query
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3.4k
document
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metadata
dict
negatives
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negative_scores
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101
document_score
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3
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document_rank
stringclasses
102 values
Set ruleset state sid
def set_state_sid_request(ruleset_name, sid): message = json.loads(request.stream.read().decode('utf-8')) message['sid'] = sid result = host.patch_state(ruleset_name, message) return jsonify(result)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def sid(self, sid):\n self._sid = sid", "def set_state(self,s):\n self.state = s", "def set_state(self, state: int):", "def __setstate__(self, state):\n\n self.set(DER = state)", "def set_rule(self, rule):\n self.rule.load_state_dict(rule, strict=True)", "def _set_state(self, ...
[ "0.6317392", "0.6268615", "0.62445796", "0.60649145", "0.58590347", "0.5837428", "0.580806", "0.58021194", "0.57980675", "0.5752198", "0.5752198", "0.5744414", "0.57234263", "0.5718662", "0.5679742", "0.5645187", "0.5636659", "0.5628161", "0.5618529", "0.5560293", "0.5513871"...
0.74748975
0
Get ruleset state sid
def get_state_sid_request(ruleset_name, sid): result = host.get_state(ruleset_name, sid) return jsonify(result)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def state_id(self):\n return self._state_id", "def get_rule_id(self):\n from .osid_errors import IllegalState\n # Someday I'll have a real implementation, but for now I just:\n raise IllegalState()", "def sid(self):\n return self._sid", "def sid(self):\n return self....
[ "0.6522904", "0.6476181", "0.6398606", "0.6354551", "0.60375524", "0.60295653", "0.60295653", "0.59568083", "0.5888084", "0.58808523", "0.58517295", "0.58414584", "0.58183634", "0.5815065", "0.57778585", "0.5670262", "0.5668822", "0.56557137", "0.56524223", "0.56524223", "0.5...
0.6936951
0
Post events to the ruleset
def post_events(ruleset_name): message = json.loads(request.stream.read().decode('utf-8')) result = host.post(ruleset_name, message) return jsonify(result)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __call__(self, event):\n post_event(event, self.baseUrl, self.filterName)", "def _do_rule_processing(self, line, events):\n\n for rule in self.rules:\n match = rule.regexp.search(line)\n if match:\n events.append(Event(self, rule.handler, LogMatch(line, matc...
[ "0.63994485", "0.6042524", "0.6003626", "0.5981115", "0.5941807", "0.5918527", "0.5845204", "0.5819378", "0.58176184", "0.58072335", "0.57101154", "0.5693851", "0.5638689", "0.56246656", "0.55693597", "0.5526446", "0.55139947", "0.54291743", "0.54178923", "0.5412167", "0.5411...
0.6678471
0
Post sid events to the ruleset
def post_sid_events(ruleset_name, sid): message = json.loads(request.stream.read().decode('utf-8')) message['sid'] = sid result = host.post(ruleset_name, message) return jsonify(result)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def post_events(ruleset_name):\n message = json.loads(request.stream.read().decode('utf-8'))\n result = host.post(ruleset_name, message)\n return jsonify(result)", "def set_state_sid_request(ruleset_name, sid):\n message = json.loads(request.stream.read().decode('utf-8'))\n message['sid'] = sid\n ...
[ "0.560759", "0.5351545", "0.5286287", "0.5215918", "0.50854534", "0.50759035", "0.5052492", "0.5019985", "0.49917015", "0.4915208", "0.4852344", "0.48465505", "0.48308286", "0.47611645", "0.47459525", "0.47393727", "0.47084105", "0.46966222", "0.46946904", "0.46800652", "0.46...
0.7941506
0
Post factss to the ruleset
def default_facts_request(ruleset_name): message = json.loads(request.stream.read().decode('utf-8')) result = host.assert_fact(ruleset_name, message) return jsonify(result)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def refactor_post(self,post_name):\n for name in list(self.rules):\n related_post = \"{}.post.{}\".format(name,post_name)\n if related_post in self.rules:\n parts = [self.MakeSymbolName(x) for x in [post_name, related_post]]\n self.rules[name] = self.MakeC...
[ "0.59369296", "0.5716443", "0.56727445", "0.56607735", "0.56607735", "0.5658255", "0.55677813", "0.5550459", "0.550259", "0.5496113", "0.5428811", "0.53964937", "0.53963", "0.53746647", "0.534213", "0.53217864", "0.53138274", "0.53119683", "0.5304553", "0.5288513", "0.5285464...
0.0
-1
Post sid facts to the ruleset
def facts_request(ruleset_name, sid): message = json.loads(request.stream.read().decode('utf-8')) message['sid'] = sid result = host.assert_fact(ruleset_name, message) return jsonify(result)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def post_sid_events(ruleset_name, sid):\n message = json.loads(request.stream.read().decode('utf-8'))\n message['sid'] = sid\n result = host.post(ruleset_name, message)\n return jsonify(result)", "def set_state_sid_request(ruleset_name, sid):\n message = json.loads(request.stream.read().decode('ut...
[ "0.681913", "0.601957", "0.6000734", "0.512934", "0.50864774", "0.50735605", "0.5069272", "0.49133524", "0.4802121", "0.47750175", "0.4747155", "0.47424155", "0.46992487", "0.46838996", "0.4672943", "0.4652938", "0.46246898", "0.46226344", "0.46224123", "0.46183434", "0.46069...
0.55748254
3
Convert network's sigmoid output into depth prediction The formula for this conversion is given in the 'additional considerations' section of the paper.
def disp_to_depth(disp): MIN_DEPTH = 0.1#0.1#1e-3#0.1 MAX_DEPTH = 100#100 min_disp = 1 / MAX_DEPTH max_disp = 1 / MIN_DEPTH scaled_disp = min_disp + (max_disp - min_disp) * disp depth = 1 / scaled_disp return scaled_disp, depth
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def predict_depth(self, input_path, output_dir):\n try:\n result = real_predict_depth(input_path, output_dir)\n reset_default_graph()\n return result\n except Exception as e:\n return '!ERROR' + str(e)", "def sigmoid2predictions(output: torch.Tensor) -> t...
[ "0.6572657", "0.65554243", "0.59933084", "0.5872708", "0.57708555", "0.570335", "0.5695602", "0.56596303", "0.5657298", "0.5650019", "0.56448084", "0.56245506", "0.5608685", "0.559613", "0.5580954", "0.5546335", "0.55431604", "0.55408496", "0.5537363", "0.5534928", "0.5526947...
0.0
-1
Computation of error metrics between predicted and ground truth depths
def compute_errors(gt, pred): thresh = np.maximum((gt / pred), (pred / gt)) a1 = (thresh < 1.25 ).mean() a2 = (thresh < 1.25 ** 2).mean() a3 = (thresh < 1.25 ** 3).mean() rmse = (gt - pred) ** 2 rmse = np.sqrt(rmse.mean()) rmse_log = (np.log(gt) - np.log(pred)) ** 2 rmse_log = np.s...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_error(self, params):\n return self.endog - self.predict(params)", "def error_in_assigned_energy(predictions, ground_truth):\n errors = {}\n both_sets_of_meters = iterate_through_submeters_of_two_metergroups(\n predictions, ground_truth)\n for pred_meter, ground_truth_meter in both_...
[ "0.67500764", "0.6546798", "0.65235454", "0.65183836", "0.6474383", "0.64611065", "0.6404243", "0.6374521", "0.6370064", "0.6316882", "0.6293244", "0.6250774", "0.62246156", "0.62059015", "0.62025636", "0.617991", "0.61761516", "0.6161752", "0.61567384", "0.614145", "0.613276...
0.6518734
3
Evaluates a pretrained model using a specified test set
def evaluate(params,dataloader): MIN_DEPTH = 1e-3 MAX_DEPTH = 80 num_gpus = 1 pred_depth_scale_factor = 1 checkpoint_path = './log_diretory/mono_depth2-102000/model-97060'#'./log_diretory/kitti_resnet_MS2_nbn_1epoch_pose_fix/model-189107' gt_path = './utils/gt/eigen_zhou' eval_stereo = Fals...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def evaluate_model(model, testset):\n\n # Sort data by top level label to ease inspection\n testset = testset.sort_using_layer(-1, reverse=True)\n\n # Feed the samples to the model to obtain each layers' activations\n v = testset.get_layer(0)\n hs = model.transform(v)[1:]\n\n # Read model weights...
[ "0.74473107", "0.741568", "0.72438216", "0.71786034", "0.7145973", "0.69723153", "0.68649215", "0.683188", "0.6808273", "0.67838573", "0.67193323", "0.6700262", "0.6699694", "0.6683093", "0.6668421", "0.6642181", "0.66375136", "0.6633465", "0.6606388", "0.6606388", "0.6606388...
0.0
-1
This function populates an instance of DeadlineTab with the UI controls that make up the submission dialog. This tab is instantiated by Katana every time the user selects "Tabs > Thinkbox > Submit to Deadline" from the menu bar in Katana. Essentially, this function serves as a deferred __init__ implementation for the t...
def PopulateSubmitter( gui ): global submissionInfo print( "Grabbing submitter info..." ) try: stringSubInfo = CallDeadlineCommand( [ "-prettyJSON", "-GetSubmissionInfo", "Pools", "Groups", "MaxPriority", "UserHomeDir", "RepoDir:submission/Katana/Main", "RepoDir:submission/Integration/Main", ], useD...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def populateUI():\n \n # Main form layout\n form = cmds.formLayout()\n\n # Tab Layout\n tabs = cmds.tabLayout(innerMarginWidth=5, innerMarginHeight=5)\n # Form attachment config\n cmds.formLayout( form, edit=True, attachForm=((tabs, 'top', 0), (tabs, 'left', 0), (tabs, 'bottom', 0), (tabs, 'ri...
[ "0.6382394", "0.5868262", "0.5825627", "0.5785208", "0.5766149", "0.57174546", "0.568647", "0.56794524", "0.5625228", "0.5619884", "0.55831283", "0.5565874", "0.5505238", "0.5494764", "0.54784214", "0.5467494", "0.54137725", "0.5401807", "0.5315046", "0.5295689", "0.52889675"...
0.65244144
0
Augments a staged job info submission file with the appropriate properties for the Pipeline Tool settings.
def ConcatenatePipelineSettingsToJob( jobInfoPath, batchName ): global submissionInfo jobWriterPath = os.path.join( submissionInfo["RepoDirs"]["submission/Integration/Main"], "JobWriter.py" ) scenePath = NodegraphAPI.GetSourceFile() argArray = ["-ExecuteScript", jobWriterPath, "Katana", "--write", "--sc...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def merge_job_info(run, seqno, slices):\n inset = {\"job_info\": [\"workscript.stdout\", \"workscript.stderr\"],\n }\n outset = {\"job_info\": [\"std_{0:06d}_{1:03d}.out\", \"std_{0:06d}_{1:03d}.err\"],\n }\n tarset = {\"job_info\": \"job_info_{0:06d}_{1:03d}.tgz\",\n }\n ba...
[ "0.54600435", "0.5365229", "0.49939448", "0.4906341", "0.48872775", "0.48601264", "0.48544395", "0.48313162", "0.48072532", "0.47882256", "0.4777496", "0.47465393", "0.47256604", "0.46529025", "0.4646404", "0.46430737", "0.46245492", "0.4616829", "0.45886663", "0.4572362", "0...
0.61063117
0
Grabs a status message from the JobWriter that indicates which pipeline tools have settings enabled for the current scene.
def RetrievePipelineToolStatus( raiseOnExitCode=False ): global submissionInfo scenePath = NodegraphAPI.GetSourceFile() jobWriterPath = os.path.join(submissionInfo["RepoDirs"]["submission/Integration/Main"], "JobWriter.py") argArray = ["-ExecuteScript", jobWriterPath, "Katana", "--status", "--scene-pa...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_tools_state(self):\n\t\treturn Job(SDK.PrlVm_GetToolsState(self.handle)[0])", "def status(self):\n return STATUSES.get(self._mower_status, {}).get('message', self._mower_status)", "def get_status(self):\n url = \"data_request?id=jobstatus&job=%d&plugin=zwave\" % self.id\n return se...
[ "0.5826462", "0.5679577", "0.56522906", "0.5546779", "0.55216604", "0.5513174", "0.54827136", "0.5467698", "0.5463971", "0.5463971", "0.5463971", "0.5425974", "0.5425974", "0.5425974", "0.5425974", "0.5425974", "0.5425974", "0.5425974", "0.5425974", "0.5425974", "0.5425974", ...
