body
stringlengths 26
98.2k
| body_hash
int64 -9,222,864,604,528,158,000
9,221,803,474B
| docstring
stringlengths 1
16.8k
| path
stringlengths 5
230
| name
stringlengths 1
96
| repository_name
stringlengths 7
89
| lang
stringclasses 1
value | body_without_docstring
stringlengths 20
98.2k
|
|---|---|---|---|---|---|---|---|
def Update(self, request, context):
'Update an existing organization.\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
| 7,422,785,294,231,557,000
|
Update an existing organization.
|
python/src/chirpstack_api/as_pb/external/api/organization_pb2_grpc.py
|
Update
|
GaiaFL/chirpstack-api
|
python
|
def Update(self, request, context):
'\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
|
def Delete(self, request, context):
'Delete an organization.\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
| 6,167,222,203,331,012,000
|
Delete an organization.
|
python/src/chirpstack_api/as_pb/external/api/organization_pb2_grpc.py
|
Delete
|
GaiaFL/chirpstack-api
|
python
|
def Delete(self, request, context):
'\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
|
def ListUsers(self, request, context):
"Get organization's user list.\n "
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
| -307,360,098,556,259,500
|
Get organization's user list.
|
python/src/chirpstack_api/as_pb/external/api/organization_pb2_grpc.py
|
ListUsers
|
GaiaFL/chirpstack-api
|
python
|
def ListUsers(self, request, context):
"\n "
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
|
def GetUser(self, request, context):
'Get data for a particular organization user.\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
| -4,923,811,219,087,573,000
|
Get data for a particular organization user.
|
python/src/chirpstack_api/as_pb/external/api/organization_pb2_grpc.py
|
GetUser
|
GaiaFL/chirpstack-api
|
python
|
def GetUser(self, request, context):
'\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
|
def AddUser(self, request, context):
'Add a new user to an organization.\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
| -4,596,004,449,455,495,700
|
Add a new user to an organization.
|
python/src/chirpstack_api/as_pb/external/api/organization_pb2_grpc.py
|
AddUser
|
GaiaFL/chirpstack-api
|
python
|
def AddUser(self, request, context):
'\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
|
def UpdateUser(self, request, context):
'Update a user in an organization.\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
| 4,537,644,656,659,038,000
|
Update a user in an organization.
|
python/src/chirpstack_api/as_pb/external/api/organization_pb2_grpc.py
|
UpdateUser
|
GaiaFL/chirpstack-api
|
python
|
def UpdateUser(self, request, context):
'\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
|
def DeleteUser(self, request, context):
'Delete a user from an organization.\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
| -8,003,106,685,235,128,000
|
Delete a user from an organization.
|
python/src/chirpstack_api/as_pb/external/api/organization_pb2_grpc.py
|
DeleteUser
|
GaiaFL/chirpstack-api
|
python
|
def DeleteUser(self, request, context):
'\n '
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
|
def has_pywin32():
"\n Does this environment have pywin32?\n Should return False even when Mercurial's Demand Import allowed import of\n win32cred.\n "
with ExceptionRaisedContext() as exc:
win32cred.__name__
return (not bool(exc))
| -7,351,007,536,469,651,000
|
Does this environment have pywin32?
Should return False even when Mercurial's Demand Import allowed import of
win32cred.
|
src/site-packages/keyrings/alt/Windows.py
|
has_pywin32
|
nficano/alexa-find-my-iphone
|
python
|
def has_pywin32():
"\n Does this environment have pywin32?\n Should return False even when Mercurial's Demand Import allowed import of\n win32cred.\n "
with ExceptionRaisedContext() as exc:
win32cred.__name__
return (not bool(exc))
|
def has_wincrypto():
"\n Does this environment have wincrypto?\n Should return False even when Mercurial's Demand Import allowed import of\n _win_crypto, so accesses an attribute of the module.\n "
with ExceptionRaisedContext() as exc:
_win_crypto.__name__
return (not bool(exc))
| -6,387,045,016,494,770,000
|
Does this environment have wincrypto?
Should return False even when Mercurial's Demand Import allowed import of
_win_crypto, so accesses an attribute of the module.
|
src/site-packages/keyrings/alt/Windows.py
|
has_wincrypto
|
nficano/alexa-find-my-iphone
|
python
|
def has_wincrypto():
"\n Does this environment have wincrypto?\n Should return False even when Mercurial's Demand Import allowed import of\n _win_crypto, so accesses an attribute of the module.\n "
with ExceptionRaisedContext() as exc:
_win_crypto.__name__
return (not bool(exc))
|
@properties.ClassProperty
@classmethod
def priority(self):
'\n Preferred over file.EncryptedKeyring but not other, more sophisticated\n Windows backends.\n '
if (not (platform.system() == 'Windows')):
raise RuntimeError('Requires Windows')
return 0.8
| 1,551,983,717,609,327,600
|
Preferred over file.EncryptedKeyring but not other, more sophisticated
Windows backends.
|
src/site-packages/keyrings/alt/Windows.py
|
priority
|
nficano/alexa-find-my-iphone
|
python
|
@properties.ClassProperty
@classmethod
def priority(self):
'\n Preferred over file.EncryptedKeyring but not other, more sophisticated\n Windows backends.\n '
if (not (platform.system() == 'Windows')):
raise RuntimeError('Requires Windows')
return 0.8
|
def encrypt(self, password):
'Encrypt the password using the CryptAPI.\n '
return _win_crypto.encrypt(password)
| -5,918,730,288,420,528,000
|
Encrypt the password using the CryptAPI.
|
src/site-packages/keyrings/alt/Windows.py
|
encrypt
|
nficano/alexa-find-my-iphone
|
python
|
def encrypt(self, password):
'\n '
return _win_crypto.encrypt(password)
|
def decrypt(self, password_encrypted):
'Decrypt the password using the CryptAPI.\n '
return _win_crypto.decrypt(password_encrypted)
| -1,593,266,222,747,378,200
|
Decrypt the password using the CryptAPI.
|
src/site-packages/keyrings/alt/Windows.py
|
decrypt
|
nficano/alexa-find-my-iphone
|
python
|
def decrypt(self, password_encrypted):
'\n '
return _win_crypto.decrypt(password_encrypted)
|
@properties.ClassProperty
@classmethod
def priority(self):
"\n Preferred on Windows when pywin32 isn't installed\n "
if (platform.system() != 'Windows'):
raise RuntimeError('Requires Windows')
if (not has_wincrypto()):
raise RuntimeError('Requires ctypes')
return 2
| 1,881,900,591,926,196,200
|
Preferred on Windows when pywin32 isn't installed
|
src/site-packages/keyrings/alt/Windows.py
|
priority
|
nficano/alexa-find-my-iphone
|
python
|
@properties.ClassProperty
@classmethod
def priority(self):
"\n \n "
if (platform.system() != 'Windows'):
raise RuntimeError('Requires Windows')
if (not has_wincrypto()):
raise RuntimeError('Requires ctypes')
return 2
|
def get_password(self, service, username):
'Get password of the username for the service\n '
try:
key = ('Software\\%s\\Keyring' % service)
hkey = winreg.OpenKey(winreg.HKEY_CURRENT_USER, key)
password_saved = winreg.QueryValueEx(hkey, username)[0]
password_base64 = password_saved.encode('ascii')
password_encrypted = base64.decodestring(password_base64)
password = _win_crypto.decrypt(password_encrypted).decode('utf-8')
except EnvironmentError:
password = None
return password
| -6,747,333,599,557,987,000
|
Get password of the username for the service
|
src/site-packages/keyrings/alt/Windows.py
|
get_password
|
nficano/alexa-find-my-iphone
|
python
|
def get_password(self, service, username):
'\n '
try:
key = ('Software\\%s\\Keyring' % service)
hkey = winreg.OpenKey(winreg.HKEY_CURRENT_USER, key)
password_saved = winreg.QueryValueEx(hkey, username)[0]
password_base64 = password_saved.encode('ascii')
password_encrypted = base64.decodestring(password_base64)
password = _win_crypto.decrypt(password_encrypted).decode('utf-8')
except EnvironmentError:
password = None
return password
|
def set_password(self, service, username, password):
'Write the password to the registry\n '
password_encrypted = _win_crypto.encrypt(password.encode('utf-8'))
password_base64 = base64.encodestring(password_encrypted)
password_saved = password_base64.decode('ascii')
key_name = ('Software\\%s\\Keyring' % service)
hkey = winreg.CreateKey(winreg.HKEY_CURRENT_USER, key_name)
winreg.SetValueEx(hkey, username, 0, winreg.REG_SZ, password_saved)
| 6,056,869,806,802,730,000
|
Write the password to the registry
|
src/site-packages/keyrings/alt/Windows.py
|
set_password
|
nficano/alexa-find-my-iphone
|
python
|
def set_password(self, service, username, password):
'\n '
password_encrypted = _win_crypto.encrypt(password.encode('utf-8'))
password_base64 = base64.encodestring(password_encrypted)
password_saved = password_base64.decode('ascii')
key_name = ('Software\\%s\\Keyring' % service)
hkey = winreg.CreateKey(winreg.HKEY_CURRENT_USER, key_name)
winreg.SetValueEx(hkey, username, 0, winreg.REG_SZ, password_saved)
|
def delete_password(self, service, username):
'Delete the password for the username of the service.\n '
try:
key_name = ('Software\\%s\\Keyring' % service)
hkey = winreg.OpenKey(winreg.HKEY_CURRENT_USER, key_name, 0, winreg.KEY_ALL_ACCESS)
winreg.DeleteValue(hkey, username)
winreg.CloseKey(hkey)
except WindowsError:
e = sys.exc_info()[1]
raise PasswordDeleteError(e)
self._delete_key_if_empty(service)
| -3,171,431,112,783,877,600
|
Delete the password for the username of the service.
|
src/site-packages/keyrings/alt/Windows.py
|
delete_password
|
nficano/alexa-find-my-iphone
|
python
|
def delete_password(self, service, username):
'\n '
try:
key_name = ('Software\\%s\\Keyring' % service)
hkey = winreg.OpenKey(winreg.HKEY_CURRENT_USER, key_name, 0, winreg.KEY_ALL_ACCESS)
winreg.DeleteValue(hkey, username)
winreg.CloseKey(hkey)
except WindowsError:
e = sys.exc_info()[1]
raise PasswordDeleteError(e)
self._delete_key_if_empty(service)
|
def __contains__(self, t):
' t in this '
for (k, v) in self.__dict__.items():
if (k == t):
return True
if isinstance(v, DictToAttrDeep):
if (t in v):
return True
| -3,873,695,675,860,616,700
|
t in this
|
rulemanager.py
|
__contains__
|
n44hernandezp/openschc
|
python
|
def __contains__(self, t):
' '
for (k, v) in self.__dict__.items():
if (k == t):
return True
if isinstance(v, DictToAttrDeep):
if (t in v):
return True
|
def __getitem__(self, t):
' this[k] '
for (k, v) in self.__dict__.items():
if (k == t):
return v
if isinstance(v, DictToAttrDeep):
if (t in v):
return v[t]
| 521,882,756,960,341,760
|
this[k]
|
rulemanager.py
|
__getitem__
|
n44hernandezp/openschc
|
python
|
def __getitem__(self, t):
' '
for (k, v) in self.__dict__.items():
if (k == t):
return v
if isinstance(v, DictToAttrDeep):
if (t in v):
return v[t]
|
def get(self, k, d=None):
' this.get(k) '
if (k not in self):
return d
return self.__getitem__(k)
| -6,245,523,393,949,625,000
|
this.get(k)
|
rulemanager.py
|
get
|
n44hernandezp/openschc
|
python
|
def get(self, k, d=None):
' '
if (k not in self):
return d
return self.__getitem__(k)
|
def _checkRuleValue(self, rule_id, rule_id_length):
'this function looks if bits specified in ruleID are not outside of\n rule_id_length'
if (rule_id_length > 32):
raise ValueError('Rule length should be less than 32')
r1 = rule_id
for k in range(32, rule_id_length, (- 1)):
if (((1 << k) & r1) != 0):
raise ValueError('rule ID too long')
| 7,033,565,771,389,573,000
|
this function looks if bits specified in ruleID are not outside of
rule_id_length
|
rulemanager.py
|
_checkRuleValue
|
n44hernandezp/openschc
|
python
|
def _checkRuleValue(self, rule_id, rule_id_length):
'this function looks if bits specified in ruleID are not outside of\n rule_id_length'
if (rule_id_length > 32):
raise ValueError('Rule length should be less than 32')
r1 = rule_id
for k in range(32, rule_id_length, (- 1)):
if (((1 << k) & r1) != 0):
raise ValueError('rule ID too long')
|
def _ruleIncluded(self, r1ID, r1l, r2ID, r2l):
'check if a conflict exists between to ruleID (i.e. same first bits equals) '
r1 = (r1ID << (32 - r1l))
r2 = (r2ID << (32 - r2l))
l = min(r1l, r2l)
for k in range((32 - l), 32):
if ((r1 & (1 << k)) != (r2 & (1 << k))):
return False
return True
| 4,394,786,761,340,157,400
|
check if a conflict exists between to ruleID (i.e. same first bits equals)
|
rulemanager.py
|
_ruleIncluded
|
n44hernandezp/openschc
|
python
|
def _ruleIncluded(self, r1ID, r1l, r2ID, r2l):
' '
r1 = (r1ID << (32 - r1l))
r2 = (r2ID << (32 - r2l))
l = min(r1l, r2l)
for k in range((32 - l), 32):
if ((r1 & (1 << k)) != (r2 & (1 << k))):
return False
return True
|
def find_rule_bypacket(self, context, packet_bbuf):
' returns a compression rule or an fragmentation rule\n in the context matching with the field value of rule id in the packet.\n '
for k in ['fragSender', 'fragReceiver', 'fragSender2', 'fragReceiver2', 'comp']:
r = context.get(k)
if (r is not None):
rule_id = packet_bbuf.get_bits(r['ruleLength'], position=0)
if (r['ruleID'] == rule_id):
print('--------------------RuleManage------------------')
print('ruleID ', rule_id)
print()
print('--------------------------------------------------')
return (k, r)
return (None, None)
| -8,728,772,641,255,952,000
|
returns a compression rule or an fragmentation rule
in the context matching with the field value of rule id in the packet.
|
rulemanager.py
|
find_rule_bypacket
|
n44hernandezp/openschc
|
python
|
def find_rule_bypacket(self, context, packet_bbuf):
' returns a compression rule or an fragmentation rule\n in the context matching with the field value of rule id in the packet.\n '
for k in ['fragSender', 'fragReceiver', 'fragSender2', 'fragReceiver2', 'comp']:
r = context.get(k)
if (r is not None):
rule_id = packet_bbuf.get_bits(r['ruleLength'], position=0)
if (r['ruleID'] == rule_id):
print('--------------------RuleManage------------------')
print('ruleID ', rule_id)
print()
print('--------------------------------------------------')
return (k, r)
return (None, None)
|
def find_context_bydevL2addr(self, dev_L2addr):
' find a context with dev_L2addr. '
for c in self._db:
if (c['devL2Addr'] == dev_L2addr):
return c
if (c['devL2Addr'] == '*'):
return c
return None
| 6,848,620,013,530,425,000
|
find a context with dev_L2addr.
|
rulemanager.py
|
find_context_bydevL2addr
|
n44hernandezp/openschc
|
python
|
def find_context_bydevL2addr(self, dev_L2addr):
' '
for c in self._db:
if (c['devL2Addr'] == dev_L2addr):
return c
if (c['devL2Addr'] == '*'):
return c
return None
|
def find_context_bydstiid(self, dst_iid):
' find a context with dst_iid, which can be a wild card. '
for c in self._db:
if (c['dstIID'] == dst_iid):
return c
if (c['dstIID'] == '*'):
return c
return None
| -5,258,360,269,052,741,000
|
find a context with dst_iid, which can be a wild card.
|
rulemanager.py
|
find_context_bydstiid
|
n44hernandezp/openschc
|
python
|
def find_context_bydstiid(self, dst_iid):
' '
for c in self._db:
if (c['dstIID'] == dst_iid):
return c
if (c['dstIID'] == '*'):
return c
return None
|
def find_context_exact(self, dev_L2addr, dst_iid):
' find a context by both devL2Addr and dstIID.\n This is mainly for internal use. '
for c in self._db:
if ((c['devL2Addr'] == dev_L2addr) and (c['dstIID'] == dst_iid)):
return c
return None
| 7,843,116,249,667,408,000
|
find a context by both devL2Addr and dstIID.
This is mainly for internal use.
|
rulemanager.py
|
find_context_exact
|
n44hernandezp/openschc
|
python
|
def find_context_exact(self, dev_L2addr, dst_iid):
' find a context by both devL2Addr and dstIID.\n This is mainly for internal use. '
for c in self._db:
if ((c['devL2Addr'] == dev_L2addr) and (c['dstIID'] == dst_iid)):
return c
return None
|
def add_context(self, context, comp=None, fragSender=None, fragReceiver=None, fragSender2=None, fragReceiver2=None):
' add context into the db. '
if (self.find_context_exact(context['devL2Addr'], context['dstIID']) is not None):
raise ValueError('the context {}/{} exist.'.format(context['devL2Addr'], context['dstIID']))
c = deepcopy(context)
self._db.append(c)
self.add_rules(c, comp, fragSender, fragReceiver, fragSender2, fragReceiver2)
| 4,446,036,947,611,640,300
|
add context into the db.
|
rulemanager.py
|
add_context
|
n44hernandezp/openschc
|
python
|
def add_context(self, context, comp=None, fragSender=None, fragReceiver=None, fragSender2=None, fragReceiver2=None):
' '
if (self.find_context_exact(context['devL2Addr'], context['dstIID']) is not None):
raise ValueError('the context {}/{} exist.'.format(context['devL2Addr'], context['dstIID']))
c = deepcopy(context)
self._db.append(c)
self.add_rules(c, comp, fragSender, fragReceiver, fragSender2, fragReceiver2)
|
def add_rules(self, context, comp=None, fragSender=None, fragReceiver=None, fragSender2=None, fragReceiver2=None):
' add rules into the context specified. '
if (comp is not None):
self.add_rule(context, 'comp', comp)
if (fragSender is not None):
self.add_rule(context, 'fragSender', fragSender)
if (fragReceiver is not None):
self.add_rule(context, 'fragReceiver', fragReceiver)
if (fragSender2 is not None):
self.add_rule(context, 'fragSender2', fragSender2)
if (fragReceiver2 is not None):
self.add_rule(context, 'fragReceiver2', fragReceiver2)
| 3,261,218,258,141,239,300
|
add rules into the context specified.
|
rulemanager.py
|
add_rules
|
n44hernandezp/openschc
|
python
|
def add_rules(self, context, comp=None, fragSender=None, fragReceiver=None, fragSender2=None, fragReceiver2=None):
' '
if (comp is not None):
self.add_rule(context, 'comp', comp)
if (fragSender is not None):
self.add_rule(context, 'fragSender', fragSender)
if (fragReceiver is not None):
self.add_rule(context, 'fragReceiver', fragReceiver)
if (fragSender2 is not None):
self.add_rule(context, 'fragSender2', fragSender2)
if (fragReceiver2 is not None):
self.add_rule(context, 'fragReceiver2', fragReceiver2)
|
def add_rule(self, context, key, rule):
' Check rule integrity and uniqueless and add it to the db '
if (not ('ruleID' in rule)):
raise ValueError('Rule ID not defined.')
if (not ('ruleLength' in rule)):
if (rule['ruleID'] < 255):
rule['ruleLength'] = 8
else:
raise ValueError('RuleID too large for default size on a byte')
if (key == 'comp'):
self.check_rule_compression(rule)
elif (key in ['fragSender', 'fragReceiver', 'fragSender2', 'fragReceiver2', 'comp']):
self.check_rule_fragmentation(rule)
else:
raise ValueError('key must be either comp, fragSender, fragReceiver, fragSender2, fragReceiver2')
rule_id = rule['ruleID']
rule_id_length = rule['ruleLength']
self._checkRuleValue(rule_id, rule_id_length)
for k in ['fragSender', 'fragReceiver', 'fragSender2', 'fragReceiver2', 'comp']:
r = context.get(k)
if (r is not None):
if ((rule_id_length == r.ruleLength) and (rule_id == r.ruleID)):
raise ValueError('Rule {}/{} exists'.format(rule_id, rule_id_length))
context[key] = DictToAttrDeep(**rule)
| 7,021,694,655,899,434,000
|
Check rule integrity and uniqueless and add it to the db
|
rulemanager.py
|
add_rule
|
n44hernandezp/openschc
|
python
|
def add_rule(self, context, key, rule):
' '
if (not ('ruleID' in rule)):
raise ValueError('Rule ID not defined.')
