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rhinstaller/anaconda | 63edc8680f1b05cbfe11bef28703acba808c5174 | pyanaconda/core/configuration/system.py | python | SystemSection.can_activate_keyboard | (self) | return self._is_boot_iso or self._is_booted_os | Can we activate the keyboard?
FIXME: This is a temporary workaround. | Can we activate the keyboard? | [
"Can",
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"activate",
"the",
"keyboard?"
] | def can_activate_keyboard(self):
"""Can we activate the keyboard?
FIXME: This is a temporary workaround.
"""
return self._is_boot_iso or self._is_booted_os | [
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crits/crits_services | c7abf91f1865d913cffad4b966599da204f8ae43 | taxii_service/handlers.py | python | has_cybox_repr | (obj) | Determine if this indicator is of a type that can
successfully be converted to a CybOX object.
:return The CybOX representation if possible, else False. | Determine if this indicator is of a type that can
successfully be converted to a CybOX object. | [
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] | def has_cybox_repr(obj):
"""
Determine if this indicator is of a type that can
successfully be converted to a CybOX object.
:return The CybOX representation if possible, else False.
"""
try:
rep = make_cybox_object(obj.ind_type, obj.value)
return rep
except:
return False | [
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fgsect/unicorefuzz | f5b27a6dda601ef18cb166f79eb121be8e7b88bd | unicorefuzz/x64utils.py | python | get_msr | (uc: Uc, scratch: int, msr: int) | return (edx << 32) | (eax & 0xFFFFFFFF) | fetch the contents of the given model-specific register (MSR).
this will clobber some memory at the given scratch address, as it emits some code. | fetch the contents of the given model-specific register (MSR).
this will clobber some memory at the given scratch address, as it emits some code. | [
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"""
fetch the contents of the given model-specific register (MSR).
this will clobber some memory at the given scratch address, as it emits some code.
"""
# save clobbered registers
orax = uc.reg_read(UC_X86_REG_RAX)
ordx = uc.reg_read(UC_X86_REG_RDX)
orcx = uc.reg_read(UC_X86_REG_RCX)
orip = uc.reg_read(UC_X86_REG_RIP)
# x86: rdmsr
buf = b"\x0f\x32"
uc.mem_write(scratch, buf)
uc.reg_write(UC_X86_REG_RCX, msr & 0xFFFFFFFF)
uc.emu_start(scratch, scratch + len(buf), count=1)
eax = uc.reg_read(UC_X86_REG_EAX)
edx = uc.reg_read(UC_X86_REG_EDX)
# restore clobbered registers
uc.reg_write(UC_X86_REG_RAX, orax)
uc.reg_write(UC_X86_REG_RDX, ordx)
uc.reg_write(UC_X86_REG_RCX, orcx)
uc.reg_write(UC_X86_REG_RIP, orip)
return (edx << 32) | (eax & 0xFFFFFFFF) | [
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epfl-lts2/pygsp | a3412ce7696c02c8a55439e89d0c9ab8ae863269 | pygsp/features.py | python | compute_norm_tig | (g, **kwargs) | return np.linalg.norm(tig, axis=1, ord=2) | r"""
Compute the :math:`\ell_2` norm of the Tig.
See :func:`compute_tig`.
Parameters
----------
g: Filter
The filter or filter bank.
kwargs: dict
Additional parameters to be passed to the
:func:`pygsp.filters.Filter.filter` method. | r"""
Compute the :math:`\ell_2` norm of the Tig.
See :func:`compute_tig`. | [
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] | def compute_norm_tig(g, **kwargs):
r"""
Compute the :math:`\ell_2` norm of the Tig.
See :func:`compute_tig`.
Parameters
----------
g: Filter
The filter or filter bank.
kwargs: dict
Additional parameters to be passed to the
:func:`pygsp.filters.Filter.filter` method.
"""
tig = compute_tig(g, **kwargs)
return np.linalg.norm(tig, axis=1, ord=2) | [
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missionpinball/mpf | 8e6b74cff4ba06d2fec9445742559c1068b88582 | mpf/platforms/pololu/pololu_ticcmd_wrapper.py | python | PololuTiccmdWrapper.set_max_acceleration | (self, acceleration) | Set the max acceleration of the stepper.
Args:
----
acceleration (number): The maximum acceleration of the stepper in microsteps per 100 s^2 | Set the max acceleration of the stepper. | [
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"stepper",
"."
] | def set_max_acceleration(self, acceleration):
"""Set the max acceleration of the stepper.
Args:
----
acceleration (number): The maximum acceleration of the stepper in microsteps per 100 s^2
"""
self._ticcmd('--max-accel', str(int(acceleration))) | [
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InvestmentSystems/static-frame | 0b19d6969bf6c17fb0599871aca79eb3b52cf2ed | static_frame/core/frame.py | python | Frame.index | (self) | return self._index | The ``IndexBase`` instance assigned for row labels. | The ``IndexBase`` instance assigned for row labels. | [
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'''The ``IndexBase`` instance assigned for row labels.
'''
return self._index | [
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thinkle/gourmet | 8af29c8ded24528030e5ae2ea3461f61c1e5a575 | gourmet/gtk_extras/dialog_extras.py | python | ImageSelectorDialog.post_dialog | (self) | [] | def post_dialog (self):
self.preview = Gtk.Image()
self.fsd.set_preview_widget(self.preview)
self.fsd.connect('selection-changed',self.update_preview)
self.preview.show() | [
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suavecode/SUAVE | 4f83c467c5662b6cc611ce2ab6c0bdd25fd5c0a5 | trunk/SUAVE/Plugins/pint/unit.py | python | UnitRegistry.disable_contexts | (self, n=None) | Disable the last n enabled contexts. | Disable the last n enabled contexts. | [
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] | def disable_contexts(self, n=None):
"""Disable the last n enabled contexts.
"""
if n is None:
n = len(self._contexts)
self._active_ctx.remove_contexts(n) | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/rsa-3.4.2/rsa/key.py | python | AbstractKey.save_pkcs1 | (self, format='PEM') | return method() | Saves the public key in PKCS#1 DER or PEM format.
:param format: the format to save; 'PEM' or 'DER'
:returns: the DER- or PEM-encoded public key. | Saves the public key in PKCS#1 DER or PEM format. | [
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"format",
"."
] | def save_pkcs1(self, format='PEM'):
"""Saves the public key in PKCS#1 DER or PEM format.
:param format: the format to save; 'PEM' or 'DER'
:returns: the DER- or PEM-encoded public key.
"""
methods = {
'PEM': self._save_pkcs1_pem,
'DER': self._save_pkcs1_der,
}
method = self._assert_format_exists(format, methods)
return method() | [
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TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e | tensorflow_dl_models/research/object_detection/core/box_list_ops.py | python | matched_intersection | (boxlist1, boxlist2, scope=None) | Compute intersection areas between corresponding boxes in two boxlists.
Args:
boxlist1: BoxList holding N boxes
boxlist2: BoxList holding N boxes
scope: name scope.
Returns:
a tensor with shape [N] representing pairwise intersections | Compute intersection areas between corresponding boxes in two boxlists. | [
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] | def matched_intersection(boxlist1, boxlist2, scope=None):
"""Compute intersection areas between corresponding boxes in two boxlists.
Args:
boxlist1: BoxList holding N boxes
boxlist2: BoxList holding N boxes
scope: name scope.
Returns:
a tensor with shape [N] representing pairwise intersections
"""
with tf.name_scope(scope, 'MatchedIntersection'):
y_min1, x_min1, y_max1, x_max1 = tf.split(
value=boxlist1.get(), num_or_size_splits=4, axis=1)
y_min2, x_min2, y_max2, x_max2 = tf.split(
value=boxlist2.get(), num_or_size_splits=4, axis=1)
min_ymax = tf.minimum(y_max1, y_max2)
max_ymin = tf.maximum(y_min1, y_min2)
intersect_heights = tf.maximum(0.0, min_ymax - max_ymin)
min_xmax = tf.minimum(x_max1, x_max2)
max_xmin = tf.maximum(x_min1, x_min2)
intersect_widths = tf.maximum(0.0, min_xmax - max_xmin)
return tf.reshape(intersect_heights * intersect_widths, [-1]) | [
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newfies-dialer/newfies-dialer | 8168b3dd43e9f5ce73a2645b3229def1b2815d47 | newfies/dialer_campaign/function_def.py | python | get_phonebook_list | (user) | return result_list | Return phonebook list of logged in user | Return phonebook list of logged in user | [
"Return",
"phonebook",
"list",
"of",
"logged",
"in",
"user"
] | def get_phonebook_list(user):
"""Return phonebook list of logged in user"""
phonebook_list = Phonebook.objects.filter(user=user).order_by('id')
result_list = []
for phonebook in phonebook_list:
contacts_in_phonebook = phonebook.phonebook_contacts()
nbcontact = " -> %d contact(s)" % (contacts_in_phonebook)
pb_string = phonebook.name + nbcontact
result_list.append((phonebook.id, pb_string))
return result_list | [
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joaomatosf/jexboss | 338b531a2a2aee9d294f394086d6718b60526350 | jexboss.py | python | is_proxy_ok | () | [] | def is_proxy_ok():
print_and_flush(GREEN + "\n ** Checking proxy: %s **\n\n" % gl_args.proxy)
headers = {"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
"Connection": "keep-alive",
"User-Agent": get_random_user_agent()}
try:
r = gl_http_pool.request('GET', gl_args.host, redirect=False, headers=headers)
except:
print_and_flush(RED + " * Error: Failed to connect to %s using proxy %s.\n"
" See logs for more details...\n" %(gl_args.host,gl_args.proxy) + ENDC)
logging.warning("Failed to connect to %s using proxy" %gl_args.host, exc_info=traceback)
return False
if r.status == 407:
print_and_flush(RED + " * Error 407: Proxy authentication is required. \n"
" Please enter the correct login and password for authentication. \n"
" Example: -P http://proxy.com:3128 -L username:password\n" + ENDC)
logging.error("Proxy authentication failed")
return False
elif r.status == 503 or r.status == 502:
print_and_flush(RED + " * Error %s: The service %s is not availabel to your proxy. \n"
" See logs for more details...\n" %(r.status,gl_args.host)+ENDC)
logging.error("Service unavailable to your proxy")
return False
else:
return True | [
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shellphish/ictf-framework | c0384f12060cf47442a52f516c6e78bd722f208a | database/api/scripts.py | python | script_new | () | return json.dumps({"result": "success",
"id": script_id}) | The ``/script/new`` endpoint requires authentication.
It add a script to the database, and initializes its state.
Note that this endpoint requires a POST request.
It can be reached at
``/script/new?secret=<API_SECRET>``.
It requires the following inputs:
- name, an optional name of the script.
- upload_id, upload which has the payload.
- filename, the name of the file
- type, the type of the script, currently exploit, benign, getflag, or
setflag.
- state, enabled, disabled. Defaults to "enabled"
- service_id
The JSON response looks like::
{
"id" : int,
"result": ("success", "failure")
}
:return: a JSON dictionary containing status information. | The ``/script/new`` endpoint requires authentication.
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] | def script_new():
"""The ``/script/new`` endpoint requires authentication.
It add a script to the database, and initializes its state.
Note that this endpoint requires a POST request.
It can be reached at
``/script/new?secret=<API_SECRET>``.
It requires the following inputs:
- name, an optional name of the script.
- upload_id, upload which has the payload.
- filename, the name of the file
- type, the type of the script, currently exploit, benign, getflag, or
setflag.
- state, enabled, disabled. Defaults to "enabled"
- service_id
The JSON response looks like::
{
"id" : int,
"result": ("success", "failure")
}
:return: a JSON dictionary containing status information.
