n dict_fixed_node = {node_init:(None,k, 0)}\n \n # In the trivial case where the two nodes are identical\n if node_init == node_end:\n cost = 0\n shortest_path = [node_init]\n no_path = False\n return cost, shortest_path, no_path\n \n # While the end node has not been reached\n while not node_end_reached:\n current_node_adj = graph.node_get_adj(current_node).copy()\n # We get rid off the nodes that have been fixed, except at the first iteration\n if k != 0:\n current_node_adj.remove(dict_fixed_node[current_node][0])\n ## Updating the distances : either the node are neighbors and \n # something might change, either they are not, and their distance \n # does not change.\n # For the neighbors node\n for e in current_node_adj:\n dist_temp = dist[current_node] + graph.weights[(current_node, e)]\n # We change the distance only if it is lower than it used to be\n # otherwise, we keep it\n if dist_temp < dist[e] or dist[e] == -np.inf:\n dist[e] = dist_temp\n # Setting the father node\n father_node[e] = current_node\n father_node[current_node] = None\n # We set the distance of the current node to 0\n dist[current_node] = 0 \n # Index and distances which are not 0 and not minus infty\n sub_dist_index = [i for i, e in enumerate(dist) if e > 0]\n sub_dist_value = np.array([e for i, e in enumerate(dist) if e > 0])\n # If these two lists are empty, we stop the algorithm and that means\n # that we cannot reach our point\n if not sub_dist_index or sub_dist_value.size == 0:\n no_path = True\n cost = 'impossible path'\n shortest_path = 'impossible path'\n break\n # Now we need to set our choice for the next node\n if np.array_equal(sub_dist_value, np.ones(len(sub_dist_value))):\n ## If there are only ones : we pick them up randomly\n current_node = int(random.choice(list(sub_dist_index)))\n min_dist = sub_dist_value.min()\n else:\n ## If not we just pick up the one with the minimum distance.\n current_node = sub_dist_index[sub_dist_value.argmin()]\n min_dist = sub_dist_value.min()\n # Adding this node to the dictionnary\n dict_fixed_node[current_node] = (int(father_node[current_node]), k, min_dist)\n # If the end_node has been reached, we stop the search algorithm\n if node_end in dict_fixed_node.keys():\n node_end_reached = True\n # Incrementing the counter\n k += 1\n #print('current_node : {}'.format(current_node))\n #print(dict_fixed_node)\n # Now we need to get the shortest path from our iterations whose information \n # are in dict_fixed_node. To do this, we need to circle back from the end_node\n # to the init_node in this dictionnary.\n # This is done only if some path between node_init and end_node exists.\n if no_path == False:\n list_father_node = list(dict_fixed_node.values())\n previous_node = list_father_node[-1][0]\n shortest_path = [node_end, previous_node]\n # While the initial node has not been reached, we add the relevant\n # nodes to our shortest path\n while previous_node != node_init:\n previous_node = dict_fixed_node[previous_node][0]\n shortest_path.append(previous_node)\n \n # Computing the cost of this shortest path in terms of weights\n cost = int(dict_fixed_node[node_end][2])\n \n return cost, shortest_path, no_path","async def calc_nodes(height, balances, zero_nodes_lookup, root_index, hash_func):\n if len(balances) == 0:\n return {root_index: zero_nodes_lookup[height]}\n if height == 0:\n assert len(balances) == 1\n _, vault_data = balances[-1]\n balance = int(vault_data['amount'])\n # A node with balance=0 is considered uninitialized.\n if balance == 0:\n return {root_index: zero_nodes_lookup[0]}\n stark_key = int(vault_data['stark_key'])\n token_id = int(vault_data['token_id'])\n return {root_index: await vault_hash_async(stark_key, token_id, balance, hash_func)}\n mid = 2 ** (height - 1)\n left_balances = [(i, data) for i, data in balances if i < mid]\n right_balances = [(i - mid, data) for i, data in balances if i >= mid]\n left, right = await asyncio.gather(\n calc_nodes(height - 1, left_balances, zero_nodes_lookup, 2 * root_index, hash_func),\n calc_nodes(height - 1, right_balances, zero_nodes_lookup, 2 * root_index + 1, hash_func))\n nodes = {root_index: await hash_func(left[2 * root_index], right[2 * root_index + 1])}\n nodes.update(left)\n nodes.update(right)\n return nodes","def transform(nodes, weights, new_corners):\n if nodes.shape[1] == 1:\n x_0 = new_corners[0, :]\n x_1 = new_corners[1, :]\n M = np.zeros((1, 1))\n M[:, 0] = 0.5 * (x_1 - x_0)\n origin = np.array([-1.0])\n elif nodes.shape[1] == 2:\n x_0 = new_corners[0, :]\n x_1 = new_corners[1, :]\n x_2 = new_corners[2, :]\n M = np.zeros((2, 2))\n M[:, 0] = 0.5 * (x_1 - x_0)\n M[:, 1] = 0.5 * (x_2 - x_0)\n origin = np.array([-1.0, -1.0])\n elif nodes.shape[1] == 3:\n x_0 = new_corners[0, :]\n x_1 = new_corners[1, :]\n x_2 = new_corners[2, :]\n x_3 = new_corners[3, :]\n M = np.zeros((3, 3))\n M[:, 0] = 0.5 * (x_1 - x_0)\n M[:, 1] = 0.5 * (x_2 - x_0)\n M[:, 2] = 0.5 * (x_3 - x_0)\n origin = np.array([-1.0, -1.0, -1.0])\n\n offset = -M @ origin + x_0\n volume_fraction = np.abs(np.linalg.det(M))\n return np.add(nodes @ M.T, offset), volume_fraction * weights","def create_leaves(self, parent_node, leaf_values):\n # TODO: find generalization!!\n import numpy as np\n # creating the leaf object\n parent_node.child_nodes[0] = self.leaf_type(0.0, 0, diagram_type=self.__class__)\n\n # creating the offsets\n # deciding on mult or add rule\n # additive_coefficient = np.mean(leaf_values)\n # new_offsets = np.array([leaf_values[i]-additive_coefficient for i in range(self.base)])\n # max_difference = np.max(np.abs(new_offsets))\n # mult_coefficient = max_difference if max_difference != 0.0 else 1.0\n # for i in range(self.base):\n # node.child_nodes[i] = node.child_nodes[0]\n # node.offsets[i] = np.array([((new_offsets[i])/mult_coefficient), mult_coefficient], dtype='float64')\n # return node, [additive_coefficient, mult_coefficient]\n if leaf_values[0] == 0 or (leaf_values[1]-leaf_values[0] < leaf_values[1]/leaf_values[0]):\n parent_node.offsets[0] = np.array([0, 1], dtype='float64')\n for i in range(1, self.base, 1):\n parent_node.child_nodes[i] = parent_node.child_nodes[0]\n parent_node.offsets[i] = np.array([(leaf_values[i]-leaf_values[0]), 1], dtype='float64')\n return parent_node, [leaf_values[0], 1]\n else:\n parent_node.offsets[0] = np.array([1, 1], dtype='float64')\n for i in range(1, self.base, 1):\n parent_node.child_nodes[i] = parent_node.child_nodes[0]\n parent_node.offsets[i] = np.array([leaf_values[i]/leaf_values[0], (leaf_values[i]/leaf_values[0])],\n dtype='float64')\n return parent_node, [0, leaf_values[0]]","def computeNodeErrors(net, inputs, actualOutputs):\n errors = {}\n squareError = 0.0\n label = net.run(inputs)\n predictedOutputs = [1.0 if l == label else 0.0 for l in net.validLabels]\n for i, node in enumerate(net.outputLayer):\n errors[node] = predictedOutputs[i] - actualOutputs[i]\n squareError += errors[node] ** 2\n if squareError == 0.0: # No need to continue\n return ({node:0.0 for node in net.nodes}, 0.0)\n reversedLayers = net.hiddenLayers[:]\n reversedLayers.reverse()\n toBeComputed = []\n for layer in reversedLayers:\n toBeComputed += layer\n while len(toBeComputed) > 0: # Repeat until all nodes processed\n newToBeComputed = []\n for node in toBeComputed:\n notComputable = False\n totalChildError = 0\n for child in node.children:\n if child not in errors: # Cannot yet compute\n notComputable = True\n break\n childError = errors[child]\n weight = child.weights[node]\n totalChildError += childError * weight\n if notComputable:\n newToBeComputed.append(node)\n else:\n activationDerivative = DERIVATIVES[node.activationFunc]\n if activationDerivative is None:\n raise TrainingError(\"Activation function of node \"\n + str(node) + \" is non-differentiable.\")\n weightedInput = node.getWeightedInputSum(BYPASS)\n nodeDerivative = activationDerivative(weightedInput)\n errors[node] = nodeDerivative * totalChildError\n toBeComputed = newToBeComputed\n return (errors, squareError)","def newton_method_bidirectional(f, bnd1, bnd2, Ep, step):\n\n while True:\n step = step + 1\n\n # print(\"bnd1=\",bnd1,\" and bnd2=\",bnd2)\n\n h_bnd1 = f(bnd1) / derivative(f, bnd1)\n bnd1 = bnd1 - h_bnd1\n if (decide(abs(h_bnd1) <= Ep)):\n # print(\"Root in Approximation: \",bnd1)\n return step\n\n h_bnd2 = f(bnd2) / derivative(f, bnd2)\n bnd2 = bnd2 - h_bnd2\n if (decide(abs(h_bnd2) <= Ep)):\n # print(\"Root in Approximation: \",bnd2)\n return step","def algorithm(self):\n t = time.clock()\n self.calculateFirstPath()\n improve = True\n while improve and (self.allowedTime > (time.clock() - t)):\n improve = False\n\n for i in range(self.NB_OF_NODES):\n for j in range(self.NB_OF_NODES):\n if j in [(i - 1) % self.NB_OF_NODES, i, (i + 1) % self.NB_OF_NODES]:\n continue\n\n if self.getDistance(i, i + 1) + self.getDistance(j, j + 1) > self.getDistance(i, j) + self.getDistance(i + 1, j + 1):\n self.exchange(i, j)\n improve = True","def evaluate(\n self, nodes, derivatives=np.array([0, 0, 0]), modes=None, unique=False\n ):","def rewire(self, new_node, near_indexes):\n\n for i in near_indexes:\n near_node = self.node_list[i]\n edge_node = self.steer(new_node, near_node)\n if not edge_node:\n continue\n edge_node.cost = self.calc_new_cost(new_node, near_node)\n\n no_collision = self.check_collision(edge_node, self.obstacle_list)\n improved_cost = near_node.cost > edge_node.cost\n\n if no_collision and improved_cost:\n self.node_list[i] = edge_node\n self.propagate_cost_to_leaves(new_node)","def find_all_feedbacks_Tarjan(ls_nodenames, lt_links):\n \n ll_SCC = SCC_analysis.decompose_SCC(ls_nodenames, lt_links)\n ll_feedbacks = []\n for l_SCC in ll_SCC:\n lt_links_in_SCC = basic_topology_functions.extract_subnet_topology(lt_links, l_SCC)\n print(\"feedbacks are calculated in subnetwork with \",len(l_SCC),\" nodes\")\n \n dic_startnode_endnodes = {}\n for s_nodename in l_SCC:\n dic_startnode_endnodes[s_nodename] = []\n for t_link in lt_links_in_SCC:\n if t_link[0] == s_nodename:\n dic_startnode_endnodes[s_nodename].append(t_link[-1])\n #print(dic_startnode_endnodes)\n dic_node_counter = {}\n for s_nodename in l_SCC:\n dic_node_counter[s_nodename] = len(dic_startnode_endnodes[s_nodename])-1\n #print(dic_node_counter)\n for s_node_trajectorystart in dic_node_counter.keys():\n if dic_node_counter[s_node_trajectorystart] == -1: #this means that all feedback containing this node are all calculated\n continue\n else:\n l_trajectory = [s_node_trajectorystart]\n while l_trajectory:\n #print(l_trajectory)\n i_counter = dic_node_counter[l_trajectory[-1]]\n if i_counter == -1:\n dic_node_counter[l_trajectory[-1]] = len(dic_startnode_endnodes[l_trajectory[-1]])-1\n l_trajectory.pop()\n if l_trajectory:\n dic_node_counter[l_trajectory[-1]] -= 1\n else:\n s_node_next = dic_startnode_endnodes[l_trajectory[-1]][i_counter]\n if s_node_next == s_node_trajectorystart:\n ll_feedbacks.append(list(l_trajectory))\n if len(ll_feedbacks)%20000 == 0:\n print(\"calculated feedbacks are now \",len(ll_feedbacks))\n \n if s_node_next in l_trajectory:\n dic_node_counter[l_trajectory[-1]] -= 1\n else:\n l_trajectory.append(s_node_next)\n dic_startnode_endnodes[s_node_trajectorystart] = []\n dic_node_counter[s_node_trajectorystart] =-1\n \n return ll_feedbacks","def perform(self, node, inputs, outputs):\r\n x, w, v, W, V = inputs\r\n N = x.shape[0]\r\n outer = numpy.outer\r\n\r\n G = lambda n: sum(v[:, m] * V.T[n].dot(v[:, m]) / (w[n] - w[m])\r\n for m in xrange(N) if m != n)\r\n g = sum(outer(v[:, n], v[:, n] * W[n] + G(n))\r\n for n in xrange(N))\r\n\r\n # Numpy's eigh(a, 'L') (eigh(a, 'U')) is a function of tril(a)\r\n # (triu(a)) only. This means that partial derivative of\r\n # eigh(a, 'L') (eigh(a, 'U')) with respect to a[i,j] is zero\r\n # for i < j (i > j). At the same time, non-zero components of\r\n # the gradient must account for the fact that variation of the\r\n # opposite triangle contributes to variation of two elements\r\n # of Hermitian (symmetric) matrix. The following line\r\n # implements the necessary logic.\r\n out = self.tri0(g) + self.tri1(g).T\r\n\r\n # The call to self.tri0 in perform upcast from float32 to\r\n # float64 or from int* to int64 in numpy 1.6.1 but not in\r\n # 1.6.2. We do not want version dependent dtype in Theano.\r\n # We think it should be the same as the output.\r\n outputs[0][0] = numpy.asarray(out, dtype=node.outputs[0].dtype)","def my_Newton( fct, df_dx, x0):\r\n xn = float(x0)\r\n eps = 1e-5\r\n N = 20\r\n i = 0\r\n while abs( fct( xn**(i + 1)) - fct( xn**i)) > eps and i < N:\r\n x_next = xn - fct(xn)/df_dx(xn)\r\n print( i, 'fct value', abs( fct(xn)), x_next)\r\n xn = x_next\r\n i += 1\r\n if abs( fct( xn)) < eps:\r\n return x_next\r\n else: #solution did not converge\r\n return np.nan","def brute_tree(XTRAIN,istopTRAIN,XTEST,istopTEST):\n \n ntrain=XTRAIN.shape[0]\n ntest=XTEST.shape[0]\n \n if np.sum(istopTRAIN)==0:\n return 0,[]\n\n cost0=np.zeros(Ngammas*Nreps)\n cost1=np.zeros(Ngammas*Nreps)\n cost0test=np.zeros(Ngammas*Nreps)\n cost1test=np.zeros(Ngammas*Nreps)\n \n precisionTRAIN=np.zeros(Ngammas*Nreps)\n precisionTEST=np.zeros(Ngammas*Nreps)\n recallTEST=np.zeros(Ngammas*Nreps)\n rate=np.zeros(Ngammas*Nreps)\n \n for iii in range(Ngammas):\n \n gamma=GAMMA[iii]\n \n for jjj in range(Nreps):\n \n \"\"\" train a tree using training data with random splitting \"\"\"\n \n tree_hyperparameters['class_weight']={0:1,1:gamma}\n clf=tree.DecisionTreeClassifier(**tree_hyperparameters)\n clf.fit(XTRAIN,istopTRAIN)\n \n \"\"\"\" record costs and precision on validation data \"\"\"\n \n pTRAIN=clf.predict(XTRAIN)\n precisionTRAIN[iii*Nreps+jjj]=np.divide(sum(1 for i in range(ntrain) if pTRAIN[i] == 1 and istopTRAIN[i]==1),sum(pTRAIN))\n cost0[iii*Nreps+jjj]=sum(1 for i in range(ntrain) if pTRAIN[i] == 1 and istopTRAIN[i]==0)\n cost1[iii*Nreps+jjj]=sum(1 for i in range(ntrain) if pTRAIN[i] == 0 and istopTRAIN[i]==1)\n \n \"\"\" record precision on test data \"\"\"\n \n pTEST=clf.predict(XTEST)\n precisionTEST[iii*Nreps+jjj]=np.divide(sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==1),sum(pTEST))\n recallTEST[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==1)/sum(istopTEST)\n cost0test[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==0)\n cost1test[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 0 and istopTEST[i]==1)\n \n \"\"\" record positive rate on full data \"\"\"\n \n rate[iii*Nreps+jjj]=(sum(pTRAIN)+sum(pTEST))/(ntrain+ntest)\n \n \"\"\" Compute Pareto front for validation data \"\"\"\n \n Pareto = Lower_Convex_Hull(np.concatenate((cost0.reshape(-1,1),cost1.reshape(-1,1)),1))\n \n \"\"\" make some nice plots for whoever is watching \"\"\"\n \n plt.figure(figsize=(10,5))\n plt.subplot(121)\n plt.plot(cost0,cost1,'.')\n plt.plot(cost0[Pareto],cost1[Pareto],'d')\n plt.xlabel('errors on class zero training data')\n plt.ylabel('errors on class one training data')\n\n plt.subplot(122)\n plt.plot(cost0test,cost1test,'.')\n plt.plot(cost0test[Pareto],cost1test[Pareto],'d')\n plt.xlabel('errors on class zero test data')\n plt.ylabel('errors on class one test data')\n plt.show()\n \n plt.figure(figsize=(15,5))\n plt.subplot(131)\n plt.semilogy(precisionTRAIN,rate,'.')\n plt.semilogy(precisionTRAIN[Pareto],rate[Pareto],'d')\n plt.xlabel('precision on training data')\n plt.ylabel('positive rate')\n\n plt.subplot(132) \n plt.semilogy(precisionTEST,rate,'.')\n plt.semilogy(precisionTEST[Pareto],rate[Pareto],'d')\n plt.xlabel('precision on test data')\n plt.ylabel('positive rate')\n\n plt.subplot(133) \n plt.plot(precisionTEST,recallTEST,'.')\n plt.plot(precisionTEST[Pareto],recallTEST[Pareto],'d')\n plt.xlabel('precision on test data')\n plt.ylabel('recall on test data')\n plt.show() \n \n return {'cost0':cost0,'cost1':cost1,'cost0test':cost0test,'cost1test':cost1test,'precisionTRAIN':precisionTRAIN,'precisionTEST':precisionTEST,'recallTEST':recallTEST,'rate':rate,'Pareto':Pareto}","def handle_leaf_nodes(self, nodes):\n max_node_id = -1\n for n in nodes:\n n.sum_hess = self.decrypt(n.sum_hess)\n n.sum_grad = self.decrypt(n.sum_grad)\n n.weight = self.splitter.node_weight(n.sum_grad, n.sum_hess)\n n.sitename = self.sitename\n if n.id > max_node_id:\n max_node_id = n.id\n new_nodes = [Node() for i in range(max_node_id + 1)]\n for n in nodes:\n new_nodes[n.id] = n\n return new_nodes","def label_correcting_algo(dt, ori_node, des_node, do_return=False):\n # Convert all labels to string\n ori = str(ori_node)\n des = str(des_node)\n dt[[\"start\", \"end\"]] = dt[[\"start\", \"end\"]].astype(str) \n \n # Initialization\n nodes = set(dt.loc[:,\"start\"].unique()) | set(dt.loc[:,\"end\"].unique())\n dist = {}.fromkeys(nodes, np.inf)\n dist[ori] = 0\n points = {}.fromkeys(nodes, ori)\n iter_set = {ori}\n \n # Main Algo\n while iter_set:\n i = iter_set.pop() # Randomly pop out a node i\n A_i = dt[dt.start == i]\n for row in A_i.index: \n j = A_i.loc[:, \"end\"][row]\n c_ij = A_i.loc[:, \"cost\"][row]\n if dist[j] > dist[i] + c_ij:\n dist[j] = dist[i] + c_ij\n points[j] = i\n iter_set = iter_set | set([j]) # Union\n \n # Print & Return the Answer\n x = pd.concat([pd.Series(points), pd.Series(dist)], axis=1)\n x.columns = [\"Front-node\", \"Distance\"]\n\n current_node = des\n front_node = \"\"\n sp = des\n while front_node != ori:\n front_node = str(x.loc[current_node, \"Front-node\"])\n sp = \"{} -> {}\".format(front_node, sp)\n current_node = front_node\n \n sp = \"From node {} to node {}, total Distance: {}\\n{}\\n\".format(ori, des, x.loc[des, \"Distance\"], sp)\n if do_return:\n print(sp)\n return x\n else:\n return sp","def calculate_distance_to_divide(\n grid, longest_path=True, add_to_grid=False, clobber=False\n):\n # check that flow__receiver nodes exists\n if \"flow__receiver_node\" not in grid.at_node:\n raise FieldError(\n \"A 'flow__receiver_node' field is required at the \"\n \"nodes of the input grid.\"\n )\n\n if \"flow__upstream_node_order\" not in grid.at_node:\n raise FieldError(\n \"A 'flow__upstream_node_order' field is required at the \"\n \"nodes of the input grid.\"\n )\n\n if \"drainage_area\" not in grid.at_node:\n raise FieldError(\n \"A 'flow__upstream_node_order' field is required at the \"\n \"nodes of the input grid.\"\n )\n\n # get the reciever nodes, depending on if this is to-one, or to-multiple,\n # we'll need to get a different at-node field.\n if grid.at_node[\"flow__receiver_node\"].size != grid.size(\"node\"):\n to_one = False\n else:\n to_one = True\n\n flow__receiver_node = grid.at_node[\"flow__receiver_node\"]\n drainage_area = grid.at_node[\"drainage_area\"]\n\n # get the upstream node order\n flow__upstream_node_order = grid.at_node[\"flow__upstream_node_order\"]\n\n # get downstream flow link lengths, result depends on type of grid.\n if isinstance(grid, RasterModelGrid):\n flow_link_lengths = grid.length_of_d8[\n grid.at_node[\"flow__link_to_receiver_node\"]\n ]\n else:\n flow_link_lengths = grid.length_of_link[\n grid.at_node[\"flow__link_to_receiver_node\"]\n ]\n\n # create an array that representes the distance to the divide.\n distance_to_divide = np.zeros(grid.nodes.size)\n\n if not longest_path:\n distance_to_divide[:] = 2 * grid.size(\"node\") * np.max(flow_link_lengths)\n\n # iterate through the flow__upstream_node_order backwards.\n for node in reversed(flow__upstream_node_order):\n # if drainage are is equal to node cell area, set distance to zeros\n # this should handle the drainage divide cells as boundary cells have\n # their area set to zero.\n if drainage_area[node] == grid.cell_area_at_node[node]:\n distance_to_divide[node] = 0\n\n # get flow recievers\n reciever = flow__receiver_node[node]\n\n if to_one:\n # if not processing an outlet node.\n if reciever != node:\n if longest_path:\n cond = (\n distance_to_divide[reciever]\n < distance_to_divide[node] + flow_link_lengths[node]\n )\n else:\n cond = (\n distance_to_divide[reciever]\n > distance_to_divide[node] + flow_link_lengths[node]\n )\n\n if cond:\n distance_to_divide[reciever] = (\n distance_to_divide[node] + flow_link_lengths[node]\n )\n\n else:\n # non-existant links are coded with -1\n useable_receivers = np.where(reciever != grid.BAD_INDEX)[0]\n\n for idx in range(len(useable_receivers)):\n r = reciever[useable_receivers][idx]\n fll = flow_link_lengths[node][useable_receivers][idx]\n\n # if not processing an outlet node.\n if r != node:\n if longest_path:\n cond = distance_to_divide[r] < distance_to_divide[node] + fll\n else:\n cond = distance_to_divide[r] > distance_to_divide[node] + fll\n\n if cond:\n distance_to_divide[r] = distance_to_divide[node] + fll\n\n # store on the grid\n if add_to_grid:\n grid.add_field(\n \"distance_to_divide\", distance_to_divide, at=\"node\", clobber=clobber\n )\n\n return distance_to_divide","def newtonsMethod(f, df, ddf, x, niter=10):\n\n points = []\n\n for i in xrange(niter):\n point = np.dot(-la.inv(ddf(x)), (df(x)))\n\n slope = np.dot(df(x), point)\n\n a = backtracking(f, slope, x, point)\n \n #update point\n x_k = x + a*point\n points.append(x_k)\n x = x_k\n\n return points"],"string":"[\n \"def calculate_divided_differences(nodes):\\n nodes_to_compute = []\\n divided_differences = []\\n for node in nodes:\\n nodes_to_compute.append(DividedDifferenceNode(x=node[0], divided_difference=node[1]))\\n\\n divided_differences.append(tuple(nodes_to_compute))\\n\\n while len(nodes_to_compute) > 1:\\n next_node_row = calculate_divided_differences_row(nodes_to_compute)\\n divided_differences.append(tuple(next_node_row))\\n nodes_to_compute = next_node_row\\n\\n return divided_differences\",\n \"def calculate_newton_interpolation(divided_differences):\\n polynomial = []\\n for i, divided_differences_row in enumerate(divided_differences):\\n polynomial_part = '({0})'.format(divided_differences_row[0].divided_difference)\\n\\n for j in range(0, i):\\n polynomial_part += '*(x-{0})'.format(divided_differences[0][j].x)\\n\\n polynomial_part += '+'\\n polynomial.append(polynomial_part)\\n polynomial_str = ''.join(polynomial)[:-1]\\n\\n print('Calculated polynomial: {0}'.format(polynomial_str))\\n # Heuristic simplification of calculated polynomial\\n simplified_polynomial = sy.simplify(polynomial_str)\\n print(\\\"Simplified polynomial: {0}\\\".format(simplified_polynomial))\\n return simplified_polynomial\",\n \"def compute_relations(nodes: List[Node]) -> None:\\n # Calculate parents\\n for node in nodes:\\n node.parents = []\\n for node in nodes:\\n for child in node.children():\\n child.parents.append(node)\\n\\n def compute_dominators(\\n entry: Node,\\n parents: Callable[[Node], List[Node]],\\n dominators: Callable[[Node], Set[Node]],\\n immediately_dominates: Callable[[Node], List[Node]],\\n set_immediate_dominator: Callable[[Node, Optional[Node]], None],\\n ) -> None:\\n # See https://en.wikipedia.org/wiki/Dominator_(graph_theory)#Algorithms\\n # Note: if `n` is unreachable from `entry`, then *every* node will\\n # vacuously belong to `n`'s dominator set.\\n for n in nodes:\\n dominators(n).clear()\\n if n == entry:\\n dominators(n).add(n)\\n else:\\n dominators(n).update(nodes)\\n\\n changes = True\\n while changes:\\n changes = False\\n for node in nodes:\\n if node == entry:\\n continue\\n nset = dominators(node)\\n for parent in parents(node):\\n nset = nset.intersection(dominators(parent))\\n nset.add(node)\\n if len(nset) < len(dominators(node)):\\n assert nset.issubset(dominators(node))\\n dominators(node).intersection_update(nset)\\n changes = True\\n\\n # Compute immediate dominator, and the inverse relation\\n for node in nodes:\\n immediately_dominates(node).clear()\\n for node in nodes:\\n doms = dominators(node).difference({node})\\n # If `node == entry` or the flow graph is not reducible, `doms` may be empty.\\n # TODO: Infinite loops could be made reducible by introducing\\n # branches like `if (false) { return; }` without breaking semantics\\n if doms:\\n # There should be a unique max `len(dominators(d))` if the flowgraph\\n # is reducible. Fall back to largest index for irreducible graphs.\\n imdom = max(doms, key=lambda d: (len(dominators(d)), d.block.index))\\n immediately_dominates(imdom).append(node)\\n set_immediate_dominator(node, imdom)\\n else:\\n set_immediate_dominator(node, None)\\n for node in nodes:\\n immediately_dominates(node).sort(key=lambda x: x.block.index)\\n\\n def _set_immediate_dominator(node: Node, imdom: Optional[Node]) -> None:\\n node.immediate_dominator = imdom\\n\\n def _set_immediate_postdominator(node: Node, impdom: Optional[Node]) -> None:\\n node.immediate_postdominator = impdom\\n\\n entry = nodes[0]\\n terminal = nodes[-1]\\n assert isinstance(terminal, TerminalNode)\\n\\n # Compute dominators & immediate dominators\\n compute_dominators(\\n entry=entry,\\n parents=lambda n: n.parents,\\n dominators=lambda n: n.dominators,\\n immediately_dominates=lambda n: n.immediately_dominates,\\n set_immediate_dominator=_set_immediate_dominator,\\n )\\n\\n # Compute postdominators & immediate postdominators\\n # This uses the same algorithm as above, but with edges reversed\\n compute_dominators(\\n entry=terminal,\\n parents=lambda n: n.children(),\\n dominators=lambda n: n.postdominators,\\n immediately_dominates=lambda n: n.immediately_postdominates,\\n set_immediate_dominator=_set_immediate_postdominator,\\n )\\n\\n # Iterate over all edges n -> c and check for backedges, which define natural loops\\n for node in nodes:\\n for child in node.children():\\n if child not in node.dominators:\\n continue\\n # Found a backedge node -> child where child dominates node; child is the \\\"head\\\" of the loop\\n if child.loop is None:\\n child.loop = NaturalLoop(child)\\n child.loop.nodes |= {child, node}\\n child.loop.backedges.add(node)\\n for parent in nodes:\\n if reachable_without(parent, node, child):\\n child.loop.nodes.add(parent)\",\n \"def compute_tree(self, tree):\\n g_list_val, g_list_h = self._build_graph(tree) # return theano variable of each node\\n list_val = self._traversal_tree(tree) #\\n f = theano.function(g_list_val, g_list_h, allow_input_downcast=True)\\n result = f(*list_val)\\n return result\",\n \"def gradients(output_node, node_list):\\r\\n\\r\\n # a map from node to a list of gradient contributions from each output node\\r\\n node_to_output_grads_list = {}\\r\\n # Special note on initializing gradient of output_node as oneslike_op(output_node):\\r\\n # We are really taking a derivative of the scalar reduce_sum(output_node)\\r\\n # instead of the vector output_node. But this is the common case for loss function.\\r\\n node_to_output_grads_list[output_node] = [oneslike_op(output_node)]\\r\\n # a map from node to the gradient of that node\\r\\n node_to_output_grad = {}\\r\\n # Traverse graph in reverse topological order given the output_node that we are taking gradient wrt.\\r\\n reverse_topo_order = list(reversed(find_topo_sort([output_node])))\\r\\n #node_to_output_grad[output_node] = oneslike_op(output_node)\\r\\n \\r\\n \\r\\n \\\"\\\"\\\"TODO: Your code here\\\"\\\"\\\"\\r\\n \\r\\n for node in reverse_topo_order:\\r\\n #print(node)\\r\\n #print(node_to_output_grad)\\r\\n if not(node in node_to_output_grad):\\r\\n #node_to_output_grad[node] = node.op.gradient(node, sum_node_list ([node_to_output_grad[node1] for node1 in node_to_output_grads_list[node] ]))\\r\\n sum_node = sum_node_list (node_to_output_grads_list[node]) \\r\\n grad = node.op.gradient(node, sum_node)\\r\\n node_to_output_grad[node] = sum_node\\r\\n #print(grad)\\r\\n #print(len(node.inputs))\\r\\n for i in range(len(node.inputs)):\\r\\n #print(i)\\r\\n if (not(node.inputs[i] in node_to_output_grads_list)):\\r\\n node_to_output_grads_list[node.inputs[i]]=[]\\r\\n node_to_output_grads_list[node.inputs[i]].append(grad[i])\\r\\n \\r\\n #input_grad = \\r\\n \\r\\n \\r\\n '''for node1 in node_to_output_grads_list[node]:\\r\\n print(node1)\\r\\n if (node in node_to_output_grad):\\r\\n node_to_output_grad[node] = node_to_output_grad[node] + node_to_output_grad[node1]\\r\\n else:\\r\\n node_to_output_grad[node] = node_to_output_grad[node1]\\r\\n '''\\r\\n #print(\\\"node to output \\\")\\r\\n #print(node_to_output_grad)\\r\\n\\r\\n del reverse_topo_order\\r\\n # Collect results for gradients requested.\\r\\n grad_node_list = [node_to_output_grad[node] for node in node_list]\\r\\n return grad_node_list\",\n \"def insert_nodes(self):\\n neighbour_max_distance = 5\\n new_nodes = []\\n for node in self.nodes:\\n left_distance = node.get_distance(node.neighbour1)\\n right_distance = node.get_distance(node.neighbour2)\\n if left_distance > neighbour_max_distance:\\n # halfway\\n half_point = (\\n node.x + (node.neighbour1.x - node.x) / 2,\\n node.y + (node.neighbour1.y - node.y) / 2\\n )\\n new_node = Node(half_point)\\n node.neighbour1.connect(node.neighbour1.neighbour1, new_node)\\n new_node.connect(node.neighbour1, node)\\n node.connect(new_node, node.neighbour2)\\n new_nodes.append(new_node)\\n new_nodes.append(node)\\n\\n if right_distance > neighbour_max_distance:\\n # halfway\\n half_point = (\\n node.x + (node.neighbour2.x - node.x) / 2,\\n node.y + (node.neighbour2.y - node.y) / 2\\n )\\n new_node = Node(half_point)\\n node.neighbour2.connect(new_node, node.neighbour2.neighbour2)\\n new_node.connect(node, node.neighbour2)\\n node.connect(node.neighbour1, new_node)\\n new_nodes.append(new_node)\\n\\n return new_nodes\",\n \"def job_tree(self):\\n\\n # 1. Enforce depth of 1 for steps\\n def depth_one(steps):\\n depth_one = []\\n for step in steps:\\n if type(step) is list:\\n if type(step[0]) is list:\\n depth_one.append(step[0])\\n else:\\n depth_one.append(step)\\n else:\\n depth_one.append([step])\\n return depth_one\\n\\n # 2. Convert steps to list of node objects (0,1,2,3...)\\n def assign_nodes(steps):\\n nodes = [i for i in range(len(steps))]\\n objects = list(\\n set([elem for sublist in steps for elem in sublist]))\\n\\n # checks for multiple src and dst objects -- added when looking for\\n # mutiples\\n split_objects = []\\n for obj in objects:\\n if len(obj) > 1:\\n new_objs = obj.split(\\\", \\\")\\n split_objects.extend(new_objs)\\n else:\\n split_objects.append(obj)\\n objects = split_objects\\n del(split_objects)\\n\\n # populate with leafless trees (Node objects, no edges)\\n for node in nodes:\\n nodes[node] = Node(str(node))\\n\\n # search for leafy trees\\n for obj in objects:\\n\\n # accounts for multiple drc/dst objects\\n leaves = []\\n for i, sublist in enumerate(steps):\\n for string in sublist:\\n if string.count(',') > 0:\\n if obj in string:\\n leaves.append(i)\\n else:\\n if obj in sublist:\\n leaves.append(i)\\n leaves = sorted(list(set(leaves)))\\n\\n if len(leaves) > 1:\\n viable_edges = []\\n\\n # compute cross-product\\n for leaf1 in leaves:\\n for leaf2 in leaves:\\n if str(leaf1) != str(leaf2) and sorted((leaf1, leaf2)) not in viable_edges:\\n viable_edges.append(sorted((leaf1, leaf2)))\\n\\n # form edge networks\\n for edge in viable_edges:\\n n1, n2 = nodes[edge[0]], nodes[edge[1]]\\n n1.add_edge(n2)\\n n2.add_edge(n1)\\n nodes[int(n1.name)], nodes[int(n2.name)] = n1, n2\\n return nodes\\n\\n # 3. Determine number of trees and regroup by connected nodes\\n def connected_nodes(nodes):\\n proto_trees = []\\n nodes = set(nodes)\\n\\n while nodes:\\n n = nodes.pop()\\n group = {n}\\n queue = [n]\\n while queue:\\n n = queue.pop(0)\\n neighbors = n.edges\\n neighbors.difference_update(group)\\n nodes.difference_update(neighbors)\\n group.update(neighbors)\\n queue.extend(neighbors)\\n proto_trees.append(group)\\n return proto_trees\\n\\n # 4. Convert nodes to nested dictionary of parent-children relations\\n # i.e. adding depth -- also deals with tree-node sorting and path\\n # optimization\\n def build_tree_dict(trees, steps):\\n # node sorting in trees\\n sorted_trees = []\\n for tree in trees:\\n sorted_trees.append(\\n sorted(tree, key=lambda x: int(x.name)))\\n\\n # retrieve values of the nodes (the protocol's containers)\\n # for each tree ... may want to use dictionary eventually\\n all_values = []\\n for tree in sorted_trees:\\n values = [steps[int(node.name)] for node in tree]\\n all_values.append(values)\\n\\n # create relational tuples:\\n all_digs = []\\n singles = []\\n dst_potentials = []\\n for tree_idx in range(len(sorted_trees)):\\n edge_flag = False\\n tree_digs = []\\n for node_idx in range(len(sorted_trees[tree_idx])):\\n\\n # digs: directed graph vectors\\n digs = []\\n dst_nodes = []\\n node_values = all_values[tree_idx][node_idx]\\n src_node = str(sorted_trees[tree_idx][node_idx].name)\\n\\n # ACTION ON MULTIPLE OBJECTS (E.G. TRANSFER FROM SRC -> DST\\n # WELLS)\\n # Outcome space: {1-1, 1-many, many-1, many-many}\\n if len(node_values) == 2:\\n # single destination (x-1)\\n if node_values[1].count(\\\",\\\") == 0:\\n dst_nodes = [i for i, sublist in enumerate(\\n steps) if node_values[1] == sublist[0]]\\n # multiple destinations (x-many)\\n elif node_values[1].count(\\\",\\\") > 0:\\n dst_nodes = []\\n for dst in node_values[1].replace(\\\", \\\", \\\"\\\"):\\n for i, sublist in enumerate(steps):\\n if i not in dst_nodes and dst == sublist[0]:\\n dst_nodes.append(i)\\n\\n # ACTION ON A SINGLE OBJECT\\n elif len(node_values) == 1:\\n dst_nodes = [i for i, sublist in enumerate(\\n steps) if node_values[0] == sublist[0]]\\n\\n # Constructing tuples in (child, parent) format\\n for dst_node in dst_nodes:\\n dig = (int(dst_node), int(src_node))\\n digs.append(dig)\\n\\n # else: an edge-case for dictionaries constructed with no edges\\n # initiates tree separation via flag\\n if digs != []:\\n edge_flag = False\\n tree_digs.append(digs)\\n else:\\n edge_flag = True\\n digs = [(int(src_node), int(src_node))]\\n tree_digs.append(digs)\\n\\n # digraph cycle detection: avoids cycles by overlooking set\\n # repeats\\n true_tree_digs = []\\n for digs in tree_digs:\\n for dig in digs:\\n if tuple(sorted(dig, reverse=True)) not in true_tree_digs:\\n true_tree_digs.append(\\n tuple(sorted(dig, reverse=True)))\\n\\n # edge-case for dictionaries constructed with no edges\\n if true_tree_digs != [] and edge_flag == False:\\n all_digs.append(true_tree_digs)\\n elif edge_flag == True:\\n all_digs.extend(tree_digs)\\n\\n # Enforces forest ordering\\n all_digs = sorted(all_digs, key=lambda x: x[0])\\n\\n # job tree traversal to find all paths:\\n forest = []\\n for digs_set in all_digs:\\n\\n # pass 1: initialize nodes dictionary\\n nodes = OrderedDict()\\n for tup in digs_set:\\n id, parent_id = tup\\n # ensure all nodes accounted for\\n nodes[id] = OrderedDict({'id': id})\\n nodes[parent_id] = OrderedDict({'id': parent_id})\\n\\n # pass 2: create trees and parent-child relations\\n for tup in digs_set:\\n id, parent_id = tup\\n node = nodes[id]\\n # links node to its parent\\n if id != parent_id:\\n # add new_node as child to parent\\n parent = nodes[parent_id]\\n if not 'children' in parent:\\n # ensure parent has a 'children' field\\n parent['children'] = []\\n children = parent['children']\\n children.append(node)\\n\\n desired_tree_idx = sorted(list(nodes.keys()))[0]\\n forest.append(nodes[desired_tree_idx])\\n return forest\\n\\n # 5. Convert dictionary-stored nodes to unflattened, nested list of\\n # parent-children relations\\n def dict_to_list(forest):\\n forest_list = []\\n for tree in forest:\\n tString = str(json.dumps(tree))\\n tString = tString.replace('\\\"id\\\": ', \\\"\\\").replace('\\\"children\\\": ', \\\"\\\").replace(\\n '[{', \\\"[\\\").replace('}]', \\\"]\\\").replace('{', \\\"[\\\").replace('}', \\\"]\\\")\\n\\n # find largest repeated branch (if applicable)\\n # maybe think about using prefix trees or SIMD extensions for better\\n # efficiency\\n x, y, length, match = 0, 0, 0, ''\\n for y in range(len(tString)):\\n for x in range(len(tString)):\\n substring = tString[y:x]\\n if len(list(re.finditer(re.escape(substring), tString))) > 1 and len(substring) > length:\\n match = substring\\n length = len(substring)\\n\\n # checking for legitimate branch repeat\\n if \\\"[\\\" in match and \\\"]\\\" in match:\\n hits = []\\n index = 0\\n if len(tString) > 3:\\n while index < len(tString):\\n index = tString.find(str(match), index)\\n if index == -1:\\n break\\n hits.append(index)\\n index += len(match)\\n\\n # find all locations of repeated branch and remove\\n if len(hits) > 1:\\n for start_loc in hits[1:]:\\n tString = tString[:start_loc] + \\\\\\n tString[start_loc:].replace(match, \\\"]\\\", 1)\\n\\n # increment all numbers in string to match the protocol\\n newString = \\\"\\\"\\n numString = \\\"\\\"\\n for el in tString:\\n if el.isdigit(): # build number\\n numString += el\\n else:\\n if numString != \\\"\\\": # convert it to int and reinstantaite numString\\n numString = str(int(numString) + 1)\\n newString += numString\\n newString += el\\n numString = \\\"\\\"\\n tString = newString\\n del newString\\n\\n forest_list.append(ast.literal_eval(tString))\\n return forest_list\\n\\n # 6. Print job tree(s)\\n def print_tree(lst, level=0):\\n print(' ' * (level - 1) + '+---' * (level > 0) + str(lst[0]))\\n for l in lst[1:]:\\n if type(l) is list:\\n print_tree(l, level + 1)\\n else:\\n print(' ' * level + '+---' + l)\\n\\n # 1\\n steps = depth_one(self.object_list)\\n # 2\\n nodes = assign_nodes(steps)\\n # 3\\n proto_forest = connected_nodes(nodes)\\n # 4\\n forest = build_tree_dict(proto_forest, steps)\\n # 5\\n self.forest_list = dict_to_list(forest)\\n # 6\\n print(\\\"\\\\n\\\" + \\\"A suggested Job Tree based on container dependency: \\\\n\\\")\\n for tree_list in self.forest_list:\\n print_tree(tree_list)\",\n \"def expand_tree(self, N=1):\\n # type: (int) -> None\\n assert self._initialized, 'Search not initialized.'\\n for _ in range(N): \\n x_rand = self.sample_free()\\n x_nearest = self.nearest(x_rand)\\n x_new = self.steer(x_nearest, x_rand)\\n if self.coll_free(x_nearest, x_new):\\n self.index+=1\\n X_near = [x for x in self.near(x_new) if self.coll_free(x, x_new)]\\n cost_min = self.costs[self.research_index(self.nodes,x_nearest)][1] + self.dist(x_nearest, x_new)\\n x_min = x_nearest\\n for x in X_near:\\n cost = self.costs[self.research_index(self.nodes,x)][1] + self.dist(x, x_new)\\n if cost < cost_min:\\n cost_min = cost\\n x_min = x\\n \\n self.nodes.append(x_new)\\n j=self.research_index(self.nodes,x_min)\\n self.parents[self.index,j]=1\\n self.costs[self.index] = (x_new,self.costs[j][1] + self.dist(x_min, x_new))\\n for x in X_near:\\n k=self.research_index(self.nodes,x)\\n if self.costs[self.index][1] + self.dist(x_new, x) < self.costs[k][1]:\\n self.parents[self.index]=np.zeros(self.N)\\n self.parents[self.index,k] = 1\\n self.costs[k] = (self.costs[k][0],self.costs[self.index][1] + self.dist(x_new, x))\",\n \"def all_nodes_dfs(log_T, initial_state, min_score, sub_info, max_depth=1000000000000000000, maxtraversals=1000000000000000000):\\n # default argument for sub_info: empty_sub_info = (np.array([], dtype=int), np.array([], dtype=int), 1000000000000000000)\\n min_score = float(min_score) # make sure numba knows this is a float (otherwise, sometimes, it doesn't (bug in numba))\\n order = np.zeros(log_T.shape, np.int64)\\n for i in xrange(order.shape[1]):\\n order[i] = (-log_T[i]).argsort()\\n n_states = log_T.shape[0]\\n node = [order[initial_state, 0]] # most likely first node\\n node_idx = [0]\\n lengths_dfs = [-1.0]\\n nodes_dfs = [[-1, ]]\\n for it in xrange(maxtraversals):\\n # score and return current node if adequate\\n score = log_T[initial_state, node[0]]\\n for p in xrange(1, len(node)):\\n score += log_T[node[p - 1], node[p]]\\n if min_score <= score and syntax_check(np.array(node), sub_info, partial=False):\\n lengths_dfs.append(-score)\\n nodes_dfs.append(list(node))\\n # next node ##\\n # try adding a value at the end\\n for next_idx, next_state in enumerate(order[node[-1]]):\\n if min_score <= score + log_T[node[-1], next_state] and len(node) < max_depth \\\\\\n and syntax_check(np.array(node + [next_state]), sub_info, partial=True):\\n node.append(next_state)\\n node_idx.append(next_idx)\\n break\\n # adding a value at the end failed, so we are a leave\\n else:\\n for p in xrange(len(node) - 1, -1, -1):\\n if node_idx[p] != n_states - 1: # find where within the node to increase (and discard all others after)\\n old_idx = node_idx[p]\\n del node_idx[p:]\\n del node[p:]\\n node_idx.append(old_idx + 1)\\n prev_state = node[p - 1] if p > 0 else initial_state\\n node.append(order[prev_state, node_idx[p]])\\n break\\n else:\\n break # end of the generator, can't increase even the root\\n else:\\n assert False, \\\"Number of traversals exceeded\\\"\\n\\n return lengths_dfs[1:], nodes_dfs[1:]\",\n \"def find_sharpest_fork_general(Nodes):\\n pair_list = []\\n Dis = np.array([])\\n for n in Nodes:\\n if n.parent is not None:\\n if n.parent.parent is not None:\\n a = n.parent.children\\n if(isinstance(a, list)):\\n if(len(a)==2):\\n n1 = a[0]\\n n2 = a[1]\\n pair_list.append([n1 , n2])\\n dis = LA.norm(a[0].xyz - a[1].xyz,2)\\n Dis = np.append(Dis,dis)\\n if(len(Dis)!= 0):\\n (b,) = np.where(Dis == Dis.min())\\n sharpest_pair = pair_list[b[0]]\\n distance = Dis.min()\\n else:\\n sharpest_pair = [0,0]\\n distance = 0.\\n return sharpest_pair, distance\",\n \"def get_locations(nodes, tl, br):\\n \\n # Base cases:\\n if len(nodes) == 1: # for singleton, only choice is to place in the single spot in 1x1 square\\n return {nodes[0]: tl}\\n if len(nodes) == 2: # for two nodes, arbitrarily chose to place the first node in top left\\n return {nodes[0]: tl, nodes[1]: br}\\n\\n # Recursive case, need to create and solve subproblems:\\n ret = {}\\n\\n num_edges = count_num_edges(nodes)\\n if num_edges == 0: # for empty graphs, no need to run METIS, just assign arbitrarily\\n i = 0\\n for x in range(tl.x, br.x+1): \\n for y in range(tl.y, br.y+1):\\n if i < len(nodes):\\n ret.update({nodes[i]: Point(x,y)})\\n i += 1\\n return ret\\n\\n filename = splitext(basename(sys.argv[1]))[0] + '.p.' + sys.argv[2] + '.yx.' + sys.argv[3] + '.drop.' + sys.argv[4] + '.' +\\\\\\n '_'.join(['delete', str(tl.x), str(tl.y), str(br.x), str(br.y)]) \\n\\n # special case for the very first call of get_locations. For example, suppose that there are\\n # 97 nodes on a 10x10 grid. Instead of dividing the 97 nodes into 2 equal partitions, we should\\n # divide them into a partition of 90 nodes and a partition of 7 nodes. The former should be\\n # placed on a 10x9 grid and te latter should be placed on a 1x7 grid.\\n if len(nodes) < (br.x - tl.x + 1) * (br.y - tl.y + 1):\\n assert tl == Point(0, 0)\\n size_tl_nodes = (br.x + 1) * int(len(nodes) / (br.x + 1))\\n if size_tl_nodes == len(nodes):\\n ret.update(get_locations(nodes, tl=Point(0, 0), br=Point(br.x, len(nodes) / (br.x + 1) - 1)))\\n return ret\\n\\n nodes_tl, nodes_br = partition(nodes, size_tl_nodes, filename)\\n # complicated indexing here. As an example, for the 97 into 10x10 case, we want to send 90 nodes\\n # to a rectangle spanned by tl=Point(0, 0) and br=Point(9, 8) and we want to send 7 nodes to a \\n # rectangle spanned by tl=Point(0, 9) and br=Point(6, 9)\\n ret.update(get_locations(nodes_tl, tl=Point(0, 0), br=Point(br.x, len(nodes) / (br.x + 1) - 1)))\\n ret.update(get_locations(nodes_br, tl=Point(0, len(nodes) / (br.x + 1)), br=Point(len(nodes) % (br.x + 1) - 1, len(nodes) / (br.x + 1))))\\n return ret\\n\\n if br.x - tl.x > br.y - tl.y: # if rectangle is wider than tall, split on y axis\\n half = tl.x + (br.x - tl.x - 1) / 2\\n size_tl_nodes = (half - tl.x + 1) * (br.y - tl.y + 1)\\n else: # split on x axis\\n half = tl.y + (br.y - tl.y - 1) / 2\\n size_tl_nodes = (br.x - tl.x + 1) * (half - tl.y + 1)\\n\\n nodes_tl, nodes_br = partition(nodes, size_tl_nodes, filename)\\n\\n if br.x - tl.x > br.y - tl.y: # if rectangle is wider than tall, split on y axis\\n ret.update(get_locations(nodes_tl, tl=tl, br=Point(half, br.y)))\\n ret.update(get_locations(nodes_br, tl=Point(half + 1,tl.y), br=br))\\n else: # split on x axis\\n ret.update(get_locations(nodes_tl, tl=tl, br=Point(br.x, half)))\\n ret.update(get_locations(nodes_br, tl=Point(tl.x, half + 1), br=br))\\n\\n return ret\",\n \"def find_sharpest_fork(nodes):\\n pair_list = []\\n Dis = np.array([])\\n for n in nodes:\\n if n.parent is not None:\\n if n.parent.parent is not None:\\n a = n.parent.children\\n if(isinstance(a, list)):\\n if(len(a)==2):\\n n1 = a[0]\\n n2 = a[1]\\n if(len(n1.children) == 0 and len(n2.children) == 0):\\n pair_list.append([n1 , n2])\\n dis = LA.norm(a[0].xyz - a[1].xyz,2)\\n Dis = np.append(Dis,dis)\\n if(len(Dis)!= 0):\\n (b,) = np.where(Dis == Dis.min())\\n sharpest_pair = pair_list[b[0]]\\n distance = Dis.min()\\n else:\\n sharpest_pair = [0,0]\\n distance = 0.\\n return sharpest_pair, distance\",\n \"def make_sidewalk_nodes(street, prev_node, curr_node, next_node):\\n if prev_node is None:\\n v = - curr_node.vector_to(next_node, normalize=False)\\n vec_prev = curr_node.vector() + v\\n prev_node = Node(None, vec_prev[0], vec_prev[1])\\n elif next_node is None:\\n v = - curr_node.vector_to(prev_node, normalize=False)\\n vec_next = curr_node.vector() + v\\n next_node = Node(None, vec_next[0], vec_next[1])\\n\\n curr_latlng = np.array(curr_node.location())\\n\\n v_cp_n = curr_node.vector_to(prev_node, normalize=True)\\n v_cn_n = curr_node.vector_to(next_node, normalize=True)\\n v_sidewalk = v_cp_n + v_cn_n\\n\\n if np.linalg.norm(v_sidewalk) < 1e-10:\\n v_sidewalk_n = np.array([v_cn_n[1], - v_cn_n[0]])\\n else:\\n v_sidewalk_n = v_sidewalk / np.linalg.norm(v_sidewalk)\\n\\n p1 = curr_latlng + street.distance_to_sidewalk * v_sidewalk_n\\n p2 = curr_latlng - street.distance_to_sidewalk * v_sidewalk_n\\n\\n p_sidewalk_1 = Node(None, p1[0], p1[1])\\n p_sidewalk_2 = Node(None, p2[0], p2[1])\\n\\n curr_node.append_sidewalk_node(street.id, p_sidewalk_1)\\n curr_node.append_sidewalk_node(street.id, p_sidewalk_2)\\n\\n # Figure out on which side you want to put each sidewalk node\\n v_c1 = curr_node.vector_to(p_sidewalk_1)\\n if np.cross(v_cn_n, v_c1) > 0:\\n return p_sidewalk_1, p_sidewalk_2\\n else:\\n return p_sidewalk_2, p_sidewalk_1\",\n \"def RegenerateWith2Nodes (failed_node, list_of_nodes, name):\\n \\n list_of_vects = []\\n\\n for node_index in list_of_nodes:\\n list_of_vects = list_of_vects + (map(lambda x: list(x), list(conf.BASIS_VECTORS [node_index])))\\n\\n array_A = numpy.array(list_of_vects).transpose()\\n arrays_B = []\\n\\n arrays_B = map(lambda x: list(x), list(conf.BASIS_VECTORS [failed_node]))\\n\\n obj_list = []\\n\\n for node_index in list_of_nodes:\\n for object_index in range(0,conf.PART_SIZE):\\n obj_list.append (dist.pull_object_from_stores (name, node_index, object_index))\\n\\n for each in arrays_B:\\n parts_to_pull = numpy.linalg.solve (array_A, numpy.array(each).transpose())\\n pack = []\\n \\n for i in range(0,len(parts_to_pull)):\\n if (parts_to_pull[i] != 0):\\n pack.append (copy.deepcopy (obj_list[i]))\\n\\n dist.push_object_to_store (name, failed_node, reduce (numpy.bitwise_xor, pack), arrays_B.index (each))\",\n \"def prepare_data_for_g(self):\\n\\n paths = []\\n for i in self.root_nodes:\\n if np.random.rand() < config.update_ratio:\\n sample, paths_from_i = self.sample(i, self.trees[i], config.n_sample_gen, for_d=False)\\n if paths_from_i is not None:\\n paths.extend(paths_from_i)\\n # for each root, we generate 20 samples, each sample is equal to one path from root to that sample\\n # So, we will get maximum (num_root x 20) paths\\n # path is a list with length = (N x num_sample), with num_sample = 20\\n # paths =[[path_root1_to_sample1],[path_root1_to_sample2],....,[path_root1_to_sample20],\\n # [path_root2_to_sample1],[path_root2_to_sample2],....,[path_root2_to sample20]\\n # .\\n # .\\n # [path_rootN_to_sample1],[path_rootN_to_sample2],....,[path_rootN_to_sample20]]\\n # get_node_pairs_from_path\\n\\n node_pairs = list(map(self.get_node_pairs_from_path, paths))\\n # node_pairs = [[node pairs for path_root1_to_sample1],[node pairs for path_root1_to_sample2],....,[node pairs for path_root1_to_sample20],\\n # [node_pairs for path_root2_to_sample1],[node pairs for path_root2_to_sample2],....,[node pairs for path_root2_to sample20],\\n # .\\n # .\\n # [node pairs for path_rootN_to_sample1],[node pairs for path_rootN_to_sample2],....,[node pairs for path_rootN_to_sample20]]\\n\\n node_1 = []\\n node_2 = []\\n for i in range(len(node_pairs)):\\n for pair in node_pairs[i]:\\n node_1.append(pair[0])\\n node_2.append(pair[1])\\n # reward = self.sess.run(self.discriminator.reward,\\n # feed_dict={self.discriminator.node_id: np.array(node_1),\\n # self.discriminator.node_neighbor_id: np.array(node_2)})\\n reward = self.discriminator.forward(node_1, node_2)\\n return node_1, node_2, reward\",\n \"def update_tree(root, executed_acts, total_rew):\\n root.value = max(total_rew, root.value)\\n root.visits += 1\\n new_nodes = 0\\n\\n node = root\\n for step, act in enumerate(executed_acts):\\n if act not in node.children:\\n node.children[act] = Node()\\n new_nodes += 1\\n node = node.children[act]\\n node.value = max(total_rew, node.value)\\n node.visits += 1\\n\\n return new_nodes\",\n \"def newton(backward_differences, max_num_iters, newton_coefficient, ode_fn_vec,\\n order, step_size, time, tol, unitary, upper):\\n initial_guess = tf.reduce_sum(\\n tf1.where(\\n tf.range(MAX_ORDER + 1) <= order,\\n backward_differences[:MAX_ORDER + 1],\\n tf.zeros_like(backward_differences)[:MAX_ORDER + 1]),\\n axis=0)\\n\\n np_dtype = np_dtype = dtype_util.as_numpy_dtype(backward_differences.dtype)\\n\\n rhs_constant_term = newton_coefficient * tf.reduce_sum(\\n tf1.where(\\n tf.range(1, MAX_ORDER + 1) <= order,\\n RECIPROCAL_SUMS[1:, np.newaxis].astype(np_dtype) *\\n backward_differences[1:MAX_ORDER + 1],\\n tf.zeros_like(backward_differences)[1:MAX_ORDER + 1]),\\n axis=0)\\n\\n next_time = time + step_size\\n step_size_cast = tf.cast(step_size, backward_differences.dtype)\\n real_dtype = tf.abs(backward_differences).dtype\\n\\n def newton_body(iterand):\\n \\\"\\\"\\\"Performs one iteration of Newton's method.\\\"\\\"\\\"\\n next_backward_difference = iterand.next_backward_difference\\n next_state_vec = iterand.next_state_vec\\n\\n rhs = newton_coefficient * step_size_cast * ode_fn_vec(\\n next_time,\\n next_state_vec) - rhs_constant_term - next_backward_difference\\n delta = tf.squeeze(\\n tf.linalg.triangular_solve(\\n upper,\\n tf.matmul(tf.transpose(unitary), rhs[:, tf.newaxis]),\\n lower=False))\\n num_iters = iterand.num_iters + 1\\n\\n next_backward_difference += delta\\n next_state_vec += delta\\n\\n delta_norm = tf.cast(tf.norm(delta), real_dtype)\\n lipschitz_const = delta_norm / iterand.prev_delta_norm\\n\\n # Stop if method has converged.\\n approx_dist_to_sol = lipschitz_const / (1. - lipschitz_const) * delta_norm\\n close_to_sol = approx_dist_to_sol < tol\\n delta_norm_is_zero = tf.equal(delta_norm, tf.constant(0., dtype=real_dtype))\\n converged = close_to_sol | delta_norm_is_zero\\n finished = converged\\n\\n # Stop if any of the following conditions are met:\\n # (A) We have hit the maximum number of iterations.\\n # (B) The method is converging too slowly.\\n # (C) The method is not expected to converge.\\n too_slow = lipschitz_const > 1.\\n finished = finished | too_slow\\n if max_num_iters is not None:\\n too_many_iters = tf.equal(num_iters, max_num_iters)\\n num_iters_left = max_num_iters - num_iters\\n num_iters_left_cast = tf.cast(num_iters_left, real_dtype)\\n wont_converge = (\\n approx_dist_to_sol * lipschitz_const**num_iters_left_cast > tol)\\n finished = finished | too_many_iters | wont_converge\\n\\n return [\\n _NewtonIterand(\\n converged=converged,\\n finished=finished,\\n next_backward_difference=next_backward_difference,\\n next_state_vec=next_state_vec,\\n num_iters=num_iters,\\n prev_delta_norm=delta_norm)\\n ]\\n\\n iterand = _NewtonIterand(\\n converged=False,\\n finished=False,\\n next_backward_difference=tf.zeros_like(initial_guess),\\n next_state_vec=tf.identity(initial_guess),\\n num_iters=0,\\n prev_delta_norm=tf.constant(np.array(-0.), dtype=real_dtype))\\n [iterand] = tf.while_loop(lambda iterand: tf.logical_not(iterand.finished),\\n newton_body, [iterand])\\n return (iterand.converged, iterand.next_backward_difference,\\n iterand.next_state_vec, iterand.num_iters)\",\n \"def recursion_loop(pulls, discount, grid_n):\\n\\n r_grid = np.linspace(0, 1, grid_n)\\n gittins, values = initial_approximation(pulls, discount, grid_n)\\n n = pulls - 2 # Note that the 2 comes from (1) the initial approximation and (2) python indexing\\n while n >= 1:\\n g, v = recursion_step(values[:n + 1, n, :], r_grid, discount)\\n values[:n, n - 1] = v\\n gittins[:n, n - 1] = g\\n n -= 1\\n return gittins, values\",\n \"def update_nodes(self, weights=None, hive_instance=None):\\n hive = hive_instance or shared_hive_instance()\\n metadata = None\\n account = None\\n cnt = 0\\n while metadata is None and cnt < 5:\\n cnt += 1\\n try:\\n account = Account(\\\"fullnodeupdate\\\", hive_instance=hive)\\n metadata = json.loads(account[\\\"json_metadata\\\"])\\n except:\\n hive.rpc.next()\\n account = None\\n metadata = None\\n if metadata is None:\\n return\\n report = metadata[\\\"report\\\"]\\n failing_nodes = metadata[\\\"failing_nodes\\\"]\\n parameter = metadata[\\\"parameter\\\"]\\n benchmarks = parameter[\\\"benchmarks\\\"]\\n if weights is None:\\n weights_dict = {}\\n for benchmark in benchmarks:\\n weights_dict[benchmark] = (1. / len(benchmarks))\\n elif isinstance(weights, list):\\n weights_dict = {}\\n i = 0\\n weight_sum = 0\\n for w in weights:\\n weight_sum += w\\n for benchmark in benchmarks:\\n if i < len(weights):\\n weights_dict[benchmark] = weights[i] / weight_sum\\n else:\\n weights_dict[benchmark] = 0.\\n i += 1\\n elif isinstance(weights, dict):\\n weights_dict = {}\\n i = 0\\n weight_sum = 0\\n for w in weights:\\n weight_sum += weights[w]\\n for benchmark in benchmarks:\\n if benchmark in weights:\\n weights_dict[benchmark] = weights[benchmark] / weight_sum\\n else:\\n weights_dict[benchmark] = 0.\\n\\n max_score = len(report) + 1\\n new_nodes = []\\n for node in self:\\n new_node = node.copy()\\n for report_node in report:\\n if node[\\\"url\\\"] == report_node[\\\"node\\\"]:\\n new_node[\\\"version\\\"] = report_node[\\\"version\\\"]\\n scores = []\\n for benchmark in benchmarks:\\n result = report_node[benchmark]\\n rank = result[\\\"rank\\\"]\\n if not result[\\\"ok\\\"]:\\n rank = max_score + 1\\n score = (max_score - rank) / (max_score - 1) * 100\\n weighted_score = score * weights_dict[benchmark]\\n scores.append(weighted_score)\\n sum_score = 0\\n for score in scores:\\n sum_score += score\\n new_node[\\\"score\\\"] = sum_score\\n for node_failing in failing_nodes:\\n if node[\\\"url\\\"] == node_failing:\\n new_node[\\\"score\\\"] = -1\\n new_nodes.append(new_node)\\n super(NodeList, self).__init__(new_nodes)\",\n \"def solve(self):\\n self.left -= len(self.nodes)\\n \\n def depths(x,depth = 0):\\n depth+=1\\n for y in self.graph[x]:\\n if y in self.nodes:\\n self.nodes.remove(y)\\n depth = depths(y,depth)\\n return depth\\n \\n while len(self.nodes):\\n x = self.nodes.pop()\\n self.firstGen.append(depths(x))\\n #print self.graph\\n #print self.nodes\\n #print self.firstGen\",\n \"def generate_children_nodes(\\n curr_node, list_of_processed_nodes,\\n running_count_of_children_dups, a_star_search=False\\n):\\n children_nodes_to_return = []\\n direction_coordinates_map = curr_node.state.get_legal_snake_movement_coords()\\n \\n for direction, legal_coords in direction_coordinates_map.iteritems():\\n curr_state_copy = copy.deepcopy(curr_node.state)\\n new_state = State(\\n dim=curr_state_copy.dim, num_obstacles=curr_state_copy.num_obstacles,\\n grid=curr_state_copy.grid, snake=curr_state_copy.snake\\n )\\n #pdb.set_trace()#\\n new_state.update_state_after_movement(new_head_coords=legal_coords)\\n\\n new_node_state = new_state\\n new_node_action = direction\\n new_node_parent_index = curr_node.index\\n new_node_depth = curr_node.depth + 1\\n\\n new_node = Node(\\n state=new_node_state, action=new_node_action,\\n parent_index=new_node_parent_index, depth=new_node_depth\\n )\\n\\n if not a_star_search:\\n if new_node in list_of_processed_nodes:\\n running_count_of_children_dups += 1\\n continue\\n\\n children_nodes_to_return.append(new_node)\\n\\n return children_nodes_to_return, running_count_of_children_dups\",\n \"def find_nodes(input_line: str) -> List[Node]:\\n li = [int(elem) for elem in input_line.split(\\\" \\\")]\\n assert len(li) >= 2\\n \\n # store Nodes in two sets, depending is their processing ready or not\\n unfinished = set()\\n finished = set()\\n \\n \\n i = 0 # points to the index where to read the input list\\n parent = None\\n \\n # add root node\\n global root # global so we can directly grab its value outside this func\\n root = Node(num_childs = li[i], num_metadata = li[i+1], children = None, parent = parent)\\n print(\\\"Added root node:\\\", root)\\n \\n # Logic for handling the root node\\n if root.num_childs > 0:\\n unfinished.add(root) # assumes more to come...\\n i += 2 # continue from child's first element\\n else: # root node does not have children\\n finished.add(root)\\n i += 2 + num_metadata\\n \\n parent = root\\n \\n \\n all_done = False # set to True when all nodes has been processed (to break out of the loop)\\n \\n # now we have a root ready\\n while i < len(li):\\n #print(i)\\n \\n while parent.num_child_processed >= parent.num_childs:\\n # backtrack a step towards root node!\\n # store metadata elements\\n parent.metadata = li[i: i+parent.num_metadata]\\n \\n # calculate node value\\n parent.value = sum(parent.children[idx - 1].value for idx in parent.metadata if idx > 0 and idx <= parent.num_childs)\\n \\n finished.add(parent)\\n unfinished.remove(parent)\\n i += parent.num_metadata\\n \\n if parent.parent:\\n parent = parent.parent\\n else: # was root\\n print(\\\"Backtracking out from root, hence all done\\\")\\n all_done = True\\n break\\n \\n if all_done:\\n break\\n \\n curr_num_childs, curr_num_metadata = li[i], li[i+1]\\n \\n # create a new node\\n curr_node = Node(num_childs = curr_num_childs, num_metadata = curr_num_metadata, children = None, parent = parent)\\n #print(\\\"Found new node:\\\", curr_num_childs, curr_num_metadata, \\\"\\\\t\\\\tparent:\\\", parent)\\n parent.children.append(curr_node)\\n parent.num_child_processed += 1\\n \\n if curr_num_childs > 0: # current node has children\\n unfinished.add(curr_node)\\n i = i + 2 # continue with the child\\n parent = curr_node # which has current node as its parent\\n else: # current node is a leaf node\\n curr_node.metadata = li[i+2: i+2+curr_num_metadata]\\n # calculate node value\\n curr_node.value = sum(curr_node.metadata)\\n \\n finished.add(curr_node)\\n i = i + 2 + curr_num_metadata\\n \\n return finished\",\n \"def getNextNodeUsingCellDiff(kGoalState):\\n \\n global fringe\\n global solutions\\n\\n \\n\\n\\n\\n minNode = None\\n minCost = 99999999999\\n minNodeIndex = -1\\n\\n \\n pnode = None\\n pcost = None\\n\\n if len(solutions)>0 and solutions[0] != None:\\n pnode = solutions[0];\\n pcost = getHValueForNode(pnode,kGoalState)\\n #print pnode, pcost\\n # raw_input()\\n \\n\\n\\n\\n for idx,node in enumerate(fringe):\\n #get the heu. function values\\n g_value = getHValueForNode(node,kGoalState)\\n \\n\\n if g_value < minCost:\\n minNode = node\\n minNodeIndex = idx\\n minCost = g_value\\n\\n\\n fringe.pop(minNodeIndex)\\n c = getHValueForNode(minNode,kGoalState)\\n if pnode != None:\\n if c > pcost:\\n minNode = None\\n \\n return minNode\",\n \"def _compute_nodes_1d(npts, ilbds1d): # pylint: disable=line-too-long\\n if npts % 2 == 0:\\n raise ValueError(\\\"Please enter odd npts\\\")\\n ind = np.arange(1, npts + 1)\\n nodes = 0.5 * (1 - np.cos(np.pi * ind / (npts + 1)))\\n return nodes * (ilbds1d[1] - ilbds1d[0]) + ilbds1d[0]\",\n \"def calculate(self, extraParams=None):\\n\\n if not self.isCalc or self.nodeClass == \\\"button\\\" or self.nodeClass == \\\"formnode\\\":\\n nodeIsCircular = self.isCircular()\\n if not self._bypassCircularEvaluator and nodeIsCircular:\\n circularNodes = self.getSortedCyclicDependencies()\\n\\n if self.dynamicEvaluator is None:\\n self.dynamicEvaluator = FactoryDynamic.createInstance(\\n circularNodes, self)\\n\\n params = self.dynamicEvaluator.generateCircularParameters(\\n self, circularNodes)\\n\\n if params['dynamicIndex'] is None:\\n raise ValueError(\\\"Cyclic dependency detected between nodes: \\\" + \\\",\\\".join(\\n circularNodes) + \\\". Please use the 'pp.dynamic' function\\\")\\n elif 'indexDic' in params and len(params['indexDic']) > 1:\\n raise ValueError(\\n f'Multiple indices were found using dynamic. Indexes: {\\\",\\\".join(params[\\\"indexDic\\\"].keys())}. Nodes involved: {\\\",\\\".join(circularNodes)}')\\n\\n self.dynamicEvaluator.circularEval(self, params)\\n else:\\n from_circular_evaluator = self._bypassCircularEvaluator\\n\\n self.sendStartCalcNode(from_circular_evaluator)\\n self.model.currentProcessingNode(self.identifier)\\n self._bypassCircularEvaluator = False\\n\\n startTime = dt.datetime.now()\\n finalDef = str(self._definition)\\n self.lastLazyTime = 0\\n\\n # CLEAR circular dependency\\n if nodeIsCircular:\\n finalDef = BaseDynamic.clearAllCircularDependency(finalDef)\\n tmpCode = self.compileDef(finalDef)\\n\\n # check for replace calls to varaibles with next rules:\\n # node1 --> change to getNode('node1').result\\n # node1.result --> change to getNode('node1').result\\n # node1.title --> change to getNode('node1').title\\n # \\\"node1\\\" --> no change\\n # 'node1' --> no change\\n if not tmpCode is None:\\n names = self.parseNames(tmpCode)\\n rx = r\\\"('[^'\\\\\\\\]*(?:\\\\\\\\.