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linhaow/TextClassify | aa479ae0941c008602631c50124d8c07d159bfb1 | hubconfs/xlnet_hubconf.1.py | python | xlnetLMHeadModel | (*args, **kwargs) | return model | xlnetModel is the basic XLNet Transformer model from
"XLNet: Generalized Autoregressive Pretraining for Language Understanding"
by Zhilin Yang, Zihang Dai1, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
with a tied (pre-trained) language modeling head on top.
Example:
# Load the tokenizer
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
# Prepare tokenized input
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load xlnetLMHeadModel
model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlnet-large-cased')
model.eval()
# Predict hidden states features for each layer
with torch.no_grad():
predictions_1, mems = model(tokens_tensor_1)
predictions_2, mems = model(tokens_tensor_2, mems=mems)
# Get the predicted last token
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
predicted_token = tokenizer.decode([predicted_index])
assert predicted_token == ' who' | xlnetModel is the basic XLNet Transformer model from
"XLNet: Generalized Autoregressive Pretraining for Language Understanding"
by Zhilin Yang, Zihang Dai1, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
with a tied (pre-trained) language modeling head on top. | [
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"Yang\u0003",
"Zihang",
"Dai\u00031",
"Yiming",
"Yang",
"Jaime",
"Carbonell",... | def xlnetLMHeadModel(*args, **kwargs):
"""
xlnetModel is the basic XLNet Transformer model from
"XLNet: Generalized Autoregressive Pretraining for Language Understanding"
by Zhilin Yang, Zihang Dai1, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le
with a tied (pre-trained) language modeling head on top.
Example:
# Load the tokenizer
import torch
tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'xlnetTokenizer', 'xlnet-large-cased')
# Prepare tokenized input
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
# Load xlnetLMHeadModel
model = torch.hub.load('huggingface/pytorch-transformers', 'xlnetLMHeadModel', 'xlnet-large-cased')
model.eval()
# Predict hidden states features for each layer
with torch.no_grad():
predictions_1, mems = model(tokens_tensor_1)
predictions_2, mems = model(tokens_tensor_2, mems=mems)
# Get the predicted last token
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
predicted_token = tokenizer.decode([predicted_index])
assert predicted_token == ' who'
"""
model = XLNetLMHeadModel.from_pretrained(*args, **kwargs)
return model | [
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/calendar.py | python | HTMLCalendar.formatmonthname | (self, theyear, themonth, withyear=True) | return '<tr><th colspan="7" class="%s">%s</th></tr>' % (
self.cssclass_month_head, s) | Return a month name as a table row. | Return a month name as a table row. | [
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"""
Return a month name as a table row.
"""
if withyear:
s = '%s %s' % (month_name[themonth], theyear)
else:
s = '%s' % month_name[themonth]
return '<tr><th colspan="7" class="%s">%s</th></tr>' % (
self.cssclass_month_head, s) | [
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tranluan/Nonlinear_Face_3DMM | 662098a602d542c3505cd16ba01dd302f33eeee8 | model_non_linear_3DMM_proxy.py | python | DCGAN.setupParaStat | (self) | [] | def setupParaStat(self):
if self.is_reduce:
self.tri = load_3DMM_tri_reduce()
self.vertex_tri = load_3DMM_vertex_tri_reduce()
self.vt2pixel_u, self.vt2pixel_v = load_FaceAlignment_vt2pixel_reduce()
self.uv_tri, self.uv_mask = load_FaceAlignment_tri_2d_reduce(with_mask = True)
else:
self.tri = load_3DMM_tri()
self.vertex_tri = load_3DMM_vertex_tri()
self.vt2pixel_u, self.vt2pixel_v = load_FaceAlignment_vt2pixel()
self.uv_tri, self.uv_mask = load_FaceAlignment_tri_2d(with_mask = True)
# Basis
mu_shape, w_shape = load_FaceAlignment_basic('shape', is_reduce = self.is_reduce)
mu_exp, w_exp = load_FaceAlignment_basic('exp', is_reduce = self.is_reduce)
self.mean_shape = mu_shape + mu_exp
if self.is_2d_normalize:
#self.mean_shape = np.tile(np.array([0, 0, 6e4]), VERTEX_NUM)
self.std_shape = np.tile(np.array([1e4, 1e4, 1e4]), self.vertexNum)
else:
#self.mean_shape = np.load('mean_shape.npy')
self.std_shape = np.load('std_shape.npy')
self.mean_shape_tf = tf.constant(self.mean_shape, tf.float32)
self.std_shape_tf = tf.constant(self.std_shape, tf.float32)
self.mean_m = np.load('mean_m.npy')
self.std_m = np.load('std_m.npy')
self.mean_m_tf = tf.constant(self.mean_m, tf.float32)
self.std_m_tf = tf.constant(self.std_m, tf.float32)
self.w_shape = w_shape
self.w_exp = w_exp | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/modules/upstart_service.py | python | start | (name) | return not __salt__["cmd.retcode"](cmd, python_shell=False) | Start the specified service
CLI Example:
.. code-block:: bash
salt '*' service.start <service name> | Start the specified service | [
"Start",
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"specified",
"service"
] | def start(name):
"""
Start the specified service
CLI Example:
.. code-block:: bash
salt '*' service.start <service name>
"""
cmd = ["service", name, "start"]
return not __salt__["cmd.retcode"](cmd, python_shell=False) | [
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los-cocos/cocos | 3b47281f95d6ee52bb2a357a767f213e670bd601 | cocos/audio/pygame/base.py | python | get_sdl_version | () | return v.major, v.minor, v.patch | Get the version of the linked SDL runtime.
:rtype: int, int, int
:return: major, minor, patch | Get the version of the linked SDL runtime. | [
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] | def get_sdl_version():
"""Get the version of the linked SDL runtime.
:rtype: int, int, int
:return: major, minor, patch
"""
v = SDL.SDL_Linked_Version()
return v.major, v.minor, v.patch | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/OpenSSL/crypto.py | python | PKey.generate_key | (self, type, bits) | Generate a key pair of the given type, with the given number of bits.
This generates a key "into" the this object.
:param type: The key type.
:type type: :py:data:`TYPE_RSA` or :py:data:`TYPE_DSA`
:param bits: The number of bits.
:type bits: :py:data:`int` ``>= 0``
:raises TypeError: If :py:data:`type` or :py:data:`bits` isn't
of the appropriate type.
:raises ValueError: If the number of bits isn't an integer of
the appropriate size.
:return: ``None`` | Generate a key pair of the given type, with the given number of bits. | [
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] | def generate_key(self, type, bits):
"""
Generate a key pair of the given type, with the given number of bits.
This generates a key "into" the this object.
:param type: The key type.
:type type: :py:data:`TYPE_RSA` or :py:data:`TYPE_DSA`
:param bits: The number of bits.
:type bits: :py:data:`int` ``>= 0``
:raises TypeError: If :py:data:`type` or :py:data:`bits` isn't
of the appropriate type.
:raises ValueError: If the number of bits isn't an integer of
the appropriate size.
:return: ``None``
"""
if not isinstance(type, int):
raise TypeError("type must be an integer")
if not isinstance(bits, int):
raise TypeError("bits must be an integer")
if type == TYPE_RSA:
if bits <= 0:
raise ValueError("Invalid number of bits")
# TODO Check error return
exponent = _lib.BN_new()
exponent = _ffi.gc(exponent, _lib.BN_free)
_lib.BN_set_word(exponent, _lib.RSA_F4)
rsa = _lib.RSA_new()
result = _lib.RSA_generate_key_ex(rsa, bits, exponent, _ffi.NULL)
_openssl_assert(result == 1)
result = _lib.EVP_PKEY_assign_RSA(self._pkey, rsa)
_openssl_assert(result == 1)
elif type == TYPE_DSA:
dsa = _lib.DSA_new()
_openssl_assert(dsa != _ffi.NULL)
dsa = _ffi.gc(dsa, _lib.DSA_free)
res = _lib.DSA_generate_parameters_ex(
dsa, bits, _ffi.NULL, 0, _ffi.NULL, _ffi.NULL, _ffi.NULL
)
_openssl_assert(res == 1)
_openssl_assert(_lib.DSA_generate_key(dsa) == 1)
_openssl_assert(_lib.EVP_PKEY_set1_DSA(self._pkey, dsa) == 1)
else:
raise Error("No such key type")
self._initialized = True | [
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etetoolkit/ete | 2b207357dc2a40ccad7bfd8f54964472c72e4726 | ete3/phylomedb/phylomeDB3.py | python | PhylomeDB3Connector.get_phylomes_for_seed_ids | (self, ids) | return phylomes | Given a list of phylomeDB IDs, return in which phylomes these IDs have
been used as a seed | Given a list of phylomeDB IDs, return in which phylomes these IDs have
been used as a seed | [
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] | def get_phylomes_for_seed_ids(self, ids):
""" Given a list of phylomeDB IDs, return in which phylomes these IDs have
been used as a seed
"""
## Check if the input parameter is well-constructed
if not self.__check_input_parameter__(list_id = ids):
raise NameError("get_phylomes_for_seed_ids: Check your input data")
## Get all phylomes where the input phylome IDs have been used as a seed
cmd = 'SELECT CONCAT("Phy", t.protid, "_", code) AS protid, t.phylome_id,'
cmd += ' ph.name FROM %s AS t, %s AS ph, ' % (self._trees, self._phylomes)
cmd += 'species AS s WHERE (t.protid IN (%s) ' % (self.__parser_ids__(ids))
cmd += 'AND t.phylome_id = ph.phylome_id AND ph.seed_taxid = s.taxid) '
cmd += 'GROUP BY t.protid, ph.phylome_id'
phylomes = {}
if self.__execute__(cmd):
for r in self._SQL.fetchall():
phylomes.setdefault(r["protid"], []).append((r["phylome_id"],r["name"]))
return phylomes | [
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NTMC-Community/MatchZoo-py | 0e5c04e1e948aa9277abd5c85ff99d9950d8527f | matchzoo/modules/attention.py | python | BidirectionalAttention.__init__ | (self) | Init. | Init. | [
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"""Init."""
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dmlc/dgl | 8d14a739bc9e446d6c92ef83eafe5782398118de | examples/pytorch/gas/dataloader.py | python | GASDataset.__len__ | (self) | return len(self.graph) | r"""Number of data examples
Return
-------
int | r"""Number of data examples
Return
-------
int | [
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r"""Number of data examples
Return
-------
int
"""
return len(self.graph) | [
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PaddlePaddle/PaddleDetection | 635e3e0a80f3d05751cdcfca8af04ee17c601a92 | ppdet/metrics/coco_utils.py | python | json_eval_results | (metric, json_directory, dataset) | cocoapi eval with already exists proposal.json, bbox.json or mask.json | cocoapi eval with already exists proposal.json, bbox.json or mask.json | [
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] | def json_eval_results(metric, json_directory, dataset):
"""
cocoapi eval with already exists proposal.json, bbox.json or mask.json
"""
assert metric == 'COCO'
anno_file = dataset.get_anno()
json_file_list = ['proposal.json', 'bbox.json', 'mask.json']
if json_directory:
assert os.path.exists(
json_directory), "The json directory:{} does not exist".format(
json_directory)
for k, v in enumerate(json_file_list):
json_file_list[k] = os.path.join(str(json_directory), v)
coco_eval_style = ['proposal', 'bbox', 'segm']
for i, v_json in enumerate(json_file_list):
if os.path.exists(v_json):
cocoapi_eval(v_json, coco_eval_style[i], anno_file=anno_file)
else:
logger.info("{} not exists!".format(v_json)) | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/sched.py | python | scheduler.enter | (self, delay, priority, action, argument) | return self.enterabs(time, priority, action, argument) | A variant that specifies the time as a relative time.