"""
upload_id = request.form.get("upload_id")
filename = request.form.get("filename")
type_ = request.form.get("type")
state = request.form.get("state", "enabled")
service_id = request.form.get("service_id")
if state not in ("enabled", "disabled"):
abort(400)
cursor = mysql.cursor()
# get the team_id from the uploads
cursor.execute("""SELECT team_id
FROM uploads WHERE id = %s LIMIT 1""",
(upload_id,))
result = cursor.fetchone()
team_id = result["team_id"]
# add the script
cursor.execute("""INSERT INTO scripts (type, team_id, service_id,
upload_id, filename, current_state)
VALUES (%s, %s, %s, %s, %s, %s)""",
(type_, team_id, service_id, upload_id,
filename, state))
script_id = cursor.lastrowid
# set it in the script state log
tick_id, _, _, _ = get_current_tick(cursor)
cursor.execute("""INSERT INTO script_state (script_id, state, reason, tick_id)
VALUES (%s, %s, %s, %s)""",
(script_id, state, "initial state", tick_id))
mysql.database.commit()
return json.dumps({"result": "success",
"id": script_id}) | [
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cbanack/comic-vine-scraper | 8a7071796c61a9483079ad0e9ade56fcb7596bcd | src/py/gui/forms/searchform.py | python | SearchForm.__key_was_released | (self, sender, args) | Called whenever the user releases any key on this form. | Called whenever the user releases any key on this form. | [
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] | def __key_was_released(self, sender, args):
''' Called whenever the user releases any key on this form. '''
# unhighlight the skip button bold whenever the user releases control key
if args.KeyCode == Keys.ControlKey:
self.__pressing_controlkey = False;
self.__skip_button.Text = i18n.get("SearchFormSkip") | [
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divamgupta/image-segmentation-keras | dc830bbd76371aaedbf8cb997bdedca388c544c4 | keras_segmentation/predict.py | python | set_video | (inp, video_name) | return cap, video, fps | [] | def set_video(inp, video_name):
cap = cv2.VideoCapture(inp)
fps = int(cap.get(cv2.CAP_PROP_FPS))
video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
size = (video_width, video_height)
fourcc = cv2.VideoWriter_fourcc(*"XVID")
video = cv2.VideoWriter(video_name, fourcc, fps, size)
return cap, video, fps | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-windows/x86/ldap3/utils/log.py | python | set_library_log_activation_level | (logging_level) | [] | def set_library_log_activation_level(logging_level):
if isinstance(logging_level, int):
global _logging_level
_logging_level = logging_level
else:
if log_enabled(ERROR):
log(ERROR, 'invalid library log activation level <%s> ', logging_level)
raise ValueError('invalid library log activation level') | [
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crdoconnor/strictyaml | b456066a763285532fd75cd274d4a275d8499d6c | strictyaml/utils.py | python | is_infinity | (value) | return compile(r"^[-+]?\.?(?:inf|Inf|INF)$").match(value) is not None | Is string a valid representation for positive or negative infinity?
Valid formats are:
[+/-]inf, [+/-]INF, [+/-]Inf, [+/-].inf, [+/-].INF and [+/-].Inf
>>> is_infinity(".inf")
True
>>> is_infinity("+.INF")
True
>>> is_infinity("-.Inf")
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>>> is_infinity("Inf")
True
>>> is_infinity("INF")
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>>> is_infinity("-INF")
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>>> is_infinity("infinitesimal")
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"""
Is string a valid representation for positive or negative infinity?
Valid formats are:
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>>> is_infinity(".inf")
True
>>> is_infinity("+.INF")
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>>> is_infinity("Inf")
True
>>> is_infinity("INF")
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>>> is_infinity("-INF")
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>>> is_infinity("infinitesimal")
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"""
return compile(r"^[-+]?\.?(?:inf|Inf|INF)$").match(value) is not None | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/Lib/sysconfig.py | python | get_config_var | (name) | return get_config_vars().get(name) | Return the value of a single variable using the dictionary returned by
'get_config_vars()'.
Equivalent to get_config_vars().get(name) | Return the value of a single variable using the dictionary returned by
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"""Return the value of a single variable using the dictionary returned by
'get_config_vars()'.
Equivalent to get_config_vars().get(name)
"""
return get_config_vars().get(name) | [
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greatscottgadgets/luna | 08b035c9c2b053d7edffb0d220d948b5c2ca927e | luna/gateware/interface/flash.py | python | ECP5ConfigurationFlashInterface.__init__ | (self, *, bus, use_cs=False) | Params:
bus -- The SPI bus object to connect to.
use_cs -- Whether or not the CS line should be passed through to the target device. | Params:
bus -- The SPI bus object to connect to.
use_cs -- Whether or not the CS line should be passed through to the target device. | [
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""" Params:
bus -- The SPI bus object to connect to.
use_cs -- Whether or not the CS line should be passed through to the target device.
"""
self.bus = bus
self.use_cs = use_cs
#
# I/O port
#
self.sck = Signal()
self.sdi = Signal()
self.sdo = Signal()
self.cs = Signal() | [
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qutebrowser/qutebrowser | 3a2aaaacbf97f4bf0c72463f3da94ed2822a5442 | qutebrowser/misc/crashdialog.py | python | dump_exception_info | (exc, pages, cmdhist, qobjects) | Dump exception info to stderr.
Args:
exc: An exception tuple (type, value, traceback)
pages: A list of lists of the open pages (URLs as strings)
cmdhist: A list with the command history (as strings)
qobjects: A list of all QObjects as string. | Dump exception info to stderr. | [
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] | def dump_exception_info(exc, pages, cmdhist, qobjects):
"""Dump exception info to stderr.
Args:
exc: An exception tuple (type, value, traceback)
pages: A list of lists of the open pages (URLs as strings)
cmdhist: A list with the command history (as strings)
qobjects: A list of all QObjects as string.
"""
print(file=sys.stderr)
print("\n\n===== Handling exception with --no-err-windows... =====\n\n",
file=sys.stderr)
print("\n---- Exceptions ----", file=sys.stderr)
print(''.join(traceback.format_exception(*exc)), file=sys.stderr)
print("\n---- Version info ----", file=sys.stderr)
try:
print(version.version_info(), file=sys.stderr)
except Exception:
traceback.print_exc()
print("\n---- Config ----", file=sys.stderr)
try:
print(config.instance.dump_userconfig(), file=sys.stderr)
except Exception:
traceback.print_exc()
print("\n---- Commandline args ----", file=sys.stderr)
print(' '.join(sys.argv[1:]), file=sys.stderr)
print("\n---- Open pages ----", file=sys.stderr)
print('\n\n'.join('\n'.join(e) for e in pages), file=sys.stderr)
print("\n---- Command history ----", file=sys.stderr)
print('\n'.join(cmdhist), file=sys.stderr)
print("\n---- Objects ----", file=sys.stderr)
print(qobjects, file=sys.stderr)
print("\n---- Environment ----", file=sys.stderr)
try:
print(_get_environment_vars(), file=sys.stderr)
except Exception:
traceback.print_exc() | [
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inasafe/inasafe | 355eb2ce63f516b9c26af0c86a24f99e53f63f87 | safe/report/extractors/composer.py | python | QGISComposerContext.substitution_map | (self) | return self._substitution_map | Substitution map.
:return: Substitution map containing dict mapping used in QGIS
Composition template
:rtype: dict | Substitution map. | [
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"""Substitution map.
:return: Substitution map containing dict mapping used in QGIS
Composition template
:rtype: dict
"""
return self._substitution_map | [
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GoSecure/pyrdp | abd8b8762b6d7fd0e49d4a927b529f892b412743 | pyrdp/player/gdi/cache.py | python | BrushCache.get | (self, idx: int) | [] | def get(self, idx: int) -> QBrush:
if idx in self.entries:
return self.entries[idx]
else:
return None | [
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/masked/timectrl.py | python | TimeCtrl.SetInsertionPoint | (self, pos) | This override records the specified position and associated cell before
calling base class' function. This is necessary to handle the optional
spin button, because the insertion point is lost when the focus shifts
to the spin button. | This override records the specified position and associated cell before
calling base class' function. This is necessary to handle the optional
spin button, because the insertion point is lost when the focus shifts
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"""
This override records the specified position and associated cell before
calling base class' function. This is necessary to handle the optional
spin button, because the insertion point is lost when the focus shifts
to the spin button.
"""
## dbg('TimeCtrl::SetInsertionPoint', pos, indent=1)
BaseMaskedTextCtrl.SetInsertionPoint(self, pos) # (causes EVT_TEXT event to fire)
self.__posCurrent = self.GetInsertionPoint() | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/idlelib/AutoComplete.py | python | AutoComplete.fetch_completions | (self, what, mode) | Return a pair of lists of completions for something. The first list
is a sublist of the second. Both are sorted.
If there is a Python subprocess, get the comp. list there. Otherwise,
either fetch_completions() is running in the subprocess itself or it
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The subprocess environment is that of the most recently run script. If
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"""Return a pair of lists of completions for something. The first list
is a sublist of the second. Both are sorted.
If there is a Python subprocess, get the comp. list there. Otherwise,
either fetch_completions() is running in the subprocess itself or it
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The subprocess environment is that of the most recently run script. If
two unrelated modules are being edited some calltips in the current
module may be inoperative if the module was not the last to run.
"""
try:
rpcclt = self.editwin.flist.pyshell.interp.rpcclt
except:
rpcclt = None
if rpcclt:
return rpcclt.remotecall("exec", "get_the_completion_list",
(what, mode), {})
else:
if mode == COMPLETE_ATTRIBUTES:
if what == "":
namespace = __main__.__dict__.copy()
namespace.update(__main__.__builtins__.__dict__)
bigl = eval("dir()", namespace)
bigl.sort()
if "__all__" in bigl:
smalll = eval("__all__", namespace)
smalll.sort()
else:
smalll = [s for s in bigl if s[:1] != '_']
else:
try:
entity = self.get_entity(what)
bigl = dir(entity)
bigl.sort()
if "__all__" in bigl:
smalll = entity.__all__
smalll.sort()
else:
smalll = [s for s in bigl if s[:1] != '_']
except:
return [], []
elif mode == COMPLETE_FILES:
if what == "":
what = "."
try:
expandedpath = os.path.expanduser(what)
bigl = os.listdir(expandedpath)
bigl.sort()
smalll = [s for s in bigl if s[:1] != '.']
except OSError:
return [], []
if not smalll:
smalll = bigl
return smalll, bigl | [
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pulp/pulp | a0a28d804f997b6f81c391378aff2e4c90183df9 | server/pulp/server/controllers/repository.py | python | download_deferred | () | Downloads all the units with entries in the DeferredDownload collection. | Downloads all the units with entries in the DeferredDownload collection. | [
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"""
Downloads all the units with entries in the DeferredDownload collection.
"""
task_description = _('Download Cached On-Demand Content')
deferred_content_units = _get_deferred_content_units()
download_requests = _create_download_requests(deferred_content_units)
download_step = LazyUnitDownloadStep(
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robcarver17/pysystemtrade | b0385705b7135c52d39cb6d2400feece881bcca9 | syscore/text.py | python | sort_dict_by_underscore_length | (other_args) | return sorted_list_of_dicts | Sort dict according to keys and presence of leading underscores
:param other_args: dict
:return: list of dict. First element is dict of all keys with no leading underscores.
Second element is dict of all keys with 1 leading underscore...and so on | Sort dict according to keys and presence of leading underscores | [
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"""
Sort dict according to keys and presence of leading underscores
:param other_args: dict
:return: list of dict. First element is dict of all keys with no leading underscores.
Second element is dict of all keys with 1 leading underscore...and so on
"""
other_arg_keys = list(other_args.keys())
other_arg_keys_sorted = sort_keywords_by_underscore_length(other_arg_keys)
sorted_list_of_dicts = []
for list_of_args in other_arg_keys_sorted:
extracted_dict = dict([(key, other_args[key]) for key in list_of_args])
sorted_list_of_dicts.append(extracted_dict)
return sorted_list_of_dicts | [
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llcshappy/Monocular-3D-Human-Pose | 29bc314737d549d112b15d6fdd144338fbf6cab0 | 3DLabelGen/Left2Right/predict_right_pose_1.py | python | create_model | ( session, batch_size ) | return model | Create model and initialize it or load its parameters in a session
Args
session: tensorflow session
actions: list of string. Actions to train/test on
batch_size: integer. Number of examples in each batch
Returns
model: The created (or loaded) model
Raises
ValueError if asked to load a model, but the checkpoint specified by
FLAGS.load cannot be found. | Create model and initialize it or load its parameters in a session | [
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"""
Create model and initialize it or load its parameters in a session
Args
session: tensorflow session
actions: list of string. Actions to train/test on
batch_size: integer. Number of examples in each batch
Returns
model: The created (or loaded) model
Raises
ValueError if asked to load a model, but the checkpoint specified by
FLAGS.load cannot be found.