if (not ('ruleLength' in rule)):
if (rule['ruleID'] < 255):
rule['ruleLength'] = 8
else:
raise ValueError('RuleID too large for default size on a byte')
if (key == 'comp'):
self.check_rule_compression(rule)
elif (key in ['fragSender', 'fragReceiver', 'fragSender2', 'fragReceiver2', 'comp']):
self.check_rule_fragmentation(rule)
else:
raise ValueError('key must be either comp, fragSender, fragReceiver, fragSender2, fragReceiver2')
rule_id = rule['ruleID']
rule_id_length = rule['ruleLength']
self._checkRuleValue(rule_id, rule_id_length)
for k in ['fragSender', 'fragReceiver', 'fragSender2', 'fragReceiver2', 'comp']:
r = context.get(k)
if (r is not None):
if ((rule_id_length == r.ruleLength) and (rule_id == r.ruleID)):
raise ValueError('Rule {}/{} exists'.format(rule_id, rule_id_length))
context[key] = DictToAttrDeep(**rule)
|
def check_rule_compression(self, rule):
' compression rule check '
if ((not ('compression' in rule)) or ('fragmentation' in rule)):
raise ValueError('{} Invalid rule'.format(self._nameRule(rule)))
canon_rule_set = []
for r in rule['compression']['rule_set']:
canon_r = {}
for (k, v) in r.items():
if isinstance(v, str):
canon_r[k.upper()] = v.upper()
else:
canon_r[k.upper()] = v
canon_rule_set.append(canon_r)
rule['compression']['rule_set'] = canon_rule_set
| 2,203,879,224,705,384,400
|
compression rule check
|
rulemanager.py
|
check_rule_compression
|
n44hernandezp/openschc
|
python
|
def check_rule_compression(self, rule):
' '
if ((not ('compression' in rule)) or ('fragmentation' in rule)):
raise ValueError('{} Invalid rule'.format(self._nameRule(rule)))
canon_rule_set = []
for r in rule['compression']['rule_set']:
canon_r = {}
for (k, v) in r.items():
if isinstance(v, str):
canon_r[k.upper()] = v.upper()
else:
canon_r[k.upper()] = v
canon_rule_set.append(canon_r)
rule['compression']['rule_set'] = canon_rule_set
|
def check_rule_fragmentation(self, rule):
' fragmentation rule check '
if ((not ('fragmentation' in rule)) or ('compression' in rule)):
raise ValueError('{} Invalid rule'.format(self._nameRule(rule)))
if ('fragmentation' in rule):
fragRule = rule['fragmentation']
if (not ('FRMode' in fragRule)):
raise ValueError('{} Fragmentation mode must be specified'.format(self._nameRule(rule)))
mode = fragRule['FRMode']
if (not (mode in ('noAck', 'ackAlways', 'ackOnError'))):
raise ValueError('{} Unknown fragmentation mode'.format(self._nameRule(rule)))
if (not ('FRModeProfile' in fragRule)):
fragRule['FRModeProfile'] = {}
profile = fragRule['FRModeProfile']
if (not ('dtagSize' in profile)):
profile['dtagSize'] = 0
if (not ('WSize' in profile)):
if (mode == 'noAck'):
profile['WSize'] = 0
elif (mode == 'ackAlways'):
profile['WSize'] = 1
elif (mode == 'ackOnError'):
profile['WSize'] = 5
if (not ('FCNSize' in profile)):
if (mode == 'noAck'):
profile['FCNSize'] = 1
elif (mode == 'ackAlways'):
profile['FCNSize'] = 3
elif (mode == 'ackOnError'):
profile['FCNSize'] = 3
if ('windowSize' in profile):
if ((profile['windowSize'] > ((1 << profile['FCNSize']) - 1)) or (profile['windowSize'] < 0)):
raise ValueError('{} illegal windowSize'.format(self._nameRule(rule)))
else:
profile['windowSize'] = ((1 << profile['FCNSize']) - 1)
if (mode == 'ackOnError'):
if (not ('ackBehavior' in profile)):
raise ValueError('Ack on error behavior must be specified (afterAll1 or afterAll0)')
if (not ('tileSize' in profile)):
profile['tileSize'] = 64
| -3,439,352,066,574,126,600
|
fragmentation rule check
|
rulemanager.py
|
check_rule_fragmentation
|
n44hernandezp/openschc
|
python
|
def check_rule_fragmentation(self, rule):
' '
if ((not ('fragmentation' in rule)) or ('compression' in rule)):
raise ValueError('{} Invalid rule'.format(self._nameRule(rule)))
if ('fragmentation' in rule):
fragRule = rule['fragmentation']
if (not ('FRMode' in fragRule)):
raise ValueError('{} Fragmentation mode must be specified'.format(self._nameRule(rule)))
mode = fragRule['FRMode']
if (not (mode in ('noAck', 'ackAlways', 'ackOnError'))):
raise ValueError('{} Unknown fragmentation mode'.format(self._nameRule(rule)))
if (not ('FRModeProfile' in fragRule)):
fragRule['FRModeProfile'] = {}
profile = fragRule['FRModeProfile']
if (not ('dtagSize' in profile)):
profile['dtagSize'] = 0
if (not ('WSize' in profile)):
if (mode == 'noAck'):
profile['WSize'] = 0
elif (mode == 'ackAlways'):
profile['WSize'] = 1
elif (mode == 'ackOnError'):
profile['WSize'] = 5
if (not ('FCNSize' in profile)):
if (mode == 'noAck'):
profile['FCNSize'] = 1
elif (mode == 'ackAlways'):
profile['FCNSize'] = 3
elif (mode == 'ackOnError'):
profile['FCNSize'] = 3
if ('windowSize' in profile):
if ((profile['windowSize'] > ((1 << profile['FCNSize']) - 1)) or (profile['windowSize'] < 0)):
raise ValueError('{} illegal windowSize'.format(self._nameRule(rule)))
else:
profile['windowSize'] = ((1 << profile['FCNSize']) - 1)
if (mode == 'ackOnError'):
if (not ('ackBehavior' in profile)):
raise ValueError('Ack on error behavior must be specified (afterAll1 or afterAll0)')
if (not ('tileSize' in profile)):
profile['tileSize'] = 64
|
def adjust_bbox(fig, format, bbox_inches):
'\n Temporarily adjust the figure so that only the specified area\n (bbox_inches) is saved.\n\n It modifies fig.bbox, fig.bbox_inches,\n fig.transFigure._boxout, and fig.patch. While the figure size\n changes, the scale of the original figure is conserved. A\n function which restores the original values are returned.\n '
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect('auto')
def restore_bbox():
for (ax, asp, loc) in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
adjust_bbox_handler = _adjust_bbox_handler_d.get(format)
if (adjust_bbox_handler is not None):
adjust_bbox_handler(fig, bbox_inches)
return restore_bbox
else:
warnings.warn(('bbox_inches option for %s backend is not implemented yet.' % format))
return None
| 8,733,391,175,678,230,000
|
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
|
editing files/Portable Python 3.2.5.1/App/Lib/site-packages/matplotlib/tight_bbox.py
|
adjust_bbox
|
mattl1598/Project-Mochachino
|
python
|
def adjust_bbox(fig, format, bbox_inches):
'\n Temporarily adjust the figure so that only the specified area\n (bbox_inches) is saved.\n\n It modifies fig.bbox, fig.bbox_inches,\n fig.transFigure._boxout, and fig.patch. While the figure size\n changes, the scale of the original figure is conserved. A\n function which restores the original values are returned.\n '
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect('auto')
def restore_bbox():
for (ax, asp, loc) in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
adjust_bbox_handler = _adjust_bbox_handler_d.get(format)
if (adjust_bbox_handler is not None):
adjust_bbox_handler(fig, bbox_inches)
return restore_bbox
else:
warnings.warn(('bbox_inches option for %s backend is not implemented yet.' % format))
return None
|
def adjust_bbox_png(fig, bbox_inches):
'\n adjust_bbox for png (Agg) format\n '
tr = fig.dpi_scale_trans
_bbox = TransformedBbox(bbox_inches, tr)
(x0, y0) = (_bbox.x0, _bbox.y0)
fig.bbox_inches = Bbox.from_bounds(0, 0, bbox_inches.width, bbox_inches.height)
(x0, y0) = (_bbox.x0, _bbox.y0)
(w1, h1) = (fig.bbox.width, fig.bbox.height)
fig.transFigure._boxout = Bbox.from_bounds((- x0), (- y0), w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds((x0 / w1), (y0 / h1), (fig.bbox.width / w1), (fig.bbox.height / h1))
| 3,590,841,682,367,379,000
|
adjust_bbox for png (Agg) format
|
editing files/Portable Python 3.2.5.1/App/Lib/site-packages/matplotlib/tight_bbox.py
|
adjust_bbox_png
|
mattl1598/Project-Mochachino
|
python
|
def adjust_bbox_png(fig, bbox_inches):
'\n \n '
tr = fig.dpi_scale_trans
_bbox = TransformedBbox(bbox_inches, tr)
(x0, y0) = (_bbox.x0, _bbox.y0)
fig.bbox_inches = Bbox.from_bounds(0, 0, bbox_inches.width, bbox_inches.height)
(x0, y0) = (_bbox.x0, _bbox.y0)
(w1, h1) = (fig.bbox.width, fig.bbox.height)
fig.transFigure._boxout = Bbox.from_bounds((- x0), (- y0), w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds((x0 / w1), (y0 / h1), (fig.bbox.width / w1), (fig.bbox.height / h1))
|
def adjust_bbox_pdf(fig, bbox_inches):
'\n adjust_bbox for pdf & eps format\n '
if (fig._cachedRenderer.__class__.__name__ == 'RendererPgf'):
tr = Affine2D().scale(fig.dpi)
f = 1.0
else:
tr = Affine2D().scale(72)
f = (72.0 / fig.dpi)
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0, bbox_inches.width, bbox_inches.height)
(x0, y0) = (_bbox.x0, _bbox.y0)
(w1, h1) = ((fig.bbox.width * f), (fig.bbox.height * f))
fig.transFigure._boxout = Bbox.from_bounds((- x0), (- y0), w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds((x0 / w1), (y0 / h1), (fig.bbox.width / w1), (fig.bbox.height / h1))
| 6,674,782,105,807,579,000
|
adjust_bbox for pdf & eps format
|
editing files/Portable Python 3.2.5.1/App/Lib/site-packages/matplotlib/tight_bbox.py
|
adjust_bbox_pdf
|
mattl1598/Project-Mochachino
|
python
|
def adjust_bbox_pdf(fig, bbox_inches):
'\n \n '
if (fig._cachedRenderer.__class__.__name__ == 'RendererPgf'):
tr = Affine2D().scale(fig.dpi)
f = 1.0
else:
tr = Affine2D().scale(72)
f = (72.0 / fig.dpi)
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0, bbox_inches.width, bbox_inches.height)
(x0, y0) = (_bbox.x0, _bbox.y0)
(w1, h1) = ((fig.bbox.width * f), (fig.bbox.height * f))
fig.transFigure._boxout = Bbox.from_bounds((- x0), (- y0), w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds((x0 / w1), (y0 / h1), (fig.bbox.width / w1), (fig.bbox.height / h1))
|
def process_figure_for_rasterizing(figure, bbox_inches_restore, mode):
'\n This need to be called when figure dpi changes during the drawing\n (e.g., rasterizing). It recovers the bbox and re-adjust it with\n the new dpi.\n '
(bbox_inches, restore_bbox) = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, mode, bbox_inches)
return (bbox_inches, r)
| 424,844,068,388,893,500
|
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
|
editing files/Portable Python 3.2.5.1/App/Lib/site-packages/matplotlib/tight_bbox.py
|
process_figure_for_rasterizing
|
mattl1598/Project-Mochachino
|
python
|
def process_figure_for_rasterizing(figure, bbox_inches_restore, mode):
'\n This need to be called when figure dpi changes during the drawing\n (e.g., rasterizing). It recovers the bbox and re-adjust it with\n the new dpi.\n '
(bbox_inches, restore_bbox) = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, mode, bbox_inches)
return (bbox_inches, r)
|
def validate_authorization_request(self):
'The client constructs the request URI by adding the following\n parameters to the query component of the authorization endpoint URI\n using the "application/x-www-form-urlencoded" format.\n Per `Section 4.2.1`_.\n\n response_type\n REQUIRED. Value MUST be set to "token".\n\n client_id\n REQUIRED. The client identifier as described in Section 2.2.\n\n redirect_uri\n OPTIONAL. As described in Section 3.1.2.\n\n scope\n OPTIONAL. The scope of the access request as described by\n Section 3.3.\n\n state\n RECOMMENDED. An opaque value used by the client to maintain\n state between the request and callback. The authorization\n server includes this value when redirecting the user-agent back\n to the client. The parameter SHOULD be used for preventing\n cross-site request forgery as described in Section 10.12.\n\n The client directs the resource owner to the constructed URI using an\n HTTP redirection response, or by other means available to it via the\n user-agent.\n\n For example, the client directs the user-agent to make the following\n HTTP request using TLS:\n\n .. code-block:: http\n\n GET /authorize?response_type=token&client_id=s6BhdRkqt3&state=xyz\n &redirect_uri=https%3A%2F%2Fclient%2Eexample%2Ecom%2Fcb HTTP/1.1\n Host: server.example.com\n\n .. _`Section 4.2.1`: https://tools.ietf.org/html/rfc6749#section-4.2.1\n '
client = self.authenticate_token_endpoint_client()
log.debug('Validate authorization request of %r', client)
redirect_uri = self.validate_authorization_redirect_uri(self.request, client)
response_type = self.request.response_type
if (not client.check_response_type(response_type)):
raise UnauthorizedClientError('The client is not authorized to use "response_type={}"'.format(response_type), state=self.request.state, redirect_uri=redirect_uri, redirect_fragment=True)
try:
self.request.client = client
self.validate_requested_scope()
self.execute_hook('after_validate_authorization_request')
except OAuth2Error as error:
error.redirect_uri = redirect_uri
error.redirect_fragment = True
raise error
return redirect_uri
| -2,264,537,786,648,840,400
|
The client constructs the request URI by adding the following
parameters to the query component of the authorization endpoint URI
using the "application/x-www-form-urlencoded" format.
Per `Section 4.2.1`_.
response_type
REQUIRED. Value MUST be set to "token".
client_id
REQUIRED. The client identifier as described in Section 2.2.
redirect_uri
OPTIONAL. As described in Section 3.1.2.
scope
OPTIONAL. The scope of the access request as described by
Section 3.3.
state
RECOMMENDED. An opaque value used by the client to maintain
state between the request and callback. The authorization
server includes this value when redirecting the user-agent back
to the client. The parameter SHOULD be used for preventing
cross-site request forgery as described in Section 10.12.
The client directs the resource owner to the constructed URI using an
HTTP redirection response, or by other means available to it via the
user-agent.
For example, the client directs the user-agent to make the following
HTTP request using TLS:
.. code-block:: http
GET /authorize?response_type=token&client_id=s6BhdRkqt3&state=xyz
&redirect_uri=https%3A%2F%2Fclient%2Eexample%2Ecom%2Fcb HTTP/1.1
Host: server.example.com
.. _`Section 4.2.1`: https://tools.ietf.org/html/rfc6749#section-4.2.1
|
authlib/oauth2/rfc6749/grants/implicit.py
|
validate_authorization_request
|
2tunnels/authlib
|
python
|
def validate_authorization_request(self):
'The client constructs the request URI by adding the following\n parameters to the query component of the authorization endpoint URI\n using the "application/x-www-form-urlencoded" format.\n Per `Section 4.2.1`_.\n\n response_type\n REQUIRED. Value MUST be set to "token".\n\n client_id\n REQUIRED. The client identifier as described in Section 2.2.\n\n redirect_uri\n OPTIONAL. As described in Section 3.1.2.\n\n scope\n OPTIONAL. The scope of the access request as described by\n Section 3.3.\n\n state\n RECOMMENDED. An opaque value used by the client to maintain\n state between the request and callback. The authorization\n server includes this value when redirecting the user-agent back\n to the client. The parameter SHOULD be used for preventing\n cross-site request forgery as described in Section 10.12.\n\n The client directs the resource owner to the constructed URI using an\n HTTP redirection response, or by other means available to it via the\n user-agent.\n\n For example, the client directs the user-agent to make the following\n HTTP request using TLS:\n\n .. code-block:: http\n\n GET /authorize?response_type=token&client_id=s6BhdRkqt3&state=xyz\n &redirect_uri=https%3A%2F%2Fclient%2Eexample%2Ecom%2Fcb HTTP/1.1\n Host: server.example.com\n\n .. _`Section 4.2.1`: https://tools.ietf.org/html/rfc6749#section-4.2.1\n '
client = self.authenticate_token_endpoint_client()
log.debug('Validate authorization request of %r', client)
redirect_uri = self.validate_authorization_redirect_uri(self.request, client)
response_type = self.request.response_type
if (not client.check_response_type(response_type)):
raise UnauthorizedClientError('The client is not authorized to use "response_type={}"'.format(response_type), state=self.request.state, redirect_uri=redirect_uri, redirect_fragment=True)
try:
self.request.client = client
self.validate_requested_scope()
self.execute_hook('after_validate_authorization_request')
except OAuth2Error as error:
error.redirect_uri = redirect_uri
error.redirect_fragment = True
raise error
return redirect_uri
|
def create_authorization_response(self, redirect_uri, grant_user):
'If the resource owner grants the access request, the authorization\n server issues an access token and delivers it to the client by adding\n the following parameters to the fragment component of the redirection\n URI using the "application/x-www-form-urlencoded" format.\n Per `Section 4.2.2`_.\n\n access_token\n REQUIRED. The access token issued by the authorization server.\n\n token_type\n REQUIRED. The type of the token issued as described in\n Section 7.1. Value is case insensitive.\n\n expires_in\n RECOMMENDED. The lifetime in seconds of the access token. For\n example, the value "3600" denotes that the access token will\n expire in one hour from the time the response was generated.\n If omitted, the authorization server SHOULD provide the\n expiration time via other means or document the default value.\n\n scope\n OPTIONAL, if identical to the scope requested by the client;\n otherwise, REQUIRED. The scope of the access token as\n described by Section 3.3.\n\n state\n REQUIRED if the "state" parameter was present in the client\n authorization request. The exact value received from the\n client.\n\n The authorization server MUST NOT issue a refresh token.\n\n For example, the authorization server redirects the user-agent by\n sending the following HTTP response:\n\n .. code-block:: http\n\n HTTP/1.1 302 Found\n Location: http://example.com/cb#access_token=2YotnFZFEjr1zCsicMWpAA\n &state=xyz&token_type=example&expires_in=3600\n\n Developers should note that some user-agents do not support the\n inclusion of a fragment component in the HTTP "Location" response\n header field. Such clients will require using other methods for\n redirecting the client than a 3xx redirection response -- for\n example, returning an HTML page that includes a \'continue\' button\n with an action linked to the redirection URI.\n\n .. _`Section 4.2.2`: https://tools.ietf.org/html/rfc6749#section-4.2.2\n\n :param redirect_uri: Redirect to the given URI for the authorization\n :param grant_user: if resource owner granted the request, pass this\n resource owner, otherwise pass None.\n :returns: (status_code, body, headers)\n '
state = self.request.state
if grant_user:
self.request.user = grant_user
client = self.request.client
token = self.generate_token(client, self.GRANT_TYPE, user=grant_user, scope=client.get_allowed_scope(self.request.scope), include_refresh_token=False)
log.debug('Grant token %r to %r', token, client)
self.save_token(token)
self.execute_hook('process_token', token=token)
params = [(k, token[k]) for k in token]
if state:
params.append(('state', state))
uri = add_params_to_uri(redirect_uri, params, fragment=True)
headers = [('Location', uri)]
return (302, '', headers)
else:
raise AccessDeniedError(state=state, redirect_uri=redirect_uri, redirect_fragment=True)
| -206,207,470,362,243,260
|
If the resource owner grants the access request, the authorization
server issues an access token and delivers it to the client by adding
the following parameters to the fragment component of the redirection
URI using the "application/x-www-form-urlencoded" format.
Per `Section 4.2.2`_.
access_token
REQUIRED. The access token issued by the authorization server.
token_type
REQUIRED. The type of the token issued as described in
Section 7.1. Value is case insensitive.
expires_in
RECOMMENDED. The lifetime in seconds of the access token. For
example, the value "3600" denotes that the access token will
expire in one hour from the time the response was generated.
If omitted, the authorization server SHOULD provide the
expiration time via other means or document the default value.
scope
OPTIONAL, if identical to the scope requested by the client;
otherwise, REQUIRED. The scope of the access token as
described by Section 3.3.
state
REQUIRED if the "state" parameter was present in the client
authorization request. The exact value received from the
client.
The authorization server MUST NOT issue a refresh token.
For example, the authorization server redirects the user-agent by
sending the following HTTP response:
.. code-block:: http
HTTP/1.1 302 Found
Location: http://example.com/cb#access_token=2YotnFZFEjr1zCsicMWpAA
&state=xyz&token_type=example&expires_in=3600
Developers should note that some user-agents do not support the
inclusion of a fragment component in the HTTP "Location" response
header field. Such clients will require using other methods for
redirecting the client than a 3xx redirection response -- for
example, returning an HTML page that includes a 'continue' button
with an action linked to the redirection URI.
.. _`Section 4.2.2`: https://tools.ietf.org/html/rfc6749#section-4.2.2
:param redirect_uri: Redirect to the given URI for the authorization
:param grant_user: if resource owner granted the request, pass this
resource owner, otherwise pass None.
:returns: (status_code, body, headers)
|
authlib/oauth2/rfc6749/grants/implicit.py
|
create_authorization_response
|
2tunnels/authlib
|
python
|
def create_authorization_response(self, redirect_uri, grant_user):
'If the resource owner grants the access request, the authorization\n server issues an access token and delivers it to the client by adding\n the following parameters to the fragment component of the redirection\n URI using the "application/x-www-form-urlencoded" format.\n Per `Section 4.2.2`_.\n\n access_token\n REQUIRED. The access token issued by the authorization server.\n\n token_type\n REQUIRED. The type of the token issued as described in\n Section 7.1. Value is case insensitive.\n\n expires_in\n RECOMMENDED. The lifetime in seconds of the access token. For\n example, the value "3600" denotes that the access token will\n expire in one hour from the time the response was generated.\n If omitted, the authorization server SHOULD provide the\n expiration time via other means or document the default value.\n\n scope\n OPTIONAL, if identical to the scope requested by the client;\n otherwise, REQUIRED. The scope of the access token as\n described by Section 3.3.\n\n state\n REQUIRED if the "state" parameter was present in the client\n authorization request. The exact value received from the\n client.\n\n The authorization server MUST NOT issue a refresh token.\n\n For example, the authorization server redirects the user-agent by\n sending the following HTTP response:\n\n .. code-block:: http\n\n HTTP/1.1 302 Found\n Location: http://example.com/cb#access_token=2YotnFZFEjr1zCsicMWpAA\n &state=xyz&token_type=example&expires_in=3600\n\n Developers should note that some user-agents do not support the\n inclusion of a fragment component in the HTTP "Location" response\n header field. Such clients will require using other methods for\n redirecting the client than a 3xx redirection response -- for\n example, returning an HTML page that includes a \'continue\' button\n with an action linked to the redirection URI.\n\n .. _`Section 4.2.2`: https://tools.ietf.org/html/rfc6749#section-4.2.2\n\n :param redirect_uri: Redirect to the given URI for the authorization\n :param grant_user: if resource owner granted the request, pass this\n resource owner, otherwise pass None.\n :returns: (status_code, body, headers)\n '
state = self.request.state
if grant_user:
self.request.user = grant_user
client = self.request.client
token = self.generate_token(client, self.GRANT_TYPE, user=grant_user, scope=client.get_allowed_scope(self.request.scope), include_refresh_token=False)
log.debug('Grant token %r to %r', token, client)
self.save_token(token)
self.execute_hook('process_token', token=token)
params = [(k, token[k]) for k in token]
if state:
params.append(('state', state))
uri = add_params_to_uri(redirect_uri, params, fragment=True)
headers = [('Location', uri)]
return (302, , headers)
else:
raise AccessDeniedError(state=state, redirect_uri=redirect_uri, redirect_fragment=True)
|
def combination(n, r):
'\n :param n: the count of different items\n :param r: the number of select\n :return: combination\n n! / (r! * (n - r)!)\n '
r = min((n - r), r)
result = 1
for i in range(n, (n - r), (- 1)):
result *= i
for i in range(1, (r + 1)):
result //= i
return result
| -5,737,441,407,772,606,000
|
:param n: the count of different items
:param r: the number of select
:return: combination
n! / (r! * (n - r)!)
|
lib/python-lib/combination.py
|
combination
|
ta7uw/atcoder
|
python
|
def combination(n, r):
'\n :param n: the count of different items\n :param r: the number of select\n :return: combination\n n! / (r! * (n - r)!)\n '
r = min((n - r), r)
result = 1
for i in range(n, (n - r), (- 1)):
result *= i
for i in range(1, (r + 1)):
result //= i
return result
|
@cell
def cdsem_straight(widths: Tuple[(float, ...)]=(0.4, 0.45, 0.5, 0.6, 0.8, 1.0), length: float=LINE_LENGTH, cross_section: CrossSectionFactory=strip, text: Optional[ComponentFactory]=text_rectangular_mini, spacing: float=3) -> Component:
'Returns straight waveguide lines width sweep.\n\n Args:\n widths: for the sweep\n length: for the line\n cross_section: for the lines\n text: optional text for labels\n spacing: edge to edge spacing\n '
lines = []
for width in widths:
cross_section = partial(cross_section, width=width)
line = straight_function(length=length, cross_section=cross_section)
if text:
line = line.copy()
t = (line << text(str(int((width * 1000.0)))))
t.xmin = (line.xmax + 5)
t.y = 0
lines.append(line)
return grid(lines, spacing=(0, spacing))
| 8,057,996,998,196,953,000
|
Returns straight waveguide lines width sweep.
Args:
widths: for the sweep
length: for the line
cross_section: for the lines
text: optional text for labels
spacing: edge to edge spacing
|
gdsfactory/components/cdsem_straight.py
|
cdsem_straight
|
gdsfactory/gdsfactory
|
python
|
@cell
def cdsem_straight(widths: Tuple[(float, ...)]=(0.4, 0.45, 0.5, 0.6, 0.8, 1.0), length: float=LINE_LENGTH, cross_section: CrossSectionFactory=strip, text: Optional[ComponentFactory]=text_rectangular_mini, spacing: float=3) -> Component:
'Returns straight waveguide lines width sweep.\n\n Args:\n widths: for the sweep\n length: for the line\n cross_section: for the lines\n text: optional text for labels\n spacing: edge to edge spacing\n '
lines = []
for width in widths:
cross_section = partial(cross_section, width=width)
line = straight_function(length=length, cross_section=cross_section)
if text:
line = line.copy()
t = (line << text(str(int((width * 1000.0)))))
t.xmin = (line.xmax + 5)
t.y = 0
lines.append(line)
return grid(lines, spacing=(0, spacing))
|
def write(self, value: int, from_hw: bool) -> int:
'Stage the effects of writing a value (see RGReg.write)'
assert (value >= 0)
masked = (value & ((1 << self.width) - 1))
if (self.read_only and (not from_hw)):
pass
elif (self.w1c and (not from_hw)):
self.next_value &= (~ masked)
else:
self.next_value = masked
return self._next_sw_read()
| 981,121,838,863,917,000
|
Stage the effects of writing a value (see RGReg.write)
|
hw/ip/otbn/dv/otbnsim/sim/ext_regs.py
|
write
|
Daasin/FOSS-fTPM
|
python
|
def write(self, value: int, from_hw: bool) -> int:
assert (value >= 0)
masked = (value & ((1 << self.width) - 1))
if (self.read_only and (not from_hw)):
pass
elif (self.w1c and (not from_hw)):
self.next_value &= (~ masked)
else:
self.next_value = masked
return self._next_sw_read()
|
def set_bits(self, value: int) -> int:
'Like write, but |=.'
masked = (value & ((1 << self.width) - 1))
self.next_value |= masked
return self._next_sw_read()
| 8,974,269,918,986,983,000
|
Like write, but |=.
|
hw/ip/otbn/dv/otbnsim/sim/ext_regs.py
|
set_bits
|
Daasin/FOSS-fTPM
|
python
|
def set_bits(self, value: int) -> int:
masked = (value & ((1 << self.width) - 1))
self.next_value |= masked
return self._next_sw_read()
|
def clear_bits(self, value: int) -> int:
'Like write, but &= ~.'
self.next_value &= (~ value)
return self._next_sw_read()
| 1,797,275,488,805,924,000
|
Like write, but &= ~.
|
hw/ip/otbn/dv/otbnsim/sim/ext_regs.py
|
clear_bits
|
Daasin/FOSS-fTPM
|
python
|
def clear_bits(self, value: int) -> int:
self.next_value &= (~ value)
return self._next_sw_read()
|
def write(self, value: int, from_hw: bool) -> None:
'Stage the effects of writing a value.\n\n If from_hw is true, this write is from OTBN hardware (rather than the\n bus).\n\n '
assert (value >= 0)
now = self._apply_fields((lambda fld, fv: fld.write(fv, from_hw)), value)
trace = (self._next_trace if self.double_flopped else self._trace)
trace.append(ExtRegChange('=', value, from_hw, now))
| 1,804,722,120,910,216,000
|
Stage the effects of writing a value.
