body_hash stringlengths 64 64 | body stringlengths 23 109k | docstring stringlengths 1 57k | path stringlengths 4 198 | name stringlengths 1 115 | repository_name stringlengths 7 111 | repository_stars float64 0 191k | lang stringclasses 1 value | body_without_docstring stringlengths 14 108k | unified stringlengths 45 133k |
|---|---|---|---|---|---|---|---|---|---|
c0c2eae7054cb1816a32c69039498bf23b980ad9bbbbba7a6b2ab1a841891c39 | @app.route('/predict', methods=['POST'])
def predict_medicine():
'\n Return prediction about medicine in image.\n\n Request Format:\n Multipart Form Request with image to run inference sent with key "image"\n\n Response Format:\n JSON Response\n {\n "authentic" : <bool>,\n "name" : <medicine name text>\n "description" : <text description about the medicine>\n }\n '
filestr = request.files['medicine_image'].read()
npimg = numpy.fromstring(filestr, numpy.uint8)
img = cv2.imdecode(npimg, cv2.IMREAD_UNCHANGED)
(authentic, med_name, med_description) = infer_medicine(img)
return jsonify({'authentic': authentic, 'name': med_name, 'description': med_description}) | Return prediction about medicine in image.
Request Format:
Multipart Form Request with image to run inference sent with key "image"
Response Format:
JSON Response
{
"authentic" : <bool>,
"name" : <medicine name text>
"description" : <text description about the medicine>
} | fake/server.py | predict_medicine | PriyanshuRj/BrigAID | 1 | python | @app.route('/predict', methods=['POST'])
def predict_medicine():
'\n Return prediction about medicine in image.\n\n Request Format:\n Multipart Form Request with image to run inference sent with key "image"\n\n Response Format:\n JSON Response\n {\n "authentic" : <bool>,\n "name" : <medicine name text>\n "description" : <text description about the medicine>\n }\n '
filestr = request.files['medicine_image'].read()
npimg = numpy.fromstring(filestr, numpy.uint8)
img = cv2.imdecode(npimg, cv2.IMREAD_UNCHANGED)
(authentic, med_name, med_description) = infer_medicine(img)
return jsonify({'authentic': authentic, 'name': med_name, 'description': med_description}) | @app.route('/predict', methods=['POST'])
def predict_medicine():
'\n Return prediction about medicine in image.\n\n Request Format:\n Multipart Form Request with image to run inference sent with key "image"\n\n Response Format:\n JSON Response\n {\n "authentic" : <bool>,\n "name" : <medicine name text>\n "description" : <text description about the medicine>\n }\n '
filestr = request.files['medicine_image'].read()
npimg = numpy.fromstring(filestr, numpy.uint8)
img = cv2.imdecode(npimg, cv2.IMREAD_UNCHANGED)
(authentic, med_name, med_description) = infer_medicine(img)
return jsonify({'authentic': authentic, 'name': med_name, 'description': med_description})<|docstring|>Return prediction about medicine in image.
Request Format:
Multipart Form Request with image to run inference sent with key "image"
Response Format:
JSON Response
{
"authentic" : <bool>,
"name" : <medicine name text>
"description" : <text description about the medicine>
}<|endoftext|> |
6c0c5dc478a4fee779a4ada7c7a37915686b8646ff507069ac0ad7466a66be79 | def test_part1() -> None:
'\n Examples for Part 1.\n '
assert (manhattan_distance((0, 0, 0, 0), (3, 0, 0, 0)) == 3)
assert (manhattan_distance((9, 0, 0, 0), (0, 3, 0, 0)) == 12)
test_input = '\n'.join(('0,0,0,0', '3,0,0,0', '0,3,0,0', '0,0,3,0', '0,0,0,3', '0,0,0,6', '9,0,0,0', '12,0,0,0'))
test1 = ((0, 0, 0, 0), (3, 0, 0, 0), (0, 3, 0, 0), (0, 0, 3, 0), (0, 0, 0, 3), (0, 0, 0, 6), (9, 0, 0, 0), (12, 0, 0, 0))
assert (read_points(test_input) == test1)
conn_map = connection_map(test1)
assert (all_reachable(conn_map, (0, 0, 0, 0)) == {(0, 0, 0, 0), (3, 0, 0, 0), (0, 3, 0, 0), (0, 0, 3, 0), (0, 0, 0, 3), (0, 0, 0, 6)})
assert (all_reachable(conn_map, (9, 0, 0, 0)) == {(9, 0, 0, 0), (12, 0, 0, 0)})
assert (constellations(test1) == 2)
test2 = (((- 1), 2, 2, 0), (0, 0, 2, (- 2)), (0, 0, 0, (- 2)), ((- 1), 2, 0, 0), ((- 2), (- 2), (- 2), 2), (3, 0, 2, (- 1)), ((- 1), 3, 2, 2), ((- 1), 0, (- 1), 0), (0, 2, 1, (- 2)), (3, 0, 0, 0))
assert (constellations(test2) == 4) | Examples for Part 1. | 2018/day25.py | test_part1 | andypymont/adventofcode | 0 | python | def test_part1() -> None:
'\n \n '
assert (manhattan_distance((0, 0, 0, 0), (3, 0, 0, 0)) == 3)
assert (manhattan_distance((9, 0, 0, 0), (0, 3, 0, 0)) == 12)
test_input = '\n'.join(('0,0,0,0', '3,0,0,0', '0,3,0,0', '0,0,3,0', '0,0,0,3', '0,0,0,6', '9,0,0,0', '12,0,0,0'))
test1 = ((0, 0, 0, 0), (3, 0, 0, 0), (0, 3, 0, 0), (0, 0, 3, 0), (0, 0, 0, 3), (0, 0, 0, 6), (9, 0, 0, 0), (12, 0, 0, 0))
assert (read_points(test_input) == test1)
conn_map = connection_map(test1)
assert (all_reachable(conn_map, (0, 0, 0, 0)) == {(0, 0, 0, 0), (3, 0, 0, 0), (0, 3, 0, 0), (0, 0, 3, 0), (0, 0, 0, 3), (0, 0, 0, 6)})
assert (all_reachable(conn_map, (9, 0, 0, 0)) == {(9, 0, 0, 0), (12, 0, 0, 0)})
assert (constellations(test1) == 2)
test2 = (((- 1), 2, 2, 0), (0, 0, 2, (- 2)), (0, 0, 0, (- 2)), ((- 1), 2, 0, 0), ((- 2), (- 2), (- 2), 2), (3, 0, 2, (- 1)), ((- 1), 3, 2, 2), ((- 1), 0, (- 1), 0), (0, 2, 1, (- 2)), (3, 0, 0, 0))
assert (constellations(test2) == 4) | def test_part1() -> None:
'\n \n '
assert (manhattan_distance((0, 0, 0, 0), (3, 0, 0, 0)) == 3)
assert (manhattan_distance((9, 0, 0, 0), (0, 3, 0, 0)) == 12)
test_input = '\n'.join(('0,0,0,0', '3,0,0,0', '0,3,0,0', '0,0,3,0', '0,0,0,3', '0,0,0,6', '9,0,0,0', '12,0,0,0'))
test1 = ((0, 0, 0, 0), (3, 0, 0, 0), (0, 3, 0, 0), (0, 0, 3, 0), (0, 0, 0, 3), (0, 0, 0, 6), (9, 0, 0, 0), (12, 0, 0, 0))
assert (read_points(test_input) == test1)
conn_map = connection_map(test1)
assert (all_reachable(conn_map, (0, 0, 0, 0)) == {(0, 0, 0, 0), (3, 0, 0, 0), (0, 3, 0, 0), (0, 0, 3, 0), (0, 0, 0, 3), (0, 0, 0, 6)})
assert (all_reachable(conn_map, (9, 0, 0, 0)) == {(9, 0, 0, 0), (12, 0, 0, 0)})
assert (constellations(test1) == 2)
test2 = (((- 1), 2, 2, 0), (0, 0, 2, (- 2)), (0, 0, 0, (- 2)), ((- 1), 2, 0, 0), ((- 2), (- 2), (- 2), 2), (3, 0, 2, (- 1)), ((- 1), 3, 2, 2), ((- 1), 0, (- 1), 0), (0, 2, 1, (- 2)), (3, 0, 0, 0))
assert (constellations(test2) == 4)<|docstring|>Examples for Part 1.<|endoftext|> |
b4c6bdc1286dc6d2067ac0b6392a84427680351d0f1284df89fb0f7bf6ddc827 | def main() -> None:
'\n Calculate and output the solutions based on the real puzzle input.\n '
data = aocd.get_data(year=2018, day=25)
points = read_points(data)
print(f'Part 1: {constellations(points)}') | Calculate and output the solutions based on the real puzzle input. | 2018/day25.py | main | andypymont/adventofcode | 0 | python | def main() -> None:
'\n \n '
data = aocd.get_data(year=2018, day=25)
points = read_points(data)
print(f'Part 1: {constellations(points)}') | def main() -> None:
'\n \n '
data = aocd.get_data(year=2018, day=25)
points = read_points(data)
print(f'Part 1: {constellations(points)}')<|docstring|>Calculate and output the solutions based on the real puzzle input.<|endoftext|> |
f65186f5fbd3373e04237f78714ffa9a94b024cb741c8bfa70e49cedebb1f8bc | def create_tarfile(source_dir: str, output_filename: str='zipped.tar.gz', exclude_function: Optional[Callable[([tarfile.TarInfo], Optional[tarfile.TarInfo])]]=None) -> None:
'Create a compressed representation of source_dir.\n\n Args:\n source_dir: Path to source dir.\n output_filename: Name of outputted gz.\n exclude_function: Function that determines whether to exclude file.\n '
if (exclude_function is None):
def exclude_function(tarinfo: tarfile.TarInfo) -> Optional[tarfile.TarInfo]:
'Exclude files from tar.\n\n Args:\n tarinfo: Any\n\n Returns:\n tarinfo required for exclude.\n '
filename = tarinfo.name
if (('.zenml/' in filename) or ('venv/' in filename)):
return None
else:
return tarinfo
with tarfile.open(output_filename, 'w:gz') as tar:
tar.add(source_dir, arcname='', filter=exclude_function) | Create a compressed representation of source_dir.
Args:
source_dir: Path to source dir.
output_filename: Name of outputted gz.
exclude_function: Function that determines whether to exclude file. | src/zenml/io/utils.py | create_tarfile | Ankur3107/zenml | 1 | python | def create_tarfile(source_dir: str, output_filename: str='zipped.tar.gz', exclude_function: Optional[Callable[([tarfile.TarInfo], Optional[tarfile.TarInfo])]]=None) -> None:
'Create a compressed representation of source_dir.\n\n Args:\n source_dir: Path to source dir.\n output_filename: Name of outputted gz.\n exclude_function: Function that determines whether to exclude file.\n '
if (exclude_function is None):
def exclude_function(tarinfo: tarfile.TarInfo) -> Optional[tarfile.TarInfo]:
'Exclude files from tar.\n\n Args:\n tarinfo: Any\n\n Returns:\n tarinfo required for exclude.\n '
filename = tarinfo.name
if (('.zenml/' in filename) or ('venv/' in filename)):
return None
else:
return tarinfo
with tarfile.open(output_filename, 'w:gz') as tar:
tar.add(source_dir, arcname=, filter=exclude_function) | def create_tarfile(source_dir: str, output_filename: str='zipped.tar.gz', exclude_function: Optional[Callable[([tarfile.TarInfo], Optional[tarfile.TarInfo])]]=None) -> None:
'Create a compressed representation of source_dir.\n\n Args:\n source_dir: Path to source dir.\n output_filename: Name of outputted gz.\n exclude_function: Function that determines whether to exclude file.\n '
if (exclude_function is None):
def exclude_function(tarinfo: tarfile.TarInfo) -> Optional[tarfile.TarInfo]:
'Exclude files from tar.\n\n Args:\n tarinfo: Any\n\n Returns:\n tarinfo required for exclude.\n '
filename = tarinfo.name
if (('.zenml/' in filename) or ('venv/' in filename)):
return None
else:
return tarinfo
with tarfile.open(output_filename, 'w:gz') as tar:
tar.add(source_dir, arcname=, filter=exclude_function)<|docstring|>Create a compressed representation of source_dir.
Args:
source_dir: Path to source dir.
output_filename: Name of outputted gz.
exclude_function: Function that determines whether to exclude file.<|endoftext|> |
0d3612760a793d292a2a4d33ecee311b6f1b13c01f3e159c030714f9b9247860 | def extract_tarfile(source_tar: str, output_dir: str) -> None:
'Extracts all files in a compressed tar file to output_dir.\n\n Args:\n source_tar: Path to a tar compressed file.\n output_dir: Directory where to extract.\n '
if is_remote(source_tar):
raise NotImplementedError('Use local tars for now.')
with tarfile.open(source_tar, 'r:gz') as tar:
tar.extractall(output_dir) | Extracts all files in a compressed tar file to output_dir.
Args:
source_tar: Path to a tar compressed file.
output_dir: Directory where to extract. | src/zenml/io/utils.py | extract_tarfile | Ankur3107/zenml | 1 | python | def extract_tarfile(source_tar: str, output_dir: str) -> None:
'Extracts all files in a compressed tar file to output_dir.\n\n Args:\n source_tar: Path to a tar compressed file.\n output_dir: Directory where to extract.\n '
if is_remote(source_tar):
raise NotImplementedError('Use local tars for now.')
with tarfile.open(source_tar, 'r:gz') as tar:
tar.extractall(output_dir) | def extract_tarfile(source_tar: str, output_dir: str) -> None:
'Extracts all files in a compressed tar file to output_dir.\n\n Args:\n source_tar: Path to a tar compressed file.\n output_dir: Directory where to extract.\n '
if is_remote(source_tar):
raise NotImplementedError('Use local tars for now.')
with tarfile.open(source_tar, 'r:gz') as tar:
tar.extractall(output_dir)<|docstring|>Extracts all files in a compressed tar file to output_dir.
Args:
source_tar: Path to a tar compressed file.
output_dir: Directory where to extract.<|endoftext|> |
453a2f67a8a7ae300a29e1b92e1e01ead44139291d4d329ee1c0a6af344420e0 | def get_global_config_directory() -> str:
'Returns the global config directory for ZenML.'
return click.get_app_dir(APP_NAME) | Returns the global config directory for ZenML. | src/zenml/io/utils.py | get_global_config_directory | Ankur3107/zenml | 1 | python | def get_global_config_directory() -> str:
return click.get_app_dir(APP_NAME) | def get_global_config_directory() -> str:
return click.get_app_dir(APP_NAME)<|docstring|>Returns the global config directory for ZenML.<|endoftext|> |
2479616ce7b8412c6b3739d62b7ad4635f347e13ebeb6dd68925fe48b15845b8 | def write_file_contents_as_string(file_path: str, content: str) -> None:
'Writes contents of file.\n\n Args:\n file_path: Path to file.\n content: Contents of file.\n '
with open(file_path, 'w') as f:
f.write(content) | Writes contents of file.
Args:
file_path: Path to file.
content: Contents of file. | src/zenml/io/utils.py | write_file_contents_as_string | Ankur3107/zenml | 1 | python | def write_file_contents_as_string(file_path: str, content: str) -> None:
'Writes contents of file.\n\n Args:\n file_path: Path to file.\n content: Contents of file.\n '
with open(file_path, 'w') as f:
f.write(content) | def write_file_contents_as_string(file_path: str, content: str) -> None:
'Writes contents of file.\n\n Args:\n file_path: Path to file.\n content: Contents of file.\n '
with open(file_path, 'w') as f:
f.write(content)<|docstring|>Writes contents of file.
Args:
file_path: Path to file.
content: Contents of file.<|endoftext|> |
9fc8a99baad7b37ea5f12379b185d086f0d460f65b0198785a4c61cb3544532f | def read_file_contents_as_string(file_path: str) -> str:
'Reads contents of file.\n\n Args:\n file_path: Path to file.\n '
if (not file_exists(file_path)):
raise FileNotFoundError(f'{file_path} does not exist!')
return open(file_path).read() | Reads contents of file.
Args:
file_path: Path to file. | src/zenml/io/utils.py | read_file_contents_as_string | Ankur3107/zenml | 1 | python | def read_file_contents_as_string(file_path: str) -> str:
'Reads contents of file.\n\n Args:\n file_path: Path to file.\n '
if (not file_exists(file_path)):
raise FileNotFoundError(f'{file_path} does not exist!')
return open(file_path).read() | def read_file_contents_as_string(file_path: str) -> str:
'Reads contents of file.\n\n Args:\n file_path: Path to file.\n '
if (not file_exists(file_path)):
raise FileNotFoundError(f'{file_path} does not exist!')
return open(file_path).read()<|docstring|>Reads contents of file.
Args:
file_path: Path to file.<|endoftext|> |
bf805c050f774054406f1d15ea00c60596ee115669c1baa50a4b3f5b33487393 | def is_gcs_path(path: str) -> bool:
'Returns True if path is on Google Cloud Storage.\n\n Args:\n path: Any path as a string.\n\n Returns:\n True if gcs path, else False.\n '
return path.startswith('gs://') | Returns True if path is on Google Cloud Storage.
Args:
path: Any path as a string.
Returns:
True if gcs path, else False. | src/zenml/io/utils.py | is_gcs_path | Ankur3107/zenml | 1 | python | def is_gcs_path(path: str) -> bool:
'Returns True if path is on Google Cloud Storage.\n\n Args:\n path: Any path as a string.\n\n Returns:\n True if gcs path, else False.\n '
return path.startswith('gs://') | def is_gcs_path(path: str) -> bool:
'Returns True if path is on Google Cloud Storage.\n\n Args:\n path: Any path as a string.\n\n Returns:\n True if gcs path, else False.\n '
return path.startswith('gs://')<|docstring|>Returns True if path is on Google Cloud Storage.
Args:
path: Any path as a string.
Returns:
True if gcs path, else False.<|endoftext|> |
306d018e419eefb4e0712af1557a288c4803ca5e4b2e138e3c4c8048e29cadbc | def exclude_function(tarinfo: tarfile.TarInfo) -> Optional[tarfile.TarInfo]:
'Exclude files from tar.\n\n Args:\n tarinfo: Any\n\n Returns:\n tarinfo required for exclude.\n '
filename = tarinfo.name
if (('.zenml/' in filename) or ('venv/' in filename)):
return None
else:
return tarinfo | Exclude files from tar.
Args:
tarinfo: Any
Returns:
tarinfo required for exclude. | src/zenml/io/utils.py | exclude_function | Ankur3107/zenml | 1 | python | def exclude_function(tarinfo: tarfile.TarInfo) -> Optional[tarfile.TarInfo]:
'Exclude files from tar.\n\n Args:\n tarinfo: Any\n\n Returns:\n tarinfo required for exclude.\n '
filename = tarinfo.name
if (('.zenml/' in filename) or ('venv/' in filename)):
return None
else:
return tarinfo | def exclude_function(tarinfo: tarfile.TarInfo) -> Optional[tarfile.TarInfo]:
'Exclude files from tar.\n\n Args:\n tarinfo: Any\n\n Returns:\n tarinfo required for exclude.\n '
filename = tarinfo.name
if (('.zenml/' in filename) or ('venv/' in filename)):
return None
else:
return tarinfo<|docstring|>Exclude files from tar.
Args:
tarinfo: Any
Returns:
tarinfo required for exclude.<|endoftext|> |
f190740da349a929d18e453ad059a358cd78583d02a22e2e43c2376a23ba68ff | def preprocess_sentence(sentence):
'\n sentence = re.sub(r\'\\*+\', \'\', sentence)\n sentence = re.sub(\n u"[’!"#$%&\'()*+,-./:;<=>?@，。?★、…【】《》？“”‘’！[\\]^_`{|}~]+", "", sentence\n )\n '
sentence = Converter('zh-hans').convert(sentence)
sentence = change_sentence(sentence)
return sentence | sentence = re.sub(r'\*+', '', sentence)
sentence = re.sub(
u"[’!"#$%&'()*+,-./:;<=>?@，。?★、…【】《》？“”‘’！[\]^_`{|}~]+", "", sentence
) | submit/vec_feat_xgb_test/test.py | preprocess_sentence | ubuntu733/SentencePairs | 0 | python | def preprocess_sentence(sentence):
'\n sentence = re.sub(r\'\\*+\', \'\', sentence)\n sentence = re.sub(\n u"[’!"#$%&\'()*+,-./:;<=>?@，。?★、…【】《》？“”‘’！[\\]^_`{|}~]+", , sentence\n )\n '
sentence = Converter('zh-hans').convert(sentence)
sentence = change_sentence(sentence)
return sentence | def preprocess_sentence(sentence):
'\n sentence = re.sub(r\'\\*+\', \'\', sentence)\n sentence = re.sub(\n u"[’!"#$%&\'()*+,-./:;<=>?@，。?★、…【】《》？“”‘’！[\\]^_`{|}~]+", , sentence\n )\n '
sentence = Converter('zh-hans').convert(sentence)
sentence = change_sentence(sentence)
return sentence<|docstring|>sentence = re.sub(r'\*+', '', sentence)
sentence = re.sub(
u"[’!"#$%&'()*+,-./:;<=>?@，。?★、…【】《》？“”‘’！[\]^_`{|}~]+", "", sentence
)<|endoftext|> |
b21716a33aeae6bcafe8c1438bfcc21b97c2c070d8484d9851d6c2615c6991d4 | def ape(y, p):
'Absolute Percentage Error (APE).\n Args:\n y (float): target\n p (float): prediction\n\n Returns:\n e (float): APE\n '
assert (np.abs(y) > EPS)
return np.abs((1 - (p / y))) | Absolute Percentage Error (APE).
Args:
y (float): target
p (float): prediction
Returns:
e (float): APE | causalml/metrics/regression.py | ape | rsoleimani/causalml | 2,919 | python | def ape(y, p):
'Absolute Percentage Error (APE).\n Args:\n y (float): target\n p (float): prediction\n\n Returns:\n e (float): APE\n '
assert (np.abs(y) > EPS)
return np.abs((1 - (p / y))) | def ape(y, p):
'Absolute Percentage Error (APE).\n Args:\n y (float): target\n p (float): prediction\n\n Returns:\n e (float): APE\n '
assert (np.abs(y) > EPS)
return np.abs((1 - (p / y)))<|docstring|>Absolute Percentage Error (APE).
Args:
y (float): target
p (float): prediction
Returns:
e (float): APE<|endoftext|> |
f01195ca4f4434ee9310c83fea36239346cfe5c532c799d01276ff6a8350bdd7 | def mape(y, p):
'Mean Absolute Percentage Error (MAPE).\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): MAPE\n '
filt = (np.abs(y) > EPS)
return np.mean(np.abs((1 - (p[filt] / y[filt])))) | Mean Absolute Percentage Error (MAPE).
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): MAPE | causalml/metrics/regression.py | mape | rsoleimani/causalml | 2,919 | python | def mape(y, p):
'Mean Absolute Percentage Error (MAPE).\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): MAPE\n '
filt = (np.abs(y) > EPS)
return np.mean(np.abs((1 - (p[filt] / y[filt])))) | def mape(y, p):
'Mean Absolute Percentage Error (MAPE).\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): MAPE\n '
filt = (np.abs(y) > EPS)
return np.mean(np.abs((1 - (p[filt] / y[filt]))))<|docstring|>Mean Absolute Percentage Error (MAPE).
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): MAPE<|endoftext|> |
b6cbc3d50460fdbfc83bc24d9a01fd872acd60ba651560c790061b6da532ce75 | def smape(y, p):
'Symmetric Mean Absolute Percentage Error (sMAPE).\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): sMAPE\n '
return (2.0 * np.mean((np.abs((y - p)) / (np.abs(y) + np.abs(p))))) | Symmetric Mean Absolute Percentage Error (sMAPE).
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): sMAPE | causalml/metrics/regression.py | smape | rsoleimani/causalml | 2,919 | python | def smape(y, p):
'Symmetric Mean Absolute Percentage Error (sMAPE).\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): sMAPE\n '
return (2.0 * np.mean((np.abs((y - p)) / (np.abs(y) + np.abs(p))))) | def smape(y, p):
'Symmetric Mean Absolute Percentage Error (sMAPE).\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): sMAPE\n '
return (2.0 * np.mean((np.abs((y - p)) / (np.abs(y) + np.abs(p)))))<|docstring|>Symmetric Mean Absolute Percentage Error (sMAPE).
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): sMAPE<|endoftext|> |
1049ef896a923930c98c837901363ef96953baaf438c6e162be1f24ad707887a | def rmse(y, p):
'Root Mean Squared Error (RMSE).\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): RMSE\n '
assert (y.shape == p.shape)
return np.sqrt(mse(y, p)) | Root Mean Squared Error (RMSE).
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): RMSE | causalml/metrics/regression.py | rmse | rsoleimani/causalml | 2,919 | python | def rmse(y, p):
'Root Mean Squared Error (RMSE).\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): RMSE\n '
assert (y.shape == p.shape)
return np.sqrt(mse(y, p)) | def rmse(y, p):
'Root Mean Squared Error (RMSE).\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): RMSE\n '
assert (y.shape == p.shape)
return np.sqrt(mse(y, p))<|docstring|>Root Mean Squared Error (RMSE).
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): RMSE<|endoftext|> |
c2d20e7358dda072b7f389923758688778f889486a18fff8fae5081b1cdc5a49 | def gini(y, p):
'Normalized Gini Coefficient.\n\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): normalized Gini coefficient\n '
assert (y.shape == p.shape)
n_samples = y.shape[0]
arr = np.array([y, p]).transpose()
true_order = arr[arr[(:, 0)].argsort()][(::(- 1), 0)]
pred_order = arr[arr[(:, 1)].argsort()][(::(- 1), 0)]
l_true = (np.cumsum(true_order) / np.sum(true_order))
l_pred = (np.cumsum(pred_order) / np.sum(pred_order))
l_ones = np.linspace((1 / n_samples), 1, n_samples)
g_true = np.sum((l_ones - l_true))
g_pred = np.sum((l_ones - l_pred))
return (g_pred / g_true) | Normalized Gini Coefficient.
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): normalized Gini coefficient | causalml/metrics/regression.py | gini | rsoleimani/causalml | 2,919 | python | def gini(y, p):
'Normalized Gini Coefficient.\n\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): normalized Gini coefficient\n '
assert (y.shape == p.shape)
n_samples = y.shape[0]
arr = np.array([y, p]).transpose()
true_order = arr[arr[(:, 0)].argsort()][(::(- 1), 0)]
pred_order = arr[arr[(:, 1)].argsort()][(::(- 1), 0)]
l_true = (np.cumsum(true_order) / np.sum(true_order))
l_pred = (np.cumsum(pred_order) / np.sum(pred_order))
l_ones = np.linspace((1 / n_samples), 1, n_samples)
g_true = np.sum((l_ones - l_true))
g_pred = np.sum((l_ones - l_pred))
return (g_pred / g_true) | def gini(y, p):
'Normalized Gini Coefficient.\n\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n\n Returns:\n e (numpy.float64): normalized Gini coefficient\n '
assert (y.shape == p.shape)
n_samples = y.shape[0]
arr = np.array([y, p]).transpose()
true_order = arr[arr[(:, 0)].argsort()][(::(- 1), 0)]
pred_order = arr[arr[(:, 1)].argsort()][(::(- 1), 0)]
l_true = (np.cumsum(true_order) / np.sum(true_order))
l_pred = (np.cumsum(pred_order) / np.sum(pred_order))
l_ones = np.linspace((1 / n_samples), 1, n_samples)
g_true = np.sum((l_ones - l_true))
g_pred = np.sum((l_ones - l_pred))
return (g_pred / g_true)<|docstring|>Normalized Gini Coefficient.
Args:
y (numpy.array): target
p (numpy.array): prediction
Returns:
e (numpy.float64): normalized Gini coefficient<|endoftext|> |
bac4f47a93ff93e943398745ab80b81d7310b8974fc410508be9115ec8f63cd8 | def regression_metrics(y, p, w=None, metrics={'RMSE': rmse, 'sMAPE': smape, 'Gini': gini}):
'Log metrics for regressors.\n\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n w (numpy.array, optional): a treatment vector (1 or True: treatment, 0 or False: control). If given, log\n metrics for the treatment and control group separately\n metrics (dict, optional): a dictionary of the metric names and functions\n '
assert metrics
assert (y.shape[0] == p.shape[0])
for (name, func) in metrics.items():
if (w is not None):
assert (y.shape[0] == w.shape[0])
if (w.dtype != bool):
w = (w == 1)
logger.info('{:>8s} (Control): {:10.4f}'.format(name, func(y[(~ w)], p[(~ w)])))
logger.info('{:>8s} (Treatment): {:10.4f}'.format(name, func(y[w], p[w])))
else:
logger.info('{:>8s}: {:10.4f}'.format(name, func(y, p))) | Log metrics for regressors.
Args:
y (numpy.array): target
p (numpy.array): prediction
w (numpy.array, optional): a treatment vector (1 or True: treatment, 0 or False: control). If given, log
metrics for the treatment and control group separately
metrics (dict, optional): a dictionary of the metric names and functions | causalml/metrics/regression.py | regression_metrics | rsoleimani/causalml | 2,919 | python | def regression_metrics(y, p, w=None, metrics={'RMSE': rmse, 'sMAPE': smape, 'Gini': gini}):
'Log metrics for regressors.\n\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n w (numpy.array, optional): a treatment vector (1 or True: treatment, 0 or False: control). If given, log\n metrics for the treatment and control group separately\n metrics (dict, optional): a dictionary of the metric names and functions\n '
assert metrics
assert (y.shape[0] == p.shape[0])
for (name, func) in metrics.items():
if (w is not None):
assert (y.shape[0] == w.shape[0])
if (w.dtype != bool):
w = (w == 1)
logger.info('{:>8s} (Control): {:10.4f}'.format(name, func(y[(~ w)], p[(~ w)])))
logger.info('{:>8s} (Treatment): {:10.4f}'.format(name, func(y[w], p[w])))
else:
logger.info('{:>8s}: {:10.4f}'.format(name, func(y, p))) | def regression_metrics(y, p, w=None, metrics={'RMSE': rmse, 'sMAPE': smape, 'Gini': gini}):
'Log metrics for regressors.\n\n Args:\n y (numpy.array): target\n p (numpy.array): prediction\n w (numpy.array, optional): a treatment vector (1 or True: treatment, 0 or False: control). If given, log\n metrics for the treatment and control group separately\n metrics (dict, optional): a dictionary of the metric names and functions\n '
assert metrics
assert (y.shape[0] == p.shape[0])
for (name, func) in metrics.items():
if (w is not None):
assert (y.shape[0] == w.shape[0])
if (w.dtype != bool):
w = (w == 1)
logger.info('{:>8s} (Control): {:10.4f}'.format(name, func(y[(~ w)], p[(~ w)])))
logger.info('{:>8s} (Treatment): {:10.4f}'.format(name, func(y[w], p[w])))
else:
logger.info('{:>8s}: {:10.4f}'.format(name, func(y, p)))<|docstring|>Log metrics for regressors.