"""
model = linear_model.LinearModel(
FLAGS.linear_size,
FLAGS.num_layers,
FLAGS.residual,
FLAGS.batch_norm,
FLAGS.max_norm,
batch_size,
FLAGS.learning_rate,
summaries_dir,
FLAGS.predict_14,
dtype=tf.float16 if FLAGS.use_fp16 else tf.float32)
if FLAGS.load <= 0:
# Create a new model from scratch
print("Creating model with fresh parameters.")
session.run( tf.global_variables_initializer() )
return model
# Load a previously saved model
ckpt = tf.train.get_checkpoint_state( train_dir, latest_filename="checkpoint")
print( "train_dir", train_dir )
if ckpt and ckpt.model_checkpoint_path:
# Check if the specific checkpoint exists
if FLAGS.load > 0:
if os.path.isfile(os.path.join(train_dir,"checkpoint-{0}.index".format(FLAGS.load))):
ckpt_name = os.path.join( os.path.join(train_dir,"checkpoint-{0}".format(FLAGS.load)) )
else:
raise ValueError("Asked to load checkpoint {0}, but it does not seem to exist".format(FLAGS.load))
else:
ckpt_name = os.path.basename( ckpt.model_checkpoint_path )
print("Loading model {0}".format( ckpt_name ))
model.saver.restore( session, ckpt.model_checkpoint_path )
return model
else:
print("Could not find checkpoint. Aborting.")
raise( ValueError, "Checkpoint {0} does not seem to exist".format( ckpt.model_checkpoint_path ) )
return model | [
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Galvant/InstrumentKit | 6d216bd7f8e9ec7918762fe5fb7a306d5bd0eb1f | instruments/newport/newportesp301.py | python | NewportESP301Axis.feedback_configuration | (self) | return int(self._newport_cmd("ZB?", target=self._axis_id)[:-2], 16) | Gets/sets the axis Feedback configuration
:type: `int` | Gets/sets the axis Feedback configuration | [
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"configuration"
] | def feedback_configuration(self):
"""
Gets/sets the axis Feedback configuration
:type: `int`
"""
return int(self._newport_cmd("ZB?", target=self._axis_id)[:-2], 16) | [
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iotaledger/iota.py | f596c1ac0d9bcbceda1cf6109cd921943a6599b3 | iota/crypto/signing.py | python | KeyIterator.advance | (self) | Advances the generator without creating a key. | Advances the generator without creating a key. | [
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"""
Advances the generator without creating a key.
"""
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SHI-Labs/Decoupled-Classification-Refinement | 16202b48eb9cbf79a9b130a98e8c209d4f24693e | faster_rcnn_dcr/symbols/resnet_v1_101_rcnn_dcn_dcr_res2.py | python | resnet_v1_101_rcnn_dcn_dcr_res2.get_symbol | (self, cfg, is_train=True) | return group | [] | def get_symbol(self, cfg, is_train=True):
if not is_train:
return self.get_symbol_test(cfg, is_train=False)
# config alias for convenient
num_classes = cfg.dataset.NUM_CLASSES
num_reg_classes = (2 if cfg.CLASS_AGNOSTIC else num_classes)
num_anchors = cfg.network.NUM_ANCHORS
# input init
if is_train:
data = mx.sym.Variable(name="data")
im_info = mx.sym.Variable(name="im_info")
gt_boxes = mx.sym.Variable(name="gt_boxes")
rpn_label = mx.sym.Variable(name='label')
rpn_bbox_target = mx.sym.Variable(name='bbox_target')
rpn_bbox_weight = mx.sym.Variable(name='bbox_weight')
else:
data = mx.sym.Variable(name="data")
im_info = mx.sym.Variable(name="im_info")
# shared convolutional layers
conv_feat, res2 = self.get_resnet_v1_conv4(data)
# res5
relu1 = self.get_resnet_v1_conv5(conv_feat)
rpn_cls_score, rpn_bbox_pred = self.get_rpn(conv_feat, num_anchors)
if is_train:
# prepare rpn data
rpn_cls_score_reshape = mx.sym.Reshape(
data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
# classification
rpn_cls_prob = mx.sym.SoftmaxOutput(data=rpn_cls_score_reshape, label=rpn_label, multi_output=True,
normalization='valid', use_ignore=True, ignore_label=-1,
name="rpn_cls_prob")
# bounding box regression
rpn_bbox_loss_ = rpn_bbox_weight * mx.sym.smooth_l1(name='rpn_bbox_loss_', scalar=3.0,
data=(rpn_bbox_pred - rpn_bbox_target))
rpn_bbox_loss = mx.sym.MakeLoss(name='rpn_bbox_loss', data=rpn_bbox_loss_,
grad_scale=1.0 / cfg.TRAIN.RPN_BATCH_SIZE)
# ROI proposal
# rpn_cls_act = mx.sym.SoftmaxActivation(
# data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_act")
rpn_cls_act = mx.sym.softmax(
data=rpn_cls_score_reshape, axis=1, name="rpn_cls_act")
rpn_cls_act_reshape = mx.sym.Reshape(
data=rpn_cls_act, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_act_reshape')
if cfg.TRAIN.CXX_PROPOSAL:
rois = mx.contrib.sym.Proposal(
cls_prob=rpn_cls_act_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
feature_stride=cfg.network.RPN_FEAT_STRIDE, scales=tuple(cfg.network.ANCHOR_SCALES),
ratios=tuple(cfg.network.ANCHOR_RATIOS),
rpn_pre_nms_top_n=cfg.TRAIN.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TRAIN.RPN_POST_NMS_TOP_N,
threshold=cfg.TRAIN.RPN_NMS_THRESH, rpn_min_size=cfg.TRAIN.RPN_MIN_SIZE)
else:
rois = mx.sym.Custom(
cls_prob=rpn_cls_act_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
op_type='proposal', feat_stride=cfg.network.RPN_FEAT_STRIDE,
scales=tuple(cfg.network.ANCHOR_SCALES), ratios=tuple(cfg.network.ANCHOR_RATIOS),
rpn_pre_nms_top_n=cfg.TRAIN.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TRAIN.RPN_POST_NMS_TOP_N,
threshold=cfg.TRAIN.RPN_NMS_THRESH, rpn_min_size=cfg.TRAIN.RPN_MIN_SIZE)
# ROI proposal target
gt_boxes_reshape = mx.sym.Reshape(data=gt_boxes, shape=(-1, 5), name='gt_boxes_reshape')
rois, label, bbox_target, bbox_weight = mx.sym.Custom(rois=rois, gt_boxes=gt_boxes_reshape,
op_type='proposal_target',
num_classes=num_reg_classes,
batch_images=cfg.TRAIN.BATCH_IMAGES,
batch_rois=cfg.TRAIN.BATCH_ROIS,
cfg=cPickle.dumps(cfg),
fg_fraction=cfg.TRAIN.FG_FRACTION)
else:
# ROI Proposal
rpn_cls_score_reshape = mx.sym.Reshape(
data=rpn_cls_score, shape=(0, 2, -1, 0), name="rpn_cls_score_reshape")
# rpn_cls_prob = mx.sym.SoftmaxActivation(
# data=rpn_cls_score_reshape, mode="channel", name="rpn_cls_prob")
rpn_cls_prob = mx.sym.softmax(
data=rpn_cls_score_reshape, axis=1, name="rpn_cls_prob")
rpn_cls_prob_reshape = mx.sym.Reshape(
data=rpn_cls_prob, shape=(0, 2 * num_anchors, -1, 0), name='rpn_cls_prob_reshape')
if cfg.TEST.CXX_PROPOSAL:
rois = mx.contrib.sym.Proposal(
cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
feature_stride=cfg.network.RPN_FEAT_STRIDE, scales=tuple(cfg.network.ANCHOR_SCALES),
ratios=tuple(cfg.network.ANCHOR_RATIOS),
rpn_pre_nms_top_n=cfg.TEST.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TEST.RPN_POST_NMS_TOP_N,
threshold=cfg.TEST.RPN_NMS_THRESH, rpn_min_size=cfg.TEST.RPN_MIN_SIZE)
else:
rois = mx.sym.Custom(
cls_prob=rpn_cls_prob_reshape, bbox_pred=rpn_bbox_pred, im_info=im_info, name='rois',
op_type='proposal', feat_stride=cfg.network.RPN_FEAT_STRIDE,
scales=tuple(cfg.network.ANCHOR_SCALES), ratios=tuple(cfg.network.ANCHOR_RATIOS),
rpn_pre_nms_top_n=cfg.TEST.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=cfg.TEST.RPN_POST_NMS_TOP_N,
threshold=cfg.TEST.RPN_NMS_THRESH, rpn_min_size=cfg.TEST.RPN_MIN_SIZE)
conv_new_1 = mx.sym.Convolution(data=relu1, kernel=(1, 1), num_filter=256, name="conv_new_1")
conv_new_1_relu = mx.sym.Activation(data=conv_new_1, act_type='relu', name='conv_new_1_relu')
offset_t = mx.contrib.sym.DeformablePSROIPooling(name='offset_t', data=conv_new_1_relu, rois=rois, group_size=1, pooled_size=7,
sample_per_part=4, no_trans=True, part_size=7, output_dim=256, spatial_scale=0.0625)
offset = mx.sym.FullyConnected(name='offset', data=offset_t, num_hidden=7 * 7 * 2, lr_mult=0.01)
offset_reshape = mx.sym.Reshape(data=offset, shape=(-1, 2, 7, 7), name="offset_reshape")
deformable_roi_pool = mx.contrib.sym.DeformablePSROIPooling(name='deformable_roi_pool', data=conv_new_1_relu, rois=rois,
trans=offset_reshape, group_size=1, pooled_size=7, sample_per_part=4,
no_trans=False, part_size=7, output_dim=256, spatial_scale=0.0625, trans_std=0.1)
# 2 fc
fc_new_1 = mx.sym.FullyConnected(name='fc_new_1', data=deformable_roi_pool, num_hidden=1024)
fc_new_1_relu = mx.sym.Activation(data=fc_new_1, act_type='relu', name='fc_new_1_relu')
fc_new_2 = mx.sym.FullyConnected(name='fc_new_2', data=fc_new_1_relu, num_hidden=1024)
fc_new_2_relu = mx.sym.Activation(data=fc_new_2, act_type='relu', name='fc_new_2_relu')
# cls_score/bbox_pred
cls_score = mx.sym.FullyConnected(name='cls_score', data=fc_new_2_relu, num_hidden=num_classes)
bbox_pred = mx.sym.FullyConnected(name='bbox_pred', data=fc_new_2_relu, num_hidden=num_reg_classes * 4)
if is_train:
if cfg.TRAIN.ENABLE_OHEM:
labels_ohem, bbox_weights_ohem = mx.sym.Custom(op_type='BoxAnnotatorOHEM', num_classes=num_classes,
num_reg_classes=num_reg_classes,
roi_per_img=cfg.TRAIN.BATCH_ROIS_OHEM,
cls_score=cls_score, bbox_pred=bbox_pred, labels=label,
bbox_targets=bbox_target, bbox_weights=bbox_weight)
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=labels_ohem,
normalization='valid', use_ignore=True, ignore_label=-1)
bbox_loss_ = bbox_weights_ohem * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0,
data=(bbox_pred - bbox_target))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_,
grad_scale=1.0 / cfg.TRAIN.BATCH_ROIS_OHEM)
rcnn_label = labels_ohem
else:
cls_prob = mx.sym.SoftmaxOutput(name='cls_prob', data=cls_score, label=label, normalization='valid')
bbox_loss_ = bbox_weight * mx.sym.smooth_l1(name='bbox_loss_', scalar=1.0,
data=(bbox_pred - bbox_target))
bbox_loss = mx.sym.MakeLoss(name='bbox_loss', data=bbox_loss_, grad_scale=1.0 / cfg.TRAIN.BATCH_ROIS)
rcnn_label = label
else:
cls_prob = mx.sym.softmax(name='cls_prob', data=cls_score)
