nthakur/cornstack-6-langs-v1-tevatron-6M · Datasets at Hugging Face

query_id stringlengths 32 32	query stringlengths 9 4.01k	positive_passages listlengths 1 1	negative_passages listlengths 88 101
54edd522ff7874290002d20c11258464	Displays information about a given dataloader. Prints the size of the dataset, the dimensions of the images and target images, and displays a batch of images and targets with `imshow_batch()`.	[ { "docid": "229d1344396f33cd0894a070ef6f73a1", "score": "0.8973931", "text": "def dataloader_info(dataloader):\n images, targets = iter(dataloader).next()\n print(\"Number of images:\", len(dataloader.dataset))\n print(\"Image size:\", images.size())\n print(\"Targets size:\", targets.size()...	[ { "docid": "c860b5e3ffea39704ce31445f96037ca", "score": "0.73829144", "text": "def print_dataset(dataloader, num_elements=3):\n for element, i in zip(dataloader, range(num_elements)):\n print('+++ Image {} +++'.format(i))\n for key in element.keys():\n print(k...
9ef2258445ccb3bdee3a53fecf5db85f	write content to a file with newline character	[ { "docid": "2e3afb6c2d524efa8778274d7d898516", "score": "0.67021", "text": "def put(file, line=''):\n\tfile.write(line + '\\n')", "title": "" } ]	[ { "docid": "4973ee05daef1745514ee87dca7aaae3", "score": "0.779091", "text": "def write_to_file(fname,content):\r\n f = open(fname, \"a\")\r\n f.write(content)\r\n f.write(\"\\n\")\r\n f.close()", "title": "" }, { "docid": "50275ecbc66e5545bbd5b6931007e09d", "score": "0.707061...
a33b8f4a6d21b020da34d58c400fba42	Adds an entry to the routing table of the Node.	[ { "docid": "0fedbf05d03351e1e6e7dc8da7a7a5c0", "score": "0.6699021", "text": "def routing_table(self, entry):\n #check if entry is a dict, or if empty list reset routing_table\n if not isinstance(entry, dict):\n if entry == []:\n self._routing_table = []\n ...	[ { "docid": "48c2432e16918993b0fb714584798688", "score": "0.6901373", "text": "def update_routing_table(self, node_id: NodeID) -> None:\n self.logger.debug(\"Updating %s in routing table\", encode_hex(node_id))\n self.routing_table.add_or_update(node_id)", "title": "" }, { "doci...
cd323382656cf93ddc488225cd8382bb	Percent point function at q, or inverse CDF. Approximated by looking up the index in the cdf table that is closest to q.	[ { "docid": "bf20399d7950fabe0303fc32e8edfabe", "score": "0.6197692", "text": "def ppf(self, q):\n q = np.atleast_1d(q)\n\n # look up the index of the quantile in the 2D CDF grid\n values = []\n for qi in q:\n # find index in grid for every dimension\n id...	[ { "docid": "f12bea694f0f6a1129ecd0f73232bc83", "score": "0.71535176", "text": "def cdf_percentile(x, p, q=50.0):\n\n # Determine index where cumulative percentage is achieved\n i = np.where(p>q/100.0)[0][0]\n\n # If at extremes of the distribution, return the limiting value\n if i==0 or i==l...
15e5fdf7b8cc1f7fa79217b67c1d0b34	Convert, as necessary, l,b,d into floats	[ { "docid": "74bacff892397f91578a1959e95a602d", "score": "0.0", "text": "def parse_lbd(gal_l, gal_b, distance):\n l = parse_units(gal_l, 'deg', 'Galactic longitude')\n b = parse_units(gal_b, 'deg', 'Galactic latitude')\n d = parse_units(distance, 'kpc', 'distance')\n return l, b, d", "tit...	[ { "docid": "8e5c2f05726d138a1343dda176d0b088", "score": "0.64943665", "text": "def str2flt(vec):\n newvec=[]\n for v in vec:\n try:\n newv=float(v)\n except:\n newv=None\n finally:\n newvec.append(newv)\n return newvec", "title": "" },...
29ac3efcaa28934ad9bb8706440c339e	bool, whether supports role mailing lists	[ { "docid": "289d3ce6018d57d5f4249092058e5749", "score": "0.5978602", "text": "def supports_mailing(self):\n return NETWORK_NAME == 'Eionet'", "title": "" } ]	[ { "docid": "293a10f48e27313337970f0560635621", "score": "0.6431647", "text": "def canAssignRole(role):", "title": "" }, { "docid": "64886deff0eadde94b2434cbb708cf61", "score": "0.6292626", "text": "def check_role(rolelist, message):\n for role in rolelist:\n # noinspection ...
cd70aef18393b85c960a4f28b6c4af6e	Initialize remote value of KNX DPT 1.001.	[ { "docid": "60bdcba32d8ddcf96df7845be210e2df", "score": "0.0", "text": "def __init__(\n self,\n xknx: XKNX,\n group_address: GroupAddressesType \| None = None,\n group_address_state: GroupAddressesType \| None = None,\n sync_state: bool \| int \| float \| str = True,\n ...	[ { "docid": "996d1505c26ab59baae9bb93bb56606b", "score": "0.5731152", "text": "def test_from_knx(self):\n xknx = XKNX()\n remote_value = RemoteValueStep(xknx)\n assert remote_value.from_knx(DPTBinary(1)) == RemoteValueStep.Direction.INCREASE\n assert remote_value.from_knx(DPTB...
356a2e713ff7fa829665edd4d2a035ec	Devuelve la similitud de jaccard	[ { "docid": "eb5f9d72fcbfef9a06e4b3bb2e174a36", "score": "0.53147584", "text": "def jaccard_similarity(list1, list2):\n intersection = len(list(set(list1).intersection(list2)))\n union = (len(list1) + len(list2)) - intersection\n return float(intersection) / union", "title": "" } ]	[ { "docid": "b28690c78a69fc481a989368dc9c914e", "score": "0.6333269", "text": "def jaccard(left, right):\n\n true_false = 0\n false_true = 0\n false_false = 0\n true_true = 0\n\n if len(left) == len(right):\n for index, val in enumerate(left):\n if left[index] == 1 and ri...
01866748369ab7177e45157e7fbc9253	Prints and returns the last digit of a number. If given something that's not a number, this returns None.	[ { "docid": "c0afb8ee1076a323ef93c4eb9f945235", "score": "0.8328857", "text": "def print_last_digit(number):\n number = (abs(number) % 10)\n print(\"{}\".format(number), end=\"\")\n return (int(number))", "title": "" } ]	[ { "docid": "b862fc37bc5cbaeaeab1578228d23fe9", "score": "0.8458338", "text": "def print_last_digit(number):\n print(abs(number) % 10, end=\"\")\n return (abs(number) % 10)", "title": "" }, { "docid": "67d1a5ad30a32391a4e44367e8b22772", "score": "0.7971203", "text": "def last_di...
9953d2c5c9bf35c5afb1bbb3d54db76c	plot pytstan submm sed output as SEDs specific to greybody models	[ { "docid": "5c59020054486de78b839e4f09a3bb5d", "score": "0.53297436", "text": "def pystan_postprocess_SED_plot(allfits, label=\"\"):\n\n labs = (\"1 comp\", \"2 comp\", r\"1 $\\beta=2$\", r\"2 $\\beta=2$\", \"thick\")\n for objname, fits in iteritems(allfits):\n for fit, lab in zip(fits, la...	[ { "docid": "2bcc1093c7f3f4c187383fab35b16510", "score": "0.62366015", "text": "def tsnescatterplot(model, word, list_names):\r\n arrays = np.empty((0, 300), dtype='f')\r\n word_labels = [word]\r\n color_list = ['red']\r\n\r\n # adds the vector of the query word\r\n arrays = np.append(arr...
c19228a3d740918fb2e3cafbd49ae231	Add a status line to the editor.	[ { "docid": "1d447c653b8fea3da5840afa452e3fd1", "score": "0.67183894", "text": "def addStatus(self, text, priority=0):\n pass", "title": "" } ]	[ { "docid": "b54316b4f7ef636bdd64ac3479e808a4", "score": "0.66472626", "text": "def add_line(self, line):\n height, width = self.window.getmaxyx()\n self.window.addstr(self.line_no, 2, line[:width-5])\n self.line_no += 1", "title": "" }, { "docid": "7900b80ea1e715119a86af...
17ba8b85a8254b519fe1b16970a97eb8	Transfers data from a file to an ndarray	[ { "docid": "3e85a6065442a3e646bad8126ed5e393", "score": "0.0", "text": "def extract_data(folder,file_name,lower,upper):\n\n csv_data = np.genfromtxt(\n fname=p(os.path.join(folder,file_name)),\n dtype=float,\n delimiter=','\n ,skip_header=1,\n usecols=range(lower,up...	[ { "docid": "1eda034ebc8d2c659548b8d7aa50c35f", "score": "0.6489842", "text": "def fread(f, n, dtype):\n if dtype is np.str:\n dt=np.uint8\n else:\n dt=dtype\n\n data_array=np.fromfile(f, dt, n)\n #data_array.shape=(n,1)\n return data_array", "title": "" }, { "doc...
a99170ae8bd5f9324da146051ce93030	Returns info string about resolution matrix.	[ { "docid": "0424d44b77317e85bf933553aae932ff", "score": "0.6297488", "text": "def __str__(self):\n info = self.par.copy()\n if self.par['kfix'] == 1:\n info['efixstr'] = 'fixed incident energy k_i = %2.4f A-1' % self.par['k']\n else:\n info['efixstr'] = 'fixed ...	[ { "docid": "a31e39add72e9642275dadc81b8580c8", "score": "0.69172955", "text": "def get_info(self):\n size_unit = self.get_display_grid_unit()\n pxy = self.grid.get_pixel_size().coordinates\n if size_unit is not None:\n if isinstance(size_unit, units.UnitBase):\n ...
3dbd503e47042cfe17541567b30f6a37	Return visible windows belonging to a process.	[ { "docid": "74ac6ca4f6cc2566322cea394c8397ae", "score": "0.77159387", "text": "def find_windows_for_process(self, process_id, display_name):\n pids = self.get_process_ids(process_id)\n if not pids:\n return []\n\n logger.info(\n 'Waiting for 30 seconds to ensure all windows appear: ...	[ { "docid": "3fd2e2789de871ce4471b156866f2db6", "score": "0.73314553", "text": "def get_hwnds(pid):\n\n def callback(hwnd, hwnds):\n if win32gui.IsWindowVisible(hwnd) and win32gui.IsWindowEnabled(hwnd):\n _, found_pid = win32process.GetWindowThreadProcessId(hwnd)\n if foun...
6125735d81cefe56f59732353a54fe8f	r"""Calculates the hydrophobic fitness of a protein. Hydrophobic fitness is an efficient centroidbased method for calculating the packing quality of your structure [3]_. For this method C, F, I, L, M, V, W and Y are considered hydrophobic. The	[ { "docid": "da3bb79821b7a97a8c5917e0e0bb199d", "score": "0.6614055", "text": "def calculate_hydrophobic_fitness(assembly):\n hydrophobic_centroids = []\n tyrosine_centroids = []\n polar_centroids = []\n for residue in [r for r in assembly.get_monomers()\n if isinstance(r, ...	[ { "docid": "bf03dd037f2a5f685f080a4bb7178227", "score": "0.63004637", "text": "def fitness(individual):\n x=individual[0]\n z=individual[1]\n return x*2-2xz+6x+z*2-6z", "title": "" }, { "docid": "5c18d3c652ff260be0aac9a138dbbd89", "score": "0.600077", "text": "def fit...
d5903c89d6dfcc07cfe76673180a10a9	Test deleting a user account without a password key	[ { "docid": "c59feb9eaa1d7a89cbd9585b060d0535", "score": "0.8261928", "text": "def test_deleting_account_without_password_key(self):\n self.create_user()\n self.create_category()\n self.create_recipe()\n with self.client:\n headers = self.helper_login_with_token()\n...	[ { "docid": "ce8c332843fdef45824dd2dfebf1fc0c", "score": "0.81951886", "text": "def test_deleting_account_with_empty_password_value(self):\n self.create_user()\n with self.client:\n headers = self.helper_login_with_token()\n response = self.client.delete('/auth/delete-...
db4f8aec007b41fb25bf66b292e606fd	Gets the identifier of this AccessPolicy. The id of the policy. Set by server at creation time.	[ { "docid": "b1ad433d654521b2cd6b094150da0ae9", "score": "0.0", "text": "def identifier(self):\n return self._identifier", "title": "" } ]	[ { "docid": "9ad0b7f8b226c09cd01ec534b4950829", "score": "0.7846321", "text": "def policy_id(self):\n return self._policy_id", "title": "" }, { "docid": "c2968e83c5829951cc14f939bb6ea7ea", "score": "0.7770658", "text": "def access_policy_id(self) -> Optional[str]:\n retu...
8f5dc2de06c33a5958ec5f8977de11e7	Return a list value for further modification.	[ { "docid": "2a441e6f8dbd5431a200434a00c53189", "score": "0.73336405", "text": "def _get_list_value_for_modification(self, key):\r\n\t\ttry:\r\n\t\t\tvalue = self.table[key]\r\n\t\texcept KeyError:\r\n\t\t\ttry: value = self.parent[key]\r\n\t\t\texcept AttributeError: value = []\r\n\t\t\tif isinstance(va...	[ { "docid": "41b6a4da55e32ff4b218fe4383986434", "score": "0.7180536", "text": "def listify(self, value):\n if isinstance(value, list):\n return value\n else:\n return [value]", "title": "" }, { "docid": "3fd078fcb797a7be3925f9dc2168d836", "score": "0.70...
53188e7e2ec681c64a362a4c2d810e21	Determine if a word is the same forwards and backwards.	[ { "docid": "75038c4e136eac304f760b5c396cb57a", "score": "0.67014337", "text": "def is_palindrome(word: str) -> bool:\n return word.casefold() == word[::-1].casefold()", "title": "" } ]	[ { "docid": "0e45a7271bc2dadb60a364e8fba5e6f2", "score": "0.78020686", "text": "def is_reverse(word1, word2):\n return (word1[::1] == word2[::-1])", "title": "" }, { "docid": "195dd0bdae702ea529fe5b342fa9b454", "score": "0.71716213", "text": "def es_palindroma(word):\n return word...
28a5c5061b080ba6988e28b43463b904	a c / \ / \ b ar > b a / \ / \ / \ bl c bl cl cr ar / \ cl cr	[ { "docid": "3409e010af32696ebec3666fd5fa4698", "score": "0.0", "text": "def __lr(self, a):\n b = a.left\n c = b.right\n cl = c.left\n cr = c.right\n\n # Rotation\n c.right = a\n c.left = b\n b.right = cl\n a.left = cr\n\n # Affected n...	[ { "docid": "9e6321f4f699dca82b8364355e8c5b8f", "score": "0.57508165", "text": "def cat_ears():\n return \"/\\\\\" + ((SIZE - 2) * \"-\") + ((SIZE - 2) * \"-\") + \"/\\\\\"", "title": "" }, { "docid": "3523727d40ae8f829a045dc98b172c26", "score": "0.5483277", "text": "def conc(left,...
84c2e88611ea74f7a6840290521a80ec	Place schematic components determined by the layout(keyswitches and diodes)	[ { "docid": "b98eb24e9d12bffc66b188815d5ada34", "score": "0.81252825", "text": "def place_schematic_components(self):\n switch_tpl = self.jinja_env.get_template(\"schematic/keyswitch.tpl\")\n\n component_count = 0\n components_section = \"\"\n\n # Place keyswitches and diodes\...	[ { "docid": "16fd92a4fdbc4fc5103d9521d2477e9b", "score": "0.7992899", "text": "def place_layout_components(self):\n switch = self.jinja_env.get_template(\"layout/keyswitch.tpl\")\n diode = self.jinja_env.get_template(\"layout/diode.tpl\")\n component_count = 0\n components_sec...
2a7f0a4955e3eb8db210fc2e3e607aef	Load the latest model and run a test episode	[ { "docid": "6d34a9cac0e3ab9c1237a1141c59f02f", "score": "0.0", "text": "def eval(dqn):\n ckpt_file = os.path.join(os.path.dirname(__file__), 'models/checkpoint')\n with open(ckpt_file, 'r') as f:\n first_line = f.readline()\n model_name = first_line.split()[-1].strip(\"\\\"\")\n d...	[ { "docid": "212b86f1917cde8b4574004fc1c33626", "score": "0.649339", "text": "def run_model(self):\n self.set_up_run_model()", "title": "" }, { "docid": "4b489c45200b9cf8f036daec4304dea6", "score": "0.64562917", "text": "def run_load_model_single(ind):\n global BEST_LOSS,BES...
0e104461cce6a58cb1527de242a9065c	Return a list of this compartments neighbors	[ { "docid": "4ec96ae6657a1638f62adf6d432fc87c", "score": "0.8468298", "text": "def neighbors(self):\n neighbors = [comp for comp in self.node0.compartments if comp != self]\n neighbors.extend(comp for comp in self.node1.compartments if \\\n comp != self and comp not in neighbors...	[ { "docid": "38fef1ab25c0cc71bec25ddb3225d301", "score": "0.84283286", "text": "def neighbors(self):\n return [comp for comp in self.node.compartments if comp != self]", "title": "" }, { "docid": "e48d37ca06666ef16c66fdb08074775f", "score": "0.7814085", "text": "def get_neighbors(s...
8ce42f3ff08b29817d4f733e56678d1c	Get basic player info.	[ { "docid": "5ac8172ffbecca36926ad6f7d44dbd0a", "score": "0.0", "text": "def get_players(self):\n for i, player in enumerate(self._header.initial.players[1:]):\n achievements = self.get_achievements(player.attributes.player_name)\n if achievements:\n winner = a...	[ { "docid": "8ece88e1fdff785b25835f2101797e0d", "score": "0.70133686", "text": "async def playerinfo(self, ctx):\n player = self.bot.wavelink.get_player(ctx.guild.id, cls=Player)\n node = player.node\n\n used = humanize.naturalsize(node.stats.memory_used)\n total = humanize.na...
a5c78dc38b73bbb7aaddd29ec72038c3	Exports mdf class data structure into hdf5 file	[ { "docid": "13d95b2364eff06e374762e8853a6aae", "score": "0.0", "text": "def export_to_hdf5(self, file_name=None, sampling=None, compression=None, compression_opts=None):\n #\n try:\n import h5py\n import os\n except ImportError:\n warn('h5py not foun...	[ { "docid": "1547453acf20dcb55fbb46fddc5fc80d", "score": "0.72480386", "text": "def export_to_hdf5(cls, h5_file, model, eids):\n #comments = []\n pids = []\n nodes = []\n thetas = []\n for eid in eids:\n element = model.elements[eid]\n #comments.ap...