Args:
y (numpy.array): target
p (numpy.array): prediction
w (numpy.array, optional): a treatment vector (1 or True: treatment, 0 or False: control). If given, log
metrics for the treatment and control group separately
metrics (dict, optional): a dictionary of the metric names and functions<|endoftext|> |
ff5afd1b20f5384889812f6003a95a2b7995a4fde7dbac37769a0f2a999b7eb0 | def is_dirty(self) -> bool:
'Indicates if the local template root is dirty.\n\n Only applicable for VCS-tracked templates.\n '
if (self.vcs == 'git'):
with local.cwd(self.local_abspath):
return bool(git('status', '--porcelain').strip())
return False | Indicates if the local template root is dirty.
Only applicable for VCS-tracked templates. | copier/subproject.py | is_dirty | jacobstr/copier | 438 | python | def is_dirty(self) -> bool:
'Indicates if the local template root is dirty.\n\n Only applicable for VCS-tracked templates.\n '
if (self.vcs == 'git'):
with local.cwd(self.local_abspath):
return bool(git('status', '--porcelain').strip())
return False | def is_dirty(self) -> bool:
'Indicates if the local template root is dirty.\n\n Only applicable for VCS-tracked templates.\n '
if (self.vcs == 'git'):
with local.cwd(self.local_abspath):
return bool(git('status', '--porcelain').strip())
return False<|docstring|>Indicates if the local template root is dirty.
Only applicable for VCS-tracked templates.<|endoftext|> |
2da28a46b70851552eb4609656212e93a3984a6e10ca7d6b35bc44fc6add1609 | @property
def _raw_answers(self) -> AnyByStrDict:
'The last answers, loaded raw as yaml.'
try:
return yaml.safe_load((self.local_abspath / self.answers_relpath).read_text())
except OSError:
return {} | The last answers, loaded raw as yaml. | copier/subproject.py | _raw_answers | jacobstr/copier | 438 | python | @property
def _raw_answers(self) -> AnyByStrDict:
try:
return yaml.safe_load((self.local_abspath / self.answers_relpath).read_text())
except OSError:
return {} | @property
def _raw_answers(self) -> AnyByStrDict:
try:
return yaml.safe_load((self.local_abspath / self.answers_relpath).read_text())
except OSError:
return {}<|docstring|>The last answers, loaded raw as yaml.<|endoftext|> |
e3bcd15241d4d513e6bcf71a1394f8be90af46557c7875ec8bcdad9642f59eea | @cached_property
def last_answers(self) -> AnyByStrDict:
'Last answers, excluding private ones (except _src_path and _commit).'
return {key: value for (key, value) in self._raw_answers.items() if ((key in {'_src_path', '_commit'}) or (not key.startswith('_')))} | Last answers, excluding private ones (except _src_path and _commit). | copier/subproject.py | last_answers | jacobstr/copier | 438 | python | @cached_property
def last_answers(self) -> AnyByStrDict:
return {key: value for (key, value) in self._raw_answers.items() if ((key in {'_src_path', '_commit'}) or (not key.startswith('_')))} | @cached_property
def last_answers(self) -> AnyByStrDict:
return {key: value for (key, value) in self._raw_answers.items() if ((key in {'_src_path', '_commit'}) or (not key.startswith('_')))}<|docstring|>Last answers, excluding private ones (except _src_path and _commit).<|endoftext|> |
675c87fb9b50d1c747d847093b3d6903ea2fabdd3bb93242bf6fb6c354111390 | @cached_property
def template(self) -> Optional[Template]:
'Template, as it was used the last time.'
last_url = self.last_answers.get('_src_path')
last_ref = self.last_answers.get('_commit')
if last_url:
return Template(url=last_url, ref=last_ref) | Template, as it was used the last time. | copier/subproject.py | template | jacobstr/copier | 438 | python | @cached_property
def template(self) -> Optional[Template]:
last_url = self.last_answers.get('_src_path')
last_ref = self.last_answers.get('_commit')
if last_url:
return Template(url=last_url, ref=last_ref) | @cached_property
def template(self) -> Optional[Template]:
last_url = self.last_answers.get('_src_path')
last_ref = self.last_answers.get('_commit')
if last_url:
return Template(url=last_url, ref=last_ref)<|docstring|>Template, as it was used the last time.<|endoftext|> |
6231a5ff8d7d44abb8621bd5a614039072ace3ae9cff0b7375dfb0c9a01f234a | @cached_property
def vcs(self) -> Optional[VCSTypes]:
'VCS type of the subproject.'
if is_in_git_repo(self.local_abspath):
return 'git' | VCS type of the subproject. | copier/subproject.py | vcs | jacobstr/copier | 438 | python | @cached_property
def vcs(self) -> Optional[VCSTypes]:
if is_in_git_repo(self.local_abspath):
return 'git' | @cached_property
def vcs(self) -> Optional[VCSTypes]:
if is_in_git_repo(self.local_abspath):
return 'git'<|docstring|>VCS type of the subproject.<|endoftext|> |
94b3e872a96ff5b9c29a57ac304496ad66e2734a198ee4d8af878609fb830460 | def _get_client(handler):
'\n Get the clients using newer methods from the CloudBolt main repo if this CB is running\n a version greater than 9.2. These internal methods implicitly take care of much of the other\n features in CloudBolt such as proxy and ssl verification.\n Otherwise, manually instantiate clients without support for those other CloudBolt settings.\n :param handler:\n :return:\n '
import settings
from common.methods import is_version_newer
set_progress('Connecting To Azure Management Service...')
cb_version = settings.VERSION_INFO['VERSION']
if is_version_newer(cb_version, '9.2'):
from resourcehandlers.azure_arm.azure_wrapper import configure_arm_client
wrapper = handler.get_api_wrapper()
web_client = configure_arm_client(wrapper, WebSiteManagementClient)
else:
credentials = ServicePrincipalCredentials(client_id=handler.client_id, secret=handler.secret, tenant=handler.tenant_id)
web_client = WebSiteManagementClient(credentials, handler.serviceaccount)
set_progress('Connection to Azure established')
return web_client | Get the clients using newer methods from the CloudBolt main repo if this CB is running
a version greater than 9.2. These internal methods implicitly take care of much of the other
features in CloudBolt such as proxy and ssl verification.
Otherwise, manually instantiate clients without support for those other CloudBolt settings.
:param handler:
:return: | blueprints/azure_web_app/create_azure_website.py | _get_client | gamethis/cloudbolt-forge | 0 | python | def _get_client(handler):
'\n Get the clients using newer methods from the CloudBolt main repo if this CB is running\n a version greater than 9.2. These internal methods implicitly take care of much of the other\n features in CloudBolt such as proxy and ssl verification.\n Otherwise, manually instantiate clients without support for those other CloudBolt settings.\n :param handler:\n :return:\n '
import settings
from common.methods import is_version_newer
set_progress('Connecting To Azure Management Service...')
cb_version = settings.VERSION_INFO['VERSION']
if is_version_newer(cb_version, '9.2'):
from resourcehandlers.azure_arm.azure_wrapper import configure_arm_client
wrapper = handler.get_api_wrapper()
web_client = configure_arm_client(wrapper, WebSiteManagementClient)
else:
credentials = ServicePrincipalCredentials(client_id=handler.client_id, secret=handler.secret, tenant=handler.tenant_id)
web_client = WebSiteManagementClient(credentials, handler.serviceaccount)
set_progress('Connection to Azure established')
return web_client | def _get_client(handler):
'\n Get the clients using newer methods from the CloudBolt main repo if this CB is running\n a version greater than 9.2. These internal methods implicitly take care of much of the other\n features in CloudBolt such as proxy and ssl verification.\n Otherwise, manually instantiate clients without support for those other CloudBolt settings.\n :param handler:\n :return:\n '
import settings
from common.methods import is_version_newer
set_progress('Connecting To Azure Management Service...')
cb_version = settings.VERSION_INFO['VERSION']
if is_version_newer(cb_version, '9.2'):
from resourcehandlers.azure_arm.azure_wrapper import configure_arm_client
wrapper = handler.get_api_wrapper()
web_client = configure_arm_client(wrapper, WebSiteManagementClient)
else:
credentials = ServicePrincipalCredentials(client_id=handler.client_id, secret=handler.secret, tenant=handler.tenant_id)
web_client = WebSiteManagementClient(credentials, handler.serviceaccount)
set_progress('Connection to Azure established')
return web_client<|docstring|>Get the clients using newer methods from the CloudBolt main repo if this CB is running
a version greater than 9.2. These internal methods implicitly take care of much of the other
features in CloudBolt such as proxy and ssl verification.
Otherwise, manually instantiate clients without support for those other CloudBolt settings.
:param handler:
:return:<|endoftext|> |
53bdbfcb9fdfaa491febc83e7689cb811020d978689c22438a314bc832ff395a | def get_generator(data_path):
'Create lists of validation and test generators'
crop_224_valid_dir = os.path.join(data_path, '224', 'crop', 'Validation')
crop_224_test_dir = os.path.join(data_path, '224', 'crop', 'Test')
uncrop_224_valid_dir = os.path.join(data_path, '224', 'uncrop', 'Validation')
uncrop_224_test_dir = os.path.join(data_path, '224', 'uncrop', 'Test')
crop_331_valid_dir = os.path.join(data_path, '331', 'crop', 'Validation')
crop_331_test_dir = os.path.join(data_path, '331', 'crop', 'Test')
uncrop_331_valid_dir = os.path.join(data_path, '331', 'uncrop', 'Validation')
uncrop_331_test_dir = os.path.join(data_path, '331', 'uncrop', 'Test')
if (not (os.path.exists(crop_224_valid_dir) and os.path.exists(crop_331_valid_dir) and os.path.exists(uncrop_224_valid_dir) and os.path.exists(uncrop_331_valid_dir) and os.path.exists(crop_224_test_dir) and os.path.exists(uncrop_224_test_dir) and os.path.exists(crop_331_test_dir) and os.path.exists(uncrop_331_test_dir))):
print('Data path is invalid. Please check that directory tree is set up as described in README file.')
exit()
valid_gen = get_datagenerators_folders(uncrop_224_valid_dir, crop_224_valid_dir, uncrop_331_valid_dir, crop_331_valid_dir, batch_size=16)
test_gen = get_datagenerators_folders(uncrop_224_test_dir, crop_224_test_dir, uncrop_331_test_dir, crop_331_test_dir, batch_size=16)
combined_gen = (valid_gen[0:4] + test_gen[0:4])
return combined_gen | Create lists of validation and test generators | ensemble.py | get_generator | jsheng7/DeepCovidXR | 0 | python | def get_generator(data_path):
crop_224_valid_dir = os.path.join(data_path, '224', 'crop', 'Validation')
crop_224_test_dir = os.path.join(data_path, '224', 'crop', 'Test')
uncrop_224_valid_dir = os.path.join(data_path, '224', 'uncrop', 'Validation')
uncrop_224_test_dir = os.path.join(data_path, '224', 'uncrop', 'Test')
crop_331_valid_dir = os.path.join(data_path, '331', 'crop', 'Validation')
crop_331_test_dir = os.path.join(data_path, '331', 'crop', 'Test')
uncrop_331_valid_dir = os.path.join(data_path, '331', 'uncrop', 'Validation')
uncrop_331_test_dir = os.path.join(data_path, '331', 'uncrop', 'Test')
if (not (os.path.exists(crop_224_valid_dir) and os.path.exists(crop_331_valid_dir) and os.path.exists(uncrop_224_valid_dir) and os.path.exists(uncrop_331_valid_dir) and os.path.exists(crop_224_test_dir) and os.path.exists(uncrop_224_test_dir) and os.path.exists(crop_331_test_dir) and os.path.exists(uncrop_331_test_dir))):
print('Data path is invalid. Please check that directory tree is set up as described in README file.')
exit()
valid_gen = get_datagenerators_folders(uncrop_224_valid_dir, crop_224_valid_dir, uncrop_331_valid_dir, crop_331_valid_dir, batch_size=16)
test_gen = get_datagenerators_folders(uncrop_224_test_dir, crop_224_test_dir, uncrop_331_test_dir, crop_331_test_dir, batch_size=16)
combined_gen = (valid_gen[0:4] + test_gen[0:4])
return combined_gen | def get_generator(data_path):
crop_224_valid_dir = os.path.join(data_path, '224', 'crop', 'Validation')
crop_224_test_dir = os.path.join(data_path, '224', 'crop', 'Test')
uncrop_224_valid_dir = os.path.join(data_path, '224', 'uncrop', 'Validation')
uncrop_224_test_dir = os.path.join(data_path, '224', 'uncrop', 'Test')
crop_331_valid_dir = os.path.join(data_path, '331', 'crop', 'Validation')
crop_331_test_dir = os.path.join(data_path, '331', 'crop', 'Test')
uncrop_331_valid_dir = os.path.join(data_path, '331', 'uncrop', 'Validation')
uncrop_331_test_dir = os.path.join(data_path, '331', 'uncrop', 'Test')
if (not (os.path.exists(crop_224_valid_dir) and os.path.exists(crop_331_valid_dir) and os.path.exists(uncrop_224_valid_dir) and os.path.exists(uncrop_331_valid_dir) and os.path.exists(crop_224_test_dir) and os.path.exists(uncrop_224_test_dir) and os.path.exists(crop_331_test_dir) and os.path.exists(uncrop_331_test_dir))):
print('Data path is invalid. Please check that directory tree is set up as described in README file.')
exit()
valid_gen = get_datagenerators_folders(uncrop_224_valid_dir, crop_224_valid_dir, uncrop_331_valid_dir, crop_331_valid_dir, batch_size=16)
test_gen = get_datagenerators_folders(uncrop_224_test_dir, crop_224_test_dir, uncrop_331_test_dir, crop_331_test_dir, batch_size=16)
combined_gen = (valid_gen[0:4] + test_gen[0:4])
return combined_gen<|docstring|>Create lists of validation and test generators<|endoftext|> |
742a65ebf3a8cf98e8397d2ec8504171c375d1f2e5be0fe7efadc214dd483f5b | def create_member(model_name, model, generator_list):
'Create a member of model ensemble'
name_parts = model_name.split('_')
if (('224' in name_parts) and ('uncrop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[0], val_batches=generator_list[4])
elif (('224' in name_parts) and ('crop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[1], val_batches=generator_list[5])
elif (('331' in name_parts) and ('uncrop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[2], val_batches=generator_list[6])
elif (('331' in name_parts) and ('crop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[3], val_batches=generator_list[7])
return member | Create a member of model ensemble | ensemble.py | create_member | jsheng7/DeepCovidXR | 0 | python | def create_member(model_name, model, generator_list):
name_parts = model_name.split('_')
if (('224' in name_parts) and ('uncrop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[0], val_batches=generator_list[4])
elif (('224' in name_parts) and ('crop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[1], val_batches=generator_list[5])
elif (('331' in name_parts) and ('uncrop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[2], val_batches=generator_list[6])
elif (('331' in name_parts) and ('crop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[3], val_batches=generator_list[7])
return member | def create_member(model_name, model, generator_list):
name_parts = model_name.split('_')
if (('224' in name_parts) and ('uncrop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[0], val_batches=generator_list[4])
elif (('224' in name_parts) and ('crop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[1], val_batches=generator_list[5])
elif (('331' in name_parts) and ('uncrop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[2], val_batches=generator_list[6])
elif (('331' in name_parts) and ('crop' in name_parts)):
member = KerasMember(name=model_name, keras_model=model, train_batches=generator_list[3], val_batches=generator_list[7])
return member<|docstring|>Create a member of model ensemble<|endoftext|> |
fe08268a20ed01bfcb71f34787423ebcb9a26ab3b325cdbf886efdd559706cd1 | def get_members(combined_generator_list, weight_path):
'Creates the list of members for ensembling from a list of data generators and corresponding model weights'
model_list = get_model_list(weight_path)
model_name_list = ['dense_224_uncrop', 'dense_224_crop', 'dense_331_uncrop', 'dense_331_crop', 'res_224_uncrop', 'res_224_crop', 'res_331_uncrop', 'res_331_crop', 'inception_224_uncrop', 'inception_224_crop', 'inception_331_uncrop', 'inception_331_crop', 'inceptionresnet_224_uncrop', 'inceptionresnet_224_crop', 'inceptionresnet_331_uncrop', 'inceptionresnet_331_crop', 'xception_224_uncrop', 'xception_224_crop', 'xception_331_uncrop', 'xception_331_crop', 'efficient_224_uncrop', 'efficient_224_crop', 'efficient_331_uncrop', 'efficient_331_crop']
member_list = []
for (model_name, model) in zip(model_name_list, model_list):
member = create_member(model_name, model, combined_generator_list)
member_list.append(member)
return member_list | Creates the list of members for ensembling from a list of data generators and corresponding model weights | ensemble.py | get_members | jsheng7/DeepCovidXR | 0 | python | def get_members(combined_generator_list, weight_path):
model_list = get_model_list(weight_path)
model_name_list = ['dense_224_uncrop', 'dense_224_crop', 'dense_331_uncrop', 'dense_331_crop', 'res_224_uncrop', 'res_224_crop', 'res_331_uncrop', 'res_331_crop', 'inception_224_uncrop', 'inception_224_crop', 'inception_331_uncrop', 'inception_331_crop', 'inceptionresnet_224_uncrop', 'inceptionresnet_224_crop', 'inceptionresnet_331_uncrop', 'inceptionresnet_331_crop', 'xception_224_uncrop', 'xception_224_crop', 'xception_331_uncrop', 'xception_331_crop', 'efficient_224_uncrop', 'efficient_224_crop', 'efficient_331_uncrop', 'efficient_331_crop']
member_list = []
for (model_name, model) in zip(model_name_list, model_list):
member = create_member(model_name, model, combined_generator_list)
member_list.append(member)
return member_list | def get_members(combined_generator_list, weight_path):
model_list = get_model_list(weight_path)
model_name_list = ['dense_224_uncrop', 'dense_224_crop', 'dense_331_uncrop', 'dense_331_crop', 'res_224_uncrop', 'res_224_crop', 'res_331_uncrop', 'res_331_crop', 'inception_224_uncrop', 'inception_224_crop', 'inception_331_uncrop', 'inception_331_crop', 'inceptionresnet_224_uncrop', 'inceptionresnet_224_crop', 'inceptionresnet_331_uncrop', 'inceptionresnet_331_crop', 'xception_224_uncrop', 'xception_224_crop', 'xception_331_uncrop', 'xception_331_crop', 'efficient_224_uncrop', 'efficient_224_crop', 'efficient_331_uncrop', 'efficient_331_crop']
member_list = []
for (model_name, model) in zip(model_name_list, model_list):
member = create_member(model_name, model, combined_generator_list)
member_list.append(member)
return member_list<|docstring|>Creates the list of members for ensembling from a list of data generators and corresponding model weights<|endoftext|> |
c967b83c5b6feaa6a051b40c5d2c858a3f8c52c4ce2c6761cb0583f8ffdca8ae | def ensemble_members(member_list):
'Calculates weights for each model of an ensemble for weighted averaging of predictions using random\n search of a Dirichlet distribution'
wAvgEnsemble = DirichletEnsemble()
wAvgEnsemble.add_members(member_list)
wAvgEnsemble.fit()
wAvgEnsemble.describe()
combined_weighted_probs = []
combined_probs = []
for (member, weight) in zip(member_list, wAvgEnsemble.bestweights):
weighted_probs = np.multiply(member.val_probs, weight)
combined_weighted_probs.append(weighted_probs)
combined_probs.append(member.val_probs)
combined_weighted_probs = np.asarray(combined_weighted_probs)
individual_preds = pd.DataFrame(np.squeeze(np.stack(combined_probs, axis=(- 1))), columns=[member.name for member in member_list])
ensemble_pred = np.sum(combined_weighted_probs, axis=0)
ensemble_pred_round = np.round(ensemble_pred)
return (wAvgEnsemble.bestweights, ensemble_pred, ensemble_pred_round, individual_preds) | Calculates weights for each model of an ensemble for weighted averaging of predictions using random
search of a Dirichlet distribution | ensemble.py | ensemble_members | jsheng7/DeepCovidXR | 0 | python | def ensemble_members(member_list):
'Calculates weights for each model of an ensemble for weighted averaging of predictions using random\n search of a Dirichlet distribution'
wAvgEnsemble = DirichletEnsemble()
wAvgEnsemble.add_members(member_list)
wAvgEnsemble.fit()
wAvgEnsemble.describe()
combined_weighted_probs = []
combined_probs = []
for (member, weight) in zip(member_list, wAvgEnsemble.bestweights):
weighted_probs = np.multiply(member.val_probs, weight)
combined_weighted_probs.append(weighted_probs)
combined_probs.append(member.val_probs)
combined_weighted_probs = np.asarray(combined_weighted_probs)
individual_preds = pd.DataFrame(np.squeeze(np.stack(combined_probs, axis=(- 1))), columns=[member.name for member in member_list])
ensemble_pred = np.sum(combined_weighted_probs, axis=0)
ensemble_pred_round = np.round(ensemble_pred)
return (wAvgEnsemble.bestweights, ensemble_pred, ensemble_pred_round, individual_preds) | def ensemble_members(member_list):
'Calculates weights for each model of an ensemble for weighted averaging of predictions using random\n search of a Dirichlet distribution'
wAvgEnsemble = DirichletEnsemble()
wAvgEnsemble.add_members(member_list)
wAvgEnsemble.fit()
wAvgEnsemble.describe()
combined_weighted_probs = []
combined_probs = []
for (member, weight) in zip(member_list, wAvgEnsemble.bestweights):
weighted_probs = np.multiply(member.val_probs, weight)
combined_weighted_probs.append(weighted_probs)
combined_probs.append(member.val_probs)
combined_weighted_probs = np.asarray(combined_weighted_probs)
individual_preds = pd.DataFrame(np.squeeze(np.stack(combined_probs, axis=(- 1))), columns=[member.name for member in member_list])
ensemble_pred = np.sum(combined_weighted_probs, axis=0)
ensemble_pred_round = np.round(ensemble_pred)
return (wAvgEnsemble.bestweights, ensemble_pred, ensemble_pred_round, individual_preds)<|docstring|>Calculates weights for each model of an ensemble for weighted averaging of predictions using random
search of a Dirichlet distribution<|endoftext|> |
e8b01d70992376b36156a78a9fcf8aba3ccbc48767d3d306385d82b8ee69ecb1 | def _cnfify(exprs):
'Convert a sequence of expressions to their CNF form.'
return [(('(' + str(to_cnf(expr))) + ')') for expr in exprs] | Convert a sequence of expressions to their CNF form. | donatello/factor_32.py | _cnfify | welchbj/donatello | 2 | python | def _cnfify(exprs):
return [(('(' + str(to_cnf(expr))) + ')') for expr in exprs] | def _cnfify(exprs):
return [(('(' + str(to_cnf(expr))) + ')') for expr in exprs]<|docstring|>Convert a sequence of expressions to their CNF form.<|endoftext|> |
bb55881e0d7cfe68adcc1e4cae7cf1c6131f35120b39c4b5b9deac8afd2e25be | @lru_cache(maxsize=None)
def _factor_bitwise(target, num_bits, bad_chars, ops, start_value):
'The engine behind everything novel in this project.\n\n Args:\n target (int): TODO\n bad_chars (Tuple[int]): TODO\n num_bits (int): TODO\n ops (List[str]): TODO\n num_factors (int): TODO\n start_value (int): TODO\n\n Returns:\n List[int]: TODO\n\n '
num_factors = len(ops)
factor_clauses = []
for i in range(num_bits):
bit_vars = iter(('b{}_f{}'.format(i, j) for j in range(num_factors)))
clause = str(int(bool((start_value & (1 << i)))))
for (op, bit_var) in zip(ops, bit_vars):
clause = '({} {} {})'.format(clause, op, bit_var)
if (not (target & (1 << i))):
clause = ('~' + clause)
factor_clauses.append(clause)
char_constraint_clauses = []
for (bad_char, j) in product(bad_chars, range(num_factors)):
bit_vars = iter(('b{}_f{}'.format(i, j) for i in range(num_bits)))
clause = [(var if (bad_char & (1 << i)) else ('~' + var)) for (i, var) in enumerate(bit_vars)]
char_constraint_clauses.append((('~(' + ' and '.join(clause)) + ')'))
cnf_clauses = chain(_cnfify(factor_clauses), _cnfify(char_constraint_clauses))
expr = ' and '.join(cnf_clauses)
b = BooleanExpression(expr)
sat_sol = b.sat_one()
if (sat_sol is None):
return None
factors = []
for j in range(num_factors):
factor = 0
for i in range(num_bits):
bit = getattr(sat_sol, 'b{}_f{}'.format(i, j))
factor |= (bit << i)
factors.append(factor)
return factors | The engine behind everything novel in this project.
Args:
target (int): TODO
bad_chars (Tuple[int]): TODO
num_bits (int): TODO
ops (List[str]): TODO
num_factors (int): TODO
start_value (int): TODO
Returns:
List[int]: TODO | donatello/factor_32.py | _factor_bitwise | welchbj/donatello | 2 | python | @lru_cache(maxsize=None)
def _factor_bitwise(target, num_bits, bad_chars, ops, start_value):
'The engine behind everything novel in this project.\n\n Args:\n target (int): TODO\n bad_chars (Tuple[int]): TODO\n num_bits (int): TODO\n ops (List[str]): TODO\n num_factors (int): TODO\n start_value (int): TODO\n\n Returns:\n List[int]: TODO\n\n '
num_factors = len(ops)
factor_clauses = []
for i in range(num_bits):
bit_vars = iter(('b{}_f{}'.format(i, j) for j in range(num_factors)))
clause = str(int(bool((start_value & (1 << i)))))
for (op, bit_var) in zip(ops, bit_vars):
clause = '({} {} {})'.format(clause, op, bit_var)
if (not (target & (1 << i))):
clause = ('~' + clause)
factor_clauses.append(clause)
char_constraint_clauses = []
for (bad_char, j) in product(bad_chars, range(num_factors)):
bit_vars = iter(('b{}_f{}'.format(i, j) for i in range(num_bits)))
clause = [(var if (bad_char & (1 << i)) else ('~' + var)) for (i, var) in enumerate(bit_vars)]
char_constraint_clauses.append((('~(' + ' and '.join(clause)) + ')'))
cnf_clauses = chain(_cnfify(factor_clauses), _cnfify(char_constraint_clauses))
expr = ' and '.join(cnf_clauses)
b = BooleanExpression(expr)
sat_sol = b.sat_one()
if (sat_sol is None):
return None
factors = []
for j in range(num_factors):
factor = 0
for i in range(num_bits):
bit = getattr(sat_sol, 'b{}_f{}'.format(i, j))
factor |= (bit << i)
factors.append(factor)
return factors | @lru_cache(maxsize=None)
def _factor_bitwise(target, num_bits, bad_chars, ops, start_value):
'The engine behind everything novel in this project.\n\n Args:\n target (int): TODO\n bad_chars (Tuple[int]): TODO\n num_bits (int): TODO\n ops (List[str]): TODO\n num_factors (int): TODO\n start_value (int): TODO\n\n Returns:\n List[int]: TODO\n\n '
num_factors = len(ops)
factor_clauses = []
for i in range(num_bits):
bit_vars = iter(('b{}_f{}'.format(i, j) for j in range(num_factors)))
clause = str(int(bool((start_value & (1 << i)))))
for (op, bit_var) in zip(ops, bit_vars):
clause = '({} {} {})'.format(clause, op, bit_var)
if (not (target & (1 << i))):
clause = ('~' + clause)
factor_clauses.append(clause)
char_constraint_clauses = []
for (bad_char, j) in product(bad_chars, range(num_factors)):
bit_vars = iter(('b{}_f{}'.format(i, j) for i in range(num_bits)))
clause = [(var if (bad_char & (1 << i)) else ('~' + var)) for (i, var) in enumerate(bit_vars)]
char_constraint_clauses.append((('~(' + ' and '.join(clause)) + ')'))
cnf_clauses = chain(_cnfify(factor_clauses), _cnfify(char_constraint_clauses))
expr = ' and '.join(cnf_clauses)
b = BooleanExpression(expr)
sat_sol = b.sat_one()
if (sat_sol is None):
return None
factors = []
for j in range(num_factors):
factor = 0
for i in range(num_bits):
bit = getattr(sat_sol, 'b{}_f{}'.format(i, j))
factor |= (bit << i)
factors.append(factor)
return factors<|docstring|>The engine behind everything novel in this project.
Args:
target (int): TODO
bad_chars (Tuple[int]): TODO
num_bits (int): TODO
ops (List[str]): TODO
num_factors (int): TODO
start_value (int): TODO
Returns:
List[int]: TODO<|endoftext|> |
0525c28dfee62961e5998e7555ba7c7ab44353b8203d4edff2ce2ef2a070841f | @lru_cache(maxsize=None)
def factor_by_byte(target, bad_chars, usable_ops=IMPLEMENTED_OPS, num_factors=2, start_value=0):
'TODO.\n\n Args:\n TODO\n\n Returns:\n List[Factor]: TODO\n\n Raises:\n DonatelloConfigurationError: If `num_factors` is less than 2.\n\n '
if (num_factors < 2):
raise DonatelloConfigurationError('`num_factors` must be >= 2')
for op_perm in product(usable_ops, repeat=num_factors):
if ((start_value == 0) and (op_perm[0] == 'and')):
continue
msb_factors = _factor_bitwise(((target >> 24) & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, ((start_value >> 24) & 255))
if (msb_factors is None):
continue
second_msb_factors = _factor_bitwise(((target >> 16) & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, ((start_value >> 16) & 255))
if (second_msb_factors is None):
continue
second_lsb_factors = _factor_bitwise(((target >> 8) & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, ((start_value >> 8) & 255))
if (second_lsb_factors is None):
continue
lsb_factors = _factor_bitwise((target & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, (start_value & 255))
if (lsb_factors is None):
continue
num_factors = len(msb_factors)
factors = []
for i in range(num_factors):
operand = 0
operand |= (msb_factors[i] << 24)
operand |= (second_msb_factors[i] << 16)
operand |= (second_lsb_factors[i] << 8)
operand |= lsb_factors[i]
factors.append(Factor(op_perm[i], operand))
return factors
return None | TODO.
