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hughw19/NOCS_CVPR2019 | 14dbce775c3c7c45bb7b19269bd53d68efb8f73f | utils.py | python | compute_coords_aps | (final_results, synset_names, iou_thresholds, coord_thresholds) | Compute Average Precision at a set IoU threshold (default 0.5).
Returns:
mAP: Mean Average Precision
precisions: List of precisions at different class score thresholds.
recalls: List of recall values at different class score thresholds.
overlaps: [pred_boxes, gt_boxes] IoU overlaps. | Compute Average Precision at a set IoU threshold (default 0.5).
Returns:
mAP: Mean Average Precision
precisions: List of precisions at different class score thresholds.
recalls: List of recall values at different class score thresholds.
overlaps: [pred_boxes, gt_boxes] IoU overlaps. | [
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"score"... | def compute_coords_aps(final_results, synset_names, iou_thresholds, coord_thresholds):
"""Compute Average Precision at a set IoU threshold (default 0.5).
Returns:
mAP: Mean Average Precision
precisions: List of precisions at different class score thresholds.
recalls: List of recall values at different class score thresholds.
overlaps: [pred_boxes, gt_boxes] IoU overlaps.
"""
num_classes = len(synset_names)
num_iou_thres = len(iou_thresholds)
num_coord_thres = len(coord_thresholds)
mean_coord_dist_cls = {}
# pred_matches_all = {}
# pred_scores_all = {}
# gt_matches_all = {}
for cls_id in range(1, num_classes):
mean_coord_dist_cls[cls_id] = []
# pred_matches_all[cls_id] = [[[] for _ in range(num_shift_thres)] for _ in range(num_degree_thres)]
# gt_matches_all[cls_id] = [[[] for _ in range(num_shift_thres)] for _ in range(num_degree_thres)]
# pred_scores_all[cls_id] = [[[] for _ in range(num_shift_thres)] for _ in range(num_degree_thres)]
progress = 0
for progress, result in enumerate(final_results):
print(progress, len(final_results))
gt_class_ids = result['gt_class_ids'].astype(np.int32)
gt_bboxes = np.array(result['gt_bboxes'])
gt_masks = np.array(result['gt_masks'])
gt_coords = np.array(result['gt_coords'])
#print(gt_class_ids.shape, gt_bboxes.shape, gt_masks.shape, gt_coords.shape)
#gt_scales = result['gt_scale']
pred_class_ids = result['pred_class_ids'].astype(np.int32)
pred_bboxes = np.array(result['pred_bboxes'])
pred_masks = np.array(result['pred_masks'])
pred_coords = np.array(result['pred_coords'])
pred_scores = result['pred_scores']
#print(pred_class_ids.shape, pred_bboxes.shape, pred_masks.shape, pred_coords.shape)
for cls_id in range(1, num_classes):
# get gt and predictions in this class
if(len(gt_class_ids)):
cls_gt_class_ids = gt_class_ids[gt_class_ids==cls_id]
cls_gt_bboxes = gt_bboxes[gt_class_ids==cls_id]
cls_gt_masks = gt_masks[..., gt_class_ids==cls_id]
cls_gt_coords = gt_coords[..., gt_class_ids==cls_id, :]
else:
cls_gt_class_ids = []
cls_gt_bboxes = []
cls_gt_masks = []
cls_gt_coords = []
if(len(pred_class_ids)):
cls_pred_class_ids = pred_class_ids[pred_class_ids==cls_id]
cls_pred_bboxes = pred_bboxes[pred_class_ids==cls_id]
cls_pred_scores = pred_scores[pred_class_ids==cls_id]
cls_pred_masks = pred_masks[:, :, pred_class_ids==cls_id]
cls_pred_coords = pred_coords[:, :, pred_class_ids==cls_id, :]
else:
cls_pred_class_ids = []
cls_pred_bboxes = []
cls_pred_scores = []
cls_pred_masks = []
cls_pred_coords = []
# calculate the overlap between each gt instance and pred instance
gt_match, pred_match, overlaps, pred_indices = compute_matches(cls_gt_bboxes, cls_gt_class_ids, cls_gt_masks,
cls_pred_bboxes, cls_pred_class_ids, cls_pred_scores,
cls_pred_masks, 0.5)
if len(gt_match) and len(pred_match):
cls_pred_masks_sorted = cls_pred_masks[..., pred_indices]
cls_pred_coords_sorted = cls_pred_coords[..., pred_indices, :]
for i in range(len(pred_match)):
if pred_match[i] > -1:
j = int(pred_match[i])
mean_coord_dist = compute_mean_l1_coord_diff(cls_pred_masks_sorted[..., i],
cls_gt_masks[..., j],
cls_pred_coords_sorted[..., i, :],
cls_gt_coords[..., j, :],
synset_names,
cls_id)
#print(mean_coord_dist)
mean_coord_dist_cls[cls_id].append(mean_coord_dist)
#print(mean_coord_dist_cls[cls_id])
for cls_id in range(1, num_classes):
mean_coord_dist_cls[cls_id] = np.array(mean_coord_dist_cls[cls_id])
print('mean coord dist of {} class: {}'.format(synset_names[cls_id], np.mean(mean_coord_dist_cls[cls_id]))) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/_pyio.py | python | FileIO.seek | (self, pos, whence=SEEK_SET) | return os.lseek(self._fd, pos, whence) | Move to new file position.
Argument offset is a byte count. Optional argument whence defaults to
SEEK_SET or 0 (offset from start of file, offset should be >= 0); other values
are SEEK_CUR or 1 (move relative to current position, positive or negative),
and SEEK_END or 2 (move relative to end of file, usually negative, although
many platforms allow seeking beyond the end of a file).
Note that not all file objects are seekable. | Move to new file position. | [
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] | def seek(self, pos, whence=SEEK_SET):
"""Move to new file position.
Argument offset is a byte count. Optional argument whence defaults to
SEEK_SET or 0 (offset from start of file, offset should be >= 0); other values
are SEEK_CUR or 1 (move relative to current position, positive or negative),
and SEEK_END or 2 (move relative to end of file, usually negative, although
many platforms allow seeking beyond the end of a file).
Note that not all file objects are seekable.
"""
if isinstance(pos, float):
raise TypeError('an integer is required')
self._checkClosed()
return os.lseek(self._fd, pos, whence) | [
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MaurizioFD/RecSys2019_DeepLearning_Evaluation | 0fb6b7f5c396f8525316ed66cf9c9fdb03a5fa9b | Base/BaseRecommender.py | python | BaseRecommender._compute_item_score | (self, user_id_array, items_to_compute = None) | :param user_id_array: array containing the user indices whose recommendations need to be computed
:param items_to_compute: array containing the items whose scores are to be computed.
If None, all items are computed, otherwise discarded items will have as score -np.inf
:return: array (len(user_id_array), n_items) with the score. | [] | def _compute_item_score(self, user_id_array, items_to_compute = None):
"""
:param user_id_array: array containing the user indices whose recommendations need to be computed
:param items_to_compute: array containing the items whose scores are to be computed.
If None, all items are computed, otherwise discarded items will have as score -np.inf
:return: array (len(user_id_array), n_items) with the score.
"""
raise NotImplementedError("BaseRecommender: compute_item_score not assigned for current recommender, unable to compute prediction scores") | [
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broadinstitute/viral-ngs | e144969e4c57060d53f38a4c3a270e8227feace1 | util/misc.py | python | load_yaml_or_json | (fname) | Load a dictionary from either a yaml or a json file | Load a dictionary from either a yaml or a json file | [
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] | def load_yaml_or_json(fname):
'''Load a dictionary from either a yaml or a json file'''
with open(fname) as f:
if fname.upper().endswith('.YAML'): return yaml.safe_load(f) or {}
if fname.upper().endswith('.JSON'): return json.load(f) or {}
raise TypeError('Unsupported dict file format: ' + fname) | [
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... | https://github.com/broadinstitute/viral-ngs/blob/e144969e4c57060d53f38a4c3a270e8227feace1/util/misc.py#L493-L498 | ||
buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/gevent/greenlet.py | python | Greenlet.start | (self) | Schedule the greenlet to run in this loop iteration | Schedule the greenlet to run in this loop iteration | [
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"""Schedule the greenlet to run in this loop iteration"""
if self._start_event is None:
self._start_event = self.parent.loop.run_callback(self.switch) | [
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leancloud/satori | 701caccbd4fe45765001ca60435c0cb499477c03 | satori-rules/plugin/libs/gevent/greenlet.py | python | Greenlet.__start_cancelled_by_kill | (self) | return self._start_event is _cancelled_start_event | [] | def __start_cancelled_by_kill(self):
return self._start_event is _cancelled_start_event | [
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] | https://github.com/leancloud/satori/blob/701caccbd4fe45765001ca60435c0cb499477c03/satori-rules/plugin/libs/gevent/greenlet.py#L209-L210 | |||
cbrgm/telegram-robot-rss | 58fe98de427121fdc152c8df0721f1891174e6c9 | util/database.py | python | DatabaseHandler.add_user | (self, telegram_id, username, firstname, lastname, language_code, is_bot, is_active) | Adds a user to sqlite database
Args:
param1 (int): The telegram_id of a user.
param2 (str): The username of a user.
param3 (str): The firstname of a user.
param4 (str): The lastname of a user.
param5 (str): The language_code of a user.
param6 (str): The is_bot flag of a user. | Adds a user to sqlite database | [
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"user",
"to",
"sqlite",
"database"
] | def add_user(self, telegram_id, username, firstname, lastname, language_code, is_bot, is_active):
"""Adds a user to sqlite database
Args:
param1 (int): The telegram_id of a user.
param2 (str): The username of a user.
param3 (str): The firstname of a user.
param4 (str): The lastname of a user.
param5 (str): The language_code of a user.
param6 (str): The is_bot flag of a user.
"""
conn = sqlite3.connect(self.database_path)
cursor = conn.cursor()
cursor.execute("INSERT OR IGNORE INTO user VALUES (?,?,?,?,?,?,?)",
(telegram_id, username, firstname, lastname, language_code, is_bot, is_active))
conn.commit()
conn.close() | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/idlelib/PyShell.py | python | PyShell.open_stack_viewer | (self, event=None) | [] | def open_stack_viewer(self, event=None):
if self.interp.rpcclt:
return self.interp.remote_stack_viewer()
try:
sys.last_traceback
except:
tkMessageBox.showerror("No stack trace",
"There is no stack trace yet.\n"
"(sys.last_traceback is not defined)",
parent=self.text)
return
from idlelib.StackViewer import StackBrowser
StackBrowser(self.root, self.flist) | [
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tijme/angularjs-csti-scanner | dcf5bf3d36a0a6ee3a9db1340e68de95b33e4615 | acstis/actions/TraverseUrlAction.py | python | TraverseUrlAction.get_action_items_derived | (self) | return items | Get new queue items based on this action.
Returns:
list(:class:`nyawc.QueueItem`): A list of possibly vulnerable queue items. | Get new queue items based on this action. | [
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] | def get_action_items_derived(self):
"""Get new queue items based on this action.
Returns:
list(:class:`nyawc.QueueItem`): A list of possibly vulnerable queue items.
"""
items = []
path = self.get_parsed_url().path
filename = self.get_filename()
if filename:
path[0:-len(filename)]
parts = list(filter(None, path.split("/")))
for index in range(0, len(parts)):
for payload in self.__payloads:
queue_item = self.get_item_copy()
verify_item = self.get_item_copy()
path = "/".join(parts[0:index])
path_with_affix = ("/" if path else "") + path + "/" + payload["value"]
parsed = self.get_parsed_url(queue_item.request.url)
parsed = parsed._replace(path=path_with_affix, query="")
queue_item.request.url = parsed.geturl()
queue_item.payload = payload
path_with_affix = ("/" if path else "") + path + "/" + Payloads.get_verify_payload(payload)["value"]
parsed = self.get_parsed_url(verify_item.request.url)
parsed = parsed._replace(path=path_with_affix, query="")
verify_item.request.url = parsed.geturl()
verify_item.payload = Payloads.get_verify_payload(payload)
queue_item.verify_item = verify_item
items.append(queue_item)
return items | [
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nwcell/psycopg2-windows | 5698844286001962f3eeeab58164301898ef48e9 | psycopg2/_range.py | python | RangeCaster._from_db | (self, name, pyrange, conn_or_curs) | return RangeCaster(name, pyrange,
oid=type, subtype_oid=subtype, array_oid=array) | Return a `RangeCaster` instance for the type *pgrange*.
Raise `ProgrammingError` if the type is not found. | Return a `RangeCaster` instance for the type *pgrange*. | [
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"""Return a `RangeCaster` instance for the type *pgrange*.
Raise `ProgrammingError` if the type is not found.
"""
from psycopg2.extensions import STATUS_IN_TRANSACTION
from psycopg2.extras import _solve_conn_curs
conn, curs = _solve_conn_curs(conn_or_curs)
if conn.server_version < 90200:
raise ProgrammingError("range types not available in version %s"
% conn.server_version)
# Store the transaction status of the connection to revert it after use
conn_status = conn.status
# Use the correct schema
if '.' in name:
schema, tname = name.split('.', 1)
else:
tname = name
schema = 'public'
# get the type oid and attributes
try:
curs.execute("""\
select rngtypid, rngsubtype,
(select typarray from pg_type where oid = rngtypid)
from pg_range r
join pg_type t on t.oid = rngtypid
join pg_namespace ns on ns.oid = typnamespace
where typname = %s and ns.nspname = %s;
""", (tname, schema))
except ProgrammingError:
if not conn.autocommit:
conn.rollback()
raise
else:
rec = curs.fetchone()
# revert the status of the connection as before the command
if (conn_status != STATUS_IN_TRANSACTION
and not conn.autocommit):
conn.rollback()
if not rec:
raise ProgrammingError(
"PostgreSQL type '%s' not found" % name)
type, subtype, array = rec
return RangeCaster(name, pyrange,
oid=type, subtype_oid=subtype, array_oid=array) | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/Python3/calendar.py | python | Calendar.yeardayscalendar | (self, year, width=3) | return [months[i:i+width] for i in range(0, len(months), width) ] | Return the data for the specified year ready for formatting (similar to
yeardatescalendar()). Entries in the week lists are day numbers.
Day numbers outside this month are zero. | Return the data for the specified year ready for formatting (similar to
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"""
Return the data for the specified year ready for formatting (similar to
yeardatescalendar()). Entries in the week lists are day numbers.
Day numbers outside this month are zero.
"""
months = [
self.monthdayscalendar(year, i)
for i in range(January, January+12)
]
return [months[i:i+width] for i in range(0, len(months), width) ] | [
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Floobits/flootty | 731fb4516da3ad34c724440787e57c97229838e8 | flootty/floo/common/lib/diff_match_patch.py | python | diff_match_patch.diff_halfMatch | (self, text1, text2) | return (text1_a, text1_b, text2_a, text2_b, mid_common) | Do the two texts share a substring which is at least half the length of
the longer text?
This speedup can produce non-minimal diffs.
Args:
text1: First string.
text2: Second string.
Returns:
Five element Array, containing the prefix of text1, the suffix of text1,
the prefix of text2, the suffix of text2 and the common middle. Or None
if there was no match. | Do the two texts share a substring which is at least half the length of
the longer text?
This speedup can produce non-minimal diffs. | [
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] | def diff_halfMatch(self, text1, text2):
"""Do the two texts share a substring which is at least half the length of
the longer text?
This speedup can produce non-minimal diffs.
