query stringlengths 9 9.05k | document stringlengths 10 222k | metadata dict | negatives listlengths 30 30 | negative_scores listlengths 30 30 | document_score stringlengths 4 10 | document_rank stringclasses 2
values |
|---|---|---|---|---|---|---|
Initialize a new linked list containing the given items. The first node in the linked list contains the first item in . | def __init__(self, items):
if len(items) == 0:
self._first = None
self._rest = None
else:
self._first = items[0]
self._rest = LinkedListRec(items[1:]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, items):\r\n if len(items) == 0: # No items, and an empty list!\r\n self._first = None\r\n else:\r\n self._first = _Node(items[0])\r\n curr = self._first\r\n for item in items[1:]:\r\n curr.next = _Node(item)\r\n ... | [
"0.83739454",
"0.7514045",
"0.74844074",
"0.7438117",
"0.70560646",
"0.6919856",
"0.67227983",
"0.6710322",
"0.6697007",
"0.66781634",
"0.6622026",
"0.65524465",
"0.65173554",
"0.6469273",
"0.6463215",
"0.64528155",
"0.64364386",
"0.64092153",
"0.6385144",
"0.63465625",
"0.63... | 0.77780277 | 1 |
Store item at position in this list. Raise IndexError if index is >= the length of . | def __setitem__(self, index, item):
# TODO: complete this function!
if index >= self.__len__():
raise IndexError
else:
if index == 0:
self._first = item
else:
self._rest.__setitem__(index-1, item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __setitem__(self, index, newItem):\r\n #if index < 0 or index >= self.size():\r\n # raise IndexError(\"Array index out of bounds\")\r\n self._items[index] = newItem",
"def __setitem__(self, index: int, item: Any) -> None:\n # If empty raise indexerror\n if self.is_empty(... | [
"0.7629131",
"0.76125854",
"0.7422154",
"0.7377137",
"0.7344196",
"0.7304132",
"0.7274706",
"0.72285616",
"0.7209512",
"0.7208999",
"0.7147858",
"0.7123275",
"0.7095537",
"0.70878774",
"0.70825756",
"0.70619637",
"0.70550114",
"0.70175976",
"0.6974228",
"0.6926821",
"0.691515... | 0.7947383 | 0 |
Return the number of times occurs in this list. Use == to compare items. | def count(self, item):
# TODO: complete this function!
if item not in self:
return 0
else:
num_occur = 0
if self._first == item:
num_occur += 1
num_occur += self._rest.count(item)
return num_occur | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count(self, value):\n self.__validate_value(value)\n counter = 0\n for v in self.__list:\n if v == value:\n counter += 1\n return counter",
"def count(self):\n return sum(1 for _ in self)",
"def count(self, element):\n count = 0\n f... | [
"0.7869159",
"0.75559103",
"0.7474671",
"0.74528986",
"0.7343283",
"0.7281859",
"0.7218688",
"0.7205522",
"0.7064695",
"0.7057718",
"0.7028781",
"0.70113736",
"0.6989967",
"0.6975656",
"0.69622695",
"0.6945205",
"0.69255155",
"0.6864829",
"0.6824261",
"0.6775264",
"0.6769184"... | 0.758594 | 1 |
Insert item in to the list at position . Raise an IndexError if index is > the length of the list. Note that it is possible to add to the end of the list (when index == len(self)). | def insert(self, index, item):
if index > len(self):
raise IndexError
elif index == 0:
self.insert_first(item)
else:
self._rest.insert(index-1, item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert(self, index, item):\n\n # Check if -len(self) <= index <= len(self)\n valid_index = (index <= len(self)) and (index >= -len(self))\n\n try:\n if not valid_index:\n raise IndexError\n\n if self.count == self.capacity:\n self._resize... | [
"0.8071151",
"0.80658376",
"0.8052258",
"0.80156034",
"0.7884237",
"0.7812524",
"0.7785804",
"0.76542145",
"0.7635252",
"0.76222324",
"0.7612091",
"0.74901724",
"0.74399555",
"0.7413813",
"0.7359655",
"0.73120105",
"0.72784495",
"0.7094499",
"0.7009153",
"0.6994207",
"0.69889... | 0.8356974 | 0 |
Return a new LinkedList whose nodes store items that are obtained by applying f to each item in this linked list. Does not change this linked list. | def map(self, f):
if self.is_empty():
pass
else:
items = []
items.append(f(self._first))
map(f._rest)
new_lst = LinkedListRec(items) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def map(self, f: Callable[[Any], Any]) -> RecursiveList:\n # If empty, return empty list\n if self.is_empty():\n return RecursiveList([])\n else:\n # Apply f to the first element and make a new list to return\n rl = RecursiveList([f(self._first)])\n ... | [
"0.6886586",
"0.62040824",
"0.58206326",
"0.5771074",
"0.5719031",
"0.57083845",
"0.5648191",
"0.5646751",
"0.5586226",
"0.5547645",
"0.5476429",
"0.54671377",
"0.5460554",
"0.5458973",
"0.5454752",
"0.54406434",
"0.5396636",
"0.5386853",
"0.53583515",
"0.52437586",
"0.520149... | 0.7892843 | 0 |
1. calculates max length of feat/gep tensors 2. padds feat/geo tensors with zeros till the max length 3. writes resulting tensor to the file | def write_filtered_pad_feat_geo(self):
length_max = self._get_length_max()
data_list = range(self.idx_write, len(self.files_refined))
# length_max = 150
progress = tqdm(data_list)
for id in progress:
progress.set_postfix({'pdb': self.files_refined[id]})
fe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compress(args):\n # This is the sequential version, processing 8 rows one after another.\n x_folders_path = args.input_file\n x_test_images_path = np.asarray(make_mat(x_folders_path))\n # Build model, restore optimized parameters.\n model = CompressionModel(args)\n checkpoint = tf.train.Chec... | [
"0.6039958",
"0.57728046",
"0.5744389",
"0.5502115",
"0.5466844",
"0.5433579",
"0.5422248",
"0.5407497",
"0.53619576",
"0.5324268",
"0.5301215",
"0.52916783",
"0.5289233",
"0.52885836",
"0.52297246",
"0.5225404",
"0.52155596",
"0.5209802",
"0.52026147",
"0.52013487",
"0.51849... | 0.59754586 | 1 |
get the max length of feature array among all pdbids | def _get_length_max(self):
# data_list = list(range(len(self.files_refined)))
data_list = range(self.idx_max_length, len(self.files_refined))
progress = tqdm(data_list)
for pdb_id in progress:
features_filt, geo_filt = self._get_features_geo_filtered(pdb_id)
lengt... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_max_rois(self):\n \n maxsize = 0\n for index in self.SampleID:\n rois = self.__getrois__(index);\n maxsize = max(maxsize, rois.shape[0])\n \n return maxsize",
"def length(a):\n return max(a.shape)",
"def determine_max_length(sequences, ids):\n... | [
"0.70063245",
"0.67031974",
"0.6578034",
"0.6577833",
"0.654353",
"0.65412873",
"0.64995086",
"0.64809895",
"0.64476216",
"0.642505",
"0.6413181",
"0.6401733",
"0.6401651",
"0.638045",
"0.6361821",
"0.6353808",
"0.6341069",
"0.6327722",
"0.6285773",
"0.628314",
"0.62640816",
... | 0.68651325 | 1 |
creates a hot vector of an atom type | def atom_to_hot_vector(self, elem: str):
hot_vector = np.zeros(22)
idx = self.dict_atoms_simple[elem]
hot_vector[idx] = 1
return hot_vector | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def as_one_hot(ind, n):\n vec = np.zeros(n)\n vec[ind] = 1\n return vec",
"def to_one_hot(v):\n n = len(v)\n m = max(v) + 1\n out = np.zeros((n, m))\n out[np.arange(n), v] = 1\n return out",
"def vec_to_onehot(vec, m=0):\r\n\r\n n = vec.shape[0]\r\n if m == 0:\r\n m = len(n... | [
"0.65515584",
"0.64342755",
"0.6431607",
"0.6298604",
"0.62674636",
"0.62252426",
"0.6173745",
"0.6168766",
"0.6096443",
"0.60652435",
"0.6063281",
"0.6023734",
"0.6019546",
"0.59940034",
"0.598831",
"0.59744394",
"0.5963999",
"0.5954198",
"0.594281",
"0.59369165",
"0.5905558... | 0.77975243 | 0 |
selects atoms of "id_pdb" protein within the distance "precision" around "center_lig" | def _get_mask_selected_atoms_pocket(
self, pdb_id: int,
):
path_protein, path_ligand = self._get_path(pdb_id)
center_ligand = self._get_ligand_center(path_ligand)
if self.type_filtering == "all" and self.h_filterig == 'h':
sel="protein and noh and sqr(x-'{0}')+sqr(y-'{1}... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def query_points(self, point, precision=5):\n # Generate the geohash.\n (latitude, longitude) = point\n\n hashcode = geohash.encode(latitude, longitude, precision)\n log.debug('Point \"%s\" geohash is: \"%s\"' % (point, hashcode))\n\n results = self.regex_query(self.spatial_index... | [
"0.5119753",
"0.5106974",
"0.5073792",
"0.49484593",
"0.49429494",
"0.49392182",
"0.49106923",
"0.4801841",
"0.47417852",
"0.46974775",
"0.46954015",
"0.46917465",
"0.4655261",
"0.46518767",
"0.45872286",
"0.45733228",
"0.45712203",
"0.45690605",
"0.4567515",
"0.45560297",
"0... | 0.53425086 | 0 |
get the geometrical center of a ligand | def _get_ligand_center(self, path_ligand):
mol_ligand = Molecule(path_ligand)
coor_lig = mol_ligand.coords
center = np.mean(coor_lig, axis=0)
center = center.reshape(1, -1)
return center | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_center(self):\n lon, lat = self.coordinates\n\n dimx = lon.shape[0]\n dimy = lon.shape[1]\n \n return (lon[dimx/2][dimy/2],lat[dimx/2][dimy/2])",
"def get_center(self,lonlat=False):\n lon, lat = np.asarray(self.rotator.rots[0][0:2])*180/pi\n if lonlat: return lon... | [
"0.73146564",
"0.7097857",
"0.7076957",
"0.7063528",
"0.70455927",
"0.6983909",
"0.69735634",
"0.6866781",
"0.6822262",
"0.6771345",
"0.6769709",
"0.6763476",
"0.674945",
"0.6733786",
"0.6720019",
"0.66451687",
"0.6616574",
"0.66154957",
"0.6583129",
"0.6547878",
"0.65317476"... | 0.81156254 | 0 |
takes all elems in protein | def _get_all_elems(self, protein_id: int):
path_protein, _ = self._get_path(protein_id)
try:
# mol_pocket = Molecule(path_protein)
mol_protein = Molecule(path_protein)
mol_protein.filter('protein')
if (self.type_feature == "bio_properties" or self.type_fea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def elems(self):",
"def possible_subpeptides(self):\n ret = [\"\"]\n protein_len = len(self.protein)\n for l in range(1, protein_len):\n for i in range(protein_len):\n if i + l <= protein_len:\n ret += [self.protein[i : i+l]]\n else... | [
"0.62303525",
"0.5552091",
"0.5510698",
"0.5500446",
"0.54802835",
"0.53920203",
"0.53920203",
"0.5362717",
"0.5361895",
"0.5349297",
"0.5304554",
"0.5291094",
"0.52732027",
"0.5205366",
"0.51758593",
"0.51736546",
"0.5152028",
"0.5149067",
"0.5142705",
"0.51384175",
"0.51371... | 0.57472456 | 1 |
get a full path to protein/ligand | def _get_path(self, protein_id: int):
protein_name = self.files_refined[protein_id]
path_protein = os.path.join(
self.init_refined, protein_name, protein_name + "_protein.pdb"
)
path_ligand = os.path.join(
self.init_refined, protein_name, protein_name + "_ligand.m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_fullpath(self, address):\n address = os.path.abspath(address)\n if len(address) < 4 or address[-4:] != \".dta\":\n address = address + \".dta\"\n return address",
"def get_plato_path():\n\treturn \"/tsi/\"",
"def lpath(file0, file1):\n return os.path.abspath(os.path.... | [
"0.66211337",
"0.63901454",
"0.63766974",
"0.63610274",
"0.63550186",
"0.63381064",
"0.6267822",
"0.617297",
"0.61711824",
"0.61469203",
"0.6130231",
"0.6094284",
"0.6089338",
"0.60851544",
"0.6083939",
"0.60728234",
"0.6053661",
"0.60350275",
"0.60183203",
"0.60183203",
"0.6... | 0.7850319 | 0 |
Renders and returns a string of the rendered menu. | def render(self, menu):
# Get the menu title.
try:
menu_title = menu["meta"]["title"]
except KeyError:
# If menu title not found, set to a default title.
menu_title = "??????????"
