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rgs1/zk_shell | zk_shell/xclient.py | XClient.sessions_info | def sessions_info(self, hosts):
"""Returns ClientInfo per session.
:param hosts: comma separated lists of members of the ZK ensemble.
:returns: A dictionary of (session_id, ClientInfo).
"""
info_by_id = {}
for server_endpoint, dump in self.dump_by_server(hosts).items():
server_ip, server_port = server_endpoint
for line in dump.split("\n"):
mat = self.IP_PORT_REGEX.match(line)
if mat is None:
continue
ip, port, sid = mat.groups()
info_by_id[sid] = ClientInfo(sid, ip, port, server_ip, server_port)
return info_by_id | python | def sessions_info(self, hosts):
"""Returns ClientInfo per session.
:param hosts: comma separated lists of members of the ZK ensemble.
:returns: A dictionary of (session_id, ClientInfo).
"""
info_by_id = {}
for server_endpoint, dump in self.dump_by_server(hosts).items():
server_ip, server_port = server_endpoint
for line in dump.split("\n"):
mat = self.IP_PORT_REGEX.match(line)
if mat is None:
continue
ip, port, sid = mat.groups()
info_by_id[sid] = ClientInfo(sid, ip, port, server_ip, server_port)
return info_by_id | [
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xflr6/concepts | concepts/definitions.py | Definition.rename_object | def rename_object(self, old, new):
"""Replace the name of an object by a new one."""
self._objects.replace(old, new)
pairs = self._pairs
pairs |= {(new, p) for p in self._properties
if (old, p) in pairs and not pairs.remove((old, p))} | python | def rename_object(self, old, new):
"""Replace the name of an object by a new one."""
self._objects.replace(old, new)
pairs = self._pairs
pairs |= {(new, p) for p in self._properties
if (old, p) in pairs and not pairs.remove((old, p))} | [
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pettarin/ipapy | ipapy/data/__init__.py | convert_unicode_field | def convert_unicode_field(string):
"""
Convert a Unicode field into the corresponding list of Unicode strings.
The (input) Unicode field is a Unicode string containing
one or more Unicode codepoints (``xxxx`` or ``U+xxxx`` or ``xxxx_yyyy``),
separated by a space.
:param str string: the (input) Unicode field
:rtype: list of Unicode strings
"""
values = []
for codepoint in [s for s in string.split(DATA_FILE_CODEPOINT_SEPARATOR) if (s != DATA_FILE_VALUE_NOT_AVAILABLE) and (len(s) > 0)]:
values.append(u"".join([hex_to_unichr(c) for c in codepoint.split(DATA_FILE_CODEPOINT_JOINER)]))
return values | python | def convert_unicode_field(string):
"""
Convert a Unicode field into the corresponding list of Unicode strings.
The (input) Unicode field is a Unicode string containing
one or more Unicode codepoints (``xxxx`` or ``U+xxxx`` or ``xxxx_yyyy``),
separated by a space.
:param str string: the (input) Unicode field
:rtype: list of Unicode strings
"""
values = []
for codepoint in [s for s in string.split(DATA_FILE_CODEPOINT_SEPARATOR) if (s != DATA_FILE_VALUE_NOT_AVAILABLE) and (len(s) > 0)]:
values.append(u"".join([hex_to_unichr(c) for c in codepoint.split(DATA_FILE_CODEPOINT_JOINER)]))
return values | [
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seatgeek/fuzzywuzzy | fuzzywuzzy/fuzz.py | _token_set | def _token_set(s1, s2, partial=True, force_ascii=True, full_process=True):
"""Find all alphanumeric tokens in each string...
- treat them as a set
- construct two strings of the form:
<sorted_intersection><sorted_remainder>
- take ratios of those two strings
- controls for unordered partial matches"""
if not full_process and s1 == s2:
return 100
p1 = utils.full_process(s1, force_ascii=force_ascii) if full_process else s1
p2 = utils.full_process(s2, force_ascii=force_ascii) if full_process else s2
if not utils.validate_string(p1):
return 0
if not utils.validate_string(p2):
return 0
# pull tokens
tokens1 = set(p1.split())
tokens2 = set(p2.split())
intersection = tokens1.intersection(tokens2)
diff1to2 = tokens1.difference(tokens2)
diff2to1 = tokens2.difference(tokens1)
sorted_sect = " ".join(sorted(intersection))
sorted_1to2 = " ".join(sorted(diff1to2))
sorted_2to1 = " ".join(sorted(diff2to1))
combined_1to2 = sorted_sect + " " + sorted_1to2
combined_2to1 = sorted_sect + " " + sorted_2to1
# strip
sorted_sect = sorted_sect.strip()
combined_1to2 = combined_1to2.strip()
combined_2to1 = combined_2to1.strip()
if partial:
ratio_func = partial_ratio
else:
ratio_func = ratio
pairwise = [
ratio_func(sorted_sect, combined_1to2),
ratio_func(sorted_sect, combined_2to1),
ratio_func(combined_1to2, combined_2to1)
]
return max(pairwise) | python | def _token_set(s1, s2, partial=True, force_ascii=True, full_process=True):
"""Find all alphanumeric tokens in each string...
- treat them as a set
- construct two strings of the form:
<sorted_intersection><sorted_remainder>
- take ratios of those two strings
- controls for unordered partial matches"""
if not full_process and s1 == s2:
return 100
p1 = utils.full_process(s1, force_ascii=force_ascii) if full_process else s1
p2 = utils.full_process(s2, force_ascii=force_ascii) if full_process else s2
if not utils.validate_string(p1):
return 0
if not utils.validate_string(p2):
return 0
# pull tokens
tokens1 = set(p1.split())
tokens2 = set(p2.split())
intersection = tokens1.intersection(tokens2)
diff1to2 = tokens1.difference(tokens2)
diff2to1 = tokens2.difference(tokens1)
sorted_sect = " ".join(sorted(intersection))
sorted_1to2 = " ".join(sorted(diff1to2))
sorted_2to1 = " ".join(sorted(diff2to1))
combined_1to2 = sorted_sect + " " + sorted_1to2
combined_2to1 = sorted_sect + " " + sorted_2to1
# strip
sorted_sect = sorted_sect.strip()
combined_1to2 = combined_1to2.strip()
combined_2to1 = combined_2to1.strip()
if partial:
ratio_func = partial_ratio
else:
ratio_func = ratio
pairwise = [
ratio_func(sorted_sect, combined_1to2),
ratio_func(sorted_sect, combined_2to1),
ratio_func(combined_1to2, combined_2to1)
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return max(pairwise) | [
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signaturit/python-sdk | signaturit_sdk/signaturit_client.py | SignaturitClient.get_subscriptions | def get_subscriptions(self, limit=100, offset=0, params={}):
"""
Get all subscriptions
"""
url = self.SUBSCRIPTIONS_URL + "?limit=%s&offset=%s" % (limit, offset)
for key, value in params.items():
if key is 'ids':
value = ",".join(value)
url += '&%s=%s' % (key, value)
connection = Connection(self.token)
connection.set_url(self.production, url)
return connection.get_request() | python | def get_subscriptions(self, limit=100, offset=0, params={}):
"""
Get all subscriptions
"""
url = self.SUBSCRIPTIONS_URL + "?limit=%s&offset=%s" % (limit, offset)
for key, value in params.items():
if key is 'ids':
value = ",".join(value)
url += '&%s=%s' % (key, value)
connection = Connection(self.token)
connection.set_url(self.production, url)
return connection.get_request() | [
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ten10solutions/Geist | geist/match_position_finder_helpers.py | find_potential_match_regions | def find_potential_match_regions(template, transformed_array, method='correlation', raw_tolerance=0.666):
"""To prevent prohibitively slow calculation of normalisation coefficient at each point in image
find potential match points, and normalise these only these.
This function uses the definitions of the matching functions to calculate the expected match value
and finds positions in the transformed array matching these- normalisation will then eliminate false positives
"""
if method == 'correlation':
match_value = np.sum(template**2) # this will be the value of the match in the
elif method == 'squared difference':
match_value = 0
elif method == 'correlation coefficient':
temp_minus_mean = template - np.mean(template)
match_value = np.sum(temp_minus_mean**2)
else:
raise ValueError('Matching method not implemented')
condition = ((np.round(transformed_array, decimals=3)>=match_value*raw_tolerance) &
(np.round(transformed_array, decimals=3)<=match_value*(1./raw_tolerance)))
return np.transpose(condition.nonzero()) | python | def find_potential_match_regions(template, transformed_array, method='correlation', raw_tolerance=0.666):
"""To prevent prohibitively slow calculation of normalisation coefficient at each point in image
find potential match points, and normalise these only these.
This function uses the definitions of the matching functions to calculate the expected match value
and finds positions in the transformed array matching these- normalisation will then eliminate false positives
"""
if method == 'correlation':
match_value = np.sum(template**2) # this will be the value of the match in the
elif method == 'squared difference':
match_value = 0
elif method == 'correlation coefficient':
temp_minus_mean = template - np.mean(template)
match_value = np.sum(temp_minus_mean**2)
else:
raise ValueError('Matching method not implemented')
condition = ((np.round(transformed_array, decimals=3)>=match_value*raw_tolerance) &
(np.round(transformed_array, decimals=3)<=match_value*(1./raw_tolerance)))
return np.transpose(condition.nonzero()) | [
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peterbrittain/asciimatics | asciimatics/effects.py | _Trail._maybe_reseed | def _maybe_reseed(self, normal):
"""
Randomly create a new column once this one is finished.
"""
self._y += self._rate
self._life -= 1
if self._life <= 0:
self._clear = not self._clear if normal else True
self._rate = randint(1, 2)
if self._clear:
self._y = 0
self._life = self._screen.height // self._rate
else:
self._y = randint(0, self._screen.height // 2) - \
self._screen.height // 4
self._life = \
randint(1, self._screen.height - self._y) // self._rate | python | def _maybe_reseed(self, normal):
"""
Randomly create a new column once this one is finished.
"""
self._y += self._rate
self._life -= 1
if self._life <= 0:
self._clear = not self._clear if normal else True
self._rate = randint(1, 2)
if self._clear:
self._y = 0
self._life = self._screen.height // self._rate
else:
self._y = randint(0, self._screen.height // 2) - \
self._screen.height // 4
self._life = \
randint(1, self._screen.height - self._y) // self._rate | [
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jonhadfield/creds | lib/creds/users.py | Users.describe_users | def describe_users(self, users_filter=None):
"""Return a list of users matching a filter (if provided)."""
user_list = Users(oktypes=User)
for user in self._user_list:
if users_filter and (users_filter.get('name') == user.name or users_filter.get('uid') == user.uid):
user_list.append(user)
return user_list | python | def describe_users(self, users_filter=None):
"""Return a list of users matching a filter (if provided)."""
user_list = Users(oktypes=User)
for user in self._user_list:
if users_filter and (users_filter.get('name') == user.name or users_filter.get('uid') == user.uid):
user_list.append(user)
return user_list | [
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dmwm/DBS | Server/Python/src/dbs/web/DBSWriterModel.py | insertBulkBlock | def insertBulkBlock(self):
"""
API to insert a bulk block
:param blockDump: Output of the block dump command
:type blockDump: dict
"""
try:
body = request.body.read()
indata = cjson.decode(body)
if (indata.get("file_parent_list", []) and indata.get("dataset_parent_list", [])):
dbsExceptionHandler("dbsException-invalid-input2", "insertBulkBlock: dataset and file parentages cannot be in the input at the same time",
self.logger.exception, "insertBulkBlock: datset and file parentages cannot be in the input at the same time.")
indata = validateJSONInputNoCopy("blockBulk", indata)
self.dbsBlockInsert.putBlock(indata)
except cjson.DecodeError as dc:
dbsExceptionHandler("dbsException-invalid-input2", "Wrong format/data from insert BulkBlock input", self.logger.exception, str(dc))
except dbsException as de:
dbsExceptionHandler(de.eCode, de.message, self.logger.exception, de.message)
except HTTPError as he:
raise he
except Exception as ex:
#illegal variable name/number
if str(ex).find("ORA-01036") != -1:
dbsExceptionHandler("dbsException-invalid-input2", "illegal variable name/number from input", self.logger.exception, str(ex))
else:
sError = "DBSWriterModel/insertBulkBlock. %s\n. Exception trace: \n %s" \
% (ex, traceback.format_exc())
dbsExceptionHandler('dbsException-server-error', dbsExceptionCode['dbsException-server-error'], self.logger.exception, sError) | python | def insertBulkBlock(self):
"""
API to insert a bulk block
:param blockDump: Output of the block dump command
:type blockDump: dict
"""
try:
body = request.body.read()
indata = cjson.decode(body)
if (indata.get("file_parent_list", []) and indata.get("dataset_parent_list", [])):
dbsExceptionHandler("dbsException-invalid-input2", "insertBulkBlock: dataset and file parentages cannot be in the input at the same time",
self.logger.exception, "insertBulkBlock: datset and file parentages cannot be in the input at the same time.")
indata = validateJSONInputNoCopy("blockBulk", indata)
self.dbsBlockInsert.putBlock(indata)
except cjson.DecodeError as dc:
dbsExceptionHandler("dbsException-invalid-input2", "Wrong format/data from insert BulkBlock input", self.logger.exception, str(dc))
except dbsException as de:
dbsExceptionHandler(de.eCode, de.message, self.logger.exception, de.message)
except HTTPError as he:
raise he
except Exception as ex:
#illegal variable name/number
if str(ex).find("ORA-01036") != -1:
dbsExceptionHandler("dbsException-invalid-input2", "illegal variable name/number from input", self.logger.exception, str(ex))
else:
sError = "DBSWriterModel/insertBulkBlock. %s\n. Exception trace: \n %s" \
% (ex, traceback.format_exc())
dbsExceptionHandler('dbsException-server-error', dbsExceptionCode['dbsException-server-error'], self.logger.exception, sError) | [
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Adarnof/adarnauth-esi | esi/managers.py | TokenManager.create_from_code | def create_from_code(self, code, user=None):
"""
Perform OAuth code exchange to retrieve a token.
:param code: OAuth grant code.
:param user: User who will own token.
:return: :class:`esi.models.Token`
"""
# perform code exchange
logger.debug("Creating new token from code {0}".format(code[:-5]))
oauth = OAuth2Session(app_settings.ESI_SSO_CLIENT_ID, redirect_uri=app_settings.ESI_SSO_CALLBACK_URL)
token = oauth.fetch_token(app_settings.ESI_TOKEN_URL, client_secret=app_settings.ESI_SSO_CLIENT_SECRET,
code=code)
r = oauth.request('get', app_settings.ESI_TOKEN_VERIFY_URL)
r.raise_for_status()
token_data = r.json()
logger.debug(token_data)
# translate returned data to a model
model = self.create(
character_id=token_data['CharacterID'],
character_name=token_data['CharacterName'],
character_owner_hash=token_data['CharacterOwnerHash'],
access_token=token['access_token'],
refresh_token=token['refresh_token'],
token_type=token_data['TokenType'],
user=user,
)
# parse scopes
if 'Scopes' in token_data:
from esi.models import Scope
for s in token_data['Scopes'].split():
try:
scope = Scope.objects.get(name=s)
model.scopes.add(scope)
except Scope.DoesNotExist:
# This scope isn't included in a data migration. Create a placeholder until it updates.
try:
help_text = s.split('.')[1].replace('_', ' ').capitalize()
except IndexError:
# Unusual scope name, missing periods.
help_text = s.replace('_', ' ').capitalize()
scope = Scope.objects.create(name=s, help_text=help_text)
model.scopes.add(scope)
logger.debug("Added {0} scopes to new token.".format(model.scopes.all().count()))
if not app_settings.ESI_ALWAYS_CREATE_TOKEN:
# see if we already have a token for this character and scope combination
# if so, we don't need a new one
queryset = self.get_queryset().equivalent_to(model)
if queryset.exists():
logger.debug(
"Identified {0} tokens equivalent to new token. Updating access and refresh tokens.".format(
queryset.count()))
queryset.update(
access_token=model.access_token,
refresh_token=model.refresh_token,
created=model.created,
)
if queryset.filter(user=model.user).exists():
logger.debug("Equivalent token with same user exists. Deleting new token.")
model.delete()
model = queryset.filter(user=model.user)[0] # pick one at random
logger.debug("Successfully created {0} for user {1}".format(repr(model), user))
return model | python | def create_from_code(self, code, user=None):
"""
Perform OAuth code exchange to retrieve a token.
:param code: OAuth grant code.
:param user: User who will own token.
:return: :class:`esi.models.Token`
"""
# perform code exchange
logger.debug("Creating new token from code {0}".format(code[:-5]))
oauth = OAuth2Session(app_settings.ESI_SSO_CLIENT_ID, redirect_uri=app_settings.ESI_SSO_CALLBACK_URL)
token = oauth.fetch_token(app_settings.ESI_TOKEN_URL, client_secret=app_settings.ESI_SSO_CLIENT_SECRET,
code=code)
r = oauth.request('get', app_settings.ESI_TOKEN_VERIFY_URL)
r.raise_for_status()
token_data = r.json()
logger.debug(token_data)
# translate returned data to a model
model = self.create(
character_id=token_data['CharacterID'],
character_name=token_data['CharacterName'],
character_owner_hash=token_data['CharacterOwnerHash'],
access_token=token['access_token'],
refresh_token=token['refresh_token'],
token_type=token_data['TokenType'],
user=user,
)
# parse scopes
if 'Scopes' in token_data:
from esi.models import Scope
for s in token_data['Scopes'].split():
try:
scope = Scope.objects.get(name=s)
model.scopes.add(scope)
except Scope.DoesNotExist:
# This scope isn't included in a data migration. Create a placeholder until it updates.
try:
help_text = s.split('.')[1].replace('_', ' ').capitalize()
except IndexError:
# Unusual scope name, missing periods.
help_text = s.replace('_', ' ').capitalize()
scope = Scope.objects.create(name=s, help_text=help_text)
model.scopes.add(scope)
logger.debug("Added {0} scopes to new token.".format(model.scopes.all().count()))
if not app_settings.ESI_ALWAYS_CREATE_TOKEN:
# see if we already have a token for this character and scope combination
# if so, we don't need a new one
queryset = self.get_queryset().equivalent_to(model)
if queryset.exists():
logger.debug(
"Identified {0} tokens equivalent to new token. Updating access and refresh tokens.".format(
queryset.count()))
queryset.update(
access_token=model.access_token,
refresh_token=model.refresh_token,
created=model.created,
)
if queryset.filter(user=model.user).exists():
logger.debug("Equivalent token with same user exists. Deleting new token.")
model.delete()
model = queryset.filter(user=model.user)[0] # pick one at random
logger.debug("Successfully created {0} for user {1}".format(repr(model), user))
return model | [
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:param code: OAuth grant code.
:param user: User who will own token.
:return: :class:`esi.models.Token` | [
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kylejusticemagnuson/pyti | pyti/vertical_horizontal_filter.py | vertical_horizontal_filter | def vertical_horizontal_filter(data, period):
"""
Vertical Horizontal Filter.
Formula:
ABS(pHIGH - pLOW) / SUM(ABS(Pi - Pi-1))
"""
catch_errors.check_for_period_error(data, period)
vhf = [abs(np.max(data[idx+1-period:idx+1]) -
np.min(data[idx+1-period:idx+1])) /
sum([abs(data[idx+1-period:idx+1][i] - data[idx+1-period:idx+1][i-1]) for i in range(0, len(data[idx+1-period:idx+1]))]) for idx in range(period - 1, len(data))]
vhf = fill_for_noncomputable_vals(data, vhf)
return vhf | python | def vertical_horizontal_filter(data, period):
"""
Vertical Horizontal Filter.
Formula:
ABS(pHIGH - pLOW) / SUM(ABS(Pi - Pi-1))
"""
catch_errors.check_for_period_error(data, period)
vhf = [abs(np.max(data[idx+1-period:idx+1]) -
np.min(data[idx+1-period:idx+1])) /
sum([abs(data[idx+1-period:idx+1][i] - data[idx+1-period:idx+1][i-1]) for i in range(0, len(data[idx+1-period:idx+1]))]) for idx in range(period - 1, len(data))]
vhf = fill_for_noncomputable_vals(data, vhf)
return vhf | [
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JukeboxPipeline/jukebox-core | src/jukeboxcore/gui/reftrackitemdata.py | reftrack_version_data | def reftrack_version_data(rt, role):
"""Return the data for the version that is loaded by the reftrack
:param rt: the :class:`jukeboxcore.reftrack.Reftrack` holds the data
:type rt: :class:`jukeboxcore.reftrack.Reftrack`
:param role: item data role
:type role: QtCore.Qt.ItemDataRole
:returns: data for the version
:rtype: depending on role
:raises: None
"""
tfi = rt.get_taskfileinfo()
if not tfi:
return
return filesysitemdata.taskfileinfo_version_data(tfi, role) | python | def reftrack_version_data(rt, role):
"""Return the data for the version that is loaded by the reftrack
:param rt: the :class:`jukeboxcore.reftrack.Reftrack` holds the data
:type rt: :class:`jukeboxcore.reftrack.Reftrack`
:param role: item data role
:type role: QtCore.Qt.ItemDataRole
:returns: data for the version
:rtype: depending on role
:raises: None
"""
tfi = rt.get_taskfileinfo()
if not tfi:
return
return filesysitemdata.taskfileinfo_version_data(tfi, role) | [
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:param role: item data role
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podio/podio-py | pypodio2/areas.py | View.update_last_view | def update_last_view(self, app_id, attributes):
"""
Updates the last view for the active user
:param app_id: the app id
:param attributes: the body of the request in dictionary format
"""
if not isinstance(attributes, dict):
raise TypeError('Must be of type dict')
attribute_data = json.dumps(attributes)
return self.transport.PUT(url='/view/app/{}/last'.format(app_id),
body=attribute_data, type='application/json') | python | def update_last_view(self, app_id, attributes):
"""
Updates the last view for the active user
:param app_id: the app id
:param attributes: the body of the request in dictionary format
"""
if not isinstance(attributes, dict):
raise TypeError('Must be of type dict')
attribute_data = json.dumps(attributes)
return self.transport.PUT(url='/view/app/{}/last'.format(app_id),
body=attribute_data, type='application/json') | [
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PyThaiNLP/pythainlp | pythainlp/soundex/udom83.py | udom83 | def udom83(text: str) -> str:
"""
Udom83 - It's a Thai soundex rule.
:param str text: Thai word
:return: Udom83 soundex
"""
if not text or not isinstance(text, str):
return ""
text = _RE_1.sub("ัน\\1", text)
text = _RE_2.sub("ั\\1", text)
text = _RE_3.sub("ัน\\1", text)
text = _RE_4.sub("ัน", text)
text = _RE_5.sub("\\1", text)
text = _RE_6.sub("\\1ย", text)
text = _RE_7.sub("ม\\1", text)
text = _RE_8.sub("ม", text)
text = _RE_9.sub("ม", text)
text = _RE_10.sub("", text)
text = _RE_11.sub("", text)
if not text:
return ""
sd = text[0].translate(_TRANS1)
sd += text[1:].translate(_TRANS2)
return (sd + "000000")[:7] | python | def udom83(text: str) -> str:
"""
Udom83 - It's a Thai soundex rule.
:param str text: Thai word
:return: Udom83 soundex
"""
if not text or not isinstance(text, str):
return ""
text = _RE_1.sub("ัน\\1", text)
text = _RE_2.sub("ั\\1", text)
text = _RE_3.sub("ัน\\1", text)
text = _RE_4.sub("ัน", text)
text = _RE_5.sub("\\1", text)
text = _RE_6.sub("\\1ย", text)
text = _RE_7.sub("ม\\1", text)
text = _RE_8.sub("ม", text)
text = _RE_9.sub("ม", text)
text = _RE_10.sub("", text)
text = _RE_11.sub("", text)
if not text:
return ""
sd = text[0].translate(_TRANS1)
sd += text[1:].translate(_TRANS2)
return (sd + "000000")[:7] | [
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SINGROUP/SOAPLite | soaplite/core.py | get_soap_locals | def get_soap_locals(obj, Hpos, alp, bet, rCut=5.0, nMax=5, Lmax=5, crossOver=True, all_atomtypes=None, eta=1.0):
"""Get the RBF basis SOAP output for the given positions in a finite system.
Args:
obj(ase.Atoms): Atomic structure for which the SOAP output is
calculated.
Hpos: Positions at which to calculate SOAP
alp: Alphas
bet: Betas
rCut: Radial cutoff.
nMax: Maximum number of radial basis functions
Lmax: Maximum spherical harmonics degree
crossOver:
all_atomtypes: Can be used to specify the atomic elements for which to
calculate the output. If given the output is calculated only for the
given species and is ordered by atomic number.
eta: The gaussian smearing width.
Returns:
np.ndarray: SOAP output for the given positions.