Args:
text1: First string.
text2: Second string.
Returns:
Five element Array, containing the prefix of text1, the suffix of text1,
the prefix of text2, the suffix of text2 and the common middle. Or None
if there was no match.
"""
if self.Diff_Timeout <= 0:
# Don't risk returning a non-optimal diff if we have unlimited time.
return None
if len(text1) > len(text2):
(longtext, shorttext) = (text1, text2)
else:
(shorttext, longtext) = (text1, text2)
if len(longtext) < 4 or len(shorttext) * 2 < len(longtext):
return None # Pointless.
def diff_halfMatchI(longtext, shorttext, i):
"""Does a substring of shorttext exist within longtext such that the
substring is at least half the length of longtext?
Closure, but does not reference any external variables.
Args:
longtext: Longer string.
shorttext: Shorter string.
i: Start index of quarter length substring within longtext.
Returns:
Five element Array, containing the prefix of longtext, the suffix of
longtext, the prefix of shorttext, the suffix of shorttext and the
common middle. Or None if there was no match.
"""
seed = longtext[i:i + len(longtext) // 4]
best_common = ''
j = shorttext.find(seed)
while j != -1:
prefixLength = self.diff_commonPrefix(longtext[i:], shorttext[j:])
suffixLength = self.diff_commonSuffix(longtext[:i], shorttext[:j])
if len(best_common) < suffixLength + prefixLength:
best_common = (shorttext[j - suffixLength:j] + shorttext[j:j + prefixLength])
best_longtext_a = longtext[:i - suffixLength]
best_longtext_b = longtext[i + prefixLength:]
best_shorttext_a = shorttext[:j - suffixLength]
best_shorttext_b = shorttext[j + prefixLength:]
j = shorttext.find(seed, j + 1)
if len(best_common) * 2 >= len(longtext):
return (best_longtext_a, best_longtext_b,
best_shorttext_a, best_shorttext_b, best_common)
else:
return None
# First check if the second quarter is the seed for a half-match.
hm1 = diff_halfMatchI(longtext, shorttext, (len(longtext) + 3) // 4)
# Check again based on the third quarter.
hm2 = diff_halfMatchI(longtext, shorttext, (len(longtext) + 1) // 2)
if not hm1 and not hm2:
return None
elif not hm2:
hm = hm1
elif not hm1:
hm = hm2
else:
# Both matched. Select the longest.
if len(hm1[4]) > len(hm2[4]):
hm = hm1
else:
hm = hm2
# A half-match was found, sort out the return data.
if len(text1) > len(text2):
(text1_a, text1_b, text2_a, text2_b, mid_common) = hm
else:
(text2_a, text2_b, text1_a, text1_b, mid_common) = hm
return (text1_a, text1_b, text2_a, text2_b, mid_common) | [
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gramps-project/gramps | 04d4651a43eb210192f40a9f8c2bad8ee8fa3753 | gramps/plugins/textreport/detancestralreport.py | python | DetAncestorReport.write_marriage | (self, person) | Output marriage sentence. | Output marriage sentence. | [
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] | def write_marriage(self, person):
"""
Output marriage sentence.
"""
is_first = True
for family_handle in person.get_family_handle_list():
family = self._db.get_family_from_handle(family_handle)
spouse_handle = utils.find_spouse(person, family)
if spouse_handle:
spouse = self._db.get_person_from_handle(spouse_handle)
spouse_mark = utils.get_person_mark(self._db, spouse)
else:
spouse_mark = None
text = self.__narrator.get_married_string(family,
is_first,
self._nd)
if text:
self.doc.write_text_citation(text, spouse_mark)
if self.want_ids:
self.doc.write_text(' (%s)' % family.get_gramps_id())
is_first = False | [
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snakemake/snakemake | 987282dde8a2db5174414988c134a39ae8836a61 | snakemake/benchmark.py | python | BenchmarkTimer._update_record | (self) | Perform the actual measurement | Perform the actual measurement | [
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] | def _update_record(self):
"""Perform the actual measurement"""
import psutil
# Memory measurements
rss, vms, uss, pss = 0, 0, 0, 0
# I/O measurements
io_in, io_out = 0, 0
check_io = True
# CPU seconds
cpu_usages = 0
# CPU usage time
cpu_time = 0
# Iterate over process and all children
try:
this_time = time.time()
for proc in chain((self.main,), self.main.children(recursive=True)):
proc = self.procs.setdefault(proc.pid, proc)
with proc.oneshot():
if self.bench_record.prev_time:
cpu_usages += proc.cpu_percent() * (
this_time - self.bench_record.prev_time
)
meminfo = proc.memory_full_info()
rss += meminfo.rss
vms += meminfo.vms
uss += meminfo.uss
pss += meminfo.pss
if check_io:
try:
ioinfo = proc.io_counters()
io_in += ioinfo.read_bytes
io_out += ioinfo.write_bytes
except NotImplementedError as nie:
# OS doesn't track IO
check_io = False
cpu_times = proc.cpu_times()
cpu_time += cpu_times.user + cpu_times.system
self.bench_record.prev_time = this_time
if not self.bench_record.first_time:
self.bench_record.prev_time = this_time
rss /= 1024 * 1024
vms /= 1024 * 1024
uss /= 1024 * 1024
pss /= 1024 * 1024
if check_io:
io_in /= 1024 * 1024
io_out /= 1024 * 1024
else:
io_in = None
io_out = None
except psutil.Error as e:
return
# Update benchmark record's RSS and VMS
self.bench_record.max_rss = max(self.bench_record.max_rss or 0, rss)
self.bench_record.max_vms = max(self.bench_record.max_vms or 0, vms)
self.bench_record.max_uss = max(self.bench_record.max_uss or 0, uss)
self.bench_record.max_pss = max(self.bench_record.max_pss or 0, pss)
self.bench_record.io_in = io_in
self.bench_record.io_out = io_out
self.bench_record.cpu_usages += cpu_usages
self.bench_record.cpu_time = cpu_time | [
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frappe/frappe | b64cab6867dfd860f10ccaf41a4ec04bc890b583 | frappe/sessions.py | python | Session.start | (self) | start a new session | start a new session | [
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] | def start(self):
"""start a new session"""
# generate sid
if self.user=='Guest':
sid = 'Guest'
else:
sid = frappe.generate_hash()
self.data.user = self.user
self.data.sid = sid
self.data.data.user = self.user
self.data.data.session_ip = frappe.local.request_ip
if self.user != "Guest":
self.data.data.update({
"last_updated": frappe.utils.now(),
"session_expiry": get_expiry_period(self.device),
"full_name": self.full_name,
"user_type": self.user_type,
"device": self.device,
"session_country": get_geo_ip_country(frappe.local.request_ip) if frappe.local.request_ip else None,
})
# insert session
if self.user!="Guest":
self.insert_session_record()
# update user
user = frappe.get_doc("User", self.data['user'])
frappe.db.sql("""UPDATE `tabUser`
SET
last_login = %(now)s,
last_ip = %(ip)s,
last_active = %(now)s
WHERE name=%(name)s""", {
'now': frappe.utils.now(),
'ip': frappe.local.request_ip,
'name': self.data['user']
})
user.run_notifications("before_change")
user.run_notifications("on_update")
frappe.db.commit() | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | dist/lib/python2.7/SimpleXMLRPCServer.py | python | SimpleXMLRPCDispatcher.register_instance | (self, instance, allow_dotted_names=False) | Registers an instance to respond to XML-RPC requests.
Only one instance can be installed at a time.
If the registered instance has a _dispatch method then that
method will be called with the name of the XML-RPC method and
its parameters as a tuple
e.g. instance._dispatch('add',(2,3))
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and, if found, will be called. Methods beginning with an '_'
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will be called instead of the registered instance.
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the name segments start with an '_'.
*** SECURITY WARNING: ***
Enabling the allow_dotted_names options allows intruders
to access your module's global variables and may allow
intruders to execute arbitrary code on your machine. Only
use this option on a secure, closed network. | Registers an instance to respond to XML-RPC requests. | [
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"XML",
"-",
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"."
] | def register_instance(self, instance, allow_dotted_names=False):
"""Registers an instance to respond to XML-RPC requests.
Only one instance can be installed at a time.
If the registered instance has a _dispatch method then that
method will be called with the name of the XML-RPC method and
its parameters as a tuple
e.g. instance._dispatch('add',(2,3))
If the registered instance does not have a _dispatch method
then the instance will be searched to find a matching method
and, if found, will be called. Methods beginning with an '_'
are considered private and will not be called by
SimpleXMLRPCServer.
If a registered function matches a XML-RPC request, then it
will be called instead of the registered instance.
If the optional allow_dotted_names argument is true and the
instance does not have a _dispatch method, method names
containing dots are supported and resolved, as long as none of
the name segments start with an '_'.
*** SECURITY WARNING: ***
Enabling the allow_dotted_names options allows intruders
to access your module's global variables and may allow
intruders to execute arbitrary code on your machine. Only
use this option on a secure, closed network.
"""
self.instance = instance
self.allow_dotted_names = allow_dotted_names | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.40/roles/lib_utils/library/oo_iam_kms.py | python | AwsIamKms.get_all_kms_info | (self) | return aliases | fetch all kms info and return them
list_keys doesn't have information regarding aliases
list_aliases doesn't have the full kms arn
fetch both and join them on the targetKeyId | fetch all kms info and return them | [
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] | def get_all_kms_info(self):
'''fetch all kms info and return them
list_keys doesn't have information regarding aliases
list_aliases doesn't have the full kms arn
fetch both and join them on the targetKeyId
'''
aliases = self.kms_client.list_aliases()['Aliases']
keys = self.kms_client.list_keys()['Keys']
for alias in aliases:
for key in keys:
if 'TargetKeyId' in alias and 'KeyId' in key:
if alias['TargetKeyId'] == key['KeyId']:
alias.update(key)
return aliases | [
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LexPredict/openedgar | 1d1b8bc8faa3c59e05d9883e53039b6328b7d831 | lexpredict_openedgar/openedgar/tasks.py | python | create_filing_error | (row, filing_path: str) | return True | Create a Filing error record from an index row.
:param row:
:param filing_path:
:return: | Create a Filing error record from an index row.
:param row:
:param filing_path:
:return: | [
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] | def create_filing_error(row, filing_path: str):
"""
Create a Filing error record from an index row.
:param row:
:param filing_path:
:return:
"""
# Get vars
cik = row["CIK"]
company_name = row["Company Name"]
form_type = row["Form Type"]
try:
date_filed = dateutil.parser.parse(str(row["Date Filed"])).date()
except ValueError:
date_filed = None
except IndexError:
date_filed = None
# Create empty error filing record
filing = Filing()
filing.form_type = form_type
filing.date_filed = date_filed
filing.s3_path = filing_path
filing.is_error = True
filing.is_processed = False
# Get company info
try:
company = Company.objects.get(cik=cik)
try:
_ = CompanyInfo.objects.get(company=company, date=date_filed)
except CompanyInfo.DoesNotExist:
# Create company info record
company_info = CompanyInfo()
company_info.company = company
company_info.name = company_name
company_info.sic = None
company_info.state_incorporation = None
company_info.state_location = None
company_info.date = date_filed
company_info.save()
except Company.DoesNotExist:
# Create company
company = Company()
company.cik = cik
try:
company.save()
except django.db.utils.IntegrityError:
return create_filing_error(row, filing_path)
# Create company info record
company_info = CompanyInfo()
company_info.company = company
company_info.name = company_name
company_info.sic = None
company_info.state_incorporation = None
company_info.state_location = None
company_info.date = date_filed
company_info.save()
# Finally update company and save
filing.company = company
filing.save()
return True | [
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adamcaudill/EquationGroupLeak | 52fa871c89008566c27159bd48f2a8641260c984 | Firewall/EXPLOITS/EPBA/EPICBANANA/pexpect.py | python | spawn.sendintr | (self) | This sends a SIGINT to the child. It does not require
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char = termios.tcgetattr(self.child_fd)[6][termios.VINTR]
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char = chr(3)
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ynhacler/RedKindle | 7c970920dc840f869e38cbda480d630cc2e7b200 | web/template.py | python | TemplateResult._prepare_body | (self) | Prepare value of __body__ by joining parts. | Prepare value of __body__ by joining parts. | [
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body = self._d.get('__body__')
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tf-encrypted/tf-encrypted | 8b7cfb32c426e9a6f56769a1b47626bd1be03a66 | tf_encrypted/protocol/pond/pond.py | python | PondTensor.add | (self, other) | return self.prot.add(self, other) | Add `other` to this PondTensor. This can be another tensor with the same
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niftools/blender_niftools_addon | fc28f567e1fa431ec6633cb2a138898136090b29 | io_scene_niftools/modules/nif_export/animation/transform.py | python | TransformAnimation.export_transforms | (self, parent_block, b_obj, b_action, bone=None) | If bone == None, object level animation is exported.
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"""
If bone == None, object level animation is exported.
If a bone is given, skeletal animation is exported.
"""
# b_action may be None, then nothing is done.
if not b_action:
return
# blender object must exist
assert b_obj
# if a bone is given, b_obj must be an armature
if bone:
assert type(b_obj.data) == bpy.types.Armature
# just for more detailed error reporting later on
bonestr = ""
# skeletal animation - with bone correction & coordinate corrections
if bone and bone.name in b_action.groups:
# get bind matrix for bone
bind_matrix = math.get_object_bind(bone)
exp_fcurves = b_action.groups[bone.name].channels
# just for more detailed error reporting later on
bonestr = f" in bone {bone.name}"
target_name = block_store.get_full_name(bone)
priority = bone.niftools.priority
# object level animation - no coordinate corrections
elif not bone:
# raise error on any objects parented to bones
if b_obj.parent and b_obj.parent_type == "BONE":
raise NifError(
f"{b_obj.name} is parented to a bone AND has animations. The nif format does not support this!")
target_name = block_store.get_full_name(b_obj)
priority = 0
# we have either a root object (Scene Root), in which case we take the coordinates without modification
# or a generic object parented to an empty = node
# objects may have an offset from their parent that is not apparent in the user input (ie. UI values and keyframes)
# we want to export matrix_local, and the keyframes are in matrix_basis, so do:
# matrix_local = matrix_parent_inverse * matrix_basis
bind_matrix = b_obj.matrix_parent_inverse
exp_fcurves = [fcu for fcu in b_action.fcurves if
fcu.data_path in ("rotation_quaternion", "rotation_euler", "location", "scale")]
else:
# bone isn't keyframed in this action, nothing to do here
return
# decompose the bind matrix
bind_scale, bind_rot, bind_trans = math.decompose_srt(bind_matrix)
n_kfc, n_kfi = self.create_controller(parent_block, target_name, priority)
# fill in the non-trivial values
start_frame, stop_frame = b_action.frame_range
self.set_flags_and_timing(n_kfc, exp_fcurves, start_frame, stop_frame)
# get the desired fcurves for each data type from exp_fcurves
quaternions = [fcu for fcu in exp_fcurves if fcu.data_path.endswith("quaternion")]
translations = [fcu for fcu in exp_fcurves if fcu.data_path.endswith("location")]
eulers = [fcu for fcu in exp_fcurves if fcu.data_path.endswith("euler")]
scales = [fcu for fcu in exp_fcurves if fcu.data_path.endswith("scale")]
# ensure that those groups that are present have all their fcurves
for fcus, num_fcus in ((quaternions, 4), (eulers, 3), (translations, 3), (scales, 3)):
if fcus and len(fcus) != num_fcus:
raise NifError(
f"Incomplete key set {bonestr} for action {b_action.name}."
f"Ensure that if a bone is keyframed for a property, all channels are keyframed.")