If from_hw is true, this write is from OTBN hardware (rather than the
bus).
|
hw/ip/otbn/dv/otbnsim/sim/ext_regs.py
|
write
|
Daasin/FOSS-fTPM
|
python
|
def write(self, value: int, from_hw: bool) -> None:
'Stage the effects of writing a value.\n\n If from_hw is true, this write is from OTBN hardware (rather than the\n bus).\n\n '
assert (value >= 0)
now = self._apply_fields((lambda fld, fv: fld.write(fv, from_hw)), value)
trace = (self._next_trace if self.double_flopped else self._trace)
trace.append(ExtRegChange('=', value, from_hw, now))
|
def write(self, reg_name: str, value: int, from_hw: bool) -> None:
'Stage the effects of writing a value to a register'
assert (value >= 0)
self._get_reg(reg_name).write(value, from_hw)
self._dirty = 2
| -2,695,661,009,836,998,000
|
Stage the effects of writing a value to a register
|
hw/ip/otbn/dv/otbnsim/sim/ext_regs.py
|
write
|
Daasin/FOSS-fTPM
|
python
|
def write(self, reg_name: str, value: int, from_hw: bool) -> None:
assert (value >= 0)
self._get_reg(reg_name).write(value, from_hw)
self._dirty = 2
|
def set_bits(self, reg_name: str, value: int) -> None:
'Set some bits of a register (HW access only)'
assert (value >= 0)
self._get_reg(reg_name).set_bits(value)
self._dirty = 2
| -3,031,869,466,492,627,000
|
Set some bits of a register (HW access only)
|
hw/ip/otbn/dv/otbnsim/sim/ext_regs.py
|
set_bits
|
Daasin/FOSS-fTPM
|
python
|
def set_bits(self, reg_name: str, value: int) -> None:
assert (value >= 0)
self._get_reg(reg_name).set_bits(value)
self._dirty = 2
|
def increment_insn_cnt(self) -> None:
'Increment the INSN_CNT register'
reg = self._get_reg('INSN_CNT')
assert (len(reg.fields) == 1)
fld = reg.fields[0]
reg.write(min((fld.value + 1), ((1 << 32) - 1)), True)
self._dirty = 2
| 6,531,003,870,117,979,000
|
Increment the INSN_CNT register
|
hw/ip/otbn/dv/otbnsim/sim/ext_regs.py
|
increment_insn_cnt
|
Daasin/FOSS-fTPM
|
python
|
def increment_insn_cnt(self) -> None:
reg = self._get_reg('INSN_CNT')
assert (len(reg.fields) == 1)
fld = reg.fields[0]
reg.write(min((fld.value + 1), ((1 << 32) - 1)), True)
self._dirty = 2
|
def rollout(self, **args):
" Return a list of dicts containing instr_id:'xx', path:[(viewpointId, heading_rad, elevation_rad)] "
raise NotImplementedError
| 5,032,299,443,237,458,000
|
Return a list of dicts containing instr_id:'xx', path:[(viewpointId, heading_rad, elevation_rad)]
|
r2r_src/agent.py
|
rollout
|
rcorona/R2R-EnvDrop
|
python
|
def rollout(self, **args):
" "
raise NotImplementedError
|
def _sort_batch(self, obs):
' Extract instructions from a list of observations and sort by descending\n sequence length (to enable PyTorch packing). '
seq_tensor = np.array([ob['instr_encoding'] for ob in obs])
seq_lengths = np.argmax((seq_tensor == padding_idx), axis=1)
seq_lengths[(seq_lengths == 0)] = seq_tensor.shape[1]
seq_tensor = torch.from_numpy(seq_tensor)
seq_lengths = torch.from_numpy(seq_lengths)
(seq_lengths, perm_idx) = seq_lengths.sort(0, True)
sorted_tensor = seq_tensor[perm_idx]
mask = (sorted_tensor == padding_idx)[:, :seq_lengths[0]]
return (Variable(sorted_tensor, requires_grad=False).long().cuda(), mask.byte().cuda(), list(seq_lengths), list(perm_idx))
| 8,841,784,052,885,048,000
|
Extract instructions from a list of observations and sort by descending
sequence length (to enable PyTorch packing).
|
r2r_src/agent.py
|
_sort_batch
|
rcorona/R2R-EnvDrop
|
python
|
def _sort_batch(self, obs):
' Extract instructions from a list of observations and sort by descending\n sequence length (to enable PyTorch packing). '
seq_tensor = np.array([ob['instr_encoding'] for ob in obs])
seq_lengths = np.argmax((seq_tensor == padding_idx), axis=1)
seq_lengths[(seq_lengths == 0)] = seq_tensor.shape[1]
seq_tensor = torch.from_numpy(seq_tensor)
seq_lengths = torch.from_numpy(seq_lengths)
(seq_lengths, perm_idx) = seq_lengths.sort(0, True)
sorted_tensor = seq_tensor[perm_idx]
mask = (sorted_tensor == padding_idx)[:, :seq_lengths[0]]
return (Variable(sorted_tensor, requires_grad=False).long().cuda(), mask.byte().cuda(), list(seq_lengths), list(perm_idx))
|
def _feature_variable(self, obs):
' Extract precomputed features into variable. '
features = np.empty((len(obs), args.views, (self.feature_size + args.angle_feat_size)), dtype=np.float32)
for (i, ob) in enumerate(obs):
features[i, :, :] = ob['feature']
return Variable(torch.from_numpy(features), requires_grad=False).cuda()
| -5,244,354,546,349,609,000
|
Extract precomputed features into variable.
|
r2r_src/agent.py
|
_feature_variable
|
rcorona/R2R-EnvDrop
|
python
|
def _feature_variable(self, obs):
' '
features = np.empty((len(obs), args.views, (self.feature_size + args.angle_feat_size)), dtype=np.float32)
for (i, ob) in enumerate(obs):
features[i, :, :] = ob['feature']
return Variable(torch.from_numpy(features), requires_grad=False).cuda()
|
def _teacher_action(self, obs, ended):
'\n Extract teacher actions into variable.\n :param obs: The observation.\n :param ended: Whether the action seq is ended\n :return:\n '
a = np.zeros(len(obs), dtype=np.int64)
for (i, ob) in enumerate(obs):
if ended[i]:
a[i] = args.ignoreid
else:
for (k, candidate) in enumerate(ob['candidate']):
if (candidate['viewpointId'] == ob['teacher']):
a[i] = k
break
else:
assert (ob['teacher'] == ob['viewpoint'])
a[i] = len(ob['candidate'])
return torch.from_numpy(a).cuda()
| -6,105,101,396,949,073,000
|
Extract teacher actions into variable.
:param obs: The observation.
:param ended: Whether the action seq is ended
:return:
|
r2r_src/agent.py
|
_teacher_action
|
rcorona/R2R-EnvDrop
|
python
|
def _teacher_action(self, obs, ended):
'\n Extract teacher actions into variable.\n :param obs: The observation.\n :param ended: Whether the action seq is ended\n :return:\n '
a = np.zeros(len(obs), dtype=np.int64)
for (i, ob) in enumerate(obs):
if ended[i]:
a[i] = args.ignoreid
else:
for (k, candidate) in enumerate(ob['candidate']):
if (candidate['viewpointId'] == ob['teacher']):
a[i] = k
break
else:
assert (ob['teacher'] == ob['viewpoint'])
a[i] = len(ob['candidate'])
return torch.from_numpy(a).cuda()
|
def make_equiv_action(self, a_t, perm_obs, perm_idx=None, traj=None):
'\n Interface between Panoramic view and Egocentric view \n It will convert the action panoramic view action a_t to equivalent egocentric view actions for the simulator\n '
def take_action(i, idx, name):
if (type(name) is int):
self.env.env.sims[idx].makeAction(name, 0, 0)
else:
self.env.env.sims[idx].makeAction(*self.env_actions[name])
state = self.env.env.sims[idx].getState()
if (traj is not None):
traj[i]['path'].append((state.location.viewpointId, state.heading, state.elevation))
if (perm_idx is None):
perm_idx = range(len(perm_obs))
for (i, idx) in enumerate(perm_idx):
action = a_t[i]
if (action != (- 1)):
select_candidate = perm_obs[i]['candidate'][action]
src_point = perm_obs[i]['viewIndex']
trg_point = select_candidate['pointId']
src_level = (src_point // 12)
trg_level = (trg_point // 12)
while (src_level < trg_level):
take_action(i, idx, 'up')
src_level += 1
while (src_level > trg_level):
take_action(i, idx, 'down')
src_level -= 1
while (self.env.env.sims[idx].getState().viewIndex != trg_point):
take_action(i, idx, 'right')
assert (select_candidate['viewpointId'] == self.env.env.sims[idx].getState().navigableLocations[select_candidate['idx']].viewpointId)
take_action(i, idx, select_candidate['idx'])
| 1,658,864,895,377,073,200
|
Interface between Panoramic view and Egocentric view
It will convert the action panoramic view action a_t to equivalent egocentric view actions for the simulator
|
r2r_src/agent.py
|
make_equiv_action
|
rcorona/R2R-EnvDrop
|
python
|
def make_equiv_action(self, a_t, perm_obs, perm_idx=None, traj=None):
'\n Interface between Panoramic view and Egocentric view \n It will convert the action panoramic view action a_t to equivalent egocentric view actions for the simulator\n '
def take_action(i, idx, name):
if (type(name) is int):
self.env.env.sims[idx].makeAction(name, 0, 0)
else:
self.env.env.sims[idx].makeAction(*self.env_actions[name])
state = self.env.env.sims[idx].getState()
if (traj is not None):
traj[i]['path'].append((state.location.viewpointId, state.heading, state.elevation))
if (perm_idx is None):
perm_idx = range(len(perm_obs))
for (i, idx) in enumerate(perm_idx):
action = a_t[i]
if (action != (- 1)):
select_candidate = perm_obs[i]['candidate'][action]
src_point = perm_obs[i]['viewIndex']
trg_point = select_candidate['pointId']
src_level = (src_point // 12)
trg_level = (trg_point // 12)
while (src_level < trg_level):
take_action(i, idx, 'up')
src_level += 1
while (src_level > trg_level):
take_action(i, idx, 'down')
src_level -= 1
while (self.env.env.sims[idx].getState().viewIndex != trg_point):
take_action(i, idx, 'right')
assert (select_candidate['viewpointId'] == self.env.env.sims[idx].getState().navigableLocations[select_candidate['idx']].viewpointId)
take_action(i, idx, select_candidate['idx'])
|
def rollout(self, train_ml=None, train_rl=True, reset=True, speaker=None):
'\n :param train_ml: The weight to train with maximum likelihood\n :param train_rl: whether use RL in training\n :param reset: Reset the environment\n :param speaker: Speaker used in back translation.\n If the speaker is not None, use back translation.\n O.w., normal training\n :return:\n '
if ((self.feedback == 'teacher') or (self.feedback == 'argmax')):
train_rl = False
if reset:
obs = np.array(self.env.reset())
else:
obs = np.array(self.env._get_obs())
batch_size = len(obs)
if (speaker is not None):
noise = self.decoder.drop_env(torch.ones(self.feature_size).cuda())
batch = self.env.batch.copy()
speaker.env = self.env
insts = speaker.infer_batch(featdropmask=noise)
boss = (np.ones((batch_size, 1), np.int64) * self.tok.word_to_index['<BOS>'])
insts = np.concatenate((boss, insts), 1)
for (i, (datum, inst)) in enumerate(zip(batch, insts)):
if (inst[(- 1)] != self.tok.word_to_index['<PAD>']):
inst[(- 1)] = self.tok.word_to_index['<EOS>']
datum.pop('instructions')
datum.pop('instr_encoding')
datum['instructions'] = self.tok.decode_sentence(inst)
datum['instr_encoding'] = inst
obs = np.array(self.env.reset(batch))
(seq, seq_mask, seq_lengths, perm_idx) = self._sort_batch(obs)
perm_obs = obs[perm_idx]
(ctx, h_t, c_t) = self.encoder(seq, seq_lengths)
ctx_mask = seq_mask
last_dist = np.zeros(batch_size, np.float32)
for (i, ob) in enumerate(perm_obs):
last_dist[i] = ob['distance']
traj = [{'instr_id': ob['instr_id'], 'path': [(ob['viewpoint'], ob['heading'], ob['elevation'])]} for ob in perm_obs]
visited = [set() for _ in perm_obs]
ended = np.array(([False] * batch_size))
rewards = []
hidden_states = []
policy_log_probs = []
masks = []
entropys = []
ml_loss = 0.0
h1 = h_t
for t in range(self.episode_len):
(input_a_t, f_t, candidate_feat, candidate_leng) = self.get_input_feat(perm_obs)
if (speaker is not None):
candidate_feat[..., :(- args.angle_feat_size)] *= noise
f_t[..., :(- args.angle_feat_size)] *= noise
(h_t, c_t, logit, h1) = self.decoder(input_a_t, f_t, candidate_feat, h_t, h1, c_t, ctx, ctx_mask, already_dropfeat=(speaker is not None))
hidden_states.append(h_t)
candidate_mask = utils.length2mask(candidate_leng)
if args.submit:
for (ob_id, ob) in enumerate(perm_obs):
visited[ob_id].add(ob['viewpoint'])
for (c_id, c) in enumerate(ob['candidate']):
if (c['viewpointId'] in visited[ob_id]):
candidate_mask[ob_id][c_id] = 1
logit.masked_fill_(candidate_mask, (- float('inf')))
target = self._teacher_action(perm_obs, ended)
ml_loss += self.criterion(logit, target)
if (self.feedback == 'teacher'):
a_t = target
elif (self.feedback == 'argmax'):
(_, a_t) = logit.max(1)
a_t = a_t.detach()
log_probs = F.log_softmax(logit, 1)
policy_log_probs.append(log_probs.gather(1, a_t.unsqueeze(1)))
elif (self.feedback == 'sample'):
probs = F.softmax(logit, 1)
c = torch.distributions.Categorical(probs)
self.logs['entropy'].append(c.entropy().sum().item())
entropys.append(c.entropy())
a_t = c.sample().detach()
policy_log_probs.append(c.log_prob(a_t))
else:
print(self.feedback)
sys.exit('Invalid feedback option')
cpu_a_t = a_t.cpu().numpy()
for (i, next_id) in enumerate(cpu_a_t):
if ((next_id == (candidate_leng[i] - 1)) or (next_id == args.ignoreid) or ended[i]):
cpu_a_t[i] = (- 1)
self.make_equiv_action(cpu_a_t, perm_obs, perm_idx, traj)
obs = np.array(self.env._get_obs())
perm_obs = obs[perm_idx]
dist = np.zeros(batch_size, np.float32)
reward = np.zeros(batch_size, np.float32)
mask = np.ones(batch_size, np.float32)
for (i, ob) in enumerate(perm_obs):
dist[i] = ob['distance']
if ended[i]:
reward[i] = 0.0
mask[i] = 0.0
else:
action_idx = cpu_a_t[i]
if (action_idx == (- 1)):
if (dist[i] < 3):
reward[i] = 2.0
else:
reward[i] = (- 2.0)
else:
reward[i] = (- (dist[i] - last_dist[i]))
if (reward[i] > 0):
reward[i] = 1
elif (reward[i] < 0):
reward[i] = (- 1)
else:
raise NameError("The action doesn't change the move")
rewards.append(reward)
masks.append(mask)
last_dist[:] = dist
ended[:] = np.logical_or(ended, (cpu_a_t == (- 1)))
if ended.all():
break
if train_rl:
(input_a_t, f_t, candidate_feat, candidate_leng) = self.get_input_feat(perm_obs)
if (speaker is not None):
candidate_feat[..., :(- args.angle_feat_size)] *= noise
f_t[..., :(- args.angle_feat_size)] *= noise
(last_h_, _, _, _) = self.decoder(input_a_t, f_t, candidate_feat, h_t, h1, c_t, ctx, ctx_mask, (speaker is not None))
rl_loss = 0.0
last_value__ = self.critic(last_h_).detach()
discount_reward = np.zeros(batch_size, np.float32)
for i in range(batch_size):
if (not ended[i]):
discount_reward[i] = last_value__[i]
length = len(rewards)
total = 0
for t in range((length - 1), (- 1), (- 1)):
discount_reward = ((discount_reward * args.gamma) + rewards[t])
mask_ = Variable(torch.from_numpy(masks[t]), requires_grad=False).cuda()
clip_reward = discount_reward.copy()
r_ = Variable(torch.from_numpy(clip_reward), requires_grad=False).cuda()
v_ = self.critic(hidden_states[t])
a_ = (r_ - v_).detach()
rl_loss += (((- policy_log_probs[t]) * a_) * mask_).sum()
rl_loss += ((((r_ - v_) ** 2) * mask_).sum() * 0.5)
if (self.feedback == 'sample'):
rl_loss += (((- 0.01) * entropys[t]) * mask_).sum()
self.logs['critic_loss'].append((((r_ - v_) ** 2) * mask_).sum().item())
total = (total + np.sum(masks[t]))
self.logs['total'].append(total)
if (args.normalize_loss == 'total'):
rl_loss /= total
elif (args.normalize_loss == 'batch'):
rl_loss /= batch_size
else:
assert (args.normalize_loss == 'none')
self.loss += rl_loss
if (train_ml is not None):
self.loss += ((ml_loss * train_ml) / batch_size)
if (type(self.loss) is int):
self.losses.append(0.0)
else:
self.losses.append((self.loss.item() / self.episode_len))
return traj
| -5,757,432,292,980,751,000
|
:param train_ml: The weight to train with maximum likelihood
:param train_rl: whether use RL in training
:param reset: Reset the environment
:param speaker: Speaker used in back translation.
If the speaker is not None, use back translation.