render = "\n" + menu_title
render += "\n" + "=" * len(menu_titl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render(self,session,context):\n #TODO: at some point this should actually get implemented, but to do so\n # would need to convert self.menu_items from list to a dict, where the\n # keys are the items that should be selected in the menu. \n # to do so would need to update MenuItem as... | [
"0.7988218",
"0.76087123",
"0.75279605",
"0.71491927",
"0.6854286",
"0.67999434",
"0.6798856",
"0.67587787",
"0.67390203",
"0.6681261",
"0.6657621",
"0.6626091",
"0.6576273",
"0.6531636",
"0.64970994",
"0.6481709",
"0.6479811",
"0.6458487",
"0.6414937",
"0.63659364",
"0.63357... | 0.77127457 | 1 |
count number of fillin edges after removing nid number of combinations of nhd existing edges (nodes in the subgraph of nhd) | def fill_count(nid):
n_edges = G.subgraph(G.neighbors(nid)).number_of_edges()
deg = G.degree[nid]
n_fill = deg*(deg-1)//2 - n_edges
return n_fill | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_fillin(graph, nodes):\n count = 0\n for v1 in nodes:\n for v2 in nodes:\n if v1 != v2 and v2 not in graph[v1]:\n count += 1\n return count / 2",
"def num_edges(g):\n total_edges_with_duplicates = sum(len(v) for v in g.values())\n return total_edges_with_d... | [
"0.68112355",
"0.6494084",
"0.6307327",
"0.6232778",
"0.62294644",
"0.6160811",
"0.61596954",
"0.6086348",
"0.60630405",
"0.60320306",
"0.6026491",
"0.59552574",
"0.594635",
"0.5942232",
"0.5926342",
"0.5921132",
"0.59107393",
"0.59103554",
"0.58853024",
"0.58828676",
"0.5837... | 0.7644923 | 0 |
Add the QListWisgetItem initialized with txt informations to the User Interface logger_list and to the save_parameters.logger array. =============== =========== ====================== Parameters Type Description txt string the log info to add. =============== =========== ====================== | def add_log(self,txt):
try:
now=datetime.datetime.now()
new_item=QtWidgets.QListWidgetItem(now.strftime('%Y/%m/%d %H:%M:%S')+": "+txt)
self.ui.logger_list.addItem(new_item)
if self.h5saver.h5_file.isopen:
self.h5saver.append(self.h5saver.logger_arr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Add_text(self, s):\n self.logger.DeleteAllItems()\n FONT_RATIO = self.GUI_RESOLUTION + (self.GUI_RESOLUTION - 1) * 5\n if self.GUI_RESOLUTION > 1.1:\n font1 = wx.Font(11, wx.SWISS, wx.NORMAL,\n wx.NORMAL, False, self.font_type)\n elif self.GUI_R... | [
"0.5881799",
"0.58458656",
"0.56814134",
"0.5619595",
"0.55748296",
"0.5516204",
"0.54515344",
"0.540663",
"0.53921944",
"0.5337886",
"0.5323092",
"0.5313274",
"0.52923506",
"0.52373755",
"0.5226844",
"0.5185388",
"0.51751226",
"0.5174729",
"0.5174729",
"0.5174729",
"0.512978... | 0.7931131 | 0 |
Remove all docks containing Moves or Viewers. See Also quit_fun, update_status | def clear_move_det_controllers(self):
try:
#remove all docks containing Moves or Viewers
if hasattr(self,'move_modules'):
if self.move_modules is not None:
for module in self.move_modules:
module.quit_fun()
self.move... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear_moves(self):\n [x.set_board() for x in self.pieces if x.get_state() == MOVE]",
"def cleanup():\n for s in [missiles, explosions, bonus]:\n\n set_to_remove = set([])\n for m in s:\n if m.isDead:\n set_to_remove.add(m)\n\n s.difference_update(set_t... | [
"0.58050376",
"0.5732785",
"0.56806105",
"0.56256336",
"0.5601662",
"0.5536522",
"0.5464107",
"0.5463185",
"0.54307175",
"0.54176515",
"0.5413854",
"0.5383799",
"0.537846",
"0.5367632",
"0.53629655",
"0.5356074",
"0.5353963",
"0.5322776",
"0.5320462",
"0.532009",
"0.5315577",... | 0.6498112 | 0 |
Load and restore a layout state from the select_file obtained pathname file. See Also utils.select_file | def load_layout_state(self, file=None):
try:
if file is None:
file=utils.select_file(save=False, ext='dock')
if file is not None:
with open(str(file), 'rb') as f:
dockstate = pickle.load(f)
self.dockarea.restoreState... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_layout(file_name):\n with open(file_name) as layout_file:\n DATABASE[\"layout\"] = ordered_load(layout_file)",
"def loadState(self, file):\n if isinstance(file, str):\n with open(file, 'r') as f:\n xml = f.read()\n else:\n xml = file.read()\n ... | [
"0.6230063",
"0.58539796",
"0.5767025",
"0.5591188",
"0.555881",
"0.5494226",
"0.5373943",
"0.5372999",
"0.53382033",
"0.53322387",
"0.5284557",
"0.52710265",
"0.52633137",
"0.52633137",
"0.52633137",
"0.5242723",
"0.5234761",
"0.52294743",
"0.5227755",
"0.52208555",
"0.52176... | 0.77486223 | 0 |
Quit the current instance of DAQ_scan and close on cascade move and detector modules. See Also quit_fun | def quit_fun(self):
try:
try:
self.h5saver.close_file()
except:
pass
for module in self.move_modules:
try:
module.quit_fun()
QtWidgets.QApplication.processEvents()
QTh... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n print 'closing'\n self.comm.close_all_serial() # Close all serial connections opened with SNAPconnect\n print 'closed'\n sys.exit(0) # Exit the program",
"def quitDataAcq(self):\r\n\t\t# stop the data acquition if threads still running\r\n\t\tglobal control, payloa... | [
"0.6674686",
"0.663018",
"0.64640576",
"0.64363",
"0.63610995",
"0.6332642",
"0.6332226",
"0.6291272",
"0.62454903",
"0.623352",
"0.61810166",
"0.61721116",
"0.6159734",
"0.6145881",
"0.6129895",
"0.61248255",
"0.611556",
"0.6087104",
"0.6085639",
"0.60234964",
"0.6013995",
... | 0.71952796 | 0 |
Save the current layout state in the select_file obtained pathname file. Once done dump the pickle. See Also utils.select_file | def save_layout_state(self, file = None):
try:
dockstate = self.dockarea.saveState()
if file is None:
file=utils.select_file(start_path=None, save=True, ext='dock')
if file is not None:
with open(str(file), 'wb') as f:
pickl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_file(self):\n if self.select_path.text() != \"\":\n filepath = self.select_path.text()\n road_network = self.map_selection.currentText()\n if self.map_selection.currentText() == \"User Defined\":\n road_network = self.map_selection_user_defined.text()... | [
"0.6506166",
"0.6454797",
"0.6402835",
"0.6275733",
"0.62479436",
"0.6210814",
"0.6192487",
"0.6136867",
"0.61263037",
"0.6086277",
"0.60623175",
"0.60475004",
"0.60043085",
"0.6003689",
"0.60000306",
"0.5965004",
"0.5953231",
"0.5944658",
"0.5938529",
"0.5885336",
"0.5870291... | 0.7611917 | 0 |
Check for changes in the given (parameter,change,information) tuple list. In case of value changed, update the DAQscan_settings tree consequently. =============== ============================================ ============================== Parameters Type Description param instance of pyqtgraph parameter the parameter t... | def parameter_tree_changed(self, param, changes):
for param, change, data in changes:
path = self.settings.childPath(param)
if path is not None:
childName = '.'.join(path)
else:
childName = param.name()
if change == 'childAdded':pas... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateParameterNodeFromGUI(self, caller=None, event=None):\n\n\t\tif self._parameterNode is None or self._updatingGUIFromParameterNode and not self.active:\n\t\t\treturn\n\n\t\t#wasModified = self._parameterNode.StartModify() # Modify all properties in a single batch\n\t\t#self._parameterNode.EndModify(wasMod... | [
"0.6044912",
"0.60374033",
"0.5927496",
"0.5887439",
"0.5804231",
"0.5773921",
"0.57223433",
"0.56518865",
"0.5639361",
"0.5627822",
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"0.5597466",
"0.5559683",
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"0.5499283",
"0.5494966",
"0.5484403",
"0.5473031",
"0.5471835",
"0.54695016",
"0.54352117... | 0.71576345 | 0 |
Set a file preset from the converted xml file given by the filename parameter. =============== =========== =================================================== Parameters Type Description filename string the name of the xml file to be converted/treated =============== =========== ========================================... | def set_file_preset(self,filename):
if os.path.splitext(filename)[1] == '.xml':
self.preset_file = filename
self.preset_manager.set_file_preset(filename, show=False)
self.move_docks = []
self.det_docks_settings = []
self.det_docks_viewer = []
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setFile(self, filename):\n self.prepare() #new call on each new file to process\n self.filename = \"%s\" % filename",
"def set_preset_mode(self,filename):\n try:\n self.mainwindow.setVisible(False)\n for area in self.dockarea.tempAreas:\n area.window... | [
"0.5829164",
"0.5519779",
"0.5461985",
"0.5307552",
"0.5232683",
"0.5231247",
"0.5187496",
"0.5112098",
"0.5074602",
"0.50464576",
"0.49168807",
"0.48866105",
"0.48481604",
"0.4804258",
"0.47996974",
"0.47971925",
"0.47929823",
"0.47869778",
"0.47837144",
"0.47798917",
"0.475... | 0.68054706 | 0 |
Send the command_DAQ signal with "set_ini_positions" list item as an attribute. | def set_ini_positions(self):
self.command_DAQ_signal.emit(["set_ini_positions"]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_ini_positions(self):\n try:\n positions=self.scan_moves[0]\n for ind_move,pos in enumerate(positions): #move all activated modules to specified positions\n # if pos[0]!=self.move_modules[ind_move].title: # check the module correspond to the name assigned in pos\n... | [
"0.65206444",
"0.5565225",
"0.54022473",
"0.5384504",
"0.5145287",
"0.5145287",
"0.5145287",
"0.5145287",
"0.5145287",
"0.5145287",
"0.5145287",
"0.5145287",
"0.5145287",
"0.5145287",
"0.5145287",
"0.50860304",
"0.5072326",
"0.5072326",
"0.49873772",
"0.49510655",
"0.4950132"... | 0.83170754 | 0 |
Set the date/time and author values of the scan_info child of the scan_attributes tree. Show the 'scan' file attributes. See Also show_file_attributes | def set_metadata_about_current_scan(self):
date=QDateTime(QDate.currentDate(),QTime.currentTime())
self.scan_attributes.child('scan_info','date_time').setValue(date)
self.scan_attributes.child('scan_info','author').setValue(self.dataset_attributes.child('dataset_info','author').value())
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_file_attributes(self,type_info='dataset'):\n dialog=QtWidgets.QDialog()\n vlayout=QtWidgets.QVBoxLayout()\n tree = ParameterTree()\n tree.setMinimumWidth(400)\n tree.setMinimumHeight(500)\n if type_info=='scan':\n tree.setParameters(self.scan_attributes... | [
"0.551714",
"0.5440282",
"0.53046125",
"0.52899736",
"0.5286924",
"0.5248003",
"0.52438426",
"0.52438426",
"0.5181869",
"0.5158462",
"0.5115434",
"0.50846976",
"0.5074747",
"0.5050444",
"0.5019831",
"0.5019831",
"0.5000268",
"0.49550933",
"0.4948447",
"0.4942758",
"0.4933988"... | 0.78501546 | 0 |
Set the date value of the data_set_infodate_time child of the data_set_attributes tree. Show the 'dataset' file attributes. See Also show_file_attributes | def set_metadata_about_dataset(self):
date=QDateTime(QDate.currentDate(),QTime.currentTime())
self.dataset_attributes.child('dataset_info','date_time').setValue(date)
res = self.show_file_attributes('dataset')
return res | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_set_get_dt():\n data = io.create_sample_dataset()\n assert data.attrs[\"dt\"] == 1.0\n assert data.piv.dt == 1.0\n data.piv.set_dt(2.0)\n assert data.attrs[\"dt\"] == 2.0",
"def setNodeDatum(self, node, value):\n\n if not cmds.attributeQuery('datum', node=node, exists=True):\n ... | [
"0.62531453",
"0.6206267",
"0.61347795",
"0.6099797",
"0.5967181",