22ee0c05930fea1d2164d13242a82399	GIVEN a source path to images files, extensions and a valid camera model WHEN the method is called THEN return a list of images files names that has such model in the exif metadata	[ { "docid": "4f012ed8064f0314e4995ff96e56b3aa", "score": "0.705926", "text": "def test_fetch_exif_images_by_model():\n files = c.fetch_images_by_camera_model(\"/home/urra/projects/pto/tests/data/dated_images/\", [\".jpg\", \".JPG\"],\"iPhone 4\")\n for file in files:\n assert c.is_exif_model...	[ { "docid": "69e1f335d6291ec8d45ac999c6f8da78", "score": "0.74459547", "text": "def test_fetch_exif_models():\n cameras = []\n files = c.read_src_files(\"/home/urra/projects/pto/tests/data/dated_images/\")\n for file in files:\n model = c.get_exif_attribute(file,\"model\")\n if not...
0ae60727fde0f48a9395014a6e53f368	Get most recent data object from local store (resultsdir) or database depending on use_local flag.	[ { "docid": "6c515790931a0e7851bab6f6a6949aeb", "score": "0.6872474", "text": "def get_latest_data(self, use_local: bool = None):\n if use_local is None:\n use_local = self.config.flags.use_local\n if use_local:\n return self.find_latest_data()\n else:\n ...	[ { "docid": "9bc5fc8af3a9c1739be2bb32accd70b3", "score": "0.6305982", "text": "def find_latest_data(self):\n jsonname = \"{}_data.json\".format(self.test_name.replace(\".\", \"_\"))\n resultsdir = os.path.expandvars(self.config.resultsdir)\n latest = None\n latest_mtime = 0.0\...
60adf11f2a9751f14a8ef65231d4c05d	r""" Return the FindStat identifier of the statistic.	[ { "docid": "a4d686e2f37ebac8c56bb38feb5e58fc", "score": "0.0", "text": "def id(self):\n return self._id", "title": "" } ]	[ { "docid": "43b0dde7ac6d869f6563a21cd3d75741", "score": "0.7452811", "text": "def id(self):\n return self._map[FINDSTAT_MAP_IDENTIFIER]", "title": "" }, { "docid": "a088cfd9a870d83188b34abc3a381360", "score": "0.6871175", "text": "def name(self):\n return self._map[FIND...
9eafee5ebddd5186dd57c2b654c81526	Returns a redirect url.	[ { "docid": "9cfd5aa97d497b11446daebd68bceb36", "score": "0.6828274", "text": "def redirect_url(default='index'):\n return request.referrer or url_for(default)", "title": "" } ]	[ { "docid": "2c096b0d65d755bd5a9a865cd350ebd4", "score": "0.8545995", "text": "def redirecturl(self) :\n\t\ttry :\n\t\t\treturn self._redirecturl\n\t\texcept Exception as e:\n\t\t\traise e", "title": "" }, { "docid": "bb3925ef9109540b34a175d0301cdb67", "score": "0.82717377", "text": "...
d872102cb6d6374073611e7e8fecba67	runs throught the code and executes it	[ { "docid": "f757c21e8c83cb674b551802f565d782", "score": "0.0", "text": "def execute(self,repl=True,to_use=''):\n \n if not to_use:\n code = self.code\n else:\n code = to_use\n code = self.updatecode(code)\n codepos = self.codepos\n cells = self.cells\n ...	[ { "docid": "1efae473535689ade6bba4a0066de701", "score": "0.7500659", "text": "def do_run(self):", "title": "" }, { "docid": "6b853038144e84eca87301e1d74ad67a", "score": "0.74891174", "text": "def run():", "title": "" }, { "docid": "6b853038144e84eca87301e1d74ad67a", "...
ea6f05dcbdaa7b397d23a33d382cf3ea	Create a simple naive multipnomial model where input X is a 1D array of floats in [0, 1.] output Y is onehot encode of three classes, A, B, C. [1,0,0] > class A [0,1,0] > class B [0,0,1] > class C Any x_i in [0, 0.3) belong to class A. Any x_i in [0.3, 0.6) belong to class B. Any x_i in [0.6, 1) belong to class C.	[ { "docid": "15bcca248399937da0adcd09acc877c2", "score": "0.6226215", "text": "def test_naive_multinomial():\n def make_y(x):\n if x <= 0.33:\n return [1,0,0]\n elif x > 0.33 and x <= 0.67:\n return [0,1,0]\n else:\n return [0,0,1]\n\n out_dir =...	[ { "docid": "10065240f9d781740b94508886ad9703", "score": "0.6501281", "text": "def multiclass_noisify(y, P, random_state=0):\n print (np.max(y), P.shape[0])\n assert P.shape[0] == P.shape[1]\n assert np.max(y) < P.shape[0]\n\n # row stochastic matrix\n assert_array_almost_equal(P.sum(axis=...
30230a45bc399c7ea7f30a6a1461c888	Retrieve genotypes by sample name	[ { "docid": "10e55688a93184e74c2c36105c726f2a", "score": "0.7850626", "text": "def genotypes_of(self, sample):\n\t\treturn self.genotypes[self._sample_to_index[sample]]", "title": "" } ]	[ { "docid": "c7c15478556ae0a2a23c4ca0fadc4713", "score": "0.7029988", "text": "def get_snp_sample_genotype(current_pos, sample_name):\n record = next((record for record in call_base.snp_record_dict[methyl_record.CHROM]\n if record.POS == current_pos), None)\n sample = ...
884cec929793e46311860e5e14300fdd	Frequency Shift This function does not perfrom a Hilbert transfrom when data is complex, NMRPipe seems to. As such the results of the imaginary channel differs from NMRPipe. In addition MAX/MIN value are slightly different than those from NMRPipe.	[ { "docid": "cb26b9cf473d587800e69fd53cbada94", "score": "0.0", "text": "def fsh(dic,data,dir,pts,sw=True):\n if dir not in [\"ls\",\"rs\"]:\n raise ValueError(\"dir must be ls or rs\")\n\n if np.iscomplexobj(data) == False: # real data\n null,data = _ht(dict(dic),data,zf=True)\n ...	[ { "docid": "e34f45944a36c1470aefbbbac1be3568", "score": "0.6343489", "text": "def freq_shift(x, freq, fs):\n x = cp.asarray(x)\n return _freq_shift_kernel(x, freq, fs)", "title": "" }, { "docid": "1fc5636acb473f7957d08fa865e58c51", "score": "0.62186146", "text": "def amplificat...
0d6919f49c0ebb2e84b311fbdd4ea193	This function returns the nth Fibonacci number.	[ { "docid": "13f70311b75a2ea21d39ab6d0482438f", "score": "0.7792695", "text": "def fib(n):\r\n i = 0\r\n j = 1\r\n n = n - 1\r\n\r\n while n >= 0:\r\n i, j = j, i + j\r\n n = n - 1\r\n \r\n return i", "title": "" } ]	[ { "docid": "270d8c585cead31811c63b02a378ee30", "score": "0.8607473", "text": "def nthFibonacci(index):\n # TODO: validate input (negatives, or non-integer)\n # happy case, weve already generated the sequence up to that point\n # we can return it in O(1)\n if len(fibonacci_sequence) > index:\...
cb42e71e00225186d6bb77be3ae9fbbc	Mask generator for coupling layers.	[ { "docid": "0212f6da636384e44ce7bdc4a481a1c7", "score": "0.53870505", "text": "def get_coupling_mask(n_dim, n_channel, n_mask, split_type='ChessBoard'):\n with jt.no_grad():\n masks = []\n if split_type == 'ChessBoard':\n if n_channel == 1:\n mask = jt.arange(0...	[ { "docid": "3864f70cbd28508ad8c3451c62984087", "score": "0.62455934", "text": "def gen_mask(word):\n return chain.from_iterable(\n [gen_mask_direction(word, mut) for mut in _directions])", "title": "" }, { "docid": "0a3928e295f68c9c0d3c5fa413c7530d", "score": "0.61573017", ...
2a35d1556d926281ff1c66b1f8595185	Automate the process of generating metadata from TeX formulas.	[ { "docid": "57d490b7a975ff5f9fac15062ce902f5", "score": "0.0", "text": "def __init__(self, length, *args):\n assert length >= 1\n self.length = length\n self.f = args\n super().__init__(self, text=None)", "title": "" } ]	[ { "docid": "c54f0bb68ce7bada5f80ae2c7db2c0f6", "score": "0.5863953", "text": "def generate_meta(tree, bill_id):\n meta = E(\"meta\")\n \n meta.append(generate_identification(tree, bill_id))\n meta.append(generate_publication(tree, bill_id))\n meta.append(generate_lifecycle(tree, bill_id))...
6ae63b9b8b80d14d27de932f9eb1e03c	Get an agent from the kqml cljson representation (KQMLList).	[ { "docid": "685395fcba5b3ca5e845987b23be1b76", "score": "0.66371286", "text": "def get_agent(cls, cl_agent):\n agent_json = cls.converter.cl_to_json(cl_agent)\n if isinstance(agent_json, list):\n return [ensure_agent_type(Agent._from_json(agj))\n for agj in ag...	[ { "docid": "4d7dd4b8753496023f532f5b4858d14a", "score": "0.5037742", "text": "def getAgent(self):\n return self._agent", "title": "" }, { "docid": "93e38bdce58e1707d76e0deb2486f062", "score": "0.5006577", "text": "def get_agent(payload, agent_id):\n agent = Agents.query.fil...
2159ce5f65118e46b83ea4fe457f9f0a	actionsarray actionTimestring actionTypestring messagestring subActionType	[ { "docid": "ddcd7ce0aedbe47e10420f7964345ddc", "score": "0.0", "text": "def client_info(self, action):\n url = self.baseurl + '/api/v1.2/statis/action'\n r = requests.post(url, json=action)\n # response = r.json()\n return r", "title": "" } ]	[ { "docid": "a1eab1b30f938463e2e5fb67b270f001", "score": "0.63483447", "text": "def define_actions( action ):\n\n actions = [\"walking\", \"eating\", \"smoking\", \"discussion\", \"directions\",\n \"greeting\", \"phoning\", \"posing\", \"purchases\", \"sitting\",\n \"sittingdo...
e544453d4703f1678a7fddec7ad18e3c	Return the current user action with the action parameters.	[ { "docid": "199f1fef2f8bdfe98639a7eb4ea04156", "score": "0.7800999", "text": "def get_current_action(self):\r\n # get current action\r\n action = self.user_action_generator.action\r\n \r\n # get current key if the action was KeyPress\r\n key = self.user_action_generato...	[ { "docid": "b8a16c06f0468742277f2b40eef86a0c", "score": "0.75212395", "text": "def _get_action(self):\n return self.__action", "title": "" }, { "docid": "5754aaab8e2accf8435431cbb23e2c13", "score": "0.739199", "text": "def get_action(self):\n return self._action", "titl...
67218f5ad982602182ad27fb6773c3c3	Checks if class attr are present	[ { "docid": "28041c011931c8d004179897589b27a8", "score": "0.6782795", "text": "def test_correct_classattr(self):\n b = User()\n attr = [\"email\", \"password\", \"first_name\", \"last_name\"]\n d = b.__dict__\n for i in attr:\n self.assertFalse(i in d)\n ...	[ { "docid": "ef997cefcb0ea532cc503fe05aebf969", "score": "0.73302776", "text": "def test_HasClassFields(self):\n for strAttr, _, _ in self.ClassFields:\n strError = 'Missing attribute {} in class {}'.format(strAttr,\n self.TestClass.__n...
fdcd639721cb599dcf895499e664212a	Returns the number of tests in the test file	[ { "docid": "c0cf8d7008a50fea2b280fef0de7ecdb", "score": "0.8652606", "text": "def number_of_tests(self):\n tests = c.file_number_of_lines(self.test_set)\n if self.test_set_header:\n tests -= 1\n return tests", "title": "" } ]	[ { "docid": "102ac0a63de87b6a11eba38139c2a37d", "score": "0.76091194", "text": "def _get_num_tests(test_output: str) -> int:\n match = re.search(r\"Tests run: (\\d+)\", test_output)\n return int(match.group(1)) if match else 0", "title": "" }, { "docid": "e8ce229ea3064a8ad2912ca74b05248...
e6dcf15390001e188afa8c958c44f8c0	Print a comment if print_it == True	[ { "docid": "496621f2535e8c405f2da0f98efe3073", "score": "0.7507166", "text": "def print_comment(text, print_it=verbose):\n prefix = \"OpenCortex >>> \"\n if not isinstance(text, str): text = text.decode('ascii')\n if print_it:\n \n print(\"%s%s\"%(prefix, text.replace(\"\\n\", \"\...	[ { "docid": "71e0ebbe3d52f7aa63197fe9c465c018", "score": "0.71713185", "text": "def print_comment_v(text):\n print_comment(text, True)", "title": "" }, { "docid": "d80b74f4cf1fc4848b45fb7609e4ec77", "score": "0.6587678", "text": "def print_comment(comment):\n shell_type = get_sh...
b60091eff4fa1a3e17b561797c93ad9a	Returns the MongoDB aggregation pipeline for the stage.	[ { "docid": "28197be9cd5f99638ac517947cc55d2f", "score": "0.0", "text": "def to_mongo(self, sample_collection):\n if not self.has_view:\n raise ValueError(\n \"%s stages use `load_view()`, not `to_mongo()`\" % type(self)\n )\n\n raise NotImplementedError...	[ { "docid": "60548f516377e285b92b4677a5d21114", "score": "0.65483505", "text": "def aggregation(self) -> Optional[pulumi.Input['AggregationArgs']]:\n return pulumi.get(self, \"aggregation\")", "title": "" }, { "docid": "60548f516377e285b92b4677a5d21114", "score": "0.65483505", ...
d6988d323edac3789be94a660f15ea1f	This method returns the name of the customer	[ { "docid": "bd3f80d095c5204b24e4f670f127c793", "score": "0.9271176", "text": "def get_customer_name(self):\n\t\treturn\tself.name", "title": "" } ]	[ { "docid": "527a7f56285a511d1e07ad302fc9925e", "score": "0.86061275", "text": "def get_customer(self) -> str:\n return self.customer", "title": "" }, { "docid": "caefd9628fac406b27c6bc3d54168670", "score": "0.8200847", "text": "def customer(self):\n return self.details[...
edcfbd83b47afb3c8a65e63a61ec03c4	ON PASSIVE DTP CREATION, NOT ON INITIAL TCP CONNECTION. For Initial TCP connection, see handle_accept in StreamFTPServer. Mainly copypasted from PassiveDTP, except that dtp_handler is run with a stream_rate.	[ { "docid": "24021e26f16b217576b13d6927ba2472", "score": "0.53463334", "text": "def handle_accept(self):\n \"\"\"Called when remote client initiates a connection.\"\"\"\n if not self.cmd_channel.connected:\n return self.close()\n try:\n sock, addr = self.accept(...	[ { "docid": "22ea0adb1e670bbe1fc11d1036a3d4aa", "score": "0.63551366", "text": "def on_dtp_connection(self):\n self.debug(\"FTPHandler.on_dtp_connection()\")\n if self.data_server:\n self.data_server.close()\n self.data_server = None\n\n # check for data to send\n ...
ea950c2fd6d934bafb035b9d5c85283d	Given an input_dir, returns the list of userids. This is to be used as the reference, in terms of ordering, for all other datasets.	[ { "docid": "d0d531e57eecbbd657cdc2c1c73acc18", "score": "0.79590195", "text": "def get_user_ids(input_dir: str) -> List[str]:\n image_files_name_pattern = os.path.join(input_dir, \"Image\", \"*.jpg\")\n image_filepaths = glob.glob(image_files_name_pattern)\n file_names = (os.path.basename(file_...	[ { "docid": "405b4402f2c8b31623706774d5d56e09", "score": "0.6556707", "text": "def get_ids(dir):\n return (f[:-4] for f in os.listdir(dir))", "title": "" }, { "docid": "6a235251084e44768351a9ca138ef4df", "score": "0.6357975", "text": "def all_train_ids(data_dir) -> np.ndarray:\n ...
5284b5b1c816e437f2dd5b007b5e4604	Load the driver from the one specified in args, or from flags.	[ { "docid": "cc5cff5c0422f40ab9537d843010485a", "score": "0.0", "text": "def __init__(self, volume_driver=None, args, kwargs):\n super(ReddwarfVolumeManager, self).__init__(args, **kwargs)", "title": "" } ]	[ { "docid": "29d364750dffbefc3df07d8c03186348", "score": "0.5825256", "text": "def _get_driver(self, path, driver):\n self._debug('Loading driver %s...' % driver)\n abs_path = os.path.abspath(os.path.join(path, driver + '.py'))\n try:\n self._drivers_loaded[driver] = imp.l...
a8a9c2682b7df65a05e26dfb11a51644	Validate snippet and device list with user	[ { "docid": "61a0d856096cd7be8b40d900987e795c", "score": "0.6050847", "text": "def confirm_deployment_validity(snippet: str, requested_device_name_list: List[str]) -> bool:\n\n # Print snippet and ask user for confirmation\n netcat.LOGGER.info(\"Displaying snippet and asking user for confirmation\"...	[ { "docid": "3b47c7e3290d05b7551f276b3bcf0573", "score": "0.5582056", "text": "def validate_device_info(self, devinfo, prefix=\"\"):\n errors = []\n\n if \"deviceType\" in devinfo:\n deviceType = devinfo[\"deviceType\"]\n if deviceType == \"network/upnp\":\n ...
67fbb6fcae3c99ed218d2119a82b1dfc	Search using the GeoLocation functionallity of GoogleAPI. This assumes its given data are infact street signs. intersections are marked by '&'	[ { "docid": "e18b0729804c87403bd9a77406bd57f5", "score": "0.66954947", "text": "def _search_geolocation(g_maps, output_path, streets):\n\n combinations_to_try = __create_suffixes_combinations(streets)\n results = []\n for comb in combinations_to_try:\n geocode_results = g_maps.geocode(com...	[ { "docid": "eceead8944cf2d534d88cf4bcd05bbf8", "score": "0.70246893", "text": "def search_location(streets, others, output_path):\n\n g_maps = googlemaps.Client(key=API_KEY)\n\n if len(streets):\n print(\"[+] Trying to match exact geo-location data...\") # Geocoding an address\n if ...
1e982dbea5d8b95f5c90b7f7b40a3e67	Sets the current tool to edit shape coordinates. Returns None	[ { "docid": "b90c2abb6c7e2c051b1f9f45e888119c", "score": "0.879929", "text": "def set_current_tool_to_edit_shape_coords(self):\n\n self.variables.active_tool = TOOLS.EDIT_SHAPE_COORDS_TOOL\n self.variables.current_tool = TOOLS.EDIT_SHAPE_COORDS_TOOL", "title": "" } ]	[ { "docid": "745d7a5ceebf910f6ee3d6fd4a96a1ef", "score": "0.8333395", "text": "def set_current_tool_to_edit_shape(self):\n\n self.variables.active_tool = TOOLS.EDIT_SHAPE_TOOL\n self.variables.current_tool = TOOLS.EDIT_SHAPE_TOOL", "title": "" }, { "docid": "1828cf63c01a3eca58d7...