Args:
TODO
Returns:
List[Factor]: TODO
Raises:
DonatelloConfigurationError: If `num_factors` is less than 2. | donatello/factor_32.py | factor_by_byte | welchbj/donatello | 2 | python | @lru_cache(maxsize=None)
def factor_by_byte(target, bad_chars, usable_ops=IMPLEMENTED_OPS, num_factors=2, start_value=0):
'TODO.\n\n Args:\n TODO\n\n Returns:\n List[Factor]: TODO\n\n Raises:\n DonatelloConfigurationError: If `num_factors` is less than 2.\n\n '
if (num_factors < 2):
raise DonatelloConfigurationError('`num_factors` must be >= 2')
for op_perm in product(usable_ops, repeat=num_factors):
if ((start_value == 0) and (op_perm[0] == 'and')):
continue
msb_factors = _factor_bitwise(((target >> 24) & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, ((start_value >> 24) & 255))
if (msb_factors is None):
continue
second_msb_factors = _factor_bitwise(((target >> 16) & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, ((start_value >> 16) & 255))
if (second_msb_factors is None):
continue
second_lsb_factors = _factor_bitwise(((target >> 8) & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, ((start_value >> 8) & 255))
if (second_lsb_factors is None):
continue
lsb_factors = _factor_bitwise((target & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, (start_value & 255))
if (lsb_factors is None):
continue
num_factors = len(msb_factors)
factors = []
for i in range(num_factors):
operand = 0
operand |= (msb_factors[i] << 24)
operand |= (second_msb_factors[i] << 16)
operand |= (second_lsb_factors[i] << 8)
operand |= lsb_factors[i]
factors.append(Factor(op_perm[i], operand))
return factors
return None | @lru_cache(maxsize=None)
def factor_by_byte(target, bad_chars, usable_ops=IMPLEMENTED_OPS, num_factors=2, start_value=0):
'TODO.\n\n Args:\n TODO\n\n Returns:\n List[Factor]: TODO\n\n Raises:\n DonatelloConfigurationError: If `num_factors` is less than 2.\n\n '
if (num_factors < 2):
raise DonatelloConfigurationError('`num_factors` must be >= 2')
for op_perm in product(usable_ops, repeat=num_factors):
if ((start_value == 0) and (op_perm[0] == 'and')):
continue
msb_factors = _factor_bitwise(((target >> 24) & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, ((start_value >> 24) & 255))
if (msb_factors is None):
continue
second_msb_factors = _factor_bitwise(((target >> 16) & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, ((start_value >> 16) & 255))
if (second_msb_factors is None):
continue
second_lsb_factors = _factor_bitwise(((target >> 8) & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, ((start_value >> 8) & 255))
if (second_lsb_factors is None):
continue
lsb_factors = _factor_bitwise((target & 255), NUM_BITS_IN_BYTE, bad_chars, op_perm, (start_value & 255))
if (lsb_factors is None):
continue
num_factors = len(msb_factors)
factors = []
for i in range(num_factors):
operand = 0
operand |= (msb_factors[i] << 24)
operand |= (second_msb_factors[i] << 16)
operand |= (second_lsb_factors[i] << 8)
operand |= lsb_factors[i]
factors.append(Factor(op_perm[i], operand))
return factors
return None<|docstring|>TODO.
Args:
TODO
Returns:
List[Factor]: TODO
Raises:
DonatelloConfigurationError: If `num_factors` is less than 2.<|endoftext|> |
b809d36775c9e3406d159766228cd2d51d1fca1b59070aaea5483187b925df18 | def run_migrations_offline():
"Run migrations in 'offline' mode.\n\n This configures the context with just a URL\n and not an Engine, though an Engine is acceptable\n here as well. By skipping the Engine creation\n we don't even need a DBAPI to be available.\n\n Calls to context.execute() here emit the given string to the\n script output.\n\n "
url = config.get_main_option('sqlalchemy.url')
context.configure(url=url, target_metadata=target_metadata, literal_binds=True, dialect_opts={'paramstyle': 'named'})
with context.begin_transaction():
context.run_migrations() | Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output. | cve_bot/migrations/env.py | run_migrations_offline | weastur/blog | 3 | python | def run_migrations_offline():
"Run migrations in 'offline' mode.\n\n This configures the context with just a URL\n and not an Engine, though an Engine is acceptable\n here as well. By skipping the Engine creation\n we don't even need a DBAPI to be available.\n\n Calls to context.execute() here emit the given string to the\n script output.\n\n "
url = config.get_main_option('sqlalchemy.url')
context.configure(url=url, target_metadata=target_metadata, literal_binds=True, dialect_opts={'paramstyle': 'named'})
with context.begin_transaction():
context.run_migrations() | def run_migrations_offline():
"Run migrations in 'offline' mode.\n\n This configures the context with just a URL\n and not an Engine, though an Engine is acceptable\n here as well. By skipping the Engine creation\n we don't even need a DBAPI to be available.\n\n Calls to context.execute() here emit the given string to the\n script output.\n\n "
url = config.get_main_option('sqlalchemy.url')
context.configure(url=url, target_metadata=target_metadata, literal_binds=True, dialect_opts={'paramstyle': 'named'})
with context.begin_transaction():
context.run_migrations()<|docstring|>Run migrations in 'offline' mode.
This configures the context with just a URL
and not an Engine, though an Engine is acceptable
here as well. By skipping the Engine creation
we don't even need a DBAPI to be available.
Calls to context.execute() here emit the given string to the
script output.<|endoftext|> |
da3ec54124d380e26618bef34e2b036d8621b6932a250c49e3f13ebc04892ad8 | def run_migrations_online():
"Run migrations in 'online' mode.\n\n In this scenario we need to create an Engine\n and associate a connection with the context.\n\n "
connectable = engine_from_config(config.get_section(config.config_ini_section), prefix='sqlalchemy.', poolclass=pool.NullPool)
with connectable.connect() as connection:
context.configure(connection=connection, target_metadata=target_metadata)
with context.begin_transaction():
context.run_migrations() | Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context. | cve_bot/migrations/env.py | run_migrations_online | weastur/blog | 3 | python | def run_migrations_online():
"Run migrations in 'online' mode.\n\n In this scenario we need to create an Engine\n and associate a connection with the context.\n\n "
connectable = engine_from_config(config.get_section(config.config_ini_section), prefix='sqlalchemy.', poolclass=pool.NullPool)
with connectable.connect() as connection:
context.configure(connection=connection, target_metadata=target_metadata)
with context.begin_transaction():
context.run_migrations() | def run_migrations_online():
"Run migrations in 'online' mode.\n\n In this scenario we need to create an Engine\n and associate a connection with the context.\n\n "
connectable = engine_from_config(config.get_section(config.config_ini_section), prefix='sqlalchemy.', poolclass=pool.NullPool)
with connectable.connect() as connection:
context.configure(connection=connection, target_metadata=target_metadata)
with context.begin_transaction():
context.run_migrations()<|docstring|>Run migrations in 'online' mode.
In this scenario we need to create an Engine
and associate a connection with the context.<|endoftext|> |
2623bd5e73af548dd8cc588fd736b419b6489028594acbe9bd3823ca4bb726dc | def convert_to_4(self):
'\n Convert a pre-4.0 configuration file to a 4.0 configuration file.\n '
from six.moves.urllib import parse
if (not self.config.has_section('backends')):
self.config.add_section('backends')
site = parse.urlparse(self.get('site', default_value=''))
backend_uri = 'zebra://{username}:{password}@{hostname}'.format(username=self.get('username', default_value=''), password=parse.quote(self.get('password', default_value=''), safe=''), hostname=site.hostname)
self.config.set('backends', 'default', backend_uri)
self.config.remove_option('default', 'username')
self.config.remove_option('default', 'password')
self.config.remove_option('default', 'site')
if (not self.config.has_section('default_aliases')):
self.config.add_section('default_aliases')
if (not self.config.has_section('default_shared_aliases')):
self.config.add_section('default_shared_aliases')
if self.config.has_section('wrmap'):
for (alias, mapping) in self.config.items('wrmap'):
self.config.set('default_aliases', alias, mapping)
self.config.remove_section('wrmap')
if self.config.has_section('shared_wrmap'):
for (alias, mapping) in self.config.items('shared_wrmap'):
self.config.set('default_shared_aliases', alias, mapping)
self.config.remove_section('shared_wrmap') | Convert a pre-4.0 configuration file to a 4.0 configuration file. | taxi/settings.py | convert_to_4 | simonbru/taxi | 0 | python | def convert_to_4(self):
'\n \n '
from six.moves.urllib import parse
if (not self.config.has_section('backends')):
self.config.add_section('backends')
site = parse.urlparse(self.get('site', default_value=))
backend_uri = 'zebra://{username}:{password}@{hostname}'.format(username=self.get('username', default_value=), password=parse.quote(self.get('password', default_value=), safe=), hostname=site.hostname)
self.config.set('backends', 'default', backend_uri)
self.config.remove_option('default', 'username')
self.config.remove_option('default', 'password')
self.config.remove_option('default', 'site')
if (not self.config.has_section('default_aliases')):
self.config.add_section('default_aliases')
if (not self.config.has_section('default_shared_aliases')):
self.config.add_section('default_shared_aliases')
if self.config.has_section('wrmap'):
for (alias, mapping) in self.config.items('wrmap'):
self.config.set('default_aliases', alias, mapping)
self.config.remove_section('wrmap')
if self.config.has_section('shared_wrmap'):
for (alias, mapping) in self.config.items('shared_wrmap'):
self.config.set('default_shared_aliases', alias, mapping)
self.config.remove_section('shared_wrmap') | def convert_to_4(self):
'\n \n '
from six.moves.urllib import parse
if (not self.config.has_section('backends')):
self.config.add_section('backends')
site = parse.urlparse(self.get('site', default_value=))
backend_uri = 'zebra://{username}:{password}@{hostname}'.format(username=self.get('username', default_value=), password=parse.quote(self.get('password', default_value=), safe=), hostname=site.hostname)
self.config.set('backends', 'default', backend_uri)
self.config.remove_option('default', 'username')
self.config.remove_option('default', 'password')
self.config.remove_option('default', 'site')
if (not self.config.has_section('default_aliases')):
self.config.add_section('default_aliases')
if (not self.config.has_section('default_shared_aliases')):
self.config.add_section('default_shared_aliases')
if self.config.has_section('wrmap'):
for (alias, mapping) in self.config.items('wrmap'):
self.config.set('default_aliases', alias, mapping)
self.config.remove_section('wrmap')
if self.config.has_section('shared_wrmap'):
for (alias, mapping) in self.config.items('shared_wrmap'):
self.config.set('default_shared_aliases', alias, mapping)
self.config.remove_section('shared_wrmap')<|docstring|>Convert a pre-4.0 configuration file to a 4.0 configuration file.<|endoftext|> |
1191c3b9275e365671ca1f6f18598afa013671b27aa6a96f008ab0ed1a50a8a4 | def update_args(args, new_args, action_groups, exclude=('Model arguments',), silent=('',), force=(), list_arguments=('relation_scorers', 'data_variants')):
"\n Update Namespace args with entries in new_args excluding action groups in 'exclude'.\n Logs updated entries at level INFO and differing entries that aren't updated at level WARNING\n :param args: Namespace to update\n :param new_args: Namespace with new values\n :param action_groups: _action_groups attribute of original argument parser\n :param exclude: tuple of action group names to exclude from the update\n :param silent: do not warn when we can't update these arguments\n :param force: update these values even if new_args has the default value and allows arguments in excluded action\n groups to be updated.\n :param list_arguments: arguments that are lists (new values will be appended)\n :return: Void\n "
for group in action_groups:
for action in group._group_actions:
dest = action.dest
if (dest == 'help'):
continue
if (dest not in args):
logger.warning(f'argument {dest} not found in old args, adding with default value {action.default}')
setattr(args, dest, action.default)
new_value = getattr(new_args, dest)
old_value = getattr(args, dest)
if ((new_value == action.default) and (dest not in force) and (getattr(args, dest, None) is not None)):
continue
if (old_value != new_value):
if ((group.title not in exclude) or (dest in force)):
if (dest in list_arguments):
changed = False
for new_scorer in new_value:
if (new_scorer not in old_value):
old_value.append(new_scorer)
changed = True
if changed:
setattr(args, dest, old_value)
logger.info(f'Appending {group.title} argument {dest} to {old_value} with {new_value}')
else:
setattr(args, dest, new_value)
logger.info(f'updating {group.title} argument {dest} from {old_value} to {new_value}')
elif (dest not in silent):
logger.warning(f"can't update {group.title} argument {dest} from {old_value} to {new_value}! It's built into the model!") | Update Namespace args with entries in new_args excluding action groups in 'exclude'.
Logs updated entries at level INFO and differing entries that aren't updated at level WARNING
:param args: Namespace to update
:param new_args: Namespace with new values
:param action_groups: _action_groups attribute of original argument parser
:param exclude: tuple of action group names to exclude from the update
:param silent: do not warn when we can't update these arguments
:param force: update these values even if new_args has the default value and allows arguments in excluded action
groups to be updated.
:param list_arguments: arguments that are lists (new values will be appended)
:return: Void | OpenKI/UtilityFunctions.py | update_args | drevicko/OpenKI | 0 | python | def update_args(args, new_args, action_groups, exclude=('Model arguments',), silent=(,), force=(), list_arguments=('relation_scorers', 'data_variants')):
"\n Update Namespace args with entries in new_args excluding action groups in 'exclude'.\n Logs updated entries at level INFO and differing entries that aren't updated at level WARNING\n :param args: Namespace to update\n :param new_args: Namespace with new values\n :param action_groups: _action_groups attribute of original argument parser\n :param exclude: tuple of action group names to exclude from the update\n :param silent: do not warn when we can't update these arguments\n :param force: update these values even if new_args has the default value and allows arguments in excluded action\n groups to be updated.\n :param list_arguments: arguments that are lists (new values will be appended)\n :return: Void\n "
for group in action_groups:
for action in group._group_actions:
dest = action.dest
if (dest == 'help'):
continue
if (dest not in args):
logger.warning(f'argument {dest} not found in old args, adding with default value {action.default}')
setattr(args, dest, action.default)
new_value = getattr(new_args, dest)
old_value = getattr(args, dest)
if ((new_value == action.default) and (dest not in force) and (getattr(args, dest, None) is not None)):
continue
if (old_value != new_value):
if ((group.title not in exclude) or (dest in force)):
if (dest in list_arguments):
changed = False
for new_scorer in new_value:
if (new_scorer not in old_value):
old_value.append(new_scorer)
changed = True
if changed:
setattr(args, dest, old_value)
logger.info(f'Appending {group.title} argument {dest} to {old_value} with {new_value}')
else:
setattr(args, dest, new_value)
logger.info(f'updating {group.title} argument {dest} from {old_value} to {new_value}')
elif (dest not in silent):
logger.warning(f"can't update {group.title} argument {dest} from {old_value} to {new_value}! It's built into the model!") | def update_args(args, new_args, action_groups, exclude=('Model arguments',), silent=(,), force=(), list_arguments=('relation_scorers', 'data_variants')):
"\n Update Namespace args with entries in new_args excluding action groups in 'exclude'.\n Logs updated entries at level INFO and differing entries that aren't updated at level WARNING\n :param args: Namespace to update\n :param new_args: Namespace with new values\n :param action_groups: _action_groups attribute of original argument parser\n :param exclude: tuple of action group names to exclude from the update\n :param silent: do not warn when we can't update these arguments\n :param force: update these values even if new_args has the default value and allows arguments in excluded action\n groups to be updated.\n :param list_arguments: arguments that are lists (new values will be appended)\n :return: Void\n "
for group in action_groups:
for action in group._group_actions:
dest = action.dest
if (dest == 'help'):
continue
if (dest not in args):
logger.warning(f'argument {dest} not found in old args, adding with default value {action.default}')
setattr(args, dest, action.default)
new_value = getattr(new_args, dest)
old_value = getattr(args, dest)
if ((new_value == action.default) and (dest not in force) and (getattr(args, dest, None) is not None)):
continue
if (old_value != new_value):
if ((group.title not in exclude) or (dest in force)):
if (dest in list_arguments):
changed = False
for new_scorer in new_value:
if (new_scorer not in old_value):
old_value.append(new_scorer)
changed = True
if changed:
setattr(args, dest, old_value)
logger.info(f'Appending {group.title} argument {dest} to {old_value} with {new_value}')
else:
setattr(args, dest, new_value)
logger.info(f'updating {group.title} argument {dest} from {old_value} to {new_value}')
elif (dest not in silent):
logger.warning(f"can't update {group.title} argument {dest} from {old_value} to {new_value}! It's built into the model!")<|docstring|>Update Namespace args with entries in new_args excluding action groups in 'exclude'.
Logs updated entries at level INFO and differing entries that aren't updated at level WARNING
:param args: Namespace to update
:param new_args: Namespace with new values
:param action_groups: _action_groups attribute of original argument parser
:param exclude: tuple of action group names to exclude from the update
:param silent: do not warn when we can't update these arguments
:param force: update these values even if new_args has the default value and allows arguments in excluded action
groups to be updated.
:param list_arguments: arguments that are lists (new values will be appended)
:return: Void<|endoftext|> |
f740b4bec8769c2d481f07bfcd4507314256ccf2de99e77654cd7f20243cfa00 | def refuse_cuda(self, is_cuda=True):
"\n Dummy function for monkey patching cuda() on an nn.Parameter (eg: an embedding), forcing it to not respond to\n calls to `cuda()`. You still need to suitably process input tensors such that the module receives cpu inputs\n and process it's outputs such that subsequent processing receives gpu.\n To monkeypatch a parameter `self.weight`, use `self.weight.cuda = refuse_cuda.__get__(self.weight)`.\n A `forward()` method like the following may also be appropriate:\n def forward_cpu(self, input: torch.Tensor) -> torch.Tensor:\n output = super().forward(input.cpu())\n if self.is_cuda_:\n output = output.cuda()\n return output\n :param self:\n :param is_cuda: The value cuda() is supposed do be set to (eg: set cuda() on model outputs to this)\n :return: self\n "
self.is_cuda_ = is_cuda
return self | Dummy function for monkey patching cuda() on an nn.Parameter (eg: an embedding), forcing it to not respond to
calls to `cuda()`. You still need to suitably process input tensors such that the module receives cpu inputs
and process it's outputs such that subsequent processing receives gpu.
To monkeypatch a parameter `self.weight`, use `self.weight.cuda = refuse_cuda.__get__(self.weight)`.
A `forward()` method like the following may also be appropriate:
def forward_cpu(self, input: torch.Tensor) -> torch.Tensor:
output = super().forward(input.cpu())
if self.is_cuda_:
output = output.cuda()
return output
:param self:
:param is_cuda: The value cuda() is supposed do be set to (eg: set cuda() on model outputs to this)
:return: self | OpenKI/UtilityFunctions.py | refuse_cuda | drevicko/OpenKI | 0 | python | def refuse_cuda(self, is_cuda=True):
"\n Dummy function for monkey patching cuda() on an nn.Parameter (eg: an embedding), forcing it to not respond to\n calls to `cuda()`. You still need to suitably process input tensors such that the module receives cpu inputs\n and process it's outputs such that subsequent processing receives gpu.\n To monkeypatch a parameter `self.weight`, use `self.weight.cuda = refuse_cuda.__get__(self.weight)`.\n A `forward()` method like the following may also be appropriate:\n def forward_cpu(self, input: torch.Tensor) -> torch.Tensor:\n output = super().forward(input.cpu())\n if self.is_cuda_:\n output = output.cuda()\n return output\n :param self:\n :param is_cuda: The value cuda() is supposed do be set to (eg: set cuda() on model outputs to this)\n :return: self\n "
self.is_cuda_ = is_cuda
return self | def refuse_cuda(self, is_cuda=True):
"\n Dummy function for monkey patching cuda() on an nn.Parameter (eg: an embedding), forcing it to not respond to\n calls to `cuda()`. You still need to suitably process input tensors such that the module receives cpu inputs\n and process it's outputs such that subsequent processing receives gpu.\n To monkeypatch a parameter `self.weight`, use `self.weight.cuda = refuse_cuda.__get__(self.weight)`.\n A `forward()` method like the following may also be appropriate:\n def forward_cpu(self, input: torch.Tensor) -> torch.Tensor:\n output = super().forward(input.cpu())\n if self.is_cuda_:\n output = output.cuda()\n return output\n :param self:\n :param is_cuda: The value cuda() is supposed do be set to (eg: set cuda() on model outputs to this)\n :return: self\n "
self.is_cuda_ = is_cuda
return self<|docstring|>Dummy function for monkey patching cuda() on an nn.Parameter (eg: an embedding), forcing it to not respond to
calls to `cuda()`. You still need to suitably process input tensors such that the module receives cpu inputs
and process it's outputs such that subsequent processing receives gpu.
To monkeypatch a parameter `self.weight`, use `self.weight.cuda = refuse_cuda.__get__(self.weight)`.
A `forward()` method like the following may also be appropriate:
def forward_cpu(self, input: torch.Tensor) -> torch.Tensor:
output = super().forward(input.cpu())
if self.is_cuda_:
output = output.cuda()
return output
:param self:
:param is_cuda: The value cuda() is supposed do be set to (eg: set cuda() on model outputs to this)
:return: self<|endoftext|> |
f0c7e46306aff33ffeb2adaaeff76b1e387eab9da31e9dd50e1ecb82d7b006d8 | def parse_args():
'\n Parses command line arguments.\n '
parser = argparse.ArgumentParser('Reading Comprehension on BaiduRC dataset')
parser.add_argument('--prepare', action='store_true', help='create the directories, prepare the vocabulary and embeddings')
parser.add_argument('--train', action='store_true', help='train the model')
parser.add_argument('--evaluate', action='store_true', help='evaluate the model on dev set')
parser.add_argument('--predict', action='store_true', help='predict the answers for test set with trained model')
parser.add_argument('--gpu', type=str, default='0', help='specify gpu device')
train_settings = parser.add_argument_group('train settings')
train_settings.add_argument('--optim', default='adam', help='optimizer type')
train_settings.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
train_settings.add_argument('--weight_decay', type=float, default=0, help='weight decay')
train_settings.add_argument('--dropout_keep_prob', type=float, default=1, help='dropout keep rate')
train_settings.add_argument('--batch_size', type=int, default=32, help='train batch size')
train_settings.add_argument('--epochs', type=int, default=10, help='train epochs')
model_settings = parser.add_argument_group('model settings')
model_settings.add_argument('--algo', choices=['BIDAF', 'MLSTM'], default='BIDAF', help='choose the algorithm to use')
model_settings.add_argument('--embed_size', type=int, default=300, help='size of the embeddings')
model_settings.add_argument('--hidden_size', type=int, default=150, help='size of LSTM hidden units')
model_settings.add_argument('--max_p_num', type=int, default=5, help='max passage num in one sample')
model_settings.add_argument('--max_p_len', type=int, default=500, help='max length of passage')
model_settings.add_argument('--max_q_len', type=int, default=60, help='max length of question')
model_settings.add_argument('--max_a_len', type=int, default=200, help='max length of answer')
path_settings = parser.add_argument_group('path settings')
path_settings.add_argument('--train_files', nargs='+', default=['../data/demo/trainset/search.train.json'], help='list of files that contain the preprocessed train data')
path_settings.add_argument('--dev_files', nargs='+', default=['../data/demo/devset/search.dev.json'], help='list of files that contain the preprocessed dev data')
path_settings.add_argument('--test_files', nargs='+', default=['../data/demo/testset/search.test.json'], help='list of files that contain the preprocessed test data')
path_settings.add_argument('--brc_dir', default='../data/baidu', help='the dir with preprocessed baidu reading comprehension data')
path_settings.add_argument('--vocab_dir', default='../data/vocab/', help='the dir to save vocabulary')
path_settings.add_argument('--model_dir', default='../data/models/', help='the dir to store models')
path_settings.add_argument('--result_dir', default='../data/results/', help='the dir to output the results')
path_settings.add_argument('--summary_dir', default='../data/summary/', help='the dir to write tensorboard summary')
path_settings.add_argument('--log_path', help='path of the log file. If not set, logs are printed to console')
return parser.parse_args() | Parses command line arguments. | tensorflow/run.py | parse_args | hhcyforever/19MRC | 971 | python | def parse_args():
'\n \n '
parser = argparse.ArgumentParser('Reading Comprehension on BaiduRC dataset')
parser.add_argument('--prepare', action='store_true', help='create the directories, prepare the vocabulary and embeddings')
parser.add_argument('--train', action='store_true', help='train the model')
parser.add_argument('--evaluate', action='store_true', help='evaluate the model on dev set')
parser.add_argument('--predict', action='store_true', help='predict the answers for test set with trained model')
parser.add_argument('--gpu', type=str, default='0', help='specify gpu device')
train_settings = parser.add_argument_group('train settings')
train_settings.add_argument('--optim', default='adam', help='optimizer type')
train_settings.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
train_settings.add_argument('--weight_decay', type=float, default=0, help='weight decay')
train_settings.add_argument('--dropout_keep_prob', type=float, default=1, help='dropout keep rate')
train_settings.add_argument('--batch_size', type=int, default=32, help='train batch size')
train_settings.add_argument('--epochs', type=int, default=10, help='train epochs')
model_settings = parser.add_argument_group('model settings')
model_settings.add_argument('--algo', choices=['BIDAF', 'MLSTM'], default='BIDAF', help='choose the algorithm to use')
model_settings.add_argument('--embed_size', type=int, default=300, help='size of the embeddings')
model_settings.add_argument('--hidden_size', type=int, default=150, help='size of LSTM hidden units')
model_settings.add_argument('--max_p_num', type=int, default=5, help='max passage num in one sample')
model_settings.add_argument('--max_p_len', type=int, default=500, help='max length of passage')
model_settings.add_argument('--max_q_len', type=int, default=60, help='max length of question')
model_settings.add_argument('--max_a_len', type=int, default=200, help='max length of answer')
path_settings = parser.add_argument_group('path settings')
path_settings.add_argument('--train_files', nargs='+', default=['../data/demo/trainset/search.train.json'], help='list of files that contain the preprocessed train data')
path_settings.add_argument('--dev_files', nargs='+', default=['../data/demo/devset/search.dev.json'], help='list of files that contain the preprocessed dev data')
path_settings.add_argument('--test_files', nargs='+', default=['../data/demo/testset/search.test.json'], help='list of files that contain the preprocessed test data')
path_settings.add_argument('--brc_dir', default='../data/baidu', help='the dir with preprocessed baidu reading comprehension data')
path_settings.add_argument('--vocab_dir', default='../data/vocab/', help='the dir to save vocabulary')
path_settings.add_argument('--model_dir', default='../data/models/', help='the dir to store models')
path_settings.add_argument('--result_dir', default='../data/results/', help='the dir to output the results')
path_settings.add_argument('--summary_dir', default='../data/summary/', help='the dir to write tensorboard summary')
path_settings.add_argument('--log_path', help='path of the log file. If not set, logs are printed to console')
return parser.parse_args() | def parse_args():
'\n \n '
parser = argparse.ArgumentParser('Reading Comprehension on BaiduRC dataset')
parser.add_argument('--prepare', action='store_true', help='create the directories, prepare the vocabulary and embeddings')
parser.add_argument('--train', action='store_true', help='train the model')
parser.add_argument('--evaluate', action='store_true', help='evaluate the model on dev set')
parser.add_argument('--predict', action='store_true', help='predict the answers for test set with trained model')
parser.add_argument('--gpu', type=str, default='0', help='specify gpu device')
train_settings = parser.add_argument_group('train settings')
train_settings.add_argument('--optim', default='adam', help='optimizer type')
train_settings.add_argument('--learning_rate', type=float, default=0.001, help='learning rate')
train_settings.add_argument('--weight_decay', type=float, default=0, help='weight decay')
train_settings.add_argument('--dropout_keep_prob', type=float, default=1, help='dropout keep rate')
train_settings.add_argument('--batch_size', type=int, default=32, help='train batch size')
train_settings.add_argument('--epochs', type=int, default=10, help='train epochs')
model_settings = parser.add_argument_group('model settings')
model_settings.add_argument('--algo', choices=['BIDAF', 'MLSTM'], default='BIDAF', help='choose the algorithm to use')
model_settings.add_argument('--embed_size', type=int, default=300, help='size of the embeddings')
model_settings.add_argument('--hidden_size', type=int, default=150, help='size of LSTM hidden units')
model_settings.add_argument('--max_p_num', type=int, default=5, help='max passage num in one sample')
model_settings.add_argument('--max_p_len', type=int, default=500, help='max length of passage')
model_settings.add_argument('--max_q_len', type=int, default=60, help='max length of question')
model_settings.add_argument('--max_a_len', type=int, default=200, help='max length of answer')
path_settings = parser.add_argument_group('path settings')
path_settings.add_argument('--train_files', nargs='+', default=['../data/demo/trainset/search.train.json'], help='list of files that contain the preprocessed train data')
path_settings.add_argument('--dev_files', nargs='+', default=['../data/demo/devset/search.dev.json'], help='list of files that contain the preprocessed dev data')
path_settings.add_argument('--test_files', nargs='+', default=['../data/demo/testset/search.test.json'], help='list of files that contain the preprocessed test data')
path_settings.add_argument('--brc_dir', default='../data/baidu', help='the dir with preprocessed baidu reading comprehension data')
path_settings.add_argument('--vocab_dir', default='../data/vocab/', help='the dir to save vocabulary')
path_settings.add_argument('--model_dir', default='../data/models/', help='the dir to store models')
path_settings.add_argument('--result_dir', default='../data/results/', help='the dir to output the results')
path_settings.add_argument('--summary_dir', default='../data/summary/', help='the dir to write tensorboard summary')
path_settings.add_argument('--log_path', help='path of the log file. If not set, logs are printed to console')
return parser.parse_args()<|docstring|>Parses command line arguments.<|endoftext|> |
639a2d567c70acedc2cba171823e04ec7cd5e349111548afcf644db59a80cfe8 | def prepare(args):
'\n checks data, creates the directories, prepare the vocabulary and embeddings\n '
logger = logging.getLogger('brc')
logger.info('Checking the data files...')
for data_path in ((args.train_files + args.dev_files) + args.test_files):
assert os.path.exists(data_path), '{} file does not exist.'.format(data_path)
logger.info('Preparing the directories...')
for dir_path in [args.vocab_dir, args.model_dir, args.result_dir, args.summary_dir]:
if (not os.path.exists(dir_path)):
os.makedirs(dir_path)
logger.info('Building vocabulary...')
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, args.train_files, args.dev_files, args.test_files)
vocab = Vocab(lower=True)
for word in brc_data.word_iter('train'):
vocab.add(word)
unfiltered_vocab_size = vocab.size()
vocab.filter_tokens_by_cnt(min_cnt=2)
filtered_num = (unfiltered_vocab_size - vocab.size())
logger.info('After filter {} tokens, the final vocab size is {}'.format(filtered_num, vocab.size()))
logger.info('Assigning embeddings...')
vocab.randomly_init_embeddings(args.embed_size)
logger.info('Saving vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'wb') as fout:
pickle.dump(vocab, fout)
logger.info('Done with preparing!') | checks data, creates the directories, prepare the vocabulary and embeddings | tensorflow/run.py | prepare | hhcyforever/19MRC | 971 | python | def prepare(args):
'\n \n '
logger = logging.getLogger('brc')
logger.info('Checking the data files...')
for data_path in ((args.train_files + args.dev_files) + args.test_files):
assert os.path.exists(data_path), '{} file does not exist.'.format(data_path)
logger.info('Preparing the directories...')
for dir_path in [args.vocab_dir, args.model_dir, args.result_dir, args.summary_dir]:
if (not os.path.exists(dir_path)):
os.makedirs(dir_path)
logger.info('Building vocabulary...')