[^'\\\\\\\\]*)*'|\\\\\\\"[^\\\\\\\"\\\\\\\\]*(?:\\\\\\\\.[^\\\\\\\"\\\\\\\\]*)*\\\\\\\")|\\\\b{0}\\\\b\\\"\\n for node in names:\\n if self._model.existNode(self._model.clearId(node)):\\n finalDef = re.sub(rx.format(node), lambda m:\\n (\\n m.group(1)\\n if m.group(1)\\n else\\n (\\n \\\"getNode('\\\"+node+\\\"')\\\"\\n if (m.endpos > m.regs[0][1]+5) and ((m.string[m.regs[0][1]:m.regs[0][1]+5] == '.node') or (m.string[m.regs[0][1]:m.regs[0][1]+8] == '.timeit('))\\n else\\n (node\\n if (m.string[m.regs[0][0]-1:m.regs[0][0]+len(node)] == ('.'+node)) or (m.string[m.regs[0][0]-7:m.regs[0][0]] == 'import ') or (m.string[m.regs[0][0]-5:m.regs[0][0]] == 'from ')\\n else \\\"getCalcNode('\\\"+node+\\\"')\\\"\\n )\\n )\\n ), finalDef, 0, re.IGNORECASE)\\n elif node == \\\"self\\\":\\n finalDef = re.sub(rx.format(node), lambda m:\\n (\\n m.group(1)\\n if m.group(1)\\n else\\n \\\"getNode('\\\" + self.identifier + \\\"')\\\"\\n if (m.endpos > m.regs[0][1]+11) and (m.string[m.regs[0][1]:m.regs[0][1]+11] != '._tryFilter')\\n else \\\"self\\\"\\n ), finalDef, 0, re.IGNORECASE)\\n\\n localRes = {\\n \\\"getNode\\\": self._model.getNode,\\n \\\"getCalcNode\\\": self._getCalcNode,\\n \\\"cp\\\": Helpers(self)\\n }\\n if not extraParams is None:\\n for keyParam in extraParams:\\n localRes[keyParam] = extraParams[keyParam]\\n\\n customImports = self.model.getCustomImports()\\n if customImports:\\n for keyParam in customImports:\\n localRes[keyParam] = customImports[keyParam]\\n\\n try:\\n # execute node definition in supervised context\\n memoryIO = io.StringIO()\\n try:\\n with redirect_stdout(memoryIO):\\n exec(compile(finalDef, '<string>', 'exec'), localRes)\\n except Exception as ex:\\n if \\\"_io.StringIO\\\" in str(ex):\\n exec(compile(finalDef, '<string>', 'exec'), localRes)\\n else:\\n raise ex\\n\\n self.lastEvaluationConsole = memoryIO.getvalue()\\n memoryIO = None\\n\\n if self.nodeClass not in [\\\"button\\\", \\\"module\\\", \\\"text\\\"]:\\n if 'this' in localRes:\\n self._result = localRes['this']\\n elif 'result' in localRes:\\n self._result = localRes['result']\\n else:\\n self._result = None\\n if self.lastEvaluationConsole != \\\"\\\":\\n self._result = str(self.lastEvaluationConsole)\\n else:\\n raise ValueError(\\n \\\"The result was not found. Did you forget to include the text 'result =' ?\\\")\\n\\n self._isCalc = self.nodeClass != \\\"button\\\"\\n self.postCalculate()\\n\\n endTime = dt.datetime.now()\\n self.lastEvaluationTime = (\\n endTime - startTime).total_seconds() - self.lastLazyTime\\n if self.lastEvaluationTime < 0:\\n self.lastEvaluationTime = 0\\n self.evaluationVersion = self.model.evaluationVersion\\n finally:\\n localRes[\\\"cp\\\"].release()\\n localRes = None\\n self.sendEndCalcNode(from_circular_evaluator)\\n else:\\n self._bypassCircularEvaluator = False\",\n \"def start_one_step(self):\\r\\n new_infected_list = []\\r\\n old_infected_list = copy.deepcopy(self.infected_list)\\r\\n new_recovered_list = []\\r\\n old_recovered_list = copy.deepcopy(self.recovered_list)\\r\\n # For each infected node\\r\\n for infected_nid in old_infected_list:\\r\\n infected_node = self.node_dict[infected_nid]\\r\\n # For each neighbor\\r\\n for dst_nid in infected_node.get_dst_nid_list(self.graph):\\r\\n dst_node = self.node_dict[dst_nid]\\r\\n # Infect susceptible nodes with probability [p]\\r\\n if dst_node.state is NodeState.SUSCEPTIBLE and random.random() < self.p:\\r\\n dst_node.infected(self.i)\\r\\n new_infected_list.append(dst_nid)\\r\\n\\r\\n # Minus 1 turn of (remaining) infected days for all infected nodes\\r\\n infected_node.minus_one_state_day()\\r\\n # If infected node is recovered\\r\\n if infected_node.check_finish_infection():\\r\\n # Infected node get recovered\\r\\n infected_node.recovered(self.r)\\r\\n # Remove from infected list\\r\\n self.infected_list.remove(infected_nid)\\r\\n # Append to recovered list\\r\\n new_recovered_list.append(infected_nid)\\r\\n\\r\\n # Add newly infected nodes into infected list\\r\\n self.infected_list += new_infected_list\\r\\n\\r\\n # For each recovered node\\r\\n for recovered_nid in old_recovered_list:\\r\\n recovered_node = self.node_dict[recovered_nid]\\r\\n # Minus 1 turn of (remaining) recovered days for all recovered nodes\\r\\n recovered_node.minus_one_state_day()\\r\\n # If infected node is recovered\\r\\n if recovered_node.check_finish_recovery():\\r\\n # Recovered node get recovered\\r\\n recovered_node.susceptible()\\r\\n # Remove from recovered list\\r\\n self.recovered_list.remove(recovered_nid)\\r\\n\\r\\n # Add newly recovered nodes into recovered list\\r\\n self.recovered_list += new_recovered_list\",\n \"def split_next(self):\\n # Consider the node with the highest loss reduction (a.k.a. gain)\\n node = heappop(self.splittable_nodes)\\n\\n tic = time()\\n (sample_indices_left,\\n sample_indices_right,\\n right_child_pos) = self.splitter.split_indices(node.split_info,\\n node.sample_indices)\\n self.total_apply_split_time += time() - tic\\n\\n depth = node.depth + 1\\n n_leaf_nodes = len(self.finalized_leaves) + len(self.splittable_nodes)\\n n_leaf_nodes += 2\\n\\n left_child_node = TreeNode(depth,\\n sample_indices_left,\\n node.split_info.sum_gradient_left,\\n node.split_info.sum_hessian_left,\\n parent=node)\\n right_child_node = TreeNode(depth,\\n sample_indices_right,\\n node.split_info.sum_gradient_right,\\n node.split_info.sum_hessian_right,\\n parent=node)\\n left_child_node.sibling = right_child_node\\n right_child_node.sibling = left_child_node\\n node.right_child = right_child_node\\n node.left_child = left_child_node\\n\\n # set start and stop indices\\n left_child_node.partition_start = node.partition_start\\n left_child_node.partition_stop = node.partition_start + right_child_pos\\n right_child_node.partition_start = left_child_node.partition_stop\\n right_child_node.partition_stop = node.partition_stop\\n\\n self.n_nodes += 2\\n\\n if self.max_depth is not None and depth == self.max_depth:\\n self._finalize_leaf(left_child_node)\\n self._finalize_leaf(right_child_node)\\n return left_child_node, right_child_node\\n\\n if (self.max_leaf_nodes is not None\\n and n_leaf_nodes == self.max_leaf_nodes):\\n self._finalize_leaf(left_child_node)\\n self._finalize_leaf(right_child_node)\\n self._finalize_splittable_nodes()\\n return left_child_node, right_child_node\\n\\n if left_child_node.n_samples < self.min_samples_leaf * 2:\\n self._finalize_leaf(left_child_node)\\n if right_child_node.n_samples < self.min_samples_leaf * 2:\\n self._finalize_leaf(right_child_node)\\n\\n # Compute histograms of childs, and compute their best possible split\\n # (if needed)\\n should_split_left = left_child_node.value is None # node isn't a leaf\\n should_split_right = right_child_node.value is None\\n if should_split_left or should_split_right:\\n\\n # We will compute the histograms of both nodes even if one of them\\n # is a leaf, since computing the second histogram is very cheap\\n # (using histogram subtraction).\\n n_samples_left = left_child_node.sample_indices.shape[0]\\n n_samples_right = right_child_node.sample_indices.shape[0]\\n if n_samples_left < n_samples_right:\\n smallest_child = left_child_node\\n largest_child = right_child_node\\n else:\\n smallest_child = right_child_node\\n largest_child = left_child_node\\n\\n # We use the brute O(n_samples) method on the child that has the\\n # smallest number of samples, and the subtraction trick O(n_bins)\\n # on the other one.\\n tic = time()\\n smallest_child.histograms = \\\\\\n self.histogram_builder.compute_histograms_brute(\\n smallest_child.sample_indices)\\n largest_child.histograms = \\\\\\n self.histogram_builder.compute_histograms_subtraction(\\n node.histograms, smallest_child.histograms)\\n self.total_compute_hist_time += time() - tic\\n\\n tic = time()\\n if should_split_left:\\n self._compute_best_split_and_push(left_child_node)\\n if should_split_right:\\n self._compute_best_split_and_push(right_child_node)\\n self.total_find_split_time += time() - tic\\n\\n return left_child_node, right_child_node\",\n \"def update_nodes(nodes, svg_h):\\n for i in range(0, len(nodes)):\\n nodes[i,2] = svg_h-nodes[i,2]\\n return nodes\",\n \"def _buildtree(self):\\n self.pricetree = np.zeros((self.steps+1,self.steps+1))\\n self.pricetree[0][0] = self.p\\n for j in range(self.steps):\\n for i in range(j+1):\\n self.pricetree[j+1][i+1] = self.pricetree[j][i]*self.down\\n self.pricetree[j+1][0] = self.pricetree[j][0]*self.up\",\n \"def run_one_step(self, dt):\\n if not self._erode_flooded_nodes:\\n flood_status = self._grid.at_node[\\\"flood_status_code\\\"]\\n flooded_nodes = np.nonzero(flood_status == _FLOODED)[0]\\n else:\\n flooded_nodes = []\\n\\n upstream_order_IDs = self._grid[\\\"node\\\"][\\\"flow__upstream_node_order\\\"]\\n\\n defined_flow_receivers = np.not_equal(\\n self._grid[\\\"node\\\"][\\\"flow__link_to_receiver_node\\\"], self._grid.BAD_INDEX\\n )\\n\\n try:\\n length_of_link = self._grid.length_of_d8\\n except AttributeError:\\n length_of_link = self._grid.length_of_link\\n\\n flow_link_lengths = length_of_link[\\n self._grid.at_node[\\\"flow__link_to_receiver_node\\\"][defined_flow_receivers]\\n ]\\n flow_receivers = self._grid[\\\"node\\\"][\\\"flow__receiver_node\\\"]\\n\\n # Operate the main function:\\n if self._use_W:\\n self._alpha[defined_flow_receivers] = (\\n self._K[defined_flow_receivers]\\n * dt\\n * self._A[defined_flow_receivers] ** self._m\\n / self._W[defined_flow_receivers]\\n / (flow_link_lengths**self._n)\\n )\\n\\n else:\\n self._alpha[defined_flow_receivers] = (\\n self._K[defined_flow_receivers]\\n * dt\\n * self._A[defined_flow_receivers] ** self._m\\n / (flow_link_lengths**self._n)\\n )\\n\\n # Handle flooded nodes, if any (no erosion there)\\n if flooded_nodes is not None:\\n self._alpha[flooded_nodes] = 0.0\\n\\n reversed_flow = self._elevs < self._elevs[flow_receivers]\\n # this check necessary if flow has been routed across\\n # depressions\\n self._alpha[reversed_flow] = 0.0\\n\\n threshdt = self._sp_crit * dt\\n\\n # solve using Brent's Method in Cython for Speed\\n if isinstance(threshdt, float):\\n brent_method_erode_fixed_threshold(\\n upstream_order_IDs,\\n flow_receivers,\\n threshdt,\\n self._alpha,\\n self._n,\\n self._elevs,\\n )\\n else:\\n brent_method_erode_variable_threshold(\\n upstream_order_IDs,\\n flow_receivers,\\n threshdt,\\n self._alpha,\\n self._n,\\n self._elevs,\\n )\",\n \"def brents(f, x0, x1, max_iter=50, tolerance=1e-5):\\n \\n fx0 = f(x0)\\n fx1 = f(x1)\\n \\n assert (fx0 * fx1) <= 0, \\\"Root not bracketed\\\" \\n \\n if abs(fx0) < abs(fx1):\\n x0, x1 = x1, x0\\n fx0, fx1 = fx1, fx0\\n \\n x2, fx2 = x0, fx0\\n \\n d = np.nan\\n mflag = True\\n steps_taken = 0\\n \\n while steps_taken < max_iter and abs(x1-x0) > tolerance:\\n fx0 = f(x0)\\n fx1 = f(x1)\\n fx2 = f(x2)\\n \\n if fx0 != fx2 and fx1 != fx2:\\n L0 = (x0 * fx1 * fx2) / ((fx0 - fx1) * (fx0 - fx2))\\n L1 = (x1 * fx0 * fx2) / ((fx1 - fx0) * (fx1 - fx2))\\n L2 = (x2 * fx1 * fx0) / ((fx2 - fx0) * (fx2 - fx1))\\n new = L0 + L1 + L2\\n \\n else:\\n new = x1 - ( (fx1 * (x1 - x0)) / (fx1 - fx0) )\\n \\n tt1 = (new < ((3 * x0 + x1) / 4) or new > x1)\\n tt2 = (mflag == True and (abs(new - x1)) >= (abs(x1 - x2) / 2))\\n tt3 = (mflag == False and (abs(new - x1)) >= (abs(x2 - d) / 2))\\n tt4 = (mflag == True and (abs(x1 - x2)) < tolerance)\\n tt5 = (mflag == False and (abs(x2 - d)) < tolerance)\\n if (tt1 or\\n tt2 or\\n tt3 or\\n tt4 or\\n tt5):\\n new = (x0 + x1) / 2\\n mflag = True\\n \\n else:\\n mflag = False\\n \\n fnew = f(new)\\n d, x2 = x2, x1\\n \\n if (fx0 * fnew) < 0:\\n x1 = new\\n else:\\n x0 = new\\n \\n if abs(fx0) < abs(fx1):\\n x0, x1 = x1, x0\\n \\n steps_taken += 1\\n \\n return x1, steps_taken\",\n \"def make_euler_circuit(start_node, updated_graph_instance):\\n\\n current_edges_on_graph_list = make_edges_list(updated_graph_instance.edges_dict)\\n\\n current_node = start_node\\n\\n node_visit_order = [current_node]\\n edge_visit_order = []\\n\\n # print(\\\"\\\\n\\\\n\\\\ncurrent_edges_on_graph_list:\\\", current_edges_on_graph_list)\\n\\n while len(current_edges_on_graph_list) > 0:\\n\\n # print(\\\"current_edges_on_graph_list:\\\", current_edges_on_graph_list)\\n # while there are still edges on the graph, keep traversing\\n\\n current_bridges_on_graph = get_bridges(current_edges_on_graph_list)\\n\\n edges_conn_to_current_node = get_all_conn_edges_remaining_in_graph(\\n current_node, current_edges_on_graph_list, updated_graph_instance.nodes_dict\\n )\\n\\n edge_to_traverse = choose_edge_to_traverse(\\n current_node, edges_conn_to_current_node, current_bridges_on_graph\\n )\\n\\n if edge_to_traverse in current_edges_on_graph_list:\\n\\n current_edges_on_graph_list.remove(edge_to_traverse)\\n\\n else:\\n\\n current_edges_on_graph_list.remove(edge_to_traverse[::-1])\\n\\n edge_to_traverse_list = list(edge_to_traverse)\\n # remove current node from edge to traverse\\n edge_to_traverse_list.remove(current_node)\\n # update current node to be the only node left in the edge list\\n\\n # update edge traveral list with edge just traversed\\n edge_traversed = (current_node, edge_to_traverse_list[0])\\n\\n edge_visit_order.append(edge_traversed)\\n\\n current_node = edge_to_traverse_list[0]\\n\\n # add the new current node to the nodes visit order list\\n node_visit_order.append(current_node)\\n\\n # add node visit order and edge_visit order to graph instance\\n\\n updated_graph_instance.node_visit_order = node_visit_order\\n\\n updated_graph_instance.edge_visit_order = edge_visit_order\\n\\n updated_graph_instance.node_geojson = make_node_geojson(updated_graph_instance)\\n\\n updated_graph_instance.edge_geojson = make_edge_geojson(updated_graph_instance)\\n\\n updated_graph_instance.route_geojson = make_route_geojson(updated_graph_instance)\\n\\n print(\\\"\\\\n\\\\n\\\\n\\\\n\\\\nROUTE COLLECTION\\\", updated_graph_instance.route_geojson)\\n\\n print(\\\"check done\\\")\\n\\n return updated_graph_instance\",\n \"def compare_nodes(G,all_match_pairs,match_pair,traversed,node1,node2, ports_weight):\\n logger.debug(f\\\"comparing {node1},{node2}, traversed {traversed}\\\")\\n nbrs1 = sorted(set(G.neighbors(node1)) - set(traversed))\\n #remove dummies\\n nbrs1 = sorted(set([nbr for nbr in nbrs1 if G.get_edge_data(node1, nbr)['weight'] !=7]))\\n nbrs2 = sorted(set(G.neighbors(node2)) - set(traversed))\\n #remove dummies\\n nbrs2 = sorted(set([nbr for nbr in nbrs2 if G.get_edge_data(node2, nbr)['weight'] !=7]))\\n logger.debug(f\\\"node1:{node1},property: {G.nodes[node1]},neigbors1: {nbrs1}\\\")\\n logger.debug(f\\\"node2:{node2},property: {G.nodes[node2]},neigbors2: {nbrs2}\\\")\\n if not nbrs1 or not nbrs2:\\n if compare_two_nodes(G, node1, node2, ports_weight):\\n match_pair[node1] = node2\\n logger.debug(f\\\"no new neihbours, returning recursion {match_pair}\\\")\\n return\\n elif len(nbrs1)> 10:\\n if \\\"start_point\\\" in match_pair.keys():\\n match_pair[\\\"start_point\\\"]+=[node1,node2]\\n else:\\n match_pair[\\\"start_point\\\"]=[node1,node2]\\n logger.debug(f\\\"skipping high fanout nets due to large computation, {node1} {nbrs1}\\\")\\n traversed.append(node1)\\n return\\n elif len(nbrs2)> 10:\\n if \\\"start_point\\\" in match_pair.keys():\\n match_pair[\\\"start_point\\\"]+=[node1,node2]\\n else:\\n match_pair[\\\"start_point\\\"]=[node1,node2]\\n traversed.append(node2)\\n logger.debug(f\\\"skipping high fanout nets due to large computation, {node2} {nbrs2}\\\")\\n return\\n\\n if node1 == node2:\\n if node1 in match_pair.keys() or node1 in match_pair.values():\\n logger.debug(\\\"avoid existing pair wise symmetry\\\")\\n return\\n logger.debug(f\\\"single node {node1}, nbrs {nbrs1}, nbr_weight {[G.get_edge_data(node1,nbr) for nbr in nbrs1]}\\\")\\n SD_nbrs= [nbr for nbr in nbrs1 if G.get_edge_data(node1, nbr)['weight'] !=2]\\n ## TBD: filter based on primitive constraints\\n ## Right now will try to figure out S/D paths\\n if len(SD_nbrs) ==0:\\n logger.debug(f\\\"No SD paths found to traverse\\\")\\n match_pair[node1]=node1\\n elif len(SD_nbrs) ==1:\\n logger.debug(f\\\"traversing single S/D path {SD_nbrs}\\\")\\n match_pair[node1]=node1\\n traversed.append(node1)\\n compare_nodes(G,all_match_pairs,match_pair,traversed,SD_nbrs[0],SD_nbrs[0],ports_weight)\\n else:\\n logger.debug(f\\\" multiple nodes diverging {SD_nbrs}\\\")\\n logger.debug(f\\\"nbr weights: {SD_nbrs} {[G.get_edge_data(node1, nbr)['weight'] for nbr in SD_nbrs ]}\\\")\\n match_pair[node1]=node1\\n traversed.append(node1)\\n new_sp=sorted(set(SD_nbrs)-set(traversed))\\n all_match_pairs_local={}\\n for nbr1,nbr2 in combinations(new_sp, 2):\\n logger.debug(f\\\"recursive pair call from single branch {nbr1} {nbr2}\\\")\\n new_pair={}\\n compare_nodes(G,all_match_pairs,new_pair,traversed.copy(),nbr1,nbr2,ports_weight)\\n if new_pair:\\n #new_pair[nbr1]=nbr2\\n all_match_pairs_local[nbr1+'_'+nbr2] = new_pair\\n all_match_pairs_local={k: v for k, v in all_match_pairs_local.items() if len(v)>0}\\n if len(all_match_pairs_local)==1:\\n match_pair.update( all_match_pairs_local[list(all_match_pairs_local.keys())[0]])\\n logger.debug(f\\\"found inline pair: {pprint.pformat(match_pair, indent=4)}\\\")\\n else:\\n for nbr1 in new_sp:\\n if (nbr1+'_'+nbr1 not in all_match_pairs.keys()):\\n logger.debug(f\\\"recursive single branch call from single branch {nbr1} {nbr1}\\\")\\n new_pair={}\\n compare_nodes(G,all_match_pairs,new_pair,traversed.copy(),nbr1,nbr1,ports_weight)\\n #filtering multiple axis of symmetries with same block, ideally they should be handled by array generation\\n if new_pair:\\n all_match_pairs[nbr1+'_'+nbr1] = new_pair\\n logger.debug(f\\\"updating match pairs: {pprint.pformat(all_match_pairs, indent=4)}\\\")\\n\\n\\n elif nbrs1 == nbrs2:\\n logger.debug(f\\\"traversing converging branch\\\")\\n match_pair[node1]=node2\\n traversed+=[node1,node2]\\n nbrs1=sorted(set(nbrs1)-set([node1,node2]))\\n logger.debug(f\\\"all non traversed neighbours: {nbrs1}\\\")\\n if len(nbrs1)==1:\\n nbr1=nbr2=nbrs1[0]\\n logger.debug(f\\\"keeping single converged branch inline {nbr1} {nbr2}\\\")\\n compare_nodes(G,all_match_pairs,match_pair,traversed.copy(),nbr1,nbr2,ports_weight)\\n else:\\n for nbr1,nbr2 in combinations_with_replacement(nbrs1,2):\\n logger.debug(f\\\"recursive call from converged branch {nbr1} {nbr2}\\\")\\n if nbr1+'_'+nbr2 not in all_match_pairs.keys():\\n new_pair={}\\n compare_nodes(G,all_match_pairs,new_pair,traversed.copy(),nbr1,nbr2,ports_weight)\\n #filtering multiple axis of symmetries with same block, ideally they should be handled by array generation\\n if new_pair:\\n all_match_pairs[nbr1+'_'+nbr2] = new_pair\\n logger.debug(f\\\"updating match pairs: {pprint.pformat(all_match_pairs, indent=4)}\\\")\\n\\n\\n elif compare_two_nodes(G,node1,node2,ports_weight):\\n nbrs1 = sorted(set([nbr for nbr in nbrs1 if G.get_edge_data(node1, nbr)['weight'] !=2]))\\n nbrs2 = sorted(set([nbr for nbr in nbrs2 if G.get_edge_data(node2, nbr)['weight'] !=2]))\\n match_pair[node1]=node2\\n traversed+=[node1,node2]\\n logger.debug(f\\\"Traversing parallel branches from {node1},{node2} {nbrs1}, {nbrs2}\\\")\\n nbrs1_wt = [G.get_edge_data(node1, nbr)['weight'] for nbr in nbrs1]\\n nbrs2_wt = [G.get_edge_data(node2, nbr)['weight'] for nbr in nbrs2]\\n unique_match=find_unique_matching_branches(G,nbrs1,nbrs2,ports_weight)\\n if len(nbrs1)==0 or len(nbrs2)==0:\\n logger.debug(f\\\"no new SD neihbours, returning recursion {match_pair}\\\")\\n elif len(nbrs1) ==1 and len(nbrs2)==1:\\n logger.debug(f\\\"traversing binary branch\\\")\\n compare_nodes(G,all_match_pairs,match_pair,traversed,nbrs1.pop(),nbrs2.pop(),ports_weight)\\n elif unique_match:\\n logger.debug(f'traversing unique matches {unique_match}')\\n match_pair[node1]=node2\\n traversed+=[node1,node2]\\n for nbr1,nbr2 in unique_match.items():\\n logger.debug(f\\\"recursive call from binary {node1}:{node2} to {nbr1}:{nbr2}\\\")\\n compare_nodes(G,all_match_pairs,match_pair,traversed.copy(),nbr1,nbr2,ports_weight)\\n elif len(nbrs1_wt)>len(set(nbrs1_wt))>1 and len(nbrs2_wt)>len(set(nbrs2_wt))>1:\\n logger.debug(f\\\"setting new start points {node1} {node2}\\\")\\n match_pair[node1]=node2\\n if \\\"start_point\\\" in match_pair.keys():\\n match_pair[\\\"start_point\\\"]+=[node1,node2]\\n else:\\n match_pair[\\\"start_point\\\"]=[node1,node2]\\n else:\\n match_pair = {}\\n logger.debug(f\\\"end all traversal from binary branch {node1} {node2}\\\")\\n\\n else:\\n match_pair = {}\\n logger.debug(f\\\"end of recursion branch, matches {match_pair}\\\")\",\n \"def flep(tree_adj, nodes_sign, edge_weight, root, return_fullcut_info=False):\\n # start = clock()\\n assert isinstance(tree_adj, dict)\\n if not (isinstance(nodes_sign, tuple) and len(nodes_sign) == 2):\\n nodes_sign = (nodes_sign, nodes_sign)\\n if root in nodes_sign[0]:\\n cutp, cutn = (MAX_WEIGHT, 0) if nodes_sign[0][root] < 0 else (0, MAX_WEIGHT)\\n val_1 = nodes_sign[0][root]*MAX_WEIGHT\\n cutp_, cutn_ = (MAX_WEIGHT, 0) if nodes_sign[1][root] < 0 else (0, MAX_WEIGHT)\\n val_2 = nodes_sign[1][root]*MAX_WEIGHT\\n return (val_1, val_2), {}, {root: (True, -1, cutp, cutn, cutp_, cutn_)}\\n stack = []\\n status = defaultdict(lambda: (False, -1, 0, 0, 0, 0))\\n stack.append(root)\\n while stack:\\n v = stack.pop()\\n if v >= 0:\\n discovered, pred, cutp, cutn, cutp_, cutn_ = status[v]\\n else:\\n v = -(v+100)\\n discovered, pred, cutp, cutn, cutp_, cutn_ = status[v]\\n for child in tree_adj[v]:\\n if status[child][1] != v:\\n continue\\n eweight = edge_weight[(child, v) if child < v else (v, child)]\\n _, _, childp, childn, childp_, childn_ = status[child]\\n cutp += min(childp, childn + eweight)\\n cutn += min(childn, childp + eweight)\\n cutp_ += min(childp_, childn_ + eweight)\\n cutn_ += min(childn_, childp_ + eweight)\\n status[v] = (discovered, pred, cutp, cutn, cutp_, cutn_)\\n # print('{}: (+: {}, -: {})'.format(v, cutp, cutn))\\n if v == root:\\n # FLEP_CALLS_TIMING.append(clock() - start)\\n intermediate = {}\\n if return_fullcut_info:\\n intermediate = {n: vals[2:6]\\n for n, vals in status.items()\\n if vals[0] and n not in nodes_sign[0]}\\n return (((cutn - cutp), (cutn_ - cutp_)), intermediate, status)\\n\\n if not discovered:\\n status[v] = (True, pred, cutp, cutn, cutp_, cutn_)\\n if v in nodes_sign[0]:\\n # don't go beyond revealed nodes\\n continue\\n stack.append(-(v+100))\\n for w in tree_adj[v]:\\n discovered, pred, cutp, cutn, cutp_, cutn_ = status[w]\\n if pred == -1 and w != root:\\n if w in nodes_sign[0]:\\n cutp, cutn = (MAX_WEIGHT, 0) if nodes_sign[0][w] < 0 else (0, MAX_WEIGHT)\\n cutp_, cutn_ = (MAX_WEIGHT, 0) if nodes_sign[1][w] < 0 else (0, MAX_WEIGHT)\\n status[w] = (discovered, v, cutp, cutn, cutp_, cutn_)\\n if not discovered:\\n stack.append(w)\\n assert False, root\",\n \"def CreateTree(d,list_L):\\r\\n \\r\\n d_new = defaultdict(dict)\\r\\n \\r\\n #labels corresponding to minimum evolutionary distance in Matrix\\r\\n a,b = minInDict(d)\\r\\n\\r\\n n = ['(',a,',',b,')']\\r\\n new_cluster = ''.join(n)\\r\\n \\r\\n #remove the entries in newly formed cluster from our list\\r\\n for k in list_L:\\r\\n if k == a:\\r\\n list_L.remove(k)\\r\\n break\\r\\n for p in list_L:\\r\\n if p == b:\\r\\n list_L.remove(p)\\r\\n break\\r\\n\\r\\n #to remove an existing cluster with those entries\\r\\n for j in list_L:\\r\\n if a in j or b in j:\\r\\n list_L.remove(j)\\r\\n\\r\\n #Base case of our recursive function \\r\\n if len(d) == 1:\\r\\n return new_cluster\\r\\n\\r\\n #calculating distances from our new cluster(eg: (a,b))to other labels/clusters\\r\\n for q in list_L:\\r\\n \\r\\n if b in d:\\r\\n if q in d[a] and q in d[b]:\\r\\n d_new[new_cluster][q] = (d[a][q] + d[b][q])/2\\r\\n continue\\r\\n if q in d:\\r\\n if b in d[q] and q in d[a]:\\r\\n d_new[new_cluster][q] = (d[a][q] + d[q][b])/2\\r\\n continue\\r\\n if b in d:\\r\\n if a in d[q] and q in d[b]:\\r\\n d_new[new_cluster][q] = (d[q][a] + d[b][q])/2\\r\\n continue\\r\\n if a in d[q] and b in d[q]:\\r\\n d_new[new_cluster][q] = (d[q][a] + d[q][b])/2\\r\\n \\r\\n list_L.insert(0,new_cluster)\\r\\n \\r\\n #deleting row and column corresponding to a and b\\r\\n del d[a]\\r\\n if b in d:\\r\\n del d[b]\\r\\n \\r\\n for p in d:\\r\\n for q in d[p]:\\r\\n if q != b and q != a:\\r\\n d_new[p][q] = d[p][q]\\r\\n\\r\\n return CreateTree(d_new,list_L)\",\n \"def fractionalPoints(totalNodeList, recNodeList, fracPoints):\\n\\n avPoint = averagePoints(recNodeList)\\n\\n for i in range(0, fracPoints):\\n closestPoint = closest_node(avPoint, totalNodeList) #Finds closest point\\n totalNodeList.remove(closestPoint)\\n recNodeList.append(closestPoint)\\n\\n printProgressBar(i, fracPoints) \\n\\n return recNodeList\",\n \"def get_nodes(final_node, discard_params=True):\\n var_nodes = []\\n func_nodes = []\\n seen_ids = set()\\n\\n # We re-do backward var iter here, with some changes\\n cand_funcs = []\\n seen = set()\\n \\n def add_cand(cand):\\n if cand not in seen:\\n # Negate since heapq is min-heap\\n heapq.heappush(cand_funcs, (-cand.rank, len(seen), cand))\\n seen.add(cand)\\n \\n add_cand(final_node.creator_node)\\n seen_ids.add(id(final_node))\\n \\n while cand_funcs:\\n _,_,func = heapq.heappop(cand_funcs)\\n inputs = func.inputs\\n target_inputs = [x for x in inputs if x.requires_grad]\\n if not target_inputs:\\n continue\\n \\n if id(func) not in seen_ids:\\n func_nodes += [func]\\n seen_ids.add(id(func))\\n \\n for x in target_inputs:\\n if discard_params and x.creator_node is None:\\n if isinstance(x.get_variable_or_none(), Parameter):\\n continue # Discard param dead ends\\n \\n if id(x) not in seen_ids:\\n var_nodes += [x]\\n seen_ids.add(id(x))\\n \\n if x.creator_node is not None:\\n add_cand(x.creator_node)\\n \\n return var_nodes, func_nodes\",\n \"def prepare_initial_nodes(x_start, x_end, nodes_y):\\n nodes_x = [float(x_start + ((x_end - x_start) / (len(nodes_y) - 1)) * i) for i in range(0, len(nodes_y))]\\n nodes_y = [float(y) for y in nodes_y]\\n print(nodes_x)\\n print(nodes_y)\\n nodes = list(zip(nodes_x, nodes_y))\\n return nodes\",\n \"def _compute_newton_step(lambdas, p_norm, w_norm):\\n return lambdas.candidate + (p_norm / w_norm) ** 2 * (p_norm - 1)\",\n \"def next_node_dfs(search_state, last_node_is_ok):\\n log_T, initial_state, min_score, max_depth, maxtraversals, node, node_idx, it, order, score, sub_info = search_state\\n min_score = float(min_score) # make sure numba knows this is a float (otherwise, sometimes, it doesn't (bug in numba))\\n n_states = log_T.shape[0]\\n if it == maxtraversals:\\n assert False, \\\"Number of traversals exceeded\\\"\\n while True:\\n # next node ##\\n # try adding a value at the end\\n for next_idx, next_state in enumerate(order[node[-1]]):\\n if last_node_is_ok and min_score <= score + log_T[node[-1], next_state] and len(node) < max_depth \\\\\\n and syntax_check(np.array(node + [next_state]), sub_info, partial=True):\\n node.append(next_state)\\n node_idx.append(next_idx)\\n break\\n # adding a value at the end failed, so we are a leave\\n else:\\n for p in xrange(len(node) - 1, -1, -1):\\n if node_idx[p] != n_states - 1: # find where within the node to increase (and discard all others after)\\n old_idx = node_idx[p]\\n del node_idx[p:]\\n del node[p:]\\n node_idx.append(old_idx + 1)\\n prev_state = node[p - 1] if p > 0 else initial_state\\n node.append(order[prev_state, node_idx[p]])\\n break\\n else:\\n search_state = log_T, initial_state, min_score, max_depth, maxtraversals, list(node), list(node_idx), it, order, score, sub_info\\n return [-1], score, search_state # end of the generator, can't increase even the root\\n last_node_is_ok = True # We can now make progress again, regardless of whether we could at the beginning\\n it += 1\\n # score and return current node if adequate\\n score = log_T[initial_state, node[0]]\\n for p in xrange(1, len(node)):\\n score += log_T[node[p - 1], node[p]]\\n if min_score <= score and syntax_check(np.array(node), sub_info, partial=False):\\n search_state = log_T, initial_state, min_score, max_depth, maxtraversals, list(node), list(node_idx), it, order, score, sub_info\\n return list(node), score, search_state # the invocation to list here is to make a copy, don't remove!\",\n \"def getNodesAndDistances():\\n\\n\\tglobal width, height\\n\\n\\t# First we generate the list\\n\\n\\tprint \\\"\\\\tGetting node list...\\\"\\n\\t\\n\\tnodeDict = {}\\n\\n\\tfor y in range(height):\\n\\t\\tfor x in range(width):\\n\\t\\t\\ttheType = getSquare(x, y)\\n\\n\\t\\t\\tprint \\\"\\\\t\\\\tGetting list for node (%d, %d) of type %d...\\\" % (x, y, theType)\\n\\n\\t\\t\\ttempList = getNodeList(x, y, theType)\\n\\n\\t\\t\\tif tempList == []:\\n\\t\\t\\t\\tprint \\\"\\\\t\\\\t\\\\tNo nodes here.\\\"\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfor i in range(len(tempList)):\\n\\t\\t\\t\\t\\tnode = tempList[i]\\n\\t\\t\\t\\t\\tnodeName = node[0]\\n\\t\\t\\t\\t\\tnodeDict[nodeName] = node[1:]\\t# Everything but the first element\\n\\t\\t\\t\\t\\tprint \\\"\\\\t\\\\t\\\\tAdded node '%s'...\\\" % nodeName\\n\\n\\tprint \\\"\\\\tDone getting node list (%d nodes)...\\\" % (len(nodeDict.keys()))\\n\\tprint \\\"\\\"\\n\\n\\t# Now that we've got that, we get a list of pairs\\n\\n\\tpairList = getPairList(nodeDict)\\n\\n\\t# Now we calculate the distance between every pair of nodes that connect\\n\\n\\tprint \\\"\\\"\\n\\tprint \\\"\\\\tCreateing dictionary of distances between connected nodes...\\\"\\n\\n\\tdistanceDict = {}\\n\\n\\tfor tuple in pairList:\\n\\t\\t(nodeA, nodeB) = tuple\\n\\t\\tprint \\\"\\\\t\\\\tCalculating distance between '%s' and '%s'...\\\" % (nodeA, nodeB)\\n\\t\\tdistance = distanceBetween(nodeA, nodeB, nodeDict)\\n\\t\\tpairName = \\\"%s%s\\\" % (nodeA, nodeB)\\n\\t\\tdistanceDict[pairName] = distance\\n\\t\\tprint \\\"\\\\t\\\\t\\\\tDistace was %f.\\\" % (distance)\\n\\n\\tprint \\\"\\\\tDone creating dictionary of node differences (%d pairs).\\\" % (len(distanceDict.keys()))\\n\\n\\treturn nodeDict, distanceDict\",\n \"def perform(self, node, inputs, outputs):\\r\\n x = inputs[0]\\r\\n L = inputs[1]\\r\\n dz = inputs[2]\\r\\n dx = outputs[0]\\r\\n N = x.shape[0]\\r\\n if self.lower:\\r\\n F = numpy.tril(dz)\\r\\n for k in xrange(N - 1, -1, -1):\\r\\n for j in xrange(k + 1, N):\\r\\n for i in xrange(j, N):\\r\\n F[i, k] -= F[i, j] * L[j, k]\\r\\n F[j, k] -= F[i, j] * L[i, k]\\r\\n for j in xrange(k + 1, N):\\r\\n F[j, k] /= L[k, k]\\r\\n F[k, k] -= L[j, k] * F[j, k]\\r\\n F[k, k] /= (2 * L[k, k])\\r\\n else:\\r\\n F = numpy.triu(dz)\\r\\n M = N - 1\\r\\n for k in xrange(N - 1, -1, -1):\\r\\n for j in xrange(k + 1, N):\\r\\n for i in xrange(j, N):\\r\\n F[k, i] -= F[j, i] * L[k, j]\\r\\n F[k, j] -= F[j, i] * L[k, i]\\r\\n for j in xrange(k + 1, N):\\r\\n F[k, j] /= L[k, k]\\r\\n F[k, k] -= L[k, j] * F[k, j]\\r\\n F[k, k] /= (2 * L[k, k])\\r\\n dx[0] = F\",\n \"def add_nodes(self, new_nodes):\\n self.nodes = self._nodes + ensure_list(new_nodes)\\n for nd in ensure_list(new_nodes):\\n self.predecessors[nd.name] = []\\n self.successors[nd.name] = []\\n if self._sorted_nodes is not None:\\n # starting from the previous sorted list, so is faster\\n self.sorting(presorted=self.sorted_nodes + ensure_list(new_nodes))\",\n \"def balances_to_path_nodes(balances_data, workers=1, hash_func=pedersen_hash):\\n height = balances_data['tree_height']\\n vaults_data = balances_data['vaults_data']\\n balances = [(vault['vault_id'], vault) for vault in vaults_data]\\n\\n event_loop = asyncio.new_event_loop()\\n\\n with parallel_hash(hash_func, workers) as async_hash_func:\\n try:\\n res = event_loop.run_until_complete(\\n calc_nodes(height, balances, calc_zero_nodes(height), 1, async_hash_func)\\n )\\n return res\\n finally:\\n event_loop.close()\",\n \"def updateClusters(LLE_node_vals, switch_penalty=1):\\r\\n (T, num_clusters) = LLE_node_vals.shape\\r\\n future_cost_vals = np.zeros(LLE_node_vals.shape)\\r\\n\\r\\n # compute future costs\\r\\n for i in range(T-2, -1, -1):\\r\\n j = i+1\\r\\n indicator = np.zeros(num_clusters)\\r\\n future_costs = future_cost_vals[j, :]\\r\\n lle_vals = LLE_node_vals[j, :]\\r\\n for cluster in range(num_clusters):\\r\\n total_vals = future_costs + lle_vals + switch_penalty\\r\\n total_vals[cluster] -= switch_penalty\\r\\n future_cost_vals[i, cluster] = np.min(total_vals)\\r\\n\\r\\n # compute the best path\\r\\n path = np.zeros(T)\\r\\n\\r\\n # the first location\\r\\n curr_location = np.argmin(future_cost_vals[0, :] + LLE_node_vals[0, :])\\r\\n path[0] = curr_location\\r\\n\\r\\n # compute the path\\r\\n for i in range(T-1):\\r\\n j = i+1\\r\\n future_costs = future_cost_vals[j, :]\\r\\n lle_vals = LLE_node_vals[j, :]\\r\\n total_vals = future_costs + lle_vals + switch_penalty\\r\\n total_vals[int(path[i])] -= switch_penalty\\r\\n\\r\\n path[i+1] = np.argmin(total_vals)\\r\\n\\r\\n # return the computed path\\r\\n return path\",\n \"def evaluate_cuts(base_tree, node):\\n config = Configuration.config # Collect configuration\\n\\n N = config.normals # Collect predefined set of normal vectors\\n N = np.append(N, node.auxiliary_normals, axis=0) # Append partition's bounding-box-aligned vectors as normals\\n N = np.unique(np.round(N, 3), axis=0) # Return sorted unique elements of input array_like\\n\\n trees = []\\n for i in range(N.shape[0]):\\n trees_of_this_normal = [] # start a list of trees for splits along this normal\\n normal = N[i] # current normal\\n for plane in bsp_tree.get_planes(node.part, normal): # iterate over all valid cutting planes for the node\\n tree, result = bsp_tree.expand_node(base_tree, node.path, plane) # split the node using the plane\\n if tree: # only keep the tree if the split is successful\\n trees_of_this_normal.append(tree)\\n logger.debug(f\\\"normal index: {i}, origin: {plane[0]}, normal: {plane[1]}, result: {result}\\\")\\n if len(trees_of_this_normal) == 0: # avoid empty list errors during objective function evaluation\\n logger.info(f\\\"normal index: {i}, trees for normal: {len(trees_of_this_normal)}, total trees: {len(trees)}\\\")\\n continue\\n # go through each objective function, evaluate the objective function for each tree in this normal's\\n # list, fill in the data in each tree object in the list\\n for evaluate_objective_func in objectives.values():\\n evaluate_objective_func(trees_of_this_normal, node.path)\\n trees += trees_of_this_normal\\n logger.info(f\\\"normal index: {i}, trees for normal: {len(trees_of_this_normal)}, total trees: {len(trees)}\\\")\\n\\n # go through the list of trees, best ones first, and throw away any that are too similar to another tree already\\n # in the result list\\n result_set = []\\n for tree in sorted(trees, key=lambda x: x.objective):\\n if tree.sufficiently_different(node, result_set):\\n result_set.append(tree)\\n logger.info(f\\\"{len(result_set)} valid trees\\\")\\n return result_set\",\n \"def fn(n):\\n if n == 1: return [TreeNode()]\\n ans = []\\n for nn in range(1, n, 2): \\n for left in fn(nn):\\n for right in fn(n-1-nn): \\n ans.append(TreeNode(left=left, right=right))\\n return ans\",\n \"def batch_predict(tree_adj, training_signs, edge_weight):\\n # since shazoo use the revealed signs as-is, it's ok to use the same name\\n training_signs, l2_values, rta_signs = training_signs\\n all_nodes_to_predict = set(tree_adj) - set(training_signs)\\n logging.debug('batch_predict has %d nodes to predict', len(all_nodes_to_predict))\\n methods = ['l2cost', 'rta', 'shazoo']\\n # fields are current_closest_hinge, current_sign, current_dst_to_closest_hinge\\n node_predictions = {m: defaultdict(lambda: (None, None, 2e9)) for m in methods}\\n hinge_value = {m: {} for m in methods}\\n total_iter = 0\\n while all_nodes_to_predict:\\n some_root_of_a_border_tree = next(iter(all_nodes_to_predict))\\n hinge_nodes, border_tree_nodes = find_hinge_nodes(tree_adj, edge_weight, training_signs,\\n some_root_of_a_border_tree,\\n with_visited=True)\\n unmarked = border_tree_nodes - hinge_nodes\\n for u in hinge_nodes:\\n if u in hinge_value['shazoo']:\\n continue\\n vals, _, status = flep(tree_adj, (training_signs, rta_signs), edge_weight, u)\\n hinge_value['shazoo'][u] = sgn(vals[0])\\n hinge_value['rta'][u] = sgn(vals[1])\\n if not USE_SCIPY:\\n continue\\n border_tree = build_border_tree_from_mincut_run(status, edge_weight)\\n _, E, El, leaves_sign, _, _ = border_tree\\n L = {u: l2_values[u] for u in leaves_sign}\\n mapped_E, mapped_El_L, mapping = preprocess_edge_and_leaves(E, El, L)\\n val = solve_by_zeroing_derivative(mapped_E, mapped_El_L, mapping, L,\\n reorder=False)[0][u]\\n hinge_value['l2cost'][u] = sgn(val)\\n predicted_in_that_border_tree = set()\\n inner_iter = 0\\n # to avoid the same fork being picked again and again\\n unmarked.add(some_root_of_a_border_tree)\\n while unmarked:\\n one_to_predict = next(iter(unmarked))\\n hinge_tree = get_hinge_tree(one_to_predict, tree_adj, hinge_nodes)\\n other_predicted = set()\\n for h, h_val in iteritems(hinge_value['shazoo']):\\n if h not in hinge_tree:\\n continue\\n predicted = propagate_hinge(hinge_tree, h, h_val, node_predictions['shazoo'],\\n edge_weight)\\n for u in predicted:\\n prediction_info = node_predictions['shazoo'][u]\\n used_hinge = prediction_info[0]\\n node_predictions['rta'][u] = (used_hinge, hinge_value['rta'][used_hinge],\\n prediction_info[2])\\n if not USE_SCIPY:\\n continue\\n node_predictions['l2cost'][u] = (used_hinge, hinge_value['l2cost'][used_hinge],\\n prediction_info[2])\\n other_predicted.update(predicted)\\n predicted_in_that_border_tree.update(other_predicted)\\n unmarked -= other_predicted\\n inner_iter += 1\\n if inner_iter > len(tree_adj):\\n import time\\n logging.critical('batch predict failed in the inner loop')\\n persistent.save_var('__fail_{}.my'.format(int(time.time())), (tree_adj, (training_signs, l2_values, rta_signs), edge_weight))\\n raise RuntimeError('batch predict failed in the inner loop')\\n all_nodes_to_predict -= predicted_in_that_border_tree\\n total_iter += 1\\n if total_iter > len(tree_adj):\\n import time\\n logging.critical('batch predict failed in the outer loop')\\n persistent.save_var('__fail_{}.my'.format(int(time.time())), (tree_adj, (training_signs, l2_values, rta_signs), edge_weight))\\n raise RuntimeError('batch predict failed in the outer loop')\\n logging.debug('batch_predict has actually predicted %d nodes', len(node_predictions) - len(training_signs))\\n return {m: {u: v[1] for u, v in iteritems(node_predictions[m]) if u not in training_signs}\\n for m in methods}\",\n \"def nj(nodeL,distD):\\n while len(nodeL) > 2:\\n nodeA,nodeB = bestPair(nodeL,distD)\\n bA, bB = branchLength(nodeA,nodeB,nodeL,distD)\\n newNode= mergeNodes(nodeA, nodeB, bA, bB)\\n updateDistances(nodeA,nodeB,newNode,nodeL,distD)\\n nodeL.remove(nodeA)\\n nodeL.remove(nodeB)\\n nodeL.append(newNode)\\n return terminate(nodeL,distD)\",\n \"def push_nodes_with_binary(self, b, step_ratio=0.1, niter=0):\\n lid = list(self._data[:, 0])\\n lpid = list(self._data[:, -2])\\n t_data = self._data.copy()\\n\\n children_idx = {\\n pid: [i for i, p in enumerate(lpid) if p == t_data[i, 0]] for pid in lpid\\n }\\n\\n for _ in range(niter):\\n for i in range(t_data.shape[0]):\\n pid, radius, (x, y, z) = (\\n int(t_data[i, -2]),\\n t_data[i, -3],\\n t_data[i, 2:5],\\n )\\n cidx = children_idx[pid]\\n if pid != i and pid in lid and len(cidx) <= 1:\\n px, py, pz = t_data[t_data[:, 0] == pid, 2:5][0]\\n vnorm = norm_vec(np.asarray([x - px, y - py, z - pz]))\\n\\n if len(cidx) == 1:\\n cx, cy, cz = t_data[cidx[0], 2:5]\\n vnorm = (\\n vnorm + norm_vec(np.asarray([cx - x, cy - y, cz - z]))\\n ) / 2\\n if all([v == 0 for v in vnorm]):\\n continue\\n\\n pt = np.asarray([x, y, z])\\n p_vectors = get_perpendicular_vectors(pt, vnorm)\\n p_distances = [\\n get_distance_to_boundary(pt, pvec, b) for pvec in p_vectors\\n ]\\n dx, dy, dz = np.sum(\\n [pv * pd for pv, pd in zip(p_vectors, p_distances)], 0\\n )\\n\\n # Constrain the displacement by the nodo radii\\n tx = x + dx * step_ratio\\n ty = y + dy * step_ratio\\n tz = z + dz * step_ratio\\n dist = (\\n (tx - self._data[i, 2]) ** 2\\n + (ty - self._data[i, 3]) ** 2\\n + (tz - self._data[i, 4]) ** 2\\n ) ** 0.5\\n if dist <= radius / 2:\\n t_data[i, 2] = tx\\n t_data[i, 3] = ty\\n t_data[i, 4] = tz\\n else:\\n pass\\n self._data = t_data\",\n \"def _compute_ptdfs(self):\\n z = self._compute_z()\\n s = np.zeros([self.n_nodes, self.n_nodes, self.n_nodes])\\n for k in range(self.n_nodes):\\n for L in range(self.n_nodes):\\n for i in range(self.n_nodes):\\n if k == 0 and L != 0:\\n s[k, L, i] = -1 * z[L-1, i-1]\\n elif k != 0 and L == 0:\\n s[k, L, i] = z[k-1, i-1]\\n elif k != 0 and L != 0 and k != L:\\n s[k, L, i] = z[k-1, i-1] - z[L-1, i-1]\\n return s\",\n \"def root_nodes(self, node1, node2, distance):\\n if node1 == node2.parent:\\n upper_node = node1\\n lower_node = node2\\n upper_dist, lower_dist = distance, lower_node.branch - distance\\n elif node2 == node1.parent:\\n upper_node = node2\\n lower_node = node1\\n upper_dist, lower_dist = lower_node.branch - distance, distance\\n else:\\n raise PhyloValueError('root_nodes() requires that one of the given nodes is the parent of the other.')\\n if len(self.root.children) <= 1:\\n raise PhyloValueError('cannot re-root a tree where the existing root has one or no children.')\\n elif len(self.root.children) == 2:\\n if upper_node == self.root:\\n # Just need to adjust branch lengths\\n root_child = self.root.children[1] if self.root.children[0] == lower_node else self.root.children[0]\\n root_child.branch += upper_dist\\n lower_node.branch = lower_dist\\n else:\\n upper_path = self.find_path_to_root(upper_node)\\n # Process the old root child after removing the root:\\n root_child = self.root.children[1] if self.root.children[0] == upper_path[1] else self.root.children[0]\\n root_child.branch += upper_path[1].branch\\n root_child.parent = upper_path[1]\\n upper_path[1].children.append(root_child)\\n # Process nodes between root and upper_node:\\n prev_node = upper_path[1]\\n for next_node in upper_path[2:]:\\n prev_node.children.remove(next_node)\\n prev_node.parent = next_node\\n next_node.children.append(prev_node)\\n prev_node.branch = next_node.branch\\n prev_node = next_node\\n # Process upper_node, lower_node, and the new root\\n upper_node.parent = lower_node.parent = self.root\\n upper_node.children.remove(lower_node)\\n self.root.children = [node1, node2] # Keeps the argument order\\n upper_node.branch = upper_dist\\n lower_node.branch = lower_dist\\n else: # If the root has 3 children it means it's an unrooted tree\\n new_root = self.new_tree_node()\\n new_root.branch = self.root.branch # Transfers any existing root branch\\n if upper_node != self.root:\\n upper_path = self.find_path_to_root(upper_node)\\n prev_node = self.root\\n for next_node in upper_path[1:]:\\n prev_node.children.remove(next_node)\\n prev_node.parent = next_node\\n next_node.children.append(prev_node)\\n prev_node.branch = next_node.branch\\n prev_node = next_node\\n upper_node.children.remove(lower_node)\\n upper_node.branch = upper_dist\\n lower_node.branch = lower_dist\\n new_root.children.append(upper_node)\\n new_root.children.append(lower_node)\\n upper_node.parent = lower_node.parent = new_root\\n self.root = new_root\\n self.process_tree_nodes()\",\n \"def sub_run1(new_node, distance):\\n optimal_value[0] = optimal_value[0] + distance\\n\\n PL = get_node_points(new_node.left)\\n P = get_node_points(new_node)\\n PR = get_node_points(new_node.right)\\n\\n line1 = matrix.linear_eq((PL, P))\\n line2 = matrix.linear_eq((PR, P))\\n\\n line1[2] = line1[2] * (-1)\\n line2[2] = line2[2] * (-1)\\n\\n linear_list.append((new_node.left, new_node, line1))\\n linear_list.append((new_node, new_node.right, line2))\\n\\n remove_eq(new_node)\\n tspturtle.tsp_draw(PL, P, PR)\\n\\n remove(new_node)\",\n \"def neighborJoining(distances):\\n\\n tree = {}\\n\\n while(len(distances.keys()) > 2):\\n\\n r = calcRs(distances)\\n M = makeMMatrix(distances, r)\\n\\n smallest = 10000\\n smallestKey = (\\\"\\\",\\\"\\\")\\n\\n #Find nearest neighbors\\n for key in M.keys():\\n for subkey in M[key].keys():\\n if M[key][subkey] < smallest:\\n smallest = M[key][subkey]\\n smallestKey = (key, subkey)\\n\\n #Add new node and update distances to rest of tree\\n newname = smallestKey[0] + \\\"-\\\" + smallestKey[1]\\n distances[newname] = {}\\n tree[smallestKey[0]] = {}\\n tree[smallestKey[1]] = {}\\n dij = distances[smallestKey[0]][smallestKey[1]]\\n for key in M.keys():\\n if key in smallestKey:\\n continue\\n distances[newname][key] = .5*(distances[smallestKey[0]][key] \\\\\\n + distances[smallestKey[1]][key] - dij)\\n distances[key][newname] = distances[newname][key]\\n\\n #Update distances to parents of node\\n dik = (dij + r[smallestKey[0]] - r[smallestKey[1]])/2\\n tree[smallestKey[0]][newname] = dik\\n tree[smallestKey[1]][newname] = dij-dik\\n detachDict(distances, smallestKey[0], smallestKey[1])\\n\\n #Connect final two nodes\\n tree[distances.keys()[0]] = {}\\n tree[distances.keys()[0]][distances[distances.keys()[0]].keys()[0]] =\\\\\\n distances[distances.keys()[0]][distances[distances.keys()[0]].keys()[0]] \\n return tree\",\n \"def compute_operator(self, snapshots):\\n\\n # To avoid recursion function, use FIFO list to simulate the tree\\n # structure\\n data_queue = [snapshots.copy()]\\n\\n current_bin = 0\\n while data_queue:\\n Xraw = data_queue.pop(0)\\n\\n n_samples = Xraw.shape[1]\\n\\n step = max(1, int(np.floor(old_div(n_samples, self._nyq))))\\n Xsub = Xraw[:, ::step]\\n Xc = Xsub[:, :-1]\\n Yc = Xsub[:, 1:]\\n\\n Xc, Yc = compute_tlsq(Xc, Yc, self._tlsq_rank)\\n\\n rho = old_div(float(self._max_cycles), n_samples)\\n sub_operator = SubMrDMDOperator(svd_rank=self._svd_rank,\\n eigs_divider=2. * np.pi * step, rho=rho)\\n sub_operator.compute_operator(Xc, Yc)\\n\\n modes = sub_operator.modes\\n eigs = sub_operator.eigenvalues\\n Atilde = sub_operator.as_numpy_array\\n b = sub_operator.compute_sub_amplitudes(Xc, self._opt)\\n\\n #---------------------------------------------------------------\\n # DMD Amplitudes and Dynamics\\n #---------------------------------------------------------------\\n Vand = np.vander(np.power(eigs, old_div(1., step)), n_samples, True)\\n\\n Psi = (Vand.T * b).T\\n\\n self._modes.append(modes)\\n self._b.append(b)\\n self._Atilde.append(Atilde)\\n self._eigenvalues.append(eigs)\\n self._nsamples.append(n_samples)\\n self._steps.append(step)\\n\\n if Xraw.dtype == 'float64':\\n Xraw -= modes.dot(Psi).real\\n else:\\n Xraw -= modes.dot(Psi)\\n\\n if current_bin < 2**(self._max_level - 1) - 1:\\n current_bin += 1\\n half = int(np.ceil(old_div(Xraw.shape[1], 2)))\\n data_queue.append(Xraw[:, :half])\\n data_queue.append(Xraw[:, half:])\\n else:\\n current_bin += 1\",\n \"def solve(self):\\n smallest_f = self.get_smallest_f_cost_unvisited_node()\\n smallest_f_node = smallest_f[0]\\n\\n if smallest_f[1] > 1:\\n current_node = self.get_smallest_h_cost_unvisited_node()\\n else:\\n current_node = smallest_f_node\\n if current_node.f_cost == self.inf:\\n return\\n\\n self.set_h_cost(current_node)\\n self.unvisited_pos.remove(current_node.pos)\\n self.visited_pos.append(current_node.pos)\\n neighbours = algo_utils.get_neighbours(current_node, self.grid, self.wall_pos)\\n\\n for neigh in neighbours:\\n neighbour_dist = neigh.g_cost\\n current_dist = current_node.g_cost\\n new_dist = current_dist + 1\\n if neighbour_dist < new_dist:\\n continue\\n neigh.g_cost = new_dist\\n self.set_h_cost(neigh)\\n mix_neigh = {neigh.pos: neigh.g_cost}\\n self.mix.update(mix_neigh)\\n mix_current = {current_node.pos: current_node.g_cost}\\n self.mix.update(mix_current)\\n\\n smallest_f = self.get_smallest_f_cost_unvisited_node()\\n smallest_f_node = smallest_f[0]\\n smallest_h_node = self.get_smallest_h_cost_unvisited_node()\\n\\n if (\\n self.end_pos not in self.unvisited_pos\\n or algo_utils.get_smallest_g_cost_unvisited_node(\\n self.grid, self.unvisited_pos\\n ).g_cost\\n == self.inf\\n ):\\n for key, value in self.mix.items():\\n self.mix[key] = round((value * 1.0) / self.end_node.g_cost, 3)\\n self.backtrack_path(self.end_node)\\n else:\\n if smallest_f[1] > 1:\\n current_node = smallest_h_node\\n else:\\n current_node = smallest_f_node\\n self.solve()\",\n \"def newton_iteration(f: Callable, df: Callable, eps: float, x0: float = None, a: float = None, b: float = None,\\n weight: float = 0.9, display: bool = False, max_iterations: int = 100) -> float:\\n x = np.inf\\n if x0 is None:\\n x0 = (a + b) / 2\\n if a is not None and b is not None and a == b:\\n return a\\n x_next = x0\\n iterations = 0\\n while abs(x - x_next) > eps and iterations < max_iterations:\\n iterations += 1\\n x = x_next\\n\\n if display:\\n import matplotlib.pyplot as plt\\n xx0 = a or x-1\\n xx1 = b or x+1\\n xx = np.linspace(xx0, xx1, 100)\\n yy = np.array(list(map(f, xx)))\\n plt.plot(xx, yy)\\n plt.axvline(x=x)\\n plt.show()\\n\\n f_x = f(x)\\n try:\\n df_x = df(x)\\n except ZeroDivisionError:\\n df_x = (f_x - f(x-eps))/eps\\n if df_x != 0:\\n x_next = x - f_x / df_x\\n\\n if a is not None and x_next < a:\\n x_next = weight * a + (1 - weight) * x\\n elif b is not None and x_next > b:\\n x_next = weight * b + (1 - weight) * x\\n\\n if a is not None and x_next < a:\\n x_next = a\\n if b is not None and x_next > b:\\n x_next = b\\n\\n return x_next\",\n \"def calc_new_cost(self, initial_node, to_node):\\n\\n cost = compute_distance(initial_node, to_node)\\n return initial_node.cost + d\",\n \"def perform(self, node, inputs, outputs):\\n x, w, v, W, V = inputs\\n N = x.shape[0]\\n outer = np.outer\\n\\n def G(n):\\n return sum(\\n v[:, m] * V.T[n].dot(v[:, m]) / (w[n] - w[m])\\n for m in range(N)\\n if m != n\\n )\\n\\n g = sum(outer(v[:, n], v[:, n] * W[n] + G(n)) for n in range(N))\\n\\n # Numpy's eigh(a, 'L') (eigh(a, 'U')) is a function of tril(a)\\n # (triu(a)) only. This means that partial derivative of\\n # eigh(a, 'L') (eigh(a, 'U')) with respect to a[i,j] is zero\\n # for i < j (i > j). At the same time, non-zero components of\\n # the gradient must account for the fact that variation of the\\n # opposite triangle contributes to variation of two elements\\n # of Hermitian (symmetric) matrix. The following line\\n # implements the necessary logic.\\n out = self.tri0(g) + self.tri1(g).T\\n\\n # Make sure we return the right dtype even if NumPy performed\\n # upcasting in self.tri0.\\n outputs[0][0] = np.asarray(out, dtype=node.outputs[0].dtype)\",\n \"def a_star_alg(self, p1: int, p2: int, max_level: int = 1000):\\r\\n \\r\\n # Create start and end node\\r\\n start_node = Node(None, p1, self.node_dict[p1])\\r\\n start_node.g = start_node.h = start_node.f = 0\\r\\n end_node = Node(None, p2, self.node_dict[p2])\\r\\n end_node.g = end_node.h = end_node.f = 0\\r\\n\\r\\n # Initialize both open and closed list\\r\\n open_list = []\\r\\n closed_list = []\\r\\n\\r\\n # Add the start node\\r\\n open_list.append(start_node)\\r\\n\\r\\n # Loop until you find the end\\r\\n level = 0\\r\\n while len(open_list) > 0 and level < max_level:\\r\\n level += 1\\r\\n\\r\\n # Get the current node (the node in open_list with the lowest cost)\\r\\n current_node = open_list[0]\\r\\n current_index = 0\\r\\n for index, item in enumerate(open_list):\\r\\n if item.f < current_node.f:\\r\\n current_node = item\\r\\n current_index = index\\r\\n\\r\\n # Pop current off open list, add to closed list\\r\\n open_list.pop(current_index)\\r\\n closed_list.append(current_node)\\r\\n\\r\\n # Found the goal\\r\\n if current_node == end_node:\\r\\n path = []\\r\\n distance = current_node.g\\r\\n current = current_node\\r\\n while current is not None:\\r\\n path.append(current.number)\\r\\n current = current.parent\\r\\n\\r\\n return path[::-1], distance # Return reversed path\\r\\n\\r\\n # Generate children\\r\\n children = []\\r\\n for new_number in self.road_tree[current_node.number]: # Adjacent nodes\\r\\n new_node = Node(current_node, new_number, self.node_dict[new_number])\\r\\n children.append(new_node)\\r\\n\\r\\n # Loop through children\\r\\n for child in children:\\r\\n append_to_open_list = False\\r\\n\\r\\n # Create the f, g, and h values\\r\\n child.g = current_node.g + self.road_dict[(current_node.number, child.number)]\\r\\n child.h = sqrt((child.x - end_node.x) ** 2 + (child.y - end_node.y) ** 2) / 200\\r\\n child.f = child.g + child.h\\r\\n\\r\\n # Child is already in the closed list\\r\\n closed_list, append_to_open_list = self.check_in_list(child, closed_list, append_to_open_list)\\r\\n\\r\\n # Child is already in the open list\\r\\n open_list, append_to_open_list = self.check_in_list(child, open_list, append_to_open_list)\\r\\n\\r\\n # Add the child to the open list\\r\\n if append_to_open_list:\\r\\n open_list.append(child)\\r\\n\\r\\n return [], 1e10\",\n \"def _reconstruct_result(self, node, ss_result, backward=False):\\n prev = ss_result['prev']\\n ele_diff = ss_result['ele_diff']\\n\\n path = [node]\\n ele_gain = 0.\\n contributions = {} # Of each node, to the cumulative elevation gain\\n dist = ss_result['dist']\\n\\n if backward:\\n # 'node' is the start point and so does not contribute to ele_gain\\n contributions[node] = 0.\\n else:\\n # The ele diff between 'node' (first on the path) and next node\\n # on the path toward the source\\n contributions[node] = max([0., ele_diff[node]])\\n ele_gain += contributions[node]\\n\\n # Follow backpointers to reconstruct path and compute statistics\\n successor = node\\n curr_node = prev[node]\\n while curr_node is not None:\\n path.append(curr_node)\\n if backward:\\n # ele_diff's were computed moving forward toward 'node', so we\\n # must be careful to get the right contribution here\\n contribution = max([0., -ele_diff[successor]])\\n else:\\n contribution = max([0., ele_diff[curr_node]])\\n contributions[curr_node] = contribution\\n ele_gain += contributions[curr_node]\\n curr_node = prev[curr_node]\\n successor = curr_node\\n\\n # Since path was built by appending and backtracking, need to\\n # reverse it if 'node' is supposed to be the terminus\\n if not backward:\\n path.reverse()\\n\\n # Not yet a SearchResult: more processing needed in merge step\\n result = {\\n 'path': path,\\n 'path_len': dist[node],\\n 'ele_gain': ele_gain,\\n 'contributions': contributions,\\n 'dist': dist\\n }\\n\\n return result\",\n \"def steepest_descent_f(init_x0, init_x1, alpha, n=5):\\n ret = [(init_x0, init_x1)]\\n a = init_x0\\n b = init_x1\\n for i in range(n):\\n a1 = a - alpha * gradF1(a, b)\\n b1 = b - alpha * gradF2(a, b)\\n ret.append((a1, b1))\\n a = a1\\n b = b1 \\n \\n return ret\",\n \"def run_trial(self, num_nodes): \\n # compute the neighbors for the newly-created node\\n new_node_neighbors = set()\\n for dummy_idx in range(num_nodes):\\n new_node_neighbors.add(random.choice(self._node_numbers))\\n # update the list of node numbers so that each node number \\n # appears in the correct ratio\\n\\n # add num_nodes (= new node number) to list\\n self._node_numbers.append(self._num_nodes)\\n\\n # add new neighbours to the list\\n self._node_numbers.extend(list(new_node_neighbors))\\n\\n # update the number of nodes\\n self._num_nodes += 1\\n\\n return new_node_neighbors\",\n \"def compute(self, node, input_vals):\\r\\n \\\"\\\"\\\"TODO: Your code here\\\"\\\"\\\"\\r\\n \\\"\\\"\\\"Given values of two input nodes, return result of element-wise multiplication.\\\"\\\"\\\"\\r\\n assert len(input_vals) == 2\\r\\n return input_vals[0] / input_vals[1]\",\n \"def deltaCalc(self, expected):\\n \\n n = len(self.structure)\\n self.delta = [None] * n\\n self.delta[n - 1] = []\\n \\n for i in xrange(len(expected)):\\n curr = self.a[n - 1][i]\\n self.delta[n - 1].append(self.derivativeFunc(curr) * (expected[i] - curr))\\n self.delta[n - 1] = np.array(self.delta[n - 1])\\n \\n # From n - 1 to 1 layer \\n for i in xrange(n - 1, 0, -1):\\n currDelta = self.delta[i]\\n if i != (n - 1):\\n currDelta = currDelta[0][:-1]\\n \\n self.delta[i - 1] = np.array(np.dot(currDelta, self.theta[i]))\\n self.delta[i - 1][0] *= self.a[i - 1]\\n \\n return\",\n \"def build(self, dist_matrix, class_map, cluster_naming_function):\\n # Update attributes\\n self.orig_dist_matrix = dist_matrix \\n self.class_map = class_map \\n self.work_dist_matrix = dist_matrix\\n\\n # Get number of elements\\n n = dist_matrix.shape[0]\\n\\n if PROGRESS:\\n print 'Starting tree build now!'\\n\\n # Loop through n-3 elements & add nodes in tree\\n for i in range(n - 3):\\n\\n if DEBUG:\\n print 'Distance Matrix'\\n pprint(self.work_dist_matrix)\\n print\\n\\n # Calculate q_matrix matrix from distances\\n q_matrix = _calculate_q_matrix(self.work_dist_matrix)\\n \\n if DEBUG:\\n print 'Q matrix:'\\n pprint(q_matrix)\\n print\\n\\n # Find pair of elements (i,j) where q_matrix(i,j) has the lowest value\\n (min_col, min_row) = _find_min_pair(q_matrix)\\n\\n # Add nodes i,j, and cluster node of i and j to this tree\\n # And update working distance matrix accordingly\\n new_cluster_name = cluster_naming_function(min_row, min_col, self.cluster_map)\\n self.cluster_leaves(min_row, min_col, new_cluster_name) \\n\\n if DEBUG:\\n print 'Tree:'\\n pprint(nx.clustering(self.tree))\\n pprint(self.cluster_dictionary)\\n print '\\\\n\\\\n'\\n \\n # View graph after each step for debugging\\n if VIEW_ALL:\\n labels = {i[0]: i[0]+'/'+i[1]['c'] for i in njt.tree.nodes(data=True)}\\n layout = nx.spring_layout(njt.tree)\\n nx.draw_networkx(njt.tree, pos=layout, with_labels=True, labels=labels) #class labels\\n plt.show()\\n\\n if PROGRESS:\\n print str(i + 1) + \\\" down, \\\" + str(n-i-4) + \\\" to go...