"""
rCutHard = rCut + 5
assert Lmax <= 9, "l cannot exceed 9. Lmax={}".format(Lmax)
assert Lmax >= 0, "l cannot be negative.Lmax={}".format(Lmax)
assert rCutHard < 17.0001, "hard radius cuttof cannot be larger than 17 Angs. rCut={}".format(rCutHard)
assert rCutHard > 1.999, "hard redius cuttof cannot be lower than 1 Ang. rCut={}".format(rCutHard)
assert nMax >= 2, "number of basis functions cannot be lower than 2. nMax={}".format(nMax)
assert nMax <= 13, "number of basis functions cannot exceed 12. nMax={}".format(nMax)
assert eta >= 0.0001, "Eta cannot be zero or negative. nMax={}".format(eta)
# get clusgeo internal format for c-code
Apos, typeNs, py_Ntypes, atomtype_lst, totalAN = _format_ase2clusgeo(obj, all_atomtypes)
Hpos = np.array(Hpos)
py_Hsize = Hpos.shape[0]
# flatten arrays
Hpos = Hpos.flatten()
alp = alp.flatten()
bet = bet.flatten()
# convert int to c_int
lMax = c_int(Lmax)
Hsize = c_int(py_Hsize)
Ntypes = c_int(py_Ntypes)
totalAN = c_int(totalAN)
rCutHard = c_double(rCutHard)
Nsize = c_int(nMax)
c_eta = c_double(eta)
#convert int array to c_int array
typeNs = (c_int * len(typeNs))(*typeNs)
# convert to c_double arrays
# alphas
alphas = (c_double * len(alp))(*alp.tolist())
# betas
betas = (c_double * len(bet))(*bet.tolist())
#Apos
axyz = (c_double * len(Apos))(*Apos.tolist())
#Hpos
hxyz = (c_double * len(Hpos))(*Hpos.tolist())
### START SOAP###
#path_to_so = os.path.dirname(os.path.abspath(__file__))
_PATH_TO_SOAPLITE_SO = os.path.dirname(os.path.abspath(__file__))
_SOAPLITE_SOFILES = glob.glob( "".join([ _PATH_TO_SOAPLITE_SO, "/../lib/libsoap*.*so"]) ) ## NOT SURE ABOUT THIS
if py_Ntypes == 1 or (not crossOver):
substring = "lib/libsoapPySig."
libsoap = CDLL(next((s for s in _SOAPLITE_SOFILES if substring in s), None))
libsoap.soap.argtypes = [POINTER (c_double),POINTER (c_double), POINTER (c_double),POINTER (c_double), POINTER (c_double), POINTER (c_int),c_double,c_int,c_int,c_int,c_int,c_int,c_double]
libsoap.soap.restype = POINTER (c_double)
c = (c_double*(int((nMax*(nMax+1))/2)*(Lmax+1)*py_Ntypes*py_Hsize))()
libsoap.soap( c, axyz, hxyz, alphas, betas, typeNs, rCutHard, totalAN, Ntypes, Nsize, lMax, Hsize,c_eta)
else:
substring = "lib/libsoapGTO."
libsoapGTO = CDLL(next((s for s in _SOAPLITE_SOFILES if substring in s), None))
libsoapGTO.soap.argtypes = [POINTER (c_double),POINTER (c_double), POINTER (c_double),POINTER (c_double), POINTER (c_double), POINTER (c_int),c_double,c_int,c_int,c_int,c_int,c_int,c_double]
libsoapGTO.soap.restype = POINTER (c_double)
c = (c_double*(int((nMax*(nMax+1))/2)*(Lmax+1)*int((py_Ntypes*(py_Ntypes +1))/2)*py_Hsize))()
libsoapGTO.soap( c, axyz, hxyz, alphas, betas, typeNs, rCutHard, totalAN, Ntypes, Nsize, lMax, Hsize,c_eta)
# return c;
if crossOver:
crosTypes = int((py_Ntypes*(py_Ntypes+1))/2)
shape = (py_Hsize, int((nMax*(nMax+1))/2)*(Lmax+1)*crosTypes)
else:
shape = (py_Hsize, int((nMax*(nMax+1))/2)*(Lmax+1)*py_Ntypes)
a = np.ctypeslib.as_array(c)
a = a.reshape(shape)
return a | python | def get_soap_locals(obj, Hpos, alp, bet, rCut=5.0, nMax=5, Lmax=5, crossOver=True, all_atomtypes=None, eta=1.0):
"""Get the RBF basis SOAP output for the given positions in a finite system.
Args:
obj(ase.Atoms): Atomic structure for which the SOAP output is
calculated.
Hpos: Positions at which to calculate SOAP
alp: Alphas
bet: Betas
rCut: Radial cutoff.
nMax: Maximum number of radial basis functions
Lmax: Maximum spherical harmonics degree
crossOver:
all_atomtypes: Can be used to specify the atomic elements for which to
calculate the output. If given the output is calculated only for the
given species and is ordered by atomic number.
eta: The gaussian smearing width.
Returns:
np.ndarray: SOAP output for the given positions.
"""
rCutHard = rCut + 5
assert Lmax <= 9, "l cannot exceed 9. Lmax={}".format(Lmax)
assert Lmax >= 0, "l cannot be negative.Lmax={}".format(Lmax)
assert rCutHard < 17.0001, "hard radius cuttof cannot be larger than 17 Angs. rCut={}".format(rCutHard)
assert rCutHard > 1.999, "hard redius cuttof cannot be lower than 1 Ang. rCut={}".format(rCutHard)
assert nMax >= 2, "number of basis functions cannot be lower than 2. nMax={}".format(nMax)
assert nMax <= 13, "number of basis functions cannot exceed 12. nMax={}".format(nMax)
assert eta >= 0.0001, "Eta cannot be zero or negative. nMax={}".format(eta)
# get clusgeo internal format for c-code
Apos, typeNs, py_Ntypes, atomtype_lst, totalAN = _format_ase2clusgeo(obj, all_atomtypes)
Hpos = np.array(Hpos)
py_Hsize = Hpos.shape[0]
# flatten arrays
Hpos = Hpos.flatten()
alp = alp.flatten()
bet = bet.flatten()
# convert int to c_int
lMax = c_int(Lmax)
Hsize = c_int(py_Hsize)
Ntypes = c_int(py_Ntypes)
totalAN = c_int(totalAN)
rCutHard = c_double(rCutHard)
Nsize = c_int(nMax)
c_eta = c_double(eta)
#convert int array to c_int array
typeNs = (c_int * len(typeNs))(*typeNs)
# convert to c_double arrays
# alphas
alphas = (c_double * len(alp))(*alp.tolist())
# betas
betas = (c_double * len(bet))(*bet.tolist())
#Apos
axyz = (c_double * len(Apos))(*Apos.tolist())
#Hpos
hxyz = (c_double * len(Hpos))(*Hpos.tolist())
### START SOAP###
#path_to_so = os.path.dirname(os.path.abspath(__file__))
_PATH_TO_SOAPLITE_SO = os.path.dirname(os.path.abspath(__file__))
_SOAPLITE_SOFILES = glob.glob( "".join([ _PATH_TO_SOAPLITE_SO, "/../lib/libsoap*.*so"]) ) ## NOT SURE ABOUT THIS
if py_Ntypes == 1 or (not crossOver):
substring = "lib/libsoapPySig."
libsoap = CDLL(next((s for s in _SOAPLITE_SOFILES if substring in s), None))
libsoap.soap.argtypes = [POINTER (c_double),POINTER (c_double), POINTER (c_double),POINTER (c_double), POINTER (c_double), POINTER (c_int),c_double,c_int,c_int,c_int,c_int,c_int,c_double]
libsoap.soap.restype = POINTER (c_double)
c = (c_double*(int((nMax*(nMax+1))/2)*(Lmax+1)*py_Ntypes*py_Hsize))()
libsoap.soap( c, axyz, hxyz, alphas, betas, typeNs, rCutHard, totalAN, Ntypes, Nsize, lMax, Hsize,c_eta)
else:
substring = "lib/libsoapGTO."
libsoapGTO = CDLL(next((s for s in _SOAPLITE_SOFILES if substring in s), None))
libsoapGTO.soap.argtypes = [POINTER (c_double),POINTER (c_double), POINTER (c_double),POINTER (c_double), POINTER (c_double), POINTER (c_int),c_double,c_int,c_int,c_int,c_int,c_int,c_double]
libsoapGTO.soap.restype = POINTER (c_double)
c = (c_double*(int((nMax*(nMax+1))/2)*(Lmax+1)*int((py_Ntypes*(py_Ntypes +1))/2)*py_Hsize))()
libsoapGTO.soap( c, axyz, hxyz, alphas, betas, typeNs, rCutHard, totalAN, Ntypes, Nsize, lMax, Hsize,c_eta)
# return c;
if crossOver:
crosTypes = int((py_Ntypes*(py_Ntypes+1))/2)
shape = (py_Hsize, int((nMax*(nMax+1))/2)*(Lmax+1)*crosTypes)
else:
shape = (py_Hsize, int((nMax*(nMax+1))/2)*(Lmax+1)*py_Ntypes)
a = np.ctypeslib.as_array(c)
a = a.reshape(shape)
return a | [
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alp: Alphas
bet: Betas
rCut: Radial cutoff.
nMax: Maximum number of radial basis functions
Lmax: Maximum spherical harmonics degree
crossOver:
all_atomtypes: Can be used to specify the atomic elements for which to
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eta: The gaussian smearing width.
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autokey/autokey | lib/autokey/scripting.py | Engine.run_script_from_macro | def run_script_from_macro(self, args):
"""
Used internally by AutoKey for phrase macros
"""
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self.run_script(args["name"])
except Exception as e:
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"""
Used internally by AutoKey for phrase macros
"""
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rootpy/rootpy | rootpy/io/pickler.py | dump | def dump(obj, root_file, proto=0, key=None):
"""Dump an object into a ROOT TFile.
`root_file` may be an open ROOT file or directory, or a string path to an
existing ROOT file.
"""
if isinstance(root_file, string_types):
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if own_file:
root_file.Close()
return ret | python | def dump(obj, root_file, proto=0, key=None):
"""Dump an object into a ROOT TFile.
`root_file` may be an open ROOT file or directory, or a string path to an
existing ROOT file.
"""
if isinstance(root_file, string_types):
root_file = root_open(root_file, 'recreate')
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own_file = False
ret = Pickler(root_file, proto).dump(obj, key)
if own_file:
root_file.Close()
return ret | [
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geronimp/graftM | graftm/sequence_search_results.py | DiamondSearchResult.import_from_daa_file | def import_from_daa_file(daa_filename):
'''Generate new results object from the output of diamond blastx/p'''
# blast m8 format is
# 'qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore
res = DiamondSearchResult()
res.fields = [
SequenceSearchResult.QUERY_ID_FIELD,
SequenceSearchResult.HIT_ID_FIELD,
SequenceSearchResult.PERCENT_ID_FIELD,
SequenceSearchResult.ALIGNMENT_LENGTH_FIELD,
SequenceSearchResult.MISMATCH_FIELD,
#skip
SequenceSearchResult.QUERY_FROM_FIELD,
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SequenceSearchResult.HIT_FROM_FIELD,
SequenceSearchResult.HIT_TO_FIELD,
SequenceSearchResult.EVALUE_FIELD,
SequenceSearchResult.ALIGNMENT_BIT_SCORE,
# extras
SequenceSearchResult.ALIGNMENT_DIRECTION,
SequenceSearchResult.HMM_NAME_FIELD
]
cmd = "diamond view -a '%s'" % daa_filename
logging.debug("Running cmd: %s" % cmd)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
stdout, stderr = process.communicate()
reader = csv.reader(stdout.decode('ascii').splitlines(),
delimiter='\t')
if process.returncode != 0:
raise Exception("Problem running diamond view with cmd: '%s',"
"stderr was %s" % (cmd, stderr))
for row in reader:
# 'qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore
# 0 1 2 3 4 5 6 7 8 9 10 11
query_start = int(row[6])
query_end = int(row[7])
res.results.append([row[0],
row[1],
row[2],
row[3],
row[4],
query_start,
query_end,
int(row[8]),
int(row[9]),
row[10],
row[11],
query_start < query_end,
os.path.basename(daa_filename)
])
return res | python | def import_from_daa_file(daa_filename):
'''Generate new results object from the output of diamond blastx/p'''
# blast m8 format is
# 'qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore
res = DiamondSearchResult()
res.fields = [
SequenceSearchResult.QUERY_ID_FIELD,
SequenceSearchResult.HIT_ID_FIELD,
SequenceSearchResult.PERCENT_ID_FIELD,
SequenceSearchResult.ALIGNMENT_LENGTH_FIELD,
SequenceSearchResult.MISMATCH_FIELD,
#skip
SequenceSearchResult.QUERY_FROM_FIELD,
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SequenceSearchResult.HIT_FROM_FIELD,
SequenceSearchResult.HIT_TO_FIELD,
SequenceSearchResult.EVALUE_FIELD,
SequenceSearchResult.ALIGNMENT_BIT_SCORE,
# extras
SequenceSearchResult.ALIGNMENT_DIRECTION,
SequenceSearchResult.HMM_NAME_FIELD
]
cmd = "diamond view -a '%s'" % daa_filename
logging.debug("Running cmd: %s" % cmd)
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True)
stdout, stderr = process.communicate()
reader = csv.reader(stdout.decode('ascii').splitlines(),
delimiter='\t')
if process.returncode != 0:
raise Exception("Problem running diamond view with cmd: '%s',"
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for row in reader:
# 'qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore
# 0 1 2 3 4 5 6 7 8 9 10 11
query_start = int(row[6])
query_end = int(row[7])
res.results.append([row[0],
row[1],
row[2],
row[3],
row[4],
query_start,
query_end,
int(row[8]),
int(row[9]),
row[10],
row[11],
query_start < query_end,
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])
return res | [
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libtcod/python-tcod | fonts/X11/bdf/bdf2png.py | Glyph.parseBits | def parseBits(self, hexcode, width):
"""enumerate over bits in a line of data"""
bitarray = []
for byte in hexcode[::-1]:
bits = int(byte, 16)
for x in range(4):
bitarray.append(bool((2 ** x) & bits))
bitarray = bitarray[::-1]
return enumerate(bitarray[:width]) | python | def parseBits(self, hexcode, width):
"""enumerate over bits in a line of data"""
bitarray = []
for byte in hexcode[::-1]:
bits = int(byte, 16)
for x in range(4):
bitarray.append(bool((2 ** x) & bits))
bitarray = bitarray[::-1]
return enumerate(bitarray[:width]) | [
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peterwittek/somoclu | src/Python/somoclu/train.py | Somoclu.view_activation_map | def view_activation_map(self, data_vector=None, data_index=None,
activation_map=None, figsize=None,
colormap=cm.Spectral_r, colorbar=False,
bestmatches=False, bestmatchcolors=None,
labels=None, zoom=None, filename=None):
"""Plot the activation map of a given data instance or a new data
vector
:param data_vector: Optional parameter for a new vector
:type data_vector: numpy.array
:param data_index: Optional parameter for the index of the data instance
:type data_index: int.
:param activation_map: Optional parameter to pass the an activation map
:type activation_map: numpy.array
:param figsize: Optional parameter to specify the size of the figure.
:type figsize: (int, int)
:param colormap: Optional parameter to specify the color map to be
used.
:type colormap: matplotlib.colors.Colormap
:param colorbar: Optional parameter to include a colormap as legend.
:type colorbar: bool.
:param bestmatches: Optional parameter to plot best matching units.
:type bestmatches: bool.
:param bestmatchcolors: Optional parameter to specify the color of each
best matching unit.
:type bestmatchcolors: list of int.
:param labels: Optional parameter to specify the label of each point.
:type labels: list of str.
:param zoom: Optional parameter to zoom into a region on the map. The
first two coordinates of the tuple are the row limits, the
second tuple contains the column limits.
:type zoom: ((int, int), (int, int))
:param filename: If specified, the plot will not be shown but saved to
this file.
:type filename: str.
"""
if data_vector is None and data_index is None:
raise Exception("Either specify a vector to see its activation "
"or give an index of the training data instances")
if data_vector is not None and data_index is not None:
raise Exception("You cannot specify both a data vector and the "
"index of a training data instance")
if data_vector is not None and activation_map is not None:
raise Exception("You cannot pass a previously computated"
"activation map with a data vector")
if data_vector is not None:
try:
d1, _ = data_vector.shape
w = data_vector.copy()
except ValueError:
d1, _ = data_vector.shape
w = data_vector.reshape(1, d1)
if w.shape[1] == 1:
w = w.T
matrix = cdist(self.codebook.reshape((self.codebook.shape[0] *
self.codebook.shape[1],
self.codebook.shape[2])),
w, 'euclidean').T
matrix.shape = (self.codebook.shape[0], self.codebook.shape[1])
else:
if activation_map is None and self.activation_map is None:
self.get_surface_state()
if activation_map is None:
activation_map = self.activation_map
matrix = activation_map[data_index].reshape((self.codebook.shape[0],
self.codebook.shape[1]))
return self._view_matrix(matrix, figsize, colormap, colorbar,
bestmatches, bestmatchcolors, labels, zoom,
filename) | python | def view_activation_map(self, data_vector=None, data_index=None,
activation_map=None, figsize=None,
colormap=cm.Spectral_r, colorbar=False,
bestmatches=False, bestmatchcolors=None,
labels=None, zoom=None, filename=None):
"""Plot the activation map of a given data instance or a new data
vector
:param data_vector: Optional parameter for a new vector
:type data_vector: numpy.array
:param data_index: Optional parameter for the index of the data instance
:type data_index: int.
:param activation_map: Optional parameter to pass the an activation map
:type activation_map: numpy.array
:param figsize: Optional parameter to specify the size of the figure.
:type figsize: (int, int)
:param colormap: Optional parameter to specify the color map to be
used.
:type colormap: matplotlib.colors.Colormap
:param colorbar: Optional parameter to include a colormap as legend.
:type colorbar: bool.
:param bestmatches: Optional parameter to plot best matching units.
:type bestmatches: bool.
:param bestmatchcolors: Optional parameter to specify the color of each
best matching unit.
:type bestmatchcolors: list of int.
:param labels: Optional parameter to specify the label of each point.
:type labels: list of str.
:param zoom: Optional parameter to zoom into a region on the map. The
first two coordinates of the tuple are the row limits, the
second tuple contains the column limits.
:type zoom: ((int, int), (int, int))
:param filename: If specified, the plot will not be shown but saved to
this file.
:type filename: str.
"""
if data_vector is None and data_index is None:
raise Exception("Either specify a vector to see its activation "
"or give an index of the training data instances")
if data_vector is not None and data_index is not None:
raise Exception("You cannot specify both a data vector and the "
"index of a training data instance")
if data_vector is not None and activation_map is not None:
raise Exception("You cannot pass a previously computated"
"activation map with a data vector")
if data_vector is not None:
try:
d1, _ = data_vector.shape
w = data_vector.copy()
except ValueError:
d1, _ = data_vector.shape
w = data_vector.reshape(1, d1)
if w.shape[1] == 1:
w = w.T
matrix = cdist(self.codebook.reshape((self.codebook.shape[0] *
self.codebook.shape[1],
self.codebook.shape[2])),
w, 'euclidean').T
matrix.shape = (self.codebook.shape[0], self.codebook.shape[1])
else:
if activation_map is None and self.activation_map is None:
self.get_surface_state()
if activation_map is None:
activation_map = self.activation_map
matrix = activation_map[data_index].reshape((self.codebook.shape[0],
self.codebook.shape[1]))
return self._view_matrix(matrix, figsize, colormap, colorbar,
bestmatches, bestmatchcolors, labels, zoom,
filename) | [
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saltstack/salt | salt/netapi/rest_tornado/event_processor.py | SaltInfo.publish | def publish(self, key, data):
'''
Publishes the data to the event stream.
'''
publish_data = {key: data}
pub = salt.utils.json.dumps(publish_data) + str('\n\n') # future lint: disable=blacklisted-function
self.handler.write_message(pub) | python | def publish(self, key, data):
'''
Publishes the data to the event stream.
'''
publish_data = {key: data}
pub = salt.utils.json.dumps(publish_data) + str('\n\n') # future lint: disable=blacklisted-function
self.handler.write_message(pub) | [
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openstack/networking-arista | networking_arista/l3Plugin/l3_arista.py | AristaL3ServicePlugin.add_router_interface | def add_router_interface(self, context, router_id, interface_info):
"""Add a subnet of a network to an existing router."""
new_router = super(AristaL3ServicePlugin, self).add_router_interface(
context, router_id, interface_info)
core = directory.get_plugin()
# Get network info for the subnet that is being added to the router.
# Check if the interface information is by port-id or subnet-id
add_by_port, add_by_sub = self._validate_interface_info(interface_info)
if add_by_sub:
subnet = core.get_subnet(context, interface_info['subnet_id'])
elif add_by_port:
port = core.get_port(context, interface_info['port_id'])
subnet_id = port['fixed_ips'][0]['subnet_id']
subnet = core.get_subnet(context, subnet_id)
network_id = subnet['network_id']
# To create SVI's in Arista HW, the segmentation Id is required
# for this network.
ml2_db = NetworkContext(self, context, {'id': network_id})
seg_id = ml2_db.network_segments[0]['segmentation_id']
# Package all the info needed for Hw programming
router = self.get_router(context, router_id)
router_info = copy.deepcopy(new_router)
router_info['seg_id'] = seg_id
router_info['name'] = router['name']
router_info['cidr'] = subnet['cidr']
router_info['gip'] = subnet['gateway_ip']
router_info['ip_version'] = subnet['ip_version']
try:
self.driver.add_router_interface(context, router_info)
return new_router
except Exception:
with excutils.save_and_reraise_exception():
LOG.error(_LE("Error Adding subnet %(subnet)s to "
"router %(router_id)s on Arista HW"),
{'subnet': subnet, 'router_id': router_id})
super(AristaL3ServicePlugin, self).remove_router_interface(
context,
router_id,
interface_info) | python | def add_router_interface(self, context, router_id, interface_info):
"""Add a subnet of a network to an existing router."""
new_router = super(AristaL3ServicePlugin, self).add_router_interface(
context, router_id, interface_info)
core = directory.get_plugin()
# Get network info for the subnet that is being added to the router.
# Check if the interface information is by port-id or subnet-id
add_by_port, add_by_sub = self._validate_interface_info(interface_info)
if add_by_sub:
subnet = core.get_subnet(context, interface_info['subnet_id'])
elif add_by_port:
port = core.get_port(context, interface_info['port_id'])
subnet_id = port['fixed_ips'][0]['subnet_id']
subnet = core.get_subnet(context, subnet_id)
network_id = subnet['network_id']
# To create SVI's in Arista HW, the segmentation Id is required
# for this network.
ml2_db = NetworkContext(self, context, {'id': network_id})
seg_id = ml2_db.network_segments[0]['segmentation_id']
# Package all the info needed for Hw programming
router = self.get_router(context, router_id)
router_info = copy.deepcopy(new_router)
router_info['seg_id'] = seg_id
router_info['name'] = router['name']
router_info['cidr'] = subnet['cidr']
router_info['gip'] = subnet['gateway_ip']
router_info['ip_version'] = subnet['ip_version']
try:
self.driver.add_router_interface(context, router_info)
return new_router
except Exception:
with excutils.save_and_reraise_exception():
LOG.error(_LE("Error Adding subnet %(subnet)s to "
"router %(router_id)s on Arista HW"),
{'subnet': subnet, 'router_id': router_id})
super(AristaL3ServicePlugin, self).remove_router_interface(
context,
router_id,
interface_info) | [
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b3j0f/utils | b3j0f/utils/property.py | put_property | def put_property(elt, key, value, ttl=None, ctx=None):
"""Put properties in elt.
:param elt: properties elt to put. Not None methods.
:param number ttl: If not None, property time to leave.
:param ctx: elt ctx from where put properties. Equals elt if None. It
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:param dict properties: properties to put in elt. elt and ttl are exclude.
:return: Timer if ttl is not None.
:rtype: Timer
"""
return put_properties(elt=elt, properties={key: value}, ttl=ttl, ctx=ctx) | python | def put_property(elt, key, value, ttl=None, ctx=None):
"""Put properties in elt.
:param elt: properties elt to put. Not None methods.
:param number ttl: If not None, property time to leave.
:param ctx: elt ctx from where put properties. Equals elt if None. It
allows to get function properties related to a class or instance if
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:param dict properties: properties to put in elt. elt and ttl are exclude.
:return: Timer if ttl is not None.
:rtype: Timer
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return put_properties(elt=elt, properties={key: value}, ttl=ttl, ctx=ctx) | [
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allows to get function properties related to a class or instance if
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gem/oq-engine | openquake/risklib/riskinput.py | CompositeRiskModel.get_dmg_csq | def get_dmg_csq(self, assets_by_site, gmf):
"""
:returns:
an array of shape (A, L, 1, D + 1) with the number of buildings
in each damage state for each asset and loss type
"""
A = sum(len(assets) for assets in assets_by_site)
L = len(self.loss_types)
D = len(self.damage_states)
out = numpy.zeros((A, L, 1, D + 1), F32)
for assets, gmv in zip(assets_by_site, gmf):
group = group_array(assets, 'taxonomy')
for taxonomy, assets in group.items():
for l, loss_type in enumerate(self.loss_types):
fracs = self[taxonomy](loss_type, assets, [gmv])
for asset, frac in zip(assets, fracs):
dmg = asset['number'] * frac[0, :D]
csq = asset['value-' + loss_type] * frac[0, D]
out[asset['ordinal'], l, 0, :D] = dmg
out[asset['ordinal'], l, 0, D] = csq
return out | python | def get_dmg_csq(self, assets_by_site, gmf):
"""
:returns:
an array of shape (A, L, 1, D + 1) with the number of buildings
in each damage state for each asset and loss type
"""
A = sum(len(assets) for assets in assets_by_site)
L = len(self.loss_types)
D = len(self.damage_states)
out = numpy.zeros((A, L, 1, D + 1), F32)
for assets, gmv in zip(assets_by_site, gmf):
group = group_array(assets, 'taxonomy')
for taxonomy, assets in group.items():
for l, loss_type in enumerate(self.loss_types):
fracs = self[taxonomy](loss_type, assets, [gmv])
for asset, frac in zip(assets, fracs):
dmg = asset['number'] * frac[0, :D]
csq = asset['value-' + loss_type] * frac[0, D]
out[asset['ordinal'], l, 0, :D] = dmg
out[asset['ordinal'], l, 0, D] = csq
return out | [
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hfaran/piazza-api | piazza_api/network.py | Network.resolve_post | def resolve_post(self, post):
"""Mark post as resolved
:type post: dict|str|int
:param post: Either the post dict returned by another API method, or
the `cid` field of that post.