"0.5845252",
"0.5718401",
"0.5666374",
"0.5655639",
"0.5612481",
"0.5601563",
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"0.55429155",
"0.54330015",
"0.5411815",
"0.5386389",
"0.5386389",
"0.52813655",
"0.52794474",
"0.52794474",
"0.5279447... | 0.74898064 | 0 |
| Set the preset mode from the given filename. | | In case of "mock" or "canon" move, set the corresponding preset calling set_()_preset procedure. | | Else set the preset file using set_file_preset function. | Once done connect the move and detector modules to logger to recipe/transmit informations. | def set_preset_mode(self,filename):
try:
self.mainwindow.setVisible(False)
for area in self.dockarea.tempAreas:
area.window().setVisible(False)
self.splash_sc.show()
QtWidgets.QApplication.processEvents()
self.splash_sc.raise_()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_file_preset(self,filename):\n if os.path.splitext(filename)[1] == '.xml':\n self.preset_file = filename\n self.preset_manager.set_file_preset(filename, show=False)\n self.move_docks = []\n self.det_docks_settings = []\n self.det_docks_viewer = [... | [
"0.7156599",
"0.6034789",
"0.58596665",
"0.5840704",
"0.5835563",
"0.57934976",
"0.5777567",
"0.5766049",
"0.5764263",
"0.57255656",
"0.5667473",
"0.5654134",
"0.56322426",
"0.5626051",
"0.5547577",
"0.55469173",
"0.55058944",
"0.5447592",
"0.5425441",
"0.53853714",
"0.537712... | 0.6626212 | 1 |
Sets the current scan given the selected settings. Makes some checks, increments the h5 file scans. In case the dialog is cancelled, return False and aborts the scan | def set_scan(self):
try:
# set the filename and path
res = self.create_new_file(False)
if not res:
return
#reinit these objects
self.scan_data_1D = []
self.scan_data_1D_average = []
self.scan_data_2D = []
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_scan(self):\n self.ui.log_message.setText('Starting acquisition')\n self.overshoot = False\n self.plot_2D_ini=False\n self.plot_1D_ini = False\n res = self.set_scan()\n if res:\n\n # save settings from move modules\n move_modules_names = [mo... | [
"0.66779613",
"0.56389666",
"0.5437402",
"0.54135776",
"0.5386053",
"0.52407885",
"0.50568384",
"0.5048242",
"0.5046069",
"0.49726388",
"0.49683028",
"0.4941879",
"0.49254397",
"0.4911938",
"0.4909159",
"0.49013782",
"0.4855569",
"0.48478332",
"0.48153368",
"0.4804125",
"0.47... | 0.6381482 | 1 |
Foreach module of the move module object list, stop motion. See Also stop_scan, DAQ_Move_main.daq_move.stop_Motion | def stop_moves(self,overshoot):
self.overshoot = overshoot
self.stop_scan()
for mod in self.move_modules:
mod.stop_Motion() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n self.stopAngMovementAll()\n self.stopMovementAll()",
"def stop_moving(self):\n self.logger.info('stop moving')\n self.anc350_instrument.stop = True\n self.anc350_instrument.stop_moving('XPiezoStepper')\n self.anc350_instrument.stop_moving('YPiezoStepper... | [
"0.6659773",
"0.6369866",
"0.63668823",
"0.630369",
"0.6286686",
"0.6217773",
"0.61441624",
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"0.60714436",
"0.60264486",
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"0.58430815",
"0.58115435",
"0.5793677",
"0.5671151",
"0.5671151",
"0.5641215",
"0.562186... | 0.7260226 | 0 |
Emit the command_DAQ signal "stop_acquisiion". See Also set_ini_positions | def stop_scan(self):
self.ui.log_message.setText('Stoping acquisition')
self.command_DAQ_signal.emit(["stop_acquisition"])
if not self.overshoot:
self.set_ini_positions() #do not set ini position again in case overshoot fired
status = 'Data Acquisition has been stopped b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n print(\"Stopping accessory.\")",
"def stop(self):\n self.scion_sh('stop')",
"def on_stop(self):\n pass",
"def on_stop(self):\n pass",
"def on_stop(self):\n pass",
"def on_stop(self):\n pass",
"def on_stop(self):\n pass",
"def on_stop(... | [
"0.61277246",
"0.61252224",
"0.61175966",
"0.61175966",
"0.61175966",
"0.61175966",
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"0.60791785",
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"0.5927264",
"0.58939093",
"0.58727676",
"0.58689684",
"0.58689684",
"0.58689684",
... | 0.6285996 | 0 |
Update the 2D graphic window in the Graphic Interface with the given datas (if not none). | def update_2D_graph(self,datas):
try:
if "2D" in self.scanner.settings.child('scan_options','scan_type').value(): #scan2D type cartography
if not(self.plot_2D_ini):#init the data
self.plot_2D_ini=True
self.scan_x_axis=self.scan_parameters.a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show2d(self, data):\n self.__plot2d.clear()\n self.__plot2d.addImage(legend=\"data\", data=data)\n self.setCurrentIndex(self.__index2d)",
"def redraw(self):\n x2, y2 = [[] for i in range(len(self.x))], \\\n [[] for i in range(len(self.x))]\n game_point = [ra... | [
"0.6652105",
"0.6480033",
"0.6392848",
"0.6373413",
"0.6372241",
"0.6316622",
"0.6263822",
"0.6242772",
"0.62332606",
"0.616997",
"0.61216587",
"0.61146516",
"0.60937005",
"0.6056487",
"0.605595",
"0.60478926",
"0.60207796",
"0.6014414",
"0.5973884",
"0.5959114",
"0.59557223"... | 0.65230453 | 1 |
Add the scan_optionsplot_form child to the DAQscan_settings tree from the selected value of the given parameter. =============== ================================= ======================== Parameters Type Description param instance of pyqtgraph parameter the parameter to treat =============== ===========================... | def update_plot_det_items(self,param):
items=param.value()['selected']
self.settings.child('scan_options', 'plot_from').setOpts(limits=items) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_scan2D_type(self, param):\n try:\n self.settings.child('scan2D_settings', 'step_2d_axis1').show()\n self.settings.child('scan2D_settings', 'step_2d_axis2').show()\n scan_subtype = self.settings.child('scan2D_settings', 'scan2D_type').value()\n self.sett... | [
"0.5666199",
"0.5596686",
"0.54091656",
"0.5221192",
"0.5203126",
"0.5030914",
"0.49853924",
"0.49624065",
"0.49295855",
"0.4927549",
"0.49155295",
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"0.48622483",
"0.48564363",
"0.48544782",
"0.48372853",
"0.48309246",
"0.4821907",
"0.4... | 0.6061984 | 0 |
Show the txt message in the status bar with a delay of wait_time ms. =============== =========== ======================= Parameters Type Description txt string The message to show wait_time int the delay of showing log_type string the type of the log =============== =========== ======================= | def update_status(self,txt,wait_time=0,log_type=None):
try:
self.ui.statusbar.showMessage(txt,wait_time)
if log_type is not None:
self.log_signal.emit(txt)
logging.info(txt)
except Exception as e:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def StatusbarTimer(self):\r\n\t\ttime.sleep(self.statusmsgTimeout)\r\n\t\tself.statusbar.SetStatusText(self.statusmsg)",
"def flash_status(text=\"\", duration=0.05):\n status_label.color = WHITE\n status_label.text = text\n time.sleep(duration)\n status_label.color = BLACK\n time.sleep(duration)\n... | [
"0.7081665",
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"0.6747514",
"0.66980165",
"0.65417576",
"0.64842546",
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"0.6144765",
"0.612524",
"0.60996556",
"0.60810894",
"0.60790706",
"0.6026205",
"0.5993... | 0.80850214 | 0 |
| Set the positions from the scan_move attribute. | | Move all activated modules to specified positions. | Check the module corresponding to the name assigned in pos. See Also DAQ_Move_main.daq_move.move_Abs | def set_ini_positions(self):
try:
positions=self.scan_moves[0]
for ind_move,pos in enumerate(positions): #move all activated modules to specified positions
# if pos[0]!=self.move_modules[ind_move].title: # check the module correspond to the name assigned in pos
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_stages(self,positions):\n for ind_move,pos in enumerate(positions): #move all activated modules to specified positions\n #self.move_modules[ind_move].move_Abs(pos)\n self.move_modules_commands[ind_move].emit(utils.ThreadCommand(command=\"move_Abs\", attributes=[pos]))",
"def... | [
"0.7467515",
"0.6947724",
"0.6550185",
"0.64504737",
"0.6210145",
"0.6199858",
"0.60810655",
"0.60117346",
"0.58760816",
"0.5873726",
"0.58373207",
"0.58349156",
"0.5817869",
"0.57891977",
"0.57215756",
"0.57186145",
"0.57167464",
"0.5713565",
"0.57004696",
"0.56890833",
"0.5... | 0.82506406 | 0 |
| Update the move_done_positions attribute if needed. | If position attribute is setted, for all move modules launched, update scan_read_positions with a [modulename, position] list. ============== ============ ================= Parameters Type Description name string the module name position float ??? ============== =... | def move_done(self,name,position):
try:
if name not in list(self.move_done_positions.keys()):
self.move_done_positions[name]=position
if len(self.move_done_positions.items())==len(self.move_modules_names):
list_tmp=[]
for name_tmp in sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_ini_positions(self):\n try:\n positions=self.scan_moves[0]\n for ind_move,pos in enumerate(positions): #move all activated modules to specified positions\n # if pos[0]!=self.move_modules[ind_move].title: # check the module correspond to the name assigned in pos\n... | [
"0.6969691",
"0.6139816",
"0.5908709",
"0.5744122",
"0.5650663",
"0.5544578",
"0.55405074",
"0.55050606",
"0.54708046",
"0.54686624",
"0.5464044",
"0.54615414",
"0.5459722",
"0.54332083",
"0.53042233",
"0.5279307",
"0.52247083",
"0.5211969",
"0.51990247",
"0.51911044",
"0.516... | 0.84063566 | 0 |
Send the status signal 'Time out during acquisition' and stop the timer. | def timeout(self):
self.timeout_scan_flag=True
self.timer.stop()
self.status_sig.emit(["Update_Status","Timeout during acquisition",'log'])
self.status_sig.emit(["Timeout"]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop_timer(self):\r\n self.countdownTimer.stop()",
"def stop_timer(self):\n self.end_time = datetime.now()",
"def stop_scan(self):\n self.ui.log_message.setText('Stoping acquisition')\n self.command_DAQ_signal.emit([\"stop_acquisition\"])\n\n if not self.overshoot:\n ... | [
"0.6732306",
"0.6624446",
"0.6623612",
"0.6596136",
"0.6428103",
"0.6352463",
"0.629017",
"0.62560344",
"0.6242175",
"0.6213506",
"0.619111",
"0.6187021",
"0.61418176",
"0.60949814",
"0.60904163",
"0.60898554",
"0.6060151",
"0.6058167",
"0.6037202",
"0.60368913",
"0.60180306"... | 0.8051863 | 0 |
Move all the activated modules to the specified positions. =============== ============ ============================================= Parameters Type Description positions tuple list The list of the positions related to indices =============== ============ ============================================= See Also DAQ_Move... | def move_stages(self,positions):
for ind_move,pos in enumerate(positions): #move all activated modules to specified positions
#self.move_modules[ind_move].move_Abs(pos)
self.move_modules_commands[ind_move].emit(utils.ThreadCommand(command="move_Abs", attributes=[pos])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_ini_positions(self):\n try:\n positions=self.scan_moves[0]\n for ind_move,pos in enumerate(positions): #move all activated modules to specified positions\n # if pos[0]!=self.move_modules[ind_move].title: # check the module correspond to the name assigned in pos\n... | [
"0.7278717",
"0.6633478",
"0.61216366",
"0.5976694",
"0.5918757",