b0e2c74cf887e7bfcc8bd8b5160d7677	Checks whether the currently logged in user can create or edit a task, when the specified role is required.	[ { "docid": "d3bcab63cb65b2d503d97fe659bfb779", "score": "0.77631235", "text": "def canCreateTaskWithRequiredRole(self, required_role):\n if required_role == 'mentor':\n valid_org_keys = [o.key() for o in self.data.mentor_for]\n elif required_role == 'org_admin':\n valid_org_keys = [o.key...	[ { "docid": "f488f928e748390b66273f859a86562a", "score": "0.7407585", "text": "def canCreateTask(self):\n return self.canCreateTaskWithRequiredRole('mentor')", "title": "" }, { "docid": "92dd1cad5bff8325c5e3ca5ccdb34116", "score": "0.7034055", "text": "def checkCanUserEditTask(self...
1bde121e390fc36fc99ed0b7fd77c9d2	Add product to the cart and update it's quantity	[ { "docid": "3cb7c65e6952b839dff8153624c77b7c", "score": "0.76845706", "text": "def add_cart(request, product_id):\n product = Product.objects.get(id=product_id)\n \n try:\n cart = Cart.objects.get(cart_id=__cart_id(request))\n except Cart.DoesNotExist:\n cart = Cart.objects.cre...	[ { "docid": "038040f6e0eb53f4d3d4004bd145c921", "score": "0.862083", "text": "def add(self, product, quantity=1, update_quantity=False): # get product,quantity,update_quantity from cart/views.py\n product_id = str(product.id) # if the item didn't in the cart, initialize...
cb9c1c4fdb48eddde66afef13b7904af	Allow use of `hash` so that a \|Particle\| instance may be used as a key in a `dict`.	[ { "docid": "a801f58462299836602c20628475aa20", "score": "0.55745435", "text": "def __hash__(self) -> int:\n return hash(self.__repr__())", "title": "" } ]	[ { "docid": "82db20635cd06c7647f81eb18910a756", "score": "0.6146902", "text": "def __hash__(self):\n return hash(self._mean) + hash(self._variance)", "title": "" }, { "docid": "1ee89e699ad8265b4902c38050171d5d", "score": "0.61150706", "text": "def __hash__():", "title": "" ...
86ac939b5df955ee10bb53581ead4b69	Reformat gridder evaluated on the nu grid returned by make_evaluation_grids into the sampled C function which has an index for the closest gridpoint and an index for the fractional distance from that gridpoint	[ { "docid": "e7f43fd4d5950f40a55473836065815b", "score": "0.0", "text": "def gridder_to_C(gridder, R):\n M = len(gridder) // (2 * R)\n C = np.zeros((2 * R, M), dtype=float)\n for r in range(0, 2 * R):\n l = r - R + 1\n indx = np.arange(M) - 2 * M * l\n # Use symmetry to deal...	[ { "docid": "c823a3cba264c1f5bea7da3f273e4960", "score": "0.59278005", "text": "def convertDistanceFromPartyGrid(d, index):\n return d*PartyGlobals.PartyGridUnitLength[index] + PartyGlobals.PartyGridToPandaOffset[index] + PartyGlobals.PartyGridUnitLength[index]/2.0", "title": "" }, { "doci...
dc8ca5ea91bb92119eca9c9fce7484a5	Set lase state to either True or False.	[ { "docid": "650f72297f6ef32dd426f087087f11b2", "score": "0.73475146", "text": "def lase(self, state: bool):\r\n self._lase = state\r\n self.lase_hist.append(state)\r\n\r\n # if the new option is the same as before don't send changes to labjack\r\n if (self.lase_hist[-2] != st...	[ { "docid": "f39497edbf7019fa15424233840455d3", "score": "0.6832224", "text": "def set_living(self, state):\n if isinstance(state, bool):\n self.living = state\n else:\n raise TypeError('state must be boolean.')", "title": "" }, { "docid": "a8ee0c74ec516994...
0a3f8726bab73ebb368b400097bed051	Initialize the networks and Ops. Assume discrete space for dqn, so action dimension will always be action_space.n	[ { "docid": "c29ac42e89f0c747d00e3c9f5352047d", "score": "0.0", "text": "def init_opt(self):\n action_dim = self.env_spec.action_space.n\n obs_dim = self.env_spec.observation_space.flat_dim\n\n self.episode_rewards = []\n self.episode_qf_losses = []\n\n with tf.name_sco...	[ { "docid": "1e53c3d8b3fa5a4ff22e11b3435270ca", "score": "0.7468827", "text": "def init(self, net_dim, state_dim, action_dim):", "title": "" }, { "docid": "bb193d6ac445ca2d0ed5820ee42a2a9a", "score": "0.68246925", "text": "def initialize_models(self):\n # this information might...
64077ca2c30dd59cbbb080a41f79cfe4	Reload the index table. Useful if the file is being written too when read	[ { "docid": "dd998432bd98573535cc5d0c24862dd6", "score": "0.6675373", "text": "def reload(self):\n self._scan_file(self.filesize, self.n_entries, self.fpos)", "title": "" } ]	[ { "docid": "56cff3ec649cd56bb356b38988b314b4", "score": "0.7886104", "text": "def reload_index(self, index_dir):\n print(\"Reloading Index...\")\n self.index.loadIndex(index_dir, load_data=True)", "title": "" }, { "docid": "604659cd191b4a69894922483f31180e", "score": "0.764...
e4c501b5a15d5938a40f9b6d7a9e3f64	SQL query for comparison	[ { "docid": "555e45c55f904218e0d7f5311567405f", "score": "0.0", "text": "def test_clean_all(self):\n print(\"TEST NETTOYAGE INTÉGRALE\")\n CleaningDB.cleaning_all_products()\n for t in TABLES:\n with connection.cursor() as cursor:\n sql = \"SELECT * FROM %s;...	[ { "docid": "e6b845161c19b8f140eda6940b5bcaf5", "score": "0.60658246", "text": "def compare_with_old_data_query(self):\n raise NotImplementedError", "title": "" }, { "docid": "6bf6e08760ea97956415d68ce530b8c7", "score": "0.59604883", "text": "def test_linear_conditional_query(s...
3001344b6fc182cc3564ae5a8062821a	Add an OpenAPI extension for marshmallow_enum.EnumField instances	[ { "docid": "9514b932c402f6c2f21dc187ca92ba11", "score": "0.67637134", "text": "def enum_to_properties(self, field, **kwargs):\n import marshmallow_enum\n\n if isinstance(field, marshmallow_enum.EnumField):\n return {\"type\": \"string\", \"enum\": [m.name for m in field.enum]}\n return {...	[ { "docid": "381e3a39199513c60cbc001503cc590d", "score": "0.6585628", "text": "def validate_enum(validator, enums, instance, schema):\n\n if isinstance(instance, tagged.Tagged):\n instance = instance.base\n\n yield from mvalidators.Draft4Validator.VALIDATORS[\"enum\"](validator, enums, insta...
61352d110ea023b6cacf94292173435c	Median number of shares or units of a given asset traded over a 21 day period.	[ { "docid": "0d2284f3fe9a982a63f69431d5e090dd", "score": "0.0", "text": "def adv22_day_pct(self) -> float:\n return self.__adv22_day_pct", "title": "" } ]	[ { "docid": "8ba43e7a940ff8dc1b498a4bbbc7fc6b", "score": "0.6867634", "text": "def median(wm):\n return statistics.median(wm)", "title": "" }, { "docid": "3febeacdf96db1146d2eda694f2c3be4", "score": "0.6539522", "text": "def mad(x):\n\n return median(abs(x - median(x)))", "t...
235106b93a4829d54c95d3f1a8f502a1	Singular value decomposition for m x n matrix and m >= n.	[ { "docid": "ca5da7e6a7c9a56b02bd75cdb26e1f9c", "score": "0.55227935", "text": "def _svd(a: jnp.ndarray,\n is_hermitian: bool,\n max_iterations: int) -> Sequence[jnp.ndarray]:\n\n u, h, _, _ = lax.linalg.qdwh(a, is_hermitian, max_iterations)\n\n v, s = lax.linalg.eigh(h)\n\n # Flips th...	[ { "docid": "db982ca250fc556cbd63ac23f542deb7", "score": "0.7124864", "text": "def singular_values(A):\n return jnp.linalg.svd(A, full_matrices=False, compute_uv=False)", "title": "" }, { "docid": "556edafe03784eb6ce50bb664b575aa8", "score": "0.6591163", "text": "def do_svd(self, k...
dd67518d84447aad41eace3b8f0145f1	Suppose we use few model instances of same id, and we must be able to update them independently on root model updated. For this we should use not get_model_by_id(), but get_model_copy_by_id().	[ { "docid": "7db3c694e8d3a4fcddd0927265e53bcf", "score": "0.69966036", "text": "def get_model_copy_by_id(cls, id):\n model = cls.get_model_by_id(id)\n return model.copy() if model else None", "title": "" } ]	[ { "docid": "6042db19415df1c676419f2b89732154", "score": "0.6511655", "text": "def _get_shallow_copy_model(model: nn.Module):\n old_to_new = dict()\n for name, module in _get_dfs_module_list(model):\n new_module = copy(module)\n new_module._modules = OrderedDict()\n for subname...
83a57e4c1e60f8cf3070ed419312c611	Get the initial arguments JSON.	[ { "docid": "2a6c308824d75aae2ffd0e84373d535d", "score": "0.86039567", "text": "def get_initial_arguments_json(self):\n # Define this in subclasses\n raise NotImplementedError", "title": "" } ]	[ { "docid": "0aa6ceae496229d0bfae3964aeeb7ca2", "score": "0.8692744", "text": "def get_initial_arguments_json(self):\n return self.get_object().arguments", "title": "" }, { "docid": "098d839e7def9d209ce24da0c6fb7d74", "score": "0.7873527", "text": "def get_initial_arguments_jso...
101c8997fedd063b35ebd1bab2cdf156	Warp frames to 84x84 as done in the Nature paper and later work.	[ { "docid": "55e1dc24ca078905558c0fcc429341b7", "score": "0.0", "text": "def __init__(self, env):\n gym.ObservationWrapper.__init__(self, env)\n self.width = 84\n self.height = 84\n\n if env.unwrapped.spec.id == 'MontezumaRevengeNoFrameskip-v4':\n self.ego_game = Mo...	[ { "docid": "441f2546b813a8c8f51f599898f61cba", "score": "0.5635383", "text": "def liftFrame(self, frame):\n\t\tframe.lift()", "title": "" }, { "docid": "81435cd2d848904bced17e4bc3157f38", "score": "0.55682594", "text": "def adjust_images(self, now=0):\n if self.direction != se...
a3d3c12ec19b6263be1c8b39b2a8c1fc	Return number of variable figure elements, assume zero.	[ { "docid": "ec10f9e111a0f970fd9481390af80121", "score": "0.63331455", "text": "def count_variable_connections(self):\r\n self.browser.implicitly_wait(1)\r\n try:\r\n figs = self.browser.find_elements_by_class_name('variable-connection')\r\n count = len(figs)\r\n ...	[ { "docid": "a376965fb7e385d6100eeb757767e5fd", "score": "0.7942516", "text": "def count_variable_figures(self):\r\n self.browser.implicitly_wait(1)\r\n try:\r\n figs = self.browser.find_elements_by_class_name('variable-figure')\r\n count = len(figs)\r\n finally...
5518a8e037777ad1a20b958e7a9b5d51	Generate the different list items by selecting a random element from each list. Needs an list of invoice names as an input.	[ { "docid": "6a4ad741be49c6bd7279709f7a92b888", "score": "0.6615224", "text": "def generate_list_items(listItemList):\n listItems = []\n numberOfListItems = random.randint(1,10)\n currencies = [\"\",\" £\", \" $\", \" CHF\"] # \" €\",\n totalSyn = [\"\", \"Gesamt \", \"Gesamt: \", \"Total \",...	[ { "docid": "2dd8a6155aa29361b6440537abf3c93f", "score": "0.5974432", "text": "def sample_generator(iterator, items_wanted=1):\n selected_items = [None] * items_wanted\n for item_index, item in enumerate(iterator):\n for selected_item_index in range(items_wanted):\n if not random....
4fa87d05fb8569c1d1b575e0e1c84c93	run the given function while collecting arguments to a target function	[ { "docid": "bfc485186e6c37622ae9b1a850793e88", "score": "0.0", "text": "def get_contexts_via_monitor(driver, caller_va, decoder_fva: int, index: viv_utils.InstructionFunctionIndex):\n try:\n caller_fva = index[caller_va]\n except KeyError:\n logger.trace(\" unknown function\")\n ...	[ { "docid": "f6aaecb9bcb2c146ba087c9f92a0222e", "score": "0.7327121", "text": "def run(self):\n if self.function:\n self.function(self.args)", "title": "" }, { "docid": "0f44c5f93b96523d3fd03ac59bdde2cc", "score": "0.7171412", "text": "def run(self):\n self.fn(self.ar...
aad0522ace98fd658bbd18bf9978bf1b	Set the callback to call when data is received from the Arduino	[ { "docid": "11166a6a9720889551bf439677821d1c", "score": "0.7634608", "text": "def set_data_received_callback(self, callback):\n self._data_received_callback = callback", "title": "" } ]	[ { "docid": "89ce51f1ed4e906a68428dcb0030d809", "score": "0.690485", "text": "def _listener_callback(self, data):\n self._dynamixel_current_state = data", "title": "" }, { "docid": "434a456c2ff3757c8479112bdc016566", "score": "0.67482555", "text": "def callback():", "title"...
ccdd6186cc6ccd4013894a7d4f98def8	find the module(s) given the name(s)	[ { "docid": "7d55dcfc22921a14d2acde0e99e59f72", "score": "0.6622528", "text": "def find_modules(self, requested_names):\n found_modules = set()\n for requested_name in requested_names:\n is_instance = ' ' in requested_name\n\n for module_name, module in self.py3_wrappe...	[ { "docid": "25fe60cd21b7004b8076805f2bb02ae5", "score": "0.7484802", "text": "def _find_package_in_modules(name: str) -> Optional[str]:\n package_specs = importlib.util.find_spec(name)\n\n try:\n importlib.util.module_from_spec(package_specs) # type: ignore\n except Attr...
1822aa152e4ae6101470c797e06ea372	Save model pipeline (tfidf + model), target names mapping and config.	[ { "docid": "5812b96de2030d1febf685852c3f1f32", "score": "0.7611598", "text": "def save_model(\n pipe: Pipeline,\n target_names_mapping: Dict[int, str],\n config: Dict[str, Any],\n) -> None:\n\n # save pipe\n joblib.dump(pipe, config[\"path_to_save_model\"])\n\n # save target names mapp...	[ { "docid": "e40de5c6aa241e21541b11fbaa5a2203", "score": "0.72795296", "text": "def save_model(self):\n joblib.dump(self.pipeline, \"model.joblib\")", "title": "" }, { "docid": "f44c48b51254da0f5311f1e7d429aa89", "score": "0.71536785", "text": "def save_model(self):\n jo...
d392101f4958355ebaa3029234bddc9f	Searches JIRA for issues using a JQL query.	[ { "docid": "6249cf1b0a95f8479077c980266af686", "score": "0.6391476", "text": "def complete_search(\r\n self, jql, validate_query=None, fields=None, expand=None,\r\n properties=None, fields_by_keys=None,\r\n batch_size=DEFAULT_BATCH_SIZE):\r\n query = {'jql': jql}\...	[ { "docid": "79d565c73706b0e9138be3112ed4be00", "score": "0.7664945", "text": "def issues_search(self, jql='order by key', start_at=0, max_results=1000, validate_query=True, fields='id'):\n\n loop_count = max_results // BATCH_SIZE_ISSUES + 1\n issues = list()\n last_loop_remainder = ...
513423d0730ce67c2ce4b29c54cea9b8	Name of the torrent	[ { "docid": "f502bdaaa7e80f70f74bffc52d4af1b2", "score": "0.6839437", "text": "def name(self):\n if 'name' not in self.metainfo['info'] and self.path is not None:\n self.metainfo['info']['name'] = os.path.basename(self.path)\n return self.metainfo['info'].get('name', None)", ...	[ { "docid": "edc7919d4d56da18ec23e5bdee1b835a", "score": "0.7028946", "text": "def getName(self) -> unicode:\n ...", "title": "" }, { "docid": "edc7919d4d56da18ec23e5bdee1b835a", "score": "0.7028946", "text": "def getName(self) -> unicode:\n ...", "title": "" }, ...
8e5f61833b87cea1b17fb804514a3fbc	Returns a list of possible board layouts, given the player whose turn it is, and a list of valid moves.	[ { "docid": "3a957289a63ff511b3ccdcd8b6daffa7", "score": "0.7096633", "text": "def get_possible_boards(board, moves, player):\n boards = []\n for move in moves:\n new_board = board[:]\n new_board[int(move)-1] = player\n boards.append(new_board)\n return boards", "title":...	[ { "docid": "0e50adc01cbb6cb681b3a100d0724f31", "score": "0.65457636", "text": "def available_moves(self, player):\n pieces = 0\n if player == Player.white:\n pieces = self._white_pieces\n elif player == Player.black:\n pieces = self._black_pieces\n else:...
fb480e403a4d5dd341c51577f1b55220	This is the normalization step generating dbt models files from the destination_catalog.json taken as input.	[ { "docid": "e11d0077aeb8e9e1aae12b5f7489774a", "score": "0.71015024", "text": "def generate_dbt_models(destination_type: DestinationType, test_resource_name: str, test_root_dir: str):\n catalog_processor = CatalogProcessor(os.path.join(test_root_dir, \"models\", \"generated\"), destination_type)\n ...	[ { "docid": "ce965d0bf5e58e59351a5e541e0e9128", "score": "0.5778649", "text": "def build_cm_models(output_dir):\n\n for roots,dirs,files in os.walk(output_dir):\n path_to_sto_list=[]\n path_to_cm_list=[]\n path_to_dir=[]\n db_exist=[]\n for name in dirs:\n ...
7b214326c84de19606bbaaccfe707110	returns a dictionary of	[ { "docid": "94d2ec278189c510866bc3aeee035468", "score": "0.0", "text": "def predict(self, at=5):\n recs = {}\n for i in range(0, self.R_hat.shape[0]):\n pl_id = self.pl_id_list[i]\n pl_row = self.R_hat.data[self.R_hat.indptr[i]:\n s...	[ { "docid": "0f82d3748c9bac7976eba45ff1d4a511", "score": "0.7530778", "text": "def _asdict(self):", "title": "" }, { "docid": "3bf041b0ae01ad45ea6f683f8414e7ee", "score": "0.7360245", "text": "def to_dict(self):", "title": "" }, { "docid": "3bf041b0ae01ad45ea6f683f8414e7ee...