:returns: True if it is successful. False otherwise
"""
try:
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except KeyError:
cid = post
params = {
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"resolved": "true"
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return self._rpc.content_mark_resolved(params) | python | def resolve_post(self, post):
"""Mark post as resolved
:type post: dict|str|int
:param post: Either the post dict returned by another API method, or
the `cid` field of that post.
:returns: True if it is successful. False otherwise
"""
try:
cid = post["id"]
except KeyError:
cid = post
params = {
"cid": cid,
"resolved": "true"
}
return self._rpc.content_mark_resolved(params) | [
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sentinel-hub/sentinelhub-py | sentinelhub/io_utils.py | get_jp2_bit_depth | def get_jp2_bit_depth(stream):
"""Reads bit encoding depth of jpeg2000 file in binary stream format
:param stream: binary stream format
:type stream: Binary I/O (e.g. io.BytesIO, io.BufferedReader, ...)
:return: bit depth
:rtype: int
"""
stream.seek(0)
while True:
read_buffer = stream.read(8)
if len(read_buffer) < 8:
raise ValueError('Image Header Box not found in Jpeg2000 file')
_, box_id = struct.unpack('>I4s', read_buffer)
if box_id == b'ihdr':
read_buffer = stream.read(14)
params = struct.unpack('>IIHBBBB', read_buffer)
return (params[3] & 0x7f) + 1 | python | def get_jp2_bit_depth(stream):
"""Reads bit encoding depth of jpeg2000 file in binary stream format
:param stream: binary stream format
:type stream: Binary I/O (e.g. io.BytesIO, io.BufferedReader, ...)
:return: bit depth
:rtype: int
"""
stream.seek(0)
while True:
read_buffer = stream.read(8)
if len(read_buffer) < 8:
raise ValueError('Image Header Box not found in Jpeg2000 file')
_, box_id = struct.unpack('>I4s', read_buffer)
if box_id == b'ihdr':
read_buffer = stream.read(14)
params = struct.unpack('>IIHBBBB', read_buffer)
return (params[3] & 0x7f) + 1 | [
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Dentosal/python-sc2 | sc2/unit.py | Unit.research | def research(self, upgrade, *args, **kwargs):
""" Requires UpgradeId to be passed instead of AbilityId """
return self(self._game_data.upgrades[upgrade.value].research_ability.id, *args, **kwargs) | python | def research(self, upgrade, *args, **kwargs):
""" Requires UpgradeId to be passed instead of AbilityId """
return self(self._game_data.upgrades[upgrade.value].research_ability.id, *args, **kwargs) | [
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jobovy/galpy | galpy/potential/Potential.py | turn_physical_off | def turn_physical_off(Pot):
"""
NAME:
turn_physical_off
PURPOSE:
turn off automatic returning of outputs in physical units
INPUT:
(none)
OUTPUT:
(none)
HISTORY:
2016-01-30 - Written - Bovy (UofT)
"""
if isinstance(Pot,list):
for pot in Pot:
turn_physical_off(pot)
else:
Pot.turn_physical_off()
return None | python | def turn_physical_off(Pot):
"""
NAME:
turn_physical_off
PURPOSE:
turn off automatic returning of outputs in physical units
INPUT:
(none)
OUTPUT:
(none)
HISTORY:
2016-01-30 - Written - Bovy (UofT)
"""
if isinstance(Pot,list):
for pot in Pot:
turn_physical_off(pot)
else:
Pot.turn_physical_off()
return None | [
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RiotGames/cloud-inquisitor | plugins/public/cinq-collector-aws/cinq_collector_aws/region.py | AWSRegionCollector.update_snapshots | def update_snapshots(self):
"""Update list of EBS Snapshots for the account / region
Returns:
`None`
"""
self.log.debug('Updating EBSSnapshots for {}/{}'.format(self.account.account_name, self.region))
ec2 = self.session.resource('ec2', region_name=self.region)
try:
existing_snapshots = EBSSnapshot.get_all(self.account, self.region)
snapshots = {x.id: x for x in ec2.snapshots.filter(OwnerIds=[self.account.account_number])}
for data in list(snapshots.values()):
if data.id in existing_snapshots:
snapshot = existing_snapshots[data.id]
if snapshot.update(data):
self.log.debug('Change detected for EBSSnapshot {}/{}/{}'.format(
self.account.account_name,
self.region,
snapshot.resource.resource_id
))
else:
properties = {
'create_time': data.start_time,
'encrypted': data.encrypted,
'kms_key_id': data.kms_key_id,
'state': data.state,
'state_message': data.state_message,
'volume_id': data.volume_id,
'volume_size': data.volume_size,
}
tags = {t['Key']: t['Value'] for t in data.tags or {}}
snapshot = EBSSnapshot.create(
data.id,
account_id=self.account.account_id,
location=self.region,
properties=properties,
tags=tags
)
self.log.debug('Added new EBSSnapshot {}/{}/{}'.format(
self.account.account_name,
self.region,
snapshot.resource.resource_id
))
db.session.commit()
vk = set(list(snapshots.keys()))
evk = set(list(existing_snapshots.keys()))
try:
for snapshotID in evk - vk:
db.session.delete(existing_snapshots[snapshotID].resource)
self.log.debug('Deleted EBSSnapshot {}/{}/{}'.format(
self.account.account_name,
self.region,
snapshotID
))
db.session.commit()
except:
self.log.exception('Failed removing deleted snapshots')
db.session.rollback()
finally:
del ec2 | python | def update_snapshots(self):
"""Update list of EBS Snapshots for the account / region
Returns:
`None`
"""
self.log.debug('Updating EBSSnapshots for {}/{}'.format(self.account.account_name, self.region))
ec2 = self.session.resource('ec2', region_name=self.region)
try:
existing_snapshots = EBSSnapshot.get_all(self.account, self.region)
snapshots = {x.id: x for x in ec2.snapshots.filter(OwnerIds=[self.account.account_number])}
for data in list(snapshots.values()):
if data.id in existing_snapshots:
snapshot = existing_snapshots[data.id]
if snapshot.update(data):
self.log.debug('Change detected for EBSSnapshot {}/{}/{}'.format(
self.account.account_name,
self.region,
snapshot.resource.resource_id
))
else:
properties = {
'create_time': data.start_time,
'encrypted': data.encrypted,
'kms_key_id': data.kms_key_id,
'state': data.state,
'state_message': data.state_message,
'volume_id': data.volume_id,
'volume_size': data.volume_size,
}
tags = {t['Key']: t['Value'] for t in data.tags or {}}
snapshot = EBSSnapshot.create(
data.id,
account_id=self.account.account_id,
location=self.region,
properties=properties,
tags=tags
)
self.log.debug('Added new EBSSnapshot {}/{}/{}'.format(
self.account.account_name,
self.region,
snapshot.resource.resource_id
))
db.session.commit()
vk = set(list(snapshots.keys()))
evk = set(list(existing_snapshots.keys()))
try:
for snapshotID in evk - vk:
db.session.delete(existing_snapshots[snapshotID].resource)
self.log.debug('Deleted EBSSnapshot {}/{}/{}'.format(
self.account.account_name,
self.region,
snapshotID
))
db.session.commit()
except:
self.log.exception('Failed removing deleted snapshots')
db.session.rollback()
finally:
del ec2 | [
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] | train | https://github.com/RiotGames/cloud-inquisitor/blob/181dc2566ca59fc855f695b7fcc2c3b934e6ee9f/plugins/public/cinq-collector-aws/cinq_collector_aws/region.py#L307-L374 | 0.002175 |
tsileo/globster | globster.py | Globster.is_pattern_valid | def is_pattern_valid(pattern):
"""Returns True if pattern is valid.
:param pattern: Normalized pattern.
is_pattern_valid() assumes pattern to be normalized.
see: globbing.normalize_pattern
"""
result = True
translator = Globster.pattern_info[Globster.identify(pattern)]["translator"]
tpattern = '(%s)' % translator(pattern)
try:
re_obj = lazy_regex.lazy_compile(tpattern, re.UNICODE)
re_obj.search("") # force compile
except Exception as e:
result = False
return result | python | def is_pattern_valid(pattern):
"""Returns True if pattern is valid.
:param pattern: Normalized pattern.
is_pattern_valid() assumes pattern to be normalized.
see: globbing.normalize_pattern
"""
result = True
translator = Globster.pattern_info[Globster.identify(pattern)]["translator"]
tpattern = '(%s)' % translator(pattern)
try:
re_obj = lazy_regex.lazy_compile(tpattern, re.UNICODE)
re_obj.search("") # force compile
except Exception as e:
result = False
return result | [
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tomislater/RandomWords | random_words/random_words.py | Random.load_nicknames | def load_nicknames(self, file):
"""
Load dict from file for random nicknames.
:param str file: filename
"""
with open(os.path.join(main_dir, file + '.dat'), 'r') as f:
self.nicknames = json.load(f) | python | def load_nicknames(self, file):
"""
Load dict from file for random nicknames.
:param str file: filename
"""
with open(os.path.join(main_dir, file + '.dat'), 'r') as f:
self.nicknames = json.load(f) | [
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tensorflow/tensor2tensor | tensor2tensor/utils/optimize.py | optimize | def optimize(loss, learning_rate, hparams, use_tpu=False, variables=None):
"""Minimize loss."""
loss = weight_decay_and_noise(loss, hparams, learning_rate)
loss = tf.identity(loss, name="total_loss")
if variables is None:
variables = tf.trainable_variables()
# Print trainable variables.
log_variable_sizes(variables, verbose=hparams.summarize_vars)
# Print non-trainable variables.
non_trainable_variables = list(
set(tf.global_variables()) - set(variables))
log_variable_sizes(non_trainable_variables, tag="Non-trainable variables",
verbose=hparams.summarize_vars)
if hparams.summarize_vars:
summarize_variables(variables)
# Summarize non-trainable variables as well
summarize_variables(non_trainable_variables, tag="Non-trainable variables")
diet_vars = [
v for v in tf.global_variables() if v.dtype == dtypes.float16_ref
]
log_variable_sizes(
diet_vars, "Diet Variables", verbose=hparams.summarize_vars)
opt = ConditionalOptimizer(hparams.optimizer, learning_rate, hparams, use_tpu)
if use_tpu:
opt = tf.contrib.tpu.CrossShardOptimizer(opt)
opt_summaries = []
if common_layers.should_generate_summaries():
tf.summary.scalar("learning_rate", learning_rate)
opt_summaries.append("loss")
if hparams.summarize_grads:
tf.logging.info("Summarizing gradients")
opt_summaries.extend(
["gradients", "gradient_norm", "global_gradient_norm"])
if hparams.clip_grad_norm:
tf.logging.info("Clipping gradients, norm: %0.5f", hparams.clip_grad_norm)
if hparams.grad_noise_scale:
tf.logging.info("Adding noise to gradients, noise scale: %0.5f",
hparams.grad_noise_scale)
train_op = tf.contrib.layers.optimize_loss(
name="training",
loss=loss,
global_step=tf.train.get_or_create_global_step(),
learning_rate=learning_rate,
clip_gradients=hparams.clip_grad_norm or None,
gradient_noise_scale=hparams.grad_noise_scale or None,
optimizer=opt,
summaries=opt_summaries,
colocate_gradients_with_ops=True,
variables=variables)
return train_op | python | def optimize(loss, learning_rate, hparams, use_tpu=False, variables=None):
"""Minimize loss."""
loss = weight_decay_and_noise(loss, hparams, learning_rate)
loss = tf.identity(loss, name="total_loss")
if variables is None:
variables = tf.trainable_variables()
# Print trainable variables.
log_variable_sizes(variables, verbose=hparams.summarize_vars)
# Print non-trainable variables.
non_trainable_variables = list(
set(tf.global_variables()) - set(variables))
log_variable_sizes(non_trainable_variables, tag="Non-trainable variables",
verbose=hparams.summarize_vars)
if hparams.summarize_vars:
summarize_variables(variables)
# Summarize non-trainable variables as well
summarize_variables(non_trainable_variables, tag="Non-trainable variables")
diet_vars = [
v for v in tf.global_variables() if v.dtype == dtypes.float16_ref
]
log_variable_sizes(
diet_vars, "Diet Variables", verbose=hparams.summarize_vars)
opt = ConditionalOptimizer(hparams.optimizer, learning_rate, hparams, use_tpu)
if use_tpu:
opt = tf.contrib.tpu.CrossShardOptimizer(opt)
opt_summaries = []
if common_layers.should_generate_summaries():
tf.summary.scalar("learning_rate", learning_rate)
opt_summaries.append("loss")
if hparams.summarize_grads:
tf.logging.info("Summarizing gradients")
opt_summaries.extend(
["gradients", "gradient_norm", "global_gradient_norm"])
if hparams.clip_grad_norm:
tf.logging.info("Clipping gradients, norm: %0.5f", hparams.clip_grad_norm)
if hparams.grad_noise_scale:
tf.logging.info("Adding noise to gradients, noise scale: %0.5f",
hparams.grad_noise_scale)
train_op = tf.contrib.layers.optimize_loss(
name="training",
loss=loss,
global_step=tf.train.get_or_create_global_step(),
learning_rate=learning_rate,
clip_gradients=hparams.clip_grad_norm or None,
gradient_noise_scale=hparams.grad_noise_scale or None,
optimizer=opt,
summaries=opt_summaries,
colocate_gradients_with_ops=True,
variables=variables)
return train_op | [
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saltstack/salt | salt/modules/smf_service.py | status | def status(name, sig=None):
'''
Return the status for a service.
If the name contains globbing, a dict mapping service name to True/False
values is returned.
.. versionchanged:: 2018.3.0
The service name can now be a glob (e.g. ``salt*``)
Args:
name (str): The name of the service to check
sig (str): Not implemented
Returns:
bool: True if running, False otherwise
dict: Maps service name to True if running, False otherwise
CLI Example:
.. code-block:: bash
salt '*' service.status <service name>
'''
contains_globbing = bool(re.search(r'\*|\?|\[.+\]', name))
if contains_globbing:
services = fnmatch.filter(get_all(), name)
else:
services = [name]
results = {}
for service in services:
cmd = '/usr/bin/svcs -H -o STATE {0}'.format(service)
line = __salt__['cmd.run'](cmd, python_shell=False)
results[service] = line == 'online'
if contains_globbing:
return results
return results[name] | python | def status(name, sig=None):
'''
Return the status for a service.
If the name contains globbing, a dict mapping service name to True/False
values is returned.
.. versionchanged:: 2018.3.0
The service name can now be a glob (e.g. ``salt*``)
Args:
name (str): The name of the service to check
sig (str): Not implemented
Returns:
bool: True if running, False otherwise
dict: Maps service name to True if running, False otherwise
CLI Example:
.. code-block:: bash
salt '*' service.status <service name>
'''
contains_globbing = bool(re.search(r'\*|\?|\[.+\]', name))
if contains_globbing:
services = fnmatch.filter(get_all(), name)
else:
services = [name]
results = {}
for service in services:
cmd = '/usr/bin/svcs -H -o STATE {0}'.format(service)
line = __salt__['cmd.run'](cmd, python_shell=False)
results[service] = line == 'online'
if contains_globbing:
return results
return results[name] | [
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Args:
name (str): The name of the service to check
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PaulHancock/Aegean | AegeanTools/regions.py | Region.vec2sky | def vec2sky(cls, vec, degrees=False):
"""
Convert [x,y,z] vectors into sky coordinates ra,dec
Parameters
----------
vec : numpy.array
Unit vectors as an array of (x,y,z)
degrees
Returns
-------
sky : numpy.array
Sky coordinates as an array of (ra,dec)
See Also
--------
:func:`AegeanTools.regions.Region.sky2vec`
"""
theta, phi = hp.vec2ang(vec)
ra = phi
dec = np.pi/2-theta
if degrees:
ra = np.degrees(ra)
dec = np.degrees(dec)
return cls.radec2sky(ra, dec) | python | def vec2sky(cls, vec, degrees=False):
"""
Convert [x,y,z] vectors into sky coordinates ra,dec
Parameters
----------
vec : numpy.array
Unit vectors as an array of (x,y,z)
degrees
Returns
-------
sky : numpy.array
Sky coordinates as an array of (ra,dec)
See Also
--------
:func:`AegeanTools.regions.Region.sky2vec`
"""
theta, phi = hp.vec2ang(vec)
ra = phi
dec = np.pi/2-theta
if degrees:
ra = np.degrees(ra)
dec = np.degrees(dec)
return cls.radec2sky(ra, dec) | [
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maxalbert/tohu | tohu/v2/custom_generator.py | add_new_init_method | def add_new_init_method(obj):
"""
Replace the existing obj.__init__() method with a new one
which calls the original one and in addition performs the
following actions:
(1) Finds all instances of tohu.BaseGenerator in the namespace
and collects them in the dictionary `self.field_gens`.
(2) ..to do..
"""
orig_init = obj.__init__
def new_init(self, *args, **kwargs):
logger.debug(f"Initialising new {self}")
# Call original __init__ function to ensure we pick up
# any tohu generators that are defined there.
orig_init(self, *args, **kwargs)
#
# Find field generator templates and attach spawned copies
#
field_gens_templates = find_field_generators(self)
logger.debug(f'Found {len(field_gens_templates)} field generator template(s):')
debug_print_dict(field_gens_templates)
def find_orig_parent(dep_gen, origs):
"""
Find name and instance of the parent of the dependent
generator `dep_gen` amongst the generators in `origs`.
"""
for parent_name, parent in origs.items():
if dep_gen.parent is parent:
return parent_name, parent
raise RuntimeError(f"Parent of dependent generator {dep_gen} not defined in the same custom generator")
logger.debug('Spawning field generator templates...')
origs = {}
spawned = {}
for name, gen in field_gens_templates.items():
if isinstance(gen, IndependentGenerator) and gen in origs.values():
logger.debug(f'Cloning generator {name}={gen} because it is an alias for an existing generator')
gen = gen.clone()
if isinstance(gen, IndependentGenerator):
origs[name] = gen
spawned[name] = gen._spawn()
logger.debug(f'Spawning generator {gen}. New spawn: {spawned[name]}')
elif isinstance(gen, DependentGenerator):
orig_parent_name, orig_parent = find_orig_parent(gen, origs)
new_parent = spawned[orig_parent_name]
#spawned[name] = new_parent.clone()
spawned[name] = gen._spawn_and_reattach_parent(new_parent)
else:
pass
self.field_gens = spawned
self.__dict__.update(self.field_gens)
logger.debug(f'Field generators attached to custom generator instance:')
debug_print_dict(self.field_gens)
#
# Add seed generator
#
self.seed_generator = SeedGenerator()
#
# Create class for the items produced by this generator
#
self.__class__.item_cls = make_item_class_for_custom_generator(self)
obj.__init__ = new_init | python | def add_new_init_method(obj):
"""
Replace the existing obj.__init__() method with a new one
which calls the original one and in addition performs the
following actions:
(1) Finds all instances of tohu.BaseGenerator in the namespace
and collects them in the dictionary `self.field_gens`.
(2) ..to do..
"""
orig_init = obj.__init__
def new_init(self, *args, **kwargs):
logger.debug(f"Initialising new {self}")
# Call original __init__ function to ensure we pick up
# any tohu generators that are defined there.
orig_init(self, *args, **kwargs)
#
# Find field generator templates and attach spawned copies
#
field_gens_templates = find_field_generators(self)
logger.debug(f'Found {len(field_gens_templates)} field generator template(s):')
debug_print_dict(field_gens_templates)
def find_orig_parent(dep_gen, origs):
"""
Find name and instance of the parent of the dependent
generator `dep_gen` amongst the generators in `origs`.
"""
for parent_name, parent in origs.items():
if dep_gen.parent is parent:
return parent_name, parent
raise RuntimeError(f"Parent of dependent generator {dep_gen} not defined in the same custom generator")
logger.debug('Spawning field generator templates...')
origs = {}
spawned = {}
for name, gen in field_gens_templates.items():
if isinstance(gen, IndependentGenerator) and gen in origs.values():
logger.debug(f'Cloning generator {name}={gen} because it is an alias for an existing generator')
gen = gen.clone()
if isinstance(gen, IndependentGenerator):
origs[name] = gen
spawned[name] = gen._spawn()
logger.debug(f'Spawning generator {gen}. New spawn: {spawned[name]}')
elif isinstance(gen, DependentGenerator):
orig_parent_name, orig_parent = find_orig_parent(gen, origs)
new_parent = spawned[orig_parent_name]
#spawned[name] = new_parent.clone()
spawned[name] = gen._spawn_and_reattach_parent(new_parent)
else:
pass
self.field_gens = spawned
self.__dict__.update(self.field_gens)
logger.debug(f'Field generators attached to custom generator instance:')
debug_print_dict(self.field_gens)
#
# Add seed generator
#
self.seed_generator = SeedGenerator()
#
# Create class for the items produced by this generator
#
self.__class__.item_cls = make_item_class_for_custom_generator(self)
obj.__init__ = new_init | [
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(2) ..to do.. | [
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gwastro/pycbc | pycbc/detector.py | Detector.optimal_orientation | def optimal_orientation(self, t_gps):
"""Return the optimal orientation in right ascension and declination
for a given GPS time.
Parameters
----------
t_gps: float
Time in gps seconds
Returns
-------
ra: float
Right ascension that is optimally oriented for the detector
dec: float
Declination that is optimally oriented for the detector
"""
ra = self.longitude + (self.gmst_estimate(t_gps) % (2.0*np.pi))
dec = self.latitude
return ra, dec | python | def optimal_orientation(self, t_gps):
"""Return the optimal orientation in right ascension and declination
for a given GPS time.
Parameters
----------
t_gps: float
Time in gps seconds
Returns
-------
ra: float
Right ascension that is optimally oriented for the detector
dec: float
Declination that is optimally oriented for the detector
"""
ra = self.longitude + (self.gmst_estimate(t_gps) % (2.0*np.pi))
dec = self.latitude
return ra, dec | [
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riggsd/davies | davies/compass/__init__.py | Project.set_base_location | def set_base_location(self, location):
"""Configure the project's base location"""
self.base_location = location
self._utm_zone = location.zone
self._utm_datum = location.datum
self._utm_convergence = location.convergence | python | def set_base_location(self, location):
"""Configure the project's base location"""
self.base_location = location
self._utm_zone = location.zone
self._utm_datum = location.datum
self._utm_convergence = location.convergence | [
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cihai/cihai | cihai/conversion.py | parse_var | def parse_var(var):
"""
Returns a tuple consisting of a string and a tag, or None, if none is
specified.
"""
bits = var.split("<", 1)
if len(bits) < 2:
tag = None
else:
tag = bits[1]
return ucn_to_unicode(bits[0]), tag | python | def parse_var(var):
"""
Returns a tuple consisting of a string and a tag, or None, if none is
specified.
"""
bits = var.split("<", 1)
if len(bits) < 2:
tag = None
else:
tag = bits[1]
return ucn_to_unicode(bits[0]), tag | [
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kalefranz/auxlib | auxlib/decorators.py | memoizemethod | def memoizemethod(method):
"""
Decorator to cause a method to cache it's results in self for each
combination of inputs and return the cached result on subsequent calls.
Does not support named arguments or arg values that are not hashable.
>>> class Foo (object):
... @memoizemethod
... def foo(self, x, y=0):
... print('running method with', x, y)
... return x + y + 3
...
>>> foo1 = Foo()
>>> foo2 = Foo()
>>> foo1.foo(10)
running method with 10 0
13
>>> foo1.foo(10)
13
>>> foo2.foo(11, y=7)
running method with 11 7
21
>>> foo2.foo(11)
running method with 11 0
14
>>> foo2.foo(11, y=7)
21
>>> class Foo (object):
... def __init__(self, lower):
... self.lower = lower
... @memoizemethod
... def range_tuple(self, upper):
... print('running function')
... return tuple(i for i in range(self.lower, upper))
... @memoizemethod
... def range_iter(self, upper):
... print('running function')
... return (i for i in range(self.lower, upper))
...
>>> foo = Foo(3)
>>> foo.range_tuple(6)
running function
(3, 4, 5)
>>> foo.range_tuple(7)
running function
(3, 4, 5, 6)
>>> foo.range_tuple(6)
(3, 4, 5)
>>> foo.range_iter(6)
Traceback (most recent call last):
TypeError: Can't memoize a generator or non-hashable object!