0.7234952
0
Modifies the Pipeline Tool status label UI element with the supplied message
def UpdatePipelineToolStatusLabel( gui, statusMessage ): gui.pipelineToolStatusLabel.setText( statusMessage )
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def update_status(self, msg):\n self.status_lbl.config(text=msg)", "def status_display(self, message, level=0, field=0):\n #print(message)\n self.statusbar_txt.set(message)", "def updateStatus(self, message):\r\n self.statusBar().showMessage(message, 5000)\r\n if self.kinfile...
[ "0.79712987", "0.7420736", "0.7228758", "0.7161831", "0.70710754", "0.69951653", "0.6985524", "0.6982464", "0.6789776", "0.6676902", "0.6615886", "0.6576708", "0.6549624", "0.653623", "0.6525615", "0.648638", "0.6450446", "0.64447117", "0.6439653", "0.6434152", "0.6397675", ...
0.8840854
0
Generic error handling when the a pipeline tools script run via deadline command returns a nonzero exit code. Generates a technical error message for a given subprocess.CalledProcessError instance and displays it in the Katana console. Similarly, a humanreadable error message is presented to the user in a modal dialog....
def HandlePipelineToolsCalledProcessError( exc ): errorMsg = StringIO() errorMsg.write( "Pipeline Tools encountered an error - the command:" ) errorMsg.write( os.linesep * 2 ) errorMsg.write( exc.cmd ) errorMsg.write( os.linesep * 2 ) errorMsg.write( "return a non-zero (%d) exit code" % exc.retu...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def handle_build_error(error):\n sys.stderr.write('Error running command `%s`. Returned %s.\\n' % (\n ' '.join(error.argv), str(error.error_code)))", "def print_unable_to_run(exc: \"CalledProcessError\"):\n _print(str(exc), level=MessageLevel.QUIET)", "def error(text, exitcode=1):\n\n # If we g...
[ "0.5881938", "0.5847716", "0.5779613", "0.5747992", "0.5723428", "0.5697356", "0.56824833", "0.5620372", "0.55943125", "0.5581835", "0.5550394", "0.55393744", "0.5523604", "0.55169374", "0.551156", "0.54878414", "0.54639775", "0.54484504", "0.54443103", "0.5437845", "0.542948...
0.75752896
0
Opens the a dialog for viewing and modifying the job's pipeline tool settings. The dialog is launched in a deadline command subprocess. All settings are maintained by the JobWriter using a combination of the application name and the scene path.
def OpenIntegrationWindow( raiseOnExitCode=False ): global submissionInfo integrationPath = os.path.join( submissionInfo["RepoDirs"]["submission/Integration/Main"], "IntegrationUIStandAlone.py" ) scenePath = NodegraphAPI.GetSourceFile() if not scenePath: raise SceneNotSavedError() argArray ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def shotWinUI(*args):\n### ---------- should check for current project\n if cmds.window(\"shotWin\", exists = True):\n cmds.deleteUI(\"shotWin\")\n\n widgets[\"win\"] = cmds.window(\"shotWin\", t= \"Charlex Shot Manager\", w=1000, h=560, s=False)\n widgets[\"mainCLO\"] = cmds.columnLayout(w=1000, h...
[ "0.5803222", "0.5764439", "0.5632621", "0.5579987", "0.55609196", "0.5531322", "0.5498198", "0.5456681", "0.5440068", "0.54032004", "0.54000485", "0.5395688", "0.5395272", "0.53947246", "0.5367619", "0.5364008", "0.53474754", "0.5346228", "0.53405243", "0.5282741", "0.5273741...
0.6004767
0
Returns the path to DeadlineCommand.
def GetDeadlineCommand( useDeadlineBg=False ): deadlineBin = "" try: deadlineBin = os.environ[ 'DEADLINE_PATH' ] except KeyError: # if the error is a key error it means that DEADLINE_PATH is not set. however Deadline command may be in the PATH or on OSX it could be in the file /Users/Shared/...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _get_deadline_command_path():\n\n deadline_bin = os.environ.get('DEADLINE_PATH', '')\n\n # On Linux, the Deadline Client installer creates a system-wide script to set the DEADLINE_PATH environment\n # variable. Cloud-init does not load system environment variables. Cherry-pick the\n ...
[ "0.753901", "0.6118858", "0.6027574", "0.58908194", "0.5830067", "0.5762068", "0.570046", "0.5663638", "0.56532186", "0.56523234", "0.5645256", "0.5634848", "0.56305516", "0.5628725", "0.5616503", "0.5605415", "0.5578746", "0.55568534", "0.55538136", "0.5526211", "0.5518902",...
0.73081684
1
Creates a utf8 encoded file with each argument in arguments on a separate line.
def CreateArgFile( arguments, tmpDir ): tmpFile = os.path.join( tmpDir, "args.txt" ) with io.open( tmpFile, 'w', encoding="utf-8-sig" ) as fileHandle: fileHandle.write( "\n".join( arguments ) ) return tmpFile
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _make_i18n_data_file(cls, filename, encoding):\n cls.cluster.fs.setuser(cls.cluster.superuser)\n f = cls.cluster.fs.open(filename, \"w\")\n for x in range(256):\n f.write(\"%d\\t%s\\n\" % (x, chr(x).encode(encoding)))\n f.close()", "def output_file(data, filename):\n with open(filename + ...
[ "0.6052145", "0.57538974", "0.567268", "0.55836433", "0.55042565", "0.5475151", "0.54015994", "0.5380762", "0.5356525", "0.5350646", "0.5287505", "0.5250849", "0.52459705", "0.5193831", "0.51840913", "0.51814663", "0.5180244", "0.5170334", "0.51647687", "0.5129749", "0.506740...
0.7158849
0
Run DeadlineCommand with the specified arguments returning the standard out
def CallDeadlineCommand(arguments, hideWindow=True, useArgFile=False, useDeadlineBg=False, raiseOnExitCode=False): deadlineCommand = GetDeadlineCommand( useDeadlineBg ) tmpdir = None if useArgFile or useDeadlineBg: tmpdir = tempfile.mkdtemp() if useDeadlineBg: arguments = [ "-outputfil...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _call_deadline_command_raw(self, arguments):\n # make a copy so we don't mutate the caller's reference\n arguments = list(arguments)\n arguments.insert(0, self._deadline_command_path)\n try:\n proc = subprocess.Popen(\n arguments,\n stdin=sub...
[ "0.6623097", "0.6296079", "0.6084702", "0.5997185", "0.59120613", "0.5874816", "0.57683825", "0.5742775", "0.57391727", "0.5712437", "0.56482756", "0.5596094", "0.558642", "0.553605", "0.553605", "0.5521694", "0.55093294", "0.54556483", "0.5436485", "0.54264593", "0.54078025"...
0.65875596
1
Get the path to the file where we will store sticky settings
def GetStickySettingsFilePath(): global submissionInfo deadlineHome = submissionInfo[ "UserHomeDir" ].strip() return os.path.join( deadlineHome, "settings", "katana_sticky.json" )
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def settingsFilePath(self):\n return self._settingsFilePath", "def get_preference_file():\n\n return \"{}/{}\".format(_MANAGER_PREFERENCE_PATH, _MANAGER_PREFERENCE_FILE)", "def get_preference_file_cache_destination_path():\n\n return read_preference_key(search_key=\"cache_manager_cache_path\")", ...
[ "0.72601885", "0.7198174", "0.69512", "0.6910759", "0.69085604", "0.68241256", "0.67362624", "0.6648517", "0.66195136", "0.6618425", "0.6611885", "0.65249866", "0.6479099", "0.64735585", "0.64711976", "0.6452971", "0.638629", "0.6381435", "0.63718975", "0.63349026", "0.631905...
0.8301903
0
Writes the current settings from Submitter UI to the sticky settings file.
def WriteStickySettings( gui ): global stickySettingWidgets, stickyWidgetSaveFunctions print( "Writing sticky settings..." ) configFile = GetStickySettingsFilePath() stickySettings = {} for setting, widgetName in stickySettingWidgets.iteritems(): try: widget = getattr( gui, wi...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def save_settings(self):\n logger.info(f'Saving settings: {self.settings_dict}')\n for k, section in self.settings_dict.items():\n for setting_name in section.keys():\n value = self.get_control_value(setting_name)\n if value is not None:\n s...
[ "0.71699524", "0.7144108", "0.6855974", "0.68193734", "0.66913515", "0.66821957", "0.64933175", "0.64606106", "0.6453299", "0.63580054", "0.63520503", "0.63510686", "0.6333627", "0.6321657", "0.6306876", "0.62875223", "0.6263997", "0.62562144", "0.62060374", "0.61840034", "0....
0.7189759
0
Reads in settings from the sticky settings file, then update the UI with the new settings
def LoadStickySettings( gui ): global stickySettingWidgets, stickyWidgetLoadFunctions configFile = GetStickySettingsFilePath() print( "Reading sticky settings from: %s" % configFile ) stickySettings = None try: with io.open( configFile, "r", encoding="utf-8" ) as fileHandle: sti...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def updateSettings(self):\n self.parser.read(self.file)\n self.showTicker = self.parser.getboolean('Settings', 'showTicker')\n self.verbose = self.parser.getboolean('Settings', 'verbose')\n self.sleepTime = self.parser.getint('Settings', 'sleeptime')\n self.saveGraph = self.parse...
[ "0.686287", "0.67737114", "0.6748555", "0.6717438", "0.6571519", "0.6435366", "0.63965124", "0.6376148", "0.6361373", "0.62330866", "0.621005", "0.62071073", "0.61983466", "0.61828625", "0.61292857", "0.6116447", "0.59903854", "0.5935539", "0.5932085", "0.58979905", "0.584024...
0.69833964
0
Converts a url patternesque string into a path, given a context dict, and splits the result.
def pathify(urlpattern, **context): repl = lambda match: context[match.group(1)] path = re.sub(r':([a-z]+)', repl, urlpattern) return tuple(path[1:].split('/'))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def split_string_path(base, path):\n for i in range(len(path)):\n if isinstance(base, string_types):\n return path[:i], path[i:]\n base = base[path[i]]\n return path, ()", "def resolveContext(self, context):\n if context is None:\n return context\n elif isinstance(contex...
[ "0.59003174", "0.5704174", "0.5683664", "0.5584328", "0.55201805", "0.546162", "0.5402494", "0.535743", "0.53368884", "0.5284471", "0.5279856", "0.52473545", "0.5235247", "0.52138245", "0.51656365", "0.5129309", "0.5124352", "0.5093053", "0.5055723", "0.5051632", "0.5041933",...
0.7681451
0
init cluster_temp for all the center point
def __initCluster(self): data_size, cluster_center = self.data_size, self.cluster_center self.cluster_temp = np.zeros(data_size, dtype=int) self.cluster_upper_bound = np.full(len(cluster_center), float('inf'), dtype=float) for center in cluster_center: self.cluster_temp[cente...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def initClusters(self):\n if len(self.labelList) != len(self.pointList):\n \traise ValueError(\"Label List and Point List not the same length!\")\n for i in range(len(self.labelList)):\n self.centroids[self.labelList[i]] = self.pointList[i]\n self.pointcounts[self.labelLi...
[ "0.69504863", "0.6859036", "0.67012495", "0.6668851", "0.6667392", "0.6468853", "0.6415132", "0.64095896", "0.63832414", "0.6361127", "0.63474107", "0.6336359", "0.62062657", "0.62016225", "0.61754805", "0.61420494", "0.6140045", "0.6138546", "0.6138051", "0.6124449", "0.6099...
0.825215
0
load data to memory
def load_dis_data(self, filename): logger.info('load data') self.distance, self.data_size = {}, 1 for line in open(path + filename, 'r'): x1, x2, d = line.strip().split(' ') x1, x2, d = int(x1), int(x2), float(d) self.data_size = max(x2 + 1, self.data_size) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def load_data(self) -> None:", "def load_data(self):", "def load_data(self):\n raise NotImplementedError()", "def _loadData(self, data):\n Movie._loadData(self, data)\n PlexSession._loadData(self, data)", "def load_data(self):\n if self.debug:\n print(\"Loading data\"...