5e06fab16bb0d071c255737bfb52f140	Return pulls tags from a given username and repository ID.	[ { "docid": "01255111111eef23e1ba5ba8a38f9085", "score": "0.6062889", "text": "def pulls_by_id(self, repository_id, access_token=None):\n return self._complete_request_by_id(\n repository_id, \"pulls\", access_token)", "title": "" } ]	[ { "docid": "182350f0750dd0612243d00a837739d5", "score": "0.71279055", "text": "def tags_by_name(self, username, repository_name, access_token=None):\n return self._complete_request_by_name(\n username, repository_name, \"tags\", access_token)", "title": "" }, { "docid": "af...
0be3860b5d6da42b962f0c7443225204	Constructor for Datacenter data structure. self.name > str self.clusters > list(Cluster)	[ { "docid": "216bee2b06cd3fac63d0b176ae82f434", "score": "0.7706204", "text": "def __init__(self, name, cluster_dict):\n\n self.name = name\n self.clusters = cluster_dict", "title": "" } ]	[ { "docid": "64611b4547d39b69e6d2ea2437c484bc", "score": "0.682774", "text": "def __init__(self,\r\n X=None,\r\n y=None,\r\n AttributeNames=None,\r\n classNames=None,\r\n N=None,\r\n M=None,\r\n ...
81ee465edfa3c2d3a25c93edc7c36b88	ComplianceViolation a model defined in Swagger	[ { "docid": "ce33712e662119a97a00e7b66fbd10d3", "score": "0.0", "text": "def __init__(self, compliance_type=None, listing_id=None, offer_id=None, sku=None, violations=None): # noqa: E501 # noqa: E501\n self._compliance_type = None\n self._listing_id = None\n self._offer_id = None\n...	[ { "docid": "05c207eb191ec3969dc106fb77be54b2", "score": "0.5712235", "text": "def missed_the_point():\n abort(422, messages={'_schema': ['Please, more parameters']})", "title": "" }, { "docid": "824e81f0df0e610efff1bc783f0cb58a", "score": "0.5648701", "text": "def validate(self, a...
5da92a368f8d5dedef47a25c95e01cc5	Fetches all incidents not marked as 'Resolved'	[ { "docid": "c01976be6a59d05bb2960bf2c8ce3f02", "score": "0.6302394", "text": "def get_all_open_incidents(self):\n self.open_incidents = []\n try:\n response = self.session.get(f'{self.base_url}/incidents')\n except RequestException as err:\n sys.exit(f'REQUEST ...	[ { "docid": "625033ffb356b956860c15ebe156935a", "score": "0.66342324", "text": "def unresolved_issues(self):\n return self.issue_set.filter(resolved_state__isnull=True)", "title": "" }, { "docid": "8aca38791acd7bc07aab9efcffe76b20", "score": "0.5845197", "text": "def get_all_un...
751e3123e3738fb33e4824f9c5d67a85	Get status of lastest submission for sample sets in the workspace	[ { "docid": "1339076c31977b1205ad36d02b201592", "score": "0.5348673", "text": "def get_sample_set_status(self, configuration):\n return self.get_entity_status('sample_set', configuration)", "title": "" } ]	[ { "docid": "3c7aeddc0da95b45b75c63b6011e29da", "score": "0.614393", "text": "def last_success(cls):\n qs = cls.objects.filter(complete=True, has_error=False)\n return qs.order_by(\"-start_run\")[0] if qs.count() else None", "title": "" }, { "docid": "c4aa6f29437eb3be907e23d9639...
33f1864dad6aa245ab722088c583c125	Anonymous function node. Inline the function definition, and return the function identifier.	[ { "docid": "cff250350e2e20c429b64454aca71c7f", "score": "0.5156287", "text": "def _(node, inline, bindings=()):\n name = next(name_generator('lambda'))\n inline_children = []\n\n # bindings in local scope\n binding_assignments = [\n python.Assign(\n targets=[\n ...	[ { "docid": "0de17cbc78992eb9c121abcf78ba8ae1", "score": "0.67060685", "text": "def _(node, inline):\n name = generate(\n tscl.Function(\n parameters=tscl.List(expressions=()),\n expressions=node.expressions,\n ),\n inline,\n node.bindings.expressions,...
53708b31bc8ffe4495009b081e518f75	Return a particular LVNF in this tenant.	[ { "docid": "6d8e99eae8651813c00b8bc5eb1e953f", "score": "0.756228", "text": "def lvnf(self, addr):\n\n if self.tenant_id not in RUNTIME.tenants:\n return None\n\n if addr not in RUNTIME.tenants[self.tenant_id].lvnfs:\n return None\n\n return RUNTIME.tenants[sel...	[ { "docid": "243aa44a45f3fde3c4066a7040d4bcc6", "score": "0.68816304", "text": "def lvnfs(self):\n\n if self.tenant_id not in RUNTIME.tenants:\n return None\n\n return RUNTIME.tenants[self.tenant_id].lvnfs.values()", "title": "" }, { "docid": "ea2ba440f1a9f95c41ee0374...
28ac0f95cb5d7de56dd58690cd1c22c2	Generates all pythagorean triples with sum (a+b+c) n, mn odd and m,n coprime, and returns the pythagorean triples (a,b,c)	[ { "docid": "a822654a02fcadae9ba4ccd9714616f7", "score": "0.7234538", "text": "def pythagorean_triples(L, give_sum=False):\n M = int((L//2)**0.5)+1\n for m, n in coprimes(M):\n # Skip m,n with (m-n) even\n if (m-n) % 2 == 0: continue\n for k in itertools.count(1):\n ...	[ { "docid": "270dba8f27498fcb432a46b9affb1de2", "score": "0.7959152", "text": "def pythagorean_triplet(n):\n\n max_value = n // 3 + 1 # set maximum for a, not exceed 1/3 of n, to prevent b < a or c < a\n for a in range(1, max_value):\n rest = n - a\n for b in range((rest-a)//2 + 1, ...
c743f7246010cbe6833d12c760ee90cc	Checks wheather coordinate (x,y) is in circle with center s and radius val	[ { "docid": "b5c4a74dcb7087397ca948845c677589", "score": "0.0", "text": "def check_incl(s,x,y, val, norm = l1_norm, use_norm=True):\n \n keki = s.split(' ')\n o = float(keki[0])\n h = float(keki[1])\n if(not use_norm):\n if((abs(o-x)<val) and (abs(h-y))<val*2):\n return T...	[ { "docid": "96969616fcf6e2eb9efb0ce4cccce6c9", "score": "0.8017797", "text": "def is_in_circle(center, radius, position):\n\n d = math.sqrt(((center[0] - position[0]) 2) +\\\n (center[1] - position[1]) 2)\n\n return d <= radius", "title": "" }, { "docid": "11ecf...
c89fd9c6e428416132183bca926ce386	The publisher of the 3rd Party Artifact that is being bought. E.g. NewRelic	[ { "docid": "5151abc3e492ac5419ad39fafb658524", "score": "0.65167165", "text": "def publisher(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"publisher\")", "title": "" } ]	[ { "docid": "5edae9fd69ceb12a313846410a240744", "score": "0.69928986", "text": "def publisher(self) -> str:\n return pulumi.get(self, \"publisher\")", "title": "" }, { "docid": "0da235a06b1f437255f5219cd6eb2efb", "score": "0.6917126", "text": "def publisher(self) -> Optional[st...
8d0ad179f81a20ff6613833cd14acd7b	Return all statistic_ids (or filtered one) and unit of measurement. Queries the database for existing statistic_ids, as well as integrations with a recorder platform for statistic_ids which will be added in the next statistics period.	[ { "docid": "184f0821fec57cb5786e98df8f012a1e", "score": "0.64374584", "text": "async def async_list_statistic_ids(\n hass: HomeAssistant,\n statistic_ids: set[str] \| None = None,\n statistic_type: Literal[\"mean\"] \| Literal[\"sum\"] \| None = None,\n) -> list[dict]:\n instance = get_instance...	[ { "docid": "f9a4da3caaebf0150c69dc96a64d5ea5", "score": "0.7760222", "text": "def list_statistic_ids(\n hass: HomeAssistant,\n statistic_ids: set[str] \| None = None,\n statistic_type: Literal[\"mean\"] \| Literal[\"sum\"] \| None = None,\n) -> list[dict]:\n result = {}\n instance = get_inst...
6e23d6d89d173283541b4f362872abe2	Sums the ASCII character values mod256 and returns the lower byte of the two's complement of that value	[ { "docid": "16e9820e599d012efae3f474349ed2fc", "score": "0.5380465", "text": "def get_checksum(file):\n sum = 0\n with open(file, 'r') as f:\n data = f.read()\n for i in range(len(data)):\n sum = sum + ord(data[i])\n temp = sum % 256\n rem = -temp\n return '%2X' %...	[ { "docid": "5219472d468a1de5c385225eaff14233", "score": "0.65635484", "text": "def SignedChar(value):\n if value < 0:\n # Perform two's complement.\n return value + 256\n return value", "title": "" }, { "docid": "535d30b8d513bdbc8168cea6c4f9e15f", "score": "0.6562...
21bc9a94854948e612cbde5974c136a5	Return a XMLRPC wrapper function which can optionally intercept a call to the given ROS master XMLRPC method. method is the desired ROS master XMLRPC function to call	[ { "docid": "a32b1731a45187225a9d21080b5d405f", "score": "0.7624827", "text": "def __getWrapper(self, method):\n def wrap(args):\n \"\"\"Callback function for an XMLRPC function.\n\n args -- the method input arguments\n\n \"\"\"\n # If this class has ...	[ { "docid": "10d52d7f9b2d1cfc71a797955024df93", "score": "0.7143489", "text": "def rpcmethod(func):\n func.rpcmethod = True\n return func", "title": "" }, { "docid": "3074070b2ffa1480ab2b169d64206e36", "score": "0.68000335", "text": "def rpc_method(func):\n func.rpc_c...
8bcfc6a93aee455d8653b8831eda99de	Run an arbitrary query by Variable Elimination. What is the analytic cost of this? You have to do K noise queries in a graph with K endog nodes + K exog nodes in normal CFI. In twin network inference, you have to do 1 query in a graph with 2K endog nodes + K exog nodes.	[ { "docid": "8164bfb7ca118009b8158e8e045cb136", "score": "0.5620961", "text": "def query(self, var, observed, counterfactual=False, twin=False):\n if not isinstance(var, list):\n var = [var]\n if twin:\n # time_start = time.time()\n infer = VariableEliminati...	[ { "docid": "9d9bd34ad7a79c09351a5f63d8b1a69e", "score": "0.64059097", "text": "def run(self, query, observed, elim_order):\n\n # CHANGE from original code:\n # Here is where my code begins\n\n # Step 1: indentifying and reducing observables/evidence\n self.overwrite_log(\"STE...
7c4d9379deed687e5ce4e2288b5a1b41	Simple JSON Formatter wrapper for consistent formatting.	[ { "docid": "6997219fa49faa74f7a7f38d7dcb8b21", "score": "0.0", "text": "def proto_to_json(proto: message.Message) -> str:\n return json_format.MessageToJson(\n message=proto, sort_keys=True, preserving_proto_field_name=True)", "title": "" } ]	[ { "docid": "0cca7b880ba3f7c97ca0b4032b21db93", "score": "0.7336949", "text": "def make_formatter(format_name):\n\n if \"json\" in format_name:\n from json import dumps\n import datetime\n\n def jsonhandler(obj):\n obj.isoformat() if isinstance(\n obj, (d...
84a2546916e464b4e9efb9ec4517028f	Generate a static plot of a population	[ { "docid": "8d12c199b22d65fbbed1d75d237cf117", "score": "0.683494", "text": "def plot_population(self,\n population_name: str,\n x: str,\n y: str,\n xlim: tuple = None,\n ylim: tuple = None...	[ { "docid": "31c58aced541f7fab74b9e4cce69790d", "score": "0.65963537", "text": "def plot_population():\n import numpy as np\n import matplotlib.pyplot as plt\n import astropy.units as u\n from astroquery.nasa_exoplanet_archive import NasaExoplanetArchive\n\n\n #First establish the rules th...
b5800e48092206b95caebfbb2b34ccd2	Intialize a generic file reader with batching for list of files	[ { "docid": "4c72d0005257cfd74efa0b155be92ac2", "score": "0.6491739", "text": "def __init__(self, records_list, image_options={}):\n print(\"Initializing Batch Dataset Reader...\")\n print(image_options)\n self.files = records_list\n self.image_options = image_options\n ...	[ { "docid": "5a66de5c5579ae4e5898e279c0a111d4", "score": "0.6745472", "text": "def reader_for_list(filelist):\n\n for filename in open(filelist):\n filename = filename.rstrip()\n logging.info(str(filename))\n \n for x in reader(filename):\n yield x", "title": "" ...
e25d912f5d2e53d37a000386bda81e4f	read data/nodes.json file from this project	[ { "docid": "9ea1887b72b94e53d863e551452aab0c", "score": "0.80150056", "text": "def readNodesJsonFile():\n fileName = \"data/nodes.json\"\n with open(fileName, \"r\") as f:\n jsonString = f.read()\n return jsonString", "title": "" } ]	[ { "docid": "59d00d70bbdea5d642c76118be182d5d", "score": "0.6795475", "text": "def read(filename):\n\twith open(filename) as data_file: \n\t data = json.load(data_file)\n\t results = []\n\tfor obj in data[\"nodes\"]:\n\t\ttemp = obj.split(\":\")\n\t\tif len(temp) == 2:\n\t\t\tresults.append(Link...
be307e889961de8b22b58dbcb9b623f4	Return max Return the peak (and/or the index of the peak) in a given 2D array	[ { "docid": "1179a2aecb80a7c30e687babc324e634", "score": "0.0", "text": "def _return_max(x, y, exp_dnu=None,):\n xx, yy = np.copy(x), np.copy(y)\n if list(yy) != []:\n if exp_dnu is not None:\n lst = list(np.absolute(xx-exp_dnu))\n idx = lst.index(min(lst))\n els...	[ { "docid": "1eb06e174c15dd61c4e2c4aa238fdf6e", "score": "0.783653", "text": "def findPeak(image) :\n maxValue = -100000.0\n maxIndex = [0,0]\n if(len(image.shape) == 2) :\n maxAxis = image.argmax(0)\n for pos in maxAxis :\n if(pos != 0) :\n for i in range...
be816247720df41620388f43b0eda2db	Recursively creates the decision tree. This method assumes that the dataTuples are sorted in descending order of feature entropy.	[ { "docid": "91b41d7e4a24be12f78539f37179ebd5", "score": "0.67282295", "text": "def createTree(dataTuples):\n\t\n\tdataTuples = dataTuples[:]\n\n\tif len(dataTuples) == 0:\n\t\t# Return a value of 0 which will \n\t\t# later be replaced with the kind of tree that will be found at this leaf\n\t\t#print \"L...	[ { "docid": "2f3829909b554b823e81942a3ca0fd1f", "score": "0.718289", "text": "def _gen_decision_tree(self, data, cur_depth):\n label = data[:, -1]\n print(type(23432))\n print(len(self.f_info))\n # ! Case 1: Leaf nodes -> generate predictions\n # Case 1.1: pure node -> ...
bfe9e75675fa5a41eb65f52393eeb1df	Import a discourse from csv file	[ { "docid": "e6147a781d4e22ec9c8ff9f560ad3b7e", "score": "0.7458001", "text": "def import_discourse_csvs(corpus_context, typed_data):\n string_set_template = 'n.{name} = csvLine.{name}'\n float_set_template = 'n.{name} = toFloat(csvLine.{name})'\n int_set_template = 'n.{name} = toInt(csvLine.{na...	[ { "docid": "f2ced21216439f9c43fe27a6ffed1504", "score": "0.63136464", "text": "def load_database():\n # copy data from csv file to database\n with connection:\n with connection.cursor() as cursor:\n with open('cleaned_collisions.csv', 'r') as f:\n next(f) # Skip th...
2f3514ca783200d98c92a95c191f312e	Build a list of adjectives starting with passed letter	[ { "docid": "6ebff2d920f2f0cc133ee2de8ed358e3", "score": "0.7244773", "text": "def get_adjectives_starting_with(first_letter):\n api_data = get_adjectives_data(first_letter)\n adjectives = parse_adjectives_data(api_data)\n return adjectives", "title": "" } ]	[ { "docid": "b38229f8f650f088818b96fc992bd8cb", "score": "0.6487598", "text": "def get_animals_starting_with(first_letter):\n animals_list = ANIMALS[first_letter]\n return animals_list", "title": "" }, { "docid": "902048fe510cff13baabc92782125940", "score": "0.6204445", "text": ...
6bd4008d04365eca02543aa406daab14	Get or set str value for 'defaultVRDEExtPack' The name of the extension pack providing the default VRDE. This attribute is for choosing between multiple extension packs providing VRDE. If only one is installed, it will automatically be the default one. The attribute value can be empty if no VRDE extension pack is insta...	[ { "docid": "9eb2b1cd771ed8dc51fd8f17732ada53", "score": "0.8275803", "text": "def default_vrde_ext_pack(self):\n ret = self._get_attr(\"defaultVRDEExtPack\")\n return ret", "title": "" } ]	[ { "docid": "003848579f7abc97ed2c7d5cde483606", "score": "0.6818636", "text": "def vrde_ext_pack(self):\n ret = self._get_attr(\"VRDEExtPack\")\n return ret", "title": "" }, { "docid": "18bc3517be47d423160a4da84adfadec", "score": "0.5376459", "text": "def part_component_...
37bede708518e1fdcba27e6f1a95117d	Implement a cnn to embed the images in relation to their tags. For a walkthough	[ { "docid": "2ead985af337a8143ff5bcb27e844058", "score": "0.6047527", "text": "def build_cnn(image_input, image_output, tag_size):\n W_conv1 = weight_variable([5, 5, 1, 32])\n b_conv1 = bias_variable([32])\n\n x_image = tf.reshape(image_input, [-1, 127, 127, 1])\n h_conv1 = tf.nn.relu(conv2d(...	[ { "docid": "e2d9ed5733792dc2e0fd49d1b1ada69f", "score": "0.58495724", "text": "def tag_cloud(link=22656, \n lim_num_tags=200, \n image_dims=(400, 200),\n skip_tags = [],\n out_filepath=\"TagCloud.png\",\n ):\n\n info = taginfo(link=link...
c16c73e55a512a5510b33a0c0f6efe0a	Save progress in crack phase.	[ { "docid": "8f4c2960e773cbb6e6f06df7299d617f", "score": "0.0", "text": "def set_cracked(self, cracked):\n self.cracked = open(cracked, \"a\")", "title": "" } ]	[ { "docid": "609015f3ffca95ee235a70fb75c8cec3", "score": "0.6551519", "text": "def save_progress(block, addresses, new_addresses, progress_file, with_db):\n print(\"Saving progress... do not close/power off computer until process is\"\n \" complete.\")\n update_progress(block, addresses, p...