"0.5745256",
"0.57365614",
"0.56655884",
"0.56013227",
"0.5571817",
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"0.55053484",
"0.5494978",
"0.5477668",
"0.54770064",
"0.5414701",
"0.54118055",
"0.54023... | 0.7948619 | 0 |
Find peak in filtered image | def peak_finder(filt_im, dist, threshold):
from skimage.feature import peak_local_max
coordinates = peak_local_max(filt_im, min_distance=dist, threshold_abs=threshold)
return coordinates | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def peakdet2d(image):\n # define an 8-connected neighborhood\n neighborhood = generate_binary_structure(2,2)\n\n #apply the local maximum filter; all pixel of maximal value \n #in their neighborhood are set to 1\n local_max = maximum_filter(image, footprint=neighborhood)==image\n #local_max is a ... | [
"0.7611833",
"0.75127107",
"0.7351917",
"0.72137237",
"0.71414274",
"0.7130999",
"0.70705295",
"0.68659514",
"0.68234545",
"0.68224365",
"0.6820093",
"0.67013717",
"0.6689779",
"0.6589693",
"0.65794873",
"0.6565677",
"0.655181",
"0.65445316",
"0.64960223",
"0.6448608",
"0.644... | 0.78542244 | 0 |
Creates vocabulary that is used to map BPEs to ids and viceversa. It iterates over data, performs tokenization with the BPE tokenizer and true caser and creates a vocabulary of unique symbols that is saved. | def create_bpe_vocabulary(bpe_vocab_fp, bpe_int_fp, data_path, truecaser_fp):
bpe = BPE(glossaries=SPECIAL_TOKENS)
bpe.load(bpcodes_fp=bpe_int_fp, merges=-1)
tcaser = MosesTruecaser(load_from=truecaser_fp, is_asr=True)
tcase_func = partial(tcaser.truecase, return_str=True, use_known=True)
unsup_tok... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_vocab(data):\n # data = _read_words(filename)\n counter = collections.Counter(data)\n # print('counter', counter) # dictionary for the occurrence number of each word, e.g. 'banknote': 1, 'photography': 1, 'kia': 1\n count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))\n # p... | [
"0.6357832",
"0.62543017",
"0.6113525",
"0.6069173",
"0.6017819",
"0.6013648",
"0.59880906",
"0.5984867",
"0.59745556",
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"0.5963113",
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"0.5936401",
"0.59178555",
"0.59036773",
"0.5891193",
"0.5887666",
"0.5862846",
"0.58609784",
"0.585865... | 0.65041125 | 0 |
Creates a Converter class instance with the CommonAPIHandler class instance. | def __init__(self, common_api_handler):
self.common_api_handler = common_api_handler | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def newConverter(self, value):\r\n return JsonConverter(value, self)",
"def converters(self) -> Iterator[Tuple[str, Type[ConverterAPI]]]:",
"def __init__(self, api_wrapper):\n\n self.api_wrapper = api_wrapper",
"def __init__(self, client):\n self._client = client\n self._argument_... | [
"0.5562537",
"0.53939396",
"0.53782076",
"0.53677505",
"0.536641",
"0.53500295",
"0.53288835",
"0.53207904",
"0.5240729",
"0.5229475",
"0.5199164",
"0.5126729",
"0.5124941",
"0.5108102",
"0.51042795",
"0.5092689",
"0.5077401",
"0.50703996",
"0.50640655",
"0.5028293",
"0.50157... | 0.6730362 | 0 |
Calculates the ACE of the model based on data (X,Y). | def ace(model, X, Y, n_bins=10, backend="google"):
if backend is None:
raise NotImplementedError
elif backend == "prototype":
raise NotImplementedError
elif backend == "google":
probabilities, labels = google_metric_formulation(model=model, X=X, Y=Y)
metric_value = um.ace(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_accels (self):\n return (self.get_ax (), self.get_ay (), self.get_az ())",
"def azizen(self):\n # x0,y0 array pixel coordinates relative to cx,cy\n# ndy0,ndx0=img.shape\n ndy0=self.ndy0\n ndx0=self.ndx0\n x0,y0=np.meshgrid(np.linspace(0,ndx0-1,ndx0)-self.cx,np.li... | [
"0.57355887",
"0.5717503",
"0.56428075",
"0.5639411",
"0.55450124",
"0.54540753",
"0.5409499",
"0.53608996",
"0.52965295",
"0.52900827",
"0.52363586",
"0.5206994",
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"0.51666576",
"0.51596075",
"0.5145973",
"0.5130768",
"0.5117612",
"0.5103825",
"0.5... | 0.6102737 | 0 |
Plugin main page. With list of all categories and search menu. | def index(request):
main_menu = [
(
buildUrl(request.basePath(), {"path": "/search"}),
xbmcgui.ListItem(
label=xbmcaddon.Addon().getLocalizedString(30009),
iconImage=getMediaResource("search.png"),
path=buildUrl(request.basePath(), {"p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def categories_menu():\n categories = ['EU-affairs', 'Economy', 'Security', 'Society', 'World']\n\n for category in categories:\n url = build_url({'mode': 'Topic', 'foldername': category, 'page': 1})\n li = xbmcgui.ListItem(category, iconImage='DefaultFolder.png')\n xbmcplugin.addDirecto... | [
"0.6400513",
"0.6388478",
"0.60347533",
"0.59958863",
"0.599536",
"0.58903766",
"0.58873385",
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"0.57688916",
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"0.560429",
"0.5595375",
"0.5581772",
"0.55693513",
"0.5544254",
"0.550893... | 0.659088 | 0 |
Print a recipe with the name `name` and return the instance | def get_recipe_by_name(self, name):
for key, val in self.recipes_list.items():
for a, b in val.items():
if name == a:
print(str(b)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_recipe_by_name(self, name):\n for key in self.recipe_list.keys():\n if name in self.get_recipes_by_types(key):\n for recipe in self.recipe_list[key]:\n if recipe.name == name:\n print(recipe.__str__())\n retur... | [
"0.6717745",
"0.6291671",
"0.6284392",
"0.5784514",
"0.5750448",
"0.56267476",
"0.561223",
"0.5574568",
"0.55019253",
"0.5437774",
"0.541985",
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"0.5318898",
"0.53134686",
"0.529557",
"0.52849776",
"0.5276027",
"0.52454233",
"0.5224742",
... | 0.65596545 | 1 |
Send a message when the command /reloadplayers is issued. | def reload_command(update: Update, context: CallbackContext) -> None:
player.saveChatID(players)
logger.info(f'Player chat ids have been saved in {config.CHAT_ID_JSON}')
player.loadPlayers(players)
logger.info(f'Players reloaded')
update.message.reply_text(f'Players reloaded') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reload_player(id):\n global database\n table = database.Tables.players\n return get_query(table.select().where(table.c.id == id))",
"async def update_players():\n if USERS:\n message = json.dumps({\"type\": \"update\", **GAME.get_state()})\n await asyncio.wait([user.send(message) fo... | [
"0.63851124",
"0.63306063",
"0.6239043",
"0.60842466",
"0.5970209",
"0.58716434",
"0.5787298",
"0.57728904",
"0.57372475",
"0.5649972",
"0.55818504",
"0.5535778",
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"0.5485581",
"0.5465095",
"0.54483074",
"0.5431276",
"0.5417135",
"0.5363092",
"0.535481",
"0.53463... | 0.7901673 | 0 |
Gets and sets the countryLetterCode | def country_letter_code(self):
if "countryLetterCode" in self._prop_dict:
return self._prop_dict["countryLetterCode"]
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def country_code(self):\n return self.__country_code",
"def country_code(self):\n return self.__country_code",
"def get_country_code(self):\n\n return self.country_code",
"def get_country_code(self):\n\n return self.country_code",
"def get_country_code(self):\n\n return s... | [
"0.7646007",
"0.7646007",
"0.74890023",
"0.74890023",
"0.74890023",
"0.74890023",
"0.74890023",
"0.74890023",
"0.74890023",
"0.74890023",
"0.7042643",
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"0.6893787",
"0.68850124",
"0.68850124",
"0.68662137",
"0.67749107",
"0.67048216",
"0... | 0.8383952 | 0 |
Gets and sets the marketingNotificationEmails | def marketing_notification_emails(self):
if "marketingNotificationEmails" in self._prop_dict:
return self._prop_dict["marketingNotificationEmails"]
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sender_email_notifications(self):\n return self._sender_email_notifications",
"def signer_email_notifications(self):\n return self._signer_email_notifications",
"def Emails(self, default=[None]):\n return self.data.get('emails', default)",
"def sender_email_notifications(self, se... | [
"0.69371665",
"0.6533384",
"0.6434023",
"0.6230268",
"0.6194945",
"0.61440927",
"0.60741955",
"0.60613334",
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"0.5590484",
"0.5568619",
"0.55109143",
"0.5490925",
"0.54866195",
"0.542... | 0.8347074 | 0 |
Gets and sets the onPremisesLastSyncDateTime | def on_premises_last_sync_date_time(self):
if "onPremisesLastSyncDateTime" in self._prop_dict:
return datetime.strptime(self._prop_dict["onPremisesLastSyncDateTime"].replace("Z", ""), "%Y-%m-%dT%H:%M:%S.%f")
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_last_sync(self):\n return self.app.sync_thread.last_sync.strftime('%H:%M')",
"def sync_time(self):\n return self.get_sync_time()",
"def on_premises_sync_enabled(self):\n if \"onPremisesSyncEnabled\" in self._prop_dict:\n return self._prop_dict[\"onPremisesSyncEnabled\"]\... | [
"0.6325699",
"0.587589",
"0.5858154",
"0.5858154",
"0.5850004",
"0.57757026",
"0.57757026",
"0.5724789",
"0.5687654",
"0.5652039",
"0.5625669",
"0.55714643",
"0.55631775",
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"0.5526351",
"0.5519201",
"0.5501846",
"0.5501846",
"0.5501846",
"0.5501846",
"0.5501846",
... | 0.80928105 | 1 |
Gets and sets the onPremisesSyncEnabled | def on_premises_sync_enabled(self):
if "onPremisesSyncEnabled" in self._prop_dict:
return self._prop_dict["onPremisesSyncEnabled"]
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_ldp_sync_enabled(self):\n return self.__ldp_sync_enabled",
"def _enable_sync(self, enable_sync: bool = True):\n self.__enable_sync = enable_sync",
"def _set_ldp_sync_enabled(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=... | [
"0.6428073",
"0.63226694",
"0.62949497",
"0.5958731",
"0.5850597",
"0.57621175",
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"0.5549039",
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"0.5244695",
"0.52350914",
"0.5211665",
"0.5182246",
"0.5170122",
"0.5141607... | 0.8503791 | 1 |
Gets and sets the privacyProfile | def privacy_profile(self):
if "privacyProfile" in self._prop_dict:
if isinstance(self._prop_dict["privacyProfile"], OneDriveObjectBase):
return self._prop_dict["privacyProfile"]
else :
self._prop_dict["privacyProfile"] = PrivacyProfile(self._prop_dict["pri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_profile(self):\n endpoint = '/profile'\n return self.get_request(endpoint)",
"def get_user_profile(self):\n return self.user.profile",
"def getProfile(self):\n self.profile = LineContact(self, self._getProfile())\n\n return self.profile",
"def profile(self) -> Profi... | [
"0.5970012",
"0.5945452",
"0.57073313",
"0.5657435",
"0.5657435",
"0.56528354",
"0.5645841",
"0.5632789",
"0.56322527",
"0.55798125",
"0.555526",
"0.5460383",
"0.54457045",
"0.54457045",
"0.5418339",
"0.53700995",
"0.536092",
"0.53463423",
"0.53403723",
"0.5308688",
"0.530533... | 0.7585365 | 0 |
Gets and sets the securityComplianceNotificationMails | def security_compliance_notification_mails(self):
if "securityComplianceNotificationMails" in self._prop_dict:
return self._prop_dict["securityComplianceNotificationMails"]
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def technical_notification_mails(self):\n if \"technicalNotificationMails\" in self._prop_dict:\n return self._prop_dict[\"technicalNotificationMails\"]\n else:\n return None",
