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text-code-galeras-code-generation-from-docstring-3k-deduped

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{ "docstring": "Swap the two given rows of the matrix in-place.\n\n Examples\n ========\n\n >>> from sympy import Matrix\n >>> M = Matrix([[0, 1], [1, 0]])\n >>> M\n Matrix([\n [0, 1],\n [1, 0]])\n >>> M.row_swap(0, 1)\n >>> M\n Matrix([\n [1, 0],\n [0, 1]])\n\n See Also\n ========\n\n row\n col_swap\n ", "language": "en", "n_whitespaces": 171, "n_words": 45, "vocab_size": 31 }

{ "docstring": "Creates a bipartite graph for use in testing matching algorithms.\n\n The bipartite graph has a maximum cardinality matching that leaves\n vertex 1 and vertex 10 unmatched. The first six numbers are the left\n vertices and the next six numbers are the right vertices.\n\n ", "language": "en", "n_whitespaces": 71, "n_words": 43, "vocab_size": 31 }

{ "docstring": "Invoke user function and return trace function for call event.\n\n If the debugger stops on this function call, invoke\n self.user_call(). Raise BdbQuit if self.quitting is set.\n Return self.trace_dispatch to continue tracing in this scope.\n ", "language": "en", "n_whitespaces": 62, "n_words": 34, "vocab_size": 31 }

{ "docstring": "\n Run a function in a sub-process.\n\n Parameters\n ----------\n func : function\n The function to be run. It must be in a module that is importable.\n *args : str\n Any additional command line arguments to be passed in\n the first argument to ``subprocess.run``.\n extra_env : dict[str, str]\n Any additional environment variables to be set for the subprocess.\n ", "language": "en", "n_whitespaces": 107, "n_words": 56, "vocab_size": 39 }

{ "docstring": "\n Parameters: State\n Returns: Manhattan Distance between Current State and Goal State\n Restrictions: State must be a self.size x self.size Array\n ", "language": "en", "n_whitespaces": 49, "n_words": 20, "vocab_size": 16 }

{ "docstring": "One of our source files changed. Schedule a rerun if appropriate.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }

{ "docstring": "\n Generates a new IntegerPartition object from a list or dictionary.\n\n Explanation\n ===========\n\n The partition can be given as a list of positive integers or a\n dictionary of (integer, multiplicity) items. If the partition is\n preceded by an integer an error will be raised if the partition\n does not sum to that given integer.\n\n Examples\n ========\n\n >>> from sympy.combinatorics.partitions import IntegerPartition\n >>> a = IntegerPartition([5, 4, 3, 1, 1])\n >>> a\n IntegerPartition(14, (5, 4, 3, 1, 1))\n >>> print(a)\n [5, 4, 3, 1, 1]\n >>> IntegerPartition({1:3, 2:1})\n IntegerPartition(5, (2, 1, 1, 1))\n\n If the value that the partition should sum to is given first, a check\n will be made to see n error will be raised if there is a discrepancy:\n\n >>> IntegerPartition(10, [5, 4, 3, 1])\n Traceback (most recent call last):\n ...\n ValueError: The partition is not valid\n\n ", "language": "en", "n_whitespaces": 307, "n_words": 138, "vocab_size": 80 }

{ "docstring": "\n Warn on usage of ci= and convert to appropriate errorbar= arg.\n\n ci was deprecated when errorbar was added in 0.12. It should not be removed\n completely for some time, but it can be moved out of function definitions\n (and extracted from kwargs) after one cycle.\n\n ", "language": "en", "n_whitespaces": 61, "n_words": 45, "vocab_size": 42 }

{ "docstring": "\n Open a new element. Attributes can be given as keyword\n arguments, or as a string/string dictionary. The method returns\n an opaque identifier that can be passed to the :meth:`close`\n method, to close all open elements up to and including this one.\n\n Parameters\n ----------\n tag\n Element tag.\n attrib\n Attribute dictionary. Alternatively, attributes can be given as\n keyword arguments.\n\n Returns\n -------\n An element identifier.\n ", "language": "en", "n_whitespaces": 182, "n_words": 62, "vocab_size": 50 }

{ "docstring": "Extract information from the provided wheel, ensuring it meets basic\n standards.\n\n Returns the name of the .dist-info directory and the parsed WHEEL metadata.\n ", "language": "en", "n_whitespaces": 32, "n_words": 23, "vocab_size": 20 }

{ "docstring": "\n Recursively reduce the tensors in a nested list/tuple/dictionary of lists of tensors across all processes by the\n mean of a given operation.\n\n Args:\n tensor (nested list/tuple/dictionary of `torch.Tensor`):\n The data to reduce.\n reduction (`str`, *optional*, defaults to `\"mean\"`):\n A reduction method. Can be of \"mean\", \"sum\", or \"none\"\n\n Returns:\n The same data structure as `data` with all the tensors reduced.\n ", "language": "en", "n_whitespaces": 119, "n_words": 60, "vocab_size": 45 }

{ "docstring": "\n Generates a `pytest.mark.parametrize` instance for the `task_runner` indirect\n fixture.\n\n Passes marks from the fixtures to the parameter so we can indicate required services\n on each task runner fixture.\n ", "language": "en", "n_whitespaces": 44, "n_words": 28, "vocab_size": 25 }

{ "docstring": "\n login_required works on a simple view wrapped in a login_required\n decorator.\n ", "language": "en", "n_whitespaces": 33, "n_words": 11, "vocab_size": 9 }

{ "docstring": "Retrieve the ``Estimator`` stored in this checkpoint.", "language": "en", "n_whitespaces": 6, "n_words": 7, "vocab_size": 7 }

{ "docstring": "\n Compute what the p_mean_variance output would have been, should the\n model's score function be conditioned by cond_fn.\n\n See condition_mean() for details on cond_fn.\n\n Unlike condition_mean(), this instead uses the conditioning strategy\n from Song et al (2020).\n ", "language": "en", "n_whitespaces": 79, "n_words": 36, "vocab_size": 33 }