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, args.train_files, args.dev_files, args.test_files)
vocab = Vocab(lower=True)
for word in brc_data.word_iter('train'):
vocab.add(word)
unfiltered_vocab_size = vocab.size()
vocab.filter_tokens_by_cnt(min_cnt=2)
filtered_num = (unfiltered_vocab_size - vocab.size())
logger.info('After filter {} tokens, the final vocab size is {}'.format(filtered_num, vocab.size()))
logger.info('Assigning embeddings...')
vocab.randomly_init_embeddings(args.embed_size)
logger.info('Saving vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'wb') as fout:
pickle.dump(vocab, fout)
logger.info('Done with preparing!') | def prepare(args):
'\n \n '
logger = logging.getLogger('brc')
logger.info('Checking the data files...')
for data_path in ((args.train_files + args.dev_files) + args.test_files):
assert os.path.exists(data_path), '{} file does not exist.'.format(data_path)
logger.info('Preparing the directories...')
for dir_path in [args.vocab_dir, args.model_dir, args.result_dir, args.summary_dir]:
if (not os.path.exists(dir_path)):
os.makedirs(dir_path)
logger.info('Building vocabulary...')
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, args.train_files, args.dev_files, args.test_files)
vocab = Vocab(lower=True)
for word in brc_data.word_iter('train'):
vocab.add(word)
unfiltered_vocab_size = vocab.size()
vocab.filter_tokens_by_cnt(min_cnt=2)
filtered_num = (unfiltered_vocab_size - vocab.size())
logger.info('After filter {} tokens, the final vocab size is {}'.format(filtered_num, vocab.size()))
logger.info('Assigning embeddings...')
vocab.randomly_init_embeddings(args.embed_size)
logger.info('Saving vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'wb') as fout:
pickle.dump(vocab, fout)
logger.info('Done with preparing!')<|docstring|>checks data, creates the directories, prepare the vocabulary and embeddings<|endoftext|> |
dce7de5528141832208fbafa9fdfe4e44058513b4257efa8244759064864aead | def train(args):
'\n trains the reading comprehension model\n '
logger = logging.getLogger('brc')
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, args.train_files, args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Initialize the model...')
rc_model = RCModel(vocab, args)
logger.info('Training the model...')
rc_model.train(brc_data, args.epochs, args.batch_size, save_dir=args.model_dir, save_prefix=args.algo, dropout_keep_prob=args.dropout_keep_prob)
logger.info('Done with model training!') | trains the reading comprehension model | tensorflow/run.py | train | hhcyforever/19MRC | 971 | python | def train(args):
'\n \n '
logger = logging.getLogger('brc')
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, args.train_files, args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Initialize the model...')
rc_model = RCModel(vocab, args)
logger.info('Training the model...')
rc_model.train(brc_data, args.epochs, args.batch_size, save_dir=args.model_dir, save_prefix=args.algo, dropout_keep_prob=args.dropout_keep_prob)
logger.info('Done with model training!') | def train(args):
'\n \n '
logger = logging.getLogger('brc')
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, args.train_files, args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Initialize the model...')
rc_model = RCModel(vocab, args)
logger.info('Training the model...')
rc_model.train(brc_data, args.epochs, args.batch_size, save_dir=args.model_dir, save_prefix=args.algo, dropout_keep_prob=args.dropout_keep_prob)
logger.info('Done with model training!')<|docstring|>trains the reading comprehension model<|endoftext|> |
4a2e76c3e3805261d40c17adfd9c601427f6007e032299ff11b767f691302152 | def evaluate(args):
'\n evaluate the trained model on dev files\n '
logger = logging.getLogger('brc')
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert (len(args.dev_files) > 0), 'No dev files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, dev_files=args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Evaluating the model on dev set...')
dev_batches = brc_data.gen_mini_batches('dev', args.batch_size, pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
(dev_loss, dev_bleu_rouge) = rc_model.evaluate(dev_batches, result_dir=args.result_dir, result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(os.path.join(args.result_dir))) | evaluate the trained model on dev files | tensorflow/run.py | evaluate | hhcyforever/19MRC | 971 | python | def evaluate(args):
'\n \n '
logger = logging.getLogger('brc')
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert (len(args.dev_files) > 0), 'No dev files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, dev_files=args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Evaluating the model on dev set...')
dev_batches = brc_data.gen_mini_batches('dev', args.batch_size, pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
(dev_loss, dev_bleu_rouge) = rc_model.evaluate(dev_batches, result_dir=args.result_dir, result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(os.path.join(args.result_dir))) | def evaluate(args):
'\n \n '
logger = logging.getLogger('brc')
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert (len(args.dev_files) > 0), 'No dev files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, dev_files=args.dev_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Evaluating the model on dev set...')
dev_batches = brc_data.gen_mini_batches('dev', args.batch_size, pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
(dev_loss, dev_bleu_rouge) = rc_model.evaluate(dev_batches, result_dir=args.result_dir, result_prefix='dev.predicted')
logger.info('Loss on dev set: {}'.format(dev_loss))
logger.info('Result on dev set: {}'.format(dev_bleu_rouge))
logger.info('Predicted answers are saved to {}'.format(os.path.join(args.result_dir)))<|docstring|>evaluate the trained model on dev files<|endoftext|> |
291f1e58d4c76578a729b358a9827146afd42eccca7569daf857bdf8ce380724 | def predict(args):
'\n predicts answers for test files\n '
logger = logging.getLogger('brc')
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert (len(args.test_files) > 0), 'No test files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, test_files=args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test', args.batch_size, pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
rc_model.evaluate(test_batches, result_dir=args.result_dir, result_prefix='test.predicted') | predicts answers for test files | tensorflow/run.py | predict | hhcyforever/19MRC | 971 | python | def predict(args):
'\n \n '
logger = logging.getLogger('brc')
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert (len(args.test_files) > 0), 'No test files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, test_files=args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test', args.batch_size, pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
rc_model.evaluate(test_batches, result_dir=args.result_dir, result_prefix='test.predicted') | def predict(args):
'\n \n '
logger = logging.getLogger('brc')
logger.info('Load data_set and vocab...')
with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
vocab = pickle.load(fin)
assert (len(args.test_files) > 0), 'No test files are provided.'
brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len, test_files=args.test_files)
logger.info('Converting text into ids...')
brc_data.convert_to_ids(vocab)
logger.info('Restoring the model...')
rc_model = RCModel(vocab, args)
rc_model.restore(model_dir=args.model_dir, model_prefix=args.algo)
logger.info('Predicting answers for test set...')
test_batches = brc_data.gen_mini_batches('test', args.batch_size, pad_id=vocab.get_id(vocab.pad_token), shuffle=False)
rc_model.evaluate(test_batches, result_dir=args.result_dir, result_prefix='test.predicted')<|docstring|>predicts answers for test files<|endoftext|> |
21f71a4f3ae0033f980dcfc7ec94bcf4fa9b877514861774bffd06ef82d05377 | def run():
'\n Prepares and runs the whole system.\n '
args = parse_args()
logger = logging.getLogger('brc')
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
if args.log_path:
file_handler = logging.FileHandler(args.log_path)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
else:
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
logger.info('Running with args : {}'.format(args))
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.prepare:
prepare(args)
if args.train:
train(args)
if args.evaluate:
evaluate(args)
if args.predict:
predict(args) | Prepares and runs the whole system. | tensorflow/run.py | run | hhcyforever/19MRC | 971 | python | def run():
'\n \n '
args = parse_args()
logger = logging.getLogger('brc')
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
if args.log_path:
file_handler = logging.FileHandler(args.log_path)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
else:
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
logger.info('Running with args : {}'.format(args))
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.prepare:
prepare(args)
if args.train:
train(args)
if args.evaluate:
evaluate(args)
if args.predict:
predict(args) | def run():
'\n \n '
args = parse_args()
logger = logging.getLogger('brc')
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
if args.log_path:
file_handler = logging.FileHandler(args.log_path)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
else:
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
logger.info('Running with args : {}'.format(args))
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.prepare:
prepare(args)
if args.train:
train(args)
if args.evaluate:
evaluate(args)
if args.predict:
predict(args)<|docstring|>Prepares and runs the whole system.<|endoftext|> |
667db9f35695ae1d7278a6a7a9a93f0b59805aa76c022a4b81e9d653aa119d6f | def __init__(self, fab=None, heavy_chains=None, light_chains=None, names=None):
'\n Fab object container that handles combinations of light/heavy Chain pairs.\n\n Args:\n fab (list):\n heavy_chains (ChainCollection):\n light_chains (ChainCollection):\n names (list):\n '
if ((heavy_chains is None) and (light_chains is None) and (fab is None)):
raise ValueError('Provide a list of Chain objects or an ChainCollection object')
if (isinstance(fab, list) and all((isinstance(fab_i, Fab) for fab_i in fab))):
self._fab = fab
self._light_chains = ChainCollection([x[0] for x in self._fab])
self._heavy_chains = ChainCollection([x[1] for x in self._fab])
if ((fab is None) and ((heavy_chains is not None) and (light_chains is not None))):
if isinstance(heavy_chains, list):
self._heavy_chains = ChainCollection(antibody_objects=heavy_chains)
elif isinstance(heavy_chains, ChainCollection):
self._heavy_chains = heavy_chains
else:
raise ValueError('Provide a list of Chain objects or an ChainCollection object')
if isinstance(light_chains, list):
self._light_chains = ChainCollection(antibody_objects=light_chains)
elif isinstance(light_chains, ChainCollection):
self._light_chains = light_chains
else:
raise ValueError('Provide a list of Chain objects or an ChainCollection object')
if (len(self._light_chains.loading_status()) == 0):
self._light_chains.load()
if (len(self._heavy_chains.loading_status()) == 0):
self._heavy_chains.load()
if (self._light_chains.n_ab != self._heavy_chains.n_ab):
raise ValueError('Number of heavy chains must be the same of light chains')
if (isinstance(names, list) and all((isinstance(name, str) for name in names))):
if (len(names) == self._heavy_chains.n_ab):
self._names = names
else:
raise ValueError('Length of name list must be the same as length of heavy_chains/light chains lists')
elif (names is None):
self._names = ['{} - {}'.format(heavy, light) for (heavy, light) in zip(self._heavy_chains.names, self._light_chains.names)]
else:
raise ValueError('Names expected a list of strings, instead got {}'.format(type(names)))
self._n_ab = self._light_chains.n_ab
self._pair_sequences = [(heavy + light) for (light, heavy) in zip(self._heavy_chains.sequences, self._light_chains.sequences)]
self._internal_heavy_name = self._heavy_chains.names
self._internal_light_name = self._light_chains.names | Fab object container that handles combinations of light/heavy Chain pairs.
Args:
fab (list):
heavy_chains (ChainCollection):
light_chains (ChainCollection):
names (list): | abpytools/core/fab_collection.py | __init__ | gf712/AbPyTools | 13 | python | def __init__(self, fab=None, heavy_chains=None, light_chains=None, names=None):
'\n Fab object container that handles combinations of light/heavy Chain pairs.\n\n Args:\n fab (list):\n heavy_chains (ChainCollection):\n light_chains (ChainCollection):\n names (list):\n '
if ((heavy_chains is None) and (light_chains is None) and (fab is None)):
raise ValueError('Provide a list of Chain objects or an ChainCollection object')
if (isinstance(fab, list) and all((isinstance(fab_i, Fab) for fab_i in fab))):
self._fab = fab
self._light_chains = ChainCollection([x[0] for x in self._fab])
self._heavy_chains = ChainCollection([x[1] for x in self._fab])
if ((fab is None) and ((heavy_chains is not None) and (light_chains is not None))):
if isinstance(heavy_chains, list):
self._heavy_chains = ChainCollection(antibody_objects=heavy_chains)
elif isinstance(heavy_chains, ChainCollection):
self._heavy_chains = heavy_chains
else:
raise ValueError('Provide a list of Chain objects or an ChainCollection object')
if isinstance(light_chains, list):
self._light_chains = ChainCollection(antibody_objects=light_chains)
elif isinstance(light_chains, ChainCollection):
self._light_chains = light_chains
else:
raise ValueError('Provide a list of Chain objects or an ChainCollection object')
if (len(self._light_chains.loading_status()) == 0):
self._light_chains.load()
if (len(self._heavy_chains.loading_status()) == 0):
self._heavy_chains.load()
if (self._light_chains.n_ab != self._heavy_chains.n_ab):
raise ValueError('Number of heavy chains must be the same of light chains')
if (isinstance(names, list) and all((isinstance(name, str) for name in names))):
if (len(names) == self._heavy_chains.n_ab):
self._names = names
else:
raise ValueError('Length of name list must be the same as length of heavy_chains/light chains lists')
elif (names is None):
self._names = ['{} - {}'.format(heavy, light) for (heavy, light) in zip(self._heavy_chains.names, self._light_chains.names)]
else:
raise ValueError('Names expected a list of strings, instead got {}'.format(type(names)))
self._n_ab = self._light_chains.n_ab
self._pair_sequences = [(heavy + light) for (light, heavy) in zip(self._heavy_chains.sequences, self._light_chains.sequences)]
self._internal_heavy_name = self._heavy_chains.names
self._internal_light_name = self._light_chains.names | def __init__(self, fab=None, heavy_chains=None, light_chains=None, names=None):
'\n Fab object container that handles combinations of light/heavy Chain pairs.\n\n Args:\n fab (list):\n heavy_chains (ChainCollection):\n light_chains (ChainCollection):\n names (list):\n '
if ((heavy_chains is None) and (light_chains is None) and (fab is None)):
raise ValueError('Provide a list of Chain objects or an ChainCollection object')
if (isinstance(fab, list) and all((isinstance(fab_i, Fab) for fab_i in fab))):
self._fab = fab
self._light_chains = ChainCollection([x[0] for x in self._fab])
self._heavy_chains = ChainCollection([x[1] for x in self._fab])
if ((fab is None) and ((heavy_chains is not None) and (light_chains is not None))):
if isinstance(heavy_chains, list):
self._heavy_chains = ChainCollection(antibody_objects=heavy_chains)
elif isinstance(heavy_chains, ChainCollection):
self._heavy_chains = heavy_chains
else:
raise ValueError('Provide a list of Chain objects or an ChainCollection object')
if isinstance(light_chains, list):
self._light_chains = ChainCollection(antibody_objects=light_chains)
elif isinstance(light_chains, ChainCollection):
self._light_chains = light_chains
else:
raise ValueError('Provide a list of Chain objects or an ChainCollection object')
if (len(self._light_chains.loading_status()) == 0):
self._light_chains.load()
if (len(self._heavy_chains.loading_status()) == 0):
self._heavy_chains.load()
if (self._light_chains.n_ab != self._heavy_chains.n_ab):
raise ValueError('Number of heavy chains must be the same of light chains')
if (isinstance(names, list) and all((isinstance(name, str) for name in names))):
if (len(names) == self._heavy_chains.n_ab):
self._names = names
else:
raise ValueError('Length of name list must be the same as length of heavy_chains/light chains lists')
elif (names is None):
self._names = ['{} - {}'.format(heavy, light) for (heavy, light) in zip(self._heavy_chains.names, self._light_chains.names)]
else:
raise ValueError('Names expected a list of strings, instead got {}'.format(type(names)))
self._n_ab = self._light_chains.n_ab
self._pair_sequences = [(heavy + light) for (light, heavy) in zip(self._heavy_chains.sequences, self._light_chains.sequences)]
self._internal_heavy_name = self._heavy_chains.names
self._internal_light_name = self._light_chains.names<|docstring|>Fab object container that handles combinations of light/heavy Chain pairs.
Args:
fab (list):
heavy_chains (ChainCollection):
light_chains (ChainCollection):
names (list):<|endoftext|> |
4ef9ea154a6d4cccbcd7fe6ef107a0b24a29763910f15e7a85ef395b4b7dc13d | def get_object(self, name):
'\n\n :param name: str\n :return:\n '
if (name in self.names):
index = self.names.index(name)
return self[index]
else:
raise ValueError('Could not find sequence with specified name') | :param name: str
:return: | abpytools/core/fab_collection.py | get_object | gf712/AbPyTools | 13 | python | def get_object(self, name):
'\n\n :param name: str\n :return:\n '
if (name in self.names):
index = self.names.index(name)
return self[index]
else:
raise ValueError('Could not find sequence with specified name') | def get_object(self, name):
'\n\n :param name: str\n :return:\n '
if (name in self.names):
index = self.names.index(name)
return self[index]
else:
raise ValueError('Could not find sequence with specified name')<|docstring|>:param name: str
:return:<|endoftext|> |
7450bb2bb8b5b5aab87765168aec578a461f4cd703296b57b0a91db1c7071cd6 | def cbServerGreeting(proto, username, password):
'\n Initial callback - invoked after the server sends us its greet message.\n '
tp = TrivialPrompter()
stdio.StandardIO(tp)
proto.prompt = tp.prompt
proto.display = tp.display
return proto.login(username, password).addCallback(cbAuthentication, proto) | Initial callback - invoked after the server sends us its greet message. | twisted/imap.py | cbServerGreeting | pengchenyu111/SpiderLearning | 3 | python | def cbServerGreeting(proto, username, password):
'\n \n '
tp = TrivialPrompter()
stdio.StandardIO(tp)
proto.prompt = tp.prompt
proto.display = tp.display
return proto.login(username, password).addCallback(cbAuthentication, proto) | def cbServerGreeting(proto, username, password):
'\n \n '
tp = TrivialPrompter()
stdio.StandardIO(tp)
proto.prompt = tp.prompt
proto.display = tp.display
return proto.login(username, password).addCallback(cbAuthentication, proto)<|docstring|>Initial callback - invoked after the server sends us its greet message.<|endoftext|> |
a6f44e94a74c07262070e471b0a2651760be7662da684d4f886d9a39699a80a8 | def ebConnection(reason):
'\n Fallback error-handler. If anything goes wrong, log it and quit.\n '
log.startLogging(sys.stdout)
log.err(reason)
return reason | Fallback error-handler. If anything goes wrong, log it and quit. | twisted/imap.py | ebConnection | pengchenyu111/SpiderLearning | 3 | python | def ebConnection(reason):
'\n \n '
log.startLogging(sys.stdout)
log.err(reason)
return reason | def ebConnection(reason):
'\n \n '
log.startLogging(sys.stdout)
log.err(reason)
return reason<|docstring|>Fallback error-handler. If anything goes wrong, log it and quit.<|endoftext|> |
3a1ff6ae716b634a9c01dc532a4f289fe734b2c9e1deba08290677a321b6756e | def buildProtocol(self, addr):
"\n Initiate the protocol instance. Since we are building a simple IMAP\n client, we don't bother checking what capabilities the server has. We\n just add all the authenticators twisted.mail has. Note: Gmail no\n longer uses any of the methods below, it's been using XOAUTH since\n 2010.\n "
assert (not self.usedUp)
self.usedUp = True
p = self.protocol()
p.factory = self
p.greetDeferred = self.onConn
p.registerAuthenticator(imap4.PLAINAuthenticator(self.username))
p.registerAuthenticator(imap4.LOGINAuthenticator(self.username))
p.registerAuthenticator(imap4.CramMD5ClientAuthenticator(self.username))
return p | Initiate the protocol instance. Since we are building a simple IMAP
client, we don't bother checking what capabilities the server has. We
just add all the authenticators twisted.mail has. Note: Gmail no
longer uses any of the methods below, it's been using XOAUTH since
2010. | twisted/imap.py | buildProtocol | pengchenyu111/SpiderLearning | 3 | python | def buildProtocol(self, addr):
"\n Initiate the protocol instance. Since we are building a simple IMAP\n client, we don't bother checking what capabilities the server has. We\n just add all the authenticators twisted.mail has. Note: Gmail no\n longer uses any of the methods below, it's been using XOAUTH since\n 2010.\n "
assert (not self.usedUp)
self.usedUp = True
p = self.protocol()
p.factory = self
p.greetDeferred = self.onConn
p.registerAuthenticator(imap4.PLAINAuthenticator(self.username))
p.registerAuthenticator(imap4.LOGINAuthenticator(self.username))
p.registerAuthenticator(imap4.CramMD5ClientAuthenticator(self.username))
return p | def buildProtocol(self, addr):
"\n Initiate the protocol instance. Since we are building a simple IMAP\n client, we don't bother checking what capabilities the server has. We\n just add all the authenticators twisted.mail has. Note: Gmail no\n longer uses any of the methods below, it's been using XOAUTH since\n 2010.\n "
assert (not self.usedUp)
self.usedUp = True
p = self.protocol()
p.factory = self
p.greetDeferred = self.onConn
p.registerAuthenticator(imap4.PLAINAuthenticator(self.username))
p.registerAuthenticator(imap4.LOGINAuthenticator(self.username))
p.registerAuthenticator(imap4.CramMD5ClientAuthenticator(self.username))
return p<|docstring|>Initiate the protocol instance. Since we are building a simple IMAP
client, we don't bother checking what capabilities the server has. We
just add all the authenticators twisted.mail has. Note: Gmail no
longer uses any of the methods below, it's been using XOAUTH since
2010.<|endoftext|> |
2eab7fcc536826d68b77adbe59a7e421e0824736a11629cf0dbd1b17401ce954 | def check_url(url: str) -> bool:
'Check if the given URL is valid.\n\n Args:\n url: The URL to check.\n\n Returns:\n True if the URL is valid, False otherwise.\n '
return bool(re.match(URL_REGEX, url)) | Check if the given URL is valid.
Args:
url: The URL to check.
Returns:
True if the URL is valid, False otherwise. | src/meltano/core/tracking/snowplow_tracker.py | check_url | Mu-L/meltano | 0 | python | def check_url(url: str) -> bool:
'Check if the given URL is valid.\n\n Args:\n url: The URL to check.\n\n Returns:\n True if the URL is valid, False otherwise.\n '
return bool(re.match(URL_REGEX, url)) | def check_url(url: str) -> bool:
'Check if the given URL is valid.\n\n Args:\n url: The URL to check.\n\n Returns:\n True if the URL is valid, False otherwise.\n '
return bool(re.match(URL_REGEX, url))<|docstring|>Check if the given URL is valid.
Args:
url: The URL to check.
Returns:
True if the URL is valid, False otherwise.<|endoftext|> |
897844e070f31bd1ad9432ff6e8ae6118f4141bf72d69f6e9e7582734d143944 | def __init__(self, project: Project, *, request_timeout: int=2.0, **kwargs: Any):
'Create a Snowplow Tracker for the Meltano project.\n\n Args:\n project: The Meltano project.\n request_timeout: The timeout for all the event emitters.\n kwargs: Additional arguments to pass to the parent snowplow Tracker class.\n '
settings_service = ProjectSettingsService(project)
endpoints = settings_service.get('snowplow.collector_endpoints')
emitters: list[Emitter] = []
for endpoint in endpoints:
if (not check_url(endpoint)):
logger.warning('invalid_snowplow_endpoint', endpoint=endpoint)
continue
parsed_url = urlparse(endpoint)
emitters.append(Emitter(endpoint=(parsed_url.hostname + parsed_url.path), protocol=(parsed_url.scheme or 'http'), port=parsed_url.port, request_timeout=request_timeout))
super().__init__(emitters=emitters, **kwargs) | Create a Snowplow Tracker for the Meltano project.
Args:
project: The Meltano project.
request_timeout: The timeout for all the event emitters.
kwargs: Additional arguments to pass to the parent snowplow Tracker class. | src/meltano/core/tracking/snowplow_tracker.py | __init__ | Mu-L/meltano | 0 | python | def __init__(self, project: Project, *, request_timeout: int=2.0, **kwargs: Any):
'Create a Snowplow Tracker for the Meltano project.\n\n Args:\n project: The Meltano project.\n request_timeout: The timeout for all the event emitters.\n kwargs: Additional arguments to pass to the parent snowplow Tracker class.\n '
settings_service = ProjectSettingsService(project)
endpoints = settings_service.get('snowplow.collector_endpoints')
emitters: list[Emitter] = []
for endpoint in endpoints:
if (not check_url(endpoint)):
logger.warning('invalid_snowplow_endpoint', endpoint=endpoint)
continue
parsed_url = urlparse(endpoint)
emitters.append(Emitter(endpoint=(parsed_url.hostname + parsed_url.path), protocol=(parsed_url.scheme or 'http'), port=parsed_url.port, request_timeout=request_timeout))
super().__init__(emitters=emitters, **kwargs) | def __init__(self, project: Project, *, request_timeout: int=2.0, **kwargs: Any):
'Create a Snowplow Tracker for the Meltano project.\n\n Args:\n project: The Meltano project.\n request_timeout: The timeout for all the event emitters.\n kwargs: Additional arguments to pass to the parent snowplow Tracker class.\n '
settings_service = ProjectSettingsService(project)
endpoints = settings_service.get('snowplow.collector_endpoints')
emitters: list[Emitter] = []
for endpoint in endpoints:
if (not check_url(endpoint)):
logger.warning('invalid_snowplow_endpoint', endpoint=endpoint)
continue
parsed_url = urlparse(endpoint)
emitters.append(Emitter(endpoint=(parsed_url.hostname + parsed_url.path), protocol=(parsed_url.scheme or 'http'), port=parsed_url.port, request_timeout=request_timeout))
super().__init__(emitters=emitters, **kwargs)<|docstring|>Create a Snowplow Tracker for the Meltano project.
Args:
project: The Meltano project.
request_timeout: The timeout for all the event emitters.