[ "0.8095919", "0.7828865", "0.7052371", "0.6787303", "0.66700774", "0.66354895", "0.6628009", "0.66243476", "0.6613523", "0.6586045", "0.65813166", "0.6575096", "0.6461651", "0.6460657", "0.64506775", "0.64494663", "0.64268064", "0.64058614", "0.6364637", "0.6342839", "0.63403...
0.0
-1
select the distance ranked if not auto, we will choose the distance at 1.8% top position as dc
def get_dc(self, auto=False, percent=0.018): data_size, distance = self.data_size, self.distance if not auto: position = int((data_size * (data_size + 1) / 2 - data_size) * percent) dc = sorted(distance.items(), key=lambda item: item[1])[position][1] logger.info("dc -...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def distances(self):", "def location_of_stops(self, choice, distance):\r\n avg_dist = 0\r\n min_dist = 1000\r\n max_dist = 0\r\n\r\n if choice == 1:\r\n #for dist_ in distance:\r\n # if int(dist_) < min_dist:\r\n # min_dist = dist_\r\n ...
[ "0.6012625", "0.60071933", "0.6004598", "0.58783704", "0.5852285", "0.57596016", "0.57278866", "0.5697758", "0.56681937", "0.56577826", "0.56478626", "0.5647478", "0.5602821", "0.5586023", "0.55812424", "0.55765444", "0.55755365", "0.55707633", "0.556011", "0.5559879", "0.555...
0.56715935
8
calculate the density of each vector and get the max_pos
def calculate_density(self, dc, cut_off=False): data_size, distance = self.data_size, self.distance logger.info('calculate density begin') func = lambda dij, dc: math.exp(- (dij / dc) ** 2) if cut_off: func = lambda dij, dc: 1 if dij < dc else 0 max_density = -1 ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_max_density(self):\n max_density = str(self.density.index(min(self.density)) + 1)\n print(max_density)\n return max_density", "def _calc_density(x: np.ndarray, y: np.ndarray):\n from scipy.stats import gaussian_kde\n\n # Calculate the point density\n xy = np.vstack([x, y])\n...
[ "0.6800876", "0.6359699", "0.6188986", "0.6152179", "0.6099716", "0.60784084", "0.5983489", "0.58815664", "0.58410376", "0.58110356", "0.58088136", "0.57901305", "0.577701", "0.575839", "0.57013303", "0.5689801", "0.56889397", "0.56295735", "0.562879", "0.562879", "0.562879",...
0.5624752
21
calculate the delta of each vector save the delta point as master
def calculate_delta(self): rho_des_index, distance, data_size = self.rho_des_index, self.distance, self.data_size self.result[rho_des_index[0]][1] = -1 for i in range(1, data_size): for j in range(0, i): old_i, old_j = rho_des_index[i], rho_des_index[j] ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def compute_velocities(self):\n Ddemo_trajs = []\n\n for demo_traj in self._demo_trajs:\n d_traj = np.diff(demo_traj, axis=0)/self._dt\n #append last element to adjust the length\n d_traj = np.hstack([d_traj, d_traj[-1]])\n #add it to the list\n ...
[ "0.65990263", "0.6551683", "0.6391416", "0.6347046", "0.6311589", "0.6305406", "0.6271971", "0.62243825", "0.61688155", "0.6113892", "0.6110934", "0.6104737", "0.6018288", "0.5975151", "0.5968072", "0.592978", "0.59040904", "0.584247", "0.57922715", "0.57737917", "0.5765913",...
0.673058
0
use the multiplication of normalized rho and delta as gamma to determine cluster center
def calculate_gamma(self): result = self.result # scaler = preprocessing.StandardScaler() # train_minmax = scaler.fit_transform(result) # st_rho, st_delta = train_minmax[:, 0], train_minmax[:, 1] # self.gamma = (st_delta + st_rho) / 2 self.gamma = result[:, 0] * result[:,...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def calculate_cluster_center(self, threshold):\n gamma = self.gamma\n self.cluster_center = np.where(gamma >= threshold)[0]", "def M_step(X, gamma):\n N = X.shape[0] # number of objects\n C = gamma.shape[1] # number of clusters\n d = X.shape[1] # dimension of each object\n\n ### YOUR CO...
[ "0.6678306", "0.6379675", "0.6162399", "0.59755903", "0.5975015", "0.59482974", "0.59137064", "0.58591443", "0.5844926", "0.5769318", "0.5760845", "0.5734815", "0.5687573", "0.56765157", "0.56329596", "0.56303257", "0.5629205", "0.558961", "0.55850154", "0.5579336", "0.557140...
0.64044535
1
Intercept a point with gamma greater than 0.2 as the cluster center
def calculate_cluster_center(self, threshold): gamma = self.gamma self.cluster_center = np.where(gamma >= threshold)[0]
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def gaussian(centre, k, intensity, xpos):\r\n\treturn intensity * np.exp(- np.power(k * (xpos - centre), 2))", "def predict_center(point):\n point_cluster_num = predict_cluster(point)\n center = centers[point_cluster_num]\n return center", "def center(x):\n return x - x.mean()", "def gauss_spot(s...
[ "0.6103443", "0.5330922", "0.529988", "0.52296835", "0.52157253", "0.5153788", "0.51438296", "0.51432735", "0.5122229", "0.5116896", "0.51106155", "0.509807", "0.50852835", "0.50819665", "0.5080408", "0.50785637", "0.50589377", "0.50488997", "0.504817", "0.50294673", "0.50135...
0.57451344
1
Initial configuration. Used to specify your username, password and domain. Configuration is stored in ~/.accountable/config.yaml.
def configure(username, password, domain): art = r''' Welcome! __ ___. .__ _____ ____ ____ ____ __ __ _____/ |______ \_ |__ | | ____ \__ \ _/ ___\/ ___\/ _ \| | \/ \ __\__ \ | __ \| | _/ __ \ / __ \\ \__\ \__( <_> ) | / | \ | / __ \| \_\ \ |_...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def configure(self):\n configurations = config.Configurations()\n self.credentials = configurations.credentials\n self.config = configurations.config", "def configure(self, conf):\n self.openam_base_url = conf.get('url')\n self.username = conf.get('user')\n self.__passwo...
[ "0.6725636", "0.65860176", "0.6572466", "0.6524407", "0.6381888", "0.625799", "0.62230504", "0.6196215", "0.6191912", "0.61845404", "0.6112712", "0.60647243", "0.6038883", "0.6033728", "0.60200953", "0.60005546", "0.5989566", "0.598916", "0.59883714", "0.5980305", "0.5948604"...
0.6898006
0
List all issue types. Optional parameter to list issue types by a given project.
def issuetypes(accountable, project_key): projects = accountable.issue_types(project_key) headers = sorted(['id', 'name', 'description']) rows = [] for key, issue_types in sorted(projects.items()): for issue_type in issue_types: rows.append( [key] + [v for k, v in sor...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def list(self, request):\n bug_types = BugType.objects.all()\n\n # Note the additional `many=True` argument to the\n # serializer. It's needed when you are serializing\n # a list of objects instead of a single object.\n serializer = BugTypeSerializer(\n bug_types, many...
[ "0.580682", "0.57997316", "0.55276394", "0.53734636", "0.53584605", "0.53383344", "0.5332019", "0.5323111", "0.53199", "0.52730525", "0.5229358", "0.5195646", "0.51418656", "0.51354766", "0.50994647", "0.5088411", "0.50732434", "0.5071402", "0.50672746", "0.5037107", "0.50092...
0.715429
0
Returns a list of all a project's components.
def components(accountable, project_key): components = accountable.project_components(project_key) headers = sorted(['id', 'name', 'self']) rows = [[v for k, v in sorted(component.items()) if k in headers] for component in components] rows.insert(0, headers) print_table(SingleTable(rows)...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def list_projects():\n if '.wcscanner' not in os.listdir(context.__BASE_PATH__):\n return []\n return os.listdir(context.__PROJECTS_PATH__)", "def get_projects(self):\n unaligned_path = self.get_unaligned_path()\n logger.debug(\"collecting list of projects\")\n return [p for p i...
[ "0.7027259", "0.68796676", "0.6818949", "0.6719623", "0.67192936", "0.6683855", "0.66704535", "0.66671795", "0.66467714", "0.6640229", "0.65798545", "0.6556899", "0.65495473", "0.65493363", "0.65490365", "0.6516722", "0.6468194", "0.64657927", "0.64543396", "0.64378566", "0.6...
0.63358796
31
Create a new issue and checkout a branch named after it.
def checkoutbranch(accountable, options): issue = accountable.checkout_branch(options) headers = sorted(['id', 'key', 'self']) rows = [headers, [itemgetter(header)(issue) for header in headers]] print_table(SingleTable(rows))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def create_branch_from_issue(jira_url, jira_username, jira_api_key, project_key, source_branch_name, issue_key):\n click.echo('Branch \"{}\" was created'.format(\n create_branch_func(\n source_branch_name, get_branch_name(jira_url, jira_username, jira_api_key, issue_key, project_key)\n ...
[ "0.76392406", "0.68339527", "0.669128", "0.6574816", "0.64436364", "0.641573", "0.6397651", "0.6355058", "0.633265", "0.63172853", "0.62088567", "0.61402905", "0.59608126", "0.59550846", "0.59550846", "0.59512776", "0.592472", "0.59242016", "0.5866487", "0.58594525", "0.58388...
0.51225036
67
Checkout a new branch or checkout to a branch for a given issue.
def checkout(accountable, issue_key): issue = accountable.checkout(issue_key) headers = issue.keys() rows = [headers, [v for k, v in issue.items()]] print_table(SingleTable(rows))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def checkout(connection, branch, rid=None, repo=None):\n\n if repo is None:\n repo = Repository(connection, rid)\n\n return repo.checkout(branch)", "def checkout2(repo, branch, overwrite=True):\n cmd = 'git checkout %s' % (branch,)\n out = repo.issue(cmd, error='return')\n if ov...
[ "0.6947186", "0.67593735", "0.6722136", "0.668372", "0.6598679", "0.6580118", "0.657252", "0.6368997", "0.6331378", "0.6195333", "0.6167681", "0.6159897", "0.6148435", "0.61310816", "0.61085874", "0.6107166", "0.61063325", "0.6099721", "0.5997142", "0.5974702", "0.5951594", ...
0.5194389
62
List metadata for a given issue key.
def issue(ctx, accountable, issue_key): accountable.issue_key = issue_key if not ctx.invoked_subcommand: issue = accountable.issue_meta() headers = issue.keys() rows = [headers, [v for k, v in issue.items()]] print_table(SingleTable(rows))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_metadata(key=''):\n response, content = httplib2.Http().request(\n '%s/%s' % (METADATA_BASE_URL, key),\n headers={'Metadata-Flavor': 'Google'},\n method='GET',\n )\n if response['status'] == '404':\n raise NotFoundError(response, content)\n return content", "def get_metadata_keys (a...
[ "0.61033624", "0.5903859", "0.5889419", "0.578731", "0.56787604", "0.56540203", "0.56326365", "0.5542737", "0.5526142", "0.54738116", "0.5469202", "0.5457471", "0.5391811", "0.53859067", "0.5359557", "0.5359515", "0.5340263", "0.5308705", "0.5304822", "0.5293067", "0.52539086...
0.57509816
4
Update an existing issue.
def update(accountable, options): issue = accountable.issue_update(options) headers = issue.keys() rows = [headers, [v for k, v in issue.items()]] print_table(SingleTable(rows))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def update(self):\n\n params = {\n \"title\": self.title,\n \"body\": self.body,\n \"state\": self.state,\n \"labels\": self.labels,\n \"assignees\": self.assignees,\n }\n\n if self.milestone:\n params[\"milestone\"] = self.mile...
[ "0.7507036", "0.6419542", "0.6255618", "0.62028265", "0.6197646", "0.6125686", "0.60750145", "0.6069679", "0.6039978", "0.5961434", "0.5903089", "0.5882173", "0.58697164", "0.58686197", "0.5837822", "0.5791016", "0.5773482", "0.57148165", "0.56193185", "0.5503607", "0.5480972...
0.59853673
9
Lists all comments for a given issue key.
def comments(accountable): comments = accountable.issue_comments() headers = sorted(['author_name', 'body', 'updated']) if comments: rows = [[v for k, v in sorted(c.items()) if k in headers] for c in comments] rows.insert(0, headers) print_table(SingleTable(rows)) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _get_comments(self, issue_id):\n data = self._get(\"/issues/{}/comments\".format(issue_id))\n comments = []\n for item in data:\n comments.append(\n Comment(item['user']['login'], item['body'])\n )\n return comments", "def problem_comments(self...