# add dcr-v2 module
# TODO: move roi pooling to earlier stage (res3, res4)?
if is_train:
dcr_rois, dcr_label = mx.sym.Custom(
rois=rois, cls_prob=cls_prob, bbox_pred=bbox_pred, im_info=im_info, gt_boxes=gt_boxes_reshape,
name='dcr_rois', op_type='dcr_target',
cfg=cPickle.dumps(cfg))
else:
# dcr_rois = mx.sym.Custom(
# rois=rois, bbox_pred=bbox_pred, im_info=im_info,
# name='dcr_rois', op_type='dcr_proposal')
dcr_rois = mx.sym.Custom(
rois=rois, cls_prob=cls_prob, bbox_pred=bbox_pred, im_info=im_info,
name='dcr_rois', op_type='dcr_proposal',
cfg=cPickle.dumps(cfg))
res2_roi_pool = mx.symbol.ROIPooling(
name='res2_roi_pool', data=res2, rois=dcr_rois, pooled_size=(56, 56), spatial_scale=0.25)
dcr_branch = self.dcr_branch(res2_roi_pool, dilate=cfg.DCR.dilate)
pool1 = mx.sym.Pooling(
data=dcr_branch, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')
flat = mx.sym.Flatten(data=pool1)
dcr_fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='dcr_fc1')
if is_train:
dcr_prob = mx.sym.SoftmaxOutput(name='dcr_prob', data=dcr_fc1, label=dcr_label, normalization='valid')
# reshape output
rcnn_label = mx.sym.Reshape(data=rcnn_label, shape=(cfg.TRAIN.BATCH_IMAGES, -1), name='label_reshape')
dcr_label = mx.sym.Reshape(data=dcr_label, shape=(cfg.TRAIN.BATCH_IMAGES, -1), name='dcr_label_reshape')
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TRAIN.BATCH_IMAGES, -1, num_classes),
name='cls_prob_reshape')
dcr_prob = mx.sym.Reshape(data=dcr_prob, shape=(cfg.TRAIN.BATCH_IMAGES, -1, num_classes),
name='dcr_prob_reshape')
bbox_loss = mx.sym.Reshape(data=bbox_loss, shape=(cfg.TRAIN.BATCH_IMAGES, -1, 4 * num_reg_classes),
name='bbox_loss_reshape')
group = mx.sym.Group([rpn_cls_prob, rpn_bbox_loss, cls_prob, bbox_loss, dcr_prob,
mx.sym.BlockGrad(rcnn_label), mx.sym.BlockGrad(dcr_label)])
else:
# cls_prob = mx.sym.SoftmaxActivation(name='cls_prob', data=cls_score)
# cls_prob = mx.sym.softmax(name='cls_prob', data=cls_score)
cls_prob = mx.sym.Reshape(data=cls_prob, shape=(cfg.TEST.BATCH_IMAGES, -1, num_classes),
name='cls_prob_reshape')
dcr_prob = mx.sym.softmax(name='dcr_prob', data=dcr_fc1)
dcr_prob = mx.sym.Reshape(data=dcr_prob, shape=(cfg.TEST.BATCH_IMAGES, -1, num_classes),
name='dcr_prob_reshape')
bbox_pred = mx.sym.Reshape(data=bbox_pred, shape=(cfg.TEST.BATCH_IMAGES, -1, 4 * num_reg_classes),
name='bbox_pred_reshape')
group = mx.sym.Group([rois, cls_prob, bbox_pred, dcr_prob])
self.sym = group
return group | [
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wwqgtxx/wwqLyParse | 33136508e52821babd9294fdecffbdf02d73a6fc | wwqLyParse/common/js_engine.py | python | VM.call | (self, function_name, *args) | return await self.communicate({
"action": "call",
"functionName": function_name,
"args": args
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:param str function_name: The function to call.
:param args: Function arguments.
function_name can include "." to call functions on an object. However,
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.. code-block:: javascript
var func = vm.run("function.to.call");
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"""Call a function and return the result.
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:param args: Function arguments.
function_name can include "." to call functions on an object. However,
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.. code-block:: javascript
var func = vm.run("function.to.call");
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google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/serene/preprocessing.py | python | filter_evidence_fn | (
example, y_input) | Filter out claims/evidence that have zero length.
Args:
example: The encoded example
y_input: Unused, contains the label, included for API compat
Returns:
True to preserve example, False to filter it out | Filter out claims/evidence that have zero length. | [
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example, y_input):
# pylint: disable=unused-argument
"""Filter out claims/evidence that have zero length.
Args:
example: The encoded example
y_input: Unused, contains the label, included for API compat
Returns:
True to preserve example, False to filter it out
"""
# Bert encodes text in evidence_text_word_ids.
# Word embedding model uses evidence_text.
if 'evidence_text_word_ids' in example:
evidence_length = len(example['evidence_text_word_ids'])
else:
evidence_length = len(example['evidence_text'])
# Explicit length check required.
# Implicit length check causes TensorFlow to fail during tracing.
if evidence_length != 0:
return True
else:
return False | [
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dictation-toolbox/aenea | dfd679720b90f92340d4a8cbd4603cab37f18804 | client/aenea/alias.py | python | Alias.discard | (self, string_or_alias) | Remove string_or_alias if it is present. | Remove string_or_alias if it is present. | [
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"""Remove string_or_alias if it is present."""
self._regex = None
sora = string_or_alias
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for alias in self._map[sora]:
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self._map[string].remove(sora)
del self._rmap[sora] | [
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WerWolv/EdiZon_CheatsConfigsAndScripts | d16d36c7509c01dca770f402babd83ff2e9ae6e7 | Scripts/lib/python3.5/inspect.py | python | getmoduleinfo | (path) | Get the module name, suffix, mode, and module type for a given file. | Get the module name, suffix, mode, and module type for a given file. | [
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"""Get the module name, suffix, mode, and module type for a given file."""
warnings.warn('inspect.getmoduleinfo() is deprecated', DeprecationWarning,
2)
with warnings.catch_warnings():
warnings.simplefilter('ignore', PendingDeprecationWarning)
import imp
filename = os.path.basename(path)
suffixes = [(-len(suffix), suffix, mode, mtype)
for suffix, mode, mtype in imp.get_suffixes()]
suffixes.sort() # try longest suffixes first, in case they overlap
for neglen, suffix, mode, mtype in suffixes:
if filename[neglen:] == suffix:
return ModuleInfo(filename[:neglen], suffix, mode, mtype) | [
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exaile/exaile | a7b58996c5c15b3aa7b9975ac13ee8f784ef4689 | xlgui/widgets/playback.py | python | SeekProgressBar.do_size_allocate | (self, allocation) | Recalculates the marker points | Recalculates the marker points | [
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"""
Recalculates the marker points
"""
oldallocation = self.get_allocation()
Gtk.EventBox.do_size_allocate(self, allocation)
if allocation != oldallocation:
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self._points[marker] = self._get_points(marker) | [
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openimages/dataset | 077282972acd0ad8628f1526760ad239a38a8a97 | downloader.py | python | download_all_images | (args) | Downloads all images specified in the input file. | Downloads all images specified in the input file. | [
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] | def download_all_images(args):
"""Downloads all images specified in the input file."""
bucket = boto3.resource(
's3', config=botocore.config.Config(
signature_version=botocore.UNSIGNED)).Bucket(BUCKET_NAME)
download_folder = args['download_folder'] or os.getcwd()
if not os.path.exists(download_folder):
os.makedirs(download_folder)
try:
image_list = list(
check_and_homogenize_image_list(
read_image_list_file(args['image_list'])))
except ValueError as exception:
sys.exit(exception)
progress_bar = tqdm.tqdm(
total=len(image_list), desc='Downloading images', leave=True)
with futures.ThreadPoolExecutor(
max_workers=args['num_processes']) as executor:
all_futures = [
executor.submit(download_one_image, bucket, split, image_id,
download_folder) for (split, image_id) in image_list
]
for future in futures.as_completed(all_futures):
future.result()
progress_bar.update(1)
progress_bar.close() | [
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google-research/tensorflow_constrained_optimization | 723d63f8567aaa988c4ce4761152beee2b462e1d | tensorflow_constrained_optimization/python/rates/deferred_tensor.py | python | DeferredTensorInputList.__add__ | (self, other) | return result | Appends two `DeferredTensorInputList`s. | Appends two `DeferredTensorInputList`s. | [
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"""Appends two `DeferredTensorInputList`s."""
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result.append(element)
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chubin/cheat.sh | 46d1a5f73c6b88da15d809154245dbf234e9479e | lib/adapter/adapter.py | python | Adapter.save_state | (cls, state) | Save state `state` of the repository.
Must be called after the cache clean up. | Save state `state` of the repository.
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Save state `state` of the repository.
Must be called after the cache clean up.
"""
local_repository_dir = cls.local_repository_location()
state_filename = os.path.join(local_repository_dir, '.cached_revision')
open(state_filename, 'w').write(state) | [
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google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/search_agents/muzero/utils.py | python | dcg_score | (relevances: List[float]) | return float(
sum([
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])) | DCG score computation.
Args:
relevances: List[float], The list of relevance scores.
Returns:
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"""DCG score computation.
Args:
relevances: List[float], The list of relevance scores.
Returns:
The discounted cumulative gain for k == len(relevances).
"""
return float(
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JustDoPython/python-100-day | 4e75007195aa4cdbcb899aeb06b9b08996a4606c | wsfriends/wxfriends.py | python | WxFriends.show | (self) | [] | def show(self):
labels = self.province_dict.keys()
means = self.province_dict.values()
index = np.arange(len(labels)) + 1
# 方块宽度
width = 0.5
# 透明度
opacity = 0.4
fig, ax = plt.subplots()
rects = ax.bar(index + width, means, width, alpha=opacity, color='blue', label='省份')
self.auto_label(rects)
ax.set_ylabel('数量', fontproperties=self.get_chinese_font())
ax.set_title('好友省份分布情况', fontproperties=self.get_chinese_font())
ax.set_xticks(index + width)
ax.set_xticklabels(labels, fontproperties=self.get_chinese_font())
# 将x轴标签竖列
plt.xticks(rotation=90)
# 设置y轴数值上下限
plt.ylim(0, 100)
plt.tight_layout()
ax.legend()
fig.tight_layout()
plt.show() | [
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TengXiaoDai/DistributedCrawling | f5c2439e6ce68dd9b49bde084d76473ff9ed4963 | Lib/site-packages/setuptools/command/bdist_egg.py | python | bdist_egg.copy_metadata_to | (self, target_dir) | Copy metadata (egg info) to the target_dir | Copy metadata (egg info) to the target_dir | [
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# normalize the path (so that a forward-slash in egg_info will
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norm_egg_info = os.path.normpath(self.egg_info)
prefix = os.path.join(norm_egg_info, '')
for path in self.ei_cmd.filelist.files:
if path.startswith(prefix):
target = os.path.join(target_dir, path[len(prefix):])
ensure_directory(target)
self.copy_file(path, target) | [
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aws-samples/aws-kube-codesuite | ab4e5ce45416b83bffb947ab8d234df5437f4fca | src/urllib3/_collections.py | python | HTTPHeaderDict.add | (self, key, val) | Adds a (name, value) pair, doesn't overwrite the value if it already
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>>> headers = HTTPHeaderDict(foo='bar')
>>> headers.add('Foo', 'baz')
>>> headers['foo']
'bar, baz' | Adds a (name, value) pair, doesn't overwrite the value if it already
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>>> headers = HTTPHeaderDict(foo='bar')
>>> headers.add('Foo', 'baz')
>>> headers['foo']
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key_lower = key.lower()
new_vals = [key, val]
# Keep the common case aka no item present as fast as possible
vals = self._container.setdefault(key_lower, new_vals)
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rwl/PYPOWER | f5be0406aa54dcebded075de075454f99e2a46e6 | pypower/dSbus_dV.py | python | dSbus_dV | (Ybus, V) | return dS_dVm, dS_dVa | Computes partial derivatives of power injection w.r.t. voltage.
Returns two matrices containing partial derivatives of the complex bus
power injections w.r.t voltage magnitude and voltage angle respectively
(for all buses). If C{Ybus} is a sparse matrix, the return values will be
also. The following explains the expressions used to form the matrices::
S = diag(V) * conj(Ibus) = diag(conj(Ibus)) * V
Partials of V & Ibus w.r.t. voltage magnitudes::
dV/dVm = diag(V / abs(V))
dI/dVm = Ybus * dV/dVm = Ybus * diag(V / abs(V))
Partials of V & Ibus w.r.t. voltage angles::
dV/dVa = j * diag(V)
dI/dVa = Ybus * dV/dVa = Ybus * j * diag(V)
Partials of S w.r.t. voltage magnitudes::
dS/dVm = diag(V) * conj(dI/dVm) + diag(conj(Ibus)) * dV/dVm
= diag(V) * conj(Ybus * diag(V / abs(V)))
+ conj(diag(Ibus)) * diag(V / abs(V))
Partials of S w.r.t. voltage angles::
dS/dVa = diag(V) * conj(dI/dVa) + diag(conj(Ibus)) * dV/dVa
= diag(V) * conj(Ybus * j * diag(V))
+ conj(diag(Ibus)) * j * diag(V)
= -j * diag(V) * conj(Ybus * diag(V))
+ conj(diag(Ibus)) * j * diag(V)
= j * diag(V) * conj(diag(Ibus) - Ybus * diag(V))
For more details on the derivations behind the derivative code used
in PYPOWER information, see:
[TN2] R. D. Zimmerman, "AC Power Flows, Generalized OPF Costs and
their Derivatives using Complex Matrix Notation", MATPOWER
Technical Note 2, February 2010.
U{http://www.pserc.cornell.edu/matpower/TN2-OPF-Derivatives.pdf}
@author: Ray Zimmerman (PSERC Cornell) | Computes partial derivatives of power injection w.r.t. voltage. | [
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"""Computes partial derivatives of power injection w.r.t. voltage.