"def marketing_notification_emails(self):\n if \"marketingNotificationEmails\" in self._prop_... | [
"0.69751585",
"0.6532241",
"0.63405067",
"0.61766267",
"0.5517822",
"0.53570926",
"0.5327254",
"0.529896",
"0.5265682",
"0.52485424",
"0.52477956",
"0.5233743",
"0.5219979",
"0.5212109",
"0.51795435",
"0.51341754",
"0.5116517",
"0.51135075",
"0.50650805",
"0.50360894",
"0.501... | 0.83950746 | 0 |
Gets and sets the securityComplianceNotificationPhones | def security_compliance_notification_phones(self):
if "securityComplianceNotificationPhones" in self._prop_dict:
return self._prop_dict["securityComplianceNotificationPhones"]
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def security_compliance_notification_mails(self):\n if \"securityComplianceNotificationMails\" in self._prop_dict:\n return self._prop_dict[\"securityComplianceNotificationMails\"]\n else:\n return None",
"def subscribed_osp_settings(self):\n return self._subscribed_osp... | [
"0.5857168",
"0.47481012",
"0.47196215",
"0.47196215",
"0.47196215",
"0.46024665",
"0.46012902",
"0.4508265",
"0.4456161",
"0.44369522",
"0.44339985",
"0.44333518",
"0.4388865",
"0.43224573",
"0.43134397",
"0.4274985",
"0.42672578",
"0.42493713",
"0.42413083",
"0.4228379",
"0... | 0.8340764 | 0 |
Gets and sets the street | def street(self):
if "street" in self._prop_dict:
return self._prop_dict["street"]
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def street(self, street):\n\n self._street = street",
"def street(self):\n return self._street",
"def street_address(self):\n if \"streetAddress\" in self._prop_dict:\n return self._prop_dict[\"streetAddress\"]\n else:\n return None",
"def set_Street(self, va... | [
"0.783183",
"0.7820453",
"0.7319929",
"0.7179174",
"0.69076365",
"0.68732244",
"0.67973804",
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"0.6677137",
"0.6618805",
"0.63332087",
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"0.6294569",
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"0.6228575",
"0.62083244",
"0.61282",
"0.60469615",
"0.5996438",
"0.5996438",
"0.5966137... | 0.7868559 | 0 |
Gets and sets the technicalNotificationMails | def technical_notification_mails(self):
if "technicalNotificationMails" in self._prop_dict:
return self._prop_dict["technicalNotificationMails"]
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def security_compliance_notification_mails(self):\n if \"securityComplianceNotificationMails\" in self._prop_dict:\n return self._prop_dict[\"securityComplianceNotificationMails\"]\n else:\n return None",
"def marketing_notification_emails(self):\n if \"marketingNotific... | [
"0.72383255",
"0.67024106",
"0.6310228",
"0.61527723",
"0.58870727",
"0.5678961",
"0.5668604",
"0.56506723",
"0.56087625",
"0.5606368",
"0.55602396",
"0.55281776",
"0.5463542",
"0.5450086",
"0.54098886",
"0.53999764",
"0.5385844",
"0.5371181",
"0.5366454",
"0.5366454",
"0.536... | 0.834292 | 0 |
Gets and sets the verifiedDomains | def verified_domains(self):
if "verifiedDomains" in self._prop_dict:
return VerifiedDomainsCollectionPage(self._prop_dict["verifiedDomains"])
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def domains(self, domains):\n\n self._domains = domains",
"def AllowedDomains(self)->list:\n return self._allowedDomains",
"def domains(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"domains\")",
"def actual_domains():\n domains = set([item.strip()\n for ... | [
"0.6851726",
"0.64443517",
"0.63583946",
"0.63004833",
"0.6298253",
"0.62720436",
"0.6231519",
"0.62004226",
"0.6135281",
"0.608775",
"0.60724044",
"0.6070492",
"0.6036177",
"0.5973979",
"0.5964414",
"0.5957335",
"0.5919933",
"0.5915844",
"0.588611",
"0.5883396",
"0.5857002",... | 0.7317793 | 0 |
Gets and sets the mobileDeviceManagementAuthority | def mobile_device_management_authority(self):
if "mobileDeviceManagementAuthority" in self._prop_dict:
if isinstance(self._prop_dict["mobileDeviceManagementAuthority"], OneDriveObjectBase):
return self._prop_dict["mobileDeviceManagementAuthority"]
else :
s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def phone_mobile(self, instance):\r\n return instance.user.profile.phone_mobile",
"def mobile_phone(self):\n if \"mobilePhone\" in self._prop_dict:\n return self._prop_dict[\"mobilePhone\"]\n else:\n return None",
"def mobile_phone(self):\n if \"mobilePhone\" i... | [
"0.4749059",
"0.4726899",
"0.4726899",
"0.4723475",
"0.4704215",
"0.46767765",
"0.4548445",
"0.4494811",
"0.44466412",
"0.44466412",
"0.44455937",
"0.4413021",
"0.43985566",
"0.43943882",
"0.43872052",
"0.437152",
"0.4362718",
"0.43555802",
"0.43432292",
"0.43409374",
"0.4315... | 0.8321553 | 0 |
Test case for resource_user_resource_add_user_groups_for_user_post | def test_resource_user_resource_add_user_groups_for_user_post(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_users_groups_post(self):\n pass",
"def test_resource_user_resource_set_user_groups_for_user_put(self):\n pass",
"def test_resource_user_resource_add_users_post(self):\n pass",
"def test_resource_user_resource_add_user_post(self):\n pass",
"def test_api_v1_groups_post(se... | [
"0.82436454",
"0.81671906",
"0.74460876",
"0.73422563",
"0.7304262",
"0.73025596",
"0.7296159",
"0.713983",
"0.70329434",
"0.70329434",
"0.6849084",
"0.6840059",
"0.67962676",
"0.6764818",
"0.6713147",
"0.6695165",
"0.6665931",
"0.6661961",
"0.6651971",
"0.66383326",
"0.66298... | 0.949568 | 0 |
Test case for resource_user_resource_add_user_post | def test_resource_user_resource_add_user_post(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_resource_user_resource_add_users_post(self):\n pass",
"def test_add_user(self):\n pass",
"def test_post_user_post(self):\n pass",
"def test_api_user_post(self):\n pass",
"def test_resource_user_resource_add_user_groups_for_user_post(self):\n pass",
"def test_us... | [
"0.883399",
"0.7554905",
"0.7362358",
"0.7336772",
"0.72329384",
"0.72166926",
"0.72166926",
"0.71635604",
"0.6990422",
"0.6944165",
"0.6894131",
"0.68870056",
"0.6824432",
"0.68076354",
"0.68076354",
"0.68076354",
"0.6781031",
"0.67799467",
"0.6756481",
"0.6751568",
"0.6748"... | 0.93442345 | 0 |
Test case for resource_user_resource_add_users_post | def test_resource_user_resource_add_users_post(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_resource_user_resource_add_user_post(self):\n pass",
"def test_users_post(self):\n pass",
"def test_users_post(self):\n pass",
"def test_add_user(self):\n pass",
"def test_resource_user_resource_add_user_groups_for_user_post(self):\n pass",
"def test_post_users... | [
"0.9141933",
"0.75679886",
"0.75679886",
"0.75640416",
"0.7506603",
"0.74060404",
"0.7228901",
"0.7180535",
"0.7083268",
"0.7073483",
"0.7039329",
"0.7013013",
"0.6911373",
"0.68960893",
"0.68714774",
"0.682104",
"0.6790674",
"0.67737126",
"0.67651474",
"0.67651474",
"0.67651... | 0.9336231 | 0 |
Test case for resource_user_resource_change_user_avatar_patch | def test_resource_user_resource_change_user_avatar_patch(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_resource_user_resource_get_avatar_file_get(self):\n pass",
"def test_resource_user_resource_change_user_patch(self):\n pass",
"def test_user_avatar_serving(self):\n User = get_user_model()\n test_user = User.objects.create_user('Bob', 'bob@bob.com', 'pass123',\n ... | [
"0.7562802",
"0.7389839",
"0.6715897",
"0.60947317",
"0.60880405",
"0.6087778",
"0.6076995",
"0.60552335",
"0.6042653",
"0.6041773",
"0.6028372",
"0.6022853",
"0.6014988",
"0.5976914",
"0.59176534",
"0.59106654",
"0.5886265",
"0.58843976",
"0.5870331",
"0.58677554",
"0.586704... | 0.95680916 | 0 |
Test case for resource_user_resource_change_user_patch | def test_resource_user_resource_change_user_patch(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_patch_user(self):\n pass",
"def test_resource_user_resource_change_user_avatar_patch(self):\n pass",
"def test_update_user(self):\n pass",
"def test_patch_user(self):\n new_user = self.make_user('new_user')\n url = reverse(\n 'projectroles:api_role_updat... | [
"0.78669316",
"0.76529086",
"0.7445579",
"0.719746",
"0.7120976",
"0.7110063",
"0.7032789",
"0.69978964",
"0.6989593",
"0.6931952",
"0.69096875",
"0.69092476",
"0.6884571",
"0.6873923",
"0.68736744",
"0.6841495",
"0.6838705",
"0.67565453",
"0.6710545",
"0.6630291",
"0.6619691... | 0.94681674 | 0 |
Test case for resource_user_resource_find_users_get | def test_resource_user_resource_find_users_get(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_resource_user_resource_get_user_get(self):\n pass",
"def test_resource_user_resource_get_user_by_email_address_get(self):\n pass",
"def test_resource_user_resource_get_current_user_get(self):\n pass",
"def test_users_get(self):\n pass",
"def test_users_get(self):\n ... | [
"0.8451211",
"0.7752305",
"0.77465975",
"0.7734184",
"0.7734184",
"0.7529441",
"0.74243814",
"0.7295259",
"0.72327244",
"0.71953094",
"0.7041438",
"0.6882189",
"0.684746",
"0.6764927",
"0.67365676",
"0.6709953",
"0.6689331",
"0.6677777",
"0.6628603",
"0.6623493",
"0.6618926",... | 0.9211182 | 0 |
Test case for resource_user_resource_get_avatar_file_get | def test_resource_user_resource_get_avatar_file_get(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_resource_user_resource_change_user_avatar_patch(self):\n pass",
"def test_user_avatar_serving(self):\n User = get_user_model()\n test_user = User.objects.create_user('Bob', 'bob@bob.com', 'pass123',\n set_default_avatar=True)\n\n av... | [
"0.7323743",
"0.6877225",
"0.6818644",
"0.6498818",
"0.6493186",
"0.63726944",
"0.6352777",
"0.63323724",
"0.6304216",
"0.6296056",
"0.628039",
"0.62724555",
"0.61974806",
"0.6175622",
"0.6132429",
"0.6130371",
"0.6130324",
"0.6102433",
"0.6083106",
"0.60774434",
"0.6063673",... | 0.95785964 | 0 |
Test case for resource_user_resource_get_current_user_get | def test_resource_user_resource_get_current_user_get(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_resource_user_resource_get_user_get(self):\n pass",
"def test_get_current(self):\n self.assertEqual(api.user.get_current().getUserName(), TEST_USER_NAME)",
"def test_resource_user_resource_find_users_get(self):\n pass",
"def get(self):\r\n return get_user(request)",
"de... | [
"0.8418081",
"0.74915165",
"0.72019815",
"0.7007737",
"0.6998898",
"0.695637",
"0.6839162",
"0.6838583",
"0.6736907",
"0.67333174",
"0.6719712",
"0.67011625",
"0.66943836",
"0.6677189",
"0.66528165",
"0.66169584",
"0.6552954",
"0.65320903",
"0.649906",
"0.6491142",
"0.6487751... | 0.94497687 | 0 |
Test case for resource_user_resource_get_user_by_email_address_get | def test_resource_user_resource_get_user_by_email_address_get(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_user_by_emailuser_email_get(self):\n pass",
"def test_resource_user_resource_get_user_get(self):\n pass",
"def user(email):\r\n return User.objects.get(email=email)",
"def test_get_user(user_store: MockStore, user_email: str):\n # GIVEN a database with a user\n\n # WHEN qu... | [
"0.86324656",
"0.75253284",
"0.72573936",
"0.71740294",
"0.7146205",
"0.7137798",
"0.7132184",
"0.7113488",
"0.70112",
"0.70053416",
"0.7004624",
"0.6986397",
"0.6980885",
"0.69266295",
"0.68897694",
"0.68832684",
"0.68475723",
"0.6841938",
"0.68417424",
"0.68409705",
"0.6803... | 0.9579092 | 0 |
Test case for resource_user_resource_get_user_get | def test_resource_user_resource_get_user_get(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_resource_user_resource_get_current_user_get(self):\n pass",
"def test_resource_user_resource_find_users_get(self):\n pass",
"def test_resource_user_resource_get_user_by_email_address_get(self):\n pass",