{ "docstring": "Test directed graph for strong connectivity.\n\n A directed graph is strongly connected if and only if every vertex in\n the graph is reachable from every other vertex.\n\n Parameters\n ----------\n G : NetworkX Graph\n A directed graph.\n\n Returns\n -------\n connected : bool\n True if the graph is strongly connected, False otherwise.\n\n Examples\n --------\n >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 0), (2, 4), (4, 2)])\n >>> nx.is_strongly_connected(G)\n True\n >>> G.remove_edge(2, 3)\n >>> nx.is_strongly_connected(G)\n False\n\n Raises\n ------\n NetworkXNotImplemented\n If G is undirected.\n\n See Also\n --------\n is_weakly_connected\n is_semiconnected\n is_connected\n is_biconnected\n strongly_connected_components\n\n Notes\n -----\n For directed graphs only.\n Connectivity is undefined for the null graph.", "language": "en", "n_whitespaces": 211, "n_words": 104, "vocab_size": 73 }

{ "docstring": "This is mostly duplicated code from the discover dataset version\n TODO: try to make this more DRY with the discover version\n ", "language": "en", "n_whitespaces": 35, "n_words": 21, "vocab_size": 18 }

{ "docstring": "\n Set the resolution of parent figure in dots-per-inch.\n \n Parameters\n ----------\n val : float\n ", "language": "en", "n_whitespaces": 57, "n_words": 13, "vocab_size": 13 }

{ "docstring": "Construct a source file from 'body' (a string containing lines\n of C/C++ code) and 'headers' (a list of header files to include)\n and run it through the preprocessor. Return true if the\n preprocessor succeeded, false if there were any errors.\n ('body' probably isn't of much use, but what the heck.)\n ", "language": "en", "n_whitespaces": 86, "n_words": 50, "vocab_size": 43 }

{ "docstring": "\n try:\n pass\n except Exception, a.foo:\n pass\n try:\n pass\n except Exception as xxx_todo_changeme:\n a.foo = xxx_todo_changeme\n pass", "language": "en", "n_whitespaces": 135, "n_words": 16, "vocab_size": 11 }

