_id
stringlengths 2
6
| partition
stringclasses 3
values | text
stringlengths 87
6.4k
| title
stringclasses 1
value | language
stringclasses 1
value | meta_information
dict |
|---|---|---|---|---|---|
d1 | train | def writeBoolean(self, n):
"""
Writes a Boolean to the stream.
"""
t = TYPE_BOOL_TRUE
if n is False:
t = TYPE_BOOL_FALSE
self.stream.write(t) | PYTHON | {
"dummy_field": ""
} |
|
d2 | train | def paste(xsel=False):
"""Returns system clipboard contents."""
selection = "primary" if xsel else "clipboard"
try:
return subprocess.Popen(["xclip", "-selection", selection, "-o"], stdout=subprocess.PIPE).communicate()[0].decode("utf-8")
except OSError as why:
raise XclipNotFound | PYTHON | {
"dummy_field": ""
} |
|
d3 | train | def _format_json(data, theme):
"""Pretty print a dict as a JSON, with colors if pygments is present."""
output = json.dumps(data, indent=2, sort_keys=True)
if pygments and sys.stdout.isatty():
style = get_style_by_name(theme)
formatter = Terminal256Formatter(style=style)
return pygments.highlight(output, JsonLexer(), formatter)
return output | PYTHON | {
"dummy_field": ""
} |
|
d4 | train | def create_path(path):
"""Creates a absolute path in the file system.
:param path: The path to be created
"""
import os
if not os.path.exists(path):
os.makedirs(path) | PYTHON | {
"dummy_field": ""
} |
|
d5 | train | def _vector_or_scalar(x, type='row'):
"""Convert an object to either a scalar or a row or column vector."""
if isinstance(x, (list, tuple)):
x = np.array(x)
if isinstance(x, np.ndarray):
assert x.ndim == 1
if type == 'column':
x = x[:, None]
return x | PYTHON | {
"dummy_field": ""
} |
|
d6 | train | def experiment_property(prop):
"""Get a property of the experiment by name."""
exp = experiment(session)
p = getattr(exp, prop)
return success_response(field=prop, data=p, request_type=prop) | PYTHON | {
"dummy_field": ""
} |
|
d7 | train | def data_from_file(file):
"""Return (first channel data, sample frequency, sample width) from a .wav
file."""
fp = wave.open(file, 'r')
data = fp.readframes(fp.getnframes())
channels = fp.getnchannels()
freq = fp.getframerate()
bits = fp.getsampwidth()
# Unpack bytes -- warning currently only tested with 16 bit wavefiles. 32
# bit not supported.
data = struct.unpack(('%sh' % fp.getnframes()) * channels, data)
# Only use first channel
channel1 = []
n = 0
for d in data:
if n % channels == 0:
channel1.append(d)
n += 1
fp.close()
return (channel1, freq, bits) | PYTHON | {
"dummy_field": ""
} |
|
d8 | train | def source_range(start, end, nr_var_dict):
"""
Given a range of source numbers, as well as a dictionary
containing the numbers of each source, returns a dictionary
containing tuples of the start and end index
for each source variable type.
"""
return OrderedDict((k, e-s)
for k, (s, e)
in source_range_tuple(start, end, nr_var_dict).iteritems()) | PYTHON | {
"dummy_field": ""
} |
|
d9 | train | def timespan(start_time):
"""Return time in milliseconds from start_time"""
timespan = datetime.datetime.now() - start_time
timespan_ms = timespan.total_seconds() * 1000
return timespan_ms | PYTHON | {
"dummy_field": ""
} |
|
d10 | train | def _convert_to_array(array_like, dtype):
"""
Convert Matrix attributes which are array-like or buffer to array.
"""
if isinstance(array_like, bytes):
return np.frombuffer(array_like, dtype=dtype)
return np.asarray(array_like, dtype=dtype) | PYTHON | {
"dummy_field": ""
} |
|
d11 | train | def get_uniques(l):
""" Returns a list with no repeated elements.