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"""A variant that specifies the time as a relative time.
This is actually the more commonly used interface.
"""
time = self.timefunc() + delay
return self.enterabs(time, priority, action, argument) | [
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jdf/processing.py | 76e48ac855fd34169a7576a5cbc396bda698e781 | mode/formatter/autopep8.py | python | detect_encoding | (filename) | return 'utf-8' | Return file encoding. | Return file encoding. | [
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"""Return file encoding."""
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constverum/ProxyBroker | d21aae8575fc3a95493233ecfd2c7cf47b36b069 | proxybroker/providers.py | python | Spys_ru._pipe | (self) | [] | async def _pipe(self):
expSession = r"'([a-z0-9]{32})'"
url = 'http://spys.one/proxies/'
page = await self.get(url)
sessionId = re.findall(expSession, page)[0]
data = {
'xf0': sessionId, # session id
'xpp': 3, # 3 - 200 proxies on page
'xf1': None,
} # 1 = ANM & HIA; 3 = ANM; 4 = HIA
method = 'POST'
urls = [
{'url': url, 'data': {**data, 'xf1': lvl}, 'method': method}
for lvl in [3, 4]
]
await self._find_on_pages(urls) | [
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pimutils/todoman | 6460032da527cd6885621b882d9f37f653412feb | todoman/cli.py | python | list | (ctx, *args, **kwargs) | List tasks (default). Filters any completed or cancelled tasks by default.
If no arguments are provided, all lists will be displayed, and only
incomplete tasks are show. Otherwise, only todos for the specified list
will be displayed.
eg:
\b
- `todo list' shows all unfinished tasks from all lists.
- `todo list work' shows all unfinished tasks from the list `work`.
This is the default action when running `todo'.
The following commands can further filter shown todos, or include those
omited by default: | List tasks (default). Filters any completed or cancelled tasks by default. | [
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] | def list(ctx, *args, **kwargs):
"""
List tasks (default). Filters any completed or cancelled tasks by default.
If no arguments are provided, all lists will be displayed, and only
incomplete tasks are show. Otherwise, only todos for the specified list
will be displayed.
eg:
\b
- `todo list' shows all unfinished tasks from all lists.
- `todo list work' shows all unfinished tasks from the list `work`.
This is the default action when running `todo'.
The following commands can further filter shown todos, or include those
omited by default:
"""
hide_list = (len([_ for _ in ctx.db.lists()]) == 1) or ( # noqa: C416
len(kwargs["lists"]) == 1
)
todos = ctx.db.todos(**kwargs)
click.echo(ctx.formatter.compact_multiple(todos, hide_list)) | [
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tlsnotary/tlsnotary | be3346ca8e754c7f1d027c33b183f7e799d2b0b0 | src/auditee/python/slowaes/slowaes.py | python | AES.rotate | (self, word) | return word[1:] + word[:1] | Rijndael's key schedule rotate operation.
Rotate a word eight bits to the left: eg, rotate(1d2c3a4f) == 2c3a4f1d
Word is an char list of size 4 (32 bits overall). | Rijndael's key schedule rotate operation. | [
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] | def rotate(self, word):
""" Rijndael's key schedule rotate operation.
Rotate a word eight bits to the left: eg, rotate(1d2c3a4f) == 2c3a4f1d
Word is an char list of size 4 (32 bits overall).
"""
return word[1:] + word[:1] | [
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scikit-fuzzy/scikit-fuzzy | 92ad3c382ac19707086204ac6cdf6e81353345a7 | docs/ext/plot2rst.py | python | Path.pjoin | (self, *args) | return self.__class__(os.path.join(self, *args)) | Join paths. `p` prefix prevents confusion with string method. | Join paths. `p` prefix prevents confusion with string method. | [
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"""Join paths. `p` prefix prevents confusion with string method."""
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fake-name/ReadableWebProxy | ed5c7abe38706acc2684a1e6cd80242a03c5f010 | WebMirror/management/rss_parser_funcs/feed_parse_extractYamtl.py | python | extractYamtl | (item) | return False | 'yamtl' | 'yamtl' | [
"yamtl"
] | def extractYamtl(item):
"""
'yamtl'
"""
vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title'])
if not (chp or vol or frag) or 'preview' in item['title'].lower():
return None
return False | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/tablib-0.12.1/tablib/packages/dbfpy3/dbf.py | python | Dbf.append | (self, record) | Append ``record`` to the database. | Append ``record`` to the database. | [
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"""Append ``record`` to the database."""
record.index = self.header.recordCount
record._write()
self.header.recordCount += 1
self._changed = True
self._new = False | [
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Trusted-AI/adversarial-robustness-toolbox | 9fabffdbb92947efa1ecc5d825d634d30dfbaf29 | art/defences/detector/poison/spectral_signature_defense.py | python | SpectralSignatureDefense.detect_poison | (self, **kwargs) | return report, is_clean_lst | Returns poison detected and a report.
:return: (report, is_clean_lst):
where a report is a dictionary containing the index as keys the outlier score of suspected poisons as
values where is_clean is a list, where is_clean_lst[i]=1 means that x_train[i] there is clean and
is_clean_lst[i]=0, means that x_train[i] was classified as poison. | Returns poison detected and a report. | [
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] | def detect_poison(self, **kwargs) -> Tuple[dict, List[int]]:
"""
Returns poison detected and a report.
:return: (report, is_clean_lst):
where a report is a dictionary containing the index as keys the outlier score of suspected poisons as
values where is_clean is a list, where is_clean_lst[i]=1 means that x_train[i] there is clean and
is_clean_lst[i]=0, means that x_train[i] was classified as poison.
"""
self.set_params(**kwargs)
if self.classifier.layer_names is not None:
nb_layers = len(self.classifier.layer_names)
else:
raise ValueError("No layer names identified.")
features_x_poisoned = self.classifier.get_activations(
self.x_train, layer=nb_layers - 1, batch_size=self.batch_size
)
features_split = segment_by_class(features_x_poisoned, self.y_train, self.classifier.nb_classes)
score_by_class = []
keep_by_class = []
for idx, feature in enumerate(features_split):
# Check for empty list
if len(feature): # pylint: disable=C1801
score = SpectralSignatureDefense.spectral_signature_scores(np.vstack(feature))
score_cutoff = np.quantile(score, max(1 - self.eps_multiplier * self.expected_pp_poison, 0.0))
score_by_class.append(score)
keep_by_class.append(score < score_cutoff)
else:
score_by_class.append([0])
keep_by_class.append([True])
base_indices_by_class = segment_by_class(
np.arange(self.y_train.shape[0]),
self.y_train,
self.classifier.nb_classes,
)
is_clean_lst = [0] * self.y_train.shape[0]
report = {}
for keep_booleans, all_scores, indices in zip(keep_by_class, score_by_class, base_indices_by_class):
for keep_boolean, all_score, idx in zip(keep_booleans, all_scores, indices):
if keep_boolean:
is_clean_lst[idx] = 1
else:
report[idx] = all_score[0]
return report, is_clean_lst | [
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/sysconfig.py | python | _init_posix | (vars) | Initialize the module as appropriate for POSIX systems. | Initialize the module as appropriate for POSIX systems. | [
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] | def _init_posix(vars):
"""Initialize the module as appropriate for POSIX systems."""
# load the installed Makefile:
makefile = _get_makefile_filename()
try:
_parse_makefile(makefile, vars)
except IOError, e:
msg = "invalid Python installation: unable to open %s" % makefile
if hasattr(e, "strerror"):
msg = msg + " (%s)" % e.strerror
raise IOError(msg)
# load the installed pyconfig.h:
config_h = get_config_h_filename()
try:
with open(config_h) as f:
parse_config_h(f, vars)
except IOError, e:
msg = "invalid Python installation: unable to open %s" % config_h
if hasattr(e, "strerror"):
msg = msg + " (%s)" % e.strerror
raise IOError(msg)
# On AIX, there are wrong paths to the linker scripts in the Makefile
# -- these paths are relative to the Python source, but when installed
# the scripts are in another directory.
if _PYTHON_BUILD:
vars['LDSHARED'] = vars['BLDSHARED'] | [
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CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idaapi.py | python | struc_t.is_varstr | (self, *args) | return _idaapi.struc_t_is_varstr(self, *args) | is_varstr(self) -> bool | is_varstr(self) -> bool | [
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"""
is_varstr(self) -> bool
"""
return _idaapi.struc_t_is_varstr(self, *args) | [
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facebookresearch/pytorch3d | fddd6a700fa9685c1ce2d4b266c111d7db424ecc | pytorch3d/renderer/mesh/textures.py | python | TexturesAtlas.join_batch | (self, textures: List["TexturesAtlas"]) | return new_tex | Join the list of textures given by `textures` to
self to create a batch of textures. Return a new
TexturesAtlas object with the combined textures.
Args:
textures: List of TexturesAtlas objects
Returns:
new_tex: TexturesAtlas object with the combined
textures from self and the list `textures`. | Join the list of textures given by `textures` to
self to create a batch of textures. Return a new
TexturesAtlas object with the combined textures. | [
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"""
Join the list of textures given by `textures` to
self to create a batch of textures. Return a new
TexturesAtlas object with the combined textures.
Args:
textures: List of TexturesAtlas objects
Returns:
new_tex: TexturesAtlas object with the combined
textures from self and the list `textures`.