{ "docstring": "coronary artery disease ( cad ) is the emerging cause of morbidity and mortality in developing world . \\n it provides an excellent resolution for visualization of the coronary arteries for catheter - based or operating interventions . \\n\n although the association of this technique with major complications such as mortality is highly uncommon , it is frequently associated with various cardiac and noncardiac complications . computed tomography ( ct ) coronary angiography is\n a promising technique for the evaluation of cad noninvasively . \\n it assesses disease within the coronary artery and provides qualitative and quantitative information about nonobstructive atherosclerotic plaque burden within the vessel\n wall . \\n thus , ct angiography - based disease evaluation may provide clinically more significant information than conventional angiography . the introduction of multi - slice computed tomography ( msct ) technology such as 64-slice , 12\n 8-slice , 256-slice , and now 320-slice msct has produced a high diagnostic accuracy of ct coronary angiography . \\n it has consistently showed to have a very high negative predictive value ( well above 90% ) in ruling out patients with s\n ignificant cad defined as coronary luminal stenosis of > 50% . \\n the american college of cardiology / american heart association recommends that coronary angiography should be performed before valve surgery in men aged > 40 years , women\n aged > 35 years with coronary risk factors and in postmenopausal women . \\n the prevalence of cad in patients undergoing valve replacement is 2040% in developed countries . in the previous studies , \\n the incidence of angiographically p\n roven cad in acquired valvular diseases has been shown to vary widely from 9% to 41% . in aortic stenosis , \\n we aimed to report the diagnostic performance of 128-slice ct coronary angiography in 50 patients undergoing for major noncoron\n ary cardiac surgery referred for diagnostic invasive coronary angiography to assess the extent and severity of coronary stenosis . \\n during january 2013 to december 2014 , we enrolled fifty major noncoronary cardiac surgery patients sche\n duled for invasive coronary angiography who fulfilled the following inclusion criteria of age 40 years , having low or intermediate probability of cad , left ventricular ejection fraction ( lvef ) > 35% , and patient giving informed conse\n nt for undergoing msct and conventional coronary angiography . \\n those having any contraindication for contrast injection , lvef < 35% , high pretest probability of cad , and hemodynamic instability were excluded from the study . \\n pati\n ents with heart rates of > 70 bpm received ( unless they had known overt heart failure or electrocardiogram ( ecg ) atrioventricular conduction abnormalities ) a single oral dose of 100 mg metoprolol 45 min before the scan . \\n patients w\n ith heart rates of > 80 bpm received an additional oral dose of metoprolol if not contraindicated . \\n all patients were scanned with a 128-slice ct scanner ( siemens , somatom definition as ) equipped with a new feature in msct technolog\n y , so - called z - axis flying - focus technology . \\n the central 32 detector rows acquire 0.6-mm slices , and the flying - focus spot switches back and forth between 2 z positions between each reading . \\n two slices per detector row a\n re acquired , which results in a higher oversampling rate in the z - axis , thereby reducing artifacts related to the spiral acquisition and improving spatial resolution down to 0.4 mm . \\n a bolus of 6580 ml contrast material ( omnipaque\n ) was injected through an arm vein at a flow rate of 5 ml / s . \\n a bolus tracking technique was used to synchronize the arrival of contrast in the coronary arteries with the initiation of the scan . to monitor the arrival of contrast m\n aterial , \\n axial scans were obtained at the level of the ascending aorta with a delay of 10 s after the start of the contrast injection . \\n the scan was automatically started when a threshold of 150 hounsfield units was reached in a re\n gion of interest positioned in the ascending aorta . \\n images were reconstructed with ecg gating to obtain optimal , motion - free image quality . \\n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a s\n ingle observer unaware of the multi - slice ct results identified coronary lesion as a single vessel , double vessel , or triple vessel disease . \\n all lesion , regardless of size , were included for comparison with ct coronary angiograp\n hy . \\n lesions were classified as having nonsignificant disease ( luminal irregularities or < 50% stenosis ) or as having significant stenosis . \\n stenosis was evaluated in two orthogonal views and classified as significant if the mean\n lumen diameter reduction was 50% using a validated quantitative coronary angiography ( qca ) . \\n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiograp\n hy . \\n total calcium scores of all patients were calculated with dedicated software and expressed as agatston scores . \\n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of th\n e number , areas , and peak hounsfield units of the detected calcified lesions . \\n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \\n maximum intensity projections were\n used to identify coronary lesions and ( curved ) multiplanar reconstructions to classify lesions as significant or nonsignificant . \\n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \\n the di\n agnostic performance of ct coronary angiography for the detection of significant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and\n positive and negative likelihood ratios with the corresponding exact 95% of confidence interval ( cis ) . \\n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease p\n er vessel ) , and patient by patient ( no or any disease per patient ) . \\n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a single observer unaware of the multi - slice ct results identified coronary\n lesion as a single vessel , double vessel , or triple vessel disease . \\n all lesion , regardless of size , were included for comparison with ct coronary angiography . \\n lesions were classified as having nonsignificant disease ( luminal\n irregularities or < 50% stenosis ) or as having significant stenosis . \\n stenosis was evaluated in two orthogonal views and classified as significant if the mean lumen diameter reduction was 50% using a validated quantitative coronary an\n giography ( qca ) . \\n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiography . \\n total calcium scores of all patients were calculated with dedicated\n software and expressed as agatston scores . \\n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of the number , areas , and peak hounsfield units of the detected calcified lesi\n ons . \\n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \\n maximum intensity projections were used to identify coronary lesions and ( curved ) multiplanar reconstruction\n s to classify lesions as significant or nonsignificant . \\n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \\n the diagnostic performance of ct coronary angiography for the detection of signif\n icant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and positive and negative likelihood ratios with the corresponding exact 95% of\n confidence interval ( cis ) . \\n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease per vessel ) , and patient by patient ( no or any disease per patient ) . \\n\n in this study , 29 ( 58% ) subjects were female , and 21 ( 42% ) were male showing an average age of 50.36 8.39 years . \\n of fifty patients 24 ( 48% ) , 13 ( 26% ) , eight ( 16% ) , and five ( 10% ) underwent mitral valve replacement ,\n double valve replacement ( dvr ) , aortic valve replacement , and other surgeries , respectively . \\n high distribution of cad risk factors such as hypertension ( 24% ) , smoking ( 22% ) , and dyslipidemia ( 18% ) was observed in the stu\n dy group . \\n the mean creatinine level was 0.766 0.17 and average dye used in conventional angiography was 48.5 26.6 whereas for ct angiography it was 72.8 6.32 . \\n average radiation dose in conventional coronary angiography and msct\n coronary angiography was 5.2 msv and 9.2 msv , respectively . \\n the majority of the patients had sinus rhythm ( 68% ) , whereas atrial fibrillation was found in 32% of the subjects . \\n patients included in the study had low to intermed\n iate probability of cad . in this study , three patients had complications after conventional angiography . \\n complications were of local site hematoma , acute kidney injury managed conservatively , and acute heart failure . \\n a patient\n who developed hematoma was obese female patients with body mass index > 30 kg / m . \\n the patient suffered from pseudoaneurysm , had hospitalized for 9 days , which leads to increased morbidity and cost of hospital stay . \\n the diagnos\n tic accuracy of ct coronary angiography was evaluated regarding true positive , true negative values and is presented in table 1 . the overall sensitivity and \\n specificity of ct angiography technique was 100% ( 95% ci : 39.76%100% ) and\n 91.30% ( 95% ci : 79.21%97.58% ) , respectively [ table 2 ] . \\n the positive predictive value ( 50% ; 95% ci : 15.70%84.30% ) and negative predictive value ( 100% ; 95% ci : 91.59%100% ) of ct angiography were also fairly high in these\n patients . \\n recent reports from multiple studies demonstrated that recent - generation msct scanners showed promise for noninvasive detection of coronary stenosis however , until now no studies were found regarding the clinical efficacy\n or prognostic value of 128-slice ct coronary angiography versus conventional invasive coronary angiography in the diagnosis of patients planned for major noncoronary surgeries such as dvr , bentall , atrial septal defect closure , etc .\n in our study , we reported 8% cad prevalence in patients planned for major noncoronary cardiac surgery . \\n we performed conventional and msct coronary angiography in all patients and the results showed that ct coronary angiography with i\n nvasive coronary angiography as the reference standard had a considerably high sensitivity ( 100% ) and specificity ( 95.65% ) . \\n the health economic model using invasive coronary angiography as the reference standard showed that at a p\n retest probability of cad of 70% or lower , ct coronary angiography resulted in lower cost per patient with a true positive diagnosis . at a pretest probability of cad of 70% or higher , invasive coronary angiography was associated with a\n lower cost per patient with a true positive diagnosis . in our study population , \\n two patients developed local site complications in the form of hematoma and pseudoaneurysm after conventional angiography . \\n hence , msct coronary ang\n iography will be more favorable in female obese patients with intermediate likelihood of cad . \\n hence , msct coronary angiography will be cost - effective in patients of valvular heart diseases . \\n however , ct angiography suffers from\n a drawback that average amount of dye used in msct coronary angiography were 72.8 6.32 ml which is higher than average amount of dye required for conventional angiography ( 48.6 26.6 ml ) . \\n hence , the use of ct coronary angiography\n could not be used in patients with known renal dysfunction , where reduction of contrast dye load is highly advocated . \\n our results show that 128-slice ct coronary angiography is a reliable technique to detect coronary stenosis in pat\n ients planned for noncoronary cardiac surgery . \\n although there has been important technological progress in the development of ct coronary angiography , its clinical application remains limited . \\n a study wth large numbers of patient\n s is required for the recommendation of only ct coronary angiography for the coronary evaluation in major non - cardiac surgeries . \\n mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , guja\n rat , india ) . \\n u.n . mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , gujarat , india ) . \\n ", "language": "en", "n_whitespaces": 2837, "n_words": 2237, "vocab_size": 651 }

{ "docstring": "\n fetches daily sector prices for S&P500 for a fixed time period\n\n Parameters\n ----------\n start_date : str ('yyyy-mm-dd') or datetime.date\n start date for fetching data\n end_date : str ('yyyy-mm-dd') or datetime.date\n end date for fetching data\n\n Returns\n -------\n sp500_tickers_data : Dictionary\n dictionary of dataframes with SPY daily sector prices\n ", "language": "en", "n_whitespaces": 97, "n_words": 48, "vocab_size": 33 }