"""
result = []
for i in l:
if i not in result:
result.append(i)
return result | PYTHON | {
"dummy_field": ""
} |
|
d12 | train | def interp(x, xp, *args, **kwargs):
"""Wrap interpolate_1d for deprecated interp."""
return interpolate_1d(x, xp, *args, **kwargs) | PYTHON | {
"dummy_field": ""
} |
|
d13 | train | def _array2cstr(arr):
""" Serializes a numpy array to a compressed base64 string """
out = StringIO()
np.save(out, arr)
return b64encode(out.getvalue()) | PYTHON | {
"dummy_field": ""
} |
|
d14 | train | def percentile(values, k):
"""Find the percentile of a list of values.
:param list values: The list of values to find the percentile of
:param int k: The percentile to find
:rtype: float or int
"""
if not values:
return None
values.sort()
index = (len(values) * (float(k) / 100)) - 1
return values[int(math.ceil(index))] | PYTHON | {
"dummy_field": ""
} |
|
d15 | train | def _string_hash(s):
"""String hash (djb2) with consistency between py2/py3 and persistency between runs (unlike `hash`)."""
h = 5381
for c in s:
h = h * 33 + ord(c)
return h | PYTHON | {
"dummy_field": ""
} |
|
d16 | train | def transform_from_rot_trans(R, t):
"""Transforation matrix from rotation matrix and translation vector."""
R = R.reshape(3, 3)
t = t.reshape(3, 1)
return np.vstack((np.hstack([R, t]), [0, 0, 0, 1])) | PYTHON | {
"dummy_field": ""
} |
|
d17 | train | def _encode_bool(name, value, dummy0, dummy1):
"""Encode a python boolean (True/False)."""
return b"\x08" + name + (value and b"\x01" or b"\x00") | PYTHON | {
"dummy_field": ""
} |
|
d18 | train | def transform_to_3d(points,normal,z=0):
"""Project points into 3d from 2d points."""
d = np.cross(normal, (0, 0, 1))
M = rotation_matrix(d)
transformed_points = M.dot(points.T).T + z
return transformed_points | PYTHON | {
"dummy_field": ""
} |
|
d19 | train | def _not(condition=None, **kwargs):
"""
Return the opposite of input condition.
:param condition: condition to process.
:result: not condition.
:rtype: bool
"""
result = True
if condition is not None:
result = not run(condition, **kwargs)
return result | PYTHON | {
"dummy_field": ""
} |
|
d20 | train | def HttpResponse403(request, template=KEY_AUTH_403_TEMPLATE,
content=KEY_AUTH_403_CONTENT, content_type=KEY_AUTH_403_CONTENT_TYPE):
"""
HTTP response for forbidden access (status code 403)
"""
return AccessFailedResponse(request, template, content, content_type, status=403) | PYTHON | {
"dummy_field": ""
} |
|
d21 | train | def items(self, section_name):
""":return: list((option, value), ...) pairs of all items in the given section"""
return [(k, v) for k, v in super(GitConfigParser, self).items(section_name) if k != '__name__'] | PYTHON | {
"dummy_field": ""
} |
|
d22 | train | def mag(z):
"""Get the magnitude of a vector."""
if isinstance(z[0], np.ndarray):
return np.array(list(map(np.linalg.norm, z)))
else:
return np.linalg.norm(z) | PYTHON | {
"dummy_field": ""
} |
|
d23 | train | def config_parser_to_dict(config_parser):
"""
Convert a ConfigParser to a dictionary.
"""
response = {}
for section in config_parser.sections():
for option in config_parser.options(section):
response.setdefault(section, {})[option] = config_parser.get(section, option)
return response | PYTHON | {
"dummy_field": ""
} |
|
d24 | train | def __add__(self, other):
"""Handle the `+` operator."""
return self._handle_type(other)(self.value + other.value) | PYTHON | {
"dummy_field": ""
} |
|
d25 | train | def connect_mysql(host, port, user, password, database):
"""Connect to MySQL with retries."""
return pymysql.connect(
host=host, port=port,
user=user, passwd=password,
db=database
) | PYTHON | {
"dummy_field": ""
} |
|
d26 | train | def get_column(self, X, column):
"""Return a column of the given matrix.