"""
tex_types_same = all(isinstance(tex, TexturesAtlas) for tex in textures)
if not tex_types_same:
raise ValueError("All textures must be of type TexturesAtlas.")
atlas_list = []
atlas_list += self.atlas_list()
num_faces_per_mesh = self._num_faces_per_mesh
for tex in textures:
atlas_list += tex.atlas_list()
num_faces_per_mesh += tex._num_faces_per_mesh
new_tex = self.__class__(atlas=atlas_list)
new_tex._num_faces_per_mesh = num_faces_per_mesh
return new_tex | [
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rotki/rotki | aafa446815cdd5e9477436d1b02bee7d01b398c8 | rotkehlchen/chain/ethereum/modules/makerdao/vaults.py | python | MakerdaoVaults.reset_last_query_ts | (self) | Reset the last query timestamps, effectively cleaning the caches | Reset the last query timestamps, effectively cleaning the caches | [
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"""Reset the last query timestamps, effectively cleaning the caches"""
super().reset_last_query_ts()
self.last_vault_mapping_query_ts = 0
self.last_vault_details_query_ts = 0 | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/userreports/reports/specs.py | python | AggregateDateColumn.get_format_fn | (self) | return _format | [] | def get_format_fn(self):
def _format(data):
if not data.get('year', None) or not data.get('month', None):
return _('Unknown Date')
format_ = self.format or '%Y-%m'
return date(year=int(data['year']), month=int(data['month']), day=1).strftime(format_)
return _format | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_vendored_deps/library/oc_adm_registry.py | python | OpenShiftCLI.openshift_cmd | (self, cmd, oadm=False, output=False, output_type='json', input_data=None) | return rval | Base command for oc | Base command for oc | [
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] | def openshift_cmd(self, cmd, oadm=False, output=False, output_type='json', input_data=None):
'''Base command for oc '''
cmds = [self.oc_binary]
if oadm:
cmds.append('adm')
cmds.extend(cmd)
if self.all_namespaces:
cmds.extend(['--all-namespaces'])
elif self.namespace is not None and self.namespace.lower() not in ['none', 'emtpy']: # E501
cmds.extend(['-n', self.namespace])
if self.verbose:
print(' '.join(cmds))
try:
returncode, stdout, stderr = self._run(cmds, input_data)
except OSError as ex:
returncode, stdout, stderr = 1, '', 'Failed to execute {}: {}'.format(subprocess.list2cmdline(cmds), ex)
rval = {"returncode": returncode,
"cmd": ' '.join(cmds)}
if output_type == 'json':
rval['results'] = {}
if output and stdout:
try:
rval['results'] = json.loads(stdout)
except ValueError as verr:
if "No JSON object could be decoded" in verr.args:
rval['err'] = verr.args
elif output_type == 'raw':
rval['results'] = stdout if output else ''
if self.verbose:
print("STDOUT: {0}".format(stdout))
print("STDERR: {0}".format(stderr))
if 'err' in rval or returncode != 0:
rval.update({"stderr": stderr,
"stdout": stdout})
return rval | [
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metaodi/osmapi | 1559827bc77889cc67ed3a2b45cb373cfaa293f5 | osmapi/OsmApi.py | python | OsmApi.NoteCreate | (self, NoteData) | return self._NoteAction(uri) | Creates a note.
Returns updated NoteData (without timestamp). | Creates a note. | [
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] | def NoteCreate(self, NoteData):
"""
Creates a note.
Returns updated NoteData (without timestamp).
"""
uri = "/api/0.6/notes"
uri += "?" + urllib.parse.urlencode(NoteData)
return self._NoteAction(uri) | [
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dagster-io/dagster | b27d569d5fcf1072543533a0c763815d96f90b8f | python_modules/libraries/dagster-aws/dagster_aws/ecs/tasks.py | python | default_ecs_task_metadata | (ec2, ecs) | return TaskMetadata(
cluster=cluster,
subnets=subnets,
security_groups=security_groups,
task_definition=task_definition,
container_definition=container_definition,
assign_public_ip="ENABLED" if public_ip else "DISABLED",
) | ECS injects an environment variable into each Fargate task. The value
of this environment variable is a url that can be queried to introspect
information about the current processes's running task:
https://docs.aws.amazon.com/AmazonECS/latest/userguide/task-metadata-endpoint-v4-fargate.html | ECS injects an environment variable into each Fargate task. The value
of this environment variable is a url that can be queried to introspect
information about the current processes's running task: | [
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"""
ECS injects an environment variable into each Fargate task. The value
of this environment variable is a url that can be queried to introspect
information about the current processes's running task:
https://docs.aws.amazon.com/AmazonECS/latest/userguide/task-metadata-endpoint-v4-fargate.html
"""
container_metadata_uri = os.environ.get("ECS_CONTAINER_METADATA_URI_V4")
name = requests.get(container_metadata_uri).json()["Name"]
task_metadata_uri = container_metadata_uri + "/task"
response = requests.get(task_metadata_uri).json()
cluster = response.get("Cluster")
task_arn = response.get("TaskARN")
def describe_task_or_raise(task_arn, cluster):
try:
return ecs.describe_tasks(tasks=[task_arn], cluster=cluster)["tasks"][0]
except IndexError:
raise EcsNoTasksFound
try:
task = backoff(
describe_task_or_raise,
retry_on=(EcsNoTasksFound,),
kwargs={"task_arn": task_arn, "cluster": cluster},
max_retries=BACKOFF_RETRIES,
)
except EcsNoTasksFound:
raise EcsEventualConsistencyTimeout
enis = []
subnets = []
for attachment in task["attachments"]:
if attachment["type"] == "ElasticNetworkInterface":
for detail in attachment["details"]:
if detail["name"] == "subnetId":
subnets.append(detail["value"])
if detail["name"] == "networkInterfaceId":
enis.append(ec2.NetworkInterface(detail["value"]))
public_ip = False
security_groups = []
for eni in enis:
if (eni.association_attribute or {}).get("PublicIp"):
public_ip = True
for group in eni.groups:
security_groups.append(group["GroupId"])
task_definition_arn = task["taskDefinitionArn"]
task_definition = ecs.describe_task_definition(taskDefinition=task_definition_arn)[
"taskDefinition"
]
container_definition = next(
iter(
[
container
for container in task_definition["containerDefinitions"]
if container["name"] == name
]
)
)
return TaskMetadata(
cluster=cluster,
subnets=subnets,
security_groups=security_groups,
task_definition=task_definition,
container_definition=container_definition,
assign_public_ip="ENABLED" if public_ip else "DISABLED",
) | [
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facebookresearch/ParlAI | e4d59c30eef44f1f67105961b82a83fd28d7d78b | parlai/agents/rag/indexers.py | python | indexer_factory | (opt: Opt) | return indexer | Build indexer.
:param opt:
Options
:return indexer:
return build indexer, according to options | Build indexer. | [
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"""
Build indexer.
:param opt:
Options
:return indexer:
return build indexer, according to options
"""
if opt['indexer_type'] == 'compressed':
if opt['path_to_index'] == WIKIPEDIA_EXACT_INDEX:
logging.warning(
f'Changing index path to compressed index: {WIKIPEDIA_COMPRESSED_INDEX}'
)
opt['path_to_index'] = modelzoo_path(
opt['datapath'], WIKIPEDIA_COMPRESSED_INDEX
)
indexer = CompressedIndexer(opt)
elif opt['indexer_type'] == 'exact':
if opt['path_to_index'] == WIKIPEDIA_COMPRESSED_INDEX:
logging.warning(
f'Changing index path to exact index: {WIKIPEDIA_EXACT_INDEX}'
)
opt['path_to_index'] = modelzoo_path(opt['datapath'], WIKIPEDIA_EXACT_INDEX)
indexer = DenseHNSWFlatIndexer(opt)
else:
raise ValueError(f"Unsupported indexer type: {opt['indexer_type']}")
return indexer | [
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JetBrains/python-skeletons | 95ad24b666e475998e5d1cc02ed53a2188036167 | numpy/core/__init__.py | python | intc.__rlshift__ | (self, *args, **kwargs) | Return value<<self. | Return value<<self. | [
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pass | [
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hydroshare/hydroshare | 7ba563b55412f283047fb3ef6da367d41dec58c6 | hs_geo_raster_resource/models.py | python | RasterResource.can_have_multiple_files | (cls) | return False | [] | def can_have_multiple_files(cls):
# can have only 1 file
return False | [
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l11x0m7/Question_Answering_Models | b53c33db08a51f8e5f8c774eb65ec29c75942c66 | cQA/qacnn/models.py | python | QACNN.add_loss_op | (self, q_ap_cosine, q_am_cosine) | return total_loss, loss, accu | 损失节点 | 损失节点 | [
"损失节点"
] | def add_loss_op(self, q_ap_cosine, q_am_cosine):
"""
损失节点
"""
original_loss = self.config.m - q_ap_cosine + q_am_cosine
l = tf.maximum(tf.zeros_like(original_loss), original_loss)
loss = tf.reduce_sum(l)
tf.add_to_collection('total_loss', loss)
total_loss = tf.add_n(tf.get_collection('total_loss'))
accu = tf.reduce_mean(tf.cast(tf.equal(0., l), tf.float32))
return total_loss, loss, accu | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_volume_attachment_status.py | python | V1VolumeAttachmentStatus.attach_error | (self) | return self._attach_error | Gets the attach_error of this V1VolumeAttachmentStatus. # noqa: E501
:return: The attach_error of this V1VolumeAttachmentStatus. # noqa: E501
:rtype: V1VolumeError | Gets the attach_error of this V1VolumeAttachmentStatus. # noqa: E501 | [
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"""Gets the attach_error of this V1VolumeAttachmentStatus. # noqa: E501
:return: The attach_error of this V1VolumeAttachmentStatus. # noqa: E501
:rtype: V1VolumeError
"""
return self._attach_error | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/PIL/ImageOps.py | python | deform | (image, deformer, resample=Image.BILINEAR) | return image.transform(
image.size, Image.MESH, deformer.getmesh(image), resample
) | Deform image using the given deformer | Deform image using the given deformer | [
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"the",
"given",
"deformer"
] | def deform(image, deformer, resample=Image.BILINEAR):
"Deform image using the given deformer"
return image.transform(
image.size, Image.MESH, deformer.getmesh(image), resample
) | [
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LinOTP/LinOTP | bb3940bbaccea99550e6c063ff824f258dd6d6d7 | linotp/lib/user.py | python | getUserDetail | (user) | return userinfo | Returns userinfo of an user
:param user: the user
:returns: the userinfo dict | Returns userinfo of an user | [
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"""
Returns userinfo of an user
:param user: the user
:returns: the userinfo dict
"""
(uid, resId, resClass) = getUserId(user)
log.debug("got uid %r, ResId %r, Class %r", uid, resId, resClass)
userinfo = getUserInfo(uid, resId, resClass)
return userinfo | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/matplotlib-3.0.3-py3.7-macosx-10.9-x86_64.egg/matplotlib/cbook/__init__.py | python | _string_to_bool | (s) | Parses the string argument as a boolean | Parses the string argument as a boolean | [
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"""Parses the string argument as a boolean"""
if not isinstance(s, str):
return bool(s)
warn_deprecated("2.2", "Passing one of 'on', 'true', 'off', 'false' as a "
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if s.lower() in ['on', 'true']:
return True
if s.lower() in ['off', 'false']:
return False
raise ValueError('String "%s" must be one of: '
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-linux/x64/PIL/Image.py | python | Image.paste | (self, im, box=None, mask=None) | Pastes another image into this image. The box argument is either
a 2-tuple giving the upper left corner, a 4-tuple defining the
left, upper, right, and lower pixel coordinate, or None (same as
(0, 0)). See :ref:`coordinate-system`. If a 4-tuple is given, the size
of the pasted image must match the size of the region.