# go over all fcurves collected above and transform and store all their keys
quat_curve = []
euler_curve = []
trans_curve = []
scale_curve = []
for frame, quat in self.iter_frame_key(quaternions, mathutils.Quaternion):
quat = math.export_keymat(bind_rot, quat.to_matrix().to_4x4(), bone).to_quaternion()
quat_curve.append((frame, quat))
for frame, euler in self.iter_frame_key(eulers, mathutils.Euler):
keymat = math.export_keymat(bind_rot, euler.to_matrix().to_4x4(), bone)
euler = keymat.to_euler("XYZ", euler)
euler_curve.append((frame, euler))
for frame, trans in self.iter_frame_key(translations, mathutils.Vector):
keymat = math.export_keymat(bind_rot, mathutils.Matrix.Translation(trans), bone)
trans = keymat.to_translation() + bind_trans
trans_curve.append((frame, trans))
for frame, scale in self.iter_frame_key(scales, mathutils.Vector):
# just use the first scale curve and assume even scale over all curves
scale_curve.append((frame, scale[0]))
if n_kfi:
# set the default transforms of the interpolator as the bone's bind pose
n_kfi.translation.x, n_kfi.translation.y, n_kfi.translation.z = bind_trans
n_kfi.rotation.w, n_kfi.rotation.x, n_kfi.rotation.y, n_kfi.rotation.z = bind_rot.to_quaternion()
n_kfi.scale = bind_scale
if max(len(c) for c in (quat_curve, euler_curve, trans_curve, scale_curve)) > 0:
# number of frames is > 0, so add transform data
n_kfd = block_store.create_block("NiTransformData", exp_fcurves)
n_kfi.data = n_kfd
else:
# no need to add any keys, done
return
else:
# add the keyframe data
n_kfd = block_store.create_block("NiKeyframeData", exp_fcurves)
n_kfc.data = n_kfd
# TODO [animation] support other interpolation modes, get interpolation from blender?
# probably requires additional data like tangents and stuff
# finally we can export the data calculated above
if euler_curve:
n_kfd.rotation_type = NifFormat.KeyType.XYZ_ROTATION_KEY
n_kfd.num_rotation_keys = 1 # *NOT* len(frames) this crashes the engine!
for i, coord in enumerate(n_kfd.xyz_rotations):
coord.num_keys = len(euler_curve)
coord.interpolation = NifFormat.KeyType.LINEAR_KEY
coord.keys.update_size()
for key, (frame, euler) in zip(coord.keys, euler_curve):
key.time = frame / self.fps
key.value = euler[i]
elif quat_curve:
n_kfd.rotation_type = NifFormat.KeyType.QUADRATIC_KEY
n_kfd.num_rotation_keys = len(quat_curve)
n_kfd.quaternion_keys.update_size()
for key, (frame, quat) in zip(n_kfd.quaternion_keys, quat_curve):
key.time = frame / self.fps
key.value.w = quat.w
key.value.x = quat.x
key.value.y = quat.y
key.value.z = quat.z
n_kfd.translations.interpolation = NifFormat.KeyType.LINEAR_KEY
n_kfd.translations.num_keys = len(trans_curve)
n_kfd.translations.keys.update_size()
for key, (frame, trans) in zip(n_kfd.translations.keys, trans_curve):
key.time = frame / self.fps
key.value.x, key.value.y, key.value.z = trans
n_kfd.scales.interpolation = NifFormat.KeyType.LINEAR_KEY
n_kfd.scales.num_keys = len(scale_curve)
n_kfd.scales.keys.update_size()
for key, (frame, scale) in zip(n_kfd.scales.keys, scale_curve):
key.time = frame / self.fps
key.value = scale | [
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statsmodels/statsmodels | debbe7ea6ba28fe5bdb78f09f8cac694bef98722 | statsmodels/tsa/statespace/mlemodel.py | python | MLEModel.clone | (self, endog, exog=None, **kwargs) | Clone state space model with new data and optionally new specification
Parameters
----------
endog : array_like
The observed time-series process :math:`y`
k_states : int
The dimension of the unobserved state process.
exog : array_like, optional
Array of exogenous regressors, shaped nobs x k. Default is no
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kwargs
Keyword arguments to pass to the new model class to change the
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Returns
-------
model : MLEModel subclass
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This method must be implemented | Clone state space model with new data and optionally new specification | [
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"""
Clone state space model with new data and optionally new specification
Parameters
----------
endog : array_like
The observed time-series process :math:`y`
k_states : int
The dimension of the unobserved state process.
exog : array_like, optional
Array of exogenous regressors, shaped nobs x k. Default is no
exogenous regressors.
kwargs
Keyword arguments to pass to the new model class to change the
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Returns
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model : MLEModel subclass
Notes
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This method must be implemented
"""
raise NotImplementedError('This method is not implemented in the base'
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awslabs/aws-config-rules | 8dfeacf9d9e5e5f0fbb1b8545ff702dea700ea7a | python/EMR_KERBEROS_ENABLED/EMR_KERBEROS_ENABLED.py | python | get_client | (service, event) | return boto3.client(service, aws_access_key_id=credentials['AccessKeyId'],
aws_secret_access_key=credentials['SecretAccessKey'],
aws_session_token=credentials['SessionToken']
) | Return the service boto client. It should be used instead of directly calling the client.
Keyword arguments:
service -- the service name used for calling the boto.client()
event -- the event variable given in the lambda handler | Return the service boto client. It should be used instead of directly calling the client. | [
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"""Return the service boto client. It should be used instead of directly calling the client.
Keyword arguments:
service -- the service name used for calling the boto.client()
event -- the event variable given in the lambda handler
"""
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pycrypto/pycrypto | 7acba5f3a6ff10f1424c309d0d34d2b713233019 | lib/Crypto/Hash/HMAC.py | python | HMAC.digest | (self) | return h.digest() | Return the **binary** (non-printable) MAC of the message that has
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This method does not change the state of the MAC object.
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"""Return the **binary** (non-printable) MAC of the message that has
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This method does not change the state of the MAC object.
You can continue updating the object after calling this function.
:Return: A byte string of `digest_size` bytes. It may contain non-ASCII
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h = self.outer.copy()
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wucng/TensorExpand | 4ea58f64f5c5082b278229b799c9f679536510b7 | TensorExpand/Object detection/faster rcnn/CharlesShang-TFFRCNN-master/lib/datasets/pascal_voc2.py | python | pascal_voc._load_rpn_roidb | (self, gt_roidb, model) | return self.create_roidb_from_box_list(box_list, gt_roidb) | [] | def _load_rpn_roidb(self, gt_roidb, model):
# set the prefix
if self._image_set == 'test':
prefix = model + '/testing'
else:
prefix = model + '/training'
box_list = []
for index in self.image_index:
filename = os.path.join(self._pascal_path, 'region_proposals', prefix, index + '.txt')
assert os.path.exists(filename), \
'RPN data not found at: {}'.format(filename)
raw_data = np.loadtxt(filename, dtype=float)
if len(raw_data.shape) == 1:
if raw_data.size == 0:
raw_data = raw_data.reshape((0, 5))
else:
raw_data = raw_data.reshape((1, 5))
x1 = raw_data[:, 0]
y1 = raw_data[:, 1]
x2 = raw_data[:, 2]
y2 = raw_data[:, 3]
score = raw_data[:, 4]
inds = np.where((x2 > x1) & (y2 > y1))[0]
raw_data = raw_data[inds,:4]
self._num_boxes_proposal += raw_data.shape[0]
box_list.append(raw_data)
return self.create_roidb_from_box_list(box_list, gt_roidb) | [
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skyfielders/python-skyfield | 0e68757a5c1081f784c58fd7a76635c6deb98451 | skyfield/timelib.py | python | Time.gmst | (self) | return sidereal_time(self) | Greenwich Mean Sidereal Time (GMST) in hours. | Greenwich Mean Sidereal Time (GMST) in hours. | [
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4shadoww/hakkuframework | 409a11fc3819d251f86faa3473439f8c19066a21 | lib/dns/rrset.py | python | RRset.match | (self, *args, **kwargs) | Does this rrset match the specified attributes?
Behaves as :py:func:`full_match()` if the first argument is a
``dns.name.Name``, and as :py:func:`dns.rdataset.Rdataset.match()`
otherwise.
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``dns.name.Name``, and as :py:func:`dns.rdataset.Rdataset.match()`
otherwise.
(This behavior fixes a design mistake where the signature of this
method became incompatible with that of its superclass. The fix
makes RRsets matchable as Rdatasets while preserving backwards
compatibility.)
"""
if isinstance(args[0], dns.name.Name):
return self.full_match(*args, **kwargs)
else:
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yuxiaokui/Intranet-Penetration | f57678a204840c83cbf3308e3470ae56c5ff514b | proxy/XX-Net/code/default/python27/1.0/lib/email/message.py | python | Message.get_charset | (self) | return self._charset | Return the Charset instance associated with the message's payload. | Return the Charset instance associated with the message's payload. | [
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Chaffelson/nipyapi | d3b186fd701ce308c2812746d98af9120955e810 | nipyapi/nifi/models/processor_diagnostics_entity.py | python | ProcessorDiagnosticsEntity.uri | (self, uri) | Sets the uri of this ProcessorDiagnosticsEntity.
The URI for futures requests to the component.
:param uri: The uri of this ProcessorDiagnosticsEntity.
:type: str | Sets the uri of this ProcessorDiagnosticsEntity.
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python-diamond/Diamond | 7000e16cfdf4508ed9291fc4b3800592557b2431 | src/collectors/iodrivesnmp/iodrivesnmp.py | python | IODriveSNMPCollector.get_bytes | (self, s) | return struct.unpack('%sB' % len(s), s) | Turns a string into a list of byte values | Turns a string into a list of byte values | [
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glitchdotcom/WebPutty | 4f5da5eb2b4668cbf3c15cf002feacd1d95d2ef7 | ziplibs/wtforms/ext/appengine/db.py | python | convert_EmailProperty | (model, prop, kwargs) | return get_TextField(kwargs) | Returns a form field for a ``db.EmailProperty``. | Returns a form field for a ``db.EmailProperty``. | [
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Azure/azure-devops-cli-extension | 11334cd55806bef0b99c3bee5a438eed71e44037 | azure-devops/azext_devops/dev/team/user.py | python | update_user_entitlement | (user, license_type, organization=None, detect=None) | return user_entitlement_update.user_entitlement | Update license type for a user.
:param user: Email ID or ID of the user.
:type user: str
:param license_type: License type for the user.
:type license_type: str
:rtype: UserEntitlementsPatchResponse | Update license type for a user.
:param user: Email ID or ID of the user.
:type user: str
:param license_type: License type for the user.
:type license_type: str
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"""Update license type for a user.
:param user: Email ID or ID of the user.
:type user: str
:param license_type: License type for the user.
:type license_type: str
:rtype: UserEntitlementsPatchResponse
"""
patch_document = []
value = {}
value['accountLicenseType'] = license_type
patch_document.append(_create_patch_operation('replace', '/accessLevel', value))
organization = resolve_instance(detect=detect, organization=organization)
if '@' in user:
user = resolve_identity_as_id(user, organization)
client = get_member_entitlement_management_client(organization)
user_entitlement_update = client.update_user_entitlement(document=patch_document, user_id=user)
if user_entitlement_update.is_success is False and \
user_entitlement_update.operation_results[0].errors[0] is not None:
raise CLIError(user_entitlement_update.operation_results[0].errors[0]['value'])
return user_entitlement_update.user_entitlement | [
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isce-framework/isce2 | 0e5114a8bede3caf1d533d98e44dfe4b983e3f48 | contrib/geo_autoRIFT/geogrid/GeogridOptical.py | python | GeogridOptical.finalize | (self) | Clean up all the C pointers. | Clean up all the C pointers. | [
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] | def finalize(self):
'''
Clean up all the C pointers.
'''
from . import geogridOptical
geogridOptical.destroyGeoGridOptical_Py(self._geogridOptical)
self._geogridOptical = None | [
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natashamjaques/neural_chat | ddb977bb4602a67c460d02231e7bbf7b2cb49a97 | ParlAI/parlai/mturk/core/legacy_2018/agents.py | python | MTurkAgent.wait_completion_timeout | (self, iterations) | return | Suspends the thread waiting for hit completion for some number of
iterations on the THREAD_MTURK_POLLING_SLEEP time | Suspends the thread waiting for hit completion for some number of
iterations on the THREAD_MTURK_POLLING_SLEEP time | [
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] | def wait_completion_timeout(self, iterations):
"""Suspends the thread waiting for hit completion for some number of
iterations on the THREAD_MTURK_POLLING_SLEEP time"""
# Determine number of sleep iterations for the amount of time
# we want to wait before syncing with MTurk. Start with 10 seconds
# of waiting
iters = (
shared_utils.THREAD_MTURK_POLLING_SLEEP / shared_utils.THREAD_MEDIUM_SLEEP
)
i = 0
# Wait for the desired number of MTURK_POLLING_SLEEP iterations
while not self.hit_is_complete and i < iters * iterations:
time.sleep(shared_utils.THREAD_SHORT_SLEEP)
i += 1
return | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/xlrd-2.0.1/xlrd/__init__.py | python | dump | (filename, outfile=sys.stdout, unnumbered=False) | For debugging: dump an XLS file's BIFF records in char & hex.
:param filename: The path to the file to be dumped.
:param outfile: An open file, to which the dump is written.
:param unnumbered: If true, omit offsets (for meaningful diffs). | For debugging: dump an XLS file's BIFF records in char & hex. | [
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] | def dump(filename, outfile=sys.stdout, unnumbered=False):
"""
For debugging: dump an XLS file's BIFF records in char & hex.
:param filename: The path to the file to be dumped.
:param outfile: An open file, to which the dump is written.
:param unnumbered: If true, omit offsets (for meaningful diffs).
"""
from .biffh import biff_dump
bk = Book()
bk.biff2_8_load(filename=filename, logfile=outfile, )
biff_dump(bk.mem, bk.base, bk.stream_len, 0, outfile, unnumbered) | [
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OCA/l10n-brazil | 6faefc04c7b0de3de3810a7ab137493d933fb579 | l10n_br_stock/hooks.py | python | pre_init_hook | (cr) | Import XML data to change core data | Import XML data to change core data | [
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"to",
"change",
"core",
"data"
] | def pre_init_hook(cr):
"""Import XML data to change core data"""
if not tools.config["without_demo"]:
_logger.info(_("Loading l10n_br_stock warehouse external ids..."))
with api.Environment.manage():
env = api.Environment(cr, SUPERUSER_ID, {})
set_stock_warehouse_external_ids(
env, "l10n_br_base.empresa_simples_nacional"
)
set_stock_warehouse_external_ids(
env, "l10n_br_base.empresa_lucro_presumido"
) | [
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keras-team/keras-applications | bc89834ed36935ab4a4994446e34ff81c0d8e1b7 | keras_applications/mobilenet.py | python | preprocess_input | (x, **kwargs) | return imagenet_utils.preprocess_input(x, mode='tf', **kwargs) | Preprocesses a numpy array encoding a batch of images.
# Arguments
x: a 4D numpy array consists of RGB values within [0, 255].
# Returns
Preprocessed array. | Preprocesses a numpy array encoding a batch of images. | [
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] | def preprocess_input(x, **kwargs):
"""Preprocesses a numpy array encoding a batch of images.