Args:
X: `numpy.ndarray` or `pandas.DataFrame`.
column: `int` or `str`.
Returns:
np.ndarray: Selected column.
"""
if isinstance(X, pd.DataFrame):
return X[column].values
return X[:, column] | PYTHON | {
"dummy_field": ""
} |
|
d27 | train | def connect(url, username, password):
"""
Return a connected Bitbucket session
"""
bb_session = stashy.connect(url, username, password)
logger.info('Connected to: %s as %s', url, username)
return bb_session | PYTHON | {
"dummy_field": ""
} |
|
d28 | train | def add_blank_row(self, label):
"""
Add a blank row with only an index value to self.df.
This is done inplace.
"""
col_labels = self.df.columns
blank_item = pd.Series({}, index=col_labels, name=label)
# use .loc to add in place (append won't do that)
self.df.loc[blank_item.name] = blank_item
return self.df | PYTHON | {
"dummy_field": ""
} |
|
d29 | train | def teardown(self):
"""
Stop and remove the container if it exists.
"""
while self._http_clients:
self._http_clients.pop().close()
if self.created:
self.halt() | PYTHON | {
"dummy_field": ""
} |
|
d30 | train | def dumped(text, level, indent=2):
"""Put curly brackets round an indented text"""
return indented("{\n%s\n}" % indented(text, level + 1, indent) or "None", level, indent) + "\n" | PYTHON | {
"dummy_field": ""
} |
|
d31 | train | def context(self):
"""
Create a context manager that ensures code runs within action's context.
The action does NOT finish when the context is exited.
"""
parent = _ACTION_CONTEXT.set(self)
try:
yield self
finally:
_ACTION_CONTEXT.reset(parent) | PYTHON | {
"dummy_field": ""
} |
|
d32 | train | def pformat(object, indent=1, width=80, depth=None):
"""Format a Python object into a pretty-printed representation."""
return PrettyPrinter(indent=indent, width=width, depth=depth).pformat(object) | PYTHON | {
"dummy_field": ""
} |
|
d33 | train | def replace_sys_args(new_args):
"""Temporarily replace sys.argv with current arguments
Restores sys.argv upon exit of the context manager.
"""
# Replace sys.argv arguments
# for module import
old_args = sys.argv
sys.argv = new_args
try:
yield
finally:
sys.argv = old_args | PYTHON | {
"dummy_field": ""
} |
|
d34 | train | def serialize(obj):
"""Takes a object and produces a dict-like representation
:param obj: the object to serialize
"""
if isinstance(obj, list):
return [serialize(o) for o in obj]
return GenericSerializer(ModelProviderImpl()).serialize(obj) | PYTHON | {
"dummy_field": ""
} |
|
d35 | train | def advance_one_line(self):
"""Advances to next line."""
current_line = self._current_token.line_number
while current_line == self._current_token.line_number:
self._current_token = ConfigParser.Token(*next(self._token_generator)) | PYTHON | {
"dummy_field": ""
} |
|
d36 | train | def generate_swagger_html(swagger_static_root, swagger_json_url):
"""
given a root directory for the swagger statics, and
a swagger json path, return back a swagger html designed
to use those values.
"""
tmpl = _get_template("swagger.html")
return tmpl.render(
swagger_root=swagger_static_root, swagger_json_url=swagger_json_url
) | PYTHON | {
"dummy_field": ""
} |
|
d37 | train | def do_next(self, args):
"""Step over the next statement
"""
self._do_print_from_last_cmd = True
self._interp.step_over()
return True | PYTHON | {
"dummy_field": ""
} |
|
d38 | train | def __add__(self,other):
"""
If the number of columns matches, we can concatenate two LabeldMatrices
with the + operator.