[ "0.6735813", "0.63723326", "0.6294947", "0.62622994", "0.61518073", "0.6067908", "0.5866216", "0.5846625", "0.5813753", "0.58126175", "0.5763556", "0.5763556", "0.56536496", "0.5609131", "0.55674785", "0.55547565", "0.55169636", "0.54941475", "0.547762", "0.5461454", "0.54559...
0.68489426
0
Add a comment to the given issue key. Accepts a body argument to be used as the comment's body.
def addcomment(accountable, body): r = accountable.issue_add_comment(body) headers = sorted(['author_name', 'body', 'updated']) rows = [[v for k, v in sorted(r.items()) if k in headers]] rows.insert(0, headers) print_table(SingleTable(rows))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def add_comment_to_issue(repo, issue_number, body, allow_duplicates):\n found = False\n issue = repo.issue(issue_number)\n\n if not allow_duplicates:\n for comment in issue.iter_comments():\n if comment.body == body:\n found = True\n break\n\n if allow_du...
[ "0.682299", "0.6740081", "0.6561953", "0.6297364", "0.6274821", "0.6229835", "0.61394274", "0.5977267", "0.5953699", "0.5946078", "0.58701116", "0.5741862", "0.57191175", "0.56251615", "0.56233865", "0.5619574", "0.5502269", "0.5478731", "0.54059154", "0.5405601", "0.5395265"...
0.7143064
0
List all worklogs for a given issue key.
def worklog(accountable): worklog = accountable.issue_worklog() headers = ['author_name', 'comment', 'time_spent'] if worklog: rows = [[v for k, v in sorted(w.items()) if k in headers] for w in worklog] rows.insert(0, headers) print_table(SingleTable(rows)) else: ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def query_worklog(self, emp_id=None):\n\n query = \"select * from worklog\"\n\n try:\n self.dbCursor.execute(query)\n return self.dbCursor.fetchall()\n except mysql.connector.Error as err:\n ErrorMessageWindow(err)", "def get_logs(job_key):\n job = Job.fet...
[ "0.6104521", "0.5450961", "0.5429597", "0.54215986", "0.53740776", "0.53375506", "0.5177321", "0.51320475", "0.509252", "0.50396067", "0.50299364", "0.50039464", "0.49709633", "0.49424547", "0.49327973", "0.49153993", "0.4895989", "0.4895989", "0.48916838", "0.48571062", "0.4...
0.68508613
0
List all possible transitions for a given issue.
def transitions(accountable): transitions = accountable.issue_transitions().get('transitions') headers = ['id', 'name'] if transitions: rows = [[v for k, v in sorted(t.items()) if k in headers] for t in transitions] rows.insert(0, headers) print_table(SingleTable(rows...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def transitions(self) -> List[Dict]:\n return []", "def transitions(self, from_state=None):\n return list(self.iter_transitions(from_state))", "def setup_transition_list():\n xn_list = []\n\n xn_list.append( Transition(3, 4, 2., 'left ejection') )\n xn_list.append( Transition(12, 2, 2., ...
[ "0.6658203", "0.64597243", "0.609803", "0.5973659", "0.59435755", "0.5658975", "0.56407136", "0.5377166", "0.5376197", "0.5366969", "0.53484374", "0.5304934", "0.52682185", "0.5264709", "0.5251384", "0.5251384", "0.5246294", "0.52343994", "0.5204525", "0.5165543", "0.5150279"...
0.7419396
0
Transition the given issue to the provided ID. The API does not return a JSON response for this call.
def dotransition(accountable, transition_id): t = accountable.issue_do_transition(transition_id) if t.status_code == 204: click.secho( 'Successfully transitioned {}'.format(accountable.issue_key), fg='green' )
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _apply_issue(self, issue):\n data = {\n \"title\" : issue._title,\n \"body\" : issue._desc,\n \"labels\" : issue._labels\n }\n state = issue._state\n resp = self._post(\n self._base + \"/issues\", data=self._format_data(data))\n iss...
[ "0.6096263", "0.5963492", "0.5913452", "0.5913452", "0.5844219", "0.563173", "0.5610131", "0.55846214", "0.5549557", "0.55340236", "0.53562486", "0.52944756", "0.52351904", "0.51000565", "0.5072759", "0.5057223", "0.504402", "0.49940005", "0.49697727", "0.49525893", "0.487762...
0.5930258
2
Executes a user search for the given query.
def users(accountable, query): users = accountable.users(query) headers = ['display_name', 'key'] if users: rows = [[v for k, v in sorted(u.items()) if k in headers] for u in users] rows.insert(0, headers) print_table(SingleTable(rows)) else: click.secho('...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def search_user(request: Request) -> Response:\n if not request.query_params.get('query'):\n return Response({'type': 'error', 'data': {'message': 'Invalid username query'}})\n\n users = User.objects.filter(\n username__contains=request.query_params.get('query'))\n return Response(UserSerial...
[ "0.6919763", "0.6878182", "0.68767464", "0.6873332", "0.674729", "0.66318774", "0.6590094", "0.65592164", "0.65443397", "0.65341306", "0.653293", "0.65029144", "0.6447364", "0.6444566", "0.63871574", "0.63288534", "0.6322284", "0.6304574", "0.6282313", "0.6281736", "0.6269824...
0.0
-1
Debug breakpoint while in curses mode
def _D(stdscr): curses.nocbreak() stdscr.keypad(0) curses.echo() curses.endwin() import pdb; pdb.set_trace()
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __exit__(self, exc_type, exc_val, exc_tb):\n self.stdscr.keypad(False)\n self.stdscr.nodelay(False)\n curses.echo()\n curses.nocbreak()\n curses.endwin()", "def gdb_breakpoint():\n _gdb_python_call_gen('gdb_breakpoint')()", "def _debug_trace():\n from PyQt4.QtCore i...
[ "0.6429854", "0.64178854", "0.6243146", "0.62248564", "0.6194091", "0.610298", "0.6085449", "0.5991171", "0.5902381", "0.58755255", "0.5865251", "0.5808573", "0.5802128", "0.57814217", "0.57667226", "0.5760795", "0.5739943", "0.572808", "0.5721066", "0.5709992", "0.5702061", ...
0.75144726
0
Simple reader for CSV files
def csv_reader(filepath): with open(filepath) as f: for row in f: row = row.strip() r = list() part = '' is_double_quoted = False for c in row: if c == ',': if is_double_quoted is False: ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def read_csv():", "def read_csv_file(self):\n pass", "def _read_csv(self):\n self.function_name = '_read_csv'\n with open(os.path.join(self.task.downloads, self.csv_name)) as csv_file:\n reader = csv.reader(csv_file, dialect='excel')\n for row in reader:\n ...
[ "0.8346554", "0.78787494", "0.7780344", "0.7574572", "0.7152502", "0.71060395", "0.71036065", "0.71036065", "0.70984864", "0.7079543", "0.7066068", "0.7056243", "0.7056243", "0.7051177", "0.70340747", "0.702111", "0.70198274", "0.7002565", "0.699316", "0.6969437", "0.69495463...
0.6942765
21
Simple writer for CSV files
def csv_writer(filepath, seqs): with open(filepath, 'w') as f: f.write('\n'.join([','.join( ['"{}"'.format(r) if (' ' in r) or (',' in r) else r for r in s]) for s in seqs]))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __create_csv(self):\n with open(self.__csv_file_name, 'w', newline='', encoding='utf-8') as csv_file:\n writer = csv.DictWriter(csv_file, fieldnames=self.__csv_fields, delimiter=';')\n writer.writeheader()", "def write_csv(self, filelike):\r\n items = self.rows()\r\n ...
[ "0.73990387", "0.73866165", "0.72805005", "0.7275408", "0.71544623", "0.71080923", "0.70806223", "0.7071333", "0.70384103", "0.7035223", "0.7030914", "0.7019498", "0.7012548", "0.69920474", "0.6981219", "0.6955778", "0.6939745", "0.6935824", "0.69006646", "0.6868536", "0.6852...
0.645615
78
Return the n answers.
def search(): question = request.get_json() question = question['questions'] prediction = pipe.run(query=question[0], top_k_retriever=3, top_k_reader=3) answer = [] for res in prediction['answers']: answer.append(res['answer']) result = {"results":[prediction]} return json.dump...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def answers(self):\n assert self._answer_count\n for ii in self._answer_count:\n yield ii", "def get_n_solutions(self, n):\n return [self.get_solution() for _ in range(n)]", "def get_n_answers(self):\n return len(self.df)", "def get_top_answers(self, N):\n return sorted(\n ...
[ "0.6906924", "0.68668944", "0.67589056", "0.6748318", "0.6720697", "0.6720697", "0.6720697", "0.6613341", "0.6404832", "0.63191617", "0.6287063", "0.61694974", "0.6149776", "0.6117479", "0.6075899", "0.60121405", "0.59699076", "0.59674805", "0.5928417", "0.59035397", "0.59035...
0.0
-1
Retrieve yaml data from a given path if file not exist, return False
def get_yaml_data(path): yaml_path = "%s%s.yml" % (CONTENT_FILE_DIR, path[:-5]) if os.path.isfile(yaml_path): f = open(yaml_path, 'r') template_data = yaml.load(f) return template_data else: return False
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def load_yaml(path):\n if os.path.exists(path):\n f = open(path)\n data = yaml.load(f)\n f.close()\n return data\n else:\n # This should maybe throw an exception or something\n return {}", "def load_yaml(path):\n if os.path.exists(path):\n f = open(path)\...
[ "0.7641903", "0.7460384", "0.69069195", "0.6766211", "0.6622035", "0.6619555", "0.6472961", "0.6431809", "0.630646", "0.6232994", "0.620704", "0.620435", "0.617769", "0.6173353", "0.6155012", "0.6154364", "0.6134782", "0.6125133", "0.6101209", "0.6087636", "0.6067218", "0.6...
0.80198294
0
Try and determine the correct _ (underscore) template matching the files directory structure
def determine_template_by_path(path): path = path.lstrip('/') path_chunks = re.split('\/', path) if len(path_chunks) <= 1: return path else: """ For now be ignorant and just return the first entry of the list as the possible template name, so in fact ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _FindTemplateFile(self, topdir):\n if topdir.endswith('..'):\n topdir = '/'.join(topdir.split('/')[:-2])\n fnames = os.listdir(topdir)\n for fname in fnames:\n filename = '%s/%s' % (topdir, fname)\n if filename.endswith('.yaml') and not os.path.isdir(filena...
[ "0.68880016", "0.65661573", "0.6463607", "0.6450778", "0.6291978", "0.6183937", "0.6181306", "0.6174625", "0.61639", "0.60519874", "0.6047972", "0.6023817", "0.60039794", "0.5952855", "0.5941049", "0.5935887", "0.5918789", "0.5912781", "0.5902953", "0.5882798", "0.5849363", ...
0.6831351
1
constructor instantiate a Document with a term_list to be converted into dict
def __init__(self, term_list, links=[]): # do type check if not isinstance(term_list, list): raise TypeError('term_list must be of type list') if not isinstance(links, list): raise TypeError('links must be of type list') self.term_dict = {x: term_list.count(x) for x in term_list} self.links = copy.deepc...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __init__(self, docs, n):\n self.n = n\n self.dict = {}\n self.vocab = set()\n self.sum_index = \"*sum*\"\n regex = re.compile(\"\\s+\")\n count = 0\n for doc in docs:\n terms = re.split(regex, doc)\n for term in terms:\n if t...
[ "0.66287744", "0.65560615", "0.64975613", "0.63856316", "0.63014597", "0.6102487", "0.6062859", "0.60123897", "0.59738135", "0.597112", "0.5929631", "0.58660865", "0.5838759", "0.5802222", "0.5794235", "0.5776823", "0.57711035", "0.5759174", "0.57271045", "0.5723895", "0.5695...
0.6789457
0
constructor instantiate a Document with a dict of word count
def __init__(self, t_dict, links=[]): # do type check if not isinstance(t_dict, dict): raise TypeError('t_dict must be of type dict') if not isinstance(links, list): raise TypeError('links must be of type list') self.term_dict = copy.deepcopy(t_dict) self.links = copy.deepcopy(links)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __init__(self, docs, n):\n self.n = n\n self.dict = {}\n self.vocab = set()\n self.sum_index = \"*sum*\"\n regex = re.compile(\"\\s+\")\n count = 0\n for doc in docs:\n terms = re.split(regex, doc)\n for term in terms:\n if t...