"""
@wraps(method)
def _wrapper(self, *args, **kwargs):
# NOTE: a __dict__ check is performed here rather than using the
# built-in hasattr function because hasattr will look up to an object's
# class if the attr is not directly found in the object's dict. That's
# bad for this if the class itself has a memoized classmethod for
# example that has been called before the memoized instance method,
# then the instance method will use the class's result cache, causing
# its results to be globally stored rather than on a per instance
# basis.
if '_memoized_results' not in self.__dict__:
self._memoized_results = {}
memoized_results = self._memoized_results
key = (method.__name__, args, tuple(sorted(kwargs.items())))
if key in memoized_results:
return memoized_results[key]
else:
try:
result = method(self, *args, **kwargs)
except KeyError as e:
if '__wrapped__' in str(e):
result = None # is this the right thing to do? happened during py3 conversion
else:
raise
if isinstance(result, GeneratorType) or not isinstance(result, Hashable):
raise TypeError("Can't memoize a generator or non-hashable object!")
return memoized_results.setdefault(key, result)
return _wrapper | python | def memoizemethod(method):
"""
Decorator to cause a method to cache it's results in self for each
combination of inputs and return the cached result on subsequent calls.
Does not support named arguments or arg values that are not hashable.
>>> class Foo (object):
... @memoizemethod
... def foo(self, x, y=0):
... print('running method with', x, y)
... return x + y + 3
...
>>> foo1 = Foo()
>>> foo2 = Foo()
>>> foo1.foo(10)
running method with 10 0
13
>>> foo1.foo(10)
13
>>> foo2.foo(11, y=7)
running method with 11 7
21
>>> foo2.foo(11)
running method with 11 0
14
>>> foo2.foo(11, y=7)
21
>>> class Foo (object):
... def __init__(self, lower):
... self.lower = lower
... @memoizemethod
... def range_tuple(self, upper):
... print('running function')
... return tuple(i for i in range(self.lower, upper))
... @memoizemethod
... def range_iter(self, upper):
... print('running function')
... return (i for i in range(self.lower, upper))
...
>>> foo = Foo(3)
>>> foo.range_tuple(6)
running function
(3, 4, 5)
>>> foo.range_tuple(7)
running function
(3, 4, 5, 6)
>>> foo.range_tuple(6)
(3, 4, 5)
>>> foo.range_iter(6)
Traceback (most recent call last):
TypeError: Can't memoize a generator or non-hashable object!
"""
@wraps(method)
def _wrapper(self, *args, **kwargs):
# NOTE: a __dict__ check is performed here rather than using the
# built-in hasattr function because hasattr will look up to an object's
# class if the attr is not directly found in the object's dict. That's
# bad for this if the class itself has a memoized classmethod for
# example that has been called before the memoized instance method,
# then the instance method will use the class's result cache, causing
# its results to be globally stored rather than on a per instance
# basis.
if '_memoized_results' not in self.__dict__:
self._memoized_results = {}
memoized_results = self._memoized_results
key = (method.__name__, args, tuple(sorted(kwargs.items())))
if key in memoized_results:
return memoized_results[key]
else:
try:
result = method(self, *args, **kwargs)
except KeyError as e:
if '__wrapped__' in str(e):
result = None # is this the right thing to do? happened during py3 conversion
else:
raise
if isinstance(result, GeneratorType) or not isinstance(result, Hashable):
raise TypeError("Can't memoize a generator or non-hashable object!")
return memoized_results.setdefault(key, result)
return _wrapper | [
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>>> class Foo (object):
... @memoizemethod
... def foo(self, x, y=0):
... print('running method with', x, y)
... return x + y + 3
...
>>> foo1 = Foo()
>>> foo2 = Foo()
>>> foo1.foo(10)
running method with 10 0
13
>>> foo1.foo(10)
13
>>> foo2.foo(11, y=7)
running method with 11 7
21
>>> foo2.foo(11)
running method with 11 0
14
>>> foo2.foo(11, y=7)
21
>>> class Foo (object):
... def __init__(self, lower):
... self.lower = lower
... @memoizemethod
... def range_tuple(self, upper):
... print('running function')
... return tuple(i for i in range(self.lower, upper))
... @memoizemethod
... def range_iter(self, upper):
... print('running function')
... return (i for i in range(self.lower, upper))
...
>>> foo = Foo(3)
>>> foo.range_tuple(6)
running function
(3, 4, 5)
>>> foo.range_tuple(7)
running function
(3, 4, 5, 6)
>>> foo.range_tuple(6)
(3, 4, 5)
>>> foo.range_iter(6)
Traceback (most recent call last):
TypeError: Can't memoize a generator or non-hashable object! | [
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klmitch/requiem | requiem/client.py | RESTClient._make_req | def _make_req(self, method, url, methname, headers=None):
"""Create a request object for the specified method and url."""
# Build up headers
hset = hdrs.HeaderDict()
# Walk through our global headers
for hdr, value in self._headers.items():
# If it's a callable, call it
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value = value(methname)
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# If it's meaningful, attach it
if value:
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# Were headers passed in?
if headers is not None:
# Update from specified headers
hset.update(headers)
# Hook method to instantiate requests
self._debug("Creating request %s.%s(%r, %r, headers=%r)",
self._req_class.__module__, self._req_class.__name__,
method, url, hset)
return self._req_class(method, url, self._client, self._procstack,
headers=hset, debug=self._debug) | python | def _make_req(self, method, url, methname, headers=None):
"""Create a request object for the specified method and url."""
# Build up headers
hset = hdrs.HeaderDict()
# Walk through our global headers
for hdr, value in self._headers.items():
# If it's a callable, call it
if callable(value):
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# If it's meaningful, attach it
if value:
hset[hdr] = value
# Were headers passed in?
if headers is not None:
# Update from specified headers
hset.update(headers)
# Hook method to instantiate requests
self._debug("Creating request %s.%s(%r, %r, headers=%r)",
self._req_class.__module__, self._req_class.__name__,
method, url, hset)
return self._req_class(method, url, self._client, self._procstack,
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pycontribs/jira | jira/client.py | JIRA.create_component | def create_component(self,
name,
project,
description=None,
leadUserName=None,
assigneeType=None,
isAssigneeTypeValid=False,
):
"""Create a component inside a project and return a Resource for it.
:param name: name of the component
:type name: str
:param project: key of the project to create the component in
:type project: str
:param description: a description of the component
:type description: str
:param leadUserName: the username of the user responsible for this component
:type leadUserName: Optional[str]
:param assigneeType: see the ComponentBean.AssigneeType class for valid values
:type assigneeType: Optional[str]
:param isAssigneeTypeValid: boolean specifying whether the assignee type is acceptable (Default: False)
:type isAssigneeTypeValid: bool
:rtype: Component
"""
data = {
'name': name,
'project': project,
'isAssigneeTypeValid': isAssigneeTypeValid}
if description is not None:
data['description'] = description
if leadUserName is not None:
data['leadUserName'] = leadUserName
if assigneeType is not None:
data['assigneeType'] = assigneeType
url = self._get_url('component')
r = self._session.post(
url, data=json.dumps(data))
component = Component(self._options, self._session, raw=json_loads(r))
return component | python | def create_component(self,
name,
project,
description=None,
leadUserName=None,
assigneeType=None,
isAssigneeTypeValid=False,
):
"""Create a component inside a project and return a Resource for it.
:param name: name of the component
:type name: str
:param project: key of the project to create the component in
:type project: str
:param description: a description of the component
:type description: str
:param leadUserName: the username of the user responsible for this component
:type leadUserName: Optional[str]
:param assigneeType: see the ComponentBean.AssigneeType class for valid values
:type assigneeType: Optional[str]
:param isAssigneeTypeValid: boolean specifying whether the assignee type is acceptable (Default: False)
:type isAssigneeTypeValid: bool
:rtype: Component
"""
data = {
'name': name,
'project': project,
'isAssigneeTypeValid': isAssigneeTypeValid}
if description is not None:
data['description'] = description
if leadUserName is not None:
data['leadUserName'] = leadUserName
if assigneeType is not None:
data['assigneeType'] = assigneeType
url = self._get_url('component')
r = self._session.post(
url, data=json.dumps(data))
component = Component(self._options, self._session, raw=json_loads(r))
return component | [
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networks-lab/metaknowledge | metaknowledge/mkCollection.py | CollectionWithIDs.dropBadEntries | def dropBadEntries(self):
"""Removes all the bad entries from the collection
"""
self._collection = set((i for i in self if not i.bad))
self.bad = False
self.errors = {} | python | def dropBadEntries(self):
"""Removes all the bad entries from the collection
"""
self._collection = set((i for i in self if not i.bad))
self.bad = False
self.errors = {} | [
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DataDog/integrations-core | process/datadog_checks/process/process.py | ProcessCheck._filter_by_user | def _filter_by_user(self, user, pids):
"""
Filter pids by it's username.
:param user: string with name of system user
:param pids: set of pids to filter
:return: set of filtered pids
"""
filtered_pids = set()
for pid in pids:
try:
proc = psutil.Process(pid)
if proc.username() == user:
self.log.debug("Collecting pid {} belonging to {}".format(pid, user))
filtered_pids.add(pid)
else:
self.log.debug("Discarding pid {} not belonging to {}".format(pid, user))
except psutil.NoSuchProcess:
pass
return filtered_pids | python | def _filter_by_user(self, user, pids):
"""
Filter pids by it's username.
:param user: string with name of system user
:param pids: set of pids to filter
:return: set of filtered pids
"""
filtered_pids = set()
for pid in pids:
try:
proc = psutil.Process(pid)
if proc.username() == user:
self.log.debug("Collecting pid {} belonging to {}".format(pid, user))
filtered_pids.add(pid)
else:
self.log.debug("Discarding pid {} not belonging to {}".format(pid, user))
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return filtered_pids | [
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JdeRobot/base | src/drivers/MAVLinkServer/MAVProxy/modules/lib/ANUGA/geo_reference.py | Geo_reference.change_points_geo_ref | def change_points_geo_ref(self, points, points_geo_ref=None):
"""Change the geo reference of a list or numeric array of points to
be this reference.(The reference used for this object)
If the points do not have a geo ref, assume 'absolute' values
"""
import copy
# remember if we got a list
is_list = isinstance(points, list)
points = ensure_numeric(points, num.float)
# sanity checks
if len(points.shape) == 1:
#One point has been passed
msg = 'Single point must have two elements'
assert len(points) == 2, msg
points = num.reshape(points, (1,2))
msg = 'Points array must be two dimensional.\n'
msg += 'I got %d dimensions' %len(points.shape)
assert len(points.shape) == 2, msg
msg = 'Input must be an N x 2 array or list of (x,y) values. '
msg += 'I got an %d x %d array' %points.shape
assert points.shape[1] == 2, msg
# FIXME (Ole): Could also check if zone, xllcorner, yllcorner
# are identical in the two geo refs.
if points_geo_ref is not self:
# If georeferences are different
points = copy.copy(points) # Don't destroy input
if not points_geo_ref is None:
# Convert points to absolute coordinates
points[:,0] += points_geo_ref.xllcorner
points[:,1] += points_geo_ref.yllcorner
# Make points relative to primary geo reference
points[:,0] -= self.xllcorner
points[:,1] -= self.yllcorner
if is_list:
points = points.tolist()
return points | python | def change_points_geo_ref(self, points, points_geo_ref=None):
"""Change the geo reference of a list or numeric array of points to
be this reference.(The reference used for this object)
If the points do not have a geo ref, assume 'absolute' values
"""
import copy
# remember if we got a list
is_list = isinstance(points, list)
points = ensure_numeric(points, num.float)
# sanity checks
if len(points.shape) == 1:
#One point has been passed
msg = 'Single point must have two elements'
assert len(points) == 2, msg
points = num.reshape(points, (1,2))
msg = 'Points array must be two dimensional.\n'
msg += 'I got %d dimensions' %len(points.shape)
assert len(points.shape) == 2, msg
msg = 'Input must be an N x 2 array or list of (x,y) values. '
msg += 'I got an %d x %d array' %points.shape
assert points.shape[1] == 2, msg
# FIXME (Ole): Could also check if zone, xllcorner, yllcorner
# are identical in the two geo refs.
if points_geo_ref is not self:
# If georeferences are different
points = copy.copy(points) # Don't destroy input
if not points_geo_ref is None:
# Convert points to absolute coordinates
points[:,0] += points_geo_ref.xllcorner
points[:,1] += points_geo_ref.yllcorner
# Make points relative to primary geo reference
points[:,0] -= self.xllcorner
points[:,1] -= self.yllcorner
if is_list:
points = points.tolist()
return points | [
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pybel/pybel | src/pybel/io/nodelink.py | to_jsons | def to_jsons(graph: BELGraph, **kwargs) -> str:
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graph_json_str = to_json(graph)
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coinbase/coinbase-python | coinbase/wallet/client.py | Client.commit_sell | def commit_sell(self, account_id, sell_id, **params):
"""https://developers.coinbase.com/api/v2#commit-a-sell"""
response = self._post(
'v2', 'accounts', account_id, 'sells', sell_id, 'commit', data=params)
return self._make_api_object(response, Sell) | python | def commit_sell(self, account_id, sell_id, **params):
"""https://developers.coinbase.com/api/v2#commit-a-sell"""
response = self._post(
'v2', 'accounts', account_id, 'sells', sell_id, 'commit', data=params)
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rkhleics/wagtailmodeladmin | wagtailmodeladmin/options.py | ModelAdmin.edit_view | def edit_view(self, request, object_id):
"""
Instantiates a class-based view to provide 'edit' functionality for the
assigned model, or redirect to Wagtail's edit view if the assigned
model extends 'Page'. The view class used can be overridden by changing
the 'edit_view_class' attribute.
"""
kwargs = {'model_admin': self, 'object_id': object_id}
view_class = self.edit_view_class
return view_class.as_view(**kwargs)(request) | python | def edit_view(self, request, object_id):
"""
Instantiates a class-based view to provide 'edit' functionality for the
assigned model, or redirect to Wagtail's edit view if the assigned
model extends 'Page'. The view class used can be overridden by changing
the 'edit_view_class' attribute.
"""
kwargs = {'model_admin': self, 'object_id': object_id}
view_class = self.edit_view_class
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] | train | https://github.com/rkhleics/wagtailmodeladmin/blob/7fddc853bab2ff3868b8c7a03329308c55f16358/wagtailmodeladmin/options.py#L354-L363 | 0.004008 |
angr/angr | angr/state_plugins/concrete.py | Concrete.sync | def sync(self):
"""
Handle the switch between the concrete execution and angr.
This method takes care of:
1- Synchronize registers.
2- Set a concrete target to the memory backer so the memory reads are redirected in the concrete process memory.
3- If possible restore the SimProcedures with the real addresses inside the concrete process.
4- Set an inspect point to sync the segments register as soon as they are read during the symbolic execution.
5- Flush all the pages loaded until now.
:return:
"""
def _sync_segments(state):
"""
Segment registers synchronization is on demand as soon as the
symbolic execution access a segment register.
"""
concr_target = state.project.concrete_target
if isinstance(state.arch, ArchAMD64):
state.project.simos.initialize_segment_register_x64(state, concr_target)
elif isinstance(state.arch, ArchX86):
gdt = state.project.simos.initialize_gdt_x86(state, concr_target)
state.concrete.whitelist.append((gdt.addr, gdt.addr + gdt.limit))
state.inspect.remove_breakpoint('reg_read', bp=state.concrete.fs_register_bp)
state.concrete.segment_registers_initialized = True
state.concrete.fs_register_bp = None
l.debug("Sync the state with the concrete memory inside the Concrete plugin")
target = self.state.project.concrete_target
# Setting a concrete memory backend
self.state.memory.mem._memory_backer.set_concrete_target(target)
# Sync angr registers with the one getting from the concrete target
# registers that we don't want to concretize.
l.debug("Synchronizing general purpose registers")
to_sync_register = list(filter(lambda x: x.concrete, self.state.arch.register_list))
for register in to_sync_register:
# before let's sync all the subregisters of the current register.
# sometimes this can be helpful ( i.e. ymmm0 e xmm0 )
if register.subregisters:
subregisters_names = map(lambda x: x[0], register.subregisters)
self._sync_registers(subregisters_names, target)
# finally let's synchronize the whole register
self._sync_registers([register.name], target)
if self.synchronize_cle:
self._sync_cle(target)
# Synchronize the imported functions addresses (.got, IAT) in the
# concrete process with ones used in the SimProcedures dictionary
if self.state.project._should_use_sim_procedures and not self.state.project.loader.main_object.pic:
l.debug("Restoring SimProc using concrete memory")
for reloc in self.state.project.loader.main_object.relocs:
if reloc.symbol: # consider only reloc with a symbol
l.debug("Trying to re-hook SimProc %s", reloc.symbol.name)
# l.debug("reloc.rebased_addr: %#x " % reloc.rebased_addr)
func_address = target.read_memory(reloc.rebased_addr, self.state.project.arch.bits / 8)
func_address = struct.unpack(self.state.project.arch.struct_fmt(), func_address)[0]
l.debug("Function address hook is now: %#x ", func_address)
self.state.project.rehook_symbol(func_address, reloc.symbol.name)
if self.synchronize_cle and not self.state.project.loader.main_object.contains_addr(func_address):
old_func_symbol = self.state.project.loader.find_symbol(reloc.symbol.name)
if old_func_symbol: # if we actually have a symbol
owner_obj = old_func_symbol.owner
# calculating the new real address
new_relative_address = func_address - owner_obj.mapped_base
new_func_symbol = cle.backends.Symbol(owner_obj, old_func_symbol.name, new_relative_address,
old_func_symbol.size, old_func_symbol.type)
for new_reloc in self.state.project.loader.find_relevant_relocations(old_func_symbol.name):
if new_reloc.symbol.name == new_func_symbol.name and \
new_reloc.value != new_func_symbol.rebased_addr:
l.debug("Updating CLE symbols metadata, moving %s from 0x%x to 0x%x",
new_reloc.symbol.name,
new_reloc.value,
new_func_symbol.rebased_addr)
new_reloc.resolve(new_func_symbol)
new_reloc.relocate([])
else:
l.debug("SimProc not restored, you are going to simulate also the code of external libraries!")
# flush the angr memory in order to synchronize them with the content of the
# concrete process memory when a read/write to the page is performed
self.state.memory.flush_pages(self.whitelist)
l.info("Exiting SimEngineConcrete: simulated address %x concrete address %x ", self.state.addr,
target.read_register("pc"))
# now we have to register a SimInspect in order to synchronize the segments register
# on demand when the symbolic execution accesses it
if not self.segment_registers_callback_initialized:
segment_register_name = self.state.project.simos.get_segment_register_name()
if segment_register_name:
self.fs_register_bp = self.state.inspect.b('reg_read',
reg_read_offset=segment_register_name,
action=_sync_segments)
self.segment_registers_callback_initialized = True
l.debug("Set SimInspect breakpoint to the new state!")
else:
l.error("Can't set breakpoint to synchronize segments registers, horrible things will happen.") | python | def sync(self):
"""
Handle the switch between the concrete execution and angr.
This method takes care of:
1- Synchronize registers.
2- Set a concrete target to the memory backer so the memory reads are redirected in the concrete process memory.
3- If possible restore the SimProcedures with the real addresses inside the concrete process.
4- Set an inspect point to sync the segments register as soon as they are read during the symbolic execution.
5- Flush all the pages loaded until now.
:return:
"""
def _sync_segments(state):
"""
Segment registers synchronization is on demand as soon as the
symbolic execution access a segment register.
"""
concr_target = state.project.concrete_target
if isinstance(state.arch, ArchAMD64):
state.project.simos.initialize_segment_register_x64(state, concr_target)
elif isinstance(state.arch, ArchX86):
gdt = state.project.simos.initialize_gdt_x86(state, concr_target)
state.concrete.whitelist.append((gdt.addr, gdt.addr + gdt.limit))
state.inspect.remove_breakpoint('reg_read', bp=state.concrete.fs_register_bp)
state.concrete.segment_registers_initialized = True
state.concrete.fs_register_bp = None
l.debug("Sync the state with the concrete memory inside the Concrete plugin")
target = self.state.project.concrete_target
# Setting a concrete memory backend
self.state.memory.mem._memory_backer.set_concrete_target(target)
# Sync angr registers with the one getting from the concrete target
# registers that we don't want to concretize.
l.debug("Synchronizing general purpose registers")
to_sync_register = list(filter(lambda x: x.concrete, self.state.arch.register_list))
for register in to_sync_register:
# before let's sync all the subregisters of the current register.
# sometimes this can be helpful ( i.e. ymmm0 e xmm0 )
if register.subregisters:
subregisters_names = map(lambda x: x[0], register.subregisters)
self._sync_registers(subregisters_names, target)
# finally let's synchronize the whole register
self._sync_registers([register.name], target)
if self.synchronize_cle:
self._sync_cle(target)
# Synchronize the imported functions addresses (.got, IAT) in the
# concrete process with ones used in the SimProcedures dictionary
if self.state.project._should_use_sim_procedures and not self.state.project.loader.main_object.pic:
l.debug("Restoring SimProc using concrete memory")
for reloc in self.state.project.loader.main_object.relocs:
if reloc.symbol: # consider only reloc with a symbol
l.debug("Trying to re-hook SimProc %s", reloc.symbol.name)
# l.debug("reloc.rebased_addr: %#x " % reloc.rebased_addr)
func_address = target.read_memory(reloc.rebased_addr, self.state.project.arch.bits / 8)
func_address = struct.unpack(self.state.project.arch.struct_fmt(), func_address)[0]
l.debug("Function address hook is now: %#x ", func_address)
self.state.project.rehook_symbol(func_address, reloc.symbol.name)
if self.synchronize_cle and not self.state.project.loader.main_object.contains_addr(func_address):
old_func_symbol = self.state.project.loader.find_symbol(reloc.symbol.name)
if old_func_symbol: # if we actually have a symbol
owner_obj = old_func_symbol.owner
# calculating the new real address
new_relative_address = func_address - owner_obj.mapped_base
new_func_symbol = cle.backends.Symbol(owner_obj, old_func_symbol.name, new_relative_address,
old_func_symbol.size, old_func_symbol.type)
for new_reloc in self.state.project.loader.find_relevant_relocations(old_func_symbol.name):
if new_reloc.symbol.name == new_func_symbol.name and \
new_reloc.value != new_func_symbol.rebased_addr:
l.debug("Updating CLE symbols metadata, moving %s from 0x%x to 0x%x",
new_reloc.symbol.name,
new_reloc.value,
new_func_symbol.rebased_addr)
new_reloc.resolve(new_func_symbol)
new_reloc.relocate([])
else:
l.debug("SimProc not restored, you are going to simulate also the code of external libraries!")
# flush the angr memory in order to synchronize them with the content of the
# concrete process memory when a read/write to the page is performed
self.state.memory.flush_pages(self.whitelist)
l.info("Exiting SimEngineConcrete: simulated address %x concrete address %x ", self.state.addr,
target.read_register("pc"))
# now we have to register a SimInspect in order to synchronize the segments register
# on demand when the symbolic execution accesses it
if not self.segment_registers_callback_initialized:
segment_register_name = self.state.project.simos.get_segment_register_name()
if segment_register_name:
self.fs_register_bp = self.state.inspect.b('reg_read',
reg_read_offset=segment_register_name,
action=_sync_segments)
self.segment_registers_callback_initialized = True
l.debug("Set SimInspect breakpoint to the new state!")
else:
l.error("Can't set breakpoint to synchronize segments registers, horrible things will happen.") | [
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inasafe/inasafe | safe/gui/tools/shake_grid/shake_grid.py | ShakeGrid.mmi_to_contours | def mmi_to_contours(self, force_flag=True, algorithm=USE_ASCII):
"""Extract contours from the event's tif file.
Contours are extracted at a 0.5 MMI interval. The resulting file will
be saved in the extract directory. In the easiest use case you can
:param force_flag: (Optional). Whether to force the
regeneration of contour product. Defaults to False.
:type force_flag: bool
:param algorithm: (Optional) Which interpolation algorithm to
use to create the underlying raster. Defaults to 'nearest'.
:type algorithm: str
**Only enforced if theForceFlag is true!**
:returns: An absolute filesystem path pointing to the generated
contour dataset.
:exception: ContourCreationError
simply do::
shake_grid = ShakeGrid()
contour_path = shake_grid.mmi_to_contours()
which will return the contour dataset for the latest event on the
ftp server.
"""
LOGGER.debug('mmi_to_contours requested.')