"""
assert self.matrix.shape[1] == other.matrix.shape[1]
return LabeledMatrix(np.concatenate([self.matrix,other.matrix],axis=0),self.labels) | PYTHON | {
"dummy_field": ""
} |
|
d39 | train | def get_line_flux(line_wave, wave, flux, **kwargs):
"""Interpolated flux at a given wavelength (calls np.interp)."""
return np.interp(line_wave, wave, flux, **kwargs) | PYTHON | {
"dummy_field": ""
} |
|
d40 | train | def send(message, request_context=None, binary=False):
"""Sends a message to websocket.
:param str message: data to send
:param request_context:
:raises IOError: If unable to send a message.
"""
if binary:
return uwsgi.websocket_send_binary(message, request_context)
return uwsgi.websocket_send(message, request_context) | PYTHON | {
"dummy_field": ""
} |
|
d41 | train | def get_number(s, cast=int):
"""
Try to get a number out of a string, and cast it.
"""
import string
d = "".join(x for x in str(s) if x in string.digits)
return cast(d) | PYTHON | {
"dummy_field": ""
} |
|
d42 | train | def get_hline():
""" gets a horiztonal line """
return Window(
width=LayoutDimension.exact(1),
height=LayoutDimension.exact(1),
content=FillControl('-', token=Token.Line)) | PYTHON | {
"dummy_field": ""
} |
|
d43 | train | def parse_cookies_str(cookies):
"""
parse cookies str to dict
:param cookies: cookies str
:type cookies: str
:return: cookie dict
:rtype: dict
"""
cookie_dict = {}
for record in cookies.split(";"):
key, value = record.strip().split("=", 1)
cookie_dict[key] = value
return cookie_dict | PYTHON | {
"dummy_field": ""
} |
|
d44 | train | def to_snake_case(name):
""" Given a name in camelCase return in snake_case """
s1 = FIRST_CAP_REGEX.sub(r'\1_\2', name)
return ALL_CAP_REGEX.sub(r'\1_\2', s1).lower() | PYTHON | {
"dummy_field": ""
} |
|
d45 | train | def populate_obj(obj, attrs):
"""Populates an object's attributes using the provided dict
"""
for k, v in attrs.iteritems():
setattr(obj, k, v) | PYTHON | {
"dummy_field": ""
} |
|
d46 | train | def wordfreq(text, is_filename=False):
"""Return a dictionary of words and word counts in a string."""
if is_filename:
with open(text) as f:
text = f.read()
freqs = {}
for word in text.split():
lword = word.lower()
freqs[lword] = freqs.get(lword, 0) + 1
return freqs | PYTHON | {
"dummy_field": ""
} |
|
d47 | train | def copyFile(input, output, replace=None):
"""Copy a file whole from input to output."""
_found = findFile(output)
if not _found or (_found and replace):
shutil.copy2(input, output) | PYTHON | {
"dummy_field": ""
} |
|
d48 | train | def push(h, x):
"""Push a new value into heap."""
h.push(x)
up(h, h.size()-1) | PYTHON | {
"dummy_field": ""
} |
|
d49 | train | def yank(event):
"""
Paste before cursor.
"""
event.current_buffer.paste_clipboard_data(
event.cli.clipboard.get_data(), count=event.arg, paste_mode=PasteMode.EMACS) | PYTHON | {
"dummy_field": ""
} |
|
d50 | train | def filter_contour(imageFile, opFile):
""" convert an image by applying a contour """
im = Image.open(imageFile)
im1 = im.filter(ImageFilter.CONTOUR)
im1.save(opFile) | PYTHON | {
"dummy_field": ""
} |
|
d51 | train | def count(lines):
""" Counts the word frequences in a list of sentences.
Note:
This is a helper function for parallel execution of `Vocabulary.from_text`
method.