If the modes don't match, the pasted image is converted to the mode of
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Instead of an image, the source can be a integer or tuple
containing pixel values. The method then fills the region
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If a mask is given, this method updates only the regions
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images (in the latter case, the alpha band is used as mask).
Where the mask is 255, the given image is copied as is. Where
the mask is 0, the current value is preserved. Intermediate
values will mix the two images together, including their alpha
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See :py:meth:`~PIL.Image.Image.alpha_composite` if you want to
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:param im: Source image or pixel value (integer or tuple).
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If a 2-tuple is used instead, it's treated as the upper left
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upper left corner.
If an image is given as the second argument and there is no
third, the box defaults to (0, 0), and the second argument
is interpreted as a mask image.
:param mask: An optional mask image. | Pastes another image into this image. The box argument is either
a 2-tuple giving the upper left corner, a 4-tuple defining the
left, upper, right, and lower pixel coordinate, or None (same as
(0, 0)). See :ref:`coordinate-system`. If a 4-tuple is given, the size
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Pastes another image into this image. The box argument is either
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left, upper, right, and lower pixel coordinate, or None (same as
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"""
if isImageType(box) and mask is None:
# abbreviated paste(im, mask) syntax
mask = box
box = None
if box is None:
box = (0, 0)
if len(box) == 2:
# upper left corner given; get size from image or mask
if isImageType(im):
size = im.size
elif isImageType(mask):
size = mask.size
else:
# FIXME: use self.size here?
raise ValueError("cannot determine region size; use 4-item box")
box += (box[0] + size[0], box[1] + size[1])
if isinstance(im, str):
from . import ImageColor
im = ImageColor.getcolor(im, self.mode)
elif isImageType(im):
im.load()
if self.mode != im.mode:
if self.mode != "RGB" or im.mode not in ("RGBA", "RGBa"):
# should use an adapter for this!
im = im.convert(self.mode)
im = im.im
self._ensure_mutable()
if mask:
mask.load()
self.im.paste(im, box, mask.im)
else:
self.im.paste(im, box) | [
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JaniceWuo/MovieRecommend | 4c86db64ca45598917d304f535413df3bc9fea65 | movierecommend/venv1/Lib/site-packages/django/db/migrations/operations/models.py | python | AlterUniqueTogether.__init__ | (self, name, unique_together) | [] | def __init__(self, name, unique_together):
unique_together = normalize_together(unique_together)
self.unique_together = set(tuple(cons) for cons in unique_together)
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/sklearn/mixture/base.py | python | _check_X | (X, n_components=None, n_features=None) | return X | Check the input data X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
n_components : int
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-------
X : array, shape (n_samples, n_features) | Check the input data X. | [
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X : array-like, shape (n_samples, n_features)
n_components : int
Returns
-------
X : array, shape (n_samples, n_features)
"""
X = check_array(X, dtype=[np.float64, np.float32])
if n_components is not None and X.shape[0] < n_components:
raise ValueError('Expected n_samples >= n_components '
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% (n_components, X.shape[0]))
if n_features is not None and X.shape[1] != n_features:
raise ValueError("Expected the input data X have %d features, "
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ctxis/canape | 5f0e03424577296bcc60c2008a60a98ec5307e4b | CANAPE.Scripting/Lib/lib2to3/fixer_util.py | python | in_special_context | (node) | return False | Returns true if node is in an environment where all that is required
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See test_map_nochange in test_fixers.py for some examples and tests. | Returns true if node is in an environment where all that is required
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... | https://github.com/ctxis/canape/blob/5f0e03424577296bcc60c2008a60a98ec5307e4b/CANAPE.Scripting/Lib/lib2to3/fixer_util.py#L208-L225 | |
NeuromorphicProcessorProject/snn_toolbox | a85ada7b5d060500703285ef8a68f06ea1ffda65 | snntoolbox/parsing/utils.py | python | AbstractModelParser.evaluate | (self, batch_size, num_to_test, x_test=None, y_test=None,
dataflow=None) | return score | Evaluate parsed Keras model.
Can use either numpy arrays ``x_test, y_test`` containing the test
samples, or generate them with a dataflow
(``keras.ImageDataGenerator.flow_from_directory`` object).
Parameters
----------
batch_size: int
Batch size
num_to_test: int
Number of samples to test
x_test: Optional[np.ndarray]
y_test: Optional[np.ndarray]
dataflow: keras.ImageDataGenerator.flow_from_directory | Evaluate parsed Keras model. | [
"Evaluate",
"parsed",
"Keras",
"model",
"."
] | def evaluate(self, batch_size, num_to_test, x_test=None, y_test=None,
dataflow=None):
"""Evaluate parsed Keras model.
Can use either numpy arrays ``x_test, y_test`` containing the test
samples, or generate them with a dataflow
(``keras.ImageDataGenerator.flow_from_directory`` object).
Parameters
----------
batch_size: int
Batch size
num_to_test: int
Number of samples to test
x_test: Optional[np.ndarray]
y_test: Optional[np.ndarray]
dataflow: keras.ImageDataGenerator.flow_from_directory
"""
assert (x_test is not None and y_test is not None or dataflow is not
None), "No testsamples provided."
if x_test is not None:
score = self.parsed_model.evaluate(x_test, y_test, batch_size,
verbose=0)
else:
steps = int(num_to_test / batch_size)
score = self.parsed_model.evaluate(dataflow, steps=steps)
print("Top-1 accuracy: {:.2%}".format(score[1]))
print("Top-5 accuracy: {:.2%}\n".format(score[2]))
return score | [
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googleapis/python-ndb | e780c81cde1016651afbfcad8180d9912722cf1b | google/cloud/ndb/_cache.py | python | _handle_transient_errors | (read=False) | return wrap | Decorator for global_XXX functions for handling transient errors.
Will log as warning or reraise transient errors according to `strict_read` and
`strict_write` attributes of the global cache and whether the operation is a read or
a write.
If in strict mode, will retry the wrapped function up to 5 times before reraising
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] | def _handle_transient_errors(read=False):
"""Decorator for global_XXX functions for handling transient errors.
Will log as warning or reraise transient errors according to `strict_read` and
`strict_write` attributes of the global cache and whether the operation is a read or
a write.
If in strict mode, will retry the wrapped function up to 5 times before reraising
the transient error.
"""
def wrap(wrapped):
def retry(wrapped, transient_errors):
@functools.wraps(wrapped)
@tasklets.tasklet
def retry_wrapper(key, *args, **kwargs):
sleep_generator = core_retry.exponential_sleep_generator(0.1, 1)
attempts = 5
for sleep_time in sleep_generator: # pragma: NO BRANCH
# pragma is required because loop never exits normally, it only gets
# raised out of.
attempts -= 1
try:
result = yield wrapped(key, *args, **kwargs)
raise tasklets.Return(result)
except transient_errors:
if not attempts:
raise
yield tasklets.sleep(sleep_time)
return retry_wrapper
@functools.wraps(wrapped)
@tasklets.tasklet
def wrapper(key, *args, **kwargs):
cache = _global_cache()
is_read = read
if not is_read:
is_read = kwargs.get("read", False)
strict = cache.strict_read if is_read else cache.strict_write
if strict:
function = retry(wrapped, cache.transient_errors)
else:
function = wrapped
try:
result = yield function(key, *args, **kwargs)
raise tasklets.Return(result)
except cache.transient_errors as error:
if strict:
raise
if not getattr(error, "_ndb_warning_logged", False):
# Same exception will be sent to every future in the batch. Only
# need to log one warning, though.
warnings.warn(
"Error connecting to global cache: {}".format(error),
RuntimeWarning,
)
error._ndb_warning_logged = True
raise tasklets.Return(None)
return wrapper
return wrap | [
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inkandswitch/livebook | 93c8d467734787366ad084fc3566bf5cbe249c51 | public/pypyjs/modules/zipfile.py | python | ZipFile.printdir | (self) | Print a table of contents for the zip file. | Print a table of contents for the zip file. | [
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"""Print a table of contents for the zip file."""
print "%-46s %19s %12s" % ("File Name", "Modified ", "Size")
for zinfo in self.filelist:
date = "%d-%02d-%02d %02d:%02d:%02d" % zinfo.date_time[:6]
print "%-46s %s %12d" % (zinfo.filename, date, zinfo.file_size) | [
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faucamp/python-gsmmodem | 834c68b1387ca2c91e2210faa8f75526b39723b5 | gsmmodem/pdu.py | python | _decodeTimestamp | (byteIter) | return datetime.strptime(dateStr[:-2], '%y%m%d%H%M%S').replace(tzinfo=SmsPduTzInfo(timeZoneStr)) | Decodes a 7-octet timestamp | Decodes a 7-octet timestamp | [
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""" Decodes a 7-octet timestamp """
dateStr = decodeSemiOctets(byteIter, 7)
timeZoneStr = dateStr[-2:]
return datetime.strptime(dateStr[:-2], '%y%m%d%H%M%S').replace(tzinfo=SmsPduTzInfo(timeZoneStr)) | [
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galaxyproject/galaxy | 4c03520f05062e0f4a1b3655dc0b7452fda69943 | lib/galaxy/webapps/util.py | python | wrap_if_allowed_or_fail | (app, stack, wrap, name=None, args=None, kwargs=None) | return wrap(app, *args, **kwargs) | Wrap the application with the given method if the application stack allows for it.
Arguments are the same as for :func:`wrap_if_allowed`.
Raises py:class:`MiddlewareWrapUnsupported` if the stack does not allow the middleware. | Wrap the application with the given method if the application stack allows for it. | [
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"""
Wrap the application with the given method if the application stack allows for it.
Arguments are the same as for :func:`wrap_if_allowed`.
Raises py:class:`MiddlewareWrapUnsupported` if the stack does not allow the middleware.
"""
name = name or wrap.__name__
if not stack.allowed_middleware(wrap):
raise MiddlewareWrapUnsupported(
"'%s' is enabled in your configuration but the %s application stack does not support it, this "
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args = args or []
kwargs = kwargs or {}
log.debug("Enabling '%s' middleware", name)
return wrap(app, *args, **kwargs) | [
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xonsh/xonsh | b76d6f994f22a4078f602f8b386f4ec280c8461f | xonsh/imphooks.py | python | XonshImportHook.find_spec | (self, fullname, path, target=None) | return spec | Finds the spec for a xonsh module if it exists. | Finds the spec for a xonsh module if it exists. | [
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"""Finds the spec for a xonsh module if it exists."""
dot = "."
spec = None
path = sys.path if path is None else path
if dot not in fullname and dot not in path:
path = [dot] + path
name = fullname.rsplit(dot, 1)[-1]
fname = name + ".xsh"
for p in path:
if not isinstance(p, str):
continue
if not os.path.isdir(p) or not os.access(p, os.R_OK):
continue
if fname not in {x.name for x in os.scandir(p)}:
continue
spec = ModuleSpec(fullname, self)
self._filenames[fullname] = os.path.abspath(os.path.join(p, fname))
break
return spec | [
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Qidian213/deep_sort_yolov3 | df913275777ecaee19b4a99ea1889ac6063c0a4b | deep_sort/nn_matching.py | python | NearestNeighborDistanceMetric.distance | (self, features, targets) | return cost_matrix | Compute distance between features and targets.