# TODO: Use sqlite rather?
output_file_base = os.path.join(
self.output_dir,
'%s-contours-%s.' % (self.output_basename, algorithm))
output_file = output_file_base + 'shp'
if os.path.exists(output_file) and force_flag is not True:
return output_file
elif os.path.exists(output_file):
try:
os.remove(output_file_base + 'shp')
os.remove(output_file_base + 'shx')
os.remove(output_file_base + 'dbf')
os.remove(output_file_base + 'prj')
except OSError:
LOGGER.exception(
'Old contour files not deleted'
' - this may indicate a file permissions issue.')
tif_path = self.mmi_to_raster(force_flag, algorithm)
# Based largely on
# http://svn.osgeo.org/gdal/trunk/autotest/alg/contour.py
driver = ogr.GetDriverByName('ESRI Shapefile')
ogr_dataset = driver.CreateDataSource(output_file)
if ogr_dataset is None:
# Probably the file existed and could not be overriden
raise ContourCreationError(
'Could not create datasource for:\n%s. Check that the file '
'does not already exist and that you do not have file system '
'permissions issues' % output_file)
layer = ogr_dataset.CreateLayer('contour')
field_definition = ogr.FieldDefn('ID', ogr.OFTInteger)
layer.CreateField(field_definition)
field_definition = ogr.FieldDefn('MMI', ogr.OFTReal)
layer.CreateField(field_definition)
# So we can fix the x pos to the same x coord as centroid of the
# feature so labels line up nicely vertically
field_definition = ogr.FieldDefn('X', ogr.OFTReal)
layer.CreateField(field_definition)
# So we can fix the y pos to the min y coord of the whole contour so
# labels line up nicely vertically
field_definition = ogr.FieldDefn('Y', ogr.OFTReal)
layer.CreateField(field_definition)
# So that we can set the html hex colour based on its MMI class
field_definition = ogr.FieldDefn('RGB', ogr.OFTString)
layer.CreateField(field_definition)
# So that we can set the label in it roman numeral form
field_definition = ogr.FieldDefn('ROMAN', ogr.OFTString)
layer.CreateField(field_definition)
# So that we can set the label horizontal alignment
field_definition = ogr.FieldDefn('ALIGN', ogr.OFTString)
layer.CreateField(field_definition)
# So that we can set the label vertical alignment
field_definition = ogr.FieldDefn('VALIGN', ogr.OFTString)
layer.CreateField(field_definition)
# So that we can set feature length to filter out small features
field_definition = ogr.FieldDefn('LEN', ogr.OFTReal)
layer.CreateField(field_definition)
tif_dataset = gdal.Open(tif_path, GA_ReadOnly)
# see http://gdal.org/java/org/gdal/gdal/gdal.html for these options
band = 1
contour_interval = 0.5
contour_base = 0
fixed_level_list = []
use_no_data_flag = 0
no_data_value = -9999
id_field = 0 # first field defined above
elevation_field = 1 # second (MMI) field defined above
try:
gdal.ContourGenerate(
tif_dataset.GetRasterBand(band),
contour_interval,
contour_base,
fixed_level_list,
use_no_data_flag,
no_data_value,
layer,
id_field,
elevation_field)
except Exception as e:
LOGGER.exception('Contour creation failed')
raise ContourCreationError(str(e))
finally:
del tif_dataset
ogr_dataset.Release()
# Copy over the standard .prj file since ContourGenerate does not
# create a projection definition
projection_path = os.path.join(
self.output_dir,
'%s-contours-%s.prj' % (self.output_basename, algorithm))
source_projection_path = resources_path(
'converter_data', 'mmi-contours.prj')
shutil.copyfile(source_projection_path, projection_path)
# Lastly copy over the standard qml (QGIS Style file)
qml_path = os.path.join(
self.output_dir,
'%s-contours-%s.qml' % (self.output_basename, algorithm))
source_qml_path = resources_path('converter_data', 'mmi-contours.qml')
shutil.copyfile(source_qml_path, qml_path)
# Now update the additional columns - X,Y, ROMAN and RGB
try:
set_contour_properties(output_file)
except InvalidLayerError:
raise
return output_file | python | def mmi_to_contours(self, force_flag=True, algorithm=USE_ASCII):
"""Extract contours from the event's tif file.
Contours are extracted at a 0.5 MMI interval. The resulting file will
be saved in the extract directory. In the easiest use case you can
:param force_flag: (Optional). Whether to force the
regeneration of contour product. Defaults to False.
:type force_flag: bool
:param algorithm: (Optional) Which interpolation algorithm to
use to create the underlying raster. Defaults to 'nearest'.
:type algorithm: str
**Only enforced if theForceFlag is true!**
:returns: An absolute filesystem path pointing to the generated
contour dataset.
:exception: ContourCreationError
simply do::
shake_grid = ShakeGrid()
contour_path = shake_grid.mmi_to_contours()
which will return the contour dataset for the latest event on the
ftp server.
"""
LOGGER.debug('mmi_to_contours requested.')
# TODO: Use sqlite rather?
output_file_base = os.path.join(
self.output_dir,
'%s-contours-%s.' % (self.output_basename, algorithm))
output_file = output_file_base + 'shp'
if os.path.exists(output_file) and force_flag is not True:
return output_file
elif os.path.exists(output_file):
try:
os.remove(output_file_base + 'shp')
os.remove(output_file_base + 'shx')
os.remove(output_file_base + 'dbf')
os.remove(output_file_base + 'prj')
except OSError:
LOGGER.exception(
'Old contour files not deleted'
' - this may indicate a file permissions issue.')
tif_path = self.mmi_to_raster(force_flag, algorithm)
# Based largely on
# http://svn.osgeo.org/gdal/trunk/autotest/alg/contour.py
driver = ogr.GetDriverByName('ESRI Shapefile')
ogr_dataset = driver.CreateDataSource(output_file)
if ogr_dataset is None:
# Probably the file existed and could not be overriden
raise ContourCreationError(
'Could not create datasource for:\n%s. Check that the file '
'does not already exist and that you do not have file system '
'permissions issues' % output_file)
layer = ogr_dataset.CreateLayer('contour')
field_definition = ogr.FieldDefn('ID', ogr.OFTInteger)
layer.CreateField(field_definition)
field_definition = ogr.FieldDefn('MMI', ogr.OFTReal)
layer.CreateField(field_definition)
# So we can fix the x pos to the same x coord as centroid of the
# feature so labels line up nicely vertically
field_definition = ogr.FieldDefn('X', ogr.OFTReal)
layer.CreateField(field_definition)
# So we can fix the y pos to the min y coord of the whole contour so
# labels line up nicely vertically
field_definition = ogr.FieldDefn('Y', ogr.OFTReal)
layer.CreateField(field_definition)
# So that we can set the html hex colour based on its MMI class
field_definition = ogr.FieldDefn('RGB', ogr.OFTString)
layer.CreateField(field_definition)
# So that we can set the label in it roman numeral form
field_definition = ogr.FieldDefn('ROMAN', ogr.OFTString)
layer.CreateField(field_definition)
# So that we can set the label horizontal alignment
field_definition = ogr.FieldDefn('ALIGN', ogr.OFTString)
layer.CreateField(field_definition)
# So that we can set the label vertical alignment
field_definition = ogr.FieldDefn('VALIGN', ogr.OFTString)
layer.CreateField(field_definition)
# So that we can set feature length to filter out small features
field_definition = ogr.FieldDefn('LEN', ogr.OFTReal)
layer.CreateField(field_definition)
tif_dataset = gdal.Open(tif_path, GA_ReadOnly)
# see http://gdal.org/java/org/gdal/gdal/gdal.html for these options
band = 1
contour_interval = 0.5
contour_base = 0
fixed_level_list = []
use_no_data_flag = 0
no_data_value = -9999
id_field = 0 # first field defined above
elevation_field = 1 # second (MMI) field defined above
try:
gdal.ContourGenerate(
tif_dataset.GetRasterBand(band),
contour_interval,
contour_base,
fixed_level_list,
use_no_data_flag,
no_data_value,
layer,
id_field,
elevation_field)
except Exception as e:
LOGGER.exception('Contour creation failed')
raise ContourCreationError(str(e))
finally:
del tif_dataset
ogr_dataset.Release()
# Copy over the standard .prj file since ContourGenerate does not
# create a projection definition
projection_path = os.path.join(
self.output_dir,
'%s-contours-%s.prj' % (self.output_basename, algorithm))
source_projection_path = resources_path(
'converter_data', 'mmi-contours.prj')
shutil.copyfile(source_projection_path, projection_path)
# Lastly copy over the standard qml (QGIS Style file)
qml_path = os.path.join(
self.output_dir,
'%s-contours-%s.qml' % (self.output_basename, algorithm))
source_qml_path = resources_path('converter_data', 'mmi-contours.qml')
shutil.copyfile(source_qml_path, qml_path)
# Now update the additional columns - X,Y, ROMAN and RGB
try:
set_contour_properties(output_file)
except InvalidLayerError:
raise
return output_file | [
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Contours are extracted at a 0.5 MMI interval. The resulting file will
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:param force_flag: (Optional). Whether to force the
regeneration of contour product. Defaults to False.
:type force_flag: bool
:param algorithm: (Optional) Which interpolation algorithm to
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:type algorithm: str
**Only enforced if theForceFlag is true!**
:returns: An absolute filesystem path pointing to the generated
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:exception: ContourCreationError
simply do::
shake_grid = ShakeGrid()
contour_path = shake_grid.mmi_to_contours()
which will return the contour dataset for the latest event on the
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ziwenxie/netease-dl | netease/start.py | cli | def cli(ctx, timeout, proxy, output, quiet, lyric, again):
"""A command tool to download NetEase-Music's songs."""
ctx.obj = NetEase(timeout, proxy, output, quiet, lyric, again) | python | def cli(ctx, timeout, proxy, output, quiet, lyric, again):
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ctx.obj = NetEase(timeout, proxy, output, quiet, lyric, again) | [
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BerkeleyAutomation/perception | perception/image.py | BinaryImage.inverse | def inverse(self):
""" Inverts image (all nonzeros become zeros and vice verse)
Returns
-------
:obj:`BinaryImage`
inverse of this binary image
"""
data = np.zeros(self.shape).astype(np.uint8)
ind = np.where(self.data == 0)
data[ind[0], ind[1], ...] = BINARY_IM_MAX_VAL
return BinaryImage(data, self._frame) | python | def inverse(self):
""" Inverts image (all nonzeros become zeros and vice verse)
Returns
-------
:obj:`BinaryImage`
inverse of this binary image
"""
data = np.zeros(self.shape).astype(np.uint8)
ind = np.where(self.data == 0)
data[ind[0], ind[1], ...] = BINARY_IM_MAX_VAL
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DataONEorg/d1_python | lib_common/src/d1_common/cert/x509.py | deserialize_pem | def deserialize_pem(cert_pem):
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Args:
cert_pem: str or bytes
PEM (Base64) encoded X.509 v3 certificate
Returns:
cert_obj: cryptography.Certificate
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"""Deserialize PEM (Base64) encoded X.509 v3 certificate.
Args:
cert_pem: str or bytes
PEM (Base64) encoded X.509 v3 certificate
Returns:
cert_obj: cryptography.Certificate
"""
if isinstance(cert_pem, str):
cert_pem = cert_pem.encode("utf-8")
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myyang/django-unixtimestampfield | unixtimestampfield/fields.py | OrdinalPatchMixin.to_naive_datetime | def to_naive_datetime(self, value):
"""
from value to datetime with tzinfo format (datetime.datetime instance)
"""
if isinstance(value, (six.integer_types, float, six.string_types)):
try:
return self.from_number(value)
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return self.datetime_str_to_datetime(value)
if isinstance(value, datetime.datetime):
return value
raise exceptions.ValidationError(
"Unable to convert value: '%s' to python data type" % value,
code="invalid_datetime"
) | python | def to_naive_datetime(self, value):
"""
from value to datetime with tzinfo format (datetime.datetime instance)
"""
if isinstance(value, (six.integer_types, float, six.string_types)):
try:
return self.from_number(value)
except ValueError:
return self.datetime_str_to_datetime(value)
if isinstance(value, datetime.datetime):
return value
raise exceptions.ValidationError(
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code="invalid_datetime"
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numenta/htmresearch | projects/l2_pooling/noise_tolerance_l2.py | createRandomObjectDescriptions | def createRandomObjectDescriptions(numObjects,
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"""
Returns {"Object 1": [(0, "C"), (1, "B"), (2, "C"), ...],
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"""
return dict(("Object %d" % i,
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for i in xrange(1, numObjects + 1)) | python | def createRandomObjectDescriptions(numObjects,
numLocationsPerObject,
featurePool=("A", "B", "C")):
"""
Returns {"Object 1": [(0, "C"), (1, "B"), (2, "C"), ...],
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"""
return dict(("Object %d" % i,
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ddorn/superprompt | superprompt/core.py | prompt_file | def prompt_file(prompt, default=None, must_exist=True, is_dir=False,
show_default=True, prompt_suffix=': ', color=None):
"""
Prompt a filename using using glob for autocompetion.
If must_exist is True (default) then you can be sure that the value returned
is an existing filename or directory name.
If is_dir is True, this will show only the directories for the completion.
"""
if must_exist:
while True:
r = prompt_autocomplete(prompt, path_complete(is_dir), default, show_default=show_default,
prompt_suffix=prompt_suffix, color=color)
if os.path.exists(r):
break
print('This path does not exist.')
else:
r = prompt_autocomplete(prompt, path_complete(is_dir), default, show_default=show_default,
prompt_suffix=prompt_suffix, color=color)
return r | python | def prompt_file(prompt, default=None, must_exist=True, is_dir=False,
show_default=True, prompt_suffix=': ', color=None):
"""
Prompt a filename using using glob for autocompetion.
If must_exist is True (default) then you can be sure that the value returned
is an existing filename or directory name.
If is_dir is True, this will show only the directories for the completion.
"""
if must_exist:
while True:
r = prompt_autocomplete(prompt, path_complete(is_dir), default, show_default=show_default,
prompt_suffix=prompt_suffix, color=color)
if os.path.exists(r):
break
print('This path does not exist.')
else:
r = prompt_autocomplete(prompt, path_complete(is_dir), default, show_default=show_default,
prompt_suffix=prompt_suffix, color=color)
return r | [
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bharadwajyarlagadda/bingmaps | bingmaps/apiservices/elevations.py | ElevationsApi.zoomlevel | def zoomlevel(self):
"""Retrieves zoomlevel from the output response
Returns:
zoomlevel (namedtuple): A namedtuple of zoomlevel from the output
response
"""
resources = self.get_resource()
zoomlevel = namedtuple('zoomlevel', 'zoomLevel')
try:
return [zoomlevel(resource['zoomLevel'])
for resource in resources]
except TypeError:
try:
if isinstance(resources['ElevationData'], dict):
return zoomlevel(resources['ElevationData']['ZoomLevel'])
except KeyError:
try:
if isinstance(resources['SeaLevelData'], dict):
zoom = resources['SeaLevelData']['ZoomLevel']
return zoomlevel(zoom)
except KeyError:
print(KeyError) | python | def zoomlevel(self):
"""Retrieves zoomlevel from the output response
Returns:
zoomlevel (namedtuple): A namedtuple of zoomlevel from the output
response
"""
resources = self.get_resource()
zoomlevel = namedtuple('zoomlevel', 'zoomLevel')
try:
return [zoomlevel(resource['zoomLevel'])
for resource in resources]
except TypeError:
try:
if isinstance(resources['ElevationData'], dict):
return zoomlevel(resources['ElevationData']['ZoomLevel'])
except KeyError:
try:
if isinstance(resources['SeaLevelData'], dict):
zoom = resources['SeaLevelData']['ZoomLevel']
return zoomlevel(zoom)
except KeyError:
print(KeyError) | [
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SiLab-Bonn/pyBAR | pybar/analysis/analysis_utils.py | smooth_differentiation | def smooth_differentiation(x, y, weigths=None, order=5, smoothness=3, derivation=1):
'''Returns the dy/dx(x) with the fit and differentiation of a spline curve
Parameters
----------
x : array like
y : array like
Returns
-------
dy/dx : array like
'''
if (len(x) != len(y)):
raise ValueError("x, y must have the same length")
f = splrep(x, y, w=weigths, k=order, s=smoothness) # spline function
return splev(x, f, der=derivation) | python | def smooth_differentiation(x, y, weigths=None, order=5, smoothness=3, derivation=1):
'''Returns the dy/dx(x) with the fit and differentiation of a spline curve
Parameters
----------
x : array like
y : array like
Returns
-------
dy/dx : array like
'''
if (len(x) != len(y)):
raise ValueError("x, y must have the same length")
f = splrep(x, y, w=weigths, k=order, s=smoothness) # spline function
return splev(x, f, der=derivation) | [
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sorgerlab/indra | indra/sources/eidos/cli.py | run_eidos | def run_eidos(endpoint, *args):
"""Run a given enpoint of Eidos through the command line.
Parameters
----------
endpoint : str
The class within the Eidos package to run, for instance
'apps.ExtractFromDirectory' will run
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*args
Any further arguments to be passed as inputs to the class
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cmd = ['java', '-Xmx12G', '-cp', eip, call_class] + list(args)
logger.info('Running Eidos with command "%s"' % (' '.join(cmd)))
subprocess.call(cmd) | python | def run_eidos(endpoint, *args):
"""Run a given enpoint of Eidos through the command line.
Parameters
----------
endpoint : str
The class within the Eidos package to run, for instance
'apps.ExtractFromDirectory' will run
'org.clulab.wm.eidos.apps.ExtractFromDirectory'
*args
Any further arguments to be passed as inputs to the class
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"""
# Make the full path to the class that should be used
call_class = '%s.%s' % (eidos_package, endpoint)
# Assemble the command line command and append optonal args
cmd = ['java', '-Xmx12G', '-cp', eip, call_class] + list(args)
logger.info('Running Eidos with command "%s"' % (' '.join(cmd)))
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rushter/heamy | heamy/dataset.py | Dataset.to_dense | def to_dense(self):
"""Convert sparse Dataset to dense matrix."""
if hasattr(self._X_train, 'todense'):
self._X_train = self._X_train.todense()
self._X_test = self._X_test.todense() | python | def to_dense(self):
"""Convert sparse Dataset to dense matrix."""
if hasattr(self._X_train, 'todense'):
self._X_train = self._X_train.todense()
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saltstack/salt | salt/modules/boto3_elasticache.py | create_cache_cluster | def create_cache_cluster(name, wait=600, security_groups=None,
region=None, key=None, keyid=None, profile=None, **args):
'''
Create a cache cluster.
Example:
.. code-block:: bash
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Engine=redis \
CacheNodeType=cache.t2.micro \
NumCacheNodes=1 \
SecurityGroupIds='[sg-11223344]' \
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'''
if security_groups:
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args['SecurityGroupIds'] = []
args['SecurityGroupIds'] += sgs
args = dict([(k, v) for k, v in args.items() if not k.startswith('_')])
return _create_resource(name, name_param='CacheClusterId', desc='cache cluster',
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Example:
.. code-block:: bash
salt myminion boto3_elasticache.create_cache_cluster name=myCacheCluster \
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CacheNodeType=cache.t2.micro \
NumCacheNodes=1 \
SecurityGroupIds='[sg-11223344]' \
CacheSubnetGroupName=myCacheSubnetGroup
'''
if security_groups:
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security_groups = [security_groups]
sgs = __salt__['boto_secgroup.convert_to_group_ids'](groups=security_groups, region=region,
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if 'SecurityGroupIds' not in args:
args['SecurityGroupIds'] = []
args['SecurityGroupIds'] += sgs
args = dict([(k, v) for k, v in args.items() if not k.startswith('_')])
return _create_resource(name, name_param='CacheClusterId', desc='cache cluster',
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lmcinnes/umap | umap/umap_.py | UMAP.transform | def transform(self, X):
"""Transform X into the existing embedded space and return that
transformed output.
Parameters
----------
X : array, shape (n_samples, n_features)
New data to be transformed.
Returns
-------
X_new : array, shape (n_samples, n_components)
Embedding of the new data in low-dimensional space.
"""
# If we fit just a single instance then error
if self.embedding_.shape[0] == 1:
raise ValueError('Transform unavailable when model was fit with'
'only a single data sample.')
# If we just have the original input then short circuit things
X = check_array(X, dtype=np.float32, accept_sparse="csr")
x_hash = joblib.hash(X)
if x_hash == self._input_hash:
return self.embedding_
if self._sparse_data:
raise ValueError("Transform not available for sparse input.")
elif self.metric == 'precomputed':
raise ValueError("Transform of new data not available for "
"precomputed metric.")
X = check_array(X, dtype=np.float32, order="C")
random_state = check_random_state(self.transform_seed)
rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64)
if self._small_data:
dmat = pairwise_distances(
X, self._raw_data, metric=self.metric, **self._metric_kwds
)
indices = np.argpartition(dmat,
self._n_neighbors)[:, :self._n_neighbors]
dmat_shortened = submatrix(dmat, indices, self._n_neighbors)
indices_sorted = np.argsort(dmat_shortened)
indices = submatrix(indices, indices_sorted, self._n_neighbors)
dists = submatrix(dmat_shortened, indices_sorted,
self._n_neighbors)
else:
init = initialise_search(
self._rp_forest,
self._raw_data,
X,
int(self._n_neighbors * self.transform_queue_size),
self._random_init,
self._tree_init,
rng_state,
)
result = self._search(
self._raw_data,
self._search_graph.indptr,
self._search_graph.indices,
init,
X,
)
indices, dists = deheap_sort(result)
indices = indices[:, : self._n_neighbors]
dists = dists[:, : self._n_neighbors]
adjusted_local_connectivity = max(0, self.local_connectivity - 1.0)
sigmas, rhos = smooth_knn_dist(
dists, self._n_neighbors, local_connectivity=adjusted_local_connectivity
)
rows, cols, vals = compute_membership_strengths(indices, dists, sigmas, rhos)
graph = scipy.sparse.coo_matrix(
(vals, (rows, cols)), shape=(X.shape[0], self._raw_data.shape[0])
)
# This was a very specially constructed graph with constant degree.
# That lets us do fancy unpacking by reshaping the csr matrix indices
# and data. Doing so relies on the constant degree assumption!
csr_graph = normalize(graph.tocsr(), norm="l1")
inds = csr_graph.indices.reshape(X.shape[0], self._n_neighbors)
weights = csr_graph.data.reshape(X.shape[0], self._n_neighbors)
embedding = init_transform(inds, weights, self.embedding_)
if self.n_epochs is None:
# For smaller datasets we can use more epochs
if graph.shape[0] <= 10000:
n_epochs = 100
else:
n_epochs = 30
else:
n_epochs = self.n_epochs // 3.0
graph.data[graph.data < (graph.data.max() / float(n_epochs))] = 0.0
graph.eliminate_zeros()
epochs_per_sample = make_epochs_per_sample(graph.data, n_epochs)
head = graph.row
tail = graph.col
embedding = optimize_layout(
embedding,
self.embedding_,
head,
tail,
n_epochs,
graph.shape[1],
epochs_per_sample,
self._a,
self._b,
rng_state,
self.repulsion_strength,
self._initial_alpha,
self.negative_sample_rate,
verbose=self.verbose,
)
return embedding | python | def transform(self, X):
"""Transform X into the existing embedded space and return that
transformed output.
Parameters
----------
X : array, shape (n_samples, n_features)
New data to be transformed.
Returns
-------
X_new : array, shape (n_samples, n_components)
Embedding of the new data in low-dimensional space.
"""
# If we fit just a single instance then error
if self.embedding_.shape[0] == 1:
raise ValueError('Transform unavailable when model was fit with'
'only a single data sample.')
# If we just have the original input then short circuit things
X = check_array(X, dtype=np.float32, accept_sparse="csr")
x_hash = joblib.hash(X)
if x_hash == self._input_hash:
return self.embedding_
if self._sparse_data:
raise ValueError("Transform not available for sparse input.")
elif self.metric == 'precomputed':
raise ValueError("Transform of new data not available for "
"precomputed metric.")
X = check_array(X, dtype=np.float32, order="C")
random_state = check_random_state(self.transform_seed)
rng_state = random_state.randint(INT32_MIN, INT32_MAX, 3).astype(np.int64)
if self._small_data:
dmat = pairwise_distances(
X, self._raw_data, metric=self.metric, **self._metric_kwds
)
indices = np.argpartition(dmat,
self._n_neighbors)[:, :self._n_neighbors]
dmat_shortened = submatrix(dmat, indices, self._n_neighbors)
indices_sorted = np.argsort(dmat_shortened)
indices = submatrix(indices, indices_sorted, self._n_neighbors)
dists = submatrix(dmat_shortened, indices_sorted,
self._n_neighbors)
else:
init = initialise_search(
self._rp_forest,
self._raw_data,
X,
int(self._n_neighbors * self.transform_queue_size),
self._random_init,
self._tree_init,
rng_state,
)
result = self._search(
self._raw_data,
self._search_graph.indptr,
self._search_graph.indices,
init,
X,
)
indices, dists = deheap_sort(result)
indices = indices[:, : self._n_neighbors]
dists = dists[:, : self._n_neighbors]
adjusted_local_connectivity = max(0, self.local_connectivity - 1.0)
sigmas, rhos = smooth_knn_dist(
dists, self._n_neighbors, local_connectivity=adjusted_local_connectivity
)
rows, cols, vals = compute_membership_strengths(indices, dists, sigmas, rhos)
graph = scipy.sparse.coo_matrix(
(vals, (rows, cols)), shape=(X.shape[0], self._raw_data.shape[0])
)
# This was a very specially constructed graph with constant degree.
# That lets us do fancy unpacking by reshaping the csr matrix indices
# and data. Doing so relies on the constant degree assumption!
csr_graph = normalize(graph.tocsr(), norm="l1")
inds = csr_graph.indices.reshape(X.shape[0], self._n_neighbors)
weights = csr_graph.data.reshape(X.shape[0], self._n_neighbors)
embedding = init_transform(inds, weights, self.embedding_)
if self.n_epochs is None:
# For smaller datasets we can use more epochs
if graph.shape[0] <= 10000:
n_epochs = 100
else:
n_epochs = 30
else:
n_epochs = self.n_epochs // 3.0
graph.data[graph.data < (graph.data.max() / float(n_epochs))] = 0.0
graph.eliminate_zeros()
epochs_per_sample = make_epochs_per_sample(graph.data, n_epochs)
head = graph.row
tail = graph.col
embedding = optimize_layout(
embedding,
self.embedding_,
head,
tail,
n_epochs,
graph.shape[1],
epochs_per_sample,
self._a,
self._b,
rng_state,
self.repulsion_strength,
self._initial_alpha,
self.negative_sample_rate,
verbose=self.verbose,
)
return embedding | [
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Returns
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elastic/elasticsearch-dsl-py | examples/alias_migration.py | migrate | def migrate(move_data=True, update_alias=True):
"""
Upgrade function that creates a new index for the data. Optionally it also can
(and by default will) reindex previous copy of the data into the new index
(specify ``move_data=False`` to skip this step) and update the alias to
point to the latest index (set ``update_alias=False`` to skip).
