blob_id stringlengths 40 40 | bodies listlengths 2 6 | bodies_text stringlengths 196 6.73k | class_docstring stringlengths 0 700 | class_name stringlengths 1 86 | detected_licenses listlengths 0 45 | format_version stringclasses 1
value | full_text stringlengths 438 7.52k | id stringlengths 40 40 | length_bytes int64 506 50k | license_type stringclasses 2
values | methods listlengths 2 6 | n_methods int64 2 6 | original_id stringlengths 38 40 ⌀ | prompt stringlengths 153 4.25k | prompted_full_text stringlengths 645 10.7k | revision_id stringlengths 40 40 | skeleton stringlengths 162 4.34k | snapshot_name stringclasses 1
value | snapshot_source_dir stringclasses 1
value | solution stringlengths 302 7.33k | source stringclasses 1
value | source_path stringlengths 4 177 | source_repo stringlengths 6 110 | split stringclasses 1
value | star_events_count int64 0 209k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
47ae6aae0321307823f97613e97d8a51af22d6e4 | [
"max_shape = self._find_max_shape(self.fovlist)\nfig = plt.figure(figsize=(20, 16))\ngs = gridspec.GridSpec(max_shape[0], 16, figure=fig)\nself._plot_traces_with_cell_img(self.fovlist, gs, max_shape)",
"shapes = []\nnum_of_labeled = 0\nfor fov in fovlist:\n shapes.append(fov.all_data.shape)\n try:\n ... | <|body_start_0|>
max_shape = self._find_max_shape(self.fovlist)
fig = plt.figure(figsize=(20, 16))
gs = gridspec.GridSpec(max_shape[0], 16, figure=fig)
self._plot_traces_with_cell_img(self.fovlist, gs, max_shape)
<|end_body_0|>
<|body_start_1|>
shapes = []
num_of_labeled... | Plots a simple comparison of the dF/F traces that originated from the unlabeled cells and the labeled cells. | ShowLabeledAndUnlabeled | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ShowLabeledAndUnlabeled:
"""Plots a simple comparison of the dF/F traces that originated from the unlabeled cells and the labeled cells."""
def run(self):
"""Main pipeline"""
<|body_0|>
def _find_max_shape(self, fovlist):
"""Iterate over the found files and decid... | stack_v2_sparse_classes_36k_train_025400 | 11,294 | permissive | [
{
"docstring": "Main pipeline",
"name": "run",
"signature": "def run(self)"
},
{
"docstring": "Iterate over the found files and decide upon the shape of the array that will hold the stacked data. This is useful when the number of measurements in each FOV was unequal.",
"name": "_find_max_sha... | 5 | stack_v2_sparse_classes_30k_train_020242 | Implement the Python class `ShowLabeledAndUnlabeled` described below.
Class description:
Plots a simple comparison of the dF/F traces that originated from the unlabeled cells and the labeled cells.
Method signatures and docstrings:
- def run(self): Main pipeline
- def _find_max_shape(self, fovlist): Iterate over the ... | Implement the Python class `ShowLabeledAndUnlabeled` described below.
Class description:
Plots a simple comparison of the dF/F traces that originated from the unlabeled cells and the labeled cells.
Method signatures and docstrings:
- def run(self): Main pipeline
- def _find_max_shape(self, fovlist): Iterate over the ... | 87fcca6fd79f65122b4010d2225d10403450da7e | <|skeleton|>
class ShowLabeledAndUnlabeled:
"""Plots a simple comparison of the dF/F traces that originated from the unlabeled cells and the labeled cells."""
def run(self):
"""Main pipeline"""
<|body_0|>
def _find_max_shape(self, fovlist):
"""Iterate over the found files and decid... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ShowLabeledAndUnlabeled:
"""Plots a simple comparison of the dF/F traces that originated from the unlabeled cells and the labeled cells."""
def run(self):
"""Main pipeline"""
max_shape = self._find_max_shape(self.fovlist)
fig = plt.figure(figsize=(20, 16))
gs = gridspec.Gr... | the_stack_v2_python_sparse | calcium_bflow_analysis/colabeled_cells/compare_labeled_unlabeled.py | PBLab/ca-analysis-bloodflow | train | 0 |
9981a9d733723cae2a904efb9b12018279e61c85 | [
"url = '{0}/consumers'.format(container_ref)\nresp = self.client.post(url, request_model=model, extra_headers=extra_headers, user_name=user_name, use_auth=use_auth)\nif resp.status_code == 401 and (not use_auth):\n return (resp, None)\nif resp.status_code == 200:\n if admin is None:\n admin = user_name... | <|body_start_0|>
url = '{0}/consumers'.format(container_ref)
resp = self.client.post(url, request_model=model, extra_headers=extra_headers, user_name=user_name, use_auth=use_auth)
if resp.status_code == 401 and (not use_auth):
return (resp, None)
if resp.status_code == 200:
... | ConsumerBehaviors | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ConsumerBehaviors:
def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True):
"""Register a consumer to a container. :param model: The metadata for the consumer :param container_ref: Full reference to a container :param extra_headers: ... | stack_v2_sparse_classes_36k_train_025401 | 4,859 | permissive | [
{
"docstring": "Register a consumer to a container. :param model: The metadata for the consumer :param container_ref: Full reference to a container :param extra_headers: Any additional headers to pass to the request :param user_name: The user name used to create the consumer :param admin: The user with permissi... | 4 | stack_v2_sparse_classes_30k_train_015173 | Implement the Python class `ConsumerBehaviors` described below.
Class description:
Implement the ConsumerBehaviors class.
Method signatures and docstrings:
- def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True): Register a consumer to a container. :param model... | Implement the Python class `ConsumerBehaviors` described below.
Class description:
Implement the ConsumerBehaviors class.
Method signatures and docstrings:
- def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True): Register a consumer to a container. :param model... | c8e3dc14e6225f1d400131434e8afec0aa410ae7 | <|skeleton|>
class ConsumerBehaviors:
def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True):
"""Register a consumer to a container. :param model: The metadata for the consumer :param container_ref: Full reference to a container :param extra_headers: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ConsumerBehaviors:
def create_consumer(self, model, container_ref, extra_headers=None, user_name=None, admin=None, use_auth=True):
"""Register a consumer to a container. :param model: The metadata for the consumer :param container_ref: Full reference to a container :param extra_headers: Any additional... | the_stack_v2_python_sparse | functionaltests/api/v1/behaviors/consumer_behaviors.py | openstack/barbican | train | 189 | |
6bc385410d626ca67296a556af1bd89ff2cf596d | [
"font = ImageFont.truetype(str(self.font_file), 45, encoding='utf-8')\nfor letter in self.hebrew.letter_li:\n (self.training_folder / letter).mkdir(parents=True, exist_ok=True)\nfor i in range(len(self.hebrew.font_li)):\n letter_path = self.training_folder / Path(self.hebrew.letter_li[i])\n text = self.heb... | <|body_start_0|>
font = ImageFont.truetype(str(self.font_file), 45, encoding='utf-8')
for letter in self.hebrew.letter_li:
(self.training_folder / letter).mkdir(parents=True, exist_ok=True)
for i in range(len(self.hebrew.font_li)):
letter_path = self.training_folder / Pat... | Creates a folder with example images for each hebrew letter using habbakuk font. | FontImages | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FontImages:
"""Creates a folder with example images for each hebrew letter using habbakuk font."""
def create_images(self):
"""Write a .jpeg image for each character in the font character set."""
<|body_0|>
def assert_data_correct(self) -> bool:
"""Assert that th... | stack_v2_sparse_classes_36k_train_025402 | 3,430 | no_license | [
{
"docstring": "Write a .jpeg image for each character in the font character set.",
"name": "create_images",
"signature": "def create_images(self)"
},
{
"docstring": "Assert that the font data exists and is in the correct format.",
"name": "assert_data_correct",
"signature": "def assert_... | 3 | stack_v2_sparse_classes_30k_train_001160 | Implement the Python class `FontImages` described below.
Class description:
Creates a folder with example images for each hebrew letter using habbakuk font.
Method signatures and docstrings:
- def create_images(self): Write a .jpeg image for each character in the font character set.
- def assert_data_correct(self) ->... | Implement the Python class `FontImages` described below.
Class description:
Creates a folder with example images for each hebrew letter using habbakuk font.
Method signatures and docstrings:
- def create_images(self): Write a .jpeg image for each character in the font character set.
- def assert_data_correct(self) ->... | 5283f8f5d00e99777ff28fe4b5f97c34c7bbeb7f | <|skeleton|>
class FontImages:
"""Creates a folder with example images for each hebrew letter using habbakuk font."""
def create_images(self):
"""Write a .jpeg image for each character in the font character set."""
<|body_0|>
def assert_data_correct(self) -> bool:
"""Assert that th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FontImages:
"""Creates a folder with example images for each hebrew letter using habbakuk font."""
def create_images(self):
"""Write a .jpeg image for each character in the font character set."""
font = ImageFont.truetype(str(self.font_file), 45, encoding='utf-8')
for letter in se... | the_stack_v2_python_sparse | src/data_handler/font_images.py | Wehzie/hwr-2021 | train | 0 |
549a30caebdb2311958ec94ee705e7089e120f6f | [
"self.name = name\nself.help = help\nself.args = args\nself.execute = None",
"@wraps(wrapped_function)\ndef wrapper(*args, **kwds):\n return wrapped_function(*args, **kwds)\nif self.name is None:\n self.name = wrapped_function.__name__\nself.execute = wrapped_function\nwrapper.decorator = self\nreturn wrapp... | <|body_start_0|>
self.name = name
self.help = help
self.args = args
self.execute = None
<|end_body_0|>
<|body_start_1|>
@wraps(wrapped_function)
def wrapper(*args, **kwds):
return wrapped_function(*args, **kwds)
if self.name is None:
self.... | The @cmd() function decorator marks a function as the implementation of a command-line command of an executable. | cmd | [
"MIT",
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class cmd:
"""The @cmd() function decorator marks a function as the implementation of a command-line command of an executable."""
def __init__(self, name=None, help=None, args=()):
"""Create a function decorator and wrapper for a function implementing a command-line command. For example, t... | stack_v2_sparse_classes_36k_train_025403 | 7,308 | permissive | [
{
"docstring": "Create a function decorator and wrapper for a function implementing a command-line command. For example, the command-line executable 'foo' may wish to implement a command-line command 'bar' with the option '--baz' invoked at the command line as 'foo bar --baz'. The arguments of this constructor ... | 2 | stack_v2_sparse_classes_30k_train_016892 | Implement the Python class `cmd` described below.
Class description:
The @cmd() function decorator marks a function as the implementation of a command-line command of an executable.
Method signatures and docstrings:
- def __init__(self, name=None, help=None, args=()): Create a function decorator and wrapper for a fun... | Implement the Python class `cmd` described below.
Class description:
The @cmd() function decorator marks a function as the implementation of a command-line command of an executable.
Method signatures and docstrings:
- def __init__(self, name=None, help=None, args=()): Create a function decorator and wrapper for a fun... | b531d5d374ee9b3b66732001eaa6923b3f97ec1b | <|skeleton|>
class cmd:
"""The @cmd() function decorator marks a function as the implementation of a command-line command of an executable."""
def __init__(self, name=None, help=None, args=()):
"""Create a function decorator and wrapper for a function implementing a command-line command. For example, t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class cmd:
"""The @cmd() function decorator marks a function as the implementation of a command-line command of an executable."""
def __init__(self, name=None, help=None, args=()):
"""Create a function decorator and wrapper for a function implementing a command-line command. For example, the command-li... | the_stack_v2_python_sparse | pylib/cmdline.py | engineeringentropy/echronos | train | 0 |
b74b97d30356952ba8928fe9043a9db14137730d | [
"self.mor_item = mor_item\nself.mor_type = mor_type\nself.uuid = uuid",
"if dictionary is None:\n return None\nmor_item = dictionary.get('morItem')\nmor_type = dictionary.get('morType')\nuuid = dictionary.get('uuid')\nreturn cls(mor_item, mor_type, uuid)"
] | <|body_start_0|>
self.mor_item = mor_item
self.mor_type = mor_type
self.uuid = uuid
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
mor_item = dictionary.get('morItem')
mor_type = dictionary.get('morType')
uuid = dictionary.get('uui... | Implementation of the 'VmwareObjectId' model. Specifies a unique Protection Source id across Cohesity Clusters. It is derived from the id of the VMware Protection Source. Attributes: mor_item (string): Specifies the Managed Object Reference Item. mor_type (string): Specifies the Managed Object Reference Type. uuid (str... | VmwareObjectId | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VmwareObjectId:
"""Implementation of the 'VmwareObjectId' model. Specifies a unique Protection Source id across Cohesity Clusters. It is derived from the id of the VMware Protection Source. Attributes: mor_item (string): Specifies the Managed Object Reference Item. mor_type (string): Specifies th... | stack_v2_sparse_classes_36k_train_025404 | 1,877 | permissive | [
{
"docstring": "Constructor for the VmwareObjectId class",
"name": "__init__",
"signature": "def __init__(self, mor_item=None, mor_type=None, uuid=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dictionary (dictionary): A dictionary representation of the objec... | 2 | stack_v2_sparse_classes_30k_train_021068 | Implement the Python class `VmwareObjectId` described below.
Class description:
Implementation of the 'VmwareObjectId' model. Specifies a unique Protection Source id across Cohesity Clusters. It is derived from the id of the VMware Protection Source. Attributes: mor_item (string): Specifies the Managed Object Referenc... | Implement the Python class `VmwareObjectId` described below.
Class description:
Implementation of the 'VmwareObjectId' model. Specifies a unique Protection Source id across Cohesity Clusters. It is derived from the id of the VMware Protection Source. Attributes: mor_item (string): Specifies the Managed Object Referenc... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class VmwareObjectId:
"""Implementation of the 'VmwareObjectId' model. Specifies a unique Protection Source id across Cohesity Clusters. It is derived from the id of the VMware Protection Source. Attributes: mor_item (string): Specifies the Managed Object Reference Item. mor_type (string): Specifies th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VmwareObjectId:
"""Implementation of the 'VmwareObjectId' model. Specifies a unique Protection Source id across Cohesity Clusters. It is derived from the id of the VMware Protection Source. Attributes: mor_item (string): Specifies the Managed Object Reference Item. mor_type (string): Specifies the Managed Obj... | the_stack_v2_python_sparse | cohesity_management_sdk/models/vmware_object_id.py | cohesity/management-sdk-python | train | 24 |
6f0a46f62d4864cc8aa07dee0b2a58c2c34a4dd5 | [
"groups = [set(), set()]\ng = defaultdict(set)\nfor i, nodes in enumerate(graph):\n for j in nodes:\n g[i].add(j)\n g[j].add(i)\n\ndef dfs(u, i):\n result = True\n for v in g[u]:\n if v in groups[i]:\n return False\n elif v not in groups[(i + 1) % 2]:\n gro... | <|body_start_0|>
groups = [set(), set()]
g = defaultdict(set)
for i, nodes in enumerate(graph):
for j in nodes:
g[i].add(j)
g[j].add(i)
def dfs(u, i):
result = True
for v in g[u]:
if v in groups[i]:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def isBipartite(self, graph: List[List[int]]) -> bool:
"""09/11/2020 23:15"""
<|body_0|>
def isBipartite(self, graph: List[List[int]]) -> bool:
"""09/11/2020 23:17"""
<|body_1|>
def isBipartite(self, graph: List[List[int]]) -> bool:
"""... | stack_v2_sparse_classes_36k_train_025405 | 3,868 | no_license | [
{
"docstring": "09/11/2020 23:15",
"name": "isBipartite",
"signature": "def isBipartite(self, graph: List[List[int]]) -> bool"
},
{
"docstring": "09/11/2020 23:17",
"name": "isBipartite",
"signature": "def isBipartite(self, graph: List[List[int]]) -> bool"
},
{
"docstring": "05/1... | 3 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isBipartite(self, graph: List[List[int]]) -> bool: 09/11/2020 23:15
- def isBipartite(self, graph: List[List[int]]) -> bool: 09/11/2020 23:17
- def isBipartite(self, graph: L... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def isBipartite(self, graph: List[List[int]]) -> bool: 09/11/2020 23:15
- def isBipartite(self, graph: List[List[int]]) -> bool: 09/11/2020 23:17
- def isBipartite(self, graph: L... | 1389a009a02e90e8700a7a00e0b7f797c129cdf4 | <|skeleton|>
class Solution:
def isBipartite(self, graph: List[List[int]]) -> bool:
"""09/11/2020 23:15"""
<|body_0|>
def isBipartite(self, graph: List[List[int]]) -> bool:
"""09/11/2020 23:17"""
<|body_1|>
def isBipartite(self, graph: List[List[int]]) -> bool:
"""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def isBipartite(self, graph: List[List[int]]) -> bool:
"""09/11/2020 23:15"""
groups = [set(), set()]
g = defaultdict(set)
for i, nodes in enumerate(graph):
for j in nodes:
g[i].add(j)
g[j].add(i)
def dfs(u, i):
... | the_stack_v2_python_sparse | leetcode/solved/801_Is_Graph_Bipartite?/solution.py | sungminoh/algorithms | train | 0 | |
f5ec7046d0dde0e18a462ca7b2e23907432c02b9 | [
"self.config = config_\nself.logger = logging.getLogger('gym_logger')\ndata_provider = DataProvider(self.config)\nself.city_states = data_provider.read_city_states()\nself.hex_attr_df = data_provider.read_hex_bin_attributes()\nself.hex_bins = self.hex_attr_df['hex_id']\nself.T = len(self.city_states)\nself.S = len(... | <|body_start_0|>
self.config = config_
self.logger = logging.getLogger('gym_logger')
data_provider = DataProvider(self.config)
self.city_states = data_provider.read_city_states()
self.hex_attr_df = data_provider.read_hex_bin_attributes()
self.hex_bins = self.hex_attr_df['... | BasicEnv | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BasicEnv:
def __init__(self, config_):
"""Constructor :param config_: :return:"""
<|body_0|>
def step(self, action):
"""Advances the environment by 1 time step :param action: :return:"""
<|body_1|>
def lookahead(self, action, t, city_state, driver_distri... | stack_v2_sparse_classes_36k_train_025406 | 4,505 | no_license | [
{
"docstring": "Constructor :param config_: :return:",
"name": "__init__",
"signature": "def __init__(self, config_)"
},
{
"docstring": "Advances the environment by 1 time step :param action: :return:",
"name": "step",
"signature": "def step(self, action)"
},
{
"docstring": "Look... | 5 | stack_v2_sparse_classes_30k_train_007125 | Implement the Python class `BasicEnv` described below.
Class description:
Implement the BasicEnv class.
Method signatures and docstrings:
- def __init__(self, config_): Constructor :param config_: :return:
- def step(self, action): Advances the environment by 1 time step :param action: :return:
- def lookahead(self, ... | Implement the Python class `BasicEnv` described below.
Class description:
Implement the BasicEnv class.
Method signatures and docstrings:
- def __init__(self, config_): Constructor :param config_: :return:
- def step(self, action): Advances the environment by 1 time step :param action: :return:
- def lookahead(self, ... | f7fcd2cc1d6ba18b199d176d4d39193f025ee281 | <|skeleton|>
class BasicEnv:
def __init__(self, config_):
"""Constructor :param config_: :return:"""
<|body_0|>
def step(self, action):
"""Advances the environment by 1 time step :param action: :return:"""
<|body_1|>
def lookahead(self, action, t, city_state, driver_distri... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BasicEnv:
def __init__(self, config_):
"""Constructor :param config_: :return:"""
self.config = config_
self.logger = logging.getLogger('gym_logger')
data_provider = DataProvider(self.config)
self.city_states = data_provider.read_city_states()
self.hex_attr_df =... | the_stack_v2_python_sparse | gym_nyc_yellow_taxi/envs/basic_env.py | transparent-framework/optimize-ride-sharing-earnings | train | 7 | |
696671acf1ae3b613457e05ab4124171105fde6f | [
"print('\\n//////////////////////////////////////////////////')\nprint('♪♪ CountGraphController Initialized ♪♪')\nprint('//////////////////////////////////////////////////\\n')\nself.controller: ElevatorController = ElevatorController(dir, start_at, end_at)",
"value_list: list[int] = []\ncreated_list: list[dateti... | <|body_start_0|>
print('\n//////////////////////////////////////////////////')
print('♪♪ CountGraphController Initialized ♪♪')
print('//////////////////////////////////////////////////\n')
self.controller: ElevatorController = ElevatorController(dir, start_at, end_at)
<|end_body_0|>
<|b... | CountGraphController | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CountGraphController:
def __init__(self, dir: str, start_at: datetime=None, end_at: datetime=None):
"""カウントのグラフを表示するためのコントローラー @params str dir エレベーター向き（left or right）"""
<|body_0|>
def show_graph(self):
"""グラフを生成・表示"""
<|body_1|>
<|end_skeleton|>
<|body_sta... | stack_v2_sparse_classes_36k_train_025407 | 2,180 | no_license | [
{
"docstring": "カウントのグラフを表示するためのコントローラー @params str dir エレベーター向き（left or right）",
"name": "__init__",
"signature": "def __init__(self, dir: str, start_at: datetime=None, end_at: datetime=None)"
},
{
"docstring": "グラフを生成・表示",
"name": "show_graph",
"signature": "def show_graph(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_012898 | Implement the Python class `CountGraphController` described below.
Class description:
Implement the CountGraphController class.
Method signatures and docstrings:
- def __init__(self, dir: str, start_at: datetime=None, end_at: datetime=None): カウントのグラフを表示するためのコントローラー @params str dir エレベーター向き（left or right）
- def show_g... | Implement the Python class `CountGraphController` described below.
Class description:
Implement the CountGraphController class.
Method signatures and docstrings:
- def __init__(self, dir: str, start_at: datetime=None, end_at: datetime=None): カウントのグラフを表示するためのコントローラー @params str dir エレベーター向き（left or right）
- def show_g... | 0de2ee9c5af2e39f15952adf3df55c227d9a3bd0 | <|skeleton|>
class CountGraphController:
def __init__(self, dir: str, start_at: datetime=None, end_at: datetime=None):
"""カウントのグラフを表示するためのコントローラー @params str dir エレベーター向き（left or right）"""
<|body_0|>
def show_graph(self):
"""グラフを生成・表示"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CountGraphController:
def __init__(self, dir: str, start_at: datetime=None, end_at: datetime=None):
"""カウントのグラフを表示するためのコントローラー @params str dir エレベーター向き（left or right）"""
print('\n//////////////////////////////////////////////////')
print('♪♪ CountGraphController Initialized ♪♪')
... | the_stack_v2_python_sparse | src/controllers/graph/count.py | Alesion30/elecon-py | train | 0 | |
cf1c816017173afb2dd1b21d8ad75706c12af189 | [
"print(\"Checkers in Surround's linter\")\nprint('=============================')\ntry:\n Run(['--list-msgs-enabled'])\nexcept SystemExit:\n pass",
"ignore_dirs = ['scripts', 'spikes', 'notebooks']\nargs = [str(p) for p in Path(project_root).glob('**/*.py')]\nargs = [p for p in args if os.path.basename(os.p... | <|body_start_0|>
print("Checkers in Surround's linter")
print('=============================')
try:
Run(['--list-msgs-enabled'])
except SystemExit:
pass
<|end_body_0|>
<|body_start_1|>
ignore_dirs = ['scripts', 'spikes', 'notebooks']
args = [str(p... | Represents the Surround linter which performs multiple checks on the surround project and displays warnings/errors found during the linting process. This class is used by the Surround CLI to perform the linting of a project via the `lint` sub-command. | Linter | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Linter:
"""Represents the Surround linter which performs multiple checks on the surround project and displays warnings/errors found during the linting process. This class is used by the Surround CLI to perform the linting of a project via the `lint` sub-command."""
def dump_checks(self):
... | stack_v2_sparse_classes_36k_train_025408 | 1,910 | permissive | [
{
"docstring": "Dumps a list of the checks in this linter to the terminal. :return: formatted list of the checkers in the linter :rtype: str",
"name": "dump_checks",
"signature": "def dump_checks(self)"
},
{
"docstring": "Runs the linter against the project specified, returning zero on success :... | 2 | stack_v2_sparse_classes_30k_train_018639 | Implement the Python class `Linter` described below.
Class description:
Represents the Surround linter which performs multiple checks on the surround project and displays warnings/errors found during the linting process. This class is used by the Surround CLI to perform the linting of a project via the `lint` sub-comm... | Implement the Python class `Linter` described below.
Class description:
Represents the Surround linter which performs multiple checks on the surround project and displays warnings/errors found during the linting process. This class is used by the Surround CLI to perform the linting of a project via the `lint` sub-comm... | effbb5353ae81d95a30306f94065ac2c8124ec44 | <|skeleton|>
class Linter:
"""Represents the Surround linter which performs multiple checks on the surround project and displays warnings/errors found during the linting process. This class is used by the Surround CLI to perform the linting of a project via the `lint` sub-command."""
def dump_checks(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Linter:
"""Represents the Surround linter which performs multiple checks on the surround project and displays warnings/errors found during the linting process. This class is used by the Surround CLI to perform the linting of a project via the `lint` sub-command."""
def dump_checks(self):
"""Dumps... | the_stack_v2_python_sparse | surround_cli/surround_cli/linter.py | a2i2/surround | train | 14 |
89ceb35496e9fec21fc66b408ed180cbb0e081b3 | [
"Agent.__init__(self, parent)\nself.server_config = CONFIG\nself.snmp_config = CONFIG_SNMP",
"delay = self.server_config.polling_interval()\nts_start = int(time.time())\nwhile True:\n log_message = 'Starting device polling sequence.'\n log.log2info(1056, log_message)\n temp_topology_directory = self.serv... | <|body_start_0|>
Agent.__init__(self, parent)
self.server_config = CONFIG
self.snmp_config = CONFIG_SNMP
<|end_body_0|>
<|body_start_1|>
delay = self.server_config.polling_interval()
ts_start = int(time.time())
while True:
log_message = 'Starting device polli... | Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post: | PollingAgent | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PollingAgent:
"""Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:"""
def __init__(self, parent):
"""Method initializing the class. Args: config_dir: Configuration directory Returns: None"""
<|body_0|>
def query(self):
... | stack_v2_sparse_classes_36k_train_025409 | 16,748 | permissive | [
{
"docstring": "Method initializing the class. Args: config_dir: Configuration directory Returns: None",
"name": "__init__",
"signature": "def __init__(self, parent)"
},
{
"docstring": "Query all remote hosts for data. Args: None Returns: None",
"name": "query",
"signature": "def query(s... | 2 | null | Implement the Python class `PollingAgent` described below.
Class description:
Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:
Method signatures and docstrings:
- def __init__(self, parent): Method initializing the class. Args: config_dir: Configuration directory Ret... | Implement the Python class `PollingAgent` described below.
Class description:
Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:
Method signatures and docstrings:
- def __init__(self, parent): Method initializing the class. Args: config_dir: Configuration directory Ret... | ae82589fbbab77fef6d6be09c1fcca5846f595a8 | <|skeleton|>
class PollingAgent:
"""Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:"""
def __init__(self, parent):
"""Method initializing the class. Args: config_dir: Configuration directory Returns: None"""
<|body_0|>
def query(self):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PollingAgent:
"""Switchmap-NG agent that gathers data. Args: None Returns: None Functions: __init__: populate: post:"""
def __init__(self, parent):
"""Method initializing the class. Args: config_dir: Configuration directory Returns: None"""
Agent.__init__(self, parent)
self.server... | the_stack_v2_python_sparse | _deprecated/switchmap-ng-poller.old | PalisadoesFoundation/switchmap-ng | train | 8 |
33202def949b6f0c4f47c192c311bcf208bb4293 | [
"super().__init__(coordinator)\nself.entity_description = description\nself.coordinator = coordinator\nself._entry_id = entry_id\nself._attrs: dict[str, Any] = {}\n_id = coordinator.data.iata\nself._attr_name = f'{_id} {description.name}'\nself._attr_unique_id = f'{_id}_{description.key}'",
"sensor_type = self.en... | <|body_start_0|>
super().__init__(coordinator)
self.entity_description = description
self.coordinator = coordinator
self._entry_id = entry_id
self._attrs: dict[str, Any] = {}
_id = coordinator.data.iata
self._attr_name = f'{_id} {description.name}'
self._a... | Define a binary sensor for FAA Delays. | FAABinarySensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FAABinarySensor:
"""Define a binary sensor for FAA Delays."""
def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def is_on(self):
"""Return the status of the sensor."""
<|... | stack_v2_sparse_classes_36k_train_025410 | 3,718 | permissive | [
{
"docstring": "Initialize the sensor.",
"name": "__init__",
"signature": "def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None"
},
{
"docstring": "Return the status of the sensor.",
"name": "is_on",
"signature": "def is_on(self)"
},
{
"do... | 3 | null | Implement the Python class `FAABinarySensor` described below.
Class description:
Define a binary sensor for FAA Delays.
Method signatures and docstrings:
- def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None: Initialize the sensor.
- def is_on(self): Return the status of the ... | Implement the Python class `FAABinarySensor` described below.
Class description:
Define a binary sensor for FAA Delays.
Method signatures and docstrings:
- def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None: Initialize the sensor.
- def is_on(self): Return the status of the ... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class FAABinarySensor:
"""Define a binary sensor for FAA Delays."""
def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None:
"""Initialize the sensor."""
<|body_0|>
def is_on(self):
"""Return the status of the sensor."""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FAABinarySensor:
"""Define a binary sensor for FAA Delays."""
def __init__(self, coordinator, entry_id, description: BinarySensorEntityDescription) -> None:
"""Initialize the sensor."""
super().__init__(coordinator)
self.entity_description = description
self.coordinator = ... | the_stack_v2_python_sparse | homeassistant/components/faa_delays/binary_sensor.py | home-assistant/core | train | 35,501 |
315911396961d52916c19292d41ac6fd4a565761 | [
"head1 = self.build_list(nums1)\nhead2 = self.build_list(nums2)\np1 = head1\np2 = head2\nres = []\nwhile p1 and p2:\n if p1.val < p2.val:\n p2 = p2.next\n elif p1.val > p2.val:\n p1 = p1.next\n else:\n res.append(p1.val)\n p1 = p1.next\n p2 = p2.next\nreturn res",
"dumm... | <|body_start_0|>
head1 = self.build_list(nums1)
head2 = self.build_list(nums2)
p1 = head1
p2 = head2
res = []
while p1 and p2:
if p1.val < p2.val:
p2 = p2.next
elif p1.val > p2.val:
p1 = p1.next
else:
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def func(self, nums1, nums2):
"""Args: nums1: list[int] nums2: list[int] Return: list[int]"""
<|body_0|>
def build_list(self, nums):
"""Args: nums: list[int] Return: ListNode"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
head1 = self.bui... | stack_v2_sparse_classes_36k_train_025411 | 1,263 | no_license | [
{
"docstring": "Args: nums1: list[int] nums2: list[int] Return: list[int]",
"name": "func",
"signature": "def func(self, nums1, nums2)"
},
{
"docstring": "Args: nums: list[int] Return: ListNode",
"name": "build_list",
"signature": "def build_list(self, nums)"
}
] | 2 | stack_v2_sparse_classes_30k_train_000073 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def func(self, nums1, nums2): Args: nums1: list[int] nums2: list[int] Return: list[int]
- def build_list(self, nums): Args: nums: list[int] Return: ListNode | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def func(self, nums1, nums2): Args: nums1: list[int] nums2: list[int] Return: list[int]
- def build_list(self, nums): Args: nums: list[int] Return: ListNode
<|skeleton|>
class S... | 101bce2fac8b188a4eb2f5e017293d21ad0ecb21 | <|skeleton|>
class Solution:
def func(self, nums1, nums2):
"""Args: nums1: list[int] nums2: list[int] Return: list[int]"""
<|body_0|>
def build_list(self, nums):
"""Args: nums: list[int] Return: ListNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def func(self, nums1, nums2):
"""Args: nums1: list[int] nums2: list[int] Return: list[int]"""
head1 = self.build_list(nums1)
head2 = self.build_list(nums2)
p1 = head1
p2 = head2
res = []
while p1 and p2:
if p1.val < p2.val:
... | the_stack_v2_python_sparse | 秋招/腾讯/1.1.py | AiZhanghan/Leetcode | train | 0 | |
3fd0f8a80e2687472efde5a91ec8037e95c57006 | [
"data = self.data\nid_ = data['entity']['id']\nreturn f'{PLATFORM_URL}orders/{id_}'",
"available = super().available\ndata = self.data\nto_role = data['to_role']\nreturn to_role == 'customer_user' and available"
] | <|body_start_0|>
data = self.data
id_ = data['entity']['id']
return f'{PLATFORM_URL}orders/{id_}'
<|end_body_0|>
<|body_start_1|>
available = super().available
data = self.data
to_role = data['to_role']
return to_role == 'customer_user' and available
<|end_body_1... | Email to customer on comment created. | CommentCreatedToCustomer | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CommentCreatedToCustomer:
"""Email to customer on comment created."""
def action_url(self) -> str:
"""Action URL."""
<|body_0|>
def available(self) -> bool:
"""Check if this action is available."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
da... | stack_v2_sparse_classes_36k_train_025412 | 5,020 | no_license | [
{
"docstring": "Action URL.",
"name": "action_url",
"signature": "def action_url(self) -> str"
},
{
"docstring": "Check if this action is available.",
"name": "available",
"signature": "def available(self) -> bool"
}
] | 2 | stack_v2_sparse_classes_30k_train_006981 | Implement the Python class `CommentCreatedToCustomer` described below.
Class description:
Email to customer on comment created.
Method signatures and docstrings:
- def action_url(self) -> str: Action URL.
- def available(self) -> bool: Check if this action is available. | Implement the Python class `CommentCreatedToCustomer` described below.
Class description:
Email to customer on comment created.
Method signatures and docstrings:
- def action_url(self) -> str: Action URL.
- def available(self) -> bool: Check if this action is available.
<|skeleton|>
class CommentCreatedToCustomer:
... | cca179f55ebc3c420426eff59b23d7c8963ca9a3 | <|skeleton|>
class CommentCreatedToCustomer:
"""Email to customer on comment created."""
def action_url(self) -> str:
"""Action URL."""