\\\"\\n \\n # Add remaining branch lengths and nodes from working distance matrix to this tree \\n previous_cluster = new_cluster_name\\n mid_edge_length = 0.5 * (self.work_dist_matrix.iat[0, 1]\\n + self.work_dist_matrix.iat[0, 2]\\n - self.work_dist_matrix.iat[1, 2])\\n (node1, node2) = (self.work_dist_matrix.columns[0], self.work_dist_matrix.columns[1])\\n new_cluster = cluster_naming_function(node1, node2, self.cluster_map)\\n self.cluster_leaves(node1, node2, new_cluster)\\n # Viz only scales based on a weight attribute, so we set that as the length\\n self.tree.add_edge(previous_cluster, new_cluster, length=mid_edge_length, weight=mid_edge_length)\\n\\n if DEBUG:\\n print 'Final tree:'\\n pprint(nx.clustering(self.tree))\\n pprint(self.cluster_dictionary)\",\n \"def _DFS_loop(nodes, edges, t_n=None):\\n\\n if t_n is not None:\\n n_t = dict((b,a) for a,b in t_n.items()) # {time: node}\\n get_node_by_time = lambda time: time if t_n is None else n_t[time]\\n get_time_by_node = lambda node: node if t_n is None else t_n[node]\\n gen_edges = lambda node: map(get_time_by_node,edges[get_node_by_time(node)])\\n\\n explored = set()\\n leader = dict()\\n _DFS_loop.t = 0 # finishing time\\n times = dict() # {time: node}\\n\\n def DFS(i):\\n explored.add(i)\\n leader[i] = s\\n for j in gen_edges(i):\\n if j not in explored:\\n DFS(j)\\n _DFS_loop.t += 1\\n times[i] = _DFS_loop.t\\n\\n for i in nodes:\\n if i not in explored:\\n s = i # leader node\\n DFS(i)\\n\\n leaders = defaultdict(list)\\n for n,l in leader.items():\\n leaders[get_node_by_time(l)].append(get_node_by_time(n))\\n\\n return times, leaders\",\n \"def solve_csp(nodes, arcs, max_steps):\\n\\n nodes = list(nodes)\\n print 'nodes:', nodes\\n\\n node_values_dict = dict(zip(nodes, '2'*len(set(nodes))))\\n print 'initial random assignment', node_values_dict\\n indexes = np.arange(len(nodes))\\n\\n graph = {}\\n for arc in arcs:\\n if not arc[0] in graph:\\n graph[arc[0]] = []\\n if not arc[1] in graph:\\n graph[arc[1]] = []\\n graph[arc[0]].append(arc[1])\\n graph[arc[1]].append(arc[0])\\n for i in indexes:\\n if i in graph:\\n continue\\n else:\\n graph[i] = []\\n graph = dict(sorted(graph.items()))\\n print 'graph:', graph\\n\\n domain = [i for i in np.arange(1, 10, 1)]\\n print 'initial domain for each node:', domain\\n\\n superAdjacency ={}\\n for i in np.arange(len(nodes)):\\n superAdjacency[i] = []\\n superAdjacency[i].append(nodes[i])\\n superAdjacency[i].append(node_values_dict[nodes[i]])\\n superAdjacency[i].append(graph[i])\\n superAdjacency[i].append(domain)\\n\\n print 'superAdjacency', superAdjacency\\n\\n def getNodeType(superAdjacency, index):\\n return list(superAdjacency[index])[0]\\n\\n def getCurrentAssignment(superAdjacency, index):\\n return list(superAdjacency[index])[1]\\n\\n def getCurrentAssignmentForList(superAdjacency, indexList):\\n return [int(list(superAdjacency[index])[1]) for index in indexList]\\n\\n def getSolution(superAdjacency):\\n return [int(list(superAdjacency[index])[1]) for index in superAdjacency]\\n\\n def getNeighbours(superAdjacency, index):\\n return list(superAdjacency[index])[2]\\n\\n def getDomain(superAdjacency, index):\\n return list(superAdjacency[index])[3]\\n\\n def updateSuperAdjacency(superAdjacency, nodeType, newValue):\\n updateList =[]\\n for i in superAdjacency:\\n if str(getNodeType(superAdjacency, i)) == nodeType:\\n updateList.append(i)\\n for i in updateList:\\n superAdjacency[i][1] = newValue\\n\\n def isSolution():\\n return graphConstraints(superAdjacency)\\n\\n def graphConstraints(superAdjacency):\\n graphEval = []\\n\\n for index in superAdjacency:\\n neighbours = getNeighbours(superAdjacency, index)\\n nodeType = getNodeType(superAdjacency, index)\\n\\n if nodeType == 'T':\\n graphEval.append(int(str(eval(str(\\n getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0]))\\n elif nodeType == 'C':\\n return 'NA'\\n elif nodeType == 'S':\\n graphEval.append(int(str(eval(str(\\n getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0]))\\n elif nodeType == 'H':\\n graphEval.append(int(str(np.sum(\\n getCurrentAssignmentForList(superAdjacency, neighbours)))[0]))\\n if nodeType == 'P':\\n graphEval.append(int(str(np.sum(\\n getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0]))\\n\\n currentAssignment = [item[1] for item in superAdjacency.values()]\\n difference = map(sub, currentAssignment, graphEval)\\n\\n if sum(difference) == 0:\\n return True\\n else:\\n return difference\\n\\n def findConflictVariable(superAdjacency, lastUpdateNode):\\n node_conflict_count = {}\\n for node in node_values_dict:\\n node_conflict_count[node] = 0\\n for index in superAdjacency:\\n neighbours = getNeighbours(superAdjacency, index)\\n nodeType = getNodeType(superAdjacency, index)\\n if nodeType == 'T':\\n try:\\n if getCurrentAssignment(superAdjacency, index) != \\\\\\n int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0]):\\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\\n except:\\n continue\\n elif nodeType == 'S':\\n try:\\n if getCurrentAssignment(superAdjacency, index) != int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0]):\\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\\n except:\\n continue\\n elif nodeType == 'H':\\n try:\\n if getCurrentAssignment(superAdjacency, index) != int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[0]):\\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\\n except:\\n continue\\n if nodeType == 'P':\\n try:\\n if getCurrentAssignment(superAdjacency, index) != int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0]):\\n node_conflict_count[nodeType] = node_conflict_count[nodeType] + 1\\n except:\\n continue\\n choices = [k for k, v in node_conflict_count.items() if v > 0]\\n if len(choices) > 0:\\n updateNode = random.choice(choices)\\n\\n if updateNode == lastUpdateNode:\\n choices.pop(choices.index(updateNode))\\n try:\\n lastUpdateNode = random.choice(choices)\\n return lastUpdateNode, lastUpdateNode\\n except:\\n return lastUpdateNode, lastUpdateNode\\n else:\\n lastUpdateNode = updateNode\\n return updateNode, lastUpdateNode\\n else:\\n return 'NA', 'NA'\\n\\n\\n\\n def valueForConflictedVariable(superAdjacency, var):\\n for index in superAdjacency:\\n nodeType = getNodeType(superAdjacency, index)\\n neighbours = getNeighbours(superAdjacency, index)\\n if not neighbours:\\n continue\\n elif str(nodeType) == str(var):\\n domain = getDomain(superAdjacency, index)\\n\\n choice = random.choice(domain)\\n if nodeType == 'T':\\n choice = int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[0])\\n elif nodeType == 'S':\\n choice = int(str(eval(str(getCurrentAssignmentForList(superAdjacency, neighbours)).replace(',', '*'))[0])[::-1][0])\\n elif nodeType == 'H':\\n choice = int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[0])\\n if nodeType == 'P':\\n choice = int(str(np.sum(getCurrentAssignmentForList(superAdjacency, neighbours)))[::-1][0])\\n\\n choice = int(choice)\\n if choice % 2 == 0:\\n return choice\\n else:\\n return choice\\n\\n def min_conflicts(nodes, arcs, max_steps):\\n lastUpdateNode = ''\\n for i in range(max_steps):\\n if isSolution() == True:\\n return\\n var, lastUpdateNode = findConflictVariable(superAdjacency, lastUpdateNode)\\n if var != 'NA':\\n value = valueForConflictedVariable(superAdjacency, var)\\n updateSuperAdjacency(superAdjacency, var, value)\\n node_values_dict[var] = value\\n else:\\n pass\\n\\n return\\n\\n min_conflicts(nodes, arcs, max_steps)\\n node_values = getSolution(superAdjacency)\\n return node_values\",\n \"def newton_raphson(f,x0,iterations): \\n current = x0\\n fdash = differentiate_polynomial(f)\\n print(fdash)\\n for i in range(iterations): \\n current = current - evaluate_polynomial(f,current)/evaluate_polynomial(fdash,current)\\n return current\",\n \"def update_nodes(self):\\n raise NotImplementedError('ERROR: sweeper has to implement update_nodes(self)')\",\n \"def test_compare_old_to_new_method_to_create_trees(self):\\n nodes = util.generate_sequence_of_points(2, 2)\\n tree1 = kdtree.createNewTree(nodes)\\n kdtree.visualize(tree1)\\n \\n sel_axis = (lambda axis: axis)\\n tree2 = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\\n tree2.split2([0.25, 0.5], axis = 1)\\n tree2.split2([0.75, 0.5], axis = 1)\\n \\n #left\\n tree2.split2([0.25, 0.25], axis = 0, sel_axis = sel_axis)\\n tree2.split2([0.25, 0.75], axis = 0, sel_axis = sel_axis)\\n \\n #right\\n tree2.split2([0.75, 0.25], axis = 0, sel_axis = sel_axis)\\n tree2.split2([0.75, 0.75], axis = 0, sel_axis = sel_axis)\\n \\n kdtree.visualize(tree2)\\n \\n for n in zip(kdtree.level_order(tree1), kdtree.level_order(tree2)):\\n self.assertEqual(n[0].data, n[1].data, \\\"elements not equal\\\")\\n \\n if n[0].data is not None and n[1].data is not None:\\n self.assertEqual(n[0].axis, n[1].axis, \\\"elements not equal\\\")\",\n \"def prepare_data_for_d(self):\\n\\n center_nodes = []\\n neighbor_nodes = []\\n labels = []\\n for i in self.root_nodes:\\n if np.random.rand() < config.update_ratio:\\n # self.graph[i] = [neighbors of i]\\n pos = self.graph[i]\\n neg, _ = self.sample(i, self.trees[i], len(pos), for_d=True)\\n # print(\\\"tree_i_d: \\\", self.trees[i])\\n # print(\\\"neg_samples: \\\", neg)\\n # print(\\\"neg is: \\\", neg)\\n if len(pos) != 0 and neg is not None:\\n # positive samples\\n center_nodes.extend([i] * len(pos))\\n neighbor_nodes.extend(pos)\\n labels.extend([1] * len(pos))\\n\\n # negative samples\\n center_nodes.extend([i] * len(pos))\\n neighbor_nodes.extend(neg)\\n labels.extend([0] * len(neg))\\n # print(\\\"cen: \\\", center_nodes)\\n return center_nodes, neighbor_nodes, labels\",\n \"def iterative_solution(list1, list2):\\n pre_root = Node(-1)\\n prev = pre_root\\n\\n while list1 and list2:\\n if list1.data <= list2.data:\\n prev.next = list1\\n list1 = list1.next\\n else:\\n prev.next = list2\\n list2 = list2.next\\n prev = prev.next\\n\\n if not list1:\\n prev.next = list2\\n else:\\n prev.next = list1\\n\\n return pre_root.next\",\n \"def startTreeBasedCoord(self):\\n self.log_message('ID {0} starts Load Propagation Optimization'.format(self.CommID))\\n #self.MsgReceiveCount_interval = 0\\n #self.MsgSendCount_interval = 0\\n\\n self.noOfTimesteps = (self.toTime - self.fromTime) / self.stepSize + 1\\n\\n # calculate pool of schedules (also saved in self.schedules) and schedule load curves\\n if self.getTER1() != 0: # if not a gas boiler\\n self.calcSchedulePool(self.fromTime, self.toTime)\\n self.calcScheduleConsumptionCurves()\\n\\n if not self.Parent: #root node\\n random.seed() # initialize pseudo random number generator\\n\\n\\n\\n # leave nodes select initial best schedule from schedule pool based on fluctuations curve and propagate their load to parent\\n if not self.Children:\\n #self.ChildrenProcessed = [0 for x in range(len(self.Children))]\\n if self.getTER1() != 0: # not a gas boiler\\n self.selectBestSchedule(copy.deepcopy(self.ERemainderLocal))\\n self.setSOC(self.SOCEnd[self.chosenScheduleIndex])\\n self.setStateModlvl(self.chosenSchedule[-1])\\n self.sendMessage(self.Parent, 40, ['newload', copy.deepcopy(self.EConsumptionChosenSchedule)])\\n else:\\n zeros = [0 for x in range(len(self.ERemainderLocal))]\\n self.sendMessage(self.Parent, 40, ['newload', copy.deepcopy(zeros)])\\n\\n else: # if not a leave node\\n self.EConsumptionChildCurves = [ [0 for x in range(len(self.EFluctuationCurve))] for y in range(len(self.Children))] # initialize array for load curves of children\\n self.EConsumptionChildCurvesRec = [ [0 for x in range(len(self.EFluctuationCurve))] for y in range(len(self.Children))] # initialize array for load curves of children\\n self.noOfConsumptionCurvesReceived = 0\\n #self.ChildLoadCurvesChosen = [0 for x in range(len(self.Children))]\\n return\",\n \"def gradients(output_node, node_list):\\n # a map from node to a list of gradient contributions from each output node\\n node_to_output_grads_list = {}\\n # Special note on initializing gradient of output_node as oneslike_op(output_node):\\n # We are really taking a derivative of the scalar reduce_sum(output_node)\\n # instead of the vector output_node. But this is the common case for loss function.\\n node_to_output_grads_list[output_node] = [oneslike_op(output_node)]\\n # a map from node to the gradient of that node\\n node_to_output_grad = {}\\n # Traverse graph in reverse topological order given the output_node that we are taking gradient wrt.\\n reverse_topo_order = list(reversed(find_topo_sort([output_node])))\\n for node in reverse_topo_order:\\n grad = sum_node_list(node_to_output_grads_list[node]) # Node\\n node_to_output_grad[node] = grad # node_to_output_grad[node]为对相应的node求导, 即文章中的v^-_n\\n grads = node.op.gradient(node, grad) # Node list\\n for i in range(len(node.inputs)):\\n ch = node.inputs[i]\\n grads_list = node_to_output_grads_list.get(ch, [])\\n grads_list.append(grads[i])\\n node_to_output_grads_list[ch] = grads_list\\n\\n # Collect results for gradients requested.\\n grad_node_list = [node_to_output_grad[node] for node in node_list]\\n return grad_node_list\",\n \"def iter_func(root_name, root, set_traverse, list_funcs, G, strings,\\n plot_nodes, cur_pos, xgrain, min_weight, max_weight):\\n set_traverse.append(root)\\n nbs = G.neighbors(root)\\n nbs = G[root]\\n\\n plot_nodes.append(cur_pos)\\n xgrain = xgrain/2.0\\n\\n flag_pn = -1\\n for nb in nbs.keys():\\n if nb in set_traverse:\\n continue\\n\\n next_pos = [0, 0, 0]\\n if root.name == root_name:\\n next_pos[0] = cur_pos[0]\\n else:\\n next_pos[0] = cur_pos[0] + xgrain*flag_pn*( 0.8+0.2*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight) ) #* (nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[1] = cur_pos[1] + 3.0*(nbs[nb][\\\"weight\\\"]-1.0/max_weight)/(1.0/min_weight-1.0/max_weight)\\n next_pos[2] = nb.name\\n\\n flag_pn = flag_pn*(-1)\\n\\n strings.append([root, nb])\\n set_traverse, strings, plot_nodes = iter_func(root_name, nb, set_traverse, list_funcs, G, strings, plot_nodes, next_pos, xgrain, min_weight, max_weight)\\n\\n return set_traverse, strings, plot_nodes\",\n \"def run_trial(self, num_nodes): \\n #compute the neighbors for the newly-created node\\n new_node_neighbors = set()\\n for dummy_idx in range(num_nodes):\\n new_node_neighbors.add(random.choice(self._node_numbers))\\n # update the list of node numbers so that each node number \\n # appears in the correct ratio\\n self._node_numbers.append(self._num_nodes)\\n self._node_numbers.extend(list(new_node_neighbors)) \\n #update the number of nodes\\n self._num_nodes += 1\\n return list(new_node_neighbors)\",\n \"def computeNodeVolumes(self):\\n for i in np.arange(0,self.ni):\\n for j in np.arange(0,self.nj):\\n for k in np.arange(0,self.nk):\\n \\n V = self.dh[0]*self.dh[1]*self.dh[2]\\n if (i==0 or i==self.ni-1): V*=0.5\\n if (j==0 or j==self.nj-1): V*=0.5\\n if (k==0 or k==self.nk-1): V*=0.5\\n \\n self.node_vol[i][j][k] = V\",\n \"def newton_body(iterand):\\n next_backward_difference = iterand.next_backward_difference\\n next_state_vec = iterand.next_state_vec\\n\\n rhs = newton_coefficient * step_size_cast * ode_fn_vec(\\n next_time,\\n next_state_vec) - rhs_constant_term - next_backward_difference\\n delta = tf.squeeze(\\n tf.linalg.triangular_solve(\\n upper,\\n tf.matmul(tf.transpose(unitary), rhs[:, tf.newaxis]),\\n lower=False))\\n num_iters = iterand.num_iters + 1\\n\\n next_backward_difference += delta\\n next_state_vec += delta\\n\\n delta_norm = tf.cast(tf.norm(delta), real_dtype)\\n lipschitz_const = delta_norm / iterand.prev_delta_norm\\n\\n # Stop if method has converged.\\n approx_dist_to_sol = lipschitz_const / (1. - lipschitz_const) * delta_norm\\n close_to_sol = approx_dist_to_sol < tol\\n delta_norm_is_zero = tf.equal(delta_norm, tf.constant(0., dtype=real_dtype))\\n converged = close_to_sol | delta_norm_is_zero\\n finished = converged\\n\\n # Stop if any of the following conditions are met:\\n # (A) We have hit the maximum number of iterations.\\n # (B) The method is converging too slowly.\\n # (C) The method is not expected to converge.\\n too_slow = lipschitz_const > 1.\\n finished = finished | too_slow\\n if max_num_iters is not None:\\n too_many_iters = tf.equal(num_iters, max_num_iters)\\n num_iters_left = max_num_iters - num_iters\\n num_iters_left_cast = tf.cast(num_iters_left, real_dtype)\\n wont_converge = (\\n approx_dist_to_sol * lipschitz_const**num_iters_left_cast > tol)\\n finished = finished | too_many_iters | wont_converge\\n\\n return [\\n _NewtonIterand(\\n converged=converged,\\n finished=finished,\\n next_backward_difference=next_backward_difference,\\n next_state_vec=next_state_vec,\\n num_iters=num_iters,\\n prev_delta_norm=delta_norm)\\n ]\",\n \"def Trees__LCA_LowestCommonDenominator():\\n # Python2 ported to Python3 via 2to3-3.7\\n # URL:# URL:https://www.hackerrank.com/challenges/binary-search-tree-lowest-common-ancestor/problem\\n '''\\n class Node:\\n def __init__(self,info): \\n self.info = info \\n self.left = None \\n self.right = None \\n // this is a node of the tree , which contains info as data, left , right\\n '''\\n def lca(root, v1, v2):\\n # Find a and b. Link child nodes to parent to be able to backtrack.\\n # (1) Note, we add 'parent' attribute to node dynamically via node.parent = ...\\n root.parent = None\\n node_stack = []\\n node_stack.append(root)\\n v1_node, v2_node = None, None\\n while node_stack:\\n node = node_stack.pop()\\n if not v1_node and node.info == v1:\\n v1_node = node\\n if not v2_node and node.info == v2:\\n v2_node = node\\n for child_node in [node.left, node.right]:\\n if child_node:\\n child_node.parent = node # (1)\\n node_stack.append(child_node)\\n\\n # Generate path from A to root.\\n curr = v1_node\\n a_to_root = set()\\n while curr:\\n a_to_root.add(curr.info)\\n curr = curr.parent\\n\\n # traverse up b until you come across an element in a's path to parent.\\n curr = v2_node\\n while curr:\\n if curr.info in a_to_root:\\n return curr\\n else:\\n curr = curr.parent\\n\\n print(\\\"Shouldn't be here, Something went wrong\\\")\\n\\n # # Recursive. (Iterative is better, but did recursive for practice.) ~15 min.\\n # # Main idea is that we count the number of v1/v2's found of the subnodes.\\n # # If a node has sum of 2, we know it's the lca.\\n # def lca(root, v1, v2):\\n # def lca_helper(node):\\n # ret_node = None\\n # if not node:\\n # return 0, None\\n # v_match_counter = 0\\n # if node.info in [v1, v2]:\\n # v_match_counter += 1\\n # left_count, left_node_ret = lca_helper(node.left)\\n # right_count, right_node_ret = lca_helper(node.right)\\n # v_match_counter += left_count + right_count\\n # if v_match_counter == 2:\\n # ret_node = node\\n # if left_node_ret:\\n # ret_node = left_node_ret\\n # if right_node_ret:\\n # ret_node = right_node_ret\\n # return v_match_counter, ret_node\\n\\n # _, node = lca_helper(root)\\n # return node\",\n \"def extractTree(nodesList, rootNode):\\n if len(nodesList) == 0:\\n return\\n if nodesList[0] == '!':\\n return nodesList[1:]\\n\\n splitAttribute, splitValue, attributeValue = nodesList[0].strip().split('-')\\n nodesList = nodesList[1:]\\n \\n if splitAttribute != splitValue or splitAttribute != '$' or splitValue != '$':\\n rootNode.setSplit((splitAttribute, splitValue))\\n else:\\n rootNode.setSplit(\\\"Base case\\\")\\n rootNode.setData(attributeValue)\\n return nodesList[2:]\\n \\n \\n leftTree = Tree()\\n rightTree = Tree()\\n rootNode.setLesser(leftTree)\\n rootNode.setGreater(rightTree)\\n nodesList = extractTree(nodesList, leftTree)\\n\\n \\n \\n nodesList = extractTree(nodesList, rightTree)\\n\\n return nodesList\",\n \"def number_nodes(tree):\\n def list_of_nodes(tree):\\n \\\"\\\"\\\"Return a list of internal nodes in postorder traversal\\n\\n @param HuffmanNode tree: a tree to traverse\\n @rtype: list\\n\\n >>> t = HuffmanNode(None, HuffmanNode(6), HuffmanNode(7))\\n >>> list_of_nodes(t) == [t]\\n True\\n >>> t = HuffmanNode(None, HuffmanNode(8), HuffmanNode(None, HuffmanNode(5), HuffmanNode(6)))\\n >>> list_of_nodes(t) == [HuffmanNode(None, HuffmanNode(5), HuffmanNode(6)), HuffmanNode(None, HuffmanNode(8), HuffmanNode(None, HuffmanNode(5), HuffmanNode(6)))]\\n True\\n \\\"\\\"\\\"\\n list_ = []\\n if tree.left.is_leaf() and tree.right.is_leaf():\\n list_.append(tree)\\n return list_\\n elif tree.left.is_leaf():\\n list_.extend(list_of_nodes(tree.right))\\n list_.append(tree)\\n return list_\\n elif tree.right.is_leaf():\\n list_.extend(list_of_nodes(tree.left))\\n list_.append(tree)\\n return list_\\n else:\\n list_.extend(list_of_nodes(tree.left))\\n list_.extend(list_of_nodes(tree.right))\\n list_.append(tree)\\n return list_\\n\\n internal_nodes = list_of_nodes(tree)\\n for i in range(len(internal_nodes)):\\n node = internal_nodes[i]\\n node.number = i\",\n \"def newCostCalc(dfNew, curCost,a,b):\\n a1,a2,a3 = dfNew.iloc[a-2],dfNew.iloc[a-1],dfNew.iloc[a]\\n b1,b2,b3 = dfNew.iloc[b-2],dfNew.iloc[b-1],dfNew.iloc[b]\\n reCalc = curCost\\n reCalc = reCalc - euclideanDistance(a1['x'],a1['y'],a2['x'],a2['y'])\\n reCalc = reCalc - euclideanDistance(a2['x'],a2['y'],a3['x'],a3['y'])\\n reCalc = reCalc - euclideanDistance(b1['x'],b1['y'],b2['x'],b2['y'])\\n reCalc = reCalc - euclideanDistance(b2['x'],b2['y'],b3['x'],b3['y'])\\n\\n x, y = dfNew.iloc[a-1].copy(), dfNew.iloc[b-1].copy()\\n dfNew.iloc[a-1],dfNew.iloc[b-1] = y,x\\n\\n a1,a2,a3 = dfNew.iloc[a-2],dfNew.iloc[a-1],dfNew.iloc[a]\\n b1,b2,b3 = dfNew.iloc[b-2],dfNew.iloc[b-1],dfNew.iloc[b]\\n\\n reCalc = reCalc + euclideanDistance(a1['x'],a1['y'],a2['x'],a2['y'])\\n reCalc = reCalc + euclideanDistance(a2['x'],a2['y'],a3['x'],a3['y'])\\n reCalc = reCalc + euclideanDistance(b1['x'],b1['y'],b2['x'],b2['y'])\\n reCalc = reCalc + euclideanDistance(b2['x'],b2['y'],b3['x'],b3['y'])\\n\\n x, y = dfNew.iloc[a-1].copy(), dfNew.iloc[b-1].copy()\\n dfNew.iloc[a-1],dfNew.iloc[b-1] = y,x\\n\\n return reCalc\",\n \"def Dijkstra2(node_init, node_end, graph):\\n\\n ### Parameter initialisation\\n node_list = list(graph.vertices.keys())\\n dist = np.full(len(node_list), -np.inf)\\n # At the beginning we have not reached the end_node\\n node_end_reached = False\\n # At the beginning, we assume there is a shortest path:\\n no_path = False\\n \\n # Initialising the distances of the nodes\\n dist[node_init] = 0\\n # Setting the father_node which contains the provenance of the nodes\\n father_node = np.full(len(node_list), -np.inf)\\n # Initialising the current node\\n current_node = node_init \\n # Initialising the dictionnary of fixed node which has the following shape:\\n #{fixed_node: (previous_node, iteration, cost)}\\n # Fixing the number of iterations\\n k = 0\\n dict_fixed_node = {node_init:(None,k, 0)}\\n \\n # In the trivial case where the two nodes are identical\\n if node_init == node_end:\\n cost = 0\\n shortest_path = [node_init]\\n no_path = False\\n return cost, shortest_path, no_path\\n \\n # While the end node has not been reached\\n while not node_end_reached:\\n current_node_adj = graph.node_get_adj(current_node).copy()\\n # We get rid off the nodes that have been fixed, except at the first iteration\\n if k != 0:\\n current_node_adj.remove(dict_fixed_node[current_node][0])\\n ## Updating the distances : either the node are neighbors and \\n # something might change, either they are not, and their distance \\n # does not change.\\n # For the neighbors node\\n for e in current_node_adj:\\n dist_temp = dist[current_node] + graph.weights[(current_node, e)]\\n # We change the distance only if it is lower than it used to be\\n # otherwise, we keep it\\n if dist_temp < dist[e] or dist[e] == -np.inf:\\n dist[e] = dist_temp\\n # Setting the father node\\n father_node[e] = current_node\\n father_node[current_node] = None\\n # We set the distance of the current node to 0\\n dist[current_node] = 0 \\n # Index and distances which are not 0 and not minus infty\\n sub_dist_index = [i for i, e in enumerate(dist) if e > 0]\\n sub_dist_value = np.array([e for i, e in enumerate(dist) if e > 0])\\n # If these two lists are empty, we stop the algorithm and that means\\n # that we cannot reach our point\\n if not sub_dist_index or sub_dist_value.size == 0:\\n no_path = True\\n cost = 'impossible path'\\n shortest_path = 'impossible path'\\n break\\n # Now we need to set our choice for the next node\\n if np.array_equal(sub_dist_value, np.ones(len(sub_dist_value))):\\n ## If there are only ones : we pick them up randomly\\n current_node = int(random.choice(list(sub_dist_index)))\\n min_dist = sub_dist_value.min()\\n else:\\n ## If not we just pick up the one with the minimum distance.\\n current_node = sub_dist_index[sub_dist_value.argmin()]\\n min_dist = sub_dist_value.min()\\n # Adding this node to the dictionnary\\n dict_fixed_node[current_node] = (int(father_node[current_node]), k, min_dist)\\n # If the end_node has been reached, we stop the search algorithm\\n if node_end in dict_fixed_node.keys():\\n node_end_reached = True\\n # Incrementing the counter\\n k += 1\\n #print('current_node : {}'.format(current_node))\\n #print(dict_fixed_node)\\n # Now we need to get the shortest path from our iterations whose information \\n # are in dict_fixed_node. To do this, we need to circle back from the end_node\\n # to the init_node in this dictionnary.\\n # This is done only if some path between node_init and end_node exists.\\n if no_path == False:\\n list_father_node = list(dict_fixed_node.values())\\n previous_node = list_father_node[-1][0]\\n shortest_path = [node_end, previous_node]\\n # While the initial node has not been reached, we add the relevant\\n # nodes to our shortest path\\n while previous_node != node_init:\\n previous_node = dict_fixed_node[previous_node][0]\\n shortest_path.append(previous_node)\\n \\n # Computing the cost of this shortest path in terms of weights\\n cost = int(dict_fixed_node[node_end][2])\\n \\n return cost, shortest_path, no_path\",\n \"async def calc_nodes(height, balances, zero_nodes_lookup, root_index, hash_func):\\n if len(balances) == 0:\\n return {root_index: zero_nodes_lookup[height]}\\n if height == 0:\\n assert len(balances) == 1\\n _, vault_data = balances[-1]\\n balance = int(vault_data['amount'])\\n # A node with balance=0 is considered uninitialized.\\n if balance == 0:\\n return {root_index: zero_nodes_lookup[0]}\\n stark_key = int(vault_data['stark_key'])\\n token_id = int(vault_data['token_id'])\\n return {root_index: await vault_hash_async(stark_key, token_id, balance, hash_func)}\\n mid = 2 ** (height - 1)\\n left_balances = [(i, data) for i, data in balances if i < mid]\\n right_balances = [(i - mid, data) for i, data in balances if i >= mid]\\n left, right = await asyncio.gather(\\n calc_nodes(height - 1, left_balances, zero_nodes_lookup, 2 * root_index, hash_func),\\n calc_nodes(height - 1, right_balances, zero_nodes_lookup, 2 * root_index + 1, hash_func))\\n nodes = {root_index: await hash_func(left[2 * root_index], right[2 * root_index + 1])}\\n nodes.update(left)\\n nodes.update(right)\\n return nodes\",\n \"def transform(nodes, weights, new_corners):\\n if nodes.shape[1] == 1:\\n x_0 = new_corners[0, :]\\n x_1 = new_corners[1, :]\\n M = np.zeros((1, 1))\\n M[:, 0] = 0.5 * (x_1 - x_0)\\n origin = np.array([-1.0])\\n elif nodes.shape[1] == 2:\\n x_0 = new_corners[0, :]\\n x_1 = new_corners[1, :]\\n x_2 = new_corners[2, :]\\n M = np.zeros((2, 2))\\n M[:, 0] = 0.5 * (x_1 - x_0)\\n M[:, 1] = 0.5 * (x_2 - x_0)\\n origin = np.array([-1.0, -1.0])\\n elif nodes.shape[1] == 3:\\n x_0 = new_corners[0, :]\\n x_1 = new_corners[1, :]\\n x_2 = new_corners[2, :]\\n x_3 = new_corners[3, :]\\n M = np.zeros((3, 3))\\n M[:, 0] = 0.5 * (x_1 - x_0)\\n M[:, 1] = 0.5 * (x_2 - x_0)\\n M[:, 2] = 0.5 * (x_3 - x_0)\\n origin = np.array([-1.0, -1.0, -1.0])\\n\\n offset = -M @ origin + x_0\\n volume_fraction = np.abs(np.linalg.det(M))\\n return np.add(nodes @ M.T, offset), volume_fraction * weights\",\n \"def create_leaves(self, parent_node, leaf_values):\\n # TODO: find generalization!!\\n import numpy as np\\n # creating the leaf object\\n parent_node.child_nodes[0] = self.leaf_type(0.0, 0, diagram_type=self.__class__)\\n\\n # creating the offsets\\n # deciding on mult or add rule\\n # additive_coefficient = np.mean(leaf_values)\\n # new_offsets = np.array([leaf_values[i]-additive_coefficient for i in range(self.base)])\\n # max_difference = np.max(np.abs(new_offsets))\\n # mult_coefficient = max_difference if max_difference != 0.0 else 1.0\\n # for i in range(self.base):\\n # node.child_nodes[i] = node.child_nodes[0]\\n # node.offsets[i] = np.array([((new_offsets[i])/mult_coefficient), mult_coefficient], dtype='float64')\\n # return node, [additive_coefficient, mult_coefficient]\\n if leaf_values[0] == 0 or (leaf_values[1]-leaf_values[0] < leaf_values[1]/leaf_values[0]):\\n parent_node.offsets[0] = np.array([0, 1], dtype='float64')\\n for i in range(1, self.base, 1):\\n parent_node.child_nodes[i] = parent_node.child_nodes[0]\\n parent_node.offsets[i] = np.array([(leaf_values[i]-leaf_values[0]), 1], dtype='float64')\\n return parent_node, [leaf_values[0], 1]\\n else:\\n parent_node.offsets[0] = np.array([1, 1], dtype='float64')\\n for i in range(1, self.base, 1):\\n parent_node.child_nodes[i] = parent_node.child_nodes[0]\\n parent_node.offsets[i] = np.array([leaf_values[i]/leaf_values[0], (leaf_values[i]/leaf_values[0])],\\n dtype='float64')\\n return parent_node, [0, leaf_values[0]]\",\n \"def computeNodeErrors(net, inputs, actualOutputs):\\n errors = {}\\n squareError = 0.0\\n label = net.run(inputs)\\n predictedOutputs = [1.0 if l == label else 0.0 for l in net.validLabels]\\n for i, node in enumerate(net.outputLayer):\\n errors[node] = predictedOutputs[i] - actualOutputs[i]\\n squareError += errors[node] ** 2\\n if squareError == 0.0: # No need to continue\\n return ({node:0.0 for node in net.nodes}, 0.0)\\n reversedLayers = net.hiddenLayers[:]\\n reversedLayers.reverse()\\n toBeComputed = []\\n for layer in reversedLayers:\\n toBeComputed += layer\\n while len(toBeComputed) > 0: # Repeat until all nodes processed\\n newToBeComputed = []\\n for node in toBeComputed:\\n notComputable = False\\n totalChildError = 0\\n for child in node.children:\\n if child not in errors: # Cannot yet compute\\n notComputable = True\\n break\\n childError = errors[child]\\n weight = child.weights[node]\\n totalChildError += childError * weight\\n if notComputable:\\n newToBeComputed.append(node)\\n else:\\n activationDerivative = DERIVATIVES[node.activationFunc]\\n if activationDerivative is None:\\n raise TrainingError(\\\"Activation function of node \\\"\\n + str(node) + \\\" is non-differentiable.\\\")\\n weightedInput = node.getWeightedInputSum(BYPASS)\\n nodeDerivative = activationDerivative(weightedInput)\\n errors[node] = nodeDerivative * totalChildError\\n toBeComputed = newToBeComputed\\n return (errors, squareError)\",\n \"def newton_method_bidirectional(f, bnd1, bnd2, Ep, step):\\n\\n while True:\\n step = step + 1\\n\\n # print(\\\"bnd1=\\\",bnd1,\\\" and bnd2=\\\",bnd2)\\n\\n h_bnd1 = f(bnd1) / derivative(f, bnd1)\\n bnd1 = bnd1 - h_bnd1\\n if (decide(abs(h_bnd1) <= Ep)):\\n # print(\\\"Root in Approximation: \\\",bnd1)\\n return step\\n\\n h_bnd2 = f(bnd2) / derivative(f, bnd2)\\n bnd2 = bnd2 - h_bnd2\\n if (decide(abs(h_bnd2) <= Ep)):\\n # print(\\\"Root in Approximation: \\\",bnd2)\\n return step\",\n \"def algorithm(self):\\n t = time.clock()\\n self.calculateFirstPath()\\n improve = True\\n while improve and (self.allowedTime > (time.clock() - t)):\\n improve = False\\n\\n for i in range(self.NB_OF_NODES):\\n for j in range(self.NB_OF_NODES):\\n if j in [(i - 1) % self.NB_OF_NODES, i, (i + 1) % self.NB_OF_NODES]:\\n continue\\n\\n if self.getDistance(i, i + 1) + self.getDistance(j, j + 1) > self.getDistance(i, j) + self.getDistance(i + 1, j + 1):\\n self.exchange(i, j)\\n improve = True\",\n \"def evaluate(\\n self, nodes, derivatives=np.array([0, 0, 0]), modes=None, unique=False\\n ):\",\n \"def rewire(self, new_node, near_indexes):\\n\\n for i in near_indexes:\\n near_node = self.node_list[i]\\n edge_node = self.steer(new_node, near_node)\\n if not edge_node:\\n continue\\n edge_node.cost = self.calc_new_cost(new_node, near_node)\\n\\n no_collision = self.check_collision(edge_node, self.obstacle_list)\\n improved_cost = near_node.cost > edge_node.cost\\n\\n if no_collision and improved_cost:\\n self.node_list[i] = edge_node\\n self.propagate_cost_to_leaves(new_node)\",\n \"def find_all_feedbacks_Tarjan(ls_nodenames, lt_links):\\n \\n ll_SCC = SCC_analysis.decompose_SCC(ls_nodenames, lt_links)\\n ll_feedbacks = []\\n for l_SCC in ll_SCC:\\n lt_links_in_SCC = basic_topology_functions.extract_subnet_topology(lt_links, l_SCC)\\n print(\\\"feedbacks are calculated in subnetwork with \\\",len(l_SCC),\\\" nodes\\\")\\n \\n dic_startnode_endnodes = {}\\n for s_nodename in l_SCC:\\n dic_startnode_endnodes[s_nodename] = []\\n for t_link in lt_links_in_SCC:\\n if t_link[0] == s_nodename:\\n dic_startnode_endnodes[s_nodename].append(t_link[-1])\\n #print(dic_startnode_endnodes)\\n dic_node_counter = {}\\n for s_nodename in l_SCC:\\n dic_node_counter[s_nodename] = len(dic_startnode_endnodes[s_nodename])-1\\n #print(dic_node_counter)\\n for s_node_trajectorystart in dic_node_counter.keys():\\n if dic_node_counter[s_node_trajectorystart] == -1: #this means that all feedback containing this node are all calculated\\n continue\\n else:\\n l_trajectory = [s_node_trajectorystart]\\n while l_trajectory:\\n #print(l_trajectory)\\n i_counter = dic_node_counter[l_trajectory[-1]]\\n if i_counter == -1:\\n dic_node_counter[l_trajectory[-1]] = len(dic_startnode_endnodes[l_trajectory[-1]])-1\\n l_trajectory.pop()\\n if l_trajectory:\\n dic_node_counter[l_trajectory[-1]] -= 1\\n else:\\n s_node_next = dic_startnode_endnodes[l_trajectory[-1]][i_counter]\\n if s_node_next == s_node_trajectorystart:\\n ll_feedbacks.append(list(l_trajectory))\\n if len(ll_feedbacks)%20000 == 0:\\n print(\\\"calculated feedbacks are now \\\",len(ll_feedbacks))\\n \\n if s_node_next in l_trajectory:\\n dic_node_counter[l_trajectory[-1]] -= 1\\n else:\\n l_trajectory.append(s_node_next)\\n dic_startnode_endnodes[s_node_trajectorystart] = []\\n dic_node_counter[s_node_trajectorystart] =-1\\n \\n return ll_feedbacks\",\n \"def perform(self, node, inputs, outputs):\\r\\n x, w, v, W, V = inputs\\r\\n N = x.shape[0]\\r\\n outer = numpy.outer\\r\\n\\r\\n G = lambda n: sum(v[:, m] * V.T[n].dot(v[:, m]) / (w[n] - w[m])\\r\\n for m in xrange(N) if m != n)\\r\\n g = sum(outer(v[:, n], v[:, n] * W[n] + G(n))\\r\\n for n in xrange(N))\\r\\n\\r\\n # Numpy's eigh(a, 'L') (eigh(a, 'U')) is a function of tril(a)\\r\\n # (triu(a)) only. This means that partial derivative of\\r\\n # eigh(a, 'L') (eigh(a, 'U')) with respect to a[i,j] is zero\\r\\n # for i < j (i > j). At the same time, non-zero components of\\r\\n # the gradient must account for the fact that variation of the\\r\\n # opposite triangle contributes to variation of two elements\\r\\n # of Hermitian (symmetric) matrix. The following line\\r\\n # implements the necessary logic.\\r\\n out = self.tri0(g) + self.tri1(g).T\\r\\n\\r\\n # The call to self.tri0 in perform upcast from float32 to\\r\\n # float64 or from int* to int64 in numpy 1.6.1 but not in\\r\\n # 1.6.2. We do not want version dependent dtype in Theano.\\r\\n # We think it should be the same as the output.\\r\\n outputs[0][0] = numpy.asarray(out, dtype=node.outputs[0].dtype)\",\n \"def my_Newton( fct, df_dx, x0):\\r\\n xn = float(x0)\\r\\n eps = 1e-5\\r\\n N = 20\\r\\n i = 0\\r\\n while abs( fct( xn**(i + 1)) - fct( xn**i)) > eps and i < N:\\r\\n x_next = xn - fct(xn)/df_dx(xn)\\r\\n print( i, 'fct value', abs( fct(xn)), x_next)\\r\\n xn = x_next\\r\\n i += 1\\r\\n if abs( fct( xn)) < eps:\\r\\n return x_next\\r\\n else: #solution did not converge\\r\\n return np.nan\",\n \"def brute_tree(XTRAIN,istopTRAIN,XTEST,istopTEST):\\n \\n ntrain=XTRAIN.shape[0]\\n ntest=XTEST.shape[0]\\n \\n if np.sum(istopTRAIN)==0:\\n return 0,[]\\n\\n cost0=np.zeros(Ngammas*Nreps)\\n cost1=np.zeros(Ngammas*Nreps)\\n cost0test=np.zeros(Ngammas*Nreps)\\n cost1test=np.zeros(Ngammas*Nreps)\\n \\n precisionTRAIN=np.zeros(Ngammas*Nreps)\\n precisionTEST=np.zeros(Ngammas*Nreps)\\n recallTEST=np.zeros(Ngammas*Nreps)\\n rate=np.zeros(Ngammas*Nreps)\\n \\n for iii in range(Ngammas):\\n \\n gamma=GAMMA[iii]\\n \\n for jjj in range(Nreps):\\n \\n \\\"\\\"\\\" train a tree using training data with random splitting \\\"\\\"\\\"\\n \\n tree_hyperparameters['class_weight']={0:1,1:gamma}\\n clf=tree.DecisionTreeClassifier(**tree_hyperparameters)\\n clf.fit(XTRAIN,istopTRAIN)\\n \\n \\\"\\\"\\\"\\\" record costs and precision on validation data \\\"\\\"\\\"\\n \\n pTRAIN=clf.predict(XTRAIN)\\n precisionTRAIN[iii*Nreps+jjj]=np.divide(sum(1 for i in range(ntrain) if pTRAIN[i] == 1 and istopTRAIN[i]==1),sum(pTRAIN))\\n cost0[iii*Nreps+jjj]=sum(1 for i in range(ntrain) if pTRAIN[i] == 1 and istopTRAIN[i]==0)\\n cost1[iii*Nreps+jjj]=sum(1 for i in range(ntrain) if pTRAIN[i] == 0 and istopTRAIN[i]==1)\\n \\n \\\"\\\"\\\" record precision on test data \\\"\\\"\\\"\\n \\n pTEST=clf.predict(XTEST)\\n precisionTEST[iii*Nreps+jjj]=np.divide(sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==1),sum(pTEST))\\n recallTEST[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==1)/sum(istopTEST)\\n cost0test[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 1 and istopTEST[i]==0)\\n cost1test[iii*Nreps+jjj]=sum(1 for i in range(ntest) if pTEST[i] == 0 and istopTEST[i]==1)\\n \\n \\\"\\\"\\\" record positive rate on full data \\\"\\\"\\\"\\n \\n rate[iii*Nreps+jjj]=(sum(pTRAIN)+sum(pTEST))/(ntrain+ntest)\\n \\n \\\"\\\"\\\" Compute Pareto front for validation data \\\"\\\"\\\"\\n \\n Pareto = Lower_Convex_Hull(np.concatenate((cost0.reshape(-1,1),cost1.reshape(-1,1)),1))\\n \\n \\\"\\\"\\\" make some nice plots for whoever is watching \\\"\\\"\\\"\\n \\n plt.figure(figsize=(10,5))\\n plt.subplot(121)\\n plt.plot(cost0,cost1,'.')\\n plt.plot(cost0[Pareto],cost1[Pareto],'d')\\n plt.xlabel('errors on class zero training data')\\n plt.ylabel('errors on class one training data')\\n\\n plt.subplot(122)\\n plt.plot(cost0test,cost1test,'.')\\n plt.plot(cost0test[Pareto],cost1test[Pareto],'d')\\n plt.xlabel('errors on class zero test data')\\n plt.ylabel('errors on class one test data')\\n plt.show()\\n \\n plt.figure(figsize=(15,5))\\n plt.subplot(131)\\n plt.semilogy(precisionTRAIN,rate,'.')\\n plt.semilogy(precisionTRAIN[Pareto],rate[Pareto],'d')\\n plt.xlabel('precision on training data')\\n plt.ylabel('positive rate')\\n\\n plt.subplot(132) \\n plt.semilogy(precisionTEST,rate,'.')\\n plt.semilogy(precisionTEST[Pareto],rate[Pareto],'d')\\n plt.xlabel('precision on test data')\\n plt.ylabel('positive rate')\\n\\n plt.subplot(133) \\n plt.plot(precisionTEST,recallTEST,'.')\\n plt.plot(precisionTEST[Pareto],recallTEST[Pareto],'d')\\n plt.xlabel('precision on test data')\\n plt.ylabel('recall on test data')\\n plt.show() \\n \\n return {'cost0':cost0,'cost1':cost1,'cost0test':cost0test,'cost1test':cost1test,'precisionTRAIN':precisionTRAIN,'precisionTEST':precisionTEST,'recallTEST':recallTEST,'rate':rate,'Pareto':Pareto}\",\n \"def handle_leaf_nodes(self, nodes):\\n max_node_id = -1\\n for n in nodes:\\n n.sum_hess = self.decrypt(n.sum_hess)\\n n.sum_grad = self.decrypt(n.sum_grad)\\n n.weight = self.splitter.node_weight(n.sum_grad, n.sum_hess)\\n n.sitename = self.sitename\\n if n.id > max_node_id:\\n max_node_id = n.id\\n new_nodes = [Node() for i in range(max_node_id + 1)]\\n for n in nodes:\\n new_nodes[n.id] = n\\n return new_nodes\",\n \"def label_correcting_algo(dt, ori_node, des_node, do_return=False):\\n # Convert all labels to string\\n ori = str(ori_node)\\n des = str(des_node)\\n dt[[\\\"start\\\", \\\"end\\\"]] = dt[[\\\"start\\\", \\\"end\\\"]].astype(str) \\n \\n # Initialization\\n nodes = set(dt.loc[:,\\\"start\\\"].unique()) | set(dt.loc[:,\\\"end\\\"].unique())\\n dist = {}.fromkeys(nodes, np.inf)\\n dist[ori] = 0\\n points = {}.fromkeys(nodes, ori)\\n iter_set = {ori}\\n \\n # Main Algo\\n while iter_set:\\n i = iter_set.pop() # Randomly pop out a node i\\n A_i = dt[dt.start == i]\\n for row in A_i.index: \\n j = A_i.loc[:, \\\"end\\\"][row]\\n c_ij = A_i.loc[:, \\\"cost\\\"][row]\\n if dist[j] > dist[i] + c_ij:\\n dist[j] = dist[i] + c_ij\\n points[j] = i\\n iter_set = iter_set | set([j]) # Union\\n \\n # Print & Return the Answer\\n x = pd.concat([pd.Series(points), pd.Series(dist)], axis=1)\\n x.columns = [\\\"Front-node\\\", \\\"Distance\\\"]\\n\\n current_node = des\\n front_node = \\\"\\\"\\n sp = des\\n while front_node != ori:\\n front_node = str(x.loc[current_node, \\\"Front-node\\\"])\\n sp = \\\"{} -> {}\\\".format(front_node, sp)\\n current_node = front_node\\n \\n sp = \\\"From node {} to node {}, total Distance: {}\\\\n{}\\\\n\\\".format(ori, des, x.loc[des, \\\"Distance\\\"], sp)\\n if do_return:\\n print(sp)\\n return x\\n else:\\n return sp\",\n \"def calculate_distance_to_divide(\\n grid, longest_path=True, add_to_grid=False, clobber=False\\n):\\n # check that flow__receiver nodes exists\\n if \\\"flow__receiver_node\\\" not in grid.at_node:\\n raise FieldError(\\n \\\"A 'flow__receiver_node' field is required at the \\\"\\n \\\"nodes of the input grid.\\\"\\n )\\n\\n if \\\"flow__upstream_node_order\\\" not in grid.at_node:\\n raise FieldError(\\n \\\"A 'flow__upstream_node_order' field is required at the \\\"\\n \\\"nodes of the input grid.\\\"\\n )\\n\\n if \\\"drainage_area\\\" not in grid.at_node:\\n raise FieldError(\\n \\\"A 'flow__upstream_node_order' field is required at the \\\"\\n \\\"nodes of the input grid.\\\"\\n )\\n\\n # get the reciever nodes, depending on if this is to-one, or to-multiple,\\n # we'll need to get a different at-node field.\\n if grid.at_node[\\\"flow__receiver_node\\\"].size != grid.size(\\\"node\\\"):\\n to_one = False\\n else:\\n to_one = True\\n\\n flow__receiver_node = grid.at_node[\\\"flow__receiver_node\\\"]\\n drainage_area = grid.at_node[\\\"drainage_area\\\"]\\n\\n # get the upstream node order\\n flow__upstream_node_order = grid.at_node[\\\"flow__upstream_node_order\\\"]\\n\\n # get downstream flow link lengths, result depends on type of grid.\\n if isinstance(grid, RasterModelGrid):\\n flow_link_lengths = grid.length_of_d8[\\n grid.at_node[\\\"flow__link_to_receiver_node\\\"]\\n ]\\n else:\\n flow_link_lengths = grid.length_of_link[\\n grid.at_node[\\\"flow__link_to_receiver_node\\\"]\\n ]\\n\\n # create an array that representes the distance to the divide.\\n distance_to_divide = np.zeros(grid.nodes.size)\\n\\n if not longest_path:\\n distance_to_divide[:] = 2 * grid.size(\\\"node\\\") * np.max(flow_link_lengths)\\n\\n # iterate through the flow__upstream_node_order backwards.\\n for node in reversed(flow__upstream_node_order):\\n # if drainage are is equal to node cell area, set distance to zeros\\n # this should handle the drainage divide cells as boundary cells have\\n # their area set to zero.\\n if drainage_area[node] == grid.cell_area_at_node[node]:\\n distance_to_divide[node] = 0\\n\\n # get flow recievers\\n reciever = flow__receiver_node[node]\\n\\n if to_one:\\n # if not processing an outlet node.\\n if reciever != node:\\n if longest_path:\\n cond = (\\n distance_to_divide[reciever]\\n < distance_to_divide[node] + flow_link_lengths[node]\\n )\\n else:\\n cond = (\\n distance_to_divide[reciever]\\n > distance_to_divide[node] + flow_link_lengths[node]\\n )\\n\\n if cond:\\n distance_to_divide[reciever] = (\\n distance_to_divide[node] + flow_link_lengths[node]\\n )\\n\\n else:\\n # non-existant links are coded with -1\\n useable_receivers = np.where(reciever != grid.BAD_INDEX)[0]\\n\\n for idx in range(len(useable_receivers)):\\n r = reciever[useable_receivers][idx]\\n fll = flow_link_lengths[node][useable_receivers][idx]\\n\\n # if not processing an outlet node.\\n if r != node:\\n if longest_path:\\n cond = distance_to_divide[r] < distance_to_divide[node] + fll\\n else:\\n cond = distance_to_divide[r] > distance_to_divide[node] + fll\\n\\n if cond:\\n distance_to_divide[r] = distance_to_divide[node] + fll\\n\\n # store on the grid\\n if add_to_grid:\\n grid.add_field(\\n \\\"distance_to_divide\\\", distance_to_divide, at=\\\"node\\\", clobber=clobber\\n )\\n\\n return distance_to_divide\",\n \"def newtonsMethod(f, df, ddf, x, niter=10):\\n\\n points = []\\n\\n for i in xrange(niter):\\n point = np.dot(-la.inv(ddf(x)), (df(x)))\\n\\n slope = np.dot(df(x), point)\\n\\n a = backtracking(f, slope, x, point)\\n \\n #update point\\n x_k = x + a*point\\n points.append(x_k)\\n x = x_k\\n\\n return points\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65839136","0.57315016","0.567347","0.55890775","0.55738044","0.5463602","0.54403263","0.5439998","0.5405277","0.53822875","0.5344752","0.5334331","0.5325484","0.5318365","0.53122985","0.53017414","0.5295547","0.5275849","0.52737594","0.52574426","0.5230346","0.52266407","0.5218263","0.5185318","0.51770127","0.5173949","0.5160851","0.5157591","0.51378775","0.5133119","0.51289505","0.5125523","0.5108133","0.51064736","0.5104259","0.5103502","0.5081732","0.5076288","0.5071857","0.50684774","0.5065425","0.50579035","0.50561845","0.5056071","0.5039873","0.50350887","0.503006","0.5029164","0.50276995","0.50238025","0.50192326","0.50077516","0.50068814","0.49979854","0.49950433","0.49872214","0.4986004","0.49854264","0.4978293","0.4977638","0.49750566","0.49637634","0.49631697","0.49588704","0.4954976","0.4951387","0.4945267","0.49444473","0.49434066","0.4940878","0.49375913","0.49338678","0.49338412","0.4928129","0.49236566","0.49182633","0.49153966","0.49121758","0.49117246","0.49077004","0.4906052","0.49051553","0.49036717","0.49035296","0.48919111","0.4884319","0.4883693","0.4875527","0.48731244","0.4872033","0.4870849","0.48670393","0.4865498","0.48618716","0.48583892","0.48449036","0.48444298","0.4838655","0.48371902","0.48324525"],"string":"[\n \"0.65839136\",\n \"0.57315016\",\n \"0.567347\",\n \"0.55890775\",\n \"0.55738044\",\n \"0.5463602\",\n \"0.54403263\",\n \"0.5439998\",\n \"0.5405277\",\n \"0.53822875\",\n \"0.5344752\",\n \"0.5334331\",\n \"0.5325484\",\n \"0.5318365\",\n \"0.53122985\",\n \"0.53017414\",\n \"0.5295547\",\n \"0.5275849\",\n \"0.52737594\",\n \"0.52574426\",\n \"0.5230346\",\n \"0.52266407\",\n \"0.5218263\",\n \"0.5185318\",\n \"0.51770127\",\n \"0.5173949\",\n \"0.5160851\",\n \"0.5157591\",\n \"0.51378775\",\n \"0.5133119\",\n \"0.51289505\",\n \"0.5125523\",\n \"0.5108133\",\n \"0.51064736\",\n \"0.5104259\",\n \"0.5103502\",\n \"0.5081732\",\n \"0.5076288\",\n \"0.5071857\",\n \"0.50684774\",\n \"0.5065425\",\n \"0.50579035\",\n \"0.50561845\",\n \"0.5056071\",\n \"0.5039873\",\n \"0.50350887\",\n \"0.503006\",\n \"0.5029164\",\n \"0.50276995\",\n \"0.50238025\",\n \"0.50192326\",\n \"0.50077516\",\n \"0.50068814\",\n \"0.49979854\",\n \"0.49950433\",\n \"0.49872214\",\n \"0.4986004\",\n \"0.49854264\",\n \"0.4978293\",\n \"0.4977638\",\n \"0.49750566\",\n \"0.49637634\",\n \"0.49631697\",\n \"0.49588704\",\n \"0.4954976\",\n \"0.4951387\",\n \"0.4945267\",\n \"0.49444473\",\n \"0.49434066\",\n \"0.4940878\",\n \"0.49375913\",\n \"0.49338678\",\n \"0.49338412\",\n \"0.4928129\",\n \"0.49236566\",\n \"0.49182633\",\n \"0.49153966\",\n \"0.49121758\",\n \"0.49117246\",\n \"0.49077004\",\n \"0.4906052\",\n \"0.49051553\",\n \"0.49036717\",\n \"0.49035296\",\n \"0.48919111\",\n \"0.4884319\",\n \"0.4883693\",\n \"0.4875527\",\n \"0.48731244\",\n \"0.4872033\",\n \"0.4870849\",\n \"0.48670393\",\n \"0.4865498\",\n \"0.48618716\",\n \"0.48583892\",\n \"0.48449036\",\n \"0.48444298\",\n \"0.4838655\",\n \"0.48371902\",\n \"0.48324525\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6325345"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":8298,"cells":{"query":{"kind":"string","value":"Calculates divided differences for given interpolation nodes. It is assumed, that at least two interpolation nodes are provided. Each tuple of returned list represents one level of divided differences tree."},"document":{"kind":"string","value":"def calculate_divided_differences(nodes):\n nodes_to_compute = []\n divided_differences = []\n for node in nodes:\n nodes_to_compute.append(DividedDifferenceNode(x=node[0], divided_difference=node[1]))\n\n divided_differences.append(tuple(nodes_to_compute))\n\n while len(nodes_to_compute) > 1:\n next_node_row = calculate_divided_differences_row(nodes_to_compute)\n divided_differences.append(tuple(next_node_row))\n nodes_to_compute = next_node_row\n\n return divided_differences"},"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 calculate_divided_differences_row(nodes_to_compute):\n divided_differences = []\n\n if len(nodes_to_compute) == 1:\n return None\n\n for i in range(0, len(nodes_to_compute) - 1):\n child = DividedDifferenceNode.create_child_node(nodes_to_compute[i], nodes_to_compute[i + 1])\n child.calculate_value()\n divided_differences.append(child)\n\n for node in divided_differences:\n print(node, end='')\n\n print('\\n')\n return divided_differences","def diff(self):\n return [node.diff for node in self]","def test_frac_diffNonGaps(self):\n s1 = self.RNA(\"AAAA\")\n s2 = self.RNA(\"AGGG\")\n s3 = self.RNA(\"GGGG\")\n s4 = self.RNA(\"AG--GA-G\")\n s5 = self.RNA(\"CU--CU-C\")\n s6 = self.RNA(\"AC--GC-G\")\n s7 = self.RNA(\"--------\")\n s8 = self.RNA(\"AAAA----\")\n s9 = self.RNA(\"A-GG-A-C\")\n e = self.RNA(\"\")\n\n def test(x, y, z):\n return self.assertFloatEqual(x.frac_diff_non_gaps(y), z)\n\n test(s1, s2, 0.75)\n test(s1, s3, 1)\n test(s2, s3, 0.25)\n test(s1, s4, 0.5)\n test(s4, s5, 1)\n test(s4, s6, 0.4)\n test(s4, s7, 0)\n test(s4, s8, 0.5)\n test(s4, s9, 1 / 3.0)\n test(e, s4, 0)","def getSubdivisionNodes(self, signature):\n x, y, z = signature[0], signature[1], signature[2]\n return [(2*x+1, 2*y, 2*z), (2*x, 2*y, 2*z),\n (2*x+1, 2*y+1, 2*z), (2*x, 2*y, 2*z+1),\n (2*x+1, 2*y+1, 2*z+1), (2*x, 2*y+1, 2*z),\n (2*x+1, 2*y, 2*z+1), (2*x, 2*y+1, 2*z+1)]","def fractionalPoints(totalNodeList, recNodeList, fracPoints):\n\n avPoint = averagePoints(recNodeList)\n\n for i in range(0, fracPoints):\n closestPoint = closest_node(avPoint, totalNodeList) #Finds closest point\n totalNodeList.remove(closestPoint)\n recNodeList.append(closestPoint)\n\n printProgressBar(i, fracPoints) \n\n return recNodeList","def equiangular_dimension_unpack(nodes, ratio):\n dim1 = int((nodes / ratio) ** 0.5)\n dim2 = int((nodes * ratio) ** 0.5)\n if dim1 * dim2 != nodes: # Try to correct dim1 or dim2 if ratio is wrong\n if nodes % dim1 == 0:\n dim2 = nodes // dim1\n if nodes % dim2 == 0:\n dim1 = nodes // dim2\n assert dim1 * dim2 == nodes, f'Unable to unpack nodes: {nodes}, ratio: {ratio}'\n return dim1, dim2","def getFractionalItems(self, startingPoint, returnFmt = 0, refinements = 1):\n\n def closest_node(node, nodes):\n\n \"\"\" returns closest node using dot vectorization, slightly faster see https://codereview.stackexchange.com/questions/28207/finding-the-closest-point-to-a-list-of-points \"\"\"\n\n if node in nodes:\n nodes.remove(node)\n\n nodes = np.asarray(nodes)\n deltas = nodes - node\n dist_2 = np.einsum('ij,ij->i', deltas, deltas)\n temp = nodes[np.argmin(dist_2)]\n return (temp[0], temp[1])\n\n def averagePoints(nodeList):\n #Consider switching to numpy mean arrays if performance is an issue\n #inits\n tempX, tempY = 0, 0\n for node in nodeList:\n tempX += node[0]\n tempY += node[1]\n \n avX, avY = tempX/len(nodeList), tempY/len(nodeList)\n avPoint = [avX, avY]\n\n return avPoint\n\n def fractionalPoints(totalNodeList, recNodeList, fracPoints):\n\n \"\"\" Starts out with one point should be in a place of high density #NOTE this is not automated yet. Keep adding points (it will add the closest)\n point to the set over and over until 50% of the points are encircled. Then it will return a list of those points \"\"\"\n\n avPoint = averagePoints(recNodeList)\n\n for i in range(0, fracPoints):\n closestPoint = closest_node(avPoint, totalNodeList) #Finds closest point\n totalNodeList.remove(closestPoint)\n recNodeList.append(closestPoint)\n\n printProgressBar(i, fracPoints) \n\n return recNodeList \n\n #Gets fractional points \n numPointsFrac = math.floor(self.numPoints * self.detectionFraction)\n fracPoints = fractionalPoints(self.points, [startingPoint], numPointsFrac)\n \n #Hull creation and getting of verticies\n hull = ConvexHull(fracPoints)\n polyVertices = [fracPoints[vertex] for vertex in hull.vertices] \n cutVertices = chaikins_corner_cutting(polyVertices, refinements)\n\n #Path creation \n polyCodes = [mppath.Path.LINETO] * len(polyVertices)\n polyCodes[0] = mppath.Path.MOVETO\n polyCodes[-1] = mppath.Path.CLOSEPOLY\n\n cutCodes = [mppath.Path.LINETO] * len(cutVertices)\n cutCodes[0] = mppath.Path.MOVETO\n cutCodes[-1] = mppath.Path.CLOSEPOLY\n\n polyPath = mppath.Path(polyVertices, polyCodes)\n cutPath = mppath.Path(cutVertices, cutCodes)\n\n #How you want the information returned \n if returnFmt == -2:\n return [[cutVertices, cutPath], fracPoints]\n if returnFmt == -1:\n return fracPoints\n if returnFmt == 0:\n return [cutVertices, cutPath]\n if returnFmt == 1:\n return [polyVertices, polyPath]\n if returnFmt == 2:\n return [[cutVertices, cutPath], [polyVertices, polyPath]]","def _computeDerivative(self,angles, distances):\n slope=[]\n slope.append(0)\n for i in xrange(1,len(angles)):\n der = (distances[i]-distances[i-1])/(angles[i]-angles[i-1])\n slope.append(der)\n #slope.append(0)\n return slope","def diff_frac(data_1, data_2):\n\n frac_1 = np.sum(data_1) / len(data_1)\n frac_2 = np.sum(data_2) / len(data_2)\n\n return frac_1 - frac_2","def _compute_diff(self, begin, end):\n d = self.diff\n x = self.x\n for i in range(begin, end):\n for j in range(i):\n d[i].append((d[i][j] - d[i-1][j]) / (x[i] - x[i-j-1]))","def rat2frac_list(x, y):\n\tcont = rat2cont_quot(x, y)\n\tfrac = []\n\tfor i in range(len(cont)):\n\t\tfrac.append(cont2frac(cont[:(i+1)]))\n\treturn frac","def differences(data: list) -> list:\n differences = []\n iterable, copy = tee(data)\n next(copy) # adjusts copy of my iterable up 1 element\n for x, y in zip(iterable, copy):\n differences.append(abs(x - y))\n\n return differences","def test_frac_diffGaps(self):\n s1 = self.RNA(\"AAAA\")\n s2 = self.RNA(\"GGGG\")\n s3 = self.RNA(\"----\")\n s4 = self.RNA(\"A-A-\")\n s5 = self.RNA(\"-G-G\")\n s6 = self.RNA(\"UU--\")\n s7 = self.RNA(\"-\")\n s8 = self.RNA(\"GGG\")\n e = self.RNA(\"\")\n self.assertEqual(s1.frac_diff_gaps(s1), 0)\n self.assertEqual(s1.frac_diff_gaps(s2), 0)\n self.assertEqual(s1.frac_diff_gaps(s3), 1)\n self.assertEqual(s1.frac_diff_gaps(s4), 0.5)\n self.assertEqual(s1.frac_diff_gaps(s5), 0.5)\n self.assertEqual(s1.frac_diff_gaps(s6), 0.5)\n self.assertEqual(s1.frac_diff_gaps(s7), 1)\n self.assertEqual(s1.frac_diff_gaps(e), 0)\n self.assertEqual(s3.frac_diff_gaps(s3), 0)\n self.assertEqual(s3.frac_diff_gaps(s4), 0.5)\n self.assertEqual(s3.frac_diff_gaps(s7), 0.0)\n self.assertEqual(e.frac_diff_gaps(e), 0.0)\n self.assertEqual(s4.frac_diff_gaps(s5), 1.0)\n self.assertEqual(s4.frac_diff_gaps(s6), 0.5)\n self.assertFloatEqual(s6.frac_diff_gaps(s8), 1 / 3.0)","def test_frac_diff(self):\n s1 = self.RNA(\"ACGU\")\n s2 = self.RNA(\"AACG\")\n s3 = self.RNA(\"GG\")\n s4 = self.RNA(\"A\")\n e = self.RNA(\"\")\n self.assertEqual(s1.frac_diff(e), 0)\n self.assertEqual(s1.frac_diff(s2), 0.75)\n self.assertEqual(s1.frac_diff(s3), 1)\n self.assertEqual(s1.frac_diff(s4), 0) # note truncation","def diff(self, value):\n if hasattr(value, \"__len__\"):\n if len(value) == len(self):\n for node, val in zip(self, value):\n node.diff = val\n return\n else:\n raise RxDException(\n \"diff must either be a scalar or an iterable of the same length as the NodeList\"\n )\n for node in self:\n node.diff = value","def deltas(L):\n return map(sub, tuple(L)[1:], L)","def __diff_internal(self):\n assert self.p > 0, \"order of Bspline must be > 0\" # we already handle the other case in diff()\n\n # https://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/B-spline/bspline-derv.html\n #\n t = self.knot_vector\n p = self.p\n bi = BsplineBasis(t[:-1], p - 1)\n bip1 = BsplineBasis(t[1:], p - 1)\n\n numer1 = +p\n numer2 = -p\n denom1 = t[p:-1] - t[:-(p + 1)]\n denom2 = t[(p + 1):] - t[1:-p]\n\n with np.errstate(divide='ignore', invalid='ignore'):\n ci = np.where(denom1 != 0., (numer1 / denom1), 0.)\n cip1 = np.where(denom2 != 0., (numer2 / denom2), 0.)\n\n return (ci, bi), (cip1, bip1)","def find_fractions():\n num_list = []\n den_list = []\n for n in range(10, 100):\n for d in range(10, 100):\n if d > n:\n x = n / d\n ln = list(str(n))\n ld = list(str(d))\n if (ln[0] == ld[1]) and (ln[0] != '0'):\n if ld[0] != '0':\n if (int(ln[1]) / int(ld[0])) == x:\n print \"n/d =\", n, d\n num_list.append(n)\n den_list.append(d)\n else:\n continue\n elif (ln[1] == ld[0]) and (ln[1] != '0'):\n if ld[1] != '0':\n if (int(ln[0]) / int(ld[1])) == x:\n print \"n/d =\", n, d\n num_list.append(n)\n den_list.append(d)\n else:\n continue\n else:\n continue\n return num_list, den_list","def diff(self):\n\t\tif len(self.v) < 4:\n\t\t\treturn None\n\t\tif self.poli == None:\n\t\t\tself.generar_polinomio()\n\t\tif x != None:\n\t\t\treturn diff(self.poli)(x)\n\t\treturn diff(self.poli)","def _extract_fraction_with_text_nl(tokens, short_scale, ordinals):\n for c in _FRACTION_MARKER_NL:\n partitions = partition_list(tokens, lambda t: t.word == c)\n\n if len(partitions) == 3:\n numbers1 = \\\n _extract_numbers_with_text_nl(partitions[0], short_scale,\n ordinals, fractional_numbers=False)\n numbers2 = \\\n _extract_numbers_with_text_nl(partitions[2], short_scale,\n ordinals, fractional_numbers=True)\n\n if not numbers1 or not numbers2:\n return None, None\n\n # ensure first is not a fraction and second is a fraction\n num1 = numbers1[-1]\n num2 = numbers2[0]\n if num1.value >= 1 and 0 < num2.value < 1:\n return num1.value + num2.value, \\\n num1.tokens + partitions[1] + num2.tokens\n\n return None, None","def calculate_deltas(tvals, tp_confidences, fp_confidences, num_samples):\n deltas = []\n tp_percentages = []\n fp_percentages = []\n for tval in tvals:\n dval, tp_pct, fp_pct = delta(\n tval,\n tp_confidences,\n fp_confidences,\n num_samples\n )\n deltas.append(dval)\n tp_percentages.append(tp_pct)\n fp_percentages.append(fp_pct)\n return deltas, tp_percentages, fp_percentages","def divide_microstructure_unit(self,point1,point2,dimensions):\n\t\tnew_sections = []\n\t\tif len(point1) < 4:\n\t\t\tdone = 0.0\n\t\t\tdtwo = 0.0\n\t\telse:\n\t\t\tdone = point1[-1]\n\t\t\tdtwo = point2[-1]\n\t\t\n\t\tp1 = np.array(point1[:3])\n\t\tp2 = np.array(point2[:3])\n\t\tvec = p2-p1\n\t\tdimslength = float(np.sum(dimensions))\n\t\tfor d,dim in enumerate(dimensions[:-1]):\n\t\t\tnearsideproportion = np.sum(dimensions[:d])/dimslength\n\t\t\tfarsideproportion = np.sum(dimensions[:d+1])/dimslength\n\t\t\tnew_sections.append([\t\n\t\t\t\t\t\tlist(np.append(p1+vec*nearsideproportion,done)),\n\t\t\t\t\t\tlist(np.append(((p1+vec*nearsideproportion)+(p1+vec*farsideproportion))/2.0,(done+dtwo)/2.0)),\n\t\t\t\t\t\tlist(np.append(p1+vec*farsideproportion,dtwo))\n\t\t\t\t\t\t])\n\t\t\n\t\tnew_sections.append([\t\n\t\t\t\t\tlist(new_sections[-1][-1]),\n\t\t\t\t\tlist((np.array(new_sections[-1][-1])+np.array(list(point2[:3])+[dtwo]))/2.0),\n\t\t\t\t\tlist(point2[:3])+[dtwo]\n\t\t\t\t\t])\n\t\t\n\t\tif len(dimensions) > 2:\n\t\t\treturn(new_sections,['node','paranode1','paranode2','internode','paranode2','paranode1'][:len(new_sections)])\n\t\t\n\t\telse:\n\t\t\treturn(new_sections,['interbouton','bouton'][:len(new_sections)])","def find_sharpest_fork_general(Nodes):\n pair_list = []\n Dis = np.array([])\n for n in Nodes:\n if n.parent is not None:\n if n.parent.parent is not None:\n a = n.parent.children\n if(isinstance(a, list)):\n if(len(a)==2):\n n1 = a[0]\n n2 = a[1]\n pair_list.append([n1 , n2])\n dis = LA.norm(a[0].xyz - a[1].xyz,2)\n Dis = np.append(Dis,dis)\n if(len(Dis)!= 0):\n (b,) = np.where(Dis == Dis.min())\n sharpest_pair = pair_list[b[0]]\n distance = Dis.min()\n else:\n sharpest_pair = [0,0]\n distance = 0.\n return sharpest_pair, distance","def divideWork(self,ratios):\r\n \r\n taskCount = 0\r\n\r\n #Sums up the ratio amounts\r\n for nodeId,ratio in ratios.iteritems():\r\n taskCount += ratio\r\n\r\n #Divide the work into 'taskCount' chunks\r\n [primer,prefix] = WorkRange(self.charset).divideMore(self.primer,self.prefix,taskCount)\r\n \r\n prim = {}\r\n pref = {}\r\n \r\n for nodeId,ratio in ratios.iteritems():\r\n prim[nodeId] = []\r\n pref[nodeId] = []\r\n\r\n #Assigns each node 'ratio' amount chunks\r\n for i in range(ratio):\r\n prim[nodeId].append(primer.pop())\r\n pref[nodeId].append(prefix.pop())\r\n\r\n\r\n return [prim,pref]","def derivative(requestContext, seriesList):\n results = []\n for series in seriesList:\n newValues = []\n prev = None\n for val in series:\n if None in (prev,val):\n newValues.append(None)\n prev = val\n continue\n newValues.append(val - prev)\n prev = val\n newName = \"derivative(%s)\" % series.name\n newSeries = TimeSeries(newName, series.start, series.end, series.step, newValues)\n newSeries.pathExpression = newName\n results.append(newSeries)\n return results","def diff_op(self, args: List[float], time: float) -> List[float]:\n v, nK, ca = args\n ca_args: List[float] = [v, ca]\n dvdt: float = self.dvdt(args=args)\n dnKdt: float = self.kvhh.dndt(v=v, n=nK)\n dCadt: float = self.dCadt(args=ca_args)\n return [dvdt, dnKdt, dCadt]","def split_distances(self, remaining: List[int], distances: List[float], mid: int) -> List[List[int]]:\n closer, farther = [], []\n for index in remaining:\n if distances[index] <= mid:\n closer.append(index)\n else:\n farther.append(index)\n return [closer, farther]","def calc_diffs(self, y, x, locs):\n res = {}\n \n for item, value in locs.iteritems():\n res[item] = self.slab_ratio * (self.grid[y, x] - self.grid[value['y'], value['x']])\n \n return res","def test_compare_old_to_new_method_to_create_trees(self):\n nodes = util.generate_sequence_of_points(2, 2)\n tree1 = kdtree.createNewTree(nodes)\n kdtree.visualize(tree1)\n \n sel_axis = (lambda axis: axis)\n tree2 = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\n tree2.split2([0.25, 0.5], axis = 1)\n tree2.split2([0.75, 0.5], axis = 1)\n \n #left\n tree2.split2([0.25, 0.25], axis = 0, sel_axis = sel_axis)\n tree2.split2([0.25, 0.75], axis = 0, sel_axis = sel_axis)\n \n #right\n tree2.split2([0.75, 0.25], axis = 0, sel_axis = sel_axis)\n tree2.split2([0.75, 0.75], axis = 0, sel_axis = sel_axis)\n \n kdtree.visualize(tree2)\n \n for n in zip(kdtree.level_order(tree1), kdtree.level_order(tree2)):\n self.assertEqual(n[0].data, n[1].data, \"elements not equal\")\n \n if n[0].data is not None and n[1].data is not None:\n self.assertEqual(n[0].axis, n[1].axis, \"elements not equal\")","def compareNodes(x, y):\n return x.pathValue - y.pathValue","def getNodesAndDistances():\n\n\tglobal width, height\n\n\t# First we generate the list\n\n\tprint \"\\tGetting node list...