Parameters
----------
features : ndarray
An NxM matrix of N features of dimensionality M.
targets : List[int]
A list of targets to match the given `features` against.
Returns
-------
ndarray
Returns a cost matrix of shape len(targets), len(features), where
element (i, j) contains the closest squared distance between
`targets[i]` and `features[j]`. | Compute distance between features and targets. | [
"Compute",
"distance",
"between",
"features",
"and",
"targets",
"."
] | def distance(self, features, targets):
"""Compute distance between features and targets.
Parameters
----------
features : ndarray
An NxM matrix of N features of dimensionality M.
targets : List[int]
A list of targets to match the given `features` against.
Returns
-------
ndarray
Returns a cost matrix of shape len(targets), len(features), where
element (i, j) contains the closest squared distance between
`targets[i]` and `features[j]`.
"""
cost_matrix = np.zeros((len(targets), len(features)))
for i, target in enumerate(targets):
cost_matrix[i, :] = self._metric(self.samples[target], features)
return cost_matrix | [
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caiiiac/Machine-Learning-with-Python | 1a26c4467da41ca4ebc3d5bd789ea942ef79422f | MachineLearning/venv/lib/python3.5/site-packages/pandas/core/dtypes/common.py | python | is_categorical_dtype | (arr_or_dtype) | return CategoricalDtype.is_dtype(arr_or_dtype) | Check whether an array-like or dtype is of the Categorical dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean : Whether or not the array-like or dtype is
of the Categorical dtype.
Examples
--------
>>> is_categorical_dtype(object)
False
>>> is_categorical_dtype(CategoricalDtype())
True
>>> is_categorical_dtype([1, 2, 3])
False
>>> is_categorical_dtype(pd.Categorical([1, 2, 3]))
True
>>> is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))
True | Check whether an array-like or dtype is of the Categorical dtype. | [
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] | def is_categorical_dtype(arr_or_dtype):
"""
Check whether an array-like or dtype is of the Categorical dtype.
Parameters
----------
arr_or_dtype : array-like
The array-like or dtype to check.
Returns
-------
boolean : Whether or not the array-like or dtype is
of the Categorical dtype.
Examples
--------
>>> is_categorical_dtype(object)
False
>>> is_categorical_dtype(CategoricalDtype())
True
>>> is_categorical_dtype([1, 2, 3])
False
>>> is_categorical_dtype(pd.Categorical([1, 2, 3]))
True
>>> is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))
True
"""
if arr_or_dtype is None:
return False
return CategoricalDtype.is_dtype(arr_or_dtype) | [
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PytLab/VASPy | 3650b3e07bcd722f9443ae1213ff4b8734c42ffa | vaspy/matstudio.py | python | XsdFile.get_name_info | (self) | 获取文件中能量，力等数据. | 获取文件中能量，力等数据. | [
"获取文件中能量，力等数据",
"."
] | def get_name_info(self):
"""
获取文件中能量，力等数据.
"""
# Get info string.
info = None
for elem in self.tree.iter("SymmetrySystem"):
info = elem.attrib.get('Name')
break
if info is None:
return
# Get thermo data.
fieldnames = ["energy", "force", "magnetism", "path"]
try:
for key, value in zip(fieldnames, info.split()):
if key != "path":
data = float(value.split(':')[-1].strip())
else:
data = value.split(":")[-1].strip()
setattr(self, key, data)
except:
# Set default values.
self.force, self.energy, self.magnetism = 0.0, 0.0, 0.0
msg = "No data info in Name property '{}'".format(info)
self.__logger.warning(msg)
finally:
self.path = getcwd() | [
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git-cola/git-cola | b48b8028e0c3baf47faf7b074b9773737358163d | cola/models/prefs.py | python | SetConfig.__init__ | (self, model, source, config, value) | [] | def __init__(self, model, source, config, value):
self.source = source
self.config = config
self.value = value
self.old_value = None
self.model = model | [
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mtianyan/FlaskMovie | 75158aa7bab6104cfb22ff4953fb5306dfe9cec9 | app/admin/views.py | python | user_del | (id=None) | return redirect(url_for('admin.user_list', page=from_page)) | 删除会员 | 删除会员 | [
"删除会员"
] | def user_del(id=None):
"""
删除会员
"""
# 因为删除当前页。假如是最后一页，这一页已经不见了。回不到。
from_page = int(request.args.get('fp')) - 1
# 此处考虑全删完了，没法前挪的情况，0被视为false
if not from_page:
from_page = 1
user = User.query.get_or_404(int(id))
db.session.delete(user)
db.session.commit()
flash("删除会员成功！", "ok")
return redirect(url_for('admin.user_list', page=from_page)) | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/twill/twill/other_packages/pyparsing.py | python | downcaseTokens | (s,l,t) | return map( str.lower, t ) | Helper parse action to convert tokens to lower case. | Helper parse action to convert tokens to lower case. | [
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] | def downcaseTokens(s,l,t):
"""Helper parse action to convert tokens to lower case."""
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r0x0r/pywebview | 7641414db75542958d1d1158b903576aafebd47c | webview/wsgi.py | python | StaticContentsApp.no_permissions | (self, environ, start_response) | return do_403(environ, start_response) | Handle if we can't open the file | Handle if we can't open the file | [
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"""
Handle if we can't open the file
"""
return do_403(environ, start_response) | [
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fluentpython/example-code-2e | 80f7f84274a47579e59c29a4657691525152c9d5 | 21-async/mojifinder/bottle.py | python | BaseRequest.method | (self) | return self.environ.get('REQUEST_METHOD', 'GET').upper() | The ``REQUEST_METHOD`` value as an uppercase string. | The ``REQUEST_METHOD`` value as an uppercase string. | [
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''' The ``REQUEST_METHOD`` value as an uppercase string. '''
return self.environ.get('REQUEST_METHOD', 'GET').upper() | [
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facebookresearch/pytext | 1a4e184b233856fcfb9997d74f167cbf5bbbfb8d | pytext/data/data_structures/annotation.py | python | Node.list_nonTerminals | (self) | return non_terminals | Returns all Intent and Slot nodes subordinate to this node | Returns all Intent and Slot nodes subordinate to this node | [
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Returns all Intent and Slot nodes subordinate to this node
"""
non_terminals = []
for child in self.children:
if type(child) != Root and type(child) != Token:
non_terminals.append(child)
non_terminals += child.list_nonTerminals()
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter8-Regression/venv/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/_vendor/distlib/metadata.py | python | LegacyMetadata.__init__ | (self, path=None, fileobj=None, mapping=None,
scheme='default') | [] | def __init__(self, path=None, fileobj=None, mapping=None,
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raise TypeError('path, fileobj and mapping are exclusive')
self._fields = {}
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dropbox/nautilus-dropbox | e5dd94738b096c1d7c131365e99b19c9daf0de29 | rst2man.py | python | Table.append_cell | (self, cell_lines) | cell_lines is an array of lines | cell_lines is an array of lines | [
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amphibian-dev/toad | 3d932169112e8474525262af15440af7f2cf8029 | toad/selection.py | python | drop_empty | (frame, threshold = 0.9, nan = None, return_drop = False,
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Args:
frame (DataFrame): dataframe that will be used
threshold (number): drop the features whose empty num is greater than threshold. if threshold is float, it will be use as percentage
nan (any): values will be look like empty
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frame (DataFrame): dataframe that will be used
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"""
cols = frame.columns.copy()
if exclude is not None:
cols = cols.drop(exclude)
if threshold < 1:
threshold = len(frame) * threshold
drop_list = []
for col in cols:
series = frame[col]
if nan is not None:
series = series.replace(nan, np.nan)
n = series.isnull().sum()
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r = frame.drop(columns = drop_list)
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aws-samples/aws-glue-samples | 13c21776350ca7996c086fb8a5bf6dfaf386054c | utilities/Hive_metastore_migration/src/hive_metastore_migration.py | python | append | (l, elem) | return l | Append list with element and return the list modified | Append list with element and return the list modified | [
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linuxscout/mishkal | 4f4ae0ebc2d6acbeb3de3f0303151ec7b54d2f76 | interfaces/web/lib/paste/wsgilib.py | python | catch_errors | (application, environ, start_response, error_callback,
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lbryio/torba | 190304344c0ff68f8a24cf50272307a11bf7f62b | torba/server/coins.py | python | Monoeci.header_hash | (cls, header) | return x11_hash.getPoWHash(header) | Given a header return the hash. | Given a header return the hash. | [
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PaddlePaddle/Research | 2da0bd6c72d60e9df403aff23a7802779561c4a1 | ST_DM/KDD2021-MSTPAC/code/ST-PAC/datasets/datasets_factory.py | python | DatasetsFactory.get_dataset | (cls, name) | return cls.datasets.get(name, None) | get class type with class name | get class type with class name | [
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numba/numba | bf480b9e0da858a65508c2b17759a72ee6a44c51 | numba/core/typeconv/castgraph.py | python | TypeGraph.safe | (self, a, b) | [] | def safe(self, a, b):
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kozec/sc-controller | ce92c773b8b26f6404882e9209aff212c4053170 | scc/lib/enum.py | python | EnumMeta.__bool__ | (cls) | return True | classes/types should always be True. | classes/types should always be True. | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/xml/dom/expatbuilder.py | python | ExpatBuilder.parseString | (self, string) | return doc | Parse a document from a string, returning the document node. | Parse a document from a string, returning the document node. | [
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"""Parse a document from a string, returning the document node."""
parser = self.getParser()
try:
parser.Parse(string, True)
self._setup_subset(string)
except ParseEscape:
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doc = self.document
self.reset()
self._parser = None
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joe42/CloudFusion | c4b94124e74a81e0634578c7754d62160081f7a1 | cloudfusion/third_party/requests_1_2_3/requests/cookies.py | python | RequestsCookieJar.update | (self, other) | Updates this jar with cookies from another CookieJar or dict-like | Updates this jar with cookies from another CookieJar or dict-like | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/hqcase/views.py | python | case_api | (request, domain, case_id=None) | return JsonResponse({'error': "Request method not allowed"}, status=405) | [] | def case_api(request, domain, case_id=None):
if request.method == 'GET' and case_id:
return _handle_individual_get(request, case_id)
if request.method == 'GET' and not case_id:
return _handle_list_view(request)
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HypothesisWorks/hypothesis | d1bfc4acc86899caa7a40f892322e1a69fbf36f4 | hypothesis-python/src/hypothesis/internal/charmap.py | python | as_general_categories | (cats, name="cats") | return tuple(c for c in cs if c in out) | Return a tuple of Unicode categories in a normalised order.