kwargs: Additional arguments to pass to the parent snowplow Tracker class.<|endoftext|> |
a83fc3d302f842819c3117a4ce0a456d95769561951eb8f59a8ab8085fbb1758 | def read_vcf(fh) -> pd.DataFrame:
'Read VCF file into a DataFrame'
vcf_cols = []
for line in fh:
if line.startswith('#CHROM'):
vcf_cols = line[1:].strip().split('\t')
break
df = pd.read_table(fh, comment='#', header=None, names=vcf_cols, dtype=VCF_COL_DTYPES, na_filter=False)
return df | Read VCF file into a DataFrame | vcf_consensus_builder/vcf_io.py | read_vcf | leoisl/vcf_consensus_builder | 0 | python | def read_vcf(fh) -> pd.DataFrame:
vcf_cols = []
for line in fh:
if line.startswith('#CHROM'):
vcf_cols = line[1:].strip().split('\t')
break
df = pd.read_table(fh, comment='#', header=None, names=vcf_cols, dtype=VCF_COL_DTYPES, na_filter=False)
return df | def read_vcf(fh) -> pd.DataFrame:
vcf_cols = []
for line in fh:
if line.startswith('#CHROM'):
vcf_cols = line[1:].strip().split('\t')
break
df = pd.read_table(fh, comment='#', header=None, names=vcf_cols, dtype=VCF_COL_DTYPES, na_filter=False)
return df<|docstring|>Read VCF file into a DataFrame<|endoftext|> |
2ba9f2665fe0d7fb70af290aebc093ba38fa7841915f97ff5ceb0b31295e8635 | @app.expanded_callback(Output('chart-holder-1', 'children'), [Input('figure-button-1', 'n_clicks')], [State('chart-type-1', 'value'), State('chart-top-n-1', 'value'), State('chart-transpose-1', 'on'), State('example-wordclass-dropdown', 'value')])
def _new_chart(n_clicks, chart_type, top_n, transpose, wordclass, **kwargs):
'\n Make new chart by kind. Do it 5 times, once for each chart space\n '
if (n_clicks is None):
return no_update
try:
corpus = _get_corpus(slug)
except TypeError:
return ([], [])
(df, _, _) = _quick_freq(corpus, wordclass=wordclass)
if transpose:
df = df.T
df = df.iloc[(:, :top_n)]
figure = _df_to_figure(df, kind=chart_type, width='100%')
chart_data = dict(id='chart-1', figure=figure, style=dict(width='100%', height='400px'))
chart = dcc.Graph(**chart_data)
return chart | Make new chart by kind. Do it 5 times, once for each chart space | example/callbacks.py | _new_chart | interrogator/buzzword | 3 | python | @app.expanded_callback(Output('chart-holder-1', 'children'), [Input('figure-button-1', 'n_clicks')], [State('chart-type-1', 'value'), State('chart-top-n-1', 'value'), State('chart-transpose-1', 'on'), State('example-wordclass-dropdown', 'value')])
def _new_chart(n_clicks, chart_type, top_n, transpose, wordclass, **kwargs):
'\n \n '
if (n_clicks is None):
return no_update
try:
corpus = _get_corpus(slug)
except TypeError:
return ([], [])
(df, _, _) = _quick_freq(corpus, wordclass=wordclass)
if transpose:
df = df.T
df = df.iloc[(:, :top_n)]
figure = _df_to_figure(df, kind=chart_type, width='100%')
chart_data = dict(id='chart-1', figure=figure, style=dict(width='100%', height='400px'))
chart = dcc.Graph(**chart_data)
return chart | @app.expanded_callback(Output('chart-holder-1', 'children'), [Input('figure-button-1', 'n_clicks')], [State('chart-type-1', 'value'), State('chart-top-n-1', 'value'), State('chart-transpose-1', 'on'), State('example-wordclass-dropdown', 'value')])
def _new_chart(n_clicks, chart_type, top_n, transpose, wordclass, **kwargs):
'\n \n '
if (n_clicks is None):
return no_update
try:
corpus = _get_corpus(slug)
except TypeError:
return ([], [])
(df, _, _) = _quick_freq(corpus, wordclass=wordclass)
if transpose:
df = df.T
df = df.iloc[(:, :top_n)]
figure = _df_to_figure(df, kind=chart_type, width='100%')
chart_data = dict(id='chart-1', figure=figure, style=dict(width='100%', height='400px'))
chart = dcc.Graph(**chart_data)
return chart<|docstring|>Make new chart by kind. Do it 5 times, once for each chart space<|endoftext|> |
6ee874392c7dc4f7c692ad1d38c0d024d17c4f643432849004289b08f4248d1f | def generate_dataset(path_org, path_trg):
'\n Function that generates a dataset in the format\n that we will use in the dataloader from torchtext\n input:\n path_org (string): location of the dataset file i\n in the SCAN format\n path_trg (string): location of where we will save \n the new files\n '
lines = open(path_org, 'r').readlines()
with open((path_trg + '.in'), 'w+') as file_in_out, open((path_trg + '.out'), 'w+') as file_out_out:
for line in lines:
line_list = line.split(':')
line_in = line_list[1][1:(- 4)]
line_out = line_list[2][1:]
file_in_out.write((line_in + '\n'))
file_out_out.write(line_out) | Function that generates a dataset in the format
that we will use in the dataloader from torchtext
input:
path_org (string): location of the dataset file i
in the SCAN format
path_trg (string): location of where we will save
the new files | build_dataset.py | generate_dataset | hec44/SCAN-reproduction | 0 | python | def generate_dataset(path_org, path_trg):
'\n Function that generates a dataset in the format\n that we will use in the dataloader from torchtext\n input:\n path_org (string): location of the dataset file i\n in the SCAN format\n path_trg (string): location of where we will save \n the new files\n '
lines = open(path_org, 'r').readlines()
with open((path_trg + '.in'), 'w+') as file_in_out, open((path_trg + '.out'), 'w+') as file_out_out:
for line in lines:
line_list = line.split(':')
line_in = line_list[1][1:(- 4)]
line_out = line_list[2][1:]
file_in_out.write((line_in + '\n'))
file_out_out.write(line_out) | def generate_dataset(path_org, path_trg):
'\n Function that generates a dataset in the format\n that we will use in the dataloader from torchtext\n input:\n path_org (string): location of the dataset file i\n in the SCAN format\n path_trg (string): location of where we will save \n the new files\n '
lines = open(path_org, 'r').readlines()
with open((path_trg + '.in'), 'w+') as file_in_out, open((path_trg + '.out'), 'w+') as file_out_out:
for line in lines:
line_list = line.split(':')
line_in = line_list[1][1:(- 4)]
line_out = line_list[2][1:]
file_in_out.write((line_in + '\n'))
file_out_out.write(line_out)<|docstring|>Function that generates a dataset in the format
that we will use in the dataloader from torchtext
input:
path_org (string): location of the dataset file i
in the SCAN format
path_trg (string): location of where we will save
the new files<|endoftext|> |
260c7a0eac50d148f42bbc09df68e1cad65953be2f3bfd36143df175d829500c | def __new__(metacls, cls, bases, namespace, lazy_method: str='get', auto_wire: Union[(bool, Iterable[str])]=None, dependencies: DEPENDENCIES_TYPE=None, use_names: Union[(bool, Iterable[str])]=None, use_type_hints: Union[(bool, Iterable[str])]=None, container: DependencyContainer=None):
"\n Metaclass used to generate class with constant dependencies.\n\n This should be used for configuration or external static resources.\n Only public uppercase class attributes will be converted to dependencies.\n\n .. doctest::\n\n >>> import antidote\n >>> class Conf(metaclass=antidote.LazyConstantsMeta):\n ... DOMAIN = 'domain'\n ... _A = 'unchanged'\n ... a = 'unchanged'\n ...\n ... def __init__(self):\n ... self._data = {'domain': 'example.com'}\n ...\n ... def get(self, key):\n ... return self._data[key]\n ...\n >>> Conf._A\n 'unchanged'\n >>> Conf.a\n 'unchanged'\n >>> Conf().DOMAIN\n 'example.com'\n >>> Conf.DOMAIN\n LazyMethodCallDependency(...)\n >>> antidote.world.get(Conf.DOMAIN)\n 'example.com'\n >>> @antidote.inject(dependencies=(Conf.DOMAIN,))\n ... def f(a):\n ... return a\n >>> f()\n 'example.com'\n\n As one can see, neither :code:`a` nor :code:`_A` are changed,\n only :code:`DOMAIN`. Constant's initial value becomes the argument given\n to the lazy method, by default :code:`__call__()`. It has two different\n behaviors depending how it is retrieved:\n\n - Used as a instance attribute, :code:`Conf().DOMAIN`, is is equivalent\n to :code:`Conf().__call__('domain')`. This lets your code stay easy to\n manipulate and test.\n - Used as a class attribute, :code:`Conf.DOMAIN`, it becomes a special\n object used by Antidote to identify a dependency. This lets you inject\n :code:`Conf.DOMAIN` anywhere in your code.\n\n The advantage of using this is that Antidote will only instantiate\n :code:`Conf` once, if and only if necessary. The same is applied for\n every constant, those are singletons. Defining your static resources or\n configuration as class constants also makes your code more maintainable,\n as any decent IDE will refactor / find the usage of those in a blink of\n an eye.\n\n Underneath it uses :py:class:`.LazyMethodCall` and :py:func:`.register`.\n It is equivalent to:\n\n .. testcode::\n\n from antidote import LazyMethodCall, register\n\n @register(auto_wire=('__init__', '__call__'))\n class Conf:\n # Required for the example as we specify __init__() explicitly\n # for auto wiring, so it has to exist.\n def __init__(self):\n pass\n\n def __call__(self, key):\n return config[key]\n\n DOMAIN = LazyMethodCall(__call__)('domain')\n\n Args:\n lazy_method: Name of the lazy method to use for the constants.\n Defaults to :code:`'__call__'`.\n auto_wire: Injects automatically the dependencies of the methods\n specified, or only of :code:`__init__()` and :code:`__call__()`\n if True.\n dependencies: Can be either a mapping of arguments name to their\n dependency, an iterable of dependencies or a function which returns\n the dependency given the arguments name. If an iterable is specified,\n the position of the arguments is used to determine their respective\n dependency. An argument may be skipped by using :code:`None` as a\n placeholder. The first argument is always ignored for methods (self)\n and class methods (cls).Type hints are overridden. Defaults to\n :code:`None`.\n use_names: Whether or not the arguments' name should be used as their\n respective dependency. An iterable of argument names may also be\n supplied to restrict this to those. Defaults to :code:`False`.\n use_type_hints: Whether or not the type hints (annotations) should be\n used as the arguments dependency. An iterable of argument names may\n also be specified to restrict this to those. Any type hints from\n the builtins (str, int...) or the typing (:py:class:`~typing.Optional`,\n ...) are ignored. Defaults to :code:`True`.\n container: :py:class:`~.core.container.DependencyContainer` to which the\n dependency should be attached. Defaults to the global container,\n :code:`antidote.world`.\n "
if (lazy_method not in namespace):
raise ValueError('Lazy method {}() is no defined in {}'.format(lazy_method, cls))
resource_class = super().__new__(metacls, cls, bases, namespace)
wire_raise_on_missing = True
if ((auto_wire is None) or isinstance(auto_wire, bool)):
if (auto_wire is False):
methods = ()
else:
methods = (lazy_method, '__init__')
wire_raise_on_missing = False
else:
methods = auto_wire
if methods:
resource_class = wire(resource_class, methods=methods, dependencies=dependencies, use_names=use_names, use_type_hints=use_type_hints, container=container, raise_on_missing=wire_raise_on_missing)
resource_class = register(resource_class, auto_wire=False, singleton=True, container=container)
func = resource_class.__dict__[lazy_method]
for (name, v) in list(resource_class.__dict__.items()):
if ((not name.startswith('_')) and name.isupper()):
setattr(resource_class, name, LazyMethodCall(func, singleton=True)(v))
return resource_class | Metaclass used to generate class with constant dependencies.
This should be used for configuration or external static resources.
Only public uppercase class attributes will be converted to dependencies.
.. doctest::
>>> import antidote
>>> class Conf(metaclass=antidote.LazyConstantsMeta):
... DOMAIN = 'domain'
... _A = 'unchanged'
... a = 'unchanged'
...
... def __init__(self):
... self._data = {'domain': 'example.com'}
...
... def get(self, key):
... return self._data[key]
...
>>> Conf._A
'unchanged'
>>> Conf.a
'unchanged'
>>> Conf().DOMAIN
'example.com'
>>> Conf.DOMAIN
LazyMethodCallDependency(...)
>>> antidote.world.get(Conf.DOMAIN)
'example.com'
>>> @antidote.inject(dependencies=(Conf.DOMAIN,))
... def f(a):
... return a
>>> f()
'example.com'
As one can see, neither :code:`a` nor :code:`_A` are changed,
only :code:`DOMAIN`. Constant's initial value becomes the argument given
to the lazy method, by default :code:`__call__()`. It has two different
behaviors depending how it is retrieved:
- Used as a instance attribute, :code:`Conf().DOMAIN`, is is equivalent
to :code:`Conf().__call__('domain')`. This lets your code stay easy to
manipulate and test.
- Used as a class attribute, :code:`Conf.DOMAIN`, it becomes a special
object used by Antidote to identify a dependency. This lets you inject
:code:`Conf.DOMAIN` anywhere in your code.
The advantage of using this is that Antidote will only instantiate
:code:`Conf` once, if and only if necessary. The same is applied for
every constant, those are singletons. Defining your static resources or
configuration as class constants also makes your code more maintainable,
as any decent IDE will refactor / find the usage of those in a blink of
an eye.
Underneath it uses :py:class:`.LazyMethodCall` and :py:func:`.register`.
It is equivalent to:
.. testcode::
from antidote import LazyMethodCall, register
@register(auto_wire=('__init__', '__call__'))
class Conf:
# Required for the example as we specify __init__() explicitly
# for auto wiring, so it has to exist.
def __init__(self):
pass
def __call__(self, key):
return config[key]
DOMAIN = LazyMethodCall(__call__)('domain')
Args:
lazy_method: Name of the lazy method to use for the constants.
Defaults to :code:`'__call__'`.
auto_wire: Injects automatically the dependencies of the methods
specified, or only of :code:`__init__()` and :code:`__call__()`
if True.
dependencies: Can be either a mapping of arguments name to their
dependency, an iterable of dependencies or a function which returns
the dependency given the arguments name. If an iterable is specified,
the position of the arguments is used to determine their respective
dependency. An argument may be skipped by using :code:`None` as a
placeholder. The first argument is always ignored for methods (self)
and class methods (cls).Type hints are overridden. Defaults to
:code:`None`.
use_names: Whether or not the arguments' name should be used as their
respective dependency. An iterable of argument names may also be
supplied to restrict this to those. Defaults to :code:`False`.
use_type_hints: Whether or not the type hints (annotations) should be
used as the arguments dependency. An iterable of argument names may
also be specified to restrict this to those. Any type hints from
the builtins (str, int...) or the typing (:py:class:`~typing.Optional`,
...) are ignored. Defaults to :code:`True`.
container: :py:class:`~.core.container.DependencyContainer` to which the
dependency should be attached. Defaults to the global container,
:code:`antidote.world`. | src/antidote/helpers/constants.py | __new__ | keelerm84/antidote | 0 | python | def __new__(metacls, cls, bases, namespace, lazy_method: str='get', auto_wire: Union[(bool, Iterable[str])]=None, dependencies: DEPENDENCIES_TYPE=None, use_names: Union[(bool, Iterable[str])]=None, use_type_hints: Union[(bool, Iterable[str])]=None, container: DependencyContainer=None):
"\n Metaclass used to generate class with constant dependencies.\n\n This should be used for configuration or external static resources.\n Only public uppercase class attributes will be converted to dependencies.\n\n .. doctest::\n\n >>> import antidote\n >>> class Conf(metaclass=antidote.LazyConstantsMeta):\n ... DOMAIN = 'domain'\n ... _A = 'unchanged'\n ... a = 'unchanged'\n ...\n ... def __init__(self):\n ... self._data = {'domain': 'example.com'}\n ...\n ... def get(self, key):\n ... return self._data[key]\n ...\n >>> Conf._A\n 'unchanged'\n >>> Conf.a\n 'unchanged'\n >>> Conf().DOMAIN\n 'example.com'\n >>> Conf.DOMAIN\n LazyMethodCallDependency(...)\n >>> antidote.world.get(Conf.DOMAIN)\n 'example.com'\n >>> @antidote.inject(dependencies=(Conf.DOMAIN,))\n ... def f(a):\n ... return a\n >>> f()\n 'example.com'\n\n As one can see, neither :code:`a` nor :code:`_A` are changed,\n only :code:`DOMAIN`. Constant's initial value becomes the argument given\n to the lazy method, by default :code:`__call__()`. It has two different\n behaviors depending how it is retrieved:\n\n - Used as a instance attribute, :code:`Conf().DOMAIN`, is is equivalent\n to :code:`Conf().__call__('domain')`. This lets your code stay easy to\n manipulate and test.\n - Used as a class attribute, :code:`Conf.DOMAIN`, it becomes a special\n object used by Antidote to identify a dependency. This lets you inject\n :code:`Conf.DOMAIN` anywhere in your code.\n\n The advantage of using this is that Antidote will only instantiate\n :code:`Conf` once, if and only if necessary. The same is applied for\n every constant, those are singletons. Defining your static resources or\n configuration as class constants also makes your code more maintainable,\n as any decent IDE will refactor / find the usage of those in a blink of\n an eye.\n\n Underneath it uses :py:class:`.LazyMethodCall` and :py:func:`.register`.\n It is equivalent to:\n\n .. testcode::\n\n from antidote import LazyMethodCall, register\n\n @register(auto_wire=('__init__', '__call__'))\n class Conf:\n # Required for the example as we specify __init__() explicitly\n # for auto wiring, so it has to exist.\n def __init__(self):\n pass\n\n def __call__(self, key):\n return config[key]\n\n DOMAIN = LazyMethodCall(__call__)('domain')\n\n Args:\n lazy_method: Name of the lazy method to use for the constants.\n Defaults to :code:`'__call__'`.\n auto_wire: Injects automatically the dependencies of the methods\n specified, or only of :code:`__init__()` and :code:`__call__()`\n if True.\n dependencies: Can be either a mapping of arguments name to their\n dependency, an iterable of dependencies or a function which returns\n the dependency given the arguments name. If an iterable is specified,\n the position of the arguments is used to determine their respective\n dependency. An argument may be skipped by using :code:`None` as a\n placeholder. The first argument is always ignored for methods (self)\n and class methods (cls).Type hints are overridden. Defaults to\n :code:`None`.\n use_names: Whether or not the arguments' name should be used as their\n respective dependency. An iterable of argument names may also be\n supplied to restrict this to those. Defaults to :code:`False`.\n use_type_hints: Whether or not the type hints (annotations) should be\n used as the arguments dependency. An iterable of argument names may\n also be specified to restrict this to those. Any type hints from\n the builtins (str, int...) or the typing (:py:class:`~typing.Optional`,\n ...) are ignored. Defaults to :code:`True`.\n container: :py:class:`~.core.container.DependencyContainer` to which the\n dependency should be attached. Defaults to the global container,\n :code:`antidote.world`.\n "
if (lazy_method not in namespace):
raise ValueError('Lazy method {}() is no defined in {}'.format(lazy_method, cls))
resource_class = super().__new__(metacls, cls, bases, namespace)
wire_raise_on_missing = True
if ((auto_wire is None) or isinstance(auto_wire, bool)):
if (auto_wire is False):
methods = ()
else:
methods = (lazy_method, '__init__')
wire_raise_on_missing = False
else:
methods = auto_wire
if methods:
resource_class = wire(resource_class, methods=methods, dependencies=dependencies, use_names=use_names, use_type_hints=use_type_hints, container=container, raise_on_missing=wire_raise_on_missing)
resource_class = register(resource_class, auto_wire=False, singleton=True, container=container)
func = resource_class.__dict__[lazy_method]
for (name, v) in list(resource_class.__dict__.items()):
if ((not name.startswith('_')) and name.isupper()):
setattr(resource_class, name, LazyMethodCall(func, singleton=True)(v))
return resource_class | def __new__(metacls, cls, bases, namespace, lazy_method: str='get', auto_wire: Union[(bool, Iterable[str])]=None, dependencies: DEPENDENCIES_TYPE=None, use_names: Union[(bool, Iterable[str])]=None, use_type_hints: Union[(bool, Iterable[str])]=None, container: DependencyContainer=None):
"\n Metaclass used to generate class with constant dependencies.\n\n This should be used for configuration or external static resources.\n Only public uppercase class attributes will be converted to dependencies.\n\n .. doctest::\n\n >>> import antidote\n >>> class Conf(metaclass=antidote.LazyConstantsMeta):\n ... DOMAIN = 'domain'\n ... _A = 'unchanged'\n ... a = 'unchanged'\n ...\n ... def __init__(self):\n ... self._data = {'domain': 'example.com'}\n ...\n ... def get(self, key):\n ... return self._data[key]\n ...\n >>> Conf._A\n 'unchanged'\n >>> Conf.a\n 'unchanged'\n >>> Conf().DOMAIN\n 'example.com'\n >>> Conf.DOMAIN\n LazyMethodCallDependency(...)\n >>> antidote.world.get(Conf.DOMAIN)\n 'example.com'\n >>> @antidote.inject(dependencies=(Conf.DOMAIN,))\n ... def f(a):\n ... return a\n >>> f()\n 'example.com'\n\n As one can see, neither :code:`a` nor :code:`_A` are changed,\n only :code:`DOMAIN`. Constant's initial value becomes the argument given\n to the lazy method, by default :code:`__call__()`. It has two different\n behaviors depending how it is retrieved:\n\n - Used as a instance attribute, :code:`Conf().DOMAIN`, is is equivalent\n to :code:`Conf().__call__('domain')`. This lets your code stay easy to\n manipulate and test.\n - Used as a class attribute, :code:`Conf.DOMAIN`, it becomes a special\n object used by Antidote to identify a dependency. This lets you inject\n :code:`Conf.DOMAIN` anywhere in your code.\n\n The advantage of using this is that Antidote will only instantiate\n :code:`Conf` once, if and only if necessary. The same is applied for\n every constant, those are singletons. Defining your static resources or\n configuration as class constants also makes your code more maintainable,\n as any decent IDE will refactor / find the usage of those in a blink of\n an eye.\n\n Underneath it uses :py:class:`.LazyMethodCall` and :py:func:`.register`.\n It is equivalent to:\n\n .. testcode::\n\n from antidote import LazyMethodCall, register\n\n @register(auto_wire=('__init__', '__call__'))\n class Conf:\n # Required for the example as we specify __init__() explicitly\n # for auto wiring, so it has to exist.\n def __init__(self):\n pass\n\n def __call__(self, key):\n return config[key]\n\n DOMAIN = LazyMethodCall(__call__)('domain')\n\n Args:\n lazy_method: Name of the lazy method to use for the constants.\n Defaults to :code:`'__call__'`.\n auto_wire: Injects automatically the dependencies of the methods\n specified, or only of :code:`__init__()` and :code:`__call__()`\n if True.\n dependencies: Can be either a mapping of arguments name to their\n dependency, an iterable of dependencies or a function which returns\n the dependency given the arguments name. If an iterable is specified,\n the position of the arguments is used to determine their respective\n dependency. An argument may be skipped by using :code:`None` as a\n placeholder. The first argument is always ignored for methods (self)\n and class methods (cls).Type hints are overridden. Defaults to\n :code:`None`.\n use_names: Whether or not the arguments' name should be used as their\n respective dependency. An iterable of argument names may also be\n supplied to restrict this to those. Defaults to :code:`False`.\n use_type_hints: Whether or not the type hints (annotations) should be\n used as the arguments dependency. An iterable of argument names may\n also be specified to restrict this to those. Any type hints from\n the builtins (str, int...) or the typing (:py:class:`~typing.Optional`,\n ...) are ignored. Defaults to :code:`True`.\n container: :py:class:`~.core.container.DependencyContainer` to which the\n dependency should be attached. Defaults to the global container,\n :code:`antidote.world`.\n "
if (lazy_method not in namespace):
raise ValueError('Lazy method {}() is no defined in {}'.format(lazy_method, cls))
resource_class = super().__new__(metacls, cls, bases, namespace)
wire_raise_on_missing = True
if ((auto_wire is None) or isinstance(auto_wire, bool)):
if (auto_wire is False):
methods = ()
else:
methods = (lazy_method, '__init__')
wire_raise_on_missing = False
else:
methods = auto_wire
if methods:
resource_class = wire(resource_class, methods=methods, dependencies=dependencies, use_names=use_names, use_type_hints=use_type_hints, container=container, raise_on_missing=wire_raise_on_missing)
resource_class = register(resource_class, auto_wire=False, singleton=True, container=container)
func = resource_class.__dict__[lazy_method]
for (name, v) in list(resource_class.__dict__.items()):
if ((not name.startswith('_')) and name.isupper()):
setattr(resource_class, name, LazyMethodCall(func, singleton=True)(v))
return resource_class<|docstring|>Metaclass used to generate class with constant dependencies.
This should be used for configuration or external static resources.
Only public uppercase class attributes will be converted to dependencies.
.. doctest::
>>> import antidote
>>> class Conf(metaclass=antidote.LazyConstantsMeta):
... DOMAIN = 'domain'
... _A = 'unchanged'
... a = 'unchanged'
...
... def __init__(self):
... self._data = {'domain': 'example.com'}
...
... def get(self, key):
... return self._data[key]
...
>>> Conf._A
'unchanged'
>>> Conf.a
'unchanged'
>>> Conf().DOMAIN
'example.com'
>>> Conf.DOMAIN
LazyMethodCallDependency(...)
>>> antidote.world.get(Conf.DOMAIN)
'example.com'
>>> @antidote.inject(dependencies=(Conf.DOMAIN,))
... def f(a):
... return a
>>> f()
'example.com'
As one can see, neither :code:`a` nor :code:`_A` are changed,
only :code:`DOMAIN`. Constant's initial value becomes the argument given
to the lazy method, by default :code:`__call__()`. It has two different
behaviors depending how it is retrieved:
- Used as a instance attribute, :code:`Conf().DOMAIN`, is is equivalent
to :code:`Conf().__call__('domain')`. This lets your code stay easy to
manipulate and test.
- Used as a class attribute, :code:`Conf.DOMAIN`, it becomes a special
object used by Antidote to identify a dependency. This lets you inject
:code:`Conf.DOMAIN` anywhere in your code.
The advantage of using this is that Antidote will only instantiate
:code:`Conf` once, if and only if necessary. The same is applied for
every constant, those are singletons. Defining your static resources or
configuration as class constants also makes your code more maintainable,
as any decent IDE will refactor / find the usage of those in a blink of
an eye.
Underneath it uses :py:class:`.LazyMethodCall` and :py:func:`.register`.
It is equivalent to:
.. testcode::
from antidote import LazyMethodCall, register
@register(auto_wire=('__init__', '__call__'))
class Conf:
# Required for the example as we specify __init__() explicitly
# for auto wiring, so it has to exist.
def __init__(self):
pass
def __call__(self, key):
return config[key]
DOMAIN = LazyMethodCall(__call__)('domain')
Args:
lazy_method: Name of the lazy method to use for the constants.
Defaults to :code:`'__call__'`.
auto_wire: Injects automatically the dependencies of the methods
specified, or only of :code:`__init__()` and :code:`__call__()`
if True.
dependencies: Can be either a mapping of arguments name to their
dependency, an iterable of dependencies or a function which returns
the dependency given the arguments name. If an iterable is specified,
the position of the arguments is used to determine their respective
dependency. An argument may be skipped by using :code:`None` as a
placeholder. The first argument is always ignored for methods (self)
and class methods (cls).Type hints are overridden. Defaults to
:code:`None`.
use_names: Whether or not the arguments' name should be used as their
respective dependency. An iterable of argument names may also be
supplied to restrict this to those. Defaults to :code:`False`.
use_type_hints: Whether or not the type hints (annotations) should be
used as the arguments dependency. An iterable of argument names may
also be specified to restrict this to those. Any type hints from
the builtins (str, int...) or the typing (:py:class:`~typing.Optional`,
...) are ignored. Defaults to :code:`True`.
container: :py:class:`~.core.container.DependencyContainer` to which the
dependency should be attached. Defaults to the global container,
:code:`antidote.world`.<|endoftext|> |
bfa60b07c8e25564054a2c9836ed79b57bb4293baaa809863a7a953ff86de751 | @commands.command()
async def export(self, ctx, *emoji: Union[(discord.PartialEmoji, discord.Emoji)]):
'\n Insult the user.\n Usage: [p]insult <Member>\n Example: [p]insult @Eris#0001\n '
if (len(emoji) == 0):
(await ctx.send('No emoji to download!'))
return
buf = io.BytesIO()
with zipfile.ZipFile(buf, 'w') as zf:
for e in emoji:
asset = e.url
url = str(asset)
name = f'{e.name}.gif'
new_buf = io.BytesIO()
(await asset.save(new_buf))
zf.writestr(name, new_buf.getvalue())
buf.seek(0)
(await ctx.send(file=discord.File(buf, filename='export.zip'))) | Insult the user.
Usage: [p]insult <Member>
Example: [p]insult @Eris#0001 | export_emoji/export_emoji.py | export | edma8378/Eris-Cogs | 0 | python | @commands.command()
async def export(self, ctx, *emoji: Union[(discord.PartialEmoji, discord.Emoji)]):
'\n Insult the user.\n Usage: [p]insult <Member>\n Example: [p]insult @Eris#0001\n '
if (len(emoji) == 0):
(await ctx.send('No emoji to download!'))
return
buf = io.BytesIO()
with zipfile.ZipFile(buf, 'w') as zf:
for e in emoji:
asset = e.url
url = str(asset)
name = f'{e.name}.gif'
new_buf = io.BytesIO()
(await asset.save(new_buf))
zf.writestr(name, new_buf.getvalue())
buf.seek(0)
(await ctx.send(file=discord.File(buf, filename='export.zip'))) | @commands.command()
async def export(self, ctx, *emoji: Union[(discord.PartialEmoji, discord.Emoji)]):
'\n Insult the user.\n Usage: [p]insult <Member>\n Example: [p]insult @Eris#0001\n '
if (len(emoji) == 0):
(await ctx.send('No emoji to download!'))
return
buf = io.BytesIO()
with zipfile.ZipFile(buf, 'w') as zf:
for e in emoji:
asset = e.url
url = str(asset)
name = f'{e.name}.gif'
new_buf = io.BytesIO()
(await asset.save(new_buf))
zf.writestr(name, new_buf.getvalue())
buf.seek(0)
(await ctx.send(file=discord.File(buf, filename='export.zip')))<|docstring|>Insult the user.