\"\n\t\n\tnodeDict = {}\n\n\tfor y in range(height):\n\t\tfor x in range(width):\n\t\t\ttheType = getSquare(x, y)\n\n\t\t\tprint \"\\t\\tGetting list for node (%d, %d) of type %d...\" % (x, y, theType)\n\n\t\t\ttempList = getNodeList(x, y, theType)\n\n\t\t\tif tempList == []:\n\t\t\t\tprint \"\\t\\t\\tNo nodes here.\"\n\t\t\telse:\n\t\t\t\tfor i in range(len(tempList)):\n\t\t\t\t\tnode = tempList[i]\n\t\t\t\t\tnodeName = node[0]\n\t\t\t\t\tnodeDict[nodeName] = node[1:]\t# Everything but the first element\n\t\t\t\t\tprint \"\\t\\t\\tAdded node '%s'...\" % nodeName\n\n\tprint \"\\tDone getting node list (%d nodes)...\" % (len(nodeDict.keys()))\n\tprint \"\"\n\n\t# Now that we've got that, we get a list of pairs\n\n\tpairList = getPairList(nodeDict)\n\n\t# Now we calculate the distance between every pair of nodes that connect\n\n\tprint \"\"\n\tprint \"\\tCreateing dictionary of distances between connected nodes...\"\n\n\tdistanceDict = {}\n\n\tfor tuple in pairList:\n\t\t(nodeA, nodeB) = tuple\n\t\tprint \"\\t\\tCalculating distance between '%s' and '%s'...\" % (nodeA, nodeB)\n\t\tdistance = distanceBetween(nodeA, nodeB, nodeDict)\n\t\tpairName = \"%s%s\" % (nodeA, nodeB)\n\t\tdistanceDict[pairName] = distance\n\t\tprint \"\\t\\t\\tDistace was %f.\" % (distance)\n\n\tprint \"\\tDone creating dictionary of node differences (%d pairs).\" % (len(distanceDict.keys()))\n\n\treturn nodeDict, distanceDict","def _compare_elements(self, old, new):\n res = None\n # We want to go through the tree post-order\n if isinstance(old, dict):\n res_dict = self.compare_dicts(old, new)\n if (len(res_dict) > 0):\n res = res_dict\n # Now we are on the same level\n # different types, new value is new\n elif (type(old) != type(new)):\n res = {'---': old, '+++': new}\n # recursive arrays\n # we can be sure now, that both new and old are\n # of the same type\n elif (isinstance(old, list)):\n res_arr = self._compare_arrays(old, new)\n if (len(res_arr) > 0):\n res = res_arr\n # the only thing remaining are scalars\n else:\n scalar_diff = self._compare_scalars(old, new)\n if scalar_diff is not None:\n res = scalar_diff\n\n return res","def find_sharpest_fork(nodes):\n pair_list = []\n Dis = np.array([])\n for n in nodes:\n if n.parent is not None:\n if n.parent.parent is not None:\n a = n.parent.children\n if(isinstance(a, list)):\n if(len(a)==2):\n n1 = a[0]\n n2 = a[1]\n if(len(n1.children) == 0 and len(n2.children) == 0):\n pair_list.append([n1 , n2])\n dis = LA.norm(a[0].xyz - a[1].xyz,2)\n Dis = np.append(Dis,dis)\n if(len(Dis)!= 0):\n (b,) = np.where(Dis == Dis.min())\n sharpest_pair = pair_list[b[0]]\n distance = Dis.min()\n else:\n sharpest_pair = [0,0]\n distance = 0.\n return sharpest_pair, distance","def calculate_distance_to_divide(\n grid, longest_path=True, add_to_grid=False, clobber=False\n):\n # check that flow__receiver nodes exists\n if \"flow__receiver_node\" not in grid.at_node:\n raise FieldError(\n \"A 'flow__receiver_node' field is required at the \"\n \"nodes of the input grid.\"\n )\n\n if \"flow__upstream_node_order\" not in grid.at_node:\n raise FieldError(\n \"A 'flow__upstream_node_order' field is required at the \"\n \"nodes of the input grid.\"\n )\n\n if \"drainage_area\" not in grid.at_node:\n raise FieldError(\n \"A 'flow__upstream_node_order' field is required at the \"\n \"nodes of the input grid.\"\n )\n\n # get the reciever nodes, depending on if this is to-one, or to-multiple,\n # we'll need to get a different at-node field.\n if grid.at_node[\"flow__receiver_node\"].size != grid.size(\"node\"):\n to_one = False\n else:\n to_one = True\n\n flow__receiver_node = grid.at_node[\"flow__receiver_node\"]\n drainage_area = grid.at_node[\"drainage_area\"]\n\n # get the upstream node order\n flow__upstream_node_order = grid.at_node[\"flow__upstream_node_order\"]\n\n # get downstream flow link lengths, result depends on type of grid.\n if isinstance(grid, RasterModelGrid):\n flow_link_lengths = grid.length_of_d8[\n grid.at_node[\"flow__link_to_receiver_node\"]\n ]\n else:\n flow_link_lengths = grid.length_of_link[\n grid.at_node[\"flow__link_to_receiver_node\"]\n ]\n\n # create an array that representes the distance to the divide.\n distance_to_divide = np.zeros(grid.nodes.size)\n\n if not longest_path:\n distance_to_divide[:] = 2 * grid.size(\"node\") * np.max(flow_link_lengths)\n\n # iterate through the flow__upstream_node_order backwards.\n for node in reversed(flow__upstream_node_order):\n # if drainage are is equal to node cell area, set distance to zeros\n # this should handle the drainage divide cells as boundary cells have\n # their area set to zero.\n if drainage_area[node] == grid.cell_area_at_node[node]:\n distance_to_divide[node] = 0\n\n # get flow recievers\n reciever = flow__receiver_node[node]\n\n if to_one:\n # if not processing an outlet node.\n if reciever != node:\n if longest_path:\n cond = (\n distance_to_divide[reciever]\n < distance_to_divide[node] + flow_link_lengths[node]\n )\n else:\n cond = (\n distance_to_divide[reciever]\n > distance_to_divide[node] + flow_link_lengths[node]\n )\n\n if cond:\n distance_to_divide[reciever] = (\n distance_to_divide[node] + flow_link_lengths[node]\n )\n\n else:\n # non-existant links are coded with -1\n useable_receivers = np.where(reciever != grid.BAD_INDEX)[0]\n\n for idx in range(len(useable_receivers)):\n r = reciever[useable_receivers][idx]\n fll = flow_link_lengths[node][useable_receivers][idx]\n\n # if not processing an outlet node.\n if r != node:\n if longest_path:\n cond = distance_to_divide[r] < distance_to_divide[node] + fll\n else:\n cond = distance_to_divide[r] > distance_to_divide[node] + fll\n\n if cond:\n distance_to_divide[r] = distance_to_divide[node] + fll\n\n # store on the grid\n if add_to_grid:\n grid.add_field(\n \"distance_to_divide\", distance_to_divide, at=\"node\", clobber=clobber\n )\n\n return distance_to_divide","def split_translation(t):\n from math import fmod\n\n def _split1(x):\n x_part = fmod(x, 1.0)\n x_whole = x - x_part\n if x_part > 0.5:\n x_part -= 1\n x_whole += 1\n elif x_part < -0.5:\n x_part += 1\n x_whole -= 1\n\n return (x_whole, x_part)\n\n _tt = [_split1(x) for x in t]\n\n return tuple(t[0] for t in _tt), tuple(t[1] for t in _tt)","def dif_prog_div(x, y):\n\n dd = np.zeros([len(y), len(y)]) \n dd[0] = y\n dd[1,:-1] = (dd[0,1:] - dd[0,:-1]) / (x[1:]-x[:-1])\n\n for i in range(2,len(y)):\n dd[i,:-i] = (dd[i-1,1:-(i-1)] - dd[i-1,:-i]) / (x[i:]-x[:-i])\n\n return dd","def alldiff():\n res = {'Computation-alldiff-0': {'Experiment': 'alldiff',\n 'Parameters': {'w': 5, 'x': 1, 'z': 4},\n 'Results': {'f1': 15, 'f2': 51}},\n 'Computation-alldiff-1': {'Experiment': 'alldiff',\n 'Parameters': {'w': 6, 'x': 1, 'z': 4},\n 'Results': {'f1': 16, 'f2': 61}},\n 'Computation-alldiff-2': {'Experiment': 'alldiff',\n 'Parameters': {'w': 5, 'x': 2, 'z': 4},\n 'Results': {'f1': 25, 'f2': 52}},\n 'Computation-alldiff-3': {'Experiment': 'alldiff',\n 'Parameters': {'w': 6, 'x': 2, 'z': 4},\n 'Results': {'f1': 26, 'f2': 62}},\n 'Computation-alldiff-4': {'Experiment': 'alldiff',\n 'Parameters': {'w': 5, 'x': 3, 'z': 4},\n 'Results': {'f1': 35, 'f2': 53}},\n 'Computation-alldiff-5': {'Experiment': 'alldiff',\n 'Parameters': {'w': 6, 'x': 3, 'z': 4},\n 'Results': {'f1': 36, 'f2': 63}}}\n\n # Notice the ordering\n domain = {'x':[\"1\", \"2\", \"3\"], 'w':[\"5\", \"6\"]}\n metadata = {'z':\"4\"}\n parameters = [\"x\", \"w\"]\n parameters.sort()\n metrics = [\"f1\", \"f2\"]\n metrics.sort()\n exp_name = \"alldiff\"\n return exp_name, metadata, parameters, domain, metrics, res","def division(i, S, Gi, NG):\n\n NGX = [dict() for _ in NG]\n\n C = set()\n\n for v in S:\n c = Gi.node[v]['class']\n NG[i][c].remove(v)\n if not NGX[i].has_key(c):\n NGX[i][c] = set()\n NGX[i][c].add(v)\n C.add(c)\n\n # split class table\n for c in C:\n # copy/move entries of NGX[j] table \n for j in xrange(len(NG)):\n if j == i:\n # remove the entry NG[i] (where S is from) if empty\n if not NG[i][c]:\n del NG[i][c]\n else:\n if NG[i].has_key(c) and NG[i][c]:\n NGX[j][c] = set(NG[j][c])\n else:\n NGX[j][c] = NG[j].pop(c)\n return NGX","def difference(*colls):\n\n # Get all the leaf paths for each collection: make each path a tuple\n leaf_paths_by_coll = list(map(lambda c: list(map(tuple, get_all_leaf_paths(c))), colls))\n\n # Find the union of all leaf paths: merge all the paths and keep only the unique paths\n union_leaf_paths = list(distinct(concat(*leaf_paths_by_coll)))\n\n # Get the values corresponding to these leaf paths in every collection: if a leaf path doesn't exist, assumes None\n values_by_coll = list(map(lambda lp: list(map(lambda coll: tz.get_in(lp, coll), colls)), union_leaf_paths))\n\n # Filter out the leaf paths that have identical values across the collections\n keep_leaf_paths = list(map(0, filter(lambda t: not allequal(t[1]), zip(union_leaf_paths, values_by_coll))))\n keep_values = list(map(1, filter(lambda t: not allequal(t[1]), zip(union_leaf_paths, values_by_coll))))\n\n # Rearrange to construct a list of dictionaries -- one per original collection.\n # Each of these dictionaries maps a 'kept' leaf path to its corresponding\n # value in the collection\n differences = list(map(lambda vals: dict(zip(keep_leaf_paths, vals)), list(zip(*keep_values))))\n\n return differences","def compute_divergence(self):\n d_tr_a = []\n d_te_a = []\n for k in self.synth_keys:\n d_tr_a.append(self.divergence('tr', k))\n d_te_a.append(self.divergence('te', k))\n\n training = np.mean(np.array(d_tr_a))\n testing = np.mean(np.array(d_te_a))\n return training, testing","def _collect_num_denom(ast, nums, denoms):\n if not (isinstance(ast, Mul) or isinstance(ast, Div)):\n # If ast is not multiplication or division, just put it in nums.\n nums.append(ast)\n return\n\n if isinstance(ast.left, Div) or isinstance(ast.left, Mul):\n # If the left argument is a multiplication or division, descend into\n # it, otherwise it is in the numerator.\n _collect_num_denom(ast.left, nums, denoms)\n else:\n nums.append(ast.left)\n\n if isinstance(ast.right, Div) or isinstance(ast.right, Mul):\n # If the left argument is a multiplication or division, descend into\n # it, otherwise it is in the denominator.\n if isinstance(ast, Mul):\n _collect_num_denom(ast.right, nums, denoms)\n elif isinstance(ast, Div):\n # Note that when we descend into the denominator of a Div, we want \n # to swap our nums and denoms lists\n _collect_num_denom(ast.right, denoms, nums)\n else:\n if isinstance(ast, Mul):\n nums.append(ast.right)\n elif isinstance(ast, Div):\n denoms.append(ast.right)","def extractTree(nodesList, rootNode):\n if len(nodesList) == 0:\n return\n if nodesList[0] == '!':\n return nodesList[1:]\n\n splitAttribute, splitValue, attributeValue = nodesList[0].strip().split('-')\n nodesList = nodesList[1:]\n \n if splitAttribute != splitValue or splitAttribute != '$' or splitValue != '$':\n rootNode.setSplit((splitAttribute, splitValue))\n else:\n rootNode.setSplit(\"Base case\")\n rootNode.setData(attributeValue)\n return nodesList[2:]\n \n \n leftTree = Tree()\n rightTree = Tree()\n rootNode.setLesser(leftTree)\n rootNode.setGreater(rightTree)\n nodesList = extractTree(nodesList, leftTree)\n\n \n \n nodesList = extractTree(nodesList, rightTree)\n\n return nodesList","def _get_packet_intervals(\n packets: Sequence[Packet],\n node: int,\n getter: Callable[[Packet, int], float]\n) -> np.ndarray:\n prev_time = 0.0\n intervals = []\n for packet in packets:\n if packet.was_served[node]:\n new_time = getter(packet, node)\n intervals.append(new_time - prev_time)\n prev_time = new_time\n return np.asarray(intervals)","def find_diff_nodes(S,T):\n diff_nodes = []\n for node in S:\n s_formula = S.formula_conj(node)\n t_formula = T.formula_conj(node)\n if s_formula != t_formula:\n diff_nodes.append(node)\n return diff_nodes","def diff(self,images):\n diffArray = [0,1,2,3]\n\n # compute the difference bewteen two adjacent images in the same ovtave\n for i in range(1,5):\n diffArray[i-1] = images[i]-images[i-1]\n\n return numpy.array(diffArray)","def divide_list(ld, division):\n buckets = []\n current = []\n for obj in ld:\n if len(current) < division:\n current.append(obj)\n else:\n buckets.append(current)\n current = [obj]\n if len(current) > 0:\n buckets.append(current)\n return buckets","def cell_division(waitlist, celllist, AgEpitope, tnow, mut_list, RNs):\n for cell in celllist:\n # get list of 0 to 2 daughters\n dlist, mut_list = divide(cell, AgEpitope, tnow, mut_list, RNs)\n # add daughters to waitlist\n waitlist = waitlist + dlist\n return waitlist, mut_list","def vars_divide ( self , var1 , var2 , name = '' , title = '' ) :\n \n f1 = isinstance ( var1 , num_types )\n f2 = isinstance ( var2 , num_types )\n\n if f1 and f2 :\n res = float ( var1 ) / float ( var2 )\n return ROOT.RooRealConstant.value ( res ) \n elif f1 :\n var1 = ROOT.RooRealConstant.value ( var1 ) \n return self.vars_divide ( var1 , var2 , name , title )\n elif f2 :\n return self.vars_multiply ( var1 , 1.0/var2 , name , title )\n \n self.aux_keep.append ( var1 )\n self.aux_keep.append ( var2 )\n\n result = Ostap.MoreRooFit.Division ( var1 , var2 )\n self.aux_keep.append ( result )\n \n return result","def divergence(tensor, dx=1, difference='central'):\n assert difference in ('central', 'forward', 'backward'), difference\n rank = spatial_rank(tensor)\n if difference == 'forward':\n return _divergence_nd(tensor, (0, 1)) / dx ** rank\n elif difference == 'backward':\n return _divergence_nd(tensor, (-1, 0)) / dx ** rank\n else:\n return _divergence_nd(tensor, (-1, 1)) / (2 * dx) ** rank","def difference(G, H):\n\n if G.order() != H.order():\n msg = \"Node sets of the two directed graphs are not equal!\"\n raise StaticGraphNotEqNodesException(msg)\n \n n_nodes = G.order()\n edges = ((u, v) for u in G.nodes()\n for v in set(G.successors(u)) - set(H.successors(u)))\n deg = make_deg(n_nodes, edges)\n edges = ((u, v) for u in G.nodes()\n for v in set(G.successors(u)) - set(H.successors(u)))\n D = make(n_nodes, G.size(), edges, deg)\n return D","def test_interpolation(self):\n\n ndx1, ndx2 = self.find_partition()\n tessellation = Delaunay(self.grid[ndx2,:])\n\n # initialisation\n results = []\n ndim = self.ndim+1\n\n for j in ndx1:\n nmodels = len(self.tracks[j].models)\n aResult = np.empty((nmodels,ndim+nglb+6),dtype=gtype)\n pt = self.tracks[j].params + [0.0,]\n\n for i in range(nmodels):\n aModel1 = self.tracks[j].models[i]\n pt[-1] = aModel1.glb[iage]\n aModel2 = interpolate_model(self,pt,tessellation,ndx2)\n aResult[i,0:ndim] = pt\n if (aModel2 is None):\n aResult[i,ndim:ndim+nglb+6] = np.nan\n else:\n aResult[i,ndim:ndim+nglb+6] = compare_models(aModel1,aModel2)\n\n results.append(aResult)\n\n return results, ndx1, ndx2, tessellation","def diffSeries(requestContext, *seriesLists):\n (seriesList,start,end,step) = normalize(seriesLists)\n name = \"diffSeries(%s)\" % formatPathExpressions(seriesList)\n values = ( safeDiff(row) for row in izip(*seriesList) )\n series = TimeSeries(name,start,end,step,values)\n series.pathExpression = name\n return [series]","def DataDiff(source, target, compare_list_as_value=True, depth=0, no_difference_value=None):\n # Ensure recursion doesnt go out of control\n if depth > 150:\n raise Exception('DataDiff recurlsion depth has hit limit (50), aborting.')\n\n # If we are not working with 2 different containers we can inspect, then do a simple check\n if type(source) not in (list, tuple, dict) or type(target) not in (list, tuple, dict):\n # If the types are different, the data is different (and cant be compared more)\n if type(source) != type(target):\n return (source, target)\n # Else, theyre the same types, if the values are different\n elif source != target:\n return (source, target)\n # Else, theyre the same types and value\n else:\n # This should only happen if this is a fresh DataDiff() call, depth==0\n if depth == 0:\n return (no_difference_value, no_difference_value)\n else:\n raise Exception('This should never happen, having a mismatching value different in anywhere but depth=0')\n\n\n if type(source) in (list, tuple):\n source_diff = []\n elif type(source) == dict:\n source_diff = {}\n else:\n raise Exception('Unhandled source_diff data type: %s' % type(source))\n\n if type(target) in (list, tuple):\n target_diff = []\n elif type(target) == dict:\n target_diff = {}\n else:\n raise Exception('Unhandled target_diff data type: %s' % type(target))\n\n # Check for incompatible types, and just return them both as theyre totally different\n if type(source_diff) != type(target_diff):\n return (source, target)\n\n # If we're handling a Dictionary compare\n if type(source_diff) == dict:\n # Process the source keys first\n for key in source.keys():\n _CompareDictValue(key, source, target, source_diff, target_diff, compare_list_as_value, no_difference_value, depth)\n\n # Process the target keys next, skipping any source keys we already processed\n for key in target.keys():\n # Skip any keys we already processed in source\n if key in source:\n continue\n\n # Reverse target/source, so that the reverse comparison/set is done\n _CompareDictValue(key, target, source, target_diff, source_diff, compare_list_as_value, no_difference_value, depth)\n\n # Else, if we're handling a List compare\n elif type(source_diff) == list:\n # If lists must be compared in total because the order of a list is important\n if compare_list_as_value:\n if source != target:\n return (list(source), list(target))\n\n # Else, compare each element of the list\n else:\n for count in range(0, len(source)):\n if count >= len(target):\n source_diff.append(source[count])\n elif source[count] != target[count]:\n source_diff.append(source[count])\n target_diff.append(target[count])\n\n # If the target has more elements than the source, add the rest \n if len(target) > len(source):\n target_diff += target[-(len(source) - len(target)):]\n\n else:\n raise Exception('Unspecified type handler for data: %s. Only dict and list/tuple types are accepted.')\n\n return (source_diff, target_diff)","def group_and_vote_fractions():\n group_share = np.array([0, 0.2, 0.4, 0.6, 0.8])\n vote_share = np.array([0, 0.2, 0.4, 0.6, 0.8])\n return group_share, vote_share","def get_deltas(temps, vals, ts_index):\n nan_array = np.full([len(temps)], np.nan)\n vals_react = nan_array\n vals_ts = nan_array\n vals_prod = nan_array\n # any files before the ts will be a reactant file\n for index in range(len(vals)):\n if index == 0:\n vals_react = vals[index]\n elif index < ts_index:\n vals_react += vals[index]\n elif index == ts_index:\n vals_ts = vals[index]\n elif index == ts_index + 1:\n vals_prod = vals[index]\n else:\n vals_prod += vals[index]\n delta_ts = vals_ts - vals_react\n delta_rxn = vals_prod - vals_react\n return delta_ts, delta_rxn","def compute(self, node, input_vals):\r\n assert len(input_vals) == 2\r\n return input_vals[0] - input_vals[1]","def diff_op(self, args: List[float], time: float) -> List[float]:\n v, h_nav, n_kvhh, h_kva, m_kvsi, s_ampar, x_nmdar, s_nmdar, s_gabar, ca = args\n ca_args: List[float] = [v, s_nmdar, ca]\n\n if self.ion:\n self.set_vCa(in_ca=ca)\n\n dvdt: float = self.dvdt(args=args)\n dhNadt: float = self.nav.dhdt(v=v, h=h_nav)\n dnKdt: float = self.kvhh.dndt(v=v, n=n_kvhh)\n dhAdt: float = self.kva.dhdt(v=v, h=h_kva)\n dmKSdt: float = self.kvsi.dmdt(v=v, m=m_kvsi)\n dsAMPAdt: float = self.ampar.dsdt(v=v, s=s_ampar)\n dxNMDAdt: float = self.nmdar.dxdt(v=v, x=x_nmdar)\n dsNMDAdt: float = self.nmdar.dsdt(v=v, s=s_nmdar, x=x_nmdar)\n dsGABAdt: float = self.gabar.dsdt(v=v, s=s_gabar)\n dCadt: float = self.dCadt(args=ca_args)\n return [dvdt, \n dhNadt, \n dnKdt, \n dhAdt, \n dmKSdt,\n dsAMPAdt, \n dxNMDAdt, \n dsNMDAdt, \n dsGABAdt, \n dCadt,\n ]","def ratio_calculator(numerator, denominator):\n ratios = []\n for i in numerator:\n for j in denominator:\n if i[0] == j[0] and j[1] != 0:\n ratios.append([i[0], round(float(i[1]) / j[1], 3)])\n break\n elif i[0] == j[0]:\n ratios.append([i[0], 0])\n return ratios","def differences(input_list):\n output_list = []\n for x in range(1,len(input_list)):\n output_list.append(input_list[x]-input_list[x-1])\n return output_list","def new_decomposition(self):\n from sage.combinat.binary_tree import BinaryTree\n t_low = self.lower_binary_tree().to_tilting()\n t_up = self.upper_binary_tree().to_tilting()\n common = [p for p in t_low if p in t_up]\n\n def extract_tree(x, y, tilt, common):\n \"\"\"\n Extract a tree with root at position xy (recursive).\n \"\"\"\n left_tree = None\n for k in range(y - 1, x, -1):\n if (x, k) in tilt:\n if (x, k) not in common:\n left_tree = extract_tree(x, k, tilt, common)\n break\n right_tree = None\n for k in range(x + 1, y):\n if (k, y) in tilt:\n if (k, y) not in common:\n right_tree = extract_tree(k, y, tilt, common)\n break\n return BinaryTree([left_tree, right_tree])\n\n TIP = self.parent()\n return [TIP.from_binary_trees(extract_tree(cx, cy, t_low, common),\n extract_tree(cx, cy, t_up, common))\n for cx, cy in common]","def _items_divide(self, numerator_data, denominator_data):\n items = {}\n if numerator_data['items'] is None:\n items = None\n else:\n for n in numerator_data['items']:\n # TODO what should we do when a matching item isn't found?\n matching_d = next((item for item in denominator_data['items'] if\n item['group'] == n['group']),\n {'group': '_unknown', 'value': None})\n if matching_d['value'] is None or n['value'] is None:\n divided = None\n else:\n divided = n['value'] / matching_d['value']\n\n # item = dict({'group': n['group'],\n # 'value': divided})\n items[n['group']] = divided\n\n return {'items': items, 'grouping': numerator_data['grouping'],\n 'data_id': numerator_data['data_id']}","def splitNodes(matching):\n outer = set(range(self.n))\n inner = set([])\n for (u, v) in matching:\n if u in outer:\n outer.remove(u)\n if v in outer:\n outer.remove(v)\n inner.add(u)\n inner.add(v)\n return list(inner), list(outer)","def group_diff(options, db):\n nested_rvals = []\n for ip in options.gmp:\n nested_rvals.append(get_ip_parents(ip, db))\n # get just the list of groups, stripping out the networks.\n group1 = [x[0] for x in nested_rvals[0]]\n group2 = [x[0] for x in nested_rvals[1]]\n common = sorted(list(set(group1) & set(group2)))\n diff1 = sorted(list(set(group1) - set(group2)))\n diff2 = sorted(list(set(group2) - set(group1)))\n return common, diff1, diff2","def get_ratios(L1, L2):\r\n ratios = []\r\n for index in range(len(L1)):\r\n try:\r\n ratios.append(L1[index]/float(L2[index]))\r\n except ZeroDivisionError:\r\n ratios.append(float('NaN')) #NaN = not a number\r\n except:\r\n raise ValueError('get_ratios called with bad arg')\r\n return ratios","def diff_eq(self,x,t,par):\n \n # setting the functions\n s = x[0]\n i = x[1]\n r = x[2]\n d = x[3]\n \n # mathematical equations\n DiffS = -par[1]*(s/10**par[4])*(i**par[0])\n DiffI = (par[1]*(s/10**par[4]) - par[2] - par[3])*(i**par[0])\n DiffR = par[2]*(i**par[0])\n DiffD = par[3]*(i**par[0])\n \n return [DiffS,DiffI,DiffR,DiffD]","def sub_division(width: float, minimum_division: float, stretch_factor: float) -> list:\n\n sum_x = 0\n next_ = minimum_division\n new_grid = []\n max_dx = 20/100\n x = width/2\n\n while sum_x < x:\n remaining = x - sum_x\n\n if next_ > max_dx:\n n = np.ceil(remaining/max_dx)\n\n if n == 0:\n new_grid.append(remaining)\n\n next_ = remaining/n\n\n for _ in range(0, int(n)):\n new_grid.append(next_)\n sum_x += next_\n\n remaining = x - sum_x\n\n if next_ < remaining:\n new_grid.append(next_)\n sum_x += next_\n else:\n remaining += new_grid[-1]\n new_grid[-1] = remaining/2\n new_grid.append(remaining/2)\n sum_x = x\n\n next_ = next_ * stretch_factor\n\n x1 = new_grid[::-1]\n x2 = new_grid+x1\n\n return x2","def convert_elemwise_div(node, **kwargs):\n return create_basic_op_node('Div', node, kwargs)","def multi_dec(f):\n\n @wraps(f)\n def wrapper(*args, **kwargs):\n args = args[0] if len(args) == 1 and isinstance(args[0], (list, tuple)) else args\n for arg in args:\n if isinstance(arg, Node) and arg.parent.name is 'root':\n arg.parent.remove_child(arg)\n arg.update_child_calls()\n return f(*args, **kwargs)\n return wrapper","def _floatdiff(x, y):\n rankx = _rank(x)\n ranky = _rank(y)\n return _diff(rankx, ranky)","def test_dividing(self):\n divider = Divider()\n\n for i in range(-10, 10):\n for j in range(-10, 10):\n if j != 0:\n self.assertEqual(i/j, divider.calc(j, i))","def split_validation_data(*data, divmod=0.5):\n n = round((len(data[0]) - 1) * divmod)\n return [(d[n:], d[:n]) for d in data]","def eval_numerical_gradient_blobs(f, inputs, output, h=1e-5):\n numeric_diffs = []\n for input_blob in inputs:\n diff = np.zeros_like(input_blob.diffs)\n it = np.nditer(input_blob.vals, flags=['multi_index'],\n op_flags=['readwrite'])\n while not it.finished:\n idx = it.multi_index\n orig = input_blob.vals[idx]\n\n input_blob.vals[idx] = orig + h\n f(*(inputs + (output,)))\n pos = np.copy(output.vals)\n input_blob.vals[idx] = orig - h\n f(*(inputs + (output,)))\n neg = np.copy(output.vals)\n input_blob.vals[idx] = orig\n\n diff[idx] = np.sum((pos - neg) * output.diffs) / (2.0 * h)\n\n it.iternext()\n numeric_diffs.append(diff)\n return numeric_diffs","def _get_nums_from_fractions(self, nums):\n\t\tnums_fraction = []\n\n\t\tfor num in nums:\n\t\t\tif re.search(\"\\d*\\(\\d+/\\d+\\)\\d*\", num):\n\t\t\t\tnums_fraction.append(num)\n\t\tnums_fraction = sorted(nums_fraction, key=lambda x: len(x), reverse=True)\n\n\t\tfloat_nums = []\n\t\tfor num in nums:\n\t\t\tif ',' in num:\n\t\t\t\t# [TODO] It can be points with comma like (0, 1)...\n\t\t\t\tnew_num = []\n\t\t\t\tfor c in num:\n\t\t\t\t\tif c == ',':\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tnew_num.append(c)\n\t\t\t\tnum = ''.join(new_num)\n\t\t\t\tfloat_num = num\n\t\t\telse:\n\t\t\t\tfloat_num = num\n\n\t\t\ttry:\n\t\t\t\tfloat_nums.append(str(float(eval(float_num))))\n\t\t\texcept:\n\t\t\t\tif float_num.startswith('0'):\n\t\t\t\t\twhile float_num.startswith('0'):\n\t\t\t\t\t\tfloat_num = float_num[1:]\n\t\t\t\t\tfloat_nums.append(str(float(eval(float_num))))\n\n\t\tfloat_nums_fraction = []\n\t\tfor num in nums_fraction:\n\t\t\tif ',' in num:\n\t\t\t\tnew_num = []\n\t\t\t\tfor c in num:\n\t\t\t\t\tif c == ',':\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tnew_num.append(c)\n\t\t\t\tnum = ''.join(new_num)\n\t\t\t\tfloat_nums_fraction.append(str(float(eval(num))))\n\t\t\telse:\n\t\t\t\tfloat_nums_fraction.append(str(float(eval(num))))\n\n\t\treturn float_nums, float_nums_fraction","def decompose_paths(self):\n if self.child_nodes == {}:\n return []\n\n import numpy as np\n\n def decompose_paths_rec(node_inner, path):\n \"\"\"\n This function does the recursive create_path of the decomposition\n :param node_inner:\n :param path:\n \"\"\"\n if node_inner.is_leaf():\n path = np.append(path, str(node_inner.value))\n return path[None]\n else:\n paths = np.array([])\n for edge_name in node_inner.child_nodes:\n new_path = np.append(path, str(edge_name))\n paths = np.append(paths, decompose_paths_rec(node_inner.child_nodes[edge_name], new_path))\n return paths\n\n decomposition = decompose_paths_rec(self, np.array([]))\n return decomposition.reshape((decomposition.shape[0]/(self.d+1), self.d+1))","def compute_relations(nodes: List[Node]) -> None:\n # Calculate parents\n for node in nodes:\n node.parents = []\n for node in nodes:\n for child in node.children():\n child.parents.append(node)\n\n def compute_dominators(\n entry: Node,\n parents: Callable[[Node], List[Node]],\n dominators: Callable[[Node], Set[Node]],\n immediately_dominates: Callable[[Node], List[Node]],\n set_immediate_dominator: Callable[[Node, Optional[Node]], None],\n ) -> None:\n # See https://en.wikipedia.org/wiki/Dominator_(graph_theory)#Algorithms\n # Note: if `n` is unreachable from `entry`, then *every* node will\n # vacuously belong to `n`'s dominator set.\n for n in nodes:\n dominators(n).clear()\n if n == entry:\n dominators(n).add(n)\n else:\n dominators(n).update(nodes)\n\n changes = True\n while changes:\n changes = False\n for node in nodes:\n if node == entry:\n continue\n nset = dominators(node)\n for parent in parents(node):\n nset = nset.intersection(dominators(parent))\n nset.add(node)\n if len(nset) < len(dominators(node)):\n assert nset.issubset(dominators(node))\n dominators(node).intersection_update(nset)\n changes = True\n\n # Compute immediate dominator, and the inverse relation\n for node in nodes:\n immediately_dominates(node).clear()\n for node in nodes:\n doms = dominators(node).difference({node})\n # If `node == entry` or the flow graph is not reducible, `doms` may be empty.\n # TODO: Infinite loops could be made reducible by introducing\n # branches like `if (false) { return; }` without breaking semantics\n if doms:\n # There should be a unique max `len(dominators(d))` if the flowgraph\n # is reducible. Fall back to largest index for irreducible graphs.\n imdom = max(doms, key=lambda d: (len(dominators(d)), d.block.index))\n immediately_dominates(imdom).append(node)\n set_immediate_dominator(node, imdom)\n else:\n set_immediate_dominator(node, None)\n for node in nodes:\n immediately_dominates(node).sort(key=lambda x: x.block.index)\n\n def _set_immediate_dominator(node: Node, imdom: Optional[Node]) -> None:\n node.immediate_dominator = imdom\n\n def _set_immediate_postdominator(node: Node, impdom: Optional[Node]) -> None:\n node.immediate_postdominator = impdom\n\n entry = nodes[0]\n terminal = nodes[-1]\n assert isinstance(terminal, TerminalNode)\n\n # Compute dominators & immediate dominators\n compute_dominators(\n entry=entry,\n parents=lambda n: n.parents,\n dominators=lambda n: n.dominators,\n immediately_dominates=lambda n: n.immediately_dominates,\n set_immediate_dominator=_set_immediate_dominator,\n )\n\n # Compute postdominators & immediate postdominators\n # This uses the same algorithm as above, but with edges reversed\n compute_dominators(\n entry=terminal,\n parents=lambda n: n.children(),\n dominators=lambda n: n.postdominators,\n immediately_dominates=lambda n: n.immediately_postdominates,\n set_immediate_dominator=_set_immediate_postdominator,\n )\n\n # Iterate over all edges n -> c and check for backedges, which define natural loops\n for node in nodes:\n for child in node.children():\n if child not in node.dominators:\n continue\n # Found a backedge node -> child where child dominates node; child is the \"head\" of the loop\n if child.loop is None:\n child.loop = NaturalLoop(child)\n child.loop.nodes |= {child, node}\n child.loop.backedges.add(node)\n for parent in nodes:\n if reachable_without(parent, node, child):\n child.loop.nodes.add(parent)","def compare_trees(first_soup: HTMLStrip, second_soup: HTMLStrip) -> float:\n first_tree = Tree.from_soup_object(first_soup.file_name, first_soup.original_soup)\n second_tree = Tree.from_soup_object(second_soup.file_name, second_soup.original_soup)\n\n common_paths_size: int = first_tree.num_of_common_paths(second_tree)\n target_size: int = second_tree.total_num_of_paths()\n similarity = float(common_paths_size)*100/target_size\n print(f'{similarity:.2f}')\n return similarity","def _group_nodes(cls, nodes, node_info_holder):\n group_nodes = [[]]\n kb_nodes = []\n for n in nodes:\n matched_scope = []\n if n.name in node_info_holder.nodes_keep:\n kb_nodes.append(n)\n continue\n for scope in node_info_holder.scopes:\n if scope in n.name and n.name[n.name.find(scope) + len(scope)] == \"/\":\n matched_scope.append(scope)\n if len(matched_scope) > 1:\n raise ValueError(\n \"More than one scope {} contained in node name {}.\".format(\n str(matched_scope), n.name))\n if matched_scope:\n curr_scope = matched_scope[0]\n if group_nodes[-1]:\n group_nodes.append(curr_scope)\n group_nodes.append([])\n node_info_holder.nodes[curr_scope].append(n)\n else:\n group_nodes[-1].append(n)\n return group_nodes, kb_nodes","def arithmeticDivision2(numbers,str_result,target):\n success = False\n str_temp=\"\"\n for x in numbers:\n if x%target==0:\n target2=x//target\n numbers.remove(x)\n numbers,str_temp,success=arithmeticBasic(numbers,str_temp,target2)\n if success:\n str_result+=str(x)+'/('+str_temp+')'\n numbers.clear()\n numbers.append(target)\n break\n else:\n numbers.insert(0,x)\n return numbers,str_result,success","def find_step(tree: Tree, template: Tree) -> list:\n if template == \"@\":\n if type(tree) == str:\n return [tree]\n return tree.leaves()\n\n if template == '*':\n return []\n\n if type(template) != str and template.label() == '*':\n res_star = []\n for temp_node in template:\n res_star.extend(find_star_step(tree, temp_node))\n return res_star\n\n if type(tree) == str or type(template) == str:\n if tree == template:\n return []\n return []\n\n if tree.label() != template.label():\n return []\n else:\n\n res = []\n for t_node in template:\n for node in get_node_by_label(tree, t_node):\n res.extend(find_step(node, t_node))\n return res","def testListDivide():\n listDivide([1, 2, 3, 4, 5])\n listDivide([2, 4, 6, 8, 10])\n listDivide([30, 54, 63, 98, 100], divide=10)\n listDivide([])\n listDivide([1, 2, 3, 4, 5], 1)","def diff(*args):\n return reduce(lambda x, y: x - y, args)","def diff(self, da, axis):\n\n def interp_function(data_left, data_right, shift):\n # linear, centered interpolation\n # TODO: generalize to higher order interpolation\n return shift*(data_right - data_left)\n return self._neighbor_binary_func(da, axis, interp_function)","def test_scalar_division(self):\n\n a1 = tuples.Tuple([\"a\", \"b\", \"c\", \"d\"], 1, -2, 3, -4)\n\n a2 = a1 / 2\n\n self.assertEqual(a2,\n tuples.Tuple([\"a\", \"b\", \"c\", \"d\"], 0.5, -1, 1.5, -2))","def get_resulting_diffs():\n diff_dirpath = application.join_abs_path(\n EMPTY_TEST_DIR, application.OUTPUT_DIR_NAME)\n diffleft_filename = application.join_abs_path(\n diff_dirpath, application.OUTPUT_DIFF_LEFT_FILENAME)\n diffright_filename = application.join_abs_path(\n diff_dirpath, application.OUTPUT_DIFF_RIGHT_FILENAME)\n\n diff_left = read_gzip_file_lines_into_set(diffleft_filename)\n diff_right = read_gzip_file_lines_into_set(diffright_filename)\n\n return diff_left, diff_right","def _divide(triangle, points):\n a, b, c = triangle\n\n # Insert points midway along each side of the triangle.\n # Form 3 new triangles.\n new_vertices = [\n _midpoint(points[a], points[b]),\n _midpoint(points[b], points[c]),\n _midpoint(points[c], points[a]),\n ]\n\n new_vertices_indices = [\n len(points),\n len(points) + 1,\n len(points) + 2,\n ]\n\n new_triangles = [\n (a, new_vertices_indices[0], new_vertices_indices[2]),\n (b, new_vertices_indices[1], new_vertices_indices[0]),\n (c, new_vertices_indices[2], new_vertices_indices[1]),\n ]\n\n return new_vertices, new_triangles","def testListDivide():\n #a\n numbers = [1,2,3,4,5]\n expected = 2\n \n try:\n assert listDivide(numbers) == expected\n except AssertionError:\n raise ListDivideException(\"Test Failed\")\n \n \n #b\n numbers = [2,4,6,8,10]\n expected = 5\n \n try:\n assert listDivide(numbers) == expected\n except AssertionError:\n raise ListDivideException(\"Test Failed\") \n \n #c\n numbers = [30, 54, 63, 98, 100]\n divide = 10\n expected = 2\n \n try:\n assert listDivide(numbers, divide) == expected\n except AssertionError:\n raise ListDivideException(\"Test Failed\") \n \n #d\n numbers = []\n expected = 0\n \n try:\n assert listDivide(numbers) == expected\n except AssertionError:\n raise ListDivideException(\"Test Failed\") \n \n #e\n numbers = [1, 2, 3, 4, 5]\n divide = 1\n expected = 5\n \n try:\n assert listDivide(numbers, divide) == expected\n except AssertionError:\n raise ListDivideException(\"Test Failed\")","def compute_tree_changes(dmt_new, dmt_old, directory_path=''):\n updated, new, deleted = set(), set(), set()\n # Base cases:\n # Both files or empty directories\n if (not dmt_new.children) and (not dmt_old.children):\n return updated, new, deleted\n # New directory\n elif not dmt_old.children:\n mutual_filesystem_items = set()\n new_filesystem_items = set(dmt_new.children.keys())\n deleted_filesystem_items = set()\n elif not dmt_new.children:\n mutual_filesystem_items = set()\n new_filesystem_items = set()\n deleted_filesystem_items = set(dmt_old.children.keys())\n else:\n mutual_filesystem_items = set(dmt_new.children.keys()).intersection(set(dmt_old.children.keys()))\n new_filesystem_items = set(dmt_new.children.keys()).difference(set(dmt_old.children.keys()))\n deleted_filesystem_items = set(dmt_old.children.keys()).difference(set(dmt_new.children.keys()))\n \n \n # Compile the set of updated files and directories, as well as any other changes within subdirectories.\n for filesystem_item in mutual_filesystem_items:\n # Always check subdirectories for e.g file renamings.\n if filesystem_item[-1] == '/':\n subdir_name = filesystem_item\n subdir_path = directory_path + subdir_name\n subdir_updated, subdir_new, subdir_deleted = \\\n compute_tree_changes(dmt_new.children[subdir_name], dmt_old.children[subdir_name], subdir_path)\n \n # Mark the subdirectory if necessary.\n if (dmt_old.children[subdir_name].dmt_hash != dmt_new.children[subdir_name].dmt_hash) or \\\n subdir_updated or subdir_new or subdir_deleted:\n updated.add(subdir_path)\n \n # Incorporate differences from within.\n updated.update(subdir_updated)\n new.update(subdir_new)\n deleted.update(subdir_deleted)\n \n # File with differing hash values.\n elif dmt_old.children[filesystem_item].dmt_hash != dmt_new.children[filesystem_item].dmt_hash:\n filename = filesystem_item\n file_path = directory_path + filename\n updated.add(file_path)\n \n # Compile the set of newly created files.\n for filesystem_item in new_filesystem_items:\n item_path = directory_path + filesystem_item\n new.add(item_path)\n new.update(get_all_paths(dmt_new.children[filesystem_item], item_path))\n \n # Compile the set of deleted files.\n for filesystem_item in deleted_filesystem_items:\n item_path = directory_path + filesystem_item\n deleted.add(item_path)\n deleted.update(get_all_paths(dmt_old.children[filesystem_item], item_path))\n \n return updated, new, deleted","def diff_eq(self,x,t,par):\n # setting the functions\n s,i,r,d = x\n a,b,r,d,logS0 = par\n\n S0 = 10**logS0\n \n # mathematical equations\n DiffS = - par[1]*(s/S0)*(i**a)\n DiffI = par[1]*(s/S0)*(i**a) -(r+d)*i\n DiffR = r*i\n DiffD = d*i\n \n return [DiffS,DiffI,DiffR,DiffD]","def get_energy_of_split(self, elem_l, elem_r):\n\n # logging.debug(f\"computing energy of split: {ch_l, ch_r}\")\n\n # elem_l = self.clusters[ch_l]\n # elem_r = self.clusters[ch_r]\n\n # To follow the convention on Ginkgo, to get the correct result, we set t==0 if we have a leaf, i.e. t<t_cut\n l_node_invM = 0\n r_node_invM =0\n l_node_invM = self.momentum[elem_l][0] ** 2 - np.linalg.norm(self.momentum[elem_l][1::]) ** 2\n r_node_invM = self.momentum[elem_r][0] ** 2 - np.linalg.norm(self.momentum[elem_r][1::]) ** 2\n\n logging.debug(f\" t_l ={l_node_invM}\")\n logging.debug(f\" t_R ={r_node_invM}\")\n\n logging.debug(f\" p_l ={self.momentum[elem_l]}\")\n logging.debug(f\" p_r ={self.momentum[elem_r]}\")\n\n split_llh = likelihood.split_logLH_with_stop_nonstop_prob(self.momentum[elem_l],\n self.momentum[elem_r],\n self.min_invM,\n self.Lambda)\n\n\n # logging.debug(f\"split_llh = {split_llh}\")\n\n # llh = split_llh + a_node.map_tree_energy + b_node.map_tree_energy\n logging.debug(f\"split likelihood ={split_llh}\")\n\n return split_llh","def testListDivide():\n assert listDivide([1,2,3,4,5]) == 2\n assert listDivide([2,4,6,8,10]) == 5\n assert listDivide([30, 54, 63,98, 100], divide = 10) == 2\n assert listDivide([]) == 0\n assert listDivide([1,2,3,4,5], 1) == 5","def div(a, x):\n return [a[i]/x for i in range(2)]","def _divide_pred(pred):\n if type(pred) == list:\n fake = []\n real = []\n for p in pred:\n fake.append([tensor[:tensor.size(0) // 2] for tensor in p])\n real.append([tensor[tensor.size(0) // 2:] for tensor in p])\n else:\n fake = pred[:pred.size(0) // 2]\n real = pred[pred.size(0) // 2:]\n\n return fake, real","def diff_eq(self,x,t,par):\n # setting the functions\n s,i,r,d = x\n b,r,d,logS0 = par\n\n S0 = 10**logS0\n \n # mathematical equations\n DiffS = 0\n DiffI = par[1]*(s/S0)*i\n DiffR = r*i\n DiffD = d*i\n \n return [DiffS,DiffI,DiffR,DiffD]","def diffs(self):\n return np.array([self.diff(i) for i in self.idx])","def compute_relative_changes(a_list):\n result = []\n\n for i in range(1,len(a_list)):\n rel = (a_list[i] - a_list[i-1])/a_list[i-1]\n result.append(rel)\n\n return result","def step(nodes, outputs, edges):\n flowed = []\n for node_name in nodes.copy():\n if node_name in flowed:\n continue\n if len(nodes[node_name]) == 2:\n if node_name in flowed:\n continue\n node = [int(value) for value in nodes[node_name]]\n low_value, high_value = min(node), max(node)\n low_flow, high_flow = edges[node_name] \n low_dictionary, low_node_name = low_flow\n high_dictionary, high_node_name = high_flow\n low_node = low_dictionary.get(low_node_name, tuple())\n high_node = high_dictionary.get(high_node_name, tuple())\n low_dictionary[low_node_name] = low_node + (str(low_value),)\n high_dictionary[high_node_name] = high_node + (str(high_value),)\n nodes[node_name] = tuple()\n if low_dictionary is nodes:\n flowed.append(low_node_name)\n if high_dictionary is nodes:\n flowed.append(high_node_name)\n return nodes, outputs, edges","def node_diff(self):\n if self.input1 is None or self.input2 is None:\n raise Exception(\"Missing input: please run the populate() method first\")\n if self.node_dict1 is None or self.node_dict2 is None:\n self.make_node_dict()\n # Initialize dictonaries to keep track of the nodes in respnse 1 and response 2\n g1={}\n g2={}\n # Set to keep track of the union of all curie ids\n curie_set = set()\n for curie in self.node_dict1.keys():\n g1[curie] = {}\n # intersection is only in the g1 dictionary\n g1[curie]['intersection'] = set()\n # node section keeps track of node ids associated with each node i.e. \"n0\"\n g1[curie]['node'] = set()\n curie_set.add(curie)\n for curie in self.node_dict2.keys():\n g2[curie] = {}\n # node section keeps track of node ids associated with each node i.e. \"n0\"\n g2[curie]['node'] = set()\n curie_set.add(curie)\n node_names1 = []\n node_names2 = []\n\n # extract all node ids (i.e. \"n0\",\"n1\",ect...)\n if len(self.input1['question_graph']['nodes'])>0:\n if 'id' in self.input1['question_graph']['nodes'][0]:\n node_names1 = [x['id'] for x in self.input1['question_graph']['nodes']]\n elif 'node_id' in self.input1['question_graph']['nodes'][0]:\n node_names1 = [x['node_id'] for x in self.input1['question_graph']['nodes']]\n if len(self.input2['question_graph']['nodes'])>0:\n if 'id' in self.input2['question_graph']['nodes'][0]:\n node_names2 = [x['id'] for x in self.input2['question_graph']['nodes']]\n elif 'node_id' in self.input2['question_graph']['nodes'][0]:\n node_names2 = [x['node_id'] for x in self.input2['question_graph']['nodes']]\n \n # initialize the result dictonary\n diff_dict = {}\n diff_dict[\"-1|-1\"] = {'intersection':[],'g1-g2':[],'g2-g1':[]}\n # initialize node id tuple keys\n for id1 in node_names1:\n for id2 in node_names2:\n diff_dict[id1+\"|\"+id2] = {'intersection':[],'g1-g2':[],'g2-g1':[]}\n # iterate through answers\n for answer1 in self.input1['answers']:\n for answer2 in self.input2['answers']:\n for id1 in answer1['node_bindings'].keys():\n # This is to handle cases where answer node id has a list or a string\n if isinstance(answer1['node_bindings'][id1], str):\n bindings1 = [answer1['node_bindings'][id1]]\n elif isinstance(answer1['node_bindings'][id1], list):\n bindings1 = answer1['node_bindings'][id1]\n for curie1 in bindings1:\n # store node id\n g1[curie1]['node'].add(id1)\n for id2 in answer2['node_bindings'].keys():\n # This is to handle cases where answer node id has a list or a string\n if isinstance(answer2['node_bindings'][id2], str):\n bindings2 = [answer2['node_bindings'][id2]]\n elif isinstance(answer2['node_bindings'][id2], list):\n bindings2 = answer2['node_bindings'][id2]\n for curie2 in bindings2:\n # store node id\n g2[curie2]['node'].add(id2)\n if curie1 == curie2:\n # stor intersection tuple\n g1[curie1]['intersection'].add(id1+\"|\"+id2)\n # iterate through all curies\n for curie in curie_set:\n # check if curie is from answer 1\n if curie in g1.keys():\n # check if in intersection\n if len(g1[curie]['intersection'])>0:\n diff_dict[\"-1|-1\"]['intersection'] += [self.node_dict1[curie]]\n for id1 in node_names1:\n for id2 in node_names2:\n node_tuple = id1+\"|\"+id2\n if id1 in g1[curie]['node'] and id2 in g2[curie]['node']:\n diff_dict[node_tuple]['intersection'] += [self.node_dict1[curie]]\n elif id1 in g1[curie]['node']:\n diff_dict[node_tuple]['g1-g2'] += [self.node_dict1[curie]]\n elif id2 in g2[curie]['node']:\n diff_dict[node_tuple]['g2-g1'] += [self.node_dict1[curie]]\n # If not in intersection store in g1-g2\n else:\n diff_dict[\"-1|-1\"]['g1-g2'] += [self.node_dict1[curie]]\n for id1 in g1[curie]['node']:\n # iterate through all answer 2 ids\n for id2 in node_names2:\n diff_dict[id1+\"|\"+id2]['g1-g2'] += [self.node_dict1[curie]]\n # if not in g1 but in g2 then in g2-g1\n elif curie in g2.keys():\n diff_dict[\"-1|-1\"]['g2-g1'] += [self.node_dict2[curie]]\n for id2 in g2[curie]['node']:\n # iterate through all answer 1 ids\n for id1 in node_names1:\n diff_dict[id1+\"|\"+id2]['g2-g1'] += [self.node_dict2[curie]]\n return diff_dict","def _findTangentSplitAuto(self, angles):\n # get angles from points\n splits = []\n\n # get average variables\n minAngle = min(angles) or 0.00001\n maxAngle = max(angles)\n average = (minAngle + maxAngle) * 0.5\n mean = sum(angles) / len(angles) * 0.5\n\n # get value at which to split\n threshold = (math.log(average) - math.log(mean)) / (math.log(maxAngle) - math.log(minAngle)) * average\n\n # if curve is relatively smooth don't split\n if mean * 10 > average:\n return []\n\n # split based on angles\n for i, angle in enumerate(angles):\n if angle > threshold:\n splits.append(i + 1)\n\n return splits","def deltaCalc(self, expected):\n \n n = len(self.structure)\n self.delta = [None] * n\n self.delta[n - 1] = []\n \n for i in xrange(len(expected)):\n curr = self.a[n - 1][i]\n self.delta[n - 1].append(self.derivativeFunc(curr) * (expected[i] - curr))\n self.delta[n - 1] = np.array(self.delta[n - 1])\n \n # From n - 1 to 1 layer \n for i in xrange(n - 1, 0, -1):\n currDelta = self.delta[i]\n if i != (n - 1):\n currDelta = currDelta[0][:-1]\n \n self.delta[i - 1] = np.array(np.dot(currDelta, self.theta[i]))\n self.delta[i - 1][0] *= self.a[i - 1]\n \n return","def subtree_distances(self, root):\r\n\r\n nodes = root.get_terminals()\r\n nodes.reverse()\r\n node_pairs = itertools.ifilter(\r\n lambda (a1, a2): a1.name < a2.name,\r\n itertools.product(nodes, nodes))\r\n\r\n distances = [self._node_distance(pair[0], pair[1])\r\n for pair in node_pairs]\r\n\r\n return distances"],"string":"[\n \"def calculate_divided_differences_row(nodes_to_compute):\\n divided_differences = []\\n\\n if len(nodes_to_compute) == 1:\\n return None\\n\\n for i in range(0, len(nodes_to_compute) - 1):\\n child = DividedDifferenceNode.create_child_node(nodes_to_compute[i], nodes_to_compute[i + 1])\\n child.calculate_value()\\n divided_differences.append(child)\\n\\n for node in divided_differences:\\n print(node, end='')\\n\\n print('\\\\n')\\n return divided_differences\",\n \"def diff(self):\\n return [node.diff for node in self]\",\n \"def test_frac_diffNonGaps(self):\\n s1 = self.RNA(\\\"AAAA\\\")\\n s2 = self.RNA(\\\"AGGG\\\")\\n s3 = self.RNA(\\\"GGGG\\\")\\n s4 = self.RNA(\\\"AG--GA-G\\\")\\n s5 = self.RNA(\\\"CU--CU-C\\\")\\n s6 = self.RNA(\\\"AC--GC-G\\\")\\n s7 = self.RNA(\\\"--------\\\")\\n s8 = self.RNA(\\\"AAAA----\\\")\\n s9 = self.RNA(\\\"A-GG-A-C\\\")\\n e = self.RNA(\\\"\\\")\\n\\n def test(x, y, z):\\n return self.assertFloatEqual(x.frac_diff_non_gaps(y), z)\\n\\n test(s1, s2, 0.75)\\n test(s1, s3, 1)\\n test(s2, s3, 0.25)\\n test(s1, s4, 0.5)\\n test(s4, s5, 1)\\n test(s4, s6, 0.4)\\n test(s4, s7, 0)\\n test(s4, s8, 0.5)\\n test(s4, s9, 1 / 3.0)\\n test(e, s4, 0)\",\n \"def getSubdivisionNodes(self, signature):\\n x, y, z = signature[0], signature[1], signature[2]\\n return [(2*x+1, 2*y, 2*z), (2*x, 2*y, 2*z),\\n (2*x+1, 2*y+1, 2*z), (2*x, 2*y, 2*z+1),\\n (2*x+1, 2*y+1, 2*z+1), (2*x, 2*y+1, 2*z),\\n (2*x+1, 2*y, 2*z+1), (2*x, 2*y+1, 2*z+1)]\",\n \"def fractionalPoints(totalNodeList, recNodeList, fracPoints):\\n\\n avPoint = averagePoints(recNodeList)\\n\\n for i in range(0, fracPoints):\\n closestPoint = closest_node(avPoint, totalNodeList) #Finds closest point\\n totalNodeList.remove(closestPoint)\\n recNodeList.append(closestPoint)\\n\\n printProgressBar(i, fracPoints) \\n\\n return recNodeList\",\n \"def equiangular_dimension_unpack(nodes, ratio):\\n dim1 = int((nodes / ratio) ** 0.5)\\n dim2 = int((nodes * ratio) ** 0.5)\\n if dim1 * dim2 != nodes: # Try to correct dim1 or dim2 if ratio is wrong\\n if nodes % dim1 == 0:\\n dim2 = nodes // dim1\\n if nodes % dim2 == 0:\\n dim1 = nodes // dim2\\n assert dim1 * dim2 == nodes, f'Unable to unpack nodes: {nodes}, ratio: {ratio}'\\n return dim1, dim2\",\n \"def getFractionalItems(self, startingPoint, returnFmt = 0, refinements = 1):\\n\\n def closest_node(node, nodes):\\n\\n \\\"\\\"\\\" returns closest node using dot vectorization, slightly faster see https://codereview.stackexchange.com/questions/28207/finding-the-closest-point-to-a-list-of-points \\\"\\\"\\\"\\n\\n if node in nodes:\\n nodes.remove(node)\\n\\n nodes = np.asarray(nodes)\\n deltas = nodes - node\\n dist_2 = np.einsum('ij,ij->i', deltas, deltas)\\n temp = nodes[np.argmin(dist_2)]\\n return (temp[0], temp[1])\\n\\n def averagePoints(nodeList):\\n #Consider switching to numpy mean arrays if performance is an issue\\n #inits\\n tempX, tempY = 0, 0\\n for node in nodeList:\\n tempX += node[0]\\n tempY += node[1]\\n \\n avX, avY = tempX/len(nodeList), tempY/len(nodeList)\\n avPoint = [avX, avY]\\n\\n return avPoint\\n\\n def fractionalPoints(totalNodeList, recNodeList, fracPoints):\\n\\n \\\"\\\"\\\" Starts out with one point should be in a place of high density #NOTE this is not automated yet. Keep adding points (it will add the closest)\\n point to the set over and over until 50% of the points are encircled. Then it will return a list of those points \\\"\\\"\\\"\\n\\n avPoint = averagePoints(recNodeList)\\n\\n for i in range(0, fracPoints):\\n closestPoint = closest_node(avPoint, totalNodeList) #Finds closest point\\n totalNodeList.remove(closestPoint)\\n recNodeList.append(closestPoint)\\n\\n printProgressBar(i, fracPoints) \\n\\n return recNodeList \\n\\n #Gets fractional points \\n numPointsFrac = math.floor(self.numPoints * self.detectionFraction)\\n fracPoints = fractionalPoints(self.points, [startingPoint], numPointsFrac)\\n \\n #Hull creation and getting of verticies\\n hull = ConvexHull(fracPoints)\\n polyVertices = [fracPoints[vertex] for vertex in hull.vertices] \\n cutVertices = chaikins_corner_cutting(polyVertices, refinements)\\n\\n #Path creation \\n polyCodes = [mppath.Path.LINETO] * len(polyVertices)\\n polyCodes[0] = mppath.Path.MOVETO\\n polyCodes[-1] = mppath.Path.CLOSEPOLY\\n\\n cutCodes = [mppath.Path.LINETO] * len(cutVertices)\\n cutCodes[0] = mppath.Path.MOVETO\\n cutCodes[-1] = mppath.Path.CLOSEPOLY\\n\\n polyPath = mppath.Path(polyVertices, polyCodes)\\n cutPath = mppath.Path(cutVertices, cutCodes)\\n\\n #How you want the information returned \\n if returnFmt == -2:\\n return [[cutVertices, cutPath], fracPoints]\\n if returnFmt == -1:\\n return fracPoints\\n if returnFmt == 0:\\n return [cutVertices, cutPath]\\n if returnFmt == 1:\\n return [polyVertices, polyPath]\\n if returnFmt == 2:\\n return [[cutVertices, cutPath], [polyVertices, polyPath]]\",\n \"def _computeDerivative(self,angles, distances):\\n slope=[]\\n slope.append(0)\\n for i in xrange(1,len(angles)):\\n der = (distances[i]-distances[i-1])/(angles[i]-angles[i-1])\\n slope.append(der)\\n #slope.append(0)\\n return slope\",\n \"def diff_frac(data_1, data_2):\\n\\n frac_1 = np.sum(data_1) / len(data_1)\\n frac_2 = np.sum(data_2) / len(data_2)\\n\\n return frac_1 - frac_2\",\n \"def _compute_diff(self, begin, end):\\n d = self.diff\\n x = self.x\\n for i in range(begin, end):\\n for j in range(i):\\n d[i].append((d[i][j] - d[i-1][j]) / (x[i] - x[i-j-1]))\",\n \"def rat2frac_list(x, y):\\n\\tcont = rat2cont_quot(x, y)\\n\\tfrac = []\\n\\tfor i in range(len(cont)):\\n\\t\\tfrac.append(cont2frac(cont[:(i+1)]))\\n\\treturn frac\",\n \"def differences(data: list) -> list:\\n differences = []\\n iterable, copy = tee(data)\\n next(copy) # adjusts copy of my iterable up 1 element\\n for x, y in zip(iterable, copy):\\n differences.append(abs(x - y))\\n\\n return differences\",\n \"def test_frac_diffGaps(self):\\n s1 = self.RNA(\\\"AAAA\\\")\\n s2 = self.RNA(\\\"GGGG\\\")\\n s3 = self.RNA(\\\"----\\\")\\n s4 = self.RNA(\\\"A-A-\\\")\\n s5 = self.RNA(\\\"-G-G\\\")\\n s6 = self.RNA(\\\"UU--\\\")\\n s7 = self.RNA(\\\"-\\\")\\n s8 = self.RNA(\\\"GGG\\\")\\n e = self.RNA(\\\"\\\")\\n self.assertEqual(s1.frac_diff_gaps(s1), 0)\\n self.assertEqual(s1.frac_diff_gaps(s2), 0)\\n self.assertEqual(s1.frac_diff_gaps(s3), 1)\\n self.assertEqual(s1.frac_diff_gaps(s4), 0.5)\\n self.assertEqual(s1.frac_diff_gaps(s5), 0.5)\\n self.assertEqual(s1.frac_diff_gaps(s6), 0.5)\\n self.assertEqual(s1.frac_diff_gaps(s7), 1)\\n self.assertEqual(s1.frac_diff_gaps(e), 0)\\n self.assertEqual(s3.frac_diff_gaps(s3), 0)\\n self.assertEqual(s3.frac_diff_gaps(s4), 0.5)\\n self.assertEqual(s3.frac_diff_gaps(s7), 0.0)\\n self.assertEqual(e.frac_diff_gaps(e), 0.0)\\n self.assertEqual(s4.frac_diff_gaps(s5), 1.0)\\n self.assertEqual(s4.frac_diff_gaps(s6), 0.5)\\n self.assertFloatEqual(s6.frac_diff_gaps(s8), 1 / 3.0)\",\n \"def test_frac_diff(self):\\n s1 = self.RNA(\\\"ACGU\\\")\\n s2 = self.RNA(\\\"AACG\\\")\\n s3 = self.RNA(\\\"GG\\\")\\n s4 = self.RNA(\\\"A\\\")\\n e = self.RNA(\\\"\\\")\\n self.assertEqual(s1.frac_diff(e), 0)\\n self.assertEqual(s1.frac_diff(s2), 0.75)\\n self.assertEqual(s1.frac_diff(s3), 1)\\n self.assertEqual(s1.frac_diff(s4), 0) # note truncation\",\n \"def diff(self, value):\\n if hasattr(value, \\\"__len__\\\"):\\n if len(value) == len(self):\\n for node, val in zip(self, value):\\n node.diff = val\\n return\\n else:\\n raise RxDException(\\n \\\"diff must either be a scalar or an iterable of the same length as the NodeList\\\"\\n )\\n for node in self:\\n node.diff = value\",\n \"def deltas(L):\\n return map(sub, tuple(L)[1:], L)\",\n \"def __diff_internal(self):\\n assert self.p > 0, \\\"order of Bspline must be > 0\\\" # we already handle the other case in diff()\\n\\n # https://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/B-spline/bspline-derv.html\\n #\\n t = self.knot_vector\\n p = self.p\\n bi = BsplineBasis(t[:-1], p - 1)\\n bip1 = BsplineBasis(t[1:], p - 1)\\n\\n numer1 = +p\\n numer2 = -p\\n denom1 = t[p:-1] - t[:-(p + 1)]\\n denom2 = t[(p + 1):] - t[1:-p]\\n\\n with np.errstate(divide='ignore', invalid='ignore'):\\n ci = np.where(denom1 != 0., (numer1 / denom1), 0.)\\n cip1 = np.where(denom2 != 0., (numer2 / denom2), 0.)\\n\\n return (ci, bi), (cip1, bip1)\",\n \"def find_fractions():\\n num_list = []\\n den_list = []\\n for n in range(10, 100):\\n for d in range(10, 100):\\n if d > n:\\n x = n / d\\n ln = list(str(n))\\n ld = list(str(d))\\n if (ln[0] == ld[1]) and (ln[0] != '0'):\\n if ld[0] != '0':\\n if (int(ln[1]) / int(ld[0])) == x:\\n print \\\"n/d =\\\", n, d\\n num_list.append(n)\\n den_list.append(d)\\n else:\\n continue\\n elif (ln[1] == ld[0]) and (ln[1] != '0'):\\n if ld[1] != '0':\\n if (int(ln[0]) / int(ld[1])) == x:\\n print \\\"n/d =\\\", n, d\\n num_list.append(n)\\n den_list.append(d)\\n else:\\n continue\\n else:\\n continue\\n return num_list, den_list\",\n \"def diff(self):\\n\\t\\tif len(self.v) < 4:\\n\\t\\t\\treturn None\\n\\t\\tif self.poli == None:\\n\\t\\t\\tself.generar_polinomio()\\n\\t\\tif x != None:\\n\\t\\t\\treturn diff(self.poli)(x)\\n\\t\\treturn diff(self.poli)\",\n \"def _extract_fraction_with_text_nl(tokens, short_scale, ordinals):\\n for c in _FRACTION_MARKER_NL:\\n partitions = partition_list(tokens, lambda t: t.word == c)\\n\\n if len(partitions) == 3:\\n numbers1 = \\\\\\n _extract_numbers_with_text_nl(partitions[0], short_scale,\\n ordinals, fractional_numbers=False)\\n numbers2 = \\\\\\n _extract_numbers_with_text_nl(partitions[2], short_scale,\\n ordinals, fractional_numbers=True)\\n\\n if not numbers1 or not numbers2:\\n return None, None\\n\\n # ensure first is not a fraction and second is a fraction\\n num1 = numbers1[-1]\\n num2 = numbers2[0]\\n if num1.value >= 1 and 0 < num2.value < 1:\\n return num1.value + num2.value, \\\\\\n num1.tokens + partitions[1] + num2.tokens\\n\\n return None, None\",\n \"def calculate_deltas(tvals, tp_confidences, fp_confidences, num_samples):\\n deltas = []\\n tp_percentages = []\\n fp_percentages = []\\n for tval in tvals:\\n dval, tp_pct, fp_pct = delta(\\n tval,\\n tp_confidences,\\n fp_confidences,\\n num_samples\\n )\\n deltas.append(dval)\\n tp_percentages.append(tp_pct)\\n fp_percentages.append(fp_pct)\\n return deltas, tp_percentages, fp_percentages\",\n \"def divide_microstructure_unit(self,point1,point2,dimensions):\\n\\t\\tnew_sections = []\\n\\t\\tif len(point1) < 4:\\n\\t\\t\\tdone = 0.0\\n\\t\\t\\tdtwo = 0.0\\n\\t\\telse:\\n\\t\\t\\tdone = point1[-1]\\n\\t\\t\\tdtwo = point2[-1]\\n\\t\\t\\n\\t\\tp1 = np.array(point1[:3])\\n\\t\\tp2 = np.array(point2[:3])\\n\\t\\tvec = p2-p1\\n\\t\\tdimslength = float(np.sum(dimensions))\\n\\t\\tfor d,dim in enumerate(dimensions[:-1]):\\n\\t\\t\\tnearsideproportion = np.sum(dimensions[:d])/dimslength\\n\\t\\t\\tfarsideproportion = np.sum(dimensions[:d+1])/dimslength\\n\\t\\t\\tnew_sections.append([\\t\\n\\t\\t\\t\\t\\t\\tlist(np.append(p1+vec*nearsideproportion,done)),\\n\\t\\t\\t\\t\\t\\tlist(np.append(((p1+vec*nearsideproportion)+(p1+vec*farsideproportion))/2.0,(done+dtwo)/2.0)),\\n\\t\\t\\t\\t\\t\\tlist(np.append(p1+vec*farsideproportion,dtwo))\\n\\t\\t\\t\\t\\t\\t])\\n\\t\\t\\n\\t\\tnew_sections.append([\\t\\n\\t\\t\\t\\t\\tlist(new_sections[-1][-1]),\\n\\t\\t\\t\\t\\tlist((np.array(new_sections[-1][-1])+np.array(list(point2[:3])+[dtwo]))/2.0),\\n\\t\\t\\t\\t\\tlist(point2[:3])+[dtwo]\\n\\t\\t\\t\\t\\t])\\n\\t\\t\\n\\t\\tif len(dimensions) > 2:\\n\\t\\t\\treturn(new_sections,['node','paranode1','paranode2','internode','paranode2','paranode1'][:len(new_sections)])\\n\\t\\t\\n\\t\\telse:\\n\\t\\t\\treturn(new_sections,['interbouton','bouton'][:len(new_sections)])\",\n \"def find_sharpest_fork_general(Nodes):\\n pair_list = []\\n Dis = np.array([])\\n for n in Nodes:\\n if n.parent is not None:\\n if n.parent.parent is not None:\\n a = n.parent.children\\n if(isinstance(a, list)):\\n if(len(a)==2):\\n n1 = a[0]\\n n2 = a[1]\\n pair_list.append([n1 , n2])\\n dis = LA.norm(a[0].xyz - a[1].xyz,2)\\n Dis = np.append(Dis,dis)\\n if(len(Dis)!= 0):\\n (b,) = np.where(Dis == Dis.min())\\n sharpest_pair = pair_list[b[0]]\\n distance = Dis.min()\\n else:\\n sharpest_pair = [0,0]\\n distance = 0.