{ "docstring": "Check the equivalence of the components found by PCA and SparsePCA.\n\n Non-regression test for:\n https://github.com/scikit-learn/scikit-learn/issues/23932\n ", "language": "en", "n_whitespaces": 24, "n_words": 15, "vocab_size": 14 }

{ "docstring": "\n Can be used as a decorator to send an event only if HAYSTACK_EXECUTION_CONTEXT is \"public_demo\"\n ", "language": "en", "n_whitespaces": 22, "n_words": 15, "vocab_size": 15 }

{ "docstring": "Ensure that the batch_dim is removed when saving.\n\n When serving or retraining, it is important to reset the batch dim.\n This can be an issue inside of tf.function. See b/132783590 for context.\n ", "language": "en", "n_whitespaces": 53, "n_words": 32, "vocab_size": 30 }

{ "docstring": "Test a user (not team) installation where the user has hidden their username from public view", "language": "en", "n_whitespaces": 15, "n_words": 16, "vocab_size": 15 }

{ "docstring": "\n Build Reduce operator that will be performed across rows/columns.\n\n It's used if `func` reduces the dimension of partitions in contrast to `Fold`.\n\n Parameters\n ----------\n reduce_function : callable(pandas.DataFrame) -> pandas.Series\n Source function.\n axis : int, optional\n Axis to apply function along.\n\n Returns\n -------\n callable\n Function that takes query compiler and executes Reduce function.\n ", "language": "en", "n_whitespaces": 156, "n_words": 52, "vocab_size": 47 }

{ "docstring": "This is to test if helpful information is displayed when\n the objects captured in trainable/training function are too\n large and RESOURCES_EXHAUSTED error of gRPC is triggered.", "language": "en", "n_whitespaces": 39, "n_words": 26, "vocab_size": 24 }

{ "docstring": "\n For each *key, value* pair in *kwargs*, check that *value* has the shape\n *_shape*, if not, raise an appropriate ValueError.\n\n *None* in the shape is treated as a \"free\" size that can have any length.\n e.g. (None, 2) -> (N, 2)\n\n The values checked must be numpy arrays.\n\n Examples\n --------\n To check for (N, 2) shaped arrays\n\n >>> _api.check_shape((None, 2), arg=arg, other_arg=other_arg)\n ", "language": "en", "n_whitespaces": 93, "n_words": 62, "vocab_size": 54 }

{ "docstring": "Returns the requested content back in unicode.\n\n :param r: Response object to get unicode content from.\n\n Tried:\n\n 1. charset from content-type\n 2. fall back and replace all unicode characters\n\n :rtype: str\n ", "language": "en", "n_whitespaces": 49, "n_words": 31, "vocab_size": 28 }

{ "docstring": "\n Renders this Graph as PDF, if `graphviz` is installed.\n\n :param filename: A filename for the rendered PDF.\n :param view: If True, rendered file will be opened.\n ", "language": "en", "n_whitespaces": 55, "n_words": 26, "vocab_size": 24 }

{ "docstring": "\n tasks are especially complex arguments so need pre-processing.\n keep it short.\n ", "language": "en", "n_whitespaces": 33, "n_words": 11, "vocab_size": 11 }

{ "docstring": "\n Validate that a custom ChangeList class can be used (#9749)\n ", "language": "en", "n_whitespaces": 25, "n_words": 10, "vocab_size": 10 }

{ "docstring": "\n Returns the first derivative of this function.\n ", "language": "en", "n_whitespaces": 22, "n_words": 7, "vocab_size": 7 }

{ "docstring": "\n Calculate year, week, and day according to the ISO 8601 standard.\n\n .. versionadded:: 1.1.0\n\n Returns\n -------\n DataFrame\n With columns year, week and day.\n\n See Also\n --------\n Timestamp.isocalendar : Function return a 3-tuple containing ISO year,\n week number, and weekday for the given Timestamp object.\n datetime.date.isocalendar : Return a named tuple object with\n three components: year, week and weekday.\n\n Examples\n --------\n >>> ser = pd.to_datetime(pd.Series([\"2010-01-01\", pd.NaT]))\n >>> ser.dt.isocalendar()\n year week day\n 0 2009 53 5\n 1 <NA> <NA> <NA>\n >>> ser.dt.isocalendar().week\n 0 53\n 1 <NA>\n Name: week, dtype: UInt32\n ", "language": "en", "n_whitespaces": 293, "n_words": 88, "vocab_size": 64 }

{ "docstring": "\n Checks if a field is solenoidal.\n\n Parameters\n ==========\n\n field : Vector\n The field to check for solenoidal property\n\n Examples\n ========\n\n >>> from sympy.physics.vector import ReferenceFrame\n >>> from sympy.physics.vector import is_solenoidal\n >>> R = ReferenceFrame('R')\n >>> is_solenoidal(R[1]*R[2]*R.x + R[0]*R[2]*R.y + R[0]*R[1]*R.z)\n True\n >>> is_solenoidal(R[1] * R.y)\n False\n\n ", "language": "en", "n_whitespaces": 96, "n_words": 46, "vocab_size": 36 }

{ "docstring": "Check if both axis may be scrolled.\n\n Returns:\n bool: True if horizontal and vertical scrolling is enabled.\n ", "language": "en", "n_whitespaces": 42, "n_words": 17, "vocab_size": 16 }

{ "docstring": "\n Tests for a bug in which constrained layout and align_ylabels on\n three unevenly sized subplots, one of whose y tick labels include\n negative numbers, drives the non-negative subplots' y labels off\n the edge of the plot\n ", "language": "en", "n_whitespaces": 52, "n_words": 36, "vocab_size": 31 }

{ "docstring": "\n Display a tag, optionally linked to a filtered list of objects.\n\n Args:\n value: A Tag instance\n viewname: If provided, the tag will be a hyperlink to the specified view's URL\n ", "language": "en", "n_whitespaces": 54, "n_words": 30, "vocab_size": 26 }

{ "docstring": " Forward function.\n Args:\n x: input features with shape of (num_windows*B, N, C)\n mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None\n ", "language": "en", "n_whitespaces": 60, "n_words": 23, "vocab_size": 20 }