54edd522ff7874290002d20c11258464

Displays information about a given dataloader. Prints the size of the dataset, the dimensions of the images and target images, and displays a batch of images and targets with `imshow_batch()`.

[ { "docid": "229d1344396f33cd0894a070ef6f73a1", "score": "0.8973931", "text": "def dataloader_info(dataloader):\n images, targets = iter(dataloader).next()\n print(\"Number of images:\", len(dataloader.dataset))\n print(\"Image size:\", images.size())\n print(\"Targets size:\", targets.size()...

[ { "docid": "c860b5e3ffea39704ce31445f96037ca", "score": "0.73829144", "text": "def print_dataset(dataloader, num_elements=3):\n for element, i in zip(dataloader, range(num_elements)):\n print('+++ Image {} +++'.format(i))\n for key in element.keys():\n print(k...

9ef2258445ccb3bdee3a53fecf5db85f

write content to a file with newline character

[ { "docid": "2e3afb6c2d524efa8778274d7d898516", "score": "0.67021", "text": "def put(file, line=''):\n\tfile.write(line + '\\n')", "title": "" } ]

[ { "docid": "4973ee05daef1745514ee87dca7aaae3", "score": "0.779091", "text": "def write_to_file(fname,content):\r\n f = open(fname, \"a\")\r\n f.write(content)\r\n f.write(\"\\n\")\r\n f.close()", "title": "" }, { "docid": "50275ecbc66e5545bbd5b6931007e09d", "score": "0.707061...

a33b8f4a6d21b020da34d58c400fba42

Adds an entry to the routing table of the Node.

[ { "docid": "0fedbf05d03351e1e6e7dc8da7a7a5c0", "score": "0.6699021", "text": "def routing_table(self, entry):\n #check if entry is a dict, or if empty list reset routing_table\n if not isinstance(entry, dict):\n if entry == []:\n self._routing_table = []\n ...

[ { "docid": "48c2432e16918993b0fb714584798688", "score": "0.6901373", "text": "def update_routing_table(self, node_id: NodeID) -> None:\n self.logger.debug(\"Updating %s in routing table\", encode_hex(node_id))\n self.routing_table.add_or_update(node_id)", "title": "" }, { "doci...

cd323382656cf93ddc488225cd8382bb

Percent point function at q, or inverse CDF. Approximated by looking up the index in the cdf table that is closest to q.

[ { "docid": "bf20399d7950fabe0303fc32e8edfabe", "score": "0.6197692", "text": "def ppf(self, q):\n q = np.atleast_1d(q)\n\n # look up the index of the quantile in the 2D CDF grid\n values = []\n for qi in q:\n # find index in grid for every dimension\n id...

[ { "docid": "f12bea694f0f6a1129ecd0f73232bc83", "score": "0.71535176", "text": "def cdf_percentile(x, p, q=50.0):\n\n # Determine index where cumulative percentage is achieved\n i = np.where(p>q/100.0)[0][0]\n\n # If at extremes of the distribution, return the limiting value\n if i==0 or i==l...

15e5fdf7b8cc1f7fa79217b67c1d0b34

Convert, as necessary, l,b,d into floats

[ { "docid": "74bacff892397f91578a1959e95a602d", "score": "0.0", "text": "def parse_lbd(gal_l, gal_b, distance):\n l = parse_units(gal_l, 'deg', 'Galactic longitude')\n b = parse_units(gal_b, 'deg', 'Galactic latitude')\n d = parse_units(distance, 'kpc', 'distance')\n return l, b, d", "tit...

[ { "docid": "8e5c2f05726d138a1343dda176d0b088", "score": "0.64943665", "text": "def str2flt(vec):\n newvec=[]\n for v in vec:\n try:\n newv=float(v)\n except:\n newv=None\n finally:\n newvec.append(newv)\n return newvec", "title": "" },...

29ac3efcaa28934ad9bb8706440c339e

bool, whether supports role mailing lists

[ { "docid": "289d3ce6018d57d5f4249092058e5749", "score": "0.5978602", "text": "def supports_mailing(self):\n return NETWORK_NAME == 'Eionet'", "title": "" } ]

[ { "docid": "293a10f48e27313337970f0560635621", "score": "0.6431647", "text": "def canAssignRole(role):", "title": "" }, { "docid": "64886deff0eadde94b2434cbb708cf61", "score": "0.6292626", "text": "def check_role(rolelist, message):\n for role in rolelist:\n # noinspection ...

cd70aef18393b85c960a4f28b6c4af6e

Initialize remote value of KNX DPT 1.001.

[ { "docid": "60bdcba32d8ddcf96df7845be210e2df", "score": "0.0", "text": "def __init__(\n self,\n xknx: XKNX,\n group_address: GroupAddressesType | None = None,\n group_address_state: GroupAddressesType | None = None,\n sync_state: bool | int | float | str = True,\n ...

[ { "docid": "996d1505c26ab59baae9bb93bb56606b", "score": "0.5731152", "text": "def test_from_knx(self):\n xknx = XKNX()\n remote_value = RemoteValueStep(xknx)\n assert remote_value.from_knx(DPTBinary(1)) == RemoteValueStep.Direction.INCREASE\n assert remote_value.from_knx(DPTB...

356a2e713ff7fa829665edd4d2a035ec

Devuelve la similitud de jaccard

[ { "docid": "eb5f9d72fcbfef9a06e4b3bb2e174a36", "score": "0.53147584", "text": "def jaccard_similarity(list1, list2):\n intersection = len(list(set(list1).intersection(list2)))\n union = (len(list1) + len(list2)) - intersection\n return float(intersection) / union", "title": "" } ]

[ { "docid": "b28690c78a69fc481a989368dc9c914e", "score": "0.6333269", "text": "def jaccard(left, right):\n\n true_false = 0\n false_true = 0\n false_false = 0\n true_true = 0\n\n if len(left) == len(right):\n for index, val in enumerate(left):\n if left[index] == 1 and ri...

01866748369ab7177e45157e7fbc9253

Prints and returns the last digit of a number. If given something that's not a number, this returns None.

[ { "docid": "c0afb8ee1076a323ef93c4eb9f945235", "score": "0.8328857", "text": "def print_last_digit(number):\n number = (abs(number) % 10)\n print(\"{}\".format(number), end=\"\")\n return (int(number))", "title": "" } ]

[ { "docid": "b862fc37bc5cbaeaeab1578228d23fe9", "score": "0.8458338", "text": "def print_last_digit(number):\n print(abs(number) % 10, end=\"\")\n return (abs(number) % 10)", "title": "" }, { "docid": "67d1a5ad30a32391a4e44367e8b22772", "score": "0.7971203", "text": "def last_di...

9953d2c5c9bf35c5afb1bbb3d54db76c

plot pytstan submm sed output as SEDs specific to greybody models

[ { "docid": "5c59020054486de78b839e4f09a3bb5d", "score": "0.53297436", "text": "def pystan_postprocess_SED_plot(allfits, label=\"\"):\n\n labs = (\"1 comp\", \"2 comp\", r\"1 $\\beta=2$\", r\"2 $\\beta=2$\", \"thick\")\n for objname, fits in iteritems(allfits):\n for fit, lab in zip(fits, la...

[ { "docid": "2bcc1093c7f3f4c187383fab35b16510", "score": "0.62366015", "text": "def tsnescatterplot(model, word, list_names):\r\n arrays = np.empty((0, 300), dtype='f')\r\n word_labels = [word]\r\n color_list = ['red']\r\n\r\n # adds the vector of the query word\r\n arrays = np.append(arr...

c19228a3d740918fb2e3cafbd49ae231

Add a status line to the editor.

[ { "docid": "1d447c653b8fea3da5840afa452e3fd1", "score": "0.67183894", "text": "def addStatus(self, text, priority=0):\n pass", "title": "" } ]

[ { "docid": "b54316b4f7ef636bdd64ac3479e808a4", "score": "0.66472626", "text": "def add_line(self, line):\n height, width = self.window.getmaxyx()\n self.window.addstr(self.line_no, 2, line[:width-5])\n self.line_no += 1", "title": "" }, { "docid": "7900b80ea1e715119a86af...

17ba8b85a8254b519fe1b16970a97eb8

Transfers data from a file to an ndarray

[ { "docid": "3e85a6065442a3e646bad8126ed5e393", "score": "0.0", "text": "def extract_data(folder,file_name,lower,upper):\n\n csv_data = np.genfromtxt(\n fname=p(os.path.join(folder,file_name)),\n dtype=float,\n delimiter=','\n ,skip_header=1,\n usecols=range(lower,up...

[ { "docid": "1eda034ebc8d2c659548b8d7aa50c35f", "score": "0.6489842", "text": "def fread(f, n, dtype):\n if dtype is np.str:\n dt=np.uint8\n else:\n dt=dtype\n\n data_array=np.fromfile(f, dt, n)\n #data_array.shape=(n,1)\n return data_array", "title": "" }, { "doc...

a99170ae8bd5f9324da146051ce93030

Returns info string about resolution matrix.

[ { "docid": "0424d44b77317e85bf933553aae932ff", "score": "0.6297488", "text": "def __str__(self):\n info = self.par.copy()\n if self.par['kfix'] == 1:\n info['efixstr'] = 'fixed incident energy k_i = %2.4f A-1' % self.par['k']\n else:\n info['efixstr'] = 'fixed ...

[ { "docid": "a31e39add72e9642275dadc81b8580c8", "score": "0.69172955", "text": "def get_info(self):\n size_unit = self.get_display_grid_unit()\n pxy = self.grid.get_pixel_size().coordinates\n if size_unit is not None:\n if isinstance(size_unit, units.UnitBase):\n ...

3dbd503e47042cfe17541567b30f6a37

Return visible windows belonging to a process.

[ { "docid": "74ac6ca4f6cc2566322cea394c8397ae", "score": "0.77159387", "text": "def find_windows_for_process(self, process_id, display_name):\n pids = self.get_process_ids(process_id)\n if not pids:\n return []\n\n logger.info(\n 'Waiting for 30 seconds to ensure all windows appear: ...

[ { "docid": "3fd2e2789de871ce4471b156866f2db6", "score": "0.73314553", "text": "def get_hwnds(pid):\n\n def callback(hwnd, hwnds):\n if win32gui.IsWindowVisible(hwnd) and win32gui.IsWindowEnabled(hwnd):\n _, found_pid = win32process.GetWindowThreadProcessId(hwnd)\n if foun...

6125735d81cefe56f59732353a54fe8f

r"""Calculates the hydrophobic fitness of a protein. Hydrophobic fitness is an efficient centroidbased method for calculating the packing quality of your structure [3]_. For this method C, F, I, L, M, V, W and Y are considered hydrophobic. The

[ { "docid": "da3bb79821b7a97a8c5917e0e0bb199d", "score": "0.6614055", "text": "def calculate_hydrophobic_fitness(assembly):\n hydrophobic_centroids = []\n tyrosine_centroids = []\n polar_centroids = []\n for residue in [r for r in assembly.get_monomers()\n if isinstance(r, ...

[ { "docid": "bf03dd037f2a5f685f080a4bb7178227", "score": "0.63004637", "text": "def fitness(individual):\n x=individual[0]\n z=individual[1]\n return x**2-2*x*z+6*x+z**2-6*z", "title": "" }, { "docid": "5c18d3c652ff260be0aac9a138dbbd89", "score": "0.600077", "text": "def fit...

d5903c89d6dfcc07cfe76673180a10a9

Test deleting a user account without a password key

[ { "docid": "c59feb9eaa1d7a89cbd9585b060d0535", "score": "0.8261928", "text": "def test_deleting_account_without_password_key(self):\n self.create_user()\n self.create_category()\n self.create_recipe()\n with self.client:\n headers = self.helper_login_with_token()\n...

[ { "docid": "ce8c332843fdef45824dd2dfebf1fc0c", "score": "0.81951886", "text": "def test_deleting_account_with_empty_password_value(self):\n self.create_user()\n with self.client:\n headers = self.helper_login_with_token()\n response = self.client.delete('/auth/delete-...

db4f8aec007b41fb25bf66b292e606fd

Gets the identifier of this AccessPolicy. The id of the policy. Set by server at creation time.

[ { "docid": "b1ad433d654521b2cd6b094150da0ae9", "score": "0.0", "text": "def identifier(self):\n return self._identifier", "title": "" } ]

[ { "docid": "9ad0b7f8b226c09cd01ec534b4950829", "score": "0.7846321", "text": "def policy_id(self):\n return self._policy_id", "title": "" }, { "docid": "c2968e83c5829951cc14f939bb6ea7ea", "score": "0.7770658", "text": "def access_policy_id(self) -> Optional[str]:\n retu...

8f5dc2de06c33a5958ec5f8977de11e7

Return a list value for further modification.

[ { "docid": "2a441e6f8dbd5431a200434a00c53189", "score": "0.73336405", "text": "def _get_list_value_for_modification(self, key):\r\n\t\ttry:\r\n\t\t\tvalue = self.table[key]\r\n\t\texcept KeyError:\r\n\t\t\ttry: value = self.parent[key]\r\n\t\t\texcept AttributeError: value = []\r\n\t\t\tif isinstance(va...

[ { "docid": "41b6a4da55e32ff4b218fe4383986434", "score": "0.7180536", "text": "def listify(self, value):\n if isinstance(value, list):\n return value\n else:\n return [value]", "title": "" }, { "docid": "3fd078fcb797a7be3925f9dc2168d836", "score": "0.70...

53188e7e2ec681c64a362a4c2d810e21

Determine if a word is the same forwards and backwards.

[ { "docid": "75038c4e136eac304f760b5c396cb57a", "score": "0.67014337", "text": "def is_palindrome(word: str) -> bool:\n return word.casefold() == word[::-1].casefold()", "title": "" } ]

[ { "docid": "0e45a7271bc2dadb60a364e8fba5e6f2", "score": "0.78020686", "text": "def is_reverse(word1, word2):\n return (word1[::1] == word2[::-1])", "title": "" }, { "docid": "195dd0bdae702ea529fe5b342fa9b454", "score": "0.71716213", "text": "def es_palindroma(word):\n return word...

28a5c5061b080ba6988e28b43463b904

a c / \ / \ b ar > b a / \ / \ / \ bl c bl cl cr ar / \ cl cr

[ { "docid": "3409e010af32696ebec3666fd5fa4698", "score": "0.0", "text": "def __lr(self, a):\n b = a.left\n c = b.right\n cl = c.left\n cr = c.right\n\n # Rotation\n c.right = a\n c.left = b\n b.right = cl\n a.left = cr\n\n # Affected n...

[ { "docid": "9e6321f4f699dca82b8364355e8c5b8f", "score": "0.57508165", "text": "def cat_ears():\n return \"/\\\\\" + ((SIZE - 2) * \"-\") + ((SIZE - 2) * \"-\") + \"/\\\\\"", "title": "" }, { "docid": "3523727d40ae8f829a045dc98b172c26", "score": "0.5483277", "text": "def conc(left,...

84c2e88611ea74f7a6840290521a80ec

Place schematic components determined by the layout(keyswitches and diodes)

[ { "docid": "b98eb24e9d12bffc66b188815d5ada34", "score": "0.81252825", "text": "def place_schematic_components(self):\n switch_tpl = self.jinja_env.get_template(\"schematic/keyswitch.tpl\")\n\n component_count = 0\n components_section = \"\"\n\n # Place keyswitches and diodes\...

[ { "docid": "16fd92a4fdbc4fc5103d9521d2477e9b", "score": "0.7992899", "text": "def place_layout_components(self):\n switch = self.jinja_env.get_template(\"layout/keyswitch.tpl\")\n diode = self.jinja_env.get_template(\"layout/diode.tpl\")\n component_count = 0\n components_sec...

2a7f0a4955e3eb8db210fc2e3e607aef

Load the latest model and run a test episode

[ { "docid": "6d34a9cac0e3ab9c1237a1141c59f02f", "score": "0.0", "text": "def eval(dqn):\n ckpt_file = os.path.join(os.path.dirname(__file__), 'models/checkpoint')\n with open(ckpt_file, 'r') as f:\n first_line = f.readline()\n model_name = first_line.split()[-1].strip(\"\\\"\")\n d...

[ { "docid": "212b86f1917cde8b4574004fc1c33626", "score": "0.649339", "text": "def run_model(self):\n self.set_up_run_model()", "title": "" }, { "docid": "4b489c45200b9cf8f036daec4304dea6", "score": "0.64562917", "text": "def run_load_model_single(ind):\n global BEST_LOSS,BES...

0e104461cce6a58cb1527de242a9065c

Return a list of this compartments neighbors

[ { "docid": "4ec96ae6657a1638f62adf6d432fc87c", "score": "0.8468298", "text": "def neighbors(self):\n neighbors = [comp for comp in self.node0.compartments if comp != self]\n neighbors.extend(comp for comp in self.node1.compartments if \\\n comp != self and comp not in neighbors...

[ { "docid": "38fef1ab25c0cc71bec25ddb3225d301", "score": "0.84283286", "text": "def neighbors(self):\n return [comp for comp in self.node.compartments if comp != self]", "title": "" }, { "docid": "e48d37ca06666ef16c66fdb08074775f", "score": "0.7814085", "text": "def get_neighbors(s...

8ce42f3ff08b29817d4f733e56678d1c

Get basic player info.

[ { "docid": "5ac8172ffbecca36926ad6f7d44dbd0a", "score": "0.0", "text": "def get_players(self):\n for i, player in enumerate(self._header.initial.players[1:]):\n achievements = self.get_achievements(player.attributes.player_name)\n if achievements:\n winner = a...

[ { "docid": "8ece88e1fdff785b25835f2101797e0d", "score": "0.70133686", "text": "async def playerinfo(self, ctx):\n player = self.bot.wavelink.get_player(ctx.guild.id, cls=Player)\n node = player.node\n\n used = humanize.naturalsize(node.stats.memory_used)\n total = humanize.na...

a5c78dc38b73bbb7aaddd29ec72038c3

Exports mdf class data structure into hdf5 file

[ { "docid": "13d95b2364eff06e374762e8853a6aae", "score": "0.0", "text": "def export_to_hdf5(self, file_name=None, sampling=None, compression=None, compression_opts=None):\n #\n try:\n import h5py\n import os\n except ImportError:\n warn('h5py not foun...

[ { "docid": "1547453acf20dcb55fbb46fddc5fc80d", "score": "0.72480386", "text": "def export_to_hdf5(cls, h5_file, model, eids):\n #comments = []\n pids = []\n nodes = []\n thetas = []\n for eid in eids:\n element = model.elements[eid]\n #comments.ap...