\\n return sharpest_pair, distance\",\n \"def divideWork(self,ratios):\\r\\n \\r\\n taskCount = 0\\r\\n\\r\\n #Sums up the ratio amounts\\r\\n for nodeId,ratio in ratios.iteritems():\\r\\n taskCount += ratio\\r\\n\\r\\n #Divide the work into 'taskCount' chunks\\r\\n [primer,prefix] = WorkRange(self.charset).divideMore(self.primer,self.prefix,taskCount)\\r\\n \\r\\n prim = {}\\r\\n pref = {}\\r\\n \\r\\n for nodeId,ratio in ratios.iteritems():\\r\\n prim[nodeId] = []\\r\\n pref[nodeId] = []\\r\\n\\r\\n #Assigns each node 'ratio' amount chunks\\r\\n for i in range(ratio):\\r\\n prim[nodeId].append(primer.pop())\\r\\n pref[nodeId].append(prefix.pop())\\r\\n\\r\\n\\r\\n return [prim,pref]\",\n \"def derivative(requestContext, seriesList):\\n results = []\\n for series in seriesList:\\n newValues = []\\n prev = None\\n for val in series:\\n if None in (prev,val):\\n newValues.append(None)\\n prev = val\\n continue\\n newValues.append(val - prev)\\n prev = val\\n newName = \\\"derivative(%s)\\\" % series.name\\n newSeries = TimeSeries(newName, series.start, series.end, series.step, newValues)\\n newSeries.pathExpression = newName\\n results.append(newSeries)\\n return results\",\n \"def diff_op(self, args: List[float], time: float) -> List[float]:\\n v, nK, ca = args\\n ca_args: List[float] = [v, ca]\\n dvdt: float = self.dvdt(args=args)\\n dnKdt: float = self.kvhh.dndt(v=v, n=nK)\\n dCadt: float = self.dCadt(args=ca_args)\\n return [dvdt, dnKdt, dCadt]\",\n \"def split_distances(self, remaining: List[int], distances: List[float], mid: int) -> List[List[int]]:\\n closer, farther = [], []\\n for index in remaining:\\n if distances[index] <= mid:\\n closer.append(index)\\n else:\\n farther.append(index)\\n return [closer, farther]\",\n \"def calc_diffs(self, y, x, locs):\\n res = {}\\n \\n for item, value in locs.iteritems():\\n res[item] = self.slab_ratio * (self.grid[y, x] - self.grid[value['y'], value['x']])\\n \\n return res\",\n \"def test_compare_old_to_new_method_to_create_trees(self):\\n nodes = util.generate_sequence_of_points(2, 2)\\n tree1 = kdtree.createNewTree(nodes)\\n kdtree.visualize(tree1)\\n \\n sel_axis = (lambda axis: axis)\\n tree2 = kdtree.createNewTree([[0.5, 0.5]],axis = 0, sel_axis= sel_axis)\\n tree2.split2([0.25, 0.5], axis = 1)\\n tree2.split2([0.75, 0.5], axis = 1)\\n \\n #left\\n tree2.split2([0.25, 0.25], axis = 0, sel_axis = sel_axis)\\n tree2.split2([0.25, 0.75], axis = 0, sel_axis = sel_axis)\\n \\n #right\\n tree2.split2([0.75, 0.25], axis = 0, sel_axis = sel_axis)\\n tree2.split2([0.75, 0.75], axis = 0, sel_axis = sel_axis)\\n \\n kdtree.visualize(tree2)\\n \\n for n in zip(kdtree.level_order(tree1), kdtree.level_order(tree2)):\\n self.assertEqual(n[0].data, n[1].data, \\\"elements not equal\\\")\\n \\n if n[0].data is not None and n[1].data is not None:\\n self.assertEqual(n[0].axis, n[1].axis, \\\"elements not equal\\\")\",\n \"def compareNodes(x, y):\\n return x.pathValue - y.pathValue\",\n \"def getNodesAndDistances():\\n\\n\\tglobal width, height\\n\\n\\t# First we generate the list\\n\\n\\tprint \\\"\\\\tGetting node list...\\\"\\n\\t\\n\\tnodeDict = {}\\n\\n\\tfor y in range(height):\\n\\t\\tfor x in range(width):\\n\\t\\t\\ttheType = getSquare(x, y)\\n\\n\\t\\t\\tprint \\\"\\\\t\\\\tGetting list for node (%d, %d) of type %d...\\\" % (x, y, theType)\\n\\n\\t\\t\\ttempList = getNodeList(x, y, theType)\\n\\n\\t\\t\\tif tempList == []:\\n\\t\\t\\t\\tprint \\\"\\\\t\\\\t\\\\tNo nodes here.\\\"\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfor i in range(len(tempList)):\\n\\t\\t\\t\\t\\tnode = tempList[i]\\n\\t\\t\\t\\t\\tnodeName = node[0]\\n\\t\\t\\t\\t\\tnodeDict[nodeName] = node[1:]\\t# Everything but the first element\\n\\t\\t\\t\\t\\tprint \\\"\\\\t\\\\t\\\\tAdded node '%s'...\\\" % nodeName\\n\\n\\tprint \\\"\\\\tDone getting node list (%d nodes)...\\\" % (len(nodeDict.keys()))\\n\\tprint \\\"\\\"\\n\\n\\t# Now that we've got that, we get a list of pairs\\n\\n\\tpairList = getPairList(nodeDict)\\n\\n\\t# Now we calculate the distance between every pair of nodes that connect\\n\\n\\tprint \\\"\\\"\\n\\tprint \\\"\\\\tCreateing dictionary of distances between connected nodes...\\\"\\n\\n\\tdistanceDict = {}\\n\\n\\tfor tuple in pairList:\\n\\t\\t(nodeA, nodeB) = tuple\\n\\t\\tprint \\\"\\\\t\\\\tCalculating distance between '%s' and '%s'...\\\" % (nodeA, nodeB)\\n\\t\\tdistance = distanceBetween(nodeA, nodeB, nodeDict)\\n\\t\\tpairName = \\\"%s%s\\\" % (nodeA, nodeB)\\n\\t\\tdistanceDict[pairName] = distance\\n\\t\\tprint \\\"\\\\t\\\\t\\\\tDistace was %f.\\\" % (distance)\\n\\n\\tprint \\\"\\\\tDone creating dictionary of node differences (%d pairs).\\\" % (len(distanceDict.keys()))\\n\\n\\treturn nodeDict, distanceDict\",\n \"def _compare_elements(self, old, new):\\n res = None\\n # We want to go through the tree post-order\\n if isinstance(old, dict):\\n res_dict = self.compare_dicts(old, new)\\n if (len(res_dict) > 0):\\n res = res_dict\\n # Now we are on the same level\\n # different types, new value is new\\n elif (type(old) != type(new)):\\n res = {'---': old, '+++': new}\\n # recursive arrays\\n # we can be sure now, that both new and old are\\n # of the same type\\n elif (isinstance(old, list)):\\n res_arr = self._compare_arrays(old, new)\\n if (len(res_arr) > 0):\\n res = res_arr\\n # the only thing remaining are scalars\\n else:\\n scalar_diff = self._compare_scalars(old, new)\\n if scalar_diff is not None:\\n res = scalar_diff\\n\\n return res\",\n \"def find_sharpest_fork(nodes):\\n pair_list = []\\n Dis = np.array([])\\n for n in nodes:\\n if n.parent is not None:\\n if n.parent.parent is not None:\\n a = n.parent.children\\n if(isinstance(a, list)):\\n if(len(a)==2):\\n n1 = a[0]\\n n2 = a[1]\\n if(len(n1.children) == 0 and len(n2.children) == 0):\\n pair_list.append([n1 , n2])\\n dis = LA.norm(a[0].xyz - a[1].xyz,2)\\n Dis = np.append(Dis,dis)\\n if(len(Dis)!= 0):\\n (b,) = np.where(Dis == Dis.min())\\n sharpest_pair = pair_list[b[0]]\\n distance = Dis.min()\\n else:\\n sharpest_pair = [0,0]\\n distance = 0.\\n return sharpest_pair, distance\",\n \"def calculate_distance_to_divide(\\n grid, longest_path=True, add_to_grid=False, clobber=False\\n):\\n # check that flow__receiver nodes exists\\n if \\\"flow__receiver_node\\\" not in grid.at_node:\\n raise FieldError(\\n \\\"A 'flow__receiver_node' field is required at the \\\"\\n \\\"nodes of the input grid.\\\"\\n )\\n\\n if \\\"flow__upstream_node_order\\\" not in grid.at_node:\\n raise FieldError(\\n \\\"A 'flow__upstream_node_order' field is required at the \\\"\\n \\\"nodes of the input grid.\\\"\\n )\\n\\n if \\\"drainage_area\\\" not in grid.at_node:\\n raise FieldError(\\n \\\"A 'flow__upstream_node_order' field is required at the \\\"\\n \\\"nodes of the input grid.\\\"\\n )\\n\\n # get the reciever nodes, depending on if this is to-one, or to-multiple,\\n # we'll need to get a different at-node field.\\n if grid.at_node[\\\"flow__receiver_node\\\"].size != grid.size(\\\"node\\\"):\\n to_one = False\\n else:\\n to_one = True\\n\\n flow__receiver_node = grid.at_node[\\\"flow__receiver_node\\\"]\\n drainage_area = grid.at_node[\\\"drainage_area\\\"]\\n\\n # get the upstream node order\\n flow__upstream_node_order = grid.at_node[\\\"flow__upstream_node_order\\\"]\\n\\n # get downstream flow link lengths, result depends on type of grid.\\n if isinstance(grid, RasterModelGrid):\\n flow_link_lengths = grid.length_of_d8[\\n grid.at_node[\\\"flow__link_to_receiver_node\\\"]\\n ]\\n else:\\n flow_link_lengths = grid.length_of_link[\\n grid.at_node[\\\"flow__link_to_receiver_node\\\"]\\n ]\\n\\n # create an array that representes the distance to the divide.\\n distance_to_divide = np.zeros(grid.nodes.size)\\n\\n if not longest_path:\\n distance_to_divide[:] = 2 * grid.size(\\\"node\\\") * np.max(flow_link_lengths)\\n\\n # iterate through the flow__upstream_node_order backwards.\\n for node in reversed(flow__upstream_node_order):\\n # if drainage are is equal to node cell area, set distance to zeros\\n # this should handle the drainage divide cells as boundary cells have\\n # their area set to zero.\\n if drainage_area[node] == grid.cell_area_at_node[node]:\\n distance_to_divide[node] = 0\\n\\n # get flow recievers\\n reciever = flow__receiver_node[node]\\n\\n if to_one:\\n # if not processing an outlet node.\\n if reciever != node:\\n if longest_path:\\n cond = (\\n distance_to_divide[reciever]\\n < distance_to_divide[node] + flow_link_lengths[node]\\n )\\n else:\\n cond = (\\n distance_to_divide[reciever]\\n > distance_to_divide[node] + flow_link_lengths[node]\\n )\\n\\n if cond:\\n distance_to_divide[reciever] = (\\n distance_to_divide[node] + flow_link_lengths[node]\\n )\\n\\n else:\\n # non-existant links are coded with -1\\n useable_receivers = np.where(reciever != grid.BAD_INDEX)[0]\\n\\n for idx in range(len(useable_receivers)):\\n r = reciever[useable_receivers][idx]\\n fll = flow_link_lengths[node][useable_receivers][idx]\\n\\n # if not processing an outlet node.\\n if r != node:\\n if longest_path:\\n cond = distance_to_divide[r] < distance_to_divide[node] + fll\\n else:\\n cond = distance_to_divide[r] > distance_to_divide[node] + fll\\n\\n if cond:\\n distance_to_divide[r] = distance_to_divide[node] + fll\\n\\n # store on the grid\\n if add_to_grid:\\n grid.add_field(\\n \\\"distance_to_divide\\\", distance_to_divide, at=\\\"node\\\", clobber=clobber\\n )\\n\\n return distance_to_divide\",\n \"def split_translation(t):\\n from math import fmod\\n\\n def _split1(x):\\n x_part = fmod(x, 1.0)\\n x_whole = x - x_part\\n if x_part > 0.5:\\n x_part -= 1\\n x_whole += 1\\n elif x_part < -0.5:\\n x_part += 1\\n x_whole -= 1\\n\\n return (x_whole, x_part)\\n\\n _tt = [_split1(x) for x in t]\\n\\n return tuple(t[0] for t in _tt), tuple(t[1] for t in _tt)\",\n \"def dif_prog_div(x, y):\\n\\n dd = np.zeros([len(y), len(y)]) \\n dd[0] = y\\n dd[1,:-1] = (dd[0,1:] - dd[0,:-1]) / (x[1:]-x[:-1])\\n\\n for i in range(2,len(y)):\\n dd[i,:-i] = (dd[i-1,1:-(i-1)] - dd[i-1,:-i]) / (x[i:]-x[:-i])\\n\\n return dd\",\n \"def alldiff():\\n res = {'Computation-alldiff-0': {'Experiment': 'alldiff',\\n 'Parameters': {'w': 5, 'x': 1, 'z': 4},\\n 'Results': {'f1': 15, 'f2': 51}},\\n 'Computation-alldiff-1': {'Experiment': 'alldiff',\\n 'Parameters': {'w': 6, 'x': 1, 'z': 4},\\n 'Results': {'f1': 16, 'f2': 61}},\\n 'Computation-alldiff-2': {'Experiment': 'alldiff',\\n 'Parameters': {'w': 5, 'x': 2, 'z': 4},\\n 'Results': {'f1': 25, 'f2': 52}},\\n 'Computation-alldiff-3': {'Experiment': 'alldiff',\\n 'Parameters': {'w': 6, 'x': 2, 'z': 4},\\n 'Results': {'f1': 26, 'f2': 62}},\\n 'Computation-alldiff-4': {'Experiment': 'alldiff',\\n 'Parameters': {'w': 5, 'x': 3, 'z': 4},\\n 'Results': {'f1': 35, 'f2': 53}},\\n 'Computation-alldiff-5': {'Experiment': 'alldiff',\\n 'Parameters': {'w': 6, 'x': 3, 'z': 4},\\n 'Results': {'f1': 36, 'f2': 63}}}\\n\\n # Notice the ordering\\n domain = {'x':[\\\"1\\\", \\\"2\\\", \\\"3\\\"], 'w':[\\\"5\\\", \\\"6\\\"]}\\n metadata = {'z':\\\"4\\\"}\\n parameters = [\\\"x\\\", \\\"w\\\"]\\n parameters.sort()\\n metrics = [\\\"f1\\\", \\\"f2\\\"]\\n metrics.sort()\\n exp_name = \\\"alldiff\\\"\\n return exp_name, metadata, parameters, domain, metrics, res\",\n \"def division(i, S, Gi, NG):\\n\\n NGX = [dict() for _ in NG]\\n\\n C = set()\\n\\n for v in S:\\n c = Gi.node[v]['class']\\n NG[i][c].remove(v)\\n if not NGX[i].has_key(c):\\n NGX[i][c] = set()\\n NGX[i][c].add(v)\\n C.add(c)\\n\\n # split class table\\n for c in C:\\n # copy/move entries of NGX[j] table \\n for j in xrange(len(NG)):\\n if j == i:\\n # remove the entry NG[i] (where S is from) if empty\\n if not NG[i][c]:\\n del NG[i][c]\\n else:\\n if NG[i].has_key(c) and NG[i][c]:\\n NGX[j][c] = set(NG[j][c])\\n else:\\n NGX[j][c] = NG[j].pop(c)\\n return NGX\",\n \"def difference(*colls):\\n\\n # Get all the leaf paths for each collection: make each path a tuple\\n leaf_paths_by_coll = list(map(lambda c: list(map(tuple, get_all_leaf_paths(c))), colls))\\n\\n # Find the union of all leaf paths: merge all the paths and keep only the unique paths\\n union_leaf_paths = list(distinct(concat(*leaf_paths_by_coll)))\\n\\n # Get the values corresponding to these leaf paths in every collection: if a leaf path doesn't exist, assumes None\\n values_by_coll = list(map(lambda lp: list(map(lambda coll: tz.get_in(lp, coll), colls)), union_leaf_paths))\\n\\n # Filter out the leaf paths that have identical values across the collections\\n keep_leaf_paths = list(map(0, filter(lambda t: not allequal(t[1]), zip(union_leaf_paths, values_by_coll))))\\n keep_values = list(map(1, filter(lambda t: not allequal(t[1]), zip(union_leaf_paths, values_by_coll))))\\n\\n # Rearrange to construct a list of dictionaries -- one per original collection.\\n # Each of these dictionaries maps a 'kept' leaf path to its corresponding\\n # value in the collection\\n differences = list(map(lambda vals: dict(zip(keep_leaf_paths, vals)), list(zip(*keep_values))))\\n\\n return differences\",\n \"def compute_divergence(self):\\n d_tr_a = []\\n d_te_a = []\\n for k in self.synth_keys:\\n d_tr_a.append(self.divergence('tr', k))\\n d_te_a.append(self.divergence('te', k))\\n\\n training = np.mean(np.array(d_tr_a))\\n testing = np.mean(np.array(d_te_a))\\n return training, testing\",\n \"def _collect_num_denom(ast, nums, denoms):\\n if not (isinstance(ast, Mul) or isinstance(ast, Div)):\\n # If ast is not multiplication or division, just put it in nums.\\n nums.append(ast)\\n return\\n\\n if isinstance(ast.left, Div) or isinstance(ast.left, Mul):\\n # If the left argument is a multiplication or division, descend into\\n # it, otherwise it is in the numerator.\\n _collect_num_denom(ast.left, nums, denoms)\\n else:\\n nums.append(ast.left)\\n\\n if isinstance(ast.right, Div) or isinstance(ast.right, Mul):\\n # If the left argument is a multiplication or division, descend into\\n # it, otherwise it is in the denominator.\\n if isinstance(ast, Mul):\\n _collect_num_denom(ast.right, nums, denoms)\\n elif isinstance(ast, Div):\\n # Note that when we descend into the denominator of a Div, we want \\n # to swap our nums and denoms lists\\n _collect_num_denom(ast.right, denoms, nums)\\n else:\\n if isinstance(ast, Mul):\\n nums.append(ast.right)\\n elif isinstance(ast, Div):\\n denoms.append(ast.right)\",\n \"def extractTree(nodesList, rootNode):\\n if len(nodesList) == 0:\\n return\\n if nodesList[0] == '!':\\n return nodesList[1:]\\n\\n splitAttribute, splitValue, attributeValue = nodesList[0].strip().split('-')\\n nodesList = nodesList[1:]\\n \\n if splitAttribute != splitValue or splitAttribute != '$' or splitValue != '$':\\n rootNode.setSplit((splitAttribute, splitValue))\\n else:\\n rootNode.setSplit(\\\"Base case\\\")\\n rootNode.setData(attributeValue)\\n return nodesList[2:]\\n \\n \\n leftTree = Tree()\\n rightTree = Tree()\\n rootNode.setLesser(leftTree)\\n rootNode.setGreater(rightTree)\\n nodesList = extractTree(nodesList, leftTree)\\n\\n \\n \\n nodesList = extractTree(nodesList, rightTree)\\n\\n return nodesList\",\n \"def _get_packet_intervals(\\n packets: Sequence[Packet],\\n node: int,\\n getter: Callable[[Packet, int], float]\\n) -> np.ndarray:\\n prev_time = 0.0\\n intervals = []\\n for packet in packets:\\n if packet.was_served[node]:\\n new_time = getter(packet, node)\\n intervals.append(new_time - prev_time)\\n prev_time = new_time\\n return np.asarray(intervals)\",\n \"def find_diff_nodes(S,T):\\n diff_nodes = []\\n for node in S:\\n s_formula = S.formula_conj(node)\\n t_formula = T.formula_conj(node)\\n if s_formula != t_formula:\\n diff_nodes.append(node)\\n return diff_nodes\",\n \"def diff(self,images):\\n diffArray = [0,1,2,3]\\n\\n # compute the difference bewteen two adjacent images in the same ovtave\\n for i in range(1,5):\\n diffArray[i-1] = images[i]-images[i-1]\\n\\n return numpy.array(diffArray)\",\n \"def divide_list(ld, division):\\n buckets = []\\n current = []\\n for obj in ld:\\n if len(current) < division:\\n current.append(obj)\\n else:\\n buckets.append(current)\\n current = [obj]\\n if len(current) > 0:\\n buckets.append(current)\\n return buckets\",\n \"def cell_division(waitlist, celllist, AgEpitope, tnow, mut_list, RNs):\\n for cell in celllist:\\n # get list of 0 to 2 daughters\\n dlist, mut_list = divide(cell, AgEpitope, tnow, mut_list, RNs)\\n # add daughters to waitlist\\n waitlist = waitlist + dlist\\n return waitlist, mut_list\",\n \"def vars_divide ( self , var1 , var2 , name = '' , title = '' ) :\\n \\n f1 = isinstance ( var1 , num_types )\\n f2 = isinstance ( var2 , num_types )\\n\\n if f1 and f2 :\\n res = float ( var1 ) / float ( var2 )\\n return ROOT.RooRealConstant.value ( res ) \\n elif f1 :\\n var1 = ROOT.RooRealConstant.value ( var1 ) \\n return self.vars_divide ( var1 , var2 , name , title )\\n elif f2 :\\n return self.vars_multiply ( var1 , 1.0/var2 , name , title )\\n \\n self.aux_keep.append ( var1 )\\n self.aux_keep.append ( var2 )\\n\\n result = Ostap.MoreRooFit.Division ( var1 , var2 )\\n self.aux_keep.append ( result )\\n \\n return result\",\n \"def divergence(tensor, dx=1, difference='central'):\\n assert difference in ('central', 'forward', 'backward'), difference\\n rank = spatial_rank(tensor)\\n if difference == 'forward':\\n return _divergence_nd(tensor, (0, 1)) / dx ** rank\\n elif difference == 'backward':\\n return _divergence_nd(tensor, (-1, 0)) / dx ** rank\\n else:\\n return _divergence_nd(tensor, (-1, 1)) / (2 * dx) ** rank\",\n \"def difference(G, H):\\n\\n if G.order() != H.order():\\n msg = \\\"Node sets of the two directed graphs are not equal!\\\"\\n raise StaticGraphNotEqNodesException(msg)\\n \\n n_nodes = G.order()\\n edges = ((u, v) for u in G.nodes()\\n for v in set(G.successors(u)) - set(H.successors(u)))\\n deg = make_deg(n_nodes, edges)\\n edges = ((u, v) for u in G.nodes()\\n for v in set(G.successors(u)) - set(H.successors(u)))\\n D = make(n_nodes, G.size(), edges, deg)\\n return D\",\n \"def test_interpolation(self):\\n\\n ndx1, ndx2 = self.find_partition()\\n tessellation = Delaunay(self.grid[ndx2,:])\\n\\n # initialisation\\n results = []\\n ndim = self.ndim+1\\n\\n for j in ndx1:\\n nmodels = len(self.tracks[j].models)\\n aResult = np.empty((nmodels,ndim+nglb+6),dtype=gtype)\\n pt = self.tracks[j].params + [0.0,]\\n\\n for i in range(nmodels):\\n aModel1 = self.tracks[j].models[i]\\n pt[-1] = aModel1.glb[iage]\\n aModel2 = interpolate_model(self,pt,tessellation,ndx2)\\n aResult[i,0:ndim] = pt\\n if (aModel2 is None):\\n aResult[i,ndim:ndim+nglb+6] = np.nan\\n else:\\n aResult[i,ndim:ndim+nglb+6] = compare_models(aModel1,aModel2)\\n\\n results.append(aResult)\\n\\n return results, ndx1, ndx2, tessellation\",\n \"def diffSeries(requestContext, *seriesLists):\\n (seriesList,start,end,step) = normalize(seriesLists)\\n name = \\\"diffSeries(%s)\\\" % formatPathExpressions(seriesList)\\n values = ( safeDiff(row) for row in izip(*seriesList) )\\n series = TimeSeries(name,start,end,step,values)\\n series.pathExpression = name\\n return [series]\",\n \"def DataDiff(source, target, compare_list_as_value=True, depth=0, no_difference_value=None):\\n # Ensure recursion doesnt go out of control\\n if depth > 150:\\n raise Exception('DataDiff recurlsion depth has hit limit (50), aborting.')\\n\\n # If we are not working with 2 different containers we can inspect, then do a simple check\\n if type(source) not in (list, tuple, dict) or type(target) not in (list, tuple, dict):\\n # If the types are different, the data is different (and cant be compared more)\\n if type(source) != type(target):\\n return (source, target)\\n # Else, theyre the same types, if the values are different\\n elif source != target:\\n return (source, target)\\n # Else, theyre the same types and value\\n else:\\n # This should only happen if this is a fresh DataDiff() call, depth==0\\n if depth == 0:\\n return (no_difference_value, no_difference_value)\\n else:\\n raise Exception('This should never happen, having a mismatching value different in anywhere but depth=0')\\n\\n\\n if type(source) in (list, tuple):\\n source_diff = []\\n elif type(source) == dict:\\n source_diff = {}\\n else:\\n raise Exception('Unhandled source_diff data type: %s' % type(source))\\n\\n if type(target) in (list, tuple):\\n target_diff = []\\n elif type(target) == dict:\\n target_diff = {}\\n else:\\n raise Exception('Unhandled target_diff data type: %s' % type(target))\\n\\n # Check for incompatible types, and just return them both as theyre totally different\\n if type(source_diff) != type(target_diff):\\n return (source, target)\\n\\n # If we're handling a Dictionary compare\\n if type(source_diff) == dict:\\n # Process the source keys first\\n for key in source.keys():\\n _CompareDictValue(key, source, target, source_diff, target_diff, compare_list_as_value, no_difference_value, depth)\\n\\n # Process the target keys next, skipping any source keys we already processed\\n for key in target.keys():\\n # Skip any keys we already processed in source\\n if key in source:\\n continue\\n\\n # Reverse target/source, so that the reverse comparison/set is done\\n _CompareDictValue(key, target, source, target_diff, source_diff, compare_list_as_value, no_difference_value, depth)\\n\\n # Else, if we're handling a List compare\\n elif type(source_diff) == list:\\n # If lists must be compared in total because the order of a list is important\\n if compare_list_as_value:\\n if source != target:\\n return (list(source), list(target))\\n\\n # Else, compare each element of the list\\n else:\\n for count in range(0, len(source)):\\n if count >= len(target):\\n source_diff.append(source[count])\\n elif source[count] != target[count]:\\n source_diff.append(source[count])\\n target_diff.append(target[count])\\n\\n # If the target has more elements than the source, add the rest \\n if len(target) > len(source):\\n target_diff += target[-(len(source) - len(target)):]\\n\\n else:\\n raise Exception('Unspecified type handler for data: %s. Only dict and list/tuple types are accepted.')\\n\\n return (source_diff, target_diff)\",\n \"def group_and_vote_fractions():\\n group_share = np.array([0, 0.2, 0.4, 0.6, 0.8])\\n vote_share = np.array([0, 0.2, 0.4, 0.6, 0.8])\\n return group_share, vote_share\",\n \"def get_deltas(temps, vals, ts_index):\\n nan_array = np.full([len(temps)], np.nan)\\n vals_react = nan_array\\n vals_ts = nan_array\\n vals_prod = nan_array\\n # any files before the ts will be a reactant file\\n for index in range(len(vals)):\\n if index == 0:\\n vals_react = vals[index]\\n elif index < ts_index:\\n vals_react += vals[index]\\n elif index == ts_index:\\n vals_ts = vals[index]\\n elif index == ts_index + 1:\\n vals_prod = vals[index]\\n else:\\n vals_prod += vals[index]\\n delta_ts = vals_ts - vals_react\\n delta_rxn = vals_prod - vals_react\\n return delta_ts, delta_rxn\",\n \"def compute(self, node, input_vals):\\r\\n assert len(input_vals) == 2\\r\\n return input_vals[0] - input_vals[1]\",\n \"def diff_op(self, args: List[float], time: float) -> List[float]:\\n v, h_nav, n_kvhh, h_kva, m_kvsi, s_ampar, x_nmdar, s_nmdar, s_gabar, ca = args\\n ca_args: List[float] = [v, s_nmdar, ca]\\n\\n if self.ion:\\n self.set_vCa(in_ca=ca)\\n\\n dvdt: float = self.dvdt(args=args)\\n dhNadt: float = self.nav.dhdt(v=v, h=h_nav)\\n dnKdt: float = self.kvhh.dndt(v=v, n=n_kvhh)\\n dhAdt: float = self.kva.dhdt(v=v, h=h_kva)\\n dmKSdt: float = self.kvsi.dmdt(v=v, m=m_kvsi)\\n dsAMPAdt: float = self.ampar.dsdt(v=v, s=s_ampar)\\n dxNMDAdt: float = self.nmdar.dxdt(v=v, x=x_nmdar)\\n dsNMDAdt: float = self.nmdar.dsdt(v=v, s=s_nmdar, x=x_nmdar)\\n dsGABAdt: float = self.gabar.dsdt(v=v, s=s_gabar)\\n dCadt: float = self.dCadt(args=ca_args)\\n return [dvdt, \\n dhNadt, \\n dnKdt, \\n dhAdt, \\n dmKSdt,\\n dsAMPAdt, \\n dxNMDAdt, \\n dsNMDAdt, \\n dsGABAdt, \\n dCadt,\\n ]\",\n \"def ratio_calculator(numerator, denominator):\\n ratios = []\\n for i in numerator:\\n for j in denominator:\\n if i[0] == j[0] and j[1] != 0:\\n ratios.append([i[0], round(float(i[1]) / j[1], 3)])\\n break\\n elif i[0] == j[0]:\\n ratios.append([i[0], 0])\\n return ratios\",\n \"def differences(input_list):\\n output_list = []\\n for x in range(1,len(input_list)):\\n output_list.append(input_list[x]-input_list[x-1])\\n return output_list\",\n \"def new_decomposition(self):\\n from sage.combinat.binary_tree import BinaryTree\\n t_low = self.lower_binary_tree().to_tilting()\\n t_up = self.upper_binary_tree().to_tilting()\\n common = [p for p in t_low if p in t_up]\\n\\n def extract_tree(x, y, tilt, common):\\n \\\"\\\"\\\"\\n Extract a tree with root at position xy (recursive).\\n \\\"\\\"\\\"\\n left_tree = None\\n for k in range(y - 1, x, -1):\\n if (x, k) in tilt:\\n if (x, k) not in common:\\n left_tree = extract_tree(x, k, tilt, common)\\n break\\n right_tree = None\\n for k in range(x + 1, y):\\n if (k, y) in tilt:\\n if (k, y) not in common:\\n right_tree = extract_tree(k, y, tilt, common)\\n break\\n return BinaryTree([left_tree, right_tree])\\n\\n TIP = self.parent()\\n return [TIP.from_binary_trees(extract_tree(cx, cy, t_low, common),\\n extract_tree(cx, cy, t_up, common))\\n for cx, cy in common]\",\n \"def _items_divide(self, numerator_data, denominator_data):\\n items = {}\\n if numerator_data['items'] is None:\\n items = None\\n else:\\n for n in numerator_data['items']:\\n # TODO what should we do when a matching item isn't found?\\n matching_d = next((item for item in denominator_data['items'] if\\n item['group'] == n['group']),\\n {'group': '_unknown', 'value': None})\\n if matching_d['value'] is None or n['value'] is None:\\n divided = None\\n else:\\n divided = n['value'] / matching_d['value']\\n\\n # item = dict({'group': n['group'],\\n # 'value': divided})\\n items[n['group']] = divided\\n\\n return {'items': items, 'grouping': numerator_data['grouping'],\\n 'data_id': numerator_data['data_id']}\",\n \"def splitNodes(matching):\\n outer = set(range(self.n))\\n inner = set([])\\n for (u, v) in matching:\\n if u in outer:\\n outer.remove(u)\\n if v in outer:\\n outer.remove(v)\\n inner.add(u)\\n inner.add(v)\\n return list(inner), list(outer)\",\n \"def group_diff(options, db):\\n nested_rvals = []\\n for ip in options.gmp:\\n nested_rvals.append(get_ip_parents(ip, db))\\n # get just the list of groups, stripping out the networks.\\n group1 = [x[0] for x in nested_rvals[0]]\\n group2 = [x[0] for x in nested_rvals[1]]\\n common = sorted(list(set(group1) & set(group2)))\\n diff1 = sorted(list(set(group1) - set(group2)))\\n diff2 = sorted(list(set(group2) - set(group1)))\\n return common, diff1, diff2\",\n \"def get_ratios(L1, L2):\\r\\n ratios = []\\r\\n for index in range(len(L1)):\\r\\n try:\\r\\n ratios.append(L1[index]/float(L2[index]))\\r\\n except ZeroDivisionError:\\r\\n ratios.append(float('NaN')) #NaN = not a number\\r\\n except:\\r\\n raise ValueError('get_ratios called with bad arg')\\r\\n return ratios\",\n \"def diff_eq(self,x,t,par):\\n \\n # setting the functions\\n s = x[0]\\n i = x[1]\\n r = x[2]\\n d = x[3]\\n \\n # mathematical equations\\n DiffS = -par[1]*(s/10**par[4])*(i**par[0])\\n DiffI = (par[1]*(s/10**par[4]) - par[2] - par[3])*(i**par[0])\\n DiffR = par[2]*(i**par[0])\\n DiffD = par[3]*(i**par[0])\\n \\n return [DiffS,DiffI,DiffR,DiffD]\",\n \"def sub_division(width: float, minimum_division: float, stretch_factor: float) -> list:\\n\\n sum_x = 0\\n next_ = minimum_division\\n new_grid = []\\n max_dx = 20/100\\n x = width/2\\n\\n while sum_x < x:\\n remaining = x - sum_x\\n\\n if next_ > max_dx:\\n n = np.ceil(remaining/max_dx)\\n\\n if n == 0:\\n new_grid.append(remaining)\\n\\n next_ = remaining/n\\n\\n for _ in range(0, int(n)):\\n new_grid.append(next_)\\n sum_x += next_\\n\\n remaining = x - sum_x\\n\\n if next_ < remaining:\\n new_grid.append(next_)\\n sum_x += next_\\n else:\\n remaining += new_grid[-1]\\n new_grid[-1] = remaining/2\\n new_grid.append(remaining/2)\\n sum_x = x\\n\\n next_ = next_ * stretch_factor\\n\\n x1 = new_grid[::-1]\\n x2 = new_grid+x1\\n\\n return x2\",\n \"def convert_elemwise_div(node, **kwargs):\\n return create_basic_op_node('Div', node, kwargs)\",\n \"def multi_dec(f):\\n\\n @wraps(f)\\n def wrapper(*args, **kwargs):\\n args = args[0] if len(args) == 1 and isinstance(args[0], (list, tuple)) else args\\n for arg in args:\\n if isinstance(arg, Node) and arg.parent.name is 'root':\\n arg.parent.remove_child(arg)\\n arg.update_child_calls()\\n return f(*args, **kwargs)\\n return wrapper\",\n \"def _floatdiff(x, y):\\n rankx = _rank(x)\\n ranky = _rank(y)\\n return _diff(rankx, ranky)\",\n \"def test_dividing(self):\\n divider = Divider()\\n\\n for i in range(-10, 10):\\n for j in range(-10, 10):\\n if j != 0:\\n self.assertEqual(i/j, divider.calc(j, i))\",\n \"def split_validation_data(*data, divmod=0.5):\\n n = round((len(data[0]) - 1) * divmod)\\n return [(d[n:], d[:n]) for d in data]\",\n \"def eval_numerical_gradient_blobs(f, inputs, output, h=1e-5):\\n numeric_diffs = []\\n for input_blob in inputs:\\n diff = np.zeros_like(input_blob.diffs)\\n it = np.nditer(input_blob.vals, flags=['multi_index'],\\n op_flags=['readwrite'])\\n while not it.finished:\\n idx = it.multi_index\\n orig = input_blob.vals[idx]\\n\\n input_blob.vals[idx] = orig + h\\n f(*(inputs + (output,)))\\n pos = np.copy(output.vals)\\n input_blob.vals[idx] = orig - h\\n f(*(inputs + (output,)))\\n neg = np.copy(output.vals)\\n input_blob.vals[idx] = orig\\n\\n diff[idx] = np.sum((pos - neg) * output.diffs) / (2.0 * h)\\n\\n it.iternext()\\n numeric_diffs.append(diff)\\n return numeric_diffs\",\n \"def _get_nums_from_fractions(self, nums):\\n\\t\\tnums_fraction = []\\n\\n\\t\\tfor num in nums:\\n\\t\\t\\tif re.search(\\\"\\\\d*\\\\(\\\\d+/\\\\d+\\\\)\\\\d*\\\", num):\\n\\t\\t\\t\\tnums_fraction.append(num)\\n\\t\\tnums_fraction = sorted(nums_fraction, key=lambda x: len(x), reverse=True)\\n\\n\\t\\tfloat_nums = []\\n\\t\\tfor num in nums:\\n\\t\\t\\tif ',' in num:\\n\\t\\t\\t\\t# [TODO] It can be points with comma like (0, 1)...\\n\\t\\t\\t\\tnew_num = []\\n\\t\\t\\t\\tfor c in num:\\n\\t\\t\\t\\t\\tif c == ',':\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tnew_num.append(c)\\n\\t\\t\\t\\tnum = ''.join(new_num)\\n\\t\\t\\t\\tfloat_num = num\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfloat_num = num\\n\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tfloat_nums.append(str(float(eval(float_num))))\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tif float_num.startswith('0'):\\n\\t\\t\\t\\t\\twhile float_num.startswith('0'):\\n\\t\\t\\t\\t\\t\\tfloat_num = float_num[1:]\\n\\t\\t\\t\\t\\tfloat_nums.append(str(float(eval(float_num))))\\n\\n\\t\\tfloat_nums_fraction = []\\n\\t\\tfor num in nums_fraction:\\n\\t\\t\\tif ',' in num:\\n\\t\\t\\t\\tnew_num = []\\n\\t\\t\\t\\tfor c in num:\\n\\t\\t\\t\\t\\tif c == ',':\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tnew_num.append(c)\\n\\t\\t\\t\\tnum = ''.join(new_num)\\n\\t\\t\\t\\tfloat_nums_fraction.append(str(float(eval(num))))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfloat_nums_fraction.append(str(float(eval(num))))\\n\\n\\t\\treturn float_nums, float_nums_fraction\",\n \"def decompose_paths(self):\\n if self.child_nodes == {}:\\n return []\\n\\n import numpy as np\\n\\n def decompose_paths_rec(node_inner, path):\\n \\\"\\\"\\\"\\n This function does the recursive create_path of the decomposition\\n :param node_inner:\\n :param path:\\n \\\"\\\"\\\"\\n if node_inner.is_leaf():\\n path = np.append(path, str(node_inner.value))\\n return path[None]\\n else:\\n paths = np.array([])\\n for edge_name in node_inner.child_nodes:\\n new_path = np.append(path, str(edge_name))\\n paths = np.append(paths, decompose_paths_rec(node_inner.child_nodes[edge_name], new_path))\\n return paths\\n\\n decomposition = decompose_paths_rec(self, np.array([]))\\n return decomposition.reshape((decomposition.shape[0]/(self.d+1), self.d+1))\",\n \"def compute_relations(nodes: List[Node]) -> None:\\n # Calculate parents\\n for node in nodes:\\n node.parents = []\\n for node in nodes:\\n for child in node.children():\\n child.parents.append(node)\\n\\n def compute_dominators(\\n entry: Node,\\n parents: Callable[[Node], List[Node]],\\n dominators: Callable[[Node], Set[Node]],\\n immediately_dominates: Callable[[Node], List[Node]],\\n set_immediate_dominator: Callable[[Node, Optional[Node]], None],\\n ) -> None:\\n # See https://en.wikipedia.org/wiki/Dominator_(graph_theory)#Algorithms\\n # Note: if `n` is unreachable from `entry`, then *every* node will\\n # vacuously belong to `n`'s dominator set.\\n for n in nodes:\\n dominators(n).clear()\\n if n == entry:\\n dominators(n).add(n)\\n else:\\n dominators(n).update(nodes)\\n\\n changes = True\\n while changes:\\n changes = False\\n for node in nodes:\\n if node == entry:\\n continue\\n nset = dominators(node)\\n for parent in parents(node):\\n nset = nset.intersection(dominators(parent))\\n nset.add(node)\\n if len(nset) < len(dominators(node)):\\n assert nset.issubset(dominators(node))\\n dominators(node).intersection_update(nset)\\n changes = True\\n\\n # Compute immediate dominator, and the inverse relation\\n for node in nodes:\\n immediately_dominates(node).clear()\\n for node in nodes:\\n doms = dominators(node).difference({node})\\n # If `node == entry` or the flow graph is not reducible, `doms` may be empty.\\n # TODO: Infinite loops could be made reducible by introducing\\n # branches like `if (false) { return; }` without breaking semantics\\n if doms:\\n # There should be a unique max `len(dominators(d))` if the flowgraph\\n # is reducible. Fall back to largest index for irreducible graphs.\\n imdom = max(doms, key=lambda d: (len(dominators(d)), d.block.index))\\n immediately_dominates(imdom).append(node)\\n set_immediate_dominator(node, imdom)\\n else:\\n set_immediate_dominator(node, None)\\n for node in nodes:\\n immediately_dominates(node).sort(key=lambda x: x.block.index)\\n\\n def _set_immediate_dominator(node: Node, imdom: Optional[Node]) -> None:\\n node.immediate_dominator = imdom\\n\\n def _set_immediate_postdominator(node: Node, impdom: Optional[Node]) -> None:\\n node.immediate_postdominator = impdom\\n\\n entry = nodes[0]\\n terminal = nodes[-1]\\n assert isinstance(terminal, TerminalNode)\\n\\n # Compute dominators & immediate dominators\\n compute_dominators(\\n entry=entry,\\n parents=lambda n: n.parents,\\n dominators=lambda n: n.dominators,\\n immediately_dominates=lambda n: n.immediately_dominates,\\n set_immediate_dominator=_set_immediate_dominator,\\n )\\n\\n # Compute postdominators & immediate postdominators\\n # This uses the same algorithm as above, but with edges reversed\\n compute_dominators(\\n entry=terminal,\\n parents=lambda n: n.children(),\\n dominators=lambda n: n.postdominators,\\n immediately_dominates=lambda n: n.immediately_postdominates,\\n set_immediate_dominator=_set_immediate_postdominator,\\n )\\n\\n # Iterate over all edges n -> c and check for backedges, which define natural loops\\n for node in nodes:\\n for child in node.children():\\n if child not in node.dominators:\\n continue\\n # Found a backedge node -> child where child dominates node; child is the \\\"head\\\" of the loop\\n if child.loop is None:\\n child.loop = NaturalLoop(child)\\n child.loop.nodes |= {child, node}\\n child.loop.backedges.add(node)\\n for parent in nodes:\\n if reachable_without(parent, node, child):\\n child.loop.nodes.add(parent)\",\n \"def compare_trees(first_soup: HTMLStrip, second_soup: HTMLStrip) -> float:\\n first_tree = Tree.from_soup_object(first_soup.file_name, first_soup.original_soup)\\n second_tree = Tree.from_soup_object(second_soup.file_name, second_soup.original_soup)\\n\\n common_paths_size: int = first_tree.num_of_common_paths(second_tree)\\n target_size: int = second_tree.total_num_of_paths()\\n similarity = float(common_paths_size)*100/target_size\\n print(f'{similarity:.2f}')\\n return similarity\",\n \"def _group_nodes(cls, nodes, node_info_holder):\\n group_nodes = [[]]\\n kb_nodes = []\\n for n in nodes:\\n matched_scope = []\\n if n.name in node_info_holder.nodes_keep:\\n kb_nodes.append(n)\\n continue\\n for scope in node_info_holder.scopes:\\n if scope in n.name and n.name[n.name.find(scope) + len(scope)] == \\\"/\\\":\\n matched_scope.append(scope)\\n if len(matched_scope) > 1:\\n raise ValueError(\\n \\\"More than one scope {} contained in node name {}.\\\".format(\\n str(matched_scope), n.name))\\n if matched_scope:\\n curr_scope = matched_scope[0]\\n if group_nodes[-1]:\\n group_nodes.append(curr_scope)\\n group_nodes.append([])\\n node_info_holder.nodes[curr_scope].append(n)\\n else:\\n group_nodes[-1].append(n)\\n return group_nodes, kb_nodes\",\n \"def arithmeticDivision2(numbers,str_result,target):\\n success = False\\n str_temp=\\\"\\\"\\n for x in numbers:\\n if x%target==0:\\n target2=x//target\\n numbers.remove(x)\\n numbers,str_temp,success=arithmeticBasic(numbers,str_temp,target2)\\n if success:\\n str_result+=str(x)+'/('+str_temp+')'\\n numbers.clear()\\n numbers.append(target)\\n break\\n else:\\n numbers.insert(0,x)\\n return numbers,str_result,success\",\n \"def find_step(tree: Tree, template: Tree) -> list:\\n if template == \\\"@\\\":\\n if type(tree) == str:\\n return [tree]\\n return tree.leaves()\\n\\n if template == '*':\\n return []\\n\\n if type(template) != str and template.label() == '*':\\n res_star = []\\n for temp_node in template:\\n res_star.extend(find_star_step(tree, temp_node))\\n return res_star\\n\\n if type(tree) == str or type(template) == str:\\n if tree == template:\\n return []\\n return []\\n\\n if tree.label() != template.label():\\n return []\\n else:\\n\\n res = []\\n for t_node in template:\\n for node in get_node_by_label(tree, t_node):\\n res.extend(find_step(node, t_node))\\n return res\",\n \"def testListDivide():\\n listDivide([1, 2, 3, 4, 5])\\n listDivide([2, 4, 6, 8, 10])\\n listDivide([30, 54, 63, 98, 100], divide=10)\\n listDivide([])\\n listDivide([1, 2, 3, 4, 5], 1)\",\n \"def diff(*args):\\n return reduce(lambda x, y: x - y, args)\",\n \"def diff(self, da, axis):\\n\\n def interp_function(data_left, data_right, shift):\\n # linear, centered interpolation\\n # TODO: generalize to higher order interpolation\\n return shift*(data_right - data_left)\\n return self._neighbor_binary_func(da, axis, interp_function)\",\n \"def test_scalar_division(self):\\n\\n a1 = tuples.Tuple([\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\"], 1, -2, 3, -4)\\n\\n a2 = a1 / 2\\n\\n self.assertEqual(a2,\\n tuples.Tuple([\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\"], 0.5, -1, 1.5, -2))\",\n \"def get_resulting_diffs():\\n diff_dirpath = application.join_abs_path(\\n EMPTY_TEST_DIR, application.OUTPUT_DIR_NAME)\\n diffleft_filename = application.join_abs_path(\\n diff_dirpath, application.OUTPUT_DIFF_LEFT_FILENAME)\\n diffright_filename = application.join_abs_path(\\n diff_dirpath, application.OUTPUT_DIFF_RIGHT_FILENAME)\\n\\n diff_left = read_gzip_file_lines_into_set(diffleft_filename)\\n diff_right = read_gzip_file_lines_into_set(diffright_filename)\\n\\n return diff_left, diff_right\",\n \"def _divide(triangle, points):\\n a, b, c = triangle\\n\\n # Insert points midway along each side of the triangle.\\n # Form 3 new triangles.\\n new_vertices = [\\n _midpoint(points[a], points[b]),\\n _midpoint(points[b], points[c]),\\n _midpoint(points[c], points[a]),\\n ]\\n\\n new_vertices_indices = [\\n len(points),\\n len(points) + 1,\\n len(points) + 2,\\n ]\\n\\n new_triangles = [\\n (a, new_vertices_indices[0], new_vertices_indices[2]),\\n (b, new_vertices_indices[1], new_vertices_indices[0]),\\n (c, new_vertices_indices[2], new_vertices_indices[1]),\\n ]\\n\\n return new_vertices, new_triangles\",\n \"def testListDivide():\\n #a\\n numbers = [1,2,3,4,5]\\n expected = 2\\n \\n try:\\n assert listDivide(numbers) == expected\\n except AssertionError:\\n raise ListDivideException(\\\"Test Failed\\\")\\n \\n \\n #b\\n numbers = [2,4,6,8,10]\\n expected = 5\\n \\n try:\\n assert listDivide(numbers) == expected\\n except AssertionError:\\n raise ListDivideException(\\\"Test Failed\\\") \\n \\n #c\\n numbers = [30, 54, 63, 98, 100]\\n divide = 10\\n expected = 2\\n \\n try:\\n assert listDivide(numbers, divide) == expected\\n except AssertionError:\\n raise ListDivideException(\\\"Test Failed\\\") \\n \\n #d\\n numbers = []\\n expected = 0\\n \\n try:\\n assert listDivide(numbers) == expected\\n except AssertionError:\\n raise ListDivideException(\\\"Test Failed\\\") \\n \\n #e\\n numbers = [1, 2, 3, 4, 5]\\n divide = 1\\n expected = 5\\n \\n try:\\n assert listDivide(numbers, divide) == expected\\n except AssertionError:\\n raise ListDivideException(\\\"Test Failed\\\")\",\n \"def compute_tree_changes(dmt_new, dmt_old, directory_path=''):\\n updated, new, deleted = set(), set(), set()\\n # Base cases:\\n # Both files or empty directories\\n if (not dmt_new.children) and (not dmt_old.children):\\n return updated, new, deleted\\n # New directory\\n elif not dmt_old.children:\\n mutual_filesystem_items = set()\\n new_filesystem_items = set(dmt_new.children.keys())\\n deleted_filesystem_items = set()\\n elif not dmt_new.children:\\n mutual_filesystem_items = set()\\n new_filesystem_items = set()\\n deleted_filesystem_items = set(dmt_old.children.keys())\\n else:\\n mutual_filesystem_items = set(dmt_new.children.keys()).intersection(set(dmt_old.children.keys()))\\n new_filesystem_items = set(dmt_new.children.keys()).difference(set(dmt_old.children.keys()))\\n deleted_filesystem_items = set(dmt_old.children.keys()).difference(set(dmt_new.children.keys()))\\n \\n \\n # Compile the set of updated files and directories, as well as any other changes within subdirectories.\\n for filesystem_item in mutual_filesystem_items:\\n # Always check subdirectories for e.g file renamings.\\n if filesystem_item[-1] == '/':\\n subdir_name = filesystem_item\\n subdir_path = directory_path + subdir_name\\n subdir_updated, subdir_new, subdir_deleted = \\\\\\n compute_tree_changes(dmt_new.children[subdir_name], dmt_old.children[subdir_name], subdir_path)\\n \\n # Mark the subdirectory if necessary.\\n if (dmt_old.children[subdir_name].dmt_hash != dmt_new.children[subdir_name].dmt_hash) or \\\\\\n subdir_updated or subdir_new or subdir_deleted:\\n updated.add(subdir_path)\\n \\n # Incorporate differences from within.\\n updated.update(subdir_updated)\\n new.update(subdir_new)\\n deleted.update(subdir_deleted)\\n \\n # File with differing hash values.\\n elif dmt_old.children[filesystem_item].dmt_hash != dmt_new.children[filesystem_item].dmt_hash:\\n filename = filesystem_item\\n file_path = directory_path + filename\\n updated.add(file_path)\\n \\n # Compile the set of newly created files.\\n for filesystem_item in new_filesystem_items:\\n item_path = directory_path + filesystem_item\\n new.add(item_path)\\n new.update(get_all_paths(dmt_new.children[filesystem_item], item_path))\\n \\n # Compile the set of deleted files.\\n for filesystem_item in deleted_filesystem_items:\\n item_path = directory_path + filesystem_item\\n deleted.add(item_path)\\n deleted.update(get_all_paths(dmt_old.children[filesystem_item], item_path))\\n \\n return updated, new, deleted\",\n \"def diff_eq(self,x,t,par):\\n # setting the functions\\n s,i,r,d = x\\n a,b,r,d,logS0 = par\\n\\n S0 = 10**logS0\\n \\n # mathematical equations\\n DiffS = - par[1]*(s/S0)*(i**a)\\n DiffI = par[1]*(s/S0)*(i**a) -(r+d)*i\\n DiffR = r*i\\n DiffD = d*i\\n \\n return [DiffS,DiffI,DiffR,DiffD]\",\n \"def get_energy_of_split(self, elem_l, elem_r):\\n\\n # logging.debug(f\\\"computing energy of split: {ch_l, ch_r}\\\")\\n\\n # elem_l = self.clusters[ch_l]\\n # elem_r = self.clusters[ch_r]\\n\\n # To follow the convention on Ginkgo, to get the correct result, we set t==0 if we have a leaf, i.e. t<t_cut\\n l_node_invM = 0\\n r_node_invM =0\\n l_node_invM = self.momentum[elem_l][0] ** 2 - np.linalg.norm(self.momentum[elem_l][1::]) ** 2\\n r_node_invM = self.momentum[elem_r][0] ** 2 - np.linalg.norm(self.momentum[elem_r][1::]) ** 2\\n\\n logging.debug(f\\\" t_l ={l_node_invM}\\\")\\n logging.debug(f\\\" t_R ={r_node_invM}\\\")\\n\\n logging.debug(f\\\" p_l ={self.momentum[elem_l]}\\\")\\n logging.debug(f\\\" p_r ={self.momentum[elem_r]}\\\")\\n\\n split_llh = likelihood.split_logLH_with_stop_nonstop_prob(self.momentum[elem_l],\\n self.momentum[elem_r],\\n self.min_invM,\\n self.Lambda)\\n\\n\\n # logging.debug(f\\\"split_llh = {split_llh}\\\")\\n\\n # llh = split_llh + a_node.map_tree_energy + b_node.map_tree_energy\\n logging.debug(f\\\"split likelihood ={split_llh}\\\")\\n\\n return split_llh\",\n \"def testListDivide():\\n assert listDivide([1,2,3,4,5]) == 2\\n assert listDivide([2,4,6,8,10]) == 5\\n assert listDivide([30, 54, 63,98, 100], divide = 10) == 2\\n assert listDivide([]) == 0\\n assert listDivide([1,2,3,4,5], 1) == 5\",\n \"def div(a, x):\\n return [a[i]/x for i in range(2)]\",\n \"def _divide_pred(pred):\\n if type(pred) == list:\\n fake = []\\n real = []\\n for p in pred:\\n fake.append([tensor[:tensor.size(0) // 2] for tensor in p])\\n real.append([tensor[tensor.size(0) // 2:] for tensor in p])\\n else:\\n fake = pred[:pred.size(0) // 2]\\n real = pred[pred.size(0) // 2:]\\n\\n return fake, real\",\n \"def diff_eq(self,x,t,par):\\n # setting the functions\\n s,i,r,d = x\\n b,r,d,logS0 = par\\n\\n S0 = 10**logS0\\n \\n # mathematical equations\\n DiffS = 0\\n DiffI = par[1]*(s/S0)*i\\n DiffR = r*i\\n DiffD = d*i\\n \\n return [DiffS,DiffI,DiffR,DiffD]\",\n \"def diffs(self):\\n return np.array([self.diff(i) for i in self.idx])\",\n \"def compute_relative_changes(a_list):\\n result = []\\n\\n for i in range(1,len(a_list)):\\n rel = (a_list[i] - a_list[i-1])/a_list[i-1]\\n result.append(rel)\\n\\n return result\",\n \"def step(nodes, outputs, edges):\\n flowed = []\\n for node_name in nodes.copy():\\n if node_name in flowed:\\n continue\\n if len(nodes[node_name]) == 2:\\n if node_name in flowed:\\n continue\\n node = [int(value) for value in nodes[node_name]]\\n low_value, high_value = min(node), max(node)\\n low_flow, high_flow = edges[node_name] \\n low_dictionary, low_node_name = low_flow\\n high_dictionary, high_node_name = high_flow\\n low_node = low_dictionary.get(low_node_name, tuple())\\n high_node = high_dictionary.get(high_node_name, tuple())\\n low_dictionary[low_node_name] = low_node + (str(low_value),)\\n high_dictionary[high_node_name] = high_node + (str(high_value),)\\n nodes[node_name] = tuple()\\n if low_dictionary is nodes:\\n flowed.append(low_node_name)\\n if high_dictionary is nodes:\\n flowed.append(high_node_name)\\n return nodes, outputs, edges\",\n \"def node_diff(self):\\n if self.input1 is None or self.input2 is None:\\n raise Exception(\\\"Missing input: please run the populate() method first\\\")\\n if self.node_dict1 is None or self.node_dict2 is None:\\n self.make_node_dict()\\n # Initialize dictonaries to keep track of the nodes in respnse 1 and response 2\\n g1={}\\n g2={}\\n # Set to keep track of the union of all curie ids\\n curie_set = set()\\n for curie in self.node_dict1.keys():\\n g1[curie] = {}\\n # intersection is only in the g1 dictionary\\n g1[curie]['intersection'] = set()\\n # node section keeps track of node ids associated with each node i.e. \\\"n0\\\"\\n g1[curie]['node'] = set()\\n curie_set.add(curie)\\n for curie in self.node_dict2.keys():\\n g2[curie] = {}\\n # node section keeps track of node ids associated with each node i.e. \\\"n0\\\"\\n g2[curie]['node'] = set()\\n curie_set.add(curie)\\n node_names1 = []\\n node_names2 = []\\n\\n # extract all node ids (i.e. \\\"n0\\\",\\\"n1\\\",ect...)\\n if len(self.input1['question_graph']['nodes'])>0:\\n if 'id' in self.input1['question_graph']['nodes'][0]:\\n node_names1 = [x['id'] for x in self.input1['question_graph']['nodes']]\\n elif 'node_id' in self.input1['question_graph']['nodes'][0]:\\n node_names1 = [x['node_id'] for x in self.input1['question_graph']['nodes']]\\n if len(self.input2['question_graph']['nodes'])>0:\\n if 'id' in self.input2['question_graph']['nodes'][0]:\\n node_names2 = [x['id'] for x in self.input2['question_graph']['nodes']]\\n elif 'node_id' in self.input2['question_graph']['nodes'][0]:\\n node_names2 = [x['node_id'] for x in self.input2['question_graph']['nodes']]\\n \\n # initialize the result dictonary\\n diff_dict = {}\\n diff_dict[\\\"-1|-1\\\"] = {'intersection':[],'g1-g2':[],'g2-g1':[]}\\n # initialize node id tuple keys\\n for id1 in node_names1:\\n for id2 in node_names2:\\n diff_dict[id1+\\\"|\\\"+id2] = {'intersection':[],'g1-g2':[],'g2-g1':[]}\\n # iterate through answers\\n for answer1 in self.input1['answers']:\\n for answer2 in self.input2['answers']:\\n for id1 in answer1['node_bindings'].keys():\\n # This is to handle cases where answer node id has a list or a string\\n if isinstance(answer1['node_bindings'][id1], str):\\n bindings1 = [answer1['node_bindings'][id1]]\\n elif isinstance(answer1['node_bindings'][id1], list):\\n bindings1 = answer1['node_bindings'][id1]\\n for curie1 in bindings1:\\n # store node id\\n g1[curie1]['node'].add(id1)\\n for id2 in answer2['node_bindings'].keys():\\n # This is to handle cases where answer node id has a list or a string\\n if isinstance(answer2['node_bindings'][id2], str):\\n bindings2 = [answer2['node_bindings'][id2]]\\n elif isinstance(answer2['node_bindings'][id2], list):\\n bindings2 = answer2['node_bindings'][id2]\\n for curie2 in bindings2:\\n # store node id\\n g2[curie2]['node'].add(id2)\\n if curie1 == curie2:\\n # stor intersection tuple\\n g1[curie1]['intersection'].add(id1+\\\"|\\\"+id2)\\n # iterate through all curies\\n for curie in curie_set:\\n # check if curie is from answer 1\\n if curie in g1.keys():\\n # check if in intersection\\n if len(g1[curie]['intersection'])>0:\\n diff_dict[\\\"-1|-1\\\"]['intersection'] += [self.node_dict1[curie]]\\n for id1 in node_names1:\\n for id2 in node_names2:\\n node_tuple = id1+\\\"|\\\"+id2\\n if id1 in g1[curie]['node'] and id2 in g2[curie]['node']:\\n diff_dict[node_tuple]['intersection'] += [self.node_dict1[curie]]\\n elif id1 in g1[curie]['node']:\\n diff_dict[node_tuple]['g1-g2'] += [self.node_dict1[curie]]\\n elif id2 in g2[curie]['node']:\\n diff_dict[node_tuple]['g2-g1'] += [self.node_dict1[curie]]\\n # If not in intersection store in g1-g2\\n else:\\n diff_dict[\\\"-1|-1\\\"]['g1-g2'] += [self.node_dict1[curie]]\\n for id1 in g1[curie]['node']:\\n # iterate through all answer 2 ids\\n for id2 in node_names2:\\n diff_dict[id1+\\\"|\\\"+id2]['g1-g2'] += [self.node_dict1[curie]]\\n # if not in g1 but in g2 then in g2-g1\\n elif curie in g2.keys():\\n diff_dict[\\\"-1|-1\\\"]['g2-g1'] += [self.node_dict2[curie]]\\n for id2 in g2[curie]['node']:\\n # iterate through all answer 1 ids\\n for id1 in node_names1:\\n diff_dict[id1+\\\"|\\\"+id2]['g2-g1'] += [self.node_dict2[curie]]\\n return diff_dict\",\n \"def _findTangentSplitAuto(self, angles):\\n # get angles from points\\n splits = []\\n\\n # get average variables\\n minAngle = min(angles) or 0.00001\\n maxAngle = max(angles)\\n average = (minAngle + maxAngle) * 0.5\\n mean = sum(angles) / len(angles) * 0.5\\n\\n # get value at which to split\\n threshold = (math.log(average) - math.log(mean)) / (math.log(maxAngle) - math.log(minAngle)) * average\\n\\n # if curve is relatively smooth don't split\\n if mean * 10 > average:\\n return []\\n\\n # split based on angles\\n for i, angle in enumerate(angles):\\n if angle > threshold:\\n splits.append(i + 1)\\n\\n return splits\",\n \"def deltaCalc(self, expected):\\n \\n n = len(self.structure)\\n self.delta = [None] * n\\n self.delta[n - 1] = []\\n \\n for i in xrange(len(expected)):\\n curr = self.a[n - 1][i]\\n self.delta[n - 1].append(self.derivativeFunc(curr) * (expected[i] - curr))\\n self.delta[n - 1] = np.array(self.delta[n - 1])\\n \\n # From n - 1 to 1 layer \\n for i in xrange(n - 1, 0, -1):\\n currDelta = self.delta[i]\\n if i != (n - 1):\\n currDelta = currDelta[0][:-1]\\n \\n self.delta[i - 1] = np.array(np.dot(currDelta, self.theta[i]))\\n self.delta[i - 1][0] *= self.a[i - 1]\\n \\n return\",\n \"def subtree_distances(self, root):\\r\\n\\r\\n nodes = root.get_terminals()\\r\\n nodes.reverse()\\r\\n node_pairs = itertools.ifilter(\\r\\n lambda (a1, a2): a1.name < a2.name,\\r\\n itertools.product(nodes, nodes))\\r\\n\\r\\n distances = [self._node_distance(pair[0], pair[1])\\r\\n for pair in node_pairs]\\r\\n\\r\\n return distances\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7081299","0.526944","0.52446645","0.5239564","0.523342","0.51974493","0.51963425","0.5140926","0.50798607","0.5076724","0.50607145","0.5054353","0.5047319","0.49978474","0.49799216","0.49528977","0.49478018","0.49372533","0.49131808","0.490018","0.48992065","0.4881139","0.483447","0.48213744","0.48211652","0.48179495","0.4774421","0.4773157","0.47703457","0.47701833","0.47622505","0.4756548","0.47551778","0.47547767","0.4747443","0.4740483","0.47289827","0.47096822","0.47052774","0.47030467","0.4702251","0.47013265","0.46887287","0.4685533","0.46786118","0.46610072","0.46476656","0.4639737","0.4637906","0.4625062","0.46239173","0.4619137","0.4612453","0.46091428","0.4601979","0.4597569","0.459671","0.45939854","0.45768806","0.4569076","0.45597997","0.4558658","0.45555708","0.45529705","0.4551819","0.4549997","0.4547624","0.45384267","0.45300558","0.4526362","0.45232844","0.4520279","0.45197383","0.45188397","0.45123148","0.45092767","0.4509249","0.45070252","0.45035177","0.45033658","0.44852942","0.4481704","0.44811776","0.44747904","0.44728574","0.44725865","0.44647738","0.44606042","0.44577897","0.4456576","0.44557968","0.4454424","0.4448886","0.4448548","0.44468504","0.44465604","0.44409955","0.44394305","0.44353747","0.4431598"],"string":"[\n \"0.7081299\",\n \"0.526944\",\n \"0.52446645\",\n \"0.5239564\",\n \"0.523342\",\n \"0.51974493\",\n \"0.51963425\",\n \"0.5140926\",\n \"0.50798607\",\n \"0.5076724\",\n \"0.50607145\",\n \"0.5054353\",\n \"0.5047319\",\n \"0.49978474\",\n \"0.49799216\",\n \"0.49528977\",\n \"0.49478018\",\n \"0.49372533\",\n \"0.49131808\",\n \"0.490018\",\n \"0.48992065\",\n \"0.4881139\",\n \"0.483447\",\n \"0.48213744\",\n \"0.48211652\",\n \"0.48179495\",\n \"0.4774421\",\n \"0.4773157\",\n \"0.47703457\",\n \"0.47701833\",\n \"0.47622505\",\n \"0.4756548\",\n \"0.47551778\",\n \"0.47547767\",\n \"0.4747443\",\n \"0.4740483\",\n \"0.47289827\",\n \"0.47096822\",\n \"0.47052774\",\n \"0.47030467\",\n \"0.4702251\",\n \"0.47013265\",\n \"0.46887287\",\n \"0.4685533\",\n \"0.46786118\",\n \"0.46610072\",\n \"0.46476656\",\n \"0.4639737\",\n \"0.4637906\",\n \"0.4625062\",\n \"0.46239173\",\n \"0.4619137\",\n \"0.4612453\",\n \"0.46091428\",\n \"0.4601979\",\n \"0.4597569\",\n \"0.459671\",\n \"0.45939854\",\n \"0.45768806\",\n \"0.4569076\",\n \"0.45597997\",\n \"0.4558658\",\n \"0.45555708\",\n \"0.45529705\",\n \"0.4551819\",\n \"0.4549997\",\n \"0.4547624\",\n \"0.45384267\",\n \"0.45300558\",\n \"0.4526362\",\n \"0.45232844\",\n \"0.4520279\",\n \"0.45197383\",\n \"0.45188397\",\n \"0.45123148\",\n \"0.45092767\",\n \"0.4509249\",\n \"0.45070252\",\n \"0.45035177\",\n \"0.45033658\",\n \"0.44852942\",\n \"0.4481704\",\n \"0.44811776\",\n \"0.44747904\",\n \"0.44728574\",\n \"0.44725865\",\n \"0.44647738\",\n \"0.44606042\",\n \"0.44577897\",\n \"0.4456576\",\n \"0.44557968\",\n \"0.4454424\",\n \"0.4448886\",\n \"0.4448548\",\n \"0.44468504\",\n \"0.44465604\",\n \"0.44409955\",\n \"0.44394305\",\n \"0.44353747\",\n \"0.4431598\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77134573"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8299,"cells":{"query":{"kind":"string","value":"Creates polynomial from given list of divided differences. Polynomial string is created according to equation provided in project docs."},"document":{"kind":"string","value":"def calculate_newton_interpolation(divided_differences):\n polynomial = []\n for i, divided_differences_row in enumerate(divided_differences):\n polynomial_part = '({0})'.format(divided_differences_row[0].divided_difference)\n\n for j in range(0, i):\n polynomial_part += '*(x-{0})'.format(divided_differences[0][j].x)\n\n polynomial_part += '+'\n polynomial.append(polynomial_part)\n polynomial_str = ''.join(polynomial)[:-1]\n\n print('Calculated polynomial: {0}'.format(polynomial_str))\n # Heuristic simplification of calculated polynomial\n simplified_polynomial = sy.simplify(polynomial_str)\n print(\"Simplified polynomial: {0}\".format(simplified_polynomial))\n return simplified_polynomial"},"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 list_to_poly(polynomial_list):\n max_degree = len(polynomial_list) - 1\n strings = []\n opts = ['x', '']\n for index, num in enumerate(polynomial_list):\n if num == 0:\n continue\n if index < max_degree - 1:\n string = '{}x^{}'.format(num, max_degree - index)\n strings.append(string)\n else:\n strings.append(str(num) + opts[index - (max_degree - 1)])\n polynomial = ' + '.join(strings).replace('+ -', '- ')\n return polynomial","def polynomial_equation(funct):\n coeff = str(differentiation.parse_coefficient(funct))\n if \"^\" not in funct:\n divisor = \"1\"\n else:\n divisor_location = str(funct.index(\"^\") + 1)\n divisor = funct[divisor_location:]\n if divisor == \"-1\":\n pass\n else:\n divisor = str(int(divisor) + 1)\n coeff += \"/\" + divisor\n return coeff + \"x^\" + str(divisor)","def compute_deriv(poly):\n derivative_of_poly = []\n for i in range(1, len(poly)):\n power = i\n coeff = poly[i]\n y = float(coeff * power)\n first = derivative_of_poly.append(y)\n return derivative_of_poly","def build_poly(x, degree):\n \"\"\"\n Assemble the 3 label vectors with the original ordering \n Inputs:\n - x (ndarray) : binary prediction for set 1\n - degree (int) : binary prediction for set 2 \n Outputs: \n - p (ndarray) : predicted labels for test set ( with the original ordering)\n \"\"\"\n # forming a matrix containing the data points\n terms = np.hstack([np.ones([x.shape[0],1]),np.tile(x,(1,degree))])\n index = np.arange(degree)+1\n \n # forming a matrix contnaining the exponents\n exponents = np.multiply(np.ones((1, x.shape[1])), index[:, np.newaxis])\n exponents = exponents.reshape([1, x.shape[1]*degree])\n exponents = np.multiply(exponents, np.ones([x.shape[0], 1]))\n exponents = np.hstack([np.ones( (x.shape[0], 1) ),exponents])\n \n # using the exponent matrix as the element-wise exponents of the terms in the terms matrix\n p=np.power(terms,exponents)\n return p","def poly_derivative(poly):\n if not poly or type(poly) is not list:\n return None\n\n response = []\n\n for order in range(1, len(poly)):\n response.append(order * poly[order])\n\n if not response:\n response.append(0)\n\n return response","def polynomial_creator(*coefficients):\n def polynomial(x):\n res = 0\n for index, coeff in enumerate(coefficients):\n res += coeff * x** index\n return res\n return polynomial","def compute_deriv(poly):\n exp = 1\n new_poly = []\n for x in poly[1:]:\n new_poly.append(x * exp)\n exp += 1\n\n return tuple(new_poly)","def build_poly_expr(query_tuple):\n print(\"query_tuple: \", query_tuple)\n expression = '0 + '\n factors = np.arange(7)\n\n for coeff, factor in zip(query_tuple, factors):\n if coeff != None:\n expression += '(' + str(np.float64(coeff)) + '*x^{}) + '.format(factor)\n\n # Remove trailing '+'\n expression = expression[:-3]\n \n # Return as a tuple.\n return (expression,)","def get_equation(self):\n self.polynomials = dict(sorted(self.polynomials.items(), reverse=True))\n string = \"\"\n\n for index, polynomial in self.polynomials.items():\n polynomial = int(polynomial)\n index = int(index)\n\n if polynomial != 0:\n if polynomial < 0:\n string_pre = \" - \"\n else:\n string_pre = \" + \"\n\n if index != 0:\n string_append = \"x\"\n elif polynomial == 1 or polynomial == -1:\n string_append = str(abs(polynomial))\n else:\n string_append = \"\"\n\n if polynomial < 0:\n polynomial = abs(polynomial)\n\n if polynomial != 1:\n string_append = str(polynomial) + string_append\n\n if index != 0 and index != 1:\n string_append += \"^\" + str(index)\n\n string += string_pre + string_append\n\n if len(string) > 0:\n string = string[3:]\n else:\n string = \"0\"\n\n return string","def coefficients_from_Weierstrass_polynomial(f):\n R = f.parent()\n cubic_variables = [x for x in R.gens() if f.degree(x) == 3]\n quadratic_variables = [y for y in R.gens() if f.degree(y) == 2]\n try:\n x = cubic_variables[0]\n y = quadratic_variables[0]\n except IndexError:\n raise ValueError('polynomial is not in long Weierstrass form')\n\n a1 = a2 = a3 = a4 = a6 = 0\n x3 = y2 = None\n for coeff, mon in f:\n if mon == x**3:\n x3 = coeff\n elif mon == x**2:\n a2 = coeff\n elif mon == x:\n a4 = coeff\n elif mon == 1:\n a6 = coeff\n elif mon == y**2:\n y2 = -coeff\n elif mon == x*y:\n a1 = -coeff\n elif mon == y:\n a3 = -coeff\n else:\n raise ValueError('polynomial is not in long Weierstrass form')\n\n if x3 != y2:\n raise ValueError('the coefficient of x^3 and -y^2 must be the same')\n elif x3 != 1:\n a1, a2, a3, a4, a6 = a1/x3, a2/x3, a3/x3, a4/x3, a6/x3\n return [a1, a2, a3, a4, a6]","def test():\n assert str(Polynomial(0, 1, 0, -1, 4, -2, 0, 1, 3, 0)) == \"3x^8 + x^7 - 2x^5 + 4x^4 - x^3 + x\"\n assert str(Polynomial([-5, 1, 0, -1, 4, -2, 0, 1, 3, 0])) == \"3x^8 + x^7 - 2x^5 + 4x^4 - x^3 + x - 5\"\n assert str(Polynomial(x7=1, x4=4, x8=3, x9=0, x0=0, x5=-2, x3=-1, x1=1)) == \"3x^8 + x^7 - 2x^5 + 4x^4 - x^3 + x\"\n assert str(Polynomial(x2=0)) == \"0\"\n assert str(Polynomial(x0=0)) == \"0\"\n assert Polynomial(x0=2, x1=0, x3=0, x2=3) == Polynomial(2, 0, 3)\n assert Polynomial(x2=0) == Polynomial(x0=0)\n assert str(Polynomial(x0=1) + Polynomial(x1=1)) == \"x + 1\"\n assert str(Polynomial([-1, 1, 1, 0]) + Polynomial(1, -1, 1)) == \"2x^2\"\n pol1 = Polynomial(x2=3, x0=1)\n pol2 = Polynomial(x1=1, x3=0)\n assert str(pol1 + pol2) == \"3x^2 + x + 1\"\n assert str(pol1 + pol2) == \"3x^2 + x + 1\"\n assert str(Polynomial(x0=-1, x1=1) ** 1) == \"x - 1\"\n assert str(Polynomial(x0=-1, x1=1) ** 2) == \"x^2 - 2x + 1\"\n pol3 = Polynomial(x0=-1, x1=1)\n assert str(pol3 ** 4) == \"x^4 - 4x^3 + 6x^2 - 4x + 1\"\n assert str(pol3 ** 4) == \"x^4 - 4x^3 + 6x^2 - 4x + 1\"\n assert str(Polynomial(x0=2).derivative()) == \"0\"\n assert str(Polynomial(x3=2, x1=3, x0=2).derivative()) == \"6x^2 + 3\"\n assert str(Polynomial(x3=2, x1=3, x0=2).derivative().derivative()) == \"12x\"\n pol4 = Polynomial(x3=2, x1=3, x0=2)\n assert str(pol4.derivative()) == \"6x^2 + 3\"\n assert str(pol4.derivative()) == \"6x^2 + 3\"\n assert Polynomial(-2, 3, 4, -5).at_value(0) == -2\n assert Polynomial(x2=3, x0=-1, x1=-2).at_value(3) == 20\n assert Polynomial(x2=3, x0=-1, x1=-2).at_value(3, 5) == 44\n pol5 = Polynomial([1, 0, -2])\n assert pol5.at_value(-2.4) == -10.52\n assert pol5.at_value(-2.4) == -10.52\n assert pol5.at_value(-1, 3.6) == -23.92\n assert pol5.at_value(-1, 3.6) == -23.92","def poly_derivative(poly):\n if type(poly) is not list or len(poly) < 1:\n return None\n if len(poly) == 1:\n return [0]\n\n derivated_coefficients = []\n\n for power, coefficient in enumerate(poly):\n if power == 0:\n pass\n\n else:\n new_coefficient = coefficient * power\n derivated_coefficients.append(new_coefficient)\n\n return(derivated_coefficients)","def _poly_func(x, a, b, c, d, e):\n return a * x ** 6 + b * x ** 5 + c * x ** 4 + d * x ** 3 + e * x ** 2","def construct_poly(data, power):\n return np.power(data, power)","def fourth_poly(a, b, c, d, e):\n return lambda z: a*z**4 + b*z**3 + c*z**2 + d*z + e","def build_poly(x, degree):\n phi = np.ones(len(x))\n phi = np.vstack((phi, [x**(j+1) for j in range(degree)]))\n \n return phi.T","def linear_simplify_poly(poly):\n if len(poly) < 4:\n return poly\n\n q = Queue()\n for v in poly:\n q.put(v)\n\n new_poly = []\n a = q.get()\n b = q.get()\n while True:\n if q.empty():\n new_poly += [a,b]\n break\n c = q.get()\n e1 = (b-a).normalized()\n e2 = (c-b).normalized()\n if abs(1.0 - e1.dot(e2)) < 1e-2:\n # colinear. skip b.