O.w., normal training
:return:
|
r2r_src/agent.py
|
rollout
|
rcorona/R2R-EnvDrop
|
python
|
def rollout(self, train_ml=None, train_rl=True, reset=True, speaker=None):
'\n :param train_ml: The weight to train with maximum likelihood\n :param train_rl: whether use RL in training\n :param reset: Reset the environment\n :param speaker: Speaker used in back translation.\n If the speaker is not None, use back translation.\n O.w., normal training\n :return:\n '
if ((self.feedback == 'teacher') or (self.feedback == 'argmax')):
train_rl = False
if reset:
obs = np.array(self.env.reset())
else:
obs = np.array(self.env._get_obs())
batch_size = len(obs)
if (speaker is not None):
noise = self.decoder.drop_env(torch.ones(self.feature_size).cuda())
batch = self.env.batch.copy()
speaker.env = self.env
insts = speaker.infer_batch(featdropmask=noise)
boss = (np.ones((batch_size, 1), np.int64) * self.tok.word_to_index['<BOS>'])
insts = np.concatenate((boss, insts), 1)
for (i, (datum, inst)) in enumerate(zip(batch, insts)):
if (inst[(- 1)] != self.tok.word_to_index['<PAD>']):
inst[(- 1)] = self.tok.word_to_index['<EOS>']
datum.pop('instructions')
datum.pop('instr_encoding')
datum['instructions'] = self.tok.decode_sentence(inst)
datum['instr_encoding'] = inst
obs = np.array(self.env.reset(batch))
(seq, seq_mask, seq_lengths, perm_idx) = self._sort_batch(obs)
perm_obs = obs[perm_idx]
(ctx, h_t, c_t) = self.encoder(seq, seq_lengths)
ctx_mask = seq_mask
last_dist = np.zeros(batch_size, np.float32)
for (i, ob) in enumerate(perm_obs):
last_dist[i] = ob['distance']
traj = [{'instr_id': ob['instr_id'], 'path': [(ob['viewpoint'], ob['heading'], ob['elevation'])]} for ob in perm_obs]
visited = [set() for _ in perm_obs]
ended = np.array(([False] * batch_size))
rewards = []
hidden_states = []
policy_log_probs = []
masks = []
entropys = []
ml_loss = 0.0
h1 = h_t
for t in range(self.episode_len):
(input_a_t, f_t, candidate_feat, candidate_leng) = self.get_input_feat(perm_obs)
if (speaker is not None):
candidate_feat[..., :(- args.angle_feat_size)] *= noise
f_t[..., :(- args.angle_feat_size)] *= noise
(h_t, c_t, logit, h1) = self.decoder(input_a_t, f_t, candidate_feat, h_t, h1, c_t, ctx, ctx_mask, already_dropfeat=(speaker is not None))
hidden_states.append(h_t)
candidate_mask = utils.length2mask(candidate_leng)
if args.submit:
for (ob_id, ob) in enumerate(perm_obs):
visited[ob_id].add(ob['viewpoint'])
for (c_id, c) in enumerate(ob['candidate']):
if (c['viewpointId'] in visited[ob_id]):
candidate_mask[ob_id][c_id] = 1
logit.masked_fill_(candidate_mask, (- float('inf')))
target = self._teacher_action(perm_obs, ended)
ml_loss += self.criterion(logit, target)
if (self.feedback == 'teacher'):
a_t = target
elif (self.feedback == 'argmax'):
(_, a_t) = logit.max(1)
a_t = a_t.detach()
log_probs = F.log_softmax(logit, 1)
policy_log_probs.append(log_probs.gather(1, a_t.unsqueeze(1)))
elif (self.feedback == 'sample'):
probs = F.softmax(logit, 1)
c = torch.distributions.Categorical(probs)
self.logs['entropy'].append(c.entropy().sum().item())
entropys.append(c.entropy())
a_t = c.sample().detach()
policy_log_probs.append(c.log_prob(a_t))
else:
print(self.feedback)
sys.exit('Invalid feedback option')
cpu_a_t = a_t.cpu().numpy()
for (i, next_id) in enumerate(cpu_a_t):
if ((next_id == (candidate_leng[i] - 1)) or (next_id == args.ignoreid) or ended[i]):
cpu_a_t[i] = (- 1)
self.make_equiv_action(cpu_a_t, perm_obs, perm_idx, traj)
obs = np.array(self.env._get_obs())
perm_obs = obs[perm_idx]
dist = np.zeros(batch_size, np.float32)
reward = np.zeros(batch_size, np.float32)
mask = np.ones(batch_size, np.float32)
for (i, ob) in enumerate(perm_obs):
dist[i] = ob['distance']
if ended[i]:
reward[i] = 0.0
mask[i] = 0.0
else:
action_idx = cpu_a_t[i]
if (action_idx == (- 1)):
if (dist[i] < 3):
reward[i] = 2.0
else:
reward[i] = (- 2.0)
else:
reward[i] = (- (dist[i] - last_dist[i]))
if (reward[i] > 0):
reward[i] = 1
elif (reward[i] < 0):
reward[i] = (- 1)
else:
raise NameError("The action doesn't change the move")
rewards.append(reward)
masks.append(mask)
last_dist[:] = dist
ended[:] = np.logical_or(ended, (cpu_a_t == (- 1)))
if ended.all():
break
if train_rl:
(input_a_t, f_t, candidate_feat, candidate_leng) = self.get_input_feat(perm_obs)
if (speaker is not None):
candidate_feat[..., :(- args.angle_feat_size)] *= noise
f_t[..., :(- args.angle_feat_size)] *= noise
(last_h_, _, _, _) = self.decoder(input_a_t, f_t, candidate_feat, h_t, h1, c_t, ctx, ctx_mask, (speaker is not None))
rl_loss = 0.0
last_value__ = self.critic(last_h_).detach()
discount_reward = np.zeros(batch_size, np.float32)
for i in range(batch_size):
if (not ended[i]):
discount_reward[i] = last_value__[i]
length = len(rewards)
total = 0
for t in range((length - 1), (- 1), (- 1)):
discount_reward = ((discount_reward * args.gamma) + rewards[t])
mask_ = Variable(torch.from_numpy(masks[t]), requires_grad=False).cuda()
clip_reward = discount_reward.copy()
r_ = Variable(torch.from_numpy(clip_reward), requires_grad=False).cuda()
v_ = self.critic(hidden_states[t])
a_ = (r_ - v_).detach()
rl_loss += (((- policy_log_probs[t]) * a_) * mask_).sum()
rl_loss += ((((r_ - v_) ** 2) * mask_).sum() * 0.5)
if (self.feedback == 'sample'):
rl_loss += (((- 0.01) * entropys[t]) * mask_).sum()
self.logs['critic_loss'].append((((r_ - v_) ** 2) * mask_).sum().item())
total = (total + np.sum(masks[t]))
self.logs['total'].append(total)
if (args.normalize_loss == 'total'):
rl_loss /= total
elif (args.normalize_loss == 'batch'):
rl_loss /= batch_size
else:
assert (args.normalize_loss == 'none')
self.loss += rl_loss
if (train_ml is not None):
self.loss += ((ml_loss * train_ml) / batch_size)
if (type(self.loss) is int):
self.losses.append(0.0)
else:
self.losses.append((self.loss.item() / self.episode_len))
return traj
|
def _dijkstra(self):
'\n The dijkstra algorithm.\n Was called beam search to be consistent with existing work.\n But it actually finds the Exact K paths with smallest listener log_prob.\n :return:\n [{\n "scan": XXX\n "instr_id":XXX,\n \'instr_encoding": XXX\n \'dijk_path\': [v1, v2, ..., vn] (The path used for find all the candidates)\n "paths": {\n "trajectory": [viewpoint_id1, viewpoint_id2, ..., ],\n "action": [act_1, act_2, ..., ],\n "listener_scores": [log_prob_act1, log_prob_act2, ..., ],\n "visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)\n }\n }]\n '
def make_state_id(viewpoint, action):
return ('%s_%s' % (viewpoint, str(action)))
def decompose_state_id(state_id):
(viewpoint, action) = state_id.split('_')
action = int(action)
return (viewpoint, action)
obs = self.env._get_obs()
batch_size = len(obs)
results = [{'scan': ob['scan'], 'instr_id': ob['instr_id'], 'instr_encoding': ob['instr_encoding'], 'dijk_path': [ob['viewpoint']], 'paths': []} for ob in obs]
(seq, seq_mask, seq_lengths, perm_idx) = self._sort_batch(obs)
recover_idx = np.zeros_like(perm_idx)
for (i, idx) in enumerate(perm_idx):
recover_idx[idx] = i
(ctx, h_t, c_t) = self.encoder(seq, seq_lengths)
(ctx, h_t, c_t, ctx_mask) = (ctx[recover_idx], h_t[recover_idx], c_t[recover_idx], seq_mask[recover_idx])
id2state = [{make_state_id(ob['viewpoint'], (- 95)): {'next_viewpoint': ob['viewpoint'], 'running_state': (h_t[i], h_t[i], c_t[i]), 'location': (ob['viewpoint'], ob['heading'], ob['elevation']), 'feature': None, 'from_state_id': None, 'score': 0, 'scores': [], 'actions': []}} for (i, ob) in enumerate(obs)]
visited = [set() for _ in range(batch_size)]
finished = [set() for _ in range(batch_size)]
graphs = [utils.FloydGraph() for _ in range(batch_size)]
ended = np.array(([False] * batch_size))
for _ in range(300):
smallest_idXstate = [(max(((state_id, state) for (state_id, state) in id2state[i].items() if (state_id not in visited[i])), key=(lambda item: item[1]['score'])) if (not ended[i]) else next(iter(id2state[i].items()))) for i in range(batch_size)]
for (i, (state_id, state)) in enumerate(smallest_idXstate):
assert (ended[i] or (state_id not in visited[i]))
if (not ended[i]):
(viewpoint, action) = decompose_state_id(state_id)
visited[i].add(state_id)
if (action == (- 1)):
finished[i].add(state_id)
if (len(finished[i]) >= args.candidates):
ended[i] = True
(h_ts, h1s, c_ts) = zip(*(idXstate[1]['running_state'] for idXstate in smallest_idXstate))
(h_t, h1, c_t) = (torch.stack(h_ts), torch.stack(h1s), torch.stack(c_ts))
for (i, (state_id, state)) in enumerate(smallest_idXstate):
next_viewpoint = state['next_viewpoint']
scan = results[i]['scan']
(from_viewpoint, heading, elevation) = state['location']
self.env.env.sims[i].newEpisode(scan, next_viewpoint, heading, elevation)
obs = self.env._get_obs()
for (i, ob) in enumerate(obs):
viewpoint = ob['viewpoint']
if (not graphs[i].visited(viewpoint)):
for c in ob['candidate']:
next_viewpoint = c['viewpointId']
dis = self.env.distances[ob['scan']][viewpoint][next_viewpoint]
graphs[i].add_edge(viewpoint, next_viewpoint, dis)
graphs[i].update(viewpoint)
results[i]['dijk_path'].extend(graphs[i].path(results[i]['dijk_path'][(- 1)], viewpoint))
(input_a_t, f_t, candidate_feat, candidate_leng) = self.get_input_feat(obs)
(h_t, c_t, alpha, logit, h1) = self.decoder(input_a_t, f_t, candidate_feat, h_t, h1, c_t, ctx, ctx_mask, False)
candidate_mask = utils.length2mask(candidate_leng)
logit.masked_fill_(candidate_mask, (- float('inf')))
log_probs = F.log_softmax(logit, 1)
(_, max_act) = log_probs.max(1)
for (i, ob) in enumerate(obs):
current_viewpoint = ob['viewpoint']
candidate = ob['candidate']
(current_state_id, current_state) = smallest_idXstate[i]
(old_viewpoint, from_action) = decompose_state_id(current_state_id)
assert (ob['viewpoint'] == current_state['next_viewpoint'])
if ((from_action == (- 1)) or ended[i]):
continue
for j in range((len(ob['candidate']) + 1)):
modified_log_prob = log_probs[i][j].detach().cpu().item()
new_score = (current_state['score'] + modified_log_prob)
if (j < len(candidate)):
next_id = make_state_id(current_viewpoint, j)
next_viewpoint = candidate[j]['viewpointId']
trg_point = candidate[j]['pointId']
heading = (((trg_point % 12) * math.pi) / 6)
elevation = ((((trg_point // 12) - 1) * math.pi) / 6)
location = (next_viewpoint, heading, elevation)
else:
next_id = make_state_id(current_viewpoint, (- 1))
next_viewpoint = current_viewpoint
location = (current_viewpoint, ob['heading'], ob['elevation'])
if ((next_id not in id2state[i]) or (new_score > id2state[i][next_id]['score'])):
id2state[i][next_id] = {'next_viewpoint': next_viewpoint, 'location': location, 'running_state': (h_t[i], h1[i], c_t[i]), 'from_state_id': current_state_id, 'feature': (f_t[i].detach().cpu(), candidate_feat[i][j].detach().cpu()), 'score': new_score, 'scores': (current_state['scores'] + [modified_log_prob]), 'actions': (current_state['actions'] + [(len(candidate) + 1)])}
for i in range(batch_size):
if (len(visited[i]) == len(id2state[i])):
ended[i] = True
if ended.all():
break
for i in range(batch_size):
results[i]['dijk_path'].extend(graphs[i].path(results[i]['dijk_path'][(- 1)], results[i]['dijk_path'][0]))
'\n "paths": {\n "trajectory": [viewpoint_id1, viewpoint_id2, ..., ],\n "action": [act_1, act_2, ..., ],\n "listener_scores": [log_prob_act1, log_prob_act2, ..., ],\n "visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)\n }\n '
for (i, result) in enumerate(results):
assert (len(finished[i]) <= args.candidates)
for state_id in finished[i]:
path_info = {'trajectory': [], 'action': [], 'listener_scores': id2state[i][state_id]['scores'], 'listener_actions': id2state[i][state_id]['actions'], 'visual_feature': []}
(viewpoint, action) = decompose_state_id(state_id)
while (action != (- 95)):
state = id2state[i][state_id]
path_info['trajectory'].append(state['location'])
path_info['action'].append(action)
path_info['visual_feature'].append(state['feature'])
state_id = id2state[i][state_id]['from_state_id']
(viewpoint, action) = decompose_state_id(state_id)
state = id2state[i][state_id]
path_info['trajectory'].append(state['location'])
for need_reverse_key in ['trajectory', 'action', 'visual_feature']:
path_info[need_reverse_key] = path_info[need_reverse_key][::(- 1)]
result['paths'].append(path_info)
return results
| -1,167,941,217,106,676,200
|
The dijkstra algorithm.
Was called beam search to be consistent with existing work.
But it actually finds the Exact K paths with smallest listener log_prob.
:return:
[{
"scan": XXX
"instr_id":XXX,
'instr_encoding": XXX
'dijk_path': [v1, v2, ..., vn] (The path used for find all the candidates)
"paths": {
"trajectory": [viewpoint_id1, viewpoint_id2, ..., ],
"action": [act_1, act_2, ..., ],
"listener_scores": [log_prob_act1, log_prob_act2, ..., ],
"visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)
}
}]
|
r2r_src/agent.py
|
_dijkstra
|
rcorona/R2R-EnvDrop
|
python
|
def _dijkstra(self):
'\n The dijkstra algorithm.\n Was called beam search to be consistent with existing work.\n But it actually finds the Exact K paths with smallest listener log_prob.\n :return:\n [{\n "scan": XXX\n "instr_id":XXX,\n \'instr_encoding": XXX\n \'dijk_path\': [v1, v2, ..., vn] (The path used for find all the candidates)\n "paths": {\n "trajectory": [viewpoint_id1, viewpoint_id2, ..., ],\n "action": [act_1, act_2, ..., ],\n "listener_scores": [log_prob_act1, log_prob_act2, ..., ],\n "visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)\n }\n }]\n '
def make_state_id(viewpoint, action):
return ('%s_%s' % (viewpoint, str(action)))
def decompose_state_id(state_id):
(viewpoint, action) = state_id.split('_')
action = int(action)
return (viewpoint, action)
obs = self.env._get_obs()
batch_size = len(obs)
results = [{'scan': ob['scan'], 'instr_id': ob['instr_id'], 'instr_encoding': ob['instr_encoding'], 'dijk_path': [ob['viewpoint']], 'paths': []} for ob in obs]
(seq, seq_mask, seq_lengths, perm_idx) = self._sort_batch(obs)
recover_idx = np.zeros_like(perm_idx)
for (i, idx) in enumerate(perm_idx):
recover_idx[idx] = i
(ctx, h_t, c_t) = self.encoder(seq, seq_lengths)
(ctx, h_t, c_t, ctx_mask) = (ctx[recover_idx], h_t[recover_idx], c_t[recover_idx], seq_mask[recover_idx])
id2state = [{make_state_id(ob['viewpoint'], (- 95)): {'next_viewpoint': ob['viewpoint'], 'running_state': (h_t[i], h_t[i], c_t[i]), 'location': (ob['viewpoint'], ob['heading'], ob['elevation']), 'feature': None, 'from_state_id': None, 'score': 0, 'scores': [], 'actions': []}} for (i, ob) in enumerate(obs)]
visited = [set() for _ in range(batch_size)]
finished = [set() for _ in range(batch_size)]
graphs = [utils.FloydGraph() for _ in range(batch_size)]
ended = np.array(([False] * batch_size))
for _ in range(300):
smallest_idXstate = [(max(((state_id, state) for (state_id, state) in id2state[i].items() if (state_id not in visited[i])), key=(lambda item: item[1]['score'])) if (not ended[i]) else next(iter(id2state[i].items()))) for i in range(batch_size)]
for (i, (state_id, state)) in enumerate(smallest_idXstate):
assert (ended[i] or (state_id not in visited[i]))
if (not ended[i]):
(viewpoint, action) = decompose_state_id(state_id)
visited[i].add(state_id)
if (action == (- 1)):
finished[i].add(state_id)
if (len(finished[i]) >= args.candidates):
ended[i] = True
(h_ts, h1s, c_ts) = zip(*(idXstate[1]['running_state'] for idXstate in smallest_idXstate))
(h_t, h1, c_t) = (torch.stack(h_ts), torch.stack(h1s), torch.stack(c_ts))
for (i, (state_id, state)) in enumerate(smallest_idXstate):
next_viewpoint = state['next_viewpoint']
scan = results[i]['scan']
(from_viewpoint, heading, elevation) = state['location']
self.env.env.sims[i].newEpisode(scan, next_viewpoint, heading, elevation)
obs = self.env._get_obs()
for (i, ob) in enumerate(obs):
viewpoint = ob['viewpoint']
if (not graphs[i].visited(viewpoint)):
for c in ob['candidate']:
next_viewpoint = c['viewpointId']
dis = self.env.distances[ob['scan']][viewpoint][next_viewpoint]
graphs[i].add_edge(viewpoint, next_viewpoint, dis)
graphs[i].update(viewpoint)
results[i]['dijk_path'].extend(graphs[i].path(results[i]['dijk_path'][(- 1)], viewpoint))
(input_a_t, f_t, candidate_feat, candidate_leng) = self.get_input_feat(obs)
(h_t, c_t, alpha, logit, h1) = self.decoder(input_a_t, f_t, candidate_feat, h_t, h1, c_t, ctx, ctx_mask, False)
candidate_mask = utils.length2mask(candidate_leng)
logit.masked_fill_(candidate_mask, (- float('inf')))
log_probs = F.log_softmax(logit, 1)
(_, max_act) = log_probs.max(1)
for (i, ob) in enumerate(obs):
current_viewpoint = ob['viewpoint']
candidate = ob['candidate']
(current_state_id, current_state) = smallest_idXstate[i]
(old_viewpoint, from_action) = decompose_state_id(current_state_id)
assert (ob['viewpoint'] == current_state['next_viewpoint'])
if ((from_action == (- 1)) or ended[i]):
continue
for j in range((len(ob['candidate']) + 1)):
modified_log_prob = log_probs[i][j].detach().cpu().item()
new_score = (current_state['score'] + modified_log_prob)
if (j < len(candidate)):
next_id = make_state_id(current_viewpoint, j)
next_viewpoint = candidate[j]['viewpointId']
trg_point = candidate[j]['pointId']
heading = (((trg_point % 12) * math.pi) / 6)
elevation = ((((trg_point // 12) - 1) * math.pi) / 6)
location = (next_viewpoint, heading, elevation)
else:
next_id = make_state_id(current_viewpoint, (- 1))
next_viewpoint = current_viewpoint
location = (current_viewpoint, ob['heading'], ob['elevation'])
if ((next_id not in id2state[i]) or (new_score > id2state[i][next_id]['score'])):
id2state[i][next_id] = {'next_viewpoint': next_viewpoint, 'location': location, 'running_state': (h_t[i], h1[i], c_t[i]), 'from_state_id': current_state_id, 'feature': (f_t[i].detach().cpu(), candidate_feat[i][j].detach().cpu()), 'score': new_score, 'scores': (current_state['scores'] + [modified_log_prob]), 'actions': (current_state['actions'] + [(len(candidate) + 1)])}
for i in range(batch_size):
if (len(visited[i]) == len(id2state[i])):
ended[i] = True
if ended.all():
break
for i in range(batch_size):
results[i]['dijk_path'].extend(graphs[i].path(results[i]['dijk_path'][(- 1)], results[i]['dijk_path'][0]))
'\n "paths": {\n "trajectory": [viewpoint_id1, viewpoint_id2, ..., ],\n "action": [act_1, act_2, ..., ],\n "listener_scores": [log_prob_act1, log_prob_act2, ..., ],\n "visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)\n }\n '
for (i, result) in enumerate(results):
assert (len(finished[i]) <= args.candidates)
for state_id in finished[i]:
path_info = {'trajectory': [], 'action': [], 'listener_scores': id2state[i][state_id]['scores'], 'listener_actions': id2state[i][state_id]['actions'], 'visual_feature': []}
(viewpoint, action) = decompose_state_id(state_id)
while (action != (- 95)):
state = id2state[i][state_id]
path_info['trajectory'].append(state['location'])
path_info['action'].append(action)
path_info['visual_feature'].append(state['feature'])
state_id = id2state[i][state_id]['from_state_id']
(viewpoint, action) = decompose_state_id(state_id)
state = id2state[i][state_id]
path_info['trajectory'].append(state['location'])
for need_reverse_key in ['trajectory', 'action', 'visual_feature']:
path_info[need_reverse_key] = path_info[need_reverse_key][::(- 1)]
result['paths'].append(path_info)
return results
|
def beam_search(self, speaker):
'\n :param speaker: The speaker to be used in searching.\n :return:\n {\n "scan": XXX\n "instr_id":XXX,\n "instr_encoding": XXX\n "dijk_path": [v1, v2, ...., vn]\n "paths": [{\n "trajectory": [viewoint_id0, viewpoint_id1, viewpoint_id2, ..., ],\n "action": [act_1, act_2, ..., ],\n "listener_scores": [log_prob_act1, log_prob_act2, ..., ],\n "speaker_scores": [log_prob_word1, log_prob_word2, ..., ],\n }]\n }\n '
self.env.reset()
results = self._dijkstra()
'\n return from self._dijkstra()\n [{\n "scan": XXX\n "instr_id":XXX,\n "instr_encoding": XXX\n "dijk_path": [v1, v2, ...., vn]\n "paths": [{\n "trajectory": [viewoint_id0, viewpoint_id1, viewpoint_id2, ..., ],\n "action": [act_1, act_2, ..., ],\n "listener_scores": [log_prob_act1, log_prob_act2, ..., ],\n "visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)\n }]\n }]\n '
for result in results:
lengths = []
num_paths = len(result['paths'])
for path in result['paths']:
assert (len(path['trajectory']) == (len(path['visual_feature']) + 1))
lengths.append(len(path['visual_feature']))
max_len = max(lengths)
img_feats = torch.zeros(num_paths, max_len, 36, (self.feature_size + args.angle_feat_size))
can_feats = torch.zeros(num_paths, max_len, (self.feature_size + args.angle_feat_size))
for (j, path) in enumerate(result['paths']):
for (k, feat) in enumerate(path['visual_feature']):
(img_feat, can_feat) = feat
img_feats[j][k] = img_feat
can_feats[j][k] = can_feat
(img_feats, can_feats) = (img_feats.cuda(), can_feats.cuda())
features = ((img_feats, can_feats), lengths)
insts = np.array([result['instr_encoding'] for _ in range(num_paths)])
seq_lengths = np.argmax((insts == self.tok.word_to_index['<EOS>']), axis=1)
insts = torch.from_numpy(insts).cuda()
speaker_scores = speaker.teacher_forcing(train=True, features=features, insts=insts, for_listener=True)
for (j, path) in enumerate(result['paths']):
path.pop('visual_feature')
path['speaker_scores'] = (- speaker_scores[j].detach().cpu().numpy()[:seq_lengths[j]])
return results
| -8,871,821,870,732,488,000
|
:param speaker: The speaker to be used in searching.