"""
words = [w for l in lines for w in l.strip().split()]
return Counter(words) | PYTHON | {
"dummy_field": ""
} |
|
d52 | train | def dictapply(d, fn):
"""
apply a function to all non-dict values in a dictionary
"""
for k, v in d.items():
if isinstance(v, dict):
v = dictapply(v, fn)
else:
d[k] = fn(v)
return d | PYTHON | {
"dummy_field": ""
} |
|
d53 | train | def count_replica(self, partition):
"""Return count of replicas of given partition."""
return sum(1 for b in partition.replicas if b in self.brokers) | PYTHON | {
"dummy_field": ""
} |
|
d54 | train | def visit_Name(self, node):
""" Get range for parameters for examples or false branching. """
return self.add(node, self.result[node.id]) | PYTHON | {
"dummy_field": ""
} |
|
d55 | train | def mkdir(dir, enter):
"""Create directory with template for topic of the current environment
"""
if not os.path.exists(dir):
os.makedirs(dir) | PYTHON | {
"dummy_field": ""
} |
|
d56 | train | def qrot(vector, quaternion):
"""Rotate a 3D vector using quaternion algebra.
Implemented by Vladimir Kulikovskiy.
Parameters
----------
vector: np.array
quaternion: np.array
Returns
-------
np.array
"""
t = 2 * np.cross(quaternion[1:], vector)
v_rot = vector + quaternion[0] * t + np.cross(quaternion[1:], t)
return v_rot | PYTHON | {
"dummy_field": ""
} |
|
d57 | train | def _numpy_char_to_bytes(arr):
"""Like netCDF4.chartostring, but faster and more flexible.
"""
# based on: http://stackoverflow.com/a/10984878/809705
arr = np.array(arr, copy=False, order='C')
dtype = 'S' + str(arr.shape[-1])
return arr.view(dtype).reshape(arr.shape[:-1]) | PYTHON | {
"dummy_field": ""
} |
|
d58 | train | def _string_hash(s):
"""String hash (djb2) with consistency between py2/py3 and persistency between runs (unlike `hash`)."""
h = 5381
for c in s:
h = h * 33 + ord(c)
return h | PYTHON | {
"dummy_field": ""
} |
|
d59 | train | def csv_to_dicts(file, header=None):
"""Reads a csv and returns a List of Dicts with keys given by header row."""
with open(file) as csvfile:
return [row for row in csv.DictReader(csvfile, fieldnames=header)] | PYTHON | {
"dummy_field": ""
} |
|
d60 | train | def get_tri_area(pts):
"""
Given a list of coords for 3 points,
Compute the area of this triangle.
Args:
pts: [a, b, c] three points
"""
a, b, c = pts[0], pts[1], pts[2]
v1 = np.array(b) - np.array(a)
v2 = np.array(c) - np.array(a)
area_tri = abs(sp.linalg.norm(sp.cross(v1, v2)) / 2)
return area_tri | PYTHON | {
"dummy_field": ""
} |
|
d61 | train | def one_hot(x, size, dtype=np.float32):
"""Make a n+1 dim one-hot array from n dim int-categorical array."""
return np.array(x[..., np.newaxis] == np.arange(size), dtype) | PYTHON | {
"dummy_field": ""
} |
|
d62 | train | def round_to_int(number, precision):
"""Round a number to a precision"""
precision = int(precision)
rounded = (int(number) + precision / 2) // precision * precision
return rounded | PYTHON | {
"dummy_field": ""
} |
|
d63 | train | def create_object(cls, members):
"""Promise an object of class `cls` with content `members`."""
obj = cls.__new__(cls)
obj.__dict__ = members
return obj | PYTHON | {
"dummy_field": ""
} |
|
d64 | train | def to_unicode_repr( _letter ):
""" helpful in situations where browser/app may recognize Unicode encoding
in the \u0b8e type syntax but not actual unicode glyph/code-point"""
# Python 2-3 compatible
return u"u'"+ u"".join( [ u"\\u%04x"%ord(l) for l in _letter ] ) + u"'" | PYTHON | {
"dummy_field": ""
} |
|
d65 | train | def create_path(path):
"""Creates a absolute path in the file system.