# Arguments
x: a 4D numpy array consists of RGB values within [0, 255].
# Returns
Preprocessed array.
"""
return imagenet_utils.preprocess_input(x, mode='tf', **kwargs) | [
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sfu-db/dataprep | 6dfb9c659e8bf73f07978ae195d0372495c6f118 | dataprep/clean/clean_duplication_utils.py | python | Clusterer.set_cluster_params | (self, ngram: int, radius: int, block_size: int) | Set clustering parameters. | Set clustering parameters. | [
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] | def set_cluster_params(self, ngram: int, radius: int, block_size: int) -> None:
"""
Set clustering parameters.
"""
self._ngram = ngram
self._radius = radius
self._block_size = block_size | [
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/extensions_v1beta1_ingress_list.py | python | ExtensionsV1beta1IngressList.api_version | (self, api_version) | Sets the api_version of this ExtensionsV1beta1IngressList.
APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources # noqa: E501
:param api_version: The api_version of this ExtensionsV1beta1IngressList. # noqa: E501
:type: str | Sets the api_version of this ExtensionsV1beta1IngressList. | [
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] | def api_version(self, api_version):
"""Sets the api_version of this ExtensionsV1beta1IngressList.
APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources # noqa: E501
:param api_version: The api_version of this ExtensionsV1beta1IngressList. # noqa: E501
:type: str
"""
self._api_version = api_version | [
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digidotcom/xbee-python | 0757f4be0017530c205175fbee8f9f61be9614d1 | digi/xbee/filesystem.py | python | FileSystemElement.path | (self) | return self._path.decode(encoding='utf8', errors='ignore') | Returns the file system element absolute path.
Returns:
String: File system element absolute path. | Returns the file system element absolute path. | [
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"""
Returns the file system element absolute path.
Returns:
String: File system element absolute path.
"""
return self._path.decode(encoding='utf8', errors='ignore') | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/asyncore.py | python | dispatcher.handle_error | (self) | [] | def handle_error(self):
nil, t, v, tbinfo = compact_traceback()
# sometimes a user repr method will crash.
try:
self_repr = repr(self)
except:
self_repr = '<__repr__(self) failed for object at %0x>' % id(self)
self.log_info(
'uncaptured python exception, closing channel %s (%s:%s %s)' % (
self_repr,
t,
v,
tbinfo
),
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self.handle_close() | [
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KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/setuptools/package_index.py | python | ContentChecker.is_valid | (self) | return True | Check the hash. Return False if validation fails. | Check the hash. Return False if validation fails. | [
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"""
Check the hash. Return False if validation fails.
"""
return True | [
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/27/Lib/xml/sax/xmlreader.py | python | InputSource.getSystemId | (self) | return self.__system_id | Returns the system identifier of this InputSource. | Returns the system identifier of this InputSource. | [
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return self.__system_id | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | examples/research_projects/rag/callbacks_rag.py | python | Seq2SeqLoggingCallback._write_logs | (
self, trainer: pl.Trainer, pl_module: pl.LightningModule, type_path: str, save_generations=True
) | [] | def _write_logs(
self, trainer: pl.Trainer, pl_module: pl.LightningModule, type_path: str, save_generations=True
) -> None:
logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****")
metrics = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]})
# Log results
od = Path(pl_module.hparams.output_dir)
if type_path == "test":
results_file = od / "test_results.txt"
generations_file = od / "test_generations.txt"
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
results_file = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
generations_file = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
results_file.parent.mkdir(exist_ok=True)
generations_file.parent.mkdir(exist_ok=True)
with open(results_file, "a+") as writer:
for key in sorted(metrics):
if key in ["log", "progress_bar", "preds"]:
continue
val = metrics[key]
if isinstance(val, torch.Tensor):
val = val.item()
msg = f"{key}: {val:.6f}\n"
writer.write(msg)
if not save_generations:
return
if "preds" in metrics:
content = "\n".join(metrics["preds"])
generations_file.open("w+").write(content) | [
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fregu856/2D_detection | 1f22a6d604d39f8f79fe916fcdbf40b5b668a39a | model.py | python | SqueezeDet_model.tensor_IOU | (self, box1, box2) | return IOU | [] | def tensor_IOU(self, box1, box2):
# intersection:
xmin = tf.maximum(box1[0], box2[0])
ymin = tf.maximum(box1[1], box2[1])
xmax = tf.minimum(box1[2], box2[2])
ymax = tf.minimum(box1[3], box2[3])
w = tf.maximum(0.0, xmax - xmin)
h = tf.maximum(0.0, ymax - ymin)
intersection_area = w*h
# union:
w1 = box1[2] - box1[0]
h1 = box1[3] - box1[1]
w2 = box2[2] - box2[0]
h2 = box2[3] - box2[1]
union_area = w1*h1 + w2*h2 - intersection_area
IOU = intersection_area/(union_area + self.epsilon)
# # (don't think self.epsilon is actually needed here)
return IOU | [
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spectacles/CodeComplice | 8ca8ee4236f72b58caa4209d2fbd5fa56bd31d62 | libs/koXMLTreeService.py | python | TreeService.getTreeForURI | (self, uri, content=None) | return tree | [] | def getTreeForURI(self, uri, content=None):
if not uri and not content:
return None
tree = None
if uri and uri in self.__treeMap:
tree = self.__treeMap[uri]
# if tree is not None:
# print "tree cache hit for [%s]"%uri
if not content:
return tree
if not tree:
if not content:
# get the content
try:
f = open(uri, 'r')
content = f.read(-1)
f.close()
except IOError as e:
# ignore file errors and return an empty tree
content = ""
if not content.startswith("<?xml"):
tree = HTMLDocument()
if not tree:
tree = XMLDocument()
# raise Exception("NOT IMPLEMENTED YET")
if content:
tree.parse(content)
if uri:
self.__treeMap[uri] = tree
return tree | [
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QuantumFractal/Data-Structure-Zoo | 3884d55fac0cccf3b17d18550ec52a285f909c2c | 0-Object-Oriented Programming/objects.py | python | Truck.vroom | (self) | return 'Vroooooom' | [] | def vroom(self):
return 'Vroooooom' | [
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JetBrains/python-skeletons | 95ad24b666e475998e5d1cc02ed53a2188036167 | builtins.py | python | str.swapcase | (self) | return '' | Return a copy of the string with uppercase characters converted
to lowercase and vice versa.
:rtype: str | Return a copy of the string with uppercase characters converted
to lowercase and vice versa. | [
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"""Return a copy of the string with uppercase characters converted
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:rtype: str
"""
return '' | [
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] | https://github.com/JetBrains/python-skeletons/blob/95ad24b666e475998e5d1cc02ed53a2188036167/builtins.py#L1701-L1707 | |
yantisj/netgrph | 0ebc72efbd970e0ce76d44d91a27d167103633b5 | nglib/query/__init__.py | python | display_mgmt_groups | () | Print all Management Groups to the Screen | Print all Management Groups to the Screen | [
"Print",
"all",
"Management",
"Groups",
"to",
"the",
"Screen"
] | def display_mgmt_groups():
"""Print all Management Groups to the Screen"""
mgmt = nglib.py2neo_ses.cypher.execute(
'MATCH (s:Switch) RETURN DISTINCT(s.mgmt) as name ORDER BY name')
if len(mgmt) > 0:
print("Available Groups:")
for s in mgmt.records:
print("> " + str(s.name))
else:
print("No management groups found in DB") | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/elasticsearch/client/indices.py | python | IndicesClient.clear_cache | (self, index=None, params=None) | return data | Clear either all caches or specific cached associated with one ore more indices.
`<http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-clearcache.html>`_
:arg index: A comma-separated list of index name to limit the operation
:arg field_data: Clear field data
:arg fielddata: Clear field data
:arg fields: A comma-separated list of fields to clear when using the
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:arg filter: Clear filter caches
:arg filter_cache: Clear filter caches
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:arg id: Clear ID caches for parent/child
:arg id_cache: Clear ID caches for parent/child
:arg allow_no_indices: Whether to ignore if a wildcard indices
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when no indices have been specified)
:arg expand_wildcards: Whether to expand wildcard expression to concrete indices
that are open, closed or both.
:arg ignore_indices: When performed on multiple indices, allows to
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:arg ignore_unavailable: Whether specified concrete indices should be ignored
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:arg index: A comma-separated list of index name to limit the operation
:arg recycler: Clear the recycler cache | Clear either all caches or specific cached associated with one ore more indices.
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"/",
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"""
Clear either all caches or specific cached associated with one ore more indices.
`<http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/indices-clearcache.html>`_
:arg index: A comma-separated list of index name to limit the operation
:arg field_data: Clear field data
:arg fielddata: Clear field data
:arg fields: A comma-separated list of fields to clear when using the
`field_data` parameter (default: all)
:arg filter: Clear filter caches
:arg filter_cache: Clear filter caches
:arg filter_keys: A comma-separated list of keys to clear when using
the `filter_cache` parameter (default: all)
:arg id: Clear ID caches for parent/child
:arg id_cache: Clear ID caches for parent/child
:arg allow_no_indices: Whether to ignore if a wildcard indices
expression resolves into no concrete indices. (This includes `_all` string or
when no indices have been specified)
:arg expand_wildcards: Whether to expand wildcard expression to concrete indices
that are open, closed or both.
:arg ignore_indices: When performed on multiple indices, allows to
ignore `missing` ones (default: none)
:arg ignore_unavailable: Whether specified concrete indices should be ignored
when unavailable (missing or closed)
:arg index: A comma-separated list of index name to limit the operation
:arg recycler: Clear the recycler cache
"""
_, data = self.transport.perform_request('POST', _make_path(index, '_cache', 'clear'),
params=params)
return data | [
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selfteaching/selfteaching-python-camp | 9982ee964b984595e7d664b07c389cddaf158f1e | exercises/1901100108/d07/mymodule/stats_word.py | python | stats_text | (text) | return stats_text_en(text) + stats_text_cn(text) | 合并 英文词频 和 中文字频 的结果 | 合并 英文词频 和 中文字频 的结果 | [
"合并",
"英文词频",
"和",
"中文字频",
"的结果"
] | def stats_text(text):
'''
合并 英文词频 和 中文字频 的结果
'''
return stats_text_en(text) + stats_text_cn(text) | [
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/site-packages/pip/_vendor/lockfile/sqlitelockfile.py | python | SQLiteLockFile.__init__ | (self, path, threaded=True, timeout=None) | >>> lock = SQLiteLockFile('somefile')
>>> lock = SQLiteLockFile('somefile', threaded=False) | >>> lock = SQLiteLockFile('somefile')
>>> lock = SQLiteLockFile('somefile', threaded=False) | [
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"(",
"somefile",
"threaded",
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"False",
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] | def __init__(self, path, threaded=True, timeout=None):
"""
>>> lock = SQLiteLockFile('somefile')
>>> lock = SQLiteLockFile('somefile', threaded=False)
"""
LockBase.__init__(self, path, threaded, timeout)
self.lock_file = unicode(self.lock_file)
self.unique_name = unicode(self.unique_name)
if SQLiteLockFile.testdb is None:
import tempfile
_fd, testdb = tempfile.mkstemp()
os.close(_fd)
os.unlink(testdb)
del _fd, tempfile
SQLiteLockFile.testdb = testdb
import sqlite3
self.connection = sqlite3.connect(SQLiteLockFile.testdb)
c = self.connection.cursor()
try:
c.execute("create table locks"
"("
" lock_file varchar(32),"
" unique_name varchar(32)"
")")
except sqlite3.OperationalError:
pass
else:
self.connection.commit()
import atexit
atexit.register(os.unlink, SQLiteLockFile.testdb) | [
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pypa/pip | 7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4 | src/pip/_vendor/rich/ansi.py | python | _ansi_tokenize | (ansi_text: str) | Tokenize a string in to plain text and ANSI codes.
Args:
ansi_text (str): A String containing ANSI codes.
Yields:
AnsiToken: A named tuple of (plain, sgr, osc) | Tokenize a string in to plain text and ANSI codes. | [
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"string",
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"to",
"plain",
"text",
"and",
"ANSI",
"codes",
"."
] | def _ansi_tokenize(ansi_text: str) -> Iterable[_AnsiToken]:
"""Tokenize a string in to plain text and ANSI codes.
Args:
ansi_text (str): A String containing ANSI codes.
Yields:
AnsiToken: A named tuple of (plain, sgr, osc)
"""
def remove_csi(ansi_text: str) -> str:
"""Remove unknown CSI sequences."""
return re_csi.sub("", ansi_text)
position = 0
for match in re_ansi.finditer(ansi_text):
start, end = match.span(0)
sgr, osc = match.groups()
if start > position:
yield _AnsiToken(remove_csi(ansi_text[position:start]))
yield _AnsiToken("", sgr, osc)
position = end
if position < len(ansi_text):
yield _AnsiToken(remove_csi(ansi_text[position:])) | [
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apache/superset | 3d829fc3c838358dd8c798ecaeefd34c502edca0 | superset/connectors/sqla/views.py | python | TableModelView.edit | (self, pk: str) | return redirect("/superset/explore/table/{}/".format(pk)) | Simple hack to redirect to explore view after saving | Simple hack to redirect to explore view after saving | [
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"to",
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"view",
"after",
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] | def edit(self, pk: str) -> FlaskResponse:
"""Simple hack to redirect to explore view after saving"""
resp = super().edit(pk)
if isinstance(resp, str):
return resp
return redirect("/superset/explore/table/{}/".format(pk)) | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/whoosh/src/whoosh/query/spans.py | python | Span.__init__ | (self, start, end=None, startchar=None, endchar=None,
boost=1.0) | [] | def __init__(self, start, end=None, startchar=None, endchar=None,
boost=1.0):
if end is None:
end = start
assert start <= end
self.start = start
self.end = end
self.startchar = startchar
self.endchar = endchar
self.boost = boost | [
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catalyst-team/catalyst | 678dc06eda1848242df010b7f34adb572def2598 | catalyst/contrib/losses/regression.py | python | _ce_with_logits | (logits, target) | return torch.sum(-target * torch.log_softmax(logits, -1), -1) | Returns cross entropy for giving logits | Returns cross entropy for giving logits | [
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"entropy",
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"giving",
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] | def _ce_with_logits(logits, target):
"""Returns cross entropy for giving logits"""
return torch.sum(-target * torch.log_softmax(logits, -1), -1) | [
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podgorniy/alfred-translate | 752a7374b7b3a4ed1f01cbad096f36202e1df77f | src/translate.py | python | get_translation_suggestions | (input_string, spelling_suggestions, vocabulary_article) | return res | Returns XML with translate suggestions | Returns XML with translate suggestions | [
"Returns",
"XML",
"with",
"translate",
"suggestions"
] | def get_translation_suggestions(input_string, spelling_suggestions, vocabulary_article):
"""Returns XML with translate suggestions"""
res = []
if len(spelling_suggestions) == 0 and len(vocabulary_article) == 0:
return res
if len(vocabulary_article['def']) != 0:
for article in vocabulary_article['def']:
for translation in article['tr']:
if 'ts' in article.keys():
subtitle = article['ts']
elif 'ts' in translation.keys():
subtitle = translation['ts']
else:
subtitle = ''
res.append({
'translation': translation['text'],
'transcription': subtitle,
})
return res | [
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... | https://github.com/podgorniy/alfred-translate/blob/752a7374b7b3a4ed1f01cbad096f36202e1df77f/src/translate.py#L57-L77 | |
yuzhoujr/leetcode | 6a2ad1fc11225db18f68bfadd21a7419d2cb52a4 | tree/EricD/111. Minimum Depth of Binary Tree - EricD.py | python | minDepth | (self, root) | :type root: TreeNode
:rtype: int | :type root: TreeNode
:rtype: int | [
":",
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":",
"TreeNode",
":",
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] | def minDepth(self, root):
"""
:type root: TreeNode
:rtype: int
"""
if root:
if root.left is None and root.right is None:
return 1
else:
l = self.minDepth(root.left) if root.left else sys.maxint
r = self.minDepth(root.right) if root.right else sys.maxint
return min(l,r)+1
else:
return 0 | [
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Komodo/KomodoEdit | 61edab75dce2bdb03943b387b0608ea36f548e8e | src/components/sciutils.py | python | genLinesBwd | (offset, styledText, position) | r"""Generate styled lines backwards from the given position.