[ "0.7427657", "0.7263681", "0.69983685", "0.6996699", "0.6939715", "0.66831976", "0.6674372", "0.667245", "0.6642106", "0.66213447", "0.6589039", "0.6532153", "0.64735675", "0.643729", "0.64181757", "0.63333464", "0.6331262", "0.63129574", "0.6303807", "0.6298997", "0.62720144...
0.0
-1
init Construct a DocumentSet with main document
def __init__(self, main_doc): if not isinstance(main_doc, Document): raise TypeError('term must be of type Document') self.main_doc = main_doc self.env_docs = []
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def create(init_document: 'Document') -> 'DocumentArray':", "def build_document(self):\n pass", "def new_document(self) -> nodes.document:\n document = super().new_document()\n document.__class__ = addnodes.document # replace the class with patched version\n\n # substitute transfor...
[ "0.6253416", "0.60488045", "0.5982539", "0.59698325", "0.5956928", "0.59276694", "0.58543664", "0.5842508", "0.5783739", "0.5778808", "0.57767564", "0.5767244", "0.57607514", "0.57084924", "0.5701809", "0.5619672", "0.56058586", "0.56026864", "0.55988246", "0.5564519", "0.556...
0.6229792
1
Add Env Page append a new env_page to env_docs
def add_env_page(self, env_page): if not isinstance(env_page, Document): raise TypeError('env_page must be of type Document') self.env_docs.append(env_page)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def add_env(self, env):\n pass", "def addPage(self, name, page, **attrs):\n page.globalConfig = self.globalConfig\n page.pageConfig['pageName'] = name\n self.globalConfig.pageList.append(name)\n self.globalConfig.pageAttributes[name] = dict(attrs)\n setattr(self,name,pag...
[ "0.5903957", "0.5708109", "0.5389904", "0.5385481", "0.52170116", "0.5199296", "0.51268643", "0.51034814", "0.5072406", "0.50699824", "0.49988046", "0.49757445", "0.4973589", "0.49586692", "0.49433592", "0.4912121", "0.4901298", "0.48945105", "0.4888683", "0.48753846", "0.487...
0.83140147
0
Count term in environment calculate idf of a term in main doc
def __count_term_in_env(self, term): # type check if not isinstance(term, str): raise TypeError('term must be of type str') total_cnt = float(len(self.env_docs)) + 1.0 if total_cnt == 1.0: return 1.0 cnt = 1.0 for doc in self.env_docs: if term in doc.term_dict: cnt += 1.0 return math.log(to...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def calc_tf(doc):\r\n tf = {}\r\n for term in doc:\r\n if term not in tf:\r\n tf[term] = doc.count(term)\r\n return tf", "def term_idf(self, term):\n idf = math.log(2 + self.count_term_distinct_documents(ANY))\\\n - math.log(1 + self.count_term_distinct_documents(term...
[ "0.729734", "0.71593374", "0.7090254", "0.6939883", "0.6922164", "0.66782546", "0.6643847", "0.65991753", "0.6548294", "0.6533882", "0.6521299", "0.6515126", "0.6509364", "0.65010506", "0.64998555", "0.6493106", "0.64863795", "0.6480846", "0.6379101", "0.6369765", "0.63460505...
0.7381723
0
Statistic TF calculate and sort terms in main doc by tf
def statistic_tf(self): return sorted(self.main_doc.term_dict.items(), key=operator.itemgetter(1), reverse=True)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def calc_tf(doc):\r\n tf = {}\r\n for term in doc:\r\n if term not in tf:\r\n tf[term] = doc.count(term)\r\n return tf", "def compute_TF(doc_info):\n tf_scores = []\n\n for idx, doc in enumerate(doc_info):\n tf_score_table = {}\n for word in doc['freq_dict'].keys():...
[ "0.7448172", "0.7352905", "0.7274512", "0.719932", "0.70984966", "0.70313746", "0.69458485", "0.68545496", "0.66986537", "0.6692026", "0.6671128", "0.662987", "0.6564009", "0.65638477", "0.65546054", "0.6543071", "0.6411876", "0.6403338", "0.6386789", "0.631672", "0.62851524"...
0.805907
0
Statistic TFIDF calculate and sort terms in main doc by tfidf
def statistic_tfidf(self): # calculate df-idf for all words count_dict = {x: self.main_doc.term_dict[x] * self.__count_term_in_env(x) for x in self.main_doc.term_dict} # sort them by df and idf return sorted(count_dict.items(), key=operator.itemgetter(1), reverse=True)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def tf_idf_score():\n\n global final_doc_set\n global final_dictionary\n final_score = []\n\n for doc_id in final_doc_set:\n score = 0\n for query_term in final_dictionary.keys():\n if final_dictionary[query_term][1].get(doc_id):\n tf = final_dictionary[query_ter...
[ "0.7582223", "0.7435247", "0.73491657", "0.73197037", "0.7230406", "0.7206604", "0.71902233", "0.71717143", "0.71699125", "0.7125617", "0.702574", "0.7018102", "0.700898", "0.69296885", "0.6926581", "0.6906138", "0.68636584", "0.68492436", "0.68379414", "0.6832129", "0.682651...
0.8353365
0
Show the menu and return either None (if an exit key was pressed) or FindTweetMenu.BACK_INDEX
def showAndGet(self): keywords = TerminalInterface.getSearchKeywords() # If user did not enter any keywords, return FindUserMenu.BACK_INDEX if keywords is None: return FindTweetMenu.BACK_INDEX tweetGeneratorMethod = lambda: TweetsTableTools.findTweets( self._connection, keywords) menu = TweetsMenu(self...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def return_menu(self):\n while True:\n number = pyip.inputNum(\"0. Back to the main menu: \")\n if number == 0:\n # Clean up the console\n self.clear_console()\n # back to the main menu\n self.run()\n else:\n ...
[ "0.6956989", "0.63773394", "0.6250696", "0.6249938", "0.6121402", "0.6083828", "0.6070606", "0.6057965", "0.6057965", "0.6047459", "0.60398436", "0.60209143", "0.5979282", "0.59761137", "0.59599715", "0.59599715", "0.59599715", "0.5945133", "0.59189636", "0.5891626", "0.58872...
0.75444674
0
Update Plex by sending signal and jumping ahead by debounce timeout.
async def trigger_plex_update(hass, server_id): async_dispatcher_send(hass, PLEX_UPDATE_PLATFORMS_SIGNAL.format(server_id)) await hass.async_block_till_done() next_update = dt_util.utcnow() + timedelta(seconds=DEBOUNCE_TIMEOUT) async_fire_time_changed(hass, next_update) await hass.async_block_till_d...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def on_update(self, event, signal):\n t = ppb.get_time() - self.start_time\n if t >= self.duration:\n signal(ppb.events.Quit())", "def pulley_activate(self):\n self.pulley(\"up\")\n time.sleep(5 * 0.7)\n self.pulley(\"stop\")\n time.sleep(2)\n self.pull...
[ "0.56693584", "0.5572143", "0.524882", "0.52033556", "0.5159521", "0.51439494", "0.5112212", "0.50803035", "0.50585544", "0.50414705", "0.49876454", "0.4983399", "0.49722165", "0.49537098", "0.49445942", "0.49259475", "0.49010918", "0.48859626", "0.48851368", "0.48832425", "0...
0.5235737
3
Uses an index array to obtain indices using an index array along an axis.
def select_indices(arr,index_arr,axis=-1): shape_list=(lambda x,y: [ 1 if dim!=x else y for dim in range(len(arr.shape))] ) indices_list=[np.reshape(np.arange(length),shape_list(length_id,length)) for length_id,length in enumerate(arr.shape)] indices_list[axis]=index_arr return arr.rav...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def pndindex(*args):\r\n return np.ndindex(*args)", "def pndindex(*args):\n return np.ndindex(*args)", "def _index(tensor_3d, tensor_2d):\n x, y, z = tensor_3d.size()\n t = tensor_3d.reshape(x * y, z)\n tt = tensor_2d.reshape(x * y)\n v = t[torch.arange(x * y), tt]\n v = v.reshape(x, y)\n ...
[ "0.7302368", "0.7263272", "0.69995314", "0.6984675", "0.68649966", "0.68557614", "0.6626734", "0.6612736", "0.64494765", "0.63717943", "0.6355618", "0.6344733", "0.6259788", "0.62565714", "0.62565714", "0.6241369", "0.62404037", "0.62190133", "0.62045544", "0.61014456", "0.60...
0.7482163
0
Load model with saved parameters
def __init__(self,data_path=None,load_quant=True,use_cuda=False): data_path = os.path.join("data", "Transformer_500k_UNK") if data_path==None else data_path if not os.path.isdir(data_path) or len(os.listdir(data_path)) == 0: raise FileNotFoundError(f"No such file or directory: {data_path}, ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def load_model(self) -> Any:", "def load_model(self):\n pass", "def load(path_to_model):\n pass", "def load_model(self, model_path: str):", "def _load_model_from_trained_params(self):\n self.ent_emb = tf.constant(self.trained_model_params[0])\n self.rel_emb = tf.constant(self.tr...
[ "0.82873255", "0.8127506", "0.7947357", "0.78214943", "0.7764831", "0.772304", "0.76998776", "0.7674535", "0.7609661", "0.75857383", "0.75005114", "0.74642503", "0.7394183", "0.7352217", "0.73177934", "0.7304131", "0.7298603", "0.7293181", "0.7292386", "0.7279392", "0.7239284...
0.0
-1
Give an answer to the input sentence using the model
def evaluateOneInput(self, input_sentence): input_sentence = process_punct(input_sentence.encode()) # Evaluate sentence output_words = self.evaluate(input_sentence) # Format and print response sentence output_words[:] = [x for x in output_words if not (x =='SOS' or x == 'EOS' or ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def insult_me(\n message : str \n ):\n \n #load model\n model = Detoxify('original')\n \n #predict toxicity\n results = model.predict(message)\n \n #echo results\n click.echo(pd.Series(results))", "def example_single(args, model, word2idx):\n #在命令行中加载和分段<目标、(推特内容)>配对\n ...
[ "0.6981685", "0.67661905", "0.67217624", "0.6701556", "0.6554767", "0.6553322", "0.6489698", "0.641607", "0.6358076", "0.6350352", "0.6321193", "0.62813115", "0.6276418", "0.6273281", "0.6234738", "0.61554706", "0.61365837", "0.61111104", "0.61026496", "0.6102064", "0.6094713...
0.6599026
4
Continous loop of inputs and answers
def evaluateCycle(self): print("Enter q or quit to exit") input_sentence = '' while(1): # Get input sentence input_sentence = input('> ') # Check if it is quit case if input_sentence == 'q' or input_sentence == 'quit': break ans = self....
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def eval_loop():\n while(True):\n decision = raw_input(\"enter some mathematical operations\")\n if(decision == \"done\"):\n break\n print eval(decision)", "def main():\n min_random = 10 #keeping constant for the min random number range\n max_random = 99 #keeping constant...
[ "0.6230928", "0.6096894", "0.6076145", "0.5999656", "0.5885386", "0.5863726", "0.5846322", "0.5829764", "0.5800593", "0.57979757", "0.5749408", "0.57334924", "0.5704362", "0.5703528", "0.56611365", "0.5657158", "0.56488985", "0.564698", "0.5644468", "0.5608744", "0.5578464", ...
0.6725195
0
Primary method to play the game & checking the solution. It is not used in solving)
def shift(self, direction): direct, pos = tuple(direction) board = {'L': self.rows, 'R': self.rows, 'D': self.cols, 'U': self.cols}[direct] board[int(pos)].shift(direction=self.direct[direct])
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def play_game():\n pass", "def play(self):\n print(\"Game is starting!!\")\n self.generate_secret_number()\n while True:\n self.get_guess_from_user()\n self.ans = self.compare_results()\n if self.ans:\n print(f\"Right Guess!! , the numbe...
[ "0.75411516", "0.7212041", "0.71931386", "0.7155727", "0.7048633", "0.7033831", "0.7021678", "0.70061886", "0.7005463", "0.6991841", "0.69794375", "0.69777775", "0.69694954", "0.6947417", "0.6946482", "0.68822587", "0.68286717", "0.68259066", "0.6824033", "0.6817702", "0.6791...