\n a = a\n b = c\n else:\n # a,b needed.\n new_poly += [a]\n a = b\n b = c\n return new_poly","def polyLS(pd, x, y, f, X, Y \\\n, coeff = [], xmc = [], ymc = [], ell = [], w = [], ELL = [], W = []) :\n xmc, ymc, ell, w, ELL, W = assignDefaults(x, y, xmc, ymc, ell, w, ELL, W)\n \n numP = int((pd + 1) * (pd + 2) / 2)\n \n if (len(xmc) == 1) and (len(ymc) == 1) :\n \n\n if coeff == [] :\n p = poly(x, y, pd)\n coeff = np.linalg.lstsq(p, f, rcond=None)[0]\n\n B = poly(X, Y, pd)\n approx = B.dot(coeff).flatten()\n coeff_copy = coeff\n \n else :\n \n approx = np.zeros(len(X), float)\n \n if coeff == [] :\n for i in range(len(xmc)) :\n IND = inSquare(x, y, xmc[i], ymc[i], ELL, W)\n if len(IND) < int(1.5 * numP) :\n raise ValueError(\"Not enough data for this polynomial \" \\\n + \"degree.\\nEither lower the polynomial degree or \" \\\n + \"decrease the number of subdivisions.\")\n p = poly(x[IND], y[IND], pd)\n lam = np.linalg.lstsq(p, f[IND], rcond=None)[0]\n coeff.append(lam)\n\n coeff_copy = coeff.copy()\n\n for i in range(len(xmc) - 1, -1, -1) :\n IND = inSquare(X, Y, xmc[i], ymc[i], ell, w)\n B = poly(X[IND], Y[IND], pd)\n lam = coeff.pop()\n approx[IND] = B.dot(lam).flatten()\n \n return approx, coeff_copy","def poly_derivative(poly):\n if not type(poly) is list or len(poly) == 0 or type(poly[0]) is not int:\n return None\n\n derivative = []\n for i in range(1, len(poly)):\n derivative.append(poly[i] * i)\n\n if derivative == []:\n derivative = [0]\n\n return derivative","def poly_desc(W, b):\n result = 'y = '\n for i, w in enumerate(W):\n result += '{:+.2f} x^{} '.format(w, len(W) - i)\n result += '{:+.2f}'.format(b[0])\n return result","def linear_polynomial(self, e: 'PFElement') -> Polynomial:\n poly = self.polynomial(-e)\n poly += poly.monic(1)\n return poly","def parse_poly(self, expr: str) -> Polynomial:\n return symbolic_polynomial(expr, self)","def poly_derivative(poly):\n res = []\n if type(poly) is not list or len(poly) == 0:\n return None\n if len(poly) == 1:\n return([0])\n for i in range(1, len(poly)):\n if type(poly[i]) is not int:\n return None\n res.append(poly[i] * i)\n return(res)","def polynomial(self, *args, indeterminate: str = 'X') -> Polynomial:\n return Polynomial([self.element(c) for c in args], base_field=self, indeterminate=indeterminate)","def newton_divided_difference(x, y):\n\n n = x.size\n q = np.zeros((n, n - 1))\n # Insert 'y' in the first column of the matrix 'q'\n q = np.concatenate((y[:, None], q), axis=1)\n\n for i in range(1, n):\n for j in range(1, i + 1):\n q[i, j] = (q[i, j - 1] - q[i - 1, j - 1]) / (x[i] - x[i - j])\n\n # Copy the diagonal values of the matrix q to the vector f\n f = np.zeros(n)\n for i in range(0, n):\n f[i] = q[i, i]\n\n # Prints the polynomial\n print(\"The polynomial is:\")\n print(\"p(x)={:+.4f}\".format(f[0]), end=\"\")\n for i in range(1, n):\n print(\"{:+.4f}\".format(f[i]), end=\"\")\n for j in range(1, i + 1):\n print(\"(x{:+.4f})\".format(x[j] * -1), end=\"\")\n print(\"\")\n\n return [f]","def poly_derivative(poly):\n result = []\n\n if poly is None or type(poly) != list or poly == []:\n return None\n\n for i in range(len(poly)):\n if type(poly[i]) not in (int, float):\n return None\n elif len(poly) == 1:\n result.append(0)\n else:\n if i == 0:\n continue\n result.append(i * poly[i])\n\n return result","def generate_polynomial():\n degree = numpy.random.choice(range(3, 7))\n x = numpy.linspace(-10, 10, 1000)\n coefficients = numpy.random.chisquare(3, size=degree) + 1\n coefficients *= numpy.random.choice([-1, 1], size=coefficients.shape)\n coefficients *= 0.5\n y = numpy.polyval(coefficients, x)\n add_noise(y, 0.1)\n return x, y","def zzX_to_poly(f, *symbols):\n from sympy.polys import Poly\n\n terms = {}\n\n for monom, coeff in zzX_to_dict(f).iteritems():\n terms[monom] = Integer(int(coeff))\n\n return Poly(terms, *symbols)","def poly_derivative(poly):\n if type(poly) != list:\n return None\n size = len(poly)\n if size == 0:\n return None\n for e in poly:\n if not isinstance(e, (int, float)):\n return None\n if size == 1:\n return [0]\n deriv = []\n for i in range(1, size):\n temp = poly[i] * i\n deriv.append(temp)\n return deriv","def base_polynome(numbers):\n\n monomes = [ x**n for n in numbers ]\n polynome = sum(monomes)\n\n return poly(polynome, x)","def _poly_desc(self, W, b):\n\t\tresult = 'y = '\n\t\tfor i, w in enumerate(W):\n\t\t\tresult += '{:+.2f} x^{} '.format(w, len(W) - i)\n\t\tresult += '{:+.2f}'.format(b[0])\n\t\treturn result","def definePolyFunction():\n lstWeights=[]\n degree = input(\"degree of polynomial in terms of highest exponent of x:\")\n degree = int(degree+1)\n for a in range (0,degree):\n string='weight for x^'+str(a)+':'\n weight = input(string)\n weight = float(weight)\n lstWeights.append(weight)\n return lstWeights","def poly_derivative(poly):\n if not isinstance(poly, list):\n return None\n elif len(poly) == 0:\n return None\n elif len(poly) == 1:\n return [0]\n else:\n deriv = [0] * (len(poly) - 1)\n for i in range(len(poly) - 1):\n if (not isinstance(poly[i], int)):\n return None\n deriv[i] = poly[i + 1] * (i + 1)\n return deriv","def general_poly(L):\n def evaluate(x):\n length=len(L)-1\n value=0\n for i in L:\n value+=i*(x**length)\n length-=1\n return value\n return evaluate","def lsf2poly(lsf):\n # Reference: A.M. Kondoz, \"Digital Speech: Coding for Low Bit Rate Communications\n # Systems\" John Wiley & Sons 1994 ,Chapter 4\n\n # Line spectral frequencies must be real.\n\n lsf = np.array(lsf)\n\n if max(lsf) > np.pi or min(lsf) < 0:\n raise ValueError('Line spectral frequencies must be between 0 and pi.')\n\n p = len(lsf) # model order\n\n # Form zeros using the LSFs and unit amplitudes\n z = np.exp(1.j * lsf)\n\n # Separate the zeros to those belonging to P and Q\n rQ = z[0::2]\n rP = z[1::2]\n\n # Include the conjugates as well\n rQ = np.concatenate((rQ, rQ.conjugate()))\n rP = np.concatenate((rP, rP.conjugate()))\n\n # Form the polynomials P and Q, note that these should be real\n Q = np.poly(rQ);\n P = np.poly(rP);\n\n # Form the sum and difference filters by including known roots at z = 1 and\n # z = -1\n\n if p % 2:\n # Odd order: z = +1 and z = -1 are roots of the difference filter, P1(z)\n P1 = np.convolve(P, [1, 0, -1])\n Q1 = Q\n else:\n # Even order: z = -1 is a root of the sum filter, Q1(z) and z = 1 is a\n # root of the difference filter, P1(z)\n P1 = np.convolve(P, [1, -1])\n Q1 = np.convolve(Q, [1, 1])\n\n # Prediction polynomial is formed by averaging P1 and Q1\n\n a = .5 * (P1 + Q1)\n return a[0:-1:1] # do not return last element","def poly_eq(self, independent, dependent):\n\n try:\n x = self.df_input[[independent]]\n y = self.df_input[[dependent]]\n\n poly = PolynomialFeatures(degree = 2)\n x_poly = poly.fit_transform(x) \n\n model = LinearRegression()\n model.fit(x_poly, y)\n coef_arr = model.coef_\n intercept_arr = model.intercept_\n \n poly_equation = \"y = \" + str(round(coef_arr[0][2], 4)) + \"x\\xB2\"\n \n if(coef_arr[0][1] < 0):\n poly_equation += \" + (\" + str(round(coef_arr[0][1], 4)) + \"x\" + \")\"\n else:\n poly_equation += \" + \" + str(round(coef_arr[0][1], 4)) + \"x\"\n \n if(intercept_arr[0] < 0):\n poly_equation += \" + (\" + str(round(intercept_arr[0], 4)) + \")\"\n else:\n poly_equation += \" + \" + str(round(intercept_arr[0], 4))\n \n return poly_equation\n except Exception as e:\n print(e)","def deriv(self):\n poly_deriv = []\n for i, val in enumerate(self.coeff):\n poly_deriv.append(i*val)\n # Removes the differentiated constant (which is always 0).\n del poly_deriv[0]\n return Poly(poly_deriv)","def Hcurl_polynomials(\n domain_dim: int, range_dim: int, order: int, variables: AxisVariablesNotSingle = x\n) -> typing.List[VectorFunction]:\n assert domain_dim == range_dim\n if domain_dim == 2:\n return [VectorFunction((\n variables[0] ** (order - 1 - j) * variables[1] ** (j + 1),\n -variables[0] ** (order - j) * variables[1] ** j,\n )) for j in range(order)]\n if domain_dim == 3:\n poly: typing.List[VectorFunction] = []\n poly += [VectorFunction((\n variables[0] ** (m - 1) * variables[1] ** n * variables[2] ** (order - m - n + 1),\n 0, -variables[0] ** m * variables[1] ** n * variables[2] ** (order - m - n)\n )) for n in range(order) for m in range(1, order + 1 - n)]\n poly += [VectorFunction((\n 0, variables[0] ** m * variables[1] ** (n - 1) * variables[2] ** (order - m - n + 1),\n -variables[0] ** m * variables[1] ** n * variables[2] ** (order - m - n)\n )) for m in range(order) for n in range(1, order + 1 - m)]\n poly += [VectorFunction((\n variables[0] ** (order - n) * variables[1] ** n,\n -variables[0] ** (order + 1 - n) * variables[1] ** (n - 1), 0\n )) for n in range(1, order + 1)]\n return poly\n raise ValueError(f\"Unsupported dimension: {domain_dim}\")","def build_poly(x, degree): \n # ***************************************************\n # COPY YOUR CODE FROM EX03 HERE\n # polynomial basis function: TODO\n # this function should return the matrix formed\n # by applying the polynomial basis to the input data\n # ***************************************************\n raise NotImplementedError","def __str__(self) -> str:\n polynomial = \"\"\n for i in range(self.degree, -1, -1):\n if self.coefficients[i] == 0:\n continue\n elif self.coefficients[i] > 0:\n if polynomial:\n polynomial += \" + \"\n else:\n polynomial += \" - \"\n\n if i == 0:\n polynomial += str(abs(self.coefficients[i]))\n elif i == 1:\n polynomial += str(abs(self.coefficients[i])) + \"x\"\n else:\n polynomial += str(abs(self.coefficients[i])) + \"x^\" + str(i)\n\n return polynomial","def horner(poly: Sequence[float], x: float) -> float:\n result = 0.0\n for coeff in reversed(poly):\n result = result * x + coeff\n return result","def simplify(self): # TODO too complex, refactor\n simplified = self.__simplifyRecurse()\n # distributed out the polynomial. Now need to collect like terms\n simplified.vars = self.vars.copy()\n orderedVars = sorted(list(self.vars))\n\n powers = {} # will have keys of tuples. The tuples will represent the power of a variable. Values will be the\n for monomial in simplified.poly[1:]:\n power = [0] * len(orderedVars)\n total = 0\n\n if monomial.isSimple():\n monomial.poly = [\"*\", monomial.poly]\n\n for term in monomial.poly[1:]:\n term = ensurePoly(term)\n if isinstance(term.poly, (int, float)):\n total += term.poly\n elif isinstance(term.poly, (Variable.Variable)):\n power[orderedVars.index(term.poly)] += 1\n\n power = tuple(power)\n if power not in powers:\n powers[power] = total\n else:\n powers[power] = min(total, powers[power])\n\n finalPoly = Polynomial()\n finalPoly.poly = [\"+\"]\n finalPoly.vars = self.vars.copy()\n for power in sorted(list(powers.keys())):\n monomial = Polynomial()\n monomial.poly = [\"*\"]\n if powers[power] != 0:\n monomial.poly.append(powers[power])\n\n for pow, ind in zip(power, range(len(power))):\n if pow == 0:\n continue\n elif pow == 1:\n monomial.poly.append(orderedVars[ind])\n else:\n monomial.poly.append(orderedVars[ind]**pow)\n finalPoly.poly.append(monomial)\n return finalPoly","def parse_polynomial(s):\n\n def parse_n(s):\n '''Parse the number part of a polynomial string term'''\n if not s:\n return 1\n elif s == '-':\n return -1\n elif s == '+':\n return 1\n return float(eval(s))\n\n def parse_p(s, powerPattern):\n '''Parse the power part of a polynomial string term'''\n if not s:\n return 0\n multipliers = powerPattern.findall(s)[0]\n if not multipliers:\n return 1\n return int(multipliers)\n s = str(s).replace(' ', '') # remove all whitespace from string\n m = re.search('[a-zA-Z]+', s)\n try:\n varLetter = m.group(0)\n except AttributeError:\n varLetter = 'P'\n termPattern = re.compile(\n '([+-]?\\d*\\.?\\d*)\\**({var}?\\^?\\d?)'.format(var=varLetter))\n powerPattern = re.compile('{var}\\^?(\\d)?'.format(var=varLetter))\n order_multipliers = {}\n\n for n, p in termPattern.findall(s):\n n, p = n.strip(), p.strip()\n if not n and not p:\n continue\n n, p = parse_n(n), parse_p(p, powerPattern)\n if p in order_multipliers:\n order_multipliers[p] += n\n else:\n order_multipliers[p] = n\n highest_order = max(\n max(order_multipliers.keys()), 1) # order must be at least linear\n multipliers = [0] * (highest_order + 1)\n for key, val in order_multipliers.items():\n multipliers[key] = val\n\n return multipliers","def __init__(self, pol1, pol2):\n\n self._pol1, self._pol2 = pol1, pol2\n self.deg = self._pol1.deg*self._pol2.deg # degree of composed polynomial\n\n # WARNING: numpy.polynomial.polynomial.polyadd and polypow considers\n # arrays as polynomials with lowest coefficient first,\n # contrarily to polyval and polyfit.\n _pol1, _pol2 = self._pol1.pol[::-1], self._pol2.pol[::-1]\n\n self.pol = np.zeros((1,)) # composed polynomial\n for i in range(pol1.deg + 1):\n self.pol = polyadd(self.pol, _pol1[i]*polypow(_pol2, i))\n\n self.pol = self.pol[::-1]","def __test_s_polynomial():\n poly_ring = PolynomialRing(QQ, 'x,y', order='deglex')\n x, y = poly_ring('x'), poly_ring('y')\n g = x ** 3 - 2 * x * y\n h = x ** 2 * y - 2 * y ** 2 + x\n print __s_polynomial(g, h) # Expected -x^2","def polynomialInterpolation(self,s):\n #print(s)\n #s[i]=xi,s[j]=xj\n return Polynomial.createFromInterpolation(s,range(len(s)))\n #return Polynomial(s,T)","def PolyDiff(u, x, deg = 3, diff = 1, width = 5):\n\n u = u.flatten()\n x = x.flatten()\n\n n = len(x)\n du = np.zeros((n - 2*width,diff))\n\n # Take the derivatives in the center of the domain\n for j in range(width, n-width):\n\n points = np.arange(j - width, j + width)\n\n # Fit to a polynomial\n poly = np.polynomial.chebyshev.Chebyshev.fit(x[points],u[points],deg)\n\n # Take derivatives\n for d in range(1,diff+1):\n du[j-width, d-1] = poly.deriv(m=d)(x[j])\n\n return du","def poly_regression(self,precision=8):\n # return empty lists if input is empty\n if self.training == []:\n return [],[]\n\n latitudes = []\n longitudes = []\n for point in self.training[:-1]:\n latitudes.append(point[0])\n longitudes.append(point[1]) \n # store everything in a dataframe\n latDf = pd.DataFrame(numpy.array(latitudes), columns=['latitudes'])\n longDf = pd.DataFrame(numpy.array(longitudes), columns=['longitudes'])\n\n # learn how to do regression\n reg = linear_model.LinearRegression()\n\n # pass the order of your polynomial here \n poly = PolynomialFeatures(precision)\n\n \n # regression with latitude as domain\n vertical_predicted_path = []\n transform = poly.fit_transform(longDf)\n\n reg.fit(transform,latDf)\n predictions = reg.predict(transform)\n\n for i in range(len(predictions)):\n vertical_predicted_path.append([predictions[i][0],longDf[\"longitudes\"][i]])\n\n \n # regression with longitude domain\n horizontal_predicted_path = []\n transform = poly.fit_transform(latDf)\n\n reg.fit(transform,longDf)\n predictions = reg.predict(transform)\n\n for i in range(len(predictions)):\n horizontal_predicted_path.append([latDf[\"latitudes\"][i], predictions[i][0]])\n\n self.horizontal = sorted(horizontal_predicted_path, key=lambda k: [k[1], k[0]])\n self.vertical = sorted(vertical_predicted_path, key=lambda k: [k[0], k[1]])\n \n # return sorted horizontal and vertical prediction\n return self.horizontal, self.vertical","def general_poly (L):\n def to_apply (x):\n n = 0\n for i in L:\n n = x*n + i\n return n\n return to_apply","def test_polynomial_from_float_list_same_as_from_float_args(self):\n coeffs = [1.0, 2.0, 3.0, 4.0, 5.0]\n\n p1 = Polynomial(coeffs)\n p2 = Polynomial(*coeffs)\n\n self.assertEqual(p1, p2)","def poly(x, y, pd) :\n # Maximum polynomial degree allowed is 7.\n maxD = 7\n if pd > maxD :\n exit(\"Please choose a reasonable polynomial degree (0 <= pd <= \" + maxD + \").\")\n \n # Make the polynomial matrix one degree at a time.\n p = np.zeros((len(x), int((pd+1)*(pd+2)/2)), float)\n count = 0\n numP = 0\n for i in range(pd + 1) :\n for j in range(numP + 1) :\n if (j == 0) and (numP == 0) :\n p[:,count] = 1\n elif (j == 0) :\n p[:,count] = x**(numP-j)\n elif (numP-j == 0) :\n p[:,count] = y**j\n else :\n p[:,count] = x**(numP-j) * y**j\n count += 1\n numP += 1\n \n return p","def build_poly(tx, degree) :\n shape = tx.shape\n poly = np.zeros((shape[0], shape[1] * degree))\n poly[:,:shape[1]] = tx\n for deg in range(2, degree + 1) :\n for j in range(0, shape[1]) :\n poly[:, shape[1] * (deg - 1) + j] = tx[:,j] ** deg\n return poly","def zzx_to_poly(f, *symbols):\n from sympy.polys import Poly\n\n terms = {}\n\n for monom, coeff in zzx_to_dict(f).iteritems():\n terms[(monom,)] = Integer(int(coeff))\n\n return Poly(terms, *symbols)","def poly_derivative(poly):\n try:\n iter(poly)\n except TypeError:\n return None\n if poly == [] or any(not isinstance(expo, (int, float)) for expo in poly):\n return None\n if len(poly) == 1:\n return [0]\n return [i*expo for i, expo in enumerate(poly)][1:]","def preprocess_roots(poly):\n coeff = S.One\n\n poly_func = poly.func\n try:\n _, poly = poly.clear_denoms(convert=True)\n except DomainError:\n return coeff, poly\n\n poly = poly.primitive()[1]\n poly = poly.retract()\n\n # TODO: This is fragile. Figure out how to make this independent of construct_domain().\n if poly.get_domain().is_Poly and all(c.is_term for c in poly.rep.coeffs()):\n poly = poly.inject()\n\n strips = list(zip(*poly.monoms()))\n gens = list(poly.gens[1:])\n\n base, strips = strips[0], strips[1:]\n\n for gen, strip in zip(list(gens), strips):\n reverse = False\n\n if strip[0] < strip[-1]:\n strip = reversed(strip)\n reverse = True\n\n ratio = None\n\n for a, b in zip(base, strip):\n if not a and not b:\n continue\n elif not a or not b:\n break\n elif b % a != 0:\n break\n else:\n _ratio = b // a\n\n if ratio is None:\n ratio = _ratio\n elif ratio != _ratio:\n break\n else:\n if reverse:\n ratio = -ratio\n\n poly = poly.eval(gen, 1)\n coeff *= gen**(-ratio)\n gens.remove(gen)\n\n if gens:\n poly = poly.eject(*gens)\n\n if poly.is_univariate and poly.get_domain().is_ZZ:\n basis = _integer_basis(poly)\n\n if basis is not None:\n n = poly.degree()\n\n def func(k, coeff):\n return coeff//basis**(n - k[0])\n\n poly = poly.termwise(func)\n coeff *= basis\n\n if not isinstance(poly, poly_func):\n poly = poly_func(poly)\n return coeff, poly","def test_polynomial_from_complex_list_same_as_from_complex_args(self):\n coeffs = [1j, 2j, 3j, 4j, 5j]\n\n p1 = Polynomial(coeffs)\n p2 = Polynomial(*coeffs)\n\n self.assertEqual(p1, p2)","def gen_rand_poly(deg_lower_limit = 1, deg_upper_limit = 10, coeff_limit = 10):\n deg = random.randint(deg_lower_limit,deg_upper_limit)\n coeffs = [random.randint(-coeff_limit, coeff_limit) for _ in range(deg+1)]\n\n # Never have 0 as leading coefficient\n if coeffs[deg] == 0:\n coeffs[deg] = 1\n\n def term(coeff, d):\n if coeff == 0:\n return ''\n elif d == 0:\n return (' + ' if coeff>0 else ' - ') + str(abs(coeff))\n elif d == 1:\n return (' + ' if coeff>0 else ' - ') + (f'{abs(coeff)}x' if abs(coeff)!=1 else 'x')\n elif d == deg:\n return ('' if coeff>0 else '-') + (f'{abs(coeff)}x^{d}' if abs(coeff)!=1 else f'x^{d}')\n else:\n return (' + ' if coeff>0 else ' - ') + (f'{abs(coeff)}x^{d}' if abs(coeff)!=1 else f'x^{d}')\n\n terms = [term(coeffs[d], d) for d in range(deg+1)]\n return deg, coeffs, ''.join([terms[d]for d in range(deg,-1,-1)]).strip('+ ')","def derivative(self) -> Polynomial:\n coefficients: list[float] = [0] * self.degree\n for i in range(self.degree):\n coefficients[i] = self.coefficients[i + 1] * (i + 1)\n return Polynomial(self.degree - 1, coefficients)","def coeffients(x, y):\n\n # ensure floating point datatypes\n x.astype(float)\n y.astype(float)\n\n # degree of interpolating polynomial\n n = len(x)\n\n # intitilize list of coeffients for interpolating polynomial to y values\n c = y.tolist()\n\n # compute coeffients\n for j in range(1, n):\n for i in range(n-1, j-1, -1):\n c[i] = float(c[i]-c[i-1])/float(x[i]-x[i-j])\n\n # return an array of polynomial coefficient, note: reverse order for np.polyval function\n return np.array(c[::-1])","def tensdot(polyList,order,trunc):\n\n def reshape(poly,expo):\n\n poly.coef = poly[:][:,expo]\n poly.expo = expo\n return poly\n\n dim = len(polyList)\n expo = indextens(order,dim,trunc)\n nbrPoly = expo.shape[1]\n coef = np.eye(nbrPoly)\n\n # Tensor product of the univariate basis\n\n for i in range(dim): polyList[i] = reshape(polyList[i],expo[i])\n for i in range(nbrPoly): coef[i] = np.prod([polyList[j][expo[j,i]] for j in range(dim)],axis=0)\n\n poly = Polynomial(expo,coef,1)\n return poly","def polygen(count=10, sum_count=10, deg=5, cof=10):\n\n s = enumi_beg\n ans = enumi_beg\n\n for i in range(count):\n s += item_beg\n ans += item_beg\n p = genpoly(sum_count, deg, cof)\n ans += p.print_out()\n s += p.rep + item_end\n ans += item_end\n s += enumi_end\n ans += enumi_end\n return s, ans","def general_poly (L):\n def inside(x):\n result = 0\n pwr = len(L) - 1\n for l in L:\n result = result + l * x ** pwr\n pwr -= 1\n return result\n return inside","def _eval_legpoly(self, t_0, t_s, p_0, p_s, geometry=None):\n\n assert geometry is not None, \"Geometry needs to be specified!\"\n\n theta_0 = sp.Symbol(\"theta_0\")\n theta_s = sp.Symbol(\"theta_s\")\n theta_ex = sp.Symbol(\"theta_ex\")\n phi_0 = sp.Symbol(\"phi_0\")\n phi_s = sp.Symbol(\"phi_s\")\n phi_ex = sp.Symbol(\"phi_ex\")\n\n res = self.legexpansion(t_0, t_s, p_0, p_s, geometry).xreplace(\n {\n theta_0: t_0,\n theta_s: t_s,\n phi_0: p_0,\n phi_s: p_s,\n theta_ex: t_s,\n phi_ex: p_s,\n }\n )\n return res.evalf()","def derivative(polinom):\n l = []\n for i in range(1, polinom.degree+1):\n l.append(polinom.coefficients[i]*i)\n return Polinom(l)","def construct_polynomial_approx(degree, weights):\n # here is a function that is created on the fly from the input feature\n # mapping and weights\n def prediction_function(xs):\n expanded_xs = np.matrix(expand_to_monomials(xs, degree))\n ys = expanded_xs*np.matrix(weights).reshape((len(weights),1))\n return np.array(ys).flatten()\n # we return the function reference (handle) itself. This can be used like\n # any other function\n return prediction_function","def _reform_poly_coefficients(fit_poly_x, fit_poly_y):\n # Extract values for CD matrix and recombining\n c11 = fit_poly_x.c1_0.value\n c12 = fit_poly_x.c0_1.value\n c21 = fit_poly_y.c1_0.value\n c22 = fit_poly_y.c0_1.value\n sip_poly_x = fit_poly_x.copy()\n sip_poly_y = fit_poly_y.copy()\n # Force low order coefficients to be 0 as defined in SIP\n sip_poly_x.c0_0 = 0\n sip_poly_y.c0_0 = 0\n sip_poly_x.c1_0 = 0\n sip_poly_x.c0_1 = 0\n sip_poly_y.c1_0 = 0\n sip_poly_y.c0_1 = 0\n\n cdmat = ((c11, c12), (c21, c22))\n invcdmat = npla.inv(np.array(cdmat))\n degree = fit_poly_x.degree\n # Now loop through all remaining coefficients\n for i in range(0, degree + 1):\n for j in range(0, degree + 1):\n if (i + j > 1) and (i + j < degree + 1):\n old_x = getattr(fit_poly_x, f'c{i}_{j}').value\n old_y = getattr(fit_poly_y, f'c{i}_{j}').value\n newcoeff = np.dot(invcdmat, np.array([[old_x], [old_y]]))\n setattr(sip_poly_x, f'c{i}_{j}', newcoeff[0, 0])\n setattr(sip_poly_y, f'c{i}_{j}', newcoeff[1, 0])\n\n return cdmat, sip_poly_x, sip_poly_y","def Hdiv_polynomials(\n domain_dim: int, range_dim: int, order: int, variables: AxisVariablesNotSingle = x\n) -> typing.List[VectorFunction]:\n assert domain_dim == range_dim\n if domain_dim == 2:\n return [VectorFunction((\n variables[0] * variables[0] ** (order - 1 - j) * variables[1] ** j,\n variables[1] * variables[0] ** (order - 1 - j) * variables[1] ** j,\n )) for j in range(order)]\n if domain_dim == 3:\n basis: typing.List[VectorFunction] = []\n for j in range(order):\n for k in range(order - j):\n p = variables[0] ** (order - 1 - j - k) * variables[1] ** j * variables[2] ** k\n basis.append(VectorFunction((variables[0] * p, variables[1] * p, variables[2] * p)))\n return basis\n\n raise ValueError(f\"Unsupported dimension: {domain_dim}\")","def equations(p):\n [x, y] = p\n list = [x - 5 , y - 5]\n return list","def _evalPoly(self,a,x):\n y = a[0]\n for i in range(1,len(a)):\n y = self.F.Multiply(y, x)\n y = self.F.Add(y, a[i])\n return y","def Hdiv_quolynomials(\n domain_dim: int, range_dim: int, order: int, variables: AxisVariablesNotSingle = x\n) -> typing.List[VectorFunction]:\n assert domain_dim == range_dim\n basis: typing.List[VectorFunction] = []\n for d in range(domain_dim):\n for j in product(range(order), repeat=domain_dim - 1):\n poly = 1\n for a, b in zip(variables, j[:d] + (order,) + j[d:]):\n poly *= a ** b\n basis.append(VectorFunction([poly if i == d else 0 for i in range(domain_dim)]))\n return basis","def construct3rdSumPoly(E):\n if E.a1() != 0 or E.a2() != 0 or E.a3() != 0:\n raise TypeError('Provided elliptic curve is not in the short Weierstrass form.') \n \n FF = E.base_field()\n A = E.a4()\n B = E.a6()\n PR.<s1, s2, s3> = PolynomialRing(FF, 3, order='degrevlex')\n smp = (s1 - s2)**2*s3**2 - 2*((s1 + s2)*(s1*s2 + A) + 2*B)*s3 + (s1*s2 - A)**2 - 4*B*(s1 + s2)\n return smp","def build_poly(x, degree):\n \n X = np.vander((x[:,0]).T, degree+1, increasing=True)\n \n for i in range(1,np.shape(x)[1],1):\n feat = (x[:,i]).T\n vander = np.vander(feat, degree+1, increasing=True)\n #remove the column of 1 at the beginning of each vander\n vander = np.delete(vander, 0,axis = 1)\n #concatenation\n X = np.concatenate((X, vander), axis=1)\n \n return X","def parameters_polynomial(cobj, prop, prop_units, alist, blist):\n for i, aval in enumerate(alist):\n if i == 0:\n param_units = prop_units\n else:\n param_units = prop_units / pyunits.K**i\n\n coeff = Var(doc=\"A parameter for CoolProp polynomial form\", units=param_units)\n cobj.add_component(prop + \"_coeff_A\" + str(i), coeff)\n coeff.fix(aval)\n\n for i, bval in enumerate(blist):\n if i == 0:\n param_units = pyunits.dimensionless\n else:\n param_units = pyunits.K**-i\n\n coeff = Var(doc=\"B parameter for CoolProp exponential form\", units=param_units)\n cobj.add_component(prop + \"_coeff_B\" + str(i), coeff)\n coeff.fix(bval)","def generate_poly(hyper, params):\n\n k, d = hyper['k'], hyper['d']\n #atoms = { \n # (h,) : symbols('h_%d'%h)\n # for h in xrange(1, k+1)\n # }\n #atoms[(k,)] = 1. - sum( symbols('h_%d'%h) for h in xrange(1, k) )\n\n atoms = {}\n for h in xrange(1,k+1):\n atoms.update({ \n (h,x1) : symbols('x_%d%d'%(h,x1))\n for x1 in xrange(1,d+1)\n })\n #atoms[(h,d)] = 1. - sum(symbols('x_%d%d'%(h,x1)) for x1 in xrange(1,d))\n\n m = {}\n for x1 in xrange(1,d+1):\n m[(x1,)] = poly( sum( atoms[(h,x1)] for h in xrange(1,k+1) ) )\n for x2 in xrange(1,d+1):\n m[(x1,x2)] = poly( sum( atoms[(h,x1)] * atoms[(h,x2)] for h in xrange(1,k+1) ) )\n for x3 in xrange(1,d+1):\n m[(x1,x2,x3)] = poly( sum( atoms[(h,x1)] * atoms[(h,x2)] * atoms[(h,x3)] for h in xrange(1,k+1) ) )\n\n return m","def general_poly (L):\r\n\r\n def secondFunc(x):\r\n total = 0\r\n listLength = len(L)-1\r\n for i in L:\r\n total += i * x**listLength\r\n listLength -= 1\r\n return(total)\r\n return secondFunc","def __polydiff__(x, dt, params, options=None):\n if isinstance(options, dict) and 'weights' in options.keys():\n w = options['weights']\n else:\n w = np.ones_like(x)\n\n if isinstance(params, list):\n order = params[0]\n else:\n order = params\n\n t = np.arange(1, len(x)+1)*dt\n\n # polyfit\n r = np.polyfit(t, x, order, w=w)[::-1]\n\n # derivative coefficients\n dr = copy.copy(r[1:])\n for i, _ in enumerate(dr):\n dr[i] = dr[i]*(i + 1)\n\n # evaluate dxdt_hat\n dxdt_hat = 0\n for i, _ in enumerate(dr):\n dxdt_hat += dr[i]*t**i\n\n # evaluate smooth x\n x_hat = 0\n for i, _ in enumerate(r):\n x_hat += r[i]*t**i\n\n return x_hat, dxdt_hat","def PolyDiffPoint(u, x, deg = 3, diff = 1, index = None):\n \n n = len(x)\n if index == None: index = int((n-1)/2)\n\n # Fit to a polynomial\n poly = np.polynomial.chebyshev.Chebyshev.fit(x,u,deg)\n \n # Take derivatives\n derivatives = []\n for d in range(1,diff+1):\n derivatives.append(poly.deriv(m=d)(x[index]))\n \n return derivatives","def _getFrac(expr):\n expr=expr.replace(' ', '')\n l = len(expr)\n frac = []; start = 0; par = 0\n pack=''; num=''\n op = ['+','-']\n operator = ['+','-','/','*']\n sym = ['x','y']\n multFrac = False\n\n for i in range(0,l):\n if expr[i]=='(' : #(\n if par==0 : start=i\n par += 1\n elif expr[i] == ')' : #)\n par -= 1\n if par==0 :\n pack += expr[start:i+1]; start = i+1\n elif expr[i]=='*'and par==0: #*\n pack += expr[start:i]; start = i+1\n if num!='' :\n frac.append((num,pack))\n frac.append(expr[i])\n pack = ''; num = ''\n else :\n pack += expr[i]\n elif expr[i]=='/'and par==0: #/\n pack += expr[start:i]\n num += pack\n pack = ''\n start = i+1\n elif expr[i] in op and par==0 and num != '': #+-\n pack += expr[start:i]\n frac.append((num,pack))\n frac.append(expr[i])\n pack = ''; num = ''; start = i+1\n elif expr[i] in op and par==0:\n pack += expr[start:i]\n frac.append((pack,''))\n frac.append(expr[i])\n pack = ''; num = ''; start = i+1\n\n if start < l : pack += expr[start:l]\n if num != '' :\n frac.append((num,pack))\n else:\n frac.append((pack,''))\n\n frac2 = [frac[0]]\n i=1\n while i<len(frac):\n if frac[i] in operator and frac[i]!='*' :\n frac2.append(frac[i])\n frac2.append(frac[i+1])\n elif frac[i]=='*' :\n (a1,b1)=frac[i-1]\n (a2,b2)=frac[i+1]\n frac2[len(frac2)-1]=(a1+'*'+a2,b1+'*'+b2)\n i+=2\n return frac2","def strMultiPoly(poly, symbol=[\"X\",\"Y\"], asc=True):\n return termorder.MultivarTermOrder(cmp).format(MultiVarPolynomial(poly,symbol), symbol, asc)","def build_poly(x, degree):\n tx = np.zeros((x.shape[0], x.shape[1]*(degree+1)))\n \n for j in range(degree+1):\n tx[:,x.shape[1]*j:x.shape[1]*(j+1)] = np.power(x,j)\n \n return tx","def nice_cubic_polynomial(p):\n tmp = \"\"\n if p[\"a\"] == 1:\n tmp += \" x^3\"\n elif p[\"a\"] != 0:\n tmp += \"%.2fx^3\" % p[\"a\"]\n if p[\"b\"] == 1:\n tmp += \"\\t+ x^2\"\n elif p[\"b\"] != 0:\n tmp += \"\\t+ %.2fx^2\" % p[\"b\"]\n else:\n tmp += \"\\t\\t\"\n if p[\"c\"] == 1:\n tmp += \"\\t+ x\"\n elif p[\"c\"] != 0:\n tmp += \"\\t+ %.2fx\" % p[\"c\"]\n else:\n tmp += \"\\t\\t\"\n if p[\"d\"] != 0:\n tmp += \"\\t+ %.2f\" % p[\"d\"]\n return tmp","def polyrecur(order,dist,trunc=1):\n\n printer(0,'Computing polynomials ...')\n if not isinstance(dist,Joint): dist = Joint(dist)\n\n nbrPoly = order+1\n dim = dist[:].shape[0]\n expo = np.arange(nbrPoly)\n coef = np.zeros((nbrPoly,nbrPoly))\n norm = np.ones((dim,nbrPoly))\n\n coef[0,0] = 1\n polyList = []\n\n # Creates the univariate polynomial basis\n\n for i in range(dim):\n\n polyList.append(Polynomial(expo,coef))\n AB = dist[i].coef(nbrPoly)\n\n for j in range(1,nbrPoly):\n\n norm[i,j] = norm[i,j-1]*AB[1,j]\n polyList[i][j] = np.roll(polyList[i][j-1],1,axis=0)\n polyList[i][j] -= AB[0,j-1]*polyList[i][j-1]+AB[1,j-1]*polyList[i][j-2]\n\n # Normalization and tensor product\n\n for i in range(dim): polyList[i][:] /= np.sqrt(norm[i,:,None])\n poly = tensdot(polyList,order,trunc)\n\n printer(1,'Computing polynomials 100 %')\n return poly","def test_polynomial_from_string_the_same_as_string_args(self):\n coeffs = \"abcdefghijklmnopqrstuvwxyz\"\n\n p1 = Polynomial(coeffs)\n p2 = Polynomial(*coeffs)\n\n self.assertEqual(p1, p2)","def part_der(f,l):\r\n \r\n der = [] #empty list which derived functions will be appended onto\r\n for i in l:\r\n \r\n der.append(sym.diff(f, lambda i: f))\r\n print(der) \r\n return der","def poly(x, coeffs):\n return np.sum([coeffs[i] * x ** i for i in range(len(coeffs))], axis=0)","def PBpoly(n, x):\n n = int(n)\n return Bpoly(n, x-math.floor(x))","def minpoly(firstterms):\n field = ring.getRing(firstterms[0])\n r_0 = uniutil.polynomial({len(firstterms):field.one}, field)\n r_1 = uniutil.polynomial(enumerate(reversed(firstterms)), field)\n poly_ring = r_0.getRing()\n v_0 = poly_ring.zero\n v_1 = poly_ring.one\n n = len(firstterms) // 2\n\n while n <= r_1.degree():\n q, r = divmod(r_0, r_1)\n v_0, v_1 = v_1, v_0 - q*v_1\n r_0, r_1 = r_1, r\n return v_1.scalar_mul(v_1.leading_coefficient().inverse())","def E_polynomial(self):\n\n from nodepy import stability_function\n p, q = self.stability_function()\n return stability_function.E_polynomial(p, q)","def generate_random_tropical_poly(max_degree, min_coefficient, max_coefficient):\n coefficients = []\n for d in range(0, random.randint(1, max_degree) + 1):\n coefficients.append(random.randint(min_coefficient, max_coefficient))\n return coefficients","def anti_deriv(self):\n poly_anti_deriv = [0]\n for i, val in enumerate(self.coeff):\n poly_anti_deriv.append(round(val/(i+1.0), 2))\n return Poly(poly_anti_deriv)","def calc_poly_linear_regression(independent, dependent):\n # Reshape for sklearn\n independent = independent.values.reshape(-1,1)\n dependent = dependent.values.reshape(-1,1)\n # Make the whole thing poly\n poly = PolynomialFeatures(degree=2)\n independent_ = poly.fit_transform(independent)\n # Do the linear regression\n model = LinearRegression()\n model.fit(independent_, dependent)\n # Calculate R2\n return model.score(independent_, dependent)","def poly(x, degree=2):\n x = np.array(x)\n X_trans = np.transpose(np.vstack((x**k for k in range(degree + 1))))\n return np.linalg.qr(X_trans)[0][:, 1:]","def poly(*args):\n if len(args) == 0 or len(args) == 2:\n raise ValueError('bad number of arguments {} passed to poly()'.format(len(args)))\n if len(args) == 1:\n if ispoly(args[0]):\n return deepcopy(args[0])\n else:\n raise VauleError('non-poly list passed to poly()')\n # args is of length 3 or greater. Check to see if args are points\n a = list(args)\n b = list(filter(lambda x: not ispoint(x),a))\n if len(b) > 0:\n raise ValueError('non-point arguments to poly(): {} '.format(b))\n return deepcopy(a)","def addPol(*pol):\n\n sum = np.zeros((1,))\n for p in pol:\n sum = polyadd(sum, p)\n\n return sum","def factor_polynomial(a,b,c):\r\n afacts = factors(abs(a))\r\n cfacts = factors(abs(c))\r\n #choose a factor of a (d), then f is a/d and so on\r\n for d in afacts:\r\n for g in cfacts:\r\n f = a/d\r\n e = c/g\r\n if e*f + d*g == b:\r\n print(\"{}*x**2 + {}*x + {} = ({}x + {})({}x + {})\".format(a,b,c,d,int(e),int(f),g))\r\n return\r\n #return False\r","def get_poly(kwargs):\n from sklearn.preprocessing import PolynomialFeatures\n return PolynomialFeatures(**kwargs)","def __merge(self, inputPoly, op):\n newPoly = Polynomial()\n\n # cast the input to a Polynomial\n inputPoly = ensurePoly(inputPoly)\n if isinstance(inputPoly.poly, (int, float, Variable.Variable)):\n input = Polynomial()\n input.poly = [op, inputPoly.poly]\n else:\n input = inputPoly\n\n if isinstance(self.poly, (int, float, Variable.Variable)):\n self.poly = [op, self.poly]\n\n if op == self.poly[0] and op == input.poly[0]:\n newPoly.poly = self.poly + input.poly[1:]\n elif op == self.poly[0]:\n newPoly.poly = self.poly\n newPoly.poly.append(input)\n elif op == input.poly[0]:\n newPoly.poly = [input.poly[0], self]\n newPoly.poly.extend(input.poly[1:])\n else:\n newPoly.poly = [op, self, input]\n\n return newPoly","def polyfit(dates, levels, p):\n x = matplotlib.dates.date2num(dates)\n\n y = levels\n\n # Using shifted x values, find coefficient of best-fit\n # polynomial f(x) of degree p\n shifted_times = [t-x[0] for t in x]\n p_coeff = np.polyfit(shifted_times, y, p)\n\n # Convert coefficient into a polynomial that can be evaluated\n # e.g. poly(0.3)\n poly = np.poly1d(p_coeff)\n\n return (poly, x[0])","def reducedFormOne(self, equation):\n splitter = re.split('(\\+|\\-)', equation)\n newEquation = str()\n state = 0\n for token in splitter:\n if '(' in token or state > 0 or '[' in token:\n state += 1\n newEquation += token\n continue\n if ')' in token or ']' in token:\n state -= 1\n continue\n if '^' + self.var in token:\n newEquation += token\n self.validPolynome = False\n continue\n find = re.findall('(\\*|\\^|\\/)?(' + self.var + ')(\\^\\d+)?' , token)\n newVar = []\n for var in find:\n newVar.append(''.join(map(str,var)))\n for var in newVar:\n token = token.replace(var, '')\n if token != '+' and token != '-' and token != '':\n try:\n newEquation += str(eval(token.replace('^', '**'))) + ''.join(newVar)\n except:\n self.error = True\n continue\n else:\n newEquation += token\n return newEquation","def _polynomial_entity(value, context):\n assert isinstance(value, Polynomial)\n coefficients = np.asarray(value.coefficients)\n num_variables = coefficients.ndim\n variables = [sympy.Symbol(context.pop()) for _ in range(num_variables)]\n function_symbol = context.pop()\n handle = FunctionHandle(function_symbol)\n handle_description = sympy.Function(function_symbol)(*variables)\n\n polynomial = polynomials.coefficients_to_polynomial(coefficients, variables)\n polynomial = polynomial.sympy()\n\n return Entity(\n context=context,\n value=value,\n expression=polynomial,\n polynomial_variables=variables,\n description='Let {function} = {polynomial}.',\n handle=handle,\n function=handle_description,\n polynomial=polynomial)"],"string":"[\n \"def list_to_poly(polynomial_list):\\n max_degree = len(polynomial_list) - 1\\n strings = []\\n opts = ['x', '']\\n for index, num in enumerate(polynomial_list):\\n if num == 0:\\n continue\\n if index < max_degree - 1:\\n string = '{}x^{}'.format(num, max_degree - index)\\n strings.append(string)\\n else:\\n strings.append(str(num) + opts[index - (max_degree - 1)])\\n polynomial = ' + '.join(strings).replace('+ -', '- ')\\n return polynomial\",\n \"def polynomial_equation(funct):\\n coeff = str(differentiation.parse_coefficient(funct))\\n if \\\"^\\\" not in funct:\\n divisor = \\\"1\\\"\\n else:\\n divisor_location = str(funct.index(\\\"^\\\") + 1)\\n divisor = funct[divisor_location:]\\n if divisor == \\\"-1\\\":\\n pass\\n else:\\n divisor = str(int(divisor) + 1)\\n coeff += \\\"/\\\" + divisor\\n return coeff + \\\"x^\\\" + str(divisor)\",\n \"def compute_deriv(poly):\\n derivative_of_poly = []\\n for i in range(1, len(poly)):\\n power = i\\n coeff = poly[i]\\n y = float(coeff * power)\\n first = derivative_of_poly.append(y)\\n return derivative_of_poly\",\n \"def build_poly(x, degree):\\n \\\"\\\"\\\"\\n Assemble the 3 label vectors with the original ordering \\n Inputs:\\n - x (ndarray) : binary prediction for set 1\\n - degree (int) : binary prediction for set 2 \\n Outputs: \\n - p (ndarray) : predicted labels for test set ( with the original ordering)\\n \\\"\\\"\\\"\\n # forming a matrix containing the data points\\n terms = np.hstack([np.ones([x.shape[0],1]),np.tile(x,(1,degree))])\\n index = np.arange(degree)+1\\n \\n # forming a matrix contnaining the exponents\\n exponents = np.multiply(np.ones((1, x.shape[1])), index[:, np.newaxis])\\n exponents = exponents.reshape([1, x.shape[1]*degree])\\n exponents = np.multiply(exponents, np.ones([x.shape[0], 1]))\\n exponents = np.hstack([np.ones( (x.shape[0], 1) ),exponents])\\n \\n # using the exponent matrix as the element-wise exponents of the terms in the terms matrix\\n p=np.power(terms,exponents)\\n return p\",\n \"def poly_derivative(poly):\\n if not poly or type(poly) is not list:\\n return None\\n\\n response = []\\n\\n for order in range(1, len(poly)):\\n response.append(order * poly[order])\\n\\n if not response:\\n response.append(0)\\n\\n return response\",\n \"def polynomial_creator(*coefficients):\\n def polynomial(x):\\n res = 0\\n for index, coeff in enumerate(coefficients):\\n res += coeff * x** index\\n return res\\n return polynomial\",\n \"def compute_deriv(poly):\\n exp = 1\\n new_poly = []\\n for x in poly[1:]:\\n new_poly.append(x * exp)\\n exp += 1\\n\\n return tuple(new_poly)\",\n \"def build_poly_expr(query_tuple):\\n print(\\\"query_tuple: \\\", query_tuple)\\n expression = '0 + '\\n factors = np.arange(7)\\n\\n for coeff, factor in zip(query_tuple, factors):\\n if coeff != None:\\n expression += '(' + str(np.float64(coeff)) + '*x^{}) + '.format(factor)\\n\\n # Remove trailing '+'\\n expression = expression[:-3]\\n \\n # Return as a tuple.\\n return (expression,)\",\n \"def get_equation(self):\\n self.polynomials = dict(sorted(self.polynomials.items(), reverse=True))\\n string = \\\"\\\"\\n\\n for index, polynomial in self.polynomials.items():\\n polynomial = int(polynomial)\\n index = int(index)\\n\\n if polynomial != 0:\\n if polynomial < 0:\\n string_pre = \\\" - \\\"\\n else:\\n string_pre = \\\" + \\\"\\n\\n if index != 0:\\n string_append = \\\"x\\\"\\n elif polynomial == 1 or polynomial == -1:\\n string_append = str(abs(polynomial))\\n else:\\n string_append = \\\"\\\"\\n\\n if polynomial < 0:\\n polynomial = abs(polynomial)\\n\\n if polynomial != 1:\\n string_append = str(polynomial) + string_append\\n\\n if index != 0 and index != 1:\\n string_append += \\\"^\\\" + str(index)\\n\\n string += string_pre + string_append\\n\\n if len(string) > 0:\\n string = string[3:]\\n else:\\n string = \\\"0\\\"\\n\\n return string\",\n \"def coefficients_from_Weierstrass_polynomial(f):\\n R = f.parent()\\n cubic_variables = [x for x in R.gens() if f.degree(x) == 3]\\n quadratic_variables = [y for y in R.gens() if f.degree(y) == 2]\\n try:\\n x = cubic_variables[0]\\n y = quadratic_variables[0]\\n except IndexError:\\n raise ValueError('polynomial is not in long Weierstrass form')\\n\\n a1 = a2 = a3 = a4 = a6 = 0\\n x3 = y2 = None\\n for coeff, mon in f:\\n if mon == x**3:\\n x3 = coeff\\n elif mon == x**2:\\n a2 = coeff\\n elif mon == x:\\n a4 = coeff\\n elif mon == 1:\\n a6 = coeff\\n elif mon == y**2:\\n y2 = -coeff\\n elif mon == x*y:\\n a1 = -coeff\\n elif mon == y:\\n a3 = -coeff\\n else:\\n raise ValueError('polynomial is not in long Weierstrass form')\\n\\n if x3 != y2:\\n raise ValueError('the coefficient of x^3 and -y^2 must be the same')\\n elif x3 != 1:\\n a1, a2, a3, a4, a6 = a1/x3, a2/x3, a3/x3, a4/x3, a6/x3\\n return [a1, a2, a3, a4, a6]\",\n \"def test():\\n assert str(Polynomial(0, 1, 0, -1, 4, -2, 0, 1, 3, 0)) == \\\"3x^8 + x^7 - 2x^5 + 4x^4 - x^3 + x\\\"\\n assert str(Polynomial([-5, 1, 0, -1, 4, -2, 0, 1, 3, 0])) == \\\"3x^8 + x^7 - 2x^5 + 4x^4 - x^3 + x - 5\\\"\\n assert str(Polynomial(x7=1, x4=4, x8=3, x9=0, x0=0, x5=-2, x3=-1, x1=1)) == \\\"3x^8 + x^7 - 2x^5 + 4x^4 - x^3 + x\\\"\\n assert str(Polynomial(x2=0)) == \\\"0\\\"\\n assert str(Polynomial(x0=0)) == \\\"0\\\"\\n assert Polynomial(x0=2, x1=0, x3=0, x2=3) == Polynomial(2, 0, 3)\\n assert Polynomial(x2=0) == Polynomial(x0=0)\\n assert str(Polynomial(x0=1) + Polynomial(x1=1)) == \\\"x + 1\\\"\\n assert str(Polynomial([-1, 1, 1, 0]) + Polynomial(1, -1, 1)) == \\\"2x^2\\\"\\n pol1 = Polynomial(x2=3, x0=1)\\n pol2 = Polynomial(x1=1, x3=0)\\n assert str(pol1 + pol2) == \\\"3x^2 + x + 1\\\"\\n assert str(pol1 + pol2) == \\\"3x^2 + x + 1\\\"\\n assert str(Polynomial(x0=-1, x1=1) ** 1) == \\\"x - 1\\\"\\n assert str(Polynomial(x0=-1, x1=1) ** 2) == \\\"x^2 - 2x + 1\\\"\\n pol3 = Polynomial(x0=-1, x1=1)\\n assert str(pol3 ** 4) == \\\"x^4 - 4x^3 + 6x^2 - 4x + 1\\\"\\n assert str(pol3 ** 4) == \\\"x^4 - 4x^3 + 6x^2 - 4x + 1\\\"\\n assert str(Polynomial(x0=2).derivative()) == \\\"0\\\"\\n assert str(Polynomial(x3=2, x1=3, x0=2).derivative()) == \\\"6x^2 + 3\\\"\\n assert str(Polynomial(x3=2, x1=3, x0=2).derivative().derivative()) == \\\"12x\\\"\\n pol4 = Polynomial(x3=2, x1=3, x0=2)\\n assert str(pol4.derivative()) == \\\"6x^2 + 3\\\"\\n assert str(pol4.derivative()) == \\\"6x^2 + 3\\\"\\n assert Polynomial(-2, 3, 4, -5).at_value(0) == -2\\n assert Polynomial(x2=3, x0=-1, x1=-2).at_value(3) == 20\\n assert Polynomial(x2=3, x0=-1, x1=-2).at_value(3, 5) == 44\\n pol5 = Polynomial([1, 0, -2])\\n assert pol5.at_value(-2.4) == -10.52\\n assert pol5.at_value(-2.4) == -10.52\\n assert pol5.at_value(-1, 3.6) == -23.92\\n assert pol5.at_value(-1, 3.6) == -23.92\",\n \"def poly_derivative(poly):\\n if type(poly) is not list or len(poly) < 1:\\n return None\\n if len(poly) == 1:\\n return [0]\\n\\n derivated_coefficients = []\\n\\n for power, coefficient in enumerate(poly):\\n if power == 0:\\n pass\\n\\n else:\\n new_coefficient = coefficient * power\\n derivated_coefficients.append(new_coefficient)\\n\\n return(derivated_coefficients)\",\n \"def _poly_func(x, a, b, c, d, e):\\n return a * x ** 6 + b * x ** 5 + c * x ** 4 + d * x ** 3 + e * x ** 2\",\n \"def construct_poly(data, power):\\n return np.power(data, power)\",\n \"def fourth_poly(a, b, c, d, e):\\n return lambda z: a*z**4 + b*z**3 + c*z**2 + d*z + e\",\n \"def build_poly(x, degree):\\n phi = np.ones(len(x))\\n phi = np.vstack((phi, [x**(j+1) for j in range(degree)]))\\n \\n return phi.T\",\n \"def linear_simplify_poly(poly):\\n if len(poly) < 4:\\n return poly\\n\\n q = Queue()\\n for v in poly:\\n q.put(v)\\n\\n new_poly = []\\n a = q.get()\\n b = q.get()\\n while True:\\n if q.empty():\\n new_poly += [a,b]\\n break\\n c = q.get()\\n e1 = (b-a).normalized()\\n e2 = (c-b).normalized()\\n if abs(1.0 - e1.dot(e2)) < 1e-2:\\n # colinear. skip b.\\n a = a\\n b = c\\n else:\\n # a,b needed.\\n new_poly += [a]\\n a = b\\n b = c\\n return new_poly\",\n \"def polyLS(pd, x, y, f, X, Y \\\\\\n, coeff = [], xmc = [], ymc = [], ell = [], w = [], ELL = [], W = []) :\\n xmc, ymc, ell, w, ELL, W = assignDefaults(x, y, xmc, ymc, ell, w, ELL, W)\\n \\n numP = int((pd + 1) * (pd + 2) / 2)\\n \\n if (len(xmc) == 1) and (len(ymc) == 1) :\\n \\n\\n if coeff == [] :\\n p = poly(x, y, pd)\\n coeff = np.linalg.lstsq(p, f, rcond=None)[0]\\n\\n B = poly(X, Y, pd)\\n approx = B.dot(coeff).flatten()\\n coeff_copy = coeff\\n \\n else :\\n \\n approx = np.zeros(len(X), float)\\n \\n if coeff == [] :\\n for i in range(len(xmc)) :\\n IND = inSquare(x, y, xmc[i], ymc[i], ELL, W)\\n if len(IND) < int(1.5 * numP) :\\n raise ValueError(\\\"Not enough data for this polynomial \\\" \\\\\\n + \\\"degree.\\\\nEither lower the polynomial degree or \\\" \\\\\\n + \\\"decrease the number of subdivisions.\\\")\\n p = poly(x[IND], y[IND], pd)\\n lam = np.linalg.lstsq(p, f[IND], rcond=None)[0]\\n coeff.append(lam)\\n\\n coeff_copy = coeff.copy()\\n\\n for i in range(len(xmc) - 1, -1, -1) :\\n IND = inSquare(X, Y, xmc[i], ymc[i], ell, w)\\n B = poly(X[IND], Y[IND], pd)\\n lam = coeff.pop()\\n approx[IND] = B.dot(lam).flatten()\\n \\n return approx, coeff_copy\",\n \"def poly_derivative(poly):\\n if not type(poly) is list or len(poly) == 0 or type(poly[0]) is not int:\\n return None\\n\\n derivative = []\\n for i in range(1, len(poly)):\\n derivative.append(poly[i] * i)\\n\\n if derivative == []:\\n derivative = [0]\\n\\n return derivative\",\n \"def poly_desc(W, b):\\n result = 'y = '\\n for i, w in enumerate(W):\\n result += '{:+.2f} x^{} '.format(w, len(W) - i)\\n result += '{:+.2f}'.format(b[0])\\n return result\",\n \"def linear_polynomial(self, e: 'PFElement') -> Polynomial:\\n poly = self.polynomial(-e)\\n poly += poly.monic(1)\\n return poly\",\n \"def parse_poly(self, expr: str) -> Polynomial:\\n return symbolic_polynomial(expr, self)\",\n \"def poly_derivative(poly):\\n res = []\\n if type(poly) is not list or len(poly) == 0:\\n return None\\n if len(poly) == 1:\\n return([0])\\n for i in range(1, len(poly)):\\n if type(poly[i]) is not int:\\n return None\\n res.append(poly[i] * i)\\n return(res)\",\n \"def polynomial(self, *args, indeterminate: str = 'X') -> Polynomial:\\n return Polynomial([self.element(c) for c in args], base_field=self, indeterminate=indeterminate)\",\n \"def newton_divided_difference(x, y):\\n\\n n = x.size\\n q = np.zeros((n, n - 1))\\n # Insert 'y' in the first column of the matrix 'q'\\n q = np.concatenate((y[:, None], q), axis=1)\\n\\n for i in range(1, n):\\n for j in range(1, i + 1):\\n q[i, j] = (q[i, j - 1] - q[i - 1, j - 1]) / (x[i] - x[i - j])\\n\\n # Copy the diagonal values of the matrix q to the vector f\\n f = np.zeros(n)\\n for i in range(0, n):\\n f[i] = q[i, i]\\n\\n # Prints the polynomial\\n print(\\\"The polynomial is:\\\")\\n print(\\\"p(x)={:+.4f}\\\".format(f[0]), end=\\\"\\\")\\n for i in range(1, n):\\n print(\\\"{:+.4f}\\\".format(f[i]), end=\\\"\\\")\\n for j in range(1, i + 1):\\n print(\\\"(x{:+.4f})\\\".format(x[j] * -1), end=\\\"\\\")\\n print(\\\"\\\")\\n\\n return [f]\",\n \"def poly_derivative(poly):\\n result = []\\n\\n if poly is None or type(poly) != list or poly == []:\\n return None\\n\\n for i in range(len(poly)):\\n if type(poly[i]) not in (int, float):\\n return None\\n elif len(poly) == 1:\\n result.append(0)\\n else:\\n if i == 0:\\n continue\\n result.append(i * poly[i])\\n\\n return result\",\n \"def generate_polynomial():\\n degree = numpy.random.choice(range(3, 7))\\n x = numpy.linspace(-10, 10, 1000)\\n coefficients = numpy.random.chisquare(3, size=degree) + 1\\n coefficients *= numpy.random.choice([-1, 1], size=coefficients.shape)\\n coefficients *= 0.5\\n y = numpy.polyval(coefficients, x)\\n add_noise(y, 0.1)\\n return x, y\",\n \"def zzX_to_poly(f, *symbols):\\n from sympy.polys import Poly\\n\\n terms = {}\\n\\n for monom, coeff in zzX_to_dict(f).iteritems():\\n terms[monom] = Integer(int(coeff))\\n\\n return Poly(terms, *symbols)\",\n \"def poly_derivative(poly):\\n if type(poly) != list:\\n return None\\n size = len(poly)\\n if size == 0:\\n return None\\n for e in poly:\\n if not isinstance(e, (int, float)):\\n return None\\n if size == 1:\\n return [0]\\n deriv = []\\n for i in range(1, size):\\n temp = poly[i] * i\\n deriv.append(temp)\\n return deriv\",\n \"def base_polynome(numbers):\\n\\n monomes = [ x**n for n in numbers ]\\n polynome = sum(monomes)\\n\\n return poly(polynome, x)\",\n \"def _poly_desc(self, W, b):\\n\\t\\tresult = 'y = '\\n\\t\\tfor i, w in enumerate(W):\\n\\t\\t\\tresult += '{:+.2f} x^{} '.format(w, len(W) - i)\\n\\t\\tresult += '{:+.2f}'.format(b[0])\\n\\t\\treturn result\",\n \"def definePolyFunction():\\n lstWeights=[]\\n degree = input(\\\"degree of polynomial in terms of highest exponent of x:\\\")\\n degree = int(degree+1)\\n for a in range (0,degree):\\n string='weight for x^'+str(a)+':'\\n weight = input(string)\\n weight = float(weight)\\n lstWeights.append(weight)\\n return lstWeights\",\n \"def poly_derivative(poly):\\n if not isinstance(poly, list):\\n return None\\n elif len(poly) == 0:\\n return None\\n elif len(poly) == 1:\\n return [0]\\n else:\\n deriv = [0] * (len(poly) - 1)\\n for i in range(len(poly) - 1):\\n if (not isinstance(poly[i], int)):\\n return None\\n deriv[i] = poly[i + 1] * (i + 1)\\n return deriv\",\n \"def general_poly(L):\\n def evaluate(x):\\n length=len(L)-1\\n value=0\\n for i in L:\\n value+=i*(x**length)\\n length-=1\\n return value\\n return evaluate\",\n \"def lsf2poly(lsf):\\n # Reference: A.M. Kondoz, \\\"Digital Speech: Coding for Low Bit Rate Communications\\n # Systems\\\" John Wiley & Sons 1994 ,Chapter 4\\n\\n # Line spectral frequencies must be real.\\n\\n lsf = np.array(lsf)\\n\\n if max(lsf) > np.pi or min(lsf) < 0:\\n raise ValueError('Line spectral frequencies must be between 0 and pi.')\\n\\n p = len(lsf) # model order\\n\\n # Form zeros using the LSFs and unit amplitudes\\n z = np.exp(1.j * lsf)\\n\\n # Separate the zeros to those belonging to P and Q\\n rQ = z[0::2]\\n rP = z[1::2]\\n\\n # Include the conjugates as well\\n rQ = np.concatenate((rQ, rQ.conjugate()))\\n rP = np.concatenate((rP, rP.conjugate()))\\n\\n # Form the polynomials P and Q, note that these should be real\\n Q = np.poly(rQ);\\n P = np.poly(rP);\\n\\n # Form the sum and difference filters by including known roots at z = 1 and\\n # z = -1\\n\\n if p % 2:\\n # Odd order: z = +1 and z = -1 are roots of the difference filter, P1(z)\\n P1 = np.convolve(P, [1, 0, -1])\\n Q1 = Q\\n else:\\n # Even order: z = -1 is a root of the sum filter, Q1(z) and z = 1 is a\\n # root of the difference filter, P1(z)\\n P1 = np.convolve(P, [1, -1])\\n Q1 = np.convolve(Q, [1, 1])\\n\\n # Prediction polynomial is formed by averaging P1 and Q1\\n\\n a = .5 * (P1 + Q1)\\n return a[0:-1:1] # do not return last element\",\n \"def poly_eq(self, independent, dependent):\\n\\n try:\\n x = self.df_input[[independent]]\\n y = self.df_input[[dependent]]\\n\\n poly = PolynomialFeatures(degree = 2)\\n x_poly = poly.fit_transform(x) \\n\\n model = LinearRegression()\\n model.fit(x_poly, y)\\n coef_arr = model.coef_\\n intercept_arr = model.intercept_\\n \\n poly_equation = \\\"y = \\\" + str(round(coef_arr[0][2], 4)) + \\\"x\\\\xB2\\\"\\n \\n if(coef_arr[0][1] < 0):\\n poly_equation += \\\" + (\\\" + str(round(coef_arr[0][1], 4)) + \\\"x\\\" + \\\")\\\"\\n else:\\n poly_equation += \\\" + \\\" + str(round(coef_arr[0][1], 4)) + \\\"x\\\"\\n \\n if(intercept_arr[0] < 0):\\n poly_equation += \\\" + (\\\" + str(round(intercept_arr[0], 4)) + \\\")\\\"\\n else:\\n poly_equation += \\\" + \\\" + str(round(intercept_arr[0], 4))\\n \\n return poly_equation\\n except Exception as e:\\n print(e)\",\n \"def deriv(self):\\n poly_deriv = []\\n for i, val in enumerate(self.coeff):\\n poly_deriv.append(i*val)\\n # Removes the differentiated constant (which is always 0).\\n del poly_deriv[0]\\n return Poly(poly_deriv)\",\n \"def Hcurl_polynomials(\\n domain_dim: int, range_dim: int, order: int, variables: AxisVariablesNotSingle = x\\n) -> typing.List[VectorFunction]:\\n assert domain_dim == range_dim\\n if domain_dim == 2:\\n return [VectorFunction((\\n variables[0] ** (order - 1 - j) * variables[1] ** (j + 1),\\n -variables[0] ** (order - j) * variables[1] ** j,\\n )) for j in range(order)]\\n if domain_dim == 3:\\n poly: typing.List[VectorFunction] = []\\n poly += [VectorFunction((\\n variables[0] ** (m - 1) * variables[1] ** n * variables[2] ** (order - m - n + 1),\\n 0, -variables[0] ** m * variables[1] ** n * variables[2] ** (order - m - n)\\n )) for n in range(order) for m in range(1, order + 1 - n)]\\n poly += [VectorFunction((\\n 0, variables[0] ** m * variables[1] ** (n - 1) * variables[2] ** (order - m - n + 1),\\n -variables[0] ** m * variables[1] ** n * variables[2] ** (order - m - n)\\n )) for m in range(order) for n in range(1, order + 1 - m)]\\n poly += [VectorFunction((\\n variables[0] ** (order - n) * variables[1] ** n,\\n -variables[0] ** (order + 1 - n) * variables[1] ** (n - 1), 0\\n )) for n in range(1, order + 1)]\\n return poly\\n raise ValueError(f\\\"Unsupported dimension: {domain_dim}\\\")\",\n \"def build_poly(x, degree): \\n # ***************************************************\\n # COPY YOUR CODE FROM EX03 HERE\\n # polynomial basis function: TODO\\n # this function should return the matrix formed\\n # by applying the polynomial basis to the input data\\n # ***************************************************\\n raise NotImplementedError\",\n \"def __str__(self) -> str:\\n polynomial = \\\"\\\"\\n for i in range(self.degree, -1, -1):\\n if self.coefficients[i] == 0:\\n continue\\n elif self.coefficients[i] > 0:\\n if polynomial:\\n polynomial += \\\" + \\\"\\n else:\\n polynomial += \\\" - \\\"\\n\\n if i == 0:\\n polynomial += str(abs(self.coefficients[i]))\\n elif i == 1:\\n polynomial += str(abs(self.coefficients[i])) + \\\"x\\\"\\n else:\\n polynomial += str(abs(self.coefficients[i])) + \\\"x^\\\" + str(i)\\n\\n return polynomial\",\n \"def horner(poly: Sequence[float], x: float) -> float:\\n result = 0.0\\n for coeff in reversed(poly):\\n result = result * x + coeff\\n return result\",\n \"def simplify(self): # TODO too complex, refactor\\n simplified = self.__simplifyRecurse()\\n # distributed out the polynomial. Now need to collect like terms\\n simplified.vars = self.vars.copy()\\n orderedVars = sorted(list(self.vars))\\n\\n powers = {} # will have keys of tuples. The tuples will represent the power of a variable. Values will be the\\n for monomial in simplified.poly[1:]:\\n power = [0] * len(orderedVars)\\n total = 0\\n\\n if monomial.isSimple():\\n monomial.poly = [\\\"*\\\", monomial.poly]\\n\\n for term in monomial.poly[1:]:\\n term = ensurePoly(term)\\n if isinstance(term.poly, (int, float)):\\n total += term.poly\\n elif isinstance(term.poly, (Variable.Variable)):\\n power[orderedVars.index(term.poly)] += 1\\n\\n power = tuple(power)\\n if power not in powers:\\n powers[power] = total\\n else:\\n powers[power] = min(total, powers[power])\\n\\n finalPoly = Polynomial()\\n finalPoly.poly = [\\\"+\\\"]\\n finalPoly.vars = self.vars.copy()\\n for power in sorted(list(powers.keys())):\\n monomial = Polynomial()\\n monomial.poly = [\\\"*\\\"]\\n if powers[power] != 0:\\n monomial.poly.append(powers[power])\\n\\n for pow, ind in zip(power, range(len(power))):\\n if pow == 0:\\n continue\\n elif pow == 1:\\n monomial.poly.append(orderedVars[ind])\\n else:\\n monomial.poly.append(orderedVars[ind]**pow)\\n finalPoly.poly.append(monomial)\\n return finalPoly\",\n \"def parse_polynomial(s):\\n\\n def parse_n(s):\\n '''Parse the number part of a polynomial string term'''\\n if not s:\\n return 1\\n elif s == '-':\\n return -1\\n elif s == '+':\\n return 1\\n return float(eval(s))\\n\\n def parse_p(s, powerPattern):\\n '''Parse the power part of a polynomial string term'''\\n if not s:\\n return 0\\n multipliers = powerPattern.findall(s)[0]\\n if not multipliers:\\n return 1\\n return int(multipliers)\\n s = str(s).replace(' ', '') # remove all whitespace from string\\n m = re.search('[a-zA-Z]+', s)\\n try:\\n varLetter = m.group(0)\\n except AttributeError:\\n varLetter = 'P'\\n termPattern = re.compile(\\n '([+-]?\\\\d*\\\\.?\\\\d*)\\\\**({var}?\\\\^?\\\\d?)'.format(var=varLetter))\\n powerPattern = re.compile('{var}\\\\^?(\\\\d)?'.format(var=varLetter))\\n order_multipliers = {}\\n\\n for n, p in termPattern.findall(s):\\n n, p = n.strip(), p.strip()\\n if not n and not p:\\n continue\\n n, p = parse_n(n), parse_p(p, powerPattern)\\n if p in order_multipliers:\\n order_multipliers[p] += n\\n else:\\n order_multipliers[p] = n\\n highest_order = max(\\n max(order_multipliers.keys()), 1) # order must be at least linear\\n multipliers = [0] * (highest_order + 1)\\n for key, val in order_multipliers.items():\\n multipliers[key] = val\\n\\n return multipliers\",\n \"def __init__(self, pol1, pol2):\\n\\n self._pol1, self._pol2 = pol1, pol2\\n self.deg = self._pol1.deg*self._pol2.deg # degree of composed polynomial\\n\\n # WARNING: numpy.polynomial.polynomial.polyadd and polypow considers\\n # arrays as polynomials with lowest coefficient first,\\n # contrarily to polyval and polyfit.