<|body_0|>
def available(self) -> bool:
"""Check if this action is available."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CommentCreatedToCustomer:
"""Email to customer on comment created."""
def action_url(self) -> str:
"""Action URL."""
data = self.data
id_ = data['entity']['id']
return f'{PLATFORM_URL}orders/{id_}'
def available(self) -> bool:
"""Check if this action is availa... | the_stack_v2_python_sparse | src/briefy/choreographer/actions/mail/leica/comment.py | BriefyHQ/briefy.choreographer | train | 0 |
5f0bc3d0f189c1a422c5dada12222f2a13e1c8f6 | [
"super().__init__(func, M, b, w, None, useNumericalDerivatives)\nself.m_Dt = Dt\nself.m_c = np.zeros(len(c) + 1)\nself.m_c[1:len(c) + 1] = c\nself.m_c[0] = -np.sum(c)",
"qn_hat = qn\npn_hat = pn\nfor c in self.m_c:\n qn_hat, pn_hat = self.m_step(c * self.m_Dt, qn_hat, pn_hat)\nfor c in self.m_c:\n qn_hat, p... | <|body_start_0|>
super().__init__(func, M, b, w, None, useNumericalDerivatives)
self.m_Dt = Dt
self.m_c = np.zeros(len(c) + 1)
self.m_c[1:len(c) + 1] = c
self.m_c[0] = -np.sum(c)
<|end_body_0|>
<|body_start_1|>
qn_hat = qn
pn_hat = pn
for c in self.m_c:
... | A derived class to create a post-processed composition method as described in L + R section 6.2.3, pp. 148. The idea here is that a composition method is applied to z after it has undergone a transformation. When the output is required the composition method is then applied to the results. This allows a higher order me... | Processing | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Processing:
"""A derived class to create a post-processed composition method as described in L + R section 6.2.3, pp. 148. The idea here is that a composition method is applied to z after it has undergone a transformation. When the output is required the composition method is then applied to the ... | stack_v2_sparse_classes_36k_train_025413 | 16,278 | no_license | [
{
"docstring": "Parameters: func - Either the potential function or its first spatial derivative, depending on whether useNumericalDerivatices is False or True. Must take a one-dimensional three element vector represent the spatial coordinates at which at which the function will evaluate. M - The mass of the pa... | 4 | stack_v2_sparse_classes_30k_train_008675 | Implement the Python class `Processing` described below.
Class description:
A derived class to create a post-processed composition method as described in L + R section 6.2.3, pp. 148. The idea here is that a composition method is applied to z after it has undergone a transformation. When the output is required the com... | Implement the Python class `Processing` described below.
Class description:
A derived class to create a post-processed composition method as described in L + R section 6.2.3, pp. 148. The idea here is that a composition method is applied to z after it has undergone a transformation. When the output is required the com... | 17bd483cbb80381edb5dbf3306035c5cd423191b | <|skeleton|>
class Processing:
"""A derived class to create a post-processed composition method as described in L + R section 6.2.3, pp. 148. The idea here is that a composition method is applied to z after it has undergone a transformation. When the output is required the composition method is then applied to the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Processing:
"""A derived class to create a post-processed composition method as described in L + R section 6.2.3, pp. 148. The idea here is that a composition method is applied to z after it has undergone a transformation. When the output is required the composition method is then applied to the results. This... | the_stack_v2_python_sparse | Steppers/scovel.py | Rhys314/Simulating_Hamiltonian_Dynamics | train | 0 |
8c11a5fda007282b39b69846cd6acaaec9141609 | [
"self.nmap_services_file = 'nemo/common/utils/nmap-services'\nself.port_service = {}\nself.custom_service_file = 'nemo/common/utils/custom-services.txt'\nself.custom_port_service = {}\nself.__load_services()",
"try:\n with open(self.nmap_services_file) as f:\n for line in f:\n if line.startsw... | <|body_start_0|>
self.nmap_services_file = 'nemo/common/utils/nmap-services'
self.port_service = {}
self.custom_service_file = 'nemo/common/utils/custom-services.txt'
self.custom_port_service = {}
self.__load_services()
<|end_body_0|>
<|body_start_1|>
try:
wi... | 解析端口的Service名称 包括通用定义和自定义 | ParsePortService | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ParsePortService:
"""解析端口的Service名称 包括通用定义和自定义"""
def __init__(self):
"""默认参数"""
<|body_0|>
def __load_services(self):
"""读取映射关系:nmap-services"""
<|body_1|>
def get_service(self, port, port_type='tcp'):
"""根据端口号查找Service名称"""
<|body_2... | stack_v2_sparse_classes_36k_train_025414 | 2,419 | no_license | [
{
"docstring": "默认参数",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "读取映射关系:nmap-services",
"name": "__load_services",
"signature": "def __load_services(self)"
},
{
"docstring": "根据端口号查找Service名称",
"name": "get_service",
"signature": "def get_se... | 3 | stack_v2_sparse_classes_30k_train_009618 | Implement the Python class `ParsePortService` described below.
Class description:
解析端口的Service名称 包括通用定义和自定义
Method signatures and docstrings:
- def __init__(self): 默认参数
- def __load_services(self): 读取映射关系:nmap-services
- def get_service(self, port, port_type='tcp'): 根据端口号查找Service名称 | Implement the Python class `ParsePortService` described below.
Class description:
解析端口的Service名称 包括通用定义和自定义
Method signatures and docstrings:
- def __init__(self): 默认参数
- def __load_services(self): 读取映射关系:nmap-services
- def get_service(self, port, port_type='tcp'): 根据端口号查找Service名称
<|skeleton|>
class ParsePortServi... | ac9b63c60a64677d4d070afe069af04be591dfcc | <|skeleton|>
class ParsePortService:
"""解析端口的Service名称 包括通用定义和自定义"""
def __init__(self):
"""默认参数"""
<|body_0|>
def __load_services(self):
"""读取映射关系:nmap-services"""
<|body_1|>
def get_service(self, port, port_type='tcp'):
"""根据端口号查找Service名称"""
<|body_2... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ParsePortService:
"""解析端口的Service名称 包括通用定义和自定义"""
def __init__(self):
"""默认参数"""
self.nmap_services_file = 'nemo/common/utils/nmap-services'
self.port_service = {}
self.custom_service_file = 'nemo/common/utils/custom-services.txt'
self.custom_port_service = {}
... | the_stack_v2_python_sparse | nemo/common/utils/parseservice.py | xiaolushuo/nemo | train | 0 |
d3190b2172dbb8b8071bf720cd25e81b48e2ef57 | [
"self.loggit = logging.getLogger('curator.validators.SchemaCheck')\nself.loggit.debug('Schema: %s', schema)\nself.loggit.debug('\"%s\" config: %s', test_what, config)\nself.config = config\nself.schema = schema\nself.test_what = test_what\nself.location = location\nself.badvalue = None\nself.error = None",
"def g... | <|body_start_0|>
self.loggit = logging.getLogger('curator.validators.SchemaCheck')
self.loggit.debug('Schema: %s', schema)
self.loggit.debug('"%s" config: %s', test_what, config)
self.config = config
self.schema = schema
self.test_what = test_what
self.location = ... | SchemaCheck | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SchemaCheck:
def __init__(self, config, schema, test_what, location):
"""Validate ``config`` with the provided :py:class:`~.voluptuous.schema_builder.Schema` from ``schema``. ``test_what`` and ``location`` are for reporting the results, in case of failure. If validation is successful, :p... | stack_v2_sparse_classes_36k_train_025415 | 3,571 | permissive | [
{
"docstring": "Validate ``config`` with the provided :py:class:`~.voluptuous.schema_builder.Schema` from ``schema``. ``test_what`` and ``location`` are for reporting the results, in case of failure. If validation is successful, :py:meth:`result` returns ``config`` a valid :py:class:`~.voluptuous.schema_builder... | 3 | stack_v2_sparse_classes_30k_train_015831 | Implement the Python class `SchemaCheck` described below.
Class description:
Implement the SchemaCheck class.
Method signatures and docstrings:
- def __init__(self, config, schema, test_what, location): Validate ``config`` with the provided :py:class:`~.voluptuous.schema_builder.Schema` from ``schema``. ``test_what``... | Implement the Python class `SchemaCheck` described below.
Class description:
Implement the SchemaCheck class.
Method signatures and docstrings:
- def __init__(self, config, schema, test_what, location): Validate ``config`` with the provided :py:class:`~.voluptuous.schema_builder.Schema` from ``schema``. ``test_what``... | b41743a061ad790820affe7acee5f71abe819357 | <|skeleton|>
class SchemaCheck:
def __init__(self, config, schema, test_what, location):
"""Validate ``config`` with the provided :py:class:`~.voluptuous.schema_builder.Schema` from ``schema``. ``test_what`` and ``location`` are for reporting the results, in case of failure. If validation is successful, :p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SchemaCheck:
def __init__(self, config, schema, test_what, location):
"""Validate ``config`` with the provided :py:class:`~.voluptuous.schema_builder.Schema` from ``schema``. ``test_what`` and ``location`` are for reporting the results, in case of failure. If validation is successful, :py:meth:`result... | the_stack_v2_python_sparse | curator/validators/schemacheck.py | volatilemolotov/curator | train | 0 | |
c9c6a74bf47b9be9792fe8b207a94906f08e48a5 | [
"self._attr_unique_id = hashlib.sha256(user.name.encode('utf-8')).hexdigest()\nself._attr_name = user.name\nself._user = user",
"self._attr_entity_picture = self._user.get_image()\nif (now_playing := self._user.get_now_playing()):\n self._attr_native_value = format_track(now_playing)\nelse:\n self._attr_nat... | <|body_start_0|>
self._attr_unique_id = hashlib.sha256(user.name.encode('utf-8')).hexdigest()
self._attr_name = user.name
self._user = user
<|end_body_0|>
<|body_start_1|>
self._attr_entity_picture = self._user.get_image()
if (now_playing := self._user.get_now_playing()):
... | A class for the Last.fm account. | LastFmSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LastFmSensor:
"""A class for the Last.fm account."""
def __init__(self, user: User, lastfm_api: LastFMNetwork) -> None:
"""Initialize the sensor."""
<|body_0|>
def update(self) -> None:
"""Update device state."""
<|body_1|>
<|end_skeleton|>
<|body_start... | stack_v2_sparse_classes_36k_train_025416 | 2,863 | permissive | [
{
"docstring": "Initialize the sensor.",
"name": "__init__",
"signature": "def __init__(self, user: User, lastfm_api: LastFMNetwork) -> None"
},
{
"docstring": "Update device state.",
"name": "update",
"signature": "def update(self) -> None"
}
] | 2 | stack_v2_sparse_classes_30k_train_009803 | Implement the Python class `LastFmSensor` described below.
Class description:
A class for the Last.fm account.
Method signatures and docstrings:
- def __init__(self, user: User, lastfm_api: LastFMNetwork) -> None: Initialize the sensor.
- def update(self) -> None: Update device state. | Implement the Python class `LastFmSensor` described below.
Class description:
A class for the Last.fm account.
Method signatures and docstrings:
- def __init__(self, user: User, lastfm_api: LastFMNetwork) -> None: Initialize the sensor.
- def update(self) -> None: Update device state.
<|skeleton|>
class LastFmSensor... | 2e65b77b2b5c17919939481f327963abdfdc53f0 | <|skeleton|>
class LastFmSensor:
"""A class for the Last.fm account."""
def __init__(self, user: User, lastfm_api: LastFMNetwork) -> None:
"""Initialize the sensor."""
<|body_0|>
def update(self) -> None:
"""Update device state."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LastFmSensor:
"""A class for the Last.fm account."""
def __init__(self, user: User, lastfm_api: LastFMNetwork) -> None:
"""Initialize the sensor."""
self._attr_unique_id = hashlib.sha256(user.name.encode('utf-8')).hexdigest()
self._attr_name = user.name
self._user = user
... | the_stack_v2_python_sparse | homeassistant/components/lastfm/sensor.py | konnected-io/home-assistant | train | 24 |
11b1b53046b8dcd84dd96e7f40e99557de9100d5 | [
"for mapping in CmsError.__dict__.items():\n if mapping[1] == code:\n return mapping[0]\nreturn None",
"for mapping in CmsError.__dict__.items():\n if mapping[0] == name:\n return mapping[1]\nreturn None"
] | <|body_start_0|>
for mapping in CmsError.__dict__.items():
if mapping[1] == code:
return mapping[0]
return None
<|end_body_0|>
<|body_start_1|>
for mapping in CmsError.__dict__.items():
if mapping[0] == name:
return mapping[1]
retu... | CMS errors | CmsError | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CmsError:
"""CMS errors"""
def getName(code):
"""Gets the CMS error name from its code :param code: The code to map :return None: Failed to map code :return String: The name of the code"""
<|body_0|>
def getCode(name):
"""Gets the CMS error code from its name :pa... | stack_v2_sparse_classes_36k_train_025417 | 6,596 | no_license | [
{
"docstring": "Gets the CMS error name from its code :param code: The code to map :return None: Failed to map code :return String: The name of the code",
"name": "getName",
"signature": "def getName(code)"
},
{
"docstring": "Gets the CMS error code from its name :param name; The name to map :re... | 2 | stack_v2_sparse_classes_30k_train_009504 | Implement the Python class `CmsError` described below.
Class description:
CMS errors
Method signatures and docstrings:
- def getName(code): Gets the CMS error name from its code :param code: The code to map :return None: Failed to map code :return String: The name of the code
- def getCode(name): Gets the CMS error c... | Implement the Python class `CmsError` described below.
Class description:
CMS errors
Method signatures and docstrings:
- def getName(code): Gets the CMS error name from its code :param code: The code to map :return None: Failed to map code :return String: The name of the code
- def getCode(name): Gets the CMS error c... | 1c87d086f15a7810b839368fdb82c0de264a7272 | <|skeleton|>
class CmsError:
"""CMS errors"""
def getName(code):
"""Gets the CMS error name from its code :param code: The code to map :return None: Failed to map code :return String: The name of the code"""
<|body_0|>
def getCode(name):
"""Gets the CMS error code from its name :pa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CmsError:
"""CMS errors"""
def getName(code):
"""Gets the CMS error name from its code :param code: The code to map :return None: Failed to map code :return String: The name of the code"""
for mapping in CmsError.__dict__.items():
if mapping[1] == code:
return ... | the_stack_v2_python_sparse | nimbelink/cell/at/cmsError.py | NimbeLink/pynl-cell | train | 0 |
27bdc6035c31eb562e1a96c5877f647de99af71a | [
"if root == None:\n return False\nreturn bool(self.helper(root, sum, 0))",
"pathSum += root.val\nif root.left:\n left = self.helper(root.left, target, pathSum)\n if left:\n return True\nif root.right:\n right = self.helper(root.right, target, pathSum)\n if right:\n return True\nif not... | <|body_start_0|>
if root == None:
return False
return bool(self.helper(root, sum, 0))
<|end_body_0|>
<|body_start_1|>
pathSum += root.val
if root.left:
left = self.helper(root.left, target, pathSum)
if left:
return True
if root... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def hasPathSum(self, root, sum):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def helper(self, root, target, pathSum):
""":type root: TreeNode :type depth: int :rtype: int"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if root == Non... | stack_v2_sparse_classes_36k_train_025418 | 1,704 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: int",
"name": "hasPathSum",
"signature": "def hasPathSum(self, root, sum)"
},
{
"docstring": ":type root: TreeNode :type depth: int :rtype: int",
"name": "helper",
"signature": "def helper(self, root, target, pathSum)"
}
] | 2 | stack_v2_sparse_classes_30k_train_003862 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasPathSum(self, root, sum): :type root: TreeNode :rtype: int
- def helper(self, root, target, pathSum): :type root: TreeNode :type depth: int :rtype: int | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def hasPathSum(self, root, sum): :type root: TreeNode :rtype: int
- def helper(self, root, target, pathSum): :type root: TreeNode :type depth: int :rtype: int
<|skeleton|>
class... | 61933e7c0b8d8ffef9bd9a4af4fddfdb77568b62 | <|skeleton|>
class Solution:
def hasPathSum(self, root, sum):
""":type root: TreeNode :rtype: int"""
<|body_0|>
def helper(self, root, target, pathSum):
""":type root: TreeNode :type depth: int :rtype: int"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def hasPathSum(self, root, sum):
""":type root: TreeNode :rtype: int"""
if root == None:
return False
return bool(self.helper(root, sum, 0))
def helper(self, root, target, pathSum):
""":type root: TreeNode :type depth: int :rtype: int"""
pathS... | the_stack_v2_python_sparse | 112-Path-Sum.py | OhMesch/Algorithm-Problems | train | 0 | |
f4b8afd0437e6be91dc002bde22a9c0dc98fdf69 | [
"mask1 = 1 << 7\nmask2 = 1 << 6\nn_byte = 0\nfor num in data:\n mask = 1 << 7\n if n_byte == 0:\n while mask & num:\n n_byte += 1\n mask >>= 1\n if n_byte == 0:\n continue\n if n_byte > 4 or n_byte == 1:\n return False\n n_byte -= 1\n ... | <|body_start_0|>
mask1 = 1 << 7
mask2 = 1 << 6
n_byte = 0
for num in data:
mask = 1 << 7
if n_byte == 0:
while mask & num:
n_byte += 1
mask >>= 1
if n_byte == 0:
continue
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def validUtf8(self, data):
""":type data: List[int] :rtype: bool"""
<|body_0|>
def validUtf8_1(self, data):
""":type data: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
mask1 = 1 << 7
mask2 = 1 << 6
... | stack_v2_sparse_classes_36k_train_025419 | 1,579 | no_license | [
{
"docstring": ":type data: List[int] :rtype: bool",
"name": "validUtf8",
"signature": "def validUtf8(self, data)"
},
{
"docstring": ":type data: List[int] :rtype: bool",
"name": "validUtf8_1",
"signature": "def validUtf8_1(self, data)"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def validUtf8(self, data): :type data: List[int] :rtype: bool
- def validUtf8_1(self, data): :type data: List[int] :rtype: bool | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def validUtf8(self, data): :type data: List[int] :rtype: bool
- def validUtf8_1(self, data): :type data: List[int] :rtype: bool
<|skeleton|>
class Solution:
def validUtf8(s... | 70bdd75b6af2e1811c1beab22050c01d28d7373e | <|skeleton|>
class Solution:
def validUtf8(self, data):
""":type data: List[int] :rtype: bool"""
<|body_0|>
def validUtf8_1(self, data):
""":type data: List[int] :rtype: bool"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def validUtf8(self, data):
""":type data: List[int] :rtype: bool"""
mask1 = 1 << 7
mask2 = 1 << 6
n_byte = 0
for num in data:
mask = 1 << 7
if n_byte == 0:
while mask & num:
n_byte += 1
... | the_stack_v2_python_sparse | python/leetcode/393_UTF-8_Validation.py | bobcaoge/my-code | train | 0 | |
d78a13d3944c2813a8128d2e495ddaedff049ecd | [
"self.x_obst = copy.deepcopy(x)\nself.gamma = copy.deepcopy(gamma)\nself.beta = copy.deepcopy(beta)\nself.n_dim = np.size(self.x_obst)",
"c = np.cos(theta)\ns = np.sin(theta)\nif self.n_dim == 2:\n R = np.array([[c, -s], [s, c]])\nelif self.n_dim == 3:\n if np.shape(u)[0] != self.n_dim:\n raise Value... | <|body_start_0|>
self.x_obst = copy.deepcopy(x)
self.gamma = copy.deepcopy(gamma)
self.beta = copy.deepcopy(beta)
self.n_dim = np.size(self.x_obst)
<|end_body_0|>
<|body_start_1|>
c = np.cos(theta)
s = np.sin(theta)
if self.n_dim == 2:
R = np.array([[... | Implementation of an obstacle for Dynamic Movement Primitives as described in [1] Hoffmann, H., Pastor, P., Park, D. H., & Schaal, S. (2009, May). Biologically-inspired dynamical systems for movement generation: automatic real-time goal adaptation and obstacle avoidance. In Robotics and Automation, 2009. ICRA'09. IEEE ... | Obstacle_Steering | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Obstacle_Steering:
"""Implementation of an obstacle for Dynamic Movement Primitives as described in [1] Hoffmann, H., Pastor, P., Park, D. H., & Schaal, S. (2009, May). Biologically-inspired dynamical systems for movement generation: automatic real-time goal adaptation and obstacle avoidance. In ... | stack_v2_sparse_classes_36k_train_025420 | 6,106 | no_license | [
{
"docstring": "Initialize the obstacle object",
"name": "__init__",
"signature": "def __init__(self, x=np.zeros(3), gamma=20.0, beta=10.0 / np.pi, **kwargs)"
},
{
"docstring": "Compute the rotation matrix of a rotation of theta around the direction given by u (if self.n_dim = 3)",
"name": "... | 3 | stack_v2_sparse_classes_30k_train_017665 | Implement the Python class `Obstacle_Steering` described below.
Class description:
Implementation of an obstacle for Dynamic Movement Primitives as described in [1] Hoffmann, H., Pastor, P., Park, D. H., & Schaal, S. (2009, May). Biologically-inspired dynamical systems for movement generation: automatic real-time goal... | Implement the Python class `Obstacle_Steering` described below.
Class description:
Implementation of an obstacle for Dynamic Movement Primitives as described in [1] Hoffmann, H., Pastor, P., Park, D. H., & Schaal, S. (2009, May). Biologically-inspired dynamical systems for movement generation: automatic real-time goal... | 41e353f91f78613cf7bea2ef2369f7589a091a01 | <|skeleton|>
class Obstacle_Steering:
"""Implementation of an obstacle for Dynamic Movement Primitives as described in [1] Hoffmann, H., Pastor, P., Park, D. H., & Schaal, S. (2009, May). Biologically-inspired dynamical systems for movement generation: automatic real-time goal adaptation and obstacle avoidance. In ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Obstacle_Steering:
"""Implementation of an obstacle for Dynamic Movement Primitives as described in [1] Hoffmann, H., Pastor, P., Park, D. H., & Schaal, S. (2009, May). Biologically-inspired dynamical systems for movement generation: automatic real-time goal adaptation and obstacle avoidance. In Robotics and ... | the_stack_v2_python_sparse | dmp/point_obstacle.py | mginesi/dmp_vol_obst | train | 23 |
3b14377946dd19af220f60f69e8419571f787ecd | [
"def tuplify(root):\n return root and (root.val, [tuplify(c) for c in root.children])\nreturn json.dumps(tuplify(root))",
"def detuplify(t):\n if t:\n root = Node(t[0], [])\n for c in t[1]:\n root.children.append(detuplify(c))\n return root\n return None\nreturn detuplify(... | <|body_start_0|>
def tuplify(root):
return root and (root.val, [tuplify(c) for c in root.children])
return json.dumps(tuplify(root))
<|end_body_0|>
<|body_start_1|>
def detuplify(t):
if t:
root = Node(t[0], [])
for c in t[1]:
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root: 'Node') -> str:
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
<|body_0|>
def deserialize(self, data: str) -> 'Node':
"""Decodes your encoded data to tree. :type data: str :rtype: Node"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k_train_025421 | 748 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: Node :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root: 'Node') -> str"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: Node",
"name": "deserialize",
"signature": "def des... | 2 | stack_v2_sparse_classes_30k_train_008184 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: 'Node') -> str: Encodes a tree to a single string. :type root: Node :rtype: str
- def deserialize(self, data: str) -> 'Node': Decodes your encoded data to tre... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root: 'Node') -> str: Encodes a tree to a single string. :type root: Node :rtype: str
- def deserialize(self, data: str) -> 'Node': Decodes your encoded data to tre... | 4b1c76729576684331a8f1cdd7ced8b757c20fea | <|skeleton|>
class Codec:
def serialize(self, root: 'Node') -> str:
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
<|body_0|>
def deserialize(self, data: str) -> 'Node':
"""Decodes your encoded data to tree. :type data: str :rtype: Node"""
<|body_1|>
<|e... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root: 'Node') -> str:
"""Encodes a tree to a single string. :type root: Node :rtype: str"""
def tuplify(root):
return root and (root.val, [tuplify(c) for c in root.children])
return json.dumps(tuplify(root))
def deserialize(self, data: str) -... | the_stack_v2_python_sparse | 0428_SerializeandDeserializeN-aryTree.py | ysonggit/leetcode_python | train | 1 | |
5ab9b49605001bc229f7829d858a52fe43fb2d7a | [
"if isinstance(transition, list):\n dbg = [cls(function=function, transition=t, target=target) for t in transition]\n return meta.CrestList.fromlist(dbg)\nelse:\n return super().__new__(cls)",
"super().__init__(name, parent)\nself.function = function\nself.transition = transition\nself.target = target"
] | <|body_start_0|>
if isinstance(transition, list):
dbg = [cls(function=function, transition=t, target=target) for t in transition]
return meta.CrestList.fromlist(dbg)
else:
return super().__new__(cls)
<|end_body_0|>
<|body_start_1|>
super().__init__(name, pare... | An Action whose function is executed when a certain automaton transition is triggered. .. note:: Pro-tip: To avoid repetition, you can initialise it with a list of transitions (or str-names). This will automatically create an action for each transition. Be careful, because the action's name will be autoamtically number... | Action | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Action:
"""An Action whose function is executed when a certain automaton transition is triggered. .. note:: Pro-tip: To avoid repetition, you can initialise it with a list of transitions (or str-names). This will automatically create an action for each transition. Be careful, because the action's... | stack_v2_sparse_classes_36k_train_025422 | 8,941 | permissive | [
{
"docstring": "this is so we can define the same update for multiple states",
"name": "__new__",
"signature": "def __new__(cls, transition, target, function, name='', parent=None)"
},
{
"docstring": "Parameters ---------- source: Transition or str The transition at which the update should be tr... | 2 | stack_v2_sparse_classes_30k_val_000562 | Implement the Python class `Action` described below.
Class description:
An Action whose function is executed when a certain automaton transition is triggered. .. note:: Pro-tip: To avoid repetition, you can initialise it with a list of transitions (or str-names). This will automatically create an action for each trans... | Implement the Python class `Action` described below.
Class description:
An Action whose function is executed when a certain automaton transition is triggered. .. note:: Pro-tip: To avoid repetition, you can initialise it with a list of transitions (or str-names). This will automatically create an action for each trans... | 7fd97c50b0c6c923e1c477105bed4f0ea032bb99 | <|skeleton|>
class Action:
"""An Action whose function is executed when a certain automaton transition is triggered. .. note:: Pro-tip: To avoid repetition, you can initialise it with a list of transitions (or str-names). This will automatically create an action for each transition. Be careful, because the action's... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Action:
"""An Action whose function is executed when a certain automaton transition is triggered. .. note:: Pro-tip: To avoid repetition, you can initialise it with a list of transitions (or str-names). This will automatically create an action for each transition. Be careful, because the action's name will be... | the_stack_v2_python_sparse | crestdsl/model/model.py | crestdsl/CREST | train | 16 |
3c6b9405778e5c3d928822ad42ab3a0378c73b2d | [
"if self.context.get('request').user.full_name != '':\n raise serializers.ValidationError('身份已被认证过！')\nidentify_instance_face = ocr.apply_async(args=(attrs.get('face'), 'face'))\nidentify_instance_back = ocr.apply_async(args=(attrs.get('back'), 'face'))\nis_success = identify_instance_face.get() and identify_ins... | <|body_start_0|>
if self.context.get('request').user.full_name != '':
raise serializers.ValidationError('身份已被认证过！')
identify_instance_face = ocr.apply_async(args=(attrs.get('face'), 'face'))
identify_instance_back = ocr.apply_async(args=(attrs.get('back'), 'face'))
is_success... | 身份认证序列化器 | VerifyIdCardSerializer | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VerifyIdCardSerializer:
"""身份认证序列化器"""
def validate(self, attrs):
"""OCR识别身份正反 验证阶段验证身份信息是否正确或是否已被验证"""
<|body_0|>
def update(self, instance, validated_data):
"""更新身份信息"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if self.context.get('request... | stack_v2_sparse_classes_36k_train_025423 | 5,759 | permissive | [
{
"docstring": "OCR识别身份正反 验证阶段验证身份信息是否正确或是否已被验证",
"name": "validate",
"signature": "def validate(self, attrs)"
},
{
"docstring": "更新身份信息",
"name": "update",
"signature": "def update(self, instance, validated_data)"
}
] | 2 | null | Implement the Python class `VerifyIdCardSerializer` described below.
Class description:
身份认证序列化器
Method signatures and docstrings:
- def validate(self, attrs): OCR识别身份正反 验证阶段验证身份信息是否正确或是否已被验证
- def update(self, instance, validated_data): 更新身份信息 | Implement the Python class `VerifyIdCardSerializer` described below.
Class description:
身份认证序列化器
Method signatures and docstrings:
- def validate(self, attrs): OCR识别身份正反 验证阶段验证身份信息是否正确或是否已被验证
- def update(self, instance, validated_data): 更新身份信息
<|skeleton|>
class VerifyIdCardSerializer:
"""身份认证序列化器"""
def v... | 13cb59130d15e782f78bc5148409bef0f1c516e0 | <|skeleton|>
class VerifyIdCardSerializer:
"""身份认证序列化器"""
def validate(self, attrs):
"""OCR识别身份正反 验证阶段验证身份信息是否正确或是否已被验证"""
<|body_0|>
def update(self, instance, validated_data):
"""更新身份信息"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VerifyIdCardSerializer:
"""身份认证序列化器"""
def validate(self, attrs):
"""OCR识别身份正反 验证阶段验证身份信息是否正确或是否已被验证"""
if self.context.get('request').user.full_name != '':
raise serializers.ValidationError('身份已被认证过！')
identify_instance_face = ocr.apply_async(args=(attrs.get('face'), ... | the_stack_v2_python_sparse | user_app/serializers/individual_info_serializers.py | lmyfzx/Django-Mall | train | 0 |
bd55fc42feba492079bc822c9fd158e63c24c03d | [
"if db_field.name == 'groups':\n kwargs['queryset'] = Group.objects.exclude(Q(name__startswith='preprint_') | Q(name__startswith='node_') | Q(name__startswith='osfgroup_') | Q(name__startswith='collections_'))\nreturn super(OSFUserAdmin, self).formfield_for_manytomany(db_field, request, **kwargs)",
"groups_to_... | <|body_start_0|>
if db_field.name == 'groups':
kwargs['queryset'] = Group.objects.exclude(Q(name__startswith='preprint_') | Q(name__startswith='node_') | Q(name__startswith='osfgroup_') | Q(name__startswith='collections_'))
return super(OSFUserAdmin, self).formfield_for_manytomany(db_field, ... | OSFUserAdmin | [
"Apache-2.0",
"LGPL-2.0-or-later",
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown",
"MIT",
"AGPL-3.0-only",
"LicenseRef-scancode-unknown-license-reference",
"MPL-1.1",
"CPAL-1.0",
"LicenseRef-scancode-proprietary-license",
"LicenseRef-scancode-warranty-disclaimer",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class OSFUserAdmin:
def formfield_for_manytomany(self, db_field, request, **kwargs):
"""Restricts preprint/node/osfgroup django groups from showing up in the user's groups list in the admin app"""
<|body_0|>
def save_related(self, request, form, formsets, change):
"""Since... | stack_v2_sparse_classes_36k_train_025424 | 2,154 | permissive | [
{
"docstring": "Restricts preprint/node/osfgroup django groups from showing up in the user's groups list in the admin app",
"name": "formfield_for_manytomany",
"signature": "def formfield_for_manytomany(self, db_field, request, **kwargs)"
},
{
"docstring": "Since m2m fields overridden with new f... | 2 | stack_v2_sparse_classes_30k_train_003710 | Implement the Python class `OSFUserAdmin` described below.
Class description:
Implement the OSFUserAdmin class.
Method signatures and docstrings:
- def formfield_for_manytomany(self, db_field, request, **kwargs): Restricts preprint/node/osfgroup django groups from showing up in the user's groups list in the admin app... | Implement the Python class `OSFUserAdmin` described below.
Class description:
Implement the OSFUserAdmin class.
Method signatures and docstrings:
- def formfield_for_manytomany(self, db_field, request, **kwargs): Restricts preprint/node/osfgroup django groups from showing up in the user's groups list in the admin app... | 5d632eb6d4566d7d31cd8d6b40d1bc93c60ddf5e | <|skeleton|>
class OSFUserAdmin:
def formfield_for_manytomany(self, db_field, request, **kwargs):
"""Restricts preprint/node/osfgroup django groups from showing up in the user's groups list in the admin app"""
<|body_0|>
def save_related(self, request, form, formsets, change):
"""Since... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class OSFUserAdmin:
def formfield_for_manytomany(self, db_field, request, **kwargs):
"""Restricts preprint/node/osfgroup django groups from showing up in the user's groups list in the admin app"""
if db_field.name == 'groups':
kwargs['queryset'] = Group.objects.exclude(Q(name__startswith... | the_stack_v2_python_sparse | osf/admin.py | RCOSDP/RDM-osf.io | train | 12 | |
eebdc26acca3c256d2e89eda60bb90bbf6538125 | [
"super(AllCurImageFC, self).__init__()\nargs.wrap_model = False\nself.args = args\nself.dropout = nn.Dropout(p=args.dropout)\nself.r_y_fc = nn.Linear(args.hidden_dim * 4, 2)\nself.l_y_fc = nn.Linear(args.hidden_dim * 4, 2)\nself.y_fc = nn.Linear(args.hidden_dim * 4, 2)",
"x = self.dropout(x)\nB, N, H = x.size()\n... | <|body_start_0|>
super(AllCurImageFC, self).__init__()
args.wrap_model = False
self.args = args
self.dropout = nn.Dropout(p=args.dropout)
self.r_y_fc = nn.Linear(args.hidden_dim * 4, 2)
self.l_y_fc = nn.Linear(args.hidden_dim * 4, 2)
self.y_fc = nn.Linear(args.hid... | AllCurImageFC | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AllCurImageFC:
def __init__(self, args):
"""Given some a patch model, add add some FC layers and a shortcut to make whole image prediction"""
<|body_0|>
def forward(self, x):
"""param x: a batch of image tensors, in the order of: [Cu L CC, Cu L MLO, Cu R CC, Cu R MLO... | stack_v2_sparse_classes_36k_train_025425 | 5,214 | permissive | [
{
"docstring": "Given some a patch model, add add some FC layers and a shortcut to make whole image prediction",
"name": "__init__",
"signature": "def __init__(self, args)"
},
{
"docstring": "param x: a batch of image tensors, in the order of: [Cu L CC, Cu L MLO, Cu R CC, Cu R MLO Pr L CC, Pr L ... | 2 | stack_v2_sparse_classes_30k_train_012231 | Implement the Python class `AllCurImageFC` described below.