Returns two matrices containing partial derivatives of the complex bus
power injections w.r.t voltage magnitude and voltage angle respectively
(for all buses). If C{Ybus} is a sparse matrix, the return values will be
also. The following explains the expressions used to form the matrices::
S = diag(V) * conj(Ibus) = diag(conj(Ibus)) * V
Partials of V & Ibus w.r.t. voltage magnitudes::
dV/dVm = diag(V / abs(V))
dI/dVm = Ybus * dV/dVm = Ybus * diag(V / abs(V))
Partials of V & Ibus w.r.t. voltage angles::
dV/dVa = j * diag(V)
dI/dVa = Ybus * dV/dVa = Ybus * j * diag(V)
Partials of S w.r.t. voltage magnitudes::
dS/dVm = diag(V) * conj(dI/dVm) + diag(conj(Ibus)) * dV/dVm
= diag(V) * conj(Ybus * diag(V / abs(V)))
+ conj(diag(Ibus)) * diag(V / abs(V))
Partials of S w.r.t. voltage angles::
dS/dVa = diag(V) * conj(dI/dVa) + diag(conj(Ibus)) * dV/dVa
= diag(V) * conj(Ybus * j * diag(V))
+ conj(diag(Ibus)) * j * diag(V)
= -j * diag(V) * conj(Ybus * diag(V))
+ conj(diag(Ibus)) * j * diag(V)
= j * diag(V) * conj(diag(Ibus) - Ybus * diag(V))
For more details on the derivations behind the derivative code used
in PYPOWER information, see:
[TN2] R. D. Zimmerman, "AC Power Flows, Generalized OPF Costs and
their Derivatives using Complex Matrix Notation", MATPOWER
Technical Note 2, February 2010.
U{http://www.pserc.cornell.edu/matpower/TN2-OPF-Derivatives.pdf}
@author: Ray Zimmerman (PSERC Cornell)
"""
ib = range(len(V))
if issparse(Ybus):
Ibus = Ybus * V
diagV = sparse((V, (ib, ib)))
diagIbus = sparse((Ibus, (ib, ib)))
diagVnorm = sparse((V / abs(V), (ib, ib)))
else:
Ibus = Ybus * asmatrix(V).T
diagV = asmatrix(diag(V))
diagIbus = asmatrix(diag( asarray(Ibus).flatten() ))
diagVnorm = asmatrix(diag(V / abs(V)))
dS_dVm = diagV * conj(Ybus * diagVnorm) + conj(diagIbus) * diagVnorm
dS_dVa = 1j * diagV * conj(diagIbus - Ybus * diagV)
return dS_dVm, dS_dVa | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/xml/sax/saxutils.py | python | XMLFilterBase.parse | (self, source) | [] | def parse(self, source):
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aleju/imgaug | 0101108d4fed06bc5056c4a03e2bcb0216dac326 | imgaug/augmenters/blend.py | python | FrequencyNoiseAlpha | (exponent=(-4, 4), first=None, second=None,
per_channel=False, size_px_max=(4, 16),
upscale_method=None,
iterations=(1, 3), aggregation_method=["avg", "max"],
sigmoid=0.5, sigmoid_thresh=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated") | return BlendAlphaFrequencyNoise(
exponent=exponent,
foreground=first,
background=second,
per_channel=per_channel,
size_px_max=size_px_max,
upscale_method=upscale_method,
iterations=iterations,
aggregation_method=aggregation_method,
sigmoid=sigmoid,
sigmoid_thresh=sigmoid_thresh,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic) | See :class:`BlendAlphaFrequencyNoise`.
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] | def FrequencyNoiseAlpha(exponent=(-4, 4), first=None, second=None,
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upscale_method=None,
iterations=(1, 3), aggregation_method=["avg", "max"],
sigmoid=0.5, sigmoid_thresh=None,
seed=None, name=None,
random_state="deprecated", deterministic="deprecated"):
"""See :class:`BlendAlphaFrequencyNoise`.
Deprecated since 0.4.0.
"""
# pylint: disable=invalid-name, dangerous-default-value
return BlendAlphaFrequencyNoise(
exponent=exponent,
foreground=first,
background=second,
per_channel=per_channel,
size_px_max=size_px_max,
upscale_method=upscale_method,
iterations=iterations,
aggregation_method=aggregation_method,
sigmoid=sigmoid,
sigmoid_thresh=sigmoid_thresh,
seed=seed, name=name,
random_state=random_state, deterministic=deterministic) | [
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sabri-zaki/EasY_HaCk | 2a39ac384dd0d6fc51c0dd22e8d38cece683fdb9 | .modules/.sqlmap/thirdparty/fcrypt/fcrypt.py | python | _test | () | return doctest.testmod(fcrypt) | Run doctest on fcrypt module. | Run doctest on fcrypt module. | [
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kubernetes-client/python | 47b9da9de2d02b2b7a34fbe05afb44afd130d73a | kubernetes/client/models/v1_container.py | python | V1Container.__ne__ | (self, other) | return self.to_dict() != other.to_dict() | Returns true if both objects are not equal | Returns true if both objects are not equal | [
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nsacyber/WALKOFF | 52d3311abe99d64cd2a902eb998c5e398efe0e07 | common/walkoff_client/walkoff_client/api/apps_api.py | python | AppsApi.create_app_api | (self, app_api, **kwargs) | return self.create_app_api_with_http_info(app_api, **kwargs) | Create app api # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_app_api(app_api, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param AppApi app_api: The app api object to be created (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: AppApi
If the method is called asynchronously,
returns the request thread. | Create app api # noqa: E501 | [
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] | def create_app_api(self, app_api, **kwargs): # noqa: E501
"""Create app api # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_app_api(app_api, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param AppApi app_api: The app api object to be created (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: AppApi
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.create_app_api_with_http_info(app_api, **kwargs) | [
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ktbyers/pynet | f01ca44afe1db1e64828fc93028f67410174719e | pyth_ans_ecourse/class8/ex5_db_show_version.py | python | main | () | Use Netmiko to connect to each of the devices in the database. Execute
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Use Netmiko to connect to each of the devices in the database. Execute
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django.setup()
start_time = datetime.now()
devices = NetworkDevice.objects.all()
for a_device in devices:
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elapsed_time = datetime.now() - start_time
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/idlelib/configDialog.py | python | ConfigDialog.AddChangedItem | (self, typ, section, item, value) | [] | def AddChangedItem(self, typ, section, item, value):
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entropy1337/infernal-twin | 10995cd03312e39a48ade0f114ebb0ae3a711bb8 | Modules/build/reportlab/src/reportlab/graphics/charts/axes.py | python | sample5c | () | return drawing | Sample drawing, xvalue/yvalue axes, y connected at right of x. | Sample drawing, xvalue/yvalue axes, y connected at right of x. | [
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"Sample drawing, xvalue/yvalue axes, y connected at right of x."
drawing = Drawing(400, 200)
data = [(10, 20, 30, 42)]
xAxis = XValueAxis()
xAxis.setPosition(50, 50, 300)
xAxis.configure(data)
yAxis = YValueAxis()
yAxis.setPosition(50, 50, 125)
yAxis.joinAxis = xAxis
yAxis.joinAxisMode = 'right'
yAxis.configure(data)
drawing.add(xAxis)
drawing.add(yAxis)
return drawing | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/pysaml2-4.9.0/src/saml2/assertion.py | python | _authn_context_decl_ref | (decl_ref, authn_auth=None) | return factory(saml.AuthnContext,
authn_context_decl_ref=decl_ref,
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KoreLogicSecurity/mastiff | 04d569e4fa59513572e77c74b049cad82f9b0310 | mastiff/plugins/output/__init__.py | python | table.addtitle | (self, title=None) | Add a title to the table. | Add a title to the table. | [
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nschloe/quadpy | c4c076d8ddfa968486a2443a95e2fb3780dcde0f | src/quadpy/t2/_lyness_jespersen.py | python | lyness_jespersen_02 | () | return T2Scheme("Lyness-Jespersen 2", d, 2, source) | [] | def lyness_jespersen_02():
d = {
"centroid": [[frac(3, 4)]],
"vertex": [[frac(1, 12)]],
}
return T2Scheme("Lyness-Jespersen 2", d, 2, source) | [
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ecdavis/pants | 88129d24020e95b71e8d0260a111dc0b457b0676 | pants/contrib/irc.py | python | BaseIRC.send_command | (self, command, *args, **kwargs) | Send a command to the remote endpoint.
========= ======== ============
Argument Default Description
========= ======== ============
command The command to send.
\*args *Optional.* A list of arguments to send with the command.
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"""
Send a command to the remote endpoint.
========= ======== ============
Argument Default Description
========= ======== ============
command The command to send.
\*args *Optional.* A list of arguments to send with the command.
_prefix None *Optional.* A string that, if provided, will be prepended to the command string before it's sent to the server.
========= ======== ============
"""
if args:
args = list(args)
for i in xrange(len(args)):
arg = args[i]
if not isinstance(arg, basestring):
args[i] = str(arg)
if not args[-1].startswith(':'):
args[-1] = ':%s' % args[-1]
out = '%s %s\r\n' % (command, ' '.join(args))
else:
out = '%s\r\n' % command
if '_prefix' in kwargs and kwargs['_prefix']:
out = '%s %s' % (kwargs['_prefix'], out)
elif self.prefix:
out = '%s %s' % (self.prefix, out)
# Send it.
log.debug('\x1B[0;32m>> %s\x1B[0m' % out.rstrip())
self.write(out.encode(self.encoding)) | [
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OpenMDAO/OpenMDAO | f47eb5485a0bb5ea5d2ae5bd6da4b94dc6b296bd | openmdao/components/linear_system_comp.py | python | LinearSystemComp.__init__ | (self, **kwargs) | Intialize the LinearSystemComp component. | Intialize the LinearSystemComp component. | [
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"""
Intialize the LinearSystemComp component.
"""
super().__init__(**kwargs)
self._lup = None
self._no_check_partials = True | [
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clinton-hall/nzbToMedia | 27669389216902d1085660167e7bda0bd8527ecf | libs/common/pytz/tzinfo.py | python | StaticTzInfo.localize | (self, dt, is_dst=False) | return dt.replace(tzinfo=self) | Convert naive time to local time | Convert naive time to local time | [
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'''Convert naive time to local time'''
if dt.tzinfo is not None:
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return dt.replace(tzinfo=self) | [
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microsoft/MPNet | 081523a788c1556f28dd90cbc629810f48b083fb | pretraining/fairseq/data/concat_dataset.py | python | ConcatDataset.ordered_indices | (self) | return np.argsort(self.sizes) | Returns indices sorted by length. So less padding is needed. | Returns indices sorted by length. So less padding is needed. | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/urllib3/contrib/_securetransport/low_level.py | python | _temporary_keychain | () | return keychain, tempdirectory | This function creates a temporary Mac keychain that we can use to work with
credentials. This keychain uses a one-time password and a temporary file to
store the data. We expect to have one keychain per socket. The returned
SecKeychainRef must be freed by the caller, including calling
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Returns a tuple of the SecKeychainRef and the path to the temporary
directory that contains it. | This function creates a temporary Mac keychain that we can use to work with
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"""
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store the data. We expect to have one keychain per socket. The returned
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Returns a tuple of the SecKeychainRef and the path to the temporary
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"""
# Unfortunately, SecKeychainCreate requires a path to a keychain. This
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# ask for 40 random bytes.
random_bytes = os.urandom(40)
filename = base64.b16encode(random_bytes[:8]).decode('utf-8')
password = base64.b16encode(random_bytes[8:]) # Must be valid UTF-8
tempdirectory = tempfile.mkdtemp()
keychain_path = os.path.join(tempdirectory, filename).encode('utf-8')
# We now want to create the keychain itself.
keychain = Security.SecKeychainRef()
status = Security.SecKeychainCreate(
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None,
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# Having created the keychain, we want to pass it off to the caller.
return keychain, tempdirectory | [
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JinnLynn/genpac | 2f466d28f403a9a5624e02edcd538475fe475fc8 | genpac/formats.py | python | FmtBase.tpl | (self) | return text_type(self._default_tpl) | [] | def tpl(self):
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benedekrozemberczki/DANMF | 6726fbfb9a1d4f8a19650ee89773e1c544329321 | src/danmf.py | python | DANMF.update_V | (self, i) | Updating right hand factors.
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"""
Updating right hand factors.
:param i: Layer index.