:return:
{
"scan": XXX
"instr_id":XXX,
"instr_encoding": XXX
"dijk_path": [v1, v2, ...., vn]
"paths": [{
"trajectory": [viewoint_id0, viewpoint_id1, viewpoint_id2, ..., ],
"action": [act_1, act_2, ..., ],
"listener_scores": [log_prob_act1, log_prob_act2, ..., ],
"speaker_scores": [log_prob_word1, log_prob_word2, ..., ],
}]
}
|
r2r_src/agent.py
|
beam_search
|
rcorona/R2R-EnvDrop
|
python
|
def beam_search(self, speaker):
'\n :param speaker: The speaker to be used in searching.\n :return:\n {\n "scan": XXX\n "instr_id":XXX,\n "instr_encoding": XXX\n "dijk_path": [v1, v2, ...., vn]\n "paths": [{\n "trajectory": [viewoint_id0, viewpoint_id1, viewpoint_id2, ..., ],\n "action": [act_1, act_2, ..., ],\n "listener_scores": [log_prob_act1, log_prob_act2, ..., ],\n "speaker_scores": [log_prob_word1, log_prob_word2, ..., ],\n }]\n }\n '
self.env.reset()
results = self._dijkstra()
'\n return from self._dijkstra()\n [{\n "scan": XXX\n "instr_id":XXX,\n "instr_encoding": XXX\n "dijk_path": [v1, v2, ...., vn]\n "paths": [{\n "trajectory": [viewoint_id0, viewpoint_id1, viewpoint_id2, ..., ],\n "action": [act_1, act_2, ..., ],\n "listener_scores": [log_prob_act1, log_prob_act2, ..., ],\n "visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)\n }]\n }]\n '
for result in results:
lengths = []
num_paths = len(result['paths'])
for path in result['paths']:
assert (len(path['trajectory']) == (len(path['visual_feature']) + 1))
lengths.append(len(path['visual_feature']))
max_len = max(lengths)
img_feats = torch.zeros(num_paths, max_len, 36, (self.feature_size + args.angle_feat_size))
can_feats = torch.zeros(num_paths, max_len, (self.feature_size + args.angle_feat_size))
for (j, path) in enumerate(result['paths']):
for (k, feat) in enumerate(path['visual_feature']):
(img_feat, can_feat) = feat
img_feats[j][k] = img_feat
can_feats[j][k] = can_feat
(img_feats, can_feats) = (img_feats.cuda(), can_feats.cuda())
features = ((img_feats, can_feats), lengths)
insts = np.array([result['instr_encoding'] for _ in range(num_paths)])
seq_lengths = np.argmax((insts == self.tok.word_to_index['<EOS>']), axis=1)
insts = torch.from_numpy(insts).cuda()
speaker_scores = speaker.teacher_forcing(train=True, features=features, insts=insts, for_listener=True)
for (j, path) in enumerate(result['paths']):
path.pop('visual_feature')
path['speaker_scores'] = (- speaker_scores[j].detach().cpu().numpy()[:seq_lengths[j]])
return results
|
def test(self, use_dropout=False, feedback='argmax', allow_cheat=False, iters=None):
' Evaluate once on each instruction in the current environment '
self.feedback = feedback
if use_dropout:
self.encoder.train()
self.decoder.train()
self.critic.train()
else:
self.encoder.eval()
self.decoder.eval()
self.critic.eval()
super(Seq2SeqAgent, self).test(iters)
| 4,978,732,549,777,399,000
|
Evaluate once on each instruction in the current environment
|
r2r_src/agent.py
|
test
|
rcorona/R2R-EnvDrop
|
python
|
def test(self, use_dropout=False, feedback='argmax', allow_cheat=False, iters=None):
' '
self.feedback = feedback
if use_dropout:
self.encoder.train()
self.decoder.train()
self.critic.train()
else:
self.encoder.eval()
self.decoder.eval()
self.critic.eval()
super(Seq2SeqAgent, self).test(iters)
|
def train(self, n_iters, feedback='teacher', **kwargs):
' Train for a given number of iterations '
self.feedback = feedback
self.encoder.train()
self.decoder.train()
self.critic.train()
self.losses = []
for iter in tqdm(range(1, (n_iters + 1))):
self.encoder_optimizer.zero_grad()
self.decoder_optimizer.zero_grad()
self.critic_optimizer.zero_grad()
self.loss = 0
if (feedback == 'teacher'):
self.feedback = 'teacher'
self.rollout(train_ml=args.teacher_weight, train_rl=False, **kwargs)
elif (feedback == 'sample'):
if (args.ml_weight != 0):
self.feedback = 'teacher'
self.rollout(train_ml=args.ml_weight, train_rl=False, **kwargs)
self.feedback = 'sample'
self.rollout(train_ml=None, train_rl=True, **kwargs)
else:
assert False
self.loss.backward()
torch.nn.utils.clip_grad_norm(self.encoder.parameters(), 40.0)
torch.nn.utils.clip_grad_norm(self.decoder.parameters(), 40.0)
self.encoder_optimizer.step()
self.decoder_optimizer.step()
self.critic_optimizer.step()
| 8,232,530,218,817,938,000
|
Train for a given number of iterations
|
r2r_src/agent.py
|
train
|
rcorona/R2R-EnvDrop
|
python
|
def train(self, n_iters, feedback='teacher', **kwargs):
' '
self.feedback = feedback
self.encoder.train()
self.decoder.train()
self.critic.train()
self.losses = []
for iter in tqdm(range(1, (n_iters + 1))):
self.encoder_optimizer.zero_grad()
self.decoder_optimizer.zero_grad()
self.critic_optimizer.zero_grad()
self.loss = 0
if (feedback == 'teacher'):
self.feedback = 'teacher'
self.rollout(train_ml=args.teacher_weight, train_rl=False, **kwargs)
elif (feedback == 'sample'):
if (args.ml_weight != 0):
self.feedback = 'teacher'
self.rollout(train_ml=args.ml_weight, train_rl=False, **kwargs)
self.feedback = 'sample'
self.rollout(train_ml=None, train_rl=True, **kwargs)
else:
assert False
self.loss.backward()
torch.nn.utils.clip_grad_norm(self.encoder.parameters(), 40.0)
torch.nn.utils.clip_grad_norm(self.decoder.parameters(), 40.0)
self.encoder_optimizer.step()
self.decoder_optimizer.step()
self.critic_optimizer.step()
|
def save(self, epoch, path):
' Snapshot models '
(the_dir, _) = os.path.split(path)
os.makedirs(the_dir, exist_ok=True)
states = {}
def create_state(name, model, optimizer):
states[name] = {'epoch': (epoch + 1), 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict()}
all_tuple = [('encoder', self.encoder, self.encoder_optimizer), ('decoder', self.decoder, self.decoder_optimizer), ('critic', self.critic, self.critic_optimizer)]
for param in all_tuple:
create_state(*param)
torch.save(states, path)
| -2,901,870,798,852,050,400
|
Snapshot models
|
r2r_src/agent.py
|
save
|
rcorona/R2R-EnvDrop
|
python
|
def save(self, epoch, path):
' '
(the_dir, _) = os.path.split(path)
os.makedirs(the_dir, exist_ok=True)
states = {}
def create_state(name, model, optimizer):
states[name] = {'epoch': (epoch + 1), 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict()}
all_tuple = [('encoder', self.encoder, self.encoder_optimizer), ('decoder', self.decoder, self.decoder_optimizer), ('critic', self.critic, self.critic_optimizer)]
for param in all_tuple:
create_state(*param)
torch.save(states, path)
|
def load(self, path):
' Loads parameters (but not training state) '
states = torch.load(path)
def recover_state(name, model, optimizer):
state = model.state_dict()
model_keys = set(state.keys())
load_keys = set(states[name]['state_dict'].keys())
if (model_keys != load_keys):
print('NOTICE: DIFFERENT KEYS IN THE LISTEREN')
state.update(states[name]['state_dict'])
model.load_state_dict(state)
if args.loadOptim:
optimizer.load_state_dict(states[name]['optimizer'])
all_tuple = [('encoder', self.encoder, self.encoder_optimizer), ('decoder', self.decoder, self.decoder_optimizer), ('critic', self.critic, self.critic_optimizer)]
for param in all_tuple:
recover_state(*param)
return (states['encoder']['epoch'] - 1)
| 3,282,851,448,400,201,700
|
Loads parameters (but not training state)
|
r2r_src/agent.py
|
load
|
rcorona/R2R-EnvDrop
|
python
|
def load(self, path):
' '
states = torch.load(path)
def recover_state(name, model, optimizer):
state = model.state_dict()
model_keys = set(state.keys())
load_keys = set(states[name]['state_dict'].keys())
if (model_keys != load_keys):
print('NOTICE: DIFFERENT KEYS IN THE LISTEREN')
state.update(states[name]['state_dict'])
model.load_state_dict(state)
if args.loadOptim:
optimizer.load_state_dict(states[name]['optimizer'])
all_tuple = [('encoder', self.encoder, self.encoder_optimizer), ('decoder', self.decoder, self.decoder_optimizer), ('critic', self.critic, self.critic_optimizer)]
for param in all_tuple:
recover_state(*param)
return (states['encoder']['epoch'] - 1)
|
def _get_local_ip_address(self):
'\n Gets the local ip address of this computer\n @returns str Local IP address\n '
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(('8.8.8.8', 80))
local_ip_address = s.getsockname()[0]
s.close()
return local_ip_address
| 7,471,134,649,321,523,000
|
Gets the local ip address of this computer
@returns str Local IP address
|
openbci/wifi.py
|
_get_local_ip_address
|
daniellasry/OpenBCI_Python
|
python
|
def _get_local_ip_address(self):
'\n Gets the local ip address of this computer\n @returns str Local IP address\n '
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(('8.8.8.8', 80))
local_ip_address = s.getsockname()[0]
s.close()
return local_ip_address
|
def getBoardType(self):
' Returns the version of the board '
return self.board_type
| -4,757,551,775,959,316,000
|
Returns the version of the board
|
openbci/wifi.py
|
getBoardType
|
daniellasry/OpenBCI_Python
|
python
|
def getBoardType(self):
' '
return self.board_type
|
def setImpedance(self, flag):
' Enable/disable impedance measure '
self.impedance = bool(flag)
| -5,800,124,032,592,154,000
|
Enable/disable impedance measure
|
openbci/wifi.py
|
setImpedance
|
daniellasry/OpenBCI_Python
|
python
|
def setImpedance(self, flag):
' '
self.impedance = bool(flag)
|
def connect(self):
' Connect to the board and configure it. Note: recreates various objects upon call. '
if (self.ip_address is None):
raise ValueError('self.ip_address cannot be None')
if self.log:
print(('Init WiFi connection with IP: ' + self.ip_address))
'\n Docs on these HTTP requests and more are found:\n https://app.swaggerhub.com/apis/pushtheworld/openbci-wifi-server/1.3.0\n '
res_board = requests.get(('http://%s/board' % self.ip_address))
if (res_board.status_code == 200):
board_info = res_board.json()
if (not board_info['board_connected']):
raise RuntimeError('No board connected to WiFi Shield. To learn how to connect to a Cyton or Ganglion visit http://docs.openbci.com/Tutorials/03-Wifi_Getting_Started_Guide')
self.board_type = board_info['board_type']
self.eeg_channels_per_sample = board_info['num_channels']
if self.log:
print(('Connected to %s with %s channels' % (self.board_type, self.eeg_channels_per_sample)))
self.gains = None
if (self.board_type == k.BOARD_CYTON):
self.gains = [24, 24, 24, 24, 24, 24, 24, 24]
self.daisy = False
elif (self.board_type == k.BOARD_DAISY):
self.gains = [24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24]
self.daisy = True
elif (self.board_type == k.BOARD_GANGLION):
self.gains = [51, 51, 51, 51]
self.daisy = False
self.local_wifi_server.set_daisy(daisy=self.daisy)
self.local_wifi_server.set_parser(ParseRaw(gains=self.gains, board_type=self.board_type))
if self.high_speed:
output_style = 'raw'
else:
output_style = 'json'
res_tcp_post = requests.post(('http://%s/tcp' % self.ip_address), json={'ip': self.local_ip_address, 'port': self.local_wifi_server_port, 'output': output_style, 'delimiter': True, 'latency': self.latency})
if (res_tcp_post.status_code == 200):
tcp_status = res_tcp_post.json()
if tcp_status['connected']:
if self.log:
print('WiFi Shield to Python TCP Socket Established')
else:
raise RuntimeWarning('WiFi Shield is not able to connect to local server. Please open an issue.')
| -1,779,810,696,293,935,000
|
Connect to the board and configure it. Note: recreates various objects upon call.
|
openbci/wifi.py
|
connect
|
daniellasry/OpenBCI_Python
|
python
|
def connect(self):
' '
if (self.ip_address is None):
raise ValueError('self.ip_address cannot be None')
if self.log:
print(('Init WiFi connection with IP: ' + self.ip_address))
'\n Docs on these HTTP requests and more are found:\n https://app.swaggerhub.com/apis/pushtheworld/openbci-wifi-server/1.3.0\n '
res_board = requests.get(('http://%s/board' % self.ip_address))
if (res_board.status_code == 200):
board_info = res_board.json()
if (not board_info['board_connected']):
raise RuntimeError('No board connected to WiFi Shield. To learn how to connect to a Cyton or Ganglion visit http://docs.openbci.com/Tutorials/03-Wifi_Getting_Started_Guide')
self.board_type = board_info['board_type']
self.eeg_channels_per_sample = board_info['num_channels']
if self.log:
print(('Connected to %s with %s channels' % (self.board_type, self.eeg_channels_per_sample)))
self.gains = None
if (self.board_type == k.BOARD_CYTON):
self.gains = [24, 24, 24, 24, 24, 24, 24, 24]
self.daisy = False
elif (self.board_type == k.BOARD_DAISY):
self.gains = [24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24]
self.daisy = True
elif (self.board_type == k.BOARD_GANGLION):
self.gains = [51, 51, 51, 51]
self.daisy = False
self.local_wifi_server.set_daisy(daisy=self.daisy)
self.local_wifi_server.set_parser(ParseRaw(gains=self.gains, board_type=self.board_type))
if self.high_speed:
output_style = 'raw'
else:
output_style = 'json'
res_tcp_post = requests.post(('http://%s/tcp' % self.ip_address), json={'ip': self.local_ip_address, 'port': self.local_wifi_server_port, 'output': output_style, 'delimiter': True, 'latency': self.latency})
if (res_tcp_post.status_code == 200):
tcp_status = res_tcp_post.json()
if tcp_status['connected']:
if self.log:
print('WiFi Shield to Python TCP Socket Established')
else:
raise RuntimeWarning('WiFi Shield is not able to connect to local server. Please open an issue.')
|
def init_streaming(self):
' Tell the board to record like crazy. '
res_stream_start = requests.get(('http://%s/stream/start' % self.ip_address))
if (res_stream_start.status_code == 200):
self.streaming = True
self.packets_dropped = 0
self.time_last_packet = timeit.default_timer()
else:
raise EnvironmentError(('Unable to start streaming. Check API for status code %d on /stream/start' % res_stream_start.status_code))
| -482,764,792,112,558,660
|
Tell the board to record like crazy.
|
openbci/wifi.py
|
init_streaming
|
daniellasry/OpenBCI_Python
|
python
|
def init_streaming(self):
' '
res_stream_start = requests.get(('http://%s/stream/start' % self.ip_address))
if (res_stream_start.status_code == 200):
self.streaming = True
self.packets_dropped = 0
self.time_last_packet = timeit.default_timer()
else:
raise EnvironmentError(('Unable to start streaming. Check API for status code %d on /stream/start' % res_stream_start.status_code))
|
def find_wifi_shield(self, shield_name=None, wifi_shield_cb=None):
'Detects Ganglion board MAC address -- if more than 1 around, will select first. Needs root privilege.'
if self.log:
print('Try to find WiFi shields on your local wireless network')
print(('Scanning for %d seconds nearby devices...' % self.timeout))
list_ip = []
list_id = []
found_shield = False
def wifi_shield_found(response):
res = requests.get(response.location, verify=False).text
device_description = xmltodict.parse(res)
cur_shield_name = str(device_description['root']['device']['serialNumber'])
cur_base_url = str(device_description['root']['URLBase'])
cur_ip_address = re.findall('[0-9]+(?:\\.[0-9]+){3}', cur_base_url)[0]
list_id.append(cur_shield_name)
list_ip.append(cur_ip_address)
found_shield = True
if (shield_name is None):
print(('Found WiFi Shield %s with IP Address %s' % (cur_shield_name, cur_ip_address)))
if (wifi_shield_cb is not None):
wifi_shield_cb(cur_ip_address)
elif (shield_name == cur_shield_name):
if (wifi_shield_cb is not None):
wifi_shield_cb(cur_ip_address)
ssdp_hits = ssdp.discover('urn:schemas-upnp-org:device:Basic:1', timeout=self.timeout, wifi_found_cb=wifi_shield_found)
nb_wifi_shields = len(list_id)
if (nb_wifi_shields < 1):
print('No WiFi Shields found ;(')
raise OSError('Cannot find OpenBCI WiFi Shield with local name')
if (nb_wifi_shields > 1):
print(((((('Found ' + str(nb_wifi_shields)) + ', selecting first named: ') + list_id[0]) + ' with IPV4: ') + list_ip[0]))
return list_ip[0]
| 9,049,252,316,412,233,000
|
Detects Ganglion board MAC address -- if more than 1 around, will select first. Needs root privilege.
|
openbci/wifi.py
|
find_wifi_shield
|
daniellasry/OpenBCI_Python
|
python
|
def find_wifi_shield(self, shield_name=None, wifi_shield_cb=None):
if self.log:
print('Try to find WiFi shields on your local wireless network')
print(('Scanning for %d seconds nearby devices...' % self.timeout))
list_ip = []
list_id = []
found_shield = False
def wifi_shield_found(response):
res = requests.get(response.location, verify=False).text
device_description = xmltodict.parse(res)
cur_shield_name = str(device_description['root']['device']['serialNumber'])
cur_base_url = str(device_description['root']['URLBase'])
cur_ip_address = re.findall('[0-9]+(?:\\.[0-9]+){3}', cur_base_url)[0]
list_id.append(cur_shield_name)
list_ip.append(cur_ip_address)
found_shield = True
if (shield_name is None):
print(('Found WiFi Shield %s with IP Address %s' % (cur_shield_name, cur_ip_address)))
if (wifi_shield_cb is not None):
wifi_shield_cb(cur_ip_address)
elif (shield_name == cur_shield_name):
if (wifi_shield_cb is not None):
wifi_shield_cb(cur_ip_address)
ssdp_hits = ssdp.discover('urn:schemas-upnp-org:device:Basic:1', timeout=self.timeout, wifi_found_cb=wifi_shield_found)
nb_wifi_shields = len(list_id)
if (nb_wifi_shields < 1):
print('No WiFi Shields found ;(')
raise OSError('Cannot find OpenBCI WiFi Shield with local name')
if (nb_wifi_shields > 1):
print(((((('Found ' + str(nb_wifi_shields)) + ', selecting first named: ') + list_id[0]) + ' with IPV4: ') + list_ip[0]))
return list_ip[0]
|
def wifi_write(self, output):
'\n Pass through commands from the WiFi Shield to the Carrier board\n :param output:\n :return:\n '
res_command_post = requests.post(('http://%s/command' % self.ip_address), json={'command': output})
if (res_command_post.status_code == 200):
ret_val = res_command_post.text
if self.log:
print(ret_val)
return ret_val
else:
if self.log:
print(('Error code: %d %s' % (res_command_post.status_code, res_command_post.text)))
raise RuntimeError(('Error code: %d %s' % (res_command_post.status_code, res_command_post.text)))
| -9,185,579,094,860,822,000
|
Pass through commands from the WiFi Shield to the Carrier board
:param output:
:return:
|
openbci/wifi.py
|
wifi_write
|
daniellasry/OpenBCI_Python
|
python
|
def wifi_write(self, output):
'\n Pass through commands from the WiFi Shield to the Carrier board\n :param output:\n :return:\n '
res_command_post = requests.post(('http://%s/command' % self.ip_address), json={'command': output})
if (res_command_post.status_code == 200):
ret_val = res_command_post.text
if self.log:
print(ret_val)
return ret_val
else:
if self.log:
print(('Error code: %d %s' % (res_command_post.status_code, res_command_post.text)))
raise RuntimeError(('Error code: %d %s' % (res_command_post.status_code, res_command_post.text)))
|
def getNbEEGChannels(self):
'Will not get new data on impedance check.'
return self.eeg_channels_per_sample
| -2,454,272,197,531,395,600
|
Will not get new data on impedance check.
|
openbci/wifi.py
|
getNbEEGChannels
|
daniellasry/OpenBCI_Python
|
python
|
def getNbEEGChannels(self):
return self.eeg_channels_per_sample
|
def start_streaming(self, callback, lapse=(- 1)):
'\n Start handling streaming data from the board. Call a provided callback\n for every single sample that is processed\n\n Args:\n callback: A callback function -- or a list of functions -- that will receive a single argument of the\n OpenBCISample object captured.\n '
start_time = timeit.default_timer()
if (not isinstance(callback, list)):
self.local_wifi_server.set_callback(callback)
else:
self.local_wifi_server.set_callback(callback[0])
if (not self.streaming):
self.init_streaming()
| 4,880,693,691,809,626,000
|
Start handling streaming data from the board. Call a provided callback
for every single sample that is processed
Args:
callback: A callback function -- or a list of functions -- that will receive a single argument of the
OpenBCISample object captured.
|
openbci/wifi.py
|
start_streaming
|
daniellasry/OpenBCI_Python
|
python
|
def start_streaming(self, callback, lapse=(- 1)):
'\n Start handling streaming data from the board. Call a provided callback\n for every single sample that is processed\n\n Args:\n callback: A callback function -- or a list of functions -- that will receive a single argument of the\n OpenBCISample object captured.\n '
start_time = timeit.default_timer()
if (not isinstance(callback, list)):
self.local_wifi_server.set_callback(callback)
else:
self.local_wifi_server.set_callback(callback[0])
if (not self.streaming):
self.init_streaming()
|
def test_signal(self, signal):
' Enable / disable test signal '
if (signal == 0):
self.warn('Disabling synthetic square wave')
try:
self.wifi_write(']')
except Exception as e:
print(('Something went wrong while setting signal: ' + str(e)))
elif (signal == 1):
self.warn('Enabling synthetic square wave')
try:
self.wifi_write('[')
except Exception as e:
print(('Something went wrong while setting signal: ' + str(e)))
else:
self.warn(('%s is not a known test signal. Valid signal is 0-1' % signal))
| 7,795,635,748,368,347,000
|
Enable / disable test signal
|
openbci/wifi.py
|
test_signal
|
daniellasry/OpenBCI_Python
|
python
|
def test_signal(self, signal):
' '
if (signal == 0):
self.warn('Disabling synthetic square wave')
try:
self.wifi_write(']')
except Exception as e:
print(('Something went wrong while setting signal: ' + str(e)))
elif (signal == 1):
self.warn('Enabling synthetic square wave')
try:
self.wifi_write('[')
except Exception as e:
print(('Something went wrong while setting signal: ' + str(e)))
else:
self.warn(('%s is not a known test signal. Valid signal is 0-1' % signal))
|
def set_channel(self, channel, toggle_position):
' Enable / disable channels '
try:
if (channel > self.num_channels):
raise ValueError('Cannot set non-existant channel')
if (toggle_position == 1):
if (channel is 1):
self.wifi_write('!')
if (channel is 2):
self.wifi_write('@')
if (channel is 3):
self.wifi_write('#')
if (channel is 4):
self.wifi_write('$')
if (channel is 5):
self.wifi_write('%')
if (channel is 6):
self.wifi_write('^')
if (channel is 7):
self.wifi_write('&')
if (channel is 8):
self.wifi_write('*')
if (channel is 9):
self.wifi_write('Q')
if (channel is 10):
self.wifi_write('W')
if (channel is 11):
self.wifi_write('E')
if (channel is 12):
self.wifi_write('R')
if (channel is 13):
self.wifi_write('T')
if (channel is 14):
self.wifi_write('Y')
if (channel is 15):
self.wifi_write('U')
if (channel is 16):
self.wifi_write('I')
elif (toggle_position == 0):
if (channel is 1):
self.wifi_write('1')
if (channel is 2):
self.wifi_write('2')
if (channel is 3):
self.wifi_write('3')
if (channel is 4):
self.wifi_write('4')
if (channel is 5):
self.wifi_write('5')
if (channel is 6):
self.wifi_write('6')
if (channel is 7):
self.wifi_write('7')
if (channel is 8):
self.wifi_write('8')
if (channel is 9):
self.wifi_write('q')
if (channel is 10):
self.wifi_write('w')
if (channel is 11):
self.wifi_write('e')
if (channel is 12):
self.wifi_write('r')
if (channel is 13):
self.wifi_write('t')
if (channel is 14):
self.wifi_write('y')
if (channel is 15):
self.wifi_write('u')
if (channel is 16):
self.wifi_write('i')
except Exception as e:
print(('Something went wrong while setting channels: ' + str(e)))
| -451,990,832,647,603,140
|
Enable / disable channels
|
openbci/wifi.py
|
set_channel
|
daniellasry/OpenBCI_Python
|
python
|
def set_channel(self, channel, toggle_position):
' '
try:
if (channel > self.num_channels):
raise ValueError('Cannot set non-existant channel')
if (toggle_position == 1):
if (channel is 1):
self.wifi_write('!')
if (channel is 2):
self.wifi_write('@')
if (channel is 3):
self.wifi_write('#')
if (channel is 4):
self.wifi_write('$')
if (channel is 5):
self.wifi_write('%')
if (channel is 6):
self.wifi_write('^')
if (channel is 7):
self.wifi_write('&')
if (channel is 8):
self.wifi_write('*')
if (channel is 9):
self.wifi_write('Q')
if (channel is 10):
self.wifi_write('W')
if (channel is 11):
self.wifi_write('E')
if (channel is 12):
self.wifi_write('R')
if (channel is 13):
self.wifi_write('T')
if (channel is 14):
self.wifi_write('Y')
if (channel is 15):
self.wifi_write('U')
if (channel is 16):
self.wifi_write('I')
elif (toggle_position == 0):
if (channel is 1):
self.wifi_write('1')
if (channel is 2):
self.wifi_write('2')
if (channel is 3):
self.wifi_write('3')
if (channel is 4):
self.wifi_write('4')
if (channel is 5):
self.wifi_write('5')
if (channel is 6):
self.wifi_write('6')
if (channel is 7):
self.wifi_write('7')
if (channel is 8):
self.wifi_write('8')
if (channel is 9):
self.wifi_write('q')
if (channel is 10):
self.wifi_write('w')
if (channel is 11):
self.wifi_write('e')
if (channel is 12):
self.wifi_write('r')
if (channel is 13):
self.wifi_write('t')
if (channel is 14):
self.wifi_write('y')
if (channel is 15):
self.wifi_write('u')
if (channel is 16):
self.wifi_write('i')
except Exception as e:
print(('Something went wrong while setting channels: ' + str(e)))
|
def set_sample_rate(self, sample_rate):
' Change sample rate '
try:
if ((self.board_type == k.BOARD_CYTON) or (self.board_type == k.BOARD_DAISY)):
if (sample_rate == 250):
self.wifi_write('~6')
elif (sample_rate == 500):
self.wifi_write('~5')
elif (sample_rate == 1000):
self.wifi_write('~4')
elif (sample_rate == 2000):
self.wifi_write('~3')
elif (sample_rate == 4000):
self.wifi_write('~2')
elif (sample_rate == 8000):
self.wifi_write('~1')
elif (sample_rate == 16000):
self.wifi_write('~0')
else:
print(('Sample rate not supported: ' + str(sample_rate)))
elif (self.board_type == k.BOARD_GANGLION):
if (sample_rate == 200):
self.wifi_write('~7')
elif (sample_rate == 400):
self.wifi_write('~6')
elif (sample_rate == 800):
self.wifi_write('~5')
elif (sample_rate == 1600):
self.wifi_write('~4')
elif (sample_rate == 3200):
self.wifi_write('~3')
elif (sample_rate == 6400):
self.wifi_write('~2')
elif (sample_rate == 12800):
self.wifi_write('~1')
elif (sample_rate == 25600):
self.wifi_write('~0')
else:
print(('Sample rate not supported: ' + str(sample_rate)))
else:
print('Board type not supported for setting sample rate')
except Exception as e:
print(('Something went wrong while setting sample rate: ' + str(e)))
| -6,714,558,995,829,764,000
|
Change sample rate
|
openbci/wifi.py
|
set_sample_rate
|
daniellasry/OpenBCI_Python
|
python
|
def set_sample_rate(self, sample_rate):
' '
try:
if ((self.board_type == k.BOARD_CYTON) or (self.board_type == k.BOARD_DAISY)):
if (sample_rate == 250):
self.wifi_write('~6')
elif (sample_rate == 500):
self.wifi_write('~5')
elif (sample_rate == 1000):
self.wifi_write('~4')
elif (sample_rate == 2000):
self.wifi_write('~3')
elif (sample_rate == 4000):
self.wifi_write('~2')
elif (sample_rate == 8000):
self.wifi_write('~1')
elif (sample_rate == 16000):
self.wifi_write('~0')
else:
print(('Sample rate not supported: ' + str(sample_rate)))
elif (self.board_type == k.BOARD_GANGLION):
if (sample_rate == 200):
self.wifi_write('~7')
elif (sample_rate == 400):
self.wifi_write('~6')
elif (sample_rate == 800):
self.wifi_write('~5')
elif (sample_rate == 1600):
self.wifi_write('~4')
elif (sample_rate == 3200):
self.wifi_write('~3')
elif (sample_rate == 6400):
self.wifi_write('~2')
elif (sample_rate == 12800):
self.wifi_write('~1')
elif (sample_rate == 25600):
self.wifi_write('~0')
else:
print(('Sample rate not supported: ' + str(sample_rate)))
else:
print('Board type not supported for setting sample rate')
except Exception as e:
print(('Something went wrong while setting sample rate: ' + str(e)))
|
def set_accelerometer(self, toggle_position):
' Enable / disable accelerometer '
try:
if (self.board_type == k.BOARD_GANGLION):
if (toggle_position == 1):
self.wifi_write('n')
elif (toggle_position == 0):
self.wifi_write('N')
else:
print('Board type not supported for setting accelerometer')
except Exception as e:
print(('Something went wrong while setting accelerometer: ' + str(e)))
| -4,555,134,248,605,669,400
|
Enable / disable accelerometer
|
openbci/wifi.py
|
set_accelerometer
|
daniellasry/OpenBCI_Python
|
python
|
def set_accelerometer(self, toggle_position):
' '
try:
if (self.board_type == k.BOARD_GANGLION):
if (toggle_position == 1):
self.wifi_write('n')
elif (toggle_position == 0):
self.wifi_write('N')
else:
print('Board type not supported for setting accelerometer')
except Exception as e:
print(('Something went wrong while setting accelerometer: ' + str(e)))
|
def check_connection(self):
' Check connection quality in term of lag and number of packets drop. Reinit connection if necessary. FIXME: parameters given to the board will be lost.'