Class description:
Implement the AllCurImageFC class.
Method signatures and docstrings:
- def __init__(self, args): Given some a patch model, add add some FC layers and a shortcut to make whole image prediction
- def forward(self, x): param x: a batch of ima... | Implement the Python class `AllCurImageFC` described below.
Class description:
Implement the AllCurImageFC class.
Method signatures and docstrings:
- def __init__(self, args): Given some a patch model, add add some FC layers and a shortcut to make whole image prediction
- def forward(self, x): param x: a batch of ima... | 12bace8fd6ce9c5bb129fd0d30a46a00a2f7b054 | <|skeleton|>
class AllCurImageFC:
def __init__(self, args):
"""Given some a patch model, add add some FC layers and a shortcut to make whole image prediction"""
<|body_0|>
def forward(self, x):
"""param x: a batch of image tensors, in the order of: [Cu L CC, Cu L MLO, Cu R CC, Cu R MLO... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AllCurImageFC:
def __init__(self, args):
"""Given some a patch model, add add some FC layers and a shortcut to make whole image prediction"""
super(AllCurImageFC, self).__init__()
args.wrap_model = False
self.args = args
self.dropout = nn.Dropout(p=args.dropout)
... | the_stack_v2_python_sparse | onconet/models/aggregate_hiddens.py | yala/Mirai | train | 66 | |
ca54b3f61cd29e0ebf226207120f4a6b4384d6c6 | [
"self.temperature = temperature\nself.scale = scale\nself.normalize = normalize\nsuper(PositionalEncodingSine, self).__init__(input_channels, **kwargs)",
"if self.normalize:\n bin_scale = position_mat.max() - position_mat.min()\n if self.input_channels > 2:\n cords = position_mat[..., :2] / bin_scale... | <|body_start_0|>
self.temperature = temperature
self.scale = scale
self.normalize = normalize
super(PositionalEncodingSine, self).__init__(input_channels, **kwargs)
<|end_body_0|>
<|body_start_1|>
if self.normalize:
bin_scale = position_mat.max() - position_mat.min()... | PositionalEncodingSine | [
"Apache-2.0",
"BSD-2-Clause-Views",
"MIT",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PositionalEncodingSine:
def __init__(self, input_channels: int, temperature: float=1000, scale: float=1.0, normalize=False, **kwargs):
"""Generate sine positional embedding. See `BasePositionalEncoding` for details. Args: input_channels (int): the dimension of input. temperature (float, ... | stack_v2_sparse_classes_36k_train_025426 | 12,251 | permissive | [
{
"docstring": "Generate sine positional embedding. See `BasePositionalEncoding` for details. Args: input_channels (int): the dimension of input. temperature (float, optional): Defaults to 1000. scale (float, optional): Defaults to 1.0. normalize (bool, optional): Defaults to False.",
"name": "__init__",
... | 2 | null | Implement the Python class `PositionalEncodingSine` described below.
Class description:
Implement the PositionalEncodingSine class.
Method signatures and docstrings:
- def __init__(self, input_channels: int, temperature: float=1000, scale: float=1.0, normalize=False, **kwargs): Generate sine positional embedding. See... | Implement the Python class `PositionalEncodingSine` described below.
Class description:
Implement the PositionalEncodingSine class.
Method signatures and docstrings:
- def __init__(self, input_channels: int, temperature: float=1000, scale: float=1.0, normalize=False, **kwargs): Generate sine positional embedding. See... | 3652b18c7ce68122dae7a32670624727d50e0914 | <|skeleton|>
class PositionalEncodingSine:
def __init__(self, input_channels: int, temperature: float=1000, scale: float=1.0, normalize=False, **kwargs):
"""Generate sine positional embedding. See `BasePositionalEncoding` for details. Args: input_channels (int): the dimension of input. temperature (float, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PositionalEncodingSine:
def __init__(self, input_channels: int, temperature: float=1000, scale: float=1.0, normalize=False, **kwargs):
"""Generate sine positional embedding. See `BasePositionalEncoding` for details. Args: input_channels (int): the dimension of input. temperature (float, optional): Def... | the_stack_v2_python_sparse | mmdet/models/utils/bvr_transformer/positional_encoding.py | shinya7y/UniverseNet | train | 407 | |
e210760c63c85cafe55b8ac84dad80440a0a6780 | [
"super(VAEReconstructionLoss, self).__init__()\nself.reconstruction_function = torch.nn.MSELoss()\nself.model = model",
"recon_x = x_hat\ngeneration_loss = self.reconstruction_function(recon_x, x)\nif self.model.embedding_module is None:\n mu, logvar = self.model.get_posteriors(x)\n KLD = -0.5 * torch.sum(1... | <|body_start_0|>
super(VAEReconstructionLoss, self).__init__()
self.reconstruction_function = torch.nn.MSELoss()
self.model = model
<|end_body_0|>
<|body_start_1|>
recon_x = x_hat
generation_loss = self.reconstruction_function(recon_x, x)
if self.model.embedding_module i... | VAEReconstructionLoss | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VAEReconstructionLoss:
def __init__(self, model):
"""Calculate the sum of the Kullback–Leibler divergence loss and the loss of any given function Parameters ---------- model : dies.autoencoder model of the autoencoder for which the loss is to be calculated"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k_train_025427 | 4,402 | no_license | [
{
"docstring": "Calculate the sum of the Kullback–Leibler divergence loss and the loss of any given function Parameters ---------- model : dies.autoencoder model of the autoencoder for which the loss is to be calculated",
"name": "__init__",
"signature": "def __init__(self, model)"
},
{
"docstri... | 2 | stack_v2_sparse_classes_30k_train_012422 | Implement the Python class `VAEReconstructionLoss` described below.
Class description:
Implement the VAEReconstructionLoss class.
Method signatures and docstrings:
- def __init__(self, model): Calculate the sum of the Kullback–Leibler divergence loss and the loss of any given function Parameters ---------- model : di... | Implement the Python class `VAEReconstructionLoss` described below.
Class description:
Implement the VAEReconstructionLoss class.
Method signatures and docstrings:
- def __init__(self, model): Calculate the sum of the Kullback–Leibler divergence loss and the loss of any given function Parameters ---------- model : di... | b686e6b28a263722a642ddca21eeb71f7c810ad3 | <|skeleton|>
class VAEReconstructionLoss:
def __init__(self, model):
"""Calculate the sum of the Kullback–Leibler divergence loss and the loss of any given function Parameters ---------- model : dies.autoencoder model of the autoencoder for which the loss is to be calculated"""
<|body_0|>
def ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VAEReconstructionLoss:
def __init__(self, model):
"""Calculate the sum of the Kullback–Leibler divergence loss and the loss of any given function Parameters ---------- model : dies.autoencoder model of the autoencoder for which the loss is to be calculated"""
super(VAEReconstructionLoss, self)... | the_stack_v2_python_sparse | dies/dies/losses.py | scribbler00/task-TCN | train | 3 | |
e9bb0b2d574bc55c41115a01ecd67313b1cd8fc0 | [
"with open(self.filename) if not self.gzip else gzip.open(self.filename) as lease_file:\n lease_data = lease_file.read()\n if self.gzip:\n lease_data = lease_data.decode('utf-8')\n for match in self.regex_leaseblock.finditer(lease_data):\n block = match.groupdict()\n properties, option... | <|body_start_0|>
with open(self.filename) if not self.gzip else gzip.open(self.filename) as lease_file:
lease_data = lease_file.read()
if self.gzip:
lease_data = lease_data.decode('utf-8')
for match in self.regex_leaseblock.finditer(lease_data):
... | IscDhcpLeasesGen | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IscDhcpLeasesGen:
def get_iter(self, include_backups=False):
"""Parse the lease file and return a generator of Lease instances."""
<|body_0|>
def _get_current_iter(self):
"""Parse the lease file and return a dict of active and valid Lease instances. The key for this ... | stack_v2_sparse_classes_36k_train_025428 | 5,648 | no_license | [
{
"docstring": "Parse the lease file and return a generator of Lease instances.",
"name": "get_iter",
"signature": "def get_iter(self, include_backups=False)"
},
{
"docstring": "Parse the lease file and return a dict of active and valid Lease instances. The key for this dict is the ethernet addr... | 2 | null | Implement the Python class `IscDhcpLeasesGen` described below.
Class description:
Implement the IscDhcpLeasesGen class.
Method signatures and docstrings:
- def get_iter(self, include_backups=False): Parse the lease file and return a generator of Lease instances.
- def _get_current_iter(self): Parse the lease file and... | Implement the Python class `IscDhcpLeasesGen` described below.
Class description:
Implement the IscDhcpLeasesGen class.
Method signatures and docstrings:
- def get_iter(self, include_backups=False): Parse the lease file and return a generator of Lease instances.
- def _get_current_iter(self): Parse the lease file and... | 10379e974d969be94a40317e8121436f03f19ca2 | <|skeleton|>
class IscDhcpLeasesGen:
def get_iter(self, include_backups=False):
"""Parse the lease file and return a generator of Lease instances."""
<|body_0|>
def _get_current_iter(self):
"""Parse the lease file and return a dict of active and valid Lease instances. The key for this ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IscDhcpLeasesGen:
def get_iter(self, include_backups=False):
"""Parse the lease file and return a generator of Lease instances."""
with open(self.filename) if not self.gzip else gzip.open(self.filename) as lease_file:
lease_data = lease_file.read()
if self.gzip:
... | the_stack_v2_python_sparse | networks/management/commands/load_from_isc_dhcp.py | playmepe/djing2 | train | 0 | |
218a54e8059e1d513866433510c2d86d07dddc41 | [
"self.parameters = parameters\nself.storage = storage\nself._instance = None\nreturn",
"if self._instance is None:\n self._instance = Reporter(header=self.parameters.header, filename=self.parameters.filename, storage=self.storage, delimiter=self.parameters.delimiter, missing=self.parameters.missing)\nreturn se... | <|body_start_0|>
self.parameters = parameters
self.storage = storage
self._instance = None
return
<|end_body_0|>
<|body_start_1|>
if self._instance is None:
self._instance = Reporter(header=self.parameters.header, filename=self.parameters.filename, storage=self.stora... | A Report builder builds an instance of a Reporter | ReportBuilder | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ReportBuilder:
"""A Report builder builds an instance of a Reporter"""
def __init__(self, parameters, storage):
""":param: - `parameters`: parameters needed to build a reporter - `storage`: a file opener"""
<|body_0|>
def instance(self):
""":return: instance of R... | stack_v2_sparse_classes_36k_train_025429 | 990 | permissive | [
{
"docstring": ":param: - `parameters`: parameters needed to build a reporter - `storage`: a file opener",
"name": "__init__",
"signature": "def __init__(self, parameters, storage)"
},
{
"docstring": ":return: instance of Reporter",
"name": "instance",
"signature": "def instance(self)"
... | 2 | stack_v2_sparse_classes_30k_train_013851 | Implement the Python class `ReportBuilder` described below.
Class description:
A Report builder builds an instance of a Reporter
Method signatures and docstrings:
- def __init__(self, parameters, storage): :param: - `parameters`: parameters needed to build a reporter - `storage`: a file opener
- def instance(self): :... | Implement the Python class `ReportBuilder` described below.
Class description:
A Report builder builds an instance of a Reporter
Method signatures and docstrings:
- def __init__(self, parameters, storage): :param: - `parameters`: parameters needed to build a reporter - `storage`: a file opener
- def instance(self): :... | b4d1c77e1d611fe2b30768b42bdc7493afb0ea95 | <|skeleton|>
class ReportBuilder:
"""A Report builder builds an instance of a Reporter"""
def __init__(self, parameters, storage):
""":param: - `parameters`: parameters needed to build a reporter - `storage`: a file opener"""
<|body_0|>
def instance(self):
""":return: instance of R... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ReportBuilder:
"""A Report builder builds an instance of a Reporter"""
def __init__(self, parameters, storage):
""":param: - `parameters`: parameters needed to build a reporter - `storage`: a file opener"""
self.parameters = parameters
self.storage = storage
self._instance... | the_stack_v2_python_sparse | apetools/builders/subbuilders/reportbuilder.py | russell-n/oldape | train | 0 |
6eeb2d6be78771a9465d6d2e7b9be50ccaa4674f | [
"self.num_vulnerabilities = num_vulnerabilities\nself.num_intensity_bins = num_intensity_bins\nself.num_damage_bins = num_damage_bins\nself.vulnerability_sparseness = vulnerability_sparseness\nself.dtypes = OrderedDict([('vulnerability_id', 'i'), ('intensity_bin_index', 'i'), ('damage_bin_index', 'i'), ('prob', 'f'... | <|body_start_0|>
self.num_vulnerabilities = num_vulnerabilities
self.num_intensity_bins = num_intensity_bins
self.num_damage_bins = num_damage_bins
self.vulnerability_sparseness = vulnerability_sparseness
self.dtypes = OrderedDict([('vulnerability_id', 'i'), ('intensity_bin_index... | Generate random data for Vulnerability dummy model file. This file shows the conditional distributions of damage for each intensity bin and for each vulnerability ID. Attributes: generate_data: Generate Vulnerability dummy model file data. | VulnerabilityFile | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class VulnerabilityFile:
"""Generate random data for Vulnerability dummy model file. This file shows the conditional distributions of damage for each intensity bin and for each vulnerability ID. Attributes: generate_data: Generate Vulnerability dummy model file data."""
def __init__(self, num_vuln... | stack_v2_sparse_classes_36k_train_025430 | 39,722 | permissive | [
{
"docstring": "Initialise VulnerabilityFile class. Args: num_vulnerabilities (int): number of vulnerabilities. num_intensity_bins (int): number of intensity bins. num_damage_bins(int): number of damage bins. vulnerability_sparseness (float): percentage of bins normalised to range [0,1] impacted for a vulnerabi... | 2 | null | Implement the Python class `VulnerabilityFile` described below.
Class description:
Generate random data for Vulnerability dummy model file. This file shows the conditional distributions of damage for each intensity bin and for each vulnerability ID. Attributes: generate_data: Generate Vulnerability dummy model file da... | Implement the Python class `VulnerabilityFile` described below.
Class description:
Generate random data for Vulnerability dummy model file. This file shows the conditional distributions of damage for each intensity bin and for each vulnerability ID. Attributes: generate_data: Generate Vulnerability dummy model file da... | 23e704c335629ccd010969b1090446cfa3f384d5 | <|skeleton|>
class VulnerabilityFile:
"""Generate random data for Vulnerability dummy model file. This file shows the conditional distributions of damage for each intensity bin and for each vulnerability ID. Attributes: generate_data: Generate Vulnerability dummy model file data."""
def __init__(self, num_vuln... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class VulnerabilityFile:
"""Generate random data for Vulnerability dummy model file. This file shows the conditional distributions of damage for each intensity bin and for each vulnerability ID. Attributes: generate_data: Generate Vulnerability dummy model file data."""
def __init__(self, num_vulnerabilities, ... | the_stack_v2_python_sparse | oasislmf/computation/data/dummy_model/generate.py | OasisLMF/OasisLMF | train | 122 |
6c2ca81f10b03dab9a20586a6bca92323d023fb3 | [
"self._count = slave_count + 1\nself._coordinator: DataUpdateCoordinator[list[int] | None] | None = None\nself._result: list[int] = []\nsuper().__init__(hub, entry)",
"name = self._attr_name if self._attr_name else 'modbus_sensor'\nself._coordinator = DataUpdateCoordinator(hass, _LOGGER, name=name)\nslaves: list[... | <|body_start_0|>
self._count = slave_count + 1
self._coordinator: DataUpdateCoordinator[list[int] | None] | None = None
self._result: list[int] = []
super().__init__(hub, entry)
<|end_body_0|>
<|body_start_1|>
name = self._attr_name if self._attr_name else 'modbus_sensor'
... | Modbus binary sensor. | ModbusBinarySensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModbusBinarySensor:
"""Modbus binary sensor."""
def __init__(self, hub: ModbusHub, entry: dict[str, Any], slave_count: int) -> None:
"""Initialize the Modbus binary sensor."""
<|body_0|>
async def async_setup_slaves(self, hass: HomeAssistant, slave_count: int, entry: dic... | stack_v2_sparse_classes_36k_train_025431 | 5,764 | permissive | [
{
"docstring": "Initialize the Modbus binary sensor.",
"name": "__init__",
"signature": "def __init__(self, hub: ModbusHub, entry: dict[str, Any], slave_count: int) -> None"
},
{
"docstring": "Add slaves as needed (1 read for multiple sensors).",
"name": "async_setup_slaves",
"signature"... | 4 | stack_v2_sparse_classes_30k_train_011576 | Implement the Python class `ModbusBinarySensor` described below.
Class description:
Modbus binary sensor.
Method signatures and docstrings:
- def __init__(self, hub: ModbusHub, entry: dict[str, Any], slave_count: int) -> None: Initialize the Modbus binary sensor.
- async def async_setup_slaves(self, hass: HomeAssista... | Implement the Python class `ModbusBinarySensor` described below.
Class description:
Modbus binary sensor.
Method signatures and docstrings:
- def __init__(self, hub: ModbusHub, entry: dict[str, Any], slave_count: int) -> None: Initialize the Modbus binary sensor.
- async def async_setup_slaves(self, hass: HomeAssista... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class ModbusBinarySensor:
"""Modbus binary sensor."""
def __init__(self, hub: ModbusHub, entry: dict[str, Any], slave_count: int) -> None:
"""Initialize the Modbus binary sensor."""
<|body_0|>
async def async_setup_slaves(self, hass: HomeAssistant, slave_count: int, entry: dic... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModbusBinarySensor:
"""Modbus binary sensor."""
def __init__(self, hub: ModbusHub, entry: dict[str, Any], slave_count: int) -> None:
"""Initialize the Modbus binary sensor."""
self._count = slave_count + 1
self._coordinator: DataUpdateCoordinator[list[int] | None] | None = None
... | the_stack_v2_python_sparse | homeassistant/components/modbus/binary_sensor.py | home-assistant/core | train | 35,501 |
d72d07557f329c79b4d38f1ddf419c1c2e139b74 | [
"args = parser.parse_args()\ntry:\n if not all([args['platform_id'], args['vm_uuid']]):\n raise Exception('Parameter error')\n data = control.network_devices.get_network_by_vm_uuid(platform_id=args['platform_id'], vm_uuid=args['vm_uuid'])\nexcept Exception as e:\n return (set_return_val(False, [], s... | <|body_start_0|>
args = parser.parse_args()
try:
if not all([args['platform_id'], args['vm_uuid']]):
raise Exception('Parameter error')
data = control.network_devices.get_network_by_vm_uuid(platform_id=args['platform_id'], vm_uuid=args['vm_uuid'])
except E... | NetWorkManage | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NetWorkManage:
def get(self):
"""获取vCenter vm_network_device 信息 --- tags: - vCenter network device parameters: - in: query name: platform_id type: integer required: true - in: query name: vm_uuid type: string required: true responses: 200: description: vCenter network device 信息 schema: p... | stack_v2_sparse_classes_36k_train_025432 | 7,551 | no_license | [
{
"docstring": "获取vCenter vm_network_device 信息 --- tags: - vCenter network device parameters: - in: query name: platform_id type: integer required: true - in: query name: vm_uuid type: string required: true responses: 200: description: vCenter network device 信息 schema: properties: ok: type: boolean description:... | 3 | stack_v2_sparse_classes_30k_train_016832 | Implement the Python class `NetWorkManage` described below.
Class description:
Implement the NetWorkManage class.
Method signatures and docstrings:
- def get(self): 获取vCenter vm_network_device 信息 --- tags: - vCenter network device parameters: - in: query name: platform_id type: integer required: true - in: query name... | Implement the Python class `NetWorkManage` described below.
Class description:
Implement the NetWorkManage class.
Method signatures and docstrings:
- def get(self): 获取vCenter vm_network_device 信息 --- tags: - vCenter network device parameters: - in: query name: platform_id type: integer required: true - in: query name... | d25871dc66dfbd9f04e3d4d95843e39de286cfc8 | <|skeleton|>
class NetWorkManage:
def get(self):
"""获取vCenter vm_network_device 信息 --- tags: - vCenter network device parameters: - in: query name: platform_id type: integer required: true - in: query name: vm_uuid type: string required: true responses: 200: description: vCenter network device 信息 schema: p... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NetWorkManage:
def get(self):
"""获取vCenter vm_network_device 信息 --- tags: - vCenter network device parameters: - in: query name: platform_id type: integer required: true - in: query name: vm_uuid type: string required: true responses: 200: description: vCenter network device 信息 schema: properties: ok:... | the_stack_v2_python_sparse | app/main/vcenter/apis/network_devices.py | zcl-organization/naguan | train | 0 | |
a5eef6f68ed1112a9350ac19109fec232028a099 | [
"pos = 0\nfor i in range(len(nums)):\n if nums[i] != 0:\n nums[i], nums[pos] = (nums[pos], nums[i])\n pos += 1",
"def sorting(n):\n return (0,) if n != 0 else (1, 0)\nnums.sort(key=sorting)",
"count = 0\nfor i in range(len(nums) - 1):\n i = i - count\n if nums[i] == 0:\n count +... | <|body_start_0|>
pos = 0
for i in range(len(nums)):
if nums[i] != 0:
nums[i], nums[pos] = (nums[pos], nums[i])
pos += 1
<|end_body_0|>
<|body_start_1|>
def sorting(n):
return (0,) if n != 0 else (1, 0)
nums.sort(key=sorting)
<|end_... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def moveZeroes3(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def moveZeroes2(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead. time complexity : O(NlogN) space ... | stack_v2_sparse_classes_36k_train_025433 | 1,082 | no_license | [
{
"docstring": "Do not return anything, modify nums in-place instead.",
"name": "moveZeroes3",
"signature": "def moveZeroes3(self, nums: List[int]) -> None"
},
{
"docstring": "Do not return anything, modify nums in-place instead. time complexity : O(NlogN) space complexity : O(1)",
"name": "... | 3 | stack_v2_sparse_classes_30k_train_004368 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def moveZeroes3(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.
- def moveZeroes2(self, nums: List[int]) -> None: Do not return anything, mo... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def moveZeroes3(self, nums: List[int]) -> None: Do not return anything, modify nums in-place instead.
- def moveZeroes2(self, nums: List[int]) -> None: Do not return anything, mo... | 29cb49a166a1dfd19c39613a0e9895c545a6bfe9 | <|skeleton|>
class Solution:
def moveZeroes3(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
<|body_0|>
def moveZeroes2(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead. time complexity : O(NlogN) space ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def moveZeroes3(self, nums: List[int]) -> None:
"""Do not return anything, modify nums in-place instead."""
pos = 0
for i in range(len(nums)):
if nums[i] != 0:
nums[i], nums[pos] = (nums[pos], nums[i])
pos += 1
def moveZeroes2(... | the_stack_v2_python_sparse | 01.Array&String/2-09.moveZeroes.py | mjmingd/study_algorithm | train | 0 | |
ba4829a196275b6f3d94f407eb058c7f2138ae8e | [
"serializer = GroupSerializer2(data=request.data)\nif serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\nelse:\n return Response(serializer.errors)",
"serializer = GroupSerializer2(data=request.data)\ntemp = Group.objects.get(id=pk)\nif serializer.is_valid():\n temp.delete(... | <|body_start_0|>
serializer = GroupSerializer2(data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data)
else:
return Response(serializer.errors)
<|end_body_0|>
<|body_start_1|>
serializer = GroupSerializer2(data=... | GroupViewSet | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GroupViewSet:
def create(self, request):
"""Створення групи"""
<|body_0|>
def delete(self, request, pk):
"""Видалення групи за айді"""
<|body_1|>
def list(self, request):
"""Вивід списку груп"""
<|body_2|>
def add_student(self, reque... | stack_v2_sparse_classes_36k_train_025434 | 3,505 | no_license | [
{
"docstring": "Створення групи",
"name": "create",
"signature": "def create(self, request)"
},
{
"docstring": "Видалення групи за айді",
"name": "delete",
"signature": "def delete(self, request, pk)"
},
{
"docstring": "Вивід списку груп",
"name": "list",
"signature": "de... | 5 | stack_v2_sparse_classes_30k_train_012027 | Implement the Python class `GroupViewSet` described below.
Class description:
Implement the GroupViewSet class.
Method signatures and docstrings:
- def create(self, request): Створення групи
- def delete(self, request, pk): Видалення групи за айді
- def list(self, request): Вивід списку груп
- def add_student(self, r... | Implement the Python class `GroupViewSet` described below.
Class description:
Implement the GroupViewSet class.
Method signatures and docstrings:
- def create(self, request): Створення групи
- def delete(self, request, pk): Видалення групи за айді
- def list(self, request): Вивід списку груп
- def add_student(self, r... | c21c0df4974ff625f78cb967edb86ec18e2d062d | <|skeleton|>
class GroupViewSet:
def create(self, request):
"""Створення групи"""
<|body_0|>
def delete(self, request, pk):
"""Видалення групи за айді"""
<|body_1|>
def list(self, request):
"""Вивід списку груп"""
<|body_2|>
def add_student(self, reque... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GroupViewSet:
def create(self, request):
"""Створення групи"""
serializer = GroupSerializer2(data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data)
else:
return Response(serializer.errors)
def del... | the_stack_v2_python_sparse | OpenEduApi/Users/views.py | AkiroToshira/OpenEdu | train | 0 | |
7ada38ce3f9e547a2bbc91c707b9c16f68211b33 | [
"exception_handler.return_value = \"it's handled\"\nresult = custom_exception_handler(None, None)\nself.assertTrue(exception_handler.called)\nself.assertEqual(result, \"it's handled\")",
"exception_handler.return_value = None\nresult = custom_exception_handler(elasticsearch.exceptions.ConnectionError('oops'), Non... | <|body_start_0|>
exception_handler.return_value = "it's handled"
result = custom_exception_handler(None, None)
self.assertTrue(exception_handler.called)
self.assertEqual(result, "it's handled")
<|end_body_0|>
<|body_start_1|>
exception_handler.return_value = None
result ... | Tests for DRF custom exception handling. | CustomExceptionHandlerTests | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CustomExceptionHandlerTests:
"""Tests for DRF custom exception handling."""
def test_drf_handled_exception(self, exception_handler):
"""Test that we pass DRF recognized exceptions through unmodified"""
<|body_0|>
def test_502_error_exceptions(self, exception_handler):
... | stack_v2_sparse_classes_36k_train_025435 | 12,045 | permissive | [
{
"docstring": "Test that we pass DRF recognized exceptions through unmodified",
"name": "test_drf_handled_exception",
"signature": "def test_drf_handled_exception(self, exception_handler)"
},
{
"docstring": "Test ES connection exception is handled",
"name": "test_502_error_exceptions",
... | 4 | stack_v2_sparse_classes_30k_train_002461 | Implement the Python class `CustomExceptionHandlerTests` described below.
Class description:
Tests for DRF custom exception handling.
Method signatures and docstrings:
- def test_drf_handled_exception(self, exception_handler): Test that we pass DRF recognized exceptions through unmodified
- def test_502_error_excepti... | Implement the Python class `CustomExceptionHandlerTests` described below.
Class description:
Tests for DRF custom exception handling.
Method signatures and docstrings:
- def test_drf_handled_exception(self, exception_handler): Test that we pass DRF recognized exceptions through unmodified
- def test_502_error_excepti... | 73d334a9f0df7c044c06989977a9a22dd2ff9b7a | <|skeleton|>
class CustomExceptionHandlerTests:
"""Tests for DRF custom exception handling."""
def test_drf_handled_exception(self, exception_handler):
"""Test that we pass DRF recognized exceptions through unmodified"""
<|body_0|>
def test_502_error_exceptions(self, exception_handler):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CustomExceptionHandlerTests:
"""Tests for DRF custom exception handling."""
def test_drf_handled_exception(self, exception_handler):
"""Test that we pass DRF recognized exceptions through unmodified"""
exception_handler.return_value = "it's handled"
result = custom_exception_handl... | the_stack_v2_python_sparse | goldstone/drfes/tests.py | bhuvan-rk/goldstone-server | train | 0 |
7a958ef7288168c82419957d30710eb71434c811 | [
"excitation = self.GetExcitation()\nresponse = self._system_response\nvariance = sumpf.modules.SignalMean(excitation * excitation).GetMean()[0]\nkernels = []\nfor branch in self._select_branches:\n input = nlsp.nonlinear_function.Power(excitation, branch)\n cross_corr = sumpf.modules.CorrelateSignals(signal1=... | <|body_start_0|>
excitation = self.GetExcitation()
response = self._system_response
variance = sumpf.modules.SignalMean(excitation * excitation).GetMean()[0]
kernels = []
for branch in self._select_branches:
input = nlsp.nonlinear_function.Power(excitation, branch)
... | WienerGapproach | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WienerGapproach:
def _GetFilterImpuleResponses(self):
"""Get the identified filter impulse responses. :return: the filter impulse responses"""
<|body_0|>
def _GetNonlinerFunctions(self):
"""Get the nonlinear functions. :return: the nonlinear functions"""
<|bo... | stack_v2_sparse_classes_36k_train_025436 | 1,917 | no_license | [
{
"docstring": "Get the identified filter impulse responses. :return: the filter impulse responses",
"name": "_GetFilterImpuleResponses",
"signature": "def _GetFilterImpuleResponses(self)"
},
{
"docstring": "Get the nonlinear functions. :return: the nonlinear functions",
"name": "_GetNonline... | 2 | stack_v2_sparse_classes_30k_train_005728 | Implement the Python class `WienerGapproach` described below.
Class description:
Implement the WienerGapproach class.
Method signatures and docstrings:
- def _GetFilterImpuleResponses(self): Get the identified filter impulse responses. :return: the filter impulse responses
- def _GetNonlinerFunctions(self): Get the n... | Implement the Python class `WienerGapproach` described below.
Class description:
Implement the WienerGapproach class.
Method signatures and docstrings:
- def _GetFilterImpuleResponses(self): Get the identified filter impulse responses. :return: the filter impulse responses
- def _GetNonlinerFunctions(self): Get the n... | 41ba79cddeb8f76ffed1d3435d629e014f7d04c5 | <|skeleton|>
class WienerGapproach:
def _GetFilterImpuleResponses(self):
"""Get the identified filter impulse responses. :return: the filter impulse responses"""
<|body_0|>
def _GetNonlinerFunctions(self):
"""Get the nonlinear functions. :return: the nonlinear functions"""
<|bo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WienerGapproach:
def _GetFilterImpuleResponses(self):
"""Get the identified filter impulse responses. :return: the filter impulse responses"""
excitation = self.GetExcitation()
response = self._system_response
variance = sumpf.modules.SignalMean(excitation * excitation).GetMean... | the_stack_v2_python_sparse | model_generator/system_identification/wgn_identification/wiener_g_approach.py | zuowanbushiwo/systemidentifier | train | 0 | |
b53e4603b387644eeccbed8a999ac75b4d1f426b | [
"f = self.dtype_f(self.init)\nself.A.mult(u, f)\nfa2 = self.init.getVecArray(f)\nxa = self.init.getVecArray(u)\nfor i in range(self.xs, self.xe):\n fa2[i] += self.lambda0 ** 2 * xa[i] * (1 - xa[i] ** self.nu)\nfa2[0] = 0\nfa2[-1] = 0\nreturn f",
"me = self.dtype_u(u0)\ntarget = Fisher_full(self.init, self, fac... | <|body_start_0|>
f = self.dtype_f(self.init)
self.A.mult(u, f)
fa2 = self.init.getVecArray(f)
xa = self.init.getVecArray(u)
for i in range(self.xs, self.xe):
fa2[i] += self.lambda0 ** 2 * xa[i] * (1 - xa[i] ** self.nu)
fa2[0] = 0
fa2[-1] = 0
re... | Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc | petsc_fisher_fullyimplicit | [
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class petsc_fisher_fullyimplicit:
"""Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc"""
def eval_f(self, u, t):
"""Routine to evaluate the RHS Args: u (dtype_u): current values t (float): current time Returns: dtype_f: the ... | stack_v2_sparse_classes_36k_train_025437 | 16,584 | permissive | [
{
"docstring": "Routine to evaluate the RHS Args: u (dtype_u): current values t (float): current time Returns: dtype_f: the RHS",
"name": "eval_f",
"signature": "def eval_f(self, u, t)"
},
{
"docstring": "Nonlinear solver for (I-factor*F)(u) = rhs Args: rhs (dtype_f): right-hand side for the lin... | 2 | null | Implement the Python class `petsc_fisher_fullyimplicit` described below.
Class description:
Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc
Method signatures and docstrings:
- def eval_f(self, u, t): Routine to evaluate the RHS Args: u (dtype_u): curr... | Implement the Python class `petsc_fisher_fullyimplicit` described below.