"""
if i < self.p-1:
Vu = 2*self.A.dot(self.P).T
Vd = self.P.T.dot(self.P).dot(self.V_s[i])+self.V_s[i]
self.V_s[i] = self.V_s[i] * Vu/np.maximum(Vd, 10**-10)
else:
Vu = 2*self.A.dot(self.P).T+(self.args.lamb*self.A.dot(self.V_s[i].T)).T
Vd = self.P.T.dot(self.P).dot(self.V_s[i])
Vd = Vd + self.V_s[i]+(self.args.lamb*self.D.dot(self.V_s[i].T)).T
self.V_s[i] = self.V_s[i] * Vu/np.maximum(Vd, 10**-10) | [
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frappe/erpnext | 9d36e30ef7043b391b5ed2523b8288bf46c45d18 | erpnext/manufacturing/doctype/work_order/work_order.py | python | WorkOrder.update_reserved_qty_for_production | (self, items=None) | update reserved_qty_for_production in bins | update reserved_qty_for_production in bins | [
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'''update reserved_qty_for_production in bins'''
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if d.source_warehouse:
stock_bin = get_bin(d.item_code, d.source_warehouse)
stock_bin.update_reserved_qty_for_production() | [
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lena-voita/the-story-of-heads | efaa0dd520400baa760654b5b85396c203d3cbb7 | lib/train/tickers.py | python | DevSubscriber.after_dev_run | (self, dev_name, dev_run_values) | Called each dev step after evaluating ops and provides their results | Called each dev step after evaluating ops and provides their results | [
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"""
Called each dev step after evaluating ops and provides their results
"""
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/bz2.py | python | BZ2File.readline | (self, size=-1) | return self._buffer.readline(size) | Read a line of uncompressed bytes from the file.
The terminating newline (if present) is retained. If size is
non-negative, no more than size bytes will be read (in which
case the line may be incomplete). Returns b'' if already at EOF. | Read a line of uncompressed bytes from the file. | [
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"""Read a line of uncompressed bytes from the file.
The terminating newline (if present) is retained. If size is
non-negative, no more than size bytes will be read (in which
case the line may be incomplete). Returns b'' if already at EOF.
"""
if not isinstance(size, int):
if not hasattr(size, "__index__"):
raise TypeError("Integer argument expected")
size = size.__index__()
self._check_can_read()
return self._buffer.readline(size) | [
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readthedocs/readthedocs.org | 0852d7c10d725d954d3e9a93513171baa1116d9f | readthedocs/projects/tasks.py | python | UpdateDocsTaskStep.setup_python_environment | (self) | Build the virtualenv and install the project into it.
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Build the virtualenv and install the project into it.
Always build projects with a virtualenv.
:param build_env: Build environment to pass commands and execution through.
"""
self.build_env.update_build(state=BUILD_STATE_INSTALLING)
# Check if the python version/build image in the current venv is the
# same to be used in this build and if it differs, wipe the venv to
# avoid conflicts.
if self.python_env.is_obsolete:
self.python_env.delete_existing_venv_dir()
else:
self.python_env.delete_existing_build_dir()
# Install all ``build.tools`` specified by the user
if self.config.using_build_tools:
self.python_env.install_build_tools()
self.python_env.setup_base()
self.python_env.save_environment_json()
self.python_env.install_core_requirements()
self.python_env.install_requirements()
if self.project.has_feature(Feature.LIST_PACKAGES_INSTALLED_ENV):
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wummel/linkchecker | c2ce810c3fb00b895a841a7be6b2e78c64e7b042 | linkcheck/checker/fileurl.py | python | FileUrl.is_directory | (self) | return os.path.isdir(filename) and not os.path.islink(filename) | Check if file is a directory.
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jasperproject/jasper-client | 85accbb30a2bc97995ab7991d71941885b848cfa | client/app_utils.py | python | sendEmail | (SUBJECT, BODY, TO, FROM, SENDER, PASSWORD, SMTP_SERVER) | Sends an HTML email. | Sends an HTML email. | [
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"""Sends an HTML email."""
for body_charset in 'US-ASCII', 'ISO-8859-1', 'UTF-8':
try:
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pass
else:
break
msg = MIMEText(BODY.encode(body_charset), 'html', body_charset)
msg['From'] = SENDER
msg['To'] = TO
msg['Subject'] = SUBJECT
SMTP_PORT = 587
session = smtplib.SMTP(SMTP_SERVER, SMTP_PORT)
session.starttls()
session.login(FROM, PASSWORD)
session.sendmail(SENDER, TO, msg.as_string())
session.quit() | [
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Map-A-Droid/MAD | 81375b5c9ccc5ca3161eb487aa81469d40ded221 | mapadroid/worker/WorkerQuests.py | python | WorkerQuests._post_move_location_routine | (self, timestamp: float) | [] | def _post_move_location_routine(self, timestamp: float):
if self._stop_worker_event.is_set():
raise InternalStopWorkerException
position_type = self._mapping_manager.routemanager_get_position_type(self._routemanager_name,
self._origin)
if position_type is None:
self.logger.warning("Mappings/Routemanagers have changed, stopping worker to be created again")
raise InternalStopWorkerException
if self.get_devicesettings_value('rotate_on_lvl_30', False) and \
self._mitm_mapper.get_playerlevel(self._origin) >= 30 and self._level_mode:
# switch if player lvl >= 30
self.switch_account()
with self._work_mutex:
if not self._mapping_manager.routemanager_get_init(self._routemanager_name):
self.logger.info("Processing Stop / Quest...")
on_main_menu = self._check_pogo_main_screen(10, False)
if not on_main_menu:
self._restart_pogo(mitm_mapper=self._mitm_mapper)
self.logger.info('Open Stop')
self._stop_process_time = math.floor(time.time())
type_received: LatestReceivedType = self._try_to_open_pokestop(timestamp)
if type_received is not None and type_received == LatestReceivedType.STOP:
self._handle_stop(self._stop_process_time)
else:
self.logger.debug('Currently in INIT Mode - no Stop processing')
time.sleep(5) | [
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saymedia/remoteobjects | d250e9a0e53c53744cb16c5fb2dc136df3785c1f | remoteobjects/promise.py | python | PromiseObject._set_api_data | (self, value) | [] | def _set_api_data(self, value):
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/polys/polyclasses.py | python | DMP.eject | (f, dom, front=False) | return f.__class__(F, dom, f.lev - len(dom.symbols)) | Eject selected generators into the ground domain. | Eject selected generators into the ground domain. | [
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F = dmp_eject(f.rep, f.lev, dom, front=front)
return f.__class__(F, dom, f.lev - len(dom.symbols)) | [
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wangheda/youtube-8m | 07e54b387ee027cb58b0c14f5eb7c88cfa516d58 | youtube-8m-zhangteng/eval_embedding.py | python | get_input_evaluation_tensors | (reader,
data_pattern,
batch_size=1024,
num_readers=1) | Creates the section of the graph which reads the evaluation data.
Args:
reader: A class which parses the training data.
data_pattern: A 'glob' style path to the data files.
batch_size: How many examples to process at a time.
num_readers: How many I/O threads to use.
Returns:
A tuple containing the features tensor, labels tensor, and optionally a
tensor containing the number of frames per video. The exact dimensions
depend on the reader being used.
Raises:
IOError: If no files matching the given pattern were found. | Creates the section of the graph which reads the evaluation data. | [
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] | def get_input_evaluation_tensors(reader,
data_pattern,
batch_size=1024,
num_readers=1):
"""Creates the section of the graph which reads the evaluation data.
Args:
reader: A class which parses the training data.
data_pattern: A 'glob' style path to the data files.
batch_size: How many examples to process at a time.
num_readers: How many I/O threads to use.
Returns:
A tuple containing the features tensor, labels tensor, and optionally a
tensor containing the number of frames per video. The exact dimensions
depend on the reader being used.
Raises:
IOError: If no files matching the given pattern were found.
"""
logging.info("Using batch size of " + str(batch_size) + " for evaluation.")
with tf.name_scope("eval_input"):
files = gfile.Glob(data_pattern)
if not files:
raise IOError("Unable to find the evaluation files.")
logging.info("number of evaluation files: " + str(len(files)))
filename_queue = tf.train.string_input_producer(
files, shuffle=False, num_epochs=1)
eval_data = [
reader.prepare_reader(filename_queue) for _ in range(num_readers)
]
return tf.train.batch_join(
eval_data,
batch_size=batch_size,
capacity=3 * batch_size,
allow_smaller_final_batch=True,
enqueue_many=True) | [
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gnuradio/pybombs | 17044241bf835b93571026b112f179f2db7448a4 | pybombs/commands/autoconfig.py | python | AutoConfigurator._auto_config_makewidth | (self) | return multiprocessing.cpu_count() | Automatically set: makewidth | Automatically set: makewidth | [
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facetoe/zenpy | e614e973aa4d3c4c2a0b91767c2d8565f48ec717 | zenpy/lib/api_objects/__init__.py | python | SuspendedTicket.brand | (self) | | Comment: The id of the brand this ticket is associated with - only applicable for enterprise accounts | | Comment: The id of the brand this ticket is associated with - only applicable for enterprise accounts | [
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"""
| Comment: The id of the brand this ticket is associated with - only applicable for enterprise accounts
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if self.api and self.brand_id:
return self.api._get_brand(self.brand_id) | [
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itayhubara/BinaryNet.pytorch | b99870af6e73992896ab5db5ea26b83d2adb1201 | models/resnet_binary.py | python | Bottleneck.forward | (self, x) | return out | [] | def forward(self, x):
residual = x
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out = self.conv1(x)
out = self.bn1(out)
out = self.tanh(out)
out = self.conv2(out)
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out += residual
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out = self.bn2(out)
out = self.tanh2(out)
return out | [
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SeldonIO/alibi | ce961caf995d22648a8338857822c90428af4765 | alibi/explainers/backends/tensorflow/cfrl_base.py | python | encode | (X: Union[tf.Tensor, np.ndarray], encoder: keras.Model, **kwargs) | return encoder(X, training=False) | Encodes the input tensor.
Parameters
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Input to be encoded.
encoder
Pretrained encoder network.
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-------
Input encoding. | Encodes the input tensor. | [
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"""
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Parameters
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X
Input to be encoded.
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Pretrained encoder network.
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Input encoding.
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XX-net/XX-Net | a9898cfcf0084195fb7e69b6bc834e59aecdf14f | python3.8.2/Lib/site-packages/pip/_internal/req/req_uninstall.py | python | compress_for_output_listing | (paths) | return will_remove, will_skip | Returns a tuple of 2 sets of which paths to display to user
The first set contains paths that would be deleted. Files of a package
are not added and the top-level directory of the package has a '*' added
at the end - to signify that all it's contents are removed.
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# type: (Iterable[str]) -> Tuple[Set[str], Set[str]]
"""Returns a tuple of 2 sets of which paths to display to user
The first set contains paths that would be deleted. Files of a package
are not added and the top-level directory of the package has a '*' added
at the end - to signify that all it's contents are removed.
The second set contains files that would have been skipped in the above
folders.
"""
will_remove = set(paths)
will_skip = set()
# Determine folders and files
folders = set()
files = set()
for path in will_remove:
if path.endswith(".pyc"):
continue
if path.endswith("__init__.py") or ".dist-info" in path:
folders.add(os.path.dirname(path))
files.add(path)
# probably this one https://github.com/python/mypy/issues/390
_normcased_files = set(map(os.path.normcase, files)) # type: ignore
folders = compact(folders)
# This walks the tree using os.walk to not miss extra folders
# that might get added.
for folder in folders:
for dirpath, _, dirfiles in os.walk(folder):
for fname in dirfiles:
if fname.endswith(".pyc"):
continue
file_ = os.path.join(dirpath, fname)
if (os.path.isfile(file_) and
os.path.normcase(file_) not in _normcased_files):
# We are skipping this file. Add it to the set.
will_skip.add(file_)
will_remove = files | {
os.path.join(folder, "*") for folder in folders
}
return will_remove, will_skip | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/geometry/hyperbolic_space/hyperbolic_geodesic.py | python | HyperbolicGeodesicKM.plot | (self, boundary=True, **options) | return pic | r"""
Plot ``self``.
EXAMPLES::
sage: HyperbolicPlane().KM().get_geodesic((0,0), (1,0)).plot() # optional - sage.plot
Graphics object consisting of 2 graphics primitives
.. PLOT::
KM = HyperbolicPlane().KM()
sphinx_plot(KM.get_geodesic((0,0), (1,0)).plot()) | r"""
Plot ``self``. | [
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r"""
Plot ``self``.