"def test_api_user_get(self):\n pass",
"def test_get_user(self):\n ... | [
"0.89160436",
"0.8220227",
"0.7937708",
"0.76886445",
"0.7485996",
"0.73820853",
"0.72005",
"0.7096893",
"0.7096893",
"0.69726586",
"0.6945443",
"0.6902139",
"0.68669623",
"0.68625176",
"0.6857196",
"0.685426",
"0.68481326",
"0.6848064",
"0.6813843",
"0.68119854",
"0.6787243"... | 0.92044413 | 0 |
Test case for resource_user_resource_remove_user_from_user_groups_delete | def test_resource_user_resource_remove_user_from_user_groups_delete(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_groups_group_users_user_delete(self):\n pass",
"def test_groups_group_users_user_delete(self):\n pass",
"def test_deluser(self):\n self.run_function(\"group.add\", [self._group], gid=self._gid)\n self.run_function(\"user.add\", [self._user])\n self.run_function(\"gro... | [
"0.8203382",
"0.8203382",
"0.805757",
"0.8017475",
"0.7963476",
"0.78263724",
"0.78263724",
"0.782173",
"0.7564547",
"0.7356291",
"0.7356291",
"0.72409266",
"0.7238935",
"0.72285354",
"0.72242224",
"0.7204366",
"0.7199144",
"0.71891713",
"0.71857566",
"0.7139048",
"0.7114398"... | 0.9579116 | 0 |
Test case for resource_user_resource_set_user_groups_for_user_put | def test_resource_user_resource_set_user_groups_for_user_put(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_resource_user_resource_add_user_groups_for_user_post(self):\n pass",
"def test_groups_group_users_put(self):\n pass",
"def test_groups_group_users_put(self):\n pass",
"def test_resource_user_resource_remove_user_from_user_groups_delete(self):\n pass",
"def test_users_gr... | [
"0.80913395",
"0.80515426",
"0.80515426",
"0.68639123",
"0.685416",
"0.6789936",
"0.67439145",
"0.66826963",
"0.66794705",
"0.66288406",
"0.6604435",
"0.65601975",
"0.65593284",
"0.6556658",
"0.65404123",
"0.6524204",
"0.6517538",
"0.64963746",
"0.6454848",
"0.64492613",
"0.6... | 0.9488719 | 0 |
Test that batch Reads will allow read_all_bins with no ops | def test_batch_read_all_bins_pos(self):
b = br.Read(("test", "demo", 1), ops=None, read_all_bins=True)
assert b.read_all_bins | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_block_missing_batch(self):\n pass",
"def test_reading_counter_without_batching(self):\n self._test_reading_counter_template(copy_options={'MAXBATCHSIZE': '1'})",
"def test_block_bad_batch(self):\n pass",
"def test_batch(self):\n pass",
"def has_full_batch(self) -> bool:... | [
"0.6690717",
"0.6221007",
"0.6105783",
"0.59919345",
"0.5908246",
"0.58860576",
"0.58761734",
"0.584706",
"0.57898694",
"0.56910086",
"0.5677327",
"0.5672258",
"0.5624047",
"0.5601044",
"0.56001467",
"0.55973536",
"0.55933803",
"0.5525978",
"0.55243194",
"0.5496909",
"0.54945... | 0.817219 | 0 |
Test that batch Reads will always use a new batch_records list. | def test_batch_default_arg_pos(self):
b = br.Read(("test", "demo", 1), ops=None)
bwr = br.BatchRecords()
bwr.batch_records.append(b)
assert len(bwr.batch_records) == 1
bwr = br.BatchRecords()
assert len(bwr.batch_records) == 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_batch(self):\n pass",
"def test_batches_are_accessible(\n monkeypatch,\n multibatch_generic_csv_generator,\n multibatch_generic_csv_generator_context,\n):\n\n context: DataContext = multibatch_generic_csv_generator_context\n data_relative_path = \"../data\"\n data_path = os.path... | [
"0.67201746",
"0.6547219",
"0.64617795",
"0.63159215",
"0.62892824",
"0.62672013",
"0.62574714",
"0.612008",
"0.61138254",
"0.6076384",
"0.6040956",
"0.603392",
"0.6027112",
"0.60223246",
"0.5984337",
"0.596963",
"0.5913834",
"0.59047127",
"0.58905107",
"0.5845407",
"0.584176... | 0.6759463 | 0 |
Update the factors, resulting in change in the dimensions | def onUpdateFactors(self, evt):
if self.blockFactorUpdate:
print "Blocking factor update"
return
x, y, z = self.dataUnits[0].dataSource.getOriginalDimensions()
fx = 1
fy = 1
fz = 1
try:
fx = float(self.factorX.GetValue())
fy = float(self.factorY.GetValue())
fz = float(self.factorZ.GetValue())... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rebalance(self):\n\n # Compute norms along columns for each factor matrix\n norms = [np.linalg.norm(f, axis=0) for f in self.factors]\n\n # Multiply norms across all modes\n lam = np.prod(norms, axis=0) ** (1/self.ndim)\n\n # Update factors\n self.factors = [f * (lam /... | [
"0.6571209",
"0.6276669",
"0.62764835",
"0.6115042",
"0.60019165",
"0.60013884",
"0.594291",
"0.59389067",
"0.5907155",
"0.5843025",
"0.5711148",
"0.5672419",
"0.5652959",
"0.5652877",
"0.5635358",
"0.55926377",
"0.55566674",
"0.55367833",
"0.55367833",
"0.5536621",
"0.553272... | 0.68815076 | 0 |
Creates the GUI for setting the resampled size | def createResample(self):
box = wx.StaticBox(self, -1, "Resample now to")
self.currDimText = u"Current dataset original dimensions: %d x %d x %d"
self.dimsLbl = wx.StaticText(self, -1, self.currDimText % (0, 0, 0))
boxsizer = wx.StaticBoxSizer(box, wx.VERTICAL)
panel = wx.Panel(self, -1)
boxsizer.Add(pan... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Pane_Resized( self, new_sizes ):\r\n if(new_sizes[0] > 200 ):\r\n cb.xtotal = new_sizes[0]-100\r\n self.canvas_one.config(width = new_sizes[0])\r\n self.canvas_scale.config(width = new_sizes[0])\r\n else:\r\n cb.xtotal = 200-100\r\n self.canv... | [
"0.6498354",
"0.63386226",
"0.6295219",
"0.6207969",
"0.6043354",
"0.60301924",
"0.60260755",
"0.60126054",
"0.6000474",
"0.5969267",
"0.5954346",
"0.5932289",
"0.5930187",
"0.5924253",
"0.5905372",
"0.59038717",
"0.58730376",
"0.5864292",
"0.5850218",
"0.58474237",
"0.584617... | 0.73525316 | 0 |
Select a half size to be used for resampling | def onSetToHalfSize(self, evt):
self.halfResampleZ.Enable(1)
if self.dataUnits:
x, y, z = self.dataUnits[0].dataSource.getOriginalDimensions()
zf = 1
if self.halfResampleZ.GetValue():
zf = 0.5
self.currSize = int(0.5 * x), int(0.5 * y), int(zf * z)
self.fourthResampleZ.Enable(0)
for obj in [... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def downsample_sam(self, factor):",
"def subsample(y, limit=256, factor=2):\n if len(y) > limit:\n return y[::factor].reset_index(drop=True)\n return y",
"def downsample(state):\n return state[::2, ::2, :]",
"def get_resized_sub_sample(self, off_x, off_y):\n sub_sample = self.original[... | [
"0.66821367",
"0.6283318",
"0.59744126",
"0.5961904",
"0.5954585",
"0.5949425",
"0.59060234",
"0.5834289",
"0.5813429",
"0.57990295",
"0.57953787",
"0.5726417",
"0.5719342",
"0.57168734",
"0.5709625",
"0.569461",
"0.56766534",
"0.5663706",
"0.56595075",
"0.56507003",
"0.56502... | 0.66162187 | 1 |
Select a fourth size to be used for resampling | def onSetToFourthSize(self, evt):
self.halfResampleZ.Enable(0)
self.fourthResampleZ.Enable(1)
if self.dataUnits:
zf = 1
x, y, z = self.dataUnits[0].dataSource.getOriginalDimensions()
if self.fourthResampleZ.GetValue():
zf = 0.25
self.currSize = int(0.25 * x), int(0.25 * y), int(zf * z)
fo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def downsample_sam(self, factor):",
"def sample(self, size=1):\n pass",
"def reduce_sample_size(data,classes,times=2):\n data=data[range(0,data.shape[0],times)]\n classes=classes[range(0,classes.shape[0],times)]\n return data,classes",
"def _choose_sample(self):\n\n \t #periodically gene... | [
"0.6436063",
"0.6256925",
"0.6241231",
"0.6079577",
"0.598289",
"0.598289",
"0.598289",
"0.598289",
"0.5905832",
"0.5845936",
"0.5776135",
"0.57633346",
"0.56861585",
"0.5645118",
"0.5645118",
"0.55884224",
"0.55766916",
"0.5545338",
"0.5502274",
"0.5496958",
"0.5489562",
"... | 0.6551757 | 0 |
Return True if the image i2 can be to the right of i1. | def right(i1, i2):
return "".join([i1[row][-1] for row in range(len(i1))]) == "".join(
[i2[row][0] for row in range(len(i2))]
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_right(self):\n if self.pupils_located:\n return self.horizontal_ratio() <= 0.35",
"def _has_right(self, j):\n return (2 * j + 2) < len(self)",
"def isRight(self):\n\t\tif self.sq(self.a) == self.sq(self.b) + self.sq(self.c):\n\t\t\treturn True\n\t\telif self.sq(self.b) == self.s... | [
"0.6519159",
"0.6493096",
"0.6476172",
"0.62810856",
"0.6208851",
"0.61317664",
"0.61116636",
"0.6103508",
"0.6087532",
"0.5999861",
"0.5941688",
"0.5938497",
"0.59380716",
"0.58896476",
"0.58713394",
"0.5852274",
"0.5774916",
"0.577145",
"0.57683116",
"0.57450664",
"0.573638... | 0.6787813 | 0 |
Converts w_global to the same dtype as w_client. PFNM procedure changes the dtype causing discrepancies between data dtype and model dtype. | def change_global_dtypes(w_global, w_client):
result = []
for i in range(len(w_client)):
dtype = w_client[i].dtype
result.append(w_global[i].astype(dtype))
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_global_weights(self, global_weights):\n self.global_transform.from_vector_inplace(global_weights)",
"def _update_dtype(self) -> None:\n self.inv_eps = np.array([self.inv_eps]).astype(self.dtype)[0]",
"def convert_global_weights(global_weights: Mapping, local_var_dict: MutableMapping) ... | [
"0.5540853",
"0.548035",
"0.5475646",
"0.54616445",
"0.51597744",
"0.51043653",
"0.5094637",
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"0.5053623",
"0.5016467",
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"0.49614197",
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"0.48159397",
"0.4805115",
"0.4801537",
"0.48007137",
"0.47899008",
"0.4788... | 0.80209565 | 0 |
Computes the network dimensions from the its weights | def compute_net_dimensions(weights):
new_dims = []
for i in range(1, len(weights), 2):
new_dims.append(weights[i].shape[0])
return new_dims | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_weight_dimensions(weight_shape: np.array) -> np.array:\n dims = len(weight_shape)\n if dims == 4:\n return weight_shape\n return np.append(weight_shape, [1 for _ in range(4 - dims)]).astype(int)",
"def __find_net_dims(self):\n\n input_params = INPUT_CHANNELS * INPUT_SIZE ** 2\n ... | [
"0.7643601",
"0.7434242",
"0.7021509",
"0.6931846",
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"0.6769189",
"0.67655206",
"0.6704349",
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"0.66505307",
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"0.64749146",
"0.64721304",
"0.646667",
"0.64608335",
"0.6393854",
"0.637714... | 0.8552961 | 0 |
Compute GMM models for each digit | def eachDigitGMM(data, cfg):
models = {}
for j in range(len(data)):
train_set = data[j][0]
for i in range(1, len(data[j])):
train_set = np.concatenate((train_set, data[j][i]), axis=0)
estimator = GaussianMixture(n_components=cfg['components'], max_iter=cfg['max_iter... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\r\n parametrized_data = loadData('files/parametrized.p')\r\n config = loadConfig('config/gmm.cfg')\r\n\r\n data_ = eachDigitGMM(parametrized_data, config)\r\n\r\n save(data_)",
"def gmm(X, k):\n mix = sklearn.mixture.GaussianMixture(n_components=k).fit(X)\n pi = mix.weights_\n m ... | [
"0.6906623",
"0.661936",
"0.64382786",
"0.62845135",
"0.60806674",
"0.593841",
"0.58753103",
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"0.58379036",
"0.5826022",
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"0.5648508",
"0.56253916",
"0.5614218",
"0.5571951",
"0.557083",
"0.5570502",
"0.55684984"... | 0.7731218 | 0 |
Creates a cuboid from min coordinates and max coordinates, scales it by a scale factor. min and max ara opposite corners of the final cuboid | def __cuboid(cls, min: np.ndarray, max: np.ndarray, scale: float = 1.) -> np.ndarray:
max = max.flatten()