Usage: [p]insult <Member>
Example: [p]insult @Eris#0001<|endoftext|> |
2371328274a0ab4ccba0e68142be003068c7f05da6a5bc34d5e950c0d3ceb87d | @abstractmethod
def _get_class(self, item: str):
'\n The subclass must have the following implementation of this method\n\n def _get_class(self, item: str):\n return globals()[item]\n\n globals() only contains the objects in the same module where self.__class__ is defined\n '
pass | The subclass must have the following implementation of this method
def _get_class(self, item: str):
return globals()[item]
globals() only contains the objects in the same module where self.__class__ is defined | stringchain/baseclass.py | _get_class | zhangyi-hu/stringchain | 1 | python | @abstractmethod
def _get_class(self, item: str):
'\n The subclass must have the following implementation of this method\n\n def _get_class(self, item: str):\n return globals()[item]\n\n globals() only contains the objects in the same module where self.__class__ is defined\n '
pass | @abstractmethod
def _get_class(self, item: str):
'\n The subclass must have the following implementation of this method\n\n def _get_class(self, item: str):\n return globals()[item]\n\n globals() only contains the objects in the same module where self.__class__ is defined\n '
pass<|docstring|>The subclass must have the following implementation of this method
def _get_class(self, item: str):
return globals()[item]
globals() only contains the objects in the same module where self.__class__ is defined<|endoftext|> |
dd5944ac396d1153022dfaafb634793a0c77227e4db79047fc7ab63a95148c33 | def clamp(val, valmin, valmax):
'Simple clamping function, limits to [min, max]'
if (val < valmin):
return valmin
if (val > valmax):
return valmax
return val | Simple clamping function, limits to [min, max] | src/tracking_turtlebot/utils.py | clamp | Christophe-Foyer/tracking_turtlebot | 0 | python | def clamp(val, valmin, valmax):
if (val < valmin):
return valmin
if (val > valmax):
return valmax
return val | def clamp(val, valmin, valmax):
if (val < valmin):
return valmin
if (val > valmax):
return valmax
return val<|docstring|>Simple clamping function, limits to [min, max]<|endoftext|> |
d3d5f89bcca0e5b32944239577e89ab2fd04a8cc2aa26fb329c728da02636111 | def makeSimpleProfile(output, input, slop):
'\n From trutlebot_teleop, adds a bit of smoothing to startup/slowdown\n '
if (input > output):
output = min(input, (output + slop))
elif (input < output):
output = max(input, (output - slop))
else:
output = input
return output | From trutlebot_teleop, adds a bit of smoothing to startup/slowdown | src/tracking_turtlebot/utils.py | makeSimpleProfile | Christophe-Foyer/tracking_turtlebot | 0 | python | def makeSimpleProfile(output, input, slop):
'\n \n '
if (input > output):
output = min(input, (output + slop))
elif (input < output):
output = max(input, (output - slop))
else:
output = input
return output | def makeSimpleProfile(output, input, slop):
'\n \n '
if (input > output):
output = min(input, (output + slop))
elif (input < output):
output = max(input, (output - slop))
else:
output = input
return output<|docstring|>From trutlebot_teleop, adds a bit of smoothing to startup/slowdown<|endoftext|> |
ce5f937a9ec454111e8b6d3f68d8388a02cfb8dbda144257f1d0b25413df010c | def derivative(self):
'Calculate the derivative, discretely'
if (len(self.state_list) > 1):
return (self.state_list[(- 1)] - self.state_list[(- 2)])
else:
return 0 | Calculate the derivative, discretely | src/tracking_turtlebot/utils.py | derivative | Christophe-Foyer/tracking_turtlebot | 0 | python | def derivative(self):
if (len(self.state_list) > 1):
return (self.state_list[(- 1)] - self.state_list[(- 2)])
else:
return 0 | def derivative(self):
if (len(self.state_list) > 1):
return (self.state_list[(- 1)] - self.state_list[(- 2)])
else:
return 0<|docstring|>Calculate the derivative, discretely<|endoftext|> |
9a937ba27c0e031d98a3ea105d8c49d5c1eebd8c56b0b3c1116afdbe6c446d07 | def _cycle_over_sample_range(start, end, sample_size):
'\n Given a range (start, end), returns a generator that will cycle over a population\n sample with size specified by ``sample_size``\n '
return itertools.cycle(random.sample(xrange(start, end), sample_size)) | Given a range (start, end), returns a generator that will cycle over a population
sample with size specified by ``sample_size`` | anon/utils.py | _cycle_over_sample_range | Tesorio/django-anon | 146 | python | def _cycle_over_sample_range(start, end, sample_size):
'\n Given a range (start, end), returns a generator that will cycle over a population\n sample with size specified by ``sample_size``\n '
return itertools.cycle(random.sample(xrange(start, end), sample_size)) | def _cycle_over_sample_range(start, end, sample_size):
'\n Given a range (start, end), returns a generator that will cycle over a population\n sample with size specified by ``sample_size``\n '
return itertools.cycle(random.sample(xrange(start, end), sample_size))<|docstring|>Given a range (start, end), returns a generator that will cycle over a population
sample with size specified by ``sample_size``<|endoftext|> |
ef332bc3bb9dbf33a55dff71571c62a3592649444749bec10276b41b982fc31b | def fake_word(min_size=_min_word_size, max_size=20):
' Return fake word\n\n :min_size: Minimum number of chars\n :max_size: Maximum number of chars\n\n Example:\n\n >>> import django_anon as anon\n >>> print(anon.fake_word())\n adipisci\n\n '
if (min_size < _min_word_size):
raise ValueError('no such word with this size < min_size')
for word in _word_generator:
if (min_size <= len(word) <= max_size):
return word | Return fake word
:min_size: Minimum number of chars
:max_size: Maximum number of chars
Example:
>>> import django_anon as anon
>>> print(anon.fake_word())
adipisci | anon/utils.py | fake_word | Tesorio/django-anon | 146 | python | def fake_word(min_size=_min_word_size, max_size=20):
' Return fake word\n\n :min_size: Minimum number of chars\n :max_size: Maximum number of chars\n\n Example:\n\n >>> import django_anon as anon\n >>> print(anon.fake_word())\n adipisci\n\n '
if (min_size < _min_word_size):
raise ValueError('no such word with this size < min_size')
for word in _word_generator:
if (min_size <= len(word) <= max_size):
return word | def fake_word(min_size=_min_word_size, max_size=20):
' Return fake word\n\n :min_size: Minimum number of chars\n :max_size: Maximum number of chars\n\n Example:\n\n >>> import django_anon as anon\n >>> print(anon.fake_word())\n adipisci\n\n '
if (min_size < _min_word_size):
raise ValueError('no such word with this size < min_size')
for word in _word_generator:
if (min_size <= len(word) <= max_size):
return word<|docstring|>Return fake word
:min_size: Minimum number of chars
:max_size: Maximum number of chars
Example:
>>> import django_anon as anon
>>> print(anon.fake_word())
adipisci<|endoftext|> |
64c1f97c011df8525f95e89abcf7325a2d0f0d71e4a1a849050598956816a5f6 | def fake_text(max_size=255, max_diff_allowed=5, separator=' '):
' Return fake text\n\n :max_size: Maximum number of chars\n :max_diff_allowed: Maximum difference (fidelity) allowed, in chars number\n :separator: Word separator\n\n Example:\n\n >>> print(anon.fake_text())\n alias aliquam aliquid amet animi aperiam architecto asperiores aspernatur assumenda at atque aut autem beatae blanditiis commodi consectetur consequatur consequuntur corporis corrupti culpa cum cumque cupiditate debitis delectus deleniti deserunt dicta\n\n '
if (max_diff_allowed < 1):
raise ValueError('max_diff_allowed must be > 0')
num_words = max(1, int((max_size / _max_word_size)))
words = itertools.islice(_word_generator, num_words)
text = separator.join(words)
try:
if (len(text) > max_size):
text = _trim_text(text, separator, max_size)
except ValueError:
text = text[:max_size]
return text | Return fake text
:max_size: Maximum number of chars
:max_diff_allowed: Maximum difference (fidelity) allowed, in chars number
:separator: Word separator
Example:
>>> print(anon.fake_text())
alias aliquam aliquid amet animi aperiam architecto asperiores aspernatur assumenda at atque aut autem beatae blanditiis commodi consectetur consequatur consequuntur corporis corrupti culpa cum cumque cupiditate debitis delectus deleniti deserunt dicta | anon/utils.py | fake_text | Tesorio/django-anon | 146 | python | def fake_text(max_size=255, max_diff_allowed=5, separator=' '):
' Return fake text\n\n :max_size: Maximum number of chars\n :max_diff_allowed: Maximum difference (fidelity) allowed, in chars number\n :separator: Word separator\n\n Example:\n\n >>> print(anon.fake_text())\n alias aliquam aliquid amet animi aperiam architecto asperiores aspernatur assumenda at atque aut autem beatae blanditiis commodi consectetur consequatur consequuntur corporis corrupti culpa cum cumque cupiditate debitis delectus deleniti deserunt dicta\n\n '
if (max_diff_allowed < 1):
raise ValueError('max_diff_allowed must be > 0')
num_words = max(1, int((max_size / _max_word_size)))
words = itertools.islice(_word_generator, num_words)
text = separator.join(words)
try:
if (len(text) > max_size):
text = _trim_text(text, separator, max_size)
except ValueError:
text = text[:max_size]
return text | def fake_text(max_size=255, max_diff_allowed=5, separator=' '):
' Return fake text\n\n :max_size: Maximum number of chars\n :max_diff_allowed: Maximum difference (fidelity) allowed, in chars number\n :separator: Word separator\n\n Example:\n\n >>> print(anon.fake_text())\n alias aliquam aliquid amet animi aperiam architecto asperiores aspernatur assumenda at atque aut autem beatae blanditiis commodi consectetur consequatur consequuntur corporis corrupti culpa cum cumque cupiditate debitis delectus deleniti deserunt dicta\n\n '
if (max_diff_allowed < 1):
raise ValueError('max_diff_allowed must be > 0')
num_words = max(1, int((max_size / _max_word_size)))
words = itertools.islice(_word_generator, num_words)
text = separator.join(words)
try:
if (len(text) > max_size):
text = _trim_text(text, separator, max_size)
except ValueError:
text = text[:max_size]
return text<|docstring|>Return fake text
:max_size: Maximum number of chars
:max_diff_allowed: Maximum difference (fidelity) allowed, in chars number
:separator: Word separator
Example:
>>> print(anon.fake_text())
alias aliquam aliquid amet animi aperiam architecto asperiores aspernatur assumenda at atque aut autem beatae blanditiis commodi consectetur consequatur consequuntur corporis corrupti culpa cum cumque cupiditate debitis delectus deleniti deserunt dicta<|endoftext|> |
b668c54138b88a73822b6e215eccbd7fecff8379d23cbd6e00d06ff778506de0 | def fake_small_text(max_size=50):
' Preset for fake_text.\n\n :max_size: Maximum number of chars\n\n Example:\n\n >>> print(anon.fake_small_text())\n Distinctio Dolor Dolore Dolorem Doloremque Dolores\n\n '
return fake_text(max_size=max_size).title() | Preset for fake_text.
:max_size: Maximum number of chars
Example:
>>> print(anon.fake_small_text())
Distinctio Dolor Dolore Dolorem Doloremque Dolores | anon/utils.py | fake_small_text | Tesorio/django-anon | 146 | python | def fake_small_text(max_size=50):
' Preset for fake_text.\n\n :max_size: Maximum number of chars\n\n Example:\n\n >>> print(anon.fake_small_text())\n Distinctio Dolor Dolore Dolorem Doloremque Dolores\n\n '
return fake_text(max_size=max_size).title() | def fake_small_text(max_size=50):
' Preset for fake_text.\n\n :max_size: Maximum number of chars\n\n Example:\n\n >>> print(anon.fake_small_text())\n Distinctio Dolor Dolore Dolorem Doloremque Dolores\n\n '
return fake_text(max_size=max_size).title()<|docstring|>Preset for fake_text.
:max_size: Maximum number of chars
Example:
>>> print(anon.fake_small_text())
Distinctio Dolor Dolore Dolorem Doloremque Dolores<|endoftext|> |
7609f36dd98d77561a5520207ac961695adf5f544fc878dca3301f41048f7759 | def fake_name(max_size=15):
' Preset for fake_text. Also returns capitalized words.\n\n :max_size: Maximum number of chars\n\n Example:\n\n >>> print(anon.fake_name())\n Doloribus Ea\n\n '
return fake_text(max_size=max_size).title() | Preset for fake_text. Also returns capitalized words.
:max_size: Maximum number of chars
Example:
>>> print(anon.fake_name())
Doloribus Ea | anon/utils.py | fake_name | Tesorio/django-anon | 146 | python | def fake_name(max_size=15):
' Preset for fake_text. Also returns capitalized words.\n\n :max_size: Maximum number of chars\n\n Example:\n\n >>> print(anon.fake_name())\n Doloribus Ea\n\n '
return fake_text(max_size=max_size).title() | def fake_name(max_size=15):
' Preset for fake_text. Also returns capitalized words.\n\n :max_size: Maximum number of chars\n\n Example:\n\n >>> print(anon.fake_name())\n Doloribus Ea\n\n '
return fake_text(max_size=max_size).title()<|docstring|>Preset for fake_text. Also returns capitalized words.
:max_size: Maximum number of chars
Example:
>>> print(anon.fake_name())
Doloribus Ea<|endoftext|> |
f3d36fd3b197d67d876deeb8b44843b3e1b523eb512192f1ab89e0291eb85220 | def fake_username(max_size=10, separator=''):
' Returns fake username\n\n :max_size: Maximum number of chars\n :separator: Word separator\n :rand_range: Range to use when generating random number\n\n Example:\n\n >>> print(anon.fake_username())\n eius54455\n\n '
random_number = str(next(_small_int_generator))
min_size_allowed = (_min_word_size + len(random_number))
if (max_size < min_size_allowed):
raise ValueError('username must be >= {}'.format(min_size_allowed))
else:
max_size -= len(random_number)
return (fake_text(max_size, separator=separator) + random_number) | Returns fake username
:max_size: Maximum number of chars
:separator: Word separator
:rand_range: Range to use when generating random number
Example:
>>> print(anon.fake_username())
eius54455 | anon/utils.py | fake_username | Tesorio/django-anon | 146 | python | def fake_username(max_size=10, separator=):
' Returns fake username\n\n :max_size: Maximum number of chars\n :separator: Word separator\n :rand_range: Range to use when generating random number\n\n Example:\n\n >>> print(anon.fake_username())\n eius54455\n\n '
random_number = str(next(_small_int_generator))
min_size_allowed = (_min_word_size + len(random_number))
if (max_size < min_size_allowed):
raise ValueError('username must be >= {}'.format(min_size_allowed))
else:
max_size -= len(random_number)
return (fake_text(max_size, separator=separator) + random_number) | def fake_username(max_size=10, separator=):
' Returns fake username\n\n :max_size: Maximum number of chars\n :separator: Word separator\n :rand_range: Range to use when generating random number\n\n Example:\n\n >>> print(anon.fake_username())\n eius54455\n\n '
random_number = str(next(_small_int_generator))
min_size_allowed = (_min_word_size + len(random_number))
if (max_size < min_size_allowed):
raise ValueError('username must be >= {}'.format(min_size_allowed))
else:
max_size -= len(random_number)
return (fake_text(max_size, separator=separator) + random_number)<|docstring|>Returns fake username
:max_size: Maximum number of chars
:separator: Word separator
:rand_range: Range to use when generating random number
Example:
>>> print(anon.fake_username())
eius54455<|endoftext|> |
f3f6ab3c703081f8712cfe020c640b1e2ffab4eb60ac6575d95c05fc3a229fb3 | def fake_email(max_size=40, suffix='@example.com'):
' Returns fake email address\n\n :max_size: Maximum number of chars\n :suffix: Suffix to add to email addresses (including @)\n\n Example:\n\n >>> print(anon.fake_email())\n example@example.com\n\n '
min_size_allowed = (_min_word_size + len(suffix))
if ((max_size + len(suffix)) > 254):
raise ValueError('email address must not exceed 254 chars')
elif (max_size < min_size_allowed):
raise ValueError('max_size must be >= {}'.format(min_size_allowed))
else:
max_size -= len(suffix)
return (fake_username(max_size, separator='.') + suffix) | Returns fake email address
:max_size: Maximum number of chars
:suffix: Suffix to add to email addresses (including @)
Example:
>>> print(anon.fake_email())
example@example.com | anon/utils.py | fake_email | Tesorio/django-anon | 146 | python | def fake_email(max_size=40, suffix='@example.com'):
' Returns fake email address\n\n :max_size: Maximum number of chars\n :suffix: Suffix to add to email addresses (including @)\n\n Example:\n\n >>> print(anon.fake_email())\n example@example.com\n\n '
min_size_allowed = (_min_word_size + len(suffix))
if ((max_size + len(suffix)) > 254):
raise ValueError('email address must not exceed 254 chars')
elif (max_size < min_size_allowed):
raise ValueError('max_size must be >= {}'.format(min_size_allowed))
else:
max_size -= len(suffix)
return (fake_username(max_size, separator='.') + suffix) | def fake_email(max_size=40, suffix='@example.com'):
' Returns fake email address\n\n :max_size: Maximum number of chars\n :suffix: Suffix to add to email addresses (including @)\n\n Example:\n\n >>> print(anon.fake_email())\n example@example.com\n\n '
min_size_allowed = (_min_word_size + len(suffix))
if ((max_size + len(suffix)) > 254):
raise ValueError('email address must not exceed 254 chars')
elif (max_size < min_size_allowed):
raise ValueError('max_size must be >= {}'.format(min_size_allowed))
else:
max_size -= len(suffix)
return (fake_username(max_size, separator='.') + suffix)<|docstring|>Returns fake email address
:max_size: Maximum number of chars
:suffix: Suffix to add to email addresses (including @)
Example:
>>> print(anon.fake_email())
example@example.com<|endoftext|> |
c24b154715f74bf0a856677ab1d5ad7459c468f236e53e23b9ed1a1b997a4621 | def fake_url(max_size=50, scheme='http://', suffix='.com'):
' Returns fake URL\n\n :max_size: Maximum number of chars\n :scheme: URL scheme (http://)\n :suffix: Suffix to add to domain (including dot)\n\n Example:\n\n >>> print(anon.fake_url())\n http://facilis.fuga.fugiat.fugit.harum.hic.id.com\n\n '
min_size_allowed = ((_min_word_size + len(scheme)) + len(suffix))
if (max_size < min_size_allowed):
raise ValueError('max_size must be >= {}'.format(min_size_allowed))
else:
max_size -= (len(scheme) + len(suffix))
domain = (fake_text(max_size=max_size, separator='.') + suffix)
return (scheme + domain) | Returns fake URL
:max_size: Maximum number of chars
:scheme: URL scheme (http://)
:suffix: Suffix to add to domain (including dot)
Example:
>>> print(anon.fake_url())
http://facilis.fuga.fugiat.fugit.harum.hic.id.com | anon/utils.py | fake_url | Tesorio/django-anon | 146 | python | def fake_url(max_size=50, scheme='http://', suffix='.com'):
' Returns fake URL\n\n :max_size: Maximum number of chars\n :scheme: URL scheme (http://)\n :suffix: Suffix to add to domain (including dot)\n\n Example:\n\n >>> print(anon.fake_url())\n http://facilis.fuga.fugiat.fugit.harum.hic.id.com\n\n '
min_size_allowed = ((_min_word_size + len(scheme)) + len(suffix))
if (max_size < min_size_allowed):
raise ValueError('max_size must be >= {}'.format(min_size_allowed))
else:
max_size -= (len(scheme) + len(suffix))
domain = (fake_text(max_size=max_size, separator='.') + suffix)
return (scheme + domain) | def fake_url(max_size=50, scheme='http://', suffix='.com'):
' Returns fake URL\n\n :max_size: Maximum number of chars\n :scheme: URL scheme (http://)\n :suffix: Suffix to add to domain (including dot)\n\n Example:\n\n >>> print(anon.fake_url())\n http://facilis.fuga.fugiat.fugit.harum.hic.id.com\n\n '
min_size_allowed = ((_min_word_size + len(scheme)) + len(suffix))
if (max_size < min_size_allowed):
raise ValueError('max_size must be >= {}'.format(min_size_allowed))
else:
max_size -= (len(scheme) + len(suffix))
domain = (fake_text(max_size=max_size, separator='.') + suffix)
return (scheme + domain)<|docstring|>Returns fake URL
:max_size: Maximum number of chars
:scheme: URL scheme (http://)
:suffix: Suffix to add to domain (including dot)
Example:
>>> print(anon.fake_url())
http://facilis.fuga.fugiat.fugit.harum.hic.id.com<|endoftext|> |
5c354b775139b293f34347243929537cd0e9315b0bf2d30ab6ecaea659106b53 | def fake_phone_number(format='999-999-9999'):
' Returns a fake phone number in the desired format\n\n :format: Format of phone number to generate\n\n Example:\n\n >>> print(anon.fake_phone_number())\n 863-068-9424\n\n '
number = []
for char in format:
if (char == '9'):
n = next(_number_generator)
if (not number):
while (n == '0'):
n = next(_number_generator)
number.append(n)
else:
number.append(char)
return ''.join(number) | Returns a fake phone number in the desired format
:format: Format of phone number to generate
Example:
>>> print(anon.fake_phone_number())
863-068-9424 | anon/utils.py | fake_phone_number | Tesorio/django-anon | 146 | python | def fake_phone_number(format='999-999-9999'):
' Returns a fake phone number in the desired format\n\n :format: Format of phone number to generate\n\n Example:\n\n >>> print(anon.fake_phone_number())\n 863-068-9424\n\n '
number = []
for char in format:
if (char == '9'):
n = next(_number_generator)
if (not number):
while (n == '0'):
n = next(_number_generator)
number.append(n)
else:
number.append(char)
return .join(number) | def fake_phone_number(format='999-999-9999'):
' Returns a fake phone number in the desired format\n\n :format: Format of phone number to generate\n\n Example:\n\n >>> print(anon.fake_phone_number())\n 863-068-9424\n\n '
number = []
for char in format:
if (char == '9'):
n = next(_number_generator)
if (not number):
while (n == '0'):
n = next(_number_generator)
number.append(n)
else:
number.append(char)
return .join(number)<|docstring|>Returns a fake phone number in the desired format
:format: Format of phone number to generate
Example:
>>> print(anon.fake_phone_number())
863-068-9424<|endoftext|> |
cbdd5bc2666302b57c1cce8c2ce04835dd34de09582de77cc15e7e19d537e499 | def _get_show_clock(self):
'\n Getter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)\n\n YANG Description: display current time for the cluster or specified switch\n '
return self.__show_clock | Getter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)
YANG Description: display current time for the cluster or specified switch | pybind/slxos/v16r_1_00b/brocade_clock_rpc/__init__.py | _get_show_clock | shivharis/pybind | 0 | python | def _get_show_clock(self):
'\n Getter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)\n\n YANG Description: display current time for the cluster or specified switch\n '
return self.__show_clock | def _get_show_clock(self):
'\n Getter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)\n\n YANG Description: display current time for the cluster or specified switch\n '
return self.__show_clock<|docstring|>Getter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)
YANG Description: display current time for the cluster or specified switch<|endoftext|> |
4b23e017503c32612d1987addd25f09eff0a95d2fa68526856416aabaa737673 | def _set_show_clock(self, v, load=False):
'\n Setter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)\n If this variable is read-only (config: false) in the\n source YANG file, then _set_show_clock is considered as a private\n method. Backends looking to populate this variable should\n do so via calling thisObj._set_show_clock() directly.\n\n YANG Description: display current time for the cluster or specified switch\n '
if hasattr(v, '_utype'):
v = v._utype(v)
try:
t = YANGDynClass(v, base=show_clock.show_clock, is_leaf=True, yang_name='show-clock', rest_name='show-clock', parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'clock-get'}}, namespace='urn:brocade.com:mgmt:brocade-clock', defining_module='brocade-clock', yang_type='rpc', is_config=True)
except (TypeError, ValueError):
raise ValueError({'error-string': 'show_clock must be of a type compatible with rpc', 'defined-type': 'rpc', 'generated-type': 'YANGDynClass(base=show_clock.show_clock, is_leaf=True, yang_name="show-clock", rest_name="show-clock", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u\'tailf-common\': {u\'hidden\': u\'rpccmd\', u\'actionpoint\': u\'clock-get\'}}, namespace=\'urn:brocade.com:mgmt:brocade-clock\', defining_module=\'brocade-clock\', yang_type=\'rpc\', is_config=True)'})
self.__show_clock = t
if hasattr(self, '_set'):
self._set() | Setter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)
If this variable is read-only (config: false) in the
source YANG file, then _set_show_clock is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_show_clock() directly.
YANG Description: display current time for the cluster or specified switch | pybind/slxos/v16r_1_00b/brocade_clock_rpc/__init__.py | _set_show_clock | shivharis/pybind | 0 | python | def _set_show_clock(self, v, load=False):
'\n Setter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)\n If this variable is read-only (config: false) in the\n source YANG file, then _set_show_clock is considered as a private\n method. Backends looking to populate this variable should\n do so via calling thisObj._set_show_clock() directly.\n\n YANG Description: display current time for the cluster or specified switch\n '
if hasattr(v, '_utype'):
v = v._utype(v)
try:
t = YANGDynClass(v, base=show_clock.show_clock, is_leaf=True, yang_name='show-clock', rest_name='show-clock', parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'clock-get'}}, namespace='urn:brocade.com:mgmt:brocade-clock', defining_module='brocade-clock', yang_type='rpc', is_config=True)
except (TypeError, ValueError):
raise ValueError({'error-string': 'show_clock must be of a type compatible with rpc', 'defined-type': 'rpc', 'generated-type': 'YANGDynClass(base=show_clock.show_clock, is_leaf=True, yang_name="show-clock", rest_name="show-clock", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u\'tailf-common\': {u\'hidden\': u\'rpccmd\', u\'actionpoint\': u\'clock-get\'}}, namespace=\'urn:brocade.com:mgmt:brocade-clock\', defining_module=\'brocade-clock\', yang_type=\'rpc\', is_config=True)'})
self.__show_clock = t
if hasattr(self, '_set'):
self._set() | def _set_show_clock(self, v, load=False):
'\n Setter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)\n If this variable is read-only (config: false) in the\n source YANG file, then _set_show_clock is considered as a private\n method. Backends looking to populate this variable should\n do so via calling thisObj._set_show_clock() directly.\n\n YANG Description: display current time for the cluster or specified switch\n '
if hasattr(v, '_utype'):
v = v._utype(v)
try:
t = YANGDynClass(v, base=show_clock.show_clock, is_leaf=True, yang_name='show-clock', rest_name='show-clock', parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u'tailf-common': {u'hidden': u'rpccmd', u'actionpoint': u'clock-get'}}, namespace='urn:brocade.com:mgmt:brocade-clock', defining_module='brocade-clock', yang_type='rpc', is_config=True)
except (TypeError, ValueError):
raise ValueError({'error-string': 'show_clock must be of a type compatible with rpc', 'defined-type': 'rpc', 'generated-type': 'YANGDynClass(base=show_clock.show_clock, is_leaf=True, yang_name="show-clock", rest_name="show-clock", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=False, extensions={u\'tailf-common\': {u\'hidden\': u\'rpccmd\', u\'actionpoint\': u\'clock-get\'}}, namespace=\'urn:brocade.com:mgmt:brocade-clock\', defining_module=\'brocade-clock\', yang_type=\'rpc\', is_config=True)'})
self.__show_clock = t
if hasattr(self, '_set'):
self._set()<|docstring|>Setter method for show_clock, mapped from YANG variable /brocade_clock_rpc/show_clock (rpc)
If this variable is read-only (config: false) in the
source YANG file, then _set_show_clock is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_show_clock() directly.
YANG Description: display current time for the cluster or specified switch<|endoftext|> |
6920710088569a497b9cf64126bd0e68ae455fcd9208259f3cb8d4fe355cb9b6 | def convex_upsample(self, flow, mask, rate=4):
'[H/rate, W/rate, 2] -> [H, W, 2]'
(N, _, H, W) = flow.shape
mask = F.reshape(mask, (N, 1, 9, rate, rate, H, W))
mask = F.softmax(mask, axis=2)
up_flow = self.unfold((rate * flow), [3, 3], padding=1)
up_flow = F.reshape(up_flow, (N, 2, 9, 1, 1, H, W))
up_flow = F.sum((mask * up_flow), axis=2)
up_flow = F.transpose(up_flow, (0, 1, 4, 2, 5, 3))
return F.reshape(up_flow, (N, 2, (rate * H), (rate * W))) | [H/rate, W/rate, 2] -> [H, W, 2] | nets/crestereo.py | convex_upsample | megvii-research/CREStereo | 80 | python | def convex_upsample(self, flow, mask, rate=4):
(N, _, H, W) = flow.shape
mask = F.reshape(mask, (N, 1, 9, rate, rate, H, W))
mask = F.softmax(mask, axis=2)
up_flow = self.unfold((rate * flow), [3, 3], padding=1)
up_flow = F.reshape(up_flow, (N, 2, 9, 1, 1, H, W))
up_flow = F.sum((mask * up_flow), axis=2)
up_flow = F.transpose(up_flow, (0, 1, 4, 2, 5, 3))
return F.reshape(up_flow, (N, 2, (rate * H), (rate * W))) | def convex_upsample(self, flow, mask, rate=4):
(N, _, H, W) = flow.shape
mask = F.reshape(mask, (N, 1, 9, rate, rate, H, W))
mask = F.softmax(mask, axis=2)
up_flow = self.unfold((rate * flow), [3, 3], padding=1)
up_flow = F.reshape(up_flow, (N, 2, 9, 1, 1, H, W))
up_flow = F.sum((mask * up_flow), axis=2)
up_flow = F.transpose(up_flow, (0, 1, 4, 2, 5, 3))
return F.reshape(up_flow, (N, 2, (rate * H), (rate * W)))<|docstring|>[H/rate, W/rate, 2] -> [H, W, 2]<|endoftext|> |
e1ed6cdcf6479c5b5dbc88d76a1f5431eb7370040625a2aeae1137b1d2938a8d | def abort_now():
'Abort the current process without doing any exception teardown'
sys.stdout.flush()
if win32api:
win32api.TerminateProcess(win32api.GetCurrentProcess(), 3)
else:
os.kill(0, 9) | Abort the current process without doing any exception teardown | tests/lit/lit/run.py | abort_now | zhengyangl/alive2 | 1,771 | python | def abort_now():
sys.stdout.flush()
if win32api:
win32api.TerminateProcess(win32api.GetCurrentProcess(), 3)
else:
os.kill(0, 9) | def abort_now():
sys.stdout.flush()
if win32api:
win32api.TerminateProcess(win32api.GetCurrentProcess(), 3)
else:
os.kill(0, 9)<|docstring|>Abort the current process without doing any exception teardown<|endoftext|> |
7be087d6b056184bdddaaacb9a3c325c6dd0889383aec68472fb14a7079f7198 | def _execute_test_impl(test, lit_config, parallelism_semaphores):
'Execute one test'
pg = test.config.parallelism_group
if callable(pg):
pg = pg(test)
result = None
semaphore = None
try:
if pg:
semaphore = parallelism_semaphores[pg]
if semaphore:
semaphore.acquire()
start_time = time.time()
result = test.config.test_format.execute(test, lit_config)
if isinstance(result, tuple):
(code, output) = result
result = lit.Test.Result(code, output)
elif (not isinstance(result, lit.Test.Result)):
raise ValueError('unexpected result from test execution')
result.elapsed = (time.time() - start_time)
except KeyboardInterrupt:
raise
except:
if lit_config.debug:
raise
output = 'Exception during script execution:\n'
output += traceback.format_exc()
output += '\n'
result = lit.Test.Result(lit.Test.UNRESOLVED, output)
finally:
if semaphore:
semaphore.release()
test.setResult(result) | Execute one test | tests/lit/lit/run.py | _execute_test_impl | zhengyangl/alive2 | 1,771 | python | def _execute_test_impl(test, lit_config, parallelism_semaphores):
pg = test.config.parallelism_group
if callable(pg):
pg = pg(test)
result = None
semaphore = None
try:
if pg:
semaphore = parallelism_semaphores[pg]
if semaphore:
semaphore.acquire()
start_time = time.time()
result = test.config.test_format.execute(test, lit_config)
if isinstance(result, tuple):
(code, output) = result
result = lit.Test.Result(code, output)
elif (not isinstance(result, lit.Test.Result)):
raise ValueError('unexpected result from test execution')
result.elapsed = (time.time() - start_time)
except KeyboardInterrupt:
raise
except:
if lit_config.debug:
raise
output = 'Exception during script execution:\n'
output += traceback.format_exc()
output += '\n'
result = lit.Test.Result(lit.Test.UNRESOLVED, output)
finally:
if semaphore:
semaphore.release()
test.setResult(result) | def _execute_test_impl(test, lit_config, parallelism_semaphores):
pg = test.config.parallelism_group
if callable(pg):
pg = pg(test)
result = None
semaphore = None
try:
if pg:
semaphore = parallelism_semaphores[pg]
if semaphore:
semaphore.acquire()
start_time = time.time()
result = test.config.test_format.execute(test, lit_config)
if isinstance(result, tuple):
(code, output) = result
result = lit.Test.Result(code, output)
elif (not isinstance(result, lit.Test.Result)):
raise ValueError('unexpected result from test execution')
result.elapsed = (time.time() - start_time)
except KeyboardInterrupt:
raise
except:
if lit_config.debug:
raise
output = 'Exception during script execution:\n'
output += traceback.format_exc()
output += '\n'
result = lit.Test.Result(lit.Test.UNRESOLVED, output)
finally:
if semaphore:
semaphore.release()
test.setResult(result)<|docstring|>Execute one test<|endoftext|> |
4968dddfc8de0e54af2998fcf259553b027e6fa5f42bf4482c26c2968e361256 | def worker_initializer(lit_config, parallelism_semaphores):
'Copy expensive repeated data into worker processes'
global child_lit_config
child_lit_config = lit_config
global child_parallelism_semaphores
child_parallelism_semaphores = parallelism_semaphores | Copy expensive repeated data into worker processes | tests/lit/lit/run.py | worker_initializer | zhengyangl/alive2 | 1,771 | python | def worker_initializer(lit_config, parallelism_semaphores):
global child_lit_config
child_lit_config = lit_config
global child_parallelism_semaphores
child_parallelism_semaphores = parallelism_semaphores | def worker_initializer(lit_config, parallelism_semaphores):
global child_lit_config
child_lit_config = lit_config
global child_parallelism_semaphores
child_parallelism_semaphores = parallelism_semaphores<|docstring|>Copy expensive repeated data into worker processes<|endoftext|> |
07021805273252b817b412bc89553cf78cfe82cad421115c859e0c8af85846c2 | def worker_run_one_test(test_index, test):
'Run one test in a multiprocessing.Pool\n\n Side effects in this function and functions it calls are not visible in the\n main lit process.\n\n Arguments and results of this function are pickled, so they should be cheap\n to copy. For efficiency, we copy all data needed to execute all tests into\n each worker and store it in the child_* global variables. This reduces the\n cost of each task.\n\n Returns an index and a Result, which the parent process uses to update\n the display.\n '
try:
_execute_test_impl(test, child_lit_config, child_parallelism_semaphores)
return (test_index, test)
except KeyboardInterrupt as e:
abort_now()
except:
traceback.print_exc() | Run one test in a multiprocessing.Pool
Side effects in this function and functions it calls are not visible in the
main lit process.
Arguments and results of this function are pickled, so they should be cheap
to copy. For efficiency, we copy all data needed to execute all tests into
each worker and store it in the child_* global variables. This reduces the
cost of each task.