Note that while this function is running the application can still perform
any and all searches without any loss of functionality. It should, however,
not perform any writes at this time as those might be lost.
"""
# construct a new index name by appending current timestamp
next_index = PATTERN.replace('*', datetime.now().strftime('%Y%m%d%H%M%S%f'))
# get the low level connection
es = connections.get_connection()
# create new index, it will use the settings from the template
es.indices.create(index=next_index)
if move_data:
# move data from current alias to the new index
es.reindex(
body={"source": {"index": ALIAS}, "dest": {"index": next_index}},
request_timeout=3600
)
# refresh the index to make the changes visible
es.indices.refresh(index=next_index)
if update_alias:
# repoint the alias to point to the newly created index
es.indices.update_aliases(body={
'actions': [
{"remove": {"alias": ALIAS, "index": PATTERN}},
{"add": {"alias": ALIAS, "index": next_index}},
]
}) | python | def migrate(move_data=True, update_alias=True):
"""
Upgrade function that creates a new index for the data. Optionally it also can
(and by default will) reindex previous copy of the data into the new index
(specify ``move_data=False`` to skip this step) and update the alias to
point to the latest index (set ``update_alias=False`` to skip).
Note that while this function is running the application can still perform
any and all searches without any loss of functionality. It should, however,
not perform any writes at this time as those might be lost.
"""
# construct a new index name by appending current timestamp
next_index = PATTERN.replace('*', datetime.now().strftime('%Y%m%d%H%M%S%f'))
# get the low level connection
es = connections.get_connection()
# create new index, it will use the settings from the template
es.indices.create(index=next_index)
if move_data:
# move data from current alias to the new index
es.reindex(
body={"source": {"index": ALIAS}, "dest": {"index": next_index}},
request_timeout=3600
)
# refresh the index to make the changes visible
es.indices.refresh(index=next_index)
if update_alias:
# repoint the alias to point to the newly created index
es.indices.update_aliases(body={
'actions': [
{"remove": {"alias": ALIAS, "index": PATTERN}},
{"add": {"alias": ALIAS, "index": next_index}},
]
}) | [
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pgmpy/pgmpy | pgmpy/readwrite/XMLBeliefNetwork.py | XBNWriter.set_variables | def set_variables(self, data):
"""
Set variables for the network.
Parameters
----------
data: dict
dict for variable in the form of example as shown.
Examples
--------
>>> from pgmpy.readwrite.XMLBeliefNetwork import XBNWriter
>>> writer = XBNWriter()
>>> writer.set_variables({'a': {'TYPE': 'discrete', 'XPOS': '13495',
... 'YPOS': '10465', 'DESCRIPTION': '(a) Metastatic Cancer',
... 'STATES': ['Present', 'Absent']}
... 'b': {'TYPE': 'discrete', 'XPOS': '11290',
... 'YPOS': '11965', 'DESCRIPTION': '(b) Serum Calcium Increase',
... 'STATES': ['Present', 'Absent']}})
"""
variables = etree.SubElement(self.bnmodel, "VARIABLES")
for var in sorted(data):
variable = etree.SubElement(variables, 'VAR', attrib={'NAME': var, 'TYPE': data[var]['TYPE'],
'XPOS': data[var]['XPOS'], 'YPOS': data[var]['YPOS']})
etree.SubElement(variable, 'DESCRIPTION', attrib={'DESCRIPTION': data[var]['DESCRIPTION']})
for state in data[var]['STATES']:
etree.SubElement(variable, 'STATENAME').text = state | python | def set_variables(self, data):
"""
Set variables for the network.
Parameters
----------
data: dict
dict for variable in the form of example as shown.
Examples
--------
>>> from pgmpy.readwrite.XMLBeliefNetwork import XBNWriter
>>> writer = XBNWriter()
>>> writer.set_variables({'a': {'TYPE': 'discrete', 'XPOS': '13495',
... 'YPOS': '10465', 'DESCRIPTION': '(a) Metastatic Cancer',
... 'STATES': ['Present', 'Absent']}
... 'b': {'TYPE': 'discrete', 'XPOS': '11290',
... 'YPOS': '11965', 'DESCRIPTION': '(b) Serum Calcium Increase',
... 'STATES': ['Present', 'Absent']}})
"""
variables = etree.SubElement(self.bnmodel, "VARIABLES")
for var in sorted(data):
variable = etree.SubElement(variables, 'VAR', attrib={'NAME': var, 'TYPE': data[var]['TYPE'],
'XPOS': data[var]['XPOS'], 'YPOS': data[var]['YPOS']})
etree.SubElement(variable, 'DESCRIPTION', attrib={'DESCRIPTION': data[var]['DESCRIPTION']})
for state in data[var]['STATES']:
etree.SubElement(variable, 'STATENAME').text = state | [
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>>> writer = XBNWriter()
>>> writer.set_variables({'a': {'TYPE': 'discrete', 'XPOS': '13495',
... 'YPOS': '10465', 'DESCRIPTION': '(a) Metastatic Cancer',
... 'STATES': ['Present', 'Absent']}
... 'b': {'TYPE': 'discrete', 'XPOS': '11290',
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pandas-dev/pandas | pandas/core/internals/blocks.py | Block.replace | def replace(self, to_replace, value, inplace=False, filter=None,
regex=False, convert=True):
"""replace the to_replace value with value, possible to create new
blocks here this is just a call to putmask. regex is not used here.
It is used in ObjectBlocks. It is here for API compatibility.
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
original_to_replace = to_replace
# try to replace, if we raise an error, convert to ObjectBlock and
# retry
try:
values, to_replace = self._try_coerce_args(self.values,
to_replace)
mask = missing.mask_missing(values, to_replace)
if filter is not None:
filtered_out = ~self.mgr_locs.isin(filter)
mask[filtered_out.nonzero()[0]] = False
blocks = self.putmask(mask, value, inplace=inplace)
if convert:
blocks = [b.convert(by_item=True, numeric=False,
copy=not inplace) for b in blocks]
return blocks
except (TypeError, ValueError):
# GH 22083, TypeError or ValueError occurred within error handling
# causes infinite loop. Cast and retry only if not objectblock.
if is_object_dtype(self):
raise
# try again with a compatible block
block = self.astype(object)
return block.replace(to_replace=original_to_replace,
value=value,
inplace=inplace,
filter=filter,
regex=regex,
convert=convert) | python | def replace(self, to_replace, value, inplace=False, filter=None,
regex=False, convert=True):
"""replace the to_replace value with value, possible to create new
blocks here this is just a call to putmask. regex is not used here.
It is used in ObjectBlocks. It is here for API compatibility.
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
original_to_replace = to_replace
# try to replace, if we raise an error, convert to ObjectBlock and
# retry
try:
values, to_replace = self._try_coerce_args(self.values,
to_replace)
mask = missing.mask_missing(values, to_replace)
if filter is not None:
filtered_out = ~self.mgr_locs.isin(filter)
mask[filtered_out.nonzero()[0]] = False
blocks = self.putmask(mask, value, inplace=inplace)
if convert:
blocks = [b.convert(by_item=True, numeric=False,
copy=not inplace) for b in blocks]
return blocks
except (TypeError, ValueError):
# GH 22083, TypeError or ValueError occurred within error handling
# causes infinite loop. Cast and retry only if not objectblock.
if is_object_dtype(self):
raise
# try again with a compatible block
block = self.astype(object)
return block.replace(to_replace=original_to_replace,
value=value,
inplace=inplace,
filter=filter,
regex=regex,
convert=convert) | [
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CLARIAH/grlc | src/swagger.py | build_spec | def build_spec(user, repo, sha=None, prov=None, extraMetadata=[]):
"""Build grlc specification for the given github user / repo."""
loader = grlc.utils.getLoader(user, repo, sha=sha, prov=prov)
files = loader.fetchFiles()
raw_repo_uri = loader.getRawRepoUri()
# Fetch all .rq files
items = []
allowed_ext = ["rq", "sparql", "json", "tpf"]
for c in files:
glogger.debug('>>>>>>>>>>>>>>>>>>>>>>>>>c_name: {}'.format(c['name']))
extension = c['name'].split('.')[-1]
if extension in allowed_ext:
call_name = c['name'].split('.')[0]
# Retrieve extra metadata from the query decorators
query_text = loader.getTextFor(c)
item = None
if extension == "json":
query_text = json.loads(query_text)
if extension in ["rq", "sparql", "json"]:
glogger.debug("===================================================================")
glogger.debug("Processing SPARQL query: {}".format(c['name']))
glogger.debug("===================================================================")
item = process_sparql_query_text(query_text, loader, call_name, extraMetadata)
elif "tpf" == extension:
glogger.debug("===================================================================")
glogger.debug("Processing TPF query: {}".format(c['name']))
glogger.debug("===================================================================")
item = process_tpf_query_text(query_text, raw_repo_uri, call_name, extraMetadata)
else:
glogger.info("Ignoring unsupported source call name: {}".format(c['name']))
if item:
items.append(item)
return items | python | def build_spec(user, repo, sha=None, prov=None, extraMetadata=[]):
"""Build grlc specification for the given github user / repo."""
loader = grlc.utils.getLoader(user, repo, sha=sha, prov=prov)
files = loader.fetchFiles()
raw_repo_uri = loader.getRawRepoUri()
# Fetch all .rq files
items = []
allowed_ext = ["rq", "sparql", "json", "tpf"]
for c in files:
glogger.debug('>>>>>>>>>>>>>>>>>>>>>>>>>c_name: {}'.format(c['name']))
extension = c['name'].split('.')[-1]
if extension in allowed_ext:
call_name = c['name'].split('.')[0]
# Retrieve extra metadata from the query decorators
query_text = loader.getTextFor(c)
item = None
if extension == "json":
query_text = json.loads(query_text)
if extension in ["rq", "sparql", "json"]:
glogger.debug("===================================================================")
glogger.debug("Processing SPARQL query: {}".format(c['name']))
glogger.debug("===================================================================")
item = process_sparql_query_text(query_text, loader, call_name, extraMetadata)
elif "tpf" == extension:
glogger.debug("===================================================================")
glogger.debug("Processing TPF query: {}".format(c['name']))
glogger.debug("===================================================================")
item = process_tpf_query_text(query_text, raw_repo_uri, call_name, extraMetadata)
else:
glogger.info("Ignoring unsupported source call name: {}".format(c['name']))
if item:
items.append(item)
return items | [
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Esri/ArcREST | src/arcrest/manageorg/_parameters.py | InvitationList.addUser | def addUser(self, username, password,
firstname, lastname,
email, role):
"""adds a user to the invitation list"""
self._invites.append({
"username":username,
"password":password,
"firstname":firstname,
"lastname":lastname,
"fullname":"%s %s" % (firstname, lastname),
"email":email,
"role":role
}) | python | def addUser(self, username, password,
firstname, lastname,
email, role):
"""adds a user to the invitation list"""
self._invites.append({
"username":username,
"password":password,
"firstname":firstname,
"lastname":lastname,
"fullname":"%s %s" % (firstname, lastname),
"email":email,
"role":role
}) | [
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ktbyers/netmiko | netmiko/fortinet/fortinet_ssh.py | FortinetSSH.disable_paging | def disable_paging(self, delay_factor=1):
"""Disable paging is only available with specific roles so it may fail."""
check_command = "get system status | grep Virtual"
output = self.send_command_timing(check_command)
self.allow_disable_global = True
self.vdoms = False
self._output_mode = "more"
if "Virtual domain configuration: enable" in output:
self.vdoms = True
vdom_additional_command = "config global"
output = self.send_command_timing(vdom_additional_command, delay_factor=2)
if "Command fail" in output:
self.allow_disable_global = False
self.remote_conn.close()
self.establish_connection(width=100, height=1000)
new_output = ""
if self.allow_disable_global:
self._retrieve_output_mode()
disable_paging_commands = [
"config system console",
"set output standard",
"end",
]
# There is an extra 'end' required if in multi-vdoms are enabled
if self.vdoms:
disable_paging_commands.append("end")
outputlist = [
self.send_command_timing(command, delay_factor=2)
for command in disable_paging_commands
]
# Should test output is valid
new_output = self.RETURN.join(outputlist)
return output + new_output | python | def disable_paging(self, delay_factor=1):
"""Disable paging is only available with specific roles so it may fail."""
check_command = "get system status | grep Virtual"
output = self.send_command_timing(check_command)
self.allow_disable_global = True
self.vdoms = False
self._output_mode = "more"
if "Virtual domain configuration: enable" in output:
self.vdoms = True
vdom_additional_command = "config global"
output = self.send_command_timing(vdom_additional_command, delay_factor=2)
if "Command fail" in output:
self.allow_disable_global = False
self.remote_conn.close()
self.establish_connection(width=100, height=1000)
new_output = ""
if self.allow_disable_global:
self._retrieve_output_mode()
disable_paging_commands = [
"config system console",
"set output standard",
"end",
]
# There is an extra 'end' required if in multi-vdoms are enabled
if self.vdoms:
disable_paging_commands.append("end")
outputlist = [
self.send_command_timing(command, delay_factor=2)
for command in disable_paging_commands
]
# Should test output is valid
new_output = self.RETURN.join(outputlist)
return output + new_output | [
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eddieantonio/perfection | perfection/getty.py | arrange_rows | def arrange_rows(row_queue, t):
"""
Takes a priority queue as generated by place_items_in_square().
Arranges the items from its conceptual square to one list. Returns
both the resultant vector, plus the displacement vector, to be used
in the final output hash function.
>>> rows = [(2, 1, [(0, 1), (1, 5)]), (3, 3, [(1, 7)])]
>>> result, displacements = arrange_rows(rows, 4)
>>> result
(1, 5, 7)
>>> displacements
(None, 0, None, 1)
>>> rows = [(1, 1, [(0, 1), (2, 7)]), (2, 2, [(1, 5)])]
>>> result, displacements = arrange_rows(rows, 3)
>>> result
(1, 5, 7)
>>> displacements
(None, 0, 0)
"""
# Create a set of all of the unoccupied columns.
max_columns = t ** 2
cols = ((x, True) for x in range(max_columns))
unoccupied_columns = collections.OrderedDict(cols)
# Create the resultant and displacement vectors.
result = [None] * max_columns
displacements = [None] * t
while row_queue:
# Get the next row to place.
_inverse_length, y, row = heapq.heappop(row_queue)
offset = find_first_fit(unoccupied_columns, row, max_columns)
# Calculate the offset of the first item.
first_item_x = row[0][0]
displacements[y] = offset
for x, item in row:
actual_x = x + offset
result[actual_x] = item
del unoccupied_columns[actual_x]
return tuple(trim_nones_from_right(result)), tuple(displacements) | python | def arrange_rows(row_queue, t):
"""
Takes a priority queue as generated by place_items_in_square().
Arranges the items from its conceptual square to one list. Returns
both the resultant vector, plus the displacement vector, to be used
in the final output hash function.
>>> rows = [(2, 1, [(0, 1), (1, 5)]), (3, 3, [(1, 7)])]
>>> result, displacements = arrange_rows(rows, 4)
>>> result
(1, 5, 7)
>>> displacements
(None, 0, None, 1)
>>> rows = [(1, 1, [(0, 1), (2, 7)]), (2, 2, [(1, 5)])]
>>> result, displacements = arrange_rows(rows, 3)
>>> result
(1, 5, 7)
>>> displacements
(None, 0, 0)
"""
# Create a set of all of the unoccupied columns.
max_columns = t ** 2
cols = ((x, True) for x in range(max_columns))
unoccupied_columns = collections.OrderedDict(cols)
# Create the resultant and displacement vectors.
result = [None] * max_columns
displacements = [None] * t
while row_queue:
# Get the next row to place.
_inverse_length, y, row = heapq.heappop(row_queue)
offset = find_first_fit(unoccupied_columns, row, max_columns)
# Calculate the offset of the first item.
first_item_x = row[0][0]
displacements[y] = offset
for x, item in row:
actual_x = x + offset
result[actual_x] = item
del unoccupied_columns[actual_x]
return tuple(trim_nones_from_right(result)), tuple(displacements) | [
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Arranges the items from its conceptual square to one list. Returns
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in the final output hash function.
>>> rows = [(2, 1, [(0, 1), (1, 5)]), (3, 3, [(1, 7)])]
>>> result, displacements = arrange_rows(rows, 4)
>>> result
(1, 5, 7)
>>> displacements
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>>> rows = [(1, 1, [(0, 1), (2, 7)]), (2, 2, [(1, 5)])]
>>> result, displacements = arrange_rows(rows, 3)
>>> result
(1, 5, 7)
>>> displacements
(None, 0, 0) | [
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totalgood/pugnlp | src/pugnlp/util.py | hist_from_values_list | def hist_from_values_list(values_list, fillers=(None,), normalize=False,
cumulative=False, to_str=False, sep=',', min_bin=None, max_bin=None):
"""Compute an emprical histogram, PMF or CDF in a list of lists or a csv string
Only works for discrete (integer) values (doesn't bin real values).
`fillers`: list or tuple of values to ignore in computing the histogram
>>> hist_from_values_list([1,1,2,1,1,1,2,3,2,4,4,5,7,7,9]) # doctest: +NORMALIZE_WHITESPACE
[(1, 5), (2, 3), (3, 1), (4, 2), (5, 1), (6, 0), (7, 2), (8, 0), (9, 1)]
>>> hist_from_values_list([(1,9),(1,8),(2,),(1,),(1,4),(2,5),(3,3),(5,0),(2,2)]) # doctest: +NORMALIZE_WHITESPACE
[[(1, 4), (2, 3), (3, 1), (4, 0), (5, 1)], [(0, 1), (1, 0), ... (6, 0), (7, 0), (8, 1), (9, 1)]]
>>> hist_from_values_list(transposed_matrix([(8,),(1,3,5),(2,),(3,4,5,8)])) # doctest: +NORMALIZE_WHITESPACE
[[(8, 1)], [(1, 1), (2, 0), (3, 1), (4, 0), (5, 1)], [(2, 1)], [(3, 1), (4, 1), (5, 1), (6, 0), (7, 0), (8, 1)]]
"""
value_types = tuple([int, float] + [type(filler) for filler in fillers])
if all(isinstance(value, value_types) for value in values_list):
# ignore all fillers and convert all floats to ints when doing counting
counters = [Counter(int(value) for value in values_list if isinstance(value, (int, float)))]
elif all(len(row) == 1 for row in values_list) and all(isinstance(row[0], value_types) for row in values_list):
return hist_from_values_list([values[0] for values in values_list], fillers=fillers,
normalize=normalize, cumulative=cumulative,
to_str=to_str, sep=sep, min_bin=min_bin, max_bin=max_bin)
else: # assume it's a row-wise table (list of rows)
return [
hist_from_values_list(col, fillers=fillers, normalize=normalize, cumulative=cumulative, to_str=to_str, sep=sep,
min_bin=min_bin, max_bin=max_bin)
for col in transposed_matrix(values_list)
]
if not values_list:
return []
intkeys_list = [[c for c in counts if (isinstance(c, int) or (isinstance(c, float) and int(c) == c))]
for counts in counters]
try:
min_bin = int(min_bin)
except (IndexError, ValueError, AttributeError, TypeError):
min_bin = min(min(intkeys) for intkeys in intkeys_list)
try:
max_bin = int(max_bin)
except (IndexError, ValueError, AttributeError, TypeError):
max_bin = max(max(intkeys) for intkeys in intkeys_list)
# FIXME: this looks slow and hazardous (like it's ignore min/max bin):
# TODO: reuse min(intkeys)
min_bin = max(min_bin, min((min(intkeys) if intkeys else 0) for intkeys in intkeys_list))
max_bin = min(max_bin, max((max(intkeys) if intkeys else 0) for intkeys in intkeys_list))
histograms = []
for intkeys, counts in zip(intkeys_list, counters):
histograms += [OrderedDict()]
if not intkeys:
continue
if normalize:
N = sum(counts[c] for c in intkeys)
for c in intkeys:
counts[c] = float(counts[c]) / N
if cumulative:
for i in range(min_bin, max_bin + 1):
histograms[-1][i] = counts.get(i, 0) + histograms[-1].get(i - 1, 0)
else:
for i in range(min_bin, max_bin + 1):
histograms[-1][i] = counts.get(i, 0)
if not histograms:
histograms = [OrderedDict()]
# fill in the zero counts between the integer bins of the histogram
aligned_histograms = []
for i in range(min_bin, max_bin + 1):
aligned_histograms += [tuple([i] + [hist.get(i, 0) for hist in histograms])]
if to_str:
# FIXME: add header row
return str_from_table(aligned_histograms, sep=sep, max_rows=365 * 2 + 1)
return aligned_histograms | python | def hist_from_values_list(values_list, fillers=(None,), normalize=False,
cumulative=False, to_str=False, sep=',', min_bin=None, max_bin=None):
"""Compute an emprical histogram, PMF or CDF in a list of lists or a csv string
Only works for discrete (integer) values (doesn't bin real values).
`fillers`: list or tuple of values to ignore in computing the histogram
>>> hist_from_values_list([1,1,2,1,1,1,2,3,2,4,4,5,7,7,9]) # doctest: +NORMALIZE_WHITESPACE
[(1, 5), (2, 3), (3, 1), (4, 2), (5, 1), (6, 0), (7, 2), (8, 0), (9, 1)]
>>> hist_from_values_list([(1,9),(1,8),(2,),(1,),(1,4),(2,5),(3,3),(5,0),(2,2)]) # doctest: +NORMALIZE_WHITESPACE
[[(1, 4), (2, 3), (3, 1), (4, 0), (5, 1)], [(0, 1), (1, 0), ... (6, 0), (7, 0), (8, 1), (9, 1)]]
>>> hist_from_values_list(transposed_matrix([(8,),(1,3,5),(2,),(3,4,5,8)])) # doctest: +NORMALIZE_WHITESPACE
[[(8, 1)], [(1, 1), (2, 0), (3, 1), (4, 0), (5, 1)], [(2, 1)], [(3, 1), (4, 1), (5, 1), (6, 0), (7, 0), (8, 1)]]
"""
value_types = tuple([int, float] + [type(filler) for filler in fillers])
if all(isinstance(value, value_types) for value in values_list):
# ignore all fillers and convert all floats to ints when doing counting
counters = [Counter(int(value) for value in values_list if isinstance(value, (int, float)))]
elif all(len(row) == 1 for row in values_list) and all(isinstance(row[0], value_types) for row in values_list):
return hist_from_values_list([values[0] for values in values_list], fillers=fillers,
normalize=normalize, cumulative=cumulative,
to_str=to_str, sep=sep, min_bin=min_bin, max_bin=max_bin)
else: # assume it's a row-wise table (list of rows)
return [
hist_from_values_list(col, fillers=fillers, normalize=normalize, cumulative=cumulative, to_str=to_str, sep=sep,
min_bin=min_bin, max_bin=max_bin)
for col in transposed_matrix(values_list)
]
if not values_list:
return []
intkeys_list = [[c for c in counts if (isinstance(c, int) or (isinstance(c, float) and int(c) == c))]
for counts in counters]
try:
min_bin = int(min_bin)
except (IndexError, ValueError, AttributeError, TypeError):
min_bin = min(min(intkeys) for intkeys in intkeys_list)
try:
max_bin = int(max_bin)
except (IndexError, ValueError, AttributeError, TypeError):
max_bin = max(max(intkeys) for intkeys in intkeys_list)
# FIXME: this looks slow and hazardous (like it's ignore min/max bin):
# TODO: reuse min(intkeys)
min_bin = max(min_bin, min((min(intkeys) if intkeys else 0) for intkeys in intkeys_list))
max_bin = min(max_bin, max((max(intkeys) if intkeys else 0) for intkeys in intkeys_list))
histograms = []
for intkeys, counts in zip(intkeys_list, counters):
histograms += [OrderedDict()]
if not intkeys:
continue
if normalize:
N = sum(counts[c] for c in intkeys)
for c in intkeys:
counts[c] = float(counts[c]) / N
if cumulative:
for i in range(min_bin, max_bin + 1):
histograms[-1][i] = counts.get(i, 0) + histograms[-1].get(i - 1, 0)
else:
for i in range(min_bin, max_bin + 1):
histograms[-1][i] = counts.get(i, 0)
if not histograms:
histograms = [OrderedDict()]
# fill in the zero counts between the integer bins of the histogram
aligned_histograms = []
for i in range(min_bin, max_bin + 1):
aligned_histograms += [tuple([i] + [hist.get(i, 0) for hist in histograms])]
if to_str:
# FIXME: add header row
return str_from_table(aligned_histograms, sep=sep, max_rows=365 * 2 + 1)
return aligned_histograms | [
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[(1, 5), (2, 3), (3, 1), (4, 2), (5, 1), (6, 0), (7, 2), (8, 0), (9, 1)]
>>> hist_from_values_list([(1,9),(1,8),(2,),(1,),(1,4),(2,5),(3,3),(5,0),(2,2)]) # doctest: +NORMALIZE_WHITESPACE
[[(1, 4), (2, 3), (3, 1), (4, 0), (5, 1)], [(0, 1), (1, 0), ... (6, 0), (7, 0), (8, 1), (9, 1)]]
>>> hist_from_values_list(transposed_matrix([(8,),(1,3,5),(2,),(3,4,5,8)])) # doctest: +NORMALIZE_WHITESPACE
[[(8, 1)], [(1, 1), (2, 0), (3, 1), (4, 0), (5, 1)], [(2, 1)], [(3, 1), (4, 1), (5, 1), (6, 0), (7, 0), (8, 1)]] | [
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nicodv/kmodes | kmodes/util/__init__.py | get_max_value_key | def get_max_value_key(dic):
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v = np.array(list(dic.values()))
k = np.array(list(dic.keys()))
maxima = np.where(v == np.max(v))[0]
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else:
# In order to be consistent, always selects the minimum key
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return k[maxima[np.argmin(k[maxima])]] | python | def get_max_value_key(dic):
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v = np.array(list(dic.values()))
k = np.array(list(dic.keys()))
maxima = np.where(v == np.max(v))[0]
if len(maxima) == 1:
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MacHu-GWU/sqlalchemy_mate-project | sqlalchemy_mate/credential.py | EngineCreator.create_mysql_oursql | def create_mysql_oursql(self, **kwargs):
"""
:rtype: Engine
"""
return self._ce(
self._ccs(self.DialectAndDriver.mysql_oursql), **kwargs
) | python | def create_mysql_oursql(self, **kwargs):
"""
:rtype: Engine
"""
return self._ce(
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pyca/pynacl | src/nacl/bindings/crypto_core.py | crypto_core_ed25519_is_valid_point | def crypto_core_ed25519_is_valid_point(p):
"""
Check if ``p`` represents a point on the edwards25519 curve, in canonical
form, on the main subgroup, and that the point doesn't have a small order.