{ "docstring": "\n Handle a ManyToManyField. Related objects are only serialized as\n references to the object's PK (i.e. the related *data* is not dumped,\n just the relation).\n ", "language": "en", "n_whitespaces": 53, "n_words": 24, "vocab_size": 22 }

{ "docstring": "\n Test that a filename can be optionally added to the end of the URL.\n ", "language": "en", "n_whitespaces": 29, "n_words": 14, "vocab_size": 13 }

{ "docstring": "Decorator for common logic related to managing temporary\n directories.\n ", "language": "en", "n_whitespaces": 15, "n_words": 9, "vocab_size": 9 }

{ "docstring": "Return 'pathname' as a name that will work on the native filesystem.\n\n The path is split on '/' and put back together again using the current\n directory separator. Needed because filenames in the setup script are\n always supplied in Unix style, and have to be converted to the local\n convention before we can actually use them in the filesystem. Raises\n ValueError on non-Unix-ish systems if 'pathname' either starts or\n ends with a slash.\n ", "language": "en", "n_whitespaces": 96, "n_words": 73, "vocab_size": 60 }

{ "docstring": "Wrap output with container based on an estimator's or global config.\n\n Parameters\n ----------\n method : {\"transform\"}\n Estimator's method to get container output for.\n\n data_to_wrap : {ndarray, dataframe}\n Data to wrap with container.\n\n original_input : {ndarray, dataframe}\n Original input of function.\n\n estimator : estimator instance\n Estimator with to get the output configuration from.\n\n Returns\n -------\n output : {ndarray, dataframe}\n If the output config is \"default\" or the estimator is not configured\n for wrapping return `data_to_wrap` unchanged.\n If the output config is \"pandas\", return `data_to_wrap` as a pandas\n DataFrame.\n ", "language": "en", "n_whitespaces": 173, "n_words": 87, "vocab_size": 55 }

{ "docstring": "Check if the point is at the origin (0, 0).\n\n Returns:\n bool: True if the offset is the origin.\n\n ", "language": "en", "n_whitespaces": 44, "n_words": 19, "vocab_size": 14 }

{ "docstring": "\n Return how much space should be reserved for text above and below the\n Axes, as a pair of floats.\n ", "language": "en", "n_whitespaces": 41, "n_words": 19, "vocab_size": 19 }

{ "docstring": "\n Create (synchronously) a mask that extracts the indices provided.\n\n Parameters\n ----------\n row_indices : list-like, slice or label\n The row labels for the rows to extract.\n col_indices : list-like, slice or label\n The column labels for the columns to extract.\n\n Returns\n -------\n PandasOnDaskDataframeVirtualPartition\n A new ``PandasOnDaskDataframeVirtualPartition`` object,\n materialized.\n ", "language": "en", "n_whitespaces": 155, "n_words": 47, "vocab_size": 35 }

{ "docstring": ":return: config path shared by the users. Only return first item, even if ``multipath`` is set to ``True``", "language": "en", "n_whitespaces": 17, "n_words": 18, "vocab_size": 18 }

{ "docstring": "Send the notification about starting or ending sales.", "language": "en", "n_whitespaces": 7, "n_words": 8, "vocab_size": 8 }

{ "docstring": "Return true if the object is an asynchronous generator function.\n\n Asynchronous generator functions are defined with \"async def\"\n syntax and have \"yield\" expressions in their body.\n ", "language": "en", "n_whitespaces": 35, "n_words": 26, "vocab_size": 25 }

{ "docstring": "\n Parse known args to ignore help flag.\n ", "language": "en", "n_whitespaces": 22, "n_words": 7, "vocab_size": 7 }

{ "docstring": "\n Callback called when the operator is killed.\n Cancel any running job.\n ", "language": "en", "n_whitespaces": 33, "n_words": 11, "vocab_size": 11 }

{ "docstring": "Print 'msg' to stdout if the global DEBUG (taken from the\n DISTUTILS_DEBUG environment variable) flag is true.\n ", "language": "en", "n_whitespaces": 31, "n_words": 17, "vocab_size": 16 }

{ "docstring": "Tests if large arguments are can be serialized by the Trainer.", "language": "en", "n_whitespaces": 10, "n_words": 11, "vocab_size": 11 }

{ "docstring": "\n The 'lataxis' property is an instance of Lataxis\n that may be specified as:\n - An instance of :class:`plotly.graph_objs.layout.geo.Lataxis`\n - A dict of string/value properties that will be passed\n to the Lataxis constructor\n\n Supported dict properties:\n\n dtick\n Sets the graticule's longitude/latitude tick\n step.\n gridcolor\n Sets the graticule's stroke color.\n gridwidth\n Sets the graticule's stroke width (in px).\n range\n Sets the range of this axis (in degrees), sets\n the map's clipped coordinates.\n showgrid\n Sets whether or not graticule are shown on the\n map.\n tick0\n Sets the graticule's starting tick\n longitude/latitude.\n\n Returns\n -------\n plotly.graph_objs.layout.geo.Lataxis\n ", "language": "en", "n_whitespaces": 454, "n_words": 91, "vocab_size": 63 }

{ "docstring": "\n Return a token that can be used once to do a password reset\n for the given user.\n ", "language": "en", "n_whitespaces": 39, "n_words": 17, "vocab_size": 16 }

{ "docstring": "Get curses data to display for a process.\n\n - p is the process to display\n - selected is a tag=True if p is the selected process\n ", "language": "en", "n_whitespaces": 47, "n_words": 26, "vocab_size": 16 }

{ "docstring": "Test availability templates with values from other entities.", "language": "en", "n_whitespaces": 7, "n_words": 8, "vocab_size": 8 }

{ "docstring": "\n Ensure that the built in delete button supports a next_url provided.\n ", "language": "en", "n_whitespaces": 26, "n_words": 11, "vocab_size": 11 }

{ "docstring": "Returns status information about the cluster.\n\n Currently contains two fields:\n autoscaling_status (str)-- a status message from the autoscaler.\n autoscaling_error (str)-- an error message from the autoscaler if\n anything has gone wrong during autoscaling.\n\n These fields are both read from the GCS, it's expected that the\n autoscaler writes them there.\n ", "language": "en", "n_whitespaces": 114, "n_words": 49, "vocab_size": 39 }