Returns an index and a Result, which the parent process uses to update
the display. | tests/lit/lit/run.py | worker_run_one_test | zhengyangl/alive2 | 1,771 | python | def worker_run_one_test(test_index, test):
'Run one test in a multiprocessing.Pool\n\n Side effects in this function and functions it calls are not visible in the\n main lit process.\n\n Arguments and results of this function are pickled, so they should be cheap\n to copy. For efficiency, we copy all data needed to execute all tests into\n each worker and store it in the child_* global variables. This reduces the\n cost of each task.\n\n Returns an index and a Result, which the parent process uses to update\n the display.\n '
try:
_execute_test_impl(test, child_lit_config, child_parallelism_semaphores)
return (test_index, test)
except KeyboardInterrupt as e:
abort_now()
except:
traceback.print_exc() | def worker_run_one_test(test_index, test):
'Run one test in a multiprocessing.Pool\n\n Side effects in this function and functions it calls are not visible in the\n main lit process.\n\n Arguments and results of this function are pickled, so they should be cheap\n to copy. For efficiency, we copy all data needed to execute all tests into\n each worker and store it in the child_* global variables. This reduces the\n cost of each task.\n\n Returns an index and a Result, which the parent process uses to update\n the display.\n '
try:
_execute_test_impl(test, child_lit_config, child_parallelism_semaphores)
return (test_index, test)
except KeyboardInterrupt as e:
abort_now()
except:
traceback.print_exc()<|docstring|>Run one test in a multiprocessing.Pool
Side effects in this function and functions it calls are not visible in the
main lit process.
Arguments and results of this function are pickled, so they should be cheap
to copy. For efficiency, we copy all data needed to execute all tests into
each worker and store it in the child_* global variables. This reduces the
cost of each task.
Returns an index and a Result, which the parent process uses to update
the display.<|endoftext|> |
1f20fc08b2d9f00b695ae0d00733d07ccaa83e39b799378a376b9f7dd5ac1ac4 | def execute_tests(self, display, jobs, max_time=None):
'\n execute_tests(display, jobs, [max_time])\n\n Execute each of the tests in the run, using up to jobs number of\n parallel tasks, and inform the display of each individual result. The\n provided tests should be a subset of the tests available in this run\n object.\n\n If max_time is non-None, it should be a time in seconds after which to\n stop executing tests.\n\n The display object will have its update method called with each test as\n it is completed. The calls are guaranteed to be locked with respect to\n one another, but are *not* guaranteed to be called on the same thread as\n this method was invoked on.\n\n Upon completion, each test in the run will have its result\n computed. Tests which were not actually executed (for any reason) will\n be given an UNRESOLVED result.\n '
if ((not self.tests) or (jobs == 0)):
return
self.display = display
self.failure_count = 0
self.hit_max_failures = False
if self.lit_config.singleProcess:
global child_lit_config
child_lit_config = self.lit_config
for (test_index, test) in enumerate(self.tests):
result = worker_run_one_test(test_index, test)
self.consume_test_result(result)
else:
self.execute_tests_in_pool(jobs, max_time)
for test in self.tests:
if (test.result is None):
test.setResult(lit.Test.Result(lit.Test.UNRESOLVED, '', 0.0)) | execute_tests(display, jobs, [max_time])
Execute each of the tests in the run, using up to jobs number of
parallel tasks, and inform the display of each individual result. The
provided tests should be a subset of the tests available in this run
object.
If max_time is non-None, it should be a time in seconds after which to
stop executing tests.
The display object will have its update method called with each test as
it is completed. The calls are guaranteed to be locked with respect to
one another, but are *not* guaranteed to be called on the same thread as
this method was invoked on.
Upon completion, each test in the run will have its result
computed. Tests which were not actually executed (for any reason) will
be given an UNRESOLVED result. | tests/lit/lit/run.py | execute_tests | zhengyangl/alive2 | 1,771 | python | def execute_tests(self, display, jobs, max_time=None):
'\n execute_tests(display, jobs, [max_time])\n\n Execute each of the tests in the run, using up to jobs number of\n parallel tasks, and inform the display of each individual result. The\n provided tests should be a subset of the tests available in this run\n object.\n\n If max_time is non-None, it should be a time in seconds after which to\n stop executing tests.\n\n The display object will have its update method called with each test as\n it is completed. The calls are guaranteed to be locked with respect to\n one another, but are *not* guaranteed to be called on the same thread as\n this method was invoked on.\n\n Upon completion, each test in the run will have its result\n computed. Tests which were not actually executed (for any reason) will\n be given an UNRESOLVED result.\n '
if ((not self.tests) or (jobs == 0)):
return
self.display = display
self.failure_count = 0
self.hit_max_failures = False
if self.lit_config.singleProcess:
global child_lit_config
child_lit_config = self.lit_config
for (test_index, test) in enumerate(self.tests):
result = worker_run_one_test(test_index, test)
self.consume_test_result(result)
else:
self.execute_tests_in_pool(jobs, max_time)
for test in self.tests:
if (test.result is None):
test.setResult(lit.Test.Result(lit.Test.UNRESOLVED, , 0.0)) | def execute_tests(self, display, jobs, max_time=None):
'\n execute_tests(display, jobs, [max_time])\n\n Execute each of the tests in the run, using up to jobs number of\n parallel tasks, and inform the display of each individual result. The\n provided tests should be a subset of the tests available in this run\n object.\n\n If max_time is non-None, it should be a time in seconds after which to\n stop executing tests.\n\n The display object will have its update method called with each test as\n it is completed. The calls are guaranteed to be locked with respect to\n one another, but are *not* guaranteed to be called on the same thread as\n this method was invoked on.\n\n Upon completion, each test in the run will have its result\n computed. Tests which were not actually executed (for any reason) will\n be given an UNRESOLVED result.\n '
if ((not self.tests) or (jobs == 0)):
return
self.display = display
self.failure_count = 0
self.hit_max_failures = False
if self.lit_config.singleProcess:
global child_lit_config
child_lit_config = self.lit_config
for (test_index, test) in enumerate(self.tests):
result = worker_run_one_test(test_index, test)
self.consume_test_result(result)
else:
self.execute_tests_in_pool(jobs, max_time)
for test in self.tests:
if (test.result is None):
test.setResult(lit.Test.Result(lit.Test.UNRESOLVED, , 0.0))<|docstring|>execute_tests(display, jobs, [max_time])
Execute each of the tests in the run, using up to jobs number of
parallel tasks, and inform the display of each individual result. The
provided tests should be a subset of the tests available in this run
object.
If max_time is non-None, it should be a time in seconds after which to
stop executing tests.
The display object will have its update method called with each test as
it is completed. The calls are guaranteed to be locked with respect to
one another, but are *not* guaranteed to be called on the same thread as
this method was invoked on.
Upon completion, each test in the run will have its result
computed. Tests which were not actually executed (for any reason) will
be given an UNRESOLVED result.<|endoftext|> |
1b83190623f33513471c14881be5cb113d039d40ec154ffe9708a3d98131e818 | def consume_test_result(self, pool_result):
'Test completion callback for worker_run_one_test\n\n Updates the test result status in the parent process. Each task in the\n pool returns the test index and the result, and we use the index to look\n up the original test object. Also updates the progress bar as tasks\n complete.\n '
if self.hit_max_failures:
return
(test_index, test_with_result) = pool_result
assert (self.tests[test_index].file_path == test_with_result.file_path), 'parent and child disagree on test path'
self.tests[test_index] = test_with_result
self.display.update(test_with_result)
self.failure_count += (test_with_result.result.code == lit.Test.FAIL)
if (self.lit_config.maxFailures and (self.failure_count == self.lit_config.maxFailures)):
self.hit_max_failures = True | Test completion callback for worker_run_one_test
Updates the test result status in the parent process. Each task in the
pool returns the test index and the result, and we use the index to look
up the original test object. Also updates the progress bar as tasks
complete. | tests/lit/lit/run.py | consume_test_result | zhengyangl/alive2 | 1,771 | python | def consume_test_result(self, pool_result):
'Test completion callback for worker_run_one_test\n\n Updates the test result status in the parent process. Each task in the\n pool returns the test index and the result, and we use the index to look\n up the original test object. Also updates the progress bar as tasks\n complete.\n '
if self.hit_max_failures:
return
(test_index, test_with_result) = pool_result
assert (self.tests[test_index].file_path == test_with_result.file_path), 'parent and child disagree on test path'
self.tests[test_index] = test_with_result
self.display.update(test_with_result)
self.failure_count += (test_with_result.result.code == lit.Test.FAIL)
if (self.lit_config.maxFailures and (self.failure_count == self.lit_config.maxFailures)):
self.hit_max_failures = True | def consume_test_result(self, pool_result):
'Test completion callback for worker_run_one_test\n\n Updates the test result status in the parent process. Each task in the\n pool returns the test index and the result, and we use the index to look\n up the original test object. Also updates the progress bar as tasks\n complete.\n '
if self.hit_max_failures:
return
(test_index, test_with_result) = pool_result
assert (self.tests[test_index].file_path == test_with_result.file_path), 'parent and child disagree on test path'
self.tests[test_index] = test_with_result
self.display.update(test_with_result)
self.failure_count += (test_with_result.result.code == lit.Test.FAIL)
if (self.lit_config.maxFailures and (self.failure_count == self.lit_config.maxFailures)):
self.hit_max_failures = True<|docstring|>Test completion callback for worker_run_one_test
Updates the test result status in the parent process. Each task in the
pool returns the test index and the result, and we use the index to look
up the original test object. Also updates the progress bar as tasks
complete.<|endoftext|> |
a86b9c57543f03d689e779901e04c4f0d2fbc34e27437790d60ac002015c7aa8 | def generate_hangul_images(label_file, fonts_dir, output_dir):
'Generate Hangul image files.\n\n This will take in the passed in labels file and will generate several\n images using the font files provided in the font directory. The font\n directory is expected to be populated with *.ttf (True Type Font) files.\n The generated images will be stored in the given output directory. Image\n paths will have their corresponding labels listed in a CSV file.\n '
with io.open(label_file, 'r', encoding='utf-8') as f:
labels = f.read().splitlines()
image_dir = os.path.join(output_dir, 'hangul-images')
if (not os.path.exists(image_dir)):
os.makedirs(os.path.join(image_dir))
fonts = glob.glob(os.path.join(fonts_dir, '*.ttf'))
labels_csv = io.open(os.path.join(output_dir, 'labels-map.csv'), 'w', encoding='utf-8')
total_count = 0
prev_count = 0
for character in labels:
if ((total_count - prev_count) > 5000):
prev_count = total_count
print('{} images generated...'.format(total_count))
for font in fonts:
total_count += 1
image = Image.new('L', (IMAGE_WIDTH, IMAGE_HEIGHT), color=0)
font = ImageFont.truetype(font, 48)
drawing = ImageDraw.Draw(image)
(w, h) = drawing.textsize(character, font=font)
drawing.text((((IMAGE_WIDTH - w) / 2), ((IMAGE_HEIGHT - h) / 2)), character, fill=255, font=font)
file_string = 'hangul_{}.jpeg'.format(total_count)
file_path = os.path.join(image_dir, file_string)
image.save(file_path, 'JPEG')
labels_csv.write(u'{},{}\n'.format(file_path, character))
for i in range(DISTORTION_COUNT):
total_count += 1
file_string = 'hangul_{}.jpeg'.format(total_count)
file_path = os.path.join(image_dir, file_string)
arr = numpy.array(image)
distorted_array = elastic_distort(arr, alpha=random.randint(30, 36), sigma=random.randint(5, 6))
distorted_image = Image.fromarray(distorted_array)
distorted_image.save(file_path, 'JPEG')
labels_csv.write(u'{},{}\n'.format(file_path, character))
print('Finished generating {} images.'.format(total_count))
labels_csv.close() | Generate Hangul image files.
This will take in the passed in labels file and will generate several
images using the font files provided in the font directory. The font
directory is expected to be populated with *.ttf (True Type Font) files.
The generated images will be stored in the given output directory. Image
paths will have their corresponding labels listed in a CSV file. | tools/hangul-image-generator.py | generate_hangul_images | g-may/tensorflow-hangul-recognition | 243 | python | def generate_hangul_images(label_file, fonts_dir, output_dir):
'Generate Hangul image files.\n\n This will take in the passed in labels file and will generate several\n images using the font files provided in the font directory. The font\n directory is expected to be populated with *.ttf (True Type Font) files.\n The generated images will be stored in the given output directory. Image\n paths will have their corresponding labels listed in a CSV file.\n '
with io.open(label_file, 'r', encoding='utf-8') as f:
labels = f.read().splitlines()
image_dir = os.path.join(output_dir, 'hangul-images')
if (not os.path.exists(image_dir)):
os.makedirs(os.path.join(image_dir))
fonts = glob.glob(os.path.join(fonts_dir, '*.ttf'))
labels_csv = io.open(os.path.join(output_dir, 'labels-map.csv'), 'w', encoding='utf-8')
total_count = 0
prev_count = 0
for character in labels:
if ((total_count - prev_count) > 5000):
prev_count = total_count
print('{} images generated...'.format(total_count))
for font in fonts:
total_count += 1
image = Image.new('L', (IMAGE_WIDTH, IMAGE_HEIGHT), color=0)
font = ImageFont.truetype(font, 48)
drawing = ImageDraw.Draw(image)
(w, h) = drawing.textsize(character, font=font)
drawing.text((((IMAGE_WIDTH - w) / 2), ((IMAGE_HEIGHT - h) / 2)), character, fill=255, font=font)
file_string = 'hangul_{}.jpeg'.format(total_count)
file_path = os.path.join(image_dir, file_string)
image.save(file_path, 'JPEG')
labels_csv.write(u'{},{}\n'.format(file_path, character))
for i in range(DISTORTION_COUNT):
total_count += 1
file_string = 'hangul_{}.jpeg'.format(total_count)
file_path = os.path.join(image_dir, file_string)
arr = numpy.array(image)
distorted_array = elastic_distort(arr, alpha=random.randint(30, 36), sigma=random.randint(5, 6))
distorted_image = Image.fromarray(distorted_array)
distorted_image.save(file_path, 'JPEG')
labels_csv.write(u'{},{}\n'.format(file_path, character))
print('Finished generating {} images.'.format(total_count))
labels_csv.close() | def generate_hangul_images(label_file, fonts_dir, output_dir):
'Generate Hangul image files.\n\n This will take in the passed in labels file and will generate several\n images using the font files provided in the font directory. The font\n directory is expected to be populated with *.ttf (True Type Font) files.\n The generated images will be stored in the given output directory. Image\n paths will have their corresponding labels listed in a CSV file.\n '
with io.open(label_file, 'r', encoding='utf-8') as f:
labels = f.read().splitlines()
image_dir = os.path.join(output_dir, 'hangul-images')
if (not os.path.exists(image_dir)):
os.makedirs(os.path.join(image_dir))
fonts = glob.glob(os.path.join(fonts_dir, '*.ttf'))
labels_csv = io.open(os.path.join(output_dir, 'labels-map.csv'), 'w', encoding='utf-8')
total_count = 0
prev_count = 0
for character in labels:
if ((total_count - prev_count) > 5000):
prev_count = total_count
print('{} images generated...'.format(total_count))
for font in fonts:
total_count += 1
image = Image.new('L', (IMAGE_WIDTH, IMAGE_HEIGHT), color=0)
font = ImageFont.truetype(font, 48)
drawing = ImageDraw.Draw(image)
(w, h) = drawing.textsize(character, font=font)
drawing.text((((IMAGE_WIDTH - w) / 2), ((IMAGE_HEIGHT - h) / 2)), character, fill=255, font=font)
file_string = 'hangul_{}.jpeg'.format(total_count)
file_path = os.path.join(image_dir, file_string)
image.save(file_path, 'JPEG')
labels_csv.write(u'{},{}\n'.format(file_path, character))
for i in range(DISTORTION_COUNT):
total_count += 1
file_string = 'hangul_{}.jpeg'.format(total_count)
file_path = os.path.join(image_dir, file_string)
arr = numpy.array(image)
distorted_array = elastic_distort(arr, alpha=random.randint(30, 36), sigma=random.randint(5, 6))
distorted_image = Image.fromarray(distorted_array)
distorted_image.save(file_path, 'JPEG')
labels_csv.write(u'{},{}\n'.format(file_path, character))
print('Finished generating {} images.'.format(total_count))
labels_csv.close()<|docstring|>Generate Hangul image files.
This will take in the passed in labels file and will generate several
images using the font files provided in the font directory. The font
directory is expected to be populated with *.ttf (True Type Font) files.
The generated images will be stored in the given output directory. Image
paths will have their corresponding labels listed in a CSV file.<|endoftext|> |
ed32d9f4d8b83e38e1ee645247199a2c54f68c4b74de8f45f5c9b6880af64e9e | def elastic_distort(image, alpha, sigma):
'Perform elastic distortion on an image.\n\n Here, alpha refers to the scaling factor that controls the intensity of the\n deformation. The sigma variable refers to the Gaussian filter standard\n deviation.\n '
random_state = numpy.random.RandomState(None)
shape = image.shape
dx = (gaussian_filter(((random_state.rand(*shape) * 2) - 1), sigma, mode='constant') * alpha)
dy = (gaussian_filter(((random_state.rand(*shape) * 2) - 1), sigma, mode='constant') * alpha)
(x, y) = numpy.meshgrid(numpy.arange(shape[0]), numpy.arange(shape[1]))
indices = (numpy.reshape((y + dy), ((- 1), 1)), numpy.reshape((x + dx), ((- 1), 1)))
return map_coordinates(image, indices, order=1).reshape(shape) | Perform elastic distortion on an image.
Here, alpha refers to the scaling factor that controls the intensity of the
deformation. The sigma variable refers to the Gaussian filter standard
deviation. | tools/hangul-image-generator.py | elastic_distort | g-may/tensorflow-hangul-recognition | 243 | python | def elastic_distort(image, alpha, sigma):
'Perform elastic distortion on an image.\n\n Here, alpha refers to the scaling factor that controls the intensity of the\n deformation. The sigma variable refers to the Gaussian filter standard\n deviation.\n '
random_state = numpy.random.RandomState(None)
shape = image.shape
dx = (gaussian_filter(((random_state.rand(*shape) * 2) - 1), sigma, mode='constant') * alpha)
dy = (gaussian_filter(((random_state.rand(*shape) * 2) - 1), sigma, mode='constant') * alpha)
(x, y) = numpy.meshgrid(numpy.arange(shape[0]), numpy.arange(shape[1]))
indices = (numpy.reshape((y + dy), ((- 1), 1)), numpy.reshape((x + dx), ((- 1), 1)))
return map_coordinates(image, indices, order=1).reshape(shape) | def elastic_distort(image, alpha, sigma):
'Perform elastic distortion on an image.\n\n Here, alpha refers to the scaling factor that controls the intensity of the\n deformation. The sigma variable refers to the Gaussian filter standard\n deviation.\n '
random_state = numpy.random.RandomState(None)
shape = image.shape
dx = (gaussian_filter(((random_state.rand(*shape) * 2) - 1), sigma, mode='constant') * alpha)
dy = (gaussian_filter(((random_state.rand(*shape) * 2) - 1), sigma, mode='constant') * alpha)
(x, y) = numpy.meshgrid(numpy.arange(shape[0]), numpy.arange(shape[1]))
indices = (numpy.reshape((y + dy), ((- 1), 1)), numpy.reshape((x + dx), ((- 1), 1)))
return map_coordinates(image, indices, order=1).reshape(shape)<|docstring|>Perform elastic distortion on an image.
Here, alpha refers to the scaling factor that controls the intensity of the
deformation. The sigma variable refers to the Gaussian filter standard
deviation.<|endoftext|> |
1489320bb5e3797fb4887f9919f13b7383d81cba56339104e28e41d4bfd897b8 | def __init__(self, text):
'\n 初始化\n :param text: \n '
self.text = text
self.pos = 0
self.current_char = self.text[self.pos] | 初始化
:param text: | Python/lsbasi/pascal1.py | __init__ | InnoFang/misc-code | 4 | python | def __init__(self, text):
'\n 初始化\n :param text: \n '
self.text = text
self.pos = 0
self.current_char = self.text[self.pos] | def __init__(self, text):
'\n 初始化\n :param text: \n '
self.text = text
self.pos = 0
self.current_char = self.text[self.pos]<|docstring|>初始化
:param text:<|endoftext|> |
4ab01d54628d223ba446af0fcc27c32ee7f53f36bf3ad44cf3d5b240edd3e720 | def error(self):
'\n 内置异常\n :return: \n '
raise Exception('Invalid character') | 内置异常
:return: | Python/lsbasi/pascal1.py | error | InnoFang/misc-code | 4 | python | def error(self):
'\n 内置异常\n :return: \n '
raise Exception('Invalid character') | def error(self):
'\n 内置异常\n :return: \n '
raise Exception('Invalid character')<|docstring|>内置异常
:return:<|endoftext|> |
88c2eebf6156dd87554eecd8625ae9b6b25f5d014dde89e9b14fcbd2319bcd44 | def advance(self):
'\n 字符下标加一，得到下一个字符\n :return: \n '
self.pos += 1
if (self.pos > (len(self.text) - 1)):
self.current_char = None
else:
self.current_char = self.text[self.pos] | 字符下标加一，得到下一个字符
:return: | Python/lsbasi/pascal1.py | advance | InnoFang/misc-code | 4 | python | def advance(self):
'\n 字符下标加一，得到下一个字符\n :return: \n '
self.pos += 1
if (self.pos > (len(self.text) - 1)):
self.current_char = None
else:
self.current_char = self.text[self.pos] | def advance(self):
'\n 字符下标加一，得到下一个字符\n :return: \n '
self.pos += 1
if (self.pos > (len(self.text) - 1)):
self.current_char = None
else:
self.current_char = self.text[self.pos]<|docstring|>字符下标加一，得到下一个字符
:return:<|endoftext|> |
7bcb361d68096f24c5aa5c96fe29f6f9271351c17d9b217cdabe7292e74e3a1e | def peek(self):
'\n 得到下一个字符，但是字符下标不变\n :return: \n '
peek_pos = (self.pos + 1)
if (peek_pos > (len(self.text) - 1)):
return None
else:
return self.text[peek_pos] | 得到下一个字符，但是字符下标不变
:return: | Python/lsbasi/pascal1.py | peek | InnoFang/misc-code | 4 | python | def peek(self):
'\n 得到下一个字符，但是字符下标不变\n :return: \n '
peek_pos = (self.pos + 1)
if (peek_pos > (len(self.text) - 1)):
return None
else:
return self.text[peek_pos] | def peek(self):
'\n 得到下一个字符，但是字符下标不变\n :return: \n '
peek_pos = (self.pos + 1)
if (peek_pos > (len(self.text) - 1)):
return None
else:
return self.text[peek_pos]<|docstring|>得到下一个字符，但是字符下标不变
:return:<|endoftext|> |
0c40d3fb286d291296a61c78ee55f9b9e600b12b37ffb4f12980111a97af85dd | def skip_whitespace(self):
'\n 跳过空字符\n :return: \n '
while ((self.current_char is not None) and self.current_char.isspace()):
self.advance() | 跳过空字符
:return: | Python/lsbasi/pascal1.py | skip_whitespace | InnoFang/misc-code | 4 | python | def skip_whitespace(self):
'\n 跳过空字符\n :return: \n '
while ((self.current_char is not None) and self.current_char.isspace()):
self.advance() | def skip_whitespace(self):
'\n 跳过空字符\n :return: \n '
while ((self.current_char is not None) and self.current_char.isspace()):
self.advance()<|docstring|>跳过空字符
:return:<|endoftext|> |
92ee14919658740841fee99eb0bea6503a78f3afd050a6b115b0a84bfd5667f8 | def integer(self):
'\n 读取数字\n :return: \n '
result = ''
while ((self.current_char is not None) and self.current_char.isdigit()):
result += self.current_char
self.advance()
return int(result) | 读取数字
:return: | Python/lsbasi/pascal1.py | integer | InnoFang/misc-code | 4 | python | def integer(self):
'\n 读取数字\n :return: \n '
result =
while ((self.current_char is not None) and self.current_char.isdigit()):
result += self.current_char
self.advance()
return int(result) | def integer(self):
'\n 读取数字\n :return: \n '
result =
while ((self.current_char is not None) and self.current_char.isdigit()):
result += self.current_char
self.advance()
return int(result)<|docstring|>读取数字
:return:<|endoftext|> |
3e2d008b95532a369ee7ef3f79ac62e4dbb1e718e5861fb6e10d6010d3b37bc4 | def _id(self):
'\n 读取标识符(变量和保留字（即BEGIN，END））\n :return: \n '
result = ''
while ((self.current_char is not None) and self.current_char.isalnum()):
result += self.current_char
self.advance()
token = RESERVED_KEYWORDS.get(result, Token(ID, result))
return token | 读取标识符(变量和保留字（即BEGIN，END））
:return: | Python/lsbasi/pascal1.py | _id | InnoFang/misc-code | 4 | python | def _id(self):
'\n 读取标识符(变量和保留字（即BEGIN，END））\n :return: \n '
result =
while ((self.current_char is not None) and self.current_char.isalnum()):
result += self.current_char
self.advance()
token = RESERVED_KEYWORDS.get(result, Token(ID, result))
return token | def _id(self):
'\n 读取标识符(变量和保留字（即BEGIN，END））\n :return: \n '
result =
while ((self.current_char is not None) and self.current_char.isalnum()):
result += self.current_char
self.advance()
token = RESERVED_KEYWORDS.get(result, Token(ID, result))
return token<|docstring|>读取标识符(变量和保留字（即BEGIN，END））
:return:<|endoftext|> |
8b620753aee5844e3b322417d6a245ccef3e9fd281c2c4c5f882a1d8922f566a | def get_next_token(self):
'\n 把句子分割成token，一次一个token\n :return: \n '
while (self.current_char is not None):
if self.current_char.isspace():
self.skip_whitespace()
continue
if self.current_char.isalpha():
return self._id()
if self.current_char.isdigit():
return Token(INTEGER, self.integer())
if ((self.current_char == ':') and (self.peek() == '=')):
self.advance()
self.advance()
return Token(ASSIGN, ':=')
if (self.current_char == ';'):
self.advance()
return Token(SEMI, ';')
if (self.current_char == '+'):
self.advance()
return Token(PLUS, '+')
if (self.current_char == '-'):
self.advance()
return Token(MINUS, '-')
if (self.current_char == '*'):
self.advance()
return Token(MUL, '*')
if (self.current_char == '/'):
self.advance()
return Token(DIV, '/')
if (self.current_char == '('):
self.advance()
return Token(LPAREN, '(')
if (self.current_char == ')'):
self.advance()
return Token(RPAREN, ')')
if (self.current_char == '.'):
self.advance()
return Token(DOT, '.')
self.error()
return Token(EOF, None) | 把句子分割成token，一次一个token
:return: | Python/lsbasi/pascal1.py | get_next_token | InnoFang/misc-code | 4 | python | def get_next_token(self):
'\n 把句子分割成token，一次一个token\n :return: \n '
while (self.current_char is not None):
if self.current_char.isspace():
self.skip_whitespace()
continue
if self.current_char.isalpha():
return self._id()
if self.current_char.isdigit():
return Token(INTEGER, self.integer())
if ((self.current_char == ':') and (self.peek() == '=')):
self.advance()
self.advance()
return Token(ASSIGN, ':=')
if (self.current_char == ';'):
self.advance()
return Token(SEMI, ';')
if (self.current_char == '+'):
self.advance()
return Token(PLUS, '+')
if (self.current_char == '-'):
self.advance()
return Token(MINUS, '-')
if (self.current_char == '*'):
self.advance()
return Token(MUL, '*')
if (self.current_char == '/'):
self.advance()
return Token(DIV, '/')
if (self.current_char == '('):
self.advance()
return Token(LPAREN, '(')
if (self.current_char == ')'):
self.advance()
return Token(RPAREN, ')')
if (self.current_char == '.'):
self.advance()
return Token(DOT, '.')
self.error()
return Token(EOF, None) | def get_next_token(self):
'\n 把句子分割成token，一次一个token\n :return: \n '
while (self.current_char is not None):
if self.current_char.isspace():
self.skip_whitespace()
continue
if self.current_char.isalpha():
return self._id()
if self.current_char.isdigit():
return Token(INTEGER, self.integer())
if ((self.current_char == ':') and (self.peek() == '=')):
self.advance()
self.advance()
return Token(ASSIGN, ':=')
if (self.current_char == ';'):
self.advance()
return Token(SEMI, ';')
if (self.current_char == '+'):
self.advance()
return Token(PLUS, '+')
if (self.current_char == '-'):
self.advance()
return Token(MINUS, '-')
if (self.current_char == '*'):
self.advance()
return Token(MUL, '*')
if (self.current_char == '/'):
self.advance()
return Token(DIV, '/')
if (self.current_char == '('):
self.advance()
return Token(LPAREN, '(')
if (self.current_char == ')'):
self.advance()
return Token(RPAREN, ')')
if (self.current_char == '.'):
self.advance()
return Token(DOT, '.')