:param path: The path to be created
"""
import os
if not os.path.exists(path):
os.makedirs(path) | PYTHON | {
"dummy_field": ""
} |
|
d66 | train | def string_input(prompt=''):
"""Python 3 input()/Python 2 raw_input()"""
v = sys.version[0]
if v == '3':
return input(prompt)
else:
return raw_input(prompt) | PYTHON | {
"dummy_field": ""
} |
|
d67 | train | def cfloat64_array_to_numpy(cptr, length):
"""Convert a ctypes double pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_double)):
return np.fromiter(cptr, dtype=np.float64, count=length)
else:
raise RuntimeError('Expected double pointer') | PYTHON | {
"dummy_field": ""
} |
|
d68 | train | def yn_prompt(msg, default=True):
"""
Prompts the user for yes or no.
"""
ret = custom_prompt(msg, ["y", "n"], "y" if default else "n")
if ret == "y":
return True
return False | PYTHON | {
"dummy_field": ""
} |
|
d69 | train | def _display(self, layout):
"""launch layouts display"""
print(file=self.out)
TextWriter().format(layout, self.out) | PYTHON | {
"dummy_field": ""
} |
|
d70 | train | def assert_list(self, putative_list, expected_type=string_types, key_arg=None):
"""
:API: public
"""
return assert_list(putative_list, expected_type, key_arg=key_arg,
raise_type=lambda msg: TargetDefinitionException(self, msg)) | PYTHON | {
"dummy_field": ""
} |
|
d71 | train | def _xxrange(self, start, end, step_count):
"""Generate n values between start and end."""
_step = (end - start) / float(step_count)
return (start + (i * _step) for i in xrange(int(step_count))) | PYTHON | {
"dummy_field": ""
} |
|
d72 | train | def assert_exactly_one_true(bool_list):
"""This method asserts that only one value of the provided list is True.
:param bool_list: List of booleans to check
:return: True if only one value is True, False otherwise
"""
assert isinstance(bool_list, list)
counter = 0
for item in bool_list:
if item:
counter += 1
return counter == 1 | PYTHON | {
"dummy_field": ""
} |
|
d73 | train | def _get_random_id():
""" Get a random (i.e., unique) string identifier"""
symbols = string.ascii_uppercase + string.ascii_lowercase + string.digits
return ''.join(random.choice(symbols) for _ in range(15)) | PYTHON | {
"dummy_field": ""
} |
|
d74 | train | async def list(source):
"""Generate a single list from an asynchronous sequence."""
result = []
async with streamcontext(source) as streamer:
async for item in streamer:
result.append(item)
yield result | PYTHON | {
"dummy_field": ""
} |
|
d75 | train | def csv_to_dicts(file, header=None):
"""Reads a csv and returns a List of Dicts with keys given by header row."""
with open(file) as csvfile:
return [row for row in csv.DictReader(csvfile, fieldnames=header)] | PYTHON | {
"dummy_field": ""
} |
|
d76 | train | def _attrprint(d, delimiter=', '):
"""Print a dictionary of attributes in the DOT format"""
return delimiter.join(('"%s"="%s"' % item) for item in sorted(d.items())) | PYTHON | {
"dummy_field": ""
} |
|
d77 | train | def get_next_scheduled_time(cron_string):
"""Calculate the next scheduled time by creating a crontab object
with a cron string"""
itr = croniter.croniter(cron_string, datetime.utcnow())
return itr.get_next(datetime) | PYTHON | {
"dummy_field": ""
} |
|
d78 | train | def exit(exit_code=0):
r"""A function to support exiting from exit hooks.
Could also be used to exit from the calling scripts in a thread safe manner.