{ "docstring": "\n Return a :class:`Piecewise` with exclusive conditions, i.e., where exactly\n one condition is True.\n\n SymPy normally represents the condition in an \"if-elif\"-fashion, which\n leads to that more than one condition can be True. This is sometimes not\n wanted when representing the :class:`Piecewise` mathematically.\n\n Note that further manipulation of the resulting :class:`Piecewise`, e.g.\n simplifying it, will most likely make it non-exclusive. Hence, this is\n primarily a function to be used in conjunction with printing the Piecewise\n or if one would like to reorder the expression-condition pairs.\n\n ``piecewise_exclusive`` will also explicitly add a final\n :class:`~sympy.core.numbers.NaN` segment to the :class:`Piecewise`, unless\n all cases are covered. This can be avoided by passing ``skip_nan=True`` as\n a final argument. It can also be used in some situations where SymPy cannot\n determine that all cases are covered.\n\n Examples\n ========\n >>> from sympy import piecewise_exclusive, Symbol, Piecewise, S\n >>> x = Symbol('x', real=True)\n >>> p = Piecewise((0, x < 0), (S.Half, x <= 0), (1, True))\n >>> piecewise_exclusive(p)\n Piecewise((0, x < 0), (1/2, Eq(x, 0)), (1, x > 0))\n >>> piecewise_exclusive(Piecewise((2, x > 1)))\n Piecewise((2, x > 1), (nan, x <= 1))\n >>> piecewise_exclusive(Piecewise((2, x > 1)), skip_nan=True)\n Piecewise((2, x > 1))\n\n ", "language": "en", "n_whitespaces": 272, "n_words": 193, "vocab_size": 124 }

{ "docstring": "Return the unit of measurement used by the platform.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 8 }

{ "docstring": "\nThe error arose for the nse argument of bcoo_fromdense. In order for BCOO.fromdense()\nto be used in traced/compiled code, you must pass a concrete value to the nse\n(number of specified elements) argument.\n", "language": "en", "n_whitespaces": 30, "n_words": 33, "vocab_size": 28 }

{ "docstring": "\n Add slashes before quotes. Useful for escaping strings in CSV, for\n example. Less useful for escaping JavaScript; use the ``escapejs``\n filter instead.\n ", "language": "en", "n_whitespaces": 35, "n_words": 22, "vocab_size": 19 }

{ "docstring": "Return a dictionary of the available cloud provider plugins.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

{ "docstring": "\n tests that given an valid ids in the form of a list that the boto_elb\n deregister_instances all members of the given list\n ", "language": "en", "n_whitespaces": 44, "n_words": 22, "vocab_size": 16 }

{ "docstring": "Return whether or not the event offset was reached.", "language": "en", "n_whitespaces": 8, "n_words": 9, "vocab_size": 9 }

{ "docstring": "\n Validate that the expression can be used to order a query\n ", "language": "en", "n_whitespaces": 26, "n_words": 11, "vocab_size": 11 }

{ "docstring": "\n Set the global options for extract\n ", "language": "en", "n_whitespaces": 21, "n_words": 6, "vocab_size": 6 }

{ "docstring": "\n Loads saved tracks from Spotify.\n Will throw an exception if users is not logged in.\n ", "language": "en", "n_whitespaces": 37, "n_words": 15, "vocab_size": 15 }

{ "docstring": "Print help\nOverview Menu:\n\nCoinGecko:\n cgglobal global crypto market info\n cgnews last news available on CoinGecko\n cgdefi global DeFi market info\n cgstables stablecoins\n cgnft non fungible token market status\n cgnftday non fungible token of the day\n cgexchanges top crypto exchanges\n cgexrates coin exchange rates\n cgplatforms crypto financial platforms\n cgproducts crypto financial products\n cgindexes crypto indexes\n cgderivatives crypto derivatives\n cgcategories crypto categories\n cghold ethereum, bitcoin holdings overview statistics\nCoinPaprika:\n cpglobal global crypto market info\n cpinfo basic info about all coins available on CoinPaprika\n cpmarkets market related info about all coins available on CoinPaprika\n cpexchanges list all exchanges\n cpexmarkets all available markets on given exchange\n cpplatforms list blockchain platforms eg. ethereum, solana, kusama, terra\n cpcontracts all smart contracts for given platform\nCoinbase:\n cbpairs info about available trading pairs on Coinbase\nCryptoPanic:\n news recent crypto news from CryptoPanic aggregator\nWithdrawalFees:\n wf overall withdrawal fees\n ewf overall exchange withdrawal fees\n wfpe crypto withdrawal fees per exchange\nBlockchainCenter:\n altindex displays altcoin season index (if 75% of top 50 coins perform better than btc)\n", "language": "en", "n_whitespaces": 482, "n_words": 168, "vocab_size": 110 }

{ "docstring": "\n\t\tSELECT\n\t\t\tso_item.item_code,\n\t\t\tso_item.item_name,\n\t\t\tso_item.description,\n\t\t\tso.name,\n\t\t\tso.transaction_date,\n\t\t\tso.customer,\n\t\t\tso.territory,\n\t\t\tsum(so_item.qty) as total_qty,\n\t\t\tso.company\n\t\tFROM `tabSales Order` so, `tabSales Order Item` so_item\n\t\tWHERE\n\t\t\tso.docstatus = 1\n\t\t\tand so.name = so_item.parent\n\t\t\tand so.status not in (\"Closed\",\"Completed\",\"Cancelled\")\n\t\tGROUP BY\n\t\t\tso.name,so_item.item_code\n\t\t", "language": "en", "n_whitespaces": 20, "n_words": 36, "vocab_size": 33 }

{ "docstring": "\nquery{\n products(first:100){\n edges{\n node{\n id\n }\n }\n }\n}\nsubscription{\n event{\n ...on ProductUpdated{\n product{\n id\n }\n }\n }\n}", "language": "en", "n_whitespaces": 65, "n_words": 19, "vocab_size": 11 }