:param p: a :py:data:`.crypto_core_ed25519_BYTES` long bytes sequence
representing a point on the edwards25519 curve
:type p: bytes
:return: point validity
:rtype: bool
"""
ensure(isinstance(p, bytes) and len(p) == crypto_core_ed25519_BYTES,
'Point must be a crypto_core_ed25519_BYTES long bytes sequence',
raising=exc.TypeError)
rc = lib.crypto_core_ed25519_is_valid_point(p)
return rc == 1 | python | def crypto_core_ed25519_is_valid_point(p):
"""
Check if ``p`` represents a point on the edwards25519 curve, in canonical
form, on the main subgroup, and that the point doesn't have a small order.
:param p: a :py:data:`.crypto_core_ed25519_BYTES` long bytes sequence
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:type p: bytes
:return: point validity
:rtype: bool
"""
ensure(isinstance(p, bytes) and len(p) == crypto_core_ed25519_BYTES,
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raising=exc.TypeError)
rc = lib.crypto_core_ed25519_is_valid_point(p)
return rc == 1 | [
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moonso/loqusdb | loqusdb/plugins/mongo/variant.py | VariantMixin.add_variant | def add_variant(self, variant):
"""Add a variant to the variant collection
If the variant exists we update the count else we insert a new variant object.
Args:
variant (dict): A variant dictionary
"""
LOG.debug("Upserting variant: {0}".format(variant.get('_id')))
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if message.modified_count == 1:
LOG.debug("Variant %s was updated", variant.get('_id'))
else:
LOG.debug("Variant was added to database for first time")
return | python | def add_variant(self, variant):
"""Add a variant to the variant collection
If the variant exists we update the count else we insert a new variant object.
Args:
variant (dict): A variant dictionary
"""
LOG.debug("Upserting variant: {0}".format(variant.get('_id')))
update = self._get_update(variant)
message = self.db.variant.update_one(
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if message.modified_count == 1:
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nameko/nameko | nameko/exceptions.py | get_module_path | def get_module_path(exc_type):
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>>> "nameko.exceptions.MethodNotFound"
"""
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return "{}.{}".format(module.__name__, exc_type.__name__) | python | def get_module_path(exc_type):
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e.g.::
>>> get_module_path(MethodNotFound)
>>> "nameko.exceptions.MethodNotFound"
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PyCQA/pydocstyle | src/pydocstyle/violations.py | ErrorRegistry.get_error_codes | def get_error_codes(cls) -> Iterable[str]:
"""Yield all registered codes."""
for group in cls.groups:
for error in group.errors:
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IBMStreams/pypi.streamsx | streamsx/rest.py | StreamsConnection.resource_url | def resource_url(self):
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UCL-INGI/INGInious | inginious/agent/docker_agent/__init__.py | DockerAgent.handle_student_job_closing | async def handle_student_job_closing(self, container_id, retval):
"""
Handle a closing student container. Do some cleaning, verify memory limits, timeouts, ... and returns data to the associated grading
container
"""
try:
self._logger.debug("Closing student %s", container_id)
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del self._student_containers_running[container_id]
except asyncio.CancelledError:
raise
except:
self._logger.warning("Student container %s that has finished(p1) was not launched by this agent", str(container_id), exc_info=True)
return
# Delete remaining student containers
if job_id in self._student_containers_for_job: # if it does not exists, then the parent container has closed
self._student_containers_for_job[job_id].remove(container_id)
killed = await self._timeout_watcher.was_killed(container_id)
if container_id in self._containers_killed:
killed = self._containers_killed[container_id]
del self._containers_killed[container_id]
if killed == "timeout":
retval = 253
elif killed == "overflow":
retval = 252
try:
await self._write_to_container_stdin(write_stream, {"type": "run_student_retval", "retval": retval, "socket_id": socket_id})
except asyncio.CancelledError:
raise
except:
pass # parent container closed
# Do not forget to remove the container
try:
await self._docker.remove_container(container_id)
except asyncio.CancelledError:
raise
except:
pass # ignore
except asyncio.CancelledError:
raise
except:
self._logger.exception("Exception in handle_student_job_closing") | python | async def handle_student_job_closing(self, container_id, retval):
"""
Handle a closing student container. Do some cleaning, verify memory limits, timeouts, ... and returns data to the associated grading
container
"""
try:
self._logger.debug("Closing student %s", container_id)
try:
job_id, parent_container_id, socket_id, write_stream = self._student_containers_running[container_id]
del self._student_containers_running[container_id]
except asyncio.CancelledError:
raise
except:
self._logger.warning("Student container %s that has finished(p1) was not launched by this agent", str(container_id), exc_info=True)
return
# Delete remaining student containers
if job_id in self._student_containers_for_job: # if it does not exists, then the parent container has closed
self._student_containers_for_job[job_id].remove(container_id)
killed = await self._timeout_watcher.was_killed(container_id)
if container_id in self._containers_killed:
killed = self._containers_killed[container_id]
del self._containers_killed[container_id]
if killed == "timeout":
retval = 253
elif killed == "overflow":
retval = 252
try:
await self._write_to_container_stdin(write_stream, {"type": "run_student_retval", "retval": retval, "socket_id": socket_id})
except asyncio.CancelledError:
raise
except:
pass # parent container closed
# Do not forget to remove the container
try:
await self._docker.remove_container(container_id)
except asyncio.CancelledError:
raise
except:
pass # ignore
except asyncio.CancelledError:
raise
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googleapis/google-cloud-python | bigtable/google/cloud/bigtable/cluster.py | Cluster.update | def update(self):
"""Update this cluster.
For example:
.. literalinclude:: snippets.py
:start-after: [START bigtable_update_cluster]
:end-before: [END bigtable_update_cluster]
.. note::
Updates the ``serve_nodes``. If you'd like to
change them before updating, reset the values via
.. code:: python
cluster.serve_nodes = 8
before calling :meth:`update`.
:type location: :str:``CreationOnly``
:param location: The location where this cluster's nodes and storage
reside. For best performance, clients should be located as
close as possible to this cluster. Currently only zones are
supported, so values should be of the form
``projects/<project>/locations/<zone>``.
:type serve_nodes: :int
:param serve_nodes: The number of nodes allocated to this cluster.
More nodes enable higher throughput and more consistent
performance.
:rtype: :class:`Operation`
:returns: The long-running operation corresponding to the
update operation.
"""
client = self._instance._client
# We are passing `None` for second argument location.
# Location is set only at the time of creation of a cluster
# and can not be changed after cluster has been created.
return client.instance_admin_client.update_cluster(
self.name, self.serve_nodes, None
) | python | def update(self):
"""Update this cluster.
For example:
.. literalinclude:: snippets.py
:start-after: [START bigtable_update_cluster]
:end-before: [END bigtable_update_cluster]
.. note::
Updates the ``serve_nodes``. If you'd like to
change them before updating, reset the values via
.. code:: python
cluster.serve_nodes = 8
before calling :meth:`update`.
:type location: :str:``CreationOnly``
:param location: The location where this cluster's nodes and storage
reside. For best performance, clients should be located as
close as possible to this cluster. Currently only zones are
supported, so values should be of the form
``projects/<project>/locations/<zone>``.
:type serve_nodes: :int
:param serve_nodes: The number of nodes allocated to this cluster.
More nodes enable higher throughput and more consistent
performance.
:rtype: :class:`Operation`
:returns: The long-running operation corresponding to the
update operation.
"""
client = self._instance._client
# We are passing `None` for second argument location.
# Location is set only at the time of creation of a cluster
# and can not be changed after cluster has been created.
return client.instance_admin_client.update_cluster(
self.name, self.serve_nodes, None
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Ouranosinc/xclim | xclim/checks.py | valid_daily_mean_discharge | def valid_daily_mean_discharge(comp):
r"""Decorator to check that a computation runs on valid discharge data."""
@wraps(comp)
def func(q, **kwds):
check_valid_discharge(q)
return comp(q, **kwds)
return func | python | def valid_daily_mean_discharge(comp):
r"""Decorator to check that a computation runs on valid discharge data."""
@wraps(comp)
def func(q, **kwds):
check_valid_discharge(q)
return comp(q, **kwds)
return func | [
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MacHu-GWU/single_file_module-project | sfm/ziplib.py | decompress | def decompress(obj, return_type="bytes"):
"""
De-compress it to it's original.
:param obj: Compressed object, could be bytes or str.
:param return_type: if bytes, then return bytes; if str, then use
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"""
if isinstance(obj, binary_type):
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elif return_type == "obj":
return pickle.loads(b)
else:
raise ValueError(
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"""
De-compress it to it's original.
:param obj: Compressed object, could be bytes or str.
:param return_type: if bytes, then return bytes; if str, then use
base64.b64decode; if obj, then use pickle.loads return an object.
"""
if isinstance(obj, binary_type):
b = zlib.decompress(obj)
elif isinstance(obj, string_types):
b = zlib.decompress(base64.b64decode(obj.encode("utf-8")))
else:
raise TypeError("input cannot be anything other than str and bytes!")
if return_type == "bytes":
return b
elif return_type == "str":
return b.decode("utf-8")
elif return_type == "obj":
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etcher-be/emiz | emiz/avwx/remarks.py | pressure_tendency | def pressure_tendency(code: str, unit: str = 'mb') -> str:
"""
Translates a 5-digit pressure outlook code
Ex: 50123 -> 12.3 mb: Increasing, then decreasing
"""
width, precision = int(code[2:4]), code[4]
return ('3-hour pressure difference: +/- '
f'{width}.{precision} {unit} - {PRESSURE_TENDENCIES[code[1]]}') | python | def pressure_tendency(code: str, unit: str = 'mb') -> str:
"""
Translates a 5-digit pressure outlook code
Ex: 50123 -> 12.3 mb: Increasing, then decreasing
"""
width, precision = int(code[2:4]), code[4]
return ('3-hour pressure difference: +/- '
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dask/dask-kubernetes | dask_kubernetes/objects.py | _set_k8s_attribute | def _set_k8s_attribute(obj, attribute, value):
"""
Set a specific value on a kubernetes object's attribute
obj
an object from Kubernetes Python API client
attribute
Should be a Kubernetes API style attribute (with camelCase)
value
Can be anything (string, list, dict, k8s objects) that can be
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"""
current_value = None
attribute_name = None
# All k8s python client objects have an 'attribute_map' property
# which has as keys python style attribute names (api_client)
# and as values the kubernetes JSON API style attribute names
# (apiClient). We want to allow users to use the JSON API style attribute
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for python_attribute, json_attribute in obj.attribute_map.items():
if json_attribute == attribute:
attribute_name = python_attribute
break
else:
raise ValueError('Attribute must be one of {}'.format(obj.attribute_map.values()))
if hasattr(obj, attribute_name):
current_value = getattr(obj, attribute_name)
if current_value is not None:
# This will ensure that current_value is something JSONable,
# so a dict, list, or scalar
current_value = SERIALIZATION_API_CLIENT.sanitize_for_serialization(
current_value
)
if isinstance(current_value, dict):
# Deep merge our dictionaries!
setattr(obj, attribute_name, merge_dictionaries(current_value, value))
elif isinstance(current_value, list):
# Just append lists
setattr(obj, attribute_name, current_value + value)
else:
# Replace everything else
setattr(obj, attribute_name, value) | python | def _set_k8s_attribute(obj, attribute, value):
"""
Set a specific value on a kubernetes object's attribute
obj
an object from Kubernetes Python API client
attribute
Should be a Kubernetes API style attribute (with camelCase)
value
Can be anything (string, list, dict, k8s objects) that can be
accepted by the k8s python client
"""
current_value = None
attribute_name = None
# All k8s python client objects have an 'attribute_map' property
# which has as keys python style attribute names (api_client)
# and as values the kubernetes JSON API style attribute names
# (apiClient). We want to allow users to use the JSON API style attribute
# names only.
for python_attribute, json_attribute in obj.attribute_map.items():
if json_attribute == attribute:
attribute_name = python_attribute
break
else:
raise ValueError('Attribute must be one of {}'.format(obj.attribute_map.values()))
if hasattr(obj, attribute_name):
current_value = getattr(obj, attribute_name)
if current_value is not None:
# This will ensure that current_value is something JSONable,
# so a dict, list, or scalar
current_value = SERIALIZATION_API_CLIENT.sanitize_for_serialization(
current_value
)
if isinstance(current_value, dict):
# Deep merge our dictionaries!
setattr(obj, attribute_name, merge_dictionaries(current_value, value))
elif isinstance(current_value, list):
# Just append lists
setattr(obj, attribute_name, current_value + value)
else:
# Replace everything else
setattr(obj, attribute_name, value) | [
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dpgaspar/Flask-AppBuilder | flask_appbuilder/views.py | RestCRUDView.show_item_dict | def show_item_dict(self, item):
"""Returns a json-able dict for show"""
d = {}
for col in self.show_columns:
v = getattr(item, col)
if not isinstance(v, (int, float, string_types)):
v = str(v)
d[col] = v
return d | python | def show_item_dict(self, item):
"""Returns a json-able dict for show"""
d = {}
for col in self.show_columns:
v = getattr(item, col)
if not isinstance(v, (int, float, string_types)):
v = str(v)
d[col] = v
return d | [
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ming060/robotframework-uiautomatorlibrary | uiautomatorlibrary/Mobile.py | Mobile.scroll_backward_vertically | def scroll_backward_vertically(self, steps=10, *args, **selectors):
"""
Perform scroll backward (vertically)action on the object which has *selectors* attributes.
Return whether the object can be Scroll or not.
See `Scroll Forward Vertically` for more details.
"""
return self.device(**selectors).scroll.vert.backward(steps=steps) | python | def scroll_backward_vertically(self, steps=10, *args, **selectors):
"""
Perform scroll backward (vertically)action on the object which has *selectors* attributes.
Return whether the object can be Scroll or not.
See `Scroll Forward Vertically` for more details.
"""
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Alignak-monitoring/alignak | alignak/external_command.py | ExternalCommandManager.send_custom_host_notification | def send_custom_host_notification(self, host, options, author, comment):
"""DOES NOTHING (Should send a custom notification)
Format of the line that triggers function call::
SEND_CUSTOM_HOST_NOTIFICATION;<host_name>;<options>;<author>;<comment>
:param host: host to send notif for
:type host: alignak.object.host.Host
:param options: notification options
:type options:
:param author: notification author
:type author: str
:param comment: notification text
:type comment: str
:return: None
"""
logger.warning("The external command 'SEND_CUSTOM_HOST_NOTIFICATION' "
"is not currently implemented in Alignak. If you really need it, "
"request for its implementation in the project repository: "
"https://github.com/Alignak-monitoring/alignak")
self.send_an_element(make_monitoring_log(
'warning', 'SEND_CUSTOM_HOST_NOTIFICATION: this command is not implemented!')) | python | def send_custom_host_notification(self, host, options, author, comment):
"""DOES NOTHING (Should send a custom notification)
Format of the line that triggers function call::
SEND_CUSTOM_HOST_NOTIFICATION;<host_name>;<options>;<author>;<comment>
:param host: host to send notif for
:type host: alignak.object.host.Host
:param options: notification options
:type options:
:param author: notification author
:type author: str
:param comment: notification text
:type comment: str
:return: None
"""
logger.warning("The external command 'SEND_CUSTOM_HOST_NOTIFICATION' "
"is not currently implemented in Alignak. If you really need it, "
"request for its implementation in the project repository: "
"https://github.com/Alignak-monitoring/alignak")
self.send_an_element(make_monitoring_log(
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cirruscluster/cirruscluster | cirruscluster/ext/ansible/runner/connection_plugins/fireball.py | Connection.exec_command | def exec_command(self, cmd, tmp_path, sudo_user, sudoable=False, executable='/bin/sh'):
''' run a command on the remote host '''
vvv("EXEC COMMAND %s" % cmd)
if self.runner.sudo and sudoable:
raise errors.AnsibleError("fireball does not use sudo, but runs as whoever it was initiated as. (That itself is where to use sudo).")
data = dict(
mode='command',
cmd=cmd,
tmp_path=tmp_path,
executable=executable,
)
data = utils.jsonify(data)
data = utils.encrypt(self.key, data)
self.socket.send(data)
response = self.socket.recv()
response = utils.decrypt(self.key, response)
response = utils.parse_json(response)
return (response.get('rc',None), '', response.get('stdout',''), response.get('stderr','')) | python | def exec_command(self, cmd, tmp_path, sudo_user, sudoable=False, executable='/bin/sh'):
''' run a command on the remote host '''
vvv("EXEC COMMAND %s" % cmd)
if self.runner.sudo and sudoable:
raise errors.AnsibleError("fireball does not use sudo, but runs as whoever it was initiated as. (That itself is where to use sudo).")
data = dict(
mode='command',
cmd=cmd,
tmp_path=tmp_path,
executable=executable,
)
data = utils.jsonify(data)
data = utils.encrypt(self.key, data)
self.socket.send(data)
response = self.socket.recv()
response = utils.decrypt(self.key, response)
response = utils.parse_json(response)
return (response.get('rc',None), '', response.get('stdout',''), response.get('stderr','')) | [
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chapel-lang/sphinxcontrib-chapeldomain | sphinxcontrib/chapeldomain.py | ChapelDomain._make_module_refnode | def _make_module_refnode(self, builder, fromdocname, name, contnode):
"""Helper function to generate new xref node based on
current environment.
"""
# Get additional info for modules.
docname, synopsis, platform, deprecated = self.data['modules'][name]
title = name
if synopsis:
title += ': ' + synopsis
if deprecated:
title += _(' (deprecated)')
if platform:
title += ' (' + platform + ')'
return make_refnode(builder, fromdocname, docname,
'module-' + name, contnode, title) | python | def _make_module_refnode(self, builder, fromdocname, name, contnode):
"""Helper function to generate new xref node based on
current environment.
"""
# Get additional info for modules.
docname, synopsis, platform, deprecated = self.data['modules'][name]
title = name
if synopsis:
title += ': ' + synopsis
if deprecated:
title += _(' (deprecated)')
if platform:
title += ' (' + platform + ')'
return make_refnode(builder, fromdocname, docname,
'module-' + name, contnode, title) | [
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Esri/ArcREST | src/arcrest/ags/_imageservice.py | ImageService.getSamples | def getSamples(self,geometry,geometryType="esriGeometryPoint",
sampleDistance=None,sampleCount=None,mosaicRule=None,
pixelSize=None,returnFirstValueOnly=None,interpolation=None,
outFields=None):
"""
The getSamples operation is performed on an image service resource.
The getSamples operation is supported by both mosaic dataset and raster
dataset image services.
The result of this operation includes sample point locations, pixel
values, and corresponding spatial resolutions of the source data for a
given geometry. When the input geometry is a polyline, envelope, or
polygon, sampling is based on sampleCount or sampleDistance; when the
input geometry is a point or multipoint, the point or points are used
directly.
The number of sample locations in the response is based on the
sampleDistance or sampleCount parameter and cannot exceed the limit of
the image service (the default is 1000, which is an approximate limit).
Inputs:
geometry - A geometry that defines the location(s) to be sampled. The
structure of the geometry is the same as the structure of the JSON
geometry objects returned by the ArcGIS REST API. Applicable geometry
types are point, multipoint, polyline, polygon, and envelope. When
spatialReference is omitted in the input geometry, it will be assumed
to be the spatial reference of the image service.
geometryType - The type of geometry specified by the geometry parameter.
The geometry type can be point, multipoint, polyline, polygon, or envelope.
Values: esriGeometryPoint | esriGeometryMultipoint | esriGeometryPolyline |
esriGeometryPolygon | esriGeometryEnvelope
sampleDistance - The distance interval used to sample points from the
provided path. The unit is the same as the input geometry. If neither
sampleCount nor sampleDistance is provided, no densification can be done
for paths (polylines), and a default sampleCount (100) is used for areas
(polygons or envelopes).
sampleCount - The approximate number of sample locations from the provided
path. If neither sampleCount nor sampleDistance is provided, no
densification can be done for paths (polylines), and a default
sampleCount (100) is used for areas (polygons or envelopes).
mosaicRule - Specifies the mosaic rule defining the image sort order.
Additional filtering can be applied to the where clause and FIDs of a
mosaic rule.
pixelSize - The raster that is visible at the specified pixel size in the
mosaic dataset will be used for sampling. If pixelSize is not specified,
the service's pixel size is used.
The structure of the esri_codephpixelSize parameter is the same as the
structure of the point object returned by the ArcGIS REST API. In addition
to the JSON structure, you can specify the pixel size with a simple
comma-separated syntax.
returnFirstValueOnly - Indicates whether to return all values at a point,
or return the first non-NoData value based on the current mosaic rule.
The default is true.
interpolation - This parameter was added at 10.3. The resampling method.
Default is nearest neighbor.
outFields - This parameter was added at 10.3. The list of fields to be
included in the response. This list is a comma-delimited list of field
names. You can also specify the wildcard character (*) as the value of
this parameter to include all the field values in the results.
"""
url = self._url + "/getSamples"
params = {
"f" : "json",
"geometry" : geometry,
"geometryType": geometryType
}
if not sampleDistance is None:
params["sampleDistance"] = sampleDistance
if not sampleCount is None:
params["sampleCount"] = sampleCount
if not mosaicRule is None:
params["mosaicRule"] = mosaicRule
if not pixelSize is None:
params["pixelSize"] = pixelSize
if not returnFirstValueOnly is None:
params["returnFirstValueOnly"] = returnFirstValueOnly
if not interpolation is None:
params["interpolation"] = interpolation
if not outFields is None:
params["outFields"] = outFields
return self._get(url=url,
param_dict=params,
securityHandler=self._securityHandler,
proxy_url=self._proxy_url,
proxy_port=self._proxy_port) | python | def getSamples(self,geometry,geometryType="esriGeometryPoint",
sampleDistance=None,sampleCount=None,mosaicRule=None,
pixelSize=None,returnFirstValueOnly=None,interpolation=None,
outFields=None):
"""
The getSamples operation is performed on an image service resource.
The getSamples operation is supported by both mosaic dataset and raster
dataset image services.
The result of this operation includes sample point locations, pixel
values, and corresponding spatial resolutions of the source data for a
given geometry. When the input geometry is a polyline, envelope, or
polygon, sampling is based on sampleCount or sampleDistance; when the
input geometry is a point or multipoint, the point or points are used
directly.
The number of sample locations in the response is based on the
sampleDistance or sampleCount parameter and cannot exceed the limit of
the image service (the default is 1000, which is an approximate limit).
Inputs:
geometry - A geometry that defines the location(s) to be sampled. The
structure of the geometry is the same as the structure of the JSON
geometry objects returned by the ArcGIS REST API. Applicable geometry
types are point, multipoint, polyline, polygon, and envelope. When
spatialReference is omitted in the input geometry, it will be assumed
to be the spatial reference of the image service.
geometryType - The type of geometry specified by the geometry parameter.
The geometry type can be point, multipoint, polyline, polygon, or envelope.
Values: esriGeometryPoint | esriGeometryMultipoint | esriGeometryPolyline |
esriGeometryPolygon | esriGeometryEnvelope
sampleDistance - The distance interval used to sample points from the
provided path. The unit is the same as the input geometry. If neither
sampleCount nor sampleDistance is provided, no densification can be done
for paths (polylines), and a default sampleCount (100) is used for areas
(polygons or envelopes).
sampleCount - The approximate number of sample locations from the provided
path. If neither sampleCount nor sampleDistance is provided, no
densification can be done for paths (polylines), and a default
sampleCount (100) is used for areas (polygons or envelopes).
mosaicRule - Specifies the mosaic rule defining the image sort order.