min = min.flatten()
avg = (max + min) / 2
cube = np.zeros((8, 3), dtype=float)
cube[0] = (np.array([max[0], max[1], max[2]], dtype=float) - avg) * scale + avg
cube[1]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalize_array(cube, new_max, new_min):\n minimum, maximum = np.min(cube), np.max(cube)\n if maximum - minimum != 0:\n m = (new_max - new_min) / (maximum - minimum)\n b = new_min - m * minimum\n cube = m * cube + b\n return cube",
"def scale(self, from_min, from_max, to_min, to... | [
"0.60914046",
"0.57667685",
"0.5735776",
"0.5717235",
"0.5637005",
"0.56177884",
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"0.55222833",
"0.55189645",
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"0.5418421",
"0.541573",
"0.54125637",
"0.54057217",
"0.54026675",
"0.5391674",
"0.536245... | 0.7557332 | 0 |
Match this set of Nodes to target set of nodes based on their coordinates (distance) if nodeID is supplied, find the closest nodes to this node or a list of nodes Returns the node ids from target that are closest to this set of nodes and the distance of the found nodes if selected | def match(self, nodes, nodeID: int | list = None, distances: bool = False) -> list:
if nodeID is None:
source = self.toarray()
source_keys = self.keys()
elif type(nodeID) is int:
source = self[nodeID].coors.reshape(1, 3)
source_keys = [nodeID]
elif... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def findClosestNodes(self, target: hash.hash.Hash):\n # TODO: make more efficient\n # See: http://stackoverflow.com/questions/30654398/implementing-find-node-on-torrent-kademlia-routing-table\n \n nodes = []\n \n for bucket in self.buckets:\n nodes = nodes + buc... | [
"0.62708193",
"0.6021189",
"0.59817636",
"0.5910058",
"0.5903073",
"0.5894784",
"0.5860802",
"0.5795183",
"0.5762382",
"0.5749643",
"0.5712168",
"0.56886685",
"0.56831497",
"0.5669823",
"0.56181604",
"0.556276",
"0.55174476",
"0.54817003",
"0.5468962",
"0.545016",
"0.5425786"... | 0.7419687 | 0 |
Creates a permutation matrix for node ordering using a Reverse CuthillMcKee algorithm | def reverse_cuthill_mckee(self, permutation: bool = True) -> [np.ndarray, np.ndarray]:
# 1. get the connectivity matrix
nodeids, C = self.node_connectivity_matrix()
# 2. compute the new node order
perm = scipy.sparse.csgraph.reverse_cuthill_mckee(C, False)
# 3. create a diction... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rcm_perm(adj_mat):\n _adj_mat = _preprocess(adj_mat)\n graph = csr_matrix(_adj_mat)\n idx = reverse_cuthill_mckee(graph, symmetric_mode=True)\n return reorder(adj_mat, idx)",
"def permutation_matrix(order):\n matrix = np.zeros([order,order])\n matrix[-1,0] = 1\n matrix[0:-1,1::] = np.ide... | [
"0.69158185",
"0.65334654",
"0.6261852",
"0.61041516",
"0.5897336",
"0.5834572",
"0.58315396",
"0.5738542",
"0.57123655",
"0.5679085",
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"0.56044054",
"0.55797595",
"0.55649453",
"0.55566525",
"0.55533034",
"0.5550322",
"0.5... | 0.75701296 | 0 |
Check if fetched series is really in series dict. | def test_seriesdict(self):
series = self.tvdb.get_series('seinfeld')
assert self.tvdb.sid_series[series['id']] == series | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_charts(self):\n return self.__charts is not None",
"def _exists(self) -> bool:\n client = MlflowClient()\n all_metrics = client._tracking_client.store.get_all_metrics(\n run_uuid=self.run_id\n )\n return any(self._is_dataset_metric(x) for x in all_metrics)",
... | [
"0.583036",
"0.5771205",
"0.5721502",
"0.56829405",
"0.5610772",
"0.56102914",
"0.55929446",
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"0.5556868",
"0.5543703",
"0.5533181",
"0.55242735",
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"0.5499089",
"0.5489801",
"0.5489597",
"0.5480643",
"0.5466936",
"0.54631066",
"0.5451241... | 0.6420335 | 0 |
Returns a trained sklearn pipeline of [csp, lda] | def get_trained_CSP_LDA(data, labels, window_size=None, preprocessing=LowpassWrapper(), step_size=None):
# slide window over trial data to generate many more data points
if step_size and window_size and window_size < data.shape[-1]:
data, labels = get_windows(data, labels, window_size, step_size)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_sklearn(self):\n import sklearn.pipeline as skp\n\n steps = []\n for step in self.steps:\n steps += [(step[0], step[1].to_sklearn())]\n return skp.Pipeline(steps)",
"def create(pdef):\n from sklearn.pipeline import Pipeline\n return [Pipeline(p) for p in pdef]"... | [
"0.7025629",
"0.69190943",
"0.67792165",
"0.6730748",
"0.670529",
"0.6454828",
"0.636034",
"0.6229045",
"0.618576",
"0.6156404",
"0.611859",
"0.5985514",
"0.59795904",
"0.5978339",
"0.59732825",
"0.59604967",
"0.5957266",
"0.5932507",
"0.5930365",
"0.5898609",
"0.5893132",
... | 0.71815854 | 0 |
takes the output form a WindowInlet and returns the probability (according to the csp+lda classifier) that the right hand was imagined | def predict_proba(self, window: np.array):
data = np.transpose(np.array(window))[self.data_channels]
print('data shape in wrapped:', data.shape)
proba = self.clf.predict_proba(data)
return proba[0][1] # proba = [[prob_left, prob_right]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _window_hitprobability(self, x, y):\n hm_count = np.zeros_like(y).astype(float)\n hm = np.zeros_like(y).astype(float)\n #skf = StratifiedShuffleSplit(n_splits=self.n_iter, test_size=self.shuffle_test_split, random_state=self.random_state)\n skf = StratifiedShuffleSplit(n_splits=self... | [
"0.62477136",
"0.60458094",
"0.59196264",
"0.5856302",
"0.58170736",
"0.5776016",
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"0.55685145",
"0.5566319",
"0.552953",
"0.5490147",
"0.54789966",
"0.5... | 0.6419543 | 0 |
calls fit(data, labels) on the csp+lda classifier | def fit(self, data, labels):
self.clf.fit(data, labels) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self, data: np.array, labels: np.array):\n raise NotImplementedError",
"def fit(self, data):\n self.labels = np.array(data['class'])\n self.num_classes = len(data['class'].unique())\n self.all_classes = np.sort(np.unique(self.labels))\n # populating the features datafra... | [
"0.6964673",
"0.6742371",
"0.6725926",
"0.66971344",
"0.66852725",
"0.663152",
"0.6579634",
"0.65666497",
"0.64772695",
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"0.64389485",
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"0.63765246",
"0.6369041",
"0.63476217",
"0.63209647",
"0.6289308",
"0.6281771",
"0.627... | 0.74599844 | 0 |
Validate the number of arguments and call a delegate handler. | def processcmd(command, args, expected_nargs, delegate):
if len(args) != expected_nargs:
raise FormatError("Wrong number of arguments for '" + command + "', expected " +
str(expected_nargs) + " but got " + str(len(args)))
delegate(userdata, *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def accept_arguments(method, number_of_arguments=1):\n if 'method' in method.__class__.__name__:\n number_of_arguments += 1\n func = getattr(method, 'im_func', getattr(method, '__func__'))\n func_defaults = getattr(func, 'func_defaults', getattr(func, '__defaults__'))\n number_of_def... | [
"0.57729924",
"0.5719926",
"0.5693527",
"0.56088734",
"0.5534906",
"0.54928994",
"0.54834074",
"0.5414944",
"0.5400435",
"0.5389888",
"0.5379765",
"0.53202635",
"0.530763",
"0.5304636",
"0.5287145",
"0.52742493",
"0.5270792",
"0.5256097",
"0.5236908",
"0.52355695",
"0.5228352... | 0.58143497 | 0 |
Transform the raw devlist into a human readable list. | def devlist_handler(userdata, *args):
for (dev, connected) in database.devlist(userdata["cursor"]):
if dev == "devmaster":
continue
if connected:
print(shlex.quote("+" + dev), end=" ")
else:
print(shlex.quote("-" + dev), end=" ")
print() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _output_vadapter_list_verbose(outf, name, vadapter_list):\n\n if vadapter_list:\n FMT = \"%s\\n%-10s\\n%-20s\\n%-5s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n%s\\n\"\n# HEADER = (\"id\",\"name\",\"io_module_id\",\"vfabric_id\",\"protocol\",\"init_type\",\"desc\",\"status\... | [
"0.62227356",
"0.5772413",
"0.57569736",
"0.5707236",
"0.5700806",
"0.56800795",
"0.5655333",
"0.5608158",
"0.55705553",
"0.5569611",
"0.55440795",
"0.5537111",
"0.5448807",
"0.5446118",
"0.5439977",
"0.5417143",
"0.54102635",
"0.5405327",
"0.5382883",
"0.53770655",
"0.537642... | 0.60405725 | 1 |
Transform the raw guestlist into a human readable list. | def guestlist_handler(userdata, *args):
for guest in userdata["guestlist"]:
print(shlex.quote(guest), end=" ")
print() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_list(self, desc, lst, writeln):\n if not lst:\n return\n val = ', '.join([list_escape(v) for v in lst])\n writeln(\"%s: %s\" % (desc, val))",
"def list_to_text(ingridients_list):\n to_return = \"List\\n\"\n for (ingridient, quantity) in ingridients_list:\n to... | [
"0.5813186",
"0.5700969",
"0.56966585",
"0.5681182",
"0.56655324",
"0.5646942",
"0.5607249",
"0.5601681",
"0.55947936",
"0.5556963",
"0.5547201",
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"0.5473723",
"0.54715216",
"0.5467049",
"0.54641753",
"0.54640037",
"0.54564065",
"0.545640... | 0.63324744 | 0 |
retrieve information about organization | def fetch_organization(organization):
return fetch_json(organization_url, organization) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_retrieve_l_organization(self):\n pass",
"def test_get_organization(self):\n pass",
"def org_info(self):\n\n response = self.postman.request('info')\n\n if (response.status_code == requests.codes.ok):\n data = response.json()\n\n self.repos = data['publ... | [
"0.7663464",
"0.7498538",
"0.742738",
"0.72355056",
"0.6984059",
"0.6942441",
"0.68556046",
"0.6813534",
"0.6796668",
"0.67712766",
"0.66672313",
"0.6606143",
"0.6533429",
"0.6531706",
"0.6509286",
"0.6505963",
"0.64864504",
"0.6475964",
"0.64553684",
"0.64510405",
"0.6366222... | 0.75476795 | 1 |
retrieve information about funding round | def fetch_funding_round(uuid):
return fetch_json(funding_round_url, uuid) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_furniture():",
"def get_ReddCoin_info(wallet: str, info: dict):\r\n\tresp = requests.get('https://live.reddcoin.com/api/addr/' + wallet)\r\n\tif resp.status_code == 200:\r\n\t\tresponse = json.loads(resp.text)\r\n\t\tinfo['received'] = float(response['totalReceived'])\r\n\t\tinfo['received_dollars'] = in... | [
"0.58193326",
"0.56235486",
"0.5595207",
"0.5539969",
"0.5518017",
"0.5504899",
"0.54936755",
"0.5480481",
"0.5456606",
"0.53925306",
"0.538725",
"0.5352123",
"0.53428936",
"0.5233922",
"0.5222662",
"0.52174175",
"0.5200093",
"0.51958585",
"0.5187113",
"0.51531327",
"0.514449... | 0.69011307 | 0 |
retrieve org logo url location | def fetch_logo_url(organization):
return fetch_json(image_url, organization) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logo_url(self):\n return self.get_url(\"logo\", \"images/logo.png\")",
"def logo_url(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"logo_url\")",
"def logo_url(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"logo_url\")",
"def logo_uri(self) -> str:\n return... | [
"0.73948944",
"0.7267919",
"0.7267919",
"0.722876",
"0.7195375",
"0.7034044",
"0.68424994",
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"0.6594823",
"0.65504336",
"0.65054303",
"0.65054303",
"0.645779",
"0.6434796",
"0.64087206"... | 0.76190805 | 0 |
r"""Calculates and stores range stats with forward values. Args | def _calculate_range_stats(self, x_copy):