EXAMPLES::
sage: HyperbolicPlane().KM().get_geodesic((0,0), (1,0)).plot() # optional - sage.plot
Graphics object consisting of 2 graphics primitives
.. PLOT::
KM = HyperbolicPlane().KM()
sphinx_plot(KM.get_geodesic((0,0), (1,0)).plot())
"""
opts = {'axes': False, 'aspect_ratio': 1}
opts.update(self.graphics_options())
opts.update(options)
pic = line([k.coordinates() for k in self.endpoints()], **opts)
if boundary:
pic += self._model.get_background_graphic()
return pic | [
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Yonsm/.homeassistant | 4d9a0070d0fcd8a5ded46e7884da9a4494bbbf26 | custom_components/xiaomi_fan_circulator/fan.py | python | XiaomiFanFA1.async_set_preset_mode | (self, preset_mode: str) | Set the speed of the fan. | Set the speed of the fan. | [
"Set",
"the",
"speed",
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] | async def async_set_preset_mode(self, preset_mode: str) -> None:
"""Set the speed of the fan."""
if self.supported_features & SUPPORT_PRESET_MODE == 0:
return
if preset_mode.isdigit():
preset_mode = int(preset_mode)
if preset_mode in [SPEED_OFF, 0]:
await self.async_turn_off()
return
# Map speed level to speed
if preset_mode in FAN_SPEED_VALUES_FA1.keys():
preset_mode = FAN_SPEED_VALUES_FA1[preset_mode]
await self._try_command(
"Setting fan speed of the miio device failed.",
self._device.send,
"set_properties",
[{"piid": 2, "siid": 2, "did": self._did, "value": preset_mode}]
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mlrun/mlrun | 4c120719d64327a34b7ee1ab08fb5e01b258b00a | mlrun/data_types/infer.py | python | infer_schema_from_df | (
df: pd.DataFrame,
features,
entities,
timestamp_key: str = None,
entity_columns=None,
options: InferOptions = InferOptions.Null,
) | return timestamp_key | infer feature set schema from dataframe | infer feature set schema from dataframe | [
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"dataframe"
] | def infer_schema_from_df(
df: pd.DataFrame,
features,
entities,
timestamp_key: str = None,
entity_columns=None,
options: InferOptions = InferOptions.Null,
):
"""infer feature set schema from dataframe"""
timestamp_fields = []
current_entities = list(entities.keys())
entity_columns = entity_columns or []
index_columns = dict()
def upsert_entity(name, value_type):
if name in current_entities:
entities[name].value_type = value_type
else:
entities[name] = {"name": name, "value_type": value_type}
schema = pyarrow.Schema.from_pandas(df)
index_type = None
for i in range(len(schema)):
column = schema.names[i]
value_type = pa_type_to_value_type(schema.types[i])
if column in df.index.names:
index_columns[column] = value_type
continue
is_entity = column in entity_columns or column in current_entities
if is_entity:
upsert_entity(column, value_type)
elif (
InferOptions.get_common_options(options, InferOptions.Features)
and column != timestamp_key
):
if column in features.keys():
features[column].value_type = value_type
else:
features[column] = {"name": column, "value_type": value_type}
if value_type == "datetime" and not is_entity:
timestamp_fields.append(column)
index_type = None
if InferOptions.get_common_options(options, InferOptions.Index):
# infer types of index fields
if df.index.name:
if df.index.name in index_columns:
index_type = index_columns[df.index.name]
if not index_type:
field = convert_pandas_type_to_json_field(df.index)
index_type = pd_schema_to_value_type(field["type"])
# Workaround to infer a boolean index correctly, and not as 'str'.
upsert_entity(df.index.name, index_type)
elif df.index.nlevels > 1:
for level, name in zip(df.index.levels, df.index.names):
if name in index_columns:
index_type = index_columns[name]
else:
field = convert_pandas_type_to_json_field(df.index)
index_type = pd_schema_to_value_type(field["type"])
upsert_entity(name, index_type)
if index_type == "datetime":
timestamp_fields.append(name)
return timestamp_key | [
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openeventdata/mordecai | 9d37110f6cd1275852548fc53fd7a21bb77593f9 | mordecai/geoparse.py | python | Geoparser._feature_most_population | (self, results) | Find the placename with the largest population and return its country.
More population is a rough measure of importance.
Paramaters
----------
results: dict
output of `query_geonames`
Returns
-------
most_pop: str
ISO code of country of place with largest population,
or empty string if none | Find the placename with the largest population and return its country.
More population is a rough measure of importance. | [
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] | def _feature_most_population(self, results):
"""
Find the placename with the largest population and return its country.
More population is a rough measure of importance.
Paramaters
----------
results: dict
output of `query_geonames`
Returns
-------
most_pop: str
ISO code of country of place with largest population,
or empty string if none
"""
try:
populations = [i['population'] for i in results['hits']['hits']]
most_pop = results['hits']['hits'][np.array(populations).astype("int").argmax()]
return most_pop['country_code3']
except Exception as e:
return "" | [
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apple/ccs-calendarserver | 13c706b985fb728b9aab42dc0fef85aae21921c3 | calendarserver/tools/obliterate.py | python | ObliterateService.removeRevisionsForHomeResourceID | (self, resourceID) | [] | def removeRevisionsForHomeResourceID(self, resourceID):
if not self.options["dry-run"]:
rev = schema.CALENDAR_OBJECT_REVISIONS
kwds = {"ResourceID": resourceID}
yield Delete(
From=rev,
Where=(
rev.CALENDAR_HOME_RESOURCE_ID == Parameter("ResourceID")
),
).on(self.txn, **kwds) | [
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IntelPython/sdc | 1ebf55c00ef38dfbd401a70b3945e352a5a38b87 | sdc/datatypes/common_functions.py | python | _sdc_asarray_overload | (data) | return None | [] | def _sdc_asarray_overload(data):
# TODO: extend with other types
if not isinstance(data, types.List):
return None
if isinstance(data.dtype, types.UnicodeType):
def _sdc_asarray_impl(data):
return create_str_arr_from_list(data)
return _sdc_asarray_impl
else:
result_dtype = data.dtype
def _sdc_asarray_impl(data):
# TODO: check if elementwise copy is needed at all
res_size = len(data)
res_arr = numpy.empty(res_size, dtype=result_dtype)
for i in numba.prange(res_size):
res_arr[i] = data[i]
return res_arr
return _sdc_asarray_impl
return None | [
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poppy-project/pypot | c5d384fe23eef9f6ec98467f6f76626cdf20afb9 | pypot/server/httpserver.py | python | HTTPRobotServer.make_app | (self) | return Application(url_paths) | [] | def make_app(self):
PoppyRequestHandler.restful_robot = self.restful_robot
return Application(url_paths) | [
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landlab/landlab | a5dd80b8ebfd03d1ba87ef6c4368c409485f222c | landlab/components/network_sediment_transporter/network_sediment_transporter.py | python | NetworkSedimentTransporter._calculate_mean_D_and_rho | (self) | Calculate mean grain size and density on each link | Calculate mean grain size and density on each link | [
"Calculate",
"mean",
"grain",
"size",
"and",
"density",
"on",
"each",
"link"
] | def _calculate_mean_D_and_rho(self):
"""Calculate mean grain size and density on each link"""
current_parcels = self._parcels.dataset.isel(time=self._time_idx)
# In the first full timestep, we need to calc grain size & rho_sed.
# Assume all parcels are in the active layer for the purposes of
# grain size and mean sediment density calculations
# FUTURE: make it possible to circumvent this if mean grain size
# has already been calculated (e.g. during 'zeroing' runs)
# Calculate mean values for density and grain size (weighted by volume).
sel_parcels = current_parcels.where(
current_parcels.element_id != self.OUT_OF_NETWORK
)
d_weighted = sel_parcels.D * sel_parcels.volume
rho_weighted = sel_parcels.density * sel_parcels.volume
d_weighted.name = "d_weighted"
rho_weighted.name = "rho_weighted"
grouped_by_element = xr.merge(
(sel_parcels.element_id, sel_parcels.volume, d_weighted, rho_weighted)
).groupby("element_id")
d_avg = grouped_by_element.sum().d_weighted / grouped_by_element.sum().volume
rho_avg = (
grouped_by_element.sum().rho_weighted / grouped_by_element.sum().volume
)
self._d_mean_active = np.zeros(self._grid.size("link"))
self._d_mean_active[d_avg.element_id.values.astype(int)] = d_avg.values
self._rhos_mean_active = np.zeros(self._grid.size("link"))
self._rhos_mean_active[rho_avg.element_id.values.astype(int)] = rho_avg.values | [
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oracle/graalpython | 577e02da9755d916056184ec441c26e00b70145c | graalpython/lib-python/3/tkinter/__init__.py | python | Variable.trace_add | (self, mode, callback) | return cbname | Define a trace callback for the variable.
Mode is one of "read", "write", "unset", or a list or tuple of
such strings.
Callback must be a function which is called when the variable is
read, written or unset.
Return the name of the callback. | Define a trace callback for the variable. | [
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] | def trace_add(self, mode, callback):
"""Define a trace callback for the variable.
Mode is one of "read", "write", "unset", or a list or tuple of
such strings.
Callback must be a function which is called when the variable is
read, written or unset.
Return the name of the callback.
"""
cbname = self._register(callback)
self._tk.call('trace', 'add', 'variable',
self._name, mode, (cbname,))
return cbname | [
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deezer/spleeter | 48e38088370bdc2997040ea3c57f96495bdf75a5 | spleeter/model/__init__.py | python | EstimatorSpecBuilder._build_manual_output_waveform | (self, masked_stft) | return output_waveform | Perform ratio mask separation
:param output_dict: dictionary of estimated spectrogram (key: instrument
name, value: estimated spectrogram of the instrument)
:returns: dictionary of separated waveforms (key: instrument name,
value: estimated waveform of the instrument) | Perform ratio mask separation | [
"Perform",
"ratio",
"mask",
"separation"
] | def _build_manual_output_waveform(self, masked_stft):
"""Perform ratio mask separation
:param output_dict: dictionary of estimated spectrogram (key: instrument
name, value: estimated spectrogram of the instrument)
:returns: dictionary of separated waveforms (key: instrument name,
value: estimated waveform of the instrument)
"""
output_waveform = {}
for instrument, stft_data in masked_stft.items():
output_waveform[instrument] = self._inverse_stft(stft_data)
return output_waveform | [
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Amulet-Team/Amulet-Map-Editor | e99619ba6aab855173b9f7c203455944ab97f89a | amulet_map_editor/api/opengl/camera/camera.py | python | Camera.orthographic_clipping | (self, clipping: Tuple[float, float]) | Set the near and far clipping distance when in orthographic mode. | Set the near and far clipping distance when in orthographic mode. | [
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] | def orthographic_clipping(self, clipping: Tuple[float, float]):
"""Set the near and far clipping distance when in orthographic mode."""
self._set_clipping(Projection.TOP_DOWN, clipping) | [
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docker-archive/docker-registry | f93b432d3fc7befa508ab27a590e6d0f78c86401 | docker_registry/index.py | python | put_repository_auth | (namespace, repository) | return toolkit.response('OK') | [] | def put_repository_auth(namespace, repository):
return toolkit.response('OK') | [
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JacquesLucke/animation_nodes | b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1 | animation_nodes/utils/blender_ui.py | python | iterActiveSpacesByType | (type) | [] | def iterActiveSpacesByType(type):
for space in iterActiveSpaces():
if space.type == type:
yield space | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/hqwebapp/views.py | python | CRUDPaginatedViewMixin.paginated_list | (self) | This should return a list (or generator object) of data formatted as follows:
[
{
'itemData': {
'id': <id of item>,
<json dict of item data for the knockout model to use>
},
'template': <knockout template id>
}
] | This should return a list (or generator object) of data formatted as follows:
[
{
'itemData': {
'id': <id of item>,
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},
'template': <knockout template id>
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"""
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[
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'itemData': {
'id': <id of item>,
<json dict of item data for the knockout model to use>
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"""
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microsoft/botbuilder-python | 3d410365461dc434df59bdfeaa2f16d28d9df868 | libraries/botframework-connector/botframework/connector/token_api/aio/operations_async/_user_token_operations_async.py | python | UserTokenOperations.exchange_async | (
self,
user_id,
connection_name,
channel_id,
uri=None,
token=None,
*,
custom_headers=None,
raw=False,
**operation_config
) | return deserialized | :param user_id:
:type user_id: str
:param connection_name:
:type connection_name: str
:param channel_id:
:type channel_id: str
:param uri:
:type uri: str
:param token:
:type token: str
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:return: object or ClientRawResponse if raw=true
:rtype: object or ~msrest.pipeline.ClientRawResponse
:raises:
:class:`ErrorResponseException<botframework.tokenapi.models.ErrorResponseException>` | [] | async def exchange_async(
self,
user_id,
connection_name,
channel_id,
uri=None,
token=None,
*,
custom_headers=None,
raw=False,
**operation_config
):
"""
:param user_id:
:type user_id: str
:param connection_name:
:type connection_name: str
:param channel_id:
:type channel_id: str
:param uri:
:type uri: str
:param token:
:type token: str
:param dict custom_headers: headers that will be added to the request
:param bool raw: returns the direct response alongside the
deserialized response
:param operation_config: :ref:`Operation configuration
overrides<msrest:optionsforoperations>`.