22ee0c05930fea1d2164d13242a82399

GIVEN a source path to images files, extensions and a valid camera model WHEN the method is called THEN return a list of images files names that has such model in the exif metadata

[ { "docid": "4f012ed8064f0314e4995ff96e56b3aa", "score": "0.705926", "text": "def test_fetch_exif_images_by_model():\n files = c.fetch_images_by_camera_model(\"/home/urra/projects/pto/tests/data/dated_images/\", [\".jpg\", \".JPG\"],\"iPhone 4\")\n for file in files:\n assert c.is_exif_model...

[ { "docid": "69e1f335d6291ec8d45ac999c6f8da78", "score": "0.74459547", "text": "def test_fetch_exif_models():\n cameras = []\n files = c.read_src_files(\"/home/urra/projects/pto/tests/data/dated_images/\")\n for file in files:\n model = c.get_exif_attribute(file,\"model\")\n if not...

0ae60727fde0f48a9395014a6e53f368

Get most recent data object from local store (resultsdir) or database depending on use_local flag.

[ { "docid": "6c515790931a0e7851bab6f6a6949aeb", "score": "0.6872474", "text": "def get_latest_data(self, use_local: bool = None):\n if use_local is None:\n use_local = self.config.flags.use_local\n if use_local:\n return self.find_latest_data()\n else:\n ...

[ { "docid": "9bc5fc8af3a9c1739be2bb32accd70b3", "score": "0.6305982", "text": "def find_latest_data(self):\n jsonname = \"{}_data.json\".format(self.test_name.replace(\".\", \"_\"))\n resultsdir = os.path.expandvars(self.config.resultsdir)\n latest = None\n latest_mtime = 0.0\...

60adf11f2a9751f14a8ef65231d4c05d

r""" Return the FindStat identifier of the statistic.

[ { "docid": "a4d686e2f37ebac8c56bb38feb5e58fc", "score": "0.0", "text": "def id(self):\n return self._id", "title": "" } ]

[ { "docid": "43b0dde7ac6d869f6563a21cd3d75741", "score": "0.7452811", "text": "def id(self):\n return self._map[FINDSTAT_MAP_IDENTIFIER]", "title": "" }, { "docid": "a088cfd9a870d83188b34abc3a381360", "score": "0.6871175", "text": "def name(self):\n return self._map[FIND...

9eafee5ebddd5186dd57c2b654c81526

Returns a redirect url.

[ { "docid": "9cfd5aa97d497b11446daebd68bceb36", "score": "0.6828274", "text": "def redirect_url(default='index'):\n return request.referrer or url_for(default)", "title": "" } ]

[ { "docid": "2c096b0d65d755bd5a9a865cd350ebd4", "score": "0.8545995", "text": "def redirecturl(self) :\n\t\ttry :\n\t\t\treturn self._redirecturl\n\t\texcept Exception as e:\n\t\t\traise e", "title": "" }, { "docid": "bb3925ef9109540b34a175d0301cdb67", "score": "0.82717377", "text": "...

d872102cb6d6374073611e7e8fecba67

runs throught the code and executes it

[ { "docid": "f757c21e8c83cb674b551802f565d782", "score": "0.0", "text": "def execute(self,repl=True,to_use=''):\n \n if not to_use:\n code = self.code\n else:\n code = to_use\n code = self.updatecode(code)\n codepos = self.codepos\n cells = self.cells\n ...

[ { "docid": "1efae473535689ade6bba4a0066de701", "score": "0.7500659", "text": "def do_run(self):", "title": "" }, { "docid": "6b853038144e84eca87301e1d74ad67a", "score": "0.74891174", "text": "def run():", "title": "" }, { "docid": "6b853038144e84eca87301e1d74ad67a", "...

ea6f05dcbdaa7b397d23a33d382cf3ea

Create a simple naive multipnomial model where input X is a 1D array of floats in [0, 1.] output Y is onehot encode of three classes, A, B, C. [1,0,0] > class A [0,1,0] > class B [0,0,1] > class C Any x_i in [0, 0.3) belong to class A. Any x_i in [0.3, 0.6) belong to class B. Any x_i in [0.6, 1) belong to class C.

[ { "docid": "15bcca248399937da0adcd09acc877c2", "score": "0.6226215", "text": "def test_naive_multinomial():\n def make_y(x):\n if x <= 0.33:\n return [1,0,0]\n elif x > 0.33 and x <= 0.67:\n return [0,1,0]\n else:\n return [0,0,1]\n\n out_dir =...

[ { "docid": "10065240f9d781740b94508886ad9703", "score": "0.6501281", "text": "def multiclass_noisify(y, P, random_state=0):\n print (np.max(y), P.shape[0])\n assert P.shape[0] == P.shape[1]\n assert np.max(y) < P.shape[0]\n\n # row stochastic matrix\n assert_array_almost_equal(P.sum(axis=...

30230a45bc399c7ea7f30a6a1461c888

Retrieve genotypes by sample name

[ { "docid": "10e55688a93184e74c2c36105c726f2a", "score": "0.7850626", "text": "def genotypes_of(self, sample):\n\t\treturn self.genotypes[self._sample_to_index[sample]]", "title": "" } ]

[ { "docid": "c7c15478556ae0a2a23c4ca0fadc4713", "score": "0.7029988", "text": "def get_snp_sample_genotype(current_pos, sample_name):\n record = next((record for record in call_base.snp_record_dict[methyl_record.CHROM]\n if record.POS == current_pos), None)\n sample = ...

884cec929793e46311860e5e14300fdd

Frequency Shift This function does not perfrom a Hilbert transfrom when data is complex, NMRPipe seems to. As such the results of the imaginary channel differs from NMRPipe. In addition MAX/MIN value are slightly different than those from NMRPipe.

[ { "docid": "cb26b9cf473d587800e69fd53cbada94", "score": "0.0", "text": "def fsh(dic,data,dir,pts,sw=True):\n if dir not in [\"ls\",\"rs\"]:\n raise ValueError(\"dir must be ls or rs\")\n\n if np.iscomplexobj(data) == False: # real data\n null,data = _ht(dict(dic),data,zf=True)\n ...

[ { "docid": "e34f45944a36c1470aefbbbac1be3568", "score": "0.6343489", "text": "def freq_shift(x, freq, fs):\n x = cp.asarray(x)\n return _freq_shift_kernel(x, freq, fs)", "title": "" }, { "docid": "1fc5636acb473f7957d08fa865e58c51", "score": "0.62186146", "text": "def amplificat...

0d6919f49c0ebb2e84b311fbdd4ea193

This function returns the nth Fibonacci number.

[ { "docid": "13f70311b75a2ea21d39ab6d0482438f", "score": "0.7792695", "text": "def fib(n):\r\n i = 0\r\n j = 1\r\n n = n - 1\r\n\r\n while n >= 0:\r\n i, j = j, i + j\r\n n = n - 1\r\n \r\n return i", "title": "" } ]

[ { "docid": "270d8c585cead31811c63b02a378ee30", "score": "0.8607473", "text": "def nthFibonacci(index):\n # TODO: validate input (negatives, or non-integer)\n # happy case, weve already generated the sequence up to that point\n # we can return it in O(1)\n if len(fibonacci_sequence) > index:\...

cb42e71e00225186d6bb77be3ae9fbbc

Mask generator for coupling layers.

[ { "docid": "0212f6da636384e44ce7bdc4a481a1c7", "score": "0.53870505", "text": "def get_coupling_mask(n_dim, n_channel, n_mask, split_type='ChessBoard'):\n with jt.no_grad():\n masks = []\n if split_type == 'ChessBoard':\n if n_channel == 1:\n mask = jt.arange(0...

[ { "docid": "3864f70cbd28508ad8c3451c62984087", "score": "0.62455934", "text": "def gen_mask(word):\n return chain.from_iterable(\n [gen_mask_direction(word, mut) for mut in _directions])", "title": "" }, { "docid": "0a3928e295f68c9c0d3c5fa413c7530d", "score": "0.61573017", ...

2a35d1556d926281ff1c66b1f8595185

Automate the process of generating metadata from TeX formulas.

[ { "docid": "57d490b7a975ff5f9fac15062ce902f5", "score": "0.0", "text": "def __init__(self, length, *args):\n assert length >= 1\n self.length = length\n self.f = args\n super().__init__(self, text=None)", "title": "" } ]

[ { "docid": "c54f0bb68ce7bada5f80ae2c7db2c0f6", "score": "0.5863953", "text": "def generate_meta(tree, bill_id):\n meta = E(\"meta\")\n \n meta.append(generate_identification(tree, bill_id))\n meta.append(generate_publication(tree, bill_id))\n meta.append(generate_lifecycle(tree, bill_id))...

6ae63b9b8b80d14d27de932f9eb1e03c

Get an agent from the kqml cljson representation (KQMLList).

[ { "docid": "685395fcba5b3ca5e845987b23be1b76", "score": "0.66371286", "text": "def get_agent(cls, cl_agent):\n agent_json = cls.converter.cl_to_json(cl_agent)\n if isinstance(agent_json, list):\n return [ensure_agent_type(Agent._from_json(agj))\n for agj in ag...

[ { "docid": "4d7dd4b8753496023f532f5b4858d14a", "score": "0.5037742", "text": "def getAgent(self):\n return self._agent", "title": "" }, { "docid": "93e38bdce58e1707d76e0deb2486f062", "score": "0.5006577", "text": "def get_agent(payload, agent_id):\n agent = Agents.query.fil...

2159ce5f65118e46b83ea4fe457f9f0a

actionsarray actionTimestring actionTypestring messagestring subActionType

[ { "docid": "ddcd7ce0aedbe47e10420f7964345ddc", "score": "0.0", "text": "def client_info(self, action):\n url = self.baseurl + '/api/v1.2/statis/action'\n r = requests.post(url, json=action)\n # response = r.json()\n return r", "title": "" } ]

[ { "docid": "a1eab1b30f938463e2e5fb67b270f001", "score": "0.63483447", "text": "def define_actions( action ):\n\n actions = [\"walking\", \"eating\", \"smoking\", \"discussion\", \"directions\",\n \"greeting\", \"phoning\", \"posing\", \"purchases\", \"sitting\",\n \"sittingdo...

e544453d4703f1678a7fddec7ad18e3c

Return the current user action with the action parameters.

[ { "docid": "199f1fef2f8bdfe98639a7eb4ea04156", "score": "0.7800999", "text": "def get_current_action(self):\r\n # get current action\r\n action = self.user_action_generator.action\r\n \r\n # get current key if the action was KeyPress\r\n key = self.user_action_generato...

[ { "docid": "b8a16c06f0468742277f2b40eef86a0c", "score": "0.75212395", "text": "def _get_action(self):\n return self.__action", "title": "" }, { "docid": "5754aaab8e2accf8435431cbb23e2c13", "score": "0.739199", "text": "def get_action(self):\n return self._action", "titl...

67218f5ad982602182ad27fb6773c3c3

Checks if class attr are present

[ { "docid": "28041c011931c8d004179897589b27a8", "score": "0.6782795", "text": "def test_correct_classattr(self):\n b = User()\n attr = [\"email\", \"password\", \"first_name\", \"last_name\"]\n d = b.__dict__\n for i in attr:\n self.assertFalse(i in d)\n ...

[ { "docid": "ef997cefcb0ea532cc503fe05aebf969", "score": "0.73302776", "text": "def test_HasClassFields(self):\n for strAttr, _, _ in self.ClassFields:\n strError = 'Missing attribute {} in class {}'.format(strAttr,\n self.TestClass.__n...

fdcd639721cb599dcf895499e664212a

Returns the number of tests in the test file

[ { "docid": "c0cf8d7008a50fea2b280fef0de7ecdb", "score": "0.8652606", "text": "def number_of_tests(self):\n tests = c.file_number_of_lines(self.test_set)\n if self.test_set_header:\n tests -= 1\n return tests", "title": "" } ]

[ { "docid": "102ac0a63de87b6a11eba38139c2a37d", "score": "0.76091194", "text": "def _get_num_tests(test_output: str) -> int:\n match = re.search(r\"Tests run: (\\d+)\", test_output)\n return int(match.group(1)) if match else 0", "title": "" }, { "docid": "e8ce229ea3064a8ad2912ca74b05248...

e6dcf15390001e188afa8c958c44f8c0

Print a comment if print_it == True

[ { "docid": "496621f2535e8c405f2da0f98efe3073", "score": "0.7507166", "text": "def print_comment(text, print_it=verbose):\n prefix = \"OpenCortex >>> \"\n if not isinstance(text, str): text = text.decode('ascii')\n if print_it:\n \n print(\"%s%s\"%(prefix, text.replace(\"\\n\", \"\...

[ { "docid": "71e0ebbe3d52f7aa63197fe9c465c018", "score": "0.71713185", "text": "def print_comment_v(text):\n print_comment(text, True)", "title": "" }, { "docid": "d80b74f4cf1fc4848b45fb7609e4ec77", "score": "0.6587678", "text": "def print_comment(comment):\n shell_type = get_sh...

b60091eff4fa1a3e17b561797c93ad9a

Returns the MongoDB aggregation pipeline for the stage.

[ { "docid": "28197be9cd5f99638ac517947cc55d2f", "score": "0.0", "text": "def to_mongo(self, sample_collection):\n if not self.has_view:\n raise ValueError(\n \"%s stages use `load_view()`, not `to_mongo()`\" % type(self)\n )\n\n raise NotImplementedError...

[ { "docid": "60548f516377e285b92b4677a5d21114", "score": "0.65483505", "text": "def aggregation(self) -> Optional[pulumi.Input['AggregationArgs']]:\n return pulumi.get(self, \"aggregation\")", "title": "" }, { "docid": "60548f516377e285b92b4677a5d21114", "score": "0.65483505", ...

d6988d323edac3789be94a660f15ea1f

This method returns the name of the customer

[ { "docid": "bd3f80d095c5204b24e4f670f127c793", "score": "0.9271176", "text": "def get_customer_name(self):\n\t\treturn\tself.name", "title": "" } ]

[ { "docid": "527a7f56285a511d1e07ad302fc9925e", "score": "0.86061275", "text": "def get_customer(self) -> str:\n return self.customer", "title": "" }, { "docid": "caefd9628fac406b27c6bc3d54168670", "score": "0.8200847", "text": "def customer(self):\n return self.details[...

edcfbd83b47afb3c8a65e63a61ec03c4

ON PASSIVE DTP CREATION, NOT ON INITIAL TCP CONNECTION. For Initial TCP connection, see handle_accept in StreamFTPServer. Mainly copypasted from PassiveDTP, except that dtp_handler is run with a stream_rate.

[ { "docid": "24021e26f16b217576b13d6927ba2472", "score": "0.53463334", "text": "def handle_accept(self):\n \"\"\"Called when remote client initiates a connection.\"\"\"\n if not self.cmd_channel.connected:\n return self.close()\n try:\n sock, addr = self.accept(...

[ { "docid": "22ea0adb1e670bbe1fc11d1036a3d4aa", "score": "0.63551366", "text": "def on_dtp_connection(self):\n self.debug(\"FTPHandler.on_dtp_connection()\")\n if self.data_server:\n self.data_server.close()\n self.data_server = None\n\n # check for data to send\n ...

ea950c2fd6d934bafb035b9d5c85283d

Given an input_dir, returns the list of userids. This is to be used as the reference, in terms of ordering, for all other datasets.

[ { "docid": "d0d531e57eecbbd657cdc2c1c73acc18", "score": "0.79590195", "text": "def get_user_ids(input_dir: str) -> List[str]:\n image_files_name_pattern = os.path.join(input_dir, \"Image\", \"*.jpg\")\n image_filepaths = glob.glob(image_files_name_pattern)\n file_names = (os.path.basename(file_...

[ { "docid": "405b4402f2c8b31623706774d5d56e09", "score": "0.6556707", "text": "def get_ids(dir):\n return (f[:-4] for f in os.listdir(dir))", "title": "" }, { "docid": "6a235251084e44768351a9ca138ef4df", "score": "0.6357975", "text": "def all_train_ids(data_dir) -> np.ndarray:\n ...

5284b5b1c816e437f2dd5b007b5e4604

Load the driver from the one specified in args, or from flags.

[ { "docid": "cc5cff5c0422f40ab9537d843010485a", "score": "0.0", "text": "def __init__(self, volume_driver=None, *args, **kwargs):\n super(ReddwarfVolumeManager, self).__init__(*args, **kwargs)", "title": "" } ]

[ { "docid": "29d364750dffbefc3df07d8c03186348", "score": "0.5825256", "text": "def _get_driver(self, path, driver):\n self._debug('Loading driver %s...' % driver)\n abs_path = os.path.abspath(os.path.join(path, driver + '.py'))\n try:\n self._drivers_loaded[driver] = imp.l...

a8a9c2682b7df65a05e26dfb11a51644

Validate snippet and device list with user

[ { "docid": "61a0d856096cd7be8b40d900987e795c", "score": "0.6050847", "text": "def confirm_deployment_validity(snippet: str, requested_device_name_list: List[str]) -> bool:\n\n # Print snippet and ask user for confirmation\n netcat.LOGGER.info(\"Displaying snippet and asking user for confirmation\"...

[ { "docid": "3b47c7e3290d05b7551f276b3bcf0573", "score": "0.5582056", "text": "def validate_device_info(self, devinfo, prefix=\"\"):\n errors = []\n\n if \"deviceType\" in devinfo:\n deviceType = devinfo[\"deviceType\"]\n if deviceType == \"network/upnp\":\n ...

67fbb6fcae3c99ed218d2119a82b1dfc

Search using the GeoLocation functionallity of GoogleAPI. This assumes its given data are infact street signs. intersections are marked by '&'

[ { "docid": "e18b0729804c87403bd9a77406bd57f5", "score": "0.66954947", "text": "def _search_geolocation(g_maps, output_path, streets):\n\n combinations_to_try = __create_suffixes_combinations(streets)\n results = []\n for comb in combinations_to_try:\n geocode_results = g_maps.geocode(com...

[ { "docid": "eceead8944cf2d534d88cf4bcd05bbf8", "score": "0.70246893", "text": "def search_location(streets, others, output_path):\n\n g_maps = googlemaps.Client(key=API_KEY)\n\n if len(streets):\n print(\"[+] Trying to match exact geo-location data...\") # Geocoding an address\n if ...

1e982dbea5d8b95f5c90b7f7b40a3e67

Sets the current tool to edit shape coordinates. Returns None

[ { "docid": "b90c2abb6c7e2c051b1f9f45e888119c", "score": "0.879929", "text": "def set_current_tool_to_edit_shape_coords(self):\n\n self.variables.active_tool = TOOLS.EDIT_SHAPE_COORDS_TOOL\n self.variables.current_tool = TOOLS.EDIT_SHAPE_COORDS_TOOL", "title": "" } ]

[ { "docid": "745d7a5ceebf910f6ee3d6fd4a96a1ef", "score": "0.8333395", "text": "def set_current_tool_to_edit_shape(self):\n\n self.variables.active_tool = TOOLS.EDIT_SHAPE_TOOL\n self.variables.current_tool = TOOLS.EDIT_SHAPE_TOOL", "title": "" }, { "docid": "1828cf63c01a3eca58d7...

b0e2c74cf887e7bfcc8bd8b5160d7677

Checks whether the currently logged in user can create or edit a task, when the specified role is required.