{ "docstring": "Same as sample() but returns the count as a separate value.\n\n Returns:\n A columnar batch of experiences (e.g., tensors) and the\n size of the collected batch.\n\n Examples:\n >>> import gym\n >>> from ray.rllib.evaluation.rollout_worker import RolloutWorker\n >>> from ray.rllib.algorithms.pg.pg_tf_policy import PGTF1Policy\n >>> worker = RolloutWorker( # doctest: +SKIP\n ... env_creator=lambda _: gym.make(\"CartPole-v0\"), # doctest: +SKIP\n ... policy_spec=PGTFPolicy) # doctest: +SKIP\n >>> print(worker.sample_with_count()) # doctest: +SKIP\n (SampleBatch({\"obs\": [...], \"action\": [...], ...}), 3)\n ", "language": "en", "n_whitespaces": 209, "n_words": 70, "vocab_size": 48 }

{ "docstring": "Generates and validates a set of config key/value pairs (passed via kwargs).\n\n Validation whether given config keys are valid is done immediately upon\n construction (by comparing against the properties of a default AlgorithmConfig\n object of this class).\n Allows combination with a full AlgorithmConfig object to yield a new\n AlgorithmConfig object.\n\n Used anywhere, we would like to enable the user to only define a few config\n settings that would change with respect to some main config, e.g. in multi-agent\n setups and evaluation configs.\n\n Examples:\n >>> from ray.rllib.algorithms.ppo import PPOConfig\n >>> from ray.rllib.policy.policy import PolicySpec\n >>> config = (\n ... PPOConfig()\n ... .multi_agent(\n ... policies={\n ... \"pol0\": PolicySpec(config=PPOConfig.overrides(lambda_=0.95))\n ... },\n ... )\n ... )\n\n >>> from ray.rllib.algorithms.algorithm_config import AlgorithmConfig\n >>> from ray.rllib.algorithms.pg import PGConfig\n >>> config = (\n ... PGConfig()\n ... .evaluation(\n ... evaluation_num_workers=1,\n ... evaluation_interval=1,\n ... evaluation_config=AlgorithmConfig.overrides(explore=False),\n ... )\n ... )\n\n Returns:\n A dict mapping valid config property-names to values.\n\n Raises:\n KeyError: In case a non-existing property name (kwargs key) is being\n passed in. Valid property names are taken from a default AlgorithmConfig\n object of `cls`.\n ", "language": "en", "n_whitespaces": 599, "n_words": 175, "vocab_size": 112 }

{ "docstring": "\n Sometimes, things like axis_direction need to be adjusted.\n ", "language": "en", "n_whitespaces": 15, "n_words": 8, "vocab_size": 8 }

{ "docstring": "\n Making a new Session at file level rather than stream level as boto3 sessions are NOT thread-safe.\n Currently grabbing last_modified across multiple files asynchronously and may implement more multi-threading in future.\n See https://boto3.amazonaws.com/v1/documentation/api/latest/guide/resources.html (anchor link broken, scroll to bottom)\n ", "language": "en", "n_whitespaces": 68, "n_words": 39, "vocab_size": 38 }

{ "docstring": "Transposes a tensor and returns it.\n\n Args:\n x: Tensor or variable.\n\n Returns:\n A tensor.\n\n Examples:\n\n >>> var = tf.keras.backend.variable([[1, 2, 3], [4, 5, 6]])\n >>> tf.keras.backend.eval(var)\n array([[1., 2., 3.],\n [4., 5., 6.]], dtype=float32)\n >>> var_transposed = tf.keras.backend.transpose(var)\n >>> tf.keras.backend.eval(var_transposed)\n array([[1., 4.],\n [2., 5.],\n [3., 6.]], dtype=float32)\n >>> input = tf.keras.backend.placeholder((2, 3))\n >>> input\n <KerasTensor: shape=(2, 3) dtype=float32 ...>\n >>> input_transposed = tf.keras.backend.transpose(input)\n >>> input_transposed\n <KerasTensor: shape=(3, 2) dtype=float32 ...>\n ", "language": "en", "n_whitespaces": 168, "n_words": 69, "vocab_size": 51 }

{ "docstring": "\n If a CSRF cookie is present but the token is missing or invalid, the\n middleware rejects the incoming request.\n ", "language": "en", "n_whitespaces": 41, "n_words": 19, "vocab_size": 16 }

{ "docstring": "\n Get the original object. Usually used together with ``trace_copy``.\n ", "language": "en", "n_whitespaces": 24, "n_words": 9, "vocab_size": 9 }

{ "docstring": "Find all matching candidates for the current requirement.\n\n Consults a finder to find all matching candidates.\n\n :param sources: Pipfile-formatted sources, defaults to None\n :param sources: list[dict], optional\n :param PackageFinder finder: A **PackageFinder** instance from pip's repository implementation\n :return: A list of Installation Candidates\n :rtype: list[ :class:`~pipenv.patched.pip._internal.index.InstallationCandidate` ]\n ", "language": "en", "n_whitespaces": 96, "n_words": 47, "vocab_size": 40 }

{ "docstring": "\n A smoke test to ensure GET on the add_view works.\n ", "language": "en", "n_whitespaces": 25, "n_words": 10, "vocab_size": 10 }

{ "docstring": "\n CreateOnlyDefault instances with a callable default should set context\n on the callable if possible\n ", "language": "en", "n_whitespaces": 36, "n_words": 14, "vocab_size": 13 }

{ "docstring": "\n GIVEN:\n - File containing barcode\n - Legacy method of detection is enabled\n WHEN:\n - File is scanned for barcodes\n THEN:\n - Barcodes are properly detected\n ", "language": "en", "n_whitespaces": 98, "n_words": 25, "vocab_size": 20 }

{ "docstring": "\n The 'selected' property is an instance of Selected\n that may be specified as:\n - An instance of :class:`plotly.graph_objs.bar.Selected`\n - A dict of string/value properties that will be passed\n to the Selected constructor\n\n Supported dict properties:\n\n marker\n :class:`plotly.graph_objects.bar.selected.Marke\n r` instance or dict with compatible properties\n textfont\n :class:`plotly.graph_objects.bar.selected.Textf\n ont` instance or dict with compatible\n properties\n\n Returns\n -------\n plotly.graph_objs.bar.Selected\n ", "language": "en", "n_whitespaces": 264, "n_words": 56, "vocab_size": 39 }