0.0
-1
method to create random tests
def shuffle(self, steps): from random import sample for s in range(steps): direction = sample('LRUD', 1)[0] if direction in 'LR': stepsize = str(sample(range(self.cdim), 1)[0]) else: stepsize = str(sample(range(self.rdim), 1)[0]) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_generate_all_testing(self):\n pass", "def create_scenarios(self, params, num_scenarios, random_seed):\n return None", "def tests():", "def random_test(self):\r\n return 1", "def random_test(self):\r\n return 1", "def test_create10(self):\n pass", "def setUp(self)...
[ "0.7360971", "0.71221584", "0.7059438", "0.697786", "0.697786", "0.683397", "0.65771455", "0.6566908", "0.6540432", "0.6537697", "0.6506476", "0.6506476", "0.6487945", "0.6480973", "0.6466432", "0.64281267", "0.6419093", "0.6400205", "0.6394195", "0.6386315", "0.6373467", "...
0.0
-1
Run all test scenario and then execute reporter if html flag exist.
def run(self): list_test_scenarios = self.__get_list_scenarios_in_folder() if not list_test_scenarios: utils.print_error( "\n{}\n".format(constant.ERR_CANNOT_FIND_ANY_TEST_SCENARIOS)) exit(1) (tests_pass, tests_fail) = self.__execute_tests(list_test_scen...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __execute_reporter(self):\n if not self.__args.report:\n return\n reporter.HTMLReporter().generate_report_from_file(\n self.__lst_json_files)", "def pytest_runtest_makereport(item, call): # pylint: disable=unused-argument\n pytest_html = item.config.pluginmanager.getpl...
[ "0.6686276", "0.65002805", "0.6378646", "0.6340691", "0.6270522", "0.62139416", "0.6112857", "0.60783136", "0.6054379", "0.60486007", "0.60120285", "0.5982267", "0.5931877", "0.59273463", "0.5872965", "0.58716303", "0.5847251", "0.5843916", "0.583857", "0.58348507", "0.583448...
0.62751275
4
Catch args for TestRunner in sys.argv.
def __catch_arg(self): arg_parser = argparse.ArgumentParser() arg_parser.add_argument("-d", "--directory", dest="directory", default="", nargs="?", help="directory of test " "scenarios (not recursive)") ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_invalidargs(clickrunner):\n for args in maincli.invalid_args:\n result = clickrunner.invoke(maincli.entrypoint, args)\n assert result.exit_code == 2\n assert \"no such option\" in result.output", "def test_validargs(clickrunner):\n for args in maincli.valid_args:\n resu...
[ "0.7225798", "0.70116264", "0.69673175", "0.6952749", "0.69509286", "0.6940995", "0.69118005", "0.69118005", "0.68684554", "0.6853725", "0.67827827", "0.67526424", "0.6737822", "0.6723002", "0.670661", "0.6685841", "0.66617244", "0.66590375", "0.6636994", "0.66235673", "0.657...
0.6579239
20
Execute all test case and collect the number of tests and pass.
def __execute_tests(self, lst_tests): tests_pass = tests_fail = 0 queue_of_result = multiprocessing.Queue() for test in lst_tests: process = multiprocessing.Process( target=TestRunner.__helper_execute_test, kwargs={"test_cls": test, ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def run_all(self):\n failures, errors = [], []\n\n # Run each test case registered with us and agglomerate the results.\n for case_ in self.cases:\n case_.run()\n update_results(failures, errors, case_)\n\n # Display our results.\n print_errors(errors)\n ...
[ "0.8066305", "0.7679223", "0.7588624", "0.75611496", "0.7493511", "0.7447741", "0.7365203", "0.73429716", "0.7243645", "0.71807855", "0.71580714", "0.7156753", "0.7147598", "0.7142445", "0.71408165", "0.71127534", "0.70493865", "0.70469147", "0.70359755", "0.70329404", "0.703...
0.7079473
16
Execute html_reporter if html flag is exist in sys.argv.
def __execute_reporter(self): if not self.__args.report: return reporter.HTMLReporter().generate_report_from_file( self.__lst_json_files)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def main(args):\n p = OptionParser()\n p.add_option('-d', '--debug',\n action='store_true', default=False, dest='debug',\n help='debug')\n p.add_option('-w', '--w3c',\n action='store_true', default=False, dest='w3c',\n help='send file to vali...
[ "0.61923707", "0.6103338", "0.57461756", "0.5729902", "0.5687042", "0.5661867", "0.5577024", "0.5549909", "0.5545881", "0.5544282", "0.5523945", "0.5516386", "0.5506726", "0.5482997", "0.545956", "0.541977", "0.5402856", "0.5398641", "0.5351573", "0.53477114", "0.5338802", ...
0.65210193
0
Get all scenario in folder. Recursive to sub folder if "rd" argument appear in sys.argv.
def __get_list_scenarios_in_folder(self): # If both directory and recur_directory are exist # then show "Invalid command" and exit. if self.__args.directory is not "" \ and self.__args.recur_directory is not "": utils.print_error("\n{}\n".format(constant.ERR_COMMAND_E...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def getImmediateSubdirectories(dir):", "def open_run_list(base_path, filter=None):\n dir_list = listdir(base_path)\n if not dir_list:\n return []\n if filter is not None:\n filter_list = glob(path.join(base_path, filter))\n filter_list = [path.basename(x) for x in filter_list]\n ...
[ "0.63146096", "0.5627409", "0.558369", "0.55248845", "0.5507381", "0.54794055", "0.54717195", "0.541972", "0.5409548", "0.5396662", "0.53879046", "0.5381494", "0.53524697", "0.534816", "0.53151697", "0.5314741", "0.5300426", "0.5292671", "0.5287332", "0.52773887", "0.5202164"...
0.6236096
1
Execute test case in a subprocess and send result to parent process
def __helper_execute_test(test_cls, channel, time_out): test_case = test_cls() test_case.execute_scenario(time_out=time_out) temp = {} if hasattr(test_case, "test_result"): temp["status"] = test_case.test_result.get_test_status() temp["json_path"] = test_case.test...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def subprocess_run(self, *args):\n return self.testdir.runpytest_subprocess(*args)", "def exec_test_command(cmd):\n process = Popen(cmd, stdout=PIPE, stderr=PIPE, close_fds=True, env=os.environ)\n result = process.communicate()\n return (\n process.returncode,\n bytes(result[0]).dec...
[ "0.7415895", "0.7005816", "0.6814329", "0.68095565", "0.6792471", "0.6724851", "0.67234045", "0.671659", "0.6664277", "0.66287655", "0.66093504", "0.6543628", "0.6516597", "0.6505767", "0.64967453", "0.649315", "0.64489895", "0.64397633", "0.63724536", "0.6358078", "0.6355825...
0.0
-1
Takes a tuple representing a circle as (x,y,radius) and returns a tuple with the x,y coordinates and width,size (x,y,w,h)
def circle_2_tuple(circle): assign_coord = lambda x,y: x - y if x > y else 0 x = assign_coord(circle[0],circle[2]) y = assign_coord(circle[1],circle[2]) assign_size = lambda x,y : y*2 if x > y else y*2 - (y-x) w = assign_size(circle[0],circle[2]) h = assign_size(circle[1],circle[2]) retur...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def circle_2_bbox(circle):\n x,y,w,h = circle_2_tuple(circle)\n return ((x,y),(x+w,y+h))", "def circleInfo(r):\n c = 2 * 3.14159 * r\n a = 3.14159 * r * r\n return (c, a)", "def _resolve_size(self, width, height, center_x, center_y):\n if self.size_type == 'explicit':\n size_x,...
[ "0.6815439", "0.67740446", "0.6597744", "0.64084023", "0.63581634", "0.6175593", "0.6125594", "0.6088099", "0.6076769", "0.60566986", "0.6024376", "0.5960171", "0.5957911", "0.5952948", "0.59458065", "0.5938926", "0.5935301", "0.59228104", "0.59222513", "0.5917145", "0.588294...
0.82615507
0
Takes a tuple representing a circle as (x,y,radius) and returns a tuple represeting a bbox ((x,y),(x',y'))
def circle_2_bbox(circle): x,y,w,h = circle_2_tuple(circle) return ((x,y),(x+w,y+h))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def circle_2_tuple(circle):\n assign_coord = lambda x,y: x - y if x > y else 0\n x = assign_coord(circle[0],circle[2])\n y = assign_coord(circle[1],circle[2])\n\n assign_size = lambda x,y : y*2 if x > y else y*2 - (y-x) \n w = assign_size(circle[0],circle[2])\n h = assign_size(circle[1],circle[2...
[ "0.7212098", "0.6748863", "0.6743238", "0.6730478", "0.67082477", "0.66678756", "0.66592455", "0.66318727", "0.6586817", "0.65842336", "0.6532223", "0.6481017", "0.6468795", "0.6422326", "0.6373362", "0.63589585", "0.635091", "0.6347281", "0.6332991", "0.63162756", "0.6307187...
0.87923753
0
Takes a tuple of tuples represeting a bbox ((x,y),(x',y')) and returns
def fix_bbox(bbox,img_shape): x = min(bbox[1][0],img_shape[1]) y = min(bbox[1][1],img_shape[0]) return ((bbox[0]),(x,y))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def circle_2_bbox(circle):\n x,y,w,h = circle_2_tuple(circle)\n return ((x,y),(x+w,y+h))", "def bbox(self):\n lower = (self.x.min(), self.y.min())\n upper = (self.x.max(), self.y.max())\n return (lower, upper)", "def bbox2points(bbox):\r\n l, x, y, w, h = bbox\r\n xmin = int(ro...
[ "0.74169517", "0.7330232", "0.73051816", "0.7260692", "0.72117823", "0.71556735", "0.711998", "0.70630515", "0.6968945", "0.6965542", "0.6959953", "0.68821084", "0.68737143", "0.68725014", "0.6858501", "0.68244123", "0.67616284", "0.67497444", "0.67070234", "0.66811466", "0.6...
0.75615424
0
Draws bboxes in a image given an array of circles [(x,y,radius)]
def bbox_from_circle(img, circles): seg_imgs = [] bboxes = [] aux = img.copy() for i,el in enumerate(circles): bbox = circle_2_bbox(el['coord']) bbox = fix_bbox(bbox,aux.shape) cv.rectangle(aux,bbox[0],bbox[1],(0,255,0)) bboxes.append(bbox) return bboxes
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def draw_bboxes(img, bboxes, color=(0, 0, 255), thick=6):\n draw_img = np.copy(img)\n # Draw rectangles given bbox coordinates as opposing coordinates\n # bboxes = opposing coordinates: (x1,y1), (x2,y2)\n [cv2.rectangle(draw_img, bbox[0], bbox[1], color, thick) for bbox in bboxes]\n return draw_img"...
[ "0.68533266", "0.68072176", "0.6805508", "0.6788925", "0.676972", "0.6738393", "0.67133397", "0.664385", "0.66165227", "0.6587222", "0.6578446", "0.65585065", "0.6551722", "0.65482426", "0.6528621", "0.65220505", "0.64468735", "0.64413995", "0.6400262", "0.6379862", "0.637677...
0.73344976
0
Calculate heterozygosity samples = list of sample names vcf = VCF file
def calHet( inFile, varType ): names = [] print("Sample\tfracHet\thetCt\thomCt") # print header with open( inFile, 'r') as files: # open sample name file for i in files: i = i.rstrip() vcf = i + "." + varType + ".vcf" ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def vcf_samples(vcffile):\n try:\n vcf_reader = vcf.Reader(open(vcffile, 'r'))\n return vcf_reader.samples\n except Exception as error:\n print(f\"Could not read vcffile {vcffile}: continuing without vcf data: {str(error)}\")\n\n return []", "def calculate_mixture_features(args):\n ...
[ "0.59488827", "0.5802758", "0.58007336", "0.57328737", "0.56093895", "0.55808663", "0.5559472", "0.5527993", "0.55187845", "0.5462843", "0.54014647", "0.5394218", "0.53905374", "0.53511345", "0.53466797", "0.5334894", "0.5314936", "0.5283237", "0.5243797", "0.5231647", "0.523...
0.6672723
0
Set up SMHI forecast as config entry.
async def async_setup_entry(opp: OpenPeerPower, entry: ConfigEntry) -> bool: opp.config_entries.async_setup_platforms(entry, PLATFORMS) return True
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __init__(self, config_file_name):\n configs = io.read_yaml(PATH_CONFIG, config_file_name)\n Logger.info('Loaded future forecasts configs from file',\n os.path.join(PATH_CONFIG, config_file_name), self.__class__.__name__)\n\n self.is_sell_in_model = configs['model'] == 's...