[ { "docid": "d3bcab63cb65b2d503d97fe659bfb779", "score": "0.77631235", "text": "def canCreateTaskWithRequiredRole(self, required_role):\n if required_role == 'mentor':\n valid_org_keys = [o.key() for o in self.data.mentor_for]\n elif required_role == 'org_admin':\n valid_org_keys = [o.key...

[ { "docid": "f488f928e748390b66273f859a86562a", "score": "0.7407585", "text": "def canCreateTask(self):\n return self.canCreateTaskWithRequiredRole('mentor')", "title": "" }, { "docid": "92dd1cad5bff8325c5e3ca5ccdb34116", "score": "0.7034055", "text": "def checkCanUserEditTask(self...

1bde121e390fc36fc99ed0b7fd77c9d2

Add product to the cart and update it's quantity

[ { "docid": "3cb7c65e6952b839dff8153624c77b7c", "score": "0.76845706", "text": "def add_cart(request, product_id):\n product = Product.objects.get(id=product_id)\n \n try:\n cart = Cart.objects.get(cart_id=__cart_id(request))\n except Cart.DoesNotExist:\n cart = Cart.objects.cre...

[ { "docid": "038040f6e0eb53f4d3d4004bd145c921", "score": "0.862083", "text": "def add(self, product, quantity=1, update_quantity=False): # get product,quantity,update_quantity from cart/views.py\n product_id = str(product.id) # if the item didn't in the cart, initialize...

cb9c1c4fdb48eddde66afef13b7904af

Allow use of `hash` so that a |Particle| instance may be used as a key in a `dict`.

[ { "docid": "a801f58462299836602c20628475aa20", "score": "0.55745435", "text": "def __hash__(self) -> int:\n return hash(self.__repr__())", "title": "" } ]

[ { "docid": "82db20635cd06c7647f81eb18910a756", "score": "0.6146902", "text": "def __hash__(self):\n return hash(self._mean) + hash(self._variance)", "title": "" }, { "docid": "1ee89e699ad8265b4902c38050171d5d", "score": "0.61150706", "text": "def __hash__():", "title": "" ...

86ac939b5df955ee10bb53581ead4b69

Reformat gridder evaluated on the nu grid returned by make_evaluation_grids into the sampled C function which has an index for the closest gridpoint and an index for the fractional distance from that gridpoint

[ { "docid": "e7f43fd4d5950f40a55473836065815b", "score": "0.0", "text": "def gridder_to_C(gridder, R):\n M = len(gridder) // (2 * R)\n C = np.zeros((2 * R, M), dtype=float)\n for r in range(0, 2 * R):\n l = r - R + 1\n indx = np.arange(M) - 2 * M * l\n # Use symmetry to deal...

[ { "docid": "c823a3cba264c1f5bea7da3f273e4960", "score": "0.59278005", "text": "def convertDistanceFromPartyGrid(d, index):\n return d*PartyGlobals.PartyGridUnitLength[index] + PartyGlobals.PartyGridToPandaOffset[index] + PartyGlobals.PartyGridUnitLength[index]/2.0", "title": "" }, { "doci...

dc8ca5ea91bb92119eca9c9fce7484a5

Set lase state to either True or False.

[ { "docid": "650f72297f6ef32dd426f087087f11b2", "score": "0.73475146", "text": "def lase(self, state: bool):\r\n self._lase = state\r\n self.lase_hist.append(state)\r\n\r\n # if the new option is the same as before don't send changes to labjack\r\n if (self.lase_hist[-2] != st...

[ { "docid": "f39497edbf7019fa15424233840455d3", "score": "0.6832224", "text": "def set_living(self, state):\n if isinstance(state, bool):\n self.living = state\n else:\n raise TypeError('state must be boolean.')", "title": "" }, { "docid": "a8ee0c74ec516994...

0a3f8726bab73ebb368b400097bed051

Initialize the networks and Ops. Assume discrete space for dqn, so action dimension will always be action_space.n

[ { "docid": "c29ac42e89f0c747d00e3c9f5352047d", "score": "0.0", "text": "def init_opt(self):\n action_dim = self.env_spec.action_space.n\n obs_dim = self.env_spec.observation_space.flat_dim\n\n self.episode_rewards = []\n self.episode_qf_losses = []\n\n with tf.name_sco...

[ { "docid": "1e53c3d8b3fa5a4ff22e11b3435270ca", "score": "0.7468827", "text": "def init(self, net_dim, state_dim, action_dim):", "title": "" }, { "docid": "bb193d6ac445ca2d0ed5820ee42a2a9a", "score": "0.68246925", "text": "def initialize_models(self):\n # this information might...

64077ca2c30dd59cbbb080a41f79cfe4

Reload the index table. Useful if the file is being written too when read

[ { "docid": "dd998432bd98573535cc5d0c24862dd6", "score": "0.6675373", "text": "def reload(self):\n self._scan_file(self.filesize, self.n_entries, self.fpos)", "title": "" } ]

[ { "docid": "56cff3ec649cd56bb356b38988b314b4", "score": "0.7886104", "text": "def reload_index(self, index_dir):\n print(\"Reloading Index...\")\n self.index.loadIndex(index_dir, load_data=True)", "title": "" }, { "docid": "604659cd191b4a69894922483f31180e", "score": "0.764...

e4c501b5a15d5938a40f9b6d7a9e3f64

SQL query for comparison

[ { "docid": "555e45c55f904218e0d7f5311567405f", "score": "0.0", "text": "def test_clean_all(self):\n print(\"TEST NETTOYAGE INTÉGRALE\")\n CleaningDB.cleaning_all_products()\n for t in TABLES:\n with connection.cursor() as cursor:\n sql = \"SELECT * FROM %s;...

[ { "docid": "e6b845161c19b8f140eda6940b5bcaf5", "score": "0.60658246", "text": "def compare_with_old_data_query(self):\n raise NotImplementedError", "title": "" }, { "docid": "6bf6e08760ea97956415d68ce530b8c7", "score": "0.59604883", "text": "def test_linear_conditional_query(s...

3001344b6fc182cc3564ae5a8062821a

Add an OpenAPI extension for marshmallow_enum.EnumField instances

[ { "docid": "9514b932c402f6c2f21dc187ca92ba11", "score": "0.67637134", "text": "def enum_to_properties(self, field, **kwargs):\n import marshmallow_enum\n\n if isinstance(field, marshmallow_enum.EnumField):\n return {\"type\": \"string\", \"enum\": [m.name for m in field.enum]}\n return {...

[ { "docid": "381e3a39199513c60cbc001503cc590d", "score": "0.6585628", "text": "def validate_enum(validator, enums, instance, schema):\n\n if isinstance(instance, tagged.Tagged):\n instance = instance.base\n\n yield from mvalidators.Draft4Validator.VALIDATORS[\"enum\"](validator, enums, insta...

61352d110ea023b6cacf94292173435c

Median number of shares or units of a given asset traded over a 21 day period.

[ { "docid": "0d2284f3fe9a982a63f69431d5e090dd", "score": "0.0", "text": "def adv22_day_pct(self) -> float:\n return self.__adv22_day_pct", "title": "" } ]

[ { "docid": "8ba43e7a940ff8dc1b498a4bbbc7fc6b", "score": "0.6867634", "text": "def median(wm):\n return statistics.median(wm)", "title": "" }, { "docid": "3febeacdf96db1146d2eda694f2c3be4", "score": "0.6539522", "text": "def mad(x):\n\n return median(abs(x - median(x)))", "t...

235106b93a4829d54c95d3f1a8f502a1

Singular value decomposition for m x n matrix and m >= n.

[ { "docid": "ca5da7e6a7c9a56b02bd75cdb26e1f9c", "score": "0.55227935", "text": "def _svd(a: jnp.ndarray,\n is_hermitian: bool,\n max_iterations: int) -> Sequence[jnp.ndarray]:\n\n u, h, _, _ = lax.linalg.qdwh(a, is_hermitian, max_iterations)\n\n v, s = lax.linalg.eigh(h)\n\n # Flips th...

[ { "docid": "db982ca250fc556cbd63ac23f542deb7", "score": "0.7124864", "text": "def singular_values(A):\n return jnp.linalg.svd(A, full_matrices=False, compute_uv=False)", "title": "" }, { "docid": "556edafe03784eb6ce50bb664b575aa8", "score": "0.6591163", "text": "def do_svd(self, k...

dd67518d84447aad41eace3b8f0145f1

Suppose we use few model instances of same id, and we must be able to update them independently on root model updated. For this we should use not get_model_by_id(), but get_model_copy_by_id().

[ { "docid": "7db3c694e8d3a4fcddd0927265e53bcf", "score": "0.69966036", "text": "def get_model_copy_by_id(cls, id):\n model = cls.get_model_by_id(id)\n return model.copy() if model else None", "title": "" } ]

[ { "docid": "6042db19415df1c676419f2b89732154", "score": "0.6511655", "text": "def _get_shallow_copy_model(model: nn.Module):\n old_to_new = dict()\n for name, module in _get_dfs_module_list(model):\n new_module = copy(module)\n new_module._modules = OrderedDict()\n for subname...

83a57e4c1e60f8cf3070ed419312c611

Get the initial arguments JSON.

[ { "docid": "2a6c308824d75aae2ffd0e84373d535d", "score": "0.86039567", "text": "def get_initial_arguments_json(self):\n # Define this in subclasses\n raise NotImplementedError", "title": "" } ]

[ { "docid": "0aa6ceae496229d0bfae3964aeeb7ca2", "score": "0.8692744", "text": "def get_initial_arguments_json(self):\n return self.get_object().arguments", "title": "" }, { "docid": "098d839e7def9d209ce24da0c6fb7d74", "score": "0.7873527", "text": "def get_initial_arguments_jso...

101c8997fedd063b35ebd1bab2cdf156

Warp frames to 84x84 as done in the Nature paper and later work.

[ { "docid": "55e1dc24ca078905558c0fcc429341b7", "score": "0.0", "text": "def __init__(self, env):\n gym.ObservationWrapper.__init__(self, env)\n self.width = 84\n self.height = 84\n\n if env.unwrapped.spec.id == 'MontezumaRevengeNoFrameskip-v4':\n self.ego_game = Mo...

[ { "docid": "441f2546b813a8c8f51f599898f61cba", "score": "0.5635383", "text": "def liftFrame(self, frame):\n\t\tframe.lift()", "title": "" }, { "docid": "81435cd2d848904bced17e4bc3157f38", "score": "0.55682594", "text": "def adjust_images(self, now=0):\n if self.direction != se...

a3d3c12ec19b6263be1c8b39b2a8c1fc

Return number of variable figure elements, assume zero.

[ { "docid": "ec10f9e111a0f970fd9481390af80121", "score": "0.63331455", "text": "def count_variable_connections(self):\r\n self.browser.implicitly_wait(1)\r\n try:\r\n figs = self.browser.find_elements_by_class_name('variable-connection')\r\n count = len(figs)\r\n ...

[ { "docid": "a376965fb7e385d6100eeb757767e5fd", "score": "0.7942516", "text": "def count_variable_figures(self):\r\n self.browser.implicitly_wait(1)\r\n try:\r\n figs = self.browser.find_elements_by_class_name('variable-figure')\r\n count = len(figs)\r\n finally...

5518a8e037777ad1a20b958e7a9b5d51

Generate the different list items by selecting a random element from each list. Needs an list of invoice names as an input.

[ { "docid": "6a4ad741be49c6bd7279709f7a92b888", "score": "0.6615224", "text": "def generate_list_items(listItemList):\n listItems = []\n numberOfListItems = random.randint(1,10)\n currencies = [\"\",\" £\", \" $\", \" CHF\"] # \" €\",\n totalSyn = [\"\", \"Gesamt \", \"Gesamt: \", \"Total \",...

[ { "docid": "2dd8a6155aa29361b6440537abf3c93f", "score": "0.5974432", "text": "def sample_generator(iterator, items_wanted=1):\n selected_items = [None] * items_wanted\n for item_index, item in enumerate(iterator):\n for selected_item_index in range(items_wanted):\n if not random....

4fa87d05fb8569c1d1b575e0e1c84c93

run the given function while collecting arguments to a target function

[ { "docid": "bfc485186e6c37622ae9b1a850793e88", "score": "0.0", "text": "def get_contexts_via_monitor(driver, caller_va, decoder_fva: int, index: viv_utils.InstructionFunctionIndex):\n try:\n caller_fva = index[caller_va]\n except KeyError:\n logger.trace(\" unknown function\")\n ...

[ { "docid": "f6aaecb9bcb2c146ba087c9f92a0222e", "score": "0.7327121", "text": "def run(self):\n if self.function:\n self.function(*self.args)", "title": "" }, { "docid": "0f44c5f93b96523d3fd03ac59bdde2cc", "score": "0.7171412", "text": "def run(self):\n self.fn(*self.ar...

aad0522ace98fd658bbd18bf9978bf1b

Set the callback to call when data is received from the Arduino

[ { "docid": "11166a6a9720889551bf439677821d1c", "score": "0.7634608", "text": "def set_data_received_callback(self, callback):\n self._data_received_callback = callback", "title": "" } ]

[ { "docid": "89ce51f1ed4e906a68428dcb0030d809", "score": "0.690485", "text": "def _listener_callback(self, data):\n self._dynamixel_current_state = data", "title": "" }, { "docid": "434a456c2ff3757c8479112bdc016566", "score": "0.67482555", "text": "def callback():", "title"...

ccdd6186cc6ccd4013894a7d4f98def8

find the module(s) given the name(s)

[ { "docid": "7d55dcfc22921a14d2acde0e99e59f72", "score": "0.6622528", "text": "def find_modules(self, requested_names):\n found_modules = set()\n for requested_name in requested_names:\n is_instance = ' ' in requested_name\n\n for module_name, module in self.py3_wrappe...

[ { "docid": "25fe60cd21b7004b8076805f2bb02ae5", "score": "0.7484802", "text": "def _find_package_in_modules(name: str) -> Optional[str]:\n package_specs = importlib.util.find_spec(name)\n\n try:\n importlib.util.module_from_spec(package_specs) # type: ignore\n except Attr...

1822aa152e4ae6101470c797e06ea372

Save model pipeline (tfidf + model), target names mapping and config.

[ { "docid": "5812b96de2030d1febf685852c3f1f32", "score": "0.7611598", "text": "def save_model(\n pipe: Pipeline,\n target_names_mapping: Dict[int, str],\n config: Dict[str, Any],\n) -> None:\n\n # save pipe\n joblib.dump(pipe, config[\"path_to_save_model\"])\n\n # save target names mapp...

[ { "docid": "e40de5c6aa241e21541b11fbaa5a2203", "score": "0.72795296", "text": "def save_model(self):\n joblib.dump(self.pipeline, \"model.joblib\")", "title": "" }, { "docid": "f44c48b51254da0f5311f1e7d429aa89", "score": "0.71536785", "text": "def save_model(self):\n jo...

d392101f4958355ebaa3029234bddc9f

Searches JIRA for issues using a JQL query.

[ { "docid": "6249cf1b0a95f8479077c980266af686", "score": "0.6391476", "text": "def complete_search(\r\n self, jql, validate_query=None, fields=None, expand=None,\r\n properties=None, fields_by_keys=None,\r\n batch_size=DEFAULT_BATCH_SIZE):\r\n query = {'jql': jql}\...

[ { "docid": "79d565c73706b0e9138be3112ed4be00", "score": "0.7664945", "text": "def issues_search(self, jql='order by key', start_at=0, max_results=1000, validate_query=True, fields='id'):\n\n loop_count = max_results // BATCH_SIZE_ISSUES + 1\n issues = list()\n last_loop_remainder = ...

513423d0730ce67c2ce4b29c54cea9b8

Name of the torrent

[ { "docid": "f502bdaaa7e80f70f74bffc52d4af1b2", "score": "0.6839437", "text": "def name(self):\n if 'name' not in self.metainfo['info'] and self.path is not None:\n self.metainfo['info']['name'] = os.path.basename(self.path)\n return self.metainfo['info'].get('name', None)", ...

[ { "docid": "edc7919d4d56da18ec23e5bdee1b835a", "score": "0.7028946", "text": "def getName(self) -> unicode:\n ...", "title": "" }, { "docid": "edc7919d4d56da18ec23e5bdee1b835a", "score": "0.7028946", "text": "def getName(self) -> unicode:\n ...", "title": "" }, ...

8e5f61833b87cea1b17fb804514a3fbc

Returns a list of possible board layouts, given the player whose turn it is, and a list of valid moves.

[ { "docid": "3a957289a63ff511b3ccdcd8b6daffa7", "score": "0.7096633", "text": "def get_possible_boards(board, moves, player):\n boards = []\n for move in moves:\n new_board = board[:]\n new_board[int(move)-1] = player\n boards.append(new_board)\n return boards", "title":...

[ { "docid": "0e50adc01cbb6cb681b3a100d0724f31", "score": "0.65457636", "text": "def available_moves(self, player):\n pieces = 0\n if player == Player.white:\n pieces = self._white_pieces\n elif player == Player.black:\n pieces = self._black_pieces\n else:...

fb480e403a4d5dd341c51577f1b55220

This is the normalization step generating dbt models files from the destination_catalog.json taken as input.

[ { "docid": "e11d0077aeb8e9e1aae12b5f7489774a", "score": "0.71015024", "text": "def generate_dbt_models(destination_type: DestinationType, test_resource_name: str, test_root_dir: str):\n catalog_processor = CatalogProcessor(os.path.join(test_root_dir, \"models\", \"generated\"), destination_type)\n ...

[ { "docid": "ce965d0bf5e58e59351a5e541e0e9128", "score": "0.5778649", "text": "def build_cm_models(output_dir):\n\n for roots,dirs,files in os.walk(output_dir):\n path_to_sto_list=[]\n path_to_cm_list=[]\n path_to_dir=[]\n db_exist=[]\n for name in dirs:\n ...

7b214326c84de19606bbaaccfe707110

returns a dictionary of

[ { "docid": "94d2ec278189c510866bc3aeee035468", "score": "0.0", "text": "def predict(self, at=5):\n recs = {}\n for i in range(0, self.R_hat.shape[0]):\n pl_id = self.pl_id_list[i]\n pl_row = self.R_hat.data[self.R_hat.indptr[i]:\n s...

[ { "docid": "0f82d3748c9bac7976eba45ff1d4a511", "score": "0.7530778", "text": "def _asdict(self):", "title": "" }, { "docid": "3bf041b0ae01ad45ea6f683f8414e7ee", "score": "0.7360245", "text": "def to_dict(self):", "title": "" }, { "docid": "3bf041b0ae01ad45ea6f683f8414e7ee...

5e06fab16bb0d071c255737bfb52f140

Return pulls tags from a given username and repository ID.