Class description:
Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc
Method signatures and docstrings:
- def eval_f(self, u, t): Routine to evaluate the RHS Args: u (dtype_u): curr... | 1a51834bedffd4472e344bed28f4d766614b1537 | <|skeleton|>
class petsc_fisher_fullyimplicit:
"""Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc"""
def eval_f(self, u, t):
"""Routine to evaluate the RHS Args: u (dtype_u): current values t (float): current time Returns: dtype_f: the ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class petsc_fisher_fullyimplicit:
"""Problem class implementing the fully-implicit 2D Gray-Scott reaction-diffusion equation with periodic BC and PETSc"""
def eval_f(self, u, t):
"""Routine to evaluate the RHS Args: u (dtype_u): current values t (float): current time Returns: dtype_f: the RHS"""
... | the_stack_v2_python_sparse | pySDC/implementations/problem_classes/GeneralizedFisher_1D_PETSc.py | Parallel-in-Time/pySDC | train | 30 |
122dc14c3630ef281f91bcdf46677b4d920a59d1 | [
"authors = ','.join([x['name'] for x in doc.artists])\nauthor = re.sub('[\\\\\\\\/:*?\"<>|]', '', authors.strip())\nmp3_name = re.sub('[\\\\\\\\/:*?\"<>|]', '', doc['name'])\nname = os.path.join(author, '%s - %s.mp4' % (author, mp3_name))\nreturn name",
"try:\n target_r = get_target_r(doc, Config().get_mv_reso... | <|body_start_0|>
authors = ','.join([x['name'] for x in doc.artists])
author = re.sub('[\\\\/:*?"<>|]', '', authors.strip())
mp3_name = re.sub('[\\\\/:*?"<>|]', '', doc['name'])
name = os.path.join(author, '%s - %s.mp4' % (author, mp3_name))
return name
<|end_body_0|>
<|body_sta... | Video | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Video:
def download_filename_full(self, doc):
"""implement pls get a path to save file, by relative path need be complete by child :param doc: :return: :rtype: str"""
<|body_0|>
def url_load(self, doc):
"""implement pls :param doc: :return: :rtype: str"""
<|b... | stack_v2_sparse_classes_36k_train_025438 | 1,545 | permissive | [
{
"docstring": "implement pls get a path to save file, by relative path need be complete by child :param doc: :return: :rtype: str",
"name": "download_filename_full",
"signature": "def download_filename_full(self, doc)"
},
{
"docstring": "implement pls :param doc: :return: :rtype: str",
"nam... | 2 | stack_v2_sparse_classes_30k_train_017154 | Implement the Python class `Video` described below.
Class description:
Implement the Video class.
Method signatures and docstrings:
- def download_filename_full(self, doc): implement pls get a path to save file, by relative path need be complete by child :param doc: :return: :rtype: str
- def url_load(self, doc): imp... | Implement the Python class `Video` described below.
Class description:
Implement the Video class.
Method signatures and docstrings:
- def download_filename_full(self, doc): implement pls get a path to save file, by relative path need be complete by child :param doc: :return: :rtype: str
- def url_load(self, doc): imp... | 68e588c0612d0ab2af3a820ff88ca24d698ceeb7 | <|skeleton|>
class Video:
def download_filename_full(self, doc):
"""implement pls get a path to save file, by relative path need be complete by child :param doc: :return: :rtype: str"""
<|body_0|>
def url_load(self, doc):
"""implement pls :param doc: :return: :rtype: str"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Video:
def download_filename_full(self, doc):
"""implement pls get a path to save file, by relative path need be complete by child :param doc: :return: :rtype: str"""
authors = ','.join([x['name'] for x in doc.artists])
author = re.sub('[\\\\/:*?"<>|]', '', authors.strip())
mp3... | the_stack_v2_python_sparse | NXSpider/spider/video.py | Z-Shuming/NXSpider | train | 0 | |
f5ec543e4fa6c611bc868851ef090ff4ccf747b2 | [
"super(ErrorCalculator, self).__init__()\nself.char_list = char_list\nself.space = sym_space\nself.pad = sym_pad\nself.report_bleu = report_bleu\nself.idx_blank = self.char_list.index(self.pad)\nif self.space in self.char_list:\n self.idx_space = self.char_list.index(self.space)\nelse:\n self.idx_space = None... | <|body_start_0|>
super(ErrorCalculator, self).__init__()
self.char_list = char_list
self.space = sym_space
self.pad = sym_pad
self.report_bleu = report_bleu
self.idx_blank = self.char_list.index(self.pad)
if self.space in self.char_list:
self.idx_space... | Calculate BLEU for ST and MT models during training. :param y_hats: numpy array with predicted text :param y_pads: numpy array with true (target) text :param char_list: vocabulary list :param sym_space: space symbol :param sym_pad: pad symbol :param report_bleu: report BLUE score if True | ErrorCalculator | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ErrorCalculator:
"""Calculate BLEU for ST and MT models during training. :param y_hats: numpy array with predicted text :param y_pads: numpy array with true (target) text :param char_list: vocabulary list :param sym_space: space symbol :param sym_pad: pad symbol :param report_bleu: report BLUE sc... | stack_v2_sparse_classes_36k_train_025439 | 4,216 | permissive | [
{
"docstring": "Construct an ErrorCalculator object.",
"name": "__init__",
"signature": "def __init__(self, char_list, sym_space, sym_pad, report_bleu=False)"
},
{
"docstring": "Calculate corpus-level BLEU score. :param torch.Tensor ys_hat: prediction (batch, seqlen) :param torch.Tensor ys_pad: ... | 4 | null | Implement the Python class `ErrorCalculator` described below.
Class description:
Calculate BLEU for ST and MT models during training. :param y_hats: numpy array with predicted text :param y_pads: numpy array with true (target) text :param char_list: vocabulary list :param sym_space: space symbol :param sym_pad: pad sy... | Implement the Python class `ErrorCalculator` described below.
Class description:
Calculate BLEU for ST and MT models during training. :param y_hats: numpy array with predicted text :param y_pads: numpy array with true (target) text :param char_list: vocabulary list :param sym_space: space symbol :param sym_pad: pad sy... | bcd20948db7846ee523443ef9fd78c7a1248c95e | <|skeleton|>
class ErrorCalculator:
"""Calculate BLEU for ST and MT models during training. :param y_hats: numpy array with predicted text :param y_pads: numpy array with true (target) text :param char_list: vocabulary list :param sym_space: space symbol :param sym_pad: pad symbol :param report_bleu: report BLUE sc... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ErrorCalculator:
"""Calculate BLEU for ST and MT models during training. :param y_hats: numpy array with predicted text :param y_pads: numpy array with true (target) text :param char_list: vocabulary list :param sym_space: space symbol :param sym_pad: pad symbol :param report_bleu: report BLUE score if True""... | the_stack_v2_python_sparse | espnet/nets/e2e_mt_common.py | espnet/espnet | train | 7,242 |
253a2a357091b0ac92a0980ab37e31c8af5e4a23 | [
"self.epochs = epochs\nself.batch_size = batch_size\nself.hidden_neurons = hidden_neurons\nself.output_neurons = output_neurons",
"input_dims = 1\nmodel = Sequential()\nmodel.add(Dense(self.hidden_neurons, activation='relu', input_shape=(input_dims,)))\nmodel.add(Dense(self.output_neurons, activation='linear'))\n... | <|body_start_0|>
self.epochs = epochs
self.batch_size = batch_size
self.hidden_neurons = hidden_neurons
self.output_neurons = output_neurons
<|end_body_0|>
<|body_start_1|>
input_dims = 1
model = Sequential()
model.add(Dense(self.hidden_neurons, activation='relu'... | ANN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ANN:
def __init__(self, epochs, batch_size, hidden_neurons, output_neurons):
"""Initialize NN settings"""
<|body_0|>
def solve(self, train_X, train_Y, test_X, test_Y):
"""Initialize NN, train and test"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_025440 | 1,636 | no_license | [
{
"docstring": "Initialize NN settings",
"name": "__init__",
"signature": "def __init__(self, epochs, batch_size, hidden_neurons, output_neurons)"
},
{
"docstring": "Initialize NN, train and test",
"name": "solve",
"signature": "def solve(self, train_X, train_Y, test_X, test_Y)"
}
] | 2 | stack_v2_sparse_classes_30k_val_000413 | Implement the Python class `ANN` described below.
Class description:
Implement the ANN class.
Method signatures and docstrings:
- def __init__(self, epochs, batch_size, hidden_neurons, output_neurons): Initialize NN settings
- def solve(self, train_X, train_Y, test_X, test_Y): Initialize NN, train and test | Implement the Python class `ANN` described below.
Class description:
Implement the ANN class.
Method signatures and docstrings:
- def __init__(self, epochs, batch_size, hidden_neurons, output_neurons): Initialize NN settings
- def solve(self, train_X, train_Y, test_X, test_Y): Initialize NN, train and test
<|skeleto... | 4a9e1166faa8af8b499cb90adbbaddcc93ad88e2 | <|skeleton|>
class ANN:
def __init__(self, epochs, batch_size, hidden_neurons, output_neurons):
"""Initialize NN settings"""
<|body_0|>
def solve(self, train_X, train_Y, test_X, test_Y):
"""Initialize NN, train and test"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ANN:
def __init__(self, epochs, batch_size, hidden_neurons, output_neurons):
"""Initialize NN settings"""
self.epochs = epochs
self.batch_size = batch_size
self.hidden_neurons = hidden_neurons
self.output_neurons = output_neurons
def solve(self, train_X, train_Y, t... | the_stack_v2_python_sparse | Tutorial 2/ann.py | clara2911/ANNProject | train | 1 | |
01d38d164a9b8314bf8d1090e64174179ddac863 | [
"if not strs:\n return None\nif strs == ['']:\n return ''\nx = chr(258).join(strs)\nreturn x",
"if s is None:\n return None\nif s == '':\n return ['']\nres = s.split(chr(258))\nreturn res"
] | <|body_start_0|>
if not strs:
return None
if strs == ['']:
return ''
x = chr(258).join(strs)
return x
<|end_body_0|>
<|body_start_1|>
if s is None:
return None
if s == '':
return ['']
res = s.split(chr(258))
... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def encode(self, strs: [str]) -> str:
"""Encodes a list of strings to a single string."""
<|body_0|>
def decode(self, s: str) -> [str]:
"""Decodes a single string to a list of strings."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not st... | stack_v2_sparse_classes_36k_train_025441 | 515 | no_license | [
{
"docstring": "Encodes a list of strings to a single string.",
"name": "encode",
"signature": "def encode(self, strs: [str]) -> str"
},
{
"docstring": "Decodes a single string to a list of strings.",
"name": "decode",
"signature": "def decode(self, s: str) -> [str]"
}
] | 2 | stack_v2_sparse_classes_30k_train_005522 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def encode(self, strs: [str]) -> str: Encodes a list of strings to a single string.
- def decode(self, s: str) -> [str]: Decodes a single string to a list of strings. | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def encode(self, strs: [str]) -> str: Encodes a list of strings to a single string.
- def decode(self, s: str) -> [str]: Decodes a single string to a list of strings.
<|skeleton|>
cla... | fb5a930b5ad27e7ed405e5787346327d9b3bf957 | <|skeleton|>
class Codec:
def encode(self, strs: [str]) -> str:
"""Encodes a list of strings to a single string."""
<|body_0|>
def decode(self, s: str) -> [str]:
"""Decodes a single string to a list of strings."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def encode(self, strs: [str]) -> str:
"""Encodes a list of strings to a single string."""
if not strs:
return None
if strs == ['']:
return ''
x = chr(258).join(strs)
return x
def decode(self, s: str) -> [str]:
"""Decodes a sin... | the_stack_v2_python_sparse | Jiuzhang_practice/encode_decode_str.py | armstrong019/coding_n_project | train | 0 | |
70895d393258b47f3d8da573e86c45916b21fa4c | [
"self.age_adjustment_functions = {}\naffected_age_indices = [i for i in AGE_INDICES if f'age_{i}' in mixing_age_adjust]\nfor age_idx in affected_age_indices:\n key = f'age_{age_idx}'\n mixing_age_adjust[key]['times'] = [(time_date - BASE_DATE).days for time_date in mixing_age_adjust[key]['times']]\n age_ti... | <|body_start_0|>
self.age_adjustment_functions = {}
affected_age_indices = [i for i in AGE_INDICES if f'age_{i}' in mixing_age_adjust]
for age_idx in affected_age_indices:
key = f'age_{age_idx}'
mixing_age_adjust[key]['times'] = [(time_date - BASE_DATE).days for time_date... | AgeMixingAdjustment | [
"BSD-2-Clause-Views"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AgeMixingAdjustment:
def __init__(self, mixing_age_adjust: dict):
"""Build the time variant age adjustment functions"""
<|body_0|>
def get_adjustment(self, time: float, mixing_matrix: np.ndarray) -> np.ndarray:
"""Apply time-varying age adjustments. Returns a new mix... | stack_v2_sparse_classes_36k_train_025442 | 1,827 | permissive | [
{
"docstring": "Build the time variant age adjustment functions",
"name": "__init__",
"signature": "def __init__(self, mixing_age_adjust: dict)"
},
{
"docstring": "Apply time-varying age adjustments. Returns a new mixing matrix, modified to adjust for dynamic mixing changes for a given point in ... | 2 | null | Implement the Python class `AgeMixingAdjustment` described below.
Class description:
Implement the AgeMixingAdjustment class.
Method signatures and docstrings:
- def __init__(self, mixing_age_adjust: dict): Build the time variant age adjustment functions
- def get_adjustment(self, time: float, mixing_matrix: np.ndarr... | Implement the Python class `AgeMixingAdjustment` described below.
Class description:
Implement the AgeMixingAdjustment class.
Method signatures and docstrings:
- def __init__(self, mixing_age_adjust: dict): Build the time variant age adjustment functions
- def get_adjustment(self, time: float, mixing_matrix: np.ndarr... | 0cbd006d1f15da414d02eed44e48bb5c06f0802e | <|skeleton|>
class AgeMixingAdjustment:
def __init__(self, mixing_age_adjust: dict):
"""Build the time variant age adjustment functions"""
<|body_0|>
def get_adjustment(self, time: float, mixing_matrix: np.ndarray) -> np.ndarray:
"""Apply time-varying age adjustments. Returns a new mix... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AgeMixingAdjustment:
def __init__(self, mixing_age_adjust: dict):
"""Build the time variant age adjustment functions"""
self.age_adjustment_functions = {}
affected_age_indices = [i for i in AGE_INDICES if f'age_{i}' in mixing_age_adjust]
for age_idx in affected_age_indices:
... | the_stack_v2_python_sparse | apps/covid_19/preprocess/mixing_matrix/adjust_age.py | malanchak/AuTuMN | train | 0 | |
7404a7b58c06514cc5aa293cb0e627371d43bff4 | [
"self._image = tfx_image\nself._k8s_core_api = kube_utils.make_core_v1_api()\nself._namespace = name_space\nself._container_name = container_name\nself._job_name = kube_utils.sanitize_pod_name(job_prefix + _generate_component_name_suffix())\nself.ttl_seconds = 5\nself._pod_name = None\nself._stream_pod_logs = strea... | <|body_start_0|>
self._image = tfx_image
self._k8s_core_api = kube_utils.make_core_v1_api()
self._namespace = name_space
self._container_name = container_name
self._job_name = kube_utils.sanitize_pod_name(job_prefix + _generate_component_name_suffix())
self.ttl_seconds = ... | A Kubernetes job runner that launches and executes pipeline components in kubernetes cluster. | KubernetesJobRunner | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class KubernetesJobRunner:
"""A Kubernetes job runner that launches and executes pipeline components in kubernetes cluster."""
def __init__(self, tfx_image, job_prefix, container_name, name_space='default', stream_logs=False):
"""Create a kubernetes model server runner. Args: tfx_image: co... | stack_v2_sparse_classes_36k_train_025443 | 7,976 | permissive | [
{
"docstring": "Create a kubernetes model server runner. Args: tfx_image: container image for tfx. job_prefix: prefix for the job. Unique hash will follow as suffix. container_name: name of the container. name_space: namespace of the run. stream_logs: whether to stream logs from the pod.",
"name": "__init__... | 6 | stack_v2_sparse_classes_30k_train_019902 | Implement the Python class `KubernetesJobRunner` described below.
Class description:
A Kubernetes job runner that launches and executes pipeline components in kubernetes cluster.
Method signatures and docstrings:
- def __init__(self, tfx_image, job_prefix, container_name, name_space='default', stream_logs=False): Cre... | Implement the Python class `KubernetesJobRunner` described below.
Class description:
A Kubernetes job runner that launches and executes pipeline components in kubernetes cluster.
Method signatures and docstrings:
- def __init__(self, tfx_image, job_prefix, container_name, name_space='default', stream_logs=False): Cre... | 1b328504fa08a70388691e4072df76f143631325 | <|skeleton|>
class KubernetesJobRunner:
"""A Kubernetes job runner that launches and executes pipeline components in kubernetes cluster."""
def __init__(self, tfx_image, job_prefix, container_name, name_space='default', stream_logs=False):
"""Create a kubernetes model server runner. Args: tfx_image: co... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class KubernetesJobRunner:
"""A Kubernetes job runner that launches and executes pipeline components in kubernetes cluster."""
def __init__(self, tfx_image, job_prefix, container_name, name_space='default', stream_logs=False):
"""Create a kubernetes model server runner. Args: tfx_image: container image... | the_stack_v2_python_sparse | tfx/orchestration/experimental/centralized_kubernetes_orchestrator/kubernetes_job_runner.py | tensorflow/tfx | train | 2,116 |
09ceeff88db61da4ecf6a84878bedc5302bdf39a | [
"if self.request.version == 'v6':\n return IngestStatusSerializerV6\nelif self.request.version == 'v7':\n return IngestStatusSerializerV6",
"if request.version == 'v6' or request.version == 'v7':\n return self.list_impl(request)\nraise Http404()",
"started = rest_util.parse_timestamp(request, 'started'... | <|body_start_0|>
if self.request.version == 'v6':
return IngestStatusSerializerV6
elif self.request.version == 'v7':
return IngestStatusSerializerV6
<|end_body_0|>
<|body_start_1|>
if request.version == 'v6' or request.version == 'v7':
return self.list_impl(r... | This view is the endpoint for retrieving summarized ingest status. | IngestsStatusView | [
"LicenseRef-scancode-free-unknown",
"Apache-2.0",
"LicenseRef-scancode-public-domain"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IngestsStatusView:
"""This view is the endpoint for retrieving summarized ingest status."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
<|body_0|>
def list(self, request):
"""Determine ap... | stack_v2_sparse_classes_36k_train_025444 | 30,689 | permissive | [
{
"docstring": "Returns the appropriate serializer based off the requests version of the REST API",
"name": "get_serializer_class",
"signature": "def get_serializer_class(self)"
},
{
"docstring": "Determine api version and call specific method :param request: the HTTP POST request :type request:... | 3 | stack_v2_sparse_classes_30k_train_001387 | Implement the Python class `IngestsStatusView` described below.
Class description:
This view is the endpoint for retrieving summarized ingest status.
Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def list(self, r... | Implement the Python class `IngestsStatusView` described below.
Class description:
This view is the endpoint for retrieving summarized ingest status.
Method signatures and docstrings:
- def get_serializer_class(self): Returns the appropriate serializer based off the requests version of the REST API
- def list(self, r... | 28618aee07ceed9e4a6eb7b8d0e6f05b31d8fd6b | <|skeleton|>
class IngestsStatusView:
"""This view is the endpoint for retrieving summarized ingest status."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
<|body_0|>
def list(self, request):
"""Determine ap... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IngestsStatusView:
"""This view is the endpoint for retrieving summarized ingest status."""
def get_serializer_class(self):
"""Returns the appropriate serializer based off the requests version of the REST API"""
if self.request.version == 'v6':
return IngestStatusSerializerV6
... | the_stack_v2_python_sparse | scale/ingest/views.py | kfconsultant/scale | train | 0 |
c3fad39e744ec77c591ae0667e9f9e319af025dd | [
"category_links = response.xpath('//a[@class=\"level-top \"]/@href').extract()\nfor link in category_links:\n yield scrapy.Request(link, self.parse_main)",
"for i in response.xpath('//li[@class=\"item product product-item\"]'):\n cost = i.xpath('.//span[@class=\"price\"]/text()').get().replace('$', '')\n ... | <|body_start_0|>
category_links = response.xpath('//a[@class="level-top "]/@href').extract()
for link in category_links:
yield scrapy.Request(link, self.parse_main)
<|end_body_0|>
<|body_start_1|>
for i in response.xpath('//li[@class="item product product-item"]'):
cost ... | JmlSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class JmlSpider:
def parse(self, response, **kwargs):
"""Grab category pages. @url https://www.jmlhomefashion.com/ @returns items 0 @returns requests 6 @request https://www.jmlhomefashion.com/bedroom.html"""
<|body_0|>
def parse_main(self, response):
"""extract some item's... | stack_v2_sparse_classes_36k_train_025445 | 7,251 | no_license | [
{
"docstring": "Grab category pages. @url https://www.jmlhomefashion.com/ @returns items 0 @returns requests 6 @request https://www.jmlhomefashion.com/bedroom.html",
"name": "parse",
"signature": "def parse(self, response, **kwargs)"
},
{
"docstring": "extract some item's data and loop item urls... | 3 | null | Implement the Python class `JmlSpider` described below.
Class description:
Implement the JmlSpider class.
Method signatures and docstrings:
- def parse(self, response, **kwargs): Grab category pages. @url https://www.jmlhomefashion.com/ @returns items 0 @returns requests 6 @request https://www.jmlhomefashion.com/bedr... | Implement the Python class `JmlSpider` described below.
Class description:
Implement the JmlSpider class.
Method signatures and docstrings:
- def parse(self, response, **kwargs): Grab category pages. @url https://www.jmlhomefashion.com/ @returns items 0 @returns requests 6 @request https://www.jmlhomefashion.com/bedr... | 025babe4a03553d720806828f89929c6e773d683 | <|skeleton|>
class JmlSpider:
def parse(self, response, **kwargs):
"""Grab category pages. @url https://www.jmlhomefashion.com/ @returns items 0 @returns requests 6 @request https://www.jmlhomefashion.com/bedroom.html"""
<|body_0|>
def parse_main(self, response):
"""extract some item's... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class JmlSpider:
def parse(self, response, **kwargs):
"""Grab category pages. @url https://www.jmlhomefashion.com/ @returns items 0 @returns requests 6 @request https://www.jmlhomefashion.com/bedroom.html"""
category_links = response.xpath('//a[@class="level-top "]/@href').extract()
for link... | the_stack_v2_python_sparse | data_scraping/gmd/spiders/jml.py | panky2202/scrapy-dev | train | 1 | |
cec325bb82d1f6ded6943c8ad925184f6e410239 | [
"cursor = self.view.sel()\nself.original_position = cursor[0]\ncursor.clear()\ncursor.add(region)\nself.view.show(region)",
"last_edits = self.view.settings().get(LAST_EDITS_SETTING, {})\nlast_edit = last_edits.get(str(self.view.id()), None)\ncurrent_position = self.view.sel()[0]\nif last_edit is None:\n retur... | <|body_start_0|>
cursor = self.view.sel()
self.original_position = cursor[0]
cursor.clear()
cursor.add(region)
self.view.show(region)
<|end_body_0|>
<|body_start_1|>
last_edits = self.view.settings().get(LAST_EDITS_SETTING, {})
last_edit = last_edits.get(str(self... | GotoLastEdit | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GotoLastEdit:
def move_cursor_to_region(self, region):
"""Clear the cursor's position and move it to `region`."""
<|body_0|>
def run(self, edit):
"""If there was a last edit recorded for the view, store the current position as self.original_position and move the curs... | stack_v2_sparse_classes_36k_train_025446 | 1,978 | no_license | [
{
"docstring": "Clear the cursor's position and move it to `region`.",
"name": "move_cursor_to_region",
"signature": "def move_cursor_to_region(self, region)"
},
{
"docstring": "If there was a last edit recorded for the view, store the current position as self.original_position and move the curs... | 2 | null | Implement the Python class `GotoLastEdit` described below.
Class description:
Implement the GotoLastEdit class.
Method signatures and docstrings:
- def move_cursor_to_region(self, region): Clear the cursor's position and move it to `region`.
- def run(self, edit): If there was a last edit recorded for the view, store... | Implement the Python class `GotoLastEdit` described below.
Class description:
Implement the GotoLastEdit class.
Method signatures and docstrings:
- def move_cursor_to_region(self, region): Clear the cursor's position and move it to `region`.
- def run(self, edit): If there was a last edit recorded for the view, store... | 8390a0139137574ab237b3ff5fe8ea61e8a0b76b | <|skeleton|>
class GotoLastEdit:
def move_cursor_to_region(self, region):
"""Clear the cursor's position and move it to `region`."""
<|body_0|>
def run(self, edit):
"""If there was a last edit recorded for the view, store the current position as self.original_position and move the curs... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GotoLastEdit:
def move_cursor_to_region(self, region):
"""Clear the cursor's position and move it to `region`."""
cursor = self.view.sel()
self.original_position = cursor[0]
cursor.clear()
cursor.add(region)
self.view.show(region)
def run(self, edit):
... | the_stack_v2_python_sparse | data/input/abrookins/GotoLastEdit/goto_last_edit.py | bopopescu/pythonanalyzer | train | 0 | |
22967d5ae9a84b89ccf0c31c0200765a1c308a72 | [
"if null_default_value is None:\n null_default_value = math.log2(min_included)\nelse:\n null_default_value = math.log2(null_default_value)\nself.min_included: float = min_included\nself.max_included: float = max_included\nself.log2_min_included = math.log2(min_included)\nself.log2_max_included = math.log2(max... | <|body_start_0|>
if null_default_value is None:
null_default_value = math.log2(min_included)
else:
null_default_value = math.log2(null_default_value)
self.min_included: float = min_included
self.max_included: float = max_included
self.log2_min_included = m... | Get a LogUniform distribution. Refer to: :class:`scipy.stats.loguniform`. .. seealso:: :func:`~neuraxle.base.BaseStep.set_hyperparams_space`, :class:`ScipyDistributionWrapper`, | ScipyLogUniform | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ScipyLogUniform:
"""Get a LogUniform distribution. Refer to: :class:`scipy.stats.loguniform`. .. seealso:: :func:`~neuraxle.base.BaseStep.set_hyperparams_space`, :class:`ScipyDistributionWrapper`,"""
def __init__(self, min_included: float, max_included: float, null_default_value=None):
... | stack_v2_sparse_classes_36k_train_025447 | 19,816 | permissive | [
{
"docstring": "Create a quantized random log uniform distribution. A random float between the two values inclusively will be returned. :param min_included: minimum integer, should be somehow included. :param max_included: maximum integer, should be somehow included. :param null_default_value: null default valu... | 2 | stack_v2_sparse_classes_30k_train_017305 | Implement the Python class `ScipyLogUniform` described below.
Class description:
Get a LogUniform distribution. Refer to: :class:`scipy.stats.loguniform`. .. seealso:: :func:`~neuraxle.base.BaseStep.set_hyperparams_space`, :class:`ScipyDistributionWrapper`,
Method signatures and docstrings:
- def __init__(self, min_i... | Implement the Python class `ScipyLogUniform` described below.
Class description:
Get a LogUniform distribution. Refer to: :class:`scipy.stats.loguniform`. .. seealso:: :func:`~neuraxle.base.BaseStep.set_hyperparams_space`, :class:`ScipyDistributionWrapper`,
Method signatures and docstrings:
- def __init__(self, min_i... | af917c984241178436a759be3b830e6d8b03245f | <|skeleton|>
class ScipyLogUniform:
"""Get a LogUniform distribution. Refer to: :class:`scipy.stats.loguniform`. .. seealso:: :func:`~neuraxle.base.BaseStep.set_hyperparams_space`, :class:`ScipyDistributionWrapper`,"""
def __init__(self, min_included: float, max_included: float, null_default_value=None):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ScipyLogUniform:
"""Get a LogUniform distribution. Refer to: :class:`scipy.stats.loguniform`. .. seealso:: :func:`~neuraxle.base.BaseStep.set_hyperparams_space`, :class:`ScipyDistributionWrapper`,"""
def __init__(self, min_included: float, max_included: float, null_default_value=None):
"""Create ... | the_stack_v2_python_sparse | neuraxle/hyperparams/scipy_distributions.py | Neuraxio/Neuraxle | train | 597 |
dfb2574187cf7489c0bfddfce99d425708130eef | [
"if preprocessor_id == 'mangoml_WordCountTransformer':\n return WordCountTransformer(**preprocessor_params)\nelif preprocessor_id == 'mangoml_StopwordCountTransformer':\n return StopwordCountTransformer(**preprocessor_params)\nelif preprocessor_id == 'mangoml_MeanWordLengthTransformer':\n return MeanWordLe... | <|body_start_0|>
if preprocessor_id == 'mangoml_WordCountTransformer':
return WordCountTransformer(**preprocessor_params)
elif preprocessor_id == 'mangoml_StopwordCountTransformer':
return StopwordCountTransformer(**preprocessor_params)
elif preprocessor_id == 'mangoml_Me... | Class used to build preprocessors | PreprocessorFactory | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PreprocessorFactory:
"""Class used to build preprocessors"""
def build_mangoml(self, preprocessor_id, preprocessor_params):
"""Method that builds preprocessors/wrappers implemented by mango"""
<|body_0|>
def build_sklearn(self, preprocessor_id, preprocessor_params):
... | stack_v2_sparse_classes_36k_train_025448 | 3,247 | permissive | [
{
"docstring": "Method that builds preprocessors/wrappers implemented by mango",
"name": "build_mangoml",
"signature": "def build_mangoml(self, preprocessor_id, preprocessor_params)"
},
{
"docstring": "Method that builds preprocessors implemented in sklearn",
"name": "build_sklearn",
"si... | 3 | stack_v2_sparse_classes_30k_train_021665 | Implement the Python class `PreprocessorFactory` described below.
Class description:
Class used to build preprocessors
Method signatures and docstrings:
- def build_mangoml(self, preprocessor_id, preprocessor_params): Method that builds preprocessors/wrappers implemented by mango
- def build_sklearn(self, preprocesso... | Implement the Python class `PreprocessorFactory` described below.
Class description:
Class used to build preprocessors
Method signatures and docstrings:
- def build_mangoml(self, preprocessor_id, preprocessor_params): Method that builds preprocessors/wrappers implemented by mango
- def build_sklearn(self, preprocesso... | 36764761418f13253359ead379c7af467a578ba7 | <|skeleton|>
class PreprocessorFactory:
"""Class used to build preprocessors"""
def build_mangoml(self, preprocessor_id, preprocessor_params):
"""Method that builds preprocessors/wrappers implemented by mango"""
<|body_0|>
def build_sklearn(self, preprocessor_id, preprocessor_params):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PreprocessorFactory:
"""Class used to build preprocessors"""
def build_mangoml(self, preprocessor_id, preprocessor_params):
"""Method that builds preprocessors/wrappers implemented by mango"""
if preprocessor_id == 'mangoml_WordCountTransformer':
return WordCountTransformer(**... | the_stack_v2_python_sparse | mangoml/mangoml/model/preprocessors/preprocessor_factory.py | gustavosantos/mango | train | 0 |
c3530a98dd01e194c42c74c839482aad8ef88ca0 | [
"true_data = ('Q4JUZ8', 'O27018', 'B7GCS7', 'B5FMN5', 'A9PQS5', 'C8A7E2', 'C5R887', 'C6QM83', 'A8XHC3', 'D1IMX6', 'D2I171', 'B3ZUA2', 'A8FYR2', 'A8NFC2', 'Q6CHT1', 'D2QE09', 'B0HSZ7', 'B2A7G7', 'Q4JUZ8', 'O27018', 'B7GCS7', 'B5FMN5', 'A9PQS5', 'C8A7E2', 'C9QA98')\nfor data in true_data:\n self.assertTrue(is_unip... | <|body_start_0|>
true_data = ('Q4JUZ8', 'O27018', 'B7GCS7', 'B5FMN5', 'A9PQS5', 'C8A7E2', 'C5R887', 'C6QM83', 'A8XHC3', 'D1IMX6', 'D2I171', 'B3ZUA2', 'A8FYR2', 'A8NFC2', 'Q6CHT1', 'D2QE09', 'B0HSZ7', 'B2A7G7', 'Q4JUZ8', 'O27018', 'B7GCS7', 'B5FMN5', 'A9PQS5', 'C8A7E2', 'C9QA98')
for data in true_data:
... | Legacy tests for PhyloFacts (Limahuli) System | IdentifiersAndAccessionsTest | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class IdentifiersAndAccessionsTest:
"""Legacy tests for PhyloFacts (Limahuli) System"""
def test_uniprot_accessions(self):
"""Verify a sample of UniProt identifiers validate"""
<|body_0|>
def test_bogus_uniprot_accessions(self):
"""Verify that a sample of bad UniProt a... | stack_v2_sparse_classes_36k_train_025449 | 11,037 | no_license | [
{
"docstring": "Verify a sample of UniProt identifiers validate",
"name": "test_uniprot_accessions",
"signature": "def test_uniprot_accessions(self)"
},
{
"docstring": "Verify that a sample of bad UniProt accessions fail",
"name": "test_bogus_uniprot_accessions",
"signature": "def test_b... | 4 | null | Implement the Python class `IdentifiersAndAccessionsTest` described below.
Class description:
Legacy tests for PhyloFacts (Limahuli) System
Method signatures and docstrings:
- def test_uniprot_accessions(self): Verify a sample of UniProt identifiers validate
- def test_bogus_uniprot_accessions(self): Verify that a sa... | Implement the Python class `IdentifiersAndAccessionsTest` described below.