{ "docstring": "Check the default usage of the LearningCurveDisplay class.", "language": "en", "n_whitespaces": 7, "n_words": 8, "vocab_size": 7 }

{ "docstring": "\n Compute the length along the specified axis of the specified partition.\n\n Parameters\n ----------\n partition : ``PandasOnDaskDataframeVirtualPartition`` or ``PandasOnDaskDataframePartition``\n The partition whose size to compute.\n axis : int, default: 0\n The axis along which to compute size.\n\n Returns\n -------\n list\n A list of lengths along the specified axis that sum to the overall length of the partition\n along the specified axis.\n\n Notes\n -----\n This utility function is used to ensure that computation occurs asynchronously across all partitions\n whether the partitions are virtual or physical partitions.\n ", "language": "en", "n_whitespaces": 220, "n_words": 84, "vocab_size": 54 }

{ "docstring": "\n Returns the components of this vector but the output includes\n also zero values components.\n\n Examples\n ========\n\n >>> from sympy.vector import CoordSys3D, Vector\n >>> C = CoordSys3D('C')\n >>> v1 = 3*C.i + 4*C.j + 5*C.k\n >>> v1._projections\n (3, 4, 5)\n >>> v2 = C.x*C.y*C.z*C.i\n >>> v2._projections\n (C.x*C.y*C.z, 0, 0)\n >>> v3 = Vector.zero\n >>> v3._projections\n (0, 0, 0)\n ", "language": "en", "n_whitespaces": 170, "n_words": 57, "vocab_size": 43 }

{ "docstring": "\n This function downloads markdown readme files for various\n ecosystem libraries, saves them in specified locations and preprocesses\n them before sphinx build starts.\n\n If you have ecosystem libraries that live in a separate repo from Ray,\n adding them here will allow for their docs to be present in Ray docs\n without the need for duplicate files. For more details, see ``doc/README.md``.\n ", "language": "en", "n_whitespaces": 82, "n_words": 60, "vocab_size": 52 }

{ "docstring": "\n Close the connection.\n \n # Connect to the server / broker.\n # ", "language": "en", "n_whitespaces": 39, "n_words": 11, "vocab_size": 9 }

{ "docstring": "\n Make invisible-handle \"subtitles\" entries look more like titles.\n\n Note: This function is not part of the public API and may be changed or removed.\n\n ", "language": "en", "n_whitespaces": 34, "n_words": 24, "vocab_size": 24 }

{ "docstring": "\n The 'ohlc' property is a tuple of instances of\n Ohlc that may be specified as:\n - A list or tuple of instances of plotly.graph_objs.layout.template.data.Ohlc\n - A list or tuple of dicts of string/value properties that\n will be passed to the Ohlc constructor\n\n Supported dict properties:\n\n Returns\n -------\n tuple[plotly.graph_objs.layout.template.data.Ohlc]\n ", "language": "en", "n_whitespaces": 131, "n_words": 48, "vocab_size": 33 }

{ "docstring": "\n Matches the files to the patterns, and returns which patterns matched\n the files.\n\n *patterns* (:class:`~collections.abc.Iterable` of :class:`~pathspec.pattern.Pattern`)\n contains the patterns to use.\n\n *files* (:class:`~collections.abc.Iterable` of :class:`str`) contains\n the normalized file paths to be matched against *patterns*.\n\n *all_matches* (:class:`boot` or :data:`None`) is whether to return all\n matches patterns (:data:`True`), or only the last matched pattern\n (:data:`False`). Default is :data:`None` for :data:`False`.\n\n Returns the matched files (:class:`dict`) which maps each matched file\n (:class:`str`) to the patterns that matched in order (:class:`.MatchDetail`).\n ", "language": "en", "n_whitespaces": 116, "n_words": 79, "vocab_size": 52 }

{ "docstring": "\n Sets the source reference on Chart Studio Cloud for\n `columnorder`.\n\n The 'columnordersrc' property must be specified as a string or\n as a plotly.grid_objs.Column object\n\n Returns\n -------\n str\n ", "language": "en", "n_whitespaces": 84, "n_words": 27, "vocab_size": 25 }

{ "docstring": "Get stocks with earnings growth rates better than 25% and relatively low PE and PEG ratios.\n [Source: Yahoo Finance]\n\n Returns\n -------\n pd.DataFrame\n Undervalued stocks\n ", "language": "en", "n_whitespaces": 46, "n_words": 24, "vocab_size": 22 }

{ "docstring": "\n Enable interactive mode.\n\n See `.pyplot.isinteractive` for more details.\n\n See Also\n --------\n ioff : Disable interactive mode.\n isinteractive : Whether interactive mode is enabled.\n show : Show all figures (and maybe block).\n pause : Show all figures, and block for a time.\n\n Notes\n -----\n For a temporary change, this can be used as a context manager::\n\n # if interactive mode is off\n # then figures will not be shown on creation\n plt.ioff()\n # This figure will not be shown immediately\n fig = plt.figure()\n\n with plt.ion():\n # interactive mode will be on\n # figures will automatically be shown\n fig2 = plt.figure()\n # ...\n\n To enable optional usage as a context manager, this function returns a\n `~contextlib.ExitStack` object, which is not intended to be stored or\n accessed by the user.\n ", "language": "en", "n_whitespaces": 259, "n_words": 127, "vocab_size": 82 }

{ "docstring": "\n Override this method to change how the values are displayed in\n the status bar.\n\n In this case, we want them to be displayed in degrees N/S/E/W.\n ", "language": "en", "n_whitespaces": 55, "n_words": 26, "vocab_size": 21 }

{ "docstring": "Invoke the function with inputs and relayout the result.\n\n Args:\n fn: the function to invoke.\n layout: if not None, the output of the fn will be relayout with this.\n *args: positional arguments to be called with fn.\n **kwargs: keyword arguments to be called with fn.\n\n Returns:\n The output of fn, with potential relayout with the layout specified.\n ", "language": "en", "n_whitespaces": 75, "n_words": 57, "vocab_size": 35 }