[ { "docid": "01255111111eef23e1ba5ba8a38f9085", "score": "0.6062889", "text": "def pulls_by_id(self, repository_id, access_token=None):\n return self._complete_request_by_id(\n repository_id, \"pulls\", access_token)", "title": "" } ]

[ { "docid": "182350f0750dd0612243d00a837739d5", "score": "0.71279055", "text": "def tags_by_name(self, username, repository_name, access_token=None):\n return self._complete_request_by_name(\n username, repository_name, \"tags\", access_token)", "title": "" }, { "docid": "af...

0be3860b5d6da42b962f0c7443225204

Constructor for Datacenter data structure. self.name > str self.clusters > list(Cluster)

[ { "docid": "216bee2b06cd3fac63d0b176ae82f434", "score": "0.7706204", "text": "def __init__(self, name, cluster_dict):\n\n self.name = name\n self.clusters = cluster_dict", "title": "" } ]

[ { "docid": "64611b4547d39b69e6d2ea2437c484bc", "score": "0.682774", "text": "def __init__(self,\r\n X=None,\r\n y=None,\r\n AttributeNames=None,\r\n classNames=None,\r\n N=None,\r\n M=None,\r\n ...

81ee465edfa3c2d3a25c93edc7c36b88

ComplianceViolation a model defined in Swagger

[ { "docid": "ce33712e662119a97a00e7b66fbd10d3", "score": "0.0", "text": "def __init__(self, compliance_type=None, listing_id=None, offer_id=None, sku=None, violations=None): # noqa: E501 # noqa: E501\n self._compliance_type = None\n self._listing_id = None\n self._offer_id = None\n...

[ { "docid": "05c207eb191ec3969dc106fb77be54b2", "score": "0.5712235", "text": "def missed_the_point():\n abort(422, messages={'_schema': ['Please, more parameters']})", "title": "" }, { "docid": "824e81f0df0e610efff1bc783f0cb58a", "score": "0.5648701", "text": "def validate(self, a...

5da92a368f8d5dedef47a25c95e01cc5

Fetches all incidents not marked as 'Resolved'

[ { "docid": "c01976be6a59d05bb2960bf2c8ce3f02", "score": "0.6302394", "text": "def get_all_open_incidents(self):\n self.open_incidents = []\n try:\n response = self.session.get(f'{self.base_url}/incidents')\n except RequestException as err:\n sys.exit(f'REQUEST ...

[ { "docid": "625033ffb356b956860c15ebe156935a", "score": "0.66342324", "text": "def unresolved_issues(self):\n return self.issue_set.filter(resolved_state__isnull=True)", "title": "" }, { "docid": "8aca38791acd7bc07aab9efcffe76b20", "score": "0.5845197", "text": "def get_all_un...

751e3123e3738fb33e4824f9c5d67a85

Get status of lastest submission for sample sets in the workspace

[ { "docid": "1339076c31977b1205ad36d02b201592", "score": "0.5348673", "text": "def get_sample_set_status(self, configuration):\n return self.get_entity_status('sample_set', configuration)", "title": "" } ]

[ { "docid": "3c7aeddc0da95b45b75c63b6011e29da", "score": "0.614393", "text": "def last_success(cls):\n qs = cls.objects.filter(complete=True, has_error=False)\n return qs.order_by(\"-start_run\")[0] if qs.count() else None", "title": "" }, { "docid": "c4aa6f29437eb3be907e23d9639...

33f1864dad6aa245ab722088c583c125

Anonymous function node. Inline the function definition, and return the function identifier.

[ { "docid": "cff250350e2e20c429b64454aca71c7f", "score": "0.5156287", "text": "def _(node, inline, bindings=()):\n name = next(name_generator('lambda'))\n inline_children = []\n\n # bindings in local scope\n binding_assignments = [\n python.Assign(\n targets=[\n ...

[ { "docid": "0de17cbc78992eb9c121abcf78ba8ae1", "score": "0.67060685", "text": "def _(node, inline):\n name = generate(\n tscl.Function(\n parameters=tscl.List(expressions=()),\n expressions=node.expressions,\n ),\n inline,\n node.bindings.expressions,...

53708b31bc8ffe4495009b081e518f75

Return a particular LVNF in this tenant.

[ { "docid": "6d8e99eae8651813c00b8bc5eb1e953f", "score": "0.756228", "text": "def lvnf(self, addr):\n\n if self.tenant_id not in RUNTIME.tenants:\n return None\n\n if addr not in RUNTIME.tenants[self.tenant_id].lvnfs:\n return None\n\n return RUNTIME.tenants[sel...

[ { "docid": "243aa44a45f3fde3c4066a7040d4bcc6", "score": "0.68816304", "text": "def lvnfs(self):\n\n if self.tenant_id not in RUNTIME.tenants:\n return None\n\n return RUNTIME.tenants[self.tenant_id].lvnfs.values()", "title": "" }, { "docid": "ea2ba440f1a9f95c41ee0374...

28ac0f95cb5d7de56dd58690cd1c22c2

Generates all pythagorean triples with sum (a+b+c) n, mn odd and m,n coprime, and returns the pythagorean triples (a,b,c)

[ { "docid": "a822654a02fcadae9ba4ccd9714616f7", "score": "0.7234538", "text": "def pythagorean_triples(L, give_sum=False):\n M = int((L//2)**0.5)+1\n for m, n in coprimes(M):\n # Skip m,n with (m-n) even\n if (m-n) % 2 == 0: continue\n for k in itertools.count(1):\n ...

[ { "docid": "270dba8f27498fcb432a46b9affb1de2", "score": "0.7959152", "text": "def pythagorean_triplet(n):\n\n max_value = n // 3 + 1 # set maximum for a, not exceed 1/3 of n, to prevent b < a or c < a\n for a in range(1, max_value):\n rest = n - a\n for b in range((rest-a)//2 + 1, ...

c743f7246010cbe6833d12c760ee90cc

Checks wheather coordinate (x,y) is in circle with center s and radius val

[ { "docid": "b5c4a74dcb7087397ca948845c677589", "score": "0.0", "text": "def check_incl(s,x,y, val, norm = l1_norm, use_norm=True):\n \n keki = s.split(' ')\n o = float(keki[0])\n h = float(keki[1])\n if(not use_norm):\n if((abs(o-x)<val) and (abs(h-y))<val*2):\n return T...

[ { "docid": "96969616fcf6e2eb9efb0ce4cccce6c9", "score": "0.8017797", "text": "def is_in_circle(center, radius, position):\n\n d = math.sqrt(((center[0] - position[0]) ** 2) +\\\n (center[1] - position[1]) ** 2)\n\n return d <= radius", "title": "" }, { "docid": "11ecf...

c89fd9c6e428416132183bca926ce386

The publisher of the 3rd Party Artifact that is being bought. E.g. NewRelic

[ { "docid": "5151abc3e492ac5419ad39fafb658524", "score": "0.65167165", "text": "def publisher(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"publisher\")", "title": "" } ]

[ { "docid": "5edae9fd69ceb12a313846410a240744", "score": "0.69928986", "text": "def publisher(self) -> str:\n return pulumi.get(self, \"publisher\")", "title": "" }, { "docid": "0da235a06b1f437255f5219cd6eb2efb", "score": "0.6917126", "text": "def publisher(self) -> Optional[st...

8d0ad179f81a20ff6613833cd14acd7b

Return all statistic_ids (or filtered one) and unit of measurement. Queries the database for existing statistic_ids, as well as integrations with a recorder platform for statistic_ids which will be added in the next statistics period.

[ { "docid": "184f0821fec57cb5786e98df8f012a1e", "score": "0.64374584", "text": "async def async_list_statistic_ids(\n hass: HomeAssistant,\n statistic_ids: set[str] | None = None,\n statistic_type: Literal[\"mean\"] | Literal[\"sum\"] | None = None,\n) -> list[dict]:\n instance = get_instance...

[ { "docid": "f9a4da3caaebf0150c69dc96a64d5ea5", "score": "0.7760222", "text": "def list_statistic_ids(\n hass: HomeAssistant,\n statistic_ids: set[str] | None = None,\n statistic_type: Literal[\"mean\"] | Literal[\"sum\"] | None = None,\n) -> list[dict]:\n result = {}\n instance = get_inst...

6e23d6d89d173283541b4f362872abe2

Sums the ASCII character values mod256 and returns the lower byte of the two's complement of that value

[ { "docid": "16e9820e599d012efae3f474349ed2fc", "score": "0.5380465", "text": "def get_checksum(file):\n sum = 0\n with open(file, 'r') as f:\n data = f.read()\n for i in range(len(data)):\n sum = sum + ord(data[i])\n temp = sum % 256\n rem = -temp\n return '%2X' %...

[ { "docid": "5219472d468a1de5c385225eaff14233", "score": "0.65635484", "text": "def SignedChar(value):\n if value < 0:\n # Perform two's complement.\n return value + 256\n return value", "title": "" }, { "docid": "535d30b8d513bdbc8168cea6c4f9e15f", "score": "0.6562...

21bc9a94854948e612cbde5974c136a5

Return a XMLRPC wrapper function which can optionally intercept a call to the given ROS master XMLRPC method. method is the desired ROS master XMLRPC function to call

[ { "docid": "a32b1731a45187225a9d21080b5d405f", "score": "0.7624827", "text": "def __getWrapper(self, method):\n def wrap(*args):\n \"\"\"Callback function for an XMLRPC function.\n\n * args -- the method input arguments\n\n \"\"\"\n # If this class has ...

[ { "docid": "10d52d7f9b2d1cfc71a797955024df93", "score": "0.7143489", "text": "def rpcmethod(func):\n func.rpcmethod = True\n return func", "title": "" }, { "docid": "3074070b2ffa1480ab2b169d64206e36", "score": "0.68000335", "text": "def rpc_method(func):\n func.rpc_c...

8bcfc6a93aee455d8653b8831eda99de

Run an arbitrary query by Variable Elimination. What is the analytic cost of this? You have to do K noise queries in a graph with K endog nodes + K exog nodes in normal CFI. In twin network inference, you have to do 1 query in a graph with 2K endog nodes + K exog nodes.

[ { "docid": "8164bfb7ca118009b8158e8e045cb136", "score": "0.5620961", "text": "def query(self, var, observed, counterfactual=False, twin=False):\n if not isinstance(var, list):\n var = [var]\n if twin:\n # time_start = time.time()\n infer = VariableEliminati...

[ { "docid": "9d9bd34ad7a79c09351a5f63d8b1a69e", "score": "0.64059097", "text": "def run(self, query, observed, elim_order):\n\n # CHANGE from original code:\n # Here is where my code begins\n\n # Step 1: indentifying and reducing observables/evidence\n self.overwrite_log(\"STE...

7c4d9379deed687e5ce4e2288b5a1b41

Simple JSON Formatter wrapper for consistent formatting.

[ { "docid": "6997219fa49faa74f7a7f38d7dcb8b21", "score": "0.0", "text": "def proto_to_json(proto: message.Message) -> str:\n return json_format.MessageToJson(\n message=proto, sort_keys=True, preserving_proto_field_name=True)", "title": "" } ]

[ { "docid": "0cca7b880ba3f7c97ca0b4032b21db93", "score": "0.7336949", "text": "def make_formatter(format_name):\n\n if \"json\" in format_name:\n from json import dumps\n import datetime\n\n def jsonhandler(obj):\n obj.isoformat() if isinstance(\n obj, (d...

84a2546916e464b4e9efb9ec4517028f

Generate a static plot of a population

[ { "docid": "8d12c199b22d65fbbed1d75d237cf117", "score": "0.683494", "text": "def plot_population(self,\n population_name: str,\n x: str,\n y: str,\n xlim: tuple = None,\n ylim: tuple = None...

[ { "docid": "31c58aced541f7fab74b9e4cce69790d", "score": "0.65963537", "text": "def plot_population():\n import numpy as np\n import matplotlib.pyplot as plt\n import astropy.units as u\n from astroquery.nasa_exoplanet_archive import NasaExoplanetArchive\n\n\n #First establish the rules th...

b5800e48092206b95caebfbb2b34ccd2

Intialize a generic file reader with batching for list of files

[ { "docid": "4c72d0005257cfd74efa0b155be92ac2", "score": "0.6491739", "text": "def __init__(self, records_list, image_options={}):\n print(\"Initializing Batch Dataset Reader...\")\n print(image_options)\n self.files = records_list\n self.image_options = image_options\n ...

[ { "docid": "5a66de5c5579ae4e5898e279c0a111d4", "score": "0.6745472", "text": "def reader_for_list(filelist):\n\n for filename in open(filelist):\n filename = filename.rstrip()\n logging.info(str(filename))\n \n for x in reader(filename):\n yield x", "title": "" ...

e25d912f5d2e53d37a000386bda81e4f

read data/nodes.json file from this project

[ { "docid": "9ea1887b72b94e53d863e551452aab0c", "score": "0.80150056", "text": "def readNodesJsonFile():\n fileName = \"data/nodes.json\"\n with open(fileName, \"r\") as f:\n jsonString = f.read()\n return jsonString", "title": "" } ]

[ { "docid": "59d00d70bbdea5d642c76118be182d5d", "score": "0.6795475", "text": "def read(filename):\n\twith open(filename) as data_file: \n\t data = json.load(data_file)\n\t results = []\n\tfor obj in data[\"nodes\"]:\n\t\ttemp = obj.split(\":\")\n\t\tif len(temp) == 2:\n\t\t\tresults.append(Link...

be307e889961de8b22b58dbcb9b623f4

Return max Return the peak (and/or the index of the peak) in a given 2D array

[ { "docid": "1179a2aecb80a7c30e687babc324e634", "score": "0.0", "text": "def _return_max(x, y, exp_dnu=None,):\n xx, yy = np.copy(x), np.copy(y)\n if list(yy) != []:\n if exp_dnu is not None:\n lst = list(np.absolute(xx-exp_dnu))\n idx = lst.index(min(lst))\n els...

[ { "docid": "1eb06e174c15dd61c4e2c4aa238fdf6e", "score": "0.783653", "text": "def findPeak(image) :\n maxValue = -100000.0\n maxIndex = [0,0]\n if(len(image.shape) == 2) :\n maxAxis = image.argmax(0)\n for pos in maxAxis :\n if(pos != 0) :\n for i in range...

be816247720df41620388f43b0eda2db

Recursively creates the decision tree. This method assumes that the dataTuples are sorted in descending order of feature entropy.

[ { "docid": "91b41d7e4a24be12f78539f37179ebd5", "score": "0.67282295", "text": "def createTree(dataTuples):\n\t\n\tdataTuples = dataTuples[:]\n\n\tif len(dataTuples) == 0:\n\t\t# Return a value of 0 which will \n\t\t# later be replaced with the kind of tree that will be found at this leaf\n\t\t#print \"L...

[ { "docid": "2f3829909b554b823e81942a3ca0fd1f", "score": "0.718289", "text": "def _gen_decision_tree(self, data, cur_depth):\n label = data[:, -1]\n print(type(23432))\n print(len(self.f_info))\n # ! Case 1: Leaf nodes -> generate predictions\n # Case 1.1: pure node -> ...

bfe9e75675fa5a41eb65f52393eeb1df

Import a discourse from csv file

[ { "docid": "e6147a781d4e22ec9c8ff9f560ad3b7e", "score": "0.7458001", "text": "def import_discourse_csvs(corpus_context, typed_data):\n string_set_template = 'n.{name} = csvLine.{name}'\n float_set_template = 'n.{name} = toFloat(csvLine.{name})'\n int_set_template = 'n.{name} = toInt(csvLine.{na...

[ { "docid": "f2ced21216439f9c43fe27a6ffed1504", "score": "0.63136464", "text": "def load_database():\n # copy data from csv file to database\n with connection:\n with connection.cursor() as cursor:\n with open('cleaned_collisions.csv', 'r') as f:\n next(f) # Skip th...

2f3514ca783200d98c92a95c191f312e

Build a list of adjectives starting with passed letter

[ { "docid": "6ebff2d920f2f0cc133ee2de8ed358e3", "score": "0.7244773", "text": "def get_adjectives_starting_with(first_letter):\n api_data = get_adjectives_data(first_letter)\n adjectives = parse_adjectives_data(api_data)\n return adjectives", "title": "" } ]

[ { "docid": "b38229f8f650f088818b96fc992bd8cb", "score": "0.6487598", "text": "def get_animals_starting_with(first_letter):\n animals_list = ANIMALS[first_letter]\n return animals_list", "title": "" }, { "docid": "902048fe510cff13baabc92782125940", "score": "0.6204445", "text": ...

6bd4008d04365eca02543aa406daab14

Get or set str value for 'defaultVRDEExtPack' The name of the extension pack providing the default VRDE. This attribute is for choosing between multiple extension packs providing VRDE. If only one is installed, it will automatically be the default one. The attribute value can be empty if no VRDE extension pack is insta...

[ { "docid": "9eb2b1cd771ed8dc51fd8f17732ada53", "score": "0.8275803", "text": "def default_vrde_ext_pack(self):\n ret = self._get_attr(\"defaultVRDEExtPack\")\n return ret", "title": "" } ]

[ { "docid": "003848579f7abc97ed2c7d5cde483606", "score": "0.6818636", "text": "def vrde_ext_pack(self):\n ret = self._get_attr(\"VRDEExtPack\")\n return ret", "title": "" }, { "docid": "18bc3517be47d423160a4da84adfadec", "score": "0.5376459", "text": "def part_component_...

37bede708518e1fdcba27e6f1a95117d

Implement a cnn to embed the images in relation to their tags. For a walkthough

[ { "docid": "2ead985af337a8143ff5bcb27e844058", "score": "0.6047527", "text": "def build_cnn(image_input, image_output, tag_size):\n W_conv1 = weight_variable([5, 5, 1, 32])\n b_conv1 = bias_variable([32])\n\n x_image = tf.reshape(image_input, [-1, 127, 127, 1])\n h_conv1 = tf.nn.relu(conv2d(...

[ { "docid": "e2d9ed5733792dc2e0fd49d1b1ada69f", "score": "0.58495724", "text": "def tag_cloud(link=22656, \n lim_num_tags=200, \n image_dims=(400, 200),\n skip_tags = [],\n out_filepath=\"TagCloud.png\",\n ):\n\n info = taginfo(link=link...

c16c73e55a512a5510b33a0c0f6efe0a

Save progress in crack phase.

[ { "docid": "8f4c2960e773cbb6e6f06df7299d617f", "score": "0.0", "text": "def set_cracked(self, cracked):\n self.cracked = open(cracked, \"a\")", "title": "" } ]

[ { "docid": "609015f3ffca95ee235a70fb75c8cec3", "score": "0.6551519", "text": "def save_progress(block, addresses, new_addresses, progress_file, with_db):\n print(\"Saving progress... do not close/power off computer until process is\"\n \" complete.\")\n update_progress(block, addresses, p...