[ "0.61663395", "0.5952376", "0.574808", "0.5706627", "0.57054985", "0.56703043", "0.5660496", "0.5641424", "0.5638844", "0.56216913", "0.5614565", "0.55751806", "0.55385953", "0.547761", "0.54702574", "0.54657686", "0.5461015", "0.5460905", "0.5451515", "0.54444194", "0.544236...
0.0
-1
Unload a config entry.
async def async_unload_entry(opp: OpenPeerPower, entry: ConfigEntry) -> bool: return await opp.config_entries.async_unload_platforms(entry, PLATFORMS)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "async def async_unload_entry(hass, config_entry):\n unload_ok = await hass.config_entries.async_forward_entry_unload(\n config_entry, \"climate\"\n )\n return unload_ok", "async def async_unload_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:\n hass.data.pop(DOMAIN)\n return True", ...
[ "0.697284", "0.6888074", "0.6779855", "0.6747459", "0.6689002", "0.6657831", "0.66162205", "0.6603433", "0.65925974", "0.65595686", "0.65411645", "0.6507643", "0.6507643", "0.6507643", "0.6507643", "0.64977276", "0.64931643", "0.6486601", "0.6486601", "0.6486601", "0.6486601"...
0.58642447
78
A convenience function for getting a single suggestion.
def get_suggestion(): global _suggestions_iterator while True: try: return next(_suggestions_iterator) except StopIteration: _suggestions_iterator = iter(suggestions)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def suggestion(self, suggestion_id):\r\n return suggestions.Suggestion(self, suggestion_id)", "def pull_suggestion(self, callback, who, arg):\n\t\t\n random_sug = self.dong.db.get_random_row('suggest')\n res = self.google_suggest(callback, who, random_sug[2], False)\n\t\t\n w = res.sp...
[ "0.70957506", "0.7064316", "0.6983561", "0.6963836", "0.6963836", "0.6800833", "0.6749406", "0.6550867", "0.6436159", "0.6428319", "0.6357224", "0.62608695", "0.62456524", "0.6239825", "0.6186077", "0.60764414", "0.6011701", "0.5944827", "0.5927803", "0.582557", "0.5824507", ...
0.7540617
0
Builds game board by retrieving a sudoku puzzle preset from a sudoku dataset and then sets up the game board. Also calls a backtracking algorithm to derive a solution for the sudoku puzzle.
def build_game_board(self): # retrieves new sudoku puzzle from dataset sudoku_set = self.data.get_sudoku_set() sudoku_problem, sudoku_solution = sudoku_set[0], sudoku_set[1] # removes old game boards self.board = [] self.puzzle = [] self.alg_solution = [] ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def solveSudoku(board):\n # represents all numbers in a specific row, col, box\n # format: if (5,9) is in rows, that means row 5 contains digit 9\n\t\t# format: if (3, 2) is in cols, that means col 3 contains digit 2\n\t\t# format: if (0,2,8) is in boxes, that means box (0,2) contains 8\n\t\t# cellsT...
[ "0.70219713", "0.6696299", "0.6669564", "0.665291", "0.6652331", "0.6648256", "0.6491506", "0.6417593", "0.64122254", "0.6406946", "0.64067495", "0.6398763", "0.6398049", "0.6371711", "0.63527167", "0.6332174", "0.63301975", "0.6281087", "0.6276849", "0.62565374", "0.62547344...
0.8129647
0
Requests user input for the row column and number input they would like to enter as the next entry to the Sudoku puzzle. Has some lightweight data validation through a try / except format and asks for another input attempt if invalid inputs were provided.
def request_number_input(self): try: self.print_board(self.board) row = int(input("Please enter row to add number to (0-8): ")) col = int(input("Please enter column to add number to (0-8): ")) num = int(input("Please enter number you wish to add (1-9): ")) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_input(self):\n while True:\n try:\n self.rows = int(input(\"Number of rows: \"))\n while self.rows < 2 or self.rows > 30:\n self.rows = int(input(\"Please enter a number between 2 and 30: \"))\n break\n except Valu...
[ "0.7249653", "0.7011725", "0.68896455", "0.65655696", "0.64116174", "0.63925433", "0.62278056", "0.6168719", "0.6091157", "0.6074832", "0.604573", "0.6043378", "0.5990928", "0.5926154", "0.5902846", "0.58928376", "0.5879233", "0.5877413", "0.5809618", "0.57965463", "0.5755129...
0.71602094
1
Checks that inputs are valid and returns informative messages if not. If input is valid, updates the game board and returns an updated game state.
def set_number(self, col, row, num): if col > 8 or row > 8 or num > 9 or num < 0: return "Invalid input, try again!" elif self.new_input_does_not_overlap_original_board(col, row): if num == 0: self.board[row][col] = 0 else: self.board[r...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def update_game_state(self):\n # if board is not filled out, returns a valid move message\n for row in self.board:\n if 0 in row:\n return \"Valid input\"\n\n # if board is filled out, verifies if solution is valid and updates game state\n self.game_state = alg...
[ "0.7908367", "0.62731576", "0.6187185", "0.6057185", "0.59081376", "0.583079", "0.58031124", "0.57425433", "0.57258314", "0.5701646", "0.56752145", "0.56365675", "0.562094", "0.5612027", "0.5599031", "0.5581615", "0.5548244", "0.55428475", "0.5534015", "0.5533693", "0.5515880...
0.0
-1
Checks if the requested square to change is an original input for the puzzle, which cannot be changed.
def new_input_does_not_overlap_original_board(self, col, row): return self.puzzle[row][col] == 0
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def is_solved(self):\n # Iterate through each square of the puzzle\n for row in range(self.sl):\n for col in range(self.sl):\n val = self.puzzle[row][col]\n\n # If any square value is blank (0), not solved, return False\n if val == 0:\n ...
[ "0.68145555", "0.66621006", "0.65014184", "0.6457396", "0.64046955", "0.6342213", "0.6310124", "0.630704", "0.6286575", "0.62758124", "0.62362766", "0.6218367", "0.62178296", "0.61827266", "0.61717474", "0.61584324", "0.61545163", "0.61536086", "0.6134026", "0.6131081", "0.61...
0.710153
0
Checks to see if the sudoku puzzle has been filed out and if it has, checks if solution is valid.
def update_game_state(self): # if board is not filled out, returns a valid move message for row in self.board: if 0 in row: return "Valid input" # if board is filled out, verifies if solution is valid and updates game state self.game_state = alg.check_solutio...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def check_if_solvable(self):\n\n self.solvable=True #status of sudoku\n for i in range(0, 9):\n for j in range(0, 9):\n if self.a[i][j]==0:\n continue\n if self.check(i, j)[self.a[i][j]]==0:\n self.solvable=False\n return False", "def test_is_solved_when_p...
[ "0.7875301", "0.73579586", "0.73001826", "0.7293104", "0.7169292", "0.70995307", "0.70965403", "0.7090685", "0.70901805", "0.70878255", "0.70585865", "0.7052431", "0.69659704", "0.69108915", "0.69038904", "0.6884344", "0.6804607", "0.6775108", "0.677041", "0.67370665", "0.669...
0.0
-1
Method for retrieving game state.
def get_game_state(self): return self.game_state
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_game_state(self):\r\n return self._game_state", "def get_game_state(self):\n return self._game_state", "def get_game_state(self):\n return self._game_state", "def get_game_state(self):\n return self._game_state", "def get_game_state(self):\n return self._current_s...
[ "0.8740767", "0.86155", "0.86155", "0.86155", "0.8482095", "0.84146124", "0.84146124", "0.8371279", "0.8281865", "0.82611275", "0.7860468", "0.7752739", "0.7565724", "0.75503594", "0.75414294", "0.75414294", "0.75414294", "0.75414294", "0.75414294", "0.75414294", "0.75414294"...
0.87546504
0
Method for retrieving current puzzle board.
def get_game_board(self): return self.board
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_board(self):\r\n return self.board", "def get_board(self):\n return self.board", "def get_board(self):\n pass", "def getBoard(self):\n return self.board", "def get_board(self):\n return self._board", "def get_board(self):\n return self._board", "def get...
[ "0.81792754", "0.8089178", "0.8084012", "0.80593", "0.8017772", "0.8017772", "0.7993326", "0.78803223", "0.7876486", "0.775667", "0.7621218", "0.72338516", "0.72066325", "0.70492387", "0.6822986", "0.68192434", "0.681123", "0.6792606", "0.6792606", "0.6792606", "0.6792606", ...
0.7823289
9
Method for printing a puzzle board, given a board input. Adds separators for readability.
def print_board(self, board): print("Sudoku Board:") count = 0 for row in board: string = "" for num in range(len(row)): if row[num] != 0: string += str(row[num]) else: string += "_" i...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def print_puzzle(board):\n\n row_size = get_row_size(board)\n output = '\\n'\n\n for idx, val in enumerate(board):\n output += \" {} \".format(val)\n if idx % row_size == row_size - 1:\n output += \"\\n\"\n\n return output", "def print_board(self):\n num_rows = len(self.bo...
[ "0.78815925", "0.7831809", "0.7829072", "0.7808938", "0.7672224", "0.76157385", "0.759019", "0.7571991", "0.7562879", "0.7542402", "0.7536376", "0.7534153", "0.75278115", "0.7526931", "0.7525567", "0.75204974", "0.75150675", "0.7491006", "0.7489625", "0.7483982", "0.7483982",...
0.7443866
27
Nethod for playing a game of sudoku. Prints out rules and instructions and asks for user inputs. If current puzzle is solved, asks player if they would like to play again and provides a new puzzle.
def play_sudoku(puzzle): print_instructions() print("For review and grading purposes purposes, here is a sample solution:") puzzle.print_board(puzzle.alg_solution) # while puzzle is not solved, continues to ask user for their next input while puzzle.get_game_state() != "Solved!": puzzle.re...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def main():\r\n print(WELCOME_MESSAGE)\r\n\r\n playing = True\r\n while playing:\r\n\r\n # Valid inputs that the user can use\r\n move_actions = (UP, DOWN, LEFT, RIGHT)\r\n other_actions = (GIVE_UP, HELP)\r\n\r\n grid_size = int(input(BOARD_SIZE_PROMPT))\r\n\r\n # Get th...
[ "0.7092953", "0.689259", "0.68871856", "0.6726647", "0.6684823", "0.6676533", "0.6589806", "0.6555856", "0.6482301", "0.63967913", "0.6358735", "0.6353301", "0.6346815", "0.63003695", "0.6269608", "0.6250014", "0.6243339", "0.62017316", "0.61864555", "0.6185452", "0.61633503"...
0.82174706
0
Prints to console a set of instructions for how to play a game of Sudoku.
def print_instructions(): print("Welcome to the game of Sudoku!") print("--------------------------------") print("The goal of the game is to fill every 'square' here with a number.") print("The rules of the game are simple:") print(" Rule No 1: You can only enter numbers 1-9 in each square.") ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def print_instructions(self):\n\t\tprint('\\n\\n==========================================================================')\n\t\tprint('==========================================================================\\n')\n\t\tprint('Welcome to Tic Tac Toe, the came you know and love. \\nThe rules are the same ones you...
[ "0.75848573", "0.71088547", "0.703432", "0.6988165", "0.69866717", "0.6634064", "0.6410708", "0.6397539", "0.6355258", "0.63541543", "0.6338774", "0.63357323", "0.6296808", "0.6284995", "0.6267385", "0.62419635", "0.6191577", "0.6185726", "0.6172115", "0.61716187", "0.6133821...
0.74090517
1
Generate a automatic configuration for Home Assistant.
def gen_ha_config(self, mqtt_base_topic): json_config = { "name": self.friendly_name, "unique_id": "DALI2MQTT_LIGHT_{}".format(self.device_name), "state_topic": MQTT_STATE_TOPIC.format(mqtt_base_topic, self.device_name), "command_topic": MQTT_COMMAND_TOPIC.format(...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def generate_config():\n\n return {\n \"email_subject\": DEFAULT_EMAIL_SUBJECT,\n \"from_email\": DEFAULT_FROM_EMAIL,\n \"to_email\": DEFAULT_TO_EMAIL,\n \"url\": DEFAULT_URL,\n \"start_value\": DEFAULT_START_VALUE,\n \"lo...