Class description:
Legacy tests for PhyloFacts (Limahuli) System
Method signatures and docstrings:
- def test_uniprot_accessions(self): Verify a sample of UniProt identifiers validate
- def test_bogus_uniprot_accessions(self): Verify that a sa... | bbf5df137a0a459598c3f9073d80f0086e5f7550 | <|skeleton|>
class IdentifiersAndAccessionsTest:
"""Legacy tests for PhyloFacts (Limahuli) System"""
def test_uniprot_accessions(self):
"""Verify a sample of UniProt identifiers validate"""
<|body_0|>
def test_bogus_uniprot_accessions(self):
"""Verify that a sample of bad UniProt a... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class IdentifiersAndAccessionsTest:
"""Legacy tests for PhyloFacts (Limahuli) System"""
def test_uniprot_accessions(self):
"""Verify a sample of UniProt identifiers validate"""
true_data = ('Q4JUZ8', 'O27018', 'B7GCS7', 'B5FMN5', 'A9PQS5', 'C8A7E2', 'C5R887', 'C6QM83', 'A8XHC3', 'D1IMX6', 'D2I1... | the_stack_v2_python_sparse | pfacts003/utils/tests.py | berkeleyphylogenomics/BPG_utilities | train | 1 |
2ac57ac4d6068159dbd7f33372238af32a5b7842 | [
"super().__init__(api, coordinator, description)\nself._previous_uptime: str | None = None\nself._last_boot: datetime | None = None",
"attr = getattr(self._api.information, self.entity_description.key)\nif attr is None:\n return None\nif self.entity_description.key == 'uptime':\n if self._previous_uptime is... | <|body_start_0|>
super().__init__(api, coordinator, description)
self._previous_uptime: str | None = None
self._last_boot: datetime | None = None
<|end_body_0|>
<|body_start_1|>
attr = getattr(self._api.information, self.entity_description.key)
if attr is None:
retur... | Representation a Synology information sensor. | SynoDSMInfoSensor | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SynoDSMInfoSensor:
"""Representation a Synology information sensor."""
def __init__(self, api: SynoApi, coordinator: SynologyDSMCentralUpdateCoordinator, description: SynologyDSMSensorEntityDescription) -> None:
"""Initialize the Synology SynoDSMInfoSensor entity."""
<|body_0... | stack_v2_sparse_classes_36k_train_025450 | 15,893 | permissive | [
{
"docstring": "Initialize the Synology SynoDSMInfoSensor entity.",
"name": "__init__",
"signature": "def __init__(self, api: SynoApi, coordinator: SynologyDSMCentralUpdateCoordinator, description: SynologyDSMSensorEntityDescription) -> None"
},
{
"docstring": "Return the state.",
"name": "n... | 2 | null | Implement the Python class `SynoDSMInfoSensor` described below.
Class description:
Representation a Synology information sensor.
Method signatures and docstrings:
- def __init__(self, api: SynoApi, coordinator: SynologyDSMCentralUpdateCoordinator, description: SynologyDSMSensorEntityDescription) -> None: Initialize t... | Implement the Python class `SynoDSMInfoSensor` described below.
Class description:
Representation a Synology information sensor.
Method signatures and docstrings:
- def __init__(self, api: SynoApi, coordinator: SynologyDSMCentralUpdateCoordinator, description: SynologyDSMSensorEntityDescription) -> None: Initialize t... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class SynoDSMInfoSensor:
"""Representation a Synology information sensor."""
def __init__(self, api: SynoApi, coordinator: SynologyDSMCentralUpdateCoordinator, description: SynologyDSMSensorEntityDescription) -> None:
"""Initialize the Synology SynoDSMInfoSensor entity."""
<|body_0... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SynoDSMInfoSensor:
"""Representation a Synology information sensor."""
def __init__(self, api: SynoApi, coordinator: SynologyDSMCentralUpdateCoordinator, description: SynologyDSMSensorEntityDescription) -> None:
"""Initialize the Synology SynoDSMInfoSensor entity."""
super().__init__(api,... | the_stack_v2_python_sparse | homeassistant/components/synology_dsm/sensor.py | home-assistant/core | train | 35,501 |
bb976bf58a7164c41d78134633e883e8b8bc9bf1 | [
"value = {'fw_version': fw_version, 'on_schedule': on_time}\nid = self.client.api.create_changeset(JOB_FOTA_NAME, value, devices)\nreturn id",
"status = self.client.api.get_current_device_status(device_id)\nmstatus = [self.prepare_model(s) for s in status]\nfor s in mstatus:\n if s.name == JOB_FOTA_NAME:\n ... | <|body_start_0|>
value = {'fw_version': fw_version, 'on_schedule': on_time}
id = self.client.api.create_changeset(JOB_FOTA_NAME, value, devices)
return id
<|end_body_0|>
<|body_start_1|>
status = self.client.api.get_current_device_status(device_id)
mstatus = [self.prepare_model(... | FotaCollection | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FotaCollection:
def schedule(self, fw_version, devices, on_time=''):
"""Schedule a FOTA. Args: fw_version (str): Firmware version. devices (list of str): Targets devices. on_time (datetime): When to schedule the fota. Returns: A :py:class:`FotaModel` object. Raises: :py:class:`adm.errors... | stack_v2_sparse_classes_36k_train_025451 | 2,177 | no_license | [
{
"docstring": "Schedule a FOTA. Args: fw_version (str): Firmware version. devices (list of str): Targets devices. on_time (datetime): When to schedule the fota. Returns: A :py:class:`FotaModel` object. Raises: :py:class:`adm.errors.APIError` If the server returns an error.",
"name": "schedule",
"signat... | 3 | null | Implement the Python class `FotaCollection` described below.
Class description:
Implement the FotaCollection class.
Method signatures and docstrings:
- def schedule(self, fw_version, devices, on_time=''): Schedule a FOTA. Args: fw_version (str): Firmware version. devices (list of str): Targets devices. on_time (datet... | Implement the Python class `FotaCollection` described below.
Class description:
Implement the FotaCollection class.
Method signatures and docstrings:
- def schedule(self, fw_version, devices, on_time=''): Schedule a FOTA. Args: fw_version (str): Firmware version. devices (list of str): Targets devices. on_time (datet... | d27b0d6ee47b9c4f320f518705074f1032fedf8a | <|skeleton|>
class FotaCollection:
def schedule(self, fw_version, devices, on_time=''):
"""Schedule a FOTA. Args: fw_version (str): Firmware version. devices (list of str): Targets devices. on_time (datetime): When to schedule the fota. Returns: A :py:class:`FotaModel` object. Raises: :py:class:`adm.errors... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FotaCollection:
def schedule(self, fw_version, devices, on_time=''):
"""Schedule a FOTA. Args: fw_version (str): Firmware version. devices (list of str): Targets devices. on_time (datetime): When to schedule the fota. Returns: A :py:class:`FotaModel` object. Raises: :py:class:`adm.errors.APIError` If ... | the_stack_v2_python_sparse | zdevicemanager/client/models/fota.py | zerynth/core-zerynth-toolchain | train | 0 | |
d5ddb217ce6ebb68f9b9a1047b0341748688c478 | [
"self.logic = None\nself.environ = None\nself.actions = 0\nself.hasArrow = True",
"self.environ = environment\nself.logic = logicEngine\nself.actions = 0\nself.hasArrow = True\nsuccess = False\ndead = False\ngoldFound = False\nx, y = (0, 0)\nwhile not goldFound and (not dead):\n breeze, stench, gilter = self.e... | <|body_start_0|>
self.logic = None
self.environ = None
self.actions = 0
self.hasArrow = True
<|end_body_0|>
<|body_start_1|>
self.environ = environment
self.logic = logicEngine
self.actions = 0
self.hasArrow = True
success = False
dead = F... | A hybrid agent that uses logic and a planner to work its way through the wumpus world | RandomAgent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RandomAgent:
"""A hybrid agent that uses logic and a planner to work its way through the wumpus world"""
def __init__(self):
"""Initializes the agent"""
<|body_0|>
def search(self, environment, logicEngine):
"""Executes a random search for the gold"""
<|b... | stack_v2_sparse_classes_36k_train_025452 | 1,170 | no_license | [
{
"docstring": "Initializes the agent",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Executes a random search for the gold",
"name": "search",
"signature": "def search(self, environment, logicEngine)"
}
] | 2 | null | Implement the Python class `RandomAgent` described below.
Class description:
A hybrid agent that uses logic and a planner to work its way through the wumpus world
Method signatures and docstrings:
- def __init__(self): Initializes the agent
- def search(self, environment, logicEngine): Executes a random search for th... | Implement the Python class `RandomAgent` described below.
Class description:
A hybrid agent that uses logic and a planner to work its way through the wumpus world
Method signatures and docstrings:
- def __init__(self): Initializes the agent
- def search(self, environment, logicEngine): Executes a random search for th... | b20bb8af5d3ef0809811d01a73cb173d075c4c3e | <|skeleton|>
class RandomAgent:
"""A hybrid agent that uses logic and a planner to work its way through the wumpus world"""
def __init__(self):
"""Initializes the agent"""
<|body_0|>
def search(self, environment, logicEngine):
"""Executes a random search for the gold"""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RandomAgent:
"""A hybrid agent that uses logic and a planner to work its way through the wumpus world"""
def __init__(self):
"""Initializes the agent"""
self.logic = None
self.environ = None
self.actions = 0
self.hasArrow = True
def search(self, environment, l... | the_stack_v2_python_sparse | project4/randomAgent.py | mqtlam/osu-cs531 | train | 0 |
5b27f794883ed18b0c95360e6ea778cb50d750a2 | [
"preorder = []\n\ndef traverse(root):\n if root is None:\n preorder.append('#')\n return\n preorder.append(root.val)\n traverse(root.left)\n traverse(root.right)\ntraverse(root)\nreturn ','.join([str(val) for val in preorder])",
"if data == '':\n return None\npreorder = data.split(','... | <|body_start_0|>
preorder = []
def traverse(root):
if root is None:
preorder.append('#')
return
preorder.append(root.val)
traverse(root.left)
traverse(root.right)
traverse(root)
return ','.join([str(val) for... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_025453 | 6,926 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | stack_v2_sparse_classes_30k_train_017508 | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 0821af55eca60084b503b5f751301048c55e4381 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
preorder = []
def traverse(root):
if root is None:
preorder.append('#')
return
preorder.append(root.val)
trav... | the_stack_v2_python_sparse | Hard/LC297.py | shuowenwei/LeetCodePython | train | 2 | |
511ba6bc33c3b211bf5bdf5d182e93513f0820ca | [
"if isinstance(configuration, dict):\n if strict:\n configuration = analysis_version.update_input_with_defaults(configuration)\n key_set = set(configuration.keys())\n if ignore_non_config:\n configuration_keys = analysis_version.input_specification.configuration_keys\n key_set = key_se... | <|body_start_0|>
if isinstance(configuration, dict):
if strict:
configuration = analysis_version.update_input_with_defaults(configuration)
key_set = set(configuration.keys())
if ignore_non_config:
configuration_keys = analysis_version.input_spe... | Manager for the :class:`~django_analyses.models.run.Run` model. Handles the creation and retrieval of runs when :class:`~django_analyses.models.pipeline.node.Node` are executed. | RunManager | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RunManager:
"""Manager for the :class:`~django_analyses.models.run.Run` model. Handles the creation and retrieval of runs when :class:`~django_analyses.models.pipeline.node.Node` are executed."""
def filter_by_configuration(self, analysis_version: AnalysisVersion, configuration: Union[Dict[s... | stack_v2_sparse_classes_36k_train_025454 | 10,200 | permissive | [
{
"docstring": "Returns a queryset of *analysis_version* runs matching the provided *configuration*. Parameters ---------- analysis_version : AnalysisVersion Analysis version runs to query configuration : Union[Dict[str, Any], Iterable[Dict[str, Any]]] Configuration options to filter by strict : bool, optional ... | 4 | null | Implement the Python class `RunManager` described below.
Class description:
Manager for the :class:`~django_analyses.models.run.Run` model. Handles the creation and retrieval of runs when :class:`~django_analyses.models.pipeline.node.Node` are executed.
Method signatures and docstrings:
- def filter_by_configuration(... | Implement the Python class `RunManager` described below.
Class description:
Manager for the :class:`~django_analyses.models.run.Run` model. Handles the creation and retrieval of runs when :class:`~django_analyses.models.pipeline.node.Node` are executed.
Method signatures and docstrings:
- def filter_by_configuration(... | 5642579660fd09dde4a23bf02ec98a7ec264bceb | <|skeleton|>
class RunManager:
"""Manager for the :class:`~django_analyses.models.run.Run` model. Handles the creation and retrieval of runs when :class:`~django_analyses.models.pipeline.node.Node` are executed."""
def filter_by_configuration(self, analysis_version: AnalysisVersion, configuration: Union[Dict[s... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RunManager:
"""Manager for the :class:`~django_analyses.models.run.Run` model. Handles the creation and retrieval of runs when :class:`~django_analyses.models.pipeline.node.Node` are executed."""
def filter_by_configuration(self, analysis_version: AnalysisVersion, configuration: Union[Dict[str, Any], Ite... | the_stack_v2_python_sparse | django_analyses/models/managers/run.py | TheLabbingProject/django_analyses | train | 1 |
7d712f2a91abb98b510b193c3769ea0264d18af7 | [
"db_obj = None\nif data.get('id') is not None:\n db_obj = cls.get(db=db, id=data['id'])\n identifier = data.get('id')\nelif data.get('key') is not None:\n db_obj = cls.get_by(db=db, field='key', value=data['key'])\n identifier = data.get('key')\nif db_obj:\n if db_obj.rule_id != data['rule_id']:\n ... | <|body_start_0|>
db_obj = None
if data.get('id') is not None:
db_obj = cls.get(db=db, id=data['id'])
identifier = data.get('id')
elif data.get('key') is not None:
db_obj = cls.get_by(db=db, field='key', value=data['key'])
identifier = data.get('key... | Which data categories to apply the referenced Rule to | RuleTarget | [
"CC-BY-4.0",
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class RuleTarget:
"""Which data categories to apply the referenced Rule to"""
def create_or_update(cls, db: Session, *, data: Dict[str, Any]) -> FidesopsBase:
"""An override of `FidesopsBase.create_or_update` that handles the specific edge case where a `RuleTarget` getting updated may be h... | stack_v2_sparse_classes_36k_train_025455 | 15,366 | permissive | [
{
"docstring": "An override of `FidesopsBase.create_or_update` that handles the specific edge case where a `RuleTarget` getting updated may be having its `rule_id` changed, potentially causing `RuleTarget`s to unexpectedly bounce between `Rule`s.",
"name": "create_or_update",
"signature": "def create_or... | 4 | stack_v2_sparse_classes_30k_train_013925 | Implement the Python class `RuleTarget` described below.
Class description:
Which data categories to apply the referenced Rule to
Method signatures and docstrings:
- def create_or_update(cls, db: Session, *, data: Dict[str, Any]) -> FidesopsBase: An override of `FidesopsBase.create_or_update` that handles the specifi... | Implement the Python class `RuleTarget` described below.
Class description:
Which data categories to apply the referenced Rule to
Method signatures and docstrings:
- def create_or_update(cls, db: Session, *, data: Dict[str, Any]) -> FidesopsBase: An override of `FidesopsBase.create_or_update` that handles the specifi... | 1ab840206a78e60673aebd5838ba567095512a58 | <|skeleton|>
class RuleTarget:
"""Which data categories to apply the referenced Rule to"""
def create_or_update(cls, db: Session, *, data: Dict[str, Any]) -> FidesopsBase:
"""An override of `FidesopsBase.create_or_update` that handles the specific edge case where a `RuleTarget` getting updated may be h... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class RuleTarget:
"""Which data categories to apply the referenced Rule to"""
def create_or_update(cls, db: Session, *, data: Dict[str, Any]) -> FidesopsBase:
"""An override of `FidesopsBase.create_or_update` that handles the specific edge case where a `RuleTarget` getting updated may be having its `ru... | the_stack_v2_python_sparse | src/fidesops/models/policy.py | nathanawmk/fidesops | train | 0 |
4cb80bf0b76dd6b8405d2b9fa83484b6bfe94cd0 | [
"if count is None:\n qs = ''\nelse:\n qs = '?limit=%s' % count\nuri = '/%s%s' % (self.uri_base, qs)\nbody = {'ttl': ttl, 'grace': grace}\nresp, resp_body = self.api.method_post(uri, body=body)\nif resp.status_code == 204:\n return None\nhref = resp_body[0]['href']\nclaim_id = href.split('claim_id=')[-1]\nr... | <|body_start_0|>
if count is None:
qs = ''
else:
qs = '?limit=%s' % count
uri = '/%s%s' % (self.uri_base, qs)
body = {'ttl': ttl, 'grace': grace}
resp, resp_body = self.api.method_post(uri, body=body)
if resp.status_code == 204:
return ... | Manager class for a Queue Claims. | QueueClaimManager | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class QueueClaimManager:
"""Manager class for a Queue Claims."""
def claim(self, ttl, grace, count=None):
"""Claims up to `count` unclaimed messages from this queue. If count is not specified, the default is to claim 10 messages. The `ttl` parameter specifies how long the server should wai... | stack_v2_sparse_classes_36k_train_025456 | 26,279 | permissive | [
{
"docstring": "Claims up to `count` unclaimed messages from this queue. If count is not specified, the default is to claim 10 messages. The `ttl` parameter specifies how long the server should wait before releasing the claim. The ttl value MUST be between 60 and 43200 seconds. The `grace` parameter is the mess... | 2 | stack_v2_sparse_classes_30k_test_000507 | Implement the Python class `QueueClaimManager` described below.
Class description:
Manager class for a Queue Claims.
Method signatures and docstrings:
- def claim(self, ttl, grace, count=None): Claims up to `count` unclaimed messages from this queue. If count is not specified, the default is to claim 10 messages. The... | Implement the Python class `QueueClaimManager` described below.
Class description:
Manager class for a Queue Claims.
Method signatures and docstrings:
- def claim(self, ttl, grace, count=None): Claims up to `count` unclaimed messages from this queue. If count is not specified, the default is to claim 10 messages. The... | 2397136b75e6fcc906ee406e9c1bc7aaef94387a | <|skeleton|>
class QueueClaimManager:
"""Manager class for a Queue Claims."""
def claim(self, ttl, grace, count=None):
"""Claims up to `count` unclaimed messages from this queue. If count is not specified, the default is to claim 10 messages. The `ttl` parameter specifies how long the server should wai... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class QueueClaimManager:
"""Manager class for a Queue Claims."""
def claim(self, ttl, grace, count=None):
"""Claims up to `count` unclaimed messages from this queue. If count is not specified, the default is to claim 10 messages. The `ttl` parameter specifies how long the server should wait before rele... | the_stack_v2_python_sparse | pyrax/queueing.py | pycontribs/pyrax | train | 10 |
b948b46f351388179dc76e1f7db4d562d85c0f3a | [
"super().__init__()\nself.type_ = type_\nself.op = op\nself.query = query\nself.description = description",
"from norm.engine import ImplType\nlam = None\nif self.op == ImplType.ASS:\n lam = self.type_.execute(session, context)\n if lam is None:\n lam = Lambda(namespace=context.context_namespace, nam... | <|body_start_0|>
super().__init__()
self.type_ = type_
self.op = op
self.query = query
self.description = description
<|end_body_0|>
<|body_start_1|>
from norm.engine import ImplType
lam = None
if self.op == ImplType.ASS:
lam = self.type_.exec... | TypeImplementation | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TypeImplementation:
def __init__(self, type_, op, query, description):
"""The implementation of a type. Depending on the operation, it can be initial implementation or incremental implementation. :param type_: the type to implement :type type_: TypeName :param op: the operation of the im... | stack_v2_sparse_classes_36k_train_025457 | 1,927 | permissive | [
{
"docstring": "The implementation of a type. Depending on the operation, it can be initial implementation or incremental implementation. :param type_: the type to implement :type type_: TypeName :param op: the operation of the implementation, i.e., ['=', '|=', '&='] :type op: ImplType :param query: the query e... | 2 | stack_v2_sparse_classes_30k_train_000238 | Implement the Python class `TypeImplementation` described below.
Class description:
Implement the TypeImplementation class.
Method signatures and docstrings:
- def __init__(self, type_, op, query, description): The implementation of a type. Depending on the operation, it can be initial implementation or incremental i... | Implement the Python class `TypeImplementation` described below.
Class description:
Implement the TypeImplementation class.
Method signatures and docstrings:
- def __init__(self, type_, op, query, description): The implementation of a type. Depending on the operation, it can be initial implementation or incremental i... | ff76e030d7cebdca51c72d5d7e789d90f0e1e565 | <|skeleton|>
class TypeImplementation:
def __init__(self, type_, op, query, description):
"""The implementation of a type. Depending on the operation, it can be initial implementation or incremental implementation. :param type_: the type to implement :type type_: TypeName :param op: the operation of the im... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TypeImplementation:
def __init__(self, type_, op, query, description):
"""The implementation of a type. Depending on the operation, it can be initial implementation or incremental implementation. :param type_: the type to implement :type type_: TypeName :param op: the operation of the implementation, ... | the_stack_v2_python_sparse | norm/executable/implementation.py | xumiao/supernorm | train | 0 | |
5fb80fbed65d0b1f9c8e4959dca6f16c5463f036 | [
"self.capabilities = capabilities\nself.risk = (1 - R / 3) / (1 + R / 3)\nself.p_win = 0\nself.k_ab = 0\nself.total_utility = 0\nsuper().__init__(*args, **kwargs)",
"adj_mat = nx.to_numpy_matrix(world.network)\npos_to_name = {}\nname_to_pos = {}\nfor i, node in enumerate(world.network.nodes()):\n pos_to_name[i... | <|body_start_0|>
self.capabilities = capabilities
self.risk = (1 - R / 3) / (1 + R / 3)
self.p_win = 0
self.k_ab = 0
self.total_utility = 0
super().__init__(*args, **kwargs)
<|end_body_0|>
<|body_start_1|>
adj_mat = nx.to_numpy_matrix(world.network)
pos_t... | Base class. | WarAgent | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class WarAgent:
"""Base class."""
def __init__(self, capabilities, R, *args, **kwargs):
"""New WarAgent"""
<|body_0|>
def start_conflict(self, world, rival):
"""Called at the beginning of a sub-model run to compute the utility and percieved probability of winning for t... | stack_v2_sparse_classes_36k_train_025458 | 12,998 | no_license | [
{
"docstring": "New WarAgent",
"name": "__init__",
"signature": "def __init__(self, capabilities, R, *args, **kwargs)"
},
{
"docstring": "Called at the beginning of a sub-model run to compute the utility and percieved probability of winning for the agent.",
"name": "start_conflict",
"sig... | 2 | null | Implement the Python class `WarAgent` described below.
Class description:
Base class.
Method signatures and docstrings:
- def __init__(self, capabilities, R, *args, **kwargs): New WarAgent
- def start_conflict(self, world, rival): Called at the beginning of a sub-model run to compute the utility and percieved probabi... | Implement the Python class `WarAgent` described below.
Class description:
Base class.
Method signatures and docstrings:
- def __init__(self, capabilities, R, *args, **kwargs): New WarAgent
- def start_conflict(self, world, rival): Called at the beginning of a sub-model run to compute the utility and percieved probabi... | 5e9a0e03aa7ddf5e5ddf89943ccc68d94b539e95 | <|skeleton|>
class WarAgent:
"""Base class."""
def __init__(self, capabilities, R, *args, **kwargs):
"""New WarAgent"""
<|body_0|>
def start_conflict(self, world, rival):
"""Called at the beginning of a sub-model run to compute the utility and percieved probability of winning for t... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class WarAgent:
"""Base class."""
def __init__(self, capabilities, R, *args, **kwargs):
"""New WarAgent"""
self.capabilities = capabilities
self.risk = (1 - R / 3) / (1 + R / 3)
self.p_win = 0
self.k_ab = 0
self.total_utility = 0
super().__init__(*args, *... | the_stack_v2_python_sparse | Predicting_Politics_Mesquita/Agents-In-Conflict-master/Code/WarReason/models/war_reason_model/war_reason.py | burakbayramli/books | train | 223 |
7e235e3e8e74ecb32ce189a3f8af9ae7ec2bceaf | [
"string = string.strip()\nsort_name, birth, death = cls._get_lifespan(string)\nextra = dict()\nif birth is not None:\n extra[Contributor.BIRTH_DATE] = birth\nif death is not None:\n extra[Contributor.DEATH_DATE] = death\nreturn ContributorData(sort_name=sort_name, extra=extra)",
"birth = None\ndeath = None\... | <|body_start_0|>
string = string.strip()
sort_name, birth, death = cls._get_lifespan(string)
extra = dict()
if birth is not None:
extra[Contributor.BIRTH_DATE] = birth
if death is not None:
extra[Contributor.DEATH_DATE] = death
return ContributorDa... | Parse VIAF-style personal names. These are used by VIAF but also (in slightly modified form) by OCLC Classify. TODO: VIAFParser has its own "name parsing" code which focuses on extractingdata from the XML generated by the VIAF API. These code bases need to be merged. | NameParser | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NameParser:
"""Parse VIAF-style personal names. These are used by VIAF but also (in slightly modified form) by OCLC Classify. TODO: VIAFParser has its own "name parsing" code which focuses on extractingdata from the XML generated by the VIAF API. These code bases need to be merged."""
def pa... | stack_v2_sparse_classes_36k_train_025459 | 40,883 | permissive | [
{
"docstring": "Parse a string into a ContributorData object. This may include sort_name, birth_date, and death_date.",
"name": "parse",
"signature": "def parse(cls, string)"
},
{
"docstring": "Extract a possible lifespan from an author string. :return: A 3-tuple (name_without_lifespan, birth, d... | 2 | null | Implement the Python class `NameParser` described below.
Class description:
Parse VIAF-style personal names. These are used by VIAF but also (in slightly modified form) by OCLC Classify. TODO: VIAFParser has its own "name parsing" code which focuses on extractingdata from the XML generated by the VIAF API. These code ... | Implement the Python class `NameParser` described below.
Class description:
Parse VIAF-style personal names. These are used by VIAF but also (in slightly modified form) by OCLC Classify. TODO: VIAFParser has its own "name parsing" code which focuses on extractingdata from the XML generated by the VIAF API. These code ... | 0ab8add4890280334779ba0862eb9290ab792bb5 | <|skeleton|>
class NameParser:
"""Parse VIAF-style personal names. These are used by VIAF but also (in slightly modified form) by OCLC Classify. TODO: VIAFParser has its own "name parsing" code which focuses on extractingdata from the XML generated by the VIAF API. These code bases need to be merged."""
def pa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NameParser:
"""Parse VIAF-style personal names. These are used by VIAF but also (in slightly modified form) by OCLC Classify. TODO: VIAFParser has its own "name parsing" code which focuses on extractingdata from the XML generated by the VIAF API. These code bases need to be merged."""
def parse(cls, stri... | the_stack_v2_python_sparse | viaf.py | NYPL-Simplified/metadata_wrangler | train | 6 |
2b5ff03bc155daaece7585ef8a03299574061b3a | [
"super().__init__(dev=dev, qubits=qubits, **kw)\nself.index_iteration = 1\nself.index_spectroscopy = 1\nself.previous_freqs = {qb.name: qb.ge_freq() for qb in qubits}\nself.results = {}\nself.final_init(**kw)",
"super().create_routine_template()\nfor i in range(self.get_param_value('n_spectroscopies', default=2))... | <|body_start_0|>
super().__init__(dev=dev, qubits=qubits, **kw)
self.index_iteration = 1
self.index_spectroscopy = 1
self.previous_freqs = {qb.name: qb.ge_freq() for qb in qubits}
self.results = {}
self.final_init(**kw)
<|end_body_0|>
<|body_start_1|>
super().cre... | Routine to find the ge transition frequency via qubit spectroscopy. A series of qubit spectroscopies is performed. A Decision step decides whether a fit failed for some qubits and whether to rerun the spectroscopy for them. The user can specify the number of spectroscopies via the keyword "n_spectroscopies" in the conf... | AdaptiveQubitSpectroscopy | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class AdaptiveQubitSpectroscopy:
"""Routine to find the ge transition frequency via qubit spectroscopy. A series of qubit spectroscopies is performed. A Decision step decides whether a fit failed for some qubits and whether to rerun the spectroscopy for them. The user can specify the number of spectros... | stack_v2_sparse_classes_36k_train_025460 | 24,662 | permissive | [
{
"docstring": "Initialize the AdaptiveQubitSpectroscopy routine. Args: dev (Device): Device to be used for the routine qubits (list): The qubits which should be calibrated. By default, all qubits of the device are selected. Configuration parameters (coming from the configuration parameter dictionary): n_spectr... | 3 | stack_v2_sparse_classes_30k_train_020194 | Implement the Python class `AdaptiveQubitSpectroscopy` described below.
Class description:
Routine to find the ge transition frequency via qubit spectroscopy. A series of qubit spectroscopies is performed. A Decision step decides whether a fit failed for some qubits and whether to rerun the spectroscopy for them. The ... | Implement the Python class `AdaptiveQubitSpectroscopy` described below.
Class description:
Routine to find the ge transition frequency via qubit spectroscopy. A series of qubit spectroscopies is performed. A Decision step decides whether a fit failed for some qubits and whether to rerun the spectroscopy for them. The ... | bc6733d774fe31a23f4c7e73e5eb0beed8d30e7d | <|skeleton|>
class AdaptiveQubitSpectroscopy:
"""Routine to find the ge transition frequency via qubit spectroscopy. A series of qubit spectroscopies is performed. A Decision step decides whether a fit failed for some qubits and whether to rerun the spectroscopy for them. The user can specify the number of spectros... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class AdaptiveQubitSpectroscopy:
"""Routine to find the ge transition frequency via qubit spectroscopy. A series of qubit spectroscopies is performed. A Decision step decides whether a fit failed for some qubits and whether to rerun the spectroscopy for them. The user can specify the number of spectroscopies via th... | the_stack_v2_python_sparse | pycqed/measurement/calibration/automatic_calibration_routines/adaptive_qubit_spectroscopy.py | QudevETH/PycQED_py3 | train | 8 |
484d0c0e3ae779ca662c3578876853f4a116d9cf | [
"logging.debug('Loading features from %s', endpath(featmap.dirname()))\nself.rdonly = rdonly\nself.featmap = FeatureMapping(featmap)\nself.featuredb = FeatureVectors(featdb)\nself.fstream = FeatureStream(fstream, rdonly)\nself.featurespace = featurespace",
"base = rc.articles_home / featurespace\nif not base.exis... | <|body_start_0|>
logging.debug('Loading features from %s', endpath(featmap.dirname()))
self.rdonly = rdonly
self.featmap = FeatureMapping(featmap)
self.featuredb = FeatureVectors(featdb)
self.fstream = FeatureStream(fstream, rdonly)
self.featurespace = featurespace
<|end_... | Wraps the L{FeatureMapping}, L{FeatureVectors} and L{FeatureStream} objects. There may be multiple L{FeatureData} indexes for the same database of articles, depending on the choice of feature extraction method. @ivar featmap: L{FeatureMapping} between feature names and feature IDs. @ivar featuredb: L{FeatureVectors} to... | FeatureData | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class FeatureData:
"""Wraps the L{FeatureMapping}, L{FeatureVectors} and L{FeatureStream} objects. There may be multiple L{FeatureData} indexes for the same database of articles, depending on the choice of feature extraction method. @ivar featmap: L{FeatureMapping} between feature names and feature IDs... | stack_v2_sparse_classes_36k_train_025461 | 8,507 | no_license | [
{
"docstring": "Constructor. @param featmap: Path to FeatureMapping. @param featdb: Path to FeatureVectors. @param fstream: Path to FeatureStream.",
"name": "__init__",
"signature": "def __init__(self, featmap, featdb, fstream, featurespace, rdonly=True)"
},
{
"docstring": "Initialise L{FeatureD... | 6 | stack_v2_sparse_classes_30k_train_000455 | Implement the Python class `FeatureData` described below.
Class description:
Wraps the L{FeatureMapping}, L{FeatureVectors} and L{FeatureStream} objects. There may be multiple L{FeatureData} indexes for the same database of articles, depending on the choice of feature extraction method. @ivar featmap: L{FeatureMapping... | Implement the Python class `FeatureData` described below.
Class description:
Wraps the L{FeatureMapping}, L{FeatureVectors} and L{FeatureStream} objects. There may be multiple L{FeatureData} indexes for the same database of articles, depending on the choice of feature extraction method. @ivar featmap: L{FeatureMapping... | a91b94917404f92c97eaa814ebaa3180af17be61 | <|skeleton|>
class FeatureData:
"""Wraps the L{FeatureMapping}, L{FeatureVectors} and L{FeatureStream} objects. There may be multiple L{FeatureData} indexes for the same database of articles, depending on the choice of feature extraction method. @ivar featmap: L{FeatureMapping} between feature names and feature IDs... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class FeatureData:
"""Wraps the L{FeatureMapping}, L{FeatureVectors} and L{FeatureStream} objects. There may be multiple L{FeatureData} indexes for the same database of articles, depending on the choice of feature extraction method. @ivar featmap: L{FeatureMapping} between feature names and feature IDs. @ivar featu... | the_stack_v2_python_sparse | medline/FeatureData.py | gpoulter/mscanner | train | 1 |
67f363ce27fd222fe32ca451394d9054c509e3a5 | [
"if not issparse(data):\n return False\nif data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:\n return False\nreturn np.issubdtype(data.dtype, np.number)",
"self.sparse = 'yes'\ndata = self.data.tocoo()\nif np.issubdtype(self.data.dtype, np.complexfloating):\n self.complex = 'yes'\n self.array_size = ... | <|body_start_0|>
if not issparse(data):
return False
if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:
return False
return np.issubdtype(data.dtype, np.number)
<|end_body_0|>
<|body_start_1|>
self.sparse = 'yes'
data = self.data.tocoo()
if np.... | Inserter for sparse, numeric arrays. | SparseInserter | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SparseInserter:
"""Inserter for sparse, numeric arrays."""
def can_insert(data):
"""Can insert numeric scalars."""
<|body_0|>
def prepare_data(self):
"""Records RecordSize metadata."""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
if not issparse... | stack_v2_sparse_classes_36k_train_025462 | 3,589 | permissive | [
{
"docstring": "Can insert numeric scalars.",
"name": "can_insert",
"signature": "def can_insert(data)"
},
{
"docstring": "Records RecordSize metadata.",
"name": "prepare_data",
"signature": "def prepare_data(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_013286 | Implement the Python class `SparseInserter` described below.
Class description:
Inserter for sparse, numeric arrays.
Method signatures and docstrings:
- def can_insert(data): Can insert numeric scalars.
- def prepare_data(self): Records RecordSize metadata. | Implement the Python class `SparseInserter` described below.
Class description:
Inserter for sparse, numeric arrays.
Method signatures and docstrings:
- def can_insert(data): Can insert numeric scalars.
- def prepare_data(self): Records RecordSize metadata.