"offset" is the position offset from the start of the source SciMoz
buffer at which "styledText" begins. I.e. the whole buffer
is not necessarily passed in.
"styledText" is the styled text from which to extract.
"position" is the position (NOT offset) from which to begin pulling
out lines.
Yields 2-tuples of the following form:
(<scimoz position at start of line>,
<line/style data of the line>) | r"""Generate styled lines backwards from the given position.
"offset" is the position offset from the start of the source SciMoz
buffer at which "styledText" begins. I.e. the whole buffer
is not necessarily passed in.
"styledText" is the styled text from which to extract.
"position" is the position (NOT offset) from which to begin pulling
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Yields 2-tuples of the following form:
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<line/style data of the line>) | [
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r"""Generate styled lines backwards from the given position.
"offset" is the position offset from the start of the source SciMoz
buffer at which "styledText" begins. I.e. the whole buffer
is not necessarily passed in.
"styledText" is the styled text from which to extract.
"position" is the position (NOT offset) from which to begin pulling
out lines.
Yields 2-tuples of the following form:
(<scimoz position at start of line>,
<line/style data of the line>)
"""
DEBUG = 0
if DEBUG:
print
_printBanner("genLinesBwd(offset=%d, styledText, position=%d)"\
% (offset, position))
_printBufferContext(offset, styledText, position)
index = position - offset
EOLCHARS = tuple("\r\n")
# The first line is slightly special in that there is no EOL to scan
# first. (If "position" is at the start of a line, then we return the
# empty string as the first line.)
i = index*2 - 2
end = index*2
while i >= 0 and styledText[i] not in EOLCHARS:
i -= 2
start = i + 2
line = styledText[start:end]
if DEBUG: print "yield:%d-%d:%s" % (start/2+offset, end/2+offset, _data2text(line))
yield (start/2+offset, line)
while i >= 0:
end = start
# Scan over the EOL.
if styledText[i] == '\n' and i-2 > 0 and styledText[i-2] == '\r':
i -= 4
else:
i -= 2
# Scan to start of line.
while i >= 0 and styledText[i] not in EOLCHARS:
i -= 2
start = i + 2
line = styledText[start:end]
if DEBUG: print "yield:%d-%d:%s" % (start/2+offset, end/2+offset, _data2text(line))
yield (start/2+offset, line)
if DEBUG: _printBanner("done") | [
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geekan/scrapy-examples | edb1cb116bd6def65a6ef01f953b58eb43e54305 | alexa_topsites/alexa_topsites/spiders/spider.py | python | alexa_topsitesSpider.parse_1 | (self, response) | [] | def parse_1(self, response):
info('Parse '+response.url)
x = self.parse_with_rules(response, self.list_css_rules, dict)
# x = self.parse_with_rules(response, self.content_css_rules, dict)
print(json.dumps(x, ensure_ascii=False, indent=2)) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/commands/__init__.py | python | _sort_commands | (cmddict, order) | return sorted(cmddict.items(), key=keyfn) | [] | def _sort_commands(cmddict, order):
def keyfn(key):
try:
return order.index(key[1])
except ValueError:
# unordered items should come last
return 0xff
return sorted(cmddict.items(), key=keyfn) | [
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hudson-and-thames/mlfinlab | 79dcc7120ec84110578f75b025a75850eb72fc73 | mlfinlab/codependence/information.py | python | get_optimal_number_of_bins | (num_obs: int, corr_coef: float = None) | Calculates optimal number of bins for discretization based on number of observations
and correlation coefficient (univariate case).
Algorithms used in this function were originally proposed in the works of Hacine-Gharbi et al. (2012)
and Hacine-Gharbi and Ravier (2018). They are described in the Cornell lecture notes:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3512994&download=yes (p.26)
:param num_obs: (int) Number of observations.
:param corr_coef: (float) Correlation coefficient, used to estimate the number of bins for univariate case.
:return: (int) Optimal number of bins. | Calculates optimal number of bins for discretization based on number of observations
and correlation coefficient (univariate case). | [
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"""
Calculates optimal number of bins for discretization based on number of observations
and correlation coefficient (univariate case).
Algorithms used in this function were originally proposed in the works of Hacine-Gharbi et al. (2012)
and Hacine-Gharbi and Ravier (2018). They are described in the Cornell lecture notes:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3512994&download=yes (p.26)
:param num_obs: (int) Number of observations.
:param corr_coef: (float) Correlation coefficient, used to estimate the number of bins for univariate case.
:return: (int) Optimal number of bins.
"""
pass | [
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i3visio/osrframework | e02a6e9b1346ab5a01244c0d19bcec8232bf1a37 | osrframework/domainfy.py | python | create_domains | (tlds, nicks=None, nicks_file=None) | return domain_candidates | Method that globally permits to generate the domains to be checked
Args:
tlds (list): List of tlds.
nicks (list): List of aliases.
nicks_file (str): The filepath to the aliases file.
Returns:
list: The list of domains to be checked. | Method that globally permits to generate the domains to be checked | [
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] | def create_domains(tlds, nicks=None, nicks_file=None):
"""Method that globally permits to generate the domains to be checked
Args:
tlds (list): List of tlds.
nicks (list): List of aliases.
nicks_file (str): The filepath to the aliases file.
Returns:
list: The list of domains to be checked.
"""
domain_candidates = []
if nicks is not None:
for nick in nicks:
for tld in tlds:
tmp = {
"domain" : nick + tld["tld"],
"type" : tld["type"],
"tld": tld["tld"]
}
domain_candidates.append(tmp)
elif nicks_file is not None:
with open(nicks_file, "r") as file:
nicks = file.read().splitlines()
for nick in nicks:
for tld in tlds:
tmp = {
"domain" : nick + tld["tld"],
"type" : tld["type"],
"tld": tld["tld"]
}
domain_candidates.append(tmp)
return domain_candidates | [
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eliben/pyelftools | 8f7a0becaface09435c4374947548b7851e3d1a2 | elftools/dwarf/callframe.py | python | CFIEntry._decode_CFI_table | (self) | return DecodedCallFrameTable(table=table, reg_order=reg_order) | Decode the instructions contained in the given CFI entry and return
a DecodedCallFrameTable. | Decode the instructions contained in the given CFI entry and return
a DecodedCallFrameTable. | [
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""" Decode the instructions contained in the given CFI entry and return
a DecodedCallFrameTable.
"""
if isinstance(self, CIE):
# For a CIE, initialize cur_line to an "empty" line
cie = self
cur_line = dict(pc=0, cfa=CFARule(reg=None, offset=0))
reg_order = []
else: # FDE
# For a FDE, we need to decode the attached CIE first, because its
# decoded table is needed. Its "initial instructions" describe a
# line that serves as the base (first) line in the FDE's table.
cie = self.cie
cie_decoded_table = cie.get_decoded()
if len(cie_decoded_table.table) > 0:
last_line_in_CIE = copy.copy(cie_decoded_table.table[-1])
cur_line = copy.copy(last_line_in_CIE)
else:
cur_line = dict(cfa=CFARule(reg=None, offset=0))
cur_line['pc'] = self['initial_location']
reg_order = copy.copy(cie_decoded_table.reg_order)
table = []
# Keeps a stack for the use of DW_CFA_{remember|restore}_state
# instructions.
line_stack = []
def _add_to_order(regnum):
# DW_CFA_restore and others remove registers from cur_line,
# but they stay in reg_order. Avoid duplicates.
if regnum not in reg_order:
reg_order.append(regnum)
for instr in self.instructions:
# Throughout this loop, cur_line is the current line. Some
# instructions add it to the table, but most instructions just
# update it without adding it to the table.
name = instruction_name(instr.opcode)
if name == 'DW_CFA_set_loc':
table.append(copy.copy(cur_line))
cur_line['pc'] = instr.args[0]
elif name in ( 'DW_CFA_advance_loc1', 'DW_CFA_advance_loc2',
'DW_CFA_advance_loc4', 'DW_CFA_advance_loc'):
table.append(copy.copy(cur_line))
cur_line['pc'] += instr.args[0] * cie['code_alignment_factor']
elif name == 'DW_CFA_def_cfa':
cur_line['cfa'] = CFARule(
reg=instr.args[0],
offset=instr.args[1])
elif name == 'DW_CFA_def_cfa_sf':
cur_line['cfa'] = CFARule(
reg=instr.args[0],
offset=instr.args[1] * cie['code_alignment_factor'])
elif name == 'DW_CFA_def_cfa_register':
cur_line['cfa'] = CFARule(
reg=instr.args[0],
offset=cur_line['cfa'].offset)
elif name == 'DW_CFA_def_cfa_offset':
cur_line['cfa'] = CFARule(
reg=cur_line['cfa'].reg,
offset=instr.args[0])
elif name == 'DW_CFA_def_cfa_expression':
cur_line['cfa'] = CFARule(expr=instr.args[0])
elif name == 'DW_CFA_undefined':
_add_to_order(instr.args[0])
cur_line[instr.args[0]] = RegisterRule(RegisterRule.UNDEFINED)
elif name == 'DW_CFA_same_value':
_add_to_order(instr.args[0])
cur_line[instr.args[0]] = RegisterRule(RegisterRule.SAME_VALUE)
elif name in ( 'DW_CFA_offset', 'DW_CFA_offset_extended',
'DW_CFA_offset_extended_sf'):
_add_to_order(instr.args[0])
cur_line[instr.args[0]] = RegisterRule(
RegisterRule.OFFSET,
instr.args[1] * cie['data_alignment_factor'])
elif name in ('DW_CFA_val_offset', 'DW_CFA_val_offset_sf'):
_add_to_order(instr.args[0])
cur_line[instr.args[0]] = RegisterRule(
RegisterRule.VAL_OFFSET,
instr.args[1] * cie['data_alignment_factor'])
elif name == 'DW_CFA_register':
_add_to_order(instr.args[0])
cur_line[instr.args[0]] = RegisterRule(
RegisterRule.REGISTER,
instr.args[1])
elif name == 'DW_CFA_expression':
_add_to_order(instr.args[0])
cur_line[instr.args[0]] = RegisterRule(
RegisterRule.EXPRESSION,
instr.args[1])
elif name == 'DW_CFA_val_expression':
_add_to_order(instr.args[0])
cur_line[instr.args[0]] = RegisterRule(
RegisterRule.VAL_EXPRESSION,
instr.args[1])
elif name in ('DW_CFA_restore', 'DW_CFA_restore_extended'):
_add_to_order(instr.args[0])
dwarf_assert(
isinstance(self, FDE),
'%s instruction must be in a FDE' % name)
if instr.args[0] in last_line_in_CIE:
cur_line[instr.args[0]] = last_line_in_CIE[instr.args[0]]
else:
cur_line.pop(instr.args[0], None)
elif name == 'DW_CFA_remember_state':
line_stack.append(copy.deepcopy(cur_line))
elif name == 'DW_CFA_restore_state':
pc = cur_line['pc']
cur_line = line_stack.pop()
cur_line['pc'] = pc
# The current line is appended to the table after all instructions
# have ended, if there were instructions.
if cur_line['cfa'].reg is not None or len(cur_line) > 2:
table.append(cur_line)
return DecodedCallFrameTable(table=table, reg_order=reg_order) | [
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hyperledger/aries-cloudagent-python | 2f36776e99f6053ae92eed8123b5b1b2e891c02a | aries_cloudagent/messaging/models/base.py | python | BaseModel.validate | (self, unknown: str = None) | return self | Validate a constructed model. | Validate a constructed model. | [
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] | def validate(self, unknown: str = None):
"""Validate a constructed model."""
schema = self.Schema(unknown=unknown)
errors = schema.validate(self.serialize())
if errors:
raise ValidationError(errors)
return self | [
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skyfielders/python-skyfield | 0e68757a5c1081f784c58fd7a76635c6deb98451 | skyfield/toposlib.py | python | GeographicPosition.lst_hours_at | (self, t) | return (t.gast + self.longitude._hours + sprime / 54000.0) % 24.0 | Return the Local Apparent Sidereal Time, in hours, at time ``t``.
This location’s Local Apparent Sidereal Time (LAST) is the right
ascension of the zenith at the time ``t``, as measured against
the “true” Earth equator and equinox (rather than the fictional
“mean” equator and equinox, which ignore the Earth’s nutation). | Return the Local Apparent Sidereal Time, in hours, at time ``t``. | [
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"Time",
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] | def lst_hours_at(self, t):
"""Return the Local Apparent Sidereal Time, in hours, at time ``t``.
This location’s Local Apparent Sidereal Time (LAST) is the right
ascension of the zenith at the time ``t``, as measured against
the “true” Earth equator and equinox (rather than the fictional
“mean” equator and equinox, which ignore the Earth’s nutation).
"""
sprime = -47.0e-6 * (t.whole - T0 + t.tdb_fraction) / 36525.0
return (t.gast + self.longitude._hours + sprime / 54000.0) % 24.0 | [
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maas/maas | db2f89970c640758a51247c59bf1ec6f60cf4ab5 | src/maasserver/models/filesystem.py | python | Filesystem.get_physical_block_devices | (self) | return devices | Return PhysicalBlockDevices backing the filesystem. | Return PhysicalBlockDevices backing the filesystem. | [
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] | def get_physical_block_devices(self):
"""Return PhysicalBlockDevices backing the filesystem."""
from maasserver.models.virtualblockdevice import VirtualBlockDevice
devices = []
parent = self.get_parent()
if isinstance(parent, PhysicalBlockDevice):
devices.append(parent)
elif isinstance(parent, VirtualBlockDevice):
for grandparent in parent.get_parents():
if isinstance(grandparent, Partition):
grandparent = grandparent.partition_table.block_device
device = grandparent.actual_instance
if isinstance(device, PhysicalBlockDevice):
devices.append(device)
return devices | [
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/analysis/phase_diagram.py | python | PhaseDiagram.get_all_chempots | (self, comp) | return chempots | Get chemical potentials at a given compositon.
Args:
comp (Composition): Composition
Returns:
Chemical potentials. | Get chemical potentials at a given compositon. | [
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] | def get_all_chempots(self, comp):
"""
Get chemical potentials at a given compositon.
Args:
comp (Composition): Composition
Returns:
Chemical potentials.