if (not self.streaming):
return
if (self.packets_dropped > self.max_packets_to_skip):
self.warn('Too many packets dropped, attempt to reconnect')
self.reconnect()
elif ((self.timeout > 0) and ((timeit.default_timer() - self.time_last_packet) > self.timeout)):
self.warn('Too long since got new data, attempt to reconnect')
self.reconnect()
| -7,697,840,872,393,702,000
|
Check connection quality in term of lag and number of packets drop. Reinit connection if necessary. FIXME: parameters given to the board will be lost.
|
openbci/wifi.py
|
check_connection
|
daniellasry/OpenBCI_Python
|
python
|
def check_connection(self):
' '
if (not self.streaming):
return
if (self.packets_dropped > self.max_packets_to_skip):
self.warn('Too many packets dropped, attempt to reconnect')
self.reconnect()
elif ((self.timeout > 0) and ((timeit.default_timer() - self.time_last_packet) > self.timeout)):
self.warn('Too long since got new data, attempt to reconnect')
self.reconnect()
|
def reconnect(self):
' In case of poor connection, will shut down and relaunch everything. FIXME: parameters given to the board will be lost.'
self.warn('Reconnecting')
self.stop()
self.disconnect()
self.connect()
self.init_streaming()
| 4,734,633,004,977,587,000
|
In case of poor connection, will shut down and relaunch everything. FIXME: parameters given to the board will be lost.
|
openbci/wifi.py
|
reconnect
|
daniellasry/OpenBCI_Python
|
python
|
def reconnect(self):
' '
self.warn('Reconnecting')
self.stop()
self.disconnect()
self.connect()
self.init_streaming()
|
@used
def type_from_ast(ast_node: ast.AST, visitor: Optional['NameCheckVisitor']=None, ctx: Optional[Context]=None) -> Value:
'Given an AST node representing an annotation, return a\n :class:`Value <pyanalyze.value.Value>`.\n\n :param ast_node: AST node to evaluate.\n\n :param visitor: Visitor class to use. This is used in the default\n :class:`Context` to resolve names and show errors.\n This is ignored if `ctx` is given.\n\n :param ctx: :class:`Context` to use for evaluation.\n\n '
if (ctx is None):
ctx = _DefaultContext(visitor, ast_node)
return _type_from_ast(ast_node, ctx)
| 6,403,837,150,855,721,000
|
Given an AST node representing an annotation, return a
:class:`Value <pyanalyze.value.Value>`.
:param ast_node: AST node to evaluate.
:param visitor: Visitor class to use. This is used in the default
:class:`Context` to resolve names and show errors.
This is ignored if `ctx` is given.
:param ctx: :class:`Context` to use for evaluation.
|
pyanalyze/annotations.py
|
type_from_ast
|
nbdaaron/pyanalyze
|
python
|
@used
def type_from_ast(ast_node: ast.AST, visitor: Optional['NameCheckVisitor']=None, ctx: Optional[Context]=None) -> Value:
'Given an AST node representing an annotation, return a\n :class:`Value <pyanalyze.value.Value>`.\n\n :param ast_node: AST node to evaluate.\n\n :param visitor: Visitor class to use. This is used in the default\n :class:`Context` to resolve names and show errors.\n This is ignored if `ctx` is given.\n\n :param ctx: :class:`Context` to use for evaluation.\n\n '
if (ctx is None):
ctx = _DefaultContext(visitor, ast_node)
return _type_from_ast(ast_node, ctx)
|
def type_from_runtime(val: object, visitor: Optional['NameCheckVisitor']=None, node: Optional[ast.AST]=None, globals: Optional[Mapping[(str, object)]]=None, ctx: Optional[Context]=None) -> Value:
'Given a runtime annotation object, return a\n :class:`Value <pyanalyze.value.Value>`.\n\n :param val: Object to evaluate. This will usually come from an\n ``__annotations__`` dictionary.\n\n :param visitor: Visitor class to use. This is used in the default\n :class:`Context` to resolve names and show errors.\n This is ignored if `ctx` is given.\n\n :param node: AST node that the annotation derives from. This is\n used for showing errors. Ignored if `ctx` is given.\n\n :param globals: Dictionary of global variables that can be used\n to resolve names. Ignored if `ctx` is given.\n\n :param ctx: :class:`Context` to use for evaluation.\n\n '
if (ctx is None):
ctx = _DefaultContext(visitor, node, globals)
return _type_from_runtime(val, ctx)
| -3,828,119,079,951,672,300
|
Given a runtime annotation object, return a
:class:`Value <pyanalyze.value.Value>`.
:param val: Object to evaluate. This will usually come from an
``__annotations__`` dictionary.
:param visitor: Visitor class to use. This is used in the default
:class:`Context` to resolve names and show errors.
This is ignored if `ctx` is given.
:param node: AST node that the annotation derives from. This is
used for showing errors. Ignored if `ctx` is given.
:param globals: Dictionary of global variables that can be used
to resolve names. Ignored if `ctx` is given.
:param ctx: :class:`Context` to use for evaluation.
|
pyanalyze/annotations.py
|
type_from_runtime
|
nbdaaron/pyanalyze
|
python
|
def type_from_runtime(val: object, visitor: Optional['NameCheckVisitor']=None, node: Optional[ast.AST]=None, globals: Optional[Mapping[(str, object)]]=None, ctx: Optional[Context]=None) -> Value:
'Given a runtime annotation object, return a\n :class:`Value <pyanalyze.value.Value>`.\n\n :param val: Object to evaluate. This will usually come from an\n ``__annotations__`` dictionary.\n\n :param visitor: Visitor class to use. This is used in the default\n :class:`Context` to resolve names and show errors.\n This is ignored if `ctx` is given.\n\n :param node: AST node that the annotation derives from. This is\n used for showing errors. Ignored if `ctx` is given.\n\n :param globals: Dictionary of global variables that can be used\n to resolve names. Ignored if `ctx` is given.\n\n :param ctx: :class:`Context` to use for evaluation.\n\n '
if (ctx is None):
ctx = _DefaultContext(visitor, node, globals)
return _type_from_runtime(val, ctx)
|
def type_from_value(value: Value, visitor: Optional['NameCheckVisitor']=None, node: Optional[ast.AST]=None, ctx: Optional[Context]=None, is_typeddict: bool=False) -> Value:
'Given a :class:`Value <pyanalyze.value.Value` representing an annotation,\n return a :class:`Value <pyanalyze.value.Value>` representing the type.\n\n The input value represents an expression, the output value represents\n a type. For example, the :term:`impl` of ``typing.cast(typ, val)``\n calls :func:`type_from_value` on the value it receives for its\n `typ` argument and returns the result.\n\n :param value: :class:`Value <pyanalyze.value.Value` to evaluate.\n\n :param visitor: Visitor class to use. This is used in the default\n :class:`Context` to resolve names and show errors.\n This is ignored if `ctx` is given.\n\n :param node: AST node that the annotation derives from. This is\n used for showing errors. Ignored if `ctx` is given.\n\n :param ctx: :class:`Context` to use for evaluation.\n\n :param is_typeddict: Whether we are at the top level of a `TypedDict`\n definition.\n\n '
if (ctx is None):
ctx = _DefaultContext(visitor, node)
return _type_from_value(value, ctx, is_typeddict=is_typeddict)
| 4,292,624,891,465,387,000
|
Given a :class:`Value <pyanalyze.value.Value` representing an annotation,
return a :class:`Value <pyanalyze.value.Value>` representing the type.
The input value represents an expression, the output value represents
a type. For example, the :term:`impl` of ``typing.cast(typ, val)``
calls :func:`type_from_value` on the value it receives for its
`typ` argument and returns the result.
:param value: :class:`Value <pyanalyze.value.Value` to evaluate.
:param visitor: Visitor class to use. This is used in the default
:class:`Context` to resolve names and show errors.
This is ignored if `ctx` is given.
:param node: AST node that the annotation derives from. This is
used for showing errors. Ignored if `ctx` is given.
:param ctx: :class:`Context` to use for evaluation.
:param is_typeddict: Whether we are at the top level of a `TypedDict`
definition.
|
pyanalyze/annotations.py
|
type_from_value
|
nbdaaron/pyanalyze
|
python
|
def type_from_value(value: Value, visitor: Optional['NameCheckVisitor']=None, node: Optional[ast.AST]=None, ctx: Optional[Context]=None, is_typeddict: bool=False) -> Value:
'Given a :class:`Value <pyanalyze.value.Value` representing an annotation,\n return a :class:`Value <pyanalyze.value.Value>` representing the type.\n\n The input value represents an expression, the output value represents\n a type. For example, the :term:`impl` of ``typing.cast(typ, val)``\n calls :func:`type_from_value` on the value it receives for its\n `typ` argument and returns the result.\n\n :param value: :class:`Value <pyanalyze.value.Value` to evaluate.\n\n :param visitor: Visitor class to use. This is used in the default\n :class:`Context` to resolve names and show errors.\n This is ignored if `ctx` is given.\n\n :param node: AST node that the annotation derives from. This is\n used for showing errors. Ignored if `ctx` is given.\n\n :param ctx: :class:`Context` to use for evaluation.\n\n :param is_typeddict: Whether we are at the top level of a `TypedDict`\n definition.\n\n '
if (ctx is None):
ctx = _DefaultContext(visitor, node)
return _type_from_value(value, ctx, is_typeddict=is_typeddict)
|
def suppress_undefined_names(self) -> ContextManager[None]:
'Temporarily suppress errors about undefined names.'
return qcore.override(self, 'should_suppress_undefined_names', True)
| -5,153,936,227,865,077,000
|
Temporarily suppress errors about undefined names.
|
pyanalyze/annotations.py
|
suppress_undefined_names
|
nbdaaron/pyanalyze
|
python
|
def suppress_undefined_names(self) -> ContextManager[None]:
return qcore.override(self, 'should_suppress_undefined_names', True)
|
def show_error(self, message: str, error_code: ErrorCode=ErrorCode.invalid_annotation, node: Optional[ast.AST]=None) -> None:
'Show an error found while evaluating an annotation.'
pass
| 8,157,879,884,960,985,000
|
Show an error found while evaluating an annotation.
|
pyanalyze/annotations.py
|
show_error
|
nbdaaron/pyanalyze
|
python
|
def show_error(self, message: str, error_code: ErrorCode=ErrorCode.invalid_annotation, node: Optional[ast.AST]=None) -> None:
pass
|
def get_name(self, node: ast.Name) -> Value:
'Return the :class:`Value <pyanalyze.value.Value>` corresponding to a name.'
return AnyValue(AnySource.inference)
| -6,227,226,071,517,584,000
|
Return the :class:`Value <pyanalyze.value.Value>` corresponding to a name.
|
pyanalyze/annotations.py
|
get_name
|
nbdaaron/pyanalyze
|
python
|
def get_name(self, node: ast.Name) -> Value:
return AnyValue(AnySource.inference)
|
def get_name(self, node: ast.Name) -> Value:
'Return the :class:`Value <pyanalyze.value.Value>` corresponding to a name.'
return self.get_name_from_globals(node.id, self.globals)
| 4,904,100,106,146,036,000
|
Return the :class:`Value <pyanalyze.value.Value>` corresponding to a name.
|
pyanalyze/annotations.py
|
get_name
|
nbdaaron/pyanalyze
|
python
|
def get_name(self, node: ast.Name) -> Value:
return self.get_name_from_globals(node.id, self.globals)
|
def get_name(self, node: ast.Name) -> Value:
'Return the :class:`Value <pyanalyze.value.Value>` corresponding to a name.'
return self.annotations_context.get_name(node)
| 7,946,218,256,446,995,000
|
Return the :class:`Value <pyanalyze.value.Value>` corresponding to a name.
|
pyanalyze/annotations.py
|
get_name
|
nbdaaron/pyanalyze
|
python
|
def get_name(self, node: ast.Name) -> Value:
return self.annotations_context.get_name(node)
|
def __virtual__():
'\n Only return if python-etcd is installed\n '
return (__virtualname__ if HAS_LIBS else False)
| 337,201,942,222,086,850
|
Only return if python-etcd is installed
|
salt/pillar/vmware_pillar.py
|
__virtual__
|
aaannz/salt-1
|
python
|
def __virtual__():
'\n \n '
return (__virtualname__ if HAS_LIBS else False)
|
def ext_pillar(minion_id, pillar, **kwargs):
'\n Check vmware/vcenter for all data\n '
vmware_pillar = {}
host = None
username = None
password = None
property_types = []
property_name = 'name'
protocol = None
port = None
pillar_key = 'vmware'
replace_default_attributes = False
type_specific_pillar_attributes = {'VirtualMachine': [{'config': ['version', 'guestId', 'files', 'tools', 'flags', 'memoryHotAddEnabled', 'cpuHotAddEnabled', 'cpuHotRemoveEnabled', 'datastoreUrl', 'swapPlacement', 'bootOptions', 'scheduledHardwareUpgradeInfo', 'memoryAllocation', 'cpuAllocation']}, {'summary': [{'runtime': [{'host': ['name', {'parent': 'name'}]}, 'bootTime']}, {'guest': ['toolsStatus', 'toolsVersionStatus', 'toolsVersionStatus2', 'toolsRunningStatus']}, {'config': ['cpuReservation', 'memoryReservation']}, {'storage': ['committed', 'uncommitted', 'unshared']}, {'dasVmProtection': ['dasProtected']}]}, {'storage': [{'perDatastoreUsage': [{'datastore': 'name'}, 'committed', 'uncommitted', 'unshared']}]}], 'HostSystem': [{'datastore': ['name', 'overallStatus', {'summary': ['url', 'freeSpace', 'maxFileSize', 'maxVirtualDiskCapacity', 'maxPhysicalRDMFileSize', 'maxVirtualRDMFileSize', {'vmfs': ['capacity', 'blockSizeMb', 'maxBlocks', 'majorVersion', 'version', 'uuid', {'extent': ['diskName', 'partition']}, 'vmfsUpgradeable', 'ssd', 'local']}]}, {'vm': 'name'}]}, {'vm': ['name', 'overallStatus', {'summary': [{'runtime': 'powerState'}]}]}]}
pillar_attributes = [{'summary': ['overallStatus']}, {'network': ['name', {'config': {'distributedVirtualSwitch': 'name'}}]}, {'datastore': ['name']}, {'parent': ['name']}]
if ('pillar_key' in kwargs):
pillar_key = kwargs['pillar_key']
vmware_pillar[pillar_key] = {}
if ('host' not in kwargs):
log.error('VMWare external pillar configured but host is not specified in ext_pillar configuration.')
return vmware_pillar
else:
host = kwargs['host']
log.debug('vmware_pillar -- host = %s', host)
if ('username' not in kwargs):
log.error('VMWare external pillar requested but username is not specified in ext_pillar configuration.')
return vmware_pillar
else:
username = kwargs['username']
log.debug('vmware_pillar -- username = %s', username)
if ('password' not in kwargs):
log.error('VMWare external pillar requested but password is not specified in ext_pillar configuration.')
return vmware_pillar
else:
password = kwargs['password']
log.debug('vmware_pillar -- password = %s', password)
if ('replace_default_attributes' in kwargs):
replace_default_attributes = kwargs['replace_default_attributes']
if replace_default_attributes:
pillar_attributes = []
type_specific_pillar_attributes = {}
if ('property_types' in kwargs):
for prop_type in kwargs['property_types']:
if isinstance(prop_type, dict):
property_types.append(getattr(vim, prop_type.keys()[0]))
if isinstance(prop_type[prop_type.keys()[0]], list):
pillar_attributes = (pillar_attributes + prop_type[prop_type.keys()[0]])
else:
log.warning('A property_type dict was specified, but its value is not a list')
else:
property_types.append(getattr(vim, prop_type))
else:
property_types = [vim.VirtualMachine]
log.debug('vmware_pillar -- property_types = %s', property_types)
if ('property_name' in kwargs):
property_name = kwargs['property_name']
else:
property_name = 'name'
log.debug('vmware_pillar -- property_name = %s', property_name)
if ('protocol' in kwargs):
protocol = kwargs['protocol']
log.debug('vmware_pillar -- protocol = %s', protocol)
if ('port' in kwargs):
port = kwargs['port']
log.debug('vmware_pillar -- port = %s', port)
virtualgrain = None
osgrain = None
if ('virtual' in __grains__):
virtualgrain = __grains__['virtual'].lower()
if ('os' in __grains__):
osgrain = __grains__['os'].lower()
if ((virtualgrain == 'vmware') or (osgrain == 'vmware esxi') or (osgrain == 'esxi')):
vmware_pillar[pillar_key] = {}
try:
_conn = salt.utils.vmware.get_service_instance(host, username, password, protocol, port, verify_ssl=kwargs.get('verify_ssl', True))
if _conn:
data = None
for prop_type in property_types:
data = salt.utils.vmware.get_mor_by_property(_conn, prop_type, minion_id, property_name=property_name)
if data:
type_name = type(data).__name__.replace('vim.', '')
if hasattr(data, 'availableField'):
vmware_pillar[pillar_key]['annotations'] = {}
for availableField in data.availableField:
for customValue in data.customValue:
if (availableField.key == customValue.key):
vmware_pillar[pillar_key]['annotations'][availableField.name] = customValue.value
type_specific_pillar_attribute = []
if (type_name in type_specific_pillar_attributes):
type_specific_pillar_attribute = type_specific_pillar_attributes[type_name]
vmware_pillar[pillar_key] = dictupdate.update(vmware_pillar[pillar_key], _crawl_attribute(data, (pillar_attributes + type_specific_pillar_attribute)))
break
Disconnect(_conn)
else:
log.error('Unable to obtain a connection with %s, please verify your vmware ext_pillar configuration', host)
except RuntimeError:
log.error('A runtime error occurred in the vmware_pillar, this is likely caused by an infinite recursion in a requested attribute. Verify your requested attributes and reconfigure the pillar.')
return vmware_pillar
else:
return {}
| -1,612,344,056,009,843,000
|
Check vmware/vcenter for all data
|
salt/pillar/vmware_pillar.py
|
ext_pillar
|
aaannz/salt-1
|
python
|
def ext_pillar(minion_id, pillar, **kwargs):
'\n \n '
vmware_pillar = {}
host = None
username = None
password = None
property_types = []
property_name = 'name'
protocol = None
port = None
pillar_key = 'vmware'
replace_default_attributes = False
type_specific_pillar_attributes = {'VirtualMachine': [{'config': ['version', 'guestId', 'files', 'tools', 'flags', 'memoryHotAddEnabled', 'cpuHotAddEnabled', 'cpuHotRemoveEnabled', 'datastoreUrl', 'swapPlacement', 'bootOptions', 'scheduledHardwareUpgradeInfo', 'memoryAllocation', 'cpuAllocation']}, {'summary': [{'runtime': [{'host': ['name', {'parent': 'name'}]}, 'bootTime']}, {'guest': ['toolsStatus', 'toolsVersionStatus', 'toolsVersionStatus2', 'toolsRunningStatus']}, {'config': ['cpuReservation', 'memoryReservation']}, {'storage': ['committed', 'uncommitted', 'unshared']}, {'dasVmProtection': ['dasProtected']}]}, {'storage': [{'perDatastoreUsage': [{'datastore': 'name'}, 'committed', 'uncommitted', 'unshared']}]}], 'HostSystem': [{'datastore': ['name', 'overallStatus', {'summary': ['url', 'freeSpace', 'maxFileSize', 'maxVirtualDiskCapacity', 'maxPhysicalRDMFileSize', 'maxVirtualRDMFileSize', {'vmfs': ['capacity', 'blockSizeMb', 'maxBlocks', 'majorVersion', 'version', 'uuid', {'extent': ['diskName', 'partition']}, 'vmfsUpgradeable', 'ssd', 'local']}]}, {'vm': 'name'}]}, {'vm': ['name', 'overallStatus', {'summary': [{'runtime': 'powerState'}]}]}]}
pillar_attributes = [{'summary': ['overallStatus']}, {'network': ['name', {'config': {'distributedVirtualSwitch': 'name'}}]}, {'datastore': ['name']}, {'parent': ['name']}]
if ('pillar_key' in kwargs):
pillar_key = kwargs['pillar_key']
vmware_pillar[pillar_key] = {}
if ('host' not in kwargs):
log.error('VMWare external pillar configured but host is not specified in ext_pillar configuration.')
return vmware_pillar
else:
host = kwargs['host']
log.debug('vmware_pillar -- host = %s', host)
if ('username' not in kwargs):
log.error('VMWare external pillar requested but username is not specified in ext_pillar configuration.')
return vmware_pillar
else:
username = kwargs['username']
log.debug('vmware_pillar -- username = %s', username)
if ('password' not in kwargs):
log.error('VMWare external pillar requested but password is not specified in ext_pillar configuration.')