:return: object or ClientRawResponse if raw=true
:rtype: object or ~msrest.pipeline.ClientRawResponse
:raises:
:class:`ErrorResponseException<botframework.tokenapi.models.ErrorResponseException>`
"""
exchange_request = models.TokenExchangeRequest(uri=uri, token=token)
# Construct URL
url = self.exchange_async.metadata["url"]
# Construct parameters
query_parameters = {}
query_parameters["userId"] = self._serialize.query("user_id", user_id, "str")
query_parameters["connectionName"] = self._serialize.query(
"connection_name", connection_name, "str"
)
query_parameters["channelId"] = self._serialize.query(
"channel_id", channel_id, "str"
)
# Construct headers
header_parameters = {}
header_parameters["Accept"] = "application/json"
header_parameters["Content-Type"] = "application/json; charset=utf-8"
if custom_headers:
header_parameters.update(custom_headers)
# Construct body
body_content = self._serialize.body(exchange_request, "TokenExchangeRequest")
# Construct and send request
request = self._client.post(
url, query_parameters, header_parameters, body_content
)
response = await self._client.async_send(
request, stream=False, **operation_config
)
if response.status_code not in [200, 400, 404]:
raise models.ErrorResponseException(self._deserialize, response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize("TokenResponse", response)
if response.status_code == 400:
deserialized = self._deserialize("ErrorResponse", response)
if response.status_code == 404:
deserialized = self._deserialize("TokenResponse", response)
if raw:
client_raw_response = ClientRawResponse(deserialized, response)
return client_raw_response
return deserialized | [
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jesparza/peepdf | c74dc65c0ac7e506bae4f2582a2435ec50741f40 | PDFCore.py | python | PDFObject.contains | (self, string) | return False | Look for the string inside the object content
@param string: A string
@return: A boolean to specify if the string has been found or not | Look for the string inside the object content | [
"Look",
"for",
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"string",
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"the",
"object",
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] | def contains(self, string):
'''
Look for the string inside the object content
@param string: A string
@return: A boolean to specify if the string has been found or not
'''
value = str(self.value)
rawValue = str(self.rawValue)
encValue = str(self.encryptedValue)
if re.findall(string,value,re.IGNORECASE) != [] or re.findall(string,rawValue,re.IGNORECASE) != [] or re.findall(string,encValue,re.IGNORECASE) != []:
return True
if self.containsJS():
for js in self.JSCode:
if re.findall(string,js,re.IGNORECASE) != []:
return True
return False | [
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".... | https://github.com/jesparza/peepdf/blob/c74dc65c0ac7e506bae4f2582a2435ec50741f40/PDFCore.py#L123-L139 | |
JoelBender/bacpypes | 41104c2b565b2ae9a637c941dfb0fe04195c5e96 | py27/bacpypes/debugging.py | python | DebugContents.debug_contents | (self, indent=1, file=sys.stdout, _ids=None) | Debug the contents of an object. | Debug the contents of an object. | [
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] | def debug_contents(self, indent=1, file=sys.stdout, _ids=None):
"""Debug the contents of an object."""
if _debug: _log.debug("debug_contents indent=%r file=%r _ids=%r", indent, file, _ids)
klasses = list(self.__class__.__mro__)
klasses.reverse()
if _debug: _log.debug(" - klasses: %r", klasses)
# loop through the classes and look for _debug_contents
attrs = []
cids = []
ownFn = []
for klass in klasses:
if klass is DebugContents:
continue
if not issubclass(klass, DebugContents) and hasattr(klass, 'debug_contents'):
for i, seenAlready in enumerate(ownFn):
if issubclass(klass, seenAlready):
del ownFn[i]
break
ownFn.append(klass)
continue
# look for a tuple of attribute names
if not hasattr(klass, '_debug_contents'):
continue
debugContents = klass._debug_contents
if not isinstance(debugContents, tuple):
raise RuntimeError("%s._debug_contents must be a tuple" % (klass.__name__,))
# already seen it?
if id(debugContents) in cids:
continue
cids.append(id(debugContents))
for attr in debugContents:
if attr not in attrs:
attrs.append(attr)
# a bit of debugging
if _debug:
_log.debug(" - attrs: %r", attrs)
_log.debug(" - ownFn: %r", ownFn)
# make/extend the list of objects already seen
if _ids is None:
_ids = []
# loop through the attributes
for attr in attrs:
# assume you're going deep, but not into lists and dicts
goDeep = True
goListDict = False
goHexed = False
# attribute list might want to go deep
if attr.endswith("-"):
goDeep = False
attr = attr[:-1]
elif attr.endswith("*"):
goHexed = True
attr = attr[:-1]
elif attr.endswith("+"):
goDeep = False
goListDict = True
attr = attr[:-1]
if attr.endswith("+"):
goDeep = True
attr = attr[:-1]
value = getattr(self, attr, None)
# skip None
if value is None:
continue
# standard output
if goListDict and isinstance(value, list) and value:
file.write("%s%s = [\n" % (' ' * indent, attr))
indent += 1
for i, elem in enumerate(value):
file.write("%s[%d] %r\n" % (' ' * indent, i, elem))
if goDeep and hasattr(elem, 'debug_contents'):
if id(elem) not in _ids:
_ids.append(id(elem))
elem.debug_contents(indent + 1, file, _ids)
indent -= 1
file.write("%s ]\n" % (' ' * indent,))
elif goListDict and isinstance(value, dict) and value:
file.write("%s%s = {\n" % (' ' * indent, attr))
indent += 1
for key, elem in value.items():
file.write("%s%r : %r\n" % (' ' * indent, key, elem))
if goDeep and hasattr(elem, 'debug_contents'):
if id(elem) not in _ids:
_ids.append(id(elem))
elem.debug_contents(indent + 1, file, _ids)
indent -= 1
file.write("%s }\n" % (' ' * indent,))
elif goHexed and isinstance(value, str):
if len(value) > 20:
hexed = btox(value[:20], '.') + "..."
else:
hexed = btox(value, '.')
file.write("%s%s = x'%s'\n" % (' ' * indent, attr, hexed))
# elif goHexed and isinstance(value, int):
# file.write("%s%s = 0x%X\n" % (' ' * indent, attr, value))
else:
file.write("%s%s = %r\n" % (' ' * indent, attr, value))
# go nested if it is debugable
if goDeep and hasattr(value, 'debug_contents'):
if id(value) not in _ids:
_ids.append(id(value))
value.debug_contents(indent + 1, file, _ids)
# go through the functions
ownFn.reverse()
for klass in ownFn:
klass.debug_contents(self, indent, file, _ids) | [
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seanbell/opensurfaces | 7f3e987560faa62cd37f821760683ccd1e053c7c | server/mturk/tasks.py | python | mturk_iteration_task | (show_progress=False) | Update votes and start new tasks | Update votes and start new tasks | [
"Update",
"votes",
"and",
"start",
"new",
"tasks"
] | def mturk_iteration_task(show_progress=False):
""" Update votes and start new tasks """
if not settings.MTURK_PIPELINE_ENABLE:
print "mturk_iteration_task: Not running since MTURK_PIPELINE_ENABLE=%s" % (
settings.MTURK_PIPELINE_ENABLE)
return
if not os.path.isfile('manage.py'):
raise RuntimeError('Worker not in server directory')
if os.path.exists('.disable-mturk'):
print "mturk_iteration_task: Not running since .disable-mturk file exists"
return
#mturk_update_votes_cubam_task(show_progress=show_progress)
consume_pending_objects_task()
if settings.MTURK_CONFIGURE_QUALIFICATIONS:
configure_qualifications_task() | [
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dulwich/dulwich | 1f66817d712e3563ce1ff53b1218491a2eae39da | dulwich/object_store.py | python | MemoryObjectStore.add_objects | (self, objects, progress=None) | Add a set of objects to this object store.
Args:
objects: Iterable over a list of (object, path) tuples | Add a set of objects to this object store. | [
"Add",
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"."
] | def add_objects(self, objects, progress=None):
"""Add a set of objects to this object store.
Args:
objects: Iterable over a list of (object, path) tuples
"""
for obj, path in objects:
self.add_object(obj) | [
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] | https://github.com/dulwich/dulwich/blob/1f66817d712e3563ce1ff53b1218491a2eae39da/dulwich/object_store.py#L996-L1003 | ||
inspurer/WorkAttendanceSystem | 1221e2d67bdf5bb15fe99517cc3ded58ccb066df | V2.0/venv/Lib/site-packages/pip-9.0.1-py3.5.egg/pip/_vendor/ipaddress.py | python | IPv6Address.ipv4_mapped | (self) | return IPv4Address(self._ip & 0xFFFFFFFF) | Return the IPv4 mapped address.
Returns:
If the IPv6 address is a v4 mapped address, return the
IPv4 mapped address. Return None otherwise. | Return the IPv4 mapped address. | [
"Return",
"the",
"IPv4",
"mapped",
"address",
"."
] | def ipv4_mapped(self):
"""Return the IPv4 mapped address.
Returns:
If the IPv6 address is a v4 mapped address, return the
IPv4 mapped address. Return None otherwise.
"""
if (self._ip >> 32) != 0xFFFF:
return None
return IPv4Address(self._ip & 0xFFFFFFFF) | [
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h2non/pook | d2970eff5ef7b611e786422145cff4f6d6df412e | pook/api.py | python | disable_network | () | Disables real traffic networking mode in the current mock engine. | Disables real traffic networking mode in the current mock engine. | [
"Disables",
"real",
"traffic",
"networking",
"mode",
"in",
"the",
"current",
"mock",
"engine",
"."
] | def disable_network():
"""
Disables real traffic networking mode in the current mock engine.
"""
_engine.disable_network() | [
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] | https://github.com/h2non/pook/blob/d2970eff5ef7b611e786422145cff4f6d6df412e/pook/api.py#L266-L270 | ||
ComplianceAsCode/content | 6760a14e7fe3fb34205bd1a165095a9334412e95 | ssg/ext/boolean/boolean.py | python | NOT.__call__ | (self, **kwargs) | return not self.args[0](**kwargs) | Return the evaluated (negated) value for this function. | Return the evaluated (negated) value for this function. | [
"Return",
"the",
"evaluated",
"(",
"negated",
")",
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] | def __call__(self, **kwargs):
"""
Return the evaluated (negated) value for this function.
"""
return not self.args[0](**kwargs) | [
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sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/physics/vector/dyadic.py | python | Dyadic.__truediv__ | (self, other) | return self.__mul__(1 / other) | Divides the Dyadic by a sympifyable expression. | Divides the Dyadic by a sympifyable expression. | [
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"the",
"Dyadic",
"by",
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] | def __truediv__(self, other):
"""Divides the Dyadic by a sympifyable expression. """
return self.__mul__(1 / other) | [
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] | https://github.com/sympy/sympy/blob/d822fcba181155b85ff2b29fe525adbafb22b448/sympy/physics/vector/dyadic.py#L110-L112 | |
rwth-i6/returnn | f2d718a197a280b0d5f0fd91a7fcb8658560dddb | returnn/tf/layers/rec.py | python | RecLayer._post_proc_output_cell_strict | (self, y, in_data) | return out_data.placeholder | :param tf.Tensor y: (time,batch,dim)
:param Data in_data:
:rtype: tf.Tensor
:return: (time,batch,dim) or (time,batch,...,dim) | :param tf.Tensor y: (time,batch,dim)
:param Data in_data:
:rtype: tf.Tensor
:return: (time,batch,dim) or (time,batch,...,dim) | [
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... | def _post_proc_output_cell_strict(self, y, in_data):
"""
:param tf.Tensor y: (time,batch,dim)
:param Data in_data:
:rtype: tf.Tensor
:return: (time,batch,dim) or (time,batch,...,dim)
"""
if in_data.batch_ndim_dense > 3:
y_shape = tf_util.get_shape(in_data.placeholder)
if not in_data.sparse:
y_shape = y_shape[:-1]
y_shape += [tf_util.get_shape_dim(y, -1)]
y = tf.reshape(y, y_shape)
out_data = in_data.copy_template_dense()
out_data = out_data.copy_template_replace_dim_tag(
axis=out_data.feature_dim_axis, new_dim_tag=self.output.feature_dim_or_sparse_dim)
out_data.placeholder = y
if not self.time_dim_tag:
out_data = out_data.copy_squeeze_axes(axes=[0])
# The output format should match now.
# If this is not the case, we should fix get_out_data_from_opts accordingly
# and avoid unnecessary further transformations here, esp any transposes.
assert out_data.dim_tags == self.output.dim_tags
return out_data.placeholder | [
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