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>>> as_general_categories(['N'])
('Nd', 'Nl', 'No')
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cs = categories()
out = set(cats)
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sqall01/alertR | e1d1a83e54f876cc4cd7bd87387e05cb75d4dc13 | sensorClientTemplate/lib/globalData/baseObjects.py | python | LocalObject.deepcopy | (obj) | This function copies all attributes of the given object to a new object.
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/locations/forms.py | python | LocationForm.get_allowed_types | (domain, parent) | return list(LocationType.objects
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parent_type=parent_type)
.all()) | [] | def get_allowed_types(domain, parent):
parent_type = parent.location_type if parent else None
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INK-USC/KagNet | b386661ac5841774b9d17cc132e991a7bef3c5ef | baselines/pytorch-pretrained-BERT/pytorch_pretrained_bert/modeling_gpt2.py | python | GPT2PreTrainedModel.__init__ | (self, config, *inputs, **kwargs) | [] | def __init__(self, config, *inputs, **kwargs):
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... | https://github.com/INK-USC/KagNet/blob/b386661ac5841774b9d17cc132e991a7bef3c5ef/baselines/pytorch-pretrained-BERT/pytorch_pretrained_bert/modeling_gpt2.py#L332-L342 | ||||
edgewall/trac | beb3e4eaf1e0a456d801a50a8614ecab06de29fc | trac/web/chrome.py | python | accesskey | (req, key) | return key if req.session.as_int('accesskeys') else None | Helper function for creating accesskey HTML attribute according
to preference values | Helper function for creating accesskey HTML attribute according
to preference values | [
"Helper",
"function",
"for",
"creating",
"accesskey",
"HTML",
"attribute",
"according",
"to",
"preference",
"values"
] | def accesskey(req, key):
"""Helper function for creating accesskey HTML attribute according
to preference values"""
return key if req.session.as_int('accesskeys') else None | [
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CalebBell/thermo | 572a47d1b03d49fe609b8d5f826fa6a7cde00828 | thermo/phases/ceos.py | python | CEOSGas.fugacities_lowest_Gibbs | (self) | return [P*zs[i]*trunc_exp(lnphis[i]) for i in range(len(zs))] | [] | def fugacities_lowest_Gibbs(self):
eos_mix = self.eos_mix
P = self.P
zs = self.zs
try:
if eos_mix.G_dep_g < eos_mix.G_dep_l:
lnphis = eos_mix.fugacity_coefficients(eos_mix.Z_g)
else:
lnphis = eos_mix.fugacity_coefficients(eos_mix.Z_l)
except:
try:
lnphis = eos_mix.fugacity_coefficients(eos_mix.Z_g)
except:
lnphis = eos_mix.fugacity_coefficients(eos_mix.Z_l)
return [P*zs[i]*trunc_exp(lnphis[i]) for i in range(len(zs))] | [
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JPStrydom/Crypto-Trading-Bot | 94b5aab261a35d99bc044267baf4735f0ee3f89a | src/app.py | python | get_settings | () | return settings_content | [] | def get_settings():
settings_file_directory = "../database/settings.json"
settings_template = {
"sound": False,
"tradeParameters": {
"tickerInterval": "TICKER_INTERVAL",
"buy": {
"btcAmount": 0,
"rsiThreshold": 0,
"24HourVolumeThreshold": 0,
"minimumUnitPrice": 0,
"maxOpenTrades": 0
},
"sell": {
"lossMarginThreshold": 0,
"rsiThreshold": 0,
"minProfitMarginThreshold": 0,
"profitMarginThreshold": 0
}
},
"pauseParameters": {
"buy": {
"rsiThreshold": 0,
"pauseTime": 0
},
"sell": {
"profitMarginThreshold": 0,
"pauseTime": 0
},
"balance": {
"pauseTime": 0
}
}
}
settings_content = get_json_from_file(settings_file_directory, settings_template)
if settings_content == settings_template:
print("Please completed the `settings.json` file in your `database` directory")
exit()
return settings_content | [
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... | https://github.com/JPStrydom/Crypto-Trading-Bot/blob/94b5aab261a35d99bc044267baf4735f0ee3f89a/src/app.py#L41-L80 | |||
MaurizioFD/RecSys2019_DeepLearning_Evaluation | 0fb6b7f5c396f8525316ed66cf9c9fdb03a5fa9b | CNN_on_embeddings/IJCAI/CFM_github/LoadData.py | python | LoadData.construct_dataset | (self, X_user, X_item) | return Data_Dic | Construct dataset
:param X_user: user structured data
:param X_item: item structured data
:return: | Construct dataset
:param X_user: user structured data
:param X_item: item structured data
:return: | [
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] | def construct_dataset(self, X_user, X_item):
'''
Construct dataset
:param X_user: user structured data
:param X_item: item structured data
:return:
'''
Data_Dic = {}
indexs = range(len(X_user))
Data_Dic['X_user'] = [X_user[i] for i in indexs]
Data_Dic['X_item'] = [X_item[i] for i in indexs]
return Data_Dic | [
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snakeztc/NeuralDialog-ZSDG | 1d1548457a16a2e07567dc8532ea8b2fba178540 | zsdg/main.py | python | LossManager.add_loss | (self, loss) | [] | def add_loss(self, loss):
for key, val in loss.items():
if val is not None and type(val) is not bool:
self.losses[key].append(val.data[0]) | [
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bashtage/linearmodels | 9256269f01ff8c5f85e65342d66149a5636661b6 | linearmodels/panel/model.py | python | PanelOLS.other_effects | (self) | return self._other_effects | Flag indicating whether other (generic) effects are included | Flag indicating whether other (generic) effects are included | [
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] | def other_effects(self) -> bool:
"""Flag indicating whether other (generic) effects are included"""
return self._other_effects | [
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] | https://github.com/bashtage/linearmodels/blob/9256269f01ff8c5f85e65342d66149a5636661b6/linearmodels/panel/model.py#L1313-L1315 | |
slinderman/pyhawkes | 0df433a40c5e6d8c1dcdb98ffc88fe3a403ac223 | pyhawkes/standard_models.py | python | _NonlinearHawkesNodeBase.fit_with_bfgs | (self) | Fit the model with BFGS | Fit the model with BFGS | [
"Fit",
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"with",
"BFGS"
] | def fit_with_bfgs(self):
"""
Fit the model with BFGS
"""
# If W_max is specified, set this as a bound
# if self.W_max is not None:
bnds = self.bias_bnds + self.weight_bnds * (self.K * self.B) \
if self.constrained else None
# else:
# bnds = [(None, None)] * (1 + self.K * self.B)
itr = [0]
def callback(w):
if itr[0] % 10 == 0:
print("Iteration: %03d\t LP: %.5f" % (itr[0], self.objective(w)))
itr[0] = itr[0] + 1
itr[0] = 0
x0 = self.w
res = minimize(self.objective, # Objective function
x0, # Initial value
jac=grad(self.objective), # Gradient of the objective
bounds=bnds, # Bounds on x
callback=callback)
self.w = res.x | [
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andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/core/ui/console/menu.py | python | menu._cmd_print | (self, params) | [] | def _cmd_print(self, params):
if not len(params):
raise BaseFrameworkException('Variable is expected')
small_locals = {'kb': kb, 'w3af_core': self._w3af}
small_globals = {}
eval_variable = ' '.join(params)
try:
res = eval(eval_variable, small_globals, small_locals)
except:
om.out.console('Unknown variable.')
else:
pp = pprint.PrettyPrinter(indent=4)
output = pp.pformat(res)
om.out.console(output) | [
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broadinstitute/viral-ngs | e144969e4c57060d53f38a4c3a270e8227feace1 | file_utils.py | python | merge_tarballs | (out_tarball, in_tarballs, threads=None, extract_to_disk_path=None, pipe_hint_in=None, pipe_hint_out=None) | return 0 | Merges separate tarballs into one tarball
data can be piped in and/or out | Merges separate tarballs into one tarball
data can be piped in and/or out | [
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"can",
"be",
"piped",
"in",
"and",
"/",
"or",
"out"
] | def merge_tarballs(out_tarball, in_tarballs, threads=None, extract_to_disk_path=None, pipe_hint_in=None, pipe_hint_out=None):
''' Merges separate tarballs into one tarball
data can be piped in and/or out
'''
util.file.repack_tarballs(out_tarball, in_tarballs, threads=threads, extract_to_disk_path=extract_to_disk_path, pipe_hint_in=pipe_hint_in, pipe_hint_out=pipe_hint_out)
return 0 | [
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... | https://github.com/broadinstitute/viral-ngs/blob/e144969e4c57060d53f38a4c3a270e8227feace1/file_utils.py#L22-L27 | |
SymbiFlow/symbiflow-arch-defs | f38793112ff78a06de9f1e3269bd22543e29729f | utils/lib/parse_pcf.py | python | parse_simple_pcf | (f) | Parse a simple PCF file object and yield PcfIoConstraint objects. | Parse a simple PCF file object and yield PcfIoConstraint objects. | [
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"."
] | def parse_simple_pcf(f):
""" Parse a simple PCF file object and yield PcfIoConstraint objects. """
for line_number, line in enumerate(f):
line_number += 1
# Remove comments.
args = re.sub(r"#.*", "", line.strip()).split()
if not args:
continue
# Ignore arguments.
args = [arg for arg in args if arg[0] != '-']
assert len(args) == 3, args
if args[0] == 'set_io':
yield PcfIoConstraint(
net=args[1],
pad=args[2],
line_str=line.strip(),
line_num=line_number,
)
if args[0] == 'set_clk':
yield PcfClkConstraint(
pin=args[1],
net=args[2],
) | [
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tenpy/tenpy | bbdd3dbbdb511948eb0e6ba7ff619ac6ca657fff | tenpy/models/model.py | python | NearestNeighborModel.bond_energies | (self, psi) | return psi.expectation_value(self.H_bond[1:], axes=(['p0', 'p1'], ['p0*', 'p1*'])) | Calculate bond energies <psi|H_bond|psi>.
Parameters
----------
psi : :class:`~tenpy.networks.mps.MPS`
The MPS for which the bond energies should be calculated.
Returns
-------
E_bond : 1D ndarray
List of bond energies: for finite bc, ``E_Bond[i]`` is the energy of bond ``i, i+1``.
(i.e. we omit bond 0 between sites L-1 and 0);
for infinite bc ``E_bond[i]`` is the energy of bond ``i-1, i``. | Calculate bond energies <psi|H_bond|psi>. | [
"Calculate",
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"energies",
"<psi|H_bond|psi",
">",
"."
] | def bond_energies(self, psi):
"""Calculate bond energies <psi|H_bond|psi>.
Parameters
----------
psi : :class:`~tenpy.networks.mps.MPS`
The MPS for which the bond energies should be calculated.
Returns
-------
E_bond : 1D ndarray
List of bond energies: for finite bc, ``E_Bond[i]`` is the energy of bond ``i, i+1``.
(i.e. we omit bond 0 between sites L-1 and 0);
for infinite bc ``E_bond[i]`` is the energy of bond ``i-1, i``.