<|skeleton|>
class SparseInserter:
"""Inserter for spar... | 5923798f61c80771d69ff7500446b711921159a7 | <|skeleton|>
class SparseInserter:
"""Inserter for sparse, numeric arrays."""
def can_insert(data):
"""Can insert numeric scalars."""
<|body_0|>
def prepare_data(self):
"""Records RecordSize metadata."""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SparseInserter:
"""Inserter for sparse, numeric arrays."""
def can_insert(data):
"""Can insert numeric scalars."""
if not issparse(data):
return False
if data.dtype.char in UNSUPPORTED_NUMERIC_TYPE_CODES:
return False
return np.issubdtype(data.dtype... | the_stack_v2_python_sparse | sdafile/numeric_inserter.py | enthought/sandia-data-archive | train | 0 |
84efa6791d6b095b31ea29e21f30daa52318552d | [
"self.num = 0\n\ndef inner_order(root):\n if root is None:\n return\n inner_order(root.right)\n self.num += root.val\n root.val = self.num\n inner_order(root.left)\ninner_order(root)",
"order_list = []\n\ndef inner_order(root, order_list):\n if root is None:\n return None\n inne... | <|body_start_0|>
self.num = 0
def inner_order(root):
if root is None:
return
inner_order(root.right)
self.num += root.val
root.val = self.num
inner_order(root.left)
inner_order(root)
<|end_body_0|>
<|body_start_1|>
... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def convertBST(self, root: TreeNode) -> TreeNode:
"""方案：中序遍历"""
<|body_0|>
def convertBST_v2(self, root: TreeNode) -> TreeNode:
"""方案：中序遍历，逆序叠加"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.num = 0
def inner_order(root):
... | stack_v2_sparse_classes_36k_train_025463 | 1,910 | no_license | [
{
"docstring": "方案：中序遍历",
"name": "convertBST",
"signature": "def convertBST(self, root: TreeNode) -> TreeNode"
},
{
"docstring": "方案：中序遍历，逆序叠加",
"name": "convertBST_v2",
"signature": "def convertBST_v2(self, root: TreeNode) -> TreeNode"
}
] | 2 | null | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def convertBST(self, root: TreeNode) -> TreeNode: 方案：中序遍历
- def convertBST_v2(self, root: TreeNode) -> TreeNode: 方案：中序遍历，逆序叠加 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def convertBST(self, root: TreeNode) -> TreeNode: 方案：中序遍历
- def convertBST_v2(self, root: TreeNode) -> TreeNode: 方案：中序遍历，逆序叠加
<|skeleton|>
class Solution:
def convertBST(se... | 2e81b871bf1db7ea7432d1ebf889c72066e64753 | <|skeleton|>
class Solution:
def convertBST(self, root: TreeNode) -> TreeNode:
"""方案：中序遍历"""
<|body_0|>
def convertBST_v2(self, root: TreeNode) -> TreeNode:
"""方案：中序遍历，逆序叠加"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def convertBST(self, root: TreeNode) -> TreeNode:
"""方案：中序遍历"""
self.num = 0
def inner_order(root):
if root is None:
return
inner_order(root.right)
self.num += root.val
root.val = self.num
inner_orde... | the_stack_v2_python_sparse | Tree/convertBST.py | NextNight/LeetCodeAndStructAndAlgorithm | train | 0 | |
4b11bbaaaa2c3d8554f629fa6da538eead1ae024 | [
"chr_location = models.ChromosomeLocation(species_id='taxonomy:9606', chr=location['chr'], interval=models.CytobandInterval(start=location['start'], end=location['end']))\nchr_location._id = ga4gh_identify(chr_location)\nreturn chr_location.as_dict()",
"if 'chr' in location and 'start' in location and ('end' in l... | <|body_start_0|>
chr_location = models.ChromosomeLocation(species_id='taxonomy:9606', chr=location['chr'], interval=models.CytobandInterval(start=location['start'], end=location['end']))
chr_location._id = ga4gh_identify(chr_location)
return chr_location.as_dict()
<|end_body_0|>
<|body_start_1|... | The class for GA4GH Chromosome Location. | ChromosomeLocation | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ChromosomeLocation:
"""The class for GA4GH Chromosome Location."""
def add_location(self, location):
"""Get a gene's Chromosome Location. :param dict location: A gene's location. :return: A dictionary of a GA4GH VRS ChromosomeLocation."""
<|body_0|>
def get_location(self... | stack_v2_sparse_classes_36k_train_025464 | 3,279 | permissive | [
{
"docstring": "Get a gene's Chromosome Location. :param dict location: A gene's location. :return: A dictionary of a GA4GH VRS ChromosomeLocation.",
"name": "add_location",
"signature": "def add_location(self, location)"
},
{
"docstring": "Transform a gene's location into a Chromosome Location.... | 3 | stack_v2_sparse_classes_30k_val_001179 | Implement the Python class `ChromosomeLocation` described below.
Class description:
The class for GA4GH Chromosome Location.
Method signatures and docstrings:
- def add_location(self, location): Get a gene's Chromosome Location. :param dict location: A gene's location. :return: A dictionary of a GA4GH VRS ChromosomeL... | Implement the Python class `ChromosomeLocation` described below.
Class description:
The class for GA4GH Chromosome Location.
Method signatures and docstrings:
- def add_location(self, location): Get a gene's Chromosome Location. :param dict location: A gene's location. :return: A dictionary of a GA4GH VRS ChromosomeL... | 6dd633c2590aa6a40247b1b48c2e3381225d89ab | <|skeleton|>
class ChromosomeLocation:
"""The class for GA4GH Chromosome Location."""
def add_location(self, location):
"""Get a gene's Chromosome Location. :param dict location: A gene's location. :return: A dictionary of a GA4GH VRS ChromosomeLocation."""
<|body_0|>
def get_location(self... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ChromosomeLocation:
"""The class for GA4GH Chromosome Location."""
def add_location(self, location):
"""Get a gene's Chromosome Location. :param dict location: A gene's location. :return: A dictionary of a GA4GH VRS ChromosomeLocation."""
chr_location = models.ChromosomeLocation(species_i... | the_stack_v2_python_sparse | gene/vrs_locations/chromosome_location.py | richardhj/gene-normalization | train | 0 |
9775c895efff4add3dfa347a907b5f3cc7bf0bc2 | [
"url = response.url\nself.logger.info('Response url is %s' % url)\nnext_btn = response.xpath('//a[contains(.//text(),\"下一页\")]/@href').extract_first()\nif next_btn:\n next_page = parse.urljoin(url, next_btn)\n yield Request(next_page, callback=self.parse)\ncoding_list = response.xpath('//div[@class=\"shizhan-... | <|body_start_0|>
url = response.url
self.logger.info('Response url is %s' % url)
next_btn = response.xpath('//a[contains(.//text(),"下一页")]/@href').extract_first()
if next_btn:
next_page = parse.urljoin(url, next_btn)
yield Request(next_page, callback=self.parse)
... | CodingSpider | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CodingSpider:
def parse(self, response):
"""抓取课程列表页面"""
<|body_0|>
def parse_detail(self, response):
"""抓取课程详情页面"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
url = response.url
self.logger.info('Response url is %s' % url)
next_btn... | stack_v2_sparse_classes_36k_train_025465 | 3,795 | no_license | [
{
"docstring": "抓取课程列表页面",
"name": "parse",
"signature": "def parse(self, response)"
},
{
"docstring": "抓取课程详情页面",
"name": "parse_detail",
"signature": "def parse_detail(self, response)"
}
] | 2 | stack_v2_sparse_classes_30k_train_017445 | Implement the Python class `CodingSpider` described below.
Class description:
Implement the CodingSpider class.
Method signatures and docstrings:
- def parse(self, response): 抓取课程列表页面
- def parse_detail(self, response): 抓取课程详情页面 | Implement the Python class `CodingSpider` described below.
Class description:
Implement the CodingSpider class.
Method signatures and docstrings:
- def parse(self, response): 抓取课程列表页面
- def parse_detail(self, response): 抓取课程详情页面
<|skeleton|>
class CodingSpider:
def parse(self, response):
"""抓取课程列表页面"""
... | f4d6a6b9bb9fd68ad3f336aa3350a22844b9ddb2 | <|skeleton|>
class CodingSpider:
def parse(self, response):
"""抓取课程列表页面"""
<|body_0|>
def parse_detail(self, response):
"""抓取课程详情页面"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CodingSpider:
def parse(self, response):
"""抓取课程列表页面"""
url = response.url
self.logger.info('Response url is %s' % url)
next_btn = response.xpath('//a[contains(.//text(),"下一页")]/@href').extract_first()
if next_btn:
next_page = parse.urljoin(url, next_btn)
... | the_stack_v2_python_sparse | scrapys/imooc/imooc/spiders/coding.py | jinjin123/CodeRecordAsPython | train | 1 | |
a45759a94a84e722f6b1cc73ed9c5055a652bf9a | [
"if not isinstance(items, list):\n items = [items]\nresponses = []\ntry:\n table = DYNAMO_DB.Table(table_name)\nexcept Exception as e:\n Logger.e('DynamoDB#put_item', f'Failed at DYNAMO_DB.Table(table_name) : {e}')\n raise e\nif use_batch_writer:\n with table.batch_writer() as batch:\n for ite... | <|body_start_0|>
if not isinstance(items, list):
items = [items]
responses = []
try:
table = DYNAMO_DB.Table(table_name)
except Exception as e:
Logger.e('DynamoDB#put_item', f'Failed at DYNAMO_DB.Table(table_name) : {e}')
raise e
if... | DynamoDB | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DynamoDB:
def put_items(table_name: str, items: List[Dict], use_batch_writer: bool=False) -> List:
"""[summary] Args: table_name (str): [description] items (List[Dict]): [description] Raises: e: [description] Returns: List: [description]"""
<|body_0|>
def partitionkey_query(... | stack_v2_sparse_classes_36k_train_025466 | 2,734 | no_license | [
{
"docstring": "[summary] Args: table_name (str): [description] items (List[Dict]): [description] Raises: e: [description] Returns: List: [description]",
"name": "put_items",
"signature": "def put_items(table_name: str, items: List[Dict], use_batch_writer: bool=False) -> List"
},
{
"docstring": ... | 2 | stack_v2_sparse_classes_30k_val_000487 | Implement the Python class `DynamoDB` described below.
Class description:
Implement the DynamoDB class.
Method signatures and docstrings:
- def put_items(table_name: str, items: List[Dict], use_batch_writer: bool=False) -> List: [summary] Args: table_name (str): [description] items (List[Dict]): [description] Raises:... | Implement the Python class `DynamoDB` described below.
Class description:
Implement the DynamoDB class.
Method signatures and docstrings:
- def put_items(table_name: str, items: List[Dict], use_batch_writer: bool=False) -> List: [summary] Args: table_name (str): [description] items (List[Dict]): [description] Raises:... | f8b3f30920825e35fbaca9c28d9ef9ae134b8d66 | <|skeleton|>
class DynamoDB:
def put_items(table_name: str, items: List[Dict], use_batch_writer: bool=False) -> List:
"""[summary] Args: table_name (str): [description] items (List[Dict]): [description] Raises: e: [description] Returns: List: [description]"""
<|body_0|>
def partitionkey_query(... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DynamoDB:
def put_items(table_name: str, items: List[Dict], use_batch_writer: bool=False) -> List:
"""[summary] Args: table_name (str): [description] items (List[Dict]): [description] Raises: e: [description] Returns: List: [description]"""
if not isinstance(items, list):
items = [... | the_stack_v2_python_sparse | fin_app/database/nosql/dynamodb.py | pondelion/FinAppBackend | train | 0 | |
d1824c9697eef4fc236e87df0b11aa251c719efa | [
"self.position_hash = {}\nself.hashwa = {}\nself.min = float('inf')\nself.max = -1",
"for w in dict:\n self.min = min(self.min, len(w))\n self.max = max(self.max, len(w))\n for pos, char in enumerate(w):\n if pos not in self.position_hash:\n self.position_hash[pos] = set()\n self... | <|body_start_0|>
self.position_hash = {}
self.hashwa = {}
self.min = float('inf')
self.max = -1
<|end_body_0|>
<|body_start_1|>
for w in dict:
self.min = min(self.min, len(w))
self.max = max(self.max, len(w))
for pos, char in enumerate(w):
... | MagicDictionary | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class MagicDictionary:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def buildDict(self, dict):
"""Build a dictionary through a list of words :type dict: List[str] :rtype: void"""
<|body_1|>
def search(self, word):
"""Return... | stack_v2_sparse_classes_36k_train_025467 | 2,297 | no_license | [
{
"docstring": "Initialize your data structure here.",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "Build a dictionary through a list of words :type dict: List[str] :rtype: void",
"name": "buildDict",
"signature": "def buildDict(self, dict)"
},
{
"docs... | 3 | stack_v2_sparse_classes_30k_val_000761 | Implement the Python class `MagicDictionary` described below.
Class description:
Implement the MagicDictionary class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def buildDict(self, dict): Build a dictionary through a list of words :type dict: List[str] :rtype: void
... | Implement the Python class `MagicDictionary` described below.
Class description:
Implement the MagicDictionary class.
Method signatures and docstrings:
- def __init__(self): Initialize your data structure here.
- def buildDict(self, dict): Build a dictionary through a list of words :type dict: List[str] :rtype: void
... | 5feb84672630a97e6af3a0327cea5c58a6fa7563 | <|skeleton|>
class MagicDictionary:
def __init__(self):
"""Initialize your data structure here."""
<|body_0|>
def buildDict(self, dict):
"""Build a dictionary through a list of words :type dict: List[str] :rtype: void"""
<|body_1|>
def search(self, word):
"""Return... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class MagicDictionary:
def __init__(self):
"""Initialize your data structure here."""
self.position_hash = {}
self.hashwa = {}
self.min = float('inf')
self.max = -1
def buildDict(self, dict):
"""Build a dictionary through a list of words :type dict: List[str] :rt... | the_stack_v2_python_sparse | Adhoc/Implement-Magic-Dictionary/location_hash_solution.py | Aditya0025/CoderChef-Kitchen | train | 1 | |
4465bedab331f4fc1378cddb2d4933ed199d76f4 | [
"super(DNET_BAW, self).__init__()\nself.nc = 1\nself.ef_dim = nef\nself.df_dim = ndf\nself.define_module()",
"ndf = self.df_dim\nself.netD_1 = nn.Sequential(conv4x4(self.nc, ndf), nn.LeakyReLU(negative_slope=0.2, inplace=True), conv4x4(ndf, ndf), nn.LeakyReLU(negative_slope=0.2, inplace=True), conv4x4(ndf, ndf * ... | <|body_start_0|>
super(DNET_BAW, self).__init__()
self.nc = 1
self.ef_dim = nef
self.df_dim = ndf
self.define_module()
<|end_body_0|>
<|body_start_1|>
ndf = self.df_dim
self.netD_1 = nn.Sequential(conv4x4(self.nc, ndf), nn.LeakyReLU(negative_slope=0.2, inplace=Tr... | Discriminator class for the Black and White stage. Args: - ndf (int, optional): dimension of Black and White discriminator's filters. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128) | DNET_BAW | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DNET_BAW:
"""Discriminator class for the Black and White stage. Args: - ndf (int, optional): dimension of Black and White discriminator's filters. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)"""
def __init__(self, ndf=64, nef=128):
"""Initializ... | stack_v2_sparse_classes_36k_train_025468 | 22,492 | no_license | [
{
"docstring": "Initialize the Black and White stage discriminator. Other attributes: - nc (int): Number of channels.",
"name": "__init__",
"signature": "def __init__(self, ndf=64, nef=128)"
},
{
"docstring": "Define the ENCOLOR discriminator module.",
"name": "define_module",
"signature... | 3 | stack_v2_sparse_classes_30k_train_016875 | Implement the Python class `DNET_BAW` described below.
Class description:
Discriminator class for the Black and White stage. Args: - ndf (int, optional): dimension of Black and White discriminator's filters. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)
Method signatures and doc... | Implement the Python class `DNET_BAW` described below.
Class description:
Discriminator class for the Black and White stage. Args: - ndf (int, optional): dimension of Black and White discriminator's filters. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)
Method signatures and doc... | 70d344d80425e7bbcc7984737dbe50a6638293c9 | <|skeleton|>
class DNET_BAW:
"""Discriminator class for the Black and White stage. Args: - ndf (int, optional): dimension of Black and White discriminator's filters. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)"""
def __init__(self, ndf=64, nef=128):
"""Initializ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DNET_BAW:
"""Discriminator class for the Black and White stage. Args: - ndf (int, optional): dimension of Black and White discriminator's filters. (Default: 64) - nef (int, optional): Projected embeddings dimensions. (Default: 128)"""
def __init__(self, ndf=64, nef=128):
"""Initialize the Black a... | the_stack_v2_python_sparse | TeleGAN/model.py | ails-lab/teleGAN | train | 1 |
58125ed5a79e1560775737532faec1359f4480de | [
"dat = pd.read_csv(src, delimiter=',')\nnp_list = np.array(dat.as_matrix())\nreturn np_list",
"with open(src, 'r') as input:\n reader = csv.reader(input, delimiter=',')\n data_holder = []\n for index, row in enumerate(reader):\n print('Reading index#' + str(index))\n data = []\n for ... | <|body_start_0|>
dat = pd.read_csv(src, delimiter=',')
np_list = np.array(dat.as_matrix())
return np_list
<|end_body_0|>
<|body_start_1|>
with open(src, 'r') as input:
reader = csv.reader(input, delimiter=',')
data_holder = []
for index, row in enumer... | CSVReader | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CSVReader:
def csv_to_numpy_list(self, src):
"""Grabs csv file and return numpy list Args: stc(str): Csv file location Returns: np_list(np(list)): numpy containing csv data"""
<|body_0|>
def csv_to_list(self, src):
"""Grabs csv file and return 2 dimension list Args: ... | stack_v2_sparse_classes_36k_train_025469 | 2,803 | no_license | [
{
"docstring": "Grabs csv file and return numpy list Args: stc(str): Csv file location Returns: np_list(np(list)): numpy containing csv data",
"name": "csv_to_numpy_list",
"signature": "def csv_to_numpy_list(self, src)"
},
{
"docstring": "Grabs csv file and return 2 dimension list Args: src(str)... | 5 | stack_v2_sparse_classes_30k_train_016312 | Implement the Python class `CSVReader` described below.
Class description:
Implement the CSVReader class.
Method signatures and docstrings:
- def csv_to_numpy_list(self, src): Grabs csv file and return numpy list Args: stc(str): Csv file location Returns: np_list(np(list)): numpy containing csv data
- def csv_to_list... | Implement the Python class `CSVReader` described below.
Class description:
Implement the CSVReader class.
Method signatures and docstrings:
- def csv_to_numpy_list(self, src): Grabs csv file and return numpy list Args: stc(str): Csv file location Returns: np_list(np(list)): numpy containing csv data
- def csv_to_list... | 1cd33aa6c5dd08b29c14b55e24c2f4ee66203a08 | <|skeleton|>
class CSVReader:
def csv_to_numpy_list(self, src):
"""Grabs csv file and return numpy list Args: stc(str): Csv file location Returns: np_list(np(list)): numpy containing csv data"""
<|body_0|>
def csv_to_list(self, src):
"""Grabs csv file and return 2 dimension list Args: ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CSVReader:
def csv_to_numpy_list(self, src):
"""Grabs csv file and return numpy list Args: stc(str): Csv file location Returns: np_list(np(list)): numpy containing csv data"""
dat = pd.read_csv(src, delimiter=',')
np_list = np.array(dat.as_matrix())
return np_list
def csv_... | the_stack_v2_python_sparse | dataset_generator/lib/CSVReader.py | amumu/noox_project_ai | train | 0 | |
d98f5c7d16d712221c0ea840e8d828f773728611 | [
"self.moles_base = moles_base\nself.moles_mapping_url = f'{moles_base}/api/v0/obs/all'\nif MOLES_MAPPING_FILE:\n with open(MOLES_MAPPING_FILE) as reader:\n self.moles_mapping = json.load(reader)\nelse:\n try:\n self.moles_mapping = requests.get(self.moles_mapping_url).json()\n except JSONDeco... | <|body_start_0|>
self.moles_base = moles_base
self.moles_mapping_url = f'{moles_base}/api/v0/obs/all'
if MOLES_MAPPING_FILE:
with open(MOLES_MAPPING_FILE) as reader:
self.moles_mapping = json.load(reader)
else:
try:
self.moles_mappi... | Class to map a filepath to the relate MOLES record :param moles_base: Base URL to the MOLES api server (default: http://api.catalogue.ceda.ac.uk). :type moles_base: str | CatalogueDatasets | [
"BSD-3-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CatalogueDatasets:
"""Class to map a filepath to the relate MOLES record :param moles_base: Base URL to the MOLES api server (default: http://api.catalogue.ceda.ac.uk). :type moles_base: str"""
def __init__(self, moles_base='http://api.catalogue.ceda.ac.uk'):
""":param moles_base: Th... | stack_v2_sparse_classes_36k_train_025470 | 3,204 | permissive | [
{
"docstring": ":param moles_base: The base URL for the MOLES API server :type moles_base: str",
"name": "__init__",
"signature": "def __init__(self, moles_base='http://api.catalogue.ceda.ac.uk')"
},
{
"docstring": "Try and find metadata for a MOLES record associated with the path. Example API r... | 2 | stack_v2_sparse_classes_30k_train_015717 | Implement the Python class `CatalogueDatasets` described below.
Class description:
Class to map a filepath to the relate MOLES record :param moles_base: Base URL to the MOLES api server (default: http://api.catalogue.ceda.ac.uk). :type moles_base: str
Method signatures and docstrings:
- def __init__(self, moles_base=... | Implement the Python class `CatalogueDatasets` described below.
Class description:
Class to map a filepath to the relate MOLES record :param moles_base: Base URL to the MOLES api server (default: http://api.catalogue.ceda.ac.uk). :type moles_base: str
Method signatures and docstrings:
- def __init__(self, moles_base=... | b0dcf7fba0f66e8f7d2c61d8ab031d2257a3742e | <|skeleton|>
class CatalogueDatasets:
"""Class to map a filepath to the relate MOLES record :param moles_base: Base URL to the MOLES api server (default: http://api.catalogue.ceda.ac.uk). :type moles_base: str"""
def __init__(self, moles_base='http://api.catalogue.ceda.ac.uk'):
""":param moles_base: Th... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CatalogueDatasets:
"""Class to map a filepath to the relate MOLES record :param moles_base: Base URL to the MOLES api server (default: http://api.catalogue.ceda.ac.uk). :type moles_base: str"""
def __init__(self, moles_base='http://api.catalogue.ceda.ac.uk'):
""":param moles_base: The base URL fo... | the_stack_v2_python_sparse | facet_scanner/collection_handlers/utils/moles_datasets.py | cedadev/facet-scanner | train | 0 |
5568c53b8e8f44243969ce8850a1e295688c95c5 | [
"queue = [root]\nres = []\nwhile queue:\n node = queue.pop(0)\n if node:\n res.append(str(node.val))\n queue.append(node.left)\n queue.append(node.right)\n else:\n res.append('#')\nreturn ','.join(res)",
"vals = data.split(',')\nroot_val = vals.pop(0)\nif root_val == '#':\n ... | <|body_start_0|>
queue = [root]
res = []
while queue:
node = queue.pop(0)
if node:
res.append(str(node.val))
queue.append(node.left)
queue.append(node.right)
else:
res.append('#')
return '... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_025471 | 1,507 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | dca40686c6a280bd394feb8e6e78d40eecf854b9 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
queue = [root]
res = []
while queue:
node = queue.pop(0)
if node:
res.append(str(node.val))
queue.append(node.left... | the_stack_v2_python_sparse | src/amazon/297. Serialize and Deserialize Binary Tree.py | 1325052669/leetcode | train | 0 | |
ba0c5a7f9c03c173a66e5523f0d872fc48d75226 | [
"if not root:\n return [None]\nres = [root.val]\nleft = self.serialize(root.left)\nright = self.serialize(root.right)\nreturn res + left + right",
"if not data:\n return None\nval = data.pop(0)\nif val == None:\n return None\nroot = TreeNode(val)\nroot.left = self.deserialize(data)\nroot.right = self.des... | <|body_start_0|>
if not root:
return [None]
res = [root.val]
left = self.serialize(root.left)
right = self.serialize(root.right)
return res + left + right
<|end_body_0|>
<|body_start_1|>
if not data:
return None
val = data.pop(0)
i... | Codec | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|>
<|body_... | stack_v2_sparse_classes_36k_train_025472 | 2,324 | no_license | [
{
"docstring": "Encodes a tree to a single string. :type root: TreeNode :rtype: str",
"name": "serialize",
"signature": "def serialize(self, root)"
},
{
"docstring": "Decodes your encoded data to tree. :type data: str :rtype: TreeNode",
"name": "deserialize",
"signature": "def deserializ... | 2 | null | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | Implement the Python class `Codec` described below.
Class description:
Implement the Codec class.
Method signatures and docstrings:
- def serialize(self, root): Encodes a tree to a single string. :type root: TreeNode :rtype: str
- def deserialize(self, data): Decodes your encoded data to tree. :type data: str :rtype:... | 9535d038bee690b7c7aeca352a4ab32d188684bb | <|skeleton|>
class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
<|body_0|>
def deserialize(self, data):
"""Decodes your encoded data to tree. :type data: str :rtype: TreeNode"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Codec:
def serialize(self, root):
"""Encodes a tree to a single string. :type root: TreeNode :rtype: str"""
if not root:
return [None]
res = [root.val]
left = self.serialize(root.left)
right = self.serialize(root.right)
return res + left + right
... | the_stack_v2_python_sparse | 0297.py | Agchai52/Leetcode1 | train | 1 | |
d870bc14e27f410951e7876aeb88f69f19ec363f | [
"self.require_collection()\nrequest = http.Request('POST', self.get_url(), self.wrap_object(text))\nreturn (request, parsers.parse_json)",
"self.require_item()\nrequest = http.Request('PUT', self.get_url(), self.wrap_object(text))\nreturn (request, parsers.parse_json)"
] | <|body_start_0|>
self.require_collection()
request = http.Request('POST', self.get_url(), self.wrap_object(text))
return (request, parsers.parse_json)
<|end_body_0|>
<|body_start_1|>
self.require_item()
request = http.Request('PUT', self.get_url(), self.wrap_object(text))
... | UserVoiceTextResource | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class UserVoiceTextResource:
def create(self, text):
"""Create a new resource. :var text: the text of the resource to be created. :vartype text: str"""
<|body_0|>
def update(self, text):
"""Update this resource. :var text: the new text of the resource. :vartype text: str""... | stack_v2_sparse_classes_36k_train_025473 | 2,920 | permissive | [
{
"docstring": "Create a new resource. :var text: the text of the resource to be created. :vartype text: str",
"name": "create",
"signature": "def create(self, text)"
},
{
"docstring": "Update this resource. :var text: the new text of the resource. :vartype text: str",
"name": "update",
... | 2 | stack_v2_sparse_classes_30k_train_016668 | Implement the Python class `UserVoiceTextResource` described below.
Class description:
Implement the UserVoiceTextResource class.
Method signatures and docstrings:
- def create(self, text): Create a new resource. :var text: the text of the resource to be created. :vartype text: str
- def update(self, text): Update th... | Implement the Python class `UserVoiceTextResource` described below.
Class description:
Implement the UserVoiceTextResource class.
Method signatures and docstrings:
- def create(self, text): Create a new resource. :var text: the text of the resource to be created. :vartype text: str
- def update(self, text): Update th... | 25caa745a104c8dc209584fa359294c65dbf88bb | <|skeleton|>
class UserVoiceTextResource:
def create(self, text):
"""Create a new resource. :var text: the text of the resource to be created. :vartype text: str"""
<|body_0|>
def update(self, text):
"""Update this resource. :var text: the new text of the resource. :vartype text: str""... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class UserVoiceTextResource:
def create(self, text):
"""Create a new resource. :var text: the text of the resource to be created. :vartype text: str"""
self.require_collection()
request = http.Request('POST', self.get_url(), self.wrap_object(text))
return (request, parsers.parse_json... | the_stack_v2_python_sparse | libsaas/services/uservoice/resource.py | piplcom/libsaas | train | 1 | |
eebce5055491523aac6d617b41b23e8efa31c585 | [
"line_separated_items = [s + '\\\\\\\\' for s in items]\nTexText.__init__(self, *line_separated_items, **kwargs)\nfor part in self:\n dot = Tex('\\\\cdot').scale(self.dot_scale_factor)\n dot.next_to(part[0], LEFT, SMALL_BUFF)\n part.add_to_back(dot)\nself.arrange(DOWN, aligned_edge=LEFT, buff=self.buff)",
... | <|body_start_0|>
line_separated_items = [s + '\\\\' for s in items]
TexText.__init__(self, *line_separated_items, **kwargs)
for part in self:
dot = Tex('\\cdot').scale(self.dot_scale_factor)
dot.next_to(part[0], LEFT, SMALL_BUFF)
part.add_to_back(dot)
... | 项目列表 | BulletedList | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class BulletedList:
"""项目列表"""
def __init__(self, *items: str, **kwargs):
"""支持多个字符串，每个一行；也支持一个字符串，使用 LaTeX 的换行（\\\\）"""
<|body_0|>
def fade_all_but(self, index_or_string: int | str, opacity: float=0.5) -> None:
"""把除了 ``index_or_string`` 之外的不透明度均设为 ``opacity`` ``index... | stack_v2_sparse_classes_36k_train_025474 | 14,403 | permissive | [
{
"docstring": "支持多个字符串，每个一行；也支持一个字符串，使用 LaTeX 的换行（\\\\\\\\）",
"name": "__init__",
"signature": "def __init__(self, *items: str, **kwargs)"
},
{
"docstring": "把除了 ``index_or_string`` 之外的不透明度均设为 ``opacity`` ``index_or_string`` 可以传入子物体的下标，也可以传入一个字符串",
"name": "fade_all_but",
"signature": "... | 2 | stack_v2_sparse_classes_30k_val_000496 | Implement the Python class `BulletedList` described below.
Class description:
项目列表
Method signatures and docstrings:
- def __init__(self, *items: str, **kwargs): 支持多个字符串，每个一行；也支持一个字符串，使用 LaTeX 的换行（\\\\）
- def fade_all_but(self, index_or_string: int | str, opacity: float=0.5) -> None: 把除了 ``index_or_string`` 之外的不透明度均设... | Implement the Python class `BulletedList` described below.
Class description:
项目列表
Method signatures and docstrings:
- def __init__(self, *items: str, **kwargs): 支持多个字符串，每个一行；也支持一个字符串，使用 LaTeX 的换行（\\\\）
- def fade_all_but(self, index_or_string: int | str, opacity: float=0.5) -> None: 把除了 ``index_or_string`` 之外的不透明度均设... | 99fe80a55cdc5c2fcc249b3645d7f1cd19852bcd | <|skeleton|>
class BulletedList:
"""项目列表"""
def __init__(self, *items: str, **kwargs):
"""支持多个字符串，每个一行；也支持一个字符串，使用 LaTeX 的换行（\\\\）"""
<|body_0|>
def fade_all_but(self, index_or_string: int | str, opacity: float=0.5) -> None:
"""把除了 ``index_or_string`` 之外的不透明度均设为 ``opacity`` ``index... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class BulletedList:
"""项目列表"""
def __init__(self, *items: str, **kwargs):
"""支持多个字符串，每个一行；也支持一个字符串，使用 LaTeX 的换行（\\\\）"""
line_separated_items = [s + '\\\\' for s in items]
TexText.__init__(self, *line_separated_items, **kwargs)
for part in self:
dot = Tex('\\cdot').s... | the_stack_v2_python_sparse | manimlib/mobject/svg/tex_mobject.py | manim-kindergarten/manim | train | 130 |
f7f0f40e4c7e3be96d5a121901d49344f01edff7 | [
"cleaned_data = self.cleaned_data\ntry:\n precio_costo = int(cleaned_data.get('precio_costo'))\n precio_venta = int(cleaned_data.get('precio_venta'))\nexcept (TypeError, ValueError):\n return cleaned_data\nif precio_costo >= precio_venta:\n msg = u'El Precio de costo debe ser menor que el precio de vent... | <|body_start_0|>
cleaned_data = self.cleaned_data
try:
precio_costo = int(cleaned_data.get('precio_costo'))
precio_venta = int(cleaned_data.get('precio_venta'))
except (TypeError, ValueError):
return cleaned_data
if precio_costo >= precio_venta:
... | PromocionForm | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class PromocionForm:
def clean(self):
"""Método de validación personalizado que válida si el precio de costo no sea mayor que el precio de venta"""
<|body_0|>
def clean_codigo(self):
"""Verifica que campo único, quitando espacio en blanco"""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_36k_train_025475 | 14,795 | permissive | [
{
"docstring": "Método de validación personalizado que válida si el precio de costo no sea mayor que el precio de venta",
"name": "clean",
"signature": "def clean(self)"
},
{
"docstring": "Verifica que campo único, quitando espacio en blanco",
"name": "clean_codigo",
"signature": "def cl... | 2 | stack_v2_sparse_classes_30k_train_013893 | Implement the Python class `PromocionForm` described below.
Class description:
Implement the PromocionForm class.
Method signatures and docstrings:
- def clean(self): Método de validación personalizado que válida si el precio de costo no sea mayor que el precio de venta
- def clean_codigo(self): Verifica que campo ún... | Implement the Python class `PromocionForm` described below.
Class description:
Implement the PromocionForm class.