"""
core.processExitHooks()
if state.isExitHooked and not hasattr(sys, 'exitfunc'): # The function is called from the exit hook
sys.stderr.flush()
sys.stdout.flush()
os._exit(exit_code) #pylint: disable=W0212
sys.exit(exit_code) | PYTHON | {
"dummy_field": ""
} |
|
d79 | train | def dot_product(self, other):
""" Return the dot product of the given vectors. """
return self.x * other.x + self.y * other.y | PYTHON | {
"dummy_field": ""
} |
|
d80 | train | def reloader_thread(softexit=False):
"""If ``soft_exit`` is True, we use sys.exit(); otherwise ``os_exit``
will be used to end the process.
"""
while RUN_RELOADER:
if code_changed():
# force reload
if softexit:
sys.exit(3)
else:
os._exit(3)
time.sleep(1) | PYTHON | {
"dummy_field": ""
} |
|
d81 | train | def list_to_csv(value):
"""
Converts list to string with comma separated values. For string is no-op.
"""
if isinstance(value, (list, tuple, set)):
value = ",".join(value)
return value | PYTHON | {
"dummy_field": ""
} |
|
d82 | train | def average(iterator):
"""Iterative mean."""
count = 0
total = 0
for num in iterator:
count += 1
total += num
return float(total)/count | PYTHON | {
"dummy_field": ""
} |
|
d83 | train | def cint32_array_to_numpy(cptr, length):
"""Convert a ctypes int pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)):
return np.fromiter(cptr, dtype=np.int32, count=length)
else:
raise RuntimeError('Expected int pointer') | PYTHON | {
"dummy_field": ""
} |
|
d84 | train | def _aws_get_instance_by_tag(region, name, tag, raw):
"""Get all instances matching a tag."""
client = boto3.session.Session().client('ec2', region)
matching_reservations = client.describe_instances(Filters=[{'Name': tag, 'Values': [name]}]).get('Reservations', [])
instances = []
[[instances.append(_aws_instance_from_dict(region, instance, raw)) # pylint: disable=expression-not-assigned
for instance in reservation.get('Instances')] for reservation in matching_reservations if reservation]
return instances | PYTHON | {
"dummy_field": ""
} |
|
d85 | train | def cfloat64_array_to_numpy(cptr, length):
"""Convert a ctypes double pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_double)):
return np.fromiter(cptr, dtype=np.float64, count=length)
else:
raise RuntimeError('Expected double pointer') | PYTHON | {
"dummy_field": ""
} |
|
d86 | train | def loganalytics_data_plane_client(cli_ctx, _):
"""Initialize Log Analytics data client for use with CLI."""
from .vendored_sdks.loganalytics import LogAnalyticsDataClient
from azure.cli.core._profile import Profile
profile = Profile(cli_ctx=cli_ctx)
cred, _, _ = profile.get_login_credentials(
resource="https://api.loganalytics.io")
return LogAnalyticsDataClient(cred) | PYTHON | {
"dummy_field": ""
} |
|
d87 | train | def cfloat32_array_to_numpy(cptr, length):
"""Convert a ctypes float pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
return np.fromiter(cptr, dtype=np.float32, count=length)
else:
raise RuntimeError('Expected float pointer') | PYTHON | {
"dummy_field": ""
} |
|
d88 | train | def underscore(text):
"""Converts text that may be camelcased into an underscored format"""
return UNDERSCORE[1].sub(r'\1_\2', UNDERSCORE[0].sub(r'\1_\2', text)).lower() | PYTHON | {
"dummy_field": ""
} |
|
d89 | train | def cint8_array_to_numpy(cptr, length):
"""Convert a ctypes int pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_int8)):
return np.fromiter(cptr, dtype=np.int8, count=length)
else:
raise RuntimeError('Expected int pointer') | PYTHON | {
"dummy_field": ""
} |
|
d90 | train | def get_stoplist(language):
"""Returns an built-in stop-list for the language as a set of words."""
file_path = os.path.join("stoplists", "%s.txt" % language)
try:
stopwords = pkgutil.get_data("justext", file_path)
except IOError:
raise ValueError(
"Stoplist for language '%s' is missing. "
"Please use function 'get_stoplists' for complete list of stoplists "
"and feel free to contribute by your own stoplist." % language
)
return frozenset(w.decode("utf8").lower() for w in stopwords.splitlines()) | PYTHON | {
"dummy_field": ""
} |
|
d91 | train | def add_str(window, line_num, str):
""" attempt to draw str on screen and ignore errors if they occur """
try:
window.addstr(line_num, 0, str)
except curses.error:
pass | PYTHON | {
"dummy_field": ""
} |
|
d92 | train | def relative_path(path):
"""
Return the given path relative to this file.