"""
if self.lat.bc_MPS == 'infinite':
return psi.expectation_value(self.H_bond, axes=(['p0', 'p1'], ['p0*', 'p1*']))
# else
return psi.expectation_value(self.H_bond[1:], axes=(['p0', 'p1'], ['p0*', 'p1*'])) | [
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... | https://github.com/tenpy/tenpy/blob/bbdd3dbbdb511948eb0e6ba7ff619ac6ca657fff/tenpy/models/model.py#L266-L284 | |
BigBrotherBot/big-brother-bot | 848823c71413c86e7f1ff9584f43e08d40a7f2c0 | b3/plugins/netblocker/__init__.py | python | NetblockerPlugin.onPlayerConnect | (self, event) | Examine players ip address and allow/deny connection. | Examine players ip address and allow/deny connection. | [
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"address",
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"/",
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"."
] | def onPlayerConnect(self, event):
"""
Examine players ip address and allow/deny connection.
"""
client = event.client
self.debug('checking client: %s, name: %s, ip: %s, level: %s', client.cid, client.name, client.ip, client.maxLevel)
# check the level of the connecting client before applying the filters
if client.maxLevel > self._maxLevel:
self.debug('%s is a higher level user, and allowed to connect', client.name)
else:
# transform ip address
ip = netblock.convert(client.ip)
# cycle through our blocks
for block in self._blocks:
# convert each block
b = netblock.convert(block)
# check if clients ip is in the disallowed range
if b[0] <= ip[0] <= b[1]:
# client not allowed to connect
self.debug('client refused: %s (%s)', client.ip, client.name)
client.kick("Netblocker: Client %s refused!" % client.name) | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/numpy-1.10.0.dev0_046311a-py3.3-win-amd64.egg/numpy/ma/core.py | python | MaskedArray.__ne__ | (self, other) | return check | Check whether other doesn't equal self elementwise | Check whether other doesn't equal self elementwise | [
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"whether",
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"doesn",
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"self",
"elementwise"
] | def __ne__(self, other):
"Check whether other doesn't equal self elementwise"
if self is masked:
return masked
omask = getattr(other, '_mask', nomask)
if omask is nomask:
check = ndarray.__ne__(self.filled(0), other)
try:
check = check.view(type(self))
check._mask = self._mask
except AttributeError:
# In case check is a boolean (or a numpy.bool)
return check
else:
odata = filled(other, 0)
check = ndarray.__ne__(self.filled(0), odata).view(type(self))
if self._mask is nomask:
check._mask = omask
else:
mask = mask_or(self._mask, omask)
if mask.dtype.names:
if mask.size > 1:
axis = 1
else:
axis = None
try:
mask = mask.view((bool_, len(self.dtype))).all(axis)
except ValueError:
mask = np.all([[f[n].all() for n in mask.dtype.names]
for f in mask], axis=axis)
check._mask = mask
return check | [
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joelibaceta/video-to-ascii | 906abad87083afbd2b13a44a31e614566446bbb9 | video_to_ascii/render_strategy/image_processor.py | python | colorize_char | (char, ansi_color) | return str_colorized | Get an appropriate char of brightness from a rgb color | Get an appropriate char of brightness from a rgb color | [
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"char",
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"brightness",
"from",
"a",
"rgb",
"color"
] | def colorize_char(char, ansi_color):
"""
Get an appropriate char of brightness from a rgb color
"""
str_colorized = colorize(char, ansi=ansi_color)
return str_colorized | [
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leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/plugins/qt_frame.py | python | LeoQtBody.hideCanvas | (self, event=None) | Hide canvas pane. | Hide canvas pane. | [
"Hide",
"canvas",
"pane",
"."
] | def hideCanvas(self, event=None):
"""Hide canvas pane."""
c, d = self.c, self.editorWrappers
wrapper = c.frame.body.wrapper
w = wrapper.widget
name = w.leo_name
assert name
assert wrapper == d.get(name), 'wrong wrapper'
assert g.isTextWrapper(wrapper), wrapper
assert g.isTextWidget(w), w
if len(list(d.keys())) <= 1:
return
# At present, can not delete the first column.
if name == '1':
g.warning('can not delete leftmost editor')
return
#
# Actually delete the widget.
del d[name]
f = c.frame.top.leo_body_inner_frame
layout = f.layout()
for z in (w, w.leo_label):
if z:
self.unpackWidget(layout, z)
#
# Select another editor.
w.leo_label = None
new_wrapper = list(d.values())[0]
self.numberOfEditors -= 1
if self.numberOfEditors == 1:
w = new_wrapper.widget
if w.leo_label:
self.unpackWidget(layout, w.leo_label)
w.leo_label = None
self.selectEditor(new_wrapper) | [
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bytedance/byteps | d0bcf1a87ee87539ceb29bcc976d4da063ffc47b | launcher/dist_launcher.py | python | start_ssh | (prog, node, port, username, fname) | return thread | [] | def start_ssh(prog, node, port, username, fname):
def run(prog):
subprocess.check_call(prog, shell=True)
dirname = 'sshlog'
if not os.path.exists(dirname):
os.mkdir(dirname)
pname = dirname + '/' + fname
if username is not None:
prog = 'ssh -o StrictHostKeyChecking=no ' + ' -l ' + username \
+ ' ' + node + ' -p ' + port + ' \'' + prog + '\'' \
+ ' > ' + pname + '.stdout' + ' 2>' + pname + '.stderr&'
else:
prog = 'ssh -o StrictHostKeyChecking=no ' + node + ' -p ' + port + ' \'' + prog + '\'' \
+ ' > ' + pname + '.stdout' + ' 2>' + pname + '.stderr&'
thread = Thread(target=run, args=(prog,))
thread.setDaemon(True)
thread.start()
return thread | [
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SamSchott/maestral | a32653bac7b5a76cb326d4fd5a4fb2c11f19a2fc | src/maestral/client.py | python | DropboxClient.remove | (self, dbx_path: str, **kwargs) | Removes a file / folder from Dropbox.
:param dbx_path: Path to file on Dropbox.
:param kwargs: Keyword arguments for the Dropbox API files_delete_v2 endpoint.
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] | def remove(self, dbx_path: str, **kwargs) -> files.Metadata:
"""
Removes a file / folder from Dropbox.
:param dbx_path: Path to file on Dropbox.
:param kwargs: Keyword arguments for the Dropbox API files_delete_v2 endpoint.
:returns: Metadata of deleted item.
"""
with convert_api_errors(dbx_path=dbx_path):
res = self.dbx.files_delete_v2(dbx_path, **kwargs)
return res.metadata | [
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beancount/fava | 69614956d3c01074403af0a07ddbaa986cf602a0 | src/fava/ext/portfolio_list/__init__.py | python | PortfolioList._account_metadata_pattern | (self, tree, metadata_key, pattern) | return title, portfolio_data | Returns portfolio info based on matching account open metadata.
Args:
tree: Ledger root tree node.
metadata_key: Metadata key to match for in account open.
pattern: Metadata value's regex pattern to match for.
Return:
Data structured for use with a querytable - (types, rows). | Returns portfolio info based on matching account open metadata. | [
"Returns",
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"open",
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] | def _account_metadata_pattern(self, tree, metadata_key, pattern):
"""
Returns portfolio info based on matching account open metadata.
Args:
tree: Ledger root tree node.
metadata_key: Metadata key to match for in account open.
pattern: Metadata value's regex pattern to match for.
Return:
Data structured for use with a querytable - (types, rows).
"""
title = (
"Accounts with '"
+ metadata_key
+ "' metadata matching: '"
+ pattern
+ "'"
)
selected_accounts = []
regexer = re.compile(pattern)
for entry in self.ledger.all_entries_by_type.Open:
if (metadata_key in entry.meta) and (
regexer.match(entry.meta[metadata_key]) is not None
):
selected_accounts.append(entry.account)
selected_nodes = [tree[x] for x in selected_accounts]
portfolio_data = self._portfolio_data(selected_nodes)
return title, portfolio_data | [
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mesalock-linux/mesapy | ed546d59a21b36feb93e2309d5c6b75aa0ad95c9 | pypy/objspace/std/unicodeobject.py | python | UnicodeDocstrings.upper | () | S.upper() -> unicode
Return a copy of S converted to uppercase. | S.upper() -> unicode | [
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"""S.upper() -> unicode
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emmetio/livestyle-sublime-old | c42833c046e9b2f53ebce3df3aa926528f5a33b5 | lsutils/websockets.py | python | off | (name, callback=None) | [] | def off(name, callback=None):
_dispatcher.off(name, callback) | [
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PRBonn/bonnet | 7bf03b06e48faec46d39ea6b4b4706a73ff6ce48 | train_py/arch/abstract_net.py | python | AbstractNetwork.loss_f | (self, lbls_pl, logits_train, gamma_focal=2, w_t="log", w_d=1e-4) | Calculates the loss from the logits and the labels. | Calculates the loss from the logits and the labels. | [
"Calculates",
"the",
"loss",
"from",
"the",
"logits",
"and",
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"labels",
"."
] | def loss_f(self, lbls_pl, logits_train, gamma_focal=2, w_t="log", w_d=1e-4):
"""Calculates the loss from the logits and the labels.
"""
print("Defining loss function")
with tf.variable_scope("loss"):
lbls_resized = self.resize_label(lbls_pl)
# Apply median freq balancing (median frec / freq(class))
w = np.empty(len(self.dataset.train.content))
if w_t == "log":
# get the frequencies and weights
for key in self.dataset.train.content:
e = 1.02 # max weight = 50
f_c = self.dataset.train.content[key]
w[self.DATA["label_remap"][key]] = 1 / np.log(f_c + e)
print("\nWeights for loss function (1/log(frec(c)+e)):\n", w)
elif w_t == "median_freq":
# get the frequencies
f = np.empty(len(self.dataset.train.content))
for key in self.dataset.train.content:
e = 0.001
f_c = self.dataset.train.content[key]
f[self.DATA["label_remap"][key]] = f_c
w[self.DATA["label_remap"][key]] = 1 / (f_c + e)
# calculate the median frequencies and normalize
median_freq = np.median(f)
print("\nFrequencies of classes:\n", f)
print("\nMedian freq:\n", median_freq)
print("\nWeights for loss function (1/frec(c)):\n", w)
w = median_freq * w
print("\nWeights for loss function (median frec/frec(c)):\n", w)
else:
print("Using natural weights, since no valid loss option was given.")