Additional filtering can be applied to the where clause and FIDs of a
mosaic rule.
pixelSize - The raster that is visible at the specified pixel size in the
mosaic dataset will be used for sampling. If pixelSize is not specified,
the service's pixel size is used.
The structure of the esri_codephpixelSize parameter is the same as the
structure of the point object returned by the ArcGIS REST API. In addition
to the JSON structure, you can specify the pixel size with a simple
comma-separated syntax.
returnFirstValueOnly - Indicates whether to return all values at a point,
or return the first non-NoData value based on the current mosaic rule.
The default is true.
interpolation - This parameter was added at 10.3. The resampling method.
Default is nearest neighbor.
outFields - This parameter was added at 10.3. The list of fields to be
included in the response. This list is a comma-delimited list of field
names. You can also specify the wildcard character (*) as the value of
this parameter to include all the field values in the results.
"""
url = self._url + "/getSamples"
params = {
"f" : "json",
"geometry" : geometry,
"geometryType": geometryType
}
if not sampleDistance is None:
params["sampleDistance"] = sampleDistance
if not sampleCount is None:
params["sampleCount"] = sampleCount
if not mosaicRule is None:
params["mosaicRule"] = mosaicRule
if not pixelSize is None:
params["pixelSize"] = pixelSize
if not returnFirstValueOnly is None:
params["returnFirstValueOnly"] = returnFirstValueOnly
if not interpolation is None:
params["interpolation"] = interpolation
if not outFields is None:
params["outFields"] = outFields
return self._get(url=url,
param_dict=params,
securityHandler=self._securityHandler,
proxy_url=self._proxy_url,
proxy_port=self._proxy_port) | [
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given geometry. When the input geometry is a polyline, envelope, or
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input geometry is a point or multipoint, the point or points are used
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The number of sample locations in the response is based on the
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Inputs:
geometry - A geometry that defines the location(s) to be sampled. The
structure of the geometry is the same as the structure of the JSON
geometry objects returned by the ArcGIS REST API. Applicable geometry
types are point, multipoint, polyline, polygon, and envelope. When
spatialReference is omitted in the input geometry, it will be assumed
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geometryType - The type of geometry specified by the geometry parameter.
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sampleDistance - The distance interval used to sample points from the
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sampleCount nor sampleDistance is provided, no densification can be done
for paths (polylines), and a default sampleCount (100) is used for areas
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sampleCount - The approximate number of sample locations from the provided
path. If neither sampleCount nor sampleDistance is provided, no
densification can be done for paths (polylines), and a default
sampleCount (100) is used for areas (polygons or envelopes).
mosaicRule - Specifies the mosaic rule defining the image sort order.
Additional filtering can be applied to the where clause and FIDs of a
mosaic rule.
pixelSize - The raster that is visible at the specified pixel size in the
mosaic dataset will be used for sampling. If pixelSize is not specified,
the service's pixel size is used.
The structure of the esri_codephpixelSize parameter is the same as the
structure of the point object returned by the ArcGIS REST API. In addition
to the JSON structure, you can specify the pixel size with a simple
comma-separated syntax.
returnFirstValueOnly - Indicates whether to return all values at a point,
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interpolation - This parameter was added at 10.3. The resampling method.
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outFields - This parameter was added at 10.3. The list of fields to be
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dropbox/stone | stone/frontend/ir_generator.py | IRGenerator._add_imports_to_env | def _add_imports_to_env(self, raw_api):
"""
Scans raw parser output for import declarations. Checks if the imports
are valid, and then creates a reference to the namespace in the
environment.
Args:
raw_api (Tuple[Namespace, List[stone.stone.parser._Element]]):
Namespace paired with raw parser output.
"""
for namespace, desc in raw_api:
for item in desc:
if isinstance(item, AstImport):
if namespace.name == item.target:
raise InvalidSpec('Cannot import current namespace.',
item.lineno, item.path)
if item.target not in self.api.namespaces:
raise InvalidSpec(
'Namespace %s is not defined in any spec.' %
quote(item.target),
item.lineno, item.path)
env = self._get_or_create_env(namespace.name)
imported_env = self._get_or_create_env(item.target)
if namespace.name in imported_env:
# Block circular imports. The Python backend can't
# easily generate code for circular references.
raise InvalidSpec(
'Circular import of namespaces %s and %s '
'detected.' %
(quote(namespace.name), quote(item.target)),
item.lineno, item.path)
env[item.target] = imported_env | python | def _add_imports_to_env(self, raw_api):
"""
Scans raw parser output for import declarations. Checks if the imports
are valid, and then creates a reference to the namespace in the
environment.
Args:
raw_api (Tuple[Namespace, List[stone.stone.parser._Element]]):
Namespace paired with raw parser output.
"""
for namespace, desc in raw_api:
for item in desc:
if isinstance(item, AstImport):
if namespace.name == item.target:
raise InvalidSpec('Cannot import current namespace.',
item.lineno, item.path)
if item.target not in self.api.namespaces:
raise InvalidSpec(
'Namespace %s is not defined in any spec.' %
quote(item.target),
item.lineno, item.path)
env = self._get_or_create_env(namespace.name)
imported_env = self._get_or_create_env(item.target)
if namespace.name in imported_env:
# Block circular imports. The Python backend can't
# easily generate code for circular references.
raise InvalidSpec(
'Circular import of namespaces %s and %s '
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(quote(namespace.name), quote(item.target)),
item.lineno, item.path)
env[item.target] = imported_env | [
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Args:
raw_api (Tuple[Namespace, List[stone.stone.parser._Element]]):
Namespace paired with raw parser output. | [
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sloria/read_env | read_env.py | parse_env | def parse_env(content):
"""Parse the content of a .env file (a line-delimited KEY=value format) into a
dictionary mapping keys to values.
"""
values = {}
for line in content.splitlines():
lexer = shlex.shlex(line, posix=True)
tokens = list(lexer)
# parses the assignment statement
if len(tokens) < 3:
continue
name, op = tokens[:2]
value = ''.join(tokens[2:])
if op != '=':
continue
if not _ITEM_RE.match(name):
continue
value = value.replace(r'\n', '\n')
value = value.replace(r'\t', '\t')
values[name] = value
return values | python | def parse_env(content):
"""Parse the content of a .env file (a line-delimited KEY=value format) into a
dictionary mapping keys to values.
"""
values = {}
for line in content.splitlines():
lexer = shlex.shlex(line, posix=True)
tokens = list(lexer)
# parses the assignment statement
if len(tokens) < 3:
continue
name, op = tokens[:2]
value = ''.join(tokens[2:])
if op != '=':
continue
if not _ITEM_RE.match(name):
continue
value = value.replace(r'\n', '\n')
value = value.replace(r'\t', '\t')
values[name] = value
return values | [
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PyPSA/PyPSA | pypsa/descriptors.py | get_switchable_as_dense | def get_switchable_as_dense(network, component, attr, snapshots=None, inds=None):
"""
Return a Dataframe for a time-varying component attribute with values for all
non-time-varying components filled in with the default values for the
attribute.
Parameters
----------
network : pypsa.Network
component : string
Component object name, e.g. 'Generator' or 'Link'
attr : string
Attribute name
snapshots : pandas.Index
Restrict to these snapshots rather than network.snapshots.
inds : pandas.Index
Restrict to these components rather than network.components.index
Returns
-------
pandas.DataFrame
Examples
--------
>>> get_switchable_as_dense(network, 'Generator', 'p_max_pu')
"""
df = network.df(component)
pnl = network.pnl(component)
index = df.index
varying_i = pnl[attr].columns
fixed_i = df.index.difference(varying_i)
if inds is not None:
index = index.intersection(inds)
varying_i = varying_i.intersection(inds)
fixed_i = fixed_i.intersection(inds)
if snapshots is None:
snapshots = network.snapshots
return (pd.concat([
pd.DataFrame(np.repeat([df.loc[fixed_i, attr].values], len(snapshots), axis=0),
index=snapshots, columns=fixed_i),
pnl[attr].loc[snapshots, varying_i]
], axis=1, sort=False).reindex(columns=index)) | python | def get_switchable_as_dense(network, component, attr, snapshots=None, inds=None):
"""
Return a Dataframe for a time-varying component attribute with values for all
non-time-varying components filled in with the default values for the
attribute.
Parameters
----------
network : pypsa.Network
component : string
Component object name, e.g. 'Generator' or 'Link'
attr : string
Attribute name
snapshots : pandas.Index
Restrict to these snapshots rather than network.snapshots.
inds : pandas.Index
Restrict to these components rather than network.components.index
Returns
-------
pandas.DataFrame
Examples
--------
>>> get_switchable_as_dense(network, 'Generator', 'p_max_pu')
"""
df = network.df(component)
pnl = network.pnl(component)
index = df.index
varying_i = pnl[attr].columns
fixed_i = df.index.difference(varying_i)
if inds is not None:
index = index.intersection(inds)
varying_i = varying_i.intersection(inds)
fixed_i = fixed_i.intersection(inds)
if snapshots is None:
snapshots = network.snapshots
return (pd.concat([
pd.DataFrame(np.repeat([df.loc[fixed_i, attr].values], len(snapshots), axis=0),
index=snapshots, columns=fixed_i),
pnl[attr].loc[snapshots, varying_i]
], axis=1, sort=False).reindex(columns=index)) | [
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Component object name, e.g. 'Generator' or 'Link'
attr : string
Attribute name
snapshots : pandas.Index
Restrict to these snapshots rather than network.snapshots.
inds : pandas.Index
Restrict to these components rather than network.components.index
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pandas.DataFrame
Examples
--------
>>> get_switchable_as_dense(network, 'Generator', 'p_max_pu') | [
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ejhigson/nestcheck | nestcheck/data_processing.py | process_error_helper | def process_error_helper(root, base_dir, process_func, errors_to_handle=(),
**func_kwargs):
"""Wrapper which applies process_func and handles some common errors so one
bad run does not spoil the whole batch.
Useful errors to handle include:
OSError: if you are not sure if all the files exist
AssertionError: if some of the many assertions fail for known reasons;
for example is there are occasional problems decomposing runs into threads
due to limited numerical precision in logls.
Parameters
----------
root: str
File root.
base_dir: str
Directory containing file.
process_func: func
Function for processing file.
errors_to_handle: error type or tuple of error types
Errors to catch without throwing an exception.
func_kwargs: dict
Kwargs to pass to process_func.
Returns
-------
run: dict
Nested sampling run dict (see the module docstring for more
details) or, if an error occured, a dict containing its type
and the file root.
"""
try:
return process_func(root, base_dir, **func_kwargs)
except errors_to_handle as err:
run = {'error': type(err).__name__,
'output': {'file_root': root}}
return run | python | def process_error_helper(root, base_dir, process_func, errors_to_handle=(),
**func_kwargs):
"""Wrapper which applies process_func and handles some common errors so one
bad run does not spoil the whole batch.
Useful errors to handle include:
OSError: if you are not sure if all the files exist
AssertionError: if some of the many assertions fail for known reasons;
for example is there are occasional problems decomposing runs into threads
due to limited numerical precision in logls.
Parameters
----------
root: str
File root.
base_dir: str
Directory containing file.
process_func: func
Function for processing file.
errors_to_handle: error type or tuple of error types
Errors to catch without throwing an exception.
func_kwargs: dict
Kwargs to pass to process_func.
Returns
-------
run: dict
Nested sampling run dict (see the module docstring for more
details) or, if an error occured, a dict containing its type
and the file root.
"""
try:
return process_func(root, base_dir, **func_kwargs)
except errors_to_handle as err:
run = {'error': type(err).__name__,
'output': {'file_root': root}}
return run | [
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Errors to catch without throwing an exception.
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HPENetworking/PYHPEIMC | archived/pyhpimc.py | get_vm_host_info | def get_vm_host_info(hostId):
"""
function takes hostId as input to RESTFUL call to HP IMC
:param hostId: int or string of HostId of Hypervisor host
:return:list of dictionatires contraining the VM Host information for the target hypervisor
"""
global r
if auth is None or url is None: # checks to see if the imc credentials are already available
set_imc_creds()
get_vm_host_info_url = "/imcrs/vrm/host?hostId=" + str(hostId)
f_url = url + get_vm_host_info_url
payload = None
r = requests.get(f_url, auth=auth,
headers=headers) # creates the URL using the payload variable as the contents
# print(r.status_code)
if r.status_code == 200:
if len(r.text) > 0:
return json.loads(r.text)
elif r.status_code == 204:
print("Device is not a supported Hypervisor")
return "Device is not a supported Hypervisor"
else:
print("An Error has occured") | python | def get_vm_host_info(hostId):
"""
function takes hostId as input to RESTFUL call to HP IMC
:param hostId: int or string of HostId of Hypervisor host
:return:list of dictionatires contraining the VM Host information for the target hypervisor
"""
global r
if auth is None or url is None: # checks to see if the imc credentials are already available
set_imc_creds()
get_vm_host_info_url = "/imcrs/vrm/host?hostId=" + str(hostId)
f_url = url + get_vm_host_info_url
payload = None
r = requests.get(f_url, auth=auth,
headers=headers) # creates the URL using the payload variable as the contents
# print(r.status_code)
if r.status_code == 200:
if len(r.text) > 0:
return json.loads(r.text)
elif r.status_code == 204:
print("Device is not a supported Hypervisor")
return "Device is not a supported Hypervisor"
else:
print("An Error has occured") | [
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rhayes777/PyAutoFit | autofit/tools/pipeline.py | ResultsCollection.from_phase | def from_phase(self, phase_name):
"""
Returns the result of a previous phase by its name
Parameters
----------
phase_name: str
The name of a previous phase
Returns
-------
result: Result
The result of that phase
Raises
------
exc.PipelineException
If no phase with the expected result is found
"""
try:
return self.__result_dict[phase_name]
except KeyError:
raise exc.PipelineException("No previous phase named {} found in results ({})".format(phase_name, ", ".join(
self.__result_dict.keys()))) | python | def from_phase(self, phase_name):
"""
Returns the result of a previous phase by its name
Parameters
----------
phase_name: str
The name of a previous phase
Returns
-------
result: Result
The result of that phase
Raises
------
exc.PipelineException
If no phase with the expected result is found
"""
try:
return self.__result_dict[phase_name]
except KeyError:
raise exc.PipelineException("No previous phase named {} found in results ({})".format(phase_name, ", ".join(
self.__result_dict.keys()))) | [
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resonai/ybt | yabt/caching.py | save_target_in_cache | def save_target_in_cache(target: Target, build_context):
"""Save `target` to build cache for future reuse.
The target hash is used to determine its cache location,
where the target metadata and artifacts metadata are seriazlied to JSON.
In addition, relevant artifacts produced by the target are copied under
the artifacts cache dir by their content hash.
TODO: pruning policy to limit cache size.
"""
cache_dir = build_context.conf.get_cache_dir(target, build_context)
if isdir(cache_dir):
rmtree(cache_dir)
makedirs(cache_dir)
logger.debug('Saving target metadata in cache under {}', cache_dir)
# write target metadata
with open(join(cache_dir, 'target.json'), 'w') as meta_file:
meta_file.write(target.json(build_context))
# copy artifacts to artifact cache by hash
artifacts = target.artifacts.get_all()
artifact_hashes = {}
for artifact_type, artifact_map in artifacts.items():
if artifact_type in (AT.docker_image,):
continue
for dst_path, src_path in artifact_map.items():
artifact_hashes[dst_path] = hash_tree(src_path)
# not caching "app" artifacts, since they're part
# of the source tree
if artifact_type not in _NO_CACHE_TYPES:
copy_artifact(src_path, artifact_hashes[dst_path],
build_context.conf)
# serialize target artifacts metadata + hashes
artifacts_desc = {
artifact_type.name:
[{'dst': dst_path, 'src': src_path,
'hash': artifact_hashes.get(dst_path)}
for dst_path, src_path in artifact_map.items()]
for artifact_type, artifact_map in artifacts.items()
}
with open(join(cache_dir, 'artifacts.json'), 'w') as artifacts_meta_file:
artifacts_meta_file.write(json.dumps(artifacts_desc, indent=4,
sort_keys=True))
# copying the summary dict so I can modify it without mutating the target
summary = dict(target.summary)
summary['name'] = target.name
summary['artifacts_hash'] = hash_tree(join(cache_dir, 'artifacts.json'))
if summary.get('created') is None:
summary['created'] = time()
write_summary(summary, cache_dir) | python | def save_target_in_cache(target: Target, build_context):
"""Save `target` to build cache for future reuse.
The target hash is used to determine its cache location,
where the target metadata and artifacts metadata are seriazlied to JSON.
In addition, relevant artifacts produced by the target are copied under
the artifacts cache dir by their content hash.
TODO: pruning policy to limit cache size.
"""
cache_dir = build_context.conf.get_cache_dir(target, build_context)
if isdir(cache_dir):
rmtree(cache_dir)
makedirs(cache_dir)
logger.debug('Saving target metadata in cache under {}', cache_dir)
# write target metadata
with open(join(cache_dir, 'target.json'), 'w') as meta_file:
meta_file.write(target.json(build_context))
# copy artifacts to artifact cache by hash
artifacts = target.artifacts.get_all()
artifact_hashes = {}
for artifact_type, artifact_map in artifacts.items():
if artifact_type in (AT.docker_image,):
continue
for dst_path, src_path in artifact_map.items():
artifact_hashes[dst_path] = hash_tree(src_path)
# not caching "app" artifacts, since they're part
# of the source tree
if artifact_type not in _NO_CACHE_TYPES:
copy_artifact(src_path, artifact_hashes[dst_path],
build_context.conf)
# serialize target artifacts metadata + hashes
artifacts_desc = {
artifact_type.name:
[{'dst': dst_path, 'src': src_path,
'hash': artifact_hashes.get(dst_path)}
for dst_path, src_path in artifact_map.items()]
for artifact_type, artifact_map in artifacts.items()
}
with open(join(cache_dir, 'artifacts.json'), 'w') as artifacts_meta_file:
artifacts_meta_file.write(json.dumps(artifacts_desc, indent=4,
sort_keys=True))
# copying the summary dict so I can modify it without mutating the target
summary = dict(target.summary)
summary['name'] = target.name
summary['artifacts_hash'] = hash_tree(join(cache_dir, 'artifacts.json'))
if summary.get('created') is None:
summary['created'] = time()
write_summary(summary, cache_dir) | [
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where the target metadata and artifacts metadata are seriazlied to JSON.
In addition, relevant artifacts produced by the target are copied under
the artifacts cache dir by their content hash.
TODO: pruning policy to limit cache size. | [
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christophertbrown/bioscripts | ctbBio/rRNA_copies.py | rna_bases | def rna_bases(rna_cov, scaffold, bases, line):
"""
determine if read overlaps with rna, if so count bases
"""
start = int(line[3])
stop = start + bases - 1
if scaffold not in rna_cov:
return rna_cov
for pos in rna_cov[scaffold][2]:
ol = get_overlap([start, stop], pos)
rna_cov[scaffold][0] += ol
return rna_cov | python | def rna_bases(rna_cov, scaffold, bases, line):
"""
determine if read overlaps with rna, if so count bases
"""
start = int(line[3])
stop = start + bases - 1
if scaffold not in rna_cov:
return rna_cov
for pos in rna_cov[scaffold][2]:
ol = get_overlap([start, stop], pos)
rna_cov[scaffold][0] += ol
return rna_cov | [
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goldmann/docker-squash | docker_squash/image.py | Image._parse_image_name | def _parse_image_name(self, image):
"""
Parses the provided image name and splits it in the
name and tag part, if possible. If no tag is provided
'latest' is used.
"""
if ':' in image and '/' not in image.split(':')[-1]:
image_tag = image.split(':')[-1]
image_name = image[:-(len(image_tag) + 1)]
else:
image_tag = "latest"
image_name = image
return (image_name, image_tag) | python | def _parse_image_name(self, image):
"""
Parses the provided image name and splits it in the
name and tag part, if possible. If no tag is provided
'latest' is used.
"""
if ':' in image and '/' not in image.split(':')[-1]:
image_tag = image.split(':')[-1]
image_name = image[:-(len(image_tag) + 1)]
else:
image_tag = "latest"
image_name = image
return (image_name, image_tag) | [
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wonambi-python/wonambi | wonambi/widgets/notes.py | Notes.get_cycle_mrkr | def get_cycle_mrkr(self, end=False):
"""Mark cycle start or end.
Parameters
----------
end : bool
If True, marks a cycle end; otherwise, it's a cycle start
"""
if self.annot is None: # remove if buttons are disabled
self.parent.statusBar().showMessage('No score file loaded')
return
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
try:
self.annot.set_cycle_mrkr(window_start, end=end)
except KeyError:
msg = ('The start of the window does not correspond to any epoch '
'in sleep scoring file')
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
else:
bound = 'start'
if end:
bound = 'end'
lg.info('User marked ' + str(window_start) + ' as cycle ' +
bound)
self.parent.overview.mark_cycles(window_start, window_length,
end=end) | python | def get_cycle_mrkr(self, end=False):
"""Mark cycle start or end.
Parameters
----------
end : bool
If True, marks a cycle end; otherwise, it's a cycle start
"""
if self.annot is None: # remove if buttons are disabled
self.parent.statusBar().showMessage('No score file loaded')
return
window_start = self.parent.value('window_start')
window_length = self.parent.value('window_length')
try:
self.annot.set_cycle_mrkr(window_start, end=end)
except KeyError:
msg = ('The start of the window does not correspond to any epoch '
'in sleep scoring file')
self.parent.statusBar().showMessage(msg)
lg.debug(msg)
else:
bound = 'start'
if end:
bound = 'end'
lg.info('User marked ' + str(window_start) + ' as cycle ' +
bound)
self.parent.overview.mark_cycles(window_start, window_length,
end=end) | [
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] | train | https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/notes.py#L995-L1027 | 0.001803 |
ARMmbed/mbed-cloud-sdk-python | src/mbed_cloud/_backends/enrollment/apis/public_api_api.py | PublicAPIApi.get_device_enrollments | def get_device_enrollments(self, **kwargs): # noqa: E501
"""Get enrollment list. # noqa: E501
Provides a list of pending and claimed enrollments. **Example usage:** ``` curl -X GET \\ -H 'Authorization: Bearer <valid access token>' \\ https://api.us-east-1.mbedcloud.com/v3/device-enrollments ``` With query parameters: ``` curl -X GET \\ -H 'Authorization: Bearer <valid access token>' \\ 'https://api.us-east-1.mbedcloud.com/v3/device-enrollments?limit=10' ``` # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass asynchronous=True
>>> thread = api.get_device_enrollments(asynchronous=True)
>>> result = thread.get()
:param asynchronous bool
:param int limit: Number of results to be returned. Between 2 and 1000, inclusive.
:param str after: Entity ID to fetch after.
:param str order: ASC or DESC
:param str include: Comma-separated additional data to return. Currently supported: total_count.
:return: EnrollmentIdentities
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('asynchronous'):
return self.get_device_enrollments_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_device_enrollments_with_http_info(**kwargs) # noqa: E501
return data | python | def get_device_enrollments(self, **kwargs): # noqa: E501
"""Get enrollment list. # noqa: E501
Provides a list of pending and claimed enrollments. **Example usage:** ``` curl -X GET \\ -H 'Authorization: Bearer <valid access token>' \\ https://api.us-east-1.mbedcloud.com/v3/device-enrollments ``` With query parameters: ``` curl -X GET \\ -H 'Authorization: Bearer <valid access token>' \\ 'https://api.us-east-1.mbedcloud.com/v3/device-enrollments?limit=10' ``` # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass asynchronous=True
>>> thread = api.get_device_enrollments(asynchronous=True)
>>> result = thread.get()
:param asynchronous bool
:param int limit: Number of results to be returned. Between 2 and 1000, inclusive.
:param str after: Entity ID to fetch after.
:param str order: ASC or DESC
:param str include: Comma-separated additional data to return. Currently supported: total_count.
:return: EnrollmentIdentities
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('asynchronous'):
return self.get_device_enrollments_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_device_enrollments_with_http_info(**kwargs) # noqa: E501
return data | [
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This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass asynchronous=True
>>> thread = api.get_device_enrollments(asynchronous=True)
>>> result = thread.get()
:param asynchronous bool
:param int limit: Number of results to be returned. Between 2 and 1000, inclusive.
:param str after: Entity ID to fetch after.
:param str order: ASC or DESC
:param str include: Comma-separated additional data to return. Currently supported: total_count.
:return: EnrollmentIdentities
If the method is called asynchronously,
returns the request thread. | [
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slaveofcode/pycrawler | pycrawler/page.py | extract_js_links | def extract_js_links(bs4):
"""Extracting js links from BeautifulSoup object
:param bs4: `BeautifulSoup`
:return: `list` List of links
"""
links = extract_links(bs4)
real_js = [anchor for anchor in links if anchor.endswith(('.js', '.JS'))]
js_tags = [anchor['src'] for anchor in bs4.select('script[type="text/javascript"]')
if anchor.has_attr('src')]
return list(set(real_js+js_tags)) | python | def extract_js_links(bs4):
"""Extracting js links from BeautifulSoup object
:param bs4: `BeautifulSoup`
:return: `list` List of links
"""
links = extract_links(bs4)
real_js = [anchor for anchor in links if anchor.endswith(('.js', '.JS'))]
js_tags = [anchor['src'] for anchor in bs4.select('script[type="text/javascript"]')
if anchor.has_attr('src')]
return list(set(real_js+js_tags)) | [
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:param bs4: `BeautifulSoup`
:return: `list` List of links | [
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