\\n _pol1, _pol2 = self._pol1.pol[::-1], self._pol2.pol[::-1]\\n\\n self.pol = np.zeros((1,)) # composed polynomial\\n for i in range(pol1.deg + 1):\\n self.pol = polyadd(self.pol, _pol1[i]*polypow(_pol2, i))\\n\\n self.pol = self.pol[::-1]\",\n \"def __test_s_polynomial():\\n poly_ring = PolynomialRing(QQ, 'x,y', order='deglex')\\n x, y = poly_ring('x'), poly_ring('y')\\n g = x ** 3 - 2 * x * y\\n h = x ** 2 * y - 2 * y ** 2 + x\\n print __s_polynomial(g, h) # Expected -x^2\",\n \"def polynomialInterpolation(self,s):\\n #print(s)\\n #s[i]=xi,s[j]=xj\\n return Polynomial.createFromInterpolation(s,range(len(s)))\\n #return Polynomial(s,T)\",\n \"def PolyDiff(u, x, deg = 3, diff = 1, width = 5):\\n\\n u = u.flatten()\\n x = x.flatten()\\n\\n n = len(x)\\n du = np.zeros((n - 2*width,diff))\\n\\n # Take the derivatives in the center of the domain\\n for j in range(width, n-width):\\n\\n points = np.arange(j - width, j + width)\\n\\n # Fit to a polynomial\\n poly = np.polynomial.chebyshev.Chebyshev.fit(x[points],u[points],deg)\\n\\n # Take derivatives\\n for d in range(1,diff+1):\\n du[j-width, d-1] = poly.deriv(m=d)(x[j])\\n\\n return du\",\n \"def poly_regression(self,precision=8):\\n # return empty lists if input is empty\\n if self.training == []:\\n return [],[]\\n\\n latitudes = []\\n longitudes = []\\n for point in self.training[:-1]:\\n latitudes.append(point[0])\\n longitudes.append(point[1]) \\n # store everything in a dataframe\\n latDf = pd.DataFrame(numpy.array(latitudes), columns=['latitudes'])\\n longDf = pd.DataFrame(numpy.array(longitudes), columns=['longitudes'])\\n\\n # learn how to do regression\\n reg = linear_model.LinearRegression()\\n\\n # pass the order of your polynomial here \\n poly = PolynomialFeatures(precision)\\n\\n \\n # regression with latitude as domain\\n vertical_predicted_path = []\\n transform = poly.fit_transform(longDf)\\n\\n reg.fit(transform,latDf)\\n predictions = reg.predict(transform)\\n\\n for i in range(len(predictions)):\\n vertical_predicted_path.append([predictions[i][0],longDf[\\\"longitudes\\\"][i]])\\n\\n \\n # regression with longitude domain\\n horizontal_predicted_path = []\\n transform = poly.fit_transform(latDf)\\n\\n reg.fit(transform,longDf)\\n predictions = reg.predict(transform)\\n\\n for i in range(len(predictions)):\\n horizontal_predicted_path.append([latDf[\\\"latitudes\\\"][i], predictions[i][0]])\\n\\n self.horizontal = sorted(horizontal_predicted_path, key=lambda k: [k[1], k[0]])\\n self.vertical = sorted(vertical_predicted_path, key=lambda k: [k[0], k[1]])\\n \\n # return sorted horizontal and vertical prediction\\n return self.horizontal, self.vertical\",\n \"def general_poly (L):\\n def to_apply (x):\\n n = 0\\n for i in L:\\n n = x*n + i\\n return n\\n return to_apply\",\n \"def test_polynomial_from_float_list_same_as_from_float_args(self):\\n coeffs = [1.0, 2.0, 3.0, 4.0, 5.0]\\n\\n p1 = Polynomial(coeffs)\\n p2 = Polynomial(*coeffs)\\n\\n self.assertEqual(p1, p2)\",\n \"def poly(x, y, pd) :\\n # Maximum polynomial degree allowed is 7.\\n maxD = 7\\n if pd > maxD :\\n exit(\\\"Please choose a reasonable polynomial degree (0 <= pd <= \\\" + maxD + \\\").\\\")\\n \\n # Make the polynomial matrix one degree at a time.\\n p = np.zeros((len(x), int((pd+1)*(pd+2)/2)), float)\\n count = 0\\n numP = 0\\n for i in range(pd + 1) :\\n for j in range(numP + 1) :\\n if (j == 0) and (numP == 0) :\\n p[:,count] = 1\\n elif (j == 0) :\\n p[:,count] = x**(numP-j)\\n elif (numP-j == 0) :\\n p[:,count] = y**j\\n else :\\n p[:,count] = x**(numP-j) * y**j\\n count += 1\\n numP += 1\\n \\n return p\",\n \"def build_poly(tx, degree) :\\n shape = tx.shape\\n poly = np.zeros((shape[0], shape[1] * degree))\\n poly[:,:shape[1]] = tx\\n for deg in range(2, degree + 1) :\\n for j in range(0, shape[1]) :\\n poly[:, shape[1] * (deg - 1) + j] = tx[:,j] ** deg\\n return poly\",\n \"def zzx_to_poly(f, *symbols):\\n from sympy.polys import Poly\\n\\n terms = {}\\n\\n for monom, coeff in zzx_to_dict(f).iteritems():\\n terms[(monom,)] = Integer(int(coeff))\\n\\n return Poly(terms, *symbols)\",\n \"def poly_derivative(poly):\\n try:\\n iter(poly)\\n except TypeError:\\n return None\\n if poly == [] or any(not isinstance(expo, (int, float)) for expo in poly):\\n return None\\n if len(poly) == 1:\\n return [0]\\n return [i*expo for i, expo in enumerate(poly)][1:]\",\n \"def preprocess_roots(poly):\\n coeff = S.One\\n\\n poly_func = poly.func\\n try:\\n _, poly = poly.clear_denoms(convert=True)\\n except DomainError:\\n return coeff, poly\\n\\n poly = poly.primitive()[1]\\n poly = poly.retract()\\n\\n # TODO: This is fragile. Figure out how to make this independent of construct_domain().\\n if poly.get_domain().is_Poly and all(c.is_term for c in poly.rep.coeffs()):\\n poly = poly.inject()\\n\\n strips = list(zip(*poly.monoms()))\\n gens = list(poly.gens[1:])\\n\\n base, strips = strips[0], strips[1:]\\n\\n for gen, strip in zip(list(gens), strips):\\n reverse = False\\n\\n if strip[0] < strip[-1]:\\n strip = reversed(strip)\\n reverse = True\\n\\n ratio = None\\n\\n for a, b in zip(base, strip):\\n if not a and not b:\\n continue\\n elif not a or not b:\\n break\\n elif b % a != 0:\\n break\\n else:\\n _ratio = b // a\\n\\n if ratio is None:\\n ratio = _ratio\\n elif ratio != _ratio:\\n break\\n else:\\n if reverse:\\n ratio = -ratio\\n\\n poly = poly.eval(gen, 1)\\n coeff *= gen**(-ratio)\\n gens.remove(gen)\\n\\n if gens:\\n poly = poly.eject(*gens)\\n\\n if poly.is_univariate and poly.get_domain().is_ZZ:\\n basis = _integer_basis(poly)\\n\\n if basis is not None:\\n n = poly.degree()\\n\\n def func(k, coeff):\\n return coeff//basis**(n - k[0])\\n\\n poly = poly.termwise(func)\\n coeff *= basis\\n\\n if not isinstance(poly, poly_func):\\n poly = poly_func(poly)\\n return coeff, poly\",\n \"def test_polynomial_from_complex_list_same_as_from_complex_args(self):\\n coeffs = [1j, 2j, 3j, 4j, 5j]\\n\\n p1 = Polynomial(coeffs)\\n p2 = Polynomial(*coeffs)\\n\\n self.assertEqual(p1, p2)\",\n \"def gen_rand_poly(deg_lower_limit = 1, deg_upper_limit = 10, coeff_limit = 10):\\n deg = random.randint(deg_lower_limit,deg_upper_limit)\\n coeffs = [random.randint(-coeff_limit, coeff_limit) for _ in range(deg+1)]\\n\\n # Never have 0 as leading coefficient\\n if coeffs[deg] == 0:\\n coeffs[deg] = 1\\n\\n def term(coeff, d):\\n if coeff == 0:\\n return ''\\n elif d == 0:\\n return (' + ' if coeff>0 else ' - ') + str(abs(coeff))\\n elif d == 1:\\n return (' + ' if coeff>0 else ' - ') + (f'{abs(coeff)}x' if abs(coeff)!=1 else 'x')\\n elif d == deg:\\n return ('' if coeff>0 else '-') + (f'{abs(coeff)}x^{d}' if abs(coeff)!=1 else f'x^{d}')\\n else:\\n return (' + ' if coeff>0 else ' - ') + (f'{abs(coeff)}x^{d}' if abs(coeff)!=1 else f'x^{d}')\\n\\n terms = [term(coeffs[d], d) for d in range(deg+1)]\\n return deg, coeffs, ''.join([terms[d]for d in range(deg,-1,-1)]).strip('+ ')\",\n \"def derivative(self) -> Polynomial:\\n coefficients: list[float] = [0] * self.degree\\n for i in range(self.degree):\\n coefficients[i] = self.coefficients[i + 1] * (i + 1)\\n return Polynomial(self.degree - 1, coefficients)\",\n \"def coeffients(x, y):\\n\\n # ensure floating point datatypes\\n x.astype(float)\\n y.astype(float)\\n\\n # degree of interpolating polynomial\\n n = len(x)\\n\\n # intitilize list of coeffients for interpolating polynomial to y values\\n c = y.tolist()\\n\\n # compute coeffients\\n for j in range(1, n):\\n for i in range(n-1, j-1, -1):\\n c[i] = float(c[i]-c[i-1])/float(x[i]-x[i-j])\\n\\n # return an array of polynomial coefficient, note: reverse order for np.polyval function\\n return np.array(c[::-1])\",\n \"def tensdot(polyList,order,trunc):\\n\\n def reshape(poly,expo):\\n\\n poly.coef = poly[:][:,expo]\\n poly.expo = expo\\n return poly\\n\\n dim = len(polyList)\\n expo = indextens(order,dim,trunc)\\n nbrPoly = expo.shape[1]\\n coef = np.eye(nbrPoly)\\n\\n # Tensor product of the univariate basis\\n\\n for i in range(dim): polyList[i] = reshape(polyList[i],expo[i])\\n for i in range(nbrPoly): coef[i] = np.prod([polyList[j][expo[j,i]] for j in range(dim)],axis=0)\\n\\n poly = Polynomial(expo,coef,1)\\n return poly\",\n \"def polygen(count=10, sum_count=10, deg=5, cof=10):\\n\\n s = enumi_beg\\n ans = enumi_beg\\n\\n for i in range(count):\\n s += item_beg\\n ans += item_beg\\n p = genpoly(sum_count, deg, cof)\\n ans += p.print_out()\\n s += p.rep + item_end\\n ans += item_end\\n s += enumi_end\\n ans += enumi_end\\n return s, ans\",\n \"def general_poly (L):\\n def inside(x):\\n result = 0\\n pwr = len(L) - 1\\n for l in L:\\n result = result + l * x ** pwr\\n pwr -= 1\\n return result\\n return inside\",\n \"def _eval_legpoly(self, t_0, t_s, p_0, p_s, geometry=None):\\n\\n assert geometry is not None, \\\"Geometry needs to be specified!\\\"\\n\\n theta_0 = sp.Symbol(\\\"theta_0\\\")\\n theta_s = sp.Symbol(\\\"theta_s\\\")\\n theta_ex = sp.Symbol(\\\"theta_ex\\\")\\n phi_0 = sp.Symbol(\\\"phi_0\\\")\\n phi_s = sp.Symbol(\\\"phi_s\\\")\\n phi_ex = sp.Symbol(\\\"phi_ex\\\")\\n\\n res = self.legexpansion(t_0, t_s, p_0, p_s, geometry).xreplace(\\n {\\n theta_0: t_0,\\n theta_s: t_s,\\n phi_0: p_0,\\n phi_s: p_s,\\n theta_ex: t_s,\\n phi_ex: p_s,\\n }\\n )\\n return res.evalf()\",\n \"def derivative(polinom):\\n l = []\\n for i in range(1, polinom.degree+1):\\n l.append(polinom.coefficients[i]*i)\\n return Polinom(l)\",\n \"def construct_polynomial_approx(degree, weights):\\n # here is a function that is created on the fly from the input feature\\n # mapping and weights\\n def prediction_function(xs):\\n expanded_xs = np.matrix(expand_to_monomials(xs, degree))\\n ys = expanded_xs*np.matrix(weights).reshape((len(weights),1))\\n return np.array(ys).flatten()\\n # we return the function reference (handle) itself. This can be used like\\n # any other function\\n return prediction_function\",\n \"def _reform_poly_coefficients(fit_poly_x, fit_poly_y):\\n # Extract values for CD matrix and recombining\\n c11 = fit_poly_x.c1_0.value\\n c12 = fit_poly_x.c0_1.value\\n c21 = fit_poly_y.c1_0.value\\n c22 = fit_poly_y.c0_1.value\\n sip_poly_x = fit_poly_x.copy()\\n sip_poly_y = fit_poly_y.copy()\\n # Force low order coefficients to be 0 as defined in SIP\\n sip_poly_x.c0_0 = 0\\n sip_poly_y.c0_0 = 0\\n sip_poly_x.c1_0 = 0\\n sip_poly_x.c0_1 = 0\\n sip_poly_y.c1_0 = 0\\n sip_poly_y.c0_1 = 0\\n\\n cdmat = ((c11, c12), (c21, c22))\\n invcdmat = npla.inv(np.array(cdmat))\\n degree = fit_poly_x.degree\\n # Now loop through all remaining coefficients\\n for i in range(0, degree + 1):\\n for j in range(0, degree + 1):\\n if (i + j > 1) and (i + j < degree + 1):\\n old_x = getattr(fit_poly_x, f'c{i}_{j}').value\\n old_y = getattr(fit_poly_y, f'c{i}_{j}').value\\n newcoeff = np.dot(invcdmat, np.array([[old_x], [old_y]]))\\n setattr(sip_poly_x, f'c{i}_{j}', newcoeff[0, 0])\\n setattr(sip_poly_y, f'c{i}_{j}', newcoeff[1, 0])\\n\\n return cdmat, sip_poly_x, sip_poly_y\",\n \"def Hdiv_polynomials(\\n domain_dim: int, range_dim: int, order: int, variables: AxisVariablesNotSingle = x\\n) -> typing.List[VectorFunction]:\\n assert domain_dim == range_dim\\n if domain_dim == 2:\\n return [VectorFunction((\\n variables[0] * variables[0] ** (order - 1 - j) * variables[1] ** j,\\n variables[1] * variables[0] ** (order - 1 - j) * variables[1] ** j,\\n )) for j in range(order)]\\n if domain_dim == 3:\\n basis: typing.List[VectorFunction] = []\\n for j in range(order):\\n for k in range(order - j):\\n p = variables[0] ** (order - 1 - j - k) * variables[1] ** j * variables[2] ** k\\n basis.append(VectorFunction((variables[0] * p, variables[1] * p, variables[2] * p)))\\n return basis\\n\\n raise ValueError(f\\\"Unsupported dimension: {domain_dim}\\\")\",\n \"def equations(p):\\n [x, y] = p\\n list = [x - 5 , y - 5]\\n return list\",\n \"def _evalPoly(self,a,x):\\n y = a[0]\\n for i in range(1,len(a)):\\n y = self.F.Multiply(y, x)\\n y = self.F.Add(y, a[i])\\n return y\",\n \"def Hdiv_quolynomials(\\n domain_dim: int, range_dim: int, order: int, variables: AxisVariablesNotSingle = x\\n) -> typing.List[VectorFunction]:\\n assert domain_dim == range_dim\\n basis: typing.List[VectorFunction] = []\\n for d in range(domain_dim):\\n for j in product(range(order), repeat=domain_dim - 1):\\n poly = 1\\n for a, b in zip(variables, j[:d] + (order,) + j[d:]):\\n poly *= a ** b\\n basis.append(VectorFunction([poly if i == d else 0 for i in range(domain_dim)]))\\n return basis\",\n \"def construct3rdSumPoly(E):\\n if E.a1() != 0 or E.a2() != 0 or E.a3() != 0:\\n raise TypeError('Provided elliptic curve is not in the short Weierstrass form.') \\n \\n FF = E.base_field()\\n A = E.a4()\\n B = E.a6()\\n PR.<s1, s2, s3> = PolynomialRing(FF, 3, order='degrevlex')\\n smp = (s1 - s2)**2*s3**2 - 2*((s1 + s2)*(s1*s2 + A) + 2*B)*s3 + (s1*s2 - A)**2 - 4*B*(s1 + s2)\\n return smp\",\n \"def build_poly(x, degree):\\n \\n X = np.vander((x[:,0]).T, degree+1, increasing=True)\\n \\n for i in range(1,np.shape(x)[1],1):\\n feat = (x[:,i]).T\\n vander = np.vander(feat, degree+1, increasing=True)\\n #remove the column of 1 at the beginning of each vander\\n vander = np.delete(vander, 0,axis = 1)\\n #concatenation\\n X = np.concatenate((X, vander), axis=1)\\n \\n return X\",\n \"def parameters_polynomial(cobj, prop, prop_units, alist, blist):\\n for i, aval in enumerate(alist):\\n if i == 0:\\n param_units = prop_units\\n else:\\n param_units = prop_units / pyunits.K**i\\n\\n coeff = Var(doc=\\\"A parameter for CoolProp polynomial form\\\", units=param_units)\\n cobj.add_component(prop + \\\"_coeff_A\\\" + str(i), coeff)\\n coeff.fix(aval)\\n\\n for i, bval in enumerate(blist):\\n if i == 0:\\n param_units = pyunits.dimensionless\\n else:\\n param_units = pyunits.K**-i\\n\\n coeff = Var(doc=\\\"B parameter for CoolProp exponential form\\\", units=param_units)\\n cobj.add_component(prop + \\\"_coeff_B\\\" + str(i), coeff)\\n coeff.fix(bval)\",\n \"def generate_poly(hyper, params):\\n\\n k, d = hyper['k'], hyper['d']\\n #atoms = { \\n # (h,) : symbols('h_%d'%h)\\n # for h in xrange(1, k+1)\\n # }\\n #atoms[(k,)] = 1. - sum( symbols('h_%d'%h) for h in xrange(1, k) )\\n\\n atoms = {}\\n for h in xrange(1,k+1):\\n atoms.update({ \\n (h,x1) : symbols('x_%d%d'%(h,x1))\\n for x1 in xrange(1,d+1)\\n })\\n #atoms[(h,d)] = 1. - sum(symbols('x_%d%d'%(h,x1)) for x1 in xrange(1,d))\\n\\n m = {}\\n for x1 in xrange(1,d+1):\\n m[(x1,)] = poly( sum( atoms[(h,x1)] for h in xrange(1,k+1) ) )\\n for x2 in xrange(1,d+1):\\n m[(x1,x2)] = poly( sum( atoms[(h,x1)] * atoms[(h,x2)] for h in xrange(1,k+1) ) )\\n for x3 in xrange(1,d+1):\\n m[(x1,x2,x3)] = poly( sum( atoms[(h,x1)] * atoms[(h,x2)] * atoms[(h,x3)] for h in xrange(1,k+1) ) )\\n\\n return m\",\n \"def general_poly (L):\\r\\n\\r\\n def secondFunc(x):\\r\\n total = 0\\r\\n listLength = len(L)-1\\r\\n for i in L:\\r\\n total += i * x**listLength\\r\\n listLength -= 1\\r\\n return(total)\\r\\n return secondFunc\",\n \"def __polydiff__(x, dt, params, options=None):\\n if isinstance(options, dict) and 'weights' in options.keys():\\n w = options['weights']\\n else:\\n w = np.ones_like(x)\\n\\n if isinstance(params, list):\\n order = params[0]\\n else:\\n order = params\\n\\n t = np.arange(1, len(x)+1)*dt\\n\\n # polyfit\\n r = np.polyfit(t, x, order, w=w)[::-1]\\n\\n # derivative coefficients\\n dr = copy.copy(r[1:])\\n for i, _ in enumerate(dr):\\n dr[i] = dr[i]*(i + 1)\\n\\n # evaluate dxdt_hat\\n dxdt_hat = 0\\n for i, _ in enumerate(dr):\\n dxdt_hat += dr[i]*t**i\\n\\n # evaluate smooth x\\n x_hat = 0\\n for i, _ in enumerate(r):\\n x_hat += r[i]*t**i\\n\\n return x_hat, dxdt_hat\",\n \"def PolyDiffPoint(u, x, deg = 3, diff = 1, index = None):\\n \\n n = len(x)\\n if index == None: index = int((n-1)/2)\\n\\n # Fit to a polynomial\\n poly = np.polynomial.chebyshev.Chebyshev.fit(x,u,deg)\\n \\n # Take derivatives\\n derivatives = []\\n for d in range(1,diff+1):\\n derivatives.append(poly.deriv(m=d)(x[index]))\\n \\n return derivatives\",\n \"def _getFrac(expr):\\n expr=expr.replace(' ', '')\\n l = len(expr)\\n frac = []; start = 0; par = 0\\n pack=''; num=''\\n op = ['+','-']\\n operator = ['+','-','/','*']\\n sym = ['x','y']\\n multFrac = False\\n\\n for i in range(0,l):\\n if expr[i]=='(' : #(\\n if par==0 : start=i\\n par += 1\\n elif expr[i] == ')' : #)\\n par -= 1\\n if par==0 :\\n pack += expr[start:i+1]; start = i+1\\n elif expr[i]=='*'and par==0: #*\\n pack += expr[start:i]; start = i+1\\n if num!='' :\\n frac.append((num,pack))\\n frac.append(expr[i])\\n pack = ''; num = ''\\n else :\\n pack += expr[i]\\n elif expr[i]=='/'and par==0: #/\\n pack += expr[start:i]\\n num += pack\\n pack = ''\\n start = i+1\\n elif expr[i] in op and par==0 and num != '': #+-\\n pack += expr[start:i]\\n frac.append((num,pack))\\n frac.append(expr[i])\\n pack = ''; num = ''; start = i+1\\n elif expr[i] in op and par==0:\\n pack += expr[start:i]\\n frac.append((pack,''))\\n frac.append(expr[i])\\n pack = ''; num = ''; start = i+1\\n\\n if start < l : pack += expr[start:l]\\n if num != '' :\\n frac.append((num,pack))\\n else:\\n frac.append((pack,''))\\n\\n frac2 = [frac[0]]\\n i=1\\n while i<len(frac):\\n if frac[i] in operator and frac[i]!='*' :\\n frac2.append(frac[i])\\n frac2.append(frac[i+1])\\n elif frac[i]=='*' :\\n (a1,b1)=frac[i-1]\\n (a2,b2)=frac[i+1]\\n frac2[len(frac2)-1]=(a1+'*'+a2,b1+'*'+b2)\\n i+=2\\n return frac2\",\n \"def strMultiPoly(poly, symbol=[\\\"X\\\",\\\"Y\\\"], asc=True):\\n return termorder.MultivarTermOrder(cmp).format(MultiVarPolynomial(poly,symbol), symbol, asc)\",\n \"def build_poly(x, degree):\\n tx = np.zeros((x.shape[0], x.shape[1]*(degree+1)))\\n \\n for j in range(degree+1):\\n tx[:,x.shape[1]*j:x.shape[1]*(j+1)] = np.power(x,j)\\n \\n return tx\",\n \"def nice_cubic_polynomial(p):\\n tmp = \\\"\\\"\\n if p[\\\"a\\\"] == 1:\\n tmp += \\\" x^3\\\"\\n elif p[\\\"a\\\"] != 0:\\n tmp += \\\"%.2fx^3\\\" % p[\\\"a\\\"]\\n if p[\\\"b\\\"] == 1:\\n tmp += \\\"\\\\t+ x^2\\\"\\n elif p[\\\"b\\\"] != 0:\\n tmp += \\\"\\\\t+ %.2fx^2\\\" % p[\\\"b\\\"]\\n else:\\n tmp += \\\"\\\\t\\\\t\\\"\\n if p[\\\"c\\\"] == 1:\\n tmp += \\\"\\\\t+ x\\\"\\n elif p[\\\"c\\\"] != 0:\\n tmp += \\\"\\\\t+ %.2fx\\\" % p[\\\"c\\\"]\\n else:\\n tmp += \\\"\\\\t\\\\t\\\"\\n if p[\\\"d\\\"] != 0:\\n tmp += \\\"\\\\t+ %.2f\\\" % p[\\\"d\\\"]\\n return tmp\",\n \"def polyrecur(order,dist,trunc=1):\\n\\n printer(0,'Computing polynomials ...')\\n if not isinstance(dist,Joint): dist = Joint(dist)\\n\\n nbrPoly = order+1\\n dim = dist[:].shape[0]\\n expo = np.arange(nbrPoly)\\n coef = np.zeros((nbrPoly,nbrPoly))\\n norm = np.ones((dim,nbrPoly))\\n\\n coef[0,0] = 1\\n polyList = []\\n\\n # Creates the univariate polynomial basis\\n\\n for i in range(dim):\\n\\n polyList.append(Polynomial(expo,coef))\\n AB = dist[i].coef(nbrPoly)\\n\\n for j in range(1,nbrPoly):\\n\\n norm[i,j] = norm[i,j-1]*AB[1,j]\\n polyList[i][j] = np.roll(polyList[i][j-1],1,axis=0)\\n polyList[i][j] -= AB[0,j-1]*polyList[i][j-1]+AB[1,j-1]*polyList[i][j-2]\\n\\n # Normalization and tensor product\\n\\n for i in range(dim): polyList[i][:] /= np.sqrt(norm[i,:,None])\\n poly = tensdot(polyList,order,trunc)\\n\\n printer(1,'Computing polynomials 100 %')\\n return poly\",\n \"def test_polynomial_from_string_the_same_as_string_args(self):\\n coeffs = \\\"abcdefghijklmnopqrstuvwxyz\\\"\\n\\n p1 = Polynomial(coeffs)\\n p2 = Polynomial(*coeffs)\\n\\n self.assertEqual(p1, p2)\",\n \"def part_der(f,l):\\r\\n \\r\\n der = [] #empty list which derived functions will be appended onto\\r\\n for i in l:\\r\\n \\r\\n der.append(sym.diff(f, lambda i: f))\\r\\n print(der) \\r\\n return der\",\n \"def poly(x, coeffs):\\n return np.sum([coeffs[i] * x ** i for i in range(len(coeffs))], axis=0)\",\n \"def PBpoly(n, x):\\n n = int(n)\\n return Bpoly(n, x-math.floor(x))\",\n \"def minpoly(firstterms):\\n field = ring.getRing(firstterms[0])\\n r_0 = uniutil.polynomial({len(firstterms):field.one}, field)\\n r_1 = uniutil.polynomial(enumerate(reversed(firstterms)), field)\\n poly_ring = r_0.getRing()\\n v_0 = poly_ring.zero\\n v_1 = poly_ring.one\\n n = len(firstterms) // 2\\n\\n while n <= r_1.degree():\\n q, r = divmod(r_0, r_1)\\n v_0, v_1 = v_1, v_0 - q*v_1\\n r_0, r_1 = r_1, r\\n return v_1.scalar_mul(v_1.leading_coefficient().inverse())\",\n \"def E_polynomial(self):\\n\\n from nodepy import stability_function\\n p, q = self.stability_function()\\n return stability_function.E_polynomial(p, q)\",\n \"def generate_random_tropical_poly(max_degree, min_coefficient, max_coefficient):\\n coefficients = []\\n for d in range(0, random.randint(1, max_degree) + 1):\\n coefficients.append(random.randint(min_coefficient, max_coefficient))\\n return coefficients\",\n \"def anti_deriv(self):\\n poly_anti_deriv = [0]\\n for i, val in enumerate(self.coeff):\\n poly_anti_deriv.append(round(val/(i+1.0), 2))\\n return Poly(poly_anti_deriv)\",\n \"def calc_poly_linear_regression(independent, dependent):\\n # Reshape for sklearn\\n independent = independent.values.reshape(-1,1)\\n dependent = dependent.values.reshape(-1,1)\\n # Make the whole thing poly\\n poly = PolynomialFeatures(degree=2)\\n independent_ = poly.fit_transform(independent)\\n # Do the linear regression\\n model = LinearRegression()\\n model.fit(independent_, dependent)\\n # Calculate R2\\n return model.score(independent_, dependent)\",\n \"def poly(x, degree=2):\\n x = np.array(x)\\n X_trans = np.transpose(np.vstack((x**k for k in range(degree + 1))))\\n return np.linalg.qr(X_trans)[0][:, 1:]\",\n \"def poly(*args):\\n if len(args) == 0 or len(args) == 2:\\n raise ValueError('bad number of arguments {} passed to poly()'.format(len(args)))\\n if len(args) == 1:\\n if ispoly(args[0]):\\n return deepcopy(args[0])\\n else:\\n raise VauleError('non-poly list passed to poly()')\\n # args is of length 3 or greater. Check to see if args are points\\n a = list(args)\\n b = list(filter(lambda x: not ispoint(x),a))\\n if len(b) > 0:\\n raise ValueError('non-point arguments to poly(): {} '.format(b))\\n return deepcopy(a)\",\n \"def addPol(*pol):\\n\\n sum = np.zeros((1,))\\n for p in pol:\\n sum = polyadd(sum, p)\\n\\n return sum\",\n \"def factor_polynomial(a,b,c):\\r\\n afacts = factors(abs(a))\\r\\n cfacts = factors(abs(c))\\r\\n #choose a factor of a (d), then f is a/d and so on\\r\\n for d in afacts:\\r\\n for g in cfacts:\\r\\n f = a/d\\r\\n e = c/g\\r\\n if e*f + d*g == b:\\r\\n print(\\\"{}*x**2 + {}*x + {} = ({}x + {})({}x + {})\\\".format(a,b,c,d,int(e),int(f),g))\\r\\n return\\r\\n #return False\\r\",\n \"def get_poly(kwargs):\\n from sklearn.preprocessing import PolynomialFeatures\\n return PolynomialFeatures(**kwargs)\",\n \"def __merge(self, inputPoly, op):\\n newPoly = Polynomial()\\n\\n # cast the input to a Polynomial\\n inputPoly = ensurePoly(inputPoly)\\n if isinstance(inputPoly.poly, (int, float, Variable.Variable)):\\n input = Polynomial()\\n input.poly = [op, inputPoly.poly]\\n else:\\n input = inputPoly\\n\\n if isinstance(self.poly, (int, float, Variable.Variable)):\\n self.poly = [op, self.poly]\\n\\n if op == self.poly[0] and op == input.poly[0]:\\n newPoly.poly = self.poly + input.poly[1:]\\n elif op == self.poly[0]:\\n newPoly.poly = self.poly\\n newPoly.poly.append(input)\\n elif op == input.poly[0]:\\n newPoly.poly = [input.poly[0], self]\\n newPoly.poly.extend(input.poly[1:])\\n else:\\n newPoly.poly = [op, self, input]\\n\\n return newPoly\",\n \"def polyfit(dates, levels, p):\\n x = matplotlib.dates.date2num(dates)\\n\\n y = levels\\n\\n # Using shifted x values, find coefficient of best-fit\\n # polynomial f(x) of degree p\\n shifted_times = [t-x[0] for t in x]\\n p_coeff = np.polyfit(shifted_times, y, p)\\n\\n # Convert coefficient into a polynomial that can be evaluated\\n # e.g. poly(0.3)\\n poly = np.poly1d(p_coeff)\\n\\n return (poly, x[0])\",\n \"def reducedFormOne(self, equation):\\n splitter = re.split('(\\\\+|\\\\-)', equation)\\n newEquation = str()\\n state = 0\\n for token in splitter:\\n if '(' in token or state > 0 or '[' in token:\\n state += 1\\n newEquation += token\\n continue\\n if ')' in token or ']' in token:\\n state -= 1\\n continue\\n if '^' + self.var in token:\\n newEquation += token\\n self.validPolynome = False\\n continue\\n find = re.findall('(\\\\*|\\\\^|\\\\/)?(' + self.var + ')(\\\\^\\\\d+)?' , token)\\n newVar = []\\n for var in find:\\n newVar.append(''.join(map(str,var)))\\n for var in newVar:\\n token = token.replace(var, '')\\n if token != '+' and token != '-' and token != '':\\n try:\\n newEquation += str(eval(token.replace('^', '**'))) + ''.join(newVar)\\n except:\\n self.error = True\\n continue\\n else:\\n newEquation += token\\n return newEquation\",\n \"def _polynomial_entity(value, context):\\n assert isinstance(value, Polynomial)\\n coefficients = np.asarray(value.coefficients)\\n num_variables = coefficients.ndim\\n variables = [sympy.Symbol(context.pop()) for _ in range(num_variables)]\\n function_symbol = context.pop()\\n handle = FunctionHandle(function_symbol)\\n handle_description = sympy.Function(function_symbol)(*variables)\\n\\n polynomial = polynomials.coefficients_to_polynomial(coefficients, variables)\\n polynomial = polynomial.sympy()\\n\\n return Entity(\\n context=context,\\n value=value,\\n expression=polynomial,\\n polynomial_variables=variables,\\n description='Let {function} = {polynomial}.',\\n handle=handle,\\n function=handle_description,\\n polynomial=polynomial)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6846461","0.65011793","0.6292352","0.6274255","0.62225056","0.61097145","0.60969347","0.6090148","0.60779357","0.60200155","0.60196537","0.59783614","0.5969226","0.5965326","0.5856345","0.5849811","0.58080703","0.5775723","0.5735005","0.5733751","0.5722096","0.5698544","0.56953406","0.56824267","0.5653592","0.5636684","0.563195","0.56265783","0.56259936","0.55919147","0.55880266","0.5585891","0.5576518","0.5575801","0.5565807","0.55617356","0.5559807","0.555813","0.5551955","0.55366033","0.5531899","0.5521932","0.55078155","0.55027956","0.54881066","0.5472018","0.5467412","0.5459621","0.54507357","0.5449399","0.54257417","0.5409083","0.53826433","0.5372734","0.53560984","0.5348208","0.53363293","0.5330159","0.53247994","0.53188366","0.53168577","0.529635","0.52777994","0.52713233","0.5267082","0.5258767","0.52576727","0.5250707","0.52387667","0.523134","0.52311265","0.521637","0.5212681","0.52106714","0.5195989","0.5189531","0.51671946","0.5159518","0.51566106","0.5144678","0.51300174","0.5122544","0.51157093","0.5110835","0.51055366","0.5098564","0.50945264","0.50908965","0.5087293","0.50872725","0.50866365","0.50831044","0.5082207","0.5081495","0.50732553","0.5072947","0.5071048","0.50679743","0.50673664","0.5057545"],"string":"[\n \"0.6846461\",\n \"0.65011793\",\n \"0.6292352\",\n \"0.6274255\",\n \"0.62225056\",\n \"0.61097145\",\n \"0.60969347\",\n \"0.6090148\",\n \"0.60779357\",\n \"0.60200155\",\n \"0.60196537\",\n \"0.59783614\",\n \"0.5969226\",\n \"0.5965326\",\n \"0.5856345\",\n \"0.5849811\",\n \"0.58080703\",\n \"0.5775723\",\n \"0.5735005\",\n \"0.5733751\",\n \"0.5722096\",\n \"0.5698544\",\n \"0.56953406\",\n \"0.56824267\",\n \"0.5653592\",\n \"0.5636684\",\n \"0.563195\",\n \"0.56265783\",\n \"0.56259936\",\n \"0.55919147\",\n \"0.55880266\",\n \"0.5585891\",\n \"0.5576518\",\n \"0.5575801\",\n \"0.5565807\",\n \"0.55617356\",\n \"0.5559807\",\n \"0.555813\",\n \"0.5551955\",\n \"0.55366033\",\n \"0.5531899\",\n \"0.5521932\",\n \"0.55078155\",\n \"0.55027956\",\n \"0.54881066\",\n \"0.5472018\",\n \"0.5467412\",\n \"0.5459621\",\n \"0.54507357\",\n \"0.5449399\",\n \"0.54257417\",\n \"0.5409083\",\n \"0.53826433\",\n \"0.5372734\",\n \"0.53560984\",\n \"0.5348208\",\n \"0.53363293\",\n \"0.5330159\",\n \"0.53247994\",\n \"0.53188366\",\n \"0.53168577\",\n \"0.529635\",\n \"0.52777994\",\n \"0.52713233\",\n \"0.5267082\",\n \"0.5258767\",\n \"0.52576727\",\n \"0.5250707\",\n \"0.52387667\",\n \"0.523134\",\n \"0.52311265\",\n \"0.521637\",\n \"0.5212681\",\n \"0.52106714\",\n \"0.5195989\",\n \"0.5189531\",\n \"0.51671946\",\n \"0.5159518\",\n \"0.51566106\",\n \"0.5144678\",\n \"0.51300174\",\n \"0.5122544\",\n \"0.51157093\",\n \"0.5110835\",\n \"0.51055366\",\n \"0.5098564\",\n \"0.50945264\",\n \"0.50908965\",\n \"0.5087293\",\n \"0.50872725\",\n \"0.50866365\",\n \"0.50831044\",\n \"0.5082207\",\n \"0.5081495\",\n \"0.50732553\",\n \"0.5072947\",\n \"0.5071048\",\n \"0.50679743\",\n \"0.50673664\",\n \"0.5057545\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70788777"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":82,"numItemsPerPage":100,"numTotalItems":95772,"offset":8200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6Im1sR1pFYktIVHRVSGRhX1RRdEczQ0N1S3k5ME9Qd25DNHY5elRjM3FVYzgifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4MjczNTU5MiwianRpIjoiZWJmYzNkMzQtNTc4My00ZmQyLTk3MWItYjYzY2NlMTU0YTM5Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODI3MzkxOTIsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.cCKM_5_IGBlk3s4BeNgQtkg2O1iBjXJNb2Cp6G-MFkkX0XNJ4wqqQKvy1X7H_OKHcPKZXZLk7hIoAmAoWQW1Bw","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 |
|---|---|---|---|---|---|---|
Calculate the likelihoods for bernoulli | def calculate_likelihoods_bernoulli(data, labels, vocab):
classes = set(labels)
likelihoods = {}
# Calculate likelihood for each class
for cls in classes:
documentsInClass = [set(map(lambda y: y[0], data[x])) for x in range(len(data)) if labels[x] == cls]
numDocsInClass = len(docum... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_prob_mle(X: np.ndarray, n: int) -> float:\n\n assert n > 1, \"for n = 1 use Bernoulli distribution.\"\n Binomial._check_input_data(X=X)\n Binomial._check_support(X=X, n=n)\n\n prob = X.mean() / n\n return prob",
"def likelihood(self, data, hypo):\n tagged, n,... | [
"0.6838529",
"0.6828888",
"0.68097115",
"0.674418",
"0.67185456",
"0.66190183",
"0.66117036",
"0.6602112",
"0.65802485",
"0.657394",
"0.6549069",
"0.65399593",
"0.6539858",
"0.65330833",
"0.65202075",
"0.64768666",
"0.647659",
"0.6444814",
"0.6444441",
"0.638896",
"0.6378039"... | 0.66105807 | 7 |
Extract the known vocabulary from our training data | def get_vocab(trainingData):
return set(reduce(lambda x,y: x+y, map(lambda x: map(lambda y: y[0], x), trainingData), [])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_vocab(self):\n\n\t\tself.parse_transcript() \n\t\tself.purge_words()\n\t\tself.analyze_words()\n\t\tself.sort_word_analysis()",
"def get_vocabulary(documents):\n cv_model = CountVectorizer(binary=True)\n cv_model.fit(documents)\n\n vocabulary = cv_model.get_feature_names()\n vocabulary = list... | [
"0.7562361",
"0.7207228",
"0.7081693",
"0.70540327",
"0.7029069",
"0.70169085",
"0.7005524",
"0.7005293",
"0.7000013",
"0.69506437",
"0.69444656",
"0.6888032",
"0.6835956",
"0.68238574",
"0.6815228",
"0.6788677",
"0.6784426",
"0.6746104",
"0.6692373",
"0.6628482",
"0.660853",... | 0.7412368 | 1 |
Parse and explode input data | def parse_data(filename):
labels = []
documents = []
with open(filename, 'r') as f:
for line in f:
values = line.split()
label = values[0]
document = []
for wordCount in values[1:]:
parsed = wordCount.split(':')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_data(self, lines: List[str]) -> List[str]:\n if self.is_tokenized:\n if self.parser == \"spacy\":\n lines = [l.split() for l in lines]\n elif self.parser == \"udpipe\":\n lines = [[l.split()] for l in lines]\n\n return lines",
"def par... | [
"0.6394949",
"0.6299175",
"0.6273174",
"0.62510943",
"0.6217176",
"0.62149054",
"0.6148983",
"0.61309695",
"0.6124276",
"0.6101856",
"0.60483354",
"0.5908739",
"0.5896337",
"0.588902",
"0.587758",
"0.58742684",
"0.5872623",
"0.5748684",
"0.57401425",
"0.57069176",
"0.5701802"... | 0.0 | -1 |
Return elements from the iterable until it is exhausted. Then repeat the sequence indefinitely. cycle(seq) ==> seq[0], seq[1], ..., seq[n 1], seq[0], seq[1], ... | def cycle(seq, n=None):
if n is not None:
return Iter(_ncycle(n, seq))
return Iter(itertools.cycle(seq)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cycle(iterator: Iterable[Any]) -> Iterable[Any]:\n while True:\n yield from iterator",
"def iter_sequence_infinite(seq):\n while True:\n for item in seq:\n yield item",
"def repeat(seq, n):\n for e in seq:\n for _ in range(n):\n yield e",
"def repeat(iterable, coun... | [
"0.77041095",
"0.73121995",
"0.68373466",
"0.68154997",
"0.64987105",
"0.64865166",
"0.64855176",
"0.61515063",
"0.61507326",
"0.6116417",
"0.585476",
"0.5825893",
"0.5797128",
"0.5789132",
"0.57815135",
"0.5774213",
"0.577342",
"0.5757885",
"0.5697775",
"0.56689405",
"0.5658... | 0.78215605 | 0 |
Returns the object for the specified number of times. If not specified, returns the object endlessly. | def repeat(obj, times=None):
if times is None:
return Iter(itertools.repeat(obj))
return Iter(itertools.repeat(obj, times)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def repeat(self, count):\n return self.Sequence((self,) * count)",
"def repeat(self, fn, *args, **kwargs):\n return repeat_n_times(self.n, fn, *args, **kwargs)",
"def twist(r, num_repeats=1):\n for i in range(num_repeats):\n r.go(0, 50)\n time.sleep(.75)\n r.stop()\n ... | [
"0.6203525",
"0.59771645",
"0.5969791",
"0.5901691",
"0.5872414",
"0.5850168",
"0.58327895",
"0.58200485",
"0.57241195",
"0.5649693",
"0.56443447",
"0.5643324",
"0.56162107",
"0.5555079",
"0.5550507",
"0.5531917",
"0.54840827",
"0.54764825",
"0.5443261",
"0.54094815",
"0.5397... | 0.64713174 | 0 |
Make infinite calls to a function with the given arguments. End sequence if func() raises StopIteration. | def repeatedly(func, /, *args, **kwargs):
func = to_callable(func)
try:
while True:
yield func(*args, **kwargs)
except StopIteration as e:
yield from stop_seq(e) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iterate(func, x):\n while True:\n x = func(x)\n yield x",
"def iterate(f, x):\n while True:\n yield x\n x = f(x)",
"def iterate(func: Callable[..., T], x: T, *args, index: Index = None):\n func = to_callable(func)\n index = to_index_seq(index)\n\n if index is None... | [
"0.7004105",
"0.6646999",
"0.63588154",
"0.633149",
"0.6150608",
"0.6094851",
"0.6073668",
"0.59957045",
"0.5961045",
"0.5894083",
"0.5876988",
"0.58702266",
"0.5862073",
"0.58489794",
"0.5841767",
"0.5814898",
"0.57949513",
"0.5713033",
"0.5708868",
"0.57078665",
"0.5697791"... | 0.7911098 | 0 |
Return iterator with a single object. | def singleton(obj: T, expand: bool = False) -> Iter[T]:
if expand:
try:
yield from obj
except TypeError:
pass
yield obj | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getIter(object):\n iterator = None\n try:\n iterator = iter(object)\n except TypeError:\n pass\n return iterator",
"def __next__(self):\n return next(self.iterator)",
"def __iter__(self):\n self._first()\n return self",
"def __iter__(self):\n return s... | [
"0.75817907",
"0.74081564",
"0.7373555",
"0.71963745",
"0.71724164",
"0.7078414",
"0.7078414",
"0.7078414",
"0.6872086",
"0.6868703",
"0.683738",
"0.67541516",
"0.665635",
"0.66446465",
"0.6599324",
"0.65719706",
"0.65708953",
"0.65270007",
"0.6459694",
"0.64549285",
"0.64549... | 0.6621986 | 14 |
Invert a fold. Similar to iterate, but expects a function of seed > (seed', x). The second value of the tuple is included in the resulting sequence while the first is used to seed func in the next iteration. Stops iteration if func returns None or raise StopIteration. | def unfold(func, seed):
try:
elem = func(seed)
while elem is not None:
seed, x = elem
yield x
elem = func(seed)
except StopIteration as e:
yield from stop_seq(e) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inverse(f):\n return lambda y: search(lambda x: f(x) == y)",
"def fold(iterable, func, base):\n acc = base\n for element in iterable:\n acc = func(acc, element)\n return acc",
"def foldl(func, start, itr):\n return _foldl(func, start, iter(itr))",
"def flip(func):\n if not callab... | [
"0.551734",
"0.54347765",
"0.5407587",
"0.53981245",
"0.5355724",
"0.5321342",
"0.5257516",
"0.52478385",
"0.50181824",
"0.5012077",
"0.49952018",
"0.49402606",
"0.493333",
"0.493333",
"0.4932859",
"0.492038",
"0.49162006",
"0.4866282",
"0.4826197",
"0.480424",
"0.48014733",
... | 0.76988935 | 0 |
f""" Repeatedly apply a function func to input. If more than one argument to func is passed, it iterate over the past n values. It requires at least one argument, if you need to iterate a zero | def iterate(func: Callable[..., T], x: T, *args, index: Index = None):
func = to_callable(func)
index = to_index_seq(index)
if index is None and not args:
out = _iterate(func, x)
elif index is None:
out = _iterate_n(func, (x, *args))
else:
if not args:
out = _ite... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _call_n(x, f, n, *args, **kwargs):\n return [f(i, x, *args, **kwargs) for i in range(n)]",
"def loop(func, n):\n for i in range(n):\n func()",
"def repeatfunc(func, n, *args):\n return starmap(func, repeat(args, n))",
"def repeat(fun, n):\n for i in range(n):\n yield fun()",
"de... | [
"0.7455248",
"0.7375146",
"0.7370228",
"0.71860796",
"0.7169171",
"0.69895",
"0.6812954",
"0.6778386",
"0.675432",
"0.6688753",
"0.6684521",
"0.665017",
"0.6537547",
"0.64917755",
"0.63972217",
"0.63972217",
"0.6348087",
"0.62363607",
"0.62081873",
"0.6142448",
"0.6037471",
... | 0.6898225 | 6 |
Create iterator from sequence of numbers. | def from_sequence(self, seq):
return Iter(self._from_sequence(seq)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def numeric_sequence_iteration(self) -> global___Statement.Iteration.NumericSequenceIteration:",
"def simple_seq(seq):\n for i in seq:\n yield i",
"def numbers():\n for number in range(1, 76):\n yield number",
"def __iter__(self):\r\n \r\n return iter(self._by_number)",
"d... | [
"0.6523413",
"0.6352263",
"0.6295878",
"0.6285677",
"0.6251581",
"0.6161719",
"0.6161719",
"0.6123521",
"0.59559906",
"0.59429246",
"0.5929433",
"0.59055716",
"0.5842463",
"0.58171993",
"0.57896346",
"0.57666224",
"0.57651085",
"0.5762295",
"0.5750994",
"0.5719213",
"0.571020... | 0.70666045 | 0 |
Create iterator from slice object. | def from_slice(self, slice):
start = 0 if slice.start is None else slice.start
step = 1 if slice.step is None else slice.step
return self.count(start, step, stop=slice.step) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def slice(iterable, *args):\n return iter(it.islice(iterable, *args))",
"def __getitem__(self, arg):\n if isinstance(arg, slice):\n # get value from slice\n start, stop, step = arg.start, arg.stop, arg.step\n # sanitize step\n if step is None:\n ... | [
"0.6592863",
"0.6384958",
"0.6363769",
"0.62649775",
"0.61393666",
"0.60202515",
"0.59986544",
"0.5966865",
"0.5952172",
"0.5857153",
"0.5757738",
"0.574337",
"0.5653791",
"0.5642721",
"0.56035215",
"0.5587913",
"0.5578734",
"0.5572186",
"0.5529783",
"0.5510107",
"0.5508437",... | 0.6573793 | 1 |
Return values starting from start advancing by the given step. | def count(self, start=0, step=1, stop=None):
out = itertools.count(start, step)
if stop is not None:
out = itertools.takewhile(lambda x: x < stop, out)
return Iter(out) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drange(start, stop, step):\n values=[]\n r = start\n while r <= stop:\n values.append(r)\n r += step\n return values",
"def make_step(self):\n self.step_vals = np.cumsum(self.vals)",
"def arange(self, start: float, stop: float, step: float = 1.0) -> None:\n self.valu... | [
"0.65679765",
"0.64803994",
"0.6422485",
"0.64052194",
"0.63530135",
"0.62748873",
"0.62696385",
"0.6264165",
"0.62514406",
"0.6197221",
"0.614684",
"0.6128096",
"0.6093126",
"0.60743475",
"0.60505",
"0.6030288",
"0.59912664",
"0.597212",
"0.5929738",
"0.59283173",
"0.5926146... | 0.0 | -1 |
Return a sequence of n evenly spaced numbers from a to b. | def evenly_spaced(self, a: Real, b: Real, n: int) -> Iter:
return Iter(_evenly_spaced(a, b, n)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_by_n( seq, n ):\n while seq:\n yield seq[:n]\n seq = seq[n:]",
"def split_by_n(seq, n):\n while seq:\n yield seq[:n]\n seq = seq[n:]",
"def genslices(n):\n return product(range(-n, n+1), range(-n, n+1), range(-n, n+1))",
"def genslices(n):\n retur... | [
"0.67545396",
"0.67385876",
"0.6609418",
"0.6586301",
"0.6527549",
"0.6490447",
"0.64674157",
"0.6464234",
"0.64498013",
"0.6438853",
"0.6431555",
"0.64294153",
"0.6413279",
"0.63846004",
"0.63330746",
"0.63312566",
"0.6319206",
"0.6290663",
"0.6289663",
"0.62633723",
"0.6244... | 0.7720257 | 0 |
Convert int to string without using builtin str() | def int_to_string(num):
if num < 0:
num, is_neg = -num, True
else:
is_neg = False
s = []
while num > 0:
s.append(chr(ord('0') + num%10))
num //= 10
return ('-' if is_neg else '') + ''.join(reversed(s)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _int2str(num):\n if num<10:\n return '00%s'%str(num)\n elif 10<=num<100:\n return '0%s'%str(num)\n else:\n return '%s'%str(num)",
"def _int_to_string(v):\n \n if not isinstance(v,int):\n raise InstrumentParameterException('Value %... | [
"0.8262509",
"0.7803891",
"0.7300844",
"0.7271281",
"0.72517717",
"0.718009",
"0.7099532",
"0.70860934",
"0.7008732",
"0.69923896",
"0.68970364",
"0.68703055",
"0.6847767",
"0.67874974",
"0.6779158",
"0.676237",
"0.67338043",
"0.6702256",
"0.6671091",
"0.6608676",
"0.65521425... | 0.78195685 | 1 |
Convert string to int | def string_to_int(s):
return functools.reduce(lambda running_sum, c: running_sum * 10 + string.digits.index(c),
s[s[0] == '-':], 0) * (-1 if s[0] == '' else 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _str_to_int(in_str):\n if in_str == '':\n return 0\n return int(in_str, 10)",
"def to_int(s: str) -> int:\n try:\n return int(s.replace('_', ''))\n except ValueError:\n return int(ast.literal_eval(s))",
"def dec2int(r: str) -> int:",
"def to_int(str_val: str) -> int:\n\n ... | [
"0.82145184",
"0.8003485",
"0.79868853",
"0.79608244",
"0.7875066",
"0.7750694",
"0.7643904",
"0.76203454",
"0.7554625",
"0.75325245",
"0.7502179",
"0.7484789",
"0.74722457",
"0.7454978",
"0.7444927",
"0.7426303",
"0.7408998",
"0.74060065",
"0.73542196",
"0.72918594",
"0.7284... | 0.7517601 | 10 |
this method retrieves a data file from an internet location. make sure it is not password protected or anything. | def get_data_file(url, data=None, headers={}):
req = urllib2.Request(url=url)
for key in headers:
req.add_header(key, headers[key])
site = urllib2.urlopen(req)
data = site.read()
headers = site.info()
site.close()
output = open(local_file, 'w')
output.write(data)
output.close()
return data, headers | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(location):\n # This is factored out so we can use a different retrieval method if required.\n # Originally used urllib2, but it had SSL issues on my machine\n response = requests.get(location)\n return response.content",
"def web_get_file(self, url):\n try:\n print(url)... | [
"0.6372641",
"0.625264",
"0.62305814",
"0.62305814",
"0.6188294",
"0.6158766",
"0.6149518",
"0.6095374",
"0.60664946",
"0.60570997",
"0.6010867",
"0.60009617",
"0.59899217",
"0.59876364",
"0.59813595",
"0.59589547",
"0.5951379",
"0.5951082",
"0.59479934",
"0.5938637",
"0.5925... | 0.60267264 | 10 |
This method takes in a results file created by ibex, strips out the comments, concatenates lines together that should go together, and outputs complete lines to the specified text file | def parse_results_file(filename):
file = open(filename, 'r')
pretext=[line for line in file.readlines() if line.strip()]
file.close()
text = []
processed = []
languages = 'NONE'
ID = 'NONE'
moreheader = raw_input('Extra header labels from question field (e.g.: item,condition,factor1,factor2): ')
stim_type =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(inputfname, outfname):\n with open(inputfname, 'rt', encoding='utf8') as fh:\n # first block\n reviews = []\n while True:\n comment = next(fh).strip()\n if not comment:\n # blank line, block separator\n break\n url_movi... | [
"0.60390484",
"0.599767",
"0.5916299",
"0.5812747",
"0.56795824",
"0.5623025",
"0.54872257",
"0.5453515",
"0.5433996",
"0.53970265",
"0.53352237",
"0.5327005",
"0.5286181",
"0.5284214",
"0.5239452",
"0.522815",
"0.5226091",
"0.5211899",
"0.52046853",
"0.51947665",
"0.5192673"... | 0.5896646 | 3 |
Helper function for counting leading ""s | def leading(self, string: str) -> int:
leading_amount = 0
while string[leading_amount] == "-":
leading_amount += 1
return leading_amount | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_blank(bd):\n count = 0\n for num in bd:\n if num == \" \":\n return count\n else:\n count += 1",
"def linecount(x):\n return sum(1 for char in x if char == \"\\n\")",
"def test_number_start_word():\n assert syllapy.count(\"4dog\") == 0",
"def srow(... | [
"0.6599933",
"0.62831575",
"0.62317944",
"0.6203771",
"0.6051275",
"0.6027196",
"0.599927",
"0.5920738",
"0.5860832",
"0.5799355",
"0.57977337",
"0.57650226",
"0.57611704",
"0.57336843",
"0.5714815",
"0.5706141",
"0.56840324",
"0.5660799",
"0.5651189",
"0.5635546",
"0.562362"... | 0.62299573 | 3 |
Structurize the list of lines into a dict by counting the leading ""s > as values. We also get rid of the "" and whitespaces afterwards! | def structure(data: list) -> dict:
structure = {}
for i in range(0, len(data)):
leading_ = Interpreter.leading(Interpreter(), data[i])
data_ele = data[i].replace("-", "").strip()
if data_ele in structure:
structure[data_ele + "%%%"] = leading_
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dictFromLines(lines,sep=None):\n reComment = re.compile('#.*')\n temp = [reComment.sub('',x).strip() for x in lines.split('\\n')]\n if sep == None or type(sep) == type(''):\n temp = dict([x.split(sep,1) for x in temp if x])\n else: #--Assume re object.\n temp = dict([sep.split(x,1) fo... | [
"0.6359959",
"0.6270492",
"0.6224244",
"0.6006112",
"0.599421",
"0.5908032",
"0.5810006",
"0.579494",
"0.57904506",
"0.57837945",
"0.5747093",
"0.57200885",
"0.56876975",
"0.56852555",
"0.5679893",
"0.56574166",
"0.56393373",
"0.5639167",
"0.55973274",
"0.55856735",
"0.556869... | 0.0 | -1 |
Entry point for the interpreter process. | def interpret(data: list) -> dict:
return Interpreter.structure(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run():\n main()",
"def main():\n run_program()",
"def main():\n pass",
"def main():\n return",
"def main() -> None:\n return",
"def main() -> None:",
"def main() -> None:",
"def main() -> None:",
"def main() -> None:",
"def main():\n args = parse_args()\n process_args... | [
"0.7234675",
"0.7215261",
"0.7081077",
"0.7078648",
"0.7007489",
"0.6970343",
"0.6970343",
"0.6970343",
"0.6970343",
"0.6953635",
"0.69507045",
"0.6907722",
"0.683263",
"0.6791568",
"0.67693144",
"0.67693144",
"0.67693144",
"0.67693144",
"0.67693144",
"0.67693144",
"0.6769314... | 0.0 | -1 |
Checks a files permissions against a permission requirement | def _does_perms_meet_req(stats, disallowed_perms):
# There's undoubtedly some simple clever binary algebra way to do this
vals_with = dict()
vals_with['r'] = [4, 5, 6, 7]
vals_with['w'] = [2, 3, 6, 7]
vals_with['x'] = [1, 3, 5, 7]
# Scopes are User, Group, and World
scope = ['U', 'G', 'W']... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_permissions(self):\n self.assertEqual(dir_perm, 0o2750)\n self.assertEqual(file_perm, 0o0440)",
"def check_files_permissions(self):\n result = []\n interesting_files = [\n # directories\n '/etc/init.d'\n '/etc/cron.d',\n '/etc/cron.... | [
"0.7191872",
"0.68649065",
"0.67243284",
"0.6699847",
"0.6546892",
"0.6490462",
"0.6473374",
"0.6473374",
"0.6460848",
"0.64598584",
"0.6364402",
"0.6364259",
"0.636375",
"0.6361546",
"0.6326846",
"0.6268052",
"0.62131613",
"0.6208553",
"0.6181878",
"0.6179629",
"0.6141624",
... | 0.66519415 | 4 |
Function to change pick uncertainties based upon correlation values (saved in pick Comment). This works inplace on the catalog. | def reweight_picks(cat):
from obspy.core.event import QuantityError
for ev in cat:
for pk in ev.picks:
if pk.phase_hint == 'P':
ccval = float(pk.comments[0].text.split('=')[-1])
# Re-weight based on some scheme (less down-weighting)
if ccval > ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def switch_cut_cor(self):\n if self.cut_cor == 41:\n self.cut_cor = 42\n elif self.cut_cor == 42:\n self.cut_cor = 41",
"def change_mix_corr(self, \n q_values, lmax,\n new_corr,new_cospsi,\n **kwargs):\n PhaseRetriever.__init__(self,\n q_valu... | [
"0.55101144",
"0.54023015",
"0.5400192",
"0.5268006",
"0.5240452",
"0.5010482",
"0.4968746",
"0.48149148",
"0.4811697",
"0.47968152",
"0.47857383",
"0.47856605",
"0.47505182",
"0.47229475",
"0.46873763",
"0.46638468",
"0.46599233",
"0.46597108",
"0.46573195",
"0.4628536",
"0.... | 0.49707213 | 6 |
Function to plot a random sample from a catalog with picks | def simple_pick_plot(cat, n_events, template_dict, st_dict, pyasdf=None, savefiles=False):
from obspy import Catalog, UTCDateTime, Stream
from obspy.core.event import ResourceIdentifier
if n_events == 'all':
rand_cat = cat
else:
rand_cat = rand_cat_sample(cat, n_events)
# Make a list... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_random_sample(pattern, num_to_select, row_no, col_no, c_map=\"viridis\"):\n mpl.rc(\"image\", cmap=c_map)\n all_images = get_image_paths(pattern)\n sampled_img = get_rand_img(num_to_select, all_images)\n plot_grid(row_no, col_no, sampled_img)",
"def sample_and_plot(self):\n fig = plt.... | [
"0.66966414",
"0.65395725",
"0.6370684",
"0.6370684",
"0.63241494",
"0.63104224",
"0.6130251",
"0.5980402",
"0.59737",
"0.5940193",
"0.5889786",
"0.58694214",
"0.5850805",
"0.5846972",
"0.5820033",
"0.581082",
"0.5789322",
"0.57852453",
"0.5775959",
"0.5773435",
"0.5740156",
... | 0.6025946 | 7 |
Plot a template over a detected stream, with picks corrected by lagcalc. | def plot_repicked(template, picks, det_stream, size=(10.5, 7.5), save=False,
savefile=None, title=False):
# _check_save_args(save, savefile)
fig, axes = plt.subplots(len(det_stream), 1, sharex=True, figsize=size)
if len(template) > 1:
axes = axes.ravel()
mintime = det_stream.so... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def xx_plot(epoch, model, features, filters, figname, fgal=0.5):\n # fetch Stripe 82 data\n X, Xcov = fetch_prepped_s82data(epoch, fgal, features, filters)\n Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, features,\n filters, use_single=False)\n N = ... | [
"0.59035116",
"0.58859456",
"0.578238",
"0.5767331",
"0.5742631",
"0.57282263",
"0.55894816",
"0.5575051",
"0.5565919",
"0.55595213",
"0.554277",
"0.5542592",
"0.55424595",
"0.5524052",
"0.5506664",
"0.55034083",
"0.5472254",
"0.54397625",
"0.54324085",
"0.5425673",
"0.541573... | 0.6715108 | 0 |
Return a dictionary of information about a person | def build_person(first_name,last_name, age =''):
person = { 'first': first_name.title(), 'last' : last_name.title()}
if age:
person['age'] = age
return person | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_person(self):\n person_dict = {\n 'firstname': self.__firstname,\n 'lastname': self.__lastname,\n 'height': self.__height,\n 'weight': self.__weight,\n 'age': self.__age\n }\n return person_dict",
"def who_am_i():\n return {'n... | [
"0.7562372",
"0.7202004",
"0.68984586",
"0.6763077",
"0.67415196",
"0.6625842",
"0.6569505",
"0.65048116",
"0.6392704",
"0.63766086",
"0.6367099",
"0.6340469",
"0.6332369",
"0.6284719",
"0.626071",
"0.62580657",
"0.62507343",
"0.6244045",
"0.62412703",
"0.6240264",
"0.623494"... | 0.5966081 | 47 |
Carga toda la pila con enteros | def cargaAutoInt(pila):
while not pila_llena(pila):
dato = random.randint(0, 10)
apilar(pila, dato) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def comenzar_nuevo_juego():\n escena_uno.cargarEscena1(screen, display_width, display_height)#Se pone a correr la escena\n #escena_uno.cargarEscena2(screen, display_width, display_height)",
"def Inicio():\n menu = \"\"\"\n Bienvenido al conversor de monedas 💰\n\n 1 - Pesos colombianos\n 2 - Pe... | [
"0.6960144",
"0.62438864",
"0.5807747",
"0.5806468",
"0.5786809",
"0.5756569",
"0.5700163",
"0.5687251",
"0.55761003",
"0.5567695",
"0.5562708",
"0.55521",
"0.5552041",
"0.5549286",
"0.5544261",
"0.5521034",
"0.5520254",
"0.54952437",
"0.54869676",
"0.5480632",
"0.54718786",
... | 0.53275734 | 40 |
Carga tola la pila con strings | def cargaAutoStr(pila):
while not pila_llena(pila):
largo = random.randint(1, 15)
apilar(pila, randString(largo)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def comenzar_nuevo_juego():\n escena_uno.cargarEscena1(screen, display_width, display_height)#Se pone a correr la escena\n #escena_uno.cargarEscena2(screen, display_width, display_height)",
"def stringToPila(palabra):\n pila = Pila()\n for elemento in palabra:\n apilar(pila, elemento)\n ret... | [
"0.58442247",
"0.5648824",
"0.56434804",
"0.56288433",
"0.5588605",
"0.5584497",
"0.5554773",
"0.5449711",
"0.54150164",
"0.53789884",
"0.5370985",
"0.532739",
"0.53188556",
"0.5307319",
"0.52647316",
"0.51832354",
"0.51682794",
"0.51462793",
"0.51443756",
"0.51443756",
"0.51... | 0.59360135 | 0 |
Apila un elemento en la pila | def apilar(pila, dato):
pila.tope += 1
pila.datos[pila.tope] = dato | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_podataka_sa_REST(self):\n urlUredjaj = self.cfg.get_konfig_element('REST', 'uredjaj')\n listaUredjaja = helper_funkcije.get_uredjaje_sa_REST(urlUredjaj)\n #inicijalizacija svih dostupnih uredjaja\n if listaUredjaja:\n for uredjaj in listaUredjaja:\n UN... | [
"0.60461986",
"0.59805244",
"0.59310794",
"0.5646558",
"0.5615027",
"0.55744654",
"0.5515526",
"0.54436487",
"0.5441662",
"0.5441662",
"0.54161835",
"0.5410337",
"0.5382265",
"0.5358372",
"0.53443366",
"0.533257",
"0.53211975",
"0.53207725",
"0.53205705",
"0.53148395",
"0.531... | 0.5313862 | 21 |
Desapila el elemento en cima | def desapilar(pila):
dato = pila.datos[pila.tope]
pila.tope -= 1
return dato | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete(self, *args, **kwargs):\n campo = Campostagimg.objects.filter(tag=self.tag, imagen=self.imagen)\n for c in campo:\n c.medidas = \"\"\n c.save()\n c.precision = 0\n c.save()\n c.v_esperado = \"\"\n c.save() \n\n ... | [
"0.61714435",
"0.61389273",
"0.6033699",
"0.5995851",
"0.5916466",
"0.5895718",
"0.58551925",
"0.5754909",
"0.5726794",
"0.5719078",
"0.5699067",
"0.56908727",
"0.5681546",
"0.56783044",
"0.5675249",
"0.5675179",
"0.56316787",
"0.55975395",
"0.55830395",
"0.55497795",
"0.5538... | 0.6247548 | 0 |
Devuelve elemento de la cima | def cima(pila):
return pila.datos[pila.tope] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def first(self):\n if self.is_empty():\n raise Empty('La cola está vacía')\n return self._head._element # frente alineado con la cabeza de la lista",
"def getFactura(self): \n return self.caja",
"def getFactura(self): \n return self.caja",
"def Cima(self):\n i... | [
"0.62369305",
"0.5968242",
"0.5968242",
"0.5929463",
"0.5875082",
"0.5795964",
"0.574635",
"0.57377017",
"0.56929976",
"0.566961",
"0.5612241",
"0.5606185",
"0.5604151",
"0.55805415",
"0.55805415",
"0.5572482",
"0.555679",
"0.5539605",
"0.5539363",
"0.5522629",
"0.55216",
"... | 0.6678378 | 0 |
Muestra todos elementos en pila | def barrido(pila):
paux = Pila()
while not pila_vacia(pila):
dato = desapilar(pila)
print(dato)
apilar(paux, dato)
while not pila_vacia(paux):
apilar(pila, desapilar(paux)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list(self):",
"def listar_gabarito():\n return GabaritoProva.listar(gabarito)",
"def listadoServicio(listServicios,codigoReserva):\n try:\n if(codigoReserva!=\"\"):\n variables.listado = listar(codigoReserva)\n listServicios.clear()\n for registro in variables.... | [
"0.6206592",
"0.6027048",
"0.60082996",
"0.5969509",
"0.5969509",
"0.5953642",
"0.5931115",
"0.58567864",
"0.5827238",
"0.5822924",
"0.5788263",
"0.5785534",
"0.578256",
"0.57585746",
"0.5745181",
"0.57410586",
"0.5723462",
"0.57228816",
"0.56660974",
"0.5607361",
"0.5586085"... | 0.0 | -1 |
Devuelve la pila invertida | def invertir(pila1):
pila2 = Pila()
while not pila_vacia(pila1):
apilar(pila2, desapilar(pila1))
return pila2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __invert__(self):\n return self.inverse()",
"def inverse(self, x, y):",
"def __invert(self, args):",
"def invert(self,el):\n return el^(self.q-2)",
"def invert(self):\n tmp = self.pvt\n self.pvt = self.nvt\n self.nvt = tmp\n tmp = self.pFace\n self.pFace... | [
"0.7260351",
"0.7015506",
"0.69790083",
"0.6911814",
"0.6806856",
"0.6755985",
"0.6703932",
"0.67010486",
"0.66792345",
"0.6677001",
"0.6664742",
"0.66548175",
"0.6640481",
"0.65897536",
"0.65876037",
"0.6559641",
"0.6545105",
"0.65431285",
"0.6482718",
"0.64756066",
"0.64310... | 0.7615787 | 0 |
Devuelve una cadena aleatoria | def randString(largo=1):
valores = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
return '' .join(random.choice(valores) for i in range(largo)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entrada_aluno(matricula):\n cod_curso = entrada_curso()\n print('> Em que ano, mês e dia você entrou na UFC? (YYYY-MM-DD)')\n data_de_ingresso = check.entrada('>>> ', check.data)\n print('> Em que data você vai concluir seu curso? (YYYY-MM-DD)')\n data_de_conclusao = check.entrada('>>> ', check.... | [
"0.6110482",
"0.58829135",
"0.58471066",
"0.5808168",
"0.5769837",
"0.5732787",
"0.571716",
"0.56439555",
"0.5630807",
"0.5551206",
"0.55446774",
"0.55200577",
"0.5448135",
"0.5437733",
"0.5413697",
"0.539102",
"0.5377001",
"0.5376293",
"0.53636044",
"0.53557664",
"0.53530836... | 0.0 | -1 |
Devuelve pila del string ingresado | def stringToPila(palabra):
pila = Pila()
for elemento in palabra:
apilar(pila, elemento)
return pila | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_string2(self):\n pass",
"def getApellidos(apellido):\n texto = f'El apellido es: {apellido}'\n return texto\n pass",
"def psea(pname): # -> str:\n ...",
"def print_as_text(pi):\n\n pi_string = str(\"%1.18f\" % pi)\n\n print(\"Definitive: \" + PI_STRING)\n\n print(\"Estimat... | [
"0.5971977",
"0.59691834",
"0.5927014",
"0.59089327",
"0.58850706",
"0.5883489",
"0.5877891",
"0.5867647",
"0.58412963",
"0.5805934",
"0.57704586",
"0.5736824",
"0.57056093",
"0.5686816",
"0.5681042",
"0.5671489",
"0.5662893",
"0.56376225",
"0.5629446",
"0.56220335",
"0.56079... | 0.599477 | 0 |
renders html and takes a screenshot | def screenshot(url, path):
# open in webpage
driver = webdriver.PhantomJS()
driver.set_window_size(1080, 800)
driver.set_page_load_timeout(30)
driver.get(url)
driver.save_screenshot(path)
driver.quit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_screenshot(html_file, year):\r\n\tdriver = webdriver.PhantomJS()\r\n\tdriver.set_window_size(800, 800)\r\n\tdriver.get(html_file)\r\n\r\n\t#allows the page to load completely\r\n\ttime.sleep(2)\r\n\r\n\timage = 'images/' + str(year) + '.png'\r\n\r\n\tdriver.save_screenshot(image)\r\n\tdriver.quit()\r\n\r\n... | [
"0.6781203",
"0.67746735",
"0.6765626",
"0.6692511",
"0.6436723",
"0.62806684",
"0.62287426",
"0.62287426",
"0.6214485",
"0.61541307",
"0.6063398",
"0.6000311",
"0.5993428",
"0.59772694",
"0.59754765",
"0.5933216",
"0.59321904",
"0.58835065",
"0.5881337",
"0.5876187",
"0.5874... | 0.6651713 | 4 |
Transform incoming data to a homogeneous 2d array. | def transform(self, X):
shape = len(X), self.max_len
Xt = self.pad_value * np.ones(shape, dtype=self.dtype)
for i, arr in enumerate(X):
m = min(self.max_len, len(arr))
if not m:
continue
arr = np.array(arr[:m])
Xt[i, :m] = arr
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(self, data):\n data = np.atleast_2d(data)\n\n if self.orientation == 'row':\n return data\n else:\n return data.T",
"def _to_numpy_ndarray(cls, data):\n if isinstance(data, np.ndarray):\n return data\n arr = np.array(data, dtype=np.f... | [
"0.69984454",
"0.69612616",
"0.69326127",
"0.68554795",
"0.68044513",
"0.6771031",
"0.6694132",
"0.6677301",
"0.65618455",
"0.64136314",
"0.64136314",
"0.6397039",
"0.6386304",
"0.63829464",
"0.63655895",
"0.63461405",
"0.6290972",
"0.6282495",
"0.6213077",
"0.6213077",
"0.61... | 0.0 | -1 |
Transform incoming data to a homogeneous 3d array. | def transform(self, X):
n = len(X)
Xt = self.pad_value * np.ones((n,) + self.max_size, dtype=self.dtype)
for i, arr in enumerate(X):
m = min(self.max_size[0], len(arr))
if not m:
continue
arr = np.array(arr[:m], dtype=object)
for j... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cvt2array(tuples):\n rc = []\n for t in tuples:\n rc.append(point3d(np.float32(t[X]), np.float32(t[Y]), np.float32(t[Z])))\n return rc",
"def mat_2d_to_3d(x, agg_num, hop):\n # Pad to at least one block. \n len_x, n_in = x.shape\n if (len_x < agg_num):\n x = np... | [
"0.6545719",
"0.64571476",
"0.6421992",
"0.6398177",
"0.6392814",
"0.632745",
"0.62147915",
"0.6207807",
"0.6147925",
"0.61356354",
"0.6131394",
"0.612939",
"0.61182773",
"0.61020154",
"0.6070016",
"0.60693353",
"0.605311",
"0.6039241",
"0.6023937",
"0.60228825",
"0.60228825"... | 0.0 | -1 |
Finalize the grades and print. Only for assessors. | def finalize(request, pk, version=0):
ts = get_timeslot()
if not hasattr(ts, 'resultoptions'):
raise PermissionDenied("Results menu is not yet visible.")