w.fill(1.0)
for key in self.dataset.train.content:
if self.dataset.train.content[key] == float("inf"):
w[self.DATA["label_remap"][key]] = 0
print("weights: ", w)
# use class weights as tf constant
w_tf = tf.constant(w, dtype=tf.float32, name='class_weights')
w_mask = w.astype(np.bool).astype(np.float32)
w_mask_tf = tf.constant(w_mask, dtype=tf.float32,
name='class_weights_mask')
# make logits softmax matrixes for loss
loss_epsilon = tf.constant(value=1e-10)
softmax = tf.nn.softmax(logits_train)
softmax_mat = tf.reshape(softmax, (-1, self.num_classes))
zerohot_softmax_mat = 1 - softmax_mat
# make the labels one-hot for the cross-entropy
onehot_mat = tf.reshape(tf.one_hot(lbls_resized, self.num_classes),
(-1, self.num_classes))
# make the zero hot to punish the false negatives, but ignore the
# zero-weight classes
masked_sum = tf.reduce_sum(onehot_mat * w_mask_tf, axis=1)
zeros = onehot_mat * 0.0
zerohot_mat = tf.where(tf.less(masked_sum, 1e-5),
x=zeros,
y=1 - onehot_mat)
# focal loss p and gamma
gamma = np.full(onehot_mat.get_shape().as_list(), fill_value=gamma_focal)
gamma_tf = tf.constant(gamma, dtype=tf.float32)
focal_softmax = tf.pow(1 - softmax_mat, gamma_tf) * \
tf.log(softmax_mat + loss_epsilon)
zerohot_focal_softmax = tf.pow(1 - zerohot_softmax_mat, gamma_tf) * \
tf.log(zerohot_softmax_mat + loss_epsilon)
# calculate xentropy
cross_entropy = - tf.reduce_sum(tf.multiply(focal_softmax * onehot_mat +
zerohot_focal_softmax * zerohot_mat, w_tf),
axis=[1])
loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
# weight decay
print("Weight decay: ", w_d)
w_d_tf = tf.constant(w_d, dtype=tf.float32, name='weight_decay')
variables = tf.trainable_variables(scope="model")
for var in variables:
if "weights" in var.name:
loss += w_d_tf * tf.nn.l2_loss(var)
return loss | [
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NTMC-Community/MatchZoo | 8a487ee5a574356fc91e4f48e219253dc11bcff2 | matchzoo/preprocessors/diin_preprocessor.py | python | DIINPreprocessor.fit | (self, data_pack: DataPack, verbose: int = 1) | return self | Fit pre-processing context for transformation.
:param data_pack: data_pack to be preprocessed.
:param verbose: Verbosity.
:return: class:'DIINPreprocessor' instance. | Fit pre-processing context for transformation. | [
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] | def fit(self, data_pack: DataPack, verbose: int = 1):
"""
Fit pre-processing context for transformation.
:param data_pack: data_pack to be preprocessed.
:param verbose: Verbosity.
:return: class:'DIINPreprocessor' instance.
"""
func = chain_transform(self._units)
data_pack = data_pack.apply_on_text(func, mode='both', verbose=verbose)
vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
vocab_size = len(vocab_unit.state['term_index'])
self._context['vocab_unit'] = vocab_unit
self._context['vocab_size'] = vocab_size
self._context['embedding_input_dim'] = vocab_size
data_pack = data_pack.apply_on_text(
units.NgramLetter(ngram=1, reduce_dim=True).transform,
mode='both', verbose=verbose)
char_unit = build_vocab_unit(data_pack, verbose=verbose)
self._context['char_unit'] = char_unit
self._context['input_shapes'] = [
(self._fixed_length_left,),
(self._fixed_length_right,),
(self._fixed_length_left, self._fixed_length_word,),
(self._fixed_length_right, self._fixed_length_word,),
(self._fixed_length_left,),
(self._fixed_length_right,)
]
return self | [
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aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/kubernetes/client/models/v1_limit_range_list.py | python | V1LimitRangeList.__eq__ | (self, other) | return self.__dict__ == other.__dict__ | Returns true if both objects are equal | Returns true if both objects are equal | [
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"are",
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] | def __eq__(self, other):
"""
Returns true if both objects are equal
"""
if not isinstance(other, V1LimitRangeList):
return False
return self.__dict__ == other.__dict__ | [
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xjsender/haoide | 717dd706db1169bfc41e818ac6fc6cd9a0aef12d | requests/packages/urllib3/response.py | python | HTTPResponse._flush_decoder | (self) | return b'' | Flushes the decoder. Should only be called if the decoder is actually
being used. | Flushes the decoder. Should only be called if the decoder is actually
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] | def _flush_decoder(self):
"""
Flushes the decoder. Should only be called if the decoder is actually
being used.
"""
if self._decoder:
buf = self._decoder.decompress(b'')
return buf + self._decoder.flush()
return b'' | [
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GitGuardian/ggshield | 94a1fa0f6402cd1df2dd3dbc5b932862e85f99e5 | ggshield/git_shell.py | python | shell_split | (command: List[str], **kwargs: Any) | return shell(command, **kwargs).split("\n") | [] | def shell_split(command: List[str], **kwargs: Any) -> List[str]:
return shell(command, **kwargs).split("\n") | [
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frescobaldi/frescobaldi | 301cc977fc4ba7caa3df9e4bf905212ad5d06912 | frescobaldi_app/gadgets/drag.py | python | Dragger.startDrag | (self, widget) | Reimplement to start a drag. | Reimplement to start a drag. | [
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] | def startDrag(self, widget):
"""Reimplement to start a drag.""" | [
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] | https://github.com/frescobaldi/frescobaldi/blob/301cc977fc4ba7caa3df9e4bf905212ad5d06912/frescobaldi_app/gadgets/drag.py#L101-L102 | ||
cuthbertLab/music21 | bd30d4663e52955ed922c10fdf541419d8c67671 | music21/analysis/transposition.py | python | TranspositionChecker.getPitchesOfDistinctTranspositions | (self) | return allNormalOrderPitchTuples | Outputs pitch tuples for each distinct transposition (normal order).
>>> pList = [pitch.Pitch('C4'), pitch.Pitch('E4'), pitch.Pitch('G#4')]
>>> tc = analysis.transposition.TranspositionChecker(pList)
>>> tc.getPitchesOfDistinctTranspositions()
[(<music21.pitch.Pitch C>, <music21.pitch.Pitch E>, <music21.pitch.Pitch G#>),
(<music21.pitch.Pitch C#>, <music21.pitch.Pitch F>, <music21.pitch.Pitch A>),
(<music21.pitch.Pitch D>, <music21.pitch.Pitch F#>, <music21.pitch.Pitch A#>),
(<music21.pitch.Pitch E->, <music21.pitch.Pitch G>, <music21.pitch.Pitch B>)] | Outputs pitch tuples for each distinct transposition (normal order). | [
"Outputs",
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"tuples",
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"each",
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"normal",
"order",
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] | def getPitchesOfDistinctTranspositions(self):
'''
Outputs pitch tuples for each distinct transposition (normal order).
>>> pList = [pitch.Pitch('C4'), pitch.Pitch('E4'), pitch.Pitch('G#4')]
>>> tc = analysis.transposition.TranspositionChecker(pList)
>>> tc.getPitchesOfDistinctTranspositions()
[(<music21.pitch.Pitch C>, <music21.pitch.Pitch E>, <music21.pitch.Pitch G#>),
(<music21.pitch.Pitch C#>, <music21.pitch.Pitch F>, <music21.pitch.Pitch A>),
(<music21.pitch.Pitch D>, <music21.pitch.Pitch F#>, <music21.pitch.Pitch A#>),
(<music21.pitch.Pitch E->, <music21.pitch.Pitch G>, <music21.pitch.Pitch B>)]
'''
chords = self.getChordsOfDistinctTranspositions()
allNormalOrderPitchTuples = [c.pitches for c in chords]
return allNormalOrderPitchTuples | [
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HenriWahl/Nagstamon | 16549c6860b51a93141d84881c6ad28c35d8581e | Nagstamon/Servers/Livestatus.py | python | LivestatusServer._update_object | (self, obj, data) | return result | populate the generic fields of obj (GenericHost or GenericService)
from data. | populate the generic fields of obj (GenericHost or GenericService)
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] | def _update_object(self, obj, data):
"""populate the generic fields of obj (GenericHost or GenericService)
from data."""
result = obj
result.server = self.name
result.last_check = format_timestamp(data['last_check'])
result.duration = duration(data['last_state_change'])
result.attempt = data['current_attempt']
result.status_information = data['plugin_output']
result.passiveonly = False
result.notifications_disabled = data['notifications_enabled'] != 1
result.flapping = data['is_flapping'] == 1
result.acknowledged = data['acknowledged'] == 1
result.scheduled_downtime = data['scheduled_downtime_depth'] == 1
if data['state'] == data['last_hard_state']:
result.status_type = 'hard'
else:
result.status_type = 'soft'
return result | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/cbs/v20170312/models.py | python | AttachDetail.__init__ | (self) | r"""
:param InstanceId: 实例ID。
:type InstanceId: str
:param AttachedDiskCount: 实例已挂载数据盘的数量。
:type AttachedDiskCount: int
:param MaxAttachCount: 实例最大可挂载数据盘的数量。
:type MaxAttachCount: int | r"""
:param InstanceId: 实例ID。
:type InstanceId: str
:param AttachedDiskCount: 实例已挂载数据盘的数量。
:type AttachedDiskCount: int
:param MaxAttachCount: 实例最大可挂载数据盘的数量。
:type MaxAttachCount: int | [
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r"""
:param InstanceId: 实例ID。
:type InstanceId: str
:param AttachedDiskCount: 实例已挂载数据盘的数量。
:type AttachedDiskCount: int
:param MaxAttachCount: 实例最大可挂载数据盘的数量。
:type MaxAttachCount: int
"""
self.InstanceId = None
self.AttachedDiskCount = None
self.MaxAttachCount = None | [
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] | https://github.com/TencentCloud/tencentcloud-sdk-python/blob/3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2/tencentcloud/cbs/v20170312/models.py#L79-L90 | ||
youfou/wxpy | ab63e12da822dc85615fa203e5be9fa28ae0b59f | wxpy/api/chats/chat.py | python | Chat.get_avatar | (self, save_path=None) | return self.bot.core.get_head_img(**kwargs) | 获取头像
:param save_path: 保存路径(后缀通常为.jpg)，若为 `None` 则返回字节数据 | 获取头像 | [
"获取头像"
] | def get_avatar(self, save_path=None):
"""
获取头像
:param save_path: 保存路径(后缀通常为.jpg)，若为 `None` 则返回字节数据
"""
logger.info('getting avatar of {}'.format(self))
from .group import Group
from .member import Member
from .friend import User
if isinstance(self, Group):
kwargs = dict(userName=None, chatroomUserName=self.user_name)
elif isinstance(self, Member):
kwargs = dict(userName=self.user_name, chatroomUserName=self.group.user_name)
elif isinstance(self, User):
kwargs = dict(userName=self.user_name, chatroomUserName=None)
else:
raise TypeError('expected `Chat`, got`{}`'.format(type(self)))
kwargs.update(dict(picDir=save_path))
return self.bot.core.get_head_img(**kwargs) | [
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"fr... | https://github.com/youfou/wxpy/blob/ab63e12da822dc85615fa203e5be9fa28ae0b59f/wxpy/api/chats/chat.py#L310-L334 |
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