return vmware_pillar
else:
password = kwargs['password']
log.debug('vmware_pillar -- password = %s', password)
if ('replace_default_attributes' in kwargs):
replace_default_attributes = kwargs['replace_default_attributes']
if replace_default_attributes:
pillar_attributes = []
type_specific_pillar_attributes = {}
if ('property_types' in kwargs):
for prop_type in kwargs['property_types']:
if isinstance(prop_type, dict):
property_types.append(getattr(vim, prop_type.keys()[0]))
if isinstance(prop_type[prop_type.keys()[0]], list):
pillar_attributes = (pillar_attributes + prop_type[prop_type.keys()[0]])
else:
log.warning('A property_type dict was specified, but its value is not a list')
else:
property_types.append(getattr(vim, prop_type))
else:
property_types = [vim.VirtualMachine]
log.debug('vmware_pillar -- property_types = %s', property_types)
if ('property_name' in kwargs):
property_name = kwargs['property_name']
else:
property_name = 'name'
log.debug('vmware_pillar -- property_name = %s', property_name)
if ('protocol' in kwargs):
protocol = kwargs['protocol']
log.debug('vmware_pillar -- protocol = %s', protocol)
if ('port' in kwargs):
port = kwargs['port']
log.debug('vmware_pillar -- port = %s', port)
virtualgrain = None
osgrain = None
if ('virtual' in __grains__):
virtualgrain = __grains__['virtual'].lower()
if ('os' in __grains__):
osgrain = __grains__['os'].lower()
if ((virtualgrain == 'vmware') or (osgrain == 'vmware esxi') or (osgrain == 'esxi')):
vmware_pillar[pillar_key] = {}
try:
_conn = salt.utils.vmware.get_service_instance(host, username, password, protocol, port, verify_ssl=kwargs.get('verify_ssl', True))
if _conn:
data = None
for prop_type in property_types:
data = salt.utils.vmware.get_mor_by_property(_conn, prop_type, minion_id, property_name=property_name)
if data:
type_name = type(data).__name__.replace('vim.', )
if hasattr(data, 'availableField'):
vmware_pillar[pillar_key]['annotations'] = {}
for availableField in data.availableField:
for customValue in data.customValue:
if (availableField.key == customValue.key):
vmware_pillar[pillar_key]['annotations'][availableField.name] = customValue.value
type_specific_pillar_attribute = []
if (type_name in type_specific_pillar_attributes):
type_specific_pillar_attribute = type_specific_pillar_attributes[type_name]
vmware_pillar[pillar_key] = dictupdate.update(vmware_pillar[pillar_key], _crawl_attribute(data, (pillar_attributes + type_specific_pillar_attribute)))
break
Disconnect(_conn)
else:
log.error('Unable to obtain a connection with %s, please verify your vmware ext_pillar configuration', host)
except RuntimeError:
log.error('A runtime error occurred in the vmware_pillar, this is likely caused by an infinite recursion in a requested attribute. Verify your requested attributes and reconfigure the pillar.')
return vmware_pillar
else:
return {}
|
def _recurse_config_to_dict(t_data):
'\n helper function to recurse through a vim object and attempt to return all child objects\n '
if (not isinstance(t_data, type(None))):
if isinstance(t_data, list):
t_list = []
for i in t_data:
t_list.append(_recurse_config_to_dict(i))
return t_list
elif isinstance(t_data, dict):
t_dict = {}
for (k, v) in six.iteritems(t_data):
t_dict[k] = _recurse_config_to_dict(v)
return t_dict
elif hasattr(t_data, '__dict__'):
return _recurse_config_to_dict(t_data.__dict__)
else:
return _serializer(t_data)
| 7,516,975,427,656,124,000
|
helper function to recurse through a vim object and attempt to return all child objects
|
salt/pillar/vmware_pillar.py
|
_recurse_config_to_dict
|
aaannz/salt-1
|
python
|
def _recurse_config_to_dict(t_data):
'\n \n '
if (not isinstance(t_data, type(None))):
if isinstance(t_data, list):
t_list = []
for i in t_data:
t_list.append(_recurse_config_to_dict(i))
return t_list
elif isinstance(t_data, dict):
t_dict = {}
for (k, v) in six.iteritems(t_data):
t_dict[k] = _recurse_config_to_dict(v)
return t_dict
elif hasattr(t_data, '__dict__'):
return _recurse_config_to_dict(t_data.__dict__)
else:
return _serializer(t_data)
|
def _crawl_attribute(this_data, this_attr):
'\n helper function to crawl an attribute specified for retrieval\n '
if isinstance(this_data, list):
t_list = []
for d in this_data:
t_list.append(_crawl_attribute(d, this_attr))
return t_list
elif isinstance(this_attr, dict):
t_dict = {}
for k in this_attr:
if hasattr(this_data, k):
t_dict[k] = _crawl_attribute(getattr(this_data, k, None), this_attr[k])
return t_dict
elif isinstance(this_attr, list):
this_dict = {}
for l in this_attr:
this_dict = dictupdate.update(this_dict, _crawl_attribute(this_data, l))
return this_dict
else:
return {this_attr: _recurse_config_to_dict(getattr(this_data, this_attr, None))}
| -6,944,335,053,640,380,000
|
helper function to crawl an attribute specified for retrieval
|
salt/pillar/vmware_pillar.py
|
_crawl_attribute
|
aaannz/salt-1
|
python
|
def _crawl_attribute(this_data, this_attr):
'\n \n '
if isinstance(this_data, list):
t_list = []
for d in this_data:
t_list.append(_crawl_attribute(d, this_attr))
return t_list
elif isinstance(this_attr, dict):
t_dict = {}
for k in this_attr:
if hasattr(this_data, k):
t_dict[k] = _crawl_attribute(getattr(this_data, k, None), this_attr[k])
return t_dict
elif isinstance(this_attr, list):
this_dict = {}
for l in this_attr:
this_dict = dictupdate.update(this_dict, _crawl_attribute(this_data, l))
return this_dict
else:
return {this_attr: _recurse_config_to_dict(getattr(this_data, this_attr, None))}
|
def _serializer(obj):
'\n helper function to serialize some objects for prettier return\n '
import datetime
if isinstance(obj, datetime.datetime):
if (obj.utcoffset() is not None):
obj = (obj - obj.utcoffset())
return obj.__str__()
return obj
| -3,098,185,855,057,940,000
|
helper function to serialize some objects for prettier return
|
salt/pillar/vmware_pillar.py
|
_serializer
|
aaannz/salt-1
|
python
|
def _serializer(obj):
'\n \n '
import datetime
if isinstance(obj, datetime.datetime):
if (obj.utcoffset() is not None):
obj = (obj - obj.utcoffset())
return obj.__str__()
return obj
|
def sjoin(left_df, right_df, how='inner', op='intersects', lsuffix='left', rsuffix='right'):
"Spatial join of two GeoDataFrames.\n\n Parameters\n ----------\n left_df, right_df : GeoDataFrames\n how : string, default 'inner'\n The type of join:\n\n * 'left': use keys from left_df; retain only left_df geometry column\n * 'right': use keys from right_df; retain only right_df geometry column\n * 'inner': use intersection of keys from both dfs; retain only\n left_df geometry column\n op : string, default 'intersects'\n Binary predicate, one of {'intersects', 'contains', 'within'}.\n See http://shapely.readthedocs.io/en/latest/manual.html#binary-predicates.\n lsuffix : string, default 'left'\n Suffix to apply to overlapping column names (left GeoDataFrame).\n rsuffix : string, default 'right'\n Suffix to apply to overlapping column names (right GeoDataFrame).\n\n "
if (not isinstance(left_df, GeoDataFrame)):
raise ValueError("'left_df' should be GeoDataFrame, got {}".format(type(left_df)))
if (not isinstance(right_df, GeoDataFrame)):
raise ValueError("'right_df' should be GeoDataFrame, got {}".format(type(right_df)))
allowed_hows = ['left', 'right', 'inner']
if (how not in allowed_hows):
raise ValueError(('`how` was "%s" but is expected to be in %s' % (how, allowed_hows)))
allowed_ops = ['contains', 'within', 'intersects']
if (op not in allowed_ops):
raise ValueError(('`op` was "%s" but is expected to be in %s' % (op, allowed_ops)))
if (not _check_crs(left_df, right_df)):
_crs_mismatch_warn(left_df, right_df, stacklevel=3)
index_left = ('index_%s' % lsuffix)
index_right = ('index_%s' % rsuffix)
if (any(left_df.columns.isin([index_left, index_right])) or any(right_df.columns.isin([index_left, index_right]))):
raise ValueError("'{0}' and '{1}' cannot be names in the frames being joined".format(index_left, index_right))
if (right_df._sindex_generated or ((not left_df._sindex_generated) and (right_df.shape[0] > left_df.shape[0]))):
tree_idx = (right_df.sindex if (len(left_df) > 0) else None)
tree_idx_right = True
else:
tree_idx = (left_df.sindex if (len(right_df) > 0) else None)
tree_idx_right = False
left_df = left_df.copy(deep=True)
try:
left_index_name = left_df.index.name
left_df.index = left_df.index.rename(index_left)
except TypeError:
index_left = [(('index_%s' % lsuffix) + str(pos)) for (pos, ix) in enumerate(left_df.index.names)]
left_index_name = left_df.index.names
left_df.index = left_df.index.rename(index_left)
left_df = left_df.reset_index()
right_df = right_df.copy(deep=True)
try:
right_index_name = right_df.index.name
right_df.index = right_df.index.rename(index_right)
except TypeError:
index_right = [(('index_%s' % rsuffix) + str(pos)) for (pos, ix) in enumerate(right_df.index.names)]
right_index_name = right_df.index.names
right_df.index = right_df.index.rename(index_right)
right_df = right_df.reset_index()
if (op == 'within'):
(left_df, right_df) = (right_df, left_df)
tree_idx_right = (not tree_idx_right)
r_idx = np.empty((0, 0))
l_idx = np.empty((0, 0))
if (tree_idx_right and tree_idx):
idxmatch = left_df.geometry.apply((lambda x: x.bounds)).apply((lambda x: (list(tree_idx.intersection(x)) if (not (x == ())) else [])))
idxmatch = idxmatch[(idxmatch.apply(len) > 0)]
if (idxmatch.shape[0] > 0):
r_idx = np.concatenate(idxmatch.values)
l_idx = np.concatenate([([i] * len(v)) for (i, v) in idxmatch.iteritems()])
elif ((not tree_idx_right) and tree_idx):
idxmatch = right_df.geometry.apply((lambda x: x.bounds)).apply((lambda x: (list(tree_idx.intersection(x)) if (not (x == ())) else [])))
idxmatch = idxmatch[(idxmatch.apply(len) > 0)]
if (idxmatch.shape[0] > 0):
l_idx = np.concatenate(idxmatch.values)
r_idx = np.concatenate([([i] * len(v)) for (i, v) in idxmatch.iteritems()])
if ((len(r_idx) > 0) and (len(l_idx) > 0)):
if compat.USE_PYGEOS:
import pygeos
predicate_d = {'intersects': pygeos.intersects, 'contains': pygeos.contains, 'within': pygeos.contains}
check_predicates = predicate_d[op]
else:
def find_intersects(a1, a2):
return a1.intersects(a2)
def find_contains(a1, a2):
return a1.contains(a2)
predicate_d = {'intersects': find_intersects, 'contains': find_contains, 'within': find_contains}
check_predicates = np.vectorize(predicate_d[op])
if compat.USE_PYGEOS:
res = check_predicates(left_df.geometry[l_idx].values.data, right_df[right_df.geometry.name][r_idx].values.data)
else:
res = check_predicates(left_df.geometry.apply((lambda x: prepared.prep(x)))[l_idx], right_df[right_df.geometry.name][r_idx])
result = pd.DataFrame(np.column_stack([l_idx, r_idx, res]))
result.columns = ['_key_left', '_key_right', 'match_bool']
result = pd.DataFrame(result[(result['match_bool'] == 1)]).drop('match_bool', axis=1)
else:
result = pd.DataFrame(columns=['_key_left', '_key_right'], dtype=float)
if (op == 'within'):
(left_df, right_df) = (right_df, left_df)
result = result.rename(columns={'_key_left': '_key_right', '_key_right': '_key_left'})
if (how == 'inner'):
result = result.set_index('_key_left')
joined = left_df.merge(result, left_index=True, right_index=True).merge(right_df.drop(right_df.geometry.name, axis=1), left_on='_key_right', right_index=True, suffixes=(('_%s' % lsuffix), ('_%s' % rsuffix))).set_index(index_left).drop(['_key_right'], axis=1)
if isinstance(index_left, list):
joined.index.names = left_index_name
else:
joined.index.name = left_index_name
elif (how == 'left'):
result = result.set_index('_key_left')
joined = left_df.merge(result, left_index=True, right_index=True, how='left').merge(right_df.drop(right_df.geometry.name, axis=1), how='left', left_on='_key_right', right_index=True, suffixes=(('_%s' % lsuffix), ('_%s' % rsuffix))).set_index(index_left).drop(['_key_right'], axis=1)
if isinstance(index_left, list):
joined.index.names = left_index_name
else:
joined.index.name = left_index_name
else:
joined = left_df.drop(left_df.geometry.name, axis=1).merge(result.merge(right_df, left_on='_key_right', right_index=True, how='right'), left_index=True, right_on='_key_left', how='right').set_index(index_right).drop(['_key_left', '_key_right'], axis=1)
if isinstance(index_right, list):
joined.index.names = right_index_name
else:
joined.index.name = right_index_name
return joined
| -7,132,081,164,258,639,000
|
Spatial join of two GeoDataFrames.
Parameters
----------
left_df, right_df : GeoDataFrames
how : string, default 'inner'
The type of join:
* 'left': use keys from left_df; retain only left_df geometry column
* 'right': use keys from right_df; retain only right_df geometry column
* 'inner': use intersection of keys from both dfs; retain only
left_df geometry column
op : string, default 'intersects'
Binary predicate, one of {'intersects', 'contains', 'within'}.
See http://shapely.readthedocs.io/en/latest/manual.html#binary-predicates.
lsuffix : string, default 'left'
Suffix to apply to overlapping column names (left GeoDataFrame).
rsuffix : string, default 'right'
Suffix to apply to overlapping column names (right GeoDataFrame).
|
geopandas/tools/sjoin.py
|
sjoin
|
anathnathphy67/geopandas
|
python
|
def sjoin(left_df, right_df, how='inner', op='intersects', lsuffix='left', rsuffix='right'):
"Spatial join of two GeoDataFrames.\n\n Parameters\n ----------\n left_df, right_df : GeoDataFrames\n how : string, default 'inner'\n The type of join:\n\n * 'left': use keys from left_df; retain only left_df geometry column\n * 'right': use keys from right_df; retain only right_df geometry column\n * 'inner': use intersection of keys from both dfs; retain only\n left_df geometry column\n op : string, default 'intersects'\n Binary predicate, one of {'intersects', 'contains', 'within'}.\n See http://shapely.readthedocs.io/en/latest/manual.html#binary-predicates.\n lsuffix : string, default 'left'\n Suffix to apply to overlapping column names (left GeoDataFrame).\n rsuffix : string, default 'right'\n Suffix to apply to overlapping column names (right GeoDataFrame).\n\n "
if (not isinstance(left_df, GeoDataFrame)):
raise ValueError("'left_df' should be GeoDataFrame, got {}".format(type(left_df)))
if (not isinstance(right_df, GeoDataFrame)):
raise ValueError("'right_df' should be GeoDataFrame, got {}".format(type(right_df)))
allowed_hows = ['left', 'right', 'inner']
if (how not in allowed_hows):
raise ValueError(('`how` was "%s" but is expected to be in %s' % (how, allowed_hows)))
allowed_ops = ['contains', 'within', 'intersects']
if (op not in allowed_ops):
raise ValueError(('`op` was "%s" but is expected to be in %s' % (op, allowed_ops)))
if (not _check_crs(left_df, right_df)):
_crs_mismatch_warn(left_df, right_df, stacklevel=3)
index_left = ('index_%s' % lsuffix)
index_right = ('index_%s' % rsuffix)
if (any(left_df.columns.isin([index_left, index_right])) or any(right_df.columns.isin([index_left, index_right]))):
raise ValueError("'{0}' and '{1}' cannot be names in the frames being joined".format(index_left, index_right))
if (right_df._sindex_generated or ((not left_df._sindex_generated) and (right_df.shape[0] > left_df.shape[0]))):
tree_idx = (right_df.sindex if (len(left_df) > 0) else None)
tree_idx_right = True
else:
tree_idx = (left_df.sindex if (len(right_df) > 0) else None)
tree_idx_right = False
left_df = left_df.copy(deep=True)
try:
left_index_name = left_df.index.name
left_df.index = left_df.index.rename(index_left)
except TypeError:
index_left = [(('index_%s' % lsuffix) + str(pos)) for (pos, ix) in enumerate(left_df.index.names)]
left_index_name = left_df.index.names
left_df.index = left_df.index.rename(index_left)
left_df = left_df.reset_index()
right_df = right_df.copy(deep=True)
try:
right_index_name = right_df.index.name
right_df.index = right_df.index.rename(index_right)
except TypeError:
index_right = [(('index_%s' % rsuffix) + str(pos)) for (pos, ix) in enumerate(right_df.index.names)]
right_index_name = right_df.index.names
right_df.index = right_df.index.rename(index_right)
right_df = right_df.reset_index()
if (op == 'within'):
(left_df, right_df) = (right_df, left_df)
tree_idx_right = (not tree_idx_right)
r_idx = np.empty((0, 0))
l_idx = np.empty((0, 0))
if (tree_idx_right and tree_idx):
idxmatch = left_df.geometry.apply((lambda x: x.bounds)).apply((lambda x: (list(tree_idx.intersection(x)) if (not (x == ())) else [])))
idxmatch = idxmatch[(idxmatch.apply(len) > 0)]
if (idxmatch.shape[0] > 0):
r_idx = np.concatenate(idxmatch.values)
l_idx = np.concatenate([([i] * len(v)) for (i, v) in idxmatch.iteritems()])
elif ((not tree_idx_right) and tree_idx):
idxmatch = right_df.geometry.apply((lambda x: x.bounds)).apply((lambda x: (list(tree_idx.intersection(x)) if (not (x == ())) else [])))
idxmatch = idxmatch[(idxmatch.apply(len) > 0)]
if (idxmatch.shape[0] > 0):
l_idx = np.concatenate(idxmatch.values)
r_idx = np.concatenate([([i] * len(v)) for (i, v) in idxmatch.iteritems()])
if ((len(r_idx) > 0) and (len(l_idx) > 0)):
if compat.USE_PYGEOS:
import pygeos
predicate_d = {'intersects': pygeos.intersects, 'contains': pygeos.contains, 'within': pygeos.contains}
check_predicates = predicate_d[op]
else:
def find_intersects(a1, a2):
return a1.intersects(a2)
def find_contains(a1, a2):
return a1.contains(a2)
predicate_d = {'intersects': find_intersects, 'contains': find_contains, 'within': find_contains}
check_predicates = np.vectorize(predicate_d[op])
if compat.USE_PYGEOS:
res = check_predicates(left_df.geometry[l_idx].values.data, right_df[right_df.geometry.name][r_idx].values.data)
else:
res = check_predicates(left_df.geometry.apply((lambda x: prepared.prep(x)))[l_idx], right_df[right_df.geometry.name][r_idx])
result = pd.DataFrame(np.column_stack([l_idx, r_idx, res]))
result.columns = ['_key_left', '_key_right', 'match_bool']
result = pd.DataFrame(result[(result['match_bool'] == 1)]).drop('match_bool', axis=1)
else:
result = pd.DataFrame(columns=['_key_left', '_key_right'], dtype=float)
if (op == 'within'):
(left_df, right_df) = (right_df, left_df)
result = result.rename(columns={'_key_left': '_key_right', '_key_right': '_key_left'})
if (how == 'inner'):
result = result.set_index('_key_left')
joined = left_df.merge(result, left_index=True, right_index=True).merge(right_df.drop(right_df.geometry.name, axis=1), left_on='_key_right', right_index=True, suffixes=(('_%s' % lsuffix), ('_%s' % rsuffix))).set_index(index_left).drop(['_key_right'], axis=1)
if isinstance(index_left, list):
joined.index.names = left_index_name
else:
joined.index.name = left_index_name
elif (how == 'left'):
result = result.set_index('_key_left')
joined = left_df.merge(result, left_index=True, right_index=True, how='left').merge(right_df.drop(right_df.geometry.name, axis=1), how='left', left_on='_key_right', right_index=True, suffixes=(('_%s' % lsuffix), ('_%s' % rsuffix))).set_index(index_left).drop(['_key_right'], axis=1)
if isinstance(index_left, list):
joined.index.names = left_index_name
else:
joined.index.name = left_index_name
else:
joined = left_df.drop(left_df.geometry.name, axis=1).merge(result.merge(right_df, left_on='_key_right', right_index=True, how='right'), left_index=True, right_on='_key_left', how='right').set_index(index_right).drop(['_key_left', '_key_right'], axis=1)
if isinstance(index_right, list):
joined.index.names = right_index_name
else:
joined.index.name = right_index_name
return joined
|
def test_minify(self):
'Tests _minify with an invalid filepath.'
with self.assertRaises(subprocess.CalledProcessError) as called_process:
build._minify(INVALID_INPUT_FILEPATH, INVALID_OUTPUT_FILEPATH)
self.assertEqual(called_process.exception.returncode, 1)
| -1,228,251,134,857,554,400
|
Tests _minify with an invalid filepath.
|
scripts/build_test.py
|
test_minify
|
muarachmann/oppia
|
python
|
def test_minify(self):
with self.assertRaises(subprocess.CalledProcessError) as called_process:
build._minify(INVALID_INPUT_FILEPATH, INVALID_OUTPUT_FILEPATH)
self.assertEqual(called_process.exception.returncode, 1)
|
def test_minify_and_create_sourcemap(self):
'Tests _minify_and_create_sourcemap with an invalid filepath.'
with self.assertRaises(subprocess.CalledProcessError) as called_process:
build._minify_and_create_sourcemap(INVALID_INPUT_FILEPATH, INVALID_OUTPUT_FILEPATH)
self.assertEqual(called_process.exception.returncode, 1)
| -7,904,411,719,584,090,000
|
Tests _minify_and_create_sourcemap with an invalid filepath.
|
scripts/build_test.py
|
test_minify_and_create_sourcemap
|
muarachmann/oppia
|
python
|
def test_minify_and_create_sourcemap(self):
with self.assertRaises(subprocess.CalledProcessError) as called_process:
build._minify_and_create_sourcemap(INVALID_INPUT_FILEPATH, INVALID_OUTPUT_FILEPATH)
self.assertEqual(called_process.exception.returncode, 1)
|
def test_ensure_files_exist(self):
'Test _ensure_files_exist raises exception with a non-existent\n filepath.\n '
non_existent_filepaths = [INVALID_INPUT_FILEPATH]
with self.assertRaisesRegexp(OSError, ('File %s does not exist.' % non_existent_filepaths[0])):
build._ensure_files_exist(non_existent_filepaths)
| 1,052,954,398,160,072,000
|
Test _ensure_files_exist raises exception with a non-existent
filepath.
|
scripts/build_test.py
|
test_ensure_files_exist
|
muarachmann/oppia
|
python
|
def test_ensure_files_exist(self):
'Test _ensure_files_exist raises exception with a non-existent\n filepath.\n '
non_existent_filepaths = [INVALID_INPUT_FILEPATH]
with self.assertRaisesRegexp(OSError, ('File %s does not exist.' % non_existent_filepaths[0])):
build._ensure_files_exist(non_existent_filepaths)
|
def test_join_files(self):
'Determine third_party.js contains the content of the first 10 JS\n files in /third_party/static.\n '
third_party_js_stream = StringIO.StringIO()
dependency_filepaths = build.get_dependencies_filepaths()
build._join_files(dependency_filepaths['js'], third_party_js_stream)
counter = 0
JS_FILE_COUNT = 10
for js_filepath in dependency_filepaths['js']:
if (counter == JS_FILE_COUNT):
break
with open(js_filepath, 'r') as js_file:
for line in js_file:
self.assertIn(line, third_party_js_stream.getvalue())
counter += 1
| 5,106,008,982,036,936,000
|
Determine third_party.js contains the content of the first 10 JS
files in /third_party/static.
|
scripts/build_test.py
|
test_join_files
|
muarachmann/oppia
|
python
|
def test_join_files(self):
'Determine third_party.js contains the content of the first 10 JS\n files in /third_party/static.\n '
third_party_js_stream = StringIO.StringIO()
dependency_filepaths = build.get_dependencies_filepaths()
build._join_files(dependency_filepaths['js'], third_party_js_stream)
counter = 0
JS_FILE_COUNT = 10
for js_filepath in dependency_filepaths['js']:
if (counter == JS_FILE_COUNT):
break
with open(js_filepath, 'r') as js_file:
for line in js_file:
self.assertIn(line, third_party_js_stream.getvalue())
counter += 1
|
def test_generate_copy_tasks_for_fonts(self):
'Test _generate_copy_tasks_for_fonts ensures that the number of copy\n tasks matches the number of font files.\n '
copy_tasks = collections.deque()
dependency_filepaths = build.get_dependencies_filepaths()
test_target = os.path.join('target', 'fonts', '')
self.assertEqual(len(copy_tasks), 0)
copy_tasks += build._generate_copy_tasks_for_fonts(dependency_filepaths['fonts'], test_target)
self.assertEqual(len(copy_tasks), len(dependency_filepaths['fonts']))
| -4,733,900,273,529,137,000
|
Test _generate_copy_tasks_for_fonts ensures that the number of copy
tasks matches the number of font files.