[ "0.603333", "0.5879031", "0.58355165", "0.58121914", "0.58121914", "0.57822585", "0.57382154", "0.57061666", "0.57061666", "0.5690142", "0.56779516", "0.5659888", "0.5630428", "0.56107765", "0.5600595", "0.5599627", "0.55983096", "0.55772704", "0.55772704", "0.55755526", "0.5...
0.0
-1
returns the number of combinations of size k that can be made from n items. >>> nchoosek(5,3) 10 >>> nchoosek(1,1) 1 >>> nchoosek(4,2) 6
def nchoosek(n, k): if (n, k) in known: return known[(n,k)] if k == 0: return 1 if n == k: return 1 if n < k: return "n must be greater than k" result = nchoosek(n - 1, k - 1) + nchoosek(n - 1, k) known[(n,k)] = result return result
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def nchoosek(n, k):\n if n < k:\n return 0\n return partition(n, [k, n - k])", "def n_choose_k(N,K):\n return factorial(N) // (factorial(N - K) * factorial(K))", "def n_choose_k(n: int, k: int) -> int:\n # Edge case, no possible way to choose.\n if k > n or k < 0 or n < 0: return 0\n #...
[ "0.8256239", "0.7943082", "0.77849466", "0.77155447", "0.75553346", "0.75553226", "0.7516673", "0.7516673", "0.7516148", "0.74694544", "0.73904", "0.7390276", "0.7361454", "0.72738117", "0.72258496", "0.7099127", "0.7037198", "0.7019336", "0.70099944", "0.7001311", "0.6997127...
0.7859963
2
Creates four plotly visualizations using the New York Times Archive API
def return_figures(): # Add New York Times API Key nyt = NYTAPI("AsjeHhqDYrePA2GMPpYoY1KAKAdG7P99") # Select Year and Month of articles data = nyt.archive_metadata( date = datetime.datetime(2020, 7, 1) ) def data_to_df(data): # Initiate list for restructured information data_list = [] ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def return_figures():\n\n graph_one = []\n df = cleanparrisdf('data/Salem-Village-Data-Set.csv')\n sources = [0,0,0,1,1,1]\n targets = [2,3,4,2,3,4]\n values = df[\"petition_count\"].tolist()\n\n data_one = dict(\n type = 'sankey',\n node = dict(\n pad = 10,\n ...
[ "0.64221996", "0.6253128", "0.61704546", "0.61357796", "0.59865403", "0.5960612", "0.5946181", "0.59333766", "0.585216", "0.58064705", "0.5785414", "0.576179", "0.5730726", "0.57133067", "0.57080656", "0.5644872", "0.56262666", "0.5618747", "0.559435", "0.559427", "0.5580006"...
0.715962
0
Since virtual steppers are virtual, we don't need pins or step sequences. We're still using delay and n_steps to resemble physical steppers.
def __init__(self, name = None, n_steps = 256, delay = 1e-3): self.fig, self.ax = plt.subplots(figsize=(3, 3)) self.n_steps = n_steps self.delay = delay self.step_size = 2 * pi / self.n_steps if name is None: self.name = 'Stepper {}'.format(VirtualStepper.count + 1) self.angle = 0.0 self.check() se...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def simulation_step(self):\n if not self.np_trajectory.size:\n #No trajectory to go to.....\n return\n closest_ind = self.find_closest_trajectory_pose()\n ref_ind = (closest_ind + 30) # closest_ind + numpy.round(self.v / 4)\n traj_len = len(self.np_trajectory[0])\n...
[ "0.61042655", "0.58270293", "0.57595366", "0.5680195", "0.566837", "0.56112635", "0.5599372", "0.55904734", "0.5564814", "0.55495346", "0.55369097", "0.5532785", "0.552632", "0.55011874", "0.5483982", "0.5469438", "0.5464263", "0.54594064", "0.54537576", "0.5439023", "0.54092...
0.5976446
1
Rotates to the angle specified (chooses the direction of minimum rotation)
def rotate_to(self, angle, degrees = False): target = angle * pi / 180 if degrees else angle curr = self.angle diff = (target - curr) % (2*pi) if abs(diff - (2*pi)) < diff: diff = diff - (2*pi) self.rotate_by(diff)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def rotate(self, angle):\n old_angle, tilt = self.rotation\n new_angle = old_angle + angle\n while new_angle > 90:\n new_angle = new_angle - 90\n while angle < -90:\n new_angle = new_angle + 90\n self.rotation = (new_angle, tilt)", "def rotate(self, angle)...
[ "0.76305664", "0.74709356", "0.7153996", "0.71267575", "0.71231437", "0.7109132", "0.71029294", "0.70860887", "0.7050864", "0.6986599", "0.69027513", "0.6902106", "0.6818123", "0.68133605", "0.67248964", "0.6705006", "0.6692359", "0.66833335", "0.6678019", "0.66422665", "0.66...
0.7034826
9
Rotate the stepper by this angle (radians unless specified) Positive angles rotate clockwise, negative angles rotate counterclockwise
def rotate_by(self, angle, degrees = False): target = angle * pi / 180 if degrees else angle if self.inv: target = -target if target > 0: n = int(target // self.step_size) + 1 for _ in range(n): self.step_c() else: n = int(-target // self.step_size) + 1 for _ in range(n): self.step_cc()...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def rotate_rad(self, angle):\n self.beam_angle += angle\n self.xy = rotate(self.xy, angle)\n self.angle += angle", "def rotate(self, direction):\n electro = pygame.mixer.Sound('resources/Electro_Motor.wav')\n electro.set_volume(0.2)\n self.rotation += min(max(direction, ...
[ "0.7070411", "0.7032392", "0.6987201", "0.6970376", "0.69328016", "0.6915016", "0.6913845", "0.68389475", "0.68369746", "0.682694", "0.6704316", "0.6675216", "0.6641125", "0.66407424", "0.66319656", "0.66140467", "0.65792656", "0.65759706", "0.6568908", "0.6567404", "0.651743...
0.730979
0
Resets the position of the stepper to 0
def zero(self): self.angle = 0.0 self.draw() time.sleep(self.delay)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def reset_position(self):\n self.goto(STARTING_POSITION)", "def reset(self):\n self.steps = 0\n self.state = 0\n self.trajectory = []", "def reset(self):\n self._position = TwoDV(0.0, 0.0)\n self._orient = TNavigator.START_ORIENTATION[self._mode]", "def reset(self):...
[ "0.74105155", "0.72891736", "0.72350556", "0.70255756", "0.70246994", "0.6999789", "0.6948243", "0.6939481", "0.6906952", "0.68637085", "0.68452317", "0.6839638", "0.6813699", "0.68041223", "0.68041223", "0.6704756", "0.6704756", "0.6704756", "0.6691812", "0.66758686", "0.665...
0.6214618
68
Add radio buttons to an `~.axes.Axes`.
def __init__(self, ax, labels, active=0, activecolor='blue', size=49, orientation="vertical", **kwargs): AxesWidget.__init__(self, ax) self.activecolor = activecolor axcolor = ax.get_facecolor() self.value_selected = None ax.set_xticks([]) ax.set...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def radioButton(*args, align: Union[AnyStr, bool]=\"\", annotation: Union[AnyStr, bool]=\"\",\n backgroundColor: Union[List[float, float, float], bool]=None, changeCommand:\n Script=None, collection: AnyStr=\"\", data: Union[int, bool]=0, defineTemplate:\n AnyStr=\"\", ...
[ "0.61411875", "0.5947201", "0.58568066", "0.58213407", "0.5773773", "0.56557375", "0.56341064", "0.56140417", "0.55877143", "0.55544215", "0.55341345", "0.55151653", "0.5453786", "0.5414262", "0.53953147", "0.5390913", "0.538077", "0.5366018", "0.53010714", "0.5281877", "0.52...
0.47138745
80
Initiate the temporal GIS and set the region
def setUpClass(cls): cls.use_temp_region() cls.runModule("g.region", raster="elev_state_500m")
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def initialize_region(self):\n self.new_region_name = \"\"\n self.map.regions.create_new_region()", "def update_temperature_region(self):\n self.linear_region.setRegion(\n self.temperature_plot_graph.getViewBox().viewRange()[0])", "def __init__(self, region):\r\n self.reg...
[ "0.6685489", "0.64153266", "0.6042711", "0.60086167", "0.60021317", "0.59598774", "0.58936393", "0.58936393", "0.5817339", "0.5815601", "0.57746816", "0.5762331", "0.57130456", "0.5645798", "0.5611731", "0.5609597", "0.5601189", "0.55973095", "0.55544496", "0.55544496", "0.55...
0.61658186
2
Remove the temporary region
def tearDownClass(cls): cls.runModule("g.remove", flags="rf", type="vector", name="gbif_poa3") cls.del_temp_region()
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def delete_this_region(self):", "def delete_region(self, region):\n\n self.contour_plot.vb.removeItem(region)\n del self.regions[id(region)]", "def remove():", "def removePick(self):\n self.pnt = None\n vtkRenWin.delMarker(self.renWin)", "def stop_region(self):\n self.reg...
[ "0.7985368", "0.6744757", "0.65855074", "0.6343354", "0.62646586", "0.62146246", "0.6214121", "0.6068292", "0.6062553", "0.6047077", "0.6027426", "0.6016135", "0.5975185", "0.5951818", "0.59466493", "0.58890456", "0.58813125", "0.58796966", "0.5872771", "0.58329594", "0.58293...
0.6391738
3
Show something if there isn't anything happening in the inventory
def test_nothing(self): response = self.client.get(reverse('device-list')) self.assertEqual(response.status_code, 200) self.assertQuerysetEqual(response.context['devices'], [])
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def inventory(self):\n\n #when the item list is 0 , print out having no items \n if len(self.items) == 0:\n \n print('The player has no items')\n\n #if not, print out the item list \n else:\n print(self.items)", "def display_inventory(self):\n h...
[ "0.7831163", "0.77966297", "0.7582839", "0.70266676", "0.6962343", "0.6900891", "0.68886095", "0.686806", "0.6751272", "0.6545909", "0.65149176", "0.64721984", "0.6435862", "0.6420513", "0.6411051", "0.6353434", "0.6313033", "0.63007027", "0.6266621", "0.6233488", "0.6225111"...
0.0
-1
convert csv into numpy
def csv_2_numpy(file, path=INPUT_PATH, sep=',', type='int8'): file_path = path + file reader = csv.reader(open(file_path, "r"), delimiter=sep) x = list(reader) dataset = numpy.array(x).astype(type) return dataset
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def parse(csvfilename):\r\n with open(csvfilename, 'r') as f:\r\n reader = csv.reader(f, delimiter=';')\r\n #reader = csv.reader(f, delimiter=';', quotechar=\"'\")\r\n data = list(reader)\r\n # transform data into numpy array\r\n data = np.array(data).astype(float)\r\n retu...
[ "0.7606892", "0.7327829", "0.727883", "0.7161398", "0.71550566", "0.69989276", "0.69635636", "0.68933666", "0.6836764", "0.6802852", "0.6801808", "0.67944103", "0.6787268", "0.67241", "0.6684425", "0.66639805", "0.6646328", "0.6636542", "0.6630825", "0.658941", "0.6581193", ...
0.81163687
0
convert numpy into csv file , for ID(libra) algothim
def numpy_2_file(narray, file, path=OUTPUT_PATH, sep=',' ): file_path = path + file narrayc = numpy.copy(narray) numpy.place(narrayc,numpy.logical_or(narrayc==-1,narrayc==-2), 2) dataset = numpy.copy(narrayc).astype(str) numpy.place(dataset,dataset=='2', '*') d=numpy.atleast_2d(dataset) nump...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def produce_solution(y):\n\n with open('out.csv', 'w', newline='') as csvfile:\n writer = csv.writer(csvfile, delimiter=',', lineterminator=\"\\n\")\n writer.writerow(['id', 'y'])\n for i in range(y.shape[0]):\n writer.writerow([i, y[i]])", "def write_csv_file(array, filename):...
[ "0.66480744", "0.66200095", "0.63522464", "0.6301102", "0.6266639", "0.6256515", "0.62520635", "0.6203554", "0.6200669", "0.6179599", "0.6171765", "0.61589", "0.61375374", "0.61303294", "0.61182237", "0.60997707", "0.6094575", "0.6092054", "0.6079023", "0.6075535", "0.6071643...
0.5817157
61