Method signatures and docstrings:
- def clean(self): Método de validación personalizado que válida si el precio de costo no sea mayor que el precio de venta
- def clean_codigo(self): Verifica que campo ún... | 1420e305f41301b8548dfbbabfc64330b74403be | <|skeleton|>
class PromocionForm:
def clean(self):
"""Método de validación personalizado que válida si el precio de costo no sea mayor que el precio de venta"""
<|body_0|>
def clean_codigo(self):
"""Verifica que campo único, quitando espacio en blanco"""
<|body_1|>
<|end_skele... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class PromocionForm:
def clean(self):
"""Método de validación personalizado que válida si el precio de costo no sea mayor que el precio de venta"""
cleaned_data = self.cleaned_data
try:
precio_costo = int(cleaned_data.get('precio_costo'))
precio_venta = int(cleaned_da... | the_stack_v2_python_sparse | AlyMoly/mantenedor/forms.py | CreceLibre/alymoly | train | 0 | |
3d2e01c99ec4c3a8cd5f88d4ced9016fa5dd17d9 | [
"m = hashlib.md5()\nm.update(str(value).strip().encode('utf-8'))\nreturn m.hexdigest()",
"for f in cols:\n df['{}_md5'.format(f)] = df[f].apply(lambda value: cls.func_md5(str(value)))\nreturn df",
"hsobj = hashlib.sha256()\nhsobj.update(value.encode('utf-8'))\nreturn hsobj.hexdigest().upper()",
"for f in c... | <|body_start_0|>
m = hashlib.md5()
m.update(str(value).strip().encode('utf-8'))
return m.hexdigest()
<|end_body_0|>
<|body_start_1|>
for f in cols:
df['{}_md5'.format(f)] = df[f].apply(lambda value: cls.func_md5(str(value)))
return df
<|end_body_1|>
<|body_start_2|>... | ENCRYPT | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ENCRYPT:
def func_md5(cls, value):
"""Get md5 code :param value: value :return: md5 code Examples: func_md5(value='杨海天')"""
<|body_0|>
def get_md5(cls, df, cols=['name', 'id_number', 'mobile_number']):
"""Do md5 encoding for specific columns :param df: dataframe :par... | stack_v2_sparse_classes_36k_train_025476 | 32,015 | no_license | [
{
"docstring": "Get md5 code :param value: value :return: md5 code Examples: func_md5(value='杨海天')",
"name": "func_md5",
"signature": "def func_md5(cls, value)"
},
{
"docstring": "Do md5 encoding for specific columns :param df: dataframe :param cols: columns to encode md5 :return: Examples: df =... | 6 | stack_v2_sparse_classes_30k_train_000350 | Implement the Python class `ENCRYPT` described below.
Class description:
Implement the ENCRYPT class.
Method signatures and docstrings:
- def func_md5(cls, value): Get md5 code :param value: value :return: md5 code Examples: func_md5(value='杨海天')
- def get_md5(cls, df, cols=['name', 'id_number', 'mobile_number']): Do... | Implement the Python class `ENCRYPT` described below.
Class description:
Implement the ENCRYPT class.
Method signatures and docstrings:
- def func_md5(cls, value): Get md5 code :param value: value :return: md5 code Examples: func_md5(value='杨海天')
- def get_md5(cls, df, cols=['name', 'id_number', 'mobile_number']): Do... | 2b6812fcd8a7414e606cc3f5964b171503683144 | <|skeleton|>
class ENCRYPT:
def func_md5(cls, value):
"""Get md5 code :param value: value :return: md5 code Examples: func_md5(value='杨海天')"""
<|body_0|>
def get_md5(cls, df, cols=['name', 'id_number', 'mobile_number']):
"""Do md5 encoding for specific columns :param df: dataframe :par... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ENCRYPT:
def func_md5(cls, value):
"""Get md5 code :param value: value :return: md5 code Examples: func_md5(value='杨海天')"""
m = hashlib.md5()
m.update(str(value).strip().encode('utf-8'))
return m.hexdigest()
def get_md5(cls, df, cols=['name', 'id_number', 'mobile_number'])... | the_stack_v2_python_sparse | model_tools/model_tools/gentools/advtools.py | yanghaitian1/risk_control | train | 3 | |
22d5fdecf6a39aa894c165eeccf682b085e569b3 | [
"self.parentTagId.append(parentTagId)\nself.path.append(path)\nsuper(HierarchicalTag, self).__init__()",
"self.validate_object()\nself.only_available_attrs(['parentTagId', 'path'])\nreturn self.validated_dict"
] | <|body_start_0|>
self.parentTagId.append(parentTagId)
self.path.append(path)
super(HierarchicalTag, self).__init__()
<|end_body_0|>
<|body_start_1|>
self.validate_object()
self.only_available_attrs(['parentTagId', 'path'])
return self.validated_dict
<|end_body_1|>
| HierarchicalTag | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class HierarchicalTag:
def __init__(self, parentTagId: str=Empty, path: list=Empty):
""":param parentTagId: :param path:"""
<|body_0|>
def __todict__(self):
""":return:"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.parentTagId.append(parentTagId)
... | stack_v2_sparse_classes_36k_train_025477 | 776 | no_license | [
{
"docstring": ":param parentTagId: :param path:",
"name": "__init__",
"signature": "def __init__(self, parentTagId: str=Empty, path: list=Empty)"
},
{
"docstring": ":return:",
"name": "__todict__",
"signature": "def __todict__(self)"
}
] | 2 | null | Implement the Python class `HierarchicalTag` described below.
Class description:
Implement the HierarchicalTag class.
Method signatures and docstrings:
- def __init__(self, parentTagId: str=Empty, path: list=Empty): :param parentTagId: :param path:
- def __todict__(self): :return: | Implement the Python class `HierarchicalTag` described below.
Class description:
Implement the HierarchicalTag class.
Method signatures and docstrings:
- def __init__(self, parentTagId: str=Empty, path: list=Empty): :param parentTagId: :param path:
- def __todict__(self): :return:
<|skeleton|>
class HierarchicalTag:... | 623d23917ecf6761e7254d7d6a4881b6a05e11f8 | <|skeleton|>
class HierarchicalTag:
def __init__(self, parentTagId: str=Empty, path: list=Empty):
""":param parentTagId: :param path:"""
<|body_0|>
def __todict__(self):
""":return:"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class HierarchicalTag:
def __init__(self, parentTagId: str=Empty, path: list=Empty):
""":param parentTagId: :param path:"""
self.parentTagId.append(parentTagId)
self.path.append(path)
super(HierarchicalTag, self).__init__()
def __todict__(self):
""":return:"""
se... | the_stack_v2_python_sparse | space_sdk/space_types/tags.py | AnthraxisBR/jetbrains-space-python-sdk | train | 0 | |
f6b1e71ade3babfa4e6a0ca4f0478177dddbdc0a | [
"self.posical = float(posical)\nself.negical = float(negical)\nsuper(flux, self).__init__(var, bad_val)",
"if abs(self.variable) >= 6999:\n self.result = self.bad_value\n return\nif self.variable >= 0:\n self.result = self.posical * self.variable\nelse:\n self.result = self.negical * self.variable"
] | <|body_start_0|>
self.posical = float(posical)
self.negical = float(negical)
super(flux, self).__init__(var, bad_val)
<|end_body_0|>
<|body_start_1|>
if abs(self.variable) >= 6999:
self.result = self.bad_value
return
if self.variable >= 0:
sel... | The function this class reprsents calculats a flux value | flux | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class flux:
"""The function this class reprsents calculats a flux value"""
def __init__(self, var, posical, negical, bad_val=6999):
"""Class initializer Arguments: var: (convertible to float) the domain value posical: (convertible to float) the multiplier for positive values negical: (conv... | stack_v2_sparse_classes_36k_train_025478 | 17,830 | no_license | [
{
"docstring": "Class initializer Arguments: var: (convertible to float) the domain value posical: (convertible to float) the multiplier for positive values negical: (convertible to float) the multiplier for negative values bad_val: (convertible to int) the value to indicate a bad data item",
"name": "__ini... | 2 | stack_v2_sparse_classes_30k_train_018158 | Implement the Python class `flux` described below.
Class description:
The function this class reprsents calculats a flux value
Method signatures and docstrings:
- def __init__(self, var, posical, negical, bad_val=6999): Class initializer Arguments: var: (convertible to float) the domain value posical: (convertible to... | Implement the Python class `flux` described below.
Class description:
The function this class reprsents calculats a flux value
Method signatures and docstrings:
- def __init__(self, var, posical, negical, bad_val=6999): Class initializer Arguments: var: (convertible to float) the domain value posical: (convertible to... | 95d0c102d649c5b028d262f5254106f997a7c77a | <|skeleton|>
class flux:
"""The function this class reprsents calculats a flux value"""
def __init__(self, var, posical, negical, bad_val=6999):
"""Class initializer Arguments: var: (convertible to float) the domain value posical: (convertible to float) the multiplier for positive values negical: (conv... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class flux:
"""The function this class reprsents calculats a flux value"""
def __init__(self, var, posical, negical, bad_val=6999):
"""Class initializer Arguments: var: (convertible to float) the domain value posical: (convertible to float) the multiplier for positive values negical: (convertible to fl... | the_stack_v2_python_sparse | csv_lib/equations.py | rwspicer/csv_utilities | train | 1 |
24da3e2f5f2a4d1053868c84264d30811ad91d2b | [
"url = 'projects/%s/tags/%s' % (project_id, tag)\nresp, body = self.put(url, '{}')\nself.expected_success(201, resp.status)\nreturn rest_client.ResponseBody(resp, body)",
"url = 'projects/%s/tags' % project_id\nresp, body = self.get(url)\nself.expected_success(200, resp.status)\nbody = json.loads(body)\nreturn re... | <|body_start_0|>
url = 'projects/%s/tags/%s' % (project_id, tag)
resp, body = self.put(url, '{}')
self.expected_success(201, resp.status)
return rest_client.ResponseBody(resp, body)
<|end_body_0|>
<|body_start_1|>
url = 'projects/%s/tags' % project_id
resp, body = self.g... | ProjectTagsClient | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ProjectTagsClient:
def update_project_tag(self, project_id, tag):
"""Updates the specified tag and adds it to the project's list of tags."""
<|body_0|>
def list_project_tags(self, project_id):
"""List tags for a project."""
<|body_1|>
def update_all_proj... | stack_v2_sparse_classes_36k_train_025479 | 3,220 | permissive | [
{
"docstring": "Updates the specified tag and adds it to the project's list of tags.",
"name": "update_project_tag",
"signature": "def update_project_tag(self, project_id, tag)"
},
{
"docstring": "List tags for a project.",
"name": "list_project_tags",
"signature": "def list_project_tags... | 6 | stack_v2_sparse_classes_30k_train_009366 | Implement the Python class `ProjectTagsClient` described below.
Class description:
Implement the ProjectTagsClient class.
Method signatures and docstrings:
- def update_project_tag(self, project_id, tag): Updates the specified tag and adds it to the project's list of tags.
- def list_project_tags(self, project_id): L... | Implement the Python class `ProjectTagsClient` described below.
Class description:
Implement the ProjectTagsClient class.
Method signatures and docstrings:
- def update_project_tag(self, project_id, tag): Updates the specified tag and adds it to the project's list of tags.
- def list_project_tags(self, project_id): L... | 3932a799e620a20d7abf7b89e21b520683a1809b | <|skeleton|>
class ProjectTagsClient:
def update_project_tag(self, project_id, tag):
"""Updates the specified tag and adds it to the project's list of tags."""
<|body_0|>
def list_project_tags(self, project_id):
"""List tags for a project."""
<|body_1|>
def update_all_proj... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ProjectTagsClient:
def update_project_tag(self, project_id, tag):
"""Updates the specified tag and adds it to the project's list of tags."""
url = 'projects/%s/tags/%s' % (project_id, tag)
resp, body = self.put(url, '{}')
self.expected_success(201, resp.status)
return r... | the_stack_v2_python_sparse | tempest/lib/services/identity/v3/project_tags_client.py | openstack/tempest | train | 270 | |
f1614b9d5702c7f7b8845016dbff547c4e6649cf | [
"url = self.action\nif url is None:\n return {}\nhalves = url.split('?')\nif len(halves) == 1:\n return {}\nkey_value_pairs = halves[1].split('&')\nreturn dict([pair.split('=') for pair in key_value_pairs])",
"url = self.action\nif url is None:\n return None\nprotocol_and_host = url.split('?')[0]\nhost =... | <|body_start_0|>
url = self.action
if url is None:
return {}
halves = url.split('?')
if len(halves) == 1:
return {}
key_value_pairs = halves[1].split('&')
return dict([pair.split('=') for pair in key_value_pairs])
<|end_body_0|>
<|body_start_1|>
... | Custom element class for <a:hlinkClick> elements. | CT_Hyperlink | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CT_Hyperlink:
"""Custom element class for <a:hlinkClick> elements."""
def action_fields(self):
"""A dictionary containing any key-value pairs present in the query portion of the `ppaction://` URL in the action attribute. For example `{'id':'0', 'return':'true'}` in 'ppaction://custom... | stack_v2_sparse_classes_36k_train_025480 | 1,603 | permissive | [
{
"docstring": "A dictionary containing any key-value pairs present in the query portion of the `ppaction://` URL in the action attribute. For example `{'id':'0', 'return':'true'}` in 'ppaction://customshow?id=0&return=true'. Returns an empty dictionary if the URL contains no query string or if no action attrib... | 2 | null | Implement the Python class `CT_Hyperlink` described below.
Class description:
Custom element class for <a:hlinkClick> elements.
Method signatures and docstrings:
- def action_fields(self): A dictionary containing any key-value pairs present in the query portion of the `ppaction://` URL in the action attribute. For ex... | Implement the Python class `CT_Hyperlink` described below.
Class description:
Custom element class for <a:hlinkClick> elements.
Method signatures and docstrings:
- def action_fields(self): A dictionary containing any key-value pairs present in the query portion of the `ppaction://` URL in the action attribute. For ex... | cabf6e4f1970dc14302f87414f170de19944bac2 | <|skeleton|>
class CT_Hyperlink:
"""Custom element class for <a:hlinkClick> elements."""
def action_fields(self):
"""A dictionary containing any key-value pairs present in the query portion of the `ppaction://` URL in the action attribute. For example `{'id':'0', 'return':'true'}` in 'ppaction://custom... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CT_Hyperlink:
"""Custom element class for <a:hlinkClick> elements."""
def action_fields(self):
"""A dictionary containing any key-value pairs present in the query portion of the `ppaction://` URL in the action attribute. For example `{'id':'0', 'return':'true'}` in 'ppaction://customshow?id=0&ret... | the_stack_v2_python_sparse | Pdf_docx_pptx_xlsx_epub_png/source/pptx/oxml/action.py | ryfeus/lambda-packs | train | 1,283 |
01748e70cb0ed5a7a8237b9c3b3175dc3827d778 | [
"kernel_width = float(kernel_width)\nif kernel is None:\n\n def kernel(d, kernel_width):\n return np.sqrt(np.exp(-d ** 2 / kernel_width ** 2))\nkernel_fn = partial(kernel, kernel_width=kernel_width)\nself.random_state = check_random_state(random_state)\nself.feature_selection = feature_selection\nself.bas... | <|body_start_0|>
kernel_width = float(kernel_width)
if kernel is None:
def kernel(d, kernel_width):
return np.sqrt(np.exp(-d ** 2 / kernel_width ** 2))
kernel_fn = partial(kernel, kernel_width=kernel_width)
self.random_state = check_random_state(random_state)... | Explains predictions on Image (i.e. matrix) data. For numerical features, perturb them by sampling from a Normal(0,1) and doing the inverse operation of mean-centering and scaling, according to the means and stds in the training data. For categorical features, perturb by sampling according to the training distribution,... | LimeImageExplainer | [
"MIT",
"BSD-2-Clause",
"LGPL-2.1-or-later",
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown",
"LicenseRef-scancode-unknown-license-reference"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LimeImageExplainer:
"""Explains predictions on Image (i.e. matrix) data. For numerical features, perturb them by sampling from a Normal(0,1) and doing the inverse operation of mean-centering and scaling, according to the means and stds in the training data. For categorical features, perturb by sa... | stack_v2_sparse_classes_36k_train_025481 | 10,934 | permissive | [
{
"docstring": "Init function. Args: kernel_width: kernel width for the exponential kernel. If None, defaults to sqrt(number of columns) * 0.75. kernel: similarity kernel that takes euclidean distances and kernel width as input and outputs weights in (0,1). If None, defaults to an exponential kernel. verbose: i... | 3 | stack_v2_sparse_classes_30k_train_002771 | Implement the Python class `LimeImageExplainer` described below.
Class description:
Explains predictions on Image (i.e. matrix) data. For numerical features, perturb them by sampling from a Normal(0,1) and doing the inverse operation of mean-centering and scaling, according to the means and stds in the training data. ... | Implement the Python class `LimeImageExplainer` described below.
Class description:
Explains predictions on Image (i.e. matrix) data. For numerical features, perturb them by sampling from a Normal(0,1) and doing the inverse operation of mean-centering and scaling, according to the means and stds in the training data. ... | f59730dc7a8735232ef417685800652372c3b5dd | <|skeleton|>
class LimeImageExplainer:
"""Explains predictions on Image (i.e. matrix) data. For numerical features, perturb them by sampling from a Normal(0,1) and doing the inverse operation of mean-centering and scaling, according to the means and stds in the training data. For categorical features, perturb by sa... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LimeImageExplainer:
"""Explains predictions on Image (i.e. matrix) data. For numerical features, perturb them by sampling from a Normal(0,1) and doing the inverse operation of mean-centering and scaling, according to the means and stds in the training data. For categorical features, perturb by sampling accord... | the_stack_v2_python_sparse | tensorwatch/saliency/lime/lime_image.py | microsoft/tensorwatch | train | 3,626 |
061a058fffce77b9c6c92eaa0c6bce66261fcb2e | [
"self.sent_id_map = {str_i.lower(): i + 1 for i, str_i in enumerate(sentence_ids)}\nself.EOD_index = len(self.sent_id_map)\nself.max_doc_length = max_doc_length + 1\nself.max_sent_length = None\nself.PAD_index = 0",
"numeric_context_docs = []\nfor doc in documents:\n doc = doc.split(' ')\n doc = [self.sent_... | <|body_start_0|>
self.sent_id_map = {str_i.lower(): i + 1 for i, str_i in enumerate(sentence_ids)}
self.EOD_index = len(self.sent_id_map)
self.max_doc_length = max_doc_length + 1
self.max_sent_length = None
self.PAD_index = 0
<|end_body_0|>
<|body_start_1|>
numeric_conte... | Creates numerical representations as input for the CIM model | Processor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Processor:
"""Creates numerical representations as input for the CIM model"""
def __init__(self, sentence_ids, max_doc_length):
"""Stores indexes of sentences, End-of-Sentence index, maximum document and sentence length, and pad index."""
<|body_0|>
def to_numeric_docume... | stack_v2_sparse_classes_36k_train_025482 | 21,540 | no_license | [
{
"docstring": "Stores indexes of sentences, End-of-Sentence index, maximum document and sentence length, and pad index.",
"name": "__init__",
"signature": "def __init__(self, sentence_ids, max_doc_length)"
},
{
"docstring": "Creates numerical representations (sentence ids) for documents (= arti... | 3 | stack_v2_sparse_classes_30k_train_001881 | Implement the Python class `Processor` described below.
Class description:
Creates numerical representations as input for the CIM model
Method signatures and docstrings:
- def __init__(self, sentence_ids, max_doc_length): Stores indexes of sentences, End-of-Sentence index, maximum document and sentence length, and pa... | Implement the Python class `Processor` described below.
Class description:
Creates numerical representations as input for the CIM model
Method signatures and docstrings:
- def __init__(self, sentence_ids, max_doc_length): Stores indexes of sentences, End-of-Sentence index, maximum document and sentence length, and pa... | 400bfa885ddbbc5f1d7c40d3a9e9df37ecc81dc9 | <|skeleton|>
class Processor:
"""Creates numerical representations as input for the CIM model"""
def __init__(self, sentence_ids, max_doc_length):
"""Stores indexes of sentences, End-of-Sentence index, maximum document and sentence length, and pad index."""
<|body_0|>
def to_numeric_docume... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Processor:
"""Creates numerical representations as input for the CIM model"""
def __init__(self, sentence_ids, max_doc_length):
"""Stores indexes of sentences, End-of-Sentence index, maximum document and sentence length, and pad index."""
self.sent_id_map = {str_i.lower(): i + 1 for i, st... | the_stack_v2_python_sparse | experiments/context_inclusive_model.py | vdenberg/context-in-informational-bias-detection | train | 3 |
3b4a8427ba328e0c119320a99e6e9fe68b3fbe35 | [
"if not x:\n return None\nreturn sum(x) / len(x)",
"if not x:\n return None\nx.sort()\nn = len(x)\nif n % 2 == 0:\n return (x[n // 2 - 1] + x[n // 2]) / 2\nelse:\n return x[n // 2]",
"if not x:\n return None\nx.sort()\nn = len(x)\nQ1 = x[n // 4]\nQ3 = x[3 * n // 4]\nreturn [Q1, Q3]",
"if not x:... | <|body_start_0|>
if not x:
return None
return sum(x) / len(x)
<|end_body_0|>
<|body_start_1|>
if not x:
return None
x.sort()
n = len(x)
if n % 2 == 0:
return (x[n // 2 - 1] + x[n // 2]) / 2
else:
return x[n // 2]
<|... | TinyStatistician | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TinyStatistician:
def mean(self, x):
"""Calcule la moyenne de la liste"""
<|body_0|>
def median(self, x):
"""Calcule la mediane"""
<|body_1|>
def quartile(self, x):
"""Calcule le premier et troisieme quartile"""
<|body_2|>
def var(se... | stack_v2_sparse_classes_36k_train_025483 | 1,649 | no_license | [
{
"docstring": "Calcule la moyenne de la liste",
"name": "mean",
"signature": "def mean(self, x)"
},
{
"docstring": "Calcule la mediane",
"name": "median",
"signature": "def median(self, x)"
},
{
"docstring": "Calcule le premier et troisieme quartile",
"name": "quartile",
... | 5 | stack_v2_sparse_classes_30k_train_018851 | Implement the Python class `TinyStatistician` described below.
Class description:
Implement the TinyStatistician class.
Method signatures and docstrings:
- def mean(self, x): Calcule la moyenne de la liste
- def median(self, x): Calcule la mediane
- def quartile(self, x): Calcule le premier et troisieme quartile
- de... | Implement the Python class `TinyStatistician` described below.
Class description:
Implement the TinyStatistician class.
Method signatures and docstrings:
- def mean(self, x): Calcule la moyenne de la liste
- def median(self, x): Calcule la mediane
- def quartile(self, x): Calcule le premier et troisieme quartile
- de... | 24358cc6807d86fe5da766bb4505eef29f1e371f | <|skeleton|>
class TinyStatistician:
def mean(self, x):
"""Calcule la moyenne de la liste"""
<|body_0|>
def median(self, x):
"""Calcule la mediane"""
<|body_1|>
def quartile(self, x):
"""Calcule le premier et troisieme quartile"""
<|body_2|>
def var(se... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TinyStatistician:
def mean(self, x):
"""Calcule la moyenne de la liste"""
if not x:
return None
return sum(x) / len(x)
def median(self, x):
"""Calcule la mediane"""
if not x:
return None
x.sort()
n = len(x)
if n % 2 =... | the_stack_v2_python_sparse | day02/ex05/TinyStatistician.py | Ghilphar/bootcamp_python | train | 0 | |
62f4fc070bb300b5137066cd86588c2bc7aed9f0 | [
"self.component = component\nself.priority = priority\nself.mtype = mtype\nself.view_box_id = view_box_id\nself.view_id = view_id",
"if dictionary is None:\n return None\ncomponent = dictionary.get('component')\npriority = dictionary.get('priority')\nmtype = dictionary.get('type')\nview_box_id = dictionary.get... | <|body_start_0|>
self.component = component
self.priority = priority
self.mtype = mtype
self.view_box_id = view_box_id
self.view_id = view_id
<|end_body_0|>
<|body_start_1|>
if dictionary is None:
return None
component = dictionary.get('component')
... | Implementation of the 'ClusterConfigProto_QoSMapping_QoSContext' model. QoSContext captures the properties that are relevant for QoS in a request. If a new field is added to QoSContext, cluster_config.h should be changed to enhance the hasher (QoSContextHash) and comparator (QoSContextEqual) for QoSContext. Attributes:... | ClusterConfigProto_QoSMapping_QoSContext | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ClusterConfigProto_QoSMapping_QoSContext:
"""Implementation of the 'ClusterConfigProto_QoSMapping_QoSContext' model. QoSContext captures the properties that are relevant for QoS in a request. If a new field is added to QoSContext, cluster_config.h should be changed to enhance the hasher (QoSConte... | stack_v2_sparse_classes_36k_train_025484 | 2,453 | permissive | [
{
"docstring": "Constructor for the ClusterConfigProto_QoSMapping_QoSContext class",
"name": "__init__",
"signature": "def __init__(self, component=None, priority=None, mtype=None, view_box_id=None, view_id=None)"
},
{
"docstring": "Creates an instance of this model from a dictionary Args: dicti... | 2 | stack_v2_sparse_classes_30k_train_005410 | Implement the Python class `ClusterConfigProto_QoSMapping_QoSContext` described below.
Class description:
Implementation of the 'ClusterConfigProto_QoSMapping_QoSContext' model. QoSContext captures the properties that are relevant for QoS in a request. If a new field is added to QoSContext, cluster_config.h should be ... | Implement the Python class `ClusterConfigProto_QoSMapping_QoSContext` described below.
Class description:
Implementation of the 'ClusterConfigProto_QoSMapping_QoSContext' model. QoSContext captures the properties that are relevant for QoS in a request. If a new field is added to QoSContext, cluster_config.h should be ... | e4973dfeb836266904d0369ea845513c7acf261e | <|skeleton|>
class ClusterConfigProto_QoSMapping_QoSContext:
"""Implementation of the 'ClusterConfigProto_QoSMapping_QoSContext' model. QoSContext captures the properties that are relevant for QoS in a request. If a new field is added to QoSContext, cluster_config.h should be changed to enhance the hasher (QoSConte... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ClusterConfigProto_QoSMapping_QoSContext:
"""Implementation of the 'ClusterConfigProto_QoSMapping_QoSContext' model. QoSContext captures the properties that are relevant for QoS in a request. If a new field is added to QoSContext, cluster_config.h should be changed to enhance the hasher (QoSContextHash) and c... | the_stack_v2_python_sparse | cohesity_management_sdk/models/cluster_config_proto_qo_s_mapping_qo_s_context.py | cohesity/management-sdk-python | train | 24 |
eec7ade4a3829cc27460793150918a5081265d99 | [
"def dfs(node, path, path_list):\n if not node:\n return\n current_path = path + [node.val]\n if not node.left and (not node.right):\n path_list.append(current_path)\n if node.left:\n dfs(node.left, current_path, path_list)\n if node.right:\n dfs(node.right, current_path, ... | <|body_start_0|>
def dfs(node, path, path_list):
if not node:
return
current_path = path + [node.val]
if not node.left and (not node.right):
path_list.append(current_path)
if node.left:
dfs(node.left, current_path, p... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def binaryTreePaths(self, root):
""":type root: TreeNode :rtype: List[str]"""
<|body_0|>
def binaryTreePaths_v2(self, root):
""":type root: TreeNode :rtype: List[str]"""
<|body_1|>
def binaryTreePaths2(self, root):
""":type root: TreeNo... | stack_v2_sparse_classes_36k_train_025485 | 3,053 | no_license | [
{
"docstring": ":type root: TreeNode :rtype: List[str]",
"name": "binaryTreePaths",
"signature": "def binaryTreePaths(self, root)"
},
{
"docstring": ":type root: TreeNode :rtype: List[str]",
"name": "binaryTreePaths_v2",
"signature": "def binaryTreePaths_v2(self, root)"
},
{
"doc... | 3 | stack_v2_sparse_classes_30k_train_003068 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def binaryTreePaths(self, root): :type root: TreeNode :rtype: List[str]
- def binaryTreePaths_v2(self, root): :type root: TreeNode :rtype: List[str]
- def binaryTreePaths2(self, ... | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def binaryTreePaths(self, root): :type root: TreeNode :rtype: List[str]
- def binaryTreePaths_v2(self, root): :type root: TreeNode :rtype: List[str]
- def binaryTreePaths2(self, ... | e60ba45fe2f2e5e3b3abfecec3db76f5ce1fde59 | <|skeleton|>
class Solution:
def binaryTreePaths(self, root):
""":type root: TreeNode :rtype: List[str]"""
<|body_0|>
def binaryTreePaths_v2(self, root):
""":type root: TreeNode :rtype: List[str]"""
<|body_1|>
def binaryTreePaths2(self, root):
""":type root: TreeNo... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def binaryTreePaths(self, root):
""":type root: TreeNode :rtype: List[str]"""
def dfs(node, path, path_list):
if not node:
return
current_path = path + [node.val]
if not node.left and (not node.right):
path_list.appe... | the_stack_v2_python_sparse | src/lt_257.py | oxhead/CodingYourWay | train | 0 | |
8a51f3dc2d318d2df430b3033bc3b90db581a878 | [
"progbar = training_utils.get_progbar(model, 'samples' if use_samples else 'steps')\nprogbar.params = callbacks.params\nprogbar.params['verbose'] = verbose\ncallbacks.model.stop_training = False\ncallbacks._call_begin_hook(mode)\nprogbar.on_train_begin()\nself.callbacks = callbacks\nself.progbar = progbar\ntry:\n ... | <|body_start_0|>
progbar = training_utils.get_progbar(model, 'samples' if use_samples else 'steps')
progbar.params = callbacks.params
progbar.params['verbose'] = verbose
callbacks.model.stop_training = False
callbacks._call_begin_hook(mode)
progbar.on_train_begin()
... | Utility object that wrap around callbacks and progress bars. | TrainingContext | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class TrainingContext:
"""Utility object that wrap around callbacks and progress bars."""
def on_start(self, model, callbacks=None, use_samples=False, verbose=0, mode=ModeKeys.TRAIN):
"""Provide a scope for the whole training process."""
<|body_0|>
def on_epoch(self, epoch=0, ... | stack_v2_sparse_classes_36k_train_025486 | 28,479 | permissive | [
{
"docstring": "Provide a scope for the whole training process.",
"name": "on_start",
"signature": "def on_start(self, model, callbacks=None, use_samples=False, verbose=0, mode=ModeKeys.TRAIN)"
},
{
"docstring": "Provide a scope for running one epoch.",
"name": "on_epoch",
"signature": "... | 3 | stack_v2_sparse_classes_30k_train_007865 | Implement the Python class `TrainingContext` described below.
Class description:
Utility object that wrap around callbacks and progress bars.
Method signatures and docstrings:
- def on_start(self, model, callbacks=None, use_samples=False, verbose=0, mode=ModeKeys.TRAIN): Provide a scope for the whole training process... | Implement the Python class `TrainingContext` described below.
Class description:
Utility object that wrap around callbacks and progress bars.
Method signatures and docstrings:
- def on_start(self, model, callbacks=None, use_samples=False, verbose=0, mode=ModeKeys.TRAIN): Provide a scope for the whole training process... | 7cbba04a2ee16d21309eefad5be6585183a2d5a9 | <|skeleton|>
class TrainingContext:
"""Utility object that wrap around callbacks and progress bars."""
def on_start(self, model, callbacks=None, use_samples=False, verbose=0, mode=ModeKeys.TRAIN):
"""Provide a scope for the whole training process."""
<|body_0|>
def on_epoch(self, epoch=0, ... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class TrainingContext:
"""Utility object that wrap around callbacks and progress bars."""
def on_start(self, model, callbacks=None, use_samples=False, verbose=0, mode=ModeKeys.TRAIN):
"""Provide a scope for the whole training process."""
progbar = training_utils.get_progbar(model, 'samples' if ... | the_stack_v2_python_sparse | tensorflow/python/keras/engine/training_v2.py | NVIDIA/tensorflow | train | 763 |
1b7672437aa119e032ee7d055fc61aab601484ee | [
"layer_name = None\nif layer_index > 0:\n dom = xml.dom.minidom.parse('utils/model_info.xml')\n root = dom.documentElement\n model = root.getElementsByTagName(model_name)[0]\n layer_name_str = model.getElementsByTagName('model_layer_name')[0].firstChild.data\n layer_name_list = eval(layer_name_str.re... | <|body_start_0|>
layer_name = None
if layer_index > 0:
dom = xml.dom.minidom.parse('utils/model_info.xml')
root = dom.documentElement
model = root.getElementsByTagName(model_name)[0]
layer_name_str = model.getElementsByTagName('model_layer_name')[0].firstC... | ModelInfo | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ModelInfo:
def get_layer_name_by_index(self, model_name, layer_index):
""":param model_name: :param layer_index: [0,layer_num] layer_index==0 means the input layer. :return:"""
<|body_0|>
def get_layer_shape_by_index(self, model_name, layer_index):
""":param model_na... | stack_v2_sparse_classes_36k_train_025487 | 1,685 | no_license | [
{
"docstring": ":param model_name: :param layer_index: [0,layer_num] layer_index==0 means the input layer. :return:",
"name": "get_layer_name_by_index",
"signature": "def get_layer_name_by_index(self, model_name, layer_index)"
},
{
"docstring": ":param model_name: :param layer_index: [0,layer_nu... | 2 | stack_v2_sparse_classes_30k_test_001025 | Implement the Python class `ModelInfo` described below.
Class description:
Implement the ModelInfo class.
Method signatures and docstrings:
- def get_layer_name_by_index(self, model_name, layer_index): :param model_name: :param layer_index: [0,layer_num] layer_index==0 means the input layer. :return:
- def get_layer_... | Implement the Python class `ModelInfo` described below.
Class description:
Implement the ModelInfo class.