|
scripts/build_test.py
|
test_generate_copy_tasks_for_fonts
|
muarachmann/oppia
|
python
|
def test_generate_copy_tasks_for_fonts(self):
'Test _generate_copy_tasks_for_fonts ensures that the number of copy\n tasks matches the number of font files.\n '
copy_tasks = collections.deque()
dependency_filepaths = build.get_dependencies_filepaths()
test_target = os.path.join('target', 'fonts', )
self.assertEqual(len(copy_tasks), 0)
copy_tasks += build._generate_copy_tasks_for_fonts(dependency_filepaths['fonts'], test_target)
self.assertEqual(len(copy_tasks), len(dependency_filepaths['fonts']))
|
def test_insert_hash(self):
'Test _insert_hash returns correct filenames with provided hashes.'
self.assertEqual(build._insert_hash('file.js', '123456'), 'file.123456.js')
self.assertEqual(build._insert_hash('path/to/file.js', '654321'), 'path/to/file.654321.js')
self.assertEqual(build._insert_hash('file.min.js', 'abcdef'), 'file.min.abcdef.js')
self.assertEqual(build._insert_hash('path/to/file.min.js', 'fedcba'), 'path/to/file.min.fedcba.js')
| 713,936,822,209,947,500
|
Test _insert_hash returns correct filenames with provided hashes.
|
scripts/build_test.py
|
test_insert_hash
|
muarachmann/oppia
|
python
|
def test_insert_hash(self):
self.assertEqual(build._insert_hash('file.js', '123456'), 'file.123456.js')
self.assertEqual(build._insert_hash('path/to/file.js', '654321'), 'path/to/file.654321.js')
self.assertEqual(build._insert_hash('file.min.js', 'abcdef'), 'file.min.abcdef.js')
self.assertEqual(build._insert_hash('path/to/file.min.js', 'fedcba'), 'path/to/file.min.fedcba.js')
|
def test_get_file_count(self):
'Test get_file_count returns the correct number of files, excluding\n file with extensions in FILE_EXTENSIONS_TO_IGNORE and files that should\n not be built.\n '
all_inclusive_file_count = 0
for (_, _, files) in os.walk(MOCK_EXTENSIONS_DEV_DIR):
all_inclusive_file_count += len(files)
ignored_file_count = 0
for (_, _, files) in os.walk(MOCK_EXTENSIONS_DEV_DIR):
for filename in files:
if ((not build.should_file_be_built(filename)) or any((filename.endswith(p) for p in build.FILE_EXTENSIONS_TO_IGNORE))):
ignored_file_count += 1
self.assertEqual((all_inclusive_file_count - ignored_file_count), build.get_file_count(MOCK_EXTENSIONS_DEV_DIR))
| -4,047,248,619,172,851,000
|
Test get_file_count returns the correct number of files, excluding
file with extensions in FILE_EXTENSIONS_TO_IGNORE and files that should
not be built.
|
scripts/build_test.py
|
test_get_file_count
|
muarachmann/oppia
|
python
|
def test_get_file_count(self):
'Test get_file_count returns the correct number of files, excluding\n file with extensions in FILE_EXTENSIONS_TO_IGNORE and files that should\n not be built.\n '
all_inclusive_file_count = 0
for (_, _, files) in os.walk(MOCK_EXTENSIONS_DEV_DIR):
all_inclusive_file_count += len(files)
ignored_file_count = 0
for (_, _, files) in os.walk(MOCK_EXTENSIONS_DEV_DIR):
for filename in files:
if ((not build.should_file_be_built(filename)) or any((filename.endswith(p) for p in build.FILE_EXTENSIONS_TO_IGNORE))):
ignored_file_count += 1
self.assertEqual((all_inclusive_file_count - ignored_file_count), build.get_file_count(MOCK_EXTENSIONS_DEV_DIR))
|
def test_compare_file_count(self):
'Test _compare_file_count raises exception when there is a\n mismatched file count between 2 dirs list.\n '
build.ensure_directory_exists(EMPTY_DIR)
source_dir_file_count = build.get_file_count(EMPTY_DIR)
assert (source_dir_file_count == 0)
target_dir_file_count = build.get_file_count(MOCK_ASSETS_DEV_DIR)
assert (target_dir_file_count > 0)
with self.assertRaisesRegexp(ValueError, ('%s files in first dir list != %s files in second dir list' % (source_dir_file_count, target_dir_file_count))):
build._compare_file_count([EMPTY_DIR], [MOCK_ASSETS_DEV_DIR])
MOCK_EXTENSIONS_DIR_LIST = [MOCK_EXTENSIONS_DEV_DIR, MOCK_EXTENSIONS_COMPILED_JS_DIR]
target_dir_file_count = (build.get_file_count(MOCK_EXTENSIONS_DEV_DIR) + build.get_file_count(MOCK_EXTENSIONS_COMPILED_JS_DIR))
assert (target_dir_file_count > 0)
with self.assertRaisesRegexp(ValueError, ('%s files in first dir list != %s files in second dir list' % (source_dir_file_count, target_dir_file_count))):
build._compare_file_count([EMPTY_DIR], MOCK_EXTENSIONS_DIR_LIST)
build.safe_delete_directory_tree(EMPTY_DIR)
| 7,889,284,212,739,964,000
|
Test _compare_file_count raises exception when there is a
mismatched file count between 2 dirs list.
|
scripts/build_test.py
|
test_compare_file_count
|
muarachmann/oppia
|
python
|
def test_compare_file_count(self):
'Test _compare_file_count raises exception when there is a\n mismatched file count between 2 dirs list.\n '
build.ensure_directory_exists(EMPTY_DIR)
source_dir_file_count = build.get_file_count(EMPTY_DIR)
assert (source_dir_file_count == 0)
target_dir_file_count = build.get_file_count(MOCK_ASSETS_DEV_DIR)
assert (target_dir_file_count > 0)
with self.assertRaisesRegexp(ValueError, ('%s files in first dir list != %s files in second dir list' % (source_dir_file_count, target_dir_file_count))):
build._compare_file_count([EMPTY_DIR], [MOCK_ASSETS_DEV_DIR])
MOCK_EXTENSIONS_DIR_LIST = [MOCK_EXTENSIONS_DEV_DIR, MOCK_EXTENSIONS_COMPILED_JS_DIR]
target_dir_file_count = (build.get_file_count(MOCK_EXTENSIONS_DEV_DIR) + build.get_file_count(MOCK_EXTENSIONS_COMPILED_JS_DIR))
assert (target_dir_file_count > 0)
with self.assertRaisesRegexp(ValueError, ('%s files in first dir list != %s files in second dir list' % (source_dir_file_count, target_dir_file_count))):
build._compare_file_count([EMPTY_DIR], MOCK_EXTENSIONS_DIR_LIST)
build.safe_delete_directory_tree(EMPTY_DIR)
|
def test_verify_filepath_hash(self):
'Test _verify_filepath_hash raises exception:\n 1) When there is an empty hash dict.\n 2) When a filename is expected to contain hash but does not.\n 3) When there is a hash in filename that cannot be found in\n hash dict.\n '
file_hashes = dict()
base_filename = 'base.html'
with self.assertRaisesRegexp(ValueError, 'Hash dict is empty'):
build._verify_filepath_hash(base_filename, file_hashes)
file_hashes = {base_filename: random.getrandbits(128)}
with self.assertRaisesRegexp(ValueError, ('%s is expected to contain MD5 hash' % base_filename)):
build._verify_filepath_hash(base_filename, file_hashes)
bad_filepath = 'README'
with self.assertRaisesRegexp(ValueError, 'Filepath has less than 2 partitions after splitting'):
build._verify_filepath_hash(bad_filepath, file_hashes)
hashed_base_filename = build._insert_hash(base_filename, random.getrandbits(128))
with self.assertRaisesRegexp(KeyError, ('Hash from file named %s does not match hash dict values' % hashed_base_filename)):
build._verify_filepath_hash(hashed_base_filename, file_hashes)
| -1,598,340,757,166,783,200
|
Test _verify_filepath_hash raises exception:
1) When there is an empty hash dict.
2) When a filename is expected to contain hash but does not.
3) When there is a hash in filename that cannot be found in
hash dict.
|
scripts/build_test.py
|
test_verify_filepath_hash
|
muarachmann/oppia
|
python
|
def test_verify_filepath_hash(self):
'Test _verify_filepath_hash raises exception:\n 1) When there is an empty hash dict.\n 2) When a filename is expected to contain hash but does not.\n 3) When there is a hash in filename that cannot be found in\n hash dict.\n '
file_hashes = dict()
base_filename = 'base.html'
with self.assertRaisesRegexp(ValueError, 'Hash dict is empty'):
build._verify_filepath_hash(base_filename, file_hashes)
file_hashes = {base_filename: random.getrandbits(128)}
with self.assertRaisesRegexp(ValueError, ('%s is expected to contain MD5 hash' % base_filename)):
build._verify_filepath_hash(base_filename, file_hashes)
bad_filepath = 'README'
with self.assertRaisesRegexp(ValueError, 'Filepath has less than 2 partitions after splitting'):
build._verify_filepath_hash(bad_filepath, file_hashes)
hashed_base_filename = build._insert_hash(base_filename, random.getrandbits(128))
with self.assertRaisesRegexp(KeyError, ('Hash from file named %s does not match hash dict values' % hashed_base_filename)):
build._verify_filepath_hash(hashed_base_filename, file_hashes)
|
def test_process_html(self):
'Test process_html removes whitespaces and adds hash to filepaths.'
BASE_HTML_SOURCE_PATH = os.path.join(MOCK_TEMPLATES_DEV_DIR, 'base.html')
BASE_JS_RELATIVE_PATH = os.path.join('pages', 'Base.js')
BASE_JS_SOURCE_PATH = os.path.join(MOCK_TEMPLATES_COMPILED_JS_DIR, BASE_JS_RELATIVE_PATH)
build._ensure_files_exist([BASE_HTML_SOURCE_PATH, BASE_JS_SOURCE_PATH])
minified_html_file_stream = StringIO.StringIO()
with self.swap(build, 'FILE_EXTENSIONS_TO_IGNORE', ('.html',)):
file_hashes = build.get_file_hashes(MOCK_TEMPLATES_DEV_DIR)
file_hashes.update(build.get_file_hashes(MOCK_TEMPLATES_COMPILED_JS_DIR))
with open(BASE_HTML_SOURCE_PATH, 'r') as source_base_file:
source_base_file_content = source_base_file.read()
self.assertRegexpMatches(source_base_file_content, '\\s{2,}', msg=('No white spaces detected in %s unexpectedly' % BASE_HTML_SOURCE_PATH))
self.assertIn(BASE_JS_RELATIVE_PATH, source_base_file_content)
with open(BASE_HTML_SOURCE_PATH, 'r') as source_base_file:
build.process_html(source_base_file, minified_html_file_stream, file_hashes)
minified_html_file_content = minified_html_file_stream.getvalue()
self.assertNotRegexpMatches(minified_html_file_content, '\\s{2,}', msg=('All white spaces must be removed from %s' % BASE_HTML_SOURCE_PATH))
final_filename = build._insert_hash(BASE_JS_RELATIVE_PATH, file_hashes[BASE_JS_RELATIVE_PATH])
self.assertIn(final_filename, minified_html_file_content)
| -218,790,766,903,421,200
|
Test process_html removes whitespaces and adds hash to filepaths.
|
scripts/build_test.py
|
test_process_html
|
muarachmann/oppia
|
python
|
def test_process_html(self):
BASE_HTML_SOURCE_PATH = os.path.join(MOCK_TEMPLATES_DEV_DIR, 'base.html')
BASE_JS_RELATIVE_PATH = os.path.join('pages', 'Base.js')
BASE_JS_SOURCE_PATH = os.path.join(MOCK_TEMPLATES_COMPILED_JS_DIR, BASE_JS_RELATIVE_PATH)
build._ensure_files_exist([BASE_HTML_SOURCE_PATH, BASE_JS_SOURCE_PATH])
minified_html_file_stream = StringIO.StringIO()
with self.swap(build, 'FILE_EXTENSIONS_TO_IGNORE', ('.html',)):
file_hashes = build.get_file_hashes(MOCK_TEMPLATES_DEV_DIR)
file_hashes.update(build.get_file_hashes(MOCK_TEMPLATES_COMPILED_JS_DIR))
with open(BASE_HTML_SOURCE_PATH, 'r') as source_base_file:
source_base_file_content = source_base_file.read()
self.assertRegexpMatches(source_base_file_content, '\\s{2,}', msg=('No white spaces detected in %s unexpectedly' % BASE_HTML_SOURCE_PATH))
self.assertIn(BASE_JS_RELATIVE_PATH, source_base_file_content)
with open(BASE_HTML_SOURCE_PATH, 'r') as source_base_file:
build.process_html(source_base_file, minified_html_file_stream, file_hashes)
minified_html_file_content = minified_html_file_stream.getvalue()
self.assertNotRegexpMatches(minified_html_file_content, '\\s{2,}', msg=('All white spaces must be removed from %s' % BASE_HTML_SOURCE_PATH))
final_filename = build._insert_hash(BASE_JS_RELATIVE_PATH, file_hashes[BASE_JS_RELATIVE_PATH])
self.assertIn(final_filename, minified_html_file_content)
|
def test_should_file_be_built(self):
'Test should_file_be_built returns the correct boolean value for\n filepath that should be built.\n '
service_js_filepath = os.path.join('local_compiled_js', 'core', 'pages', 'AudioService.js')
generated_parser_js_filepath = os.path.join('core', 'expressions', 'ExpressionParserService.js')
compiled_generated_parser_js_filepath = os.path.join('local_compiled_js', 'core', 'expressions', 'ExpressionParserService.js')
service_ts_filepath = os.path.join('core', 'pages', 'AudioService.ts')
spec_js_filepath = os.path.join('core', 'pages', 'AudioServiceSpec.js')
protractor_filepath = os.path.join('extensions', 'protractor.js')
python_controller_filepath = os.path.join('base.py')
pyc_test_filepath = os.path.join('core', 'controllers', 'base.pyc')
python_test_filepath = os.path.join('core', 'tests', 'base_test.py')
self.assertFalse(build.should_file_be_built(spec_js_filepath))
self.assertFalse(build.should_file_be_built(protractor_filepath))
self.assertTrue(build.should_file_be_built(service_js_filepath))
self.assertFalse(build.should_file_be_built(service_ts_filepath))
self.assertFalse(build.should_file_be_built(python_test_filepath))
self.assertFalse(build.should_file_be_built(pyc_test_filepath))
self.assertTrue(build.should_file_be_built(python_controller_filepath))
with self.swap(build, 'JS_FILENAME_SUFFIXES_TO_IGNORE', ('Service.js',)):
self.assertFalse(build.should_file_be_built(service_js_filepath))
self.assertTrue(build.should_file_be_built(spec_js_filepath))
with self.swap(build, 'JS_FILEPATHS_NOT_TO_BUILD', ('core/expressions/ExpressionParserService.js',)):
self.assertFalse(build.should_file_be_built(generated_parser_js_filepath))
self.assertTrue(build.should_file_be_built(compiled_generated_parser_js_filepath))
| 1,018,427,264,036,538,800
|
Test should_file_be_built returns the correct boolean value for
filepath that should be built.
|
scripts/build_test.py
|
test_should_file_be_built
|
muarachmann/oppia
|
python
|
def test_should_file_be_built(self):
'Test should_file_be_built returns the correct boolean value for\n filepath that should be built.\n '
service_js_filepath = os.path.join('local_compiled_js', 'core', 'pages', 'AudioService.js')
generated_parser_js_filepath = os.path.join('core', 'expressions', 'ExpressionParserService.js')
compiled_generated_parser_js_filepath = os.path.join('local_compiled_js', 'core', 'expressions', 'ExpressionParserService.js')
service_ts_filepath = os.path.join('core', 'pages', 'AudioService.ts')
spec_js_filepath = os.path.join('core', 'pages', 'AudioServiceSpec.js')
protractor_filepath = os.path.join('extensions', 'protractor.js')
python_controller_filepath = os.path.join('base.py')
pyc_test_filepath = os.path.join('core', 'controllers', 'base.pyc')
python_test_filepath = os.path.join('core', 'tests', 'base_test.py')
self.assertFalse(build.should_file_be_built(spec_js_filepath))
self.assertFalse(build.should_file_be_built(protractor_filepath))
self.assertTrue(build.should_file_be_built(service_js_filepath))
self.assertFalse(build.should_file_be_built(service_ts_filepath))
self.assertFalse(build.should_file_be_built(python_test_filepath))
self.assertFalse(build.should_file_be_built(pyc_test_filepath))
self.assertTrue(build.should_file_be_built(python_controller_filepath))
with self.swap(build, 'JS_FILENAME_SUFFIXES_TO_IGNORE', ('Service.js',)):
self.assertFalse(build.should_file_be_built(service_js_filepath))
self.assertTrue(build.should_file_be_built(spec_js_filepath))
with self.swap(build, 'JS_FILEPATHS_NOT_TO_BUILD', ('core/expressions/ExpressionParserService.js',)):
self.assertFalse(build.should_file_be_built(generated_parser_js_filepath))
self.assertTrue(build.should_file_be_built(compiled_generated_parser_js_filepath))
|
def test_hash_should_be_inserted(self):
'Test hash_should_be_inserted returns the correct boolean value\n for filepath that should be hashed.\n '
with self.swap(build, 'FILEPATHS_NOT_TO_RENAME', ('*.py', 'path/to/fonts/*', 'path/to/third_party.min.js.map', 'path/to/third_party.min.css.map')):
self.assertFalse(build.hash_should_be_inserted('path/to/fonts/fontawesome-webfont.svg'))
self.assertFalse(build.hash_should_be_inserted('path/to/third_party.min.css.map'))
self.assertFalse(build.hash_should_be_inserted('path/to/third_party.min.js.map'))
self.assertTrue(build.hash_should_be_inserted('path/to/wrongFonts/fonta.eot'))
self.assertTrue(build.hash_should_be_inserted('rich_text_components/Video/protractor.js'))
self.assertFalse(build.hash_should_be_inserted('main.py'))
self.assertFalse(build.hash_should_be_inserted('extensions/domain.py'))
| -5,054,451,901,803,749,000
|
Test hash_should_be_inserted returns the correct boolean value
for filepath that should be hashed.
|
scripts/build_test.py
|
test_hash_should_be_inserted
|
muarachmann/oppia
|
python
|
def test_hash_should_be_inserted(self):
'Test hash_should_be_inserted returns the correct boolean value\n for filepath that should be hashed.\n '
with self.swap(build, 'FILEPATHS_NOT_TO_RENAME', ('*.py', 'path/to/fonts/*', 'path/to/third_party.min.js.map', 'path/to/third_party.min.css.map')):
self.assertFalse(build.hash_should_be_inserted('path/to/fonts/fontawesome-webfont.svg'))
self.assertFalse(build.hash_should_be_inserted('path/to/third_party.min.css.map'))
self.assertFalse(build.hash_should_be_inserted('path/to/third_party.min.js.map'))
self.assertTrue(build.hash_should_be_inserted('path/to/wrongFonts/fonta.eot'))
self.assertTrue(build.hash_should_be_inserted('rich_text_components/Video/protractor.js'))
self.assertFalse(build.hash_should_be_inserted('main.py'))
self.assertFalse(build.hash_should_be_inserted('extensions/domain.py'))
|
def test_generate_copy_tasks_to_copy_from_source_to_target(self):
'Test generate_copy_tasks_to_copy_from_source_to_target queues up\n the same number of copy tasks as the number of files in the directory.\n '
assets_hashes = build.get_file_hashes(MOCK_ASSETS_DEV_DIR)
total_file_count = build.get_file_count(MOCK_ASSETS_DEV_DIR)
copy_tasks = collections.deque()
self.assertEqual(len(copy_tasks), 0)
copy_tasks += build.generate_copy_tasks_to_copy_from_source_to_target(MOCK_ASSETS_DEV_DIR, MOCK_ASSETS_OUT_DIR, assets_hashes)
self.assertEqual(len(copy_tasks), total_file_count)
| 8,098,721,213,208,466,000
|
Test generate_copy_tasks_to_copy_from_source_to_target queues up
the same number of copy tasks as the number of files in the directory.
|
scripts/build_test.py
|
test_generate_copy_tasks_to_copy_from_source_to_target
|
muarachmann/oppia
|
python
|
def test_generate_copy_tasks_to_copy_from_source_to_target(self):
'Test generate_copy_tasks_to_copy_from_source_to_target queues up\n the same number of copy tasks as the number of files in the directory.\n '
assets_hashes = build.get_file_hashes(MOCK_ASSETS_DEV_DIR)
total_file_count = build.get_file_count(MOCK_ASSETS_DEV_DIR)
copy_tasks = collections.deque()
self.assertEqual(len(copy_tasks), 0)
copy_tasks += build.generate_copy_tasks_to_copy_from_source_to_target(MOCK_ASSETS_DEV_DIR, MOCK_ASSETS_OUT_DIR, assets_hashes)
self.assertEqual(len(copy_tasks), total_file_count)
|
def test_is_file_hash_provided_to_frontend(self):
'Test is_file_hash_provided_to_frontend returns the correct boolean\n value for filepath that should be provided to frontend.\n '
with self.swap(build, 'FILEPATHS_PROVIDED_TO_FRONTEND', ('path/to/file.js', 'path/to/file.html', 'file.js')):
self.assertTrue(build.is_file_hash_provided_to_frontend('path/to/file.js'))
self.assertTrue(build.is_file_hash_provided_to_frontend('path/to/file.html'))
self.assertTrue(build.is_file_hash_provided_to_frontend('file.js'))
with self.swap(build, 'FILEPATHS_PROVIDED_TO_FRONTEND', ('path/to/*', '*.js', '*_end.html')):
self.assertTrue(build.is_file_hash_provided_to_frontend('path/to/file.js'))
self.assertTrue(build.is_file_hash_provided_to_frontend('path/to/file.html'))
self.assertTrue(build.is_file_hash_provided_to_frontend('file.js'))
self.assertFalse(build.is_file_hash_provided_to_frontend('path/file.css'))
self.assertTrue(build.is_file_hash_provided_to_frontend('good_end.html'))
self.assertFalse(build.is_file_hash_provided_to_frontend('bad_end.css'))
| -9,103,280,922,856,293,000
|
Test is_file_hash_provided_to_frontend returns the correct boolean
value for filepath that should be provided to frontend.
|
scripts/build_test.py
|
test_is_file_hash_provided_to_frontend
|
muarachmann/oppia
|
python
|
def test_is_file_hash_provided_to_frontend(self):
'Test is_file_hash_provided_to_frontend returns the correct boolean\n value for filepath that should be provided to frontend.\n '
with self.swap(build, 'FILEPATHS_PROVIDED_TO_FRONTEND', ('path/to/file.js', 'path/to/file.html', 'file.js')):
self.assertTrue(build.is_file_hash_provided_to_frontend('path/to/file.js'))
self.assertTrue(build.is_file_hash_provided_to_frontend('path/to/file.html'))
self.assertTrue(build.is_file_hash_provided_to_frontend('file.js'))
with self.swap(build, 'FILEPATHS_PROVIDED_TO_FRONTEND', ('path/to/*', '*.js', '*_end.html')):
self.assertTrue(build.is_file_hash_provided_to_frontend('path/to/file.js'))
self.assertTrue(build.is_file_hash_provided_to_frontend('path/to/file.html'))
self.assertTrue(build.is_file_hash_provided_to_frontend('file.js'))
self.assertFalse(build.is_file_hash_provided_to_frontend('path/file.css'))
self.assertTrue(build.is_file_hash_provided_to_frontend('good_end.html'))
self.assertFalse(build.is_file_hash_provided_to_frontend('bad_end.css'))
|
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