"""
all_facets = self._get_all_facets_and_simplexes(comp)
chempots = {}
for facet in all_facets:
facet_name = "-".join([self.qhull_entries[j].name for j in facet])
chempots[facet_name] = self._get_facet_chempots(facet)
return chempots | [
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asciidisco/plugin.video.netflix | ceb2638a9676f5839250dadfd079b9e4e4bdd759 | resources/lib/NetflixSession.py | python | NetflixSession.parse_video_list_ids_entry | (self, id, entry) | return {
id: {
'id': id,
'index': entry['index'],
'name': entry['context'],
'displayName': entry['displayName'],
'size': entry['length']
}
} | Parse a video id entry e.g. rip out the parts we need
Parameters
----------
response_data : :obj:`dict` of :obj:`str`
Dictionary entry from the ´fetch_video_list_ids´ call
Returns
-------
id : :obj:`str`
Unique id of the video list
entry : :obj:`dict` of :obj:`str`
Video list entry in the format:
"3589e2c6-ca3b-48b4-a72d-34f2c09ffbf4_11568382": {
"displayName": "Passend zu Family Guy",
"id": "3589e2c6-ca3b-48b4-a72d-34f2c09ffbf4_11568382",
"index": 18,
"name": "similars",
"size": 33
} | Parse a video id entry e.g. rip out the parts we need | [
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"""Parse a video id entry e.g. rip out the parts we need
Parameters
----------
response_data : :obj:`dict` of :obj:`str`
Dictionary entry from the ´fetch_video_list_ids´ call
Returns
-------
id : :obj:`str`
Unique id of the video list
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"""
return {
id: {
'id': id,
'index': entry['index'],
'name': entry['context'],
'displayName': entry['displayName'],
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/lib-tk/Tix.py | python | Tk.destroy | (self) | [] | def destroy(self):
# For safety, remove an delete_window binding before destroy
self.protocol("WM_DELETE_WINDOW", "")
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kuri65536/python-for-android | 26402a08fc46b09ef94e8d7a6bbc3a54ff9d0891 | python-build/python-libs/gdata/src/gdata/spreadsheet/service.py | python | SpreadsheetsService.GetListFeed | (self, key, wksht_id='default', row_id=None, query=None,
visibility='private', projection='full') | Gets a list feed or a specific entry if a row_id is defined
Args:
key: string The spreadsheet key defined in /ccc?key=
wksht_id: string The id for a specific worksheet entry
row_id: string (optional) The row_id of a row in the list
query: DocumentQuery (optional) Query parameters
Returns:
If there is no row_id, then a SpreadsheetsListFeed.
If there is a row_id, then a SpreadsheetsList. | Gets a list feed or a specific entry if a row_id is defined
Args:
key: string The spreadsheet key defined in /ccc?key=
wksht_id: string The id for a specific worksheet entry
row_id: string (optional) The row_id of a row in the list
query: DocumentQuery (optional) Query parameters
Returns:
If there is no row_id, then a SpreadsheetsListFeed.
If there is a row_id, then a SpreadsheetsList. | [
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visibility='private', projection='full'):
"""Gets a list feed or a specific entry if a row_id is defined
Args:
key: string The spreadsheet key defined in /ccc?key=
wksht_id: string The id for a specific worksheet entry
row_id: string (optional) The row_id of a row in the list
query: DocumentQuery (optional) Query parameters
Returns:
If there is no row_id, then a SpreadsheetsListFeed.
If there is a row_id, then a SpreadsheetsList.
"""
uri = ('http://%s/feeds/list/%s/%s/%s/%s'
% (self.server, key, wksht_id, visibility, projection))
if row_id is not None:
uri = '%s/%s' % (uri, row_id)
if query is not None:
query.feed = uri
uri = query.ToUri()
if row_id:
return self.Get(uri,
converter=gdata.spreadsheet.SpreadsheetsListFromString)
else:
return self.Get(uri,
converter=gdata.spreadsheet.SpreadsheetsListFeedFromString) | [
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brightmart/xlnet_zh | 75e39630ec0856509cde45aa0505195e32564466 | src/run_classifier.py | python | ImdbProcessor.get_dev_examples | (self, data_dir) | return self._create_examples(os.path.join(data_dir, "test")) | [] | def get_dev_examples(self, data_dir):
return self._create_examples(os.path.join(data_dir, "test")) | [
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elfi-dev/elfi | 07ac0ed5e81d5d5fb42de63db3cf9ccc9135b88c | elfi/methods/inference/romc.py | python | ROMC._filter_solutions | (self, eps_filter) | Filter out the solutions over eps threshold.
Parameters
----------
eps_filter: float
the threshold for filtering out solutions | Filter out the solutions over eps threshold. | [
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] | def _filter_solutions(self, eps_filter):
"""Filter out the solutions over eps threshold.
Parameters
----------
eps_filter: float
the threshold for filtering out solutions
"""
# checks
assert self.inference_state["_has_solved_problems"]
# getters
n1 = self.inference_args["N1"]
solved = self.inference_state["solved"]
optim_problems = self.optim_problems
accepted = []
for i in range(n1):
if solved[i] and (optim_problems[i].result.f_min < eps_filter):
accepted.append(True)
else:
accepted.append(False)
# update status
self.inference_args["eps_filter"] = eps_filter
self.inference_state["accepted"] = accepted
self.inference_state["_has_filtered_solutions"] = True | [
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google-research/tensorflow_constrained_optimization | 723d63f8567aaa988c4ce4761152beee2b462e1d | tensorflow_constrained_optimization/python/rates/loss.py | python | HingeLoss.__init__ | (self, margin=1.0) | Creates a new HingeLoss object with the given margin.
The margin determines how far a prediction must be from the decision
boundary in order for it to be penalized. When the margin is zero, this
threshold is exactly the decision boundary. When the margin is at least one,
the hinge loss upper bounds the zero-one loss.
Args:
margin: non-negative float, the margin of the hinge loss. Defaults to 1.
Raises:
ValueError: if the margin is negative. | Creates a new HingeLoss object with the given margin. | [
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"""Creates a new HingeLoss object with the given margin.
The margin determines how far a prediction must be from the decision
boundary in order for it to be penalized. When the margin is zero, this
threshold is exactly the decision boundary. When the margin is at least one,
the hinge loss upper bounds the zero-one loss.
Args:
margin: non-negative float, the margin of the hinge loss. Defaults to 1.
Raises:
ValueError: if the margin is negative.
"""
super(HingeLoss, self).__init__()
self._margin = float(margin)
if margin < 0.0:
raise ValueError("margin must be non-negative") | [
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Epistimio/orion | 732e739d99561020dbe620760acf062ade746006 | src/orion/algo/asha.py | python | ASHABracket.is_filled | (self) | return False | ASHA's first rung can always sample new trials | ASHA's first rung can always sample new trials | [
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"""ASHA's first rung can always sample new trials"""
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minerllabs/minerl | 0123527c334c96ebb3f0cf313df1552fa4302691 | minerl/herobraine/hero/handlers/agent/observations/location_stats.py | python | _ZPositionObservation.__init__ | (self) | [] | def __init__(self):
super().__init__(key_list=['zpos'], space=spaces.Box(low=-640000.0, high=640000.0, shape=(), dtype=np.float),
default_if_missing=0.0) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/finite_state_machine.py | python | FiniteStateMachine.prepone_output | (self) | For all paths, shift the output of the path from one
transition to the earliest possible preceding transition of
the path.
INPUT:
Nothing.
OUTPUT:
Nothing.
Apply the following to each state `s` (except initial states) of the
finite state machine as often as possible:
If the letter `a` is a prefix of the output label of all transitions from
`s` (including the final output of `s`), then remove it from all these
labels and append it to all output labels of all transitions leading
to `s`.
We assume that the states have no output labels, but final outputs are
allowed.
EXAMPLES::
sage: A = Transducer([('A', 'B', 1, 1),
....: ('B', 'B', 0, 0),
....: ('B', 'C', 1, 0)],
....: initial_states=['A'],
....: final_states=['C'])
sage: A.prepone_output()
sage: A.transitions()
[Transition from 'A' to 'B': 1|1,0,
Transition from 'B' to 'B': 0|0,
Transition from 'B' to 'C': 1|-]
::
sage: B = Transducer([('A', 'B', 0, 1),
....: ('B', 'C', 1, [1, 1]),
....: ('B', 'C', 0, 1)],
....: initial_states=['A'],
....: final_states=['C'])
sage: B.prepone_output()
sage: B.transitions()
[Transition from 'A' to 'B': 0|1,1,
Transition from 'B' to 'C': 1|1,
Transition from 'B' to 'C': 0|-]
If initial states are not labeled as such, unexpected results may be
obtained::
sage: C = Transducer([(0,1,0,0)])
sage: C.prepone_output()
verbose 0 (...: finite_state_machine.py, prepone_output)
All transitions leaving state 0 have an output label with
prefix 0. However, there is no inbound transition and it
is not an initial state. This routine (possibly called by
simplification) therefore erased this prefix from all
outbound transitions.
sage: C.transitions()
[Transition from 0 to 1: 0|-]
Also the final output of final states can be changed::
sage: T = Transducer([('A', 'B', 0, 1),
....: ('B', 'C', 1, [1, 1]),
....: ('B', 'C', 0, 1)],
....: initial_states=['A'],
....: final_states=['B'])
sage: T.state('B').final_word_out = [1]
sage: T.prepone_output()
sage: T.transitions()
[Transition from 'A' to 'B': 0|1,1,
Transition from 'B' to 'C': 1|1,
Transition from 'B' to 'C': 0|-]
sage: T.state('B').final_word_out
[]
::
sage: S = Transducer([('A', 'B', 0, 1),
....: ('B', 'C', 1, [1, 1]),
....: ('B', 'C', 0, 1)],
....: initial_states=['A'],
....: final_states=['B'])
sage: S.state('B').final_word_out = [0]
sage: S.prepone_output()
sage: S.transitions()
[Transition from 'A' to 'B': 0|1,
Transition from 'B' to 'C': 1|1,1,
Transition from 'B' to 'C': 0|1]
sage: S.state('B').final_word_out
[0]
Output labels do not have to be hashable::
sage: C = Transducer([(0, 1, 0, []),
....: (1, 0, 0, [vector([0, 0]), 0]),
....: (1, 1, 1, [vector([0, 0]), 1]),
....: (0, 0, 1, 0)],
....: determine_alphabets=False,
....: initial_states=[0])
sage: C.prepone_output()
sage: sorted(C.transitions())
[Transition from 0 to 1: 0|(0, 0),
Transition from 0 to 0: 1|0,
Transition from 1 to 0: 0|0,
Transition from 1 to 1: 1|1,(0, 0)] | For all paths, shift the output of the path from one
transition to the earliest possible preceding transition of
the path. | [
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] | def prepone_output(self):
"""
For all paths, shift the output of the path from one
transition to the earliest possible preceding transition of
the path.
INPUT:
Nothing.
OUTPUT:
Nothing.
Apply the following to each state `s` (except initial states) of the
finite state machine as often as possible:
If the letter `a` is a prefix of the output label of all transitions from
`s` (including the final output of `s`), then remove it from all these
labels and append it to all output labels of all transitions leading
to `s`.
We assume that the states have no output labels, but final outputs are
allowed.
EXAMPLES::
sage: A = Transducer([('A', 'B', 1, 1),
....: ('B', 'B', 0, 0),
....: ('B', 'C', 1, 0)],
....: initial_states=['A'],
....: final_states=['C'])
sage: A.prepone_output()
sage: A.transitions()
[Transition from 'A' to 'B': 1|1,0,
Transition from 'B' to 'B': 0|0,
Transition from 'B' to 'C': 1|-]
::
sage: B = Transducer([('A', 'B', 0, 1),
....: ('B', 'C', 1, [1, 1]),
....: ('B', 'C', 0, 1)],
....: initial_states=['A'],
....: final_states=['C'])
sage: B.prepone_output()
sage: B.transitions()
[Transition from 'A' to 'B': 0|1,1,
Transition from 'B' to 'C': 1|1,
Transition from 'B' to 'C': 0|-]
If initial states are not labeled as such, unexpected results may be
obtained::
sage: C = Transducer([(0,1,0,0)])
sage: C.prepone_output()
verbose 0 (...: finite_state_machine.py, prepone_output)
All transitions leaving state 0 have an output label with
prefix 0. However, there is no inbound transition and it
is not an initial state. This routine (possibly called by
simplification) therefore erased this prefix from all
outbound transitions.
sage: C.transitions()
[Transition from 0 to 1: 0|-]
Also the final output of final states can be changed::
sage: T = Transducer([('A', 'B', 0, 1),
....: ('B', 'C', 1, [1, 1]),
....: ('B', 'C', 0, 1)],
....: initial_states=['A'],
....: final_states=['B'])
sage: T.state('B').final_word_out = [1]
sage: T.prepone_output()
sage: T.transitions()
[Transition from 'A' to 'B': 0|1,1,
Transition from 'B' to 'C': 1|1,
Transition from 'B' to 'C': 0|-]
sage: T.state('B').final_word_out
[]
::
sage: S = Transducer([('A', 'B', 0, 1),
....: ('B', 'C', 1, [1, 1]),
....: ('B', 'C', 0, 1)],
....: initial_states=['A'],
....: final_states=['B'])
sage: S.state('B').final_word_out = [0]
sage: S.prepone_output()
sage: S.transitions()
[Transition from 'A' to 'B': 0|1,
Transition from 'B' to 'C': 1|1,1,
Transition from 'B' to 'C': 0|1]
sage: S.state('B').final_word_out
[0]
Output labels do not have to be hashable::
sage: C = Transducer([(0, 1, 0, []),
....: (1, 0, 0, [vector([0, 0]), 0]),
....: (1, 1, 1, [vector([0, 0]), 1]),
....: (0, 0, 1, 0)],
....: determine_alphabets=False,
....: initial_states=[0])
sage: C.prepone_output()
sage: sorted(C.transitions())
[Transition from 0 to 1: 0|(0, 0),
Transition from 0 to 0: 1|0,
Transition from 1 to 0: 0|0,
Transition from 1 to 1: 1|1,(0, 0)]
"""
def find_common_output(state):
if (any(transition for transition in self.transitions(state)
if not transition.word_out)
or state.is_final and not state.final_word_out):
return tuple()
first_letters = [transition.word_out[0]
for transition in self.transitions(state)]
if state.is_final:
first_letters = first_letters + [state.final_word_out[0]]
if not first_letters:
return tuple()
first_item = first_letters.pop()
if all(item == first_item for item in first_letters):
return (first_item,)
return tuple()
changed = 1
iteration = 0
while changed > 0:
changed = 0
iteration += 1
for state in self.iter_states():
if state.is_initial:
continue
if state.word_out:
raise NotImplementedError(
"prepone_output assumes that all states have "
"empty output word, but state %s has output "
"word %s" % (state, state.word_out))
common_output = find_common_output(state)
if common_output:
changed += 1
if state.is_final:
assert state.final_word_out[0] == common_output[0]
state.final_word_out = state.final_word_out[1:]
for transition in self.transitions(state):
assert transition.word_out[0] == common_output[0]
transition.word_out = transition.word_out[1:]
found_inbound_transition = False
for transition in self.iter_transitions():
if transition.to_state == state:
transition.word_out = transition.word_out \
+ [common_output[0]]
found_inbound_transition = True
if not found_inbound_transition:
verbose(
"All transitions leaving state %s have an "
"output label with prefix %s. However, "
"there is no inbound transition and it is "
"not an initial state. This routine "
"(possibly called by simplification) "
"therefore erased this prefix from all "
"outbound transitions." %
(state, common_output[0]),
level=0) | [
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lazylibrarian/LazyLibrarian | ae3c14e9db9328ce81765e094ab2a14ed7155624 | lib/pythontwitter/__init__.py | python | User.GetStatus | (self) | return self._status | Get the latest twitter.Status of this user.