self.error()
return Token(EOF, None)<|docstring|>把句子分割成token，一次一个token
:return:<|endoftext|> |
ae3d4e16b328db288476238431309e327b64b28a6df133018e29805afc7f3ed8 | def program(self):
'program : compound_statement DOT'
node = self.compound_statement()
self.eat(DOT)
return node | program : compound_statement DOT | Python/lsbasi/pascal1.py | program | InnoFang/misc-code | 4 | python | def program(self):
node = self.compound_statement()
self.eat(DOT)
return node | def program(self):
node = self.compound_statement()
self.eat(DOT)
return node<|docstring|>program : compound_statement DOT<|endoftext|> |
03a1884e4c0ddf1c04d095859ea9dd37e97985e4e5f3e883294b7b3f7d6b29fd | def compound_statement(self):
'compound_statement : BEGIN statement_list END'
self.eat(BEGIN)
nodes = self.statement_list()
self.eat(END)
root = Compound()
for node in nodes:
root.children.append(node)
return root | compound_statement : BEGIN statement_list END | Python/lsbasi/pascal1.py | compound_statement | InnoFang/misc-code | 4 | python | def compound_statement(self):
self.eat(BEGIN)
nodes = self.statement_list()
self.eat(END)
root = Compound()
for node in nodes:
root.children.append(node)
return root | def compound_statement(self):
self.eat(BEGIN)
nodes = self.statement_list()
self.eat(END)
root = Compound()
for node in nodes:
root.children.append(node)
return root<|docstring|>compound_statement : BEGIN statement_list END<|endoftext|> |
e127493312c499cad94cdec6eb21036547e403d5cea39761ad3641be88c94e3b | def statement_list(self):
'\n statement_list : statement\n | statement SEMI statement_list\n '
node = self.statement()
results = [node]
while (self.current_token.type == SEMI):
self.eat(SEMI)
results.append(self.statement())
if (self.current_token.type == ID):
self.error()
return results | statement_list : statement
| statement SEMI statement_list | Python/lsbasi/pascal1.py | statement_list | InnoFang/misc-code | 4 | python | def statement_list(self):
'\n statement_list : statement\n | statement SEMI statement_list\n '
node = self.statement()
results = [node]
while (self.current_token.type == SEMI):
self.eat(SEMI)
results.append(self.statement())
if (self.current_token.type == ID):
self.error()
return results | def statement_list(self):
'\n statement_list : statement\n | statement SEMI statement_list\n '
node = self.statement()
results = [node]
while (self.current_token.type == SEMI):
self.eat(SEMI)
results.append(self.statement())
if (self.current_token.type == ID):
self.error()
return results<|docstring|>statement_list : statement
| statement SEMI statement_list<|endoftext|> |
ac019ece37abd33ee1ecf724653290a0799e39f0792b7b47f8462ab7388cc1d3 | def statement(self):
'\n statement : compound_statement\n | assignment_statement\n | empty\n '
if (self.current_token.type == BEGIN):
node = self.compound_statement()
elif (self.current_token.type == ID):
node = self.assignment_statement()
else:
node = self.empty()
return node | statement : compound_statement
| assignment_statement
| empty | Python/lsbasi/pascal1.py | statement | InnoFang/misc-code | 4 | python | def statement(self):
'\n statement : compound_statement\n | assignment_statement\n | empty\n '
if (self.current_token.type == BEGIN):
node = self.compound_statement()
elif (self.current_token.type == ID):
node = self.assignment_statement()
else:
node = self.empty()
return node | def statement(self):
'\n statement : compound_statement\n | assignment_statement\n | empty\n '
if (self.current_token.type == BEGIN):
node = self.compound_statement()
elif (self.current_token.type == ID):
node = self.assignment_statement()
else:
node = self.empty()
return node<|docstring|>statement : compound_statement
| assignment_statement
| empty<|endoftext|> |
b1b724fe52e31039526df630f021b957fc9ed992c8b8e609aabd9bdb3c8ebbc8 | def assignment_statement(self):
'assignment_statement : variable ASSIGN expr'
left = self.variable()
token = self.current_token
self.eat(ASSIGN)
right = self.expr()
node = Assign(left, token, right)
return node | assignment_statement : variable ASSIGN expr | Python/lsbasi/pascal1.py | assignment_statement | InnoFang/misc-code | 4 | python | def assignment_statement(self):
left = self.variable()
token = self.current_token
self.eat(ASSIGN)
right = self.expr()
node = Assign(left, token, right)
return node | def assignment_statement(self):
left = self.variable()
token = self.current_token
self.eat(ASSIGN)
right = self.expr()
node = Assign(left, token, right)
return node<|docstring|>assignment_statement : variable ASSIGN expr<|endoftext|> |
20eeb5ea56608d39e5fc11ad62a20fa65db31bb647833d90f93a9dcf6dedc384 | def variable(self):
'variable : ID'
node = Var(self.current_token)
self.eat(ID)
return node | variable : ID | Python/lsbasi/pascal1.py | variable | InnoFang/misc-code | 4 | python | def variable(self):
node = Var(self.current_token)
self.eat(ID)
return node | def variable(self):
node = Var(self.current_token)
self.eat(ID)
return node<|docstring|>variable : ID<|endoftext|> |
cb40daee72466b237fa3fd098cb63473de368d0cf49a33a95eafd38e27cf3fe1 | def expr(self):
'\n expr : term ((PLUS | MINUS) term) *\n '
node = self.term()
while (self.current_token.type in (PLUS, MINUS)):
token = self.current_token
if (token.type == PLUS):
self.eat(PLUS)
elif (token.type == MINUS):
self.eat(MINUS)
node = BinOp(left=node, op=token, right=self.term())
return node | expr : term ((PLUS | MINUS) term) * | Python/lsbasi/pascal1.py | expr | InnoFang/misc-code | 4 | python | def expr(self):
'\n \n '
node = self.term()
while (self.current_token.type in (PLUS, MINUS)):
token = self.current_token
if (token.type == PLUS):
self.eat(PLUS)
elif (token.type == MINUS):
self.eat(MINUS)
node = BinOp(left=node, op=token, right=self.term())
return node | def expr(self):
'\n \n '
node = self.term()
while (self.current_token.type in (PLUS, MINUS)):
token = self.current_token
if (token.type == PLUS):
self.eat(PLUS)
elif (token.type == MINUS):
self.eat(MINUS)
node = BinOp(left=node, op=token, right=self.term())
return node<|docstring|>expr : term ((PLUS | MINUS) term) *<|endoftext|> |
33d021f98a3fda1c542a06fc2f55a2f2f7b93695711d57f628248da121053e39 | def term(self):
'\n term : factor ((MUL | DIV) factor) *\n '
node = self.factor()
while (self.current_token.type in (MUL, DIV)):
token = self.current_token
if (token.type == MUL):
self.eat(MUL)
elif (token.type == DIV):
self.eat(DIV)
node = BinOp(left=node, op=token, right=self.factor())
return node | term : factor ((MUL | DIV) factor) * | Python/lsbasi/pascal1.py | term | InnoFang/misc-code | 4 | python | def term(self):
'\n \n '
node = self.factor()
while (self.current_token.type in (MUL, DIV)):
token = self.current_token
if (token.type == MUL):
self.eat(MUL)
elif (token.type == DIV):
self.eat(DIV)
node = BinOp(left=node, op=token, right=self.factor())
return node | def term(self):
'\n \n '
node = self.factor()
while (self.current_token.type in (MUL, DIV)):
token = self.current_token
if (token.type == MUL):
self.eat(MUL)
elif (token.type == DIV):
self.eat(DIV)
node = BinOp(left=node, op=token, right=self.factor())
return node<|docstring|>term : factor ((MUL | DIV) factor) *<|endoftext|> |
162afa4af143113767ff8001091df50b95542dbbdaa73eaf1a769ebd2233f83f | def factor(self):
'\n factor : PLUS factor\n | MINUS factor\n | INTEGER\n | LPAREN expr RPAReN\n | variable\n '
token = self.current_token
if (token.type == PLUS):
self.eat(PLUS)
node = UnaryOp(op=token, expr=self.factor())
return node
elif (token.type == MINUS):
self.eat(MINUS)
node = UnaryOp(op=token, expr=self.factor())
return node
elif (token.type == INTEGER):
self.eat(INTEGER)
return Num(token)
elif (token.type == LPAREN):
self.eat(LPAREN)
node = self.expr()
self.eat(RPAREN)
return node
else:
node = self.variable()
return node | factor : PLUS factor
| MINUS factor
| INTEGER
| LPAREN expr RPAReN
| variable | Python/lsbasi/pascal1.py | factor | InnoFang/misc-code | 4 | python | def factor(self):
'\n factor : PLUS factor\n | MINUS factor\n | INTEGER\n | LPAREN expr RPAReN\n | variable\n '
token = self.current_token
if (token.type == PLUS):
self.eat(PLUS)
node = UnaryOp(op=token, expr=self.factor())
return node
elif (token.type == MINUS):
self.eat(MINUS)
node = UnaryOp(op=token, expr=self.factor())
return node
elif (token.type == INTEGER):
self.eat(INTEGER)
return Num(token)
elif (token.type == LPAREN):
self.eat(LPAREN)
node = self.expr()
self.eat(RPAREN)
return node
else:
node = self.variable()
return node | def factor(self):
'\n factor : PLUS factor\n | MINUS factor\n | INTEGER\n | LPAREN expr RPAReN\n | variable\n '
token = self.current_token
if (token.type == PLUS):
self.eat(PLUS)
node = UnaryOp(op=token, expr=self.factor())
return node
elif (token.type == MINUS):
self.eat(MINUS)
node = UnaryOp(op=token, expr=self.factor())
return node
elif (token.type == INTEGER):
self.eat(INTEGER)
return Num(token)
elif (token.type == LPAREN):
self.eat(LPAREN)
node = self.expr()
self.eat(RPAREN)
return node
else:
node = self.variable()
return node<|docstring|>factor : PLUS factor
| MINUS factor
| INTEGER
| LPAREN expr RPAReN
| variable<|endoftext|> |
3469a047b6dfc8524d63c3b206d4c1b1a994dc3ec685262e951fddc34f9bf6f1 | def parse(self):
'\n program : compound_statement DOT\n compound_statement : BEGIN statement_list END\n statement_list : statement\n | statement SEMI statement_list\n statement : compound_statement\n | assignment_statement\n | empty\n assignment_statement : variable ASSIGN expr\n empty :\n expr: term ((PLUS | MINUS) term)*\n term: factor ((MUL | DIV) factor)*\n factor : PLUS factor\n | MINUS factor\n | INTEGER\n | LPAREN expr RPAREN\n | variable\n variable: ID\n '
node = self.program()
if (self.current_token.type != EOF):
self.error()
return node | program : compound_statement DOT
compound_statement : BEGIN statement_list END
statement_list : statement
| statement SEMI statement_list
statement : compound_statement
| assignment_statement
| empty
assignment_statement : variable ASSIGN expr
empty :
expr: term ((PLUS | MINUS) term)*
term: factor ((MUL | DIV) factor)*
factor : PLUS factor
| MINUS factor
| INTEGER
| LPAREN expr RPAREN
| variable
variable: ID | Python/lsbasi/pascal1.py | parse | InnoFang/misc-code | 4 | python | def parse(self):
'\n program : compound_statement DOT\n compound_statement : BEGIN statement_list END\n statement_list : statement\n | statement SEMI statement_list\n statement : compound_statement\n | assignment_statement\n | empty\n assignment_statement : variable ASSIGN expr\n empty :\n expr: term ((PLUS | MINUS) term)*\n term: factor ((MUL | DIV) factor)*\n factor : PLUS factor\n | MINUS factor\n | INTEGER\n | LPAREN expr RPAREN\n | variable\n variable: ID\n '
node = self.program()
if (self.current_token.type != EOF):
self.error()
return node | def parse(self):
'\n program : compound_statement DOT\n compound_statement : BEGIN statement_list END\n statement_list : statement\n | statement SEMI statement_list\n statement : compound_statement\n | assignment_statement\n | empty\n assignment_statement : variable ASSIGN expr\n empty :\n expr: term ((PLUS | MINUS) term)*\n term: factor ((MUL | DIV) factor)*\n factor : PLUS factor\n | MINUS factor\n | INTEGER\n | LPAREN expr RPAREN\n | variable\n variable: ID\n '
node = self.program()
if (self.current_token.type != EOF):
self.error()
return node<|docstring|>program : compound_statement DOT
compound_statement : BEGIN statement_list END
statement_list : statement
| statement SEMI statement_list
statement : compound_statement
| assignment_statement
| empty
assignment_statement : variable ASSIGN expr
empty :
expr: term ((PLUS | MINUS) term)*
term: factor ((MUL | DIV) factor)*
factor : PLUS factor
| MINUS factor
| INTEGER
| LPAREN expr RPAREN
| variable
variable: ID<|endoftext|> |
1a78458e0500bb4c82efe4e7f8dd0909796b1e7d0b76f68e8adc35a6bc3b2752 | def export_material_property(self, b_mat, flags=1):
'Return existing material property with given settings, or create\n a new one if a material property with these settings is not found.'
if (bpy.context.scene.niftools_scene.game in ('SKYRIM',)):
return
name = block_store.get_full_name(b_mat)
n_mat_prop = NifFormat.NiMaterialProperty()
specialnames = ('EnvMap2', 'EnvMap', 'skin', 'Hair', 'dynalpha', 'HideSecret', 'Lava')
if (bpy.context.scene.niftools_scene.game in ('OBLIVION', 'FALLOUT_3', 'SKYRIM')):
for specialname in specialnames:
if ((name.lower() == specialname.lower()) or name.lower().startswith((specialname.lower() + '.'))):
if (name != specialname):
NifLog.warn(f"Renaming material '{name}' to '{specialname}'")
name = specialname
if name.lower().startswith('noname'):
NifLog.warn(f"Renaming material '{name}' to ''")
name = ''
n_mat_prop.name = name
n_mat_prop.flags = flags
ambient = b_mat.niftools.ambient_color
n_mat_prop.ambient_color.r = ambient.r
n_mat_prop.ambient_color.g = ambient.g
n_mat_prop.ambient_color.b = ambient.b
(n_mat_prop.diffuse_color.r, n_mat_prop.diffuse_color.g, n_mat_prop.diffuse_color.b, _) = b_mat.diffuse_color
(n_mat_prop.specular_color.r, n_mat_prop.specular_color.g, n_mat_prop.specular_color.b) = b_mat.specular_color
emissive = b_mat.niftools.emissive_color
n_mat_prop.emissive_color.r = emissive.r
n_mat_prop.emissive_color.g = emissive.g
n_mat_prop.emissive_color.b = emissive.b
n_mat_prop.glossiness = (min(((1 / b_mat.roughness) - 1), 128) if (b_mat.roughness != 0) else 128)
n_mat_prop.alpha = b_mat.niftools.emissive_alpha.v
for n_block in block_store.block_to_obj:
if (not isinstance(n_block, NifFormat.NiMaterialProperty)):
continue
if EXPORT_OPTIMIZE_MATERIALS:
ignore_strings = (not (n_block.name in specialnames))
else:
ignore_strings = False
first_index = (1 if ignore_strings else 0)
if (n_block.get_hash()[first_index:] == n_mat_prop.get_hash()[first_index:]):
NifLog.warn(f"Merging materials '{n_mat_prop.name}' and '{n_block.name}' (they are identical in nif)")
n_mat_prop = n_block
break
block_store.register_block(n_mat_prop)
self.material_anim.export_material(b_mat, n_mat_prop)
return n_mat_prop | Return existing material property with given settings, or create
a new one if a material property with these settings is not found. | io_scene_niftools/modules/nif_export/property/material/__init__.py | export_material_property | BlenderAddonsArchive/blender_niftools_addon | 94 | python | def export_material_property(self, b_mat, flags=1):
'Return existing material property with given settings, or create\n a new one if a material property with these settings is not found.'
if (bpy.context.scene.niftools_scene.game in ('SKYRIM',)):
return
name = block_store.get_full_name(b_mat)
n_mat_prop = NifFormat.NiMaterialProperty()
specialnames = ('EnvMap2', 'EnvMap', 'skin', 'Hair', 'dynalpha', 'HideSecret', 'Lava')
if (bpy.context.scene.niftools_scene.game in ('OBLIVION', 'FALLOUT_3', 'SKYRIM')):
for specialname in specialnames:
if ((name.lower() == specialname.lower()) or name.lower().startswith((specialname.lower() + '.'))):
if (name != specialname):
NifLog.warn(f"Renaming material '{name}' to '{specialname}'")
name = specialname
if name.lower().startswith('noname'):
NifLog.warn(f"Renaming material '{name}' to ")
name =
n_mat_prop.name = name
n_mat_prop.flags = flags
ambient = b_mat.niftools.ambient_color
n_mat_prop.ambient_color.r = ambient.r
n_mat_prop.ambient_color.g = ambient.g
n_mat_prop.ambient_color.b = ambient.b
(n_mat_prop.diffuse_color.r, n_mat_prop.diffuse_color.g, n_mat_prop.diffuse_color.b, _) = b_mat.diffuse_color
(n_mat_prop.specular_color.r, n_mat_prop.specular_color.g, n_mat_prop.specular_color.b) = b_mat.specular_color
emissive = b_mat.niftools.emissive_color
n_mat_prop.emissive_color.r = emissive.r
n_mat_prop.emissive_color.g = emissive.g
n_mat_prop.emissive_color.b = emissive.b
n_mat_prop.glossiness = (min(((1 / b_mat.roughness) - 1), 128) if (b_mat.roughness != 0) else 128)
n_mat_prop.alpha = b_mat.niftools.emissive_alpha.v
for n_block in block_store.block_to_obj:
if (not isinstance(n_block, NifFormat.NiMaterialProperty)):
continue
if EXPORT_OPTIMIZE_MATERIALS:
ignore_strings = (not (n_block.name in specialnames))
else:
ignore_strings = False
first_index = (1 if ignore_strings else 0)
if (n_block.get_hash()[first_index:] == n_mat_prop.get_hash()[first_index:]):
NifLog.warn(f"Merging materials '{n_mat_prop.name}' and '{n_block.name}' (they are identical in nif)")
n_mat_prop = n_block
break
block_store.register_block(n_mat_prop)
self.material_anim.export_material(b_mat, n_mat_prop)
return n_mat_prop | def export_material_property(self, b_mat, flags=1):
'Return existing material property with given settings, or create\n a new one if a material property with these settings is not found.'
if (bpy.context.scene.niftools_scene.game in ('SKYRIM',)):
return
name = block_store.get_full_name(b_mat)
n_mat_prop = NifFormat.NiMaterialProperty()
specialnames = ('EnvMap2', 'EnvMap', 'skin', 'Hair', 'dynalpha', 'HideSecret', 'Lava')
if (bpy.context.scene.niftools_scene.game in ('OBLIVION', 'FALLOUT_3', 'SKYRIM')):
for specialname in specialnames:
if ((name.lower() == specialname.lower()) or name.lower().startswith((specialname.lower() + '.'))):
if (name != specialname):
NifLog.warn(f"Renaming material '{name}' to '{specialname}'")
name = specialname
if name.lower().startswith('noname'):
NifLog.warn(f"Renaming material '{name}' to ")
name =
n_mat_prop.name = name
n_mat_prop.flags = flags
ambient = b_mat.niftools.ambient_color
n_mat_prop.ambient_color.r = ambient.r
n_mat_prop.ambient_color.g = ambient.g
n_mat_prop.ambient_color.b = ambient.b
(n_mat_prop.diffuse_color.r, n_mat_prop.diffuse_color.g, n_mat_prop.diffuse_color.b, _) = b_mat.diffuse_color
(n_mat_prop.specular_color.r, n_mat_prop.specular_color.g, n_mat_prop.specular_color.b) = b_mat.specular_color
emissive = b_mat.niftools.emissive_color
n_mat_prop.emissive_color.r = emissive.r
n_mat_prop.emissive_color.g = emissive.g
n_mat_prop.emissive_color.b = emissive.b
n_mat_prop.glossiness = (min(((1 / b_mat.roughness) - 1), 128) if (b_mat.roughness != 0) else 128)
n_mat_prop.alpha = b_mat.niftools.emissive_alpha.v
for n_block in block_store.block_to_obj:
if (not isinstance(n_block, NifFormat.NiMaterialProperty)):
continue
if EXPORT_OPTIMIZE_MATERIALS:
ignore_strings = (not (n_block.name in specialnames))
else:
ignore_strings = False
first_index = (1 if ignore_strings else 0)
if (n_block.get_hash()[first_index:] == n_mat_prop.get_hash()[first_index:]):
NifLog.warn(f"Merging materials '{n_mat_prop.name}' and '{n_block.name}' (they are identical in nif)")
n_mat_prop = n_block
break
block_store.register_block(n_mat_prop)
self.material_anim.export_material(b_mat, n_mat_prop)
return n_mat_prop<|docstring|>Return existing material property with given settings, or create
a new one if a material property with these settings is not found.<|endoftext|> |
c5a2a441c5016388e7920f68ae8913fae37047a0d722662cb03f0e0af72513e9 | @tree.command()
async def help(intr: dc.Interaction):
'Show help.'
help_str = "Use `/measure` to have the bot join your current voice channel, and measure everyone's voice loudness. While measuring, everyone should talk at their usual loudness. (Talking at the same is ok.)\nAfter that, bot recommends percentages that you should set everyone else's volume at. (You can ignore your own percentage.)"
(await intr.response.send_message(help_str)) | Show help. | voice_eq_bot/__init__.py | help | OdielDomanie/voice_eq_bot | 0 | python | @tree.command()
async def help(intr: dc.Interaction):
help_str = "Use `/measure` to have the bot join your current voice channel, and measure everyone's voice loudness. While measuring, everyone should talk at their usual loudness. (Talking at the same is ok.)\nAfter that, bot recommends percentages that you should set everyone else's volume at. (You can ignore your own percentage.)"
(await intr.response.send_message(help_str)) | @tree.command()
async def help(intr: dc.Interaction):
help_str = "Use `/measure` to have the bot join your current voice channel, and measure everyone's voice loudness. While measuring, everyone should talk at their usual loudness. (Talking at the same is ok.)\nAfter that, bot recommends percentages that you should set everyone else's volume at. (You can ignore your own percentage.)"
(await intr.response.send_message(help_str))<|docstring|>Show help.<|endoftext|> |
6bafddbf2e9b2a21aab84788363222408f5520315dfe4ac8ca4bce2459512c30 | @tree.command(guild=test_guild)
async def measure(intr: dc.Interaction, duration: int=10):
"Join the voice channel to measure each member's voice level,\n and recommend volume percentages.\n "
if (not intr.guild):
return
duration = min(duration, 30)
try:
voice_chn = intr.user.voice.channel
assert voice_chn
except (AttributeError, AssertionError):
(await intr.response.send_message('You need to be in a voice channel', ephemeral=True))
return
if intr.guild.voice_client:
(await intr.response.send_message('Already measuring.', ephemeral=True))
return
permissions = voice_chn.permissions_for(intr.guild.me)
if (not permissions.connect):
(await intr.response.send_message("The bot doesn't have permission to join the voice channel.", ephemeral=True))
return
async with (await voice_chn.connect(timeout=5, cls=dc.VoiceClient)) as voice_client:
resp = intr.response.send_message('Measuring voice levels, everyone should speak now.')
resp_task = asyncio.create_task(resp)
voice_receiver: dc.VoiceReceiver = (await voice_client.start_receiving(buffer=10, output_type='float'))
(await resp_task)
user_pcms: dict[(int, MemberPCM)] = {}
async for (member, _, pcm) in voice_receiver(duration):
member_pcm = user_pcms.setdefault(member.id, MemberPCM(member, bytearray()))
member_pcm.member = member
member_pcm.pcm.extend(pcm)
loudnesses = {m_pcm.member: loudness(float_to_array(m_pcm.pcm, voice_receiver.channels), voice_receiver.sampling_rate) for m_pcm in user_pcms.values()}
adjustments = {user: db_to_dc_percent((TARGET_LUFS - loud)) for (user, loud) in loudnesses.items()}
reply_lines = []
for (vc_user, adj) in adjustments.items():
ADJ_CUTOFF = 3.0
if (adj > ADJ_CUTOFF):
pass
else:
adj_perc_str = f'{adj:.0%}'
rel_loudness = (loudnesses[vc_user] - TARGET_LUFS)
if isinstance(vc_user, dc.Object):
try:
member = (await intr.guild.fetch_member(vc_user.id))
except (dc.NotFound, dc.HTTPException):
continue
name = member.display_name
else:
name = vc_user.display_name
reply_lines.append(f"`{name}`: `{adj_perc_str}` (` {(- rel_loudness):+3.1f} dB {('🔉' if (rel_loudness > 0) else '🔊')}`)")
reply_lines.sort()
if (len(reply_lines) == 0):
(await intr.followup.send('No one talked.'))
else:
(await intr.followup.send(('__Optimal volume settings:__\n\n' + '\n'.join(reply_lines)))) | Join the voice channel to measure each member's voice level,
and recommend volume percentages. | voice_eq_bot/__init__.py | measure | OdielDomanie/voice_eq_bot | 0 | python | @tree.command(guild=test_guild)
async def measure(intr: dc.Interaction, duration: int=10):
"Join the voice channel to measure each member's voice level,\n and recommend volume percentages.\n "
if (not intr.guild):
return
duration = min(duration, 30)
try:
voice_chn = intr.user.voice.channel
assert voice_chn
except (AttributeError, AssertionError):
(await intr.response.send_message('You need to be in a voice channel', ephemeral=True))
return
if intr.guild.voice_client:
(await intr.response.send_message('Already measuring.', ephemeral=True))
return
permissions = voice_chn.permissions_for(intr.guild.me)
if (not permissions.connect):
(await intr.response.send_message("The bot doesn't have permission to join the voice channel.", ephemeral=True))
return
async with (await voice_chn.connect(timeout=5, cls=dc.VoiceClient)) as voice_client:
resp = intr.response.send_message('Measuring voice levels, everyone should speak now.')
resp_task = asyncio.create_task(resp)
voice_receiver: dc.VoiceReceiver = (await voice_client.start_receiving(buffer=10, output_type='float'))
(await resp_task)
user_pcms: dict[(int, MemberPCM)] = {}
async for (member, _, pcm) in voice_receiver(duration):
member_pcm = user_pcms.setdefault(member.id, MemberPCM(member, bytearray()))
member_pcm.member = member
member_pcm.pcm.extend(pcm)
loudnesses = {m_pcm.member: loudness(float_to_array(m_pcm.pcm, voice_receiver.channels), voice_receiver.sampling_rate) for m_pcm in user_pcms.values()}
adjustments = {user: db_to_dc_percent((TARGET_LUFS - loud)) for (user, loud) in loudnesses.items()}
reply_lines = []
for (vc_user, adj) in adjustments.items():
ADJ_CUTOFF = 3.0
if (adj > ADJ_CUTOFF):
pass
else:
adj_perc_str = f'{adj:.0%}'
rel_loudness = (loudnesses[vc_user] - TARGET_LUFS)
if isinstance(vc_user, dc.Object):
try:
member = (await intr.guild.fetch_member(vc_user.id))
except (dc.NotFound, dc.HTTPException):
continue
name = member.display_name
else:
name = vc_user.display_name
reply_lines.append(f"`{name}`: `{adj_perc_str}` (` {(- rel_loudness):+3.1f} dB {('🔉' if (rel_loudness > 0) else '🔊')}`)")
reply_lines.sort()
if (len(reply_lines) == 0):
(await intr.followup.send('No one talked.'))
else:
(await intr.followup.send(('__Optimal volume settings:__\n\n' + '\n'.join(reply_lines)))) | @tree.command(guild=test_guild)
async def measure(intr: dc.Interaction, duration: int=10):
"Join the voice channel to measure each member's voice level,\n and recommend volume percentages.\n "
if (not intr.guild):
return
duration = min(duration, 30)
try:
voice_chn = intr.user.voice.channel
assert voice_chn
except (AttributeError, AssertionError):
(await intr.response.send_message('You need to be in a voice channel', ephemeral=True))
return
if intr.guild.voice_client:
(await intr.response.send_message('Already measuring.', ephemeral=True))
return
permissions = voice_chn.permissions_for(intr.guild.me)
if (not permissions.connect):
(await intr.response.send_message("The bot doesn't have permission to join the voice channel.", ephemeral=True))
return
async with (await voice_chn.connect(timeout=5, cls=dc.VoiceClient)) as voice_client:
resp = intr.response.send_message('Measuring voice levels, everyone should speak now.')
resp_task = asyncio.create_task(resp)
voice_receiver: dc.VoiceReceiver = (await voice_client.start_receiving(buffer=10, output_type='float'))
(await resp_task)
user_pcms: dict[(int, MemberPCM)] = {}
async for (member, _, pcm) in voice_receiver(duration):
member_pcm = user_pcms.setdefault(member.id, MemberPCM(member, bytearray()))
member_pcm.member = member
member_pcm.pcm.extend(pcm)
loudnesses = {m_pcm.member: loudness(float_to_array(m_pcm.pcm, voice_receiver.channels), voice_receiver.sampling_rate) for m_pcm in user_pcms.values()}
adjustments = {user: db_to_dc_percent((TARGET_LUFS - loud)) for (user, loud) in loudnesses.items()}
reply_lines = []
for (vc_user, adj) in adjustments.items():
ADJ_CUTOFF = 3.0
if (adj > ADJ_CUTOFF):
pass
else:
adj_perc_str = f'{adj:.0%}'
rel_loudness = (loudnesses[vc_user] - TARGET_LUFS)
if isinstance(vc_user, dc.Object):
try:
member = (await intr.guild.fetch_member(vc_user.id))
except (dc.NotFound, dc.HTTPException):
continue
name = member.display_name
else:
name = vc_user.display_name
reply_lines.append(f"`{name}`: `{adj_perc_str}` (` {(- rel_loudness):+3.1f} dB {('🔉' if (rel_loudness > 0) else '🔊')}`)")
reply_lines.sort()
if (len(reply_lines) == 0):
(await intr.followup.send('No one talked.'))
else:
(await intr.followup.send(('__Optimal volume settings:__\n\n' + '\n'.join(reply_lines))))<|docstring|>Join the voice channel to measure each member's voice level,
and recommend volume percentages.<|endoftext|> |
f897c7328dc36a44dc66ca67cb13d23eec7f766af1c478369211d9dd0909e8a7 | def __init__(self, description, errorMessage, resolutionMessage, show=True, messageLevel=1):
'Initialize a new L{RunningAction}\n\n @param description: Action description\n @type description: string\n @param resolutionMessage: Action resolution message\n @type resolutionMessage: string\n @param show: Display action\n @type show: bool\n @param messageLevel: Message level\n @type messageLevel: number\n '
self.description = description
self.resolutionMessage = resolutionMessage
self.errorMessage = errorMessage
self.show = show
self.messageLevel = messageLevel | Initialize a new L{RunningAction}
@param description: Action description
@type description: string
@param resolutionMessage: Action resolution message
@type resolutionMessage: string
@param show: Display action
@type show: bool
@param messageLevel: Message level
@type messageLevel: number | lib/JumpScale/baselib/actionsold/action/RunningAction.py | __init__ | rudecs/jumpscale_core7 | 1 | python | def __init__(self, description, errorMessage, resolutionMessage, show=True, messageLevel=1):
'Initialize a new L{RunningAction}\n\n @param description: Action description\n @type description: string\n @param resolutionMessage: Action resolution message\n @type resolutionMessage: string\n @param show: Display action\n @type show: bool\n @param messageLevel: Message level\n @type messageLevel: number\n '
self.description = description
self.resolutionMessage = resolutionMessage
self.errorMessage = errorMessage
self.show = show
self.messageLevel = messageLevel | def __init__(self, description, errorMessage, resolutionMessage, show=True, messageLevel=1):
'Initialize a new L{RunningAction}\n\n @param description: Action description\n @type description: string\n @param resolutionMessage: Action resolution message\n @type resolutionMessage: string\n @param show: Display action\n @type show: bool\n @param messageLevel: Message level\n @type messageLevel: number\n '
self.description = description
self.resolutionMessage = resolutionMessage
self.errorMessage = errorMessage
self.show = show
self.messageLevel = messageLevel<|docstring|>Initialize a new L{RunningAction}
@param description: Action description
@type description: string
@param resolutionMessage: Action resolution message
@type resolutionMessage: string
@param show: Display action
@type show: bool
@param messageLevel: Message level
@type messageLevel: number<|endoftext|> |
ff43b5dd856bef54edad2131696508b9ffe15a0fe324a6feaa6e30abf0482692 | @pytest.fixture
def source_path():
'Get the xonsh source path.'
pwd = os.path.dirname(__file__)
return os.path.dirname(pwd) | Get the xonsh source path. | tests/conftest.py | source_path | jmoranos/xonsh | 3 | python | @pytest.fixture
def source_path():
pwd = os.path.dirname(__file__)
return os.path.dirname(pwd) | @pytest.fixture
def source_path():
pwd = os.path.dirname(__file__)
return os.path.dirname(pwd)<|docstring|>Get the xonsh source path.<|endoftext|> |
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