# get the min, max values of the data
min_val_cur, max_val_cur = torch.aminmax(x_copy)
# calculate new epoch range values
epoch_min_val = torch.min(self.epoch_activation_min, min_val_cur)
epoch_max_val = torch.max(self.epoch_activation_m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateRange(self):\n if self.autoFollow:\n self.xrange = self.param.activeRange()\n self.xrange = self.xrange # call getter & setter again to verify limits",
"def range(self) -> ty.Tuple[float, float]:\r\n ...",
"def range(self):\n\n return time_stat(self, stat=\"range\")... | [
"0.63491815",
"0.6051725",
"0.6040286",
"0.6033649",
"0.59845704",
"0.5981012",
"0.57792556",
"0.57289696",
"0.5723835",
"0.5707534",
"0.5624634",
"0.5598468",
"0.5587254",
"0.55702907",
"0.5562683",
"0.55540055",
"0.5521237",
"0.5515974",
"0.55151474",
"0.54963857",
"0.54827... | 0.6593853 | 0 |
r"""Calculates and stores the per_channel min, max stats with forward values. | def _calculate_min_max_stats(self, x_copy):
# get the current min and max vals
min_val = self.min_val
max_val = self.max_val
x_dim = x_copy.size()
new_axis_list = [i for i in range(len(x_dim))] # noqa: C416
new_axis_list[self.ch_axis] = 0
new_axis_list[0] = self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def nudged_min_max_compute(min_broadcast, max_broadcast, num_bits, narrow_range):\n\n\n dtype = min_broadcast.dtype\n quant_min = 1 if narrow_range else 0\n quant_max = (2 ** num_bits) - 1\n\n # because of need compute each channel, so quant_min and quant_max need to broadcast.\n quant_min_float = t... | [
"0.59609085",
"0.5887487",
"0.588346",
"0.58295965",
"0.5721455",
"0.5709935",
"0.56678444",
"0.54986614",
"0.5493992",
"0.5468857",
"0.54509926",
"0.5431024",
"0.5413653",
"0.54097015",
"0.54077744",
"0.54034144",
"0.54014933",
"0.54001987",
"0.5393418",
"0.53803116",
"0.534... | 0.67637366 | 0 |
Tests a progress value source cannot refer to the current section or the current block | def test_invalid_current_location_progress():
schema_path = (
"schemas/invalid/test_invalid_progress_value_source_current_location.json"
)
questionnaire_validator = QuestionnaireValidator(
_open_and_load_schema_file(schema_path)
)
questionnaire_validator.validate()
expected_erro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_invalid_non_linear_progress_references():\n schema_path = \"schemas/invalid/test_invalid_progress_value_source_non_linear.json\"\n questionnaire_validator = QuestionnaireValidator(\n _open_and_load_schema_file(schema_path)\n )\n questionnaire_validator.validate()\n\n expected_error_m... | [
"0.5731956",
"0.5678855",
"0.5546579",
"0.55384487",
"0.5506011",
"0.54968566",
"0.549316",
"0.5454782",
"0.54516715",
"0.5400708",
"0.5395454",
"0.5379806",
"0.5356293",
"0.5338861",
"0.5338861",
"0.5338861",
"0.5338861",
"0.53379166",
"0.53287905",
"0.5304942",
"0.5277991",... | 0.62779844 | 0 |
Tests a progress value source cannot refer to a block in a repeating section except if it is the parent seciont of the value source Nor to a repeating section | def test_invalid_block_in_repeating_section():
schema_path = "schemas/invalid/test_invalid_progress_value_source_block_in_past_repeating_section.json"
questionnaire_validator = QuestionnaireValidator(
_open_and_load_schema_file(schema_path)
)
questionnaire_validator.validate()
expected_erro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_value_source_dependency_enable_section_by_progress_hub_flow(self):\n\n self.launchSurvey(\"test_progress_value_source_section_enabled_hub\")\n\n self.assertInBody(\"Choose another section to complete\")\n self.assertInBody(\"Section 1\")\n self.assertNotInBody(\"Section 2\")\n\... | [
"0.5689437",
"0.5404265",
"0.5371533",
"0.53694695",
"0.5331952",
"0.52998984",
"0.5252168",
"0.5219894",
"0.5195026",
"0.5187279",
"0.5172216",
"0.5122417",
"0.5104856",
"0.5073841",
"0.5040793",
"0.5039039",
"0.5017145",
"0.4977092",
"0.49540687",
"0.49109757",
"0.48936257"... | 0.67029315 | 0 |
This is the function which is called when playing Pokemon is selected In terms of variables, anything prefixed with u is supposed to mean your and anything prefixed with o is supposed to mean opponents | def pokemon_function(pokemon, oppo_pokemon, uhp=100, ohp=100):
print(Figlet(font='barbwire', width=200).renderText("HP : " + str(ohp) + "/100"))
print(Figlet(font='banner3', width=200).renderText(oppo_pokemon))
print(Figlet(font='barbwire', width=200).renderText("HP : " + str(uhp) + "/100"))
print(Figl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def chooseAPokemon(available: typing.Set[str], opponent: bool=False) -> pokemon.Pokemon:\n\n\tutils.setCompleter(available)\n\n\tchoiceStr = \"Choose %s Pokémon\" % (\"your opponent's\" if opponent else 'a')\n\n\twhile True:\n\t\tprint(choiceStr)\n\t\tchoice = input(\"(Pokémon Name, or 'l' list available Pokémon) ... | [
"0.64406544",
"0.6069692",
"0.604916",
"0.59736186",
"0.5835608",
"0.58242834",
"0.580983",
"0.58009094",
"0.57892954",
"0.5730977",
"0.5716335",
"0.57114285",
"0.57090443",
"0.5662691",
"0.5657727",
"0.56185436",
"0.5612823",
"0.5612479",
"0.5574806",
"0.55203336",
"0.551313... | 0.67738265 | 0 |
This is the rock paper scissor function. | def rps_function():
randNumber = random.randint(1,3)
companswer = "Scissor"
if randNumber == 1:
companswer = "Rock"
elif randNumber == 2:
companswer = "Paper"
answer = q.select("Select Rock, Paper or Scissors", choices=["Rock", "Paper", "Scissor"]).ask()
print(answer, companswe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rock_p_scissor(user_input, comp_choice):\r\n if user_input.lower() == comp_choice:\r\n compare = results[2]\r\n score_update(scoreboard, compare)\r\n banner_text(\"*\")\r\n banner_text(\"Tie! We both chose {0}\".format(user_input))\r\n banner_text(\"*\")\r\n print(s... | [
"0.7926187",
"0.73703116",
"0.7320837",
"0.666478",
"0.64771086",
"0.64553434",
"0.6445867",
"0.6426473",
"0.64188707",
"0.6385392",
"0.6307137",
"0.6290227",
"0.6273507",
"0.6219182",
"0.615628",
"0.612083",
"0.6099643",
"0.604132",
"0.60298204",
"0.6012675",
"0.59969074",
... | 0.75502604 | 1 |
takes username and password, confirms password matches db password, returns True or False | def verify_password(self, username, password):
try:
self.c.execute('SELECT password FROM profiles WHERE name=(?)', (username,))
db_pw = self.c.fetchone()[0]
print(password)
return db_pw == password
except TypeError:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def correct_password(username, password, db):\n\tquery = db((db.User.username == username) & (db.User.password == password))\n\treturn query.count() > 0",
"def check_password(self, username, password): # tested\r\n conn = self.get_db()\r\n with conn:\r\n c = conn.cursor()\r\n ... | [
"0.861218",
"0.80400485",
"0.7986726",
"0.7928326",
"0.7914342",
"0.7905069",
"0.7875336",
"0.7870443",
"0.78657377",
"0.7841633",
"0.78376216",
"0.78015494",
"0.7758104",
"0.7755465",
"0.7719777",
"0.7701607",
"0.76928484",
"0.7687092",
"0.76870865",
"0.7686983",
"0.7653624"... | 0.83543 | 1 |
Tests that ``Serializer`` can be enabled and disabled correctly. | def test_serializer():
my_serializer = Serializer()
assert my_serializer.serialize("test") == "test"
my_serializer.serialize = Mock()
my_serializer.disable()
assert my_serializer("test") == "test"
my_serializer.serialize.assert_not_called()
my_serializer.enable()
my_serializer("test"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_boolean_in_serializer() -> None:\n assert cv.custom_serializer(cv.boolean) == {\n \"type\": \"boolean\",\n }",
"def test_write_with_reader_serializer(self):\n transaction = TransactionFactory.build()\n serialized_data = TransactionSerializer(transaction).data\n serializ... | [
"0.64906883",
"0.6326578",
"0.6096442",
"0.58584917",
"0.5802942",
"0.5798236",
"0.576286",
"0.5724059",
"0.572344",
"0.572009",
"0.57130533",
"0.5687323",
"0.5676055",
"0.5551296",
"0.5540367",
"0.5537138",
"0.54916525",
"0.5475495",
"0.54611784",
"0.5454063",
"0.54074776",
... | 0.7458672 | 0 |
Tests that ``SerializerMapping`` correctly passes its inputs to the underlying serializers. Also checks that state is retrieved / loaded correctly. | def test_serializer_mapping():
serializer1 = Serializer()
serializer1.serialize = Mock(return_value="test1")
class Serializer1State(ProcessState):
pass
serializer2 = Serializer()
serializer2.serialize = Mock(return_value="test2")
class Serializer2State(ProcessState):
pass
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_dumps(self):\n result = self.mapper.dumps(self.serialization)\n self.mapper.to_dict.assert_called_once_with(\"custom\")\n self.serialization.assert_called_once_with(\n self.mapper.to_dict.return_value\n )\n self.assertIs(result, self.serialization.return_value... | [
"0.66895884",
"0.6504514",
"0.6407172",
"0.6218765",
"0.6105272",
"0.6071749",
"0.60594106",
"0.60369617",
"0.5934668",
"0.581106",
"0.57840157",
"0.57135254",
"0.5675424",
"0.5662678",
"0.5645501",
"0.56267715",
"0.5593252",
"0.5585447",
"0.5581585",
"0.5577482",
"0.5569688"... | 0.7634884 | 0 |
Helper function to save pytorch tensor as jpg image. | def save_tensor(tensor, path, title="NONAME"):
image = to_image(tensor)
image.save(os.path.join(path, "{}.jpg".format(title))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_tensor_image(fn, x):\n\n if ( 3 == len( x.size() ) ):\n x = x.permute((1, 2, 0))\n \n # Get the CPU NumPy version.\n x = torch.clamp(x, 0, 255)\n x = x.cpu().numpy().astype(np.uint8)\n\n # Save the iamge.\n cv2.imwrite(fn, x, [cv2.IMWRITE_PNG_COMPRESSION, 0])",
"def store_ten... | [
"0.7346332",
"0.6988003",
"0.670727",
"0.66723216",
"0.65957105",
"0.65615886",
"0.6526508",
"0.6357446",
"0.62757653",
"0.6271442",
"0.62078923",
"0.61825883",
"0.6181295",
"0.6175947",
"0.61734766",
"0.6105162",
"0.6091011",
"0.6061136",
"0.6027312",
"0.60250825",
"0.592948... | 0.7574364 | 0 |
Imports all controllers and register pages | def init_controllers(app):
for controller in os.listdir(os.getcwd() + "/controllers"):
module_name, ext = os.path.splitext(controller)
if module_name.endswith('_controller') and ext == '.py':
module = __import__("controllers.%s" % module_name)
PYSTHClient.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_one_time_controllers(self):\n self.index_controller = IndexController(self)\n self.opds_feeds = OPDSFeedController(self)\n self.marc_records = MARCRecordController(self)\n self.loans = LoanController(self)\n self.annotations = AnnotationController(self)\n self.ur... | [
"0.65316725",
"0.6427963",
"0.63743657",
"0.6279803",
"0.62757796",
"0.61138576",
"0.6015108",
"0.6001349",
"0.59471786",
"0.5825195",
"0.5786634",
"0.5748616",
"0.57210726",
"0.5682311",
"0.56795263",
"0.5677514",
"0.5623811",
"0.5607892",
"0.5607618",
"0.5568051",
"0.556537... | 0.7429925 | 0 |
Call the bootstrap function | def bootstrap():
Bootstrap() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bootstrap(self):\n None",
"def _do_bootstrap(self, configs=None):\n pass",
"def boot(self):\n\n pass",
"def bootstrap(environment: Environment):\n pass",
"def network_bootstrap(self, args):\n pass",
"def write_bootstrap(self):\n\n for line in Bootstrap.BOOTSTRAP:... | [
"0.80182284",
"0.80140054",
"0.70418626",
"0.6950032",
"0.66638505",
"0.6519575",
"0.648273",
"0.64663976",
"0.64168626",
"0.63539934",
"0.63502586",
"0.6342842",
"0.62229264",
"0.616589",
"0.6163845",
"0.60530704",
"0.60484225",
"0.60060966",
"0.5983313",
"0.59652656",
"0.59... | 0.85955906 | 0 |
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