Returns:
The latest twitter.Status of this user | Get the latest twitter.Status of this user. | [
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] | def GetStatus(self):
'''Get the latest twitter.Status of this user.
Returns:
The latest twitter.Status of this user
'''
return self._status | [
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ustayready/CredKing | 68b612e4cdf01d2b65b14ab2869bb8a5531056ee | plugins/gmail/lxml/html/diff.py | python | cleanup_html | (html) | return html | This 'cleans' the HTML, meaning that any page structure is removed
(only the contents of <body> are used, if there is any <body).
Also <ins> and <del> tags are removed. | This 'cleans' the HTML, meaning that any page structure is removed
(only the contents of <body> are used, if there is any <body).
Also <ins> and <del> tags are removed. | [
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... | def cleanup_html(html):
""" This 'cleans' the HTML, meaning that any page structure is removed
(only the contents of <body> are used, if there is any <body).
Also <ins> and <del> tags are removed. """
match = _body_re.search(html)
if match:
html = html[match.end():]
match = _end_body_re.search(html)
if match:
html = html[:match.start()]
html = _ins_del_re.sub('', html)
return html | [
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Ulauncher/Ulauncher | 44735d8d4c2b4b8a8d28f95ab6a6c6d577c859e5 | ulauncher/utils/desktop/reader.py | python | filter_app | (app, disable_desktop_filters=False) | return app and app.get_string('Name') and app.get_string('Type') == 'Application' \
and (app.get_show_in() or disable_desktop_filters) and not app.get_nodisplay() and not app.get_is_hidden() | :param Gio.DesktopAppInfo app:
:returns: True if app can be added to the database | :param Gio.DesktopAppInfo app:
:returns: True if app can be added to the database | [
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] | def filter_app(app, disable_desktop_filters=False):
"""
:param Gio.DesktopAppInfo app:
:returns: True if app can be added to the database
"""
return app and app.get_string('Name') and app.get_string('Type') == 'Application' \
and (app.get_show_in() or disable_desktop_filters) and not app.get_nodisplay() and not app.get_is_hidden() | [
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aianaconda/TensorFlow_Engineering_Implementation | cb787e359da9ac5a08d00cd2458fecb4cb5a3a31 | tf2code/Chapter8/code8.4/Code8-9/backend.py | python | moving_average_update | (x, value, momentum) | return moving_averages.assign_moving_average(
x, value, momentum, zero_debias=zero_debias) | Compute the moving average of a variable.
Arguments:
x: A Variable.
value: A tensor with the same shape as `variable`.
momentum: The moving average momentum.
Returns:
An Operation to update the variable. | Compute the moving average of a variable. | [
"Compute",
"the",
"moving",
"average",
"of",
"a",
"variable",
"."
] | def moving_average_update(x, value, momentum):
"""Compute the moving average of a variable.
Arguments:
x: A Variable.
value: A tensor with the same shape as `variable`.
momentum: The moving average momentum.
Returns:
An Operation to update the variable.
"""
zero_debias = not tf2.enabled()
return moving_averages.assign_moving_average(
x, value, momentum, zero_debias=zero_debias) | [
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asyml/texar-pytorch | b83d3ec17e19da08fc5f81996d02f91176e55e54 | texar/torch/core/attention_mechanism.py | python | monotonic_attention | (p_choose_i: torch.Tensor,
previous_attention: torch.Tensor,
mode: str) | return attention | r"""Compute monotonic attention distribution from choosing probabilities.
Monotonic attention implies that the input sequence is processed in an
explicitly left-to-right manner when generating the output sequence. In
addition, once an input sequence element is attended to at a given output
time step, elements occurring before it cannot be attended to at subsequent
output time steps. This function generates attention distributions
according to these assumptions. For more information, see `Online and
Linear-Time Attention by Enforcing Monotonic Alignments`.
Args:
p_choose_i: Probability of choosing input sequence/memory element i.
Should be of shape (batch_size, input_sequence_length), and should
all be in the range [0, 1].
previous_attention: The attention distribution from the previous output
time step. Should be of shape (batch_size, input_sequence_length).
For the first output time step, `previous_attention[n]` should be
`[1, 0, 0, ..., 0] for all n in [0, ... batch_size - 1]`.
mode: How to compute the attention distribution.
Must be one of ``"recursive"``, ``"parallel"``, or ``"hard"``:
- ``"recursive"`` recursively computes the distribution.
This is slowest but is exact, general, and does not suffer
from numerical instabilities.
- ``"parallel"`` uses parallelized cumulative-sum and
cumulative-product operations to compute a closed-form
solution to the recurrence relation defining the attention
distribution. This makes it more efficient than
``"recursive"``, but it requires numerical checks which make
the distribution non-exact. This can be a problem in
particular when input sequence is long and/or
:attr:`p_choose_i` has entries very close to 0 or 1.
- ``"hard"`` requires that the probabilities in
:attr:`p_choose_i` are all either 0 or 1, and subsequently
uses a more efficient and exact solution.
Returns:
A tensor of shape (batch_size, input_sequence_length) representing the
attention distributions for each sequence in the batch.
Raises:
ValueError: mode is not one of ``"recursive"``, ``"parallel"``,
``"hard"``. | r"""Compute monotonic attention distribution from choosing probabilities.
Monotonic attention implies that the input sequence is processed in an
explicitly left-to-right manner when generating the output sequence. In
addition, once an input sequence element is attended to at a given output
time step, elements occurring before it cannot be attended to at subsequent
output time steps. This function generates attention distributions
according to these assumptions. For more information, see `Online and
Linear-Time Attention by Enforcing Monotonic Alignments`. | [
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previous_attention: torch.Tensor,
mode: str) -> torch.Tensor:
r"""Compute monotonic attention distribution from choosing probabilities.
Monotonic attention implies that the input sequence is processed in an
explicitly left-to-right manner when generating the output sequence. In
addition, once an input sequence element is attended to at a given output
time step, elements occurring before it cannot be attended to at subsequent
output time steps. This function generates attention distributions
according to these assumptions. For more information, see `Online and
Linear-Time Attention by Enforcing Monotonic Alignments`.
Args:
p_choose_i: Probability of choosing input sequence/memory element i.
Should be of shape (batch_size, input_sequence_length), and should
all be in the range [0, 1].
previous_attention: The attention distribution from the previous output
time step. Should be of shape (batch_size, input_sequence_length).
For the first output time step, `previous_attention[n]` should be
`[1, 0, 0, ..., 0] for all n in [0, ... batch_size - 1]`.
mode: How to compute the attention distribution.
Must be one of ``"recursive"``, ``"parallel"``, or ``"hard"``:
- ``"recursive"`` recursively computes the distribution.
This is slowest but is exact, general, and does not suffer
from numerical instabilities.
- ``"parallel"`` uses parallelized cumulative-sum and
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solution to the recurrence relation defining the attention
distribution. This makes it more efficient than
``"recursive"``, but it requires numerical checks which make
the distribution non-exact. This can be a problem in
particular when input sequence is long and/or
:attr:`p_choose_i` has entries very close to 0 or 1.
- ``"hard"`` requires that the probabilities in
:attr:`p_choose_i` are all either 0 or 1, and subsequently
uses a more efficient and exact solution.
Returns:
A tensor of shape (batch_size, input_sequence_length) representing the
attention distributions for each sequence in the batch.
Raises:
ValueError: mode is not one of ``"recursive"``, ``"parallel"``,
``"hard"``.
"""
# Force things to be tensors
if not isinstance(p_choose_i, torch.Tensor):
p_choose_i = torch.tensor(p_choose_i)
if not isinstance(previous_attention, torch.Tensor):
previous_attention = torch.tensor(previous_attention)
if mode == "recursive":
# Use .shape[0] when it's not None, or fall back on symbolic shape
batch_size = p_choose_i.shape[0]
# Compute [1, 1 - p_choose_i[0], 1 - p_choose_i[1], ...,
# 1 - p_choose_i[-2]]
shifted_1mp_choose_i = torch.cat((p_choose_i.new_ones(batch_size, 1),
1 - p_choose_i[:, :-1]), 1)
# Compute attention distribution recursively as
# q[i] = (1 - p_choose_i[i - 1])*q[i - 1] + previous_attention[i]
# attention[i] = p_choose_i[i]*q[i]
def f(x, yz):
return torch.reshape(yz[0] * x + yz[1], (batch_size,))
x_tmp = f(torch.zeros((batch_size,)), torch.transpose(
shifted_1mp_choose_i, 0, 1))
x_tmp = f(x_tmp, torch.transpose(previous_attention, 0, 1))
attention = p_choose_i * torch.transpose(x_tmp, 0, 1)
elif mode == "parallel":
batch_size = p_choose_i.shape[0]
shifted_1mp_choose_i = torch.cat((p_choose_i.new_ones(batch_size, 1),
1 - p_choose_i[:, :-1]), 1)
# safe_cumprod computes cumprod in logspace with numeric checks
cumprod_1mp_choose_i = safe_cumprod(shifted_1mp_choose_i, dim=1)
# Compute recurrence relation solution
attention = p_choose_i * cumprod_1mp_choose_i * torch.cumsum(
previous_attention / cumprod_1mp_choose_i.clamp(min=1e-10, max=1.),
dim=1)
elif mode == "hard":
# Remove any probabilities before the index chosen last time step
p_choose_i *= torch.cumsum(previous_attention, dim=1)
# Now, use exclusive cumprod to remove probabilities after the first
# chosen index, like so:
# p_choose_i = [0, 0, 0, 1, 1, 0, 1, 1]
# cumprod(1 - p_choose_i, exclusive=True) = [1, 1, 1, 1, 0, 0, 0, 0]
# Product of above: [0, 0, 0, 1, 0, 0, 0, 0]
batch_size = p_choose_i.shape[0]
shifted_1mp_choose_i = torch.cat((p_choose_i.new_ones(batch_size, 1),
1 - p_choose_i[:, :-1]), 1)
attention = p_choose_i * torch.cumprod(shifted_1mp_choose_i, dim=1)
else:
raise ValueError("mode must be 'recursive', 'parallel', or 'hard'.")
return attention | [
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andyzsf/TuShare | 92787ad0cd492614bdb6389b71a19c80d1c8c9ae | tushare/datayes/fundamental.py | python | Fundamental.FdmtCFInsu | (self, reportType='', secID='', ticker='', beginDate='', endDate='',
publishDateBegin='', publishDateEnd='', field='') | return _ret_data(code, result) | 1、根据2007年新会计准则制定的保险业现金流量表模板，收集了2007年以来沪深上市公司定期报告中所有以此模板披露的现金流量表数据；（主要是保险业上市公司）
2、仅收集合并报表数据，包括本期和上期数据；
3、如果上市公司对外财务报表进行更正，调整，均有采集并对外展示；
4、本表中单位为人民币元；
5、每季更新。 | 1、根据2007年新会计准则制定的保险业现金流量表模板，收集了2007年以来沪深上市公司定期报告中所有以此模板披露的现金流量表数据；（主要是保险业上市公司）
2、仅收集合并报表数据，包括本期和上期数据；
3、如果上市公司对外财务报表进行更正，调整，均有采集并对外展示；
4、本表中单位为人民币元；
5、每季更新。 | [
"1、根据2007年新会计准则制定的保险业现金流量表模板，收集了2007年以来沪深上市公司定期报告中所有以此模板披露的现金流量表数据；（主要是保险业上市公司）",
"2、仅收集合并报表数据，包括本期和上期数据；",
"3、如果上市公司对外财务报表进行更正，调整，均有采集并对外展示；",
"4、本表中单位为人民币元；",
"5、每季更新。"
] | def FdmtCFInsu(self, reportType='', secID='', ticker='', beginDate='', endDate='',
publishDateBegin='', publishDateEnd='', field=''):
"""
1、根据2007年新会计准则制定的保险业现金流量表模板，收集了2007年以来沪深上市公司定期报告中所有以此模板披露的现金流量表数据；（主要是保险业上市公司）
2、仅收集合并报表数据，包括本期和上期数据；
3、如果上市公司对外财务报表进行更正，调整，均有采集并对外展示；
4、本表中单位为人民币元；
5、每季更新。
"""
code, result = self.client.getData(vs.FDMTCFINSU%(reportType, secID, ticker,
beginDate, endDate,
publishDateBegin, publishDateEnd, field))
return _ret_data(code, result) | [
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | tokumx/datadog_checks/tokumx/vendor/bson/__init__.py | python | _get_binary | (data, position, obj_end, opts, dummy1) | return value, end | Decode a BSON binary to bson.binary.Binary or python UUID. | Decode a BSON binary to bson.binary.Binary or python UUID. | [
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] | def _get_binary(data, position, obj_end, opts, dummy1):
"""Decode a BSON binary to bson.binary.Binary or python UUID."""
length, subtype = _UNPACK_LENGTH_SUBTYPE(data[position:position + 5])
position += 5
if subtype == 2:
length2 = _UNPACK_INT(data[position:position + 4])[0]
position += 4
if length2 != length - 4:
raise InvalidBSON("invalid binary (st 2) - lengths don't match!")
length = length2
end = position + length
if length < 0 or end > obj_end:
raise InvalidBSON('bad binary object length')
if subtype in (3, 4):
# Java Legacy
uuid_representation = opts.uuid_representation
if uuid_representation == JAVA_LEGACY:
java = data[position:end]
value = uuid.UUID(bytes=java[0:8][::-1] + java[8:16][::-1])
# C# legacy
elif uuid_representation == CSHARP_LEGACY:
value = uuid.UUID(bytes_le=data[position:end])
# Python
else:
value = uuid.UUID(bytes=data[position:end])
return value, end
# Python3 special case. Decode subtype 0 to 'bytes'.
if PY3 and subtype == 0:
value = data[position:end]
else:
value = Binary(data[position:end], subtype)
return value, end | [
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mrlesmithjr/Ansible | d44f0dc0d942bdf3bf7334b307e6048f0ee16e36 | roles/ansible-vsphere-management/scripts/pdns/lib/python2.7/site-packages/pip/_vendor/html5lib/treebuilders/base.py | python | TreeBuilder.getDocument | (self) | return self.document | Return the final tree | Return the final tree | [
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] | def getDocument(self):
"Return the final tree"
return self.document | [
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wulczer/txpostgres | bd56a3648574bfd0c24543e4d9e4a611458c2da1 | txpostgres/retrying.py | python | RetryingCall.start | (self, backoffIterator=None, failureTester=None) | return self._deferred | Start the call and retry it until it succeeds and fails.
:param backoffIterator: A zero-argument callable that should
return a iterator yielding reconnection delay periods. If
:class:`None` then :func:`.simpleBackoffIterator` will be
used.
:type backoffIterator: callable
:param failureTester: A one-argument callable that will be called with
a :tm:`Failure <python.failure.Failure>` instance each time the
function being retried fails. It should return
:class:`None` if the call should be retried or a
:tm:`Failure <python.failure.Failure>` if the retrying process
should be stopped. If :class:`None` is used for this parameter,
retrying will never stop until the backoff iterator is exhausted.
:type failureTester: callable | Start the call and retry it until it succeeds and fails. | [
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"""
Start the call and retry it until it succeeds and fails.
:param backoffIterator: A zero-argument callable that should
return a iterator yielding reconnection delay periods. If
:class:`None` then :func:`.simpleBackoffIterator` will be
used.