"""
return os.path.join(os.path.dirname(__file__), path) | PYTHON | {
"dummy_field": ""
} |
|
d93 | train | def dictfetchall(cursor):
"""Returns all rows from a cursor as a dict (rather than a headerless table)
From Django Documentation: https://docs.djangoproject.com/en/dev/topics/db/sql/
"""
desc = cursor.description
return [dict(zip([col[0] for col in desc], row)) for row in cursor.fetchall()] | PYTHON | {
"dummy_field": ""
} |
|
d94 | train | def xmltreefromfile(filename):
"""Internal function to read an XML file"""
try:
return ElementTree.parse(filename, ElementTree.XMLParser(collect_ids=False))
except TypeError:
return ElementTree.parse(filename, ElementTree.XMLParser()) | PYTHON | {
"dummy_field": ""
} |
|
d95 | train | def _dictfetchall(self, cursor):
""" Return all rows from a cursor as a dict. """
columns = [col[0] for col in cursor.description]
return [
dict(zip(columns, row))
for row in cursor.fetchall()
] | PYTHON | {
"dummy_field": ""
} |
|
d96 | train | def beta_pdf(x, a, b):
"""Beta distirbution probability density function."""
bc = 1 / beta(a, b)
fc = x ** (a - 1)
sc = (1 - x) ** (b - 1)
return bc * fc * sc | PYTHON | {
"dummy_field": ""
} |
|
d97 | train | def filter_out(queryset, setting_name):
"""
Remove unwanted results from queryset
"""
kwargs = helpers.get_settings().get(setting_name, {}).get('FILTER_OUT', {})
queryset = queryset.exclude(**kwargs)
return queryset | PYTHON | {
"dummy_field": ""
} |
|
d98 | train | def intToBin(i):
""" Integer to two bytes """
# divide in two parts (bytes)
i1 = i % 256
i2 = int(i / 256)
# make string (little endian)
return i.to_bytes(2, byteorder='little') | PYTHON | {
"dummy_field": ""
} |
|
d99 | train | def listlike(obj):
"""Is an object iterable like a list (and not a string)?"""
return hasattr(obj, "__iter__") \
and not issubclass(type(obj), str)\
and not issubclass(type(obj), unicode) | PYTHON | {
"dummy_field": ""
} |
|
d100 | train | def table_top_abs(self):
"""Returns the absolute position of table top"""
table_height = np.array([0, 0, self.table_full_size[2]])
return string_to_array(self.floor.get("pos")) + table_height | PYTHON | {
"dummy_field": ""
} |
End of preview. Expand
in Dataset Viewer.
Employing the CoIR evaluation framework's dataset version, utilize the code below for assessment:
import coir
from coir.data_loader import get_tasks
from coir.evaluation import COIR
from coir.models import YourCustomDEModel
model_name = "intfloat/e5-base-v2"
# Load the model
model = YourCustomDEModel(model_name=model_name)
# Get tasks
#all task ["codetrans-dl","stackoverflow-qa","apps","codefeedback-mt","codefeedback-st","codetrans-contest","synthetic-
# text2sql","cosqa","codesearchnet","codesearchnet-ccr"]
tasks = get_tasks(tasks=["codetrans-dl"])
# Initialize evaluation
evaluation = COIR(tasks=tasks,batch_size=128)
# Run evaluation
results = evaluation.run(model, output_folder=f"results/{model_name}")
print(results)
- Downloads last month
- 350