else:
if not get_timeslot().resultoptions.Visible:
raise PermissionDenied("Results menu is not yet visible.")
dstr = get_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n\n students = [\"Chris\", \"Jesse\", \"Sally\"]\n grades = [90, 80, 70]\n print_gradebook(students, grades)",
"def finalize():\n\n print(\"\"\"\n The script analysis/sedov_compare.py can be used to analyze these\n results. That will perform an average at constant radiu... | [
"0.59876657",
"0.57981944",
"0.564417",
"0.55673975",
"0.55103207",
"0.55028576",
"0.5473704",
"0.5460755",
"0.5440873",
"0.5409173",
"0.53980476",
"0.53825235",
"0.5363283",
"0.5363283",
"0.5361439",
"0.5361439",
"0.5361439",
"0.5361439",
"0.5361439",
"0.5361439",
"0.5349366... | 0.5862954 | 1 |
Edit grade for a category as indexed by step. For each student as given by pk. Also edit the individual aspects of each grade category. For trackheads and responsible staff | def finalize_preview(request, pk, step=0):
ts = get_timeslot()
if not hasattr(ts, 'resultoptions'):
raise PermissionDenied("Results menu is not yet visible.")
else:
if not get_timeslot().resultoptions.Visible:
raise PermissionDenied("Results menu is not yet visible.")
dstr = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_course_enrollment(self, student_id, course_id, course_section_id, term):\n conn = sqlite3.connect(self.db_path)\n cursor = conn.cursor()\n try:\n with conn:\n cursor.execute(\n \"\"\"\n UPDATE course_enrollments\n ... | [
"0.56488323",
"0.5607644",
"0.5588034",
"0.5586747",
"0.555214",
"0.5505774",
"0.5498233",
"0.5445095",
"0.52898294",
"0.52345645",
"0.518912",
"0.51739746",
"0.5155556",
"0.51250327",
"0.50697607",
"0.50619745",
"0.5057049",
"0.5050862",
"0.5034237",
"0.5033281",
"0.4990466"... | 0.0 | -1 |
Edit grade for a category as indexed by step. For each student as given by pk. Also edit the individual aspects of each grade category. For trackheads and responsible staff | def staff_form(request, pk, step=0):
ts = get_timeslot()
if not hasattr(ts, 'resultoptions'):
raise PermissionDenied("Results menu is not yet visible.")
else:
if not get_timeslot().resultoptions.Visible:
raise PermissionDenied("Results menu is not yet visible.")
dstr = get_ob... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_course_enrollment(self, student_id, course_id, course_section_id, term):\n conn = sqlite3.connect(self.db_path)\n cursor = conn.cursor()\n try:\n with conn:\n cursor.execute(\n \"\"\"\n UPDATE course_enrollments\n ... | [
"0.5649238",
"0.5606058",
"0.55875236",
"0.55865836",
"0.5551528",
"0.5505576",
"0.5498032",
"0.54429334",
"0.52882886",
"0.5234603",
"0.5189582",
"0.51738596",
"0.5154065",
"0.51239836",
"0.5069172",
"0.50602853",
"0.50570875",
"0.5049785",
"0.50334764",
"0.5032367",
"0.4990... | 0.46072274 | 66 |
Explanation about grading and grade categories. | def about(request, pk=None):
if pk and get_grouptype('3') in request.user.groups.all():
ts = get_object_or_404(TimeSlot, pk=pk)
else:
ts = get_timeslot()
return render(request, "results/about_grades.html", {
'scores': CategoryAspectResult.ResultOptions,
"categories": GradeCat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_knowledge_category_terms(self):\n return # osid.grading.GradeQueryInspector",
"def test_classify_grade(self):\n\t\ts = Student_Analytics()\n\t\tself.assertEqual(s.classify_grade(5.00),\"A+\")",
"def what_is_the_grade(self):\n\t\treturn_dict = {\n\t\t\t'section_title': self.title, \n\t\t\t'secti... | [
"0.625208",
"0.6094908",
"0.60138935",
"0.5881098",
"0.5708122",
"0.5681825",
"0.5661043",
"0.56477875",
"0.5588508",
"0.5543916",
"0.552775",
"0.5490752",
"0.5484218",
"0.54239833",
"0.5411527",
"0.5353282",
"0.53336465",
"0.5323209",
"0.5313484",
"0.53094536",
"0.5239492",
... | 0.0 | -1 |
List all aspects of a given grade category in the current timeslot | def list_aspects(request, pk):
category = get_object_or_404(GradeCategory, pk=pk)
aspects = GradeCategoryAspect.objects.filter(Category=category)
ts = get_timeslot()
return render(request, "results/list_aspects.html", {
"aspects": aspects,
'ts': ts,
'cat': category,
}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def time_budget_analysis(cursor, plot_parameters, by_category=False):\n\n categories, out = {}, []\n for subject in plot_parameters[\"selected subjects\"]:\n out_cat, categories[subject] = [], {}\n\n for behavior in plot_parameters[\"selected behaviors\"]:\n\n ... | [
"0.5233529",
"0.5082282",
"0.5039836",
"0.49076796",
"0.49012667",
"0.4887644",
"0.4880797",
"0.48579165",
"0.48460177",
"0.4806707",
"0.48065493",
"0.47938785",
"0.478363",
"0.47725368",
"0.47473636",
"0.47454002",
"0.47437078",
"0.47360337",
"0.47296265",
"0.47234464",
"0.4... | 0.71396613 | 0 |
Show a list of timeslots to import grades from. | def copy(request, pk=None):
# do a copy
if pk:
ts = get_object_or_404(TimeSlot, pk=pk)
if ts == get_timeslot():
raise PermissionDenied("It is not possible to copy the grades from the current timeslot.")
if get_timeslot().gradecategories.exists():
return render(req... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_grades(state, from_dir):\n grading_manager = GradingManager(state.get_assignment(), from_dir)\n print_grades(grading_manager.grades(), state.user_name)",
"def show_time(self):\n hour = str(datetime.datetime.now().strftime(\"%H\"))\n minute = str(datetime.datetime.now().strftime(\"%M\... | [
"0.5666389",
"0.5338114",
"0.5279143",
"0.51949066",
"0.51755744",
"0.51437485",
"0.51287425",
"0.51153266",
"0.51038504",
"0.5092183",
"0.5066873",
"0.504833",
"0.5036777",
"0.5032361",
"0.50242746",
"0.5023967",
"0.50215155",
"0.4967114",
"0.496651",
"0.49573854",
"0.493722... | 0.0 | -1 |
Error thrown when formatting a string but a config value is missing. | def __init__(self, key, parent=None):
if parent:
msg = f"Missing config while rendering {parent}: {key}"
else:
msg = f"Missing config: {key}"
super(MissingConfiguration, self).__init__(msg) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _raise_format_error(self, name: str, format_str: str, source_format: str):\n\n raise ValueError(f\"The '{ name }' should be { format_str }, rather than { source_format }\")",
"def test_single_specifier_missing(self):\n template = 'missing'\n value_count = 1\n msg = 'The formatter ... | [
"0.6889008",
"0.65927273",
"0.6343142",
"0.6240334",
"0.60607684",
"0.60268956",
"0.5999315",
"0.5850035",
"0.58428705",
"0.5835579",
"0.5832608",
"0.58293426",
"0.58019817",
"0.57879996",
"0.5760329",
"0.5742128",
"0.57138693",
"0.5656415",
"0.5598235",
"0.5593386",
"0.55930... | 0.0 | -1 |
Overloaded to implement recursive lazy evaluation of properties. | def __getattribute__(self, key):
value = super(Config, self).__getattribute__(key)
if key == "reserved" or key in self.reserved:
return value
else:
return self.format(value, key) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lazyprop(fn):\n\n @property\n def _lazyprop(self):\n if not hasattr(self, _LAZY_PROP_VALUES):\n setattr(self, _LAZY_PROP_VALUES, {})\n lazy_props_dict = self.__dict__[_LAZY_PROP_VALUES]\n if fn.__name__ not in lazy_props_dict:\n lazy_props_dict[fn.__name__] = fn... | [
"0.6558812",
"0.62286186",
"0.6222235",
"0.6111081",
"0.6004813",
"0.5979281",
"0.5608828",
"0.5590664",
"0.5550107",
"0.5419193",
"0.53930444",
"0.5368933",
"0.5345204",
"0.53392524",
"0.5152866",
"0.5152002",
"0.514644",
"0.5144571",
"0.51304764",
"0.5085058",
"0.50584644",... | 0.0 | -1 |
Add a child config | def add(self, key, child_config):
self.__dict__[key] = child_config
child_config.root = self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_config(self, parent_node, child_value):\n edge_cost = self.cost(parent_node.value, child_value)\n child_node = Node(\n child_value,\n parent=parent_node,\n cost=parent_node.cost + edge_cost,\n depth=parent_node.depth + 1\n )\n parent_n... | [
"0.7265803",
"0.6487871",
"0.64664084",
"0.62878114",
"0.6264923",
"0.6200054",
"0.6032241",
"0.59954774",
"0.5990614",
"0.59605205",
"0.59455884",
"0.59455884",
"0.591581",
"0.5893812",
"0.5794489",
"0.5790334",
"0.57764447",
"0.5702759",
"0.56942874",
"0.5688074",
"0.568399... | 0.7865205 | 0 |
Format strings using CONFIG object. This method uses python's builtin `str.format()` method. All root properties in CONFIG are passed in as kwargs. The properties lazy evaluate and recursively expand. | def format(self, value, key=None, **kwargs):
if not isinstance(value, str):
return value
# always format strings using the root so the full path is available
if self.root:
return self.root.format(value, key, **kwargs)
variables = CONFIG_VARIABLE_PATTERN.findall(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recursively_update_config(config, string_formatting_dict):\n\n for k in _iterate_list_or_dict(config):\n v = config[k]\n if isinstance(v, dict) or isinstance(v, list):\n recursively_update_config(v, string_formatting_dict)\n else:\n if _key_in_string(v, string_form... | [
"0.61522377",
"0.61236",
"0.60001415",
"0.57900184",
"0.57206047",
"0.57040524",
"0.5618517",
"0.5617663",
"0.56145966",
"0.56098795",
"0.5547251",
"0.55374193",
"0.5514143",
"0.54919505",
"0.5468669",
"0.54580975",
"0.54474247",
"0.54033774",
"0.5401085",
"0.53975976",
"0.53... | 0.75447696 | 0 |
Directory where ixian is installed | def IXIAN(cls):
import ixian
return os.path.dirname(os.path.realpath(ixian.__file__)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_teamocil_dir() -> pathlib.Path:\n return pathlib.Path(\"~/.teamocil/\").expanduser()",
"def get_install_dir(self):\n return EventGenerator.get_install_dir(self) + \"/madgraph5/src\"",
"def get_installdir(self):\n import mewlo\n path = os.path.dirname(os.path.realpath(mewlo.__fil... | [
"0.59720445",
"0.5938821",
"0.5927095",
"0.59131306",
"0.58979726",
"0.586261",
"0.5823516",
"0.58234173",
"0.5788261",
"0.57610697",
"0.5754324",
"0.57422614",
"0.5687996",
"0.565429",
"0.5623156",
"0.56043833",
"0.5588141",
"0.5566707",
"0.55413353",
"0.55124944",
"0.550046... | 0.7647922 | 0 |
Directory where ixian was run from | def PWD(cls):
return pwd() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def IXIAN(cls):\n import ixian\n\n return os.path.dirname(os.path.realpath(ixian.__file__))",
"def thisdir():\n if getattr(sys, 'frozen', False):\n # The application is frozen\n return os.path.dirname(sys.executable)\n else:\n # The application is not frozen\n # Ch... | [
"0.7374015",
"0.6456549",
"0.6438797",
"0.64322007",
"0.6419231",
"0.6372672",
"0.6354939",
"0.635183",
"0.6337567",
"0.6308413",
"0.6282552",
"0.62738544",
"0.62561786",
"0.62519723",
"0.62439907",
"0.6243649",
"0.62358207",
"0.623114",
"0.6230654",
"0.6224367",
"0.6190694",... | 0.0 | -1 |
Overloaded to implement recursive lazy evaluation of properties. | def __getattribute__(self, key):
from ixian.task import TASKS
formatted_key = key.lower()
if formatted_key in TASKS:
task = TASKS[formatted_key]
return TaskConfig(task)
else:
return super(TasksConfig, self).__getattribute__(key) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lazyprop(fn):\n\n @property\n def _lazyprop(self):\n if not hasattr(self, _LAZY_PROP_VALUES):\n setattr(self, _LAZY_PROP_VALUES, {})\n lazy_props_dict = self.__dict__[_LAZY_PROP_VALUES]\n if fn.__name__ not in lazy_props_dict:\n lazy_props_dict[fn.__name__] = fn... | [
"0.6558812",
"0.62286186",
"0.6222235",
"0.6111081",
"0.6004813",
"0.5979281",
"0.5608828",
"0.5590664",
"0.5550107",
"0.5419193",
"0.53930444",
"0.5368933",
"0.5345204",
"0.53392524",
"0.5152866",
"0.5152002",
"0.514644",
"0.5144571",
"0.51304764",
"0.5085058",
"0.50584644",... | 0.0 | -1 |
Tutte le possibili permutazioni di una lista | def all_perms(elements):
if len(elements) <=1:
yield elements
else:
for perm in all_perms(elements[1:]):
for i in range(len(elements)):
# nb elements[0:1] works in both string and list contexts
yield perm[:i] + elements[0:1] + perm[i:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def permutations(lst):\n pass # Replace this with your implementation of the function.",
"def permuta(L):\n return list(permuta_aux(L))",
"def listDuplicate(self,permutations=True):\n ind,ok = self.testDuplicate(permutations)\n return ind[~ok]",
"def unique():\r\n \r\n lista = main... | [
"0.6468891",
"0.636794",
"0.62925",
"0.6233623",
"0.61992514",
"0.6176376",
"0.61639607",
"0.6095989",
"0.60220444",
"0.5991031",
"0.59071577",
"0.58907956",
"0.5854479",
"0.5828909",
"0.57634",
"0.57418424",
"0.57263476",
"0.5719343",
"0.5716875",
"0.56898516",
"0.56706095",... | 0.0 | -1 |
Trova uno zero della funzione f tra i punti a e b, dove la f assume segno discorde. Il parametro opzionale toll indica la precisione con cui si vuole calcolare il valore dello zero | def bisezione(f,a,b,toll=10**-5):
m = (a+b)/2
f_m = f(m)
while abs(f_m) > toll:
if f(a)*f_m < 0:
b = m
elif f(b)*f_m < 0:
a = m
elif f_m == 0:
print("Trovata solzione esatta")
return m
else:
print("Metodo fallito")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Calcular(a: float) ->float:\n \n return (a*2)",
"def p() -> float:\n return 0.9",
"def f(x0: float, x1: float) -> float:\n return 8 - (x0 - 2) ** 2 - (x1 - 2) ** 2",
"def p2f (p):\n #return 11000**((p+1)/2)\n #return (p+1)*11000\n return (p+1)*5500",
"def p2f(self):\n\n stale = self.m_... | [
"0.65174574",
"0.6448639",
"0.63864565",
"0.6370828",
"0.6296283",
"0.6275515",
"0.6270769",
"0.6238707",
"0.6191305",
"0.61809313",
"0.618049",
"0.6167555",
"0.6149057",
"0.61406493",
"0.61188513",
"0.6118763",
"0.61178863",
"0.60342443",
"0.6006515",
"0.6004016",
"0.6002765... | 0.6965852 | 0 |
Incrementa la lista l intesa come numero (sequenza di cifre) contando in base b | def counter(l,b):
if l == [b-1]*len(l): return
i = 1
while(True):
if l[-i] < b-1:
l[-i] += 1
break
else:
l[-i] = 0
i += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def increment(b): \n if b == 11111111:\n return 00000000\n else:\n b = bin_to_dec(b)\n b = b + 1\n res = dec_to_bin (b)\n if len(res) == 8:\n return res\n else:\n c = 8 - len(res)\n return c*'0' + res",
"def next_num():\r\n C... | [
"0.67676866",
"0.62549233",
"0.62549233",
"0.6254061",
"0.62319183",
"0.6182245",
"0.6147752",
"0.608569",
"0.60396475",
"0.60262215",
"0.5960267",
"0.5946531",
"0.5906772",
"0.58969176",
"0.58864105",
"0.5877408",
"0.58636934",
"0.58469784",
"0.5830072",
"0.5778208",
"0.5726... | 0.62286186 | 5 |
combinations('ABCD', 2) > AB AC AD BC BD CD combinations(range(4), 3) > 012 013 023 123 | def combinations(iterable, r):
pool = tuple(iterable)
n = len(pool)
if r > n:
return
indices = list(range(r))
yield tuple(pool[i] for i in indices)
while True:
for i in reversed(range(r)):
if indices[i] != i + n - r:
break
else:
ret... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def combinations(s, n):\n return (\"\".join(x) for x in tuples(s,n))",
"def combinations(*comb, **kw):\n return _fixture_functions.combinations(*comb, **kw)",
"def part_2():\n return itertools.permutations(range(5, 10))",
"def get_combinations(text):\n combinations = []\n arr = []\n slen = ... | [
"0.718348",
"0.7005977",
"0.6993959",
"0.6917465",
"0.6894283",
"0.68004334",
"0.67992324",
"0.6771768",
"0.67690665",
"0.67308486",
"0.6717386",
"0.6692334",
"0.667273",
"0.66375154",
"0.6628557",
"0.6606142",
"0.6602187",
"0.65536106",
"0.65413445",
"0.6535008",
"0.6530812"... | 0.5852057 | 65 |
Fornisce i divisori di n | def divisori(n):
div=set()
for i in range(1,int(n**0.5+1)):
if n%i==0:
div.add(int(n/i))
div.add(i)
return sorted(div) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getDivisors(n):",
"def diviseur(n):\n s = 0\n for i in range (1, n):\n if n%i == 0:\n s += 1\n print(i)\n return \"Le nombre de diviseurs est\", s",
"def divisior(n: int) -> list:\n j = [n]\n for d in range(n+1): #loop bis n\n d > 0",
"def ge... | [
"0.8429677",
"0.7996387",
"0.78186643",
"0.7653951",
"0.75979793",
"0.75530833",
"0.74734193",
"0.7455468",
"0.74018323",
"0.73676103",
"0.73581624",
"0.7303397",
"0.7295638",
"0.72913414",
"0.72630566",
"0.72216606",
"0.7220337",
"0.721529",
"0.71952325",
"0.71851534",
"0.71... | 0.73575246 | 11 |
Prime n cifre del numero num | def first_n_digits(num, n):
return num // 10 ** (int(math.log(num, 10)) - n + 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isprime(n):\r\n\treturn is_prime(n)",
"def comprobar_primo(num):\n primo = True\n for i in range(2, num):\n if num%i == 0:\n primo = False\n return primo",
"def isprime(n):\n\treturn is_prime(n)",
"def get_prime_factor(n):\n if n % 2 == 0:\n return 2\n for num in r... | [
"0.7530166",
"0.7529172",
"0.74141574",
"0.7411982",
"0.74033934",
"0.7400811",
"0.73903537",
"0.7340318",
"0.7339235",
"0.7208471",
"0.72076946",
"0.71999794",
"0.71911615",
"0.7184746",
"0.7175246",
"0.71705663",
"0.7161727",
"0.71484905",
"0.714309",
"0.7138867",
"0.713257... | 0.0 | -1 |
Verifica che un intero positivo sia un quadrato | def is_square(apositiveint):
x = apositiveint // 2
seen = set([x])
while x * x != apositiveint:
x = (x + (apositiveint // x)) // 2
if x in seen: return False
seen.add(x)
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quadrant(xcoord, ycoord):\n\n xneg = bool(xcoord < 0)\n yneg = bool(ycoord < 0)\n if xneg is True:\n if yneg is False:\n return 2\n return 3\n if yneg is False:\n return 1\n return 4",
"def _point_in_tri(self, pos, tri):\n signs = np.sign([np.cross(tri[np... | [
"0.7044399",
"0.68214154",
"0.67046845",
"0.668237",
"0.65902036",
"0.6427196",
"0.6391145",
"0.637763",
"0.6324196",
"0.6229131",
"0.6208887",
"0.62083405",
"0.61662257",
"0.6166056",
"0.61422956",
"0.6140599",
"0.6139746",
"0.61388415",
"0.6134873",
"0.61307234",
"0.611599"... | 0.0 | -1 |
Ultime n cifre del numero num | def last_n_digits(num, n):
return num%(10**n) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def digit(number: int, n: int) -> int:\n return number // 10 ** n % 10",
"def getNumber():",
"def residuo_cero(numero):\n for x in range (1,10):\n if(numero % x == 0):\n return x \n return numero",
"def CLng(num):\n return int(round(float(num)))",
"def digito_verificacao(n... | [
"0.66533923",
"0.6577843",
"0.6550088",
"0.6524935",
"0.6478077",
"0.6407476",
"0.64042294",
"0.63701874",
"0.63546324",
"0.6320073",
"0.63194585",
"0.63091135",
"0.62533474",
"0.6244983",
"0.62103593",
"0.61851996",
"0.6173337",
"0.61667633",
"0.6162774",
"0.6149299",
"0.612... | 0.62598634 | 12 |
Massimo comune denominatore tra a e b | def mcd(a, b):
while(b != 0):
a,b = b,a%b
return a | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def denom(self, a):\n return self.one",
"def denom(self, a):\n raise NotImplementedError",
"def ppcm_denominateurs(self):\n\t\tl = []\n\t\tn = 1\n\t\tif self.__valide:\n\t\t\tfor m in self.liste_decroissante():\n\t\t\t\t\"\"\" les denominateurs sont positifs \"\"\"\n\t\t\t\te = m.get_coefficient(... | [
"0.64670295",
"0.63709563",
"0.6305553",
"0.6273303",
"0.62383693",
"0.62260216",
"0.6183728",
"0.61380094",
"0.61126524",
"0.61095035",
"0.6071675",
"0.6056668",
"0.60076785",
"0.5992859",
"0.5985773",
"0.5965909",
"0.5937595",
"0.5930666",
"0.5926027",
"0.59098005",
"0.5906... | 0.0 | -1 |
Restituisce una lista con tutti i numeri primi fino a n compreso col metodo del crivello di Eratostene | def primi(n):
numVec = []
for x in range(n-1):
numVec.append(x+2)
for num in numVec[:(n//2-1)]:
if numVec[num-2] != 0:
numVec[slice(2*num-2, n-1, num)] = [0]*(n//num-1)
numVec = [x for x in numVec if x!=0]
return numVec | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mk_lst_atnum(self):\n\t\telem_rnge=[]\n\t\tfor i in self.atom_num_lst:\n\t\t\tel_strt=i[0]\n\t\t\tel_end=i[1]\n\t\t\trnge_sect=range(el_strt,el_end+1)\n\t\t\telem_rnge.extend(rnge_sect)\n\t\telements=[]\n\t\tfor i in elem_rnge:\n\t\t\telement=Element.from_Z(i)\t# Indice -> pymatgen element object\n\t\t\telemen... | [
"0.6289262",
"0.62472546",
"0.6225951",
"0.61801213",
"0.60700405",
"0.59977514",
"0.5939416",
"0.5908262",
"0.5887603",
"0.585121",
"0.5849568",
"0.5843982",
"0.5811617",
"0.57871395",
"0.57503945",
"0.5743789",
"0.57214135",
"0.5715634",
"0.56780475",
"0.56768674",
"0.56632... | 0.0 | -1 |
Restituisce il radicale di n | def radicale(n):
r = 1
for p in primi(n+1):
if p>n:
break
if n%p==0:
r *= p
n = n//p
return r | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def n_wyraz(a1,nr_wyrazu,r):\n return a1+(nr_wyrazu-1)*r",
"def Arn(r, n):\n ret = 1\n for t in range(n, n-r+1-1, -1):\n ret *= t\n return ret",
"def _rnm(self, n, m, r):\n r_sum = 0\n m = int(abs(m))\n u = int((n-m)/2)\n v = int((n+m)/2)\n for s in range(0... | [
"0.6703785",
"0.65736157",
"0.6541312",
"0.6525386",
"0.6515335",
"0.63054556",
"0.6147716",
"0.61371934",
"0.61207694",
"0.6074879",
"0.60558325",
"0.605559",
"0.6001547",
"0.59967726",
"0.5936649",
"0.5906539",
"0.58998317",
"0.5871071",
"0.58641",
"0.5848409",
"0.58474976"... | 0.68493146 | 0 |
Fornisce l'elemento successivo nella sequenza di Collatz | def collatz(n):
if n%2==0: return n/2
else: return 3*n+1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def busca_sequencial_sentinela(lista, elemento):\n contador = 0\n lista.append(contador) \n try: \n while lista[contador] != elemento:\n contador += 1\n if contador == len(lista) - 1:\n del lista[-1]\n return -1\n \n del lista[-1]\n ... | [
"0.5769282",
"0.5743955",
"0.5429377",
"0.53927815",
"0.53786826",
"0.5377348",
"0.5373288",
"0.5235085",
"0.5226564",
"0.5222602",
"0.51664275",
"0.5165773",
"0.51485234",
"0.51367635",
"0.5131511",
"0.5119195",
"0.5055059",
"0.5043993",
"0.5040059",
"0.5032709",
"0.5030647"... | 0.0 | -1 |
Algoritmo di kruskal per la ricerca dell'MST di un grafo, fornito in tramite la sua matrice di adiacenza, usa la funzione ring_finder per cercare anelli nel grafo e di min_nonzero_idx per trovare gli inidici dei rami con costo minimo | def kruskal(m):
n = m.shape[0]
m_ret = np.zeros([n,n], dtype=int)
while np.count_nonzero(m_ret) != 2*(n-1):
i_min, j_min = min_nonzero_idx(m)
n_min = m[i_min, j_min]
m[i_min, j_min], m[j_min, i_min] = 0, 0
m_ret[i_min, j_min], m_ret[j_min, i_min] = n_min, n_min
if rin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def kruskal(self):\n AGM = []\n i = j = 0\n \n self.grafo = sorted(self.grafo,key=lambda item:item[2])\n\n pai = []\n nivel = []\n\n for vertice in range(self.nVer):\n pai.append(vertice)\n nivel.append(0)\n\n while j < self.nVer-1:\n ... | [
"0.615799",
"0.5902298",
"0.58482414",
"0.57372916",
"0.57352954",
"0.57080084",
"0.56059325",
"0.55901784",
"0.5569681",
"0.55468035",
"0.5539385",
"0.5525096",
"0.5479864",
"0.546164",
"0.54158294",
"0.5414072",
"0.53997874",
"0.53977036",
"0.5389939",
"0.5384784",
"0.53628... | 0.68557984 | 0 |
Implementation uses the MillerRabin Primality Test The optimal number of rounds for this test is 40 | def miller_rabin(n,k):
if n == 2:
return True
if n % 2 == 0:
return False
r, s = 0, n - 1
while s % 2 == 0:
r += 1
s //= 2
for _ in range(k):
a = random.randrange(2, n - 1)
x = pow(a, s, n)
if x == 1 or x == n - 1:
continue
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rabin_miller_is_prime(n, k=100):\n\n def basic_is_prime(_n):\n \"\"\"Basic check to see if input is a prime.\n Returns False if input number is a composite with at least one term being one of the primes below 10.000.\n Returns True if the number is a prime (can only be known if it is in... | [
"0.761824",
"0.7428767",
"0.73708177",
"0.73257506",
"0.73257506",
"0.7152389",
"0.704289",
"0.69547087",
"0.69087857",
"0.6902767",
"0.69024384",
"0.68879527",
"0.6800044",
"0.6790292",
"0.6718005",
"0.66617966",
"0.65989155",
"0.65562785",
"0.6515376",
"0.6470473",
"0.64568... | 0.7301876 | 5 |
Get current NFL season After March, returns year of upcoming season. | def current_season() -> int:
now = datetime.now()
month, year = now.month, now.year
if month < 4:
year -= 1
return year | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def current_season():\n td = datetime.datetime.today()\n if td.month > 8:\n return td.year\n return td.year - 1",
"def latest_season_before(date):\n\tif date.month < 9:\n\t\treturn date.year - 1\n\treturn date.year",
"def return_football_season(date=datetime.datetime.today()):\n date_aux = s... | [
"0.81641465",
"0.7565352",
"0.74286443",
"0.6773904",
"0.66487664",
"0.6576127",
"0.65571433",
"0.6517643",
"0.6495419",
"0.64488924",
"0.64429444",
"0.64425147",
"0.64404756",
"0.6364527",
"0.62375706",
"0.61532784",
"0.61332804",
"0.6104992",
"0.5971628",
"0.5910558",
"0.58... | 0.8059089 | 1 |
Returns first Monday in September of given year | def _labor_day(year):
day = datetime(year, 9, 1)
delta = timedelta(days=1)
while day.weekday() != 0:
day += delta
return day | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wkday_on_first(yr, mon): # returns day of week of first of month of the given year (1/1/2016)\r\n TotalDays = 0\r\n for x in range(1754, yr):\r\n YearNum = yeardays(x)\r\n TotalDays += YearNum\r\n for x in range(1, mon):\r\n MonNum = monthdays(yr, x)\r\n TotalDays += MonNu... | [
"0.74452096",
"0.7358948",
"0.71944577",
"0.6750746",
"0.66948074",
"0.6441872",
"0.63434887",
"0.6304483",
"0.6295698",
"0.6277809",
"0.62662905",
"0.62631595",
"0.6171451",
"0.6164381",
"0.6106714",
"0.60991955",
"0.60991955",
"0.6070933",
"0.60079587",
"0.59685105",
"0.593... | 0.64837617 | 5 |
Get NFL week (ESPN scoring period) from date The year of the given date determines the relevant NFL season. Assumes week 1 begins the week of Labor Day and ends the following Wednesday. Does not cap value, so may be below 1 or above 17. | def get_week_from_date(date) -> int:
month, year = date.month, date.year
if month < 4:
year -= 1
ld = _labor_day(year)
wk1_wed = ld + timedelta(days=2)
days_since = (date - wk1_wed).days
weeks_since = days_since / 7.
week = math.floor(weeks_since) + 1
return int(week) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_week(date):\n\n # TODO: the API seems broken. It returns week, year not year, week as documentef\n # why not use date.isocalendar() from the stdlib?\n\n date = date_trunc('week', date)\n\n first_monday = date_trunc('week', date_trunc('year', date))\n if first_monday.year < date.year:\n ... | [
"0.7253059",
"0.6891157",
"0.68909645",
"0.6705168",
"0.66147095",
"0.6535417",
"0.63276374",
"0.6250827",
"0.62457407",
"0.61644757",
"0.6102457",
"0.60741466",
"0.59655815",
"0.59634364",
"0.59619147",
"0.5926681",
"0.5889611",
"0.5864661",
"0.5860158",
"0.5819684",
"0.5810... | 0.7307077 | 0 |
Get current NFL week (ESPN scoring period) | def current_week() -> int:
now = datetime.now()
return get_week_from_date(now) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def current_week(self):\n\n if not self.iso_equal() and self.time_stamp.weekday() == 6:\n return self.time_stamp_iso[1] + 2\n if not self.iso_equal() or self.time_stamp.weekday() == 6:\n return self.time_stamp_iso[1] + 1 \n return self.time_stamp_iso[1]",
"def weekly():... | [
"0.7158167",
"0.71226656",
"0.7076009",
"0.6954984",
"0.6877098",
"0.6862873",
"0.6779078",
"0.67491233",
"0.66814524",
"0.66672885",
"0.6630186",
"0.66270226",
"0.642512",
"0.6424101",
"0.64128476",
"0.6316389",
"0.6287629",
"0.62338805",
"0.62046766",
"0.61931086",
"0.61917... | 0.6960671 | 3 |
Find list of edl directories in all dependencies for the passed module | def get_edl_dirs(mod, gen_cfg):
log.info("Fetching dependencies for %s", coordinates.as_path(mod.coords))
dependencies = mod.get_dependencies()
edl_dirs = [mod.get_edl_path()]
for dep, dep_coords in dependencies.items():
dep_cfg = gen_cfg.get_mod_cfg(dep)
log.info("Dependency: %s", coor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compute_module_search_path(self, pkg_name):\n pkg_location = self.get_package_location(pkg_name)\n module_search_path = [pkg_location, os.path.join(pkg_location,'lib')]\n st, cycle = graph.dfs(self.package_dependency, pkg_name)\n # computed packages on which this task depends\n ... | [
"0.65620816",
"0.63926107",
"0.6388437",
"0.63874215",
"0.6298563",
"0.6170511",
"0.6139222",
"0.6134263",
"0.6119241",
"0.61146545",
"0.608592",
"0.6056361",
"0.603119",
"0.6020695",
"0.5978215",
"0.5966075",
"0.5961092",
"0.5923319",
"0.5875171",
"0.5873686",
"0.58671385",
... | 0.76784027 | 0 |
Update the symbol XML node | def edit_symbol_node(node, filename):
size = int(re.findall('\d+', filename)[-1])
log.info('New filename %s; size %s', filename, size)
node.set('typeId', SYMBOL_ID)
node.find('name').text = 'DLS symbol'
# Use PV name from rule in control PV for tooltip etc.
# Reference that PV in rule to avoid... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_symbol(self, symbol):\r\n self.symbol = symbol",
"def symbol(self, symbol):\n self._symbol = symbol",
"def symbol(self, symbol):\n\n self._symbol = symbol",
"def set_symbol(self, row, col, symbol):\n self.field[row, col] = symbol",
"def setSymbolProps(self, name, symbol)... | [
"0.617891",
"0.61759514",
"0.61127055",
"0.5838499",
"0.56468177",
"0.56205124",
"0.5558976",
"0.5554636",
"0.5539334",
"0.5484995",
"0.54771584",
"0.5455788",
"0.54166734",
"0.53877443",
"0.5364725",
"0.53485316",
"0.5334409",
"0.5323229",
"0.53168017",
"0.5301254",
"0.51267... | 0.65849906 | 0 |
Grep on the basepath to find all files that contain an EDM symbol widget. control | def build_filelist(basepath):
log.info("Building list of files containing EDM symbols in %s", basepath)
symbol_files = []
for dir_path, _, filenames in os.walk(basepath):
for filename in filenames:
filepath = os.path.join(dir_path, filename)
if filename.endswith(".opi") and u... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def searchfiles(pattern='C:\\\\RoboDK\\\\Library\\\\*.rdk'):\n import glob\n return glob.glob(pattern)",
"def find_files(config, slot='*'):\n f_pattern = os.path.join(os.path.join(config['path'],config['led_name']), slot+'*' + config['led_name'] + '*'\n + config['current'] + ... | [
"0.6017925",
"0.56027436",
"0.5597905",
"0.555413",
"0.5544122",
"0.54559",
"0.5441576",
"0.5366995",
"0.53383917",
"0.5280805",
"0.5255213",
"0.5246665",
"0.5238796",
"0.52089924",
"0.5191325",
"0.5161962",
"0.51558715",
"0.5154216",
"0.51451254",
"0.513593",
"0.5125903",
... | 0.60150164 | 1 |
Process one symbol file and convert to PNG. | def process_symbol(filename, mod, mod_cfg, mirror_root, prod_root):
working_path = os.path.join(mirror_root, prod_root[1:])
log.debug("Finding version from %s", working_path)
mod_version = utils.get_module_version(working_path, mod_cfg.area, mod, mod_cfg.version)
log.info("Found version %s", mod_version... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decode():\r\n # Open the file with binary instructions\r\n with open(file_name) as file:\r\n lines = file.readlines()\r\n with open(PATH + file_name, \"w\") as file_write:\r\n for line in lines:\r\n file_write.write(line + \"\\n\")\r\n\r\n # Read the instruction... | [
"0.5763709",
"0.55842566",
"0.5484444",
"0.54430854",
"0.5442242",
"0.5435863",
"0.53775424",
"0.53518033",
"0.53421825",
"0.53008115",
"0.52987564",
"0.5291246",
"0.5290452",
"0.5279297",
"0.52617425",
"0.5226984",
"0.5217233",
"0.52096176",
"0.51862746",
"0.51822567",
"0.51... | 0.61083233 | 0 |
calculate_angles(chunk) calculates elevation and azimuth given a jsonformatted chunk from ODAS | def calculate_angles(self,chunk):
import math
import collections
Angles = collections.namedtuple("Angles", "ev az")
x = float(chunk['x'])
y = float(chunk['y'])
z = float(chunk['z'])
ev = round(90 - math.acos(z/math.sqrt(x*x+y*y+z*z))*180/math.pi)
az = rou... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse(all_blobs, all_angles):",
"def extract_angles(self):\n atom_ids = self.contents['ID']\n angle_list = []\n for key, value in self.angles.items():\n a = value[0]\n b = value[1]\n c = value[2]\n\n lst = [a, b, c]\n\n A_ = np.asarr... | [
"0.56423277",
"0.55796504",
"0.5485317",
"0.532156",
"0.5300278",
"0.50899595",
"0.50719124",
"0.50677234",
"0.5040855",
"0.5008711",
"0.5001167",
"0.50002366",
"0.4993435",
"0.49813247",
"0.4962279",
"0.49208593",
"0.49165124",
"0.49086887",
"0.48858005",
"0.48731053",
"0.48... | 0.7734531 | 0 |
Create a logger with console logging (info level) + log file (debug level). | def create_logger(log_dir):
logger = logging.getLogger(__file__)
logger.setLevel(logging.INFO)
# file logger
log_filename = "probabilist_connectogram_%s.log" % time.strftime("%Y-%m-%d_%H:%M:%S")
if log_dir:
log_path = os.path.join(log_dir, log_filename)
else:
log_path = log_file... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_logger():\n logging.basicConfig(level = logging.INFO, filename='logging', filemode='w')\n logger = logging.getLogger(\" \")\n admin_handler = logging.FileHandler('logging')\n admin_handler.setLevel(logging.INFO)\n logger.addHandler(admin_handler)\n logger.warning(f'{admin_handler} crea... | [
"0.7268376",
"0.70308805",
"0.7029211",
"0.7027607",
"0.6950778",
"0.6924888",
"0.6906252",
"0.6870268",
"0.6851774",
"0.68211913",
"0.6813843",
"0.6808933",
"0.68085176",
"0.6753689",
"0.67525923",
"0.6736932",
"0.67316926",
"0.67220473",
"0.67183745",
"0.6701308",
"0.669111... | 0.65178096 | 41 |
Create a command line argument parser, run it and return a dict mapping > . | def get_cmd_line_args():
usage = "%(prog)s <subject id> <nodif_brain> <bedpostx_dir> <outdir> [options]"
parser = argparse.ArgumentParser(prog = "python probabilist_connectogram.py",
usage = usage)
# Required arguments
parser.add_argument("subject_id",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main_parse_args():\n parser = ArgumentParser()\n parser.add_argument('infile', help='path to the file to be mapped.It should\\\n contain one identifer on each line.')\n parser.add_argument('-rh', '--redis_host', default=DEFAULT_REDIS_URL,\n help='url of Re... | [
"0.7854356",
"0.7411294",
"0.738185",
"0.7369688",
"0.725296",
"0.72444576",
"0.7242427",
"0.7233522",
"0.7119903",
"0.7105029",
"0.70896465",
"0.7079773",
"0.7079739",
"0.7069191",
"0.706641",
"0.7060959",
"0.7060555",
"0.7030648",
"0.70134765",
"0.69964945",
"0.6994354",
... | 0.0 | -1 |
What are the most popular three articles of all time? | def getPopularArticles():
db = psycopg2.connect("dbname=news")
c = db.cursor()
c.execute(" select count (*) as views, title from articles "
+ "left join "
+ "log on concat('/article/', articles.slug) = log.path "
+ "group by title order by views desc limit 3")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def most_popular_articles():\n\n results = query_database(QUERIES[0])\n print('\\nWhat are the most popular three articles of all time?\\n')\n for title, views in results:\n print(' * \"{}\" -- {} views'.format(title, views))",
"def most_popular_articles():\n print '1. The most popular article... | [
"0.7968826",
"0.77808386",
"0.7766518",
"0.77630246",
"0.7703104",
"0.7636034",
"0.7507648",
"0.7505851",
"0.74948984",
"0.7427754",
"0.74145854",
"0.71255904",
"0.71188134",
"0.7111447",
"0.7081126",
"0.7029817",
"0.69994766",
"0.6986983",
"0.69304246",
"0.6920505",
"0.68825... | 0.7005649 | 16 |
Who are the most popular article authors of all time? | def getPopualrAuthors():
db = psycopg2.connect("dbname=news")
c = db.cursor()
c.execute(" select count(*) as views , authors.name from articles "
+ " inner join "
+ "log on concat('/article/', articles.slug) = log.path "
+ " inner join authors on articles.author =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_mostPopularAuthors():\n\n query = \"\"\"\n SELECT authors.name,COUNT(*) as views\n FROM articles join authors\n ON articles.author=authors.id\n JOIN log ON log.path LIKE ('/article/' || articles.slug)\n GROUP BY authors.name\n ORDER BY views DE... | [
"0.8464116",
"0.8395627",
"0.8325832",
"0.8321721",
"0.81574434",
"0.8025931",
"0.7990663",
"0.77915514",
"0.778007",
"0.76863235",
"0.7682032",
"0.7626295",
"0.755735",
"0.75209177",
"0.74950725",
"0.7390242",
"0.7380563",
"0.7352149",
"0.73245054",
"0.7269289",
"0.7168644",... | 0.67932266 | 28 |
Who are the most popular article authors of all time? | def getWorstDays():
db = psycopg2.connect("dbname=news")
c = db.cursor()
c.execute(" select c.* from"
+ "(select a.* , b.* , "
+ "(cast( b.total as decimal(16,4))/a.total)*100 as percent from"
+ " (select count(*) total , time::timestamp::date as timea "
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_mostPopularAuthors():\n\n query = \"\"\"\n SELECT authors.name,COUNT(*) as views\n FROM articles join authors\n ON articles.author=authors.id\n JOIN log ON log.path LIKE ('/article/' || articles.slug)\n GROUP BY authors.name\n ORDER BY views DE... | [
"0.84637415",
"0.8395699",
"0.8325273",
"0.8321845",
"0.8157658",
"0.80273575",
"0.7990549",
"0.7792252",
"0.77818847",
"0.7686615",
"0.7682264",
"0.76261973",
"0.75585544",
"0.7522089",
"0.7496455",
"0.73912114",
"0.7381132",
"0.735024",
"0.73232555",
"0.7270634",
"0.7169818... | 0.0 | -1 |
Returns an "absolute" value for a timedelta, always representing a time distance. | def abs_timedelta(delta):
if delta.days < 0:
now = datetime.datetime.now()
return now - (now + delta)
return delta | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def abs_timedelta(delta):\r\n if delta.days < 0:\r\n now = _now()\r\n return now - (now + delta)\r\n return delta",
"def delta(self, abs_value=False):\n return self.current - self.last if not abs_value else np.abs(self.current - self.last)",
"def timedelta(self) -> datetime.timedelta... | [
"0.75997627",
"0.6525403",
"0.64160365",
"0.6255736",
"0.58985287",
"0.58082616",
"0.57936674",
"0.573392",
"0.569296",
"0.56611925",
"0.56431794",
"0.56412184",
"0.56401443",
"0.5639377",
"0.5544877",
"0.5541361",
"0.5524112",
"0.5501755",
"0.547358",
"0.54723006",
"0.545224... | 0.7604547 | 0 |
Turn a value into a date and a timedelta which represents how long ago it was. If that's not possible, return (None, value). | def date_and_delta(value):
now = datetime.datetime.now()
if isinstance(value, datetime.datetime):
date = value
delta = now - value
elif isinstance(value, datetime.timedelta):
date = now - value
delta = value
else:
try:
value = int(value)
de... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def date_and_delta(value):\r\n now = _now()\r\n if isinstance(value, datetime):\r\n date = value\r\n delta = now - value\r\n elif isinstance(value, timedelta):\r\n date = now - value\r\n delta = value\r\n else:\r\n try:\r\n value = int(value)\r\n ... | [
"0.77345294",
"0.6244832",
"0.6088585",
"0.577737",
"0.5701926",
"0.56636906",
"0.56362903",
"0.559285",
"0.5573411",
"0.5541884",
"0.5530724",
"0.55193424",
"0.55110997",
"0.54977155",
"0.54975235",
"0.54929054",
"0.5488044",
"0.54846984",
"0.545197",
"0.5361616",
"0.5360478... | 0.7786376 | 0 |
Return the Hamming distance between equallength sequences | def __hamming_distance(s1, s2):
if len(s1) != len(s2):
raise ValueError("Undefined for sequences of unequal length")
return sum(el1 != el2 for el1, el2 in zip(s1, s2)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hamming_distance(s1, s2):\n if len(s1) != len(s2):\n raise ValueError(\"Undefined for sequences of unequal lenght.\")\n return sum(ch1 != ch2 for ch1, ch2 in zip(s1, s2))",
"def hammingDistance(s1 = \"\", s2 = \"\"):\n # if len(s1) != len(s2):\n # raise ValueError(\"Undefined for sequen... | [
"0.7564989",
"0.7524423",
"0.7510748",
"0.7498424",
"0.73834527",
"0.72914463",
"0.7283258",
"0.72027653",
"0.719384",
"0.7186409",
"0.7184023",
"0.7142286",
"0.7130274",
"0.71298635",
"0.70760477",
"0.70056623",
"0.6964983",
"0.69032145",
"0.6894841",
"0.68800247",
"0.686782... | 0.7553281 | 1 |
Implementation to serialize ``o`` argument. | def default(self, o: Decimal) -> Union[int, float]:
if isinstance(o, Decimal):
# NOTE: The below is potentially a HUGE MISTAKE and an
# unnecessary OVER ENGINEERING! but this works. This is
# not required as such because we can get around this by
# converting ever... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serialize(self, obj):\n pass",
"def serialize(obj):\n return serialization_manager.serialize(obj)",
"def serialize(self, obj):\n return obj",
"def serialize(obj):\n return pickle.dumps(obj)",
"def serialize_forstorage(cls, obj):\n return misc.serialize_forstorage(obj)",
"def ... | [
"0.76455027",
"0.71864474",
"0.71055853",
"0.6807475",
"0.6794227",
"0.66385746",
"0.66352147",
"0.6566663",
"0.65611637",
"0.6546735",
"0.6452253",
"0.64283395",
"0.63991266",
"0.6386263",
"0.63441694",
"0.6334144",
"0.6327756",
"0.6327756",
"0.6322545",
"0.6322545",
"0.6317... | 0.0 | -1 |
Fundamental pretrained Ernie model | def __init__(self, cfg, name=''):
nn.Layer.__init__(self)
self.cfg = cfg
d_model = cfg['hidden_size']
d_emb = cfg.get('emb_size', cfg['hidden_size'])
d_vocab = cfg['vocab_size']
d_pos = cfg['max_position_embeddings']
# d_sent = cfg.get("sent_type_vocab_size", 4) o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pretrained():\n launch_training_on_all_splits(experiment='full', splits=NAME_SPLIT, base_model='pretrained', dropout=0.7987, learning_rate=0.00009659)",
"def load_pretrained_model(model_name):\n if model_name==\"AlexNet\":\n print(\"Loading pretrained AlexNet Model\")\n model_ft = models.... | [
"0.64619243",
"0.63850045",
"0.63135374",
"0.6303723",
"0.62750506",
"0.62338805",
"0.6216982",
"0.6216971",
"0.6207287",
"0.61998534",
"0.6164519",
"0.6160022",
"0.61489826",
"0.6124723",
"0.61036414",
"0.60924315",
"0.6061764",
"0.60476965",
"0.60332507",
"0.60278857",
"0.6... | 0.0 | -1 |
return checkpoints for recomputing | def get_checkpoints(self):
# recompute checkpoints
return self._checkpoints | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkpoint():",
"def checkpoint_set():\n checkpoints.append(list())",
"def get_all_overall_checkpoint(cls):\n return cls.create_all_overall_checkpoint()",
"def get_checkpoint_list(cls):\n return cls.create_checkpoint_list()",
"def checkpoint(self):\r\n return self._checkpoint",
"d... | [
"0.744063",
"0.70907867",
"0.7052293",
"0.69212705",
"0.68812984",
"0.664399",
"0.66409785",
"0.6579985",
"0.6512506",
"0.65115094",
"0.65036887",
"0.63288766",
"0.624046",
"0.6220209",
"0.62004256",
"0.61839545",
"0.61744016",
"0.6170368",
"0.6168887",
"0.61377853",
"0.61085... | 0.7678584 | 0 |
append name with postfix | def append_name(name, postfix):
if name is None:
ret = None
elif name == '':
ret = postfix
else:
ret = '%s_%s' % (name, postfix)
return ret | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_name(name):\r\n\r\n\r\n return name + \"-apple\"",
"def add_name(self, node):\n if 'name' in self.options:\n name = nodes.fully_normalize_name(self.options.pop('name'))\n if 'name' in node:\n del(node['name'])\n node['names'].append(name)\n ... | [
"0.6697877",
"0.662184",
"0.6614897",
"0.6473923",
"0.6254014",
"0.6248267",
"0.6226696",
"0.6212746",
"0.62123245",
"0.60669625",
"0.6044965",
"0.6044965",
"0.6044435",
"0.60033596",
"0.5903342",
"0.5898558",
"0.588601",
"0.5874177",
"0.5855147",
"0.58351374",
"0.5803622",
... | 0.8279311 | 0 |
Lands the rover, and makes it part of the grid Throws an exception if A rover with that name already existed The rover being landed has a bad direction The rovers coordinates are off the grid A rover already exists on the gird at the rover's coordinates | def land_rover(self, rover):
if self.rovers.get(rover.name):
raise RoverException(ExceptionMessages.ROVER_ALREADY_LANDED)
if not Rover.valid_direction(rover.direction):
raise RoverException(ExceptionMessages.BAD_DIRECTION)
if not self._is_coordinate_in_the_grid(rover.c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_move(self):\n self.owner = self.game.current_turn\n self.status = 'X' if self.owner == self.game.creator else 'O'\n ####\n #Random turn??\n ####\n self.save(update_fields=['status', 'owner'])\n\n # Add log entry for move\n self.game.add_log(f'cell ma... | [
"0.62105554",
"0.620282",
"0.59880394",
"0.5978847",
"0.5967129",
"0.5945851",
"0.5943394",
"0.59243786",
"0.5899155",
"0.5898589",
"0.5803447",
"0.5776576",
"0.5759452",
"0.5755554",
"0.5738934",
"0.57336867",
"0.5733618",
"0.5726307",
"0.57162607",
"0.5699025",
"0.5692675",... | 0.68636113 | 0 |
Tries to navigate and reposition the rover on the gird. Throws an exception if It cannot find that rover on the grid A bad instruction is passed Executing the instruction string will cause a collision with another rover on the gird | def navigate_rover(self, name, instruction_str):
rover = self.rovers.get(name)
if not rover:
raise RoverException(ExceptionMessages.BAD_NAME)
coordinate = copy.deepcopy(rover.coordinate)
direction = rover.direction
for instruction in instruction_str:
i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_rover(grid, start_at, instructions, name='rover'):\n plateu = None\n try:\n if isinstance(grid, str):\n x_end, y_end = grid.split(' ')\n x_end = int(x_end)\n y_end = int(y_end)\n plateu = Plateu(x_end, y_end, name)\n\n elif isinstance(grid... | [
"0.6312876",
"0.60473263",
"0.5894628",
"0.5894628",
"0.5836864",
"0.5819794",
"0.581422",
"0.5812412",
"0.5812412",
"0.58097583",
"0.58019125",
"0.5792342",
"0.57617646",
"0.5753274",
"0.57401913",
"0.5738901",
"0.5717236",
"0.5713577",
"0.57098556",
"0.57092357",
"0.5694141... | 0.6905907 | 0 |
Basically a state machine Given a instruction('R' or 'L') and a direction('N' or 'S' or 'E' or 'W'), returns the new direction Throws an exception in case of bad instruction | def _direction_after_turning(self, direction, instruction):
next_left_states = {'N':'W', 'W': 'S', 'S': 'E', 'E': 'N'}
next_right_states = {'N': 'E', 'E': 'S', 'S': 'W', 'W': 'N'}
if instruction == 'R':
return next_right_states[direction]
elif instruction == 'L':
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def go_one_step(old_state, direction):\n assert direction in ['R', 'L', 'U', 'D']\n\n x, y = old_state\n if direction == 'R':\n return (x+1, y)\n if direction == 'L':\n return (x-1, y)\n if direction == 'U':\n return (x, y+1)\n if direction == 'D':\n return (x, y-1)",
... | [
"0.6593464",
"0.62368506",
"0.61913526",
"0.5976613",
"0.57964754",
"0.5683937",
"0.5672923",
"0.5668834",
"0.5567708",
"0.5457504",
"0.5439745",
"0.5427397",
"0.54177237",
"0.53752434",
"0.5332933",
"0.53058296",
"0.53014976",
"0.52971196",
"0.5290954",
"0.5269749",
"0.52675... | 0.77884203 | 0 |
Returns a new coordinate after moving the rover, Based on the direction, it applies a movement of one grid and calculates the new coordinates. Its throws an exception if the new coordinate is off grid the new coordinate results in an collision with another rover | def _coordinate_after_moving(self, direction, coordinate):
if direction == 'N':
new_coordinate = Coordinate(coordinate.x, coordinate.y + 1)
elif direction == 'S':
new_coordinate = Coordinate(coordinate.x, coordinate.y - 1)
elif direction == 'W':
new_coordinat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _calculate_move_location(self, direction):\n current_row = self._current_loc.get_row()\n current_column = self._current_loc.get_column()\n\n # Calculate the new location for a left move\n if (direction == \"l\"):\n return Location(current_row, current_column - 1)\n ... | [
"0.6662709",
"0.6605978",
"0.6600228",
"0.6573474",
"0.6549251",
"0.65067387",
"0.6477407",
"0.6455114",
"0.64177567",
"0.6393052",
"0.6361281",
"0.6359388",
"0.6349698",
"0.63481045",
"0.63376284",
"0.63300747",
"0.63202614",
"0.6316886",
"0.630494",
"0.62923414",
"0.6275278... | 0.7444775 | 0 |
Return any binary tree that matches the given preorder and postorder traversals. Values in the traversals pre and post are distinct positive integers. | def constructFromPrePost(self, pre, post):
if not pre and not post:
return None
root = TreeNode(pre[0])
if len(pre) == 1 and len(post) == 1:
return root
if pre[1] == post[-2]:
lpre, lpost = pre[1:], post[:len(post)-1]
ltree = self.construc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buildTree(self, inorder: 'List[int]', postorder: 'List[int]') -> 'TreeNode':\n self.post_index = len(postorder) - 1\n dict = {}\n for i, num in enumerate(inorder):\n dict[num] = i\n \n def helper(in_left, in_right):\n if in_left > in_right:\n ... | [
"0.6668872",
"0.6449099",
"0.6396742",
"0.6088152",
"0.60584265",
"0.5841729",
"0.57979167",
"0.5787835",
"0.5776373",
"0.5711486",
"0.55507797",
"0.5525655",
"0.5513274",
"0.5498472",
"0.54892987",
"0.5478768",
"0.54738367",
"0.5418973",
"0.53901404",
"0.53882676",
"0.536626... | 0.66620654 | 1 |
Create and return an instance of the Isort plugin. | def setup_isort_tool_plugin(custom_rsc_path=None):
arg_parser = argparse.ArgumentParser()
if custom_rsc_path is not None:
resources = Resources([custom_rsc_path])
else:
resources = Resources(
[os.path.join(os.path.dirname(statick_tool.__file__), "plugins")]
)
config ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sorter(Plugin):\n return Plugin.order",
"def create_plugin(self, **kwargs):\n return self.plugin_class(**kwargs)",
"def new(self, sort, properties=None):\n if sort is None:\n sort = UNKNOWNSORT\n # find next available vid\n vid, index = self.vid, self.index... | [
"0.5880008",
"0.572811",
"0.5263633",
"0.5135936",
"0.5086754",
"0.5085847",
"0.5052518",
"0.5052518",
"0.5028125",
"0.5018764",
"0.5011754",
"0.49714696",
"0.49462602",
"0.48368236",
"0.48352364",
"0.4823109",
"0.48152107",
"0.48114508",
"0.48113042",
"0.479689",
"0.47876537... | 0.59617186 | 0 |
Test that the plugin manager can find the Isort plugin. | def test_isort_tool_plugin_found():
if sys.version_info.major == 3 and sys.version_info.minor < 6:
pytest.skip("isort is only available for Python 3.6+, unable to test")
manager = PluginManager()
# Get the path to statick_tool/__init__.py, get the directory part, and
# add 'plugins' to that to g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_plugins(self):\n from omtk import plugin_manager\n pm = plugin_manager.plugin_manager\n\n loaded_plugin_names = [plugin.cls.__name__ for plugin in pm.get_loaded_plugins_by_type('modules')]\n\n builtin_plugin_names = (\n 'Arm',\n 'FK',\n 'Additiv... | [
"0.66552377",
"0.6554768",
"0.6273206",
"0.62442213",
"0.6233943",
"0.62220126",
"0.60991585",
"0.60933506",
"0.6089562",
"0.5941484",
"0.59386533",
"0.59242934",
"0.5922501",
"0.58955973",
"0.58816534",
"0.58793914",
"0.58021533",
"0.57796365",
"0.57335883",
"0.57034636",
"0... | 0.8355158 | 0 |
Verify that we can parse the normal output of isort. | def test_isort_tool_plugin_parse_valid():
itp = setup_isort_tool_plugin()
total_output = []
output = "/tmp/x.py"
total_output.append(output)
output = "/tmp/y.py"
total_output.append(output)
issues = itp.parse_output(total_output)
assert len(issues) == 2
assert issues[0].filename == "... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_isort(self):\n chdir(REPO_ROOT)\n cmd = [\"isort\", \"-df\", \"-rc\", \"-c\", *SRC_DIRS]\n print(\"running:\", \" \".join(str(part) for part in cmd))\n proc = run(cmd, capture_output=True)\n assert proc.returncode == 0, f\"isort issues:\\n{proc.stdout.decode('utf-8')}\""... | [
"0.6781174",
"0.6635181",
"0.6309134",
"0.62981886",
"0.61402726",
"0.6060493",
"0.60508925",
"0.59607303",
"0.5936084",
"0.59126383",
"0.587762",
"0.58522105",
"0.5729688",
"0.56102586",
"0.5577316",
"0.55231833",
"0.5521064",
"0.55136585",
"0.55019987",
"0.54842657",
"0.548... | 0.77473277 | 0 |
Test what happens when an OSError is raised (usually means isort doesn't exist). | def test_isort_tool_plugin_scan_oserror(mock_subprocess_check_output):
mock_subprocess_check_output.side_effect = OSError("mocked error")
itp = setup_isort_tool_plugin()
package = Package(
"valid_package", os.path.join(os.path.dirname(__file__), "valid_package")
)
package["python_src"] = [
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def os_error():\n en = ctypes.get_errno()\n ctypes.set_errno(0)\n if en == 0:\n return OSError(en, \"(no errno found)\")\n else:\n return OSError(en, errno.errorcode[en])",
"def test_dump_handles_os_error(mocker):\n\tmocker.patch('subprocess.Popen', side_effect=OSError('no such file'))\... | [
"0.7091949",
"0.68408364",
"0.6782386",
"0.6650079",
"0.66032916",
"0.6333252",
"0.6230165",
"0.6217626",
"0.61586803",
"0.6140074",
"0.60988116",
"0.6090791",
"0.6002094",
"0.60008836",
"0.598052",
"0.5965969",
"0.5965969",
"0.59647274",
"0.595298",
"0.59336317",
"0.59047925... | 0.65513504 | 5 |
Test what happens when a CalledProcessError is raised (usually means isort hit an error). | def test_isort_tool_plugin_scan_calledprocesserror(mock_subprocess_check_output):
mock_subprocess_check_output.side_effect = subprocess.CalledProcessError(
0, "", output="mocked error"
)
itp = setup_isort_tool_plugin()
package = Package(
"valid_package", os.path.join(os.path.dirname(__fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_make_tool_plugin_scan_calledprocesserror(mock_subprocess_check_output):\n mock_subprocess_check_output.side_effect = subprocess.CalledProcessError(1, '', output=\"mocked error\")\n mtp = setup_make_tool_plugin()\n package = Package('valid_package', os.path.join(os.path.dirname(__file__),\n ... | [
"0.66911113",
"0.64319515",
"0.63943857",
"0.63668126",
"0.6338667",
"0.63044673",
"0.63034093",
"0.6283694",
"0.62171775",
"0.6183645",
"0.61580575",
"0.6093192",
"0.6079444",
"0.603348",
"0.6027173",
"0.5997922",
"0.59800065",
"0.59751534",
"0.59546334",
"0.5954266",
"0.595... | 0.6745141 | 0 |
Get the release history from pypi Use the json API to get the release history from pypi. The returned json structure includes a 'releases' dictionary which has keys that are release numbers and the value is an array of uploaded files. While we don't have a 'release time' per say (only the upload time on each of the fil... | def get_releases_for_package(name, since):
f = urlreq.urlopen("http://pypi.org/project/%s/json" % name)
jsondata = f.read()
data = json.loads(jsondata)
releases = []
for relname, rellist in data['releases'].iteritems():
for rel in rellist:
if rel['python_version'] == 'source':
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_releases():\n response = requests.get(PYPI_URL.format(package=PYPI_PACKAGE_NAME))\n if response:\n data = response.json()\n\n releases_dict = data.get('releases', {})\n\n if releases_dict:\n for version, release in releases_dict.items():\n release_forma... | [
"0.6942631",
"0.66807014",
"0.6496823",
"0.6251065",
"0.6223727",
"0.61681485",
"0.607848",
"0.605974",
"0.6022348",
"0.5989354",
"0.59570056",
"0.59270537",
"0.5916981",
"0.58646035",
"0.5862971",
"0.5855767",
"0.5854523",
"0.5853826",
"0.58354694",
"0.5833912",
"0.58016866"... | 0.6868364 | 1 |
Calculates X values for given list of Y values in range defined by a and b parameters. X values are simply calculated by dividing given X range by number of nodes, so they are distributed in even range. | def prepare_initial_nodes(x_start, x_end, nodes_y):
nodes_x = [float(x_start + ((x_end - x_start) / (len(nodes_y) - 1)) * i) for i in range(0, len(nodes_y))]
nodes_y = [float(y) for y in nodes_y]
print(nodes_x)
print(nodes_y)
nodes = list(zip(nodes_x, nodes_y))
return nodes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def projectionX(xdata, ydata, nbins, xrange=None, yrange=None):\n xmin, xmax = (np.min(xdata), np.max(xdata)) if xrange is None else xrange\n ymin, ymax = (np.min(ydata), np.max(ydata)) if yrange is None else yrange\n\n x_out = np.linspace(xmin, xmax, nbins+1)\n y_out = np.empty(nbins)\n dx = np.dif... | [
"0.58776325",
"0.5662537",
"0.5642036",
"0.5495832",
"0.5469611",
"0.5420076",
"0.5375376",
"0.53669316",
"0.53453207",
"0.53439814",
"0.53289247",
"0.5299425",
"0.5295588",
"0.52585924",
"0.52468276",
"0.5246785",
"0.5245637",
"0.5245567",
"0.52296543",
"0.5221779",
"0.52137... | 0.58207065 | 1 |
Takes list of divided differences nodes and calculates new divided differences node from each pair of nodes_to_compute. In other words, it computes next level of so called Newton's second interpolation form tree. | def calculate_divided_differences_row(nodes_to_compute):
divided_differences = []
if len(nodes_to_compute) == 1:
return None
for i in range(0, len(nodes_to_compute) - 1):
child = DividedDifferenceNode.create_child_node(nodes_to_compute[i], nodes_to_compute[i + 1])
child.calculate_v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_divided_differences(nodes):\n nodes_to_compute = []\n divided_differences = []\n for node in nodes:\n nodes_to_compute.append(DividedDifferenceNode(x=node[0], divided_difference=node[1]))\n\n divided_differences.append(tuple(nodes_to_compute))\n\n while len(nodes_to_compute) > 1... | [
"0.65839136",
"0.57315016",
"0.567347",
"0.55890775",
"0.55738044",
"0.5463602",
"0.54403263",
"0.5439998",
"0.5405277",
"0.53822875",
"0.5344752",
"0.5334331",
"0.5325484",
"0.5318365",
"0.53122985",
"0.53017414",
"0.5295547",
"0.5275849",
"0.52737594",
"0.52574426",
"0.5230... | 0.6325345 | 1 |
Calculates divided differences for given interpolation nodes. It is assumed, that at least two interpolation nodes are provided. Each tuple of returned list represents one level of divided differences tree. | def calculate_divided_differences(nodes):
nodes_to_compute = []
divided_differences = []
for node in nodes:
nodes_to_compute.append(DividedDifferenceNode(x=node[0], divided_difference=node[1]))
divided_differences.append(tuple(nodes_to_compute))
while len(nodes_to_compute) > 1:
nex... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_divided_differences_row(nodes_to_compute):\n divided_differences = []\n\n if len(nodes_to_compute) == 1:\n return None\n\n for i in range(0, len(nodes_to_compute) - 1):\n child = DividedDifferenceNode.create_child_node(nodes_to_compute[i], nodes_to_compute[i + 1])\n chil... | [
"0.7081299",
"0.526944",
"0.52446645",
"0.5239564",
"0.523342",
"0.51974493",
"0.51963425",
"0.5140926",
"0.50798607",
"0.5076724",
"0.50607145",
"0.5054353",
"0.5047319",
"0.49978474",
"0.49799216",
"0.49528977",
"0.49478018",
"0.49372533",
"0.49131808",
"0.490018",
"0.48992... | 0.77134573 | 0 |
Creates polynomial from given list of divided differences. Polynomial string is created according to equation provided in project docs. | def calculate_newton_interpolation(divided_differences):
polynomial = []
for i, divided_differences_row in enumerate(divided_differences):
polynomial_part = '({0})'.format(divided_differences_row[0].divided_difference)
for j in range(0, i):
polynomial_part += '*(x-{0})'.format(divid... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_to_poly(polynomial_list):\n max_degree = len(polynomial_list) - 1\n strings = []\n opts = ['x', '']\n for index, num in enumerate(polynomial_list):\n if num == 0:\n continue\n if index < max_degree - 1:\n string = '{}x^{}'.format(num, max_degree - index)\n ... | [
"0.6846461",
"0.65011793",
"0.6292352",
"0.6274255",
"0.62225056",
"0.61097145",
"0.60969347",
"0.6090148",
"0.60779357",
"0.60200155",
"0.60196537",
"0.59783614",
"0.5969226",
"0.5965326",
"0.5856345",
"0.5849811",
"0.58080703",
"0.5775723",
"0.5735005",
"0.5733751",
"0.5722... | 0.70788777 | 0 |
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