Method signatures and docstrings:
- def get_layer_name_by_index(self, model_name, layer_index): :param model_name: :param layer_index: [0,layer_num] layer_index==0 means the input layer. :return:
- def get_layer_... | 9076a813c803bc9c47054fff7bae2824304da282 | <|skeleton|>
class ModelInfo:
def get_layer_name_by_index(self, model_name, layer_index):
""":param model_name: :param layer_index: [0,layer_num] layer_index==0 means the input layer. :return:"""
<|body_0|>
def get_layer_shape_by_index(self, model_name, layer_index):
""":param model_na... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ModelInfo:
def get_layer_name_by_index(self, model_name, layer_index):
""":param model_name: :param layer_index: [0,layer_num] layer_index==0 means the input layer. :return:"""
layer_name = None
if layer_index > 0:
dom = xml.dom.minidom.parse('utils/model_info.xml')
... | the_stack_v2_python_sparse | old_code/model_info_bk.py | JaneWuNEU/hitdl_server | train | 0 | |
a3f321dea5c763417b3855ba6200a16ed9363cef | [
"super(DQN, self).__init__()\nself.hidden_size = lstm_hidden_size\nself.lstm_layers = lstm_layers\nself.lstm = LSTM(input_size=input_size, hidden_size=self.hidden_size, num_layers=self.lstm_layers, batch_first=True)\nself.network = Sequential(Linear(self.hidden_size, 128), LeakyReLU(inplace=True), BatchNorm1d(num_f... | <|body_start_0|>
super(DQN, self).__init__()
self.hidden_size = lstm_hidden_size
self.lstm_layers = lstm_layers
self.lstm = LSTM(input_size=input_size, hidden_size=self.hidden_size, num_layers=self.lstm_layers, batch_first=True)
self.network = Sequential(Linear(self.hidden_size, ... | DQN | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class DQN:
def __init__(self, input_size, num_actions, lstm_hidden_size=64, lstm_layers=1, get_action_by_boltzmann=True):
"""Double Q-Network. Used for training the Robot behaviour. :param input_size: Int. Size of the Input. :param num_actions: Int. Number of possible actions that can be taken... | stack_v2_sparse_classes_36k_train_025488 | 4,428 | no_license | [
{
"docstring": "Double Q-Network. Used for training the Robot behaviour. :param input_size: Int. Size of the Input. :param num_actions: Int. Number of possible actions that can be taken. :param lstm_hidden_size: Int. Hidden size of the LSTM layers. :param lstm_layers: Int. Number of LSTM layers. :param get_acti... | 5 | stack_v2_sparse_classes_30k_train_002513 | Implement the Python class `DQN` described below.
Class description:
Implement the DQN class.
Method signatures and docstrings:
- def __init__(self, input_size, num_actions, lstm_hidden_size=64, lstm_layers=1, get_action_by_boltzmann=True): Double Q-Network. Used for training the Robot behaviour. :param input_size: I... | Implement the Python class `DQN` described below.
Class description:
Implement the DQN class.
Method signatures and docstrings:
- def __init__(self, input_size, num_actions, lstm_hidden_size=64, lstm_layers=1, get_action_by_boltzmann=True): Double Q-Network. Used for training the Robot behaviour. :param input_size: I... | 5103b2bd78ffbbb42afb892bdca67859324726e9 | <|skeleton|>
class DQN:
def __init__(self, input_size, num_actions, lstm_hidden_size=64, lstm_layers=1, get_action_by_boltzmann=True):
"""Double Q-Network. Used for training the Robot behaviour. :param input_size: Int. Size of the Input. :param num_actions: Int. Number of possible actions that can be taken... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class DQN:
def __init__(self, input_size, num_actions, lstm_hidden_size=64, lstm_layers=1, get_action_by_boltzmann=True):
"""Double Q-Network. Used for training the Robot behaviour. :param input_size: Int. Size of the Input. :param num_actions: Int. Number of possible actions that can be taken. :param lstm_... | the_stack_v2_python_sparse | RobotController/MovementControl/ReinforcementLearningController/DQN.py | Eric-Canas/BabyRobot | train | 1 | |
1222def6f047d91f358fe9173f7af36c86d64538 | [
"self.nums = nums\nself.size_bucket = int(math.ceil(math.sqrt(len(nums))))\nself.bucket = [0 for _ in range(self.size_bucket)]\nfor idx, num in enumerate(nums):\n self.bucket[idx // self.size_bucket] += num",
"self.bucket[i // self.size_bucket] -= self.nums[i]\nself.bucket[i // self.size_bucket] += val\nself.n... | <|body_start_0|>
self.nums = nums
self.size_bucket = int(math.ceil(math.sqrt(len(nums))))
self.bucket = [0 for _ in range(self.size_bucket)]
for idx, num in enumerate(nums):
self.bucket[idx // self.size_bucket] += num
<|end_body_0|>
<|body_start_1|>
self.bucket[i // ... | NumArray | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class NumArray:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def update(self, i, val):
""":type i: int :type val: int :rtype: void"""
<|body_1|>
def sumRange(self, i, j):
""":type i: int :type j: int :rtype: int"""
<|body_2|... | stack_v2_sparse_classes_36k_train_025489 | 1,666 | no_license | [
{
"docstring": ":type nums: List[int]",
"name": "__init__",
"signature": "def __init__(self, nums)"
},
{
"docstring": ":type i: int :type val: int :rtype: void",
"name": "update",
"signature": "def update(self, i, val)"
},
{
"docstring": ":type i: int :type j: int :rtype: int",
... | 3 | null | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def update(self, i, val): :type i: int :type val: int :rtype: void
- def sumRange(self, i, j): :type i: int :type j: int :rtype:... | Implement the Python class `NumArray` described below.
Class description:
Implement the NumArray class.
Method signatures and docstrings:
- def __init__(self, nums): :type nums: List[int]
- def update(self, i, val): :type i: int :type val: int :rtype: void
- def sumRange(self, i, j): :type i: int :type j: int :rtype:... | 11d6bf2ba7b50c07e048df37c4e05c8f46b92241 | <|skeleton|>
class NumArray:
def __init__(self, nums):
""":type nums: List[int]"""
<|body_0|>
def update(self, i, val):
""":type i: int :type val: int :rtype: void"""
<|body_1|>
def sumRange(self, i, j):
""":type i: int :type j: int :rtype: int"""
<|body_2|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class NumArray:
def __init__(self, nums):
""":type nums: List[int]"""
self.nums = nums
self.size_bucket = int(math.ceil(math.sqrt(len(nums))))
self.bucket = [0 for _ in range(self.size_bucket)]
for idx, num in enumerate(nums):
self.bucket[idx // self.size_bucket] ... | the_stack_v2_python_sparse | LeetCodes/Google/RangeSumQueryMutable.py | chutianwen/LeetCodes | train | 0 | |
1eb062ef04791542a0364bf1df407388783c742a | [
"disc_fake_X_hat = disc_X(fake_X.detach())\ndisc_fake_X_loss = adv_criterion(disc_fake_X_hat, torch.zeros_like(disc_fake_X_hat))\ndisc_real_X_hat = disc_X(real_X)\ndisc_real_X_loss = adv_criterion(disc_real_X_hat, torch.ones_like(disc_real_X_hat))\ndisc_loss = (disc_fake_X_loss + disc_real_X_loss) / 2\nreturn disc_... | <|body_start_0|>
disc_fake_X_hat = disc_X(fake_X.detach())
disc_fake_X_loss = adv_criterion(disc_fake_X_hat, torch.zeros_like(disc_fake_X_hat))
disc_real_X_hat = disc_X(real_X)
disc_real_X_loss = adv_criterion(disc_real_X_hat, torch.ones_like(disc_real_X_hat))
disc_loss = (disc_f... | GANLoss | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GANLoss:
def get_disc_loss(self, real_X, fake_X, disc_X, adv_criterion):
"""Return the loss of the discriminator given inputs. Parameters: real_X: the real images from pile X fake_X: the generated images of class X disc_X: the discriminator for class X; takes images and returns real/fake... | stack_v2_sparse_classes_36k_train_025490 | 8,946 | no_license | [
{
"docstring": "Return the loss of the discriminator given inputs. Parameters: real_X: the real images from pile X fake_X: the generated images of class X disc_X: the discriminator for class X; takes images and returns real/fake class X prediction matrices adv_criterion: the adversarial loss function; takes the... | 5 | stack_v2_sparse_classes_30k_train_020797 | Implement the Python class `GANLoss` described below.
Class description:
Implement the GANLoss class.
Method signatures and docstrings:
- def get_disc_loss(self, real_X, fake_X, disc_X, adv_criterion): Return the loss of the discriminator given inputs. Parameters: real_X: the real images from pile X fake_X: the gener... | Implement the Python class `GANLoss` described below.
Class description:
Implement the GANLoss class.
Method signatures and docstrings:
- def get_disc_loss(self, real_X, fake_X, disc_X, adv_criterion): Return the loss of the discriminator given inputs. Parameters: real_X: the real images from pile X fake_X: the gener... | d65bd1b2a6c6310e3107b7bc21639925ee9438e8 | <|skeleton|>
class GANLoss:
def get_disc_loss(self, real_X, fake_X, disc_X, adv_criterion):
"""Return the loss of the discriminator given inputs. Parameters: real_X: the real images from pile X fake_X: the generated images of class X disc_X: the discriminator for class X; takes images and returns real/fake... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GANLoss:
def get_disc_loss(self, real_X, fake_X, disc_X, adv_criterion):
"""Return the loss of the discriminator given inputs. Parameters: real_X: the real images from pile X fake_X: the generated images of class X disc_X: the discriminator for class X; takes images and returns real/fake class X predi... | the_stack_v2_python_sparse | Losses.py | kartikprabhu20/3dReconstruction | train | 0 | |
b4dbb0dbac0b1998c26dba5f2ff9a5a9f95ed488 | [
"ignore = {}\nfor character in ' \\t\\r\\n' + ignorable:\n ignore[character] = None\ncounter = 0\nfor character in text[:position]:\n if ignore.has_key(character):\n counter = counter + 1\nself.position = position - counter",
"ignore = {}\nfor character in ' \\t\\r\\n' + ignorable:\n ignore[charac... | <|body_start_0|>
ignore = {}
for character in ' \t\r\n' + ignorable:
ignore[character] = None
counter = 0
for character in text[:position]:
if ignore.has_key(character):
counter = counter + 1
self.position = position - counter
<|end_body_0|... | Marker | [
"ICU"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Marker:
def save_position(self, text, position, ignorable):
"""Given a TEXT and a POSITION in that text, save the adjusted position by faking that all IGNORABLE characters before POSITION were removed."""
<|body_0|>
def get_position(self, text, ignorable, latest=0):
... | stack_v2_sparse_classes_36k_train_025491 | 45,021 | permissive | [
{
"docstring": "Given a TEXT and a POSITION in that text, save the adjusted position by faking that all IGNORABLE characters before POSITION were removed.",
"name": "save_position",
"signature": "def save_position(self, text, position, ignorable)"
},
{
"docstring": "Given a TEXT, return the valu... | 2 | stack_v2_sparse_classes_30k_train_002953 | Implement the Python class `Marker` described below.
Class description:
Implement the Marker class.
Method signatures and docstrings:
- def save_position(self, text, position, ignorable): Given a TEXT and a POSITION in that text, save the adjusted position by faking that all IGNORABLE characters before POSITION were ... | Implement the Python class `Marker` described below.
Class description:
Implement the Marker class.
Method signatures and docstrings:
- def save_position(self, text, position, ignorable): Given a TEXT and a POSITION in that text, save the adjusted position by faking that all IGNORABLE characters before POSITION were ... | dd054e802d6d8ad80baeccee0396da68144f2a26 | <|skeleton|>
class Marker:
def save_position(self, text, position, ignorable):
"""Given a TEXT and a POSITION in that text, save the adjusted position by faking that all IGNORABLE characters before POSITION were removed."""
<|body_0|>
def get_position(self, text, ignorable, latest=0):
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Marker:
def save_position(self, text, position, ignorable):
"""Given a TEXT and a POSITION in that text, save the adjusted position by faking that all IGNORABLE characters before POSITION were removed."""
ignore = {}
for character in ' \t\r\n' + ignorable:
ignore[character]... | the_stack_v2_python_sparse | ide-integration/Pymacs-0.20/Pymacs/rebox.py | debiancn/bicyclerepair | train | 3 | |
1ff5cf19221fcaf3017c0cc3f48325da8afe2ce5 | [
"try:\n db.show_by_id(show_id, session=session)\nexcept NoResultFound:\n raise NotFoundError('show with ID %s not found' % show_id)\ntry:\n season = db.season_by_id(season_id, session)\nexcept NoResultFound:\n raise NotFoundError('season with ID %s not found' % season_id)\nif not db.season_in_show(show_... | <|body_start_0|>
try:
db.show_by_id(show_id, session=session)
except NoResultFound:
raise NotFoundError('show with ID %s not found' % show_id)
try:
season = db.season_by_id(season_id, session)
except NoResultFound:
raise NotFoundError('seas... | SeriesSeasonAPI | [
"MIT"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class SeriesSeasonAPI:
def get(self, show_id, season_id, session):
"""Get season by show ID and season ID"""
<|body_0|>
def delete(self, show_id, season_id, session):
"""Forgets season by show ID and season ID"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
... | stack_v2_sparse_classes_36k_train_025492 | 47,001 | permissive | [
{
"docstring": "Get season by show ID and season ID",
"name": "get",
"signature": "def get(self, show_id, season_id, session)"
},
{
"docstring": "Forgets season by show ID and season ID",
"name": "delete",
"signature": "def delete(self, show_id, season_id, session)"
}
] | 2 | stack_v2_sparse_classes_30k_train_010247 | Implement the Python class `SeriesSeasonAPI` described below.
Class description:
Implement the SeriesSeasonAPI class.
Method signatures and docstrings:
- def get(self, show_id, season_id, session): Get season by show ID and season ID
- def delete(self, show_id, season_id, session): Forgets season by show ID and seaso... | Implement the Python class `SeriesSeasonAPI` described below.
Class description:
Implement the SeriesSeasonAPI class.
Method signatures and docstrings:
- def get(self, show_id, season_id, session): Get season by show ID and season ID
- def delete(self, show_id, season_id, session): Forgets season by show ID and seaso... | ea95ff60041beaea9aacbc2d93549e3a6b981dc5 | <|skeleton|>
class SeriesSeasonAPI:
def get(self, show_id, season_id, session):
"""Get season by show ID and season ID"""
<|body_0|>
def delete(self, show_id, season_id, session):
"""Forgets season by show ID and season ID"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class SeriesSeasonAPI:
def get(self, show_id, season_id, session):
"""Get season by show ID and season ID"""
try:
db.show_by_id(show_id, session=session)
except NoResultFound:
raise NotFoundError('show with ID %s not found' % show_id)
try:
season =... | the_stack_v2_python_sparse | flexget/components/series/api.py | BrutuZ/Flexget | train | 1 | |
d9631b04a48de845c1965c996a9ec019e42d513b | [
"pre_head = ListNode(-1)\nprev = pre_head\nwhile l1 and l2:\n if l1.val <= l2.val:\n prev.next = l1\n l1 = l1.next\n else:\n prev.next = l2\n l2 = l2.next\n prev = prev.next\nprev.next = l1 if l1 else l2\nreturn pre_head.next",
"if not l1:\n return l2\nelif not l2:\n ret... | <|body_start_0|>
pre_head = ListNode(-1)
prev = pre_head
while l1 and l2:
if l1.val <= l2.val:
prev.next = l1
l1 = l1.next
else:
prev.next = l2
l2 = l2.next
prev = prev.next
prev.next = l1... | LinkedList | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class LinkedList:
def merge_(self, l1: 'ListNode', l2: 'ListNode'):
"""Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param l1: :param l2: :return:"""
<|body_0|>
def merge(self, l1: 'ListNode', l2: 'ListNode'):
"""Approach: Recursion Time Complexity: O(M... | stack_v2_sparse_classes_36k_train_025493 | 1,190 | no_license | [
{
"docstring": "Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param l1: :param l2: :return:",
"name": "merge_",
"signature": "def merge_(self, l1: 'ListNode', l2: 'ListNode')"
},
{
"docstring": "Approach: Recursion Time Complexity: O(M + N) Space Complexity: O(N) :param l1: :... | 2 | null | Implement the Python class `LinkedList` described below.
Class description:
Implement the LinkedList class.
Method signatures and docstrings:
- def merge_(self, l1: 'ListNode', l2: 'ListNode'): Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param l1: :param l2: :return:
- def merge(self, l1: 'ListN... | Implement the Python class `LinkedList` described below.
Class description:
Implement the LinkedList class.
Method signatures and docstrings:
- def merge_(self, l1: 'ListNode', l2: 'ListNode'): Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param l1: :param l2: :return:
- def merge(self, l1: 'ListN... | 65cc78b5afa0db064f9fe8f06597e3e120f7363d | <|skeleton|>
class LinkedList:
def merge_(self, l1: 'ListNode', l2: 'ListNode'):
"""Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param l1: :param l2: :return:"""
<|body_0|>
def merge(self, l1: 'ListNode', l2: 'ListNode'):
"""Approach: Recursion Time Complexity: O(M... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class LinkedList:
def merge_(self, l1: 'ListNode', l2: 'ListNode'):
"""Approach: Iterative Time Complexity: O(N) Space Complexity: O(1) :param l1: :param l2: :return:"""
pre_head = ListNode(-1)
prev = pre_head
while l1 and l2:
if l1.val <= l2.val:
prev.nex... | the_stack_v2_python_sparse | amazon/linked_list/merge_two_sorted_list.py | Shiv2157k/leet_code | train | 1 | |
98d8c72e2d00bfcdfb19067cc89e2f95753ec4f9 | [
"FeatureExtractor.__init__(self)\nself.width = 224\nself.height = 224\nself.channels = 3\nself.final_pools = 7 * 7 * 2048\nself.model = ResNet50(weights='imagenet', include_top=False)\nprint('[INFO] loaded model ok: ResNet50 + imagenet')",
"imgs_p = []\nfor img in imgs:\n if img.shape[0] != self.height or img.... | <|body_start_0|>
FeatureExtractor.__init__(self)
self.width = 224
self.height = 224
self.channels = 3
self.final_pools = 7 * 7 * 2048
self.model = ResNet50(weights='imagenet', include_top=False)
print('[INFO] loaded model ok: ResNet50 + imagenet')
<|end_body_0|>
... | ResNet50特征提取器 es 2018-11-06 | ResNet50FeatureExtractor | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class ResNet50FeatureExtractor:
"""ResNet50特征提取器 es 2018-11-06"""
def __init__(self):
"""初始化"""
<|body_0|>
def preprocess(self, imgs):
"""图像预处理"""
<|body_1|>
def extract_features(self, imgs, batch_size=128, normalizing=False):
"""批量图像特征提取"""
... | stack_v2_sparse_classes_36k_train_025494 | 2,176 | no_license | [
{
"docstring": "初始化",
"name": "__init__",
"signature": "def __init__(self)"
},
{
"docstring": "图像预处理",
"name": "preprocess",
"signature": "def preprocess(self, imgs)"
},
{
"docstring": "批量图像特征提取",
"name": "extract_features",
"signature": "def extract_features(self, imgs, ... | 3 | stack_v2_sparse_classes_30k_train_013320 | Implement the Python class `ResNet50FeatureExtractor` described below.
Class description:
ResNet50特征提取器 es 2018-11-06
Method signatures and docstrings:
- def __init__(self): 初始化
- def preprocess(self, imgs): 图像预处理
- def extract_features(self, imgs, batch_size=128, normalizing=False): 批量图像特征提取 | Implement the Python class `ResNet50FeatureExtractor` described below.
Class description:
ResNet50特征提取器 es 2018-11-06
Method signatures and docstrings:
- def __init__(self): 初始化
- def preprocess(self, imgs): 图像预处理
- def extract_features(self, imgs, batch_size=128, normalizing=False): 批量图像特征提取
<|skeleton|>
class ResN... | 3c756d00c83cd0a8dd745fd32a074c9121977ab8 | <|skeleton|>
class ResNet50FeatureExtractor:
"""ResNet50特征提取器 es 2018-11-06"""
def __init__(self):
"""初始化"""
<|body_0|>
def preprocess(self, imgs):
"""图像预处理"""
<|body_1|>
def extract_features(self, imgs, batch_size=128, normalizing=False):
"""批量图像特征提取"""
... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class ResNet50FeatureExtractor:
"""ResNet50特征提取器 es 2018-11-06"""
def __init__(self):
"""初始化"""
FeatureExtractor.__init__(self)
self.width = 224
self.height = 224
self.channels = 3
self.final_pools = 7 * 7 * 2048
self.model = ResNet50(weights='imagenet', ... | the_stack_v2_python_sparse | feature/resnet50_feature_extractor.py | esfamely/es_face_server | train | 0 |
24aa4eacf1619d0b6886f15e59ec7ddf35b2e85f | [
"self.name = name\nself.units = units\nself.short_desc = short_desc\nself.long_desc = long_desc\nself.query = query\nself._data = dict()\nself._metadata = dict()",
"date = (year, month)\nif date not in self._metadata:\n self._metadata[date] = dict()\nself._metadata[date]['last_request_time'] = datetime.now()\n... | <|body_start_0|>
self.name = name
self.units = units
self.short_desc = short_desc
self.long_desc = long_desc
self.query = query
self._data = dict()
self._metadata = dict()
<|end_body_0|>
<|body_start_1|>
date = (year, month)
if date not in self._m... | A single metric, including its definition and all associated data. Note that this class also deals with the filesystem and makes certain assumptions about how data (and definitions) are represented. Also, data is loaded on demand instead of up front. This is intended to mitigate RAM usage at the expense of load time, b... | Metric | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Metric:
"""A single metric, including its definition and all associated data. Note that this class also deals with the filesystem and makes certain assumptions about how data (and definitions) are represented. Also, data is loaded on demand instead of up front. This is intended to mitigate RAM us... | stack_v2_sparse_classes_36k_train_025495 | 11,452 | permissive | [
{
"docstring": "Constructor. Args: name (string): This metric's name. units (string): Units the metric is measured in. short_desc (string): Short description. long_desc (string): Long description. query (string): BigQuery query string to compute this metric.",
"name": "__init__",
"signature": "def __ini... | 3 | stack_v2_sparse_classes_30k_train_010015 | Implement the Python class `Metric` described below.
Class description:
A single metric, including its definition and all associated data. Note that this class also deals with the filesystem and makes certain assumptions about how data (and definitions) are represented. Also, data is loaded on demand instead of up fro... | Implement the Python class `Metric` described below.
Class description:
A single metric, including its definition and all associated data. Note that this class also deals with the filesystem and makes certain assumptions about how data (and definitions) are represented. Also, data is loaded on demand instead of up fro... | 32027575bdea49c0b645754c7a60666af0c86ba6 | <|skeleton|>
class Metric:
"""A single metric, including its definition and all associated data. Note that this class also deals with the filesystem and makes certain assumptions about how data (and definitions) are represented. Also, data is loaded on demand instead of up front. This is intended to mitigate RAM us... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Metric:
"""A single metric, including its definition and all associated data. Note that this class also deals with the filesystem and makes certain assumptions about how data (and definitions) are represented. Also, data is loaded on demand instead of up front. This is intended to mitigate RAM usage at the ex... | the_stack_v2_python_sparse | common/metrics.py | m-lab/mlab-metrics-api | train | 0 |
f398e5b6d3a97e2f039feea93f2450ed05c87e59 | [
"self.rad = radius\nself.xc = x_center\nself.yc = y_center",
"while True:\n x = self.xc + random.uniform(-1, 1) * self.rad\n y = self.yc + random.uniform(-1, 1) * self.rad\n if (x - self.xc) ** 2 + (y - self.yc) ** 2 <= self.rad ** 2:\n return [x, y]"
] | <|body_start_0|>
self.rad = radius
self.xc = x_center
self.yc = y_center
<|end_body_0|>
<|body_start_1|>
while True:
x = self.xc + random.uniform(-1, 1) * self.rad
y = self.yc + random.uniform(-1, 1) * self.rad
if (x - self.xc) ** 2 + (y - self.yc) **... | Solution | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class Solution:
def __init__(self, radius, x_center, y_center):
""":type radius: float :type x_center: float :type y_center: float"""
<|body_0|>
def randPoint(self):
""":rtype: List[float]"""
<|body_1|>
<|end_skeleton|>
<|body_start_0|>
self.rad = radius
... | stack_v2_sparse_classes_36k_train_025496 | 1,035 | no_license | [
{
"docstring": ":type radius: float :type x_center: float :type y_center: float",
"name": "__init__",
"signature": "def __init__(self, radius, x_center, y_center)"
},
{
"docstring": ":rtype: List[float]",
"name": "randPoint",
"signature": "def randPoint(self)"
}
] | 2 | stack_v2_sparse_classes_30k_train_002848 | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, radius, x_center, y_center): :type radius: float :type x_center: float :type y_center: float
- def randPoint(self): :rtype: List[float] | Implement the Python class `Solution` described below.
Class description:
Implement the Solution class.
Method signatures and docstrings:
- def __init__(self, radius, x_center, y_center): :type radius: float :type x_center: float :type y_center: float
- def randPoint(self): :rtype: List[float]
<|skeleton|>
class Sol... | 6fec95b9b4d735727160905e754a698513bfb7d8 | <|skeleton|>
class Solution:
def __init__(self, radius, x_center, y_center):
""":type radius: float :type x_center: float :type y_center: float"""
<|body_0|>
def randPoint(self):
""":rtype: List[float]"""
<|body_1|>
<|end_skeleton|> | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class Solution:
def __init__(self, radius, x_center, y_center):
""":type radius: float :type x_center: float :type y_center: float"""
self.rad = radius
self.xc = x_center
self.yc = y_center
def randPoint(self):
""":rtype: List[float]"""
while True:
x ... | the_stack_v2_python_sparse | leetcode/math/generate-random-point-in-a-circle.py | jwyx3/practices | train | 2 | |
9fb9f1d57e27dbdff5cd70e19cb4e6db17f944bb | [
"feature = csmlDataset.dataset.getFeature(csmlFeatureId)\ntry:\n long_name = feature.description.CONTENT\nexcept AttributeError:\n long_name = id\nsuper(CsmlLayer, self).__init__(long_name)\nself.dimensions = dict(time=CsmlTimeDimension(feature.getDomain()['time']))\nself.minValue = minValue\nself.maxValue = ... | <|body_start_0|>
feature = csmlDataset.dataset.getFeature(csmlFeatureId)
try:
long_name = feature.description.CONTENT
except AttributeError:
long_name = id
super(CsmlLayer, self).__init__(long_name)
self.dimensions = dict(time=CsmlTimeDimension(feature.get... | This layer extracts selected grids to a temporary NetCDF file and then uses CDMS to serve the data. | CsmlLayer | [
"BSD-3-Clause",
"BSD-2-Clause"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class CsmlLayer:
"""This layer extracts selected grids to a temporary NetCDF file and then uses CDMS to serve the data."""
def __init__(self, csmlFeatureId, csmlDataset, minValue, maxValue):
"""@param csmlFeatureId: The id of the feature instance. @param csmlDataset: A CsmlDataset instance... | stack_v2_sparse_classes_36k_train_025497 | 5,018 | permissive | [
{
"docstring": "@param csmlFeatureId: The id of the feature instance. @param csmlDataset: A CsmlDataset instance. @todo: Get units @todo: tidy up where features are referenced.",
"name": "__init__",
"signature": "def __init__(self, csmlFeatureId, csmlDataset, minValue, maxValue)"
},
{
"docstring... | 2 | stack_v2_sparse_classes_30k_train_020828 | Implement the Python class `CsmlLayer` described below.
Class description:
This layer extracts selected grids to a temporary NetCDF file and then uses CDMS to serve the data.
Method signatures and docstrings:
- def __init__(self, csmlFeatureId, csmlDataset, minValue, maxValue): @param csmlFeatureId: The id of the fea... | Implement the Python class `CsmlLayer` described below.
Class description:
This layer extracts selected grids to a temporary NetCDF file and then uses CDMS to serve the data.
Method signatures and docstrings:
- def __init__(self, csmlFeatureId, csmlDataset, minValue, maxValue): @param csmlFeatureId: The id of the fea... | db9ed729c886b271ce85355b97e39243081e8246 | <|skeleton|>
class CsmlLayer:
"""This layer extracts selected grids to a temporary NetCDF file and then uses CDMS to serve the data."""
def __init__(self, csmlFeatureId, csmlDataset, minValue, maxValue):
"""@param csmlFeatureId: The id of the feature instance. @param csmlDataset: A CsmlDataset instance... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class CsmlLayer:
"""This layer extracts selected grids to a temporary NetCDF file and then uses CDMS to serve the data."""
def __init__(self, csmlFeatureId, csmlDataset, minValue, maxValue):
"""@param csmlFeatureId: The id of the feature instance. @param csmlDataset: A CsmlDataset instance. @todo: Get ... | the_stack_v2_python_sparse | cows/service/imps/pywms/wms_csml.py | cedadev/cows | train | 2 |
fedef0e420a8c8a0895dae29fc3ac74b1676fb4f | [
"self.entity_description = description\nserial_number = coordinator.envoy.serial_number\nassert serial_number is not None\nself.envoy_serial_num = serial_number\nsuper().__init__(coordinator)",
"data = self.coordinator.envoy.data\nassert data is not None\nreturn data"
] | <|body_start_0|>
self.entity_description = description
serial_number = coordinator.envoy.serial_number
assert serial_number is not None
self.envoy_serial_num = serial_number
super().__init__(coordinator)
<|end_body_0|>
<|body_start_1|>
data = self.coordinator.envoy.data
... | Defines a base envoy entity. | EnvoyBaseEntity | [
"Apache-2.0"
] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class EnvoyBaseEntity:
"""Defines a base envoy entity."""
def __init__(self, coordinator: EnphaseUpdateCoordinator, description: EntityDescription) -> None:
"""Init the Enphase base entity."""
<|body_0|>
def data(self) -> EnvoyData:
"""Return envoy data."""
<|b... | stack_v2_sparse_classes_36k_train_025498 | 1,029 | permissive | [
{
"docstring": "Init the Enphase base entity.",
"name": "__init__",
"signature": "def __init__(self, coordinator: EnphaseUpdateCoordinator, description: EntityDescription) -> None"
},
{
"docstring": "Return envoy data.",
"name": "data",
"signature": "def data(self) -> EnvoyData"
}
] | 2 | stack_v2_sparse_classes_30k_train_010000 | Implement the Python class `EnvoyBaseEntity` described below.
Class description:
Defines a base envoy entity.
Method signatures and docstrings:
- def __init__(self, coordinator: EnphaseUpdateCoordinator, description: EntityDescription) -> None: Init the Enphase base entity.
- def data(self) -> EnvoyData: Return envoy... | Implement the Python class `EnvoyBaseEntity` described below.
Class description:
Defines a base envoy entity.
Method signatures and docstrings:
- def __init__(self, coordinator: EnphaseUpdateCoordinator, description: EntityDescription) -> None: Init the Enphase base entity.
- def data(self) -> EnvoyData: Return envoy... | 80caeafcb5b6e2f9da192d0ea6dd1a5b8244b743 | <|skeleton|>
class EnvoyBaseEntity:
"""Defines a base envoy entity."""
def __init__(self, coordinator: EnphaseUpdateCoordinator, description: EntityDescription) -> None:
"""Init the Enphase base entity."""
<|body_0|>
def data(self) -> EnvoyData:
"""Return envoy data."""
<|b... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class EnvoyBaseEntity:
"""Defines a base envoy entity."""
def __init__(self, coordinator: EnphaseUpdateCoordinator, description: EntityDescription) -> None:
"""Init the Enphase base entity."""
self.entity_description = description
serial_number = coordinator.envoy.serial_number
... | the_stack_v2_python_sparse | homeassistant/components/enphase_envoy/entity.py | home-assistant/core | train | 35,501 |
3b1e4071344cc3940cd0e2d77bc1a5a821aaae3c | [
"self.setModel(params)\nchi2 = ((self.model_vals - self.data.d) ** 2 / self.data.sig2).sum()\nreturn -chi2 / 2",
"norm = (self.data.n - 1) * log(2 * pi)\nnorm2 = self.data.lnDetN + dNid\nreturn -0.5 * (norm + norm2)"
] | <|body_start_0|>
self.setModel(params)
chi2 = ((self.model_vals - self.data.d) ** 2 / self.data.sig2).sum()
return -chi2 / 2
<|end_body_0|>
<|body_start_1|>
norm = (self.data.n - 1) * log(2 * pi)
norm2 = self.data.lnDetN + dNid
return -0.5 * (norm + norm2)
<|end_body_1|>... | Represents the likelihood for CMB C_l data. | GaussianLikelihood | [] | stack_v2_sparse_python_classes_v1 | <|skeleton|>
class GaussianLikelihood:
"""Represents the likelihood for CMB C_l data."""
def lnLike(self, params):
"""return ln p(data|params) up to a param-independent term"""
<|body_0|>
def lnNorm(self):
"""return the parameter-independent part of the ln-likelihood."""
<|... | stack_v2_sparse_classes_36k_train_025499 | 784 | no_license | [
{
"docstring": "return ln p(data|params) up to a param-independent term",
"name": "lnLike",
"signature": "def lnLike(self, params)"
},
{
"docstring": "return the parameter-independent part of the ln-likelihood.",
"name": "lnNorm",
"signature": "def lnNorm(self)"
}
] | 2 | null | Implement the Python class `GaussianLikelihood` described below.
Class description:
Represents the likelihood for CMB C_l data.
Method signatures and docstrings:
- def lnLike(self, params): return ln p(data|params) up to a param-independent term
- def lnNorm(self): return the parameter-independent part of the ln-like... | Implement the Python class `GaussianLikelihood` described below.
Class description:
Represents the likelihood for CMB C_l data.
Method signatures and docstrings:
- def lnLike(self, params): return ln p(data|params) up to a param-independent term
- def lnNorm(self): return the parameter-independent part of the ln-like... | e6f49c4a986a70ce040d8b7c09879665d5f81f78 | <|skeleton|>
class GaussianLikelihood:
"""Represents the likelihood for CMB C_l data."""
def lnLike(self, params):
"""return ln p(data|params) up to a param-independent term"""
<|body_0|>
def lnNorm(self):
"""return the parameter-independent part of the ln-likelihood."""
<|... | stack_v2_sparse_classes_36k | data/stack_v2_sparse_classes_30k | class GaussianLikelihood:
"""Represents the likelihood for CMB C_l data."""
def lnLike(self, params):
"""return ln p(data|params) up to a param-independent term"""
self.setModel(params)
chi2 = ((self.model_vals - self.data.d) ** 2 / self.data.sig2).sum()
return -chi2 / 2
de... | the_stack_v2_python_sparse | unused/ClLikelihood.py | defjaf/MCMC | train | 1 |
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