:type backoffIterator: callable
:param failureTester: A one-argument callable that will be called with
a :tm:`Failure <python.failure.Failure>` instance each time the
function being retried fails. It should return
:class:`None` if the call should be retried or a
:tm:`Failure <python.failure.Failure>` if the retrying process
should be stopped. If :class:`None` is used for this parameter,
retrying will never stop until the backoff iterator is exhausted.
:type failureTester: callable
"""
self.resetBackoff(backoffIterator)
if failureTester is None:
failureTester = lambda _: None
self._failureTester = failureTester
self._deferred = defer.Deferred(self._cancel)
self._inProgress = None
self.failure = None
self.cancelled = False
self._call()
return self._deferred | [
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bubbliiiing/Semantic-Segmentation | 4cc89a22ffc9018d2b44e69e85672c7bdd1ab706 | deeplab_Mobile/nets/deeplab.py | python | SepConv_BN | (x, filters, prefix, stride=1, kernel_size=3, rate=1, depth_activation=False, epsilon=1e-3) | return x | [] | def SepConv_BN(x, filters, prefix, stride=1, kernel_size=3, rate=1, depth_activation=False, epsilon=1e-3):
if stride == 1:
depth_padding = 'same'
else:
kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
pad_total = kernel_size_effective - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
x = ZeroPadding2D((pad_beg, pad_end))(x)
depth_padding = 'valid'
if not depth_activation:
x = Activation('relu')(x)
# 首先使用3x3的深度可分离卷积
x = DepthwiseConv2D((kernel_size, kernel_size), strides=(stride, stride), dilation_rate=(rate, rate),
padding=depth_padding, use_bias=False, name=prefix + '_depthwise')(x)
x = BatchNormalization(name=prefix + '_depthwise_BN', epsilon=epsilon)(x)
if depth_activation:
x = Activation('relu')(x)
# 利用1x1卷积进行通道数调整
x = Conv2D(filters, (1, 1), padding='same', use_bias=False, name=prefix + '_pointwise')(x)
x = BatchNormalization(name=prefix + '_pointwise_BN', epsilon=epsilon)(x)
if depth_activation:
x = Activation('relu')(x)
return x | [
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google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/conpono/evals/classifier_utils.py | python | LCQMCPairClassificationProcessor.get_labels | (self) | return ["0", "1"] | See base class. | See base class. | [
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PokemonGoF/PokemonGo-Bot-Desktop | 4bfa94f0183406c6a86f93645eff7abd3ad4ced8 | build/pywin/Lib/email/charset.py | python | Charset.to_splittable | (self, s) | Convert a possibly multibyte string to a safely splittable format.
Uses the input_codec to try and convert the string to Unicode, so it
can be safely split on character boundaries (even for multibyte
characters).
Returns the string as-is if it isn't known how to convert it to
Unicode with the input_charset.
Characters that could not be converted to Unicode will be replaced
with the Unicode replacement character U+FFFD. | Convert a possibly multibyte string to a safely splittable format. | [
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] | def to_splittable(self, s):
"""Convert a possibly multibyte string to a safely splittable format.
Uses the input_codec to try and convert the string to Unicode, so it
can be safely split on character boundaries (even for multibyte
characters).
Returns the string as-is if it isn't known how to convert it to
Unicode with the input_charset.
Characters that could not be converted to Unicode will be replaced
with the Unicode replacement character U+FFFD.
"""
if isinstance(s, unicode) or self.input_codec is None:
return s
try:
return unicode(s, self.input_codec, 'replace')
except LookupError:
# Input codec not installed on system, so return the original
# string unchanged.
return s | [
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pydata/patsy | 5fc881104b749b720b08e393a5505d6e69d72f95 | patsy/missing.py | python | NAAction.is_numerical_NA | (self, arr) | return mask | Returns a 1-d mask array indicating which rows in an array of
numerical values contain at least one NA value.
Note that here `arr` is a numpy array or pandas DataFrame. | Returns a 1-d mask array indicating which rows in an array of
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"""Returns a 1-d mask array indicating which rows in an array of
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mask = np.zeros(arr.shape, dtype=bool)
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jhetherly/EnglishSpeechUpsampler | 1265fa3ec68fa1e662b5d7f9c61ca730f9beee99 | models.py | python | build_downsampling_block | (input_tensor,
filter_size, stride,
layer_number,
act=tf.nn.relu,
is_training=True,
depth=None,
padding='VALID',
tensorboard_output=False,
name=None) | return l | [] | def build_downsampling_block(input_tensor,
filter_size, stride,
layer_number,
act=tf.nn.relu,
is_training=True,
depth=None,
padding='VALID',
tensorboard_output=False,
name=None):
# assume this layer is twice the depth of the previous layer if no depth
# information is given
if depth is None:
depth = 2*input_tensor.get_shape().as_list()[-1]
with tf.name_scope('{}_layer_weights'.format(layer_number)):
W = weight_variable([filter_size,
input_tensor.get_shape().as_list()[-1],
depth])
if tensorboard_output:
histogram_variable_summaries(W)
with tf.name_scope('{}_layer_biases'.format(layer_number)):
b = bias_variable([depth])
if tensorboard_output:
histogram_variable_summaries(b)
with tf.name_scope('{}_layer_conv_preactivation'.format(layer_number)):
l = tf.nn.conv1d(input_tensor, W, stride=stride,
padding=padding, name=name) + b
if tensorboard_output:
histogram_variable_summaries(l)
with tf.name_scope('{}_layer_batch_norm'.format(layer_number)) as scope:
# l = tf.nn.dropout(l, keep_prob=0.25)
l = batch_norm(l, is_training, scope)
with tf.name_scope('{}_layer_conv_activation'.format(layer_number)):
l = act(l, name=name)
if tensorboard_output:
histogram_variable_summaries(l)
return l | [
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... | https://github.com/jhetherly/EnglishSpeechUpsampler/blob/1265fa3ec68fa1e662b5d7f9c61ca730f9beee99/models.py#L176-L214 | |||
saturday06/VRM_Addon_for_Blender | 0fc59703bb203dca760501221d34ecc4a566e64f | io_scene_vrm/editor/vrm0/migration.py | python | migrate_vrm0_first_person | (
first_person_props: bpy.types.PropertyGroup,
first_person_dict: Any,
) | [] | def migrate_vrm0_first_person(
first_person_props: bpy.types.PropertyGroup,
first_person_dict: Any,
) -> None:
if not isinstance(first_person_dict, dict):
return
first_person_bone = first_person_dict.get("firstPersonBone")
if isinstance(first_person_bone, str):
first_person_props.first_person_bone.value = first_person_bone
first_person_bone_offset = convert.vrm_json_vector3_to_tuple(
first_person_dict.get("firstPersonBoneOffset")
)
if first_person_bone_offset is not None:
# Axis confusing
(x, y, z) = first_person_bone_offset
first_person_props.first_person_bone_offset = (x, z, y)
mesh_annotations = first_person_dict.get("meshAnnotations")
if isinstance(mesh_annotations, collections.Iterable):
for mesh_annotation_dict in mesh_annotations:
mesh_annotation_props = first_person_props.mesh_annotations.add()
if not isinstance(mesh_annotation_dict, dict):
continue
mesh = mesh_annotation_dict.get("mesh")
if isinstance(mesh, str) and mesh in bpy.data.meshes:
mesh_annotation_props.mesh.value = bpy.data.meshes[mesh].name
first_person_flag = mesh_annotation_dict.get("firstPersonFlag")
if isinstance(first_person_flag, str):
mesh_annotation_props.first_person_flag = first_person_flag
look_at_type_name = first_person_dict.get("lookAtTypeName")
if look_at_type_name in ["Bone", "BlendShape"]:
first_person_props.look_at_type_name = look_at_type_name
for (look_at_props, look_at_dict) in [
(
first_person_props.look_at_horizontal_inner,
first_person_dict.get("lookAtHorizontalInner"),
),
(
first_person_props.look_at_horizontal_outer,
first_person_dict.get("lookAtHorizontalOuter"),
),
(
first_person_props.look_at_vertical_down,
first_person_dict.get("lookAtVerticalDown"),
),
(
first_person_props.look_at_vertical_up,
first_person_dict.get("lookAtVerticalUp"),
),
]:
if not isinstance(look_at_dict, dict):
continue
curve = convert.vrm_json_curve_to_list(look_at_dict.get("curve"))
if curve is not None:
look_at_props.curve = curve
x_range = look_at_dict.get("xRange")
if isinstance(x_range, (float, int)):
look_at_props.x_range = x_range
y_range = look_at_dict.get("yRange")
if isinstance(y_range, (float, int)):
look_at_props.y_range = y_range | [
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"retur... | https://github.com/saturday06/VRM_Addon_for_Blender/blob/0fc59703bb203dca760501221d34ecc4a566e64f/io_scene_vrm/editor/vrm0/migration.py#L125-L195 | ||||
bshillingford/python-torchfile | fbd434a5b5562c88b91a95e6476e11dbb7735436 | torchfile.py | python | TorchObject.__dir__ | (self) | return keys | [] | def __dir__(self):
keys = list(self._obj.keys())
keys.append('torch_typename')
return keys | [
"def",
"__dir__",
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")",
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"(",
"'torch_typename'",
")",
"return",
"keys"
] | https://github.com/bshillingford/python-torchfile/blob/fbd434a5b5562c88b91a95e6476e11dbb7735436/torchfile.py#L120-L123 | |||
pencil1/ApiTestManage | 851a54d5629456b7e967e15186244409ddf783cc | app/models.py | python | User.init_user | () | [] | def init_user():
user = User.query.filter_by(name='管理员').first()
if user:
print('The administrator account already exists')
print('--' * 30)
return
else:
user = User(name=u'管理员', account='admin', password='123456', status=1, role_id=2)
db.session.add(user)
db.session.commit()
print('Administrator account created successfully')
print('--' * 30) | [
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"*",... | https://github.com/pencil1/ApiTestManage/blob/851a54d5629456b7e967e15186244409ddf783cc/app/models.py#L126-L137 | ||||
nucleic/enaml | 65c2a2a2d765e88f2e1103046680571894bb41ed | enaml/qt/qt_color_dialog.py | python | QtColorDialog.set_custom_color | (index, color) | Set the custom color for the given index. | Set the custom color for the given index. | [
"Set",
"the",
"custom",
"color",
"for",
"the",
"given",
"index",
"."
] | def set_custom_color(index, color):
""" Set the custom color for the given index.
"""
QColorDialog.setCustomColor(index, color.argb) | [
"def",
"set_custom_color",
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"(",
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",",
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".",
"argb",
")"
] | https://github.com/nucleic/enaml/blob/65c2a2a2d765e88f2e1103046680571894bb41ed/enaml/qt/qt_color_dialog.py#L145-L149 | ||
PyHDI/veriloggen | 2382d200deabf59cfcfd741f5eba371010aaf2bb | veriloggen/seq/seq.py | python | Seq.implement | (self) | [] | def implement(self):
if self.as_module:
self.make_module()
return
self.make_always() | [
"def",
"implement",
"(",
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"(",
")",
"return",
"self",
".",
"make_always",
"(",
")"
] | https://github.com/PyHDI/veriloggen/blob/2382d200deabf59cfcfd741f5eba371010aaf2bb/veriloggen/seq/seq.py#L363-L368 | ||||
openstack/neutron | fb229fb527ac8b95526412f7762d90826ac41428 | neutron/db/l3_db.py | python | L3_NAT_db_mixin.disassociate_floatingips | (self, context, port_id, do_notify=True) | return router_ids | Disassociate all floating IPs linked to specific port.
@param port_id: ID of the port to disassociate floating IPs.
@param do_notify: whether we should notify routers right away.
@return: set of router-ids that require notification updates
if do_notify is False, otherwise None. | Disassociate all floating IPs linked to specific port. | [
"Disassociate",
"all",
"floating",
"IPs",
"linked",
"to",
"specific",
"port",
"."
] | def disassociate_floatingips(self, context, port_id, do_notify=True):
"""Disassociate all floating IPs linked to specific port.
@param port_id: ID of the port to disassociate floating IPs.
@param do_notify: whether we should notify routers right away.
@return: set of router-ids that require notification updates
if do_notify is False, otherwise None.
"""
router_ids = super(L3_NAT_db_mixin, self).disassociate_floatingips(
context, port_id, do_notify)
if do_notify:
self.notify_routers_updated(context, router_ids)
# since caller assumes that we handled notifications on its
# behalf, return nothing
return
return router_ids | [
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",",... | https://github.com/openstack/neutron/blob/fb229fb527ac8b95526412f7762d90826ac41428/neutron/db/l3_db.py#L2155-L2171 | |
pandaproject/panda | 133baa47882a289773a30c9656e2ea4efe569387 | panda/models/dataset.py | python | Dataset.import_data | (self, user, upload, external_id_field_index=None) | Import data into this ``Dataset`` from a given ``DataUpload``. | Import data into this ``Dataset`` from a given ``DataUpload``. | [
"Import",
"data",
"into",
"this",
"Dataset",
"from",
"a",
"given",
"DataUpload",
"."
] | def import_data(self, user, upload, external_id_field_index=None):
"""
Import data into this ``Dataset`` from a given ``DataUpload``.
"""
self.lock()
try:
if upload.imported:
raise DataImportError(_('This file has already been imported.'))
task_type = get_import_task_type_for_upload(upload)
if not task_type:
# This is normally caught on the client.
raise DataImportError(_('This file type is not supported for data import.'))
if self.column_schema:
# This is normally caught on the client.
if upload.columns != [c['name'] for c in self.column_schema]:
raise DataImportError(_('The columns in this file do not match those in the dataset.'))
else:
self.column_schema = make_column_schema(upload.columns, types=upload.guessed_types)
if self.sample_data is None:
self.sample_data = upload.sample_data
# If this is the first import and the API hasn't been used, save that information
if self.initial_upload is None and self.row_count is None:
self.initial_upload = upload
self.current_task = TaskStatus.objects.create(
task_name=task_type.name,
task_description=_('Import data from %(filename)s into %(slug)s.') \
% {'filename': upload.filename, 'slug': self.slug},
creator=user
)
self.save()
task_type.apply_async(
args=[self.slug, upload.id],
kwargs={ 'external_id_field_index': external_id_field_index },
task_id=self.current_task.id
)
except:
self.unlock()
raise | [
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"'This file ... | https://github.com/pandaproject/panda/blob/133baa47882a289773a30c9656e2ea4efe569387/panda/models/dataset.py#L180-L225 | ||
dgilland/pydash | 24ad0e43b51b367d00447c45baa68c9c03ad1a52 | src/pydash/helpers.py | python | parse_iteratee | (iteratee_keyword, *args, **kwargs) | return iteratee, args | Try to find iteratee function passed in either as a keyword argument or as the last
positional argument in `args`. | Try to find iteratee function passed in either as a keyword argument or as the last
positional argument in `args`. | [
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"argument",
"or",
"as",
"the",
"last",
"positional",
"argument",
"in",
"args",
"."
] | def parse_iteratee(iteratee_keyword, *args, **kwargs):
"""Try to find iteratee function passed in either as a keyword argument or as the last
positional argument in `args`."""
iteratee = kwargs.get(iteratee_keyword)
last_arg = args[-1]
if iteratee is None and (
callable(last_arg)
or isinstance(last_arg, str)
or isinstance(last_arg, dict)
or last_arg is None
):
iteratee = last_arg
args = args[:-1]
return iteratee, args | [
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"-",
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"]",
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"an... | https://github.com/dgilland/pydash/blob/24ad0e43b51b367d00447c45baa68c9c03ad1a52/src/pydash/helpers.py#L236-L251 |
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